diff --git a/README.md b/README.md
index f349ca2..dfc0769 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,10 @@
 # madify
 
-This is the source code for https://madi.fi. This project serves as a demonstration of using a custom [CamanJS](http://camanjs.com/) filter (in this case, the [customscale filter](https://www.npmjs.com/package/@jmu-cs/customscale-camanjs-filter), which defaults to [JMU colors](https://www.jmu.edu/identity/our-style/color.shtml)) to modify a user's image and offer it back for them to save it, all done (client-side) in the user's own browser.
\ No newline at end of file
+This is the source code for https://madi.fi. This project serves as a demonstration of using a custom [CamanJS](http://camanjs.com/) filter (in this case, the [customscale filter](https://www.npmjs.com/package/@jmu-cs/customscale-camanjs-filter), which defaults to [JMU colors](https://www.jmu.edu/identity/our-style/color.shtml)) to modify a user's image and offer it back for them to save it, all done (client-side) in the user's own browser.
+
+## Local usage
+- Run `python -m SimpleHTTPServer` from the root of the repository
+- Navigate to `localhost:8000`
+- Drag and drop (or upload) an image 
+  - It may take a few moments for your madified image to appear
+- Click on the image to download your madified image
diff --git a/img/duke-dog.png b/img/duke-dog.png
new file mode 100644
index 0000000..60bd105
Binary files /dev/null and b/img/duke-dog.png differ
diff --git a/index.html b/index.html
index 8ead69c..9ec915a 100644
--- a/index.html
+++ b/index.html
@@ -31,27 +31,22 @@
             </ul>
         </div>
     </nav>
-
     
-    <!-- <div class="editor-buttons"> -->
-        <form id="my-awesome-dropzone" class="dropzone dz-clickable" action="/dev/null">
-            <div class="preview-wrapper hidden mx-auto">
-                <canvas id="canvas" class="mx-auto"></canvas>
-                <p class="download-message">Click to save</p>
-            </div>
-            <!-- <label for="upload-file">Upload a Picture</label> -->
-            <input class="hidden dz-message" type="file" id="upload-file" placeholder="Upload a Picture" />
-            <!-- <button id="download-btn">Download Image</button> -->
-        </form>
-        <!-- <br /> -->
-        <!-- </div> -->
-        
+    <form id="my-awesome-dropzone" class="dropzone dz-clickable" action="/dev/null">
+        <div class="preview-wrapper hidden mx-auto">
+            <canvas id="canvas" class="mx-auto"></canvas>
+            <p class="download-message">Click to save</p>
+        </div>
+
+        <input class="hidden dz-message" type="file" id="upload-file" placeholder="Upload a Picture" />
+    </form>
     
     <script src="node_modules/caman/dist/caman.min.js"></script>
     <script src="node_modules/@jmu-cs/customscale-camanjs-filter/index.js"></script>
     <script src="node_modules/jquery/dist/jquery.min.js"></script>
     <script src="node_modules/dropzone/dist/min/dropzone.min.js"></script>
     <script src="node_modules/bootstrap/dist/js/bootstrap.bundle.min.js"></script>
+    <script src="node_modules/face-api.js/dist/face-api.min.js"></script>
     <script src="js/index.js"></script>
 </body>
-</html>
\ No newline at end of file
+</html>
diff --git a/js/index.js b/js/index.js
index 0b638e2..cde9704 100644
--- a/js/index.js
+++ b/js/index.js
@@ -1,42 +1,65 @@
-var img = new Image()
 var canvas = document.getElementById('canvas')
 var ctx = canvas.getContext('2d')
-var fileName = ''
+var reader = newFileReader()
+
+var fileName
 
 Caman.allowRevert = false
 
+faceapi.loadSsdMobilenetv1Model('./weights')
+
 Dropzone.options.myAwesomeDropzone = {
   autoProcessQueue: false,
   autoQueue: false,
   createImageThumbnails: false,
-  init: function () {
-    this.on("addedfile", function (file) {
-      var reader = new FileReader()
-      if (file) {
-        fileName = file.name
-        reader.readAsDataURL(file)
-      }
-      reader.addEventListener('load', function () {
-        img = new Image()
-        img.src = reader.result
-        img.onload = function () {
-          canvas.width = img.width
-          canvas.height = img.height
-          $(".preview-wrapper").css("width", img.width)
-          $(".preview-wrapper").css("height", img.height)
-          ctx.drawImage(img, 0, 0, img.width, img.height)
-          $('#canvas').removeAttr('data-caman-id')
-          Caman('#canvas', function () {
-            // this.revert(false)
-            this.customscale()
-            this.render()
-            $(".preview-wrapper").removeClass("hidden")
-            $(".navbar-text").toggleClass("removed")
-          })
-        }
-      }, false)
-    })
-  }
+  init: initDropzone
+}
+
+const filters = [
+  faceReplaceFilter,
+  camanFilter
+]
+
+function initDropzone () {
+  this.on("addedfile", function (file) {
+    if (file) {
+      fileName = file.name
+      reader.readAsDataURL(file)
+    }
+  })
+}
+
+function newFileReader () {
+  r = new FileReader()
+
+  r.addEventListener('load', function () {
+    img = new Image()
+    img.src = r.result
+    img.onload = async () => filters.forEach(async filter => await filter())
+  }, false)
+
+  return r
+}
+
+async function camanFilter () {
+  canvas.width = img.width
+  canvas.height = img.height
+  $(".preview-wrapper").css("width", img.width)
+  $(".preview-wrapper").css("height", img.height)
+  ctx.drawImage(img, 0, 0)
+  $('#canvas').removeAttr('data-caman-id')
+  Caman('#canvas', function () {
+    this.customscale()
+    this.render()
+    $(".preview-wrapper").removeClass("hidden")
+    $(".navbar-text").toggleClass("removed")
+  })
+}
+
+async function faceReplaceFilter () {
+  const detections = await faceapi.detectAllFaces(canvas)
+  faces = detections.map(d => d.box)
+  faces.forEach(drawDukeDog)
 }
 
 $('#canvas').on('click', function (e) {
@@ -47,6 +70,12 @@ $('#canvas').on('click', function (e) {
   download(canvas, actualName + '-madifyed.jpg')
 })
 
+async function drawDukeDog (face) {
+  i = new Image()
+  i.onload = () => { ctx.drawImage(i, face.x, face.y, face.width, face.height) }
+  i.src = './img/duke-dog.png'
+}
+
 function download (canvas, filename) {
   var e
   var lnk = document.createElement('a')
@@ -62,4 +91,4 @@ function download (canvas, filename) {
   else if (lnk.fireEvent) {
     lnk.fireEvent('onclick')
   }
-}
\ No newline at end of file
+}
diff --git a/node_modules/@tensorflow/tfjs-core/LICENSE b/node_modules/@tensorflow/tfjs-core/LICENSE
new file mode 100644
index 0000000..d645695
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/LICENSE
@@ -0,0 +1,202 @@
+
+                                 Apache License
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+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
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+       http://www.apache.org/licenses/LICENSE-2.0
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+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
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diff --git a/node_modules/@tensorflow/tfjs-core/README.md b/node_modules/@tensorflow/tfjs-core/README.md
new file mode 100644
index 0000000..1010a37
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/README.md
@@ -0,0 +1,45 @@
+<a id="travis-badge" href="https://travis-ci.org/tensorflow/tfjs-core" alt="Build Status">
+  <img src="https://travis-ci.org/tensorflow/tfjs-core.svg?branch=master" />
+</a>
+
+# TensorFlow.js Core API
+
+A part of the TensorFlow.js ecosystem, this repo hosts `@tensorflow/tfjs-core`,
+the TensorFlow.js Core API, which provides low-level, hardware-accelerated
+linear algebra operations and an eager API for automatic differentiation.
+
+Check out [js.tensorflow.org](https://js.tensorflow.org) for more
+information about the library, tutorials and API docs.
+
+To keep track of issues we use the [tensorflow/tfjs](https://github.com/tensorflow/tfjs) Github repo.
+
+## Importing
+
+You can install TensorFlow.js via yarn or npm. We recommend using the
+[@tensorflow/tfjs](https://www.npmjs.com/package/@tensorflow/tfjs) npm package,
+which gives you both this Core API and the higher-level
+[Layers API](https://github.com/tensorflow/tfjs-layers):
+
+```js
+import * as tf from '@tensorflow/tfjs';
+// You have the Core API: tf.matMul(), tf.softmax(), ...
+// You also have Layers API: tf.model(), tf.layers.dense(), ...
+```
+
+On the other hand, if you care about the bundle size and you do not use the
+Layers API, you can import only the Core API:
+
+```js
+import * as tfc from '@tensorflow/tfjs-core';
+// You have the Core API: tfc.matMul(), tfc.softmax(), ...
+// No Layers API.
+```
+
+For info about development, check out [DEVELOPMENT.md](./DEVELOPMENT.md).
+
+## For more information
+
+- [TensorFlow.js API documentation](https://js.tensorflow.org/api/latest/)
+- [TensorFlow.js Tutorials](https://js.tensorflow.org/tutorials/)
+
+Thanks <a href="https://www.browserstack.com/">BrowserStack</a> for providing testing support.
diff --git a/node_modules/@tensorflow/tfjs-core/dist/browser_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/browser_util.d.ts
new file mode 100644
index 0000000..e971f5a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/browser_util.d.ts
@@ -0,0 +1,2 @@
+declare function nextFrame(): Promise<void>;
+export { nextFrame };
diff --git a/node_modules/@tensorflow/tfjs-core/dist/browser_util.js b/node_modules/@tensorflow/tfjs-core/dist/browser_util.js
new file mode 100644
index 0000000..0f3dcb7
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/browser_util.js
@@ -0,0 +1,10 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var delayCallback = typeof requestAnimationFrame !== 'undefined' ?
+    requestAnimationFrame :
+    setImmediate;
+function nextFrame() {
+    return new Promise(function (resolve) { return delayCallback(function () { return resolve(); }); });
+}
+exports.nextFrame = nextFrame;
+//# sourceMappingURL=browser_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/browser_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/browser_util.js.map
new file mode 100644
index 0000000..f88177d
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/browser_util.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"browser_util.js","sourceRoot":"","sources":["../src/browser_util.ts"],"names":[],"mappings":";;AAiBA,IAAM,aAAa,GAAa,OAAO,qBAAqB,KAAK,WAAW,CAAC,CAAC;IAC1E,qBAAqB,CAAC,CAAC;IACvB,YAAY,CAAC;AAWjB;IACE,OAAO,IAAI,OAAO,CAAO,UAAA,OAAO,IAAI,OAAA,aAAa,CAAC,cAAM,OAAA,OAAO,EAAE,EAAT,CAAS,CAAC,EAA9B,CAA8B,CAAC,CAAC;AACtE,CAAC;AAEO,8BAAS"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/browser_util_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/browser_util_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/browser_util_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/browser_util_test.js b/node_modules/@tensorflow/tfjs-core/dist/browser_util_test.js
new file mode 100644
index 0000000..0883abf
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/browser_util_test.js
@@ -0,0 +1,83 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+var _this = this;
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("./index");
+var jasmine_util_1 = require("./jasmine_util");
+var test_util_1 = require("./test_util");
+jasmine_util_1.describeWithFlags('nextFrame', test_util_1.ALL_ENVS, function () {
+    it('basic usage', function () { return __awaiter(_this, void 0, void 0, function () {
+        var t0, t1;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    t0 = tf.util.now();
+                    return [4, tf.nextFrame()];
+                case 1:
+                    _a.sent();
+                    t1 = tf.util.now();
+                    expect(t1 > t0);
+                    return [2];
+            }
+        });
+    }); });
+    it('does not block timers', function () { return __awaiter(_this, void 0, void 0, function () {
+        var flag, t0;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    flag = false;
+                    setTimeout(function () {
+                        flag = true;
+                    }, 50);
+                    t0 = tf.util.now();
+                    expect(flag).toBe(false);
+                    _a.label = 1;
+                case 1:
+                    if (!(tf.util.now() - t0 < 1000 && !flag)) return [3, 3];
+                    return [4, tf.nextFrame()];
+                case 2:
+                    _a.sent();
+                    return [3, 1];
+                case 3:
+                    expect(flag).toBe(true);
+                    return [2];
+            }
+        });
+    }); });
+});
+//# sourceMappingURL=browser_util_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/browser_util_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/browser_util_test.js.map
new file mode 100644
index 0000000..5d035b6
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/browser_util_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"browser_util_test.js","sourceRoot":"","sources":["../src/browser_util_test.ts"],"names":[],"mappings":";;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;AAiBA,iBA0BA;;AA1BA,4BAA8B;AAC9B,+CAAiD;AACjD,yCAAqC;AAErC,gCAAiB,CAAC,WAAW,EAAE,oBAAQ,EAAE;IACvC,EAAE,CAAC,aAAa,EAAE;;;;;oBACV,EAAE,GAAG,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE,CAAC;oBACzB,WAAM,EAAE,CAAC,SAAS,EAAE,EAAA;;oBAApB,SAAoB,CAAC;oBACf,EAAE,GAAG,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE,CAAC;oBAEzB,MAAM,CAAC,EAAE,GAAG,EAAE,CAAC,CAAC;;;;SACjB,CAAC,CAAC;IAEH,EAAE,CAAC,uBAAuB,EAAE;;;;;oBACtB,IAAI,GAAG,KAAK,CAAC;oBACjB,UAAU,CAAC;wBACT,IAAI,GAAG,IAAI,CAAC;oBACd,CAAC,EAAE,EAAE,CAAC,CAAC;oBACD,EAAE,GAAG,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE,CAAC;oBACzB,MAAM,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC;;;yBAClB,CAAA,EAAE,CAAC,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,IAAI,IAAI,CAAC,IAAI,CAAA;oBACvC,WAAM,EAAE,CAAC,SAAS,EAAE,EAAA;;oBAApB,SAAoB,CAAC;;;oBAEvB,MAAM,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;;;;SACzB,CAAC,CAAC;AACL,CAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/buffer_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/buffer_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/buffer_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/buffer_test.js b/node_modules/@tensorflow/tfjs-core/dist/buffer_test.js
new file mode 100644
index 0000000..713baec
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/buffer_test.js
@@ -0,0 +1,55 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("./index");
+var jasmine_util_1 = require("./jasmine_util");
+var test_util_1 = require("./test_util");
+jasmine_util_1.describeWithFlags('tf.buffer', test_util_1.ALL_ENVS, function () {
+    it('float32', function () {
+        var buff = tf.buffer([1, 2, 3], 'float32');
+        buff.set(1.3, 0, 0, 0);
+        buff.set(2.9, 0, 1, 0);
+        expect(buff.get(0, 0, 0)).toBeCloseTo(1.3);
+        expect(buff.get(0, 0, 1)).toBeCloseTo(0);
+        expect(buff.get(0, 0, 2)).toBeCloseTo(0);
+        expect(buff.get(0, 1, 0)).toBeCloseTo(2.9);
+        expect(buff.get(0, 1, 1)).toBeCloseTo(0);
+        expect(buff.get(0, 1, 2)).toBeCloseTo(0);
+        test_util_1.expectArraysClose(buff.toTensor(), [1.3, 0, 0, 2.9, 0, 0]);
+        test_util_1.expectArraysClose(buff.values, new Float32Array([1.3, 0, 0, 2.9, 0, 0]));
+    });
+    it('int32', function () {
+        var buff = tf.buffer([2, 3], 'int32');
+        buff.set(1.3, 0, 0);
+        buff.set(2.1, 1, 1);
+        expect(buff.get(0, 0)).toEqual(1);
+        expect(buff.get(0, 1)).toEqual(0);
+        expect(buff.get(0, 2)).toEqual(0);
+        expect(buff.get(1, 0)).toEqual(0);
+        expect(buff.get(1, 1)).toEqual(2);
+        expect(buff.get(1, 2)).toEqual(0);
+        test_util_1.expectArraysClose(buff.toTensor(), [1, 0, 0, 0, 2, 0]);
+        test_util_1.expectArraysClose(buff.values, new Int32Array([1, 0, 0, 0, 2, 0]));
+    });
+    it('bool', function () {
+        var buff = tf.buffer([4], 'bool');
+        buff.set(true, 1);
+        buff.set(true, 2);
+        expect(buff.get(0)).toBeFalsy();
+        expect(buff.get(1)).toBeTruthy();
+        expect(buff.get(2)).toBeTruthy();
+        expect(buff.get(3)).toBeFalsy();
+        test_util_1.expectArraysClose(buff.toTensor(), [0, 1, 1, 0]);
+        test_util_1.expectArraysClose(buff.values, new Uint8Array([0, 1, 1, 0]));
+    });
+    it('string', function () {
+        var buff = tf.buffer([2, 2], 'string');
+        buff.set('first', 0, 0);
+        buff.set('third', 1, 0);
+        expect(buff.get(0, 0)).toEqual('first');
+        expect(buff.get(0, 1)).toBeFalsy();
+        expect(buff.get(1, 0)).toEqual('third');
+        expect(buff.get(1, 1)).toBeFalsy();
+        test_util_1.expectArraysEqual(buff.toTensor(), ['first', null, 'third', null]);
+    });
+});
+//# sourceMappingURL=buffer_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/buffer_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/buffer_test.js.map
new file mode 100644
index 0000000..18225e0
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/buffer_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"buffer_test.js","sourceRoot":"","sources":["../src/buffer_test.ts"],"names":[],"mappings":";;AAiBA,4BAA8B;AAC9B,+CAAiD;AACjD,yCAA2E;AAE3E,gCAAiB,CAAC,WAAW,EAAE,oBAAQ,EAAE;IACvC,EAAE,CAAC,SAAS,EAAE;QACZ,IAAM,IAAI,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,SAAS,CAAC,CAAC;QAC7C,IAAI,CAAC,GAAG,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC;QACvB,IAAI,CAAC,GAAG,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC;QACvB,MAAM,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,GAAG,CAAC,CAAC;QAC3C,MAAM,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC;QACzC,MAAM,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC;QACzC,MAAM,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,GAAG,CAAC,CAAC;QAC3C,MAAM,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC;QACzC,MAAM,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC;QACzC,6BAAiB,CAAC,IAAI,CAAC,QAAQ,EAAE,EAAE,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,EAAE,GAAG,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC3D,6BAAiB,CAAC,IAAI,CAAC,MAAM,EAAE,IAAI,YAAY,CAAC,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,EAAE,GAAG,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;IAC3E,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,OAAO,EAAE;QACV,IAAM,IAAI,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;QACxC,IAAI,CAAC,GAAG,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC;QACpB,IAAI,CAAC,GAAG,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC;QACpB,MAAM,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC;QAClC,MAAM,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC;QAClC,MAAM,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC;QAClC,MAAM,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC;QAClC,MAAM,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC;QAClC,MAAM,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC;QAClC,6BAAiB,CAAC,IAAI,CAAC,QAAQ,EAAE,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACvD,6BAAiB,CAAC,IAAI,CAAC,MAAM,EAAE,IAAI,UAAU,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;IACrE,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,MAAM,EAAE;QACT,IAAM,IAAI,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,CAAC;QACpC,IAAI,CAAC,GAAG,CAAC,IAAI,EAAE,CAAC,CAAC,CAAC;QAClB,IAAI,CAAC,GAAG,CAAC,IAAI,EAAE,CAAC,CAAC,CAAC;QAClB,MAAM,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,EAAE,CAAC;QAChC,MAAM,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,EAAE,CAAC;QACjC,MAAM,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,UAAU,EAAE,CAAC;QACjC,MAAM,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,EAAE,CAAC;QAChC,6BAAiB,CAAC,IAAI,CAAC,QAAQ,EAAE,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACjD,6BAAiB,CAAC,IAAI,CAAC,MAAM,EAAE,IAAI,UAAU,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;IAC/D,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,QAAQ,EAAE;QACX,IAAM,IAAI,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,QAAQ,CAAC,CAAC;QACzC,IAAI,CAAC,GAAG,CAAC,OAAO,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC;QACxB,IAAI,CAAC,GAAG,CAAC,OAAO,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC;QACxB,MAAM,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,OAAO,CAAC,CAAC;QACxC,MAAM,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,SAAS,EAAE,CAAC;QACnC,MAAM,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,OAAO,CAAC,CAAC;QACxC,MAAM,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,SAAS,EAAE,CAAC;QACnC,6BAAiB,CAAC,IAAI,CAAC,QAAQ,EAAE,EAAE,CAAC,OAAO,EAAE,IAAI,EAAE,OAAO,EAAE,IAAI,CAAC,CAAC,CAAC;IACrE,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/canvas_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/canvas_util.d.ts
new file mode 100644
index 0000000..d0bc9f3
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/canvas_util.d.ts
@@ -0,0 +1 @@
+export declare function getWebGLContext(webGLVersion: number): WebGLRenderingContext;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/canvas_util.js b/node_modules/@tensorflow/tfjs-core/dist/canvas_util.js
new file mode 100644
index 0000000..27329a2
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/canvas_util.js
@@ -0,0 +1,50 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var contexts = {};
+var WEBGL_ATTRIBUTES = {
+    alpha: false,
+    antialias: false,
+    premultipliedAlpha: false,
+    preserveDrawingBuffer: false,
+    depth: false,
+    stencil: false,
+    failIfMajorPerformanceCaveat: true
+};
+function getWebGLContext(webGLVersion) {
+    if (!(webGLVersion in contexts)) {
+        var canvas = document.createElement('canvas');
+        canvas.addEventListener('webglcontextlost', function (ev) {
+            ev.preventDefault();
+            delete contexts[webGLVersion];
+        }, false);
+        contexts[webGLVersion] = getWebGLRenderingContext(webGLVersion);
+    }
+    var gl = contexts[webGLVersion];
+    if (gl.isContextLost()) {
+        delete contexts[webGLVersion];
+        return getWebGLContext(webGLVersion);
+    }
+    gl.disable(gl.DEPTH_TEST);
+    gl.disable(gl.STENCIL_TEST);
+    gl.disable(gl.BLEND);
+    gl.disable(gl.DITHER);
+    gl.disable(gl.POLYGON_OFFSET_FILL);
+    gl.disable(gl.SAMPLE_COVERAGE);
+    gl.enable(gl.SCISSOR_TEST);
+    gl.enable(gl.CULL_FACE);
+    gl.cullFace(gl.BACK);
+    return contexts[webGLVersion];
+}
+exports.getWebGLContext = getWebGLContext;
+function getWebGLRenderingContext(webGLVersion) {
+    if (webGLVersion !== 1 && webGLVersion !== 2) {
+        throw new Error('Cannot get WebGL rendering context, WebGL is disabled.');
+    }
+    var canvas = document.createElement('canvas');
+    if (webGLVersion === 1) {
+        return (canvas.getContext('webgl', WEBGL_ATTRIBUTES) ||
+            canvas.getContext('experimental-webgl', WEBGL_ATTRIBUTES));
+    }
+    return canvas.getContext('webgl2', WEBGL_ATTRIBUTES);
+}
+//# sourceMappingURL=canvas_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/canvas_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/canvas_util.js.map
new file mode 100644
index 0000000..7845fdc
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/canvas_util.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"canvas_util.js","sourceRoot":"","sources":["../src/canvas_util.ts"],"names":[],"mappings":";;AAiBA,IAAM,QAAQ,GAA2C,EAAE,CAAC;AAE5D,IAAM,gBAAgB,GAA2B;IAC/C,KAAK,EAAE,KAAK;IACZ,SAAS,EAAE,KAAK;IAChB,kBAAkB,EAAE,KAAK;IACzB,qBAAqB,EAAE,KAAK;IAC5B,KAAK,EAAE,KAAK;IACZ,OAAO,EAAE,KAAK;IACd,4BAA4B,EAAE,IAAI;CACnC,CAAC;AAEF,yBAAgC,YAAoB;IAClD,IAAI,CAAC,CAAC,YAAY,IAAI,QAAQ,CAAC,EAAE;QAC/B,IAAM,MAAM,GAAG,QAAQ,CAAC,aAAa,CAAC,QAAQ,CAAC,CAAC;QAChD,MAAM,CAAC,gBAAgB,CAAC,kBAAkB,EAAE,UAAA,EAAE;YAC5C,EAAE,CAAC,cAAc,EAAE,CAAC;YACpB,OAAO,QAAQ,CAAC,YAAY,CAAC,CAAC;QAChC,CAAC,EAAE,KAAK,CAAC,CAAC;QACV,QAAQ,CAAC,YAAY,CAAC,GAAG,wBAAwB,CAAC,YAAY,CAAC,CAAC;KACjE;IACD,IAAM,EAAE,GAAG,QAAQ,CAAC,YAAY,CAAC,CAAC;IAClC,IAAI,EAAE,CAAC,aAAa,EAAE,EAAE;QACtB,OAAO,QAAQ,CAAC,YAAY,CAAC,CAAC;QAC9B,OAAO,eAAe,CAAC,YAAY,CAAC,CAAC;KACtC;IAED,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,UAAU,CAAC,CAAC;IAC1B,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,YAAY,CAAC,CAAC;IAC5B,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,KAAK,CAAC,CAAC;IACrB,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,MAAM,CAAC,CAAC;IACtB,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,mBAAmB,CAAC,CAAC;IACnC,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,eAAe,CAAC,CAAC;IAC/B,EAAE,CAAC,MAAM,CAAC,EAAE,CAAC,YAAY,CAAC,CAAC;IAC3B,EAAE,CAAC,MAAM,CAAC,EAAE,CAAC,SAAS,CAAC,CAAC;IACxB,EAAE,CAAC,QAAQ,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC;IAErB,OAAO,QAAQ,CAAC,YAAY,CAAC,CAAC;AAChC,CAAC;AA1BD,0CA0BC;AAED,kCAAkC,YAAoB;IACpD,IAAI,YAAY,KAAK,CAAC,IAAI,YAAY,KAAK,CAAC,EAAE;QAC5C,MAAM,IAAI,KAAK,CAAC,wDAAwD,CAAC,CAAC;KAC3E;IAED,IAAM,MAAM,GAAG,QAAQ,CAAC,aAAa,CAAC,QAAQ,CAAC,CAAC;IAChD,IAAI,YAAY,KAAK,CAAC,EAAE;QACtB,OAAO,CAAC,MAAM,CAAC,UAAU,CAAC,OAAO,EAAE,gBAAgB,CAAC;YAC5C,MAAM,CAAC,UAAU,CAAC,oBAAoB,EAAE,gBAAgB,CAAC,CACxC,CAAC;KAC3B;IACD,OAAO,MAAM,CAAC,UAAU,CAAC,QAAQ,EAAE,gBAAgB,CAA0B,CAAC;AAChF,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/canvas_util_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/canvas_util_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/canvas_util_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/canvas_util_test.js b/node_modules/@tensorflow/tfjs-core/dist/canvas_util_test.js
new file mode 100644
index 0000000..57cbceb
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/canvas_util_test.js
@@ -0,0 +1,23 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var canvas_util_1 = require("./canvas_util");
+var environment_1 = require("./environment");
+var jasmine_util_1 = require("./jasmine_util");
+var test_util_1 = require("./test_util");
+jasmine_util_1.describeWithFlags('canvas_util', test_util_1.BROWSER_ENVS, function () {
+    it('Returns a valid canvas', function () {
+        var canvas = canvas_util_1.getWebGLContext(environment_1.ENV.get('WEBGL_VERSION')).canvas;
+        expect(canvas instanceof HTMLCanvasElement).toBe(true);
+    });
+    it('Returns a valid gl context', function () {
+        var gl = canvas_util_1.getWebGLContext(environment_1.ENV.get('WEBGL_VERSION'));
+        expect(gl.isContextLost()).toBe(false);
+    });
+});
+jasmine_util_1.describeWithFlags('canvas_util webgl2', { WEBGL_VERSION: 2 }, function () {
+    it('is ok when the user requests webgl 1 canvas', function () {
+        var canvas = canvas_util_1.getWebGLContext(1).canvas;
+        expect(canvas instanceof HTMLCanvasElement).toBe(true);
+    });
+});
+//# sourceMappingURL=canvas_util_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/canvas_util_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/canvas_util_test.js.map
new file mode 100644
index 0000000..2dfbe68
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/canvas_util_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"canvas_util_test.js","sourceRoot":"","sources":["../src/canvas_util_test.ts"],"names":[],"mappings":";;AAiBA,6CAA8C;AAC9C,6CAAkC;AAClC,+CAAiD;AACjD,yCAAyC;AAEzC,gCAAiB,CAAC,aAAa,EAAE,wBAAY,EAAE;IAC7C,EAAE,CAAC,wBAAwB,EAAE;QAC3B,IAAM,MAAM,GAAG,6BAAe,CAAC,iBAAG,CAAC,GAAG,CAAC,eAAe,CAAC,CAAC,CAAC,MAAM,CAAC;QAChE,MAAM,CAAC,MAAM,YAAY,iBAAiB,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;IACzD,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,4BAA4B,EAAE;QAC/B,IAAM,EAAE,GAAG,6BAAe,CAAC,iBAAG,CAAC,GAAG,CAAC,eAAe,CAAC,CAAC,CAAC;QACrD,MAAM,CAAC,EAAE,CAAC,aAAa,EAAE,CAAC,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC;IACzC,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC;AAEH,gCAAiB,CAAC,oBAAoB,EAAE,EAAC,aAAa,EAAE,CAAC,EAAC,EAAE;IAC1D,EAAE,CAAC,6CAA6C,EAAE;QAChD,IAAM,MAAM,GAAG,6BAAe,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC;QACzC,MAAM,CAAC,MAAM,YAAY,iBAAiB,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;IACzD,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/debug_mode_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/debug_mode_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/debug_mode_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/debug_mode_test.js b/node_modules/@tensorflow/tfjs-core/dist/debug_mode_test.js
new file mode 100644
index 0000000..04dd2f4
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/debug_mode_test.js
@@ -0,0 +1,58 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("./index");
+var jasmine_util_1 = require("./jasmine_util");
+var tensor_util_env_1 = require("./tensor_util_env");
+var test_util_1 = require("./test_util");
+jasmine_util_1.describeWithFlags('debug on', test_util_1.ALL_ENVS, function () {
+    beforeAll(function () {
+        tf.ENV.set('DEBUG', true);
+    });
+    afterAll(function () {
+        tf.ENV.set('DEBUG', false);
+    });
+    it('debug mode does not error when no nans', function () {
+        var a = tf.tensor1d([2, -1, 0, 3]);
+        var res = tf.relu(a);
+        test_util_1.expectArraysClose(res, [2, 0, 0, 3]);
+    });
+    it('debug mode errors when there are nans, float32', function () {
+        var a = tf.tensor1d([2, NaN]);
+        var f = function () { return tf.relu(a); };
+        expect(f).toThrowError();
+    });
+    it('debug mode errors when nans in tensor construction, int32', function () {
+        var a = function () { return tf.tensor1d([2, NaN], 'int32'); };
+        expect(a).toThrowError();
+    });
+    it('debug mode errors when nans in oneHot op (tensorlike), int32', function () {
+        var f = function () { return tf.oneHot([2, NaN], 3); };
+        expect(f).toThrowError();
+    });
+    it('debug mode errors when nan in convertToTensor, int32', function () {
+        var a = function () { return tensor_util_env_1.convertToTensor(NaN, 'a', 'test', 'int32'); };
+        expect(a).toThrowError();
+    });
+    it('debug mode errors when nan in convertToTensor array input, int32', function () {
+        var a = function () { return tensor_util_env_1.convertToTensor([NaN], 'a', 'test', 'int32'); };
+        expect(a).toThrowError();
+    });
+    it('A x B', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
+        var c = tf.matMul(a, b);
+        expect(c.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(c, [0, 8, -3, 20]);
+    });
+});
+jasmine_util_1.describeWithFlags('debug off', test_util_1.ALL_ENVS, function () {
+    beforeAll(function () {
+        tf.ENV.set('DEBUG', false);
+    });
+    it('no errors where there are nans, and debug mode is disabled', function () {
+        var a = tf.tensor1d([2, NaN]);
+        var res = tf.relu(a);
+        test_util_1.expectArraysClose(res, [2, NaN]);
+    });
+});
+//# sourceMappingURL=debug_mode_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/debug_mode_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/debug_mode_test.js.map
new file mode 100644
index 0000000..b7f084b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/debug_mode_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"debug_mode_test.js","sourceRoot":"","sources":["../src/debug_mode_test.ts"],"names":[],"mappings":";;AAiBA,4BAA8B;AAC9B,+CAAiD;AACjD,qDAAkD;AAClD,yCAAwD;AAExD,gCAAiB,CAAC,UAAU,EAAE,oBAAQ,EAAE;IACtC,SAAS,CAAC;QACR,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,OAAO,EAAE,IAAI,CAAC,CAAC;IAC5B,CAAC,CAAC,CAAC;IAEH,QAAQ,CAAC;QACP,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,OAAO,EAAE,KAAK,CAAC,CAAC;IAC7B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,wCAAwC,EAAE;QAC3C,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACrC,IAAM,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;QACvB,6BAAiB,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IACvC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,gDAAgD,EAAE;QACnD,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,CAAC;QAChC,IAAM,CAAC,GAAG,cAAM,OAAA,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC,EAAV,CAAU,CAAC;QAC3B,MAAM,CAAC,CAAC,CAAC,CAAC,YAAY,EAAE,CAAC;IAC3B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,2DAA2D,EAAE;QAC9D,IAAM,CAAC,GAAG,cAAM,OAAA,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,EAAE,OAAO,CAAC,EAA9B,CAA8B,CAAC;QAC/C,MAAM,CAAC,CAAC,CAAC,CAAC,YAAY,EAAE,CAAC;IAC3B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,8DAA8D,EAAE;QACjE,IAAM,CAAC,GAAG,cAAM,OAAA,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,EAAE,CAAC,CAAC,EAAtB,CAAsB,CAAC;QACvC,MAAM,CAAC,CAAC,CAAC,CAAC,YAAY,EAAE,CAAC;IAC3B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,sDAAsD,EAAE;QACzD,IAAM,CAAC,GAAG,cAAM,OAAA,iCAAe,CAAC,GAAG,EAAE,GAAG,EAAE,MAAM,EAAE,OAAO,CAAC,EAA1C,CAA0C,CAAC;QAC3D,MAAM,CAAC,CAAC,CAAC,CAAC,YAAY,EAAE,CAAC;IAC3B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,kEAAkE,EAAE;QACrE,IAAM,CAAC,GAAG,cAAM,OAAA,iCAAe,CAAC,CAAC,GAAG,CAAC,EAAE,GAAG,EAAE,MAAM,EAAE,OAAO,CAAC,EAA5C,CAA4C,CAAC;QAC7D,MAAM,CAAC,CAAC,CAAC,CAAC,YAAY,EAAE,CAAC;IAC3B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,OAAO,EAAE;QACV,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAClD,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAEnD,IAAM,CAAC,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QAE1B,MAAM,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAChC,6BAAiB,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC;IACvC,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC;AAEH,gCAAiB,CAAC,WAAW,EAAE,oBAAQ,EAAE;IACvC,SAAS,CAAC;QACR,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,OAAO,EAAE,KAAK,CAAC,CAAC;IAC7B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,4DAA4D,EAAE;QAC/D,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,CAAC;QAChC,IAAM,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;QACvB,6BAAiB,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,CAAC;IACnC,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/device_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/device_util.d.ts
new file mode 100644
index 0000000..58c9046
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/device_util.d.ts
@@ -0,0 +1 @@
+export declare function isMobile(): boolean;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/device_util.js b/node_modules/@tensorflow/tfjs-core/dist/device_util.js
new file mode 100644
index 0000000..6bf155e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/device_util.js
@@ -0,0 +1,11 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+function isMobile() {
+    var a = navigator.userAgent || navigator.vendor || window.opera;
+    return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i
+        .test(a) ||
+        /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i
+            .test(a.substr(0, 4));
+}
+exports.isMobile = isMobile;
+//# sourceMappingURL=device_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/device_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/device_util.js.map
new file mode 100644
index 0000000..b6db30c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/device_util.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"device_util.js","sourceRoot":"","sources":["../src/device_util.ts"],"names":[],"mappings":";;AAiBA;IAEE,IAAM,CAAC,GAAG,SAAS,CAAC,SAAS,IAAI,SAAS,CAAC,MAAM,IAAK,MAAc,CAAC,KAAK,CAAC;IAE3E,OAAO,0TAA0T;SACrT,IAAI,CAAC,CAAC,CAAC;QAEf,ykDAAykD;aACpkD,IAAI,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;AAChC,CAAC;AATD,4BASC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/engine.d.ts b/node_modules/@tensorflow/tfjs-core/dist/engine.d.ts
new file mode 100644
index 0000000..0351ded
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/engine.d.ts
@@ -0,0 +1,100 @@
+import { BackendTimingInfo, DataMover, KernelBackend } from './kernels/backend';
+import { DataId, Tensor, Tensor3D, Variable } from './tensor';
+import { NamedTensorMap, NamedVariableMap, TensorContainer } from './tensor_types';
+import { DataValues } from './types';
+export declare type ForwardFunc<T> = (backend: KernelBackend, save?: <S extends Tensor>(tensor: S) => S) => T;
+export declare type CustomGradientFunc<T extends Tensor> = (...args: Tensor[]) => {
+    value: T;
+    gradFunc: (dy: T) => Tensor | Tensor[];
+};
+export declare type MemoryInfo = {
+    numTensors: number;
+    numDataBuffers: number;
+    numBytes: number;
+    unreliable?: boolean;
+    reasons: string[];
+};
+declare type KernelProfile = {
+    name: string;
+    bytesAdded: number;
+    totalBytesSnapshot: number;
+    tensorsAdded: number;
+    totalTensorsSnapshot: number;
+    inputShapes: number[][];
+    outputShape: number[] | number[][];
+};
+export declare type ProfileInfo = {
+    newBytes: number;
+    newTensors: number;
+    peakBytes: number;
+    kernels: KernelProfile[];
+    result: TensorContainer;
+};
+export interface TimingInfo extends BackendTimingInfo {
+    wallMs: number;
+}
+export declare type ScopeFn<T extends TensorContainer> = () => T;
+export interface TensorManager {
+    registerTensor(a: Tensor): void;
+    registerVariable(v: Variable): void;
+    disposeTensor(a: Tensor): void;
+    memory(): {
+        numDataBuffers: number;
+        numBytes: number;
+    };
+}
+export declare class Engine implements TensorManager, DataMover {
+    backend: KernelBackend;
+    safeMode: boolean;
+    private debugMode;
+    registeredVariables: NamedVariableMap;
+    private nextTapeNodeId;
+    private numBytes;
+    private numTensors;
+    private numStringTensors;
+    private numDataBuffers;
+    private profiling;
+    private activeProfile;
+    private activeTape;
+    private gradientScopeCount;
+    private customGradientDepth;
+    private activeScope;
+    private scopeStack;
+    private keepTensors;
+    private profiler;
+    private tensorInfo;
+    constructor(backend: KernelBackend, safeMode: boolean, debugMode: () => boolean);
+    moveData(dataId: DataId): void;
+    tidy<T extends TensorContainer>(nameOrFn: string | ScopeFn<T>, fn?: ScopeFn<T>, gradMode?: boolean): T;
+    private scopedRun;
+    private static nextTensorId;
+    nextTensorId(): number;
+    private static nextVariableId;
+    nextVariableId(): number;
+    runKernel<T extends Tensor | Tensor[], I extends NamedTensorMap>(forwardFunc: ForwardFunc<T>, inputs: I, backwardsFunc?: (dy: T, saved: Tensor[]) => {
+        [P in keyof I]: () => I[P];
+    }): T;
+    registerTensor(a: Tensor | Variable): void;
+    registerVariable(v: Variable): void;
+    disposeTensor(a: Tensor): void;
+    disposeVariables(): void;
+    memory(): MemoryInfo;
+    profile(query: () => TensorContainer): Promise<ProfileInfo>;
+    private shouldRecord;
+    private addTapeNode;
+    keep<T extends Tensor>(result: T): T;
+    startScope(name?: string, gradientsMode?: boolean): void;
+    endScope(result?: TensorContainer, gradientsMode?: boolean): void;
+    gradients<T extends Tensor>(f: () => T, xs: Tensor[], dy?: T, allowNoGradients?: boolean): {
+        value: T;
+        grads: Tensor[];
+    };
+    customGrad<T extends Tensor>(f: CustomGradientFunc<T>): (...args: Tensor[]) => T;
+    write(dataId: DataId, values: DataValues): void;
+    readSync(dataId: DataId): DataValues;
+    read(dataId: DataId): Promise<DataValues>;
+    fromPixels(pixels: ImageData | HTMLImageElement | HTMLCanvasElement | HTMLVideoElement, numChannels: number): Tensor3D;
+    time(query: () => void): Promise<TimingInfo>;
+    private track;
+}
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/engine.js b/node_modules/@tensorflow/tfjs-core/dist/engine.js
new file mode 100644
index 0000000..0e38b41
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/engine.js
@@ -0,0 +1,468 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+Object.defineProperty(exports, "__esModule", { value: true });
+var profiler_1 = require("./profiler");
+var tape_1 = require("./tape");
+var tensor_1 = require("./tensor");
+var tensor_util_1 = require("./tensor_util");
+var util = require("./util");
+var util_1 = require("./util");
+var Engine = (function () {
+    function Engine(backend, safeMode, debugMode) {
+        this.backend = backend;
+        this.safeMode = safeMode;
+        this.debugMode = debugMode;
+        this.registeredVariables = {};
+        this.nextTapeNodeId = 0;
+        this.numBytes = 0;
+        this.numTensors = 0;
+        this.numStringTensors = 0;
+        this.numDataBuffers = 0;
+        this.profiling = false;
+        this.gradientScopeCount = 0;
+        this.customGradientDepth = 0;
+        this.scopeStack = [];
+        this.keepTensors = new Set();
+        this.tensorInfo = new WeakMap();
+        this.profiler = new profiler_1.Profiler(backend);
+        this.activeProfile =
+            { newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null };
+    }
+    Engine.prototype.moveData = function (dataId) {
+        this.write(dataId, this.readSync(dataId));
+    };
+    Engine.prototype.tidy = function (nameOrFn, fn, gradMode) {
+        var _this = this;
+        if (gradMode === void 0) { gradMode = false; }
+        var name = null;
+        if (fn == null) {
+            if (typeof nameOrFn !== 'function') {
+                throw new Error('Please provide a function to tidy()');
+            }
+            fn = nameOrFn;
+        }
+        else {
+            if (typeof nameOrFn !== 'string' && !(nameOrFn instanceof String)) {
+                throw new Error('When calling with two arguments, the first argument ' +
+                    'to tidy() must be a string');
+            }
+            if (typeof fn !== 'function') {
+                throw new Error('When calling with two arguments, the 2nd argument ' +
+                    'to tidy() must be a function');
+            }
+            name = nameOrFn;
+        }
+        var result;
+        return this.scopedRun(function () { return _this.startScope(name, gradMode); }, function () { return _this.endScope(result, gradMode); }, function () {
+            result = fn();
+            if (result instanceof Promise) {
+                console.error('Cannot return a Promise inside of tidy.');
+            }
+            return result;
+        });
+    };
+    Engine.prototype.scopedRun = function (start, end, f) {
+        start();
+        try {
+            var res = f();
+            end();
+            return res;
+        }
+        catch (ex) {
+            end();
+            throw ex;
+        }
+    };
+    Engine.prototype.nextTensorId = function () {
+        return Engine.nextTensorId++;
+    };
+    Engine.prototype.nextVariableId = function () {
+        return Engine.nextVariableId++;
+    };
+    Engine.prototype.runKernel = function (forwardFunc, inputs, backwardsFunc) {
+        var _this = this;
+        var result;
+        var saved = [];
+        var saveFunc = function (x) {
+            saved.push(x);
+            return x;
+        };
+        var scopeName = this.activeScope.name;
+        var startingBytecount = this.numBytes;
+        var startingNumTensors = this.numTensors;
+        this.scopedRun(function () { return _this.customGradientDepth++; }, function () { return _this.customGradientDepth--; }, function () {
+            if (!_this.debugMode()) {
+                result = forwardFunc(_this.backend, saveFunc);
+            }
+            else {
+                result = _this.profiler.profileKernel(scopeName, function () { return forwardFunc(_this.backend, saveFunc); });
+            }
+        });
+        if (this.shouldRecord()) {
+            var tapeNode = {
+                id: this.nextTapeNodeId++,
+                name: scopeName,
+                inputs: inputs,
+                outputs: Array.isArray(result) ? result : [result]
+            };
+            if (backwardsFunc != null) {
+                tapeNode.gradient =
+                    (function (dy) { return backwardsFunc(dy, saved); });
+            }
+            this.activeTape.push(tapeNode);
+        }
+        if (this.profiling) {
+            this.activeProfile.kernels.push({
+                name: scopeName,
+                bytesAdded: this.numBytes - startingBytecount,
+                totalBytesSnapshot: this.numBytes,
+                tensorsAdded: this.numTensors - startingNumTensors,
+                totalTensorsSnapshot: this.numTensors,
+                inputShapes: Object.keys(inputs).map(function (key) { return inputs[key].shape; }),
+                outputShape: Array.isArray(result) ?
+                    result.map(function (item) { return item.shape; }) :
+                    result.shape
+            });
+        }
+        return result;
+    };
+    Engine.prototype.registerTensor = function (a) {
+        var refCount = this.tensorInfo.has(a.dataId) ?
+            this.tensorInfo.get(a.dataId).refCount :
+            0;
+        this.numTensors++;
+        if (a.dtype === 'string') {
+            this.numStringTensors++;
+        }
+        if (refCount === 0) {
+            this.numDataBuffers++;
+            var bytes = 0;
+            if (a.dtype !== 'complex64' && a.dtype !== 'string') {
+                bytes = util.sizeFromShape(a.shape) * util.bytesPerElement(a.dtype);
+            }
+            this.tensorInfo.set(a.dataId, {
+                backend: this.backend,
+                dtype: a.dtype,
+                shape: a.shape,
+                bytes: bytes,
+                refCount: 0
+            });
+            this.numBytes += bytes;
+            this.backend.register(a.dataId, a.shape, a.dtype);
+        }
+        this.tensorInfo.get(a.dataId).refCount++;
+        if (!(a instanceof tensor_1.Variable)) {
+            this.track(a);
+        }
+    };
+    Engine.prototype.registerVariable = function (v) {
+        if (this.registeredVariables[v.name] != null) {
+            throw new Error("Variable with name " + v.name + " was already registered");
+        }
+        this.registeredVariables[v.name] = v;
+    };
+    Engine.prototype.disposeTensor = function (a) {
+        if (!this.tensorInfo.has(a.dataId)) {
+            return;
+        }
+        if (this.keepTensors.has(a.id)) {
+            this.keepTensors.delete(a.id);
+        }
+        this.numTensors--;
+        if (a.dtype === 'string') {
+            this.numStringTensors--;
+        }
+        var info = this.tensorInfo.get(a.dataId);
+        var refCount = info.refCount;
+        if (refCount <= 1) {
+            if (a.dtype !== 'complex64') {
+                this.numBytes -= info.bytes;
+            }
+            this.numDataBuffers--;
+            info.backend.disposeData(a.dataId);
+            this.tensorInfo.delete(a.dataId);
+        }
+        else {
+            this.tensorInfo.get(a.dataId).refCount--;
+        }
+    };
+    Engine.prototype.disposeVariables = function () {
+        for (var varName in this.registeredVariables) {
+            var v = this.registeredVariables[varName];
+            this.disposeTensor(v);
+            delete this.registeredVariables[varName];
+        }
+    };
+    Engine.prototype.memory = function () {
+        var info = this.backend.memory();
+        info.numTensors = this.numTensors;
+        info.numDataBuffers = this.numDataBuffers;
+        info.numBytes = this.numBytes;
+        if (this.numStringTensors > 0) {
+            info.unreliable = true;
+            if (info.reasons == null) {
+                info.reasons = [];
+            }
+            info.reasons.push('Memory usage by string tensors is approximate ' +
+                '(2 bytes per character)');
+        }
+        return info;
+    };
+    Engine.prototype.profile = function (query) {
+        return __awaiter(this, void 0, void 0, function () {
+            var startBytes, startNumTensors;
+            return __generator(this, function (_a) {
+                this.profiling = true;
+                startBytes = this.numBytes;
+                startNumTensors = this.numTensors;
+                this.activeProfile.kernels = [];
+                this.activeProfile.result = query();
+                this.profiling = false;
+                this.activeProfile.peakBytes = Math.max.apply(Math, this.activeProfile.kernels.map(function (d) { return d.totalBytesSnapshot; }));
+                this.activeProfile.newBytes = this.numBytes - startBytes;
+                this.activeProfile.newTensors = this.numTensors - startNumTensors;
+                return [2, this.activeProfile];
+            });
+        });
+    };
+    Engine.prototype.shouldRecord = function () {
+        return this.activeTape != null && this.customGradientDepth === 0;
+    };
+    Engine.prototype.addTapeNode = function (inputs, result, gradientsFunc) {
+        var inputsMap = {};
+        inputs.forEach(function (input, idx) {
+            inputsMap[idx] = input;
+        });
+        var gradient = function (dy) {
+            var res = gradientsFunc(dy);
+            var resMap = {};
+            res.forEach(function (r, idx) {
+                resMap[idx] = function () { return r; };
+            });
+            return resMap;
+        };
+        var tapeNode = {
+            id: this.nextTapeNodeId++,
+            name: this.activeScope.name,
+            inputs: inputsMap,
+            outputs: [result],
+            gradient: gradient
+        };
+        this.activeTape.push(tapeNode);
+    };
+    Engine.prototype.keep = function (result) {
+        if (this.scopeStack.length === 1 && this.safeMode) {
+            throw new Error('Safe mode is ON. Enclose all tensor operations inside tf.tidy(): ' +
+                'tf.tidy(() => {...}) to avoid memory leaks.');
+        }
+        this.keepTensors.add(result.id);
+        return result;
+    };
+    Engine.prototype.startScope = function (name, gradientsMode) {
+        if (gradientsMode === void 0) { gradientsMode = false; }
+        if (gradientsMode && this.gradientScopeCount === 0) {
+            this.activeTape = [];
+        }
+        if (gradientsMode) {
+            this.gradientScopeCount++;
+        }
+        var scopeInfo = { track: [], name: 'unnamed scope' };
+        if (name) {
+            scopeInfo.name = name;
+        }
+        this.scopeStack.push(scopeInfo);
+        this.activeScope = scopeInfo;
+    };
+    Engine.prototype.endScope = function (result, gradientsMode) {
+        var _this = this;
+        if (gradientsMode === void 0) { gradientsMode = false; }
+        if (gradientsMode) {
+            this.gradientScopeCount--;
+            if (this.gradientScopeCount === 0) {
+                this.activeTape = null;
+            }
+        }
+        var tensorsToKeep = new Set(this.keepTensors);
+        var tensorsToTrackInParent = tensor_util_1.getTensorsInContainer(result);
+        tensorsToTrackInParent.forEach(function (tensor) { return tensorsToKeep.add(tensor.id); });
+        for (var i = 0; i < this.activeScope.track.length; i++) {
+            var tensor = this.activeScope.track[i];
+            if (tensorsToKeep.has(tensor.id)) {
+                continue;
+            }
+            if (this.activeTape != null) {
+                tensorsToTrackInParent.push(tensor);
+            }
+            else {
+                tensor.dispose();
+            }
+        }
+        var oldScope = this.scopeStack.pop();
+        this.activeScope = this.scopeStack.length === 0 ?
+            null :
+            this.scopeStack[this.scopeStack.length - 1];
+        tensorsToTrackInParent.forEach(function (tensor) {
+            if (!_this.keepTensors.has(tensor.id) &&
+                tensor_util_1.isTensorInList(tensor, oldScope.track)) {
+                _this.track(tensor);
+            }
+        });
+    };
+    Engine.prototype.gradients = function (f, xs, dy, allowNoGradients) {
+        var _this = this;
+        if (allowNoGradients === void 0) { allowNoGradients = false; }
+        util.assert(xs.length > 0, 'gradients() received an empty list of xs.');
+        if (dy != null && dy.dtype !== 'float32') {
+            throw new Error("dy must have 'float32' dtype, but has '" + dy.dtype + "'");
+        }
+        return this.tidy('gradients', function () {
+            var y = f();
+            util.assert(y instanceof tensor_1.Tensor, 'The result y returned by f() must be a tensor.');
+            var filteredTape = tape_1.getFilteredNodesXToY(_this.activeTape, xs, y);
+            if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {
+                throw new Error('Cannot compute gradient of y=f(x) with respect to x. Make sure ' +
+                    'that the f you passed encloses all operations that lead from x ' +
+                    'to y.');
+            }
+            var accumulatedGradientMap = {};
+            accumulatedGradientMap[y.id] = (dy == null) ? ones(y.shape) : dy;
+            tape_1.backpropagateGradients(accumulatedGradientMap, filteredTape);
+            var grads = xs.map(function (x) { return accumulatedGradientMap[x.id]; });
+            return { value: y, grads: grads };
+        }, true);
+    };
+    Engine.prototype.customGrad = function (f) {
+        var _this = this;
+        util.assert(util.isFunction(f), 'The f passed in customGrad(f) must be a function.');
+        return function () {
+            var inputs = [];
+            for (var _i = 0; _i < arguments.length; _i++) {
+                inputs[_i] = arguments[_i];
+            }
+            util.assert(inputs.every(function (t) { return t instanceof tensor_1.Tensor; }), 'The args passed in customGrad(f)(x1, x2,...) must all be tensors');
+            var gradientsFunc;
+            var result;
+            _this.scopedRun(function () { return _this.customGradientDepth++; }, function () { return _this.customGradientDepth--; }, function () {
+                var gradientsMode = true;
+                result = _this.tidy(f.name, function () {
+                    var _a = f.apply(void 0, inputs), value = _a.value, gradFunc = _a.gradFunc;
+                    util.assert(value instanceof tensor_1.Tensor, 'The function f passed in customGrad(f) must return an ' +
+                        'object where `obj.value` is a tensor');
+                    util.assert(util.isFunction(gradFunc), 'The function f passed in customGrad(f) must return an ' +
+                        'object where `obj.gradFunc` is a function.');
+                    gradientsFunc = gradFunc;
+                    return value;
+                }, gradientsMode);
+            });
+            if (_this.shouldRecord()) {
+                var gradFunc = function (dy) {
+                    var res = gradientsFunc(dy);
+                    var grads = Array.isArray(res) ? res : [res];
+                    util.assert(grads.length === inputs.length, 'The function f passed in customGrad(f) must return an object ' +
+                        'where `obj.gradFunc` is a function that returns the same ' +
+                        'number of tensors as inputs passed to f(...).');
+                    util.assert(grads.every(function (t) { return t instanceof tensor_1.Tensor; }), 'The function f passed in customGrad(f) must return an object ' +
+                        'where `obj.gradFunc` is a function that returns a list of ' +
+                        'only tensors.');
+                    return grads;
+                };
+                _this.addTapeNode(inputs, result, gradFunc);
+            }
+            return result;
+        };
+    };
+    Engine.prototype.write = function (dataId, values) {
+        var info = this.tensorInfo.get(dataId);
+        if (info.dtype === 'string') {
+            var newBytes = util_1.bytesFromStringArray(values);
+            this.numBytes += newBytes - info.bytes;
+            info.bytes = newBytes;
+        }
+        if (this.backend !== info.backend) {
+            info.backend.disposeData(dataId);
+            info.backend = this.backend;
+            this.backend.register(dataId, info.shape, info.dtype);
+        }
+        this.backend.write(dataId, values);
+    };
+    Engine.prototype.readSync = function (dataId) {
+        var info = this.tensorInfo.get(dataId);
+        return info.backend.readSync(dataId);
+    };
+    Engine.prototype.read = function (dataId) {
+        var info = this.tensorInfo.get(dataId);
+        return info.backend.read(dataId);
+    };
+    Engine.prototype.fromPixels = function (pixels, numChannels) {
+        return this.backend.fromPixels(pixels, numChannels);
+    };
+    Engine.prototype.time = function (query) {
+        return __awaiter(this, void 0, void 0, function () {
+            var start, timingInfo;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        start = util_1.now();
+                        return [4, this.backend.time(query)];
+                    case 1:
+                        timingInfo = _a.sent();
+                        timingInfo.wallMs = util_1.now() - start;
+                        return [2, timingInfo];
+                }
+            });
+        });
+    };
+    Engine.prototype.track = function (result) {
+        if (this.scopeStack.length === 1 && this.safeMode) {
+            throw new Error('Safe mode is ON. Enclose all tensor operations inside tf.tidy(): ' +
+                'tf.tidy(() => {op();...}); to avoid memory leaks.');
+        }
+        if (this.activeScope != null) {
+            this.activeScope.track.push(result);
+        }
+        return result;
+    };
+    Engine.nextTensorId = 0;
+    Engine.nextVariableId = 0;
+    return Engine;
+}());
+exports.Engine = Engine;
+function ones(shape) {
+    var values = util_1.makeOnesTypedArray(util_1.sizeFromShape(shape), 'float32');
+    return tensor_1.Tensor.make(shape, { values: values });
+}
+//# sourceMappingURL=engine.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/engine.js.map b/node_modules/@tensorflow/tfjs-core/dist/engine.js.map
new file mode 100644
index 0000000..9e432d5
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/engine.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/engine_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/engine_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/engine_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/engine_test.js b/node_modules/@tensorflow/tfjs-core/dist/engine_test.js
new file mode 100644
index 0000000..f8df7f1
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/engine_test.js
@@ -0,0 +1,547 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+var _this = this;
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("./index");
+var jasmine_util_1 = require("./jasmine_util");
+var backend_cpu_1 = require("./kernels/backend_cpu");
+var backend_webgl_1 = require("./kernels/backend_webgl");
+var test_util_1 = require("./test_util");
+jasmine_util_1.describeWithFlags('fromPixels + regular math op', test_util_1.WEBGL_ENVS, function () {
+    it('debug mode does not error when no nans', function () {
+        var pixels = new ImageData(2, 2);
+        for (var i = 0; i < 8; i++) {
+            pixels.data[i] = 100;
+        }
+        for (var i = 8; i < 16; i++) {
+            pixels.data[i] = 250;
+        }
+        var a = tf.fromPixels(pixels, 4);
+        var b = tf.scalar(20, 'int32');
+        var res = tf.add(a, b);
+        test_util_1.expectArraysEqual(res, [
+            120, 120, 120, 120, 120, 120, 120, 120, 270, 270, 270, 270, 270, 270, 270,
+            270
+        ]);
+    });
+});
+jasmine_util_1.describeWithFlags('gradients', test_util_1.ALL_ENVS, function () {
+    it('matmul + relu', function () {
+        var a = tf.tensor2d([-1, 2, -3, 10, -20, 30], [2, 3]);
+        var b = tf.tensor2d([2, -3, 4, -1, 2, -3], [3, 2]);
+        var _a = tf.grads(function (a, b) {
+            var m = tf.matMul(a, b);
+            var y = tf.relu(m);
+            return tf.sum(y);
+        })([a, b]), da = _a[0], db = _a[1];
+        var dedm = tf.step(tf.matMul(a, b));
+        expect(da.shape).toEqual(a.shape);
+        var transposeA = false;
+        var transposeB = true;
+        test_util_1.expectArraysClose(da, tf.matMul(dedm, b, transposeA, transposeB));
+        expect(db.shape).toEqual(b.shape);
+        transposeA = true;
+        transposeB = false;
+        test_util_1.expectArraysClose(db, tf.matMul(a, dedm, transposeA, transposeB));
+    });
+    it('grad(f)', function () {
+        var grad = tf.grad(function (x) { return x.square(); });
+        var result = grad(tf.tensor1d([.1, .2]));
+        test_util_1.expectArraysClose(result, [.2, .4]);
+    });
+    it('calling grad(f) twice works', function () {
+        var grad = tf.grad(function (x) { return x.square(); });
+        var result = grad(tf.tensor1d([.1, .2]));
+        var result2 = grad(tf.tensor1d([.1, .4]));
+        test_util_1.expectArraysClose(result, [.2, .4]);
+        test_util_1.expectArraysClose(result2, [.2, .8]);
+    });
+    it('grads(f)', function () {
+        var grads = tf.grads(function (x) { return x.square(); });
+        var result = grads([tf.tensor1d([.1, .2])]);
+        test_util_1.expectArraysClose(result[0], [.2, .4]);
+    });
+    it('calling grads(f) twice works', function () {
+        var grads = tf.grads(function (x) { return x.square(); });
+        var result = grads([tf.tensor1d([.1, .2])]);
+        var result2 = grads([tf.tensor1d([.1, .4])]);
+        test_util_1.expectArraysClose(result[0], [.2, .4]);
+        test_util_1.expectArraysClose(result2[0], [.2, .8]);
+    });
+    it('works with reshape', function () {
+        var a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var exponent = tf.tensor1d([2, 2, 2, 2], 'int32');
+        var da = tf.grad(function (a) {
+            var b = a.flatten();
+            var m = tf.pow(b, exponent);
+            return tf.sum(m);
+        })(a);
+        expect(da.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(da, [2, 4, 6, 8]);
+    });
+    it('reshape outside tf.grads() throws error', function () {
+        var a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var b = a.flatten();
+        var exponent = tf.tensor1d([2, 2, 2, 2], 'int32');
+        var f = function () {
+            tf.grads(function (a, b) {
+                var m = tf.pow(b, exponent);
+                return tf.sum(m);
+            })([a, b]);
+        };
+        expect(f).toThrowError();
+    });
+    it('does not error if irrelevant (pruned) ops are missing grads', function () {
+        var a = tf.tensor1d([true, true], 'bool');
+        var b = tf.tensor1d([false, true], 'bool');
+        var da = tf.grad(function (a) {
+            a.logicalAnd(b);
+            return a.sum();
+        })(a);
+        test_util_1.expectArraysClose(da, [1, 1]);
+    });
+    it('errors if relevant ops are missing grads', function () {
+        var a = tf.tensor1d([true, true], 'bool');
+        var b = tf.tensor1d([false, true], 'bool');
+        var dfda = tf.grad(function (a) {
+            return a.logicalAnd(b);
+        });
+        expect(function () { return dfda(a); }).toThrowError();
+    });
+    it('works with asType', function () {
+        var a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'int32');
+        var exponent = tf.tensor2d([2, 2, 2, 2], [2, 2], 'int32');
+        var da = tf.grad(function (a) {
+            var b = a.toFloat();
+            var m = tf.pow(b, exponent);
+            return tf.sum(m);
+        })(a);
+        expect(da.shape).toEqual([2, 2]);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [2, 4, 6, 8]);
+    });
+    it('asType outside of tf.grads() throws error', function () {
+        var a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'int32');
+        var b = a.toFloat();
+        var exponent = tf.tensor2d([2, 2, 2, 2], [2, 2], 'int32');
+        var f = function () {
+            tf.grad(function (a) {
+                var m = tf.pow(b, exponent);
+                return tf.sum(m);
+            })(a);
+        };
+        expect(f).toThrowError();
+    });
+});
+jasmine_util_1.describeWithFlags('valueAndGradients', test_util_1.ALL_ENVS, function () {
+    it('matmul + relu', function () {
+        var a = tf.tensor2d([-1, 2, -3, 10, -20, 30], [2, 3]);
+        var b = tf.tensor2d([2, -3, 4, -1, 2, -3], [3, 2]);
+        var _a = tf.valueAndGrads(function (a, b) {
+            var m = tf.matMul(a, b);
+            var y = tf.relu(m);
+            return tf.sum(y);
+        })([a, b]), value = _a.value, grads = _a.grads;
+        test_util_1.expectNumbersClose(value.get(), 10);
+        var dedm = tf.step(tf.matMul(a, b));
+        var da = grads[0], db = grads[1];
+        var transposeA = false;
+        var transposeB = true;
+        test_util_1.expectArraysClose(da, tf.matMul(dedm, b, transposeA, transposeB));
+        transposeA = true;
+        transposeB = false;
+        test_util_1.expectArraysClose(db, tf.matMul(a, dedm, transposeA, transposeB));
+    });
+    it('matmul + relu + inner tidy', function () {
+        var a = tf.tensor2d([-1, 2, -3, 10, -20, 30], [2, 3]);
+        var b = tf.tensor2d([2, -3, 4, -1, 2, -3], [3, 2]);
+        var _a = tf.valueAndGrads(function (a, b) {
+            var m = tf.matMul(a, b);
+            return tf.tidy(function () {
+                var y = tf.relu(m);
+                return tf.sum(y);
+            });
+        })([a, b]), value = _a.value, grads = _a.grads;
+        test_util_1.expectNumbersClose(value.get(), 10);
+        var dedm = tf.step(tf.matMul(a, b));
+        var da = grads[0], db = grads[1];
+        var transposeA = false;
+        var transposeB = true;
+        test_util_1.expectArraysClose(da, tf.matMul(dedm, b, transposeA, transposeB));
+        transposeA = true;
+        transposeB = false;
+        test_util_1.expectArraysClose(db, tf.matMul(a, dedm, transposeA, transposeB));
+    });
+});
+jasmine_util_1.describeWithFlags('higher-order gradients', test_util_1.ALL_ENVS, function () {
+    it('grad(grad(f))', function () {
+        var gradgrad = tf.grad(tf.grad(function (x) { return x.mul(x).mul(x); }));
+        var result = gradgrad(tf.tensor1d([.1, .2]));
+        test_util_1.expectArraysClose(result, [.6, 1.2]);
+    });
+    it('grads(grads(f))', function () {
+        var grads = tf.grads(function (x) { return x.mul(x).mul(x); });
+        var gradsgrads = tf.grads(function (x) { return grads([x])[0]; });
+        var result = gradsgrads([tf.tensor1d([.1, .2])]);
+        test_util_1.expectArraysClose(result[0], [.6, 1.2]);
+    });
+});
+jasmine_util_1.describeWithFlags('customGradient', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var a = tf.scalar(3);
+        var b = tf.scalar(2, 'int32');
+        var dy = tf.scalar(4);
+        var customPow = tf.customGrad(function (a) {
+            var value = tf.pow(a, b);
+            var gradFunc = function (dy) { return dy.mul(tf.scalar(0.1)); };
+            return { value: value, gradFunc: gradFunc };
+        });
+        var _a = tf.valueAndGrad(function (a) { return customPow(a); })(a, dy), value = _a.value, grad = _a.grad;
+        expect(value.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(value, [9]);
+        expect(grad.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(grad, [.4]);
+    });
+    it('second order derivative through customGradient', function () {
+        var a = tf.scalar(3);
+        var b = tf.scalar(2, 'int32');
+        var dy = tf.scalar(5);
+        var customPow = tf.customGrad(function (a) {
+            var value = tf.pow(a, b);
+            var gradFunc = function (dy) { return dy.mul(a); };
+            return { value: value, gradFunc: gradFunc };
+        });
+        var dda = tf.grad(tf.grad(function (a) { return customPow(a); }))(a, dy);
+        expect(dda.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(dda, dy);
+    });
+    it('calling gradient of custom op twice works', function () {
+        var customOp = tf.customGrad(function (x) {
+            return { value: x.square(), gradFunc: function (dy) { return dy.mul(x.abs()); } };
+        });
+        var x = tf.tensor1d([-1, -2, 3]);
+        var grad = tf.grad(function (x) { return customOp(x); });
+        test_util_1.expectArraysClose(grad(x), [1, 2, 3]);
+        test_util_1.expectArraysClose(grad(x), [1, 2, 3]);
+    });
+});
+jasmine_util_1.describeWithFlags('memory', test_util_1.ALL_ENVS, function () {
+    it('Sum(float)', function () {
+        expect(tf.memory().numTensors).toBe(0);
+        expect(tf.memory().numBytes).toBe(0);
+        var sum = tf.tidy(function () {
+            var a = tf.tensor1d([1, 2, 3, 4]);
+            expect(tf.memory().numTensors).toBe(1);
+            expect(tf.memory().numBytes).toBe(4 * 4);
+            return a.sum();
+        });
+        expect(tf.memory().numTensors).toBe(1);
+        expect(tf.memory().numBytes).toBe(4);
+        test_util_1.expectArraysClose(sum, [1 + 2 + 3 + 4]);
+    });
+    it('Sum(bool)', function () {
+        var sum = tf.tidy(function () {
+            var a = tf.tensor1d([true, true, false, true], 'bool');
+            expect(tf.memory().numTensors).toBe(1);
+            expect(tf.memory().numBytes).toBe(4);
+            return a.sum();
+        });
+        expect(tf.memory().numTensors).toBe(1);
+        expect(tf.memory().numBytes).toBe(4);
+        expect(sum.dtype).toBe('int32');
+        test_util_1.expectArraysClose(sum, [1 + 1 + 0 + 1]);
+    });
+    it('Sum(int32)', function () {
+        var sum = tf.tidy(function () {
+            var a = tf.tensor1d([1, 1, 0, 1], 'int32');
+            expect(tf.memory().numTensors).toBe(1);
+            expect(tf.memory().numBytes).toBe(4 * 4);
+            return a.sum();
+        });
+        expect(tf.memory().numTensors).toBe(1);
+        expect(tf.memory().numBytes).toBe(4);
+        expect(sum.dtype).toBe('int32');
+        test_util_1.expectArraysClose(sum, [1 + 1 + 0 + 1]);
+    });
+    it('string tensor', function () {
+        var a = tf.tensor([['a', 'bb'], ['c', 'd']]);
+        expect(tf.memory().numTensors).toBe(1);
+        expect(tf.memory().numBytes).toBe(10);
+        a.dispose();
+        expect(tf.memory().numTensors).toBe(0);
+        expect(tf.memory().numBytes).toBe(0);
+    });
+    it('unreliable is true for string tensors', function () {
+        tf.tensor('a');
+        var mem = tf.memory();
+        expect(mem.unreliable).toBe(true);
+        var expectedReason = 'Memory usage by string tensors is approximate ' +
+            '(2 bytes per character)';
+        expect(mem.reasons.indexOf(expectedReason) >= 0).toBe(true);
+    });
+});
+jasmine_util_1.describeWithFlags('memory webgl', test_util_1.WEBGL_ENVS, function () {
+    it('unreliable is falsy/not present when all tensors are numeric', function () {
+        tf.tensor(1);
+        var mem = tf.memory();
+        expect(mem.numTensors).toBe(1);
+        expect(mem.numDataBuffers).toBe(1);
+        expect(mem.numBytes).toBe(4);
+        expect(mem.unreliable).toBeFalsy();
+    });
+});
+jasmine_util_1.describeWithFlags('memory cpu', test_util_1.CPU_ENVS, function () {
+    it('unreliable is true due to auto gc', function () {
+        tf.tensor(1);
+        var mem = tf.memory();
+        expect(mem.numTensors).toBe(1);
+        expect(mem.numDataBuffers).toBe(1);
+        expect(mem.numBytes).toBe(4);
+        expect(mem.unreliable).toBe(true);
+        var expectedReason = 'The reported memory is an upper bound. Due to automatic garbage ' +
+            'collection, the true allocated memory may be less.';
+        expect(mem.reasons.indexOf(expectedReason) >= 0).toBe(true);
+    });
+    it('unreliable is true due to both auto gc and string tensors', function () {
+        tf.tensor(1);
+        tf.tensor('a');
+        var mem = tf.memory();
+        expect(mem.numTensors).toBe(2);
+        expect(mem.numDataBuffers).toBe(2);
+        expect(mem.numBytes).toBe(6);
+        expect(mem.unreliable).toBe(true);
+        var expectedReasonGC = 'The reported memory is an upper bound. Due to automatic garbage ' +
+            'collection, the true allocated memory may be less.';
+        expect(mem.reasons.indexOf(expectedReasonGC) >= 0).toBe(true);
+        var expectedReasonString = 'Memory usage by string tensors is approximate ' +
+            '(2 bytes per character)';
+        expect(mem.reasons.indexOf(expectedReasonString) >= 0).toBe(true);
+    });
+});
+jasmine_util_1.describeWithFlags('profile', test_util_1.ALL_ENVS, function () {
+    it('squaring', function () { return __awaiter(_this, void 0, void 0, function () {
+        var profile, result;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0: return [4, tf.profile(function () {
+                        var x = tf.tensor1d([1, 2, 3]);
+                        var x2 = x.square();
+                        x2.dispose();
+                        x2 = x.square();
+                        x2.dispose();
+                        return x;
+                    })];
+                case 1:
+                    profile = _a.sent();
+                    result = profile.result;
+                    expect(profile.newBytes).toBe(12);
+                    expect(profile.peakBytes).toBe(24);
+                    expect(profile.newTensors).toBe(1);
+                    test_util_1.expectArraysClose(result, [1, 2, 3]);
+                    expect(profile.kernels).toEqual([
+                        {
+                            'name': 'square',
+                            'bytesAdded': 12,
+                            'totalBytesSnapshot': 24,
+                            'tensorsAdded': 1,
+                            'totalTensorsSnapshot': 2,
+                            'inputShapes': [[3]],
+                            'outputShape': [3]
+                        },
+                        {
+                            'name': 'square',
+                            'bytesAdded': 12,
+                            'totalBytesSnapshot': 24,
+                            'tensorsAdded': 1,
+                            'totalTensorsSnapshot': 2,
+                            'inputShapes': [[3]],
+                            'outputShape': [3]
+                        }
+                    ]);
+                    return [2];
+            }
+        });
+    }); });
+    it('squaring without disposing', function () { return __awaiter(_this, void 0, void 0, function () {
+        var profile, result;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0: return [4, tf.profile(function () {
+                        var x = tf.tensor1d([1, 2, 3]);
+                        var x2 = x.square();
+                        return x2;
+                    })];
+                case 1:
+                    profile = _a.sent();
+                    result = profile.result;
+                    expect(profile.newBytes).toBe(24);
+                    expect(profile.peakBytes).toBe(24);
+                    expect(profile.newTensors).toBe(2);
+                    test_util_1.expectArraysClose(result, [1, 4, 9]);
+                    expect(profile.kernels).toEqual([{
+                            'name': 'square',
+                            'bytesAdded': 12,
+                            'totalBytesSnapshot': 24,
+                            'tensorsAdded': 1,
+                            'totalTensorsSnapshot': 2,
+                            'inputShapes': [[3]],
+                            'outputShape': [3]
+                        }]);
+                    return [2];
+            }
+        });
+    }); });
+});
+jasmine_util_1.describeWithFlags('disposeVariables', test_util_1.ALL_ENVS, function () {
+    it('reuse same name variable', function () {
+        tf.tensor1d([1, 2, 3]).variable(true, 'v1');
+        tf.tensor1d([1, 2, 3]).variable(true, 'v2');
+        expect(function () {
+            tf.tensor1d([1, 2, 3]).variable(true, 'v1');
+        }).toThrowError();
+        tf.disposeVariables();
+        tf.tensor1d([1, 2, 3]).variable(true, 'v1');
+        tf.tensor1d([1, 2, 3]).variable(true, 'v2');
+    });
+});
+describe('Switching cpu backends', function () {
+    beforeEach(function () {
+        tf.ENV.registerBackend('cpu1', function () { return new backend_cpu_1.MathBackendCPU(); });
+        tf.ENV.registerBackend('cpu2', function () { return new backend_cpu_1.MathBackendCPU(); });
+    });
+    afterEach(function () {
+        tf.ENV.removeBackend('cpu1');
+        tf.ENV.removeBackend('cpu2');
+    });
+    it('Move data from cpu1 to cpu2 backend', function () {
+        tf.setBackend('cpu1');
+        var a = tf.scalar(5);
+        tf.setBackend('cpu2');
+        var b = tf.scalar(3);
+        expect(tf.memory().numDataBuffers).toBe(2);
+        expect(tf.memory().numTensors).toBe(2);
+        expect(tf.memory().numBytes).toBe(8);
+        test_util_1.expectArraysClose(a, [5]);
+        test_util_1.expectArraysClose(b, [3]);
+        tf.setBackend('cpu1');
+        test_util_1.expectArraysClose(a, [5]);
+        test_util_1.expectArraysClose(b, [3]);
+        tf.dispose([a, b]);
+        expect(tf.memory().numDataBuffers).toBe(0);
+        expect(tf.memory().numTensors).toBe(0);
+        expect(tf.memory().numBytes).toBe(0);
+    });
+    it('can execute op with data from mixed backends', function () {
+        tf.setBackend('cpu1');
+        var a = tf.scalar(5);
+        tf.setBackend('cpu2');
+        var b = tf.scalar(3);
+        tf.tidy(function () {
+            tf.setBackend('cpu1');
+            test_util_1.expectArraysClose(tf.add(a, b), [8]);
+            tf.setBackend('cpu2');
+            test_util_1.expectArraysClose(tf.add(a, b), [8]);
+        });
+        expect(tf.memory().numTensors).toBe(2);
+        expect(tf.memory().numDataBuffers).toBe(2);
+        tf.dispose([a, b]);
+        expect(tf.memory().numTensors).toBe(0);
+        expect(tf.memory().numDataBuffers).toBe(0);
+    });
+});
+jasmine_util_1.describeWithFlags('Switching WebGL + CPU backends', test_util_1.WEBGL_ENVS, function () {
+    beforeEach(function () {
+        tf.ENV.registerBackend('webgl1', function () { return new backend_webgl_1.MathBackendWebGL(); });
+        tf.ENV.registerBackend('webgl2', function () { return new backend_webgl_1.MathBackendWebGL(); });
+        tf.ENV.registerBackend('cpu1', function () { return new backend_cpu_1.MathBackendCPU(); });
+    });
+    afterEach(function () {
+        tf.ENV.removeBackend('webgl1');
+        tf.ENV.removeBackend('webgl2');
+        tf.ENV.removeBackend('cpu1');
+    });
+    it('can execute op with data from mixed backends', function () {
+        tf.setBackend('webgl1');
+        var a = tf.scalar(5);
+        tf.setBackend('webgl2');
+        var b = tf.scalar(3);
+        tf.setBackend('cpu1');
+        var c = tf.scalar(2);
+        tf.tidy(function () {
+            tf.setBackend('webgl1');
+            test_util_1.expectArraysClose(tf.addN([a, b, c]), [10]);
+            tf.setBackend('webgl2');
+            test_util_1.expectArraysClose(tf.addN([a, b, c]), [10]);
+            tf.setBackend('cpu1');
+            test_util_1.expectArraysClose(tf.addN([a, b, c]), [10]);
+        });
+        expect(tf.memory().numTensors).toBe(3);
+        expect(tf.memory().numDataBuffers).toBe(3);
+        tf.dispose([a, b, c]);
+        expect(tf.memory().numTensors).toBe(0);
+        expect(tf.memory().numDataBuffers).toBe(0);
+    });
+    it('fromPixels with mixed backends works', function () {
+        tf.setBackend('webgl1');
+        var a = tf.fromPixels(new ImageData(new Uint8ClampedArray([1, 2, 3, 4]), 1, 1));
+        tf.setBackend('webgl2');
+        var b = tf.fromPixels(new ImageData(new Uint8ClampedArray([5, 6, 7, 8]), 1, 1));
+        test_util_1.expectArraysClose(tf.add(a, b), [6, 8, 10]);
+    });
+    it('single tidy multiple backends', function () {
+        expect(tf.memory().numTensors).toBe(0);
+        tf.tidy(function () {
+            tf.setBackend('webgl1');
+            var a = tf.scalar(1);
+            a.square();
+            tf.setBackend('webgl2');
+            var b = tf.scalar(1);
+            b.square();
+            expect(tf.memory().numTensors).toBe(4);
+        });
+        expect(tf.memory().numTensors).toBe(0);
+    });
+});
+describe('Memory allocation outside a test scope', function () {
+    it('constructing a tensor works', function () {
+        tf.setBackend('cpu');
+        var a = tf.tensor1d([1, 2, 3]);
+        test_util_1.expectArraysClose(a, [1, 2, 3]);
+        a.dispose();
+    });
+});
+//# sourceMappingURL=engine_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/engine_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/engine_test.js.map
new file mode 100644
index 0000000..c2f06cf
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/engine_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/environment.d.ts b/node_modules/@tensorflow/tfjs-core/dist/environment.d.ts
new file mode 100644
index 0000000..cf4ec9c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/environment.d.ts
@@ -0,0 +1,37 @@
+import { Engine, MemoryInfo, ProfileInfo, ScopeFn, TimingInfo } from './engine';
+import { Features } from './environment_util';
+import { KernelBackend } from './kernels/backend';
+import { Tensor, TensorTracker } from './tensor';
+import { TensorContainer } from './tensor_types';
+export declare const EPSILON_FLOAT16 = 0.0001;
+export declare const EPSILON_FLOAT32 = 1e-7;
+export declare class Environment {
+    private features;
+    private globalEngine;
+    private registry;
+    backendName: string;
+    constructor(features?: Features);
+    static setBackend(backendName: string, safeMode?: boolean): void;
+    static getBackend(): string;
+    static disposeVariables(): void;
+    static memory(): MemoryInfo;
+    static profile(f: () => TensorContainer): Promise<ProfileInfo>;
+    static tidy<T extends TensorContainer>(nameOrFn: string | ScopeFn<T>, fn?: ScopeFn<T>): T;
+    static dispose(container: TensorContainer): void;
+    static keep<T extends Tensor>(result: T): T;
+    static time(f: () => void): Promise<TimingInfo>;
+    get<K extends keyof Features>(feature: K): Features[K];
+    getFeatures(): Features;
+    set<K extends keyof Features>(feature: K, value: Features[K]): void;
+    private getBestBackendName;
+    private evaluateFeature;
+    setFeatures(features: Features): void;
+    reset(): void;
+    readonly backend: KernelBackend;
+    findBackend(name: string): KernelBackend;
+    registerBackend(name: string, factory: () => KernelBackend, priority?: number, setTensorTrackerFn?: (f: () => TensorTracker) => void): boolean;
+    removeBackend(name: string): void;
+    readonly engine: Engine;
+    private initEngine;
+}
+export declare let ENV: Environment;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/environment.js b/node_modules/@tensorflow/tfjs-core/dist/environment.js
new file mode 100644
index 0000000..5018d51
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/environment.js
@@ -0,0 +1,279 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var device_util = require("./device_util");
+var engine_1 = require("./engine");
+var environment_util_1 = require("./environment_util");
+var tensor_1 = require("./tensor");
+var tensor_util_1 = require("./tensor_util");
+exports.EPSILON_FLOAT16 = 1e-4;
+var TEST_EPSILON_FLOAT16 = 1e-1;
+exports.EPSILON_FLOAT32 = 1e-7;
+var TEST_EPSILON_FLOAT32 = 1e-3;
+var Environment = (function () {
+    function Environment(features) {
+        this.features = {};
+        this.registry = {};
+        if (features != null) {
+            this.features = features;
+        }
+        if (this.get('DEBUG')) {
+            console.warn('Debugging mode is ON. The output of every math call will ' +
+                'be downloaded to CPU and checked for NaNs. ' +
+                'This significantly impacts performance.');
+        }
+    }
+    Environment.setBackend = function (backendName, safeMode) {
+        if (safeMode === void 0) { safeMode = false; }
+        if (!(backendName in exports.ENV.registry)) {
+            throw new Error("Backend name '" + backendName + "' not found in registry");
+        }
+        exports.ENV.engine.backend = exports.ENV.findBackend(backendName);
+        exports.ENV.backendName = backendName;
+    };
+    Environment.getBackend = function () {
+        exports.ENV.initEngine();
+        return exports.ENV.backendName;
+    };
+    Environment.disposeVariables = function () {
+        exports.ENV.engine.disposeVariables();
+    };
+    Environment.memory = function () {
+        return exports.ENV.engine.memory();
+    };
+    Environment.profile = function (f) {
+        return exports.ENV.engine.profile(f);
+    };
+    Environment.tidy = function (nameOrFn, fn) {
+        return exports.ENV.engine.tidy(nameOrFn, fn);
+    };
+    Environment.dispose = function (container) {
+        var tensors = tensor_util_1.getTensorsInContainer(container);
+        tensors.forEach(function (tensor) { return tensor.dispose(); });
+    };
+    Environment.keep = function (result) {
+        return exports.ENV.engine.keep(result);
+    };
+    Environment.time = function (f) {
+        return exports.ENV.engine.time(f);
+    };
+    Environment.prototype.get = function (feature) {
+        if (feature in this.features) {
+            return this.features[feature];
+        }
+        this.features[feature] = this.evaluateFeature(feature);
+        return this.features[feature];
+    };
+    Environment.prototype.getFeatures = function () {
+        return this.features;
+    };
+    Environment.prototype.set = function (feature, value) {
+        this.features[feature] = value;
+    };
+    Environment.prototype.getBestBackendName = function () {
+        var _this = this;
+        if (Object.keys(this.registry).length === 0) {
+            throw new Error('No backend found in registry.');
+        }
+        var sortedBackends = Object.keys(this.registry)
+            .map(function (name) {
+            return { name: name, entry: _this.registry[name] };
+        })
+            .sort(function (a, b) {
+            return b.entry.priority - a.entry.priority;
+        });
+        return sortedBackends[0].name;
+    };
+    Environment.prototype.evaluateFeature = function (feature) {
+        if (feature === 'DEBUG') {
+            return false;
+        }
+        else if (feature === 'IS_BROWSER') {
+            return typeof window !== 'undefined';
+        }
+        else if (feature === 'IS_NODE') {
+            return (typeof process !== 'undefined') &&
+                (typeof process.versions !== 'undefined') &&
+                (typeof process.versions.node !== 'undefined');
+        }
+        else if (feature === 'IS_CHROME') {
+            return environment_util_1.isChrome();
+        }
+        else if (feature === 'WEBGL_CPU_FORWARD') {
+            return true;
+        }
+        else if (feature === 'WEBGL_PACK') {
+            return false;
+        }
+        else if (feature === 'WEBGL_PACK_BATCHNORMALIZATION') {
+            return this.get('WEBGL_PACK');
+        }
+        else if (feature === 'WEBGL_PACK_CLIP') {
+            return this.get('WEBGL_PACK');
+        }
+        else if (feature === 'WEBGL_PACK_DEPTHWISECONV') {
+            return this.get('WEBGL_PACK');
+        }
+        else if (feature === 'WEBGL_LAZILY_UNPACK') {
+            return this.get('WEBGL_PACK');
+        }
+        else if (feature === 'WEBGL_CONV_IM2COL') {
+            return this.get('WEBGL_PACK');
+        }
+        else if (feature === 'WEBGL_PAGING_ENABLED') {
+            return this.get('IS_BROWSER') && !this.get('PROD');
+        }
+        else if (feature === 'WEBGL_MAX_TEXTURE_SIZE') {
+            return environment_util_1.getWebGLMaxTextureSize(this.get('WEBGL_VERSION'));
+        }
+        else if (feature === 'IS_TEST') {
+            return false;
+        }
+        else if (feature === 'BACKEND') {
+            return this.getBestBackendName();
+        }
+        else if (feature === 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') {
+            var webGLVersion = this.get('WEBGL_VERSION');
+            if (webGLVersion === 0) {
+                return 0;
+            }
+            return environment_util_1.getWebGLDisjointQueryTimerVersion(webGLVersion);
+        }
+        else if (feature === 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') {
+            return this.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0 &&
+                !device_util.isMobile();
+        }
+        else if (feature === 'HAS_WEBGL') {
+            return this.get('WEBGL_VERSION') > 0;
+        }
+        else if (feature === 'WEBGL_VERSION') {
+            if (environment_util_1.isWebGLVersionEnabled(2)) {
+                return 2;
+            }
+            else if (environment_util_1.isWebGLVersionEnabled(1)) {
+                return 1;
+            }
+            return 0;
+        }
+        else if (feature === 'WEBGL_RENDER_FLOAT32_ENABLED') {
+            return environment_util_1.isRenderToFloatTextureEnabled(this.get('WEBGL_VERSION'));
+        }
+        else if (feature === 'WEBGL_DOWNLOAD_FLOAT_ENABLED') {
+            return environment_util_1.isDownloadFloatTextureEnabled(this.get('WEBGL_VERSION'));
+        }
+        else if (feature === 'WEBGL_FENCE_API_ENABLED') {
+            return environment_util_1.isWebGLFenceEnabled(this.get('WEBGL_VERSION'));
+        }
+        else if (feature === 'WEBGL_SIZE_UPLOAD_UNIFORM') {
+            var useUniforms = this.get('WEBGL_RENDER_FLOAT32_ENABLED');
+            return useUniforms ? 4 : 0;
+        }
+        else if (feature === 'TEST_EPSILON') {
+            return this.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 :
+                TEST_EPSILON_FLOAT16;
+        }
+        else if (feature === 'EPSILON') {
+            return this.backend.floatPrecision() === 32 ? exports.EPSILON_FLOAT32 :
+                exports.EPSILON_FLOAT16;
+        }
+        else if (feature === 'PROD') {
+            return false;
+        }
+        else if (feature === 'TENSORLIKE_CHECK_SHAPE_CONSISTENCY') {
+            return !this.get('PROD');
+        }
+        throw new Error("Unknown feature " + feature + ".");
+    };
+    Environment.prototype.setFeatures = function (features) {
+        this.features = Object.assign({}, features);
+    };
+    Environment.prototype.reset = function () {
+        this.features = environment_util_1.getFeaturesFromURL();
+        if (this.globalEngine != null) {
+            this.globalEngine = null;
+        }
+    };
+    Object.defineProperty(Environment.prototype, "backend", {
+        get: function () {
+            return this.engine.backend;
+        },
+        enumerable: true,
+        configurable: true
+    });
+    Environment.prototype.findBackend = function (name) {
+        if (!(name in this.registry)) {
+            return null;
+        }
+        return this.registry[name].backend;
+    };
+    Environment.prototype.registerBackend = function (name, factory, priority, setTensorTrackerFn) {
+        var _this = this;
+        if (priority === void 0) { priority = 1; }
+        if (name in this.registry) {
+            console.warn(name + " backend was already registered. Reusing existing backend");
+            if (setTensorTrackerFn != null) {
+                setTensorTrackerFn(function () { return _this.engine; });
+            }
+            return false;
+        }
+        try {
+            var backend = factory();
+            backend.setDataMover({ moveData: function (dataId) { return _this.engine.moveData(dataId); } });
+            this.registry[name] = { backend: backend, priority: priority };
+            return true;
+        }
+        catch (err) {
+            console.warn("Registration of backend " + name + " failed");
+            console.warn(err.stack || err.message);
+            return false;
+        }
+    };
+    Environment.prototype.removeBackend = function (name) {
+        if (!(name in this.registry)) {
+            throw new Error(name + " backend not found in registry");
+        }
+        this.registry[name].backend.dispose();
+        delete this.registry[name];
+    };
+    Object.defineProperty(Environment.prototype, "engine", {
+        get: function () {
+            this.initEngine();
+            return this.globalEngine;
+        },
+        enumerable: true,
+        configurable: true
+    });
+    Environment.prototype.initEngine = function () {
+        var _this = this;
+        if (this.globalEngine == null) {
+            this.backendName = this.get('BACKEND');
+            var backend = this.findBackend(this.backendName);
+            this.globalEngine =
+                new engine_1.Engine(backend, false, function () { return _this.get('DEBUG'); });
+        }
+    };
+    return Environment;
+}());
+exports.Environment = Environment;
+function getGlobalNamespace() {
+    var ns;
+    if (typeof (window) !== 'undefined') {
+        ns = window;
+    }
+    else if (typeof (process) !== 'undefined') {
+        ns = process;
+    }
+    else {
+        throw new Error('Could not find a global object');
+    }
+    return ns;
+}
+function getOrMakeEnvironment() {
+    var ns = getGlobalNamespace();
+    if (ns.ENV == null) {
+        ns.ENV = new Environment(environment_util_1.getFeaturesFromURL());
+        tensor_1.setTensorTracker(function () { return ns.ENV.engine; });
+    }
+    return ns.ENV;
+}
+exports.ENV = getOrMakeEnvironment();
+//# sourceMappingURL=environment.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/environment.js.map b/node_modules/@tensorflow/tfjs-core/dist/environment.js.map
new file mode 100644
index 0000000..1555e7c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/environment.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/environment_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/environment_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/environment_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/environment_test.js b/node_modules/@tensorflow/tfjs-core/dist/environment_test.js
new file mode 100644
index 0000000..b9a3aef
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/environment_test.js
@@ -0,0 +1,140 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var device_util = require("./device_util");
+var environment_1 = require("./environment");
+var environment_util_1 = require("./environment_util");
+var tf = require("./index");
+var jasmine_util_1 = require("./jasmine_util");
+var backend_cpu_1 = require("./kernels/backend_cpu");
+var backend_webgl_1 = require("./kernels/backend_webgl");
+var test_util_1 = require("./test_util");
+jasmine_util_1.describeWithFlags('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE', test_util_1.WEBGL_ENVS, function () {
+    it('disjoint query timer disabled', function () {
+        var features = { 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION': 0 };
+        var env = new environment_1.Environment(features);
+        expect(env.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE'))
+            .toBe(false);
+    });
+    it('disjoint query timer enabled, mobile', function () {
+        var features = { 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION': 1 };
+        spyOn(device_util, 'isMobile').and.returnValue(true);
+        var env = new environment_1.Environment(features);
+        expect(env.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE'))
+            .toBe(false);
+    });
+    it('disjoint query timer enabled, not mobile', function () {
+        var features = { 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION': 1 };
+        spyOn(device_util, 'isMobile').and.returnValue(false);
+        var env = new environment_1.Environment(features);
+        expect(env.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE'))
+            .toBe(true);
+    });
+});
+jasmine_util_1.describeWithFlags('WEBGL_PAGING_ENABLED', test_util_1.WEBGL_ENVS, function (testEnv) {
+    it('should be true if in a browser', function () {
+        var features = { 'IS_BROWSER': true };
+        var env = new environment_1.Environment(features);
+        expect(env.get('WEBGL_PAGING_ENABLED')).toBe(true);
+    });
+    it('should not cause errors when paging is turned off', function () {
+        environment_1.ENV.set('WEBGL_PAGING_ENABLED', false);
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
+        var c = tf.matMul(a, b);
+        test_util_1.expectArraysClose(c, [0, 8, -3, 20]);
+    });
+    it('should be false when the environment is prod', function () {
+        var features = { 'IS_BROWSER': true };
+        var env = new environment_1.Environment(features);
+        env.set('PROD', true);
+        expect(env.get('WEBGL_PAGING_ENABLED')).toBe(false);
+    });
+});
+describe('Backend', function () {
+    beforeAll(function () {
+        spyOn(console, 'warn');
+    });
+    afterEach(function () {
+        environment_1.ENV.reset();
+    });
+    it('custom cpu registration', function () {
+        var backend;
+        environment_1.ENV.registerBackend('custom-cpu', function () {
+            backend = new backend_cpu_1.MathBackendCPU();
+            return backend;
+        });
+        expect(environment_1.ENV.findBackend('custom-cpu')).toBe(backend);
+        environment_1.Environment.setBackend('custom-cpu');
+        expect(environment_1.ENV.backend).toBe(backend);
+        environment_1.ENV.removeBackend('custom-cpu');
+    });
+    it('webgl not supported, falls back to cpu', function () {
+        environment_1.ENV.setFeatures({ 'WEBGL_VERSION': 0 });
+        var cpuBackend;
+        environment_1.ENV.registerBackend('custom-cpu', function () {
+            cpuBackend = new backend_cpu_1.MathBackendCPU();
+            return cpuBackend;
+        }, 103);
+        var success = environment_1.ENV.registerBackend('custom-webgl', function () { return new backend_webgl_1.MathBackendWebGL(); }, 104);
+        expect(success).toBe(false);
+        expect(environment_1.ENV.findBackend('custom-webgl') == null).toBe(true);
+        expect(environment_1.Environment.getBackend()).toBe('custom-cpu');
+        expect(environment_1.ENV.backend).toBe(cpuBackend);
+        environment_1.ENV.removeBackend('custom-cpu');
+    });
+    it('default custom background null', function () {
+        expect(environment_1.ENV.findBackend('custom')).toBeNull();
+    });
+    it('allow custom backend', function () {
+        var backend = new backend_cpu_1.MathBackendCPU();
+        var success = environment_1.ENV.registerBackend('custom', function () { return backend; });
+        expect(success).toBeTruthy();
+        expect(environment_1.ENV.findBackend('custom')).toEqual(backend);
+        environment_1.ENV.removeBackend('custom');
+    });
+});
+describe('environment_util.getQueryParams', function () {
+    it('basic', function () {
+        expect(environment_util_1.getQueryParams('?a=1&b=hi&f=animal'))
+            .toEqual({ 'a': '1', 'b': 'hi', 'f': 'animal' });
+    });
+});
+jasmine_util_1.describeWithFlags('max texture size', test_util_1.WEBGL_ENVS, function () {
+    it('should not throw exception', function () {
+        expect(function () { return environment_1.ENV.get('WEBGL_MAX_TEXTURE_SIZE'); }).not.toThrow();
+    });
+});
+jasmine_util_1.describeWithFlags('epsilon', {}, function () {
+    it('Epsilon is a function of float precision', function () {
+        var epsilonValue = environment_1.ENV.backend.floatPrecision() === 32 ? environment_1.EPSILON_FLOAT32 : environment_1.EPSILON_FLOAT16;
+        expect(environment_1.ENV.get('EPSILON')).toBe(epsilonValue);
+    });
+    it('abs(epsilon) > 0', function () {
+        expect(tf.abs(environment_1.ENV.get('EPSILON')).get()).toBeGreaterThan(0);
+    });
+});
+jasmine_util_1.describeWithFlags('TENSORLIKE_CHECK_SHAPE_CONSISTENCY', test_util_1.ALL_ENVS, function () {
+    it('disabled when prod is enabled', function () {
+        var env = new environment_1.Environment();
+        env.set('PROD', true);
+        expect(env.get('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')).toBe(false);
+    });
+    it('enabled when prod is disabled', function () {
+        var env = new environment_1.Environment();
+        env.set('PROD', false);
+        expect(env.get('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')).toBe(true);
+    });
+});
+jasmine_util_1.describeWithFlags('WEBGL_SIZE_UPLOAD_UNIFORM', test_util_1.WEBGL_ENVS, function () {
+    it('is 0 when there is no float32 bit support', function () {
+        var env = new environment_1.Environment();
+        env.set('WEBGL_RENDER_FLOAT32_ENABLED', false);
+        expect(env.get('WEBGL_SIZE_UPLOAD_UNIFORM')).toBe(0);
+    });
+    it('is > 0 when there is float32 bit support', function () {
+        var env = new environment_1.Environment();
+        env.set('WEBGL_RENDER_FLOAT32_ENABLED', true);
+        expect(env.get('WEBGL_SIZE_UPLOAD_UNIFORM')).toBeGreaterThan(0);
+    });
+});
+//# sourceMappingURL=environment_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/environment_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/environment_test.js.map
new file mode 100644
index 0000000..ba7c272
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/environment_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"environment_test.js","sourceRoot":"","sources":["../src/environment_test.ts"],"names":[],"mappings":";;AAiBA,2CAA6C;AAC7C,6CAAiF;AACjF,uDAA4D;AAC5D,4BAA8B;AAC9B,+CAAiD;AAEjD,qDAAqD;AACrD,yDAAyD;AACzD,yCAAoE;AAEpE,gCAAiB,CACb,+CAA+C,EAAE,sBAAU,EAAE;IAC3D,EAAE,CAAC,+BAA+B,EAAE;QAClC,IAAM,QAAQ,GACC,EAAC,8CAA8C,EAAE,CAAC,EAAC,CAAC;QAEnE,IAAM,GAAG,GAAG,IAAI,yBAAW,CAAC,QAAQ,CAAC,CAAC;QAEtC,MAAM,CAAC,GAAG,CAAC,GAAG,CAAC,+CAA+C,CAAC,CAAC;aAC3D,IAAI,CAAC,KAAK,CAAC,CAAC;IACnB,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,sCAAsC,EAAE;QACzC,IAAM,QAAQ,GACC,EAAC,8CAA8C,EAAE,CAAC,EAAC,CAAC;QACnE,KAAK,CAAC,WAAW,EAAE,UAAU,CAAC,CAAC,GAAG,CAAC,WAAW,CAAC,IAAI,CAAC,CAAC;QAErD,IAAM,GAAG,GAAG,IAAI,yBAAW,CAAC,QAAQ,CAAC,CAAC;QAEtC,MAAM,CAAC,GAAG,CAAC,GAAG,CAAC,+CAA+C,CAAC,CAAC;aAC3D,IAAI,CAAC,KAAK,CAAC,CAAC;IACnB,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,0CAA0C,EAAE;QAC7C,IAAM,QAAQ,GACC,EAAC,8CAA8C,EAAE,CAAC,EAAC,CAAC;QACnE,KAAK,CAAC,WAAW,EAAE,UAAU,CAAC,CAAC,GAAG,CAAC,WAAW,CAAC,KAAK,CAAC,CAAC;QAEtD,IAAM,GAAG,GAAG,IAAI,yBAAW,CAAC,QAAQ,CAAC,CAAC;QAEtC,MAAM,CAAC,GAAG,CAAC,GAAG,CAAC,+CAA+C,CAAC,CAAC;aAC3D,IAAI,CAAC,IAAI,CAAC,CAAC;IAClB,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC;AAEP,gCAAiB,CAAC,sBAAsB,EAAE,sBAAU,EAAE,UAAA,OAAO;IAC3D,EAAE,CAAC,gCAAgC,EAAE;QACnC,IAAM,QAAQ,GAAa,EAAC,YAAY,EAAE,IAAI,EAAC,CAAC;QAChD,IAAM,GAAG,GAAG,IAAI,yBAAW,CAAC,QAAQ,CAAC,CAAC;QACtC,MAAM,CAAC,GAAG,CAAC,GAAG,CAAC,sBAAsB,CAAC,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;IACrD,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,mDAAmD,EAAE;QACtD,iBAAG,CAAC,GAAG,CAAC,sBAAsB,EAAE,KAAK,CAAC,CAAC;QAEvC,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAClD,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAEnD,IAAM,CAAC,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QAE1B,6BAAiB,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC;IACvC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,8CAA8C,EAAE;QACjD,IAAM,QAAQ,GAAa,EAAC,YAAY,EAAE,IAAI,EAAC,CAAC;QAChD,IAAM,GAAG,GAAG,IAAI,yBAAW,CAAC,QAAQ,CAAC,CAAC;QACtC,GAAG,CAAC,GAAG,CAAC,MAAM,EAAE,IAAI,CAAC,CAAC;QACtB,MAAM,CAAC,GAAG,CAAC,GAAG,CAAC,sBAAsB,CAAC,CAAC,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC;IACtD,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC;AAEH,QAAQ,CAAC,SAAS,EAAE;IAClB,SAAS,CAAC;QAER,KAAK,CAAC,OAAO,EAAE,MAAM,CAAC,CAAC;IACzB,CAAC,CAAC,CAAC;IAEH,SAAS,CAAC;QACR,iBAAG,CAAC,KAAK,EAAE,CAAC;IACd,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,yBAAyB,EAAE;QAC5B,IAAI,OAAsB,CAAC;QAC3B,iBAAG,CAAC,eAAe,CAAC,YAAY,EAAE;YAChC,OAAO,GAAG,IAAI,4BAAc,EAAE,CAAC;YAC/B,OAAO,OAAO,CAAC;QACjB,CAAC,CAAC,CAAC;QAEH,MAAM,CAAC,iBAAG,CAAC,WAAW,CAAC,YAAY,CAAC,CAAC,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC;QACpD,yBAAW,CAAC,UAAU,CAAC,YAAY,CAAC,CAAC;QACrC,MAAM,CAAC,iBAAG,CAAC,OAAO,CAAC,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC;QAElC,iBAAG,CAAC,aAAa,CAAC,YAAY,CAAC,CAAC;IAClC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,wCAAwC,EAAE;QAC3C,iBAAG,CAAC,WAAW,CAAC,EAAC,eAAe,EAAE,CAAC,EAAC,CAAC,CAAC;QACtC,IAAI,UAAyB,CAAC;QAC9B,iBAAG,CAAC,eAAe,CAAC,YAAY,EAAE;YAChC,UAAU,GAAG,IAAI,4BAAc,EAAE,CAAC;YAClC,OAAO,UAAU,CAAC;QACpB,CAAC,EAAE,GAAG,CAAC,CAAC;QACR,IAAM,OAAO,GACT,iBAAG,CAAC,eAAe,CAAC,cAAc,EAAE,cAAM,OAAA,IAAI,gCAAgB,EAAE,EAAtB,CAAsB,EAAE,GAAG,CAAC,CAAC;QAC3E,MAAM,CAAC,OAAO,CAAC,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC;QAC5B,MAAM,CAAC,iBAAG,CAAC,WAAW,CAAC,cAAc,CAAC,IAAI,IAAI,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;QAC3D,MAAM,CAAC,yBAAW,CAAC,UAAU,EAAE,CAAC,CAAC,IAAI,CAAC,YAAY,CAAC,CAAC;QACpD,MAAM,CAAC,iBAAG,CAAC,OAAO,CAAC,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC;QAErC,iBAAG,CAAC,aAAa,CAAC,YAAY,CAAC,CAAC;IAClC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,gCAAgC,EAAE;QACnC,MAAM,CAAC,iBAAG,CAAC,WAAW,CAAC,QAAQ,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC;IAC/C,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,sBAAsB,EAAE;QACzB,IAAM,OAAO,GAAG,IAAI,4BAAc,EAAE,CAAC;QACrC,IAAM,OAAO,GAAG,iBAAG,CAAC,eAAe,CAAC,QAAQ,EAAE,cAAM,OAAA,OAAO,EAAP,CAAO,CAAC,CAAC;QAC7D,MAAM,CAAC,OAAO,CAAC,CAAC,UAAU,EAAE,CAAC;QAC7B,MAAM,CAAC,iBAAG,CAAC,WAAW,CAAC,QAAQ,CAAC,CAAC,CAAC,OAAO,CAAC,OAAO,CAAC,CAAC;QACnD,iBAAG,CAAC,aAAa,CAAC,QAAQ,CAAC,CAAC;IAC9B,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC;AAEH,QAAQ,CAAC,iCAAiC,EAAE;IAC1C,EAAE,CAAC,OAAO,EAAE;QACV,MAAM,CAAC,iCAAc,CAAC,oBAAoB,CAAC,CAAC;aACvC,OAAO,CAAC,EAAC,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,IAAI,EAAE,GAAG,EAAE,QAAQ,EAAC,CAAC,CAAC;IACrD,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC;AAEH,gCAAiB,CAAC,kBAAkB,EAAE,sBAAU,EAAE;IAChD,EAAE,CAAC,4BAA4B,EAAE;QAC/B,MAAM,CAAC,cAAM,OAAA,iBAAG,CAAC,GAAG,CAAC,wBAAwB,CAAC,EAAjC,CAAiC,CAAC,CAAC,GAAG,CAAC,OAAO,EAAE,CAAC;IAChE,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC;AAEH,gCAAiB,CAAC,SAAS,EAAE,EAAE,EAAE;IAC/B,EAAE,CAAC,0CAA0C,EAAE;QAC7C,IAAM,YAAY,GACd,iBAAG,CAAC,OAAO,CAAC,cAAc,EAAE,KAAK,EAAE,CAAC,CAAC,CAAC,6BAAe,CAAC,CAAC,CAAC,6BAAe,CAAC;QAC5E,MAAM,CAAC,iBAAG,CAAC,GAAG,CAAC,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,YAAY,CAAC,CAAC;IAChD,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,kBAAkB,EAAE;QACrB,MAAM,CAAC,EAAE,CAAC,GAAG,CAAC,iBAAG,CAAC,GAAG,CAAC,SAAS,CAAC,CAAC,CAAC,GAAG,EAAE,CAAC,CAAC,eAAe,CAAC,CAAC,CAAC,CAAC;IAC9D,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC;AAEH,gCAAiB,CAAC,oCAAoC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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/environment_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/environment_util.d.ts
new file mode 100644
index 0000000..b5fc860
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/environment_util.d.ts
@@ -0,0 +1,50 @@
+export interface Features {
+    'DEBUG'?: boolean;
+    'IS_BROWSER'?: boolean;
+    'IS_NODE'?: boolean;
+    'WEBGL_LAZILY_UNPACK'?: boolean;
+    'WEBGL_CPU_FORWARD'?: boolean;
+    'WEBGL_PACK'?: boolean;
+    'WEBGL_PACK_BATCHNORMALIZATION'?: boolean;
+    'WEBGL_PACK_CLIP'?: boolean;
+    'WEBGL_PACK_DEPTHWISECONV'?: boolean;
+    'WEBGL_CONV_IM2COL'?: boolean;
+    'WEBGL_PAGING_ENABLED'?: boolean;
+    'WEBGL_MAX_TEXTURE_SIZE'?: number;
+    'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'?: number;
+    'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE'?: boolean;
+    'WEBGL_VERSION'?: number;
+    'HAS_WEBGL'?: boolean;
+    'WEBGL_RENDER_FLOAT32_ENABLED'?: boolean;
+    'WEBGL_DOWNLOAD_FLOAT_ENABLED'?: boolean;
+    'WEBGL_FENCE_API_ENABLED'?: boolean;
+    'WEBGL_SIZE_UPLOAD_UNIFORM'?: number;
+    'BACKEND'?: string;
+    'TEST_EPSILON'?: number;
+    'IS_CHROME'?: boolean;
+    'IS_TEST'?: boolean;
+    'EPSILON'?: number;
+    'PROD'?: boolean;
+    'TENSORLIKE_CHECK_SHAPE_CONSISTENCY'?: boolean;
+}
+export declare enum Type {
+    NUMBER = 0,
+    BOOLEAN = 1,
+    STRING = 2
+}
+export declare const URL_PROPERTIES: URLProperty[];
+export interface URLProperty {
+    name: keyof Features;
+    type: Type;
+}
+export declare function isWebGLVersionEnabled(webGLVersion: 1 | 2): boolean;
+export declare function getWebGLMaxTextureSize(webGLVersion: number): number;
+export declare function getWebGLDisjointQueryTimerVersion(webGLVersion: number): number;
+export declare function isRenderToFloatTextureEnabled(webGLVersion: number): boolean;
+export declare function isDownloadFloatTextureEnabled(webGLVersion: number): boolean;
+export declare function isWebGLFenceEnabled(webGLVersion: number): boolean;
+export declare function isChrome(): boolean;
+export declare function getFeaturesFromURL(): Features;
+export declare function getQueryParams(queryString: string): {
+    [key: string]: string;
+};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/environment_util.js b/node_modules/@tensorflow/tfjs-core/dist/environment_util.js
new file mode 100644
index 0000000..e9d962a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/environment_util.js
@@ -0,0 +1,203 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var canvas_util_1 = require("./canvas_util");
+var Type;
+(function (Type) {
+    Type[Type["NUMBER"] = 0] = "NUMBER";
+    Type[Type["BOOLEAN"] = 1] = "BOOLEAN";
+    Type[Type["STRING"] = 2] = "STRING";
+})(Type = exports.Type || (exports.Type = {}));
+exports.URL_PROPERTIES = [
+    { name: 'DEBUG', type: Type.BOOLEAN },
+    { name: 'IS_BROWSER', type: Type.BOOLEAN },
+    { name: 'WEBGL_LAZILY_UNPACK', type: Type.BOOLEAN },
+    { name: 'WEBGL_CPU_FORWARD', type: Type.BOOLEAN },
+    { name: 'WEBGL_PACK', type: Type.BOOLEAN },
+    { name: 'WEBGL_PACK_BATCHNORMALIZATION', type: Type.BOOLEAN },
+    { name: 'WEBGL_PACK_CLIP', type: Type.BOOLEAN },
+    { name: 'WEBGL_PACK_DEPTHWISECONV', type: Type.BOOLEAN },
+    { name: 'WEBGL_CONV_IM2COL', type: Type.BOOLEAN },
+    { name: 'WEBGL_MAX_TEXTURE_SIZE', type: Type.NUMBER },
+    { name: 'WEBGL_PAGING_ENABLED', type: Type.BOOLEAN },
+    { name: 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION', type: Type.NUMBER },
+    { name: 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE', type: Type.BOOLEAN },
+    { name: 'WEBGL_VERSION', type: Type.NUMBER },
+    { name: 'WEBGL_RENDER_FLOAT32_ENABLED', type: Type.BOOLEAN },
+    { name: 'WEBGL_DOWNLOAD_FLOAT_ENABLED', type: Type.BOOLEAN },
+    { name: 'WEBGL_FENCE_API_ENABLED', type: Type.BOOLEAN },
+    { name: 'WEBGL_SIZE_UPLOAD_UNIFORM', type: Type.NUMBER },
+    { name: 'BACKEND', type: Type.STRING },
+    { name: 'EPSILON', type: Type.NUMBER },
+    { name: 'PROD', type: Type.BOOLEAN },
+    { name: 'TENSORLIKE_CHECK_SHAPE_CONSISTENCY', type: Type.BOOLEAN },
+];
+function isWebGLVersionEnabled(webGLVersion) {
+    try {
+        var gl = canvas_util_1.getWebGLContext(webGLVersion);
+        if (gl != null) {
+            return true;
+        }
+    }
+    catch (e) {
+        return false;
+    }
+    return false;
+}
+exports.isWebGLVersionEnabled = isWebGLVersionEnabled;
+var MAX_TEXTURE_SIZE;
+function getWebGLMaxTextureSize(webGLVersion) {
+    if (MAX_TEXTURE_SIZE == null) {
+        var gl = canvas_util_1.getWebGLContext(webGLVersion);
+        MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);
+    }
+    return MAX_TEXTURE_SIZE;
+}
+exports.getWebGLMaxTextureSize = getWebGLMaxTextureSize;
+function getWebGLDisjointQueryTimerVersion(webGLVersion) {
+    if (webGLVersion === 0) {
+        return 0;
+    }
+    var queryTimerVersion;
+    var gl = canvas_util_1.getWebGLContext(webGLVersion);
+    if (hasExtension(gl, 'EXT_disjoint_timer_query_webgl2') &&
+        webGLVersion === 2) {
+        queryTimerVersion = 2;
+    }
+    else if (hasExtension(gl, 'EXT_disjoint_timer_query')) {
+        queryTimerVersion = 1;
+    }
+    else {
+        queryTimerVersion = 0;
+    }
+    return queryTimerVersion;
+}
+exports.getWebGLDisjointQueryTimerVersion = getWebGLDisjointQueryTimerVersion;
+function isRenderToFloatTextureEnabled(webGLVersion) {
+    if (webGLVersion === 0) {
+        return false;
+    }
+    var gl = canvas_util_1.getWebGLContext(webGLVersion);
+    if (webGLVersion === 1) {
+        if (!hasExtension(gl, 'OES_texture_float')) {
+            return false;
+        }
+    }
+    else {
+        if (!hasExtension(gl, 'EXT_color_buffer_float')) {
+            return false;
+        }
+    }
+    var isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl, webGLVersion);
+    return isFrameBufferComplete;
+}
+exports.isRenderToFloatTextureEnabled = isRenderToFloatTextureEnabled;
+function isDownloadFloatTextureEnabled(webGLVersion) {
+    if (webGLVersion === 0) {
+        return false;
+    }
+    var gl = canvas_util_1.getWebGLContext(webGLVersion);
+    if (webGLVersion === 1) {
+        if (!hasExtension(gl, 'OES_texture_float')) {
+            return false;
+        }
+        if (!hasExtension(gl, 'WEBGL_color_buffer_float')) {
+            return false;
+        }
+    }
+    else {
+        if (!hasExtension(gl, 'EXT_color_buffer_float')) {
+            return false;
+        }
+    }
+    var isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl, webGLVersion);
+    return isFrameBufferComplete;
+}
+exports.isDownloadFloatTextureEnabled = isDownloadFloatTextureEnabled;
+function isWebGLFenceEnabled(webGLVersion) {
+    if (webGLVersion !== 2) {
+        return false;
+    }
+    var gl = canvas_util_1.getWebGLContext(webGLVersion);
+    var isEnabled = gl.fenceSync != null;
+    return isEnabled;
+}
+exports.isWebGLFenceEnabled = isWebGLFenceEnabled;
+function isChrome() {
+    return typeof navigator !== 'undefined' && navigator != null &&
+        navigator.userAgent != null && /Chrome/.test(navigator.userAgent) &&
+        /Google Inc/.test(navigator.vendor);
+}
+exports.isChrome = isChrome;
+var TENSORFLOWJS_FLAGS_PREFIX = 'tfjsflags';
+function getFeaturesFromURL() {
+    var features = {};
+    if (typeof window === 'undefined' || typeof window.location === 'undefined' ||
+        typeof window.location.search === 'undefined') {
+        return features;
+    }
+    var urlParams = getQueryParams(window.location.search);
+    if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {
+        var urlFlags_1 = {};
+        var keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(',');
+        keyValues.forEach(function (keyValue) {
+            var _a = keyValue.split(':'), key = _a[0], value = _a[1];
+            urlFlags_1[key] = value;
+        });
+        exports.URL_PROPERTIES.forEach(function (urlProperty) {
+            if (urlProperty.name in urlFlags_1) {
+                console.log("Setting feature override from URL " + urlProperty.name + ": " +
+                    ("" + urlFlags_1[urlProperty.name]));
+                if (urlProperty.type === Type.NUMBER) {
+                    features[urlProperty.name] = +urlFlags_1[urlProperty.name];
+                }
+                else if (urlProperty.type === Type.BOOLEAN) {
+                    features[urlProperty.name] = urlFlags_1[urlProperty.name] === 'true';
+                }
+                else if (urlProperty.type === Type.STRING) {
+                    features[urlProperty.name] = urlFlags_1[urlProperty.name];
+                }
+                else {
+                    console.warn("Unknown URL param: " + urlProperty.name + ".");
+                }
+            }
+        });
+    }
+    return features;
+}
+exports.getFeaturesFromURL = getFeaturesFromURL;
+function hasExtension(gl, extensionName) {
+    var ext = gl.getExtension(extensionName);
+    return ext != null;
+}
+function createFloatTextureAndBindToFramebuffer(gl, webGLVersion) {
+    var frameBuffer = gl.createFramebuffer();
+    var texture = gl.createTexture();
+    gl.bindTexture(gl.TEXTURE_2D, texture);
+    var internalFormat = webGLVersion === 2 ? gl.RGBA32F : gl.RGBA;
+    gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, 1, 1, 0, gl.RGBA, gl.FLOAT, null);
+    gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);
+    gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);
+    var isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;
+    gl.bindTexture(gl.TEXTURE_2D, null);
+    gl.bindFramebuffer(gl.FRAMEBUFFER, null);
+    gl.deleteTexture(texture);
+    gl.deleteFramebuffer(frameBuffer);
+    return isFrameBufferComplete;
+}
+function getQueryParams(queryString) {
+    var params = {};
+    queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, function (s) {
+        var t = [];
+        for (var _i = 1; _i < arguments.length; _i++) {
+            t[_i - 1] = arguments[_i];
+        }
+        decodeParam(params, t[0], t[1]);
+        return t.join('=');
+    });
+    return params;
+}
+exports.getQueryParams = getQueryParams;
+function decodeParam(params, name, value) {
+    params[decodeURIComponent(name)] = decodeURIComponent(value || '');
+}
+//# sourceMappingURL=environment_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/environment_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/environment_util.js.map
new file mode 100644
index 0000000..1f592c5
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/environment_util.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"environment_util.js","sourceRoot":"","sources":["../src/environment_util.ts"],"names":[],"mappings":";;AAiBA,6CAA8C;AAqE9C,IAAY,IAIX;AAJD,WAAY,IAAI;IACd,mCAAM,CAAA;IACN,qCAAO,CAAA;IACP,mCAAM,CAAA;AACR,CAAC,EAJW,IAAI,GAAJ,YAAI,KAAJ,YAAI,QAIf;AAEY,QAAA,cAAc,GAAkB;IAC3C,EAAC,IAAI,EAAE,OAAO,EAAE,IAAI,EAAE,IAAI,CAAC,OAAO,EAAC;IACnC,EAAC,IAAI,EAAE,YAAY,EAAE,IAAI,EAAE,IAAI,CAAC,OAAO,EAAC;IACxC,EAAC,IAAI,EAAE,qBAAqB,EAAE,IAAI,EAAE,IAAI,CAAC,OAAO,EAAC;IACjD,EAAC,IAAI,EAAE,mBAAmB,EAAE,IAAI,EAAE,IAAI,CAAC,OAAO,EAAC;IAC/C,EAAC,IAAI,EAAE,YAAY,EAAE,IAAI,EAAE,IAAI,CAAC,OAAO,EAAC;IACxC,EAAC,IAAI,EAAE,+BAA+B,EAAE,IAAI,EAAE,IAAI,CAAC,OAAO,EAAC;IAC3D,EAAC,IAAI,EAAE,iBAAiB,EAAE,IAAI,EAAE,IAAI,CAAC,OAAO,EAAC;IAC7C,EAAC,IAAI,EAAE,0BAA0B,EAAE,IAAI,EAAE,IAAI,CAAC,OAAO,EAAC;IACtD,EAAC,IAAI,EAAE,mBAAmB,EAAE,IAAI,EAAE,IAAI,CAAC,OAAO,EAAC;IAC/C,EAAC,IAAI,EAAE,wBAAwB,EAAE,IAAI,EAAE,IAAI,CAAC,MAAM,EAAC;IACnD,EAAC,IAAI,EAAE,sBAAsB,EAAE,IAAI,EAAE,IAAI,CAAC,OAAO,EAAC;IAClD,EAAC,IAAI,EAAE,8CAA8C,EAAE,IAAI,EAAE,IAAI,CAAC,MAAM,EAAC;IACzE,EAAC,IAAI,EAAE,+CAA+C,EAAE,IAAI,EAAE,IAAI,CAAC,OAAO,EAAC;IAC3E,EAAC,IAAI,EAAE,eAAe,EAAE,IAAI,EAAE,IAAI,CAAC,MAAM,EAAC;IAC1C,EAAC,IAAI,EAAE,8BAA8B,EAAE,IAAI,EAAE,IAAI,CAAC,OAAO,EAAC;IAC1D,EAAC,IAAI,EAAE,8BAA8B,EAAE,IAAI,EAAE,IAAI,CAAC,OAAO,EAAC;IAC1D,EAAC,IAAI,EAAE,yBAAyB,EAAE,IAAI,EAAE,IAAI,CAAC,OAAO,EAAC;IACrD,EAAC,IAAI,EAAE,2BAA2B,EAAE,IAAI,EAAE,IAAI,CAAC,MAAM,EAAC;IACtD,EAAC,IAAI,EAAE,SAAS,EAAE,IAAI,EAAE,IAAI,CAAC,MAAM,EAAC;IACpC,EAAC,IAAI,EAAE,SAAS,EAAE,IAAI,EAAE,IAAI,CAAC,MAAM,EAAC;IACpC,EAAC,IAAI,EAAE,MAAM,EAAE,IAAI,EAAE,IAAI,CAAC,OAAO,EAAC;IAClC,EAAC,IAAI,EAAE,oCAAoC,EAAE,IAAI,EAAE,IAAI,CAAC,OAAO,EAAC;CACjE,CAAC;AAOF,+BAAsC,YAAiB;IACrD,IAAI;QACF,IAAM,EAAE,GAAG,6BAAe,CAAC,YAAY,CAAC,CAAC;QACzC,IAAI,EAAE,IAAI,IAAI,EAAE;YACd,OAAO,IAAI,CAAC;SACb;KACF;IAAC,OAAO,CAAC,EAAE;QACV,OAAO,KAAK,CAAC;KACd;IACD,OAAO,KAAK,CAAC;AACf,CAAC;AAVD,sDAUC;AAED,IAAI,gBAAwB,CAAC;AAI7B,gCAAuC,YAAoB;IACzD,IAAI,gBAAgB,IAAI,IAAI,EAAE;QAC5B,IAAM,EAAE,GAAG,6BAAe,CAAC,YAAY,CAAC,CAAC;QACzC,gBAAgB,GAAG,EAAE,CAAC,YAAY,CAAC,EAAE,CAAC,gBAAgB,CAAC,CAAC;KACzD;IACD,OAAO,gBAAgB,CAAC;AAC1B,CAAC;AAND,wDAMC;AAED,2CAAkD,YAAoB;IAEpE,IAAI,YAAY,KAAK,CAAC,EAAE;QACtB,OAAO,CAAC,CAAC;KACV;IAED,IAAI,iBAAyB,CAAC;IAC9B,IAAM,EAAE,GAAG,6BAAe,CAAC,YAAY,CAAC,CAAC;IAEzC,IAAI,YAAY,CAAC,EAAE,EAAE,iCAAiC,CAAC;QACnD,YAAY,KAAK,CAAC,EAAE;QACtB,iBAAiB,GAAG,CAAC,CAAC;KACvB;SAAM,IAAI,YAAY,CAAC,EAAE,EAAE,0BAA0B,CAAC,EAAE;QACvD,iBAAiB,GAAG,CAAC,CAAC;KACvB;SAAM;QACL,iBAAiB,GAAG,CAAC,CAAC;KACvB;IACD,OAAO,iBAAiB,CAAC;AAC3B,CAAC;AAlBD,8EAkBC;AAED,uCAA8C,YAAoB;IAChE,IAAI,YAAY,KAAK,CAAC,EAAE;QACtB,OAAO,KAAK,CAAC;KACd;IAED,IAAM,EAAE,GAAG,6BAAe,CAAC,YAAY,CAAC,CAAC;IAEzC,IAAI,YAAY,KAAK,CAAC,EAAE;QACtB,IAAI,CAAC,YAAY,CAAC,EAAE,EAAE,mBAAmB,CAAC,EAAE;YAC1C,OAAO,KAAK,CAAC;SACd;KACF;SAAM;QACL,IAAI,CAAC,YAAY,CAAC,EAAE,EAAE,wBAAwB,CAAC,EAAE;YAC/C,OAAO,KAAK,CAAC;SACd;KACF;IAED,IAAM,qBAAqB,GACvB,sCAAsC,CAAC,EAAE,EAAE,YAAY,CAAC,CAAC;IAC7D,OAAO,qBAAqB,CAAC;AAC/B,CAAC;AApBD,sEAoBC;AAED,uCAA8C,YAAoB;IAChE,IAAI,YAAY,KAAK,CAAC,EAAE;QACtB,OAAO,KAAK,CAAC;KACd;IAED,IAAM,EAAE,GAAG,6BAAe,CAAC,YAAY,CAAC,CAAC;IAEzC,IAAI,YAAY,KAAK,CAAC,EAAE;QACtB,IAAI,CAAC,YAAY,CAAC,EAAE,EAAE,mBAAmB,CAAC,EAAE;YAC1C,OAAO,KAAK,CAAC;SACd;QACD,IAAI,CAAC,YAAY,CAAC,EAAE,EAAE,0BAA0B,CAAC,EAAE;YACjD,OAAO,KAAK,CAAC;SACd;KACF;SAAM;QACL,IAAI,CAAC,YAAY,CAAC,EAAE,EAAE,wBAAwB,CAAC,EAAE;YAC/C,OAAO,KAAK,CAAC;SACd;KACF;IAED,IAAM,qBAAqB,GACvB,sCAAsC,CAAC,EAAE,EAAE,YAAY,CAAC,CAAC;IAC7D,OAAO,qBAAqB,CAAC;AAC/B,CAAC;AAvBD,sEAuBC;AAED,6BAAoC,YAAoB;IACtD,IAAI,YAAY,KAAK,CAAC,EAAE;QACtB,OAAO,KAAK,CAAC;KACd;IACD,IAAM,EAAE,GAAG,6BAAe,CAAC,YAAY,CAAC,CAAC;IAGzC,IAAM,SAAS,GAAI,EAAU,CAAC,SAAS,IAAI,IAAI,CAAC;IAChD,OAAO,SAAS,CAAC;AACnB,CAAC;AATD,kDASC;AAED;IACE,OAAO,OAAO,SAAS,KAAK,WAAW,IAAI,SAAS,IAAI,IAAI;QACxD,SAAS,CAAC,SAAS,IAAI,IAAI,IAAI,QAAQ,CAAC,IAAI,CAAC,SAAS,CAAC,SAAS,CAAC;QACjE,YAAY,CAAC,IAAI,CAAC,SAAS,CAAC,MAAM,CAAC,CAAC;AAC1C,CAAC;AAJD,4BAIC;AAGD,IAAM,yBAAyB,GAAG,WAAW,CAAC;AAC9C;IACE,IAAM,QAAQ,GAAa,EAAE,CAAC;IAE9B,IAAI,OAAO,MAAM,KAAK,WAAW,IAAI,OAAO,MAAM,CAAC,QAAQ,KAAK,WAAW;QACvE,OAAO,MAAM,CAAC,QAAQ,CAAC,MAAM,KAAK,WAAW,EAAE;QACjD,OAAO,QAAQ,CAAC;KACjB;IAED,IAAM,SAAS,GAAG,cAAc,CAAC,MAAM,CAAC,QAAQ,CAAC,MAAM,CAAC,CAAC;IACzD,IAAI,yBAAyB,IAAI,SAAS,EAAE;QAC1C,IAAM,UAAQ,GAA4B,EAAE,CAAC;QAE7C,IAAM,SAAS,GAAG,SAAS,CAAC,yBAAyB,CAAC,CAAC,KAAK,CAAC,GAAG,CAAC,CAAC;QAClE,SAAS,CAAC,OAAO,CAAC,UAAA,QAAQ;YAClB,IAAA,wBAAsD,EAArD,WAAG,EAAE,aAAK,CAA4C;YAC7D,UAAQ,CAAC,GAAG,CAAC,GAAG,KAAK,CAAC;QACxB,CAAC,CAAC,CAAC;QAEH,sBAAc,CAAC,OAAO,CAAC,UAAA,WAAW;YAChC,IAAI,WAAW,CAAC,IAAI,IAAI,UAAQ,EAAE;gBAChC,OAAO,CAAC,GAAG,CACP,uCAAqC,WAAW,CAAC,IAAI,OAAI;qBACzD,KAAG,UAAQ,CAAC,WAAW,CAAC,IAAI,CAAG,CAAA,CAAC,CAAC;gBACrC,IAAI,WAAW,CAAC,IAAI,KAAK,IAAI,CAAC,MAAM,EAAE;oBACpC,QAAQ,CAAC,WAAW,CAAC,IAAI,CAAC,GAAG,CAAC,UAAQ,CAAC,WAAW,CAAC,IAAI,CAAC,CAAC;iBAC1D;qBAAM,IAAI,WAAW,CAAC,IAAI,KAAK,IAAI,CAAC,OAAO,EAAE;oBAC5C,QAAQ,CAAC,WAAW,CAAC,IAAI,CAAC,GAAG,UAAQ,CAAC,WAAW,CAAC,IAAI,CAAC,KAAK,MAAM,CAAC;iBACpE;qBAAM,IAAI,WAAW,CAAC,IAAI,KAAK,IAAI,CAAC,MAAM,EAAE;oBAE3C,QAAQ,CAAC,WAAW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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/globals.d.ts b/node_modules/@tensorflow/tfjs-core/dist/globals.d.ts
new file mode 100644
index 0000000..7202e5d
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/globals.d.ts
@@ -0,0 +1,7 @@
+import { Environment } from './environment';
+export { customGrad, grad, grads, valueAndGrad, valueAndGrads, variableGrads } from './gradients';
+export declare const tidy: typeof Environment.tidy;
+export declare const keep: typeof Environment.keep;
+export declare const dispose: typeof Environment.dispose;
+export declare const time: typeof Environment.time;
+export declare const profile: typeof Environment.profile;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/globals.js b/node_modules/@tensorflow/tfjs-core/dist/globals.js
new file mode 100644
index 0000000..e3c9e17
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/globals.js
@@ -0,0 +1,16 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("./environment");
+var gradients_1 = require("./gradients");
+exports.customGrad = gradients_1.customGrad;
+exports.grad = gradients_1.grad;
+exports.grads = gradients_1.grads;
+exports.valueAndGrad = gradients_1.valueAndGrad;
+exports.valueAndGrads = gradients_1.valueAndGrads;
+exports.variableGrads = gradients_1.variableGrads;
+exports.tidy = environment_1.Environment.tidy;
+exports.keep = environment_1.Environment.keep;
+exports.dispose = environment_1.Environment.dispose;
+exports.time = environment_1.Environment.time;
+exports.profile = environment_1.Environment.profile;
+//# sourceMappingURL=globals.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/globals.js.map b/node_modules/@tensorflow/tfjs-core/dist/globals.js.map
new file mode 100644
index 0000000..9ed8a8f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/globals.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"globals.js","sourceRoot":"","sources":["../src/globals.ts"],"names":[],"mappings":";;AAiBA,6CAA0C;AAC1C,yCAAgG;AAAxF,iCAAA,UAAU,CAAA;AAAE,2BAAA,IAAI,CAAA;AAAE,4BAAA,KAAK,CAAA;AAAE,mCAAA,YAAY,CAAA;AAAE,oCAAA,aAAa,CAAA;AAAE,oCAAA,aAAa,CAAA;AAE9D,QAAA,IAAI,GAAG,yBAAW,CAAC,IAAI,CAAC;AACxB,QAAA,IAAI,GAAG,yBAAW,CAAC,IAAI,CAAC;AACxB,QAAA,OAAO,GAAG,yBAAW,CAAC,OAAO,CAAC;AAC9B,QAAA,IAAI,GAAG,yBAAW,CAAC,IAAI,CAAC;AACxB,QAAA,OAAO,GAAG,yBAAW,CAAC,OAAO,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/gradients.d.ts b/node_modules/@tensorflow/tfjs-core/dist/gradients.d.ts
new file mode 100644
index 0000000..689a0e4
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/gradients.d.ts
@@ -0,0 +1,20 @@
+import { CustomGradientFunc, ScopeFn } from './engine';
+import { Scalar, Tensor, Variable } from './tensor';
+import { NamedTensorMap, TensorContainer } from './tensor_types';
+declare function gradScope<T extends TensorContainer>(nameOrScopeFn: string | ScopeFn<T>, scopeFn?: ScopeFn<T>): T;
+declare function grad<I extends Tensor, O extends Tensor>(f: (x: I) => O): (x: I, dy?: O) => I;
+declare function grads<O extends Tensor>(f: (...args: Tensor[]) => O): (args: Tensor[], dy?: O) => Tensor[];
+declare function valueAndGrad<I extends Tensor, O extends Tensor>(f: (x: I) => O): (x: I, dy?: O) => {
+    value: O;
+    grad: I;
+};
+declare function valueAndGrads<O extends Tensor>(f: (...args: Tensor[]) => O): (args: Tensor[], dy?: O) => {
+    grads: Tensor[];
+    value: O;
+};
+declare function variableGrads(f: () => Scalar, varList?: Variable[]): {
+    value: Scalar;
+    grads: NamedTensorMap;
+};
+declare function customGrad<T extends Tensor>(f: CustomGradientFunc<T>): (...args: Tensor[]) => T;
+export { gradScope, customGrad, variableGrads, valueAndGrad, valueAndGrads, grad, grads, };
diff --git a/node_modules/@tensorflow/tfjs-core/dist/gradients.js b/node_modules/@tensorflow/tfjs-core/dist/gradients.js
new file mode 100644
index 0000000..d74545a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/gradients.js
@@ -0,0 +1,112 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("./environment");
+var tensor_1 = require("./tensor");
+var util = require("./util");
+function gradScope(nameOrScopeFn, scopeFn) {
+    return environment_1.ENV.engine.tidy(nameOrScopeFn, scopeFn, true);
+}
+exports.gradScope = gradScope;
+function grad(f) {
+    util.assert(util.isFunction(f), 'The f passed in grad(f) must be a function');
+    return function (x, dy) {
+        util.assert(x instanceof tensor_1.Tensor, 'The x passed in grad(f)(x) must be a tensor');
+        util.assert(dy == null || dy instanceof tensor_1.Tensor, 'The dy passed in grad(f)(x, dy) must be a tensor');
+        return environment_1.ENV.engine.tidy(function () {
+            var _a = environment_1.ENV.engine.gradients(function () { return f(x); }, [x], dy), value = _a.value, grads = _a.grads;
+            if (dy != null) {
+                util.assertShapesMatch(value.shape, dy.shape, 'The shape of dy passed in grad(f)(x, dy) must match the shape ' +
+                    'returned by f(x)');
+            }
+            checkGrads(grads);
+            return grads[0];
+        });
+    };
+}
+exports.grad = grad;
+function grads(f) {
+    util.assert(util.isFunction(f), 'The f passed in grads(f) must be a function');
+    return function (args, dy) {
+        util.assert(Array.isArray(args) && args.every(function (arg) { return arg instanceof tensor_1.Tensor; }), 'The args passed in grads(f)(args) must be an array of tensors');
+        util.assert(dy == null || dy instanceof tensor_1.Tensor, 'The dy passed in grads(f)(args, dy) must be a tensor');
+        return environment_1.ENV.engine.tidy(function () {
+            var _a = environment_1.ENV.engine.gradients(function () { return f.apply(void 0, args); }, args, dy), value = _a.value, grads = _a.grads;
+            if (dy != null) {
+                util.assertShapesMatch(value.shape, dy.shape, 'The shape of dy passed in grads(f)([x1,...], dy) must ' +
+                    'match the shape returned by f([x1,...])');
+            }
+            checkGrads(grads);
+            return grads;
+        });
+    };
+}
+exports.grads = grads;
+function valueAndGrad(f) {
+    util.assert(util.isFunction(f), 'The f passed in valueAndGrad(f) must be a function');
+    return function (x, dy) {
+        util.assert(x instanceof tensor_1.Tensor, 'The x passed in valueAndGrad(f)(x) must be a tensor');
+        util.assert(dy == null || dy instanceof tensor_1.Tensor, 'The dy passed in valueAndGrad(f)(x, dy) must be a tensor');
+        var _a = environment_1.ENV.engine.gradients(function () { return f(x); }, [x], dy), grads = _a.grads, value = _a.value;
+        checkGrads(grads);
+        return { grad: grads[0], value: value };
+    };
+}
+exports.valueAndGrad = valueAndGrad;
+function valueAndGrads(f) {
+    util.assert(util.isFunction(f), 'The f passed in valueAndGrads(f) must be a function');
+    return function (args, dy) {
+        util.assert(Array.isArray(args) && args.every(function (arg) { return arg instanceof tensor_1.Tensor; }), 'The args passed in valueAndGrads(f)(args) must be array of tensors');
+        util.assert(dy == null || dy instanceof tensor_1.Tensor, 'The dy passed in valueAndGrads(f)(args, dy) must be a tensor');
+        var res = environment_1.ENV.engine.gradients(function () { return f.apply(void 0, args); }, args, dy);
+        if (dy != null) {
+            util.assertShapesMatch(res.value.shape, dy.shape, 'The shape of dy passed in valueAndGrads(f)([x1,...], dy) must ' +
+                'match the shape returned by f([x1,...])');
+        }
+        checkGrads(res.grads);
+        return res;
+    };
+}
+exports.valueAndGrads = valueAndGrads;
+function variableGrads(f, varList) {
+    util.assert(util.isFunction(f), 'The f passed in variableGrads(f) must be a function');
+    util.assert(varList == null ||
+        Array.isArray(varList) && varList.every(function (v) { return v instanceof tensor_1.Variable; }), 'The varList passed in variableGrads(f, varList) must be an array ' +
+        'of variables');
+    if (varList == null) {
+        varList = [];
+        for (var varName in environment_1.ENV.engine.registeredVariables) {
+            varList.push(environment_1.ENV.engine.registeredVariables[varName]);
+        }
+    }
+    var originalVarCount = varList.length;
+    varList = varList.filter(function (variable) { return variable.trainable; });
+    util.assert(varList.length > 0, "variableGrads() expects at least one of the input variables to be " +
+        ("trainable, but none of the " + originalVarCount + " variables is ") +
+        "trainable.");
+    var allowNoGradients = true;
+    var _a = environment_1.ENV.engine.gradients(f, varList, null, allowNoGradients), value = _a.value, grads = _a.grads;
+    util.assert(grads.some(function (g) { return g != null; }), 'Cannot find a connection between any variable and the result of the ' +
+        'loss function y=f(x). Please make sure the operations that use ' +
+        'variables are inside the function f passed to minimize().');
+    util.assert(value.rank === 0, "The f passed in variableGrads(f) must return a scalar, but it " +
+        ("returned a rank-" + value.rank + " tensor"));
+    var namedGrads = {};
+    varList.forEach(function (v, i) {
+        if (grads[i] != null) {
+            namedGrads[v.name] = grads[i];
+        }
+    });
+    return { value: value, grads: namedGrads };
+}
+exports.variableGrads = variableGrads;
+function customGrad(f) {
+    return environment_1.ENV.engine.customGrad(f);
+}
+exports.customGrad = customGrad;
+function checkGrads(grads) {
+    var numNullGradients = grads.filter(function (g) { return g == null; }).length;
+    if (numNullGradients > 0) {
+        throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that\n    the f you passed encloses all operations that lead from x to y.");
+    }
+}
+//# sourceMappingURL=gradients.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/gradients.js.map b/node_modules/@tensorflow/tfjs-core/dist/gradients.js.map
new file mode 100644
index 0000000..6d35893
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/gradients.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/index.d.ts b/node_modules/@tensorflow/tfjs-core/dist/index.d.ts
new file mode 100644
index 0000000..27ed0d2
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/index.d.ts
@@ -0,0 +1,39 @@
+import './kernels/backend_webgl';
+import './kernels/backend_cpu';
+import { nextFrame } from './browser_util';
+import * as environment from './environment';
+import * as io from './io/io';
+import * as math from './math';
+import * as serialization from './serialization';
+import * as test_util from './test_util';
+import * as util from './util';
+import { version } from './version';
+import * as webgl from './webgl';
+export { InferenceModel, ModelPredictConfig } from './model_types';
+export { AdadeltaOptimizer } from './optimizers/adadelta_optimizer';
+export { AdagradOptimizer } from './optimizers/adagrad_optimizer';
+export { AdamOptimizer } from './optimizers/adam_optimizer';
+export { AdamaxOptimizer } from './optimizers/adamax_optimizer';
+export { MomentumOptimizer } from './optimizers/momentum_optimizer';
+export { Optimizer } from './optimizers/optimizer';
+export { RMSPropOptimizer } from './optimizers/rmsprop_optimizer';
+export { SGDOptimizer } from './optimizers/sgd_optimizer';
+export { Scalar, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, TensorBuffer, variable, Variable } from './tensor';
+export { NamedTensorMap } from './tensor_types';
+export { DataType, DataTypeMap, DataValues, Rank, ShapeMap } from './types';
+export * from './ops/ops';
+export { LSTMCellFunc } from './ops/lstm';
+export { Reduction } from './ops/loss_ops';
+export * from './train';
+export * from './globals';
+export { Features } from './environment_util';
+export { TimingInfo } from './engine';
+export { ENV, Environment } from './environment';
+export declare const setBackend: typeof environment.Environment.setBackend;
+export declare const getBackend: typeof environment.Environment.getBackend;
+export declare const disposeVariables: typeof environment.Environment.disposeVariables;
+export declare const memory: typeof environment.Environment.memory;
+export { version as version_core };
+export { nextFrame };
+export { environment, io, math, serialization, test_util, util, webgl };
+export { KernelBackend, BackendTimingInfo, DataMover, DataStorage } from './kernels/backend';
diff --git a/node_modules/@tensorflow/tfjs-core/dist/index.js b/node_modules/@tensorflow/tfjs-core/dist/index.js
new file mode 100644
index 0000000..dd25d08
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/index.js
@@ -0,0 +1,68 @@
+"use strict";
+function __export(m) {
+    for (var p in m) if (!exports.hasOwnProperty(p)) exports[p] = m[p];
+}
+Object.defineProperty(exports, "__esModule", { value: true });
+require("./kernels/backend_webgl");
+require("./kernels/backend_cpu");
+var browser_util_1 = require("./browser_util");
+exports.nextFrame = browser_util_1.nextFrame;
+var environment = require("./environment");
+exports.environment = environment;
+var environment_1 = require("./environment");
+var io = require("./io/io");
+exports.io = io;
+var math = require("./math");
+exports.math = math;
+var serialization = require("./serialization");
+exports.serialization = serialization;
+var tensor_1 = require("./tensor");
+var test_util = require("./test_util");
+exports.test_util = test_util;
+var util = require("./util");
+exports.util = util;
+var version_1 = require("./version");
+exports.version_core = version_1.version;
+var webgl = require("./webgl");
+exports.webgl = webgl;
+var adadelta_optimizer_1 = require("./optimizers/adadelta_optimizer");
+exports.AdadeltaOptimizer = adadelta_optimizer_1.AdadeltaOptimizer;
+var adagrad_optimizer_1 = require("./optimizers/adagrad_optimizer");
+exports.AdagradOptimizer = adagrad_optimizer_1.AdagradOptimizer;
+var adam_optimizer_1 = require("./optimizers/adam_optimizer");
+exports.AdamOptimizer = adam_optimizer_1.AdamOptimizer;
+var adamax_optimizer_1 = require("./optimizers/adamax_optimizer");
+exports.AdamaxOptimizer = adamax_optimizer_1.AdamaxOptimizer;
+var momentum_optimizer_1 = require("./optimizers/momentum_optimizer");
+exports.MomentumOptimizer = momentum_optimizer_1.MomentumOptimizer;
+var optimizer_1 = require("./optimizers/optimizer");
+exports.Optimizer = optimizer_1.Optimizer;
+var rmsprop_optimizer_1 = require("./optimizers/rmsprop_optimizer");
+exports.RMSPropOptimizer = rmsprop_optimizer_1.RMSPropOptimizer;
+var sgd_optimizer_1 = require("./optimizers/sgd_optimizer");
+exports.SGDOptimizer = sgd_optimizer_1.SGDOptimizer;
+var tensor_2 = require("./tensor");
+exports.Tensor = tensor_2.Tensor;
+exports.TensorBuffer = tensor_2.TensorBuffer;
+exports.variable = tensor_2.variable;
+exports.Variable = tensor_2.Variable;
+var types_1 = require("./types");
+exports.Rank = types_1.Rank;
+__export(require("./ops/ops"));
+var loss_ops_1 = require("./ops/loss_ops");
+exports.Reduction = loss_ops_1.Reduction;
+__export(require("./train"));
+__export(require("./globals"));
+var environment_2 = require("./environment");
+exports.ENV = environment_2.ENV;
+exports.Environment = environment_2.Environment;
+exports.setBackend = environment_1.Environment.setBackend;
+exports.getBackend = environment_1.Environment.getBackend;
+exports.disposeVariables = environment_1.Environment.disposeVariables;
+exports.memory = environment_1.Environment.memory;
+var backend_1 = require("./kernels/backend");
+exports.KernelBackend = backend_1.KernelBackend;
+exports.DataStorage = backend_1.DataStorage;
+var ops = require("./ops/ops");
+tensor_1.setOpHandler(ops);
+//# sourceMappingURL=index.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/index.js.map b/node_modules/@tensorflow/tfjs-core/dist/index.js.map
new file mode 100644
index 0000000..cfaa4fa
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/index.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"index.js","sourceRoot":"","sources":["../src/index.ts"],"names":[],"mappings":";;;;;AAoBA,mCAAiC;AACjC,iCAA+B;AAE/B,+CAAyC;AA8CjC,oBA9CA,wBAAS,CA8CA;AA7CjB,2CAA6C;AAgDrC,kCAAW;AA/CnB,6CAA0C;AAG1C,4BAA8B;AA4CT,gBAAE;AA3CvB,6BAA+B;AA2CN,oBAAI;AA1C7B,+CAAiD;AA0ClB,sCAAa;AAzC5C,mCAAsC;AACtC,uCAAyC;AAwCK,8BAAS;AAvCvD,6BAA+B;AAuC0B,oBAAI;AAtC7D,qCAAkC;AAiCf,uBAjCX,iBAAO,CAiCgB;AAhC/B,+BAAiC;AAqC8B,sBAAK;AAhCpE,sEAAkE;AAA1D,iDAAA,iBAAiB,CAAA;AACzB,oEAAgE;AAAxD,+CAAA,gBAAgB,CAAA;AACxB,8DAA0D;AAAlD,yCAAA,aAAa,CAAA;AACrB,kEAA8D;AAAtD,6CAAA,eAAe,CAAA;AACvB,sEAAkE;AAA1D,iDAAA,iBAAiB,CAAA;AACzB,oDAAiD;AAAzC,gCAAA,SAAS,CAAA;AACjB,oEAAgE;AAAxD,+CAAA,gBAAgB,CAAA;AACxB,4DAAwD;AAAhD,uCAAA,YAAY,CAAA;AACpB,mCAAkH;AAAlG,0BAAA,MAAM,CAAA;AAA0C,gCAAA,YAAY,CAAA;AAAE,4BAAA,QAAQ,CAAA;AAAE,4BAAA,QAAQ,CAAA;AAEhG,iCAA0E;AAA/B,uBAAA,IAAI,CAAA;AAE/C,+BAA0B;AAE1B,2CAAyC;AAAjC,+BAAA,SAAS,CAAA;AAEjB,6BAAwB;AACxB,+BAA0B;AAI1B,6CAA+C;AAAvC,4BAAA,GAAG,CAAA;AAAE,oCAAA,WAAW,CAAA;AAEX,QAAA,UAAU,GAAG,yBAAW,CAAC,UAAU,CAAC;AACpC,QAAA,UAAU,GAAG,yBAAW,CAAC,UAAU,CAAC;AACpC,QAAA,gBAAgB,GAAG,yBAAW,CAAC,gBAAgB,CAAC;AAChD,QAAA,MAAM,GAAG,yBAAW,CAAC,MAAM,CAAC;AASzC,6CAA2F;AAAnF,kCAAA,aAAa,CAAA;AAAgC,gCAAA,WAAW,CAAA;AAEhE,+BAAiC;AACjC,qBAAY,CAAC,GAAG,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/browser_files.d.ts b/node_modules/@tensorflow/tfjs-core/dist/io/browser_files.d.ts
new file mode 100644
index 0000000..92ab5d1
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/browser_files.d.ts
@@ -0,0 +1,14 @@
+import { IORouter } from './router_registry';
+import { IOHandler, ModelArtifacts, SaveResult } from './types';
+export declare class BrowserDownloads implements IOHandler {
+    private readonly modelTopologyFileName;
+    private readonly weightDataFileName;
+    private readonly jsonAnchor;
+    private readonly weightDataAnchor;
+    static readonly URL_SCHEME: string;
+    constructor(fileNamePrefix?: string);
+    save(modelArtifacts: ModelArtifacts): Promise<SaveResult>;
+}
+export declare const browserDownloadsRouter: IORouter;
+export declare function browserDownloads(fileNamePrefix?: string): IOHandler;
+export declare function browserFiles(files: File[]): IOHandler;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/browser_files.js b/node_modules/@tensorflow/tfjs-core/dist/io/browser_files.js
new file mode 100644
index 0000000..1e9b65d
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/browser_files.js
@@ -0,0 +1,236 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var io_utils_1 = require("./io_utils");
+var router_registry_1 = require("./router_registry");
+var DEFAULT_FILE_NAME_PREFIX = 'model';
+var DEFAULT_JSON_EXTENSION_NAME = '.json';
+var DEFAULT_WEIGHT_DATA_EXTENSION_NAME = '.weights.bin';
+var BrowserDownloads = (function () {
+    function BrowserDownloads(fileNamePrefix) {
+        if (!environment_1.ENV.get('IS_BROWSER')) {
+            throw new Error('browserDownloads() cannot proceed because the current environment ' +
+                'is not a browser.');
+        }
+        if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {
+            fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);
+        }
+        if (fileNamePrefix == null || fileNamePrefix.length === 0) {
+            fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;
+        }
+        this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;
+        this.weightDataFileName =
+            fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;
+    }
+    BrowserDownloads.prototype.save = function (modelArtifacts) {
+        return __awaiter(this, void 0, void 0, function () {
+            var weightsURL, weightsManifest, modelTopologyAndWeightManifest, modelTopologyAndWeightManifestURL, jsonAnchor, weightDataAnchor;
+            return __generator(this, function (_a) {
+                weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], { type: 'application/octet-stream' }));
+                if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
+                    throw new Error('BrowserDownloads.save() does not support saving model topology ' +
+                        'in binary formats yet.');
+                }
+                else {
+                    weightsManifest = [{
+                            paths: ['./' + this.weightDataFileName],
+                            weights: modelArtifacts.weightSpecs
+                        }];
+                    modelTopologyAndWeightManifest = {
+                        modelTopology: modelArtifacts.modelTopology,
+                        weightsManifest: weightsManifest
+                    };
+                    modelTopologyAndWeightManifestURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: 'application/json' }));
+                    jsonAnchor = this.jsonAnchor == null ? document.createElement('a') :
+                        this.jsonAnchor;
+                    jsonAnchor.download = this.modelTopologyFileName;
+                    jsonAnchor.href = modelTopologyAndWeightManifestURL;
+                    jsonAnchor.click();
+                    if (modelArtifacts.weightData != null) {
+                        weightDataAnchor = this.weightDataAnchor == null ?
+                            document.createElement('a') :
+                            this.weightDataAnchor;
+                        weightDataAnchor.download = this.weightDataFileName;
+                        weightDataAnchor.href = weightsURL;
+                        weightDataAnchor.click();
+                    }
+                    return [2, { modelArtifactsInfo: io_utils_1.getModelArtifactsInfoForJSON(modelArtifacts) }];
+                }
+                return [2];
+            });
+        });
+    };
+    BrowserDownloads.URL_SCHEME = 'downloads://';
+    return BrowserDownloads;
+}());
+exports.BrowserDownloads = BrowserDownloads;
+var BrowserFiles = (function () {
+    function BrowserFiles(files) {
+        if (files == null || files.length < 1) {
+            throw new Error("When calling browserFiles, at least 1 file is required, " +
+                ("but received " + files));
+        }
+        this.files = files;
+    }
+    BrowserFiles.prototype.load = function () {
+        return __awaiter(this, void 0, void 0, function () {
+            var jsonFile, weightFiles;
+            var _this = this;
+            return __generator(this, function (_a) {
+                jsonFile = this.files[0];
+                weightFiles = this.files.slice(1);
+                return [2, new Promise(function (resolve, reject) {
+                        var jsonReader = new FileReader();
+                        jsonReader.onload = function (event) {
+                            var modelJSON = JSON.parse(event.target.result);
+                            var modelTopology = modelJSON.modelTopology;
+                            if (modelTopology == null) {
+                                reject(new Error("modelTopology field is missing from file " + jsonFile.name));
+                                return;
+                            }
+                            if (weightFiles.length === 0) {
+                                resolve({ modelTopology: modelTopology });
+                            }
+                            var weightsManifest = modelJSON.weightsManifest;
+                            if (weightsManifest == null) {
+                                reject(new Error("weightManifest field is missing from file " + jsonFile.name));
+                                return;
+                            }
+                            var pathToFile;
+                            try {
+                                pathToFile =
+                                    _this.checkManifestAndWeightFiles(weightsManifest, weightFiles);
+                            }
+                            catch (err) {
+                                reject(err);
+                                return;
+                            }
+                            var weightSpecs = [];
+                            var paths = [];
+                            var perFileBuffers = [];
+                            weightsManifest.forEach(function (weightsGroup) {
+                                weightsGroup.paths.forEach(function (path) {
+                                    paths.push(path);
+                                    perFileBuffers.push(null);
+                                });
+                                weightSpecs.push.apply(weightSpecs, weightsGroup.weights);
+                            });
+                            weightsManifest.forEach(function (weightsGroup) {
+                                weightsGroup.paths.forEach(function (path) {
+                                    var weightFileReader = new FileReader();
+                                    weightFileReader.onload = function (event) {
+                                        var weightData = event.target.result;
+                                        var index = paths.indexOf(path);
+                                        perFileBuffers[index] = weightData;
+                                        if (perFileBuffers.indexOf(null) === -1) {
+                                            resolve({
+                                                modelTopology: modelTopology,
+                                                weightSpecs: weightSpecs,
+                                                weightData: io_utils_1.concatenateArrayBuffers(perFileBuffers),
+                                            });
+                                        }
+                                    };
+                                    weightFileReader.onerror = function (error) {
+                                        return reject("Failed to weights data from file of path '" + path + "'.");
+                                    };
+                                    weightFileReader.readAsArrayBuffer(pathToFile[path]);
+                                });
+                            });
+                        };
+                        jsonReader.onerror = function (error) { return reject("Failed to read model topology and weights manifest JSON " +
+                            ("from file '" + jsonFile.name + "'. BrowserFiles supports loading ") +
+                            "Keras-style tf.Model artifacts only."); };
+                        jsonReader.readAsText(jsonFile);
+                    })];
+            });
+        });
+    };
+    BrowserFiles.prototype.checkManifestAndWeightFiles = function (manifest, files) {
+        var basenames = [];
+        var fileNames = files.map(function (file) { return io_utils_1.basename(file.name); });
+        var pathToFile = {};
+        for (var _i = 0, manifest_1 = manifest; _i < manifest_1.length; _i++) {
+            var group = manifest_1[_i];
+            group.paths.forEach(function (path) {
+                var pathBasename = io_utils_1.basename(path);
+                if (basenames.indexOf(pathBasename) !== -1) {
+                    throw new Error("Duplicate file basename found in weights manifest: " +
+                        ("'" + pathBasename + "'"));
+                }
+                basenames.push(pathBasename);
+                if (fileNames.indexOf(pathBasename) === -1) {
+                    throw new Error("Weight file with basename '" + pathBasename + "' is not provided.");
+                }
+                else {
+                    pathToFile[path] = files[fileNames.indexOf(pathBasename)];
+                }
+            });
+        }
+        if (basenames.length !== files.length) {
+            throw new Error("Mismatch in the number of files in weights manifest " +
+                ("(" + basenames.length + ") and the number of weight files provided ") +
+                ("(" + files.length + ")."));
+        }
+        return pathToFile;
+    };
+    return BrowserFiles;
+}());
+exports.browserDownloadsRouter = function (url) {
+    if (!environment_1.ENV.get('IS_BROWSER')) {
+        return null;
+    }
+    else {
+        if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {
+            return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));
+        }
+        else {
+            return null;
+        }
+    }
+};
+router_registry_1.IORouterRegistry.registerSaveRouter(exports.browserDownloadsRouter);
+function browserDownloads(fileNamePrefix) {
+    if (fileNamePrefix === void 0) { fileNamePrefix = 'model'; }
+    return new BrowserDownloads(fileNamePrefix);
+}
+exports.browserDownloads = browserDownloads;
+function browserFiles(files) {
+    return new BrowserFiles(files);
+}
+exports.browserFiles = browserFiles;
+//# sourceMappingURL=browser_files.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/browser_files.js.map b/node_modules/@tensorflow/tfjs-core/dist/io/browser_files.js.map
new file mode 100644
index 0000000..a57e4e6
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/browser_files.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/browser_files_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/io/browser_files_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/browser_files_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/browser_files_test.js b/node_modules/@tensorflow/tfjs-core/dist/io/browser_files_test.js
new file mode 100644
index 0000000..277a145
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/browser_files_test.js
@@ -0,0 +1,689 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+var _this = this;
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+var browser_files_1 = require("./browser_files");
+var modelTopology1 = {
+    'class_name': 'Sequential',
+    'keras_version': '2.1.4',
+    'config': [{
+            'class_name': 'Dense',
+            'config': {
+                'kernel_initializer': {
+                    'class_name': 'VarianceScaling',
+                    'config': {
+                        'distribution': 'uniform',
+                        'scale': 1.0,
+                        'seed': null,
+                        'mode': 'fan_avg'
+                    }
+                },
+                'name': 'dense',
+                'kernel_constraint': null,
+                'bias_regularizer': null,
+                'bias_constraint': null,
+                'dtype': 'float32',
+                'activation': 'linear',
+                'trainable': true,
+                'kernel_regularizer': null,
+                'bias_initializer': { 'class_name': 'Zeros', 'config': {} },
+                'units': 1,
+                'batch_input_shape': [null, 3],
+                'use_bias': true,
+                'activity_regularizer': null
+            }
+        }],
+    'backend': 'tensorflow'
+};
+var weightSpecs1 = [
+    {
+        name: 'dense/kernel',
+        shape: [3, 1],
+        dtype: 'float32',
+    },
+    {
+        name: 'dense/bias',
+        shape: [1],
+        dtype: 'float32',
+    }
+];
+var weightData1 = new ArrayBuffer(16);
+var artifacts1 = {
+    modelTopology: modelTopology1,
+    weightSpecs: weightSpecs1,
+    weightData: weightData1,
+};
+jasmine_util_1.describeWithFlags('browserDownloads', test_util_1.BROWSER_ENVS, function () {
+    var FakeHTMLAnchorElement = (function () {
+        function FakeHTMLAnchorElement() {
+            this.clicked = 0;
+        }
+        FakeHTMLAnchorElement.prototype.click = function () {
+            this.clicked++;
+        };
+        return FakeHTMLAnchorElement;
+    }());
+    var fakeAnchors = [];
+    var fakeAnchorCount = 0;
+    beforeEach(function () {
+        fakeAnchorCount = 0;
+        fakeAnchors = [new FakeHTMLAnchorElement(), new FakeHTMLAnchorElement()];
+        spyOn(document, 'createElement').and.callFake(function (tag) {
+            return fakeAnchors[fakeAnchorCount++];
+        });
+    });
+    it('Explicit file name prefix, with existing anchors', function () { return __awaiter(_this, void 0, void 0, function () {
+        var testStartDate, downloadTrigger, saveResult, artifactsInfo, jsonAnchor, weightDataAnchor, jsonContent, modelTopologyAndWeightsManifest, _a, _b, weightsManifest, response, buffer;
+        return __generator(this, function (_c) {
+            switch (_c.label) {
+                case 0:
+                    testStartDate = new Date();
+                    downloadTrigger = tf.io.getSaveHandlers('downloads://test-model')[0];
+                    return [4, downloadTrigger.save(artifacts1)];
+                case 1:
+                    saveResult = _c.sent();
+                    expect(saveResult.errors).toEqual(undefined);
+                    artifactsInfo = saveResult.modelArtifactsInfo;
+                    expect(artifactsInfo.dateSaved.getTime())
+                        .toBeGreaterThanOrEqual(testStartDate.getTime());
+                    expect(saveResult.modelArtifactsInfo.modelTopologyBytes)
+                        .toEqual(JSON.stringify(modelTopology1).length);
+                    expect(saveResult.modelArtifactsInfo.weightSpecsBytes)
+                        .toEqual(JSON.stringify(weightSpecs1).length);
+                    expect(saveResult.modelArtifactsInfo.weightDataBytes).toEqual(16);
+                    jsonAnchor = fakeAnchors[0];
+                    weightDataAnchor = fakeAnchors[1];
+                    expect(jsonAnchor.download).toEqual('test-model.json');
+                    expect(weightDataAnchor.download).toEqual('test-model.weights.bin');
+                    return [4, fetch(jsonAnchor.href)];
+                case 2:
+                    jsonContent = _c.sent();
+                    _b = (_a = JSON).parse;
+                    return [4, jsonContent.text()];
+                case 3:
+                    modelTopologyAndWeightsManifest = _b.apply(_a, [_c.sent()]);
+                    expect(modelTopologyAndWeightsManifest.modelTopology)
+                        .toEqual(modelTopology1);
+                    weightsManifest = modelTopologyAndWeightsManifest.weightsManifest;
+                    expect(weightsManifest.length).toEqual(1);
+                    expect(weightsManifest[0].paths).toEqual(['./test-model.weights.bin']);
+                    expect(weightsManifest[0].weights).toEqual(weightSpecs1);
+                    return [4, fetch(weightDataAnchor.href)];
+                case 4:
+                    response = _c.sent();
+                    return [4, response.arrayBuffer()];
+                case 5:
+                    buffer = _c.sent();
+                    expect(buffer).toEqual(weightData1);
+                    expect(jsonAnchor.clicked).toEqual(1);
+                    expect(weightDataAnchor.clicked).toEqual(1);
+                    return [2];
+            }
+        });
+    }); });
+    it('URL scheme in explicit name gets stripped', function () { return __awaiter(_this, void 0, void 0, function () {
+        var testStartDate, downloadTrigger, saveResult, artifactsInfo, jsonAnchor, weightDataAnchor, jsonContent, modelTopologyAndWeightsManifest, _a, _b, weightsManifest, response, buffer;
+        return __generator(this, function (_c) {
+            switch (_c.label) {
+                case 0:
+                    testStartDate = new Date();
+                    downloadTrigger = browser_files_1.browserDownloads('downloads://test-model');
+                    return [4, downloadTrigger.save(artifacts1)];
+                case 1:
+                    saveResult = _c.sent();
+                    expect(saveResult.errors).toEqual(undefined);
+                    artifactsInfo = saveResult.modelArtifactsInfo;
+                    expect(artifactsInfo.dateSaved.getTime())
+                        .toBeGreaterThanOrEqual(testStartDate.getTime());
+                    expect(saveResult.modelArtifactsInfo.modelTopologyBytes)
+                        .toEqual(JSON.stringify(modelTopology1).length);
+                    expect(saveResult.modelArtifactsInfo.weightSpecsBytes)
+                        .toEqual(JSON.stringify(weightSpecs1).length);
+                    expect(saveResult.modelArtifactsInfo.weightDataBytes).toEqual(16);
+                    jsonAnchor = fakeAnchors[0];
+                    weightDataAnchor = fakeAnchors[1];
+                    expect(jsonAnchor.download).toEqual('test-model.json');
+                    expect(weightDataAnchor.download).toEqual('test-model.weights.bin');
+                    return [4, fetch(jsonAnchor.href)];
+                case 2:
+                    jsonContent = _c.sent();
+                    _b = (_a = JSON).parse;
+                    return [4, jsonContent.text()];
+                case 3:
+                    modelTopologyAndWeightsManifest = _b.apply(_a, [_c.sent()]);
+                    expect(modelTopologyAndWeightsManifest.modelTopology)
+                        .toEqual(modelTopology1);
+                    weightsManifest = modelTopologyAndWeightsManifest.weightsManifest;
+                    expect(weightsManifest.length).toEqual(1);
+                    expect(weightsManifest[0].paths).toEqual(['./test-model.weights.bin']);
+                    expect(weightsManifest[0].weights).toEqual(weightSpecs1);
+                    return [4, fetch(weightDataAnchor.href)];
+                case 4:
+                    response = _c.sent();
+                    return [4, response.arrayBuffer()];
+                case 5:
+                    buffer = _c.sent();
+                    expect(buffer).toEqual(weightData1);
+                    expect(jsonAnchor.clicked).toEqual(1);
+                    expect(weightDataAnchor.clicked).toEqual(1);
+                    return [2];
+            }
+        });
+    }); });
+    it('No file name provided, with existing anchors', function () { return __awaiter(_this, void 0, void 0, function () {
+        var testStartDate, downloadTrigger, saveResult, artifactsInfo, jsonAnchor, weightDataAnchor, jsonContent, modelTopologyAndWeightsManifest, _a, _b, weightsManifest, response, buffer;
+        return __generator(this, function (_c) {
+            switch (_c.label) {
+                case 0:
+                    testStartDate = new Date();
+                    downloadTrigger = browser_files_1.browserDownloads();
+                    return [4, downloadTrigger.save(artifacts1)];
+                case 1:
+                    saveResult = _c.sent();
+                    expect(saveResult.errors).toEqual(undefined);
+                    artifactsInfo = saveResult.modelArtifactsInfo;
+                    expect(artifactsInfo.dateSaved.getTime())
+                        .toBeGreaterThanOrEqual(testStartDate.getTime());
+                    expect(saveResult.modelArtifactsInfo.modelTopologyBytes)
+                        .toEqual(JSON.stringify(modelTopology1).length);
+                    expect(saveResult.modelArtifactsInfo.weightSpecsBytes)
+                        .toEqual(JSON.stringify(weightSpecs1).length);
+                    expect(saveResult.modelArtifactsInfo.weightDataBytes).toEqual(16);
+                    jsonAnchor = fakeAnchors[0];
+                    weightDataAnchor = fakeAnchors[1];
+                    expect(jsonAnchor.download).toEqual('model.json');
+                    expect(weightDataAnchor.download).toEqual('model.weights.bin');
+                    return [4, fetch(jsonAnchor.href)];
+                case 2:
+                    jsonContent = _c.sent();
+                    _b = (_a = JSON).parse;
+                    return [4, jsonContent.text()];
+                case 3:
+                    modelTopologyAndWeightsManifest = _b.apply(_a, [_c.sent()]);
+                    expect(modelTopologyAndWeightsManifest.modelTopology)
+                        .toEqual(modelTopology1);
+                    weightsManifest = modelTopologyAndWeightsManifest.weightsManifest;
+                    expect(weightsManifest.length).toEqual(1);
+                    expect(weightsManifest[0].paths).toEqual(['./model.weights.bin']);
+                    expect(weightsManifest[0].weights).toEqual(weightSpecs1);
+                    return [4, fetch(weightDataAnchor.href)];
+                case 4:
+                    response = _c.sent();
+                    return [4, response.arrayBuffer()];
+                case 5:
+                    buffer = _c.sent();
+                    expect(buffer).toEqual(weightData1);
+                    return [2];
+            }
+        });
+    }); });
+    it('Download only model topology', function () { return __awaiter(_this, void 0, void 0, function () {
+        var testStartDate, downloadTrigger, modelTopologyOnlyArtifacts, saveResult, artifactsInfo, jsonAnchor, weightDataAnchor, jsonContent, modelTopologyAndWeightsManifest, _a, _b;
+        return __generator(this, function (_c) {
+            switch (_c.label) {
+                case 0:
+                    testStartDate = new Date();
+                    downloadTrigger = browser_files_1.browserDownloads();
+                    modelTopologyOnlyArtifacts = {
+                        modelTopology: modelTopology1,
+                    };
+                    return [4, downloadTrigger.save(modelTopologyOnlyArtifacts)];
+                case 1:
+                    saveResult = _c.sent();
+                    expect(saveResult.errors).toEqual(undefined);
+                    artifactsInfo = saveResult.modelArtifactsInfo;
+                    expect(artifactsInfo.dateSaved.getTime())
+                        .toBeGreaterThanOrEqual(testStartDate.getTime());
+                    expect(saveResult.modelArtifactsInfo.modelTopologyBytes)
+                        .toEqual(JSON.stringify(modelTopology1).length);
+                    expect(saveResult.modelArtifactsInfo.weightSpecsBytes).toEqual(0);
+                    expect(saveResult.modelArtifactsInfo.weightDataBytes).toEqual(0);
+                    jsonAnchor = fakeAnchors[0];
+                    weightDataAnchor = fakeAnchors[1];
+                    expect(jsonAnchor.download).toEqual('model.json');
+                    expect(jsonAnchor.clicked).toEqual(1);
+                    expect(weightDataAnchor.download).toEqual(undefined);
+                    expect(weightDataAnchor.clicked).toEqual(0);
+                    return [4, fetch(jsonAnchor.href)];
+                case 2:
+                    jsonContent = _c.sent();
+                    _b = (_a = JSON).parse;
+                    return [4, jsonContent.text()];
+                case 3:
+                    modelTopologyAndWeightsManifest = _b.apply(_a, [_c.sent()]);
+                    expect(modelTopologyAndWeightsManifest.modelTopology)
+                        .toEqual(modelTopology1);
+                    return [2];
+            }
+        });
+    }); });
+    it('browserDownloadsRouter', function () {
+        expect(browser_files_1.browserDownloadsRouter('downloads://foo') instanceof browser_files_1.BrowserDownloads)
+            .toEqual(true);
+        expect(browser_files_1.browserDownloadsRouter('invaliddownloads://foo')).toBeNull();
+        expect(browser_files_1.browserDownloadsRouter('foo')).toBeNull();
+    });
+});
+jasmine_util_1.describeWithFlags('browserFiles', test_util_1.BROWSER_ENVS, function () {
+    var weightsFile = new File([weightData1], 'model.weights.bin', { type: 'application/octet-stream' });
+    it('One group, one path', function () { return __awaiter(_this, void 0, void 0, function () {
+        var weightsManifest, weightsTopologyAndManifest, jsonFile, filesHandler, modelArtifacts;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    weightsManifest = [{
+                            paths: ['./model.weights.bin'],
+                            weights: weightSpecs1,
+                        }];
+                    weightsTopologyAndManifest = {
+                        modelTopology: modelTopology1,
+                        weightsManifest: weightsManifest,
+                    };
+                    jsonFile = new File([JSON.stringify(weightsTopologyAndManifest)], 'model.json', { type: 'application/json' });
+                    filesHandler = tf.io.browserFiles([jsonFile, weightsFile]);
+                    return [4, filesHandler.load()];
+                case 1:
+                    modelArtifacts = _a.sent();
+                    expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
+                    expect(modelArtifacts.weightSpecs).toEqual(weightSpecs1);
+                    expect(new Uint8Array(modelArtifacts.weightData))
+                        .toEqual(new Uint8Array(weightData1));
+                    return [2];
+            }
+        });
+    }); });
+    it("One group, two paths", function () { return __awaiter(_this, void 0, void 0, function () {
+        var weightSpecs, weightsManifest, weightsTopologyAndManifest, weightsFile1, weightsFile2, jsonFile, filesHandler, modelArtifacts;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    weightSpecs = [
+                        {
+                            name: 'foo',
+                            shape: [1, 1],
+                            dtype: 'float32',
+                        },
+                        {
+                            name: 'bar',
+                            shape: [1, 1],
+                            dtype: 'float32',
+                        }
+                    ];
+                    weightsManifest = [{
+                            paths: ['./dir1/model.weights.1.bin', './dir2/model.weights.2.bin'],
+                            weights: weightSpecs,
+                        }];
+                    weightsTopologyAndManifest = {
+                        modelTopology: modelTopology1,
+                        weightsManifest: weightsManifest,
+                    };
+                    weightsFile1 = new File([new Uint8Array([1, 2, 3, 4]).buffer], 'model.weights.1.bin', { type: 'application/octet-stream' });
+                    weightsFile2 = new File([new Uint8Array([10, 20, 30, 40]).buffer], 'model.weights.2.bin', { type: 'application/octet-stream' });
+                    jsonFile = new File([JSON.stringify(weightsTopologyAndManifest)], 'model.json', { type: 'application/json' });
+                    filesHandler = tf.io.browserFiles([jsonFile, weightsFile1, weightsFile2]);
+                    return [4, filesHandler.load()];
+                case 1:
+                    modelArtifacts = _a.sent();
+                    expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
+                    expect(modelArtifacts.weightSpecs).toEqual(weightSpecs);
+                    expect(new Uint8Array(modelArtifacts.weightData)).toEqual(new Uint8Array([
+                        1, 2, 3, 4, 10, 20, 30, 40
+                    ]));
+                    return [2];
+            }
+        });
+    }); });
+    it("Two groups, four paths, reverseOrder=false", function () { return __awaiter(_this, void 0, void 0, function () {
+        var weightSpecs1, weightSpecs2, weightsManifest, weightsTopologyAndManifest, weightsFile1, weightsFile2, weightsFile3, weightsFile4, jsonFile, filesHandler, modelArtifacts;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    weightSpecs1 = [
+                        {
+                            name: 'foo',
+                            shape: [1, 1],
+                            dtype: 'float32',
+                        },
+                        {
+                            name: 'bar',
+                            shape: [1, 1],
+                            dtype: 'float32',
+                        }
+                    ];
+                    weightSpecs2 = [
+                        {
+                            name: 'baz',
+                            shape: [1, 1],
+                            dtype: 'float32',
+                        },
+                        {
+                            name: 'qux',
+                            shape: [1, 1],
+                            dtype: 'float32',
+                        }
+                    ];
+                    weightsManifest = [
+                        {
+                            paths: ['./model.weights.1.bin', './model.weights.2.bin'],
+                            weights: weightSpecs1,
+                        },
+                        {
+                            paths: ['./model.weights.3.bin', './model.weights.4.bin'],
+                            weights: weightSpecs2,
+                        }
+                    ];
+                    weightsTopologyAndManifest = {
+                        modelTopology: modelTopology1,
+                        weightsManifest: weightsManifest,
+                    };
+                    weightsFile1 = new File([new Uint8Array([1, 3, 5, 7]).buffer], 'model.weights.1.bin', { type: 'application/octet-stream' });
+                    weightsFile2 = new File([new Uint8Array([10, 30, 50, 70]).buffer], 'model.weights.2.bin', { type: 'application/octet-stream' });
+                    weightsFile3 = new File([new Uint8Array([2, 4, 6, 8]).buffer], 'model.weights.3.bin', { type: 'application/octet-stream' });
+                    weightsFile4 = new File([new Uint8Array([20, 40, 60, 80]).buffer], 'model.weights.4.bin', { type: 'application/octet-stream' });
+                    jsonFile = new File([JSON.stringify(weightsTopologyAndManifest)], 'model.json', { type: 'application/json' });
+                    filesHandler = tf.io.browserFiles([jsonFile, weightsFile1, weightsFile2, weightsFile3, weightsFile4]);
+                    return [4, filesHandler.load()];
+                case 1:
+                    modelArtifacts = _a.sent();
+                    expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
+                    expect(modelArtifacts.weightSpecs)
+                        .toEqual(weightSpecs1.concat(weightSpecs2));
+                    expect(new Uint8Array(modelArtifacts.weightData)).toEqual(new Uint8Array([
+                        1, 3, 5, 7, 10, 30, 50, 70, 2, 4, 6, 8, 20, 40, 60, 80
+                    ]));
+                    return [2];
+            }
+        });
+    }); });
+    it("Two groups, four paths, reverseOrder=true", function () { return __awaiter(_this, void 0, void 0, function () {
+        var weightSpecs1, weightSpecs2, weightsManifest, weightsTopologyAndManifest, weightsFile1, weightsFile2, weightsFile3, weightsFile4, jsonFile, filesHandler, modelArtifacts;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    weightSpecs1 = [
+                        {
+                            name: 'foo',
+                            shape: [1, 1],
+                            dtype: 'float32',
+                        },
+                        {
+                            name: 'bar',
+                            shape: [1, 1],
+                            dtype: 'float32',
+                        }
+                    ];
+                    weightSpecs2 = [
+                        {
+                            name: 'baz',
+                            shape: [1, 1],
+                            dtype: 'float32',
+                        },
+                        {
+                            name: 'qux',
+                            shape: [1, 1],
+                            dtype: 'float32',
+                        }
+                    ];
+                    weightsManifest = [
+                        {
+                            paths: ['./model.weights.1.bin', './model.weights.2.bin'],
+                            weights: weightSpecs1,
+                        },
+                        {
+                            paths: ['./model.weights.3.bin', './model.weights.4.bin'],
+                            weights: weightSpecs2,
+                        }
+                    ];
+                    weightsTopologyAndManifest = {
+                        modelTopology: modelTopology1,
+                        weightsManifest: weightsManifest,
+                    };
+                    weightsFile1 = new File([new Uint8Array([1, 3, 5, 7]).buffer], 'model.weights.1.bin', { type: 'application/octet-stream' });
+                    weightsFile2 = new File([new Uint8Array([10, 30, 50, 70]).buffer], 'model.weights.2.bin', { type: 'application/octet-stream' });
+                    weightsFile3 = new File([new Uint8Array([2, 4, 6, 8]).buffer], 'model.weights.3.bin', { type: 'application/octet-stream' });
+                    weightsFile4 = new File([new Uint8Array([20, 40, 60, 80]).buffer], 'model.weights.4.bin', { type: 'application/octet-stream' });
+                    jsonFile = new File([JSON.stringify(weightsTopologyAndManifest)], 'model.json', { type: 'application/json' });
+                    filesHandler = tf.io.browserFiles([jsonFile, weightsFile4, weightsFile3, weightsFile2, weightsFile1]);
+                    return [4, filesHandler.load()];
+                case 1:
+                    modelArtifacts = _a.sent();
+                    expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
+                    expect(modelArtifacts.weightSpecs)
+                        .toEqual(weightSpecs1.concat(weightSpecs2));
+                    expect(new Uint8Array(modelArtifacts.weightData)).toEqual(new Uint8Array([
+                        1, 3, 5, 7, 10, 30, 50, 70, 2, 4, 6, 8, 20, 40, 60, 80
+                    ]));
+                    return [2];
+            }
+        });
+    }); });
+    it('Upload model topology only', function () { return __awaiter(_this, void 0, void 0, function () {
+        var weightsManifest, weightsTopologyAndManifest, jsonFile, filesHandler, modelArtifacts;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    weightsManifest = [{
+                            paths: ['./model.weights.bin'],
+                            weights: weightSpecs1,
+                        }];
+                    weightsTopologyAndManifest = {
+                        modelTopology: modelTopology1,
+                        weightsManifest: weightsManifest,
+                    };
+                    jsonFile = new File([JSON.stringify(weightsTopologyAndManifest)], 'model.json', { type: 'application/json' });
+                    filesHandler = tf.io.browserFiles([jsonFile]);
+                    return [4, filesHandler.load()];
+                case 1:
+                    modelArtifacts = _a.sent();
+                    expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
+                    expect(modelArtifacts.weightSpecs).toEqual(undefined);
+                    return [2];
+            }
+        });
+    }); });
+    it('Mismatch in number of paths and number of files', function () { return __awaiter(_this, void 0, void 0, function () {
+        var weightsManifest, weightsTopologyAndManifest, weightsFile1, weightsFile2, jsonFile, filesHandler, err_1;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    weightsManifest = [{
+                            paths: ['./model.weights.1.bin'],
+                            weights: weightSpecs1,
+                        }];
+                    weightsTopologyAndManifest = {
+                        modelTopology: weightSpecs1,
+                        weightsManifest: weightsManifest,
+                    };
+                    weightsFile1 = new File([new Uint8Array([1, 2, 3, 4]).buffer], 'model.weights.1.bin', { type: 'application/octet-stream' });
+                    weightsFile2 = new File([new Uint8Array([10, 20, 30, 40]).buffer], 'model.weights.2.bin', { type: 'application/octet-stream' });
+                    jsonFile = new File([JSON.stringify(weightsTopologyAndManifest)], 'model.json', { type: 'application/json' });
+                    filesHandler = tf.io.browserFiles([jsonFile, weightsFile2, weightsFile1]);
+                    _a.label = 1;
+                case 1:
+                    _a.trys.push([1, 3, , 4]);
+                    return [4, filesHandler.load()];
+                case 2:
+                    _a.sent();
+                    fail('Loading with mismatch in number of paths and number of files ' +
+                        'succeeded unexpectedly.');
+                    return [3, 4];
+                case 3:
+                    err_1 = _a.sent();
+                    expect(err_1.message)
+                        .toEqual('Mismatch in the number of files in weights manifest (1) ' +
+                        'and the number of weight files provided (2).');
+                    return [3, 4];
+                case 4: return [2];
+            }
+        });
+    }); });
+    it('Mismatch in manifest paths and file names', function () { return __awaiter(_this, void 0, void 0, function () {
+        var weightSpecs, weightsManifest, weightsTopologyAndManifest, weightsFile1, weightsFile2, jsonFile, filesHandler, err_2;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    weightSpecs = [
+                        {
+                            name: 'foo',
+                            shape: [1, 1],
+                            dtype: 'float32',
+                        },
+                        {
+                            name: 'bar',
+                            shape: [1, 1],
+                            dtype: 'float32',
+                        }
+                    ];
+                    weightsManifest = [{
+                            paths: ['./model.weights.1.bin', './model.weights.2.bin'],
+                            weights: weightSpecs,
+                        }];
+                    weightsTopologyAndManifest = {
+                        modelTopology: modelTopology1,
+                        weightsManifest: weightsManifest,
+                    };
+                    weightsFile1 = new File([new Uint8Array([1, 2, 3, 4]).buffer], 'model.weights.1.bin', { type: 'application/octet-stream' });
+                    weightsFile2 = new File([new Uint8Array([10, 20, 30, 40]).buffer], 'model.weights.3.bin', { type: 'application/octet-stream' });
+                    jsonFile = new File([JSON.stringify(weightsTopologyAndManifest)], 'model.json', { type: 'application/json' });
+                    filesHandler = tf.io.browserFiles([jsonFile, weightsFile1, weightsFile2]);
+                    _a.label = 1;
+                case 1:
+                    _a.trys.push([1, 3, , 4]);
+                    return [4, filesHandler.load()];
+                case 2:
+                    _a.sent();
+                    fail('Loading with mismatching paths and file names ' +
+                        'succeeded unexpectedly.');
+                    return [3, 4];
+                case 3:
+                    err_2 = _a.sent();
+                    expect(err_2.message)
+                        .toEqual('Weight file with basename \'model.weights.2.bin\' is not ' +
+                        'provided.');
+                    return [3, 4];
+                case 4: return [2];
+            }
+        });
+    }); });
+    it('Duplicate basenames in paths fails', function () { return __awaiter(_this, void 0, void 0, function () {
+        var weightSpecs, weightsManifest, weightsTopologyAndManifest, weightsFile1, weightsFile2, jsonFile, filesHandler, err_3;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    weightSpecs = [
+                        {
+                            name: 'foo',
+                            shape: [1, 1],
+                            dtype: 'float32',
+                        },
+                        {
+                            name: 'bar',
+                            shape: [1, 1],
+                            dtype: 'float32',
+                        }
+                    ];
+                    weightsManifest = [{
+                            paths: ['./dir1/model.weights.1.bin', './dir2/model.weights.1.bin'],
+                            weights: weightSpecs,
+                        }];
+                    weightsTopologyAndManifest = {
+                        modelTopology: modelTopology1,
+                        weightsManifest: weightsManifest,
+                    };
+                    weightsFile1 = new File([new Uint8Array([1, 2, 3, 4]).buffer], 'model.weights.1.bin', { type: 'application/octet-stream' });
+                    weightsFile2 = new File([new Uint8Array([10, 20, 30, 40]).buffer], 'model.weights.2.bin', { type: 'application/octet-stream' });
+                    jsonFile = new File([JSON.stringify(weightsTopologyAndManifest)], 'model.json', { type: 'application/json' });
+                    filesHandler = tf.io.browserFiles([jsonFile, weightsFile1, weightsFile2]);
+                    _a.label = 1;
+                case 1:
+                    _a.trys.push([1, 3, , 4]);
+                    return [4, filesHandler.load()];
+                case 2:
+                    _a.sent();
+                    fail('Loading with duplicate basenames in paths succeeded unexpectedly.');
+                    return [3, 4];
+                case 3:
+                    err_3 = _a.sent();
+                    expect(err_3.message)
+                        .toEqual('Duplicate file basename found in weights manifest: ' +
+                        '\'model.weights.1.bin\'');
+                    return [3, 4];
+                case 4: return [2];
+            }
+        });
+    }); });
+    it('Missing modelTopology from JSON leads to Error', function () { return __awaiter(_this, void 0, void 0, function () {
+        var weightsManifest, weightsTopologyAndManifest, jsonFile, filesHandler, err_4;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    weightsManifest = [{
+                            paths: ['./model.weights.bin'],
+                            weights: weightSpecs1,
+                        }];
+                    weightsTopologyAndManifest = {
+                        weightsManifest: weightsManifest,
+                    };
+                    jsonFile = new File([JSON.stringify(weightsTopologyAndManifest)], 'model.json', { type: 'application/json' });
+                    filesHandler = tf.io.browserFiles([jsonFile, weightsFile]);
+                    _a.label = 1;
+                case 1:
+                    _a.trys.push([1, 3, , 4]);
+                    return [4, filesHandler.load()];
+                case 2:
+                    _a.sent();
+                    fail('Loading with Files IOHandler with missing modelTopology ' +
+                        'succeeded unexpectedly.');
+                    return [3, 4];
+                case 3:
+                    err_4 = _a.sent();
+                    expect(err_4.message)
+                        .toMatch(/modelTopology field is missing from file model\.json/);
+                    return [3, 4];
+                case 4: return [2];
+            }
+        });
+    }); });
+    it('Incorrect number of files leads to Error', function () {
+        expect(function () { return tf.io.browserFiles(null); }).toThrowError(/at least 1 file/);
+        expect(function () { return tf.io.browserFiles([]); }).toThrowError(/at least 1 file/);
+    });
+});
+//# sourceMappingURL=browser_files_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/browser_files_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/io/browser_files_test.js.map
new file mode 100644
index 0000000..4ff3006
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/browser_files_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/browser_http.d.ts b/node_modules/@tensorflow/tfjs-core/dist/io/browser_http.d.ts
new file mode 100644
index 0000000..6c5e9f7
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/browser_http.d.ts
@@ -0,0 +1,22 @@
+import { IORouter } from './router_registry';
+import { IOHandler, ModelArtifacts, SaveResult } from './types';
+export declare class BrowserHTTPRequest implements IOHandler {
+    private readonly weightPathPrefix?;
+    protected readonly path: string | string[];
+    protected readonly requestInit: RequestInit;
+    private readonly fetchFunc;
+    readonly DEFAULT_METHOD: string;
+    static readonly URL_SCHEME_REGEX: RegExp;
+    constructor(path: string | string[], requestInit?: RequestInit, weightPathPrefix?: string, fetchFunc?: Function);
+    save(modelArtifacts: ModelArtifacts): Promise<SaveResult>;
+    load(): Promise<ModelArtifacts>;
+    private loadBinaryTopology;
+    protected loadBinaryModel(): Promise<ModelArtifacts>;
+    protected loadJSONModel(): Promise<ModelArtifacts>;
+    private loadWeights;
+    private getFetchFunc;
+}
+export declare function parseUrl(url: string): [string, string];
+export declare function isHTTPScheme(url: string): boolean;
+export declare const httpRequestRouter: IORouter;
+export declare function browserHTTPRequest(path: string | string[], requestInit?: RequestInit, weightPathPrefix?: string, fetchFunc?: Function): IOHandler;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/browser_http.js b/node_modules/@tensorflow/tfjs-core/dist/io/browser_http.js
new file mode 100644
index 0000000..5dc7470
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/browser_http.js
@@ -0,0 +1,281 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+Object.defineProperty(exports, "__esModule", { value: true });
+var util_1 = require("../util");
+var io_utils_1 = require("./io_utils");
+var router_registry_1 = require("./router_registry");
+var weights_loader_1 = require("./weights_loader");
+var BrowserHTTPRequest = (function () {
+    function BrowserHTTPRequest(path, requestInit, weightPathPrefix, fetchFunc) {
+        this.weightPathPrefix = weightPathPrefix;
+        this.DEFAULT_METHOD = 'POST';
+        if (fetchFunc == null) {
+            if (typeof fetch === 'undefined') {
+                throw new Error('browserHTTPRequest is not supported outside the web browser ' +
+                    'without a fetch polyfill.');
+            }
+            this.fetchFunc =
+                fetch.bind(typeof window === 'undefined' ? null : window);
+        }
+        else {
+            util_1.assert(typeof fetchFunc === 'function', 'Must pass a function that matches the signature of ' +
+                '`fetch` (see ' +
+                'https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)');
+            this.fetchFunc = fetchFunc;
+        }
+        util_1.assert(path != null && path.length > 0, 'URL path for browserHTTPRequest must not be null, undefined or ' +
+            'empty.');
+        if (Array.isArray(path)) {
+            util_1.assert(path.length === 2, 'URL paths for browserHTTPRequest must have a length of 2, ' +
+                ("(actual length is " + path.length + ")."));
+        }
+        this.path = path;
+        if (requestInit != null && requestInit.body != null) {
+            throw new Error('requestInit is expected to have no pre-existing body, but has one.');
+        }
+        this.requestInit = requestInit || {};
+    }
+    BrowserHTTPRequest.prototype.save = function (modelArtifacts) {
+        return __awaiter(this, void 0, void 0, function () {
+            var init, weightsManifest, modelTopologyAndWeightManifest, response;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
+                            throw new Error('BrowserHTTPRequest.save() does not support saving model topology ' +
+                                'in binary formats yet.');
+                        }
+                        init = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);
+                        init.body = new FormData();
+                        weightsManifest = [{
+                                paths: ['./model.weights.bin'],
+                                weights: modelArtifacts.weightSpecs,
+                            }];
+                        modelTopologyAndWeightManifest = {
+                            modelTopology: modelArtifacts.modelTopology,
+                            weightsManifest: weightsManifest
+                        };
+                        init.body.append('model.json', new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: 'application/json' }), 'model.json');
+                        if (modelArtifacts.weightData != null) {
+                            init.body.append('model.weights.bin', new Blob([modelArtifacts.weightData], { type: 'application/octet-stream' }), 'model.weights.bin');
+                        }
+                        return [4, this.getFetchFunc()(this.path, init)];
+                    case 1:
+                        response = _a.sent();
+                        if (response.ok) {
+                            return [2, {
+                                    modelArtifactsInfo: io_utils_1.getModelArtifactsInfoForJSON(modelArtifacts),
+                                    responses: [response],
+                                }];
+                        }
+                        else {
+                            throw new Error("BrowserHTTPRequest.save() failed due to HTTP response status " +
+                                (response.status + "."));
+                        }
+                        return [2];
+                }
+            });
+        });
+    };
+    BrowserHTTPRequest.prototype.load = function () {
+        return __awaiter(this, void 0, void 0, function () {
+            return __generator(this, function (_a) {
+                return [2, Array.isArray(this.path) ? this.loadBinaryModel() :
+                        this.loadJSONModel()];
+            });
+        });
+    };
+    BrowserHTTPRequest.prototype.loadBinaryTopology = function () {
+        return __awaiter(this, void 0, void 0, function () {
+            var response, error_1;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        _a.trys.push([0, 3, , 4]);
+                        return [4, this.getFetchFunc()(this.path[0], this.requestInit)];
+                    case 1:
+                        response = _a.sent();
+                        if (!response.ok) {
+                            throw new Error("BrowserHTTPRequest.load() failed due to HTTP response: " + response.statusText);
+                        }
+                        return [4, response.arrayBuffer()];
+                    case 2: return [2, _a.sent()];
+                    case 3:
+                        error_1 = _a.sent();
+                        throw new Error(this.path[0] + " not found. " + error_1);
+                    case 4: return [2];
+                }
+            });
+        });
+    };
+    BrowserHTTPRequest.prototype.loadBinaryModel = function () {
+        return __awaiter(this, void 0, void 0, function () {
+            var graphPromise, manifestPromise, results, modelTopology, weightsManifestResponse, weightsManifest, weightSpecs, weightData, results_1;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        graphPromise = this.loadBinaryTopology();
+                        return [4, this.getFetchFunc()(this.path[1], this.requestInit)];
+                    case 1:
+                        manifestPromise = _a.sent();
+                        if (!manifestPromise.ok) {
+                            throw new Error("BrowserHTTPRequest.load() failed due to HTTP response: " + manifestPromise.statusText);
+                        }
+                        return [4, Promise.all([graphPromise, manifestPromise])];
+                    case 2:
+                        results = _a.sent();
+                        modelTopology = results[0], weightsManifestResponse = results[1];
+                        return [4, weightsManifestResponse.json()];
+                    case 3:
+                        weightsManifest = _a.sent();
+                        if (!(weightsManifest != null)) return [3, 5];
+                        return [4, this.loadWeights(weightsManifest)];
+                    case 4:
+                        results_1 = _a.sent();
+                        weightSpecs = results_1[0], weightData = results_1[1];
+                        _a.label = 5;
+                    case 5: return [2, { modelTopology: modelTopology, weightSpecs: weightSpecs, weightData: weightData }];
+                }
+            });
+        });
+    };
+    BrowserHTTPRequest.prototype.loadJSONModel = function () {
+        return __awaiter(this, void 0, void 0, function () {
+            var modelConfigRequest, modelConfig, modelTopology, weightsManifest, weightSpecs, weightData, weightsManifest_1, results;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0: return [4, this.getFetchFunc()(this.path, this.requestInit)];
+                    case 1:
+                        modelConfigRequest = _a.sent();
+                        if (!modelConfigRequest.ok) {
+                            throw new Error("BrowserHTTPRequest.load() failed due to HTTP response: " + modelConfigRequest.statusText);
+                        }
+                        return [4, modelConfigRequest.json()];
+                    case 2:
+                        modelConfig = _a.sent();
+                        modelTopology = modelConfig['modelTopology'];
+                        weightsManifest = modelConfig['weightsManifest'];
+                        if (modelTopology == null && weightsManifest == null) {
+                            throw new Error("The JSON from HTTP path " + this.path + " contains neither model " +
+                                "topology or manifest for weights.");
+                        }
+                        if (!(weightsManifest != null)) return [3, 4];
+                        weightsManifest_1 = modelConfig['weightsManifest'];
+                        return [4, this.loadWeights(weightsManifest_1)];
+                    case 3:
+                        results = _a.sent();
+                        weightSpecs = results[0], weightData = results[1];
+                        _a.label = 4;
+                    case 4: return [2, { modelTopology: modelTopology, weightSpecs: weightSpecs, weightData: weightData }];
+                }
+            });
+        });
+    };
+    BrowserHTTPRequest.prototype.loadWeights = function (weightsManifest) {
+        return __awaiter(this, void 0, void 0, function () {
+            var weightPath, _a, prefix, suffix, pathPrefix, weightSpecs, _i, weightsManifest_2, entry, fetchURLs, _b, _c;
+            return __generator(this, function (_d) {
+                switch (_d.label) {
+                    case 0:
+                        weightPath = Array.isArray(this.path) ? this.path[1] : this.path;
+                        _a = parseUrl(weightPath), prefix = _a[0], suffix = _a[1];
+                        pathPrefix = this.weightPathPrefix || prefix;
+                        weightSpecs = [];
+                        for (_i = 0, weightsManifest_2 = weightsManifest; _i < weightsManifest_2.length; _i++) {
+                            entry = weightsManifest_2[_i];
+                            weightSpecs.push.apply(weightSpecs, entry.weights);
+                        }
+                        fetchURLs = [];
+                        weightsManifest.forEach(function (weightsGroup) {
+                            weightsGroup.paths.forEach(function (path) {
+                                fetchURLs.push(pathPrefix + path + suffix);
+                            });
+                        });
+                        _b = [weightSpecs];
+                        _c = io_utils_1.concatenateArrayBuffers;
+                        return [4, weights_loader_1.loadWeightsAsArrayBuffer(fetchURLs, this.requestInit, this.getFetchFunc())];
+                    case 1: return [2, _b.concat([
+                            _c.apply(void 0, [_d.sent()])
+                        ])];
+                }
+            });
+        });
+    };
+    BrowserHTTPRequest.prototype.getFetchFunc = function () {
+        return this.fetchFunc;
+    };
+    BrowserHTTPRequest.URL_SCHEME_REGEX = /^https?:\/\//;
+    return BrowserHTTPRequest;
+}());
+exports.BrowserHTTPRequest = BrowserHTTPRequest;
+function parseUrl(url) {
+    var lastSlash = url.lastIndexOf('/');
+    var lastSearchParam = url.lastIndexOf('?');
+    var prefix = url.substring(0, lastSlash);
+    var suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : '';
+    return [prefix + '/', suffix];
+}
+exports.parseUrl = parseUrl;
+function isHTTPScheme(url) {
+    return url.match(BrowserHTTPRequest.URL_SCHEME_REGEX) != null;
+}
+exports.isHTTPScheme = isHTTPScheme;
+exports.httpRequestRouter = function (url) {
+    if (typeof fetch === 'undefined') {
+        return null;
+    }
+    else {
+        var isHTTP = true;
+        if (Array.isArray(url)) {
+            isHTTP = url.every(function (urlItem) { return isHTTPScheme(urlItem); });
+        }
+        else {
+            isHTTP = isHTTPScheme(url);
+        }
+        if (isHTTP) {
+            return browserHTTPRequest(url);
+        }
+    }
+    return null;
+};
+router_registry_1.IORouterRegistry.registerSaveRouter(exports.httpRequestRouter);
+router_registry_1.IORouterRegistry.registerLoadRouter(exports.httpRequestRouter);
+function browserHTTPRequest(path, requestInit, weightPathPrefix, fetchFunc) {
+    return new BrowserHTTPRequest(path, requestInit, weightPathPrefix, fetchFunc);
+}
+exports.browserHTTPRequest = browserHTTPRequest;
+//# sourceMappingURL=browser_http.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/browser_http.js.map b/node_modules/@tensorflow/tfjs-core/dist/io/browser_http.js.map
new file mode 100644
index 0000000..21ea9bb
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/browser_http.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/browser_http_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/io/browser_http_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/browser_http_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/browser_http_test.js b/node_modules/@tensorflow/tfjs-core/dist/io/browser_http_test.js
new file mode 100644
index 0000000..c3e6fdc
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/browser_http_test.js
@@ -0,0 +1,1053 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+var _this = this;
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+var browser_http_1 = require("./browser_http");
+var modelTopology1 = {
+    'class_name': 'Sequential',
+    'keras_version': '2.1.4',
+    'config': [{
+            'class_name': 'Dense',
+            'config': {
+                'kernel_initializer': {
+                    'class_name': 'VarianceScaling',
+                    'config': {
+                        'distribution': 'uniform',
+                        'scale': 1.0,
+                        'seed': null,
+                        'mode': 'fan_avg'
+                    }
+                },
+                'name': 'dense',
+                'kernel_constraint': null,
+                'bias_regularizer': null,
+                'bias_constraint': null,
+                'dtype': 'float32',
+                'activation': 'linear',
+                'trainable': true,
+                'kernel_regularizer': null,
+                'bias_initializer': { 'class_name': 'Zeros', 'config': {} },
+                'units': 1,
+                'batch_input_shape': [null, 3],
+                'use_bias': true,
+                'activity_regularizer': null
+            }
+        }],
+    'backend': 'tensorflow'
+};
+jasmine_util_1.describeWithFlags('browserHTTPRequest-load fetch', test_util_1.NODE_ENVS, function () {
+    var requestInits;
+    beforeEach(function () {
+        global.fetch = function () { };
+        requestInits = [];
+    });
+    afterAll(function () {
+        delete global.fetch;
+    });
+    var fakeResponse = function (body) { return ({
+        ok: true,
+        json: function () {
+            return Promise.resolve(JSON.parse(body));
+        },
+        arrayBuffer: function () {
+            var buf = body.buffer ?
+                body.buffer :
+                body;
+            return Promise.resolve(buf);
+        }
+    }); };
+    var setupFakeWeightFiles = function (fileBufferMap) {
+        spyOn(global, 'fetch')
+            .and.callFake(function (path, init) {
+            requestInits.push(init);
+            return fakeResponse(fileBufferMap[path]);
+        });
+    };
+    it('1 group, 2 weights, 1 path', function () { return __awaiter(_this, void 0, void 0, function () {
+        var weightManifest1, floatData, handler, modelArtifacts;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    weightManifest1 = [{
+                            paths: ['weightfile0'],
+                            weights: [
+                                {
+                                    name: 'dense/kernel',
+                                    shape: [3, 1],
+                                    dtype: 'float32',
+                                },
+                                {
+                                    name: 'dense/bias',
+                                    shape: [2],
+                                    dtype: 'float32',
+                                }
+                            ]
+                        }];
+                    floatData = new Float32Array([1, 3, 3, 7, 4]);
+                    setupFakeWeightFiles({
+                        './model.json': JSON.stringify({ modelTopology: modelTopology1, weightsManifest: weightManifest1 }),
+                        './weightfile0': floatData,
+                    });
+                    handler = tf.io.browserHTTPRequest('./model.json');
+                    return [4, handler.load()];
+                case 1:
+                    modelArtifacts = _a.sent();
+                    expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
+                    expect(modelArtifacts.weightSpecs).toEqual(weightManifest1[0].weights);
+                    expect(new Float32Array(modelArtifacts.weightData)).toEqual(floatData);
+                    expect(requestInits).toEqual([{}, {}]);
+                    return [2];
+            }
+        });
+    }); });
+    it('throw exception if no fetch polyfill', function () {
+        delete global.fetch;
+        try {
+            tf.io.browserHTTPRequest('./model.json');
+        }
+        catch (err) {
+            expect(err.message)
+                .toMatch(/not supported outside the web browser without a fetch polyfill/);
+        }
+    });
+});
+jasmine_util_1.describeWithFlags('browserHTTPRequest-save', test_util_1.CHROME_ENVS, function () {
+    var weightSpecs1 = [
+        {
+            name: 'dense/kernel',
+            shape: [3, 1],
+            dtype: 'float32',
+        },
+        {
+            name: 'dense/bias',
+            shape: [1],
+            dtype: 'float32',
+        }
+    ];
+    var weightData1 = new ArrayBuffer(16);
+    var artifacts1 = {
+        modelTopology: modelTopology1,
+        weightSpecs: weightSpecs1,
+        weightData: weightData1,
+    };
+    var requestInits = [];
+    beforeEach(function () {
+        requestInits = [];
+        spyOn(window, 'fetch').and.callFake(function (path, init) {
+            if (path === 'model-upload-test' || path === 'http://model-upload-test') {
+                requestInits.push(init);
+                return new Response(null, { status: 200 });
+            }
+            else {
+                return new Response(null, { status: 404 });
+            }
+        });
+    });
+    it('Save topology and weights, default POST method', function (done) {
+        var testStartDate = new Date();
+        var handler = tf.io.getSaveHandlers('http://model-upload-test')[0];
+        handler.save(artifacts1)
+            .then(function (saveResult) {
+            expect(saveResult.modelArtifactsInfo.dateSaved.getTime())
+                .toBeGreaterThanOrEqual(testStartDate.getTime());
+            expect(saveResult.modelArtifactsInfo.modelTopologyBytes)
+                .toEqual(JSON.stringify(modelTopology1).length);
+            expect(saveResult.modelArtifactsInfo.weightSpecsBytes)
+                .toEqual(JSON.stringify(weightSpecs1).length);
+            expect(saveResult.modelArtifactsInfo.weightDataBytes)
+                .toEqual(weightData1.byteLength);
+            expect(requestInits.length).toEqual(1);
+            var init = requestInits[0];
+            expect(init.method).toEqual('POST');
+            var body = init.body;
+            var jsonFile = body.get('model.json');
+            var jsonFileReader = new FileReader();
+            jsonFileReader.onload = function (event) {
+                var modelJSON = JSON.parse(event.target.result);
+                expect(modelJSON.modelTopology).toEqual(modelTopology1);
+                expect(modelJSON.weightsManifest.length).toEqual(1);
+                expect(modelJSON.weightsManifest[0].weights).toEqual(weightSpecs1);
+                var weightsFile = body.get('model.weights.bin');
+                var weightsFileReader = new FileReader();
+                weightsFileReader.onload = function (event) {
+                    var weightData = event.target.result;
+                    expect(new Uint8Array(weightData))
+                        .toEqual(new Uint8Array(weightData1));
+                    done();
+                };
+                weightsFileReader.onerror = function (error) {
+                    done.fail(error.target.error.message);
+                };
+                weightsFileReader.readAsArrayBuffer(weightsFile);
+            };
+            jsonFileReader.onerror = function (error) {
+                done.fail(error.target.error.message);
+            };
+            jsonFileReader.readAsText(jsonFile);
+        })
+            .catch(function (err) {
+            done.fail(err.stack);
+        });
+    });
+    it('Save topology only, default POST method', function (done) {
+        var testStartDate = new Date();
+        var handler = tf.io.getSaveHandlers('http://model-upload-test')[0];
+        var topologyOnlyArtifacts = { modelTopology: modelTopology1 };
+        handler.save(topologyOnlyArtifacts)
+            .then(function (saveResult) {
+            expect(saveResult.modelArtifactsInfo.dateSaved.getTime())
+                .toBeGreaterThanOrEqual(testStartDate.getTime());
+            expect(saveResult.modelArtifactsInfo.modelTopologyBytes)
+                .toEqual(JSON.stringify(modelTopology1).length);
+            expect(saveResult.modelArtifactsInfo.weightSpecsBytes).toEqual(0);
+            expect(saveResult.modelArtifactsInfo.weightDataBytes).toEqual(0);
+            expect(requestInits.length).toEqual(1);
+            var init = requestInits[0];
+            expect(init.method).toEqual('POST');
+            var body = init.body;
+            var jsonFile = body.get('model.json');
+            var jsonFileReader = new FileReader();
+            jsonFileReader.onload = function (event) {
+                var modelJSON = JSON.parse(event.target.result);
+                expect(modelJSON.modelTopology).toEqual(modelTopology1);
+                expect(body.get('model.weights.bin')).toEqual(null);
+                done();
+            };
+            jsonFileReader.onerror = function (error) {
+                done.fail(error.target.error.message);
+            };
+            jsonFileReader.readAsText(jsonFile);
+        })
+            .catch(function (err) {
+            done.fail(err.stack);
+        });
+    });
+    it('Save topology and weights, PUT method, extra headers', function (done) {
+        var testStartDate = new Date();
+        var handler = tf.io.browserHTTPRequest('model-upload-test', {
+            method: 'PUT',
+            headers: { 'header_key_1': 'header_value_1', 'header_key_2': 'header_value_2' }
+        });
+        handler.save(artifacts1)
+            .then(function (saveResult) {
+            expect(saveResult.modelArtifactsInfo.dateSaved.getTime())
+                .toBeGreaterThanOrEqual(testStartDate.getTime());
+            expect(saveResult.modelArtifactsInfo.modelTopologyBytes)
+                .toEqual(JSON.stringify(modelTopology1).length);
+            expect(saveResult.modelArtifactsInfo.weightSpecsBytes)
+                .toEqual(JSON.stringify(weightSpecs1).length);
+            expect(saveResult.modelArtifactsInfo.weightDataBytes)
+                .toEqual(weightData1.byteLength);
+            expect(requestInits.length).toEqual(1);
+            var init = requestInits[0];
+            expect(init.method).toEqual('PUT');
+            expect(init.headers).toEqual({
+                'header_key_1': 'header_value_1',
+                'header_key_2': 'header_value_2'
+            });
+            var body = init.body;
+            var jsonFile = body.get('model.json');
+            var jsonFileReader = new FileReader();
+            jsonFileReader.onload = function (event) {
+                var modelJSON = JSON.parse(event.target.result);
+                expect(modelJSON.modelTopology).toEqual(modelTopology1);
+                expect(modelJSON.weightsManifest.length).toEqual(1);
+                expect(modelJSON.weightsManifest[0].weights).toEqual(weightSpecs1);
+                var weightsFile = body.get('model.weights.bin');
+                var weightsFileReader = new FileReader();
+                weightsFileReader.onload = function (event) {
+                    var weightData = event.target.result;
+                    expect(new Uint8Array(weightData))
+                        .toEqual(new Uint8Array(weightData1));
+                    done();
+                };
+                weightsFileReader.onerror = function (error) {
+                    done.fail(error.target.error.message);
+                };
+                weightsFileReader.readAsArrayBuffer(weightsFile);
+            };
+            jsonFileReader.onerror = function (error) {
+                done.fail(error.target.error.message);
+            };
+            jsonFileReader.readAsText(jsonFile);
+        })
+            .catch(function (err) {
+            done.fail(err.stack);
+        });
+    });
+    it('404 response causes Error', function (done) {
+        var handler = tf.io.getSaveHandlers('http://invalid/path')[0];
+        handler.save(artifacts1)
+            .then(function (saveResult) {
+            done.fail('Calling browserHTTPRequest at invalid URL succeeded ' +
+                'unexpectedly');
+        })
+            .catch(function (err) {
+            done();
+        });
+    });
+    it('getLoadHandlers with one URL string', function () {
+        var handlers = tf.io.getLoadHandlers('http://foo/model.json');
+        expect(handlers.length).toEqual(1);
+        expect(handlers[0] instanceof browser_http_1.BrowserHTTPRequest).toEqual(true);
+    });
+    it('getLoadHandlers with two URL strings', function () {
+        var handlers = tf.io.getLoadHandlers(['https://foo/graph.pb', 'https://foo/weights_manifest.json']);
+        expect(handlers.length).toEqual(1);
+        expect(handlers[0] instanceof browser_http_1.BrowserHTTPRequest).toEqual(true);
+    });
+    it('Existing body leads to Error', function () {
+        expect(function () { return tf.io.browserHTTPRequest('model-upload-test', {
+            body: 'existing body'
+        }); }).toThrowError(/requestInit is expected to have no pre-existing body/);
+    });
+    it('Empty, null or undefined URL paths lead to Error', function () {
+        expect(function () { return tf.io.browserHTTPRequest(null); })
+            .toThrowError(/must not be null, undefined or empty/);
+        expect(function () { return tf.io.browserHTTPRequest(undefined); })
+            .toThrowError(/must not be null, undefined or empty/);
+        expect(function () { return tf.io.browserHTTPRequest(''); })
+            .toThrowError(/must not be null, undefined or empty/);
+    });
+    it('router', function () {
+        expect(browser_http_1.httpRequestRouter('http://bar/foo') instanceof browser_http_1.BrowserHTTPRequest)
+            .toEqual(true);
+        expect(browser_http_1.httpRequestRouter('https://localhost:5000/upload') instanceof
+            browser_http_1.BrowserHTTPRequest)
+            .toEqual(true);
+        expect(browser_http_1.httpRequestRouter('localhost://foo')).toBeNull();
+        expect(browser_http_1.httpRequestRouter('foo:5000/bar')).toBeNull();
+    });
+});
+jasmine_util_1.describeWithFlags('parseUrl', test_util_1.BROWSER_ENVS, function () {
+    it('should parse url with no suffix', function () {
+        var url = 'http://google.com/file';
+        var _a = browser_http_1.parseUrl(url), prefix = _a[0], suffix = _a[1];
+        expect(prefix).toEqual('http://google.com/');
+        expect(suffix).toEqual('');
+    });
+    it('should parse url with suffix', function () {
+        var url = 'http://google.com/file?param=1';
+        var _a = browser_http_1.parseUrl(url), prefix = _a[0], suffix = _a[1];
+        expect(prefix).toEqual('http://google.com/');
+        expect(suffix).toEqual('?param=1');
+    });
+    it('should parse url with multiple serach params', function () {
+        var url = 'http://google.com/a?x=1/file?param=1';
+        var _a = browser_http_1.parseUrl(url), prefix = _a[0], suffix = _a[1];
+        expect(prefix).toEqual('http://google.com/a?x=1/');
+        expect(suffix).toEqual('?param=1');
+    });
+});
+jasmine_util_1.describeWithFlags('browserHTTPRequest-load', test_util_1.BROWSER_ENVS, function () {
+    describe('JSON model', function () {
+        var requestInits;
+        var windowFetchSpy;
+        var setupFakeWeightFiles = function (fileBufferMap) {
+            windowFetchSpy = spyOn(window, 'fetch')
+                .and.callFake(function (path, init) {
+                requestInits.push(init);
+                return new Response(fileBufferMap[path]);
+            });
+        };
+        beforeEach(function () {
+            requestInits = [];
+        });
+        it('1 group, 2 weights, 1 path', function () { return __awaiter(_this, void 0, void 0, function () {
+            var weightManifest1, floatData, handler, modelArtifacts;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        weightManifest1 = [{
+                                paths: ['weightfile0'],
+                                weights: [
+                                    {
+                                        name: 'dense/kernel',
+                                        shape: [3, 1],
+                                        dtype: 'float32',
+                                    },
+                                    {
+                                        name: 'dense/bias',
+                                        shape: [2],
+                                        dtype: 'float32',
+                                    }
+                                ]
+                            }];
+                        floatData = new Float32Array([1, 3, 3, 7, 4]);
+                        setupFakeWeightFiles({
+                            './model.json': JSON.stringify({ modelTopology: modelTopology1, weightsManifest: weightManifest1 }),
+                            './weightfile0': floatData,
+                        });
+                        handler = tf.io.browserHTTPRequest('./model.json');
+                        return [4, handler.load()];
+                    case 1:
+                        modelArtifacts = _a.sent();
+                        expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
+                        expect(modelArtifacts.weightSpecs).toEqual(weightManifest1[0].weights);
+                        expect(new Float32Array(modelArtifacts.weightData)).toEqual(floatData);
+                        expect(requestInits).toEqual([{}, {}]);
+                        expect(windowFetchSpy.calls.mostRecent().object).toEqual(window);
+                        return [2];
+                }
+            });
+        }); });
+        it('1 group, 2 weights, 1 path, with requestInit', function () { return __awaiter(_this, void 0, void 0, function () {
+            var weightManifest1, floatData, handler, modelArtifacts;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        weightManifest1 = [{
+                                paths: ['weightfile0'],
+                                weights: [
+                                    {
+                                        name: 'dense/kernel',
+                                        shape: [3, 1],
+                                        dtype: 'float32',
+                                    },
+                                    {
+                                        name: 'dense/bias',
+                                        shape: [2],
+                                        dtype: 'float32',
+                                    }
+                                ]
+                            }];
+                        floatData = new Float32Array([1, 3, 3, 7, 4]);
+                        setupFakeWeightFiles({
+                            './model.json': JSON.stringify({ modelTopology: modelTopology1, weightsManifest: weightManifest1 }),
+                            './weightfile0': floatData,
+                        });
+                        handler = tf.io.browserHTTPRequest('./model.json', { headers: { 'header_key_1': 'header_value_1' } });
+                        return [4, handler.load()];
+                    case 1:
+                        modelArtifacts = _a.sent();
+                        expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
+                        expect(modelArtifacts.weightSpecs).toEqual(weightManifest1[0].weights);
+                        expect(new Float32Array(modelArtifacts.weightData)).toEqual(floatData);
+                        expect(requestInits).toEqual([
+                            { headers: { 'header_key_1': 'header_value_1' } },
+                            { headers: { 'header_key_1': 'header_value_1' } }
+                        ]);
+                        expect(windowFetchSpy.calls.mostRecent().object).toEqual(window);
+                        return [2];
+                }
+            });
+        }); });
+        it('1 group, 2 weight, 2 paths', function () { return __awaiter(_this, void 0, void 0, function () {
+            var weightManifest1, floatData1, floatData2, handler, modelArtifacts;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        weightManifest1 = [{
+                                paths: ['weightfile0', 'weightfile1'],
+                                weights: [
+                                    {
+                                        name: 'dense/kernel',
+                                        shape: [3, 1],
+                                        dtype: 'float32',
+                                    },
+                                    {
+                                        name: 'dense/bias',
+                                        shape: [2],
+                                        dtype: 'float32',
+                                    }
+                                ]
+                            }];
+                        floatData1 = new Float32Array([1, 3, 3]);
+                        floatData2 = new Float32Array([7, 4]);
+                        setupFakeWeightFiles({
+                            './model.json': JSON.stringify({ modelTopology: modelTopology1, weightsManifest: weightManifest1 }),
+                            './weightfile0': floatData1,
+                            './weightfile1': floatData2,
+                        });
+                        handler = tf.io.browserHTTPRequest('./model.json');
+                        return [4, handler.load()];
+                    case 1:
+                        modelArtifacts = _a.sent();
+                        expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
+                        expect(modelArtifacts.weightSpecs).toEqual(weightManifest1[0].weights);
+                        expect(new Float32Array(modelArtifacts.weightData))
+                            .toEqual(new Float32Array([1, 3, 3, 7, 4]));
+                        return [2];
+                }
+            });
+        }); });
+        it('2 groups, 2 weight, 2 paths', function () { return __awaiter(_this, void 0, void 0, function () {
+            var weightsManifest, floatData1, floatData2, handler, modelArtifacts;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        weightsManifest = [
+                            {
+                                paths: ['weightfile0'],
+                                weights: [{
+                                        name: 'dense/kernel',
+                                        shape: [3, 1],
+                                        dtype: 'float32',
+                                    }]
+                            },
+                            {
+                                paths: ['weightfile1'],
+                                weights: [{
+                                        name: 'dense/bias',
+                                        shape: [2],
+                                        dtype: 'float32',
+                                    }],
+                            }
+                        ];
+                        floatData1 = new Float32Array([1, 3, 3]);
+                        floatData2 = new Float32Array([7, 4]);
+                        setupFakeWeightFiles({
+                            './model.json': JSON.stringify({ modelTopology: modelTopology1, weightsManifest: weightsManifest }),
+                            './weightfile0': floatData1,
+                            './weightfile1': floatData2,
+                        });
+                        handler = tf.io.browserHTTPRequest('./model.json');
+                        return [4, handler.load()];
+                    case 1:
+                        modelArtifacts = _a.sent();
+                        expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
+                        expect(modelArtifacts.weightSpecs)
+                            .toEqual(weightsManifest[0].weights.concat(weightsManifest[1].weights));
+                        expect(new Float32Array(modelArtifacts.weightData))
+                            .toEqual(new Float32Array([1, 3, 3, 7, 4]));
+                        return [2];
+                }
+            });
+        }); });
+        it('2 groups, 2 weight, 2 paths, Int32 and Uint8 Data', function () { return __awaiter(_this, void 0, void 0, function () {
+            var weightsManifest, floatData1, floatData2, handler, modelArtifacts;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        weightsManifest = [
+                            {
+                                paths: ['weightfile0'],
+                                weights: [{
+                                        name: 'fooWeight',
+                                        shape: [3, 1],
+                                        dtype: 'int32',
+                                    }]
+                            },
+                            {
+                                paths: ['weightfile1'],
+                                weights: [{
+                                        name: 'barWeight',
+                                        shape: [2],
+                                        dtype: 'bool',
+                                    }],
+                            }
+                        ];
+                        floatData1 = new Int32Array([1, 3, 3]);
+                        floatData2 = new Uint8Array([7, 4]);
+                        setupFakeWeightFiles({
+                            'path1/model.json': JSON.stringify({ modelTopology: modelTopology1, weightsManifest: weightsManifest }),
+                            'path1/weightfile0': floatData1,
+                            'path1/weightfile1': floatData2,
+                        });
+                        handler = tf.io.browserHTTPRequest('path1/model.json');
+                        return [4, handler.load()];
+                    case 1:
+                        modelArtifacts = _a.sent();
+                        expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
+                        expect(modelArtifacts.weightSpecs)
+                            .toEqual(weightsManifest[0].weights.concat(weightsManifest[1].weights));
+                        expect(new Int32Array(modelArtifacts.weightData.slice(0, 12)))
+                            .toEqual(new Int32Array([1, 3, 3]));
+                        expect(new Uint8Array(modelArtifacts.weightData.slice(12, 14)))
+                            .toEqual(new Uint8Array([7, 4]));
+                        return [2];
+                }
+            });
+        }); });
+        it('topology only', function () { return __awaiter(_this, void 0, void 0, function () {
+            var handler, modelArtifacts;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        setupFakeWeightFiles({
+                            './model.json': JSON.stringify({ modelTopology: modelTopology1 }),
+                        });
+                        handler = tf.io.browserHTTPRequest('./model.json');
+                        return [4, handler.load()];
+                    case 1:
+                        modelArtifacts = _a.sent();
+                        expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
+                        expect(modelArtifacts.weightSpecs).toBeUndefined();
+                        expect(modelArtifacts.weightData).toBeUndefined();
+                        return [2];
+                }
+            });
+        }); });
+        it('weights only', function () { return __awaiter(_this, void 0, void 0, function () {
+            var weightsManifest, floatData1, floatData2, handler, modelArtifacts;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        weightsManifest = [
+                            {
+                                paths: ['weightfile0'],
+                                weights: [{
+                                        name: 'fooWeight',
+                                        shape: [3, 1],
+                                        dtype: 'int32',
+                                    }]
+                            },
+                            {
+                                paths: ['weightfile1'],
+                                weights: [{
+                                        name: 'barWeight',
+                                        shape: [2],
+                                        dtype: 'float32',
+                                    }],
+                            }
+                        ];
+                        floatData1 = new Int32Array([1, 3, 3]);
+                        floatData2 = new Float32Array([-7, -4]);
+                        setupFakeWeightFiles({
+                            'path1/model.json': JSON.stringify({ weightsManifest: weightsManifest }),
+                            'path1/weightfile0': floatData1,
+                            'path1/weightfile1': floatData2,
+                        });
+                        handler = tf.io.browserHTTPRequest('path1/model.json');
+                        return [4, handler.load()];
+                    case 1:
+                        modelArtifacts = _a.sent();
+                        expect(modelArtifacts.modelTopology).toBeUndefined();
+                        expect(modelArtifacts.weightSpecs)
+                            .toEqual(weightsManifest[0].weights.concat(weightsManifest[1].weights));
+                        expect(new Int32Array(modelArtifacts.weightData.slice(0, 12)))
+                            .toEqual(new Int32Array([1, 3, 3]));
+                        expect(new Float32Array(modelArtifacts.weightData.slice(12, 20)))
+                            .toEqual(new Float32Array([-7, -4]));
+                        return [2];
+                }
+            });
+        }); });
+        it('Missing modelTopology and weightsManifest leads to error', function (done) { return __awaiter(_this, void 0, void 0, function () {
+            var handler;
+            return __generator(this, function (_a) {
+                setupFakeWeightFiles({ 'path1/model.json': JSON.stringify({}) });
+                handler = tf.io.browserHTTPRequest('path1/model.json');
+                handler.load()
+                    .then(function (modelTopology1) {
+                    done.fail('Loading from missing modelTopology and weightsManifest ' +
+                        'succeeded expectedly.');
+                })
+                    .catch(function (err) {
+                    expect(err.message)
+                        .toMatch(/contains neither model topology or manifest/);
+                    done();
+                });
+                return [2];
+            });
+        }); });
+    });
+    describe('Binary model', function () {
+        var requestInits;
+        var modelData;
+        var setupFakeWeightFiles = function (fileBufferMap) {
+            spyOn(window, 'fetch').and.callFake(function (path, init) {
+                requestInits.push(init);
+                return new Response(fileBufferMap[path]);
+            });
+        };
+        beforeEach(function () {
+            requestInits = [];
+            modelData = new ArrayBuffer(5);
+        });
+        it('1 group, 2 weights, 1 path', function (done) {
+            var weightManifest1 = [{
+                    paths: ['weightfile0'],
+                    weights: [
+                        {
+                            name: 'dense/kernel',
+                            shape: [3, 1],
+                            dtype: 'float32',
+                        },
+                        {
+                            name: 'dense/bias',
+                            shape: [2],
+                            dtype: 'float32',
+                        }
+                    ]
+                }];
+            var floatData = new Float32Array([1, 3, 3, 7, 4]);
+            setupFakeWeightFiles({
+                './model.pb': modelData,
+                './weights_manifest.json': JSON.stringify(weightManifest1),
+                './weightfile0': floatData,
+            });
+            var handler = tf.io.browserHTTPRequest(['./model.pb', './weights_manifest.json']);
+            handler.load()
+                .then(function (modelArtifacts) {
+                expect(modelArtifacts.modelTopology).toEqual(modelData);
+                expect(modelArtifacts.weightSpecs)
+                    .toEqual(weightManifest1[0].weights);
+                expect(new Float32Array(modelArtifacts.weightData))
+                    .toEqual(floatData);
+                expect(requestInits).toEqual([{}, {}, {}]);
+                done();
+            })
+                .catch(function (err) { return done.fail(err.stack); });
+        });
+        it('1 group, 2 weights, 1 path with suffix', function () { return __awaiter(_this, void 0, void 0, function () {
+            var weightManifest1, floatData, handler, modelArtifacts;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        weightManifest1 = [{
+                                paths: ['weightfile0'],
+                                weights: [
+                                    {
+                                        name: 'dense/kernel',
+                                        shape: [3, 1],
+                                        dtype: 'float32',
+                                    },
+                                    {
+                                        name: 'dense/bias',
+                                        shape: [2],
+                                        dtype: 'float32',
+                                    }
+                                ]
+                            }];
+                        floatData = new Float32Array([1, 3, 3, 7, 4]);
+                        setupFakeWeightFiles({
+                            './model.pb?tfjs-format=file': modelData,
+                            './weights_manifest.json?tfjs-format=file': JSON.stringify(weightManifest1),
+                            './weightfile0?tfjs-format=file': floatData,
+                        });
+                        handler = tf.io.browserHTTPRequest([
+                            './model.pb?tfjs-format=file',
+                            './weights_manifest.json?tfjs-format=file'
+                        ]);
+                        return [4, handler.load()];
+                    case 1:
+                        modelArtifacts = _a.sent();
+                        expect(modelArtifacts.modelTopology).toEqual(modelData);
+                        expect(modelArtifacts.weightSpecs).toEqual(weightManifest1[0].weights);
+                        expect(new Float32Array(modelArtifacts.weightData)).toEqual(floatData);
+                        expect(requestInits).toEqual([{}, {}, {}]);
+                        return [2];
+                }
+            });
+        }); });
+        it('1 group, 2 weights, 1 path, with requestInit', function () { return __awaiter(_this, void 0, void 0, function () {
+            var weightManifest1, floatData, handler, modelArtifacts;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        weightManifest1 = [{
+                                paths: ['weightfile0'],
+                                weights: [
+                                    {
+                                        name: 'dense/kernel',
+                                        shape: [3, 1],
+                                        dtype: 'float32',
+                                    },
+                                    {
+                                        name: 'dense/bias',
+                                        shape: [2],
+                                        dtype: 'float32',
+                                    }
+                                ]
+                            }];
+                        floatData = new Float32Array([1, 3, 3, 7, 4]);
+                        setupFakeWeightFiles({
+                            './model.pb': modelData,
+                            './weights_manifest.json': JSON.stringify(weightManifest1),
+                            './weightfile0': floatData,
+                        });
+                        handler = tf.io.browserHTTPRequest(['./model.pb', './weights_manifest.json'], { headers: { 'header_key_1': 'header_value_1' } });
+                        return [4, handler.load()];
+                    case 1:
+                        modelArtifacts = _a.sent();
+                        expect(modelArtifacts.modelTopology).toEqual(modelData);
+                        expect(modelArtifacts.weightSpecs).toEqual(weightManifest1[0].weights);
+                        expect(new Float32Array(modelArtifacts.weightData)).toEqual(floatData);
+                        expect(requestInits).toEqual([
+                            { headers: { 'header_key_1': 'header_value_1' } },
+                            { headers: { 'header_key_1': 'header_value_1' } },
+                            { headers: { 'header_key_1': 'header_value_1' } },
+                        ]);
+                        return [2];
+                }
+            });
+        }); });
+        it('1 group, 2 weight, 2 paths', function () { return __awaiter(_this, void 0, void 0, function () {
+            var weightManifest1, floatData1, floatData2, handler, modelArtifacts;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        weightManifest1 = [{
+                                paths: ['weightfile0', 'weightfile1'],
+                                weights: [
+                                    {
+                                        name: 'dense/kernel',
+                                        shape: [3, 1],
+                                        dtype: 'float32',
+                                    },
+                                    {
+                                        name: 'dense/bias',
+                                        shape: [2],
+                                        dtype: 'float32',
+                                    }
+                                ]
+                            }];
+                        floatData1 = new Float32Array([1, 3, 3]);
+                        floatData2 = new Float32Array([7, 4]);
+                        setupFakeWeightFiles({
+                            './model.pb': modelData,
+                            './weights_manifest.json': JSON.stringify(weightManifest1),
+                            './weightfile0': floatData1,
+                            './weightfile1': floatData2,
+                        });
+                        handler = tf.io.browserHTTPRequest(['./model.pb', './weights_manifest.json']);
+                        return [4, handler.load()];
+                    case 1:
+                        modelArtifacts = _a.sent();
+                        expect(modelArtifacts.modelTopology).toEqual(modelData);
+                        expect(modelArtifacts.weightSpecs).toEqual(weightManifest1[0].weights);
+                        expect(new Float32Array(modelArtifacts.weightData))
+                            .toEqual(new Float32Array([1, 3, 3, 7, 4]));
+                        return [2];
+                }
+            });
+        }); });
+        it('2 groups, 2 weight, 2 paths', function () { return __awaiter(_this, void 0, void 0, function () {
+            var weightsManifest, floatData1, floatData2, handler, modelArtifacts;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        weightsManifest = [
+                            {
+                                paths: ['weightfile0'],
+                                weights: [{
+                                        name: 'dense/kernel',
+                                        shape: [3, 1],
+                                        dtype: 'float32',
+                                    }]
+                            },
+                            {
+                                paths: ['weightfile1'],
+                                weights: [{
+                                        name: 'dense/bias',
+                                        shape: [2],
+                                        dtype: 'float32',
+                                    }],
+                            }
+                        ];
+                        floatData1 = new Float32Array([1, 3, 3]);
+                        floatData2 = new Float32Array([7, 4]);
+                        setupFakeWeightFiles({
+                            './model.pb': modelData,
+                            './weights_manifest.json': JSON.stringify(weightsManifest),
+                            './weightfile0': floatData1,
+                            './weightfile1': floatData2,
+                        });
+                        handler = tf.io.browserHTTPRequest(['./model.pb', './weights_manifest.json']);
+                        return [4, handler.load()];
+                    case 1:
+                        modelArtifacts = _a.sent();
+                        expect(modelArtifacts.modelTopology).toEqual(modelData);
+                        expect(modelArtifacts.weightSpecs)
+                            .toEqual(weightsManifest[0].weights.concat(weightsManifest[1].weights));
+                        expect(new Float32Array(modelArtifacts.weightData))
+                            .toEqual(new Float32Array([1, 3, 3, 7, 4]));
+                        return [2];
+                }
+            });
+        }); });
+        it('2 groups, 2 weight, 2 paths, Int32 and Uint8 Data', function () { return __awaiter(_this, void 0, void 0, function () {
+            var weightsManifest, floatData1, floatData2, handler, modelArtifacts;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        weightsManifest = [
+                            {
+                                paths: ['weightfile0'],
+                                weights: [{
+                                        name: 'fooWeight',
+                                        shape: [3, 1],
+                                        dtype: 'int32',
+                                    }]
+                            },
+                            {
+                                paths: ['weightfile1'],
+                                weights: [{
+                                        name: 'barWeight',
+                                        shape: [2],
+                                        dtype: 'bool',
+                                    }],
+                            }
+                        ];
+                        floatData1 = new Int32Array([1, 3, 3]);
+                        floatData2 = new Uint8Array([7, 4]);
+                        setupFakeWeightFiles({
+                            'path1/model.pb': modelData,
+                            'path2/weights_manifest.json': JSON.stringify(weightsManifest),
+                            'path2/weightfile0': floatData1,
+                            'path2/weightfile1': floatData2,
+                        });
+                        handler = tf.io.browserHTTPRequest(['path1/model.pb', 'path2/weights_manifest.json']);
+                        return [4, handler.load()];
+                    case 1:
+                        modelArtifacts = _a.sent();
+                        expect(modelArtifacts.modelTopology).toEqual(modelData);
+                        expect(modelArtifacts.weightSpecs)
+                            .toEqual(weightsManifest[0].weights.concat(weightsManifest[1].weights));
+                        expect(new Int32Array(modelArtifacts.weightData.slice(0, 12)))
+                            .toEqual(new Int32Array([1, 3, 3]));
+                        expect(new Uint8Array(modelArtifacts.weightData.slice(12, 14)))
+                            .toEqual(new Uint8Array([7, 4]));
+                        return [2];
+                }
+            });
+        }); });
+        it('2 groups, 2 weight, weight path prefix, Int32 and Uint8 Data', function () { return __awaiter(_this, void 0, void 0, function () {
+            var weightsManifest, floatData1, floatData2, handler, modelArtifacts;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        weightsManifest = [
+                            {
+                                paths: ['weightfile0'],
+                                weights: [{
+                                        name: 'fooWeight',
+                                        shape: [3, 1],
+                                        dtype: 'int32',
+                                    }]
+                            },
+                            {
+                                paths: ['weightfile1'],
+                                weights: [{
+                                        name: 'barWeight',
+                                        shape: [2],
+                                        dtype: 'bool',
+                                    }],
+                            }
+                        ];
+                        floatData1 = new Int32Array([1, 3, 3]);
+                        floatData2 = new Uint8Array([7, 4]);
+                        setupFakeWeightFiles({
+                            'path1/model.pb': modelData,
+                            'path2/weights_manifest.json': JSON.stringify(weightsManifest),
+                            'path3/weightfile0': floatData1,
+                            'path3/weightfile1': floatData2,
+                        });
+                        handler = tf.io.browserHTTPRequest(['path1/model.pb', 'path2/weights_manifest.json'], {}, 'path3/');
+                        return [4, handler.load()];
+                    case 1:
+                        modelArtifacts = _a.sent();
+                        expect(modelArtifacts.modelTopology).toEqual(modelData);
+                        expect(modelArtifacts.weightSpecs)
+                            .toEqual(weightsManifest[0].weights.concat(weightsManifest[1].weights));
+                        expect(new Int32Array(modelArtifacts.weightData.slice(0, 12)))
+                            .toEqual(new Int32Array([1, 3, 3]));
+                        expect(new Uint8Array(modelArtifacts.weightData.slice(12, 14)))
+                            .toEqual(new Uint8Array([7, 4]));
+                        return [2];
+                }
+            });
+        }); });
+        it('the url path length is not 2 should leads to error', function () {
+            expect(function () { return tf.io.browserHTTPRequest(['path1/model.pb']); }).toThrow();
+        });
+    });
+    it('Overriding BrowserHTTPRequest fetchFunc', function () { return __awaiter(_this, void 0, void 0, function () {
+        function customFetch(input, init) {
+            return __awaiter(this, void 0, void 0, function () {
+                return __generator(this, function (_a) {
+                    fetchInputs.push(input);
+                    fetchInits.push(init);
+                    if (input === './model.json') {
+                        return [2, new Response(JSON.stringify({
+                                modelTopology: modelTopology1,
+                                weightsManifest: weightManifest1
+                            }), { status: 200 })];
+                    }
+                    else if (input === './weightfile0') {
+                        return [2, new Response(floatData, { status: 200 })];
+                    }
+                    else {
+                        return [2, new Response(null, { status: 404 })];
+                    }
+                    return [2];
+                });
+            });
+        }
+        var weightManifest1, floatData, fetchInputs, fetchInits, handler, modelArtifacts;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    weightManifest1 = [{
+                            paths: ['weightfile0'],
+                            weights: [
+                                {
+                                    name: 'dense/kernel',
+                                    shape: [3, 1],
+                                    dtype: 'float32',
+                                },
+                                {
+                                    name: 'dense/bias',
+                                    shape: [2],
+                                    dtype: 'float32',
+                                }
+                            ]
+                        }];
+                    floatData = new Float32Array([1, 3, 3, 7, 4]);
+                    fetchInputs = [];
+                    fetchInits = [];
+                    handler = tf.io.browserHTTPRequest('./model.json', { credentials: 'include' }, null, customFetch);
+                    return [4, handler.load()];
+                case 1:
+                    modelArtifacts = _a.sent();
+                    expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
+                    expect(modelArtifacts.weightSpecs).toEqual(weightManifest1[0].weights);
+                    expect(new Float32Array(modelArtifacts.weightData)).toEqual(floatData);
+                    expect(fetchInputs).toEqual(['./model.json', './weightfile0']);
+                    expect(fetchInits).toEqual([
+                        { credentials: 'include' }, { credentials: 'include' }
+                    ]);
+                    return [2];
+            }
+        });
+    }); });
+});
+//# sourceMappingURL=browser_http_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/browser_http_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/io/browser_http_test.js.map
new file mode 100644
index 0000000..47b7ce0
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/browser_http_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db.d.ts b/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db.d.ts
new file mode 100644
index 0000000..5cc3596
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db.d.ts
@@ -0,0 +1,22 @@
+import { IORouter } from './router_registry';
+import { IOHandler, ModelArtifacts, ModelArtifactsInfo, ModelStoreManager, SaveResult } from './types';
+export declare function deleteDatabase(): Promise<void>;
+export declare class BrowserIndexedDB implements IOHandler {
+    protected readonly indexedDB: IDBFactory;
+    protected readonly modelPath: string;
+    static readonly URL_SCHEME: string;
+    constructor(modelPath: string);
+    save(modelArtifacts: ModelArtifacts): Promise<SaveResult>;
+    load(): Promise<ModelArtifacts>;
+    private databaseAction;
+}
+export declare const indexedDBRouter: IORouter;
+export declare function browserIndexedDB(modelPath: string): IOHandler;
+export declare class BrowserIndexedDBManager implements ModelStoreManager {
+    private indexedDB;
+    constructor();
+    listModels(): Promise<{
+        [path: string]: ModelArtifactsInfo;
+    }>;
+    removeModel(path: string): Promise<ModelArtifactsInfo>;
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db.js b/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db.js
new file mode 100644
index 0000000..fb7d540
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db.js
@@ -0,0 +1,309 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var io_utils_1 = require("./io_utils");
+var model_management_1 = require("./model_management");
+var router_registry_1 = require("./router_registry");
+var DATABASE_NAME = 'tensorflowjs';
+var DATABASE_VERSION = 1;
+var MODEL_STORE_NAME = 'models_store';
+var INFO_STORE_NAME = 'model_info_store';
+function deleteDatabase() {
+    return __awaiter(this, void 0, void 0, function () {
+        var idbFactory;
+        return __generator(this, function (_a) {
+            idbFactory = getIndexedDBFactory();
+            return [2, new Promise(function (resolve, reject) {
+                    var deleteRequest = idbFactory.deleteDatabase(DATABASE_NAME);
+                    deleteRequest.onsuccess = function () { return resolve(); };
+                    deleteRequest.onerror = function (error) { return reject(error); };
+                })];
+        });
+    });
+}
+exports.deleteDatabase = deleteDatabase;
+function getIndexedDBFactory() {
+    if (!environment_1.ENV.get('IS_BROWSER')) {
+        throw new Error('Failed to obtain IndexedDB factory because the current environment' +
+            'is not a web browser.');
+    }
+    var theWindow = window;
+    var factory = theWindow.indexedDB || theWindow.mozIndexedDB ||
+        theWindow.webkitIndexedDB || theWindow.msIndexedDB ||
+        theWindow.shimIndexedDB;
+    if (factory == null) {
+        throw new Error('The current browser does not appear to support IndexedDB.');
+    }
+    return factory;
+}
+function setUpDatabase(openRequest) {
+    var db = openRequest.result;
+    db.createObjectStore(MODEL_STORE_NAME, { keyPath: 'modelPath' });
+    db.createObjectStore(INFO_STORE_NAME, { keyPath: 'modelPath' });
+}
+var BrowserIndexedDB = (function () {
+    function BrowserIndexedDB(modelPath) {
+        this.indexedDB = getIndexedDBFactory();
+        if (modelPath == null || !modelPath) {
+            throw new Error('For IndexedDB, modelPath must not be null, undefined or empty.');
+        }
+        this.modelPath = modelPath;
+    }
+    BrowserIndexedDB.prototype.save = function (modelArtifacts) {
+        return __awaiter(this, void 0, void 0, function () {
+            return __generator(this, function (_a) {
+                if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
+                    throw new Error('BrowserLocalStorage.save() does not support saving model topology ' +
+                        'in binary formats yet.');
+                }
+                return [2, this.databaseAction(this.modelPath, modelArtifacts)];
+            });
+        });
+    };
+    BrowserIndexedDB.prototype.load = function () {
+        return __awaiter(this, void 0, void 0, function () {
+            return __generator(this, function (_a) {
+                return [2, this.databaseAction(this.modelPath)];
+            });
+        });
+    };
+    BrowserIndexedDB.prototype.databaseAction = function (modelPath, modelArtifacts) {
+        var _this = this;
+        return new Promise(function (resolve, reject) {
+            var openRequest = _this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
+            openRequest.onupgradeneeded = function () { return setUpDatabase(openRequest); };
+            openRequest.onsuccess = function () {
+                var db = openRequest.result;
+                if (modelArtifacts == null) {
+                    var modelTx = db.transaction(MODEL_STORE_NAME, 'readonly');
+                    var modelStore = modelTx.objectStore(MODEL_STORE_NAME);
+                    var getRequest_1 = modelStore.get(_this.modelPath);
+                    getRequest_1.onsuccess = function () {
+                        if (getRequest_1.result == null) {
+                            db.close();
+                            return reject(new Error("Cannot find model with path '" + _this.modelPath + "' " +
+                                "in IndexedDB."));
+                        }
+                        else {
+                            resolve(getRequest_1.result.modelArtifacts);
+                        }
+                    };
+                    getRequest_1.onerror = function (error) {
+                        db.close();
+                        return reject(getRequest_1.error);
+                    };
+                    modelTx.oncomplete = function () { return db.close(); };
+                }
+                else {
+                    var modelArtifactsInfo_1 = io_utils_1.getModelArtifactsInfoForJSON(modelArtifacts);
+                    var infoTx_1 = db.transaction(INFO_STORE_NAME, 'readwrite');
+                    var infoStore_1 = infoTx_1.objectStore(INFO_STORE_NAME);
+                    var putInfoRequest_1 = infoStore_1.put({ modelPath: _this.modelPath, modelArtifactsInfo: modelArtifactsInfo_1 });
+                    var modelTx_1;
+                    putInfoRequest_1.onsuccess = function () {
+                        modelTx_1 = db.transaction(MODEL_STORE_NAME, 'readwrite');
+                        var modelStore = modelTx_1.objectStore(MODEL_STORE_NAME);
+                        var putModelRequest = modelStore.put({
+                            modelPath: _this.modelPath,
+                            modelArtifacts: modelArtifacts,
+                            modelArtifactsInfo: modelArtifactsInfo_1
+                        });
+                        putModelRequest.onsuccess = function () { return resolve({ modelArtifactsInfo: modelArtifactsInfo_1 }); };
+                        putModelRequest.onerror = function (error) {
+                            infoStore_1 = infoTx_1.objectStore(INFO_STORE_NAME);
+                            var deleteInfoRequest = infoStore_1.delete(_this.modelPath);
+                            deleteInfoRequest.onsuccess = function () {
+                                db.close();
+                                return reject(putModelRequest.error);
+                            };
+                            deleteInfoRequest.onerror = function (error) {
+                                db.close();
+                                return reject(putModelRequest.error);
+                            };
+                        };
+                    };
+                    putInfoRequest_1.onerror = function (error) {
+                        db.close();
+                        return reject(putInfoRequest_1.error);
+                    };
+                    infoTx_1.oncomplete = function () {
+                        if (modelTx_1 == null) {
+                            db.close();
+                        }
+                        else {
+                            modelTx_1.oncomplete = function () { return db.close(); };
+                        }
+                    };
+                }
+            };
+            openRequest.onerror = function (error) { return reject(openRequest.error); };
+        });
+    };
+    BrowserIndexedDB.URL_SCHEME = 'indexeddb://';
+    return BrowserIndexedDB;
+}());
+exports.BrowserIndexedDB = BrowserIndexedDB;
+exports.indexedDBRouter = function (url) {
+    if (!environment_1.ENV.get('IS_BROWSER')) {
+        return null;
+    }
+    else {
+        if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {
+            return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));
+        }
+        else {
+            return null;
+        }
+    }
+};
+router_registry_1.IORouterRegistry.registerSaveRouter(exports.indexedDBRouter);
+router_registry_1.IORouterRegistry.registerLoadRouter(exports.indexedDBRouter);
+function browserIndexedDB(modelPath) {
+    return new BrowserIndexedDB(modelPath);
+}
+exports.browserIndexedDB = browserIndexedDB;
+function maybeStripScheme(key) {
+    return key.startsWith(BrowserIndexedDB.URL_SCHEME) ?
+        key.slice(BrowserIndexedDB.URL_SCHEME.length) :
+        key;
+}
+var BrowserIndexedDBManager = (function () {
+    function BrowserIndexedDBManager() {
+        this.indexedDB = getIndexedDBFactory();
+    }
+    BrowserIndexedDBManager.prototype.listModels = function () {
+        return __awaiter(this, void 0, void 0, function () {
+            var _this = this;
+            return __generator(this, function (_a) {
+                return [2, new Promise(function (resolve, reject) {
+                        var openRequest = _this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
+                        openRequest.onupgradeneeded = function () { return setUpDatabase(openRequest); };
+                        openRequest.onsuccess = function () {
+                            var db = openRequest.result;
+                            var tx = db.transaction(INFO_STORE_NAME, 'readonly');
+                            var store = tx.objectStore(INFO_STORE_NAME);
+                            var getAllInfoRequest = store.getAll();
+                            getAllInfoRequest.onsuccess = function () {
+                                var out = {};
+                                for (var _i = 0, _a = getAllInfoRequest.result; _i < _a.length; _i++) {
+                                    var item = _a[_i];
+                                    out[item.modelPath] = item.modelArtifactsInfo;
+                                }
+                                resolve(out);
+                            };
+                            getAllInfoRequest.onerror = function (error) {
+                                db.close();
+                                return reject(getAllInfoRequest.error);
+                            };
+                            tx.oncomplete = function () { return db.close(); };
+                        };
+                        openRequest.onerror = function (error) { return reject(openRequest.error); };
+                    })];
+            });
+        });
+    };
+    BrowserIndexedDBManager.prototype.removeModel = function (path) {
+        return __awaiter(this, void 0, void 0, function () {
+            var _this = this;
+            return __generator(this, function (_a) {
+                path = maybeStripScheme(path);
+                return [2, new Promise(function (resolve, reject) {
+                        var openRequest = _this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
+                        openRequest.onupgradeneeded = function () { return setUpDatabase(openRequest); };
+                        openRequest.onsuccess = function () {
+                            var db = openRequest.result;
+                            var infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');
+                            var infoStore = infoTx.objectStore(INFO_STORE_NAME);
+                            var getInfoRequest = infoStore.get(path);
+                            var modelTx;
+                            getInfoRequest.onsuccess = function () {
+                                if (getInfoRequest.result == null) {
+                                    db.close();
+                                    return reject(new Error("Cannot find model with path '" + path + "' " +
+                                        "in IndexedDB."));
+                                }
+                                else {
+                                    var deleteInfoRequest = infoStore.delete(path);
+                                    var deleteModelData_1 = function () {
+                                        modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');
+                                        var modelStore = modelTx.objectStore(MODEL_STORE_NAME);
+                                        var deleteModelRequest = modelStore.delete(path);
+                                        deleteModelRequest.onsuccess = function () {
+                                            return resolve(getInfoRequest.result.modelArtifactsInfo);
+                                        };
+                                        deleteModelRequest.onerror = function (error) {
+                                            return reject(getInfoRequest.error);
+                                        };
+                                    };
+                                    deleteInfoRequest.onsuccess = deleteModelData_1;
+                                    deleteInfoRequest.onerror = function (error) {
+                                        deleteModelData_1();
+                                        db.close();
+                                        return reject(getInfoRequest.error);
+                                    };
+                                }
+                            };
+                            getInfoRequest.onerror = function (error) {
+                                db.close();
+                                return reject(getInfoRequest.error);
+                            };
+                            infoTx.oncomplete = function () {
+                                if (modelTx == null) {
+                                    db.close();
+                                }
+                                else {
+                                    modelTx.oncomplete = function () { return db.close(); };
+                                }
+                            };
+                        };
+                        openRequest.onerror = function (error) { return reject(openRequest.error); };
+                    })];
+            });
+        });
+    };
+    return BrowserIndexedDBManager;
+}());
+exports.BrowserIndexedDBManager = BrowserIndexedDBManager;
+if (environment_1.ENV.get('IS_BROWSER')) {
+    try {
+        model_management_1.ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());
+    }
+    catch (err) {
+    }
+}
+//# sourceMappingURL=indexed_db.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db.js.map b/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db.js.map
new file mode 100644
index 0000000..260b98b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"indexed_db.js","sourceRoot":"","sources":["../../src/io/indexed_db.ts"],"names":[],"mappings":";;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;AAiBA,8CAAmC;AACnC,uCAAwD;AACxD,uDAA6D;AAC7D,qDAA6D;AAG7D,IAAM,aAAa,GAAG,cAAc,CAAC;AACrC,IAAM,gBAAgB,GAAG,CAAC,CAAC;AAK3B,IAAM,gBAAgB,GAAG,cAAc,CAAC;AAIxC,IAAM,eAAe,GAAG,kBAAkB,CAAC;AAK3C;;;;YACQ,UAAU,GAAG,mBAAmB,EAAE,CAAC;YAEzC,WAAO,IAAI,OAAO,CAAO,UAAC,OAAO,EAAE,MAAM;oBACvC,IAAM,aAAa,GAAG,UAAU,CAAC,cAAc,CAAC,aAAa,CAAC,CAAC;oBAC/D,aAAa,CAAC,SAAS,GAAG,cAAM,OAAA,OAAO,EAAE,EAAT,CAAS,CAAC;oBAC1C,aAAa,CAAC,OAAO,GAAG,UAAA,KAAK,IAAI,OAAA,MAAM,CAAC,KAAK,CAAC,EAAb,CAAa,CAAC;gBACjD,CAAC,CAAC,EAAC;;;CACJ;AARD,wCAQC;AAED;IACE,IAAI,CAAC,iBAAG,CAAC,GAAG,CAAC,YAAY,CAAC,EAAE;QAI1B,MAAM,IAAI,KAAK,CACX,oEAAoE;YACpE,uBAAuB,CAAC,CAAC;KAC9B;IAED,IAAM,SAAS,GAAQ,MAAM,CAAC;IAC9B,IAAM,OAAO,GAAG,SAAS,CAAC,SAAS,IAAI,SAAS,CAAC,YAAY;QACzD,SAAS,CAAC,eAAe,IAAI,SAAS,CAAC,WAAW;QAClD,SAAS,CAAC,aAAa,CAAC;IAC5B,IAAI,OAAO,IAAI,IAAI,EAAE;QACnB,MAAM,IAAI,KAAK,CACX,2DAA2D,CAAC,CAAC;KAClE;IACD,OAAO,OAAO,CAAC;AACjB,CAAC;AAED,uBAAuB,WAAuB;IAC5C,IAAM,EAAE,GAAG,WAAW,CAAC,MAAqB,CAAC;IAC7C,EAAE,CAAC,iBAAiB,CAAC,gBAAgB,EAAE,EAAC,OAAO,EAAE,WAAW,EAAC,CAAC,CAAC;IAC/D,EAAE,CAAC,iBAAiB,CAAC,eAAe,EAAE,EAAC,OAAO,EAAE,WAAW,EAAC,CAAC,CAAC;AAChE,CAAC;AAOD;IAME,0BAAY,SAAiB;QAC3B,IAAI,CAAC,SAAS,GAAG,mBAAmB,EAAE,CAAC;QAEvC,IAAI,SAAS,IAAI,IAAI,IAAI,CAAC,SAAS,EAAE;YACnC,MAAM,IAAI,KAAK,CACX,gEAAgE,CAAC,CAAC;SACvE;QACD,IAAI,CAAC,SAAS,GAAG,SAAS,CAAC;IAC7B,CAAC;IAEK,+BAAI,GAAV,UAAW,cAA8B;;;gBAEvC,IAAI,cAAc,CAAC,aAAa,YAAY,WAAW,EAAE;oBACvD,MAAM,IAAI,KAAK,CACX,oEAAoE;wBACpE,wBAAwB,CAAC,CAAC;iBAC/B;gBAED,WAAO,IAAI,CAAC,cAAc,CAAC,IAAI,CAAC,SAAS,EAAE,cAAc,CAClC,EAAC;;;KACzB;IAEK,+BAAI,GAAV;;;gBACE,WAAO,IAAI,CAAC,cAAc,CAAC,IAAI,CAAC,SAAS,CAA4B,EAAC;;;KACvE;IAgBO,yCAAc,GAAtB,UAAuB,SAAiB,EAAE,cAA+B;QAAzE,iBA+EC;QA7EC,OAAO,IAAI,OAAO,CAA4B,UAAC,OAAO,EAAE,MAAM;YAC5D,IAAM,WAAW,GAAG,KAAI,CAAC,SAAS,CAAC,IAAI,CAAC,aAAa,EAAE,gBAAgB,CAAC,CAAC;YACzE,WAAW,CAAC,eAAe,GAAG,cAAM,OAAA,aAAa,CAAC,WAAW,CAAC,EAA1B,CAA0B,CAAC;YAE/D,WAAW,CAAC,SAAS,GAAG;gBACtB,IAAM,EAAE,GAAG,WAAW,CAAC,MAAqB,CAAC;gBAE7C,IAAI,cAAc,IAAI,IAAI,EAAE;oBAE1B,IAAM,OAAO,GAAG,EAAE,CAAC,WAAW,CAAC,gBAAgB,EAAE,UAAU,CAAC,CAAC;oBAC7D,IAAM,UAAU,GAAG,OAAO,CAAC,WAAW,CAAC,gBAAgB,CAAC,CAAC;oBACzD,IAAM,YAAU,GAAG,UAAU,CAAC,GAAG,CAAC,KAAI,CAAC,SAAS,CAAC,CAAC;oBAClD,YAAU,CAAC,SAAS,GAAG;wBACrB,IAAI,YAAU,CAAC,MAAM,IAAI,IAAI,EAAE;4BAC7B,EAAE,CAAC,KAAK,EAAE,CAAC;4BACX,OAAO,MAAM,CAAC,IAAI,KAAK,CACnB,kCAAgC,KAAI,CAAC,SAAS,OAAI;gCAClD,eAAe,CAAC,CAAC,CAAC;yBACvB;6BAAM;4BACL,OAAO,CAAC,YAAU,CAAC,MAAM,CAAC,cAAc,CAAC,CAAC;yBAC3C;oBACH,CAAC,CAAC;oBACF,YAAU,CAAC,OAAO,GAAG,UAAA,KAAK;wBACxB,EAAE,CAAC,KAAK,EAAE,CAAC;wBACX,OAAO,MAAM,CAAC,YAAU,CAAC,KAAK,CAAC,CAAC;oBAClC,CAAC,CAAC;oBACF,OAAO,CAAC,UAAU,GAAG,cAAM,OAAA,EAAE,CAAC,KAAK,EAAE,EAAV,CAAU,CAAC;iBACvC;qBAAM;oBAEL,IAAM,oBAAkB,GACpB,uCAA4B,CAAC,cAAc,CAAC,CAAC;oBAEjD,IAAM,QAAM,GAAG,EAAE,CAAC,WAAW,CAAC,eAAe,EAAE,WAAW,CAAC,CAAC;oBAC5D,IAAI,WAAS,GAAG,QAAM,CAAC,WAAW,CAAC,eAAe,CAAC,CAAC;oBACpD,IAAM,gBAAc,GAChB,WAAS,CAAC,GAAG,CAAC,EAAC,SAAS,EAAE,KAAI,CAAC,SAAS,EAAE,kBAAkB,sBAAA,EAAC,CAAC,CAAC;oBACnE,IAAI,SAAuB,CAAC;oBAC5B,gBAAc,CAAC,SAAS,GAAG;wBAEzB,SAAO,GAAG,EAAE,CAAC,WAAW,CAAC,gBAAgB,EAAE,WAAW,CAAC,CAAC;wBACxD,IAAM,UAAU,GAAG,SAAO,CAAC,WAAW,CAAC,gBAAgB,CAAC,CAAC;wBACzD,IAAM,eAAe,GAAG,UAAU,CAAC,GAAG,CAAC;4BACrC,SAAS,EAAE,KAAI,CAAC,SAAS;4BACzB,cAAc,gBAAA;4BACd,kBAAkB,sBAAA;yBACnB,CAAC,CAAC;wBACH,eAAe,CAAC,SAAS,GAAG,cAAM,OAAA,OAAO,CAAC,EAAC,kBAAkB,sBAAA,EAAC,CAAC,EAA7B,CAA6B,CAAC;wBAChE,eAAe,CAAC,OAAO,GAAG,UAAA,KAAK;4BAG7B,WAAS,GAAG,QAAM,CAAC,WAAW,CAAC,eAAe,CAAC,CAAC;4BAChD,IAAM,iBAAiB,GAAG,WAAS,CAAC,MAAM,CAAC,KAAI,CAAC,SAAS,CAAC,CAAC;4BAC3D,iBAAiB,CAAC,SAAS,GAAG;gCAC5B,EAAE,CAAC,KAAK,EAAE,CAAC;gCACX,OAAO,MAAM,CAAC,eAAe,CAAC,KAAK,CAAC,CAAC;4BACvC,CAAC,CAAC;4BACF,iBAAiB,CAAC,OAAO,GAAG,UAAA,KAAK;gCAC/B,EAAE,CAAC,KAAK,EAAE,CAAC;gCACX,OAAO,MAAM,CAAC,eAAe,CAAC,KAAK,CAAC,CAAC;4BACvC,CAAC,CAAC;wBACJ,CAAC,CAAC;oBACJ,CAAC,CAAC;oBACF,gBAAc,CAAC,OAAO,GAAG,UAAA,KAAK;wBAC5B,EAAE,CAAC,KAAK,EAAE,CAAC;wBACX,OAAO,MAAM,CAAC,gBAAc,CAAC,KAAK,CAAC,CAAC;oBACtC,CAAC,CAAC;oBACF,QAAM,CAAC,UAAU,GAAG;wBAClB,IAAI,SAAO,IAAI,IAAI,EAAE;4BACnB,EAAE,CAAC,KAAK,EAAE,CAAC;yBACZ;6BAAM;4BACL,SAAO,CAAC,UAAU,GAAG,cAAM,OAAA,EAAE,CAAC,KAAK,EAAE,EAAV,CAAU,CAAC;yBACvC;oBACH,CAAC,CAAC;iBACH;YACH,CAAC,CAAC;YACF,WAAW,CAAC,OAAO,GAAG,UAAA,KAAK,IAAI,OAAA,MAAM,CAAC,WAAW,CAAC,KAAK,CAAC,EAAzB,CAAyB,CAAC;QAC3D,CAAC,CAAC,CAAC;IACL,CAAC;IAzHe,2BAAU,GAAG,cAAc,CAAC;IA0H9C,uBAAC;CAAA,AA9HD,IA8HC;AA9HY,4CAAgB;AAgIhB,QAAA,eAAe,GAAa,UAAC,GAAoB;IAC5D,IAAI,CAAC,iBAAG,CAAC,GAAG,CAAC,YAAY,CAAC,EAAE;QAC1B,OAAO,IAAI,CAAC;KACb;SAAM;QACL,IAAI,CAAC,KAAK,CAAC,OAAO,CAAC,GAAG,CAAC,IAAI,GAAG,CAAC,UAAU,CAAC,gBAAgB,CAAC,UAAU,CAAC,EAAE;YACtE,OAAO,gBAAgB,CAAC,GAAG,CAAC,KAAK,CAAC,gBAAgB,CAAC,UAAU,CAAC,MAAM,CAAC,CAAC,CAAC;SACxE;aAAM;YACL,OAAO,IAAI,CAAC;SACb;KACF;AACH,CAAC,CAAC;AACF,kCAAgB,CAAC,kBAAkB,CAAC,uBAAe,CAAC,CAAC;AACrD,kCAAgB,CAAC,kBAAkB,CAAC,uBAAe,CAAC,CAAC;AAmBrD,0BAAiC,SAAiB;IAChD,OAAO,IAAI,gBAAgB,CAAC,SAAS,CAAC,CAAC;AACzC,CAAC;AAFD,4CAE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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db_test.js b/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db_test.js
new file mode 100644
index 0000000..1112865
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db_test.js
@@ -0,0 +1,359 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+var _this = this;
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+var indexed_db_1 = require("./indexed_db");
+jasmine_util_1.describeWithFlags('IndexedDB', test_util_1.BROWSER_ENVS, function () {
+    var modelTopology1 = {
+        'class_name': 'Sequential',
+        'keras_version': '2.1.4',
+        'config': [{
+                'class_name': 'Dense',
+                'config': {
+                    'kernel_initializer': {
+                        'class_name': 'VarianceScaling',
+                        'config': {
+                            'distribution': 'uniform',
+                            'scale': 1.0,
+                            'seed': null,
+                            'mode': 'fan_avg'
+                        }
+                    },
+                    'name': 'dense',
+                    'kernel_constraint': null,
+                    'bias_regularizer': null,
+                    'bias_constraint': null,
+                    'dtype': 'float32',
+                    'activation': 'linear',
+                    'trainable': true,
+                    'kernel_regularizer': null,
+                    'bias_initializer': { 'class_name': 'Zeros', 'config': {} },
+                    'units': 1,
+                    'batch_input_shape': [null, 3],
+                    'use_bias': true,
+                    'activity_regularizer': null
+                }
+            }],
+        'backend': 'tensorflow'
+    };
+    var weightSpecs1 = [
+        {
+            name: 'dense/kernel',
+            shape: [3, 1],
+            dtype: 'float32',
+        },
+        {
+            name: 'dense/bias',
+            shape: [1],
+            dtype: 'float32',
+        }
+    ];
+    var weightData1 = new ArrayBuffer(16);
+    var artifacts1 = {
+        modelTopology: modelTopology1,
+        weightSpecs: weightSpecs1,
+        weightData: weightData1,
+    };
+    var weightSpecs2 = [
+        {
+            name: 'dense/new_kernel',
+            shape: [5, 1],
+            dtype: 'float32',
+        },
+        {
+            name: 'dense/new_bias',
+            shape: [1],
+            dtype: 'float32',
+        }
+    ];
+    beforeEach(indexed_db_1.deleteDatabase);
+    afterEach(indexed_db_1.deleteDatabase);
+    it('Save-load round trip', function () { return __awaiter(_this, void 0, void 0, function () {
+        var testStartDate, handler, saveResult, loadedArtifacts;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    testStartDate = new Date();
+                    handler = tf.io.getSaveHandlers('indexeddb://FooModel')[0];
+                    return [4, handler.save(artifacts1)];
+                case 1:
+                    saveResult = _a.sent();
+                    expect(saveResult.modelArtifactsInfo.dateSaved.getTime())
+                        .toBeGreaterThanOrEqual(testStartDate.getTime());
+                    expect(saveResult.modelArtifactsInfo.modelTopologyBytes)
+                        .toEqual(JSON.stringify(modelTopology1).length);
+                    expect(saveResult.modelArtifactsInfo.weightSpecsBytes)
+                        .toEqual(JSON.stringify(weightSpecs1).length);
+                    expect(saveResult.modelArtifactsInfo.weightDataBytes)
+                        .toEqual(weightData1.byteLength);
+                    return [4, handler.load()];
+                case 2:
+                    loadedArtifacts = _a.sent();
+                    expect(loadedArtifacts.modelTopology).toEqual(modelTopology1);
+                    expect(loadedArtifacts.weightSpecs).toEqual(weightSpecs1);
+                    test_util_1.expectArrayBuffersEqual(loadedArtifacts.weightData, weightData1);
+                    return [2];
+            }
+        });
+    }); });
+    it('Save two models and load one', function () { return __awaiter(_this, void 0, void 0, function () {
+        var weightData2, artifacts2, handler1, saveResult1, handler2, saveResult2, loadedArtifacts;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    weightData2 = new ArrayBuffer(24);
+                    artifacts2 = {
+                        modelTopology: modelTopology1,
+                        weightSpecs: weightSpecs2,
+                        weightData: weightData2,
+                    };
+                    handler1 = tf.io.getSaveHandlers('indexeddb://Model/1')[0];
+                    return [4, handler1.save(artifacts1)];
+                case 1:
+                    saveResult1 = _a.sent();
+                    expect(saveResult1.modelArtifactsInfo.modelTopologyBytes)
+                        .toEqual(JSON.stringify(modelTopology1).length);
+                    expect(saveResult1.modelArtifactsInfo.weightSpecsBytes)
+                        .toEqual(JSON.stringify(weightSpecs1).length);
+                    expect(saveResult1.modelArtifactsInfo.weightDataBytes)
+                        .toEqual(weightData1.byteLength);
+                    handler2 = tf.io.getSaveHandlers('indexeddb://Model/2')[0];
+                    return [4, handler2.save(artifacts2)];
+                case 2:
+                    saveResult2 = _a.sent();
+                    expect(saveResult2.modelArtifactsInfo.dateSaved.getTime())
+                        .toBeGreaterThanOrEqual(saveResult1.modelArtifactsInfo.dateSaved.getTime());
+                    expect(saveResult2.modelArtifactsInfo.modelTopologyBytes)
+                        .toEqual(JSON.stringify(modelTopology1).length);
+                    expect(saveResult2.modelArtifactsInfo.weightSpecsBytes)
+                        .toEqual(JSON.stringify(weightSpecs2).length);
+                    expect(saveResult2.modelArtifactsInfo.weightDataBytes)
+                        .toEqual(weightData2.byteLength);
+                    return [4, handler1.load()];
+                case 3:
+                    loadedArtifacts = _a.sent();
+                    expect(loadedArtifacts.modelTopology).toEqual(modelTopology1);
+                    expect(loadedArtifacts.weightSpecs).toEqual(weightSpecs1);
+                    test_util_1.expectArrayBuffersEqual(loadedArtifacts.weightData, weightData1);
+                    return [2];
+            }
+        });
+    }); });
+    it('Loading nonexistent model fails', function () { return __awaiter(_this, void 0, void 0, function () {
+        var handler, err_1;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    handler = tf.io.getSaveHandlers('indexeddb://NonexistentModel')[0];
+                    _a.label = 1;
+                case 1:
+                    _a.trys.push([1, 3, , 4]);
+                    return [4, handler.load()];
+                case 2:
+                    _a.sent();
+                    fail('Loading nonexistent model from IndexedDB succeeded unexpectly');
+                    return [3, 4];
+                case 3:
+                    err_1 = _a.sent();
+                    expect(err_1.message)
+                        .toEqual('Cannot find model with path \'NonexistentModel\' in IndexedDB.');
+                    return [3, 4];
+                case 4: return [2];
+            }
+        });
+    }); });
+    it('Null, undefined or empty modelPath throws Error', function () {
+        expect(function () { return indexed_db_1.browserIndexedDB(null); })
+            .toThrowError(/IndexedDB, modelPath must not be null, undefined or empty/);
+        expect(function () { return indexed_db_1.browserIndexedDB(undefined); })
+            .toThrowError(/IndexedDB, modelPath must not be null, undefined or empty/);
+        expect(function () { return indexed_db_1.browserIndexedDB(''); })
+            .toThrowError(/IndexedDB, modelPath must not be null, undefined or empty./);
+    });
+    it('router', function () {
+        expect(indexed_db_1.indexedDBRouter('indexeddb://bar') instanceof indexed_db_1.BrowserIndexedDB)
+            .toEqual(true);
+        expect(indexed_db_1.indexedDBRouter('localstorage://bar')).toBeNull();
+        expect(indexed_db_1.indexedDBRouter('qux')).toBeNull();
+    });
+    it('Manager: List models: 0 result', function () { return __awaiter(_this, void 0, void 0, function () {
+        var models;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0: return [4, new indexed_db_1.BrowserIndexedDBManager().listModels()];
+                case 1:
+                    models = _a.sent();
+                    expect(models).toEqual({});
+                    return [2];
+            }
+        });
+    }); });
+    it('Manager: List models: 1 result', function () { return __awaiter(_this, void 0, void 0, function () {
+        var handler, saveResult, models;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    handler = tf.io.getSaveHandlers('indexeddb://baz/QuxModel')[0];
+                    return [4, handler.save(artifacts1)];
+                case 1:
+                    saveResult = _a.sent();
+                    return [4, new indexed_db_1.BrowserIndexedDBManager().listModels()];
+                case 2:
+                    models = _a.sent();
+                    expect(Object.keys(models).length).toEqual(1);
+                    expect(models['baz/QuxModel'].modelTopologyType)
+                        .toEqual(saveResult.modelArtifactsInfo.modelTopologyType);
+                    expect(models['baz/QuxModel'].modelTopologyBytes)
+                        .toEqual(saveResult.modelArtifactsInfo.modelTopologyBytes);
+                    expect(models['baz/QuxModel'].weightSpecsBytes)
+                        .toEqual(saveResult.modelArtifactsInfo.weightSpecsBytes);
+                    expect(models['baz/QuxModel'].weightDataBytes)
+                        .toEqual(saveResult.modelArtifactsInfo.weightDataBytes);
+                    return [2];
+            }
+        });
+    }); });
+    it('Manager: List models: 2 results', function () { return __awaiter(_this, void 0, void 0, function () {
+        var handler1, saveResult1, handler2, saveResult2, models;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    handler1 = tf.io.getSaveHandlers('indexeddb://QuxModel')[0];
+                    return [4, handler1.save(artifacts1)];
+                case 1:
+                    saveResult1 = _a.sent();
+                    handler2 = tf.io.getSaveHandlers('indexeddb://repeat/QuxModel')[0];
+                    return [4, handler2.save(artifacts1)];
+                case 2:
+                    saveResult2 = _a.sent();
+                    return [4, new indexed_db_1.BrowserIndexedDBManager().listModels()];
+                case 3:
+                    models = _a.sent();
+                    expect(Object.keys(models).length).toEqual(2);
+                    expect(models['QuxModel'].modelTopologyType)
+                        .toEqual(saveResult1.modelArtifactsInfo.modelTopologyType);
+                    expect(models['QuxModel'].modelTopologyBytes)
+                        .toEqual(saveResult1.modelArtifactsInfo.modelTopologyBytes);
+                    expect(models['QuxModel'].weightSpecsBytes)
+                        .toEqual(saveResult1.modelArtifactsInfo.weightSpecsBytes);
+                    expect(models['QuxModel'].weightDataBytes)
+                        .toEqual(saveResult1.modelArtifactsInfo.weightDataBytes);
+                    expect(models['repeat/QuxModel'].modelTopologyType)
+                        .toEqual(saveResult2.modelArtifactsInfo.modelTopologyType);
+                    expect(models['repeat/QuxModel'].modelTopologyBytes)
+                        .toEqual(saveResult2.modelArtifactsInfo.modelTopologyBytes);
+                    expect(models['repeat/QuxModel'].weightSpecsBytes)
+                        .toEqual(saveResult2.modelArtifactsInfo.weightSpecsBytes);
+                    expect(models['repeat/QuxModel'].weightDataBytes)
+                        .toEqual(saveResult2.modelArtifactsInfo.weightDataBytes);
+                    return [2];
+            }
+        });
+    }); });
+    it('Manager: Successful removeModel', function () { return __awaiter(_this, void 0, void 0, function () {
+        var handler1, handler2, manager, models;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    handler1 = tf.io.getSaveHandlers('indexeddb://QuxModel')[0];
+                    return [4, handler1.save(artifacts1)];
+                case 1:
+                    _a.sent();
+                    handler2 = tf.io.getSaveHandlers('indexeddb://repeat/QuxModel')[0];
+                    return [4, handler2.save(artifacts1)];
+                case 2:
+                    _a.sent();
+                    manager = new indexed_db_1.BrowserIndexedDBManager();
+                    return [4, manager.removeModel('QuxModel')];
+                case 3:
+                    _a.sent();
+                    return [4, manager.listModels()];
+                case 4:
+                    models = _a.sent();
+                    expect(Object.keys(models)).toEqual(['repeat/QuxModel']);
+                    return [2];
+            }
+        });
+    }); });
+    it('Manager: Successful removeModel with URL scheme', function () { return __awaiter(_this, void 0, void 0, function () {
+        var handler1, handler2, manager, models;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    handler1 = tf.io.getSaveHandlers('indexeddb://QuxModel')[0];
+                    return [4, handler1.save(artifacts1)];
+                case 1:
+                    _a.sent();
+                    handler2 = tf.io.getSaveHandlers('indexeddb://repeat/QuxModel')[0];
+                    return [4, handler2.save(artifacts1)];
+                case 2:
+                    _a.sent();
+                    manager = new indexed_db_1.BrowserIndexedDBManager();
+                    manager.removeModel('indexeddb://QuxModel');
+                    return [4, manager.listModels()];
+                case 3:
+                    models = _a.sent();
+                    expect(Object.keys(models)).toEqual(['repeat/QuxModel']);
+                    return [2];
+            }
+        });
+    }); });
+    it('Manager: Failed removeModel', function () { return __awaiter(_this, void 0, void 0, function () {
+        var err_2;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    _a.trys.push([0, 2, , 3]);
+                    return [4, new indexed_db_1.BrowserIndexedDBManager().removeModel('nonexistent')];
+                case 1:
+                    _a.sent();
+                    fail('Deleting nonexistent model succeeded unexpectedly.');
+                    return [3, 3];
+                case 2:
+                    err_2 = _a.sent();
+                    expect(err_2.message)
+                        .toEqual('Cannot find model with path \'nonexistent\' in IndexedDB.');
+                    return [3, 3];
+                case 3: return [2];
+            }
+        });
+    }); });
+});
+//# sourceMappingURL=indexed_db_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db_test.js.map
new file mode 100644
index 0000000..09188f8
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/indexed_db_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/io.d.ts b/node_modules/@tensorflow/tfjs-core/dist/io/io.d.ts
new file mode 100644
index 0000000..d67fa2d
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/io.d.ts
@@ -0,0 +1,15 @@
+import './indexed_db';
+import './local_storage';
+import { browserFiles } from './browser_files';
+import { browserHTTPRequest, isHTTPScheme } from './browser_http';
+import { concatenateArrayBuffers, decodeWeights, encodeWeights, getModelArtifactsInfoForJSON } from './io_utils';
+import { fromMemory, withSaveHandler } from './passthrough';
+import { IORouterRegistry } from './router_registry';
+import { IOHandler, LoadHandler, ModelArtifacts, ModelStoreManager, SaveConfig, SaveHandler, SaveResult, WeightsManifestConfig, WeightsManifestEntry } from './types';
+import { loadWeights, weightsLoaderFactory } from './weights_loader';
+declare const registerSaveRouter: typeof IORouterRegistry.registerSaveRouter;
+declare const registerLoadRouter: typeof IORouterRegistry.registerLoadRouter;
+declare const getSaveHandlers: typeof IORouterRegistry.getSaveHandlers;
+declare const getLoadHandlers: typeof IORouterRegistry.getLoadHandlers;
+export { copyModel, listModels, moveModel, removeModel } from './model_management';
+export { browserFiles, browserHTTPRequest, concatenateArrayBuffers, decodeWeights, encodeWeights, fromMemory, getLoadHandlers, getModelArtifactsInfoForJSON, getSaveHandlers, IOHandler, isHTTPScheme, LoadHandler, loadWeights, ModelArtifacts, ModelStoreManager, registerLoadRouter, registerSaveRouter, SaveConfig, SaveHandler, SaveResult, WeightsManifestConfig, WeightsManifestEntry, weightsLoaderFactory, withSaveHandler };
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/io.js b/node_modules/@tensorflow/tfjs-core/dist/io/io.js
new file mode 100644
index 0000000..0cfcf5d
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/io.js
@@ -0,0 +1,35 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+require("./indexed_db");
+require("./local_storage");
+var browser_files_1 = require("./browser_files");
+exports.browserFiles = browser_files_1.browserFiles;
+var browser_http_1 = require("./browser_http");
+exports.browserHTTPRequest = browser_http_1.browserHTTPRequest;
+exports.isHTTPScheme = browser_http_1.isHTTPScheme;
+var io_utils_1 = require("./io_utils");
+exports.concatenateArrayBuffers = io_utils_1.concatenateArrayBuffers;
+exports.decodeWeights = io_utils_1.decodeWeights;
+exports.encodeWeights = io_utils_1.encodeWeights;
+exports.getModelArtifactsInfoForJSON = io_utils_1.getModelArtifactsInfoForJSON;
+var passthrough_1 = require("./passthrough");
+exports.fromMemory = passthrough_1.fromMemory;
+exports.withSaveHandler = passthrough_1.withSaveHandler;
+var router_registry_1 = require("./router_registry");
+var weights_loader_1 = require("./weights_loader");
+exports.loadWeights = weights_loader_1.loadWeights;
+exports.weightsLoaderFactory = weights_loader_1.weightsLoaderFactory;
+var registerSaveRouter = router_registry_1.IORouterRegistry.registerSaveRouter;
+exports.registerSaveRouter = registerSaveRouter;
+var registerLoadRouter = router_registry_1.IORouterRegistry.registerLoadRouter;
+exports.registerLoadRouter = registerLoadRouter;
+var getSaveHandlers = router_registry_1.IORouterRegistry.getSaveHandlers;
+exports.getSaveHandlers = getSaveHandlers;
+var getLoadHandlers = router_registry_1.IORouterRegistry.getLoadHandlers;
+exports.getLoadHandlers = getLoadHandlers;
+var model_management_1 = require("./model_management");
+exports.copyModel = model_management_1.copyModel;
+exports.listModels = model_management_1.listModels;
+exports.moveModel = model_management_1.moveModel;
+exports.removeModel = model_management_1.removeModel;
+//# sourceMappingURL=io.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/io.js.map b/node_modules/@tensorflow/tfjs-core/dist/io/io.js.map
new file mode 100644
index 0000000..d3d28f7
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/io.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"io.js","sourceRoot":"","sources":["../../src/io/io.ts"],"names":[],"mappings":";;AAmBA,wBAAsB;AACtB,2BAAyB;AAEzB,iDAA6C;AAgB3C,uBAhBM,4BAAY,CAgBN;AAfd,+CAAgE;AAgB9D,6BAhBM,iCAAkB,CAgBN;AASlB,uBAzB0B,2BAAY,CAyB1B;AAxBd,uCAA+G;AAgB7G,kCAhBM,kCAAuB,CAgBN;AACvB,wBAjB+B,wBAAa,CAiB/B;AACb,wBAlB8C,wBAAa,CAkB9C;AAGb,uCArB6D,uCAA4B,CAqB7D;AApB9B,6CAA0D;AAkBxD,qBAlBM,wBAAU,CAkBN;AAkBV,0BApCkB,6BAAe,CAoClB;AAnCjB,qDAAmD;AAEnD,mDAAmE;AAsBjE,sBAtBM,4BAAW,CAsBN;AAUX,+BAhCmB,qCAAoB,CAgCnB;AA9BtB,IAAM,kBAAkB,GAAG,kCAAgB,CAAC,kBAAkB,CAAC;AAwB7D,gDAAkB;AAvBpB,IAAM,kBAAkB,GAAG,kCAAgB,CAAC,kBAAkB,CAAC;AAsB7D,gDAAkB;AArBpB,IAAM,eAAe,GAAG,kCAAgB,CAAC,eAAe,CAAC;AAcvD,0CAAe;AAbjB,IAAM,eAAe,GAAG,kCAAgB,CAAC,eAAe,CAAC;AAWvD,0CAAe;AATjB,uDAAiF;AAAzE,uCAAA,SAAS,CAAA;AAAE,wCAAA,UAAU,CAAA;AAAE,uCAAA,SAAS,CAAA;AAAE,yCAAA,WAAW,CAAA"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/io_utils.d.ts b/node_modules/@tensorflow/tfjs-core/dist/io/io_utils.d.ts
new file mode 100644
index 0000000..089b15a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/io_utils.d.ts
@@ -0,0 +1,15 @@
+import { NamedTensorMap } from '../tensor_types';
+import { TypedArray } from '../types';
+import { ModelArtifacts, ModelArtifactsInfo, WeightsManifestEntry } from './types';
+export declare function encodeWeights(tensors: NamedTensorMap): Promise<{
+    data: ArrayBuffer;
+    specs: WeightsManifestEntry[];
+}>;
+export declare function decodeWeights(buffer: ArrayBuffer, specs: WeightsManifestEntry[]): NamedTensorMap;
+export declare function concatenateTypedArrays(xs: TypedArray[]): ArrayBuffer;
+export declare function stringByteLength(str: string): number;
+export declare function arrayBufferToBase64String(buffer: ArrayBuffer): string;
+export declare function base64StringToArrayBuffer(str: string): ArrayBuffer;
+export declare function concatenateArrayBuffers(buffers: ArrayBuffer[]): ArrayBuffer;
+export declare function basename(path: string): string;
+export declare function getModelArtifactsInfoForJSON(modelArtifacts: ModelArtifacts): ModelArtifactsInfo;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/io_utils.js b/node_modules/@tensorflow/tfjs-core/dist/io/io_utils.js
new file mode 100644
index 0000000..e1a4130
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/io_utils.js
@@ -0,0 +1,234 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+Object.defineProperty(exports, "__esModule", { value: true });
+var tensor_ops_1 = require("../ops/tensor_ops");
+var util_1 = require("../util");
+var types_1 = require("./types");
+function encodeWeights(tensors) {
+    return __awaiter(this, void 0, void 0, function () {
+        var specs, dataPromises, name_1, t, tensorValues;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    specs = [];
+                    dataPromises = [];
+                    for (name_1 in tensors) {
+                        t = tensors[name_1];
+                        if (t.dtype !== 'float32' && t.dtype !== 'int32' && t.dtype !== 'bool') {
+                            throw new Error("Unsupported dtype in weight '" + name_1 + "': " + t.dtype);
+                        }
+                        specs.push({ name: name_1, shape: t.shape, dtype: t.dtype });
+                        dataPromises.push(t.data());
+                    }
+                    return [4, Promise.all(dataPromises)];
+                case 1:
+                    tensorValues = _a.sent();
+                    return [2, { data: concatenateTypedArrays(tensorValues), specs: specs }];
+            }
+        });
+    });
+}
+exports.encodeWeights = encodeWeights;
+function decodeWeights(buffer, specs) {
+    var out = {};
+    var offset = 0;
+    var _loop_1 = function (spec) {
+        var name_2 = spec.name;
+        var dtype = spec.dtype;
+        var shape = spec.shape;
+        var size = util_1.sizeFromShape(shape);
+        var typedArray = void 0;
+        if ('quantization' in spec) {
+            var quantization_1 = spec.quantization;
+            if (quantization_1.dtype !== 'uint8' && quantization_1.dtype !== 'uint16') {
+                throw new Error("Weight " + spec.name + " has unknown " +
+                    ("quantization dtype " + quantization_1.dtype + ". ") +
+                    "Supported quantization dtypes are: 'uint8' and 'uint16'.");
+            }
+            var quantizationSizeFactor = types_1.DTYPE_VALUE_SIZE_MAP[quantization_1.dtype];
+            var byteBuffer = buffer.slice(offset, offset + size * quantizationSizeFactor);
+            var quantizedArray = (quantization_1.dtype === 'uint8') ?
+                new Uint8Array(byteBuffer) :
+                new Uint16Array(byteBuffer);
+            if (dtype === 'float32') {
+                typedArray = Float32Array.from(quantizedArray, function (v) { return v * quantization_1.scale + quantization_1.min; });
+            }
+            else if (dtype === 'int32') {
+                typedArray = Int32Array.from(quantizedArray, function (v) { return Math.round(v * quantization_1.scale + quantization_1.min); });
+            }
+            else {
+                throw new Error("Unsupported dtype in weight '" + name_2 + "': " + dtype);
+            }
+            offset += size * quantizationSizeFactor;
+        }
+        else {
+            var dtypeFactor = types_1.DTYPE_VALUE_SIZE_MAP[dtype];
+            var byteBuffer = buffer.slice(offset, offset + size * dtypeFactor);
+            if (dtype === 'float32') {
+                typedArray = new Float32Array(byteBuffer);
+            }
+            else if (dtype === 'int32') {
+                typedArray = new Int32Array(byteBuffer);
+            }
+            else if (dtype === 'bool') {
+                typedArray = new Uint8Array(byteBuffer);
+            }
+            else {
+                throw new Error("Unsupported dtype in weight '" + name_2 + "': " + dtype);
+            }
+            offset += size * dtypeFactor;
+        }
+        var value = void 0;
+        if (dtype === 'float32') {
+            value = tensor_ops_1.tensor(typedArray, shape, 'float32');
+        }
+        else if (dtype === 'int32') {
+            value = tensor_ops_1.tensor(typedArray, shape, 'int32');
+        }
+        else if (dtype === 'bool') {
+            value = tensor_ops_1.tensor(typedArray, shape, 'bool');
+        }
+        else {
+            throw new Error("Unsupported dtype in weight '" + name_2 + "': " + dtype);
+        }
+        out[name_2] = value;
+    };
+    for (var _i = 0, specs_1 = specs; _i < specs_1.length; _i++) {
+        var spec = specs_1[_i];
+        _loop_1(spec);
+    }
+    return out;
+}
+exports.decodeWeights = decodeWeights;
+function concatenateTypedArrays(xs) {
+    if (xs === null) {
+        throw new Error("Invalid input value: " + JSON.stringify(xs));
+    }
+    var totalByteLength = 0;
+    var normalizedXs = [];
+    xs.forEach(function (x) {
+        totalByteLength += x.byteLength;
+        normalizedXs.push(x.byteLength === x.buffer.byteLength ? x :
+            new x.constructor(x));
+        if (!(x instanceof Float32Array || x instanceof Int32Array ||
+            x instanceof Uint8Array)) {
+            throw new Error("Unsupported TypedArray subtype: " + x.constructor.name);
+        }
+    });
+    var y = new Uint8Array(totalByteLength);
+    var offset = 0;
+    normalizedXs.forEach(function (x) {
+        y.set(new Uint8Array(x.buffer), offset);
+        offset += x.byteLength;
+    });
+    return y.buffer;
+}
+exports.concatenateTypedArrays = concatenateTypedArrays;
+var useNodeBuffer = typeof Buffer !== 'undefined' &&
+    (typeof Blob === 'undefined' || typeof atob === 'undefined' ||
+        typeof btoa === 'undefined');
+function stringByteLength(str) {
+    if (useNodeBuffer) {
+        return Buffer.byteLength(str);
+    }
+    return new Blob([str]).size;
+}
+exports.stringByteLength = stringByteLength;
+function arrayBufferToBase64String(buffer) {
+    if (useNodeBuffer) {
+        return Buffer.from(buffer).toString('base64');
+    }
+    return btoa(String.fromCharCode.apply(null, new Uint8Array(buffer)));
+}
+exports.arrayBufferToBase64String = arrayBufferToBase64String;
+function base64StringToArrayBuffer(str) {
+    if (useNodeBuffer) {
+        var buf = Buffer.from(str, 'base64');
+        return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);
+    }
+    var s = atob(str);
+    var buffer = new Uint8Array(s.length);
+    for (var i = 0; i < s.length; ++i) {
+        buffer.set([s.charCodeAt(i)], i);
+    }
+    return buffer.buffer;
+}
+exports.base64StringToArrayBuffer = base64StringToArrayBuffer;
+function concatenateArrayBuffers(buffers) {
+    var totalByteLength = 0;
+    buffers.forEach(function (buffer) {
+        totalByteLength += buffer.byteLength;
+    });
+    var temp = new Uint8Array(totalByteLength);
+    var offset = 0;
+    buffers.forEach(function (buffer) {
+        temp.set(new Uint8Array(buffer), offset);
+        offset += buffer.byteLength;
+    });
+    return temp.buffer;
+}
+exports.concatenateArrayBuffers = concatenateArrayBuffers;
+function basename(path) {
+    var SEPARATOR = '/';
+    path = path.trim();
+    while (path.endsWith(SEPARATOR)) {
+        path = path.slice(0, path.length - 1);
+    }
+    var items = path.split(SEPARATOR);
+    return items[items.length - 1];
+}
+exports.basename = basename;
+function getModelArtifactsInfoForJSON(modelArtifacts) {
+    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
+        throw new Error('Expected JSON model topology, received ArrayBuffer.');
+    }
+    return {
+        dateSaved: new Date(),
+        modelTopologyType: 'JSON',
+        modelTopologyBytes: modelArtifacts.modelTopology == null ?
+            0 :
+            stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),
+        weightSpecsBytes: modelArtifacts.weightSpecs == null ?
+            0 :
+            stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),
+        weightDataBytes: modelArtifacts.weightData == null ?
+            0 :
+            modelArtifacts.weightData.byteLength,
+    };
+}
+exports.getModelArtifactsInfoForJSON = getModelArtifactsInfoForJSON;
+//# sourceMappingURL=io_utils.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/io_utils.js.map b/node_modules/@tensorflow/tfjs-core/dist/io/io_utils.js.map
new file mode 100644
index 0000000..04c1e4a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/io_utils.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/io_utils_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/io/io_utils_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/io_utils_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/io_utils_test.js b/node_modules/@tensorflow/tfjs-core/dist/io/io_utils_test.js
new file mode 100644
index 0000000..c5435e2
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/io_utils_test.js
@@ -0,0 +1,519 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+var _this = this;
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var ops_1 = require("../ops/ops");
+var test_util_1 = require("../test_util");
+var test_util_2 = require("../test_util");
+var io_utils_1 = require("./io_utils");
+describe('concatenateTypedArrays', function () {
+    it('Single float arrays', function () {
+        var x = new Float32Array([1.1, 2.2, 3.3]);
+        var buffer = io_utils_1.concatenateTypedArrays([x]);
+        expect(buffer.byteLength).toEqual(12);
+        expect(new Float32Array(buffer, 0, 3)).toEqual(x);
+    });
+    it('Float arrays', function () {
+        var x = new Float32Array([1.1, 2.2, 3.3]);
+        var y = new Float32Array([-1.1, -2.2, -3.3]);
+        var buffer = io_utils_1.concatenateTypedArrays([x, y]);
+        expect(buffer.byteLength).toEqual(24);
+        expect(new Float32Array(buffer, 0, 3)).toEqual(x);
+        expect(new Float32Array(buffer, 12, 3)).toEqual(y);
+    });
+    it('Single int32 arrays', function () {
+        var x = new Int32Array([11, 22, 33]);
+        var buffer = io_utils_1.concatenateTypedArrays([x]);
+        expect(buffer.byteLength).toEqual(12);
+        expect(new Int32Array(buffer, 0, 3)).toEqual(x);
+    });
+    it('Int32 arrays', function () {
+        var x = new Int32Array([11, 22, 33]);
+        var y = new Int32Array([-11, -22, -33]);
+        var buffer = io_utils_1.concatenateTypedArrays([x, y]);
+        expect(buffer.byteLength).toEqual(24);
+        expect(new Int32Array(buffer, 0, 3)).toEqual(x);
+        expect(new Int32Array(buffer, 12, 3)).toEqual(y);
+    });
+    it('Single uint8 arrays', function () {
+        var x = new Uint8Array([11, 22, 33]);
+        var buffer = io_utils_1.concatenateTypedArrays([x]);
+        expect(buffer.byteLength).toEqual(3);
+        expect(new Uint8Array(buffer, 0, 3)).toEqual(x);
+    });
+    it('Uint8 arrays', function () {
+        var x = new Uint8Array([11, 22, 33]);
+        var y = new Uint8Array([111, 122, 133]);
+        var buffer = io_utils_1.concatenateTypedArrays([x, y]);
+        expect(buffer.byteLength).toEqual(6);
+        expect(new Uint8Array(buffer, 0, 3)).toEqual(x);
+        expect(new Uint8Array(buffer, 3, 3)).toEqual(y);
+    });
+    it('Mixed Uint8, Int32 and Float32 arrays', function () {
+        var x = new Uint8Array([0, 1, 1, 0]);
+        var y = new Int32Array([10, 20, 30, 40]);
+        var z = new Float32Array([-1.1, -2.2, -3.3, -4.4]);
+        var buffer = io_utils_1.concatenateTypedArrays([x, y, z]);
+        expect(buffer.byteLength).toEqual(1 * 4 + 4 * 4 + 4 * 4);
+        expect(new Uint8Array(buffer, 0, 4)).toEqual(x);
+        expect(new Int32Array(buffer, 4, 4)).toEqual(y);
+        expect(new Float32Array(buffer, 20, 4)).toEqual(z);
+    });
+    it('Concatenate Float32Arrays from SubArrays', function () {
+        var x1 = new Float32Array([1.1, 2.2, 3.3]);
+        var x2 = new Float32Array([-1.1, -2.2, -3.3]);
+        var xConcatenated = io_utils_1.concatenateTypedArrays([x1, x2]);
+        var y1 = new Float32Array(xConcatenated, 0, 3);
+        var y2 = new Float32Array(xConcatenated, 3 * 4, 3);
+        expect(y1.buffer.byteLength).toEqual(6 * 4);
+        expect(y2.buffer.byteLength).toEqual(6 * 4);
+        var yConcatenated = io_utils_1.concatenateTypedArrays([y1, y2]);
+        expect(yConcatenated.byteLength).toEqual(6 * 4);
+        expect(new Float32Array(yConcatenated, 0, 3)).toEqual(x1);
+        expect(new Float32Array(yConcatenated, 3 * 4, 3)).toEqual(x2);
+    });
+    it('Concatenate Int32Array from SubArrays', function () {
+        var x1 = new Int32Array([11, 22, 33]);
+        var x2 = new Int32Array([-11, -22, -33]);
+        var xConcatenated = io_utils_1.concatenateTypedArrays([x1, x2]);
+        var y1 = new Int32Array(xConcatenated, 0, 3);
+        var y2 = new Int32Array(xConcatenated, 3 * 4, 3);
+        expect(y1.buffer.byteLength).toEqual(6 * 4);
+        expect(y2.buffer.byteLength).toEqual(6 * 4);
+        var yConcatenated = io_utils_1.concatenateTypedArrays([y1, y2]);
+        expect(yConcatenated.byteLength).toEqual(6 * 4);
+        expect(new Int32Array(yConcatenated, 0, 3)).toEqual(x1);
+        expect(new Int32Array(yConcatenated, 3 * 4, 3)).toEqual(x2);
+    });
+    it('Concatenate Uint8Array from SubArrays', function () {
+        var x1 = new Uint8Array([11, 22, 33]);
+        var x2 = new Uint8Array([44, 55, 66]);
+        var xConcatenated = io_utils_1.concatenateTypedArrays([x1, x2]);
+        var y1 = new Uint8Array(xConcatenated, 0, 3);
+        var y2 = new Uint8Array(xConcatenated, 3, 3);
+        expect(y1.buffer.byteLength).toEqual(6);
+        expect(y2.buffer.byteLength).toEqual(6);
+        var yConcatenated = io_utils_1.concatenateTypedArrays([y1, y2]);
+        expect(yConcatenated.byteLength).toEqual(6);
+        expect(new Uint8Array(yConcatenated, 0, 3)).toEqual(x1);
+        expect(new Uint8Array(yConcatenated, 3, 3)).toEqual(x2);
+    });
+    it('Concatenate mixed TypedArrays from SubArrays', function () {
+        var x1 = new Uint8Array([11, 22, 33, 44]);
+        var x2 = new Int32Array([-44, -55, -66]);
+        var x3 = new Float32Array([1.1, 2.2, 3.3]);
+        var xConcatenated = io_utils_1.concatenateTypedArrays([x1, x2, x3]);
+        var y1 = new Uint8Array(xConcatenated, 0, 4);
+        var y2 = new Int32Array(xConcatenated, 4, 3);
+        var y3 = new Float32Array(xConcatenated, 4 + 3 * 4, 3);
+        expect(y1.buffer.byteLength).toEqual(4 + 3 * 4 + 3 * 4);
+        expect(y2.buffer.byteLength).toEqual(4 + 3 * 4 + 3 * 4);
+        expect(y3.buffer.byteLength).toEqual(4 + 3 * 4 + 3 * 4);
+        var yConcatenated = io_utils_1.concatenateTypedArrays([y1, y2, y3]);
+        expect(yConcatenated.byteLength).toEqual(4 + 3 * 4 + 3 * 4);
+        expect(new Uint8Array(yConcatenated, 0, 4)).toEqual(x1);
+        expect(new Int32Array(yConcatenated, 4, 3)).toEqual(x2);
+        expect(new Float32Array(yConcatenated, 4 + 3 * 4, 3)).toEqual(x3);
+    });
+    it('null and undefined inputs', function () {
+        expect(function () { return io_utils_1.concatenateTypedArrays(null); }).toThrow();
+        expect(function () { return io_utils_1.concatenateTypedArrays(undefined); }).toThrow();
+    });
+    it('empty input array', function () {
+        expect(io_utils_1.concatenateTypedArrays([]).byteLength).toEqual(0);
+    });
+    it('Unsupported dtype', function () {
+        var x = new Int16Array([0, 1, 1, 0]);
+        expect(function () { return io_utils_1.concatenateTypedArrays([x]); })
+            .toThrowError(/Unsupported TypedArray subtype: Int16Array/);
+    });
+});
+describe('encodeWeights', function () {
+    it('Float32 tensors', function (done) { return __awaiter(_this, void 0, void 0, function () {
+        var tensors;
+        return __generator(this, function (_a) {
+            tensors = {
+                x1: ops_1.tensor2d([[10, 20], [30, 40]]),
+                x2: ops_1.scalar(42),
+                x3: ops_1.tensor1d([-1.3, -3.7, 1.3, 3.7]),
+            };
+            tf.io.encodeWeights(tensors)
+                .then(function (dataAndSpecs) {
+                var data = dataAndSpecs.data;
+                var specs = dataAndSpecs.specs;
+                expect(data.byteLength).toEqual(4 * (4 + 1 + 4));
+                expect(new Float32Array(data, 0, 4)).toEqual(new Float32Array([
+                    10, 20, 30, 40
+                ]));
+                expect(new Float32Array(data, 16, 1)).toEqual(new Float32Array([42]));
+                expect(new Float32Array(data, 20, 4)).toEqual(new Float32Array([
+                    -1.3, -3.7, 1.3, 3.7
+                ]));
+                expect(specs).toEqual([
+                    {
+                        name: 'x1',
+                        dtype: 'float32',
+                        shape: [2, 2],
+                    },
+                    {
+                        name: 'x2',
+                        dtype: 'float32',
+                        shape: [],
+                    },
+                    {
+                        name: 'x3',
+                        dtype: 'float32',
+                        shape: [4],
+                    }
+                ]);
+                done();
+            })
+                .catch(function (err) {
+                console.error(err.stack);
+            });
+            return [2];
+        });
+    }); });
+    it('Int32 tensors', function (done) { return __awaiter(_this, void 0, void 0, function () {
+        var tensors;
+        return __generator(this, function (_a) {
+            tensors = {
+                x1: ops_1.tensor2d([[10, 20], [30, 40]], [2, 2], 'int32'),
+                x2: ops_1.scalar(42, 'int32'),
+                x3: ops_1.tensor1d([-1, -3, -3, -7], 'int32'),
+            };
+            tf.io.encodeWeights(tensors)
+                .then(function (dataAndSpecs) {
+                var data = dataAndSpecs.data;
+                var specs = dataAndSpecs.specs;
+                expect(data.byteLength).toEqual(4 * (4 + 1 + 4));
+                expect(new Int32Array(data, 0, 4)).toEqual(new Int32Array([
+                    10, 20, 30, 40
+                ]));
+                expect(new Int32Array(data, 16, 1)).toEqual(new Int32Array([42]));
+                expect(new Int32Array(data, 20, 4)).toEqual(new Int32Array([
+                    -1, -3, -3, -7
+                ]));
+                expect(specs).toEqual([
+                    {
+                        name: 'x1',
+                        dtype: 'int32',
+                        shape: [2, 2],
+                    },
+                    {
+                        name: 'x2',
+                        dtype: 'int32',
+                        shape: [],
+                    },
+                    {
+                        name: 'x3',
+                        dtype: 'int32',
+                        shape: [4],
+                    }
+                ]);
+                done();
+            })
+                .catch(function (err) {
+                console.error(err.stack);
+            });
+            return [2];
+        });
+    }); });
+    it('Bool tensors', function (done) { return __awaiter(_this, void 0, void 0, function () {
+        var tensors;
+        return __generator(this, function (_a) {
+            tensors = {
+                x1: ops_1.tensor2d([[true, false], [false, true]], [2, 2], 'bool'),
+                x2: ops_1.scalar(false, 'bool'),
+                x3: ops_1.tensor1d([false, true, true, false], 'bool'),
+            };
+            tf.io.encodeWeights(tensors)
+                .then(function (dataAndSpecs) {
+                var data = dataAndSpecs.data;
+                var specs = dataAndSpecs.specs;
+                expect(data.byteLength).toEqual(4 + 1 + 4);
+                expect(new Uint8Array(data, 0, 4)).toEqual(new Uint8Array([
+                    1, 0, 0, 1
+                ]));
+                expect(new Uint8Array(data, 4, 1)).toEqual(new Uint8Array([0]));
+                expect(new Uint8Array(data, 5, 4)).toEqual(new Uint8Array([
+                    0, 1, 1, 0
+                ]));
+                expect(specs).toEqual([
+                    {
+                        name: 'x1',
+                        dtype: 'bool',
+                        shape: [2, 2],
+                    },
+                    {
+                        name: 'x2',
+                        dtype: 'bool',
+                        shape: [],
+                    },
+                    {
+                        name: 'x3',
+                        dtype: 'bool',
+                        shape: [4],
+                    }
+                ]);
+                done();
+            })
+                .catch(function (err) {
+                console.error(err.stack);
+            });
+            return [2];
+        });
+    }); });
+    it('Mixed dtype tensors', function (done) { return __awaiter(_this, void 0, void 0, function () {
+        var tensors;
+        return __generator(this, function (_a) {
+            tensors = {
+                x1: ops_1.tensor2d([[10, 20], [30, 40]], [2, 2], 'int32'),
+                x2: ops_1.scalar(13.37, 'float32'),
+                x3: ops_1.tensor1d([true, false, false, true], 'bool'),
+            };
+            tf.io.encodeWeights(tensors)
+                .then(function (dataAndSpecs) {
+                var data = dataAndSpecs.data;
+                var specs = dataAndSpecs.specs;
+                expect(data.byteLength).toEqual(4 * 4 + 4 * 1 + 1 * 4);
+                expect(new Int32Array(data, 0, 4)).toEqual(new Int32Array([
+                    10, 20, 30, 40
+                ]));
+                expect(new Float32Array(data, 16, 1))
+                    .toEqual(new Float32Array([13.37]));
+                expect(new Uint8Array(data, 20, 4)).toEqual(new Uint8Array([
+                    1, 0, 0, 1
+                ]));
+                expect(specs).toEqual([
+                    {
+                        name: 'x1',
+                        dtype: 'int32',
+                        shape: [2, 2],
+                    },
+                    {
+                        name: 'x2',
+                        dtype: 'float32',
+                        shape: [],
+                    },
+                    {
+                        name: 'x3',
+                        dtype: 'bool',
+                        shape: [4],
+                    }
+                ]);
+                done();
+            })
+                .catch(function (err) {
+                console.error(err.stack);
+            });
+            return [2];
+        });
+    }); });
+});
+jasmine_util_1.describeWithFlags('decodeWeights', {}, function () {
+    it('Mixed dtype tensors', function (done) { return __awaiter(_this, void 0, void 0, function () {
+        var tensors;
+        return __generator(this, function (_a) {
+            tensors = {
+                x1: ops_1.tensor2d([[10, 20], [30, 40]], [2, 2], 'int32'),
+                x2: ops_1.scalar(13.37, 'float32'),
+                x3: ops_1.tensor1d([true, false, false], 'bool'),
+                y1: ops_1.tensor2d([-10, -20, -30], [3, 1], 'float32'),
+            };
+            tf.io.encodeWeights(tensors)
+                .then(function (dataAndSpecs) {
+                var data = dataAndSpecs.data;
+                var specs = dataAndSpecs.specs;
+                expect(data.byteLength).toEqual(4 * 4 + 4 * 1 + 1 * 3 + 4 * 3);
+                var decoded = tf.io.decodeWeights(data, specs);
+                expect(Object.keys(decoded).length).toEqual(4);
+                test_util_1.expectArraysEqual(decoded['x1'], tensors['x1']);
+                test_util_1.expectArraysEqual(decoded['x2'], tensors['x2']);
+                test_util_1.expectArraysEqual(decoded['x3'], tensors['x3']);
+                test_util_1.expectArraysEqual(decoded['y1'], tensors['y1']);
+                done();
+            })
+                .catch(function (err) {
+                console.error(err.stack);
+            });
+            return [2];
+        });
+    }); });
+    it('Unsupported dtype raises Error', function () {
+        var buffer = new ArrayBuffer(4);
+        var specs = [
+            {
+                name: 'x',
+                dtype: 'int16',
+                shape: [],
+            },
+            { name: 'y', dtype: 'int16', shape: [] }
+        ];
+        expect(function () { return tf.io.decodeWeights(buffer, specs); })
+            .toThrowError(/Unsupported dtype in weight \'x\': int16/);
+    });
+    it('support quantization uint8 weights', function () {
+        var manifestSpecs = [
+            {
+                'name': 'weight0',
+                'dtype': 'float32',
+                'shape': [3],
+                'quantization': { 'min': -1, 'scale': 0.1, 'dtype': 'uint8' }
+            },
+            {
+                'name': 'weight1',
+                'dtype': 'int32',
+                'shape': [3],
+                'quantization': { 'min': -1, 'scale': 0.1, 'dtype': 'uint8' }
+            }
+        ];
+        var data = new Uint8Array([0, 48, 255, 0, 48, 255]);
+        var decoded = tf.io.decodeWeights(data.buffer, manifestSpecs);
+        var weight0 = decoded['weight0'];
+        test_util_2.expectArraysClose(weight0, [-1, 3.8, 24.5]);
+        expect(weight0.shape).toEqual([3]);
+        expect(weight0.dtype).toEqual('float32');
+        var weight1 = decoded['weight1'];
+        test_util_1.expectArraysEqual(weight1, [-1, 4, 25]);
+        expect(weight1.shape).toEqual([3]);
+        expect(weight1.dtype).toEqual('int32');
+    });
+    it('support quantization uint16 weights', function () {
+        var manifestSpecs = [
+            {
+                'name': 'weight0',
+                'dtype': 'float32',
+                'shape': [3],
+                'quantization': { 'min': -1, 'scale': 0.1, 'dtype': 'uint16' }
+            },
+            {
+                'name': 'weight1',
+                'dtype': 'int32',
+                'shape': [3],
+                'quantization': { 'min': -1, 'scale': 0.1, 'dtype': 'uint16' }
+            }
+        ];
+        var data = new Uint16Array([0, 48, 255, 0, 48, 255]);
+        var decoded = tf.io.decodeWeights(data.buffer, manifestSpecs);
+        var weight0 = decoded['weight0'];
+        test_util_2.expectArraysClose(weight0, [-1, 3.8, 24.5]);
+        expect(weight0.shape).toEqual([3]);
+        expect(weight0.dtype).toEqual('float32');
+        var weight1 = decoded['weight1'];
+        test_util_1.expectArraysEqual(weight1, [-1, 4, 25]);
+        expect(weight1.shape).toEqual([3]);
+        expect(weight1.dtype).toEqual('int32');
+    });
+});
+describe('stringByteLength', function () {
+    it('ASCII only', function () {
+        var str = '_Lorem ipsum 1337!';
+        expect(io_utils_1.stringByteLength(str)).toEqual(str.length);
+    });
+    it('Mixed narrow and wide chars', function () {
+        var str = 'aЖ文1';
+        expect(io_utils_1.stringByteLength(str.slice(0, 1))).toEqual(1);
+        expect(io_utils_1.stringByteLength(str.slice(0, 2))).toEqual(3);
+        expect(io_utils_1.stringByteLength(str.slice(0, 3))).toEqual(6);
+        expect(io_utils_1.stringByteLength(str.slice(0, 4))).toEqual(7);
+    });
+});
+describe('arrayBufferToBase64String-base64StringToArrayBuffer', function () {
+    it('Round trip', function () {
+        var x = [];
+        for (var k = 0; k < 2; ++k) {
+            for (var i = 0; i < 254; ++i) {
+                x.push(i + k);
+            }
+            for (var i = 254; i >= 0; --i) {
+                x.push(i + k);
+            }
+        }
+        var buffer = Uint8Array.from(x).buffer;
+        var base64Str = io_utils_1.arrayBufferToBase64String(buffer);
+        var decoded = Array.from(new Uint8Array(io_utils_1.base64StringToArrayBuffer(base64Str)));
+        expect(decoded).toEqual(x);
+    });
+});
+describe('concatenateArrayBuffers', function () {
+    it('Concatenate 3 non-empty ArrayBuffers', function () {
+        var buffer1 = new Uint8Array([1, 2, 3]);
+        var buffer2 = new Uint8Array([11, 22, 33, 44]);
+        var buffer3 = new Uint8Array([111, 222, 100]);
+        var out = io_utils_1.concatenateArrayBuffers([buffer1.buffer, buffer2.buffer, buffer3.buffer]);
+        expect(new Uint8Array(out)).toEqual(new Uint8Array([
+            1, 2, 3, 11, 22, 33, 44, 111, 222, 100
+        ]));
+    });
+    it('Concatenate non-empty and empty ArrayBuffers', function () {
+        var buffer1 = new Uint8Array([1, 2, 3]);
+        var buffer2 = new Uint8Array([11, 22, 33, 44]);
+        var buffer3 = new Uint8Array([]);
+        var buffer4 = new Uint8Array([150, 100, 50]);
+        var out = io_utils_1.concatenateArrayBuffers([buffer1.buffer, buffer2.buffer, buffer3.buffer, buffer4.buffer]);
+        expect(new Uint8Array(out)).toEqual(new Uint8Array([
+            1, 2, 3, 11, 22, 33, 44, 150, 100, 50
+        ]));
+    });
+    it('A single ArrayBuffer', function () {
+        var buffer1 = new Uint8Array([1, 3, 3, 7]);
+        var out = io_utils_1.concatenateArrayBuffers([buffer1.buffer]);
+        expect(new Uint8Array(out)).toEqual(buffer1);
+    });
+    it('Zero ArrayBuffers', function () {
+        expect(new Uint8Array(io_utils_1.concatenateArrayBuffers([])))
+            .toEqual(new Uint8Array([]));
+    });
+});
+describe('basename', function () {
+    it('Paths without slashes', function () {
+        expect(io_utils_1.basename('foo.txt')).toEqual('foo.txt');
+        expect(io_utils_1.basename('bar')).toEqual('bar');
+    });
+    it('Paths with slashes', function () {
+        expect(io_utils_1.basename('qux/foo.txt')).toEqual('foo.txt');
+        expect(io_utils_1.basename('qux/My Model.json')).toEqual('My Model.json');
+        expect(io_utils_1.basename('foo/bar/baz')).toEqual('baz');
+        expect(io_utils_1.basename('/foo/bar/baz')).toEqual('baz');
+        expect(io_utils_1.basename('foo/bar/baz/')).toEqual('baz');
+        expect(io_utils_1.basename('foo/bar/baz//')).toEqual('baz');
+    });
+});
+//# sourceMappingURL=io_utils_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/io_utils_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/io/io_utils_test.js.map
new file mode 100644
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+++ b/node_modules/@tensorflow/tfjs-core/dist/io/io_utils_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/local_storage.d.ts b/node_modules/@tensorflow/tfjs-core/dist/io/local_storage.d.ts
new file mode 100644
index 0000000..f8d3a9e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/local_storage.d.ts
@@ -0,0 +1,24 @@
+import { IORouter } from './router_registry';
+import { IOHandler, ModelArtifacts, ModelArtifactsInfo, ModelStoreManager, SaveResult } from './types';
+export declare function purgeLocalStorageArtifacts(): string[];
+export declare class BrowserLocalStorage implements IOHandler {
+    protected readonly LS: Storage;
+    protected readonly modelPath: string;
+    protected readonly keys: {
+        [key: string]: string;
+    };
+    static readonly URL_SCHEME: string;
+    constructor(modelPath: string);
+    save(modelArtifacts: ModelArtifacts): Promise<SaveResult>;
+    load(): Promise<ModelArtifacts>;
+}
+export declare const localStorageRouter: IORouter;
+export declare function browserLocalStorage(modelPath: string): IOHandler;
+export declare class BrowserLocalStorageManager implements ModelStoreManager {
+    private readonly LS;
+    constructor();
+    listModels(): Promise<{
+        [path: string]: ModelArtifactsInfo;
+    }>;
+    removeModel(path: string): Promise<ModelArtifactsInfo>;
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/local_storage.js b/node_modules/@tensorflow/tfjs-core/dist/io/local_storage.js
new file mode 100644
index 0000000..493a779
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/local_storage.js
@@ -0,0 +1,247 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var util_1 = require("../util");
+var io_utils_1 = require("./io_utils");
+var model_management_1 = require("./model_management");
+var router_registry_1 = require("./router_registry");
+var PATH_SEPARATOR = '/';
+var PATH_PREFIX = 'tensorflowjs_models';
+var INFO_SUFFIX = 'info';
+var MODEL_TOPOLOGY_SUFFIX = 'model_topology';
+var WEIGHT_SPECS_SUFFIX = 'weight_specs';
+var WEIGHT_DATA_SUFFIX = 'weight_data';
+function purgeLocalStorageArtifacts() {
+    if (!environment_1.ENV.get('IS_BROWSER') || typeof window.localStorage === 'undefined') {
+        throw new Error('purgeLocalStorageModels() cannot proceed because local storage is ' +
+            'unavailable in the current environment.');
+    }
+    var LS = window.localStorage;
+    var purgedModelPaths = [];
+    for (var i = 0; i < LS.length; ++i) {
+        var key = LS.key(i);
+        var prefix = PATH_PREFIX + PATH_SEPARATOR;
+        if (key.startsWith(prefix) && key.length > prefix.length) {
+            LS.removeItem(key);
+            var modelName = getModelPathFromKey(key);
+            if (purgedModelPaths.indexOf(modelName) === -1) {
+                purgedModelPaths.push(modelName);
+            }
+        }
+    }
+    return purgedModelPaths;
+}
+exports.purgeLocalStorageArtifacts = purgeLocalStorageArtifacts;
+function getModelKeys(path) {
+    return {
+        info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),
+        topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),
+        weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),
+        weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR)
+    };
+}
+function getModelPathFromKey(key) {
+    var items = key.split(PATH_SEPARATOR);
+    if (items.length < 3) {
+        throw new Error("Invalid key format: " + key);
+    }
+    return items.slice(1, items.length - 1).join(PATH_SEPARATOR);
+}
+function maybeStripScheme(key) {
+    return key.startsWith(BrowserLocalStorage.URL_SCHEME) ?
+        key.slice(BrowserLocalStorage.URL_SCHEME.length) :
+        key;
+}
+var BrowserLocalStorage = (function () {
+    function BrowserLocalStorage(modelPath) {
+        if (!environment_1.ENV.get('IS_BROWSER') || typeof window.localStorage === 'undefined') {
+            throw new Error('The current environment does not support local storage.');
+        }
+        this.LS = window.localStorage;
+        if (modelPath == null || !modelPath) {
+            throw new Error('For local storage, modelPath must not be null, undefined or empty.');
+        }
+        this.modelPath = modelPath;
+        this.keys = getModelKeys(this.modelPath);
+    }
+    BrowserLocalStorage.prototype.save = function (modelArtifacts) {
+        return __awaiter(this, void 0, void 0, function () {
+            var topology, weightSpecs, modelArtifactsInfo, key;
+            return __generator(this, function (_a) {
+                if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
+                    throw new Error('BrowserLocalStorage.save() does not support saving model topology ' +
+                        'in binary formats yet.');
+                }
+                else {
+                    topology = JSON.stringify(modelArtifacts.modelTopology);
+                    weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);
+                    modelArtifactsInfo = io_utils_1.getModelArtifactsInfoForJSON(modelArtifacts);
+                    try {
+                        this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));
+                        this.LS.setItem(this.keys.topology, topology);
+                        this.LS.setItem(this.keys.weightSpecs, weightSpecs);
+                        this.LS.setItem(this.keys.weightData, io_utils_1.arrayBufferToBase64String(modelArtifacts.weightData));
+                        return [2, { modelArtifactsInfo: modelArtifactsInfo }];
+                    }
+                    catch (err) {
+                        for (key in this.keys) {
+                            this.LS.removeItem(this.keys[key]);
+                        }
+                        throw new Error("Failed to save model '" + this.modelPath + "' to local storage: " +
+                            "size quota being exceeded is a possible cause of this failure: " +
+                            ("modelTopologyBytes=" + modelArtifactsInfo.modelTopologyBytes + ", ") +
+                            ("weightSpecsBytes=" + modelArtifactsInfo.weightSpecsBytes + ", ") +
+                            ("weightDataBytes=" + modelArtifactsInfo.weightDataBytes + "."));
+                    }
+                }
+                return [2];
+            });
+        });
+    };
+    BrowserLocalStorage.prototype.load = function () {
+        return __awaiter(this, void 0, void 0, function () {
+            var info, out, topology, weightSpecs, weightDataBase64;
+            return __generator(this, function (_a) {
+                info = JSON.parse(this.LS.getItem(this.keys.info));
+                if (info == null) {
+                    throw new Error("In local storage, there is no model with name '" + this.modelPath + "'");
+                }
+                if (info.modelTopologyType !== 'JSON') {
+                    throw new Error('BrowserLocalStorage does not support loading non-JSON model ' +
+                        'topology yet.');
+                }
+                out = {};
+                topology = JSON.parse(this.LS.getItem(this.keys.topology));
+                if (topology == null) {
+                    throw new Error("In local storage, the topology of model '" + this.modelPath + "' " +
+                        "is missing.");
+                }
+                out.modelTopology = topology;
+                weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));
+                if (weightSpecs == null) {
+                    throw new Error("In local storage, the weight specs of model '" + this.modelPath + "' " +
+                        "are missing.");
+                }
+                out.weightSpecs = weightSpecs;
+                weightDataBase64 = this.LS.getItem(this.keys.weightData);
+                if (weightDataBase64 == null) {
+                    throw new Error("In local storage, the binary weight values of model " +
+                        ("'" + this.modelPath + "' are missing."));
+                }
+                out.weightData = io_utils_1.base64StringToArrayBuffer(weightDataBase64);
+                return [2, out];
+            });
+        });
+    };
+    BrowserLocalStorage.URL_SCHEME = 'localstorage://';
+    return BrowserLocalStorage;
+}());
+exports.BrowserLocalStorage = BrowserLocalStorage;
+exports.localStorageRouter = function (url) {
+    if (!environment_1.ENV.get('IS_BROWSER')) {
+        return null;
+    }
+    else {
+        if (!Array.isArray(url) &&
+            url.startsWith(BrowserLocalStorage.URL_SCHEME)) {
+            return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length));
+        }
+        else {
+            return null;
+        }
+    }
+};
+router_registry_1.IORouterRegistry.registerSaveRouter(exports.localStorageRouter);
+router_registry_1.IORouterRegistry.registerLoadRouter(exports.localStorageRouter);
+function browserLocalStorage(modelPath) {
+    return new BrowserLocalStorage(modelPath);
+}
+exports.browserLocalStorage = browserLocalStorage;
+var BrowserLocalStorageManager = (function () {
+    function BrowserLocalStorageManager() {
+        util_1.assert(environment_1.ENV.get('IS_BROWSER'), 'Current environment is not a web browser');
+        util_1.assert(typeof window.localStorage !== 'undefined', 'Current browser does not appear to support localStorage');
+        this.LS = window.localStorage;
+    }
+    BrowserLocalStorageManager.prototype.listModels = function () {
+        return __awaiter(this, void 0, void 0, function () {
+            var out, prefix, suffix, i, key, modelPath;
+            return __generator(this, function (_a) {
+                out = {};
+                prefix = PATH_PREFIX + PATH_SEPARATOR;
+                suffix = PATH_SEPARATOR + INFO_SUFFIX;
+                for (i = 0; i < this.LS.length; ++i) {
+                    key = this.LS.key(i);
+                    if (key.startsWith(prefix) && key.endsWith(suffix)) {
+                        modelPath = getModelPathFromKey(key);
+                        out[modelPath] = JSON.parse(this.LS.getItem(key));
+                    }
+                }
+                return [2, out];
+            });
+        });
+    };
+    BrowserLocalStorageManager.prototype.removeModel = function (path) {
+        return __awaiter(this, void 0, void 0, function () {
+            var keys, info;
+            return __generator(this, function (_a) {
+                path = maybeStripScheme(path);
+                keys = getModelKeys(path);
+                if (this.LS.getItem(keys.info) == null) {
+                    throw new Error("Cannot find model at path '" + path + "'");
+                }
+                info = JSON.parse(this.LS.getItem(keys.info));
+                this.LS.removeItem(keys.info);
+                this.LS.removeItem(keys.topology);
+                this.LS.removeItem(keys.weightSpecs);
+                this.LS.removeItem(keys.weightData);
+                return [2, info];
+            });
+        });
+    };
+    return BrowserLocalStorageManager;
+}());
+exports.BrowserLocalStorageManager = BrowserLocalStorageManager;
+if (environment_1.ENV.get('IS_BROWSER')) {
+    try {
+        model_management_1.ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());
+    }
+    catch (err) {
+    }
+}
+//# sourceMappingURL=local_storage.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/local_storage.js.map b/node_modules/@tensorflow/tfjs-core/dist/io/local_storage.js.map
new file mode 100644
index 0000000..323922b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/local_storage.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"local_storage.js","sourceRoot":"","sources":["../../src/io/local_storage.ts"],"names":[],"mappings":";;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;AAiBA,8CAAmC;AACnC,gCAA+B;AAC/B,uCAA8G;AAC9G,uDAA6D;AAC7D,qDAA6D;AAG7D,IAAM,cAAc,GAAG,GAAG,CAAC;AAC3B,IAAM,WAAW,GAAG,qBAAqB,CAAC;AAC1C,IAAM,WAAW,GAAG,MAAM,CAAC;AAC3B,IAAM,qBAAqB,GAAG,gBAAgB,CAAC;AAC/C,IAAM,mBAAmB,GAAG,cAAc,CAAC;AAC3C,IAAM,kBAAkB,GAAG,aAAa,CAAC;AAOzC;IACE,IAAI,CAAC,iBAAG,CAAC,GAAG,CAAC,YAAY,CAAC,IAAI,OAAO,MAAM,CAAC,YAAY,KAAK,WAAW,EAAE;QACxE,MAAM,IAAI,KAAK,CACX,oEAAoE;YACpE,yCAAyC,CAAC,CAAC;KAChD;IACD,IAAM,EAAE,GAAG,MAAM,CAAC,YAAY,CAAC;IAC/B,IAAM,gBAAgB,GAAa,EAAE,CAAC;IACtC,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,EAAE,CAAC,MAAM,EAAE,EAAE,CAAC,EAAE;QAClC,IAAM,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC;QACtB,IAAM,MAAM,GAAG,WAAW,GAAG,cAAc,CAAC;QAC5C,IAAI,GAAG,CAAC,UAAU,CAAC,MAAM,CAAC,IAAI,GAAG,CAAC,MAAM,GAAG,MAAM,CAAC,MAAM,EAAE;YACxD,EAAE,CAAC,UAAU,CAAC,GAAG,CAAC,CAAC;YACnB,IAAM,SAAS,GAAG,mBAAmB,CAAC,GAAG,CAAC,CAAC;YAC3C,IAAI,gBAAgB,CAAC,OAAO,CAAC,SAAS,CAAC,KAAK,CAAC,CAAC,EAAE;gBAC9C,gBAAgB,CAAC,IAAI,CAAC,SAAS,CAAC,CAAC;aAClC;SACF;KACF;IACD,OAAO,gBAAgB,CAAC;AAC1B,CAAC;AApBD,gEAoBC;AAED,sBAAsB,IAAY;IAEhC,OAAO;QACL,IAAI,EAAE,CAAC,WAAW,EAAE,IAAI,EAAE,WAAW,CAAC,CAAC,IAAI,CAAC,cAAc,CAAC;QAC3D,QAAQ,EAAE,CAAC,WAAW,EAAE,IAAI,EAAE,qBAAqB,CAAC,CAAC,IAAI,CAAC,cAAc,CAAC;QACzE,WAAW,EAAE,CAAC,WAAW,EAAE,IAAI,EAAE,mBAAmB,CAAC,CAAC,IAAI,CAAC,cAAc,CAAC;QAC1E,UAAU,EAAE,CAAC,WAAW,EAAE,IAAI,EAAE,kBAAkB,CAAC,CAAC,IAAI,CAAC,cAAc,CAAC;KACzE,CAAC;AACJ,CAAC;AASD,6BAA6B,GAAW;IACtC,IAAM,KAAK,GAAG,GAAG,CAAC,KAAK,CAAC,cAAc,CAAC,CAAC;IACxC,IAAI,KAAK,CAAC,MAAM,GAAG,CAAC,EAAE;QACpB,MAAM,IAAI,KAAK,CAAC,yBAAuB,GAAK,CAAC,CAAC;KAC/C;IACD,OAAO,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,KAAK,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC,IAAI,CAAC,cAAc,CAAC,CAAC;AAC/D,CAAC;AAED,0BAA0B,GAAW;IACnC,OAAO,GAAG,CAAC,UAAU,CAAC,mBAAmB,CAAC,UAAU,CAAC,CAAC,CAAC;QACnD,GAAG,CAAC,KAAK,CAAC,mBAAmB,CAAC,UAAU,CAAC,MAAM,CAAC,CAAC,CAAC;QAClD,GAAG,CAAC;AACV,CAAC;AAOD;IAOE,6BAAY,SAAiB;QAC3B,IAAI,CAAC,iBAAG,CAAC,GAAG,CAAC,YAAY,CAAC,IAAI,OAAO,MAAM,CAAC,YAAY,KAAK,WAAW,EAAE;YAKxE,MAAM,IAAI,KAAK,CACX,yDAAyD,CAAC,CAAC;SAChE;QACD,IAAI,CAAC,EAAE,GAAG,MAAM,CAAC,YAAY,CAAC;QAE9B,IAAI,SAAS,IAAI,IAAI,IAAI,CAAC,SAAS,EAAE;YACnC,MAAM,IAAI,KAAK,CACX,oEAAoE,CAAC,CAAC;SAC3E;QACD,IAAI,CAAC,SAAS,GAAG,SAAS,CAAC;QAC3B,IAAI,CAAC,IAAI,GAAG,YAAY,CAAC,IAAI,CAAC,SAAS,CAAC,CAAC;IAC3C,CAAC;IAWK,kCAAI,GAAV,UAAW,cAA8B;;;;gBACvC,IAAI,cAAc,CAAC,aAAa,YAAY,WAAW,EAAE;oBACvD,MAAM,IAAI,KAAK,CACX,oEAAoE;wBACpE,wBAAwB,CAAC,CAAC;iBAC/B;qBAAM;oBACC,QAAQ,GAAG,IAAI,CAAC,SAAS,CAAC,cAAc,CAAC,aAAa,CAAC,CAAC;oBACxD,WAAW,GAAG,IAAI,CAAC,SAAS,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC;oBAEzD,kBAAkB,GACpB,uCAA4B,CAAC,cAAc,CAAC,CAAC;oBAEjD,IAAI;wBACF,IAAI,CAAC,EAAE,CAAC,OAAO,CAAC,IAAI,CAAC,IAAI,CAAC,IAAI,EAAE,IAAI,CAAC,SAAS,CAAC,kBAAkB,CAAC,CAAC,CAAC;wBACpE,IAAI,CAAC,EAAE,CAAC,OAAO,CAAC,IAAI,CAAC,IAAI,CAAC,QAAQ,EAAE,QAAQ,CAAC,CAAC;wBAC9C,IAAI,CAAC,EAAE,CAAC,OAAO,CAAC,IAAI,CAAC,IAAI,CAAC,WAAW,EAAE,WAAW,CAAC,CAAC;wBACpD,IAAI,CAAC,EAAE,CAAC,OAAO,CACX,IAAI,CAAC,IAAI,CAAC,UAAU,EACpB,oCAAyB,CAAC,cAAc,CAAC,UAAU,CAAC,CAAC,CAAC;wBAE1D,WAAO,EAAC,kBAAkB,oBAAA,EAAC,EAAC;qBAC7B;oBAAC,OAAO,GAAG,EAAE;wBAEZ,KAAW,GAAG,IAAI,IAAI,CAAC,IAAI,EAAE;4BAC3B,IAAI,CAAC,EAAE,CAAC,UAAU,CAAC,IAAI,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC;yBACpC;wBAED,MAAM,IAAI,KAAK,CACX,2BAAyB,IAAI,CAAC,SAAS,yBAAsB;4BAC7D,iEAAiE;6BACjE,wBAAsB,kBAAkB,CAAC,kBAAkB,OAAI,CAAA;6BAC/D,sBAAoB,kBAAkB,CAAC,gBAAgB,OAAI,CAAA;6BAC3D,qBAAmB,kBAAkB,CAAC,eAAe,MAAG,CAAA,CAAC,CAAC;qBAC/D;iBACF;;;;KACF;IAUK,kCAAI,GAAV;;;;gBACQ,IAAI,GACN,IAAI,CAAC,KAAK,CAAC,IAAI,CAAC,EAAE,CAAC,OAAO,CAAC,IAAI,CAAC,IAAI,CAAC,IAAI,CAAC,CAAuB,CAAC;gBACtE,IAAI,IAAI,IAAI,IAAI,EAAE;oBAChB,MAAM,IAAI,KAAK,CACX,oDAAkD,IAAI,CAAC,SAAS,MAAG,CAAC,CAAC;iBAC1E;gBAED,IAAI,IAAI,CAAC,iBAAiB,KAAK,MAAM,EAAE;oBACrC,MAAM,IAAI,KAAK,CACX,8DAA8D;wBAC9D,eAAe,CAAC,CAAC;iBACtB;gBAEK,GAAG,GAAmB,EAAE,CAAC;gBAGzB,QAAQ,GAAG,IAAI,CAAC,KAAK,CAAC,IAAI,CAAC,EAAE,CAAC,OAAO,CAAC,IAAI,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC,CAAC;gBACjE,IAAI,QAAQ,IAAI,IAAI,EAAE;oBACpB,MAAM,IAAI,KAAK,CACX,8CAA4C,IAAI,CAAC,SAAS,OAAI;wBAC9D,aAAa,CAAC,CAAC;iBACpB;gBACD,GAAG,CAAC,aAAa,GAAG,QAAQ,CAAC;gBAGvB,WAAW,GAAG,IAAI,CAAC,KAAK,CAAC,IAAI,CAAC,EAAE,CAAC,OAAO,CAAC,IAAI,CAAC,IAAI,CAAC,WAAW,CAAC,CAAC,CAAC;gBACvE,IAAI,WAAW,IAAI,IAAI,EAAE;oBACvB,MAAM,IAAI,KAAK,CACX,kDAAgD,IAAI,CAAC,SAAS,OAAI;wBAClE,cAAc,CAAC,CAAC;iBACrB;gBACD,GAAG,CAAC,WAAW,GAAG,WAAW,CAAC;gBAGxB,gBAAgB,GAAG,IAAI,CAAC,EAAE,CAAC,OAAO,CAAC,IAAI,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC;gBAC/D,IAAI,gBAAgB,IAAI,IAAI,EAAE;oBAC5B,MAAM,IAAI,KAAK,CACX,sDAAsD;yBACtD,MAAI,IAAI,CAAC,SAAS,mBAAgB,CAAA,CAAC,CAAC;iBACzC;gBACD,GAAG,CAAC,UAAU,GAAG,oCAAyB,CAAC,gBAAgB,CAAC,CAAC;gBAE7D,WAAO,GAAG,EAAC;;;KACZ;IAvHe,8BAAU,GAAG,iBAAiB,CAAC;IAwHjD,0BAAC;CAAA,AA7HD,IA6HC;AA7HY,kDAAmB;AA+HnB,QAAA,kBAAkB,GAAa,UAAC,GAAoB;IAC/D,IAAI,CAAC,iBAAG,CAAC,GAAG,CAAC,YAAY,CAAC,EAAE;QAC1B,OAAO,IAAI,CAAC;KACb;SAAM;QACL,IAAI,CAAC,KAAK,CAAC,OAAO,CAAC,GAAG,CAAC;YACnB,GAAG,CAAC,UAAU,CAAC,mBAAmB,CAAC,UAAU,CAAC,EAAE;YAClD,OAAO,mBAAmB,CACtB,GAAG,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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/local_storage_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/io/local_storage_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/local_storage_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/local_storage_test.js b/node_modules/@tensorflow/tfjs-core/dist/io/local_storage_test.js
new file mode 100644
index 0000000..7a00113
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/local_storage_test.js
@@ -0,0 +1,379 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+var _this = this;
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+var io_utils_1 = require("./io_utils");
+var local_storage_1 = require("./local_storage");
+jasmine_util_1.describeWithFlags('LocalStorage', test_util_1.BROWSER_ENVS, function () {
+    var modelTopology1 = {
+        'class_name': 'Sequential',
+        'keras_version': '2.1.4',
+        'config': [{
+                'class_name': 'Dense',
+                'config': {
+                    'kernel_initializer': {
+                        'class_name': 'VarianceScaling',
+                        'config': {
+                            'distribution': 'uniform',
+                            'scale': 1.0,
+                            'seed': null,
+                            'mode': 'fan_avg'
+                        }
+                    },
+                    'name': 'dense',
+                    'kernel_constraint': null,
+                    'bias_regularizer': null,
+                    'bias_constraint': null,
+                    'dtype': 'float32',
+                    'activation': 'linear',
+                    'trainable': true,
+                    'kernel_regularizer': null,
+                    'bias_initializer': { 'class_name': 'Zeros', 'config': {} },
+                    'units': 1,
+                    'batch_input_shape': [null, 3],
+                    'use_bias': true,
+                    'activity_regularizer': null
+                }
+            }],
+        'backend': 'tensorflow'
+    };
+    var weightSpecs1 = [
+        {
+            name: 'dense/kernel',
+            shape: [3, 1],
+            dtype: 'float32',
+        },
+        {
+            name: 'dense/bias',
+            shape: [1],
+            dtype: 'float32',
+        }
+    ];
+    var weightData1 = new ArrayBuffer(16);
+    var artifacts1 = {
+        modelTopology: modelTopology1,
+        weightSpecs: weightSpecs1,
+        weightData: weightData1,
+    };
+    function findOverflowingByteSize() {
+        var LS = window.localStorage;
+        var probeKey = "tfjs_test_probe_values_" + new Date().getTime() + "_" + Math.random();
+        var minKilobytes = 200;
+        var stepKilobytes = 200;
+        var maxKilobytes = 40000;
+        for (var kilobytes = minKilobytes; kilobytes < maxKilobytes; kilobytes += stepKilobytes) {
+            var bytes = kilobytes * 1024;
+            var data = new ArrayBuffer(bytes);
+            try {
+                var encoded = io_utils_1.arrayBufferToBase64String(data);
+                LS.setItem(probeKey, encoded);
+            }
+            catch (err) {
+                return bytes;
+            }
+            LS.removeItem(probeKey);
+        }
+        throw new Error("Unable to determined overflowing byte size up to " + maxKilobytes + " kB.");
+    }
+    beforeEach(function () {
+        local_storage_1.purgeLocalStorageArtifacts();
+    });
+    afterEach(function () {
+        local_storage_1.purgeLocalStorageArtifacts();
+    });
+    it('Save artifacts succeeds', function (done) {
+        var testStartDate = new Date();
+        var handler = tf.io.getSaveHandlers('localstorage://foo/FooModel')[0];
+        handler.save(artifacts1)
+            .then(function (saveResult) {
+            expect(saveResult.modelArtifactsInfo.dateSaved.getTime())
+                .toBeGreaterThanOrEqual(testStartDate.getTime());
+            expect(saveResult.modelArtifactsInfo.modelTopologyBytes)
+                .toEqual(JSON.stringify(modelTopology1).length);
+            expect(saveResult.modelArtifactsInfo.weightSpecsBytes)
+                .toEqual(JSON.stringify(weightSpecs1).length);
+            expect(saveResult.modelArtifactsInfo.weightDataBytes).toEqual(16);
+            var LS = window.localStorage;
+            var info = JSON.parse(LS.getItem('tensorflowjs_models/foo/FooModel/info'));
+            expect(Date.parse(info.dateSaved))
+                .toEqual(saveResult.modelArtifactsInfo.dateSaved.getTime());
+            expect(info.modelTopologyBytes)
+                .toEqual(saveResult.modelArtifactsInfo.modelTopologyBytes);
+            expect(info.weightSpecsBytes)
+                .toEqual(saveResult.modelArtifactsInfo.weightSpecsBytes);
+            expect(info.weightDataBytes)
+                .toEqual(saveResult.modelArtifactsInfo.weightDataBytes);
+            var topologyString = LS.getItem('tensorflowjs_models/foo/FooModel/model_topology');
+            expect(JSON.stringify(modelTopology1)).toEqual(topologyString);
+            var weightSpecsString = LS.getItem('tensorflowjs_models/foo/FooModel/weight_specs');
+            expect(JSON.stringify(weightSpecs1)).toEqual(weightSpecsString);
+            var weightDataBase64String = LS.getItem('tensorflowjs_models/foo/FooModel/weight_data');
+            expect(io_utils_1.base64StringToArrayBuffer(weightDataBase64String))
+                .toEqual(weightData1);
+            done();
+        })
+            .catch(function (err) {
+            console.error(err.stack);
+        });
+    });
+    it('Save-load round trip succeeds', function () { return __awaiter(_this, void 0, void 0, function () {
+        var handler1, handler2, loaded;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    handler1 = tf.io.getSaveHandlers('localstorage://FooModel')[0];
+                    return [4, handler1.save(artifacts1)];
+                case 1:
+                    _a.sent();
+                    handler2 = tf.io.getLoadHandlers('localstorage://FooModel')[0];
+                    return [4, handler2.load()];
+                case 2:
+                    loaded = _a.sent();
+                    expect(loaded.modelTopology).toEqual(modelTopology1);
+                    expect(loaded.weightSpecs).toEqual(weightSpecs1);
+                    expect(loaded.weightData).toEqual(weightData1);
+                    return [2];
+            }
+        });
+    }); });
+    it('Loading nonexistent model fails.', function (done) {
+        var handler = tf.io.getSaveHandlers('localstorage://NonexistentModel')[0];
+        handler.load()
+            .then(function (artifacts) {
+            fail('Loading nonexistent model succeeded unexpectedly.');
+        })
+            .catch(function (err) {
+            expect(err.message)
+                .toEqual('In local storage, there is no model with name ' +
+                '\'NonexistentModel\'');
+            done();
+        });
+    });
+    it('Loading model with missing topology fails.', function (done) {
+        var handler1 = tf.io.getSaveHandlers('localstorage://FooModel')[0];
+        handler1.save(artifacts1)
+            .then(function (saveResult) {
+            window.localStorage.removeItem('tensorflowjs_models/FooModel/model_topology');
+            var handler2 = tf.io.getLoadHandlers('localstorage://FooModel')[0];
+            handler2.load()
+                .then(function (artifacts) {
+                fail('Loading of model with missing topology succeeded ' +
+                    'unexpectedly.');
+            })
+                .catch(function (err) {
+                expect(err.message)
+                    .toEqual('In local storage, the topology of model ' +
+                    '\'FooModel\' is missing.');
+                done();
+            });
+        })
+            .catch(function (err) {
+            console.error(err.stack);
+        });
+    });
+    it('Loading model with missing weight specs fails.', function (done) {
+        var handler1 = tf.io.getSaveHandlers('localstorage://FooModel')[0];
+        handler1.save(artifacts1)
+            .then(function (saveResult) {
+            window.localStorage.removeItem('tensorflowjs_models/FooModel/weight_specs');
+            var handler2 = tf.io.getLoadHandlers('localstorage://FooModel')[0];
+            handler2.load()
+                .then(function (artifacts) {
+                fail('Loading of model with missing weight specs succeeded ' +
+                    'unexpectedly.');
+            })
+                .catch(function (err) {
+                expect(err.message)
+                    .toEqual('In local storage, the weight specs of model ' +
+                    '\'FooModel\' are missing.');
+                done();
+            });
+        })
+            .catch(function (err) {
+            console.error(err.stack);
+        });
+    });
+    it('Loading model with missing weight data fails.', function (done) {
+        var handler1 = tf.io.getSaveHandlers('localstorage://FooModel')[0];
+        handler1.save(artifacts1)
+            .then(function (saveResult) {
+            window.localStorage.removeItem('tensorflowjs_models/FooModel/weight_data');
+            var handler2 = tf.io.getLoadHandlers('localstorage://FooModel')[0];
+            handler2.load()
+                .then(function (artifacts) {
+                fail('Loading of model with missing weight data succeeded ' +
+                    'unexpectedly.');
+            })
+                .catch(function (err) {
+                expect(err.message)
+                    .toEqual('In local storage, the binary weight values of model ' +
+                    '\'FooModel\' are missing.');
+                done();
+            });
+        })
+            .catch(function (err) {
+            console.error(err.stack);
+        });
+    });
+    it('Data size too large leads to error thrown', function (done) {
+        var overflowByteSize = findOverflowingByteSize();
+        var overflowArtifacts = {
+            modelTopology: modelTopology1,
+            weightSpecs: weightSpecs1,
+            weightData: new ArrayBuffer(overflowByteSize),
+        };
+        var handler1 = tf.io.getSaveHandlers('localstorage://FooModel')[0];
+        handler1.save(overflowArtifacts)
+            .then(function (saveResult) {
+            fail('Saving of model of overflowing-size weight data succeeded ' +
+                'unexpectedly.');
+        })
+            .catch(function (err) {
+            expect(err.message
+                .indexOf('Failed to save model \'FooModel\' to local storage'))
+                .toEqual(0);
+            done();
+        });
+    });
+    it('Null, undefined or empty modelPath throws Error', function () {
+        expect(function () { return local_storage_1.browserLocalStorage(null); })
+            .toThrowError(/local storage, modelPath must not be null, undefined or empty/);
+        expect(function () { return local_storage_1.browserLocalStorage(undefined); })
+            .toThrowError(/local storage, modelPath must not be null, undefined or empty/);
+        expect(function () { return local_storage_1.browserLocalStorage(''); })
+            .toThrowError(/local storage, modelPath must not be null, undefined or empty./);
+    });
+    it('router', function () {
+        expect(local_storage_1.localStorageRouter('localstorage://bar') instanceof local_storage_1.BrowserLocalStorage)
+            .toEqual(true);
+        expect(local_storage_1.localStorageRouter('indexeddb://bar')).toBeNull();
+        expect(local_storage_1.localStorageRouter('qux')).toBeNull();
+    });
+    it('Manager: List models: 0 result', function (done) {
+        new local_storage_1.BrowserLocalStorageManager()
+            .listModels()
+            .then(function (out) {
+            expect(out).toEqual({});
+            done();
+        })
+            .catch(function (err) { return done.fail(err.stack); });
+    });
+    it('Manager: List models: 1 result', function (done) {
+        var handler = tf.io.getSaveHandlers('localstorage://baz/QuxModel')[0];
+        handler.save(artifacts1)
+            .then(function (saveResult) {
+            new local_storage_1.BrowserLocalStorageManager()
+                .listModels()
+                .then(function (out) {
+                expect(Object.keys(out).length).toEqual(1);
+                expect(out['baz/QuxModel'].modelTopologyType)
+                    .toEqual(saveResult.modelArtifactsInfo.modelTopologyType);
+                expect(out['baz/QuxModel'].modelTopologyBytes)
+                    .toEqual(saveResult.modelArtifactsInfo.modelTopologyBytes);
+                expect(out['baz/QuxModel'].weightSpecsBytes)
+                    .toEqual(saveResult.modelArtifactsInfo.weightSpecsBytes);
+                expect(out['baz/QuxModel'].weightDataBytes)
+                    .toEqual(saveResult.modelArtifactsInfo.weightDataBytes);
+                done();
+            })
+                .catch(function (err) { return done.fail(err.stack); });
+        })
+            .catch(function (err) { return done.fail(err.stack); });
+    });
+    it('Manager: List models: 2 results', function (done) {
+        var handler1 = tf.io.getSaveHandlers('localstorage://QuxModel')[0];
+        handler1.save(artifacts1)
+            .then(function (saveResult1) {
+            var handler2 = tf.io.getSaveHandlers('localstorage://repeat/QuxModel')[0];
+            handler2.save(artifacts1)
+                .then(function (saveResult2) {
+                new local_storage_1.BrowserLocalStorageManager()
+                    .listModels()
+                    .then(function (out) {
+                    expect(Object.keys(out).length).toEqual(2);
+                    expect(out['QuxModel'].modelTopologyType)
+                        .toEqual(saveResult1.modelArtifactsInfo.modelTopologyType);
+                    expect(out['QuxModel'].modelTopologyBytes)
+                        .toEqual(saveResult1.modelArtifactsInfo
+                        .modelTopologyBytes);
+                    expect(out['QuxModel'].weightSpecsBytes)
+                        .toEqual(saveResult1.modelArtifactsInfo.weightSpecsBytes);
+                    expect(out['QuxModel'].weightDataBytes)
+                        .toEqual(saveResult1.modelArtifactsInfo.weightDataBytes);
+                    expect(out['repeat/QuxModel'].modelTopologyType)
+                        .toEqual(saveResult2.modelArtifactsInfo.modelTopologyType);
+                    expect(out['repeat/QuxModel'].modelTopologyBytes)
+                        .toEqual(saveResult2.modelArtifactsInfo
+                        .modelTopologyBytes);
+                    expect(out['repeat/QuxModel'].weightSpecsBytes)
+                        .toEqual(saveResult2.modelArtifactsInfo.weightSpecsBytes);
+                    expect(out['repeat/QuxModel'].weightDataBytes)
+                        .toEqual(saveResult2.modelArtifactsInfo.weightDataBytes);
+                    done();
+                })
+                    .catch(function (err) { return done.fail(err.stack); });
+            })
+                .catch(function (err) { return done.fail(err.stack); });
+        })
+            .catch(function (err) { return done.fail(err.stack); });
+    });
+    it('Manager: Successful deleteModel', function (done) {
+        var handler1 = tf.io.getSaveHandlers('localstorage://QuxModel')[0];
+        handler1.save(artifacts1)
+            .then(function (saveResult1) {
+            var handler2 = tf.io.getSaveHandlers('localstorage://repeat/QuxModel')[0];
+            handler2.save(artifacts1)
+                .then(function (saveResult2) {
+                var manager = new local_storage_1.BrowserLocalStorageManager();
+                manager.removeModel('QuxModel')
+                    .then(function (deletedInfo) {
+                    manager.listModels().then(function (out) {
+                        expect(Object.keys(out)).toEqual(['repeat/QuxModel']);
+                    });
+                    done();
+                })
+                    .catch(function (err) { return done.fail(err.stack); });
+            })
+                .catch(function (err) { return done.fail(err.stack); });
+        })
+            .catch(function (err) { return done.fail(err.stack); });
+    });
+});
+//# sourceMappingURL=local_storage_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/local_storage_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/io/local_storage_test.js.map
new file mode 100644
index 0000000..bf5218d
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/local_storage_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/model_management.d.ts b/node_modules/@tensorflow/tfjs-core/dist/io/model_management.d.ts
new file mode 100644
index 0000000..b36039a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/model_management.d.ts
@@ -0,0 +1,17 @@
+import { ModelArtifactsInfo, ModelStoreManager } from './types';
+export declare class ModelStoreManagerRegistry {
+    private static instance;
+    private managers;
+    private constructor();
+    private static getInstance;
+    static registerManager(scheme: string, manager: ModelStoreManager): void;
+    static getManager(scheme: string): ModelStoreManager;
+    static getSchemes(): string[];
+}
+declare function listModels(): Promise<{
+    [url: string]: ModelArtifactsInfo;
+}>;
+declare function removeModel(url: string): Promise<ModelArtifactsInfo>;
+declare function copyModel(sourceURL: string, destURL: string): Promise<ModelArtifactsInfo>;
+declare function moveModel(sourceURL: string, destURL: string): Promise<ModelArtifactsInfo>;
+export { moveModel, copyModel, removeModel, listModels };
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/model_management.js b/node_modules/@tensorflow/tfjs-core/dist/io/model_management.js
new file mode 100644
index 0000000..e1c7fa4
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/model_management.js
@@ -0,0 +1,203 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+Object.defineProperty(exports, "__esModule", { value: true });
+var util_1 = require("../util");
+var router_registry_1 = require("./router_registry");
+var URL_SCHEME_SUFFIX = '://';
+var ModelStoreManagerRegistry = (function () {
+    function ModelStoreManagerRegistry() {
+        this.managers = {};
+    }
+    ModelStoreManagerRegistry.getInstance = function () {
+        if (ModelStoreManagerRegistry.instance == null) {
+            ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();
+        }
+        return ModelStoreManagerRegistry.instance;
+    };
+    ModelStoreManagerRegistry.registerManager = function (scheme, manager) {
+        util_1.assert(scheme != null, 'scheme must not be undefined or null.');
+        if (scheme.endsWith(URL_SCHEME_SUFFIX)) {
+            scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));
+        }
+        util_1.assert(scheme.length > 0, 'scheme must not be an empty string.');
+        var registry = ModelStoreManagerRegistry.getInstance();
+        util_1.assert(registry.managers[scheme] == null, "A model store manager is already registered for scheme '" + scheme + "'.");
+        registry.managers[scheme] = manager;
+    };
+    ModelStoreManagerRegistry.getManager = function (scheme) {
+        var manager = this.getInstance().managers[scheme];
+        if (manager == null) {
+            throw new Error("Cannot find model manager for scheme '" + scheme + "'");
+        }
+        return manager;
+    };
+    ModelStoreManagerRegistry.getSchemes = function () {
+        return Object.keys(this.getInstance().managers);
+    };
+    return ModelStoreManagerRegistry;
+}());
+exports.ModelStoreManagerRegistry = ModelStoreManagerRegistry;
+function parseURL(url) {
+    if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {
+        throw new Error("The url string provided does not contain a scheme. " +
+            "Supported schemes are: " +
+            ("" + ModelStoreManagerRegistry.getSchemes().join(',')));
+    }
+    return {
+        scheme: url.split(URL_SCHEME_SUFFIX)[0],
+        path: url.split(URL_SCHEME_SUFFIX)[1],
+    };
+}
+function cloneModelInternal(sourceURL, destURL, deleteSource) {
+    if (deleteSource === void 0) { deleteSource = false; }
+    return __awaiter(this, void 0, void 0, function () {
+        var loadHandlers, loadHandler, saveHandlers, saveHandler, sourceScheme, sourcePath, sameMedium, modelArtifacts, saveResult;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    util_1.assert(sourceURL !== destURL, "Old path and new path are the same: '" + sourceURL + "'");
+                    loadHandlers = router_registry_1.IORouterRegistry.getLoadHandlers(sourceURL);
+                    util_1.assert(loadHandlers.length > 0, "Copying failed because no load handler is found for source URL " + sourceURL + ".");
+                    util_1.assert(loadHandlers.length < 2, "Copying failed because more than one (" + loadHandlers.length + ") " +
+                        ("load handlers for source URL " + sourceURL + "."));
+                    loadHandler = loadHandlers[0];
+                    saveHandlers = router_registry_1.IORouterRegistry.getSaveHandlers(destURL);
+                    util_1.assert(saveHandlers.length > 0, "Copying failed because no save handler is found for destination URL " +
+                        (destURL + "."));
+                    util_1.assert(saveHandlers.length < 2, "Copying failed because more than one (" + loadHandlers.length + ") " +
+                        ("save handlers for destination URL " + destURL + "."));
+                    saveHandler = saveHandlers[0];
+                    sourceScheme = parseURL(sourceURL).scheme;
+                    sourcePath = parseURL(sourceURL).path;
+                    sameMedium = sourceScheme === parseURL(sourceURL).scheme;
+                    return [4, loadHandler.load()];
+                case 1:
+                    modelArtifacts = _a.sent();
+                    if (!(deleteSource && sameMedium)) return [3, 3];
+                    return [4, ModelStoreManagerRegistry.getManager(sourceScheme)
+                            .removeModel(sourcePath)];
+                case 2:
+                    _a.sent();
+                    _a.label = 3;
+                case 3: return [4, saveHandler.save(modelArtifacts)];
+                case 4:
+                    saveResult = _a.sent();
+                    if (!(deleteSource && !sameMedium)) return [3, 6];
+                    return [4, ModelStoreManagerRegistry.getManager(sourceScheme)
+                            .removeModel(sourcePath)];
+                case 5:
+                    _a.sent();
+                    _a.label = 6;
+                case 6: return [2, saveResult.modelArtifactsInfo];
+            }
+        });
+    });
+}
+function listModels() {
+    return __awaiter(this, void 0, void 0, function () {
+        var schemes, out, _i, schemes_1, scheme, schemeOut, path, url;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    schemes = ModelStoreManagerRegistry.getSchemes();
+                    out = {};
+                    _i = 0, schemes_1 = schemes;
+                    _a.label = 1;
+                case 1:
+                    if (!(_i < schemes_1.length)) return [3, 4];
+                    scheme = schemes_1[_i];
+                    return [4, ModelStoreManagerRegistry.getManager(scheme).listModels()];
+                case 2:
+                    schemeOut = _a.sent();
+                    for (path in schemeOut) {
+                        url = scheme + URL_SCHEME_SUFFIX + path;
+                        out[url] = schemeOut[path];
+                    }
+                    _a.label = 3;
+                case 3:
+                    _i++;
+                    return [3, 1];
+                case 4: return [2, out];
+            }
+        });
+    });
+}
+exports.listModels = listModels;
+function removeModel(url) {
+    return __awaiter(this, void 0, void 0, function () {
+        var schemeAndPath, manager;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    schemeAndPath = parseURL(url);
+                    manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);
+                    return [4, manager.removeModel(schemeAndPath.path)];
+                case 1: return [2, _a.sent()];
+            }
+        });
+    });
+}
+exports.removeModel = removeModel;
+function copyModel(sourceURL, destURL) {
+    return __awaiter(this, void 0, void 0, function () {
+        var deleteSource;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    deleteSource = false;
+                    return [4, cloneModelInternal(sourceURL, destURL, deleteSource)];
+                case 1: return [2, _a.sent()];
+            }
+        });
+    });
+}
+exports.copyModel = copyModel;
+function moveModel(sourceURL, destURL) {
+    return __awaiter(this, void 0, void 0, function () {
+        var deleteSource;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    deleteSource = true;
+                    return [4, cloneModelInternal(sourceURL, destURL, deleteSource)];
+                case 1: return [2, _a.sent()];
+            }
+        });
+    });
+}
+exports.moveModel = moveModel;
+//# sourceMappingURL=model_management.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/model_management.js.map b/node_modules/@tensorflow/tfjs-core/dist/io/model_management.js.map
new file mode 100644
index 0000000..a45712f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/model_management.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"model_management.js","sourceRoot":"","sources":["../../src/io/model_management.ts"],"names":[],"mappings":";;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;AA4BA,gCAA+B;AAE/B,qDAAmD;AAGnD,IAAM,iBAAiB,GAAG,KAAK,CAAC;AAEhC;IAME;QACE,IAAI,CAAC,QAAQ,GAAG,EAAE,CAAC;IACrB,CAAC;IAEc,qCAAW,GAA1B;QACE,IAAI,yBAAyB,CAAC,QAAQ,IAAI,IAAI,EAAE;YAC9C,yBAAyB,CAAC,QAAQ,GAAG,IAAI,yBAAyB,EAAE,CAAC;SACtE;QACD,OAAO,yBAAyB,CAAC,QAAQ,CAAC;IAC5C,CAAC;IAQM,yCAAe,GAAtB,UAAuB,MAAc,EAAE,OAA0B;QAC/D,aAAM,CAAC,MAAM,IAAI,IAAI,EAAE,uCAAuC,CAAC,CAAC;QAChE,IAAI,MAAM,CAAC,QAAQ,CAAC,iBAAiB,CAAC,EAAE;YACtC,MAAM,GAAG,MAAM,CAAC,KAAK,CAAC,CAAC,EAAE,MAAM,CAAC,OAAO,CAAC,iBAAiB,CAAC,CAAC,CAAC;SAC7D;QACD,aAAM,CAAC,MAAM,CAAC,MAAM,GAAG,CAAC,EAAE,qCAAqC,CAAC,CAAC;QACjE,IAAM,QAAQ,GAAG,yBAAyB,CAAC,WAAW,EAAE,CAAC;QACzD,aAAM,CACF,QAAQ,CAAC,QAAQ,CAAC,MAAM,CAAC,IAAI,IAAI,EACjC,6DAA2D,MAAM,OAAI,CAAC,CAAC;QAC3E,QAAQ,CAAC,QAAQ,CAAC,MAAM,CAAC,GAAG,OAAO,CAAC;IACtC,CAAC;IAEM,oCAAU,GAAjB,UAAkB,MAAc;QAC9B,IAAM,OAAO,GAAG,IAAI,CAAC,WAAW,EAAE,CAAC,QAAQ,CAAC,MAAM,CAAC,CAAC;QACpD,IAAI,OAAO,IAAI,IAAI,EAAE;YACnB,MAAM,IAAI,KAAK,CAAC,2CAAyC,MAAM,MAAG,CAAC,CAAC;SACrE;QACD,OAAO,OAAO,CAAC;IACjB,CAAC;IAEM,oCAAU,GAAjB;QACE,OAAO,MAAM,CAAC,IAAI,CAAC,IAAI,CAAC,WAAW,EAAE,CAAC,QAAQ,CAAC,CAAC;IAClD,CAAC;IACH,gCAAC;AAAD,CAAC,AA/CD,IA+CC;AA/CY,8DAAyB;AAyDtC,kBAAkB,GAAW;IAC3B,IAAI,GAAG,CAAC,OAAO,CAAC,iBAAiB,CAAC,KAAK,CAAC,CAAC,EAAE;QACzC,MAAM,IAAI,KAAK,CACX,qDAAqD;YACrD,yBAAyB;aACzB,KAAG,yBAAyB,CAAC,UAAU,EAAE,CAAC,IAAI,CAAC,GAAG,CAAG,CAAA,CAAC,CAAC;KAC5D;IACD,OAAO;QACL,MAAM,EAAE,GAAG,CAAC,KAAK,CAAC,iBAAiB,CAAC,CAAC,CAAC,CAAC;QACvC,IAAI,EAAE,GAAG,CAAC,KAAK,CAAC,iBAAiB,CAAC,CAAC,CAAC,CAAC;KACtC,CAAC;AACJ,CAAC;AAED,4BACI,SAAiB,EAAE,OAAe,EAClC,YAAoB;IAApB,6BAAA,EAAA,oBAAoB;;;;;;oBACtB,aAAM,CACF,SAAS,KAAK,OAAO,EACrB,0CAAwC,SAAS,MAAG,CAAC,CAAC;oBAEpD,YAAY,GAAG,kCAAgB,CAAC,eAAe,CAAC,SAAS,CAAC,CAAC;oBACjE,aAAM,CACF,YAAY,CAAC,MAAM,GAAG,CAAC,EACvB,oEACI,SAAS,MAAG,CAAC,CAAC;oBACtB,aAAM,CACF,YAAY,CAAC,MAAM,GAAG,CAAC,EACvB,2CAAyC,YAAY,CAAC,MAAM,OAAI;yBAC5D,kCAAgC,SAAS,MAAG,CAAA,CAAC,CAAC;oBAChD,WAAW,GAAG,YAAY,CAAC,CAAC,CAAC,CAAC;oBAE9B,YAAY,GAAG,kCAAgB,CAAC,eAAe,CAAC,OAAO,CAAC,CAAC;oBAC/D,aAAM,CACF,YAAY,CAAC,MAAM,GAAG,CAAC,EACvB,sEAAsE;yBAC/D,OAAO,MAAG,CAAA,CAAC,CAAC;oBACvB,aAAM,CACF,YAAY,CAAC,MAAM,GAAG,CAAC,EACvB,2CAAyC,YAAY,CAAC,MAAM,OAAI;yBAC5D,uCAAqC,OAAO,MAAG,CAAA,CAAC,CAAC;oBACnD,WAAW,GAAG,YAAY,CAAC,CAAC,CAAC,CAAC;oBAE9B,YAAY,GAAG,QAAQ,CAAC,SAAS,CAAC,CAAC,MAAM,CAAC;oBAC1C,UAAU,GAAG,QAAQ,CAAC,SAAS,CAAC,CAAC,IAAI,CAAC;oBACtC,UAAU,GAAG,YAAY,KAAK,QAAQ,CAAC,SAAS,CAAC,CAAC,MAAM,CAAC;oBAExC,WAAM,WAAW,CAAC,IAAI,EAAE,EAAA;;oBAAzC,cAAc,GAAG,SAAwB;yBAK3C,CAAA,YAAY,IAAI,UAAU,CAAA,EAA1B,cAA0B;oBAC5B,WAAM,yBAAyB,CAAC,UAAU,CAAC,YAAY,CAAC;6BACnD,WAAW,CAAC,UAAU,CAAC,EAAA;;oBAD5B,SAC4B,CAAC;;wBAGZ,WAAM,WAAW,CAAC,IAAI,CAAC,cAAc,CAAC,EAAA;;oBAAnD,UAAU,GAAG,SAAsC;yBAKrD,CAAA,YAAY,IAAI,CAAC,UAAU,CAAA,EAA3B,cAA2B;oBAC7B,WAAM,yBAAyB,CAAC,UAAU,CAAC,YAAY,CAAC;6BACnD,WAAW,CAAC,UAAU,CAAC,EAAA;;oBAD5B,SAC4B,CAAC;;wBAG/B,WAAO,UAAU,CAAC,kBAAkB,EAAC;;;;CACtC;AA+BD;;;;;;oBACQ,OAAO,GAAG,yBAAyB,CAAC,UAAU,EAAE,CAAC;oBACjD,GAAG,GAAwC,EAAE,CAAC;0BACxB,EAAP,mBAAO;;;yBAAP,CAAA,qBAAO,CAAA;oBAAjB,MAAM;oBAEX,WAAM,yBAAyB,CAAC,UAAU,CAAC,MAAM,CAAC,CAAC,UAAU,EAAE,EAAA;;oBAD7D,SAAS,GACX,SAA+D;oBACnE,KAAW,IAAI,IAAI,SAAS,EAAE;wBACtB,GAAG,GAAG,MAAM,GAAG,iBAAiB,GAAG,IAAI,CAAC;wBAC9C,GAAG,CAAC,GAAG,CAAC,GAAG,SAAS,CAAC,IAAI,CAAC,CAAC;qBAC5B;;;oBANkB,IAAO,CAAA;;wBAQ5B,WAAO,GAAG,EAAC;;;;CACZ;AAgI0C,gCAAU;AAnGrD,qBAA2B,GAAW;;;;;;oBAC9B,aAAa,GAAG,QAAQ,CAAC,GAAG,CAAC,CAAC;oBAC9B,OAAO,GAAG,yBAAyB,CAAC,UAAU,CAAC,aAAa,CAAC,MAAM,CAAC,CAAC;oBACpE,WAAM,OAAO,CAAC,WAAW,CAAC,aAAa,CAAC,IAAI,CAAC,EAAA;wBAApD,WAAO,SAA6C,EAAC;;;;CACtD;AA+F6B,kCAAW;AApDzC,mBACI,SAAiB,EAAE,OAAe;;;;;;oBAC9B,YAAY,GAAG,KAAK,CAAC;oBACpB,WAAM,kBAAkB,CAAC,SAAS,EAAE,OAAO,EAAE,YAAY,CAAC,EAAA;wBAAjE,WAAO,SAA0D,EAAC;;;;CACnE;AAgDkB,8BAAS;AAN5B,mBACI,SAAiB,EAAE,OAAe;;;;;;oBAC9B,YAAY,GAAG,IAAI,CAAC;oBACnB,WAAM,kBAAkB,CAAC,SAAS,EAAE,OAAO,EAAE,YAAY,CAAC,EAAA;wBAAjE,WAAO,SAA0D,EAAC;;;;CACnE;AAEO,8BAAS"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/model_management_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/io/model_management_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/model_management_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/model_management_test.js b/node_modules/@tensorflow/tfjs-core/dist/io/model_management_test.js
new file mode 100644
index 0000000..5753160
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/model_management_test.js
@@ -0,0 +1,376 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+var indexed_db_1 = require("./indexed_db");
+var local_storage_1 = require("./local_storage");
+jasmine_util_1.describeWithFlags('ModelManagement', test_util_1.CHROME_ENVS, function () {
+    var modelTopology1 = {
+        'class_name': 'Sequential',
+        'keras_version': '2.1.4',
+        'config': [{
+                'class_name': 'Dense',
+                'config': {
+                    'kernel_initializer': {
+                        'class_name': 'VarianceScaling',
+                        'config': {
+                            'distribution': 'uniform',
+                            'scale': 1.0,
+                            'seed': null,
+                            'mode': 'fan_avg'
+                        }
+                    },
+                    'name': 'dense',
+                    'kernel_constraint': null,
+                    'bias_regularizer': null,
+                    'bias_constraint': null,
+                    'dtype': 'float32',
+                    'activation': 'linear',
+                    'trainable': true,
+                    'kernel_regularizer': null,
+                    'bias_initializer': { 'class_name': 'Zeros', 'config': {} },
+                    'units': 1,
+                    'batch_input_shape': [null, 3],
+                    'use_bias': true,
+                    'activity_regularizer': null
+                }
+            }],
+        'backend': 'tensorflow'
+    };
+    var weightSpecs1 = [
+        {
+            name: 'dense/kernel',
+            shape: [3, 1],
+            dtype: 'float32',
+        },
+        {
+            name: 'dense/bias',
+            shape: [1],
+            dtype: 'float32',
+        }
+    ];
+    var weightData1 = new ArrayBuffer(16);
+    var artifacts1 = {
+        modelTopology: modelTopology1,
+        weightSpecs: weightSpecs1,
+        weightData: weightData1,
+    };
+    beforeEach(function (done) {
+        local_storage_1.purgeLocalStorageArtifacts();
+        indexed_db_1.deleteDatabase().then(function () {
+            done();
+        });
+    });
+    afterEach(function (done) {
+        local_storage_1.purgeLocalStorageArtifacts();
+        indexed_db_1.deleteDatabase().then(function () {
+            done();
+        });
+    });
+    it('List models: 0 result', function (done) {
+        tf.io.listModels()
+            .then(function (out) {
+            expect(out).toEqual({});
+            done();
+        })
+            .catch(function (err) { return done.fail(err.stack); });
+    });
+    it('List models: 1 result', function (done) {
+        var url = 'localstorage://baz/QuxModel';
+        var handler = tf.io.getSaveHandlers(url)[0];
+        handler.save(artifacts1)
+            .then(function (saveResult) {
+            tf.io.listModels()
+                .then(function (out) {
+                expect(Object.keys(out).length).toEqual(1);
+                expect(out[url].modelTopologyType)
+                    .toEqual(saveResult.modelArtifactsInfo.modelTopologyType);
+                expect(out[url].modelTopologyBytes)
+                    .toEqual(saveResult.modelArtifactsInfo.modelTopologyBytes);
+                expect(out[url].weightSpecsBytes)
+                    .toEqual(saveResult.modelArtifactsInfo.weightSpecsBytes);
+                expect(out[url].weightDataBytes)
+                    .toEqual(saveResult.modelArtifactsInfo.weightDataBytes);
+                done();
+            })
+                .catch(function (err) { return done.fail(err.stack); });
+        })
+            .catch(function (err) { return done.fail(err.stack); });
+    });
+    it('Manager: List models: 2 results in 2 mediums', function (done) {
+        var url1 = 'localstorage://QuxModel';
+        var url2 = 'indexeddb://QuxModel';
+        var handler1 = tf.io.getSaveHandlers(url1)[0];
+        handler1.save(artifacts1)
+            .then(function (saveResult1) {
+            var handler2 = tf.io.getSaveHandlers(url2)[0];
+            handler2.save(artifacts1)
+                .then(function (saveResult2) {
+                tf.io.listModels()
+                    .then(function (out) {
+                    expect(Object.keys(out).length).toEqual(2);
+                    expect(out[url1].modelTopologyType)
+                        .toEqual(saveResult1.modelArtifactsInfo.modelTopologyType);
+                    expect(out[url1].modelTopologyBytes)
+                        .toEqual(saveResult1.modelArtifactsInfo
+                        .modelTopologyBytes);
+                    expect(out[url1].weightSpecsBytes)
+                        .toEqual(saveResult1.modelArtifactsInfo.weightSpecsBytes);
+                    expect(out[url1].weightDataBytes)
+                        .toEqual(saveResult1.modelArtifactsInfo.weightDataBytes);
+                    expect(out[url2].modelTopologyType)
+                        .toEqual(saveResult2.modelArtifactsInfo.modelTopologyType);
+                    expect(out[url2].modelTopologyBytes)
+                        .toEqual(saveResult2.modelArtifactsInfo
+                        .modelTopologyBytes);
+                    expect(out[url2].weightSpecsBytes)
+                        .toEqual(saveResult2.modelArtifactsInfo.weightSpecsBytes);
+                    expect(out[url2].weightDataBytes)
+                        .toEqual(saveResult2.modelArtifactsInfo.weightDataBytes);
+                    done();
+                })
+                    .catch(function (err) { return done.fail(err.stack); });
+            })
+                .catch(function (err) { return done.fail(err.stack); });
+        })
+            .catch(function (err) { return done.fail(err.stack); });
+    });
+    it('Successful removeModel', function (done) {
+        var handler1 = tf.io.getSaveHandlers('localstorage://QuxModel')[0];
+        handler1.save(artifacts1)
+            .then(function (saveResult1) {
+            var handler2 = tf.io.getSaveHandlers('indexeddb://repeat/QuxModel')[0];
+            handler2.save(artifacts1)
+                .then(function (saveResult2) {
+                tf.io.removeModel('indexeddb://repeat/QuxModel')
+                    .then(function (deletedInfo) {
+                    tf.io.listModels()
+                        .then(function (out) {
+                        expect(Object.keys(out)).toEqual([
+                            'localstorage://QuxModel'
+                        ]);
+                        tf.io.removeModel('localstorage://QuxModel')
+                            .then(function (out) {
+                            tf.io.listModels()
+                                .then(function (out) {
+                                expect(Object.keys(out)).toEqual([]);
+                                done();
+                            })
+                                .catch(function (err) { return done.fail(err); });
+                        })
+                            .catch(function (err) { return done.fail(err); });
+                    })
+                        .catch(function (err) { return done.fail(err); });
+                })
+                    .catch(function (err) { return done.fail(err.stack); });
+            })
+                .catch(function (err) { return done.fail(err.stack); });
+        })
+            .catch(function (err) { return done.fail(err.stack); });
+    });
+    it('Successful copyModel between mediums', function (done) {
+        var url1 = 'localstorage://a1/FooModel';
+        var url2 = 'indexeddb://a1/FooModel';
+        var handler1 = tf.io.getSaveHandlers(url1)[0];
+        handler1.save(artifacts1)
+            .then(function (saveResult) {
+            tf.io.copyModel(url1, url2)
+                .then(function (modelInfo) {
+                tf.io.listModels().then(function (out) {
+                    expect(Object.keys(out).length).toEqual(2);
+                    expect(out[url1].modelTopologyType)
+                        .toEqual(saveResult.modelArtifactsInfo.modelTopologyType);
+                    expect(out[url1].modelTopologyBytes)
+                        .toEqual(saveResult.modelArtifactsInfo.modelTopologyBytes);
+                    expect(out[url1].weightSpecsBytes)
+                        .toEqual(saveResult.modelArtifactsInfo.weightSpecsBytes);
+                    expect(out[url1].weightDataBytes)
+                        .toEqual(saveResult.modelArtifactsInfo.weightDataBytes);
+                    expect(out[url2].modelTopologyType)
+                        .toEqual(saveResult.modelArtifactsInfo.modelTopologyType);
+                    expect(out[url2].modelTopologyBytes)
+                        .toEqual(saveResult.modelArtifactsInfo.modelTopologyBytes);
+                    expect(out[url2].weightSpecsBytes)
+                        .toEqual(saveResult.modelArtifactsInfo.weightSpecsBytes);
+                    expect(out[url2].weightDataBytes)
+                        .toEqual(saveResult.modelArtifactsInfo.weightDataBytes);
+                    var handler2 = tf.io.getLoadHandlers(url2)[0];
+                    handler2.load()
+                        .then(function (loaded) {
+                        expect(loaded.modelTopology).toEqual(modelTopology1);
+                        expect(loaded.weightSpecs).toEqual(weightSpecs1);
+                        expect(new Uint8Array(loaded.weightData))
+                            .toEqual(new Uint8Array(weightData1));
+                        done();
+                    })
+                        .catch(function (err) { return done.fail(err.stack); });
+                });
+            })
+                .catch(function (err) { return done.fail(err.stack); });
+        })
+            .catch(function (err) { return done.fail(err.stack); });
+    });
+    it('Successful moveModel between mediums', function (done) {
+        var url1 = 'localstorage://a1/FooModel';
+        var url2 = 'indexeddb://a1/FooModel';
+        var handler1 = tf.io.getSaveHandlers(url1)[0];
+        handler1.save(artifacts1)
+            .then(function (saveResult) {
+            tf.io.moveModel(url1, url2)
+                .then(function (modelInfo) {
+                tf.io.listModels().then(function (out) {
+                    expect(Object.keys(out)).toEqual([url2]);
+                    expect(out[url2].modelTopologyType)
+                        .toEqual(saveResult.modelArtifactsInfo.modelTopologyType);
+                    expect(out[url2].modelTopologyBytes)
+                        .toEqual(saveResult.modelArtifactsInfo.modelTopologyBytes);
+                    expect(out[url2].weightSpecsBytes)
+                        .toEqual(saveResult.modelArtifactsInfo.weightSpecsBytes);
+                    expect(out[url2].weightDataBytes)
+                        .toEqual(saveResult.modelArtifactsInfo.weightDataBytes);
+                    var handler2 = tf.io.getLoadHandlers(url2)[0];
+                    handler2.load()
+                        .then(function (loaded) {
+                        expect(loaded.modelTopology).toEqual(modelTopology1);
+                        expect(loaded.weightSpecs).toEqual(weightSpecs1);
+                        expect(new Uint8Array(loaded.weightData))
+                            .toEqual(new Uint8Array(weightData1));
+                        done();
+                    })
+                        .catch(function (err) {
+                        done.fail(err.stack);
+                    });
+                });
+            })
+                .catch(function (err) { return done.fail(err.stack); });
+        })
+            .catch(function (err) { return done.fail(err.stack); });
+    });
+    it('Failed copyModel to invalid source URL', function (done) {
+        var url1 = 'invalidurl';
+        var url2 = 'localstorage://a1/FooModel';
+        tf.io.copyModel(url1, url2)
+            .then(function (out) {
+            done.fail('Copying from invalid URL succeeded unexpectedly.');
+        })
+            .catch(function (err) {
+            expect(err.message)
+                .toEqual('Copying failed because no load handler is found for ' +
+                'source URL invalidurl.');
+            done();
+        });
+    });
+    it('Failed copyModel to invalid destination URL', function (done) {
+        var url1 = 'localstorage://a1/FooModel';
+        var url2 = 'invalidurl';
+        var handler1 = tf.io.getSaveHandlers(url1)[0];
+        handler1.save(artifacts1)
+            .then(function (saveResult) {
+            tf.io.copyModel(url1, url2)
+                .then(function (out) {
+                done.fail('Copying to invalid URL succeeded unexpectedly.');
+            })
+                .catch(function (err) {
+                expect(err.message)
+                    .toEqual('Copying failed because no save handler is found for ' +
+                    'destination URL invalidurl.');
+                done();
+            });
+        })
+            .catch(function (err) { return done.fail(err.stack); });
+    });
+    it('Failed moveModel to invalid destination URL', function (done) {
+        var url1 = 'localstorage://a1/FooModel';
+        var url2 = 'invalidurl';
+        var handler1 = tf.io.getSaveHandlers(url1)[0];
+        handler1.save(artifacts1)
+            .then(function (saveResult) {
+            tf.io.moveModel(url1, url2)
+                .then(function (out) {
+                done.fail('Copying to invalid URL succeeded unexpectedly.');
+            })
+                .catch(function (err) {
+                expect(err.message)
+                    .toEqual('Copying failed because no save handler is found for ' +
+                    'destination URL invalidurl.');
+                tf.io.listModels()
+                    .then(function (out) {
+                    expect(Object.keys(out)).toEqual([url1]);
+                    done();
+                })
+                    .catch(function (err) { return done.fail(err.stack); });
+            });
+        })
+            .catch(function (err) { return done.fail(err.stack); });
+    });
+    it('Failed deletedModel: Absent scheme', function (done) {
+        tf.io.removeModel('foo')
+            .then(function (out) {
+            done.fail('Removing model with missing scheme succeeded unexpectedly.');
+        })
+            .catch(function (err) {
+            expect(err.message)
+                .toMatch(/The url string provided does not contain a scheme/);
+            expect(err.message.indexOf('localstorage')).toBeGreaterThan(0);
+            expect(err.message.indexOf('indexeddb')).toBeGreaterThan(0);
+            done();
+        });
+    });
+    it('Failed deletedModel: Invalid scheme', function (done) {
+        tf.io.removeModel('invalidscheme://foo')
+            .then(function (out) {
+            done.fail('Removing nonexistent model succeeded unexpectedly.');
+        })
+            .catch(function (err) {
+            expect(err.message)
+                .toEqual('Cannot find model manager for scheme \'invalidscheme\'');
+            done();
+        });
+    });
+    it('Failed deletedModel: Nonexistent model', function (done) {
+        tf.io.removeModel('indexeddb://nonexistent')
+            .then(function (out) {
+            done.fail('Removing nonexistent model succeeded unexpectedly.');
+        })
+            .catch(function (err) {
+            expect(err.message)
+                .toEqual('Cannot find model with path \'nonexistent\' in IndexedDB.');
+            done();
+        });
+    });
+    it('Failed copyModel', function (done) {
+        tf.io.copyModel('indexeddb://nonexistent', 'indexeddb://destination')
+            .then(function (out) {
+            done.fail('Copying nonexistent model succeeded unexpectedly.');
+        })
+            .catch(function (err) {
+            expect(err.message)
+                .toEqual('Cannot find model with path \'nonexistent\' in IndexedDB.');
+            done();
+        });
+    });
+    it('copyModel: Identical oldPath and newPath leads to Error', function (done) {
+        tf.io.copyModel('a/1', 'a/1')
+            .then(function (out) {
+            done.fail('Copying with identical old & new paths succeeded unexpectedly.');
+        })
+            .catch(function (err) {
+            expect(err.message)
+                .toEqual('Old path and new path are the same: \'a/1\'');
+            done();
+        });
+    });
+    it('moveModel: Identical oldPath and newPath leads to Error', function (done) {
+        tf.io.moveModel('a/1', 'a/1')
+            .then(function (out) {
+            done.fail('Copying with identical old & new paths succeeded unexpectedly.');
+        })
+            .catch(function (err) {
+            expect(err.message)
+                .toEqual('Old path and new path are the same: \'a/1\'');
+            done();
+        });
+    });
+});
+//# sourceMappingURL=model_management_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/model_management_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/io/model_management_test.js.map
new file mode 100644
index 0000000..0601d55
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/model_management_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/passthrough.d.ts b/node_modules/@tensorflow/tfjs-core/dist/io/passthrough.d.ts
new file mode 100644
index 0000000..68f42c5
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/passthrough.d.ts
@@ -0,0 +1,3 @@
+import { IOHandler, ModelArtifacts, SaveResult, WeightsManifestEntry } from './types';
+export declare function fromMemory(modelTopology: {}, weightSpecs?: WeightsManifestEntry[], weightData?: ArrayBuffer): IOHandler;
+export declare function withSaveHandler(saveHandler: (artifacts: ModelArtifacts) => Promise<SaveResult>): IOHandler;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/passthrough.js b/node_modules/@tensorflow/tfjs-core/dist/io/passthrough.js
new file mode 100644
index 0000000..91b9757
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/passthrough.js
@@ -0,0 +1,93 @@
+"use strict";
+var __assign = (this && this.__assign) || Object.assign || function(t) {
+    for (var s, i = 1, n = arguments.length; i < n; i++) {
+        s = arguments[i];
+        for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p))
+            t[p] = s[p];
+    }
+    return t;
+};
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+Object.defineProperty(exports, "__esModule", { value: true });
+var PassthroughLoader = (function () {
+    function PassthroughLoader(modelTopology, weightSpecs, weightData) {
+        this.modelTopology = modelTopology;
+        this.weightSpecs = weightSpecs;
+        this.weightData = weightData;
+    }
+    PassthroughLoader.prototype.load = function () {
+        return __awaiter(this, void 0, void 0, function () {
+            var result;
+            return __generator(this, function (_a) {
+                result = {};
+                if (this.modelTopology != null) {
+                    result = __assign({ modelTopology: this.modelTopology }, result);
+                }
+                if (this.weightSpecs != null && this.weightSpecs.length > 0) {
+                    result = __assign({ weightSpecs: this.weightSpecs }, result);
+                }
+                if (this.weightData != null && this.weightData.byteLength > 0) {
+                    result = __assign({ weightData: this.weightData }, result);
+                }
+                return [2, result];
+            });
+        });
+    };
+    return PassthroughLoader;
+}());
+var PassthroughSaver = (function () {
+    function PassthroughSaver(saveHandler) {
+        this.saveHandler = saveHandler;
+    }
+    PassthroughSaver.prototype.save = function (modelArtifacts) {
+        return __awaiter(this, void 0, void 0, function () {
+            return __generator(this, function (_a) {
+                return [2, this.saveHandler(modelArtifacts)];
+            });
+        });
+    };
+    return PassthroughSaver;
+}());
+function fromMemory(modelTopology, weightSpecs, weightData) {
+    return new PassthroughLoader(modelTopology, weightSpecs, weightData);
+}
+exports.fromMemory = fromMemory;
+function withSaveHandler(saveHandler) {
+    return new PassthroughSaver(saveHandler);
+}
+exports.withSaveHandler = withSaveHandler;
+//# sourceMappingURL=passthrough.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/passthrough.js.map b/node_modules/@tensorflow/tfjs-core/dist/io/passthrough.js.map
new file mode 100644
index 0000000..4def1ac
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/passthrough.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"passthrough.js","sourceRoot":"","sources":["../../src/io/passthrough.ts"],"names":[],"mappings":";;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;AAuBA;IACE,2BACqB,aAA8B,EAC9B,WAAoC,EACpC,UAAwB;QAFxB,kBAAa,GAAb,aAAa,CAAiB;QAC9B,gBAAW,GAAX,WAAW,CAAyB;QACpC,eAAU,GAAV,UAAU,CAAc;IAAG,CAAC;IAE3C,gCAAI,GAAV;;;;gBACM,MAAM,GAAG,EAAE,CAAC;gBAChB,IAAI,IAAI,CAAC,aAAa,IAAI,IAAI,EAAE;oBAC9B,MAAM,cAAI,aAAa,EAAE,IAAI,CAAC,aAAa,IAAK,MAAM,CAAC,CAAC;iBACzD;gBACD,IAAI,IAAI,CAAC,WAAW,IAAI,IAAI,IAAI,IAAI,CAAC,WAAW,CAAC,MAAM,GAAG,CAAC,EAAE;oBAC3D,MAAM,cAAI,WAAW,EAAE,IAAI,CAAC,WAAW,IAAK,MAAM,CAAC,CAAC;iBACrD;gBACD,IAAI,IAAI,CAAC,UAAU,IAAI,IAAI,IAAI,IAAI,CAAC,UAAU,CAAC,UAAU,GAAG,CAAC,EAAE;oBAC7D,MAAM,cAAI,UAAU,EAAE,IAAI,CAAC,UAAU,IAAK,MAAM,CAAC,CAAC;iBACnD;gBACD,WAAO,MAAM,EAAC;;;KACf;IACH,wBAAC;AAAD,CAAC,AAnBD,IAmBC;AAED;IACE,0BACqB,WACqC;QADrC,gBAAW,GAAX,WAAW,CAC0B;IAAG,CAAC;IAExD,+BAAI,GAAV,UAAW,cAA8B;;;gBACvC,WAAO,IAAI,CAAC,WAAW,CAAC,cAAc,CAAC,EAAC;;;KACzC;IACH,uBAAC;AAAD,CAAC,AARD,IAQC;AAsBD,oBACI,aAAiB,EAAE,WAAoC,EACvD,UAAwB;IAC1B,OAAO,IAAI,iBAAiB,CAAC,aAAa,EAAE,WAAW,EAAE,UAAU,CAAC,CAAC;AACvE,CAAC;AAJD,gCAIC;AAiBD,yBACI,WACuB;IACzB,OAAO,IAAI,gBAAgB,CAAC,WAAW,CAAC,CAAC;AAC3C,CAAC;AAJD,0CAIC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/passthrough_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/io/passthrough_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/passthrough_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/passthrough_test.js b/node_modules/@tensorflow/tfjs-core/dist/io/passthrough_test.js
new file mode 100644
index 0000000..2e964a6
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/passthrough_test.js
@@ -0,0 +1,171 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+var _this = this;
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+var modelTopology1 = {
+    'class_name': 'Sequential',
+    'keras_version': '2.1.4',
+    'config': [{
+            'class_name': 'Dense',
+            'config': {
+                'kernel_initializer': {
+                    'class_name': 'VarianceScaling',
+                    'config': {
+                        'distribution': 'uniform',
+                        'scale': 1.0,
+                        'seed': null,
+                        'mode': 'fan_avg'
+                    }
+                },
+                'name': 'dense',
+                'kernel_constraint': null,
+                'bias_regularizer': null,
+                'bias_constraint': null,
+                'dtype': 'float32',
+                'activation': 'linear',
+                'trainable': true,
+                'kernel_regularizer': null,
+                'bias_initializer': { 'class_name': 'Zeros', 'config': {} },
+                'units': 1,
+                'batch_input_shape': [null, 3],
+                'use_bias': true,
+                'activity_regularizer': null
+            }
+        }],
+    'backend': 'tensorflow'
+};
+var weightSpecs1 = [
+    {
+        name: 'dense/kernel',
+        shape: [3, 1],
+        dtype: 'float32',
+    },
+    {
+        name: 'dense/bias',
+        shape: [1],
+        dtype: 'float32',
+    }
+];
+var weightData1 = new ArrayBuffer(16);
+var artifacts1 = {
+    modelTopology: modelTopology1,
+    weightSpecs: weightSpecs1,
+    weightData: weightData1,
+};
+jasmine_util_1.describeWithFlags('Passthrough Saver', test_util_1.BROWSER_ENVS, function () {
+    it('passes provided arguments through on save', function () { return __awaiter(_this, void 0, void 0, function () {
+        function saveHandler(artifacts) {
+            return __awaiter(this, void 0, void 0, function () {
+                return __generator(this, function (_a) {
+                    savedArtifacts = artifacts;
+                    return [2, {
+                            modelArtifactsInfo: {
+                                dateSaved: testStartDate,
+                                modelTopologyType: 'JSON',
+                                modelTopologyBytes: JSON.stringify(modelTopology1).length,
+                                weightSpecsBytes: JSON.stringify(weightSpecs1).length,
+                                weightDataBytes: weightData1.byteLength,
+                            }
+                        }];
+                });
+            });
+        }
+        var testStartDate, savedArtifacts, saveTrigger, saveResult, artifactsInfo;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    testStartDate = new Date();
+                    savedArtifacts = null;
+                    saveTrigger = tf.io.withSaveHandler(saveHandler);
+                    return [4, saveTrigger.save(artifacts1)];
+                case 1:
+                    saveResult = _a.sent();
+                    expect(saveResult.errors).toEqual(undefined);
+                    artifactsInfo = saveResult.modelArtifactsInfo;
+                    expect(artifactsInfo.dateSaved.getTime())
+                        .toBeGreaterThanOrEqual(testStartDate.getTime());
+                    expect(saveResult.modelArtifactsInfo.modelTopologyBytes)
+                        .toEqual(JSON.stringify(modelTopology1).length);
+                    expect(saveResult.modelArtifactsInfo.weightSpecsBytes)
+                        .toEqual(JSON.stringify(weightSpecs1).length);
+                    expect(saveResult.modelArtifactsInfo.weightDataBytes).toEqual(16);
+                    expect(savedArtifacts.modelTopology).toEqual(modelTopology1);
+                    expect(savedArtifacts.weightSpecs).toEqual(weightSpecs1);
+                    expect(savedArtifacts.weightData).toEqual(weightData1);
+                    return [2];
+            }
+        });
+    }); });
+});
+jasmine_util_1.describeWithFlags('Passthrough Loader', test_util_1.BROWSER_ENVS, function () {
+    it('load topology and weights', function () { return __awaiter(_this, void 0, void 0, function () {
+        var passthroughHandler, modelArtifacts;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    passthroughHandler = tf.io.fromMemory(modelTopology1, weightSpecs1, weightData1);
+                    return [4, passthroughHandler.load()];
+                case 1:
+                    modelArtifacts = _a.sent();
+                    expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
+                    expect(modelArtifacts.weightSpecs).toEqual(weightSpecs1);
+                    expect(modelArtifacts.weightData).toEqual(weightData1);
+                    return [2];
+            }
+        });
+    }); });
+    it('load model topology only', function () { return __awaiter(_this, void 0, void 0, function () {
+        var passthroughHandler, modelArtifacts;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    passthroughHandler = tf.io.fromMemory(modelTopology1);
+                    return [4, passthroughHandler.load()];
+                case 1:
+                    modelArtifacts = _a.sent();
+                    expect(modelArtifacts.modelTopology).toEqual(modelTopology1);
+                    expect(modelArtifacts.weightSpecs).toEqual(undefined);
+                    expect(modelArtifacts.weightData).toEqual(undefined);
+                    return [2];
+            }
+        });
+    }); });
+});
+//# sourceMappingURL=passthrough_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/passthrough_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/io/passthrough_test.js.map
new file mode 100644
index 0000000..953e46c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/passthrough_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"passthrough_test.js","sourceRoot":"","sources":["../../src/io/passthrough_test.ts"],"names":[],"mappings":";;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;AAqBA,iBAiHA;;AAjHA,6BAA+B;AAC/B,gDAAkD;AAClD,0CAA0C;AAE1C,IAAM,cAAc,GAAO;IACzB,YAAY,EAAE,YAAY;IAC1B,eAAe,EAAE,OAAO;IACxB,QAAQ,EAAE,CAAC;YACT,YAAY,EAAE,OAAO;YACrB,QAAQ,EAAE;gBACR,oBAAoB,EAAE;oBACpB,YAAY,EAAE,iBAAiB;oBAC/B,QAAQ,EAAE;wBACR,cAAc,EAAE,SAAS;wBACzB,OAAO,EAAE,GAAG;wBACZ,MAAM,EAAE,IAAI;wBACZ,MAAM,EAAE,SAAS;qBAClB;iBACF;gBACD,MAAM,EAAE,OAAO;gBACf,mBAAmB,EAAE,IAAI;gBACzB,kBAAkB,EAAE,IAAI;gBACxB,iBAAiB,EAAE,IAAI;gBACvB,OAAO,EAAE,SAAS;gBAClB,YAAY,EAAE,QAAQ;gBACtB,WAAW,EAAE,IAAI;gBACjB,oBAAoB,EAAE,IAAI;gBAC1B,kBAAkB,EAAE,EAAC,YAAY,EAAE,OAAO,EAAE,QAAQ,EAAE,EAAE,EAAC;gBACzD,OAAO,EAAE,CAAC;gBACV,mBAAmB,EAAE,CAAC,IAAI,EAAE,CAAC,CAAC;gBAC9B,UAAU,EAAE,IAAI;gBAChB,sBAAsB,EAAE,IAAI;aAC7B;SACF,CAAC;IACF,SAAS,EAAE,YAAY;CACxB,CAAC;AAEF,IAAM,YAAY,GAAiC;IACjD;QACE,IAAI,EAAE,cAAc;QACpB,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC;QACb,KAAK,EAAE,SAAS;KACjB;IACD;QACE,IAAI,EAAE,YAAY;QAClB,KAAK,EAAE,CAAC,CAAC,CAAC;QACV,KAAK,EAAE,SAAS;KACjB;CACF,CAAC;AAEF,IAAM,WAAW,GAAG,IAAI,WAAW,CAAC,EAAE,CAAC,CAAC;AACxC,IAAM,UAAU,GAAyB;IACvC,aAAa,EAAE,cAAc;IAC7B,WAAW,EAAE,YAAY;IACzB,UAAU,EAAE,WAAW;CACxB,CAAC;AAEF,gCAAiB,CAAC,mBAAmB,EAAE,wBAAY,EAAE;IACnD,EAAE,CAAC,2CAA2C,EAAE;QAI9C,qBAA2B,SAA+B;;;oBAExD,cAAc,GAAG,SAAS,CAAC;oBAC3B,WAAO;4BACL,kBAAkB,EAAE;gCAClB,SAAS,EAAE,aAAa;gCACxB,iBAAiB,EAAE,MAAM;gCACzB,kBAAkB,EAAE,IAAI,CAAC,SAAS,CAAC,cAAc,CAAC,CAAC,MAAM;gCACzD,gBAAgB,EAAE,IAAI,CAAC,SAAS,CAAC,YAAY,CAAC,CAAC,MAAM;gCACrD,eAAe,EAAE,WAAW,CAAC,UAAU;6BACxC;yBACF,EAAC;;;SACH;;;;;oBAfK,aAAa,GAAG,IAAI,IAAI,EAAE,CAAC;oBAC7B,cAAc,GAAyB,IAAI,CAAC;oBAgB1C,WAAW,GAAG,EAAE,CAAC,EAAE,CAAC,eAAe,CAAC,WAAW,CAAC,CAAC;oBACpC,WAAM,WAAW,CAAC,IAAI,CAAC,UAAU,CAAC,EAAA;;oBAA/C,UAAU,GAAG,SAAkC;oBAErD,MAAM,CAAC,UAAU,CAAC,MAAM,CAAC,CAAC,OAAO,CAAC,SAAS,CAAC,CAAC;oBACvC,aAAa,GAAG,UAAU,CAAC,kBAAkB,CAAC;oBACpD,MAAM,CAAC,aAAa,CAAC,SAAS,CAAC,OAAO,EAAE,CAAC;yBACpC,sBAAsB,CAAC,aAAa,CAAC,OAAO,EAAE,CAAC,CAAC;oBACrD,MAAM,CAAC,UAAU,CAAC,kBAAkB,CAAC,kBAAkB,CAAC;yBACnD,OAAO,CAAC,IAAI,CAAC,SAAS,CAAC,cAAc,CAAC,CAAC,MAAM,CAAC,CAAC;oBACpD,MAAM,CAAC,UAAU,CAAC,kBAAkB,CAAC,gBAAgB,CAAC;yBACjD,OAAO,CAAC,IAAI,CAAC,SAAS,CAAC,YAAY,CAAC,CAAC,MAAM,CAAC,CAAC;oBAClD,MAAM,CAAC,UAAU,CAAC,kBAAkB,CAAC,eAAe,CAAC,CAAC,OAAO,CAAC,EAAE,CAAC,CAAC;oBAElE,MAAM,CAAC,cAAc,CAAC,aAAa,CAAC,CAAC,OAAO,CAAC,cAAc,CAAC,CAAC;oBAC7D,MAAM,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,OAAO,CAAC,YAAY,CAAC,CAAC;oBACzD,MAAM,CAAC,cAAc,CAAC,UAAU,CAAC,CAAC,OAAO,CAAC,WAAW,CAAC,CAAC;;;;SACxD,CAAC,CAAC;AACL,CAAC,CAAC,CAAC;AAEH,gCAAiB,CAAC,oBAAoB,EAAE,wBAAY,EAAE;IACpD,EAAE,CAAC,2BAA2B,EAAE;;;;;oBACxB,kBAAkB,GACpB,EAAE,CAAC,EAAE,CAAC,UAAU,CAAC,cAAc,EAAE,YAAY,EAAE,WAAW,CAAC,CAAC;oBACzC,WAAM,kBAAkB,CAAC,IAAI,EAAE,EAAA;;oBAAhD,cAAc,GAAG,SAA+B;oBACtD,MAAM,CAAC,cAAc,CAAC,aAAa,CAAC,CAAC,OAAO,CAAC,cAAc,CAAC,CAAC;oBAC7D,MAAM,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,OAAO,CAAC,YAAY,CAAC,CAAC;oBACzD,MAAM,CAAC,cAAc,CAAC,UAAU,CAAC,CAAC,OAAO,CAAC,WAAW,CAAC,CAAC;;;;SACxD,CAAC,CAAC;IAEH,EAAE,CAAC,0BAA0B,EAAE;;;;;oBACvB,kBAAkB,GAAG,EAAE,CAAC,EAAE,CAAC,UAAU,CAAC,cAAc,CAAC,CAAC;oBACrC,WAAM,kBAAkB,CAAC,IAAI,EAAE,EAAA;;oBAAhD,cAAc,GAAG,SAA+B;oBACtD,MAAM,CAAC,cAAc,CAAC,aAAa,CAAC,CAAC,OAAO,CAAC,cAAc,CAAC,CAAC;oBAC7D,MAAM,CAAC,cAAc,CAAC,WAAW,CAAC,CAAC,OAAO,CAAC,SAAS,CAAC,CAAC;oBACtD,MAAM,CAAC,cAAc,CAAC,UAAU,CAAC,CAAC,OAAO,CAAC,SAAS,CAAC,CAAC;;;;SACtD,CAAC,CAAC;AACL,CAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/router_registry.d.ts b/node_modules/@tensorflow/tfjs-core/dist/io/router_registry.d.ts
new file mode 100644
index 0000000..fe40da8
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/router_registry.d.ts
@@ -0,0 +1,14 @@
+import { IOHandler } from './types';
+export declare type IORouter = (url: string | string[]) => IOHandler;
+export declare class IORouterRegistry {
+    private static instance;
+    private saveRouters;
+    private loadRouters;
+    private constructor();
+    private static getInstance;
+    static registerSaveRouter(saveRouter: IORouter): void;
+    static registerLoadRouter(loadRouter: IORouter): void;
+    static getSaveHandlers(url: string | string[]): IOHandler[];
+    static getLoadHandlers(url: string | string[]): IOHandler[];
+    private static getHandlers;
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/router_registry.js b/node_modules/@tensorflow/tfjs-core/dist/io/router_registry.js
new file mode 100644
index 0000000..e57dd46
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/router_registry.js
@@ -0,0 +1,41 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var IORouterRegistry = (function () {
+    function IORouterRegistry() {
+        this.saveRouters = [];
+        this.loadRouters = [];
+    }
+    IORouterRegistry.getInstance = function () {
+        if (IORouterRegistry.instance == null) {
+            IORouterRegistry.instance = new IORouterRegistry();
+        }
+        return IORouterRegistry.instance;
+    };
+    IORouterRegistry.registerSaveRouter = function (saveRouter) {
+        IORouterRegistry.getInstance().saveRouters.push(saveRouter);
+    };
+    IORouterRegistry.registerLoadRouter = function (loadRouter) {
+        IORouterRegistry.getInstance().loadRouters.push(loadRouter);
+    };
+    IORouterRegistry.getSaveHandlers = function (url) {
+        return IORouterRegistry.getHandlers(url, 'save');
+    };
+    IORouterRegistry.getLoadHandlers = function (url) {
+        return IORouterRegistry.getHandlers(url, 'load');
+    };
+    IORouterRegistry.getHandlers = function (url, handlerType) {
+        var validHandlers = [];
+        var routers = handlerType === 'load' ? this.getInstance().loadRouters :
+            this.getInstance().saveRouters;
+        routers.forEach(function (router) {
+            var handler = router(url);
+            if (handler !== null) {
+                validHandlers.push(handler);
+            }
+        });
+        return validHandlers;
+    };
+    return IORouterRegistry;
+}());
+exports.IORouterRegistry = IORouterRegistry;
+//# sourceMappingURL=router_registry.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/router_registry.js.map b/node_modules/@tensorflow/tfjs-core/dist/io/router_registry.js.map
new file mode 100644
index 0000000..af54051
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/router_registry.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"router_registry.js","sourceRoot":"","sources":["../../src/io/router_registry.ts"],"names":[],"mappings":";;AAqBA;IAOE;QACE,IAAI,CAAC,WAAW,GAAG,EAAE,CAAC;QACtB,IAAI,CAAC,WAAW,GAAG,EAAE,CAAC;IACxB,CAAC;IAEc,4BAAW,GAA1B;QACE,IAAI,gBAAgB,CAAC,QAAQ,IAAI,IAAI,EAAE;YACrC,gBAAgB,CAAC,QAAQ,GAAG,IAAI,gBAAgB,EAAE,CAAC;SACpD;QACD,OAAO,gBAAgB,CAAC,QAAQ,CAAC;IACnC,CAAC;IAQM,mCAAkB,GAAzB,UAA0B,UAAoB;QAC5C,gBAAgB,CAAC,WAAW,EAAE,CAAC,WAAW,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC;IAC9D,CAAC;IAQM,mCAAkB,GAAzB,UAA0B,UAAoB;QAC5C,gBAAgB,CAAC,WAAW,EAAE,CAAC,WAAW,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC;IAC9D,CAAC;IAUM,gCAAe,GAAtB,UAAuB,GAAoB;QACzC,OAAO,gBAAgB,CAAC,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,CAAC;IACnD,CAAC;IASM,gCAAe,GAAtB,UAAuB,GAAoB;QACzC,OAAO,gBAAgB,CAAC,WAAW,CAAC,GAAG,EAAE,MAAM,CAAC,CAAC;IACnD,CAAC;IAEc,4BAAW,GAA1B,UAA2B,GAAoB,EAAE,WAA0B;QAEzE,IAAM,aAAa,GAAgB,EAAE,CAAC;QACtC,IAAM,OAAO,GAAG,WAAW,KAAK,MAAM,CAAC,CAAC,CAAC,IAAI,CAAC,WAAW,EAAE,CAAC,WAAW,CAAC,CAAC;YAChC,IAAI,CAAC,WAAW,EAAE,CAAC,WAAW,CAAC;QACxE,OAAO,CAAC,OAAO,CAAC,UAAA,MAAM;YACpB,IAAM,OAAO,GAAG,MAAM,CAAC,GAAG,CAAC,CAAC;YAC5B,IAAI,OAAO,KAAK,IAAI,EAAE;gBACpB,aAAa,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC;aAC7B;QACH,CAAC,CAAC,CAAC;QACH,OAAO,aAAa,CAAC;IACvB,CAAC;IACH,uBAAC;AAAD,CAAC,AA3ED,IA2EC;AA3EY,4CAAgB"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/router_registry_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/io/router_registry_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/router_registry_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/router_registry_test.js b/node_modules/@tensorflow/tfjs-core/dist/io/router_registry_test.js
new file mode 100644
index 0000000..be674e7
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/router_registry_test.js
@@ -0,0 +1,90 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+var indexed_db_1 = require("./indexed_db");
+var local_storage_1 = require("./local_storage");
+var router_registry_1 = require("./router_registry");
+jasmine_util_1.describeWithFlags('IORouterRegistry', test_util_1.BROWSER_ENVS, function () {
+    var localStorageRouter = function (url) {
+        var scheme = 'localstorage://';
+        if (url.startsWith(scheme)) {
+            return local_storage_1.browserLocalStorage(url.slice(scheme.length));
+        }
+        else {
+            return null;
+        }
+    };
+    var indexedDBRouter = function (url) {
+        var scheme = 'indexeddb://';
+        if (url.startsWith(scheme)) {
+            return indexed_db_1.browserIndexedDB(url.slice(scheme.length));
+        }
+        else {
+            return null;
+        }
+    };
+    var FakeIOHandler = (function () {
+        function FakeIOHandler(url1, url2) {
+        }
+        return FakeIOHandler;
+    }());
+    var fakeMultiStringRouter = function (url) {
+        var scheme = 'foo://';
+        if (Array.isArray(url) && url.length === 2) {
+            if (url[0].startsWith(scheme) && url[1].startsWith(scheme)) {
+                return new FakeIOHandler(url[0], url[1]);
+            }
+            else {
+                return null;
+            }
+        }
+        else {
+            return null;
+        }
+    };
+    var tempRegistryInstance = null;
+    beforeEach(function () {
+        tempRegistryInstance = router_registry_1.IORouterRegistry.instance;
+        router_registry_1.IORouterRegistry.instance = null;
+    });
+    afterEach(function () {
+        router_registry_1.IORouterRegistry.instance = tempRegistryInstance;
+    });
+    it('getSaveHandler succeeds', function () {
+        router_registry_1.IORouterRegistry.registerSaveRouter(localStorageRouter);
+        router_registry_1.IORouterRegistry.registerSaveRouter(indexedDBRouter);
+        var out1 = tf.io.getSaveHandlers('localstorage://foo-model');
+        expect(out1.length).toEqual(1);
+        expect(out1[0] instanceof local_storage_1.BrowserLocalStorage).toEqual(true);
+        var out2 = tf.io.getSaveHandlers('indexeddb://foo-model');
+        expect(out2.length).toEqual(1);
+        expect(out2[0] instanceof indexed_db_1.BrowserIndexedDB).toEqual(true);
+    });
+    it('getLoadHandler succeeds', function () {
+        router_registry_1.IORouterRegistry.registerLoadRouter(localStorageRouter);
+        router_registry_1.IORouterRegistry.registerLoadRouter(indexedDBRouter);
+        var out1 = tf.io.getLoadHandlers('localstorage://foo-model');
+        expect(out1.length).toEqual(1);
+        expect(out1[0] instanceof local_storage_1.BrowserLocalStorage).toEqual(true);
+        var out2 = tf.io.getLoadHandlers('indexeddb://foo-model');
+        expect(out2.length).toEqual(1);
+        expect(out2[0] instanceof indexed_db_1.BrowserIndexedDB).toEqual(true);
+    });
+    it('getLoadHandler with string array argument succeeds', function () {
+        router_registry_1.IORouterRegistry.registerLoadRouter(fakeMultiStringRouter);
+        var loadHandler = router_registry_1.IORouterRegistry.getLoadHandlers(['foo:///123', 'foo:///456']);
+        expect(loadHandler[0] instanceof FakeIOHandler).toEqual(true);
+        expect(router_registry_1.IORouterRegistry.getLoadHandlers(['foo:///123', 'bar:///456']))
+            .toEqual([]);
+        expect(router_registry_1.IORouterRegistry.getLoadHandlers(['foo:///123'])).toEqual([]);
+        expect(router_registry_1.IORouterRegistry.getLoadHandlers('foo:///123')).toEqual([]);
+    });
+    it('getSaveHandler fails', function () {
+        router_registry_1.IORouterRegistry.registerSaveRouter(localStorageRouter);
+        expect(tf.io.getSaveHandlers('invalidscheme://foo-model')).toEqual([]);
+        expect(tf.io.getLoadHandlers('localstorage://foo-model')).toEqual([]);
+    });
+});
+//# sourceMappingURL=router_registry_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/router_registry_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/io/router_registry_test.js.map
new file mode 100644
index 0000000..1589fa2
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/router_registry_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"router_registry_test.js","sourceRoot":"","sources":["../../src/io/router_registry_test.ts"],"names":[],"mappings":";;AAiBA,6BAA+B;AAC/B,gDAAkD;AAClD,0CAA0C;AAC1C,2CAAgE;AAChE,iDAAyE;AACzE,qDAAmD;AAGnD,gCAAiB,CAAC,kBAAkB,EAAE,wBAAY,EAAE;IAClD,IAAM,kBAAkB,GAAG,UAAC,GAAW;QACrC,IAAM,MAAM,GAAG,iBAAiB,CAAC;QACjC,IAAI,GAAG,CAAC,UAAU,CAAC,MAAM,CAAC,EAAE;YAC1B,OAAO,mCAAmB,CAAC,GAAG,CAAC,KAAK,CAAC,MAAM,CAAC,MAAM,CAAC,CAAC,CAAC;SACtD;aAAM;YACL,OAAO,IAAI,CAAC;SACb;IACH,CAAC,CAAC;IAEF,IAAM,eAAe,GAAG,UAAC,GAAW;QAClC,IAAM,MAAM,GAAG,cAAc,CAAC;QAC9B,IAAI,GAAG,CAAC,UAAU,CAAC,MAAM,CAAC,EAAE;YAC1B,OAAO,6BAAgB,CAAC,GAAG,CAAC,KAAK,CAAC,MAAM,CAAC,MAAM,CAAC,CAAC,CAAC;SACnD;aAAM;YACL,OAAO,IAAI,CAAC;SACb;IACH,CAAC,CAAC;IAEF;QACE,uBAAY,IAAY,EAAE,IAAY;QAAG,CAAC;QAC5C,oBAAC;IAAD,CAAC,AAFD,IAEC;IAED,IAAM,qBAAqB,GAAG,UAAC,GAAoB;QACjD,IAAM,MAAM,GAAG,QAAQ,CAAC;QACxB,IAAI,KAAK,CAAC,OAAO,CAAC,GAAG,CAAC,IAAI,GAAG,CAAC,MAAM,KAAK,CAAC,EAAE;YAC1C,IAAI,GAAG,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,MAAM,CAAC,IAAI,GAAG,CAAC,CAAC,CAAC,CAAC,UAAU,CAAC,MAAM,CAAC,EAAE;gBAC1D,OAAO,IAAI,aAAa,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC;aAC1C;iBAAM;gBACL,OAAO,IAAI,CAAC;aACb;SACF;aAAM;YACL,OAAO,IAAI,CAAC;SACb;IACH,CAAC,CAAC;IAEF,IAAI,oBAAoB,GAAqB,IAAI,CAAC;IAClD,UAAU,CAAC;QAGT,oBAAoB,GAAI,kCAAwB,CAAC,QAAQ,CAAC;QACzD,kCAAwB,CAAC,QAAQ,GAAG,IAAI,CAAC;IAE5C,CAAC,CAAC,CAAC;IAEH,SAAS,CAAC;QAEP,kCAAwB,CAAC,QAAQ,GAAG,oBAAoB,CAAC;IAC5D,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,yBAAyB,EAAE;QAC5B,kCAAgB,CAAC,kBAAkB,CAAC,kBAAkB,CAAC,CAAC;QACxD,kCAAgB,CAAC,kBAAkB,CAAC,eAAe,CAAC,CAAC;QAErD,IAAM,IAAI,GAAG,EAAE,CAAC,EAAE,CAAC,eAAe,CAAC,0BAA0B,CAAC,CAAC;QAC/D,MAAM,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC;QAC/B,MAAM,CAAC,IAAI,CAAC,CAAC,CAAC,YAAY,mCAAmB,CAAC,CAAC,OAAO,CAAC,IAAI,CAAC,CAAC;QAC7D,IAAM,IAAI,GAAG,EAAE,CAAC,EAAE,CAAC,eAAe,CAAC,uBAAuB,CAAC,CAAC;QAC5D,MAAM,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC;QAC/B,MAAM,CAAC,IAAI,CAAC,CAAC,CAAC,YAAY,6BAAgB,CAAC,CAAC,OAAO,CAAC,IAAI,CAAC,CAAC;IAC5D,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,yBAAyB,EAAE;QAC5B,kCAAgB,CAAC,kBAAkB,CAAC,kBAAkB,CAAC,CAAC;QACxD,kCAAgB,CAAC,kBAAkB,CAAC,eAAe,CAAC,CAAC;QAErD,IAAM,IAAI,GAAG,EAAE,CAAC,EAAE,CAAC,eAAe,CAAC,0BAA0B,CAAC,CAAC;QAC/D,MAAM,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC;QAC/B,MAAM,CAAC,IAAI,CAAC,CAAC,CAAC,YAAY,mCAAmB,CAAC,CAAC,OAAO,CAAC,IAAI,CAAC,CAAC;QAC7D,IAAM,IAAI,GAAG,EAAE,CAAC,EAAE,CAAC,eAAe,CAAC,uBAAuB,CAAC,CAAC;QAC5D,MAAM,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC;QAC/B,MAAM,CAAC,IAAI,CAAC,CAAC,CAAC,YAAY,6BAAgB,CAAC,CAAC,OAAO,CAAC,IAAI,CAAC,CAAC;IAC5D,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,oDAAoD,EAAE;QACvD,kCAAgB,CAAC,kBAAkB,CAAC,qBAAqB,CAAC,CAAC;QAC3D,IAAM,WAAW,GACb,kCAAgB,CAAC,eAAe,CAAC,CAAC,YAAY,EAAE,YAAY,CAAC,CAAC,CAAC;QACnE,MAAM,CAAC,WAAW,CAAC,CAAC,CAAC,YAAY,aAAa,CAAC,CAAC,OAAO,CAAC,IAAI,CAAC,CAAC;QAE9D,MAAM,CAAC,kCAAgB,CAAC,eAAe,CAAC,CAAC,YAAY,EAAE,YAAY,CAAC,CAAC,CAAC;aACjE,OAAO,CAAC,EAAE,CAAC,CAAC;QACjB,MAAM,CAAC,kCAAgB,CAAC,eAAe,CAAC,CAAC,YAAY,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,EAAE,CAAC,CAAC;QACrE,MAAM,CAAC,kCAAgB,CAAC,eAAe,CAAC,YAAY,CAAC,CAAC,CAAC,OAAO,CAAC,EAAE,CAAC,CAAC;IACrE,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,sBAAsB,EAAE;QACzB,kCAAgB,CAAC,kBAAkB,CAAC,kBAAkB,CAAC,CAAC;QAExD,MAAM,CAAC,EAAE,CAAC,EAAE,CAAC,eAAe,CAAC,2BAA2B,CAAC,CAAC,CAAC,OAAO,CAAC,EAAE,CAAC,CAAC;QAEvE,MAAM,CAAC,EAAE,CAAC,EAAE,CAAC,eAAe,CAAC,0BAA0B,CAAC,CAAC,CAAC,OAAO,CAAC,EAAE,CAAC,CAAC;IACxE,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/types.d.ts b/node_modules/@tensorflow/tfjs-core/dist/io/types.d.ts
new file mode 100644
index 0000000..9f0eca0
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/types.d.ts
@@ -0,0 +1,50 @@
+export declare const DTYPE_VALUE_SIZE_MAP: {
+    [dtype: string]: number;
+};
+export declare type WeightsManifestConfig = WeightsManifestGroupConfig[];
+export declare interface WeightsManifestGroupConfig {
+    paths: string[];
+    weights: WeightsManifestEntry[];
+}
+export declare interface WeightsManifestEntry {
+    name: string;
+    shape: number[];
+    dtype: 'float32' | 'int32' | 'bool';
+    quantization?: {
+        scale: number;
+        min: number;
+        dtype: 'uint16' | 'uint8';
+    };
+}
+export interface SaveConfig {
+    trainableOnly?: boolean;
+}
+export interface SaveResult {
+    modelArtifactsInfo: ModelArtifactsInfo;
+    responses?: Response[];
+    errors?: Array<{} | string>;
+}
+export declare interface ModelArtifactsInfo {
+    dateSaved: Date;
+    modelTopologyType: 'JSON' | 'GraphDef';
+    modelTopologyBytes?: number;
+    weightSpecsBytes?: number;
+    weightDataBytes?: number;
+}
+export declare interface ModelArtifacts {
+    modelTopology?: {} | ArrayBuffer;
+    weightSpecs?: WeightsManifestEntry[];
+    weightData?: ArrayBuffer;
+}
+export declare type LoadHandler = () => Promise<ModelArtifacts>;
+export declare type SaveHandler = (modelArtifact: ModelArtifacts) => Promise<SaveResult>;
+export interface IOHandler {
+    save?: SaveHandler;
+    load?: LoadHandler;
+}
+export interface ModelStoreManager {
+    listModels(): Promise<{
+        [path: string]: ModelArtifactsInfo;
+    }>;
+    removeModel(path: string): Promise<ModelArtifactsInfo>;
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/types.js b/node_modules/@tensorflow/tfjs-core/dist/io/types.js
new file mode 100644
index 0000000..cf52e5c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/types.js
@@ -0,0 +1,10 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+exports.DTYPE_VALUE_SIZE_MAP = {
+    'float32': 4,
+    'int32': 4,
+    'uint16': 2,
+    'uint8': 1,
+    'bool': 1,
+};
+//# sourceMappingURL=types.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/types.js.map b/node_modules/@tensorflow/tfjs-core/dist/io/types.js.map
new file mode 100644
index 0000000..cc5eec8
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/types.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"types.js","sourceRoot":"","sources":["../../src/io/types.ts"],"names":[],"mappings":";;AAsBa,QAAA,oBAAoB,GAA8B;IAC7D,SAAS,EAAE,CAAC;IACZ,OAAO,EAAE,CAAC;IACV,QAAQ,EAAE,CAAC;IACX,OAAO,EAAE,CAAC;IACV,MAAM,EAAE,CAAC;CACV,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.d.ts b/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.d.ts
new file mode 100644
index 0000000..1f2465f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.d.ts
@@ -0,0 +1,5 @@
+import { NamedTensorMap } from '../tensor_types';
+import { WeightsManifestConfig } from './types';
+export declare function loadWeightsAsArrayBuffer(fetchURLs: string[], requestOptions?: RequestInit, fetchFunc?: Function): Promise<ArrayBuffer[]>;
+export declare function loadWeights(manifest: WeightsManifestConfig, filePathPrefix?: string, weightNames?: string[], requestOptions?: RequestInit): Promise<NamedTensorMap>;
+export declare function weightsLoaderFactory(fetchWeightsFunction: (fetchUrls: string[]) => Promise<ArrayBuffer[]>): (manifest: WeightsManifestConfig, filePathPrefix?: string, weightNames?: string[]) => Promise<NamedTensorMap>;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.js b/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.js
new file mode 100644
index 0000000..4f903f9
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.js
@@ -0,0 +1,183 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+Object.defineProperty(exports, "__esModule", { value: true });
+var util = require("../util");
+var io_utils_1 = require("./io_utils");
+var types_1 = require("./types");
+function loadWeightsAsArrayBuffer(fetchURLs, requestOptions, fetchFunc) {
+    return __awaiter(this, void 0, void 0, function () {
+        var requests, responses, buffers;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    if (fetchFunc == null) {
+                        fetchFunc = fetch;
+                    }
+                    requests = fetchURLs.map(function (fetchURL) { return fetchFunc(fetchURL, requestOptions); });
+                    return [4, Promise.all(requests)];
+                case 1:
+                    responses = _a.sent();
+                    return [4, Promise.all(responses.map(function (response) { return response.arrayBuffer(); }))];
+                case 2:
+                    buffers = _a.sent();
+                    return [2, buffers];
+            }
+        });
+    });
+}
+exports.loadWeightsAsArrayBuffer = loadWeightsAsArrayBuffer;
+function loadWeights(manifest, filePathPrefix, weightNames, requestOptions) {
+    if (filePathPrefix === void 0) { filePathPrefix = ''; }
+    return __awaiter(this, void 0, void 0, function () {
+        var fetchWeights, loadWeights;
+        return __generator(this, function (_a) {
+            fetchWeights = function (fetchUrls) {
+                return loadWeightsAsArrayBuffer(fetchUrls, requestOptions);
+            };
+            loadWeights = weightsLoaderFactory(fetchWeights);
+            return [2, loadWeights(manifest, filePathPrefix, weightNames)];
+        });
+    });
+}
+exports.loadWeights = loadWeights;
+function weightsLoaderFactory(fetchWeightsFunction) {
+    var _this = this;
+    return function (manifest, filePathPrefix, weightNames) {
+        if (filePathPrefix === void 0) { filePathPrefix = ''; }
+        return __awaiter(_this, void 0, void 0, function () {
+            var groupIndicesToFetchMap, groupWeightsToFetch, weightsFound, allManifestWeightNames, weightsNotFound, groupIndicesToFetch, fetchUrls, buffers, weightsTensorMap, bufferIndexOffset;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        groupIndicesToFetchMap = manifest.map(function () { return false; });
+                        groupWeightsToFetch = {};
+                        weightsFound = weightNames != null
+                            ? weightNames.map(function () { return false; })
+                            : [];
+                        allManifestWeightNames = [];
+                        manifest.forEach(function (manifestGroupConfig, groupIndex) {
+                            var groupOffset = 0;
+                            manifestGroupConfig.weights.forEach(function (weightsEntry) {
+                                var rawDtype = ('quantization' in weightsEntry) ?
+                                    weightsEntry.quantization.dtype :
+                                    weightsEntry.dtype;
+                                var weightsBytes = types_1.DTYPE_VALUE_SIZE_MAP[rawDtype] *
+                                    util.sizeFromShape(weightsEntry.shape);
+                                var enqueueWeightsForFetchingFn = function () {
+                                    groupIndicesToFetchMap[groupIndex] = true;
+                                    if (groupWeightsToFetch[groupIndex] == null) {
+                                        groupWeightsToFetch[groupIndex] = [];
+                                    }
+                                    groupWeightsToFetch[groupIndex].push({
+                                        manifestEntry: weightsEntry,
+                                        groupOffset: groupOffset,
+                                        sizeBytes: weightsBytes
+                                    });
+                                };
+                                if (weightNames != null) {
+                                    weightNames.forEach(function (weightName, weightIndex) {
+                                        if (weightName === weightsEntry.name) {
+                                            enqueueWeightsForFetchingFn();
+                                            weightsFound[weightIndex] = true;
+                                        }
+                                    });
+                                }
+                                else {
+                                    enqueueWeightsForFetchingFn();
+                                }
+                                allManifestWeightNames.push(weightsEntry.name);
+                                groupOffset += weightsBytes;
+                            });
+                        });
+                        if (!weightsFound.every(function (found) { return found; })) {
+                            weightsNotFound = weightNames.filter(function (_, i) { return !weightsFound[i]; });
+                            throw new Error("Could not find weights in manifest with names: " +
+                                (weightsNotFound.join(', ') + ". \n") +
+                                "Manifest JSON has weights with names: " +
+                                (allManifestWeightNames.join(', ') + "."));
+                        }
+                        groupIndicesToFetch = groupIndicesToFetchMap.reduce(function (accumulator, shouldFetch, i) {
+                            if (shouldFetch) {
+                                accumulator.push(i);
+                            }
+                            return accumulator;
+                        }, []);
+                        fetchUrls = [];
+                        groupIndicesToFetch.forEach(function (i) {
+                            manifest[i].paths.forEach(function (filepath) {
+                                var fetchUrl = filePathPrefix +
+                                    (!filePathPrefix.endsWith('/') ? '/' : '') + filepath;
+                                fetchUrls.push(fetchUrl);
+                            });
+                        });
+                        return [4, fetchWeightsFunction(fetchUrls)];
+                    case 1:
+                        buffers = _a.sent();
+                        weightsTensorMap = {};
+                        bufferIndexOffset = 0;
+                        groupIndicesToFetch.forEach(function (i) {
+                            var numBuffers = manifest[i].paths.length;
+                            var groupBytes = 0;
+                            for (var i_1 = 0; i_1 < numBuffers; i_1++) {
+                                groupBytes += buffers[bufferIndexOffset + i_1].byteLength;
+                            }
+                            var groupBuffer = new ArrayBuffer(groupBytes);
+                            var groupByteBuffer = new Uint8Array(groupBuffer);
+                            var groupBufferOffset = 0;
+                            for (var i_2 = 0; i_2 < numBuffers; i_2++) {
+                                var buffer = new Uint8Array(buffers[bufferIndexOffset + i_2]);
+                                groupByteBuffer.set(buffer, groupBufferOffset);
+                                groupBufferOffset += buffer.byteLength;
+                            }
+                            var weightsEntries = groupWeightsToFetch[i];
+                            weightsEntries.forEach(function (weightsEntry) {
+                                var byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);
+                                var nameToTensorMap = io_utils_1.decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);
+                                for (var name_1 in nameToTensorMap) {
+                                    weightsTensorMap[name_1] = nameToTensorMap[name_1];
+                                }
+                            });
+                            bufferIndexOffset += numBuffers;
+                        });
+                        return [2, weightsTensorMap];
+                }
+            });
+        });
+    };
+}
+exports.weightsLoaderFactory = weightsLoaderFactory;
+//# sourceMappingURL=weights_loader.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.js.map b/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.js.map
new file mode 100644
index 0000000..c86cb2b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"weights_loader.js","sourceRoot":"","sources":["../../src/io/weights_loader.ts"],"names":[],"mappings":";;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;AAkBA,8BAAgC;AAEhC,uCAAyC;AACzC,iCAA0F;AAW1F,kCACI,SAAmB,EAAE,cAA4B,EAAE,SAAoB;;;;;;oBAEzE,IAAI,SAAS,IAAI,IAAI,EAAE;wBACrB,SAAS,GAAG,KAAK,CAAC;qBACnB;oBAGK,QAAQ,GAAG,SAAS,CAAC,GAAG,CAC1B,UAAA,QAAQ,IAAI,OAAA,SAAS,CAAC,QAAQ,EAAE,cAAc,CAAC,EAAnC,CAAmC,CAAC,CAAC;oBACnC,WAAM,OAAO,CAAC,GAAG,CAAC,QAAQ,CAAC,EAAA;;oBAAvC,SAAS,GAAG,SAA2B;oBAEzC,WAAM,OAAO,CAAC,GAAG,CAAC,SAAS,CAAC,GAAG,CAAC,UAAA,QAAQ,IAAI,OAAA,QAAQ,CAAC,WAAW,EAAE,EAAtB,CAAsB,CAAC,CAAC,EAAA;;oBADlE,OAAO,GACT,SAAoE;oBACxE,WAAO,OAAO,EAAC;;;;CAChB;AAdD,4DAcC;AAWD,qBACI,QAA+B,EAC/B,cAAmB,EACnB,WAAsB,EACtB,cAA4B;IAF5B,+BAAA,EAAA,mBAAmB;;;;YASf,YAAY,GAAG,UAAC,SAAmB;gBACvC,OAAA,wBAAwB,CAAC,SAAS,EAAE,cAAc,CAAC;YAAnD,CAAmD,CAAC;YAChD,WAAW,GAAG,oBAAoB,CAAC,YAAY,CAAC,CAAC;YAEvD,WAAO,WAAW,CAAC,QAAQ,EAAE,cAAc,EAAE,WAAW,CAAC,EAAC;;;CAC3D;AAhBD,kCAgBC;AA0BD,8BACE,oBAAqE;IADvE,iBAoIC;IA5HC,OAAO,UACL,QAA+B,EAC/B,cAAmB,EACnB,WAAsB;QADtB,+BAAA,EAAA,mBAAmB;;;;;;wBAMb,sBAAsB,GAAG,QAAQ,CAAC,GAAG,CAAC,cAAM,OAAA,KAAK,EAAL,CAAK,CAAC,CAAC;wBACnD,mBAAmB,GAKrB,EAAE,CAAC;wBACD,YAAY,GAAG,WAAW,IAAI,IAAI;4BACtC,CAAC,CAAC,WAAW,CAAC,GAAG,CAAC,cAAM,OAAA,KAAK,EAAL,CAAK,CAAC;4BAC9B,CAAC,CAAC,EAAE,CAAC;wBACD,sBAAsB,GAAa,EAAE,CAAC;wBAC5C,QAAQ,CAAC,OAAO,CAAC,UAAC,mBAAmB,EAAE,UAAU;4BAC/C,IAAI,WAAW,GAAG,CAAC,CAAC;4BACpB,mBAAmB,CAAC,OAAO,CAAC,OAAO,CAAC,UAAA,YAAY;gCAC9C,IAAM,QAAQ,GAAG,CAAC,cAAc,IAAI,YAAY,CAAC,CAAC,CAAC;oCAC/C,YAAY,CAAC,YAAY,CAAC,KAAK,CAAC,CAAC;oCACjC,YAAY,CAAC,KAAK,CAAC;gCAEvB,IAAM,YAAY,GAAG,4BAAoB,CAAC,QAAQ,CAAC;oCAC/C,IAAI,CAAC,aAAa,CAAC,YAAY,CAAC,KAAK,CAAC,CAAC;gCAE3C,IAAM,2BAA2B,GAAG;oCAClC,sBAAsB,CAAC,UAAU,CAAC,GAAG,IAAI,CAAC;oCAC1C,IAAI,mBAAmB,CAAC,UAAU,CAAC,IAAI,IAAI,EAAE;wCAC3C,mBAAmB,CAAC,UAAU,CAAC,GAAG,EAAE,CAAC;qCACtC;oCAED,mBAAmB,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC;wCACnC,aAAa,EAAE,YAAY;wCAC3B,WAAW,aAAA;wCACX,SAAS,EAAE,YAAY;qCACxB,CAAC,CAAC;gCACL,CAAC,CAAC;gCAEF,IAAI,WAAW,IAAI,IAAI,EAAE;oCACvB,WAAW,CAAC,OAAO,CAAC,UAAC,UAAU,EAAE,WAAW;wCAC1C,IAAI,UAAU,KAAK,YAAY,CAAC,IAAI,EAAE;4CACpC,2BAA2B,EAAE,CAAC;4CAC9B,YAAY,CAAC,WAAW,CAAC,GAAG,IAAI,CAAC;yCAClC;oCACH,CAAC,CAAC,CAAC;iCACJ;qCAAM;oCACL,2BAA2B,EAAE,CAAC;iCAC/B;gCAED,sBAAsB,CAAC,IAAI,CAAC,YAAY,CAAC,IAAI,CAAC,CAAC;gCAC/C,WAAW,IAAI,YAAY,CAAC;4BAC9B,CAAC,CAAC,CAAC;wBACL,CAAC,CAAC,CAAC;wBAEH,IAAI,CAAC,YAAY,CAAC,KAAK,CAAC,UAAA,KAAK,IAAI,OAAA,KAAK,EAAL,CAAK,CAAC,EAAE;4BACjC,eAAe,GAAG,WAAW,CAAC,MAAM,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,CAAC,YAAY,CAAC,CAAC,CAAC,EAAhB,CAAgB,CAAC,CAAC;4BACvE,MAAM,IAAI,KAAK,CACX,iDAAiD;iCAC9C,eAAe,CAAC,IAAI,CAAC,IAAI,CAAC,SAAM,CAAA;gCACnC,wCAAwC;iCACrC,sBAAsB,CAAC,IAAI,CAAC,IAAI,CAAC,MAAG,CAAA,CAAC,CAAC;yBAC9C;wBAIK,mBAAmB,GACrB,sBAAsB,CAAC,MAAM,CAAC,UAAC,WAAW,EAAE,WAAW,EAAE,CAAC;4BACxD,IAAI,WAAW,EAAE;gCACf,WAAW,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;6BACrB;4BACD,OAAO,WAAW,CAAC;wBACrB,CAAC,EAAE,EAAE,CAAC,CAAC;wBAEL,SAAS,GAAa,EAAE,CAAC;wBAC/B,mBAAmB,CAAC,OAAO,CAAC,UAAA,CAAC;4BAC3B,QAAQ,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,OAAO,CAAC,UAAA,QAAQ;gCAChC,IAAM,QAAQ,GAAG,cAAc;oCAC3B,CAAC,CAAC,cAAc,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,GAAG,QAAQ,CAAC;gCAC1D,SAAS,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC;4BAC3B,CAAC,CAAC,CAAC;wBACL,CAAC,CAAC,CAAC;wBACa,WAAM,oBAAoB,CAAC,SAAS,CAAC,EAAA;;wBAA/C,OAAO,GAAG,SAAqC;wBAE/C,gBAAgB,GAAmB,EAAE,CAAC;wBACxC,iBAAiB,GAAG,CAAC,CAAC;wBAC1B,mBAAmB,CAAC,OAAO,CAAC,UAAA,CAAC;4BAC3B,IAAM,UAAU,GAAG,QAAQ,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,MAAM,CAAC;4BAE5C,IAAI,UAAU,GAAG,CAAC,CAAC;4BACnB,KAAK,IAAI,GAAC,GAAG,CAAC,EAAE,GAAC,GAAG,UAAU,EAAE,GAAC,EAAE,EAAE;gCACnC,UAAU,IAAI,OAAO,CAAC,iBAAiB,GAAG,GAAC,CAAC,CAAC,UAAU,CAAC;6BACzD;4BAGD,IAAM,WAAW,GAAG,IAAI,WAAW,CAAC,UAAU,CAAC,CAAC;4BAChD,IAAM,eAAe,GAAG,IAAI,UAAU,CAAC,WAAW,CAAC,CAAC;4BACpD,IAAI,iBAAiB,GAAG,CAAC,CAAC;4BAC1B,KAAK,IAAI,GAAC,GAAG,CAAC,EAAE,GAAC,GAAG,UAAU,EAAE,GAAC,EAAE,EAAE;gCACnC,IAAM,MAAM,GAAG,IAAI,UAAU,CAAC,OAAO,CAAC,iBAAiB,GAAG,GAAC,CAAC,CAAC,CAAC;gCAC9D,eAAe,CAAC,GAAG,CAAC,MAAM,EAAE,iBAAiB,CAAC,CAAC;gCAC/C,iBAAiB,IAAI,MAAM,CAAC,UAAU,CAAC;6BACxC;4BAED,IAAM,cAAc,GAAG,mBAAmB,CAAC,CAAC,CAAC,CAAC;4BAC9C,cAAc,CAAC,OAAO,CAAC,UAAA,YAAY;gCACjC,IAAM,UAAU,GAAG,WAAW,CAAC,KAAK,CAChC,YAAY,CAAC,WAAW,EACxB,YAAY,CAAC,WAAW,GAAG,YAAY,CAAC,SAAS,CAAC,CAAC;gCACvD,IAAM,eAAe,GACjB,wBAAa,CAAC,UAAU,EAAE,CAAC,YAAY,CAAC,aAAa,CAAC,CAAC,CAAC;gCAC5D,KAAK,IAAM,MAAI,IAAI,eAAe,EAAE;oCAClC,gBAAgB,CAAC,MAAI,CAAC,GAAG,eAAe,CAAC,MAAI,CAAC,CAAC;iCAChD;4BACH,CAAC,CAAC,CAAC;4BAEH,iBAAiB,IAAI,UAAU,CAAC;wBAClC,CAAC,CAAC,CAAC;wBAEH,WAAO,gBAAgB,EAAC;;;;KACzB,CAAC;AACJ,CAAC;AApID,oDAoIC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader_test.js b/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader_test.js
new file mode 100644
index 0000000..e7ce1de
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader_test.js
@@ -0,0 +1,531 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+var _this = this;
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('loadWeights', test_util_1.BROWSER_ENVS, function () {
+    var setupFakeWeightFiles = function (fileBufferMap) {
+        spyOn(window, 'fetch').and.callFake(function (path) {
+            return new Response(fileBufferMap[path]);
+        });
+    };
+    it('1 group, 1 weight, 1 requested weight', function (done) {
+        setupFakeWeightFiles({ './weightfile0': new Float32Array([1, 2, 3]) });
+        var manifest = [{
+                'paths': ['weightfile0'],
+                'weights': [{ 'name': 'weight0', 'dtype': 'float32', 'shape': [3] }]
+            }];
+        var weightsNamesToFetch = ['weight0'];
+        tf.io.loadWeights(manifest, './', weightsNamesToFetch)
+            .then(function (weights) {
+            expect(window.fetch.calls.count()).toBe(1);
+            var weightNames = Object.keys(weights);
+            expect(weightNames.length).toEqual(weightsNamesToFetch.length);
+            var weight0 = weights['weight0'];
+            test_util_1.expectArraysClose(weight0, [1, 2, 3]);
+            expect(weight0.shape).toEqual([3]);
+            expect(weight0.dtype).toEqual('float32');
+        })
+            .then(done)
+            .catch(done.fail);
+    });
+    it('1 group, 2 weights, fetch 1st weight', function (done) {
+        setupFakeWeightFiles({ './weightfile0': new Float32Array([1, 2, 3, 4, 5]) });
+        var manifest = [{
+                'paths': ['weightfile0'],
+                'weights': [
+                    { 'name': 'weight0', 'dtype': 'float32', 'shape': [2] },
+                    { 'name': 'weight1', 'dtype': 'float32', 'shape': [3] }
+                ]
+            }];
+        tf.io.loadWeights(manifest, './', ['weight0'])
+            .then(function (weights) {
+            expect(window.fetch.calls.count()).toBe(1);
+            var weightNames = Object.keys(weights);
+            expect(weightNames.length).toEqual(1);
+            var weight0 = weights['weight0'];
+            test_util_1.expectArraysClose(weight0, [1, 2]);
+            expect(weight0.shape).toEqual([2]);
+            expect(weight0.dtype).toEqual('float32');
+        })
+            .then(done)
+            .catch(done.fail);
+    });
+    it('1 group, 2 weights, fetch 2nd weight', function (done) {
+        setupFakeWeightFiles({ './weightfile0': new Float32Array([1, 2, 3, 4, 5]) });
+        var manifest = [{
+                'paths': ['weightfile0'],
+                'weights': [
+                    { 'name': 'weight0', 'dtype': 'float32', 'shape': [2] },
+                    { 'name': 'weight1', 'dtype': 'float32', 'shape': [3] }
+                ]
+            }];
+        tf.io.loadWeights(manifest, './', ['weight1'])
+            .then(function (weights) {
+            expect(window.fetch.calls.count()).toBe(1);
+            var weightNames = Object.keys(weights);
+            expect(weightNames.length).toEqual(1);
+            var weight1 = weights['weight1'];
+            test_util_1.expectArraysClose(weight1, [3, 4, 5]);
+            expect(weight1.shape).toEqual([3]);
+            expect(weight1.dtype).toEqual('float32');
+        })
+            .then(done)
+            .catch(done.fail);
+    });
+    it('1 group, 2 weights, fetch all weights', function (done) {
+        setupFakeWeightFiles({ './weightfile0': new Float32Array([1, 2, 3, 4, 5]) });
+        var manifest = [{
+                'paths': ['weightfile0'],
+                'weights': [
+                    { 'name': 'weight0', 'dtype': 'float32', 'shape': [2] },
+                    { 'name': 'weight1', 'dtype': 'float32', 'shape': [3] }
+                ]
+            }];
+        tf.io.loadWeights(manifest, './', ['weight0', 'weight1'])
+            .then(function (weights) {
+            expect(window.fetch.calls.count()).toBe(1);
+            var weightNames = Object.keys(weights);
+            expect(weightNames.length).toEqual(2);
+            var weight0 = weights['weight0'];
+            test_util_1.expectArraysClose(weight0, [1, 2]);
+            expect(weight0.shape).toEqual([2]);
+            expect(weight0.dtype).toEqual('float32');
+            var weight1 = weights['weight1'];
+            test_util_1.expectArraysClose(weight1, [3, 4, 5]);
+            expect(weight1.shape).toEqual([3]);
+            expect(weight1.dtype).toEqual('float32');
+        })
+            .then(done)
+            .catch(done.fail);
+    });
+    it('1 group, multiple weights, different dtypes', function (done) {
+        var buffer = new ArrayBuffer(5 * 4 + 1);
+        var view = new DataView(buffer);
+        view.setInt32(0, 1, true);
+        view.setInt32(4, 2, true);
+        view.setUint8(8, 1);
+        view.setFloat32(9, 3., true);
+        view.setFloat32(13, 4., true);
+        view.setFloat32(17, 5., true);
+        setupFakeWeightFiles({ './weightfile0': buffer });
+        var manifest = [{
+                'paths': ['weightfile0'],
+                'weights': [
+                    { 'name': 'weight0', 'dtype': 'int32', 'shape': [2] },
+                    { 'name': 'weight1', 'dtype': 'bool', 'shape': [] },
+                    { 'name': 'weight2', 'dtype': 'float32', 'shape': [3] },
+                ]
+            }];
+        tf.io.loadWeights(manifest, './', ['weight0', 'weight1', 'weight2'])
+            .then(function (weights) {
+            expect(window.fetch.calls.count()).toBe(1);
+            var weightNames = Object.keys(weights);
+            expect(weightNames.length).toEqual(3);
+            var weight0 = weights['weight0'];
+            test_util_1.expectArraysClose(weight0, [1, 2]);
+            expect(weight0.shape).toEqual([2]);
+            expect(weight0.dtype).toEqual('int32');
+            var weight1 = weights['weight1'];
+            test_util_1.expectArraysClose(weight1, [1]);
+            expect(weight1.shape).toEqual([]);
+            expect(weight1.dtype).toEqual('bool');
+            var weight2 = weights['weight2'];
+            test_util_1.expectArraysClose(weight2, [3, 4, 5]);
+            expect(weight2.shape).toEqual([3]);
+            expect(weight2.dtype).toEqual('float32');
+        })
+            .then(done)
+            .catch(done.fail);
+    });
+    it('1 group, sharded 1 weight across multiple files', function (done) {
+        var shard0 = new Float32Array([1, 2, 3, 4, 5]);
+        var shard1 = new Float32Array([1.1, 2.2]);
+        var shard2 = new Float32Array([10, 20, 30]);
+        setupFakeWeightFiles({
+            './weightfile0': shard0,
+            './weightsfile1': shard1,
+            './weightsfile2': shard2
+        });
+        var manifest = [{
+                'paths': ['weightfile0', 'weightsfile1', 'weightsfile2'],
+                'weights': [{ 'name': 'weight0', 'dtype': 'float32', 'shape': [5, 2] }]
+            }];
+        tf.io.loadWeights(manifest, './', ['weight0'])
+            .then(function (weights) {
+            expect(window.fetch.calls.count()).toBe(3);
+            var weightNames = Object.keys(weights);
+            expect(weightNames.length).toEqual(1);
+            var weight0 = weights['weight0'];
+            test_util_1.expectArraysClose(weight0, [1, 2, 3, 4, 5, 1.1, 2.2, 10, 20, 30]);
+            expect(weight0.shape).toEqual([5, 2]);
+            expect(weight0.dtype).toEqual('float32');
+        })
+            .then(done)
+            .catch(done.fail);
+    });
+    it('1 group, sharded 2 weights across multiple files', function (done) {
+        var shard0 = new Int32Array([1, 2, 3, 4, 5]);
+        var shard1 = new ArrayBuffer(5 * 4);
+        var intBuffer = new Int32Array(shard1, 0, 2);
+        intBuffer.set([10, 20]);
+        var floatBuffer = new Float32Array(shard1, intBuffer.byteLength, 3);
+        floatBuffer.set([3.0, 4.0, 5.0]);
+        var shard2 = new Float32Array([10, 20, 30]);
+        setupFakeWeightFiles({
+            './weightfile0': shard0,
+            './weightsfile1': shard1,
+            './weightsfile2': shard2
+        });
+        var manifest = [{
+                'paths': ['weightfile0', 'weightsfile1', 'weightsfile2'],
+                'weights': [
+                    { 'name': 'weight0', 'dtype': 'int32', 'shape': [7, 1] },
+                    { 'name': 'weight1', 'dtype': 'float32', 'shape': [3, 2] }
+                ]
+            }];
+        tf.io.loadWeights(manifest, './', ['weight0', 'weight1'])
+            .then(function (weights) {
+            expect(window.fetch.calls.count()).toBe(3);
+            var weightNames = Object.keys(weights);
+            expect(weightNames.length).toEqual(2);
+            var weight0 = weights['weight0'];
+            test_util_1.expectArraysClose(weight0, [1, 2, 3, 4, 5, 10, 20]);
+            expect(weight0.shape).toEqual([7, 1]);
+            expect(weight0.dtype).toEqual('int32');
+            var weight1 = weights['weight1'];
+            test_util_1.expectArraysClose(weight1, [3.0, 4.0, 5.0, 10, 20, 30]);
+            expect(weight1.shape).toEqual([3, 2]);
+            expect(weight1.dtype).toEqual('float32');
+        })
+            .then(done)
+            .catch(done.fail);
+    });
+    it('2 group, 4 weights, fetches one group', function (done) {
+        setupFakeWeightFiles({
+            './weightfile0': new Float32Array([1, 2, 3, 4, 5]),
+            './weightfile1': new Float32Array([6, 7, 8, 9])
+        });
+        var manifest = [
+            {
+                'paths': ['weightfile0'],
+                'weights': [
+                    { 'name': 'weight0', 'dtype': 'float32', 'shape': [2] },
+                    { 'name': 'weight1', 'dtype': 'float32', 'shape': [3] }
+                ]
+            },
+            {
+                'paths': ['weightfile1'],
+                'weights': [
+                    { 'name': 'weight2', 'dtype': 'float32', 'shape': [3, 1] },
+                    { 'name': 'weight3', 'dtype': 'float32', 'shape': [] }
+                ]
+            }
+        ];
+        tf.io.loadWeights(manifest, './', ['weight0', 'weight1'])
+            .then(function (weights) {
+            expect(window.fetch.calls.count()).toBe(1);
+            var weightNames = Object.keys(weights);
+            expect(weightNames.length).toEqual(2);
+            var weight0 = weights['weight0'];
+            test_util_1.expectArraysClose(weight0, [1, 2]);
+            expect(weight0.shape).toEqual([2]);
+            expect(weight0.dtype).toEqual('float32');
+            var weight1 = weights['weight1'];
+            test_util_1.expectArraysClose(weight1, [3, 4, 5]);
+            expect(weight1.shape).toEqual([3]);
+            expect(weight1.dtype).toEqual('float32');
+        })
+            .then(done)
+            .catch(done.fail);
+    });
+    it('2 group, 4 weights, one weight from each group', function (done) {
+        setupFakeWeightFiles({
+            './weightfile0': new Float32Array([1, 2, 3, 4, 5]),
+            './weightfile1': new Float32Array([6, 7, 8, 9])
+        });
+        var manifest = [
+            {
+                'paths': ['weightfile0'],
+                'weights': [
+                    { 'name': 'weight0', 'dtype': 'float32', 'shape': [2] },
+                    { 'name': 'weight1', 'dtype': 'float32', 'shape': [3] }
+                ]
+            },
+            {
+                'paths': ['weightfile1'],
+                'weights': [
+                    { 'name': 'weight2', 'dtype': 'float32', 'shape': [3, 1] },
+                    { 'name': 'weight3', 'dtype': 'float32', 'shape': [] }
+                ]
+            }
+        ];
+        tf.io.loadWeights(manifest, './', ['weight0', 'weight2'])
+            .then(function (weights) {
+            expect(window.fetch.calls.count()).toBe(2);
+            var weightNames = Object.keys(weights);
+            expect(weightNames.length).toEqual(2);
+            var weight0 = weights['weight0'];
+            test_util_1.expectArraysClose(weight0, [1, 2]);
+            expect(weight0.shape).toEqual([2]);
+            expect(weight0.dtype).toEqual('float32');
+            var weight2 = weights['weight2'];
+            test_util_1.expectArraysClose(weight2, [6, 7, 8]);
+            expect(weight2.shape).toEqual([3, 1]);
+            expect(weight2.dtype).toEqual('float32');
+        })
+            .then(done)
+            .catch(done.fail);
+    });
+    it('2 group, 4 weights, dont specify weights fetchs all', function (done) {
+        setupFakeWeightFiles({
+            './weightfile0': new Float32Array([1, 2, 3, 4, 5]),
+            './weightfile1': new Float32Array([6, 7, 8, 9])
+        });
+        var manifest = [
+            {
+                'paths': ['weightfile0'],
+                'weights': [
+                    { 'name': 'weight0', 'dtype': 'float32', 'shape': [2] },
+                    { 'name': 'weight1', 'dtype': 'float32', 'shape': [3] }
+                ]
+            },
+            {
+                'paths': ['weightfile1'],
+                'weights': [
+                    { 'name': 'weight2', 'dtype': 'float32', 'shape': [3, 1] },
+                    { 'name': 'weight3', 'dtype': 'float32', 'shape': [] }
+                ]
+            }
+        ];
+        tf.io.loadWeights(manifest, './')
+            .then(function (weights) {
+            expect(window.fetch.calls.count()).toBe(2);
+            var weightNames = Object.keys(weights);
+            expect(weightNames.length).toEqual(4);
+            var weight0 = weights['weight0'];
+            test_util_1.expectArraysClose(weight0, [1, 2]);
+            expect(weight0.shape).toEqual([2]);
+            expect(weight0.dtype).toEqual('float32');
+            var weight1 = weights['weight1'];
+            test_util_1.expectArraysClose(weight1, [3, 4, 5]);
+            expect(weight1.shape).toEqual([3]);
+            expect(weight1.dtype).toEqual('float32');
+            var weight2 = weights['weight2'];
+            test_util_1.expectArraysClose(weight2, [6, 7, 8]);
+            expect(weight2.shape).toEqual([3, 1]);
+            expect(weight2.dtype).toEqual('float32');
+            var weight3 = weights['weight3'];
+            test_util_1.expectArraysClose(weight3, [9]);
+            expect(weight3.shape).toEqual([]);
+            expect(weight3.dtype).toEqual('float32');
+        })
+            .then(done)
+            .catch(done.fail);
+    });
+    it('throws if requested weight not found', function (done) { return __awaiter(_this, void 0, void 0, function () {
+        var manifest, weightsNamesToFetch, e_1;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    setupFakeWeightFiles({ './weightfile0': new Float32Array([1, 2, 3]) });
+                    manifest = [{
+                            'paths': ['weightfile0'],
+                            'weights': [{ 'name': 'weight0', 'dtype': 'float32', 'shape': [3] }]
+                        }];
+                    weightsNamesToFetch = ['doesntexist'];
+                    _a.label = 1;
+                case 1:
+                    _a.trys.push([1, 3, , 4]);
+                    return [4, tf.io.loadWeights(manifest, './', weightsNamesToFetch)];
+                case 2:
+                    _a.sent();
+                    done.fail();
+                    return [3, 4];
+                case 3:
+                    e_1 = _a.sent();
+                    done();
+                    return [3, 4];
+                case 4: return [2];
+            }
+        });
+    }); });
+    it('throws if requested weight has unknown dtype', function (done) { return __awaiter(_this, void 0, void 0, function () {
+        var manifest, weightsNamesToFetch, e_2;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    setupFakeWeightFiles({ './weightfile0': new Float32Array([1, 2, 3]) });
+                    manifest = [{
+                            'paths': ['weightfile0'],
+                            'weights': [{
+                                    'name': 'weight0',
+                                    'dtype': 'null',
+                                    'shape': [3]
+                                }]
+                        }];
+                    weightsNamesToFetch = ['weight0'];
+                    _a.label = 1;
+                case 1:
+                    _a.trys.push([1, 3, , 4]);
+                    return [4, tf.io.loadWeights(manifest, './', weightsNamesToFetch)];
+                case 2:
+                    _a.sent();
+                    done.fail();
+                    return [3, 4];
+                case 3:
+                    e_2 = _a.sent();
+                    done();
+                    return [3, 4];
+                case 4: return [2];
+            }
+        });
+    }); });
+    it('should use request option', function (done) {
+        setupFakeWeightFiles({ './weightfile0': new Float32Array([1, 2, 3]) });
+        var manifest = [{
+                'paths': ['weightfile0'],
+                'weights': [{ 'name': 'weight0', 'dtype': 'float32', 'shape': [3] }]
+            }];
+        var weightsNamesToFetch = ['weight0'];
+        tf.io
+            .loadWeights(manifest, './', weightsNamesToFetch, { credentials: 'include' })
+            .then(function (weights) {
+            expect(window.fetch.calls.count()).toBe(1);
+            expect(window.fetch).toHaveBeenCalledWith('./weightfile0', {
+                credentials: 'include'
+            });
+        })
+            .then(done)
+            .catch(done.fail);
+    });
+    var quantizationTest = function (quantizationDtype, done) {
+        var arrayType = quantizationDtype === 'uint8' ? Uint8Array : Uint16Array;
+        setupFakeWeightFiles({ './weightfile0': new arrayType([0, 48, 255, 0, 48, 255]) });
+        var manifest = [{
+                'paths': ['weightfile0'],
+                'weights': [
+                    {
+                        'name': 'weight0',
+                        'dtype': 'float32',
+                        'shape': [3],
+                        'quantization': { 'min': -1, 'scale': 0.1, 'dtype': quantizationDtype }
+                    },
+                    {
+                        'name': 'weight1',
+                        'dtype': 'int32',
+                        'shape': [3],
+                        'quantization': { 'min': -1, 'scale': 0.1, 'dtype': quantizationDtype }
+                    }
+                ]
+            }];
+        var weightsNamesToFetch = ['weight0', 'weight1'];
+        tf.io.loadWeights(manifest, './', weightsNamesToFetch)
+            .then(function (weights) {
+            expect(window.fetch.calls.count()).toBe(1);
+            var weightNames = Object.keys(weights);
+            expect(weightNames.length).toEqual(weightsNamesToFetch.length);
+            var weight0 = weights['weight0'];
+            test_util_1.expectArraysClose(weight0, [-1, 3.8, 24.5]);
+            expect(weight0.shape).toEqual([3]);
+            expect(weight0.dtype).toEqual('float32');
+            var weight1 = weights['weight1'];
+            test_util_1.expectArraysEqual(weight1, [-1, 4, 25]);
+            expect(weight1.shape).toEqual([3]);
+            expect(weight1.dtype).toEqual('int32');
+        })
+            .then(done)
+            .catch(done.fail);
+    };
+    it('quantized weights (uint8)', function (done) {
+        quantizationTest('uint8', done);
+    });
+    it('quantized weights (uint16)', function (done) {
+        quantizationTest('uint16', done);
+    });
+    it('2 groups, 1 quantized, 1 unquantized', function (done) {
+        setupFakeWeightFiles({
+            './weightfile0': new Uint8Array([0, 48, 255, 0, 48, 255]),
+            './weightfile1': new Float32Array([6, 7, 8, 9])
+        });
+        var manifest = [
+            {
+                'paths': ['weightfile0'],
+                'weights': [
+                    {
+                        'name': 'weight0',
+                        'dtype': 'float32',
+                        'shape': [3],
+                        'quantization': { 'min': -1, 'scale': 0.1, 'dtype': 'uint8' }
+                    },
+                    {
+                        'name': 'weight1',
+                        'dtype': 'int32',
+                        'shape': [3],
+                        'quantization': { 'min': -1, 'scale': 0.1, 'dtype': 'uint8' }
+                    }
+                ]
+            },
+            {
+                'paths': ['weightfile1'],
+                'weights': [
+                    { 'name': 'weight2', 'dtype': 'float32', 'shape': [3, 1] },
+                    { 'name': 'weight3', 'dtype': 'float32', 'shape': [] }
+                ]
+            }
+        ];
+        tf.io.loadWeights(manifest, './', ['weight0', 'weight2'])
+            .then(function (weights) {
+            expect(window.fetch.calls.count()).toBe(2);
+            var weightNames = Object.keys(weights);
+            expect(weightNames.length).toEqual(2);
+            var weight0 = weights['weight0'];
+            test_util_1.expectArraysClose(weight0, [-1, 3.8, 24.5]);
+            expect(weight0.shape).toEqual([3]);
+            expect(weight0.dtype).toEqual('float32');
+            var weight2 = weights['weight2'];
+            test_util_1.expectArraysClose(weight2, [6, 7, 8]);
+            expect(weight2.shape).toEqual([3, 1]);
+            expect(weight2.dtype).toEqual('float32');
+        })
+            .then(done)
+            .catch(done.fail);
+    });
+});
+//# sourceMappingURL=weights_loader_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader_test.js.map
new file mode 100644
index 0000000..4487850
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/io/weights_loader_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/jasmine_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/jasmine_util.d.ts
new file mode 100644
index 0000000..fac5e0e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/jasmine_util.d.ts
@@ -0,0 +1,14 @@
+import { Features } from './environment_util';
+import { KernelBackend } from './kernels/backend';
+import { MathBackendCPU } from './kernels/backend_cpu';
+export declare function envSatisfiesConstraints(constraints: Features): boolean;
+export declare function parseKarmaFlags(args: string[]): TestEnv;
+export declare function describeWithFlags(name: string, constraints: Features, tests: (env: TestEnv) => void): void;
+export interface TestEnv {
+    name: string;
+    factory: () => KernelBackend;
+    features: Features;
+}
+export declare let TEST_ENVS: TestEnv[];
+export declare const CPU_FACTORY: () => MathBackendCPU;
+export declare function setTestEnvs(testEnvs: TestEnv[]): void;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/jasmine_util.js b/node_modules/@tensorflow/tfjs-core/dist/jasmine_util.js
new file mode 100644
index 0000000..44a84a7
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/jasmine_util.js
@@ -0,0 +1,122 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("./environment");
+var backend_cpu_1 = require("./kernels/backend_cpu");
+var backend_webgl_1 = require("./kernels/backend_webgl");
+Error.stackTraceLimit = Infinity;
+function envSatisfiesConstraints(constraints) {
+    for (var key in constraints) {
+        var value = constraints[key];
+        if (environment_1.ENV.get(key) !== value) {
+            return false;
+        }
+    }
+    return true;
+}
+exports.envSatisfiesConstraints = envSatisfiesConstraints;
+function parseKarmaFlags(args) {
+    var features;
+    var backend;
+    var name = '';
+    args.forEach(function (arg, i) {
+        if (arg === '--features') {
+            features = JSON.parse(args[i + 1]);
+        }
+        else if (arg === '--backend') {
+            var type = args[i + 1];
+            name = type;
+            if (type.toLowerCase() === 'cpu') {
+                backend = function () { return new backend_cpu_1.MathBackendCPU(); };
+                features = features || {};
+                features['HAS_WEBGL'] = false;
+            }
+            else if (type.toLowerCase() === 'webgl') {
+                backend = function () { return new backend_webgl_1.MathBackendWebGL(); };
+            }
+            else {
+                throw new Error("Unknown value " + type + " for flag --backend. " +
+                    "Allowed values are 'cpu' or 'webgl'.");
+            }
+        }
+    });
+    if (features == null && backend == null) {
+        return null;
+    }
+    if (features != null && backend == null) {
+        throw new Error('--backend flag is required when --features is present. ' +
+            'Available values are "webgl" or "cpu".');
+    }
+    return { features: features || {}, factory: backend, name: name };
+}
+exports.parseKarmaFlags = parseKarmaFlags;
+function describeWithFlags(name, constraints, tests) {
+    exports.TEST_ENVS.forEach(function (testEnv) {
+        environment_1.ENV.setFeatures(testEnv.features);
+        if (envSatisfiesConstraints(constraints)) {
+            var testName = name + ' ' + testEnv.name + ' ' + JSON.stringify(testEnv.features);
+            executeTests(testName, tests, testEnv);
+        }
+    });
+}
+exports.describeWithFlags = describeWithFlags;
+exports.TEST_ENVS = [
+    {
+        name: 'webgl1',
+        factory: function () { return new backend_webgl_1.MathBackendWebGL(); },
+        features: {
+            'WEBGL_VERSION': 1,
+            'WEBGL_CPU_FORWARD': false,
+            'WEBGL_SIZE_UPLOAD_UNIFORM': 0,
+        }
+    },
+    {
+        name: 'webgl2',
+        factory: function () { return new backend_webgl_1.MathBackendWebGL(); },
+        features: {
+            'WEBGL_VERSION': 2,
+            'WEBGL_CPU_FORWARD': false,
+            'WEBGL_SIZE_UPLOAD_UNIFORM': 0,
+        }
+    },
+    {
+        name: 'cpu',
+        factory: function () { return new backend_cpu_1.MathBackendCPU(); },
+        features: { 'HAS_WEBGL': false }
+    }
+];
+exports.CPU_FACTORY = function () { return new backend_cpu_1.MathBackendCPU(); };
+if (typeof __karma__ !== 'undefined') {
+    var testEnv = parseKarmaFlags(__karma__.config.args);
+    if (testEnv) {
+        setTestEnvs([testEnv]);
+    }
+}
+function setTestEnvs(testEnvs) {
+    exports.TEST_ENVS = testEnvs;
+}
+exports.setTestEnvs = setTestEnvs;
+function executeTests(testName, tests, testEnv) {
+    describe(testName, function () {
+        var backendName = 'test-' + testEnv.name;
+        beforeAll(function () {
+            environment_1.ENV.reset();
+            environment_1.ENV.setFeatures(testEnv.features);
+            environment_1.ENV.set('IS_TEST', true);
+            environment_1.ENV.registerBackend(backendName, testEnv.factory, 1000);
+            environment_1.Environment.setBackend(backendName);
+        });
+        beforeEach(function () {
+            environment_1.ENV.engine.startScope();
+        });
+        afterEach(function () {
+            environment_1.ENV.engine.endScope();
+            environment_1.Environment.disposeVariables();
+        });
+        afterAll(function () {
+            environment_1.ENV.removeBackend(backendName);
+            environment_1.ENV.reset();
+        });
+        tests(testEnv);
+    });
+}
+//# sourceMappingURL=jasmine_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/jasmine_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/jasmine_util.js.map
new file mode 100644
index 0000000..c6ec25f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/jasmine_util.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"jasmine_util.js","sourceRoot":"","sources":["../src/jasmine_util.ts"],"names":[],"mappings":";;AAiBA,6CAA+C;AAG/C,qDAAqD;AACrD,yDAAyD;AAEzD,KAAK,CAAC,eAAe,GAAG,QAAQ,CAAC;AAGjC,iCAAwC,WAAqB;IAC3D,KAAK,IAAM,GAAG,IAAI,WAAW,EAAE;QAC7B,IAAM,KAAK,GAAG,WAAW,CAAC,GAAqB,CAAC,CAAC;QACjD,IAAI,iBAAG,CAAC,GAAG,CAAC,GAAqB,CAAC,KAAK,KAAK,EAAE;YAC5C,OAAO,KAAK,CAAC;SACd;KACF;IACD,OAAO,IAAI,CAAC;AACd,CAAC;AARD,0DAQC;AAKD,yBAAgC,IAAc;IAC5C,IAAI,QAAkB,CAAC;IACvB,IAAI,OAA4B,CAAC;IACjC,IAAI,IAAI,GAAG,EAAE,CAAC;IAEd,IAAI,CAAC,OAAO,CAAC,UAAC,GAAG,EAAE,CAAC;QAClB,IAAI,GAAG,KAAK,YAAY,EAAE;YACxB,QAAQ,GAAG,IAAI,CAAC,KAAK,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC;SACpC;aAAM,IAAI,GAAG,KAAK,WAAW,EAAE;YAC9B,IAAM,IAAI,GAAG,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC;YACzB,IAAI,GAAG,IAAI,CAAC;YACZ,IAAI,IAAI,CAAC,WAAW,EAAE,KAAK,KAAK,EAAE;gBAChC,OAAO,GAAG,cAAM,OAAA,IAAI,4BAAc,EAAE,EAApB,CAAoB,CAAC;gBACrC,QAAQ,GAAG,QAAQ,IAAI,EAAE,CAAC;gBAC1B,QAAQ,CAAC,WAAW,CAAC,GAAG,KAAK,CAAC;aAC/B;iBAAM,IAAI,IAAI,CAAC,WAAW,EAAE,KAAK,OAAO,EAAE;gBACzC,OAAO,GAAG,cAAM,OAAA,IAAI,gCAAgB,EAAE,EAAtB,CAAsB,CAAC;aACxC;iBAAM;gBACL,MAAM,IAAI,KAAK,CACX,mBAAiB,IAAI,0BAAuB;oBAC5C,sCAAsC,CAAC,CAAC;aAC7C;SACF;IACH,CAAC,CAAC,CAAC;IAEH,IAAI,QAAQ,IAAI,IAAI,IAAI,OAAO,IAAI,IAAI,EAAE;QACvC,OAAO,IAAI,CAAC;KACb;IACD,IAAI,QAAQ,IAAI,IAAI,IAAI,OAAO,IAAI,IAAI,EAAE;QACvC,MAAM,IAAI,KAAK,CACX,yDAAyD;YACzD,wCAAwC,CAAC,CAAC;KAC/C;IACD,OAAO,EAAC,QAAQ,EAAE,QAAQ,IAAI,EAAE,EAAE,OAAO,EAAE,OAAO,EAAE,IAAI,MAAA,EAAC,CAAC;AAC5D,CAAC;AAlCD,0CAkCC;AAED,2BACI,IAAY,EAAE,WAAqB,EAAE,KAA6B;IACpE,iBAAS,CAAC,OAAO,CAAC,UAAA,OAAO;QACvB,iBAAG,CAAC,WAAW,CAAC,OAAO,CAAC,QAAQ,CAAC,CAAC;QAClC,IAAI,uBAAuB,CAAC,WAAW,CAAC,EAAE;YACxC,IAAM,QAAQ,GACV,IAAI,GAAG,GAAG,GAAG,OAAO,CAAC,IAAI,GAAG,GAAG,GAAG,IAAI,CAAC,SAAS,CAAC,OAAO,CAAC,QAAQ,CAAC,CAAC;YACvE,YAAY,CAAC,QAAQ,EAAE,KAAK,EAAE,OAAO,CAAC,CAAC;SACxC;IACH,CAAC,CAAC,CAAC;AACL,CAAC;AAVD,8CAUC;AAQU,QAAA,SAAS,GAAc;IAChC;QACE,IAAI,EAAE,QAAQ;QACd,OAAO,EAAE,cAAM,OAAA,IAAI,gCAAgB,EAAE,EAAtB,CAAsB;QACrC,QAAQ,EAAE;YACR,eAAe,EAAE,CAAC;YAClB,mBAAmB,EAAE,KAAK;YAC1B,2BAA2B,EAAE,CAAC;SAC/B;KACF;IACD;QACE,IAAI,EAAE,QAAQ;QACd,OAAO,EAAE,cAAM,OAAA,IAAI,gCAAgB,EAAE,EAAtB,CAAsB;QACrC,QAAQ,EAAE;YACR,eAAe,EAAE,CAAC;YAClB,mBAAmB,EAAE,KAAK;YAC1B,2BAA2B,EAAE,CAAC;SAC/B;KACF;IACD;QACE,IAAI,EAAE,KAAK;QACX,OAAO,EAAE,cAAM,OAAA,IAAI,4BAAc,EAAE,EAApB,CAAoB;QACnC,QAAQ,EAAE,EAAC,WAAW,EAAE,KAAK,EAAC;KAC/B;CACF,CAAC;AAEW,QAAA,WAAW,GAAG,cAAM,OAAA,IAAI,4BAAc,EAAE,EAApB,CAAoB,CAAC;AAEtD,IAAI,OAAO,SAAS,KAAK,WAAW,EAAE;IACpC,IAAM,OAAO,GAAG,eAAe,CAAC,SAAS,CAAC,MAAM,CAAC,IAAI,CAAC,CAAC;IACvD,IAAI,OAAO,EAAE;QACX,WAAW,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC;KACxB;CACF;AAED,qBAA4B,QAAmB;IAC7C,iBAAS,GAAG,QAAQ,CAAC;AACvB,CAAC;AAFD,kCAEC;AAED,sBACI,QAAgB,EAAE,KAA6B,EAAE,OAAgB;IACnE,QAAQ,CAAC,QAAQ,EAAE;QACjB,IAAM,WAAW,GAAG,OAAO,GAAG,OAAO,CAAC,IAAI,CAAC;QAE3C,SAAS,CAAC;YACR,iBAAG,CAAC,KAAK,EAAE,CAAC;YACZ,iBAAG,CAAC,WAAW,CAAC,OAAO,CAAC,QAAQ,CAAC,CAAC;YAClC,iBAAG,CAAC,GAAG,CAAC,SAAS,EAAE,IAAI,CAAC,CAAC;YACzB,iBAAG,CAAC,eAAe,CAAC,WAAW,EAAE,OAAO,CAAC,OAAO,EAAE,IAAI,CAAC,CAAC;YACxD,yBAAW,CAAC,UAAU,CAAC,WAAW,CAAC,CAAC;QACtC,CAAC,CAAC,CAAC;QAEH,UAAU,CAAC;YACT,iBAAG,CAAC,MAAM,CAAC,UAAU,EAAE,CAAC;QAC1B,CAAC,CAAC,CAAC;QAEH,SAAS,CAAC;YACR,iBAAG,CAAC,MAAM,CAAC,QAAQ,EAAE,CAAC;YACtB,yBAAW,CAAC,gBAAgB,EAAE,CAAC;QACjC,CAAC,CAAC,CAAC;QAEH,QAAQ,CAAC;YACP,iBAAG,CAAC,aAAa,CAAC,WAAW,CAAC,CAAC;YAC/B,iBAAG,CAAC,KAAK,EAAE,CAAC;QACd,CAAC,CAAC,CAAC;QAEH,KAAK,CAAC,OAAO,CAAC,CAAC;IACjB,CAAC,CAAC,CAAC;AACL,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/jasmine_util_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/jasmine_util_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/jasmine_util_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/jasmine_util_test.js b/node_modules/@tensorflow/tfjs-core/dist/jasmine_util_test.js
new file mode 100644
index 0000000..14ca864
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/jasmine_util_test.js
@@ -0,0 +1,61 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("./index");
+var jasmine_util_1 = require("./jasmine_util");
+var backend_cpu_1 = require("./kernels/backend_cpu");
+var backend_webgl_1 = require("./kernels/backend_webgl");
+var test_util_1 = require("./test_util");
+jasmine_util_1.describeWithFlags('jasmine_util.envSatisfiesConstraints', {}, function () {
+    it('ENV satisfies empty constraints', function () {
+        expect(jasmine_util_1.envSatisfiesConstraints({})).toBe(true);
+    });
+    it('ENV satisfies matching constraints', function () {
+        var c = { TEST_EPSILON: tf.ENV.get('TEST_EPSILON') };
+        expect(jasmine_util_1.envSatisfiesConstraints(c)).toBe(true);
+    });
+    it('ENV does not satisfy mismatching constraints', function () {
+        var c = { TEST_EPSILON: tf.ENV.get('TEST_EPSILON') + 0.1 };
+        expect(jasmine_util_1.envSatisfiesConstraints(c)).toBe(false);
+    });
+});
+describe('jasmine_util.parseKarmaFlags', function () {
+    it('parse empty args', function () {
+        var res = jasmine_util_1.parseKarmaFlags([]);
+        expect(res).toBeNull();
+    });
+    it('--backend cpu', function () {
+        var res = jasmine_util_1.parseKarmaFlags(['--backend', 'cpu']);
+        expect(res.name).toBe('cpu');
+        expect(res.features).toEqual({ 'HAS_WEBGL': false });
+        expect(res.factory() instanceof backend_cpu_1.MathBackendCPU).toBe(true);
+    });
+    it('--backend cpu --features {"IS_NODE": true}', function () {
+        var res = jasmine_util_1.parseKarmaFlags(['--backend', 'cpu', '--features', '{"IS_NODE": true}']);
+        expect(res.name).toBe('cpu');
+        expect(res.features).toEqual({ IS_NODE: true });
+        expect(res.factory() instanceof backend_cpu_1.MathBackendCPU).toBe(true);
+    });
+    it('"--backend unknown" throws error', function () {
+        expect(function () { return jasmine_util_1.parseKarmaFlags(['--backend', 'unknown']); }).toThrowError();
+    });
+    it('"--features {}" throws error since --backend is missing', function () {
+        expect(function () { return jasmine_util_1.parseKarmaFlags(['--features', '{}']); }).toThrowError();
+    });
+    it('"--backend cpu --features" throws error since features value is missing', function () {
+        expect(function () { return jasmine_util_1.parseKarmaFlags(['--backend', 'cpu', '--features']); })
+            .toThrowError();
+    });
+    it('"--backend cpu --features notJson" throws error', function () {
+        expect(function () { return jasmine_util_1.parseKarmaFlags(['--backend', 'cpu', '--features', 'notJson']); })
+            .toThrowError();
+    });
+});
+jasmine_util_1.describeWithFlags('jasmine_util.envSatisfiesConstraints', test_util_1.WEBGL_ENVS, function () {
+    it('--backend webgl', function () {
+        var res = jasmine_util_1.parseKarmaFlags(['--backend', 'webgl']);
+        expect(res.name).toBe('webgl');
+        expect(res.features).toEqual({});
+        expect(res.factory() instanceof backend_webgl_1.MathBackendWebGL).toBe(true);
+    });
+});
+//# sourceMappingURL=jasmine_util_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/jasmine_util_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/jasmine_util_test.js.map
new file mode 100644
index 0000000..49c06b4
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/jasmine_util_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"jasmine_util_test.js","sourceRoot":"","sources":["../src/jasmine_util_test.ts"],"names":[],"mappings":";;AAiBA,4BAA8B;AAC9B,+CAA2F;AAC3F,qDAAqD;AACrD,yDAAyD;AACzD,yCAAuC;AAEvC,gCAAiB,CAAC,sCAAsC,EAAE,EAAE,EAAE;IAC5D,EAAE,CAAC,iCAAiC,EAAE;QACpC,MAAM,CAAC,sCAAuB,CAAC,EAAE,CAAC,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;IACjD,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,oCAAoC,EAAE;QACvC,IAAM,CAAC,GAAG,EAAC,YAAY,EAAE,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,cAAc,CAAC,EAAC,CAAC;QACrD,MAAM,CAAC,sCAAuB,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;IAChD,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,8CAA8C,EAAE;QACjD,IAAM,CAAC,GAAG,EAAC,YAAY,EAAE,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,cAAc,CAAC,GAAG,GAAG,EAAC,CAAC;QAC3D,MAAM,CAAC,sCAAuB,CAAC,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC;IACjD,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC;AAEH,QAAQ,CAAC,8BAA8B,EAAE;IACvC,EAAE,CAAC,kBAAkB,EAAE;QACrB,IAAM,GAAG,GAAG,8BAAe,CAAC,EAAE,CAAC,CAAC;QAChC,MAAM,CAAC,GAAG,CAAC,CAAC,QAAQ,EAAE,CAAC;IACzB,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,eAAe,EAAE;QAClB,IAAM,GAAG,GAAG,8BAAe,CAAC,CAAC,WAAW,EAAE,KAAK,CAAC,CAAC,CAAC;QAClD,MAAM,CAAC,GAAG,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC;QAC7B,MAAM,CAAC,GAAG,CAAC,QAAQ,CAAC,CAAC,OAAO,CAAC,EAAC,WAAW,EAAE,KAAK,EAAC,CAAC,CAAC;QACnD,MAAM,CAAC,GAAG,CAAC,OAAO,EAAE,YAAY,4BAAc,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;IAC7D,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,4CAA4C,EAAE;QAC/C,IAAM,GAAG,GAAG,8BAAe,CACvB,CAAC,WAAW,EAAE,KAAK,EAAE,YAAY,EAAE,mBAAmB,CAAC,CAAC,CAAC;QAC7D,MAAM,CAAC,GAAG,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC;QAC7B,MAAM,CAAC,GAAG,CAAC,QAAQ,CAAC,CAAC,OAAO,CAAC,EAAC,OAAO,EAAE,IAAI,EAAC,CAAC,CAAC;QAC9C,MAAM,CAAC,GAAG,CAAC,OAAO,EAAE,YAAY,4BAAc,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;IAC7D,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,kCAAkC,EAAE;QACrC,MAAM,CAAC,cAAM,OAAA,8BAAe,CAAC,CAAC,WAAW,EAAE,SAAS,CAAC,CAAC,EAAzC,CAAyC,CAAC,CAAC,YAAY,EAAE,CAAC;IACzE,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,yDAAyD,EAAE;QAC5D,MAAM,CAAC,cAAM,OAAA,8BAAe,CAAC,CAAC,YAAY,EAAE,IAAI,CAAC,CAAC,EAArC,CAAqC,CAAC,CAAC,YAAY,EAAE,CAAC;IACrE,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,yEAAyE,EACzE;QACE,MAAM,CAAC,cAAM,OAAA,8BAAe,CAAC,CAAC,WAAW,EAAE,KAAK,EAAE,YAAY,CAAC,CAAC,EAAnD,CAAmD,CAAC;aAC5D,YAAY,EAAE,CAAC;IACtB,CAAC,CAAC,CAAC;IAEN,EAAE,CAAC,iDAAiD,EAAE;QACpD,MAAM,CAAC,cAAM,OAAA,8BAAe,CAAC,CAAC,WAAW,EAAE,KAAK,EAAE,YAAY,EAAE,SAAS,CAAC,CAAC,EAA9D,CAA8D,CAAC;aACvE,YAAY,EAAE,CAAC;IACtB,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC;AAEH,gCAAiB,CAAC,sCAAsC,EAAE,sBAAU,EAAE;IACpE,EAAE,CAAC,iBAAiB,EAAE;QACpB,IAAM,GAAG,GAAG,8BAAe,CAAC,CAAC,WAAW,EAAE,OAAO,CAAC,CAAC,CAAC;QACpD,MAAM,CAAC,GAAG,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC;QAC/B,MAAM,CAAC,GAAG,CAAC,QAAQ,CAAC,CAAC,OAAO,CAAC,EAAE,CAAC,CAAC;QACjC,MAAM,CAAC,GAAG,CAAC,OAAO,EAAE,YAAY,gCAAgB,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;IAC/D,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/backend.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend.d.ts
new file mode 100644
index 0000000..5b88883
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend.d.ts
@@ -0,0 +1,167 @@
+import { Conv2DInfo, Conv3DInfo } from '../ops/conv_util';
+import { DataId, Scalar, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, Tensor5D } from '../tensor';
+import { DataType, DataValues, Rank, ShapeMap } from '../types';
+export interface BackendTimingInfo {
+    kernelMs: number;
+    getExtraProfileInfo?(): string;
+}
+export interface TensorStorage {
+    read(dataId: DataId): Promise<DataValues>;
+    readSync(dataId: DataId): DataValues;
+    disposeData(dataId: DataId): void;
+    write(dataId: DataId, values: DataValues): void;
+    fromPixels(pixels: ImageData | HTMLImageElement | HTMLCanvasElement | HTMLVideoElement, numChannels: number): Tensor3D;
+    register(dataId: DataId, shape: number[], dtype: DataType): void;
+    memory(): {
+        unreliable: boolean;
+    };
+}
+export declare class DataStorage<T> {
+    private dataMover;
+    private data;
+    constructor(dataMover: DataMover);
+    get(dataId: DataId): T;
+    set(dataId: DataId, value: T): void;
+    has(dataId: DataId): boolean;
+    delete(dataId: DataId): boolean;
+}
+export interface DataMover {
+    moveData(dataId: DataId): void;
+}
+export interface BackendTimer {
+    time(f: () => void): Promise<BackendTimingInfo>;
+}
+export declare class KernelBackend implements TensorStorage, BackendTimer {
+    time(f: () => void): Promise<BackendTimingInfo>;
+    read(dataId: object): Promise<DataValues>;
+    readSync(dataId: object): DataValues;
+    disposeData(dataId: object): void;
+    write(dataId: object, values: DataValues): void;
+    fromPixels(pixels: ImageData | HTMLImageElement | HTMLCanvasElement | HTMLVideoElement, numChannels: number): Tensor<Rank.R3>;
+    register(dataId: object, shape: number[], dtype: DataType): void;
+    memory(): {
+        unreliable: boolean;
+        reasons?: string[];
+    };
+    floatPrecision(): number;
+    batchMatMul(a: Tensor3D, b: Tensor3D, transposeA: boolean, transposeB: boolean): Tensor3D;
+    slice<T extends Tensor>(x: T, begin: number[], size: number[]): T;
+    stridedSlice<T extends Tensor>(x: T, begin: number[], end: number[], strides: number[], beginMask: number, endMask: number, ellipsisMask: number, newAxisMask: number, shrinkAxisMask: number): T;
+    reverse<T extends Tensor>(a: T, axis: number[]): T;
+    concat(tensors: Tensor[], axis: number): Tensor;
+    neg<T extends Tensor>(a: T): T;
+    add(a: Tensor, b: Tensor): Tensor;
+    addN<T extends Tensor>(tensors: T[]): T;
+    subtract(a: Tensor, b: Tensor): Tensor;
+    multiply(a: Tensor, b: Tensor): Tensor;
+    realDivide(a: Tensor, b: Tensor): Tensor;
+    floorDiv(a: Tensor, b: Tensor): Tensor;
+    sum(x: Tensor, axes: number[]): Tensor;
+    prod(x: Tensor, axes: number[]): Tensor;
+    unsortedSegmentSum<T extends Tensor>(x: T, segmentIds: Tensor1D, numSegments: number): Tensor;
+    argMin(x: Tensor, axis: number): Tensor;
+    argMax(x: Tensor, axis: number): Tensor;
+    equal(a: Tensor, b: Tensor): Tensor;
+    notEqual(a: Tensor, b: Tensor): Tensor;
+    less(a: Tensor, b: Tensor): Tensor;
+    lessEqual(a: Tensor, b: Tensor): Tensor;
+    greater(a: Tensor, b: Tensor): Tensor;
+    greaterEqual(a: Tensor, b: Tensor): Tensor;
+    logicalNot<T extends Tensor>(a: T): T;
+    logicalAnd(a: Tensor, b: Tensor): Tensor;
+    logicalOr(a: Tensor, b: Tensor): Tensor;
+    where(condition: Tensor): Tensor2D;
+    select(condition: Tensor, a: Tensor, b: Tensor): Tensor;
+    topk<T extends Tensor>(x: T, k: number, sorted: boolean): [T, T];
+    min(x: Tensor, axes: number[]): Tensor;
+    minimum(a: Tensor, b: Tensor): Tensor;
+    mod(a: Tensor, b: Tensor): Tensor;
+    max(x: Tensor, axes: number[]): Tensor;
+    maximum(a: Tensor, b: Tensor): Tensor;
+    all(x: Tensor, axes: number[]): Tensor;
+    any(x: Tensor, axes: number[]): Tensor;
+    squaredDifference(a: Tensor, b: Tensor): Tensor;
+    ceil<T extends Tensor>(x: T): T;
+    floor<T extends Tensor>(x: T): T;
+    round<T extends Tensor>(x: T): T;
+    sign<T extends Tensor>(x: T): T;
+    pow<T extends Tensor>(a: T, b: Tensor): T;
+    exp<T extends Tensor>(x: T): T;
+    expm1<T extends Tensor>(x: T): T;
+    log<T extends Tensor>(x: T): T;
+    log1p<T extends Tensor>(x: T): T;
+    sqrt<T extends Tensor>(x: T): T;
+    rsqrt<T extends Tensor>(x: T): T;
+    square<T extends Tensor>(x: T): T;
+    reciprocal<T extends Tensor>(x: T): T;
+    relu<T extends Tensor>(x: T): T;
+    elu<T extends Tensor>(x: T): T;
+    eluDer<T extends Tensor>(dy: T, y: T): T;
+    selu<T extends Tensor>(x: T): T;
+    int<T extends Tensor>(x: T): T;
+    clip<T extends Tensor>(x: T, min: number, max: number): T;
+    abs<T extends Tensor>(x: T): T;
+    complexAbs<T extends Tensor>(x: T): T;
+    sigmoid<T extends Tensor>(x: T): T;
+    softplus<T extends Tensor>(x: T): T;
+    sin<T extends Tensor>(x: T): T;
+    cos<T extends Tensor>(x: T): T;
+    tan<T extends Tensor>(x: T): T;
+    asin<T extends Tensor>(x: T): T;
+    acos<T extends Tensor>(x: T): T;
+    atan<T extends Tensor>(x: T): T;
+    atan2<T extends Tensor>(a: T, b: T): T;
+    sinh<T extends Tensor>(x: T): T;
+    cosh<T extends Tensor>(x: T): T;
+    tanh<T extends Tensor>(x: T): T;
+    asinh<T extends Tensor>(x: T): T;
+    acosh<T extends Tensor>(x: T): T;
+    atanh<T extends Tensor>(x: T): T;
+    erf<T extends Tensor>(x: T): T;
+    step<T extends Tensor>(x: T, alpha: number): T;
+    conv2d(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    conv2dDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    conv2dDerFilter(x: Tensor4D, dY: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    depthwiseConv2D(input: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    depthwiseConv2DDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    depthwiseConv2DDerFilter(x: Tensor4D, dY: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    conv3d(x: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo): Tensor5D;
+    conv3dDerInput(dy: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo): Tensor5D;
+    conv3dDerFilter(x: Tensor5D, dY: Tensor5D, convInfo: Conv3DInfo): Tensor5D;
+    maxPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    maxPoolBackprop(dy: Tensor4D, x: Tensor4D, y: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    avgPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    avgPoolBackprop(dy: Tensor4D, x: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    reshape<T extends Tensor, R extends Rank>(x: T, shape: ShapeMap[R]): Tensor<R>;
+    cast<T extends Tensor>(x: T, dtype: DataType): T;
+    tile<T extends Tensor>(x: T, reps: number[]): T;
+    pad<T extends Tensor>(x: T, paddings: Array<[number, number]>, constantValue: number): T;
+    transpose<T extends Tensor>(x: T, perm: number[]): T;
+    gather<T extends Tensor>(x: T, indices: Tensor1D, axis: number): T;
+    gatherND(x: Tensor, indices: Tensor): Tensor;
+    scatterND<R extends Rank>(indices: Tensor, updates: Tensor, shape: ShapeMap[R]): Tensor<R>;
+    batchToSpaceND<T extends Tensor>(x: T, blockShape: number[], crops: number[][]): T;
+    spaceToBatchND<T extends Tensor>(x: T, blockShape: number[], paddings: number[][]): T;
+    resizeBilinear(x: Tensor4D, newHeight: number, newWidth: number, alignCorners: boolean): Tensor4D;
+    resizeBilinearBackprop(dy: Tensor4D, x: Tensor4D, alignCorners: boolean): Tensor4D;
+    resizeNearestNeighbor(x: Tensor4D, newHEight: number, newWidth: number, alignCorners: boolean): Tensor4D;
+    resizeNearestNeighborBackprop(dy: Tensor4D, x: Tensor4D, alignCorners: boolean): Tensor4D;
+    batchNormalization(x: Tensor4D, mean: Tensor4D | Tensor1D, variance: Tensor4D | Tensor1D, varianceEpsilon: number, scale?: Tensor4D | Tensor1D, offset?: Tensor4D | Tensor1D): Tensor4D;
+    localResponseNormalization4D(x: Tensor4D, radius: number, bias: number, alpha: number, beta: number): Tensor4D;
+    LRNGrad(dy: Tensor4D, inputImage: Tensor4D, outputImage: Tensor4D, radius: number, bias: number, alpha: number, beta: number): Tensor4D;
+    multinomial(logits: Tensor2D, normalized: boolean, numSamples: number, seed: number): Tensor2D;
+    oneHot(indices: Tensor1D, depth: number, onValue: number, offValue: number): Tensor2D;
+    cumsum(x: Tensor, axis: number, exclusive: boolean, reverse: boolean): Tensor;
+    nonMaxSuppression(boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number, iouThreshold: number, scoreThreshold?: number): Tensor1D;
+    fft(x: Tensor2D): Tensor2D;
+    ifft(x: Tensor2D): Tensor2D;
+    complex<T extends Tensor>(real: T, imag: T): T;
+    real<T extends Tensor>(input: T): T;
+    imag<T extends Tensor>(input: T): T;
+    cropAndResize(image: Tensor4D, boxes: Tensor2D, boxIndex: Tensor1D, cropSize: [number, number], method: 'bilinear' | 'nearest', extrapolationValue: number): Tensor4D;
+    depthToSpace(x: Tensor4D, blockSize: number, dataFormat: string): Tensor4D;
+    split<T extends Tensor>(value: T, sizeSplits: number[], axis: number): T[];
+    sparseToDense<R extends Rank>(sparseIndices: Tensor, sparseValues: Tensor, outputShape: ShapeMap[R], defaultValue: Scalar): Tensor<R>;
+    setDataMover(dataMover: DataMover): void;
+    dispose(): void;
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/backend.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend.js
new file mode 100644
index 0000000..e5f0eb2
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend.js
@@ -0,0 +1,419 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var DataStorage = (function () {
+    function DataStorage(dataMover) {
+        this.dataMover = dataMover;
+        this.data = new WeakMap();
+    }
+    DataStorage.prototype.get = function (dataId) {
+        if (!this.data.has(dataId)) {
+            this.dataMover.moveData(dataId);
+        }
+        return this.data.get(dataId);
+    };
+    DataStorage.prototype.set = function (dataId, value) {
+        this.data.set(dataId, value);
+    };
+    DataStorage.prototype.has = function (dataId) {
+        return this.data.has(dataId);
+    };
+    DataStorage.prototype.delete = function (dataId) {
+        return this.data.delete(dataId);
+    };
+    return DataStorage;
+}());
+exports.DataStorage = DataStorage;
+var KernelBackend = (function () {
+    function KernelBackend() {
+    }
+    KernelBackend.prototype.time = function (f) {
+        throw new Error('Not yet implemented.');
+    };
+    KernelBackend.prototype.read = function (dataId) {
+        throw new Error('Not yet implemented.');
+    };
+    KernelBackend.prototype.readSync = function (dataId) {
+        throw new Error('Not yet implemented.');
+    };
+    KernelBackend.prototype.disposeData = function (dataId) {
+        throw new Error('Not yet implemented.');
+    };
+    KernelBackend.prototype.write = function (dataId, values) {
+        throw new Error('Not yet implemented.');
+    };
+    KernelBackend.prototype.fromPixels = function (pixels, numChannels) {
+        throw new Error('Not yet implemented.');
+    };
+    KernelBackend.prototype.register = function (dataId, shape, dtype) {
+        throw new Error('Not yet implemented.');
+    };
+    KernelBackend.prototype.memory = function () {
+        throw new Error('Not yet implemented.');
+    };
+    KernelBackend.prototype.floatPrecision = function () {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.batchMatMul = function (a, b, transposeA, transposeB) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.slice = function (x, begin, size) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.stridedSlice = function (x, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.reverse = function (a, axis) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.concat = function (tensors, axis) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.neg = function (a) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.add = function (a, b) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.addN = function (tensors) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.subtract = function (a, b) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.multiply = function (a, b) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.realDivide = function (a, b) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.floorDiv = function (a, b) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.sum = function (x, axes) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.prod = function (x, axes) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.unsortedSegmentSum = function (x, segmentIds, numSegments) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.argMin = function (x, axis) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.argMax = function (x, axis) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.equal = function (a, b) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.notEqual = function (a, b) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.less = function (a, b) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.lessEqual = function (a, b) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.greater = function (a, b) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.greaterEqual = function (a, b) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.logicalNot = function (a) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.logicalAnd = function (a, b) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.logicalOr = function (a, b) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.where = function (condition) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.select = function (condition, a, b) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.topk = function (x, k, sorted) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.min = function (x, axes) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.minimum = function (a, b) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.mod = function (a, b) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.max = function (x, axes) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.maximum = function (a, b) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.all = function (x, axes) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.any = function (x, axes) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.squaredDifference = function (a, b) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.ceil = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.floor = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.round = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.sign = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.pow = function (a, b) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.exp = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.expm1 = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.log = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.log1p = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.sqrt = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.rsqrt = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.square = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.reciprocal = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.relu = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.elu = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.eluDer = function (dy, y) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.selu = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.int = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.clip = function (x, min, max) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.abs = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.complexAbs = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.sigmoid = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.softplus = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.sin = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.cos = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.tan = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.asin = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.acos = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.atan = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.atan2 = function (a, b) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.sinh = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.cosh = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.tanh = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.asinh = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.acosh = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.atanh = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.erf = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.step = function (x, alpha) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.conv2d = function (x, filter, convInfo) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.conv2dDerInput = function (dy, filter, convInfo) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.conv2dDerFilter = function (x, dY, convInfo) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.depthwiseConv2D = function (input, filter, convInfo) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.depthwiseConv2DDerInput = function (dy, filter, convInfo) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.depthwiseConv2DDerFilter = function (x, dY, convInfo) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.conv3d = function (x, filter, convInfo) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.conv3dDerInput = function (dy, filter, convInfo) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.conv3dDerFilter = function (x, dY, convInfo) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.maxPool = function (x, convInfo) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.maxPoolBackprop = function (dy, x, y, convInfo) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.avgPool = function (x, convInfo) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.avgPoolBackprop = function (dy, x, convInfo) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.reshape = function (x, shape) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.cast = function (x, dtype) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.tile = function (x, reps) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.pad = function (x, paddings, constantValue) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.transpose = function (x, perm) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.gather = function (x, indices, axis) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.gatherND = function (x, indices) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.scatterND = function (indices, updates, shape) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.batchToSpaceND = function (x, blockShape, crops) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.spaceToBatchND = function (x, blockShape, paddings) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.resizeBilinear = function (x, newHeight, newWidth, alignCorners) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.resizeBilinearBackprop = function (dy, x, alignCorners) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.resizeNearestNeighbor = function (x, newHEight, newWidth, alignCorners) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.resizeNearestNeighborBackprop = function (dy, x, alignCorners) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.batchNormalization = function (x, mean, variance, varianceEpsilon, scale, offset) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.localResponseNormalization4D = function (x, radius, bias, alpha, beta) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.LRNGrad = function (dy, inputImage, outputImage, radius, bias, alpha, beta) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.multinomial = function (logits, normalized, numSamples, seed) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.oneHot = function (indices, depth, onValue, offValue) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.cumsum = function (x, axis, exclusive, reverse) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.nonMaxSuppression = function (boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.fft = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.ifft = function (x) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.complex = function (real, imag) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.real = function (input) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.imag = function (input) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.cropAndResize = function (image, boxes, boxIndex, cropSize, method, extrapolationValue) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.depthToSpace = function (x, blockSize, dataFormat) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.split = function (value, sizeSplits, axis) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.sparseToDense = function (sparseIndices, sparseValues, outputShape, defaultValue) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.setDataMover = function (dataMover) {
+        throw new Error('Not yet implemented');
+    };
+    KernelBackend.prototype.dispose = function () {
+        throw new Error('Not yet implemented');
+    };
+    return KernelBackend;
+}());
+exports.KernelBackend = KernelBackend;
+//# sourceMappingURL=backend.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/backend.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend.js.map
new file mode 100644
index 0000000..81820ca
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_cpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_cpu.d.ts
new file mode 100644
index 0000000..526738d
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_cpu.d.ts
@@ -0,0 +1,154 @@
+import { Conv2DInfo, Conv3DInfo } from '../ops/conv_util';
+import { DataId, Scalar, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, Tensor5D } from '../tensor';
+import { DataType, DataValues, Rank, ShapeMap } from '../types';
+import { BackendTimingInfo, DataMover, KernelBackend } from './backend';
+export declare class MathBackendCPU implements KernelBackend {
+    blockSize: number;
+    private data;
+    private fromPixels2DContext;
+    private firstUse;
+    constructor();
+    setDataMover(dataMover: DataMover): void;
+    register(dataId: DataId, shape: number[], dtype: DataType): void;
+    write(dataId: DataId, values: DataValues): void;
+    fromPixels(pixels: ImageData | HTMLImageElement | HTMLCanvasElement | HTMLVideoElement, numChannels: number): Tensor3D;
+    read(dataId: DataId): Promise<DataValues>;
+    readSync(dataId: DataId): DataValues;
+    disposeData(dataId: DataId): void;
+    time(f: () => void): Promise<BackendTimingInfo>;
+    memory(): {
+        unreliable: boolean;
+        reasons: string[];
+    };
+    complex<T extends Tensor>(real: T, imag: T): T;
+    real<T extends Tensor>(input: T): T;
+    imag<T extends Tensor>(input: T): T;
+    private assertNotComplex;
+    slice<T extends Tensor>(x: T, begin: number[], size: number[]): T;
+    stridedSlice<T extends Tensor>(x: T, begin: number[], end: number[], strides: number[], beginMask: number, endMask: number, ellipsisMask: number, newAxisMask: number, shrinkAxisMask: number): T;
+    reverse<T extends Tensor>(x: T, axis: number[]): T;
+    concat(tensors: Tensor[], axis: number): Tensor;
+    neg<T extends Tensor>(x: T): T;
+    add(a: Tensor, b: Tensor): Tensor;
+    addN<T extends Tensor>(tensors: T[]): T;
+    subtract(a: Tensor, b: Tensor): Tensor;
+    pow<T extends Tensor>(a: T, b: Tensor): T;
+    batchMatMul(a: Tensor3D, b: Tensor3D, transposeA: boolean, transposeB: boolean): Tensor3D;
+    multiply(a: Tensor, b: Tensor): Tensor;
+    realDivide(a: Tensor, b: Tensor): Tensor;
+    floorDiv(a: Tensor, b: Tensor): Tensor;
+    sum(x: Tensor, axes: number[]): Tensor;
+    prod(x: Tensor, axes: number[]): Tensor;
+    unsortedSegmentSum<T extends Tensor>(x: T, segmentIds: Tensor1D, numSegments: number): Tensor;
+    argMin(x: Tensor, axis: number): Tensor;
+    argMax(x: Tensor, axis: number): Tensor;
+    cumsum(x: Tensor, axis: number, exclusive: boolean, reverse: boolean): Tensor;
+    equal(a: Tensor, b: Tensor): Tensor;
+    notEqual(a: Tensor, b: Tensor): Tensor;
+    less(a: Tensor, b: Tensor): Tensor;
+    lessEqual(a: Tensor, b: Tensor): Tensor;
+    greater(a: Tensor, b: Tensor): Tensor;
+    greaterEqual(a: Tensor, b: Tensor): Tensor;
+    logicalNot<T extends Tensor>(x: T): T;
+    logicalAnd(a: Tensor, b: Tensor): Tensor;
+    logicalOr(a: Tensor, b: Tensor): Tensor;
+    select(condition: Tensor, a: Tensor, b: Tensor): Tensor;
+    where(condition: Tensor): Tensor2D;
+    topk<T extends Tensor>(x: T, k: number, sorted: boolean): [T, T];
+    min(x: Tensor, axes: number[]): Tensor;
+    minimum(a: Tensor, b: Tensor): Tensor;
+    mod(a: Tensor, b: Tensor): Tensor;
+    max(x: Tensor, axes: number[]): Tensor;
+    maximum(a: Tensor, b: Tensor): Tensor;
+    all(x: Tensor, axes: number[]): Tensor;
+    any(x: Tensor, axes: number[]): Tensor;
+    squaredDifference(a: Tensor, b: Tensor): Tensor;
+    ceil<T extends Tensor>(x: T): T;
+    floor<T extends Tensor>(x: T): T;
+    sign<T extends Tensor>(x: T): T;
+    round<T extends Tensor>(x: T): T;
+    exp<T extends Tensor>(x: T): T;
+    expm1<T extends Tensor>(x: T): T;
+    log<T extends Tensor>(x: T): T;
+    log1p<T extends Tensor>(x: T): T;
+    sqrt<T extends Tensor>(x: T): T;
+    rsqrt<T extends Tensor>(x: T): T;
+    square<T extends Tensor>(x: T): T;
+    reciprocal<T extends Tensor>(x: T): T;
+    relu<T extends Tensor>(x: T): T;
+    elu<T extends Tensor>(x: T): T;
+    eluDer<T extends Tensor>(dy: T, y: T): T;
+    selu<T extends Tensor>(x: T): T;
+    clip<T extends Tensor>(x: T, min: number, max: number): T;
+    abs<T extends Tensor>(x: T): T;
+    complexAbs<T extends Tensor>(x: T): T;
+    int<T extends Tensor>(x: T): T;
+    sigmoid<T extends Tensor>(x: T): T;
+    softplus<T extends Tensor>(x: T): T;
+    sin<T extends Tensor>(x: T): T;
+    cos<T extends Tensor>(x: T): T;
+    tan<T extends Tensor>(x: T): T;
+    asin<T extends Tensor>(x: T): T;
+    acos<T extends Tensor>(x: T): T;
+    atan<T extends Tensor>(x: T): T;
+    atan2<T extends Tensor>(a: T, b: T): T;
+    sinh<T extends Tensor>(x: T): T;
+    cosh<T extends Tensor>(x: T): T;
+    tanh<T extends Tensor>(x: T): T;
+    asinh<T extends Tensor>(x: T): T;
+    acosh<T extends Tensor>(x: T): T;
+    atanh<T extends Tensor>(x: T): T;
+    erf<T extends Tensor>(x: T): T;
+    step<T extends Tensor>(x: T, alpha?: number): T;
+    conv2d(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    conv3d(x: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo): Tensor5D;
+    conv2dDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    conv3dDerInput(dy: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo): Tensor5D;
+    conv2dDerFilter(x: Tensor4D, dy: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    conv3dDerFilter(x: Tensor5D, dy: Tensor5D, convInfo: Conv3DInfo): Tensor5D;
+    depthwiseConv2D(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    depthwiseConv2DDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    depthwiseConv2DDerFilter(x: Tensor4D, dy: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    tile<T extends Tensor>(x: T, reps: number[]): T;
+    pad<T extends Tensor>(x: T, paddings: Array<[number, number]>, constantValue: number): T;
+    transpose<T extends Tensor>(x: T, perm: number[]): T;
+    gather<T extends Tensor>(x: T, indices: Tensor1D, axis: number): T;
+    batchToSpaceND<T extends Tensor>(x: T, blockShape: number[], crops: number[][]): T;
+    spaceToBatchND<T extends Tensor>(x: T, blockShape: number[], paddings: Array<[number, number]>): T;
+    private pool;
+    maxPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    private maxPoolPositions;
+    maxPoolBackprop(dy: Tensor4D, x: Tensor4D, y: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    avgPoolBackprop(dy: Tensor4D, x: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    cast<T extends Tensor>(x: T, dtype: DataType): T;
+    reshape<R extends Rank>(x: Tensor, shape: ShapeMap[R]): Tensor<R>;
+    avgPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    resizeBilinear(x: Tensor4D, newHeight: number, newWidth: number, alignCorners: boolean): Tensor4D;
+    resizeBilinearBackprop(dy: Tensor4D, x: Tensor4D, alignCorners: boolean): Tensor<Rank.R4>;
+    resizeNearestNeighbor(x: Tensor4D, newHeight: number, newWidth: number, alignCorners: boolean): Tensor4D;
+    resizeNearestNeighborBackprop(dy: Tensor4D, x: Tensor4D, alignCorners: boolean): Tensor<Rank.R4>;
+    batchNormalization(x: Tensor4D, mean: Tensor4D | Tensor1D, variance: Tensor4D | Tensor1D, varianceEpsilon: number, scale?: Tensor4D | Tensor1D, offset?: Tensor4D | Tensor1D): Tensor4D;
+    localResponseNormalization4D(x: Tensor4D, depthRadius: number, bias: number, alpha: number, beta: number): Tensor4D;
+    LRNGrad(dy: Tensor4D, inputImage: Tensor4D, outputImage: Tensor4D, depthRadius: number, bias: number, alpha: number, beta: number): Tensor4D;
+    multinomial(logits: Tensor2D, normalized: boolean, numSamples: number, seed: number): Tensor2D;
+    oneHot(indices: Tensor1D, depth: number, onValue: number, offValue: number): Tensor2D;
+    nonMaxSuppression(boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number, iouThreshold: number, scoreThreshold: number): Tensor1D;
+    fft(x: Tensor2D): Tensor2D;
+    ifft(x: Tensor2D): Tensor2D;
+    private fftBatch;
+    private fftImpl;
+    private isExponentOf2;
+    private fftRadix2;
+    private fourierTransformByMatmul;
+    depthToSpace(x: Tensor4D, blockSize: number, dataFormat: 'NHWC' | 'NCHW'): Tensor4D;
+    private broadcastedBinaryOp;
+    private broadcastedBinaryComplexOp;
+    split<T extends Tensor>(x: T, sizeSplits: number[], axis: number): T[];
+    dispose(): void;
+    floatPrecision(): number;
+    cropAndResize(images: Tensor4D, boxes: Tensor2D, boxIndex: Tensor1D, cropSize: [number, number], method: string, extrapolationValue: number): Tensor<Rank.R4>;
+    sparseToDense<R extends Rank>(sparseIndices: Tensor, sparseValues: Tensor, outputShape: ShapeMap[R], defaultValue: Scalar): Tensor<R>;
+    gatherND(x: Tensor, indices: Tensor): Tensor<Rank>;
+    scatterND<R extends Rank>(indices: Tensor, updates: Tensor, shape: ShapeMap[R]): Tensor<R>;
+    private scatter;
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_cpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_cpu.js
new file mode 100644
index 0000000..5c5bc67
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_cpu.js
@@ -0,0 +1,2608 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+Object.defineProperty(exports, "__esModule", { value: true });
+var seedrandom = require("seedrandom");
+var environment_1 = require("../environment");
+var log_1 = require("../log");
+var array_ops_util = require("../ops/array_ops_util");
+var axis_util = require("../ops/axis_util");
+var broadcast_util = require("../ops/broadcast_util");
+var concat_util = require("../ops/concat_util");
+var erf_util = require("../ops/erf_util");
+var gather_nd_util = require("../ops/gather_nd_util");
+var ops = require("../ops/ops");
+var ops_1 = require("../ops/ops");
+var scatter_nd_util = require("../ops/scatter_nd_util");
+var selu_util = require("../ops/selu_util");
+var slice_util_1 = require("../ops/slice_util");
+var tensor_1 = require("../tensor");
+var types_1 = require("../types");
+var util = require("../util");
+var util_1 = require("../util");
+var backend_1 = require("./backend");
+var backend_util = require("./backend_util");
+var complex_util = require("./complex_util");
+var non_max_suppression_impl_1 = require("./non_max_suppression_impl");
+var split_shared_1 = require("./split_shared");
+var topk_impl_1 = require("./topk_impl");
+var where_impl_1 = require("./where_impl");
+var MathBackendCPU = (function () {
+    function MathBackendCPU() {
+        this.blockSize = 48;
+        this.firstUse = true;
+        if (environment_1.ENV.get('IS_BROWSER')) {
+            this.fromPixels2DContext =
+                document.createElement('canvas').getContext('2d');
+        }
+    }
+    MathBackendCPU.prototype.setDataMover = function (dataMover) {
+        this.data = new backend_1.DataStorage(dataMover);
+    };
+    MathBackendCPU.prototype.register = function (dataId, shape, dtype) {
+        if (this.firstUse) {
+            this.firstUse = false;
+            if (environment_1.ENV.get('IS_NODE')) {
+                log_1.warn('\n============================\n' +
+                    'Hi there 👋. Looks like you are running TensorFlow.js in ' +
+                    'Node.js. To speed things up dramatically, install our node ' +
+                    'backend, which binds to TensorFlow C++, by running ' +
+                    'npm i @tensorflow/tfjs-node, ' +
+                    'or npm i @tensorflow/tfjs-node-gpu if you have CUDA. ' +
+                    'Then call require(\'@tensorflow/tfjs-node\'); (-gpu ' +
+                    'suffix for CUDA) at the start of your program. ' +
+                    'Visit https://github.com/tensorflow/tfjs-node for more details.' +
+                    '\n============================\n');
+            }
+        }
+        if (this.data.has(dataId)) {
+            throw new Error("Data buffer is already registered");
+        }
+        this.data.set(dataId, { dtype: dtype });
+    };
+    MathBackendCPU.prototype.write = function (dataId, values) {
+        if (values == null) {
+            throw new Error('MathBackendCPU.write(): values can not be null');
+        }
+        this.data.get(dataId).values = values;
+    };
+    MathBackendCPU.prototype.fromPixels = function (pixels, numChannels) {
+        if (pixels == null) {
+            throw new Error('pixels passed to tf.fromPixels() can not be null');
+        }
+        var vals;
+        if (environment_1.ENV.get('IS_NODE') && pixels.getContext == null) {
+            throw new Error('When running in node, pixels must be an HTMLCanvasElement ' +
+                'like the one returned by the `canvas` npm package');
+        }
+        if (pixels.getContext != null) {
+            vals = pixels
+                .getContext('2d')
+                .getImageData(0, 0, pixels.width, pixels.height)
+                .data;
+        }
+        else if (pixels instanceof ImageData) {
+            vals = pixels.data;
+        }
+        else if (pixels instanceof HTMLImageElement ||
+            pixels instanceof HTMLVideoElement) {
+            if (this.fromPixels2DContext == null) {
+                throw new Error('Can\'t read pixels from HTMLImageElement outside ' +
+                    'the browser.');
+            }
+            this.fromPixels2DContext.canvas.width = pixels.width;
+            this.fromPixels2DContext.canvas.height = pixels.height;
+            this.fromPixels2DContext.drawImage(pixels, 0, 0, pixels.width, pixels.height);
+            vals = this.fromPixels2DContext
+                .getImageData(0, 0, pixels.width, pixels.height)
+                .data;
+        }
+        else {
+            throw new Error('pixels passed to tf.fromPixels() must be either an ' +
+                "HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or " +
+                ("ImageData, but was " + pixels.constructor.name));
+        }
+        var values;
+        if (numChannels === 4) {
+            values = new Int32Array(vals);
+        }
+        else {
+            var numPixels = pixels.width * pixels.height;
+            values = new Int32Array(numPixels * numChannels);
+            for (var i = 0; i < numPixels; i++) {
+                for (var channel = 0; channel < numChannels; ++channel) {
+                    values[i * numChannels + channel] = vals[i * 4 + channel];
+                }
+            }
+        }
+        var outShape = [pixels.height, pixels.width, numChannels];
+        return ops_1.tensor3d(values, outShape, 'int32');
+    };
+    MathBackendCPU.prototype.read = function (dataId) {
+        return __awaiter(this, void 0, void 0, function () {
+            return __generator(this, function (_a) {
+                return [2, this.readSync(dataId)];
+            });
+        });
+    };
+    MathBackendCPU.prototype.readSync = function (dataId) {
+        var _a = this.data.get(dataId), dtype = _a.dtype, complexTensors = _a.complexTensors;
+        if (dtype === 'complex64') {
+            var realValues = complexTensors.real.dataSync();
+            var imagValues = complexTensors.imag.dataSync();
+            return complex_util.mergeRealAndImagArrays(realValues, imagValues);
+        }
+        return this.data.get(dataId).values;
+    };
+    MathBackendCPU.prototype.disposeData = function (dataId) {
+        if (this.data.has(dataId)) {
+            var complexTensors = this.data.get(dataId).complexTensors;
+            if (complexTensors != null) {
+                complexTensors.real.dispose();
+                complexTensors.imag.dispose();
+            }
+            this.data.delete(dataId);
+        }
+    };
+    MathBackendCPU.prototype.time = function (f) {
+        return __awaiter(this, void 0, void 0, function () {
+            var start, kernelMs;
+            return __generator(this, function (_a) {
+                start = util_1.now();
+                f();
+                kernelMs = util_1.now() - start;
+                return [2, { kernelMs: kernelMs }];
+            });
+        });
+    };
+    MathBackendCPU.prototype.memory = function () {
+        return {
+            unreliable: true,
+            reasons: ['The reported memory is an upper bound. Due to automatic garbage ' +
+                    'collection, the true allocated memory may be less.']
+        };
+    };
+    MathBackendCPU.prototype.complex = function (real, imag) {
+        var result = tensor_1.Tensor.make(real.shape, {}, 'complex64');
+        var resultData = this.data.get(result.dataId);
+        resultData.complexTensors = {
+            real: environment_1.ENV.engine.keep(real.clone()),
+            imag: environment_1.ENV.engine.keep(imag.clone())
+        };
+        return result;
+    };
+    MathBackendCPU.prototype.real = function (input) {
+        var resultData = this.data.get(input.dataId);
+        return resultData.complexTensors.real.clone();
+    };
+    MathBackendCPU.prototype.imag = function (input) {
+        var resultData = this.data.get(input.dataId);
+        return resultData.complexTensors.imag.clone();
+    };
+    MathBackendCPU.prototype.assertNotComplex = function (tensor, opName) {
+        if (!Array.isArray(tensor)) {
+            tensor = [tensor];
+        }
+        tensor.forEach(function (t) {
+            if (t != null) {
+                util.assert(t.dtype !== 'complex64', opName + " does not support complex64 tensors.");
+            }
+        });
+    };
+    MathBackendCPU.prototype.slice = function (x, begin, size) {
+        this.assertNotComplex(x, 'slice');
+        var buffer = ops.buffer(size, x.dtype);
+        for (var i = 0; i < buffer.size; ++i) {
+            var loc = buffer.indexToLoc(i);
+            var xLoc = loc.map(function (idx, j) { return idx + begin[j]; });
+            buffer.set.apply(buffer, [x.get.apply(x, xLoc)].concat(loc));
+        }
+        return buffer.toTensor();
+    };
+    MathBackendCPU.prototype.stridedSlice = function (x, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {
+        this.assertNotComplex(x, 'stridedSlice');
+        var _a = slice_util_1.getStridedSlicedInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask), beginIndex = _a[0], size = _a[1], shrinkAxis = _a[2];
+        var shape = size.filter(function (v, index) { return shrinkAxis.indexOf(index) === -1; });
+        if (shape.some(function (axis) { return axis === 0; })) {
+            return ops.tensor([], shape);
+        }
+        var buffer = ops.buffer(size, x.dtype);
+        for (var i = 0; i < buffer.size; i++) {
+            var loc = buffer.indexToLoc(i);
+            var newLoc = new Array(loc.length);
+            for (var j = 0; j < newLoc.length; j++) {
+                newLoc[j] = loc[j] * strides[j] + beginIndex[j];
+            }
+            buffer.set.apply(buffer, [x.get.apply(x, newLoc)].concat(loc));
+        }
+        return buffer.toTensor().reshape(shape);
+    };
+    MathBackendCPU.prototype.reverse = function (x, axis) {
+        this.assertNotComplex(x, 'reverse');
+        var buffer = ops.buffer(x.shape, x.dtype);
+        var xBuffer = x.buffer();
+        var _loop_1 = function (i) {
+            var outLoc = buffer.indexToLoc(i);
+            var inLoc = outLoc.slice();
+            axis.forEach(function (ax) { return inLoc[ax] = x.shape[ax] - 1 - inLoc[ax]; });
+            buffer.set.apply(buffer, [xBuffer.get.apply(xBuffer, inLoc)].concat(outLoc));
+        };
+        for (var i = 0; i < buffer.size; i++) {
+            _loop_1(i);
+        }
+        return buffer.toTensor();
+    };
+    MathBackendCPU.prototype.concat = function (tensors, axis) {
+        this.assertNotComplex(tensors, 'concat');
+        var tensors2D = tensors.map(function (t) {
+            var innerSize = util.sizeFromShape(t.shape.slice(axis));
+            return t.as2D(-1, innerSize);
+        });
+        var outShape = concat_util.computeOutShape(tensors2D.map(function (t) { return t.shape; }), 1);
+        var values = ops.buffer(outShape, tensors[0].dtype)
+            .values;
+        if (tensors2D[0].shape[0] === 1) {
+            var offset_1 = 0;
+            tensors2D.forEach(function (t) {
+                values.set(t.dataSync(), offset_1);
+                offset_1 += t.size;
+            });
+        }
+        else {
+            var colOffset_1 = 0;
+            tensors2D.forEach(function (t) {
+                var tVals = t.dataSync();
+                var tIdx = 0;
+                for (var row = 0; row < t.shape[0]; ++row) {
+                    var resIdx = row * outShape[1] + colOffset_1;
+                    for (var col = 0; col < t.shape[1]; ++col) {
+                        values[resIdx + col] = tVals[tIdx++];
+                    }
+                }
+                colOffset_1 += t.shape[1];
+            });
+        }
+        var finalOutShape = concat_util.computeOutShape(tensors.map(function (t) { return t.shape; }), axis);
+        return ops_1.tensor(values, finalOutShape, tensors[0].dtype);
+    };
+    MathBackendCPU.prototype.neg = function (x) {
+        this.assertNotComplex(x, 'neg');
+        return this.multiply(ops.scalar(-1), x);
+    };
+    MathBackendCPU.prototype.add = function (a, b) {
+        if (a.dtype === 'complex64' || b.dtype === 'complex64') {
+            return this.broadcastedBinaryComplexOp(a.cast('complex64'), b.cast('complex64'), function (aReal, aImag, bReal, bImag) {
+                return { real: aReal + bReal, imag: aImag + bImag };
+            });
+        }
+        return this.broadcastedBinaryOp(a, b, types_1.upcastType(a.dtype, b.dtype), function (aValue, bValue) { return aValue + bValue; });
+    };
+    MathBackendCPU.prototype.addN = function (tensors) {
+        this.assertNotComplex(tensors, 'addN');
+        var vals = tensors.map(function (t) { return t.dataSync(); });
+        var result = ops.buffer(tensors[0].shape, tensors[0].dtype);
+        var resultVals = result.values;
+        for (var i = 0; i < tensors.length; i++) {
+            var currVals = vals[i];
+            for (var j = 0; j < resultVals.length; j++) {
+                resultVals[j] += currVals[j];
+            }
+        }
+        return result.toTensor();
+    };
+    MathBackendCPU.prototype.subtract = function (a, b) {
+        if (a.dtype === 'complex64' || b.dtype === 'complex64') {
+            return this.broadcastedBinaryComplexOp(a.cast('complex64'), b.cast('complex64'), function (aReal, aImag, bReal, bImag) {
+                return { real: aReal - bReal, imag: aImag - bImag };
+            });
+        }
+        return this.broadcastedBinaryOp(a, b, types_1.upcastType(a.dtype, b.dtype), function (aValue, bValue) { return aValue - bValue; });
+    };
+    MathBackendCPU.prototype.pow = function (a, b) {
+        this.assertNotComplex([a, b], 'pow');
+        return this.broadcastedBinaryOp(a, b, a.dtype, function (aValue, bValue) { return Math.pow(aValue, bValue); });
+    };
+    MathBackendCPU.prototype.batchMatMul = function (a, b, transposeA, transposeB) {
+        this.assertNotComplex([a, b], 'matMul');
+        var sharedDim = transposeA ? a.shape[1] : a.shape[2];
+        var leftDim = transposeA ? a.shape[2] : a.shape[1];
+        var rightDim = transposeB ? b.shape[1] : b.shape[2];
+        var batchDim = a.shape[0];
+        var aValues = a.dataSync();
+        var bValues = b.dataSync();
+        var _a = transposeA ?
+            [a.strides[0], 1, a.strides[1]] :
+            [a.strides[0], a.strides[1], 1], aBatch = _a[0], aOuterStep = _a[1], aInnerStep = _a[2];
+        var _b = transposeB ?
+            [1, b.strides[1], b.strides[0]] :
+            [b.strides[1], 1, b.strides[0]], bInnerStep = _b[0], bOuterStep = _b[1], bBatch = _b[2];
+        var size = leftDim * rightDim;
+        var result = ops_1.buffer([batchDim, leftDim, rightDim], a.dtype);
+        var resVals = result.values;
+        var blockSize = this.blockSize;
+        for (var b_1 = 0; b_1 < batchDim; b_1++) {
+            for (var i0 = 0; i0 < leftDim; i0 += blockSize) {
+                for (var j0 = 0; j0 < rightDim; j0 += blockSize) {
+                    for (var k0 = 0; k0 < sharedDim; k0 += blockSize) {
+                        var iBlock = Math.min(i0 + blockSize, leftDim);
+                        var jBlock = Math.min(j0 + blockSize, rightDim);
+                        var kBlock = Math.min(k0 + blockSize, sharedDim);
+                        for (var i = i0; i < iBlock; i++) {
+                            for (var j = j0; j < jBlock; j++) {
+                                var sum = 0.0;
+                                for (var k = k0; k < kBlock; k++) {
+                                    sum += aValues[b_1 * aBatch + i * aOuterStep + k * aInnerStep] *
+                                        bValues[k * bInnerStep + j * bOuterStep + b_1 * bBatch];
+                                }
+                                resVals[b_1 * size + (i * rightDim + j)] += sum;
+                            }
+                        }
+                    }
+                }
+            }
+        }
+        return result.toTensor();
+    };
+    MathBackendCPU.prototype.multiply = function (a, b) {
+        if (a.dtype === 'complex64' || b.dtype === 'complex64') {
+            return this.broadcastedBinaryComplexOp(a.cast('complex64'), b.cast('complex64'), function (aReal, aImag, bReal, bImag) {
+                return {
+                    real: aReal * bReal - aImag * bImag,
+                    imag: aReal * bImag + aImag * bReal
+                };
+            });
+        }
+        return this.broadcastedBinaryOp(a, b, types_1.upcastType(a.dtype, b.dtype), function (aValue, bValue) { return aValue * bValue; });
+    };
+    MathBackendCPU.prototype.realDivide = function (a, b) {
+        this.assertNotComplex([a, b], 'realDivide');
+        var op = function (a, b) { return a / b; };
+        var outputDtype = 'float32';
+        return this.broadcastedBinaryOp(a, b, outputDtype, op);
+    };
+    MathBackendCPU.prototype.floorDiv = function (a, b) {
+        this.assertNotComplex([a, b], 'floorDiv');
+        var op = function (a, b) { return Math.floor(a / b); };
+        var outputDtype = 'int32';
+        return this.broadcastedBinaryOp(a, b, outputDtype, op);
+    };
+    MathBackendCPU.prototype.sum = function (x, axes) {
+        this.assertNotComplex(x, 'sum');
+        axis_util.assertAxesAreInnerMostDims('sum', axes, x.rank);
+        var _a = axis_util.computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+        var resultDtype = types_1.upcastType(x.dtype, 'int32');
+        var result = ops.zeros(outShape, resultDtype);
+        var reduceSize = util.sizeFromShape(reduceShape);
+        var vals = result.dataSync();
+        var aVals = x.dataSync();
+        for (var i = 0; i < vals.length; ++i) {
+            var offset = i * reduceSize;
+            var sum = 0;
+            for (var j = 0; j < reduceSize; ++j) {
+                sum += aVals[offset + j];
+            }
+            vals[i] = sum;
+        }
+        return result;
+    };
+    MathBackendCPU.prototype.prod = function (x, axes) {
+        this.assertNotComplex(x, 'sum');
+        var _a = axis_util.computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+        var resultDtype = types_1.upcastType(x.dtype, 'int32');
+        var result = ops.zeros(outShape, resultDtype);
+        var reduceSize = util.sizeFromShape(reduceShape);
+        var vals = result.dataSync();
+        var aVals = x.dataSync();
+        for (var i = 0; i < vals.length; ++i) {
+            var offset = i * reduceSize;
+            var prod = 1;
+            for (var j = 0; j < reduceSize; ++j) {
+                prod *= aVals[offset + j];
+            }
+            vals[i] = prod;
+        }
+        return result;
+    };
+    MathBackendCPU.prototype.unsortedSegmentSum = function (x, segmentIds, numSegments) {
+        this.assertNotComplex(x, 'unsortedSegmentSum');
+        var res = [];
+        var numIters = x.rank - segmentIds.rank;
+        for (var i = 0; i < numIters; ++i) {
+            segmentIds = segmentIds.expandDims(i + 1);
+        }
+        for (var i = 0; i < numSegments; ++i) {
+            var segmentId = ops.scalar(i, 'int32');
+            var mask = ops.equal(segmentId, segmentIds).asType('float32');
+            var sum = mask.mul(x).sum(0);
+            res.push(sum);
+        }
+        return ops.stack(res);
+    };
+    MathBackendCPU.prototype.argMin = function (x, axis) {
+        this.assertNotComplex(x, 'argMin');
+        var axes = [axis];
+        axis_util.assertAxesAreInnerMostDims('argMin', axes, x.rank);
+        var _a = axis_util.computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+        var result = ops.zeros(outShape, 'int32');
+        var reduceSize = util.sizeFromShape(reduceShape);
+        var vals = result.dataSync();
+        var aVals = x.dataSync();
+        for (var i = 0; i < vals.length; ++i) {
+            var offset = i * reduceSize;
+            var min = aVals[offset];
+            var minIndex = 0;
+            for (var j = 0; j < reduceSize; ++j) {
+                var value = aVals[offset + j];
+                if (value < min) {
+                    min = value;
+                    minIndex = j;
+                }
+            }
+            vals[i] = minIndex;
+        }
+        return result;
+    };
+    MathBackendCPU.prototype.argMax = function (x, axis) {
+        this.assertNotComplex(x, 'argMax');
+        var axes = [axis];
+        axis_util.assertAxesAreInnerMostDims('argMax', axes, x.rank);
+        var _a = axis_util.computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+        var result = ops.zeros(outShape, 'int32');
+        var reduceSize = util.sizeFromShape(reduceShape);
+        var vals = result.dataSync();
+        var aVals = x.dataSync();
+        for (var i = 0; i < vals.length; ++i) {
+            var offset = i * reduceSize;
+            var max = aVals[offset];
+            var maxIndex = 0;
+            for (var j = 0; j < reduceSize; ++j) {
+                var value = aVals[offset + j];
+                if (value > max) {
+                    max = value;
+                    maxIndex = j;
+                }
+            }
+            vals[i] = maxIndex;
+        }
+        return result;
+    };
+    MathBackendCPU.prototype.cumsum = function (x, axis, exclusive, reverse) {
+        this.assertNotComplex(x, 'cumsum');
+        if (axis !== x.rank - 1) {
+            throw new Error("backend.cumsum in CPU expects an inner-most axis=" + (x.rank - 1) + " " +
+                ("but got axis=" + axis));
+        }
+        var resultDtype = types_1.upcastType(x.dtype, 'int32');
+        var result = ops.zeros(x.shape, resultDtype);
+        var vals = result.dataSync();
+        var aVals = x.dataSync();
+        var finalDim = x.shape[x.rank - 1];
+        var indexAdjuster = reverse ?
+            function (i, j) { return i + finalDim - j - 1; } :
+            function (i, j) { return i + j; };
+        for (var i = 0; i < aVals.length; i += finalDim) {
+            for (var j = 0; j < finalDim; j++) {
+                var idx = indexAdjuster(i, j);
+                if (j === 0) {
+                    vals[idx] = exclusive ? 0 : aVals[idx];
+                }
+                else {
+                    var prevIdx = indexAdjuster(i, j - 1);
+                    vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] :
+                        aVals[idx] + vals[prevIdx];
+                }
+            }
+        }
+        return result;
+    };
+    MathBackendCPU.prototype.equal = function (a, b) {
+        this.assertNotComplex([a, b], 'equal');
+        return this.broadcastedBinaryOp(a, b, 'bool', function (aVal, bVal) {
+            return (aVal === bVal) ? 1 : 0;
+        });
+    };
+    MathBackendCPU.prototype.notEqual = function (a, b) {
+        this.assertNotComplex([a, b], 'notEqual');
+        return this.broadcastedBinaryOp(a, b, 'bool', function (aVal, bVal) {
+            return (aVal !== bVal) ? 1 : 0;
+        });
+    };
+    MathBackendCPU.prototype.less = function (a, b) {
+        this.assertNotComplex([a, b], 'less');
+        return this.broadcastedBinaryOp(a, b, 'bool', function (aVal, bVal) {
+            return (aVal < bVal) ? 1 : 0;
+        });
+    };
+    MathBackendCPU.prototype.lessEqual = function (a, b) {
+        this.assertNotComplex([a, b], 'lessEqual');
+        return this.broadcastedBinaryOp(a, b, 'bool', function (aVal, bVal) {
+            return (aVal <= bVal) ? 1 : 0;
+        });
+    };
+    MathBackendCPU.prototype.greater = function (a, b) {
+        this.assertNotComplex([a, b], 'greater');
+        return this.broadcastedBinaryOp(a, b, 'bool', function (aVal, bVal) {
+            return (aVal > bVal) ? 1 : 0;
+        });
+    };
+    MathBackendCPU.prototype.greaterEqual = function (a, b) {
+        this.assertNotComplex([a, b], 'greaterEqual');
+        return this.broadcastedBinaryOp(a, b, 'bool', function (aVal, bVal) {
+            return (aVal >= bVal) ? 1 : 0;
+        });
+    };
+    MathBackendCPU.prototype.logicalNot = function (x) {
+        this.assertNotComplex(x, 'logicalNot');
+        var values = x.dataSync();
+        var newValues = new Uint8Array(values.length);
+        for (var i = 0; i < values.length; ++i) {
+            newValues[i] = values[i] ? 0 : 1;
+        }
+        return tensor_1.Tensor.make(x.shape, { values: newValues }, 'bool');
+    };
+    MathBackendCPU.prototype.logicalAnd = function (a, b) {
+        this.assertNotComplex([a, b], 'logicalAnd');
+        return this.broadcastedBinaryOp(a, b, 'bool', function (aVal, bVal) {
+            return aVal && bVal;
+        });
+    };
+    MathBackendCPU.prototype.logicalOr = function (a, b) {
+        this.assertNotComplex([a, b], 'logicalOr');
+        return this.broadcastedBinaryOp(a, b, 'bool', function (aVal, bVal) {
+            return aVal || bVal;
+        });
+    };
+    MathBackendCPU.prototype.select = function (condition, a, b) {
+        this.assertNotComplex([condition, a, b], 'select');
+        var values = condition.dataSync();
+        var aValues = a.dataSync();
+        var bValues = b.dataSync();
+        var result = ops.zeros(a.shape, types_1.upcastType(a.dtype, b.dtype));
+        var newValues = result.dataSync();
+        var index = 0;
+        var offset = condition.rank === 0 || condition.rank > 1 || a.rank === 1 ?
+            1 :
+            a.shape[1];
+        for (var i = 0; i < values.length; i++) {
+            for (var j = 0; j < offset; j++) {
+                if (values[i] === 1) {
+                    newValues[index++] = aValues[i];
+                }
+                else {
+                    newValues[index++] = bValues[i];
+                }
+            }
+        }
+        return result;
+    };
+    MathBackendCPU.prototype.where = function (condition) {
+        this.assertNotComplex([condition], 'where');
+        var condVals = condition.dataSync();
+        return where_impl_1.whereImpl(condition.shape, condVals);
+    };
+    MathBackendCPU.prototype.topk = function (x, k, sorted) {
+        this.assertNotComplex(x, 'topk');
+        var xVals = x.dataSync();
+        return topk_impl_1.topkImpl(xVals, x.shape, x.dtype, k, sorted);
+    };
+    MathBackendCPU.prototype.min = function (x, axes) {
+        this.assertNotComplex(x, 'min');
+        axis_util.assertAxesAreInnerMostDims('min', axes, x.rank);
+        var _a = axis_util.computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+        var result = ops.zeros(outShape, x.dtype);
+        var reduceSize = util.sizeFromShape(reduceShape);
+        var vals = result.dataSync();
+        var aVals = x.dataSync();
+        for (var i = 0; i < vals.length; ++i) {
+            var offset = i * reduceSize;
+            var min = aVals[offset];
+            for (var j = 0; j < reduceSize; ++j) {
+                var value = aVals[offset + j];
+                if (value < min) {
+                    min = value;
+                }
+            }
+            vals[i] = min;
+        }
+        return result;
+    };
+    MathBackendCPU.prototype.minimum = function (a, b) {
+        this.assertNotComplex([a, b], 'minimum');
+        return this.broadcastedBinaryOp(a, b, a.dtype, function (aVal, bVal) { return Math.min(aVal, bVal); });
+    };
+    MathBackendCPU.prototype.mod = function (a, b) {
+        this.assertNotComplex([a, b], 'mod');
+        return this.broadcastedBinaryOp(a, b, a.dtype, function (aVal, bVal) {
+            var rem = aVal % bVal;
+            if ((aVal < 0 && bVal < 0) || (aVal >= 0 && bVal >= 0)) {
+                return rem;
+            }
+            else {
+                return (rem + bVal) % bVal;
+            }
+        });
+    };
+    MathBackendCPU.prototype.max = function (x, axes) {
+        this.assertNotComplex(x, 'max');
+        axis_util.assertAxesAreInnerMostDims('max', axes, x.rank);
+        var _a = axis_util.computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+        var result = ops.zeros(outShape, x.dtype);
+        var reduceSize = util.sizeFromShape(reduceShape);
+        var vals = result.dataSync();
+        var aVals = x.dataSync();
+        for (var i = 0; i < vals.length; ++i) {
+            var offset = i * reduceSize;
+            var max = aVals[offset];
+            for (var j = 0; j < reduceSize; ++j) {
+                var value = aVals[offset + j];
+                if (value > max) {
+                    max = value;
+                }
+            }
+            vals[i] = max;
+        }
+        return result;
+    };
+    MathBackendCPU.prototype.maximum = function (a, b) {
+        this.assertNotComplex([a, b], 'maximum');
+        return this.broadcastedBinaryOp(a, b, a.dtype, function (aVal, bVal) { return Math.max(aVal, bVal); });
+    };
+    MathBackendCPU.prototype.all = function (x, axes) {
+        this.assertNotComplex(x, 'all');
+        axis_util.assertAxesAreInnerMostDims('all', axes, x.rank);
+        var _a = axis_util.computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+        var result = ops.zeros(outShape, x.dtype);
+        var reduceSize = util.sizeFromShape(reduceShape);
+        var vals = result.dataSync();
+        var aVals = x.dataSync();
+        for (var i = 0; i < vals.length; ++i) {
+            var offset = i * reduceSize;
+            var all = aVals[offset];
+            for (var j = 0; j < reduceSize; ++j) {
+                var value = aVals[offset + j];
+                all = all && value;
+            }
+            vals[i] = all;
+        }
+        return result;
+    };
+    MathBackendCPU.prototype.any = function (x, axes) {
+        this.assertNotComplex(x, 'any');
+        axis_util.assertAxesAreInnerMostDims('any', axes, x.rank);
+        var _a = axis_util.computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+        var result = ops.zeros(outShape, x.dtype);
+        var reduceSize = util.sizeFromShape(reduceShape);
+        var vals = result.dataSync();
+        var aVals = x.dataSync();
+        for (var i = 0; i < vals.length; ++i) {
+            var offset = i * reduceSize;
+            var anyVal = aVals[offset];
+            for (var j = 0; j < reduceSize; ++j) {
+                var value = aVals[offset + j];
+                anyVal = anyVal || value;
+            }
+            vals[i] = anyVal;
+        }
+        return result;
+    };
+    MathBackendCPU.prototype.squaredDifference = function (a, b) {
+        this.assertNotComplex([a, b], 'squaredDifference');
+        return this.broadcastedBinaryOp(a, b, a.dtype, function (aVal, bVal) {
+            var diff = aVal - bVal;
+            return diff * diff;
+        });
+    };
+    MathBackendCPU.prototype.ceil = function (x) {
+        this.assertNotComplex(x, 'ceil');
+        var values = x.dataSync();
+        var newValues = new Float32Array(values.length);
+        for (var i = 0; i < values.length; ++i) {
+            newValues[i] = Math.ceil(values[i]);
+        }
+        return tensor_1.Tensor.make(x.shape, { values: newValues });
+    };
+    MathBackendCPU.prototype.floor = function (x) {
+        this.assertNotComplex(x, 'floor');
+        var values = x.dataSync();
+        var newValues = new Float32Array(values.length);
+        for (var i = 0; i < values.length; ++i) {
+            newValues[i] = Math.floor(values[i]);
+        }
+        return tensor_1.Tensor.make(x.shape, { values: newValues });
+    };
+    MathBackendCPU.prototype.sign = function (x) {
+        this.assertNotComplex(x, 'x');
+        var values = x.dataSync();
+        var newValues = new Float32Array(values.length);
+        for (var i = 0; i < values.length; ++i) {
+            if (values[i] < 0) {
+                newValues[i] = -1;
+            }
+            else if (values[i] > 0) {
+                newValues[i] = 1;
+            }
+            else {
+                newValues[i] = 0;
+            }
+        }
+        return tensor_1.Tensor.make(x.shape, { values: newValues });
+    };
+    MathBackendCPU.prototype.round = function (x) {
+        this.assertNotComplex(x, 'round');
+        var values = x.dataSync();
+        var newValues = new Float32Array(values.length);
+        for (var i = 0; i < values.length; ++i) {
+            var base = Math.floor(values[i]);
+            if (values[i] - base < 0.5) {
+                newValues[i] = Math.floor(values[i]);
+            }
+            else if (values[i] - base > 0.5) {
+                newValues[i] = Math.ceil(values[i]);
+            }
+            else {
+                if (base % 2.0 === 0.0) {
+                    newValues[i] = base;
+                }
+                else {
+                    newValues[i] = base + 1.0;
+                }
+            }
+        }
+        return tensor_1.Tensor.make(x.shape, { values: newValues });
+    };
+    MathBackendCPU.prototype.exp = function (x) {
+        this.assertNotComplex(x, 'exp');
+        var values = x.dataSync();
+        var newValues = new Float32Array(values.length);
+        for (var i = 0; i < values.length; ++i) {
+            newValues[i] = Math.exp(values[i]);
+        }
+        return tensor_1.Tensor.make(x.shape, { values: newValues });
+    };
+    MathBackendCPU.prototype.expm1 = function (x) {
+        this.assertNotComplex(x, 'expm1');
+        var values = x.dataSync();
+        var newValues = new Float32Array(values.length);
+        for (var i = 0; i < values.length; ++i) {
+            newValues[i] = Math.expm1(values[i]);
+        }
+        return tensor_1.Tensor.make(x.shape, { values: newValues });
+    };
+    MathBackendCPU.prototype.log = function (x) {
+        this.assertNotComplex(x, 'log');
+        var values = x.dataSync();
+        var newValues = new Float32Array(values.length);
+        for (var i = 0; i < values.length; ++i) {
+            var value = values[i];
+            newValues[i] = Math.log(value);
+        }
+        return tensor_1.Tensor.make(x.shape, { values: newValues });
+    };
+    MathBackendCPU.prototype.log1p = function (x) {
+        this.assertNotComplex(x, 'log1p');
+        var values = x.dataSync();
+        var newValues = new Float32Array(values.length);
+        for (var i = 0; i < values.length; ++i) {
+            var value = values[i];
+            newValues[i] = Math.log1p(value);
+        }
+        return tensor_1.Tensor.make(x.shape, { values: newValues });
+    };
+    MathBackendCPU.prototype.sqrt = function (x) {
+        this.assertNotComplex(x, 'sqrt');
+        var values = x.dataSync();
+        var newValues = new Float32Array(values.length);
+        for (var i = 0; i < values.length; ++i) {
+            var value = values[i];
+            newValues[i] = Math.sqrt(value);
+        }
+        return tensor_1.Tensor.make(x.shape, { values: newValues });
+    };
+    MathBackendCPU.prototype.rsqrt = function (x) {
+        this.assertNotComplex(x, 'rsqrt');
+        var values = x.dataSync();
+        var newValues = new Float32Array(values.length);
+        for (var i = 0; i < values.length; ++i) {
+            var value = values[i];
+            newValues[i] = 1 / Math.sqrt(value);
+        }
+        return tensor_1.Tensor.make(x.shape, { values: newValues });
+    };
+    MathBackendCPU.prototype.square = function (x) {
+        this.assertNotComplex(x, 'square');
+        var values = x.dataSync();
+        var newValues = new Float32Array(values.length);
+        for (var i = 0; i < values.length; ++i) {
+            var value = values[i];
+            newValues[i] = value * value;
+        }
+        return tensor_1.Tensor.make(x.shape, { values: newValues });
+    };
+    MathBackendCPU.prototype.reciprocal = function (x) {
+        this.assertNotComplex(x, 'reciprocal');
+        var values = x.dataSync();
+        var newValues = new Float32Array(values.length);
+        for (var i = 0; i < values.length; ++i) {
+            newValues[i] = 1 / values[i];
+        }
+        return tensor_1.Tensor.make(x.shape, { values: newValues });
+    };
+    MathBackendCPU.prototype.relu = function (x) {
+        this.assertNotComplex(x, 'relu');
+        var res = ops.zeros(x.shape, x.dtype);
+        var resVals = res.dataSync();
+        var inVals = x.dataSync();
+        for (var i = 0; i < inVals.length; ++i) {
+            resVals[i] = Math.max(0, inVals[i]);
+        }
+        return res;
+    };
+    MathBackendCPU.prototype.elu = function (x) {
+        this.assertNotComplex(x, 'elu');
+        var resultValues = new Float32Array(x.size);
+        var values = x.dataSync();
+        for (var i = 0; i < values.length; ++i) {
+            var v = values[i];
+            if (v >= 0) {
+                resultValues[i] = v;
+            }
+            else {
+                resultValues[i] = (Math.exp(v) - 1);
+            }
+        }
+        return tensor_1.Tensor.make(x.shape, { values: resultValues });
+    };
+    MathBackendCPU.prototype.eluDer = function (dy, y) {
+        this.assertNotComplex([dy, y], 'eluDer');
+        var resultValues = new Float32Array(y.size);
+        var values = y.dataSync();
+        var dyValues = dy.dataSync();
+        for (var i = 0; i < values.length; ++i) {
+            var v = values[i];
+            if (v >= 1) {
+                resultValues[i] = dyValues[i];
+            }
+            else {
+                resultValues[i] = dyValues[i] * (v + 1);
+            }
+        }
+        return tensor_1.Tensor.make(y.shape, { values: resultValues });
+    };
+    MathBackendCPU.prototype.selu = function (x) {
+        this.assertNotComplex(x, 'selu');
+        var scaleAlpha = selu_util.SELU_SCALEALPHA;
+        var scale = selu_util.SELU_SCALE;
+        var resultValues = new Float32Array(x.size);
+        var values = x.dataSync();
+        for (var i = 0; i < values.length; ++i) {
+            var v = values[i];
+            if (v >= 0) {
+                resultValues[i] = scale * v;
+            }
+            else {
+                resultValues[i] = scaleAlpha * (Math.exp(v) - 1);
+            }
+        }
+        return tensor_1.Tensor.make(x.shape, { values: resultValues });
+    };
+    MathBackendCPU.prototype.clip = function (x, min, max) {
+        this.assertNotComplex(x, 'clip');
+        var resultValues = new Float32Array(x.size);
+        var values = x.dataSync();
+        for (var i = 0; i < values.length; ++i) {
+            var v = values[i];
+            resultValues[i] = v > max ? max : (v < min ? min : v);
+        }
+        return tensor_1.Tensor.make(x.shape, { values: resultValues });
+    };
+    MathBackendCPU.prototype.abs = function (x) {
+        var resultValues = new Float32Array(x.size);
+        var values = x.dataSync();
+        for (var i = 0; i < values.length; ++i) {
+            resultValues[i] = Math.abs(values[i]);
+        }
+        return tensor_1.Tensor.make(x.shape, { values: resultValues });
+    };
+    MathBackendCPU.prototype.complexAbs = function (x) {
+        var resultValues = new Float32Array(x.size);
+        var values = x.dataSync();
+        for (var i = 0; i < x.size; ++i) {
+            var real = values[i * 2];
+            var imag = values[i * 2 + 1];
+            resultValues[i] = Math.hypot(real, imag);
+        }
+        return tensor_1.Tensor.make(x.shape, { values: resultValues });
+    };
+    MathBackendCPU.prototype.int = function (x) {
+        this.assertNotComplex(x, 'int');
+        var resultValues = new Int32Array(x.size);
+        var values = x.dataSync();
+        for (var i = 0; i < values.length; ++i) {
+            resultValues[i] = values[i];
+        }
+        return tensor_1.Tensor.make(x.shape, { values: resultValues }, 'int32');
+    };
+    MathBackendCPU.prototype.sigmoid = function (x) {
+        this.assertNotComplex(x, 'sigmoid');
+        var resultValues = new Float32Array(x.size);
+        var values = x.dataSync();
+        for (var i = 0; i < values.length; ++i) {
+            resultValues[i] = 1 / (1 + Math.exp(-values[i]));
+        }
+        return tensor_1.Tensor.make(x.shape, { values: resultValues });
+    };
+    MathBackendCPU.prototype.softplus = function (x) {
+        this.assertNotComplex(x, 'softplus');
+        var epsilon = 1.1920928955078125e-7;
+        var threshold = Math.log(epsilon) + 2.0;
+        var resultValues = new Float32Array(x.size);
+        var values = x.dataSync();
+        for (var i = 0; i < values.length; ++i) {
+            var tooLarge = values[i] > -threshold;
+            var tooSmall = values[i] < threshold;
+            var expX = Math.exp(values[i]);
+            var result = void 0;
+            if (tooSmall) {
+                result = expX;
+            }
+            else if (tooLarge) {
+                result = values[i];
+            }
+            else {
+                result = Math.log(1.0 + expX);
+            }
+            resultValues[i] = result;
+        }
+        return tensor_1.Tensor.make(x.shape, { values: resultValues });
+    };
+    MathBackendCPU.prototype.sin = function (x) {
+        this.assertNotComplex(x, 'sin');
+        var resultValues = new Float32Array(x.size);
+        var values = x.dataSync();
+        for (var i = 0; i < values.length; ++i) {
+            resultValues[i] = Math.sin(values[i]);
+        }
+        return tensor_1.Tensor.make(x.shape, { values: resultValues });
+    };
+    MathBackendCPU.prototype.cos = function (x) {
+        this.assertNotComplex(x, 'cos');
+        var resultValues = new Float32Array(x.size);
+        var values = x.dataSync();
+        for (var i = 0; i < values.length; ++i) {
+            resultValues[i] = Math.cos(values[i]);
+        }
+        return tensor_1.Tensor.make(x.shape, { values: resultValues });
+    };
+    MathBackendCPU.prototype.tan = function (x) {
+        this.assertNotComplex(x, 'tan');
+        var resultValues = new Float32Array(x.size);
+        var values = x.dataSync();
+        for (var i = 0; i < values.length; ++i) {
+            resultValues[i] = Math.tan(values[i]);
+        }
+        return tensor_1.Tensor.make(x.shape, { values: resultValues });
+    };
+    MathBackendCPU.prototype.asin = function (x) {
+        this.assertNotComplex(x, 'asin');
+        var resultValues = new Float32Array(x.size);
+        var values = x.dataSync();
+        for (var i = 0; i < values.length; ++i) {
+            resultValues[i] = Math.asin(values[i]);
+        }
+        return tensor_1.Tensor.make(x.shape, { values: resultValues });
+    };
+    MathBackendCPU.prototype.acos = function (x) {
+        this.assertNotComplex(x, 'acos');
+        var resultValues = new Float32Array(x.size);
+        var values = x.dataSync();
+        for (var i = 0; i < values.length; ++i) {
+            resultValues[i] = Math.acos(values[i]);
+        }
+        return tensor_1.Tensor.make(x.shape, { values: resultValues });
+    };
+    MathBackendCPU.prototype.atan = function (x) {
+        this.assertNotComplex(x, 'atan');
+        var resultValues = new Float32Array(x.size);
+        var values = x.dataSync();
+        for (var i = 0; i < values.length; ++i) {
+            resultValues[i] = Math.atan(values[i]);
+        }
+        return tensor_1.Tensor.make(x.shape, { values: resultValues });
+    };
+    MathBackendCPU.prototype.atan2 = function (a, b) {
+        this.assertNotComplex([a, b], 'atan2');
+        return this.broadcastedBinaryOp(a, b, a.dtype, function (aValue, bValue) { return Math.atan2(aValue, bValue); });
+    };
+    MathBackendCPU.prototype.sinh = function (x) {
+        this.assertNotComplex(x, 'sinh');
+        var resultValues = new Float32Array(x.size);
+        var values = x.dataSync();
+        for (var i = 0; i < values.length; ++i) {
+            resultValues[i] = Math.sinh(values[i]);
+        }
+        return tensor_1.Tensor.make(x.shape, { values: resultValues });
+    };
+    MathBackendCPU.prototype.cosh = function (x) {
+        this.assertNotComplex(x, 'cosh');
+        var resultValues = new Float32Array(x.size);
+        var values = x.dataSync();
+        for (var i = 0; i < values.length; ++i) {
+            resultValues[i] = Math.cosh(values[i]);
+        }
+        return tensor_1.Tensor.make(x.shape, { values: resultValues });
+    };
+    MathBackendCPU.prototype.tanh = function (x) {
+        this.assertNotComplex(x, 'tanh');
+        var resultValues = new Float32Array(x.size);
+        var values = x.dataSync();
+        for (var i = 0; i < values.length; ++i) {
+            resultValues[i] = util.tanh(values[i]);
+        }
+        return tensor_1.Tensor.make(x.shape, { values: resultValues });
+    };
+    MathBackendCPU.prototype.asinh = function (x) {
+        this.assertNotComplex(x, 'asinh');
+        var resultValues = new Float32Array(x.size);
+        var values = x.dataSync();
+        for (var i = 0; i < values.length; ++i) {
+            resultValues[i] = Math.asinh(values[i]);
+        }
+        return tensor_1.Tensor.make(x.shape, { values: resultValues });
+    };
+    MathBackendCPU.prototype.acosh = function (x) {
+        this.assertNotComplex(x, 'acosh');
+        var resultValues = new Float32Array(x.size);
+        var values = x.dataSync();
+        for (var i = 0; i < values.length; ++i) {
+            resultValues[i] = Math.acosh(values[i]);
+        }
+        return tensor_1.Tensor.make(x.shape, { values: resultValues });
+    };
+    MathBackendCPU.prototype.atanh = function (x) {
+        this.assertNotComplex(x, 'atanh');
+        var resultValues = new Float32Array(x.size);
+        var values = x.dataSync();
+        for (var i = 0; i < values.length; ++i) {
+            resultValues[i] = Math.atanh(values[i]);
+        }
+        return tensor_1.Tensor.make(x.shape, { values: resultValues });
+    };
+    MathBackendCPU.prototype.erf = function (x) {
+        this.assertNotComplex(x, 'erf');
+        var resultValues = new Float32Array(x.size);
+        var values = x.dataSync();
+        var p = erf_util.ERF_P;
+        var a1 = erf_util.ERF_A1;
+        var a2 = erf_util.ERF_A2;
+        var a3 = erf_util.ERF_A3;
+        var a4 = erf_util.ERF_A4;
+        var a5 = erf_util.ERF_A5;
+        for (var i = 0; i < values.length; ++i) {
+            var v = values[i];
+            var t = 1.0 / (1.0 + p * v);
+            resultValues[i] = 1.0 -
+                (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t *
+                    Math.exp(-v * v);
+        }
+        return tensor_1.Tensor.make(x.shape, { values: resultValues });
+    };
+    MathBackendCPU.prototype.step = function (x, alpha) {
+        if (alpha === void 0) { alpha = 0; }
+        this.assertNotComplex(x, 'step');
+        var resultValues = new Float32Array(x.size);
+        var values = x.dataSync();
+        for (var i = 0; i < values.length; ++i) {
+            var value = values[i];
+            if (isNaN(value)) {
+                resultValues[i] = NaN;
+            }
+            else {
+                resultValues[i] = value > 0 ? 1 : alpha;
+            }
+        }
+        return tensor_1.Tensor.make(x.shape, { values: resultValues });
+    };
+    MathBackendCPU.prototype.conv2d = function (x, filter, convInfo) {
+        this.assertNotComplex([x, filter], 'conv2d');
+        var filterHeight = convInfo.filterHeight;
+        var filterWidth = convInfo.filterWidth;
+        var dilationHeight = convInfo.dilationHeight;
+        var dilationWidth = convInfo.dilationWidth;
+        var padLeft = convInfo.padInfo.left;
+        var padTop = convInfo.padInfo.top;
+        var y = ops.buffer(convInfo.outShape, x.dtype);
+        var xVals = x.dataSync();
+        var wVals = filter.dataSync();
+        var yVals = y.values;
+        for (var b = 0; b < convInfo.batchSize; ++b) {
+            var xOffset1 = b * x.strides[0];
+            var yOffset1 = b * y.strides[0];
+            for (var yR = 0; yR < convInfo.outHeight; ++yR) {
+                var yOffset2 = yOffset1 + yR * y.strides[1];
+                var xRCorner = yR * convInfo.strideHeight - padLeft;
+                for (var wR = 0; wR < filterHeight; wR++) {
+                    var xR = xRCorner + wR * dilationHeight;
+                    if (xR < 0 || xR >= convInfo.inHeight) {
+                        continue;
+                    }
+                    var wOffset1 = wR * filter.strides[0];
+                    var xOffset2 = xOffset1 + xR * x.strides[1];
+                    for (var yC = 0; yC < convInfo.outWidth; ++yC) {
+                        var yOffset3 = yOffset2 + yC * convInfo.outChannels;
+                        var xCCorner = yC * convInfo.strideWidth - padTop;
+                        for (var wC = 0; wC < filterWidth; wC++) {
+                            var xC = xCCorner + wC * dilationWidth;
+                            if (xC < 0 || xC >= convInfo.inWidth) {
+                                continue;
+                            }
+                            var wOffset2 = wOffset1 + wC * filter.strides[1];
+                            var xOffset3 = xOffset2 + xC * convInfo.inChannels;
+                            var wOffset3 = wOffset2;
+                            for (var d1 = 0; d1 < convInfo.inChannels; ++d1) {
+                                var xVal = xVals[xOffset3 + d1];
+                                for (var d2 = 0; d2 < convInfo.outChannels; ++d2) {
+                                    yVals[yOffset3 + d2] += xVal * wVals[wOffset3 + d2];
+                                }
+                                wOffset3 += convInfo.outChannels;
+                            }
+                        }
+                    }
+                }
+            }
+        }
+        return y.toTensor();
+    };
+    MathBackendCPU.prototype.conv3d = function (x, filter, convInfo) {
+        var filterDepth = convInfo.filterDepth;
+        var filterHeight = convInfo.filterHeight;
+        var filterWidth = convInfo.filterWidth;
+        var dilationDepth = convInfo.dilationDepth;
+        var dilationHeight = convInfo.dilationHeight;
+        var dilationWidth = convInfo.dilationWidth;
+        var padFront = convInfo.padInfo.front;
+        var padLeft = convInfo.padInfo.left;
+        var padTop = convInfo.padInfo.top;
+        var y = ops.buffer(convInfo.outShape, x.dtype);
+        var xVals = x.dataSync();
+        var wVals = filter.dataSync();
+        var yVals = y.values;
+        for (var b = 0; b < convInfo.batchSize; ++b) {
+            var xOffset1 = b * x.strides[0];
+            var yOffset1 = b * y.strides[0];
+            for (var yF = 0; yF < convInfo.outDepth; ++yF) {
+                var yOffset2 = yOffset1 + yF * y.strides[1];
+                var xFCorner = yF * convInfo.strideDepth - padFront;
+                for (var wF = 0; wF < filterDepth; wF++) {
+                    var xF = xFCorner + wF * dilationDepth;
+                    if (xF < 0 || xF >= convInfo.inDepth) {
+                        continue;
+                    }
+                    var wOffset1 = wF * filter.strides[0];
+                    var xOffset2 = xOffset1 + xF * x.strides[1];
+                    for (var yR = 0; yR < convInfo.outHeight; ++yR) {
+                        var yOffset3 = yOffset2 + yR * y.strides[2];
+                        var xRCorner = yR * convInfo.strideHeight - padTop;
+                        for (var wR = 0; wR < filterHeight; wR++) {
+                            var xR = xRCorner + wR * dilationHeight;
+                            if (xR < 0 || xR >= convInfo.inHeight) {
+                                continue;
+                            }
+                            var wOffset2 = wOffset1 + wR * filter.strides[1];
+                            var xOffset3 = xOffset2 + xR * x.strides[2];
+                            for (var yC = 0; yC < convInfo.outWidth; ++yC) {
+                                var yOffset4 = yOffset3 + yC * convInfo.outChannels;
+                                var xCCorner = yC * convInfo.strideWidth - padLeft;
+                                for (var wC = 0; wC < filterWidth; wC++) {
+                                    var xC = xCCorner + wC * dilationWidth;
+                                    if (xC < 0 || xC >= convInfo.inWidth) {
+                                        continue;
+                                    }
+                                    var wOffset3 = wOffset2 + wC * filter.strides[2];
+                                    var xOffset4 = xOffset3 + xC * convInfo.inChannels;
+                                    var wOffset4 = wOffset3;
+                                    for (var d1 = 0; d1 < convInfo.inChannels; ++d1) {
+                                        var xVal = xVals[xOffset4 + d1];
+                                        for (var d2 = 0; d2 < convInfo.outChannels; ++d2) {
+                                            yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];
+                                        }
+                                        wOffset4 += convInfo.outChannels;
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+        }
+        return y.toTensor();
+    };
+    MathBackendCPU.prototype.conv2dDerInput = function (dy, filter, convInfo) {
+        this.assertNotComplex([dy, filter], 'conv2dDerInput');
+        var dx = ops.buffer(convInfo.inShape, 'float32');
+        var dxValues = dx.values;
+        var _a = dx.strides, dxS0 = _a[0], dxS1 = _a[1], dxS2 = _a[2];
+        var dyValues = dy.dataSync();
+        var _b = dy.strides, dyS0 = _b[0], dyS1 = _b[1], dyS2 = _b[2];
+        var fltValues = filter.dataSync();
+        var _c = filter.strides, fltS0 = _c[0], fltS1 = _c[1], fltS2 = _c[2];
+        var batchSize = convInfo.batchSize, filterHeight = convInfo.filterHeight, filterWidth = convInfo.filterWidth, inChannels = convInfo.inChannels, inHeight = convInfo.inHeight, inWidth = convInfo.inWidth, outChannels = convInfo.outChannels, outHeight = convInfo.outHeight, outWidth = convInfo.outWidth, strideHeight = convInfo.strideHeight, strideWidth = convInfo.strideWidth;
+        var topPad = filterHeight - 1 - convInfo.padInfo.top;
+        var leftPad = filterWidth - 1 - convInfo.padInfo.left;
+        for (var b = 0; b < batchSize; ++b) {
+            for (var d1 = 0; d1 < inChannels; ++d1) {
+                for (var xR = 0; xR < inHeight; ++xR) {
+                    var xRCorner = xR - topPad;
+                    var xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
+                    var yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
+                    for (var xC = 0; xC < inWidth; ++xC) {
+                        var xCCorner = xC - leftPad;
+                        var xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
+                        var yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
+                        var dotProd = 0;
+                        for (var yR = xRMin; yR < yRMax; ++yR) {
+                            var wR = yR * strideHeight - xRCorner;
+                            for (var yC = xCMin; yC < yCMax; ++yC) {
+                                var wC = yC * strideWidth - xCCorner;
+                                var dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;
+                                var fltOffset = fltS0 * (filterHeight - 1 - wR) +
+                                    fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;
+                                for (var d2 = 0; d2 < outChannels; ++d2) {
+                                    var pixel = dyValues[dyOffset + d2];
+                                    var weight = fltValues[fltOffset + d2];
+                                    dotProd += pixel * weight;
+                                }
+                            }
+                        }
+                        dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;
+                    }
+                }
+            }
+        }
+        return dx.toTensor();
+    };
+    MathBackendCPU.prototype.conv3dDerInput = function (dy, filter, convInfo) {
+        var dx = ops.buffer(convInfo.inShape, 'float32');
+        var dxValues = dx.values;
+        var _a = dx.strides, dxS0 = _a[0], dxS1 = _a[1], dxS2 = _a[2], dxS3 = _a[3];
+        var dyValues = dy.dataSync();
+        var _b = dy.strides, dyS0 = _b[0], dyS1 = _b[1], dyS2 = _b[2], dyS3 = _b[3];
+        var fltValues = filter.dataSync();
+        var _c = filter.strides, fltS0 = _c[0], fltS1 = _c[1], fltS2 = _c[2], fltS3 = _c[3];
+        var batchSize = convInfo.batchSize, filterDepth = convInfo.filterDepth, filterHeight = convInfo.filterHeight, filterWidth = convInfo.filterWidth, inChannels = convInfo.inChannels, inDepth = convInfo.inDepth, inHeight = convInfo.inHeight, inWidth = convInfo.inWidth, outChannels = convInfo.outChannels, outDepth = convInfo.outDepth, outHeight = convInfo.outHeight, outWidth = convInfo.outWidth, strideDepth = convInfo.strideDepth, strideHeight = convInfo.strideHeight, strideWidth = convInfo.strideWidth;
+        var frontPad = filterDepth - 1 - convInfo.padInfo.front;
+        var topPad = filterHeight - 1 - convInfo.padInfo.top;
+        var leftPad = filterWidth - 1 - convInfo.padInfo.left;
+        for (var b = 0; b < batchSize; ++b) {
+            for (var d1 = 0; d1 < inChannels; ++d1) {
+                for (var xF = 0; xF < inDepth; ++xF) {
+                    var xFCorner = xF - frontPad;
+                    var xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));
+                    var yFMax = Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);
+                    for (var xR = 0; xR < inHeight; ++xR) {
+                        var xRCorner = xR - topPad;
+                        var xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
+                        var yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
+                        for (var xC = 0; xC < inWidth; ++xC) {
+                            var xCCorner = xC - leftPad;
+                            var xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
+                            var yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
+                            var dotProd = 0;
+                            for (var yF = xFMin; yF < yFMax; ++yF) {
+                                var wF = yF * strideDepth - xFCorner;
+                                for (var yR = xRMin; yR < yRMax; ++yR) {
+                                    var wR = yR * strideHeight - xRCorner;
+                                    for (var yC = xCMin; yC < yCMax; ++yC) {
+                                        var wC = yC * strideWidth - xCCorner;
+                                        var dyOffset = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;
+                                        var fltOffset = fltS0 * (filterDepth - 1 - wF) +
+                                            fltS1 * (filterHeight - 1 - wR) +
+                                            fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;
+                                        for (var d2 = 0; d2 < outChannels; ++d2) {
+                                            var pixel = dyValues[dyOffset + d2];
+                                            var weight = fltValues[fltOffset + d2];
+                                            dotProd += pixel * weight;
+                                        }
+                                    }
+                                }
+                            }
+                            dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] =
+                                dotProd;
+                        }
+                    }
+                }
+            }
+        }
+        return dx.toTensor();
+    };
+    MathBackendCPU.prototype.conv2dDerFilter = function (x, dy, convInfo) {
+        this.assertNotComplex([x, dy], 'conv2dDerFilter');
+        var strideHeight = convInfo.strideHeight;
+        var strideWidth = convInfo.strideWidth;
+        var filterHeight = convInfo.filterHeight;
+        var filterWidth = convInfo.filterWidth;
+        var dW = ops.buffer(convInfo.filterShape, 'float32');
+        var leftPad = convInfo.padInfo.left;
+        var topPad = convInfo.padInfo.top;
+        for (var wR = 0; wR < filterHeight; ++wR) {
+            var yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
+            var yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
+            for (var wC = 0; wC < filterWidth; ++wC) {
+                var yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
+                var yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
+                for (var d1 = 0; d1 < convInfo.inChannels; ++d1) {
+                    for (var d2 = 0; d2 < convInfo.outChannels; ++d2) {
+                        var dotProd = 0;
+                        for (var b = 0; b < convInfo.batchSize; ++b) {
+                            for (var yR = yRMin; yR < yRMax; ++yR) {
+                                var xR = wR + yR * strideHeight - topPad;
+                                for (var yC = yCMin; yC < yCMax; ++yC) {
+                                    var xC = wC + yC * strideWidth - leftPad;
+                                    dotProd += x.get(b, xR, xC, d1) * dy.get(b, yR, yC, d2);
+                                }
+                            }
+                        }
+                        dW.set(dotProd, wR, wC, d1, d2);
+                    }
+                }
+            }
+        }
+        return dW.toTensor();
+    };
+    MathBackendCPU.prototype.conv3dDerFilter = function (x, dy, convInfo) {
+        var strideDepth = convInfo.strideDepth;
+        var strideHeight = convInfo.strideHeight;
+        var strideWidth = convInfo.strideWidth;
+        var filterDepth = convInfo.filterDepth;
+        var filterHeight = convInfo.filterHeight;
+        var filterWidth = convInfo.filterWidth;
+        var dw = ops.buffer(convInfo.filterShape, 'float32');
+        var dwValues = dw.values;
+        var _a = dw.strides, dwS0 = _a[0], dwS1 = _a[1], dwS2 = _a[2], dwS3 = _a[3];
+        var dyValues = dy.dataSync();
+        var _b = dy.strides, dyS0 = _b[0], dyS1 = _b[1], dyS2 = _b[2], dyS3 = _b[3];
+        var xValues = x.dataSync();
+        var _c = x.strides, xS0 = _c[0], xS1 = _c[1], xS2 = _c[2], xS3 = _c[3];
+        var frontPad = convInfo.padInfo.front;
+        var leftPad = convInfo.padInfo.left;
+        var topPad = convInfo.padInfo.top;
+        for (var wF = 0; wF < filterDepth; ++wF) {
+            var yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));
+            var yFMax = Math.min(convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);
+            var wOffset1 = wF * dwS0;
+            for (var wR = 0; wR < filterHeight; ++wR) {
+                var yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
+                var yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
+                var wOffset2 = wR * dwS1 + wOffset1;
+                for (var wC = 0; wC < filterWidth; ++wC) {
+                    var yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
+                    var yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
+                    var wOffset3 = wC * dwS2 + wOffset2;
+                    for (var d1 = 0; d1 < convInfo.inChannels; ++d1) {
+                        var wOffset4 = d1 * dwS3 + wOffset3;
+                        for (var d2 = 0; d2 < convInfo.outChannels; ++d2) {
+                            var dotProd = 0;
+                            for (var b = 0; b < convInfo.batchSize; ++b) {
+                                var xOffset1 = b * xS0;
+                                var yOffset1 = b * dyS0;
+                                for (var yF = yFMin; yF < yFMax; ++yF) {
+                                    var xF = wF + yF * strideDepth - frontPad;
+                                    var xOffset2 = xF * xS1 + xOffset1;
+                                    var yOffset2 = yF * dyS1 + yOffset1;
+                                    for (var yR = yRMin; yR < yRMax; ++yR) {
+                                        var xR = wR + yR * strideHeight - topPad;
+                                        var xOffset3 = xR * xS2 + xOffset2;
+                                        var yOffset3 = yR * dyS2 + yOffset2;
+                                        for (var yC = yCMin; yC < yCMax; ++yC) {
+                                            var xC = wC + yC * strideWidth - leftPad;
+                                            var xOffset4 = xC * xS3 + xOffset3;
+                                            var yOffset4 = yC * dyS3 + yOffset3;
+                                            dotProd +=
+                                                xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];
+                                        }
+                                    }
+                                }
+                            }
+                            dwValues[wOffset4 + d2] = dotProd;
+                        }
+                    }
+                }
+            }
+        }
+        return dw.toTensor();
+    };
+    MathBackendCPU.prototype.depthwiseConv2D = function (x, filter, convInfo) {
+        this.assertNotComplex([x, filter], 'depthwiseConv2D');
+        var filterHeight = convInfo.filterHeight;
+        var filterWidth = convInfo.filterWidth;
+        var dilationHeight = convInfo.dilationHeight;
+        var dilationWidth = convInfo.dilationWidth;
+        var padLeft = convInfo.padInfo.left;
+        var padTop = convInfo.padInfo.top;
+        var chMul = convInfo.outChannels / convInfo.inChannels;
+        var y = ops.buffer(convInfo.outShape, x.dtype);
+        var xVals = x.dataSync();
+        var wVals = filter.dataSync();
+        var yVals = y.values;
+        for (var b = 0; b < convInfo.batchSize; ++b) {
+            var xOffset1 = b * x.strides[0];
+            var yOffset1 = b * y.strides[0];
+            for (var yR = 0; yR < convInfo.outHeight; ++yR) {
+                var yOffset2 = yOffset1 + yR * y.strides[1];
+                var xRCorner = yR * convInfo.strideHeight - padLeft;
+                for (var wR = 0; wR < filterHeight; ++wR) {
+                    var xR = xRCorner + wR * dilationHeight;
+                    if (xR < 0 || xR >= convInfo.inHeight) {
+                        continue;
+                    }
+                    var wOffset1 = wR * filter.strides[0];
+                    var xOffset2 = xOffset1 + xR * x.strides[1];
+                    for (var yC = 0; yC < convInfo.outWidth; ++yC) {
+                        var yOffset3 = yOffset2 + yC * y.strides[2];
+                        var xCCorner = yC * convInfo.strideWidth - padTop;
+                        for (var wC = 0; wC < filterWidth; ++wC) {
+                            var xC = xCCorner + wC * dilationWidth;
+                            if (xC < 0 || xC >= convInfo.inWidth) {
+                                continue;
+                            }
+                            var wOffset2 = wOffset1 + wC * filter.strides[1];
+                            var xOffset3 = xOffset2 + xC * convInfo.inChannels;
+                            var yOffset4 = yOffset3;
+                            var wOffset3 = wOffset2;
+                            for (var d1 = 0; d1 < convInfo.inChannels; ++d1) {
+                                var xVal = xVals[xOffset3 + d1];
+                                for (var q = 0; q < chMul; ++q) {
+                                    yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q];
+                                }
+                                yOffset4 += chMul;
+                                wOffset3 += chMul;
+                            }
+                        }
+                    }
+                }
+            }
+        }
+        return y.toTensor();
+    };
+    MathBackendCPU.prototype.depthwiseConv2DDerInput = function (dy, filter, convInfo) {
+        this.assertNotComplex([dy, filter], 'depthwiseConv2DDerInput');
+        var dx = ops.buffer(convInfo.inShape, 'float32');
+        var dxValues = dx.values;
+        var _a = dx.strides, dxS0 = _a[0], dxS1 = _a[1], dxS2 = _a[2];
+        var dyValues = dy.dataSync();
+        var _b = dy.strides, dyS0 = _b[0], dyS1 = _b[1], dyS2 = _b[2];
+        var fltValues = filter.dataSync();
+        var _c = filter.strides, fltS0 = _c[0], fltS1 = _c[1], fltS2 = _c[2];
+        var batchSize = convInfo.batchSize, filterHeight = convInfo.filterHeight, filterWidth = convInfo.filterWidth, inChannels = convInfo.inChannels, inHeight = convInfo.inHeight, inWidth = convInfo.inWidth, outChannels = convInfo.outChannels, outHeight = convInfo.outHeight, outWidth = convInfo.outWidth, strideHeight = convInfo.strideHeight, strideWidth = convInfo.strideWidth;
+        var topPad = filterHeight - 1 - convInfo.padInfo.top;
+        var leftPad = filterWidth - 1 - convInfo.padInfo.left;
+        var chMul = outChannels / inChannels;
+        for (var b = 0; b < batchSize; ++b) {
+            for (var d1 = 0; d1 < inChannels; ++d1) {
+                for (var xR = 0; xR < inHeight; ++xR) {
+                    var xRCorner = xR - topPad;
+                    var xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
+                    var yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
+                    for (var xC = 0; xC < inWidth; ++xC) {
+                        var xCCorner = xC - leftPad;
+                        var xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
+                        var yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
+                        var dotProd = 0;
+                        for (var yR = xRMin; yR < yRMax; ++yR) {
+                            var wR = yR * strideHeight - xRCorner;
+                            for (var yC = xCMin; yC < yCMax; ++yC) {
+                                var wC = yC * strideWidth - xCCorner;
+                                var dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;
+                                var fltOffset = fltS0 * (filterHeight - 1 - wR) +
+                                    fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;
+                                for (var dm = 0; dm < chMul; ++dm) {
+                                    var d2 = d1 * chMul + dm;
+                                    var pixel = dyValues[dyOffset + d2];
+                                    var weight = fltValues[fltOffset + dm];
+                                    dotProd += pixel * weight;
+                                }
+                            }
+                        }
+                        dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;
+                    }
+                }
+            }
+        }
+        return dx.toTensor();
+    };
+    MathBackendCPU.prototype.depthwiseConv2DDerFilter = function (x, dy, convInfo) {
+        this.assertNotComplex([x, dy], 'depthwiseConv2DDerFilter');
+        var strideHeight = convInfo.strideHeight;
+        var strideWidth = convInfo.strideWidth;
+        var filterHeight = convInfo.filterHeight;
+        var filterWidth = convInfo.filterWidth;
+        var dW = ops.buffer(convInfo.filterShape, 'float32');
+        var leftPad = convInfo.padInfo.left;
+        var topPad = convInfo.padInfo.top;
+        var chMul = convInfo.outChannels / convInfo.inChannels;
+        for (var wR = 0; wR < filterHeight; ++wR) {
+            var yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
+            var yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
+            for (var wC = 0; wC < filterWidth; ++wC) {
+                var yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
+                var yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
+                for (var d2 = 0; d2 < convInfo.outChannels; ++d2) {
+                    var d1 = Math.trunc(d2 / chMul);
+                    var dm = d2 % chMul;
+                    var dotProd = 0;
+                    for (var b = 0; b < convInfo.batchSize; ++b) {
+                        for (var yR = yRMin; yR < yRMax; ++yR) {
+                            var xR = wR + yR * strideHeight - topPad;
+                            for (var yC = yCMin; yC < yCMax; ++yC) {
+                                var xC = wC + yC * strideWidth - leftPad;
+                                dotProd += x.get(b, xR, xC, d1) * dy.get(b, yR, yC, d2);
+                            }
+                        }
+                    }
+                    dW.set(dotProd, wR, wC, d1, dm);
+                }
+            }
+        }
+        return dW.toTensor();
+    };
+    MathBackendCPU.prototype.tile = function (x, reps) {
+        this.assertNotComplex(x, 'tile');
+        var newShape = new Array(x.rank);
+        for (var i = 0; i < newShape.length; i++) {
+            newShape[i] = x.shape[i] * reps[i];
+        }
+        var result = ops.buffer(newShape, x.dtype);
+        var xBuf = x.buffer();
+        for (var i = 0; i < result.values.length; ++i) {
+            var newLoc = result.indexToLoc(i);
+            var originalLoc = new Array(x.rank);
+            for (var i_1 = 0; i_1 < originalLoc.length; i_1++) {
+                originalLoc[i_1] = newLoc[i_1] % x.shape[i_1];
+            }
+            var originalIndex = xBuf.locToIndex(originalLoc);
+            result.values[i] = xBuf.values[originalIndex];
+        }
+        return result.toTensor();
+    };
+    MathBackendCPU.prototype.pad = function (x, paddings, constantValue) {
+        this.assertNotComplex(x, 'pad');
+        var outShape = paddings.map(function (p, i) { return p[0] + x.shape[i] + p[1]; });
+        var start = paddings.map(function (p) { return p[0]; });
+        var xBuffer = x.buffer();
+        var buffer = ops.buffer(outShape, x.dtype);
+        if (constantValue !== 0) {
+            buffer.values.fill(constantValue);
+        }
+        for (var i = 0; i < x.size; i++) {
+            var coords = xBuffer.indexToLoc(i);
+            var outCoords = coords.map(function (c, i) { return c + start[i]; });
+            buffer.set.apply(buffer, [x.get.apply(x, coords)].concat(outCoords));
+        }
+        return buffer.toTensor();
+    };
+    MathBackendCPU.prototype.transpose = function (x, perm) {
+        this.assertNotComplex(x, 'transpose');
+        var newShape = new Array(x.rank);
+        for (var i = 0; i < newShape.length; i++) {
+            newShape[i] = x.shape[perm[i]];
+        }
+        var values = x.dataSync();
+        var result = ops_1.buffer(newShape, x.dtype);
+        var xBuf = x.buffer();
+        for (var i = 0; i < x.size; ++i) {
+            var loc = xBuf.indexToLoc(i);
+            var newLoc = new Array(loc.length);
+            for (var i_2 = 0; i_2 < newLoc.length; i_2++) {
+                newLoc[i_2] = loc[perm[i_2]];
+            }
+            var newIndex = result.locToIndex(newLoc);
+            result.values[newIndex] = values[i];
+        }
+        return result.toTensor();
+    };
+    MathBackendCPU.prototype.gather = function (x, indices, axis) {
+        this.assertNotComplex([x, indices], 'gather');
+        var newShape = x.shape.slice();
+        var indicesValues = indices.dataSync();
+        newShape[axis] = indicesValues.length;
+        var result = ops_1.buffer(newShape, x.dtype);
+        var xBuf = x.buffer();
+        for (var i = 0; i < result.size; ++i) {
+            var newLoc = result.indexToLoc(i);
+            var originalLoc = newLoc.slice();
+            originalLoc[axis] = indicesValues[newLoc[axis]];
+            var originalIndex = xBuf.locToIndex(originalLoc);
+            result.values[i] = xBuf.values[originalIndex];
+        }
+        return result.toTensor();
+    };
+    MathBackendCPU.prototype.batchToSpaceND = function (x, blockShape, crops) {
+        this.assertNotComplex([x], 'batchToSpaceND');
+        var prod = blockShape.reduce(function (a, b) { return a * b; });
+        var reshaped = array_ops_util.getReshaped(x.shape, blockShape, prod);
+        var permuted = array_ops_util.getPermuted(reshaped.length, blockShape.length);
+        var reshapedPermuted = array_ops_util.getReshapedPermuted(x.shape, blockShape, prod);
+        var sliceBeginCoords = array_ops_util.getSliceBeginCoords(crops, blockShape.length);
+        var sliceSize = array_ops_util.getSliceSize(reshapedPermuted, crops, blockShape.length);
+        return x.reshape(reshaped)
+            .transpose(permuted)
+            .reshape(reshapedPermuted)
+            .slice(sliceBeginCoords, sliceSize);
+    };
+    MathBackendCPU.prototype.spaceToBatchND = function (x, blockShape, paddings) {
+        this.assertNotComplex([x], 'spaceToBatchND');
+        var prod = blockShape.reduce(function (a, b) { return a * b; });
+        var completePaddings = [[0, 0]];
+        completePaddings.push.apply(completePaddings, paddings);
+        for (var i = 1 + blockShape.length; i < x.shape.length; ++i) {
+            completePaddings.push([0, 0]);
+        }
+        var paddedX = x.pad(completePaddings);
+        var reshapedPaddedShape = array_ops_util.getReshaped(paddedX.shape, blockShape, prod, false);
+        var permutedReshapedPaddedPermutation = array_ops_util.getPermuted(reshapedPaddedShape.length, blockShape.length, false);
+        var flattenShape = array_ops_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false);
+        return paddedX.reshape(reshapedPaddedShape)
+            .transpose(permutedReshapedPaddedPermutation)
+            .reshape(flattenShape);
+    };
+    MathBackendCPU.prototype.pool = function (x, convInfo, poolType) {
+        this.assertNotComplex(x, 'pool');
+        var strideHeight = convInfo.strideHeight;
+        var strideWidth = convInfo.strideWidth;
+        var dilationHeight = convInfo.dilationHeight;
+        var dilationWidth = convInfo.dilationWidth;
+        var effectiveFilterHeight = convInfo.effectiveFilterHeight;
+        var effectiveFilterWidth = convInfo.effectiveFilterWidth;
+        var padTop = convInfo.padInfo.top;
+        var padLeft = convInfo.padInfo.left;
+        var initialValue = (poolType === 'max' ? Number.NEGATIVE_INFINITY :
+            Number.POSITIVE_INFINITY);
+        var xValues = x.dataSync();
+        var output = ops.buffer(convInfo.outShape, x.dtype);
+        var outputVals = output.values;
+        var outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3];
+        var outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3];
+        var outputColStrides = convInfo.outShape[3];
+        for (var b = 0; b < convInfo.batchSize; ++b) {
+            var outputBatchOffset = b * outputBatchStrides;
+            var inputBatchOffset = b * x.strides[0];
+            for (var d = 0; d < convInfo.inChannels; ++d) {
+                for (var yR = 0; yR < convInfo.outHeight; ++yR) {
+                    var xRCorner = yR * strideHeight - padTop;
+                    var xRMin = Math.max(0, xRCorner);
+                    var xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);
+                    var outputRowOffset = outputBatchOffset + yR * outputRowStrides;
+                    for (var yC = 0; yC < convInfo.outWidth; ++yC) {
+                        var xCCorner = yC * strideWidth - padLeft;
+                        var xCMin = Math.max(0, xCCorner);
+                        var xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);
+                        var minMaxValue = initialValue;
+                        var avgValue = 0;
+                        var count = 0;
+                        for (var xR = xRMin; xR < xRMax; xR += dilationHeight) {
+                            var xROffset = inputBatchOffset + xR * x.strides[1];
+                            for (var xC = xCMin; xC < xCMax; xC += dilationWidth) {
+                                var xCOffset = xROffset + xC * x.strides[2];
+                                var pixel = xValues[xCOffset + d];
+                                if ((poolType === 'max' && pixel > minMaxValue)) {
+                                    minMaxValue = pixel;
+                                }
+                                else if (poolType === 'avg') {
+                                    avgValue += pixel;
+                                    count++;
+                                }
+                            }
+                            if (isNaN(minMaxValue)) {
+                                break;
+                            }
+                        }
+                        var outputOffset = outputRowOffset + yC * outputColStrides + d;
+                        outputVals[outputOffset] =
+                            poolType === 'avg' ? avgValue / count : minMaxValue;
+                    }
+                }
+            }
+        }
+        return output.toTensor();
+    };
+    MathBackendCPU.prototype.maxPool = function (x, convInfo) {
+        return this.pool(x, convInfo, 'max');
+    };
+    MathBackendCPU.prototype.maxPoolPositions = function (x, convInfo) {
+        var maxPositions = ops.buffer(convInfo.outShape, 'int32');
+        var strideHeight = convInfo.strideHeight;
+        var strideWidth = convInfo.strideWidth;
+        var dilationHeight = convInfo.dilationHeight;
+        var dilationWidth = convInfo.dilationWidth;
+        var effectiveFilterHeight = convInfo.effectiveFilterHeight;
+        var effectiveFilterWidth = convInfo.effectiveFilterWidth;
+        var padTop = convInfo.padInfo.top;
+        var padLeft = convInfo.padInfo.left;
+        for (var b = 0; b < convInfo.batchSize; ++b) {
+            for (var d = 0; d < convInfo.inChannels; ++d) {
+                for (var yR = 0; yR < convInfo.outHeight; ++yR) {
+                    var xRCorner = yR * strideHeight - padTop;
+                    var xRMin = xRCorner;
+                    while (xRMin < 0) {
+                        xRMin += dilationHeight;
+                    }
+                    var xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);
+                    for (var yC = 0; yC < convInfo.outWidth; ++yC) {
+                        var xCCorner = yC * strideWidth - padLeft;
+                        var xCMin = xCCorner;
+                        while (xCMin < 0) {
+                            xCMin += dilationWidth;
+                        }
+                        var xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);
+                        var maxValue = Number.NEGATIVE_INFINITY;
+                        var maxPosition = -1;
+                        for (var xR = xRMin; xR < xRMax; xR += dilationHeight) {
+                            var wR = xR - xRCorner;
+                            for (var xC = xCMin; xC < xCMax; xC += dilationWidth) {
+                                var wC = xC - xCCorner;
+                                var pixel = x.get(b, xR, xC, d);
+                                if (pixel > maxValue) {
+                                    maxValue = pixel;
+                                    maxPosition = wR * effectiveFilterWidth + wC;
+                                }
+                            }
+                        }
+                        maxPositions.set(maxPosition, b, yR, yC, d);
+                    }
+                }
+            }
+        }
+        return maxPositions.toTensor();
+    };
+    MathBackendCPU.prototype.maxPoolBackprop = function (dy, x, y, convInfo) {
+        this.assertNotComplex([x, y], 'maxPoolBackprop');
+        var maxPositions = this.maxPoolPositions(x, convInfo);
+        var strideHeight = convInfo.strideHeight;
+        var strideWidth = convInfo.strideWidth;
+        var dilationHeight = convInfo.dilationHeight;
+        var dilationWidth = convInfo.dilationWidth;
+        var effectiveFilterHeight = convInfo.effectiveFilterHeight;
+        var effectiveFilterWidth = convInfo.effectiveFilterWidth;
+        var padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+        var padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+        var dx = ops.buffer(x.shape, 'float32');
+        for (var b = 0; b < convInfo.batchSize; ++b) {
+            for (var d = 0; d < convInfo.inChannels; ++d) {
+                for (var dxR = 0; dxR < convInfo.inHeight; ++dxR) {
+                    for (var dxC = 0; dxC < convInfo.inWidth; ++dxC) {
+                        var dyRCorner = dxR - padTop;
+                        var dyCCorner = dxC - padLeft;
+                        var dotProd = 0;
+                        for (var wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {
+                            var dyR = (dyRCorner + wR) / strideHeight;
+                            if (dyR < 0 || dyR >= convInfo.outHeight ||
+                                Math.floor(dyR) !== dyR) {
+                                continue;
+                            }
+                            for (var wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {
+                                var dyC = (dyCCorner + wC) / strideWidth;
+                                if (dyC < 0 || dyC >= convInfo.outWidth ||
+                                    Math.floor(dyC) !== dyC) {
+                                    continue;
+                                }
+                                var maxPos = effectiveFilterHeight * effectiveFilterWidth -
+                                    1 - maxPositions.get(b, dyR, dyC, d);
+                                var curPos = wR * effectiveFilterWidth + wC;
+                                var mask = maxPos === curPos ? 1 : 0;
+                                if (mask === 0) {
+                                    continue;
+                                }
+                                var pixel = dy.get(b, dyR, dyC, d);
+                                dotProd += pixel * mask;
+                            }
+                        }
+                        dx.set(dotProd, b, dxR, dxC, d);
+                    }
+                }
+            }
+        }
+        return dx.toTensor();
+    };
+    MathBackendCPU.prototype.avgPoolBackprop = function (dy, x, convInfo) {
+        this.assertNotComplex([dy, x], 'avgPoolBackprop');
+        var strideHeight = convInfo.strideHeight;
+        var strideWidth = convInfo.strideWidth;
+        var filterHeight = convInfo.filterHeight;
+        var filterWidth = convInfo.filterWidth;
+        var dilationHeight = convInfo.dilationHeight;
+        var dilationWidth = convInfo.dilationWidth;
+        var effectiveFilterHeight = convInfo.effectiveFilterHeight;
+        var effectiveFilterWidth = convInfo.effectiveFilterWidth;
+        var padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+        var padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+        var dx = ops.buffer(x.shape, 'float32');
+        var avgMultiplier = 1 / (filterHeight * filterWidth);
+        for (var b = 0; b < convInfo.batchSize; ++b) {
+            for (var d = 0; d < convInfo.inChannels; ++d) {
+                for (var dxR = 0; dxR < convInfo.inHeight; ++dxR) {
+                    for (var dxC = 0; dxC < convInfo.inWidth; ++dxC) {
+                        var dyRCorner = dxR - padTop;
+                        var dyCCorner = dxC - padLeft;
+                        var dotProd = 0;
+                        for (var wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {
+                            var dyR = (dyRCorner + wR) / strideHeight;
+                            if (dyR < 0 || dyR >= convInfo.outHeight ||
+                                Math.floor(dyR) !== dyR) {
+                                continue;
+                            }
+                            for (var wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {
+                                var dyC = (dyCCorner + wC) / strideWidth;
+                                if (dyC < 0 || dyC >= convInfo.outWidth ||
+                                    Math.floor(dyC) !== dyC) {
+                                    continue;
+                                }
+                                var pixel = dy.get(b, dyR, dyC, d);
+                                dotProd += pixel;
+                            }
+                        }
+                        dx.set(dotProd * avgMultiplier, b, dxR, dxC, d);
+                    }
+                }
+            }
+        }
+        return dx.toTensor();
+    };
+    MathBackendCPU.prototype.cast = function (x, dtype) {
+        return backend_util.castTensor(x, dtype, this);
+    };
+    MathBackendCPU.prototype.reshape = function (x, shape) {
+        return backend_util.reshapeTensor(x, shape);
+    };
+    MathBackendCPU.prototype.avgPool = function (x, convInfo) {
+        this.assertNotComplex(x, 'avgPool');
+        return this.pool(x, convInfo, 'avg').toFloat();
+    };
+    MathBackendCPU.prototype.resizeBilinear = function (x, newHeight, newWidth, alignCorners) {
+        this.assertNotComplex(x, 'resizeBilinear');
+        var _a = x.shape, batch = _a[0], oldHeight = _a[1], oldWidth = _a[2], numChannels = _a[3];
+        var xValues = x.dataSync();
+        var result = new Float32Array(util.sizeFromShape([batch, newHeight, newWidth, numChannels]));
+        var effectiveInputSize = [
+            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,
+            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth
+        ];
+        var effectiveOutputSize = [
+            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,
+            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth
+        ];
+        var outputIdx = 0;
+        var effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];
+        var effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];
+        for (var b = 0; b < batch; b++) {
+            for (var r = 0; r < newHeight; r++) {
+                var sourceFracRow = effectiveRowSizeRatio * r;
+                var sourceRowFloor = Math.floor(sourceFracRow);
+                var rowFrac = sourceFracRow - sourceRowFloor;
+                var sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));
+                var topRowOffset = b * x.strides[0] + sourceRowFloor * x.strides[1];
+                var botRowOffset = b * x.strides[0] + sourceRowCeil * x.strides[1];
+                for (var c = 0; c < newWidth; c++) {
+                    var sourceFracCol = effectiveColSizeRatio * c;
+                    var sourceColFloor = Math.floor(sourceFracCol);
+                    var colFrac = sourceFracCol - sourceColFloor;
+                    var sourceColCeil = Math.min(oldWidth - 1, Math.ceil(sourceFracCol));
+                    var topLeftOffest = topRowOffset + sourceColFloor * x.strides[2];
+                    var botLeftOffset = botRowOffset + sourceColFloor * x.strides[2];
+                    var topRightOffset = topRowOffset + +sourceColCeil * x.strides[2];
+                    var botRightOffest = botRowOffset + sourceColCeil * x.strides[2];
+                    for (var d = 0; d < numChannels; d++) {
+                        var topLeft = xValues[topLeftOffest + d];
+                        var bottomLeft = xValues[botLeftOffset + d];
+                        var topRight = xValues[topRightOffset + d];
+                        var bottomRight = xValues[botRightOffest + d];
+                        var top_1 = topLeft + (topRight - topLeft) * colFrac;
+                        var bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;
+                        var newValue = top_1 + (bottom - top_1) * rowFrac;
+                        result[outputIdx++] = newValue;
+                    }
+                }
+            }
+        }
+        return ops.tensor(result, [batch, newHeight, newWidth, numChannels]);
+    };
+    MathBackendCPU.prototype.resizeBilinearBackprop = function (dy, x, alignCorners) {
+        this.assertNotComplex([dy, x], 'resizeBilinearBackprop');
+        var _a = x.shape, batch = _a[0], xHeight = _a[1], xWidth = _a[2], depth = _a[3];
+        var _b = dy.shape, yHeight = _b[1], yWidth = _b[2];
+        var output = new Float32Array(batch * xHeight * xWidth * depth);
+        var effectiveXSize = [
+            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,
+            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth
+        ];
+        var effectiveYSize = [
+            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,
+            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth
+        ];
+        var heightScale = effectiveXSize[0] / effectiveYSize[0];
+        var widthScale = effectiveXSize[1] / effectiveYSize[1];
+        var dyValues = dy.dataSync();
+        var offset = 0;
+        for (var b = 0; b < batch; b++) {
+            var bOffset = b * x.strides[0];
+            for (var r = 0; r < yHeight; r++) {
+                var dxR = r * heightScale;
+                var topDxRIndex = Math.floor(dxR);
+                var bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);
+                var topDxROffset = bOffset + topDxRIndex * x.strides[1];
+                var bottomDxROffset = bOffset + bottomDxRIndex * x.strides[1];
+                var dxRLerp = dxR - topDxRIndex;
+                var inverseDxRLerp = 1.0 - dxRLerp;
+                for (var c = 0; c < yWidth; c++) {
+                    var dxC = c * widthScale;
+                    var leftDxCIndex = Math.floor(dxC);
+                    var rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1);
+                    var dxCLerp = dxC - leftDxCIndex;
+                    var inverseDxCLerp = 1.0 - dxCLerp;
+                    var topLeftRCOffset = topDxROffset + leftDxCIndex * x.strides[2];
+                    var topRightRCOffset = topDxROffset + rightDxCIndex * x.strides[2];
+                    var bottomLeftRCOffset = bottomDxROffset + leftDxCIndex * x.strides[2];
+                    var bottomRightRCOffset = bottomDxROffset + rightDxCIndex * x.strides[2];
+                    var inverseDxRLerpTimesInverseDxCLerp = inverseDxRLerp * inverseDxCLerp;
+                    var inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp;
+                    var dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp;
+                    var dxRLerpTimesDxCLerp = dxRLerp * dxCLerp;
+                    for (var d = 0; d < depth; d++) {
+                        var dyVal = dyValues[offset++];
+                        output[topLeftRCOffset + d] +=
+                            dyVal * inverseDxRLerpTimesInverseDxCLerp;
+                        output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp;
+                        output[bottomLeftRCOffset + d] +=
+                            dyVal * dxRLerpTimesInverseDxCLerp;
+                        output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp;
+                    }
+                }
+            }
+        }
+        return ops.tensor4d(output, [batch, xWidth, xHeight, depth], x.dtype);
+    };
+    MathBackendCPU.prototype.resizeNearestNeighbor = function (x, newHeight, newWidth, alignCorners) {
+        this.assertNotComplex(x, 'resizeNearestNeighbor');
+        var _a = x.shape, batch = _a[0], oldHeight = _a[1], oldWidth = _a[2], numChannels = _a[3];
+        var xValues = x.dataSync();
+        var output = new Float32Array(batch * newHeight * newWidth * numChannels);
+        var effectiveInputSize = [
+            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,
+            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth
+        ];
+        var effectiveOutputSize = [
+            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,
+            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth
+        ];
+        var effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];
+        var effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];
+        var outputOffset = 0;
+        for (var b = 0; b < batch; b++) {
+            var batchOffset = b * x.strides[0];
+            for (var r = 0; r < newHeight; r++) {
+                var sourceFracRow = effectiveRowSizeRatio * r;
+                var sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) :
+                    Math.floor(sourceFracRow));
+                var rowOffset = batchOffset + sourceNearestRow * x.strides[1];
+                for (var c = 0; c < newWidth; c++) {
+                    var sourceFracCol = effectiveColSizeRatio * c;
+                    var sourceNearestCol = Math.min(oldWidth - 1, alignCorners ? Math.round(sourceFracCol) :
+                        Math.floor(sourceFracCol));
+                    var colOffset = rowOffset + sourceNearestCol * x.strides[2];
+                    for (var d = 0; d < numChannels; d++) {
+                        var newVal = xValues[colOffset + d];
+                        output[outputOffset++] = newVal;
+                    }
+                }
+            }
+        }
+        return ops.tensor(output, [batch, newHeight, newWidth, numChannels], x.dtype);
+    };
+    MathBackendCPU.prototype.resizeNearestNeighborBackprop = function (dy, x, alignCorners) {
+        this.assertNotComplex([dy, x], 'resizeNearestNeighborBackprop');
+        var _a = x.shape, batch = _a[0], xHeight = _a[1], xWidth = _a[2], depth = _a[3];
+        var _b = dy.shape, yHeight = _b[1], yWidth = _b[2];
+        var output = new Float32Array(batch * xHeight * xWidth * depth);
+        var dyValues = dy.dataSync();
+        var effectiveXSize = [
+            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,
+            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth
+        ];
+        var effectiveYSize = [
+            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,
+            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth
+        ];
+        var heightScale = effectiveXSize[0] / effectiveYSize[0];
+        var widthScale = effectiveXSize[1] / effectiveYSize[1];
+        var invHeightScale = 1 / heightScale;
+        var invWidthScale = 1 / widthScale;
+        var winHeight = (Math.ceil(invHeightScale) * 2) + 2;
+        var winWidth = (Math.ceil(invWidthScale) * 2) + 2;
+        for (var b = 0; b < batch; b++) {
+            var batchOffset = b * x.strides[0];
+            for (var r = 0; r < xHeight; r++) {
+                var rowOffset = batchOffset + r * x.strides[1];
+                var startRLerp = Math.floor(r * invHeightScale);
+                var startDyR = Math.floor(startRLerp - (winHeight / 2));
+                for (var c = 0; c < xWidth; c++) {
+                    var colOffset = rowOffset + c * x.strides[2];
+                    var startCLerp = Math.floor(c * invWidthScale);
+                    var startDyC = Math.floor(startCLerp - (winWidth / 2));
+                    for (var d = 0; d < depth; d++) {
+                        var accum = 0;
+                        for (var dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {
+                            var dyR = dyRIndex + startDyR;
+                            if (dyR < 0 || dyR >= yHeight) {
+                                continue;
+                            }
+                            var dyROffset = batchOffset + dyR * dy.strides[1];
+                            var sourceFracRow = dyR * heightScale;
+                            var sourceNearestRow = Math.min(xHeight - 1, alignCorners ? Math.round(sourceFracRow) :
+                                Math.floor(sourceFracRow));
+                            if (r !== sourceNearestRow) {
+                                continue;
+                            }
+                            for (var dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {
+                                var dyC = dyCIndex + startDyC;
+                                if (dyC < 0 || dyC >= yWidth) {
+                                    continue;
+                                }
+                                var dyCOffset = dyROffset + dyC * dy.strides[2];
+                                var sourceFracCol = dyC * widthScale;
+                                var sourceNearestCol = Math.min(xWidth - 1, alignCorners ? Math.round(sourceFracCol) :
+                                    Math.floor(sourceFracCol));
+                                if (c === sourceNearestCol) {
+                                    accum += dyValues[dyCOffset + d];
+                                }
+                            }
+                        }
+                        output[colOffset + d] = accum;
+                    }
+                }
+            }
+        }
+        return ops.tensor4d(output, x.shape, x.dtype);
+    };
+    MathBackendCPU.prototype.batchNormalization = function (x, mean, variance, varianceEpsilon, scale, offset) {
+        this.assertNotComplex([x, mean, variance, scale, offset], 'batchNormalization');
+        var xVals = x.dataSync();
+        var mVals = mean.dataSync();
+        var varVals = variance.dataSync();
+        var sVals = scale ? scale.dataSync() : new Float32Array([1]);
+        var offVals = offset ? offset.dataSync() : new Float32Array([0]);
+        var outVals = new Float32Array(xVals.length);
+        var offValsLength = offVals.length;
+        var sValsLength = sVals.length;
+        var varValsLength = varVals.length;
+        var mValsLength = mVals.length;
+        var offi = 0;
+        var mi = 0;
+        var si = 0;
+        var vi = 0;
+        for (var i = 0; i < xVals.length; ++i) {
+            outVals[i] = offVals[offi++] +
+                (xVals[i] - mVals[mi++]) * sVals[si++] /
+                    Math.sqrt(varVals[vi++] + varianceEpsilon);
+            if (offi >= offValsLength) {
+                offi = 0;
+            }
+            if (mi >= mValsLength) {
+                mi = 0;
+            }
+            if (si >= sValsLength) {
+                si = 0;
+            }
+            if (vi >= varValsLength) {
+                vi = 0;
+            }
+        }
+        return ops_1.tensor4d(outVals, x.shape);
+    };
+    MathBackendCPU.prototype.localResponseNormalization4D = function (x, depthRadius, bias, alpha, beta) {
+        this.assertNotComplex(x, 'localResponseNormalization4D');
+        var channels = x.shape[3];
+        var maxD = channels - 1;
+        var xValues = x.dataSync();
+        var size = util.sizeFromShape(x.shape);
+        var result = new Float32Array(size);
+        function sumAcrossChannels(offset) {
+            var currentChannel = offset % channels;
+            var beginSumOffset = offset - currentChannel + Math.max(0, currentChannel - depthRadius);
+            var endSumOffset = offset - currentChannel +
+                Math.min(currentChannel + depthRadius, maxD);
+            var sum = 0.0;
+            for (; beginSumOffset <= endSumOffset; beginSumOffset++) {
+                var z = xValues[beginSumOffset];
+                sum += z * z;
+            }
+            return sum;
+        }
+        for (var offset = 0; offset < size; offset++) {
+            var sum = sumAcrossChannels(offset);
+            var val = xValues[offset] * Math.pow(bias + alpha * sum, -beta);
+            result[offset] = val;
+        }
+        return ops.tensor4d(result, x.shape);
+    };
+    MathBackendCPU.prototype.LRNGrad = function (dy, inputImage, outputImage, depthRadius, bias, alpha, beta) {
+        this.assertNotComplex(dy, 'LRNGrad');
+        var channels = dy.shape[3];
+        var dyValues = dy.dataSync();
+        var inputImageValues = inputImage.dataSync();
+        var outputImageValues = outputImage.dataSync();
+        var result = new Float32Array(util.sizeFromShape(dy.shape));
+        var size = util.sizeFromShape(dy.shape);
+        for (var offset = 0; offset < size; offset++) {
+            var currentChannel = offset % channels;
+            var depthBegin = (offset - currentChannel) + Math.max(0, currentChannel - depthRadius);
+            var depthEnd = (offset - currentChannel) +
+                Math.min(channels, currentChannel + depthRadius + 1);
+            var norm = 0;
+            for (var k = depthBegin; k < depthEnd; k++) {
+                norm += Math.pow(inputImageValues[k], 2);
+            }
+            norm = alpha * norm + bias;
+            for (var k = depthBegin; k < depthEnd; k++) {
+                var dyi = -2 * alpha * beta * inputImageValues[k] *
+                    outputImageValues[offset] / norm;
+                if (offset === k) {
+                    dyi += Math.pow(norm, -beta);
+                }
+                dyi *= dyValues[offset];
+                result[k] += dyi;
+            }
+        }
+        return ops.tensor4d(result, dy.shape);
+    };
+    MathBackendCPU.prototype.multinomial = function (logits, normalized, numSamples, seed) {
+        this.assertNotComplex(logits, 'multinomial');
+        var probabilities = normalized ? logits : ops.softmax(logits);
+        var batchSize = probabilities.shape[0];
+        var numEvents = probabilities.shape[1];
+        var res = ops.zeros([batchSize, numSamples], 'int32');
+        var resVals = res.dataSync();
+        var probVals = probabilities.dataSync();
+        for (var b = 0; b < batchSize; ++b) {
+            var offset = b * numEvents;
+            var cdf = new Float32Array(numEvents - 1);
+            cdf[0] = probVals[offset];
+            for (var event_1 = 1; event_1 < cdf.length; ++event_1) {
+                cdf[event_1] = cdf[event_1 - 1] + probVals[offset + event_1];
+            }
+            var random = seedrandom.alea(seed.toString());
+            var outOffset = b * numSamples;
+            for (var sampleId = 0; sampleId < numSamples; ++sampleId) {
+                var r = random();
+                resVals[outOffset + sampleId] = cdf.length;
+                for (var event_2 = 0; event_2 < cdf.length; event_2++) {
+                    if (r < cdf[event_2]) {
+                        resVals[outOffset + sampleId] = event_2;
+                        break;
+                    }
+                }
+            }
+        }
+        return res;
+    };
+    MathBackendCPU.prototype.oneHot = function (indices, depth, onValue, offValue) {
+        this.assertNotComplex(indices, 'oneHot');
+        var res = new Float32Array(indices.size * depth);
+        res.fill(offValue);
+        for (var event_3 = 0; event_3 < indices.size; ++event_3) {
+            if (indices.get(event_3) >= 0 && indices.get(event_3) < depth) {
+                res[event_3 * depth + indices.get(event_3)] = onValue;
+            }
+        }
+        return ops.tensor2d(res, [indices.size, depth], 'int32');
+    };
+    MathBackendCPU.prototype.nonMaxSuppression = function (boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+        this.assertNotComplex(boxes, 'nonMaxSuppression');
+        var boxesVals = boxes.dataSync();
+        var scoresVals = scores.dataSync();
+        return non_max_suppression_impl_1.nonMaxSuppressionImpl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
+    };
+    MathBackendCPU.prototype.fft = function (x) {
+        return this.fftBatch(x, false);
+    };
+    MathBackendCPU.prototype.ifft = function (x) {
+        return this.fftBatch(x, true);
+    };
+    MathBackendCPU.prototype.fftBatch = function (x, inverse) {
+        var batch = x.shape[0];
+        var innerDim = x.shape[1];
+        var realResult = ops.buffer(x.shape, 'float32');
+        var imagResult = ops.buffer(x.shape, 'float32');
+        var real = ops.real(x).as2D(batch, innerDim);
+        var imag = ops.imag(x).as2D(batch, innerDim);
+        for (var b = 0; b < batch; b++) {
+            var r = real.slice([b, 0], [1, innerDim]);
+            var i = imag.slice([b, 0], [1, innerDim]);
+            var input = ops.complex(r, i);
+            var res = this.fftImpl(input, inverse).dataSync();
+            for (var d = 0; d < innerDim; d++) {
+                var c = complex_util.getComplexWithIndex(res, d);
+                realResult.values[b * innerDim + d] = c.real;
+                imagResult.values[b * innerDim + d] = c.imag;
+            }
+        }
+        var t = ops.complex(realResult.toTensor(), imagResult.toTensor());
+        return t.as2D(batch, innerDim);
+    };
+    MathBackendCPU.prototype.fftImpl = function (x, inverse) {
+        var x1D = x.as1D();
+        var n = x1D.size;
+        if (this.isExponentOf2(n)) {
+            var result = this.fftRadix2(x1D, n, inverse).as2D(x.shape[0], x.shape[1]);
+            if (inverse) {
+                result = ops.complex(ops.real(result).div(ops_1.scalar(n)), ops.imag(result).div(ops_1.scalar(n)));
+            }
+            return result;
+        }
+        else {
+            var data = x.dataSync();
+            var rawOutput = this.fourierTransformByMatmul(data, n, inverse);
+            var output = complex_util.splitRealAndImagArrays(rawOutput);
+            return ops.complex(output.real, output.imag).as2D(x.shape[0], x.shape[1]);
+        }
+    };
+    MathBackendCPU.prototype.isExponentOf2 = function (size) {
+        return (size & size - 1) === 0;
+    };
+    MathBackendCPU.prototype.fftRadix2 = function (input, size, inverse) {
+        if (size === 1) {
+            return input;
+        }
+        var data = input.dataSync();
+        var half = size / 2;
+        var evenComplex = complex_util.complexWithEvenIndex(data);
+        var evenTensor = ops.complex(evenComplex.real, evenComplex.imag).as1D();
+        var oddComplex = complex_util.complexWithOddIndex(data);
+        var oddTensor = ops.complex(oddComplex.real, oddComplex.imag).as1D();
+        evenTensor = this.fftRadix2(evenTensor, half, inverse);
+        oddTensor = this.fftRadix2(oddTensor, half, inverse);
+        var e = complex_util.exponents(size, inverse);
+        var exponent = ops.complex(e.real, e.imag).mul(oddTensor);
+        var addPart = evenTensor.add(exponent);
+        var subPart = evenTensor.sub(exponent);
+        var realTensor = ops.real(addPart).concat(ops.real(subPart));
+        var imagTensor = ops.imag(addPart).concat(ops.imag(subPart));
+        return ops.complex(realTensor, imagTensor).as1D();
+    };
+    MathBackendCPU.prototype.fourierTransformByMatmul = function (data, size, inverse) {
+        var ret = new Float32Array(size * 2);
+        for (var r = 0; r < size; r++) {
+            var real = 0.0;
+            var imag = 0.0;
+            for (var c = 0; c < size; c++) {
+                var e = complex_util.exponent(r * c, size, inverse);
+                var term = complex_util.getComplexWithIndex(data, c);
+                real += term.real * e.real - term.imag * e.imag;
+                imag += term.real * e.imag + term.imag * e.real;
+            }
+            if (inverse) {
+                real /= size;
+                imag /= size;
+            }
+            complex_util.assignToTypedArray(ret, real, imag, r);
+        }
+        return ret;
+    };
+    MathBackendCPU.prototype.depthToSpace = function (x, blockSize, dataFormat) {
+        util.assert(dataFormat === 'NHWC', "Only NHWC dataFormat supported on CPU for depthToSpace. Got " + dataFormat);
+        util.assert(blockSize > 1, "blockSize should be > 1 for depthToSpace, but was: " + blockSize);
+        var batchSize = x.shape[0];
+        var inputHeight = x.shape[1];
+        var inputWidth = x.shape[2];
+        var inputDepth = x.shape[3];
+        var outputHeight = inputHeight * blockSize;
+        var outputWidth = inputWidth * blockSize;
+        var outputDepth = inputDepth / (blockSize * blockSize);
+        var xValues = x.dataSync();
+        var result = new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);
+        var outputIdx = 0;
+        for (var b = 0; b < batchSize; ++b) {
+            for (var h = 0; h < outputHeight; ++h) {
+                var inH = Math.floor(h / blockSize);
+                var offsetH = (h % blockSize);
+                for (var w = 0; w < outputWidth; ++w) {
+                    var inW = Math.floor(w / blockSize);
+                    var offsetW = (w % blockSize);
+                    var offsetD = (offsetH * blockSize + offsetW) * outputDepth;
+                    for (var d = 0; d < outputDepth; ++d) {
+                        var inD = d + offsetD;
+                        var inputIdx = inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));
+                        result[outputIdx++] = xValues[inputIdx];
+                    }
+                }
+            }
+        }
+        return ops.tensor4d(result, [batchSize, outputHeight, outputWidth, outputDepth]);
+    };
+    MathBackendCPU.prototype.broadcastedBinaryOp = function (a, b, dtype, op) {
+        var newShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);
+        var result = ops.buffer(newShape, dtype);
+        var aVals = a.dataSync();
+        var bVals = b.dataSync();
+        var aBroadcastDims = broadcast_util.getBroadcastDims(a.shape, newShape);
+        var bBroadcastDims = broadcast_util.getBroadcastDims(b.shape, newShape);
+        var resVals = result.values;
+        if (aBroadcastDims.length + bBroadcastDims.length === 0) {
+            for (var i = 0; i < resVals.length; ++i) {
+                resVals[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);
+            }
+        }
+        else {
+            var aBuf = a.buffer();
+            var bBuf = b.buffer();
+            var _loop_2 = function (i) {
+                var loc = result.indexToLoc(i);
+                var aLoc = loc.slice(-a.rank);
+                aBroadcastDims.forEach(function (d) { return aLoc[d] = 0; });
+                var aIndex = aBuf.locToIndex(aLoc);
+                var bLoc = loc.slice(-b.rank);
+                bBroadcastDims.forEach(function (d) { return bLoc[d] = 0; });
+                var bIndex = bBuf.locToIndex(bLoc);
+                resVals[i] = op(aVals[aIndex], bVals[bIndex]);
+            };
+            for (var i = 0; i < resVals.length; ++i) {
+                _loop_2(i);
+            }
+        }
+        return result.toTensor();
+    };
+    MathBackendCPU.prototype.broadcastedBinaryComplexOp = function (a, b, op) {
+        var newShape = broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);
+        var realResult = ops.buffer(newShape, 'float32');
+        var imagResult = ops.buffer(newShape, 'float32');
+        var aVals = a.dataSync();
+        var bVals = b.dataSync();
+        var aBroadcastDims = broadcast_util.getBroadcastDims(a.shape, newShape);
+        var bBroadcastDims = broadcast_util.getBroadcastDims(b.shape, newShape);
+        var realVals = realResult.values;
+        var imagVals = imagResult.values;
+        if (aBroadcastDims.length + bBroadcastDims.length === 0) {
+            for (var i = 0; i < realVals.length; i++) {
+                var aIdx = i % aVals.length;
+                var bIdx = i % bVals.length;
+                var result = op(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2], bVals[bIdx * 2 + 1]);
+                realVals[i] = result.real;
+                imagVals[i] = result.imag;
+            }
+        }
+        else {
+            var aRealBuf = this.data.get(a.dataId).complexTensors.real.buffer();
+            var bRealBuf = this.data.get(b.dataId).complexTensors.real.buffer();
+            var _loop_3 = function (i) {
+                var loc = realResult.indexToLoc(i);
+                var aLoc = loc.slice(-a.rank);
+                aBroadcastDims.forEach(function (d) { return aLoc[d] = 0; });
+                var aIndex = aRealBuf.locToIndex(aLoc);
+                var bLoc = loc.slice(-b.rank);
+                bBroadcastDims.forEach(function (d) { return bLoc[d] = 0; });
+                var bIndex = bRealBuf.locToIndex(bLoc);
+                var opResult = op(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2], bVals[bIndex * 2 + 1]);
+                realVals[i] = opResult.real;
+                imagVals[i] = opResult.imag;
+            };
+            for (var i = 0; i < realVals.length; i++) {
+                _loop_3(i);
+            }
+        }
+        return this.complex(realResult.toTensor(), imagResult.toTensor());
+    };
+    MathBackendCPU.prototype.split = function (x, sizeSplits, axis) {
+        return split_shared_1.split(x, sizeSplits, axis);
+    };
+    MathBackendCPU.prototype.dispose = function () { };
+    MathBackendCPU.prototype.floatPrecision = function () {
+        return 32;
+    };
+    MathBackendCPU.prototype.cropAndResize = function (images, boxes, boxIndex, cropSize, method, extrapolationValue) {
+        var _a = images.shape, batch = _a[0], imageHeight = _a[1], imageWidth = _a[2], numChannels = _a[3];
+        var numBoxes = boxes.shape[0];
+        var cropHeight = cropSize[0], cropWidth = cropSize[1];
+        var output = ops.buffer([numBoxes, cropHeight, cropWidth, numChannels], images.dtype);
+        var boxVals = boxes.dataSync();
+        var boxIndVals = boxIndex.dataSync();
+        var imageVals = images.dataSync();
+        var inStride = images.strides;
+        var outStride = output.strides;
+        for (var b = 0; b < numBoxes; b++) {
+            var startInd = b * 4;
+            var y1 = boxVals[startInd];
+            var x1 = boxVals[startInd + 1];
+            var y2 = boxVals[startInd + 2];
+            var x2 = boxVals[startInd + 3];
+            var bInd = boxIndVals[b];
+            if (bInd >= batch) {
+                continue;
+            }
+            var heightScale = (cropHeight > 1) ?
+                (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) :
+                0;
+            var widthScale = (cropWidth > 1) ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;
+            for (var y = 0; y < cropHeight; y++) {
+                var yInd = (cropHeight > 1) ?
+                    y1 * (imageHeight - 1) + y * (heightScale) :
+                    0.5 * (y1 + y2) * (imageHeight - 1);
+                if (yInd < 0 || yInd > imageHeight - 1) {
+                    for (var x = 0; x < cropWidth; x++) {
+                        for (var c = 0; c < numChannels; c++) {
+                            var ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
+                            output.values[ind] = extrapolationValue;
+                        }
+                    }
+                    continue;
+                }
+                if (method === 'bilinear') {
+                    var topInd = Math.floor(yInd);
+                    var bottomInd = Math.ceil(yInd);
+                    var yLerp = yInd - topInd;
+                    for (var x = 0; x < cropWidth; x++) {
+                        var xInd = (cropWidth > 1) ?
+                            x1 * (imageWidth - 1) + x * widthScale :
+                            0.5 * (x1 + x2) * (imageWidth - 1);
+                        if (xInd < 0 || xInd > imageWidth - 1) {
+                            for (var c = 0; c < numChannels; c++) {
+                                var ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
+                                output.values[ind] = extrapolationValue;
+                            }
+                            continue;
+                        }
+                        var leftInd = Math.floor(xInd);
+                        var rightInd = Math.ceil(xInd);
+                        var xLerp = xInd - leftInd;
+                        for (var c = 0; c < numChannels; c++) {
+                            var ind = c + leftInd * inStride[2] + topInd * inStride[1] +
+                                bInd * inStride[0];
+                            var topLeft = imageVals[ind];
+                            ind = c + rightInd * inStride[2] + topInd * inStride[1] +
+                                bInd * inStride[0];
+                            var topRight = imageVals[ind];
+                            ind = c + leftInd * inStride[2] + bottomInd * inStride[1] +
+                                bInd * inStride[0];
+                            var bottomLeft = imageVals[ind];
+                            ind = c + rightInd * inStride[2] + bottomInd * inStride[1] +
+                                bInd * inStride[0];
+                            var bottomRight = imageVals[ind];
+                            var top_2 = topLeft + (topRight - topLeft) * xLerp;
+                            var bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;
+                            ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
+                            output.values[ind] = top_2 + ((bottom - top_2) * yLerp);
+                        }
+                    }
+                }
+                else {
+                    for (var x = 0; x < cropWidth; ++x) {
+                        var xInd = (cropWidth > 1) ?
+                            x1 * (imageWidth - 1) + x * widthScale :
+                            0.5 * (x1 + x2) * (imageWidth - 1);
+                        if (xInd < 0 || xInd > imageWidth - 1) {
+                            for (var c = 0; c < numChannels; c++) {
+                                var ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
+                                output.values[ind] = extrapolationValue;
+                            }
+                            continue;
+                        }
+                        var closestX = Math.round(xInd);
+                        var closestY = Math.round(yInd);
+                        for (var c = 0; c < numChannels; c++) {
+                            var inInd = c + closestX * inStride[2] +
+                                closestY * inStride[1] + bInd * inStride[0];
+                            var outInd = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
+                            output.values[outInd] = imageVals[inInd];
+                        }
+                    }
+                }
+            }
+        }
+        return output.toTensor();
+    };
+    MathBackendCPU.prototype.sparseToDense = function (sparseIndices, sparseValues, outputShape, defaultValue) {
+        var _a = scatter_nd_util.calculateShapes(sparseValues, sparseIndices, outputShape), sliceRank = _a.sliceRank, numUpdates = _a.numUpdates, sliceSize = _a.sliceSize, strides = _a.strides, outputSize = _a.outputSize;
+        var sumDupeIndices = false;
+        return this.scatter(sparseIndices, sparseValues, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);
+    };
+    MathBackendCPU.prototype.gatherND = function (x, indices) {
+        var indicesShape = indices.shape;
+        var sliceRank = indicesShape[indicesShape.length - 1];
+        var _a = gather_nd_util.prepareAndValidate(x, indices), resultShape = _a[0], numSlices = _a[1], sliceSize = _a[2], strides = _a[3];
+        if (numSlices === 0) {
+            return ops_1.tensor([], resultShape, x.dtype);
+        }
+        var buffer = new tensor_1.TensorBuffer([numSlices, sliceSize], x.dtype);
+        var indicesData = indices.dataSync();
+        var xData = x.dataSync();
+        for (var i = 0; i < numSlices; i++) {
+            var index = [];
+            var flattenIndex = 0;
+            for (var j = 0; j < sliceRank; j++) {
+                var dim = indicesData[i * sliceRank + j];
+                flattenIndex += dim * strides[j];
+                index.push(dim);
+            }
+            if (flattenIndex < 0 || flattenIndex >= x.size / sliceSize) {
+                throw new Error("Invalid indices: " + index + " does not index into " + x.shape);
+            }
+            for (var k = 0; k < sliceSize; k++) {
+                buffer.values[i * sliceSize + k] = xData[flattenIndex * sliceSize + k];
+            }
+        }
+        return buffer.toTensor().reshape(resultShape);
+    };
+    MathBackendCPU.prototype.scatterND = function (indices, updates, shape) {
+        var _a = scatter_nd_util.calculateShapes(updates, indices, shape), sliceRank = _a.sliceRank, numUpdates = _a.numUpdates, sliceSize = _a.sliceSize, strides = _a.strides, outputSize = _a.outputSize;
+        var defaultValue = ops_1.scalar(0);
+        var sumDupeIndices = true;
+        return this.scatter(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);
+    };
+    MathBackendCPU.prototype.scatter = function (indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices) {
+        var flattenShape = [outputSize / sliceSize, sliceSize];
+        var indicesData = indices.dataSync();
+        var updatesData = updates.dataSync();
+        if (outputSize === 0) {
+            return ops_1.tensor([], shape, updates.dtype);
+        }
+        var buffer = new tensor_1.TensorBuffer(flattenShape, updates.dtype);
+        buffer.values.fill(defaultValue.dataSync()[0]);
+        for (var i = 0; i < numUpdates; i++) {
+            var index = [];
+            var flattenIndex = 0;
+            for (var j = 0; j < sliceRank; j++) {
+                var dim = indicesData[i * sliceRank + j];
+                index.push(dim);
+                flattenIndex += dim * strides[j];
+            }
+            if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) {
+                throw new Error("Invalid indices: " + index + " does not index into " + shape);
+            }
+            for (var k = 0; k < sliceSize; k++) {
+                if (sumDupeIndices) {
+                    buffer.values[flattenIndex * sliceSize + k] +=
+                        updatesData[i * sliceSize + k];
+                }
+                else {
+                    buffer.values[flattenIndex * sliceSize + k] = updates.rank === 0 ?
+                        updatesData[0] :
+                        updatesData[i * sliceSize + k];
+                }
+            }
+        }
+        return buffer.toTensor().reshape(shape);
+    };
+    return MathBackendCPU;
+}());
+exports.MathBackendCPU = MathBackendCPU;
+environment_1.ENV.registerBackend('cpu', function () { return new MathBackendCPU(); }, 1, tensor_1.setTensorTracker);
+//# sourceMappingURL=backend_cpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_cpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_cpu.js.map
new file mode 100644
index 0000000..c890baa
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_cpu.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_cpu_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_cpu_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_cpu_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_cpu_test.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_cpu_test.js
new file mode 100644
index 0000000..5294de4
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_cpu_test.js
@@ -0,0 +1,44 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var test_util_1 = require("../test_util");
+var backend_cpu_1 = require("./backend_cpu");
+describe('backendCPU', function () {
+    var prevBackend;
+    var backend;
+    beforeAll(function () {
+        prevBackend = tf.getBackend();
+    });
+    beforeEach(function () {
+        backend = new backend_cpu_1.MathBackendCPU();
+        tf.ENV.registerBackend('test-storage', function () { return backend; });
+        tf.setBackend('test-storage');
+    });
+    afterEach(function () {
+        backend.dispose();
+        tf.setBackend(prevBackend);
+        tf.ENV.removeBackend('test-storage');
+    });
+    it('register empty string tensor', function () {
+        var t = tf.Tensor.make([3], {}, 'string');
+        expect(backend.readSync(t.dataId) == null).toBe(true);
+    });
+    it('register empty string tensor and write', function () {
+        var t = tf.Tensor.make([3], {}, 'string');
+        backend.write(t.dataId, ['c', 'a', 'b']);
+        test_util_1.expectArraysEqual(backend.readSync(t.dataId), ['c', 'a', 'b']);
+    });
+    it('register string tensor with values', function () {
+        var t = tf.Tensor.make([3], { values: ['a', 'b', 'c'] }, 'string');
+        test_util_1.expectArraysEqual(backend.readSync(t.dataId), ['a', 'b', 'c']);
+    });
+    it('register string tensor with values and overwrite', function () {
+        var t = tf.Tensor.make([3], { values: ['a', 'b', 'c'] }, 'string');
+        backend.write(t.dataId, ['c', 'a', 'b']);
+        test_util_1.expectArraysEqual(backend.readSync(t.dataId), ['c', 'a', 'b']);
+    });
+    it('register string tensor with values and mismatched shape', function () {
+        expect(function () { return tf.tensor(['a', 'b', 'c'], [4], 'string'); }).toThrowError();
+    });
+});
+//# sourceMappingURL=backend_cpu_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_cpu_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_cpu_test.js.map
new file mode 100644
index 0000000..6b66470
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_cpu_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"backend_cpu_test.js","sourceRoot":"","sources":["../../src/kernels/backend_cpu_test.ts"],"names":[],"mappings":";;AAiBA,6BAA+B;AAC/B,0CAA+C;AAG/C,6CAA6C;AAE7C,QAAQ,CAAC,YAAY,EAAE;IACrB,IAAI,WAAmB,CAAC;IACxB,IAAI,OAAsB,CAAC;IAE3B,SAAS,CAAC;QACR,WAAW,GAAG,EAAE,CAAC,UAAU,EAAE,CAAC;IAChC,CAAC,CAAC,CAAC;IAEH,UAAU,CAAC;QACT,OAAO,GAAG,IAAI,4BAAc,EAAE,CAAC;QAC/B,EAAE,CAAC,GAAG,CAAC,eAAe,CAAC,cAAc,EAAE,cAAM,OAAA,OAAO,EAAP,CAAO,CAAC,CAAC;QACtD,EAAE,CAAC,UAAU,CAAC,cAAc,CAAC,CAAC;IAChC,CAAC,CAAC,CAAC;IAEH,SAAS,CAAC;QACR,OAAO,CAAC,OAAO,EAAE,CAAC;QAClB,EAAE,CAAC,UAAU,CAAC,WAAW,CAAC,CAAC;QAC3B,EAAE,CAAC,GAAG,CAAC,aAAa,CAAC,cAAc,CAAC,CAAC;IACvC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,8BAA8B,EAAE;QACjC,IAAM,CAAC,GAAG,EAAE,CAAC,MAAM,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,EAAE,EAAE,EAAE,QAAQ,CAAC,CAAC;QAC5C,MAAM,CAAC,OAAO,CAAC,QAAQ,CAAC,CAAC,CAAC,MAAM,CAAC,IAAI,IAAI,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;IACxD,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,wCAAwC,EAAE;QAC3C,IAAM,CAAC,GAAG,EAAE,CAAC,MAAM,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,EAAE,EAAE,EAAE,QAAQ,CAAC,CAAC;QAC5C,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,MAAM,EAAE,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC,CAAC,CAAC;QACzC,6BAAiB,CAAC,OAAO,CAAC,QAAQ,CAAC,CAAC,CAAC,MAAM,CAAC,EAAE,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC,CAAC,CAAC;IACjE,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,oCAAoC,EAAE;QACvC,IAAM,CAAC,GAAG,EAAE,CAAC,MAAM,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,EAAE,EAAC,MAAM,EAAE,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC,EAAC,EAAE,QAAQ,CAAC,CAAC;QACnE,6BAAiB,CAAC,OAAO,CAAC,QAAQ,CAAC,CAAC,CAAC,MAAM,CAAC,EAAE,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC,CAAC,CAAC;IACjE,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,kDAAkD,EAAE;QACrD,IAAM,CAAC,GAAG,EAAE,CAAC,MAAM,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,EAAE,EAAC,MAAM,EAAE,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC,EAAC,EAAE,QAAQ,CAAC,CAAC;QACnE,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,MAAM,EAAE,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC,CAAC,CAAC;QACzC,6BAAiB,CAAC,OAAO,CAAC,QAAQ,CAAC,CAAC,CAAC,MAAM,CAAC,EAAE,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC,CAAC,CAAC;IACjE,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,yDAAyD,EAAE;QAC5D,MAAM,CAAC,cAAM,OAAA,EAAE,CAAC,MAAM,CAAC,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,QAAQ,CAAC,EAAzC,CAAyC,CAAC,CAAC,YAAY,EAAE,CAAC;IACzE,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_util.d.ts
new file mode 100644
index 0000000..7d5c4c7
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_util.d.ts
@@ -0,0 +1,6 @@
+import { Tensor } from '../tensor';
+import { Rank } from '../types';
+import { DataType, ShapeMap } from '../types';
+import { KernelBackend } from './backend';
+export declare function castTensor<T extends Tensor>(x: T, dtype: DataType, backend: KernelBackend): T;
+export declare function reshapeTensor<T extends Tensor, R extends Rank>(x: T, shape: ShapeMap[R]): Tensor<R>;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_util.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_util.js
new file mode 100644
index 0000000..2e58015
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_util.js
@@ -0,0 +1,45 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tensor_ops_1 = require("../ops/tensor_ops");
+var tensor_1 = require("../tensor");
+var util_1 = require("../util");
+function castTensor(x, dtype, backend) {
+    if (dtype === 'complex64') {
+        if (x.dtype === 'complex64') {
+            return x.clone();
+        }
+        var zerosTensor = tensor_ops_1.zeros(x.shape);
+        var floatX = x.toFloat();
+        var result = backend.complex(floatX, zerosTensor);
+        zerosTensor.dispose();
+        floatX.dispose();
+        return result;
+    }
+    if (!util_1.hasEncodingLoss(x.dtype, dtype)) {
+        return tensor_1.Tensor.make(x.shape, { dataId: x.dataId }, dtype);
+    }
+    if (x.dtype === 'complex64') {
+        var real = backend.real(x);
+        var result = real.cast(dtype);
+        real.dispose();
+        return result;
+    }
+    if (dtype === 'int32') {
+        return backend.int(x);
+    }
+    else if (dtype === 'bool') {
+        var zero = tensor_ops_1.scalar(0, x.dtype);
+        var result = backend.notEqual(x, zero);
+        zero.dispose();
+        return result;
+    }
+    else {
+        throw new Error("Error in Cast: unknown dtype argument (" + dtype + ")");
+    }
+}
+exports.castTensor = castTensor;
+function reshapeTensor(x, shape) {
+    return tensor_1.Tensor.make(shape, { dataId: x.dataId }, x.dtype);
+}
+exports.reshapeTensor = reshapeTensor;
+//# sourceMappingURL=backend_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_util.js.map
new file mode 100644
index 0000000..2691731
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_util.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"backend_util.js","sourceRoot":"","sources":["../../src/kernels/backend_util.ts"],"names":[],"mappings":";;AAiBA,gDAAgD;AAChD,oCAAiC;AAGjC,gCAAwC;AAGxC,oBACI,CAAI,EAAE,KAAe,EAAE,OAAsB;IAC/C,IAAI,KAAK,KAAK,WAAW,EAAE;QACzB,IAAI,CAAC,CAAC,KAAK,KAAK,WAAW,EAAE;YAC3B,OAAO,CAAC,CAAC,KAAK,EAAE,CAAC;SAClB;QACD,IAAM,WAAW,GAAG,kBAAK,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC;QACnC,IAAM,MAAM,GAAG,CAAC,CAAC,OAAO,EAAE,CAAC;QAC3B,IAAM,MAAM,GAAG,OAAO,CAAC,OAAO,CAAC,MAAM,EAAE,WAAW,CAAC,CAAC;QACpD,WAAW,CAAC,OAAO,EAAE,CAAC;QACtB,MAAM,CAAC,OAAO,EAAE,CAAC;QACjB,OAAO,MAAW,CAAC;KACpB;IAED,IAAI,CAAC,sBAAe,CAAC,CAAC,CAAC,KAAK,EAAE,KAAK,CAAC,EAAE;QAGpC,OAAO,eAAM,CAAC,IAAI,CAAC,CAAC,CAAC,KAAK,EAAE,EAAC,MAAM,EAAE,CAAC,CAAC,MAAM,EAAC,EAAE,KAAK,CAAM,CAAC;KAC7D;IACD,IAAI,CAAC,CAAC,KAAK,KAAK,WAAW,EAAE;QAC3B,IAAM,IAAI,GAAG,OAAO,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;QAC7B,IAAM,MAAM,GAAG,IAAI,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC;QAChC,IAAI,CAAC,OAAO,EAAE,CAAC;QACf,OAAO,MAAM,CAAC;KACf;IACD,IAAI,KAAK,KAAK,OAAO,EAAE;QACrB,OAAO,OAAO,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC;KACvB;SAAM,IAAI,KAAK,KAAK,MAAM,EAAE;QAC3B,IAAM,IAAI,GAAG,mBAAM,CAAC,CAAC,EAAE,CAAC,CAAC,KAAK,CAAC,CAAC;QAChC,IAAM,MAAM,GAAG,OAAO,CAAC,QAAQ,CAAC,CAAC,EAAE,IAAI,CAAM,CAAC;QAC9C,IAAI,CAAC,OAAO,EAAE,CAAC;QACf,OAAO,MAAM,CAAC;KACf;SAAM;QACL,MAAM,IAAI,KAAK,CAAC,4CAA0C,KAAK,MAAG,CAAC,CAAC;KACrE;AACH,CAAC;AAnCD,gCAmCC;AAED,uBACI,CAAI,EAAE,KAAkB;IAC1B,OAAO,eAAM,CAAC,IAAI,CAAC,KAAK,EAAE,EAAC,MAAM,EAAE,CAAC,CAAC,MAAM,EAAC,EAAE,CAAC,CAAC,KAAK,CAAC,CAAC;AACzD,CAAC;AAHD,sCAGC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_webgl.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_webgl.d.ts
new file mode 100644
index 0000000..62911ae
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_webgl.d.ts
@@ -0,0 +1,221 @@
+import { MemoryInfo, TimingInfo } from '../engine';
+import { Conv2DInfo, Conv3DInfo } from '../ops/conv_util';
+import { DataId, Scalar, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, Tensor5D } from '../tensor';
+import { DataType, DataValues, Rank, RecursiveArray, ShapeMap } from '../types';
+import { DataMover, KernelBackend } from './backend';
+import { GPGPUContext } from './webgl/gpgpu_context';
+import { GPGPUProgram } from './webgl/gpgpu_math';
+import { TextureManager } from './webgl/texture_manager';
+declare type KernelInfo = {
+    name: string;
+    query: Promise<number>;
+};
+export declare type TimerNode = RecursiveArray<KernelInfo> | KernelInfo;
+export interface CPUTimerQuery {
+    startMs: number;
+    endMs?: number;
+}
+export interface WebGLMemoryInfo extends MemoryInfo {
+    numBytesInGPU: number;
+    unreliable: boolean;
+}
+export interface WebGLTimingInfo extends TimingInfo {
+    uploadWaitMs: number;
+    downloadWaitMs: number;
+}
+export interface TensorHandle {
+    dataId: DataId;
+    shape: number[];
+    dtype: DataType;
+}
+export declare const SIZE_UPLOAD_UNIFORM = 4;
+export declare const MATMUL_SHARED_DIM_THRESHOLD = 1000;
+export declare class MathBackendWebGL implements KernelBackend {
+    private gpgpu?;
+    private delayedStorage;
+    private texData;
+    private pendingRead;
+    private pendingDisposal;
+    private lruDataGPU;
+    private numBytesInGPU;
+    private NUM_BYTES_BEFORE_PAGING;
+    private canvas;
+    private fromPixels2DContext;
+    private programTimersStack;
+    private activeTimers;
+    private uploadWaitMs;
+    private downloadWaitMs;
+    private cpuBackend;
+    register(dataId: DataId, shape: number[], dtype: DataType): void;
+    setDataMover(dataMover: DataMover): void;
+    fromPixels(pixels: ImageData | HTMLImageElement | HTMLCanvasElement | HTMLVideoElement, numChannels: number): Tensor3D;
+    private makeTensorHandle;
+    write(dataId: DataId, values: DataValues): void;
+    readSync(dataId: DataId): DataValues;
+    read(dataId: DataId): Promise<DataValues>;
+    private getValuesFromTexture;
+    time(f: () => void): Promise<WebGLTimingInfo>;
+    memory(): WebGLMemoryInfo;
+    private startTimer;
+    private endTimer;
+    private getQueryTime;
+    disposeData(dataId: DataId): void;
+    getTexture(dataId: DataId): WebGLTexture;
+    private textureManager;
+    private binaryCache;
+    private gpgpuCreatedLocally;
+    constructor(gpgpu?: GPGPUContext, delayedStorage?: boolean);
+    private getCPUBackend;
+    private shouldExecuteOnCPU;
+    getGPGPUContext(): GPGPUContext;
+    getCanvas(): HTMLCanvasElement;
+    complex<T extends Tensor>(real: T, imag: T): T;
+    real<T extends Tensor>(input: T): T;
+    imag<T extends Tensor>(input: T): T;
+    slice<T extends Tensor>(x: T, begin: number[], size: number[]): T;
+    stridedSlice<T extends Tensor>(x: T, begin: number[], end: number[], strides: number[], beginMask: number, endMask: number, ellipsisMask: number, newAxisMask: number, shrinkAxisMask: number): T;
+    reverse<T extends Tensor>(x: T, axis: number[]): T;
+    private concat2Tensors;
+    concat(tensors: Tensor[], axis: number): Tensor;
+    neg<T extends Tensor>(x: T): T;
+    batchMatMul(a: Tensor3D, b: Tensor3D, transposeA: boolean, transposeB: boolean): Tensor3D;
+    multiply(a: Tensor, b: Tensor): Tensor;
+    batchNormalization(x: Tensor4D, mean: Tensor4D | Tensor1D, variance: Tensor4D | Tensor1D, varianceEpsilon: number, scale?: Tensor4D | Tensor1D, offset?: Tensor4D | Tensor1D): Tensor4D;
+    localResponseNormalization4D(x: Tensor4D, radius: number, bias: number, alpha: number, beta: number): Tensor4D;
+    LRNGrad(dy: Tensor4D, inputImage: Tensor4D, outputImage: Tensor4D, depthRadius: number, bias: number, alpha: number, beta: number): Tensor4D;
+    tile<T extends Tensor>(x: T, reps: number[]): T;
+    pad<T extends Tensor>(x: T, paddings: Array<[number, number]>, constantValue: number): T;
+    transpose<T extends Tensor>(x: T, perm: number[]): T;
+    gather<T extends Tensor>(x: T, indices: Tensor1D, axis: number): T;
+    batchToSpaceND<T extends Tensor>(x: T, blockShape: number[], crops: number[][]): T;
+    spaceToBatchND<T extends Tensor>(x: T, blockShape: number[], paddings: Array<[number, number]>): T;
+    private reduce;
+    private argReduce;
+    sum(x: Tensor, axes: number[]): Tensor;
+    prod(x: Tensor, axes: number[]): Tensor;
+    unsortedSegmentSum<T extends Tensor>(x: T, segmentIds: Tensor1D, numSegments: number): Tensor;
+    private segOpCompute;
+    argMin(x: Tensor, axis: number): Tensor;
+    argMax(x: Tensor, axis: number): Tensor;
+    cumsum(x: Tensor, axis: number, exclusive: boolean, reverse: boolean): Tensor;
+    equal(a: Tensor, b: Tensor): Tensor;
+    notEqual(a: Tensor, b: Tensor): Tensor;
+    less(a: Tensor, b: Tensor): Tensor;
+    lessEqual(a: Tensor, b: Tensor): Tensor;
+    greater(a: Tensor, b: Tensor): Tensor;
+    greaterEqual(a: Tensor, b: Tensor): Tensor;
+    logicalNot<T extends Tensor>(x: T): T;
+    logicalAnd(a: Tensor, b: Tensor): Tensor;
+    logicalOr(a: Tensor, b: Tensor): Tensor;
+    select(condition: Tensor, a: Tensor, b: Tensor): Tensor;
+    where(condition: Tensor): Tensor2D;
+    topk<T extends Tensor>(x: T, k: number, sorted: boolean): [T, T];
+    min(x: Tensor, axes: number[]): Tensor;
+    minimum(a: Tensor, b: Tensor): Tensor;
+    mod(a: Tensor, b: Tensor): Tensor;
+    max(x: Tensor, axes: number[]): Tensor;
+    maximum(a: Tensor, b: Tensor): Tensor;
+    all(x: Tensor, axes: number[]): Tensor;
+    any(x: Tensor, axes: number[]): Tensor;
+    squaredDifference(a: Tensor, b: Tensor): Tensor;
+    realDivide(a: Tensor, b: Tensor): Tensor;
+    floorDiv(a: Tensor, b: Tensor): Tensor;
+    add(a: Tensor, b: Tensor): Tensor;
+    private complexSeparableBinaryOp;
+    private makeComplexComponentTensorHandle;
+    addN<T extends Tensor>(tensors: T[]): T;
+    subtract(a: Tensor, b: Tensor): Tensor;
+    pow<T extends Tensor>(a: T, b: Tensor): T;
+    ceil<T extends Tensor>(x: T): T;
+    floor<T extends Tensor>(x: T): T;
+    sign<T extends Tensor>(x: T): T;
+    round<T extends Tensor>(x: T): T;
+    exp<T extends Tensor>(x: T): T;
+    expm1<T extends Tensor>(x: T): T;
+    log<T extends Tensor>(x: T): T;
+    log1p<T extends Tensor>(x: T): T;
+    sqrt<T extends Tensor>(x: T): T;
+    rsqrt<T extends Tensor>(x: T): T;
+    square<T extends Tensor>(x: T): T;
+    reciprocal<T extends Tensor>(x: T): T;
+    relu<T extends Tensor>(x: T): T;
+    elu<T extends Tensor>(x: T): T;
+    eluDer<T extends Tensor>(dy: T, y: T): T;
+    selu<T extends Tensor>(x: T): T;
+    int<T extends Tensor>(x: T): T;
+    clip<T extends Tensor>(x: T, min: number, max: number): T;
+    abs<T extends Tensor>(x: T): T;
+    complexAbs<T extends Tensor>(x: T): T;
+    sigmoid<T extends Tensor>(x: T): T;
+    softplus<T extends Tensor>(x: T): T;
+    sin<T extends Tensor>(x: T): T;
+    cos<T extends Tensor>(x: T): T;
+    tan<T extends Tensor>(x: T): T;
+    asin<T extends Tensor>(x: T): T;
+    acos<T extends Tensor>(x: T): T;
+    atan<T extends Tensor>(x: T): T;
+    atan2<T extends Tensor>(a: T, b: T): T;
+    sinh<T extends Tensor>(x: T): T;
+    cosh<T extends Tensor>(x: T): T;
+    tanh<T extends Tensor>(x: T): T;
+    asinh<T extends Tensor>(x: T): T;
+    acosh<T extends Tensor>(x: T): T;
+    atanh<T extends Tensor>(x: T): T;
+    erf<T extends Tensor>(x: T): T;
+    step<T extends Tensor>(x: T, alpha: number): T;
+    conv2dWithIm2Row(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    conv2d(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    conv2dDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    conv2dDerFilter(x: Tensor4D, dy: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    depthwiseConv2D(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    depthwiseConv2DDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    depthwiseConv2DDerFilter(x: Tensor4D, dy: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    conv3d(x: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo): Tensor5D;
+    conv3dDerInput(dy: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo): Tensor5D;
+    conv3dDerFilter(x: Tensor5D, dy: Tensor5D, convInfo: Conv3DInfo): Tensor5D;
+    maxPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    avgPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    maxPoolBackprop(dy: Tensor4D, x: Tensor4D, y: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    avgPoolBackprop(dy: Tensor4D, x: Tensor4D, convInfo: Conv2DInfo): Tensor4D;
+    cast<T extends Tensor>(x: T, dtype: DataType): T;
+    reshape<R extends Rank>(x: Tensor, shape: ShapeMap[R]): Tensor<R>;
+    resizeBilinear(x: Tensor4D, newHeight: number, newWidth: number, alignCorners: boolean): Tensor4D;
+    resizeBilinearBackprop(dy: Tensor4D, x: Tensor4D, alignCorners: boolean): Tensor4D;
+    resizeNearestNeighbor(x: Tensor4D, newHeight: number, newWidth: number, alignCorners: boolean): Tensor4D;
+    resizeNearestNeighborBackprop(dy: Tensor4D, x: Tensor4D, alignCorners: boolean): Tensor4D;
+    multinomial(logits: Tensor2D, normalized: boolean, numSamples: number, seed: number): Tensor2D;
+    oneHot(indices: Tensor1D, depth: number, onValue: number, offValue: number): Tensor2D;
+    nonMaxSuppression(boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number, iouThreshold: number, scoreThreshold: number): Tensor1D;
+    cropAndResize(image: Tensor4D, boxes: Tensor2D, boxIndex: Tensor1D, cropSize: [number, number], method: 'bilinear' | 'nearest', extrapolationValue: number): Tensor4D;
+    depthToSpace(x: Tensor4D, blockSize: number, dataFormat: 'NHWC' | 'NCHW'): Tensor4D;
+    split<T extends Tensor>(x: T, sizeSplits: number[], axis: number): T[];
+    scatterND<R extends Rank>(indices: Tensor, updates: Tensor, shape: ShapeMap[R]): Tensor<R>;
+    sparseToDense<R extends Rank>(sparseIndices: Tensor, sparseValues: Tensor, outputShape: ShapeMap[R], defaultValue: Scalar): Tensor<R>;
+    fft(x: Tensor2D): Tensor2D;
+    ifft(x: Tensor2D): Tensor2D;
+    private fftImpl;
+    gatherND(x: Tensor, indices: Tensor): Tensor<Rank>;
+    private makeOutputArray;
+    private makePackedTensor;
+    private unpackTensor;
+    private getBatchDim;
+    private getRowsCols;
+    private packedReshape;
+    compileAndRun<K extends {
+        dtype: DataType;
+        size: number;
+        dataId: {};
+        shape: number[];
+    }>(program: GPGPUProgram, inputs: TensorHandle[], output?: K, customSetup?: (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => void, pageToCpu?: boolean): K;
+    private getAndSaveBinary;
+    getTextureManager(): TextureManager;
+    private disposed;
+    dispose(): void;
+    floatPrecision(): number;
+    private uploadToGPU;
+    private convertAndCacheOnCPU;
+    private releaseTexture;
+    private acquireTexture;
+    private computeBytes;
+}
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_webgl.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_webgl.js
new file mode 100644
index 0000000..323878b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_webgl.js
@@ -0,0 +1,1607 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+Object.defineProperty(exports, "__esModule", { value: true });
+var canvas_util_1 = require("../canvas_util");
+var environment_1 = require("../environment");
+var globals_1 = require("../globals");
+var log_1 = require("../log");
+var array_ops_util = require("../ops/array_ops_util");
+var axis_util = require("../ops/axis_util");
+var concat_util_1 = require("../ops/concat_util");
+var gather_nd_util = require("../ops/gather_nd_util");
+var reduce_util = require("../ops/reduce_util");
+var scatter_nd_util = require("../ops/scatter_nd_util");
+var segment_util = require("../ops/segment_util");
+var slice_util_1 = require("../ops/slice_util");
+var softmax_1 = require("../ops/softmax");
+var tensor_ops_1 = require("../ops/tensor_ops");
+var tensor_1 = require("../tensor");
+var types_1 = require("../types");
+var util = require("../util");
+var util_1 = require("../util");
+var backend_1 = require("./backend");
+var backend_util = require("./backend_util");
+var complex_util_1 = require("./complex_util");
+var non_max_suppression_impl_1 = require("./non_max_suppression_impl");
+var split_shared_1 = require("./split_shared");
+var topk_impl_1 = require("./topk_impl");
+var argminmax_gpu_1 = require("./webgl/argminmax_gpu");
+var avg_pool_backprop_gpu_1 = require("./webgl/avg_pool_backprop_gpu");
+var batchnorm_gpu_1 = require("./webgl/batchnorm_gpu");
+var batchnorm_packed_gpu_1 = require("./webgl/batchnorm_packed_gpu");
+var binaryop_complex_gpu = require("./webgl/binaryop_complex_gpu");
+var binaryop_complex_gpu_1 = require("./webgl/binaryop_complex_gpu");
+var binaryop_gpu = require("./webgl/binaryop_gpu");
+var binaryop_gpu_1 = require("./webgl/binaryop_gpu");
+var clip_gpu_1 = require("./webgl/clip_gpu");
+var clip_packed_gpu_1 = require("./webgl/clip_packed_gpu");
+var complex_abs_gpu_1 = require("./webgl/complex_abs_gpu");
+var concat_gpu_1 = require("./webgl/concat_gpu");
+var conv_backprop_gpu_1 = require("./webgl/conv_backprop_gpu");
+var conv_backprop_gpu_depthwise_1 = require("./webgl/conv_backprop_gpu_depthwise");
+var conv_gpu_1 = require("./webgl/conv_gpu");
+var conv_gpu_depthwise_1 = require("./webgl/conv_gpu_depthwise");
+var conv_packed_gpu_depthwise_1 = require("./webgl/conv_packed_gpu_depthwise");
+var crop_and_resize_gpu_1 = require("./webgl/crop_and_resize_gpu");
+var cumsum_gpu_1 = require("./webgl/cumsum_gpu");
+var depth_to_space_gpu_1 = require("./webgl/depth_to_space_gpu");
+var encode_float_gpu_1 = require("./webgl/encode_float_gpu");
+var fft_gpu = require("./webgl/fft_gpu");
+var fft_gpu_1 = require("./webgl/fft_gpu");
+var from_pixels_gpu_1 = require("./webgl/from_pixels_gpu");
+var gather_gpu_1 = require("./webgl/gather_gpu");
+var gather_nd_gpu_1 = require("./webgl/gather_nd_gpu");
+var gpgpu_context_1 = require("./webgl/gpgpu_context");
+var gpgpu_math = require("./webgl/gpgpu_math");
+var im2col_gpu_1 = require("./webgl/im2col_gpu");
+var lrn_gpu_1 = require("./webgl/lrn_gpu");
+var lrn_grad_gpu_1 = require("./webgl/lrn_grad_gpu");
+var max_pool_backprop_gpu_1 = require("./webgl/max_pool_backprop_gpu");
+var mulmat_gpu_1 = require("./webgl/mulmat_gpu");
+var mulmat_packed_gpu_1 = require("./webgl/mulmat_packed_gpu");
+var multinomial_gpu_1 = require("./webgl/multinomial_gpu");
+var onehot_gpu_1 = require("./webgl/onehot_gpu");
+var pack_gpu_1 = require("./webgl/pack_gpu");
+var pad_gpu_1 = require("./webgl/pad_gpu");
+var pool_gpu_1 = require("./webgl/pool_gpu");
+var reduce_gpu_1 = require("./webgl/reduce_gpu");
+var reshape_packed_gpu_1 = require("./webgl/reshape_packed_gpu");
+var resize_bilinear_backprop_gpu_1 = require("./webgl/resize_bilinear_backprop_gpu");
+var resize_bilinear_gpu_1 = require("./webgl/resize_bilinear_gpu");
+var resize_nearest_neighbor_backprop_gpu_1 = require("./webgl/resize_nearest_neighbor_backprop_gpu");
+var resize_nearest_neighbor_gpu_1 = require("./webgl/resize_nearest_neighbor_gpu");
+var reverse_gpu_1 = require("./webgl/reverse_gpu");
+var scatter_gpu_1 = require("./webgl/scatter_gpu");
+var segment_gpu_1 = require("./webgl/segment_gpu");
+var select_gpu_1 = require("./webgl/select_gpu");
+var slice_gpu_1 = require("./webgl/slice_gpu");
+var strided_slice_gpu_1 = require("./webgl/strided_slice_gpu");
+var tex_util = require("./webgl/tex_util");
+var tex_util_1 = require("./webgl/tex_util");
+var texture_manager_1 = require("./webgl/texture_manager");
+var tile_gpu_1 = require("./webgl/tile_gpu");
+var transpose_gpu_1 = require("./webgl/transpose_gpu");
+var unary_op = require("./webgl/unaryop_gpu");
+var unaryop_gpu_1 = require("./webgl/unaryop_gpu");
+var unpack_gpu_1 = require("./webgl/unpack_gpu");
+var webgl_util = require("./webgl/webgl_util");
+var where_impl_1 = require("./where_impl");
+var CPU_HANDOFF_SIZE_THRESHOLD = 10;
+var BEFORE_PAGING_CONSTANT = 300;
+exports.SIZE_UPLOAD_UNIFORM = 4;
+exports.MATMUL_SHARED_DIM_THRESHOLD = 1000;
+var MathBackendWebGL = (function () {
+    function MathBackendWebGL(gpgpu, delayedStorage) {
+        if (delayedStorage === void 0) { delayedStorage = true; }
+        this.gpgpu = gpgpu;
+        this.delayedStorage = delayedStorage;
+        this.pendingRead = new WeakMap();
+        this.pendingDisposal = new WeakSet();
+        this.lruDataGPU = [];
+        this.numBytesInGPU = 0;
+        this.uploadWaitMs = 0;
+        this.downloadWaitMs = 0;
+        this.binaryCache = {};
+        this.disposed = false;
+        if (environment_1.ENV.get('WEBGL_VERSION') < 1) {
+            throw new Error('WebGL is not supported on this device');
+        }
+        if (gpgpu == null) {
+            var gl = canvas_util_1.getWebGLContext(environment_1.ENV.get('WEBGL_VERSION'));
+            this.gpgpu = new gpgpu_context_1.GPGPUContext(gl);
+            this.canvas = gl.canvas;
+            this.gpgpuCreatedLocally = true;
+        }
+        else {
+            this.gpgpuCreatedLocally = false;
+            this.canvas = gpgpu.gl.canvas;
+        }
+        if (environment_1.ENV.get('WEBGL_PAGING_ENABLED')) {
+            this.NUM_BYTES_BEFORE_PAGING =
+                (window.screen.height * window.screen.width *
+                    window.devicePixelRatio) *
+                    BEFORE_PAGING_CONSTANT;
+        }
+        this.textureManager = new texture_manager_1.TextureManager(this.gpgpu);
+    }
+    MathBackendWebGL.prototype.register = function (dataId, shape, dtype) {
+        if (this.texData.has(dataId)) {
+            throw new Error('Data buffer is already registered');
+        }
+        this.texData.set(dataId, { shape: shape, dtype: dtype });
+    };
+    MathBackendWebGL.prototype.setDataMover = function (dataMover) {
+        this.texData = new backend_1.DataStorage(dataMover);
+    };
+    MathBackendWebGL.prototype.fromPixels = function (pixels, numChannels) {
+        if (pixels == null) {
+            throw new Error('pixels passed to tf.fromPixels() can not be null');
+        }
+        var texShape = [pixels.height, pixels.width];
+        var outShape = [pixels.height, pixels.width, numChannels];
+        if (!(pixels instanceof HTMLVideoElement) &&
+            !(pixels instanceof HTMLImageElement) &&
+            !(pixels instanceof HTMLCanvasElement) &&
+            !(pixels instanceof ImageData)) {
+            throw new Error('pixels passed to tf.fromPixels() must be either an ' +
+                "HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or " +
+                ("ImageData, but was " + pixels.constructor.name));
+        }
+        if (pixels instanceof HTMLVideoElement) {
+            if (this.fromPixels2DContext == null) {
+                if (!environment_1.ENV.get('IS_BROWSER')) {
+                    throw new Error('Can\'t read pixels from HTMLImageElement outside the browser.');
+                }
+                if (document.readyState !== 'complete') {
+                    throw new Error('The DOM is not ready yet. Please call tf.fromPixels() ' +
+                        'once the DOM is ready. One way to do that is to add an event ' +
+                        'listener for `DOMContentLoaded` on the document object');
+                }
+                this.fromPixels2DContext =
+                    document.createElement('canvas').getContext('2d');
+            }
+            this.fromPixels2DContext.canvas.width = pixels.width;
+            this.fromPixels2DContext.canvas.height = pixels.height;
+            this.fromPixels2DContext.drawImage(pixels, 0, 0, pixels.width, pixels.height);
+            pixels = this.fromPixels2DContext.canvas;
+        }
+        var tempPixelHandle = this.makeTensorHandle(texShape, 'int32');
+        this.texData.get(tempPixelHandle.dataId).usage = tex_util_1.TextureUsage.PIXELS;
+        this.gpgpu.uploadPixelDataToTexture(this.getTexture(tempPixelHandle.dataId), pixels);
+        var program = new from_pixels_gpu_1.FromPixelsProgram(outShape);
+        var res = this.compileAndRun(program, [tempPixelHandle]);
+        this.disposeData(tempPixelHandle.dataId);
+        return res;
+    };
+    MathBackendWebGL.prototype.makeTensorHandle = function (shape, dtype) {
+        var dataId = {};
+        this.register(dataId, shape, dtype);
+        return { dataId: dataId, shape: shape, dtype: dtype };
+    };
+    MathBackendWebGL.prototype.write = function (dataId, values) {
+        if (values == null) {
+            throw new Error('MathBackendWebGL.write(): values can not be null');
+        }
+        var texData = this.texData.get(dataId);
+        var texture = texData.texture, texShape = texData.texShape, usage = texData.usage, dtype = texData.dtype, isPacked = texData.isPacked;
+        if (dtype === 'complex64') {
+            throw new Error("Cannot write to a complex64 dtype. " +
+                "Please use tf.complex(real, imag).");
+        }
+        if (texture != null) {
+            this.releaseTexture(dataId, texture, texShape, usage, isPacked);
+            texData.texture = null;
+            texData.texShape = null;
+        }
+        texData.usage = tex_util_1.TextureUsage.UPLOAD;
+        texData.values = values;
+        if (!this.delayedStorage) {
+            this.uploadToGPU(dataId);
+        }
+    };
+    MathBackendWebGL.prototype.readSync = function (dataId) {
+        var texData = this.texData.get(dataId);
+        var values = texData.values, dtype = texData.dtype, complexTensors = texData.complexTensors;
+        if (values != null) {
+            return this.convertAndCacheOnCPU(dataId);
+        }
+        if (dtype === 'string') {
+            return values;
+        }
+        var shouldTimeProgram = this.activeTimers != null;
+        var start;
+        if (shouldTimeProgram) {
+            start = performance.now();
+        }
+        var result;
+        if (dtype === 'complex64') {
+            var realValues = complexTensors.real.dataSync();
+            var imagValues = complexTensors.imag.dataSync();
+            result = complex_util_1.mergeRealAndImagArrays(realValues, imagValues);
+        }
+        else {
+            result = this.getValuesFromTexture(dataId);
+        }
+        if (shouldTimeProgram) {
+            this.downloadWaitMs += performance.now() - start;
+        }
+        return this.convertAndCacheOnCPU(dataId, result);
+    };
+    MathBackendWebGL.prototype.read = function (dataId) {
+        return __awaiter(this, void 0, void 0, function () {
+            var _a, _b, subscribers_1, texData, texture, values, texShape, isPacked, shape, width, height, bufferOrTexture, vals, batch, rows, cols, dTypeVals, subscribers;
+            return __generator(this, function (_c) {
+                switch (_c.label) {
+                    case 0:
+                        if (this.pendingRead.has(dataId)) {
+                            subscribers_1 = this.pendingRead.get(dataId);
+                            return [2, new Promise(function (resolve) { return subscribers_1.push(resolve); })];
+                        }
+                        texData = this.texData.get(dataId);
+                        texture = texData.texture, values = texData.values, texShape = texData.texShape, isPacked = texData.isPacked, shape = texData.shape;
+                        if (values != null) {
+                            return [2, this.convertAndCacheOnCPU(dataId)];
+                        }
+                        this.pendingRead.set(dataId, []);
+                        if (!environment_1.ENV.get('WEBGL_DOWNLOAD_FLOAT_ENABLED') &&
+                            environment_1.ENV.get('WEBGL_VERSION') === 2) {
+                            throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and " +
+                                "WEBGL_VERSION=2 not yet supported.");
+                        }
+                        width = texShape[1];
+                        height = texShape[0];
+                        if (isPacked) {
+                            _a = tex_util.getPackedMatrixTextureShapeWidthHeight(texShape[0], texShape[1]), width = _a[0], height = _a[1];
+                        }
+                        bufferOrTexture = this.gpgpu.maybeCreateBufferFromTexture(texture, height, width);
+                        return [4, this.gpgpu.createAndWaitForFence()];
+                    case 1:
+                        _c.sent();
+                        if (bufferOrTexture instanceof WebGLTexture) {
+                            vals = this.getValuesFromTexture(dataId);
+                        }
+                        else {
+                            if (isPacked) {
+                                batch = this.getBatchDim(shape);
+                                rows = 1, cols = 1;
+                                if (shape.length) {
+                                    _b = this.getRowsCols(shape), rows = _b[0], cols = _b[1];
+                                }
+                                vals = this.gpgpu.downloadPackedMatrixFromBuffer(bufferOrTexture, batch, rows, cols, texShape[0], texShape[1]);
+                            }
+                            else {
+                                vals = this.gpgpu.downloadFloat32MatrixFromBuffer(bufferOrTexture, texShape[0], texShape[1]);
+                            }
+                        }
+                        dTypeVals = this.convertAndCacheOnCPU(dataId, vals);
+                        subscribers = this.pendingRead.get(dataId);
+                        this.pendingRead.delete(dataId);
+                        subscribers.forEach(function (resolve) { return resolve(dTypeVals); });
+                        if (this.pendingDisposal.has(dataId)) {
+                            this.pendingDisposal.delete(dataId);
+                            this.disposeData(dataId);
+                        }
+                        return [2, dTypeVals];
+                }
+            });
+        });
+    };
+    MathBackendWebGL.prototype.getValuesFromTexture = function (dataId) {
+        var _a;
+        var _b = this.texData.get(dataId), shape = _b.shape, dtype = _b.dtype, texture = _b.texture, texShape = _b.texShape;
+        if (environment_1.ENV.get('WEBGL_DOWNLOAD_FLOAT_ENABLED')) {
+            if (this.texData.get(dataId).isPacked) {
+                var batch = this.getBatchDim(shape);
+                var rows = 1, cols = 1;
+                if (shape.length) {
+                    _a = this.getRowsCols(shape), rows = _a[0], cols = _a[1];
+                }
+                return this.gpgpu.downloadMatrixFromPackedTexture(texture, batch, rows, cols, texShape[0], texShape[1]);
+            }
+            else {
+                return this.gpgpu.downloadFloat32MatrixFromOutputTexture(texture, texShape[0], texShape[1]);
+            }
+        }
+        var tmpTarget = this.makeTensorHandle(shape, 'float32');
+        tmpTarget.size = util_1.sizeFromShape(shape);
+        this.texData.get(tmpTarget.dataId).usage = tex_util_1.TextureUsage.DOWNLOAD;
+        var program = new encode_float_gpu_1.EncodeFloatProgram(shape);
+        var pageToCpu = false;
+        this.compileAndRun(program, [{ shape: shape, dtype: dtype, dataId: dataId }], tmpTarget, null, pageToCpu);
+        var tmpData = this.texData.get(tmpTarget.dataId);
+        var vals = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture, tmpData.texShape[0], tmpData.texShape[1]);
+        this.disposeData(tmpTarget.dataId);
+        return vals;
+    };
+    MathBackendWebGL.prototype.time = function (f) {
+        return __awaiter(this, void 0, void 0, function () {
+            var oldActiveTimers, newActiveTimers, outerMostTime, flattenedActiveTimerQueries, flattenedActiveTimerNames, kernelMs, res;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        oldActiveTimers = this.activeTimers;
+                        newActiveTimers = [];
+                        outerMostTime = false;
+                        if (this.programTimersStack == null) {
+                            this.programTimersStack = newActiveTimers;
+                            outerMostTime = true;
+                        }
+                        else {
+                            this.activeTimers.push(newActiveTimers);
+                        }
+                        this.activeTimers = newActiveTimers;
+                        f();
+                        flattenedActiveTimerQueries = util.flatten(this.activeTimers.map(function (d) { return d.query; }))
+                            .filter(function (d) { return d != null; });
+                        flattenedActiveTimerNames = util.flatten(this.activeTimers.map(function (d) { return d.name; }))
+                            .filter(function (d) { return d != null; });
+                        this.activeTimers = oldActiveTimers;
+                        if (outerMostTime) {
+                            this.programTimersStack = null;
+                        }
+                        return [4, Promise.all(flattenedActiveTimerQueries)];
+                    case 1:
+                        kernelMs = _a.sent();
+                        res = {
+                            uploadWaitMs: this.uploadWaitMs,
+                            downloadWaitMs: this.downloadWaitMs,
+                            kernelMs: util.sum(kernelMs),
+                            getExtraProfileInfo: function () {
+                                return kernelMs.map(function (d, i) { return ({ name: flattenedActiveTimerNames[i], ms: d }); })
+                                    .map(function (d) { return d.name + ": " + d.ms; })
+                                    .join(', ');
+                            },
+                            wallMs: null
+                        };
+                        this.uploadWaitMs = 0;
+                        this.downloadWaitMs = 0;
+                        return [2, res];
+                }
+            });
+        });
+    };
+    MathBackendWebGL.prototype.memory = function () {
+        return { unreliable: false, numBytesInGPU: this.numBytesInGPU };
+    };
+    MathBackendWebGL.prototype.startTimer = function () {
+        if (environment_1.ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {
+            return this.gpgpu.beginQuery();
+        }
+        return { startMs: performance.now(), endMs: null };
+    };
+    MathBackendWebGL.prototype.endTimer = function (query) {
+        if (environment_1.ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {
+            this.gpgpu.endQuery();
+            return query;
+        }
+        query.endMs = performance.now();
+        return query;
+    };
+    MathBackendWebGL.prototype.getQueryTime = function (query) {
+        return __awaiter(this, void 0, void 0, function () {
+            var timerQuery;
+            return __generator(this, function (_a) {
+                if (environment_1.ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {
+                    return [2, this.gpgpu.waitForQueryAndGetTime(query)];
+                }
+                timerQuery = query;
+                return [2, timerQuery.endMs - timerQuery.startMs];
+            });
+        });
+    };
+    MathBackendWebGL.prototype.disposeData = function (dataId) {
+        if (this.pendingDisposal.has(dataId)) {
+            return;
+        }
+        if (this.pendingRead.has(dataId)) {
+            this.pendingDisposal.add(dataId);
+            return;
+        }
+        if (this.texData.has(dataId)) {
+            var _a = this.texData.get(dataId), texture = _a.texture, texShape = _a.texShape, usage = _a.usage, complexTensors = _a.complexTensors, isPacked = _a.isPacked;
+            if (texture != null) {
+                this.releaseTexture(dataId, texture, texShape, usage, isPacked);
+            }
+            if (complexTensors != null) {
+                complexTensors.real.dispose();
+                complexTensors.imag.dispose();
+            }
+            this.texData.delete(dataId);
+        }
+    };
+    MathBackendWebGL.prototype.getTexture = function (dataId) {
+        this.uploadToGPU(dataId);
+        return this.texData.get(dataId).texture;
+    };
+    MathBackendWebGL.prototype.getCPUBackend = function () {
+        if (!environment_1.ENV.get('WEBGL_CPU_FORWARD')) {
+            return null;
+        }
+        if (this.cpuBackend == null) {
+            this.cpuBackend = environment_1.ENV.findBackend('cpu');
+        }
+        return this.cpuBackend;
+    };
+    MathBackendWebGL.prototype.shouldExecuteOnCPU = function (inputs, sizeThreshold) {
+        var _this = this;
+        if (sizeThreshold === void 0) { sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD; }
+        return this.getCPUBackend() != null &&
+            inputs.every(function (input) { return _this.texData.get(input.dataId).texture == null &&
+                input.size < sizeThreshold; });
+    };
+    MathBackendWebGL.prototype.getGPGPUContext = function () {
+        return this.gpgpu;
+    };
+    MathBackendWebGL.prototype.getCanvas = function () {
+        return this.canvas;
+    };
+    MathBackendWebGL.prototype.complex = function (real, imag) {
+        var result = this.makeOutputArray(real.shape, 'complex64');
+        var resultData = this.texData.get(result.dataId);
+        resultData.complexTensors = {
+            real: environment_1.ENV.engine.keep(real.clone()),
+            imag: environment_1.ENV.engine.keep(imag.clone())
+        };
+        return result;
+    };
+    MathBackendWebGL.prototype.real = function (input) {
+        var resultData = this.texData.get(input.dataId);
+        return resultData.complexTensors.real.clone();
+    };
+    MathBackendWebGL.prototype.imag = function (input) {
+        var resultData = this.texData.get(input.dataId);
+        return resultData.complexTensors.imag.clone();
+    };
+    MathBackendWebGL.prototype.slice = function (x, begin, size) {
+        if (this.shouldExecuteOnCPU([x])) {
+            return this.cpuBackend.slice(x, begin, size);
+        }
+        var program = new slice_gpu_1.SliceProgram(size);
+        var customSetup = program.getCustomSetupFunc(begin);
+        return this.compileAndRun(program, [x], null, customSetup);
+    };
+    MathBackendWebGL.prototype.stridedSlice = function (x, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {
+        if (this.shouldExecuteOnCPU([x])) {
+            return this.cpuBackend.stridedSlice(x, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);
+        }
+        var _a = slice_util_1.getStridedSlicedInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask), beginIndex = _a[0], size = _a[1], shrinkAxis = _a[2];
+        var shape = size.filter(function (v, index) { return shrinkAxis.indexOf(index) === -1; });
+        if (shape.some(function (axis) { return axis === 0; })) {
+            return tensor_ops_1.tensor([], shape);
+        }
+        var program = new strided_slice_gpu_1.StridedSliceProgram(beginIndex, strides, size, shrinkAxis);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.reverse = function (x, axis) {
+        var program = new reverse_gpu_1.ReverseProgram(x.shape, axis);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.concat2Tensors = function (a, b, axis) {
+        var outShape = concat_util_1.computeOutShape([a.shape, b.shape], axis);
+        var a2D = a.as2D(-1, util_1.sizeFromShape(a.shape.slice(axis)));
+        var b2D = b.as2D(-1, util_1.sizeFromShape(b.shape.slice(axis)));
+        var program = new concat_gpu_1.ConcatProgram(a2D.shape, b2D.shape);
+        var res = this.compileAndRun(program, [a2D, b2D]);
+        return res.reshape(outShape);
+    };
+    MathBackendWebGL.prototype.concat = function (tensors, axis) {
+        if (this.shouldExecuteOnCPU(tensors)) {
+            return this.cpuBackend.concat(tensors, axis);
+        }
+        if (tensors.length === 1) {
+            return tensors[0];
+        }
+        var result = tensors[0];
+        for (var i = 1; i < tensors.length; ++i) {
+            result = this.concat2Tensors(result, tensors[i], axis);
+        }
+        return result;
+    };
+    MathBackendWebGL.prototype.neg = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.NEG);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.batchMatMul = function (a, b, transposeA, transposeB) {
+        var outerShapeA = transposeA ? a.shape[2] : a.shape[1];
+        var outerShapeB = transposeB ? b.shape[1] : b.shape[2];
+        var sharedDim = transposeA ? a.shape[1] : a.shape[2];
+        var _a = a.shape, batch = _a[0];
+        if ((outerShapeA === 1 || outerShapeB === 1) &&
+            sharedDim > exports.MATMUL_SHARED_DIM_THRESHOLD) {
+            if (transposeA) {
+                a = a.transpose([0, 2, 1]);
+            }
+            if (transposeB) {
+                b = b.transpose([0, 2, 1]);
+            }
+            var a3D = outerShapeB === 1 ? a : a.as3D(batch, sharedDim, 1);
+            var axis = outerShapeB === 1 ? 2 : 1;
+            var b3D = outerShapeB === 1 ? b.as3D(batch, 1, sharedDim) : b;
+            return this.multiply(a3D, b3D).sum(axis, true);
+        }
+        var dtype = types_1.upcastType(a.dtype, b.dtype);
+        if (batch === 1) {
+            var aSqueezed = a.as2D(a.shape[1], a.shape[2]);
+            var bSqueezed = b.as2D(b.shape[1], b.shape[2]);
+            var program = new mulmat_packed_gpu_1.MatMulPackedProgram(aSqueezed.shape, bSqueezed.shape, [outerShapeA, outerShapeB], transposeA, transposeB);
+            var output = this.makePackedTensor(program.outputShape, dtype);
+            var result = this.compileAndRun(program, [aSqueezed, bSqueezed], output);
+            return result.reshape([1, result.shape[0], result.shape[1]]);
+        }
+        else {
+            var program = new mulmat_gpu_1.MatMulProgram(a.shape, b.shape, transposeA, transposeB);
+            var output = this.makeOutputArray(program.outputShape, dtype);
+            return this.compileAndRun(program, [a, b], output);
+        }
+    };
+    MathBackendWebGL.prototype.multiply = function (a, b) {
+        if (a.dtype === 'complex64') {
+            var aData = this.texData.get(a.dataId);
+            var bData = this.texData.get(b.dataId);
+            var realProgram = new binaryop_complex_gpu_1.BinaryOpComplexProgram(binaryop_complex_gpu.COMPLEX_MULTIPLY.REAL, a.shape, b.shape);
+            var imagProgram = new binaryop_complex_gpu_1.BinaryOpComplexProgram(binaryop_complex_gpu.COMPLEX_MULTIPLY.IMAG, a.shape, b.shape);
+            var inputs = [
+                this.makeComplexComponentTensorHandle(a, aData.complexTensors.real),
+                this.makeComplexComponentTensorHandle(a, aData.complexTensors.imag),
+                this.makeComplexComponentTensorHandle(b, bData.complexTensors.real),
+                this.makeComplexComponentTensorHandle(b, bData.complexTensors.imag)
+            ];
+            var real = this.compileAndRun(realProgram, inputs);
+            var imag = this.compileAndRun(imagProgram, inputs);
+            var complex = this.complex(real, imag);
+            real.dispose();
+            imag.dispose();
+            return complex;
+        }
+        if (this.shouldExecuteOnCPU([a, b])) {
+            return this.cpuBackend.multiply(a, b);
+        }
+        var program = new binaryop_gpu_1.BinaryOpProgram(binaryop_gpu.MUL, a.shape, b.shape);
+        var output = this.makeOutputArray(program.outputShape, a.dtype);
+        return this.compileAndRun(program, [a, b], output);
+    };
+    MathBackendWebGL.prototype.batchNormalization = function (x, mean, variance, varianceEpsilon, scale, offset) {
+        var inputs = [x, mean, variance];
+        var offsetShape = null;
+        if (offset != null) {
+            offsetShape = offset.shape;
+            inputs.push(offset);
+        }
+        var scaleShape = null;
+        if (scale != null) {
+            scaleShape = scale.shape;
+            inputs.push(scale);
+        }
+        if (environment_1.ENV.get('WEBGL_PACK_BATCHNORMALIZATION')) {
+            var batchNormPackedProgram = new batchnorm_packed_gpu_1.BatchNormPackedProgram(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon);
+            return this.compileAndRun(batchNormPackedProgram, inputs);
+        }
+        var batchNormProgram = new batchnorm_gpu_1.BatchNormProgram(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon);
+        return this.compileAndRun(batchNormProgram, inputs);
+    };
+    MathBackendWebGL.prototype.localResponseNormalization4D = function (x, radius, bias, alpha, beta) {
+        var program = new lrn_gpu_1.LRNProgram(x.shape, radius, bias, alpha, beta);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.LRNGrad = function (dy, inputImage, outputImage, depthRadius, bias, alpha, beta) {
+        var program = new lrn_grad_gpu_1.LRNGradProgram(inputImage.shape, depthRadius, bias, alpha, beta);
+        return this.compileAndRun(program, [inputImage, outputImage, dy]);
+    };
+    MathBackendWebGL.prototype.tile = function (x, reps) {
+        var program = new tile_gpu_1.TileProgram(x.shape, reps);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.pad = function (x, paddings, constantValue) {
+        var program = new pad_gpu_1.PadProgram(x.shape, paddings, constantValue);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.transpose = function (x, perm) {
+        var program = new transpose_gpu_1.TransposeProgram(x.shape, perm);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.gather = function (x, indices, axis) {
+        var program = new gather_gpu_1.GatherProgram(x.shape, indices.size, axis);
+        return this.compileAndRun(program, [x, indices]);
+    };
+    MathBackendWebGL.prototype.batchToSpaceND = function (x, blockShape, crops) {
+        util.assert(x.rank <= 4, 'batchToSpaceND for rank > 4 with a WebGL backend not implemented yet');
+        var prod = blockShape.reduce(function (a, b) { return a * b; });
+        var reshaped = array_ops_util.getReshaped(x.shape, blockShape, prod);
+        var permuted = array_ops_util.getPermuted(reshaped.length, blockShape.length);
+        var reshapedPermuted = array_ops_util.getReshapedPermuted(x.shape, blockShape, prod);
+        var sliceBeginCoords = array_ops_util.getSliceBeginCoords(crops, blockShape.length);
+        var sliceSize = array_ops_util.getSliceSize(reshapedPermuted, crops, blockShape.length);
+        return x.reshape(reshaped)
+            .transpose(permuted)
+            .reshape(reshapedPermuted)
+            .slice(sliceBeginCoords, sliceSize);
+    };
+    MathBackendWebGL.prototype.spaceToBatchND = function (x, blockShape, paddings) {
+        util.assert(x.rank <= 4, 'spaceToBatchND for rank > 4 with a WebGL backend not implemented yet');
+        var prod = blockShape.reduce(function (a, b) { return a * b; });
+        var completePaddings = [[0, 0]];
+        completePaddings.push.apply(completePaddings, paddings);
+        for (var i = 1 + blockShape.length; i < x.shape.length; ++i) {
+            completePaddings.push([0, 0]);
+        }
+        var paddedX = x.pad(completePaddings);
+        var reshapedPaddedShape = array_ops_util.getReshaped(paddedX.shape, blockShape, prod, false);
+        var permutedReshapedPaddedPermutation = array_ops_util.getPermuted(reshapedPaddedShape.length, blockShape.length, false);
+        var flattenShape = array_ops_util.getReshapedPermuted(paddedX.shape, blockShape, prod, false);
+        return paddedX.reshape(reshapedPaddedShape)
+            .transpose(permutedReshapedPaddedPermutation)
+            .reshape(flattenShape);
+    };
+    MathBackendWebGL.prototype.reduce = function (x, reduceType, dtype) {
+        var batchSize = x.shape[0];
+        var inSize = x.shape[1];
+        var windowSize = reduce_util.computeOptimalWindowSize(inSize);
+        var reduceInfo = { windowSize: windowSize, inSize: inSize, batchSize: batchSize };
+        var program = new reduce_gpu_1.ReduceProgram(reduceInfo, reduceType);
+        var _a = program.outputShape, rows = _a[0], cols = _a[1];
+        var output = this.makeOutputArray([rows, cols], dtype);
+        this.compileAndRun(program, [x], output);
+        if (output.shape[1] === 1) {
+            return output;
+        }
+        return this.reduce(output, reduceType, dtype);
+    };
+    MathBackendWebGL.prototype.argReduce = function (x, reduceType, bestIndicesA) {
+        if (bestIndicesA === void 0) { bestIndicesA = null; }
+        var batchSize = x.shape[0];
+        var inSize = x.shape[1];
+        if (bestIndicesA != null) {
+            batchSize = bestIndicesA.shape[0];
+            inSize = bestIndicesA.shape[1];
+        }
+        var windowSize = reduce_util.computeOptimalWindowSize(inSize);
+        var reduceInfo = { windowSize: windowSize, inSize: inSize, batchSize: batchSize };
+        var program = new argminmax_gpu_1.ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null);
+        var _a = program.outputShape, rows = _a[0], cols = _a[1];
+        var output = this.makeOutputArray([rows, cols], 'int32');
+        var inputs = [x];
+        if (bestIndicesA != null) {
+            inputs.push(bestIndicesA);
+        }
+        this.compileAndRun(program, inputs, output);
+        if (output.shape[1] === 1) {
+            return output;
+        }
+        return this.argReduce(x, reduceType, output);
+    };
+    MathBackendWebGL.prototype.sum = function (x, axes) {
+        axis_util.assertAxesAreInnerMostDims('sum', axes, x.rank);
+        var _a = axis_util.computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+        var inSize = util.sizeFromShape(reduceShape);
+        var a2D = x.as2D(-1, inSize);
+        var outputDType = types_1.sumOutType(x.dtype);
+        return this.reduce(a2D, 'sum', outputDType).reshape(outShape);
+    };
+    MathBackendWebGL.prototype.prod = function (x, axes) {
+        var _a = axis_util.computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+        var inSize = util.sizeFromShape(reduceShape);
+        var a2D = x.as2D(-1, inSize);
+        var outputDType = types_1.sumOutType(x.dtype);
+        return this.reduce(a2D, 'prod', outputDType).reshape(outShape);
+    };
+    MathBackendWebGL.prototype.unsortedSegmentSum = function (x, segmentIds, numSegments) {
+        var axis = 0;
+        var permutation = axis_util.getAxesPermutation([axis], x.rank);
+        var permutedX = x;
+        if (permutation != null) {
+            permutedX = x.transpose(permutation);
+            axis = axis_util.getInnerMostAxes(1, x.rank)[0];
+        }
+        var outShape = segment_util.computeOutShape(permutedX.shape, axis, numSegments);
+        var inSize = util.sizeFromShape([permutedX.shape[axis]]);
+        var a2D = permutedX.as2D(-1, inSize);
+        var outputDType = types_1.sumOutType(x.dtype);
+        var result = this.segOpCompute(a2D, 'unsortedSegmentSum', segmentIds, outputDType, numSegments)
+            .reshape(outShape);
+        if (permutation != null) {
+            result = result.transpose(axis_util.getUndoAxesPermutation(permutation));
+        }
+        return result;
+    };
+    MathBackendWebGL.prototype.segOpCompute = function (x, segOpType, segmentIds, dtype, numSegments) {
+        var batchSize = x.shape[0];
+        var inSize = x.shape[1];
+        var windowSize = segment_util.segOpComputeOptimalWindowSize(inSize, numSegments);
+        var segOpInfo = { windowSize: windowSize, inSize: inSize, batchSize: batchSize, numSegments: numSegments };
+        var program = new segment_gpu_1.SegmentOpProgram(segOpInfo, segOpType);
+        var _a = program.outputShape, rows = _a[0], cols = _a[1];
+        var output = this.makeOutputArray([rows, cols], dtype);
+        this.compileAndRun(program, [x, segmentIds], output);
+        if (output.shape[1] === numSegments) {
+            return output;
+        }
+        segmentIds = tensor_ops_1.range(0, numSegments).tile([inSize / windowSize]);
+        return this.segOpCompute(output, segOpType, segmentIds, dtype, numSegments);
+    };
+    MathBackendWebGL.prototype.argMin = function (x, axis) {
+        var axes = [axis];
+        axis_util.assertAxesAreInnerMostDims('argMin', axes, x.rank);
+        var _a = axis_util.computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+        var inSize = util.sizeFromShape(reduceShape);
+        var a2D = x.as2D(-1, inSize);
+        return this.argReduce(a2D, 'min').reshape(outShape);
+    };
+    MathBackendWebGL.prototype.argMax = function (x, axis) {
+        var axes = [axis];
+        axis_util.assertAxesAreInnerMostDims('argMax', axes, x.rank);
+        var _a = axis_util.computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+        var inSize = util.sizeFromShape(reduceShape);
+        var a2D = x.as2D(-1, inSize);
+        return this.argReduce(a2D, 'max').reshape(outShape);
+    };
+    MathBackendWebGL.prototype.cumsum = function (x, axis, exclusive, reverse) {
+        if (axis !== x.rank - 1) {
+            throw new Error("WebGL cumsum shader expects an inner-most axis=" + (x.rank - 1) + " " +
+                ("but got axis=" + axis));
+        }
+        var program = new cumsum_gpu_1.CumSumProgram(x.shape, exclusive, reverse);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.equal = function (a, b) {
+        var program = new binaryop_gpu_1.BinaryOpProgram(binaryop_gpu.EQUAL, a.shape, b.shape);
+        var output = this.makeOutputArray(program.outputShape, 'bool');
+        return this.compileAndRun(program, [a, b], output);
+    };
+    MathBackendWebGL.prototype.notEqual = function (a, b) {
+        var program = new binaryop_gpu_1.BinaryOpProgram(binaryop_gpu.NOT_EQUAL, a.shape, b.shape);
+        var output = this.makeOutputArray(program.outputShape, 'bool');
+        return this.compileAndRun(program, [a, b], output);
+    };
+    MathBackendWebGL.prototype.less = function (a, b) {
+        if (this.shouldExecuteOnCPU([a, b])) {
+            return this.cpuBackend.less(a, b);
+        }
+        var program = new binaryop_gpu_1.BinaryOpProgram(binaryop_gpu.LESS, a.shape, b.shape);
+        var output = this.makeOutputArray(program.outputShape, 'bool');
+        return this.compileAndRun(program, [a, b], output);
+    };
+    MathBackendWebGL.prototype.lessEqual = function (a, b) {
+        var program = new binaryop_gpu_1.BinaryOpProgram(binaryop_gpu.LESS_EQUAL, a.shape, b.shape);
+        var output = this.makeOutputArray(program.outputShape, 'bool');
+        return this.compileAndRun(program, [a, b], output);
+    };
+    MathBackendWebGL.prototype.greater = function (a, b) {
+        if (this.shouldExecuteOnCPU([a, b])) {
+            return this.cpuBackend.greater(a, b);
+        }
+        var program = new binaryop_gpu_1.BinaryOpProgram(binaryop_gpu.GREATER, a.shape, b.shape);
+        var output = this.makeOutputArray(program.outputShape, 'bool');
+        return this.compileAndRun(program, [a, b], output);
+    };
+    MathBackendWebGL.prototype.greaterEqual = function (a, b) {
+        var program = new binaryop_gpu_1.BinaryOpProgram(binaryop_gpu.GREATER_EQUAL, a.shape, b.shape);
+        var output = this.makeOutputArray(program.outputShape, 'bool');
+        return this.compileAndRun(program, [a, b], output);
+    };
+    MathBackendWebGL.prototype.logicalNot = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.LOGICAL_NOT);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.logicalAnd = function (a, b) {
+        var program = new binaryop_gpu_1.BinaryOpProgram(binaryop_gpu.LOGICAL_AND, a.shape, b.shape);
+        var output = this.makeOutputArray(program.outputShape, 'bool');
+        return this.compileAndRun(program, [a, b], output);
+    };
+    MathBackendWebGL.prototype.logicalOr = function (a, b) {
+        var program = new binaryop_gpu_1.BinaryOpProgram(binaryop_gpu.LOGICAL_OR, a.shape, b.shape);
+        var output = this.makeOutputArray(program.outputShape, 'bool');
+        return this.compileAndRun(program, [a, b], output);
+    };
+    MathBackendWebGL.prototype.select = function (condition, a, b) {
+        var program = new select_gpu_1.SelectProgram(condition.rank, a.shape, a.rank);
+        var output = this.makeOutputArray(program.outputShape, types_1.upcastType(a.dtype, b.dtype));
+        return this.compileAndRun(program, [condition, a, b], output);
+    };
+    MathBackendWebGL.prototype.where = function (condition) {
+        log_1.warn('tf.where() in webgl locks the UI thread. ' +
+            'Call tf.whereAsync() instead');
+        var condVals = condition.dataSync();
+        return where_impl_1.whereImpl(condition.shape, condVals);
+    };
+    MathBackendWebGL.prototype.topk = function (x, k, sorted) {
+        var xVals = x.dataSync();
+        return topk_impl_1.topkImpl(xVals, x.shape, x.dtype, k, sorted);
+    };
+    MathBackendWebGL.prototype.min = function (x, axes) {
+        axis_util.assertAxesAreInnerMostDims('min', axes, x.rank);
+        var _a = axis_util.computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+        var inSize = util.sizeFromShape(reduceShape);
+        var a2D = x.as2D(-1, inSize);
+        return this.reduce(a2D, 'min', a2D.dtype).reshape(outShape);
+    };
+    MathBackendWebGL.prototype.minimum = function (a, b) {
+        if (this.shouldExecuteOnCPU([a, b])) {
+            return this.cpuBackend.minimum(a, b);
+        }
+        var program = new binaryop_gpu_1.BinaryOpProgram(binaryop_gpu.MIN, a.shape, b.shape);
+        return this.compileAndRun(program, [a, b]);
+    };
+    MathBackendWebGL.prototype.mod = function (a, b) {
+        var program = new binaryop_gpu_1.BinaryOpProgram(binaryop_gpu.MOD, a.shape, b.shape);
+        var customSetup = program.getCustomSetupFunc();
+        return this.compileAndRun(program, [a, b], null, customSetup);
+    };
+    MathBackendWebGL.prototype.max = function (x, axes) {
+        axis_util.assertAxesAreInnerMostDims('max', axes, x.rank);
+        var _a = axis_util.computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+        var inSize = util.sizeFromShape(reduceShape);
+        var a2D = x.as2D(-1, inSize);
+        return this.reduce(a2D, 'max', a2D.dtype).reshape(outShape);
+    };
+    MathBackendWebGL.prototype.maximum = function (a, b) {
+        if (this.shouldExecuteOnCPU([a, b])) {
+            return this.cpuBackend.maximum(a, b);
+        }
+        var program = new binaryop_gpu_1.BinaryOpProgram(binaryop_gpu.MAX, a.shape, b.shape);
+        return this.compileAndRun(program, [a, b]);
+    };
+    MathBackendWebGL.prototype.all = function (x, axes) {
+        axis_util.assertAxesAreInnerMostDims('all', axes, x.rank);
+        var _a = axis_util.computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+        var inSize = util.sizeFromShape(reduceShape);
+        var a2D = x.as2D(-1, inSize);
+        return this.reduce(a2D, 'all', a2D.dtype).reshape(outShape);
+    };
+    MathBackendWebGL.prototype.any = function (x, axes) {
+        axis_util.assertAxesAreInnerMostDims('any', axes, x.rank);
+        var _a = axis_util.computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+        var inSize = util.sizeFromShape(reduceShape);
+        var a2D = x.as2D(-1, inSize);
+        return this.reduce(a2D, 'any', a2D.dtype).reshape(outShape);
+    };
+    MathBackendWebGL.prototype.squaredDifference = function (a, b) {
+        var program = new binaryop_gpu_1.BinaryOpProgram(binaryop_gpu.SQUARED_DIFFERENCE, a.shape, b.shape);
+        return this.compileAndRun(program, [a, b]);
+    };
+    MathBackendWebGL.prototype.realDivide = function (a, b) {
+        var op = binaryop_gpu.DIV;
+        var outputDtype = 'float32';
+        var program = new binaryop_gpu_1.BinaryOpProgram(op, a.shape, b.shape);
+        var output = this.makeOutputArray(program.outputShape, outputDtype);
+        return this.compileAndRun(program, [a, b], output);
+    };
+    MathBackendWebGL.prototype.floorDiv = function (a, b) {
+        var op = binaryop_gpu.INT_DIV;
+        var outputDtype = 'int32';
+        var program = new binaryop_gpu_1.BinaryOpProgram(op, a.shape, b.shape);
+        var output = this.makeOutputArray(program.outputShape, outputDtype);
+        return this.compileAndRun(program, [a, b], output);
+    };
+    MathBackendWebGL.prototype.add = function (a, b) {
+        if (a.dtype === 'complex64' && b.dtype === 'complex64') {
+            return this.complexSeparableBinaryOp(a, b, binaryop_gpu.ADD);
+        }
+        var program = new binaryop_gpu_1.BinaryOpProgram(binaryop_gpu.ADD, a.shape, b.shape);
+        var output = this.makeOutputArray(program.outputShape, types_1.upcastType(a.dtype, b.dtype));
+        return this.compileAndRun(program, [a, b], output);
+    };
+    MathBackendWebGL.prototype.complexSeparableBinaryOp = function (a, b, op) {
+        var _this = this;
+        var aData = this.texData.get(a.dataId);
+        var bData = this.texData.get(b.dataId);
+        var _a = [
+            [aData.complexTensors.real, bData.complexTensors.real],
+            [aData.complexTensors.imag, bData.complexTensors.imag]
+        ].map(function (complexParts) {
+            var aPart = complexParts[0], bPart = complexParts[1];
+            var program = new binaryop_gpu_1.BinaryOpProgram(op, a.shape, b.shape);
+            var output = _this.makeOutputArray(program.outputShape, types_1.upcastType(aPart.dtype, bPart.dtype));
+            var aHandle = _this.makeComplexComponentTensorHandle(a, aPart);
+            var bHandle = _this.makeComplexComponentTensorHandle(b, bPart);
+            return _this.compileAndRun(program, [aHandle, bHandle], output);
+        }), real = _a[0], imag = _a[1];
+        var complex = this.complex(real, imag);
+        real.dispose();
+        imag.dispose();
+        return complex;
+    };
+    MathBackendWebGL.prototype.makeComplexComponentTensorHandle = function (complexTensor, complexPart) {
+        return {
+            dataId: complexPart.dataId,
+            dtype: complexPart.dtype,
+            shape: complexTensor.shape
+        };
+    };
+    MathBackendWebGL.prototype.addN = function (tensors) {
+        var res = tensors[0];
+        for (var i = 1; i < tensors.length; i++) {
+            res = this.add(res, tensors[i]);
+        }
+        return res;
+    };
+    MathBackendWebGL.prototype.subtract = function (a, b) {
+        if (a.dtype === 'complex64' && b.dtype === 'complex64') {
+            return this.complexSeparableBinaryOp(a, b, binaryop_gpu.SUB);
+        }
+        if (this.shouldExecuteOnCPU([a, b])) {
+            return this.cpuBackend.subtract(a, b);
+        }
+        var program = new binaryop_gpu_1.BinaryOpProgram(binaryop_gpu.SUB, a.shape, b.shape);
+        var output = this.makeOutputArray(program.outputShape, types_1.upcastType(a.dtype, b.dtype));
+        return this.compileAndRun(program, [a, b], output);
+    };
+    MathBackendWebGL.prototype.pow = function (a, b) {
+        var program = new binaryop_gpu_1.BinaryOpProgram(binaryop_gpu.POW, a.shape, b.shape);
+        var customSetup = program.getCustomSetupFunc();
+        var output = this.makeOutputArray(program.outputShape, types_1.upcastType(a.dtype, b.dtype));
+        return this.compileAndRun(program, [a, b], output, customSetup);
+    };
+    MathBackendWebGL.prototype.ceil = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.CEIL);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.floor = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.FLOOR);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.sign = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.SIGN);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.round = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.ROUND);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.exp = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.EXP);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.expm1 = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.EXPM1);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.log = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.LOG);
+        var customSetup = program.getCustomSetupFunc();
+        return this.compileAndRun(program, [x], null, customSetup);
+    };
+    MathBackendWebGL.prototype.log1p = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.LOG1P);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.sqrt = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.SQRT);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.rsqrt = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.RSQRT);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.square = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.SQUARE);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.reciprocal = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.RECIPROCAL);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.relu = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.RELU);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.elu = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.ELU);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.eluDer = function (dy, y) {
+        var program = new binaryop_gpu_1.BinaryOpProgram(binaryop_gpu.ELU_DER, dy.shape, y.shape);
+        return this.compileAndRun(program, [dy, y]);
+    };
+    MathBackendWebGL.prototype.selu = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.SELU);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.int = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.TO_INT);
+        var output = this.makeOutputArray(program.outputShape, 'int32');
+        return this.compileAndRun(program, [x], output);
+    };
+    MathBackendWebGL.prototype.clip = function (x, min, max) {
+        var program;
+        if (environment_1.ENV.get('WEBGL_PACK_CLIP')) {
+            program = new clip_packed_gpu_1.ClipPackedProgram(x.shape, min, max);
+        }
+        else {
+            program = new clip_gpu_1.ClipProgram(x.shape, min, max);
+        }
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.abs = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.ABS);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.complexAbs = function (x) {
+        var xData = this.texData.get(x.dataId);
+        var program = new complex_abs_gpu_1.ComplexAbsProgram(x.shape);
+        var inputs = [
+            this.makeComplexComponentTensorHandle(x, xData.complexTensors.real),
+            this.makeComplexComponentTensorHandle(x, xData.complexTensors.imag),
+        ];
+        return this.compileAndRun(program, inputs);
+    };
+    MathBackendWebGL.prototype.sigmoid = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.SIGMOID);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.softplus = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.SOFTPLUS);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.sin = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.SIN);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.cos = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.COS);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.tan = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.TAN);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.asin = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.ASIN);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.acos = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.ACOS);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.atan = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.ATAN);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.atan2 = function (a, b) {
+        var program = new binaryop_gpu_1.BinaryOpProgram(binaryop_gpu.ATAN2, a.shape, b.shape);
+        return this.compileAndRun(program, [a, b]);
+    };
+    MathBackendWebGL.prototype.sinh = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.SINH);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.cosh = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.COSH);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.tanh = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.TANH);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.asinh = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.ASINH);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.acosh = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.ACOSH);
+        var customSetup = program.getCustomSetupFunc();
+        return this.compileAndRun(program, [x], null, customSetup);
+    };
+    MathBackendWebGL.prototype.atanh = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.ATANH);
+        var customSetup = program.getCustomSetupFunc();
+        return this.compileAndRun(program, [x], null, customSetup);
+    };
+    MathBackendWebGL.prototype.erf = function (x) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.ERF);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.step = function (x, alpha) {
+        var program = new unaryop_gpu_1.UnaryOpProgram(x.shape, unary_op.STEP(alpha));
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.conv2dWithIm2Row = function (x, filter, convInfo) {
+        var filterWidth = convInfo.filterWidth, filterHeight = convInfo.filterHeight, inChannels = convInfo.inChannels, outWidth = convInfo.outWidth, outHeight = convInfo.outHeight;
+        var sharedDim = filterWidth * filterHeight * inChannels;
+        var numCols = outHeight * outWidth;
+        var x2ColShape = [sharedDim, numCols];
+        var xSqueezed = x.squeeze([0]);
+        var w2Row = filter.reshape([sharedDim, -1]);
+        var im2ColProgram = new im2col_gpu_1.Im2ColProgram(x2ColShape, xSqueezed.shape, convInfo);
+        var im2Col = this.compileAndRun(im2ColProgram, [xSqueezed]);
+        var matmulProgram = new mulmat_packed_gpu_1.MatMulPackedProgram(im2Col.shape, w2Row.shape, [numCols, convInfo.outChannels], true, false);
+        var product = this.compileAndRun(matmulProgram, [im2Col, w2Row]);
+        return product.reshape([1, outHeight, outWidth, convInfo.outChannels]);
+    };
+    MathBackendWebGL.prototype.conv2d = function (x, filter, convInfo) {
+        if (environment_1.ENV.get('WEBGL_CONV_IM2COL') && x.shape[0] === 1) {
+            return this.conv2dWithIm2Row(x, filter, convInfo);
+        }
+        var program = new conv_gpu_1.Conv2DProgram(convInfo);
+        return this.compileAndRun(program, [x, filter]);
+    };
+    MathBackendWebGL.prototype.conv2dDerInput = function (dy, filter, convInfo) {
+        var program = new conv_backprop_gpu_1.Conv2DDerInputProgram(convInfo);
+        return this.compileAndRun(program, [dy, filter]);
+    };
+    MathBackendWebGL.prototype.conv2dDerFilter = function (x, dy, convInfo) {
+        var program = new conv_backprop_gpu_1.Conv2DDerFilterProgram(convInfo);
+        return this.compileAndRun(program, [x, dy]);
+    };
+    MathBackendWebGL.prototype.depthwiseConv2D = function (x, filter, convInfo) {
+        var program;
+        if (environment_1.ENV.get('WEBGL_PACK_DEPTHWISECONV') && convInfo.dilationWidth === 1 &&
+            convInfo.dilationHeight === 1 && convInfo.padInfo.left <= 1 &&
+            convInfo.strideWidth <= 2 &&
+            convInfo.outChannels / convInfo.inChannels === 1) {
+            program = new conv_packed_gpu_depthwise_1.DepthwiseConvPacked2DProgram(convInfo);
+            return this.compileAndRun(program, [x, filter], this.makePackedTensor(convInfo.outShape, x.dtype));
+        }
+        program = new conv_gpu_depthwise_1.DepthwiseConv2DProgram(convInfo);
+        return this.compileAndRun(program, [x, filter]);
+    };
+    MathBackendWebGL.prototype.depthwiseConv2DDerInput = function (dy, filter, convInfo) {
+        var program = new conv_backprop_gpu_depthwise_1.DepthwiseConv2DDerInputProgram(convInfo);
+        return this.compileAndRun(program, [dy, filter]);
+    };
+    MathBackendWebGL.prototype.depthwiseConv2DDerFilter = function (x, dy, convInfo) {
+        var program = new conv_backprop_gpu_depthwise_1.DepthwiseConv2DDerFilterProgram(convInfo);
+        return this.compileAndRun(program, [x, dy]);
+    };
+    MathBackendWebGL.prototype.conv3d = function (x, filter, convInfo) {
+        var program = new conv_gpu_1.Conv3DProgram(convInfo);
+        return this.compileAndRun(program, [x, filter]);
+    };
+    MathBackendWebGL.prototype.conv3dDerInput = function (dy, filter, convInfo) {
+        var program = new conv_backprop_gpu_1.Conv3DDerInputProgram(convInfo);
+        return this.compileAndRun(program, [dy, filter]);
+    };
+    MathBackendWebGL.prototype.conv3dDerFilter = function (x, dy, convInfo) {
+        var program = new conv_backprop_gpu_1.Conv3DDerFilterProgram(convInfo);
+        return this.compileAndRun(program, [x, dy]);
+    };
+    MathBackendWebGL.prototype.maxPool = function (x, convInfo) {
+        var program = new pool_gpu_1.Pool2DProgram(convInfo, 'max', false);
+        var output = this.makeOutputArray(program.outputShape, x.dtype);
+        return this.compileAndRun(program, [x], output);
+    };
+    MathBackendWebGL.prototype.avgPool = function (x, convInfo) {
+        var program = new pool_gpu_1.Pool2DProgram(convInfo, 'avg', false);
+        var output = this.makeOutputArray(program.outputShape, 'float32');
+        return this.compileAndRun(program, [x], output);
+    };
+    MathBackendWebGL.prototype.maxPoolBackprop = function (dy, x, y, convInfo) {
+        var getPositions = true;
+        var maxPoolPositionsProgram = new pool_gpu_1.Pool2DProgram(convInfo, 'max', getPositions);
+        var maxPoolPositions = this.compileAndRun(maxPoolPositionsProgram, [x]);
+        var maxPoolBackPropProgram = new max_pool_backprop_gpu_1.MaxPool2DBackpropProgram(convInfo);
+        var output = this.makeOutputArray(maxPoolBackPropProgram.outputShape, x.dtype);
+        var result = this.compileAndRun(maxPoolBackPropProgram, [dy, maxPoolPositions], output);
+        maxPoolPositions.dispose();
+        return result;
+    };
+    MathBackendWebGL.prototype.avgPoolBackprop = function (dy, x, convInfo) {
+        var avgPoolBackpropProgram = new avg_pool_backprop_gpu_1.AvgPool2DBackpropProgram(convInfo);
+        var output = this.makeOutputArray(avgPoolBackpropProgram.outputShape, x.dtype);
+        return this.compileAndRun(avgPoolBackpropProgram, [dy], output);
+    };
+    MathBackendWebGL.prototype.cast = function (x, dtype) {
+        return backend_util.castTensor(x, dtype, this);
+    };
+    MathBackendWebGL.prototype.reshape = function (x, shape) {
+        if (this.texData.get(x.dataId).isPacked &&
+            !webgl_util.isReshapeFree(x.shape, shape)) {
+            return this.packedReshape(x, shape);
+        }
+        return backend_util.reshapeTensor(x, shape);
+    };
+    MathBackendWebGL.prototype.resizeBilinear = function (x, newHeight, newWidth, alignCorners) {
+        var program = new resize_bilinear_gpu_1.ResizeBilinearProgram(x.shape, newHeight, newWidth, alignCorners);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.resizeBilinearBackprop = function (dy, x, alignCorners) {
+        var program = new resize_bilinear_backprop_gpu_1.ResizeBilinearBackpropProgram(dy, x, alignCorners);
+        return this.compileAndRun(program, [dy]);
+    };
+    MathBackendWebGL.prototype.resizeNearestNeighbor = function (x, newHeight, newWidth, alignCorners) {
+        var program = new resize_nearest_neighbor_gpu_1.ResizeNearestNeighborProgram(x.shape, newHeight, newWidth, alignCorners);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.resizeNearestNeighborBackprop = function (dy, x, alignCorners) {
+        var program = new resize_nearest_neighbor_backprop_gpu_1.ResizeNearestNeigborBackpropProgram(dy, x, alignCorners);
+        return this.compileAndRun(program, [dy]);
+    };
+    MathBackendWebGL.prototype.multinomial = function (logits, normalized, numSamples, seed) {
+        var probs = normalized ? logits : softmax_1.softmax(logits);
+        var batchSize = probs.shape[0];
+        var numOutcomes = probs.shape[1];
+        var program = new multinomial_gpu_1.MultinomialProgram(batchSize, numOutcomes, numSamples);
+        var output = this.makeOutputArray(program.outputShape, 'int32');
+        var customSetup = program.getCustomSetupFunc(seed);
+        return this.compileAndRun(program, [probs], output, customSetup);
+    };
+    MathBackendWebGL.prototype.oneHot = function (indices, depth, onValue, offValue) {
+        var program = new onehot_gpu_1.OneHotProgram(indices.size, depth, onValue, offValue);
+        return this.compileAndRun(program, [indices]);
+    };
+    MathBackendWebGL.prototype.nonMaxSuppression = function (boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+        log_1.warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +
+            'Call tf.nonMaxSuppressionAsync() instead');
+        var boxesVals = boxes.dataSync();
+        var scoresVals = scores.dataSync();
+        return non_max_suppression_impl_1.nonMaxSuppressionImpl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
+    };
+    MathBackendWebGL.prototype.cropAndResize = function (image, boxes, boxIndex, cropSize, method, extrapolationValue) {
+        var program = new crop_and_resize_gpu_1.CropAndResizeProgram(image.shape, boxes.shape, cropSize, method, extrapolationValue);
+        return this.compileAndRun(program, [image, boxes, boxIndex]);
+    };
+    MathBackendWebGL.prototype.depthToSpace = function (x, blockSize, dataFormat) {
+        util.assert(blockSize > 1, "blockSize should be > 1 for depthToSpace, but was: " + blockSize);
+        var batchSize = x.shape[0];
+        var inputHeight = (dataFormat === 'NHWC') ? x.shape[1] : x.shape[2];
+        var inputWidth = (dataFormat === 'NHWC') ? x.shape[2] : x.shape[3];
+        var inputDepth = (dataFormat === 'NHWC') ? x.shape[3] : x.shape[1];
+        var outputHeight = inputHeight * blockSize;
+        var outputWidth = inputWidth * blockSize;
+        var outputDepth = inputDepth / (blockSize * blockSize);
+        var outputShape = (dataFormat === 'NHWC') ?
+            [batchSize, outputHeight, outputWidth, outputDepth] :
+            [batchSize, outputDepth, outputHeight, outputWidth];
+        var program = new depth_to_space_gpu_1.DepthToSpaceProgram(outputShape, blockSize, dataFormat);
+        return this.compileAndRun(program, [x]);
+    };
+    MathBackendWebGL.prototype.split = function (x, sizeSplits, axis) {
+        return split_shared_1.split(x, sizeSplits, axis);
+    };
+    MathBackendWebGL.prototype.scatterND = function (indices, updates, shape) {
+        var _a = scatter_nd_util.calculateShapes(updates, indices, shape), sliceRank = _a.sliceRank, numUpdates = _a.numUpdates, sliceSize = _a.sliceSize, strides = _a.strides, outputSize = _a.outputSize;
+        var flattenShape = [outputSize / sliceSize, sliceSize];
+        var flattenIndices = indices.reshape([numUpdates, sliceRank]);
+        var flattenX = updates.reshape([numUpdates, sliceSize]);
+        if (outputSize === 0) {
+            return backend_util.reshapeTensor(tensor_ops_1.tensor([]), shape);
+        }
+        var defaultValue = tensor_ops_1.scalar(0);
+        var program = new scatter_gpu_1.ScatterProgram(numUpdates, sliceRank, flattenIndices.rank, flattenX.rank, strides, flattenShape);
+        return this.compileAndRun(program, [flattenX, flattenIndices, defaultValue])
+            .reshape(shape);
+    };
+    MathBackendWebGL.prototype.sparseToDense = function (sparseIndices, sparseValues, outputShape, defaultValue) {
+        var _a = scatter_nd_util.calculateShapes(sparseValues, sparseIndices, outputShape), sliceRank = _a.sliceRank, numUpdates = _a.numUpdates, strides = _a.strides, outputSize = _a.outputSize;
+        var sumDupeIndices = false;
+        var program = new scatter_gpu_1.ScatterProgram(numUpdates, sliceRank, sparseIndices.rank, sparseValues.rank, strides, [outputSize, 1], sumDupeIndices);
+        return this.compileAndRun(program, [sparseValues, sparseIndices, defaultValue])
+            .reshape(outputShape);
+    };
+    MathBackendWebGL.prototype.fft = function (x) {
+        var inverse = false;
+        return this.fftImpl(x, inverse);
+    };
+    MathBackendWebGL.prototype.ifft = function (x) {
+        var inverse = true;
+        return this.fftImpl(x, inverse);
+    };
+    MathBackendWebGL.prototype.fftImpl = function (x, inverse) {
+        var xData = this.texData.get(x.dataId);
+        var realProgram = new fft_gpu_1.FFTProgram(fft_gpu.COMPLEX_FFT.REAL, x.shape, inverse);
+        var imagProgram = new fft_gpu_1.FFTProgram(fft_gpu.COMPLEX_FFT.IMAG, x.shape, inverse);
+        var inputs = [
+            this.makeComplexComponentTensorHandle(x, xData.complexTensors.real),
+            this.makeComplexComponentTensorHandle(x, xData.complexTensors.imag),
+        ];
+        var real = this.compileAndRun(realProgram, inputs);
+        var imag = this.compileAndRun(imagProgram, inputs);
+        var complex = this.complex(real, imag).as2D(x.shape[0], x.shape[1]);
+        real.dispose();
+        imag.dispose();
+        return complex;
+    };
+    MathBackendWebGL.prototype.gatherND = function (x, indices) {
+        var indicesShape = indices.shape;
+        var sliceRank = indicesShape[indicesShape.length - 1];
+        var _a = gather_nd_util.prepareAndValidate(x, indices), resultShape = _a[0], numSlices = _a[1], sliceSize = _a[2], strides = _a[3];
+        var flattenIndices = indices.reshape([numSlices, sliceRank]);
+        var flattenX = x.reshape([x.size / sliceSize, sliceSize]);
+        var program = new gather_nd_gpu_1.GatherNDProgram(sliceRank, strides, [numSlices, sliceSize]);
+        return this.compileAndRun(program, [flattenX, flattenIndices])
+            .reshape(resultShape);
+    };
+    MathBackendWebGL.prototype.makeOutputArray = function (shape, dtype) {
+        return tensor_1.Tensor.make(shape, {}, dtype);
+    };
+    MathBackendWebGL.prototype.makePackedTensor = function (shape, dtype) {
+        var packedTensor = tensor_1.Tensor.make(shape, {}, dtype);
+        this.texData.get(packedTensor.dataId).isPacked = true;
+        return packedTensor;
+    };
+    MathBackendWebGL.prototype.unpackTensor = function (input) {
+        var program = new unpack_gpu_1.UnpackProgram(input.shape);
+        return this.compileAndRun(program, [input], tensor_1.Tensor.make(program.outputShape, {}, input.dtype));
+    };
+    MathBackendWebGL.prototype.getBatchDim = function (shape, dimsToSkip) {
+        if (dimsToSkip === void 0) { dimsToSkip = 2; }
+        return util.sizeFromShape(shape.slice(0, shape.length - dimsToSkip));
+    };
+    MathBackendWebGL.prototype.getRowsCols = function (shape) {
+        if (shape.length === 0) {
+            throw Error('Cannot get rows and columns of an empty shape array.');
+        }
+        return [
+            shape.length > 1 ? shape[shape.length - 2] : 1, shape[shape.length - 1]
+        ];
+    };
+    MathBackendWebGL.prototype.packedReshape = function (input, afterShape) {
+        var inputAs3D = input.reshape([this.getBatchDim(input.shape)].concat(this.getRowsCols(input.shape)));
+        var afterShapeAs3D = [this.getBatchDim(afterShape)].concat(this.getRowsCols(afterShape));
+        var program = new reshape_packed_gpu_1.ReshapePackedProgram(afterShapeAs3D, inputAs3D.shape);
+        return this.compileAndRun(program, [inputAs3D])
+            .reshape(afterShape);
+    };
+    MathBackendWebGL.prototype.compileAndRun = function (program, inputs, output, customSetup, pageToCpu) {
+        var _this = this;
+        if (pageToCpu === void 0) { pageToCpu = true; }
+        if (output == null) {
+            if (program.usesPackedTextures) {
+                output = this.makePackedTensor(program.outputShape, inputs[0].dtype);
+            }
+            else {
+                output = this.makeOutputArray(program.outputShape, inputs[0].dtype);
+            }
+        }
+        if (output.size === 0) {
+            this.texData.get(output.dataId).values =
+                util_1.getTypedArrayFromDType(output.dtype, 0);
+            return output;
+        }
+        var inputsData = inputs.map(function (input) {
+            if (input.dtype === 'complex64') {
+                throw new Error("GPGPUProgram does not support complex64 input. For complex64 " +
+                    "dtypes, please separate the program into real and imaginary " +
+                    "parts.");
+            }
+            var texData = _this.texData.get(input.dataId);
+            if (texData.texture == null) {
+                if (!program.usesPackedTextures &&
+                    util.sizeFromShape(input.shape) <=
+                        environment_1.ENV.get('WEBGL_SIZE_UPLOAD_UNIFORM')) {
+                    return {
+                        shape: input.shape,
+                        texData: null,
+                        isUniform: true,
+                        uniformValues: _this.readSync(input.dataId)
+                    };
+                }
+                if (program.usesPackedTextures) {
+                    texData.isPacked = true;
+                    texData.shape = input.shape;
+                }
+            }
+            else if (!!texData.isPacked !== !!program.usesPackedTextures) {
+                var preProcessProgram = void 0;
+                var processedInput = void 0;
+                if (texData.isPacked) {
+                    preProcessProgram = new unpack_gpu_1.UnpackProgram(input.shape);
+                    processedInput = _this.compileAndRun(preProcessProgram, [input], tensor_1.Tensor.make(preProcessProgram.outputShape, {}, input.dtype));
+                }
+                else {
+                    preProcessProgram = new pack_gpu_1.PackProgram(input.shape);
+                    processedInput = _this.compileAndRun(preProcessProgram, [input], _this.makePackedTensor(input.shape, input.dtype));
+                }
+                texData = _this.texData.get(processedInput.dataId);
+                input = processedInput;
+            }
+            else if (texData.isPacked &&
+                !webgl_util.isReshapeFree(texData.shape, input.shape)) {
+                _this.delayedStorage = false;
+                var inputValues = input.dataSync();
+                _this.delayedStorage = true;
+                input = tensor_1.Tensor.make(input.shape, { values: inputValues }, input.dtype);
+                texData = _this.texData.get(input.dataId);
+                texData.isPacked = true;
+            }
+            _this.uploadToGPU(input.dataId);
+            return { shape: input.shape, texData: texData, isUniform: false };
+        });
+        this.uploadToGPU(output.dataId);
+        var outputData = {
+            shape: output.shape,
+            texData: this.texData.get(output.dataId),
+            isUniform: false
+        };
+        var key = gpgpu_math.makeShaderKey(program, inputsData, outputData);
+        var binary = this.getAndSaveBinary(key, function () {
+            return gpgpu_math.compileProgram(_this.gpgpu, program, inputsData, outputData);
+        });
+        var shouldTimeProgram = this.activeTimers != null;
+        var query;
+        if (shouldTimeProgram) {
+            query = this.startTimer();
+        }
+        gpgpu_math.runProgram(binary, inputsData, outputData, customSetup);
+        if (environment_1.ENV.get('WEBGL_PAGING_ENABLED') && pageToCpu &&
+            this.numBytesInGPU > this.NUM_BYTES_BEFORE_PAGING) {
+            var numBytesToPage = this.numBytesInGPU - this.NUM_BYTES_BEFORE_PAGING;
+            while (numBytesToPage > 0 && this.lruDataGPU.length > 0) {
+                var dataId = this.lruDataGPU.shift();
+                var _a = this.texData.get(dataId), shape = _a.shape, dtype = _a.dtype;
+                numBytesToPage -= this.computeBytes(shape, dtype);
+                this.read(dataId);
+            }
+        }
+        if (shouldTimeProgram) {
+            query = this.endTimer(query);
+            this.activeTimers.push({ name: program.constructor.name, query: this.getQueryTime(query) });
+        }
+        if (!environment_1.ENV.get('WEBGL_LAZILY_UNPACK') &&
+            this.texData.get(output.dataId).isPacked && !program.isPackShader) {
+            return this.unpackTensor(output);
+        }
+        return output;
+    };
+    MathBackendWebGL.prototype.getAndSaveBinary = function (key, getBinary) {
+        if (!(key in this.binaryCache)) {
+            this.binaryCache[key] = getBinary();
+        }
+        return this.binaryCache[key];
+    };
+    MathBackendWebGL.prototype.getTextureManager = function () {
+        return this.textureManager;
+    };
+    MathBackendWebGL.prototype.dispose = function () {
+        if (this.disposed) {
+            return;
+        }
+        for (var key in this.binaryCache) {
+            this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram);
+        }
+        this.textureManager.dispose();
+        this.canvas.remove();
+        if (this.fromPixels2DContext != null) {
+            this.fromPixels2DContext.canvas.remove();
+        }
+        if (this.gpgpuCreatedLocally) {
+            this.gpgpu.dispose();
+        }
+        this.disposed = true;
+    };
+    MathBackendWebGL.prototype.floatPrecision = function () {
+        var _this = this;
+        return globals_1.tidy(function () {
+            if (_this.abs(tensor_ops_1.scalar(1e-8)).get() > 0) {
+                return 32;
+            }
+            return 16;
+        });
+    };
+    MathBackendWebGL.prototype.uploadToGPU = function (dataId) {
+        var _a;
+        var texData = this.texData.get(dataId);
+        var shape = texData.shape, values = texData.values, texture = texData.texture, usage = texData.usage, isPacked = texData.isPacked;
+        if (texture != null) {
+            if (environment_1.ENV.get('WEBGL_PAGING_ENABLED')) {
+                var index = this.lruDataGPU.indexOf(dataId);
+                if (index >= 0) {
+                    this.lruDataGPU.splice(this.lruDataGPU.indexOf(dataId), 1);
+                    this.lruDataGPU.push(dataId);
+                }
+            }
+            return;
+        }
+        var shouldTimeProgram = this.activeTimers != null;
+        var start;
+        if (shouldTimeProgram) {
+            start = performance.now();
+        }
+        var texShape = webgl_util.getTextureShapeFromLogicalShape(shape, isPacked);
+        texData.texShape = texShape;
+        var newTexture = this.acquireTexture(dataId, texShape, usage, isPacked);
+        texData.texture = newTexture;
+        if (values != null) {
+            if (isPacked) {
+                var batch = this.getBatchDim(shape);
+                var rows = 1, cols = 1;
+                if (shape.length) {
+                    _a = this.getRowsCols(shape), rows = _a[0], cols = _a[1];
+                }
+                this.gpgpu.uploadMatrixToPackedTexture(newTexture, batch, rows, cols, texShape[0], texShape[1], typedArrayToFloat32(values));
+            }
+            else {
+                this.gpgpu.uploadMatrixToTexture(newTexture, texShape[0], texShape[1], typedArrayToFloat32(values));
+            }
+            texData.values = null;
+            if (shouldTimeProgram) {
+                this.uploadWaitMs += performance.now() - start;
+            }
+        }
+    };
+    MathBackendWebGL.prototype.convertAndCacheOnCPU = function (dataId, float32Values) {
+        var dontKeepCopyOnGPU = this.delayedStorage;
+        var texData = this.texData.get(dataId);
+        var texture = texData.texture, texShape = texData.texShape, dtype = texData.dtype, usage = texData.usage, isPacked = texData.isPacked;
+        if (dontKeepCopyOnGPU && texture != null) {
+            this.releaseTexture(dataId, texture, texShape, usage, isPacked);
+            texData.texture = null;
+            texData.texShape = null;
+        }
+        texData.usage = tex_util_1.TextureUsage.UPLOAD;
+        if (float32Values != null) {
+            texData.values = float32ToTypedArray(float32Values, dtype);
+        }
+        return texData.values;
+    };
+    MathBackendWebGL.prototype.releaseTexture = function (dataId, texture, texShape, texType, isPacked) {
+        var _a = this.texData.get(dataId), shape = _a.shape, dtype = _a.dtype;
+        if (environment_1.ENV.get('WEBGL_PAGING_ENABLED')) {
+            var idx = this.lruDataGPU.indexOf(dataId);
+            if (idx >= 0) {
+                this.lruDataGPU.splice(idx, 1);
+            }
+        }
+        this.numBytesInGPU -= this.computeBytes(shape, dtype);
+        this.textureManager.releaseTexture(texture, texShape, texType, isPacked);
+    };
+    MathBackendWebGL.prototype.acquireTexture = function (dataId, texShape, texType, isPacked) {
+        var _a = this.texData.get(dataId), shape = _a.shape, dtype = _a.dtype;
+        if (environment_1.ENV.get('WEBGL_PAGING_ENABLED')) {
+            this.lruDataGPU.push(dataId);
+        }
+        this.numBytesInGPU += this.computeBytes(shape, dtype);
+        return this.textureManager.acquireTexture(texShape, texType, isPacked);
+    };
+    MathBackendWebGL.prototype.computeBytes = function (shape, dtype) {
+        return util.sizeFromShape(shape) * util.bytesPerElement(dtype);
+    };
+    return MathBackendWebGL;
+}());
+exports.MathBackendWebGL = MathBackendWebGL;
+if (environment_1.ENV.get('IS_BROWSER')) {
+    environment_1.ENV.registerBackend('webgl', function () { return new MathBackendWebGL(); }, 2, tensor_1.setTensorTracker);
+}
+function float32ToTypedArray(a, dtype) {
+    if (dtype === 'float32' || dtype === 'complex64') {
+        return a;
+    }
+    else if (dtype === 'int32' || dtype === 'bool') {
+        var result = (dtype === 'int32') ? new Int32Array(a.length) :
+            new Uint8Array(a.length);
+        for (var i = 0; i < result.length; ++i) {
+            result[i] = Math.round(a[i]);
+        }
+        return result;
+    }
+    else {
+        throw new Error("Unknown dtype " + dtype);
+    }
+}
+function typedArrayToFloat32(a) {
+    return (a instanceof Float32Array) ? a : new Float32Array(a);
+}
+//# sourceMappingURL=backend_webgl.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_webgl.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_webgl.js.map
new file mode 100644
index 0000000..96b24c4
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_webgl.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_webgl_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_webgl_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_webgl_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_webgl_test.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_webgl_test.js
new file mode 100644
index 0000000..964ef50
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_webgl_test.js
@@ -0,0 +1,262 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+var _this = this;
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+var backend_webgl_1 = require("./backend_webgl");
+jasmine_util_1.describeWithFlags('lazy packing and unpacking', test_util_1.WEBGL_ENVS, function () {
+    var webglLazilyUnpackFlagSaved;
+    beforeAll(function () {
+        webglLazilyUnpackFlagSaved = tf.ENV.get('WEBGL_LAZILY_UNPACK');
+        tf.ENV.set('WEBGL_LAZILY_UNPACK', true);
+    });
+    afterAll(function () {
+        tf.ENV.set('WEBGL_LAZILY_UNPACK', webglLazilyUnpackFlagSaved);
+    });
+    it('should not leak memory when lazily unpacking', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
+        var c = tf.matMul(a, b);
+        var startNumBytes = tf.memory().numBytes;
+        var startNumTensors = tf.memory().numTensors;
+        tf.add(c, 1);
+        expect(tf.memory().numBytes - startNumBytes).toEqual(16);
+        expect(tf.memory().numTensors - startNumTensors).toEqual(1);
+    });
+    it('should not leak memory when lazily packing', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
+        var c = tf.add(a, 1);
+        var startNumBytes = tf.memory().numBytes;
+        var startNumTensors = tf.memory().numTensors;
+        tf.matMul(b, c);
+        expect(tf.memory().numBytes - startNumBytes).toEqual(36);
+        expect(tf.memory().numTensors - startNumTensors).toEqual(1);
+    });
+    it('should work when the same input must be represented by' +
+        'different textures', function () {
+        var a = tf.tensor1d([1, 2]);
+        var res = tf.dot(a, a);
+        test_util_1.expectArraysClose(res, [5]);
+    });
+});
+jasmine_util_1.describeWithFlags('backendWebGL', test_util_1.WEBGL_ENVS, function () {
+    var prevBackend;
+    beforeAll(function () {
+        prevBackend = tf.getBackend();
+    });
+    afterEach(function () {
+        tf.setBackend(prevBackend);
+        tf.ENV.removeBackend('test-storage');
+    });
+    it('register empty string tensor', function () {
+        var backend = new backend_webgl_1.MathBackendWebGL();
+        tf.ENV.registerBackend('test-storage', function () { return backend; });
+        tf.setBackend('test-storage');
+        var t = tf.Tensor.make([3], {}, 'string');
+        expect(backend.readSync(t.dataId) == null).toBe(true);
+    });
+    it('register empty string tensor and write', function () {
+        var backend = new backend_webgl_1.MathBackendWebGL();
+        tf.ENV.registerBackend('test-storage', function () { return backend; });
+        tf.setBackend('test-storage');
+        var t = tf.Tensor.make([3], {}, 'string');
+        backend.write(t.dataId, ['c', 'a', 'b']);
+        test_util_1.expectArraysEqual(backend.readSync(t.dataId), ['c', 'a', 'b']);
+    });
+    it('register string tensor with values', function () {
+        var backend = new backend_webgl_1.MathBackendWebGL();
+        tf.ENV.registerBackend('test-storage', function () { return backend; });
+        tf.setBackend('test-storage');
+        var t = tf.Tensor.make([3], { values: ['a', 'b', 'c'] }, 'string');
+        test_util_1.expectArraysEqual(backend.readSync(t.dataId), ['a', 'b', 'c']);
+    });
+    it('register string tensor with values and overwrite', function () {
+        var backend = new backend_webgl_1.MathBackendWebGL();
+        tf.ENV.registerBackend('test-storage', function () { return backend; });
+        tf.setBackend('test-storage');
+        var t = tf.Tensor.make([3], { values: ['a', 'b', 'c'] }, 'string');
+        backend.write(t.dataId, ['c', 'a', 'b']);
+        test_util_1.expectArraysEqual(backend.readSync(t.dataId), ['c', 'a', 'b']);
+    });
+    it('register string tensor with values and wrong shape throws error', function () {
+        var backend = new backend_webgl_1.MathBackendWebGL();
+        tf.ENV.registerBackend('test-storage', function () { return backend; });
+        tf.setBackend('test-storage');
+        expect(function () { return tf.tensor(['a', 'b', 'c'], [4], 'string'); }).toThrowError();
+    });
+    it('delayed storage, reading', function () {
+        var delayedStorage = true;
+        var backend = new backend_webgl_1.MathBackendWebGL(null, delayedStorage);
+        tf.ENV.registerBackend('test-storage', function () { return backend; });
+        tf.setBackend('test-storage');
+        var texManager = backend.getTextureManager();
+        var t = tf.Tensor.make([3], {}, 'float32');
+        backend.write(t.dataId, new Float32Array([1, 2, 3]));
+        expect(texManager.getNumUsedTextures()).toBe(0);
+        backend.getTexture(t.dataId);
+        expect(texManager.getNumUsedTextures()).toBe(1);
+        test_util_1.expectArraysClose(backend.readSync(t.dataId), new Float32Array([1, 2, 3]));
+        expect(texManager.getNumUsedTextures()).toBe(0);
+        backend.getTexture(t.dataId);
+        expect(texManager.getNumUsedTextures()).toBe(1);
+        backend.disposeData(t.dataId);
+        expect(texManager.getNumUsedTextures()).toBe(0);
+    });
+    it('delayed storage, overwriting', function () {
+        var delayedStorage = true;
+        var backend = new backend_webgl_1.MathBackendWebGL(null, delayedStorage);
+        tf.ENV.registerBackend('test-storage', function () { return backend; });
+        tf.setBackend('test-storage');
+        var texManager = backend.getTextureManager();
+        var t = tf.Tensor.make([3], {}, 'float32');
+        backend.write(t.dataId, new Float32Array([1, 2, 3]));
+        backend.getTexture(t.dataId);
+        expect(texManager.getNumUsedTextures()).toBe(1);
+        backend.write(t.dataId, new Float32Array([4, 5, 6]));
+        expect(texManager.getNumUsedTextures()).toBe(0);
+        test_util_1.expectArraysClose(backend.readSync(t.dataId), new Float32Array([4, 5, 6]));
+        backend.getTexture(t.dataId);
+        expect(texManager.getNumUsedTextures()).toBe(1);
+        test_util_1.expectArraysClose(backend.readSync(t.dataId), new Float32Array([4, 5, 6]));
+        expect(texManager.getNumUsedTextures()).toBe(0);
+    });
+    it('immediate storage reading', function () {
+        var delayedStorage = false;
+        var backend = new backend_webgl_1.MathBackendWebGL(null, delayedStorage);
+        tf.ENV.registerBackend('test-storage', function () { return backend; });
+        tf.setBackend('test-storage');
+        var texManager = backend.getTextureManager();
+        var t = tf.Tensor.make([3], {}, 'float32');
+        backend.write(t.dataId, new Float32Array([1, 2, 3]));
+        expect(texManager.getNumUsedTextures()).toBe(1);
+        test_util_1.expectArraysClose(backend.readSync(t.dataId), new Float32Array([1, 2, 3]));
+        expect(texManager.getNumUsedTextures()).toBe(1);
+        backend.disposeData(t.dataId);
+        expect(texManager.getNumUsedTextures()).toBe(0);
+    });
+    it('immediate storage overwriting', function () {
+        var delayedStorage = false;
+        var backend = new backend_webgl_1.MathBackendWebGL(null, delayedStorage);
+        tf.ENV.registerBackend('test-storage', function () { return backend; });
+        tf.setBackend('test-storage');
+        var texManager = backend.getTextureManager();
+        var t = tf.Tensor.make([3], {}, 'float32');
+        backend.write(t.dataId, new Float32Array([1, 2, 3]));
+        expect(texManager.getNumUsedTextures()).toBe(1);
+        backend.write(t.dataId, new Float32Array([4, 5, 6]));
+        expect(texManager.getNumUsedTextures()).toBe(1);
+        test_util_1.expectArraysClose(backend.readSync(t.dataId), new Float32Array([4, 5, 6]));
+        expect(texManager.getNumUsedTextures()).toBe(1);
+        backend.disposeData(t.dataId);
+        expect(texManager.getNumUsedTextures()).toBe(0);
+    });
+    it('disposal of backend disposes all textures', function () {
+        var delayedStorage = false;
+        var backend = new backend_webgl_1.MathBackendWebGL(null, delayedStorage);
+        var texManager = backend.getTextureManager();
+        tf.ENV.registerBackend('test-storage', function () { return backend; });
+        tf.setBackend('test-storage');
+        var t = tf.Tensor.make([3], {}, 'float32');
+        backend.write(t.dataId, new Float32Array([1, 2, 3]));
+        var t2 = tf.Tensor.make([3], {}, 'float32');
+        backend.write(t2.dataId, new Float32Array([4, 5, 6]));
+        expect(texManager.getNumUsedTextures()).toBe(2);
+        backend.dispose();
+        expect(texManager.getNumUsedTextures()).toBe(0);
+    });
+});
+jasmine_util_1.describeWithFlags('Custom window size', test_util_1.WEBGL_ENVS, function () {
+    it('Set screen area to be 1x1', function () { return __awaiter(_this, void 0, void 0, function () {
+        var oldBackend, a;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    spyOnProperty(window, 'screen', 'get')
+                        .and.returnValue({ height: 1, width: 1 });
+                    oldBackend = tf.getBackend();
+                    tf.ENV.registerBackend('custom-webgl', function () { return new backend_webgl_1.MathBackendWebGL(); });
+                    tf.setBackend('custom-webgl');
+                    a = tf.ones([100, 100]);
+                    expect(tf.memory().numBytesInGPU).toBe(0);
+                    return [4, a.square().data()];
+                case 1:
+                    _a.sent();
+                    expect(tf.memory().numBytesInGPU).toBe(0);
+                    test_util_1.expectArraysEqual(a, new Float32Array(100 * 100).fill(1));
+                    tf.setBackend(oldBackend);
+                    tf.ENV.removeBackend('custom-webgl');
+                    return [2];
+            }
+        });
+    }); });
+});
+var FLOAT32_WEBGL_ENVS = Object.assign({
+    'WEBGL_RENDER_FLOAT32_ENABLED': true,
+    'WEBGL_SIZE_UPLOAD_UNIFORM': backend_webgl_1.SIZE_UPLOAD_UNIFORM
+}, test_util_1.WEBGL_ENVS);
+jasmine_util_1.describeWithFlags('upload tensors as uniforms', FLOAT32_WEBGL_ENVS, function () {
+    it('small tensor gets uploaded as scalar', function () {
+        var m = tf.memory();
+        expect(m.numBytesInGPU).toBe(0);
+        var a = tf.zeros([backend_webgl_1.SIZE_UPLOAD_UNIFORM - 1]);
+        a.square();
+        m = tf.memory();
+        expect(m.numBytesInGPU).toBe(a.size * 4);
+    });
+    it('large tensor gets uploaded to gpu', function () {
+        var m = tf.memory();
+        expect(m.numBytesInGPU).toBe(0);
+        var a = tf.zeros([backend_webgl_1.SIZE_UPLOAD_UNIFORM + 1]);
+        a.square();
+        m = tf.memory();
+        expect(m.numBytesInGPU).toBe(a.size * 4 * 2);
+    });
+    it('download and re-upload an output of a shader', function () {
+        var vals = new Float32Array(backend_webgl_1.SIZE_UPLOAD_UNIFORM + 1);
+        vals.fill(2);
+        var a = tf.square(vals);
+        a.dataSync();
+        var res = a.square();
+        var expected = new Float32Array(backend_webgl_1.SIZE_UPLOAD_UNIFORM + 1);
+        expected.fill(16);
+        test_util_1.expectArraysClose(res, expected);
+    });
+});
+//# sourceMappingURL=backend_webgl_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_webgl_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_webgl_test.js.map
new file mode 100644
index 0000000..3485f8b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/backend_webgl_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/complex_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/complex_util.d.ts
new file mode 100644
index 0000000..a963c3b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/complex_util.d.ts
@@ -0,0 +1,27 @@
+import { TypedArray } from '../types';
+export declare function mergeRealAndImagArrays(real: Float32Array, imag: Float32Array): Float32Array;
+export declare function splitRealAndImagArrays(complex: Float32Array): {
+    real: Float32Array;
+    imag: Float32Array;
+};
+export declare function complexWithEvenIndex(complex: Float32Array): {
+    real: Float32Array;
+    imag: Float32Array;
+};
+export declare function complexWithOddIndex(complex: Float32Array): {
+    real: Float32Array;
+    imag: Float32Array;
+};
+export declare function getComplexWithIndex(complex: Float32Array, index: number): {
+    real: number;
+    imag: number;
+};
+export declare function assignToTypedArray(data: TypedArray, real: number, imag: number, index: number): void;
+export declare function exponents(n: number, inverse: boolean): {
+    real: Float32Array;
+    imag: Float32Array;
+};
+export declare function exponent(k: number, n: number, inverse: boolean): {
+    real: number;
+    imag: number;
+};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/complex_util.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/complex_util.js
new file mode 100644
index 0000000..7567572
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/complex_util.js
@@ -0,0 +1,77 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+function mergeRealAndImagArrays(real, imag) {
+    if (real.length !== imag.length) {
+        throw new Error("Cannot merge real and imag arrays of different lengths. real:" +
+            (real.length + ", imag: " + imag.length + "."));
+    }
+    var result = new Float32Array(real.length * 2);
+    for (var i = 0; i < result.length; i += 2) {
+        result[i] = real[i / 2];
+        result[i + 1] = imag[i / 2];
+    }
+    return result;
+}
+exports.mergeRealAndImagArrays = mergeRealAndImagArrays;
+function splitRealAndImagArrays(complex) {
+    var real = new Float32Array(complex.length / 2);
+    var imag = new Float32Array(complex.length / 2);
+    for (var i = 0; i < complex.length; i += 2) {
+        real[i / 2] = complex[i];
+        imag[i / 2] = complex[i + 1];
+    }
+    return { real: real, imag: imag };
+}
+exports.splitRealAndImagArrays = splitRealAndImagArrays;
+function complexWithEvenIndex(complex) {
+    var len = Math.ceil(complex.length / 4);
+    var real = new Float32Array(len);
+    var imag = new Float32Array(len);
+    for (var i = 0; i < complex.length; i += 4) {
+        real[Math.floor(i / 4)] = complex[i];
+        imag[Math.floor(i / 4)] = complex[i + 1];
+    }
+    return { real: real, imag: imag };
+}
+exports.complexWithEvenIndex = complexWithEvenIndex;
+function complexWithOddIndex(complex) {
+    var len = Math.floor(complex.length / 4);
+    var real = new Float32Array(len);
+    var imag = new Float32Array(len);
+    for (var i = 2; i < complex.length; i += 4) {
+        real[Math.floor(i / 4)] = complex[i];
+        imag[Math.floor(i / 4)] = complex[i + 1];
+    }
+    return { real: real, imag: imag };
+}
+exports.complexWithOddIndex = complexWithOddIndex;
+function getComplexWithIndex(complex, index) {
+    var real = complex[index * 2];
+    var imag = complex[index * 2 + 1];
+    return { real: real, imag: imag };
+}
+exports.getComplexWithIndex = getComplexWithIndex;
+function assignToTypedArray(data, real, imag, index) {
+    data[index * 2] = real;
+    data[index * 2 + 1] = imag;
+}
+exports.assignToTypedArray = assignToTypedArray;
+function exponents(n, inverse) {
+    var real = new Float32Array(n / 2);
+    var imag = new Float32Array(n / 2);
+    for (var i = 0; i < Math.ceil(n / 2); i++) {
+        var x = (inverse ? 2 : -2) * Math.PI * (i / n);
+        real[i] = Math.cos(x);
+        imag[i] = Math.sin(x);
+    }
+    return { real: real, imag: imag };
+}
+exports.exponents = exponents;
+function exponent(k, n, inverse) {
+    var x = (inverse ? 2 : -2) * Math.PI * (k / n);
+    var real = Math.cos(x);
+    var imag = Math.sin(x);
+    return { real: real, imag: imag };
+}
+exports.exponent = exponent;
+//# sourceMappingURL=complex_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/complex_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/complex_util.js.map
new file mode 100644
index 0000000..fb40e3a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/complex_util.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"complex_util.js","sourceRoot":"","sources":["../../src/kernels/complex_util.ts"],"names":[],"mappings":";;AAgCA,gCACI,IAAkB,EAAE,IAAkB;IACxC,IAAI,IAAI,CAAC,MAAM,KAAK,IAAI,CAAC,MAAM,EAAE;QAC/B,MAAM,IAAI,KAAK,CACX,+DAA+D;aAC5D,IAAI,CAAC,MAAM,gBAAW,IAAI,CAAC,MAAM,MAAG,CAAA,CAAC,CAAC;KAC9C;IACD,IAAM,MAAM,GAAG,IAAI,YAAY,CAAC,IAAI,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC;IACjD,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,MAAM,CAAC,MAAM,EAAE,CAAC,IAAI,CAAC,EAAE;QACzC,MAAM,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC;QACxB,MAAM,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC;KAC7B;IACD,OAAO,MAAM,CAAC;AAChB,CAAC;AAbD,wDAaC;AAgBD,gCAAuC,OAAqB;IAE1D,IAAM,IAAI,GAAG,IAAI,YAAY,CAAC,OAAO,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC;IAClD,IAAM,IAAI,GAAG,IAAI,YAAY,CAAC,OAAO,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC;IAClD,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,OAAO,CAAC,MAAM,EAAE,CAAC,IAAI,CAAC,EAAE;QAC1C,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,OAAO,CAAC,CAAC,CAAC,CAAC;QACzB,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,OAAO,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC;KAC9B;IACD,OAAO,EAAC,IAAI,MAAA,EAAE,IAAI,MAAA,EAAC,CAAC;AACtB,CAAC;AATD,wDASC;AAMD,8BAAqC,OAAqB;IAExD,IAAM,GAAG,GAAG,IAAI,CAAC,IAAI,CAAC,OAAO,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC;IAC1C,IAAM,IAAI,GAAG,IAAI,YAAY,CAAC,GAAG,CAAC,CAAC;IACnC,IAAM,IAAI,GAAG,IAAI,YAAY,CAAC,GAAG,CAAC,CAAC;IACnC,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,OAAO,CAAC,MAAM,EAAE,CAAC,IAAI,CAAC,EAAE;QAC1C,IAAI,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,OAAO,CAAC,CAAC,CAAC,CAAC;QACrC,IAAI,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,OAAO,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC;KAC1C;IACD,OAAO,EAAC,IAAI,MAAA,EAAE,IAAI,MAAA,EAAC,CAAC;AACtB,CAAC;AAVD,oDAUC;AAMD,6BAAoC,OAAqB;IAEvD,IAAM,GAAG,GAAG,IAAI,CAAC,KAAK,CAAC,OAAO,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC;IAC3C,IAAM,IAAI,GAAG,IAAI,YAAY,CAAC,GAAG,CAAC,CAAC;IACnC,IAAM,IAAI,GAAG,IAAI,YAAY,CAAC,GAAG,CAAC,CAAC;IACnC,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,OAAO,CAAC,MAAM,EAAE,CAAC,IAAI,CAAC,EAAE;QAC1C,IAAI,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,OAAO,CAAC,CAAC,CAAC,CAAC;QACrC,IAAI,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,OAAO,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC;KAC1C;IACD,OAAO,EAAC,IAAI,MAAA,EAAE,IAAI,MAAA,EAAC,CAAC;AACtB,CAAC;AAVD,kDAUC;AAOD,6BACI,OAAqB,EAAE,KAAa;IACtC,IAAM,IAAI,GAAG,OAAO,CAAC,KAAK,GAAG,CAAC,CAAC,CAAC;IAChC,IAAM,IAAI,GAAG,OAAO,CAAC,KAAK,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC;IACpC,OAAO,EAAC,IAAI,MAAA,EAAE,IAAI,MAAA,EAAC,CAAC;AACtB,CAAC;AALD,kDAKC;AAQD,4BACI,IAAgB,EAAE,IAAY,EAAE,IAAY,EAAE,KAAa;IAC7D,IAAI,CAAC,KAAK,GAAG,CAAC,CAAC,GAAG,IAAI,CAAC;IACvB,IAAI,CAAC,KAAK,GAAG,CAAC,GAAG,CAAC,CAAC,GAAG,IAAI,CAAC;AAC7B,CAAC;AAJD,gDAIC;AAKD,mBACI,CAAS,EAAE,OAAgB;IAC7B,IAAM,IAAI,GAAG,IAAI,YAAY,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC;IACrC,IAAM,IAAI,GAAG,IAAI,YAAY,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC;IACrC,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,IAAI,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE;QACzC,IAAM,CAAC,GAAG,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,EAAE,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC;QACjD,IAAI,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC;QACtB,IAAI,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC;KACvB;IACD,OAAO,EAAC,IAAI,MAAA,EAAE,IAAI,MAAA,EAAC,CAAC;AACtB,CAAC;AAVD,8BAUC;AAKD,kBACI,CAAS,EAAE,CAAS,EAAE,OAAgB;IACxC,IAAM,CAAC,GAAG,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,EAAE,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC;IACjD,IAAM,IAAI,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC;IACzB,IAAM,IAAI,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC;IACzB,OAAO,EAAC,IAAI,MAAA,EAAE,IAAI,MAAA,EAAC,CAAC;AACtB,CAAC;AAND,4BAMC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/complex_util_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/complex_util_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/complex_util_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/complex_util_test.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/complex_util_test.js
new file mode 100644
index 0000000..5f3bafa
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/complex_util_test.js
@@ -0,0 +1,54 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+var complex_util = require("./complex_util");
+describe('complex_util', function () {
+    it('mergeRealAndImagArrays', function () {
+        var real = new Float32Array([1, 2, 3]);
+        var imag = new Float32Array([4, 5, 6]);
+        var complex = complex_util.mergeRealAndImagArrays(real, imag);
+        expect(complex).toEqual(new Float32Array([1, 4, 2, 5, 3, 6]));
+    });
+    it('splitRealAndImagArrays', function () {
+        var complex = new Float32Array([1, 4, 2, 5, 3, 6]);
+        var result = complex_util.splitRealAndImagArrays(complex);
+        expect(result.real).toEqual(new Float32Array([1, 2, 3]));
+        expect(result.imag).toEqual(new Float32Array([4, 5, 6]));
+    });
+    it('complexWithEvenIndex', function () {
+        var complex = new Float32Array([1, 2, 3, 4, 5, 6]);
+        var result = complex_util.complexWithEvenIndex(complex);
+        expect(result.real).toEqual(new Float32Array([1, 5]));
+        expect(result.imag).toEqual(new Float32Array([2, 6]));
+    });
+    it('complexWithOddIndex', function () {
+        var complex = new Float32Array([1, 2, 3, 4, 5, 6]);
+        var result = complex_util.complexWithOddIndex(complex);
+        expect(result.real).toEqual(new Float32Array([3]));
+        expect(result.imag).toEqual(new Float32Array([4]));
+    });
+});
+jasmine_util_1.describeWithFlags('complex_util exponents', test_util_1.ALL_ENVS, function () {
+    it('exponents inverse=false', function () {
+        var inverse = false;
+        var result = complex_util.exponents(5, inverse);
+        test_util_1.expectArraysClose(result.real, new Float32Array([1, 0.30901700258255005]));
+        test_util_1.expectArraysClose(result.imag, new Float32Array([0, -0.9510565400123596]));
+    });
+    it('exponents inverse=true', function () {
+        var inverse = true;
+        var result = complex_util.exponents(5, inverse);
+        test_util_1.expectArraysClose(result.real, new Float32Array([1, 0.30901700258255005]));
+        test_util_1.expectArraysClose(result.imag, new Float32Array([0, 0.9510565400123596]));
+    });
+});
+jasmine_util_1.describeWithFlags('complex_util assignment', test_util_1.ALL_ENVS, function () {
+    it('assign complex value in TypedArray', function () {
+        var t = new Float32Array(4);
+        complex_util.assignToTypedArray(t, 1, 2, 0);
+        complex_util.assignToTypedArray(t, 3, 4, 1);
+        test_util_1.expectArraysClose(t, new Float32Array([1, 2, 3, 4]));
+    });
+});
+//# sourceMappingURL=complex_util_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/complex_util_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/complex_util_test.js.map
new file mode 100644
index 0000000..70633bb
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/complex_util_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"complex_util_test.js","sourceRoot":"","sources":["../../src/kernels/complex_util_test.ts"],"names":[],"mappings":";;AAgBA,gDAAkD;AAClD,0CAAyD;AAEzD,6CAA+C;AAE/C,QAAQ,CAAC,cAAc,EAAE;IACvB,EAAE,CAAC,wBAAwB,EAAE;QAC3B,IAAM,IAAI,GAAG,IAAI,YAAY,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACzC,IAAM,IAAI,GAAG,IAAI,YAAY,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACzC,IAAM,OAAO,GAAG,YAAY,CAAC,sBAAsB,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC;QAChE,MAAM,CAAC,OAAO,CAAC,CAAC,OAAO,CAAC,IAAI,YAAY,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;IAChE,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,wBAAwB,EAAE;QAC3B,IAAM,OAAO,GAAG,IAAI,YAAY,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACrD,IAAM,MAAM,GAAG,YAAY,CAAC,sBAAsB,CAAC,OAAO,CAAC,CAAC;QAC5D,MAAM,CAAC,MAAM,CAAC,IAAI,CAAC,CAAC,OAAO,CAAC,IAAI,YAAY,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QACzD,MAAM,CAAC,MAAM,CAAC,IAAI,CAAC,CAAC,OAAO,CAAC,IAAI,YAAY,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;IAC3D,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,sBAAsB,EAAE;QACzB,IAAM,OAAO,GAAG,IAAI,YAAY,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACrD,IAAM,MAAM,GAAG,YAAY,CAAC,oBAAoB,CAAC,OAAO,CAAC,CAAC;QAC1D,MAAM,CAAC,MAAM,CAAC,IAAI,CAAC,CAAC,OAAO,CAAC,IAAI,YAAY,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QACtD,MAAM,CAAC,MAAM,CAAC,IAAI,CAAC,CAAC,OAAO,CAAC,IAAI,YAAY,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;IACxD,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,qBAAqB,EAAE;QACxB,IAAM,OAAO,GAAG,IAAI,YAAY,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACrD,IAAM,MAAM,GAAG,YAAY,CAAC,mBAAmB,CAAC,OAAO,CAAC,CAAC;QACzD,MAAM,CAAC,MAAM,CAAC,IAAI,CAAC,CAAC,OAAO,CAAC,IAAI,YAAY,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QACnD,MAAM,CAAC,MAAM,CAAC,IAAI,CAAC,CAAC,OAAO,CAAC,IAAI,YAAY,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;IACrD,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC;AAEH,gCAAiB,CAAC,wBAAwB,EAAE,oBAAQ,EAAE;IACpD,EAAE,CAAC,yBAAyB,EAAE;QAC5B,IAAM,OAAO,GAAG,KAAK,CAAC;QACtB,IAAM,MAAM,GAAG,YAAY,CAAC,SAAS,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;QAClD,6BAAiB,CAAC,MAAM,CAAC,IAAI,EAAE,IAAI,YAAY,CAAC,CAAC,CAAC,EAAE,mBAAmB,CAAC,CAAC,CAAC,CAAC;QAC3E,6BAAiB,CAAC,MAAM,CAAC,IAAI,EAAE,IAAI,YAAY,CAAC,CAAC,CAAC,EAAE,CAAC,kBAAkB,CAAC,CAAC,CAAC,CAAC;IAC7E,CAAC,CAAC,CAAC;IACH,EAAE,CAAC,wBAAwB,EAAE;QAC3B,IAAM,OAAO,GAAG,IAAI,CAAC;QACrB,IAAM,MAAM,GAAG,YAAY,CAAC,SAAS,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;QAClD,6BAAiB,CAAC,MAAM,CAAC,IAAI,EAAE,IAAI,YAAY,CAAC,CAAC,CAAC,EAAE,mBAAmB,CAAC,CAAC,CAAC,CAAC;QAC3E,6BAAiB,CAAC,MAAM,CAAC,IAAI,EAAE,IAAI,YAAY,CAAC,CAAC,CAAC,EAAE,kBAAkB,CAAC,CAAC,CAAC,CAAC;IAC5E,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC;AAEH,gCAAiB,CAAC,yBAAyB,EAAE,oBAAQ,EAAE;IACrD,EAAE,CAAC,oCAAoC,EAAE;QACvC,IAAM,CAAC,GAAG,IAAI,YAAY,CAAC,CAAC,CAAC,CAAC;QAE9B,YAAY,CAAC,kBAAkB,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC;QAC5C,YAAY,CAAC,kBAAkB,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC;QAE5C,6BAAiB,CAAC,CAAC,EAAE,IAAI,YAAY,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;IACvD,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/non_max_suppression_impl.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/non_max_suppression_impl.d.ts
new file mode 100644
index 0000000..b681fb2
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/non_max_suppression_impl.d.ts
@@ -0,0 +1,3 @@
+import { Tensor1D } from '../tensor';
+import { TypedArray } from '../types';
+export declare function nonMaxSuppressionImpl(boxes: TypedArray, scores: TypedArray, maxOutputSize: number, iouThreshold: number, scoreThreshold: number): Tensor1D;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/non_max_suppression_impl.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/non_max_suppression_impl.js
new file mode 100644
index 0000000..77c973e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/non_max_suppression_impl.js
@@ -0,0 +1,57 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tensor_ops_1 = require("../ops/tensor_ops");
+function nonMaxSuppressionImpl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+    var candidates = Array.from(scores)
+        .map(function (score, boxIndex) { return ({ score: score, boxIndex: boxIndex }); })
+        .filter(function (c) { return c.score > scoreThreshold; })
+        .sort(function (c1, c2) { return c2.score - c1.score; });
+    var selected = [];
+    for (var i = 0; i < candidates.length; i++) {
+        var _a = candidates[i], score = _a.score, boxIndex = _a.boxIndex;
+        if (score < scoreThreshold) {
+            break;
+        }
+        var ignoreCandidate = false;
+        for (var j = selected.length - 1; j >= 0; --j) {
+            var iou = intersectionOverUnion(boxes, boxIndex, selected[j]);
+            if (iou >= iouThreshold) {
+                ignoreCandidate = true;
+                break;
+            }
+        }
+        if (!ignoreCandidate) {
+            selected.push(boxIndex);
+            if (selected.length >= maxOutputSize) {
+                break;
+            }
+        }
+    }
+    return tensor_ops_1.tensor1d(selected, 'int32');
+}
+exports.nonMaxSuppressionImpl = nonMaxSuppressionImpl;
+function intersectionOverUnion(boxes, i, j) {
+    var iCoord = boxes.subarray(i * 4, i * 4 + 4);
+    var jCoord = boxes.subarray(j * 4, j * 4 + 4);
+    var yminI = Math.min(iCoord[0], iCoord[2]);
+    var xminI = Math.min(iCoord[1], iCoord[3]);
+    var ymaxI = Math.max(iCoord[0], iCoord[2]);
+    var xmaxI = Math.max(iCoord[1], iCoord[3]);
+    var yminJ = Math.min(jCoord[0], jCoord[2]);
+    var xminJ = Math.min(jCoord[1], jCoord[3]);
+    var ymaxJ = Math.max(jCoord[0], jCoord[2]);
+    var xmaxJ = Math.max(jCoord[1], jCoord[3]);
+    var areaI = (ymaxI - yminI) * (xmaxI - xminI);
+    var areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
+    if (areaI <= 0 || areaJ <= 0) {
+        return 0.0;
+    }
+    var intersectionYmin = Math.max(yminI, yminJ);
+    var intersectionXmin = Math.max(xminI, xminJ);
+    var intersectionYmax = Math.min(ymaxI, ymaxJ);
+    var intersectionXmax = Math.min(xmaxI, xmaxJ);
+    var intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *
+        Math.max(intersectionXmax - intersectionXmin, 0.0);
+    return intersectionArea / (areaI + areaJ - intersectionArea);
+}
+//# sourceMappingURL=non_max_suppression_impl.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/non_max_suppression_impl.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/non_max_suppression_impl.js.map
new file mode 100644
index 0000000..621675d
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/non_max_suppression_impl.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"non_max_suppression_impl.js","sourceRoot":"","sources":["../../src/kernels/non_max_suppression_impl.ts"],"names":[],"mappings":";;AAqBA,gDAA2C;AAI3C,+BACI,KAAiB,EAAE,MAAkB,EAAE,aAAqB,EAC5D,YAAoB,EAAE,cAAsB;IAC9C,IAAM,UAAU,GAAG,KAAK,CAAC,IAAI,CAAC,MAAM,CAAC;SACb,GAAG,CAAC,UAAC,KAAK,EAAE,QAAQ,IAAK,OAAA,CAAC,EAAC,KAAK,OAAA,EAAE,QAAQ,UAAA,EAAC,CAAC,EAAnB,CAAmB,CAAC;SAC7C,MAAM,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,KAAK,GAAG,cAAc,EAAxB,CAAwB,CAAC;SACrC,IAAI,CAAC,UAAC,EAAE,EAAE,EAAE,IAAK,OAAA,EAAE,CAAC,KAAK,GAAG,EAAE,CAAC,KAAK,EAAnB,CAAmB,CAAC,CAAC;IAE9D,IAAM,QAAQ,GAAa,EAAE,CAAC;IAE9B,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,UAAU,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;QACpC,IAAA,kBAAiC,EAAhC,gBAAK,EAAE,sBAAQ,CAAkB;QACxC,IAAI,KAAK,GAAG,cAAc,EAAE;YAC1B,MAAM;SACP;QAED,IAAI,eAAe,GAAG,KAAK,CAAC;QAC5B,KAAK,IAAI,CAAC,GAAG,QAAQ,CAAC,MAAM,GAAG,CAAC,EAAE,CAAC,IAAI,CAAC,EAAE,EAAE,CAAC,EAAE;YAC7C,IAAM,GAAG,GAAG,qBAAqB,CAAC,KAAK,EAAE,QAAQ,EAAE,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAC;YAChE,IAAI,GAAG,IAAI,YAAY,EAAE;gBACvB,eAAe,GAAG,IAAI,CAAC;gBACvB,MAAM;aACP;SACF;QAED,IAAI,CAAC,eAAe,EAAE;YACpB,QAAQ,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC;YACxB,IAAI,QAAQ,CAAC,MAAM,IAAI,aAAa,EAAE;gBACpC,MAAM;aACP;SACF;KACF;IAED,OAAO,qBAAQ,CAAC,QAAQ,EAAE,OAAO,CAAC,CAAC;AACrC,CAAC;AAlCD,sDAkCC;AAED,+BAA+B,KAAiB,EAAE,CAAS,EAAE,CAAS;IACpE,IAAM,MAAM,GAAG,KAAK,CAAC,QAAQ,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC;IAChD,IAAM,MAAM,GAAG,KAAK,CAAC,QAAQ,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC;IAChD,IAAM,KAAK,GAAG,IAAI,CAAC,GAAG,CAAC,MAAM,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC;IAC7C,IAAM,KAAK,GAAG,IAAI,CAAC,GAAG,CAAC,MAAM,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC;IAC7C,IAAM,KAAK,GAAG,IAAI,CAAC,GAAG,CAAC,MAAM,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC;IAC7C,IAAM,KAAK,GAAG,IAAI,CAAC,GAAG,CAAC,MAAM,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC;IAC7C,IAAM,KAAK,GAAG,IAAI,CAAC,GAAG,CAAC,MAAM,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC;IAC7C,IAAM,KAAK,GAAG,IAAI,CAAC,GAAG,CAAC,MAAM,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC;IAC7C,IAAM,KAAK,GAAG,IAAI,CAAC,GAAG,CAAC,MAAM,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC;IAC7C,IAAM,KAAK,GAAG,IAAI,CAAC,GAAG,CAAC,MAAM,CAAC,CAAC,CAAC,EAAE,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC;IAC7C,IAAM,KAAK,GAAG,CAAC,KAAK,GAAG,KAAK,CAAC,GAAG,CAAC,KAAK,GAAG,KAAK,CAAC,CAAC;IAChD,IAAM,KAAK,GAAG,CAAC,KAAK,GAAG,KAAK,CAAC,GAAG,CAAC,KAAK,GAAG,KAAK,CAAC,CAAC;IAChD,IAAI,KAAK,IAAI,CAAC,IAAI,KAAK,IAAI,CAAC,EAAE;QAC5B,OAAO,GAAG,CAAC;KACZ;IACD,IAAM,gBAAgB,GAAG,IAAI,CAAC,GAAG,CAAC,KAAK,EAAE,KAAK,CAAC,CAAC;IAChD,IAAM,gBAAgB,GAAG,IAAI,CAAC,GAAG,CAAC,KAAK,EAAE,KAAK,CAAC,CAAC;IAChD,IAAM,gBAAgB,GAAG,IAAI,CAAC,GAAG,CAAC,KAAK,EAAE,KAAK,CAAC,CAAC;IAChD,IAAM,gBAAgB,GAAG,IAAI,CAAC,GAAG,CAAC,KAAK,EAAE,KAAK,CAAC,CAAC;IAChD,IAAM,gBAAgB,GAAG,IAAI,CAAC,GAAG,CAAC,gBAAgB,GAAG,gBAAgB,EAAE,GAAG,CAAC;QACvE,IAAI,CAAC,GAAG,CAAC,gBAAgB,GAAG,gBAAgB,EAAE,GAAG,CAAC,CAAC;IACvD,OAAO,gBAAgB,GAAG,CAAC,KAAK,GAAG,KAAK,GAAG,gBAAgB,CAAC,CAAC;AAC/D,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/packing_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/packing_util.d.ts
new file mode 100644
index 0000000..dfbd658
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/packing_util.d.ts
@@ -0,0 +1,3 @@
+export declare function getVecChannels(name: string, rank: number): string[];
+export declare function getChannels(name: string, rank: number): string[];
+export declare function getSourceCoords(rank: number, dims: string[]): string;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/packing_util.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/packing_util.js
new file mode 100644
index 0000000..4578677
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/packing_util.js
@@ -0,0 +1,28 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+function getVecChannels(name, rank) {
+    return ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank).map(function (d) { return name + "." + d; });
+}
+exports.getVecChannels = getVecChannels;
+function getChannels(name, rank) {
+    if (rank === 1) {
+        return [name];
+    }
+    return getVecChannels(name, rank);
+}
+exports.getChannels = getChannels;
+function getSourceCoords(rank, dims) {
+    if (rank === 1) {
+        return 'rc';
+    }
+    var coords = '';
+    for (var i = 0; i < rank; i++) {
+        coords += dims[i];
+        if (i < rank - 1) {
+            coords += ',';
+        }
+    }
+    return coords;
+}
+exports.getSourceCoords = getSourceCoords;
+//# sourceMappingURL=packing_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/packing_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/packing_util.js.map
new file mode 100644
index 0000000..7412b95
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/packing_util.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"packing_util.js","sourceRoot":"","sources":["../../src/kernels/packing_util.ts"],"names":[],"mappings":";;AAiBA,wBAA+B,IAAY,EAAE,IAAY;IACvD,OAAO,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC,CAAC,KAAK,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC,GAAG,CAAC,UAAA,CAAC,IAAI,OAAG,IAAI,SAAI,CAAG,EAAd,CAAc,CAAC,CAAC;AAChF,CAAC;AAFD,wCAEC;AAED,qBAA4B,IAAY,EAAE,IAAY;IACpD,IAAI,IAAI,KAAK,CAAC,EAAE;QACd,OAAO,CAAC,IAAI,CAAC,CAAC;KACf;IACD,OAAO,cAAc,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC;AACpC,CAAC;AALD,kCAKC;AAED,yBAAgC,IAAY,EAAE,IAAc;IAC1D,IAAI,IAAI,KAAK,CAAC,EAAE;QACd,OAAO,IAAI,CAAC;KACb;IAED,IAAI,MAAM,GAAG,EAAE,CAAC;IAChB,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,IAAI,EAAE,CAAC,EAAE,EAAE;QAC7B,MAAM,IAAI,IAAI,CAAC,CAAC,CAAC,CAAC;QAClB,IAAI,CAAC,GAAG,IAAI,GAAG,CAAC,EAAE;YAChB,MAAM,IAAI,GAAG,CAAC;SACf;KACF;IACD,OAAO,MAAM,CAAC;AAChB,CAAC;AAbD,0CAaC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/split_shared.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/split_shared.d.ts
new file mode 100644
index 0000000..cb7e14a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/split_shared.d.ts
@@ -0,0 +1,2 @@
+import { Tensor } from '../tensor';
+export declare function split<T extends Tensor>(x: T, sizeSplits: number[], axis: number): T[];
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/split_shared.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/split_shared.js
new file mode 100644
index 0000000..576292d
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/split_shared.js
@@ -0,0 +1,14 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+function split(x, sizeSplits, axis) {
+    var begin = Array(x.rank).fill(0);
+    var size = x.shape.slice();
+    return sizeSplits.map(function (s) {
+        size[axis] = s;
+        var slice = x.slice(begin, size);
+        begin[axis] += s;
+        return slice;
+    });
+}
+exports.split = split;
+//# sourceMappingURL=split_shared.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/split_shared.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/split_shared.js.map
new file mode 100644
index 0000000..604394a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/split_shared.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"split_shared.js","sourceRoot":"","sources":["../../src/kernels/split_shared.ts"],"names":[],"mappings":";;AAoBA,eACI,CAAI,EAAE,UAAoB,EAAE,IAAY;IAC1C,IAAM,KAAK,GAAG,KAAK,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;IACpC,IAAM,IAAI,GAAG,CAAC,CAAC,KAAK,CAAC,KAAK,EAAE,CAAC;IAC7B,OAAO,UAAU,CAAC,GAAG,CAAC,UAAA,CAAC;QACrB,IAAI,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC;QACf,IAAM,KAAK,GAAG,CAAC,CAAC,KAAK,CAAC,KAAK,EAAE,IAAI,CAAC,CAAC;QACnC,KAAK,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;QACjB,OAAO,KAAK,CAAC;IACf,CAAC,CAAC,CAAC;AACL,CAAC;AAVD,sBAUC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/topk_impl.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/topk_impl.d.ts
new file mode 100644
index 0000000..a16a54e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/topk_impl.d.ts
@@ -0,0 +1,3 @@
+import { Tensor } from '../tensor';
+import { NumericDataType, TypedArray } from '../types';
+export declare function topkImpl<T extends Tensor>(x: TypedArray, xShape: number[], xDtype: NumericDataType, k: number, sorted: boolean): [T, T];
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/topk_impl.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/topk_impl.js
new file mode 100644
index 0000000..4036c1f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/topk_impl.js
@@ -0,0 +1,34 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tensor_ops_1 = require("../ops/tensor_ops");
+var util_1 = require("../util");
+function topkImpl(x, xShape, xDtype, k, sorted) {
+    var lastDim = xShape[xShape.length - 1];
+    var _a = [x.length / lastDim, lastDim], batch = _a[0], size = _a[1];
+    var allTopKVals = util_1.getTypedArrayFromDType(xDtype, batch * k);
+    var allTopKIndices = util_1.getTypedArrayFromDType('int32', batch * k);
+    for (var b = 0; b < batch; b++) {
+        var offset = b * size;
+        var vals = x.subarray(offset, offset + size);
+        var valAndInd = [];
+        for (var i = 0; i < vals.length; i++) {
+            valAndInd.push({ value: vals[i], index: i });
+        }
+        valAndInd.sort(function (a, b) { return b.value - a.value; });
+        var outOffset = b * k;
+        var topKVals = allTopKVals.subarray(outOffset, outOffset + k);
+        var topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);
+        for (var i = 0; i < k; i++) {
+            topKVals[i] = valAndInd[i].value;
+            topKIndices[i] = valAndInd[i].index;
+        }
+    }
+    var outputShape = xShape.slice();
+    outputShape[outputShape.length - 1] = k;
+    return [
+        tensor_ops_1.tensor(allTopKVals, outputShape, xDtype),
+        tensor_ops_1.tensor(allTopKIndices, outputShape, 'int32')
+    ];
+}
+exports.topkImpl = topkImpl;
+//# sourceMappingURL=topk_impl.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/topk_impl.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/topk_impl.js.map
new file mode 100644
index 0000000..ea7be09
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/topk_impl.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"topk_impl.js","sourceRoot":"","sources":["../../src/kernels/topk_impl.ts"],"names":[],"mappings":";;AAmBA,gDAAyC;AAGzC,gCAA+C;AAE/C,kBACI,CAAa,EAAE,MAAgB,EAAE,MAAuB,EAAE,CAAS,EACnE,MAAe;IAEjB,IAAM,OAAO,GAAG,MAAM,CAAC,MAAM,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC;IACpC,IAAA,kCAA6C,EAA5C,aAAK,EAAE,YAAI,CAAkC;IACpD,IAAM,WAAW,GAAG,6BAAsB,CAAC,MAAM,EAAE,KAAK,GAAG,CAAC,CAAC,CAAC;IAC9D,IAAM,cAAc,GAAG,6BAAsB,CAAC,OAAO,EAAE,KAAK,GAAG,CAAC,CAAC,CAAC;IAElE,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,KAAK,EAAE,CAAC,EAAE,EAAE;QAC9B,IAAM,MAAM,GAAG,CAAC,GAAG,IAAI,CAAC;QACxB,IAAM,IAAI,GAAG,CAAC,CAAC,QAAQ,CAAC,MAAM,EAAE,MAAM,GAAG,IAAI,CAAC,CAAC;QAC/C,IAAM,SAAS,GAA0C,EAAE,CAAC;QAC5D,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,IAAI,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;YACpC,SAAS,CAAC,IAAI,CAAC,EAAC,KAAK,EAAE,IAAI,CAAC,CAAC,CAAC,EAAE,KAAK,EAAE,CAAC,EAAC,CAAC,CAAC;SAC5C;QACD,SAAS,CAAC,IAAI,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,CAAC,CAAC,KAAK,GAAG,CAAC,CAAC,KAAK,EAAjB,CAAiB,CAAC,CAAC;QAE5C,IAAM,SAAS,GAAG,CAAC,GAAG,CAAC,CAAC;QACxB,IAAM,QAAQ,GAAG,WAAW,CAAC,QAAQ,CAAC,SAAS,EAAE,SAAS,GAAG,CAAC,CAAC,CAAC;QAChE,IAAM,WAAW,GAAG,cAAc,CAAC,QAAQ,CAAC,SAAS,EAAE,SAAS,GAAG,CAAC,CAAC,CAAC;QACtE,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,EAAE,EAAE;YAC1B,QAAQ,CAAC,CAAC,CAAC,GAAG,SAAS,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC;YACjC,WAAW,CAAC,CAAC,CAAC,GAAG,SAAS,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC;SACrC;KACF;IAGD,IAAM,WAAW,GAAG,MAAM,CAAC,KAAK,EAAE,CAAC;IACnC,WAAW,CAAC,WAAW,CAAC,MAAM,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC;IACxC,OAAO;QACL,mBAAM,CAAC,WAAW,EAAE,WAAW,EAAE,MAAM,CAAM;QAC7C,mBAAM,CAAC,cAAc,EAAE,WAAW,EAAE,OAAO,CAAM;KAClD,CAAC;AACJ,CAAC;AAlCD,4BAkCC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/argminmax_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/argminmax_gpu.d.ts
new file mode 100644
index 0000000..08f0596
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/argminmax_gpu.d.ts
@@ -0,0 +1,8 @@
+import { ReduceInfo } from '../../ops/reduce_util';
+import { GPGPUProgram } from './gpgpu_math';
+export declare class ArgMinMaxProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(reduceInfo: ReduceInfo, op: 'max' | 'min', firstPass: boolean);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/argminmax_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/argminmax_gpu.js
new file mode 100644
index 0000000..dca7d47
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/argminmax_gpu.js
@@ -0,0 +1,23 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var ArgMinMaxProgram = (function () {
+    function ArgMinMaxProgram(reduceInfo, op, firstPass) {
+        this.variableNames = ['A'];
+        var windowSize = reduceInfo.windowSize;
+        var batchSize = reduceInfo.batchSize;
+        var inSize = reduceInfo.inSize;
+        var outSize = Math.ceil(inSize / windowSize);
+        if (!firstPass) {
+            this.variableNames.push('bestIndicesA');
+        }
+        this.outputShape = [batchSize, outSize];
+        var compOp = (op === 'max') ? '>' : '<';
+        var indexSnippet = firstPass ?
+            'inOffset + i;' :
+            'round(getBestIndicesA(batch, inOffset + i));';
+        this.userCode = "\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * " + windowSize + ";\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < " + windowSize + "; i++) {\n          int inIdx = " + indexSnippet + ";\n          float candidate = getA(batch, inIdx);\n          if (candidate " + compOp + " bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    ";
+    }
+    return ArgMinMaxProgram;
+}());
+exports.ArgMinMaxProgram = ArgMinMaxProgram;
+//# sourceMappingURL=argminmax_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/argminmax_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/argminmax_gpu.js.map
new file mode 100644
index 0000000..dcfa28e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/argminmax_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"argminmax_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/argminmax_gpu.ts"],"names":[],"mappings":";;AAoBA;IAKE,0BAAY,UAAsB,EAAE,EAAe,EAAE,SAAkB;QAJvE,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QAKpB,IAAM,UAAU,GAAG,UAAU,CAAC,UAAU,CAAC;QACzC,IAAM,SAAS,GAAG,UAAU,CAAC,SAAS,CAAC;QACvC,IAAM,MAAM,GAAG,UAAU,CAAC,MAAM,CAAC;QACjC,IAAM,OAAO,GAAG,IAAI,CAAC,IAAI,CAAC,MAAM,GAAG,UAAU,CAAC,CAAC;QAC/C,IAAI,CAAC,SAAS,EAAE;YACd,IAAI,CAAC,aAAa,CAAC,IAAI,CAAC,cAAc,CAAC,CAAC;SACzC;QACD,IAAI,CAAC,WAAW,GAAG,CAAC,SAAS,EAAE,OAAO,CAAC,CAAC;QACxC,IAAM,MAAM,GAAG,CAAC,EAAE,KAAK,KAAK,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,CAAC;QAC1C,IAAM,YAAY,GAAG,SAAS,CAAC,CAAC;YAC5B,eAAe,CAAC,CAAC;YACjB,8CAA8C,CAAC;QAEnD,IAAI,CAAC,QAAQ,GAAG,wKAKc,UAAU,iIAKd,UAAU,wCAChB,YAAY,oFAEV,MAAM,mKAO3B,CAAC;IACJ,CAAC;IACH,uBAAC;AAAD,CAAC,AAzCD,IAyCC;AAzCY,4CAAgB"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/avg_pool_backprop_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/avg_pool_backprop_gpu.d.ts
new file mode 100644
index 0000000..17fb345
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/avg_pool_backprop_gpu.d.ts
@@ -0,0 +1,8 @@
+import { Conv2DInfo } from '../../ops/conv_util';
+import { GPGPUProgram } from './gpgpu_math';
+export declare class AvgPool2DBackpropProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(convInfo: Conv2DInfo);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/avg_pool_backprop_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/avg_pool_backprop_gpu.js
new file mode 100644
index 0000000..3a23ebd
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/avg_pool_backprop_gpu.js
@@ -0,0 +1,23 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var AvgPool2DBackpropProgram = (function () {
+    function AvgPool2DBackpropProgram(convInfo) {
+        this.variableNames = ['dy'];
+        this.outputShape = convInfo.inShape;
+        var filterHeight = convInfo.filterHeight;
+        var filterWidth = convInfo.filterWidth;
+        var strideHeight = convInfo.strideHeight;
+        var strideWidth = convInfo.strideWidth;
+        var dilationHeight = convInfo.dilationHeight;
+        var dilationWidth = convInfo.dilationWidth;
+        var effectiveFilterHeight = convInfo.effectiveFilterHeight;
+        var effectiveFilterWidth = convInfo.effectiveFilterWidth;
+        var padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+        var padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+        var avgMultiplier = 1 / (filterHeight * filterWidth);
+        this.userCode = "\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n      const float avgMultiplier = float(" + avgMultiplier + ");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < " + effectiveFilterHeight + ";\n            wR += " + dilationHeight + ") {\n          float dyR = float(dyRCorner + wR) / " + strideHeight + ".0;\n\n          if (dyR < 0.0 || dyR >= " + convInfo.outHeight + ".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < " + effectiveFilterWidth + ";\n            wC+= " + dilationWidth + ") {\n            float dyC = float(dyCCorner + wC) / " + strideWidth + ".0;\n\n            if (dyC < 0.0 || dyC >= " + convInfo.outWidth + ".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+    }
+    return AvgPool2DBackpropProgram;
+}());
+exports.AvgPool2DBackpropProgram = AvgPool2DBackpropProgram;
+//# sourceMappingURL=avg_pool_backprop_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/avg_pool_backprop_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/avg_pool_backprop_gpu.js.map
new file mode 100644
index 0000000..7f9d7cf
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/avg_pool_backprop_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"avg_pool_backprop_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/avg_pool_backprop_gpu.ts"],"names":[],"mappings":";;AAqBA;IAKE,kCAAY,QAAoB;QAJhC,kBAAa,GAAG,CAAC,IAAI,CAAC,CAAC;QAKrB,IAAI,CAAC,WAAW,GAAG,QAAQ,CAAC,OAAO,CAAC;QACpC,IAAM,YAAY,GAAG,QAAQ,CAAC,YAAY,CAAC;QAC3C,IAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QACzC,IAAM,YAAY,GAAG,QAAQ,CAAC,YAAY,CAAC;QAC3C,IAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QACzC,IAAM,cAAc,GAAG,QAAQ,CAAC,cAAc,CAAC;QAC/C,IAAM,aAAa,GAAG,QAAQ,CAAC,aAAa,CAAC;QAC7C,IAAM,qBAAqB,GAAG,QAAQ,CAAC,qBAAqB,CAAC;QAC7D,IAAM,oBAAoB,GAAG,QAAQ,CAAC,oBAAoB,CAAC;QAE3D,IAAM,MAAM,GAAG,qBAAqB,GAAG,CAAC,GAAG,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC;QAChE,IAAM,OAAO,GAAG,oBAAoB,GAAG,CAAC,GAAG,QAAQ,CAAC,OAAO,CAAC,IAAI,CAAC;QAEjE,IAAM,aAAa,GAAG,CAAC,GAAG,CAAC,YAAY,GAAG,WAAW,CAAC,CAAC;QAEvD,IAAI,CAAC,QAAQ,GAAG,sCACa,MAAM,UAAK,OAAO,oDACT,aAAa,sdAcvB,qBAAqB,6BACjC,cAAc,2DACc,YAAY,iDAExB,QAAQ,CAAC,SAAS,yIAKpB,oBAAoB,4BACnC,aAAa,6DACkB,WAAW,mDAEvB,QAAQ,CAAC,QAAQ,wSAalD,CAAC;IACJ,CAAC;IACH,+BAAC;AAAD,CAAC,AAjED,IAiEC;AAjEY,4DAAwB"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/batchnorm_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/batchnorm_gpu.d.ts
new file mode 100644
index 0000000..7475070
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/batchnorm_gpu.d.ts
@@ -0,0 +1,8 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class BatchNormProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    supportsBroadcasting: boolean;
+    constructor(xShape: number[], meanShape: number[], varianceShape: number[], offsetShape: number[] | null, scaleShape: number[] | null, varianceEpsilon: number);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/batchnorm_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/batchnorm_gpu.js
new file mode 100644
index 0000000..9a4835e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/batchnorm_gpu.js
@@ -0,0 +1,29 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var broadcast_util = require("../../ops/broadcast_util");
+var BatchNormProgram = (function () {
+    function BatchNormProgram(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {
+        this.outputShape = [];
+        this.supportsBroadcasting = true;
+        this.variableNames = ['x', 'mean', 'variance'];
+        broadcast_util.assertAndGetBroadcastShape(xShape, meanShape);
+        broadcast_util.assertAndGetBroadcastShape(xShape, varianceShape);
+        var offsetSnippet = '0.0';
+        if (offsetShape != null) {
+            broadcast_util.assertAndGetBroadcastShape(xShape, offsetShape);
+            this.variableNames.push('offset');
+            offsetSnippet = 'getOffsetAtOutCoords()';
+        }
+        var scaleSnippet = '1.0';
+        if (scaleShape != null) {
+            broadcast_util.assertAndGetBroadcastShape(xShape, scaleShape);
+            this.variableNames.push('scale');
+            scaleSnippet = 'getScaleAtOutCoords()';
+        }
+        this.outputShape = xShape;
+        this.userCode = "\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = " + offsetSnippet + ";\n        float scale = " + scaleSnippet + ";\n        float inv = scale * inversesqrt(variance + float(" + varianceEpsilon + "));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    ";
+    }
+    return BatchNormProgram;
+}());
+exports.BatchNormProgram = BatchNormProgram;
+//# sourceMappingURL=batchnorm_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/batchnorm_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/batchnorm_gpu.js.map
new file mode 100644
index 0000000..a35fd20
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/batchnorm_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"batchnorm_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/batchnorm_gpu.ts"],"names":[],"mappings":";;AAiBA,yDAA2D;AAG3D;IAME,0BACI,MAAgB,EAAE,SAAmB,EAAE,aAAuB,EAC9D,WAA0B,EAAE,UAAyB,EACrD,eAAuB;QAP3B,gBAAW,GAAa,EAAE,CAAC;QAE3B,yBAAoB,GAAG,IAAI,CAAC;QAM1B,IAAI,CAAC,aAAa,GAAG,CAAC,GAAG,EAAE,MAAM,EAAE,UAAU,CAAC,CAAC;QAC/C,cAAc,CAAC,0BAA0B,CAAC,MAAM,EAAE,SAAS,CAAC,CAAC;QAC7D,cAAc,CAAC,0BAA0B,CAAC,MAAM,EAAE,aAAa,CAAC,CAAC;QAEjE,IAAI,aAAa,GAAG,KAAK,CAAC;QAC1B,IAAI,WAAW,IAAI,IAAI,EAAE;YACvB,cAAc,CAAC,0BAA0B,CAAC,MAAM,EAAE,WAAW,CAAC,CAAC;YAC/D,IAAI,CAAC,aAAa,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC;YAClC,aAAa,GAAG,wBAAwB,CAAC;SAC1C;QAED,IAAI,YAAY,GAAG,KAAK,CAAC;QACzB,IAAI,UAAU,IAAI,IAAI,EAAE;YACtB,cAAc,CAAC,0BAA0B,CAAC,MAAM,EAAE,UAAU,CAAC,CAAC;YAC9D,IAAI,CAAC,aAAa,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC;YACjC,YAAY,GAAG,uBAAuB,CAAC;SACxC;QAED,IAAI,CAAC,WAAW,GAAG,MAAM,CAAC;QAC1B,IAAI,CAAC,QAAQ,GAAG,yLAKK,aAAa,iCACd,YAAY,oEACuB,eAAe,2FAGrE,CAAC;IACJ,CAAC;IACH,uBAAC;AAAD,CAAC,AAzCD,IAyCC;AAzCY,4CAAgB"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/batchnorm_packed_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/batchnorm_packed_gpu.d.ts
new file mode 100644
index 0000000..2df05ce
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/batchnorm_packed_gpu.d.ts
@@ -0,0 +1,9 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class BatchNormPackedProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    supportsBroadcasting: boolean;
+    usesPackedTextures: boolean;
+    constructor(xShape: number[], meanShape: number[], varianceShape: number[], offsetShape: number[] | null, scaleShape: number[] | null, varianceEpsilon: number);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/batchnorm_packed_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/batchnorm_packed_gpu.js
new file mode 100644
index 0000000..2f2e790
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/batchnorm_packed_gpu.js
@@ -0,0 +1,29 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var broadcast_util = require("../../ops/broadcast_util");
+var BatchNormPackedProgram = (function () {
+    function BatchNormPackedProgram(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {
+        this.supportsBroadcasting = true;
+        this.usesPackedTextures = true;
+        this.variableNames = ['x', 'mean', 'variance'];
+        broadcast_util.assertAndGetBroadcastShape(xShape, meanShape);
+        broadcast_util.assertAndGetBroadcastShape(xShape, varianceShape);
+        var offsetSnippet = 'vec4(0.0)';
+        if (offsetShape != null) {
+            broadcast_util.assertAndGetBroadcastShape(xShape, offsetShape);
+            this.variableNames.push('offset');
+            offsetSnippet = 'getOffsetAtOutCoords()';
+        }
+        var scaleSnippet = 'vec4(1.0)';
+        if (scaleShape != null) {
+            broadcast_util.assertAndGetBroadcastShape(xShape, scaleShape);
+            this.variableNames.push('scale');
+            scaleSnippet = 'getScaleAtOutCoords()';
+        }
+        this.outputShape = xShape;
+        this.userCode = "\n      void main() {\n        vec4 offset = " + offsetSnippet + ";\n        vec4 scale = " + scaleSnippet + ";\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(" + varianceEpsilon + "));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    ";
+    }
+    return BatchNormPackedProgram;
+}());
+exports.BatchNormPackedProgram = BatchNormPackedProgram;
+//# sourceMappingURL=batchnorm_packed_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/batchnorm_packed_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/batchnorm_packed_gpu.js.map
new file mode 100644
index 0000000..6470976
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/batchnorm_packed_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"batchnorm_packed_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/batchnorm_packed_gpu.ts"],"names":[],"mappings":";;AAiBA,yDAA2D;AAG3D;IAOE,gCACI,MAAgB,EAAE,SAAmB,EAAE,aAAuB,EAC9D,WAA0B,EAAE,UAAyB,EACrD,eAAuB;QAN3B,yBAAoB,GAAG,IAAI,CAAC;QAC5B,uBAAkB,GAAG,IAAI,CAAC;QAMxB,IAAI,CAAC,aAAa,GAAG,CAAC,GAAG,EAAE,MAAM,EAAE,UAAU,CAAC,CAAC;QAC/C,cAAc,CAAC,0BAA0B,CAAC,MAAM,EAAE,SAAS,CAAC,CAAC;QAC7D,cAAc,CAAC,0BAA0B,CAAC,MAAM,EAAE,aAAa,CAAC,CAAC;QAEjE,IAAI,aAAa,GAAG,WAAW,CAAC;QAChC,IAAI,WAAW,IAAI,IAAI,EAAE;YACvB,cAAc,CAAC,0BAA0B,CAAC,MAAM,EAAE,WAAW,CAAC,CAAC;YAC/D,IAAI,CAAC,aAAa,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC;YAClC,aAAa,GAAG,wBAAwB,CAAC;SAC1C;QAED,IAAI,YAAY,GAAG,WAAW,CAAC;QAC/B,IAAI,UAAU,IAAI,IAAI,EAAE;YACtB,cAAc,CAAC,0BAA0B,CAAC,MAAM,EAAE,UAAU,CAAC,CAAC;YAC9D,IAAI,CAAC,aAAa,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC;YACjC,YAAY,GAAG,uBAAuB,CAAC;SACxC;QAED,IAAI,CAAC,WAAW,GAAG,MAAM,CAAC;QAC1B,IAAI,CAAC,QAAQ,GAAG,kDAEI,aAAa,gCACd,YAAY,yMAMsB,eAAe,wEAInE,CAAC;IACJ,CAAC;IACH,6BAAC;AAAD,CAAC,AA7CD,IA6CC;AA7CY,wDAAsB"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/binaryop_complex_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/binaryop_complex_gpu.d.ts
new file mode 100644
index 0000000..d4dc534
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/binaryop_complex_gpu.d.ts
@@ -0,0 +1,12 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare const COMPLEX_MULTIPLY: {
+    REAL: string;
+    IMAG: string;
+};
+export declare class BinaryOpComplexProgram implements GPGPUProgram {
+    variableNames: string[];
+    userCode: string;
+    outputShape: number[];
+    supportsBroadcasting: boolean;
+    constructor(op: string, aShape: number[], bShape: number[]);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/binaryop_complex_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/binaryop_complex_gpu.js
new file mode 100644
index 0000000..d1ec4a3
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/binaryop_complex_gpu.js
@@ -0,0 +1,19 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var broadcast_util = require("../../ops/broadcast_util");
+exports.COMPLEX_MULTIPLY = {
+    REAL: 'return areal * breal - aimag * bimag;',
+    IMAG: 'return areal * bimag + aimag * breal;'
+};
+var BinaryOpComplexProgram = (function () {
+    function BinaryOpComplexProgram(op, aShape, bShape) {
+        this.variableNames = ['AReal', 'AImag', 'BReal', 'BImag'];
+        this.supportsBroadcasting = true;
+        this.outputShape =
+            broadcast_util.assertAndGetBroadcastShape(aShape, bShape);
+        this.userCode = "\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        " + op + "\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    ";
+    }
+    return BinaryOpComplexProgram;
+}());
+exports.BinaryOpComplexProgram = BinaryOpComplexProgram;
+//# sourceMappingURL=binaryop_complex_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/binaryop_complex_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/binaryop_complex_gpu.js.map
new file mode 100644
index 0000000..ed779d7
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/binaryop_complex_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"binaryop_complex_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/binaryop_complex_gpu.ts"],"names":[],"mappings":";;AAiBA,yDAA2D;AAO9C,QAAA,gBAAgB,GAAG;IAC9B,IAAI,EAAE,uCAAuC;IAC7C,IAAI,EAAE,uCAAuC;CAC9C,CAAC;AAEF;IAME,gCAAY,EAAU,EAAE,MAAgB,EAAE,MAAgB;QAL1D,kBAAa,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,OAAO,EAAE,OAAO,CAAC,CAAC;QAGrD,yBAAoB,GAAG,IAAI,CAAC;QAG1B,IAAI,CAAC,WAAW;YACZ,cAAc,CAAC,0BAA0B,CAAC,MAAM,EAAE,MAAM,CAAC,CAAC;QAE9D,IAAI,CAAC,QAAQ,GAAG,8GAGV,EAAE,6SAUP,CAAC;IACJ,CAAC;IACH,6BAAC;AAAD,CAAC,AAzBD,IAyBC;AAzBY,wDAAsB"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/binaryop_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/binaryop_gpu.d.ts
new file mode 100644
index 0000000..fbfeb9c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/binaryop_gpu.d.ts
@@ -0,0 +1,31 @@
+import { GPGPUContext } from './gpgpu_context';
+import { GPGPUProgram } from './gpgpu_math';
+export declare const ADD = "return a + b;";
+export declare const SUB = "return a - b;";
+export declare const MUL = "return a * b;";
+export declare const DIV = "if (a == b) return 1.0;\n  return a / b;";
+export declare const INT_DIV = "\n  float resultSign = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  int result = ia / ib;\n  int amodb = ia - ib * result;\n\n  if (resultSign < 0.0 && amodb != 0) {\n    result -= 1;\n  }\n  return float(result);\n";
+export declare const POW = "\nif(a < 0.0 && floor(b) < b){\n  return NAN;\n}\nreturn (round(mod(b, 2.0)) == 0 || round(mod(b, 2.0)) == 2) ?\n    pow(abs(a), b) : sign(a) * pow(abs(a), b);\n";
+export declare const SQUARED_DIFFERENCE = "return (a - b) * (a - b);";
+export declare const EQUAL = "return float(a == b);";
+export declare const NOT_EQUAL = "return float(a != b);";
+export declare const LESS = "return float(a < b);";
+export declare const LESS_EQUAL = "return float(a <= b);";
+export declare const GREATER = "return float(a > b);";
+export declare const GREATER_EQUAL = "return float(a >= b);";
+export declare const LOGICAL_AND = "return float(a >= 1.0 && b >= 1.0);";
+export declare const LOGICAL_OR = "return float(a >= 1.0 || b >= 1.0);";
+export declare const MAX: string;
+export declare const MIN: string;
+export declare const MOD = "if (b == 0.0) return NAN;\n  return mod(a, b);";
+export declare const ATAN2: string;
+export declare const ELU_DER = "return (b >= 1.0) ? a : a * (b + 1.0);";
+export declare class BinaryOpProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    supportsBroadcasting: boolean;
+    startLoc: WebGLUniformLocation;
+    constructor(op: string, aShape: number[], bShape: number[]);
+    getCustomSetupFunc(): (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => void;
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/binaryop_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/binaryop_gpu.js
new file mode 100644
index 0000000..fbacce4
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/binaryop_gpu.js
@@ -0,0 +1,48 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var broadcast_util = require("../../ops/broadcast_util");
+var CHECK_NAN_SNIPPET = "\n  if (isNaN(a)) return a;\n  if (isNaN(b)) return b;\n";
+exports.ADD = 'return a + b;';
+exports.SUB = 'return a - b;';
+exports.MUL = 'return a * b;';
+exports.DIV = "if (a == b) return 1.0;\n  return a / b;";
+exports.INT_DIV = "\n  float resultSign = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  int result = ia / ib;\n  int amodb = ia - ib * result;\n\n  if (resultSign < 0.0 && amodb != 0) {\n    result -= 1;\n  }\n  return float(result);\n";
+exports.POW = "\nif(a < 0.0 && floor(b) < b){\n  return NAN;\n}\nreturn (round(mod(b, 2.0)) == 0 || round(mod(b, 2.0)) == 2) ?\n    pow(abs(a), b) : sign(a) * pow(abs(a), b);\n";
+exports.SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';
+exports.EQUAL = "return float(a == b);";
+exports.NOT_EQUAL = "return float(a != b);";
+exports.LESS = "return float(a < b);";
+exports.LESS_EQUAL = "return float(a <= b);";
+exports.GREATER = "return float(a > b);";
+exports.GREATER_EQUAL = "return float(a >= b);";
+exports.LOGICAL_AND = "return float(a >= 1.0 && b >= 1.0);";
+exports.LOGICAL_OR = "return float(a >= 1.0 || b >= 1.0);";
+exports.MAX = CHECK_NAN_SNIPPET + "\n  return max(a, b);\n";
+exports.MIN = CHECK_NAN_SNIPPET + "\n  return min(a, b);\n";
+exports.MOD = "if (b == 0.0) return NAN;\n  return mod(a, b);";
+exports.ATAN2 = CHECK_NAN_SNIPPET + "\n  return atan(a, b);\n";
+exports.ELU_DER = "return (b >= 1.0) ? a : a * (b + 1.0);";
+var BinaryOpProgram = (function () {
+    function BinaryOpProgram(op, aShape, bShape) {
+        this.variableNames = ['A', 'B'];
+        this.supportsBroadcasting = true;
+        this.outputShape =
+            broadcast_util.assertAndGetBroadcastShape(aShape, bShape);
+        this.userCode = "\n      uniform float NAN;\n      float binaryOperation(float a, float b) {\n        " + op + "\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    ";
+    }
+    BinaryOpProgram.prototype.getCustomSetupFunc = function () {
+        var _this = this;
+        return function (gpgpu, webGLProgram) {
+            if (_this.startLoc == null) {
+                _this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'NAN');
+                if (_this.startLoc == null) {
+                    return;
+                }
+            }
+            gpgpu.gl.uniform1f(_this.startLoc, NaN);
+        };
+    };
+    return BinaryOpProgram;
+}());
+exports.BinaryOpProgram = BinaryOpProgram;
+//# sourceMappingURL=binaryop_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/binaryop_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/binaryop_gpu.js.map
new file mode 100644
index 0000000..63fd68f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/binaryop_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"binaryop_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/binaryop_gpu.ts"],"names":[],"mappings":";;AAiBA,yDAA2D;AAK3D,IAAM,iBAAiB,GAAG,0DAGzB,CAAC;AAEW,QAAA,GAAG,GAAG,eAAe,CAAC;AACtB,QAAA,GAAG,GAAG,eAAe,CAAC;AACtB,QAAA,GAAG,GAAG,eAAe,CAAC;AACtB,QAAA,GAAG,GAAG,0CACH,CAAC;AAMJ,QAAA,OAAO,GAAG,8OAWtB,CAAC;AAEW,QAAA,GAAG,GAAG,mKAMlB,CAAC;AACW,QAAA,kBAAkB,GAAG,2BAA2B,CAAC;AAEjD,QAAA,KAAK,GAAG,uBAAuB,CAAC;AAEhC,QAAA,SAAS,GAAG,uBAAuB,CAAC;AAEpC,QAAA,IAAI,GAAG,sBAAsB,CAAC;AAE9B,QAAA,UAAU,GAAG,uBAAuB,CAAC;AAErC,QAAA,OAAO,GAAG,sBAAsB,CAAC;AAEjC,QAAA,aAAa,GAAG,uBAAuB,CAAC;AAExC,QAAA,WAAW,GAAG,qCAAqC,CAAC;AAEpD,QAAA,UAAU,GAAG,qCAAqC,CAAC;AAEnD,QAAA,GAAG,GAAG,iBAAiB,GAAG,yBAEtC,CAAC;AACW,QAAA,GAAG,GAAG,iBAAiB,GAAG,yBAEtC,CAAC;AACW,QAAA,GAAG,GAAG,gDACC,CAAC;AAER,QAAA,KAAK,GAAG,iBAAiB,GAAG,0BAExC,CAAC;AAEW,QAAA,OAAO,GAAG,wCAAwC,CAAC;AAEhE;IASE,yBAAY,EAAU,EAAE,MAAgB,EAAE,MAAgB;QAR1D,kBAAa,GAAG,CAAC,GAAG,EAAE,GAAG,CAAC,CAAC;QAG3B,yBAAoB,GAAG,IAAI,CAAC;QAM1B,IAAI,CAAC,WAAW;YACZ,cAAc,CAAC,0BAA0B,CAAC,MAAM,EAAE,MAAM,CAAC,CAAC;QAC9D,IAAI,CAAC,QAAQ,GAAG,0FAGV,EAAE,2KAQP,CAAC;IACJ,CAAC;IAED,4CAAkB,GAAlB;QAAA,iBAYC;QAXC,OAAO,UAAC,KAAmB,EAAE,YAA0B;YACrD,IAAI,KAAI,CAAC,QAAQ,IAAI,IAAI,EAAE;gBACzB,KAAI,CAAC,QAAQ,GAAG,KAAK,CAAC,yBAAyB,CAAC,YAAY,EAAE,KAAK,CAAC,CAAC;gBACrE,IAAI,KAAI,CAAC,QAAQ,IAAI,IAAI,EAAE;oBAGzB,OAAO;iBACR;aACF;YACD,KAAK,CAAC,EAAE,CAAC,SAAS,CAAC,KAAI,CAAC,QAAQ,EAAE,GAAG,CAAC,CAAC;QACzC,CAAC,CAAC;IACJ,CAAC;IACH,sBAAC;AAAD,CAAC,AAvCD,IAuCC;AAvCY,0CAAe"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/clip_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/clip_gpu.d.ts
new file mode 100644
index 0000000..a33bc1f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/clip_gpu.d.ts
@@ -0,0 +1,7 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class ClipProgram implements GPGPUProgram {
+    variableNames: string[];
+    userCode: string;
+    outputShape: number[];
+    constructor(aShape: number[], min: number, max: number);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/clip_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/clip_gpu.js
new file mode 100644
index 0000000..315783b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/clip_gpu.js
@@ -0,0 +1,12 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var ClipProgram = (function () {
+    function ClipProgram(aShape, min, max) {
+        this.variableNames = ['A'];
+        this.outputShape = aShape;
+        this.userCode = "\n      void main() {\n        float value = getAAtOutCoords();\n        if (isNaN(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, float(" + min + "), float(" + max + ")));\n      }\n    ";
+    }
+    return ClipProgram;
+}());
+exports.ClipProgram = ClipProgram;
+//# sourceMappingURL=clip_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/clip_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/clip_gpu.js.map
new file mode 100644
index 0000000..5d561ff
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/clip_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"clip_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/clip_gpu.ts"],"names":[],"mappings":";;AAmBA;IAKE,qBAAY,MAAgB,EAAE,GAAW,EAAE,GAAW;QAJtD,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QAKpB,IAAI,CAAC,WAAW,GAAG,MAAM,CAAC;QAC1B,IAAI,CAAC,QAAQ,GAAG,qMAQmB,GAAG,iBAAY,GAAG,wBAEpD,CAAC;IACJ,CAAC;IACH,kBAAC;AAAD,CAAC,AAnBD,IAmBC;AAnBY,kCAAW"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/clip_packed_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/clip_packed_gpu.d.ts
new file mode 100644
index 0000000..11930e5
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/clip_packed_gpu.d.ts
@@ -0,0 +1,8 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class ClipPackedProgram implements GPGPUProgram {
+    variableNames: string[];
+    usesPackedTextures: boolean;
+    userCode: string;
+    outputShape: number[];
+    constructor(aShape: number[], min: number, max: number);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/clip_packed_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/clip_packed_gpu.js
new file mode 100644
index 0000000..01b7ed9
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/clip_packed_gpu.js
@@ -0,0 +1,13 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var ClipPackedProgram = (function () {
+    function ClipPackedProgram(aShape, min, max) {
+        this.variableNames = ['A'];
+        this.usesPackedTextures = true;
+        this.outputShape = aShape;
+        this.userCode = "\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (hasNaN(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(" + min + "), vec4(" + max + ")));\n      }\n    ";
+    }
+    return ClipPackedProgram;
+}());
+exports.ClipPackedProgram = ClipPackedProgram;
+//# sourceMappingURL=clip_packed_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/clip_packed_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/clip_packed_gpu.js.map
new file mode 100644
index 0000000..533d34e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/clip_packed_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"clip_packed_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/clip_packed_gpu.ts"],"names":[],"mappings":";;AAmBA;IAME,2BAAY,MAAgB,EAAE,GAAW,EAAE,GAAW;QALtD,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QACtB,uBAAkB,GAAG,IAAI,CAAC;QAKxB,IAAI,CAAC,WAAW,GAAG,MAAM,CAAC;QAC1B,IAAI,CAAC,QAAQ,GAAG,sMASkB,GAAG,gBAAW,GAAG,wBAElD,CAAC;IACJ,CAAC;IACH,wBAAC;AAAD,CAAC,AArBD,IAqBC;AArBY,8CAAiB"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/complex_abs_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/complex_abs_gpu.d.ts
new file mode 100644
index 0000000..730ed6b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/complex_abs_gpu.d.ts
@@ -0,0 +1,7 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class ComplexAbsProgram implements GPGPUProgram {
+    variableNames: string[];
+    userCode: string;
+    outputShape: number[];
+    constructor(shape: number[]);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/complex_abs_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/complex_abs_gpu.js
new file mode 100644
index 0000000..75fc790
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/complex_abs_gpu.js
@@ -0,0 +1,12 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var ComplexAbsProgram = (function () {
+    function ComplexAbsProgram(shape) {
+        this.variableNames = ['real', 'imag'];
+        this.outputShape = shape;
+        this.userCode = "\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    ";
+    }
+    return ComplexAbsProgram;
+}());
+exports.ComplexAbsProgram = ComplexAbsProgram;
+//# sourceMappingURL=complex_abs_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/complex_abs_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/complex_abs_gpu.js.map
new file mode 100644
index 0000000..129e07c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/complex_abs_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"complex_abs_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/complex_abs_gpu.ts"],"names":[],"mappings":";;AAmBA;IAKE,2BAAY,KAAe;QAJ3B,kBAAa,GAAG,CAAC,MAAM,EAAE,MAAM,CAAC,CAAC;QAK/B,IAAI,CAAC,WAAW,GAAG,KAAK,CAAC;QACzB,IAAI,CAAC,QAAQ,GAAG,ocAaf,CAAC;IACJ,CAAC;IACH,wBAAC;AAAD,CAAC,AAtBD,IAsBC;AAtBY,8CAAiB"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/concat_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/concat_gpu.d.ts
new file mode 100644
index 0000000..e1b38cc
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/concat_gpu.d.ts
@@ -0,0 +1,7 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class ConcatProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(aShape: [number, number], bShape: [number, number]);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/concat_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/concat_gpu.js
new file mode 100644
index 0000000..e65c396
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/concat_gpu.js
@@ -0,0 +1,15 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var concat_util = require("../../ops/concat_util");
+var ConcatProgram = (function () {
+    function ConcatProgram(aShape, bShape) {
+        this.variableNames = ['A', 'B'];
+        this.outputShape = [];
+        this.outputShape =
+            concat_util.computeOutShape([aShape, bShape], 1);
+        this.userCode = "\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        float value = 0.0;\n        if (yC < " + aShape[1] + ") {\n          value = getA(yR, yC);\n        } else {\n          yC -= " + aShape[1] + ";\n          value = getB(yR, yC);\n        }\n\n        setOutput(value);\n      }\n    ";
+    }
+    return ConcatProgram;
+}());
+exports.ConcatProgram = ConcatProgram;
+//# sourceMappingURL=concat_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/concat_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/concat_gpu.js.map
new file mode 100644
index 0000000..027880e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/concat_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"concat_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/concat_gpu.ts"],"names":[],"mappings":";;AAiBA,mDAAqD;AAGrD;IAME,uBAAY,MAAwB,EAAE,MAAwB;QAL9D,kBAAa,GAAG,CAAC,GAAG,EAAE,GAAG,CAAC,CAAC;QAC3B,gBAAW,GAAa,EAAE,CAAC;QAKzB,IAAI,CAAC,WAAW;YACZ,WAAW,CAAC,eAAe,CAAC,CAAC,MAAM,EAAE,MAAM,CAAC,EAAE,CAAC,CAAY,CAAC;QAEhE,IAAI,CAAC,QAAQ,GAAG,8KAOD,MAAM,CAAC,CAAC,CAAC,gFAGV,MAAM,CAAC,CAAC,CAAC,8FAMtB,CAAC;IACJ,CAAC;IACH,oBAAC;AAAD,CAAC,AA5BD,IA4BC;AA5BY,sCAAa"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_backprop_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_backprop_gpu.d.ts
new file mode 100644
index 0000000..5cd40ce
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_backprop_gpu.d.ts
@@ -0,0 +1,26 @@
+import { Conv2DInfo, Conv3DInfo } from '../../ops/conv_util';
+import { GPGPUProgram } from './gpgpu_math';
+export declare class Conv2DDerFilterProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(convInfo: Conv2DInfo);
+}
+export declare class Conv2DDerInputProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(convInfo: Conv2DInfo);
+}
+export declare class Conv3DDerFilterProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(convInfo: Conv3DInfo);
+}
+export declare class Conv3DDerInputProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(convInfo: Conv3DInfo);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_backprop_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_backprop_gpu.js
new file mode 100644
index 0000000..1392b5f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_backprop_gpu.js
@@ -0,0 +1,64 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var Conv2DDerFilterProgram = (function () {
+    function Conv2DDerFilterProgram(convInfo) {
+        this.variableNames = ['x', 'dy'];
+        this.outputShape = convInfo.filterShape;
+        var strideHeight = convInfo.strideHeight;
+        var strideWidth = convInfo.strideWidth;
+        var padTop = convInfo.padInfo.top;
+        var padLeft = convInfo.padInfo.left;
+        this.userCode = "\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < " + convInfo.batchSize + "; b++) {\n          for (int yR = 0; yR < " + convInfo.outHeight + "; yR++) {\n            int xR = wR + yR * " + strideHeight + " - " + padTop + ";\n\n            if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n              continue;\n            }\n\n            for (int yC = 0; yC < " + convInfo.outWidth + "; yC++) {\n              int xC = wC + yC * " + strideWidth + " - " + padLeft + ";\n\n              if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+    }
+    return Conv2DDerFilterProgram;
+}());
+exports.Conv2DDerFilterProgram = Conv2DDerFilterProgram;
+var Conv2DDerInputProgram = (function () {
+    function Conv2DDerInputProgram(convInfo) {
+        this.variableNames = ['dy', 'W'];
+        this.outputShape = convInfo.inShape;
+        var filterHeight = convInfo.filterHeight;
+        var filterWidth = convInfo.filterWidth;
+        var strideHeight = convInfo.strideHeight;
+        var strideWidth = convInfo.strideWidth;
+        var padTop = filterHeight - 1 - convInfo.padInfo.top;
+        var padLeft = filterWidth - 1 - convInfo.padInfo.left;
+        this.userCode = "\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < " + filterHeight + "; wR++) {\n          float dyR = float(dyRCorner + wR) / " + strideHeight + ".0;\n\n          if (dyR < 0.0 || dyR >= " + convInfo.outHeight + ".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = " + filterHeight + " - 1 - wR;\n\n          for (int wC = 0; wC < " + filterWidth + "; wC++) {\n            float dyC = float(dyCCorner + wC) / " + strideWidth + ".0;\n\n            if (dyC < 0.0 || dyC >= " + convInfo.outWidth + ".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = " + filterWidth + " - 1 - wC;\n\n            for (int d2 = 0; d2 < " + convInfo.outChannels + "; d2++) {\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, d2);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+    }
+    return Conv2DDerInputProgram;
+}());
+exports.Conv2DDerInputProgram = Conv2DDerInputProgram;
+var Conv3DDerFilterProgram = (function () {
+    function Conv3DDerFilterProgram(convInfo) {
+        this.variableNames = ['x', 'dy'];
+        this.outputShape = convInfo.filterShape;
+        var strideDepth = convInfo.strideDepth;
+        var strideHeight = convInfo.strideHeight;
+        var strideWidth = convInfo.strideWidth;
+        var padFront = convInfo.padInfo.front;
+        var padTop = convInfo.padInfo.top;
+        var padLeft = convInfo.padInfo.left;
+        this.userCode = "\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < " + convInfo.batchSize + "; b++) {\n          for (int yF = 0; yF < " + convInfo.outDepth + "; yF++) {\n            int xF = wF + yF * " + strideDepth + " - " + padFront + ";\n\n            if (xF < 0 || xF >= " + convInfo.inDepth + ") {\n              continue;\n            }\n\n            for (int yR = 0; yR < " + convInfo.outHeight + "; yR++) {\n              int xR = wR + yR * " + strideHeight + " - " + padTop + ";\n\n              if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n                continue;\n              }\n\n              for (int yC = 0; yC < " + convInfo.outWidth + "; yC++) {\n                int xC = wC + yC * " + strideWidth + " - " + padLeft + ";\n\n                if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+    }
+    return Conv3DDerFilterProgram;
+}());
+exports.Conv3DDerFilterProgram = Conv3DDerFilterProgram;
+var Conv3DDerInputProgram = (function () {
+    function Conv3DDerInputProgram(convInfo) {
+        this.variableNames = ['dy', 'W'];
+        this.outputShape = convInfo.inShape;
+        var filterDepth = convInfo.filterDepth;
+        var filterHeight = convInfo.filterHeight;
+        var filterWidth = convInfo.filterWidth;
+        var strideDepth = convInfo.strideDepth;
+        var strideHeight = convInfo.strideHeight;
+        var strideWidth = convInfo.strideWidth;
+        var padFront = filterDepth - 1 - convInfo.padInfo.front;
+        var padTop = filterHeight - 1 - convInfo.padInfo.top;
+        var padLeft = filterWidth - 1 - convInfo.padInfo.left;
+        this.userCode = "\n      const ivec3 pads = ivec3(" + padFront + ", " + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < " + filterDepth + "; wF++) {\n          float dyF = float(dyFCorner + wF) / " + strideDepth + ".0;\n\n          if (dyF < 0.0 || dyF >= " + convInfo.outDepth + ".0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = " + filterDepth + " - 1 - wF;\n\n          for (int wR = 0; wR < " + filterHeight + "; wR++) {\n            float dyR = float(dyRCorner + wR) / " + strideHeight + ".0;\n\n            if (dyR < 0.0 || dyR >= " + convInfo.outHeight + ".0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = " + filterHeight + " - 1 - wR;\n\n            for (int wC = 0; wC < " + filterWidth + "; wC++) {\n              float dyC = float(dyCCorner + wC) / " + strideWidth + ".0;\n\n              if (dyC < 0.0 || dyC >= " + convInfo.outWidth + ".0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = " + filterWidth + " - 1 - wC;\n\n              for (int d2 = 0; d2 < " + convInfo.outChannels + "; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+    }
+    return Conv3DDerInputProgram;
+}());
+exports.Conv3DDerInputProgram = Conv3DDerInputProgram;
+//# sourceMappingURL=conv_backprop_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_backprop_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_backprop_gpu.js.map
new file mode 100644
index 0000000..1709d69
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_backprop_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"conv_backprop_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/conv_backprop_gpu.ts"],"names":[],"mappings":";;AAoBA;IAKE,gCAAY,QAAoB;QAJhC,kBAAa,GAAG,CAAC,GAAG,EAAE,IAAI,CAAC,CAAC;QAK1B,IAAI,CAAC,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QAExC,IAAM,YAAY,GAAG,QAAQ,CAAC,YAAY,CAAC;QAC3C,IAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QACzC,IAAM,MAAM,GAAG,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC;QACpC,IAAM,OAAO,GAAG,QAAQ,CAAC,OAAO,CAAC,IAAI,CAAC;QAEtC,IAAI,CAAC,QAAQ,GAAG,yYAYU,QAAQ,CAAC,SAAS,kDACd,QAAQ,CAAC,SAAS,kDACnB,YAAY,WAAM,MAAM,6CAEvB,QAAQ,CAAC,QAAQ,yFAIf,QAAQ,CAAC,QAAQ,oDAClB,WAAW,WAAM,OAAO,+CAEvB,QAAQ,CAAC,OAAO,+RAY/C,CAAC;IACJ,CAAC;IACH,6BAAC;AAAD,CAAC,AAlDD,IAkDC;AAlDY,wDAAsB;AAoDnC;IAKE,+BAAY,QAAoB;QAJhC,kBAAa,GAAG,CAAC,IAAI,EAAE,GAAG,CAAC,CAAC;QAK1B,IAAI,CAAC,WAAW,GAAG,QAAQ,CAAC,OAAO,CAAC;QAEpC,IAAM,YAAY,GAAG,QAAQ,CAAC,YAAY,CAAC;QAC3C,IAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QACzC,IAAM,YAAY,GAAG,QAAQ,CAAC,YAAY,CAAC;QAC3C,IAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QAEzC,IAAM,MAAM,GAAG,YAAY,GAAG,CAAC,GAAG,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC;QACvD,IAAM,OAAO,GAAG,WAAW,GAAG,CAAC,GAAG,QAAQ,CAAC,OAAO,CAAC,IAAI,CAAC;QAExD,IAAI,CAAC,QAAQ,GAAG,sCACa,MAAM,UAAK,OAAO,ydAcnB,YAAY,iEACI,YAAY,iDAExB,QAAQ,CAAC,SAAS,gIAK7B,YAAY,sDAEH,WAAW,mEACK,WAAW,mDAEvB,QAAQ,CAAC,QAAQ,yJAM5B,WAAW,wDAEF,QAAQ,CAAC,WAAW,oQASnD,CAAC;IACJ,CAAC;IACH,4BAAC;AAAD,CAAC,AA/DD,IA+DC;AA/DY,sDAAqB;AAiElC;IAKE,gCAAY,QAAoB;QAJhC,kBAAa,GAAG,CAAC,GAAG,EAAE,IAAI,CAAC,CAAC;QAK1B,IAAI,CAAC,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QAExC,IAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QACzC,IAAM,YAAY,GAAG,QAAQ,CAAC,YAAY,CAAC;QAC3C,IAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QACzC,IAAM,QAAQ,GAAG,QAAQ,CAAC,OAAO,CAAC,KAAK,CAAC;QACxC,IAAM,MAAM,GAAG,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC;QACpC,IAAM,OAAO,GAAG,QAAQ,CAAC,OAAO,CAAC,IAAI,CAAC;QAEtC,IAAI,CAAC,QAAQ,GAAG,iRAWU,QAAQ,CAAC,SAAS,kDACd,QAAQ,CAAC,QAAQ,kDAClB,WAAW,WAAM,QAAQ,6CAExB,QAAQ,CAAC,OAAO,yFAId,QAAQ,CAAC,SAAS,oDACnB,YAAY,WAAM,MAAM,+CAEvB,QAAQ,CAAC,QAAQ,+FAIf,QAAQ,CAAC,QAAQ,sDAClB,WAAW,WAAM,OAAO,iDAEvB,QAAQ,CAAC,OAAO,kUAajD,CAAC;IACJ,CAAC;IACH,6BAAC;AAAD,CAAC,AA3DD,IA2DC;AA3DY,wDAAsB;AA6DnC;IAKE,+BAAY,QAAoB;QAJhC,kBAAa,GAAG,CAAC,IAAI,EAAE,GAAG,CAAC,CAAC;QAK1B,IAAI,CAAC,WAAW,GAAG,QAAQ,CAAC,OAAO,CAAC;QAEpC,IAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QACzC,IAAM,YAAY,GAAG,QAAQ,CAAC,YAAY,CAAC;QAC3C,IAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QACzC,IAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QACzC,IAAM,YAAY,GAAG,QAAQ,CAAC,YAAY,CAAC;QAC3C,IAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QAEzC,IAAM,QAAQ,GAAG,WAAW,GAAG,CAAC,GAAG,QAAQ,CAAC,OAAO,CAAC,KAAK,CAAC;QAC1D,IAAM,MAAM,GAAG,YAAY,GAAG,CAAC,GAAG,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC;QACvD,IAAM,OAAO,GAAG,WAAW,GAAG,CAAC,GAAG,QAAQ,CAAC,OAAO,CAAC,IAAI,CAAC;QAExD,IAAI,CAAC,QAAQ,GAAG,sCACa,QAAQ,UAAK,MAAM,UAAK,OAAO,gYAchC,WAAW,iEACK,WAAW,iDAEvB,QAAQ,CAAC,QAAQ,gIAK5B,WAAW,sDAEF,YAAY,mEACI,YAAY,mDAExB,QAAQ,CAAC,SAAS,uJAM7B,YAAY,wDAEH,WAAW,qEACK,WAAW,qDAEvB,QAAQ,CAAC,QAAQ,mKAM5B,WAAW,0DAEF,QAAQ,CAAC,WAAW,ySAUrD,CAAC;IACJ,CAAC;IACH,4BAAC;AAAD,CAAC,AA9ED,IA8EC;AA9EY,sDAAqB"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_backprop_gpu_depthwise.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_backprop_gpu_depthwise.d.ts
new file mode 100644
index 0000000..63bc702
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_backprop_gpu_depthwise.d.ts
@@ -0,0 +1,14 @@
+import { Conv2DInfo } from '../../ops/conv_util';
+import { GPGPUProgram } from './gpgpu_math';
+export declare class DepthwiseConv2DDerFilterProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(convInfo: Conv2DInfo);
+}
+export declare class DepthwiseConv2DDerInputProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(convInfo: Conv2DInfo);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_backprop_gpu_depthwise.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_backprop_gpu_depthwise.js
new file mode 100644
index 0000000..47cb27e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_backprop_gpu_depthwise.js
@@ -0,0 +1,33 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var DepthwiseConv2DDerFilterProgram = (function () {
+    function DepthwiseConv2DDerFilterProgram(convInfo) {
+        this.variableNames = ['x', 'dy'];
+        this.outputShape = convInfo.filterShape;
+        var strideHeight = convInfo.strideHeight;
+        var strideWidth = convInfo.strideWidth;
+        var padTop = convInfo.padInfo.top;
+        var padLeft = convInfo.padInfo.left;
+        var channelMul = convInfo.outChannels / convInfo.inChannels;
+        this.userCode = "\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * " + channelMul + " + dm;\n\n        float dotProd = 0.0;\n\n        // TODO: Vec4 over the batch size\n        for (int b = 0; b < " + convInfo.batchSize + "; b++) {\n          for (int yR = 0; yR < " + convInfo.outHeight + "; yR++) {\n            int xR = wR + yR * " + strideHeight + " - " + padTop + ";\n\n            if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n              continue;\n            }\n\n            for (int yC = 0; yC < " + convInfo.outWidth + "; yC++) {\n              int xC = wC + yC * " + strideWidth + " - " + padLeft + ";\n\n              if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+    }
+    return DepthwiseConv2DDerFilterProgram;
+}());
+exports.DepthwiseConv2DDerFilterProgram = DepthwiseConv2DDerFilterProgram;
+var DepthwiseConv2DDerInputProgram = (function () {
+    function DepthwiseConv2DDerInputProgram(convInfo) {
+        this.variableNames = ['dy', 'W'];
+        this.outputShape = convInfo.inShape;
+        var filterHeight = convInfo.filterHeight;
+        var filterWidth = convInfo.filterWidth;
+        var strideHeight = convInfo.strideHeight;
+        var strideWidth = convInfo.strideWidth;
+        var padTop = filterHeight - 1 - convInfo.padInfo.top;
+        var padLeft = filterWidth - 1 - convInfo.padInfo.left;
+        var channelMul = convInfo.outChannels / convInfo.inChannels;
+        this.userCode = "\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < " + filterHeight + "; wR++) {\n          float dyR = float(dyRCorner + wR) / " + strideHeight + ".0;\n\n          if (dyR < 0.0 || dyR >= " + convInfo.outHeight + ".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = " + filterHeight + " - 1 - wR;\n\n          for (int wC = 0; wC < " + filterWidth + "; wC++) {\n            float dyC = float(dyCCorner + wC) / " + strideWidth + ".0;\n\n            if (dyC < 0.0 || dyC >= " + convInfo.outWidth + ".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = " + filterWidth + " - 1 - wC;\n\n            // TODO: Vec4 over the channelMul\n            for (int dm = 0; dm < " + channelMul + "; dm++) {\n              int d2 = d1 * " + channelMul + " + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+    }
+    return DepthwiseConv2DDerInputProgram;
+}());
+exports.DepthwiseConv2DDerInputProgram = DepthwiseConv2DDerInputProgram;
+//# sourceMappingURL=conv_backprop_gpu_depthwise.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_backprop_gpu_depthwise.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_backprop_gpu_depthwise.js.map
new file mode 100644
index 0000000..d82a2a9
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_backprop_gpu_depthwise.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"conv_backprop_gpu_depthwise.js","sourceRoot":"","sources":["../../../src/kernels/webgl/conv_backprop_gpu_depthwise.ts"],"names":[],"mappings":";;AAoBA;IAKE,yCAAY,QAAoB;QAJhC,kBAAa,GAAG,CAAC,GAAG,EAAE,IAAI,CAAC,CAAC;QAK1B,IAAI,CAAC,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QAExC,IAAM,YAAY,GAAG,QAAQ,CAAC,YAAY,CAAC;QAC3C,IAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QACzC,IAAM,MAAM,GAAG,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC;QACpC,IAAM,OAAO,GAAG,QAAQ,CAAC,OAAO,CAAC,IAAI,CAAC;QACtC,IAAM,UAAU,GAAG,QAAQ,CAAC,WAAW,GAAG,QAAQ,CAAC,UAAU,CAAC;QAE9D,IAAI,CAAC,QAAQ,GAAG,6MAOI,UAAU,yHAKJ,QAAQ,CAAC,SAAS,kDACd,QAAQ,CAAC,SAAS,kDACnB,YAAY,WAAM,MAAM,6CAEvB,QAAQ,CAAC,QAAQ,yFAIf,QAAQ,CAAC,QAAQ,oDAClB,WAAW,WAAM,OAAO,+CAEvB,QAAQ,CAAC,OAAO,+RAY/C,CAAC;IACJ,CAAC;IACH,sCAAC;AAAD,CAAC,AAnDD,IAmDC;AAnDY,0EAA+B;AAqD5C;IAKE,wCAAY,QAAoB;QAJhC,kBAAa,GAAG,CAAC,IAAI,EAAE,GAAG,CAAC,CAAC;QAK1B,IAAI,CAAC,WAAW,GAAG,QAAQ,CAAC,OAAO,CAAC;QAEpC,IAAM,YAAY,GAAG,QAAQ,CAAC,YAAY,CAAC;QAC3C,IAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QACzC,IAAM,YAAY,GAAG,QAAQ,CAAC,YAAY,CAAC;QAC3C,IAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QAEzC,IAAM,MAAM,GAAG,YAAY,GAAG,CAAC,GAAG,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC;QACvD,IAAM,OAAO,GAAG,WAAW,GAAG,CAAC,GAAG,QAAQ,CAAC,OAAO,CAAC,IAAI,CAAC;QACxD,IAAM,UAAU,GAAG,QAAQ,CAAC,WAAW,GAAG,QAAQ,CAAC,UAAU,CAAC;QAE9D,IAAI,CAAC,QAAQ,GAAG,sCACa,MAAM,UAAK,OAAO,iUAYnB,YAAY,iEACI,YAAY,iDAExB,QAAQ,CAAC,SAAS,gIAK7B,YAAY,sDAEH,WAAW,mEACK,WAAW,mDAEvB,QAAQ,CAAC,QAAQ,yJAM5B,WAAW,uGAGF,UAAU,+CAChB,UAAU,iQASnC,CAAC;IACJ,CAAC;IACH,qCAAC;AAAD,CAAC,AAhED,IAgEC;AAhEY,wEAA8B"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_gpu.d.ts
new file mode 100644
index 0000000..d01b468
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_gpu.d.ts
@@ -0,0 +1,14 @@
+import { Conv2DInfo, Conv3DInfo } from '../../ops/conv_util';
+import { GPGPUProgram } from './gpgpu_math';
+export declare class Conv2DProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(convInfo: Conv2DInfo);
+}
+export declare class Conv3DProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(convInfo: Conv3DInfo);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_gpu.js
new file mode 100644
index 0000000..9a2666c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_gpu.js
@@ -0,0 +1,45 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var Conv2DProgram = (function () {
+    function Conv2DProgram(convInfo) {
+        this.variableNames = ['x', 'W'];
+        this.outputShape = convInfo.outShape;
+        var padTop = convInfo.padInfo.top;
+        var padLeft = convInfo.padInfo.left;
+        var strideHeight = convInfo.strideHeight;
+        var strideWidth = convInfo.strideWidth;
+        var dilationHeight = convInfo.dilationHeight;
+        var dilationWidth = convInfo.dilationWidth;
+        var filterHeight = convInfo.filterHeight;
+        var filterWidth = convInfo.filterWidth;
+        var inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;
+        var inputDepthVec4Remainder = convInfo.inChannels % 4;
+        this.userCode = "\n      const ivec2 strides = ivec2(" + strideHeight + ", " + strideWidth + ");\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < " + filterHeight + "; wR++) {\n          int xR = xRCorner + wR * " + dilationHeight + ";\n\n          if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n            continue;\n          }\n\n          for (int wC = 0; wC < " + filterWidth + "; wC++) {\n            int xC = xCCorner + wC * " + dilationWidth + ";\n\n            if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < " + inputDepthNearestVec4 + "; d1 += 4) {\n              vec4 xValues = vec4(\n                getX(batch, xR, xC, d1),\n                getX(batch, xR, xC, d1 + 1),\n                getX(batch, xR, xC, d1 + 2),\n                getX(batch, xR, xC, d1 + 3)\n              );\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              dotProd += dot(xValues, wValues);\n            }\n\n            if (" + (inputDepthVec4Remainder === 1) + ") {\n              dotProd +=\n                getX(batch, xR, xC, " + inputDepthNearestVec4 + ") *\n                getW(wR, wC, " + inputDepthNearestVec4 + ", d2);\n            } else if (" + (inputDepthVec4Remainder === 2) + ") {\n              vec2 xValues = vec2(\n                getX(batch, xR, xC, " + inputDepthNearestVec4 + "),\n                getX(batch, xR, xC, " + inputDepthNearestVec4 + " + 1)\n              );\n              vec2 wValues = vec2(\n                getW(wR, wC, " + inputDepthNearestVec4 + ", d2),\n                getW(wR, wC, " + inputDepthNearestVec4 + " + 1, d2)\n              );\n              dotProd += dot(xValues, wValues);\n            } else if (" + (inputDepthVec4Remainder === 3) + ") {\n              vec3 xValues = vec3(\n                getX(batch, xR, xC, " + inputDepthNearestVec4 + "),\n                getX(batch, xR, xC, " + inputDepthNearestVec4 + " + 1),\n                getX(batch, xR, xC, " + inputDepthNearestVec4 + " + 2)\n              );\n              vec3 wValues = vec3(\n                getW(wR, wC, " + inputDepthNearestVec4 + ", d2),\n                getW(wR, wC, " + inputDepthNearestVec4 + " + 1, d2),\n                getW(wR, wC, " + inputDepthNearestVec4 + " + 2, d2)\n              );\n              dotProd += dot(xValues, wValues);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+    }
+    return Conv2DProgram;
+}());
+exports.Conv2DProgram = Conv2DProgram;
+var Conv3DProgram = (function () {
+    function Conv3DProgram(convInfo) {
+        this.variableNames = ['x', 'W'];
+        this.outputShape = convInfo.outShape;
+        var padFront = convInfo.padInfo.front;
+        var padTop = convInfo.padInfo.top;
+        var padLeft = convInfo.padInfo.left;
+        var strideDepth = convInfo.strideDepth;
+        var strideHeight = convInfo.strideHeight;
+        var strideWidth = convInfo.strideWidth;
+        var dilationDepth = convInfo.dilationDepth;
+        var dilationHeight = convInfo.dilationHeight;
+        var dilationWidth = convInfo.dilationWidth;
+        var filterDepth = convInfo.filterDepth;
+        var filterHeight = convInfo.filterHeight;
+        var filterWidth = convInfo.filterWidth;
+        var inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;
+        var inputDepthVec4Remainder = convInfo.inChannels % 4;
+        this.userCode = "\n      const ivec3 strides = ivec3(" + strideDepth + ", " + strideHeight + ", " + strideWidth + ");\n      const ivec3 pads = ivec3(" + padFront + ", " + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < " + filterDepth + "; wF++) {\n          int xF = xFCorner + wF * " + dilationDepth + ";\n\n          if (xF < 0 || xF >= " + convInfo.inDepth + ") {\n            continue;\n          }\n\n          for (int wR = 0; wR < " + filterHeight + "; wR++) {\n            int xR = xRCorner + wR * " + dilationHeight + ";\n\n            if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n              continue;\n            }\n\n            for (int wC = 0; wC < " + filterWidth + "; wC++) {\n              int xC = xCCorner + wC * " + dilationWidth + ";\n\n              if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < " + inputDepthNearestVec4 + "; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (" + (inputDepthVec4Remainder === 1) + ") {\n                dotProd +=\n                  getX(batch, xF, xR, xC, " + inputDepthNearestVec4 + ") *\n                  getW(wF, wR, wC, " + inputDepthNearestVec4 + ", d2);\n              } else if (" + (inputDepthVec4Remainder === 2) + ") {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, " + inputDepthNearestVec4 + "),\n                  getX(batch, xF, xR, xC, " + inputDepthNearestVec4 + " + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, " + inputDepthNearestVec4 + ", d2),\n                  getW(wF, wR, wC, " + inputDepthNearestVec4 + " + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (" + (inputDepthVec4Remainder === 3) + ") {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, " + inputDepthNearestVec4 + "),\n                  getX(batch, xF, xR, xC, " + inputDepthNearestVec4 + " + 1),\n                  getX(batch, xF, xR, xC, " + inputDepthNearestVec4 + " + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, " + inputDepthNearestVec4 + ", d2),\n                  getW(wF, wR, wC, " + inputDepthNearestVec4 + " + 1, d2),\n                  getW(wF, wR, wC, " + inputDepthNearestVec4 + " + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+    }
+    return Conv3DProgram;
+}());
+exports.Conv3DProgram = Conv3DProgram;
+//# sourceMappingURL=conv_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_gpu.js.map
new file mode 100644
index 0000000..4191102
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"conv_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/conv_gpu.ts"],"names":[],"mappings":";;AAoBA;IAKE,uBAAY,QAAoB;QAJhC,kBAAa,GAAG,CAAC,GAAG,EAAE,GAAG,CAAC,CAAC;QAKzB,IAAI,CAAC,WAAW,GAAG,QAAQ,CAAC,QAAQ,CAAC;QACrC,IAAM,MAAM,GAAG,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC;QACpC,IAAM,OAAO,GAAG,QAAQ,CAAC,OAAO,CAAC,IAAI,CAAC;QACtC,IAAM,YAAY,GAAG,QAAQ,CAAC,YAAY,CAAC;QAC3C,IAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QACzC,IAAM,cAAc,GAAG,QAAQ,CAAC,cAAc,CAAC;QAC/C,IAAM,aAAa,GAAG,QAAQ,CAAC,aAAa,CAAC;QAC7C,IAAM,YAAY,GAAG,QAAQ,CAAC,YAAY,CAAC;QAC3C,IAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QAEzC,IAAM,qBAAqB,GAAG,IAAI,CAAC,KAAK,CAAC,QAAQ,CAAC,UAAU,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC;QACtE,IAAM,uBAAuB,GAAG,QAAQ,CAAC,UAAU,GAAG,CAAC,CAAC;QAExD,IAAI,CAAC,QAAQ,GAAG,yCACgB,YAAY,UAAK,WAAW,2CAC/B,MAAM,UAAK,OAAO,8dAcnB,YAAY,sDACP,cAAc,2CAEnB,QAAQ,CAAC,QAAQ,mFAIf,WAAW,wDACN,aAAa,6CAElB,QAAQ,CAAC,OAAO,yFAId,qBAAqB,ijBAiBvC,uBAAuB,KAAK,CAAC,4EAET,qBAAqB,0CAC5B,qBAAqB,wCAC3B,uBAAuB,KAAK,CAAC,sFAEhB,qBAAqB,gDACrB,qBAAqB,kGAG5B,qBAAqB,6CACrB,qBAAqB,8GAG3B,uBAAuB,KAAK,CAAC,sFAEhB,qBAAqB,gDACrB,qBAAqB,oDACrB,qBAAqB,kGAG5B,qBAAqB,6CACrB,qBAAqB,iDACrB,qBAAqB,oKAQ/C,CAAC;IACJ,CAAC;IACH,oBAAC;AAAD,CAAC,AAnGD,IAmGC;AAnGY,sCAAa;AAqG1B;IAKE,uBAAY,QAAoB;QAJhC,kBAAa,GAAG,CAAC,GAAG,EAAE,GAAG,CAAC,CAAC;QAKzB,IAAI,CAAC,WAAW,GAAG,QAAQ,CAAC,QAAQ,CAAC;QACrC,IAAM,QAAQ,GAAG,QAAQ,CAAC,OAAO,CAAC,KAAK,CAAC;QACxC,IAAM,MAAM,GAAG,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC;QACpC,IAAM,OAAO,GAAG,QAAQ,CAAC,OAAO,CAAC,IAAI,CAAC;QACtC,IAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QACzC,IAAM,YAAY,GAAG,QAAQ,CAAC,YAAY,CAAC;QAC3C,IAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QACzC,IAAM,aAAa,GAAG,QAAQ,CAAC,aAAa,CAAC;QAC7C,IAAM,cAAc,GAAG,QAAQ,CAAC,cAAc,CAAC;QAC/C,IAAM,aAAa,GAAG,QAAQ,CAAC,aAAa,CAAC;QAC7C,IAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QACzC,IAAM,YAAY,GAAG,QAAQ,CAAC,YAAY,CAAC;QAC3C,IAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QAEzC,IAAM,qBAAqB,GAAG,IAAI,CAAC,KAAK,CAAC,QAAQ,CAAC,UAAU,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC;QACtE,IAAM,uBAAuB,GAAG,QAAQ,CAAC,UAAU,GAAG,CAAC,CAAC;QAExD,IAAI,CAAC,QAAQ,GAAG,yCACgB,WAAW,UAAK,YAAY,UACxD,WAAW,2CACc,QAAQ,UAAK,MAAM,UAAK,OAAO,qjBAgBhC,WAAW,sDACN,aAAa,2CAElB,QAAQ,CAAC,OAAO,mFAId,YAAY,wDACP,cAAc,6CAEnB,QAAQ,CAAC,QAAQ,yFAIf,WAAW,0DACN,aAAa,+CAElB,QAAQ,CAAC,OAAO,+FAId,qBAAqB,+mBAiBvC,uBAAuB,KAAK,CAAC,oFAEL,qBAAqB,gDAC5B,qBAAqB,0CAC/B,uBAAuB,KAAK,CAAC,8FAEZ,qBAAqB,sDACrB,qBAAqB,4GAG5B,qBAAqB,mDACrB,qBAAqB,oHAG/B,uBAAuB,KAAK,CAAC,8FAEZ,qBAAqB,sDACrB,qBAAqB,0DACrB,qBAAqB,4GAG5B,qBAAqB,mDACrB,qBAAqB,uDACrB,qBAAqB,yLASrD,CAAC;IACJ,CAAC;IACH,oBAAC;AAAD,CAAC,AAlHD,IAkHC;AAlHY,sCAAa"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_gpu_depthwise.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_gpu_depthwise.d.ts
new file mode 100644
index 0000000..0f9d72d
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_gpu_depthwise.d.ts
@@ -0,0 +1,8 @@
+import { Conv2DInfo } from '../../ops/conv_util';
+import { GPGPUProgram } from './gpgpu_math';
+export declare class DepthwiseConv2DProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(convInfo: Conv2DInfo);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_gpu_depthwise.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_gpu_depthwise.js
new file mode 100644
index 0000000..1ffb6c6
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_gpu_depthwise.js
@@ -0,0 +1,23 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var DepthwiseConv2DProgram = (function () {
+    function DepthwiseConv2DProgram(convInfo) {
+        this.variableNames = ['x', 'W'];
+        this.outputShape = convInfo.outShape;
+        var xNumRows = convInfo.inHeight;
+        var xNumCols = convInfo.inWidth;
+        var padTop = convInfo.padInfo.top;
+        var padLeft = convInfo.padInfo.left;
+        var strideHeight = convInfo.strideHeight;
+        var strideWidth = convInfo.strideWidth;
+        var dilationHeight = convInfo.dilationHeight;
+        var dilationWidth = convInfo.dilationWidth;
+        var filterHeight = convInfo.filterHeight;
+        var filterWidth = convInfo.filterWidth;
+        var channelMul = convInfo.outChannels / convInfo.inChannels;
+        this.userCode = "\n      const ivec2 strides = ivec2(" + strideHeight + ", " + strideWidth + ");\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / " + channelMul + ";\n        int q = d2 - d1 * " + channelMul + ";\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TODO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < " + filterHeight + "; wR++) {\n          int xR = xRCorner + wR * " + dilationHeight + ";\n\n          if (xR < 0 || xR >= " + xNumRows + ") {\n            continue;\n          }\n\n          for (int wC = 0; wC < " + filterWidth + "; wC++) {\n            int xC = xCCorner + wC * " + dilationWidth + ";\n\n            if (xC < 0 || xC >= " + xNumCols + ") {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+    }
+    return DepthwiseConv2DProgram;
+}());
+exports.DepthwiseConv2DProgram = DepthwiseConv2DProgram;
+//# sourceMappingURL=conv_gpu_depthwise.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_gpu_depthwise.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_gpu_depthwise.js.map
new file mode 100644
index 0000000..d63f2f0
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_gpu_depthwise.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"conv_gpu_depthwise.js","sourceRoot":"","sources":["../../../src/kernels/webgl/conv_gpu_depthwise.ts"],"names":[],"mappings":";;AAoBA;IAKE,gCAAY,QAAoB;QAJhC,kBAAa,GAAG,CAAC,GAAG,EAAE,GAAG,CAAC,CAAC;QAKzB,IAAI,CAAC,WAAW,GAAG,QAAQ,CAAC,QAAQ,CAAC;QAErC,IAAM,QAAQ,GAAG,QAAQ,CAAC,QAAQ,CAAC;QACnC,IAAM,QAAQ,GAAG,QAAQ,CAAC,OAAO,CAAC;QAClC,IAAM,MAAM,GAAG,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC;QACpC,IAAM,OAAO,GAAG,QAAQ,CAAC,OAAO,CAAC,IAAI,CAAC;QACtC,IAAM,YAAY,GAAG,QAAQ,CAAC,YAAY,CAAC;QAC3C,IAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QACzC,IAAM,cAAc,GAAG,QAAQ,CAAC,cAAc,CAAC;QAC/C,IAAM,aAAa,GAAG,QAAQ,CAAC,aAAa,CAAC;QAC7C,IAAM,YAAY,GAAG,QAAQ,CAAC,YAAY,CAAC;QAC3C,IAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QACzC,IAAM,UAAU,GAAG,QAAQ,CAAC,WAAW,GAAG,QAAQ,CAAC,UAAU,CAAC;QAE9D,IAAI,CAAC,QAAQ,GAAG,yCACgB,YAAY,UAAK,WAAW,2CAC/B,MAAM,UAAK,OAAO,sNAO3B,UAAU,qCACN,UAAU,qXASN,YAAY,sDACP,cAAc,2CAEnB,QAAQ,mFAIN,WAAW,wDACN,aAAa,6CAElB,QAAQ,6PAWrC,CAAC;IACJ,CAAC;IACH,6BAAC;AAAD,CAAC,AA9DD,IA8DC;AA9DY,wDAAsB"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_packed_gpu_depthwise.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_packed_gpu_depthwise.d.ts
new file mode 100644
index 0000000..07e2ab0
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_packed_gpu_depthwise.d.ts
@@ -0,0 +1,9 @@
+import { Conv2DInfo } from '../../ops/conv_util';
+import { GPGPUProgram } from './gpgpu_math';
+export declare class DepthwiseConvPacked2DProgram implements GPGPUProgram {
+    variableNames: string[];
+    usesPackedTextures: boolean;
+    outputShape: number[];
+    userCode: string;
+    constructor(convInfo: Conv2DInfo);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_packed_gpu_depthwise.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_packed_gpu_depthwise.js
new file mode 100644
index 0000000..9eb8cca
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_packed_gpu_depthwise.js
@@ -0,0 +1,81 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var DepthwiseConvPacked2DProgram = (function () {
+    function DepthwiseConvPacked2DProgram(convInfo) {
+        this.variableNames = ['x', 'W'];
+        this.usesPackedTextures = true;
+        this.outputShape = convInfo.outShape;
+        var xNumRows = convInfo.inHeight;
+        var xNumCols = convInfo.inWidth;
+        var padTop = convInfo.padInfo.top;
+        var padLeft = convInfo.padInfo.left;
+        var strideHeight = convInfo.strideHeight;
+        var strideWidth = convInfo.strideWidth;
+        var filterHeight = convInfo.filterHeight;
+        var filterWidth = convInfo.filterWidth;
+        var texelsAcross = Math.ceil((filterWidth + 1) / 2);
+        var mainLoop = "int xR; int xC;";
+        for (var r = 0; r < filterHeight; r++) {
+            for (var c = -padLeft; c < texelsAcross * 2; c++) {
+                mainLoop += "vec4 " + xTexelName(r, c) + " = vec4(0.);";
+            }
+            for (var c = 0; c < filterWidth; c++) {
+                mainLoop += "\n          vec4 wR" + r + "C" + c + " = vec4(0.);\n          vec4 xR" + r + "C" + c + " = vec4(0.);";
+            }
+        }
+        for (var r = 0; r < filterHeight; r++) {
+            for (var c = 0; c < texelsAcross; c++) {
+                var col = c * 2;
+                var left = c * 2 + padLeft;
+                mainLoop += "\n          xR = xRCorner + " + r + ";\n          xC = xCCorner + " + left + ";\n\n          if(xR >= 0 && xR < " + xNumRows + " && xC >= 0 && xC < " + xNumCols + ") {\n            " + xTexelName(r, left) + " = getX(batch, xR, xC, d1);\n          }";
+                if (padLeft === 0) {
+                    if (col < filterWidth && c === texelsAcross - 1) {
+                        if (strideWidth > 1) {
+                            mainLoop += "\n                vec4 " + xTexelName(r, left + 2) + " = vec4(0.);\n\n                if(xR >= 0 && xR < " + xNumRows + " && xC + 2 < " + xNumCols + ") {\n                  " + xTexelName(r, left + 2) + " = getX(batch, xR, xC + 2, d1);\n                }";
+                        }
+                        mainLoop += "\n              xR" + r + "C" + left + " = " + constructTexel(r, left, strideWidth, padLeft) + ";\n            ";
+                    }
+                }
+                else if (c === 0) {
+                    mainLoop += "\n            if(xR >= 0 && xR < " + xNumRows + " && xC - 2 >= 0) {\n              " + xTexelName(r, left - 2) + " = getX(batch, xR, xC - 2, d1);\n            }";
+                }
+                if (col > 0) {
+                    mainLoop += "xR" + r + "C" + (left - 2) + " =\n            " + constructTexel(r, left - 2, strideWidth, padLeft) + ";";
+                }
+                if (left - 1 >= 0 && left - 1 < filterWidth) {
+                    mainLoop += "xR" + r + "C" + (left - 1) + " =\n              " + constructTexel(r, left - 1, strideWidth, padLeft) + ";";
+                }
+                if (col < filterWidth) {
+                    mainLoop += "\n            vec4 wTexel" + r + "C" + col + " = getW(" + r + ", " + col + ", d1, q);\n            wR" + r + "C" + col + " = vec4(wTexel" + r + "C" + col + ".xz, wTexel" + r + "C" + col + ".xz);\n          ";
+                    if (col + 1 < filterWidth) {
+                        mainLoop += "\n              vec4 wTexelR" + r + "C" + (col + 1) + " = getW(" + r + ", " + (col + 1) + ", d1, q);\n              wR" + r + "C" + (col + 1) + " =\n                vec4(wTexelR" + r + "C" + (col + 1) + ".xz, wTexelR" + r + "C" + (col + 1) + ".xz);";
+                    }
+                }
+            }
+        }
+        for (var r = 0; r < filterHeight; r++) {
+            for (var c = 0; c < filterWidth; c++) {
+                mainLoop += "result += xR" + r + "C" + c + " * wR" + r + "C" + c + ";";
+            }
+        }
+        this.userCode = "\n      const ivec2 strides = ivec2(" + strideHeight + ", " + strideWidth + ");\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2;\n        int q = 0;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        vec4 result = vec4(0.);\n\n        " + mainLoop + "\n\n        setOutput(result);\n      }\n    ";
+    }
+    return DepthwiseConvPacked2DProgram;
+}());
+exports.DepthwiseConvPacked2DProgram = DepthwiseConvPacked2DProgram;
+function xTexelName(r, c) {
+    return "xTexelR" + r + "C" + (c < 0 ? 'minus' + Math.abs(c).toString() : c);
+}
+function constructTexel(r, c, stride, padLeft) {
+    if (stride === 1) {
+        if (padLeft % 2 === c % 2) {
+            return xTexelName(r, c);
+        }
+        return "vec4(" + xTexelName(r, c - 1) + ".zw, " + xTexelName(r, c + 1) + ".xy)";
+    }
+    if (padLeft % 2 === c % 2) {
+        return "vec4(" + xTexelName(r, c) + ".xy, " + xTexelName(r, c + 2) + ".xy)";
+    }
+    return "vec4(" + xTexelName(r, c - 1) + ".zw, " + xTexelName(r, c + 1) + ".zw)";
+}
+//# sourceMappingURL=conv_packed_gpu_depthwise.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_packed_gpu_depthwise.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_packed_gpu_depthwise.js.map
new file mode 100644
index 0000000..9234ddf
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/conv_packed_gpu_depthwise.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"conv_packed_gpu_depthwise.js","sourceRoot":"","sources":["../../../src/kernels/webgl/conv_packed_gpu_depthwise.ts"],"names":[],"mappings":";;AAoBA;IAME,sCAAY,QAAoB;QALhC,kBAAa,GAAG,CAAC,GAAG,EAAE,GAAG,CAAC,CAAC;QAC3B,uBAAkB,GAAG,IAAI,CAAC;QAKxB,IAAI,CAAC,WAAW,GAAG,QAAQ,CAAC,QAAQ,CAAC;QAErC,IAAM,QAAQ,GAAG,QAAQ,CAAC,QAAQ,CAAC;QACnC,IAAM,QAAQ,GAAG,QAAQ,CAAC,OAAO,CAAC;QAClC,IAAM,MAAM,GAAG,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC;QACpC,IAAM,OAAO,GAAG,QAAQ,CAAC,OAAO,CAAC,IAAI,CAAC;QACtC,IAAM,YAAY,GAAG,QAAQ,CAAC,YAAY,CAAC;QAC3C,IAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QACzC,IAAM,YAAY,GAAG,QAAQ,CAAC,YAAY,CAAC;QAC3C,IAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QACzC,IAAM,YAAY,GAAG,IAAI,CAAC,IAAI,CAAC,CAAC,WAAW,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC;QAEtD,IAAI,QAAQ,GAAG,iBAAiB,CAAC;QAEjC,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,YAAY,EAAE,CAAC,EAAE,EAAE;YACrC,KAAK,IAAI,CAAC,GAAG,CAAC,OAAO,EAAE,CAAC,GAAG,YAAY,GAAG,CAAC,EAAE,CAAC,EAAE,EAAE;gBAChD,QAAQ,IAAI,UAAQ,UAAU,CAAC,CAAC,EAAE,CAAC,CAAC,iBAAc,CAAC;aACpD;YAED,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,WAAW,EAAE,CAAC,EAAE,EAAE;gBACpC,QAAQ,IAAI,wBACD,CAAC,SAAI,CAAC,uCACN,CAAC,SAAI,CAAC,iBAAc,CAAC;aACjC;SACF;QAUD,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,YAAY,EAAE,CAAC,EAAE,EAAE;YACrC,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,YAAY,EAAE,CAAC,EAAE,EAAE;gBACrC,IAAM,GAAG,GAAG,CAAC,GAAG,CAAC,CAAC;gBAClB,IAAM,IAAI,GAAG,CAAC,GAAG,CAAC,GAAG,OAAO,CAAC;gBAE7B,QAAQ,IAAI,iCACQ,CAAC,qCACD,IAAI,0CAED,QAAQ,4BAAuB,QAAQ,yBACxD,UAAU,CAAC,CAAC,EAAE,IAAI,CAAC,6CACrB,CAAC;gBAEL,IAAI,OAAO,KAAK,CAAC,EAAE;oBACjB,IAAI,GAAG,GAAG,WAAW,IAAI,CAAC,KAAK,YAAY,GAAG,CAAC,EAAE;wBAC/C,IAAI,WAAW,GAAG,CAAC,EAAE;4BACnB,QAAQ,IAAI,4BACH,UAAU,CAAC,CAAC,EAAE,IAAI,GAAG,CAAC,CAAC,2DAET,QAAQ,qBAAgB,QAAQ,+BACjD,UAAU,CAAC,CAAC,EAAE,IAAI,GAAG,CAAC,CAAC,uDACzB,CAAC;yBACN;wBAED,QAAQ,IAAI,uBACN,CAAC,SAAI,IAAI,WAAM,cAAc,CAAC,CAAC,EAAE,IAAI,EAAE,WAAW,EAAE,OAAO,CAAC,oBACjE,CAAC;qBACH;iBACF;qBAAM,IAAI,CAAC,KAAK,CAAC,EAAE;oBAClB,QAAQ,IAAI,sCACW,QAAQ,0CACzB,UAAU,CAAC,CAAC,EAAE,IAAI,GAAG,CAAC,CAAC,mDACzB,CAAC;iBACN;gBAED,IAAI,GAAG,GAAG,CAAC,EAAE;oBACX,QAAQ,IAAI,OAAK,CAAC,UAAI,IAAI,GAAG,CAAC,yBAC1B,cAAc,CAAC,CAAC,EAAE,IAAI,GAAG,CAAC,EAAE,WAAW,EAAE,OAAO,CAAC,MAAG,CAAC;iBAC1D;gBAED,IAAI,IAAI,GAAG,CAAC,IAAI,CAAC,IAAI,IAAI,GAAG,CAAC,GAAG,WAAW,EAAE;oBAC3C,QAAQ,IAAI,OAAK,CAAC,UAAI,IAAI,GAAG,CAAC,2BACxB,cAAc,CAAC,CAAC,EAAE,IAAI,GAAG,CAAC,EAAE,WAAW,EAAE,OAAO,CAAC,MAAG,CAAC;iBAC5D;gBAED,IAAI,GAAG,GAAG,WAAW,EAAE;oBACrB,QAAQ,IAAI,8BACG,CAAC,SAAI,GAAG,gBAAW,CAAC,UAAK,GAAG,iCACrC,CAAC,SAAI,GAAG,sBAAiB,CAAC,SAAI,GAAG,mBAAc,CAAC,SAAI,GAAG,sBAC5D,CAAC;oBAEF,IAAI,GAAG,GAAG,CAAC,GAAG,WAAW,EAAE;wBACzB,QAAQ,IAAI,iCACI,CAAC,UAAI,GAAG,GAAG,CAAC,iBAAW,CAAC,WAAK,GAAG,GAAG,CAAC,oCAC9C,CAAC,UAAI,GAAG,GAAG,CAAC,yCACA,CAAC,UAAI,GAAG,GAAG,CAAC,qBAAe,CAAC,UAAI,GAAG,GAAG,CAAC,WAAO,CAAC;qBAClE;iBACF;aACF;SACF;QAED,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,YAAY,EAAE,CAAC,EAAE,EAAE;YACrC,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,WAAW,EAAE,CAAC,EAAE,EAAE;gBACpC,QAAQ,IAAI,iBAAe,CAAC,SAAI,CAAC,aAAQ,CAAC,SAAI,CAAC,MAAG,CAAC;aACpD;SACF;QAED,IAAI,CAAC,QAAQ,GAAG,yCACgB,YAAY,UAAK,WAAW,2CAC/B,MAAM,UAAK,OAAO,iWAczC,QAAQ,kDAIb,CAAC;IACJ,CAAC;IACH,mCAAC;AAAD,CAAC,AAlID,IAkIC;AAlIY,oEAA4B;AAoIzC,oBAAoB,CAAS,EAAE,CAAS;IACtC,OAAO,YAAU,CAAC,UAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,OAAO,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC,CAAC,CAAC,CAAC,CAAE,CAAC;AACvE,CAAC;AAQD,wBACI,CAAS,EAAE,CAAS,EAAE,MAAc,EAAE,OAAe;IACvD,IAAI,MAAM,KAAK,CAAC,EAAE;QAChB,IAAI,OAAO,GAAG,CAAC,KAAK,CAAC,GAAG,CAAC,EAAE;YACzB,OAAO,UAAU,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;SACzB;QACD,OAAO,UAAQ,UAAU,CAAC,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,aAAQ,UAAU,CAAC,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,SAAM,CAAC;KACvE;IAED,IAAI,OAAO,GAAG,CAAC,KAAK,CAAC,GAAG,CAAC,EAAE;QACzB,OAAO,UAAQ,UAAU,CAAC,CAAC,EAAE,CAAC,CAAC,aAAQ,UAAU,CAAC,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,SAAM,CAAC;KACnE;IACD,OAAO,UAAQ,UAAU,CAAC,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,aAAQ,UAAU,CAAC,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,SAAM,CAAC;AACxE,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/crop_and_resize_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/crop_and_resize_gpu.d.ts
new file mode 100644
index 0000000..d705901
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/crop_and_resize_gpu.d.ts
@@ -0,0 +1,7 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class CropAndResizeProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(imageShape: [number, number, number, number], boxShape: [number, number], cropSize: [number, number], method: 'bilinear' | 'nearest', extrapolationValue: number);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/crop_and_resize_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/crop_and_resize_gpu.js
new file mode 100644
index 0000000..7c8a692
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/crop_and_resize_gpu.js
@@ -0,0 +1,40 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var CropAndResizeProgram = (function () {
+    function CropAndResizeProgram(imageShape, boxShape, cropSize, method, extrapolationValue) {
+        this.variableNames = ['Image', 'Boxes', 'BoxInd'];
+        this.outputShape = [];
+        var batch = imageShape[0], imageHeight = imageShape[1], imageWidth = imageShape[2], depth = imageShape[3];
+        var numBoxes = boxShape[0];
+        var cropHeight = cropSize[0], cropWidth = cropSize[1];
+        this.outputShape = [numBoxes, cropHeight, cropWidth, depth];
+        var methodId = method === 'bilinear' ? 1 : 0;
+        var _a = [imageHeight - 1 + ".0", imageWidth - 1 + ".0"], inputHeightFloat = _a[0], inputWidthFloat = _a[1];
+        var _b = cropHeight > 1 ?
+            [
+                "" + (imageHeight - 1) / (cropHeight - 1),
+                '(y2-y1) * height_ratio',
+                "y1*" + inputHeightFloat + " + float(y)*(height_scale)",
+            ] :
+            [
+                '0.0',
+                '0.0',
+                "0.5 * (y1+y2) * " + inputHeightFloat,
+            ], heightRatio = _b[0], heightScale = _b[1], inY = _b[2];
+        var _c = cropWidth > 1 ?
+            [
+                "" + (imageWidth - 1) / (cropWidth - 1),
+                '(x2-x1) * width_ratio',
+                "x1*" + inputWidthFloat + " + float(x)*(width_scale)",
+            ] :
+            [
+                '0.0',
+                '0.0',
+                "0.5 * (x1+x2) * " + inputWidthFloat,
+            ], widthRatio = _c[0], widthScale = _c[1], inX = _c[2];
+        this.userCode = "\n      const float height_ratio = float(" + heightRatio + ");\n      const float width_ratio = float(" + widthRatio + ");\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= " + batch + ") {\n          return;\n        }\n\n        float height_scale = " + heightScale + ";\n        float width_scale = " + widthScale + ";\n\n        float in_y = " + inY + ";\n        if( in_y < 0.0 || in_y > " + inputHeightFloat + " ) {\n          setOutput(float(" + extrapolationValue + "));\n          return;\n        }\n        float in_x = " + inX + ";\n        if( in_x < 0.0 || in_x > " + inputWidthFloat + " ) {\n          setOutput(float(" + extrapolationValue + "));\n          return;\n        }\n\n        vec2 sourceFracIndexRC = vec2(in_y,in_x);\n        if(" + methodId + " == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n          ivec2 sourceCeilRC = ivec2(ceil(sourceFracIndexRC));\n\n          float topLeft = getImage(b, sourceFloorRC.x, sourceFloorRC.y, d);\n          float bottomLeft = getImage(b, sourceCeilRC.x, sourceFloorRC.y, d);\n          float topRight = getImage(b, sourceFloorRC.x, sourceCeilRC.y, d);\n          float bottomRight = getImage(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n          vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n          float top = topLeft + (topRight - topLeft) * fracRC.y;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n          float newValue = top + (bottom - top) * fracRC.x;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestRC = ivec2(floor(\n            sourceFracIndexRC + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestRC.x, sourceNearestRC.y, d);\n          setOutput(newValue);\n        }\n      }\n    ";
+    }
+    return CropAndResizeProgram;
+}());
+exports.CropAndResizeProgram = CropAndResizeProgram;
+//# sourceMappingURL=crop_and_resize_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/crop_and_resize_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/crop_and_resize_gpu.js.map
new file mode 100644
index 0000000..071b779
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/crop_and_resize_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"crop_and_resize_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/crop_and_resize_gpu.ts"],"names":[],"mappings":";;AAmBA;IAKE,8BACE,UAA4C,EAAE,QAA0B,EACxE,QAA0B,EAAE,MAA8B,EAC1D,kBAA0B;QAP5B,kBAAa,GAAG,CAAC,OAAO,EAAE,OAAO,EAAE,QAAQ,CAAC,CAAC;QAC7C,gBAAW,GAAa,EAAE,CAAC;QAOlB,IAAA,qBAAK,EAAE,2BAAW,EAAE,0BAAU,EAAE,qBAAK,CAAe;QACpD,IAAA,sBAAQ,CAAc;QACtB,IAAA,wBAAU,EAAE,uBAAS,CAAa;QACzC,IAAI,CAAC,WAAW,GAAG,CAAC,QAAQ,EAAE,UAAU,EAAE,SAAS,EAAE,KAAK,CAAC,CAAC;QAC5D,IAAM,QAAQ,GAAG,MAAM,KAAK,UAAU,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QAEzC,IAAA,oDAC2C,EAD1C,wBAAgB,EAAE,uBAAe,CACU;QAE5C,IAAA;;;;;;;;;;aAUH,EAVI,mBAAW,EAAE,mBAAW,EAAE,WAAG,CAUhC;QACE,IAAA;;;;;;;;;;aAUH,EAVI,kBAAU,EAAE,kBAAU,EAAE,WAAG,CAU9B;QAKJ,IAAI,CAAC,QAAQ,GAAG,8CACqB,WAAW,kDACZ,UAAU,kdAgBjB,KAAK,0EAIP,WAAW,uCACZ,UAAU,kCAEjB,GAAG,4CACS,gBAAgB,wCACvB,kBAAkB,gEAGvB,GAAG,4CACS,eAAe,wCACtB,kBAAkB,2GAKjC,QAAQ,umCAwBhB,CAAC;IACJ,CAAC;IACH,2BAAC;AAAD,CAAC,AA3GD,IA2GC;AA3GY,oDAAoB"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/cumsum_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/cumsum_gpu.d.ts
new file mode 100644
index 0000000..ed75d92
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/cumsum_gpu.d.ts
@@ -0,0 +1,7 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class CumSumProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(shape: number[], exclusive: boolean, reverse: boolean);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/cumsum_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/cumsum_gpu.js
new file mode 100644
index 0000000..9ca0f0f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/cumsum_gpu.js
@@ -0,0 +1,50 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var shader_compiler_1 = require("./shader_compiler");
+var CumSumProgram = (function () {
+    function CumSumProgram(shape, exclusive, reverse) {
+        this.variableNames = ['x'];
+        this.outputShape = shape;
+        var rank = shape.length;
+        var finalDim = shape[shape.length - 1];
+        var comparator = reverse ? '<' : '>';
+        this.userCode = "\n      int getIndex(int i) {\n        " + (reverse ? "return " + finalDim + " -i - 1;" : 'return i;') + "\n      }\n\n      void main() {\n        " + shader_compiler_1.getCoordsDataType(rank) + " coords = getOutputCoords();\n        int end = " + getFinalCoord(rank, 'coords') + ";\n        float val = 0.0;\n        for (int i = " + finalDim + " - 1; i >= 0; i -= 1) {\n          int idx = getIndex(i);\n          if (idx " + comparator + " end) {\n            continue;\n          }\n          if (idx == end && " + exclusive + ") {\n            continue;\n          }\n          " + getFinalCoord(rank, 'coords') + " = idx;\n          val += getX(" + getCoords(rank, 'coords') + ");\n        }\n        setOutput(val);\n      }\n    ";
+    }
+    return CumSumProgram;
+}());
+exports.CumSumProgram = CumSumProgram;
+function getCoords(rank, name) {
+    if (rank === 1) {
+        return "" + name;
+    }
+    else if (rank === 2) {
+        return name + ".x, " + name + ".y";
+    }
+    else if (rank === 3) {
+        return name + ".x, " + name + ".y, " + name + ".z";
+    }
+    else if (rank === 4) {
+        return name + ".x, " + name + ".y, " + name + ".z, " + name + ".w";
+    }
+    else {
+        throw Error("Cumulative sum for rank " + rank + " is not yet supported");
+    }
+}
+function getFinalCoord(rank, name) {
+    if (rank === 1) {
+        return "" + name;
+    }
+    else if (rank === 2) {
+        return name + ".y";
+    }
+    else if (rank === 3) {
+        return name + ".z";
+    }
+    else if (rank === 4) {
+        return name + ".w";
+    }
+    else {
+        throw Error("Cumulative sum for rank " + rank + " is not yet supported");
+    }
+}
+//# sourceMappingURL=cumsum_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/cumsum_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/cumsum_gpu.js.map
new file mode 100644
index 0000000..d056393
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/cumsum_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"cumsum_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/cumsum_gpu.ts"],"names":[],"mappings":";;AAkBA,qDAAoD;AAEpD;IAKE,uBAAY,KAAe,EAAE,SAAkB,EAAE,OAAgB;QAJjE,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QAKpB,IAAI,CAAC,WAAW,GAAG,KAAK,CAAC;QACzB,IAAM,IAAI,GAAG,KAAK,CAAC,MAAM,CAAC;QAC1B,IAAM,QAAQ,GAAG,KAAK,CAAC,KAAK,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC;QACzC,IAAM,UAAU,GAAG,OAAO,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,CAAC;QAEvC,IAAI,CAAC,QAAQ,GAAG,6CAEV,OAAO,CAAC,CAAC,CAAC,YAAU,QAAQ,aAAU,CAAC,CAAC,CAAC,WAAW,mDAIpD,mCAAiB,CAAC,IAAI,CAAC,wDACb,aAAa,CAAC,IAAI,EAAE,QAAQ,CAAC,0DAE1B,QAAQ,qFAEX,UAAU,iFAGA,SAAS,2DAG3B,aAAa,CAAC,IAAI,EAAE,QAAQ,CAAC,uCACjB,SAAS,CAAC,IAAI,EAAE,QAAQ,CAAC,0DAI5C,CAAC;IACJ,CAAC;IACH,oBAAC;AAAD,CAAC,AAnCD,IAmCC;AAnCY,sCAAa;AAqC1B,mBAAmB,IAAY,EAAE,IAAY;IAC3C,IAAI,IAAI,KAAK,CAAC,EAAE;QACd,OAAO,KAAG,IAAM,CAAC;KAClB;SAAM,IAAI,IAAI,KAAK,CAAC,EAAE;QACrB,OAAU,IAAI,YAAO,IAAI,OAAI,CAAC;KAC/B;SAAM,IAAI,IAAI,KAAK,CAAC,EAAE;QACrB,OAAU,IAAI,YAAO,IAAI,YAAO,IAAI,OAAI,CAAC;KAC1C;SAAM,IAAI,IAAI,KAAK,CAAC,EAAE;QACrB,OAAU,IAAI,YAAO,IAAI,YAAO,IAAI,YAAO,IAAI,OAAI,CAAC;KACrD;SAAM;QACL,MAAM,KAAK,CAAC,6BAA2B,IAAI,0BAAuB,CAAC,CAAC;KACrE;AACH,CAAC;AAED,uBAAuB,IAAY,EAAE,IAAY;IAC/C,IAAI,IAAI,KAAK,CAAC,EAAE;QACd,OAAO,KAAG,IAAM,CAAC;KAClB;SAAM,IAAI,IAAI,KAAK,CAAC,EAAE;QACrB,OAAU,IAAI,OAAI,CAAC;KACpB;SAAM,IAAI,IAAI,KAAK,CAAC,EAAE;QACrB,OAAU,IAAI,OAAI,CAAC;KACpB;SAAM,IAAI,IAAI,KAAK,CAAC,EAAE;QACrB,OAAU,IAAI,OAAI,CAAC;KACpB;SAAM;QACL,MAAM,KAAK,CAAC,6BAA2B,IAAI,0BAAuB,CAAC,CAAC;KACrE;AACH,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/depth_to_space_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/depth_to_space_gpu.d.ts
new file mode 100644
index 0000000..259cf5d
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/depth_to_space_gpu.d.ts
@@ -0,0 +1,14 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class DepthToSpaceProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    blockSize: number;
+    dataFormat: string;
+    constructor(outputShape: number[], blockSize: number, dataFormat: 'NHWC' | 'NCHW');
+    private getHeightCoordString;
+    private getWidthCoordString;
+    private getDepthCoordString;
+    private getOutputDepthSize;
+    private getInputSamplingString;
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/depth_to_space_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/depth_to_space_gpu.js
new file mode 100644
index 0000000..e74a814
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/depth_to_space_gpu.js
@@ -0,0 +1,55 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var DepthToSpaceProgram = (function () {
+    function DepthToSpaceProgram(outputShape, blockSize, dataFormat) {
+        this.variableNames = ['x'];
+        this.outputShape = [];
+        this.outputShape = outputShape;
+        this.blockSize = blockSize;
+        this.dataFormat = dataFormat;
+        this.userCode = "\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = " + this.getHeightCoordString() + ";\n      int w = " + this.getWidthCoordString() + ";\n      int d = " + this.getDepthCoordString() + ";\n\n      int in_h = h / " + blockSize + ";\n      int offset_h = imod(h, " + blockSize + ");\n      int in_w = w / " + blockSize + ";\n      int offset_w = imod(w, " + blockSize + ");\n      int offset_d = (offset_h * " + blockSize + " + offset_w) *\n        " + this.getOutputDepthSize() + ";\n      int in_d = d + offset_d;\n\n      float result = " + this.getInputSamplingString() + ";\n      setOutput(result);\n    }\n  ";
+    }
+    DepthToSpaceProgram.prototype.getHeightCoordString = function () {
+        if (this.dataFormat === 'NHWC') {
+            return "coords[1]";
+        }
+        else {
+            return "coords[2]";
+        }
+    };
+    DepthToSpaceProgram.prototype.getWidthCoordString = function () {
+        if (this.dataFormat === 'NHWC') {
+            return "coords[2]";
+        }
+        else {
+            return "coords[3]";
+        }
+    };
+    DepthToSpaceProgram.prototype.getDepthCoordString = function () {
+        if (this.dataFormat === 'NHWC') {
+            return "coords[3]";
+        }
+        else {
+            return "coords[1]";
+        }
+    };
+    DepthToSpaceProgram.prototype.getOutputDepthSize = function () {
+        if (this.dataFormat === 'NHWC') {
+            return this.outputShape[3];
+        }
+        else {
+            return this.outputShape[1];
+        }
+    };
+    DepthToSpaceProgram.prototype.getInputSamplingString = function () {
+        if (this.dataFormat === 'NHWC') {
+            return "getX(b, in_h, in_w, in_d)";
+        }
+        else {
+            return "getX(b, in_d, in_h, in_w)";
+        }
+    };
+    return DepthToSpaceProgram;
+}());
+exports.DepthToSpaceProgram = DepthToSpaceProgram;
+//# sourceMappingURL=depth_to_space_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/depth_to_space_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/depth_to_space_gpu.js.map
new file mode 100644
index 0000000..99bdb07
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/depth_to_space_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"depth_to_space_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/depth_to_space_gpu.ts"],"names":[],"mappings":";;AAmBA;IAOE,6BACI,WAAqB,EAAE,SAAiB,EAAE,UAAyB;QAPvE,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QACtB,gBAAW,GAAa,EAAE,CAAC;QAOzB,IAAI,CAAC,WAAW,GAAG,WAAW,CAAC;QAC/B,IAAI,CAAC,SAAS,GAAG,SAAS,CAAC;QAC3B,IAAI,CAAC,UAAU,GAAG,UAAU,CAAC;QAC7B,IAAI,CAAC,QAAQ,GAAG,2GAIJ,IAAI,CAAC,oBAAoB,EAAE,yBAC3B,IAAI,CAAC,mBAAmB,EAAE,yBAC1B,IAAI,CAAC,mBAAmB,EAAE,kCAEnB,SAAS,wCACD,SAAS,iCACjB,SAAS,wCACD,SAAS,6CACL,SAAS,gCAClC,IAAI,CAAC,kBAAkB,EAAE,kEAGZ,IAAI,CAAC,sBAAsB,EAAE,2CAGjD,CAAC;IACF,CAAC;IAEO,kDAAoB,GAA5B;QACE,IAAI,IAAI,CAAC,UAAU,KAAK,MAAM,EAAE;YAC9B,OAAO,WAAW,CAAC;SACpB;aAAM;YACL,OAAO,WAAW,CAAC;SACpB;IACH,CAAC;IAEO,iDAAmB,GAA3B;QACE,IAAI,IAAI,CAAC,UAAU,KAAK,MAAM,EAAE;YAC9B,OAAO,WAAW,CAAC;SACpB;aAAM;YACL,OAAO,WAAW,CAAC;SACpB;IACH,CAAC;IAEO,iDAAmB,GAA3B;QACE,IAAI,IAAI,CAAC,UAAU,KAAK,MAAM,EAAE;YAC9B,OAAO,WAAW,CAAC;SACpB;aAAM;YACL,OAAO,WAAW,CAAC;SACpB;IACH,CAAC;IAEO,gDAAkB,GAA1B;QACE,IAAI,IAAI,CAAC,UAAU,KAAK,MAAM,EAAE;YAC9B,OAAO,IAAI,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC;SAC5B;aAAM;YACL,OAAO,IAAI,CAAC,WAAW,CAAC,CAAC,CAAC,CAAC;SAC5B;IACH,CAAC;IAEO,oDAAsB,GAA9B;QACE,IAAI,IAAI,CAAC,UAAU,KAAK,MAAM,EAAE;YAC9B,OAAO,2BAA2B,CAAC;SACpC;aAAM;YACL,OAAO,2BAA2B,CAAC;SACpC;IACH,CAAC;IACH,0BAAC;AAAD,CAAC,AAzED,IAyEC;AAzEY,kDAAmB"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/encode_float_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/encode_float_gpu.d.ts
new file mode 100644
index 0000000..9926d6a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/encode_float_gpu.d.ts
@@ -0,0 +1,7 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class EncodeFloatProgram implements GPGPUProgram {
+    variableNames: string[];
+    userCode: string;
+    outputShape: number[];
+    constructor(outputShape: number[]);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/encode_float_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/encode_float_gpu.js
new file mode 100644
index 0000000..9f724b3
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/encode_float_gpu.js
@@ -0,0 +1,12 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var EncodeFloatProgram = (function () {
+    function EncodeFloatProgram(outputShape) {
+        this.variableNames = ['A'];
+        this.outputShape = outputShape;
+        this.userCode = "\n      const float FLOAT_MAX = 1.70141184e38;\n      const float FLOAT_MIN = 1.17549435e-38;\n\n      lowp vec4 encode_float(highp float v) {\n        if (isNaN(v)) {\n          return vec4(255, 255, 255, 255);\n        }\n\n        highp float av = abs(v);\n\n        if(av < FLOAT_MIN) {\n          return vec4(0.0, 0.0, 0.0, 0.0);\n        } else if(v > FLOAT_MAX) {\n          return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n        } else if(v < -FLOAT_MAX) {\n          return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n        }\n\n        highp vec4 c = vec4(0,0,0,0);\n\n        highp float e = floor(log2(av));\n        highp float m = exp2(fract(log2(av))) - 1.0;\n\n        c[2] = floor(128.0 * m);\n        m -= c[2] / 128.0;\n        c[1] = floor(32768.0 * m);\n        m -= c[1] / 32768.0;\n        c[0] = floor(8388608.0 * m);\n\n        highp float ebias = e + 127.0;\n        c[3] = floor(ebias / 2.0);\n        ebias -= c[3] * 2.0;\n        c[2] += floor(ebias) * 128.0;\n\n        c[3] += 128.0 * step(0.0, -v);\n\n        return c / 255.0;\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        gl_FragColor = encode_float(x);\n      }\n    ";
+    }
+    return EncodeFloatProgram;
+}());
+exports.EncodeFloatProgram = EncodeFloatProgram;
+//# sourceMappingURL=encode_float_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/encode_float_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/encode_float_gpu.js.map
new file mode 100644
index 0000000..0f4b518
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/encode_float_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"encode_float_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/encode_float_gpu.ts"],"names":[],"mappings":";;AAmBA;IAKE,4BAAY,WAAqB;QAJjC,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QAKpB,IAAI,CAAC,WAAW,GAAG,WAAW,CAAC;QAC/B,IAAI,CAAC,QAAQ,GAAG,gqCA4Cf,CAAC;IACJ,CAAC;IACH,yBAAC;AAAD,CAAC,AArDD,IAqDC;AArDY,gDAAkB"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/fft_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/fft_gpu.d.ts
new file mode 100644
index 0000000..4cb382c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/fft_gpu.d.ts
@@ -0,0 +1,11 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare const COMPLEX_FFT: {
+    REAL: string;
+    IMAG: string;
+};
+export declare class FFTProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(op: string, inputShape: [number, number], inverse: boolean);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/fft_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/fft_gpu.js
new file mode 100644
index 0000000..2490a3b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/fft_gpu.js
@@ -0,0 +1,19 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+exports.COMPLEX_FFT = {
+    REAL: 'return real * expR - imag * expI;',
+    IMAG: 'return real * expI + imag * expR;'
+};
+var FFTProgram = (function () {
+    function FFTProgram(op, inputShape, inverse) {
+        this.variableNames = ['real', 'imag'];
+        var innerDim = inputShape[1];
+        this.outputShape = inputShape;
+        var exponentMultiplierSnippet = inverse ? "2.0 * " + Math.PI : "-2.0 * " + Math.PI;
+        var resultDenominator = inverse ? innerDim + ".0" : '1.0';
+        this.userCode = "\n      const float exponentMultiplier = " + exponentMultiplierSnippet + ";\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        " + op + "\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float(" + innerDim + ");\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < " + innerDim + "; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / " + resultDenominator + ";\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    ";
+    }
+    return FFTProgram;
+}());
+exports.FFTProgram = FFTProgram;
+//# sourceMappingURL=fft_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/fft_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/fft_gpu.js.map
new file mode 100644
index 0000000..9cf4cc6
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/fft_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"fft_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/fft_gpu.ts"],"names":[],"mappings":";;AAmBa,QAAA,WAAW,GAAG;IACzB,IAAI,EAAE,mCAAmC;IACzC,IAAI,EAAE,mCAAmC;CAC1C,CAAC;AAEF;IAKE,oBAAY,EAAU,EAAE,UAA4B,EAAE,OAAgB;QAJtE,kBAAa,GAAG,CAAC,MAAM,EAAE,MAAM,CAAC,CAAC;QAK/B,IAAM,QAAQ,GAAG,UAAU,CAAC,CAAC,CAAC,CAAC;QAC/B,IAAI,CAAC,WAAW,GAAG,UAAU,CAAC;QAE9B,IAAM,yBAAyB,GAC3B,OAAO,CAAC,CAAC,CAAC,WAAS,IAAI,CAAC,EAAI,CAAC,CAAC,CAAC,YAAU,IAAI,CAAC,EAAI,CAAC;QACvD,IAAM,iBAAiB,GAAG,OAAO,CAAC,CAAC,CAAI,QAAQ,OAAI,CAAC,CAAC,CAAC,KAAK,CAAC;QAE5D,IAAI,CAAC,QAAQ,GAAG,8CACqB,yBAAyB,mGAGxD,EAAE,oHAIsC,QAAQ,0KAM5B,QAAQ,wWASmB,iBAAiB,0LAUrE,CAAC;IACJ,CAAC;IACH,iBAAC;AAAD,CAAC,AAhDD,IAgDC;AAhDY,gCAAU"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/from_pixels_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/from_pixels_gpu.d.ts
new file mode 100644
index 0000000..3b3e5cb
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/from_pixels_gpu.d.ts
@@ -0,0 +1,7 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class FromPixelsProgram implements GPGPUProgram {
+    variableNames: string[];
+    userCode: string;
+    outputShape: number[];
+    constructor(outputShape: number[]);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/from_pixels_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/from_pixels_gpu.js
new file mode 100644
index 0000000..e0c8a20
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/from_pixels_gpu.js
@@ -0,0 +1,13 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var FromPixelsProgram = (function () {
+    function FromPixelsProgram(outputShape) {
+        this.variableNames = ['A'];
+        var height = outputShape[0], width = outputShape[1];
+        this.outputShape = outputShape;
+        this.userCode = "\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(" + width + ".0, " + height + ".0);\n\n        vec4 values = texture2D(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    ";
+    }
+    return FromPixelsProgram;
+}());
+exports.FromPixelsProgram = FromPixelsProgram;
+//# sourceMappingURL=from_pixels_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/from_pixels_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/from_pixels_gpu.js.map
new file mode 100644
index 0000000..eedb16e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/from_pixels_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"from_pixels_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/from_pixels_gpu.ts"],"names":[],"mappings":";;AAmBA;IAKE,2BAAY,WAAqB;QAJjC,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QAKb,IAAA,uBAAM,EAAE,sBAAK,CAAkB;QACtC,IAAI,CAAC,WAAW,GAAG,WAAW,CAAC;QAC/B,IAAI,CAAC,QAAQ,GAAG,0NAMmC,KAAK,YAAO,MAAM,2YAgBpE,CAAC;IACJ,CAAC;IACH,wBAAC;AAAD,CAAC,AAhCD,IAgCC;AAhCY,8CAAiB"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gather_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gather_gpu.d.ts
new file mode 100644
index 0000000..976477c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gather_gpu.d.ts
@@ -0,0 +1,8 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class GatherProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    rank: number;
+    constructor(aShape: number[], indicesLength: number, axis: number);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gather_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gather_gpu.js
new file mode 100644
index 0000000..2d70034
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gather_gpu.js
@@ -0,0 +1,38 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var shader_compiler_1 = require("./shader_compiler");
+var GatherProgram = (function () {
+    function GatherProgram(aShape, indicesLength, axis) {
+        this.variableNames = ['A', 'indices'];
+        var outputShape = aShape.slice();
+        outputShape[axis] = indicesLength;
+        this.outputShape = outputShape;
+        this.rank = outputShape.length;
+        var dtype = shader_compiler_1.getCoordsDataType(this.rank);
+        var sourceCoords = getSourceCoords(aShape, axis);
+        this.userCode = "\n      void main() {\n        " + dtype + " resRC = getOutputCoords();\n        setOutput(getA(" + sourceCoords + "));\n      }\n    ";
+    }
+    return GatherProgram;
+}());
+exports.GatherProgram = GatherProgram;
+function getSourceCoords(aShape, axis) {
+    var rank = aShape.length;
+    if (rank > 4) {
+        throw Error("Gather for rank " + rank + " is not yet supported");
+    }
+    if (rank === 1) {
+        return "int(getIndices(resRC))";
+    }
+    var currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];
+    var sourceCoords = [];
+    for (var i = 0; i < aShape.length; i++) {
+        if (i === axis) {
+            sourceCoords.push("int(getIndices(" + currentCoords[i] + "))");
+        }
+        else {
+            sourceCoords.push("" + currentCoords[i]);
+        }
+    }
+    return sourceCoords.join();
+}
+//# sourceMappingURL=gather_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gather_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gather_gpu.js.map
new file mode 100644
index 0000000..eb5fed5
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gather_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"gather_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/gather_gpu.ts"],"names":[],"mappings":";;AAkBA,qDAAoD;AAEpD;IAME,uBAAY,MAAgB,EAAE,aAAqB,EAAE,IAAY;QALjE,kBAAa,GAAG,CAAC,GAAG,EAAE,SAAS,CAAC,CAAC;QAM/B,IAAM,WAAW,GAAa,MAAM,CAAC,KAAK,EAAE,CAAC;QAC7C,WAAW,CAAC,IAAI,CAAC,GAAG,aAAa,CAAC;QAClC,IAAI,CAAC,WAAW,GAAG,WAAW,CAAC;QAC/B,IAAI,CAAC,IAAI,GAAG,WAAW,CAAC,MAAM,CAAC;QAC/B,IAAM,KAAK,GAAG,mCAAiB,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;QAC3C,IAAM,YAAY,GAAG,eAAe,CAAC,MAAM,EAAE,IAAI,CAAC,CAAC;QAEnD,IAAI,CAAC,QAAQ,GAAG,oCAEV,KAAK,4DACU,YAAY,uBAEhC,CAAC;IACJ,CAAC;IACH,oBAAC;AAAD,CAAC,AArBD,IAqBC;AArBY,sCAAa;AAuB1B,yBAAyB,MAAgB,EAAE,IAAY;IACrD,IAAM,IAAI,GAAG,MAAM,CAAC,MAAM,CAAC;IAC3B,IAAI,IAAI,GAAG,CAAC,EAAE;QACZ,MAAM,KAAK,CAAC,qBAAmB,IAAI,0BAAuB,CAAC,CAAC;KAC7D;IACD,IAAI,IAAI,KAAK,CAAC,EAAE;QACd,OAAO,wBAAwB,CAAC;KACjC;IAED,IAAM,aAAa,GAAG,CAAC,SAAS,EAAE,SAAS,EAAE,SAAS,EAAE,SAAS,CAAC,CAAC;IAEnE,IAAM,YAAY,GAAG,EAAE,CAAC;IACxB,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,MAAM,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;QACtC,IAAI,CAAC,KAAK,IAAI,EAAE;YACd,YAAY,CAAC,IAAI,CAAC,oBAAkB,aAAa,CAAC,CAAC,CAAC,OAAI,CAAC,CAAC;SAC3D;aAAM;YACL,YAAY,CAAC,IAAI,CAAC,KAAG,aAAa,CAAC,CAAC,CAAG,CAAC,CAAC;SAC1C;KACF;IACD,OAAO,YAAY,CAAC,IAAI,EAAE,CAAC;AAC7B,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gather_nd_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gather_nd_gpu.d.ts
new file mode 100644
index 0000000..114eefd
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gather_nd_gpu.d.ts
@@ -0,0 +1,9 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class GatherNDProgram implements GPGPUProgram {
+    private sliceDim;
+    private strides;
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(sliceDim: number, strides: number[], shape: number[]);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gather_nd_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gather_nd_gpu.js
new file mode 100644
index 0000000..b2999c6
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gather_nd_gpu.js
@@ -0,0 +1,18 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var shader_compiler_1 = require("./shader_compiler");
+var GatherNDProgram = (function () {
+    function GatherNDProgram(sliceDim, strides, shape) {
+        this.sliceDim = sliceDim;
+        this.strides = strides;
+        this.variableNames = ['x', 'indices'];
+        this.outputShape = shape;
+        var stridesType = shader_compiler_1.getCoordsDataType(strides.length);
+        var dtype = shader_compiler_1.getCoordsDataType(shape.length);
+        var strideString = this.sliceDim > 1 ? 'strides[j]' : 'strides';
+        this.userCode = "\n        " + stridesType + " strides = " + stridesType + "(" + this.strides + ");\n         void main() {\n          " + dtype + " coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < " + this.sliceDim + "; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * " + strideString + ";\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      ";
+    }
+    return GatherNDProgram;
+}());
+exports.GatherNDProgram = GatherNDProgram;
+//# sourceMappingURL=gather_nd_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gather_nd_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gather_nd_gpu.js.map
new file mode 100644
index 0000000..29ac9b7
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gather_nd_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"gather_nd_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/gather_nd_gpu.ts"],"names":[],"mappings":";;AAiBA,qDAAoD;AAEpD;IAIE,yBACY,QAAgB,EAAU,OAAiB,EAAE,KAAe;QAA5D,aAAQ,GAAR,QAAQ,CAAQ;QAAU,YAAO,GAAP,OAAO,CAAU;QAJvD,kBAAa,GAAG,CAAC,GAAG,EAAE,SAAS,CAAC,CAAC;QAK/B,IAAI,CAAC,WAAW,GAAG,KAAK,CAAC;QACzB,IAAM,WAAW,GAAG,mCAAiB,CAAC,OAAO,CAAC,MAAM,CAAC,CAAC;QACtD,IAAM,KAAK,GAAG,mCAAiB,CAAC,KAAK,CAAC,MAAM,CAAC,CAAC;QAC9C,IAAM,YAAY,GAAG,IAAI,CAAC,QAAQ,GAAG,CAAC,CAAC,CAAC,CAAC,YAAY,CAAC,CAAC,CAAC,SAAS,CAAC;QAClE,IAAI,CAAC,QAAQ,GAAG,eACV,WAAW,mBAAc,WAAW,SAAI,IAAI,CAAC,OAAO,8CAElD,KAAK,qGAEe,IAAI,CAAC,QAAQ,gHAEP,YAAY,2FAI3C,CAAC;IACN,CAAC;IACH,sBAAC;AAAD,CAAC,AAvBD,IAuBC;AAvBY,0CAAe"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_context.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_context.d.ts
new file mode 100644
index 0000000..d7a4076
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_context.d.ts
@@ -0,0 +1,79 @@
+/// <reference types="webgl2" />
+import { WebGL1DisjointQueryTimerExtension, WebGL2DisjointQueryTimerExtension } from './webgl_types';
+export interface FenceContext {
+    query: WebGLQuery | WebGLSync;
+    isFencePassed(): boolean;
+}
+export declare class GPGPUContext {
+    gl: WebGLRenderingContext;
+    textureFloatExtension: {};
+    textureHalfFloatExtension: {};
+    colorBufferFloatExtension: {};
+    colorBufferHalfFloatExtension: {};
+    getBufferSubDataAsyncExtension: {};
+    disjointQueryTimerExtension: WebGL2DisjointQueryTimerExtension | WebGL1DisjointQueryTimerExtension;
+    vertexBuffer: WebGLBuffer;
+    indexBuffer: WebGLBuffer;
+    framebuffer: WebGLFramebuffer;
+    outputTexture: WebGLTexture | null;
+    program: WebGLProgram | null;
+    private disposed;
+    private autoDebugValidate;
+    private disjoint;
+    private textureConfig;
+    constructor(gl?: WebGLRenderingContext);
+    dispose(): void;
+    enableAutomaticDebugValidation(enabled: boolean): void;
+    createFloat32MatrixTexture(rows: number, columns: number): WebGLTexture;
+    createFloat16MatrixTexture(rows: number, columns: number): WebGLTexture;
+    createUnsignedBytesMatrixTexture(rows: number, columns: number): WebGLTexture;
+    uploadPixelDataToTexture(texture: WebGLTexture, pixels: ImageData | HTMLImageElement | HTMLCanvasElement): void;
+    createFloat16PackedMatrixTexture(rows: number, columns: number): WebGLTexture;
+    createPackedMatrixTexture(rows: number, columns: number): WebGLTexture;
+    deleteMatrixTexture(texture: WebGLTexture): void;
+    uploadMatrixToTexture(texture: WebGLTexture, rows: number, columns: number, matrix: Float32Array): void;
+    uploadMatrixToPackedTexture(texture: WebGLTexture, batch: number, rows: number, columns: number, physicalRows: number, physicalCols: number, matrix: Float32Array): void;
+    downloadFloat32MatrixFromOutputTexture(texture: WebGLTexture, rows: number, columns: number): Float32Array;
+    downloadByteEncodedFloatMatrixFromOutputTexture(texture: WebGLTexture, rows: number, columns: number): Float32Array;
+    downloadPackedMatrixFromBuffer(buffer: WebGLBuffer, batch: number, rows: number, columns: number, physicalRows: number, physicalCols: number): Float32Array;
+    downloadFloat32MatrixFromBuffer(buffer: WebGLBuffer, rows: number, columns: number): Float32Array;
+    maybeCreateBufferFromTexture(texture: WebGLTexture, rows: number, columns: number): WebGLBuffer | WebGLTexture;
+    createAndWaitForFence(): Promise<void>;
+    private createFence;
+    downloadMatrixFromPackedTexture(texture: WebGLTexture, batch: number, rows: number, columns: number, physicalRows: number, physicalCols: number): Float32Array;
+    private vertexAttrsAreBound;
+    createProgram(fragmentShaderSource: string): WebGLProgram;
+    deleteProgram(program: WebGLProgram): void;
+    setProgram(program: WebGLProgram | null): void;
+    getUniformLocation(program: WebGLProgram, uniformName: string, shouldThrow?: boolean): WebGLUniformLocation;
+    getAttributeLocation(program: WebGLProgram, attribute: string): number;
+    getUniformLocationNoThrow(program: WebGLProgram, uniformName: string): WebGLUniformLocation;
+    setInputMatrixTexture(inputMatrixTexture: WebGLTexture, uniformLocation: WebGLUniformLocation, textureUnit: number): void;
+    setOutputMatrixTexture(outputMatrixTexture: WebGLTexture, rows: number, columns: number): void;
+    setOutputPackedMatrixTexture(outputPackedMatrixTexture: WebGLTexture, rows: number, columns: number): void;
+    setOutputMatrixWriteRegion(startRow: number, numRows: number, startColumn: number, numColumns: number): void;
+    setOutputPackedMatrixWriteRegion(startRow: number, numRows: number, startColumn: number, numColumns: number): void;
+    debugValidate(): void;
+    executeProgram(): void;
+    blockUntilAllProgramsCompleted(): void;
+    private getQueryTimerExtension;
+    private getQueryTimerExtensionWebGL2;
+    private getQueryTimerExtensionWebGL1;
+    beginQuery(): WebGLQuery;
+    endQuery(): void;
+    waitForQueryAndGetTime(query: WebGLQuery): Promise<number>;
+    private getQueryTime;
+    private isQueryAvailable;
+    pollFence(fenceContext: FenceContext): Promise<void>;
+    private itemsToPoll;
+    pollItems(): void;
+    private addItemToPoll;
+    private bindTextureToFrameBuffer;
+    private unbindTextureToFrameBuffer;
+    private downloadMatrixDriver;
+    private setOutputMatrixTextureDriver;
+    private setOutputMatrixWriteRegionDriver;
+    private throwIfDisposed;
+    private throwIfNoProgram;
+}
+export declare function binSearchLastTrue(arr: Array<() => boolean>): number;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_context.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_context.js
new file mode 100644
index 0000000..0cfd591
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_context.js
@@ -0,0 +1,484 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+Object.defineProperty(exports, "__esModule", { value: true });
+var canvas_util_1 = require("../../canvas_util");
+var environment_1 = require("../../environment");
+var util = require("../../util");
+var gpgpu_util = require("./gpgpu_util");
+var tex_util = require("./tex_util");
+var webgl_util = require("./webgl_util");
+var GPGPUContext = (function () {
+    function GPGPUContext(gl) {
+        this.outputTexture = null;
+        this.program = null;
+        this.disposed = false;
+        this.autoDebugValidate = false;
+        this.vertexAttrsAreBound = false;
+        this.itemsToPoll = [];
+        if (gl != null) {
+            this.gl = gl;
+        }
+        else {
+            this.gl = canvas_util_1.getWebGLContext(environment_1.ENV.get('WEBGL_VERSION'));
+        }
+        if (environment_1.ENV.get('WEBGL_VERSION') === 1) {
+            this.textureFloatExtension =
+                webgl_util.getExtensionOrThrow(this.gl, 'OES_texture_float');
+            this.colorBufferFloatExtension =
+                this.gl.getExtension('WEBGL_color_buffer_float');
+            if (!environment_1.ENV.get('WEBGL_RENDER_FLOAT32_ENABLED')) {
+                this.textureHalfFloatExtension =
+                    webgl_util.getExtensionOrThrow(this.gl, 'OES_texture_half_float');
+                this.colorBufferHalfFloatExtension =
+                    this.gl.getExtension('EXT_color_buffer_half_float');
+            }
+        }
+        else {
+            this.colorBufferFloatExtension =
+                webgl_util.getExtensionOrThrow(this.gl, 'EXT_color_buffer_float');
+        }
+        this.vertexBuffer = gpgpu_util.createVertexBuffer(this.gl);
+        this.indexBuffer = gpgpu_util.createIndexBuffer(this.gl);
+        this.framebuffer = webgl_util.createFramebuffer(this.gl);
+        this.textureConfig =
+            gpgpu_util.getTextureConfig(this.gl, this.textureHalfFloatExtension);
+    }
+    GPGPUContext.prototype.dispose = function () {
+        var _this = this;
+        if (this.disposed) {
+            return;
+        }
+        if (this.program != null) {
+            console.warn('Disposing a GPGPUContext that still has a bound WebGLProgram.' +
+                ' This is probably a resource leak, delete the program with ' +
+                'GPGPUContext.deleteProgram before disposing.');
+        }
+        if (this.outputTexture != null) {
+            console.warn('Disposing a GPGPUContext that still has a bound output matrix ' +
+                'texture.  This is probably a resource leak, delete the output ' +
+                'matrix texture with GPGPUContext.deleteMatrixTexture before ' +
+                'disposing.');
+        }
+        var gl = this.gl;
+        webgl_util.callAndCheck(gl, function () { return gl.finish(); });
+        webgl_util.callAndCheck(gl, function () { return gl.bindFramebuffer(gl.FRAMEBUFFER, null); });
+        webgl_util.callAndCheck(gl, function () { return gl.deleteFramebuffer(_this.framebuffer); });
+        webgl_util.callAndCheck(gl, function () { return gl.bindBuffer(gl.ARRAY_BUFFER, null); });
+        webgl_util.callAndCheck(gl, function () { return gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null); });
+        webgl_util.callAndCheck(gl, function () { return gl.deleteBuffer(_this.indexBuffer); });
+        this.disposed = true;
+    };
+    GPGPUContext.prototype.enableAutomaticDebugValidation = function (enabled) {
+        this.autoDebugValidate = enabled;
+        webgl_util.enableDebugWebGLErrorChecking(enabled);
+    };
+    GPGPUContext.prototype.createFloat32MatrixTexture = function (rows, columns) {
+        this.throwIfDisposed();
+        return gpgpu_util.createFloat32MatrixTexture(this.gl, rows, columns, this.textureConfig);
+    };
+    GPGPUContext.prototype.createFloat16MatrixTexture = function (rows, columns) {
+        this.throwIfDisposed();
+        return gpgpu_util.createFloat16MatrixTexture(this.gl, rows, columns, this.textureConfig);
+    };
+    GPGPUContext.prototype.createUnsignedBytesMatrixTexture = function (rows, columns) {
+        this.throwIfDisposed();
+        return gpgpu_util.createUnsignedBytesMatrixTexture(this.gl, rows, columns, this.textureConfig);
+    };
+    GPGPUContext.prototype.uploadPixelDataToTexture = function (texture, pixels) {
+        this.throwIfDisposed();
+        gpgpu_util.uploadPixelDataToTexture(this.gl, texture, pixels);
+    };
+    GPGPUContext.prototype.createFloat16PackedMatrixTexture = function (rows, columns) {
+        this.throwIfDisposed();
+        return gpgpu_util.createFloat16PackedMatrixTexture(this.gl, rows, columns, this.textureConfig);
+    };
+    GPGPUContext.prototype.createPackedMatrixTexture = function (rows, columns) {
+        this.throwIfDisposed();
+        return gpgpu_util.createPackedMatrixTexture(this.gl, rows, columns, this.textureConfig);
+    };
+    GPGPUContext.prototype.deleteMatrixTexture = function (texture) {
+        var _this = this;
+        this.throwIfDisposed();
+        if (this.outputTexture === texture) {
+            webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);
+            this.outputTexture = null;
+        }
+        webgl_util.callAndCheck(this.gl, function () { return _this.gl.deleteTexture(texture); });
+    };
+    GPGPUContext.prototype.uploadMatrixToTexture = function (texture, rows, columns, matrix) {
+        this.throwIfDisposed();
+        var numChannels = webgl_util.getNumChannels();
+        return gpgpu_util.uploadMatrixToTexture(this.gl, texture, rows, columns, matrix, numChannels, this.textureConfig);
+    };
+    GPGPUContext.prototype.uploadMatrixToPackedTexture = function (texture, batch, rows, columns, physicalRows, physicalCols, matrix) {
+        this.throwIfDisposed();
+        return gpgpu_util.uploadMatrixToPackedTexture(this.gl, texture, batch, rows, columns, physicalRows, physicalCols, matrix, this.textureConfig);
+    };
+    GPGPUContext.prototype.downloadFloat32MatrixFromOutputTexture = function (texture, rows, columns) {
+        var _this = this;
+        return this.downloadMatrixDriver(texture, function () { return gpgpu_util.downloadFloat32MatrixFromOutputTexture(_this.gl, rows, columns, _this.textureConfig); });
+    };
+    GPGPUContext.prototype.downloadByteEncodedFloatMatrixFromOutputTexture = function (texture, rows, columns) {
+        var _this = this;
+        return this.downloadMatrixDriver(texture, function () { return gpgpu_util.downloadByteEncodedFloatMatrixFromOutputTexture(_this.gl, rows, columns, _this.textureConfig); });
+    };
+    GPGPUContext.prototype.downloadPackedMatrixFromBuffer = function (buffer, batch, rows, columns, physicalRows, physicalCols) {
+        return gpgpu_util.downloadPackedMatrixFromBuffer(this.gl, buffer, batch, rows, columns, physicalRows, physicalCols, this.textureConfig);
+    };
+    GPGPUContext.prototype.downloadFloat32MatrixFromBuffer = function (buffer, rows, columns) {
+        return gpgpu_util.downloadFloat32MatrixFromBuffer(this.gl, buffer, rows, columns, this.textureConfig);
+    };
+    GPGPUContext.prototype.maybeCreateBufferFromTexture = function (texture, rows, columns) {
+        this.bindTextureToFrameBuffer(texture);
+        var result = gpgpu_util.maybeCreateBufferFromOutputTexture(this.gl, texture, rows, columns, this.textureConfig);
+        this.unbindTextureToFrameBuffer();
+        return result;
+    };
+    GPGPUContext.prototype.createAndWaitForFence = function () {
+        var fenceContext = this.createFence(this.gl);
+        return this.pollFence(fenceContext);
+    };
+    GPGPUContext.prototype.createFence = function (gl) {
+        var _this = this;
+        var query;
+        var isFencePassed;
+        if (environment_1.ENV.get('WEBGL_FENCE_API_ENABLED')) {
+            var gl2_1 = gl;
+            var sync_1 = gl2_1.fenceSync(gl2_1.SYNC_GPU_COMMANDS_COMPLETE, 0);
+            gl.flush();
+            isFencePassed = function () {
+                var status = gl2_1.clientWaitSync(sync_1, 0, 0);
+                return status === gl2_1.ALREADY_SIGNALED ||
+                    status === gl2_1.CONDITION_SATISFIED;
+            };
+            query = sync_1;
+        }
+        else if (environment_1.ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {
+            query = this.beginQuery();
+            this.endQuery();
+            isFencePassed = function () { return _this.isQueryAvailable(query, environment_1.ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION')); };
+        }
+        else {
+            isFencePassed = function () { return true; };
+        }
+        return { query: query, isFencePassed: isFencePassed };
+    };
+    GPGPUContext.prototype.downloadMatrixFromPackedTexture = function (texture, batch, rows, columns, physicalRows, physicalCols) {
+        var _this = this;
+        return this.downloadMatrixDriver(texture, function () { return gpgpu_util.downloadMatrixFromPackedOutputTexture(_this.gl, batch, rows, columns, physicalRows, physicalCols, _this.textureConfig); });
+    };
+    GPGPUContext.prototype.createProgram = function (fragmentShaderSource) {
+        this.throwIfDisposed();
+        var gl = this.gl;
+        var fragmentShader = webgl_util.createFragmentShader(gl, fragmentShaderSource);
+        var vertexShader = gpgpu_util.createVertexShader(gl);
+        var program = webgl_util.createProgram(gl);
+        webgl_util.callAndCheck(gl, function () { return gl.attachShader(program, vertexShader); });
+        webgl_util.callAndCheck(gl, function () { return gl.attachShader(program, fragmentShader); });
+        webgl_util.linkProgram(gl, program);
+        if (this.autoDebugValidate) {
+            webgl_util.validateProgram(gl, program);
+        }
+        if (!this.vertexAttrsAreBound) {
+            this.setProgram(program);
+            this.vertexAttrsAreBound = gpgpu_util.bindVertexProgramAttributeStreams(gl, this.program, this.vertexBuffer);
+        }
+        return program;
+    };
+    GPGPUContext.prototype.deleteProgram = function (program) {
+        var _this = this;
+        this.throwIfDisposed();
+        if (program === this.program) {
+            this.program = null;
+        }
+        if (program != null) {
+            webgl_util.callAndCheck(this.gl, function () { return _this.gl.deleteProgram(program); });
+        }
+    };
+    GPGPUContext.prototype.setProgram = function (program) {
+        var _this = this;
+        this.throwIfDisposed();
+        this.program = program;
+        if ((this.program != null) && this.autoDebugValidate) {
+            webgl_util.validateProgram(this.gl, this.program);
+        }
+        webgl_util.callAndCheck(this.gl, function () { return _this.gl.useProgram(program); });
+    };
+    GPGPUContext.prototype.getUniformLocation = function (program, uniformName, shouldThrow) {
+        if (shouldThrow === void 0) { shouldThrow = true; }
+        this.throwIfDisposed();
+        if (shouldThrow) {
+            return webgl_util.getProgramUniformLocationOrThrow(this.gl, program, uniformName);
+        }
+        else {
+            return webgl_util.getProgramUniformLocation(this.gl, program, uniformName);
+        }
+    };
+    GPGPUContext.prototype.getAttributeLocation = function (program, attribute) {
+        var _this = this;
+        this.throwIfDisposed();
+        return webgl_util.callAndCheck(this.gl, function () { return _this.gl.getAttribLocation(program, attribute); });
+    };
+    GPGPUContext.prototype.getUniformLocationNoThrow = function (program, uniformName) {
+        this.throwIfDisposed();
+        return this.gl.getUniformLocation(program, uniformName);
+    };
+    GPGPUContext.prototype.setInputMatrixTexture = function (inputMatrixTexture, uniformLocation, textureUnit) {
+        this.throwIfDisposed();
+        this.throwIfNoProgram();
+        webgl_util.bindTextureToProgramUniformSampler(this.gl, this.program, inputMatrixTexture, uniformLocation, textureUnit);
+    };
+    GPGPUContext.prototype.setOutputMatrixTexture = function (outputMatrixTexture, rows, columns) {
+        this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);
+    };
+    GPGPUContext.prototype.setOutputPackedMatrixTexture = function (outputPackedMatrixTexture, rows, columns) {
+        this.throwIfDisposed();
+        var _a = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns), width = _a[0], height = _a[1];
+        this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);
+    };
+    GPGPUContext.prototype.setOutputMatrixWriteRegion = function (startRow, numRows, startColumn, numColumns) {
+        this.setOutputMatrixWriteRegionDriver(startColumn, startRow, numColumns, numRows);
+    };
+    GPGPUContext.prototype.setOutputPackedMatrixWriteRegion = function (startRow, numRows, startColumn, numColumns) {
+        throw new Error('setOutputPackedMatrixWriteRegion not implemented.');
+    };
+    GPGPUContext.prototype.debugValidate = function () {
+        if (this.program != null) {
+            webgl_util.validateProgram(this.gl, this.program);
+        }
+        webgl_util.validateFramebuffer(this.gl);
+    };
+    GPGPUContext.prototype.executeProgram = function () {
+        this.throwIfDisposed();
+        this.throwIfNoProgram();
+        var gl = this.gl;
+        if (this.autoDebugValidate) {
+            this.debugValidate();
+        }
+        webgl_util.callAndCheck(gl, function () { return gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0); });
+    };
+    GPGPUContext.prototype.blockUntilAllProgramsCompleted = function () {
+        var _this = this;
+        this.throwIfDisposed();
+        webgl_util.callAndCheck(this.gl, function () { return _this.gl.finish(); });
+    };
+    GPGPUContext.prototype.getQueryTimerExtension = function () {
+        if (this.disjointQueryTimerExtension == null) {
+            this.disjointQueryTimerExtension =
+                webgl_util.getExtensionOrThrow(this.gl, environment_1.ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2 ?
+                    'EXT_disjoint_timer_query_webgl2' :
+                    'EXT_disjoint_timer_query');
+        }
+        return this.disjointQueryTimerExtension;
+    };
+    GPGPUContext.prototype.getQueryTimerExtensionWebGL2 = function () {
+        return this.getQueryTimerExtension();
+    };
+    GPGPUContext.prototype.getQueryTimerExtensionWebGL1 = function () {
+        return this.getQueryTimerExtension();
+    };
+    GPGPUContext.prototype.beginQuery = function () {
+        if (environment_1.ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {
+            var gl2 = this.gl;
+            var ext_1 = this.getQueryTimerExtensionWebGL2();
+            var query_1 = gl2.createQuery();
+            gl2.beginQuery(ext_1.TIME_ELAPSED_EXT, query_1);
+            return query_1;
+        }
+        var ext = this.getQueryTimerExtensionWebGL1();
+        var query = ext.createQueryEXT();
+        ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);
+        return query;
+    };
+    GPGPUContext.prototype.endQuery = function () {
+        if (environment_1.ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {
+            var gl2 = this.gl;
+            var ext_2 = this.getQueryTimerExtensionWebGL2();
+            gl2.endQuery(ext_2.TIME_ELAPSED_EXT);
+            return;
+        }
+        var ext = this.getQueryTimerExtensionWebGL1();
+        ext.endQueryEXT(ext.TIME_ELAPSED_EXT);
+    };
+    GPGPUContext.prototype.waitForQueryAndGetTime = function (query) {
+        return __awaiter(this, void 0, void 0, function () {
+            var _this = this;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0: return [4, util.repeatedTry(function () { return _this.disposed ||
+                            _this.isQueryAvailable(query, environment_1.ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION')); })];
+                    case 1:
+                        _a.sent();
+                        return [2, this.getQueryTime(query, environment_1.ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'))];
+                }
+            });
+        });
+    };
+    GPGPUContext.prototype.getQueryTime = function (query, queryTimerVersion) {
+        if (queryTimerVersion === 0) {
+            return null;
+        }
+        if (queryTimerVersion === 2) {
+            var gl2 = this.gl;
+            var timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);
+            return timeElapsedNanos / 1000000;
+        }
+        else {
+            var ext = this.getQueryTimerExtensionWebGL1();
+            var timeElapsedNanos = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);
+            return timeElapsedNanos / 1000000;
+        }
+    };
+    GPGPUContext.prototype.isQueryAvailable = function (query, queryTimerVersion) {
+        if (queryTimerVersion === 0) {
+            return true;
+        }
+        if (queryTimerVersion === 2) {
+            var gl2 = this.gl;
+            var ext = this.getQueryTimerExtensionWebGL2();
+            var available = gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);
+            if (this.disjoint == null) {
+                this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);
+            }
+            return available && !this.disjoint;
+        }
+        else {
+            var ext = this.getQueryTimerExtensionWebGL1();
+            var available = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);
+            if (this.disjoint == null) {
+                this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);
+            }
+            return available && !this.disjoint;
+        }
+    };
+    GPGPUContext.prototype.pollFence = function (fenceContext) {
+        var _this = this;
+        return new Promise(function (resolve) {
+            _this.addItemToPoll(function () { return fenceContext.isFencePassed(); }, function () { return resolve(); });
+        });
+    };
+    GPGPUContext.prototype.pollItems = function () {
+        var index = binSearchLastTrue(this.itemsToPoll.map(function (x) { return x.isDoneFn; }));
+        for (var i = 0; i <= index; ++i) {
+            var resolveFn = this.itemsToPoll[i].resolveFn;
+            resolveFn();
+        }
+        this.itemsToPoll = this.itemsToPoll.slice(index + 1);
+    };
+    GPGPUContext.prototype.addItemToPoll = function (isDoneFn, resolveFn) {
+        var _this = this;
+        this.itemsToPoll.push({ isDoneFn: isDoneFn, resolveFn: resolveFn });
+        if (this.itemsToPoll.length > 1) {
+            return;
+        }
+        util.repeatedTry(function () {
+            _this.pollItems();
+            return _this.itemsToPoll.length === 0;
+        });
+    };
+    GPGPUContext.prototype.bindTextureToFrameBuffer = function (texture) {
+        this.throwIfDisposed();
+        webgl_util.bindColorTextureToFramebuffer(this.gl, texture, this.framebuffer);
+        if (this.autoDebugValidate) {
+            webgl_util.validateFramebuffer(this.gl);
+        }
+    };
+    GPGPUContext.prototype.unbindTextureToFrameBuffer = function () {
+        if (this.outputTexture != null) {
+            webgl_util.bindColorTextureToFramebuffer(this.gl, this.outputTexture, this.framebuffer);
+            if (this.autoDebugValidate) {
+                webgl_util.validateFramebuffer(this.gl);
+            }
+        }
+        else {
+            webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);
+        }
+    };
+    GPGPUContext.prototype.downloadMatrixDriver = function (texture, downloadAndDecode) {
+        this.bindTextureToFrameBuffer(texture);
+        var result = downloadAndDecode();
+        this.unbindTextureToFrameBuffer();
+        return result;
+    };
+    GPGPUContext.prototype.setOutputMatrixTextureDriver = function (outputMatrixTextureMaybePacked, width, height) {
+        this.throwIfDisposed();
+        var gl = this.gl;
+        webgl_util.bindColorTextureToFramebuffer(gl, outputMatrixTextureMaybePacked, this.framebuffer);
+        if (this.autoDebugValidate) {
+            webgl_util.validateFramebuffer(gl);
+        }
+        this.outputTexture = outputMatrixTextureMaybePacked;
+        webgl_util.callAndCheck(gl, function () { return gl.viewport(0, 0, width, height); });
+        webgl_util.callAndCheck(gl, function () { return gl.scissor(0, 0, width, height); });
+    };
+    GPGPUContext.prototype.setOutputMatrixWriteRegionDriver = function (x, y, width, height) {
+        var _this = this;
+        this.throwIfDisposed();
+        webgl_util.callAndCheck(this.gl, function () { return _this.gl.scissor(x, y, width, height); });
+    };
+    GPGPUContext.prototype.throwIfDisposed = function () {
+        if (this.disposed) {
+            throw new Error('Attempted to use disposed GPGPUContext.');
+        }
+    };
+    GPGPUContext.prototype.throwIfNoProgram = function () {
+        if (this.program == null) {
+            throw new Error('No GPU program is currently set.');
+        }
+    };
+    return GPGPUContext;
+}());
+exports.GPGPUContext = GPGPUContext;
+function binSearchLastTrue(arr) {
+    var start = 0;
+    var end = arr.length - 1;
+    var best = -1;
+    while (start <= end) {
+        var mid = (start + end) >> 1;
+        var isDone = arr[mid]();
+        if (isDone) {
+            best = mid;
+            start = mid + 1;
+        }
+        else {
+            end = mid - 1;
+        }
+    }
+    return best;
+}
+exports.binSearchLastTrue = binSearchLastTrue;
+//# sourceMappingURL=gpgpu_context.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_context.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_context.js.map
new file mode 100644
index 0000000..8ccdbe3
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_context.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_context_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_context_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_context_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_context_test.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_context_test.js
new file mode 100644
index 0000000..5f5b92f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_context_test.js
@@ -0,0 +1,302 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var jasmine_util_1 = require("../../jasmine_util");
+var test_util_1 = require("../../test_util");
+var gpgpu_context_1 = require("./gpgpu_context");
+var tex_util = require("./tex_util");
+var DOWNLOAD_FLOAT_ENVS = {
+    'WEBGL_DOWNLOAD_FLOAT_ENABLED': true
+};
+jasmine_util_1.describeWithFlags('GPGPUContext downloadMatrixFromTexture', DOWNLOAD_FLOAT_ENVS, function () {
+    var gpgpu;
+    var texture;
+    beforeEach(function () {
+        gpgpu = new gpgpu_context_1.GPGPUContext();
+        gpgpu.enableAutomaticDebugValidation(true);
+        texture = gpgpu.createFloat32MatrixTexture(1, 1);
+    });
+    afterEach(function () {
+        gpgpu.deleteMatrixTexture(texture);
+        gpgpu.dispose();
+    });
+    it('returns 1x1 matrix that was uploaded', function () {
+        gpgpu.uploadMatrixToTexture(texture, 1, 1, new Float32Array([1.234]));
+        var result = gpgpu.downloadFloat32MatrixFromOutputTexture(texture, 1, 1);
+        test_util_1.expectNumbersClose(result[0], 1.234);
+    });
+    it('returns 2x2 matrix that was uploaded', function () {
+        var texture2 = gpgpu.createFloat32MatrixTexture(2, 2);
+        gpgpu.uploadMatrixToTexture(texture2, 2, 2, new Float32Array([1.234, 2, 3, 4]));
+        var result = gpgpu.downloadFloat32MatrixFromOutputTexture(texture2, 2, 2);
+        test_util_1.expectArraysClose(result, new Float32Array([1.234, 2, 3, 4]));
+        gpgpu.deleteMatrixTexture(texture2);
+    });
+    it('uses texture parameter', function () {
+        var texture2 = gpgpu.createFloat32MatrixTexture(1, 1);
+        gpgpu.uploadMatrixToTexture(texture, 1, 1, new Float32Array([1]));
+        gpgpu.uploadMatrixToTexture(texture2, 1, 1, new Float32Array([2]));
+        var read1 = gpgpu.downloadFloat32MatrixFromOutputTexture(texture, 1, 1);
+        var read2 = gpgpu.downloadFloat32MatrixFromOutputTexture(texture2, 1, 1);
+        test_util_1.expectNumbersClose(read1[0], 1);
+        test_util_1.expectNumbersClose(read2[0], 2);
+        gpgpu.deleteMatrixTexture(texture2);
+    });
+});
+jasmine_util_1.describeWithFlags('GPGPUContext color texture with float', DOWNLOAD_FLOAT_ENVS, function () {
+    var gpgpu;
+    var texture;
+    afterEach(function () {
+        gpgpu.deleteMatrixTexture(texture);
+        gpgpu.dispose();
+    });
+    it('basic', function () {
+        gpgpu = new gpgpu_context_1.GPGPUContext();
+        gpgpu.enableAutomaticDebugValidation(true);
+        texture = gpgpu.createFloat32MatrixTexture(1, 1);
+        gpgpu.setOutputMatrixTexture(texture, 1, 1);
+        gpgpu.gl.clearColor(0.123, 0, 0, 0);
+        gpgpu.gl.clear(gpgpu.gl.COLOR_BUFFER_BIT);
+        var result = gpgpu.downloadFloat32MatrixFromOutputTexture(texture, 1, 1);
+        test_util_1.expectNumbersClose(result[0], 0.123);
+    });
+});
+jasmine_util_1.describeWithFlags('GPGPUContext setOutputMatrixTexture', DOWNLOAD_FLOAT_ENVS, function () {
+    var gpgpu;
+    var texture;
+    beforeEach(function () {
+        gpgpu = new gpgpu_context_1.GPGPUContext();
+        gpgpu.enableAutomaticDebugValidation(true);
+        texture = gpgpu.createFloat32MatrixTexture(1, 1);
+    });
+    afterEach(function () {
+        gpgpu.deleteMatrixTexture(texture);
+        gpgpu.dispose();
+    });
+    it('sets the output texture property to the output texture', function () {
+        gpgpu.setOutputMatrixTexture(texture, 1, 1);
+        expect(gpgpu.outputTexture).toBe(texture);
+    });
+    it('rebinds the output texture to the color buffer target', function () {
+        var output = gpgpu.createFloat32MatrixTexture(1, 1);
+        gpgpu.uploadMatrixToTexture(texture, 1, 1, new Float32Array([10]));
+        gpgpu.setOutputMatrixTexture(output, 1, 1);
+        var tBeforeClear = gpgpu.downloadFloat32MatrixFromOutputTexture(texture, 1, 1);
+        test_util_1.expectNumbersClose(tBeforeClear[0], 10);
+        gpgpu.gl.clearColor(1, 0, 0, 0);
+        gpgpu.gl.clear(gpgpu.gl.COLOR_BUFFER_BIT);
+        var tAfterClear = gpgpu.downloadFloat32MatrixFromOutputTexture(texture, 1, 1);
+        test_util_1.expectNumbersClose(tAfterClear[0], 10);
+        gpgpu.deleteMatrixTexture(output);
+    });
+    it('resets output texture to null if nothing was previously bound', function () {
+        expect(gpgpu.outputTexture).toBeNull();
+        gpgpu.downloadFloat32MatrixFromOutputTexture(texture, 1, 1);
+        expect(gpgpu.outputTexture).toBeNull();
+    });
+    it('sets the gl viewport to the output texture dimensions', function () {
+        var columns = 456;
+        var rows = 123;
+        var output = gpgpu.createFloat32MatrixTexture(rows, columns);
+        gpgpu.setOutputMatrixTexture(output, rows, columns);
+        var expected = new Int32Array([0, 0, columns, rows]);
+        expect(gpgpu.gl.getParameter(gpgpu.gl.VIEWPORT)).toEqual(expected);
+        gpgpu.deleteMatrixTexture(output);
+    });
+    it('doesn\'t change gl viewport when downloading a non-output tex', function () {
+        var output = gpgpu.createFloat32MatrixTexture(128, 128);
+        gpgpu.setOutputMatrixTexture(output, 128, 128);
+        gpgpu.downloadFloat32MatrixFromOutputTexture(texture, 1, 1);
+        var expected = new Int32Array([0, 0, 128, 128]);
+        expect(gpgpu.gl.getParameter(gpgpu.gl.VIEWPORT)).toEqual(expected);
+        gpgpu.deleteMatrixTexture(output);
+    });
+});
+jasmine_util_1.describeWithFlags('GPGPUContext setOutputPackedMatrixTexture', DOWNLOAD_FLOAT_ENVS, function () {
+    var gpgpu;
+    var texture;
+    beforeEach(function () {
+        gpgpu = new gpgpu_context_1.GPGPUContext();
+        gpgpu.enableAutomaticDebugValidation(true);
+    });
+    afterEach(function () {
+        if (texture != null) {
+            gpgpu.deleteMatrixTexture(texture);
+        }
+        gpgpu.dispose();
+    });
+    it('sets the output texture property to the output texture', function () {
+        texture = gpgpu.createPackedMatrixTexture(1, 1);
+        gpgpu.setOutputPackedMatrixTexture(texture, 1, 1);
+        expect(gpgpu.outputTexture).toBe(texture);
+    });
+    it('sets the gl viewport to the output packed texture dimensions', function () {
+        var columns = 456;
+        var rows = 123;
+        texture = gpgpu.createPackedMatrixTexture(rows, columns);
+        gpgpu.setOutputPackedMatrixTexture(texture, rows, columns);
+        var _a = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns), width = _a[0], height = _a[1];
+        var expected = new Int32Array([0, 0, width, height]);
+        expect(gpgpu.gl.getParameter(gpgpu.gl.VIEWPORT)).toEqual(expected);
+    });
+});
+jasmine_util_1.describeWithFlags('GPGPUContext setOutputMatrixWriteRegion', DOWNLOAD_FLOAT_ENVS, function () {
+    var gpgpu;
+    var program;
+    var output;
+    beforeEach(function () {
+        gpgpu = new gpgpu_context_1.GPGPUContext();
+        gpgpu.enableAutomaticDebugValidation(true);
+        var src = 'precision highp float; void main(){gl_FragColor = vec4(2,0,0,0);}';
+        program = gpgpu.createProgram(src);
+        output = gpgpu.createFloat32MatrixTexture(4, 4);
+        gpgpu.uploadMatrixToTexture(output, 4, 4, new Float32Array(16));
+        gpgpu.setOutputMatrixTexture(output, 4, 4);
+        gpgpu.setProgram(program);
+    });
+    afterEach(function () {
+        gpgpu.deleteMatrixTexture(output);
+        gpgpu.deleteProgram(program);
+        gpgpu.dispose();
+    });
+    it('writes to all pixels by default', function () {
+        gpgpu.executeProgram();
+        var result = gpgpu.downloadFloat32MatrixFromOutputTexture(output, 4, 4);
+        var expected = new Float32Array(4 * 4);
+        expected.fill(2);
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('sets the scissor box to the requested parameters', function () {
+        gpgpu.setOutputMatrixWriteRegion(0, 1, 2, 3);
+        var scissorBox = gpgpu.gl.getParameter(gpgpu.gl.SCISSOR_BOX);
+        expect(scissorBox[0]).toEqual(2);
+        expect(scissorBox[1]).toEqual(0);
+        expect(scissorBox[2]).toEqual(3);
+        expect(scissorBox[3]).toEqual(1);
+    });
+    it('writes only to center 2x2 region of 4x4 texture', function () {
+        gpgpu.setOutputMatrixWriteRegion(1, 2, 1, 2);
+        gpgpu.executeProgram();
+        var result = gpgpu.downloadFloat32MatrixFromOutputTexture(output, 4, 4);
+        var expected = new Float32Array([0, 0, 0, 0, 0, 2, 2, 0, 0, 2, 2, 0, 0, 0, 0, 0]);
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('preserves data from previous writes outside of write region', function () {
+        gpgpu.setOutputMatrixWriteRegion(0, 1, 0, 4);
+        gpgpu.executeProgram();
+        gpgpu.setOutputMatrixWriteRegion(3, 1, 0, 4);
+        gpgpu.executeProgram();
+        var result = gpgpu.downloadFloat32MatrixFromOutputTexture(output, 4, 4);
+        var expected = new Float32Array([2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 2, 2, 2]);
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('writes adjacent cells across multiple calls', function () {
+        for (var row = 0; row < 4; ++row) {
+            for (var col = 0; col < 4; ++col) {
+                gpgpu.setOutputMatrixWriteRegion(row, 1, col, 1);
+                gpgpu.executeProgram();
+            }
+        }
+        var result = gpgpu.downloadFloat32MatrixFromOutputTexture(output, 4, 4);
+        var expected = new Float32Array(4 * 4);
+        expected.fill(2);
+        test_util_1.expectArraysClose(result, expected);
+    });
+});
+jasmine_util_1.describeWithFlags('GPGPUContext', DOWNLOAD_FLOAT_ENVS, function () {
+    var gpgpu;
+    beforeEach(function () {
+        gpgpu = new gpgpu_context_1.GPGPUContext();
+        gpgpu.enableAutomaticDebugValidation(true);
+    });
+    afterEach(function () {
+        gpgpu.dispose();
+    });
+    it('throws an error if used after dispose', function () {
+        var gpgpuContext = new gpgpu_context_1.GPGPUContext();
+        gpgpuContext.dispose();
+        expect(gpgpuContext.dispose).toThrowError();
+    });
+    it('throws an error if validation is on and framebuffer incomplete', function () {
+        var src = "precision highp float; void main() {}";
+        var program = gpgpu.createProgram(src);
+        var result = gpgpu.createFloat32MatrixTexture(1, 1);
+        gpgpu.setOutputMatrixTexture(result, 1, 1);
+        gpgpu.setProgram(program);
+        gpgpu.deleteMatrixTexture(result);
+        expect(gpgpu.executeProgram).toThrowError();
+        gpgpu.deleteProgram(program);
+    });
+});
+describe('gpgpu_context binSearchLastTrue', function () {
+    it('[false]', function () {
+        var a = [false];
+        var arr = a.map(function (x) { return function () { return x; }; });
+        expect(gpgpu_context_1.binSearchLastTrue(arr)).toBe(-1);
+    });
+    it('[true]', function () {
+        var a = [true];
+        var arr = a.map(function (x) { return function () { return x; }; });
+        expect(gpgpu_context_1.binSearchLastTrue(arr)).toBe(0);
+    });
+    it('[false, false]', function () {
+        var a = [false, false];
+        var arr = a.map(function (x) { return function () { return x; }; });
+        expect(gpgpu_context_1.binSearchLastTrue(arr)).toBe(-1);
+    });
+    it('[true, false]', function () {
+        var a = [true, false];
+        var arr = a.map(function (x) { return function () { return x; }; });
+        expect(gpgpu_context_1.binSearchLastTrue(arr)).toBe(0);
+    });
+    it('[true, true]', function () {
+        var a = [true, true];
+        var arr = a.map(function (x) { return function () { return x; }; });
+        expect(gpgpu_context_1.binSearchLastTrue(arr)).toBe(1);
+    });
+    it('[false, false, false]', function () {
+        var a = [false, false, false];
+        var arr = a.map(function (x) { return function () { return x; }; });
+        expect(gpgpu_context_1.binSearchLastTrue(arr)).toBe(-1);
+    });
+    it('[true, false, false]', function () {
+        var a = [true, false, false];
+        var arr = a.map(function (x) { return function () { return x; }; });
+        expect(gpgpu_context_1.binSearchLastTrue(arr)).toBe(0);
+    });
+    it('[true, true, false]', function () {
+        var a = [true, true, false];
+        var arr = a.map(function (x) { return function () { return x; }; });
+        expect(gpgpu_context_1.binSearchLastTrue(arr)).toBe(1);
+    });
+    it('[true, true, true]', function () {
+        var a = [true, true, true];
+        var arr = a.map(function (x) { return function () { return x; }; });
+        expect(gpgpu_context_1.binSearchLastTrue(arr)).toBe(2);
+    });
+    it('[false, false, false, false]', function () {
+        var a = [false, false, false, false];
+        var arr = a.map(function (x) { return function () { return x; }; });
+        expect(gpgpu_context_1.binSearchLastTrue(arr)).toBe(-1);
+    });
+    it('[true, false, false, false]', function () {
+        var a = [true, false, false, false];
+        var arr = a.map(function (x) { return function () { return x; }; });
+        expect(gpgpu_context_1.binSearchLastTrue(arr)).toBe(0);
+    });
+    it('[true, true, false, false]', function () {
+        var a = [true, true, false, false];
+        var arr = a.map(function (x) { return function () { return x; }; });
+        expect(gpgpu_context_1.binSearchLastTrue(arr)).toBe(1);
+    });
+    it('[true, true, true, false]', function () {
+        var a = [true, true, true, false];
+        var arr = a.map(function (x) { return function () { return x; }; });
+        expect(gpgpu_context_1.binSearchLastTrue(arr)).toBe(2);
+    });
+    it('[true, true, true, true]', function () {
+        var a = [true, true, true, true];
+        var arr = a.map(function (x) { return function () { return x; }; });
+        expect(gpgpu_context_1.binSearchLastTrue(arr)).toBe(3);
+    });
+});
+//# sourceMappingURL=gpgpu_context_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_context_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_context_test.js.map
new file mode 100644
index 0000000..7b4b9a8
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_context_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_math.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_math.d.ts
new file mode 100644
index 0000000..271a53f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_math.d.ts
@@ -0,0 +1,33 @@
+import { Tensor } from '../../tensor';
+import { TypedArray } from '../../types';
+import { GPGPUContext } from './gpgpu_context';
+import { ShapeInfo } from './shader_compiler';
+import { TextureData } from './tex_util';
+export interface GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    usesPackedTextures?: boolean;
+    supportsBroadcasting?: boolean;
+    isPackShader?: boolean;
+}
+export interface GPGPUBinary {
+    webGLProgram: WebGLProgram;
+    program: GPGPUProgram;
+    uniformLocations: {
+        [name: string]: WebGLUniformLocation;
+    };
+    gpgpu: GPGPUContext;
+    source: string;
+    inShapeInfos: ShapeInfo[];
+    outShapeInfo: ShapeInfo;
+}
+export interface TensorData {
+    shape: number[];
+    texData: TextureData;
+    isUniform: boolean;
+    uniformValues?: TypedArray;
+}
+export declare function compileProgram<T extends Tensor, K extends Tensor>(gpgpu: GPGPUContext, program: GPGPUProgram, inputs: TensorData[], output: TensorData): GPGPUBinary;
+export declare function runProgram<T extends Tensor, K extends Tensor>(binary: GPGPUBinary, inputs: TensorData[], output: TensorData, customSetup?: (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => void): void;
+export declare function makeShaderKey(program: GPGPUProgram, inputs: TensorData[], output: TensorData): string;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_math.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_math.js
new file mode 100644
index 0000000..5e74c90
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_math.js
@@ -0,0 +1,119 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var util = require("../../util");
+var shader_compiler = require("./shader_compiler");
+function compileProgram(gpgpu, program, inputs, output) {
+    var userCode = program.userCode;
+    var inputInfos = inputs.map(function (input, i) {
+        var shapeInfo = {
+            logicalShape: input.shape,
+            texShape: input.isUniform ? null : input.texData.texShape,
+            isUniform: input.isUniform,
+            isPacked: input.isUniform ? false : input.texData.isPacked
+        };
+        return { name: program.variableNames[i], shapeInfo: shapeInfo };
+    });
+    var inShapeInfos = inputInfos.map(function (x) { return x.shapeInfo; });
+    var outShapeInfo = {
+        logicalShape: output.shape,
+        texShape: output.texData.texShape,
+        isUniform: false,
+        isPacked: output.texData.isPacked
+    };
+    var source = shader_compiler.makeShader(inputInfos, outShapeInfo, userCode, program.supportsBroadcasting === true, program.usesPackedTextures);
+    var webGLProgram = gpgpu.createProgram(source);
+    var uniformLocations = {};
+    for (var i = 0; i < program.variableNames.length; i++) {
+        var uniformName = program.variableNames[i];
+        var shouldThrow = false;
+        uniformLocations[uniformName] =
+            gpgpu.getUniformLocation(webGLProgram, uniformName, shouldThrow);
+    }
+    return {
+        program: program,
+        source: source,
+        webGLProgram: webGLProgram,
+        uniformLocations: uniformLocations,
+        gpgpu: gpgpu,
+        inShapeInfos: inShapeInfos,
+        outShapeInfo: outShapeInfo
+    };
+}
+exports.compileProgram = compileProgram;
+function validateBinaryAndProgram(shapeInfos, inputs) {
+    if (shapeInfos.length !== inputs.length) {
+        throw Error("Binary was compiled with " + shapeInfos.length + " inputs, but " +
+            ("was executed with " + inputs.length + " inputs"));
+    }
+    shapeInfos.forEach(function (s, i) {
+        var shapeA = s.logicalShape;
+        var input = inputs[i];
+        var shapeB = input.shape;
+        if (!util.arraysEqual(shapeA, shapeB)) {
+            throw Error("Binary was compiled with different shapes than " +
+                ("the current args. Shapes " + shapeA + " and " + shapeB + " must match"));
+        }
+        if (s.isUniform && input.isUniform) {
+            return;
+        }
+        var texShapeA = s.texShape;
+        var texShapeB = input.isUniform ? null : input.texData.texShape;
+        if (!util.arraysEqual(texShapeA, texShapeB)) {
+            throw Error("Binary was compiled with different texture shapes than the" +
+                (" current args. Shape " + texShapeA + " and " + texShapeB + " must match"));
+        }
+    });
+}
+function runProgram(binary, inputs, output, customSetup) {
+    validateBinaryAndProgram(binary.inShapeInfos, inputs);
+    validateBinaryAndProgram([binary.outShapeInfo], [output]);
+    var outTex = output.texData.texture;
+    var outTexShape = output.texData.texShape;
+    var gpgpu = binary.gpgpu;
+    if (output.texData.isPacked) {
+        gpgpu.setOutputPackedMatrixTexture(outTex, outTexShape[0], outTexShape[1]);
+    }
+    else {
+        gpgpu.setOutputMatrixTexture(outTex, outTexShape[0], outTexShape[1]);
+    }
+    gpgpu.setProgram(binary.webGLProgram);
+    inputs.forEach(function (input, i) {
+        var variableName = binary.program.variableNames[i];
+        var variableUniformLocation = binary.uniformLocations[variableName];
+        if (variableUniformLocation != null) {
+            if (input.isUniform) {
+                if (util.sizeFromShape(input.shape) === 1) {
+                    gpgpu.gl.uniform1f(variableUniformLocation, input.uniformValues[0]);
+                }
+                else {
+                    var vals = input.uniformValues;
+                    if (!(vals instanceof Float32Array)) {
+                        vals = new Float32Array(vals);
+                    }
+                    gpgpu.gl.uniform1fv(variableUniformLocation, vals);
+                }
+                return;
+            }
+            var tex = input.texData.texture;
+            gpgpu.setInputMatrixTexture(tex, variableUniformLocation, i);
+        }
+    });
+    if (customSetup != null) {
+        customSetup(gpgpu, binary.webGLProgram);
+    }
+    gpgpu.executeProgram();
+}
+exports.runProgram = runProgram;
+function makeShaderKey(program, inputs, output) {
+    var keyInputs = '';
+    inputs.concat(output).forEach(function (x) {
+        keyInputs += x.shape + "_" + (x.isUniform ? 'uniform' : x.texData.texShape);
+    });
+    var keyUserCode = program.userCode;
+    var keyBroadcast = (program.supportsBroadcasting === true).toString();
+    var key = program.constructor.name;
+    key += '_' + keyBroadcast + '_' + keyInputs + '_' + keyUserCode;
+    return key;
+}
+exports.makeShaderKey = makeShaderKey;
+//# sourceMappingURL=gpgpu_math.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_math.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_math.js.map
new file mode 100644
index 0000000..75415c2
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_math.js.map
@@ -0,0 +1 @@
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diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_util.d.ts
new file mode 100644
index 0000000..04784bc
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_util.d.ts
@@ -0,0 +1,29 @@
+export interface TextureConfig {
+    internalFormatFloat: number;
+    textureFormatFloat: number;
+    internalFormatHalfFloat: number;
+    internalFormatPackedFloat: number;
+    downloadTextureFormat: number;
+    downloadUnpackNumChannels: number;
+    defaultNumChannels: number;
+    textureTypeHalfFloat: number;
+}
+export declare function createVertexShader(gl: WebGLRenderingContext): WebGLShader;
+export declare function createVertexBuffer(gl: WebGLRenderingContext): WebGLBuffer;
+export declare function createIndexBuffer(gl: WebGLRenderingContext): WebGLBuffer;
+export declare function getTextureConfig(gl: WebGLRenderingContext, textureHalfFloatExtension?: any): TextureConfig;
+export declare function createFloat32MatrixTexture(gl: WebGLRenderingContext, rows: number, columns: number, textureConfig: TextureConfig): WebGLTexture;
+export declare function createFloat16MatrixTexture(gl: WebGLRenderingContext, rows: number, columns: number, textureConfig: TextureConfig): WebGLTexture;
+export declare function createUnsignedBytesMatrixTexture(gl: WebGLRenderingContext, rows: number, columns: number, textureConfig: TextureConfig): WebGLTexture;
+export declare function createPackedMatrixTexture(gl: WebGLRenderingContext, rows: number, columns: number, textureConfig: TextureConfig): WebGLTexture;
+export declare function createFloat16PackedMatrixTexture(gl: WebGLRenderingContext, rows: number, columns: number, textureConfig: TextureConfig): WebGLTexture;
+export declare function bindVertexProgramAttributeStreams(gl: WebGLRenderingContext, program: WebGLProgram, vertexBuffer: WebGLBuffer): boolean;
+export declare function uploadPixelDataToTexture(gl: WebGLRenderingContext, texture: WebGLTexture, pixels: ImageData | HTMLImageElement | HTMLCanvasElement | HTMLVideoElement): void;
+export declare function uploadMatrixToTexture(gl: WebGLRenderingContext, texture: WebGLTexture, rows: number, columns: number, matrix: Float32Array, numChannels: number, textureConfig: TextureConfig): void;
+export declare function uploadMatrixToPackedTexture(gl: WebGLRenderingContext, texture: WebGLTexture, batch: number, rows: number, columns: number, physicalRows: number, physicalCols: number, matrix: Float32Array, textureConfig: TextureConfig): void;
+export declare function maybeCreateBufferFromOutputTexture(gl: WebGLRenderingContext, texture: WebGLTexture, rows: number, columns: number, textureConfig: TextureConfig): WebGLBuffer | WebGLTexture;
+export declare function downloadFloat32MatrixFromBuffer(gl: WebGLRenderingContext, buffer: WebGLBuffer, rows: number, columns: number, textureConfig: TextureConfig): Float32Array;
+export declare function downloadFloat32MatrixFromOutputTexture(gl: WebGLRenderingContext, rows: number, columns: number, textureConfig: TextureConfig): Float32Array;
+export declare function downloadByteEncodedFloatMatrixFromOutputTexture(gl: WebGLRenderingContext, rows: number, columns: number, textureConfig: TextureConfig): Float32Array;
+export declare function downloadPackedMatrixFromBuffer(gl: WebGLRenderingContext, buffer: WebGLBuffer, batch: number, rows: number, cols: number, physicalRows: number, physicalCols: number, textureConfig: TextureConfig): Float32Array;
+export declare function downloadMatrixFromPackedOutputTexture(gl: WebGLRenderingContext, batch: number, rows: number, cols: number, physicalRows: number, physicalCols: number, textureConfig: TextureConfig): Float32Array;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_util.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_util.js
new file mode 100644
index 0000000..541c603
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_util.js
@@ -0,0 +1,210 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../../environment");
+var util = require("../../util");
+var tex_util = require("./tex_util");
+var webgl_util = require("./webgl_util");
+function createVertexShader(gl) {
+    var vertexShaderSource = "\n    precision highp float;\n    attribute vec3 clipSpacePos;\n    attribute vec2 uv;\n    varying vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }";
+    return webgl_util.createVertexShader(gl, vertexShaderSource);
+}
+exports.createVertexShader = createVertexShader;
+function createVertexBuffer(gl) {
+    var vertexArray = new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);
+    return webgl_util.createStaticVertexBuffer(gl, vertexArray);
+}
+exports.createVertexBuffer = createVertexBuffer;
+function createIndexBuffer(gl) {
+    var triangleVertexIndices = new Uint16Array([0, 1, 2, 2, 1, 3]);
+    return webgl_util.createStaticIndexBuffer(gl, triangleVertexIndices);
+}
+exports.createIndexBuffer = createIndexBuffer;
+function getTextureConfig(gl, textureHalfFloatExtension) {
+    var glany = gl;
+    var internalFormatFloat;
+    var internalFormatHalfFloat;
+    var internalFormatPackedFloat;
+    var textureFormatFloat;
+    var downloadTextureFormat;
+    var downloadUnpackNumChannels;
+    var defaultNumChannels;
+    var textureTypeHalfFloat;
+    if (environment_1.ENV.get('WEBGL_VERSION') === 2) {
+        internalFormatFloat = glany.R32F;
+        internalFormatHalfFloat = glany.R16F;
+        internalFormatPackedFloat = glany.RGBA32F;
+        textureFormatFloat = glany.RED;
+        downloadUnpackNumChannels = 4;
+        defaultNumChannels = 1;
+        textureTypeHalfFloat = glany.HALF_FLOAT;
+    }
+    else {
+        internalFormatFloat = gl.RGBA;
+        internalFormatHalfFloat = gl.RGBA;
+        internalFormatPackedFloat = glany.RGBA;
+        textureFormatFloat = gl.RGBA;
+        downloadUnpackNumChannels = 4;
+        defaultNumChannels = 4;
+        textureTypeHalfFloat = textureHalfFloatExtension != null ?
+            textureHalfFloatExtension.HALF_FLOAT_OES :
+            null;
+    }
+    downloadTextureFormat = gl.RGBA;
+    return {
+        internalFormatFloat: internalFormatFloat,
+        internalFormatHalfFloat: internalFormatHalfFloat,
+        internalFormatPackedFloat: internalFormatPackedFloat,
+        textureFormatFloat: textureFormatFloat,
+        downloadTextureFormat: downloadTextureFormat,
+        downloadUnpackNumChannels: downloadUnpackNumChannels,
+        defaultNumChannels: defaultNumChannels,
+        textureTypeHalfFloat: textureTypeHalfFloat
+    };
+}
+exports.getTextureConfig = getTextureConfig;
+function createAndConfigureTexture(gl, width, height, internalFormat, textureFormat, textureType) {
+    webgl_util.validateTextureSize(width, height);
+    var texture = webgl_util.createTexture(gl);
+    var tex2d = gl.TEXTURE_2D;
+    webgl_util.callAndCheck(gl, function () { return gl.bindTexture(tex2d, texture); });
+    webgl_util.callAndCheck(gl, function () { return gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); });
+    webgl_util.callAndCheck(gl, function () { return gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); });
+    webgl_util.callAndCheck(gl, function () { return gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST); });
+    webgl_util.callAndCheck(gl, function () { return gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST); });
+    webgl_util.callAndCheck(gl, function () { return gl.texImage2D(tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType, null); });
+    webgl_util.callAndCheck(gl, function () { return gl.bindTexture(gl.TEXTURE_2D, null); });
+    return texture;
+}
+function createFloat32MatrixTexture(gl, rows, columns, textureConfig) {
+    var _a = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns), width = _a[0], height = _a[1];
+    return createAndConfigureTexture(gl, width, height, textureConfig.internalFormatFloat, textureConfig.textureFormatFloat, gl.FLOAT);
+}
+exports.createFloat32MatrixTexture = createFloat32MatrixTexture;
+function createFloat16MatrixTexture(gl, rows, columns, textureConfig) {
+    var _a = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns), width = _a[0], height = _a[1];
+    return createAndConfigureTexture(gl, width, height, textureConfig.internalFormatFloat, textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);
+}
+exports.createFloat16MatrixTexture = createFloat16MatrixTexture;
+function createUnsignedBytesMatrixTexture(gl, rows, columns, textureConfig) {
+    var _a = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns), width = _a[0], height = _a[1];
+    return createAndConfigureTexture(gl, width, height, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE);
+}
+exports.createUnsignedBytesMatrixTexture = createUnsignedBytesMatrixTexture;
+function createPackedMatrixTexture(gl, rows, columns, textureConfig) {
+    var _a = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns), width = _a[0], height = _a[1];
+    return createAndConfigureTexture(gl, width, height, textureConfig.internalFormatPackedFloat, gl.RGBA, gl.FLOAT);
+}
+exports.createPackedMatrixTexture = createPackedMatrixTexture;
+function createFloat16PackedMatrixTexture(gl, rows, columns, textureConfig) {
+    var _a = tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns), width = _a[0], height = _a[1];
+    return createAndConfigureTexture(gl, width, height, textureConfig.internalFormatHalfFloat, gl.RGBA, textureConfig.textureTypeHalfFloat);
+}
+exports.createFloat16PackedMatrixTexture = createFloat16PackedMatrixTexture;
+function bindVertexProgramAttributeStreams(gl, program, vertexBuffer) {
+    var posOffset = 0;
+    var uvOffset = 3 * 4;
+    var stride = (3 * 4) + (2 * 4);
+    webgl_util.callAndCheck(gl, function () { return gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer); });
+    var success = webgl_util.bindVertexBufferToProgramAttribute(gl, program, 'clipSpacePos', vertexBuffer, 3, stride, posOffset);
+    return success &&
+        webgl_util.bindVertexBufferToProgramAttribute(gl, program, 'uv', vertexBuffer, 2, stride, uvOffset);
+}
+exports.bindVertexProgramAttributeStreams = bindVertexProgramAttributeStreams;
+function uploadPixelDataToTexture(gl, texture, pixels) {
+    webgl_util.callAndCheck(gl, function () { return gl.bindTexture(gl.TEXTURE_2D, texture); });
+    webgl_util.callAndCheck(gl, function () { return gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels); });
+    webgl_util.callAndCheck(gl, function () { return gl.bindTexture(gl.TEXTURE_2D, null); });
+}
+exports.uploadPixelDataToTexture = uploadPixelDataToTexture;
+function uploadDataToTexture(gl, texture, width, height, data, textureFormat) {
+    webgl_util.validateTextureSize(width, height);
+    webgl_util.callAndCheck(gl, function () { return gl.bindTexture(gl.TEXTURE_2D, texture); });
+    webgl_util.callAndCheck(gl, function () { return gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, width, height, textureFormat, gl.FLOAT, data); });
+    webgl_util.callAndCheck(gl, function () { return gl.bindTexture(gl.TEXTURE_2D, null); });
+}
+function uploadMatrixToTexture(gl, texture, rows, columns, matrix, numChannels, textureConfig) {
+    var _a = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns), w = _a[0], h = _a[1];
+    var unpackedArray;
+    if (textureConfig.defaultNumChannels === 1) {
+        unpackedArray = matrix;
+    }
+    else {
+        unpackedArray =
+            new Float32Array(tex_util.getUnpackedArraySizeFromMatrixSize(matrix.length, numChannels));
+        tex_util.encodeMatrixToUnpackedArray(matrix, unpackedArray, numChannels);
+    }
+    uploadDataToTexture(gl, texture, w, h, unpackedArray, textureConfig.textureFormatFloat);
+}
+exports.uploadMatrixToTexture = uploadMatrixToTexture;
+function uploadMatrixToPackedTexture(gl, texture, batch, rows, columns, physicalRows, physicalCols, matrix, textureConfig) {
+    var _a = tex_util.getPackedMatrixTextureShapeWidthHeight(physicalRows, physicalCols), w = _a[0], h = _a[1];
+    var packedRGBA = new Float32Array(batch * tex_util.getPackedRGBAArraySizeFromMatrixShape(rows, columns));
+    tex_util.encodeMatrixToPackedRGBA(matrix, batch, rows, columns, packedRGBA);
+    uploadDataToTexture(gl, texture, w, h, packedRGBA, gl.RGBA);
+}
+exports.uploadMatrixToPackedTexture = uploadMatrixToPackedTexture;
+function maybeCreateBufferFromOutputTexture(gl, texture, rows, columns, textureConfig) {
+    var bufferOrTexture = texture;
+    if (environment_1.ENV.get('WEBGL_VERSION') === 2) {
+        var gl2_1 = gl;
+        var buffer_1 = gl2_1.createBuffer();
+        webgl_util.callAndCheck(gl, function () { return gl.bindBuffer(gl2_1.PIXEL_PACK_BUFFER, buffer_1); });
+        var bytesPerFloat = 4;
+        var bufferSizeBytes_1 = bytesPerFloat *
+            tex_util.getUnpackedArraySizeFromMatrixSize(rows * columns, textureConfig.downloadUnpackNumChannels);
+        webgl_util.callAndCheck(gl, function () { return gl.bufferData(gl2_1.PIXEL_PACK_BUFFER, bufferSizeBytes_1, gl.STATIC_DRAW); });
+        webgl_util.callAndCheck(gl, function () { return gl2_1.readPixels(0, 0, columns, rows, gl.RGBA, gl.FLOAT, 0); });
+        webgl_util.callAndCheck(gl, function () { return gl.bindBuffer(gl2_1.PIXEL_PACK_BUFFER, null); });
+        bufferOrTexture = buffer_1;
+    }
+    return bufferOrTexture;
+}
+exports.maybeCreateBufferFromOutputTexture = maybeCreateBufferFromOutputTexture;
+function downloadFloat32MatrixFromBuffer(gl, buffer, rows, columns, textureConfig) {
+    var gl2 = gl;
+    var downloadTarget = new Float32Array(tex_util.getUnpackedArraySizeFromMatrixSize(rows * columns, textureConfig.downloadUnpackNumChannels));
+    gl2.bindBuffer(gl.ARRAY_BUFFER, buffer);
+    gl2.getBufferSubData(gl.ARRAY_BUFFER, 0, downloadTarget);
+    gl2.bindBuffer(gl.ARRAY_BUFFER, null);
+    var matrix = new Float32Array(rows * columns);
+    tex_util.decodeMatrixFromUnpackedArray(downloadTarget, matrix, textureConfig.downloadUnpackNumChannels);
+    return matrix;
+}
+exports.downloadFloat32MatrixFromBuffer = downloadFloat32MatrixFromBuffer;
+function downloadFloat32MatrixFromOutputTexture(gl, rows, columns, textureConfig) {
+    var _a = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns), w = _a[0], h = _a[1];
+    var downloadTarget = new Float32Array(tex_util.getUnpackedArraySizeFromMatrixSize(rows * columns, textureConfig.downloadUnpackNumChannels));
+    webgl_util.callAndCheck(gl, function () { return gl.readPixels(0, 0, w, h, textureConfig.downloadTextureFormat, gl.FLOAT, downloadTarget); });
+    var matrix = new Float32Array(rows * columns);
+    tex_util.decodeMatrixFromUnpackedArray(downloadTarget, matrix, textureConfig.downloadUnpackNumChannels);
+    return matrix;
+}
+exports.downloadFloat32MatrixFromOutputTexture = downloadFloat32MatrixFromOutputTexture;
+function downloadByteEncodedFloatMatrixFromOutputTexture(gl, rows, columns, textureConfig) {
+    var _a = tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns), w = _a[0], h = _a[1];
+    var numChannels = 4;
+    var downloadTarget = new Uint8Array(tex_util.getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));
+    webgl_util.callAndCheck(gl, function () { return gl.readPixels(0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE, downloadTarget); });
+    return new Float32Array(downloadTarget.buffer);
+}
+exports.downloadByteEncodedFloatMatrixFromOutputTexture = downloadByteEncodedFloatMatrixFromOutputTexture;
+function downloadPackedMatrixFromBuffer(gl, buffer, batch, rows, cols, physicalRows, physicalCols, textureConfig) {
+    var gl2 = gl;
+    var downloadTarget = new Float32Array(tex_util.getPackedRGBAArraySizeFromMatrixShape(physicalRows, physicalCols));
+    gl2.bindBuffer(gl.ARRAY_BUFFER, buffer);
+    gl2.getBufferSubData(gl.ARRAY_BUFFER, 0, downloadTarget);
+    gl2.bindBuffer(gl.ARRAY_BUFFER, null);
+    var matrix = new Float32Array(util.sizeFromShape([batch, rows, cols]));
+    tex_util.decodeMatrixFromPackedRGBA(downloadTarget, batch, rows, cols, matrix);
+    return matrix;
+}
+exports.downloadPackedMatrixFromBuffer = downloadPackedMatrixFromBuffer;
+function downloadMatrixFromPackedOutputTexture(gl, batch, rows, cols, physicalRows, physicalCols, textureConfig) {
+    var _a = tex_util.getPackedMatrixTextureShapeWidthHeight(physicalRows, physicalCols), w = _a[0], h = _a[1];
+    var packedRGBA = new Float32Array(tex_util.getPackedRGBAArraySizeFromMatrixShape(physicalRows, physicalCols));
+    webgl_util.callAndCheck(gl, function () { return gl.readPixels(0, 0, w, h, gl.RGBA, gl.FLOAT, packedRGBA); });
+    var matrix = new Float32Array(util.sizeFromShape([batch, rows, cols]));
+    return tex_util.decodeMatrixFromPackedRGBA(packedRGBA, batch, rows, cols, matrix);
+}
+exports.downloadMatrixFromPackedOutputTexture = downloadMatrixFromPackedOutputTexture;
+//# sourceMappingURL=gpgpu_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_util.js.map
new file mode 100644
index 0000000..fa01c96
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_util.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_util_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_util_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_util_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_util_test.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_util_test.js
new file mode 100644
index 0000000..10cb758
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_util_test.js
@@ -0,0 +1,148 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../../index");
+var jasmine_util_1 = require("../../jasmine_util");
+var test_util_1 = require("../../test_util");
+var gpgpu_context_1 = require("./gpgpu_context");
+var gpgpu_util = require("./gpgpu_util");
+var DOWNLOAD_FLOAT_ENVS = {
+    'WEBGL_DOWNLOAD_FLOAT_ENABLED': true
+};
+jasmine_util_1.describeWithFlags('gpgpu_util createWebGLContext', test_util_1.WEBGL_ENVS, function () {
+    var gpgpu;
+    beforeEach(function () {
+        gpgpu = new gpgpu_context_1.GPGPUContext();
+    });
+    afterEach(function () {
+        gpgpu.dispose();
+    });
+    it('disables DEPTH_TEST and STENCIL_TEST', function () {
+        expect(gpgpu.gl.getParameter(gpgpu.gl.DEPTH_TEST)).toEqual(false);
+        expect(gpgpu.gl.getParameter(gpgpu.gl.STENCIL_TEST)).toEqual(false);
+    });
+    it('disables BLEND', function () {
+        expect(gpgpu.gl.getParameter(gpgpu.gl.BLEND)).toEqual(false);
+    });
+    it('disables DITHER, POLYGON_OFFSET_FILL', function () {
+        expect(gpgpu.gl.getParameter(gpgpu.gl.DITHER)).toEqual(false);
+        expect(gpgpu.gl.getParameter(gpgpu.gl.POLYGON_OFFSET_FILL)).toEqual(false);
+    });
+    it('enables CULL_FACE with BACK', function () {
+        expect(gpgpu.gl.getParameter(gpgpu.gl.CULL_FACE)).toEqual(true);
+        expect(gpgpu.gl.getParameter(gpgpu.gl.CULL_FACE_MODE))
+            .toEqual(gpgpu.gl.BACK);
+    });
+    it('enables SCISSOR_TEST', function () {
+        expect(gpgpu.gl.getParameter(gpgpu.gl.SCISSOR_TEST)).toEqual(true);
+    });
+});
+jasmine_util_1.describeWithFlags('gpgpu_util createFloat32MatrixTexture', test_util_1.WEBGL_ENVS, function () {
+    it('sets the TEXTURE_WRAP S+T parameters to CLAMP_TO_EDGE', function () {
+        var gpgpu = new gpgpu_context_1.GPGPUContext();
+        var textureConfig = gpgpu_util.getTextureConfig(gpgpu.gl);
+        var tex = gpgpu_util.createFloat32MatrixTexture(gpgpu.gl, 32, 32, textureConfig);
+        gpgpu.gl.bindTexture(gpgpu.gl.TEXTURE_2D, tex);
+        expect(gpgpu.gl.getTexParameter(gpgpu.gl.TEXTURE_2D, gpgpu.gl.TEXTURE_WRAP_S))
+            .toEqual(gpgpu.gl.CLAMP_TO_EDGE);
+        expect(gpgpu.gl.getTexParameter(gpgpu.gl.TEXTURE_2D, gpgpu.gl.TEXTURE_WRAP_T))
+            .toEqual(gpgpu.gl.CLAMP_TO_EDGE);
+        gpgpu.gl.bindTexture(gpgpu.gl.TEXTURE_2D, null);
+        gpgpu.deleteMatrixTexture(tex);
+        gpgpu.dispose();
+    });
+    it('sets the TEXTURE_[MIN|MAG]_FILTER parameters to NEAREST', function () {
+        var gpgpu = new gpgpu_context_1.GPGPUContext();
+        var textureConfig = gpgpu_util.getTextureConfig(gpgpu.gl);
+        var tex = gpgpu_util.createFloat32MatrixTexture(gpgpu.gl, 32, 32, textureConfig);
+        gpgpu.gl.bindTexture(gpgpu.gl.TEXTURE_2D, tex);
+        expect(gpgpu.gl.getTexParameter(gpgpu.gl.TEXTURE_2D, gpgpu.gl.TEXTURE_MIN_FILTER))
+            .toEqual(gpgpu.gl.NEAREST);
+        expect(gpgpu.gl.getTexParameter(gpgpu.gl.TEXTURE_2D, gpgpu.gl.TEXTURE_MAG_FILTER))
+            .toEqual(gpgpu.gl.NEAREST);
+        gpgpu.gl.bindTexture(gpgpu.gl.TEXTURE_2D, null);
+        gpgpu.deleteMatrixTexture(tex);
+        gpgpu.dispose();
+    });
+});
+jasmine_util_1.describeWithFlags('gpgpu_util createPackedMatrixTexture', test_util_1.WEBGL_ENVS, function () {
+    it('sets the TEXTURE_WRAP S+T parameters to CLAMP_TO_EDGE', function () {
+        var gpgpu = new gpgpu_context_1.GPGPUContext();
+        var textureConfig = gpgpu_util.getTextureConfig(gpgpu.gl);
+        var tex = gpgpu_util.createPackedMatrixTexture(gpgpu.gl, 32, 32, textureConfig);
+        gpgpu.gl.bindTexture(gpgpu.gl.TEXTURE_2D, tex);
+        expect(gpgpu.gl.getTexParameter(gpgpu.gl.TEXTURE_2D, gpgpu.gl.TEXTURE_WRAP_S))
+            .toEqual(gpgpu.gl.CLAMP_TO_EDGE);
+        expect(gpgpu.gl.getTexParameter(gpgpu.gl.TEXTURE_2D, gpgpu.gl.TEXTURE_WRAP_T))
+            .toEqual(gpgpu.gl.CLAMP_TO_EDGE);
+        gpgpu.gl.bindTexture(gpgpu.gl.TEXTURE_2D, null);
+        gpgpu.deleteMatrixTexture(tex);
+        gpgpu.dispose();
+    });
+    it('sets the TEXTURE_[MIN|MAG]_FILTER parameters to NEAREST', function () {
+        var gpgpu = new gpgpu_context_1.GPGPUContext();
+        var textureConfig = gpgpu_util.getTextureConfig(gpgpu.gl);
+        var tex = gpgpu_util.createPackedMatrixTexture(gpgpu.gl, 32, 32, textureConfig);
+        gpgpu.gl.bindTexture(gpgpu.gl.TEXTURE_2D, tex);
+        expect(gpgpu.gl.getTexParameter(gpgpu.gl.TEXTURE_2D, gpgpu.gl.TEXTURE_MIN_FILTER))
+            .toEqual(gpgpu.gl.NEAREST);
+        expect(gpgpu.gl.getTexParameter(gpgpu.gl.TEXTURE_2D, gpgpu.gl.TEXTURE_MAG_FILTER))
+            .toEqual(gpgpu.gl.NEAREST);
+        gpgpu.gl.bindTexture(gpgpu.gl.TEXTURE_2D, null);
+        gpgpu.deleteMatrixTexture(tex);
+        gpgpu.dispose();
+    });
+});
+jasmine_util_1.describeWithFlags('gpgpu_util downloadMatrixFromPackedOutputTexture', DOWNLOAD_FLOAT_ENVS, function () {
+    it('should work when texture shape != logical shape', function () {
+        var gpgpu = new gpgpu_context_1.GPGPUContext();
+        var textureConfig = gpgpu_util.getTextureConfig(gpgpu.gl);
+        var tex = gpgpu_util.createPackedMatrixTexture(gpgpu.gl, 4, 6, textureConfig);
+        var mat = tf.tensor2d([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [1, 12]);
+        gpgpu.gl.bindTexture(gpgpu.gl.TEXTURE_2D, tex);
+        gpgpu.gl.texSubImage2D(gpgpu.gl.TEXTURE_2D, 0, 0, 0, 3, 2, gpgpu.gl.RGBA, gpgpu.gl.FLOAT, new Float32Array([
+            0, 1, 0, 0, 2, 3, 0, 0, 4, 5, 0, 0,
+            6, 7, 0, 0, 8, 9, 0, 0, 10, 11, 0, 0
+        ]));
+        var result = gpgpu.downloadMatrixFromPackedTexture(tex, 1, 1, 12, 4, 6);
+        test_util_1.expectArraysClose(result, mat.dataSync());
+    });
+    it('should work when different batches occupy the same physical row', function () {
+        var gpgpu = new gpgpu_context_1.GPGPUContext();
+        var textureConfig = gpgpu_util.getTextureConfig(gpgpu.gl);
+        var physicalRows = 10;
+        var physicalCols = 16;
+        var tex = gpgpu_util.createPackedMatrixTexture(gpgpu.gl, physicalRows, physicalCols, textureConfig);
+        var mat = tf.tensor3d([
+            1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
+            13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,
+            25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
+            37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48,
+            49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
+            61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72,
+            73, 74, 75, 76, 77, 78, 79, 80, 81, 82, 83, 84,
+            85, 86, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96,
+            97, 98, 99, 100, 101, 102, 103, 104, 105, 106, 107, 108,
+            109, 110, 111, 112, 113, 114, 115, 116, 117, 118, 119, 120
+        ], [2, 20, 3]);
+        gpgpu.gl.bindTexture(gpgpu.gl.TEXTURE_2D, tex);
+        gpgpu.gl.texSubImage2D(gpgpu.gl.TEXTURE_2D, 0, 0, 0, 8, 5, gpgpu.gl.RGBA, gpgpu.gl.FLOAT, new Float32Array([
+            1, 2, 4, 5, 3, 0, 6, 0, 7, 8, 10, 11,
+            9, 0, 12, 0, 13, 14, 16, 17, 15, 0, 18, 0,
+            19, 20, 22, 23, 21, 0, 24, 0, 25, 26, 28, 29,
+            27, 0, 30, 0, 31, 32, 34, 35, 33, 0, 36, 0,
+            37, 38, 40, 41, 39, 0, 42, 0, 43, 44, 46, 47,
+            45, 0, 48, 0, 49, 50, 52, 53, 51, 0, 54, 0,
+            55, 56, 58, 59, 57, 0, 60, 0, 61, 62, 64, 65,
+            63, 0, 66, 0, 67, 68, 70, 71, 69, 0, 72, 0,
+            73, 74, 76, 77, 75, 0, 78, 0, 79, 80, 82, 83,
+            81, 0, 84, 0, 85, 86, 88, 89, 87, 0, 90, 0,
+            91, 92, 94, 95, 93, 0, 96, 0, 97, 98, 100, 101,
+            99, 0, 102, 0, 103, 104, 106, 107, 105, 0, 108, 0,
+            109, 110, 112, 113, 111, 0, 114, 0, 115, 116, 118, 119,
+            117, 0, 120, 0
+        ]));
+        var result = gpgpu.downloadMatrixFromPackedTexture(tex, 2, 20, 3, physicalRows, physicalCols);
+        test_util_1.expectArraysClose(result, mat.dataSync());
+    });
+});
+//# sourceMappingURL=gpgpu_util_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_util_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_util_test.js.map
new file mode 100644
index 0000000..296b7d1
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/gpgpu_util_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/im2col_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/im2col_gpu.d.ts
new file mode 100644
index 0000000..6fc6dee
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/im2col_gpu.d.ts
@@ -0,0 +1,8 @@
+import { Conv2DInfo } from '../../ops/conv_util';
+import { GPGPUProgram } from './gpgpu_math';
+export declare class Im2ColProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(outputShape: number[], inputShape: number[], convInfo: Conv2DInfo);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/im2col_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/im2col_gpu.js
new file mode 100644
index 0000000..e93f962
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/im2col_gpu.js
@@ -0,0 +1,15 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var Im2ColProgram = (function () {
+    function Im2ColProgram(outputShape, inputShape, convInfo) {
+        this.variableNames = ['A'];
+        this.outputShape = outputShape;
+        var filterWidth = convInfo.filterWidth, inChannels = convInfo.inChannels, strideWidth = convInfo.strideWidth, strideHeight = convInfo.strideHeight, padInfo = convInfo.padInfo, outWidth = convInfo.outWidth, dilationWidth = convInfo.dilationWidth, dilationHeight = convInfo.dilationHeight;
+        var left = padInfo.left, top = padInfo.top;
+        var itemsPerBlockRow = inChannels * filterWidth;
+        this.userCode = "\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            int blockIndex = rc.y + col;\n            int pos = rc.x + row;\n\n            if(blockIndex >= " + outputShape[1] + " || pos >= " + outputShape[0] + ") continue;\n\n            int offsetY = int(blockIndex / (" + outWidth + ")) * " + strideHeight + " - " + top + ";\n            int d0 = offsetY + " + dilationHeight + " * (pos / " + itemsPerBlockRow + ");\n\n            if(d0 >= " + inputShape[0] + " || d0 < 0) continue;\n\n            int offsetX = int(mod(float(blockIndex), " + outWidth + ".) * " + strideWidth + ". - " + left + ".);\n            int d1 = offsetX + " + dilationWidth + " * (int(mod(float(pos), " + itemsPerBlockRow + ".) / " + inChannels + ".));\n\n            if(d1 >= " + inputShape[1] + " || d1 < 0) continue;\n\n            result[row * 2 + col] = getA(d0, d1, int(mod(float(pos), " + inChannels + ".)));\n          }\n        }\n\n        gl_FragColor = result;\n      }\n    ";
+    }
+    return Im2ColProgram;
+}());
+exports.Im2ColProgram = Im2ColProgram;
+//# sourceMappingURL=im2col_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/im2col_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/im2col_gpu.js.map
new file mode 100644
index 0000000..0d695b4
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/im2col_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"im2col_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/im2col_gpu.ts"],"names":[],"mappings":";;AAoBA;IAKE,uBACI,WAAqB,EAAE,UAAoB,EAAE,QAAoB;QALrE,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QAMpB,IAAI,CAAC,WAAW,GAAG,WAAW,CAAC;QAG7B,IAAA,kCAAW,EACX,gCAAU,EACV,kCAAW,EACX,oCAAY,EACZ,0BAAO,EACP,4BAAQ,EACR,sCAAa,EACb,wCAAc,CACH;QACN,IAAA,mBAAI,EAAE,iBAAG,CAAY;QAC5B,IAAM,gBAAgB,GAAG,UAAU,GAAG,WAAW,CAAC;QAElD,IAAI,CAAC,QAAQ,GAAG,uSAWW,WAAW,CAAC,CAAC,CAAC,mBACrC,WAAW,CAAC,CAAC,CAAC,mEAEwB,QAAQ,aAAQ,YAAY,WAClE,GAAG,0CACsB,cAAc,kBAAa,gBAAgB,mCAErD,UAAU,CAAC,CAAC,CAAC,sFAEmB,QAAQ,aACvD,WAAW,YAAO,IAAI,4CACG,aAAa,gCACtC,gBAAgB,aAAQ,UAAU,qCAEnB,UAAU,CAAC,CAAC,CAAC,sGAG5B,UAAU,mFAMb,CAAC;IACJ,CAAC;IACH,oBAAC;AAAD,CAAC,AA1DD,IA0DC;AA1DY,sCAAa"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/lrn_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/lrn_gpu.d.ts
new file mode 100644
index 0000000..37d209b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/lrn_gpu.d.ts
@@ -0,0 +1,7 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class LRNProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(xShape: number[], radius: number, bias: number, alpha: number, beta: number);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/lrn_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/lrn_gpu.js
new file mode 100644
index 0000000..788b819
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/lrn_gpu.js
@@ -0,0 +1,26 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var LRNProgram = (function () {
+    function LRNProgram(xShape, radius, bias, alpha, beta) {
+        this.variableNames = ['x'];
+        this.outputShape = [];
+        var rad = radius;
+        var maxD = xShape[3] - 1;
+        this.outputShape = xShape;
+        var powOperator;
+        var basis = "float(" + bias + ") + float(" + alpha + ") * sum";
+        if (beta === 0.5) {
+            powOperator = "inversesqrt(" + basis + ")";
+        }
+        else if (beta === 1.0) {
+            powOperator = "1.0/(" + basis + ")";
+        }
+        else {
+            powOperator = "exp(log(" + basis + ") * float(-" + beta + "));";
+        }
+        this.userCode = "\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -" + rad + "; j <= " + rad + "; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  " + maxD + ") {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * " + powOperator + ";\n        setOutput(val);\n      }\n    ";
+    }
+    return LRNProgram;
+}());
+exports.LRNProgram = LRNProgram;
+//# sourceMappingURL=lrn_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/lrn_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/lrn_gpu.js.map
new file mode 100644
index 0000000..175f38e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/lrn_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"lrn_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/lrn_gpu.ts"],"names":[],"mappings":";;AAmBA;IAKE,oBACI,MAAgB,EAAE,MAAc,EAAE,IAAY,EAAE,KAAa,EAC7D,IAAY;QANhB,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QACtB,gBAAW,GAAa,EAAE,CAAC;QAMzB,IAAM,GAAG,GAAG,MAAM,CAAC;QACnB,IAAM,IAAI,GAAG,MAAM,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC;QAC3B,IAAI,CAAC,WAAW,GAAG,MAAM,CAAC;QAM1B,IAAI,WAAW,CAAC;QAChB,IAAM,KAAK,GAAG,WAAS,IAAI,kBAAa,KAAK,YAAS,CAAC;QACvD,IAAI,IAAI,KAAK,GAAG,EAAE;YAChB,WAAW,GAAG,iBAAe,KAAK,MAAG,CAAC;SACvC;aAAM,IAAI,IAAI,KAAK,GAAG,EAAE;YACvB,WAAW,GAAG,UAAQ,KAAK,MAAG,CAAC;SAChC;aAAM;YACL,WAAW,GAAG,aAAW,KAAK,mBAAc,IAAI,QAAK,CAAC;SACvD;QAED,IAAI,CAAC,QAAQ,GAAG,4QASI,GAAG,eAAU,GAAG,gFAEJ,IAAI,mIAKd,WAAW,8CAGhC,CAAC;IACJ,CAAC;IACH,iBAAC;AAAD,CAAC,AA/CD,IA+CC;AA/CY,gCAAU"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/lrn_grad_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/lrn_grad_gpu.d.ts
new file mode 100644
index 0000000..a93714b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/lrn_grad_gpu.d.ts
@@ -0,0 +1,12 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class LRNGradProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    depthRadius: number;
+    bias: number;
+    alpha: number;
+    beta: number;
+    depth: number;
+    constructor(inputShape: number[], depthRadius: number, bias: number, alpha: number, beta: number);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/lrn_grad_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/lrn_grad_gpu.js
new file mode 100644
index 0000000..930b18b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/lrn_grad_gpu.js
@@ -0,0 +1,18 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var LRNGradProgram = (function () {
+    function LRNGradProgram(inputShape, depthRadius, bias, alpha, beta) {
+        this.variableNames = ['inputImage', 'outputImage', 'dy'];
+        this.outputShape = [];
+        this.outputShape = inputShape;
+        this.depth = inputShape[3];
+        this.depthRadius = depthRadius;
+        this.bias = bias;
+        this.alpha = alpha;
+        this.beta = beta;
+        this.userCode = "\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < " + this.depth + "; ++d) {\n          int depthBegin = int(max(0.0, float(d - " + depthRadius + ")));\n          int depthEnd = int(min(float(" + this.depth + "),\n              float(d + " + depthRadius + " + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = " + this.depth + ";\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(" + alpha + ") * norm + float(" + bias + ");\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(" + alpha + ")\n                * float(" + beta + ")\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * " + beta + ");\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    ";
+    }
+    return LRNGradProgram;
+}());
+exports.LRNGradProgram = LRNGradProgram;
+//# sourceMappingURL=lrn_grad_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/lrn_grad_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/lrn_grad_gpu.js.map
new file mode 100644
index 0000000..4abede5
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/lrn_grad_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"lrn_grad_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/lrn_grad_gpu.ts"],"names":[],"mappings":";;AAmBA;IAUE,wBACI,UAAoB,EAAE,WAAmB,EAAE,IAAY,EAAE,KAAa,EACtE,IAAY;QAXhB,kBAAa,GAAG,CAAC,YAAY,EAAE,aAAa,EAAE,IAAI,CAAC,CAAC;QACpD,gBAAW,GAAa,EAAE,CAAC;QAWzB,IAAI,CAAC,WAAW,GAAG,UAAU,CAAC;QAC9B,IAAI,CAAC,KAAK,GAAG,UAAU,CAAC,CAAC,CAAC,CAAC;QAC3B,IAAI,CAAC,WAAW,GAAG,WAAW,CAAC;QAC/B,IAAI,CAAC,IAAI,GAAG,IAAI,CAAC;QACjB,IAAI,CAAC,KAAK,GAAG,KAAK,CAAC;QACnB,IAAI,CAAC,IAAI,GAAG,IAAI,CAAC;QACjB,IAAI,CAAC,QAAQ,GAAG,sNAQU,IAAI,CAAC,KAAK,oEACY,WAAW,qDACtB,IAAI,CAAC,KAAK,oCACzB,WAAW,kGAGC,IAAI,CAAC,KAAK,4aAevB,KAAK,yBAAoB,IAAI,wPAOb,KAAK,mCACpB,IAAI,mLAIY,IAAI,gSAczC,CAAC;IACJ,CAAC;IACH,qBAAC;AAAD,CAAC,AA5ED,IA4EC;AA5EY,wCAAc"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/max_pool_backprop_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/max_pool_backprop_gpu.d.ts
new file mode 100644
index 0000000..a098805
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/max_pool_backprop_gpu.d.ts
@@ -0,0 +1,8 @@
+import { Conv2DInfo } from '../../ops/conv_util';
+import { GPGPUProgram } from './gpgpu_math';
+export declare class MaxPool2DBackpropProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(convInfo: Conv2DInfo);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/max_pool_backprop_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/max_pool_backprop_gpu.js
new file mode 100644
index 0000000..1a2b0fd
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/max_pool_backprop_gpu.js
@@ -0,0 +1,20 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var MaxPool2DBackpropProgram = (function () {
+    function MaxPool2DBackpropProgram(convInfo) {
+        this.variableNames = ['dy', 'maxPos'];
+        this.outputShape = convInfo.inShape;
+        var strideHeight = convInfo.strideHeight;
+        var strideWidth = convInfo.strideWidth;
+        var dilationHeight = convInfo.dilationHeight;
+        var effectiveFilterHeight = convInfo.effectiveFilterHeight;
+        var effectiveFilterWidth = convInfo.effectiveFilterWidth;
+        var padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+        var padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+        var lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;
+        this.userCode = "\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < " + effectiveFilterHeight + ";\n          wR += " + dilationHeight + ") {\n          float dyR = float(dyRCorner + wR) / " + strideHeight + ".0;\n\n          if (dyR < 0.0 || dyR >= " + convInfo.outHeight + ".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < " + effectiveFilterWidth + "; wC++) {\n            float dyC = float(dyCCorner + wC) / " + strideWidth + ".0;\n\n            if (dyC < 0.0 || dyC >= " + convInfo.outWidth + ".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = " + lastIndex + " - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * " + effectiveFilterWidth + " + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+    }
+    return MaxPool2DBackpropProgram;
+}());
+exports.MaxPool2DBackpropProgram = MaxPool2DBackpropProgram;
+//# sourceMappingURL=max_pool_backprop_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/max_pool_backprop_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/max_pool_backprop_gpu.js.map
new file mode 100644
index 0000000..286a5bc
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/max_pool_backprop_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"max_pool_backprop_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/max_pool_backprop_gpu.ts"],"names":[],"mappings":";;AAqBA;IAKE,kCAAY,QAAoB;QAJhC,kBAAa,GAAG,CAAC,IAAI,EAAE,QAAQ,CAAC,CAAC;QAK/B,IAAI,CAAC,WAAW,GAAG,QAAQ,CAAC,OAAO,CAAC;QACpC,IAAM,YAAY,GAAG,QAAQ,CAAC,YAAY,CAAC;QAC3C,IAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QACzC,IAAM,cAAc,GAAG,QAAQ,CAAC,cAAc,CAAC;QAC/C,IAAM,qBAAqB,GAAG,QAAQ,CAAC,qBAAqB,CAAC;QAC7D,IAAM,oBAAoB,GAAG,QAAQ,CAAC,oBAAoB,CAAC;QAE3D,IAAM,MAAM,GAAG,qBAAqB,GAAG,CAAC,GAAG,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC;QAChE,IAAM,OAAO,GAAG,oBAAoB,GAAG,CAAC,GAAG,QAAQ,CAAC,OAAO,CAAC,IAAI,CAAC;QAEjE,IAAM,SAAS,GAAG,qBAAqB,GAAG,oBAAoB,GAAG,CAAC,CAAC;QACnE,IAAI,CAAC,QAAQ,GAAG,sCACa,MAAM,UAAK,OAAO,sdAcnB,qBAAqB,2BACnC,cAAc,2DACgB,YAAY,iDAExB,QAAQ,CAAC,SAAS,yIAKpB,oBAAoB,mEACJ,WAAW,mDAEvB,QAAQ,CAAC,QAAQ,oNAOvB,SAAS,+LAIJ,oBAAoB,kMAQpD,CAAC;IACJ,CAAC;IACH,+BAAC;AAAD,CAAC,AAjED,IAiEC;AAjEY,4DAAwB"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/mulmat_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/mulmat_gpu.d.ts
new file mode 100644
index 0000000..da62f01
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/mulmat_gpu.d.ts
@@ -0,0 +1,7 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class MatMulProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(aShape: [number, number, number], bShape: [number, number, number], transposeA?: boolean, transposeB?: boolean);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/mulmat_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/mulmat_gpu.js
new file mode 100644
index 0000000..943aedc
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/mulmat_gpu.js
@@ -0,0 +1,28 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var MatMulProgram = (function () {
+    function MatMulProgram(aShape, bShape, transposeA, transposeB) {
+        if (transposeA === void 0) { transposeA = false; }
+        if (transposeB === void 0) { transposeB = false; }
+        this.variableNames = ['matrixA', 'matrixB'];
+        var batchSize = aShape[0];
+        var outerShapeA = transposeA ? aShape[2] : aShape[1];
+        var outerShapeB = transposeB ? bShape[1] : bShape[2];
+        var sharedDim = transposeA ? aShape[1] : aShape[2];
+        this.outputShape = [batchSize, outerShapeA, outerShapeB];
+        var aSnippetFromOffset = function (vec4Offset, indexVar) {
+            return transposeA ? "batch, " + indexVar + " + " + vec4Offset + ", aRow" :
+                "batch, aRow, " + indexVar + " + " + vec4Offset;
+        };
+        var bSnippetFromOffset = function (vec4Offset, indexVar) {
+            return transposeB ? "batch, bCol, " + indexVar + " + " + vec4Offset :
+                "batch, " + indexVar + " + " + vec4Offset + ", bCol";
+        };
+        var sharedDimNearestVec4 = Math.floor(sharedDim / 4) * 4;
+        var sharedDimVec4Remainder = sharedDim % 4;
+        this.userCode = " float dotARowBCol(int batch, int aRow, int bCol) {\n      float result = 0.0;\n      for (int i = 0; i < " + sharedDimNearestVec4 + "; i += 4) {\n        vec4 a = vec4(\n          getMatrixA(" + aSnippetFromOffset(0, 'i') + "),\n          getMatrixA(" + aSnippetFromOffset(1, 'i') + "),\n          getMatrixA(" + aSnippetFromOffset(2, 'i') + "),\n          getMatrixA(" + aSnippetFromOffset(3, 'i') + ")\n        );\n        vec4 b = vec4(\n          getMatrixB(" + bSnippetFromOffset(0, 'i') + "),\n          getMatrixB(" + bSnippetFromOffset(1, 'i') + "),\n          getMatrixB(" + bSnippetFromOffset(2, 'i') + "),\n          getMatrixB(" + bSnippetFromOffset(3, 'i') + ")\n        );\n\n        result += dot(a, b);\n      }\n\n      if (" + (sharedDimVec4Remainder === 1) + ") {\n        result += getMatrixA(" + aSnippetFromOffset(0, sharedDimNearestVec4) + ") *\n          getMatrixB(" + bSnippetFromOffset(0, sharedDimNearestVec4) + ");\n      } else if (" + (sharedDimVec4Remainder === 2) + ") {\n        vec2 a = vec2(\n          getMatrixA(" + aSnippetFromOffset(0, sharedDimNearestVec4) + "),\n          getMatrixA(" + aSnippetFromOffset(1, sharedDimNearestVec4) + ")\n        );\n        vec2 b = vec2(\n          getMatrixB(" + bSnippetFromOffset(0, sharedDimNearestVec4) + "),\n          getMatrixB(" + bSnippetFromOffset(1, sharedDimNearestVec4) + ")\n        );\n        result += dot(a, b);\n      } else if (" + (sharedDimVec4Remainder === 3) + ") {\n        vec3 a = vec3(\n          getMatrixA(" + aSnippetFromOffset(0, sharedDimNearestVec4) + "),\n          getMatrixA(" + aSnippetFromOffset(1, sharedDimNearestVec4) + "),\n          getMatrixA(" + aSnippetFromOffset(2, sharedDimNearestVec4) + ")\n        );\n        vec3 b = vec3(\n          getMatrixB(" + bSnippetFromOffset(0, sharedDimNearestVec4) + "),\n          getMatrixB(" + bSnippetFromOffset(1, sharedDimNearestVec4) + "),\n          getMatrixB(" + bSnippetFromOffset(2, sharedDimNearestVec4) + ")\n        );\n        result += dot(a, b);\n      }\n\n      return result;\n    }\n\n    void main() {\n      ivec3 resBRC = getOutputCoords();\n      setOutput(dotARowBCol(resBRC.x, resBRC.y, resBRC.z));\n    }\n    ";
+    }
+    return MatMulProgram;
+}());
+exports.MatMulProgram = MatMulProgram;
+//# sourceMappingURL=mulmat_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/mulmat_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/mulmat_gpu.js.map
new file mode 100644
index 0000000..3e8a17c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/mulmat_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"mulmat_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/mulmat_gpu.ts"],"names":[],"mappings":";;AAmBA;IAKE,uBACI,MAAgC,EAAE,MAAgC,EAClE,UAAkB,EAAE,UAAkB;QAAtC,2BAAA,EAAA,kBAAkB;QAAE,2BAAA,EAAA,kBAAkB;QAN1C,kBAAa,GAAG,CAAC,SAAS,EAAE,SAAS,CAAC,CAAC;QAOrC,IAAM,SAAS,GAAG,MAAM,CAAC,CAAC,CAAC,CAAC;QAC5B,IAAM,WAAW,GAAG,UAAU,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC;QACvD,IAAM,WAAW,GAAG,UAAU,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC;QACvD,IAAM,SAAS,GAAG,UAAU,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC;QACrD,IAAI,CAAC,WAAW,GAAG,CAAC,SAAS,EAAE,WAAW,EAAE,WAAW,CAAC,CAAC;QAEzD,IAAM,kBAAkB,GAAG,UAAC,UAAkB,EAAE,QAAuB;YACnE,OAAA,UAAU,CAAC,CAAC,CAAC,YAAU,QAAQ,WAAM,UAAU,WAAQ,CAAC,CAAC;gBAC5C,kBAAgB,QAAQ,WAAM,UAAY;QADvD,CACuD,CAAC;QAC5D,IAAM,kBAAkB,GAAG,UAAC,UAAkB,EAAE,QAAuB;YACnE,OAAA,UAAU,CAAC,CAAC,CAAC,kBAAgB,QAAQ,WAAM,UAAY,CAAC,CAAC;gBAC5C,YAAU,QAAQ,WAAM,UAAU,WAAQ;QADvD,CACuD,CAAC;QAE5D,IAAM,oBAAoB,GAAG,IAAI,CAAC,KAAK,CAAC,SAAS,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC;QAC3D,IAAM,sBAAsB,GAAG,SAAS,GAAG,CAAC,CAAC;QAE7C,IAAI,CAAC,QAAQ,GAAG,+GAEQ,oBAAoB,kEAEzB,kBAAkB,CAAC,CAAC,EAAE,GAAG,CAAC,iCAC1B,kBAAkB,CAAC,CAAC,EAAE,GAAG,CAAC,iCAC1B,kBAAkB,CAAC,CAAC,EAAE,GAAG,CAAC,iCAC1B,kBAAkB,CAAC,CAAC,EAAE,GAAG,CAAC,oEAG1B,kBAAkB,CAAC,CAAC,EAAE,GAAG,CAAC,iCAC1B,kBAAkB,CAAC,CAAC,EAAE,GAAG,CAAC,iCAC1B,kBAAkB,CAAC,CAAC,EAAE,GAAG,CAAC,iCAC1B,kBAAkB,CAAC,CAAC,EAAE,GAAG,CAAC,6EAMrC,sBAAsB,KAAK,CAAC,2CACT,kBAAkB,CAAC,CAAC,EAAE,oBAAoB,CAAC,kCACnD,kBAAkB,CAAC,CAAC,EAAE,oBAAoB,CAAC,8BAC/C,sBAAsB,KAAK,CAAC,2DAExB,kBAAkB,CAAC,CAAC,EAAE,oBAAoB,CAAC,iCAC3C,kBAAkB,CAAC,CAAC,EAAE,oBAAoB,CAAC,oEAG3C,kBAAkB,CAAC,CAAC,EAAE,oBAAoB,CAAC,iCAC3C,kBAAkB,CAAC,CAAC,EAAE,oBAAoB,CAAC,uEAG/C,sBAAsB,KAAK,CAAC,2DAExB,kBAAkB,CAAC,CAAC,EAAE,oBAAoB,CAAC,iCAC3C,kBAAkB,CAAC,CAAC,EAAE,oBAAoB,CAAC,iCAC3C,kBAAkB,CAAC,CAAC,EAAE,oBAAoB,CAAC,oEAG3C,kBAAkB,CAAC,CAAC,EAAE,oBAAoB,CAAC,iCAC3C,kBAAkB,CAAC,CAAC,EAAE,oBAAoB,CAAC,iCAC3C,kBAAkB,CAAC,CAAC,EAAE,oBAAoB,CAAC,gOAY7D,CAAC;IACJ,CAAC;IACH,oBAAC;AAAD,CAAC,AA/ED,IA+EC;AA/EY,sCAAa"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/mulmat_packed_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/mulmat_packed_gpu.d.ts
new file mode 100644
index 0000000..f7c30c2
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/mulmat_packed_gpu.d.ts
@@ -0,0 +1,8 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class MatMulPackedProgram implements GPGPUProgram {
+    variableNames: string[];
+    usesPackedTextures: boolean;
+    outputShape: number[];
+    userCode: string;
+    constructor(aShape: [number, number], bShape: [number, number], outputShape: [number, number], transposeA?: boolean, transposeB?: boolean);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/mulmat_packed_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/mulmat_packed_gpu.js
new file mode 100644
index 0000000..b70130f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/mulmat_packed_gpu.js
@@ -0,0 +1,21 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var MatMulPackedProgram = (function () {
+    function MatMulPackedProgram(aShape, bShape, outputShape, transposeA, transposeB) {
+        if (transposeA === void 0) { transposeA = false; }
+        if (transposeB === void 0) { transposeB = false; }
+        this.variableNames = ['matrixA', 'matrixB'];
+        this.usesPackedTextures = true;
+        this.outputShape = outputShape;
+        var sharedDim = transposeA ? aShape[0] : aShape[1];
+        var sharedDimensionPacked = Math.ceil(sharedDim / 2);
+        var aSample = transposeA ? 'i * 2, rc.x' : 'rc.x, i * 2';
+        var bSample = transposeB ? 'rc.y, i * 2' : 'i * 2, rc.y';
+        var aSwizzle = transposeA ? ['a.xxyy', 'a.zzww'] : ['a.xxzz', 'a.yyww'];
+        var bSwizzle = transposeB ? ['b.xzxz', 'b.ywyw'] : ['b.xyxy', 'b.zwzw'];
+        this.userCode = "\n      const float sharedDimension = " + sharedDimensionPacked + ".0;\n\n      vec4 dot2x2ARowBCol(ivec2 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < " + sharedDimensionPacked + "; i++) {\n          vec4 a = getMatrixA(" + aSample + ");\n          vec4 b = getMatrixB(" + bSample + ");\n\n          result += (" + aSwizzle[0] + " * " + bSwizzle[0] + ") + (" + aSwizzle[1] + " * " + bSwizzle[1] + ");\n        }\n        return result;\n      }\n\n      void main() {\n        ivec2 rc = getOutputCoords();\n        setOutput(dot2x2ARowBCol(rc));\n      }\n    ";
+    }
+    return MatMulPackedProgram;
+}());
+exports.MatMulPackedProgram = MatMulPackedProgram;
+//# sourceMappingURL=mulmat_packed_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/mulmat_packed_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/mulmat_packed_gpu.js.map
new file mode 100644
index 0000000..ac4a4b2
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/mulmat_packed_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"mulmat_packed_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/mulmat_packed_gpu.ts"],"names":[],"mappings":";;AAmBA;IAME,6BACI,MAAwB,EAAE,MAAwB,EAClD,WAA6B,EAAE,UAAkB,EAAE,UAAkB;QAAtC,2BAAA,EAAA,kBAAkB;QAAE,2BAAA,EAAA,kBAAkB;QAPzE,kBAAa,GAAG,CAAC,SAAS,EAAE,SAAS,CAAC,CAAC;QACvC,uBAAkB,GAAG,IAAI,CAAC;QAOxB,IAAI,CAAC,WAAW,GAAG,WAAW,CAAC;QAE/B,IAAM,SAAS,GAAG,UAAU,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC;QACrD,IAAM,qBAAqB,GAAG,IAAI,CAAC,IAAI,CAAC,SAAS,GAAG,CAAC,CAAC,CAAC;QAEvD,IAAM,OAAO,GAAG,UAAU,CAAC,CAAC,CAAC,aAAa,CAAC,CAAC,CAAC,aAAa,CAAC;QAC3D,IAAM,OAAO,GAAG,UAAU,CAAC,CAAC,CAAC,aAAa,CAAC,CAAC,CAAC,aAAa,CAAC;QAC3D,IAAM,QAAQ,GAAG,UAAU,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,QAAQ,CAAC,CAAC;QAC1E,IAAM,QAAQ,GAAG,UAAU,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,QAAQ,CAAC,CAAC;QAE1E,IAAI,CAAC,QAAQ,GAAG,2CACkB,qBAAqB,kHAI7B,qBAAqB,gDACnB,OAAO,0CACP,OAAO,mCAEhB,QAAQ,CAAC,CAAC,CAAC,WAAM,QAAQ,CAAC,CAAC,CAAC,aAAQ,QAAQ,CAAC,CAAC,CAAC,WAC9D,QAAQ,CAAC,CAAC,CAAC,wKASd,CAAC;IACJ,CAAC;IACH,0BAAC;AAAD,CAAC,AAxCD,IAwCC;AAxCY,kDAAmB"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/multinomial_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/multinomial_gpu.d.ts
new file mode 100644
index 0000000..6858aff
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/multinomial_gpu.d.ts
@@ -0,0 +1,10 @@
+import { GPGPUContext } from './gpgpu_context';
+import { GPGPUProgram } from './gpgpu_math';
+export declare class MultinomialProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    seedLoc: WebGLUniformLocation;
+    constructor(batchSize: number, numOutcomes: number, numSamples: number);
+    getCustomSetupFunc(seed: number): (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => void;
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/multinomial_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/multinomial_gpu.js
new file mode 100644
index 0000000..d26cc7b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/multinomial_gpu.js
@@ -0,0 +1,21 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var MultinomialProgram = (function () {
+    function MultinomialProgram(batchSize, numOutcomes, numSamples) {
+        this.variableNames = ['probs'];
+        this.outputShape = [batchSize, numSamples];
+        this.userCode = "\n      uniform float seed;\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < " + (numOutcomes - 1) + "; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float(" + (numOutcomes - 1) + "));\n      }\n    ";
+    }
+    MultinomialProgram.prototype.getCustomSetupFunc = function (seed) {
+        var _this = this;
+        return function (gpgpu, webGLProgram) {
+            if (_this.seedLoc == null) {
+                _this.seedLoc = gpgpu.getUniformLocation(webGLProgram, 'seed');
+            }
+            gpgpu.gl.uniform1f(_this.seedLoc, seed);
+        };
+    };
+    return MultinomialProgram;
+}());
+exports.MultinomialProgram = MultinomialProgram;
+//# sourceMappingURL=multinomial_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/multinomial_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/multinomial_gpu.js.map
new file mode 100644
index 0000000..74bd948
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/multinomial_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"multinomial_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/multinomial_gpu.ts"],"names":[],"mappings":";;AAoBA;IAQE,4BAAY,SAAiB,EAAE,WAAmB,EAAE,UAAkB;QAPtE,kBAAa,GAAG,CAAC,OAAO,CAAC,CAAC;QAQxB,IAAI,CAAC,WAAW,GAAG,CAAC,SAAS,EAAE,UAAU,CAAC,CAAC;QAE3C,IAAI,CAAC,QAAQ,GAAG,gOAUU,WAAW,GAAG,CAAC,4PAUnB,WAAW,GAAG,CAAC,wBAEpC,CAAC;IACJ,CAAC;IAED,+CAAkB,GAAlB,UAAmB,IAAY;QAA/B,iBAOC;QANC,OAAO,UAAC,KAAmB,EAAE,YAA0B;YACrD,IAAI,KAAI,CAAC,OAAO,IAAI,IAAI,EAAE;gBACxB,KAAI,CAAC,OAAO,GAAG,KAAK,CAAC,kBAAkB,CAAC,YAAY,EAAE,MAAM,CAAC,CAAC;aAC/D;YACD,KAAK,CAAC,EAAE,CAAC,SAAS,CAAC,KAAI,CAAC,OAAO,EAAE,IAAI,CAAC,CAAC;QACzC,CAAC,CAAC;IACJ,CAAC;IACH,yBAAC;AAAD,CAAC,AA5CD,IA4CC;AA5CY,gDAAkB"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/onehot_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/onehot_gpu.d.ts
new file mode 100644
index 0000000..e12c273
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/onehot_gpu.d.ts
@@ -0,0 +1,8 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class OneHotProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    seedLoc: WebGLUniformLocation;
+    constructor(numIndices: number, depth: number, onValue: number, offValue: number);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/onehot_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/onehot_gpu.js
new file mode 100644
index 0000000..4e0e660
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/onehot_gpu.js
@@ -0,0 +1,12 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var OneHotProgram = (function () {
+    function OneHotProgram(numIndices, depth, onValue, offValue) {
+        this.variableNames = ['indices'];
+        this.outputShape = [numIndices, depth];
+        this.userCode = "\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float(" + offValue + "), float(" + onValue + "),\n                      float(index == coords.y)));\n      }\n    ";
+    }
+    return OneHotProgram;
+}());
+exports.OneHotProgram = OneHotProgram;
+//# sourceMappingURL=onehot_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/onehot_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/onehot_gpu.js.map
new file mode 100644
index 0000000..69fa907
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/onehot_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"onehot_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/onehot_gpu.ts"],"names":[],"mappings":";;AAmBA;IAQE,uBACI,UAAkB,EAAE,KAAa,EAAE,OAAe,EAAE,QAAgB;QARxE,kBAAa,GAAG,CAAC,SAAS,CAAC,CAAC;QAS1B,IAAI,CAAC,WAAW,GAAG,CAAC,UAAU,EAAE,KAAK,CAAC,CAAC;QAEvC,IAAI,CAAC,QAAQ,GAAG,qJAIU,QAAQ,iBAAY,OAAO,yEAGpD,CAAC;IACJ,CAAC;IACH,oBAAC;AAAD,CAAC,AArBD,IAqBC;AArBY,sCAAa"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/pack_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/pack_gpu.d.ts
new file mode 100644
index 0000000..4d7df78
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/pack_gpu.d.ts
@@ -0,0 +1,8 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class PackProgram implements GPGPUProgram {
+    variableNames: string[];
+    isPackShader: boolean;
+    outputShape: number[];
+    userCode: string;
+    constructor(outputShape: number[]);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/pack_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/pack_gpu.js
new file mode 100644
index 0000000..afa673e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/pack_gpu.js
@@ -0,0 +1,67 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var packing_util_1 = require("../packing_util");
+var shader_compiler_1 = require("./shader_compiler");
+var PackProgram = (function () {
+    function PackProgram(outputShape) {
+        this.variableNames = ['A'];
+        this.isPackShader = true;
+        this.outputShape = outputShape;
+        var rank = outputShape.length;
+        if (rank === 0) {
+            this.userCode = "\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      ";
+        }
+        else {
+            var channels = packing_util_1.getChannels('rc', rank);
+            var dtype = shader_compiler_1.getCoordsDataType(rank);
+            var outOfBoundsCondition = getOutOfBoundsCondition(rank, outputShape, channels);
+            var setup = getSetup(rank, outputShape[outputShape.length - 1], outputShape[outputShape.length - 2], channels);
+            var output = getOutput(outputShape, channels);
+            this.userCode = "\n        void main() {\n          " + dtype + " rc = getOutputCoords();\n\n          if(" + outOfBoundsCondition + ") {\n            setOutput(vec4(0));\n          } else {\n            " + setup + "\n\n            setOutput(vec4(" + output + "));\n          }\n        }\n      ";
+        }
+    }
+    return PackProgram;
+}());
+exports.PackProgram = PackProgram;
+function getSourceCoordsArr(rank, dims) {
+    var coords = [];
+    for (var row = 0; row <= 1; row++) {
+        for (var col = 0; col <= 1; col++) {
+            var coord = (row === 0 ? 'r' : 'rp1') + ", " + (col === 0 ? 'c' : 'cp1');
+            for (var d = 2; d < rank; d++) {
+                coord = dims[dims.length - 1 - d] + "," + coord;
+            }
+            coords.push(coord);
+        }
+    }
+    return coords;
+}
+function getOutOfBoundsCondition(rank, shape, dims) {
+    if (rank === 1) {
+        return "rc > " + shape[0];
+    }
+    var cond = '';
+    for (var i = rank - 2; i < rank; i++) {
+        cond += dims[i] + " >= " + shape[i];
+        if (i < rank - 1) {
+            cond += '||';
+        }
+    }
+    return cond;
+}
+function getSetup(rank, cols, rows, dims) {
+    if (rank === 1) {
+        return '';
+    }
+    var innerDims = dims.slice(-2);
+    return "\n    int r = " + innerDims[0] + ";\n    int c = " + innerDims[1] + ";\n    int rp1 = r + 1;\n    int cp1 = c + 1;\n\n    bool cEdge = cp1 >= " + cols + ";\n    bool rEdge = rp1 >= " + rows + ";\n  ";
+}
+function getOutput(shape, dims) {
+    var rank = shape.length;
+    var sourceCoords = getSourceCoordsArr(rank, dims);
+    if (rank === 1) {
+        return "getA(rc),\n            rc + 1 >= " + shape[0] + " ? 0. : getA(rc + 1),\n            0, 0";
+    }
+    return "getA(" + sourceCoords[0] + "),\n          cEdge ? 0. : getA(" + sourceCoords[1] + "),\n          rEdge ? 0. : getA(" + sourceCoords[2] + "),\n          rEdge || cEdge ? 0. : getA(" + sourceCoords[3] + ")";
+}
+//# sourceMappingURL=pack_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/pack_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/pack_gpu.js.map
new file mode 100644
index 0000000..9ef6408
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/pack_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"pack_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/pack_gpu.ts"],"names":[],"mappings":";;AAiBA,gDAA4C;AAG5C,qDAAoD;AAEpD;IAME,qBACI,WACY;QAPhB,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QACtB,iBAAY,GAAG,IAAI,CAAC;QAQlB,IAAI,CAAC,WAAW,GAAG,WAAW,CAAC;QAC/B,IAAM,IAAI,GAAG,WAAW,CAAC,MAAM,CAAC;QAEhC,IAAI,IAAI,KAAK,CAAC,EAAE;YACd,IAAI,CAAC,QAAQ,GAAG,4FAIf,CAAC;SACH;aAAM;YACL,IAAM,QAAQ,GAAG,0BAAW,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC;YACzC,IAAM,KAAK,GAAG,mCAAiB,CAAC,IAAI,CAAC,CAAC;YACtC,IAAM,oBAAoB,GACtB,uBAAuB,CAAC,IAAI,EAAE,WAAW,EAAE,QAAQ,CAAC,CAAC;YACzD,IAAM,KAAK,GAAG,QAAQ,CAClB,IAAI,EAAE,WAAW,CAAC,WAAW,CAAC,MAAM,GAAG,CAAC,CAAC,EACzC,WAAW,CAAC,WAAW,CAAC,MAAM,GAAG,CAAC,CAAC,EAAE,QAAQ,CAAC,CAAC;YACnD,IAAM,MAAM,GAAG,SAAS,CAAC,WAAW,EAAE,QAAQ,CAAC,CAAC;YAEhD,IAAI,CAAC,QAAQ,GAAG,wCAEV,KAAK,iDAEF,oBAAoB,8EAGrB,KAAK,uCAEU,MAAM,wCAG5B,CAAC;SACH;IACH,CAAC;IACH,kBAAC;AAAD,CAAC,AA5CD,IA4CC;AA5CY,kCAAW;AA8CxB,4BAA4B,IAAY,EAAE,IAAc;IACtD,IAAM,MAAM,GAAG,EAAE,CAAC;IAElB,KAAK,IAAI,GAAG,GAAG,CAAC,EAAE,GAAG,IAAI,CAAC,EAAE,GAAG,EAAE,EAAE;QACjC,KAAK,IAAI,GAAG,GAAG,CAAC,EAAE,GAAG,IAAI,CAAC,EAAE,GAAG,EAAE,EAAE;YACjC,IAAI,KAAK,GAAG,CAAG,GAAG,KAAK,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,KAAK,YAAK,GAAG,KAAK,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,KAAK,CAAE,CAAC;YAErE,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,IAAI,EAAE,CAAC,EAAE,EAAE;gBAC7B,KAAK,GAAM,IAAI,CAAC,IAAI,CAAC,MAAM,GAAG,CAAC,GAAG,CAAC,CAAC,MAAG,GAAG,KAAK,CAAC;aACjD;YAED,MAAM,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC;SACpB;KACF;IACD,OAAO,MAAM,CAAC;AAChB,CAAC;AAED,iCACI,IAAY,EAAE,KAAe,EAAE,IAAc;IAC/C,IAAI,IAAI,KAAK,CAAC,EAAE;QACd,OAAO,UAAQ,KAAK,CAAC,CAAC,CAAG,CAAC;KAC3B;IAED,IAAI,IAAI,GAAG,EAAE,CAAC;IACd,KAAK,IAAI,CAAC,GAAG,IAAI,GAAG,CAAC,EAAE,CAAC,GAAG,IAAI,EAAE,CAAC,EAAE,EAAE;QACpC,IAAI,IAAO,IAAI,CAAC,CAAC,CAAC,YAAO,KAAK,CAAC,CAAC,CAAG,CAAC;QACpC,IAAI,CAAC,GAAG,IAAI,GAAG,CAAC,EAAE;YAChB,IAAI,IAAI,IAAI,CAAC;SACd;KACF;IAED,OAAO,IAAI,CAAC;AACd,CAAC;AAED,kBACI,IAAY,EAAE,IAAY,EAAE,IAAY,EAAE,IAAc;IAC1D,IAAI,IAAI,KAAK,CAAC,EAAE;QACd,OAAO,EAAE,CAAC;KACX;IAED,IAAM,SAAS,GAAG,IAAI,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC;IAEjC,OAAO,mBACK,SAAS,CAAC,CAAC,CAAC,uBACZ,SAAS,CAAC,CAAC,CAAC,iFAIA,IAAI,mCACJ,IAAI,UAC3B,CAAC;AACJ,CAAC;AAED,mBAAmB,KAAe,EAAE,IAAc;IAChD,IAAM,IAAI,GAAG,KAAK,CAAC,MAAM,CAAC;IAC1B,IAAM,YAAY,GAAG,kBAAkB,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC;IACpD,IAAI,IAAI,KAAK,CAAC,EAAE;QACd,OAAO,sCACa,KAAK,CAAC,CAAC,CAAC,4CACf,CAAC;KACf;IAED,OAAO,UAAQ,YAAY,CAAC,CAAC,CAAC,wCACF,YAAY,CAAC,CAAC,CAAC,wCACf,YAAY,CAAC,CAAC,CAAC,iDACN,YAAY,CAAC,CAAC,CAAC,MAAG,CAAC;AAC1D,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/pad_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/pad_gpu.d.ts
new file mode 100644
index 0000000..b21e476
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/pad_gpu.d.ts
@@ -0,0 +1,7 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class PadProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(xShape: number[], paddings: Array<[number, number]>, constantValue: number);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/pad_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/pad_gpu.js
new file mode 100644
index 0000000..8f274b3
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/pad_gpu.js
@@ -0,0 +1,22 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var shader_compiler_1 = require("./shader_compiler");
+var PadProgram = (function () {
+    function PadProgram(xShape, paddings, constantValue) {
+        this.variableNames = ['x'];
+        this.outputShape = paddings.map(function (p, i) { return p[0] + xShape[i] + p[1]; });
+        var rank = xShape.length;
+        var type = shader_compiler_1.getCoordsDataType(rank);
+        var start = paddings.map(function (p) { return p[0]; }).join(',');
+        var end = paddings.map(function (p, i) { return p[0] + xShape[i]; }).join(',');
+        var unpackedCoords = ['coords[0]', 'coords[1]', 'coords[2]', 'coords[3]'].slice(0, rank);
+        if (rank === 1) {
+            this.userCode = "\n        int start = " + start + ";\n        int end = " + end + ";\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(float(" + constantValue + "));\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      ";
+            return;
+        }
+        this.userCode = "\n      " + type + " start = " + type + "(" + start + ");\n      " + type + " end = " + type + "(" + end + ");\n\n      void main() {\n        " + type + " outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(float(" + constantValue + "));\n        } else {\n          " + type + " coords = outC - start;\n          setOutput(getX(" + unpackedCoords + "));\n        }\n      }\n    ";
+    }
+    return PadProgram;
+}());
+exports.PadProgram = PadProgram;
+//# sourceMappingURL=pad_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/pad_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/pad_gpu.js.map
new file mode 100644
index 0000000..e60edfe
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/pad_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"pad_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/pad_gpu.ts"],"names":[],"mappings":";;AAkBA,qDAAoD;AAEpD;IAKE,oBACI,MAAgB,EAAE,QAAiC,EACnD,aAAqB;QANzB,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QAOpB,IAAI,CAAC,WAAW,GAAG,QAAQ,CAAC,GAAG,CAC3B,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,CAAC,CAAC,CAAC,CAAC,GAAmB,MAAM,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,EAAvC,CAAuC,CAAgB,CAAC;QACtE,IAAM,IAAI,GAAG,MAAM,CAAC,MAAM,CAAC;QAC3B,IAAM,IAAI,GAAG,mCAAiB,CAAC,IAAI,CAAC,CAAC;QAErC,IAAM,KAAK,GAAG,QAAQ,CAAC,GAAG,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,CAAC,CAAC,EAAJ,CAAI,CAAC,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC;QAChD,IAAM,GAAG,GAAG,QAAQ,CAAC,GAAG,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,CAAC,CAAC,CAAC,CAAC,GAAG,MAAM,CAAC,CAAC,CAAC,EAAhB,CAAgB,CAAC,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC;QAC/D,IAAM,cAAc,GACf,CAAC,WAAW,EAAE,WAAW,EAAE,WAAW,EAAC,WAAW,CAAC,CAAC,KAAK,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC;QAExE,IAAI,IAAI,KAAK,CAAC,EAAE;YACd,IAAI,CAAC,QAAQ,GAAG,2BACA,KAAK,6BACP,GAAG,uJAKO,aAAa,wGAKpC,CAAC;YACF,OAAO;SACR;QACD,IAAI,CAAC,QAAQ,GAAG,aACZ,IAAI,iBAAY,IAAI,SAAI,KAAK,kBAC7B,IAAI,eAAU,IAAI,SAAI,GAAG,2CAGvB,IAAI,6IAEc,aAAa,yCAE7B,IAAI,0DACW,cAAc,kCAGpC,CAAC;IACJ,CAAC;IACH,iBAAC;AAAD,CAAC,AAjDD,IAiDC;AAjDY,gCAAU"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/pool_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/pool_gpu.d.ts
new file mode 100644
index 0000000..95c4c7b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/pool_gpu.d.ts
@@ -0,0 +1,8 @@
+import { Conv2DInfo } from '../../ops/conv_util';
+import { GPGPUProgram } from './gpgpu_math';
+export declare class Pool2DProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(convInfo: Conv2DInfo, poolType: 'max' | 'avg', computePositions: boolean);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/pool_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/pool_gpu.js
new file mode 100644
index 0000000..ce60de4
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/pool_gpu.js
@@ -0,0 +1,43 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var Pool2DProgram = (function () {
+    function Pool2DProgram(convInfo, poolType, computePositions) {
+        this.variableNames = ['x'];
+        if (poolType === 'avg' && computePositions) {
+            throw new Error('Cannot compute positions for average pool.');
+        }
+        var filterWidth = convInfo.filterWidth;
+        var strideHeight = convInfo.strideHeight;
+        var strideWidth = convInfo.strideWidth;
+        var dilationHeight = convInfo.dilationHeight;
+        var dilationWidth = convInfo.dilationWidth;
+        var effectiveFilterHeight = convInfo.effectiveFilterHeight;
+        var effectiveFilterWidth = convInfo.effectiveFilterWidth;
+        var padTop = convInfo.padInfo.top;
+        var padLeft = convInfo.padInfo.left;
+        this.outputShape = convInfo.outShape;
+        var isAvgPool = poolType === 'avg';
+        var initializationValue = '0.0';
+        if (!isAvgPool) {
+            initializationValue = '-1.0 / 0.0';
+        }
+        if (computePositions) {
+            var compareOp_1 = '>=';
+            this.userCode = "\n        const ivec2 strides = ivec2(" + strideHeight + ", " + strideWidth + ");\n        const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < " + effectiveFilterHeight + ";\n              wR += " + dilationHeight + ") {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n              continue;\n            }\n\n            for (int wC = 0; wC < " + effectiveFilterWidth + ";\n                wC += " + dilationWidth + ") {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value " + compareOp_1 + " currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = wR * " + effectiveFilterWidth + " + wC;\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      ";
+            return;
+        }
+        var compareOp = 'max';
+        var returnValue = poolType + "(" + poolType + "(" + poolType + "(" +
+            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';
+        if (poolType === 'avg') {
+            returnValue = "avgValue / count";
+        }
+        var filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;
+        var filterWidthVec4Remainder = filterWidth % 4;
+        var updateSnippet = "\n      if (" + isAvgPool + ") {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = " + compareOp + "(values, minMaxValue);\n      }\n    ";
+        this.userCode = "\n      const ivec2 strides = ivec2(" + strideHeight + ", " + strideWidth + ");\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n      const float initializationValue = " + initializationValue + ";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(" + initializationValue + ");\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < " + effectiveFilterHeight + ";\n            wR += " + dilationHeight + ") {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n            continue;\n          }\n\n          for (int wC = 0; wC < " + filterWidthNearestVec4 + "; wC += 4) {\n            int xC = xCCorner + wC * " + dilationWidth + ";\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + " + dilationWidth + ", d),\n              getValue(batch, xR, xC + 2 * " + dilationWidth + ", d),\n              getValue(batch, xR, xC + 3 * " + dilationWidth + ", d)\n            );\n\n            " + updateSnippet + "\n          }\n\n          int xC = xCCorner + " + filterWidthNearestVec4 + ";\n          if (" + (filterWidthVec4Remainder === 1) + ") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            " + updateSnippet + "\n          } else if (" + (filterWidthVec4Remainder === 2) + ") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + " + dilationWidth + ", d),\n              initializationValue,\n              initializationValue\n            );\n\n            " + updateSnippet + "\n          } else if (" + (filterWidthVec4Remainder === 3) + ") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + " + dilationWidth + ", d),\n              getValue(batch, xR, xC + 2 * " + dilationWidth + ", d),\n              initializationValue\n            );\n\n            " + updateSnippet + "\n          }\n        }\n        setOutput(" + returnValue + ");\n      }\n    ";
+    }
+    return Pool2DProgram;
+}());
+exports.Pool2DProgram = Pool2DProgram;
+//# sourceMappingURL=pool_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/pool_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/pool_gpu.js.map
new file mode 100644
index 0000000..ffea014
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/pool_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"pool_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/pool_gpu.ts"],"names":[],"mappings":";;AAoBA;IAKE,uBACI,QAAoB,EAAE,QAAqB,EAAE,gBAAyB;QAL1E,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QAMpB,IAAI,QAAQ,KAAK,KAAK,IAAI,gBAAgB,EAAE;YAC1C,MAAM,IAAI,KAAK,CAAC,4CAA4C,CAAC,CAAC;SAC/D;QAED,IAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QACzC,IAAM,YAAY,GAAG,QAAQ,CAAC,YAAY,CAAC;QAC3C,IAAM,WAAW,GAAG,QAAQ,CAAC,WAAW,CAAC;QACzC,IAAM,cAAc,GAAG,QAAQ,CAAC,cAAc,CAAC;QAC/C,IAAM,aAAa,GAAG,QAAQ,CAAC,aAAa,CAAC;QAC7C,IAAM,qBAAqB,GAAG,QAAQ,CAAC,qBAAqB,CAAC;QAC7D,IAAM,oBAAoB,GAAG,QAAQ,CAAC,oBAAoB,CAAC;QAE3D,IAAM,MAAM,GAAG,QAAQ,CAAC,OAAO,CAAC,GAAG,CAAC;QACpC,IAAM,OAAO,GAAG,QAAQ,CAAC,OAAO,CAAC,IAAI,CAAC;QACtC,IAAI,CAAC,WAAW,GAAG,QAAQ,CAAC,QAAQ,CAAC;QAErC,IAAM,SAAS,GAAG,QAAQ,KAAK,KAAK,CAAC;QAErC,IAAI,mBAAmB,GAAG,KAAK,CAAC;QAChC,IAAI,CAAC,SAAS,EAAE;YACd,mBAAmB,GAAG,YAAY,CAAC;SACpC;QAED,IAAI,gBAAgB,EAAE;YACpB,IAAM,WAAS,GAAG,IAAI,CAAC;YAEvB,IAAI,CAAC,QAAQ,GAAG,2CACgB,YAAY,UAAK,WAAW,6CAC/B,MAAM,UAAK,OAAO,yiBAkBnB,qBAAqB,+BACjC,cAAc,oFAGF,QAAQ,CAAC,QAAQ,yFAIf,oBAAoB,iCAChC,aAAa,wFAGD,QAAQ,CAAC,OAAO,wWAU1B,WAAS,kJAGK,oBAAoB,wHAMrD,CAAC;YACF,OAAO;SACR;QAED,IAAM,SAAS,GAAG,KAAK,CAAC;QAExB,IAAI,WAAW,GAAM,QAAQ,SAAI,QAAQ,SAAI,QAAQ,MAAG;YACpD,mEAAmE,CAAC;QACxE,IAAI,QAAQ,KAAK,KAAK,EAAE;YACtB,WAAW,GAAG,kBAAkB,CAAC;SAClC;QAED,IAAM,sBAAsB,GAAG,IAAI,CAAC,KAAK,CAAC,WAAW,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC;QAC/D,IAAM,wBAAwB,GAAG,WAAW,GAAG,CAAC,CAAC;QAEjD,IAAM,aAAa,GAAG,iBACd,SAAS,2FAGG,SAAS,0CAE5B,CAAC;QAEF,IAAI,CAAC,QAAQ,GAAG,yCACgB,YAAY,UAAK,WAAW,2CAC/B,MAAM,UAAK,OAAO,oDACT,mBAAmB,kLAM/B,QAAQ,CAAC,OAAO,+fAkBZ,mBAAmB,iGAIrB,qBAAqB,6BACjC,cAAc,gFAGF,QAAQ,CAAC,QAAQ,mFAIf,sBAAsB,2DACjB,aAAa,gIAIX,aAAa,0DACT,aAAa,0DACb,aAAa,4CAG5C,aAAa,uDAGK,sBAAsB,0BACtC,wBAAwB,KAAK,CAAC,mOAQhC,aAAa,gCACJ,wBAAwB,KAAK,CAAC,iIAGZ,aAAa,oHAKxC,aAAa,gCACJ,wBAAwB,KAAK,CAAC,iIAGZ,aAAa,0DACT,aAAa,gFAI5C,aAAa,oDAGP,WAAW,sBAE1B,CAAC;IACJ,CAAC;IACH,oBAAC;AAAD,CAAC,AAjMD,IAiMC;AAjMY,sCAAa"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reduce_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reduce_gpu.d.ts
new file mode 100644
index 0000000..f5dd65a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reduce_gpu.d.ts
@@ -0,0 +1,8 @@
+import { ReduceInfo } from '../../ops/reduce_util';
+import { GPGPUProgram } from './gpgpu_math';
+export declare class ReduceProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(reduceInfo: ReduceInfo, reduceType: 'all' | 'any' | 'max' | 'min' | 'sum' | 'prod');
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reduce_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reduce_gpu.js
new file mode 100644
index 0000000..cdd15be
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reduce_gpu.js
@@ -0,0 +1,61 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var ReduceProgram = (function () {
+    function ReduceProgram(reduceInfo, reduceType) {
+        this.variableNames = ['x'];
+        var windowSize = reduceInfo.windowSize;
+        var batchSize = reduceInfo.batchSize;
+        var inSize = reduceInfo.inSize;
+        var outSize = Math.ceil(inSize / windowSize);
+        this.outputShape = [batchSize, outSize];
+        var initializationValue = '0.0';
+        var compareOp = "";
+        if (reduceType === 'prod') {
+            initializationValue = '1.0';
+        }
+        else if (reduceType === 'min') {
+            initializationValue = '1.0 / 0.0';
+            compareOp = "min";
+        }
+        else if (reduceType === 'max') {
+            initializationValue = '-1.0 / 0.0';
+            compareOp = "max";
+        }
+        var returnValue = reduceType + "(" + reduceType + "(" + reduceType + "(" +
+            'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';
+        if (reduceType === 'sum') {
+            returnValue = "sumValue";
+        }
+        else if (reduceType === 'prod') {
+            returnValue = "prodValue";
+        }
+        else if (reduceType === 'all') {
+            returnValue = "allValue";
+        }
+        else if (reduceType === 'any') {
+            returnValue = "anyValue";
+        }
+        var windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;
+        var windowSizeVec4Remainder = windowSize % 4;
+        var updateSnippet = "\n      if (" + (reduceType === 'sum') + ") {\n        sumValue += dot(values, ones);\n      } else if (" + (reduceType === 'prod') + ") {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = " + compareOp + "(values, minMaxValue);\n      }\n    ";
+        var vecType = "vec4";
+        if (reduceType === 'all') {
+            initializationValue = '1.0';
+            updateSnippet = "\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      ";
+            vecType = "bvec4";
+        }
+        else if (reduceType === 'any') {
+            initializationValue = '0.0';
+            updateSnippet = "\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      ";
+            vecType = "bvec4";
+        }
+        var checkOutOfBounds = '';
+        if (inSize % windowSize > 0) {
+            checkOutOfBounds = "\n        if (inIdx < 0 || inIdx >= " + inSize + ") {\n          return initializationValue;\n        }\n      ";
+        }
+        this.userCode = "\n      const float initializationValue = " + initializationValue + ";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        " + checkOutOfBounds + "\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * " + windowSize + ";\n\n        vec4 minMaxValue = vec4(" + initializationValue + ");\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < " + windowSizeNearestVec4 + "; i += 4) {\n          int inIdx = inOffset + i;\n          " + vecType + " values = " + vecType + "(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          " + updateSnippet + "\n        }\n\n        int inIdx = inOffset + " + windowSizeNearestVec4 + ";\n        if (" + (windowSizeVec4Remainder === 1) + ") {\n          " + vecType + " values = " + vecType + "(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          " + updateSnippet + "\n        } else if (" + (windowSizeVec4Remainder === 2) + ") {\n          " + vecType + " values = " + vecType + "(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          " + updateSnippet + "\n        } else if (" + (windowSizeVec4Remainder === 3) + ") {\n          " + vecType + " values = " + vecType + "(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          " + updateSnippet + "\n        }\n        setOutput(" + returnValue + ");\n      }\n    ";
+    }
+    return ReduceProgram;
+}());
+exports.ReduceProgram = ReduceProgram;
+//# sourceMappingURL=reduce_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reduce_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reduce_gpu.js.map
new file mode 100644
index 0000000..3bdba85
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reduce_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"reduce_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/reduce_gpu.ts"],"names":[],"mappings":";;AAoBA;IAKE,uBACI,UAAsB,EACtB,UAAgD;QANpD,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QAOpB,IAAM,UAAU,GAAG,UAAU,CAAC,UAAU,CAAC;QACzC,IAAM,SAAS,GAAG,UAAU,CAAC,SAAS,CAAC;QACvC,IAAM,MAAM,GAAG,UAAU,CAAC,MAAM,CAAC;QACjC,IAAM,OAAO,GAAG,IAAI,CAAC,IAAI,CAAC,MAAM,GAAG,UAAU,CAAC,CAAC;QAC/C,IAAI,CAAC,WAAW,GAAG,CAAC,SAAS,EAAE,OAAO,CAAC,CAAC;QAExC,IAAI,mBAAmB,GAAG,KAAK,CAAC;QAChC,IAAI,SAAS,GAAG,EAAE,CAAC;QAEnB,IAAI,UAAU,KAAK,MAAM,EAAE;YACzB,mBAAmB,GAAG,KAAK,CAAC;SAC7B;aAAM,IAAI,UAAU,KAAK,KAAK,EAAE;YAC/B,mBAAmB,GAAG,WAAW,CAAC;YAClC,SAAS,GAAG,KAAK,CAAC;SACnB;aAAM,IAAI,UAAU,KAAK,KAAK,EAAE;YAC/B,mBAAmB,GAAG,YAAY,CAAC;YACnC,SAAS,GAAG,KAAK,CAAC;SACnB;QAED,IAAI,WAAW,GAAM,UAAU,SAAI,UAAU,SAAI,UAAU,MAAG;YAC1D,mEAAmE,CAAC;QAExE,IAAI,UAAU,KAAK,KAAK,EAAE;YACxB,WAAW,GAAG,UAAU,CAAC;SAC1B;aAAM,IAAI,UAAU,KAAK,MAAM,EAAE;YAChC,WAAW,GAAG,WAAW,CAAC;SAC3B;aAAM,IAAI,UAAU,KAAK,KAAK,EAAE;YAC/B,WAAW,GAAG,UAAU,CAAC;SAC1B;aAAM,IAAI,UAAU,KAAK,KAAK,EAAE;YAC/B,WAAW,GAAG,UAAU,CAAC;SAC1B;QAED,IAAM,qBAAqB,GAAG,IAAI,CAAC,KAAK,CAAC,UAAU,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC;QAC7D,IAAM,uBAAuB,GAAG,UAAU,GAAG,CAAC,CAAC;QAE/C,IAAI,aAAa,GAAG,kBACZ,UAAU,KAAK,KAAK,wEAEb,UAAU,KAAK,MAAM,wKAIhB,SAAS,0CAE5B,CAAC;QAEF,IAAI,OAAO,GAAG,MAAM,CAAC;QAErB,IAAI,UAAU,KAAK,KAAK,EAAE;YACxB,mBAAmB,GAAG,KAAK,CAAC;YAC5B,aAAa,GAAG,oMAIf,CAAC;YACF,OAAO,GAAG,OAAO,CAAC;SACnB;aAAM,IAAI,UAAU,KAAK,KAAK,EAAE;YAC/B,mBAAmB,GAAG,KAAK,CAAC;YAC5B,aAAa,GAAG,oMAIf,CAAC;YACF,OAAO,GAAG,OAAO,CAAC;SACnB;QAED,IAAI,gBAAgB,GAAG,EAAE,CAAC;QAC1B,IAAI,MAAM,GAAG,UAAU,GAAG,CAAC,EAAE;YAC3B,gBAAgB,GAAG,yCACW,MAAM,kEAGnC,CAAC;SACH;QACD,IAAI,CAAC,QAAQ,GAAG,+CACsB,mBAAmB,sHAInD,gBAAgB,0NAQQ,UAAU,6CAEV,mBAAmB,uKAMvB,qBAAqB,oEAEvC,OAAO,kBAAa,OAAO,oMAO3B,aAAa,sDAGQ,qBAAqB,wBACxC,uBAAuB,KAAK,CAAC,wBAC/B,OAAO,kBAAa,OAAO,+KAO3B,aAAa,8BACJ,uBAAuB,KAAK,CAAC,wBACtC,OAAO,kBAAa,OAAO,sLAO3B,aAAa,8BACJ,uBAAuB,KAAK,CAAC,wBACtC,OAAO,kBAAa,OAAO,6LAO3B,aAAa,uCAEL,WAAW,sBAE1B,CAAC;IACJ,CAAC;IACH,oBAAC;AAAD,CAAC,AApJD,IAoJC;AApJY,sCAAa"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reshape_packed_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reshape_packed_gpu.d.ts
new file mode 100644
index 0000000..4b29a51
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reshape_packed_gpu.d.ts
@@ -0,0 +1,8 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class ReshapePackedProgram implements GPGPUProgram {
+    variableNames: string[];
+    usesPackedTextures: boolean;
+    outputShape: number[];
+    userCode: string;
+    constructor(outputShape: [number, number, number], inputShape: [number, number, number]);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reshape_packed_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reshape_packed_gpu.js
new file mode 100644
index 0000000..c1bda13
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reshape_packed_gpu.js
@@ -0,0 +1,34 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var util = require("../../util");
+var shader_util = require("./shader_compiler_util");
+var ReshapePackedProgram = (function () {
+    function ReshapePackedProgram(outputShape, inputShape) {
+        this.variableNames = ['A'];
+        this.usesPackedTextures = true;
+        this.outputShape = outputShape;
+        var mainLoop = "";
+        for (var i = 0; i < 4; i++) {
+            var thisRC = "thisRC = rc;";
+            if (i % 2 === 1) {
+                thisRC += "thisRC.z += 1;";
+            }
+            if (i > 1) {
+                thisRC += "thisRC.y += 1;";
+            }
+            mainLoop += "\n        " + thisRC + "\n        " + (i > 0 ? "if(thisRC.y < rows && thisRC.z < cols){" : '') + "\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[" + i + "] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        " + (i > 0 ? '}' : '') + "\n      ";
+        }
+        this.userCode = "\n      " + getReshapedInputCoords(inputShape) + "\n      " + getFlatIndex(outputShape) + "\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = " + outputShape[1] + ";\n        int cols = " + outputShape[2] + ";\n\n        " + mainLoop + "\n\n        setOutput(result);\n      }\n    ";
+    }
+    return ReshapePackedProgram;
+}());
+exports.ReshapePackedProgram = ReshapePackedProgram;
+function getFlatIndex(shape) {
+    var dotCoordsWithStrides = shader_util.dotify(['coords.x', 'coords.y', 'coords.z'], util.computeStrides(shape).map(function (d) { return d.toString(); }).concat(['1.']));
+    return "\n    int getFlatIndex(ivec3 coords) {\n      return round(" + dotCoordsWithStrides + ");\n    }\n  ";
+}
+function getReshapedInputCoords(shape) {
+    var coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);
+    return "\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      " + coordsFromIndexSnippet + "\n      return ivec3(r, c, d);\n    }\n  ";
+}
+//# sourceMappingURL=reshape_packed_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reshape_packed_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reshape_packed_gpu.js.map
new file mode 100644
index 0000000..fca88b6
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reshape_packed_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"reshape_packed_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/reshape_packed_gpu.ts"],"names":[],"mappings":";;AAiBA,iCAAmC;AAEnC,oDAAsD;AAEtD;IAME,8BAAY,WAAqC,EAAE,UAElD;QAPD,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QACtB,uBAAkB,GAAG,IAAI,CAAC;QAOxB,IAAI,CAAC,WAAW,GAAG,WAAW,CAAC;QAE/B,IAAI,QAAQ,GAAG,EAAE,CAAC;QAClB,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,EAAE,EAAE;YAC1B,IAAI,MAAM,GAAG,cAAc,CAAC;YAC5B,IAAI,CAAC,GAAG,CAAC,KAAK,CAAC,EAAE;gBACf,MAAM,IAAI,gBAAgB,CAAC;aAC5B;YACD,IAAI,CAAC,GAAG,CAAC,EAAE;gBACT,MAAM,IAAI,gBAAgB,CAAC;aAC5B;YAED,QAAQ,IAAI,eACR,MAAM,mBACN,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,yCAAyC,CAAC,CAAC,CAAC,EAAE,qOAM7C,CAAC,wGAEV,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,cACnB,CAAC;SACH;QAED,IAAI,CAAC,QAAQ,GAAG,aACZ,sBAAsB,CAAC,UAAU,CAAC,gBAClC,YAAY,CAAC,WAAW,CAAC,uJAQZ,WAAW,CAAC,CAAC,CAAC,8BACd,WAAW,CAAC,CAAC,CAAC,qBAEzB,QAAQ,kDAIb,CAAC;IACJ,CAAC;IACH,2BAAC;AAAD,CAAC,AAtDD,IAsDC;AAtDY,oDAAoB;AAwDjC,sBAAsB,KAA+B;IACnD,IAAM,oBAAoB,GAAG,WAAW,CAAC,MAAM,CAC3C,CAAC,UAAU,EAAE,UAAU,EAAE,UAAU,CAAC,EACpC,IAAI,CAAC,cAAc,CAAC,KAAK,CAAC,CAAC,GAAG,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,QAAQ,EAAE,EAAZ,CAAY,CAAC,CAAC,MAAM,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;IAEtE,OAAO,gEAEY,oBAAoB,kBAEtC,CAAC;AACJ,CAAC;AAED,gCAAgC,KAA+B;IAC7D,IAAM,sBAAsB,GACxB,WAAW,CAAC,kCAAkC,CAAC,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC,EAAE,KAAK,CAAC,CAAC;IAE3E,OAAO,sEAED,sBAAsB,8CAG3B,CAAC;AACJ,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reshape_packed_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reshape_packed_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reshape_packed_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reshape_packed_test.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reshape_packed_test.js
new file mode 100644
index 0000000..3a9d9ec
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reshape_packed_test.js
@@ -0,0 +1,52 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../../index");
+var jasmine_util_1 = require("../../jasmine_util");
+var test_util_1 = require("../../test_util");
+jasmine_util_1.describeWithFlags('expensive reshape', test_util_1.WEBGL_ENVS, function () {
+    var webglLazilyUnpackFlagSaved;
+    beforeAll(function () {
+        webglLazilyUnpackFlagSaved = tf.ENV.get('WEBGL_LAZILY_UNPACK');
+        tf.ENV.set('WEBGL_LAZILY_UNPACK', true);
+    });
+    afterAll(function () {
+        tf.ENV.set('WEBGL_LAZILY_UNPACK', webglLazilyUnpackFlagSaved);
+    });
+    var cValues = [46, 52, 58, 64, 70, 100, 115, 130, 145, 160, 154, 178, 202, 226, 250];
+    var c;
+    beforeEach(function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9], [3, 3]);
+        var b = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [3, 5]);
+        c = tf.matMul(a, b);
+    });
+    it('6d --> 1d', function () {
+        var cAs6D = tf.reshape(c, [1, 1, 1, 3, 1, 5]);
+        var cAs1D = tf.reshape(cAs6D, [-1, cValues.length]);
+        test_util_1.expectArraysClose(cAs1D, cValues);
+    });
+    it('1d --> 2d', function () {
+        var cAs1D = tf.reshape(c, [cValues.length]);
+        var cAs2D = tf.reshape(cAs1D, [5, -1]);
+        test_util_1.expectArraysClose(cAs2D, cValues);
+    });
+    it('2d --> 3d', function () {
+        var cAs3D = tf.reshape(c, [3, 1, 5]);
+        test_util_1.expectArraysClose(cAs3D, cValues);
+    });
+    it('3d --> 4d', function () {
+        var cAs3D = tf.reshape(c, [3, 1, 5]);
+        var cAs4D = tf.reshape(cAs3D, [3, 5, 1, 1]);
+        test_util_1.expectArraysClose(cAs4D, cValues);
+    });
+    it('4d --> 5d', function () {
+        var cAs4D = tf.reshape(c, [3, 5, 1, 1]);
+        var cAs5D = tf.reshape(cAs4D, [1, 1, 1, 5, 3]);
+        test_util_1.expectArraysClose(cAs5D, cValues);
+    });
+    it('5d --> 6d', function () {
+        var cAs5D = tf.reshape(c, [1, 1, 1, 5, 3]);
+        var cAs6D = tf.reshape(cAs5D, [3, 5, 1, 1, 1, 1]);
+        test_util_1.expectArraysClose(cAs6D, cValues);
+    });
+});
+//# sourceMappingURL=reshape_packed_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reshape_packed_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reshape_packed_test.js.map
new file mode 100644
index 0000000..5254e34
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reshape_packed_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"reshape_packed_test.js","sourceRoot":"","sources":["../../../src/kernels/webgl/reshape_packed_test.ts"],"names":[],"mappings":";;AAiBA,gCAAkC;AAClC,mDAAqD;AACrD,6CAA8D;AAE9D,gCAAiB,CAAC,mBAAmB,EAAE,sBAAU,EAAE;IACjD,IAAI,0BAAmC,CAAC;IAExC,SAAS,CAAC;QACR,0BAA0B,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,qBAAqB,CAAC,CAAC;QAC/D,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,qBAAqB,EAAE,IAAI,CAAC,CAAC;IAC1C,CAAC,CAAC,CAAC;IAEH,QAAQ,CAAC;QACP,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,qBAAqB,EAAE,0BAA0B,CAAC,CAAC;IAChE,CAAC,CAAC,CAAC;IAEH,IAAM,OAAO,GACT,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC,CAAC;IAC3E,IAAI,CAAY,CAAC;IAEjB,UAAU,CAAC;QACT,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC3D,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CACjB,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACjE,CAAC,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;IACtB,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,WAAW,EAAE;QACd,IAAM,KAAK,GAAG,EAAE,CAAC,OAAO,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAChD,IAAM,KAAK,GAAG,EAAE,CAAC,OAAO,CAAC,KAAK,EAAE,CAAC,CAAC,CAAC,EAAE,OAAO,CAAC,MAAM,CAAC,CAAC,CAAC;QACtD,6BAAiB,CAAC,KAAK,EAAE,OAAO,CAAC,CAAC;IACpC,CAAC,CAAC,CAAC;IACH,EAAE,CAAC,WAAW,EAAE;QACd,IAAM,KAAK,GAAG,EAAE,CAAC,OAAO,CAAC,CAAC,EAAE,CAAC,OAAO,CAAC,MAAM,CAAC,CAAC,CAAC;QAC9C,IAAM,KAAK,GAAG,EAAE,CAAC,OAAO,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QACzC,6BAAiB,CAAC,KAAK,EAAE,OAAO,CAAC,CAAC;IACpC,CAAC,CAAC,CAAC;IACH,EAAE,CAAC,WAAW,EAAE;QACd,IAAM,KAAK,GAAG,EAAE,CAAC,OAAO,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACvC,6BAAiB,CAAC,KAAK,EAAE,OAAO,CAAC,CAAC;IACpC,CAAC,CAAC,CAAC;IACH,EAAE,CAAC,WAAW,EAAE;QACd,IAAM,KAAK,GAAG,EAAE,CAAC,OAAO,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACvC,IAAM,KAAK,GAAG,EAAE,CAAC,OAAO,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC9C,6BAAiB,CAAC,KAAK,EAAE,OAAO,CAAC,CAAC;IACpC,CAAC,CAAC,CAAC;IACH,EAAE,CAAC,WAAW,EAAE;QACd,IAAM,KAAK,GAAG,EAAE,CAAC,OAAO,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC1C,IAAM,KAAK,GAAG,EAAE,CAAC,OAAO,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACjD,6BAAiB,CAAC,KAAK,EAAE,OAAO,CAAC,CAAC;IACpC,CAAC,CAAC,CAAC;IACH,EAAE,CAAC,WAAW,EAAE;QACd,IAAM,KAAK,GAAG,EAAE,CAAC,OAAO,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC7C,IAAM,KAAK,GAAG,EAAE,CAAC,OAAO,CAAC,KAAK,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACpD,6BAAiB,CAAC,KAAK,EAAE,OAAO,CAAC,CAAC;IACpC,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_bilinear_backprop_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_bilinear_backprop_gpu.d.ts
new file mode 100644
index 0000000..505eb18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_bilinear_backprop_gpu.d.ts
@@ -0,0 +1,8 @@
+import { Tensor4D } from '../../tensor';
+import { GPGPUProgram } from './gpgpu_math';
+export declare class ResizeBilinearBackpropProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(dy: Tensor4D, x: Tensor4D, alignCorners: boolean);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_bilinear_backprop_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_bilinear_backprop_gpu.js
new file mode 100644
index 0000000..0185b3c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_bilinear_backprop_gpu.js
@@ -0,0 +1,29 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var ResizeBilinearBackpropProgram = (function () {
+    function ResizeBilinearBackpropProgram(dy, x, alignCorners) {
+        this.variableNames = ['dy'];
+        this.outputShape = [];
+        this.outputShape = x.shape;
+        var _a = x.shape, xHeight = _a[1], xWidth = _a[2];
+        var _b = dy.shape, yHeight = _b[1], yWidth = _b[2];
+        var effectiveXSize = [
+            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,
+            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth
+        ];
+        var effectiveYSize = [
+            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,
+            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth
+        ];
+        var heightScale = effectiveXSize[0] / effectiveYSize[0];
+        var widthScale = effectiveXSize[1] / effectiveYSize[1];
+        var invHeightScale = 1 / heightScale;
+        var invWidthScale = 1 / widthScale;
+        var winHeight = (Math.ceil(invHeightScale) * 2) + 2;
+        var winWidth = (Math.ceil(invWidthScale) * 2) + 2;
+        this.userCode = "\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(" + heightScale + ");\n        const float widthScale = float(" + widthScale + ");\n\n        const float invHeightScale = float(" + invHeightScale + ");\n        const float invWidthScale = float(" + invWidthScale + ");\n\n        const int winHeight = int(" + winHeight + ");\n        const int winWidth = int(" + winWidth + ");\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= " + yHeight + ") {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= " + yWidth + ") {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), " + (xHeight - 1) + ".0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), " + (xWidth - 1) + ".0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    ";
+    }
+    return ResizeBilinearBackpropProgram;
+}());
+exports.ResizeBilinearBackpropProgram = ResizeBilinearBackpropProgram;
+//# sourceMappingURL=resize_bilinear_backprop_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_bilinear_backprop_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_bilinear_backprop_gpu.js.map
new file mode 100644
index 0000000..2f72c70
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_bilinear_backprop_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"resize_bilinear_backprop_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/resize_bilinear_backprop_gpu.ts"],"names":[],"mappings":";;AAoBA;IAKE,uCAAY,EAAY,EAAE,CAAW,EAAE,YAAqB;QAJ5D,kBAAa,GAAG,CAAC,IAAI,CAAC,CAAC;QACvB,gBAAW,GAAa,EAAE,CAAC;QAIzB,IAAI,CAAC,WAAW,GAAG,CAAC,CAAC,KAAK,CAAC;QACrB,IAAA,YAA+B,EAA5B,eAAO,EAAE,cAAM,CAAc;QAChC,IAAA,aAA8B,EAA3B,eAAO,EAAE,cAAM,CAAa;QAMrC,IAAM,cAAc,GAAqB;YACvC,CAAC,YAAY,IAAI,OAAO,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,GAAG,CAAC,CAAC,CAAC,CAAC,OAAO;YACrD,CAAC,YAAY,IAAI,MAAM,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC,CAAC,MAAM;SACnD,CAAC;QAEF,IAAM,cAAc,GAAqB;YACvC,CAAC,YAAY,IAAI,OAAO,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,GAAG,CAAC,CAAC,CAAC,CAAC,OAAO;YACrD,CAAC,YAAY,IAAI,MAAM,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC,CAAC,MAAM;SACnD,CAAC;QAEF,IAAM,WAAW,GAAG,cAAc,CAAC,CAAC,CAAC,GAAG,cAAc,CAAC,CAAC,CAAC,CAAC;QAC1D,IAAM,UAAU,GAAG,cAAc,CAAC,CAAC,CAAC,GAAG,cAAc,CAAC,CAAC,CAAC,CAAC;QAEzD,IAAM,cAAc,GAAG,CAAC,GAAG,WAAW,CAAC;QACvC,IAAM,aAAa,GAAG,CAAC,GAAG,UAAU,CAAC;QAIrC,IAAM,SAAS,GAAG,CAAC,IAAI,CAAC,IAAI,CAAC,cAAc,CAAC,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC;QACtD,IAAM,QAAQ,GAAG,CAAC,IAAI,CAAC,IAAI,CAAC,aAAa,CAAC,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC;QAEpD,IAAI,CAAC,QAAQ,GAAG,qQAUsB,WAAW,mDACZ,UAAU,yDAEN,cAAc,sDACf,aAAa,gDAErB,SAAS,6CACV,QAAQ,mjBAcT,OAAO,gRAQL,MAAM,+MAMY,OAAO,GAAG,CAAC,mRAMZ,MAAM,GAAG,CAAC,k9BA8B1D,CAAC;IACJ,CAAC;IACH,oCAAC;AAAD,CAAC,AAtHD,IAsHC;AAtHY,sEAA6B"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_bilinear_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_bilinear_gpu.d.ts
new file mode 100644
index 0000000..46fee90
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_bilinear_gpu.d.ts
@@ -0,0 +1,7 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class ResizeBilinearProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(inputShape: [number, number, number, number], newHeight: number, newWidth: number, alignCorners: boolean);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_bilinear_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_bilinear_gpu.js
new file mode 100644
index 0000000..a6d0edc
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_bilinear_gpu.js
@@ -0,0 +1,22 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var ResizeBilinearProgram = (function () {
+    function ResizeBilinearProgram(inputShape, newHeight, newWidth, alignCorners) {
+        this.variableNames = ['A'];
+        this.outputShape = [];
+        var batch = inputShape[0], oldHeight = inputShape[1], oldWidth = inputShape[2], depth = inputShape[3];
+        this.outputShape = [batch, newHeight, newWidth, depth];
+        var effectiveInSize = [
+            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,
+            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth
+        ];
+        var effectiveOutSize = [
+            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,
+            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth
+        ];
+        this.userCode = "\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          " + effectiveInSize[0] / effectiveOutSize[0] + ",\n          " + effectiveInSize[1] / effectiveOutSize[1] + ");\n      const vec2 inputShapeRC = vec2(" + oldHeight + ".0, " + oldWidth + ".0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    ";
+    }
+    return ResizeBilinearProgram;
+}());
+exports.ResizeBilinearProgram = ResizeBilinearProgram;
+//# sourceMappingURL=resize_bilinear_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_bilinear_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_bilinear_gpu.js.map
new file mode 100644
index 0000000..ba09b6b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_bilinear_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"resize_bilinear_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/resize_bilinear_gpu.ts"],"names":[],"mappings":";;AAmBA;IAKE,+BACI,UAA4C,EAAE,SAAiB,EAC/D,QAAgB,EAAE,YAAqB;QAN3C,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QACtB,gBAAW,GAAa,EAAE,CAAC;QAMlB,IAAA,qBAAK,EAAE,yBAAS,EAAE,wBAAQ,EAAE,qBAAK,CAAe;QACvD,IAAI,CAAC,WAAW,GAAG,CAAC,KAAK,EAAE,SAAS,EAAE,QAAQ,EAAE,KAAK,CAAC,CAAC;QAEvD,IAAM,eAAe,GAAqB;YACxC,CAAC,YAAY,IAAI,SAAS,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,GAAG,CAAC,CAAC,CAAC,CAAC,SAAS;YAC3D,CAAC,YAAY,IAAI,QAAQ,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,GAAG,CAAC,CAAC,CAAC,CAAC,QAAQ;SACzD,CAAC;QAEF,IAAM,gBAAgB,GAAqB;YACzC,CAAC,YAAY,IAAI,SAAS,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,GAAG,CAAC,CAAC,CAAC,CAAC,SAAS;YAC3D,CAAC,YAAY,IAAI,QAAQ,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,GAAG,CAAC,CAAC,CAAC,CAAC,QAAQ;SACzD,CAAC;QAEF,IAAI,CAAC,QAAQ,GAAG,2EAER,eAAe,CAAC,CAAC,CAAC,GAAG,gBAAgB,CAAC,CAAC,CAAC,qBACxC,eAAe,CAAC,CAAC,CAAC,GAAG,gBAAgB,CAAC,CAAC,CAAC,iDACb,SAAS,YAAO,QAAQ,ikCA6B1D,CAAC;IACJ,CAAC;IACH,4BAAC;AAAD,CAAC,AAxDD,IAwDC;AAxDY,sDAAqB"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_nearest_neighbor_backprop_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_nearest_neighbor_backprop_gpu.d.ts
new file mode 100644
index 0000000..7fb543a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_nearest_neighbor_backprop_gpu.d.ts
@@ -0,0 +1,8 @@
+import { Tensor4D } from '../../tensor';
+import { GPGPUProgram } from './gpgpu_math';
+export declare class ResizeNearestNeigborBackpropProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(dy: Tensor4D, x: Tensor4D, alignCorners: boolean);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_nearest_neighbor_backprop_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_nearest_neighbor_backprop_gpu.js
new file mode 100644
index 0000000..6a9896a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_nearest_neighbor_backprop_gpu.js
@@ -0,0 +1,29 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var ResizeNearestNeigborBackpropProgram = (function () {
+    function ResizeNearestNeigborBackpropProgram(dy, x, alignCorners) {
+        this.variableNames = ['dy'];
+        this.outputShape = [];
+        this.outputShape = x.shape;
+        var _a = x.shape, xHeight = _a[1], xWidth = _a[2];
+        var _b = dy.shape, yHeight = _b[1], yWidth = _b[2];
+        var effectiveXSize = [
+            (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,
+            (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth
+        ];
+        var effectiveYSize = [
+            (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,
+            (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth
+        ];
+        var heightScale = effectiveXSize[0] / effectiveYSize[0];
+        var widthScale = effectiveXSize[1] / effectiveYSize[1];
+        var invHeightScale = 1 / heightScale;
+        var invWidthScale = 1 / widthScale;
+        var winHeight = (Math.ceil(invHeightScale) * 2) + 2;
+        var winWidth = (Math.ceil(invWidthScale) * 2) + 2;
+        this.userCode = "\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(" + heightScale + ");\n        const float widthScale = float(" + widthScale + ");\n\n        const float invHeightScale = float(" + invHeightScale + ");\n        const float invWidthScale = float(" + invWidthScale + ");\n\n        const int winHeight = int(" + winHeight + ");\n        const int winWidth = int(" + winWidth + ");\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= " + yHeight + ") {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= " + yWidth + ") {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(" + effectiveXSize[0] + ") *\n                (float(dyR) / float(" + effectiveYSize[0] + "));\n\n            float sourceFracCol =\n                float(" + effectiveXSize[1] + ") *\n                  (float(dyC) / float(" + effectiveYSize[1] + "));\n\n            int sourceNearestRow = int(min(\n                float(int(" + xHeight + ") - 1),\n                " + alignCorners + " ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(" + xWidth + ") - 1),\n                " + alignCorners + " ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    ";
+    }
+    return ResizeNearestNeigborBackpropProgram;
+}());
+exports.ResizeNearestNeigborBackpropProgram = ResizeNearestNeigborBackpropProgram;
+//# sourceMappingURL=resize_nearest_neighbor_backprop_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_nearest_neighbor_backprop_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_nearest_neighbor_backprop_gpu.js.map
new file mode 100644
index 0000000..52e660d
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_nearest_neighbor_backprop_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"resize_nearest_neighbor_backprop_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/resize_nearest_neighbor_backprop_gpu.ts"],"names":[],"mappings":";;AAoBA;IAKE,6CAAY,EAAY,EAAE,CAAW,EAAE,YAAqB;QAJ5D,kBAAa,GAAG,CAAC,IAAI,CAAC,CAAC;QACvB,gBAAW,GAAa,EAAE,CAAC;QAIzB,IAAI,CAAC,WAAW,GAAG,CAAC,CAAC,KAAK,CAAC;QACrB,IAAA,YAA+B,EAA5B,eAAO,EAAE,cAAM,CAAc;QAChC,IAAA,aAA8B,EAA3B,eAAO,EAAE,cAAM,CAAa;QAMrC,IAAM,cAAc,GAAqB;YACvC,CAAC,YAAY,IAAI,OAAO,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,GAAG,CAAC,CAAC,CAAC,CAAC,OAAO;YACrD,CAAC,YAAY,IAAI,MAAM,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC,CAAC,MAAM;SACnD,CAAC;QAEF,IAAM,cAAc,GAAqB;YACvC,CAAC,YAAY,IAAI,OAAO,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,GAAG,CAAC,CAAC,CAAC,CAAC,OAAO;YACrD,CAAC,YAAY,IAAI,MAAM,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC,CAAC,MAAM;SACnD,CAAC;QAEF,IAAM,WAAW,GAAG,cAAc,CAAC,CAAC,CAAC,GAAG,cAAc,CAAC,CAAC,CAAC,CAAC;QAC1D,IAAM,UAAU,GAAG,cAAc,CAAC,CAAC,CAAC,GAAG,cAAc,CAAC,CAAC,CAAC,CAAC;QAEzD,IAAM,cAAc,GAAG,CAAC,GAAG,WAAW,CAAC;QACvC,IAAM,aAAa,GAAG,CAAC,GAAG,UAAU,CAAC;QAIrC,IAAM,SAAS,GAAG,CAAC,IAAI,CAAC,IAAI,CAAC,cAAc,CAAC,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC;QACtD,IAAM,QAAQ,GAAG,CAAC,IAAI,CAAC,IAAI,CAAC,aAAa,CAAC,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC;QAEpD,IAAI,CAAC,QAAQ,GAAG,qQAUsB,WAAW,mDACZ,UAAU,yDAEN,cAAc,sDACf,aAAa,gDAErB,SAAS,6CACV,QAAQ,ikBAcT,OAAO,gRAQL,MAAM,8GAKpB,cAAc,CAAC,CAAC,CAAC,iDACD,cAAc,CAAC,CAAC,CAAC,wEAG/B,cAAc,CAAC,CAAC,CAAC,mDACD,cAAc,CAAC,CAAC,CAAC,sFAG7B,OAAO,iCACjB,YAAY,qLAIF,MAAM,iCAChB,YAAY,uVAYzB,CAAC;IACJ,CAAC;IACH,0CAAC;AAAD,CAAC,AA3GD,IA2GC;AA3GY,kFAAmC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_nearest_neighbor_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_nearest_neighbor_gpu.d.ts
new file mode 100644
index 0000000..4a120fe
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_nearest_neighbor_gpu.d.ts
@@ -0,0 +1,7 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class ResizeNearestNeighborProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(inputShape: [number, number, number, number], newHeight: number, newWidth: number, alignCorners: boolean);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_nearest_neighbor_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_nearest_neighbor_gpu.js
new file mode 100644
index 0000000..66aa7d6
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_nearest_neighbor_gpu.js
@@ -0,0 +1,23 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var ResizeNearestNeighborProgram = (function () {
+    function ResizeNearestNeighborProgram(inputShape, newHeight, newWidth, alignCorners) {
+        this.variableNames = ['A'];
+        this.outputShape = [];
+        var batch = inputShape[0], oldHeight = inputShape[1], oldWidth = inputShape[2], depth = inputShape[3];
+        this.outputShape = [batch, newHeight, newWidth, depth];
+        var effectiveInSize = [
+            (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,
+            (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth
+        ];
+        var effectiveOutSize = [
+            (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,
+            (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth
+        ];
+        var roundBase = alignCorners ? '0.5' : '0.0';
+        this.userCode = "\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          " + effectiveInSize[0] / effectiveOutSize[0] + ",\n          " + effectiveInSize[1] / effectiveOutSize[1] + ");\n      const vec2 inputShapeRC = vec2(" + oldHeight + ".0, " + oldWidth + ".0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + " + roundBase + ")));\n\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    ";
+    }
+    return ResizeNearestNeighborProgram;
+}());
+exports.ResizeNearestNeighborProgram = ResizeNearestNeighborProgram;
+//# sourceMappingURL=resize_nearest_neighbor_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_nearest_neighbor_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_nearest_neighbor_gpu.js.map
new file mode 100644
index 0000000..8a22298
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/resize_nearest_neighbor_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"resize_nearest_neighbor_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/resize_nearest_neighbor_gpu.ts"],"names":[],"mappings":";;AAmBA;IAKE,sCACI,UAA4C,EAAE,SAAiB,EAC/D,QAAgB,EAAE,YAAqB;QAN3C,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QACtB,gBAAW,GAAa,EAAE,CAAC;QAMlB,IAAA,qBAAK,EAAE,yBAAS,EAAE,wBAAQ,EAAE,qBAAK,CAAe;QACvD,IAAI,CAAC,WAAW,GAAG,CAAC,KAAK,EAAE,SAAS,EAAE,QAAQ,EAAE,KAAK,CAAC,CAAC;QAEvD,IAAM,eAAe,GAAqB;YACxC,CAAC,YAAY,IAAI,SAAS,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,GAAG,CAAC,CAAC,CAAC,CAAC,SAAS;YAC3D,CAAC,YAAY,IAAI,QAAQ,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,GAAG,CAAC,CAAC,CAAC,CAAC,QAAQ;SACzD,CAAC;QAEF,IAAM,gBAAgB,GAAqB;YACzC,CAAC,YAAY,IAAI,SAAS,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,GAAG,CAAC,CAAC,CAAC,CAAC,SAAS;YAC3D,CAAC,YAAY,IAAI,QAAQ,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,GAAG,CAAC,CAAC,CAAC,CAAC,QAAQ;SACzD,CAAC;QAGF,IAAM,SAAS,GAAG,YAAY,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,CAAC;QAE/C,IAAI,CAAC,QAAQ,GAAG,2EAER,eAAe,CAAC,CAAC,CAAC,GAAG,gBAAgB,CAAC,CAAC,CAAC,qBACxC,eAAe,CAAC,CAAC,CAAC,GAAG,gBAAgB,CAAC,CAAC,CAAC,iDACb,SAAS,YAAO,QAAQ,ocAaD,SAAS,sIAMlE,CAAC;IACJ,CAAC;IACH,mCAAC;AAAD,CAAC,AAjDD,IAiDC;AAjDY,oEAA4B"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reverse_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reverse_gpu.d.ts
new file mode 100644
index 0000000..d9561ac
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reverse_gpu.d.ts
@@ -0,0 +1,7 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class ReverseProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(xShape: number[], axis: number[]);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reverse_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reverse_gpu.js
new file mode 100644
index 0000000..f3d3663
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reverse_gpu.js
@@ -0,0 +1,29 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var shader_compiler_1 = require("./shader_compiler");
+var ReverseProgram = (function () {
+    function ReverseProgram(xShape, axis) {
+        this.variableNames = ['x'];
+        var rank = xShape.length;
+        if (rank > 4) {
+            throw new Error("WebGL backend: Reverse of rank-" + rank + " tensor is not yet supported");
+        }
+        this.outputShape = xShape;
+        if (rank === 1) {
+            this.userCode = "\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(" + xShape[0] + " - coord - 1));\n        }\n      ";
+            return;
+        }
+        var getInCoord = function (i) {
+            if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {
+                return xShape[i] + " - coords[" + i + "] - 1";
+            }
+            return "coords[" + i + "]";
+        };
+        var inCoords = xShape.map(function (_, i) { return getInCoord(i); }).join(',');
+        var type = shader_compiler_1.getCoordsDataType(rank);
+        this.userCode = "\n      void main() {\n        " + type + " coords = getOutputCoords();\n        setOutput(getX(" + inCoords + "));\n      }\n    ";
+    }
+    return ReverseProgram;
+}());
+exports.ReverseProgram = ReverseProgram;
+//# sourceMappingURL=reverse_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reverse_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reverse_gpu.js.map
new file mode 100644
index 0000000..bdf4aa8
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/reverse_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"reverse_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/reverse_gpu.ts"],"names":[],"mappings":";;AAkBA,qDAAoD;AAEpD;IAKE,wBAAY,MAAgB,EAAE,IAAc;QAJ5C,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QAKpB,IAAM,IAAI,GAAG,MAAM,CAAC,MAAM,CAAC;QAC3B,IAAI,IAAI,GAAG,CAAC,EAAE;YACZ,MAAM,IAAI,KAAK,CACX,oCAAkC,IAAI,iCAA8B,CAAC,CAAC;SAC3E;QACD,IAAI,CAAC,WAAW,GAAG,MAAM,CAAC;QAE1B,IAAI,IAAI,KAAK,CAAC,EAAE;YACd,IAAI,CAAC,QAAQ,GAAG,iGAGK,MAAM,CAAC,CAAC,CAAC,uCAE7B,CAAC;YACF,OAAO;SACR;QACD,IAAM,UAAU,GAAG,UAAC,CAAS;YAC3B,IAAI,IAAI,CAAC,OAAO,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,IAAI,MAAM,CAAC,CAAC,CAAC,KAAK,CAAC,EAAE;gBAC7C,OAAU,MAAM,CAAC,CAAC,CAAC,kBAAa,CAAC,UAAO,CAAC;aAC1C;YACD,OAAO,YAAU,CAAC,MAAG,CAAC;QACxB,CAAC,CAAC;QACF,IAAM,QAAQ,GAAG,MAAM,CAAC,GAAG,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,UAAU,CAAC,CAAC,CAAC,EAAb,CAAa,CAAC,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC;QAC/D,IAAM,IAAI,GAAG,mCAAiB,CAAC,IAAI,CAAC,CAAC;QAErC,IAAI,CAAC,QAAQ,GAAG,oCAEV,IAAI,6DACW,QAAQ,uBAE5B,CAAC;IACJ,CAAC;IACH,qBAAC;AAAD,CAAC,AAtCD,IAsCC;AAtCY,wCAAc"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/scatter_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/scatter_gpu.d.ts
new file mode 100644
index 0000000..1542672
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/scatter_gpu.d.ts
@@ -0,0 +1,7 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class ScatterProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(updateSize: number, sliceDim: number, indicesRank: number, updatesRank: number, strides: number[], shape: number[], summingDupeIndex?: boolean);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/scatter_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/scatter_gpu.js
new file mode 100644
index 0000000..66e5743
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/scatter_gpu.js
@@ -0,0 +1,33 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var shader_compiler_1 = require("./shader_compiler");
+var ScatterProgram = (function () {
+    function ScatterProgram(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex) {
+        if (summingDupeIndex === void 0) { summingDupeIndex = true; }
+        this.variableNames = ['updates', 'indices', 'defaultValue'];
+        this.outputShape = shape;
+        var stridesType = shader_compiler_1.getCoordsDataType(strides.length);
+        var dtype = shader_compiler_1.getCoordsDataType(shape.length);
+        var indicesString = '';
+        if (indicesRank === 1) {
+            indicesString = 'i';
+        }
+        else if (indicesRank === 2) {
+            indicesString = 'i, j';
+        }
+        var indicesSnippet = "getIndices(" + indicesString + ")";
+        var updatesString = '';
+        if (updatesRank === 1) {
+            updatesString = 'i';
+        }
+        else if (updatesRank === 2) {
+            updatesString = 'i, coords[1]';
+        }
+        var updatesSnippet = "getUpdates(" + updatesString + ")";
+        var strideString = sliceDim > 1 ? 'strides[j]' : 'strides';
+        this.userCode = "\n        " + stridesType + " strides = " + stridesType + "(" + strides + ");\n\n        void main() {\n          " + dtype + " coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < " + updateSize + "; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < " + sliceDim + "; j++) {\n              int index = round(" + indicesSnippet + ");\n              flattenedIndex += index * " + strideString + ";\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += " + updatesSnippet + ";\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      ";
+    }
+    return ScatterProgram;
+}());
+exports.ScatterProgram = ScatterProgram;
+//# sourceMappingURL=scatter_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/scatter_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/scatter_gpu.js.map
new file mode 100644
index 0000000..7beca8d
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/scatter_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"scatter_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/scatter_gpu.ts"],"names":[],"mappings":";;AAkBA,qDAAoD;AAEpD;IAKE,wBACI,UAAkB,EAAE,QAAgB,EAAE,WAAmB,EACzD,WAAmB,EAAE,OAAiB,EAAE,KAAe,EACvD,gBAAuB;QAAvB,iCAAA,EAAA,uBAAuB;QAP3B,kBAAa,GAAG,CAAC,SAAS,EAAE,SAAS,EAAE,cAAc,CAAC,CAAC;QAQrD,IAAI,CAAC,WAAW,GAAG,KAAK,CAAC;QACzB,IAAM,WAAW,GAAG,mCAAiB,CAAC,OAAO,CAAC,MAAM,CAAC,CAAC;QACtD,IAAM,KAAK,GAAG,mCAAiB,CAAC,KAAK,CAAC,MAAM,CAAC,CAAC;QAC9C,IAAI,aAAa,GAAG,EAAE,CAAC;QACvB,IAAI,WAAW,KAAK,CAAC,EAAE;YACrB,aAAa,GAAG,GAAG,CAAC;SACrB;aAAM,IAAI,WAAW,KAAK,CAAC,EAAE;YAC5B,aAAa,GAAG,MAAM,CAAC;SACxB;QACD,IAAM,cAAc,GAAG,gBAAc,aAAa,MAAG,CAAC;QAEtD,IAAI,aAAa,GAAG,EAAE,CAAC;QACvB,IAAI,WAAW,KAAK,CAAC,EAAE;YACrB,aAAa,GAAG,GAAG,CAAC;SACrB;aAAM,IAAI,WAAW,KAAK,CAAC,EAAE;YAC5B,aAAa,GAAG,cAAc,CAAC;SAChC;QACD,IAAM,cAAc,GAAG,gBAAc,aAAa,MAAG,CAAC;QAEtD,IAAM,YAAY,GAAG,QAAQ,GAAG,CAAC,CAAC,CAAC,CAAC,YAAY,CAAC,CAAC,CAAC,SAAS,CAAC;QAC7D,IAAI,CAAC,QAAQ,GAAG,eACV,WAAW,mBAAc,WAAW,SAAI,OAAO,+CAG7C,KAAK,+HAGe,UAAU,uFAER,QAAQ,kDACR,cAAc,oDACN,YAAY,+FAG/B,cAAc,mJAM9B,CAAC;IACN,CAAC;IACH,qBAAC;AAAD,CAAC,AAnDD,IAmDC;AAnDY,wCAAc"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/segment_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/segment_gpu.d.ts
new file mode 100644
index 0000000..37ea532
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/segment_gpu.d.ts
@@ -0,0 +1,8 @@
+import { SegOpInfo } from '../../ops/segment_util';
+import { GPGPUProgram } from './gpgpu_math';
+export declare class SegmentOpProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(segOpInfo: SegOpInfo, segOpType: 'unsortedSegmentSum');
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/segment_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/segment_gpu.js
new file mode 100644
index 0000000..ac1c74a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/segment_gpu.js
@@ -0,0 +1,30 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var SegmentOpProgram = (function () {
+    function SegmentOpProgram(segOpInfo, segOpType) {
+        this.variableNames = ['x', 'segmentIds'];
+        var windowSize = segOpInfo.windowSize;
+        var batchSize = segOpInfo.batchSize;
+        var inSize = segOpInfo.inSize;
+        var numSegments = segOpInfo.numSegments;
+        var outSize = numSegments * Math.ceil(inSize / windowSize);
+        this.outputShape = [batchSize, outSize];
+        var initializationValue = '0.0';
+        var returnValue = "sumValue";
+        var windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;
+        var windowSizeVec4Remainder = windowSize % 4;
+        var updateSnippet = "\n        sumValue += dot(values, filter);\n    ";
+        var checkValueOutOfBounds = '';
+        if (inSize % windowSize > 0) {
+            checkValueOutOfBounds = "\n        if (inIdx < 0 || inIdx >= " + inSize + ") {\n          return initializationValue;\n        }\n      ";
+        }
+        var checkSegmentIdOutOfBounds = '';
+        if (inSize % windowSize > 0) {
+            checkSegmentIdOutOfBounds = "\n        if (inIdx < 0 || inIdx >= " + inSize + ") {\n          return -1.0;\n        }\n      ";
+        }
+        this.userCode = "\n      const float initializationValue = " + initializationValue + ";\n\n      float getValue(int batch, int inIdx) {\n        " + checkValueOutOfBounds + "\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        " + checkSegmentIdOutOfBounds + "\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          " + numSegments + ")) * float(" + windowSize + "));\n        int currentSeg = int(mod(float(outIdx), float(" + numSegments + ")));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < " + windowSizeNearestVec4 + "; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          " + updateSnippet + "\n        }\n\n        int inIdx = inOffset + " + windowSizeNearestVec4 + ";\n        if (" + (windowSizeVec4Remainder === 1) + ") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          " + updateSnippet + "\n        } else if (" + (windowSizeVec4Remainder === 2) + ") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          " + updateSnippet + "\n        } else if (" + (windowSizeVec4Remainder === 3) + ") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          " + updateSnippet + "\n        }\n        setOutput(" + returnValue + ");\n      }\n    ";
+    }
+    return SegmentOpProgram;
+}());
+exports.SegmentOpProgram = SegmentOpProgram;
+//# sourceMappingURL=segment_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/segment_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/segment_gpu.js.map
new file mode 100644
index 0000000..94b943b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/segment_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"segment_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/segment_gpu.ts"],"names":[],"mappings":";;AAoBA;IAKE,0BAAY,SAAoB,EAAE,SAA+B;QAJjE,kBAAa,GAAG,CAAC,GAAG,EAAE,YAAY,CAAC,CAAC;QAKlC,IAAM,UAAU,GAAG,SAAS,CAAC,UAAU,CAAC;QACxC,IAAM,SAAS,GAAG,SAAS,CAAC,SAAS,CAAC;QACtC,IAAM,MAAM,GAAG,SAAS,CAAC,MAAM,CAAC;QAChC,IAAM,WAAW,GAAG,SAAS,CAAC,WAAW,CAAC;QAC1C,IAAM,OAAO,GAAG,WAAW,GAAG,IAAI,CAAC,IAAI,CAAC,MAAM,GAAG,UAAU,CAAC,CAAC;QAC7D,IAAI,CAAC,WAAW,GAAG,CAAC,SAAS,EAAE,OAAO,CAAC,CAAC;QAExC,IAAM,mBAAmB,GAAG,KAAK,CAAC;QAClC,IAAM,WAAW,GAAG,UAAU,CAAC;QAE/B,IAAM,qBAAqB,GAAG,IAAI,CAAC,KAAK,CAAC,UAAU,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC;QAC7D,IAAM,uBAAuB,GAAG,UAAU,GAAG,CAAC,CAAC;QAE/C,IAAM,aAAa,GAAG,kDAErB,CAAC;QAEF,IAAI,qBAAqB,GAAG,EAAE,CAAC;QAC/B,IAAI,MAAM,GAAG,UAAU,GAAG,CAAC,EAAE;YAC3B,qBAAqB,GAAG,yCACM,MAAM,kEAGnC,CAAC;SACH;QAED,IAAI,yBAAyB,GAAG,EAAE,CAAC;QACnC,IAAI,MAAM,GAAG,UAAU,GAAG,CAAC,EAAE;YAC3B,yBAAyB,GAAG,yCACE,MAAM,mDAGnC,CAAC;SACH;QAED,IAAI,CAAC,QAAQ,GAAG,+CACsB,mBAAmB,mEAGnD,qBAAqB,+GAKrB,yBAAyB,+PASvB,WAAW,mBAAc,UAAU,mEACS,WAAW,6EAIrC,qBAAqB,4lBAgBvC,aAAa,sDAGQ,qBAAqB,wBACxC,uBAAuB,KAAK,CAAC,gbAiB/B,aAAa,8BACJ,uBAAuB,KAAK,CAAC,sbAetC,aAAa,8BACJ,uBAAuB,KAAK,CAAC,ifAetC,aAAa,uCAEL,WAAW,sBAE1B,CAAC;IACJ,CAAC;IACH,uBAAC;AAAD,CAAC,AA3ID,IA2IC;AA3IY,4CAAgB"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/select_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/select_gpu.d.ts
new file mode 100644
index 0000000..e754031
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/select_gpu.d.ts
@@ -0,0 +1,7 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class SelectProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(cRank: number, shape: number[], rank: number);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/select_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/select_gpu.js
new file mode 100644
index 0000000..b100f05
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/select_gpu.js
@@ -0,0 +1,36 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var shader_compiler_1 = require("./shader_compiler");
+var SelectProgram = (function () {
+    function SelectProgram(cRank, shape, rank) {
+        this.variableNames = ['c', 'a', 'b'];
+        this.outputShape = shape;
+        var cCoords;
+        var abCoords;
+        if (rank > 4) {
+            throw Error("Where for rank " + rank + " is not yet supported");
+        }
+        if (rank === 1) {
+            abCoords = "resRC";
+            cCoords = "resRC";
+        }
+        else {
+            var currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];
+            var cCoordVars = [];
+            var abCoordVars = [];
+            for (var i = 0; i < shape.length; i++) {
+                abCoordVars.push("" + currentCoords[i]);
+                if (i < cRank) {
+                    cCoordVars.push("" + currentCoords[i]);
+                }
+            }
+            cCoords = cCoordVars.join();
+            abCoords = abCoordVars.join();
+        }
+        var dtype = shader_compiler_1.getCoordsDataType(rank);
+        this.userCode = "\n      void main() {\n        " + dtype + " resRC = getOutputCoords();\n        float cVal = getC(" + cCoords + ");\n        if (cVal >= 1.0) {\n          setOutput(getA(" + abCoords + "));\n        } else {\n          setOutput(getB(" + abCoords + "));\n        }\n      }\n    ";
+    }
+    return SelectProgram;
+}());
+exports.SelectProgram = SelectProgram;
+//# sourceMappingURL=select_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/select_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/select_gpu.js.map
new file mode 100644
index 0000000..304ede0
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/select_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"select_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/select_gpu.ts"],"names":[],"mappings":";;AAkBA,qDAAoD;AAEpD;IAKE,uBAAY,KAAa,EAAE,KAAe,EAAE,IAAY;QAJxD,kBAAa,GAAG,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC,CAAC;QAK9B,IAAI,CAAC,WAAW,GAAG,KAAK,CAAC;QAEzB,IAAI,OAAO,CAAC;QACZ,IAAI,QAAQ,CAAC;QACb,IAAI,IAAI,GAAG,CAAC,EAAE;YACZ,MAAM,KAAK,CAAC,oBAAkB,IAAI,0BAAuB,CAAC,CAAC;SAC5D;QAED,IAAI,IAAI,KAAK,CAAC,EAAE;YACd,QAAQ,GAAG,OAAO,CAAC;YACnB,OAAO,GAAG,OAAO,CAAC;SACnB;aAAM;YACL,IAAM,aAAa,GAAG,CAAC,SAAS,EAAE,SAAS,EAAE,SAAS,EAAE,SAAS,CAAC,CAAC;YACnE,IAAM,UAAU,GAAG,EAAE,CAAC;YACtB,IAAM,WAAW,GAAG,EAAE,CAAC;YACvB,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,KAAK,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;gBACrC,WAAW,CAAC,IAAI,CAAC,KAAG,aAAa,CAAC,CAAC,CAAG,CAAC,CAAC;gBACxC,IAAI,CAAC,GAAG,KAAK,EAAE;oBACb,UAAU,CAAC,IAAI,CAAC,KAAG,aAAa,CAAC,CAAC,CAAG,CAAC,CAAC;iBACxC;aACF;YACD,OAAO,GAAG,UAAU,CAAC,IAAI,EAAE,CAAC;YAC5B,QAAQ,GAAG,WAAW,CAAC,IAAI,EAAE,CAAC;SAC/B;QAED,IAAM,KAAK,GAAG,mCAAiB,CAAC,IAAI,CAAC,CAAC;QAEtC,IAAI,CAAC,QAAQ,GAAG,oCAEV,KAAK,+DACa,OAAO,iEAER,QAAQ,wDAER,QAAQ,kCAG9B,CAAC;IACJ,CAAC;IACH,oBAAC;AAAD,CAAC,AA7CD,IA6CC;AA7CY,sCAAa"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/shader_compiler.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/shader_compiler.d.ts
new file mode 100644
index 0000000..b95ae9c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/shader_compiler.d.ts
@@ -0,0 +1,12 @@
+export declare type ShapeInfo = {
+    logicalShape: number[];
+    texShape: [number, number];
+    isUniform: boolean;
+    isPacked: boolean;
+};
+export declare type InputInfo = {
+    name: string;
+    shapeInfo: ShapeInfo;
+};
+export declare function makeShader(inputsInfo: InputInfo[], outputShape: ShapeInfo, userCode: string, broadcast: boolean, usesPackedTextures: boolean): string;
+export declare function getCoordsDataType(rank: number): string;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/shader_compiler.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/shader_compiler.js
new file mode 100644
index 0000000..9b6e751
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/shader_compiler.js
@@ -0,0 +1,598 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../../environment");
+var broadcast_util = require("../../ops/broadcast_util");
+var util = require("../../util");
+var shader_util = require("./shader_compiler_util");
+function makeShader(inputsInfo, outputShape, userCode, broadcast, usesPackedTextures) {
+    var inputPrefixSnippet = inputsInfo.map(function (x) {
+        var size = util.sizeFromShape(x.shapeInfo.logicalShape);
+        if (x.shapeInfo.isUniform) {
+            return "uniform float " + x.name + (size > 1 ? "[" + size + "]" : '') + ";";
+        }
+        return "uniform sampler2D " + x.name + ";";
+    });
+    inputPrefixSnippet = inputPrefixSnippet.join('\n');
+    var inputSamplingSnippet = inputsInfo
+        .map(function (x) { return getInputSamplingSnippet(x, outputShape, broadcast, usesPackedTextures); })
+        .join('\n');
+    var outTexShape = outputShape.texShape;
+    var outputSamplingSnippet;
+    var floatTextureSetOutputSnippet;
+    var shaderPrefix = SHADER_PREFIX;
+    if (outputShape.isPacked) {
+        outputSamplingSnippet =
+            getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape);
+        floatTextureSetOutputSnippet = FLOAT_TEXTURE_SET_RGBA_SNIPPET;
+    }
+    else {
+        outputSamplingSnippet =
+            getOutputSamplingSnippet(outputShape.logicalShape, outTexShape);
+        floatTextureSetOutputSnippet = FLOAT_TEXTURE_SET_R_SNIPPET;
+    }
+    if (usesPackedTextures) {
+        shaderPrefix += SHADER_PACKED_PREFIX;
+    }
+    var source = [
+        shaderPrefix, FLOAT_TEXTURE_SAMPLE_SNIPPET, floatTextureSetOutputSnippet,
+        inputPrefixSnippet, outputSamplingSnippet, inputSamplingSnippet, userCode
+    ].join('\n');
+    return source;
+}
+exports.makeShader = makeShader;
+function getSamplerFromInInfo(inInfo) {
+    var shape = inInfo.shapeInfo.logicalShape;
+    switch (shape.length) {
+        case 0:
+            return getSamplerScalar(inInfo);
+        case 1:
+            return getSampler1D(inInfo);
+        case 2:
+            return getSampler2D(inInfo);
+        case 3:
+            return getSampler3D(inInfo);
+        case 4:
+            return getSampler4D(inInfo);
+        case 5:
+            return getSampler5D(inInfo);
+        case 6:
+            return getSampler6D(inInfo);
+        default:
+            throw new Error(shape.length + "-D input sampling" +
+                " is not yet supported");
+    }
+}
+function getPackedSamplerFromInInfo(inInfo) {
+    var shape = inInfo.shapeInfo.logicalShape;
+    switch (shape.length) {
+        case 0:
+            return getPackedSamplerScalar(inInfo);
+        case 1:
+            return getPackedSampler1D(inInfo);
+        case 2:
+            return getPackedSampler2D(inInfo);
+        case 3:
+            return getPackedSampler3D(inInfo);
+        case 4:
+            return getPackedSampler4D(inInfo);
+        default:
+            throw new Error("Packed " + shape.length + "-D input sampling" +
+                " is not yet supported");
+    }
+}
+function getInputSamplingSnippet(inInfo, outShapeInfo, broadcast, usesPackedTextures) {
+    if (usesPackedTextures === void 0) { usesPackedTextures = false; }
+    var res = getSamplerFlat(inInfo);
+    if (usesPackedTextures) {
+        res += getPackedSamplerFromInInfo(inInfo);
+    }
+    else {
+        res += getSamplerFromInInfo(inInfo);
+    }
+    if (broadcast ||
+        util.arraysEqual(inInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape)) {
+        if (usesPackedTextures) {
+            res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo, broadcast);
+        }
+        else {
+            res += getSamplerAtOutputCoords(inInfo, outShapeInfo, broadcast);
+        }
+    }
+    return res;
+}
+function getPackedOutputSamplingSnippet(outShape, outTexShape) {
+    switch (outShape.length) {
+        case 0:
+            return getOutputScalarCoords();
+        case 1:
+            return getOutputPacked1DCoords(outShape, outTexShape);
+        case 2:
+            return getOutputPacked2DCoords(outShape, outTexShape);
+        case 3:
+            return getOutputPacked3DCoords(outShape, outTexShape);
+        case 4:
+            return getOutputPacked4DCoords(outShape, outTexShape);
+        default:
+            throw new Error(outShape.length + "-D packed output " +
+                "coordinate fetching is not yet supported");
+    }
+}
+function getOutputSamplingSnippet(outShape, outTexShape) {
+    switch (outShape.length) {
+        case 0:
+            return getOutputScalarCoords();
+        case 1:
+            return getOutput1DCoords(outShape, outTexShape);
+        case 2:
+            return getOutput2DCoords(outShape, outTexShape);
+        case 3:
+            return getOutput3DCoords(outShape, outTexShape);
+        case 4:
+            return getOutput4DCoords(outShape, outTexShape);
+        case 5:
+            return getOutput5DCoords(outShape, outTexShape);
+        case 6:
+            return getOutput6DCoords(outShape, outTexShape);
+        default:
+            throw new Error(outShape.length + "-D output sampling is not yet supported");
+    }
+}
+var SAMPLE_1D_SNIPPET = "\nvec2 UVfrom1D(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n";
+var SAMPLE_2D_SNIPPET = "\nvec2 UVfrom2D(int texNumR, int texNumC, int numC, int row, int col) {\n  int index = row * numC + col;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n";
+var SAMPLE_3D_SNIPPET = "\nvec2 UVfrom3D(int texNumR, int texNumC, int stride0,\n    int stride1, int row, int col, int depth) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n";
+var SAMPLE_4D_SNIPPET = "\nvec2 UVfrom4D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int row, int col, int depth,\n    int depth2) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth * stride2 + depth2;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom4D(int texNumR, int texNumC, int texelsInBatch2,\n    int texelsInBatch, int texelsInLogicalRow, int b2, int b,\n    int row, int col) {\n  int index = b2 * texelsInBatch2 + b * texelsInBatch +\n    (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n";
+var SAMPLE_5D_SNIPPET = "\nvec2 UVfrom5D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int stride3, int row, int col, int depth,\n    int depth2, int depth3) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 +\n              depth * stride2 + depth2 * stride3 + depth3;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n";
+var SAMPLE_6D_SNIPPET = "\nvec2 UVfrom6D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int stride3, int stride4,\n    int row, int col, int depth, int depth2, int depth3, int depth4) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth * stride2 + depth2 *\n    stride3 + depth3 * stride4 + depth4;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n";
+var FLOAT_TEXTURE_SAMPLE_SNIPPET = "\n  float sampleTexture(sampler2D textureSampler, vec2 uv) {\n    return texture2D(textureSampler, uv).r;\n  }\n";
+var FLOAT_TEXTURE_SET_R_SNIPPET = "\n  void setOutput(float val) {\n    gl_FragColor = vec4(val, 0, 0, 0);\n  }\n";
+var FLOAT_TEXTURE_SET_RGBA_SNIPPET = "\n  void setOutput(vec4 val) {\n    gl_FragColor = val;\n  }\n";
+var NAN_CHECKS = '';
+if (environment_1.ENV.get('PROD')) {
+    NAN_CHECKS = "\n    bool isNaN(float val) {\n      return false;\n    }\n\n    bool hasNaN(vec4 values) {\n      return false;\n    }\n  ";
+}
+else {
+    NAN_CHECKS = "\n    bool isNaN(float val) {\n      return (val < 1.0 || 0.0 < val || val == 0.0) ? false : true;\n    }\n\n    bool hasNaN(vec4 values) {\n      return any(bvec4(\n        isNaN(values.x),\n        isNaN(values.y),\n        isNaN(values.z),\n        isNaN(values.w)\n      ));\n    }\n  ";
+}
+var SHADER_PREFIX = "\n  precision highp float;\n  precision highp int;\n  varying vec2 resultUV;\n  const vec2 halfCR = vec2(0.5, 0.5);\n\n  struct ivec5\n  {\n    int x;\n    int y;\n    int z;\n    int w;\n    int u;\n  };\n\n  struct ivec6\n  {\n    int x;\n    int y;\n    int z;\n    int w;\n    int u;\n    int v;\n  };\n\n  " + NAN_CHECKS + "\n\n  float getNaN(vec4 values) {\n    return dot(vec4(1), values);\n  }\n\n  int round(float value) {\n    return int(floor(value + 0.5));\n  }\n\n  int imod(int x, int y) {\n    return x - y * (x / y);\n  }\n\n  //Based on the work of Dave Hoskins\n  //https://www.shadertoy.com/view/4djSRW\n  #define HASHSCALE1 443.8975\n  float random(float seed){\n    vec2 p = resultUV * seed;\n    vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n    p3 += dot(p3, p3.yzx + 19.19);\n    return fract((p3.x + p3.y) * p3.z);\n  }\n\n  " + SAMPLE_1D_SNIPPET + "\n  " + SAMPLE_2D_SNIPPET + "\n  " + SAMPLE_3D_SNIPPET + "\n  " + SAMPLE_4D_SNIPPET + "\n  " + SAMPLE_5D_SNIPPET + "\n  " + SAMPLE_6D_SNIPPET + "\n";
+var SHADER_PACKED_PREFIX = "\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n";
+function getOutputScalarCoords() {
+    return "\n    int getOutputCoords() {\n      return 0;\n    }\n  ";
+}
+function getOutputPacked1DCoords(shape, texShape) {
+    var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+    if (texShape[0] === 1) {
+        return "\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * " + packedTexShape[1] + ".0);\n      }\n    ";
+    }
+    if (texShape[1] === 1) {
+        return "\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * " + packedTexShape[0] + ".0);\n      }\n    ";
+    }
+    return "\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + packedTexShape[0] + ", " + packedTexShape[1] + "));\n      return resTexRC.x * " + packedTexShape[1] + " + resTexRC.y;\n    }\n  ";
+}
+function getOutput1DCoords(shape, texShape) {
+    if (texShape[0] === 1) {
+        return "\n      int getOutputCoords() {\n        return int(resultUV.x * " + texShape[1] + ".0);\n      }\n    ";
+    }
+    if (texShape[1] === 1) {
+        return "\n      int getOutputCoords() {\n        return int(resultUV.y * " + texShape[0] + ".0);\n      }\n    ";
+    }
+    return "\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + texShape[0] + ", " + texShape[1] + "));\n      return resTexRC.x * " + texShape[1] + " + resTexRC.y;\n    }\n  ";
+}
+function getOutputPacked3DCoords(shape, texShape) {
+    var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+    var texelsInLogicalRow = Math.ceil(shape[2] / 2);
+    var texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);
+    return "\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + packedTexShape[0] + ", " + packedTexShape[1] + "));\n      int index = resTexRC.x * " + packedTexShape[1] + " + resTexRC.y;\n\n      int b = index / " + texelsInBatch + ";\n      index -= b * " + texelsInBatch + ";\n\n      int r = 2 * (index / " + texelsInLogicalRow + ");\n      int c = imod(index, " + texelsInLogicalRow + ") * 2;\n\n      return ivec3(b, r, c);\n    }\n  ";
+}
+function getOutput3DCoords(shape, texShape) {
+    var coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);
+    return "\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + texShape[0] + ", " + texShape[1] + "));\n      int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n      " + coordsFromIndexSnippet + "\n      return ivec3(r, c, d);\n    }\n  ";
+}
+function getOutputPacked4DCoords(shape, texShape) {
+    var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+    var texelsInLogicalRow = Math.ceil(shape[3] / 2);
+    var texelsInBatch = texelsInLogicalRow * Math.ceil(shape[2] / 2);
+    var texelsInBatch2 = texelsInBatch * shape[1];
+    return "\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + packedTexShape[0] + ", " + packedTexShape[1] + "));\n      int index = resTexRC.x * " + packedTexShape[1] + " + resTexRC.y;\n\n      int b2 = index / " + texelsInBatch2 + ";\n      index -= b2 * " + texelsInBatch2 + ";\n\n      int b = index / " + texelsInBatch + ";\n      index -= b * " + texelsInBatch + ";\n\n      int r = 2 * (index / " + texelsInLogicalRow + ");\n      int c = imod(index, " + texelsInLogicalRow + ") * 2;\n\n      return ivec4(b2, b, r, c);\n    }\n  ";
+}
+function getOutput4DCoords(shape, texShape) {
+    var coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2'], shape);
+    return "\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(" + texShape[0] + ", " + texShape[1] + "));\n      int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n      " + coordsFromIndexSnippet + "\n      return ivec4(r, c, d, d2);\n    }\n  ";
+}
+function getOutput5DCoords(shape, texShape) {
+    var coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2', 'd3'], shape);
+    return "\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(" + texShape[0] + ",\n                             " + texShape[1] + "));\n\n      int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n\n      " + coordsFromIndexSnippet + "\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  ";
+}
+function getOutput6DCoords(shape, texShape) {
+    var coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2', 'd3', 'd4'], shape);
+    return "\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(" + texShape[0] + ", " + texShape[1] + "));\n      int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n\n      " + coordsFromIndexSnippet + "\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  ";
+}
+function getOutputPacked2DCoords(shape, texShape) {
+    var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+    if (util.arraysEqual(shape, texShape)) {
+        return "\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(" + packedTexShape[0] + ", " + packedTexShape[1] + "));\n      }\n    ";
+    }
+    var texelsInLogicalRow = Math.ceil(shape[1] / 2);
+    return "\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + packedTexShape[0] + ", " + packedTexShape[1] + "));\n\n      int index = resTexRC.x * " + packedTexShape[1] + " + resTexRC.y;\n      int r = 2 * (index / " + texelsInLogicalRow + ");\n      int c = imod(index, " + texelsInLogicalRow + ") * 2;\n\n      return ivec2(r, c);\n    }\n  ";
+}
+function getOutput2DCoords(shape, texShape) {
+    if (util.arraysEqual(shape, texShape)) {
+        return "\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(" + texShape[0] + ", " + texShape[1] + "));\n      }\n    ";
+    }
+    if (shape[1] === 1) {
+        return "\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(" + texShape[0] + ", " + texShape[1] + "));\n        int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    ";
+    }
+    if (shape[0] === 1) {
+        return "\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(" + texShape[0] + ", " + texShape[1] + "));\n        int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n        return ivec2(0, index);\n      }\n    ";
+    }
+    return "\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + texShape[0] + ", " + texShape[1] + "));\n      int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n      int r = index / " + shape[1] + ";\n      int c = index - r * " + shape[1] + ";\n      return ivec2(r, c);\n    }\n  ";
+}
+function getPackedSamplerScalar(inputInfo) {
+    var texName = inputInfo.name;
+    var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+    return "\n    vec4 " + funcName + "() {\n      return texture2D(" + texName + ", halfCR);\n    }\n  ";
+}
+function getSamplerScalar(inputInfo) {
+    var texName = inputInfo.name;
+    var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+    if (inputInfo.shapeInfo.isUniform) {
+        return "float " + funcName + "() {return " + texName + ";}";
+    }
+    return "\n    float " + funcName + "() {\n      return sampleTexture(" + texName + ", halfCR);\n    }\n  ";
+}
+function getPackedSampler1D(inputInfo) {
+    var texName = inputInfo.name;
+    var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+    var texShape = inputInfo.shapeInfo.texShape;
+    var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+    return "\n    vec4 " + funcName + "(int index) {\n      vec2 uv = packedUVfrom1D(\n        " + packedTexShape[0] + ", " + packedTexShape[1] + ", index);\n      return texture2D(" + texName + ", uv);\n    }\n  ";
+}
+function getSampler1D(inputInfo) {
+    var texName = inputInfo.name;
+    var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+    return "\n    float " + funcName + "(int index) {\n      return " + funcName + "Flat(index);\n    }\n  ";
+}
+function getPackedSampler2D(inputInfo) {
+    var shape = inputInfo.shapeInfo.logicalShape;
+    var texName = inputInfo.name;
+    var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+    var texShape = inputInfo.shapeInfo.texShape;
+    var texNumR = texShape[0];
+    var texNumC = texShape[1];
+    if (texShape != null && util.arraysEqual(shape, texShape)) {
+        return "\n      vec4 " + funcName + "(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(" + texNumC + ".0, " + texNumR + ".0);\n\n        return texture2D(" + texName + ", uv);\n      }\n    ";
+    }
+    var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+    var valuesPerRow = Math.ceil(shape[1] / 2);
+    return "\n    vec4 " + funcName + "(int row, int col) {\n      vec2 uv = packedUVfrom2D(" + valuesPerRow + ", " + packedTexShape[0] + ", " + packedTexShape[1] + ", row, col);\n      return texture2D(" + texName + ", uv);\n    }\n  ";
+}
+function getSampler2D(inputInfo) {
+    var shape = inputInfo.shapeInfo.logicalShape;
+    var texName = inputInfo.name;
+    var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+    var texShape = inputInfo.shapeInfo.texShape;
+    if (texShape != null && util.arraysEqual(shape, texShape)) {
+        var texNumR_1 = texShape[0];
+        var texNumC_1 = texShape[1];
+        return "\n    float " + funcName + "(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(" + texNumC_1 + ".0, " + texNumR_1 + ".0);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
+    }
+    var _a = util.squeezeShape(shape), newShape = _a.newShape, keptDims = _a.keptDims;
+    var squeezedShape = newShape;
+    if (squeezedShape.length < shape.length) {
+        var newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
+        var params = ['row', 'col'];
+        return "\n      " + getSamplerFromInInfo(newInputInfo) + "\n      float " + funcName + "(int row, int col) {\n        return " + funcName + "(" + getSqueezedParams(params, keptDims) + ");\n      }\n    ";
+    }
+    if (inputInfo.shapeInfo.isUniform) {
+        return "\n      float " + funcName + "(int row, int col) {\n        float index = dot(vec2(row, col), vec2(" + shape[1] + ", 1));\n        return " + funcName + "Flat(round(index));\n      }\n    ";
+    }
+    var texNumR = texShape[0];
+    var texNumC = texShape[1];
+    if (texNumC === 1) {
+        return "\n    float " + funcName + "(int row, int col) {\n      float index = dot(vec2(row, col), vec2(" + shape[1] + ", 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / " + texNumR + ".0);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
+    }
+    if (texNumR === 1) {
+        return "\n    float " + funcName + "(int row, int col) {\n      float index = dot(vec2(row, col), vec2(" + shape[1] + ", 1));\n      vec2 uv = vec2((index + 0.5) / " + texNumC + ".0, 0.5);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
+    }
+    return "\n  float " + funcName + "(int row, int col) {\n    vec2 uv = UVfrom2D(" + texNumR + ", " + texNumC + ", " + shape[1] + ", row, col);\n    return sampleTexture(" + texName + ", uv);\n  }\n";
+}
+function getPackedSampler3D(inputInfo) {
+    var shape = inputInfo.shapeInfo.logicalShape;
+    var texName = inputInfo.name;
+    var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+    var texShape = inputInfo.shapeInfo.texShape;
+    var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+    if (shape[0] === 1) {
+        var squeezedShape = shape.slice(1);
+        var keptDims = [1, 2];
+        var newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
+        var params = ['b', 'row', 'col'];
+        return "\n        " + getPackedSamplerFromInInfo(newInputInfo) + "\n        vec4 " + funcName + "(int b, int row, int col) {\n          return " + funcName + "(" + getSqueezedParams(params, keptDims) + ");\n        }\n      ";
+    }
+    var texNumR = packedTexShape[0];
+    var texNumC = packedTexShape[1];
+    var valuesPerRow = Math.ceil(shape[2] / 2);
+    var texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);
+    return "\n    vec4 " + funcName + "(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        " + texNumR + ", " + texNumC + ", " + texelsInBatch + ", " + valuesPerRow + ", b, row, col);\n      return texture2D(" + texName + ", uv);\n    }\n  ";
+}
+function getSampler3D(inputInfo) {
+    var shape = inputInfo.shapeInfo.logicalShape;
+    var texName = inputInfo.name;
+    var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+    var stride0 = shape[1] * shape[2];
+    var stride1 = shape[2];
+    var _a = util.squeezeShape(shape), newShape = _a.newShape, keptDims = _a.keptDims;
+    var squeezedShape = newShape;
+    if (squeezedShape.length < shape.length) {
+        var newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
+        var params = ['row', 'col', 'depth'];
+        return "\n        " + getSamplerFromInInfo(newInputInfo) + "\n        float " + funcName + "(int row, int col, int depth) {\n          return " + funcName + "(" + getSqueezedParams(params, keptDims) + ");\n        }\n      ";
+    }
+    if (inputInfo.shapeInfo.isUniform) {
+        return "\n      float " + funcName + "(int row, int col, int depth) {\n        float index = dot(vec3(row, col, depth),\n                          vec3(" + stride0 + ", " + stride1 + ", 1));\n        return " + funcName + "Flat(round(index));\n      }\n    ";
+    }
+    var texShape = inputInfo.shapeInfo.texShape;
+    var texNumR = texShape[0];
+    var texNumC = texShape[1];
+    if (texNumC === stride0) {
+        return "\n        float " + funcName + "(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(" + stride1 + ", 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(" + texNumC + ".0, " + texNumR + ".0);\n          return sampleTexture(" + texName + ", uv);\n        }\n      ";
+    }
+    if (texNumC === stride1) {
+        return "\n    float " + funcName + "(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(" + shape[1] + ", 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(" + texNumC + ".0, " + texNumR + ".0);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
+    }
+    return "\n      float " + funcName + "(int row, int col, int depth) {\n        vec2 uv = UVfrom3D(\n            " + texNumR + ", " + texNumC + ", " + stride0 + ", " + stride1 + ", row, col, depth);\n        return sampleTexture(" + texName + ", uv);\n      }\n  ";
+}
+function getPackedSampler4D(inputInfo) {
+    var shape = inputInfo.shapeInfo.logicalShape;
+    var texName = inputInfo.name;
+    var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+    var texShape = inputInfo.shapeInfo.texShape;
+    var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+    var texNumR = packedTexShape[0];
+    var texNumC = packedTexShape[1];
+    var valuesPerRow = Math.ceil(shape[3] / 2);
+    var texelsInBatch = valuesPerRow * Math.ceil(shape[2] / 2);
+    var texelsInBatch2 = texelsInBatch * shape[1];
+    return "\n    vec4 " + funcName + "(int b2, int b, int row, int col) {\n      vec2 uv = packedUVfrom4D(\n        " + texNumR + ", " + texNumC + ", " + texelsInBatch2 + ",\n        " + texelsInBatch + ", " + valuesPerRow + ", b2, b, row, col);\n      return texture2D(" + texName + ", uv);\n    }\n  ";
+}
+function getSampler4D(inputInfo) {
+    var shape = inputInfo.shapeInfo.logicalShape;
+    var texName = inputInfo.name;
+    var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+    var stride2 = shape[3];
+    var stride1 = shape[2] * stride2;
+    var stride0 = shape[1] * stride1;
+    var _a = util.squeezeShape(shape), newShape = _a.newShape, keptDims = _a.keptDims;
+    if (newShape.length < shape.length) {
+        var newInputInfo = squeezeInputInfo(inputInfo, newShape);
+        var params = ['row', 'col', 'depth', 'depth2'];
+        return "\n      " + getSamplerFromInInfo(newInputInfo) + "\n      float " + funcName + "(int row, int col, int depth, int depth2) {\n        return " + funcName + "(" + getSqueezedParams(params, keptDims) + ");\n      }\n    ";
+    }
+    if (inputInfo.shapeInfo.isUniform) {
+        return "\n      float " + funcName + "(int row, int col, int depth, int depth2) {\n        float index = dot(vec4(row, col, depth, depth2),\n                          vec4(" + stride0 + ", " + stride1 + ", " + stride2 + ", 1));\n        return " + funcName + "Flat(round(index));\n      }\n    ";
+    }
+    var texShape = inputInfo.shapeInfo.texShape;
+    var texNumR = texShape[0];
+    var texNumC = texShape[1];
+    if (texNumC === stride0) {
+        return "\n      float " + funcName + "(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2), vec3(" + stride1 + ", " + stride2 + ", 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(" + texNumC + ".0, " + texNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
+    }
+    if (texNumC === stride2) {
+        return "\n      float " + funcName + "(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(" + shape[1] * shape[2] + ", " + shape[2] + ", 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(" + texNumC + ".0, " + texNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
+    }
+    return "\n    float " + funcName + "(int row, int col, int depth, int depth2) {\n      vec2 uv = UVfrom4D(" + texNumR + ", " + texNumC + ", " + stride0 + ", " + stride1 + ",\n          " + stride2 + ", row, col, depth, depth2);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
+}
+function getSampler5D(inputInfo) {
+    var shape = inputInfo.shapeInfo.logicalShape;
+    var texName = inputInfo.name;
+    var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+    var stride3 = shape[4];
+    var stride2 = shape[3] * stride3;
+    var stride1 = shape[2] * stride2;
+    var stride0 = shape[1] * stride1;
+    var _a = util.squeezeShape(shape), newShape = _a.newShape, keptDims = _a.keptDims;
+    if (newShape.length < shape.length) {
+        var newInputInfo = squeezeInputInfo(inputInfo, newShape);
+        var params = ['row', 'col', 'depth', 'depth2', 'depth3'];
+        return "\n      " + getSamplerFromInInfo(newInputInfo) + "\n      float " + funcName + "(int row, int col, int depth, int depth2, int depth3) {\n        return " + funcName + "(" + getSqueezedParams(params, keptDims) + ");\n      }\n    ";
+    }
+    if (inputInfo.shapeInfo.isUniform) {
+        return "\n      float " + funcName + "(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(" + stride0 + ", " + stride1 + ", " + stride2 + ", " + stride3 + ")) +\n          depth3;\n        return " + funcName + "Flat(index);\n      }\n    ";
+    }
+    var texShape = inputInfo.shapeInfo.texShape;
+    var texNumR = texShape[0];
+    var texNumC = texShape[1];
+    if (texNumC === stride0) {
+        return "\n      float " + funcName + "(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(\n          vec4(col, depth, depth2, depth3),\n          vec4(" + stride1 + ", " + stride2 + ", " + stride3 + ", 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(" + texNumC + ".0, " + texNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
+    }
+    if (texNumC === stride3) {
+        return "\n      float " + funcName + "(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(" + shape[1] * shape[2] * shape[3] + ", " + shape[2] * shape[3] + ",\n            " + shape[3] + ", 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(" + texNumC + ".0, " + texNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
+    }
+    return "\n    float " + funcName + "(int row, int col, int depth, int depth2, int depth3) {\n      vec2 uv = UVfrom5D(" + texNumR + ", " + texNumC + ", " + stride0 + ", " + stride1 + ",\n          " + stride2 + ", " + stride3 + ", row, col, depth, depth2, depth3);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
+}
+function getSampler6D(inputInfo) {
+    var shape = inputInfo.shapeInfo.logicalShape;
+    var texName = inputInfo.name;
+    var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+    var stride4 = shape[5];
+    var stride3 = shape[4] * stride4;
+    var stride2 = shape[3] * stride3;
+    var stride1 = shape[2] * stride2;
+    var stride0 = shape[1] * stride1;
+    var _a = util.squeezeShape(shape), newShape = _a.newShape, keptDims = _a.keptDims;
+    if (newShape.length < shape.length) {
+        var newInputInfo = squeezeInputInfo(inputInfo, newShape);
+        var params = ['row', 'col', 'depth', 'depth2', 'depth3', 'depth4'];
+        return "\n      " + getSamplerFromInInfo(newInputInfo) + "\n      float " + funcName + "(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return " + funcName + "(" + getSqueezedParams(params, keptDims) + ");\n      }\n    ";
+    }
+    if (inputInfo.shapeInfo.isUniform) {
+        return "\n      float " + funcName + "(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(" + stride0 + ", " + stride1 + ", " + stride2 + ", " + stride3 + ")) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(" + stride4 + ", 1));\n        return " + funcName + "Flat(index);\n      }\n    ";
+    }
+    var texShape = inputInfo.shapeInfo.texShape;
+    var texNumR = texShape[0];
+    var texNumC = texShape[1];
+    if (texNumC === stride0) {
+        return "\n      float " + funcName + "(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(\n          vec4(col, depth, depth2, depth3),\n          vec4(" + stride1 + ", " + stride2 + ", " + stride3 + ", " + stride4 + ")) + depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(" + texNumC + ".0, " + texNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
+    }
+    if (texNumC === stride4) {
+        return "\n      float " + funcName + "(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(" + shape[1] * shape[2] * shape[3] * shape[4] + ",\n               " + shape[2] * shape[3] * shape[4] + ",\n               " + shape[3] * shape[4] + ",\n               " + shape[4] + ")) + depth3;\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(" + texNumC + ".0, " + texNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
+    }
+    return "\n    float " + funcName + "(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      vec2 uv = UVfrom6D(" + texNumR + ", " + texNumC + ", " + stride0 + ", " + stride1 + ",\n          " + stride2 + ", " + stride3 + ", " + stride4 + "\n          ,row, col, depth, depth2, depth3, depth4);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
+}
+function getSamplerFlat(inputInfo) {
+    var texName = inputInfo.name;
+    var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1) + 'Flat';
+    var inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);
+    if (inputInfo.shapeInfo.isUniform) {
+        if (inSize === 1) {
+            return "float " + funcName + "(int index) {return " + texName + ";}";
+        }
+        return "\n      float " + funcName + "(int index) {\n        for (int i = 0; i < " + inSize + "; i++) {\n          if (i == index) {\n            return " + texName + "[i];\n          }\n        }\n      }\n    ";
+    }
+    var texShape = inputInfo.shapeInfo.texShape;
+    var tNumR = texShape[0];
+    var tNumC = texShape[1];
+    if (tNumC === 1 && tNumR === 1) {
+        return "\n      float " + funcName + "(int index) {\n        return sampleTexture(" + texName + ", halfCR);\n      }\n    ";
+    }
+    if (tNumC === 1) {
+        return "\n      float " + funcName + "(int index) {\n        vec2 uv = vec2(0.5, (float(index) + 0.5) / " + tNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
+    }
+    if (tNumR === 1) {
+        return "\n      float " + funcName + "(int index) {\n        vec2 uv = vec2((float(index) + 0.5) / " + tNumC + ".0, 0.5);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
+    }
+    return "\n    float " + funcName + "(int index) {\n      vec2 uv = UVfrom1D(" + tNumR + ", " + tNumC + ", index);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
+}
+function getBroadcastOutputCoordsSampler(inputInfo, outShapeInfo, texFuncSnippet, funcName) {
+    var inRank = inputInfo.shapeInfo.logicalShape.length;
+    var outRank = outShapeInfo.logicalShape.length;
+    var type = 'int';
+    if (outRank === 2) {
+        type = 'ivec2';
+    }
+    else if (outRank === 3) {
+        type = 'ivec3';
+    }
+    else if (outRank === 4) {
+        type = 'ivec4';
+    }
+    var broadcastDims = broadcast_util.getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);
+    var rankDiff = outRank - inRank;
+    var coordsSnippet;
+    if (inRank === 0) {
+        coordsSnippet = '';
+    }
+    else if (outRank < 2 && broadcastDims.length >= 1) {
+        coordsSnippet = 'coords = 0;';
+    }
+    else {
+        coordsSnippet =
+            broadcastDims.map(function (d) { return "coords[" + (d + rankDiff) + "] = 0;"; }).join('\n');
+    }
+    var unpackedCoordsSnippet = '';
+    if (outRank < 2 && inRank > 0) {
+        unpackedCoordsSnippet = 'coords';
+    }
+    else {
+        unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape
+            .map(function (s, i) { return "coords[" + (i + rankDiff) + "]"; })
+            .join(', ');
+    }
+    return "\n    float " + funcName + "() {\n      " + type + " coords = getOutputCoords();\n      " + coordsSnippet + "\n      return get" + texFuncSnippet + "(" + unpackedCoordsSnippet + ");\n    }\n  ";
+}
+function getPackedSamplerAtOutputCoords(inputInfo, outShapeInfo, supportsBroadcasting) {
+    var texName = inputInfo.name;
+    var texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);
+    var texShape = inputInfo.shapeInfo.texShape;
+    var funcName = 'get' + texFuncSnippet + 'AtOutCoords';
+    var outTexShape = outShapeInfo.texShape;
+    var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+    var texNumR = packedTexShape[0];
+    var texNumC = packedTexShape[1];
+    var broadcastDims = broadcast_util.getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);
+    var inRank = inputInfo.shapeInfo.logicalShape.length;
+    var outRank = outShapeInfo.logicalShape.length;
+    if (broadcastDims.length) {
+        throw Error('Packed broadcast sampling is not implemented yet.');
+    }
+    var inTexShape = inputInfo.shapeInfo.texShape;
+    if (util.arraysEqual(inTexShape, outTexShape)) {
+        return "\n      vec4 " + funcName + "() {\n        return texture2D(" + texName + ", resultUV);\n      }\n    ";
+    }
+    var output = "return texture2D(" + texName + ", uv)";
+    if (inRank === 1 && outRank > 1) {
+        output = "\n      vec4 sample = texture2D(" + texName + ", uv);\n      return vec4(sample.xy, sample.xy);\n    ";
+    }
+    else if (inRank === 0 && outRank > 0) {
+        if (outRank === 1) {
+            output = "\n        vec4 sample = texture2D(" + texName + ", uv);\n        return vec4(sample.x, sample.x, 0., 0.);\n      ";
+        }
+        else {
+            output = "\n        vec4 sample = texture2D(" + texName + ", uv);\n        return vec4(sample.x);\n      ";
+        }
+    }
+    return "\n    vec4 " + funcName + "() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + packedTexShape[0] + ", " + packedTexShape[1] + "));\n      int index = resTexRC.x * " + packedTexShape[1] + " + resTexRC.y;\n\n      int texR = index / " + texNumC + ";\n      int texC = index - texR * " + texNumC + ";\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(" + texNumC + ", " + texNumR + ");\n\n      " + output + ";\n    }\n  ";
+}
+function getSamplerAtOutputCoords(inputInfo, outShapeInfo, supportsBroadcasting) {
+    var texName = inputInfo.name;
+    var texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);
+    var funcName = 'get' + texFuncSnippet + 'AtOutCoords';
+    var broadcastDims = broadcast_util.getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);
+    var inRank = inputInfo.shapeInfo.logicalShape.length;
+    var outRank = outShapeInfo.logicalShape.length;
+    var doBroadcast = supportsBroadcasting && ((outRank > inRank) || broadcastDims.length > 0);
+    var broadcastOverOuter = broadcast_util.broadcastDimsAreOuter(broadcastDims);
+    var isUniform = inputInfo.shapeInfo.isUniform;
+    if (doBroadcast && !broadcastOverOuter) {
+        return getBroadcastOutputCoordsSampler(inputInfo, outShapeInfo, texFuncSnippet, funcName);
+    }
+    var inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);
+    var broadcastSnippet = '';
+    if (doBroadcast && broadcastOverOuter) {
+        broadcastSnippet = "\n        int mainPart = index / " + inSize + ";\n        index -= mainPart * " + inSize + ";\n      ";
+    }
+    var outTexShape = outShapeInfo.texShape;
+    if (isUniform) {
+        if (inSize === 1) {
+            return "float " + funcName + "() {return " + texName + ";}";
+        }
+        return "\n      float " + funcName + "() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                              vec2(" + outTexShape[0] + ", " + outTexShape[1] + "));\n        int index = resTexRC.x * " + outTexShape[1] + " + resTexRC.y;\n        " + broadcastSnippet + "\n        return get" + texFuncSnippet + "Flat(index);\n      }\n    ";
+    }
+    var inTexShape = inputInfo.shapeInfo.texShape;
+    if (util.arraysEqual(inTexShape, outTexShape)) {
+        return "\n      float " + funcName + "() {\n        return sampleTexture(" + texName + ", resultUV);\n      }\n    ";
+    }
+    return "\n    float " + funcName + "() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + outTexShape[0] + ", " + outTexShape[1] + "));\n      int index = resTexRC.x * " + outTexShape[1] + " + resTexRC.y;\n      " + broadcastSnippet + "\n      int texR = index / " + inTexShape[1] + ";\n      int texC = index - texR * " + inTexShape[1] + ";\n      vec2 uv = (vec2(texC, texR) + halfCR) /\n                 vec2(" + inTexShape[1] + ".0, " + inTexShape[0] + ".0);\n\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
+}
+function getCoordsDataType(rank) {
+    if (rank <= 1) {
+        return 'int';
+    }
+    else if (rank === 2) {
+        return 'ivec2';
+    }
+    else if (rank === 3) {
+        return 'ivec3';
+    }
+    else if (rank === 4) {
+        return 'ivec4';
+    }
+    else if (rank === 5) {
+        return 'ivec5';
+    }
+    else if (rank === 6) {
+        return 'ivec6';
+    }
+    else {
+        throw Error("GPU for rank " + rank + " is not yet supported");
+    }
+}
+exports.getCoordsDataType = getCoordsDataType;
+function squeezeInputInfo(inInfo, squeezedShape) {
+    var newInputInfo = JSON.parse(JSON.stringify(inInfo));
+    newInputInfo.shapeInfo.logicalShape = squeezedShape;
+    return newInputInfo;
+}
+function getSqueezedParams(params, keptDims) {
+    return keptDims.map(function (d) { return params[d]; }).join(', ');
+}
+//# sourceMappingURL=shader_compiler.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/shader_compiler.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/shader_compiler.js.map
new file mode 100644
index 0000000..9a09817
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/shader_compiler.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/shader_compiler_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/shader_compiler_util.d.ts
new file mode 100644
index 0000000..fa08c05
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/shader_compiler_util.d.ts
@@ -0,0 +1,2 @@
+export declare function getLogicalCoordinatesFromFlatIndex(coords: string[], shape: number[], index?: string): string;
+export declare function dotify(x: string[], y: string[]): string;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/shader_compiler_util.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/shader_compiler_util.js
new file mode 100644
index 0000000..3411efd
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/shader_compiler_util.js
@@ -0,0 +1,49 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var util = require("../../util");
+function getLogicalCoordinatesFromFlatIndex(coords, shape, index) {
+    if (index === void 0) { index = 'index'; }
+    var strides = util.computeStrides(shape);
+    return strides
+        .map(function (stride, i) {
+        var line1 = "int " + coords[i] + " = " + index + " / " + stride;
+        var line2 = i === strides.length - 1 ?
+            "int " + coords[i + 1] + " = " + index + " - " + coords[i] + " * " + stride :
+            "index -= " + coords[i] + " * " + stride;
+        return line1 + "; " + line2 + ";";
+    })
+        .join('');
+}
+exports.getLogicalCoordinatesFromFlatIndex = getLogicalCoordinatesFromFlatIndex;
+function buildVec(x) {
+    if (x.length === 1) {
+        return "" + x[0];
+    }
+    return "vec" + x.length + "(" + x.join(',') + ")";
+}
+function dotify(x, y) {
+    if (x.length !== y.length) {
+        throw new Error("Vectors to be dotted must be of the same length -" +
+            ("got " + x.length + " and " + y.length));
+    }
+    var slices = [];
+    var nearestVec4 = Math.floor(x.length / 4);
+    var nearestVec4Remainder = x.length % 4;
+    for (var i = 0; i < nearestVec4; i++) {
+        var xSlice = x.slice(i * 4, i * 4 + 4);
+        var ySlice = y.slice(i * 4, i * 4 + 4);
+        slices.push(buildVec(xSlice) + ", " + buildVec(ySlice));
+    }
+    if (nearestVec4Remainder !== 0) {
+        var xSlice = x.slice(nearestVec4 * 4);
+        var ySlice = y.slice(nearestVec4 * 4);
+        if (xSlice.length === 1) {
+            xSlice = xSlice.map(function (d) { return "float(" + d + ")"; });
+            ySlice = ySlice.map(function (d) { return "float(" + d + ")"; });
+        }
+        slices.push(buildVec(xSlice) + ", " + buildVec(ySlice));
+    }
+    return slices.map(function (d, i) { return "dot(" + d + ")"; }).join('+');
+}
+exports.dotify = dotify;
+//# sourceMappingURL=shader_compiler_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/shader_compiler_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/shader_compiler_util.js.map
new file mode 100644
index 0000000..509b251
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/shader_compiler_util.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"shader_compiler_util.js","sourceRoot":"","sources":["../../../src/kernels/webgl/shader_compiler_util.ts"],"names":[],"mappings":";;AAiBA,iCAAmC;AAOnC,4CACI,MAAgB,EAAE,KAAe,EAAE,KAAe;IAAf,sBAAA,EAAA,eAAe;IACpD,IAAM,OAAO,GAAG,IAAI,CAAC,cAAc,CAAC,KAAK,CAAC,CAAC;IAC3C,OAAO,OAAO;SACT,GAAG,CAAC,UAAC,MAAM,EAAE,CAAC;QACb,IAAM,KAAK,GAAG,SAAO,MAAM,CAAC,CAAC,CAAC,WAAM,KAAK,WAAM,MAAQ,CAAC;QACxD,IAAM,KAAK,GAAG,CAAC,KAAK,OAAO,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC;YACpC,SAAO,MAAM,CAAC,CAAC,GAAG,CAAC,CAAC,WAAM,KAAK,WAAM,MAAM,CAAC,CAAC,CAAC,WAAM,MAAQ,CAAC,CAAC;YAC9D,cAAY,MAAM,CAAC,CAAC,CAAC,WAAM,MAAQ,CAAC;QACxC,OAAU,KAAK,UAAK,KAAK,MAAG,CAAC;IAC/B,CAAC,CAAC;SACD,IAAI,CAAC,EAAE,CAAC,CAAC;AAChB,CAAC;AAZD,gFAYC;AAED,kBAAkB,CAAW;IAC3B,IAAI,CAAC,CAAC,MAAM,KAAK,CAAC,EAAE;QAClB,OAAO,KAAG,CAAC,CAAC,CAAC,CAAG,CAAC;KAClB;IACD,OAAO,QAAM,CAAC,CAAC,MAAM,SAAI,CAAC,CAAC,IAAI,CAAC,GAAG,CAAC,MAAG,CAAC;AAC1C,CAAC;AAMD,gBAAuB,CAAW,EAAE,CAAW;IAC7C,IAAI,CAAC,CAAC,MAAM,KAAK,CAAC,CAAC,MAAM,EAAE;QACzB,MAAM,IAAI,KAAK,CACX,mDAAmD;aACnD,SAAO,CAAC,CAAC,MAAM,aAAQ,CAAC,CAAC,MAAQ,CAAA,CAAC,CAAC;KACxC;IAED,IAAM,MAAM,GAAa,EAAE,CAAC;IAC5B,IAAM,WAAW,GAAG,IAAI,CAAC,KAAK,CAAC,CAAC,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC;IAC7C,IAAM,oBAAoB,GAAG,CAAC,CAAC,MAAM,GAAG,CAAC,CAAC;IAE1C,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,WAAW,EAAE,CAAC,EAAE,EAAE;QACpC,IAAM,MAAM,GAAG,CAAC,CAAC,KAAK,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC;QACzC,IAAM,MAAM,GAAG,CAAC,CAAC,KAAK,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC;QACzC,MAAM,CAAC,IAAI,CAAI,QAAQ,CAAC,MAAM,CAAC,UAAK,QAAQ,CAAC,MAAM,CAAG,CAAC,CAAC;KACzD;IAED,IAAI,oBAAoB,KAAK,CAAC,EAAE;QAC9B,IAAI,MAAM,GAAG,CAAC,CAAC,KAAK,CAAC,WAAW,GAAG,CAAC,CAAC,CAAC;QACtC,IAAI,MAAM,GAAG,CAAC,CAAC,KAAK,CAAC,WAAW,GAAG,CAAC,CAAC,CAAC;QACtC,IAAI,MAAM,CAAC,MAAM,KAAK,CAAC,EAAE;YACvB,MAAM,GAAG,MAAM,CAAC,GAAG,CAAC,UAAA,CAAC,IAAI,OAAA,WAAS,CAAC,MAAG,EAAb,CAAa,CAAC,CAAC;YACxC,MAAM,GAAG,MAAM,CAAC,GAAG,CAAC,UAAA,CAAC,IAAI,OAAA,WAAS,CAAC,MAAG,EAAb,CAAa,CAAC,CAAC;SACzC;QACD,MAAM,CAAC,IAAI,CAAI,QAAQ,CAAC,MAAM,CAAC,UAAK,QAAQ,CAAC,MAAM,CAAG,CAAC,CAAC;KACzD;IAED,OAAO,MAAM,CAAC,GAAG,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,SAAO,CAAC,MAAG,EAAX,CAAW,CAAC,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC;AACrD,CAAC;AA5BD,wBA4BC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/shader_compiler_util_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/shader_compiler_util_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/shader_compiler_util_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/shader_compiler_util_test.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/shader_compiler_util_test.js
new file mode 100644
index 0000000..069737e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/shader_compiler_util_test.js
@@ -0,0 +1,29 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var jasmine_util_1 = require("../../jasmine_util");
+var test_util_1 = require("../../test_util");
+var shader_compiler_util_1 = require("./shader_compiler_util");
+jasmine_util_1.describeWithFlags('shader compiler', test_util_1.WEBGL_ENVS, function () {
+    it('dotify takes two arrays of coordinates and produces' +
+        'the glsl that finds the dot product of those coordinates', function () {
+        var coords1 = ['r', 'g', 'b', 'a'];
+        var coords2 = ['x', 'y', 'z', 'w'];
+        expect(shader_compiler_util_1.dotify(coords1, coords2))
+            .toEqual('dot(vec4(r,g,b,a), vec4(x,y,z,w))');
+    });
+    it('dotify should split up arrays into increments of vec4s', function () {
+        var coords1 = ['a', 'b', 'c', 'd', 'e', 'f', 'g'];
+        var coords2 = ['h', 'i', 'j', 'k', 'l', 'm', 'n'];
+        expect(shader_compiler_util_1.dotify(coords1, coords2))
+            .toEqual('dot(vec4(a,b,c,d), vec4(h,i,j,k))+dot(vec3(e,f,g), vec3(l,m,n))');
+    });
+    it('getLogicalCoordinatesFromFlatIndex produces glsl that takes' +
+        'a flat index and finds its coordinates within that shape', function () {
+        var coords = ['r', 'c', 'd'];
+        var shape = [1, 2, 3];
+        expect(shader_compiler_util_1.getLogicalCoordinatesFromFlatIndex(coords, shape))
+            .toEqual('int r = index / 6; index -= r * 6;' +
+            'int c = index / 3; int d = index - c * 3;');
+    });
+});
+//# sourceMappingURL=shader_compiler_util_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/shader_compiler_util_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/shader_compiler_util_test.js.map
new file mode 100644
index 0000000..265c717
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/shader_compiler_util_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"shader_compiler_util_test.js","sourceRoot":"","sources":["../../../src/kernels/webgl/shader_compiler_util_test.ts"],"names":[],"mappings":";;AAiBA,mDAAqD;AACrD,6CAA2C;AAC3C,+DAAkF;AAElF,gCAAiB,CAAC,iBAAiB,EAAE,sBAAU,EAAE;IAC/C,EAAE,CAAC,qDAAqD;QACjD,0DAA0D,EAC9D;QACE,IAAM,OAAO,GAAG,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC,CAAC;QACrC,IAAM,OAAO,GAAG,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC,CAAC;QAErC,MAAM,CAAC,6BAAM,CAAC,OAAO,EAAE,OAAO,CAAC,CAAC;aAC3B,OAAO,CAAC,mCAAmC,CAAC,CAAC;IACpD,CAAC,CAAC,CAAC;IAEN,EAAE,CAAC,wDAAwD,EAAE;QAC3D,IAAM,OAAO,GAAG,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC,CAAC;QACpD,IAAM,OAAO,GAAG,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC,CAAC;QAEpD,MAAM,CAAC,6BAAM,CAAC,OAAO,EAAE,OAAO,CAAC,CAAC;aAC3B,OAAO,CACJ,iEAAiE,CAAC,CAAC;IAC7E,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,6DAA6D;QACzD,0DAA0D,EAC9D;QACE,IAAM,MAAM,GAAG,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC,CAAC;QAC/B,IAAM,KAAK,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC;QAExB,MAAM,CAAC,yDAAkC,CAAC,MAAM,EAAE,KAAK,CAAC,CAAC;aACpD,OAAO,CACJ,oCAAoC;YACpC,2CAA2C,CAAC,CAAC;IACvD,CAAC,CAAC,CAAC;AACR,CAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/slice_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/slice_gpu.d.ts
new file mode 100644
index 0000000..a8fc474
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/slice_gpu.d.ts
@@ -0,0 +1,11 @@
+import { GPGPUContext } from './gpgpu_context';
+import { GPGPUProgram } from './gpgpu_math';
+export declare class SliceProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    rank: number;
+    startLoc: WebGLUniformLocation;
+    constructor(destSize: number[]);
+    getCustomSetupFunc(start: number[]): (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => void;
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/slice_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/slice_gpu.js
new file mode 100644
index 0000000..9fff0aa
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/slice_gpu.js
@@ -0,0 +1,63 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var shader_compiler_1 = require("./shader_compiler");
+var SliceProgram = (function () {
+    function SliceProgram(destSize) {
+        this.variableNames = ['source'];
+        this.outputShape = destSize;
+        this.rank = destSize.length;
+        var dtype = shader_compiler_1.getCoordsDataType(this.rank);
+        var sourceCoords = getCoords(this.rank);
+        this.userCode = "\n      uniform " + dtype + " start;\n\n      void main() {\n        " + dtype + " sourceLoc = start + getOutputCoords();\n        setOutput(getSource(" + sourceCoords + "));\n      }\n    ";
+    }
+    SliceProgram.prototype.getCustomSetupFunc = function (start) {
+        var _this = this;
+        if (start.length !== this.rank) {
+            throw Error("The rank (" + this.rank + ") of the program must match the " +
+                ("length of start (" + start.length + ")"));
+        }
+        return function (gpgpu, webGLProgram) {
+            if (_this.startLoc == null) {
+                _this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');
+                if (_this.startLoc == null) {
+                    return;
+                }
+            }
+            if (_this.rank === 1) {
+                gpgpu.gl.uniform1i(_this.startLoc, start[0]);
+            }
+            else if (_this.rank === 2) {
+                gpgpu.gl.uniform2i(_this.startLoc, start[0], start[1]);
+            }
+            else if (_this.rank === 3) {
+                gpgpu.gl.uniform3i(_this.startLoc, start[0], start[1], start[2]);
+            }
+            else if (_this.rank === 4) {
+                gpgpu.gl.uniform4i(_this.startLoc, start[0], start[1], start[2], start[3]);
+            }
+            else {
+                throw Error("Slicing for rank " + _this.rank + " is not yet supported");
+            }
+        };
+    };
+    return SliceProgram;
+}());
+exports.SliceProgram = SliceProgram;
+function getCoords(rank) {
+    if (rank === 1) {
+        return 'sourceLoc';
+    }
+    else if (rank === 2) {
+        return 'sourceLoc.x, sourceLoc.y';
+    }
+    else if (rank === 3) {
+        return 'sourceLoc.x, sourceLoc.y, sourceLoc.z';
+    }
+    else if (rank === 4) {
+        return 'sourceLoc.x, sourceLoc.y, sourceLoc.z, sourceLoc.w';
+    }
+    else {
+        throw Error("Slicing for rank " + rank + " is not yet supported");
+    }
+}
+//# sourceMappingURL=slice_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/slice_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/slice_gpu.js.map
new file mode 100644
index 0000000..f7247f9
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/slice_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"slice_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/slice_gpu.ts"],"names":[],"mappings":";;AAmBA,qDAAoD;AAEpD;IASE,sBAAY,QAAkB;QAR9B,kBAAa,GAAG,CAAC,QAAQ,CAAC,CAAC;QASzB,IAAI,CAAC,WAAW,GAAG,QAAQ,CAAC;QAC5B,IAAI,CAAC,IAAI,GAAG,QAAQ,CAAC,MAAM,CAAC;QAE5B,IAAM,KAAK,GAAG,mCAAiB,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;QAC3C,IAAM,YAAY,GAAG,SAAS,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;QAE1C,IAAI,CAAC,QAAQ,GAAG,qBACJ,KAAK,gDAGX,KAAK,6EACe,YAAY,uBAErC,CAAC;IACJ,CAAC;IAED,yCAAkB,GAAlB,UAAmB,KAAe;QAAlC,iBA4BC;QA3BC,IAAI,KAAK,CAAC,MAAM,KAAK,IAAI,CAAC,IAAI,EAAE;YAC9B,MAAM,KAAK,CACP,eAAa,IAAI,CAAC,IAAI,qCAAkC;iBACxD,sBAAoB,KAAK,CAAC,MAAM,MAAG,CAAA,CAAC,CAAC;SAC1C;QACD,OAAO,UAAC,KAAmB,EAAE,YAA0B;YACrD,IAAI,KAAI,CAAC,QAAQ,IAAI,IAAI,EAAE;gBACzB,KAAI,CAAC,QAAQ,GAAG,KAAK,CAAC,yBAAyB,CAAC,YAAY,EAAE,OAAO,CAAC,CAAC;gBACvE,IAAI,KAAI,CAAC,QAAQ,IAAI,IAAI,EAAE;oBAGzB,OAAO;iBACR;aACF;YACD,IAAI,KAAI,CAAC,IAAI,KAAK,CAAC,EAAE;gBACnB,KAAK,CAAC,EAAE,CAAC,SAAS,CAAC,KAAI,CAAC,QAAQ,EAAE,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC;aAC7C;iBAAM,IAAI,KAAI,CAAC,IAAI,KAAK,CAAC,EAAE;gBAC1B,KAAK,CAAC,EAAE,CAAC,SAAS,CAAC,KAAI,CAAC,QAAQ,EAAE,KAAK,CAAC,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC;aACvD;iBAAM,IAAI,KAAI,CAAC,IAAI,KAAK,CAAC,EAAE;gBAC1B,KAAK,CAAC,EAAE,CAAC,SAAS,CAAC,KAAI,CAAC,QAAQ,EAAE,KAAK,CAAC,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC;aACjE;iBAAM,IAAI,KAAI,CAAC,IAAI,KAAK,CAAC,EAAE;gBAC1B,KAAK,CAAC,EAAE,CAAC,SAAS,CACd,KAAI,CAAC,QAAQ,EAAE,KAAK,CAAC,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC,CAAC,EAAE,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC;aAC5D;iBAAM;gBACL,MAAM,KAAK,CAAC,sBAAoB,KAAI,CAAC,IAAI,0BAAuB,CAAC,CAAC;aACnE;QACH,CAAC,CAAC;IACJ,CAAC;IACH,mBAAC;AAAD,CAAC,AAvDD,IAuDC;AAvDY,oCAAY;AAyDzB,mBAAmB,IAAY;IAC7B,IAAI,IAAI,KAAK,CAAC,EAAE;QACd,OAAO,WAAW,CAAC;KACpB;SAAM,IAAI,IAAI,KAAK,CAAC,EAAE;QACrB,OAAO,0BAA0B,CAAC;KACnC;SAAM,IAAI,IAAI,KAAK,CAAC,EAAE;QACrB,OAAO,uCAAuC,CAAC;KAChD;SAAM,IAAI,IAAI,KAAK,CAAC,EAAE;QACrB,OAAO,oDAAoD,CAAC;KAC7D;SAAM;QACL,MAAM,KAAK,CAAC,sBAAoB,IAAI,0BAAuB,CAAC,CAAC;KAC9D;AACH,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/strided_slice_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/strided_slice_gpu.d.ts
new file mode 100644
index 0000000..6648243
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/strided_slice_gpu.d.ts
@@ -0,0 +1,7 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class StridedSliceProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    constructor(begin: number[], strides: number[], size: number[], shrinkAxis: number[]);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/strided_slice_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/strided_slice_gpu.js
new file mode 100644
index 0000000..de1c0bd
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/strided_slice_gpu.js
@@ -0,0 +1,37 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var shader_compiler_1 = require("./shader_compiler");
+var StridedSliceProgram = (function () {
+    function StridedSliceProgram(begin, strides, size, shrinkAxis) {
+        this.variableNames = ['x'];
+        var shape = size.filter(function (v, index) { return shrinkAxis.indexOf(index) === -1; });
+        this.outputShape = shape;
+        var rank = size.length;
+        var inputDtype = shader_compiler_1.getCoordsDataType(size.length);
+        var dtype = shader_compiler_1.getCoordsDataType(shape.length);
+        var newCoords = '';
+        if (rank === 1) {
+            newCoords = 'coords * strides + begin';
+        }
+        else {
+            var outputAxis_1 = 0;
+            newCoords =
+                size.map(function (_, i) {
+                    if (shrinkAxis.indexOf(i) === -1) {
+                        outputAxis_1++;
+                        return shape.length === 1 ?
+                            "coords * strides[" + i + "] + begin[" + i + "]" :
+                            "coords[" + (outputAxis_1 - 1) + "] * strides[" + i + "] + begin[" + i + "]";
+                    }
+                    else {
+                        return "begin[" + i + "]";
+                    }
+                })
+                    .join(',');
+        }
+        this.userCode = "\n      " + inputDtype + " begin = " + inputDtype + "(" + begin + ");\n      " + inputDtype + " strides = " + inputDtype + "(" + strides + ");\n\n      void main() {\n        " + dtype + " coords = getOutputCoords();\n        setOutput(getX(" + newCoords + "));\n      }\n    ";
+    }
+    return StridedSliceProgram;
+}());
+exports.StridedSliceProgram = StridedSliceProgram;
+//# sourceMappingURL=strided_slice_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/strided_slice_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/strided_slice_gpu.js.map
new file mode 100644
index 0000000..341933f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/strided_slice_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"strided_slice_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/strided_slice_gpu.ts"],"names":[],"mappings":";;AAkBA,qDAAoD;AAEpD;IAKE,6BACI,KAAe,EAAE,OAAiB,EAAE,IAAc,EAClD,UAAoB;QANxB,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QAOpB,IAAM,KAAK,GAAG,IAAI,CAAC,MAAM,CAAC,UAAC,CAAC,EAAE,KAAK,IAAK,OAAA,UAAU,CAAC,OAAO,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAhC,CAAgC,CAAC,CAAC;QAC1E,IAAI,CAAC,WAAW,GAAG,KAAK,CAAC;QACzB,IAAM,IAAI,GAAG,IAAI,CAAC,MAAM,CAAC;QACzB,IAAM,UAAU,GAAG,mCAAiB,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC;QAClD,IAAM,KAAK,GAAG,mCAAiB,CAAC,KAAK,CAAC,MAAM,CAAC,CAAC;QAE9C,IAAI,SAAS,GAAG,EAAE,CAAC;QACnB,IAAI,IAAI,KAAK,CAAC,EAAE;YACd,SAAS,GAAG,0BAA0B,CAAC;SACxC;aAAM;YACL,IAAI,YAAU,GAAG,CAAC,CAAC;YACnB,SAAS;gBACL,IAAI,CAAC,GAAG,CAAC,UAAC,CAAC,EAAE,CAAC;oBACR,IAAI,UAAU,CAAC,OAAO,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,EAAE;wBAChC,YAAU,EAAE,CAAC;wBACb,OAAO,KAAK,CAAC,MAAM,KAAK,CAAC,CAAC,CAAC;4BACvB,sBAAoB,CAAC,kBAAa,CAAC,MAAG,CAAC,CAAC;4BACxC,aAAU,YAAU,GAAG,CAAC,qBAAe,CAAC,kBAAa,CAAC,MAAG,CAAC;qBAC/D;yBAAM;wBACL,OAAO,WAAS,CAAC,MAAG,CAAC;qBACtB;gBACH,CAAC,CAAC;qBACD,IAAI,CAAC,GAAG,CAAC,CAAC;SACpB;QAED,IAAI,CAAC,QAAQ,GAAG,aACZ,UAAU,iBAAY,UAAU,SAAI,KAAK,kBACzC,UAAU,mBAAc,UAAU,SAAI,OAAO,2CAG3C,KAAK,6DACU,SAAS,uBAE7B,CAAC;IACJ,CAAC;IACH,0BAAC;AAAD,CAAC,AA3CD,IA2CC;AA3CY,kDAAmB"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/tex_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/tex_util.d.ts
new file mode 100644
index 0000000..d165445
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/tex_util.d.ts
@@ -0,0 +1,39 @@
+import { Tensor } from '../../tensor';
+import { DataType, DataValues } from '../../types';
+export declare enum TextureUsage {
+    RENDER = 0,
+    UPLOAD = 1,
+    PIXELS = 2,
+    DOWNLOAD = 3
+}
+export declare enum PhysicalTextureType {
+    UNPACKED_FLOAT16 = 0,
+    UNPACKED_FLOAT32 = 1,
+    PACKED_4X1_UNSIGNED_BYTE = 2,
+    PACKED_2X2_FLOAT32 = 3,
+    PACKED_2X2_FLOAT16 = 4
+}
+export interface TextureData {
+    shape: number[];
+    dtype: DataType;
+    values?: DataValues;
+    texture?: WebGLTexture;
+    complexTensors?: {
+        real: Tensor;
+        imag: Tensor;
+    };
+    texShape?: [number, number];
+    usage?: TextureUsage;
+    isPacked?: boolean;
+}
+export declare function getUnpackedMatrixTextureShapeWidthHeight(rows: number, columns: number): [number, number];
+export declare function getUnpackedArraySizeFromMatrixSize(matrixSize: number, channelsPerTexture: number): number;
+export declare function getColorMatrixTextureShapeWidthHeight(rows: number, columns: number): [number, number];
+export declare function getMatrixSizeFromUnpackedArraySize(unpackedSize: number, channelsPerTexture: number): number;
+export declare function encodeMatrixToUnpackedArray(matrix: Float32Array | Uint8Array, unpackedArray: Float32Array | Uint8Array, channelsPerTexture: number): void;
+export declare function decodeMatrixFromUnpackedArray(unpackedArray: Float32Array, matrix: Float32Array, channelsPerTexture: number): void;
+export declare function decodeMatrixFromUnpackedColorRGBAArray(unpackedArray: Float32Array, matrix: Float32Array, channels: number): void;
+export declare function getPackedMatrixTextureShapeWidthHeight(rows: number, columns: number): [number, number];
+export declare function getPackedRGBAArraySizeFromMatrixShape(rows: number, columns: number): number;
+export declare function encodeMatrixToPackedRGBA(matrix: Float32Array, batches: number, rows: number, columns: number, packedRGBA: Float32Array): Float32Array;
+export declare function decodeMatrixFromPackedRGBA(packedRGBA: Float32Array, batches: number, rows: number, columns: number, matrix: Float32Array): Float32Array;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/tex_util.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/tex_util.js
new file mode 100644
index 0000000..7553c54
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/tex_util.js
@@ -0,0 +1,209 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var util = require("../../util");
+var TextureUsage;
+(function (TextureUsage) {
+    TextureUsage[TextureUsage["RENDER"] = 0] = "RENDER";
+    TextureUsage[TextureUsage["UPLOAD"] = 1] = "UPLOAD";
+    TextureUsage[TextureUsage["PIXELS"] = 2] = "PIXELS";
+    TextureUsage[TextureUsage["DOWNLOAD"] = 3] = "DOWNLOAD";
+})(TextureUsage = exports.TextureUsage || (exports.TextureUsage = {}));
+var PhysicalTextureType;
+(function (PhysicalTextureType) {
+    PhysicalTextureType[PhysicalTextureType["UNPACKED_FLOAT16"] = 0] = "UNPACKED_FLOAT16";
+    PhysicalTextureType[PhysicalTextureType["UNPACKED_FLOAT32"] = 1] = "UNPACKED_FLOAT32";
+    PhysicalTextureType[PhysicalTextureType["PACKED_4X1_UNSIGNED_BYTE"] = 2] = "PACKED_4X1_UNSIGNED_BYTE";
+    PhysicalTextureType[PhysicalTextureType["PACKED_2X2_FLOAT32"] = 3] = "PACKED_2X2_FLOAT32";
+    PhysicalTextureType[PhysicalTextureType["PACKED_2X2_FLOAT16"] = 4] = "PACKED_2X2_FLOAT16";
+})(PhysicalTextureType = exports.PhysicalTextureType || (exports.PhysicalTextureType = {}));
+function getUnpackedMatrixTextureShapeWidthHeight(rows, columns) {
+    return [columns, rows];
+}
+exports.getUnpackedMatrixTextureShapeWidthHeight = getUnpackedMatrixTextureShapeWidthHeight;
+function getUnpackedArraySizeFromMatrixSize(matrixSize, channelsPerTexture) {
+    return matrixSize * channelsPerTexture;
+}
+exports.getUnpackedArraySizeFromMatrixSize = getUnpackedArraySizeFromMatrixSize;
+function getColorMatrixTextureShapeWidthHeight(rows, columns) {
+    return [columns * 4, rows];
+}
+exports.getColorMatrixTextureShapeWidthHeight = getColorMatrixTextureShapeWidthHeight;
+function getMatrixSizeFromUnpackedArraySize(unpackedSize, channelsPerTexture) {
+    if (unpackedSize % channelsPerTexture !== 0) {
+        throw new Error("unpackedSize (" + unpackedSize + ") must be a multiple of " +
+            ("" + channelsPerTexture));
+    }
+    return unpackedSize / channelsPerTexture;
+}
+exports.getMatrixSizeFromUnpackedArraySize = getMatrixSizeFromUnpackedArraySize;
+function encodeMatrixToUnpackedArray(matrix, unpackedArray, channelsPerTexture) {
+    var requiredSize = getUnpackedArraySizeFromMatrixSize(matrix.length, channelsPerTexture);
+    if (unpackedArray.length < requiredSize) {
+        throw new Error("unpackedArray length (" + unpackedArray.length + ") must be >= " +
+            ("" + requiredSize));
+    }
+    var dst = 0;
+    for (var src = 0; src < matrix.length; ++src) {
+        unpackedArray[dst] = matrix[src];
+        dst += channelsPerTexture;
+    }
+}
+exports.encodeMatrixToUnpackedArray = encodeMatrixToUnpackedArray;
+function decodeMatrixFromUnpackedArray(unpackedArray, matrix, channelsPerTexture) {
+    var requiredSize = getMatrixSizeFromUnpackedArraySize(unpackedArray.length, channelsPerTexture);
+    if (matrix.length < requiredSize) {
+        throw new Error("matrix length (" + matrix.length + ") must be >= " + requiredSize);
+    }
+    var dst = 0;
+    for (var src = 0; src < unpackedArray.length; src += channelsPerTexture) {
+        matrix[dst++] = unpackedArray[src];
+    }
+}
+exports.decodeMatrixFromUnpackedArray = decodeMatrixFromUnpackedArray;
+function decodeMatrixFromUnpackedColorRGBAArray(unpackedArray, matrix, channels) {
+    var requiredSize = unpackedArray.length * channels / 4;
+    if (matrix.length < requiredSize) {
+        throw new Error("matrix length (" + matrix.length + ") must be >= " + requiredSize);
+    }
+    var dst = 0;
+    for (var src = 0; src < unpackedArray.length; src += 4) {
+        for (var c = 0; c < channels; c++) {
+            matrix[dst++] = unpackedArray[src + c];
+        }
+    }
+}
+exports.decodeMatrixFromUnpackedColorRGBAArray = decodeMatrixFromUnpackedColorRGBAArray;
+function getPackedMatrixTextureShapeWidthHeight(rows, columns) {
+    return [
+        Math.max(1, Math.ceil(columns / 2)), Math.max(1, Math.ceil(rows / 2))
+    ];
+}
+exports.getPackedMatrixTextureShapeWidthHeight = getPackedMatrixTextureShapeWidthHeight;
+function getPackedRGBAArraySizeFromMatrixShape(rows, columns) {
+    var _a = getPackedMatrixTextureShapeWidthHeight(rows, columns), w = _a[0], h = _a[1];
+    return w * h * 4;
+}
+exports.getPackedRGBAArraySizeFromMatrixShape = getPackedRGBAArraySizeFromMatrixShape;
+function encodeMatrixToPackedRGBA(matrix, batches, rows, columns, packedRGBA) {
+    var requiredSize = getPackedRGBAArraySizeFromMatrixShape(rows, columns);
+    if (packedRGBA.length < requiredSize) {
+        throw new Error("packedRGBA length (" + packedRGBA.length + ") must be >=\n        " + requiredSize);
+    }
+    var oddWidth = (columns % 2) === 1;
+    var oddHeight = (rows % 2) === 1;
+    var widthInFullBlocks = Math.floor(columns / 2);
+    var heightInFullBlocks = Math.floor(rows / 2);
+    var texelsPerRow = Math.ceil(columns / 2);
+    var texelsPerBatch = texelsPerRow * Math.ceil(rows / 2);
+    var flattenedMatrixSize = util.nearestLargerEven(rows) * util.nearestLargerEven(columns);
+    for (var batch = 0; batch < batches; batch++) {
+        var sourceOffset = batch * rows * columns;
+        var batchOffset = batch * flattenedMatrixSize;
+        {
+            var dstStride = (oddWidth ? 4 : 0);
+            var oneRow = columns;
+            var dst = batchOffset;
+            for (var blockY = 0; blockY < heightInFullBlocks; ++blockY) {
+                var matrixSrcRow = (blockY * 2 * columns);
+                for (var blockX = 0; blockX < widthInFullBlocks; ++blockX) {
+                    var matrixSrcCol = blockX * 2;
+                    var src = sourceOffset + matrixSrcRow + matrixSrcCol;
+                    packedRGBA[dst] = matrix[src];
+                    packedRGBA[dst + 1] = matrix[src + 1];
+                    packedRGBA[dst + 2] = matrix[src + oneRow];
+                    packedRGBA[dst + 3] = matrix[src + oneRow + 1];
+                    dst += 4;
+                }
+                dst += dstStride;
+            }
+        }
+        if (oddWidth) {
+            var src = sourceOffset + columns - 1;
+            var dst = batchOffset + (texelsPerRow - 1) * 4;
+            var srcStride = 2 * columns;
+            var dstStride = texelsPerRow * 4;
+            for (var blockY = 0; blockY < heightInFullBlocks; ++blockY) {
+                packedRGBA[dst] = matrix[src];
+                packedRGBA[dst + 2] = matrix[src + columns];
+                src += srcStride;
+                dst += dstStride;
+            }
+        }
+        if (oddHeight) {
+            var src = sourceOffset + (rows - 1) * columns;
+            var dst = batchOffset + (texelsPerBatch - texelsPerRow) * 4;
+            for (var blockX = 0; blockX < widthInFullBlocks; ++blockX) {
+                packedRGBA[dst++] = matrix[src++];
+                packedRGBA[dst++] = matrix[src++];
+                dst += 2;
+            }
+            if (oddWidth && oddHeight) {
+                packedRGBA[batchOffset + flattenedMatrixSize - 4] = matrix[src];
+            }
+        }
+    }
+    return packedRGBA;
+}
+exports.encodeMatrixToPackedRGBA = encodeMatrixToPackedRGBA;
+function decodeMatrixFromPackedRGBA(packedRGBA, batches, rows, columns, matrix) {
+    var requiredSize = rows * columns;
+    if (matrix.length < requiredSize) {
+        throw new Error("matrix length (" + matrix.length + ") must be >= " + requiredSize);
+    }
+    var oddWidth = (columns % 2) === 1;
+    var oddHeight = (rows % 2) === 1;
+    var widthInFullBlocks = Math.floor(columns / 2);
+    var heightInFullBlocks = Math.floor(rows / 2);
+    var texelsPerRow = Math.ceil(columns / 2);
+    var texelsPerBatch = texelsPerRow * Math.ceil(rows / 2);
+    var flattenedMatrixSize = util.nearestLargerEven(rows) * util.nearestLargerEven(columns);
+    for (var batch = 0; batch < batches; batch++) {
+        var batchOffset = batch * rows * columns;
+        var sourceOffset = batch * flattenedMatrixSize;
+        {
+            var srcStride = oddWidth ? 4 : 0;
+            var dstStride = columns + (oddWidth ? 1 : 0);
+            var src = sourceOffset;
+            var dstRow1 = batchOffset;
+            var dstRow2 = batchOffset + columns;
+            for (var blockY = 0; blockY < heightInFullBlocks; ++blockY) {
+                for (var blockX = 0; blockX < widthInFullBlocks; ++blockX) {
+                    matrix[dstRow1++] = packedRGBA[src++];
+                    matrix[dstRow1++] = packedRGBA[src++];
+                    matrix[dstRow2++] = packedRGBA[src++];
+                    matrix[dstRow2++] = packedRGBA[src++];
+                }
+                src += srcStride;
+                dstRow1 += dstStride;
+                dstRow2 += dstStride;
+            }
+        }
+        if (oddWidth) {
+            var src = sourceOffset + (texelsPerRow - 1) * 4;
+            var dst = batchOffset + columns - 1;
+            var srcStride = texelsPerRow * 4;
+            var dstStride = 2 * columns;
+            for (var blockY = 0; blockY < heightInFullBlocks; ++blockY) {
+                matrix[dst] = packedRGBA[src];
+                matrix[dst + columns] = packedRGBA[src + 2];
+                src += srcStride;
+                dst += dstStride;
+            }
+        }
+        if (oddHeight) {
+            var src = sourceOffset + (texelsPerBatch - texelsPerRow) * 4;
+            var dst = batchOffset + (rows - 1) * columns;
+            for (var blockX = 0; blockX < widthInFullBlocks; ++blockX) {
+                matrix[dst++] = packedRGBA[src++];
+                matrix[dst++] = packedRGBA[src++];
+                src += 2;
+            }
+            if (oddWidth) {
+                matrix[batchOffset + (rows * columns) - 1] = packedRGBA[src];
+            }
+        }
+    }
+    return matrix;
+}
+exports.decodeMatrixFromPackedRGBA = decodeMatrixFromPackedRGBA;
+//# sourceMappingURL=tex_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/tex_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/tex_util.js.map
new file mode 100644
index 0000000..33cc7c3
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/tex_util.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"tex_util.js","sourceRoot":"","sources":["../../../src/kernels/webgl/tex_util.ts"],"names":[],"mappings":";;AAmBA,iCAAmC;AAEnC,IAAY,YAKX;AALD,WAAY,YAAY;IACtB,mDAAM,CAAA;IACN,mDAAM,CAAA;IACN,mDAAM,CAAA;IACN,uDAAQ,CAAA;AACV,CAAC,EALW,YAAY,GAAZ,oBAAY,KAAZ,oBAAY,QAKvB;AAED,IAAY,mBAMX;AAND,WAAY,mBAAmB;IAC7B,qFAAgB,CAAA;IAChB,qFAAgB,CAAA;IAChB,qGAAwB,CAAA;IACxB,yFAAkB,CAAA;IAClB,yFAAkB,CAAA;AACpB,CAAC,EANW,mBAAmB,GAAnB,2BAAmB,KAAnB,2BAAmB,QAM9B;AAoBD,kDACI,IAAY,EAAE,OAAe;IAC/B,OAAO,CAAC,OAAO,EAAE,IAAI,CAAC,CAAC;AACzB,CAAC;AAHD,4FAGC;AAED,4CACI,UAAkB,EAAE,kBAA0B;IAChD,OAAO,UAAU,GAAG,kBAAkB,CAAC;AACzC,CAAC;AAHD,gFAGC;AAED,+CACI,IAAY,EAAE,OAAe;IAC/B,OAAO,CAAC,OAAO,GAAG,CAAC,EAAE,IAAI,CAAC,CAAC;AAC7B,CAAC;AAHD,sFAGC;AAED,4CACI,YAAoB,EAAE,kBAA0B;IAClD,IAAI,YAAY,GAAG,kBAAkB,KAAK,CAAC,EAAE;QAC3C,MAAM,IAAI,KAAK,CACX,mBAAiB,YAAY,6BAA0B;aACvD,KAAG,kBAAoB,CAAA,CAAC,CAAC;KAC9B;IACD,OAAO,YAAY,GAAG,kBAAkB,CAAC;AAC3C,CAAC;AARD,gFAQC;AAED,qCACI,MAA+B,EAAE,aAAsC,EACvE,kBAA0B;IAC5B,IAAM,YAAY,GACd,kCAAkC,CAAC,MAAM,CAAC,MAAM,EAAE,kBAAkB,CAAC,CAAC;IAC1E,IAAI,aAAa,CAAC,MAAM,GAAG,YAAY,EAAE;QACvC,MAAM,IAAI,KAAK,CACX,2BAAyB,aAAa,CAAC,MAAM,kBAAe;aAC5D,KAAG,YAAc,CAAA,CAAC,CAAC;KACxB;IACD,IAAI,GAAG,GAAG,CAAC,CAAC;IACZ,KAAK,IAAI,GAAG,GAAG,CAAC,EAAE,GAAG,GAAG,MAAM,CAAC,MAAM,EAAE,EAAE,GAAG,EAAE;QAC5C,aAAa,CAAC,GAAG,CAAC,GAAG,MAAM,CAAC,GAAG,CAAC,CAAC;QACjC,GAAG,IAAI,kBAAkB,CAAC;KAC3B;AACH,CAAC;AAfD,kEAeC;AAED,uCACI,aAA2B,EAAE,MAAoB,EACjD,kBAA0B;IAC5B,IAAM,YAAY,GAAG,kCAAkC,CACnD,aAAa,CAAC,MAAM,EAAE,kBAAkB,CAAC,CAAC;IAC9C,IAAI,MAAM,CAAC,MAAM,GAAG,YAAY,EAAE;QAChC,MAAM,IAAI,KAAK,CACX,oBAAkB,MAAM,CAAC,MAAM,qBAAgB,YAAc,CAAC,CAAC;KACpE;IACD,IAAI,GAAG,GAAG,CAAC,CAAC;IACZ,KAAK,IAAI,GAAG,GAAG,CAAC,EAAE,GAAG,GAAG,aAAa,CAAC,MAAM,EAAE,GAAG,IAAI,kBAAkB,EAAE;QACvE,MAAM,CAAC,GAAG,EAAE,CAAC,GAAG,aAAa,CAAC,GAAG,CAAC,CAAC;KACpC;AACH,CAAC;AAbD,sEAaC;AAED,gDACI,aAA2B,EAAE,MAAoB,EAAE,QAAgB;IACrE,IAAM,YAAY,GAAG,aAAa,CAAC,MAAM,GAAG,QAAQ,GAAG,CAAC,CAAC;IACzD,IAAI,MAAM,CAAC,MAAM,GAAG,YAAY,EAAE;QAChC,MAAM,IAAI,KAAK,CACX,oBAAkB,MAAM,CAAC,MAAM,qBAAgB,YAAc,CAAC,CAAC;KACpE;IACD,IAAI,GAAG,GAAG,CAAC,CAAC;IACZ,KAAK,IAAI,GAAG,GAAG,CAAC,EAAE,GAAG,GAAG,aAAa,CAAC,MAAM,EAAE,GAAG,IAAI,CAAC,EAAE;QACtD,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,QAAQ,EAAE,CAAC,EAAE,EAAE;YACjC,MAAM,CAAC,GAAG,EAAE,CAAC,GAAG,aAAa,CAAC,GAAG,GAAG,CAAC,CAAC,CAAC;SACxC;KACF;AACH,CAAC;AAbD,wFAaC;AAED,gDACI,IAAY,EAAE,OAAe;IAC/B,OAAO;QACL,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,IAAI,CAAC,IAAI,CAAC,OAAO,GAAG,CAAC,CAAC,CAAC,EAAE,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,IAAI,CAAC,IAAI,CAAC,IAAI,GAAG,CAAC,CAAC,CAAC;KACtE,CAAC;AACJ,CAAC;AALD,wFAKC;AAED,+CACI,IAAY,EAAE,OAAe;IACzB,IAAA,0DAA8D,EAA7D,SAAC,EAAE,SAAC,CAA0D;IACrE,OAAO,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC;AACnB,CAAC;AAJD,sFAIC;AAqBD,kCACI,MAAoB,EAAE,OAAe,EAAE,IAAY,EAAE,OAAe,EACpE,UAAwB;IAC1B,IAAM,YAAY,GAAG,qCAAqC,CAAC,IAAI,EAAE,OAAO,CAAC,CAAC;IAC1E,IAAI,UAAU,CAAC,MAAM,GAAG,YAAY,EAAE;QACpC,MAAM,IAAI,KAAK,CAAC,wBAAsB,UAAU,CAAC,MAAM,8BACjD,YAAc,CAAC,CAAC;KACvB;IAED,IAAM,QAAQ,GAAG,CAAC,OAAO,GAAG,CAAC,CAAC,KAAK,CAAC,CAAC;IACrC,IAAM,SAAS,GAAG,CAAC,IAAI,GAAG,CAAC,CAAC,KAAK,CAAC,CAAC;IACnC,IAAM,iBAAiB,GAAG,IAAI,CAAC,KAAK,CAAC,OAAO,GAAG,CAAC,CAAC,CAAC;IAClD,IAAM,kBAAkB,GAAG,IAAI,CAAC,KAAK,CAAC,IAAI,GAAG,CAAC,CAAC,CAAC;IAEhD,IAAM,YAAY,GAAG,IAAI,CAAC,IAAI,CAAC,OAAO,GAAG,CAAC,CAAC,CAAC;IAC5C,IAAM,cAAc,GAAG,YAAY,GAAG,IAAI,CAAC,IAAI,CAAC,IAAI,GAAG,CAAC,CAAC,CAAC;IAE1D,IAAM,mBAAmB,GACrB,IAAI,CAAC,iBAAiB,CAAC,IAAI,CAAC,GAAG,IAAI,CAAC,iBAAiB,CAAC,OAAO,CAAC,CAAC;IAEnE,KAAK,IAAI,KAAK,GAAG,CAAC,EAAE,KAAK,GAAG,OAAO,EAAE,KAAK,EAAE,EAAE;QAC5C,IAAM,YAAY,GAAG,KAAK,GAAG,IAAI,GAAG,OAAO,CAAC;QAC5C,IAAM,WAAW,GAAG,KAAK,GAAG,mBAAmB,CAAC;QAGhD;YACE,IAAM,SAAS,GAAG,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;YACrC,IAAM,MAAM,GAAG,OAAO,CAAC;YACvB,IAAI,GAAG,GAAG,WAAW,CAAC;YACtB,KAAK,IAAI,MAAM,GAAG,CAAC,EAAE,MAAM,GAAG,kBAAkB,EAAE,EAAE,MAAM,EAAE;gBAC1D,IAAM,YAAY,GAAG,CAAC,MAAM,GAAG,CAAC,GAAG,OAAO,CAAC,CAAC;gBAC5C,KAAK,IAAI,MAAM,GAAG,CAAC,EAAE,MAAM,GAAG,iBAAiB,EAAE,EAAE,MAAM,EAAE;oBACzD,IAAM,YAAY,GAAG,MAAM,GAAG,CAAC,CAAC;oBAChC,IAAM,GAAG,GAAG,YAAY,GAAG,YAAY,GAAG,YAAY,CAAC;oBACvD,UAAU,CAAC,GAAG,CAAC,GAAG,MAAM,CAAC,GAAG,CAAC,CAAC;oBAC9B,UAAU,CAAC,GAAG,GAAG,CAAC,CAAC,GAAG,MAAM,CAAC,GAAG,GAAG,CAAC,CAAC,CAAC;oBACtC,UAAU,CAAC,GAAG,GAAG,CAAC,CAAC,GAAG,MAAM,CAAC,GAAG,GAAG,MAAM,CAAC,CAAC;oBAC3C,UAAU,CAAC,GAAG,GAAG,CAAC,CAAC,GAAG,MAAM,CAAC,GAAG,GAAG,MAAM,GAAG,CAAC,CAAC,CAAC;oBAC/C,GAAG,IAAI,CAAC,CAAC;iBACV;gBACD,GAAG,IAAI,SAAS,CAAC;aAClB;SACF;QAGD,IAAI,QAAQ,EAAE;YACZ,IAAI,GAAG,GAAG,YAAY,GAAG,OAAO,GAAG,CAAC,CAAC;YACrC,IAAI,GAAG,GAAG,WAAW,GAAG,CAAC,YAAY,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC;YAC/C,IAAM,SAAS,GAAG,CAAC,GAAG,OAAO,CAAC;YAC9B,IAAM,SAAS,GAAG,YAAY,GAAG,CAAC,CAAC;YACnC,KAAK,IAAI,MAAM,GAAG,CAAC,EAAE,MAAM,GAAG,kBAAkB,EAAE,EAAE,MAAM,EAAE;gBAC1D,UAAU,CAAC,GAAG,CAAC,GAAG,MAAM,CAAC,GAAG,CAAC,CAAC;gBAC9B,UAAU,CAAC,GAAG,GAAG,CAAC,CAAC,GAAG,MAAM,CAAC,GAAG,GAAG,OAAO,CAAC,CAAC;gBAC5C,GAAG,IAAI,SAAS,CAAC;gBACjB,GAAG,IAAI,SAAS,CAAC;aAClB;SACF;QAGD,IAAI,SAAS,EAAE;YACb,IAAI,GAAG,GAAG,YAAY,GAAG,CAAC,IAAI,GAAG,CAAC,CAAC,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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/tex_util_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/tex_util_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/tex_util_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/tex_util_test.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/tex_util_test.js
new file mode 100644
index 0000000..d90eddc
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/tex_util_test.js
@@ -0,0 +1,308 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var jasmine_util_1 = require("../../jasmine_util");
+var test_util_1 = require("../../test_util");
+var tex_util = require("./tex_util");
+describe('tex_util getUnpackedMatrixTextureShapeWidthHeight', function () {
+    it('[1x1] => [1x1]', function () {
+        expect(tex_util.getUnpackedMatrixTextureShapeWidthHeight(1, 1)).toEqual([
+            1, 1
+        ]);
+    });
+    it('[MxN] => [NxM]', function () {
+        expect(tex_util.getUnpackedMatrixTextureShapeWidthHeight(123, 456))
+            .toEqual([456, 123]);
+    });
+});
+describe('tex_util getPackedMatrixTextureShapeWidthHeight', function () {
+    it('[1x1] => [1x1]', function () {
+        var shape = tex_util.getPackedMatrixTextureShapeWidthHeight(1, 1);
+        expect(shape).toEqual([1, 1]);
+    });
+    it('[1x2] => [1x1]', function () {
+        var shape = tex_util.getPackedMatrixTextureShapeWidthHeight(1, 2);
+        expect(shape).toEqual([1, 1]);
+    });
+    it('[2x1] => [1x1]', function () {
+        var shape = tex_util.getPackedMatrixTextureShapeWidthHeight(2, 1);
+        expect(shape).toEqual([1, 1]);
+    });
+    it('[2x2] => [1x1]', function () {
+        var shape = tex_util.getPackedMatrixTextureShapeWidthHeight(2, 2);
+        expect(shape).toEqual([1, 1]);
+    });
+    it('[3x3] => [2x2]', function () {
+        var shape = tex_util.getPackedMatrixTextureShapeWidthHeight(3, 3);
+        expect(shape).toEqual([2, 2]);
+    });
+    it('[4x3] => [2x2]', function () {
+        var shape = tex_util.getPackedMatrixTextureShapeWidthHeight(4, 3);
+        expect(shape).toEqual([2, 2]);
+    });
+    it('[3x4] => [2x2]', function () {
+        var shape = tex_util.getPackedMatrixTextureShapeWidthHeight(3, 4);
+        expect(shape).toEqual([2, 2]);
+    });
+    it('[4x4] => [2x2]', function () {
+        var shape = tex_util.getPackedMatrixTextureShapeWidthHeight(4, 4);
+        expect(shape).toEqual([2, 2]);
+    });
+    it('[1024x1024] => [512x512]', function () {
+        var shape = tex_util.getPackedMatrixTextureShapeWidthHeight(1024, 1024);
+        expect(shape).toEqual([512, 512]);
+    });
+    it('[MxN] => [ceil(N/2)xceil(M/2)]', function () {
+        var M = 123;
+        var N = 5013;
+        var shape = tex_util.getPackedMatrixTextureShapeWidthHeight(M, N);
+        expect(shape).toEqual([Math.ceil(N / 2), Math.ceil(M / 2)]);
+    });
+});
+jasmine_util_1.describeWithFlags('tex_util encodeMatrixToUnpackedArray, channels = 4', test_util_1.WEBGL_ENVS, function () {
+    it('1x1 writes the only matrix array value to the only texel', function () {
+        var matrix = new Float32Array([1]);
+        var unpackedRGBA = new Float32Array([0, 0, 0, 0]);
+        tex_util.encodeMatrixToUnpackedArray(matrix, unpackedRGBA, 4);
+        test_util_1.expectArraysClose(unpackedRGBA, new Float32Array([1, 0, 0, 0]));
+    });
+    it('1x1 can upload texels with values greater than 1', function () {
+        var matrix = new Float32Array([100]);
+        var unpackedRGBA = new Float32Array([0, 0, 0, 0]);
+        tex_util.encodeMatrixToUnpackedArray(matrix, unpackedRGBA, 4);
+        test_util_1.expectArraysClose(unpackedRGBA, new Float32Array([100, 0, 0, 0]));
+    });
+    it('1x4 each texel has 4 elements with matrix value in R channel', function () {
+        var matrix = new Float32Array([1, 2, 3, 4]);
+        var unpackedRGBA = new Float32Array(16);
+        tex_util.encodeMatrixToUnpackedArray(matrix, unpackedRGBA, 4);
+        test_util_1.expectArraysClose(unpackedRGBA, new Float32Array([1, 0, 0, 0, 2, 0, 0, 0, 3, 0, 0, 0, 4, 0, 0, 0]));
+    });
+});
+jasmine_util_1.describeWithFlags('tex_util encodeMatrixToUnpackedArray, channels = 1', test_util_1.WEBGL_ENVS, function () {
+    it('1x1 writes the only matrix array value to the only texel', function () {
+        var matrix = new Float32Array([1]);
+        var unpackedRGBA = new Float32Array([0]);
+        tex_util.encodeMatrixToUnpackedArray(matrix, unpackedRGBA, 1);
+        test_util_1.expectArraysClose(unpackedRGBA, new Float32Array([1]));
+    });
+    it('1x1 can upload texels with values greater than 1', function () {
+        var matrix = new Float32Array([100]);
+        var unpackedRGBA = new Float32Array([0]);
+        tex_util.encodeMatrixToUnpackedArray(matrix, unpackedRGBA, 1);
+        test_util_1.expectArraysClose(unpackedRGBA, new Float32Array([100]));
+    });
+    it('1x4 each texel has 4 elements with matrix value in R channel', function () {
+        var matrix = new Float32Array([1, 2, 3, 4]);
+        var unpackedRGBA = new Float32Array(4);
+        tex_util.encodeMatrixToUnpackedArray(matrix, unpackedRGBA, 1);
+        test_util_1.expectArraysClose(unpackedRGBA, new Float32Array([1, 2, 3, 4]));
+    });
+});
+jasmine_util_1.describeWithFlags('tex_util decodeMatrixFromUnpackedArray', test_util_1.WEBGL_ENVS, function () {
+    it('1x1 writes the only matrix array value to the first element', function () {
+        var unpackedRGBA = new Float32Array([1, 0, 0, 0]);
+        var matrix = new Float32Array(1);
+        tex_util.decodeMatrixFromUnpackedArray(unpackedRGBA, matrix, 4);
+        expect(matrix.length).toEqual(1);
+        expect(matrix[0]).toEqual(1);
+    });
+    it('1x2 writes the second texel R component to the second element', function () {
+        var unpackedRGBA = new Float32Array([1, 0, 0, 0, 2, 0, 0, 0]);
+        var matrix = new Float32Array(2);
+        tex_util.decodeMatrixFromUnpackedArray(unpackedRGBA, matrix, 4);
+        expect(matrix.length).toEqual(2);
+        test_util_1.expectArraysClose(matrix, new Float32Array([1, 2]));
+    });
+});
+jasmine_util_1.describeWithFlags('tex_util encodeMatrixToPackedRGBA', test_util_1.WEBGL_ENVS, function () {
+    it('1x1 loads the element into R and 0\'s into GBA', function () {
+        var matrix = new Float32Array([1]);
+        var packedRGBA = new Float32Array(4);
+        tex_util.encodeMatrixToPackedRGBA(matrix, 1, 1, 1, packedRGBA);
+        test_util_1.expectArraysClose(packedRGBA, new Float32Array([1, 0, 0, 0]));
+    });
+    it('1x2 loads the second element into G and 0\'s into BA', function () {
+        var matrix = new Float32Array([1, 2]);
+        var packedRGBA = new Float32Array(4);
+        tex_util.encodeMatrixToPackedRGBA(matrix, 1, 1, 2, packedRGBA);
+        test_util_1.expectArraysClose(packedRGBA, new Float32Array([1, 2, 0, 0]));
+    });
+    it('2x1 loads the second element into G and 0\'s into BA', function () {
+        var matrix = new Float32Array([1, 2]);
+        var packedRGBA = new Float32Array(4);
+        tex_util.encodeMatrixToPackedRGBA(matrix, 1, 2, 1, packedRGBA);
+        test_util_1.expectArraysClose(packedRGBA, new Float32Array([1, 0, 2, 0]));
+    });
+    it('2x2 exactly fills one texel', function () {
+        var matrix = new Float32Array([1, 2, 3, 4]);
+        var packedRGBA = new Float32Array(4);
+        tex_util.encodeMatrixToPackedRGBA(matrix, 1, 2, 2, packedRGBA);
+        test_util_1.expectArraysClose(packedRGBA, new Float32Array([1, 2, 3, 4]));
+    });
+    it('4x3 pads the final column G and A channels with 0', function () {
+        var matrix = new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]);
+        var packedRGBA = new Float32Array(16);
+        tex_util.encodeMatrixToPackedRGBA(matrix, 1, 4, 3, packedRGBA);
+        test_util_1.expectArraysClose(packedRGBA, new Float32Array([1, 2, 4, 5, 3, 0, 6, 0, 7, 8, 10, 11, 9, 0, 12, 0]));
+    });
+    it('3x4 pads the final row B and A channels with 0', function () {
+        var matrix = new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]);
+        var packedRGBA = new Float32Array(16);
+        tex_util.encodeMatrixToPackedRGBA(matrix, 1, 3, 4, packedRGBA);
+        test_util_1.expectArraysClose(packedRGBA, new Float32Array([1, 2, 5, 6, 3, 4, 7, 8, 9, 10, 0, 0, 11, 12, 0, 0]));
+    });
+    it('3x3 bottom-right texel is R000', function () {
+        var matrix = new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9]);
+        var packedRGBA = new Float32Array(16);
+        tex_util.encodeMatrixToPackedRGBA(matrix, 1, 3, 3, packedRGBA);
+        test_util_1.expectArraysClose(packedRGBA, new Float32Array([1, 2, 4, 5, 3, 0, 6, 0, 7, 8, 0, 0, 9, 0, 0, 0]));
+    });
+    it('2x3x4 texels in the last row of each batch are RG00', function () {
+        var matrix = new Float32Array([
+            1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
+            13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24
+        ]);
+        var packedRGBA = new Float32Array(32);
+        tex_util.encodeMatrixToPackedRGBA(matrix, 2, 3, 4, packedRGBA);
+        test_util_1.expectArraysClose(packedRGBA, new Float32Array([
+            1, 2, 5, 6, 3, 4, 7, 8, 9, 10, 0, 0, 11, 12, 0, 0,
+            13, 14, 17, 18, 15, 16, 19, 20, 21, 22, 0, 0, 23, 24, 0, 0
+        ]));
+    });
+    it('2x4x3 texels in the last column of each batch are R0B0', function () {
+        var matrix = new Float32Array([
+            1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
+            13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24
+        ]);
+        var packedRGBA = new Float32Array(32);
+        tex_util.encodeMatrixToPackedRGBA(matrix, 2, 4, 3, packedRGBA);
+        test_util_1.expectArraysClose(packedRGBA, new Float32Array([
+            1, 2, 4, 5, 3, 0, 6, 0, 7, 8, 10, 11, 9, 0, 12, 0,
+            13, 14, 16, 17, 15, 0, 18, 0, 19, 20, 22, 23, 21, 0, 24, 0
+        ]));
+    });
+    it('2x3x3 bottom right texel in each batch is R000', function () {
+        var matrix = new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]);
+        var packedRGBA = new Float32Array(32);
+        tex_util.encodeMatrixToPackedRGBA(matrix, 2, 3, 3, packedRGBA);
+        test_util_1.expectArraysClose(packedRGBA, new Float32Array([
+            1, 2, 4, 5, 3, 0, 6, 0, 7, 8, 0, 0, 9, 0, 0, 0,
+            10, 11, 13, 14, 12, 0, 15, 0, 16, 17, 0, 0, 18, 0, 0, 0
+        ]));
+    });
+    it('4D (2x3x3x4) is properly encoded', function () {
+        var matrix = new Float32Array([
+            1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
+            19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
+            37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+            55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72
+        ]);
+        var packedRGBA = new Float32Array(96);
+        tex_util.encodeMatrixToPackedRGBA(matrix, 6, 3, 4, packedRGBA);
+        test_util_1.expectArraysClose(packedRGBA, new Float32Array([
+            1, 2, 5, 6, 3, 4, 7, 8, 9, 10, 0, 0, 11, 12, 0, 0,
+            13, 14, 17, 18, 15, 16, 19, 20, 21, 22, 0, 0, 23, 24, 0, 0,
+            25, 26, 29, 30, 27, 28, 31, 32, 33, 34, 0, 0, 35, 36, 0, 0,
+            37, 38, 41, 42, 39, 40, 43, 44, 45, 46, 0, 0, 47, 48, 0, 0,
+            49, 50, 53, 54, 51, 52, 55, 56, 57, 58, 0, 0, 59, 60, 0, 0,
+            61, 62, 65, 66, 63, 64, 67, 68, 69, 70, 0, 0, 71, 72, 0, 0
+        ]));
+    });
+});
+jasmine_util_1.describeWithFlags('tex_util decodeMatrixFromPackedRGBA', test_util_1.WEBGL_ENVS, function () {
+    it('1x1 matrix only loads R component from only texel', function () {
+        var packedRGBA = new Float32Array([1, 0, 0, 0]);
+        var matrix = new Float32Array(1);
+        tex_util.decodeMatrixFromPackedRGBA(packedRGBA, 1, 1, 1, matrix);
+        expect(matrix[0]).toEqual(1);
+    });
+    it('1x2 matrix loads RG from only texel', function () {
+        var packedRGBA = new Float32Array([1, 2, 0, 0]);
+        var matrix = new Float32Array(2);
+        tex_util.decodeMatrixFromPackedRGBA(packedRGBA, 1, 1, 2, matrix);
+        test_util_1.expectArraysClose(matrix, new Float32Array([1, 2]));
+    });
+    it('2x1 matrix loads RB from only texel', function () {
+        var packedRGBA = new Float32Array([1, 0, 2, 0]);
+        var matrix = new Float32Array(2);
+        tex_util.decodeMatrixFromPackedRGBA(packedRGBA, 1, 2, 1, matrix);
+        test_util_1.expectArraysClose(matrix, new Float32Array([1, 2]));
+    });
+    it('2x2 matrix loads RGBA from only texel', function () {
+        var packedRGBA = new Float32Array([1, 2, 3, 4]);
+        var matrix = new Float32Array(4);
+        tex_util.decodeMatrixFromPackedRGBA(packedRGBA, 1, 2, 2, matrix);
+        test_util_1.expectArraysClose(matrix, new Float32Array([1, 2, 3, 4]));
+    });
+    it('4x3 final column only reads RB from edge texels', function () {
+        var packedRGBA = new Float32Array([1, 2, 4, 5, 3, 0, 6, 0, 7, 8, 10, 11, 9, 0, 12, 0]);
+        var matrix = new Float32Array(12);
+        tex_util.decodeMatrixFromPackedRGBA(packedRGBA, 1, 4, 3, matrix);
+        test_util_1.expectArraysClose(matrix, new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]));
+    });
+    it('3x4 final row only reads RG from edge texels', function () {
+        var packedRGBA = new Float32Array([1, 2, 5, 6, 3, 4, 7, 8, 9, 10, 0, 0, 11, 12, 0, 0]);
+        var matrix = new Float32Array(12);
+        tex_util.decodeMatrixFromPackedRGBA(packedRGBA, 1, 3, 4, matrix);
+        test_util_1.expectArraysClose(matrix, new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]));
+    });
+    it('3x3 bottom-right only reads R from corner texel', function () {
+        var packedRGBA = new Float32Array([1, 2, 4, 5, 3, 0, 6, 0, 7, 8, 0, 0, 9, 0, 0, 0]);
+        var matrix = new Float32Array(9);
+        tex_util.decodeMatrixFromPackedRGBA(packedRGBA, 1, 3, 3, matrix);
+        test_util_1.expectArraysClose(matrix, new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9]));
+    });
+    it('2x3x4 bottom row in each batch only reads RG', function () {
+        var packedRGBA = new Float32Array([
+            1, 2, 5, 6, 3, 4, 7, 8, 9, 10, 0, 0, 11, 12, 0, 0,
+            13, 14, 17, 18, 15, 16, 19, 20, 21, 22, 0, 0, 23, 24, 0, 0
+        ]);
+        var matrix = new Float32Array(24);
+        tex_util.decodeMatrixFromPackedRGBA(packedRGBA, 2, 3, 4, matrix);
+        test_util_1.expectArraysClose(matrix, new Float32Array([
+            1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
+            13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24
+        ]));
+    });
+    it('2x4x3 final column in each batch only reads RB', function () {
+        var packedRGBA = new Float32Array([
+            1, 2, 4, 5, 3, 0, 6, 0, 7, 8, 10, 11, 9, 0, 12, 0,
+            13, 14, 16, 17, 15, 0, 18, 0, 19, 20, 22, 23, 21, 0, 24, 0
+        ]);
+        var matrix = new Float32Array(24);
+        tex_util.decodeMatrixFromPackedRGBA(packedRGBA, 2, 4, 3, matrix);
+        test_util_1.expectArraysClose(matrix, new Float32Array([
+            1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
+            13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24
+        ]));
+    });
+    it('2x3x3 bottom right texel in each batch only reads R', function () {
+        var packedRGBA = new Float32Array([
+            1, 2, 4, 5, 3, 0, 6, 0, 7, 8, 0, 0, 9, 0, 0, 0,
+            10, 11, 13, 14, 12, 0, 15, 0, 16, 17, 0, 0, 18, 0, 0, 0
+        ]);
+        var matrix = new Float32Array(18);
+        tex_util.decodeMatrixFromPackedRGBA(packedRGBA, 2, 3, 3, matrix);
+        test_util_1.expectArraysClose(matrix, new Float32Array([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18]));
+    });
+    it('4D (2x3x3x4) is properly decoded', function () {
+        var packedRGBA = new Float32Array([
+            1, 2, 5, 6, 3, 4, 7, 8, 9, 10, 0, 0, 11, 12, 0, 0,
+            13, 14, 17, 18, 15, 16, 19, 20, 21, 22, 0, 0, 23, 24, 0, 0,
+            25, 26, 29, 30, 27, 28, 31, 32, 33, 34, 0, 0, 35, 36, 0, 0,
+            37, 38, 41, 42, 39, 40, 43, 44, 45, 46, 0, 0, 47, 48, 0, 0,
+            49, 50, 53, 54, 51, 52, 55, 56, 57, 58, 0, 0, 59, 60, 0, 0,
+            61, 62, 65, 66, 63, 64, 67, 68, 69, 70, 0, 0, 71, 72, 0, 0
+        ]);
+        var matrix = new Float32Array(72);
+        tex_util.decodeMatrixFromPackedRGBA(packedRGBA, 6, 3, 4, matrix);
+        test_util_1.expectArraysClose(matrix, new Float32Array([
+            1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
+            16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
+            31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,
+            46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
+            61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72
+        ]));
+    });
+});
+//# sourceMappingURL=tex_util_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/tex_util_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/tex_util_test.js.map
new file mode 100644
index 0000000..fc4158e
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+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/tex_util_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/texture_manager.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/texture_manager.d.ts
new file mode 100644
index 0000000..4ce7683
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/texture_manager.d.ts
@@ -0,0 +1,17 @@
+import { GPGPUContext } from './gpgpu_context';
+import { TextureUsage } from './tex_util';
+export declare class TextureManager {
+    private gpgpu;
+    private numUsedTextures;
+    private numFreeTextures;
+    private freeTextures;
+    private logEnabled;
+    private usedTextures;
+    constructor(gpgpu: GPGPUContext);
+    acquireTexture(shapeRC: [number, number], usage: TextureUsage, isPacked: boolean): WebGLTexture;
+    releaseTexture(texture: WebGLTexture, shape: [number, number], logicalTexType: TextureUsage, isPacked: boolean): void;
+    private log;
+    getNumUsedTextures(): number;
+    getNumFreeTextures(): number;
+    dispose(): void;
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/texture_manager.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/texture_manager.js
new file mode 100644
index 0000000..8e4415a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/texture_manager.js
@@ -0,0 +1,137 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../../environment");
+var tex_util_1 = require("./tex_util");
+var TextureManager = (function () {
+    function TextureManager(gpgpu) {
+        this.gpgpu = gpgpu;
+        this.numUsedTextures = 0;
+        this.numFreeTextures = 0;
+        this.freeTextures = {};
+        this.logEnabled = false;
+        this.usedTextures = {};
+    }
+    TextureManager.prototype.acquireTexture = function (shapeRC, usage, isPacked) {
+        var physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);
+        var shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);
+        if (!(shapeKey in this.freeTextures)) {
+            this.freeTextures[shapeKey] = [];
+        }
+        if (!(shapeKey in this.usedTextures)) {
+            this.usedTextures[shapeKey] = [];
+        }
+        if (this.freeTextures[shapeKey].length > 0) {
+            this.numFreeTextures--;
+            this.numUsedTextures++;
+            this.log();
+            var newTexture_1 = this.freeTextures[shapeKey].shift();
+            this.usedTextures[shapeKey].push(newTexture_1);
+            return newTexture_1;
+        }
+        this.numUsedTextures++;
+        this.log();
+        var newTexture;
+        if (physicalTexType === tex_util_1.PhysicalTextureType.PACKED_2X2_FLOAT32) {
+            newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);
+        }
+        else if (physicalTexType === tex_util_1.PhysicalTextureType.PACKED_2X2_FLOAT16) {
+            newTexture =
+                this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);
+        }
+        else if (physicalTexType === tex_util_1.PhysicalTextureType.UNPACKED_FLOAT32) {
+            newTexture =
+                this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);
+        }
+        else if (physicalTexType === tex_util_1.PhysicalTextureType.UNPACKED_FLOAT16) {
+            newTexture =
+                this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);
+        }
+        else if (physicalTexType === tex_util_1.PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) {
+            newTexture =
+                this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);
+        }
+        this.usedTextures[shapeKey].push(newTexture);
+        return newTexture;
+    };
+    TextureManager.prototype.releaseTexture = function (texture, shape, logicalTexType, isPacked) {
+        if (this.freeTextures == null) {
+            return;
+        }
+        var physicalTexType = getPhysicalFromLogicalTextureType(logicalTexType, isPacked);
+        var shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);
+        if (!(shapeKey in this.freeTextures)) {
+            this.freeTextures[shapeKey] = [];
+        }
+        this.freeTextures[shapeKey].push(texture);
+        this.numFreeTextures++;
+        this.numUsedTextures--;
+        var texList = this.usedTextures[shapeKey];
+        var texIndex = texList.indexOf(texture);
+        if (texIndex < 0) {
+            throw new Error('Cannot release a texture that was never provided by this ' +
+                'texture manager');
+        }
+        texList.splice(texIndex, 1);
+        this.log();
+    };
+    TextureManager.prototype.log = function () {
+        if (!this.logEnabled) {
+            return;
+        }
+        var total = this.numFreeTextures + this.numUsedTextures;
+        console.log('Free/Used', this.numFreeTextures + " / " + this.numUsedTextures, "(" + total + ")");
+    };
+    TextureManager.prototype.getNumUsedTextures = function () {
+        return this.numUsedTextures;
+    };
+    TextureManager.prototype.getNumFreeTextures = function () {
+        return this.numFreeTextures;
+    };
+    TextureManager.prototype.dispose = function () {
+        var _this = this;
+        if (this.freeTextures == null) {
+            return;
+        }
+        for (var texShape in this.freeTextures) {
+            this.freeTextures[texShape].forEach(function (tex) {
+                _this.gpgpu.deleteMatrixTexture(tex);
+            });
+        }
+        for (var texShape in this.usedTextures) {
+            this.usedTextures[texShape].forEach(function (tex) {
+                _this.gpgpu.deleteMatrixTexture(tex);
+            });
+        }
+        this.freeTextures = null;
+        this.usedTextures = null;
+        this.numUsedTextures = 0;
+        this.numFreeTextures = 0;
+    };
+    return TextureManager;
+}());
+exports.TextureManager = TextureManager;
+function getPhysicalFromLogicalTextureType(logicalTexType, isPacked) {
+    if (logicalTexType === tex_util_1.TextureUsage.UPLOAD) {
+        return isPacked ? tex_util_1.PhysicalTextureType.PACKED_2X2_FLOAT32 :
+            tex_util_1.PhysicalTextureType.UNPACKED_FLOAT32;
+    }
+    else if (logicalTexType === tex_util_1.TextureUsage.RENDER || logicalTexType == null) {
+        if (isPacked) {
+            return environment_1.ENV.get('WEBGL_RENDER_FLOAT32_ENABLED') ?
+                tex_util_1.PhysicalTextureType.PACKED_2X2_FLOAT32 :
+                tex_util_1.PhysicalTextureType.PACKED_2X2_FLOAT16;
+        }
+        return environment_1.ENV.get('WEBGL_RENDER_FLOAT32_ENABLED') ?
+            tex_util_1.PhysicalTextureType.UNPACKED_FLOAT32 :
+            tex_util_1.PhysicalTextureType.UNPACKED_FLOAT16;
+    }
+    else if (logicalTexType === tex_util_1.TextureUsage.DOWNLOAD ||
+        logicalTexType === tex_util_1.TextureUsage.PIXELS) {
+        return tex_util_1.PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;
+    }
+    throw new Error("Unknown logical texture type " + logicalTexType);
+}
+function getKeyFromTextureShape(shapeRowsCol, physicalTexType, isPacked) {
+    return shapeRowsCol[0] + "_" + shapeRowsCol[1] + "_" + physicalTexType + "_" + isPacked;
+}
+//# sourceMappingURL=texture_manager.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/texture_manager.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/texture_manager.js.map
new file mode 100644
index 0000000..b9a4cd2
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/texture_manager.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"texture_manager.js","sourceRoot":"","sources":["../../../src/kernels/webgl/texture_manager.ts"],"names":[],"mappings":";;AAiBA,iDAAsC;AAGtC,uCAA6D;AAE7D;IAOE,wBAAoB,KAAmB;QAAnB,UAAK,GAAL,KAAK,CAAc;QAN/B,oBAAe,GAAG,CAAC,CAAC;QACpB,oBAAe,GAAG,CAAC,CAAC;QACpB,iBAAY,GAAsC,EAAE,CAAC;QACrD,eAAU,GAAG,KAAK,CAAC;QACnB,iBAAY,GAAsC,EAAE,CAAC;IAEnB,CAAC;IAE3C,uCAAc,GAAd,UACI,OAAyB,EAAE,KAAmB,EAC9C,QAAiB;QACnB,IAAM,eAAe,GAAG,iCAAiC,CAAC,KAAK,EAAE,QAAQ,CAAC,CAAC;QAE3E,IAAM,QAAQ,GAAG,sBAAsB,CAAC,OAAO,EAAE,eAAe,EAAE,QAAQ,CAAC,CAAC;QAC5E,IAAI,CAAC,CAAC,QAAQ,IAAI,IAAI,CAAC,YAAY,CAAC,EAAE;YACpC,IAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,GAAG,EAAE,CAAC;SAClC;QACD,IAAI,CAAC,CAAC,QAAQ,IAAI,IAAI,CAAC,YAAY,CAAC,EAAE;YACpC,IAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,GAAG,EAAE,CAAC;SAClC;QAED,IAAI,IAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAC,MAAM,GAAG,CAAC,EAAE;YAC1C,IAAI,CAAC,eAAe,EAAE,CAAC;YACvB,IAAI,CAAC,eAAe,EAAE,CAAC;YACvB,IAAI,CAAC,GAAG,EAAE,CAAC;YACX,IAAM,YAAU,GAAG,IAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAC,KAAK,EAAE,CAAC;YACvD,IAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAC,IAAI,CAAC,YAAU,CAAC,CAAC;YAC7C,OAAO,YAAU,CAAC;SACnB;QACD,IAAI,CAAC,eAAe,EAAE,CAAC;QACvB,IAAI,CAAC,GAAG,EAAE,CAAC;QAEX,IAAI,UAAwB,CAAC;QAC7B,IAAI,eAAe,KAAK,8BAAmB,CAAC,kBAAkB,EAAE;YAC9D,UAAU,GAAG,IAAI,CAAC,KAAK,CAAC,yBAAyB,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC;SAC3E;aAAM,IAAI,eAAe,KAAK,8BAAmB,CAAC,kBAAkB,EAAE;YACrE,UAAU;gBACN,IAAI,CAAC,KAAK,CAAC,gCAAgC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC;SACzE;aAAM,IAAI,eAAe,KAAK,8BAAmB,CAAC,gBAAgB,EAAE;YACnE,UAAU;gBACN,IAAI,CAAC,KAAK,CAAC,0BAA0B,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC;SACnE;aAAM,IAAI,eAAe,KAAK,8BAAmB,CAAC,gBAAgB,EAAE;YACnE,UAAU;gBACN,IAAI,CAAC,KAAK,CAAC,0BAA0B,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC;SAEnE;aAAM,IACH,eAAe,KAAK,8BAAmB,CAAC,wBAAwB,EAAE;YACpE,UAAU;gBACN,IAAI,CAAC,KAAK,CAAC,gCAAgC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC;SACzE;QACD,IAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC;QAE7C,OAAO,UAAU,CAAC;IACpB,CAAC;IAED,uCAAc,GAAd,UACI,OAAqB,EAAE,KAAuB,EAC9C,cAA4B,EAAE,QAAiB;QACjD,IAAI,IAAI,CAAC,YAAY,IAAI,IAAI,EAAE;YAE7B,OAAO;SACR;QACD,IAAM,eAAe,GACjB,iCAAiC,CAAC,cAAc,EAAE,QAAQ,CAAC,CAAC;QAChE,IAAM,QAAQ,GAAG,sBAAsB,CAAC,KAAK,EAAE,eAAe,EAAE,QAAQ,CAAC,CAAC;QAC1E,IAAI,CAAC,CAAC,QAAQ,IAAI,IAAI,CAAC,YAAY,CAAC,EAAE;YACpC,IAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,GAAG,EAAE,CAAC;SAClC;QACD,IAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC;QAC1C,IAAI,CAAC,eAAe,EAAE,CAAC;QACvB,IAAI,CAAC,eAAe,EAAE,CAAC;QACvB,IAAM,OAAO,GAAG,IAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAC;QAC5C,IAAM,QAAQ,GAAG,OAAO,CAAC,OAAO,CAAC,OAAO,CAAC,CAAC;QAC1C,IAAI,QAAQ,GAAG,CAAC,EAAE;YAChB,MAAM,IAAI,KAAK,CACX,2DAA2D;gBAC3D,iBAAiB,CAAC,CAAC;SACxB;QACD,OAAO,CAAC,MAAM,CAAC,QAAQ,EAAE,CAAC,CAAC,CAAC;QAC5B,IAAI,CAAC,GAAG,EAAE,CAAC;IACb,CAAC;IAEO,4BAAG,GAAX;QACE,IAAI,CAAC,IAAI,CAAC,UAAU,EAAE;YACpB,OAAO;SACR;QACD,IAAM,KAAK,GAAG,IAAI,CAAC,eAAe,GAAG,IAAI,CAAC,eAAe,CAAC;QAC1D,OAAO,CAAC,GAAG,CACP,WAAW,EAAK,IAAI,CAAC,eAAe,WAAM,IAAI,CAAC,eAAiB,EAChE,MAAI,KAAK,MAAG,CAAC,CAAC;IACpB,CAAC;IAED,2CAAkB,GAAlB;QACE,OAAO,IAAI,CAAC,eAAe,CAAC;IAC9B,CAAC;IAED,2CAAkB,GAAlB;QACE,OAAO,IAAI,CAAC,eAAe,CAAC;IAC9B,CAAC;IAED,gCAAO,GAAP;QAAA,iBAmBC;QAlBC,IAAI,IAAI,CAAC,YAAY,IAAI,IAAI,EAAE;YAE7B,OAAO;SACR;QACD,KAAK,IAAM,QAAQ,IAAI,IAAI,CAAC,YAAY,EAAE;YACxC,IAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAC,OAAO,CAAC,UAAA,GAAG;gBACrC,KAAI,CAAC,KAAK,CAAC,mBAAmB,CAAC,GAAG,CAAC,CAAC;YACtC,CAAC,CAAC,CAAC;SACJ;QACD,KAAK,IAAM,QAAQ,IAAI,IAAI,CAAC,YAAY,EAAE;YACxC,IAAI,CAAC,YAAY,CAAC,QAAQ,CAAC,CAAC,OAAO,CAAC,UAAA,GAAG;gBACrC,KAAI,CAAC,KAAK,CAAC,mBAAmB,CAAC,GAAG,CAAC,CAAC;YACtC,CAAC,CAAC,CAAC;SACJ;QACD,IAAI,CAAC,YAAY,GAAG,IAAI,CAAC;QACzB,IAAI,CAAC,YAAY,GAAG,IAAI,CAAC;QACzB,IAAI,CAAC,eAAe,GAAG,CAAC,CAAC;QACzB,IAAI,CAAC,eAAe,GAAG,CAAC,CAAC;IAC3B,CAAC;IACH,qBAAC;AAAD,CAAC,AAzHD,IAyHC;AAzHY,wCAAc;AA2H3B,2CACI,cAA4B,EAAE,QAAiB;IACjD,IAAI,cAAc,KAAK,uBAAY,CAAC,MAAM,EAAE;QAC1C,OAAO,QAAQ,CAAC,CAAC,CAAC,8BAAmB,CAAC,kBAAkB,CAAC,CAAC;YACxC,8BAAmB,CAAC,gBAAgB,CAAC;KACxD;SAAM,IAAI,cAAc,KAAK,uBAAY,CAAC,MAAM,IAAI,cAAc,IAAI,IAAI,EAAE;QAC3E,IAAI,QAAQ,EAAE;YACZ,OAAO,iBAAG,CAAC,GAAG,CAAC,8BAA8B,CAAC,CAAC,CAAC;gBAC5C,8BAAmB,CAAC,kBAAkB,CAAC,CAAC;gBACxC,8BAAmB,CAAC,kBAAkB,CAAC;SAC5C;QACD,OAAO,iBAAG,CAAC,GAAG,CAAC,8BAA8B,CAAC,CAAC,CAAC;YAC5C,8BAAmB,CAAC,gBAAgB,CAAC,CAAC;YACtC,8BAAmB,CAAC,gBAAgB,CAAC;KAC1C;SAAM,IACH,cAAc,KAAK,uBAAY,CAAC,QAAQ;QACxC,cAAc,KAAK,uBAAY,CAAC,MAAM,EAAE;QAC1C,OAAO,8BAAmB,CAAC,wBAAwB,CAAC;KACrD;IACD,MAAM,IAAI,KAAK,CAAC,kCAAgC,cAAgB,CAAC,CAAC;AACpE,CAAC;AAED,gCACI,YAA8B,EAAE,eAAoC,EACpE,QAAiB;IACnB,OAAU,YAAY,CAAC,CAAC,CAAC,SAAI,YAAY,CAAC,CAAC,CAAC,SAAI,eAAe,SAAI,QAAU,CAAC;AAChF,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/tile_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/tile_gpu.d.ts
new file mode 100644
index 0000000..5173363
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/tile_gpu.d.ts
@@ -0,0 +1,8 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class TileProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    rank: number;
+    constructor(aShape: number[], reps: number[]);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/tile_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/tile_gpu.js
new file mode 100644
index 0000000..8107e12
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/tile_gpu.js
@@ -0,0 +1,35 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var shader_compiler_1 = require("./shader_compiler");
+var TileProgram = (function () {
+    function TileProgram(aShape, reps) {
+        this.variableNames = ['A'];
+        var outputShape = new Array(aShape.length);
+        for (var i = 0; i < outputShape.length; i++) {
+            outputShape[i] = aShape[i] * reps[i];
+        }
+        this.outputShape = outputShape;
+        this.rank = outputShape.length;
+        var dtype = shader_compiler_1.getCoordsDataType(this.rank);
+        var sourceCoords = getSourceCoords(aShape);
+        this.userCode = "\n      void main() {\n        " + dtype + " resRC = getOutputCoords();\n        setOutput(getA(" + sourceCoords + "));\n      }\n    ";
+    }
+    return TileProgram;
+}());
+exports.TileProgram = TileProgram;
+function getSourceCoords(aShape) {
+    var rank = aShape.length;
+    if (rank > 5) {
+        throw Error("Tile for rank " + rank + " is not yet supported");
+    }
+    if (rank === 1) {
+        return "imod(resRC, " + aShape[0] + ")";
+    }
+    var currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u'];
+    var sourceCoords = [];
+    for (var i = 0; i < aShape.length; i++) {
+        sourceCoords.push("imod(" + currentCoords[i] + ", " + aShape[i] + ")");
+    }
+    return sourceCoords.join();
+}
+//# sourceMappingURL=tile_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/tile_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/tile_gpu.js.map
new file mode 100644
index 0000000..8b48e6f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/tile_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"tile_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/tile_gpu.ts"],"names":[],"mappings":";;AAkBA,qDAAoD;AAEpD;IAME,qBAAY,MAAgB,EAAE,IAAc;QAL5C,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QAMpB,IAAM,WAAW,GAAa,IAAI,KAAK,CAAC,MAAM,CAAC,MAAM,CAAC,CAAC;QACvD,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,WAAW,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;YAC3C,WAAW,CAAC,CAAC,CAAC,GAAG,MAAM,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC;SACtC;QACD,IAAI,CAAC,WAAW,GAAG,WAAW,CAAC;QAC/B,IAAI,CAAC,IAAI,GAAG,WAAW,CAAC,MAAM,CAAC;QAC/B,IAAM,KAAK,GAAG,mCAAiB,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;QAC3C,IAAM,YAAY,GAAG,eAAe,CAAC,MAAM,CAAC,CAAC;QAE7C,IAAI,CAAC,QAAQ,GAAG,oCAEV,KAAK,4DACU,YAAY,uBAEhC,CAAC;IACJ,CAAC;IACH,kBAAC;AAAD,CAAC,AAvBD,IAuBC;AAvBY,kCAAW;AAyBxB,yBAAyB,MAAgB;IACvC,IAAM,IAAI,GAAG,MAAM,CAAC,MAAM,CAAC;IAC3B,IAAI,IAAI,GAAG,CAAC,EAAE;QACZ,MAAM,KAAK,CAAC,mBAAiB,IAAI,0BAAuB,CAAC,CAAC;KAC3D;IACD,IAAI,IAAI,KAAK,CAAC,EAAE;QACd,OAAO,iBAAe,MAAM,CAAC,CAAC,CAAC,MAAG,CAAC;KACpC;IAED,IAAM,aAAa,GAAG,CAAC,SAAS,EAAE,SAAS,EAAE,SAAS,EAAE,SAAS,EAAE,SAAS,CAAC,CAAC;IAE9E,IAAM,YAAY,GAAG,EAAE,CAAC;IACxB,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,MAAM,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;QACtC,YAAY,CAAC,IAAI,CAAC,UAAQ,aAAa,CAAC,CAAC,CAAC,UAAK,MAAM,CAAC,CAAC,CAAC,MAAG,CAAC,CAAC;KAC9D;IACD,OAAO,YAAY,CAAC,IAAI,EAAE,CAAC;AAC7B,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/transpose_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/transpose_gpu.d.ts
new file mode 100644
index 0000000..f172e46
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/transpose_gpu.d.ts
@@ -0,0 +1,8 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class TransposeProgram implements GPGPUProgram {
+    variableNames: string[];
+    outputShape: number[];
+    userCode: string;
+    rank: number;
+    constructor(aShape: number[], newDim: number[]);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/transpose_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/transpose_gpu.js
new file mode 100644
index 0000000..753cc5c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/transpose_gpu.js
@@ -0,0 +1,32 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var shader_compiler_1 = require("./shader_compiler");
+var TransposeProgram = (function () {
+    function TransposeProgram(aShape, newDim) {
+        this.variableNames = ['A'];
+        var outputShape = new Array(aShape.length);
+        for (var i = 0; i < outputShape.length; i++) {
+            outputShape[i] = aShape[newDim[i]];
+        }
+        this.outputShape = outputShape;
+        this.rank = outputShape.length;
+        var dtype = shader_compiler_1.getCoordsDataType(this.rank);
+        var switched = getSwitchedCoords(newDim);
+        this.userCode = "\n    void main() {\n      " + dtype + " resRC = getOutputCoords();\n      setOutput(getA(" + switched + "));\n    }\n    ";
+    }
+    return TransposeProgram;
+}());
+exports.TransposeProgram = TransposeProgram;
+function getSwitchedCoords(newDim) {
+    var rank = newDim.length;
+    if (rank > 6) {
+        throw Error("Transpose for rank " + rank + " is not yet supported");
+    }
+    var originalOrder = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u', 'resRC.v'];
+    var switchedCoords = new Array(rank);
+    for (var i = 0; i < newDim.length; i++) {
+        switchedCoords[newDim[i]] = originalOrder[i];
+    }
+    return switchedCoords.join();
+}
+//# sourceMappingURL=transpose_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/transpose_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/transpose_gpu.js.map
new file mode 100644
index 0000000..20369f6
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/transpose_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"transpose_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/transpose_gpu.ts"],"names":[],"mappings":";;AAkBA,qDAAoD;AAEpD;IAME,0BAAY,MAAgB,EAAE,MAAgB;QAL9C,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QAMpB,IAAM,WAAW,GAAa,IAAI,KAAK,CAAC,MAAM,CAAC,MAAM,CAAC,CAAC;QACvD,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,WAAW,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;YAC3C,WAAW,CAAC,CAAC,CAAC,GAAG,MAAM,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC;SACpC;QACD,IAAI,CAAC,WAAW,GAAG,WAAW,CAAC;QAC/B,IAAI,CAAC,IAAI,GAAG,WAAW,CAAC,MAAM,CAAC;QAC/B,IAAM,KAAK,GAAG,mCAAiB,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;QAC3C,IAAM,QAAQ,GAAG,iBAAiB,CAAC,MAAM,CAAC,CAAC;QAE3C,IAAI,CAAC,QAAQ,GAAG,gCAEZ,KAAK,0DACU,QAAQ,qBAE1B,CAAC;IACJ,CAAC;IACH,uBAAC;AAAD,CAAC,AAvBD,IAuBC;AAvBY,4CAAgB;AAyB7B,2BAA2B,MAAgB;IACzC,IAAM,IAAI,GAAG,MAAM,CAAC,MAAM,CAAC;IAC3B,IAAI,IAAI,GAAG,CAAC,EAAE;QACZ,MAAM,KAAK,CAAC,wBAAsB,IAAI,0BAAuB,CAAC,CAAC;KAChE;IACD,IAAM,aAAa,GACf,CAAC,SAAS,EAAE,SAAS,EAAE,SAAS,EAAE,SAAS,EAAE,SAAS,EAAE,SAAS,CAAC,CAAC;IACvE,IAAM,cAAc,GAAG,IAAI,KAAK,CAAC,IAAI,CAAC,CAAC;IACvC,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,MAAM,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;QACtC,cAAc,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,GAAG,aAAa,CAAC,CAAC,CAAC,CAAC;KAC9C;IACD,OAAO,cAAc,CAAC,IAAI,EAAE,CAAC;AAC/B,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/unaryop_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/unaryop_gpu.d.ts
new file mode 100644
index 0000000..07d77a6
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/unaryop_gpu.d.ts
@@ -0,0 +1,45 @@
+import { GPGPUProgram } from './gpgpu_math';
+import { GPGPUContext } from './gpgpu_context';
+export declare class UnaryOpProgram implements GPGPUProgram {
+    variableNames: string[];
+    userCode: string;
+    outputShape: number[];
+    startLoc: WebGLUniformLocation;
+    constructor(aShape: number[], opSnippet: string);
+    getCustomSetupFunc(): (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => void;
+}
+export declare const ABS = "return abs(x);";
+export declare const RELU: string;
+export declare const ELU = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
+export declare const SELU: string;
+export declare function STEP(alpha?: number): string;
+export declare const NEG = "return -x;";
+export declare const CEIL = "return ceil(x);";
+export declare const FLOOR = "return floor(x);";
+export declare const SIGN = "\n  if (isNaN(x)) { return 0.0; }\n  return sign(x);\n";
+export declare const ROUND = "\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n";
+export declare const EXP = "return exp(x);";
+export declare const EXPM1 = "return exp(x) - 1.0;";
+export declare const LOG = "if (x < 0.0) return NAN;\n  return log(x);";
+export declare const LOG1P = "return log(1.0 + x);";
+export declare const SQRT = "return sqrt(x);";
+export declare const RSQRT = "return inversesqrt(x);";
+export declare const SIGMOID = "return 1.0 / (1.0 + exp(-1.0 * x));";
+export declare const SOFTPLUS = "\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n";
+export declare const SIN: string;
+export declare const COS: string;
+export declare const TAN = "return tan(x);";
+export declare const ASIN = "return asin(x);";
+export declare const ACOS = "return acos(x);";
+export declare const ATAN: string;
+export declare const SINH = "\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n";
+export declare const COSH = "\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n";
+export declare const TANH = "\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n";
+export declare const ASINH = "return log(x + sqrt(x * x + 1.0));";
+export declare const ACOSH: string;
+export declare const ATANH: string;
+export declare const ERF: string;
+export declare const SQUARE = "return x * x;";
+export declare const RECIPROCAL = "return 1.0 / x;";
+export declare const LOGICAL_NOT = "return float(!(x >= 1.0));";
+export declare const TO_INT = "return float(int(x));";
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/unaryop_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/unaryop_gpu.js
new file mode 100644
index 0000000..8160ca9
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/unaryop_gpu.js
@@ -0,0 +1,66 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var erf_util = require("../../ops/erf_util");
+var selu_util = require("../../ops/selu_util");
+var UnaryOpProgram = (function () {
+    function UnaryOpProgram(aShape, opSnippet) {
+        this.variableNames = ['A'];
+        this.outputShape = aShape;
+        this.userCode = "\n      uniform float NAN;\n      float unaryOperation(float x) {\n        " + opSnippet + "\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    ";
+    }
+    UnaryOpProgram.prototype.getCustomSetupFunc = function () {
+        var _this = this;
+        return function (gpgpu, webGLProgram) {
+            if (_this.startLoc == null) {
+                _this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'NAN');
+                if (_this.startLoc == null) {
+                    return;
+                }
+            }
+            gpgpu.gl.uniform1f(_this.startLoc, NaN);
+        };
+    };
+    return UnaryOpProgram;
+}());
+exports.UnaryOpProgram = UnaryOpProgram;
+var CHECK_NAN_SNIPPET = "if (isNaN(x)) return x;";
+exports.ABS = "return abs(x);";
+exports.RELU = CHECK_NAN_SNIPPET + "\n  return (x < 0.0) ? 0.0 : x;\n";
+exports.ELU = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
+exports.SELU = "\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = " + selu_util.SELU_SCALEALPHA + ";\n  float scale = " + selu_util.SELU_SCALE + ";\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n";
+function STEP(alpha) {
+    if (alpha === void 0) { alpha = 0.0; }
+    return CHECK_NAN_SNIPPET + ("\n    return x > 0.0 ? 1.0 : float(" + alpha + ");\n  ");
+}
+exports.STEP = STEP;
+exports.NEG = "return -x;";
+exports.CEIL = "return ceil(x);";
+exports.FLOOR = "return floor(x);";
+exports.SIGN = "\n  if (isNaN(x)) { return 0.0; }\n  return sign(x);\n";
+exports.ROUND = "\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n";
+exports.EXP = "return exp(x);";
+exports.EXPM1 = "return exp(x) - 1.0;";
+exports.LOG = "if (x < 0.0) return NAN;\n  return log(x);";
+exports.LOG1P = "return log(1.0 + x);";
+exports.SQRT = "return sqrt(x);";
+exports.RSQRT = "return inversesqrt(x);";
+exports.SIGMOID = "return 1.0 / (1.0 + exp(-1.0 * x));";
+exports.SOFTPLUS = "\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n";
+exports.SIN = CHECK_NAN_SNIPPET + "\n  return sin(x);\n";
+exports.COS = CHECK_NAN_SNIPPET + "\n  return cos(x);\n";
+exports.TAN = "return tan(x);";
+exports.ASIN = "return asin(x);";
+exports.ACOS = "return acos(x);";
+exports.ATAN = CHECK_NAN_SNIPPET + "\n  return atan(x);\n";
+exports.SINH = "\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n";
+exports.COSH = "\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n";
+exports.TANH = "\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n";
+exports.ASINH = "return log(x + sqrt(x * x + 1.0));";
+exports.ACOSH = CHECK_NAN_SNIPPET + "\n  if (x < 1.0) return NAN;\n  return log(x + sqrt(x * x - 1.0));";
+exports.ATANH = CHECK_NAN_SNIPPET + "\n  if ((x < -1.0) || (x > 1.0)) return NAN;\n  return (log(1.0 + x) - log(1.0 - x)) / 2.0;";
+exports.ERF = "\n  // Error function is calculated approximately with elementary function.\n  // See \"Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n  float p = " + erf_util.ERF_P + ";\n  float a1 = " + erf_util.ERF_A1 + ";\n  float a2 = " + erf_util.ERF_A2 + ";\n  float a3 = " + erf_util.ERF_A3 + ";\n  float a4 = " + erf_util.ERF_A4 + ";\n  float a5 = " + erf_util.ERF_A5 + ";\n\n  float t = 1.0 / (1.0 + p * x);\n  return 1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x);\n";
+exports.SQUARE = "return x * x;";
+exports.RECIPROCAL = "return 1.0 / x;";
+exports.LOGICAL_NOT = "return float(!(x >= 1.0));";
+exports.TO_INT = "return float(int(x));";
+//# sourceMappingURL=unaryop_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/unaryop_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/unaryop_gpu.js.map
new file mode 100644
index 0000000..90e3e52
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/unaryop_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"unaryop_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/unaryop_gpu.ts"],"names":[],"mappings":";;AAiBA,6CAA+C;AAC/C,+CAAiD;AAKjD;IAQE,wBAAY,MAAgB,EAAE,SAAiB;QAP/C,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QAQpB,IAAI,CAAC,WAAW,GAAG,MAAM,CAAC;QAC1B,IAAI,CAAC,QAAQ,GAAG,gFAGV,SAAS,yJASd,CAAC;IACJ,CAAC;IAED,2CAAkB,GAAlB;QAAA,iBAYC;QAXC,OAAO,UAAC,KAAmB,EAAE,YAA0B;YACrD,IAAI,KAAI,CAAC,QAAQ,IAAI,IAAI,EAAE;gBACzB,KAAI,CAAC,QAAQ,GAAG,KAAK,CAAC,yBAAyB,CAAC,YAAY,EAAE,KAAK,CAAC,CAAC;gBACrE,IAAI,KAAI,CAAC,QAAQ,IAAI,IAAI,EAAE;oBAGzB,OAAO;iBACR;aACF;YACD,KAAK,CAAC,EAAE,CAAC,SAAS,CAAC,KAAI,CAAC,QAAQ,EAAE,GAAG,CAAC,CAAC;QACzC,CAAC,CAAC;IACJ,CAAC;IACH,qBAAC;AAAD,CAAC,AAtCD,IAsCC;AAtCY,wCAAc;AAwC3B,IAAM,iBAAiB,GAAG,yBAAyB,CAAC;AAEvC,QAAA,GAAG,GAAG,gBAAgB,CAAC;AAEvB,QAAA,IAAI,GAAG,iBAAiB,GAAG,mCAEvC,CAAC;AAEW,QAAA,GAAG,GAAG,yCAAyC,CAAC;AAEhD,QAAA,IAAI,GAAG,+IAGG,SAAS,CAAC,eAAe,2BAC9B,SAAS,CAAC,UAAU,wEAErC,CAAC;AAEF,cAAqB,KAAW;IAAX,sBAAA,EAAA,WAAW;IAC9B,OAAO,iBAAiB,IAAG,wCACM,KAAK,WACrC,CAAA,CAAC;AACJ,CAAC;AAJD,oBAIC;AAEY,QAAA,GAAG,GAAG,YAAY,CAAC;AAEnB,QAAA,IAAI,GAAG,iBAAiB,CAAC;AAEzB,QAAA,KAAK,GAAG,kBAAkB,CAAC;AAE3B,QAAA,IAAI,GAAG,wDAGnB,CAAC;AAEW,QAAA,KAAK,GAAG,gWAepB,CAAC;AAEW,QAAA,GAAG,GAAG,gBAAgB,CAAC;AAEvB,QAAA,KAAK,GAAG,sBAAsB,CAAC;AAE/B,QAAA,GAAG,GAAG,4CACF,CAAC;AAEL,QAAA,KAAK,GAAG,sBAAsB,CAAC;AAE/B,QAAA,IAAI,GAAG,iBAAiB,CAAC;AAEzB,QAAA,KAAK,GAAG,wBAAwB,CAAC;AAEjC,QAAA,OAAO,GAAG,qCAAqC,CAAC;AAehD,QAAA,QAAQ,GAAG,yWAoBvB,CAAC;AAEW,QAAA,GAAG,GAAG,iBAAiB,GAAG,sBAEtC,CAAC;AAEW,QAAA,GAAG,GAAG,iBAAiB,GAAG,sBAEtC,CAAC;AAEW,QAAA,GAAG,GAAG,gBAAgB,CAAC;AAEvB,QAAA,IAAI,GAAG,iBAAiB,CAAC;AAEzB,QAAA,IAAI,GAAG,iBAAiB,CAAC;AAEzB,QAAA,IAAI,GAAG,iBAAiB,GAAG,uBAEvC,CAAC;AAEW,QAAA,IAAI,GAAG,8DAGnB,CAAC;AAEW,QAAA,IAAI,GAAG,+DAGnB,CAAC;AAEW,QAAA,IAAI,GAAG,sFAGnB,CAAC;AAEW,QAAA,KAAK,GAAG,oCAAoC,CAAC;AAE7C,QAAA,KAAK,GAAG,iBAAiB,GAAG,oEAEJ,CAAC;AAEzB,QAAA,KAAK,GAAG,iBAAiB,GAAG,6FAEK,CAAC;AAElC,QAAA,GAAG,GAAG,4NAIL,QAAQ,CAAC,KAAK,wBACb,QAAQ,CAAC,MAAM,wBACf,QAAQ,CAAC,MAAM,wBACf,QAAQ,CAAC,MAAM,wBACf,QAAQ,CAAC,MAAM,wBACf,QAAQ,CAAC,MAAM,kHAI7B,CAAC;AAEW,QAAA,MAAM,GAAG,eAAe,CAAC;AAEzB,QAAA,UAAU,GAAG,iBAAiB,CAAC;AAE/B,QAAA,WAAW,GAAG,4BAA4B,CAAC;AAE3C,QAAA,MAAM,GAAG,uBAAuB,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/unpack_gpu.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/unpack_gpu.d.ts
new file mode 100644
index 0000000..f9d69b1
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/unpack_gpu.d.ts
@@ -0,0 +1,8 @@
+import { GPGPUProgram } from './gpgpu_math';
+export declare class UnpackProgram implements GPGPUProgram {
+    variableNames: string[];
+    usesPackedTextures: boolean;
+    outputShape: number[];
+    userCode: string;
+    constructor(outputShape: number[]);
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/unpack_gpu.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/unpack_gpu.js
new file mode 100644
index 0000000..2d4257a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/unpack_gpu.js
@@ -0,0 +1,21 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var packing_util_1 = require("../packing_util");
+var shader_compiler_1 = require("./shader_compiler");
+var UnpackProgram = (function () {
+    function UnpackProgram(outputShape) {
+        this.variableNames = ['A'];
+        this.usesPackedTextures = true;
+        this.outputShape = outputShape;
+        var rank = outputShape.length;
+        var channels = packing_util_1.getChannels('rc', rank);
+        var dtype = shader_compiler_1.getCoordsDataType(rank);
+        var sourceCoords = packing_util_1.getSourceCoords(rank, channels);
+        var innerDims = channels.slice(-2);
+        var coords = rank <= 1 ? 'rc' : "vec2(" + innerDims.join(',') + ")";
+        this.userCode = "\n      void main() {\n        " + dtype + " rc = getOutputCoords();\n        vec4 packedInput = getA(" + sourceCoords + ");\n\n        setOutput(getChannel(packedInput, " + coords + "));\n      }\n    ";
+    }
+    return UnpackProgram;
+}());
+exports.UnpackProgram = UnpackProgram;
+//# sourceMappingURL=unpack_gpu.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/unpack_gpu.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/unpack_gpu.js.map
new file mode 100644
index 0000000..748be69
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/unpack_gpu.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"unpack_gpu.js","sourceRoot":"","sources":["../../../src/kernels/webgl/unpack_gpu.ts"],"names":[],"mappings":";;AAiBA,gDAA6D;AAG7D,qDAAoD;AAEpD;IAME,uBAAY,WAAqB;QALjC,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;QACtB,uBAAkB,GAAG,IAAI,CAAC;QAKxB,IAAI,CAAC,WAAW,GAAG,WAAW,CAAC;QAC/B,IAAM,IAAI,GAAG,WAAW,CAAC,MAAM,CAAC;QAEhC,IAAM,QAAQ,GAAG,0BAAW,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC;QACzC,IAAM,KAAK,GAAG,mCAAiB,CAAC,IAAI,CAAC,CAAC;QACtC,IAAM,YAAY,GAAG,8BAAe,CAAC,IAAI,EAAE,QAAQ,CAAC,CAAC;QACrD,IAAM,SAAS,GAAG,QAAQ,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC;QACrC,IAAM,MAAM,GAAG,IAAI,IAAI,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,UAAQ,SAAS,CAAC,IAAI,CAAC,GAAG,CAAC,MAAG,CAAC;QAEjE,IAAI,CAAC,QAAQ,GAAG,oCAEV,KAAK,kEACmB,YAAY,wDAEF,MAAM,uBAE7C,CAAC;IACJ,CAAC;IACH,oBAAC;AAAD,CAAC,AAzBD,IAyBC;AAzBY,sCAAa"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_custom_op_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_custom_op_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_custom_op_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_custom_op_test.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_custom_op_test.js
new file mode 100644
index 0000000..8987488
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_custom_op_test.js
@@ -0,0 +1,59 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../../index");
+var jasmine_util_1 = require("../../jasmine_util");
+var test_util_1 = require("../../test_util");
+jasmine_util_1.describeWithFlags('custom-op webgl', test_util_1.WEBGL_ENVS, function () {
+    var SquareAndAddKernel = (function () {
+        function SquareAndAddKernel(inputShape) {
+            this.variableNames = ['X'];
+            this.outputShape = inputShape.slice();
+            this.userCode = "\n          void main() {\n            float x = getXAtOutCoords();\n            float value = x * x + x;\n            setOutput(value);\n          }\n        ";
+        }
+        return SquareAndAddKernel;
+    }());
+    var SquareAndAddBackpropKernel = (function () {
+        function SquareAndAddBackpropKernel(inputShape) {
+            this.variableNames = ['X'];
+            this.outputShape = inputShape.slice();
+            this.userCode = "\n          void main() {\n            float x = getXAtOutCoords();\n            float value = 2.0 * x + 1.0;\n            setOutput(value);\n          }\n        ";
+        }
+        return SquareAndAddBackpropKernel;
+    }());
+    function squareAndAdd(x) {
+        var fn = tf.customGrad(function (x) {
+            var webglBackend = tf.ENV.backend;
+            var program = new SquareAndAddKernel(x.shape);
+            var backpropProgram = new SquareAndAddBackpropKernel(x.shape);
+            var value = webglBackend.compileAndRun(program, [x]);
+            var gradFunc = function (dy) {
+                return webglBackend.compileAndRun(backpropProgram, [x]).mul(dy);
+            };
+            return { value: value, gradFunc: gradFunc };
+        });
+        return fn(x);
+    }
+    it('lets users use custom operations', function () {
+        var inputArr = [1, 2, 3, 4];
+        var input = tf.tensor(inputArr);
+        var output = squareAndAdd(input);
+        test_util_1.expectArraysClose(output, inputArr.map(function (x) { return x * x + x; }));
+    });
+    it('lets users define gradients for operations', function () {
+        var inputArr = [1, 2, 3, 4];
+        var input = tf.tensor(inputArr);
+        var grads = tf.valueAndGrad(function (x) { return squareAndAdd(x); });
+        var _a = grads(input), value = _a.value, grad = _a.grad;
+        test_util_1.expectArraysClose(value, inputArr.map(function (x) { return x * x + x; }));
+        test_util_1.expectArraysClose(grad, inputArr.map(function (x) { return 2 * x + 1; }));
+    });
+    it('multiplies by dy parameter when it is passed', function () {
+        var inputArr = [1, 2, 3, 4];
+        var input = tf.tensor(inputArr);
+        var grads = tf.valueAndGrad(function (x) { return squareAndAdd(x); });
+        var _a = grads(input, tf.zerosLike(input)), value = _a.value, grad = _a.grad;
+        test_util_1.expectArraysClose(value, inputArr.map(function (x) { return x * x + x; }));
+        test_util_1.expectArraysClose(grad, inputArr.map(function () { return 0.0; }));
+    });
+});
+//# sourceMappingURL=webgl_custom_op_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_custom_op_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_custom_op_test.js.map
new file mode 100644
index 0000000..c482e29
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_custom_op_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"webgl_custom_op_test.js","sourceRoot":"","sources":["../../../src/kernels/webgl/webgl_custom_op_test.ts"],"names":[],"mappings":";;AAiBA,gCAAkC;AAClC,mDAAqD;AACrD,6CAA8D;AAE9D,gCAAiB,CAAC,iBAAiB,EAAE,sBAAU,EAAE;IAC/C;QAIE,4BAAY,UAAoB;YAHhC,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;YAIpB,IAAI,CAAC,WAAW,GAAG,UAAU,CAAC,KAAK,EAAE,CAAC;YAEtC,IAAI,CAAC,QAAQ,GAAG,iKAMb,CAAC;QACN,CAAC;QACH,yBAAC;IAAD,CAAC,AAfD,IAeC;IAED;QAIE,oCAAY,UAAoB;YAHhC,kBAAa,GAAG,CAAC,GAAG,CAAC,CAAC;YAIpB,IAAI,CAAC,WAAW,GAAG,UAAU,CAAC,KAAK,EAAE,CAAC;YAEtC,IAAI,CAAC,QAAQ,GAAG,qKAMb,CAAC;QACN,CAAC;QACH,iCAAC;IAAD,CAAC,AAfD,IAeC;IAED,sBAA2C,CAAI;QAC7C,IAAM,EAAE,GAAG,EAAE,CAAC,UAAU,CAAC,UAAA,CAAC;YACxB,IAAM,YAAY,GAAG,EAAE,CAAC,GAAG,CAAC,OAAoC,CAAC;YACjE,IAAM,OAAO,GAAG,IAAI,kBAAkB,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC;YAChD,IAAM,eAAe,GAAG,IAAI,0BAA0B,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC;YAEhE,IAAM,KAAK,GAAG,YAAY,CAAC,aAAa,CAAC,OAAO,EAAE,CAAC,CAAC,CAAC,CAAc,CAAC;YAEpE,IAAM,QAAQ,GAAG,UAAC,EAAK;gBACnB,OAAC,YAAY,CAAC,aAAa,CAAC,eAAe,EAAE,CAAC,CAAC,CAAC,CAAO,CAAC,GAAG,CAAC,EAAE,CAAC;YAA/D,CAA+D,CAAC;YACpE,OAAO,EAAC,KAAK,OAAA,EAAE,QAAQ,UAAA,EAAC,CAAC;QAC3B,CAAC,CAAC,CAAC;QACH,OAAO,EAAE,CAAC,CAAC,CAAM,CAAC;IACpB,CAAC;IAED,EAAE,CAAC,kCAAkC,EAAE;QACrC,IAAM,QAAQ,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC;QAC9B,IAAM,KAAK,GAAG,EAAE,CAAC,MAAM,CAAC,QAAQ,CAAC,CAAC;QAClC,IAAM,MAAM,GAAG,YAAY,CAAC,KAAK,CAAC,CAAC;QACnC,6BAAiB,CAAC,MAAM,EAAE,QAAQ,CAAC,GAAG,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,GAAG,CAAC,GAAG,CAAC,EAAT,CAAS,CAAC,CAAC,CAAC;IAC1D,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,4CAA4C,EAAE;QAC/C,IAAM,QAAQ,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC;QAC9B,IAAM,KAAK,GAAG,EAAE,CAAC,MAAM,CAAC,QAAQ,CAAC,CAAC;QAClC,IAAM,KAAK,GAAG,EAAE,CAAC,YAAY,CAAC,UAAA,CAAC,IAAI,OAAA,YAAY,CAAC,CAAC,CAAC,EAAf,CAAe,CAAC,CAAC;QAC9C,IAAA,iBAA4B,EAA3B,gBAAK,EAAE,cAAI,CAAiB;QACnC,6BAAiB,CAAC,KAAK,EAAE,QAAQ,CAAC,GAAG,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,GAAG,CAAC,GAAG,CAAC,EAAT,CAAS,CAAC,CAAC,CAAC;QACvD,6BAAiB,CAAC,IAAI,EAAE,QAAQ,CAAC,GAAG,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,GAAG,CAAC,GAAG,CAAC,EAAT,CAAS,CAAC,CAAC,CAAC;IACxD,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,8CAA8C,EAAE;QACjD,IAAM,QAAQ,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC;QAC9B,IAAM,KAAK,GAAG,EAAE,CAAC,MAAM,CAAC,QAAQ,CAAC,CAAC;QAClC,IAAM,KAAK,GAAG,EAAE,CAAC,YAAY,CAAC,UAAA,CAAC,IAAI,OAAA,YAAY,CAAC,CAAC,CAAC,EAAf,CAAe,CAAC,CAAC;QAC9C,IAAA,sCAAiD,EAAhD,gBAAK,EAAE,cAAI,CAAsC;QACxD,6BAAiB,CAAC,KAAK,EAAE,QAAQ,CAAC,GAAG,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,GAAG,CAAC,GAAG,CAAC,EAAT,CAAS,CAAC,CAAC,CAAC;QACvD,6BAAiB,CAAC,IAAI,EAAE,QAAQ,CAAC,GAAG,CAAC,cAAM,OAAA,GAAG,EAAH,CAAG,CAAC,CAAC,CAAC;IACnD,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_types.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_types.d.ts
new file mode 100644
index 0000000..7c117f0
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_types.d.ts
@@ -0,0 +1,26 @@
+/// <reference types="webgl2" />
+export interface WebGL2DisjointQueryTimerExtension {
+    TIME_ELAPSED_EXT: number;
+    GPU_DISJOINT_EXT: number;
+}
+export interface WebGL1DisjointQueryTimerExtension {
+    TIME_ELAPSED_EXT: number;
+    QUERY_RESULT_AVAILABLE_EXT: number;
+    GPU_DISJOINT_EXT: number;
+    QUERY_RESULT_EXT: number;
+    createQueryEXT: () => {};
+    beginQueryEXT: (ext: number, query: WebGLQuery) => void;
+    endQueryEXT: (ext: number) => void;
+    deleteQueryEXT: (query: WebGLQuery) => void;
+    isQueryEXT: (query: WebGLQuery) => boolean;
+    getQueryObjectEXT: (query: WebGLQuery, queryResultAvailableExt: number) => number;
+}
+export interface WebGLContextAttributes {
+    alpha?: boolean;
+    antialias?: boolean;
+    premultipliedAlpha?: boolean;
+    preserveDrawingBuffer?: boolean;
+    depth?: boolean;
+    stencil?: boolean;
+    failIfMajorPerformanceCaveat?: boolean;
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_types.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_types.js
new file mode 100644
index 0000000..2c46246
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_types.js
@@ -0,0 +1,3 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+//# sourceMappingURL=webgl_types.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_types.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_types.js.map
new file mode 100644
index 0000000..e4ce532
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_types.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"webgl_types.js","sourceRoot":"","sources":["../../../src/kernels/webgl/webgl_types.ts"],"names":[],"mappings":""}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_util.d.ts
new file mode 100644
index 0000000..bbe1b8f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_util.d.ts
@@ -0,0 +1,29 @@
+export declare function callAndCheck<T>(gl: WebGLRenderingContext, func: () => T): T;
+export declare function enableDebugWebGLErrorChecking(enabled: boolean): void;
+export declare function checkWebGLError(gl: WebGLRenderingContext): void;
+export declare function getWebGLErrorMessage(gl: WebGLRenderingContext, status: number): string;
+export declare function getExtensionOrThrow(gl: WebGLRenderingContext, extensionName: string): {};
+export declare function createVertexShader(gl: WebGLRenderingContext, vertexShaderSource: string): WebGLShader;
+export declare function createFragmentShader(gl: WebGLRenderingContext, fragmentShaderSource: string): WebGLShader;
+export declare function createProgram(gl: WebGLRenderingContext): WebGLProgram;
+export declare function linkProgram(gl: WebGLRenderingContext, program: WebGLProgram): void;
+export declare function validateProgram(gl: WebGLRenderingContext, program: WebGLProgram): void;
+export declare function createStaticVertexBuffer(gl: WebGLRenderingContext, data: Float32Array): WebGLBuffer;
+export declare function createStaticIndexBuffer(gl: WebGLRenderingContext, data: Uint16Array): WebGLBuffer;
+export declare function getNumChannels(): number;
+export declare function createTexture(gl: WebGLRenderingContext): WebGLTexture;
+export declare function validateTextureSize(width: number, height: number): void;
+export declare function createFramebuffer(gl: WebGLRenderingContext): WebGLFramebuffer;
+export declare function bindVertexBufferToProgramAttribute(gl: WebGLRenderingContext, program: WebGLProgram, attribute: string, buffer: WebGLBuffer, arrayEntriesPerItem: number, itemStrideInBytes: number, itemOffsetInBytes: number): boolean;
+export declare function bindTextureUnit(gl: WebGLRenderingContext, texture: WebGLTexture, textureUnit: number): void;
+export declare function unbindTextureUnit(gl: WebGLRenderingContext, textureUnit: number): void;
+export declare function getProgramUniformLocationOrThrow(gl: WebGLRenderingContext, program: WebGLProgram, uniformName: string): WebGLUniformLocation;
+export declare function getProgramUniformLocation(gl: WebGLRenderingContext, program: WebGLProgram, uniformName: string): WebGLUniformLocation;
+export declare function bindTextureToProgramUniformSampler(gl: WebGLRenderingContext, program: WebGLProgram, texture: WebGLTexture, uniformSamplerLocation: WebGLUniformLocation, textureUnit: number): void;
+export declare function bindCanvasToFramebuffer(gl: WebGLRenderingContext): void;
+export declare function bindColorTextureToFramebuffer(gl: WebGLRenderingContext, texture: WebGLTexture, framebuffer: WebGLFramebuffer): void;
+export declare function unbindColorTextureFromFramebuffer(gl: WebGLRenderingContext, framebuffer: WebGLFramebuffer): void;
+export declare function validateFramebuffer(gl: WebGLRenderingContext): void;
+export declare function getFramebufferErrorMessage(gl: WebGLRenderingContext, status: number): string;
+export declare function getTextureShapeFromLogicalShape(logShape: number[], isPacked?: boolean): [number, number];
+export declare function isReshapeFree(shape1: number[], shape2: number[]): boolean;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_util.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_util.js
new file mode 100644
index 0000000..1a7a068
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_util.js
@@ -0,0 +1,333 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../../environment");
+var util = require("../../util");
+function callAndCheck(gl, func) {
+    var returnValue = func();
+    checkWebGLError(gl);
+    return returnValue;
+}
+exports.callAndCheck = callAndCheck;
+var webGLDebugErrorCheckingEnabled = false;
+function enableDebugWebGLErrorChecking(enabled) {
+    webGLDebugErrorCheckingEnabled = enabled;
+}
+exports.enableDebugWebGLErrorChecking = enableDebugWebGLErrorChecking;
+function checkWebGLError(gl) {
+    if (webGLDebugErrorCheckingEnabled) {
+        var error = gl.getError();
+        if (error !== gl.NO_ERROR) {
+            throw new Error('WebGL Error: ' + getWebGLErrorMessage(gl, error));
+        }
+    }
+}
+exports.checkWebGLError = checkWebGLError;
+function getWebGLErrorMessage(gl, status) {
+    switch (status) {
+        case gl.NO_ERROR:
+            return 'NO_ERROR';
+        case gl.INVALID_ENUM:
+            return 'INVALID_ENUM';
+        case gl.INVALID_VALUE:
+            return 'INVALID_VALUE';
+        case gl.INVALID_OPERATION:
+            return 'INVALID_OPERATION';
+        case gl.INVALID_FRAMEBUFFER_OPERATION:
+            return 'INVALID_FRAMEBUFFER_OPERATION';
+        case gl.OUT_OF_MEMORY:
+            return 'OUT_OF_MEMORY';
+        case gl.CONTEXT_LOST_WEBGL:
+            return 'CONTEXT_LOST_WEBGL';
+        default:
+            return "Unknown error code " + status;
+    }
+}
+exports.getWebGLErrorMessage = getWebGLErrorMessage;
+function getExtensionOrThrow(gl, extensionName) {
+    return throwIfNull(gl, function () { return gl.getExtension(extensionName); }, 'Extension "' + extensionName + '" not supported on this browser.');
+}
+exports.getExtensionOrThrow = getExtensionOrThrow;
+function createVertexShader(gl, vertexShaderSource) {
+    var vertexShader = throwIfNull(gl, function () { return gl.createShader(gl.VERTEX_SHADER); }, 'Unable to create vertex WebGLShader.');
+    callAndCheck(gl, function () { return gl.shaderSource(vertexShader, vertexShaderSource); });
+    callAndCheck(gl, function () { return gl.compileShader(vertexShader); });
+    if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {
+        console.log(gl.getShaderInfoLog(vertexShader));
+        throw new Error('Failed to compile vertex shader.');
+    }
+    return vertexShader;
+}
+exports.createVertexShader = createVertexShader;
+function createFragmentShader(gl, fragmentShaderSource) {
+    var fragmentShader = throwIfNull(gl, function () { return gl.createShader(gl.FRAGMENT_SHADER); }, 'Unable to create fragment WebGLShader.');
+    callAndCheck(gl, function () { return gl.shaderSource(fragmentShader, fragmentShaderSource); });
+    callAndCheck(gl, function () { return gl.compileShader(fragmentShader); });
+    if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {
+        logShaderSourceAndInfoLog(fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));
+        throw new Error('Failed to compile fragment shader.');
+    }
+    return fragmentShader;
+}
+exports.createFragmentShader = createFragmentShader;
+var lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;
+function logShaderSourceAndInfoLog(shaderSource, shaderInfoLog) {
+    var lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);
+    if (lineNumberRegexResult == null) {
+        console.log("Couldn't parse line number in error: " + shaderInfoLog);
+        console.log(shaderSource);
+        return;
+    }
+    var lineNumber = +lineNumberRegexResult[1];
+    var shaderLines = shaderSource.split('\n');
+    var pad = shaderLines.length.toString().length + 2;
+    var linesWithLineNumbers = shaderLines.map(function (line, lineNumber) {
+        return util.rightPad((lineNumber + 1).toString(), pad) + line;
+    });
+    var maxLineLength = 0;
+    for (var i = 0; i < linesWithLineNumbers.length; i++) {
+        maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength);
+    }
+    var beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);
+    var errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);
+    var afterErrorLines = linesWithLineNumbers.slice(lineNumber);
+    console.log(beforeErrorLines.join('\n'));
+    console.log(shaderInfoLog.split('\n')[0]);
+    console.log("%c " + util.rightPad(errorLine[0], maxLineLength), 'border:1px solid red; background-color:#e3d2d2; color:#a61717');
+    console.log(afterErrorLines.join('\n'));
+}
+function createProgram(gl) {
+    return throwIfNull(gl, function () { return gl.createProgram(); }, 'Unable to create WebGLProgram.');
+}
+exports.createProgram = createProgram;
+function linkProgram(gl, program) {
+    callAndCheck(gl, function () { return gl.linkProgram(program); });
+    if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {
+        console.log(gl.getProgramInfoLog(program));
+        throw new Error('Failed to link vertex and fragment shaders.');
+    }
+}
+exports.linkProgram = linkProgram;
+function validateProgram(gl, program) {
+    callAndCheck(gl, function () { return gl.validateProgram(program); });
+    if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {
+        console.log(gl.getProgramInfoLog(program));
+        throw new Error('Shader program validation failed.');
+    }
+}
+exports.validateProgram = validateProgram;
+function createStaticVertexBuffer(gl, data) {
+    var buffer = throwIfNull(gl, function () { return gl.createBuffer(); }, 'Unable to create WebGLBuffer');
+    callAndCheck(gl, function () { return gl.bindBuffer(gl.ARRAY_BUFFER, buffer); });
+    callAndCheck(gl, function () { return gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW); });
+    return buffer;
+}
+exports.createStaticVertexBuffer = createStaticVertexBuffer;
+function createStaticIndexBuffer(gl, data) {
+    var buffer = throwIfNull(gl, function () { return gl.createBuffer(); }, 'Unable to create WebGLBuffer');
+    callAndCheck(gl, function () { return gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer); });
+    callAndCheck(gl, function () { return gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW); });
+    return buffer;
+}
+exports.createStaticIndexBuffer = createStaticIndexBuffer;
+function getNumChannels() {
+    if (environment_1.ENV.get('WEBGL_VERSION') === 2) {
+        return 1;
+    }
+    return 4;
+}
+exports.getNumChannels = getNumChannels;
+function createTexture(gl) {
+    return throwIfNull(gl, function () { return gl.createTexture(); }, 'Unable to create WebGLTexture.');
+}
+exports.createTexture = createTexture;
+function validateTextureSize(width, height) {
+    var maxTextureSize = environment_1.ENV.get('WEBGL_MAX_TEXTURE_SIZE');
+    if ((width <= 0) || (height <= 0)) {
+        var requested = "[" + width + "x" + height + "]";
+        throw new Error('Requested texture size ' + requested + ' is invalid.');
+    }
+    if ((width > maxTextureSize) || (height > maxTextureSize)) {
+        var requested = "[" + width + "x" + height + "]";
+        var max = "[" + maxTextureSize + "x" + maxTextureSize + "]";
+        throw new Error('Requested texture size ' + requested +
+            ' greater than WebGL maximum on this browser / GPU ' + max + '.');
+    }
+}
+exports.validateTextureSize = validateTextureSize;
+function createFramebuffer(gl) {
+    return throwIfNull(gl, function () { return gl.createFramebuffer(); }, 'Unable to create WebGLFramebuffer.');
+}
+exports.createFramebuffer = createFramebuffer;
+function bindVertexBufferToProgramAttribute(gl, program, attribute, buffer, arrayEntriesPerItem, itemStrideInBytes, itemOffsetInBytes) {
+    var loc = gl.getAttribLocation(program, attribute);
+    if (loc === -1) {
+        return false;
+    }
+    callAndCheck(gl, function () { return gl.bindBuffer(gl.ARRAY_BUFFER, buffer); });
+    callAndCheck(gl, function () { return gl.vertexAttribPointer(loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes, itemOffsetInBytes); });
+    callAndCheck(gl, function () { return gl.enableVertexAttribArray(loc); });
+    return true;
+}
+exports.bindVertexBufferToProgramAttribute = bindVertexBufferToProgramAttribute;
+function bindTextureUnit(gl, texture, textureUnit) {
+    validateTextureUnit(gl, textureUnit);
+    callAndCheck(gl, function () { return gl.activeTexture(gl.TEXTURE0 + textureUnit); });
+    callAndCheck(gl, function () { return gl.bindTexture(gl.TEXTURE_2D, texture); });
+}
+exports.bindTextureUnit = bindTextureUnit;
+function unbindTextureUnit(gl, textureUnit) {
+    validateTextureUnit(gl, textureUnit);
+    callAndCheck(gl, function () { return gl.activeTexture(gl.TEXTURE0 + textureUnit); });
+    callAndCheck(gl, function () { return gl.bindTexture(gl.TEXTURE_2D, null); });
+}
+exports.unbindTextureUnit = unbindTextureUnit;
+function getProgramUniformLocationOrThrow(gl, program, uniformName) {
+    return throwIfNull(gl, function () { return gl.getUniformLocation(program, uniformName); }, 'uniform "' + uniformName + '" not present in program.');
+}
+exports.getProgramUniformLocationOrThrow = getProgramUniformLocationOrThrow;
+function getProgramUniformLocation(gl, program, uniformName) {
+    return gl.getUniformLocation(program, uniformName);
+}
+exports.getProgramUniformLocation = getProgramUniformLocation;
+function bindTextureToProgramUniformSampler(gl, program, texture, uniformSamplerLocation, textureUnit) {
+    callAndCheck(gl, function () { return bindTextureUnit(gl, texture, textureUnit); });
+    callAndCheck(gl, function () { return gl.uniform1i(uniformSamplerLocation, textureUnit); });
+}
+exports.bindTextureToProgramUniformSampler = bindTextureToProgramUniformSampler;
+function bindCanvasToFramebuffer(gl) {
+    callAndCheck(gl, function () { return gl.bindFramebuffer(gl.FRAMEBUFFER, null); });
+    callAndCheck(gl, function () { return gl.viewport(0, 0, gl.canvas.width, gl.canvas.height); });
+    callAndCheck(gl, function () { return gl.scissor(0, 0, gl.canvas.width, gl.canvas.height); });
+}
+exports.bindCanvasToFramebuffer = bindCanvasToFramebuffer;
+function bindColorTextureToFramebuffer(gl, texture, framebuffer) {
+    callAndCheck(gl, function () { return gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer); });
+    callAndCheck(gl, function () { return gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0); });
+}
+exports.bindColorTextureToFramebuffer = bindColorTextureToFramebuffer;
+function unbindColorTextureFromFramebuffer(gl, framebuffer) {
+    callAndCheck(gl, function () { return gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer); });
+    callAndCheck(gl, function () { return gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0); });
+}
+exports.unbindColorTextureFromFramebuffer = unbindColorTextureFromFramebuffer;
+function validateFramebuffer(gl) {
+    var status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);
+    if (status !== gl.FRAMEBUFFER_COMPLETE) {
+        throw new Error('Error binding framebuffer: ' + getFramebufferErrorMessage(gl, status));
+    }
+}
+exports.validateFramebuffer = validateFramebuffer;
+function getFramebufferErrorMessage(gl, status) {
+    switch (status) {
+        case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:
+            return 'FRAMEBUFFER_INCOMPLETE_ATTACHMENT';
+        case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:
+            return 'FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT';
+        case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:
+            return 'FRAMEBUFFER_INCOMPLETE_DIMENSIONS';
+        case gl.FRAMEBUFFER_UNSUPPORTED:
+            return 'FRAMEBUFFER_UNSUPPORTED';
+        default:
+            return "unknown error " + status;
+    }
+}
+exports.getFramebufferErrorMessage = getFramebufferErrorMessage;
+function throwIfNull(gl, returnTOrNull, failureMessage) {
+    var tOrNull = callAndCheck(gl, function () { return returnTOrNull(); });
+    if (tOrNull == null) {
+        throw new Error(failureMessage);
+    }
+    return tOrNull;
+}
+function validateTextureUnit(gl, textureUnit) {
+    var maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;
+    var glTextureUnit = textureUnit + gl.TEXTURE0;
+    if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {
+        var textureUnitRange = "[gl.TEXTURE0, gl.TEXTURE" + maxTextureUnit + "]";
+        throw new Error("textureUnit must be in " + textureUnitRange + ".");
+    }
+}
+function getTextureShapeFromLogicalShape(logShape, isPacked) {
+    if (isPacked === void 0) { isPacked = false; }
+    var maxTexSize = environment_1.ENV.get('WEBGL_MAX_TEXTURE_SIZE');
+    if (isPacked) {
+        maxTexSize = maxTexSize * 2;
+        logShape = logShape.map(function (d, i) { return i >= logShape.length - 2 ?
+            util.nearestLargerEven(logShape[i]) :
+            logShape[i]; });
+    }
+    if (logShape.length !== 2) {
+        var squeezeResult = util.squeezeShape(logShape);
+        logShape = squeezeResult.newShape;
+    }
+    var size = util.sizeFromShape(logShape);
+    if (logShape.length <= 1 && size <= maxTexSize) {
+        return [1, size];
+    }
+    else if (logShape.length === 2 && logShape[0] <= maxTexSize &&
+        logShape[1] <= maxTexSize) {
+        return logShape;
+    }
+    else if (logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize &&
+        logShape[2] <= maxTexSize) {
+        return [logShape[0] * logShape[1], logShape[2]];
+    }
+    else if (logShape.length === 3 && logShape[0] <= maxTexSize &&
+        logShape[1] * logShape[2] <= maxTexSize) {
+        return [logShape[0], logShape[1] * logShape[2]];
+    }
+    else if (logShape.length === 4 &&
+        logShape[0] * logShape[1] * logShape[2] <= maxTexSize &&
+        logShape[3] <= maxTexSize) {
+        return [logShape[0] * logShape[1] * logShape[2], logShape[3]];
+    }
+    else if (logShape.length === 4 && logShape[0] <= maxTexSize &&
+        logShape[1] * logShape[2] * logShape[3] <= maxTexSize) {
+        return [logShape[0], logShape[1] * logShape[2] * logShape[3]];
+    }
+    else {
+        return util.sizeToSquarishShape(size);
+    }
+}
+exports.getTextureShapeFromLogicalShape = getTextureShapeFromLogicalShape;
+function isEven(n) {
+    return n % 2 === 0;
+}
+function isReshapeFree(shape1, shape2) {
+    shape1 = shape1.slice(-2);
+    shape2 = shape2.slice(-2);
+    if (util.arraysEqual(shape1, shape2)) {
+        return true;
+    }
+    if (!shape1.length || !shape2.length) {
+        return true;
+    }
+    if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 ||
+        shape2[1] === 0) {
+        return true;
+    }
+    if (shape1.length !== shape2.length) {
+        var shape1Cols = shape1.slice(-1)[0];
+        var shape2Cols = shape2.slice(-1)[0];
+        if (shape1Cols === shape2Cols) {
+            return true;
+        }
+        if (isEven(shape1Cols) && isEven(shape2Cols) &&
+            (shape1[0] === 1 || shape2[0] === 1)) {
+            return true;
+        }
+    }
+    else {
+        if (isEven(shape1[0]) && isEven(shape2[0])) {
+            if (isEven(shape1[1]) && isEven(shape2[1])) {
+                return true;
+            }
+            if (shape1[1] === shape2[1]) {
+                return true;
+            }
+        }
+    }
+    return false;
+}
+exports.isReshapeFree = isReshapeFree;
+//# sourceMappingURL=webgl_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_util.js.map
new file mode 100644
index 0000000..07096af
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_util.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_util_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_util_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_util_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_util_test.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_util_test.js
new file mode 100644
index 0000000..dde3aea
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_util_test.js
@@ -0,0 +1,126 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../../index");
+var jasmine_util_1 = require("../../jasmine_util");
+var test_util_1 = require("../../test_util");
+var util = require("../../util");
+var webgl_util = require("./webgl_util");
+jasmine_util_1.describeWithFlags('getTextureShapeFromLogicalShape', test_util_1.WEBGL_ENVS, function () {
+    it('scalar', function () {
+        var texShape = webgl_util.getTextureShapeFromLogicalShape([]);
+        expect(texShape).toEqual([1, 1]);
+    });
+    it('1d', function () {
+        var texShape = webgl_util.getTextureShapeFromLogicalShape([4]);
+        expect(texShape).toEqual([1, 4]);
+    });
+    it('2d stays same', function () {
+        var texShape = webgl_util.getTextureShapeFromLogicalShape([5, 2]);
+        expect(texShape).toEqual([5, 2]);
+        texShape = webgl_util.getTextureShapeFromLogicalShape([5, 1]);
+        expect(texShape).toEqual([5, 1]);
+        texShape = webgl_util.getTextureShapeFromLogicalShape([1, 5]);
+        expect(texShape).toEqual([1, 5]);
+    });
+    it('3d 2x3x4', function () {
+        var texShape = webgl_util.getTextureShapeFromLogicalShape([2, 3, 4]);
+        expect(texShape).toEqual([6, 4]);
+    });
+    it('3d 3x256x256', function () {
+        var texShape = webgl_util.getTextureShapeFromLogicalShape([3, 256, 256]);
+        expect(texShape).toEqual([3 * 256, 256]);
+    });
+    it('3d 2x1x4 got squeezed', function () {
+        var texShape = webgl_util.getTextureShapeFromLogicalShape([2, 1, 4]);
+        expect(texShape).toEqual([2, 4]);
+    });
+    it('3d 1x8x2 got squeezed', function () {
+        var texShape = webgl_util.getTextureShapeFromLogicalShape([1, 8, 2]);
+        expect(texShape).toEqual([8, 2]);
+    });
+    it('4d 2x2x256x256 got squeezed', function () {
+        var texShape = webgl_util.getTextureShapeFromLogicalShape([2, 2, 256, 256]);
+        expect(texShape).toEqual([2 * 2 * 256, 256]);
+    });
+    it('4d 1x8x1x3 got squeezed', function () {
+        var texShape = webgl_util.getTextureShapeFromLogicalShape([1, 8, 1, 3]);
+        expect(texShape).toEqual([8, 3]);
+    });
+    it('4d 1x3x1x8 got squeezed', function () {
+        var texShape = webgl_util.getTextureShapeFromLogicalShape([1, 3, 1, 8]);
+        expect(texShape).toEqual([3, 8]);
+    });
+});
+jasmine_util_1.describeWithFlags('getTextureShapeFromLogicalShape packed', test_util_1.WEBGL_ENVS, function () {
+    it('textures less than 2x max size of platform preserve their shapes', function () {
+        var isPacked = true;
+        var logicalShape = [2, util.nearestLargerEven(tf.ENV.get('WEBGL_MAX_TEXTURE_SIZE') + 1)];
+        var texShape = webgl_util.getTextureShapeFromLogicalShape(logicalShape, isPacked);
+        expect(texShape).toEqual(logicalShape);
+    });
+    it('rows/columns do not get squeezed', function () {
+        var isPacked = true;
+        var logicalShape = [1, 1, 1];
+        var texShape = webgl_util.getTextureShapeFromLogicalShape(logicalShape, isPacked);
+        expect(texShape).toEqual([2, 2]);
+    });
+    it('squarified texture shapes account for packing constraints', function () {
+        var isPacked = true;
+        var max = tf.ENV.get('WEBGL_MAX_TEXTURE_SIZE');
+        tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', 5);
+        var logicalShape = [1, 12];
+        var texShape = webgl_util.getTextureShapeFromLogicalShape(logicalShape, isPacked);
+        tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', max);
+        expect(texShape).toEqual([4, 6]);
+    });
+});
+jasmine_util_1.describeWithFlags('isReshapeFree', test_util_1.WEBGL_ENVS, function () {
+    it('is free when shapes have the same inner dimensions', function () {
+        var before = [1, 2, 3];
+        var after = [5, 2, 3];
+        expect(webgl_util.isReshapeFree(before, after)).toBe(true);
+    });
+    it('is free when one of the shapes is a scalar', function () {
+        var before = [];
+        var after = [1, 2, 3];
+        expect(webgl_util.isReshapeFree(before, after)).toBe(true);
+    });
+    it('is free when one of the dimensions equals 0', function () {
+        var before = [1, 0];
+        var after = [1, 2, 3];
+        expect(webgl_util.isReshapeFree(before, after)).toBe(true);
+    });
+    it('is free when one shape is a vector and the final dimensions match', function () {
+        var before = [9];
+        var after = [1, 1, 9];
+        expect(webgl_util.isReshapeFree(before, after)).toBe(true);
+    });
+    it('is free when one shape is a vector and the other has 1 row' +
+        'in every batch and the final dimensions are even', function () {
+        var before = [10];
+        var after = [5, 1, 2];
+        expect(webgl_util.isReshapeFree(before, after)).toBe(true);
+    });
+    it('is not free when one shape is a vector and the final dimensions' +
+        'do not match and are not even', function () {
+        var before = [18];
+        var after = [2, 1, 9];
+        expect(webgl_util.isReshapeFree(before, after)).toBe(false);
+    });
+    it('is free when the inner dimensions are divisible by 2', function () {
+        var before = [1, 2, 4];
+        var after = [1, 8, 10];
+        expect(webgl_util.isReshapeFree(before, after)).toBe(true);
+    });
+    it('is free if the rows are divisible by two and the columns are the same', function () {
+        var before = [1, 2, 3];
+        var after = [1, 4, 3];
+        expect(webgl_util.isReshapeFree(before, after)).toBe(true);
+    });
+    it('is not free when the inner dimensions are different and not all even', function () {
+        var before = [1, 2, 3];
+        var after = [1, 3, 2];
+        expect(webgl_util.isReshapeFree(before, after)).toBe(false);
+    });
+});
+//# sourceMappingURL=webgl_util_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_util_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_util_test.js.map
new file mode 100644
index 0000000..6a01289
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/webgl/webgl_util_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"webgl_util_test.js","sourceRoot":"","sources":["../../../src/kernels/webgl/webgl_util_test.ts"],"names":[],"mappings":";;AAiBA,gCAAkC;AAClC,mDAAqD;AACrD,6CAA2C;AAC3C,iCAAmC;AAEnC,yCAA2C;AAE3C,gCAAiB,CAAC,iCAAiC,EAAE,sBAAU,EAAE;IAC/D,EAAE,CAAC,QAAQ,EAAE;QACX,IAAM,QAAQ,GAAG,UAAU,CAAC,+BAA+B,CAAC,EAAE,CAAC,CAAC;QAChE,MAAM,CAAC,QAAQ,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IACnC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,IAAI,EAAE;QACP,IAAM,QAAQ,GAAG,UAAU,CAAC,+BAA+B,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QACjE,MAAM,CAAC,QAAQ,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IACnC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,eAAe,EAAE;QAClB,IAAI,QAAQ,GAAG,UAAU,CAAC,+BAA+B,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAClE,MAAM,CAAC,QAAQ,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAEjC,QAAQ,GAAG,UAAU,CAAC,+BAA+B,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC9D,MAAM,CAAC,QAAQ,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAEjC,QAAQ,GAAG,UAAU,CAAC,+BAA+B,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC9D,MAAM,CAAC,QAAQ,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IACnC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,UAAU,EAAE;QACb,IAAM,QAAQ,GAAG,UAAU,CAAC,+BAA+B,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACvE,MAAM,CAAC,QAAQ,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IACnC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,cAAc,EAAE;QACjB,IAAM,QAAQ,GAAG,UAAU,CAAC,+BAA+B,CAAC,CAAC,CAAC,EAAE,GAAG,EAAE,GAAG,CAAC,CAAC,CAAC;QAC3E,MAAM,CAAC,QAAQ,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,GAAG,GAAG,EAAE,GAAG,CAAC,CAAC,CAAC;IAC3C,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,uBAAuB,EAAE;QAC1B,IAAM,QAAQ,GAAG,UAAU,CAAC,+BAA+B,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACvE,MAAM,CAAC,QAAQ,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IACnC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,uBAAuB,EAAE;QAC1B,IAAM,QAAQ,GAAG,UAAU,CAAC,+BAA+B,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACvE,MAAM,CAAC,QAAQ,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IACnC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,6BAA6B,EAAE;QAChC,IAAM,QAAQ,GACV,UAAU,CAAC,+BAA+B,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,GAAG,EAAE,GAAG,CAAC,CAAC,CAAC;QACjE,MAAM,CAAC,QAAQ,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,GAAG,CAAC,GAAG,GAAG,EAAE,GAAG,CAAC,CAAC,CAAC;IAC/C,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,yBAAyB,EAAE;QAC5B,IAAM,QAAQ,GAAG,UAAU,CAAC,+BAA+B,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC1E,MAAM,CAAC,QAAQ,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IACnC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,yBAAyB,EAAE;QAC5B,IAAM,QAAQ,GAAG,UAAU,CAAC,+BAA+B,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC1E,MAAM,CAAC,QAAQ,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IACnC,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC;AAEH,gCAAiB,CAAC,wCAAwC,EAAE,sBAAU,EAAE;IACtE,EAAE,CAAC,kEAAkE,EAAE;QACrE,IAAM,QAAQ,GAAG,IAAI,CAAC;QACtB,IAAM,YAAY,GACd,CAAC,CAAC,EAAE,IAAI,CAAC,iBAAiB,CAAC,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,wBAAwB,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC;QAC1E,IAAM,QAAQ,GACV,UAAU,CAAC,+BAA+B,CAAC,YAAY,EAAE,QAAQ,CAAC,CAAC;QACvE,MAAM,CAAC,QAAQ,CAAC,CAAC,OAAO,CAAC,YAAY,CAAC,CAAC;IACzC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,kCAAkC,EAAE;QACrC,IAAM,QAAQ,GAAG,IAAI,CAAC;QACtB,IAAM,YAAY,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC;QAC/B,IAAM,QAAQ,GACV,UAAU,CAAC,+BAA+B,CAAC,YAAY,EAAE,QAAQ,CAAC,CAAC;QACvE,MAAM,CAAC,QAAQ,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IACnC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,2DAA2D,EAAE;QAC9D,IAAM,QAAQ,GAAG,IAAI,CAAC;QACtB,IAAM,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,wBAAwB,CAAC,CAAC;QAEjD,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,wBAAwB,EAAE,CAAC,CAAC,CAAC;QACxC,IAAM,YAAY,GAAG,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC;QAC7B,IAAM,QAAQ,GACV,UAAU,CAAC,+BAA+B,CAAC,YAAY,EAAE,QAAQ,CAAC,CAAC;QAEvE,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,wBAAwB,EAAE,GAAG,CAAC,CAAC;QAC1C,MAAM,CAAC,QAAQ,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IACnC,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC;AAEH,gCAAiB,CAAC,eAAe,EAAE,sBAAU,EAAE;IAC7C,EAAE,CAAC,oDAAoD,EAAE;QACvD,IAAM,MAAM,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC;QACzB,IAAM,KAAK,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC;QACxB,MAAM,CAAC,UAAU,CAAC,aAAa,CAAC,MAAM,EAAE,KAAK,CAAC,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;IAC7D,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,4CAA4C,EAAE;QAC/C,IAAM,MAAM,GAAa,EAAE,CAAC;QAC5B,IAAM,KAAK,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC;QACxB,MAAM,CAAC,UAAU,CAAC,aAAa,CAAC,MAAM,EAAE,KAAK,CAAC,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;IAC7D,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,6CAA6C,EAAE;QAChD,IAAM,MAAM,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACtB,IAAM,KAAK,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC;QACxB,MAAM,CAAC,UAAU,CAAC,aAAa,CAAC,MAAM,EAAE,KAAK,CAAC,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;IAC7D,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,mEAAmE,EACnE;QACE,IAAM,MAAM,GAAG,CAAC,CAAC,CAAC,CAAC;QACnB,IAAM,KAAK,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC;QACxB,MAAM,CAAC,UAAU,CAAC,aAAa,CAAC,MAAM,EAAE,KAAK,CAAC,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;IAC7D,CAAC,CAAC,CAAC;IAEN,EAAE,CAAC,4DAA4D;QACxD,kDAAkD,EACtD;QACE,IAAM,MAAM,GAAG,CAAC,EAAE,CAAC,CAAC;QACpB,IAAM,KAAK,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC;QACxB,MAAM,CAAC,UAA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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/where_impl.d.ts b/node_modules/@tensorflow/tfjs-core/dist/kernels/where_impl.d.ts
new file mode 100644
index 0000000..bd3e418
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/where_impl.d.ts
@@ -0,0 +1,3 @@
+import { Tensor2D } from '../tensor';
+import { TypedArray } from '../types';
+export declare function whereImpl(condShape: number[], condVals: TypedArray): Tensor2D;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/where_impl.js b/node_modules/@tensorflow/tfjs-core/dist/kernels/where_impl.js
new file mode 100644
index 0000000..b2a12ca
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/where_impl.js
@@ -0,0 +1,21 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var array_ops_1 = require("../ops/array_ops");
+function whereImpl(condShape, condVals) {
+    var indices = [];
+    for (var i = 0; i < condVals.length; i++) {
+        if (condVals[i]) {
+            indices.push(i);
+        }
+    }
+    var inBuffer = array_ops_1.buffer(condShape, 'int32');
+    var out = array_ops_1.buffer([indices.length, condShape.length], 'int32');
+    for (var i = 0; i < indices.length; i++) {
+        var loc = inBuffer.indexToLoc(indices[i]);
+        var offset = i * condShape.length;
+        out.values.set(loc, offset);
+    }
+    return out.toTensor();
+}
+exports.whereImpl = whereImpl;
+//# sourceMappingURL=where_impl.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/kernels/where_impl.js.map b/node_modules/@tensorflow/tfjs-core/dist/kernels/where_impl.js.map
new file mode 100644
index 0000000..6e108fb
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/kernels/where_impl.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"where_impl.js","sourceRoot":"","sources":["../../src/kernels/where_impl.ts"],"names":[],"mappings":";;AAmBA,8CAAwC;AAIxC,mBAA0B,SAAmB,EAAE,QAAoB;IACjE,IAAM,OAAO,GAAG,EAAE,CAAC;IACnB,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,QAAQ,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;QACxC,IAAI,QAAQ,CAAC,CAAC,CAAC,EAAE;YACf,OAAO,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;SACjB;KACF;IAED,IAAM,QAAQ,GAAG,kBAAM,CAAC,SAAS,EAAE,OAAO,CAAC,CAAC;IAE5C,IAAM,GAAG,GAAG,kBAAM,CAAC,CAAC,OAAO,CAAC,MAAM,EAAE,SAAS,CAAC,MAAM,CAAC,EAAE,OAAO,CAAC,CAAC;IAChE,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,OAAO,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;QACvC,IAAM,GAAG,GAAG,QAAQ,CAAC,UAAU,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC;QAC5C,IAAM,MAAM,GAAG,CAAC,GAAG,SAAS,CAAC,MAAM,CAAC;QACpC,GAAG,CAAC,MAAM,CAAC,GAAG,CAAC,GAAG,EAAE,MAAM,CAAC,CAAC;KAC7B;IACD,OAAO,GAAG,CAAC,QAAQ,EAAc,CAAC;AACpC,CAAC;AAjBD,8BAiBC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/log.d.ts b/node_modules/@tensorflow/tfjs-core/dist/log.d.ts
new file mode 100644
index 0000000..453afcc
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/log.d.ts
@@ -0,0 +1,2 @@
+export declare function warn(...msg: Array<{}>): void;
+export declare function log(...msg: Array<{}>): void;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/log.js b/node_modules/@tensorflow/tfjs-core/dist/log.js
new file mode 100644
index 0000000..f85c06f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/log.js
@@ -0,0 +1,24 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("./environment");
+function warn() {
+    var msg = [];
+    for (var _i = 0; _i < arguments.length; _i++) {
+        msg[_i] = arguments[_i];
+    }
+    if (!environment_1.ENV.get('IS_TEST')) {
+        console.warn.apply(console, msg);
+    }
+}
+exports.warn = warn;
+function log() {
+    var msg = [];
+    for (var _i = 0; _i < arguments.length; _i++) {
+        msg[_i] = arguments[_i];
+    }
+    if (!environment_1.ENV.get('IS_TEST')) {
+        console.log.apply(console, msg);
+    }
+}
+exports.log = log;
+//# sourceMappingURL=log.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/log.js.map b/node_modules/@tensorflow/tfjs-core/dist/log.js.map
new file mode 100644
index 0000000..156cdc8
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/log.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"log.js","sourceRoot":"","sources":["../src/log.ts"],"names":[],"mappings":";;AAiBA,6CAAkC;AAElC;IAAqB,aAAiB;SAAjB,UAAiB,EAAjB,qBAAiB,EAAjB,IAAiB;QAAjB,wBAAiB;;IACpC,IAAI,CAAC,iBAAG,CAAC,GAAG,CAAC,SAAS,CAAC,EAAE;QACvB,OAAO,CAAC,IAAI,OAAZ,OAAO,EAAS,GAAG,EAAE;KACtB;AACH,CAAC;AAJD,oBAIC;AAED;IAAoB,aAAiB;SAAjB,UAAiB,EAAjB,qBAAiB,EAAjB,IAAiB;QAAjB,wBAAiB;;IACnC,IAAI,CAAC,iBAAG,CAAC,GAAG,CAAC,SAAS,CAAC,EAAE;QACvB,OAAO,CAAC,GAAG,OAAX,OAAO,EAAQ,GAAG,EAAE;KACrB;AACH,CAAC;AAJD,kBAIC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/math.d.ts b/node_modules/@tensorflow/tfjs-core/dist/math.d.ts
new file mode 100644
index 0000000..a72e79c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/math.d.ts
@@ -0,0 +1,2 @@
+import { confusionMatrix } from './ops/confusion_matrix';
+export { confusionMatrix };
diff --git a/node_modules/@tensorflow/tfjs-core/dist/math.js b/node_modules/@tensorflow/tfjs-core/dist/math.js
new file mode 100644
index 0000000..46f443f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/math.js
@@ -0,0 +1,5 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var confusion_matrix_1 = require("./ops/confusion_matrix");
+exports.confusionMatrix = confusion_matrix_1.confusionMatrix;
+//# sourceMappingURL=math.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/math.js.map b/node_modules/@tensorflow/tfjs-core/dist/math.js.map
new file mode 100644
index 0000000..c559ab6
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/math.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"math.js","sourceRoot":"","sources":["../src/math.ts"],"names":[],"mappings":";;AAqBA,2DAAuD;AAE/C,0BAFA,kCAAe,CAEA"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/model_types.d.ts b/node_modules/@tensorflow/tfjs-core/dist/model_types.d.ts
new file mode 100644
index 0000000..a6b0792
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/model_types.d.ts
@@ -0,0 +1,12 @@
+import { Tensor } from './tensor';
+import { NamedTensorMap, TensorInfo } from './tensor_types';
+export interface ModelPredictConfig {
+    batchSize?: number;
+    verbose?: boolean;
+}
+export interface InferenceModel {
+    readonly inputs: TensorInfo[];
+    readonly outputs: TensorInfo[];
+    predict(inputs: Tensor | Tensor[] | NamedTensorMap, config: ModelPredictConfig): Tensor | Tensor[] | NamedTensorMap;
+    execute(inputs: Tensor | Tensor[] | NamedTensorMap, outputs: string | string[]): Tensor | Tensor[];
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/model_types.js b/node_modules/@tensorflow/tfjs-core/dist/model_types.js
new file mode 100644
index 0000000..aaad700
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/model_types.js
@@ -0,0 +1,3 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+//# sourceMappingURL=model_types.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/model_types.js.map b/node_modules/@tensorflow/tfjs-core/dist/model_types.js.map
new file mode 100644
index 0000000..e36142b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/model_types.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"model_types.js","sourceRoot":"","sources":["../src/model_types.ts"],"names":[],"mappings":""}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/arithmetic_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/arithmetic_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/arithmetic_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/arithmetic_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/arithmetic_test.js
new file mode 100644
index 0000000..f8c50e2
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/arithmetic_test.js
@@ -0,0 +1,1168 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('div', test_util_1.ALL_ENVS, function () {
+    it('same shape', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var c = tf.tensor2d([1, 2, 3, 4, 2, 5], [2, 3]);
+        var r = tf.div(a, c);
+        test_util_1.expectArraysClose(r, [1, 1, 1, 1, 2.5, 6 / 5]);
+    });
+    it('TensorLike', function () {
+        var a = [0, 1, -2, -4, 4, -4];
+        var b = [0.15, 0.2, 0.25, 0.5, 0.7, 1.2];
+        var result = tf.div(a, b);
+        expect(result.shape).toEqual([6]);
+        test_util_1.expectArraysClose(result, [0, 5.0, -8.0, -8.0, 5.714285850524902, -3.3333332538604736]);
+    });
+    it('TensorLike chained', function () {
+        var a = tf.tensor1d([0, 1, -2, -4, 4, -4]);
+        var b = [0.15, 0.2, 0.25, 0.5, 0.7, 1.2];
+        var result = a.div(b);
+        expect(result.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(result, [0, 5.0, -8.0, -8.0, 5.714285850524902, -3.3333332538604736]);
+    });
+    it('integer division implements floor divide', function () {
+        var a = tf.tensor1d([-6, -6, -5, -4, -3, -3, 3, 3, 2], 'int32');
+        var c = tf.tensor1d([-2, 2, 3, 2, -3, 3, 2, 3, 2], 'int32');
+        var r = tf.div(a, c);
+        expect(r.dtype).toEqual('int32');
+        test_util_1.expectArraysClose(r, [3, -3, -2, -2, 1, -1, 1, 1, 1]);
+    });
+    it('integer division broadcasts', function () {
+        var a = tf.tensor1d([-5, -4, 3, 2], 'int32');
+        var c = tf.scalar(2, 'int32');
+        var r = tf.div(a, c);
+        expect(r.dtype).toEqual('int32');
+        test_util_1.expectArraysClose(r, [-3, -2, 1, 1]);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor2d([1, 2], [2, 1]);
+        var c = tf.tensor2d([3, NaN], [2, 1]);
+        var r = tf.div(a, c);
+        test_util_1.expectArraysClose(r, [1 / 3, NaN]);
+    });
+    it('broadcasting same rank Tensors different shape', function () {
+        var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
+        var b = tf.tensor2d([2, 3], [2, 1]);
+        var result = tf.div(a, b);
+        expect(result.shape).toEqual([2, 2]);
+        var expected = [1 / 2, 1, -1, -4 / 3];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('broadcast 2D + 1D', function () {
+        var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
+        var b = tf.tensor1d([1, 2]);
+        var result = tf.div(a, b);
+        expect(result.shape).toEqual([2, 2]);
+        var expected = [1, 1, -3, -2];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('upcasts when dtypes dont match', function () {
+        var res = tf.div(tf.scalar(6, 'int32'), tf.scalar(3, 'float32'));
+        expect(res.dtype).toBe('float32');
+        test_util_1.expectArraysClose(res, [2]);
+        res = tf.div(tf.scalar(6, 'int32'), tf.scalar(true, 'bool'));
+        expect(res.dtype).toBe('int32');
+        test_util_1.expectArraysClose(res, [6]);
+    });
+    it('throws when passed tensors of different shapes', function () {
+        var a = tf.tensor2d([1, 2, -3, -4, 5, 6], [2, 3]);
+        var b = tf.tensor2d([5, 3, 4, -7], [2, 2]);
+        expect(function () { return tf.div(a, b); }).toThrowError();
+        expect(function () { return tf.div(b, a); }).toThrowError();
+    });
+    it('scalar divided by array', function () {
+        var c = tf.scalar(2);
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var r = tf.div(c, a);
+        test_util_1.expectArraysClose(r, [2 / 1, 2 / 2, 2 / 3, 2 / 4, 2 / 5, 2 / 6]);
+    });
+    it('scalar divided by array propagates NaNs', function () {
+        var c = tf.scalar(NaN);
+        var a = tf.tensor2d([1, 2, 3], [1, 3]);
+        var r = tf.div(c, a);
+        test_util_1.expectArraysEqual(r, [NaN, NaN, NaN]);
+    });
+    it('array divided by scalar', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var c = tf.scalar(2);
+        var r = tf.div(a, c);
+        test_util_1.expectArraysClose(r, [1 / 2, 2 / 2, 3 / 2, 4 / 2, 5 / 2, 6 / 2]);
+    });
+    it('array divided by scalar propagates NaNs', function () {
+        var a = tf.tensor2d([1, 2, NaN], [1, 3]);
+        var c = tf.scalar(2);
+        var r = tf.div(a, c);
+        test_util_1.expectArraysClose(r, [1 / 2, 2 / 2, NaN]);
+    });
+    it('gradient: Scalar', function () {
+        var a = tf.scalar(5);
+        var b = tf.scalar(2);
+        var dy = tf.scalar(4);
+        var grads = tf.grads(function (a, b) { return tf.div(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [4 / 2]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [-4 * 5 / (2 * 2)]);
+    });
+    it('gradient: Tensor1D', function () {
+        var a = tf.tensor1d([1, 2, 3]);
+        var b = tf.tensor1d([3, 4, 5]);
+        var dy = tf.tensor1d([1, 10, 20]);
+        var grads = tf.grads(function (a, b) { return tf.div(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [1 / 3, 10 / 4, 20 / 5]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [-1 * 1 / 9, -10 * 2 / 16, -20 * 3 / 25]);
+    });
+    it('gradient: Tensor1D with int32', function () {
+        var a = tf.tensor1d([1, 2, 3], 'int32');
+        var b = tf.tensor1d([3, 4, 5], 'int32');
+        var dy = tf.tensor1d([1, 10, 20]);
+        var grads = tf.grads(function (a, b) { return tf.div(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [1 / 3, 10 / 4, 20 / 5]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [-1 * 1 / 9, -10 * 2 / 16, -20 * 3 / 25]);
+    });
+    it('gradient: 1d<int32> with 1d<bool> ', function () {
+        var a = tf.tensor1d([true, false, true], 'bool');
+        var b = tf.tensor1d([1, 2, 3], 'int32');
+        var dy = tf.tensor1d([1, 19, 20]);
+        var grads = tf.grads(function (a, b) { return tf.div(a.toInt(), b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [1, 19 / 2, 20 / 3]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [-1 / 1, 0, -20 / 9]);
+    });
+    it('gradient: Tensor2D', function () {
+        var a = tf.tensor2d([3, 1, 2, 3], [2, 2]);
+        var b = tf.tensor2d([1, 3, 4, 5], [2, 2]);
+        var dy = tf.tensor2d([1, 10, 15, 20], [2, 2]);
+        var grads = tf.grads(function (a, b) { return tf.div(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [1 / 1, 10 / 3, 15 / 4, 20 / 5]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [-1 * 3 / 1, -10 * 1 / 9, -15 * 2 / 16, -20 * 3 / 25]);
+    });
+    it('gradient: scalar / Tensor1D', function () {
+        var a = tf.scalar(2);
+        var b = tf.tensor1d([3, 4, 5]);
+        var dy = tf.tensor1d([6, 7, 8]);
+        var grads = tf.grads(function (a, b) { return tf.div(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [6 / 3 + 7 / 4 + 8 / 5]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [-6 * 2 / 9, -7 * 2 / 16, -8 * 2 / 25]);
+    });
+    it('gradient: Tensor2D / scalar', function () {
+        var a = tf.tensor2d([[2, 3], [4, 5]], [2, 2]);
+        var b = tf.scalar(2);
+        var dy = tf.tensor2d([[6, 7], [8, 9]], [2, 2]);
+        var grads = tf.grads(function (a, b) { return tf.div(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [6 / 2, 7 / 2, 8 / 2, 9 / 2]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [-6 * 2 / 4 + -7 * 3 / 4 + -8 * 4 / 4 + -9 * 5 / 4]);
+    });
+    it('gradient: Tensor2D / Tensor2D w/ broadcast', function () {
+        var a = tf.tensor2d([3, 4], [2, 1]);
+        var b = tf.tensor2d([[2, 3], [4, 5]], [2, 2]);
+        var dy = tf.tensor2d([[6, 7], [8, 9]], [2, 2]);
+        var grads = tf.grads(function (a, b) { return tf.div(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [6 / 2 + 7 / 3, 8 / 4 + 9 / 5]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [-6 * 3 / 4, -7 * 3 / 9, -8 * 4 / 16, -9 * 4 / 25]);
+    });
+    it('throws when passed a as a non-tensor', function () {
+        expect(function () { return tf.div({}, tf.scalar(1)); })
+            .toThrowError(/Argument 'a' passed to 'div' must be a Tensor/);
+    });
+    it('throws when passed b as a non-tensor', function () {
+        expect(function () { return tf.div(tf.scalar(1), {}); })
+            .toThrowError(/Argument 'b' passed to 'div' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var r = tf.div([[1, 2, 3], [4, 5, 6]], 2);
+        expect(r.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(r, [1 / 2, 2 / 2, 3 / 2, 4 / 2, 5 / 2, 6 / 2]);
+    });
+});
+jasmine_util_1.describeWithFlags('mul', test_util_1.ALL_ENVS, function () {
+    it('strict same-shaped tensors', function () {
+        var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
+        var b = tf.tensor2d([5, 3, 4, -7], [2, 2]);
+        var expected = [5, 6, -12, 28];
+        var result = tf.mulStrict(a, b);
+        expect(result.shape).toEqual([2, 2]);
+        expect(result.dtype).toBe('float32');
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('strict propagates NaNs', function () {
+        var a = tf.tensor2d([1, 3, 4, 0], [2, 2]);
+        var b = tf.tensor2d([NaN, 3, NaN, 3], [2, 2]);
+        var result = tf.mulStrict(a, b);
+        expect(result.dtype).toBe('float32');
+        test_util_1.expectArraysClose(result, [NaN, 9, NaN, 0]);
+    });
+    it('strict throws when passed tensors of different shapes', function () {
+        var a = tf.tensor2d([1, 2, -3, -4, 5, 6], [2, 3]);
+        var b = tf.tensor2d([5, 3, 4, -7], [2, 2]);
+        expect(function () { return tf.mulStrict(a, b); }).toThrowError();
+        expect(function () { return tf.mulStrict(b, a); }).toThrowError();
+    });
+    it('strict throws when dtypes do not match', function () {
+        var a = tf.tensor2d([1, 2, -3, -4, 5, 6], [2, 3], 'float32');
+        var b = tf.tensor2d([5, 3, 4, -7], [2, 2], 'int32');
+        expect(function () { return tf.mulStrict(a, b); }).toThrowError();
+        expect(function () { return tf.mulStrict(b, a); }).toThrowError();
+    });
+    it('strict int32 * int32', function () {
+        var a = tf.tensor2d([1, 2, -3, -4], [2, 2], 'int32');
+        var b = tf.tensor2d([2, 1, 3, -4], [2, 2], 'int32');
+        var res = tf.mulStrict(a, b);
+        expect(res.dtype).toBe('int32');
+        test_util_1.expectArraysClose(res, [2, 2, -9, 16]);
+    });
+    it('same-shaped tensors', function () {
+        var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
+        var b = tf.tensor2d([5, 3, 4, -7], [2, 2]);
+        var expected = [5, 6, -12, 28];
+        var result = tf.mul(a, b);
+        expect(result.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('TensorLike', function () {
+        var a = [[1, 2], [-3, -4]];
+        var b = [[5, 3], [4, -7]];
+        var expected = [5, 6, -12, 28];
+        var result = tf.mul(a, b);
+        expect(result.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('TensorLike chained', function () {
+        var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
+        var b = [[5, 3], [4, -7]];
+        var expected = [5, 6, -12, 28];
+        var result = a.mul(b);
+        expect(result.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('broadcasting tensors', function () {
+        var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
+        var b = tf.scalar(2);
+        var expected = [2, 4, -6, -8];
+        var result = tf.mul(a, b);
+        expect(result.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('broadcasting same rank Tensors different shape', function () {
+        var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
+        var b = tf.tensor2d([2, 3], [2, 1]);
+        var result = tf.mul(a, b);
+        expect(result.shape).toEqual([2, 2]);
+        var expected = [2, 4, -9, -12];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('broadcast 2D + 1D', function () {
+        var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
+        var b = tf.tensor1d([1, 2]);
+        var result = tf.mul(a, b);
+        expect(result.shape).toEqual([2, 2]);
+        var expected = [1, 4, -3, -8];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('gradient: Scalar', function () {
+        var a = tf.scalar(5);
+        var b = tf.scalar(2);
+        var dy = tf.scalar(4);
+        var grads = tf.grads(function (a, b) { return tf.mul(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [b.get() * dy.get()]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [a.get() * dy.get()]);
+    });
+    it('gradient: Tensor1D', function () {
+        var a = tf.tensor1d([1, 2, 3]);
+        var b = tf.tensor1d([3, 4, 5]);
+        var dy = tf.tensor1d([1, 10, 20]);
+        var grads = tf.grads(function (a, b) { return tf.mul(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [3 * 1, 4 * 10, 5 * 20]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [1 * 1, 2 * 10, 3 * 20]);
+    });
+    it('gradient: Tensor1D with dtype int32', function () {
+        var a = tf.tensor1d([1, 2, 3], 'int32');
+        var b = tf.tensor1d([3, 4, 5], 'int32');
+        var dy = tf.tensor1d([1, 10, 20]);
+        var grads = tf.grads(function (a, b) { return tf.mul(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [3 * 1, 4 * 10, 5 * 20]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [1 * 1, 2 * 10, 3 * 20]);
+    });
+    it('gradient: Tensor2D', function () {
+        var a = tf.tensor2d([3, 1, 2, 3], [2, 2]);
+        var b = tf.tensor2d([1, 3, 4, 5], [2, 2]);
+        var dy = tf.tensor2d([1, 10, 15, 20], [2, 2]);
+        var grads = tf.grads(function (a, b) { return tf.mul(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [1 * 1, 3 * 10, 4 * 15, 5 * 20]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [3 * 1, 1 * 10, 2 * 15, 3 * 20]);
+    });
+    it('gradient: scalar * Tensor1D', function () {
+        var a = tf.scalar(2);
+        var b = tf.tensor1d([3, 4, 5]);
+        var dy = tf.tensor1d([6, 7, 8]);
+        var grads = tf.grads(function (a, b) { return tf.mul(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [3 * 6 + 4 * 7 + 5 * 8]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [2 * 6, 2 * 7, 2 * 8]);
+    });
+    it('gradient: Tensor2D * scalar', function () {
+        var a = tf.tensor2d([[2, 3], [4, 5]], [2, 2]);
+        var b = tf.scalar(2);
+        var dy = tf.tensor2d([[6, 7], [8, 9]], [2, 2]);
+        var grads = tf.grads(function (a, b) { return tf.mul(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [2 * 6, 2 * 7, 2 * 8, 2 * 9]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [2 * 6 + 3 * 7 + 4 * 8 + 5 * 9]);
+    });
+    it('gradient: Tensor2D * Tensor2D w/ broadcast', function () {
+        var a = tf.tensor2d([3, 4], [2, 1]);
+        var b = tf.tensor2d([[2, 3], [4, 5]], [2, 2]);
+        var dy = tf.tensor2d([[6, 7], [8, 9]], [2, 2]);
+        var grads = tf.grads(function (a, b) { return tf.mul(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [2 * 6 + 3 * 7, 4 * 8 + 5 * 9]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [6 * 3, 7 * 3, 8 * 4, 9 * 4]);
+    });
+    it('complex number multiplication', function () {
+        var real1 = tf.tensor1d([2]);
+        var imag1 = tf.tensor1d([3]);
+        var complex1 = tf.complex(real1, imag1);
+        var real2 = tf.tensor1d([4]);
+        var imag2 = tf.tensor1d([5]);
+        var complex2 = tf.complex(real2, imag2);
+        var result = complex1.mul(complex2);
+        expect(result.dtype).toBe('complex64');
+        expect(result.shape).toEqual([1]);
+        test_util_1.expectArraysClose(result, [2 * 4 - 3 * 5, 2 * 5 + 3 * 4]);
+    });
+    it('complex number broadcasting multiplication', function () {
+        var real1 = tf.tensor2d([1, 2, -3, -4], [2, 2]);
+        var imag1 = tf.tensor2d([10, 20, -30, -40], [2, 2]);
+        var complex1 = tf.complex(real1, imag1);
+        var real2 = tf.tensor1d([4]);
+        var imag2 = tf.tensor1d([5]);
+        var complex2 = tf.complex(real2, imag2);
+        var result = tf.mul(complex1, complex2);
+        expect(result.dtype).toEqual('complex64');
+        expect(result.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(result, [
+            1 * 4 - 10 * 5, 1 * 5 + 10 * 4, 2 * 4 - 20 * 5, 2 * 5 + 20 * 4,
+            -3 * 4 + 30 * 5, -3 * 5 + -30 * 4, -4 * 4 + 40 * 5, -4 * 5 + -40 * 4
+        ]);
+    });
+    it('throws when passed a as a non-tensor', function () {
+        expect(function () { return tf.mul({}, tf.scalar(1)); })
+            .toThrowError(/Argument 'a' passed to 'mul' must be a Tensor/);
+    });
+    it('throws when passed b as a non-tensor', function () {
+        expect(function () { return tf.mul(tf.scalar(1), {}); })
+            .toThrowError(/Argument 'b' passed to 'mul' must be a Tensor/);
+    });
+    it('upcasts when dtypes dont match', function () {
+        var res = tf.mul(tf.scalar(2, 'int32'), tf.scalar(3, 'float32'));
+        expect(res.dtype).toBe('float32');
+        test_util_1.expectArraysClose(res, [6]);
+        res = tf.mul(tf.scalar(2, 'int32'), tf.scalar(true, 'bool'));
+        expect(res.dtype).toBe('int32');
+        test_util_1.expectArraysClose(res, [2]);
+        res = tf.mul(tf.scalar(2, 'int32'), tf.scalar(false, 'bool'));
+        expect(res.dtype).toBe('int32');
+        test_util_1.expectArraysClose(res, [0]);
+    });
+    it('accepts a tensor-like object', function () {
+        var result = tf.mul([[1, 2], [-3, -4]], 2);
+        expect(result.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(result, [2, 4, -6, -8]);
+    });
+});
+jasmine_util_1.describeWithFlags('pow', test_util_1.ALL_ENVS, function () {
+    it('same-shaped tensors', function () {
+        var a = tf.tensor2d([1, -2, -3, 0, 7, 1], [2, 3]);
+        var b = tf.tensor2d([5, 3, 4, 5, 2, -3], [2, 3], 'int32');
+        var expected = [1, -8, 81, 0, 49, 1];
+        var result = tf.pow(a, b);
+        expect(result.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(result, expected, 0.01);
+    });
+    it('TensorLike', function () {
+        var a = [1, 2, 3];
+        var exp = 2;
+        var result = tf.pow(a, exp);
+        expect(result.shape).toEqual([3]);
+        expect(result.dtype).toBe('float32');
+        test_util_1.expectArraysEqual(result, [1, 4, 9]);
+    });
+    it('TensorLike chained', function () {
+        var a = tf.tensor1d([1, 2, 3]);
+        var exp = 2;
+        var result = a.pow(exp);
+        expect(result.shape).toEqual([3]);
+        expect(result.dtype).toBe('float32');
+        test_util_1.expectArraysEqual(result, [1, 4, 9]);
+    });
+    it('int32^int32 returns int32', function () {
+        var a = tf.tensor1d([1, 2, 3], 'int32');
+        var exp = tf.scalar(2, 'int32');
+        var result = tf.pow(a, exp);
+        expect(result.shape).toEqual([3]);
+        expect(result.dtype).toBe('int32');
+        test_util_1.expectArraysEqual(result, [1, 4, 9]);
+    });
+    it('different-shaped tensors', function () {
+        var a = tf.tensor2d([1, -2, -3, 0, 7, 1], [2, 3]);
+        var b = tf.scalar(2, 'int32');
+        var expected = [1, 4, 9, 0, 49, 1];
+        var result = tf.pow(a, b);
+        expect(result.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(result, expected, 0.05);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor2d([NaN, 3, NaN, 0], [2, 2]);
+        var b = tf.tensor2d([1, 3, 2, 3], [2, 2], 'int32');
+        var result = tf.pow(a, b);
+        test_util_1.expectArraysClose(result, [NaN, 27, NaN, 0], 0.05);
+    });
+    it('handles non int32 exponent param', function () {
+        var a = tf.tensor1d([2, 4]);
+        var b = tf.tensor1d([.5, 1.2]);
+        var result = tf.pow(a, b);
+        var expected = [Math.pow(2, 0.5), Math.pow(4, 1.2)];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('broadcasting same rank Tensors different shape', function () {
+        var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
+        var b = tf.tensor2d([2, 1], [2, 1], 'int32');
+        var result = tf.pow(a, b);
+        expect(result.shape).toEqual([2, 2]);
+        var expected = [1, 4, -3, -4];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('broadcast 2D + 1D', function () {
+        var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
+        var b = tf.tensor1d([1, 2], 'int32');
+        var result = tf.pow(a, b);
+        expect(result.shape).toEqual([2, 2]);
+        var expected = [1, 4, -3, 16];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('powStrict same-shaped tensors', function () {
+        var a = tf.tensor2d([1, -2, -3, 0, 7, 1], [2, 3]);
+        var b = tf.tensor2d([5, 3, 4, 5, 2, -3], [2, 3], 'int32');
+        var expected = [1, -8, 81, 0, 49, 1];
+        var result = tf.powStrict(a, b);
+        expect(result.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(result, expected, 0.01);
+    });
+    it('powStrict throws when passed tensors of different shapes', function () {
+        var a = tf.tensor2d([1, 2, -3, -4, 5, 6], [2, 3]);
+        var b = tf.tensor2d([5, 3, 4, -7], [2, 2], 'int32');
+        expect(function () { return tf.powStrict(a, b); }).toThrowError();
+    });
+    it('powStrict handles non int32 exponent param', function () {
+        var a = tf.tensor1d([2, 4]);
+        var b = tf.tensor1d([.5, 1.2]);
+        var result = tf.powStrict(a, b);
+        var expected = [Math.pow(2, 0.5), Math.pow(4, 1.2)];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('gradients: Scalar ^ Scalar', function () {
+        var a = tf.scalar(5);
+        var b = tf.scalar(2, 'int32');
+        var dy = tf.scalar(3);
+        var grads = tf.grads(function (a, b) { return tf.pow(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [2 * 5 * 3]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [3 * Math.pow(5, 2) * Math.log(5)]);
+    });
+    it('gradients: x ^ 2 where x = 0', function () {
+        var f = function (x) { return x.pow(tf.scalar(2)).asScalar(); };
+        var g = tf.grad(f)(tf.scalar(0));
+        test_util_1.expectArraysClose(g, [0]);
+    });
+    it('gradients: Scalar ^ Scalar fractional exponent', function () {
+        var a = tf.scalar(4.0);
+        var b = tf.scalar(1.5);
+        var dy = tf.scalar(3.0);
+        var grads = tf.grads(function (a, b) { return tf.pow(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [1.5 * Math.pow(4, 0.5) * 3]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [3.0 * Math.pow(4, 1.5) * Math.log(4.0)]);
+    });
+    it('gradients: Tensor ^ Tensor', function () {
+        var a = tf.tensor1d([-1, .5, 2]);
+        var b = tf.tensor1d([3, 2, -1], 'int32');
+        var dy = tf.tensor1d([1, 5, 10]);
+        var grads = tf.grads(function (a, b) { return tf.pow(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [
+            3 * Math.pow(-1, 2) * 1, 2 * Math.pow(.5, 1) * 5,
+            -1 * Math.pow(2, -2) * 10
+        ], 1e-1);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [
+            NaN, 5 * Math.pow(.5, 2) * Math.log(.5),
+            10 * Math.pow(2, -1) * Math.log(2)
+        ]);
+    });
+    it('gradient: scalar / Tensor1D', function () {
+        var a = tf.scalar(2);
+        var b = tf.tensor1d([3, 4, 5]);
+        var dy = tf.tensor1d([6, 7, 8]);
+        var grads = tf.grads(function (a, b) { return tf.pow(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [
+            6 * 3 * Math.pow(2, 2) + 7 * 4 * Math.pow(2, 3) + 8 * 5 * Math.pow(2, 4)
+        ]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [
+            6 * Math.pow(2, 3) * Math.log(2), 7 * Math.pow(2, 4) * Math.log(2),
+            8 * Math.pow(2, 5) * Math.log(2)
+        ]);
+    });
+    it('gradient: Tensor2D / scalar', function () {
+        var a = tf.tensor2d([[2, 3], [4, 5]], [2, 2]);
+        var b = tf.scalar(2);
+        var dy = tf.tensor2d([[6, 7], [8, 9]], [2, 2]);
+        var grads = tf.grads(function (a, b) { return tf.pow(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [
+            6 * 2 * Math.pow(2, 1), 7 * 2 * Math.pow(3, 1), 8 * 2 * Math.pow(4, 1),
+            9 * 2 * Math.pow(5, 1)
+        ]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [6 * Math.pow(2, 2) * Math.log(2) + 7 * Math.pow(3, 2) * Math.log(3) +
+                8 * Math.pow(4, 2) * Math.log(4) + 9 * Math.pow(5, 2) * Math.log(5)]);
+    });
+    it('gradient: Tensor2D / Tensor2D w/ broadcast', function () {
+        var a = tf.tensor2d([3, 4], [2, 1]);
+        var b = tf.tensor2d([[2, 3], [.4, .5]], [2, 2]);
+        var dy = tf.tensor2d([[6, 7], [8, 9]], [2, 2]);
+        var grads = tf.grads(function (a, b) { return tf.pow(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [
+            6 * 2 * Math.pow(3, 1) + 7 * 3 * Math.pow(3, 2),
+            8 * .4 * Math.pow(4, .4 - 1) + 9 * .5 * Math.pow(4, .5 - 1)
+        ]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [
+            6 * Math.pow(3, 2) * Math.log(3), 7 * Math.pow(3, 3) * Math.log(3),
+            8 * Math.pow(4, .4) * Math.log(4), 9 * Math.pow(4, .5) * Math.log(4)
+        ]);
+    });
+    it('throws when passed base as a non-tensor', function () {
+        expect(function () { return tf.pow({}, tf.scalar(1)); })
+            .toThrowError(/Argument 'base' passed to 'pow' must be a Tensor/);
+    });
+    it('throws when passed exp as a non-tensor', function () {
+        expect(function () { return tf.pow(tf.scalar(1), {}); })
+            .toThrowError(/Argument 'exp' passed to 'pow' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var result = tf.pow([1, 2, 3], 2);
+        expect(result.shape).toEqual([3]);
+        expect(result.dtype).toBe('float32');
+        test_util_1.expectArraysEqual(result, [1, 4, 9]);
+    });
+    it('negative base and whole exponent not NaN', function () {
+        var a = tf.tensor1d([-2, -3, -4], 'float32');
+        var b = tf.tensor1d([2, -3, 4], 'float32');
+        var expected = [Math.pow(-2, 2), Math.pow(-3, -3), Math.pow(-4, 4)];
+        var result = tf.pow(a, b);
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('negative base and fract exponent NaN', function () {
+        var a = tf.tensor1d([-2, -3, -4], 'float32');
+        var b = tf.tensor1d([2.1, -3.01, 4.1], 'float32');
+        var expected = [NaN, NaN, NaN];
+        var result = tf.pow(a, b);
+        test_util_1.expectArraysClose(result, expected);
+    });
+});
+jasmine_util_1.describeWithFlags('add', test_util_1.ALL_ENVS, function () {
+    it('c + A', function () {
+        var c = tf.scalar(5);
+        var a = tf.tensor1d([1, 2, 3]);
+        var result = tf.add(c, a);
+        test_util_1.expectArraysClose(result, [6, 7, 8]);
+    });
+    it('c + A propagates NaNs', function () {
+        var c = tf.scalar(NaN);
+        var a = tf.tensor1d([1, 2, 3]);
+        var res = tf.add(c, a);
+        test_util_1.expectArraysEqual(res, [NaN, NaN, NaN]);
+    });
+    it('A + B broadcasting same rank Tensors different shape', function () {
+        var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
+        var b = tf.tensor2d([2, 3], [2, 1]);
+        var result = tf.add(a, b);
+        expect(result.shape).toEqual([2, 2]);
+        var expected = [3, 4, 0, -1];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('A + B broadcast 2D + 1D', function () {
+        var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
+        var b = tf.tensor1d([1, 2]);
+        var result = tf.add(a, b);
+        expect(result.shape).toEqual([2, 2]);
+        var expected = [2, 4, -2, -2];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('A + B', function () {
+        var a = tf.tensor1d([2, 5, 1]);
+        var b = tf.tensor1d([4, 2, -1]);
+        var result = tf.add(a, b);
+        var expected = [6, 7, 0];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('TensorLike', function () {
+        var a = [2, 5, 1];
+        var b = [4, 2, -1];
+        var result = tf.add(a, b);
+        var expected = [6, 7, 0];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('TensorLike chained', function () {
+        var a = tf.tensor1d([2, 5, 1]);
+        var b = [4, 2, -1];
+        var result = a.add(b);
+        var expected = [6, 7, 0];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('A + B propagates NaNs', function () {
+        var a = tf.tensor1d([2, 5, NaN]);
+        var b = tf.tensor1d([4, 2, -1]);
+        var res = tf.add(a, b);
+        test_util_1.expectArraysClose(res, [6, 7, NaN]);
+    });
+    it('A + B throws when passed tensors with different shape', function () {
+        var a = tf.tensor1d([2, 5, 1, 5]);
+        var b = tf.tensor1d([4, 2, -1]);
+        expect(function () { return tf.add(a, b); }).toThrowError();
+        expect(function () { return tf.add(b, a); }).toThrowError();
+    });
+    it('2D+scalar broadcast', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var b = tf.scalar(2);
+        var res = tf.add(a, b);
+        expect(res.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(res, [3, 4, 5, 6, 7, 8]);
+    });
+    it('scalar+1D broadcast', function () {
+        var a = tf.scalar(2);
+        var b = tf.tensor1d([1, 2, 3, 4, 5, 6]);
+        var res = tf.add(a, b);
+        expect(res.shape).toEqual([6]);
+        test_util_1.expectArraysClose(res, [3, 4, 5, 6, 7, 8]);
+    });
+    it('2D+2D broadcast each with 1 dim', function () {
+        var a = tf.tensor2d([1, 2, 5], [1, 3]);
+        var b = tf.tensor2d([7, 3], [2, 1]);
+        var res = tf.add(a, b);
+        expect(res.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(res, [8, 9, 12, 4, 5, 8]);
+    });
+    it('2D+2D broadcast inner dim of b', function () {
+        var a = tf.tensor2d([1, 2, 5, 4, 5, 6], [2, 3]);
+        var b = tf.tensor2d([7, 3], [2, 1]);
+        var res = tf.add(a, b);
+        expect(res.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(res, [8, 9, 12, 7, 8, 9]);
+    });
+    it('3D+scalar', function () {
+        var a = tf.tensor3d([1, 2, 3, 4, 5, 6], [2, 3, 1]);
+        var b = tf.scalar(-1);
+        var res = tf.add(a, b);
+        expect(res.shape).toEqual([2, 3, 1]);
+        test_util_1.expectArraysClose(res, [0, 1, 2, 3, 4, 5]);
+    });
+    it('6D+scalar', function () {
+        var a = tf.range(0, 64).reshape([2, 2, 2, 2, 2, 2]);
+        var b = tf.scalar(-1);
+        var res = tf.add(a, b);
+        expect(res.shape).toEqual([2, 2, 2, 2, 2, 2]);
+        var expectedResult = [
+            -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
+            15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
+            31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46,
+            47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62
+        ];
+        test_util_1.expectArraysClose(res, expectedResult);
+    });
+    it('6D+2D', function () {
+        var a = tf.range(0, 64).reshape([2, 2, 2, 2, 2, 2]);
+        var b = tf.tensor2d([11, 13, 17, 19], [2, 2]);
+        var res = tf.add(a, b);
+        expect(res.shape).toEqual([2, 2, 2, 2, 2, 2]);
+        var expectedResult = [
+            11, 14, 19, 22, 15, 18, 23, 26, 19, 22, 27, 30, 23, 26, 31, 34,
+            27, 30, 35, 38, 31, 34, 39, 42, 35, 38, 43, 46, 39, 42, 47, 50,
+            43, 46, 51, 54, 47, 50, 55, 58, 51, 54, 59, 62, 55, 58, 63, 66,
+            59, 62, 67, 70, 63, 66, 71, 74, 67, 70, 75, 78, 71, 74, 79, 82
+        ];
+        test_util_1.expectArraysClose(res, expectedResult);
+    });
+    it('add tensors with 0 in shape', function () {
+        var a = tf.tensor1d([1]);
+        var b = tf.tensor3d([], [0, 0, 5]);
+        var res = tf.add(a, b);
+        expect(res.shape).toEqual([0, 0, 5]);
+        test_util_1.expectArraysEqual(res, []);
+    });
+    it('gradient: scalar + 1D broadcast', function () {
+        var a = tf.scalar(2);
+        var b = tf.tensor1d([3, 4, 5]);
+        var dy = tf.tensor1d([7, 8, 9]);
+        var grads = tf.grads(function (a, b) { return tf.add(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [7 + 8 + 9]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [7, 8, 9]);
+    });
+    it('gradient: 2D + 2D broadcast', function () {
+        var a = tf.tensor2d([2, 3], [2, 1]);
+        var b = tf.tensor2d([4, 5, 6, 7], [2, 2]);
+        var dy = tf.tensor2d([5, 4, 3, 2], [2, 2]);
+        var grads = tf.grads(function (a, b) { return tf.add(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [5 + 4, 3 + 2]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [5, 4, 3, 2]);
+    });
+    it('complex number addition', function () {
+        var real1 = tf.tensor1d([1]);
+        var imag1 = tf.tensor1d([2]);
+        var complex1 = tf.complex(real1, imag1);
+        var real2 = tf.tensor1d([3]);
+        var imag2 = tf.tensor1d([4]);
+        var complex2 = tf.complex(real2, imag2);
+        var result = complex1.add(complex2);
+        expect(result.dtype).toBe('complex64');
+        expect(result.shape).toEqual([1]);
+        test_util_1.expectArraysClose(result, [4, 6]);
+    });
+    it('complex number reshape and then addition', function () {
+        var real1 = tf.tensor1d([1]);
+        var imag1 = tf.tensor1d([2]);
+        var complex1 = tf.complex(real1, imag1);
+        var real2 = tf.tensor1d([3]);
+        var imag2 = tf.tensor1d([4]);
+        var complex2 = tf.complex(real2, imag2);
+        var complex1Reshaped = complex1.reshape([1, 1, 1]);
+        var complex2Reshaped = complex2.reshape([1, 1, 1]);
+        var result = complex1Reshaped.add(complex2Reshaped);
+        expect(result.dtype).toBe('complex64');
+        expect(result.shape).toEqual([1, 1, 1]);
+        test_util_1.expectArraysClose(result, [4, 6]);
+    });
+    it('complex number broadcasting addition', function () {
+        var real1 = tf.tensor2d([1, 2, -3, -4], [2, 2]);
+        var imag1 = tf.tensor2d([10, 20, -30, -40], [2, 2]);
+        var complex1 = tf.complex(real1, imag1);
+        var real2 = tf.tensor1d([4]);
+        var imag2 = tf.tensor1d([5]);
+        var complex2 = tf.complex(real2, imag2);
+        var result = tf.add(complex1, complex2);
+        expect(result.dtype).toEqual('complex64');
+        expect(result.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(result, [1 + 4, 10 + 5, 2 + 4, 20 + 5, -3 + 4, -30 + 5, -4 + 4, -40 + 5]);
+    });
+    it('throws when passed a as a non-tensor', function () {
+        expect(function () { return tf.add({}, tf.scalar(1)); })
+            .toThrowError(/Argument 'a' passed to 'add' must be a Tensor/);
+    });
+    it('throws when passed b as a non-tensor', function () {
+        expect(function () { return tf.add(tf.scalar(1), {}); })
+            .toThrowError(/Argument 'b' passed to 'add' must be a Tensor/);
+    });
+    it('upcasts when dtypes dont match', function () {
+        var res = tf.add(tf.scalar(1, 'int32'), tf.scalar(1, 'float32'));
+        expect(res.dtype).toBe('float32');
+        test_util_1.expectArraysClose(res, [2]);
+        res = tf.add(tf.scalar(1, 'int32'), tf.scalar(true, 'bool'));
+        expect(res.dtype).toBe('int32');
+        test_util_1.expectArraysClose(res, [2]);
+        res = tf.add(tf.scalar(1, 'int32'), tf.scalar(false, 'bool'));
+        expect(res.dtype).toBe('int32');
+        test_util_1.expectArraysClose(res, [1]);
+        res = tf.add(tf.complex(4, 7), tf.scalar(1, 'float32'));
+        expect(res.dtype).toBe('complex64');
+        test_util_1.expectArraysClose(res, [5, 7]);
+        res = tf.add(tf.complex(4, 7), tf.scalar(1, 'int32'));
+        expect(res.dtype).toBe('complex64');
+        test_util_1.expectArraysClose(res, [5, 7]);
+    });
+    it('accepts a tensor-like object', function () {
+        var result = tf.add(5, [1, 2, 3]);
+        test_util_1.expectArraysClose(result, [6, 7, 8]);
+    });
+});
+jasmine_util_1.describeWithFlags('addN', test_util_1.ALL_ENVS, function () {
+    it('a single tensor', function () {
+        var res = tf.addN([tf.tensor1d([1, 2, 3])]);
+        test_util_1.expectArraysClose(res, [1, 2, 3]);
+    });
+    it('two tensors, int32', function () {
+        var res = tf.addN([
+            tf.tensor1d([1, 2, -1], 'int32'),
+            tf.tensor1d([5, 3, 2], 'int32'),
+        ]);
+        test_util_1.expectArraysClose(res, [6, 5, 1]);
+        expect(res.dtype).toBe('int32');
+        expect(res.shape).toEqual([3]);
+    });
+    it('three tensors', function () {
+        var res = tf.addN([
+            tf.tensor1d([1, 2]),
+            tf.tensor1d([5, 3]),
+            tf.tensor1d([-5, -2]),
+        ]);
+        test_util_1.expectArraysClose(res, [1, 3]);
+        expect(res.dtype).toBe('float32');
+        expect(res.shape).toEqual([2]);
+    });
+    it('accepts a tensor-like object', function () {
+        var res = tf.addN([[1, 2], [3, 4]]);
+        test_util_1.expectArraysClose(res, [4, 6]);
+        expect(res.dtype).toBe('float32');
+        expect(res.shape).toEqual([2]);
+    });
+    it('list of numbers gets treated as a list of scalars', function () {
+        var res = tf.addN([1, 2, 3, 4]);
+        test_util_1.expectArraysClose(res, [10]);
+        expect(res.dtype).toBe('float32');
+        expect(res.shape).toEqual([]);
+    });
+    it('errors if list is empty', function () {
+        expect(function () { return tf.addN([]); })
+            .toThrowError(/Must pass at least one tensor to tf.addN\(\), but got 0/);
+    });
+    it('errors if argument is not an array', function () {
+        expect(function () { return tf.addN(tf.scalar(3)); })
+            .toThrowError(/The argument passed to tf.addN\(\) must be a list of tensors/);
+    });
+    it('errors if arguments not of same dtype', function () {
+        expect(function () { return tf.addN([tf.scalar(1, 'int32'), tf.scalar(2, 'float32')]); })
+            .toThrowError(/All tensors passed to tf.addN\(\) must have the same dtype/);
+    });
+    it('errors if arguments not of same shape', function () {
+        expect(function () { return tf.addN([tf.scalar(1), tf.tensor1d([2])]); })
+            .toThrowError(/All tensors passed to tf.addN\(\) must have the same shape/);
+    });
+});
+jasmine_util_1.describeWithFlags('sub', test_util_1.ALL_ENVS, function () {
+    it('c - A', function () {
+        var c = tf.scalar(5);
+        var a = tf.tensor1d([7, 2, 3]);
+        var result = tf.sub(c, a);
+        test_util_1.expectArraysClose(result, [-2, 3, 2]);
+    });
+    it('A - c', function () {
+        var a = tf.tensor1d([1, 2, -3]);
+        var c = tf.scalar(5);
+        var result = tf.sub(a, c);
+        test_util_1.expectArraysClose(result, [-4, -3, -8]);
+    });
+    it('A - c propagates NaNs', function () {
+        var a = tf.tensor1d([1, NaN, 3]);
+        var c = tf.scalar(5);
+        var res = tf.sub(a, c);
+        test_util_1.expectArraysClose(res, [-4, NaN, -2]);
+    });
+    it('A - B', function () {
+        var a = tf.tensor1d([2, 5, 1]);
+        var b = tf.tensor1d([4, 2, -1]);
+        var result = tf.sub(a, b);
+        var expected = [-2, 3, 2];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('TensorLike', function () {
+        var a = [2, 5, 1];
+        var b = [4, 2, -1];
+        var result = tf.sub(a, b);
+        var expected = [-2, 3, 2];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('TensorLike chained', function () {
+        var a = tf.tensor1d([2, 5, 1]);
+        var b = [4, 2, -1];
+        var result = a.sub(b);
+        var expected = [-2, 3, 2];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('A - B propagates NaNs', function () {
+        var a = tf.tensor1d([2, 5, 1]);
+        var b = tf.tensor1d([4, NaN, -1]);
+        var res = tf.sub(a, b);
+        test_util_1.expectArraysClose(res, [-2, NaN, 2]);
+    });
+    it('A - B throws when passed tensors with different shape', function () {
+        var a = tf.tensor1d([2, 5, 1, 5]);
+        var b = tf.tensor1d([4, 2, -1]);
+        expect(function () { return tf.sub(a, b); }).toThrowError();
+        expect(function () { return tf.sub(b, a); }).toThrowError();
+    });
+    it('A - B broadcasting same rank Tensors different shape', function () {
+        var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
+        var b = tf.tensor2d([2, 3], [2, 1]);
+        var result = tf.sub(a, b);
+        expect(result.shape).toEqual([2, 2]);
+        var expected = [-1, 0, -6, -7];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('A - B broadcast 2D + 1D', function () {
+        var a = tf.tensor2d([1, 2, -3, -4], [2, 2]);
+        var b = tf.tensor1d([1, 2]);
+        var result = tf.sub(a, b);
+        expect(result.shape).toEqual([2, 2]);
+        var expected = [0, 0, -4, -6];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('2D-scalar broadcast', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var b = tf.scalar(2);
+        var res = tf.sub(a, b);
+        expect(res.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(res, [-1, 0, 1, 2, 3, 4]);
+    });
+    it('scalar-1D broadcast', function () {
+        var a = tf.scalar(2);
+        var b = tf.tensor1d([1, 2, 3, 4, 5, 6]);
+        var res = tf.sub(a, b);
+        expect(res.shape).toEqual([6]);
+        test_util_1.expectArraysClose(res, [1, 0, -1, -2, -3, -4]);
+    });
+    it('2D-2D broadcast each with 1 dim', function () {
+        var a = tf.tensor2d([1, 2, 5], [1, 3]);
+        var b = tf.tensor2d([7, 3], [2, 1]);
+        var res = tf.sub(a, b);
+        expect(res.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(res, [-6, -5, -2, -2, -1, 2]);
+    });
+    it('2D-2D broadcast inner dim of b', function () {
+        var a = tf.tensor2d([1, 2, 5, 4, 5, 6], [2, 3]);
+        var b = tf.tensor2d([7, 3], [2, 1]);
+        var res = tf.sub(a, b);
+        expect(res.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(res, [-6, -5, -2, 1, 2, 3]);
+    });
+    it('3D-scalar', function () {
+        var a = tf.tensor3d([1, 2, 3, 4, 5, 6], [2, 3, 1]);
+        var b = tf.scalar(-1);
+        var res = tf.sub(a, b);
+        expect(res.shape).toEqual([2, 3, 1]);
+        test_util_1.expectArraysClose(res, [2, 3, 4, 5, 6, 7]);
+    });
+    it('gradients: basic 1D arrays', function () {
+        var a = tf.tensor1d([1, 2, 3]);
+        var b = tf.tensor1d([3, 2, 1]);
+        var dy = tf.tensor1d([1, 10, 20]);
+        var grads = tf.grads(function (a, b) { return tf.sub(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [1, 10, 20]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [-1, -10, -20]);
+    });
+    it('gradients: basic 2D arrays', function () {
+        var a = tf.tensor2d([0, 1, 2, 3], [2, 2]);
+        var b = tf.tensor2d([3, 2, 1, 0], [2, 2]);
+        var dy = tf.tensor2d([1, 10, 15, 20], [2, 2]);
+        var grads = tf.grads(function (a, b) { return tf.sub(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [1, 10, 15, 20]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [-1, -10, -15, -20]);
+    });
+    it('gradient: 1D - scalar broadcast', function () {
+        var a = tf.tensor1d([3, 4, 5]);
+        var b = tf.scalar(2);
+        var dy = tf.tensor1d([7, 8, 9]);
+        var grads = tf.grads(function (a, b) { return tf.sub(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [7, 8, 9]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [-7 - 8 - 9]);
+    });
+    it('gradient: scalar - 1D broadcast', function () {
+        var a = tf.scalar(2);
+        var b = tf.tensor1d([3, 4, 5]);
+        var dy = tf.tensor1d([7, 8, 9]);
+        var grads = tf.grads(function (a, b) { return tf.sub(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [7 + 8 + 9]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [-7, -8, -9]);
+    });
+    it('gradient: 2D - 2D broadcast', function () {
+        var a = tf.tensor2d([4, 5, 6, 7], [2, 2]);
+        var b = tf.tensor2d([2, 3], [2, 1]);
+        var dy = tf.tensor2d([5, 4, 3, 2], [2, 2]);
+        var grads = tf.grads(function (a, b) { return tf.sub(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [5, 4, 3, 2]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [-5 - 4, -3 - 2]);
+    });
+    it('complex number subtraction', function () {
+        var real1 = tf.tensor1d([3]);
+        var imag1 = tf.tensor1d([5]);
+        var complex1 = tf.complex(real1, imag1);
+        var real2 = tf.tensor1d([1]);
+        var imag2 = tf.tensor1d([0]);
+        var complex2 = tf.complex(real2, imag2);
+        var result = complex1.sub(complex2);
+        expect(result.dtype).toBe('complex64');
+        expect(result.shape).toEqual([1]);
+        test_util_1.expectArraysClose(result, [2, 5]);
+    });
+    it('complex number broadcasting subtraction', function () {
+        var real1 = tf.tensor2d([1, 2, -3, -4], [2, 2]);
+        var imag1 = tf.tensor2d([10, 20, -30, -40], [2, 2]);
+        var complex1 = tf.complex(real1, imag1);
+        var real2 = tf.tensor1d([4]);
+        var imag2 = tf.tensor1d([5]);
+        var complex2 = tf.complex(real2, imag2);
+        var result = tf.sub(complex1, complex2);
+        expect(result.dtype).toEqual('complex64');
+        expect(result.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(result, [1 - 4, 10 - 5, 2 - 4, 20 - 5, -3 - 4, -30 - 5, -4 - 4, -40 - 5]);
+    });
+    it('throws when passed a as a non-tensor', function () {
+        expect(function () { return tf.sub({}, tf.scalar(1)); })
+            .toThrowError(/Argument 'a' passed to 'sub' must be a Tensor/);
+    });
+    it('throws when passed b as a non-tensor', function () {
+        expect(function () { return tf.sub(tf.scalar(1), {}); })
+            .toThrowError(/Argument 'b' passed to 'sub' must be a Tensor/);
+    });
+    it('upcasts when dtypes dont match', function () {
+        var res = tf.sub(tf.scalar(1, 'int32'), tf.scalar(1, 'float32'));
+        expect(res.dtype).toBe('float32');
+        test_util_1.expectArraysClose(res, [0]);
+        res = tf.sub(tf.scalar(1, 'int32'), tf.scalar(true, 'bool'));
+        expect(res.dtype).toBe('int32');
+        test_util_1.expectArraysClose(res, [0]);
+        res = tf.sub(tf.scalar(1, 'int32'), tf.scalar(false, 'bool'));
+        expect(res.dtype).toBe('int32');
+        test_util_1.expectArraysClose(res, [1]);
+        res = tf.sub(tf.complex(4, 7), tf.scalar(1, 'float32'));
+        expect(res.dtype).toBe('complex64');
+        test_util_1.expectArraysClose(res, [3, 7]);
+        res = tf.sub(tf.complex(4, 7), tf.scalar(1, 'int32'));
+        expect(res.dtype).toBe('complex64');
+        test_util_1.expectArraysClose(res, [3, 7]);
+    });
+    it('accepts a tensor-like object', function () {
+        var result = tf.sub(5, [7, 2, 3]);
+        test_util_1.expectArraysClose(result, [-2, 3, 2]);
+    });
+});
+//# sourceMappingURL=arithmetic_test.js.map
\ No newline at end of file
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+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/arithmetic_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops.d.ts
new file mode 100644
index 0000000..f5c2cfb
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops.d.ts
@@ -0,0 +1,58 @@
+import { Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, TensorBuffer } from '../tensor';
+import { DataType, DataTypeMap, Rank, ShapeMap, TensorLike, TensorLike1D, TensorLike4D } from '../types';
+declare function clone_<T extends Tensor>(x: T | TensorLike): T;
+declare function eye_(numRows: number, numColumns?: number, batchShape?: [number] | [number, number] | [number, number, number] | [number, number, number, number], dtype?: DataType): Tensor2D;
+declare function randomNormal_<R extends Rank>(shape: ShapeMap[R], mean?: number, stdDev?: number, dtype?: 'float32' | 'int32', seed?: number): Tensor<R>;
+declare function truncatedNormal_<R extends Rank>(shape: ShapeMap[R], mean?: number, stdDev?: number, dtype?: 'float32' | 'int32', seed?: number): Tensor<R>;
+declare function randomUniform_<R extends Rank>(shape: ShapeMap[R], minval?: number, maxval?: number, dtype?: DataType): Tensor<R>;
+declare function rand_<R extends Rank>(shape: ShapeMap[R], randFunction: () => number, dtype?: DataType): Tensor<R>;
+declare function multinomial_(logits: Tensor1D | Tensor2D | TensorLike, numSamples: number, seed?: number, normalized?: boolean): Tensor1D | Tensor2D;
+declare function oneHot_(indices: Tensor1D | TensorLike1D, depth: number, onValue?: number, offValue?: number): Tensor2D;
+declare function fromPixels_(pixels: ImageData | HTMLImageElement | HTMLCanvasElement | HTMLVideoElement, numChannels?: number): Tensor3D;
+declare function toPixels(img: Tensor2D | Tensor3D | TensorLike, canvas?: HTMLCanvasElement): Promise<Uint8ClampedArray>;
+declare function reshape_<R2 extends Rank>(x: Tensor | TensorLike, shape: ShapeMap[R2]): Tensor<R2>;
+declare function squeeze_<T extends Tensor>(x: Tensor | TensorLike, axis?: number[]): T;
+declare function cast_<T extends Tensor>(x: T | TensorLike, dtype: DataType): T;
+declare function tile_<T extends Tensor>(x: T | TensorLike, reps: number[]): T;
+declare function pad1d_(x: Tensor1D | TensorLike, paddings: [number, number], constantValue?: number): Tensor1D;
+declare function pad2d_(x: Tensor2D | TensorLike, paddings: [[number, number], [number, number]], constantValue?: number): Tensor2D;
+declare function pad3d_(x: Tensor3D | TensorLike, paddings: [[number, number], [number, number], [number, number]], constantValue?: number): Tensor3D;
+declare function pad4d_(x: Tensor4D | TensorLike, paddings: [[number, number], [number, number], [number, number], [number, number]], constantValue?: number): Tensor4D;
+declare function pad_<T extends Tensor>(x: T | TensorLike, paddings: Array<[number, number]>, constantValue?: number): T;
+declare function stack_<T extends Tensor>(tensors: Array<T | TensorLike>, axis?: number): Tensor;
+declare function batchToSpaceND_<T extends Tensor>(x: T | TensorLike, blockShape: number[], crops: number[][]): T;
+declare function spaceToBatchND_<T extends Tensor>(x: T | TensorLike, blockShape: number[], paddings: number[][]): T;
+declare function unstack_<T extends Tensor>(x: T | TensorLike, axis?: number): Tensor[];
+declare function cumsum_<T extends Tensor>(x: Tensor | TensorLike, axis?: number, exclusive?: boolean, reverse?: boolean): T;
+declare function expandDims_<R2 extends Rank>(x: Tensor | TensorLike, axis?: number): Tensor<R2>;
+declare function depthToSpace_(x: Tensor4D | TensorLike4D, blockSize: number, dataFormat?: 'NHWC' | 'NCHW'): Tensor4D;
+declare function setdiff1dAsync_(x: Tensor | TensorLike, y: Tensor | TensorLike): Promise<[Tensor, Tensor]>;
+declare function buffer<R extends Rank, D extends DataType = 'float32'>(shape: ShapeMap[R], dtype?: D, values?: DataTypeMap[D]): TensorBuffer<R, D>;
+declare function print<T extends Tensor>(x: T, verbose?: boolean): void;
+export { buffer, toPixels, print };
+export declare const batchToSpaceND: typeof batchToSpaceND_;
+export declare const cast: typeof cast_;
+export declare const clone: typeof clone_;
+export declare const cumsum: typeof cumsum_;
+export declare const depthToSpace: typeof depthToSpace_;
+export declare const expandDims: typeof expandDims_;
+export declare const eye: typeof eye_;
+export declare const fromPixels: typeof fromPixels_;
+export declare const multinomial: typeof multinomial_;
+export declare const oneHot: typeof oneHot_;
+export declare const pad: typeof pad_;
+export declare const pad1d: typeof pad1d_;
+export declare const pad2d: typeof pad2d_;
+export declare const pad3d: typeof pad3d_;
+export declare const pad4d: typeof pad4d_;
+export declare const rand: typeof rand_;
+export declare const randomNormal: typeof randomNormal_;
+export declare const randomUniform: typeof randomUniform_;
+export declare const reshape: typeof reshape_;
+export declare const spaceToBatchND: typeof spaceToBatchND_;
+export declare const squeeze: typeof squeeze_;
+export declare const stack: typeof stack_;
+export declare const tile: typeof tile_;
+export declare const truncatedNormal: typeof truncatedNormal_;
+export declare const unstack: typeof unstack_;
+export declare const setdiff1dAsync: typeof setdiff1dAsync_;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops.js b/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops.js
new file mode 100644
index 0000000..c6100de
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops.js
@@ -0,0 +1,570 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var tensor_1 = require("../tensor");
+var tensor_util_env_1 = require("../tensor_util_env");
+var util = require("../util");
+var axis_util_1 = require("./axis_util");
+var concat_split_1 = require("./concat_split");
+var operation_1 = require("./operation");
+var rand_1 = require("./rand");
+var tensor_ops_1 = require("./tensor_ops");
+function clone_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'clone', null);
+    var der = function (dy) {
+        return { $x: function () { return dy.toFloat(); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) {
+        return tensor_1.Tensor.make($x.shape, { dataId: $x.dataId }, $x.dtype);
+    }, { $x: $x }, der);
+}
+function eye_(numRows, numColumns, batchShape, dtype) {
+    if (dtype === void 0) { dtype = 'float32'; }
+    if (numColumns == null) {
+        numColumns = numRows;
+    }
+    var buff = buffer([numRows, numColumns], dtype);
+    var n = numRows <= numColumns ? numRows : numColumns;
+    for (var i = 0; i < n; ++i) {
+        buff.set(1, i, i);
+    }
+    var out = buff.toTensor().as2D(numRows, numColumns);
+    if (batchShape == null) {
+        return out;
+    }
+    else {
+        if (batchShape.length === 1) {
+            return exports.tile(exports.expandDims(out, 0), [batchShape[0], 1, 1]);
+        }
+        else if (batchShape.length === 2) {
+            return exports.tile(exports.expandDims(exports.expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);
+        }
+        else if (batchShape.length === 3) {
+            return exports.tile(exports.expandDims(exports.expandDims(exports.expandDims(out, 0), 0), 0), [batchShape[0], batchShape[1], batchShape[2], 1, 1]);
+        }
+        else {
+            throw new Error("eye() currently supports only 1D and 2D " +
+                ("batchShapes, but received " + batchShape.length + "D."));
+        }
+    }
+}
+function randomNormal_(shape, mean, stdDev, dtype, seed) {
+    if (mean === void 0) { mean = 0; }
+    if (stdDev === void 0) { stdDev = 1; }
+    if (dtype != null && dtype === 'bool') {
+        throw new Error("Unsupported data type " + dtype);
+    }
+    var randGauss = new rand_1.MPRandGauss(mean, stdDev, dtype, false, seed);
+    var res = buffer(shape, dtype);
+    for (var i = 0; i < res.values.length; i++) {
+        res.values[i] = randGauss.nextValue();
+    }
+    return res.toTensor();
+}
+function truncatedNormal_(shape, mean, stdDev, dtype, seed) {
+    if (mean === void 0) { mean = 0; }
+    if (stdDev === void 0) { stdDev = 1; }
+    if (dtype != null && dtype === 'bool') {
+        throw new Error("Unsupported data type " + dtype);
+    }
+    var randGauss = new rand_1.MPRandGauss(mean, stdDev, dtype, true, seed);
+    var res = buffer(shape, dtype);
+    for (var i = 0; i < res.values.length; i++) {
+        res.values[i] = randGauss.nextValue();
+    }
+    return res.toTensor();
+}
+function randomUniform_(shape, minval, maxval, dtype) {
+    if (minval === void 0) { minval = 0; }
+    if (maxval === void 0) { maxval = 1; }
+    if (dtype === void 0) { dtype = 'float32'; }
+    var res = buffer(shape, dtype);
+    for (var i = 0; i < res.values.length; i++) {
+        res.values[i] = util.randUniform(minval, maxval);
+    }
+    return res.toTensor();
+}
+function rand_(shape, randFunction, dtype) {
+    var size = util.sizeFromShape(shape);
+    var values = null;
+    if (dtype == null || dtype === 'float32') {
+        values = new Float32Array(size);
+    }
+    else if (dtype === 'int32') {
+        values = new Int32Array(size);
+    }
+    else if (dtype === 'bool') {
+        values = new Uint8Array(size);
+    }
+    else {
+        throw new Error("Unknown data type " + dtype);
+    }
+    for (var i = 0; i < size; i++) {
+        values[i] = randFunction();
+    }
+    return tensor_1.Tensor.make(shape, { values: values }, dtype);
+}
+function multinomial_(logits, numSamples, seed, normalized) {
+    if (normalized === void 0) { normalized = false; }
+    var $logits = tensor_util_env_1.convertToTensor(logits, 'logits', 'multinomial');
+    var numOutcomes = $logits.size;
+    var origRank = $logits.rank;
+    if (numOutcomes < 2) {
+        throw new Error("Error in multinomial: you need at least 2 outcomes, but got " +
+            (numOutcomes + "."));
+    }
+    if (origRank > 2) {
+        throw new Error("Rank of probabilities must be 1 or 2, but is " + origRank);
+    }
+    seed = seed || Math.random();
+    var logits2D = origRank === 1 ? $logits.as2D(1, -1) : $logits;
+    var res = environment_1.ENV.engine.runKernel(function (backend) { return backend.multinomial(logits2D, normalized, numSamples, seed); }, { logits2D: logits2D });
+    return origRank === 1 ? res.as1D() : res;
+}
+function oneHot_(indices, depth, onValue, offValue) {
+    if (onValue === void 0) { onValue = 1; }
+    if (offValue === void 0) { offValue = 0; }
+    var $indices = tensor_util_env_1.convertToTensor(indices, 'indices', 'oneHot', 'int32');
+    if (depth < 2) {
+        throw new Error("Error in oneHot: depth must be >=2, but it is " + depth);
+    }
+    var grad = function (dy) {
+        return { $indices: function () { return tensor_ops_1.zeros($indices.shape, 'float32'); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.oneHot($indices, depth, onValue, offValue); }, { $indices: $indices }, grad);
+}
+function fromPixels_(pixels, numChannels) {
+    if (numChannels === void 0) { numChannels = 3; }
+    if (numChannels > 4) {
+        throw new Error('Cannot construct Tensor with more than 4 channels from pixels.');
+    }
+    return environment_1.ENV.engine.fromPixels(pixels, numChannels);
+}
+function toPixels(img, canvas) {
+    return __awaiter(this, void 0, void 0, function () {
+        var $img, _a, height, width, depth, minTensor, maxTensor, min, max, data, multiplier, bytes, i, r, g, b, a, j, ctx, imageData;
+        return __generator(this, function (_b) {
+            switch (_b.label) {
+                case 0:
+                    $img = tensor_util_env_1.convertToTensor(img, 'img', 'toPixels');
+                    if (!(img instanceof tensor_1.Tensor)) {
+                        $img = $img.toInt();
+                    }
+                    if ($img.rank !== 2 && $img.rank !== 3) {
+                        throw new Error("toPixels only supports rank 2 or 3 tensors, got rank " + $img.rank + ".");
+                    }
+                    _a = $img.shape.slice(0, 2), height = _a[0], width = _a[1];
+                    depth = $img.rank === 2 ? 1 : $img.shape[2];
+                    if (depth > 4 || depth === 2) {
+                        throw new Error("toPixels only supports depth of size " +
+                            ("1, 3 or 4 but got " + depth));
+                    }
+                    minTensor = $img.min();
+                    maxTensor = $img.max();
+                    return [4, minTensor.data()];
+                case 1:
+                    min = (_b.sent())[0];
+                    return [4, maxTensor.data()];
+                case 2:
+                    max = (_b.sent())[0];
+                    minTensor.dispose();
+                    maxTensor.dispose();
+                    if ($img.dtype === 'float32') {
+                        if (min < 0 || max > 1) {
+                            throw new Error("Tensor values for a float32 Tensor must be in the " +
+                                ("range [0 - 1] but got range [" + min + " - " + max + "]."));
+                        }
+                    }
+                    else if ($img.dtype === 'int32') {
+                        if (min < 0 || max > 255) {
+                            throw new Error("Tensor values for a int32 Tensor must be in the " +
+                                ("range [0 - 255] but got range [" + min + " - " + max + "]."));
+                        }
+                    }
+                    else {
+                        throw new Error("Unsupported type for toPixels: " + $img.dtype + "." +
+                            " Please use float32 or int32 tensors.");
+                    }
+                    return [4, $img.data()];
+                case 3:
+                    data = _b.sent();
+                    multiplier = $img.dtype === 'float32' ? 255 : 1;
+                    bytes = new Uint8ClampedArray(width * height * 4);
+                    for (i = 0; i < height * width; ++i) {
+                        r = void 0, g = void 0, b = void 0, a = void 0;
+                        if (depth === 1) {
+                            r = data[i] * multiplier;
+                            g = data[i] * multiplier;
+                            b = data[i] * multiplier;
+                            a = 255;
+                        }
+                        else if (depth === 3) {
+                            r = data[i * 3] * multiplier;
+                            g = data[i * 3 + 1] * multiplier;
+                            b = data[i * 3 + 2] * multiplier;
+                            a = 255;
+                        }
+                        else if (depth === 4) {
+                            r = data[i * 4] * multiplier;
+                            g = data[i * 4 + 1] * multiplier;
+                            b = data[i * 4 + 2] * multiplier;
+                            a = data[i * 4 + 3] * multiplier;
+                        }
+                        j = i * 4;
+                        bytes[j + 0] = Math.round(r);
+                        bytes[j + 1] = Math.round(g);
+                        bytes[j + 2] = Math.round(b);
+                        bytes[j + 3] = Math.round(a);
+                    }
+                    if (canvas != null) {
+                        canvas.width = width;
+                        canvas.height = height;
+                        ctx = canvas.getContext('2d');
+                        imageData = new ImageData(bytes, width, height);
+                        ctx.putImageData(imageData, 0, 0);
+                    }
+                    if ($img !== img) {
+                        $img.dispose();
+                    }
+                    return [2, bytes];
+            }
+        });
+    });
+}
+exports.toPixels = toPixels;
+function reshape_(x, shape) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'reshape', null);
+    shape = util.inferFromImplicitShape(shape, $x.size);
+    util.assert($x.size === util.sizeFromShape(shape), 'new shape and old shape must have the same number of elements.');
+    var grad = function (dy) {
+        return { $x: function () { return dy.reshape($x.shape); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.reshape($x, shape); }, { $x: $x }, grad);
+}
+function squeeze_(x, axis) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'squeeze');
+    return exports.reshape($x, util.squeezeShape($x.shape, axis).newShape);
+}
+function cast_(x, dtype) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'cast');
+    var grad = function (dy) {
+        return { $x: function () { return dy.clone(); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.cast($x, dtype); }, { $x: $x }, grad);
+}
+function tile_(x, reps) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'tile');
+    util.assert($x.rank === reps.length, "Error in transpose: rank of input " + $x.rank + " " +
+        ("must match length of reps " + reps + "."));
+    var grad = function (dy) {
+        var derX = function () {
+            var xGrad = tensor_ops_1.zerosLike($x);
+            if ($x.rank === 1) {
+                for (var i = 0; i < reps[0]; ++i) {
+                    xGrad = xGrad.add(dy.slice([i * $x.shape[0]], [$x.shape[0]]));
+                }
+            }
+            else if ($x.rank === 2) {
+                for (var i = 0; i < reps[0]; ++i) {
+                    for (var j = 0; j < reps[1]; ++j) {
+                        xGrad = xGrad.add(dy.slice([i * $x.shape[0], j * $x.shape[1]], [$x.shape[0], $x.shape[1]]));
+                    }
+                }
+            }
+            else if ($x.rank === 3) {
+                for (var i = 0; i < reps[0]; ++i) {
+                    for (var j = 0; j < reps[1]; ++j) {
+                        for (var k = 0; k < reps[2]; ++k) {
+                            xGrad = xGrad.add(dy.slice([i * $x.shape[0], j * $x.shape[1], k * $x.shape[2]], [$x.shape[0], $x.shape[1], $x.shape[2]]));
+                        }
+                    }
+                }
+            }
+            else if ($x.rank === 4) {
+                for (var i = 0; i < reps[0]; ++i) {
+                    for (var j = 0; j < reps[1]; ++j) {
+                        for (var k = 0; k < reps[2]; ++k) {
+                            for (var l = 0; l < reps[3]; ++l) {
+                                xGrad = xGrad.add(dy.slice([
+                                    i * $x.shape[0], j * $x.shape[1], k * $x.shape[2],
+                                    l * $x.shape[3]
+                                ], [$x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]));
+                            }
+                        }
+                    }
+                }
+            }
+            else {
+                throw new Error("Gradient for tile operation is not implemented for rank-" +
+                    ($x.rank + " tensors yet."));
+            }
+            return xGrad;
+        };
+        return { $x: derX };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.tile($x, reps); }, { $x: $x }, grad);
+}
+function pad1d_(x, paddings, constantValue) {
+    if (constantValue === void 0) { constantValue = 0; }
+    util.assert(paddings.length === 2, 'Invalid number of paddings. Must be length of 2.');
+    return exports.pad(x, [paddings], constantValue);
+}
+function pad2d_(x, paddings, constantValue) {
+    if (constantValue === void 0) { constantValue = 0; }
+    util.assert(paddings.length === 2 && paddings[0].length === 2 &&
+        paddings[1].length === 2, 'Invalid number of paddings. Must be length of 2 each.');
+    return exports.pad(x, paddings, constantValue);
+}
+function pad3d_(x, paddings, constantValue) {
+    if (constantValue === void 0) { constantValue = 0; }
+    util.assert(paddings.length === 3 && paddings[0].length === 2 &&
+        paddings[1].length === 2 && paddings[2].length === 2, 'Invalid number of paddings. Must be length of 2 each.');
+    return exports.pad(x, paddings, constantValue);
+}
+function pad4d_(x, paddings, constantValue) {
+    if (constantValue === void 0) { constantValue = 0; }
+    util.assert(paddings.length === 4 && paddings[0].length === 2 &&
+        paddings[1].length === 2 && paddings[2].length === 2 &&
+        paddings[3].length === 2, 'Invalid number of paddings. Must be length of 2 each.');
+    return exports.pad(x, paddings, constantValue);
+}
+function pad_(x, paddings, constantValue) {
+    if (constantValue === void 0) { constantValue = 0; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'pad');
+    if ($x.rank === 0) {
+        throw new Error('pad(scalar) is not defined. Pass non-scalar to pad');
+    }
+    var begin = paddings.map(function (p) { return p[0]; });
+    var grad = function (dy) {
+        return { $x: function () { return dy.slice(begin, $x.shape); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.pad($x, paddings, constantValue); }, { $x: $x }, grad);
+}
+function stack_(tensors, axis) {
+    if (axis === void 0) { axis = 0; }
+    var $tensors = tensor_util_env_1.convertToTensorArray(tensors, 'tensors', 'stack');
+    util.assert($tensors.length >= 1, 'Pass at least one tensor to tf.stack');
+    if ($tensors.length === 1) {
+        return $tensors[0].expandDims(axis);
+    }
+    var rank = $tensors[0].rank;
+    var shape = $tensors[0].shape;
+    var dtype = $tensors[0].dtype;
+    util.assert(axis <= rank, 'Axis must be <= rank of the tensor');
+    $tensors.forEach(function (t) {
+        util.assertShapesMatch(shape, t.shape, 'All tensors passed to stack must have matching shapes');
+    });
+    $tensors.forEach(function (t) {
+        util.assert(dtype === t.dtype, 'All tensors passed to stack must have matching dtypes');
+    });
+    var expandedTensors = $tensors.map(function (t) { return t.expandDims(axis); });
+    return concat_split_1.concat(expandedTensors, axis);
+}
+function batchToSpaceND_(x, blockShape, crops) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'batchToSpaceND');
+    var prod = blockShape.reduce(function (a, b) { return a * b; });
+    util.assert($x.rank >= 1 + blockShape.length, "input rank is " + $x.rank + " but should be > than blockShape.length " + blockShape.length);
+    util.assert(crops.length === blockShape.length, "crops.length is " + crops.length + " but should be equal to blockShape.length  " + blockShape.length);
+    util.assert($x.shape[0] % prod === 0, "input tensor batch is " + $x.shape[0] + " but is not divisible by the product of " +
+        ("the elements of blockShape " + blockShape.join(' * ') + " === " + prod));
+    var grad = function (dy) {
+        return { $x: function () { return dy.spaceToBatchND(blockShape, crops); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.batchToSpaceND($x, blockShape, crops); }, { $x: $x }, grad);
+}
+function spaceToBatchND_(x, blockShape, paddings) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'spaceToBatchND');
+    util.assert($x.rank >= 1 + blockShape.length, "input rank " + $x.rank + " should be > than [blockShape] " + blockShape.length);
+    util.assert(paddings.length === blockShape.length, "paddings.shape[0] " + paddings.length + " must be equal to [blockShape] " + blockShape.length);
+    util.assert($x.shape.reduce(function (a, b, i) {
+        if (i > 0 && i <= blockShape.length) {
+            return a &&
+                ((b + paddings[i - 1][0] + paddings[i - 1][1]) %
+                    blockShape[i - 1] ===
+                    0);
+        }
+        return a;
+    }, true), "input spatial dimensions " + $x.shape.slice(1) + " with paddings " + paddings.toString() + " must be divisible by blockShapes " + blockShape.toString());
+    var grad = function (dy) {
+        return { $x: function () { return dy.batchToSpaceND(blockShape, paddings); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.spaceToBatchND($x, blockShape, paddings); }, { $x: $x }, grad);
+}
+function unstack_(x, axis) {
+    if (axis === void 0) { axis = 0; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'unstack');
+    var num = $x.shape[axis];
+    var outputShape = Array($x.rank - 1).fill(0);
+    var outIndex = 0;
+    for (var i = 0; i < $x.rank; i++) {
+        if (i !== axis) {
+            outputShape[outIndex] = $x.shape[i];
+            outIndex++;
+        }
+    }
+    var splitSizes;
+    splitSizes = Array(num).fill(1);
+    var begin = Array($x.rank).fill(0);
+    var size = $x.shape.slice();
+    return splitSizes.map(function (s) {
+        size[axis] = s;
+        var slice = $x.slice(begin, size);
+        begin[axis] += s;
+        return slice.reshape(outputShape);
+    });
+}
+function cumsum_(x, axis, exclusive, reverse) {
+    if (axis === void 0) { axis = 0; }
+    if (exclusive === void 0) { exclusive = false; }
+    if (reverse === void 0) { reverse = false; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'cumsum');
+    axis = axis | 0;
+    var permutation = axis_util_1.getAxesPermutation([axis], $x.rank);
+    var permutedX = $x;
+    if (permutation != null) {
+        permutedX = $x.transpose(permutation);
+    }
+    var permutedAxis = axis_util_1.getInnerMostAxes(1, $x.rank)[0];
+    var grad = function (dy) {
+        return { permutedX: function () { return dy.cumsum(axis, exclusive, !reverse); } };
+    };
+    var value = environment_1.ENV.engine.runKernel(function (backend) { return backend.cumsum(permutedX, permutedAxis, exclusive, reverse); }, { permutedX: permutedX }, grad);
+    if (permutation != null) {
+        value = value.transpose(permutation);
+    }
+    return value;
+}
+function expandDims_(x, axis) {
+    if (axis === void 0) { axis = 0; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'expandDims');
+    util.assert(axis <= $x.rank, 'Axis must be <= rank of the tensor');
+    var newShape = $x.shape.slice();
+    if (axis < 0) {
+        util.assert(-($x.rank + 1) <= axis, "Axis must be in the interval [" + -($x.rank + 1) + ", " + $x.rank + "]");
+        axis = $x.rank + axis + 1;
+    }
+    newShape.splice(axis, 0, 1);
+    return exports.reshape($x, newShape);
+}
+function depthToSpace_(x, blockSize, dataFormat) {
+    if (dataFormat === void 0) { dataFormat = 'NHWC'; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'depthToSpace');
+    var inputHeight = (dataFormat === 'NHWC') ? $x.shape[1] : $x.shape[2];
+    var inputWidth = (dataFormat === 'NHWC') ? $x.shape[2] : $x.shape[3];
+    var inputDepth = (dataFormat === 'NHWC') ? $x.shape[3] : $x.shape[1];
+    util.assert(inputHeight * blockSize >= 0, "Negative dimension size caused by overflow when multiplying\n      " + inputHeight + " and " + blockSize + "  for depthToSpace with input shape\n      " + $x.shape);
+    util.assert(inputWidth * blockSize >= 0, "Negative dimension size caused by overflow when multiplying\n      " + inputWidth + " and " + blockSize + " for depthToSpace with input shape\n          " + $x.shape);
+    util.assert((inputDepth % (blockSize * blockSize) === 0), "Dimension size must be evenly divisible by " + blockSize * blockSize + " but is " + inputDepth + " for depthToSpace with input shape " + $x.shape);
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.depthToSpace($x, blockSize, dataFormat); }, { $x: $x });
+}
+function setdiff1dAsync_(x, y) {
+    return __awaiter(this, void 0, void 0, function () {
+        var $x, $y, xVals, yVals, ySet, outputSize, i, buffer, indices, i, p;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    $x = tensor_util_env_1.convertToTensor(x, 'x', 'setdiff1d');
+                    $y = tensor_util_env_1.convertToTensor(y, 'y', 'setdiff1d');
+                    util.assert($x.dtype === $y.dtype, "x and y should have the same dtype, but got x (" + $x.dtype + ") and y (" + $y.dtype + ").");
+                    util.assert($x.rank === 1, "x should be 1D tensor, but got x (" + $x.shape + ").");
+                    util.assert($y.rank === 1, "y should be 1D tensor, but got y (" + $y.shape + ").");
+                    return [4, $x.data()];
+                case 1:
+                    xVals = _a.sent();
+                    return [4, $y.data()];
+                case 2:
+                    yVals = _a.sent();
+                    ySet = new Set(yVals);
+                    outputSize = 0;
+                    for (i = 0; i < xVals.length; i++) {
+                        if (!ySet.has(xVals[i])) {
+                            outputSize++;
+                        }
+                    }
+                    buffer = new tensor_1.TensorBuffer([outputSize], $x.dtype);
+                    indices = new tensor_1.TensorBuffer([outputSize], 'int32');
+                    for (i = 0, p = 0; i < xVals.length; i++) {
+                        if (!ySet.has(xVals[i])) {
+                            buffer.values[p] = xVals[i];
+                            indices.values[p] = i;
+                            p++;
+                        }
+                    }
+                    return [2, [buffer.toTensor(), indices.toTensor()]];
+            }
+        });
+    });
+}
+function buffer(shape, dtype, values) {
+    if (dtype === void 0) { dtype = 'float32'; }
+    dtype = dtype || 'float32';
+    return new tensor_1.TensorBuffer(shape, dtype, values);
+}
+exports.buffer = buffer;
+function print(x, verbose) {
+    if (verbose === void 0) { verbose = false; }
+    console.log(x.toString(verbose));
+}
+exports.print = print;
+exports.batchToSpaceND = operation_1.op({ batchToSpaceND_: batchToSpaceND_ });
+exports.cast = operation_1.op({ cast_: cast_ });
+exports.clone = operation_1.op({ clone_: clone_ });
+exports.cumsum = operation_1.op({ cumsum_: cumsum_ });
+exports.depthToSpace = operation_1.op({ depthToSpace_: depthToSpace_ });
+exports.expandDims = operation_1.op({ expandDims_: expandDims_ });
+exports.eye = operation_1.op({ eye_: eye_ });
+exports.fromPixels = operation_1.op({ fromPixels_: fromPixels_ });
+exports.multinomial = operation_1.op({ multinomial_: multinomial_ });
+exports.oneHot = operation_1.op({ oneHot_: oneHot_ });
+exports.pad = operation_1.op({ pad_: pad_ });
+exports.pad1d = operation_1.op({ pad1d_: pad1d_ });
+exports.pad2d = operation_1.op({ pad2d_: pad2d_ });
+exports.pad3d = operation_1.op({ pad3d_: pad3d_ });
+exports.pad4d = operation_1.op({ pad4d_: pad4d_ });
+exports.rand = operation_1.op({ rand_: rand_ });
+exports.randomNormal = operation_1.op({ randomNormal_: randomNormal_ });
+exports.randomUniform = operation_1.op({ randomUniform_: randomUniform_ });
+exports.reshape = operation_1.op({ reshape_: reshape_ });
+exports.spaceToBatchND = operation_1.op({ spaceToBatchND_: spaceToBatchND_ });
+exports.squeeze = operation_1.op({ squeeze_: squeeze_ });
+exports.stack = operation_1.op({ stack_: stack_ });
+exports.tile = operation_1.op({ tile_: tile_ });
+exports.truncatedNormal = operation_1.op({ truncatedNormal_: truncatedNormal_ });
+exports.unstack = operation_1.op({ unstack_: unstack_ });
+exports.setdiff1dAsync = setdiff1dAsync_;
+//# sourceMappingURL=array_ops.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops.js.map
new file mode 100644
index 0000000..10300ec
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_test.js
new file mode 100644
index 0000000..e4e8335
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_test.js
@@ -0,0 +1,3246 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+var _this = this;
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+var util = require("../util");
+var rand_util_1 = require("./rand_util");
+jasmine_util_1.describeWithFlags('zeros', test_util_1.ALL_ENVS, function () {
+    it('1D default dtype', function () {
+        var a = tf.zeros([3]);
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([3]);
+        test_util_1.expectArraysClose(a, [0, 0, 0]);
+    });
+    it('1D float32 dtype', function () {
+        var a = tf.zeros([3], 'float32');
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([3]);
+        test_util_1.expectArraysClose(a, [0, 0, 0]);
+    });
+    it('1D int32 dtype', function () {
+        var a = tf.zeros([3], 'int32');
+        expect(a.dtype).toBe('int32');
+        expect(a.shape).toEqual([3]);
+        test_util_1.expectArraysEqual(a, [0, 0, 0]);
+    });
+    it('1D bool dtype', function () {
+        var a = tf.zeros([3], 'bool');
+        expect(a.dtype).toBe('bool');
+        expect(a.shape).toEqual([3]);
+        test_util_1.expectArraysEqual(a, [0, 0, 0]);
+    });
+    it('2D default dtype', function () {
+        var a = tf.zeros([3, 2]);
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([3, 2]);
+        test_util_1.expectArraysClose(a, [0, 0, 0, 0, 0, 0]);
+    });
+    it('2D float32 dtype', function () {
+        var a = tf.zeros([3, 2], 'float32');
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([3, 2]);
+        test_util_1.expectArraysClose(a, [0, 0, 0, 0, 0, 0]);
+    });
+    it('2D int32 dtype', function () {
+        var a = tf.zeros([3, 2], 'int32');
+        expect(a.dtype).toBe('int32');
+        expect(a.shape).toEqual([3, 2]);
+        test_util_1.expectArraysEqual(a, [0, 0, 0, 0, 0, 0]);
+    });
+    it('2D bool dtype', function () {
+        var a = tf.zeros([3, 2], 'bool');
+        expect(a.dtype).toBe('bool');
+        expect(a.shape).toEqual([3, 2]);
+        test_util_1.expectArraysEqual(a, [0, 0, 0, 0, 0, 0]);
+    });
+    it('3D default dtype', function () {
+        var a = tf.zeros([2, 2, 2]);
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([2, 2, 2]);
+        test_util_1.expectArraysClose(a, [0, 0, 0, 0, 0, 0, 0, 0]);
+    });
+    it('3D float32 dtype', function () {
+        var a = tf.zeros([2, 2, 2], 'float32');
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([2, 2, 2]);
+        test_util_1.expectArraysClose(a, [0, 0, 0, 0, 0, 0, 0, 0]);
+    });
+    it('3D int32 dtype', function () {
+        var a = tf.zeros([2, 2, 2], 'int32');
+        expect(a.dtype).toBe('int32');
+        expect(a.shape).toEqual([2, 2, 2]);
+        test_util_1.expectArraysEqual(a, [0, 0, 0, 0, 0, 0, 0, 0]);
+    });
+    it('3D bool dtype', function () {
+        var a = tf.zeros([2, 2, 2], 'bool');
+        expect(a.dtype).toBe('bool');
+        expect(a.shape).toEqual([2, 2, 2]);
+        test_util_1.expectArraysEqual(a, [0, 0, 0, 0, 0, 0, 0, 0]);
+    });
+    it('4D default dtype', function () {
+        var a = tf.zeros([3, 2, 1, 1]);
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([3, 2, 1, 1]);
+        test_util_1.expectArraysClose(a, [0, 0, 0, 0, 0, 0]);
+    });
+    it('4D float32 dtype', function () {
+        var a = tf.zeros([3, 2, 1, 1], 'float32');
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([3, 2, 1, 1]);
+        test_util_1.expectArraysClose(a, [0, 0, 0, 0, 0, 0]);
+    });
+    it('4D int32 dtype', function () {
+        var a = tf.zeros([3, 2, 1, 1], 'int32');
+        expect(a.dtype).toBe('int32');
+        expect(a.shape).toEqual([3, 2, 1, 1]);
+        test_util_1.expectArraysEqual(a, [0, 0, 0, 0, 0, 0]);
+    });
+    it('4D bool dtype', function () {
+        var a = tf.zeros([3, 2, 1, 1], 'bool');
+        expect(a.dtype).toBe('bool');
+        expect(a.shape).toEqual([3, 2, 1, 1]);
+        test_util_1.expectArraysEqual(a, [0, 0, 0, 0, 0, 0]);
+    });
+});
+jasmine_util_1.describeWithFlags('ones', test_util_1.ALL_ENVS, function () {
+    it('1D default dtype', function () {
+        var a = tf.ones([3]);
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([3]);
+        test_util_1.expectArraysClose(a, [1, 1, 1]);
+    });
+    it('1D float32 dtype', function () {
+        var a = tf.ones([3], 'float32');
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([3]);
+        test_util_1.expectArraysClose(a, [1, 1, 1]);
+    });
+    it('1D int32 dtype', function () {
+        var a = tf.ones([3], 'int32');
+        expect(a.dtype).toBe('int32');
+        expect(a.shape).toEqual([3]);
+        test_util_1.expectArraysEqual(a, [1, 1, 1]);
+    });
+    it('1D bool dtype', function () {
+        var a = tf.ones([3], 'bool');
+        expect(a.dtype).toBe('bool');
+        expect(a.shape).toEqual([3]);
+        test_util_1.expectArraysEqual(a, [1, 1, 1]);
+    });
+    it('2D default dtype', function () {
+        var a = tf.ones([3, 2]);
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([3, 2]);
+        test_util_1.expectArraysClose(a, [1, 1, 1, 1, 1, 1]);
+    });
+    it('2D float32 dtype', function () {
+        var a = tf.ones([3, 2], 'float32');
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([3, 2]);
+        test_util_1.expectArraysClose(a, [1, 1, 1, 1, 1, 1]);
+    });
+    it('2D int32 dtype', function () {
+        var a = tf.ones([3, 2], 'int32');
+        expect(a.dtype).toBe('int32');
+        expect(a.shape).toEqual([3, 2]);
+        test_util_1.expectArraysEqual(a, [1, 1, 1, 1, 1, 1]);
+    });
+    it('2D bool dtype', function () {
+        var a = tf.ones([3, 2], 'bool');
+        expect(a.dtype).toBe('bool');
+        expect(a.shape).toEqual([3, 2]);
+        test_util_1.expectArraysEqual(a, [1, 1, 1, 1, 1, 1]);
+    });
+    it('3D default dtype', function () {
+        var a = tf.ones([2, 2, 2]);
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([2, 2, 2]);
+        test_util_1.expectArraysClose(a, [1, 1, 1, 1, 1, 1, 1, 1]);
+    });
+    it('3D float32 dtype', function () {
+        var a = tf.ones([2, 2, 2], 'float32');
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([2, 2, 2]);
+        test_util_1.expectArraysClose(a, [1, 1, 1, 1, 1, 1, 1, 1]);
+    });
+    it('3D int32 dtype', function () {
+        var a = tf.ones([2, 2, 2], 'int32');
+        expect(a.dtype).toBe('int32');
+        expect(a.shape).toEqual([2, 2, 2]);
+        test_util_1.expectArraysEqual(a, [1, 1, 1, 1, 1, 1, 1, 1]);
+    });
+    it('3D bool dtype', function () {
+        var a = tf.ones([2, 2, 2], 'bool');
+        expect(a.dtype).toBe('bool');
+        expect(a.shape).toEqual([2, 2, 2]);
+        test_util_1.expectArraysEqual(a, [1, 1, 1, 1, 1, 1, 1, 1]);
+    });
+    it('4D default dtype', function () {
+        var a = tf.ones([3, 2, 1, 1]);
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([3, 2, 1, 1]);
+        test_util_1.expectArraysClose(a, [1, 1, 1, 1, 1, 1]);
+    });
+    it('4D float32 dtype', function () {
+        var a = tf.ones([3, 2, 1, 1], 'float32');
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([3, 2, 1, 1]);
+        test_util_1.expectArraysClose(a, [1, 1, 1, 1, 1, 1]);
+    });
+    it('4D int32 dtype', function () {
+        var a = tf.ones([3, 2, 1, 1], 'int32');
+        expect(a.dtype).toBe('int32');
+        expect(a.shape).toEqual([3, 2, 1, 1]);
+        test_util_1.expectArraysEqual(a, [1, 1, 1, 1, 1, 1]);
+    });
+    it('4D bool dtype', function () {
+        var a = tf.ones([3, 2, 1, 1], 'bool');
+        expect(a.dtype).toBe('bool');
+        expect(a.shape).toEqual([3, 2, 1, 1]);
+        test_util_1.expectArraysEqual(a, [1, 1, 1, 1, 1, 1]);
+    });
+});
+jasmine_util_1.describeWithFlags('zerosLike', test_util_1.ALL_ENVS, function () {
+    it('1D default dtype', function () {
+        var a = tf.tensor1d([1, 2, 3]);
+        var b = tf.zerosLike(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([3]);
+        test_util_1.expectArraysClose(b, [0, 0, 0]);
+    });
+    it('chainable 1D default dtype', function () {
+        var a = tf.tensor1d([1, 2, 3]);
+        var b = a.zerosLike();
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([3]);
+        test_util_1.expectArraysClose(b, [0, 0, 0]);
+    });
+    it('1D float32 dtype', function () {
+        var a = tf.tensor1d([1, 2, 3], 'float32');
+        var b = tf.zerosLike(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([3]);
+        test_util_1.expectArraysClose(b, [0, 0, 0]);
+    });
+    it('1D int32 dtype', function () {
+        var a = tf.tensor1d([1, 2, 3], 'int32');
+        var b = tf.zerosLike(a);
+        expect(b.dtype).toBe('int32');
+        expect(b.shape).toEqual([3]);
+        test_util_1.expectArraysEqual(b, [0, 0, 0]);
+    });
+    it('1D bool dtype', function () {
+        var a = tf.tensor1d([1, 2, 3], 'bool');
+        var b = tf.zerosLike(a);
+        expect(b.dtype).toBe('bool');
+        expect(b.shape).toEqual([3]);
+        test_util_1.expectArraysEqual(b, [0, 0, 0]);
+    });
+    it('2D default dtype', function () {
+        var a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var b = tf.zerosLike(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(b, [0, 0, 0, 0]);
+    });
+    it('2D float32 dtype', function () {
+        var a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'float32');
+        var b = tf.zerosLike(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(b, [0, 0, 0, 0]);
+    });
+    it('2D int32 dtype', function () {
+        var a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'int32');
+        var b = tf.zerosLike(a);
+        expect(b.dtype).toBe('int32');
+        expect(b.shape).toEqual([2, 2]);
+        test_util_1.expectArraysEqual(b, [0, 0, 0, 0]);
+    });
+    it('2D bool dtype', function () {
+        var a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'bool');
+        var b = tf.zerosLike(a);
+        expect(b.dtype).toBe('bool');
+        expect(b.shape).toEqual([2, 2]);
+        test_util_1.expectArraysEqual(b, [0, 0, 0, 0]);
+    });
+    it('3D default dtype', function () {
+        var a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
+        var b = tf.zerosLike(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(b, [0, 0, 0, 0]);
+    });
+    it('3D float32 dtype', function () {
+        var a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1], 'float32');
+        var b = tf.zerosLike(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(b, [0, 0, 0, 0]);
+    });
+    it('3D int32 dtype', function () {
+        var a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1], 'int32');
+        var b = tf.zerosLike(a);
+        expect(b.dtype).toBe('int32');
+        expect(b.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysEqual(b, [0, 0, 0, 0]);
+    });
+    it('3D bool dtype', function () {
+        var a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1], 'bool');
+        var b = tf.zerosLike(a);
+        expect(b.dtype).toBe('bool');
+        expect(b.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysEqual(b, [0, 0, 0, 0]);
+    });
+    it('4D default dtype', function () {
+        var a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1]);
+        var b = tf.zerosLike(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([2, 2, 1, 1]);
+        test_util_1.expectArraysClose(b, [0, 0, 0, 0]);
+    });
+    it('4D float32 dtype', function () {
+        var a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1], 'float32');
+        var b = tf.zerosLike(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([2, 2, 1, 1]);
+        test_util_1.expectArraysClose(b, [0, 0, 0, 0]);
+    });
+    it('4D int32 dtype', function () {
+        var a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1], 'int32');
+        var b = tf.zerosLike(a);
+        expect(b.dtype).toBe('int32');
+        expect(b.shape).toEqual([2, 2, 1, 1]);
+        test_util_1.expectArraysEqual(b, [0, 0, 0, 0]);
+    });
+    it('4D bool dtype', function () {
+        var a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1], 'bool');
+        var b = tf.zerosLike(a);
+        expect(b.dtype).toBe('bool');
+        expect(b.shape).toEqual([2, 2, 1, 1]);
+        test_util_1.expectArraysEqual(b, [0, 0, 0, 0]);
+    });
+    it('4D default dtype', function () {
+        var a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1]);
+        var b = tf.zerosLike(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([2, 2, 1, 1]);
+        test_util_1.expectArraysClose(b, [0, 0, 0, 0]);
+    });
+    it('5D float32 dtype', function () {
+        var a = tf.tensor5d([1, 2, 3, 4], [1, 2, 2, 1, 1], 'float32');
+        var b = tf.zerosLike(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([1, 2, 2, 1, 1]);
+        test_util_1.expectArraysClose(b, [0, 0, 0, 0]);
+    });
+    it('5D int32 dtype', function () {
+        var a = tf.tensor5d([1, 2, 3, 4], [1, 2, 2, 1, 1], 'int32');
+        var b = tf.zerosLike(a);
+        expect(b.dtype).toBe('int32');
+        expect(b.shape).toEqual([1, 2, 2, 1, 1]);
+        test_util_1.expectArraysEqual(b, [0, 0, 0, 0]);
+    });
+    it('5D bool dtype', function () {
+        var a = tf.tensor5d([1, 2, 3, 4], [1, 2, 2, 1, 1], 'bool');
+        var b = tf.zerosLike(a);
+        expect(b.dtype).toBe('bool');
+        expect(b.shape).toEqual([1, 2, 2, 1, 1]);
+        test_util_1.expectArraysEqual(b, [0, 0, 0, 0]);
+    });
+    it('5D default dtype', function () {
+        var a = tf.tensor5d([1, 2, 3, 4], [1, 2, 2, 1, 1]);
+        var b = tf.zerosLike(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([1, 2, 2, 1, 1]);
+        test_util_1.expectArraysClose(b, [0, 0, 0, 0]);
+    });
+    it('6D float32 dtype', function () {
+        var a = tf.tensor6d([1, 2, 3, 4], [1, 2, 2, 1, 1, 1], 'float32');
+        var b = tf.zerosLike(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([1, 2, 2, 1, 1, 1]);
+        test_util_1.expectArraysClose(b, [0, 0, 0, 0]);
+    });
+    it('6D int32 dtype', function () {
+        var a = tf.tensor6d([1, 2, 3, 4], [1, 2, 2, 1, 1, 1], 'int32');
+        var b = tf.zerosLike(a);
+        expect(b.dtype).toBe('int32');
+        expect(b.shape).toEqual(a.shape);
+        test_util_1.expectArraysEqual(b, [0, 0, 0, 0]);
+    });
+    it('6D bool dtype', function () {
+        var a = tf.tensor6d([1, 2, 3, 4], [1, 2, 2, 1, 1, 1], 'bool');
+        var b = tf.zerosLike(a);
+        expect(b.dtype).toBe('bool');
+        expect(b.shape).toEqual(a.shape);
+        test_util_1.expectArraysEqual(b, [0, 0, 0, 0]);
+    });
+    it('6D default dtype', function () {
+        var a = tf.tensor6d([1, 2, 3, 4], [1, 2, 2, 1, 1, 1]);
+        var b = tf.zerosLike(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(b, [0, 0, 0, 0]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.zerosLike({}); })
+            .toThrowError(/Argument 'x' passed to 'zerosLike' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var res = tf.zerosLike([[1, 2], [3, 4]]);
+        expect(res.shape).toEqual([2, 2]);
+        test_util_1.expectArraysEqual(res, [0, 0, 0, 0]);
+    });
+});
+jasmine_util_1.describeWithFlags('onesLike', test_util_1.ALL_ENVS, function () {
+    it('1D default dtype', function () {
+        var a = tf.tensor1d([1, 2, 3]);
+        var b = tf.onesLike(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([3]);
+        test_util_1.expectArraysClose(b, [1, 1, 1]);
+    });
+    it('chainable 1D default dtype', function () {
+        var a = tf.tensor1d([1, 2, 3]);
+        var b = a.onesLike();
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([3]);
+        test_util_1.expectArraysClose(b, [1, 1, 1]);
+    });
+    it('1D float32 dtype', function () {
+        var a = tf.tensor1d([1, 2, 3], 'float32');
+        var b = tf.onesLike(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([3]);
+        test_util_1.expectArraysClose(b, [1, 1, 1]);
+    });
+    it('1D int32 dtype', function () {
+        var a = tf.tensor1d([1, 2, 3], 'int32');
+        var b = tf.onesLike(a);
+        expect(b.dtype).toBe('int32');
+        expect(b.shape).toEqual([3]);
+        test_util_1.expectArraysEqual(b, [1, 1, 1]);
+    });
+    it('1D bool dtype', function () {
+        var a = tf.tensor1d([1, 2, 3], 'bool');
+        var b = tf.onesLike(a);
+        expect(b.dtype).toBe('bool');
+        expect(b.shape).toEqual([3]);
+        test_util_1.expectArraysEqual(b, [1, 1, 1]);
+    });
+    it('2D default dtype', function () {
+        var a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var b = tf.onesLike(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(b, [1, 1, 1, 1]);
+    });
+    it('2D float32 dtype', function () {
+        var a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'float32');
+        var b = tf.onesLike(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(b, [1, 1, 1, 1]);
+    });
+    it('2D int32 dtype', function () {
+        var a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'int32');
+        var b = tf.onesLike(a);
+        expect(b.dtype).toBe('int32');
+        expect(b.shape).toEqual([2, 2]);
+        test_util_1.expectArraysEqual(b, [1, 1, 1, 1]);
+    });
+    it('2D bool dtype', function () {
+        var a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'bool');
+        var b = tf.onesLike(a);
+        expect(b.dtype).toBe('bool');
+        expect(b.shape).toEqual([2, 2]);
+        test_util_1.expectArraysEqual(b, [1, 1, 1, 1]);
+    });
+    it('3D default dtype', function () {
+        var a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
+        var b = tf.onesLike(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(b, [1, 1, 1, 1]);
+    });
+    it('3D float32 dtype', function () {
+        var a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1], 'float32');
+        var b = tf.onesLike(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(b, [1, 1, 1, 1]);
+    });
+    it('3D int32 dtype', function () {
+        var a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1], 'int32');
+        var b = tf.onesLike(a);
+        expect(b.dtype).toBe('int32');
+        expect(b.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysEqual(b, [1, 1, 1, 1]);
+    });
+    it('3D bool dtype', function () {
+        var a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1], 'bool');
+        var b = tf.onesLike(a);
+        expect(b.dtype).toBe('bool');
+        expect(b.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysEqual(b, [1, 1, 1, 1]);
+    });
+    it('4D default dtype', function () {
+        var a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1]);
+        var b = tf.onesLike(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([2, 2, 1, 1]);
+        test_util_1.expectArraysClose(b, [1, 1, 1, 1]);
+    });
+    it('4D float32 dtype', function () {
+        var a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1], 'float32');
+        var b = tf.onesLike(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([2, 2, 1, 1]);
+        test_util_1.expectArraysClose(b, [1, 1, 1, 1]);
+    });
+    it('4D int32 dtype', function () {
+        var a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1], 'int32');
+        var b = tf.onesLike(a);
+        expect(b.dtype).toBe('int32');
+        expect(b.shape).toEqual([2, 2, 1, 1]);
+        test_util_1.expectArraysEqual(b, [1, 1, 1, 1]);
+    });
+    it('4D bool dtype', function () {
+        var a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1], 'bool');
+        var b = tf.onesLike(a);
+        expect(b.dtype).toBe('bool');
+        expect(b.shape).toEqual([2, 2, 1, 1]);
+        test_util_1.expectArraysEqual(b, [1, 1, 1, 1]);
+    });
+    it('4D default dtype', function () {
+        var a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1]);
+        var b = tf.onesLike(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([2, 2, 1, 1]);
+        test_util_1.expectArraysClose(b, [1, 1, 1, 1]);
+    });
+    it('5D float32 dtype', function () {
+        var a = tf.tensor5d([1, 2, 3, 4], [1, 2, 2, 1, 1], 'float32');
+        var b = tf.onesLike(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([1, 2, 2, 1, 1]);
+        test_util_1.expectArraysClose(b, [1, 1, 1, 1]);
+    });
+    it('5D int32 dtype', function () {
+        var a = tf.tensor5d([1, 2, 3, 4], [1, 2, 2, 1, 1], 'int32');
+        var b = tf.onesLike(a);
+        expect(b.dtype).toBe('int32');
+        expect(b.shape).toEqual([1, 2, 2, 1, 1]);
+        test_util_1.expectArraysEqual(b, [1, 1, 1, 1]);
+    });
+    it('5D bool dtype', function () {
+        var a = tf.tensor5d([1, 2, 3, 4], [1, 2, 2, 1, 1], 'bool');
+        var b = tf.onesLike(a);
+        expect(b.dtype).toBe('bool');
+        expect(b.shape).toEqual([1, 2, 2, 1, 1]);
+        test_util_1.expectArraysEqual(b, [1, 1, 1, 1]);
+    });
+    it('5D default dtype', function () {
+        var a = tf.tensor5d([1, 2, 3, 4], [1, 2, 2, 1, 1]);
+        var b = tf.onesLike(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([1, 2, 2, 1, 1]);
+        test_util_1.expectArraysClose(b, [1, 1, 1, 1]);
+    });
+    it('6D int32 dtype', function () {
+        var a = tf.tensor6d([1, 2, 3, 4], [1, 2, 2, 1, 1, 1], 'int32');
+        var b = tf.onesLike(a);
+        expect(b.dtype).toBe('int32');
+        expect(b.shape).toEqual(a.shape);
+        test_util_1.expectArraysEqual(b, [1, 1, 1, 1]);
+    });
+    it('6D bool dtype', function () {
+        var a = tf.tensor6d([1, 2, 3, 4], [1, 2, 2, 1, 1, 1], 'bool');
+        var b = tf.onesLike(a);
+        expect(b.dtype).toBe('bool');
+        expect(b.shape).toEqual(a.shape);
+        test_util_1.expectArraysEqual(b, [1, 1, 1, 1]);
+    });
+    it('6D default dtype', function () {
+        var a = tf.tensor6d([1, 2, 3, 4], [1, 2, 2, 1, 1, 1]);
+        var b = tf.onesLike(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(b, [1, 1, 1, 1]);
+    });
+    it('6D float32 dtype', function () {
+        var a = tf.tensor6d([1, 2, 3, 4], [1, 2, 2, 1, 1, 1], 'float32');
+        var b = tf.onesLike(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(b, [1, 1, 1, 1]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.onesLike({}); })
+            .toThrowError(/Argument 'x' passed to 'onesLike' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var res = tf.onesLike([[1, 2], [3, 4]]);
+        expect(res.shape).toEqual([2, 2]);
+        test_util_1.expectArraysEqual(res, [1, 1, 1, 1]);
+    });
+});
+jasmine_util_1.describeWithFlags('rand', test_util_1.ALL_ENVS, function () {
+    it('should return a random 1D float32 array', function () {
+        var shape = [10];
+        var result = tf.rand(shape, function () { return util.randUniform(0, 2); });
+        expect(result.dtype).toBe('float32');
+        test_util_1.expectValuesInRange(result, 0, 2);
+        result = tf.rand(shape, function () { return util.randUniform(0, 1.5); });
+        expect(result.dtype).toBe('float32');
+        test_util_1.expectValuesInRange(result, 0, 1.5);
+    });
+    it('should return a random 1D int32 array', function () {
+        var shape = [10];
+        var result = tf.rand(shape, function () { return util.randUniform(0, 2); }, 'int32');
+        expect(result.dtype).toBe('int32');
+        test_util_1.expectValuesInRange(result, 0, 2);
+    });
+    it('should return a random 1D bool array', function () {
+        var shape = [10];
+        var result = tf.rand(shape, function () { return util.randUniform(0, 1); }, 'bool');
+        expect(result.dtype).toBe('bool');
+        test_util_1.expectValuesInRange(result, 0, 1);
+    });
+    it('should return a random 2D float32 array', function () {
+        var shape = [3, 4];
+        var result = tf.rand(shape, function () { return util.randUniform(0, 2.5); });
+        expect(result.dtype).toBe('float32');
+        test_util_1.expectValuesInRange(result, 0, 2.5);
+        result = tf.rand(shape, function () { return util.randUniform(0, 1.5); }, 'float32');
+        expect(result.dtype).toBe('float32');
+        test_util_1.expectValuesInRange(result, 0, 1.5);
+    });
+    it('should return a random 2D int32 array', function () {
+        var shape = [3, 4];
+        var result = tf.rand(shape, function () { return util.randUniform(0, 2); }, 'int32');
+        expect(result.dtype).toBe('int32');
+        test_util_1.expectValuesInRange(result, 0, 2);
+    });
+    it('should return a random 2D bool array', function () {
+        var shape = [3, 4];
+        var result = tf.rand(shape, function () { return util.randUniform(0, 1); }, 'bool');
+        expect(result.dtype).toBe('bool');
+        test_util_1.expectValuesInRange(result, 0, 1);
+    });
+    it('should return a random 3D float32 array', function () {
+        var shape = [3, 4, 5];
+        var result = tf.rand(shape, function () { return util.randUniform(0, 2.5); });
+        expect(result.dtype).toBe('float32');
+        test_util_1.expectValuesInRange(result, 0, 2.5);
+        result = tf.rand(shape, function () { return util.randUniform(0, 1.5); }, 'float32');
+        expect(result.dtype).toBe('float32');
+        test_util_1.expectValuesInRange(result, 0, 1.5);
+    });
+    it('should return a random 3D int32 array', function () {
+        var shape = [3, 4, 5];
+        var result = tf.rand(shape, function () { return util.randUniform(0, 2); }, 'int32');
+        expect(result.dtype).toBe('int32');
+        test_util_1.expectValuesInRange(result, 0, 2);
+    });
+    it('should return a random 3D bool array', function () {
+        var shape = [3, 4, 5];
+        var result = tf.rand(shape, function () { return util.randUniform(0, 1); }, 'bool');
+        expect(result.dtype).toBe('bool');
+        test_util_1.expectValuesInRange(result, 0, 1);
+    });
+    it('should return a random 4D float32 array', function () {
+        var shape = [3, 4, 5, 6];
+        var result = tf.rand(shape, function () { return util.randUniform(0, 2.5); });
+        expect(result.dtype).toBe('float32');
+        test_util_1.expectValuesInRange(result, 0, 2.5);
+        result = tf.rand(shape, function () { return util.randUniform(0, 1.5); });
+        expect(result.dtype).toBe('float32');
+        test_util_1.expectValuesInRange(result, 0, 1.5);
+    });
+    it('should return a random 4D int32 array', function () {
+        var shape = [3, 4, 5, 6];
+        var result = tf.rand(shape, function () { return util.randUniform(0, 2); }, 'int32');
+        expect(result.dtype).toBe('int32');
+        test_util_1.expectValuesInRange(result, 0, 2);
+    });
+    it('should return a random 4D bool array', function () {
+        var shape = [3, 4, 5, 6];
+        var result = tf.rand(shape, function () { return util.randUniform(0, 1); }, 'bool');
+        expect(result.dtype).toBe('bool');
+        test_util_1.expectValuesInRange(result, 0, 1);
+    });
+});
+jasmine_util_1.describeWithFlags('eye', test_util_1.ALL_ENVS, function () {
+    it('1x1', function () {
+        test_util_1.expectArraysClose(tf.eye(1), tf.tensor2d([[1]]));
+    });
+    it('2x2', function () {
+        test_util_1.expectArraysClose(tf.eye(2), tf.tensor2d([[1, 0], [0, 1]]));
+    });
+    it('3x3', function () {
+        test_util_1.expectArraysClose(tf.eye(3), tf.tensor2d([[1, 0, 0], [0, 1, 0], [0, 0, 1]]));
+    });
+    it('3x4', function () {
+        test_util_1.expectArraysClose(tf.eye(3, 4), tf.tensor2d([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0]]));
+    });
+    it('4x3', function () {
+        test_util_1.expectArraysClose(tf.eye(4, 3), tf.tensor2d([[1, 0, 0], [0, 1, 0], [0, 0, 1], [0, 0, 0]]));
+    });
+    it('with 1D batchShape', function () {
+        test_util_1.expectArraysClose(tf.eye(2, 2, [3]), tf.tensor3d([[[1, 0], [0, 1]], [[1, 0], [0, 1]], [[1, 0], [0, 1]]]));
+    });
+    it('with 2D batchShape', function () {
+        test_util_1.expectArraysClose(tf.eye(2, 2, [2, 3]), tf.tensor4d([
+            [[[1, 0], [0, 1]], [[1, 0], [0, 1]], [[1, 0], [0, 1]]],
+            [[[1, 0], [0, 1]], [[1, 0], [0, 1]], [[1, 0], [0, 1]]]
+        ]));
+    });
+    it('with 3D batchShape', function () {
+        test_util_1.expectArraysClose(tf.eye(2, 2, [2, 2, 3]), tf.tensor5d([
+            [
+                [[[1, 0], [0, 1]], [[1, 0], [0, 1]], [[1, 0], [0, 1]]],
+                [[[1, 0], [0, 1]], [[1, 0], [0, 1]], [[1, 0], [0, 1]]]
+            ],
+            [
+                [[[1, 0], [0, 1]], [[1, 0], [0, 1]], [[1, 0], [0, 1]]],
+                [[[1, 0], [0, 1]], [[1, 0], [0, 1]], [[1, 0], [0, 1]]]
+            ]
+        ]));
+    });
+    it('3x3, int32', function () {
+        test_util_1.expectArraysClose(tf.eye(3, 3, null, 'int32'), tf.tensor2d([[1, 0, 0], [0, 1, 0], [0, 0, 1]], [3, 3], 'int32'));
+    });
+    it('3x3, bool', function () {
+        test_util_1.expectArraysClose(tf.eye(3, 3, null, 'bool'), tf.tensor2d([[1, 0, 0], [0, 1, 0], [0, 0, 1]], [3, 3], 'bool'));
+    });
+});
+jasmine_util_1.describeWithFlags('randomNormal', test_util_1.ALL_ENVS, function () {
+    var SEED = 2002;
+    var EPSILON = 0.05;
+    it('should return a float32 1D of random normal values', function () {
+        var SAMPLES = 10000;
+        var result = tf.randomNormal([SAMPLES], 0, 0.5, null, SEED);
+        expect(result.dtype).toBe('float32');
+        expect(result.shape).toEqual([SAMPLES]);
+        rand_util_1.jarqueBeraNormalityTest(result);
+        rand_util_1.expectArrayInMeanStdRange(result, 0, 0.5, EPSILON);
+        result = tf.randomNormal([SAMPLES], 0, 1.5, 'float32', SEED);
+        expect(result.dtype).toBe('float32');
+        expect(result.shape).toEqual([SAMPLES]);
+        rand_util_1.jarqueBeraNormalityTest(result);
+        rand_util_1.expectArrayInMeanStdRange(result, 0, 1.5, EPSILON);
+    });
+    it('should return a int32 1D of random normal values', function () {
+        var SAMPLES = 10000;
+        var result = tf.randomNormal([SAMPLES], 0, 2, 'int32', SEED);
+        expect(result.dtype).toBe('int32');
+        expect(result.shape).toEqual([SAMPLES]);
+        rand_util_1.jarqueBeraNormalityTest(result);
+        rand_util_1.expectArrayInMeanStdRange(result, 0, 2, EPSILON);
+    });
+    it('should return a float32 2D of random normal values', function () {
+        var SAMPLES = 100;
+        var result = tf.randomNormal([SAMPLES, SAMPLES], 0, 2.5, null, SEED);
+        expect(result.dtype).toBe('float32');
+        expect(result.shape).toEqual([SAMPLES, SAMPLES]);
+        rand_util_1.jarqueBeraNormalityTest(result);
+        rand_util_1.expectArrayInMeanStdRange(result, 0, 2.5, EPSILON);
+        result = tf.randomNormal([SAMPLES, SAMPLES], 0, 3.5, 'float32', SEED);
+        expect(result.dtype).toBe('float32');
+        expect(result.shape).toEqual([SAMPLES, SAMPLES]);
+        rand_util_1.jarqueBeraNormalityTest(result);
+        rand_util_1.expectArrayInMeanStdRange(result, 0, 3.5, EPSILON);
+    });
+    it('should return a int32 2D of random normal values', function () {
+        var SAMPLES = 100;
+        var result = tf.randomNormal([SAMPLES, SAMPLES], 0, 2, 'int32', SEED);
+        expect(result.dtype).toBe('int32');
+        expect(result.shape).toEqual([SAMPLES, SAMPLES]);
+        rand_util_1.jarqueBeraNormalityTest(result);
+        rand_util_1.expectArrayInMeanStdRange(result, 0, 2, EPSILON);
+    });
+    it('should return a float32 3D of random normal values', function () {
+        var SAMPLES_SHAPE = [20, 20, 20];
+        var result = tf.randomNormal(SAMPLES_SHAPE, 0, 0.5, null, SEED);
+        expect(result.dtype).toBe('float32');
+        expect(result.shape).toEqual(SAMPLES_SHAPE);
+        rand_util_1.jarqueBeraNormalityTest(result);
+        rand_util_1.expectArrayInMeanStdRange(result, 0, 0.5, EPSILON);
+        result = tf.randomNormal(SAMPLES_SHAPE, 0, 1.5, 'float32', SEED);
+        expect(result.dtype).toBe('float32');
+        expect(result.shape).toEqual(SAMPLES_SHAPE);
+        rand_util_1.jarqueBeraNormalityTest(result);
+        rand_util_1.expectArrayInMeanStdRange(result, 0, 1.5, EPSILON);
+    });
+    it('should return a int32 3D of random normal values', function () {
+        var SAMPLES_SHAPE = [20, 20, 20];
+        var result = tf.randomNormal(SAMPLES_SHAPE, 0, 2, 'int32', SEED);
+        expect(result.dtype).toBe('int32');
+        expect(result.shape).toEqual(SAMPLES_SHAPE);
+        rand_util_1.jarqueBeraNormalityTest(result);
+        rand_util_1.expectArrayInMeanStdRange(result, 0, 2, EPSILON);
+    });
+    it('should return a float32 4D of random normal values', function () {
+        var SAMPLES_SHAPE = [10, 10, 10, 10];
+        var result = tf.randomNormal(SAMPLES_SHAPE, 0, 0.5, null, SEED);
+        expect(result.dtype).toBe('float32');
+        expect(result.shape).toEqual(SAMPLES_SHAPE);
+        rand_util_1.jarqueBeraNormalityTest(result);
+        rand_util_1.expectArrayInMeanStdRange(result, 0, 0.5, EPSILON);
+        result = tf.randomNormal(SAMPLES_SHAPE, 0, 1.5, 'float32', SEED);
+        expect(result.dtype).toBe('float32');
+        expect(result.shape).toEqual(SAMPLES_SHAPE);
+        rand_util_1.jarqueBeraNormalityTest(result);
+        rand_util_1.expectArrayInMeanStdRange(result, 0, 1.5, EPSILON);
+    });
+    it('should return a int32 4D of random normal values', function () {
+        var SAMPLES_SHAPE = [10, 10, 10, 10];
+        var result = tf.randomNormal(SAMPLES_SHAPE, 0, 2, 'int32', SEED);
+        expect(result.dtype).toBe('int32');
+        expect(result.shape).toEqual(SAMPLES_SHAPE);
+        rand_util_1.jarqueBeraNormalityTest(result);
+        rand_util_1.expectArrayInMeanStdRange(result, 0, 2, EPSILON);
+    });
+    it('should return a int32 5D of random normal values', function () {
+        var SAMPLES_SHAPE = [10, 10, 10, 10, 10];
+        var result = tf.randomNormal(SAMPLES_SHAPE, 0, 2, 'int32', SEED);
+        expect(result.dtype).toBe('int32');
+        expect(result.shape).toEqual(SAMPLES_SHAPE);
+        rand_util_1.jarqueBeraNormalityTest(result);
+        rand_util_1.expectArrayInMeanStdRange(result, 0, 2, EPSILON);
+    });
+});
+jasmine_util_1.describeWithFlags('truncatedNormal', test_util_1.ALL_ENVS, function () {
+    var EPSILON = 0.60;
+    var SEED = 2002;
+    function assertTruncatedValues(array, mean, stdv) {
+        var bounds = mean + stdv * 2;
+        var values = array.dataSync();
+        for (var i = 0; i < values.length; i++) {
+            expect(Math.abs(values[i])).toBeLessThanOrEqual(bounds);
+        }
+    }
+    it('should return a random 1D float32 array', function () {
+        var shape = [1000];
+        var result = tf.truncatedNormal(shape, 0, 3.5, null, SEED);
+        expect(result.dtype).toBe('float32');
+        assertTruncatedValues(result, 0, 3.5);
+        rand_util_1.expectArrayInMeanStdRange(result, 0, 3.5, EPSILON);
+        result = tf.truncatedNormal(shape, 0, 4.5, 'float32', SEED);
+        expect(result.dtype).toBe('float32');
+        assertTruncatedValues(result, 0, 4.5);
+        rand_util_1.expectArrayInMeanStdRange(result, 0, 4.5, EPSILON);
+    });
+    it('should return a randon 1D int32 array', function () {
+        var shape = [1000];
+        var result = tf.truncatedNormal(shape, 0, 5, 'int32', SEED);
+        expect(result.dtype).toBe('int32');
+        assertTruncatedValues(result, 0, 5);
+        rand_util_1.expectArrayInMeanStdRange(result, 0, 5, EPSILON);
+    });
+    it('should return a 2D float32 array', function () {
+        var shape = [50, 50];
+        var result = tf.truncatedNormal(shape, 0, 3.5, null, SEED);
+        expect(result.dtype).toBe('float32');
+        assertTruncatedValues(result, 0, 3.5);
+        rand_util_1.expectArrayInMeanStdRange(result, 0, 3.5, EPSILON);
+        result = tf.truncatedNormal(shape, 0, 4.5, 'float32', SEED);
+        expect(result.dtype).toBe('float32');
+        assertTruncatedValues(result, 0, 4.5);
+        rand_util_1.expectArrayInMeanStdRange(result, 0, 4.5, EPSILON);
+    });
+    it('should return a 2D int32 array', function () {
+        var shape = [50, 50];
+        var result = tf.truncatedNormal(shape, 0, 5, 'int32', SEED);
+        expect(result.dtype).toBe('int32');
+        assertTruncatedValues(result, 0, 5);
+        rand_util_1.expectArrayInMeanStdRange(result, 0, 5, EPSILON);
+    });
+    it('should return a 3D float32 array', function () {
+        var shape = [10, 10, 10];
+        var result = tf.truncatedNormal(shape, 0, 3.5, null, SEED);
+        expect(result.dtype).toBe('float32');
+        assertTruncatedValues(result, 0, 3.5);
+        rand_util_1.expectArrayInMeanStdRange(result, 0, 3.5, EPSILON);
+        result = tf.truncatedNormal(shape, 0, 4.5, 'float32', SEED);
+        expect(result.dtype).toBe('float32');
+        assertTruncatedValues(result, 0, 4.5);
+        rand_util_1.expectArrayInMeanStdRange(result, 0, 4.5, EPSILON);
+    });
+    it('should return a 3D int32 array', function () {
+        var shape = [10, 10, 10];
+        var result = tf.truncatedNormal(shape, 0, 5, 'int32', SEED);
+        expect(result.dtype).toBe('int32');
+        assertTruncatedValues(result, 0, 5);
+        rand_util_1.expectArrayInMeanStdRange(result, 0, 5, EPSILON);
+    });
+    it('should return a 4D float32 array', function () {
+        var shape = [5, 5, 5, 5];
+        var result = tf.truncatedNormal(shape, 0, 3.5, null, SEED);
+        expect(result.dtype).toBe('float32');
+        assertTruncatedValues(result, 0, 3.5);
+        rand_util_1.expectArrayInMeanStdRange(result, 0, 3.5, EPSILON);
+        result = tf.truncatedNormal(shape, 0, 4.5, 'float32', SEED);
+        expect(result.dtype).toBe('float32');
+        assertTruncatedValues(result, 0, 4.5);
+        rand_util_1.expectArrayInMeanStdRange(result, 0, 4.5, EPSILON);
+    });
+    it('should return a 4D int32 array', function () {
+        var shape = [5, 5, 5, 5];
+        var result = tf.truncatedNormal(shape, 0, 5, 'int32', SEED);
+        expect(result.dtype).toBe('int32');
+        assertTruncatedValues(result, 0, 5);
+        rand_util_1.expectArrayInMeanStdRange(result, 0, 5, EPSILON);
+    });
+});
+jasmine_util_1.describeWithFlags('randomUniform', test_util_1.ALL_ENVS, function () {
+    it('should return a random 1D float32 array', function () {
+        var shape = [10];
+        var result = tf.randomUniform(shape, 0, 2.5);
+        expect(result.dtype).toBe('float32');
+        test_util_1.expectValuesInRange(result, 0, 2.5);
+        result = tf.randomUniform(shape, 0, 1.5, 'float32');
+        expect(result.dtype).toBe('float32');
+        test_util_1.expectValuesInRange(result, 0, 1.5);
+    });
+    it('should return a random 1D int32 array', function () {
+        var shape = [10];
+        var result = tf.randomUniform(shape, 0, 2, 'int32');
+        expect(result.dtype).toBe('int32');
+        test_util_1.expectValuesInRange(result, 0, 2);
+    });
+    it('should return a random 1D bool array', function () {
+        var shape = [10];
+        var result = tf.randomUniform(shape, 0, 1, 'bool');
+        expect(result.dtype).toBe('bool');
+        test_util_1.expectValuesInRange(result, 0, 1);
+    });
+    it('should return a random 2D float32 array', function () {
+        var shape = [3, 4];
+        var result = tf.randomUniform(shape, 0, 2.5);
+        expect(result.dtype).toBe('float32');
+        test_util_1.expectValuesInRange(result, 0, 2.5);
+        result = tf.randomUniform(shape, 0, 1.5, 'float32');
+        expect(result.dtype).toBe('float32');
+        test_util_1.expectValuesInRange(result, 0, 1.5);
+    });
+    it('should return a random 2D int32 array', function () {
+        var shape = [3, 4];
+        var result = tf.randomUniform(shape, 0, 2, 'int32');
+        expect(result.dtype).toBe('int32');
+        test_util_1.expectValuesInRange(result, 0, 2);
+    });
+    it('should return a random 2D bool array', function () {
+        var shape = [3, 4];
+        var result = tf.randomUniform(shape, 0, 1, 'bool');
+        expect(result.dtype).toBe('bool');
+        test_util_1.expectValuesInRange(result, 0, 1);
+    });
+    it('should return a random 3D float32 array', function () {
+        var shape = [3, 4, 5];
+        var result = tf.randomUniform(shape, 0, 2.5);
+        expect(result.dtype).toBe('float32');
+        test_util_1.expectValuesInRange(result, 0, 2.5);
+        result = tf.randomUniform(shape, 0, 1.5, 'float32');
+        expect(result.dtype).toBe('float32');
+        test_util_1.expectValuesInRange(result, 0, 1.5);
+    });
+    it('should return a random 3D int32 array', function () {
+        var shape = [3, 4, 5];
+        var result = tf.randomUniform(shape, 0, 2, 'int32');
+        expect(result.dtype).toBe('int32');
+        test_util_1.expectValuesInRange(result, 0, 2);
+    });
+    it('should return a random 3D bool array', function () {
+        var shape = [3, 4, 5];
+        var result = tf.randomUniform(shape, 0, 1, 'bool');
+        expect(result.dtype).toBe('bool');
+        test_util_1.expectValuesInRange(result, 0, 1);
+    });
+    it('should return a random 4D float32 array', function () {
+        var shape = [3, 4, 5, 6];
+        var result = tf.randomUniform(shape, 0, 2.5);
+        expect(result.dtype).toBe('float32');
+        test_util_1.expectValuesInRange(result, 0, 2.5);
+        result = tf.randomUniform(shape, 0, 1.5, 'float32');
+        expect(result.dtype).toBe('float32');
+        test_util_1.expectValuesInRange(result, 0, 1.5);
+    });
+    it('should return a random 4D int32 array', function () {
+        var shape = [3, 4, 5, 6];
+        var result = tf.randomUniform(shape, 0, 2, 'int32');
+        expect(result.dtype).toBe('int32');
+        test_util_1.expectValuesInRange(result, 0, 2);
+    });
+    it('should return a random 4D bool array', function () {
+        var shape = [3, 4, 5, 6];
+        var result = tf.randomUniform(shape, 0, 1, 'bool');
+        expect(result.dtype).toBe('bool');
+        test_util_1.expectValuesInRange(result, 0, 1);
+    });
+    it('should return a random 5D float32 array', function () {
+        var shape = [2, 3, 4, 5, 6];
+        var result = tf.randomUniform(shape, 0, 2.5);
+        expect(result.dtype).toBe('float32');
+        test_util_1.expectValuesInRange(result, 0, 2.5);
+        result = tf.randomUniform(shape, 0, 1.5, 'float32');
+        expect(result.dtype).toBe('float32');
+        test_util_1.expectValuesInRange(result, 0, 1.5);
+    });
+    it('should return a random 5D int32 array', function () {
+        var shape = [2, 3, 4, 5, 6];
+        var result = tf.randomUniform(shape, 0, 2, 'int32');
+        expect(result.dtype).toBe('int32');
+        test_util_1.expectValuesInRange(result, 0, 2);
+    });
+    it('should return a random 5D bool array', function () {
+        var shape = [2, 3, 4, 5, 6];
+        var result = tf.randomUniform(shape, 0, 1, 'bool');
+        expect(result.dtype).toBe('bool');
+        test_util_1.expectValuesInRange(result, 0, 1);
+    });
+});
+var MockContext = (function () {
+    function MockContext() {
+    }
+    MockContext.prototype.getImageData = function (x, y, width, height) {
+        var data = new Uint8ClampedArray(width * height * 4);
+        for (var i = 0; i < data.length; ++i) {
+            data[i] = i + 1;
+        }
+        return { data: data };
+    };
+    return MockContext;
+}());
+var MockCanvas = (function () {
+    function MockCanvas(width, height) {
+        this.width = width;
+        this.height = height;
+    }
+    MockCanvas.prototype.getContext = function (type) {
+        return new MockContext();
+    };
+    return MockCanvas;
+}());
+jasmine_util_1.describeWithFlags('fromPixels, mock canvas', test_util_1.NODE_ENVS, function () {
+    it('accepts a canvas-like element', function () {
+        var c = new MockCanvas(2, 2);
+        var t = tf.fromPixels(c);
+        expect(t.dtype).toBe('int32');
+        expect(t.shape).toEqual([2, 2, 3]);
+        tf.test_util.expectArraysEqual(t, [1, 2, 3, 5, 6, 7, 9, 10, 11, 13, 14, 15]);
+    });
+    it('accepts a canvas-like element, numChannels=4', function () {
+        var c = new MockCanvas(2, 2);
+        var t = tf.fromPixels(c, 4);
+        expect(t.dtype).toBe('int32');
+        expect(t.shape).toEqual([2, 2, 4]);
+        tf.test_util.expectArraysEqual(t, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]);
+    });
+    it('errors when passed a non-canvas object', function () {
+        expect(function () { return tf.fromPixels(5); }).toThrowError();
+    });
+});
+jasmine_util_1.describeWithFlags('fromPixels', test_util_1.BROWSER_ENVS, function () {
+    it('ImageData 1x1x3', function () {
+        var pixels = new ImageData(1, 1);
+        pixels.data[0] = 0;
+        pixels.data[1] = 80;
+        pixels.data[2] = 160;
+        pixels.data[3] = 240;
+        var array = tf.fromPixels(pixels, 3);
+        test_util_1.expectArraysEqual(array, [0, 80, 160]);
+    });
+    it('ImageData 1x1x4', function () {
+        var pixels = new ImageData(1, 1);
+        pixels.data[0] = 0;
+        pixels.data[1] = 80;
+        pixels.data[2] = 160;
+        pixels.data[3] = 240;
+        var array = tf.fromPixels(pixels, 4);
+        test_util_1.expectArraysEqual(array, [0, 80, 160, 240]);
+    });
+    it('ImageData 2x2x3', function () {
+        var pixels = new ImageData(2, 2);
+        for (var i = 0; i < 8; i++) {
+            pixels.data[i] = i * 2;
+        }
+        for (var i = 8; i < 16; i++) {
+            pixels.data[i] = i * 2;
+        }
+        var array = tf.fromPixels(pixels, 3);
+        test_util_1.expectArraysEqual(array, [0, 2, 4, 8, 10, 12, 16, 18, 20, 24, 26, 28]);
+    });
+    it('ImageData 2x2x4', function () {
+        var pixels = new ImageData(2, 2);
+        for (var i = 0; i < 8; i++) {
+            pixels.data[i] = i * 2;
+        }
+        for (var i = 8; i < 16; i++) {
+            pixels.data[i] = i * 2;
+        }
+        var array = tf.fromPixels(pixels, 4);
+        test_util_1.expectArraysClose(array, new Int32Array([0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28, 30]));
+    });
+    it('fromPixels, 3 channels', function () {
+        var pixels = new ImageData(1, 2);
+        pixels.data[0] = 2;
+        pixels.data[1] = 3;
+        pixels.data[2] = 4;
+        pixels.data[3] = 255;
+        pixels.data[4] = 5;
+        pixels.data[5] = 6;
+        pixels.data[6] = 7;
+        pixels.data[7] = 255;
+        var res = tf.fromPixels(pixels, 3);
+        expect(res.shape).toEqual([2, 1, 3]);
+        expect(res.dtype).toBe('int32');
+        test_util_1.expectArraysClose(res, [2, 3, 4, 5, 6, 7]);
+    });
+    it('fromPixels, reshape, then do tf.add()', function () {
+        var pixels = new ImageData(1, 1);
+        pixels.data[0] = 2;
+        pixels.data[1] = 3;
+        pixels.data[2] = 4;
+        pixels.data[3] = 255;
+        var a = tf.fromPixels(pixels, 3).reshape([1, 1, 1, 3]);
+        var res = a.add(tf.scalar(2, 'int32'));
+        expect(res.shape).toEqual([1, 1, 1, 3]);
+        expect(res.dtype).toBe('int32');
+        test_util_1.expectArraysClose(res, [4, 5, 6]);
+    });
+    it('fromPixels + fromPixels', function () {
+        var pixelsA = new ImageData(1, 1);
+        pixelsA.data[0] = 255;
+        pixelsA.data[1] = 3;
+        pixelsA.data[2] = 4;
+        pixelsA.data[3] = 255;
+        var pixelsB = new ImageData(1, 1);
+        pixelsB.data[0] = 5;
+        pixelsB.data[1] = 6;
+        pixelsB.data[2] = 7;
+        pixelsB.data[3] = 255;
+        var a = tf.fromPixels(pixelsA, 3).toFloat();
+        var b = tf.fromPixels(pixelsB, 3).toFloat();
+        var res = a.add(b);
+        expect(res.shape).toEqual([1, 1, 3]);
+        expect(res.dtype).toBe('float32');
+        test_util_1.expectArraysClose(res, [260, 9, 11]);
+    });
+    it('throws when passed a primitive number', function () {
+        expect(function () { return tf.fromPixels(3); })
+            .toThrowError(/pixels passed to tf.fromPixels\(\) must be either/);
+    });
+    it('throws when passed a string', function () {
+        expect(function () { return tf.fromPixels('test'); })
+            .toThrowError(/pixels passed to tf.fromPixels\(\) must be either/);
+    });
+});
+jasmine_util_1.describeWithFlags('toPixels no canvas', test_util_1.ALL_ENVS, function () {
+    it('draws a rank-2 float32 tensor', function () { return __awaiter(_this, void 0, void 0, function () {
+        var x, data, expected;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    x = tf.tensor2d([.15, .2], [2, 1], 'float32');
+                    return [4, tf.toPixels(x)];
+                case 1:
+                    data = _a.sent();
+                    expected = new Uint8ClampedArray([
+                        Math.round(.15 * 255), Math.round(.15 * 255), Math.round(.15 * 255),
+                        255, Math.round(.2 * 255), Math.round(.2 * 255), Math.round(.2 * 255),
+                        255
+                    ]);
+                    expect(data).toEqual(expected);
+                    return [2];
+            }
+        });
+    }); });
+    it('draws a rank-2 int32 tensor', function () { return __awaiter(_this, void 0, void 0, function () {
+        var x, data, expected;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    x = tf.tensor2d([10, 20], [2, 1], 'int32');
+                    return [4, tf.toPixels(x)];
+                case 1:
+                    data = _a.sent();
+                    expected = new Uint8ClampedArray([10, 10, 10, 255, 20, 20, 20, 255]);
+                    expect(data).toEqual(expected);
+                    return [2];
+            }
+        });
+    }); });
+    it('draws a rank-3 float32 tensor, 1 channel', function () { return __awaiter(_this, void 0, void 0, function () {
+        var x, data, expected;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    x = tf.tensor3d([.15, .2], [2, 1, 1], 'float32');
+                    return [4, tf.toPixels(x)];
+                case 1:
+                    data = _a.sent();
+                    expected = new Uint8ClampedArray([
+                        Math.round(.15 * 255), Math.round(.15 * 255), Math.round(.15 * 255),
+                        255, Math.round(.2 * 255), Math.round(.2 * 255), Math.round(.2 * 255),
+                        255
+                    ]);
+                    expect(data).toEqual(expected);
+                    return [2];
+            }
+        });
+    }); });
+    it('draws a rank-3 int32 tensor, 1 channel', function () { return __awaiter(_this, void 0, void 0, function () {
+        var x, data, expected;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    x = tf.tensor3d([10, 20], [2, 1, 1], 'int32');
+                    return [4, tf.toPixels(x)];
+                case 1:
+                    data = _a.sent();
+                    expected = new Uint8ClampedArray([10, 10, 10, 255, 20, 20, 20, 255]);
+                    expect(data).toEqual(expected);
+                    return [2];
+            }
+        });
+    }); });
+    it('draws a rank-3 float32 tensor, 3 channel', function () { return __awaiter(_this, void 0, void 0, function () {
+        var x, data, expected;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    x = tf.tensor3d([.05, .1001, .15, .2, .25, .3001], [2, 1, 3], 'float32');
+                    return [4, tf.toPixels(x)];
+                case 1:
+                    data = _a.sent();
+                    expected = new Uint8ClampedArray([
+                        Math.round(.05 * 255), Math.round(.1001 * 255), Math.round(.15 * 255),
+                        255, Math.round(.2 * 255), Math.round(.25 * 255),
+                        Math.round(.3001 * 255), 255
+                    ]);
+                    expect(data).toEqual(expected);
+                    return [2];
+            }
+        });
+    }); });
+    it('draws a rank-3 int32 tensor, 3 channel', function () { return __awaiter(_this, void 0, void 0, function () {
+        var x, data, expected;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    x = tf.tensor3d([10, 20, 30, 40, 50, 60], [2, 1, 3], 'int32');
+                    return [4, tf.toPixels(x)];
+                case 1:
+                    data = _a.sent();
+                    expected = new Uint8ClampedArray([10, 20, 30, 255, 40, 50, 60, 255]);
+                    expect(data).toEqual(expected);
+                    return [2];
+            }
+        });
+    }); });
+    it('draws a rank-3 float32 tensor, 4 channel', function () { return __awaiter(_this, void 0, void 0, function () {
+        var x, data, expected;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    x = tf.tensor3d([.05, .1001, .15, .2, .25, .3001, .35, .4], [2, 1, 4], 'float32');
+                    return [4, tf.toPixels(x)];
+                case 1:
+                    data = _a.sent();
+                    expected = new Uint8ClampedArray([
+                        Math.round(.05 * 255), Math.round(.1001 * 255), Math.round(.15 * 255),
+                        Math.round(.20 * 255), Math.round(.25 * 255), Math.round(.3001 * 255),
+                        Math.round(.35 * 255), Math.round(.4 * 255)
+                    ]);
+                    expect(data).toEqual(expected);
+                    return [2];
+            }
+        });
+    }); });
+    it('draws a rank-3 int32 tensor, 4 channel', function () { return __awaiter(_this, void 0, void 0, function () {
+        var x, data, expected;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    x = tf.tensor3d([10, 20, 30, 40, 50, 60, 70, 80], [2, 1, 4], 'int32');
+                    return [4, tf.toPixels(x)];
+                case 1:
+                    data = _a.sent();
+                    expected = new Uint8ClampedArray([10, 20, 30, 40, 50, 60, 70, 80]);
+                    expect(data).toEqual(expected);
+                    return [2];
+            }
+        });
+    }); });
+    it('throws for scalars', function (done) {
+        test_util_1.expectPromiseToFail(function () { return tf.toPixels(tf.scalar(1)); }, done);
+    });
+    it('throws for rank-1 tensors', function (done) {
+        test_util_1.expectPromiseToFail(function () { return tf.toPixels(tf.tensor1d([1])); }, done);
+    });
+    it('throws for rank-4 tensors', function (done) {
+        test_util_1.expectPromiseToFail(function () { return tf.toPixels(tf.tensor4d([1], [1, 1, 1, 1])); }, done);
+    });
+    it('throws for bool dtype', function (done) {
+        test_util_1.expectPromiseToFail(function () { return tf.toPixels(tf.tensor2d([1], [1, 1], 'bool')); }, done);
+    });
+    it('throws for rank-3 depth = 2', function (done) {
+        test_util_1.expectPromiseToFail(function () { return tf.toPixels(tf.tensor3d([1, 2], [1, 1, 2])); }, done);
+    });
+    it('throws for rank-3 depth = 5', function (done) {
+        test_util_1.expectPromiseToFail(function () { return tf.toPixels(tf.tensor3d([1, 2, 3, 4, 5], [1, 1, 5])); }, done);
+    });
+    it('throws for float32 tensor with values not in [0 - 1]', function (done) {
+        test_util_1.expectPromiseToFail(function () { return tf.toPixels(tf.tensor2d([-1, .5], [1, 2])); }, done);
+    });
+    it('throws for int32 tensor with values not in [0 - 255]', function (done) {
+        test_util_1.expectPromiseToFail(function () { return tf.toPixels(tf.tensor2d([-1, 100], [1, 2], 'int32')); }, done);
+    });
+    it('throws when passed a non-tensor', function (done) {
+        test_util_1.expectPromiseToFail(function () { return tf.toPixels({}); }, done);
+    });
+    it('accepts a tensor-like object', function () { return __awaiter(_this, void 0, void 0, function () {
+        var x, data, expected;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    x = [[10], [20]];
+                    return [4, tf.toPixels(x)];
+                case 1:
+                    data = _a.sent();
+                    expected = new Uint8ClampedArray([10, 10, 10, 255, 20, 20, 20, 255]);
+                    expect(data).toEqual(expected);
+                    return [2];
+            }
+        });
+    }); });
+});
+jasmine_util_1.describeWithFlags('toPixels', test_util_1.WEBGL_ENVS, function () {
+    it('draws a rank-2 float32 tensor, canvas', function (done) {
+        var x = tf.tensor2d([.15, .2], [2, 1], 'float32');
+        var canvas = document.createElement('canvas');
+        tf.toPixels(x, canvas).then(function (data) {
+            var expected = new Uint8ClampedArray([
+                Math.round(.15 * 255), Math.round(.15 * 255), Math.round(.15 * 255),
+                255, Math.round(.2 * 255), Math.round(.2 * 255), Math.round(.2 * 255),
+                255
+            ]);
+            expect(data).toEqual(expected);
+            var ctx = canvas.getContext('2d');
+            var imgData = ctx.getImageData(0, 0, 1, 2);
+            expect(imgData.data).toEqual(expected);
+            done();
+        });
+    });
+    it('draws a rank-2 int32 tensor, canvas', function (done) {
+        var x = tf.tensor2d([10, 20], [2, 1], 'int32');
+        var canvas = document.createElement('canvas');
+        tf.toPixels(x, canvas).then(function (data) {
+            var expected = new Uint8ClampedArray([10, 10, 10, 255, 20, 20, 20, 255]);
+            expect(data).toEqual(expected);
+            var ctx = canvas.getContext('2d');
+            var imgData = ctx.getImageData(0, 0, 1, 2);
+            expect(imgData.data).toEqual(expected);
+            done();
+        });
+    });
+    it('draws a rank-3 float32 tensor, 1 channel, canvas', function (done) {
+        var x = tf.tensor3d([.15, .2], [2, 1, 1], 'float32');
+        var canvas = document.createElement('canvas');
+        tf.toPixels(x, canvas).then(function (data) {
+            var expected = new Uint8ClampedArray([
+                Math.round(.15 * 255), Math.round(.15 * 255), Math.round(.15 * 255),
+                255, Math.round(.2 * 255), Math.round(.2 * 255), Math.round(.2 * 255),
+                255
+            ]);
+            expect(data).toEqual(expected);
+            var ctx = canvas.getContext('2d');
+            var imgData = ctx.getImageData(0, 0, 1, 2);
+            expect(imgData.data).toEqual(expected);
+            done();
+        });
+    });
+    it('draws a rank-3 int32 tensor, 1 channel, canvas', function (done) {
+        var x = tf.tensor3d([10, 20], [2, 1, 1], 'int32');
+        var canvas = document.createElement('canvas');
+        tf.toPixels(x, canvas).then(function (data) {
+            var expected = new Uint8ClampedArray([10, 10, 10, 255, 20, 20, 20, 255]);
+            expect(data).toEqual(expected);
+            var ctx = canvas.getContext('2d');
+            var imgData = ctx.getImageData(0, 0, 1, 2);
+            expect(imgData.data).toEqual(expected);
+            done();
+        });
+    });
+    it('draws a rank-3 float32 tensor, 3 channel, canvas', function (done) {
+        var x = tf.tensor3d([.05, .1001, .15, .20, .25, .3001], [2, 1, 3], 'float32');
+        var canvas = document.createElement('canvas');
+        tf.toPixels(x, canvas).then(function (data) {
+            var expected = new Uint8ClampedArray([
+                Math.round(.05 * 255), Math.round(.1001 * 255), Math.round(.15 * 255),
+                255, Math.round(.2 * 255), Math.round(.25 * 255),
+                Math.round(.3001 * 255), 255
+            ]);
+            expect(data).toEqual(expected);
+            var ctx = canvas.getContext('2d');
+            var imgData = ctx.getImageData(0, 0, 1, 2);
+            expect(imgData.data).toEqual(expected);
+            done();
+        });
+    });
+    it('draws a rank-3 int32 tensor, 3 channel, canvas', function (done) {
+        var x = tf.tensor3d([10, 20, 30, 40, 50, 60], [2, 1, 3], 'int32');
+        var canvas = document.createElement('canvas');
+        tf.toPixels(x, canvas).then(function (data) {
+            var expected = new Uint8ClampedArray([10, 20, 30, 255, 40, 50, 60, 255]);
+            expect(data).toEqual(expected);
+            var ctx = canvas.getContext('2d');
+            var imgData = ctx.getImageData(0, 0, 1, 2);
+            expect(imgData.data).toEqual(expected);
+            done();
+        });
+    });
+    it('draws a rank-3 float32 tensor, 4 channel, canvas', function (done) {
+        var x = tf.tensor3d([.05, .1001, .15, 1, .20, .25, .3001, 1], [2, 1, 4], 'float32');
+        var canvas = document.createElement('canvas');
+        tf.toPixels(x, canvas).then(function (data) {
+            var expected = new Uint8ClampedArray([
+                Math.round(.05 * 255), Math.round(.1001 * 255), Math.round(.15 * 255),
+                255, Math.round(.20 * 255), Math.round(.25 * 255),
+                Math.round(.3001 * 255), 255
+            ]);
+            expect(data).toEqual(expected);
+            var ctx = canvas.getContext('2d');
+            var imgData = ctx.getImageData(0, 0, 1, 2);
+            expect(imgData.data).toEqual(expected);
+            done();
+        });
+    });
+    it('draws a rank-3 int32 tensor, 4 channel, canvas', function (done) {
+        var x = tf.tensor3d([10, 20, 30, 255, 50, 60, 70, 255], [2, 1, 4], 'int32');
+        var canvas = document.createElement('canvas');
+        tf.toPixels(x, canvas).then(function (data) {
+            var expected = new Uint8ClampedArray([10, 20, 30, 255, 50, 60, 70, 255]);
+            expect(data).toEqual(expected);
+            var ctx = canvas.getContext('2d');
+            var imgData = ctx.getImageData(0, 0, 1, 2);
+            expect(imgData.data).toEqual(expected);
+            done();
+        });
+    });
+    it('accepts a tensor-like object', function () { return __awaiter(_this, void 0, void 0, function () {
+        var x, canvas, data, expected, ctx, imgData;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    x = [[127], [100]];
+                    canvas = document.createElement('canvas');
+                    return [4, tf.toPixels(x, canvas)];
+                case 1:
+                    data = _a.sent();
+                    expected = new Uint8ClampedArray([127, 127, 127, 255, 100, 100, 100, 255]);
+                    expect(data).toEqual(expected);
+                    ctx = canvas.getContext('2d');
+                    imgData = ctx.getImageData(0, 0, 1, 2);
+                    expect(imgData.data).toEqual(expected);
+                    return [2];
+            }
+        });
+    }); });
+});
+jasmine_util_1.describeWithFlags('clone', test_util_1.ALL_ENVS, function () {
+    it('1D default dtype', function () {
+        var a = tf.tensor1d([1, 2, 3]);
+        var b = tf.clone(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([3]);
+        test_util_1.expectArraysClose(b, [1, 2, 3]);
+    });
+    it('1D float32 dtype', function () {
+        var a = tf.tensor1d([1, 2, 3], 'float32');
+        var b = tf.clone(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([3]);
+        test_util_1.expectArraysClose(b, [1, 2, 3]);
+    });
+    it('1D int32 dtype', function () {
+        var a = tf.tensor1d([1, 2, 3], 'int32');
+        var b = tf.clone(a);
+        expect(b.dtype).toBe('int32');
+        expect(b.shape).toEqual([3]);
+        test_util_1.expectArraysEqual(b, [1, 2, 3]);
+    });
+    it('1D bool dtype', function () {
+        var a = tf.tensor1d([1, 1, 0], 'bool');
+        var b = tf.clone(a);
+        expect(b.dtype).toBe('bool');
+        expect(b.shape).toEqual([3]);
+        test_util_1.expectArraysEqual(b, [1, 1, 0]);
+    });
+    it('1D complex64 dtype', function () {
+        var a = tf.complex([1], [1]);
+        var b = tf.clone(a);
+        expect(b.dtype).toBe('complex64');
+        expect(b.shape).toEqual([1]);
+        test_util_1.expectArraysEqual(b, [1, 1]);
+    });
+    it('1D string dtype', function () {
+        var a = tf.tensor1d(['a', 'b', 'c'], 'string');
+        var b = tf.clone(a);
+        expect(b.dtype).toBe('string');
+        expect(b.shape).toEqual([3]);
+        test_util_1.expectArraysEqual(b, ['a', 'b', 'c']);
+    });
+    it('2D default dtype', function () {
+        var a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var b = tf.clone(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(b, [1, 2, 3, 4]);
+    });
+    it('2D float32 dtype', function () {
+        var a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'float32');
+        var b = tf.clone(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(b, [1, 2, 3, 4]);
+    });
+    it('2D int32 dtype', function () {
+        var a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'int32');
+        var b = tf.clone(a);
+        expect(b.dtype).toBe('int32');
+        expect(b.shape).toEqual([2, 2]);
+        test_util_1.expectArraysEqual(b, [1, 2, 3, 4]);
+    });
+    it('2D bool dtype', function () {
+        var a = tf.tensor2d([1, 1, 1, 0], [2, 2], 'bool');
+        var b = tf.clone(a);
+        expect(b.dtype).toBe('bool');
+        expect(b.shape).toEqual([2, 2]);
+        test_util_1.expectArraysEqual(b, [1, 1, 1, 0]);
+    });
+    it('2D complex64 dtype', function () {
+        var a = tf.complex([[1, 3], [5, 7]], [[2, 4], [6, 8]]);
+        var b = tf.clone(a);
+        expect(b.dtype).toBe('complex64');
+        expect(b.shape).toEqual([2, 2]);
+        test_util_1.expectArraysEqual(b, [1, 2, 3, 4, 5, 6, 7, 8]);
+    });
+    it('2D string dtype', function () {
+        var a = tf.tensor2d(['a', 'b', 'c', 'd'], [2, 2], 'string');
+        var b = tf.clone(a);
+        expect(b.dtype).toBe('string');
+        expect(b.shape).toEqual([2, 2]);
+        test_util_1.expectArraysEqual(b, ['a', 'b', 'c', 'd']);
+    });
+    it('3D default dtype', function () {
+        var a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
+        var b = tf.clone(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(b, [1, 2, 3, 4]);
+    });
+    it('3D float32 dtype', function () {
+        var a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1], 'float32');
+        var b = tf.clone(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(b, [1, 2, 3, 4]);
+    });
+    it('3D int32 dtype', function () {
+        var a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1], 'int32');
+        var b = tf.clone(a);
+        expect(b.dtype).toBe('int32');
+        expect(b.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysEqual(b, [1, 2, 3, 4]);
+    });
+    it('3D bool dtype', function () {
+        var a = tf.tensor3d([1, 1, 1, 0], [2, 2, 1], 'bool');
+        var b = tf.clone(a);
+        expect(b.dtype).toBe('bool');
+        expect(b.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysEqual(b, [1, 1, 1, 0]);
+    });
+    it('3D complex64 dtype', function () {
+        var a = tf.complex([[[1], [3]], [[5], [7]]], [[[2], [4]], [[6], [8]]]);
+        var b = tf.clone(a);
+        expect(b.dtype).toBe('complex64');
+        expect(b.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysEqual(b, [1, 2, 3, 4, 5, 6, 7, 8]);
+    });
+    it('3D string dtype', function () {
+        var a = tf.tensor3d(['a', 'b', 'c', 'd'], [2, 2, 1], 'string');
+        var b = tf.clone(a);
+        expect(b.dtype).toBe('string');
+        expect(b.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysEqual(b, ['a', 'b', 'c', 'd']);
+    });
+    it('4D default dtype', function () {
+        var a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1]);
+        var b = tf.clone(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([2, 2, 1, 1]);
+        test_util_1.expectArraysClose(b, [1, 2, 3, 4]);
+    });
+    it('4D float32 dtype', function () {
+        var a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1], 'float32');
+        var b = tf.clone(a);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([2, 2, 1, 1]);
+        test_util_1.expectArraysClose(b, [1, 2, 3, 4]);
+    });
+    it('4D int32 dtype', function () {
+        var a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1], 'int32');
+        var b = tf.clone(a);
+        expect(b.dtype).toBe('int32');
+        expect(b.shape).toEqual([2, 2, 1, 1]);
+        test_util_1.expectArraysEqual(b, [1, 2, 3, 4]);
+    });
+    it('4D bool dtype', function () {
+        var a = tf.tensor4d([1, 1, 1, 0], [2, 2, 1, 1], 'bool');
+        var b = tf.clone(a);
+        expect(b.dtype).toBe('bool');
+        expect(b.shape).toEqual([2, 2, 1, 1]);
+        test_util_1.expectArraysEqual(b, [1, 1, 1, 0]);
+    });
+    it('4D complex64 dtype', function () {
+        var a = tf.complex([[[[1]], [[3]]], [[[5]], [[7]]]], [[[[2]], [[4]]], [[[6]], [[8]]]]);
+        var b = tf.clone(a);
+        expect(b.dtype).toBe('complex64');
+        expect(b.shape).toEqual([2, 2, 1, 1]);
+        test_util_1.expectArraysEqual(b, [1, 2, 3, 4, 5, 6, 7, 8]);
+    });
+    it('4D string dtype', function () {
+        var a = tf.tensor4d(['a', 'b', 'c', 'd'], [2, 2, 1, 1], 'string');
+        var b = tf.clone(a);
+        expect(b.dtype).toBe('string');
+        expect(b.shape).toEqual([2, 2, 1, 1]);
+        test_util_1.expectArraysEqual(b, ['a', 'b', 'c', 'd']);
+    });
+    it('gradient: 1D', function () {
+        var a = tf.tensor1d([1, 2, 3]);
+        var dy = tf.tensor1d([4, 5, 6]);
+        var da = tf.grad(function (x) { return tf.clone(x); })(a, dy);
+        expect(da.dtype).toBe('float32');
+        expect(da.shape).toEqual([3]);
+        test_util_1.expectArraysClose(da, [4, 5, 6]);
+    });
+    it('gradient: 1D string throws error with string dy', function () {
+        var a = tf.tensor1d(['a', 'b', 'c'], 'string');
+        var dy = tf.tensor1d(['d', 'e', 'f']);
+        expect(function () { return tf.grad(function (x) { return tf.clone(x); })(a, dy); }).toThrowError();
+    });
+    it('gradient: 1D string throws error with bool dy', function () {
+        var a = tf.tensor1d(['a', 'b', 'c'], 'string');
+        var dy = tf.tensor1d([false, true, false], 'bool');
+        expect(function () { return tf.grad(function (x) { return tf.clone(x); })(a, dy); }).toThrowError();
+    });
+    it('gradient: 1D string throws error with int32 dy', function () {
+        var a = tf.tensor1d(['a', 'b', 'c'], 'string');
+        var dy = tf.tensor1d([4, 5, 6], 'int32');
+        expect(function () { return tf.grad(function (x) { return tf.clone(x); })(a, dy); }).toThrowError();
+    });
+    it('gradient: 1D string works with float32 dy', function () {
+        var a = tf.tensor1d(['a', 'b', 'c'], 'string');
+        var dy = tf.tensor1d([4, 5, 6]);
+        var da = tf.grad(function (x) { return tf.clone(x); })(a, dy);
+        expect(da.dtype).toBe('float32');
+        expect(da.shape).toEqual([3]);
+        test_util_1.expectArraysClose(da, [4, 5, 6]);
+    });
+    it('gradient: 2D int32', function () {
+        var a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'int32');
+        var dy = tf.tensor2d([5, 6, 7, 8], [2, 2], 'float32');
+        var da = tf.grad(function (x) { return tf.clone(x); })(a, dy);
+        expect(da.dtype).toBe('float32');
+        expect(da.shape).toEqual([2, 2]);
+        test_util_1.expectArraysEqual(da, [5, 6, 7, 8]);
+    });
+    it('gradient: 4D bool', function () {
+        var a = tf.tensor4d([1, 1, 1, 0], [2, 2, 1, 1], 'bool');
+        var dy = tf.tensor4d([5, 6, 7, 8], [2, 2, 1, 1], 'float32');
+        var da = tf.grad(function (x) { return tf.clone(x); })(a, dy);
+        expect(da.dtype).toBe('float32');
+        expect(da.shape).toEqual([2, 2, 1, 1]);
+        test_util_1.expectArraysEqual(da, [5, 6, 7, 8]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.clone({}); })
+            .toThrowError(/Argument 'x' passed to 'clone' must be a Tensor/);
+    });
+});
+jasmine_util_1.describeWithFlags('tile', test_util_1.ALL_ENVS, function () {
+    it('1D (tile)', function () {
+        var t = tf.tensor1d([1, 2, 3]);
+        var t2 = tf.tile(t, [2]);
+        expect(t2.shape).toEqual([6]);
+        test_util_1.expectArraysClose(t2, [1, 2, 3, 1, 2, 3]);
+    });
+    it('2D (tile)', function () {
+        var t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
+        var t2 = tf.tile(t, [1, 2]);
+        expect(t2.shape).toEqual([2, 4]);
+        test_util_1.expectArraysClose(t2, [1, 11, 1, 11, 2, 22, 2, 22]);
+        t2 = tf.tile(t, [2, 1]);
+        expect(t2.shape).toEqual([4, 2]);
+        test_util_1.expectArraysClose(t2, [1, 11, 2, 22, 1, 11, 2, 22]);
+        t2 = tf.tile(t, [2, 2]);
+        expect(t2.shape).toEqual([4, 4]);
+        test_util_1.expectArraysClose(t2, [1, 11, 1, 11, 2, 22, 2, 22, 1, 11, 1, 11, 2, 22, 2, 22]);
+    });
+    it('3D (tile)', function () {
+        var t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
+        var t2 = tf.tile(t, [1, 2, 1]);
+        expect(t2.shape).toEqual([2, 4, 2]);
+        test_util_1.expectArraysClose(t2, [1, 2, 3, 4, 1, 2, 3, 4, 5, 6, 7, 8, 5, 6, 7, 8]);
+    });
+    it('4D (tile)', function () {
+        var t = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2]);
+        var t2 = tf.tile(t, [1, 2, 1, 1]);
+        expect(t2.shape).toEqual([1, 4, 2, 2]);
+        test_util_1.expectArraysClose(t2, [1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8]);
+    });
+    it('5D (tile)', function () {
+        var t = tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 2, 2, 2]);
+        var t2 = tf.tile(t, [1, 2, 1, 1, 1]);
+        expect(t2.shape).toEqual([1, 2, 2, 2, 2]);
+        test_util_1.expectArraysClose(t2, [1, 2, 3, 4, 5, 6, 7, 8, 1, 2, 3, 4, 5, 6, 7, 8]);
+    });
+    it('propagates NaNs', function () {
+        var t = tf.tensor1d([1, 2, NaN]);
+        var t2 = tf.tile(t, [2]);
+        expect(t2.shape).toEqual([6]);
+        test_util_1.expectArraysClose(t2, [1, 2, NaN, 1, 2, NaN]);
+    });
+    it('1D bool (tile)', function () {
+        var t = tf.tensor1d([true, false, true], 'bool');
+        var t2 = tf.tile(t, [2]);
+        expect(t2.shape).toEqual([6]);
+        expect(t2.dtype).toBe('bool');
+        test_util_1.expectArraysEqual(t2, [1, 0, 1, 1, 0, 1]);
+    });
+    it('2D bool (tile)', function () {
+        var t = tf.tensor2d([true, false, true, true], [2, 2], 'bool');
+        var t2 = tf.tile(t, [1, 2]);
+        expect(t2.shape).toEqual([2, 4]);
+        expect(t2.dtype).toBe('bool');
+        test_util_1.expectArraysEqual(t2, [1, 0, 1, 0, 1, 1, 1, 1]);
+        t2 = tf.tile(t, [2, 1]);
+        expect(t2.shape).toEqual([4, 2]);
+        expect(t2.dtype).toBe('bool');
+        test_util_1.expectArraysEqual(t2, [1, 0, 1, 1, 1, 0, 1, 1]);
+        t2 = tf.tile(t, [2, 2]);
+        expect(t2.shape).toEqual([4, 4]);
+        expect(t2.dtype).toBe('bool');
+        test_util_1.expectArraysEqual(t2, [1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 1, 1, 1, 1]);
+    });
+    it('3D bool (tile)', function () {
+        var t = tf.tensor3d([true, false, true, false, true, false, true, false], [2, 2, 2], 'bool');
+        var t2 = tf.tile(t, [1, 2, 1]);
+        expect(t2.shape).toEqual([2, 4, 2]);
+        expect(t2.dtype).toBe('bool');
+        test_util_1.expectArraysEqual(t2, [1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0]);
+    });
+    it('1D int32 (tile)', function () {
+        var t = tf.tensor1d([1, 2, 5], 'int32');
+        var t2 = tf.tile(t, [2]);
+        expect(t2.shape).toEqual([6]);
+        expect(t2.dtype).toBe('int32');
+        test_util_1.expectArraysEqual(t2, [1, 2, 5, 1, 2, 5]);
+    });
+    it('2D int32 (tile)', function () {
+        var t = tf.tensor2d([1, 2, 3, 4], [2, 2], 'int32');
+        var t2 = tf.tile(t, [1, 2]);
+        expect(t2.shape).toEqual([2, 4]);
+        expect(t2.dtype).toBe('int32');
+        test_util_1.expectArraysEqual(t2, [1, 2, 1, 2, 3, 4, 3, 4]);
+        t2 = tf.tile(t, [2, 1]);
+        expect(t2.shape).toEqual([4, 2]);
+        expect(t2.dtype).toBe('int32');
+        test_util_1.expectArraysEqual(t2, [1, 2, 3, 4, 1, 2, 3, 4]);
+        t2 = tf.tile(t, [2, 2]);
+        expect(t2.shape).toEqual([4, 4]);
+        expect(t2.dtype).toBe('int32');
+        test_util_1.expectArraysEqual(t2, [1, 2, 1, 2, 3, 4, 3, 4, 1, 2, 1, 2, 3, 4, 3, 4]);
+    });
+    it('3D int32 (tile)', function () {
+        var t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2], 'int32');
+        var t2 = tf.tile(t, [1, 2, 1]);
+        expect(t2.shape).toEqual([2, 4, 2]);
+        expect(t2.dtype).toBe('int32');
+        test_util_1.expectArraysEqual(t2, [1, 2, 3, 4, 1, 2, 3, 4, 5, 6, 7, 8, 5, 6, 7, 8]);
+    });
+    it('1D (tile) gradient', function () {
+        var x = tf.tensor1d([1, 2, 3]);
+        var dy = tf.tensor1d([0.1, 0.2, 0.3, 1, 2, 3, 10, 20, 30]);
+        var gradients = tf.grad(function (x) { return tf.tile(x, [3]); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor1d([11.1, 22.2, 33.3]));
+    });
+    it('2D (tile) gradient', function () {
+        var x = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
+        var dy = tf.tensor2d([[1, 2, 10, 20], [3, 4, 30, 40]], [2, 4]);
+        var gradients = tf.grad(function (x) { return tf.tile(x, [1, 2]); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor2d([[11, 22], [33, 44]], [2, 2]));
+    });
+    it('3D (tile) gradient', function () {
+        var x = tf.tensor3d([[[1], [2]], [[3], [4]]], [2, 2, 1]);
+        var dy = tf.tensor3d([[[1, 10], [2, 20]], [[3, 30], [4, 40]]], [2, 2, 2]);
+        var gradients = tf.grad(function (x) { return tf.tile(x, [1, 1, 2]); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor3d([[[11], [22]], [[33], [44]]], [2, 2, 1]));
+    });
+    it('4D (tile) gradient', function () {
+        var x = tf.tensor4d([[[[1]], [[2]]], [[[3]], [[4]]]], [2, 2, 1, 1]);
+        var dy = tf.tensor4d([
+            [[[.01, .1], [1, 10]], [[.02, .2], [2, 20]]],
+            [[[.03, .3], [3, 30]], [[.04, .4], [4, 40]]]
+        ], [2, 2, 2, 2]);
+        var gradients = tf.grad(function (x) { return tf.tile(x, [1, 1, 2, 2]); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor4d([[[[11.11]], [[22.22]]], [[[33.33]], [[44.44]]]], [2, 2, 1, 1]));
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.tile({}, [1]); })
+            .toThrowError(/Argument 'x' passed to 'tile' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var res = tf.tile([1, 2, 3], [2]);
+        expect(res.shape).toEqual([6]);
+        test_util_1.expectArraysClose(res, [1, 2, 3, 1, 2, 3]);
+    });
+});
+jasmine_util_1.describeWithFlags('gather', test_util_1.ALL_ENVS, function () {
+    it('1D (gather)', function () {
+        var t = tf.tensor1d([1, 2, 3]);
+        var t2 = tf.gather(t, tf.tensor1d([0, 2, 0, 1], 'int32'), 0);
+        expect(t2.shape).toEqual([4]);
+        test_util_1.expectArraysClose(t2, [1, 3, 1, 2]);
+    });
+    it('2D (gather)', function () {
+        var t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
+        var t2 = tf.gather(t, tf.tensor1d([1, 0, 0, 1], 'int32'), 0);
+        expect(t2.shape).toEqual([4, 2]);
+        test_util_1.expectArraysClose(t2, [2, 22, 1, 11, 1, 11, 2, 22]);
+        t2 = tf.gather(t, tf.tensor1d([1, 0, 0, 1], 'int32'), 1);
+        expect(t2.shape).toEqual([2, 4]);
+        test_util_1.expectArraysClose(t2, [11, 1, 1, 11, 22, 2, 2, 22]);
+    });
+    it('3D (gather)', function () {
+        var t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
+        var t2 = tf.gather(t, tf.tensor1d([1, 0, 0, 1], 'int32'), 2);
+        expect(t2.shape).toEqual([2, 2, 4]);
+        test_util_1.expectArraysClose(t2, [2, 1, 1, 2, 4, 3, 3, 4, 6, 5, 5, 6, 8, 7, 7, 8]);
+    });
+    it('bool (gather)', function () {
+        var t = tf.tensor1d([true, false, true], 'bool');
+        var t2 = tf.gather(t, tf.tensor1d([0, 2, 0, 1], 'int32'), 0);
+        expect(t2.shape).toEqual([4]);
+        expect(t2.dtype).toBe('bool');
+        expect(t2.dataSync()).toEqual(new Uint8Array([1, 1, 1, 0]));
+    });
+    it('int32 (gather)', function () {
+        var t = tf.tensor1d([1, 2, 5], 'int32');
+        var t2 = tf.gather(t, tf.tensor1d([0, 2, 0, 1], 'int32'), 0);
+        expect(t2.shape).toEqual([4]);
+        expect(t2.dtype).toBe('int32');
+        expect(t2.dataSync()).toEqual(new Int32Array([1, 5, 1, 2]));
+    });
+    it('propagates NaNs', function () {
+        var t = tf.tensor1d([1, 2, NaN]);
+        var t2 = tf.gather(t, tf.tensor1d([0, 2, 0, 1], 'int32'), 0);
+        expect(t2.shape).toEqual([4]);
+        test_util_1.expectArraysClose(t2, [1, NaN, 1, 2]);
+    });
+    it('chaining, axis=1', function () {
+        var x = tf.zeros([2, 4, 6]);
+        var indices = tf.range(0, 6, 2, 'int32');
+        var axis = 2;
+        expect(x.gather(indices, axis).shape).toEqual([2, 4, 3]);
+    });
+    it('indices not int32 throws error', function () {
+        var x = tf.zeros([2, 4, 6]);
+        var indices = tf.range(0, 6, 2);
+        var axis = 2;
+        expect(function () { return x.gather(indices, axis); }).toThrowError();
+    });
+    it('throws when passed x as a non-tensor', function () {
+        expect(function () { return tf.gather({}, tf.tensor1d([1])); })
+            .toThrowError(/Argument 'x' passed to 'gather' must be a Tensor/);
+    });
+    it('throws when passed indices as a non-tensor', function () {
+        expect(function () { return tf.gather(tf.tensor1d([1]), {}); })
+            .toThrowError(/Argument 'indices' passed to 'gather' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var res = tf.gather([1, 2, 3], [0, 2, 0, 1], 0);
+        expect(res.shape).toEqual([4]);
+        test_util_1.expectArraysClose(res, [1, 3, 1, 2]);
+    });
+    it('gradient 1D (gather)', function () {
+        var t = tf.tensor1d([1, 2, 3]);
+        var indices = tf.tensor1d([0, 2, 0, 1], 'int32');
+        var dy = tf.tensor([3, 4, 5, 6]);
+        var gradients = tf.grad(function (t) { return tf.gather(t, indices); })(t, dy);
+        expect(gradients.shape).toEqual(t.shape);
+        test_util_1.expectArraysClose(gradients, [8, 6, 4]);
+    });
+    it('gradient 2D (gather) axis=0 shape=[2, 2]', function () {
+        var t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
+        var indices = tf.tensor1d([1, 0, 0, 1], 'int32');
+        var dy = tf.tensor([3, 4, 5, 6, 7, 8, 9, 10], [4, 2]);
+        var axis = 0;
+        var gradients = tf.grad(function (t) { return tf.gather(t, indices, axis); })(t, dy);
+        expect(gradients.shape).toEqual(t.shape);
+        test_util_1.expectArraysClose(gradients, [12, 14, 12, 14]);
+    });
+    it('gradient 2D (gather) axis=0 shape=[4, 1]', function () {
+        var t = tf.tensor2d([1, 11, 2, 22], [4, 1]);
+        var indices = tf.tensor1d([1, 0, 0, 1], 'int32');
+        var dy = tf.tensor([23, 7, 19, 13], [4, 1]);
+        var axis = 0;
+        var gradients = tf.grad(function (t) { return tf.gather(t, indices, axis); })(t, dy);
+        expect(gradients.shape).toEqual(t.shape);
+        test_util_1.expectArraysClose(gradients, [26, 36, 0, 0]);
+    });
+    it('gradient 2D (gather) axis=1 shape=[2, 2]', function () {
+        var t = tf.tensor2d([1, 11, 2, 22], [2, 2]);
+        var indices = tf.tensor1d([1, 0, 0, 1], 'int32');
+        var dy = tf.tensor([3, 4, 5, 6, 7, 8, 9, 10], [2, 4]);
+        var axis = 1;
+        var gradients = tf.grad(function (t) { return tf.gather(t, indices, axis); })(t, dy);
+        expect(gradients.shape).toEqual(t.shape);
+        test_util_1.expectArraysClose(gradients, [9, 9, 17, 17]);
+    });
+    it('gradient 2D (gather) axis=1 shape=[4, 1]', function () {
+        var t = tf.tensor2d([1, 11, 2, 22], [4, 1]);
+        var indices = tf.tensor1d([0, 0, 0, 0], 'int32');
+        var dy = tf.tensor([3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18], [4, 4]);
+        var axis = 1;
+        var gradients = tf.grad(function (t) { return tf.gather(t, indices, axis); })(t, dy);
+        expect(gradients.shape).toEqual(t.shape);
+        test_util_1.expectArraysClose(gradients, [18, 34, 50, 66]);
+    });
+    it('gradient 3D (gather) axis=0 shape=[2, 3, 2]', function () {
+        var t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
+        var indices = tf.tensor1d([1, 0, 0, 1], 'int32');
+        var dy = tf.tensor([
+            2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13,
+            4, 15, 12, -7, 18, 19, 2, 21, 6, 23, 24, 25
+        ], [4, 3, 2]);
+        var axis = 0;
+        var gradients = tf.grad(function (t) { return tf.gather(t, indices, axis); })(t, dy);
+        expect(gradients.shape).toEqual(t.shape);
+        test_util_1.expectArraysClose(gradients, [5, 33, 12.01, -7, 30, 32, 4, 18, 10, 38, 30, 25.7]);
+    });
+    it('gradient 3D (gather) axis=0 shape=[1, 4, 4]', function () {
+        var t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 4, 4]);
+        var indices = tf.tensor1d([0, 0], 'int32');
+        var dy = tf.tensor([
+            2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13, 4, 15, 12, -7,
+            18, 19, 2, 21, 6, 23, 24, 25, 101, 31, 34, 54, 1, 0, -3, -4
+        ], [2, 4, 4]);
+        var axis = 0;
+        var gradients = tf.grad(function (t) { return tf.gather(t, indices, axis); })(t, dy);
+        expect(gradients.shape).toEqual(t.shape);
+        test_util_1.expectArraysClose(gradients, [20, 16, 6, 36, 12, 23.7, 25, 43, 101.01, 31, 46, 67, 5, 15, 9, -11]);
+    });
+    it('gradient 3D (gather) axis=1 shape=[2, 3, 2]', function () {
+        var t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
+        var indices = tf.tensor1d([1, 2, 2, 1], 'int32');
+        var dy = tf.tensor([2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13, 4, 15, 12, -7], [2, 4, 2]);
+        var axis = 1;
+        var gradients = tf.grad(function (t) { return tf.gather(t, indices, axis); })(t, dy);
+        expect(gradients.shape).toEqual(t.shape);
+        test_util_1.expectArraysClose(gradients, [0, 0, 3, 15, 10, 15.7, 0, 0, 12.01, -7, 16, 28]);
+    });
+    it('gradient 3D (gather) axis=1 shape=[1, 4, 4]', function () {
+        var t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 4, 4]);
+        var indices = tf.tensor1d([1, 2, 2, 1], 'int32');
+        var dy = tf.tensor([2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13, 4, 15, 12, -7], [1, 4, 4]);
+        var axis = 1;
+        var gradients = tf.grad(function (t) { return tf.gather(t, indices, axis); })(t, dy);
+        expect(gradients.shape).toEqual(t.shape);
+        test_util_1.expectArraysClose(gradients, [0, 0, 0, 0, 6, 12, 16, 8, 6.01, .7, 13, 31, 0, 0, 0, 0]);
+    });
+    it('gradient 3D (gather) axis=2 shape=[2, 3, 2]', function () {
+        var t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
+        var indices = tf.tensor1d([1, 0, 1, 0], 'int32');
+        var dy = tf.tensor([
+            2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 12, 13,
+            4, 15, 12, -7, 18, 19, 2, 21, 6, 23, 24, 25
+        ], [2, 3, 4]);
+        var axis = 2;
+        var gradients = tf.grad(function (t) { return tf.gather(t, indices, axis); })(t, dy);
+        expect(gradients.shape).toEqual(t.shape);
+        test_util_1.expectArraysClose(gradients, [12, 6, 18.7, 7, 13, 12.01, 8, 16, 40, 20, 48, 30]);
+    });
+    it('gradient 3D (gather) axis=2 shape=[4, 1, 4]', function () {
+        var t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [4, 1, 4]);
+        var indices = tf.tensor1d([1, 3, 1], 'int32');
+        var dy = tf.tensor([2, -3, 4, 15, 6, 0.7, 1, 18, 0.01, 0, 4, 15], [4, 1, 3]);
+        var axis = 2;
+        var gradients = tf.grad(function (t) { return tf.gather(t, indices, axis); })(t, dy);
+        expect(gradients.shape).toEqual(t.shape);
+        test_util_1.expectArraysClose(gradients, [0, 6, 0, -3, 0, 15.7, 0, 6, 0, 1.01, 0, 18, 0, 15, 0, 4]);
+    });
+});
+jasmine_util_1.describeWithFlags('oneHot', test_util_1.ALL_ENVS, function () {
+    it('Depth 1 throws error', function () {
+        var indices = tf.tensor1d([0, 0, 0], 'int32');
+        expect(function () { return tf.oneHot(indices, 1); }).toThrowError();
+    });
+    it('Depth 2, diagonal', function () {
+        var indices = tf.tensor1d([0, 1], 'int32');
+        var res = tf.oneHot(indices, 2);
+        expect(res.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(res, [1, 0, 0, 1]);
+    });
+    it('Depth 2, transposed diagonal', function () {
+        var indices = tf.tensor1d([1, 0], 'int32');
+        var res = tf.oneHot(indices, 2);
+        expect(res.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(res, [0, 1, 1, 0]);
+    });
+    it('Depth 3, 4 events', function () {
+        var indices = tf.tensor1d([2, 1, 2, 0], 'int32');
+        var res = tf.oneHot(indices, 3);
+        expect(res.shape).toEqual([4, 3]);
+        test_util_1.expectArraysClose(res, [0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 0, 0]);
+    });
+    it('Out of range events do not trigger onValue', function () {
+        var indices = tf.tensor1d([-1, 5, 12345], 'int32');
+        var res = tf.oneHot(indices, 5);
+        expect(res.shape).toEqual([3, 5]);
+        test_util_1.expectArraysClose(res, [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]);
+    });
+    it('Depth 2 onValue=3, offValue=-2', function () {
+        var indices = tf.tensor1d([0, 1], 'int32');
+        var res = tf.oneHot(indices, 2, 3, -2);
+        expect(res.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(res, [3, -2, -2, 3]);
+    });
+    it('indices not int32 throws error', function () {
+        var indices = tf.tensor1d([0, 1], 'float32');
+        expect(function () { return tf.oneHot(indices, 2); }).toThrowError();
+    });
+    it('check output dtype', function () {
+        var expectedType = 'int32';
+        var indices = tf.tensor1d([0, 1], 'int32');
+        var res = tf.oneHot(indices, 2);
+        expect(res.dtype).toEqual(expectedType);
+    });
+    it('oneHot accepts a tensor-like object', function () {
+        var res = tf.oneHot([0, 1], 2);
+        expect(res.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(res, [1, 0, 0, 1]);
+    });
+    it('has gradient', function () {
+        var a = tf.tensor1d([0, 1, 2], 'int32');
+        var dy = tf.ones([3, 3], 'float32');
+        var da = tf.grad(function (x) { return tf.oneHot(x, 3); })(a, dy);
+        expect(da.dtype).toBe('float32');
+        expect(da.shape).toEqual([3]);
+        test_util_1.expectArraysClose(da, [0, 0, 0]);
+    });
+});
+jasmine_util_1.describeWithFlags('linspace', test_util_1.ALL_ENVS, function () {
+    it('start stop', function () {
+        var a = tf.linspace(1, 10, 10);
+        test_util_1.expectArraysEqual(a, [1., 2., 3., 4., 5., 6., 7., 8., 9., 10.]);
+        expect(a.shape).toEqual([10]);
+        var b = tf.linspace(12, 17, 8);
+        test_util_1.expectArraysClose(b, [
+            12., 12.71428571, 13.42857143, 14.14285714, 14.85714286, 15.57142857,
+            16.28571429, 17.
+        ]);
+        expect(b.shape).toEqual([8]);
+        var c = tf.linspace(9, 0, 6);
+        test_util_1.expectArraysClose(c, [9., 7.2, 5.4, 3.6, 1.8, 0.]);
+        expect(c.shape).toEqual([6]);
+    });
+    it('negative start stop', function () {
+        var a = tf.linspace(-4, 5, 6);
+        test_util_1.expectArraysClose(a, [-4., -2.2, -0.4, 1.4, 3.2, 5.]);
+        expect(a.shape).toEqual([6]);
+    });
+    it('start negative stop', function () {
+        var a = tf.linspace(4, -5, 6);
+        test_util_1.expectArraysClose(a, [4., 2.2, 0.4, -1.4, -3.2, -5.]);
+        expect(a.shape).toEqual([6]);
+    });
+    it('negative start negative stop', function () {
+        var a = tf.linspace(-4, -5, 6);
+        test_util_1.expectArraysClose(a, [-4., -4.2, -4.4, -4.6, -4.8, -5.]);
+        expect(a.shape).toEqual([6]);
+        var b = tf.linspace(-9, -4, 5);
+        test_util_1.expectArraysClose(b, [-9., -7.75, -6.5, -5.25, -4.]);
+        expect(b.shape).toEqual([5]);
+    });
+    it('should throw with no samples', function () {
+        expect(function () { return tf.linspace(2, 10, 0); }).toThrow();
+    });
+});
+jasmine_util_1.describeWithFlags('range', test_util_1.ALL_ENVS, function () {
+    it('start stop', function () {
+        var a = tf.range(0, 3);
+        test_util_1.expectArraysEqual(a, [0, 1, 2]);
+        expect(a.shape).toEqual([3]);
+        var b = tf.range(3, 8);
+        test_util_1.expectArraysEqual(b, [3, 4, 5, 6, 7]);
+        expect(b.shape).toEqual([5]);
+    });
+    it('start stop negative', function () {
+        var a = tf.range(-2, 3);
+        test_util_1.expectArraysEqual(a, [-2, -1, 0, 1, 2]);
+        expect(a.shape).toEqual([5]);
+        var b = tf.range(4, -2);
+        test_util_1.expectArraysEqual(b, [4, 3, 2, 1, 0, -1]);
+        expect(b.shape).toEqual([6]);
+    });
+    it('start stop step', function () {
+        var a = tf.range(4, 15, 4);
+        test_util_1.expectArraysEqual(a, [4, 8, 12]);
+        expect(a.shape).toEqual([3]);
+        var b = tf.range(4, 11, 4);
+        test_util_1.expectArraysEqual(b, [4, 8]);
+        expect(b.shape).toEqual([2]);
+        var c = tf.range(4, 17, 4);
+        test_util_1.expectArraysEqual(c, [4, 8, 12, 16]);
+        expect(c.shape).toEqual([4]);
+        var d = tf.range(0, 30, 5);
+        test_util_1.expectArraysEqual(d, [0, 5, 10, 15, 20, 25]);
+        expect(d.shape).toEqual([6]);
+        var e = tf.range(-3, 9, 2);
+        test_util_1.expectArraysEqual(e, [-3, -1, 1, 3, 5, 7]);
+        expect(e.shape).toEqual([6]);
+        var f = tf.range(3, 3);
+        test_util_1.expectArraysEqual(f, new Float32Array(0));
+        expect(f.shape).toEqual([0]);
+        var g = tf.range(3, 3, 1);
+        test_util_1.expectArraysEqual(g, new Float32Array(0));
+        expect(g.shape).toEqual([0]);
+        var h = tf.range(3, 3, 4);
+        test_util_1.expectArraysEqual(h, new Float32Array(0));
+        expect(h.shape).toEqual([0]);
+        var i = tf.range(-18, -2, 5);
+        test_util_1.expectArraysEqual(i, [-18, -13, -8, -3]);
+        expect(i.shape).toEqual([4]);
+    });
+    it('start stop large step', function () {
+        var a = tf.range(3, 10, 150);
+        test_util_1.expectArraysEqual(a, [3]);
+        expect(a.shape).toEqual([1]);
+        var b = tf.range(10, 500, 205);
+        test_util_1.expectArraysEqual(b, [10, 215, 420]);
+        expect(b.shape).toEqual([3]);
+        var c = tf.range(3, -10, -150);
+        test_util_1.expectArraysEqual(c, [3]);
+        expect(c.shape).toEqual([1]);
+        var d = tf.range(-10, -500, -205);
+        test_util_1.expectArraysEqual(d, [-10, -215, -420]);
+        expect(d.shape).toEqual([3]);
+    });
+    it('start stop negative step', function () {
+        var a = tf.range(0, -10, -1);
+        test_util_1.expectArraysEqual(a, [0, -1, -2, -3, -4, -5, -6, -7, -8, -9]);
+        expect(a.shape).toEqual([10]);
+        var b = tf.range(0, -10);
+        test_util_1.expectArraysEqual(b, [0, -1, -2, -3, -4, -5, -6, -7, -8, -9]);
+        expect(b.shape).toEqual([10]);
+        var c = tf.range(3, -4, -2);
+        test_util_1.expectArraysEqual(c, [3, 1, -1, -3]);
+        expect(c.shape).toEqual([4]);
+        var d = tf.range(-3, -18, -5);
+        test_util_1.expectArraysEqual(d, [-3, -8, -13]);
+        expect(d.shape).toEqual([3]);
+    });
+    it('start stop incompatible step', function () {
+        var a = tf.range(3, 10, -2);
+        test_util_1.expectArraysEqual(a, new Float32Array(0));
+        expect(a.shape).toEqual([0]);
+        var b = tf.range(40, 3, 2);
+        test_util_1.expectArraysEqual(b, new Float32Array(0));
+        expect(b.shape).toEqual([0]);
+    });
+    it('zero step', function () {
+        expect(function () { return tf.range(2, 10, 0); }).toThrow();
+    });
+    it('should have default dtype', function () {
+        var a = tf.range(1, 4);
+        test_util_1.expectArraysEqual(a, [1, 2, 3]);
+        expect(a.dtype).toEqual('float32');
+        expect(a.shape).toEqual([3]);
+    });
+    it('should have float32 dtype', function () {
+        var a = tf.range(1, 4, undefined, 'float32');
+        test_util_1.expectArraysEqual(a, [1, 2, 3]);
+        expect(a.dtype).toEqual('float32');
+        expect(a.shape).toEqual([3]);
+    });
+    it('should have int32 dtype', function () {
+        var a = tf.range(1, 4, undefined, 'int32');
+        test_util_1.expectArraysEqual(a, [1, 2, 3]);
+        expect(a.dtype).toEqual('int32');
+        expect(a.shape).toEqual([3]);
+    });
+});
+jasmine_util_1.describeWithFlags('fill', test_util_1.ALL_ENVS, function () {
+    it('1D fill', function () {
+        var a = tf.fill([3], 2);
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([3]);
+        test_util_1.expectArraysClose(a, [2, 2, 2]);
+    });
+    it('1D fill string', function () {
+        var a = tf.fill([3], 'aa');
+        expect(a.dtype).toBe('string');
+        expect(a.shape).toEqual([3]);
+        test_util_1.expectArraysEqual(a, ['aa', 'aa', 'aa']);
+    });
+    it('2D fill', function () {
+        var a = tf.fill([3, 2], 2);
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([3, 2]);
+        test_util_1.expectArraysClose(a, [2, 2, 2, 2, 2, 2]);
+    });
+    it('2D fill string', function () {
+        var a = tf.fill([3, 2], 'a');
+        expect(a.dtype).toBe('string');
+        expect(a.shape).toEqual([3, 2]);
+        test_util_1.expectArraysEqual(a, ['a', 'a', 'a', 'a', 'a', 'a']);
+    });
+    it('3D fill', function () {
+        var a = tf.fill([3, 2, 1], 2);
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([3, 2, 1]);
+        test_util_1.expectArraysClose(a, [2, 2, 2, 2, 2, 2]);
+    });
+    it('4D fill', function () {
+        var a = tf.fill([3, 2, 1, 2], 2);
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([3, 2, 1, 2]);
+        test_util_1.expectArraysClose(a, [2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2]);
+    });
+    it('5D fill', function () {
+        var a = tf.fill([2, 1, 2, 1, 2], 2);
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([2, 1, 2, 1, 2]);
+        test_util_1.expectArraysClose(a, [2, 2, 2, 2, 2, 2, 2, 2]);
+    });
+});
+jasmine_util_1.describeWithFlags('stack', test_util_1.ALL_ENVS, function () {
+    it('scalars 3, 5 and 7', function () {
+        var a = tf.scalar(3);
+        var b = tf.scalar(5);
+        var c = tf.scalar(7);
+        var res = tf.stack([a, b, c]);
+        expect(res.shape).toEqual([3]);
+        test_util_1.expectArraysClose(res, [3, 5, 7]);
+    });
+    it('scalars 3, 5 and 7 along axis=1 throws error', function () {
+        var a = tf.scalar(3);
+        var b = tf.scalar(5);
+        var c = tf.scalar(7);
+        var f = function () { return tf.stack([a, b, c], 1); };
+        expect(f).toThrowError();
+    });
+    it('non matching shapes throws error', function () {
+        var a = tf.scalar(3);
+        var b = tf.tensor1d([5]);
+        var f = function () { return tf.stack([a, b]); };
+        expect(f).toThrowError();
+    });
+    it('non matching dtypes throws error', function () {
+        var a = tf.scalar(3);
+        var b = tf.scalar(5, 'bool');
+        var f = function () { return tf.stack([a, b]); };
+        expect(f).toThrowError();
+    });
+    it('2d but axis=3 throws error', function () {
+        var a = tf.zeros([2, 2]);
+        var b = tf.zeros([2, 2]);
+        var f = function () { return tf.stack([a, b], 3); };
+        expect(f).toThrowError();
+    });
+    it('[1,2], [3,4] and [5,6], axis=0', function () {
+        var a = tf.tensor1d([1, 2]);
+        var b = tf.tensor1d([3, 4]);
+        var c = tf.tensor1d([5, 6]);
+        var res = tf.stack([a, b, c], 0);
+        expect(res.shape).toEqual([3, 2]);
+        test_util_1.expectArraysClose(res, [1, 2, 3, 4, 5, 6]);
+    });
+    it('[1,2], [3,4] and [5,6], axis=1', function () {
+        var a = tf.tensor1d([1, 2]);
+        var b = tf.tensor1d([3, 4]);
+        var c = tf.tensor1d([5, 6]);
+        var res = tf.stack([a, b, c], 1);
+        expect(res.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(res, [1, 3, 5, 2, 4, 6]);
+    });
+    it('[[1,2],[3,4]] and [[5, 6], [7, 8]], axis=0', function () {
+        var a = tf.tensor2d([[1, 2], [3, 4]]);
+        var b = tf.tensor2d([[5, 6], [7, 8]]);
+        var res = tf.stack([a, b], 0);
+        expect(res.shape).toEqual([2, 2, 2]);
+        test_util_1.expectArraysClose(res, [1, 2, 3, 4, 5, 6, 7, 8]);
+    });
+    it('[[1,2],[3,4]] and [[5, 6], [7, 8]], axis=2', function () {
+        var a = tf.tensor2d([[1, 2], [3, 4]]);
+        var b = tf.tensor2d([[5, 6], [7, 8]]);
+        var c = tf.tensor2d([[9, 10], [11, 12]]);
+        var res = tf.stack([a, b, c], 2);
+        expect(res.shape).toEqual([2, 2, 3]);
+        test_util_1.expectArraysClose(res, [1, 5, 9, 2, 6, 10, 3, 7, 11, 4, 8, 12]);
+    });
+    it('single tensor', function () {
+        var a = tf.tensor2d([[1, 2], [3, 4]]);
+        var res = tf.stack([a], 2);
+        expect(res.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(res, [1, 2, 3, 4]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.stack([{}]); })
+            .toThrowError(/Argument 'tensors\[0\]' passed to 'stack' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [[1, 2], [3, 4]];
+        var res = tf.stack([a], 2);
+        expect(res.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(res, [1, 2, 3, 4]);
+    });
+});
+jasmine_util_1.describeWithFlags('unstack', test_util_1.ALL_ENVS, function () {
+    it('unstack by default', function () {
+        var x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
+        var res = tf.unstack(x);
+        expect(res.length).toEqual(2);
+        expect(res[0].rank).toEqual(1);
+        expect(res[0].shape).toEqual([4]);
+        test_util_1.expectArraysClose(res[0], [1, 2, 3, 4]);
+        expect(res[1].rank).toEqual(1);
+        expect(res[1].shape).toEqual([4]);
+        test_util_1.expectArraysClose(res[1], [5, 6, 7, 8]);
+    });
+    it('unstack into 3 tensors', function () {
+        var x = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
+        var res = tf.unstack(x, 0);
+        expect(res.length).toEqual(3);
+        expect(res[0].rank).toEqual(1);
+        expect(res[0].shape).toEqual([2]);
+        test_util_1.expectArraysClose(res[0], [1, 2]);
+        expect(res[1].rank).toEqual(1);
+        expect(res[1].shape).toEqual([2]);
+        test_util_1.expectArraysClose(res[1], [3, 4]);
+        expect(res[2].rank).toEqual(1);
+        expect(res[2].shape).toEqual([2]);
+        test_util_1.expectArraysClose(res[2], [5, 6]);
+    });
+    it('unstack by axis=1', function () {
+        var x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
+        var res = tf.unstack(x, 1);
+        expect(res.length).toEqual(4);
+        expect(res[0].rank).toEqual(1);
+        expect(res[0].shape).toEqual([2]);
+        test_util_1.expectArraysClose(res[0], [1, 5]);
+        expect(res[1].rank).toEqual(1);
+        expect(res[1].shape).toEqual([2]);
+        test_util_1.expectArraysClose(res[1], [2, 6]);
+        expect(res[2].rank).toEqual(1);
+        expect(res[2].shape).toEqual([2]);
+        test_util_1.expectArraysClose(res[2], [3, 7]);
+        expect(res[3].rank).toEqual(1);
+        expect(res[3].shape).toEqual([2]);
+        test_util_1.expectArraysClose(res[3], [4, 8]);
+    });
+    it('unstack rank 3 tensor', function () {
+        var x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
+        var res = tf.unstack(x);
+        expect(res.length).toEqual(2);
+        expect(res[0].rank).toEqual(2);
+        expect(res[0].shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(res[0], [1, 2, 3, 4]);
+        expect(res[1].rank).toEqual(2);
+        expect(res[1].shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(res[1], [5, 6, 7, 8]);
+    });
+    it('unstack rank 3 tensor with axis=1', function () {
+        var x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
+        var res = tf.unstack(x, 1);
+        expect(res.length).toEqual(2);
+        expect(res[0].rank).toEqual(2);
+        expect(res[0].shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(res[0], [1, 2, 5, 6]);
+        expect(res[1].rank).toEqual(2);
+        expect(res[1].shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(res[1], [3, 4, 7, 8]);
+    });
+    it('unstack rank 3 tensor with axis=2', function () {
+        var x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
+        var res = tf.unstack(x, 2);
+        expect(res.length).toEqual(2);
+        expect(res[0].rank).toEqual(2);
+        expect(res[0].shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(res[0], [1, 3, 5, 7]);
+        expect(res[1].rank).toEqual(2);
+        expect(res[1].shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(res[1], [2, 4, 6, 8]);
+    });
+    it('unstack rank 4 tensor', function () {
+        var x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
+        var res = tf.unstack(x);
+        expect(res.length).toEqual(2);
+        expect(res[0].rank).toEqual(3);
+        expect(res[0].shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(res[0], [1, 2, 3, 4]);
+        expect(res[1].rank).toEqual(3);
+        expect(res[1].shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(res[1], [5, 6, 7, 8]);
+    });
+    it('unstack rank 4 tensor with axis=1', function () {
+        var x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
+        var res = tf.unstack(x, 1);
+        expect(res.length).toEqual(2);
+        expect(res[0].rank).toEqual(3);
+        expect(res[0].shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(res[0], [1, 2, 5, 6]);
+        expect(res[1].rank).toEqual(3);
+        expect(res[1].shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(res[1], [3, 4, 7, 8]);
+    });
+    it('unstack rank 4 tensor with axis=2', function () {
+        var x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
+        var res = tf.unstack(x, 2);
+        expect(res.length).toEqual(2);
+        expect(res[0].rank).toEqual(3);
+        expect(res[0].shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(res[0], [1, 3, 5, 7]);
+        expect(res[1].rank).toEqual(3);
+        expect(res[1].shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(res[1], [2, 4, 6, 8]);
+    });
+    it('unstack rank 4 tensor with axis=3', function () {
+        var x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
+        var res = tf.unstack(x, 3);
+        expect(res.length).toEqual(1);
+        expect(res[0].rank).toEqual(3);
+        expect(res[0].shape).toEqual([2, 2, 2]);
+        test_util_1.expectArraysClose(res[0], [1, 2, 3, 4, 5, 6, 7, 8]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.unstack({}); })
+            .toThrowError(/Argument 'x' passed to 'unstack' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var x = [[1, 2, 3, 4], [5, 6, 7, 8]];
+        var res = tf.unstack(x);
+        expect(res.length).toEqual(2);
+        expect(res[0].rank).toEqual(1);
+        expect(res[0].shape).toEqual([4]);
+        test_util_1.expectArraysClose(res[0], [1, 2, 3, 4]);
+        expect(res[1].rank).toEqual(1);
+        expect(res[1].shape).toEqual([4]);
+        test_util_1.expectArraysClose(res[1], [5, 6, 7, 8]);
+    });
+});
+jasmine_util_1.describeWithFlags('split', test_util_1.ALL_ENVS, function () {
+    it('split by number', function () {
+        var x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
+        var res = tf.split(x, 2, 1);
+        expect(res.length).toEqual(2);
+        expect(res[0].shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(res[0], [1, 2, 5, 6]);
+        expect(res[1].shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(res[1], [3, 4, 7, 8]);
+    });
+    it('split by sizes', function () {
+        var x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
+        var res = tf.split(x, [1, 2, 1], 1);
+        expect(res.length).toEqual(3);
+        expect(res[0].shape).toEqual([2, 1]);
+        test_util_1.expectArraysClose(res[0], [1, 5]);
+        expect(res[1].shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(res[1], [2, 3, 6, 7]);
+        expect(res[2].shape).toEqual([2, 1]);
+        test_util_1.expectArraysClose(res[2], [4, 8]);
+    });
+    it('chainable split by sizes', function () {
+        var x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
+        var res = x.split([1, 2, 1], 1);
+        expect(res.length).toEqual(3);
+        expect(res[0].shape).toEqual([2, 1]);
+        test_util_1.expectArraysClose(res[0], [1, 5]);
+        expect(res[1].shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(res[1], [2, 3, 6, 7]);
+        expect(res[2].shape).toEqual([2, 1]);
+        test_util_1.expectArraysClose(res[2], [4, 8]);
+    });
+    it('sizes to not sum to axis size throws error', function () {
+        var x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
+        var f = function () { return tf.split(x, [1, 2], 1); };
+        expect(f).toThrowError();
+    });
+    it('number of splits does not evenly divide axis', function () {
+        var x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);
+        var f = function () { return tf.split(x, 3, 1); };
+        expect(f).toThrowError();
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.split({}, 1); })
+            .toThrowError(/Argument 'x' passed to 'split' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var x = [[1, 2, 3, 4], [5, 6, 7, 8]];
+        var res = tf.split(x, 2, 1);
+        expect(res.length).toEqual(2);
+        expect(res[0].shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(res[0], [1, 2, 5, 6]);
+        expect(res[1].shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(res[1], [3, 4, 7, 8]);
+    });
+    it('gradient of 1st output', function () {
+        var a = tf.tensor1d([1, 2, 3]);
+        var da = tf.grad(function (x) { return tf.split(x, [1, 2])[0]; })(a);
+        expect(da.shape).toEqual([3]);
+        test_util_1.expectArraysClose(da, [1, 0, 0]);
+    });
+    it('gradient of 2nd output', function () {
+        var a = tf.tensor1d([1, 2, 3]);
+        var da = tf.grad(function (x) { return tf.split(x, [1, 2])[1]; })(a);
+        expect(da.shape).toEqual([3]);
+        test_util_1.expectArraysClose(da, [0, 1, 1]);
+    });
+});
+jasmine_util_1.describeWithFlags('expandDims', test_util_1.ALL_ENVS, function () {
+    it('scalar, default axis is 0', function () {
+        var res = tf.scalar(1).expandDims();
+        expect(res.shape).toEqual([1]);
+        test_util_1.expectArraysClose(res, [1]);
+    });
+    it('scalar, axis is out of bounds throws error', function () {
+        var f = function () { return tf.scalar(1).expandDims(1); };
+        expect(f).toThrowError();
+    });
+    it('1d, axis=-3', function () {
+        expect(function () {
+            tf.tensor1d([1, 2, 3]).expandDims(-3);
+        }).toThrowError('Axis must be in the interval [-2, 1]');
+    });
+    it('1d, axis=-2', function () {
+        var res = tf.tensor1d([1, 2, 3]).expandDims(-2);
+        expect(res.shape).toEqual([1, 3]);
+        test_util_1.expectArraysClose(res, [1, 2, 3]);
+    });
+    it('1d, axis=-1', function () {
+        var res = tf.tensor1d([1, 2, 3]).expandDims(-1);
+        expect(res.shape).toEqual([3, 1]);
+        test_util_1.expectArraysClose(res, [1, 2, 3]);
+    });
+    it('1d, axis=0', function () {
+        var res = tf.tensor1d([1, 2, 3]).expandDims(0);
+        expect(res.shape).toEqual([1, 3]);
+        test_util_1.expectArraysClose(res, [1, 2, 3]);
+    });
+    it('1d, axis=1', function () {
+        var res = tf.tensor1d([1, 2, 3]).expandDims(1);
+        expect(res.shape).toEqual([3, 1]);
+        test_util_1.expectArraysClose(res, [1, 2, 3]);
+    });
+    it('2d, axis=-4', function () {
+        expect(function () {
+            tf.tensor2d([[1, 2], [3, 4], [5, 6]]).expandDims(-4);
+        }).toThrowError('Axis must be in the interval [-3, 2]');
+    });
+    it('2d, axis=-3', function () {
+        var res = tf.tensor2d([[1, 2], [3, 4], [5, 6]]).expandDims(-3);
+        expect(res.shape).toEqual([1, 3, 2]);
+        test_util_1.expectArraysClose(res, [1, 2, 3, 4, 5, 6]);
+    });
+    it('2d, axis=-2', function () {
+        var res = tf.tensor2d([[1, 2], [3, 4], [5, 6]]).expandDims(-2);
+        expect(res.shape).toEqual([3, 1, 2]);
+        test_util_1.expectArraysClose(res, [1, 2, 3, 4, 5, 6]);
+    });
+    it('2d, axis=-1', function () {
+        var res = tf.tensor2d([[1, 2], [3, 4], [5, 6]]).expandDims(-1);
+        expect(res.shape).toEqual([3, 2, 1]);
+        test_util_1.expectArraysClose(res, [1, 2, 3, 4, 5, 6]);
+    });
+    it('2d, axis=0', function () {
+        var res = tf.tensor2d([[1, 2], [3, 4], [5, 6]]).expandDims(0);
+        expect(res.shape).toEqual([1, 3, 2]);
+        test_util_1.expectArraysClose(res, [1, 2, 3, 4, 5, 6]);
+    });
+    it('2d, axis=1', function () {
+        var res = tf.tensor2d([[1, 2], [3, 4], [5, 6]]).expandDims(1);
+        expect(res.shape).toEqual([3, 1, 2]);
+        test_util_1.expectArraysClose(res, [1, 2, 3, 4, 5, 6]);
+    });
+    it('2d, axis=2', function () {
+        var res = tf.tensor2d([[1, 2], [3, 4], [5, 6]]).expandDims(2);
+        expect(res.shape).toEqual([3, 2, 1]);
+        test_util_1.expectArraysClose(res, [1, 2, 3, 4, 5, 6]);
+    });
+    it('4d, axis=0', function () {
+        var res = tf.tensor4d([[[[4]]]]).expandDims();
+        expect(res.shape).toEqual([1, 1, 1, 1, 1]);
+        test_util_1.expectArraysClose(res, [4]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.expandDims({}); })
+            .toThrowError(/Argument 'x' passed to 'expandDims' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var res = tf.expandDims(7);
+        expect(res.shape).toEqual([1]);
+        test_util_1.expectArraysClose(res, [7]);
+    });
+    it('works with 0 in shape', function () {
+        var a = tf.tensor2d([], [0, 3]);
+        var res = a.expandDims();
+        expect(res.shape).toEqual([1, 0, 3]);
+        test_util_1.expectArraysClose(res, []);
+        var res2 = a.expandDims(1);
+        expect(res2.shape).toEqual([0, 1, 3]);
+        test_util_1.expectArraysClose(res2, []);
+        var res3 = a.expandDims(2);
+        expect(res3.shape).toEqual([0, 3, 1]);
+        test_util_1.expectArraysClose(res3, []);
+    });
+});
+jasmine_util_1.describeWithFlags('cumsum', test_util_1.ALL_ENVS, function () {
+    it('1D standard', function () {
+        var res = tf.tensor1d([1, 2, 3, 4]).cumsum();
+        expect(res.shape).toEqual([4]);
+        test_util_1.expectArraysClose(res, [1, 3, 6, 10]);
+    });
+    it('1D reverse', function () {
+        var reverse = true;
+        var exclusive = false;
+        var res = tf.tensor1d([1, 2, 3, 4]).cumsum(0, exclusive, reverse);
+        expect(res.shape).toEqual([4]);
+        test_util_1.expectArraysClose(res, [10, 9, 7, 4]);
+    });
+    it('1D exclusive', function () {
+        var exclusive = true;
+        var res = tf.tensor1d([1, 2, 3, 4]).cumsum(0, exclusive);
+        expect(res.shape).toEqual([4]);
+        test_util_1.expectArraysClose(res, [0, 1, 3, 6]);
+    });
+    it('1D exclusive reverse', function () {
+        var reverse = true;
+        var exclusive = true;
+        var res = tf.tensor1d([1, 2, 3, 4]).cumsum(0, exclusive, reverse);
+        expect(res.shape).toEqual([4]);
+        test_util_1.expectArraysClose(res, [9, 7, 4, 0]);
+    });
+    it('gradient: 1D', function () {
+        var a = tf.tensor1d([1, 2, 3]);
+        var dy = tf.tensor1d([4, 5, 6]);
+        var da = tf.grad(function (x) { return tf.cumsum(x); })(a, dy);
+        expect(da.shape).toEqual([3]);
+        test_util_1.expectArraysClose(da, [15, 11, 6]);
+    });
+    it('2D standard', function () {
+        var res = tf.tensor2d([[1, 2], [3, 4]]).cumsum(1);
+        expect(res.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(res, [1, 3, 3, 7]);
+    });
+    it('2D reverse exclusive', function () {
+        var reverse = true;
+        var exclusive = true;
+        var res = tf.tensor2d([[1, 2], [3, 4]]).cumsum(1, exclusive, reverse);
+        expect(res.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(res, [2, 0, 4, 0]);
+    });
+    it('2D axis=0', function () {
+        var res = tf.tensor2d([[1, 2], [3, 4]]).cumsum();
+        expect(res.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(res, [1, 2, 4, 6]);
+    });
+    it('3D standard', function () {
+        var res = tf.tensor3d([[[0, 1], [2, 3]], [[4, 5], [6, 7]]]).cumsum(2);
+        expect(res.shape).toEqual([2, 2, 2]);
+        test_util_1.expectArraysClose(res, [0, 1, 2, 5, 4, 9, 6, 13]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.cumsum({}); })
+            .toThrowError(/Argument 'x' passed to 'cumsum' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var res = tf.cumsum([1, 2, 3, 4]);
+        expect(res.shape).toEqual([4]);
+        test_util_1.expectArraysClose(res, [1, 3, 6, 10]);
+    });
+    it('throws error for string tensor', function () {
+        expect(function () { return tf.cumsum(['a', 'b', 'c']); })
+            .toThrowError(/Argument 'x' passed to 'cumsum' must be numeric tensor/);
+    });
+});
+jasmine_util_1.describeWithFlags('batchToSpaceND', test_util_1.ALL_ENVS, function () {
+    it('tensor4d, input shape=[4, 1, 1, 1], blockShape=[2, 2]', function () {
+        var t = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);
+        var blockShape = [2, 2];
+        var crops = [[0, 0], [0, 0]];
+        var res = tf.batchToSpaceND(t, blockShape, crops);
+        expect(res.shape).toEqual([1, 2, 2, 1]);
+        test_util_1.expectArraysClose(res, [1, 2, 3, 4]);
+    });
+    it('tensor4d, input shape=[4, 1, 1, 3], blockShape=[2, 2]', function () {
+        var t = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [4, 1, 1, 3]);
+        var blockShape = [2, 2];
+        var crops = [[0, 0], [0, 0]];
+        var res = tf.batchToSpaceND(t, blockShape, crops);
+        expect(res.shape).toEqual([1, 2, 2, 3]);
+        test_util_1.expectArraysClose(res, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]);
+    });
+    it('tensor4d, input shape=[4, 2, 2, 1], blockShape=[2, 2]', function () {
+        var t = tf.tensor4d([1, 3, 9, 11, 2, 4, 10, 12, 5, 7, 13, 15, 6, 8, 14, 16], [4, 2, 2, 1]);
+        var blockShape = [2, 2];
+        var crops = [[0, 0], [0, 0]];
+        var res = tf.batchToSpaceND(t, blockShape, crops);
+        expect(res.shape).toEqual([1, 4, 4, 1]);
+        test_util_1.expectArraysClose(res, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]);
+    });
+    it('tensor4d, input shape=[8, 1, 3, 1], blockShape=[2, 2]', function () {
+        var t = tf.tensor4d([
+            0, 1, 3, 0, 9, 11, 0, 2, 4, 0, 10, 12,
+            0, 5, 7, 0, 13, 15, 0, 6, 8, 0, 14, 16
+        ], [8, 1, 3, 1]);
+        var blockShape = [2, 2];
+        var crops = [[0, 0], [2, 0]];
+        var res = tf.batchToSpaceND(t, blockShape, crops);
+        expect(res.shape).toEqual([2, 2, 4, 1]);
+        test_util_1.expectArraysClose(res, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16]);
+    });
+    it('tensor2d, blockShape [1]', function () {
+        var t = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var blockShape = [2];
+        var crops = [[0, 0]];
+        var res = tf.batchToSpaceND(t, blockShape, crops);
+        expect(res.shape).toEqual([1, 4]);
+        test_util_1.expectArraysClose(res, [1, 3, 2, 4]);
+    });
+    it('tensor3d,  blockSHape [1]', function () {
+        var t = tf.tensor([
+            -61, 37, -68, 72, 31, 62, 0, -13, 28, 54, 96,
+            44, -55, -64, -88, -94, 65, -32, -96, -73, -2, -77,
+            -14, 47, 33, 15, 70, 20, 75, 28, 84, -13
+        ], [8, 2, 2]);
+        var blockShape = [2];
+        var crops = [[0, 2]];
+        var res = tf.batchToSpaceND(t, blockShape, crops);
+        expect(res.shape).toEqual([4, 2, 2]);
+        test_util_1.expectArraysClose(res, [-61, 37, 65, -32, 31, 62, -2, -77, 28, 54, 33, 15, -55, -64, 75, 28]);
+    });
+    it('tensor3d, blockShape [2]', function () {
+        var t = tf.tensor([
+            -61, 37, -68, 72, 31, 62, 0, -13, 28, 54, 96,
+            44, -55, -64, -88, -94, 65, -32, -96, -73, -2, -77,
+            -14, 47, 33, 15, 70, 20, 75, 28, 84, -13
+        ], [8, 2, 2]);
+        var blockShape = [2, 2];
+        var crops = [[2, 0], [2, 0]];
+        var res = tf.batchToSpaceND(t, blockShape, crops);
+        expect(res.shape).toEqual([2, 2, 2]);
+        test_util_1.expectArraysClose(res, [72, 44, -73, 20, -13, -94, 47, -13]);
+    });
+    it('throws when blockShape equal to input rank', function () {
+        var t = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);
+        var blockShape = [2, 2, 2, 2];
+        var crops = [[0, 0], [0, 0], [0, 0], [0, 0]];
+        expect(function () { return tf.batchToSpaceND(t, blockShape, crops); })
+            .toThrowError("input rank is " + t.rank + " but should be > than blockShape.length " + blockShape.length);
+    });
+    it('throws when crops row dimension not equal to blockshape', function () {
+        var t = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);
+        var blockShape = [2, 2];
+        var crops = [[0, 0]];
+        expect(function () { return tf.batchToSpaceND(t, blockShape, crops); })
+            .toThrowError("crops.length is " + crops.length + " but should be equal to blockShape.length  " + blockShape.length);
+    });
+    it('throws when input tensor batch not divisible by prod(blockShape)', function () {
+        var t = tf.tensor4d([1, 2, 3, 4, 5], [5, 1, 1, 1]);
+        var blockShape = [2, 2];
+        var crops = [[0, 0], [0, 0]];
+        var prod = blockShape.reduce(function (a, b) { return a * b; });
+        expect(function () { return tf.batchToSpaceND(t, blockShape, crops); })
+            .toThrowError("input tensor batch is " + t.shape[0] + " but is not divisible by the " +
+            ("product of the elements of blockShape " + blockShape.join(' * ') + " === " + prod));
+    });
+    it('accepts a tensor-like object', function () {
+        var t = [[[[1]]], [[[2]]], [[[3]]], [[[4]]]];
+        var blockShape = [2, 2];
+        var crops = [[0, 0], [0, 0]];
+        var res = tf.batchToSpaceND(t, blockShape, crops);
+        expect(res.shape).toEqual([1, 2, 2, 1]);
+        test_util_1.expectArraysClose(res, [1, 2, 3, 4]);
+    });
+    it('gradients,  input shape=[4, 2, 2], block shape=[2]', function () {
+        var t = tf.tensor([-61, 37, -68, 72, 31, 62, 0, -13, 28, 54, 96, 44, -55, -64, -88, -94], [4, 2, 2]);
+        var blockShape = [2];
+        var crops = [[0, 2]];
+        var dy = tf.tensor([.01, .02, .03, .04, .05, .06, .07, .08], [2, 2, 2]);
+        var gradient = tf.grad(function (t) { return tf.batchToSpaceND(t, blockShape, crops); })(t, dy);
+        expect(gradient.shape).toEqual([4, 2, 2]);
+        test_util_1.expectArraysClose(gradient, [
+            0.01, 0.02, 0, 0, 0.05, 0.06, 0, 0, 0.03, 0.04, 0, 0, 0.07, 0.08, 0, 0
+        ]);
+    });
+    it('gradients, input shape=[4, 2, 2, 1], block shape=[2, 2]', function () {
+        var t = tf.tensor4d([1, 3, 9, 11, 2, 4, 10, 12, 5, 7, 13, 15, 6, 8, 14, 16], [4, 2, 2, 1]);
+        var blockShape = [2, 2];
+        var crops = [[0, 0], [0, 0]];
+        var dy = tf.tensor([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 4, 4, 1]);
+        var gradient = tf.grad(function (t) { return tf.batchToSpaceND(t, blockShape, crops); })(t, dy);
+        expect(gradient.shape).toEqual([4, 2, 2, 1]);
+        test_util_1.expectArraysClose(gradient, [1, 3, 9, 11, 2, 4, 10, 12, 5, 7, 13, 15, 6, 8, 14, 16]);
+    });
+});
+jasmine_util_1.describeWithFlags('spaceToBatchND', test_util_1.ALL_ENVS, function () {
+    it('tensor4d, input shape=[1, 2, 2, 1], blockShape=[2, 2]', function () {
+        var t = tf.tensor4d([[[[1], [2]], [[3], [4]]]], [1, 2, 2, 1]);
+        var blockShape = [2, 2];
+        var paddings = [[0, 0], [0, 0]];
+        var res = tf.spaceToBatchND(t, blockShape, paddings);
+        expect(res.shape).toEqual([4, 1, 1, 1]);
+        test_util_1.expectArraysClose(res, [1, 2, 3, 4]);
+    });
+    it('tensor4d, input shape=[1, 2, 2, 3], blockShape=[2, 2]', function () {
+        var t = tf.tensor4d([[[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]]], [1, 2, 2, 3]);
+        var blockShape = [2, 2];
+        var paddings = [[0, 0], [0, 0]];
+        var res = tf.spaceToBatchND(t, blockShape, paddings);
+        expect(res.shape).toEqual([4, 1, 1, 3]);
+        test_util_1.expectArraysClose(res, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]);
+    });
+    it('tensor4d, input shape=[1, 4, 4, 1], blockShape=[2, 2]', function () {
+        var t = tf.tensor4d([[
+                [[1], [2], [3], [4]], [[5], [6], [7], [8]], [[9], [10], [11], [12]],
+                [[13], [14], [15], [16]]
+            ]], [1, 4, 4, 1]);
+        var blockShape = [2, 2];
+        var paddings = [[0, 0], [0, 0]];
+        var res = tf.spaceToBatchND(t, blockShape, paddings);
+        expect(res.shape).toEqual([4, 2, 2, 1]);
+        test_util_1.expectArraysClose(res, [1, 3, 9, 11, 2, 4, 10, 12, 5, 7, 13, 15, 6, 8, 14, 16]);
+    });
+    it('tensor4d, input shape=[2, 6, 6, 1], blockShape=[2, 2]', function () {
+        var t = tf.tensor4d([
+            1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
+            16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
+            31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,
+            46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
+            61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72
+        ], [2, 6, 6, 1]);
+        var blockShape = [2, 2];
+        var paddings = [[0, 0], [0, 0]];
+        var res = tf.spaceToBatchND(t, blockShape, paddings);
+        expect(res.shape).toEqual([8, 3, 3, 1]);
+        test_util_1.expectArraysClose(res, [
+            1, 3, 5, 13, 15, 17, 25, 27, 29, 37, 39, 41, 49, 51, 53, 61, 63, 65,
+            2, 4, 6, 14, 16, 18, 26, 28, 30, 38, 40, 42, 50, 52, 54, 62, 64, 66,
+            7, 9, 11, 19, 21, 23, 31, 33, 35, 43, 45, 47, 55, 57, 59, 67, 69, 71,
+            8, 10, 12, 20, 22, 24, 32, 34, 36, 44, 46, 48, 56, 58, 60, 68, 70, 72
+        ]);
+    });
+    it('tensor4d, input shape=[2, 2, 4, 1], blockShape=[2, 2]', function () {
+        var t = tf.tensor4d([
+            [[[1], [2], [3], [4]], [[5], [6], [7], [8]]],
+            [[[9], [10], [11], [12]], [[13], [14], [15], [16]]]
+        ], [2, 2, 4, 1]);
+        var blockShape = [2, 2];
+        var paddings = [[0, 0], [2, 0]];
+        var res = tf.spaceToBatchND(t, blockShape, paddings);
+        expect(res.shape).toEqual([8, 1, 3, 1]);
+        test_util_1.expectArraysClose(res, [
+            0, 1, 3, 0, 9, 11, 0, 2, 4, 0, 10, 12,
+            0, 5, 7, 0, 13, 15, 0, 6, 8, 0, 14, 16
+        ]);
+    });
+    it('tensor2d, blockShape [2]', function () {
+        var t = tf.tensor2d([1, 3, 2, 4], [1, 4]);
+        var blockShape = [2];
+        var paddings = [[0, 0]];
+        var res = tf.spaceToBatchND(t, blockShape, paddings);
+        expect(res.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(res, [1, 2, 3, 4]);
+    });
+    it('throws when blockShape equal to input rank', function () {
+        var t = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
+        var blockShape = [2, 2, 2, 2];
+        var paddings = [[0, 0], [0, 0], [0, 0], [0, 0]];
+        expect(function () { return tf.spaceToBatchND(t, blockShape, paddings); })
+            .toThrowError('input rank 4 should be > than [blockShape] 4');
+    });
+    it('throws when paddings row dimension not equal to blockshape', function () {
+        var t = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
+        var blockShape = [2, 2];
+        var paddings = [[0, 0]];
+        expect(function () { return tf.spaceToBatchND(t, blockShape, paddings); })
+            .toThrowError('paddings.shape[0] 1 must be equal to [blockShape] 2');
+    });
+    it('throws when input tensor spatial dimension not divisible by blockshapes', function () {
+        var t = tf.tensor4d([1, 2, 3, 4, 5, 6], [1, 2, 3, 1]);
+        var blockShape = [2, 2];
+        var paddings = [[0, 0], [0, 0]];
+        expect(function () { return tf.spaceToBatchND(t, blockShape, paddings); })
+            .toThrowError('input spatial dimensions 2,3,1 with paddings 0,0,0,0 must be ' +
+            'divisible by blockShapes 2,2');
+    });
+    it('accepts a tensor-like object', function () {
+        var t = [[[[1], [2]], [[3], [4]]]];
+        var blockShape = [2, 2];
+        var paddings = [[0, 0], [0, 0]];
+        var res = tf.spaceToBatchND(t, blockShape, paddings);
+        expect(res.shape).toEqual([4, 1, 1, 1]);
+        test_util_1.expectArraysClose(res, [1, 2, 3, 4]);
+    });
+});
+jasmine_util_1.describeWithFlags('batchToSpaceND X spaceToBatchND', test_util_1.ALL_ENVS, function () {
+    it('tensor4d, input shape=[4, 1, 1, 1], blockShape=[2, 2]', function () {
+        var t = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);
+        var blockShape = [2, 2];
+        var crops = [[0, 0], [0, 0]];
+        var paddings = [[0, 0], [0, 0]];
+        var b2s = tf.batchToSpaceND(t, blockShape, crops);
+        expect(b2s.shape).toEqual([1, 2, 2, 1]);
+        test_util_1.expectArraysClose(b2s, [1, 2, 3, 4]);
+        var s2b = tf.spaceToBatchND(b2s, blockShape, paddings);
+        expect(s2b.shape).toEqual([4, 1, 1, 1]);
+        test_util_1.expectArraysClose(s2b, [1, 2, 3, 4]);
+    });
+    it('tensor4d, input shape=[2, 6, 6, 1], blockShape=[2, 2]', function () {
+        var t = tf.tensor4d([
+            1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15,
+            16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,
+            31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45,
+            46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60,
+            61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72
+        ], [2, 6, 6, 1]);
+        var blockShape = [2, 2];
+        var crops = [[0, 0], [0, 0]];
+        var paddings = [[0, 0], [0, 0]];
+        var s2b = tf.spaceToBatchND(t, blockShape, paddings);
+        expect(s2b.shape).toEqual([8, 3, 3, 1]);
+        test_util_1.expectArraysClose(s2b, [
+            1, 3, 5, 13, 15, 17, 25, 27, 29, 37, 39, 41, 49, 51, 53, 61, 63, 65,
+            2, 4, 6, 14, 16, 18, 26, 28, 30, 38, 40, 42, 50, 52, 54, 62, 64, 66,
+            7, 9, 11, 19, 21, 23, 31, 33, 35, 43, 45, 47, 55, 57, 59, 67, 69, 71,
+            8, 10, 12, 20, 22, 24, 32, 34, 36, 44, 46, 48, 56, 58, 60, 68, 70, 72
+        ]);
+        var b2s = tf.batchToSpaceND(s2b, blockShape, crops);
+        expect(b2s.shape).toEqual([2, 6, 6, 1]);
+        test_util_1.expectArraysClose(b2s, [
+            1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18,
+            19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36,
+            37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54,
+            55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72
+        ]);
+    });
+    it('gradients,  input shape=[4, 2, 2], block shape=[2]', function () {
+        var t = tf.tensor([-61, 37, -68, 72, 31, 62, 0, -13, 28, 54, 96, 44, -55, -64, -88, -94], [4, 2, 2]);
+        var blockShape = [2];
+        var paddings = [[0, 2]];
+        var dy = tf.tensor([
+            1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
+            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32
+        ], [8, 2, 2]);
+        var gradient = tf.grad(function (t) { return tf.spaceToBatchND(t, blockShape, paddings); })(t, dy);
+        expect(gradient.shape).toEqual([4, 2, 2]);
+        test_util_1.expectArraysClose(gradient, [1, 2, 17, 18, 5, 6, 21, 22, 9, 10, 25, 26, 13, 14, 29, 30]);
+    });
+    it('gradients, input shape=[2, 2, 4, 1], block shape=[2, 2]', function () {
+        var t = tf.tensor4d([
+            [[[1], [2], [3], [4]], [[5], [6], [7], [8]]],
+            [[[9], [10], [11], [12]], [[13], [14], [15], [16]]]
+        ], [2, 2, 4, 1]);
+        var blockShape = [2, 2];
+        var paddings = [[0, 0], [2, 0]];
+        var dy = tf.tensor([
+            1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
+            13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24
+        ], [8, 1, 3, 1]);
+        var gradient = tf.grad(function (t) { return tf.spaceToBatchND(t, blockShape, paddings); })(t, dy);
+        expect(gradient.shape).toEqual([2, 2, 4, 1]);
+        test_util_1.expectArraysClose(gradient, [2, 8, 3, 9, 14, 20, 15, 21, 5, 11, 6, 12, 17, 23, 18, 24]);
+    });
+});
+jasmine_util_1.describeWithFlags('depthToSpace', test_util_1.ALL_ENVS, function () {
+    it('tensor4d, input shape=[1, 1, 1, 4], blockSize=2, format=NHWC', function () {
+        var t = tf.tensor4d([[[[1, 2, 3, 4]]]]);
+        var blockSize = 2;
+        var dataFormat = 'NHWC';
+        var res = tf.depthToSpace(t, blockSize, dataFormat);
+        expect(res.shape).toEqual([1, 2, 2, 1]);
+        test_util_1.expectArraysClose(res, [1, 2, 3, 4]);
+    });
+    it('tensor4d, input shape=[1, 1, 1, 12], blockSize=2, format=NHWC', function () {
+        var t = tf.tensor4d([[[[1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]]]]);
+        var blockSize = 2;
+        var dataFormat = 'NHWC';
+        var res = tf.depthToSpace(t, blockSize, dataFormat);
+        expect(res.shape).toEqual([1, 2, 2, 3]);
+        test_util_1.expectArraysClose(res, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]);
+    });
+    it('tensor4d, input shape=[1, 2, 2, 4], blockSize=2, format=NHWC', function () {
+        var t = tf.tensor4d([[[[1, 2, 3, 4], [5, 6, 7, 8]], [[9, 10, 11, 12], [13, 14, 15, 16]]]]);
+        var blockSize = 2;
+        var dataFormat = 'NHWC';
+        var res = tf.depthToSpace(t, blockSize, dataFormat);
+        expect(res.shape).toEqual([1, 4, 4, 1]);
+        test_util_1.expectArraysClose(res, [1, 2, 5, 6, 3, 4, 7, 8, 9, 10, 13, 14, 11, 12, 15, 16]);
+    });
+    it('throws when depth not divisible by blockSize * blockSize', function () {
+        var t = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]);
+        var blockSize = 3;
+        expect(function () { return tf.depthToSpace(t, blockSize); })
+            .toThrowError("Dimension size must be evenly divisible by " + blockSize * blockSize + " but is " + t.shape[3] + " for depthToSpace with input shape " + t.shape);
+    });
+});
+jasmine_util_1.describeWithFlags('depthToSpace', test_util_1.BROWSER_ENVS, function () {
+    it('throws when blocksize < 2', function () {
+        var t = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]);
+        var blockSize = 1;
+        expect(function () { return tf.depthToSpace(t, blockSize); })
+            .toThrowError("blockSize should be > 1 for depthToSpace, but was: " + blockSize);
+    });
+});
+jasmine_util_1.describeWithFlags('depthToSpace', test_util_1.CPU_ENVS, function () {
+    it('throws when CPU backend used with data format NCHW', function () {
+        var t = tf.tensor4d([1, 2, 3, 4], [1, 4, 1, 1]);
+        var blockSize = 2;
+        var dataFormat = 'NCHW';
+        expect(function () { return tf.depthToSpace(t, blockSize, dataFormat); })
+            .toThrowError("Only NHWC dataFormat supported on CPU for depthToSpace. Got " + dataFormat);
+    });
+});
+jasmine_util_1.describeWithFlags('depthToSpace', test_util_1.WEBGL_ENVS, function () {
+    it('tensor4d, input shape=[1, 4, 1, 1], blockSize=2, format=NCHW', function () {
+        var t = tf.tensor4d([1, 2, 3, 4], [1, 4, 1, 1]);
+        var blockSize = 2;
+        var dataFormat = 'NCHW';
+        var res = tf.depthToSpace(t, blockSize, dataFormat);
+        expect(res.shape).toEqual([1, 1, 2, 2]);
+        test_util_1.expectArraysClose(res, [1, 2, 3, 4]);
+    });
+    it('tensor4d, input shape=[1, 12, 1, 1], blockSize=2, format=NCHW', function () {
+        var t = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [1, 12, 1, 1]);
+        var blockSize = 2;
+        var dataFormat = 'NCHW';
+        var res = tf.depthToSpace(t, blockSize, dataFormat);
+        expect(res.shape).toEqual([1, 3, 2, 2]);
+        test_util_1.expectArraysClose(res, [1, 4, 7, 10, 2, 5, 8, 11, 3, 6, 9, 12]);
+    });
+    it('tensor4d, input shape=[1, 4, 2, 2], blockSize=2, format=NCHW', function () {
+        var t = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [1, 4, 2, 2]);
+        var blockSize = 2;
+        var dataFormat = 'NCHW';
+        var res = tf.depthToSpace(t, blockSize, dataFormat);
+        expect(res.shape).toEqual([1, 1, 4, 4]);
+        test_util_1.expectArraysClose(res, [1, 5, 2, 6, 9, 13, 10, 14, 3, 7, 4, 8, 11, 15, 12, 16]);
+    });
+    it('tensor4d, input shape=[1, 8, 2, 2], blockSize=2, format=NCHW', function () {
+        var t = tf.tensor4d([
+            1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
+            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32
+        ], [1, 8, 2, 2]);
+        var blockSize = 2;
+        var dataFormat = 'NCHW';
+        var res = tf.depthToSpace(t, blockSize, dataFormat);
+        expect(res.shape).toEqual([1, 2, 4, 4]);
+        test_util_1.expectArraysClose(res, [
+            1, 9, 2, 10, 17, 25, 18, 26, 3, 11, 4, 12, 19, 27, 20, 28,
+            5, 13, 6, 14, 21, 29, 22, 30, 7, 15, 8, 16, 23, 31, 24, 32
+        ]);
+    });
+});
+jasmine_util_1.describeWithFlags('setdiff1dAsync', test_util_1.ALL_ENVS, function () {
+    it('1d int32 tensor', function () { return __awaiter(_this, void 0, void 0, function () {
+        var x, y, _a, out, indices;
+        return __generator(this, function (_b) {
+            switch (_b.label) {
+                case 0:
+                    x = tf.tensor1d([1, 2, 3, 4], 'int32');
+                    y = tf.tensor1d([1, 2], 'int32');
+                    return [4, tf.setdiff1dAsync(x, y)];
+                case 1:
+                    _a = _b.sent(), out = _a[0], indices = _a[1];
+                    expect(out.dtype).toBe('int32');
+                    expect(indices.dtype).toBe('int32');
+                    expect(out.shape).toEqual([2]);
+                    expect(indices.shape).toEqual([2]);
+                    test_util_1.expectArraysClose(out, [3, 4]);
+                    test_util_1.expectArraysClose(indices, [2, 3]);
+                    return [2];
+            }
+        });
+    }); });
+    it('1d float32 tensor', function () { return __awaiter(_this, void 0, void 0, function () {
+        var x, y, _a, out, indices;
+        return __generator(this, function (_b) {
+            switch (_b.label) {
+                case 0:
+                    x = tf.tensor1d([1, 2, 3, 4], 'float32');
+                    y = tf.tensor1d([1, 3], 'float32');
+                    return [4, tf.setdiff1dAsync(x, y)];
+                case 1:
+                    _a = _b.sent(), out = _a[0], indices = _a[1];
+                    expect(out.dtype).toBe('float32');
+                    expect(indices.dtype).toBe('int32');
+                    expect(out.shape).toEqual([2]);
+                    expect(indices.shape).toEqual([2]);
+                    test_util_1.expectArraysClose(out, [2, 4]);
+                    test_util_1.expectArraysClose(indices, [1, 3]);
+                    return [2];
+            }
+        });
+    }); });
+    it('empty output', function () { return __awaiter(_this, void 0, void 0, function () {
+        var x, y, _a, out, indices;
+        return __generator(this, function (_b) {
+            switch (_b.label) {
+                case 0:
+                    x = tf.tensor1d([1, 2, 3, 4], 'float32');
+                    y = tf.tensor1d([1, 2, 3, 4], 'float32');
+                    return [4, tf.setdiff1dAsync(x, y)];
+                case 1:
+                    _a = _b.sent(), out = _a[0], indices = _a[1];
+                    expect(out.dtype).toBe('float32');
+                    expect(indices.dtype).toBe('int32');
+                    expect(out.shape).toEqual([0]);
+                    expect(indices.shape).toEqual([0]);
+                    test_util_1.expectArraysClose(out, []);
+                    test_util_1.expectArraysClose(indices, []);
+                    return [2];
+            }
+        });
+    }); });
+    it('tensor like', function () { return __awaiter(_this, void 0, void 0, function () {
+        var x, y, _a, out, indices;
+        return __generator(this, function (_b) {
+            switch (_b.label) {
+                case 0:
+                    x = [1, 2, 3, 4];
+                    y = [1, 3];
+                    return [4, tf.setdiff1dAsync(x, y)];
+                case 1:
+                    _a = _b.sent(), out = _a[0], indices = _a[1];
+                    expect(out.dtype).toBe('float32');
+                    expect(indices.dtype).toBe('int32');
+                    expect(out.shape).toEqual([2]);
+                    expect(indices.shape).toEqual([2]);
+                    test_util_1.expectArraysClose(out, [2, 4]);
+                    test_util_1.expectArraysClose(indices, [1, 3]);
+                    return [2];
+            }
+        });
+    }); });
+    it('should throw if x is not 1d', function () { return __awaiter(_this, void 0, void 0, function () {
+        var x, y, ex_1;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    x = tf.tensor2d([1, 2, 3, 4], [4, 1], 'float32');
+                    y = tf.tensor1d([1, 2, 3, 4], 'float32');
+                    _a.label = 1;
+                case 1:
+                    _a.trys.push([1, 3, , 4]);
+                    return [4, tf.setdiff1dAsync(x, y)];
+                case 2:
+                    _a.sent();
+                    throw new Error('The line above should have thrown an error');
+                case 3:
+                    ex_1 = _a.sent();
+                    expect(ex_1.message).toBe('x should be 1D tensor, but got x (4,1).');
+                    return [3, 4];
+                case 4: return [2];
+            }
+        });
+    }); });
+    it('should throw if y is not 1d', function () { return __awaiter(_this, void 0, void 0, function () {
+        var x, y, ex_2;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    x = tf.tensor1d([1, 2, 3, 4], 'float32');
+                    y = tf.tensor2d([1, 2, 3, 4], [4, 1], 'float32');
+                    _a.label = 1;
+                case 1:
+                    _a.trys.push([1, 3, , 4]);
+                    return [4, tf.setdiff1dAsync(x, y)];
+                case 2:
+                    _a.sent();
+                    throw new Error('The line above should have thrown an error');
+                case 3:
+                    ex_2 = _a.sent();
+                    expect(ex_2.message).toBe('y should be 1D tensor, but got y (4,1).');
+                    return [3, 4];
+                case 4: return [2];
+            }
+        });
+    }); });
+    it('should throw if x and y dtype mismatch', function () { return __awaiter(_this, void 0, void 0, function () {
+        var x, y, ex_3;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    x = tf.tensor1d([1, 2, 3, 4], 'float32');
+                    y = tf.tensor1d([1, 2, 3, 4], 'int32');
+                    _a.label = 1;
+                case 1:
+                    _a.trys.push([1, 3, , 4]);
+                    return [4, tf.setdiff1dAsync(x, y)];
+                case 2:
+                    _a.sent();
+                    throw new Error('The line above should have thrown an error');
+                case 3:
+                    ex_3 = _a.sent();
+                    expect(ex_3.message)
+                        .toBe('x and y should have the same dtype,' +
+                        ' but got x (float32) and y (int32).');
+                    return [3, 4];
+                case 4: return [2];
+            }
+        });
+    }); });
+});
+//# sourceMappingURL=array_ops_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_test.js.map
new file mode 100644
index 0000000..952dd7e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_util.d.ts
new file mode 100644
index 0000000..850061b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_util.d.ts
@@ -0,0 +1,5 @@
+export declare function getReshaped(inputShape: number[], blockShape: number[], prod: number, batchToSpace?: boolean): number[];
+export declare function getPermuted(reshapedRank: number, blockShapeRank: number, batchToSpace?: boolean): number[];
+export declare function getReshapedPermuted(inputShape: number[], blockShape: number[], prod: number, batchToSpace?: boolean): number[];
+export declare function getSliceBeginCoords(crops: number[][], blockShape: number): number[];
+export declare function getSliceSize(uncroppedShape: number[], crops: number[][], blockShape: number): number[];
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_util.js b/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_util.js
new file mode 100644
index 0000000..cd655b9
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_util.js
@@ -0,0 +1,97 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+function getReshaped(inputShape, blockShape, prod, batchToSpace) {
+    if (batchToSpace === void 0) { batchToSpace = true; }
+    var reshaped = [];
+    if (batchToSpace) {
+        reshaped = reshaped.concat(blockShape.slice(0));
+        reshaped.push(inputShape[0] / prod);
+        reshaped = reshaped.concat(inputShape.slice(1));
+    }
+    else {
+        reshaped = reshaped.concat(inputShape[0]);
+        var spatialLength = blockShape.length;
+        for (var i = 0; i < spatialLength; ++i) {
+            reshaped =
+                reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);
+        }
+        reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));
+    }
+    return reshaped;
+}
+exports.getReshaped = getReshaped;
+function getPermuted(reshapedRank, blockShapeRank, batchToSpace) {
+    if (batchToSpace === void 0) { batchToSpace = true; }
+    var permuted = [];
+    if (batchToSpace) {
+        permuted.push(blockShapeRank);
+        for (var i = blockShapeRank + 1; i < reshapedRank; ++i) {
+            if (i <= 2 * blockShapeRank) {
+                permuted.push(i);
+                permuted.push(i - (blockShapeRank + 1));
+            }
+            else {
+                permuted.push(i);
+            }
+        }
+    }
+    else {
+        var permutedBeforeBatch = [];
+        var permutedAfterBatch = [];
+        for (var i = 1; i < reshapedRank; ++i) {
+            if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {
+                permutedAfterBatch.push(i);
+            }
+            else {
+                permutedBeforeBatch.push(i);
+            }
+        }
+        permuted.push.apply(permuted, permutedBeforeBatch);
+        permuted.push(0);
+        permuted.push.apply(permuted, permutedAfterBatch);
+    }
+    return permuted;
+}
+exports.getPermuted = getPermuted;
+function getReshapedPermuted(inputShape, blockShape, prod, batchToSpace) {
+    if (batchToSpace === void 0) { batchToSpace = true; }
+    var reshapedPermuted = [];
+    if (batchToSpace) {
+        reshapedPermuted.push(inputShape[0] / prod);
+    }
+    else {
+        reshapedPermuted.push(inputShape[0] * prod);
+    }
+    for (var i = 1; i < inputShape.length; ++i) {
+        if (i <= blockShape.length) {
+            if (batchToSpace) {
+                reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);
+            }
+            else {
+                reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);
+            }
+        }
+        else {
+            reshapedPermuted.push(inputShape[i]);
+        }
+    }
+    return reshapedPermuted;
+}
+exports.getReshapedPermuted = getReshapedPermuted;
+function getSliceBeginCoords(crops, blockShape) {
+    var sliceBeginCoords = [0];
+    for (var i = 0; i < blockShape; ++i) {
+        sliceBeginCoords.push(crops[i][0]);
+    }
+    return sliceBeginCoords;
+}
+exports.getSliceBeginCoords = getSliceBeginCoords;
+function getSliceSize(uncroppedShape, crops, blockShape) {
+    var sliceSize = uncroppedShape.slice(0, 1);
+    for (var i = 0; i < blockShape; ++i) {
+        sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);
+    }
+    return sliceSize;
+}
+exports.getSliceSize = getSliceSize;
+//# sourceMappingURL=array_ops_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_util.js.map
new file mode 100644
index 0000000..501debc
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/array_ops_util.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"array_ops_util.js","sourceRoot":"","sources":["../../src/ops/array_ops_util.ts"],"names":[],"mappings":";;AAyBA,qBACI,UAAoB,EAAE,UAAoB,EAAE,IAAY,EACxD,YAAmB;IAAnB,6BAAA,EAAA,mBAAmB;IACrB,IAAI,QAAQ,GAAa,EAAE,CAAC;IAC5B,IAAI,YAAY,EAAE;QAChB,QAAQ,GAAG,QAAQ,CAAC,MAAM,CAAC,UAAU,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC;QAChD,QAAQ,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC;QACpC,QAAQ,GAAG,QAAQ,CAAC,MAAM,CAAC,UAAU,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC;KACjD;SAAM;QACL,QAAQ,GAAG,QAAQ,CAAC,MAAM,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,CAAC;QAC1C,IAAM,aAAa,GAAG,UAAU,CAAC,MAAM,CAAC;QACxC,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,aAAa,EAAE,EAAE,CAAC,EAAE;YACtC,QAAQ;gBACJ,QAAQ,CAAC,MAAM,CAAC,CAAC,UAAU,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,UAAU,CAAC,CAAC,CAAC,EAAE,UAAU,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;SACzE;QACD,QAAQ,GAAG,QAAQ,CAAC,MAAM,CAAC,UAAU,CAAC,KAAK,CAAC,aAAa,GAAG,CAAC,CAAC,CAAC,CAAC;KACjE;IACD,OAAO,QAAQ,CAAC;AAClB,CAAC;AAlBD,kCAkBC;AAWD,qBACI,YAAoB,EAAE,cAAsB,EAC5C,YAAmB;IAAnB,6BAAA,EAAA,mBAAmB;IACrB,IAAM,QAAQ,GAAG,EAAE,CAAC;IACpB,IAAI,YAAY,EAAE;QAChB,QAAQ,CAAC,IAAI,CAAC,cAAc,CAAC,CAAC;QAC9B,KAAK,IAAI,CAAC,GAAG,cAAc,GAAG,CAAC,EAAE,CAAC,GAAG,YAAY,EAAE,EAAE,CAAC,EAAE;YACtD,IAAI,CAAC,IAAI,CAAC,GAAG,cAAc,EAAE;gBAC3B,QAAQ,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;gBACjB,QAAQ,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,cAAc,GAAG,CAAC,CAAC,CAAC,CAAC;aACzC;iBAAM;gBACL,QAAQ,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;aAClB;SACF;KACF;SAAM;QACL,IAAM,mBAAmB,GAAG,EAAE,CAAC;QAC/B,IAAM,kBAAkB,GAAG,EAAE,CAAC;QAC9B,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,YAAY,EAAE,EAAE,CAAC,EAAE;YACrC,IAAI,CAAC,IAAI,cAAc,GAAG,CAAC,GAAG,CAAC,IAAI,CAAC,GAAG,CAAC,KAAK,CAAC,EAAE;gBAC9C,kBAAkB,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;aAC5B;iBAAM;gBACL,mBAAmB,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;aAC7B;SACF;QACD,QAAQ,CAAC,IAAI,OAAb,QAAQ,EAAS,mBAAmB,EAAE;QACtC,QAAQ,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;QACjB,QAAQ,CAAC,IAAI,OAAb,QAAQ,EAAS,kBAAkB,EAAE;KACtC;IACD,OAAO,QAAQ,CAAC;AAClB,CAAC;AA7BD,kCA6BC;AAWD,6BACI,UAAoB,EAAE,UAAoB,EAAE,IAAY,EACxD,YAAmB;IAAnB,6BAAA,EAAA,mBAAmB;IACrB,IAAM,gBAAgB,GAAG,EAAE,CAAC;IAE5B,IAAI,YAAY,EAAE;QAChB,gBAAgB,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC;KAC7C;SAAM;QACL,gBAAgB,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,CAAC;KAC7C;IAED,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,UAAU,CAAC,MAAM,EAAE,EAAE,CAAC,EAAE;QAC1C,IAAI,CAAC,IAAI,UAAU,CAAC,MAAM,EAAE;YAC1B,IAAI,YAAY,EAAE;gBAChB,gBAAgB,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,UAAU,CAAC,CAAC,CAAC,CAAC,CAAC;aAC1D;iBAAM;gBACL,gBAAgB,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC,CAAC,GAAG,UAAU,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC;aAC1D;SACF;aAAM;YACL,gBAAgB,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,CAAC;SACtC;KACF;IAED,OAAO,gBAAgB,CAAC;AAC1B,CAAC;AAxBD,kDAwBC;AAMD,6BACI,KAAiB,EAAE,UAAkB;IACvC,IAAM,gBAAgB,GAAG,CAAC,CAAC,CAAC,CAAC;IAC7B,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,UAAU,EAAE,EAAE,CAAC,EAAE;QACnC,gBAAgB,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;KACpC;IACD,OAAO,gBAAgB,CAAC;AAC1B,CAAC;AAPD,kDAOC;AAaD,sBACI,cAAwB,EAAE,KAAiB,EAAE,UAAkB;IACjE,IAAM,SAAS,GAAG,cAAc,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;IAC7C,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,UAAU,EAAE,EAAE,CAAC,EAAE;QACnC,SAAS,CAAC,IAAI,CAAC,cAAc,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;KACnE;IAED,OAAO,SAAS,CAAC;AACnB,CAAC;AARD,oCAQC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util.d.ts
new file mode 100644
index 0000000..657fa58
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util.d.ts
@@ -0,0 +1,9 @@
+export declare function axesAreInnerMostDims(axes: number[], rank: number): boolean;
+export declare function combineLocations(outputLoc: number[], reduceLoc: number[], axes: number[]): number[];
+export declare function computeOutAndReduceShapes(aShape: number[], axes: number[]): [number[], number[]];
+export declare function expandShapeToKeepDim(shape: number[], axes: number[]): number[];
+export declare function parseAxisParam(axis: number | number[], shape: number[]): number[];
+export declare function assertAxesAreInnerMostDims(msg: string, axes: number[], rank: number): void;
+export declare function getAxesPermutation(axes: number[], rank: number): number[] | null;
+export declare function getUndoAxesPermutation(axes: number[]): number[];
+export declare function getInnerMostAxes(numAxes: number, rank: number): number[];
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util.js b/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util.js
new file mode 100644
index 0000000..9203f74
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util.js
@@ -0,0 +1,89 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var util = require("../util");
+function axesAreInnerMostDims(axes, rank) {
+    for (var i = 0; i < axes.length; ++i) {
+        if (axes[axes.length - i - 1] !== rank - 1 - i) {
+            return false;
+        }
+    }
+    return true;
+}
+exports.axesAreInnerMostDims = axesAreInnerMostDims;
+function combineLocations(outputLoc, reduceLoc, axes) {
+    var rank = outputLoc.length + reduceLoc.length;
+    var loc = [];
+    var outIdx = 0;
+    var reduceIdx = 0;
+    for (var dim = 0; dim < rank; dim++) {
+        if (axes.indexOf(dim) === -1) {
+            loc.push(outputLoc[outIdx++]);
+        }
+        else {
+            loc.push(reduceLoc[reduceIdx++]);
+        }
+    }
+    return loc;
+}
+exports.combineLocations = combineLocations;
+function computeOutAndReduceShapes(aShape, axes) {
+    var outShape = [];
+    var rank = aShape.length;
+    for (var dim = 0; dim < rank; dim++) {
+        if (axes.indexOf(dim) === -1) {
+            outShape.push(aShape[dim]);
+        }
+    }
+    var reduceShape = axes.map(function (dim) { return aShape[dim]; });
+    return [outShape, reduceShape];
+}
+exports.computeOutAndReduceShapes = computeOutAndReduceShapes;
+function expandShapeToKeepDim(shape, axes) {
+    var reduceSubShape = axes.map(function (x) { return 1; });
+    return combineLocations(shape, reduceSubShape, axes);
+}
+exports.expandShapeToKeepDim = expandShapeToKeepDim;
+function parseAxisParam(axis, shape) {
+    var rank = shape.length;
+    axis = axis == null ? shape.map(function (s, i) { return i; }) : [].concat(axis);
+    util.assert(axis.every(function (ax) { return ax >= -rank && ax < rank; }), "All values in axis param must be in range [-" + rank + ", " + rank + ") but " +
+        ("got axis " + axis));
+    util.assert(axis.every(function (ax) { return util.isInt(ax); }), "All values in axis param must be integers but " +
+        ("got axis " + axis));
+    return axis.map(function (a) { return a < 0 ? rank + a : a; });
+}
+exports.parseAxisParam = parseAxisParam;
+function assertAxesAreInnerMostDims(msg, axes, rank) {
+    util.assert(axesAreInnerMostDims(axes, rank), msg + " supports only inner-most axes for now. " +
+        ("Got axes " + axes + " and rank-" + rank + " input."));
+}
+exports.assertAxesAreInnerMostDims = assertAxesAreInnerMostDims;
+function getAxesPermutation(axes, rank) {
+    if (axesAreInnerMostDims(axes, rank)) {
+        return null;
+    }
+    var result = [];
+    for (var i = 0; i < rank; ++i) {
+        if (axes.indexOf(i) === -1) {
+            result.push(i);
+        }
+    }
+    axes.forEach(function (axis) { return result.push(axis); });
+    return result;
+}
+exports.getAxesPermutation = getAxesPermutation;
+function getUndoAxesPermutation(axes) {
+    return axes.map(function (axis, i) { return [i, axis]; })
+        .sort(function (a, b) { return a[1] - b[1]; })
+        .map(function (x) { return x[0]; });
+}
+exports.getUndoAxesPermutation = getUndoAxesPermutation;
+function getInnerMostAxes(numAxes, rank) {
+    var res = [];
+    for (var i = rank - numAxes; i < rank; ++i) {
+        res.push(i);
+    }
+    return res;
+}
+exports.getInnerMostAxes = getInnerMostAxes;
+//# sourceMappingURL=axis_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util.js.map
new file mode 100644
index 0000000..2c34c07
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"axis_util.js","sourceRoot":"","sources":["../../src/ops/axis_util.ts"],"names":[],"mappings":";;AAiBA,8BAAgC;AAMhC,8BAAqC,IAAc,EAAE,IAAY;IAC/D,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,IAAI,CAAC,MAAM,EAAE,EAAE,CAAC,EAAE;QACpC,IAAI,IAAI,CAAC,IAAI,CAAC,MAAM,GAAG,CAAC,GAAG,CAAC,CAAC,KAAK,IAAI,GAAG,CAAC,GAAG,CAAC,EAAE;YAC9C,OAAO,KAAK,CAAC;SACd;KACF;IACD,OAAO,IAAI,CAAC;AACd,CAAC;AAPD,oDAOC;AAED,0BACI,SAAmB,EAAE,SAAmB,EAAE,IAAc;IAC1D,IAAM,IAAI,GAAG,SAAS,CAAC,MAAM,GAAG,SAAS,CAAC,MAAM,CAAC;IACjD,IAAM,GAAG,GAAG,EAAE,CAAC;IACf,IAAI,MAAM,GAAG,CAAC,CAAC;IACf,IAAI,SAAS,GAAG,CAAC,CAAC;IAChB,KAAK,IAAI,GAAG,GAAG,CAAC,EAAE,GAAG,GAAG,IAAI,EAAE,GAAG,EAAE,EAAE;QACrC,IAAI,IAAI,CAAC,OAAO,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,EAAE;YAC5B,GAAG,CAAC,IAAI,CAAC,SAAS,CAAC,MAAM,EAAE,CAAC,CAAC,CAAC;SAC/B;aAAM;YACL,GAAG,CAAC,IAAI,CAAC,SAAS,CAAC,SAAS,EAAE,CAAC,CAAC,CAAC;SAClC;KACF;IACD,OAAO,GAAG,CAAC;AACb,CAAC;AAdD,4CAcC;AAED,mCACI,MAAgB,EAAE,IAAc;IAClC,IAAM,QAAQ,GAAG,EAAE,CAAC;IACpB,IAAM,IAAI,GAAG,MAAM,CAAC,MAAM,CAAC;IAC3B,KAAK,IAAI,GAAG,GAAG,CAAC,EAAE,GAAG,GAAG,IAAI,EAAE,GAAG,EAAE,EAAE;QACnC,IAAI,IAAI,CAAC,OAAO,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,EAAE;YAC5B,QAAQ,CAAC,IAAI,CAAC,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC;SAC5B;KACF;IACD,IAAM,WAAW,GAAG,IAAI,CAAC,GAAG,CAAC,UAAA,GAAG,IAAI,OAAA,MAAM,CAAC,GAAG,CAAC,EAAX,CAAW,CAAC,CAAC;IACjD,OAAO,CAAC,QAAQ,EAAE,WAAW,CAAC,CAAC;AACjC,CAAC;AAXD,8DAWC;AAED,8BACI,KAAe,EAAE,IAAc;IACjC,IAAM,cAAc,GAAG,IAAI,CAAC,GAAG,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,EAAD,CAAC,CAAC,CAAC;IACxC,OAAO,gBAAgB,CAAC,KAAK,EAAE,cAAc,EAAE,IAAI,CAAC,CAAC;AACvD,CAAC;AAJD,oDAIC;AAED,wBACI,IAAqB,EAAE,KAAe;IACxC,IAAM,IAAI,GAAG,KAAK,CAAC,MAAM,CAAC;IAG1B,IAAI,GAAG,IAAI,IAAI,IAAI,CAAC,CAAC,CAAC,KAAK,CAAC,GAAG,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,CAAC,EAAD,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,MAAM,CAAC,IAAI,CAAC,CAAC;IAG/D,IAAI,CAAC,MAAM,CACP,IAAI,CAAC,KAAK,CAAC,UAAA,EAAE,IAAI,OAAA,EAAE,IAAI,CAAC,IAAI,IAAI,EAAE,GAAG,IAAI,EAAxB,CAAwB,CAAC,EAC1C,iDAA+C,IAAI,UAAK,IAAI,WAAQ;SAChE,cAAY,IAAM,CAAA,CAAC,CAAC;IAG5B,IAAI,CAAC,MAAM,CACP,IAAI,CAAC,KAAK,CAAC,UAAA,EAAE,IAAI,OAAA,IAAI,CAAC,KAAK,CAAC,EAAE,CAAC,EAAd,CAAc,CAAC,EAChC,gDAAgD;SAC5C,cAAY,IAAM,CAAA,CAAC,CAAC;IAG5B,OAAO,IAAI,CAAC,GAAG,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,IAAI,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,EAApB,CAAoB,CAAC,CAAC;AAC7C,CAAC;AArBD,wCAqBC;AAED,oCACI,GAAW,EAAE,IAAc,EAAE,IAAY;IAC3C,IAAI,CAAC,MAAM,CACP,oBAAoB,CAAC,IAAI,EAAE,IAAI,CAAC,EAC7B,GAAG,6CAA0C;SAC5C,cAAY,IAAI,kBAAa,IAAI,YAAS,CAAA,CAAC,CAAC;AACtD,CAAC;AAND,gEAMC;AAOD,4BAAmC,IAAc,EAAE,IAAY;IAE7D,IAAI,oBAAoB,CAAC,IAAI,EAAE,IAAI,CAAC,EAAE;QACpC,OAAO,IAAI,CAAC;KACb;IACD,IAAM,MAAM,GAAa,EAAE,CAAC;IAC5B,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,IAAI,EAAE,EAAE,CAAC,EAAE;QAC7B,IAAI,IAAI,CAAC,OAAO,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,EAAE;YAC1B,MAAM,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;SAChB;KACF;IACD,IAAI,CAAC,OAAO,CAAC,UAAA,IAAI,IAAI,OAAA,MAAM,CAAC,IAAI,CAAC,IAAI,CAAC,EAAjB,CAAiB,CAAC,CAAC;IACxC,OAAO,MAAM,CAAC;AAChB,CAAC;AAbD,gDAaC;AAGD,gCAAuC,IAAc;IACnD,OAAO,IAAI,CAAC,GAAG,CAAC,UAAC,IAAI,EAAE,CAAC,IAAK,OAAA,CAAC,CAAC,EAAE,IAAI,CAAC,EAAT,CAAS,CAAC;SAClC,IAAI,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,EAAX,CAAW,CAAC;SAC3B,GAAG,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,CAAC,CAAC,EAAJ,CAAI,CAAC,CAAC;AACtB,CAAC;AAJD,wDAIC;AAED,0BAAiC,OAAe,EAAE,IAAY;IAC5D,IAAM,GAAG,GAAa,EAAE,CAAC;IACzB,KAAK,IAAI,CAAC,GAAG,IAAI,GAAG,OAAO,EAAE,CAAC,GAAG,IAAI,EAAE,EAAE,CAAC,EAAE;QAC1C,GAAG,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;KACb;IACD,OAAO,GAAG,CAAC;AACb,CAAC;AAND,4CAMC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util_test.js
new file mode 100644
index 0000000..f07e2f5
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util_test.js
@@ -0,0 +1,224 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+var axis_util = require("./axis_util");
+describe('axis_util combineLocations', function () {
+    it('rank 4, reduce last 2 dims', function () {
+        var loc = axis_util.combineLocations([4, 1], [3, 7], [2, 3]);
+        expect(loc).toEqual([4, 1, 3, 7]);
+    });
+    it('rank 4, reduce first two dims', function () {
+        var loc = axis_util.combineLocations([4, 1], [3, 7], [0, 1]);
+        expect(loc).toEqual([3, 7, 4, 1]);
+    });
+    it('rank 4, reduce 1st and 3rd dims', function () {
+        var loc = axis_util.combineLocations([4, 1], [3, 7], [0, 2]);
+        expect(loc).toEqual([3, 4, 7, 1]);
+    });
+    it('rank 4, reduce 1st and 4th dims', function () {
+        var loc = axis_util.combineLocations([4, 1], [3, 7], [0, 3]);
+        expect(loc).toEqual([3, 4, 1, 7]);
+    });
+    it('rank 3, reduce all dims', function () {
+        var loc = axis_util.combineLocations([], [3, 7, 1], [0, 1, 2]);
+        expect(loc).toEqual([3, 7, 1]);
+    });
+    it('rank 2, reduce last dim', function () {
+        var loc = axis_util.combineLocations([3], [5], [1]);
+        expect(loc).toEqual([3, 5]);
+    });
+    it('rank 2, reduce first dim', function () {
+        var loc = axis_util.combineLocations([3], [5], [0]);
+        expect(loc).toEqual([5, 3]);
+    });
+});
+describe('axis_util computeOutAndReduceShapes', function () {
+    it('rank 4, reduce all dims', function () {
+        var _a = axis_util.computeOutAndReduceShapes([3, 7, 2, 4], [0, 1, 2, 3]), out = _a[0], red = _a[1];
+        expect(out).toEqual([]);
+        expect(red).toEqual([3, 7, 2, 4]);
+    });
+    it('rank 4, reduce last 2 dims', function () {
+        var _a = axis_util.computeOutAndReduceShapes([3, 7, 2, 4], [2, 3]), out = _a[0], red = _a[1];
+        expect(out).toEqual([3, 7]);
+        expect(red).toEqual([2, 4]);
+    });
+    it('rank 4, reduce first 2 dims', function () {
+        var _a = axis_util.computeOutAndReduceShapes([3, 7, 2, 4], [0, 1]), out = _a[0], red = _a[1];
+        expect(out).toEqual([2, 4]);
+        expect(red).toEqual([3, 7]);
+    });
+    it('rank 4, reduce last 3 dims', function () {
+        var _a = axis_util.computeOutAndReduceShapes([3, 7, 2, 4], [1, 2, 3]), out = _a[0], red = _a[1];
+        expect(out).toEqual([3]);
+        expect(red).toEqual([7, 2, 4]);
+    });
+    it('rank 4, reduce 1st and 3rd dims', function () {
+        var _a = axis_util.computeOutAndReduceShapes([3, 7, 2, 4], [0, 2]), out = _a[0], red = _a[1];
+        expect(out).toEqual([7, 4]);
+        expect(red).toEqual([3, 2]);
+    });
+    it('rank 3, reduce all dims', function () {
+        var _a = axis_util.computeOutAndReduceShapes([3, 7, 2], [0, 1, 2]), out = _a[0], red = _a[1];
+        expect(out).toEqual([]);
+        expect(red).toEqual([3, 7, 2]);
+    });
+});
+describe('axis_util axesAreInnerMostDims', function () {
+    it('rank 4, reduce last dim', function () {
+        var res = axis_util.axesAreInnerMostDims([3], 4);
+        expect(res).toBe(true);
+    });
+    it('rank 4, reduce last 2 dims', function () {
+        var res = axis_util.axesAreInnerMostDims([2, 3], 4);
+        expect(res).toBe(true);
+    });
+    it('rank 4, reduce last 3 dims', function () {
+        var res = axis_util.axesAreInnerMostDims([1, 2, 3], 4);
+        expect(res).toBe(true);
+    });
+    it('rank 4, reduce all dims', function () {
+        var res = axis_util.axesAreInnerMostDims([0, 1, 2, 3], 4);
+        expect(res).toBe(true);
+    });
+    it('rank 4, reduce all but 2nd', function () {
+        var res = axis_util.axesAreInnerMostDims([0, 2, 3], 4);
+        expect(res).toBe(false);
+    });
+    it('rank 4, reduce all but 3rd', function () {
+        var res = axis_util.axesAreInnerMostDims([0, 1, 3], 4);
+        expect(res).toBe(false);
+    });
+    it('rank 4, reduce all but last', function () {
+        var res = axis_util.axesAreInnerMostDims([0, 1, 2], 4);
+        expect(res).toBe(false);
+    });
+});
+describe('axis_util expandShapeToKeepDim', function () {
+    it('2d -> 1d axis=0', function () {
+        var shape = axis_util.expandShapeToKeepDim([2], [0]);
+        expect(shape).toEqual([1, 2]);
+    });
+    it('2d -> 1d axis=1', function () {
+        var shape = axis_util.expandShapeToKeepDim([4], [1]);
+        expect(shape).toEqual([4, 1]);
+    });
+    it('3d -> 1d axis=1,2', function () {
+        var shape = axis_util.expandShapeToKeepDim([7], [1, 2]);
+        expect(shape).toEqual([7, 1, 1]);
+    });
+    it('3d -> 2d axis=1', function () {
+        var shape = axis_util.expandShapeToKeepDim([7, 3], [1]);
+        expect(shape).toEqual([7, 1, 3]);
+    });
+});
+describe('axis_util getPermAxes', function () {
+    it('all axes, no perm is needed', function () {
+        var perm = axis_util.getAxesPermutation([0, 1, 2], 3);
+        expect(perm).toBeNull();
+    });
+    it('no axes, no perm is needed', function () {
+        var perm = axis_util.getAxesPermutation([], 3);
+        expect(perm).toBeNull();
+    });
+    it('inner most 2 axes, no perm is needed', function () {
+        var perm = axis_util.getAxesPermutation([2, 3], 4);
+        expect(perm).toBeNull();
+    });
+    it('outer most axis, perm is needed', function () {
+        var perm = axis_util.getAxesPermutation([0], 4);
+        expect(perm).toEqual([1, 2, 3, 0]);
+    });
+    it('2 outer most axes, perm is needed', function () {
+        var perm = axis_util.getAxesPermutation([0, 1], 4);
+        expect(perm).toEqual([2, 3, 0, 1]);
+    });
+});
+describe('axis_util parseAxisParam', function () {
+    it('axis=null returns no axes for scalar', function () {
+        var axis = null;
+        var shape = [];
+        expect(axis_util.parseAxisParam(axis, shape)).toEqual([]);
+    });
+    it('axis=null returns 0 axis for Tensor1D', function () {
+        var axis = null;
+        var shape = [4];
+        expect(axis_util.parseAxisParam(axis, shape)).toEqual([0]);
+    });
+    it('axis=null returns all axes for Tensor3D', function () {
+        var axis = null;
+        var shape = [3, 1, 2];
+        expect(axis_util.parseAxisParam(axis, shape)).toEqual([0, 1, 2]);
+    });
+    it('axis as a single number', function () {
+        var axis = 1;
+        var shape = [3, 1, 2];
+        expect(axis_util.parseAxisParam(axis, shape)).toEqual([1]);
+    });
+    it('axis as single negative number', function () {
+        var axis = -1;
+        var shape = [3, 1, 2];
+        expect(axis_util.parseAxisParam(axis, shape)).toEqual([2]);
+        var axis2 = -2;
+        expect(axis_util.parseAxisParam(axis2, shape)).toEqual([1]);
+        var axis3 = -3;
+        expect(axis_util.parseAxisParam(axis3, shape)).toEqual([0]);
+    });
+    it('axis as list of negative numbers', function () {
+        var axis = [-1, -3];
+        var shape = [3, 1, 2];
+        expect(axis_util.parseAxisParam(axis, shape)).toEqual([2, 0]);
+    });
+    it('axis as list of positive numbers', function () {
+        var axis = [0, 2];
+        var shape = [3, 1, 2];
+        expect(axis_util.parseAxisParam(axis, shape)).toEqual([0, 2]);
+    });
+    it('axis as combo of positive and negative numbers', function () {
+        var axis = [0, -1];
+        var shape = [3, 1, 2];
+        expect(axis_util.parseAxisParam(axis, shape)).toEqual([0, 2]);
+    });
+    it('axis out of range throws error', function () {
+        var axis = -4;
+        var shape = [3, 1, 2];
+        expect(function () { return axis_util.parseAxisParam(axis, shape); }).toThrowError();
+        var axis2 = 4;
+        expect(function () { return axis_util.parseAxisParam(axis2, shape); }).toThrowError();
+    });
+    it('axis a list with one number out of range throws error', function () {
+        var axis = [0, 4];
+        var shape = [3, 1, 2];
+        expect(function () { return axis_util.parseAxisParam(axis, shape); }).toThrowError();
+    });
+    it('axis with decimal value throws error', function () {
+        var axis = 0.5;
+        var shape = [3, 1, 2];
+        expect(function () { return axis_util.parseAxisParam(axis, shape); }).toThrowError();
+    });
+});
+jasmine_util_1.describeWithFlags('axis_util getUndoAxesPermutation', test_util_1.ALL_ENVS, function () {
+    it('4d axes', function () {
+        var axes = [2, 0, 1, 3];
+        expect(axis_util.getUndoAxesPermutation(axes)).toEqual([1, 2, 0, 3]);
+    });
+    it('3d axes, no perm', function () {
+        var axes = [0, 1, 2];
+        expect(axis_util.getUndoAxesPermutation(axes)).toEqual([0, 1, 2]);
+    });
+    it('3d axes, complete flip', function () {
+        var axes = [2, 1, 0];
+        expect(axis_util.getUndoAxesPermutation(axes)).toEqual([2, 1, 0]);
+    });
+    it('4d array with values', function () {
+        var axes = [2, 0, 1, 3];
+        var undoPermutation = axis_util.getUndoAxesPermutation(axes);
+        var a = tf.randomNormal([2, 3, 4, 5]);
+        var aT = tf.transpose(a, axes);
+        var aTT = tf.transpose(aT, undoPermutation);
+        test_util_1.expectArraysClose(a, aTT);
+    });
+});
+//# sourceMappingURL=axis_util_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util_test.js.map
new file mode 100644
index 0000000..8a2cfcc
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/axis_util_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm.d.ts
new file mode 100644
index 0000000..779425a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm.d.ts
@@ -0,0 +1,11 @@
+import { Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D } from '../tensor';
+import { Rank, TensorLike } from '../types';
+declare function batchNormalization2d_(x: Tensor2D | TensorLike, mean: Tensor2D | Tensor1D | TensorLike, variance: Tensor2D | Tensor1D | TensorLike, varianceEpsilon?: number, scale?: Tensor2D | Tensor1D | TensorLike, offset?: Tensor2D | Tensor1D | TensorLike): Tensor2D;
+declare function batchNormalization3d_(x: Tensor3D | TensorLike, mean: Tensor3D | Tensor1D | TensorLike, variance: Tensor3D | Tensor1D | TensorLike, varianceEpsilon?: number, scale?: Tensor3D | Tensor1D | TensorLike, offset?: Tensor3D | Tensor1D | TensorLike): Tensor3D;
+declare function batchNormalization4d_(x: Tensor4D | TensorLike, mean: Tensor4D | Tensor1D | TensorLike, variance: Tensor4D | Tensor1D | TensorLike, varianceEpsilon?: number, scale?: Tensor4D | Tensor1D | TensorLike, offset?: Tensor4D | Tensor1D | TensorLike): Tensor4D;
+declare function batchNormalization_<R extends Rank>(x: Tensor<R> | Tensor1D | TensorLike, mean: Tensor<R> | Tensor1D | TensorLike, variance: Tensor<R> | Tensor1D | TensorLike, varianceEpsilon?: number, scale?: Tensor<R> | Tensor1D | TensorLike, offset?: Tensor<R> | Tensor1D | TensorLike): Tensor<R>;
+export declare const batchNormalization2d: typeof batchNormalization2d_;
+export declare const batchNormalization3d: typeof batchNormalization3d_;
+export declare const batchNormalization4d: typeof batchNormalization4d_;
+export declare const batchNormalization: typeof batchNormalization_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm.js b/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm.js
new file mode 100644
index 0000000..a2d8f54
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm.js
@@ -0,0 +1,219 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var tensor_util_env_1 = require("../tensor_util_env");
+var util = require("../util");
+var array_ops_1 = require("./array_ops");
+var broadcast_util_1 = require("./broadcast_util");
+var operation_1 = require("./operation");
+var tensor_ops_1 = require("./tensor_ops");
+var unary_ops_1 = require("./unary_ops");
+function batchNormalization2d_(x, mean, variance, varianceEpsilon, scale, offset) {
+    if (varianceEpsilon === void 0) { varianceEpsilon = .001; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'batchNormalization');
+    var $mean = tensor_util_env_1.convertToTensor(mean, 'mean', 'batchNormalization');
+    var $variance = tensor_util_env_1.convertToTensor(variance, 'variance', 'batchNormalization');
+    var $scale;
+    if (scale != null) {
+        $scale = tensor_util_env_1.convertToTensor(scale, 'scale', 'batchNormalization');
+    }
+    var $offset;
+    if (offset != null) {
+        $offset = tensor_util_env_1.convertToTensor(offset, 'offset', 'batchNormalization');
+    }
+    util.assert($x.rank === 2, "Error in batchNormalization3D: x must be rank 3 but got rank " +
+        ($x.rank + "."));
+    util.assert($mean.rank === 2 || $mean.rank === 1, "Error in batchNormalization2D: mean must be rank 2 or rank 1 but " +
+        ("got rank " + $mean.rank + "."));
+    util.assert($variance.rank === 2 || $variance.rank === 1, "Error in batchNormalization2D: variance must be rank 2 or rank 1 " +
+        ("but got rank " + $variance.rank + "."));
+    if ($scale != null) {
+        util.assert($scale.rank === 2 || $scale.rank === 1, "Error in batchNormalization2D: scale must be rank 2 or rank 1 " +
+            ("but got rank " + $scale.rank + "."));
+    }
+    if ($offset != null) {
+        util.assert($offset.rank === 2 || $offset.rank === 1, "Error in batchNormalization2D: offset must be rank 2 or rank 1 " +
+            ("but got rank " + $offset.rank + "."));
+    }
+    return exports.batchNormalization($x, $mean, $variance, varianceEpsilon, $scale, $offset);
+}
+function batchNormalization3d_(x, mean, variance, varianceEpsilon, scale, offset) {
+    if (varianceEpsilon === void 0) { varianceEpsilon = .001; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'batchNormalization');
+    var $mean = tensor_util_env_1.convertToTensor(mean, 'mean', 'batchNormalization');
+    var $variance = tensor_util_env_1.convertToTensor(variance, 'variance', 'batchNormalization');
+    var $scale;
+    if (scale != null) {
+        $scale = tensor_util_env_1.convertToTensor(scale, 'scale', 'batchNormalization');
+    }
+    var $offset;
+    if (offset != null) {
+        $offset = tensor_util_env_1.convertToTensor(offset, 'offset', 'batchNormalization');
+    }
+    util.assert($x.rank === 3, "Error in batchNormalization3D: x must be rank 3 but got rank " +
+        ($x.rank + "."));
+    util.assert($mean.rank === 3 || $mean.rank === 1, "Error in batchNormalization3D: mean must be rank 3 or rank 1 but " +
+        ("got rank " + $mean.rank + "."));
+    util.assert($variance.rank === 3 || $variance.rank === 1, "Error in batchNormalization3D: variance must be rank 3 or rank 1 " +
+        ("but got rank " + $variance.rank + "."));
+    if ($scale != null) {
+        util.assert($scale.rank === 3 || $scale.rank === 1, "Error in batchNormalization3D: scale must be rank 3 or rank 1 " +
+            ("but got rank " + $scale.rank + "."));
+    }
+    if ($offset != null) {
+        util.assert($offset.rank === 3 || $offset.rank === 1, "Error in batchNormalization3D: offset must be rank 3 or rank 1 " +
+            ("but got rank " + $offset.rank + "."));
+    }
+    return exports.batchNormalization($x, $mean, $variance, varianceEpsilon, $scale, $offset);
+}
+function batchNormalization4d_(x, mean, variance, varianceEpsilon, scale, offset) {
+    if (varianceEpsilon === void 0) { varianceEpsilon = .001; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'batchNormalization');
+    var $mean = tensor_util_env_1.convertToTensor(mean, 'mean', 'batchNormalization');
+    var $variance = tensor_util_env_1.convertToTensor(variance, 'variance', 'batchNormalization');
+    var $scale;
+    if (scale != null) {
+        $scale = tensor_util_env_1.convertToTensor(scale, 'scale', 'batchNormalization');
+    }
+    var $offset;
+    if (offset != null) {
+        $offset = tensor_util_env_1.convertToTensor(offset, 'offset', 'batchNormalization');
+    }
+    util.assert($x.rank === 4, "Error in batchNormalization4D: x must be rank 4 but got rank " +
+        ($x.rank + "."));
+    util.assert($mean.rank === 4 || $mean.rank === 1, "Error in batchNormalization4D: mean must be rank 4 or rank 1 but " +
+        ("got rank " + $mean.rank + "."));
+    util.assert($variance.rank === 4 || $variance.rank === 1, "Error in batchNormalization4D: variance must be rank 4 or rank 1 " +
+        ("but got rank " + $variance.rank + "."));
+    if ($scale != null) {
+        util.assert($scale.rank === 4 || $scale.rank === 1, "Error in batchNormalization4D: scale must be rank 4 or rank 1 " +
+            ("but got rank " + $scale.rank + "."));
+    }
+    if ($offset != null) {
+        util.assert($offset.rank === 4 || $offset.rank === 1, "Error in batchNormalization4D: offset must be rank 4 or rank 1 " +
+            ("but got rank " + $offset.rank + "."));
+    }
+    return exports.batchNormalization($x, $mean, $variance, varianceEpsilon, $scale, $offset);
+}
+function batchNormalization_(x, mean, variance, varianceEpsilon, scale, offset) {
+    if (varianceEpsilon === void 0) { varianceEpsilon = .001; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'batchNormalization');
+    var $mean = tensor_util_env_1.convertToTensor(mean, 'mean', 'batchNormalization');
+    var $variance = tensor_util_env_1.convertToTensor(variance, 'variance', 'batchNormalization');
+    var $scale;
+    if (scale != null) {
+        $scale = tensor_util_env_1.convertToTensor(scale, 'scale', 'batchNormalization');
+    }
+    var $offset;
+    if (offset != null) {
+        $offset = tensor_util_env_1.convertToTensor(offset, 'offset', 'batchNormalization');
+    }
+    util.assert($mean.rank === $variance.rank, 'Batch normalization gradient requires mean and variance to have ' +
+        'equal ranks.');
+    util.assert($offset == null || $mean.rank === $offset.rank, 'Batch normalization gradient requires mean and offset to have ' +
+        'equal ranks.');
+    util.assert($scale == null || $mean.rank === $scale.rank, 'Batch normalization gradient requires mean and scale to have ' +
+        'equal ranks.');
+    var x4D;
+    if ($x.rank === 0 || $x.rank === 1) {
+        x4D = $x.as4D(1, 1, 1, $x.size);
+    }
+    else if ($x.rank === 2) {
+        x4D = $x.as4D(1, 1, $x.shape[0], $x.shape[1]);
+    }
+    else if ($x.rank === 3) {
+        x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
+    }
+    else {
+        x4D = $x;
+    }
+    var der = function (dy) {
+        var scaleValue = $scale == null ? tensor_ops_1.scalar(1) : $scale;
+        var reductionAxes = broadcast_util_1.getReductionAxes($mean.shape, x4D.shape);
+        var tileShape = [];
+        if ($mean.rank === 1) {
+            for (var i = 0; i < x4D.shape.length - 1; ++i) {
+                tileShape.push(x4D.shape[i]);
+            }
+            tileShape.push(1);
+        }
+        var xMinusMean = $x.sub($mean);
+        var dyTimesScaleValue = dy.mul(scaleValue);
+        var oneOverSqrtVariance = unary_ops_1.rsqrt($variance.add(tensor_ops_1.scalar(varianceEpsilon)));
+        var minusHalfRCube = oneOverSqrtVariance.mul(oneOverSqrtVariance)
+            .mul(oneOverSqrtVariance)
+            .mul(tensor_ops_1.scalar(-0.5));
+        var derX = function () {
+            if ($mean.rank === 1) {
+                return dy
+                    .mul(array_ops_1.tile(oneOverSqrtVariance.as4D(1, 1, 1, $mean.shape[0]), tileShape))
+                    .mul(scaleValue)
+                    .reshape($x.shape);
+            }
+            else {
+                return dy.mul(oneOverSqrtVariance).mul(scaleValue).reshape($x.shape);
+            }
+        };
+        var derMean = function () {
+            var meanDer = oneOverSqrtVariance.mul(tensor_ops_1.scalar(-1)).mul(dyTimesScaleValue);
+            if ($mean.rank === 1) {
+                meanDer = meanDer.sum(reductionAxes);
+            }
+            return meanDer.reshape($mean.shape);
+        };
+        var derVariance = function () {
+            var varianceDer = minusHalfRCube.mul(xMinusMean).mul(dyTimesScaleValue);
+            if ($mean.rank === 1) {
+                varianceDer = varianceDer.sum(reductionAxes);
+            }
+            return varianceDer.reshape($mean.shape);
+        };
+        var derScale = function () {
+            var xMinusMean2TimesRsqrt = xMinusMean.mul(oneOverSqrtVariance);
+            var scaleDer = dy.mul(xMinusMean2TimesRsqrt);
+            if ($mean.rank === 1) {
+                scaleDer = scaleDer.sum(reductionAxes);
+            }
+            return scaleDer.reshape($mean.shape);
+        };
+        var derOffset = function () {
+            var offsetDer = dy;
+            if ($mean.rank === 1) {
+                offsetDer = offsetDer.sum(reductionAxes);
+            }
+            return offsetDer.reshape($mean.shape);
+        };
+        return {
+            $x: derX,
+            $mean: derMean,
+            $variance: derVariance,
+            $scale: derScale,
+            $offset: derOffset
+        };
+    };
+    var res = environment_1.ENV.engine.runKernel(function (backend) { return backend.batchNormalization(x4D, batchnormReshape4D($mean), batchnormReshape4D($variance), varianceEpsilon, batchnormReshape4D($scale), batchnormReshape4D($offset)); }, { $x: $x, $mean: $mean, $variance: $variance, $scale: $scale, $offset: $offset }, der);
+    return res.reshape($x.shape);
+}
+function batchnormReshape4D(x) {
+    if (x == null) {
+        return null;
+    }
+    if (x.rank === 0) {
+        return x.as1D();
+    }
+    else if (x.rank === 1) {
+        return x;
+    }
+    else if (x.rank === 2) {
+        return x.as4D(1, 1, x.shape[0], x.shape[1]);
+    }
+    else if (x.rank === 3) {
+        return x.as4D(1, x.shape[0], x.shape[1], x.shape[2]);
+    }
+    return x;
+}
+exports.batchNormalization2d = operation_1.op({ batchNormalization2d_: batchNormalization2d_ });
+exports.batchNormalization3d = operation_1.op({ batchNormalization3d_: batchNormalization3d_ });
+exports.batchNormalization4d = operation_1.op({ batchNormalization4d_: batchNormalization4d_ });
+exports.batchNormalization = operation_1.op({ batchNormalization_: batchNormalization_ });
+//# sourceMappingURL=batchnorm.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm.js.map
new file mode 100644
index 0000000..2ba80f4
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm_test.js
new file mode 100644
index 0000000..5cfc8aa
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm_test.js
@@ -0,0 +1,678 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('packed batchNormalization', test_util_1.WEBGL_ENVS, function () {
+    var webglPackedBatchNormalizationSavedFlag = tf.ENV.get('WEBGL_PACK_BATCHNORMALIZATION');
+    beforeAll(function () {
+        tf.ENV.set('WEBGL_PACK_BATCHNORMALIZATION', true);
+    });
+    afterAll(function () {
+        tf.ENV.set('WEBGL_PACK_BATCHNORMALIZATION', webglPackedBatchNormalizationSavedFlag);
+    });
+    it('should not leak memory', function () {
+        var x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
+        var mean = tf.tensor1d([1, 2]);
+        var variance = tf.tensor1d([2, 3]);
+        var varianceEpsilon = .001;
+        var startNumBytes = tf.memory().numBytes;
+        var startNumTensors = tf.memory().numTensors;
+        tf.batchNormalization4d(x, mean, variance, varianceEpsilon, undefined, undefined);
+        var endNumBytes = tf.memory().numBytes;
+        var endNumTensors = tf.memory().numTensors;
+        expect(endNumBytes - startNumBytes).toEqual(16);
+        expect(endNumTensors - startNumTensors).toEqual(1);
+    });
+    it('simple batchnorm4D, no offset or scale, 2x1x1x2', function () {
+        var x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
+        var mean = tf.tensor1d([1, 2]);
+        var variance = tf.tensor1d([2, 3]);
+        var varianceEpsilon = .001;
+        var result = tf.batchNormalization4d(x, mean, variance, varianceEpsilon, undefined, undefined);
+        test_util_1.expectArraysClose(result, [
+            (x.get(0, 0, 0, 0) - mean.get(0)) * 1 /
+                Math.sqrt(variance.get(0) + varianceEpsilon),
+            (x.get(0, 0, 0, 1) - mean.get(1)) * 1 /
+                Math.sqrt(variance.get(1) + varianceEpsilon),
+            (x.get(1, 0, 0, 0) - mean.get(0)) * 1 /
+                Math.sqrt(variance.get(0) + varianceEpsilon),
+            (x.get(1, 0, 0, 1) - mean.get(1)) * 1 /
+                Math.sqrt(variance.get(1) + varianceEpsilon)
+        ]);
+    });
+    it('simple batchnorm4D, 2x1x1x2', function () {
+        var x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
+        var mean = tf.tensor1d([1, 2]);
+        var variance = tf.tensor1d([2, 3]);
+        var offset = tf.tensor1d([3, 4]);
+        var scale = tf.tensor1d([4, 5]);
+        var varianceEpsilon = .001;
+        var result = tf.batchNormalization4d(x, mean, variance, varianceEpsilon, scale, offset);
+        test_util_1.expectArraysClose(result, [
+            offset.get(0) +
+                (x.get(0, 0, 0, 0) - mean.get(0)) * scale.get(0) /
+                    Math.sqrt(variance.get(0) + varianceEpsilon),
+            offset.get(1) +
+                (x.get(0, 0, 0, 1) - mean.get(1)) * scale.get(1) /
+                    Math.sqrt(variance.get(1) + varianceEpsilon),
+            offset.get(0) +
+                (x.get(1, 0, 0, 0) - mean.get(0)) * scale.get(0) /
+                    Math.sqrt(variance.get(0) + varianceEpsilon),
+            offset.get(1) +
+                (x.get(1, 0, 0, 1) - mean.get(1)) * scale.get(1) /
+                    Math.sqrt(variance.get(1) + varianceEpsilon)
+        ]);
+    });
+    it('batchnorm3D gradients, same shapes in x, mean and variance', function () {
+        var x = tf.tensor3d([10, 20, 30, 40], [2, 1, 2]);
+        var mean = tf.tensor3d([0, 5, 10, 15], [2, 1, 2]);
+        var variance = tf.tensor3d([2, 4, 6, 8], [2, 1, 2]);
+        var scale = tf.tensor3d([2, 5, 2, 5], [2, 1, 2]);
+        var offset = tf.tensor3d([0, 0, 0, 0], [2, 1, 2]);
+        var varianceEpsilon = .001;
+        var dy = tf.tensor3d([1, 1, 1, 1], [2, 1, 2]);
+        var gradX = tf.grad(function (x) { return tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset); })(x, dy);
+        test_util_1.expectArraysClose(gradX, tf.tensor3d([1.414, 2.500, 0.816, 1.768], [2, 1, 2]));
+        var gradMean = tf.grad(function (mean) { return tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset); })(mean, dy);
+        test_util_1.expectArraysClose(gradMean, tf.tensor3d([-1.414, -2.500, -0.816, -1.768], [2, 1, 2]));
+        var gradVariance = tf.grad(function (variance) { return tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset); })(variance, dy);
+        test_util_1.expectArraysClose(gradVariance, tf.tensor3d([-3.533, -4.686, -1.360, -2.762], [2, 1, 2]));
+        var gradOffset = tf.grad(function (offset) { return tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset); })(offset, dy);
+        test_util_1.expectArraysClose(gradOffset, tf.onesLike(offset));
+        var gradScale = tf.grad(function (scale) { return tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset); })(scale, dy);
+        test_util_1.expectArraysClose(gradScale, tf.tensor3d([7.069, 7.499, 8.164, 8.838], [2, 1, 2]));
+    });
+    it('batchnorm matches tensorflow, 2x3x3', function () {
+        var x = tf.tensor3d([
+            0.49955603, 0.04158615, -1.09440524, 2.03854165, -0.61578344,
+            2.87533573, 1.18105987, 0.807462, 1.87888837, 2.26563962, -0.37040935,
+            1.35848753, -0.75347094, 0.15683117, 0.91925946, 0.34121279,
+            0.92717143, 1.89683965
+        ], [2, 3, 3]);
+        var mean = tf.tensor1d([0.39745062, -0.48062894, 0.4847822]);
+        var variance = tf.tensor1d([0.32375343, 0.67117643, 1.08334653]);
+        var offset = tf.tensor1d([0.69398749, -1.29056387, 0.9429723]);
+        var scale = tf.tensor1d([-0.5607271, 0.9878457, 0.25181573]);
+        var varianceEpsilon = .001;
+        var result = tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset);
+        test_util_1.expectArraysClose(result, [
+            0.59352049, -0.66135202, 0.5610874, -0.92077015, -1.45341019, 1.52106473,
+            -0.07704776, 0.26144429, 1.28010017, -1.14422404, -1.15776136, 1.15425493,
+            1.82644104, -0.52249442, 1.04803919, 0.74932291, 0.40568101, 1.2844412
+        ]);
+    });
+});
+jasmine_util_1.describeWithFlags('batchNormalization4D', test_util_1.ALL_ENVS, function () {
+    it('simple batchnorm4D, no offset or scale, 2x1x1x2', function () {
+        var x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
+        var mean = tf.tensor1d([1, 2]);
+        var variance = tf.tensor1d([2, 3]);
+        var varianceEpsilon = .001;
+        var result = tf.batchNormalization4d(x, mean, variance, varianceEpsilon, undefined, undefined);
+        test_util_1.expectArraysClose(result, [
+            (x.get(0, 0, 0, 0) - mean.get(0)) * 1 /
+                Math.sqrt(variance.get(0) + varianceEpsilon),
+            (x.get(0, 0, 0, 1) - mean.get(1)) * 1 /
+                Math.sqrt(variance.get(1) + varianceEpsilon),
+            (x.get(1, 0, 0, 0) - mean.get(0)) * 1 /
+                Math.sqrt(variance.get(0) + varianceEpsilon),
+            (x.get(1, 0, 0, 1) - mean.get(1)) * 1 /
+                Math.sqrt(variance.get(1) + varianceEpsilon)
+        ]);
+    });
+    it('simple batchnorm4D, no offset, 2x1x1x2', function () {
+        var x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
+        var mean = tf.tensor1d([1, 2]);
+        var variance = tf.tensor1d([2, 3]);
+        var scale = tf.tensor1d([4, 5]);
+        var varianceEpsilon = .001;
+        var result = tf.batchNormalization4d(x, mean, variance, varianceEpsilon, scale, undefined);
+        test_util_1.expectArraysClose(result, [
+            (x.get(0, 0, 0, 0) - mean.get(0)) * scale.get(0) /
+                Math.sqrt(variance.get(0) + varianceEpsilon),
+            (x.get(0, 0, 0, 1) - mean.get(1)) * scale.get(1) /
+                Math.sqrt(variance.get(1) + varianceEpsilon),
+            (x.get(1, 0, 0, 0) - mean.get(0)) * scale.get(0) /
+                Math.sqrt(variance.get(0) + varianceEpsilon),
+            (x.get(1, 0, 0, 1) - mean.get(1)) * scale.get(1) /
+                Math.sqrt(variance.get(1) + varianceEpsilon)
+        ]);
+    });
+    it('simple batchnorm4D, no scale, 2x1x1x2', function () {
+        var x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
+        var mean = tf.tensor1d([1, 2]);
+        var variance = tf.tensor1d([2, 3]);
+        var offset = tf.tensor1d([4, 5]);
+        var varianceEpsilon = .001;
+        var result = tf.batchNormalization4d(x, mean, variance, varianceEpsilon, undefined, offset);
+        test_util_1.expectArraysClose(result, [
+            offset.get(0) +
+                (x.get(0, 0, 0, 0) - mean.get(0)) * 1 /
+                    Math.sqrt(variance.get(0) + varianceEpsilon),
+            offset.get(1) +
+                (x.get(0, 0, 0, 1) - mean.get(1)) * 1 /
+                    Math.sqrt(variance.get(1) + varianceEpsilon),
+            offset.get(0) +
+                (x.get(1, 0, 0, 0) - mean.get(0)) * 1 /
+                    Math.sqrt(variance.get(0) + varianceEpsilon),
+            offset.get(1) +
+                (x.get(1, 0, 0, 1) - mean.get(1)) * 1 /
+                    Math.sqrt(variance.get(1) + varianceEpsilon)
+        ]);
+    });
+    it('simple batchnorm4D, 2x1x1x2', function () {
+        var x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
+        var mean = tf.tensor1d([1, 2]);
+        var variance = tf.tensor1d([2, 3]);
+        var offset = tf.tensor1d([3, 4]);
+        var scale = tf.tensor1d([4, 5]);
+        var varianceEpsilon = .001;
+        var result = tf.batchNormalization4d(x, mean, variance, varianceEpsilon, scale, offset);
+        test_util_1.expectArraysClose(result, [
+            offset.get(0) +
+                (x.get(0, 0, 0, 0) - mean.get(0)) * scale.get(0) /
+                    Math.sqrt(variance.get(0) + varianceEpsilon),
+            offset.get(1) +
+                (x.get(0, 0, 0, 1) - mean.get(1)) * scale.get(1) /
+                    Math.sqrt(variance.get(1) + varianceEpsilon),
+            offset.get(0) +
+                (x.get(1, 0, 0, 0) - mean.get(0)) * scale.get(0) /
+                    Math.sqrt(variance.get(0) + varianceEpsilon),
+            offset.get(1) +
+                (x.get(1, 0, 0, 1) - mean.get(1)) * scale.get(1) /
+                    Math.sqrt(variance.get(1) + varianceEpsilon)
+        ]);
+    });
+    it('accepts a tensor-like object', function () {
+        var x = [[[[2, 4]]], [[[9, 23]]]];
+        var mean = [1, 2];
+        var variance = [2, 3];
+        var offset = [3, 4];
+        var scale = [4, 5];
+        var varianceEpsilon = .001;
+        var result = tf.batchNormalization4d(x, mean, variance, varianceEpsilon, scale, offset);
+        test_util_1.expectArraysClose(result, [
+            offset[0] +
+                (x[0][0][0][0] - mean[0]) * scale[0] /
+                    Math.sqrt(variance[0] + varianceEpsilon),
+            offset[1] +
+                (x[0][0][0][1] - mean[1]) * scale[1] /
+                    Math.sqrt(variance[1] + varianceEpsilon),
+            offset[0] +
+                (x[1][0][0][0] - mean[0]) * scale[0] /
+                    Math.sqrt(variance[0] + varianceEpsilon),
+            offset[1] +
+                (x[1][0][0][1] - mean[1]) * scale[1] /
+                    Math.sqrt(variance[1] + varianceEpsilon)
+        ]);
+    });
+    it('simple batchnorm4D gradients, 2x1x1x2', function () {
+        var x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
+        var mean = tf.tensor1d([1, 2]);
+        var variance = tf.tensor1d([2, 3]);
+        var offset = tf.tensor1d([3, 4]);
+        var scale = tf.tensor1d([2, 5]);
+        var varianceEpsilon = .001;
+        var dy = tf.tensor4d([-1, -1, -1, -1], [2, 1, 1, 2]);
+        var gradX = tf.grad(function (x) { return tf.batchNormalization4d(x, mean, variance, varianceEpsilon, scale, offset); })(x, dy);
+        test_util_1.expectArraysClose(gradX, tf.tensor4d([-1.414, -2.887, -1.414, -2.887], [2, 1, 1, 2]));
+        var gradMean = tf.grad(function (mean) { return tf.batchNormalization4d(x, mean, variance, varianceEpsilon, scale, offset); })(mean, dy);
+        test_util_1.expectArraysClose(gradMean, tf.tensor1d([2.828, 5.773]));
+        var gradVariance = tf.grad(function (variance) { return tf.batchNormalization4d(x, mean, variance, varianceEpsilon, scale, offset); })(variance, dy);
+        test_util_1.expectArraysClose(gradVariance, tf.tensor1d([3.180, 11.060]));
+        var gradOffset = tf.grad(function (offset) { return tf.batchNormalization4d(x, mean, variance, varianceEpsilon, scale, offset); })(offset, dy);
+        test_util_1.expectArraysClose(gradOffset, dy.sum([0, 1, 2]));
+        var gradScale = tf.grad(function (scale) { return tf.batchNormalization4d(x, mean, variance, varianceEpsilon, scale, offset); })(scale, dy);
+        test_util_1.expectArraysClose(gradScale, tf.tensor1d([-6.362, -13.277]));
+    });
+    it('batchnorm4D gradients, same shapes in x, mean and variance', function () {
+        var x = tf.tensor4d([10, 20, 30, 40], [2, 1, 1, 2]);
+        var mean = tf.tensor4d([0, 5, 10, 15], [2, 1, 1, 2]);
+        var variance = tf.tensor4d([2, 4, 6, 8], [2, 1, 1, 2]);
+        var scale = tf.tensor4d([2, 5, 2, 5], [2, 1, 1, 2]);
+        var offset = tf.tensor4d([0, 0, 0, 0], [2, 1, 1, 2]);
+        var varianceEpsilon = .001;
+        var dy = tf.tensor4d([-1, -1, -1, -1], [2, 1, 1, 2]);
+        var gradX = tf.grad(function (x) { return tf.batchNormalization4d(x, mean, variance, varianceEpsilon, scale, offset); })(x, dy);
+        test_util_1.expectArraysClose(gradX, tf.tensor4d([-1.414, -2.500, -0.816, -1.768], [2, 1, 1, 2]));
+        var gradMean = tf.grad(function (mean) { return tf.batchNormalization4d(x, mean, variance, varianceEpsilon, scale, offset); })(mean, dy);
+        test_util_1.expectArraysClose(gradMean, tf.tensor4d([1.414, 2.500, 0.816, 1.768], [2, 1, 1, 2]));
+        var gradVariance = tf.grad(function (variance) { return tf.batchNormalization4d(x, mean, variance, varianceEpsilon, scale, offset); })(variance, dy);
+        test_util_1.expectArraysClose(gradVariance, tf.tensor4d([3.533, 4.686, 1.360, 2.762], [2, 1, 1, 2]));
+        var gradOffset = tf.grad(function (offset) { return tf.batchNormalization4d(x, mean, variance, varianceEpsilon, scale, offset); })(offset, dy);
+        test_util_1.expectArraysClose(gradOffset, dy);
+        var gradScale = tf.grad(function (scale) { return tf.batchNormalization4d(x, mean, variance, varianceEpsilon, scale, offset); })(scale, dy);
+        test_util_1.expectArraysClose(gradScale, tf.tensor4d([-7.069, -7.499, -8.164, -8.838], [2, 1, 1, 2]));
+    });
+});
+jasmine_util_1.describeWithFlags('batchNormalization3D', test_util_1.ALL_ENVS, function () {
+    it('simple batchnorm3D, no offset or scale, 2x1x2', function () {
+        var x = tf.tensor3d([2, 4, 9, 23], [2, 1, 2]);
+        var mean = tf.tensor1d([1, 2]);
+        var variance = tf.tensor1d([2, 3]);
+        var varianceEpsilon = .001;
+        var result = tf.batchNormalization3d(x, mean, variance, varianceEpsilon, undefined, undefined);
+        test_util_1.expectArraysClose(result, [
+            (x.get(0, 0, 0) - mean.get(0)) * 1 /
+                Math.sqrt(variance.get(0) + varianceEpsilon),
+            (x.get(0, 0, 1) - mean.get(1)) * 1 /
+                Math.sqrt(variance.get(1) + varianceEpsilon),
+            (x.get(1, 0, 0) - mean.get(0)) * 1 /
+                Math.sqrt(variance.get(0) + varianceEpsilon),
+            (x.get(1, 0, 1) - mean.get(1)) * 1 /
+                Math.sqrt(variance.get(1) + varianceEpsilon)
+        ]);
+    });
+    it('simple batchnorm3D, no offset, 2x1x2', function () {
+        var x = tf.tensor3d([2, 4, 9, 23], [2, 1, 2]);
+        var mean = tf.tensor1d([1, 2]);
+        var variance = tf.tensor1d([2, 3]);
+        var scale = tf.tensor1d([4, 5]);
+        var varianceEpsilon = .001;
+        var result = tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, undefined);
+        test_util_1.expectArraysClose(result, [
+            (x.get(0, 0, 0) - mean.get(0)) * scale.get(0) /
+                Math.sqrt(variance.get(0) + varianceEpsilon),
+            (x.get(0, 0, 1) - mean.get(1)) * scale.get(1) /
+                Math.sqrt(variance.get(1) + varianceEpsilon),
+            (x.get(1, 0, 0) - mean.get(0)) * scale.get(0) /
+                Math.sqrt(variance.get(0) + varianceEpsilon),
+            (x.get(1, 0, 1) - mean.get(1)) * scale.get(1) /
+                Math.sqrt(variance.get(1) + varianceEpsilon)
+        ]);
+    });
+    it('simple batchnorm3D, no scale, 2x1x2', function () {
+        var x = tf.tensor3d([2, 4, 9, 23], [2, 1, 2]);
+        var mean = tf.tensor1d([1, 2]);
+        var variance = tf.tensor1d([2, 3]);
+        var offset = tf.tensor1d([4, 5]);
+        var varianceEpsilon = .001;
+        var result = tf.batchNormalization3d(x, mean, variance, varianceEpsilon, undefined, offset);
+        test_util_1.expectArraysClose(result, [
+            offset.get(0) +
+                (x.get(0, 0, 0) - mean.get(0)) * 1 /
+                    Math.sqrt(variance.get(0) + varianceEpsilon),
+            offset.get(1) +
+                (x.get(0, 0, 1) - mean.get(1)) * 1 /
+                    Math.sqrt(variance.get(1) + varianceEpsilon),
+            offset.get(0) +
+                (x.get(1, 0, 0) - mean.get(0)) * 1 /
+                    Math.sqrt(variance.get(0) + varianceEpsilon),
+            offset.get(1) +
+                (x.get(1, 0, 1) - mean.get(1)) * 1 /
+                    Math.sqrt(variance.get(1) + varianceEpsilon)
+        ]);
+    });
+    it('simple batchnorm3D, 2x1x2', function () {
+        var x = tf.tensor3d([2, 4, 9, 23], [2, 1, 2]);
+        var mean = tf.tensor1d([1, 2]);
+        var variance = tf.tensor1d([2, 3]);
+        var offset = tf.tensor1d([3, 4]);
+        var scale = tf.tensor1d([4, 5]);
+        var varianceEpsilon = .001;
+        var result = tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset);
+        test_util_1.expectArraysClose(result, [
+            offset.get(0) +
+                (x.get(0, 0, 0) - mean.get(0)) * scale.get(0) /
+                    Math.sqrt(variance.get(0) + varianceEpsilon),
+            offset.get(1) +
+                (x.get(0, 0, 1) - mean.get(1)) * scale.get(1) /
+                    Math.sqrt(variance.get(1) + varianceEpsilon),
+            offset.get(0) +
+                (x.get(1, 0, 0) - mean.get(0)) * scale.get(0) /
+                    Math.sqrt(variance.get(0) + varianceEpsilon),
+            offset.get(1) +
+                (x.get(1, 0, 1) - mean.get(1)) * scale.get(1) /
+                    Math.sqrt(variance.get(1) + varianceEpsilon)
+        ]);
+    });
+    it('accepts a tensor-like object', function () {
+        var x = [[[2, 4]], [[9, 23]]];
+        var mean = [1, 2];
+        var variance = [2, 3];
+        var offset = [3, 4];
+        var scale = [4, 5];
+        var varianceEpsilon = .001;
+        var result = tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset);
+        test_util_1.expectArraysClose(result, [
+            offset[0] +
+                (x[0][0][0] - mean[0]) * scale[0] /
+                    Math.sqrt(variance[0] + varianceEpsilon),
+            offset[1] +
+                (x[0][0][1] - mean[1]) * scale[1] /
+                    Math.sqrt(variance[1] + varianceEpsilon),
+            offset[0] +
+                (x[1][0][0] - mean[0]) * scale[0] /
+                    Math.sqrt(variance[0] + varianceEpsilon),
+            offset[1] +
+                (x[1][0][1] - mean[1]) * scale[1] /
+                    Math.sqrt(variance[1] + varianceEpsilon)
+        ]);
+    });
+    it('batchnorm3D, x,mean,var,offset,scale are all 3D', function () {
+        var shape = [2, 1, 2];
+        var x = tf.tensor3d([2, 4, 9, 23], shape);
+        var mean = tf.tensor3d([1, 2, 3, 4], shape);
+        var variance = tf.tensor3d([2, 3, 4, 5], shape);
+        var offset = tf.tensor3d([3, 4, 5, 6], shape);
+        var scale = tf.tensor3d([4, 5, 6, 7], shape);
+        var varianceEpsilon = .001;
+        var result = tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset);
+        test_util_1.expectArraysClose(result, [
+            offset.get(0, 0, 0) +
+                (x.get(0, 0, 0) - mean.get(0, 0, 0)) * scale.get(0, 0, 0) /
+                    Math.sqrt(variance.get(0, 0, 0) + varianceEpsilon),
+            offset.get(0, 0, 1) +
+                (x.get(0, 0, 1) - mean.get(0, 0, 1)) * scale.get(0, 0, 1) /
+                    Math.sqrt(variance.get(0, 0, 1) + varianceEpsilon),
+            offset.get(1, 0, 0) +
+                (x.get(1, 0, 0) - mean.get(1, 0, 0)) * scale.get(1, 0, 0) /
+                    Math.sqrt(variance.get(1, 0, 0) + varianceEpsilon),
+            offset.get(1, 0, 1) +
+                (x.get(1, 0, 1) - mean.get(1, 0, 1)) * scale.get(1, 0, 1) /
+                    Math.sqrt(variance.get(1, 0, 1) + varianceEpsilon)
+        ]);
+    });
+    it('simple batchnorm3D gradients, 2x1x2', function () {
+        var x = tf.tensor3d([2, 4, 9, 23], [2, 1, 2]);
+        var mean = tf.tensor1d([1, 2]);
+        var variance = tf.tensor1d([2, 3]);
+        var offset = tf.tensor1d([3, 4]);
+        var scale = tf.tensor1d([2, 5]);
+        var varianceEpsilon = .001;
+        var dy = tf.tensor3d([1, 1, 1, 1], [2, 1, 2]);
+        var gradX = tf.grad(function (x) { return tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset); })(x, dy);
+        test_util_1.expectArraysClose(gradX, tf.tensor3d([1.414, 2.887, 1.414, 2.887], [2, 1, 2]));
+        var gradMean = tf.grad(function (mean) { return tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset); })(mean, dy);
+        test_util_1.expectArraysClose(gradMean, tf.tensor1d([-2.828, -5.773]));
+        var gradVariance = tf.grad(function (variance) { return tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset); })(variance, dy);
+        test_util_1.expectArraysClose(gradVariance, tf.tensor1d([-3.180, -11.060]));
+        var gradOffset = tf.grad(function (offset) { return tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset); })(offset, dy);
+        test_util_1.expectArraysClose(gradOffset, tf.onesLike(offset).mul(tf.scalar(2)));
+        var gradScale = tf.grad(function (scale) { return tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset); })(scale, dy);
+        test_util_1.expectArraysClose(gradScale, tf.tensor1d([6.362, 13.277]));
+    });
+    it('batchnorm3D gradients, same shapes in x, mean and variance', function () {
+        var x = tf.tensor3d([10, 20, 30, 40], [2, 1, 2]);
+        var mean = tf.tensor3d([0, 5, 10, 15], [2, 1, 2]);
+        var variance = tf.tensor3d([2, 4, 6, 8], [2, 1, 2]);
+        var scale = tf.tensor3d([2, 5, 2, 5], [2, 1, 2]);
+        var offset = tf.tensor3d([0, 0, 0, 0], [2, 1, 2]);
+        var varianceEpsilon = .001;
+        var dy = tf.tensor3d([1, 1, 1, 1], [2, 1, 2]);
+        var gradX = tf.grad(function (x) { return tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset); })(x, dy);
+        test_util_1.expectArraysClose(gradX, tf.tensor3d([1.414, 2.500, 0.816, 1.768], [2, 1, 2]));
+        var gradMean = tf.grad(function (mean) { return tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset); })(mean, dy);
+        test_util_1.expectArraysClose(gradMean, tf.tensor3d([-1.414, -2.500, -0.816, -1.768], [2, 1, 2]));
+        var gradVariance = tf.grad(function (variance) { return tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset); })(variance, dy);
+        test_util_1.expectArraysClose(gradVariance, tf.tensor3d([-3.533, -4.686, -1.360, -2.762], [2, 1, 2]));
+        var gradOffset = tf.grad(function (offset) { return tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset); })(offset, dy);
+        test_util_1.expectArraysClose(gradOffset, tf.onesLike(offset));
+        var gradScale = tf.grad(function (scale) { return tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset); })(scale, dy);
+        test_util_1.expectArraysClose(gradScale, tf.tensor3d([7.069, 7.499, 8.164, 8.838], [2, 1, 2]));
+    });
+    it('batchnorm matches tensorflow, 2x3x3', function () {
+        var x = tf.tensor3d([
+            0.49955603, 0.04158615, -1.09440524, 2.03854165, -0.61578344,
+            2.87533573, 1.18105987, 0.807462, 1.87888837, 2.26563962, -0.37040935,
+            1.35848753, -0.75347094, 0.15683117, 0.91925946, 0.34121279,
+            0.92717143, 1.89683965
+        ], [2, 3, 3]);
+        var mean = tf.tensor1d([0.39745062, -0.48062894, 0.4847822]);
+        var variance = tf.tensor1d([0.32375343, 0.67117643, 1.08334653]);
+        var offset = tf.tensor1d([0.69398749, -1.29056387, 0.9429723]);
+        var scale = tf.tensor1d([-0.5607271, 0.9878457, 0.25181573]);
+        var varianceEpsilon = .001;
+        var result = tf.batchNormalization3d(x, mean, variance, varianceEpsilon, scale, offset);
+        test_util_1.expectArraysClose(result, [
+            0.59352049, -0.66135202, 0.5610874, -0.92077015, -1.45341019, 1.52106473,
+            -0.07704776, 0.26144429, 1.28010017, -1.14422404, -1.15776136, 1.15425493,
+            1.82644104, -0.52249442, 1.04803919, 0.74932291, 0.40568101, 1.2844412
+        ]);
+    });
+});
+jasmine_util_1.describeWithFlags('batchNormalization2D', test_util_1.ALL_ENVS, function () {
+    it('simple batchnorm2D, no offset or scale, 2x2', function () {
+        var x = tf.tensor2d([2, 4, 9, 23], [2, 2]);
+        var mean = tf.tensor1d([1, 2]);
+        var variance = tf.tensor1d([2, 3]);
+        var varianceEpsilon = .001;
+        var result = tf.batchNormalization2d(x, mean, variance, varianceEpsilon, undefined, undefined);
+        test_util_1.expectArraysClose(result, [
+            (x.get(0, 0) - mean.get(0)) * 1 /
+                Math.sqrt(variance.get(0) + varianceEpsilon),
+            (x.get(0, 1) - mean.get(1)) * 1 /
+                Math.sqrt(variance.get(1) + varianceEpsilon),
+            (x.get(1, 0) - mean.get(0)) * 1 /
+                Math.sqrt(variance.get(0) + varianceEpsilon),
+            (x.get(1, 1) - mean.get(1)) * 1 /
+                Math.sqrt(variance.get(1) + varianceEpsilon)
+        ]);
+    });
+    it('simple batchnorm2D, no offset, 2x2', function () {
+        var x = tf.tensor2d([2, 4, 9, 23], [2, 2]);
+        var mean = tf.tensor1d([1, 2]);
+        var variance = tf.tensor1d([2, 3]);
+        var scale = tf.tensor1d([4, 5]);
+        var varianceEpsilon = .001;
+        var result = tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, undefined);
+        test_util_1.expectArraysClose(result, [
+            (x.get(0, 0) - mean.get(0)) * scale.get(0) /
+                Math.sqrt(variance.get(0) + varianceEpsilon),
+            (x.get(0, 1) - mean.get(1)) * scale.get(1) /
+                Math.sqrt(variance.get(1) + varianceEpsilon),
+            (x.get(1, 0) - mean.get(0)) * scale.get(0) /
+                Math.sqrt(variance.get(0) + varianceEpsilon),
+            (x.get(1, 1) - mean.get(1)) * scale.get(1) /
+                Math.sqrt(variance.get(1) + varianceEpsilon)
+        ]);
+    });
+    it('simple batchnorm2D, no scale, 2x2', function () {
+        var x = tf.tensor2d([2, 4, 9, 23], [2, 2]);
+        var mean = tf.tensor1d([1, 2]);
+        var variance = tf.tensor1d([2, 3]);
+        var offset = tf.tensor1d([4, 5]);
+        var varianceEpsilon = .001;
+        var result = tf.batchNormalization2d(x, mean, variance, varianceEpsilon, undefined, offset);
+        test_util_1.expectArraysClose(result, [
+            offset.get(0) +
+                (x.get(0, 0) - mean.get(0)) * 1 /
+                    Math.sqrt(variance.get(0) + varianceEpsilon),
+            offset.get(1) +
+                (x.get(0, 1) - mean.get(1)) * 1 /
+                    Math.sqrt(variance.get(1) + varianceEpsilon),
+            offset.get(0) +
+                (x.get(1, 0) - mean.get(0)) * 1 /
+                    Math.sqrt(variance.get(0) + varianceEpsilon),
+            offset.get(1) +
+                (x.get(1, 1) - mean.get(1)) * 1 /
+                    Math.sqrt(variance.get(1) + varianceEpsilon)
+        ]);
+    });
+    it('simple batchnorm2D, 2x2', function () {
+        var x = tf.tensor2d([2, 4, 9, 23], [2, 2]);
+        var mean = tf.tensor1d([1, 2]);
+        var variance = tf.tensor1d([2, 3]);
+        var offset = tf.tensor1d([3, 4]);
+        var scale = tf.tensor1d([4, 5]);
+        var varianceEpsilon = .001;
+        var result = tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, offset);
+        test_util_1.expectArraysClose(result, [
+            offset.get(0) +
+                (x.get(0, 0) - mean.get(0)) * scale.get(0) /
+                    Math.sqrt(variance.get(0) + varianceEpsilon),
+            offset.get(1) +
+                (x.get(0, 1) - mean.get(1)) * scale.get(1) /
+                    Math.sqrt(variance.get(1) + varianceEpsilon),
+            offset.get(0) +
+                (x.get(1, 0) - mean.get(0)) * scale.get(0) /
+                    Math.sqrt(variance.get(0) + varianceEpsilon),
+            offset.get(1) +
+                (x.get(1, 1) - mean.get(1)) * scale.get(1) /
+                    Math.sqrt(variance.get(1) + varianceEpsilon)
+        ]);
+    });
+    it('simple batchnorm2D gradients, 2x2', function () {
+        var x = tf.tensor2d([2, 4, 9, 23], [2, 2]);
+        var mean = tf.tensor1d([1, 2]);
+        var variance = tf.tensor1d([2, 3]);
+        var offset = tf.tensor1d([3, 4]);
+        var scale = tf.tensor1d([2, 5]);
+        var varianceEpsilon = .001;
+        var dy = tf.tensor2d([1, 1, 1, 1], [2, 2]);
+        var gradX = tf.grad(function (x) { return tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, offset); })(x, dy);
+        test_util_1.expectArraysClose(gradX, tf.tensor2d([1.414, 2.887, 1.414, 2.887], [2, 2]));
+        var gradMean = tf.grad(function (mean) { return tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, offset); })(mean, dy);
+        test_util_1.expectArraysClose(gradMean, tf.tensor1d([-2.828, -5.773]));
+        var gradVariance = tf.grad(function (variance) { return tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, offset); })(variance, dy);
+        test_util_1.expectArraysClose(gradVariance, tf.tensor1d([-3.180, -11.060]));
+        var gradOffset = tf.grad(function (offset) { return tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, offset); })(offset, dy);
+        test_util_1.expectArraysClose(gradOffset, tf.onesLike(offset).mul(tf.scalar(2)));
+        var gradScale = tf.grad(function (scale) { return tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, offset); })(scale, dy);
+        test_util_1.expectArraysClose(gradScale, tf.tensor1d([6.362, 13.277]));
+    });
+    it('batchnorm2D gradients, same shapes in x, mean and variance', function () {
+        var x = tf.tensor2d([10, 20, 30, 40], [2, 2]);
+        var mean = tf.tensor2d([0, 5, 10, 15], [2, 2]);
+        var variance = tf.tensor2d([2, 4, 6, 8], [2, 2]);
+        var scale = tf.tensor2d([2, 5, 2, 5], [2, 2]);
+        var offset = tf.tensor2d([0, 0, 0, 0], [2, 2]);
+        var varianceEpsilon = .001;
+        var dy = tf.tensor2d([1, 1, 1, 1], [2, 2]);
+        var gradX = tf.grad(function (x) { return tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, offset); })(x, dy);
+        test_util_1.expectArraysClose(gradX, tf.tensor2d([1.414, 2.500, 0.816, 1.768], [2, 2]));
+        var gradMean = tf.grad(function (mean) { return tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, offset); })(mean, dy);
+        test_util_1.expectArraysClose(gradMean, tf.tensor2d([-1.414, -2.500, -0.816, -1.768], [2, 2]));
+        var gradVariance = tf.grad(function (variance) { return tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, offset); })(variance, dy);
+        test_util_1.expectArraysClose(gradVariance, tf.tensor2d([-3.533, -4.686, -1.360, -2.762], [2, 2]));
+        var gradOffset = tf.grad(function (offset) { return tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, offset); })(offset, dy);
+        test_util_1.expectArraysClose(gradOffset, tf.onesLike(offset));
+        var gradScale = tf.grad(function (scale) { return tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, offset); })(scale, dy);
+        test_util_1.expectArraysClose(gradScale, tf.tensor2d([7.069, 7.499, 8.164, 8.838], [2, 2]));
+    });
+    it('batchnorm2D matches tensorflow, 3x3', function () {
+        var x = tf.tensor2d([
+            0.3136892, 0.92389025, 0.594782, 0.05021042, 0.67545404, 0.93910035,
+            0.13277993, 0.96474269, 0.88608916
+        ], [3, 3]);
+        var mean = tf.tensor1d([0.19526312, 0.74857256, 0.45166398]);
+        var variance = tf.tensor1d([0.22963001, 0.61521992, 0.46623685]);
+        var offset = tf.tensor1d([0.43098484, 0.77712237, 0.47916298]);
+        var scale = tf.tensor1d([0.62186907, 0.85673736, 0.19201061]);
+        var varianceEpsilon = .001;
+        var result = tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, offset);
+        test_util_1.expectArraysClose(result, [
+            0.58433646, 0.96846228, 0.51936529, 0.24315402, 0.69732157, 0.61608542,
+            0.35007446, 1.01304821, 0.60119441
+        ]);
+    });
+    it('throws when passed x as a non-tensor', function () {
+        var mean = tf.tensor1d([1, 2]);
+        var variance = tf.tensor1d([2, 3]);
+        expect(function () { return tf.batchNormalization({}, mean, variance); })
+            .toThrowError(/Argument 'x' passed to 'batchNormalization' must be a Tensor/);
+    });
+    it('throws when passed mean as a non-tensor', function () {
+        var x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
+        var variance = tf.tensor1d([2, 3]);
+        expect(function () { return tf.batchNormalization(x, {}, variance); })
+            .toThrowError(/Argument 'mean' passed to 'batchNormalization' must be a Tensor/);
+    });
+    it('throws when passed variance as a non-tensor', function () {
+        var x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
+        var mean = tf.tensor1d([1, 2]);
+        var e = /Argument 'variance' passed to 'batchNormalization' must be a Tensor/;
+        expect(function () { return tf.batchNormalization(x, mean, {}); })
+            .toThrowError(e);
+    });
+    it('throws when passed scale as a non-tensor', function () {
+        var x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
+        var mean = tf.tensor1d([1, 2]);
+        var variance = tf.tensor1d([2, 3]);
+        var epsilon = .001;
+        expect(function () {
+            return tf.batchNormalization(x, mean, variance, epsilon, {});
+        })
+            .toThrowError(/Argument 'scale' passed to 'batchNormalization' must be a Tensor/);
+    });
+    it('throws when passed offset as a non-tensor', function () {
+        var x = tf.tensor4d([2, 4, 9, 23], [2, 1, 1, 2]);
+        var mean = tf.tensor1d([1, 2]);
+        var variance = tf.tensor1d([2, 3]);
+        var epsilon = .001;
+        var scale = tf.tensor1d([0.62186907, 0.85673736, 0.19201061]);
+        var e = /Argument 'offset' passed to 'batchNormalization' must be a Tensor/;
+        expect(function () { return tf.batchNormalization(x, mean, variance, epsilon, scale, {}); })
+            .toThrowError(e);
+    });
+    it('accepts a tensor-like object', function () {
+        var x = [[2, 4], [9, 23]];
+        var mean = [1, 2];
+        var variance = [2, 3];
+        var offset = [3, 4];
+        var scale = [4, 5];
+        var varianceEpsilon = .001;
+        var result = tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, offset);
+        test_util_1.expectArraysClose(result, [
+            offset[0] +
+                (x[0][0] - mean[0]) * scale[0] /
+                    Math.sqrt(variance[0] + varianceEpsilon),
+            offset[1] +
+                (x[0][1] - mean[1]) * scale[1] /
+                    Math.sqrt(variance[1] + varianceEpsilon),
+            offset[0] +
+                (x[1][0] - mean[0]) * scale[0] /
+                    Math.sqrt(variance[0] + varianceEpsilon),
+            offset[1] +
+                (x[1][1] - mean[1]) * scale[1] /
+                    Math.sqrt(variance[1] + varianceEpsilon)
+        ]);
+    });
+    it('throws error when x is a string tensor', function () {
+        var mean = [1, 2];
+        var variance = [2, 3];
+        var offset = [3, 4];
+        var scale = [4, 5];
+        var varianceEpsilon = .001;
+        var f = function () { return tf.batchNormalization2d([['a', 'b'], ['c', 'd']], mean, variance, varianceEpsilon, scale, offset); };
+        expect(f).toThrowError(/Argument 'x' passed to 'batchNormalization' must be numeric/);
+    });
+    it('throws error when mean is a string tensor', function () {
+        var x = [[2, 4], [9, 23]];
+        var variance = [2, 3];
+        var offset = [3, 4];
+        var scale = [4, 5];
+        var varianceEpsilon = .001;
+        var f = function () { return tf.batchNormalization2d(x, ['a', 'b'], variance, varianceEpsilon, scale, offset); };
+        expect(f).toThrowError(/Argument 'mean' passed to 'batchNormalization' must be numeric/);
+    });
+    it('throws error when variance is a string tensor', function () {
+        var x = [[2, 4], [9, 23]];
+        var mean = [1, 2];
+        var offset = [3, 4];
+        var scale = [4, 5];
+        var varianceEpsilon = .001;
+        var f = function () { return tf.batchNormalization2d(x, mean, ['a', 'b'], varianceEpsilon, scale, offset); };
+        expect(f).toThrowError(/'variance' passed to 'batchNormalization' must be numeric/);
+    });
+    it('throws error when scale is a string tensor', function () {
+        var x = [[2, 4], [9, 23]];
+        var mean = [1, 2];
+        var variance = [2, 3];
+        var offset = [3, 4];
+        var varianceEpsilon = .001;
+        var f = function () { return tf.batchNormalization2d(x, mean, variance, varianceEpsilon, ['a', 'b'], offset); };
+        expect(f).toThrowError(/'scale' passed to 'batchNormalization' must be numeric/);
+    });
+    it('throws error when offset is a string tensor', function () {
+        var x = [[2, 4], [9, 23]];
+        var mean = [1, 2];
+        var variance = [2, 3];
+        var scale = [4, 5];
+        var varianceEpsilon = .001;
+        var f = function () { return tf.batchNormalization2d(x, mean, variance, varianceEpsilon, scale, ['a', 'b']); };
+        expect(f).toThrowError(/'offset' passed to 'batchNormalization' must be numeric/);
+    });
+});
+//# sourceMappingURL=batchnorm_test.js.map
\ No newline at end of file
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+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/batchnorm_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/binary_ops.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/binary_ops.d.ts
new file mode 100644
index 0000000..0b706eb
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/binary_ops.d.ts
@@ -0,0 +1,45 @@
+import { Tensor } from '../tensor';
+import { TensorLike } from '../types';
+declare function add_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
+declare function addN_<T extends Tensor>(tensors: Array<T | TensorLike>): T;
+declare function addStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
+declare function sub_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
+declare function subStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
+declare function pow_<T extends Tensor>(base: T | TensorLike, exp: Tensor | TensorLike): T;
+declare function powStrict_<T extends Tensor>(base: T, exp: Tensor): T;
+declare function mul_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
+declare function mulStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
+declare function div_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
+declare function floorDiv_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
+declare function divStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
+declare function mod_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
+declare function modStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
+declare function minimum_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
+declare function minimumStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
+declare function maximum_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
+declare function maximumStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
+declare function squaredDifference_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
+declare function squaredDifferenceStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
+declare function atan2_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
+export declare const add: typeof add_;
+export declare const addN: typeof addN_;
+export declare const addStrict: typeof addStrict_;
+export declare const atan2: typeof atan2_;
+export declare const div: typeof div_;
+export declare const divStrict: typeof divStrict_;
+export declare const floorDiv: typeof floorDiv_;
+export declare const maximum: typeof maximum_;
+export declare const maximumStrict: typeof maximumStrict_;
+export declare const minimum: typeof minimum_;
+export declare const minimumStrict: typeof minimumStrict_;
+export declare const mod: typeof mod_;
+export declare const modStrict: typeof modStrict_;
+export declare const mul: typeof mul_;
+export declare const mulStrict: typeof mulStrict_;
+export declare const pow: typeof pow_;
+export declare const powStrict: typeof powStrict_;
+export declare const squaredDifference: typeof squaredDifference_;
+export declare const squaredDifferenceStrict: typeof squaredDifferenceStrict_;
+export declare const sub: typeof sub_;
+export declare const subStrict: typeof subStrict_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/binary_ops.js b/node_modules/@tensorflow/tfjs-core/dist/ops/binary_ops.js
new file mode 100644
index 0000000..0ef2775
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/binary_ops.js
@@ -0,0 +1,388 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var tensor_util_1 = require("../tensor_util");
+var tensor_util_env_1 = require("../tensor_util_env");
+var types_1 = require("../types");
+var util = require("../util");
+var broadcast_util = require("./broadcast_util");
+var operation_1 = require("./operation");
+var tensor_ops_1 = require("./tensor_ops");
+var unary_ops_1 = require("./unary_ops");
+function add_(a, b) {
+    var _a;
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'add');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'add');
+    _a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+    var outShape = broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
+    var der = function (dy) {
+        var derA = function () {
+            var res = dy;
+            var reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);
+            if (reduceAxes.length > 0) {
+                res = res.sum(reduceAxes);
+            }
+            return res.reshape($a.shape);
+        };
+        var derB = function () {
+            var res = dy;
+            var reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);
+            if (reduceAxes.length > 0) {
+                res = res.sum(reduceAxes);
+            }
+            return res.reshape($b.shape);
+        };
+        return { $a: derA, $b: derB };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.add($a, $b); }, { $a: $a, $b: $b }, der);
+}
+function addN_(tensors) {
+    util.assert(Array.isArray(tensors), function () { return 'The argument passed to tf.addN() must be a list of tensors'; });
+    util.assert(tensors.length >= 1, function () { return "Must pass at least one tensor to tf.addN(), but got " +
+        ("" + tensors.length); });
+    var $tensors = tensors.map(function (t, i) { return tensor_util_env_1.convertToTensor(t, "tensors" + i, 'addN'); });
+    var firstTensor = $tensors[0];
+    $tensors.forEach(function (t) {
+        if (t.dtype !== firstTensor.dtype) {
+            throw new Error('All tensors passed to tf.addN() must have the same dtype');
+        }
+    });
+    $tensors.forEach(function (t) {
+        if (!util.arraysEqual(t.shape, firstTensor.shape)) {
+            throw new Error('All tensors passed to tf.addN() must have the same shape');
+        }
+    });
+    var der = function (dy) {
+        var ders = {};
+        $tensors.forEach(function (t, i) {
+            ders[i] = function () { return dy.clone(); };
+        });
+        return ders;
+    };
+    var inputs = $tensors;
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.addN($tensors); }, inputs, der);
+}
+function addStrict_(a, b) {
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'addStrict');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'addStrict');
+    util.assertShapesMatch($a.shape, $b.shape, 'Error in addStrict: ');
+    return $a.add($b);
+}
+function sub_(a, b) {
+    var _a;
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'sub');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'sub');
+    _a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+    var outShape = broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
+    var der = function (dy) {
+        var derA = function () {
+            var res = dy;
+            var reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);
+            if (reduceAxes.length > 0) {
+                res = res.sum(reduceAxes);
+            }
+            return res.reshape($a.shape);
+        };
+        var derB = function () {
+            var res = dy;
+            var reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);
+            if (reduceAxes.length > 0) {
+                res = res.sum(reduceAxes);
+            }
+            return res.neg().reshape($b.shape);
+        };
+        return { $a: derA, $b: derB };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.subtract($a, $b); }, { $a: $a, $b: $b }, der);
+}
+function subStrict_(a, b) {
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'subStrict');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'subStrict');
+    util.assertShapesMatch($a.shape, $b.shape, 'Error in subStrict: ');
+    return $a.sub($b);
+}
+function pow_(base, exp) {
+    var $base = tensor_util_env_1.convertToTensor(base, 'base', 'pow');
+    var $exp = tensor_util_env_1.convertToTensor(exp, 'exp', 'pow');
+    var outShape = broadcast_util.assertAndGetBroadcastShape($base.shape, $exp.shape);
+    base = $base.cast(types_1.upcastType($base.dtype, $exp.dtype));
+    exp = $exp.cast(types_1.upcastType($base.dtype, $exp.dtype));
+    var grad = function (dy, saved) {
+        var y = saved[0];
+        var derBase = function () {
+            var expFloat = $exp.toFloat();
+            var res = dy.mul(expFloat.mul($base.pow(expFloat.sub(tensor_ops_1.scalar(1)))));
+            var reduceAxes = broadcast_util.getReductionAxes($base.shape, outShape);
+            if (reduceAxes.length > 0) {
+                res = res.sum(reduceAxes);
+            }
+            return res.reshape($base.shape);
+        };
+        var derExp = function () {
+            var res = dy.mul(y.mul($base.log()).toFloat());
+            var reduceAxes = broadcast_util.getReductionAxes($exp.shape, outShape);
+            if (reduceAxes.length > 0) {
+                res = res.sum(reduceAxes);
+            }
+            return res.reshape($exp.shape);
+        };
+        return { $base: derBase, $exp: derExp };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend, save) { return save(backend.pow($base, $exp)); }, { $base: $base, $exp: $exp }, grad);
+}
+function powStrict_(base, exp) {
+    util.assertShapesMatch(base.shape, exp.shape, 'Error in powStrict: ');
+    return base.pow(exp);
+}
+function mul_(a, b) {
+    var _a;
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'mul');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'mul');
+    _a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+    var outShape = broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
+    var der = function (dy) {
+        var derA = function () {
+            var res = dy.mul($b.toFloat());
+            var reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);
+            if (reduceAxes.length > 0) {
+                return res.sum(reduceAxes).reshape($a.shape);
+            }
+            return res;
+        };
+        var derB = function () {
+            var res = dy.mul($a.toFloat());
+            var reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);
+            if (reduceAxes.length > 0) {
+                return res.sum(reduceAxes).reshape($b.shape);
+            }
+            return res;
+        };
+        return { $a: derA, $b: derB };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.multiply($a, $b); }, { $a: $a, $b: $b }, der);
+}
+function mulStrict_(a, b) {
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'mul');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'mul');
+    util.assertShapesMatch($a.shape, $b.shape, 'Error in multiplyStrict: ');
+    return $a.mul($b);
+}
+function div_(a, b) {
+    var _a;
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'div');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'div');
+    _a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+    var forwardFunc;
+    if ($a.dtype === 'int32' && $b.dtype === 'int32') {
+        return exports.floorDiv($a, $b);
+    }
+    else {
+        forwardFunc = function (backend) { return backend.realDivide($a, $b); };
+    }
+    var outShape = broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
+    var der = function (dy) {
+        var derA = function () {
+            var res = dy.div($b.toFloat());
+            var reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);
+            if (reduceAxes.length > 0) {
+                return res.sum(reduceAxes).reshape($a.shape);
+            }
+            return res;
+        };
+        var derB = function () {
+            var res = dy.mul($a.toFloat());
+            var reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);
+            if (reduceAxes.length > 0) {
+                res = res.sum(reduceAxes).reshape($b.shape);
+            }
+            var tmp = $b.square();
+            return res.div(tmp.toFloat()).neg();
+        };
+        return { $a: derA, $b: derB };
+    };
+    return environment_1.ENV.engine.runKernel(forwardFunc, { $a: $a, $b: $b }, der);
+}
+function floorDiv_(a, b) {
+    var _a;
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'floorDiv');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'floorDiv');
+    _a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+    var forwardFunc = function (backend) { return backend.floorDiv($a, $b); };
+    var outShape = broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
+    var der = function (dy) {
+        var derA = function () {
+            var res = dy.div($b.toFloat());
+            var reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);
+            if (reduceAxes.length > 0) {
+                return res.sum(reduceAxes).reshape($a.shape);
+            }
+            return res;
+        };
+        var derB = function () {
+            var res = dy.mul($a.toFloat());
+            var reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);
+            if (reduceAxes.length > 0) {
+                res = res.sum(reduceAxes).reshape($b.shape);
+            }
+            var tmp = $b.square();
+            return res.div(tmp.toFloat()).neg();
+        };
+        return { $a: derA, $b: derB };
+    };
+    return environment_1.ENV.engine.runKernel(forwardFunc, { $a: $a, $b: $b }, der);
+}
+function divStrict_(a, b) {
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'div');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'div');
+    util.assertShapesMatch($a.shape, $b.shape, 'Error in divideStrict: ');
+    return $a.div($b);
+}
+function mod_(a, b) {
+    var _a;
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'mod');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'mod');
+    _a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+    var outShape = broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
+    var der = function (dy) {
+        var derA = function () {
+            var reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);
+            if (reduceAxes.length > 0) {
+                return dy.sum(reduceAxes).reshape($a.shape);
+            }
+            return dy;
+        };
+        var derB = function () {
+            var res = dy.mul($a.div($b).floor().neg());
+            var reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);
+            if (reduceAxes.length > 0) {
+                return res.sum(reduceAxes).reshape($b.shape);
+            }
+            return res;
+        };
+        return { $a: derA, $b: derB };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.mod($a, $b); }, { $a: $a, $b: $b }, der);
+}
+function modStrict_(a, b) {
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'modStrict');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'modStrict');
+    util.assertShapesMatch($a.shape, $b.shape, 'Error in modStrict: ');
+    return $a.mod($b);
+}
+function minimum_(a, b) {
+    var _a;
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'minimum');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'minimum');
+    _a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+    if ($a.dtype === 'bool') {
+        $a = $a.toInt();
+        $b = $b.toInt();
+    }
+    broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
+    var der = function (dy) {
+        var derA = function () { return dy.mul($a.lessEqual($b).toFloat()); };
+        var derB = function () { return dy.mul($a.greater($b).toFloat()); };
+        return { $a: derA, $b: derB };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.minimum($a, $b); }, { $a: $a, $b: $b }, der);
+}
+function minimumStrict_(a, b) {
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'minimumStrict');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'minimumStrict');
+    util.assertShapesMatch($a.shape, $b.shape, 'Error in minimumStrict: ');
+    return $a.minimum($b);
+}
+function maximum_(a, b) {
+    var _a;
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'maximum');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'maximum');
+    _a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+    if ($a.dtype === 'bool') {
+        $a = $a.toInt();
+        $b = $b.toInt();
+    }
+    broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
+    var der = function (dy) {
+        var derA = function () { return dy.mul($a.greaterEqual($b).toFloat()); };
+        var derB = function () { return dy.mul($a.less($b).toFloat()); };
+        return { $a: derA, $b: derB };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.maximum($a, $b); }, { $a: $a, $b: $b }, der);
+}
+function maximumStrict_(a, b) {
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'maximumStrict');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'maximumStrict');
+    util.assertShapesMatch($a.shape, $b.shape, 'Error in maximumStrict: ');
+    return $a.maximum($b);
+}
+function squaredDifference_(a, b) {
+    var _a;
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'squaredDifference');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'squaredDifference');
+    _a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+    broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
+    var der = function (dy) {
+        var two = tensor_ops_1.scalar(2);
+        var derA = function () { return dy.mul($a.sub($b).mul(two)); };
+        var derB = function () { return dy.mul($b.sub($a).mul(two)); };
+        return { $a: derA, $b: derB };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.squaredDifference($a, $b); }, { $a: $a, $b: $b }, der);
+}
+function squaredDifferenceStrict_(a, b) {
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'squaredDifferenceStrict');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'squaredDifferenceStrict');
+    util.assertShapesMatch($a.shape, $b.shape, 'Error in squaredDifferenceStrict: ');
+    return $a.squaredDifference($b);
+}
+function atan2_(a, b) {
+    var _a;
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'atan2');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'atan2');
+    _a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+    var outShape = broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);
+    var der = function (dy) {
+        var derA = function () {
+            var d = exports.add($a.square(), $b.square());
+            var res = dy.mul($b.div(d));
+            var reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);
+            if (reduceAxes.length > 0) {
+                res = res.sum(reduceAxes);
+            }
+            return res.reshape($a.shape);
+        };
+        var derB = function () {
+            var d = exports.add($a.square(), $b.square());
+            var res = unary_ops_1.neg(dy.mul($a.div(d)));
+            var reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);
+            if (reduceAxes.length > 0) {
+                res = res.sum(reduceAxes);
+            }
+            return res.reshape($b.shape);
+        };
+        return { $a: derA, $b: derB };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.atan2($a, $b); }, { $a: $a, $b: $b }, der);
+}
+exports.add = operation_1.op({ add_: add_ });
+exports.addN = operation_1.op({ addN_: addN_ });
+exports.addStrict = operation_1.op({ addStrict_: addStrict_ });
+exports.atan2 = operation_1.op({ atan2_: atan2_ });
+exports.div = operation_1.op({ div_: div_ });
+exports.divStrict = operation_1.op({ divStrict_: divStrict_ });
+exports.floorDiv = operation_1.op({ floorDiv_: floorDiv_ });
+exports.maximum = operation_1.op({ maximum_: maximum_ });
+exports.maximumStrict = operation_1.op({ maximumStrict_: maximumStrict_ });
+exports.minimum = operation_1.op({ minimum_: minimum_ });
+exports.minimumStrict = operation_1.op({ minimumStrict_: minimumStrict_ });
+exports.mod = operation_1.op({ mod_: mod_ });
+exports.modStrict = operation_1.op({ modStrict_: modStrict_ });
+exports.mul = operation_1.op({ mul_: mul_ });
+exports.mulStrict = operation_1.op({ mulStrict_: mulStrict_ });
+exports.pow = operation_1.op({ pow_: pow_ });
+exports.powStrict = operation_1.op({ powStrict_: powStrict_ });
+exports.squaredDifference = operation_1.op({ squaredDifference_: squaredDifference_ });
+exports.squaredDifferenceStrict = operation_1.op({ squaredDifferenceStrict_: squaredDifferenceStrict_ });
+exports.sub = operation_1.op({ sub_: sub_ });
+exports.subStrict = operation_1.op({ subStrict_: subStrict_ });
+//# sourceMappingURL=binary_ops.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/binary_ops.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/binary_ops.js.map
new file mode 100644
index 0000000..6d5069d
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/binary_ops.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/binary_ops_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/binary_ops_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/binary_ops_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/binary_ops_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/binary_ops_test.js
new file mode 100644
index 0000000..1f8d959
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/binary_ops_test.js
@@ -0,0 +1,866 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('prelu', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var x = tf.tensor1d([0, 1, -2, -4]);
+        var a = tf.tensor1d([0.15, 0.2, 0.25, 0.15]);
+        var result = tf.prelu(x, a);
+        expect(result.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(result, [0, 1, -0.5, -0.6]);
+    });
+    it('basic TensorLike', function () {
+        var x = [0, 1, -2, -4];
+        var a = [0.15, 0.2, 0.25, 0.15];
+        var result = tf.prelu(x, a);
+        expect(result.shape).toEqual([4]);
+        test_util_1.expectArraysClose(result, [0, 1, -0.5, -0.6]);
+    });
+    it('basic TensorLike chained', function () {
+        var x = tf.tensor1d([0, 1, -2, -4]);
+        var a = [0.15, 0.2, 0.25, 0.15];
+        var result = x.prelu(a);
+        expect(result.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(result, [0, 1, -0.5, -0.6]);
+    });
+    it('derivative', function () {
+        var x = tf.tensor1d([0.5, 3, -0.1, -4]);
+        var a = tf.tensor1d([0.2, 0.4, 0.25, 0.15]);
+        var dy = tf.tensor1d([1, 1, 1, 1]);
+        var dx = tf.grad(function (x) { return tf.prelu(x, a); })(x, dy);
+        expect(dx.shape).toEqual(x.shape);
+        expect(dx.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(dx, [1, 1, 0.25, 0.15]);
+    });
+    it('throws when passed x as a non-tensor', function () {
+        expect(function () { return tf.prelu({}, tf.scalar(1)); })
+            .toThrowError(/Argument 'x' passed to 'prelu' must be a Tensor/);
+    });
+    it('throws when passed alpha as a non-tensor', function () {
+        expect(function () { return tf.prelu(tf.scalar(1), {}); })
+            .toThrowError(/Argument 'alpha' passed to 'prelu' must be a Tensor/);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.prelu(['a'], 0.1); })
+            .toThrowError(/Argument 'x' passed to 'prelu' must be numeric tensor/);
+    });
+});
+jasmine_util_1.describeWithFlags('maximum', test_util_1.ALL_ENVS, function () {
+    it('float32 and float32', function () {
+        var a = tf.tensor1d([0.5, 3, -0.1, -4]);
+        var b = tf.tensor1d([0.2, 0.4, 0.25, 0.15]);
+        var result = tf.maximum(a, b);
+        expect(result.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(result, [0.5, 3, 0.25, 0.15]);
+    });
+    it('TensorLike', function () {
+        var a = [0.5, 3, -0.1, -4];
+        var b = [0.2, 0.4, 0.25, 0.15];
+        var result = tf.maximum(a, b);
+        expect(result.shape).toEqual([4]);
+        test_util_1.expectArraysClose(result, [0.5, 3, 0.25, 0.15]);
+    });
+    it('TensorLike chained', function () {
+        var a = tf.tensor1d([0.5, 3, -0.1, -4]);
+        var b = [0.2, 0.4, 0.25, 0.15];
+        var result = a.maximum(b);
+        expect(result.shape).toEqual([4]);
+        test_util_1.expectArraysClose(result, [0.5, 3, 0.25, 0.15]);
+    });
+    it('int32 and int32', function () {
+        var a = tf.tensor1d([1, 5, 2, 3], 'int32');
+        var b = tf.tensor1d([2, 3, 1, 4], 'int32');
+        var result = tf.maximum(a, b);
+        expect(result.shape).toEqual(a.shape);
+        expect(result.dtype).toBe('int32');
+        test_util_1.expectArraysEqual(result, [2, 5, 2, 4]);
+    });
+    it('bool and bool', function () {
+        var a = tf.tensor1d([true, false, false, true], 'bool');
+        var b = tf.tensor1d([false, false, true, true], 'bool');
+        var result = tf.maximum(a, b);
+        expect(result.shape).toEqual(a.shape);
+        expect(result.dtype).toBe('int32');
+        test_util_1.expectArraysEqual(result, [1, 0, 1, 1]);
+    });
+    it('upcasts when dtypes dont match', function () {
+        var a = tf.tensor1d([1, 0, 0, 1], 'float32');
+        var b = tf.tensor1d([0, 0, 1, 1], 'int32');
+        var res = tf.maximum(a, b);
+        expect(res.shape).toEqual(a.shape);
+        expect(res.dtype).toBe('float32');
+        test_util_1.expectArraysEqual(res, [1, 0, 1, 1]);
+    });
+    it('propagates NaN', function () {
+        var a = tf.tensor1d([0.5, -0.1, NaN]);
+        var b = tf.tensor1d([0.2, 0.3, 0.25]);
+        var result = tf.maximum(a, b);
+        expect(result.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(result, [0.5, 0.3, NaN]);
+    });
+    it('broadcasts Tensor1D and scalar', function () {
+        var a = tf.tensor1d([0.5, 3, -0.1, -4]);
+        var b = tf.scalar(0.6);
+        var result = tf.maximum(a, b);
+        expect(result.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(result, [0.6, 3, 0.6, 0.6]);
+    });
+    it('broadcasts scalar and Tensor1D', function () {
+        var a = tf.scalar(0.6);
+        var b = tf.tensor1d([0.5, 3, -0.1, -4]);
+        var result = tf.maximum(a, b);
+        expect(result.shape).toEqual(b.shape);
+        test_util_1.expectArraysClose(result, [0.6, 3, 0.6, 0.6]);
+    });
+    it('broadcasts Tensor1D and Tensor2D', function () {
+        var a = tf.tensor1d([0.5, 0.3]);
+        var b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
+        var result = tf.maximum(a, b);
+        expect(result.shape).toEqual(b.shape);
+        test_util_1.expectArraysClose(result, [0.5, 0.4, 0.6, 0.3]);
+    });
+    it('broadcasts 2x1 Tensor2D and 2x2 Tensor2D', function () {
+        var a = tf.tensor2d([0.5, 0.3], [2, 1]);
+        var b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
+        var result = tf.maximum(a, b);
+        expect(result.shape).toEqual(b.shape);
+        test_util_1.expectArraysClose(result, [0.5, 0.5, 0.6, 0.3]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(5.2);
+        var b = tf.scalar(0.6);
+        var dy = tf.scalar(3);
+        var grads = tf.grads(function (a, b) { return tf.maximum(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(db.shape).toEqual(b.shape);
+        expect(da.dtype).toEqual('float32');
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [3 * 1]);
+        test_util_1.expectArraysClose(db, [3 * 0]);
+    });
+    it('gradients: Tensor1D', function () {
+        var a = tf.tensor1d([1.1, 2.6, 3, 5.9]);
+        var b = tf.tensor1d([1.0, 2.7, 3, 5.8]);
+        var dy = tf.tensor1d([1, 2, 3, 4]);
+        var grads = tf.grads(function (a, b) { return tf.maximum(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(db.shape).toEqual(b.shape);
+        expect(da.dtype).toEqual('float32');
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [1 * 1, 2 * 0, 3 * 1, 4 * 1]);
+        test_util_1.expectArraysClose(db, [1 * 0, 2 * 1, 3 * 0, 4 * 0]);
+    });
+    it('gradients: Tensor2D', function () {
+        var a = tf.tensor2d([0.5, 0.3, 0.7, 0.9], [2, 2]);
+        var b = tf.tensor2d([0.2, 0.4, 0.7, 0.15], [2, 2]);
+        var dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var grads = tf.grads(function (a, b) { return tf.maximum(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(db.shape).toEqual(b.shape);
+        expect(da.dtype).toEqual('float32');
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [1 * 1, 2 * 0, 3 * 1, 4 * 1]);
+        test_util_1.expectArraysClose(db, [1 * 0, 2 * 1, 3 * 0, 4 * 0]);
+    });
+    it('throws when passed a as a non-tensor', function () {
+        expect(function () { return tf.maximum({}, tf.scalar(1)); })
+            .toThrowError(/Argument 'a' passed to 'maximum' must be a Tensor/);
+    });
+    it('throws when passed b as a non-tensor', function () {
+        expect(function () { return tf.maximum(tf.scalar(1), {}); })
+            .toThrowError(/Argument 'b' passed to 'maximum' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [[0.5, 3], [-0.1, -4]];
+        var b = [[0.2, 0.4], [0.25, 0.15]];
+        var result = tf.maximum(a, b);
+        expect(result.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(result, [0.5, 3, 0.25, 0.15]);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.maximum('q', 3); })
+            .toThrowError(/Argument 'a' passed to 'maximum' must be numeric tensor/);
+        expect(function () { return tf.maximum(3, 'q'); })
+            .toThrowError(/Argument 'b' passed to 'maximum' must be numeric tensor/);
+    });
+});
+jasmine_util_1.describeWithFlags('squaredDifference', test_util_1.ALL_ENVS, function () {
+    it('float32 and float32', function () {
+        var a = tf.tensor1d([0.5, 3, -0.1, -4]);
+        var b = tf.tensor1d([0.2, 0.4, 0.25, 0.15]);
+        var result = tf.squaredDifference(a, b);
+        expect(result.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(result, [
+            Math.pow(0.5 - 0.2, 2), Math.pow(3 - 0.4, 2), Math.pow(-0.1 - 0.25, 2),
+            Math.pow(-4 - 0.15, 2)
+        ]);
+    });
+    it('TensorLike', function () {
+        var a = [0.5, 3, -0.1, -4];
+        var b = [0.2, 0.4, 0.25, 0.15];
+        var result = tf.squaredDifference(a, b);
+        expect(result.shape).toEqual([4]);
+        test_util_1.expectArraysClose(result, [
+            Math.pow(0.5 - 0.2, 2), Math.pow(3 - 0.4, 2), Math.pow(-0.1 - 0.25, 2),
+            Math.pow(-4 - 0.15, 2)
+        ]);
+    });
+    it('TensorLike chained', function () {
+        var a = tf.tensor1d([0.5, 3, -0.1, -4]);
+        var b = [0.2, 0.4, 0.25, 0.15];
+        var result = a.squaredDifference(b);
+        expect(result.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(result, [
+            Math.pow(0.5 - 0.2, 2), Math.pow(3 - 0.4, 2), Math.pow(-0.1 - 0.25, 2),
+            Math.pow(-4 - 0.15, 2)
+        ]);
+    });
+    it('int32 and int32', function () {
+        var a = tf.tensor1d([1, 5, 2, 3], 'int32');
+        var b = tf.tensor1d([2, 3, 1, 4], 'int32');
+        var result = tf.squaredDifference(a, b);
+        expect(result.shape).toEqual(a.shape);
+        expect(result.dtype).toBe('int32');
+        test_util_1.expectArraysEqual(result, [
+            Math.pow(1 - 2, 2), Math.pow(5 - 3, 2), Math.pow(2 - 1, 2),
+            Math.pow(3 - 4, 2)
+        ]);
+    });
+    it('upcasts when dtypes dont match', function () {
+        var res = tf.squaredDifference(tf.scalar(5, 'int32'), tf.scalar(2, 'float32'));
+        expect(res.dtype).toBe('float32');
+        test_util_1.expectArraysClose(res, [9]);
+        res = tf.squaredDifference(tf.scalar(5, 'int32'), tf.scalar(true, 'bool'));
+        expect(res.dtype).toBe('int32');
+        test_util_1.expectArraysClose(res, [16]);
+        res = tf.squaredDifference(tf.scalar(5, 'int32'), tf.scalar(false, 'bool'));
+        expect(res.dtype).toBe('int32');
+        test_util_1.expectArraysClose(res, [25]);
+    });
+    it('propagates NaN', function () {
+        var a = tf.tensor1d([0.5, -0.1, NaN]);
+        var b = tf.tensor1d([0.2, 0.3, 0.25]);
+        var result = tf.squaredDifference(a, b);
+        expect(result.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(result, [Math.pow(0.5 - 0.2, 2), Math.pow(-0.1 - 0.3, 2), NaN]);
+    });
+    it('broadcasts Tensor1D and scalar', function () {
+        var a = tf.tensor1d([0.5, 3, -0.1, -4]);
+        var b = tf.scalar(0.6);
+        var result = tf.squaredDifference(a, b);
+        expect(result.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(result, [
+            Math.pow(0.5 - 0.6, 2), Math.pow(3 - 0.6, 2), Math.pow(-0.1 - 0.6, 2),
+            Math.pow(-4 - 0.6, 2)
+        ]);
+    });
+    it('broadcasts scalar and Tensor1D', function () {
+        var a = tf.scalar(0.6);
+        var b = tf.tensor1d([0.5, 3, -0.1, -4]);
+        var result = tf.squaredDifference(a, b);
+        expect(result.shape).toEqual(b.shape);
+        test_util_1.expectArraysClose(result, [
+            Math.pow(0.6 - 0.5, 2), Math.pow(0.6 - 3, 2), Math.pow(0.6 - (-0.1), 2),
+            Math.pow(0.6 - (-4), 2)
+        ]);
+    });
+    it('broadcasts Tensor1D and Tensor2D', function () {
+        var a = tf.tensor1d([0.5, 0.3]);
+        var b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
+        var result = tf.squaredDifference(a, b);
+        expect(result.shape).toEqual(b.shape);
+        test_util_1.expectArraysClose(result, [
+            Math.pow(0.5 - 0.2, 2), Math.pow(0.3 - 0.4, 2), Math.pow(0.5 - 0.6, 2),
+            Math.pow(0.3 - 0.15, 2)
+        ]);
+    });
+    it('broadcasts 2x1 Tensor2D and 2x2 Tensor2D', function () {
+        var a = tf.tensor2d([0.5, 0.3], [2, 1]);
+        var b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
+        var result = tf.squaredDifference(a, b);
+        expect(result.shape).toEqual(b.shape);
+        test_util_1.expectArraysClose(result, [
+            Math.pow(0.5 - 0.2, 2), Math.pow(0.5 - 0.4, 2), Math.pow(0.3 - 0.6, 2),
+            Math.pow(0.3 - 0.15, 2)
+        ]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(5.2);
+        var b = tf.scalar(0.6);
+        var dy = tf.scalar(3);
+        var grads = tf.grads(function (a, b) { return tf.squaredDifference(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(db.shape).toEqual(b.shape);
+        expect(da.dtype).toEqual('float32');
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [3 * 2 * (5.2 - 0.6)]);
+        test_util_1.expectArraysClose(db, [3 * 2 * (0.6 - 5.2)]);
+    });
+    it('gradients: Tensor1D', function () {
+        var a = tf.tensor1d([1.1, 2.6, 3, 5.9]);
+        var b = tf.tensor1d([1.0, 2.7, 3, 5.8]);
+        var dy = tf.tensor1d([1, 2, 3, 1]);
+        var grads = tf.grads(function (a, b) { return tf.squaredDifference(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(db.shape).toEqual(b.shape);
+        expect(da.dtype).toEqual('float32');
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [
+            1 * 2 * (1.1 - 1.0), 2 * 2 * (2.6 - 2.7), 3 * 2 * (3 - 3),
+            1 * 2 * (5.9 - 5.8)
+        ]);
+        test_util_1.expectArraysClose(db, [
+            1 * 2 * (1.0 - 1.1), 2 * 2 * (2.7 - 2.6), 3 * 2 * (3 - 3),
+            1 * 2 * (5.8 - 5.9)
+        ]);
+    });
+    it('gradients: Tensor2D', function () {
+        var a = tf.tensor2d([0.5, 0.3, 0.7, 0.9], [2, 2]);
+        var b = tf.tensor2d([0.2, 0.4, 0.7, 0.15], [2, 2]);
+        var dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var grads = tf.grads(function (a, b) { return tf.squaredDifference(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(db.shape).toEqual(b.shape);
+        expect(da.dtype).toEqual('float32');
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [
+            1 * 2 * (0.5 - 0.2), 2 * 2 * (0.3 - 0.4), 3 * 2 * (0.7 - 0.7),
+            4 * 2 * (0.9 - 0.15)
+        ]);
+        test_util_1.expectArraysClose(db, [
+            1 * 2 * (0.2 - 0.5), 2 * 2 * (0.4 - 0.3), 3 * 2 * (0.7 - 0.7),
+            4 * 2 * (0.15 - 0.9)
+        ]);
+    });
+    it('throws when passed a as a non-tensor', function () {
+        expect(function () { return tf.squaredDifference({}, tf.scalar(1)); })
+            .toThrowError(/Argument 'a' passed to 'squaredDifference' must be a Tensor/);
+    });
+    it('throws when passed b as a non-tensor', function () {
+        expect(function () { return tf.squaredDifference(tf.scalar(1), {}); })
+            .toThrowError(/Argument 'b' passed to 'squaredDifference' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [[0.5, 3], [-0.1, -4]];
+        var b = 0.6;
+        var result = tf.squaredDifference(a, b);
+        expect(result.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(result, [
+            Math.pow(0.5 - 0.6, 2), Math.pow(3 - 0.6, 2), Math.pow(-0.1 - 0.6, 2),
+            Math.pow(-4 - 0.6, 2)
+        ]);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.squaredDifference('q', 3); })
+            .toThrowError(/Argument 'a' passed to 'squaredDifference' must be numeric/);
+        expect(function () { return tf.squaredDifference(3, 'q'); })
+            .toThrowError(/Argument 'b' passed to 'squaredDifference' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('minimum', test_util_1.ALL_ENVS, function () {
+    it('float32 and float32', function () {
+        var a = tf.tensor1d([0.5, 3, -0.1, -4]);
+        var b = tf.tensor1d([0.2, 0.4, 0.25, 0.15]);
+        var result = tf.minimum(a, b);
+        expect(result.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(result, [0.2, 0.4, -0.1, -4]);
+    });
+    it('TensorLike', function () {
+        var a = [0.5, 3, -0.1, -4];
+        var b = [0.2, 0.4, 0.25, 0.15];
+        var result = tf.minimum(a, b);
+        expect(result.shape).toEqual([4]);
+        test_util_1.expectArraysClose(result, [0.2, 0.4, -0.1, -4]);
+    });
+    it('TensorLike chained', function () {
+        var a = tf.tensor1d([0.5, 3, -0.1, -4]);
+        var b = [0.2, 0.4, 0.25, 0.15];
+        var result = a.minimum(b);
+        expect(result.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(result, [0.2, 0.4, -0.1, -4]);
+    });
+    it('int32 and int32', function () {
+        var a = tf.tensor1d([1, 5, 2, 3], 'int32');
+        var b = tf.tensor1d([2, 3, 1, 4], 'int32');
+        var result = tf.minimum(a, b);
+        expect(result.shape).toEqual(a.shape);
+        expect(result.dtype).toBe('int32');
+        test_util_1.expectArraysEqual(result, [1, 3, 1, 3]);
+    });
+    it('bool and bool', function () {
+        var a = tf.tensor1d([true, false, false, true], 'bool');
+        var b = tf.tensor1d([false, false, true, true], 'bool');
+        var result = tf.minimum(a, b);
+        expect(result.shape).toEqual(a.shape);
+        expect(result.dtype).toBe('int32');
+        test_util_1.expectArraysEqual(result, [0, 0, 0, 1]);
+    });
+    it('upcasts when dtypes dont match', function () {
+        var a = tf.tensor1d([1, 0, 0, 1], 'float32');
+        var b = tf.tensor1d([0, 0, 1, 1], 'int32');
+        var res = tf.minimum(a, b);
+        expect(res.shape).toEqual(a.shape);
+        expect(res.dtype).toBe('float32');
+        test_util_1.expectArraysEqual(res, [0, 0, 0, 1]);
+    });
+    it('propagates NaN', function () {
+        var a = tf.tensor1d([0.5, -0.1, NaN]);
+        var b = tf.tensor1d([0.2, 0.3, 0.25]);
+        var result = tf.minimum(a, b);
+        expect(result.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(result, [0.2, -0.1, NaN]);
+    });
+    it('broadcasts Tensor1D and scalar', function () {
+        var a = tf.tensor1d([0.5, 3, -0.1, -4]);
+        var b = tf.scalar(0.6);
+        var result = tf.minimum(a, b);
+        expect(result.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(result, [0.5, 0.6, -0.1, -4]);
+    });
+    it('broadcasts scalar and Tensor1D', function () {
+        var a = tf.scalar(0.6);
+        var b = tf.tensor1d([0.5, 3, -0.1, -4]);
+        var result = tf.minimum(a, b);
+        expect(result.shape).toEqual(b.shape);
+        test_util_1.expectArraysClose(result, [0.5, 0.6, -0.1, -4]);
+    });
+    it('broadcasts Tensor1D and Tensor2D', function () {
+        var a = tf.tensor1d([0.5, 0.3]);
+        var b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
+        var result = tf.minimum(a, b);
+        expect(result.shape).toEqual(b.shape);
+        test_util_1.expectArraysClose(result, [0.2, 0.3, 0.5, 0.15]);
+    });
+    it('broadcasts 2x1 Tensor2D and 2x2 Tensor2D', function () {
+        var a = tf.tensor2d([0.5, 0.3], [2, 1]);
+        var b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
+        var result = tf.minimum(a, b);
+        expect(result.shape).toEqual(b.shape);
+        test_util_1.expectArraysClose(result, [0.2, 0.4, 0.3, 0.15]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(5.2);
+        var b = tf.scalar(0.6);
+        var dy = tf.scalar(3);
+        var grads = tf.grads(function (a, b) { return tf.minimum(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(db.shape).toEqual(b.shape);
+        expect(da.dtype).toEqual('float32');
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [3 * 0]);
+        test_util_1.expectArraysClose(db, [3 * 1]);
+    });
+    it('gradients: Tensor1D', function () {
+        var a = tf.tensor1d([1.1, 2.6, 3, 5.9]);
+        var b = tf.tensor1d([1.0, 2.7, 3, 5.8]);
+        var dy = tf.tensor1d([1, 2, 3, 4]);
+        var grads = tf.grads(function (a, b) { return tf.minimum(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(db.shape).toEqual(b.shape);
+        expect(da.dtype).toEqual('float32');
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [1 * 0, 2 * 1, 3 * 1, 4 * 0]);
+        test_util_1.expectArraysClose(db, [1 * 1, 2 * 0, 3 * 0, 4 * 1]);
+    });
+    it('gradients: Tensor2D', function () {
+        var a = tf.tensor2d([0.5, 0.3, 0.7, 0.9], [2, 2]);
+        var b = tf.tensor2d([0.2, 0.4, 0.7, 0.15], [2, 2]);
+        var dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var grads = tf.grads(function (a, b) { return tf.minimum(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(db.shape).toEqual(b.shape);
+        expect(da.dtype).toEqual('float32');
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [1 * 0, 2 * 1, 3 * 1, 4 * 0]);
+        test_util_1.expectArraysClose(db, [1 * 1, 2 * 0, 3 * 0, 4 * 1]);
+    });
+    it('throws when passed a as a non-tensor', function () {
+        expect(function () { return tf.minimum({}, tf.scalar(1)); })
+            .toThrowError(/Argument 'a' passed to 'minimum' must be a Tensor/);
+    });
+    it('throws when passed b as a non-tensor', function () {
+        expect(function () { return tf.minimum(tf.scalar(1), {}); })
+            .toThrowError(/Argument 'b' passed to 'minimum' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [[0.5, 3], [-0.1, -4]];
+        var b = [[0.2, 0.4], [0.25, 0.15]];
+        var result = tf.minimum(a, b);
+        expect(result.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(result, [0.2, 0.4, -0.1, -4]);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.minimum('q', 3); })
+            .toThrowError(/Argument 'a' passed to 'minimum' must be numeric/);
+        expect(function () { return tf.minimum(3, 'q'); })
+            .toThrowError(/Argument 'b' passed to 'minimum' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('mod', test_util_1.ALL_ENVS, function () {
+    it('float32 and float32', function () {
+        var a = tf.tensor1d([0.5, 3, -0.1, -4]);
+        var b = tf.tensor1d([0.2, 0.4, 0.25, 0.15]);
+        var result = tf.mod(a, b);
+        expect(result.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(result, [0.1, 0.2, 0.15, 0.05]);
+    });
+    it('TensorLike', function () {
+        var a = [0.5, 3, -0.1, -4];
+        var b = [0.2, 0.4, 0.25, 0.15];
+        var result = tf.mod(a, b);
+        expect(result.shape).toEqual([4]);
+        test_util_1.expectArraysClose(result, [0.1, 0.2, 0.15, 0.05]);
+    });
+    it('TensorLike chained', function () {
+        var a = tf.tensor1d([0.5, 3, -0.1, -4]);
+        var b = [0.2, 0.4, 0.25, 0.15];
+        var result = a.mod(b);
+        expect(result.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(result, [0.1, 0.2, 0.15, 0.05]);
+    });
+    it('int32 and int32', function () {
+        var a = tf.tensor1d([1, 5, 2, 3], 'int32');
+        var b = tf.tensor1d([2, 3, 1, 4], 'int32');
+        var result = tf.mod(a, b);
+        expect(result.shape).toEqual(a.shape);
+        expect(result.dtype).toBe('int32');
+        test_util_1.expectArraysEqual(result, [1, 2, 0, 3]);
+    });
+    it('upcasts when dtypes dont match', function () {
+        var res = tf.mod(tf.scalar(5, 'int32'), tf.scalar(2, 'float32'));
+        expect(res.dtype).toBe('float32');
+        test_util_1.expectArraysClose(res, [1]);
+        res = tf.mod(tf.scalar(5, 'int32'), tf.scalar(true, 'bool'));
+        expect(res.dtype).toBe('int32');
+        test_util_1.expectArraysClose(res, [0]);
+    });
+    it('propagates NaN', function () {
+        var a = tf.tensor1d([5, -1, NaN]);
+        var b = tf.tensor1d([2, 3, 0.25]);
+        var result = tf.mod(a, b);
+        expect(result.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(result, [1, 2, NaN]);
+    });
+    it('broadcasts Tensor1D and scalar', function () {
+        var a = tf.tensor1d([0.5, 2.5, -0.1, -4], 'float32');
+        var b = tf.scalar(0.6);
+        var result = tf.mod(a, b);
+        expect(result.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(result, [0.5, 0.1, 0.5, 0.2]);
+    });
+    it('broadcasts scalar and Tensor1D', function () {
+        var a = tf.scalar(2);
+        var b = tf.tensor1d([3, 3, -1, -4]);
+        var result = tf.mod(a, b);
+        expect(result.shape).toEqual(b.shape);
+        test_util_1.expectArraysClose(result, [2, 2, 0, -2]);
+    });
+    it('broadcasts Tensor1D and Tensor2D', function () {
+        var a = tf.tensor1d([0.5, 0.3]);
+        var b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
+        var result = tf.mod(a, b);
+        expect(result.shape).toEqual(b.shape);
+        test_util_1.expectArraysClose(result, [0.1, 0.3, 0.5, 0.0]);
+    });
+    it('broadcasts 2x1 Tensor2D and 2x2 Tensor2D', function () {
+        var a = tf.tensor2d([0.5, 0.3], [2, 1]);
+        var b = tf.tensor2d([0.2, 0.4, 0.6, 0.15], [2, 2]);
+        var result = tf.mod(a, b);
+        expect(result.shape).toEqual(b.shape);
+        test_util_1.expectArraysClose(result, [0.1, 0.1, 0.3, 0.0]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(5.2);
+        var b = tf.scalar(0.6);
+        var dy = tf.scalar(3);
+        var grads = tf.grads(function (a, b) { return tf.mod(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(db.shape).toEqual(b.shape);
+        expect(da.dtype).toEqual('float32');
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [3]);
+        test_util_1.expectArraysClose(db, [3 * -1 * Math.floor(5.2 / 0.6)]);
+    });
+    it('gradients: Tensor1D', function () {
+        var a = tf.tensor1d([1.1, 2.6, 3, 5.9]);
+        var b = tf.tensor1d([1.0, 2.7, 3, 5.8]);
+        var dy = tf.tensor1d([1, 2, 3, 4]);
+        var grads = tf.grads(function (a, b) { return tf.mod(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(db.shape).toEqual(b.shape);
+        expect(da.dtype).toEqual('float32');
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [1 * 1, 2 * 1, 3 * 1, 4 * 1]);
+        test_util_1.expectArraysClose(db, [
+            1 * -1 * Math.floor(1.1 / 1.0), 2 * -1 * Math.floor(2.6 / 2.7),
+            3 * -1 * Math.floor(3 / 3), 4 * -1 * Math.floor(5.9 / 5.8)
+        ]);
+    });
+    it('gradients: Tensor2D', function () {
+        var a = tf.tensor2d([0.5, 0.3, 0.7, 0.91], [2, 2]);
+        var b = tf.tensor2d([0.2, 0.4, 0.7, 0.15], [2, 2]);
+        var dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var grads = tf.grads(function (a, b) { return tf.mod(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(db.shape).toEqual(b.shape);
+        expect(da.dtype).toEqual('float32');
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [1 * 1, 2 * 1, 3 * 1, 4 * 1]);
+        test_util_1.expectArraysClose(db, [
+            1 * -1 * Math.floor(0.5 / 0.2), 2 * -1 * Math.floor(0.3 / 0.4),
+            3 * -1 * Math.floor(0.7 / 0.7), 4 * -1 * Math.floor(0.91 / 0.15)
+        ]);
+    });
+    it('gradients: broadcasts scalar and Tensor1D', function () {
+        var a = tf.scalar(0.7);
+        var b = tf.tensor1d([0.2, 0.3, 0.4, 0.5]);
+        var dy = tf.tensor1d([1, 2, 3, 4]);
+        var grads = tf.grads(function (a, b) { return tf.mod(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(db.shape).toEqual(b.shape);
+        expect(da.dtype).toEqual('float32');
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [1 + 2 + 3 + 4]);
+        test_util_1.expectArraysClose(db, [
+            1 * -1 * Math.floor(0.7 / 0.2), 2 * -1 * Math.floor(0.7 / 0.3),
+            3 * -1 * Math.floor(0.7 / 0.4), 4 * -1 * Math.floor(0.7 / 0.5)
+        ]);
+    });
+    it('broadcasts Tensor1D and Tensor2D', function () {
+        var a = tf.tensor1d([0.5, 0.3]);
+        var b = tf.tensor2d([0.2, 0.4, 0.7, 0.15], [2, 2]);
+        var dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var grads = tf.grads(function (a, b) { return tf.mod(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(db.shape).toEqual(b.shape);
+        expect(da.dtype).toEqual('float32');
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [1 * 1 + 3 * 1, 2 * 1 + 4 * 1]);
+        test_util_1.expectArraysClose(db, [
+            1 * -1 * Math.floor(0.5 / 0.2), 2 * -1 * Math.floor(0.3 / 0.4),
+            3 * -1 * Math.floor(0.5 / 0.7), 4 * -1 * Math.floor(0.3 / 0.15)
+        ]);
+    });
+    it('throws when passed a as a non-tensor', function () {
+        expect(function () { return tf.mod({}, tf.scalar(1)); })
+            .toThrowError(/Argument 'a' passed to 'mod' must be a Tensor/);
+    });
+    it('throws when passed b as a non-tensor', function () {
+        expect(function () { return tf.mod(tf.scalar(1), {}); })
+            .toThrowError(/Argument 'b' passed to 'mod' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [[0.5, 3], [-0.1, -4]];
+        var b = [[0.2, 0.4], [0.25, 0.15]];
+        var result = tf.mod(a, b);
+        expect(result.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(result, [0.1, 0.2, 0.15, 0.05]);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.mod('q', 3); })
+            .toThrowError(/Argument 'a' passed to 'mod' must be numeric/);
+        expect(function () { return tf.mod(3, 'q'); })
+            .toThrowError(/Argument 'b' passed to 'mod' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('atan2', test_util_1.ALL_ENVS, function () {
+    it('same shape', function () {
+        var aValues = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0];
+        var bValues = [1.0, 2.5, 3.5, 4.5, 2.0, 5.0];
+        var a = tf.tensor2d(aValues, [2, 3]);
+        var c = tf.tensor2d(bValues, [2, 3]);
+        var r = tf.atan2(a, c);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = Math.atan2(aValues[i], bValues[i]);
+        }
+        test_util_1.expectArraysClose(r, expected);
+    });
+    it('uses chaining', function () {
+        var aValues = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0];
+        var bValues = [1.0, 2.5, 3.5, 4.5, 2.0, 5.0];
+        var a = tf.tensor2d(aValues, [2, 3]);
+        var b = tf.tensor2d(bValues, [2, 3]);
+        var r = a.atan2(b);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = Math.atan2(aValues[i], bValues[i]);
+        }
+        test_util_1.expectArraysClose(r, expected);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor2d([1.0, 2.0], [2, 1]);
+        var c = tf.tensor2d([3.0, NaN], [2, 1]);
+        var r = tf.atan2(a, c);
+        test_util_1.expectArraysClose(r, [Math.atan2(1.0, 3.0), NaN]);
+    });
+    it('broadcasting same rank Tensors different shape', function () {
+        var aValues = [1.0, 2.0, -3.0, -4.0];
+        var bValues = [2.0, 3.0];
+        var a = tf.tensor2d(aValues, [2, 2]);
+        var b = tf.tensor2d(bValues, [2, 1]);
+        var result = tf.atan2(a, b);
+        expect(result.shape).toEqual([2, 2]);
+        var expected = [
+            Math.atan2(1.0, 2.0), Math.atan2(2.0, 2.0), Math.atan2(-3.0, 3.0),
+            Math.atan2(-4.0, 3.0)
+        ];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('throws when passed tensors of different shapes', function () {
+        var a = tf.tensor2d([1, 2, -3, -4, 5, 6], [2, 3]);
+        var b = tf.tensor2d([5, 3, 4, -7], [2, 2]);
+        expect(function () { return tf.atan2(a, b); }).toThrowError();
+        expect(function () { return tf.atan2(b, a); }).toThrowError();
+    });
+    it('upcasts when dtypes dont match', function () {
+        var aValues = [1.0, 2.0, 3.0, 4.0, 5.0, 6.0];
+        var bValues = [1, 2, 3, 4, 2, 5];
+        var a = tf.tensor2d(aValues, [2, 3], 'float32');
+        var c = tf.tensor2d(bValues, [2, 3], 'int32');
+        var r = tf.atan2(a, c);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = Math.atan2(aValues[i], bValues[i]);
+        }
+        expect(r.shape).toEqual([2, 3]);
+        expect(r.dtype).toBe('float32');
+        test_util_1.expectArraysClose(r, expected);
+    });
+    it('atan2 of scalar and array propagates NaNs', function () {
+        var c = tf.scalar(NaN);
+        var a = tf.tensor2d([1, 2, 3], [1, 3]);
+        var r = tf.atan2(c, a);
+        test_util_1.expectArraysEqual(r, [NaN, NaN, NaN]);
+    });
+    it('atan2 of scalar and array', function () {
+        var aValues = [1, 2, 3, 4, 5, 6];
+        var a = tf.tensor2d(aValues, [2, 3]);
+        var c = tf.scalar(2);
+        var r = tf.atan2(a, c);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = Math.atan2(aValues[i], 2);
+        }
+        test_util_1.expectArraysClose(r, expected);
+    });
+    it('gradient: Scalar', function () {
+        var a = tf.scalar(5);
+        var b = tf.scalar(2);
+        var dy = tf.scalar(4);
+        var grads = tf.grads(function (a, b) { return tf.atan2(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [4 * 2 / 29]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [4 * -5 / 29]);
+    });
+    it('gradient: Tensor1D', function () {
+        var a = tf.tensor1d([1, 2, 3]);
+        var b = tf.tensor1d([3, 4, 5]);
+        var dy = tf.tensor1d([1, 10, 20]);
+        var grads = tf.grads(function (a, b) { return tf.atan2(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [1 * 3 / 10, 10 * 4 / 20, 20 * 5 / 34]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [-1 * 1 / 10, -10 * 2 / 20, -20 * 3 / 34]);
+    });
+    it('gradient: Tensor2D', function () {
+        var a = tf.tensor2d([3, 1, 2, 3], [2, 2]);
+        var b = tf.tensor2d([1, 3, 4, 5], [2, 2]);
+        var dy = tf.tensor2d([1, 10, 15, 20], [2, 2]);
+        var grads = tf.grads(function (a, b) { return tf.atan2(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [1 * 1 / 10, 10 * 3 / 10, 15 * 4 / 20, 20 * 5 / 34]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [-1 * 3 / 10, -10 * 1 / 10, -15 * 2 / 20, -20 * 3 / 34]);
+    });
+    it('gradient: scalar / Tensor1D', function () {
+        var a = tf.scalar(2);
+        var b = tf.tensor1d([3, 4, 5]);
+        var dy = tf.tensor1d([6, 7, 8]);
+        var grads = tf.grads(function (a, b) { return tf.atan2(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [6 * 3 / 13 + 7 * 4 / 20 + 8 * 5 / 29]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [-6 * 2 / 13, -7 * 2 / 20, -8 * 2 / 29]);
+    });
+    it('gradient: Tensor2D / scalar', function () {
+        var a = tf.tensor2d([[2, 3], [4, 5]], [2, 2]);
+        var b = tf.scalar(2);
+        var dy = tf.tensor2d([[6, 7], [8, 9]], [2, 2]);
+        var grads = tf.grads(function (a, b) { return tf.atan2(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [6 * 2 / 8, 7 * 2 / 13, 8 * 2 / 20, 9 * 2 / 29]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [-6 * 2 / 8 + -7 * 3 / 13 + -8 * 4 / 20 + -9 * 5 / 29]);
+    });
+    it('gradient: Tensor2D / Tensor2D w/ broadcast', function () {
+        var a = tf.tensor2d([3, 4], [2, 1]);
+        var b = tf.tensor2d([[2, 3], [4, 5]], [2, 2]);
+        var dy = tf.tensor2d([[6, 7], [8, 9]], [2, 2]);
+        var grads = tf.grads(function (a, b) { return tf.atan2(a, b); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [6 * 2 / 13 + 7 * 3 / 18, 8 * 4 / 32 + 9 * 5 / 41]);
+        expect(db.shape).toEqual(b.shape);
+        expect(db.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(db, [-6 * 3 / 13, -7 * 3 / 18, -8 * 4 / 32, -9 * 4 / 41]);
+    });
+    it('throws when passed a as a non-tensor', function () {
+        expect(function () { return tf.atan2({}, tf.scalar(1)); })
+            .toThrowError(/Argument 'a' passed to 'atan2' must be a Tensor/);
+    });
+    it('throws when passed b as a non-tensor', function () {
+        expect(function () { return tf.atan2(tf.scalar(1), {}); })
+            .toThrowError(/Argument 'b' passed to 'atan2' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [[1, 2, 3], [4, 5, 6]];
+        var c = 2;
+        var r = tf.atan2(a, c);
+        var expected = [];
+        for (var i = 0; i < 6; i++) {
+            expected[i] = Math.atan2(i + 1, 2);
+        }
+        test_util_1.expectArraysClose(r, expected);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.atan2('q', 3); })
+            .toThrowError(/Argument 'a' passed to 'atan2' must be numeric/);
+        expect(function () { return tf.atan2(3, 'q'); })
+            .toThrowError(/Argument 'b' passed to 'atan2' must be numeric/);
+    });
+});
+//# sourceMappingURL=binary_ops_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/binary_ops_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/binary_ops_test.js.map
new file mode 100644
index 0000000..fc58c53
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/binary_ops_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util.d.ts
new file mode 100644
index 0000000..f8078fa
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util.d.ts
@@ -0,0 +1,4 @@
+export declare function getBroadcastDims(inShape: number[], outShape: number[]): number[];
+export declare function getReductionAxes(inShape: number[], outShape: number[]): number[];
+export declare function broadcastDimsAreOuter(dims: number[]): boolean;
+export declare function assertAndGetBroadcastShape(shapeA: number[], shapeB: number[]): number[];
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util.js b/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util.js
new file mode 100644
index 0000000..f4bb7bc
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util.js
@@ -0,0 +1,69 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+function getBroadcastDims(inShape, outShape) {
+    var inRank = inShape.length;
+    var dims = [];
+    for (var i = 0; i < inRank; i++) {
+        var dim = inRank - 1 - i;
+        var a = inShape[dim] || 1;
+        var b = outShape[outShape.length - 1 - i] || 1;
+        if (b > 1 && a === 1) {
+            dims.unshift(dim);
+        }
+    }
+    return dims;
+}
+exports.getBroadcastDims = getBroadcastDims;
+function getReductionAxes(inShape, outShape) {
+    var result = [];
+    for (var i = 0; i < outShape.length; i++) {
+        var inDim = inShape[inShape.length - i - 1];
+        var outAxis = outShape.length - i - 1;
+        var outDim = outShape[outAxis];
+        if (inDim == null || (inDim === 1 && outDim > 1)) {
+            result.unshift(outAxis);
+        }
+    }
+    return result;
+}
+exports.getReductionAxes = getReductionAxes;
+function broadcastDimsAreOuter(dims) {
+    for (var i = 0; i < dims.length; i++) {
+        if (dims[i] !== i) {
+            return false;
+        }
+    }
+    return true;
+}
+exports.broadcastDimsAreOuter = broadcastDimsAreOuter;
+function assertAndGetBroadcastShape(shapeA, shapeB) {
+    var result = [];
+    var l = Math.max(shapeA.length, shapeB.length);
+    for (var i = 0; i < l; i++) {
+        var a = shapeA[shapeA.length - i - 1];
+        if (a == null) {
+            a = 1;
+        }
+        var b = shapeB[shapeB.length - i - 1];
+        if (b == null) {
+            b = 1;
+        }
+        if (a === 1) {
+            result.unshift(b);
+        }
+        else if (b === 1) {
+            result.unshift(a);
+        }
+        else if (a !== b) {
+            var errMsg = "Operands could not be broadcast together with shapes " +
+                (shapeA + " and " + shapeB + ".");
+            throw Error(errMsg);
+        }
+        else {
+            result.unshift(a);
+        }
+    }
+    return result;
+}
+exports.assertAndGetBroadcastShape = assertAndGetBroadcastShape;
+//# sourceMappingURL=broadcast_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util.js.map
new file mode 100644
index 0000000..4e29199
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"broadcast_util.js","sourceRoot":"","sources":["../../src/ops/broadcast_util.ts"],"names":[],"mappings":";;AA0BA,0BACI,OAAiB,EAAE,QAAkB;IACvC,IAAM,MAAM,GAAG,OAAO,CAAC,MAAM,CAAC;IAC9B,IAAM,IAAI,GAAa,EAAE,CAAC;IAC1B,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,MAAM,EAAE,CAAC,EAAE,EAAE;QAC/B,IAAM,GAAG,GAAG,MAAM,GAAG,CAAC,GAAG,CAAC,CAAC;QAC3B,IAAM,CAAC,GAAG,OAAO,CAAC,GAAG,CAAC,IAAI,CAAC,CAAC;QAC5B,IAAM,CAAC,GAAG,QAAQ,CAAC,QAAQ,CAAC,MAAM,GAAG,CAAC,GAAG,CAAC,CAAC,IAAI,CAAC,CAAC;QACjD,IAAI,CAAC,GAAG,CAAC,IAAI,CAAC,KAAK,CAAC,EAAE;YACpB,IAAI,CAAC,OAAO,CAAC,GAAG,CAAC,CAAC;SACnB;KACF;IACD,OAAO,IAAI,CAAC;AACd,CAAC;AAbD,4CAaC;AAMD,0BACI,OAAiB,EAAE,QAAkB;IACvC,IAAM,MAAM,GAAa,EAAE,CAAC;IAC5B,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,QAAQ,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;QACxC,IAAM,KAAK,GAAG,OAAO,CAAC,OAAO,CAAC,MAAM,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC;QAC9C,IAAM,OAAO,GAAG,QAAQ,CAAC,MAAM,GAAG,CAAC,GAAG,CAAC,CAAC;QACxC,IAAM,MAAM,GAAG,QAAQ,CAAC,OAAO,CAAC,CAAC;QACjC,IAAI,KAAK,IAAI,IAAI,IAAI,CAAC,KAAK,KAAK,CAAC,IAAI,MAAM,GAAG,CAAC,CAAC,EAAE;YAChD,MAAM,CAAC,OAAO,CAAC,OAAO,CAAC,CAAC;SACzB;KACF;IACD,OAAO,MAAM,CAAC;AAChB,CAAC;AAZD,4CAYC;AAMD,+BAAsC,IAAc;IAClD,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,IAAI,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;QACpC,IAAI,IAAI,CAAC,CAAC,CAAC,KAAK,CAAC,EAAE;YACjB,OAAO,KAAK,CAAC;SACd;KACF;IACD,OAAO,IAAI,CAAC;AACd,CAAC;AAPD,sDAOC;AAED,oCACI,MAAgB,EAAE,MAAgB;IACpC,IAAM,MAAM,GAAa,EAAE,CAAC;IAC5B,IAAM,CAAC,GAAG,IAAI,CAAC,GAAG,CAAC,MAAM,CAAC,MAAM,EAAE,MAAM,CAAC,MAAM,CAAC,CAAC;IAEjD,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,EAAE,EAAE;QAC1B,IAAI,CAAC,GAAG,MAAM,CAAC,MAAM,CAAC,MAAM,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC;QACtC,IAAI,CAAC,IAAI,IAAI,EAAE;YACb,CAAC,GAAG,CAAC,CAAC;SACP;QACD,IAAI,CAAC,GAAG,MAAM,CAAC,MAAM,CAAC,MAAM,GAAG,CAAC,GAAG,CAAC,CAAC,CAAC;QACtC,IAAI,CAAC,IAAI,IAAI,EAAE;YACb,CAAC,GAAG,CAAC,CAAC;SACP;QACD,IAAI,CAAC,KAAK,CAAC,EAAE;YACX,MAAM,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC;SACnB;aAAM,IAAI,CAAC,KAAK,CAAC,EAAE;YAClB,MAAM,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC;SACnB;aAAM,IAAI,CAAC,KAAK,CAAC,EAAE;YAClB,IAAM,MAAM,GAAG,uDAAuD;iBAC/D,MAAM,aAAQ,MAAM,MAAG,CAAA,CAAC;YAC/B,MAAM,KAAK,CAAC,MAAM,CAAC,CAAC;SACrB;aAAM;YACL,MAAM,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC;SACnB;KACF;IACD,OAAO,MAAM,CAAC;AAChB,CAAC;AA3BD,gEA2BC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util_test.js
new file mode 100644
index 0000000..8a03632
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util_test.js
@@ -0,0 +1,169 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var broadcast_util = require("./broadcast_util");
+describe('broadcast_util.getBroadcastShape', function () {
+    it('two scalars', function () {
+        var res = broadcast_util.assertAndGetBroadcastShape([], []);
+        expect(res).toEqual([]);
+    });
+    it('scalar and 1d', function () {
+        var res = broadcast_util.assertAndGetBroadcastShape([6], []);
+        expect(res).toEqual([6]);
+    });
+    it('scalar and 2d', function () {
+        var res = broadcast_util.assertAndGetBroadcastShape([2, 6], []);
+        expect(res).toEqual([2, 6]);
+    });
+    it('1d and 2d', function () {
+        var res = broadcast_util.assertAndGetBroadcastShape([6], [2, 6]);
+        expect(res).toEqual([2, 6]);
+    });
+    it('2d and 3d', function () {
+        var res = broadcast_util.assertAndGetBroadcastShape([2, 6], [7, 2, 6]);
+        expect(res).toEqual([7, 2, 6]);
+    });
+    it('3d and 3d', function () {
+        var res = broadcast_util.assertAndGetBroadcastShape([1, 1, 6], [7, 2, 6]);
+        expect(res).toEqual([7, 2, 6]);
+    });
+    it('incompatible inner shape', function () {
+        var f = function () {
+            return broadcast_util.assertAndGetBroadcastShape([7, 2, 5], [7, 2, 6]);
+        };
+        expect(f).toThrowError();
+    });
+    it('incompatible middle shape', function () {
+        var f = function () {
+            return broadcast_util.assertAndGetBroadcastShape([7, 3, 6], [7, 2, 6]);
+        };
+        expect(f).toThrowError();
+    });
+    it('compatible with broadcasting support', function () {
+        var res = broadcast_util.assertAndGetBroadcastShape([7, 1, 1], [7, 1, 1]);
+        expect(res).toEqual([7, 1, 1]);
+    });
+    it('3d and 3d, each gets broadcasted', function () {
+        var res = broadcast_util.assertAndGetBroadcastShape([4, 1, 7], [1, 3, 1]);
+        expect(res).toEqual([4, 3, 7]);
+    });
+    it('[0] and [1] = [0]', function () {
+        var res = broadcast_util.assertAndGetBroadcastShape([0], [1]);
+        expect(res).toEqual([0]);
+        var res2 = broadcast_util.assertAndGetBroadcastShape([1], [0]);
+        expect(res2).toEqual([0]);
+    });
+    it('[0] and [0] = [0]', function () {
+        var res = broadcast_util.assertAndGetBroadcastShape([0], [0]);
+        expect(res).toEqual([0]);
+    });
+    it('[0, 1] and [1, 3] = [0, 3]', function () {
+        var res = broadcast_util.assertAndGetBroadcastShape([0, 1], [1, 3]);
+        expect(res).toEqual([0, 3]);
+    });
+    it('[5, 0, 3] and [5, 1, 1] = [5, 0, 3]', function () {
+        var res = broadcast_util.assertAndGetBroadcastShape([5, 0, 3], [5, 1, 1]);
+        expect(res).toEqual([5, 0, 3]);
+    });
+    it('[1] and [0, 0, 4] = [0, 0, 4]', function () {
+        var res = broadcast_util.assertAndGetBroadcastShape([1], [0, 0, 4]);
+        expect(res).toEqual([0, 0, 4]);
+    });
+});
+describe('broadcast_util.getBroadcastDims', function () {
+    it('[] => []', function () {
+        var dims = broadcast_util.getBroadcastDims([], []);
+        expect(dims.length).toBe(0);
+    });
+    it('[] => [5, 4]', function () {
+        var dims = broadcast_util.getBroadcastDims([], [5, 4]);
+        expect(dims.length).toBe(0);
+    });
+    it('[1] => [5]', function () {
+        var dims = broadcast_util.getBroadcastDims([1], [5]);
+        expect(dims).toEqual([0]);
+    });
+    it('[5, 1] => [5, 3]', function () {
+        var dims = broadcast_util.getBroadcastDims([5, 1], [5, 3]);
+        expect(dims).toEqual([1]);
+    });
+    it('[1, 3] => [5, 3]', function () {
+        var dims = broadcast_util.getBroadcastDims([1, 3], [5, 3]);
+        expect(dims).toEqual([0]);
+    });
+    it('[1, 1] => [5, 3]', function () {
+        var dims = broadcast_util.getBroadcastDims([1, 1], [5, 3]);
+        expect(dims).toEqual([0, 1]);
+    });
+    it('[4, 1, 3] => [4, 5, 3]', function () {
+        var dims = broadcast_util.getBroadcastDims([4, 1, 3], [4, 5, 3]);
+        expect(dims).toEqual([1]);
+    });
+});
+describe('broadcast_util.getReductionAxes', function () {
+    it('[] => []', function () {
+        var axes = broadcast_util.getReductionAxes([], []);
+        expect(axes).toEqual([]);
+    });
+    it('[] => [5, 4]', function () {
+        var axes = broadcast_util.getReductionAxes([], [5, 4]);
+        expect(axes).toEqual([0, 1]);
+    });
+    it('[1] => [5]', function () {
+        var axes = broadcast_util.getReductionAxes([1], [5]);
+        expect(axes).toEqual([0]);
+    });
+    it('[5, 1] => [5, 3]', function () {
+        var axes = broadcast_util.getReductionAxes([5, 1], [5, 3]);
+        expect(axes).toEqual([1]);
+    });
+    it('[1, 3] => [5, 3]', function () {
+        var axes = broadcast_util.getReductionAxes([1, 3], [5, 3]);
+        expect(axes).toEqual([0]);
+    });
+    it('[1, 1] => [5, 3]', function () {
+        var axes = broadcast_util.getReductionAxes([1, 1], [5, 3]);
+        expect(axes).toEqual([0, 1]);
+    });
+    it('[4, 1, 3] => [4, 5, 3]', function () {
+        var axes = broadcast_util.getReductionAxes([4, 1, 3], [4, 5, 3]);
+        expect(axes).toEqual([1]);
+    });
+});
+describe('broadcast_util.broadcastDimsAreOuter', function () {
+    it('[] => []', function () {
+        var dims = broadcast_util.getBroadcastDims([], []);
+        var areOuter = broadcast_util.broadcastDimsAreOuter(dims);
+        expect(areOuter).toBe(true);
+    });
+    it('[] => [5, 4]', function () {
+        var dims = broadcast_util.getBroadcastDims([], [5, 4]);
+        var areOuter = broadcast_util.broadcastDimsAreOuter(dims);
+        expect(areOuter).toBe(true);
+    });
+    it('[1] => [5]', function () {
+        var dims = broadcast_util.getBroadcastDims([1], [5]);
+        var areOuter = broadcast_util.broadcastDimsAreOuter(dims);
+        expect(areOuter).toBe(true);
+    });
+    it('[5, 1] => [5, 3]', function () {
+        var dims = broadcast_util.getBroadcastDims([5, 1], [5, 3]);
+        var areOuter = broadcast_util.broadcastDimsAreOuter(dims);
+        expect(areOuter).toBe(false);
+    });
+    it('[1, 1] => [5, 3]', function () {
+        var dims = broadcast_util.getBroadcastDims([1, 1], [5, 3]);
+        var areOuter = broadcast_util.broadcastDimsAreOuter(dims);
+        expect(areOuter).toBe(true);
+    });
+    it('[4, 1, 3] => [4, 5, 3]', function () {
+        var dims = broadcast_util.getBroadcastDims([4, 1, 3], [4, 5, 3]);
+        var areOuter = broadcast_util.broadcastDimsAreOuter(dims);
+        expect(areOuter).toBe(false);
+    });
+    it('[1, 1, 3] => [4, 5, 3]', function () {
+        var dims = broadcast_util.getBroadcastDims([1, 1, 3], [4, 5, 3]);
+        var areOuter = broadcast_util.broadcastDimsAreOuter(dims);
+        expect(areOuter).toBe(true);
+    });
+});
+//# sourceMappingURL=broadcast_util_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util_test.js.map
new file mode 100644
index 0000000..16f0dde
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/broadcast_util_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"broadcast_util_test.js","sourceRoot":"","sources":["../../src/ops/broadcast_util_test.ts"],"names":[],"mappings":";;AAiBA,iDAAmD;AAEnD,QAAQ,CAAC,kCAAkC,EAAE;IAC3C,EAAE,CAAC,aAAa,EAAE;QAChB,IAAM,GAAG,GAAG,cAAc,CAAC,0BAA0B,CAAC,EAAE,EAAE,EAAE,CAAC,CAAC;QAC9D,MAAM,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,EAAE,CAAC,CAAC;IAC1B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,eAAe,EAAE;QAClB,IAAM,GAAG,GAAG,cAAc,CAAC,0BAA0B,CAAC,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC;QAC/D,MAAM,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;IAC3B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,eAAe,EAAE;QAClB,IAAM,GAAG,GAAG,cAAc,CAAC,0BAA0B,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC;QAClE,MAAM,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IAC9B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,WAAW,EAAE;QACd,IAAM,GAAG,GAAG,cAAc,CAAC,0BAA0B,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACnE,MAAM,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IAC9B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,WAAW,EAAE;QACd,IAAM,GAAG,GAAG,cAAc,CAAC,0BAA0B,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACzE,MAAM,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IACjC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,WAAW,EAAE;QACd,IAAM,GAAG,GAAG,cAAc,CAAC,0BAA0B,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC5E,MAAM,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IACjC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,0BAA0B,EAAE;QAC7B,IAAM,CAAC,GAAG;YACN,OAAA,cAAc,CAAC,0BAA0B,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC;QAA/D,CAA+D,CAAC;QACpE,MAAM,CAAC,CAAC,CAAC,CAAC,YAAY,EAAE,CAAC;IAC3B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,2BAA2B,EAAE;QAC9B,IAAM,CAAC,GAAG;YACN,OAAA,cAAc,CAAC,0BAA0B,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC;QAA/D,CAA+D,CAAC;QACpE,MAAM,CAAC,CAAC,CAAC,CAAC,YAAY,EAAE,CAAC;IAC3B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,sCAAsC,EAAE;QACzC,IAAM,GAAG,GAAG,cAAc,CAAC,0BAA0B,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC5E,MAAM,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IACjC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,kCAAkC,EAAE;QACrC,IAAM,GAAG,GAAG,cAAc,CAAC,0BAA0B,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC5E,MAAM,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IACjC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,mBAAmB,EAAE;QACtB,IAAM,GAAG,GAAG,cAAc,CAAC,0BAA0B,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QAChE,MAAM,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QAEzB,IAAM,IAAI,GAAG,cAAc,CAAC,0BAA0B,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QACjE,MAAM,CAAC,IAAI,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;IAC5B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,mBAAmB,EAAE;QACtB,IAAM,GAAG,GAAG,cAAc,CAAC,0BAA0B,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QAChE,MAAM,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;IAC3B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,4BAA4B,EAAE;QAC/B,IAAM,GAAG,GAAG,cAAc,CAAC,0BAA0B,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACtE,MAAM,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IAC9B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,qCAAqC,EAAE;QACxC,IAAM,GAAG,GAAG,cAAc,CAAC,0BAA0B,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC5E,MAAM,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IACjC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,+BAA+B,EAAE;QAClC,IAAM,GAAG,GAAG,cAAc,CAAC,0BAA0B,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACtE,MAAM,CAAC,GAAG,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IACjC,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC;AAEH,QAAQ,CAAC,iCAAiC,EAAE;IAC1C,EAAE,CAAC,UAAU,EAAE;QACb,IAAM,IAAI,GAAG,cAAc,CAAC,gBAAgB,CAAC,EAAE,EAAE,EAAE,CAAC,CAAC;QACrD,MAAM,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;IAC9B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,cAAc,EAAE;QACjB,IAAM,IAAI,GAAG,cAAc,CAAC,gBAAgB,CAAC,EAAE,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACzD,MAAM,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;IAC9B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,YAAY,EAAE;QACf,IAAM,IAAI,GAAG,cAAc,CAAC,gBAAgB,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QACvD,MAAM,CAAC,IAAI,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;IAC5B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,kBAAkB,EAAE;QACrB,IAAM,IAAI,GAAG,cAAc,CAAC,gBAAgB,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC7D,MAAM,CAAC,IAAI,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;IAC5B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,kBAAkB,EAAE;QACrB,IAAM,IAAI,GAAG,cAAc,CAAC,gBAAgB,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC7D,MAAM,CAAC,IAAI,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;IAC5B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,kBAAkB,EAAE;QACrB,IAAM,IAAI,GAAG,cAAc,CAAC,gBAAgB,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC7D,MAAM,C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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/clone_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/clone_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/clone_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/clone_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/clone_test.js
new file mode 100644
index 0000000..7b72a24
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/clone_test.js
@@ -0,0 +1,20 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('clone', test_util_1.ALL_ENVS, function () {
+    it('returns a tensor with the same shape and value', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9], [3, 3]);
+        var aPrime = tf.clone(a);
+        expect(aPrime.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(aPrime, a);
+    });
+    it('accepts a tensor-like object', function () {
+        var res = tf.clone([[1, 2, 3], [4, 5, 6]]);
+        expect(res.dtype).toBe('float32');
+        expect(res.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(res, [1, 2, 3, 4, 5, 6]);
+    });
+});
+//# sourceMappingURL=clone_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/clone_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/clone_test.js.map
new file mode 100644
index 0000000..e0fc2fa
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/clone_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"clone_test.js","sourceRoot":"","sources":["../../src/ops/clone_test.ts"],"names":[],"mappings":";;AAiBA,6BAA+B;AAC/B,gDAAkD;AAClD,0CAAyD;AAEzD,gCAAiB,CAAC,OAAO,EAAE,oBAAQ,EAAE;IACnC,EAAE,CAAC,gDAAgD,EAAE;QACnD,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC3D,IAAM,MAAM,GAAG,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC;QAC3B,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC;QACtC,6BAAiB,CAAC,MAAM,EAAE,CAAC,CAAC,CAAC;IAC/B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,8BAA8B,EAAE;QACjC,IAAM,GAAG,GAAG,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QAC7C,MAAM,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,IAAI,CAAC,SAAS,CAAC,CAAC;QAClC,MAAM,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAClC,6BAAiB,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IAC7C,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/compare.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/compare.d.ts
new file mode 100644
index 0000000..6d72a84
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/compare.d.ts
@@ -0,0 +1,27 @@
+import { Tensor } from '../tensor';
+import { TensorLike } from '../types';
+declare function notEqual_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
+declare function notEqualStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
+declare function less_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
+declare function lessStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
+declare function equal_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
+declare function equalStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
+declare function lessEqual_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
+declare function lessEqualStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
+declare function greater_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
+declare function greaterStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
+declare function greaterEqual_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
+declare function greaterEqualStrict_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike): T;
+export declare const equal: typeof equal_;
+export declare const equalStrict: typeof equalStrict_;
+export declare const greater: typeof greater_;
+export declare const greaterEqual: typeof greaterEqual_;
+export declare const greaterEqualStrict: typeof greaterEqualStrict_;
+export declare const greaterStrict: typeof greaterStrict_;
+export declare const less: typeof less_;
+export declare const lessEqual: typeof lessEqual_;
+export declare const lessEqualStrict: typeof lessEqualStrict_;
+export declare const lessStrict: typeof lessStrict_;
+export declare const notEqual: typeof notEqual_;
+export declare const notEqualStrict: typeof notEqualStrict_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/compare.js b/node_modules/@tensorflow/tfjs-core/dist/ops/compare.js
new file mode 100644
index 0000000..a9add3e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/compare.js
@@ -0,0 +1,109 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var tensor_util_1 = require("../tensor_util");
+var tensor_util_env_1 = require("../tensor_util_env");
+var util_1 = require("../util");
+var broadcast_util_1 = require("./broadcast_util");
+var operation_1 = require("./operation");
+var tensor_ops_1 = require("./tensor_ops");
+function notEqual_(a, b) {
+    var _a;
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'notEqual');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'notEqual');
+    _a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+    broadcast_util_1.assertAndGetBroadcastShape($a.shape, $b.shape);
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.notEqual($a, $b); }, { $a: $a, $b: $b });
+}
+function notEqualStrict_(a, b) {
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'notEqualStrict');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'notEqualStrict');
+    util_1.assertShapesMatch($a.shape, $b.shape, 'Error in notEqualStrict: ');
+    return $a.notEqual($b);
+}
+function less_(a, b) {
+    var _a;
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'less');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'less');
+    _a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+    broadcast_util_1.assertAndGetBroadcastShape($a.shape, $b.shape);
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.less($a, $b); }, { $a: $a, $b: $b });
+}
+function lessStrict_(a, b) {
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'lessStrict');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'lessStrict');
+    util_1.assertShapesMatch($a.shape, $b.shape, 'Error in lessStrict: ');
+    return $a.less($b);
+}
+function equal_(a, b) {
+    var _a;
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'equal');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'equal');
+    _a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+    broadcast_util_1.assertAndGetBroadcastShape($a.shape, $b.shape);
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.equal($a, $b); }, { $a: $a, $b: $b });
+}
+function equalStrict_(a, b) {
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'equalStrict');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'equalStrict');
+    util_1.assertShapesMatch($a.shape, $b.shape, 'Error in equalStrict: ');
+    return $a.equal($b);
+}
+function lessEqual_(a, b) {
+    var _a;
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'lessEqual');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'lessEqual');
+    _a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+    broadcast_util_1.assertAndGetBroadcastShape($a.shape, $b.shape);
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.lessEqual($a, $b); }, { $a: $a, $b: $b });
+}
+function lessEqualStrict_(a, b) {
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'lessEqualStrict');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'lessEqualStrict');
+    util_1.assertShapesMatch($a.shape, $b.shape, 'Error in lessEqualStrict: ');
+    return $a.lessEqual($b);
+}
+function greater_(a, b) {
+    var _a;
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'greater');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'greater');
+    _a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+    broadcast_util_1.assertAndGetBroadcastShape($a.shape, $b.shape);
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.greater($a, $b); }, { $a: $a, $b: $b });
+}
+function greaterStrict_(a, b) {
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'greaterStrict');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'greaterStrict');
+    util_1.assertShapesMatch($a.shape, $b.shape, 'Error in greaterStrict: ');
+    return $a.greater($b);
+}
+function greaterEqual_(a, b) {
+    var _a;
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'greaterEqual');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'greaterEqual');
+    _a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+    broadcast_util_1.assertAndGetBroadcastShape($a.shape, $b.shape);
+    var grad = function (dy) {
+        return { $a: function () { return tensor_ops_1.zerosLike($a); }, $b: function () { return tensor_ops_1.zerosLike($b); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.greaterEqual($a, $b); }, { $a: $a, $b: $b }, grad);
+}
+function greaterEqualStrict_(a, b) {
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'greaterEqualStrict');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'greaterEqualStrict');
+    util_1.assertShapesMatch($a.shape, $b.shape, 'Error in greaterEqualStrict: ');
+    return $a.greaterEqual($b);
+}
+exports.equal = operation_1.op({ equal_: equal_ });
+exports.equalStrict = operation_1.op({ equalStrict_: equalStrict_ });
+exports.greater = operation_1.op({ greater_: greater_ });
+exports.greaterEqual = operation_1.op({ greaterEqual_: greaterEqual_ });
+exports.greaterEqualStrict = operation_1.op({ greaterEqualStrict_: greaterEqualStrict_ });
+exports.greaterStrict = operation_1.op({ greaterStrict_: greaterStrict_ });
+exports.less = operation_1.op({ less_: less_ });
+exports.lessEqual = operation_1.op({ lessEqual_: lessEqual_ });
+exports.lessEqualStrict = operation_1.op({ lessEqualStrict_: lessEqualStrict_ });
+exports.lessStrict = operation_1.op({ lessStrict_: lessStrict_ });
+exports.notEqual = operation_1.op({ notEqual_: notEqual_ });
+exports.notEqualStrict = operation_1.op({ notEqualStrict_: notEqualStrict_ });
+//# sourceMappingURL=compare.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/compare.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/compare.js.map
new file mode 100644
index 0000000..84824b4
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/compare.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"compare.js","sourceRoot":"","sources":["../../src/ops/compare.ts"],"names":[],"mappings":";;AAiBA,8CAAmC;AAEnC,8CAA8C;AAC9C,sDAAmD;AAEnD,gCAA0C;AAC1C,mDAA4D;AAC5D,yCAA+B;AAC/B,2CAAuC;AAkBvC,mBACI,CAAoB,EAAE,CAAoB;;IAC5C,IAAI,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,UAAU,CAAC,CAAC;IAC7C,IAAI,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,UAAU,CAAC,CAAC;IAC7C,yCAAiC,EAAhC,UAAE,EAAE,UAAE,CAA2B;IAClC,2CAA0B,CAAC,EAAE,CAAC,KAAK,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC;IAC/C,OAAO,iBAAG,CAAC,MAAM,CAAC,SAAS,CAAC,UAAA,OAAO,IAAI,OAAA,OAAO,CAAC,QAAQ,CAAC,EAAE,EAAE,EAAE,CAAC,EAAxB,CAAwB,EAAE,EAAC,EAAE,IAAA,EAAE,EAAE,IAAA,EAAC,CACpE,CAAC;AACR,CAAC;AAUD,yBACI,CAAe,EAAE,CAAe;IAClC,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,gBAAgB,CAAC,CAAC;IACrD,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,gBAAgB,CAAC,CAAC;IACrD,wBAAiB,CAAC,EAAE,CAAC,KAAK,EAAE,EAAE,CAAC,KAAK,EAAE,2BAA2B,CAAC,CAAC;IACnE,OAAO,EAAE,CAAC,QAAQ,CAAC,EAAE,CAAC,CAAC;AACzB,CAAC;AAkBD,eACI,CAAoB,EAAE,CAAoB;;IAC5C,IAAI,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,MAAM,CAAC,CAAC;IACzC,IAAI,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,MAAM,CAAC,CAAC;IACzC,yCAAiC,EAAhC,UAAE,EAAE,UAAE,CAA2B;IAClC,2CAA0B,CAAC,EAAE,CAAC,KAAK,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC;IAE/C,OAAO,iBAAG,CAAC,MAAM,CAAC,SAAS,CAAC,UAAA,OAAO,IAAI,OAAA,OAAO,CAAC,IAAI,CAAC,EAAE,EAAE,EAAE,CAAC,EAApB,CAAoB,EAAE,EAAC,EAAE,IAAA,EAAE,EAAE,IAAA,EAAC,CAAM,CAAC;AAC9E,CAAC;AAUD,qBAAuC,CAAe,EAAE,CAAe;IACrE,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,YAAY,CAAC,CAAC;IACjD,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,YAAY,CAAC,CAAC;IACjD,wBAAiB,CAAC,EAAE,CAAC,KAAK,EAAE,EAAE,CAAC,KAAK,EAAE,uBAAuB,CAAC,CAAC;IAC/D,OAAO,EAAE,CAAC,IAAI,CAAC,EAAE,CAAC,CAAC;AACrB,CAAC;AAmBD,gBACI,CAAoB,EAAE,CAAoB;;IAC5C,IAAI,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,OAAO,CAAC,CAAC;IAC1C,IAAI,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,OAAO,CAAC,CAAC;IAC1C,yCAAiC,EAAhC,UAAE,EAAE,UAAE,CAA2B;IAClC,2CAA0B,CAAC,EAAE,CAAC,KAAK,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC;IAE/C,OAAO,iBAAG,CAAC,MAAM,CAAC,SAAS,CAAC,UAAA,OAAO,IAAI,OAAA,OAAO,CAAC,KAAK,CAAC,EAAE,EAAE,EAAE,CAAC,EAArB,CAAqB,EAAE,EAAC,EAAE,IAAA,EAAE,EAAE,IAAA,EAAC,CAAM,CAAC;AAC/E,CAAC;AAED,sBAAwC,CAAe,EAAE,CAAe;IACtE,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,aAAa,CAAC,CAAC;IAClD,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,aAAa,CAAC,CAAC;IAClD,wBAAiB,CAAC,EAAE,CAAC,KAAK,EAAE,EAAE,CAAC,KAAK,EAAE,wBAAwB,CAAC,CAAC;IAChE,OAAO,EAAE,CAAC,KAAK,CAAC,EAAE,CAAC,CAAC;AACtB,CAAC;AAmBD,oBACI,CAAoB,EAAE,CAAoB;;IAC5C,IAAI,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,WAAW,CAAC,CAAC;IAC9C,IAAI,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,WAAW,CAAC,CAAC;IAC9C,yCAAiC,EAAhC,UAAE,EAAE,UAAE,CAA2B;IAClC,2CAA0B,CAAC,EAAE,CAAC,KAAK,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC;IAE/C,OAAO,iBAAG,CAAC,MAAM,CAAC,SAAS,CAAC,UAAA,OAAO,IAAI,OAAA,OAAO,CAAC,SAAS,CAAC,EAAE,EAAE,EAAE,CAAC,EAAzB,CAAyB,EAAE,EAAC,EAAE,IAAA,EAAE,EAAE,IAAA,EAAC,CACrE,CAAC;AACR,CAAC;AAED,0BACI,CAAe,EAAE,CAAe;IAClC,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,iBAAiB,CAAC,CAAC;IACtD,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,iBAAiB,CAAC,CAAC;IACtD,wBAAiB,CAAC,EAAE,CAAC,KAAK,EAAE,EAAE,CAAC,KAAK,EAAE,4BAA4B,CAAC,CAAC;IACpE,OAAO,EAAE,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC;AAC1B,CAAC;AAmBD,kBACI,CAAoB,EAAE,CAAoB;;IAC5C,IAAI,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,SAAS,CAAC,CAAC;IAC5C,IAAI,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,SAAS,CAAC,CAAC;IAC5C,yCAAiC,EAAhC,UAAE,EAAE,UAAE,CAA2B;IAClC,2CAA0B,CAAC,EAAE,CAAC,KAAK,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC;IAE/C,OAAO,iBAAG,CAAC,MAAM,CAAC,SAAS,CAAC,UAAA,OAAO,IAAI,OAAA,OAAO,CAAC,OAAO,CAAC,EAAE,EAAE,EAAE,CAAC,EAAvB,CAAuB,EAAE,EAAC,EAAE,IAAA,EAAE,EAAE,IAAA,EAAC,CACnE,CAAC;AACR,CAAC;AAED,wBAA0C,CAAe,EAAE,CAAe;IACxE,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,eAAe,CAAC,CAAC;IACpD,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,eAAe,CAAC,CAAC;IACpD,wBAAiB,CAAC,EAAE,CAAC,KAAK,EAAE,EAAE,CAAC,KAAK,EAAE,0BAA0B,CAAC,CAAC;IAClE,OAAO,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,CAAC;AACxB,CAAC;AAmBD,uBACI,CAAoB,EAAE,CAAoB;;IAC5C,IAAI,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,cAAc,CAAC,CAAC;IACjD,IAAI,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,cAAc,CAAC,CAAC;IACjD,yCAAiC,EAAhC,UAAE,EAAE,UAAE,CAA2B;IAClC,2CAA0B,CAAC,EAAE,CAAC,KAAK,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC;IAE/C,IAAM,IAAI,GAAG,UAAC,EAAK;QACjB,OAAO,EAAC,EAAE,EAAE,cAAM,OAAA,sBAAS,CAAC,EAAE,CAAC,EAAb,CAAa,EAAE,EAAE,EAAE,cAAM,OAAA,sBAAS,CAAC,EAAE,CAAC,EAAb,CAAa,EAAC,CAAC;IAC5D,CAAC,CAAC;IACF,OAAO,iBAAG,CAAC,MAAM,CAAC,SAAS,CAChB,UAAA,OAAO,IAAI,OAAA,OAAO,CAAC,YAAY,CAAC,EAAE,EAAE,EAAE,CAAC,EAA5B,CAA4B,EAAE,EAAC,EAAE,IAAA,EAAE,EAAE,IAAA,EAAC,EAAE,IAAI,CAAM,CAAC;AAC3E,CAAC;AAED,6BACI,CAAe,EAAE,CAAe;IAClC,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,oBAAoB,CAAC,CAAC;IACzD,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,oBAAoB,CAAC,CAAC;IACzD,wBAAiB,CAAC,EAAE,CAAC,KAAK,EAAE,EAAE,CAAC,KAAK,EAAE,+BAA+B,CAAC,CAAC;IACvE,OAAO,EAAE,CAAC,YAAY,CAAC,EAAE,CAAC,CAAC;AAC7B,CAAC;AAEY,QAAA,KAAK,GAAG,cAAE,CAAC,EAAC,MAAM,QAAA,EAAC,CAAC,CAAC;AACrB,QAAA,WAAW,GAAG,cAAE,CAAC,EAAC,YAAY,cAAA,EAAC,CAAC,CAAC;AACjC,QAAA,OAAO,GAAG,cAAE,CAAC,EAAC,QAAQ,UAAA,EAAC,CAAC,CAAC;AACz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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/compare_ops_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/compare_ops_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/compare_ops_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/compare_ops_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/compare_ops_test.js
new file mode 100644
index 0000000..30e700e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/compare_ops_test.js
@@ -0,0 +1,1932 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var test_util_1 = require("../test_util");
+var jasmine_util_1 = require("../jasmine_util");
+jasmine_util_1.describeWithFlags('equal', test_util_1.ALL_ENVS, function () {
+    it('Tensor1D - int32', function () {
+        var a = tf.tensor1d([1, 4, 5], 'int32');
+        var b = tf.tensor1d([2, 3, 5], 'int32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [0, 0, 1]);
+        a = tf.tensor1d([2, 2, 2], 'int32');
+        b = tf.tensor1d([2, 2, 2], 'int32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [1, 1, 1]);
+        a = tf.tensor1d([0, 0], 'int32');
+        b = tf.tensor1d([3, 3], 'int32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [0, 0]);
+    });
+    it('Tensor1D - float32', function () {
+        var a = tf.tensor1d([1.1, 4.1, 5.1], 'float32');
+        var b = tf.tensor1d([2.2, 3.2, 5.1], 'float32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [0, 0, 1]);
+        a = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
+        b = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [1, 1, 1]);
+        a = tf.tensor1d([0.45, 0.123], 'float32');
+        b = tf.tensor1d([3.123, 3.321], 'float32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [0, 0]);
+    });
+    it('upcasts when dtypes dont match', function () {
+        var a = [1.1, 4.1, 5];
+        var b = [2.2, 3.2, 5];
+        var res = tf.equal(tf.tensor(a, [3], 'float32'), tf.tensor(b, [3], 'int32'));
+        expect(res.dtype).toBe('bool');
+        expect(res.shape).toEqual([3]);
+        test_util_1.expectArraysClose(res, [0, 0, 1]);
+        res =
+            tf.equal(tf.tensor(a, [3], 'int32'), tf.tensor(b, [3], 'bool'));
+        expect(res.dtype).toBe('bool');
+        expect(res.shape).toEqual([3]);
+        test_util_1.expectArraysClose(res, [1, 0, 0]);
+    });
+    it('TensorLike', function () {
+        var a = [1.1, 4.1, 5.1];
+        var b = [2.2, 3.2, 5.1];
+        test_util_1.expectArraysClose(tf.equal(a, b), [0, 0, 1]);
+    });
+    it('TensorLike chained', function () {
+        var a = tf.tensor1d([1.1, 4.1, 5.1], 'float32');
+        var b = [2.2, 3.2, 5.1];
+        test_util_1.expectArraysClose(a.equal(b), [0, 0, 1]);
+    });
+    it('mismatched Tensor1D shapes - int32', function () {
+        var a = tf.tensor1d([1, 2], 'int32');
+        var b = tf.tensor1d([1, 2, 3], 'int32');
+        var f = function () {
+            tf.equal(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('mismatched Tensor1D shapes - float32', function () {
+        var a = tf.tensor1d([1.1, 2.1], 'float32');
+        var b = tf.tensor1d([1.1, 2.1, 3.1], 'float32');
+        var f = function () {
+            tf.equal(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('NaNs in Tensor1D - float32', function () {
+        var a = tf.tensor1d([1.1, NaN, 2.1], 'float32');
+        var b = tf.tensor1d([2.1, 3.1, NaN], 'float32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [0, 0, 0]);
+    });
+    it('scalar and 1D broadcast', function () {
+        var a = tf.scalar(2);
+        var b = tf.tensor1d([1, 2, 3, 4, 5, 2]);
+        var res = tf.equal(a, b);
+        expect(res.dtype).toBe('bool');
+        expect(res.shape).toEqual([6]);
+        test_util_1.expectArraysEqual(res, [0, 1, 0, 0, 0, 1]);
+    });
+    it('Tensor2D - int32', function () {
+        var a = tf.tensor2d([[1, 4, 5], [8, 9, 12]], [2, 3], 'int32');
+        var b = tf.tensor2d([[2, 3, 6], [7, 10, 11]], [2, 3], 'int32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [0, 0, 0, 0, 0, 0]);
+        a = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
+        b = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [1, 1, 1, 1]);
+    });
+    it('Tensor2D - float32', function () {
+        var a = tf.tensor2d([[1.1, 4.1, 5.1], [8.1, 9.1, 12.1]], [2, 3], 'float32');
+        var b = tf.tensor2d([[2.1, 4.1, 5.1], [7.1, 10.1, 11.1]], [2, 3], 'float32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [0, 1, 1, 0, 0, 0]);
+        a = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
+        b = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [1, 1, 1, 1]);
+    });
+    it('broadcasting Tensor2D shapes - int32', function () {
+        var a = tf.tensor2d([[3], [7]], [2, 1], 'int32');
+        var b = tf.tensor2d([[2, 3, 4], [7, 8, 9]], [2, 3], 'int32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [0, 1, 0, 1, 0, 0]);
+    });
+    it('broadcasting Tensor2D shapes - float32', function () {
+        var a = tf.tensor2d([[1.1], [7.1]], [2, 1], 'float32');
+        var b = tf.tensor2d([[0.1, 1.1, 2.1], [7.1, 8.1, 9.1]], [2, 3], 'float32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [0, 1, 0, 1, 0, 0]);
+    });
+    it('NaNs in Tensor2D - float32', function () {
+        var a = tf.tensor2d([[1.1, NaN], [1.1, NaN]], [2, 2], 'float32');
+        var b = tf.tensor2d([[0.1, NaN], [1.1, NaN]], [2, 2], 'float32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [0, 0, 1, 0]);
+    });
+    it('2D and 2D broadcast each with 1 dim', function () {
+        var a = tf.tensor2d([1, 2, 5], [1, 3]);
+        var b = tf.tensor2d([5, 1], [2, 1]);
+        var res = tf.equal(a, b);
+        expect(res.dtype).toBe('bool');
+        expect(res.shape).toEqual([2, 3]);
+        test_util_1.expectArraysEqual(res, [0, 0, 1, 1, 0, 0]);
+    });
+    it('2D and scalar broadcast', function () {
+        var a = tf.tensor2d([1, 2, 3, 2, 5, 6], [2, 3]);
+        var b = tf.scalar(2);
+        var res = tf.equal(a, b);
+        expect(res.dtype).toBe('bool');
+        expect(res.shape).toEqual([2, 3]);
+        test_util_1.expectArraysEqual(res, [0, 1, 0, 1, 0, 0]);
+    });
+    it('Tensor3D - int32', function () {
+        var a = tf.tensor3d([[[1], [4], [5]], [[8], [9], [12]]], [2, 3, 1], 'int32');
+        var b = tf.tensor3d([[[2], [3], [6]], [[7], [10], [12]]], [2, 3, 1], 'int32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [0, 0, 0, 0, 0, 1]);
+        a = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
+        b = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [1, 1, 1, 1, 1, 1]);
+    });
+    it('Tensor3D - float32', function () {
+        var a = tf.tensor3d([[[1.1], [4.1], [5.1]], [[8.1], [9.1], [12.1]]], [2, 3, 1], 'float32');
+        var b = tf.tensor3d([[[2.1], [3.1], [6.1]], [[7.1], [10.1], [12.1]]], [2, 3, 1], 'float32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [0, 0, 0, 0, 0, 1]);
+        a = tf.tensor3d([[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.1]]], [2, 3, 1], 'float32');
+        b = tf.tensor3d([[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.1]]], [2, 3, 1], 'float32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [1, 1, 1, 1, 1, 1]);
+    });
+    it('broadcasting Tensor3D shapes - int32', function () {
+        var a = tf.tensor3d([[[1, 0], [2, 3], [4, 5]], [[6, 7], [9, 8], [10, 11]]], [2, 3, 2], 'int32');
+        var b = tf.tensor3d([[[1], [2], [3]], [[7], [10], [9]]], [2, 3, 1], 'int32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0]);
+    });
+    it('broadcasting Tensor3D shapes - float32', function () {
+        var a = tf.tensor3d([
+            [[1.1, 0.1], [2.1, 3.1], [4.1, 5.1]],
+            [[6.1, 7.1], [9.1, 8.1], [10.1, 11.1]]
+        ], [2, 3, 2], 'float32');
+        var b = tf.tensor3d([[[1.1], [2.1], [3.1]], [[7.1], [10.1], [9.1]]], [2, 3, 1], 'float32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0]);
+    });
+    it('NaNs in Tensor3D - float32', function () {
+        var a = tf.tensor3d([[[1.1], [NaN], [1.1]], [[0.1], [0.1], [0.1]]], [2, 3, 1], 'float32');
+        var b = tf.tensor3d([[[0.1], [0.1], [1.1]], [[1.1], [0.1], [NaN]]], [2, 3, 1], 'float32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [0, 0, 1, 0, 1, 0]);
+    });
+    it('3D and scalar', function () {
+        var a = tf.tensor3d([1, 2, 3, 4, 5, -1], [2, 3, 1]);
+        var b = tf.scalar(-1);
+        var res = tf.equal(a, b);
+        expect(res.dtype).toBe('bool');
+        expect(res.shape).toEqual([2, 3, 1]);
+        test_util_1.expectArraysEqual(res, [0, 0, 0, 0, 0, 1]);
+    });
+    it('Tensor4D - int32', function () {
+        var a = tf.tensor4d([1, 4, 5, 8], [2, 2, 1, 1], 'int32');
+        var b = tf.tensor4d([2, 3, 6, 8], [2, 2, 1, 1], 'int32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [0, 0, 0, 1]);
+        a = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
+        b = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [1, 1, 1, 1]);
+        a = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'int32');
+        b = tf.tensor4d([2, 2, 2, 2], [2, 2, 1, 1], 'int32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [0, 0, 0, 0]);
+    });
+    it('Tensor4D - float32', function () {
+        var a = tf.tensor4d([1.1, 4.1, 5.1, 8.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([2.1, 3.1, 6.1, 8.1], [2, 2, 1, 1], 'float32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [0, 0, 0, 1]);
+        a = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
+        b = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [1, 1, 1, 1]);
+        a = tf.tensor4d([0.1, 0.1, 0.1, 0.1], [2, 2, 1, 1], 'float32');
+        b = tf.tensor4d([1.1, 1.1, 1.1, 1.1], [2, 2, 1, 1], 'float32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [0, 0, 0, 0]);
+    });
+    it('broadcasting Tensor4D shapes - int32', function () {
+        var a = tf.tensor4d([1, 2, 5, 9], [2, 2, 1, 1], 'int32');
+        var b = tf.tensor4d([[[[1, 2]], [[3, 4]]], [[[5, 6]], [[7, 8]]]], [2, 2, 1, 2], 'int32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [1, 0, 0, 0, 1, 0, 0, 0]);
+    });
+    it('broadcasting Tensor4D shapes - float32', function () {
+        var a = tf.tensor4d([1.1, 2.1, 5.1, 9.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([[[[1.1, 2.1]], [[3.1, 4.1]]], [[[5.1, 6.1]], [[7.1, 8.1]]]], [2, 2, 1, 2], 'float32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [1, 0, 0, 0, 1, 0, 0, 0]);
+    });
+    it('NaNs in Tensor4D - float32', function () {
+        var a = tf.tensor4d([1.1, NaN, 1.1, 0.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([0.1, 1.1, 1.1, NaN], [2, 2, 1, 1], 'float32');
+        test_util_1.expectArraysClose(tf.equal(a, b), [0, 0, 1, 0]);
+    });
+    it('throws when passed a as a non-tensor', function () {
+        expect(function () { return tf.equal({}, tf.scalar(1)); })
+            .toThrowError(/Argument 'a' passed to 'equal' must be a Tensor/);
+    });
+    it('throws when passed b as a non-tensor', function () {
+        expect(function () { return tf.equal(tf.scalar(1), {}); })
+            .toThrowError(/Argument 'b' passed to 'equal' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [1, 4, 5];
+        var b = [2, 3, 5];
+        test_util_1.expectArraysClose(tf.equal(a, b), [0, 0, 1]);
+    });
+});
+jasmine_util_1.describeWithFlags('equalStrict', test_util_1.ALL_ENVS, function () {
+    it('Tensor1D - int32', function () {
+        var a = tf.tensor1d([1, 4, 5], 'int32');
+        var b = tf.tensor1d([2, 3, 5], 'int32');
+        test_util_1.expectArraysClose(tf.equalStrict(a, b), [0, 0, 1]);
+        a = tf.tensor1d([2, 2, 2], 'int32');
+        b = tf.tensor1d([2, 2, 2], 'int32');
+        test_util_1.expectArraysClose(tf.equalStrict(a, b), [1, 1, 1]);
+        a = tf.tensor1d([0, 0], 'int32');
+        b = tf.tensor1d([3, 3], 'int32');
+        test_util_1.expectArraysClose(tf.equalStrict(a, b), [0, 0]);
+    });
+    it('Tensor1D - float32', function () {
+        var a = tf.tensor1d([1.1, 4.1, 5.1], 'float32');
+        var b = tf.tensor1d([2.2, 3.2, 5.1], 'float32');
+        test_util_1.expectArraysClose(tf.equalStrict(a, b), [0, 0, 1]);
+        a = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
+        b = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
+        test_util_1.expectArraysClose(tf.equalStrict(a, b), [1, 1, 1]);
+        a = tf.tensor1d([0.45, 0.123], 'float32');
+        b = tf.tensor1d([3.123, 3.321], 'float32');
+        test_util_1.expectArraysClose(tf.equalStrict(a, b), [0, 0]);
+    });
+    it('mismatched Tensor1D shapes - int32', function () {
+        var a = tf.tensor1d([1, 2], 'int32');
+        var b = tf.tensor1d([1, 2, 3], 'int32');
+        var f = function () {
+            tf.equalStrict(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('mismatched Tensor1D shapes - float32', function () {
+        var a = tf.tensor1d([1.1, 2.1], 'float32');
+        var b = tf.tensor1d([1.1, 2.1, 3.1], 'float32');
+        var f = function () {
+            tf.equalStrict(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('NaNs in Tensor1D - float32', function () {
+        var a = tf.tensor1d([1.1, NaN, 2.1], 'float32');
+        var b = tf.tensor1d([2.1, 3.1, NaN], 'float32');
+        test_util_1.expectArraysClose(tf.equalStrict(a, b), [0, 0, 0]);
+    });
+    it('Tensor2D - int32', function () {
+        var a = tf.tensor2d([[1, 4, 5], [8, 9, 12]], [2, 3], 'int32');
+        var b = tf.tensor2d([[2, 3, 6], [7, 10, 11]], [2, 3], 'int32');
+        test_util_1.expectArraysClose(tf.equalStrict(a, b), [0, 0, 0, 0, 0, 0]);
+        a = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
+        b = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
+        test_util_1.expectArraysClose(tf.equalStrict(a, b), [1, 1, 1, 1]);
+    });
+    it('Tensor2D - float32', function () {
+        var a = tf.tensor2d([[1.1, 4.1, 5.1], [8.1, 9.1, 12.1]], [2, 3], 'float32');
+        var b = tf.tensor2d([[2.1, 4.1, 5.1], [7.1, 10.1, 11.1]], [2, 3], 'float32');
+        test_util_1.expectArraysClose(tf.equalStrict(a, b), [0, 1, 1, 0, 0, 0]);
+        a = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
+        b = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
+        test_util_1.expectArraysClose(tf.equalStrict(a, b), [1, 1, 1, 1]);
+    });
+    it('mismatch Tensor2D shapes - int32', function () {
+        var a = tf.tensor2d([[3], [7]], [2, 1], 'int32');
+        var b = tf.tensor2d([[2, 3, 4], [7, 8, 9]], [2, 3], 'int32');
+        var f = function () {
+            tf.equalStrict(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('mismatch Tensor2D shapes - float32', function () {
+        var a = tf.tensor2d([[1.1], [7.1]], [2, 1], 'float32');
+        var b = tf.tensor2d([[0.1, 1.1, 2.1], [7.1, 8.1, 9.1]], [2, 3], 'float32');
+        var f = function () {
+            tf.equalStrict(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('NaNs in Tensor2D - float32', function () {
+        var a = tf.tensor2d([[1.1, NaN], [1.1, NaN]], [2, 2], 'float32');
+        var b = tf.tensor2d([[0.1, NaN], [1.1, NaN]], [2, 2], 'float32');
+        test_util_1.expectArraysClose(tf.equalStrict(a, b), [0, 0, 1, 0]);
+    });
+    it('Tensor3D - int32', function () {
+        var a = tf.tensor3d([[[1], [4], [5]], [[8], [9], [12]]], [2, 3, 1], 'int32');
+        var b = tf.tensor3d([[[2], [3], [6]], [[7], [10], [12]]], [2, 3, 1], 'int32');
+        test_util_1.expectArraysClose(tf.equalStrict(a, b), [0, 0, 0, 0, 0, 1]);
+        a = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
+        b = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
+        test_util_1.expectArraysClose(tf.equalStrict(a, b), [1, 1, 1, 1, 1, 1]);
+    });
+    it('Tensor3D - float32', function () {
+        var a = tf.tensor3d([[[1.1], [4.1], [5.1]], [[8.1], [9.1], [12.1]]], [2, 3, 1], 'float32');
+        var b = tf.tensor3d([[[2.1], [3.1], [6.1]], [[7.1], [10.1], [12.1]]], [2, 3, 1], 'float32');
+        test_util_1.expectArraysClose(tf.equalStrict(a, b), [0, 0, 0, 0, 0, 1]);
+        a = tf.tensor3d([[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.1]]], [2, 3, 1], 'float32');
+        b = tf.tensor3d([[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.1]]], [2, 3, 1], 'float32');
+        test_util_1.expectArraysClose(tf.equalStrict(a, b), [1, 1, 1, 1, 1, 1]);
+    });
+    it('mismatch Tensor3D shapes - int32', function () {
+        var a = tf.tensor3d([[[1, 0], [2, 3], [4, 5]], [[6, 7], [9, 8], [10, 11]]], [2, 3, 2], 'int32');
+        var b = tf.tensor3d([[[1], [2], [3]], [[7], [10], [9]]], [2, 3, 1], 'int32');
+        var f = function () {
+            tf.equalStrict(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('mismatch Tensor3D shapes - float32', function () {
+        var a = tf.tensor3d([
+            [[1.1, 0.1], [2.1, 3.1], [4.1, 5.1]],
+            [[6.1, 7.1], [9.1, 8.1], [10.1, 11.1]]
+        ], [2, 3, 2], 'float32');
+        var b = tf.tensor3d([[[1.1], [2.1], [3.1]], [[7.1], [10.1], [9.1]]], [2, 3, 1], 'float32');
+        var f = function () {
+            tf.equalStrict(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('NaNs in Tensor3D - float32', function () {
+        var a = tf.tensor3d([[[1.1], [NaN], [1.1]], [[0.1], [0.1], [0.1]]], [2, 3, 1], 'float32');
+        var b = tf.tensor3d([[[0.1], [0.1], [1.1]], [[1.1], [0.1], [NaN]]], [2, 3, 1], 'float32');
+        test_util_1.expectArraysClose(tf.equalStrict(a, b), [0, 0, 1, 0, 1, 0]);
+    });
+    it('Tensor4D - int32', function () {
+        var a = tf.tensor4d([1, 4, 5, 8], [2, 2, 1, 1], 'int32');
+        var b = tf.tensor4d([2, 3, 6, 8], [2, 2, 1, 1], 'int32');
+        test_util_1.expectArraysClose(tf.equalStrict(a, b), [0, 0, 0, 1]);
+        a = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
+        b = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
+        test_util_1.expectArraysClose(tf.equalStrict(a, b), [1, 1, 1, 1]);
+        a = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'int32');
+        b = tf.tensor4d([2, 2, 2, 2], [2, 2, 1, 1], 'int32');
+        test_util_1.expectArraysClose(tf.equalStrict(a, b), [0, 0, 0, 0]);
+    });
+    it('Tensor4D - float32', function () {
+        var a = tf.tensor4d([1.1, 4.1, 5.1, 8.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([2.1, 3.1, 6.1, 8.1], [2, 2, 1, 1], 'float32');
+        test_util_1.expectArraysClose(tf.equalStrict(a, b), [0, 0, 0, 1]);
+        a = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
+        b = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
+        test_util_1.expectArraysClose(tf.equalStrict(a, b), [1, 1, 1, 1]);
+        a = tf.tensor4d([0.1, 0.1, 0.1, 0.1], [2, 2, 1, 1], 'float32');
+        b = tf.tensor4d([1.1, 1.1, 1.1, 1.1], [2, 2, 1, 1], 'float32');
+        test_util_1.expectArraysClose(tf.equalStrict(a, b), [0, 0, 0, 0]);
+    });
+    it('mismatch Tensor4D shapes - int32', function () {
+        var a = tf.tensor4d([1, 2, 5, 9], [2, 2, 1, 1], 'int32');
+        var b = tf.tensor4d([[[[1, 2]], [[3, 4]]], [[[5, 6]], [[7, 8]]]], [2, 2, 1, 2], 'int32');
+        var f = function () {
+            tf.equalStrict(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('mismatch Tensor4D shapes - float32', function () {
+        var a = tf.tensor4d([1.1, 2.1, 5.1, 9.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([[[[1.1, 2.1]], [[3.1, 4.1]]], [[[5.1, 6.1]], [[7.1, 8.1]]]], [2, 2, 1, 2], 'float32');
+        var f = function () {
+            tf.equalStrict(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('NaNs in Tensor4D - float32', function () {
+        var a = tf.tensor4d([1.1, NaN, 1.1, 0.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([0.1, 1.1, 1.1, NaN], [2, 2, 1, 1], 'float32');
+        test_util_1.expectArraysClose(tf.equalStrict(a, b), [0, 0, 1, 0]);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [1, 4, 5];
+        var b = [2, 3, 5];
+        test_util_1.expectArraysClose(tf.equalStrict(a, b), [0, 0, 1]);
+    });
+});
+jasmine_util_1.describeWithFlags('notEqual', test_util_1.ALL_ENVS, function () {
+    it('Tensor1D - int32', function () {
+        var a = tf.tensor1d([1, 4, 5], 'int32');
+        var b = tf.tensor1d([2, 3, 5], 'int32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [1, 1, 0]);
+        a = tf.tensor1d([2, 2, 2], 'int32');
+        b = tf.tensor1d([2, 2, 2], 'int32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [0, 0, 0]);
+        a = tf.tensor1d([0, 0], 'int32');
+        b = tf.tensor1d([3, 3], 'int32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [1, 1]);
+    });
+    it('Tensor1D - float32', function () {
+        var a = tf.tensor1d([1.1, 4.1, 5.1], 'float32');
+        var b = tf.tensor1d([2.2, 3.2, 5.1], 'float32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [1, 1, 0]);
+        a = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
+        b = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [0, 0, 0]);
+        a = tf.tensor1d([0.45, 0.123], 'float32');
+        b = tf.tensor1d([3.123, 3.321], 'float32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [1, 1]);
+    });
+    it('upcasts when dtypes dont match', function () {
+        var a = [1.1, 4.1, 5];
+        var b = [2.2, 3.2, 5];
+        var res = tf.notEqual(tf.tensor(a, [3], 'float32'), tf.tensor(b, [3], 'int32'));
+        expect(res.dtype).toBe('bool');
+        expect(res.shape).toEqual([3]);
+        test_util_1.expectArraysClose(res, [1, 1, 0]);
+        res =
+            tf.notEqual(tf.tensor(a, [3], 'int32'), tf.tensor(b, [3], 'bool'));
+        expect(res.dtype).toBe('bool');
+        expect(res.shape).toEqual([3]);
+        test_util_1.expectArraysClose(res, [0, 1, 1]);
+    });
+    it('TensorLike', function () {
+        var a = [1.1, 4.1, 5.1];
+        var b = [2.2, 3.2, 5.1];
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [1, 1, 0]);
+    });
+    it('TensorLike Chained', function () {
+        var a = tf.tensor1d([1.1, 4.1, 5.1], 'float32');
+        var b = [2.2, 3.2, 5.1];
+        test_util_1.expectArraysClose(a.notEqual(b), [1, 1, 0]);
+    });
+    it('mismatched Tensor1D shapes - int32', function () {
+        var a = tf.tensor1d([1, 2], 'int32');
+        var b = tf.tensor1d([1, 2, 3], 'int32');
+        var f = function () {
+            tf.notEqual(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('mismatched Tensor1D shapes - float32', function () {
+        var a = tf.tensor1d([1.1, 2.1], 'float32');
+        var b = tf.tensor1d([1.1, 2.1, 3.1], 'float32');
+        var f = function () {
+            tf.notEqual(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('NaNs in Tensor1D - float32', function () {
+        var a = tf.tensor1d([1.1, NaN, 2.1], 'float32');
+        var b = tf.tensor1d([2.1, 3.1, NaN], 'float32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [1, 1, 1]);
+    });
+    it('works with NaNs', function () {
+        var a = tf.tensor1d([2, 5, NaN]);
+        var b = tf.tensor1d([4, 5, -1]);
+        var res = tf.notEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysEqual(res, [1, 0, 1]);
+    });
+    it('scalar and 1D broadcast', function () {
+        var a = tf.scalar(2);
+        var b = tf.tensor1d([1, 2, 3, 4, 5, 2]);
+        var res = tf.notEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        expect(res.shape).toEqual([6]);
+        test_util_1.expectArraysEqual(res, [1, 0, 1, 1, 1, 0]);
+    });
+    it('Tensor2D - int32', function () {
+        var a = tf.tensor2d([[1, 4, 5], [8, 9, 12]], [2, 3], 'int32');
+        var b = tf.tensor2d([[2, 3, 6], [7, 10, 11]], [2, 3], 'int32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [1, 1, 1, 1, 1, 1]);
+        a = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
+        b = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [0, 0, 0, 0]);
+    });
+    it('Tensor2D - float32', function () {
+        var a = tf.tensor2d([[1.1, 4.1, 5.1], [8.1, 9.1, 12.1]], [2, 3], 'float32');
+        var b = tf.tensor2d([[2.1, 4.1, 5.1], [7.1, 10.1, 11.1]], [2, 3], 'float32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [1, 0, 0, 1, 1, 1]);
+        a = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
+        b = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [0, 0, 0, 0]);
+    });
+    it('broadcasting Tensor2D shapes - int32', function () {
+        var a = tf.tensor2d([[3], [7]], [2, 1], 'int32');
+        var b = tf.tensor2d([[2, 3, 4], [7, 8, 9]], [2, 3], 'int32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [1, 0, 1, 0, 1, 1]);
+    });
+    it('broadcasting Tensor2D shapes - float32', function () {
+        var a = tf.tensor2d([[1.1], [7.1]], [2, 1], 'float32');
+        var b = tf.tensor2d([[0.1, 1.1, 2.1], [7.1, 8.1, 9.1]], [2, 3], 'float32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [1, 0, 1, 0, 1, 1]);
+    });
+    it('NaNs in Tensor2D - float32', function () {
+        var a = tf.tensor2d([[1.1, NaN], [1.1, NaN]], [2, 2], 'float32');
+        var b = tf.tensor2d([[0.1, NaN], [1.1, NaN]], [2, 2], 'float32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [1, 1, 0, 1]);
+    });
+    it('2D and scalar broadcast', function () {
+        var a = tf.tensor2d([1, 2, 3, 2, 5, 6], [2, 3]);
+        var b = tf.scalar(2);
+        var res = tf.notEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        expect(res.shape).toEqual([2, 3]);
+        test_util_1.expectArraysEqual(res, [1, 0, 1, 0, 1, 1]);
+    });
+    it('2D and 2D broadcast each with 1 dim', function () {
+        var a = tf.tensor2d([1, 2, 5], [1, 3]);
+        var b = tf.tensor2d([5, 1], [2, 1]);
+        var res = tf.notEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        expect(res.shape).toEqual([2, 3]);
+        test_util_1.expectArraysEqual(res, [1, 1, 0, 0, 1, 1]);
+    });
+    it('Tensor3D - int32', function () {
+        var a = tf.tensor3d([[[1], [4], [5]], [[8], [9], [12]]], [2, 3, 1], 'int32');
+        var b = tf.tensor3d([[[2], [3], [6]], [[7], [10], [12]]], [2, 3, 1], 'int32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [1, 1, 1, 1, 1, 0]);
+        a = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
+        b = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [0, 0, 0, 0, 0, 0]);
+    });
+    it('Tensor3D - float32', function () {
+        var a = tf.tensor3d([[[1.1], [4.1], [5.1]], [[8.1], [9.1], [12.1]]], [2, 3, 1], 'float32');
+        var b = tf.tensor3d([[[2.1], [3.1], [6.1]], [[7.1], [10.1], [12.1]]], [2, 3, 1], 'float32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [1, 1, 1, 1, 1, 0]);
+        a = tf.tensor3d([[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.1]]], [2, 3, 1], 'float32');
+        b = tf.tensor3d([[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.1]]], [2, 3, 1], 'float32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [0, 0, 0, 0, 0, 0]);
+    });
+    it('broadcasting Tensor3D shapes - int32', function () {
+        var a = tf.tensor3d([[[1, 0], [2, 3], [4, 5]], [[6, 7], [9, 8], [10, 11]]], [2, 3, 2], 'int32');
+        var b = tf.tensor3d([[[1], [2], [3]], [[7], [10], [9]]], [2, 3, 1], 'int32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1]);
+    });
+    it('broadcasting Tensor3D shapes - float32', function () {
+        var a = tf.tensor3d([
+            [[1.1, 0.1], [2.1, 3.1], [4.1, 5.1]],
+            [[6.1, 7.1], [9.1, 8.1], [10.1, 11.1]]
+        ], [2, 3, 2], 'float32');
+        var b = tf.tensor3d([[[1.1], [2.1], [3.1]], [[7.1], [10.1], [9.1]]], [2, 3, 1], 'float32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 1, 1]);
+    });
+    it('NaNs in Tensor3D - float32', function () {
+        var a = tf.tensor3d([[[1.1], [NaN], [1.1]], [[0.1], [0.1], [0.1]]], [2, 3, 1], 'float32');
+        var b = tf.tensor3d([[[0.1], [0.1], [1.1]], [[1.1], [0.1], [NaN]]], [2, 3, 1], 'float32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [1, 1, 0, 1, 0, 1]);
+    });
+    it('3D and scalar', function () {
+        var a = tf.tensor3d([1, 2, 3, 4, 5, -1], [2, 3, 1]);
+        var b = tf.scalar(-1);
+        var res = tf.notEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        expect(res.shape).toEqual([2, 3, 1]);
+        test_util_1.expectArraysEqual(res, [1, 1, 1, 1, 1, 0]);
+    });
+    it('Tensor4D - int32', function () {
+        var a = tf.tensor4d([1, 4, 5, 8], [2, 2, 1, 1], 'int32');
+        var b = tf.tensor4d([2, 3, 6, 8], [2, 2, 1, 1], 'int32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [1, 1, 1, 0]);
+        a = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
+        b = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [0, 0, 0, 0]);
+        a = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'int32');
+        b = tf.tensor4d([2, 2, 2, 2], [2, 2, 1, 1], 'int32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [1, 1, 1, 1]);
+    });
+    it('Tensor4D - float32', function () {
+        var a = tf.tensor4d([1.1, 4.1, 5.1, 8.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([2.1, 3.1, 6.1, 8.1], [2, 2, 1, 1], 'float32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [1, 1, 1, 0]);
+        a = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
+        b = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [0, 0, 0, 0]);
+        a = tf.tensor4d([0.1, 0.1, 0.1, 0.1], [2, 2, 1, 1], 'float32');
+        b = tf.tensor4d([1.1, 1.1, 1.1, 1.1], [2, 2, 1, 1], 'float32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [1, 1, 1, 1]);
+    });
+    it('broadcasting Tensor4D shapes - int32', function () {
+        var a = tf.tensor4d([1, 2, 5, 9], [2, 2, 1, 1], 'int32');
+        var b = tf.tensor4d([[[[1, 2]], [[3, 4]]], [[[5, 6]], [[7, 8]]]], [2, 2, 1, 2], 'int32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [0, 1, 1, 1, 0, 1, 1, 1]);
+    });
+    it('broadcasting Tensor4D shapes - float32', function () {
+        var a = tf.tensor4d([1.1, 2.1, 5.1, 9.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([[[[1.1, 2.1]], [[3.1, 4.1]]], [[[5.1, 6.1]], [[7.1, 8.1]]]], [2, 2, 1, 2], 'float32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [0, 1, 1, 1, 0, 1, 1, 1]);
+    });
+    it('NaNs in Tensor4D - float32', function () {
+        var a = tf.tensor4d([1.1, NaN, 1.1, 0.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([0.1, 1.1, 1.1, NaN], [2, 2, 1, 1], 'float32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [1, 1, 0, 1]);
+    });
+    it('throws when passed a as a non-tensor', function () {
+        expect(function () { return tf.notEqual({}, tf.scalar(1)); })
+            .toThrowError(/Argument 'a' passed to 'notEqual' must be a Tensor/);
+    });
+    it('throws when passed b as a non-tensor', function () {
+        expect(function () { return tf.notEqual(tf.scalar(1), {}); })
+            .toThrowError(/Argument 'b' passed to 'notEqual' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = tf.tensor1d([1, 4, 5], 'int32');
+        var b = tf.tensor1d([2, 3, 5], 'int32');
+        test_util_1.expectArraysClose(tf.notEqual(a, b), [1, 1, 0]);
+    });
+});
+jasmine_util_1.describeWithFlags('notEqualStrict', test_util_1.ALL_ENVS, function () {
+    it('Tensor1D - int32', function () {
+        var a = tf.tensor1d([1, 4, 5], 'int32');
+        var b = tf.tensor1d([2, 3, 5], 'int32');
+        test_util_1.expectArraysClose(tf.notEqualStrict(a, b), [1, 1, 0]);
+        a = tf.tensor1d([2, 2, 2], 'int32');
+        b = tf.tensor1d([2, 2, 2], 'int32');
+        test_util_1.expectArraysClose(tf.notEqualStrict(a, b), [0, 0, 0]);
+        a = tf.tensor1d([0, 0], 'int32');
+        b = tf.tensor1d([3, 3], 'int32');
+        test_util_1.expectArraysClose(tf.notEqualStrict(a, b), [1, 1]);
+    });
+    it('Tensor1D - float32', function () {
+        var a = tf.tensor1d([1.1, 4.1, 5.1], 'float32');
+        var b = tf.tensor1d([2.2, 3.2, 5.1], 'float32');
+        test_util_1.expectArraysClose(tf.notEqualStrict(a, b), [1, 1, 0]);
+        a = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
+        b = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
+        test_util_1.expectArraysClose(tf.notEqualStrict(a, b), [0, 0, 0]);
+        a = tf.tensor1d([0.45, 0.123], 'float32');
+        b = tf.tensor1d([3.123, 3.321], 'float32');
+        test_util_1.expectArraysClose(tf.notEqualStrict(a, b), [1, 1]);
+    });
+    it('mismatched Tensor1D shapes - int32', function () {
+        var a = tf.tensor1d([1, 2], 'int32');
+        var b = tf.tensor1d([1, 2, 3], 'int32');
+        var f = function () {
+            tf.notEqualStrict(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('mismatched Tensor1D shapes - float32', function () {
+        var a = tf.tensor1d([1.1, 2.1], 'float32');
+        var b = tf.tensor1d([1.1, 2.1, 3.1], 'float32');
+        var f = function () {
+            tf.notEqualStrict(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('NaNs in Tensor1D - float32', function () {
+        var a = tf.tensor1d([1.1, NaN, 2.1], 'float32');
+        var b = tf.tensor1d([2.1, 3.1, NaN], 'float32');
+        test_util_1.expectArraysClose(tf.notEqualStrict(a, b), [1, 1, 1]);
+    });
+    it('strict version throws when x and y are different shape', function () {
+        var a = tf.tensor1d([2]);
+        var b = tf.tensor1d([4, 2, -1]);
+        expect(function () { return tf.notEqualStrict(a, b); }).toThrowError();
+        expect(function () { return tf.notEqualStrict(b, a); }).toThrowError();
+    });
+    it('Tensor2D - int32', function () {
+        var a = tf.tensor2d([[1, 4, 5], [8, 9, 12]], [2, 3], 'int32');
+        var b = tf.tensor2d([[2, 3, 6], [7, 10, 11]], [2, 3], 'int32');
+        test_util_1.expectArraysClose(tf.notEqualStrict(a, b), [1, 1, 1, 1, 1, 1]);
+        a = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
+        b = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
+        test_util_1.expectArraysClose(tf.notEqualStrict(a, b), [0, 0, 0, 0]);
+    });
+    it('Tensor2D - float32', function () {
+        var a = tf.tensor2d([[1.1, 4.1, 5.1], [8.1, 9.1, 12.1]], [2, 3], 'float32');
+        var b = tf.tensor2d([[2.1, 4.1, 5.1], [7.1, 10.1, 11.1]], [2, 3], 'float32');
+        test_util_1.expectArraysClose(tf.notEqualStrict(a, b), [1, 0, 0, 1, 1, 1]);
+        a = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
+        b = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
+        test_util_1.expectArraysClose(tf.notEqualStrict(a, b), [0, 0, 0, 0]);
+    });
+    it('mismatch Tensor2D shapes - int32', function () {
+        var a = tf.tensor2d([[3], [7]], [2, 1], 'int32');
+        var b = tf.tensor2d([[2, 3, 4], [7, 8, 9]], [2, 3], 'int32');
+        var f = function () {
+            tf.notEqualStrict(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('mismatch Tensor2D shapes - float32', function () {
+        var a = tf.tensor2d([[1.1], [7.1]], [2, 1], 'float32');
+        var b = tf.tensor2d([[0.1, 1.1, 2.1], [7.1, 8.1, 9.1]], [2, 3], 'float32');
+        var f = function () {
+            tf.notEqualStrict(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('NaNs in Tensor2D - float32', function () {
+        var a = tf.tensor2d([[1.1, NaN], [1.1, NaN]], [2, 2], 'float32');
+        var b = tf.tensor2d([[0.1, NaN], [1.1, NaN]], [2, 2], 'float32');
+        test_util_1.expectArraysClose(tf.notEqualStrict(a, b), [1, 1, 0, 1]);
+    });
+    it('Tensor3D - int32', function () {
+        var a = tf.tensor3d([[[1], [4], [5]], [[8], [9], [12]]], [2, 3, 1], 'int32');
+        var b = tf.tensor3d([[[2], [3], [6]], [[7], [10], [12]]], [2, 3, 1], 'int32');
+        test_util_1.expectArraysClose(tf.notEqualStrict(a, b), [1, 1, 1, 1, 1, 0]);
+        a = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
+        b = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
+        test_util_1.expectArraysClose(tf.notEqualStrict(a, b), [0, 0, 0, 0, 0, 0]);
+    });
+    it('Tensor3D - float32', function () {
+        var a = tf.tensor3d([[[1.1], [4.1], [5.1]], [[8.1], [9.1], [12.1]]], [2, 3, 1], 'float32');
+        var b = tf.tensor3d([[[2.1], [3.1], [6.1]], [[7.1], [10.1], [12.1]]], [2, 3, 1], 'float32');
+        test_util_1.expectArraysClose(tf.notEqualStrict(a, b), [1, 1, 1, 1, 1, 0]);
+        a = tf.tensor3d([[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.1]]], [2, 3, 1], 'float32');
+        b = tf.tensor3d([[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.1]]], [2, 3, 1], 'float32');
+        test_util_1.expectArraysClose(tf.notEqualStrict(a, b), [0, 0, 0, 0, 0, 0]);
+    });
+    it('mismatch Tensor3D shapes - int32', function () {
+        var a = tf.tensor3d([[[1, 0], [2, 3], [4, 5]], [[6, 7], [9, 8], [10, 11]]], [2, 3, 2], 'int32');
+        var b = tf.tensor3d([[[1], [2], [3]], [[7], [10], [9]]], [2, 3, 1], 'int32');
+        var f = function () {
+            tf.notEqualStrict(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('mismatch Tensor3D shapes - float32', function () {
+        var a = tf.tensor3d([
+            [[1.1, 0.1], [2.1, 3.1], [4.1, 5.1]],
+            [[6.1, 7.1], [9.1, 8.1], [10.1, 11.1]]
+        ], [2, 3, 2], 'float32');
+        var b = tf.tensor3d([[[1.1], [2.1], [3.1]], [[7.1], [10.1], [9.1]]], [2, 3, 1], 'float32');
+        var f = function () {
+            tf.notEqualStrict(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('NaNs in Tensor3D - float32', function () {
+        var a = tf.tensor3d([[[1.1], [NaN], [1.1]], [[0.1], [0.1], [0.1]]], [2, 3, 1], 'float32');
+        var b = tf.tensor3d([[[0.1], [0.1], [1.1]], [[1.1], [0.1], [NaN]]], [2, 3, 1], 'float32');
+        test_util_1.expectArraysClose(tf.notEqualStrict(a, b), [1, 1, 0, 1, 0, 1]);
+    });
+    it('Tensor4D - int32', function () {
+        var a = tf.tensor4d([1, 4, 5, 8], [2, 2, 1, 1], 'int32');
+        var b = tf.tensor4d([2, 3, 6, 8], [2, 2, 1, 1], 'int32');
+        test_util_1.expectArraysClose(tf.notEqualStrict(a, b), [1, 1, 1, 0]);
+        a = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
+        b = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
+        test_util_1.expectArraysClose(tf.notEqualStrict(a, b), [0, 0, 0, 0]);
+        a = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'int32');
+        b = tf.tensor4d([2, 2, 2, 2], [2, 2, 1, 1], 'int32');
+        test_util_1.expectArraysClose(tf.notEqualStrict(a, b), [1, 1, 1, 1]);
+    });
+    it('Tensor4D - float32', function () {
+        var a = tf.tensor4d([1.1, 4.1, 5.1, 8.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([2.1, 3.1, 6.1, 8.1], [2, 2, 1, 1], 'float32');
+        test_util_1.expectArraysClose(tf.notEqualStrict(a, b), [1, 1, 1, 0]);
+        a = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
+        b = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
+        test_util_1.expectArraysClose(tf.notEqualStrict(a, b), [0, 0, 0, 0]);
+        a = tf.tensor4d([0.1, 0.1, 0.1, 0.1], [2, 2, 1, 1], 'float32');
+        b = tf.tensor4d([1.1, 1.1, 1.1, 1.1], [2, 2, 1, 1], 'float32');
+        test_util_1.expectArraysClose(tf.notEqualStrict(a, b), [1, 1, 1, 1]);
+    });
+    it('mismatch Tensor4D shapes - int32', function () {
+        var a = tf.tensor4d([1, 2, 5, 9], [2, 2, 1, 1], 'int32');
+        var b = tf.tensor4d([[[[1, 2]], [[3, 4]]], [[[5, 6]], [[7, 8]]]], [2, 2, 1, 2], 'int32');
+        var f = function () {
+            tf.notEqualStrict(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('mismatch Tensor4D shapes - float32', function () {
+        var a = tf.tensor4d([1.1, 2.1, 5.1, 9.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([[[[1.1, 2.1]], [[3.1, 4.1]]], [[[5.1, 6.1]], [[7.1, 8.1]]]], [2, 2, 1, 2], 'float32');
+        var f = function () {
+            tf.notEqualStrict(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('NaNs in Tensor4D - float32', function () {
+        var a = tf.tensor4d([1.1, NaN, 1.1, 0.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([0.1, 1.1, 1.1, NaN], [2, 2, 1, 1], 'float32');
+        test_util_1.expectArraysClose(tf.notEqualStrict(a, b), [1, 1, 0, 1]);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [1, 4, 5];
+        var b = [2, 3, 5];
+        test_util_1.expectArraysClose(tf.notEqualStrict(a, b), [1, 1, 0]);
+    });
+});
+jasmine_util_1.describeWithFlags('less', test_util_1.ALL_ENVS, function () {
+    it('Tensor1D - int32', function () {
+        var a = tf.tensor1d([1, 4, 5], 'int32');
+        var b = tf.tensor1d([2, 3, 5], 'int32');
+        var res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 0]);
+        a = tf.tensor1d([2, 2, 2], 'int32');
+        b = tf.tensor1d([2, 2, 2], 'int32');
+        res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 0]);
+        a = tf.tensor1d([0, 0], 'int32');
+        b = tf.tensor1d([3, 3], 'int32');
+        res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1]);
+    });
+    it('Tensor1D - float32', function () {
+        var a = tf.tensor1d([1.1, 4.1, 5.1], 'float32');
+        var b = tf.tensor1d([2.2, 3.2, 5.1], 'float32');
+        var res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 0]);
+        a = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
+        b = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
+        res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 0]);
+        a = tf.tensor1d([0.45, 0.123], 'float32');
+        b = tf.tensor1d([3.123, 3.321], 'float32');
+        res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1]);
+    });
+    it('TensorLike', function () {
+        var a = [1.1, 4.1, 5.1];
+        var b = [2.2, 3.2, 5.1];
+        var res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 0]);
+    });
+    it('TensorLike Chained', function () {
+        var a = tf.tensor1d([1.1, 4.1, 5.1], 'float32');
+        var b = [2.2, 3.2, 5.1];
+        var res = a.less(b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 0]);
+    });
+    it('upcasts when dtypes dont match', function () {
+        var a = [1.1, 4.1, 5.2];
+        var b = [2.2, 3.2, 5.1];
+        var res = tf.less(tf.tensor(a, [3], 'float32'), tf.tensor(b, [3], 'int32'));
+        expect(res.dtype).toBe('bool');
+        expect(res.shape).toEqual([3]);
+        test_util_1.expectArraysClose(res, [1, 0, 0]);
+        res =
+            tf.less(tf.tensor(a, [3], 'int32'), tf.tensor(b, [3], 'bool'));
+        expect(res.dtype).toBe('bool');
+        expect(res.shape).toEqual([3]);
+        test_util_1.expectArraysClose(res, [0, 0, 0]);
+    });
+    it('mismatched Tensor1D shapes - int32', function () {
+        var a = tf.tensor1d([1, 2], 'int32');
+        var b = tf.tensor1d([1, 2, 3], 'int32');
+        var f = function () {
+            tf.less(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('mismatched Tensor1D shapes - float32', function () {
+        var a = tf.tensor1d([1.1, 2.1], 'float32');
+        var b = tf.tensor1d([1.1, 2.1, 3.1], 'float32');
+        var f = function () {
+            tf.less(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('NaNs in Tensor1D - float32', function () {
+        var a = tf.tensor1d([1.1, NaN, 2.1], 'float32');
+        var b = tf.tensor1d([2.1, 3.1, NaN], 'float32');
+        var res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 0]);
+    });
+    it('Tensor2D - int32', function () {
+        var a = tf.tensor2d([[1, 4, 5], [8, 9, 12]], [2, 3], 'int32');
+        var b = tf.tensor2d([[2, 3, 6], [7, 10, 11]], [2, 3], 'int32');
+        var res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 1, 0, 1, 0]);
+        a = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
+        b = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
+        res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 0, 0]);
+    });
+    it('Tensor2D - float32', function () {
+        var a = tf.tensor2d([[1.1, 4.1, 5.1], [8.1, 9.1, 12.1]], [2, 3], 'float32');
+        var b = tf.tensor2d([[2.1, 3.1, 6.1], [7.1, 10.1, 11.1]], [2, 3], 'float32');
+        var res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 1, 0, 1, 0]);
+        a = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
+        b = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
+        res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 0, 0]);
+    });
+    it('broadcasting Tensor2D shapes - int32', function () {
+        var a = tf.tensor2d([[3], [7]], [2, 1], 'int32');
+        var b = tf.tensor2d([[2, 3, 4], [7, 8, 9]], [2, 3], 'int32');
+        var res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 1, 0, 1, 1]);
+    });
+    it('broadcasting Tensor2D shapes - float32', function () {
+        var a = tf.tensor2d([[1.1], [7.1]], [2, 1], 'float32');
+        var b = tf.tensor2d([[0.1, 1.1, 2.1], [7.1, 8.1, 9.1]], [2, 3], 'float32');
+        var res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 1, 0, 1, 1]);
+    });
+    it('NaNs in Tensor2D - float32', function () {
+        var a = tf.tensor2d([[1.1, NaN], [0.1, NaN]], [2, 2], 'float32');
+        var b = tf.tensor2d([[0.1, NaN], [1.1, NaN]], [2, 2], 'float32');
+        var res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 1, 0]);
+    });
+    it('Tensor3D - int32', function () {
+        var a = tf.tensor3d([[[1], [4], [5]], [[8], [9], [12]]], [2, 3, 1], 'int32');
+        var b = tf.tensor3d([[[2], [3], [6]], [[7], [10], [11]]], [2, 3, 1], 'int32');
+        var res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 1, 0, 1, 0]);
+        a = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
+        b = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
+        res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 0, 0, 0, 0]);
+    });
+    it('Tensor3D - float32', function () {
+        var a = tf.tensor3d([[[1.1], [4.1], [5.1]], [[8.1], [9.1], [12.1]]], [2, 3, 1], 'float32');
+        var b = tf.tensor3d([[[2.1], [3.1], [6.1]], [[7.1], [10.1], [11.1]]], [2, 3, 1], 'float32');
+        var res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 1, 0, 1, 0]);
+        a = tf.tensor3d([[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.0]]], [2, 3, 1], 'float32');
+        b = tf.tensor3d([[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.1]]], [2, 3, 1], 'float32');
+        res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 0, 0, 0, 1]);
+    });
+    it('broadcasting Tensor3D shapes - int32', function () {
+        var a = tf.tensor3d([[[1, 0], [2, 3], [4, 5]], [[6, 7], [9, 8], [10, 11]]], [2, 3, 2], 'int32');
+        var b = tf.tensor3d([[[1], [2], [3]], [[7], [10], [9]]], [2, 3, 1], 'int32');
+        var res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0]);
+    });
+    it('broadcasting Tensor3D float32', function () {
+        var a = tf.tensor3d([
+            [[1.1, 0.1], [2.1, 3.1], [4.1, 5.1]],
+            [[6.1, 7.1], [9.1, 8.1], [10.1, 11.1]]
+        ], [2, 3, 2], 'float32');
+        var b = tf.tensor3d([[[1.1], [2.1], [3.1]], [[7.1], [10.1], [9.1]]], [2, 3, 1], 'float32');
+        var res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 0, 0, 0, 0, 1, 0, 1, 1, 0, 0]);
+    });
+    it('NaNs in Tensor3D - float32', function () {
+        var a = tf.tensor3d([[[1.1], [NaN], [1.1]], [[0.1], [0.1], [0.1]]], [2, 3, 1], 'float32');
+        var b = tf.tensor3d([[[0.1], [0.1], [1.1]], [[1.1], [0.1], [NaN]]], [2, 3, 1], 'float32');
+        var res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 0, 1, 0, 0]);
+    });
+    it('Tensor4D - int32', function () {
+        var a = tf.tensor4d([1, 4, 5, 8], [2, 2, 1, 1], 'int32');
+        var b = tf.tensor4d([2, 3, 6, 7], [2, 2, 1, 1], 'int32');
+        var res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 1, 0]);
+        a = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
+        b = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
+        res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 0, 0]);
+        a = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'int32');
+        b = tf.tensor4d([2, 2, 2, 2], [2, 2, 1, 1], 'int32');
+        res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 1, 1]);
+    });
+    it('Tensor4D - float32', function () {
+        var a = tf.tensor4d([1.1, 4.1, 5.1, 8.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([2.1, 3.1, 6.1, 7.1], [2, 2, 1, 1], 'float32');
+        var res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 1, 0]);
+        a = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
+        b = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
+        res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 0, 0]);
+        a = tf.tensor4d([0.1, 0.1, 0.1, 0.1], [2, 2, 1, 1], 'float32');
+        b = tf.tensor4d([1.1, 1.1, 1.1, 1.1], [2, 2, 1, 1], 'float32');
+        res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 1, 1]);
+    });
+    it('broadcasting Tensor4D shapes - int32', function () {
+        var a = tf.tensor4d([1, 2, 5, 9], [2, 2, 1, 1], 'int32');
+        var b = tf.tensor4d([[[[1, 2]], [[3, 4]]], [[[5, 6]], [[7, 8]]]], [2, 2, 1, 2], 'int32');
+        var res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 1, 1, 0, 1, 0, 0]);
+    });
+    it('broadcasting Tensor4D shapes - float32', function () {
+        var a = tf.tensor4d([1.1, 2.1, 5.1, 9.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([[[[1.1, 2.1]], [[3.1, 4.1]]], [[[5.1, 6.1]], [[7.1, 8.1]]]], [2, 2, 1, 2], 'float32');
+        var res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 1, 1, 0, 1, 0, 0]);
+    });
+    it('NaNs in Tensor4D - float32', function () {
+        var a = tf.tensor4d([1.1, NaN, 0.1, 0.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([0.1, 1.1, 1.1, NaN], [2, 2, 1, 1], 'float32');
+        var res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 1, 0]);
+    });
+    it('throws when passed a as a non-tensor', function () {
+        expect(function () { return tf.less({}, tf.scalar(1)); })
+            .toThrowError(/Argument 'a' passed to 'less' must be a Tensor/);
+    });
+    it('throws when passed b as a non-tensor', function () {
+        expect(function () { return tf.less(tf.scalar(1), {}); })
+            .toThrowError(/Argument 'b' passed to 'less' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [1, 4, 5];
+        var b = [2, 3, 5];
+        var res = tf.less(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 0]);
+    });
+});
+jasmine_util_1.describeWithFlags('lessStrict', test_util_1.ALL_ENVS, function () {
+    it('Tensor1D - strict version throws when a and b are different shape', function () {
+        var a = tf.tensor1d([2]);
+        var b = tf.tensor1d([4, 2, -1]);
+        expect(function () { return tf.lessStrict(a, b); }).toThrowError();
+        expect(function () { return tf.lessStrict(b, a); }).toThrowError();
+    });
+    it('Tensor2D - strict version throws when a and b are different shape', function () {
+        var a = tf.tensor2d([[1.1], [7.1]], [2, 1], 'float32');
+        var b = tf.tensor2d([[0.1, 1.1, 2.1], [7.1, 8.1, 9.1]], [2, 3], 'float32');
+        expect(function () { return tf.lessStrict(a, b); }).toThrowError();
+        expect(function () { return tf.lessStrict(b, a); }).toThrowError();
+    });
+    it('Tensor3D - strict version throws when a and b are different shape', function () {
+        var a = tf.tensor3d([
+            [[1.1, 0.1], [2.1, 3.1], [4.1, 5.1]],
+            [[6.1, 7.1], [9.1, 8.1], [10.1, 11.1]]
+        ], [2, 3, 2], 'float32');
+        var b = tf.tensor3d([[[1.1], [2.1], [3.1]], [[7.1], [10.1], [9.1]]], [2, 3, 1], 'float32');
+        expect(function () { return tf.lessStrict(a, b); }).toThrowError();
+        expect(function () { return tf.lessStrict(b, a); }).toThrowError();
+    });
+    it('Tensor4D - strict version throws when a and b are different shape', function () {
+        var a = tf.tensor4d([1.1, 2.1, 5.1, 9.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([[[[1.1, 2.1]], [[3.1, 4.1]]], [[[5.1, 6.1]], [[7.1, 8.1]]]], [2, 2, 1, 2], 'float32');
+        expect(function () { return tf.lessStrict(a, b); }).toThrowError();
+        expect(function () { return tf.lessStrict(b, a); }).toThrowError();
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [1, 4, 5];
+        var b = [2, 3, 5];
+        var res = tf.lessStrict(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 0]);
+    });
+});
+jasmine_util_1.describeWithFlags('lessEqual', test_util_1.ALL_ENVS, function () {
+    it('Tensor1D - int32', function () {
+        var a = tf.tensor1d([1, 4, 5], 'int32');
+        var b = tf.tensor1d([2, 3, 5], 'int32');
+        var res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 1]);
+        a = tf.tensor1d([2, 2, 2], 'int32');
+        b = tf.tensor1d([2, 2, 2], 'int32');
+        res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 1]);
+        a = tf.tensor1d([0, 0], 'int32');
+        b = tf.tensor1d([3, 3], 'int32');
+        res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1]);
+    });
+    it('Tensor1D - float32', function () {
+        var a = tf.tensor1d([1.1, 4.1, 5.1], 'float32');
+        var b = tf.tensor1d([2.2, 3.2, 5.1], 'float32');
+        var res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 1]);
+        a = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
+        b = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
+        res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 1]);
+        a = tf.tensor1d([0.45, 0.123], 'float32');
+        b = tf.tensor1d([3.123, 3.321], 'float32');
+        res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1]);
+    });
+    it('upcasts when dtypes dont match', function () {
+        var a = [1.1, 4.1, 5];
+        var b = [2.2, 3.2, 5];
+        var res = tf.lessEqual(tf.tensor(a, [3], 'float32'), tf.tensor(b, [3], 'int32'));
+        expect(res.dtype).toBe('bool');
+        expect(res.shape).toEqual([3]);
+        test_util_1.expectArraysClose(res, [1, 0, 1]);
+        res =
+            tf.lessEqual(tf.tensor(a, [3], 'int32'), tf.tensor(b, [3], 'bool'));
+        expect(res.dtype).toBe('bool');
+        expect(res.shape).toEqual([3]);
+        test_util_1.expectArraysClose(res, [1, 0, 0]);
+    });
+    it('TensorLike', function () {
+        var a = [1.1, 4.1, 5.1];
+        var b = [2.2, 3.2, 5.1];
+        var res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 1]);
+    });
+    it('TensorLike Chained', function () {
+        var a = tf.tensor1d([1.1, 4.1, 5.1], 'float32');
+        var b = [2.2, 3.2, 5.1];
+        var res = a.lessEqual(b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 1]);
+    });
+    it('mismatched Tensor1D shapes - int32', function () {
+        var a = tf.tensor1d([1, 2], 'int32');
+        var b = tf.tensor1d([1, 2, 3], 'int32');
+        var f = function () {
+            tf.lessEqual(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('mismatched Tensor1D shapes - float32', function () {
+        var a = tf.tensor1d([1.1, 2.1], 'float32');
+        var b = tf.tensor1d([1.1, 2.1, 3.1], 'float32');
+        var f = function () {
+            tf.lessEqual(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('NaNs in Tensor1D - float32', function () {
+        var a = tf.tensor1d([1.1, NaN, 2.1], 'float32');
+        var b = tf.tensor1d([2.1, 3.1, NaN], 'float32');
+        var res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 0]);
+    });
+    it('Tensor2D - int32', function () {
+        var a = tf.tensor2d([[1, 4, 5], [8, 9, 12]], [2, 3], 'int32');
+        var b = tf.tensor2d([[2, 3, 6], [7, 10, 11]], [2, 3], 'int32');
+        var res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 1, 0, 1, 0]);
+        a = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
+        b = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
+        res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 1, 1]);
+    });
+    it('Tensor2D - float32', function () {
+        var a = tf.tensor2d([[1.1, 4.1, 5.1], [8.1, 9.1, 12.1]], [2, 3], 'float32');
+        var b = tf.tensor2d([[2.1, 3.1, 6.1], [7.1, 10.1, 11.1]], [2, 3], 'float32');
+        var res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 1, 0, 1, 0]);
+        a = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
+        b = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
+        res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 1, 1]);
+    });
+    it('broadcasting Tensor2D shapes - int32', function () {
+        var a = tf.tensor2d([[3], [7]], [2, 1], 'int32');
+        var b = tf.tensor2d([[2, 3, 4], [7, 8, 9]], [2, 3], 'int32');
+        var res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 1, 1, 1, 1]);
+    });
+    it('broadcasting Tensor2D shapes - float32', function () {
+        var a = tf.tensor2d([[1.1], [7.1]], [2, 1], 'float32');
+        var b = tf.tensor2d([[0.1, 1.1, 2.1], [7.1, 8.1, 9.1]], [2, 3], 'float32');
+        var res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 1, 1, 1, 1]);
+    });
+    it('NaNs in Tensor2D - float32', function () {
+        var a = tf.tensor2d([[1.1, NaN], [0.1, NaN]], [2, 2], 'float32');
+        var b = tf.tensor2d([[0.1, NaN], [1.1, NaN]], [2, 2], 'float32');
+        var res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 1, 0]);
+    });
+    it('Tensor3D - int32', function () {
+        var a = tf.tensor3d([[[1], [4], [5]], [[8], [9], [12]]], [2, 3, 1], 'int32');
+        var b = tf.tensor3d([[[2], [3], [6]], [[7], [10], [11]]], [2, 3, 1], 'int32');
+        var res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 1, 0, 1, 0]);
+        a = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
+        b = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
+        res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 1, 1, 1, 1]);
+    });
+    it('Tensor3D - float32', function () {
+        var a = tf.tensor3d([[[1.1], [4.1], [5.1]], [[8.1], [9.1], [12.1]]], [2, 3, 1], 'float32');
+        var b = tf.tensor3d([[[2.1], [3.1], [6.1]], [[7.1], [10.1], [11.1]]], [2, 3, 1], 'float32');
+        var res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 1, 0, 1, 0]);
+        a = tf.tensor3d([[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.2]]], [2, 3, 1], 'float32');
+        b = tf.tensor3d([[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.1]]], [2, 3, 1], 'float32');
+        res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 1, 1, 1, 0]);
+    });
+    it('broadcasting Tensor3D shapes - int32', function () {
+        var a = tf.tensor3d([[[1, 0], [2, 3], [4, 5]], [[6, 7], [9, 8], [10, 11]]], [2, 3, 2], 'int32');
+        var b = tf.tensor3d([[[1], [2], [3]], [[7], [10], [9]]], [2, 3, 1], 'int32');
+        var res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0]);
+    });
+    it('broadcasting Tensor3D float32', function () {
+        var a = tf.tensor3d([
+            [[1.1, 0.1], [2.1, 3.1], [4.1, 5.1]],
+            [[6.1, 7.1], [9.1, 8.1], [10.1, 11.1]]
+        ], [2, 3, 2], 'float32');
+        var b = tf.tensor3d([[[1.1], [2.1], [3.1]], [[7.1], [10.1], [9.1]]], [2, 3, 1], 'float32');
+        var res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0]);
+    });
+    it('NaNs in Tensor3D - float32', function () {
+        var a = tf.tensor3d([[[1.1], [NaN], [1.1]], [[0.1], [0.1], [0.1]]], [2, 3, 1], 'float32');
+        var b = tf.tensor3d([[[0.1], [0.1], [1.1]], [[1.1], [0.1], [NaN]]], [2, 3, 1], 'float32');
+        var res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 1, 1, 1, 0]);
+    });
+    it('Tensor4D - int32', function () {
+        var a = tf.tensor4d([1, 4, 5, 8], [2, 2, 1, 1], 'int32');
+        var b = tf.tensor4d([2, 3, 6, 7], [2, 2, 1, 1], 'int32');
+        var res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 1, 0]);
+        a = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
+        b = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
+        res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 1, 1]);
+        a = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'int32');
+        b = tf.tensor4d([2, 2, 2, 2], [2, 2, 1, 1], 'int32');
+        res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 1, 1]);
+    });
+    it('Tensor4D - float32', function () {
+        var a = tf.tensor4d([1.1, 4.1, 5.1, 8.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([2.1, 3.1, 6.1, 7.1], [2, 2, 1, 1], 'float32');
+        var res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 1, 0]);
+        a = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
+        b = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
+        res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 1, 1]);
+        a = tf.tensor4d([0.1, 0.1, 0.1, 0.1], [2, 2, 1, 1], 'float32');
+        b = tf.tensor4d([1.1, 1.1, 1.1, 1.1], [2, 2, 1, 1], 'float32');
+        res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 1, 1]);
+    });
+    it('broadcasting Tensor4D shapes - int32', function () {
+        var a = tf.tensor4d([1, 2, 5, 9], [2, 2, 1, 1], 'int32');
+        var b = tf.tensor4d([[[[1, 2]], [[3, 4]]], [[[5, 6]], [[7, 8]]]], [2, 2, 1, 2], 'int32');
+        var res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 1, 1, 1, 1, 0, 0]);
+    });
+    it('broadcasting Tensor4D shapes - float32', function () {
+        var a = tf.tensor4d([1.1, 2.1, 5.1, 9.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([[[[1.1, 2.1]], [[3.1, 4.1]]], [[[5.1, 6.1]], [[7.1, 8.1]]]], [2, 2, 1, 2], 'float32');
+        var res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 1, 1, 1, 1, 0, 0]);
+    });
+    it('NaNs in Tensor4D - float32', function () {
+        var a = tf.tensor4d([1.1, NaN, 0.1, 0.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([0.1, 1.1, 1.1, NaN], [2, 2, 1, 1], 'float32');
+        var res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 1, 0]);
+    });
+    it('throws when passed a as a non-tensor', function () {
+        expect(function () { return tf.lessEqual({}, tf.scalar(1)); })
+            .toThrowError(/Argument 'a' passed to 'lessEqual' must be a Tensor/);
+    });
+    it('throws when passed b as a non-tensor', function () {
+        expect(function () { return tf.lessEqual(tf.scalar(1), {}); })
+            .toThrowError(/Argument 'b' passed to 'lessEqual' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [1, 4, 5];
+        var b = [2, 3, 5];
+        var res = tf.lessEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 1]);
+    });
+});
+jasmine_util_1.describeWithFlags('lessEqualStrict', test_util_1.ALL_ENVS, function () {
+    it('Tensor1D - strict version throws when a and b are different shape', function () {
+        var a = tf.tensor1d([2]);
+        var b = tf.tensor1d([4, 2, -1]);
+        expect(function () { return tf.lessEqualStrict(a, b); }).toThrowError();
+        expect(function () { return tf.lessEqualStrict(b, a); }).toThrowError();
+    });
+    it('Tensor2D - strict version throws when a and b are different shape', function () {
+        var a = tf.tensor2d([[1.1], [7.1]], [2, 1], 'float32');
+        var b = tf.tensor2d([[0.1, 1.1, 2.1], [7.1, 8.1, 9.1]], [2, 3], 'float32');
+        expect(function () { return tf.lessEqualStrict(a, b); }).toThrowError();
+        expect(function () { return tf.lessEqualStrict(b, a); }).toThrowError();
+    });
+    it('Tensor3D - strict version throws when a and b are different shape', function () {
+        var a = tf.tensor3d([
+            [[1.1, 0.1], [2.1, 3.1], [4.1, 5.1]],
+            [[6.1, 7.1], [9.1, 8.1], [10.1, 11.1]]
+        ], [2, 3, 2], 'float32');
+        var b = tf.tensor3d([[[1.1], [2.1], [3.1]], [[7.1], [10.1], [9.1]]], [2, 3, 1], 'float32');
+        expect(function () { return tf.lessEqualStrict(a, b); }).toThrowError();
+        expect(function () { return tf.lessEqualStrict(b, a); }).toThrowError();
+    });
+    it('Tensor4D - strict version throws when a and b are different shape', function () {
+        var a = tf.tensor4d([1.1, 2.1, 5.1, 9.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([[[[1.1, 2.1]], [[3.1, 4.1]]], [[[5.1, 6.1]], [[7.1, 8.1]]]], [2, 2, 1, 2], 'float32');
+        expect(function () { return tf.lessEqualStrict(a, b); }).toThrowError();
+        expect(function () { return tf.lessEqualStrict(b, a); }).toThrowError();
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [1, 4, 5];
+        var b = [2, 3, 5];
+        var res = tf.lessEqualStrict(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 1]);
+    });
+});
+jasmine_util_1.describeWithFlags('greater', test_util_1.ALL_ENVS, function () {
+    it('Tensor1D - int32', function () {
+        var a = tf.tensor1d([1, 4, 5], 'int32');
+        var b = tf.tensor1d([2, 3, 5], 'int32');
+        var res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 0]);
+        a = tf.tensor1d([2, 2, 2], 'int32');
+        b = tf.tensor1d([2, 2, 2], 'int32');
+        res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 0]);
+        a = tf.tensor1d([3, 3], 'int32');
+        b = tf.tensor1d([0, 0], 'int32');
+        res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1]);
+    });
+    it('Tensor1D - float32', function () {
+        var a = tf.tensor1d([1.1, 4.1, 5.1], 'float32');
+        var b = tf.tensor1d([2.2, 3.2, 5.1], 'float32');
+        var res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 0]);
+        a = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
+        b = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
+        res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 0]);
+        a = tf.tensor1d([3.123, 3.321], 'float32');
+        b = tf.tensor1d([0.45, 0.123], 'float32');
+        res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1]);
+    });
+    it('upcasts when dtypes dont match', function () {
+        var a = [1.1, 4.1, 5.2];
+        var b = [2.2, 3.2, 5.1];
+        var res = tf.greater(tf.tensor(a, [3], 'float32'), tf.tensor(b, [3], 'int32'));
+        expect(res.dtype).toBe('bool');
+        expect(res.shape).toEqual([3]);
+        test_util_1.expectArraysClose(res, [0, 1, 1]);
+        res =
+            tf.greater(tf.tensor(a, [3], 'int32'), tf.tensor(b, [3], 'bool'));
+        expect(res.dtype).toBe('bool');
+        expect(res.shape).toEqual([3]);
+        test_util_1.expectArraysClose(res, [0, 1, 1]);
+    });
+    it('TensorLike', function () {
+        var a = [1.1, 4.1, 5.1];
+        var b = [2.2, 3.2, 5.1];
+        var res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 0]);
+    });
+    it('TensorLike Chained', function () {
+        var a = tf.tensor1d([1.1, 4.1, 5.1], 'float32');
+        var b = [2.2, 3.2, 5.1];
+        var res = a.greater(b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 0]);
+    });
+    it('mismatched Tensor1D shapes - int32', function () {
+        var a = tf.tensor1d([1, 2], 'int32');
+        var b = tf.tensor1d([1, 2, 3], 'int32');
+        var f = function () {
+            tf.greater(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('mismatched Tensor1D shapes - float32', function () {
+        var a = tf.tensor1d([1.1, 2.1], 'float32');
+        var b = tf.tensor1d([1.1, 2.1, 3.1], 'float32');
+        var f = function () {
+            tf.greater(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('NaNs in Tensor1D - float32', function () {
+        var a = tf.tensor1d([1.1, NaN, 2.1], 'float32');
+        var b = tf.tensor1d([2.1, 3.1, NaN], 'float32');
+        var res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 0]);
+    });
+    it('Tensor2D - int32', function () {
+        var a = tf.tensor2d([[1, 4, 5], [8, 9, 11]], [2, 3], 'int32');
+        var b = tf.tensor2d([[2, 3, 6], [7, 10, 11]], [2, 3], 'int32');
+        var res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 0, 1, 0, 0]);
+        a = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
+        b = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
+        res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 0, 0]);
+    });
+    it('Tensor2D - float32', function () {
+        var a = tf.tensor2d([[1.1, 4.1, 5.1], [8.1, 9.1, 11.1]], [2, 3], 'float32');
+        var b = tf.tensor2d([[2.1, 3.1, 6.1], [7.1, 10.1, 11.1]], [2, 3], 'float32');
+        var res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 0, 1, 0, 0]);
+        a = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
+        b = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
+        res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 0, 0]);
+    });
+    it('broadcasting Tensor2D shapes - int32', function () {
+        var a = tf.tensor2d([[3], [7]], [2, 1], 'int32');
+        var b = tf.tensor2d([[2, 3, 4], [7, 8, 9]], [2, 3], 'int32');
+        var res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 0, 0, 0, 0]);
+    });
+    it('broadcasting Tensor2D shapes - float32', function () {
+        var a = tf.tensor2d([[1.1], [7.1]], [2, 1], 'float32');
+        var b = tf.tensor2d([[0.1, 1.1, 2.1], [7.1, 8.1, 9.1]], [2, 3], 'float32');
+        var res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 0, 0, 0, 0]);
+    });
+    it('NaNs in Tensor2D - float32', function () {
+        var a = tf.tensor2d([[1.1, NaN], [0.1, NaN]], [2, 2], 'float32');
+        var b = tf.tensor2d([[0.1, NaN], [1.1, NaN]], [2, 2], 'float32');
+        var res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 0, 0]);
+    });
+    it('Tensor3D - int32', function () {
+        var a = tf.tensor3d([[[1], [4], [5]], [[8], [9], [11]]], [2, 3, 1], 'int32');
+        var b = tf.tensor3d([[[2], [3], [6]], [[7], [10], [11]]], [2, 3, 1], 'int32');
+        var res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 0, 1, 0, 0]);
+        a = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
+        b = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
+        res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 0, 0, 0, 0]);
+    });
+    it('Tensor3D - float32', function () {
+        var a = tf.tensor3d([[[1.1], [4.1], [5.1]], [[8.1], [9.1], [11.1]]], [2, 3, 1], 'float32');
+        var b = tf.tensor3d([[[2.1], [3.1], [6.1]], [[7.1], [10.1], [11.1]]], [2, 3, 1], 'float32');
+        var res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 0, 1, 0, 0]);
+        a = tf.tensor3d([[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.2]]], [2, 3, 1], 'float32');
+        b = tf.tensor3d([[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.1]]], [2, 3, 1], 'float32');
+        res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 0, 0, 0, 1]);
+    });
+    it('broadcasting Tensor3D shapes - int32', function () {
+        var a = tf.tensor3d([[[1, 0], [2, 3], [4, 5]], [[6, 7], [9, 8], [10, 11]]], [2, 3, 2], 'int32');
+        var b = tf.tensor3d([[[1], [2], [3]], [[7], [10], [9]]], [2, 3, 1], 'int32');
+        var res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1]);
+    });
+    it('broadcasting Tensor3D float32', function () {
+        var a = tf.tensor3d([
+            [[1.1, 0.1], [2.1, 3.1], [4.1, 5.1]],
+            [[6.1, 7.1], [9.1, 8.1], [10.1, 11.1]]
+        ], [2, 3, 2], 'float32');
+        var b = tf.tensor3d([[[1.1], [2.1], [3.1]], [[7.1], [10.1], [9.1]]], [2, 3, 1], 'float32');
+        var res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 0, 1, 1, 1, 0, 0, 0, 0, 1, 1]);
+    });
+    it('NaNs in Tensor3D - float32', function () {
+        var a = tf.tensor3d([[[1.1], [NaN], [1.1]], [[0.1], [0.1], [0.1]]], [2, 3, 1], 'float32');
+        var b = tf.tensor3d([[[0.1], [0.1], [1.1]], [[1.1], [0.1], [NaN]]], [2, 3, 1], 'float32');
+        var res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 0, 0, 0, 0]);
+    });
+    it('Tensor4D - int32', function () {
+        var a = tf.tensor4d([1, 4, 5, 8], [2, 2, 1, 1], 'int32');
+        var b = tf.tensor4d([2, 3, 6, 8], [2, 2, 1, 1], 'int32');
+        var res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 0, 0]);
+        a = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
+        b = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
+        res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 0, 0]);
+        a = tf.tensor4d([2, 2, 2, 2], [2, 2, 1, 1], 'int32');
+        b = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'int32');
+        res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 1, 1]);
+    });
+    it('Tensor4D - float32', function () {
+        var a = tf.tensor4d([1.1, 4.1, 5.1, 8.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([2.1, 3.1, 6.1, 8.1], [2, 2, 1, 1], 'float32');
+        var res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 0, 0]);
+        a = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
+        b = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
+        res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 0, 0]);
+        a = tf.tensor4d([1.1, 1.1, 1.1, 1.1], [2, 2, 1, 1], 'float32');
+        b = tf.tensor4d([0.1, 0.1, 0.1, 0.1], [2, 2, 1, 1], 'float32');
+        res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 1, 1]);
+    });
+    it('broadcasting Tensor4D shapes - int32', function () {
+        var a = tf.tensor4d([1, 2, 5, 9], [2, 2, 1, 1], 'int32');
+        var b = tf.tensor4d([[[[1, 2]], [[3, 4]]], [[[5, 6]], [[7, 8]]]], [2, 2, 1, 2], 'int32');
+        var res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 0, 0, 0, 0, 1, 1]);
+    });
+    it('broadcasting Tensor4D shapes - float32', function () {
+        var a = tf.tensor4d([1.1, 2.1, 5.1, 9.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([[[[1.1, 2.1]], [[3.1, 4.1]]], [[[5.1, 6.1]], [[7.1, 8.1]]]], [2, 2, 1, 2], 'float32');
+        var res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 0, 0, 0, 0, 1, 1]);
+    });
+    it('NaNs in Tensor4D - float32', function () {
+        var a = tf.tensor4d([1.1, NaN, 0.1, 0.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([0.1, 1.1, 1.1, NaN], [2, 2, 1, 1], 'float32');
+        var res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 0, 0]);
+    });
+    it('throws when passed a as a non-tensor', function () {
+        expect(function () { return tf.greater({}, tf.scalar(1)); })
+            .toThrowError(/Argument 'a' passed to 'greater' must be a Tensor/);
+    });
+    it('throws when passed b as a non-tensor', function () {
+        expect(function () { return tf.greater(tf.scalar(1), {}); })
+            .toThrowError(/Argument 'b' passed to 'greater' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [1, 4, 5];
+        var b = [2, 3, 5];
+        var res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 0]);
+    });
+    it('works with 0 sized tensors', function () {
+        var a = tf.tensor2d([], [0, 5]);
+        var b = tf.tensor1d([1, 2, 3, 4, 5]);
+        var res = tf.greater(a, b);
+        expect(res.dtype).toBe('bool');
+        expect(res.shape).toEqual([0, 5]);
+        test_util_1.expectArraysClose(res, []);
+    });
+});
+jasmine_util_1.describeWithFlags('greaterStrict', test_util_1.ALL_ENVS, function () {
+    it('Tensor1D - strict version throws when a and b are different shape', function () {
+        var a = tf.tensor1d([2]);
+        var b = tf.tensor1d([4, 2, -1]);
+        expect(function () { return tf.greaterStrict(a, b); }).toThrowError();
+        expect(function () { return tf.greaterStrict(b, a); }).toThrowError();
+    });
+    it('Tensor2D - strict version throws when a and b are different shape', function () {
+        var a = tf.tensor2d([[1.1], [7.1]], [2, 1], 'float32');
+        var b = tf.tensor2d([[0.1, 1.1, 2.1], [7.1, 8.1, 9.1]], [2, 3], 'float32');
+        expect(function () { return tf.greaterStrict(a, b); }).toThrowError();
+        expect(function () { return tf.greaterStrict(b, a); }).toThrowError();
+    });
+    it('Tensor3D - strict version throws when a and b are different shape', function () {
+        var a = tf.tensor3d([
+            [[1.1, 0.1], [2.1, 3.1], [4.1, 5.1]],
+            [[6.1, 7.1], [9.1, 8.1], [10.1, 11.1]]
+        ], [2, 3, 2], 'float32');
+        var b = tf.tensor3d([[[1.1], [2.1], [3.1]], [[7.1], [10.1], [9.1]]], [2, 3, 1], 'float32');
+        expect(function () { return tf.greaterStrict(a, b); }).toThrowError();
+        expect(function () { return tf.greaterStrict(b, a); }).toThrowError();
+    });
+    it('Tensor4D - strict version throws when a and b are different shape', function () {
+        var a = tf.tensor4d([1.1, 2.1, 5.1, 9.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([[[[1.1, 2.1]], [[3.1, 4.1]]], [[[5.1, 6.1]], [[7.1, 8.1]]]], [2, 2, 1, 2], 'float32');
+        expect(function () { return tf.greaterStrict(a, b); }).toThrowError();
+        expect(function () { return tf.greaterStrict(b, a); }).toThrowError();
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [1, 4, 5];
+        var b = [2, 3, 5];
+        var res = tf.greaterStrict(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 0]);
+    });
+});
+jasmine_util_1.describeWithFlags('greaterEqual', test_util_1.ALL_ENVS, function () {
+    it('Tensor1D - int32', function () {
+        var a = tf.tensor1d([1, 4, 5], 'int32');
+        var b = tf.tensor1d([2, 3, 5], 'int32');
+        var res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 1]);
+        a = tf.tensor1d([2, 2, 2], 'int32');
+        b = tf.tensor1d([2, 2, 2], 'int32');
+        res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 1]);
+        a = tf.tensor1d([0, 0], 'int32');
+        b = tf.tensor1d([3, 3], 'int32');
+        res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0]);
+    });
+    it('Tensor1D - float32', function () {
+        var a = tf.tensor1d([1.1, 4.1, 5.1], 'float32');
+        var b = tf.tensor1d([2.2, 3.2, 5.1], 'float32');
+        var res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 1]);
+        a = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
+        b = tf.tensor1d([2.31, 2.31, 2.31], 'float32');
+        res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 1]);
+        a = tf.tensor1d([0.45, 0.123], 'float32');
+        b = tf.tensor1d([3.123, 3.321], 'float32');
+        res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0]);
+    });
+    it('upcasts when dtypes dont match', function () {
+        var a = [1.1, 4.1, 5];
+        var b = [2.2, 3.2, 5];
+        var res = tf.greaterEqual(tf.tensor(a, [3], 'float32'), tf.tensor(b, [3], 'int32'));
+        expect(res.dtype).toBe('bool');
+        expect(res.shape).toEqual([3]);
+        test_util_1.expectArraysClose(res, [0, 1, 1]);
+        res =
+            tf.greaterEqual(tf.tensor(a, [3], 'int32'), tf.tensor(b, [3], 'bool'));
+        expect(res.dtype).toBe('bool');
+        expect(res.shape).toEqual([3]);
+        test_util_1.expectArraysClose(res, [1, 1, 1]);
+    });
+    it('mismatched Tensor1D shapes - int32', function () {
+        var a = tf.tensor1d([1, 2], 'int32');
+        var b = tf.tensor1d([1, 2, 3], 'int32');
+        var f = function () {
+            tf.greaterEqual(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('mismatched Tensor1D shapes - float32', function () {
+        var a = tf.tensor1d([1.1, 2.1], 'float32');
+        var b = tf.tensor1d([1.1, 2.1, 3.1], 'float32');
+        var f = function () {
+            tf.greaterEqual(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('NaNs in Tensor1D - float32', function () {
+        var a = tf.tensor1d([1.1, NaN, 2.1], 'float32');
+        var b = tf.tensor1d([2.1, 3.1, NaN], 'float32');
+        var res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 0]);
+    });
+    it('Tensor2D - int32', function () {
+        var a = tf.tensor2d([[1, 4, 5], [8, 9, 12]], [2, 3], 'int32');
+        var b = tf.tensor2d([[2, 3, 6], [7, 10, 11]], [2, 3], 'int32');
+        var res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 0, 1, 0, 1]);
+        a = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
+        b = tf.tensor2d([[0, 0], [1, 1]], [2, 2], 'int32');
+        res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 1, 1]);
+    });
+    it('Tensor2D - float32', function () {
+        var a = tf.tensor2d([[1.1, 4.1, 5.1], [8.1, 9.1, 12.1]], [2, 3], 'float32');
+        var b = tf.tensor2d([[2.1, 3.1, 6.1], [7.1, 10.1, 11.1]], [2, 3], 'float32');
+        var res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 0, 1, 0, 1]);
+        a = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
+        b = tf.tensor2d([[0.2, 0.2], [1.2, 1.2]], [2, 2], 'float32');
+        res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 1, 1]);
+    });
+    it('broadcasting Tensor2D shapes - int32', function () {
+        var a = tf.tensor2d([[3], [7]], [2, 1], 'int32');
+        var b = tf.tensor2d([[2, 3, 4], [7, 8, 9]], [2, 3], 'int32');
+        var res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 0, 1, 0, 0]);
+    });
+    it('broadcasting Tensor2D shapes - float32', function () {
+        var a = tf.tensor2d([[1.1], [7.1]], [2, 1], 'float32');
+        var b = tf.tensor2d([[0.1, 1.1, 2.1], [7.1, 8.1, 9.1]], [2, 3], 'float32');
+        var res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 0, 1, 0, 0]);
+    });
+    it('NaNs in Tensor2D - float32', function () {
+        var a = tf.tensor2d([[1.1, NaN], [0.1, NaN]], [2, 2], 'float32');
+        var b = tf.tensor2d([[0.1, NaN], [1.1, NaN]], [2, 2], 'float32');
+        var res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 0, 0]);
+    });
+    it('Tensor3D - int32', function () {
+        var a = tf.tensor3d([[[1], [4], [5]], [[8], [9], [12]]], [2, 3, 1], 'int32');
+        var b = tf.tensor3d([[[2], [3], [6]], [[7], [10], [11]]], [2, 3, 1], 'int32');
+        var res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 0, 1, 0, 1]);
+        a = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
+        b = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'int32');
+        res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 1, 1, 1, 1]);
+    });
+    it('Tensor3D - float32', function () {
+        var a = tf.tensor3d([[[1.1], [4.1], [5.1]], [[8.1], [9.1], [12.1]]], [2, 3, 1], 'float32');
+        var b = tf.tensor3d([[[2.1], [3.1], [6.1]], [[7.1], [10.1], [11.1]]], [2, 3, 1], 'float32');
+        var res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 0, 1, 0, 1]);
+        a = tf.tensor3d([[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.2]]], [2, 3, 1], 'float32');
+        b = tf.tensor3d([[[0.1], [0.1], [0.1]], [[1.1], [1.1], [1.1]]], [2, 3, 1], 'float32');
+        res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 1, 1, 1, 1]);
+    });
+    it('broadcasting Tensor3D shapes - int32', function () {
+        var a = tf.tensor3d([[[1, 0], [2, 3], [4, 5]], [[6, 7], [9, 8], [10, 11]]], [2, 3, 2], 'int32');
+        var b = tf.tensor3d([[[1], [2], [3]], [[7], [10], [9]]], [2, 3, 1], 'int32');
+        var res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1]);
+    });
+    it('broadcasting Tensor3D float32', function () {
+        var a = tf.tensor3d([
+            [[1.1, 0.1], [2.1, 3.1], [4.1, 5.1]],
+            [[6.1, 7.1], [9.1, 8.1], [10.1, 11.1]]
+        ], [2, 3, 2], 'float32');
+        var b = tf.tensor3d([[[1.1], [2.1], [3.1]], [[7.1], [10.1], [9.1]]], [2, 3, 1], 'float32');
+        var res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1]);
+    });
+    it('NaNs in Tensor3D - float32', function () {
+        var a = tf.tensor3d([[[1.1], [NaN], [1.1]], [[0.1], [0.1], [0.1]]], [2, 3, 1], 'float32');
+        var b = tf.tensor3d([[[0.1], [0.1], [1.1]], [[1.1], [0.1], [NaN]]], [2, 3, 1], 'float32');
+        var res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 1, 0, 1, 0]);
+    });
+    it('Tensor4D - int32', function () {
+        var a = tf.tensor4d([1, 4, 5, 8], [2, 2, 1, 1], 'int32');
+        var b = tf.tensor4d([2, 3, 6, 7], [2, 2, 1, 1], 'int32');
+        var res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 0, 1]);
+        a = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
+        b = tf.tensor4d([0, 1, 2, 3], [2, 2, 1, 1], 'int32');
+        res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 1, 1]);
+        a = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'int32');
+        b = tf.tensor4d([2, 2, 2, 2], [2, 2, 1, 1], 'int32');
+        res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 0, 0]);
+    });
+    it('Tensor4D - float32', function () {
+        var a = tf.tensor4d([1.1, 4.1, 5.1, 8.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([2.1, 3.1, 6.1, 7.1], [2, 2, 1, 1], 'float32');
+        var res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 0, 1]);
+        a = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
+        b = tf.tensor4d([0.1, 1.1, 2.2, 3.3], [2, 2, 1, 1], 'float32');
+        res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 1, 1, 1]);
+        a = tf.tensor4d([0.1, 0.1, 0.1, 0.1], [2, 2, 1, 1], 'float32');
+        b = tf.tensor4d([1.1, 1.1, 1.1, 1.1], [2, 2, 1, 1], 'float32');
+        res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 0, 0, 0]);
+    });
+    it('broadcasting Tensor4D shapes - int32', function () {
+        var a = tf.tensor4d([1, 2, 5, 9], [2, 2, 1, 1], 'int32');
+        var b = tf.tensor4d([[[[1, 2]], [[3, 4]]], [[[5, 6]], [[7, 8]]]], [2, 2, 1, 2], 'int32');
+        var res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 0, 0, 1, 0, 1, 1]);
+    });
+    it('broadcasting Tensor4D shapes - float32', function () {
+        var a = tf.tensor4d([1.1, 2.1, 5.1, 9.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([[[[1.1, 2.1]], [[3.1, 4.1]]], [[[5.1, 6.1]], [[7.1, 8.1]]]], [2, 2, 1, 2], 'float32');
+        var res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 0, 0, 1, 0, 1, 1]);
+    });
+    it('NaNs in Tensor4D - float32', function () {
+        var a = tf.tensor4d([1.1, NaN, 0.1, 0.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([0.1, 1.1, 1.1, NaN], [2, 2, 1, 1], 'float32');
+        var res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [1, 0, 0, 0]);
+    });
+    it('throws when passed a as a non-tensor', function () {
+        expect(function () { return tf.greaterEqual({}, tf.scalar(1)); })
+            .toThrowError(/Argument 'a' passed to 'greaterEqual' must be a Tensor/);
+    });
+    it('throws when passed b as a non-tensor', function () {
+        expect(function () { return tf.greaterEqual(tf.scalar(1), {}); })
+            .toThrowError(/Argument 'b' passed to 'greaterEqual' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [1, 4, 5];
+        var b = [2, 3, 5];
+        var res = tf.greaterEqual(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 1]);
+    });
+    it('has gradient', function () {
+        var a = tf.tensor1d([3, 2, 5]);
+        var b = tf.tensor1d([4, 1, 5]);
+        var dy = tf.ones([3], 'float32');
+        var da = tf.grad(function (a) { return tf.greaterEqual(a, b); })(a, dy);
+        expect(da.dtype).toBe('float32');
+        expect(da.shape).toEqual([3]);
+        test_util_1.expectArraysClose(da, [0, 0, 0]);
+    });
+});
+jasmine_util_1.describeWithFlags('greaterEqualStrict', test_util_1.ALL_ENVS, function () {
+    it('Tensor1D - strict version throws when a and b are different shape', function () {
+        var a = tf.tensor1d([2]);
+        var b = tf.tensor1d([4, 2, -1]);
+        expect(function () { return tf.greaterEqualStrict(a, b); }).toThrowError();
+        expect(function () { return tf.greaterEqualStrict(b, a); }).toThrowError();
+    });
+    it('Tensor2D - strict version throws when a and b are different shape', function () {
+        var a = tf.tensor2d([[1.1], [7.1]], [2, 1], 'float32');
+        var b = tf.tensor2d([[0.1, 1.1, 2.1], [7.1, 8.1, 9.1]], [2, 3], 'float32');
+        expect(function () { return tf.greaterEqualStrict(a, b); }).toThrowError();
+        expect(function () { return tf.greaterEqualStrict(b, a); }).toThrowError();
+    });
+    it('Tensor3D - strict version throws when a and b are different shape', function () {
+        var a = tf.tensor3d([
+            [[1.1, 0.1], [2.1, 3.1], [4.1, 5.1]],
+            [[6.1, 7.1], [9.1, 8.1], [10.1, 11.1]]
+        ], [2, 3, 2], 'float32');
+        var b = tf.tensor3d([[[1.1], [2.1], [3.1]], [[7.1], [10.1], [9.1]]], [2, 3, 1], 'float32');
+        expect(function () { return tf.greaterEqualStrict(a, b); }).toThrowError();
+        expect(function () { return tf.greaterEqualStrict(b, a); }).toThrowError();
+    });
+    it('Tensor4D - strict version throws when a and b are different shape', function () {
+        var a = tf.tensor4d([1.1, 2.1, 5.1, 9.1], [2, 2, 1, 1], 'float32');
+        var b = tf.tensor4d([[[[1.1, 2.1]], [[3.1, 4.1]]], [[[5.1, 6.1]], [[7.1, 8.1]]]], [2, 2, 1, 2], 'float32');
+        expect(function () { return tf.greaterEqualStrict(a, b); }).toThrowError();
+        expect(function () { return tf.greaterEqualStrict(b, a); }).toThrowError();
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [1, 4, 5];
+        var b = [2, 3, 5];
+        var res = tf.greaterEqualStrict(a, b);
+        expect(res.dtype).toBe('bool');
+        test_util_1.expectArraysClose(res, [0, 1, 1]);
+    });
+});
+//# sourceMappingURL=compare_ops_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/compare_ops_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/compare_ops_test.js.map
new file mode 100644
index 0000000..f223f64
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/compare_ops_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/complex_ops.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/complex_ops.d.ts
new file mode 100644
index 0000000..0470471
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/complex_ops.d.ts
@@ -0,0 +1,9 @@
+import { Tensor } from '../tensor';
+import { TensorLike } from '../types';
+declare function complex_<T extends Tensor>(real: T | TensorLike, imag: T | TensorLike): T;
+declare function real_<T extends Tensor>(input: T | TensorLike): T;
+declare function imag_<T extends Tensor>(input: T | TensorLike): T;
+export declare const complex: typeof complex_;
+export declare const real: typeof real_;
+export declare const imag: typeof imag_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/complex_ops.js b/node_modules/@tensorflow/tfjs-core/dist/ops/complex_ops.js
new file mode 100644
index 0000000..fa40b1e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/complex_ops.js
@@ -0,0 +1,25 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var tensor_util_env_1 = require("../tensor_util_env");
+var util = require("../util");
+var operation_1 = require("./operation");
+function complex_(real, imag) {
+    var $real = tensor_util_env_1.convertToTensor(real, 'real', 'complex');
+    var $imag = tensor_util_env_1.convertToTensor(imag, 'imag', 'complex');
+    util.assertShapesMatch($real.shape, $imag.shape, "real and imag shapes, " + $real.shape + " and " + $imag.shape + ", " +
+        "must match in call to tf.complex().");
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.complex($real, $imag); }, { $real: $real, $imag: $imag });
+}
+function real_(input) {
+    var $input = tensor_util_env_1.convertToTensor(input, 'input', 'real');
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.real($input); }, { $input: $input });
+}
+function imag_(input) {
+    var $input = tensor_util_env_1.convertToTensor(input, 'input', 'imag');
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.imag($input); }, { $input: $input });
+}
+exports.complex = operation_1.op({ complex_: complex_ });
+exports.real = operation_1.op({ real_: real_ });
+exports.imag = operation_1.op({ imag_: imag_ });
+//# sourceMappingURL=complex_ops.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/complex_ops.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/complex_ops.js.map
new file mode 100644
index 0000000..e3bebc4
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/complex_ops.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"complex_ops.js","sourceRoot":"","sources":["../../src/ops/complex_ops.ts"],"names":[],"mappings":";;AAgBA,8CAAmC;AAEnC,sDAAmD;AAEnD,8BAAgC;AAChC,yCAA+B;AAqB/B,kBAAoC,IAAkB,EAAE,IAAkB;IACxE,IAAM,KAAK,GAAG,iCAAe,CAAC,IAAI,EAAE,MAAM,EAAE,SAAS,CAAC,CAAC;IACvD,IAAM,KAAK,GAAG,iCAAe,CAAC,IAAI,EAAE,MAAM,EAAE,SAAS,CAAC,CAAC;IACvD,IAAI,CAAC,iBAAiB,CAClB,KAAK,CAAC,KAAK,EAAE,KAAK,CAAC,KAAK,EACxB,2BAAyB,KAAK,CAAC,KAAK,aAAQ,KAAK,CAAC,KAAK,OAAI;QACvD,qCAAqC,CAAC,CAAC;IAE/C,OAAO,iBAAG,CAAC,MAAM,CAAC,SAAS,CAChB,UAAA,OAAO,IAAI,OAAA,OAAO,CAAC,OAAO,CAAC,KAAK,EAAE,KAAK,CAAC,EAA7B,CAA6B,EAAE,EAAC,KAAK,OAAA,EAAE,KAAK,OAAA,EAAC,CAAM,CAAC;AAC5E,CAAC;AAgBD,eAAiC,KAAmB;IAClD,IAAM,MAAM,GAAG,iCAAe,CAAC,KAAK,EAAE,OAAO,EAAE,MAAM,CAAC,CAAC;IAEvD,OAAO,iBAAG,CAAC,MAAM,CAAC,SAAS,CAAC,UAAA,OAAO,IAAI,OAAA,OAAO,CAAC,IAAI,CAAC,MAAM,CAAC,EAApB,CAAoB,EAAE,EAAC,MAAM,QAAA,EAAC,CAAM,CAAC;AAC9E,CAAC;AAeD,eAAiC,KAAmB;IAClD,IAAM,MAAM,GAAG,iCAAe,CAAC,KAAK,EAAE,OAAO,EAAE,MAAM,CAAC,CAAC;IAEvD,OAAO,iBAAG,CAAC,MAAM,CAAC,SAAS,CAAC,UAAA,OAAO,IAAI,OAAA,OAAO,CAAC,IAAI,CAAC,MAAM,CAAC,EAApB,CAAoB,EAAE,EAAC,MAAM,QAAA,EAAC,CAAM,CAAC;AAC9E,CAAC;AAEY,QAAA,OAAO,GAAG,cAAE,CAAC,EAAC,QAAQ,UAAA,EAAC,CAAC,CAAC;AACzB,QAAA,IAAI,GAAG,cAAE,CAAC,EAAC,KAAK,OAAA,EAAC,CAAC,CAAC;AACnB,QAAA,IAAI,GAAG,cAAE,CAAC,EAAC,KAAK,OAAA,EAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/complex_ops_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/complex_ops_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/complex_ops_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/complex_ops_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/complex_ops_test.js
new file mode 100644
index 0000000..849c1af
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/complex_ops_test.js
@@ -0,0 +1,139 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('complex64', test_util_1.ALL_ENVS, function () {
+    it('tf.complex', function () {
+        var real = tf.tensor1d([3, 30]);
+        var imag = tf.tensor1d([4, 40]);
+        var complex = tf.complex(real, imag);
+        expect(complex.dtype).toBe('complex64');
+        expect(complex.shape).toEqual(real.shape);
+        test_util_1.expectArraysClose(complex, [3, 4, 30, 40]);
+    });
+    it('tf.real', function () {
+        var complex = tf.complex([3, 30], [4, 40]);
+        var real = tf.real(complex);
+        expect(real.dtype).toBe('float32');
+        expect(real.shape).toEqual([2]);
+        test_util_1.expectArraysClose(real, [3, 30]);
+    });
+    it('tf.imag', function () {
+        var complex = tf.complex([3, 30], [4, 40]);
+        var imag = tf.imag(complex);
+        expect(imag.dtype).toBe('float32');
+        expect(imag.shape).toEqual([2]);
+        test_util_1.expectArraysClose(imag, [4, 40]);
+    });
+    it('throws when shapes dont match', function () {
+        var real = tf.tensor1d([3, 30]);
+        var imag = tf.tensor1d([4, 40, 50]);
+        var re = /real and imag shapes, 2 and 3, must match in call to tf.complex\(\)/;
+        expect(function () { return tf.complex(real, imag); }).toThrowError(re);
+    });
+});
+var BYTES_PER_COMPLEX_ELEMENT = 4 * 2;
+jasmine_util_1.describeWithFlags('complex64 memory', test_util_1.BROWSER_ENVS, function () {
+    it('usage', function () {
+        var numTensors = tf.memory().numTensors;
+        var numBuffers = tf.memory().numDataBuffers;
+        var startTensors = numTensors;
+        var real1 = tf.tensor1d([1]);
+        var imag1 = tf.tensor1d([2]);
+        var complex1 = tf.complex(real1, imag1);
+        expect(tf.memory().numTensors).toBe(numTensors + 5);
+        expect(tf.memory().numDataBuffers).toBe(numBuffers + 3);
+        numTensors = tf.memory().numTensors;
+        numBuffers = tf.memory().numDataBuffers;
+        var real2 = tf.tensor1d([3]);
+        var imag2 = tf.tensor1d([4]);
+        var complex2 = tf.complex(real2, imag2);
+        expect(tf.memory().numTensors).toBe(numTensors + 5);
+        expect(tf.memory().numDataBuffers).toBe(numBuffers + 3);
+        numTensors = tf.memory().numTensors;
+        numBuffers = tf.memory().numDataBuffers;
+        var result = complex1.add(complex2);
+        expect(tf.memory().numTensors).toBe(numTensors + 3);
+        numTensors = tf.memory().numTensors;
+        expect(result.dtype).toBe('complex64');
+        expect(result.shape).toEqual([1]);
+        test_util_1.expectArraysClose(result, [4, 6]);
+        var real = tf.real(result);
+        expect(tf.memory().numTensors).toBe(numTensors + 1);
+        numTensors = tf.memory().numTensors;
+        test_util_1.expectArraysClose(real, [4]);
+        var imag = tf.imag(result);
+        expect(tf.memory().numTensors).toBe(numTensors + 1);
+        numTensors = tf.memory().numTensors;
+        test_util_1.expectArraysClose(imag, [6]);
+        real1.dispose();
+        imag1.dispose();
+        real2.dispose();
+        imag2.dispose();
+        complex1.dispose();
+        complex2.dispose();
+        result.dispose();
+        real.dispose();
+        imag.dispose();
+        expect(tf.memory().numTensors).toBe(startTensors);
+    });
+    it('tf.complex disposing underlying tensors', function () {
+        var numTensors = tf.memory().numTensors;
+        var real = tf.tensor1d([3, 30]);
+        var imag = tf.tensor1d([4, 40]);
+        expect(tf.memory().numTensors).toEqual(numTensors + 2);
+        var complex = tf.complex(real, imag);
+        expect(tf.memory().numTensors).toEqual(numTensors + 5);
+        real.dispose();
+        imag.dispose();
+        expect(tf.memory().numTensors).toEqual(numTensors + 3);
+        expect(complex.dtype).toBe('complex64');
+        expect(complex.shape).toEqual(real.shape);
+        test_util_1.expectArraysClose(complex, [3, 4, 30, 40]);
+        complex.dispose();
+        expect(tf.memory().numTensors).toEqual(numTensors);
+    });
+    it('reshape', function () {
+        var memoryBefore = tf.memory();
+        var a = tf.complex([[1, 3, 5], [7, 9, 11]], [[2, 4, 6], [8, 10, 12]]);
+        expect(tf.memory().numTensors).toBe(memoryBefore.numTensors + 3);
+        expect(tf.memory().numBytes)
+            .toBe(memoryBefore.numBytes + 6 * BYTES_PER_COMPLEX_ELEMENT);
+        var b = a.reshape([6]);
+        expect(tf.memory().numTensors).toBe(memoryBefore.numTensors + 4);
+        expect(tf.memory().numBytes)
+            .toBe(memoryBefore.numBytes + 6 * BYTES_PER_COMPLEX_ELEMENT);
+        expect(b.dtype).toBe('complex64');
+        expect(b.shape).toEqual([6]);
+        test_util_1.expectArraysClose(a.dataSync(), b.dataSync());
+        b.dispose();
+        expect(tf.memory().numTensors).toBe(memoryBefore.numTensors + 3);
+        expect(tf.memory().numBytes)
+            .toBe(memoryBefore.numBytes + 6 * BYTES_PER_COMPLEX_ELEMENT);
+        a.dispose();
+        expect(tf.memory().numTensors).toBe(memoryBefore.numTensors);
+        expect(tf.memory().numBytes).toBe(memoryBefore.numBytes);
+    });
+    it('clone', function () {
+        var memoryBefore = tf.memory();
+        var a = tf.complex([[1, 3, 5], [7, 9, 11]], [[2, 4, 6], [8, 10, 12]]);
+        expect(tf.memory().numTensors).toBe(memoryBefore.numTensors + 3);
+        expect(tf.memory().numBytes)
+            .toBe(memoryBefore.numBytes + 6 * BYTES_PER_COMPLEX_ELEMENT);
+        var b = a.clone();
+        expect(tf.memory().numTensors).toBe(memoryBefore.numTensors + 4);
+        expect(tf.memory().numBytes)
+            .toBe(memoryBefore.numBytes + 6 * BYTES_PER_COMPLEX_ELEMENT);
+        expect(b.dtype).toBe('complex64');
+        test_util_1.expectArraysClose(a, b);
+        b.dispose();
+        expect(tf.memory().numTensors).toBe(memoryBefore.numTensors + 3);
+        expect(tf.memory().numBytes)
+            .toBe(memoryBefore.numBytes + 6 * BYTES_PER_COMPLEX_ELEMENT);
+        a.dispose();
+        expect(tf.memory().numTensors).toBe(memoryBefore.numTensors);
+        expect(tf.memory().numBytes).toBe(memoryBefore.numBytes);
+    });
+});
+//# sourceMappingURL=complex_ops_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/complex_ops_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/complex_ops_test.js.map
new file mode 100644
index 0000000..2450c84
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/complex_ops_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"complex_ops_test.js","sourceRoot":"","sources":["../../src/ops/complex_ops_test.ts"],"names":[],"mappings":";;AAgBA,6BAA+B;AAC/B,gDAAkD;AAClD,0CAAuE;AAEvE,gCAAiB,CAAC,WAAW,EAAE,oBAAQ,EAAE;IACvC,EAAE,CAAC,YAAY,EAAE;QACf,IAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC;QAClC,IAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC;QAClC,IAAM,OAAO,GAAG,EAAE,CAAC,OAAO,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC;QAEvC,MAAM,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC,IAAI,CAAC,WAAW,CAAC,CAAC;QACxC,MAAM,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC;QAC1C,6BAAiB,CAAC,OAAO,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,EAAE,EAAE,CAAC,CAAC,CAAC;IAC7C,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,SAAS,EAAE;QACZ,IAAM,OAAO,GAAG,EAAE,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC;QAC7C,IAAM,IAAI,GAAG,EAAE,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC;QAE9B,MAAM,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC,IAAI,CAAC,SAAS,CAAC,CAAC;QACnC,MAAM,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QAChC,6BAAiB,CAAC,IAAI,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC;IACnC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,SAAS,EAAE;QACZ,IAAM,OAAO,GAAG,EAAE,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC;QAC7C,IAAM,IAAI,GAAG,EAAE,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC;QAE9B,MAAM,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC,IAAI,CAAC,SAAS,CAAC,CAAC;QACnC,MAAM,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QAChC,6BAAiB,CAAC,IAAI,EAAE,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC;IACnC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,+BAA+B,EAAE;QAClC,IAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC;QAClC,IAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,EAAE,EAAE,EAAE,CAAC,CAAC,CAAC;QAEtC,IAAM,EAAE,GACJ,qEAAqE,CAAC;QAC1E,MAAM,CAAC,cAAM,OAAA,EAAE,CAAC,OAAO,CAAC,IAAI,EAAE,IAAI,CAAC,EAAtB,CAAsB,CAAC,CAAC,YAAY,CAAC,EAAE,CAAC,CAAC;IACxD,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC;AAEH,IAAM,yBAAyB,GAAG,CAAC,GAAG,CAAC,CAAC;AACxC,gCAAiB,CAAC,kBAAkB,EAAE,wBAAY,EAAE;IAClD,EAAE,CAAC,OAAO,EAAE;QACV,IAAI,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC;QACxC,IAAI,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,cAAc,CAAC;QAC5C,IAAM,YAAY,GAAG,UAAU,CAAC;QAEhC,IAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QAC/B,IAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QAC/B,IAAM,QAAQ,GAAG,EAAE,CAAC,OAAO,CAAC,KAAK,EAAE,KAAK,CAAC,CAAC;QAG1C,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,UAAU,GAAG,CAAC,CAAC,CAAC;QAEpD,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,cAAc,CAAC,CAAC,IAAI,CAAC,UAAU,GAAG,CAAC,CAAC,CAAC;QACxD,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC;QACpC,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,cAAc,CAAC;QAExC,IAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QAC/B,IAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QAC/B,IAAM,QAAQ,GAAG,EAAE,CAAC,OAAO,CAAC,KAAK,EAAE,KAAK,CAAC,CAAC;QAG1C,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,UAAU,GAAG,CAAC,CAAC,CAAC;QAEpD,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,cAAc,CAAC,CAAC,IAAI,CAAC,UAAU,GAAG,CAAC,CAAC,CAAC;QACxD,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC;QACpC,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,cAAc,CAAC;QAExC,IAAM,MAAM,GAAG,QAAQ,CAAC,GAAG,CAAC,QAAQ,CAAC,CAAC;QAGtC,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,UAAU,GAAG,CAAC,CAAC,CAAC;QACpD,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC;QAEpC,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,IAAI,CAAC,WAAW,CAAC,CAAC;QACvC,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QAClC,6BAAiB,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAElC,IAAM,IAAI,GAAG,EAAE,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC;QAE7B,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,UAAU,GAAG,CAAC,CAAC,CAAC;QACpD,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC;QAEpC,6BAAiB,CAAC,IAAI,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QAE7B,IAAM,IAAI,GAAG,EAAE,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC;QAE7B,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,UAAU,GAAG,CAAC,CAAC,CAAC;QACpD,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC;QAEpC,6BAAiB,CAAC,IAAI,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QAG7B,KAAK,CAAC,OAAO,EAAE,CAAC;QAChB,KAAK,CAAC,OAAO,EAAE,CAAC;QAChB,KAAK,CAAC,OAAO,EAAE,CAAC;QAChB,KAAK,CAAC,OAAO,EAAE,CAAC;QAChB,QAAQ,CAAC,OAAO,EAAE,CAAC;QACnB,QAAQ,CAAC,OAAO,EAAE,CAAC;QACnB,MAAM,CAAC,OAAO,EAAE,CAAC;QACjB,IAAI,CAAC,OAAO,EAAE,CAAC;QACf,IAAI,CAAC,OAAO,EAAE,CAAC;QACf,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,YAAY,CAAC,CAAC;IACpD,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,yCAAyC,EAAE;QAC5C,IAAM,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC;QAE1C,IAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC;QAClC,IAAM,IAAI,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC;QAClC,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,OAAO,CAAC,UAAU,GAAG,CAAC,CAAC,CAAC;QAEvD,IAAM,OAAO,GAAG,EAAE,CAAC,OAAO,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC;QAGvC,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,OAAO,CAAC,UAAU,GAAG,CAAC,CAAC,CAAC;QAEvD,IAAI,CAAC,OAAO,EAAE,CAAC;QACf,IAAI,CAAC,OAAO,EAAE,CAAC;QAGf,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,OAA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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/concat_split.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/concat_split.d.ts
new file mode 100644
index 0000000..0bafe0f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/concat_split.d.ts
@@ -0,0 +1,15 @@
+import { Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D } from '../tensor';
+import { TensorLike } from '../types';
+declare function concat1d_(tensors: Array<Tensor1D | TensorLike>): Tensor1D;
+declare function concat2d_(tensors: Array<Tensor2D | TensorLike>, axis: number): Tensor2D;
+declare function concat3d_(tensors: Array<Tensor3D | TensorLike>, axis: number): Tensor3D;
+declare function concat4d_(tensors: Array<Tensor4D | TensorLike>, axis: number): Tensor4D;
+declare function concat_<T extends Tensor>(tensors: Array<T | TensorLike>, axis?: number): T;
+declare function split_<T extends Tensor>(x: T | TensorLike, numOrSizeSplits: number[] | number, axis?: number): T[];
+export declare const concat: typeof concat_;
+export declare const concat1d: typeof concat1d_;
+export declare const concat2d: typeof concat2d_;
+export declare const concat3d: typeof concat3d_;
+export declare const concat4d: typeof concat4d_;
+export declare const split: typeof split_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/concat_split.js b/node_modules/@tensorflow/tfjs-core/dist/ops/concat_split.js
new file mode 100644
index 0000000..08ad46b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/concat_split.js
@@ -0,0 +1,67 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var tensor_util_env_1 = require("../tensor_util_env");
+var util_1 = require("../util");
+var axis_util_1 = require("./axis_util");
+var concat_util_1 = require("./concat_util");
+var operation_1 = require("./operation");
+var tensor_ops_1 = require("./tensor_ops");
+function concat1d_(tensors) {
+    return exports.concat(tensors, 0);
+}
+function concat2d_(tensors, axis) {
+    return exports.concat(tensors, axis);
+}
+function concat3d_(tensors, axis) {
+    return exports.concat(tensors, axis);
+}
+function concat4d_(tensors, axis) {
+    return exports.concat(tensors, axis);
+}
+function concat_(tensors, axis) {
+    if (axis === void 0) { axis = 0; }
+    util_1.assert(tensors.length >= 1, 'Pass at least one tensor to concat');
+    var $tensors = tensor_util_env_1.convertToTensorArray(tensors, 'tensors', 'concat');
+    axis = axis_util_1.parseAxisParam(axis, $tensors[0].shape)[0];
+    var outShape = concat_util_1.computeOutShape($tensors.map(function (t) { return t.shape; }), axis);
+    if (util_1.sizeFromShape(outShape) === 0) {
+        return tensor_ops_1.tensor([], outShape);
+    }
+    $tensors = $tensors.filter(function (t) { return t.size > 0; });
+    if ($tensors.length === 1) {
+        return $tensors[0];
+    }
+    var shapes = $tensors.map(function (t) { return t.shape; });
+    concat_util_1.assertParamsConsistent(shapes, axis);
+    var der = function (dy) {
+        var sizeSplits = shapes.map(function (s) { return s[axis]; });
+        var derTensors = exports.split(dy, sizeSplits, axis);
+        return derTensors.map(function (t) { return function () { return t; }; });
+    };
+    var inputs = $tensors;
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.concat($tensors, axis); }, inputs, der);
+}
+function split_(x, numOrSizeSplits, axis) {
+    if (axis === void 0) { axis = 0; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'split');
+    axis = axis_util_1.parseAxisParam(axis, $x.shape)[0];
+    var splitSizes;
+    if (typeof (numOrSizeSplits) === 'number') {
+        util_1.assert($x.shape[axis] % numOrSizeSplits === 0, 'Number of splits must evenly divide the axis.');
+        splitSizes = Array(numOrSizeSplits).fill($x.shape[axis] / numOrSizeSplits);
+    }
+    else {
+        util_1.assert($x.shape[axis] === numOrSizeSplits.reduce(function (a, b) { return a + b; }), 'The sum of sizes must match the size of the axis dimension.');
+        splitSizes = numOrSizeSplits;
+    }
+    var der = function (dy) { return ({ $x: function () { return exports.concat(dy, axis); } }); };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.split($x, splitSizes, axis); }, { $x: $x }, der);
+}
+exports.concat = operation_1.op({ concat_: concat_ });
+exports.concat1d = operation_1.op({ concat1d_: concat1d_ });
+exports.concat2d = operation_1.op({ concat2d_: concat2d_ });
+exports.concat3d = operation_1.op({ concat3d_: concat3d_ });
+exports.concat4d = operation_1.op({ concat4d_: concat4d_ });
+exports.split = operation_1.op({ split_: split_ });
+//# sourceMappingURL=concat_split.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/concat_split.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/concat_split.js.map
new file mode 100644
index 0000000..25b85e4
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/concat_split.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"concat_split.js","sourceRoot":"","sources":["../../src/ops/concat_split.ts"],"names":[],"mappings":";;AAiBA,8CAAmC;AAEnC,sDAAyE;AAEzE,gCAA8C;AAC9C,yCAA2C;AAC3C,6CAAsE;AACtE,yCAA+B;AAC/B,2CAAoC;AAapC,mBAAmB,OAAmC;IACpD,OAAO,cAAM,CAAC,OAAO,EAAE,CAAC,CAAY,CAAC;AACvC,CAAC;AA6BD,mBACI,OAAmC,EAAE,IAAY;IACnD,OAAO,cAAM,CAAC,OAAO,EAAE,IAAI,CAAC,CAAC;AAC/B,CAAC;AAgCD,mBACI,OAAmC,EAAE,IAAY;IACnD,OAAO,cAAM,CAAC,OAAO,EAAE,IAAI,CAAC,CAAC;AAC/B,CAAC;AASD,mBACI,OAAmC,EAAE,IAAY;IACnD,OAAO,cAAM,CAAC,OAAO,EAAE,IAAI,CAAC,CAAC;AAC/B,CAAC;AAyCD,iBAAmC,OAA4B,EAAE,IAAQ;IAAR,qBAAA,EAAA,QAAQ;IACvE,aAAM,CAAC,OAAO,CAAC,MAAM,IAAI,CAAC,EAAE,oCAAoC,CAAC,CAAC;IAClE,IAAI,QAAQ,GAAG,sCAAoB,CAAC,OAAO,EAAE,SAAS,EAAE,QAAQ,CAAC,CAAC;IAClE,IAAI,GAAG,0BAAc,CAAC,IAAI,EAAE,QAAQ,CAAC,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC;IAClD,IAAM,QAAQ,GAAG,6BAAe,CAAC,QAAQ,CAAC,GAAG,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,KAAK,EAAP,CAAO,CAAC,EAAE,IAAI,CAAC,CAAC;IACnE,IAAI,oBAAa,CAAC,QAAQ,CAAC,KAAK,CAAC,EAAE;QACjC,OAAO,mBAAM,CAAC,EAAE,EAAE,QAAQ,CAAM,CAAC;KAClC;IAED,QAAQ,GAAG,QAAQ,CAAC,MAAM,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,IAAI,GAAG,CAAC,EAAV,CAAU,CAAC,CAAC;IAC5C,IAAI,QAAQ,CAAC,MAAM,KAAK,CAAC,EAAE;QACzB,OAAO,QAAQ,CAAC,CAAC,CAAC,CAAC;KACpB;IAED,IAAM,MAAM,GAAG,QAAQ,CAAC,GAAG,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,KAAK,EAAP,CAAO,CAAC,CAAC;IAC1C,oCAAsB,CAAC,MAAM,EAAE,IAAI,CAAC,CAAC;IACrC,IAAM,GAAG,GAAG,UAAC,EAAK;QAChB,IAAM,UAAU,GAAG,MAAM,CAAC,GAAG,CAAC,UAAA,CAAC,IAAI,OAAA,CAAC,CAAC,IAAI,CAAC,EAAP,CAAO,CAAC,CAAC;QAC5C,IAAM,UAAU,GAAG,aAAK,CAAC,EAAE,EAAE,UAAU,EAAE,IAAI,CAAC,CAAC;QAC/C,OAAO,UAAU,CAAC,GAAG,CAAC,UAAA,CAAC,IAAI,OAAA,cAAM,OAAA,CAAC,EAAD,CAAC,EAAP,CAAO,CAAO,CAAC;IAC5C,CAAC,CAAC;IACF,IAAM,MAAM,GAAG,QAAc,CAAC;IAC9B,OAAO,iBAAG,CAAC,MAAM,CAAC,SAAS,CACvB,UAAA,OAAO,IAAI,OAAA,OAAO,CAAC,MAAM,CAAC,QAAQ,EAAE,IAAI,CAAM,EAAnC,CAAmC,EAAE,MAAM,EAAE,GAAG,CAAC,CAAC;AACnE,CAAC;AAmCD,gBACI,CAAe,EAAE,eAAgC,EAAE,IAAQ;IAAR,qBAAA,EAAA,QAAQ;IAC7D,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,OAAO,CAAC,CAAC;IAE5C,IAAI,GAAG,0BAAc,CAAC,IAAI,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC;IACzC,IAAI,UAAoB,CAAC;IACzB,IAAI,OAAO,CAAC,eAAe,CAAC,KAAK,QAAQ,EAAE;QACzC,aAAM,CACF,EAAE,CAAC,KAAK,CAAC,IAAI,CAAC,GAAG,eAAe,KAAK,CAAC,EACtC,+CAA+C,CAAC,CAAC;QACrD,UAAU,GAAG,KAAK,CAAC,eAAe,CAAC,CAAC,IAAI,CAAC,EAAE,CAAC,KAAK,CAAC,IAAI,CAAC,GAAG,eAAe,CAAC,CAAC;KAC5E;SAAM;QACL,aAAM,CACF,EAAE,CAAC,KAAK,CAAC,IAAI,CAAC,KAAK,eAAe,CAAC,MAAM,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,CAAC,GAAG,CAAC,EAAL,CAAK,CAAC,EAC1D,6DAA6D,CAAC,CAAC;QACnE,UAAU,GAAG,eAAe,CAAC;KAC9B;IACD,IAAM,GAAG,GAAG,UAAC,EAAO,IAAK,OAAA,CAAC,EAAC,EAAE,EAAE,cAAM,OAAA,cAAM,CAAC,EAAE,EAAE,IAAI,CAAC,EAAhB,CAAgB,EAAC,CAAC,EAA9B,CAA8B,CAAC;IACxD,OAAO,iBAAG,CAAC,MAAM,CAAC,SAAS,CACvB,UAAA,OAAO,IAAI,OAAA,OAAO,CAAC,KAAK,CAAC,EAAE,EAAE,UAAU,EAAE,IAAI,CAAC,EAAnC,CAAmC,EAAE,EAAC,EAAE,IAAA,EAAC,EAAE,GAAG,CAAC,CAAC;AACjE,CAAC;AAEY,QAAA,MAAM,GAAG,cAAE,CAAC,EAAC,OAAO,SAAA,EAAC,CAAC,CAAC;AACvB,QAAA,QAAQ,GAAG,cAAE,CAAC,EAAC,SAAS,WAAA,EAAC,CAAC,CAAC;AAC3B,QAAA,QAAQ,GAAG,cAAE,CAAC,EAAC,SAAS,WAAA,EAAC,CAAC,CAAC;AAC3B,QAAA,QAAQ,GAAG,cAAE,CAAC,EAAC,SAAS,WAAA,EAAC,CAAC,CAAC;AAC3B,QAAA,QAAQ,GAAG,cAAE,CAAC,EAAC,SAAS,WAAA,EAAC,CAAC,CAAC;AAC3B,QAAA,KAAK,GAAG,cAAE,CAAC,EAAC,MAAM,QAAA,EAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/concat_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/concat_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/concat_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/concat_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/concat_test.js
new file mode 100644
index 0000000..0a8249b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/concat_test.js
@@ -0,0 +1,327 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('concat1d', test_util_1.ALL_ENVS, function () {
+    it('3 + 5', function () {
+        var a = tf.tensor1d([3]);
+        var b = tf.tensor1d([5]);
+        var result = tf.concat1d([a, b]);
+        var expected = [3, 5];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('TensorLike 3 + 5', function () {
+        var a = [3];
+        var b = [5];
+        var result = tf.concat1d([a, b]);
+        var expected = [3, 5];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('TensorLike Chained 3 + 5', function () {
+        var a = tf.tensor1d([3]);
+        var b = [5];
+        var result = a.concat([b]);
+        var expected = [3, 5];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('3 + [5,7]', function () {
+        var a = tf.tensor1d([3]);
+        var b = tf.tensor1d([5, 7]);
+        var result = tf.concat1d([a, b]);
+        var expected = [3, 5, 7];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('[3,5] + 7', function () {
+        var a = tf.tensor1d([3, 5]);
+        var b = tf.tensor1d([7]);
+        var result = tf.concat1d([a, b]);
+        var expected = [3, 5, 7];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('3 + 5 + 7 + 9', function () {
+        var a = tf.tensor1d([3]);
+        var b = tf.tensor1d([5]);
+        var c = tf.tensor1d([7]);
+        var d = tf.tensor1d([9]);
+        var result = tf.concat1d([a, b, c, d]);
+        test_util_1.expectArraysClose(result, [3, 5, 7, 9]);
+    });
+    it('single tensor', function () {
+        var a = tf.tensor1d([3]);
+        var result = tf.concat1d([a]);
+        test_util_1.expectArraysClose(result, [3]);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [3];
+        var b = [5];
+        var result = tf.concat1d([a, b]);
+        var expected = [3, 5];
+        test_util_1.expectArraysClose(result, expected);
+    });
+});
+jasmine_util_1.describeWithFlags('concat2d', test_util_1.ALL_ENVS, function () {
+    it('[[3]] + [[5]], axis=0', function () {
+        var axis = 0;
+        var a = tf.tensor2d([3], [1, 1]);
+        var b = tf.tensor2d([5], [1, 1]);
+        var result = tf.concat2d([a, b], axis);
+        var expected = [3, 5];
+        expect(result.shape).toEqual([2, 1]);
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('TensorLike [[3]] + [[5]], axis=0', function () {
+        var axis = 0;
+        var a = [[3]];
+        var b = [[5]];
+        var result = tf.concat2d([a, b], axis);
+        var expected = [3, 5];
+        expect(result.shape).toEqual([2, 1]);
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('TensorLike Chained [[3]] + [[5]], axis=0', function () {
+        var axis = 0;
+        var a = tf.tensor2d([3], [1, 1]);
+        var b = [[5]];
+        var result = a.concat([b], axis);
+        var expected = [3, 5];
+        expect(result.shape).toEqual([2, 1]);
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('[[3]] + [[5]], axis=1', function () {
+        var axis = 1;
+        var a = tf.tensor2d([3], [1, 1]);
+        var b = tf.tensor2d([5], [1, 1]);
+        var result = tf.concat2d([a, b], axis);
+        var expected = [3, 5];
+        expect(result.shape).toEqual([1, 2]);
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('[[1, 2], [3, 4]] + [[5, 6]], axis=0', function () {
+        var axis = 0;
+        var a = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
+        var b = tf.tensor2d([[5, 6]], [1, 2]);
+        var result = tf.concat2d([a, b], axis);
+        var expected = [1, 2, 3, 4, 5, 6];
+        expect(result.shape).toEqual([3, 2]);
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('[[1, 2],[3, 4]] + [[5, 6]] + [[7, 8]], axis=0', function () {
+        var axis = 0;
+        var a = tf.tensor2d([[1, 2], [3, 4]]);
+        var b = tf.tensor2d([[5, 6]]);
+        var c = tf.tensor2d([[7, 8]]);
+        var result = tf.concat2d([a, b, c], axis);
+        var expected = [1, 2, 3, 4, 5, 6, 7, 8];
+        expect(result.shape).toEqual([4, 2]);
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('[[1, 2], [3, 4]] + [[5, 6]], axis=1 throws error', function () {
+        var axis = 1;
+        var a = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
+        var b = tf.tensor2d([[5, 6]], [1, 2]);
+        expect(function () { return tf.concat2d([a, b], axis); }).toThrowError();
+    });
+    it('[[1, 2], [3, 4]] + [[5, 6], [7, 8]], axis=1', function () {
+        var axis = 1;
+        var a = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
+        var b = tf.tensor2d([[5, 6], [7, 8]], [2, 2]);
+        var result = tf.concat2d([a, b], axis);
+        var expected = [1, 2, 5, 6, 3, 4, 7, 8];
+        expect(result.shape).toEqual([2, 4]);
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('[[1, 2],[3, 4]] + [[5, 6],[7, 8]] + [[9, 10],[11, 12]], axis=1', function () {
+        var axis = 1;
+        var a = tf.tensor2d([[1, 2], [3, 4]]);
+        var b = tf.tensor2d([[5, 6], [7, 8]]);
+        var c = tf.tensor2d([[9, 10], [11, 12]]);
+        var result = tf.concat2d([a, b, c], axis);
+        var expected = [1, 2, 5, 6, 9, 10, 3, 4, 7, 8, 11, 12];
+        expect(result.shape).toEqual([2, 6]);
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('accepts a tensor-like object', function () {
+        var axis = 0;
+        var a = [[3]];
+        var b = [[5]];
+        var result = tf.concat2d([a, b], axis);
+        var expected = [3, 5];
+        expect(result.shape).toEqual([2, 1]);
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('concat zero-sized tensors', function () {
+        var a = tf.tensor2d([], [0, 5]);
+        var b = tf.tensor2d([], [0, 5]);
+        var c = tf.tensor2d([], [0, 5]);
+        var res = tf.concat([a, b, c], 0);
+        expect(res.shape).toEqual([0, 5]);
+        test_util_1.expectArraysEqual(res, []);
+        var res2 = tf.concat([a, b, c], 1);
+        expect(res2.shape).toEqual([0, 15]);
+        test_util_1.expectArraysEqual(res2, []);
+    });
+});
+jasmine_util_1.describeWithFlags('concat3d', test_util_1.ALL_ENVS, function () {
+    it('shapes correct concat axis=0', function () {
+        var tensor1 = tf.tensor3d([1, 2, 3], [1, 1, 3]);
+        var tensor2 = tf.tensor3d([4, 5, 6], [1, 1, 3]);
+        var values = tf.concat3d([tensor1, tensor2], 0);
+        expect(values.shape).toEqual([2, 1, 3]);
+        test_util_1.expectArraysClose(values, [1, 2, 3, 4, 5, 6]);
+    });
+    it('concat axis=0', function () {
+        var tensor1 = tf.tensor3d([1, 11, 111, 2, 22, 222], [1, 2, 3]);
+        var tensor2 = tf.tensor3d([5, 55, 555, 6, 66, 666, 7, 77, 777, 8, 88, 888], [2, 2, 3]);
+        var values = tf.concat3d([tensor1, tensor2], 0);
+        expect(values.shape).toEqual([3, 2, 3]);
+        test_util_1.expectArraysClose(values, [
+            1, 11, 111, 2, 22, 222, 5, 55, 555, 6, 66, 666, 7, 77, 777, 8, 88, 888
+        ]);
+    });
+    it('TensorLike concat axis=0', function () {
+        var tensor1 = [[[1, 11, 111], [2, 22, 222]]];
+        var tensor2 = [[[5, 55, 555], [6, 66, 666]], [[7, 77, 777], [8, 88, 888]]];
+        var values = tf.concat3d([tensor1, tensor2], 0);
+        expect(values.shape).toEqual([3, 2, 3]);
+        test_util_1.expectArraysClose(values, [
+            1, 11, 111, 2, 22, 222, 5, 55, 555, 6, 66, 666, 7, 77, 777, 8, 88, 888
+        ]);
+    });
+    it('TensorLike Chained concat axis=0', function () {
+        var tensor1 = tf.tensor3d([1, 11, 111, 2, 22, 222], [1, 2, 3]);
+        var tensor2 = [[[5, 55, 555], [6, 66, 666]], [[7, 77, 777], [8, 88, 888]]];
+        var values = tensor1.concat([tensor2], 0);
+        expect(values.shape).toEqual([3, 2, 3]);
+        test_util_1.expectArraysClose(values, [
+            1, 11, 111, 2, 22, 222, 5, 55, 555, 6, 66, 666, 7, 77, 777, 8, 88, 888
+        ]);
+    });
+    it('shapes correct concat axis=1', function () {
+        var tensor1 = tf.tensor3d([1, 2, 3], [1, 1, 3]);
+        var tensor2 = tf.tensor3d([4, 5, 6], [1, 1, 3]);
+        var values = tf.concat3d([tensor1, tensor2], 1);
+        expect(values.shape).toEqual([1, 2, 3]);
+        test_util_1.expectArraysClose(values, [1, 2, 3, 4, 5, 6]);
+    });
+    it('concat axis=1', function () {
+        var tensor1 = tf.tensor3d([1, 11, 111, 3, 33, 333], [2, 1, 3]);
+        var tensor2 = tf.tensor3d([5, 55, 555, 6, 66, 666, 7, 77, 777, 8, 88, 888], [2, 2, 3]);
+        var values = tf.concat3d([tensor1, tensor2], 1);
+        expect(values.shape).toEqual([2, 3, 3]);
+        test_util_1.expectArraysClose(values, [
+            1, 11, 111, 5, 55, 555, 6, 66, 666, 3, 33, 333, 7, 77, 777, 8, 88, 888
+        ]);
+    });
+    it('shapes correct concat axis=2', function () {
+        var tensor1 = tf.tensor3d([1, 2, 3], [1, 1, 3]);
+        var tensor2 = tf.tensor3d([4, 5, 6], [1, 1, 3]);
+        var values = tf.concat3d([tensor1, tensor2], 2);
+        expect(values.shape).toEqual([1, 1, 6]);
+        test_util_1.expectArraysClose(values, [1, 2, 3, 4, 5, 6]);
+    });
+    it('concat axis=2', function () {
+        var tensor1 = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [2, 2, 2]);
+        var tensor2 = tf.tensor3d([5, 55, 555, 6, 66, 666, 7, 77, 777, 8, 88, 888], [2, 2, 3]);
+        var values = tf.concat3d([tensor1, tensor2], 2);
+        expect(values.shape).toEqual([2, 2, 5]);
+        test_util_1.expectArraysClose(values, [
+            1, 11, 5, 55, 555, 2, 22, 6, 66, 666,
+            3, 33, 7, 77, 777, 4, 44, 8, 88, 888
+        ]);
+    });
+    it('concat throws when invalid non-axis shapes, axis=0', function () {
+        var axis = 0;
+        var x1 = tf.tensor3d([1, 11, 111], [1, 1, 3]);
+        var x2 = tf.tensor3d([5, 55, 555, 6, 66, 666, 7, 77, 777, 8, 88, 888], [2, 2, 3]);
+        expect(function () { return tf.concat3d([x1, x2], axis); }).toThrowError();
+    });
+    it('concat throws when invalid non-axis shapes, axis=1', function () {
+        var axis = 1;
+        var x1 = tf.tensor3d([1, 11, 111], [1, 1, 3]);
+        var x2 = tf.tensor3d([5, 55, 555, 6, 66, 666, 7, 77, 777, 8, 88, 888], [2, 2, 3]);
+        expect(function () { return tf.concat3d([x1, x2], axis); }).toThrowError();
+    });
+    it('concat throws when invalid non-axis shapes, axis=2', function () {
+        var axis = 2;
+        var x1 = tf.tensor3d([1, 11, 2, 22], [1, 2, 2]);
+        var x2 = tf.tensor3d([5, 55, 555, 6, 66, 666, 7, 77, 777, 8, 88, 888], [2, 2, 3]);
+        expect(function () { return tf.concat3d([x1, x2], axis); }).toThrowError();
+    });
+    it('gradient concat axis=0', function () {
+        var x1 = tf.tensor3d([1, 11, 2, 22], [1, 2, 2]);
+        var x2 = tf.tensor3d([5, 55, 6, 66, 7, 77, 8, 88], [2, 2, 2]);
+        var dy = tf.tensor3d([66, 6, 55, 5, 44, 4, 33, 3, 22, 2, 11, 1], [3, 2, 2]);
+        var axis = 0;
+        var grads = tf.grads(function (x1, x2) { return tf.concat3d([x1, x2], axis); });
+        var _a = grads([x1, x2], dy), dx1 = _a[0], dx2 = _a[1];
+        expect(dx1.shape).toEqual(x1.shape);
+        test_util_1.expectArraysClose(dx1, [66, 6, 55, 5]);
+        expect(dx2.shape).toEqual(x2.shape);
+        test_util_1.expectArraysClose(dx2, [44, 4, 33, 3, 22, 2, 11, 1]);
+    });
+    it('gradient concat axis=1', function () {
+        var x1 = tf.tensor3d([1, 11, 2, 22], [2, 1, 2]);
+        var x2 = tf.tensor3d([3, 33, 4, 44, 5, 55, 6, 66], [2, 2, 2]);
+        var dy = tf.tensor3d([66, 6, 55, 5, 44, 4, 33, 3, 22, 2, 11, 1], [2, 3, 2]);
+        var axis = 1;
+        var grads = tf.grads(function (x1, x2) { return tf.concat3d([x1, x2], axis); });
+        var _a = grads([x1, x2], dy), dx1 = _a[0], dx2 = _a[1];
+        expect(dx1.shape).toEqual(x1.shape);
+        test_util_1.expectArraysClose(dx1, [66, 6, 33, 3]);
+        expect(dx2.shape).toEqual(x2.shape);
+        test_util_1.expectArraysClose(dx2, [55, 5, 44, 4, 22, 2, 11, 1]);
+    });
+    it('gradient concat axis=2', function () {
+        var x1 = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
+        var x2 = tf.tensor3d([5, 55, 6, 66, 7, 77, 8, 88], [2, 2, 2]);
+        var dy = tf.tensor3d([4, 40, 400, 3, 30, 300, 2, 20, 200, 1, 10, 100], [2, 2, 3]);
+        var axis = 2;
+        var grads = tf.grads(function (x1, x2) { return tf.concat3d([x1, x2], axis); });
+        var _a = grads([x1, x2], dy), dx1 = _a[0], dx2 = _a[1];
+        expect(dx1.shape).toEqual(x1.shape);
+        test_util_1.expectArraysClose(dx1, [4, 3, 2, 1]);
+        expect(dx2.shape).toEqual(x2.shape);
+        test_util_1.expectArraysClose(dx2, [40, 400, 30, 300, 20, 200, 10, 100]);
+    });
+    it('gradient concat axis=-1', function () {
+        var x1 = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
+        var x2 = tf.tensor3d([5, 55, 6, 66, 7, 77, 8, 88], [2, 2, 2]);
+        var dy = tf.tensor3d([4, 40, 400, 3, 30, 300, 2, 20, 200, 1, 10, 100], [2, 2, 3]);
+        var axis = -1;
+        var grads = tf.grads(function (x1, x2) { return tf.concat3d([x1, x2], axis); });
+        var _a = grads([x1, x2], dy), dx1 = _a[0], dx2 = _a[1];
+        expect(dx1.shape).toEqual(x1.shape);
+        test_util_1.expectArraysClose(dx1, [4, 3, 2, 1]);
+        expect(dx2.shape).toEqual(x2.shape);
+        test_util_1.expectArraysClose(dx2, [40, 400, 30, 300, 20, 200, 10, 100]);
+    });
+    it('accepts a tensor-like object', function () {
+        var tensor1 = [[[1, 2, 3]]];
+        var tensor2 = [[[4, 5, 6]]];
+        var values = tf.concat3d([tensor1, tensor2], 0);
+        expect(values.shape).toEqual([2, 1, 3]);
+        test_util_1.expectArraysClose(values, [1, 2, 3, 4, 5, 6]);
+    });
+    it('concat tensors with 0 in their shape', function () {
+        var tensor1 = tf.tensor3d([1, 2, 3, 4, 5, 6], [2, 3, 1]);
+        var tensor2 = tf.tensor3d([], [0, 3, 1]);
+        var values = tf.concat3d([tensor1, tensor2], 0);
+        expect(values.shape).toEqual([2, 3, 1]);
+        test_util_1.expectArraysClose(values, [1, 2, 3, 4, 5, 6]);
+    });
+});
+jasmine_util_1.describeWithFlags('concat throws for non-tensors', test_util_1.ALL_ENVS, function () {
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.concat([{}]); })
+            .toThrowError(/Argument 'tensors\[0\]' passed to 'concat' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var tensor1 = [[[1, 2, 3, 4]]];
+        var tensor2 = [[[4, 5, 6, 7]]];
+        var values = tf.concat([tensor1, tensor2], 0);
+        expect(values.shape).toEqual([2, 1, 4]);
+        test_util_1.expectArraysClose(values, [1, 2, 3, 4, 4, 5, 6, 7]);
+    });
+});
+//# sourceMappingURL=concat_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/concat_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/concat_test.js.map
new file mode 100644
index 0000000..1913e35
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/concat_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util.d.ts
new file mode 100644
index 0000000..bc18cae
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util.d.ts
@@ -0,0 +1,2 @@
+export declare function assertParamsConsistent(shapes: number[][], axis: number): void;
+export declare function computeOutShape(shapes: number[][], axis: number): number[];
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util.js b/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util.js
new file mode 100644
index 0000000..461e00b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util.js
@@ -0,0 +1,29 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var util = require("../util");
+function assertParamsConsistent(shapes, axis) {
+    var rank = shapes[0].length;
+    shapes.forEach(function (shape, i) {
+        util.assert(shape.length === rank, "Error in concat" + rank + "D: rank of tensors[" + i + "] must be the same " +
+            ("as the rank of the rest (" + rank + ")"));
+    });
+    util.assert(axis >= 0 && axis < rank, "Error in concat" + rank + "D: axis must be between 0 and " + (rank - 1) + ".");
+    var firstShape = shapes[0];
+    shapes.forEach(function (shape, i) {
+        for (var r = 0; r < rank; r++) {
+            util.assert((r === axis) || (shape[r] === firstShape[r]), "Error in concat" + rank + "D: Shape of tensors[" + i + "] (" + shape + ") " +
+                ("does not match the shape of the rest (" + firstShape + ") ") +
+                ("along the non-concatenated axis " + i + "."));
+        }
+    });
+}
+exports.assertParamsConsistent = assertParamsConsistent;
+function computeOutShape(shapes, axis) {
+    var outputShape = shapes[0].slice();
+    for (var i = 1; i < shapes.length; i++) {
+        outputShape[axis] += shapes[i][axis];
+    }
+    return outputShape;
+}
+exports.computeOutShape = computeOutShape;
+//# sourceMappingURL=concat_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util.js.map
new file mode 100644
index 0000000..7c099da
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"concat_util.js","sourceRoot":"","sources":["../../src/ops/concat_util.ts"],"names":[],"mappings":";;AAiBA,8BAAgC;AAEhC,gCAAuC,MAAkB,EAAE,IAAY;IACrE,IAAM,IAAI,GAAG,MAAM,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC;IAC9B,MAAM,CAAC,OAAO,CAAC,UAAC,KAAK,EAAE,CAAC;QACtB,IAAI,CAAC,MAAM,CACP,KAAK,CAAC,MAAM,KAAK,IAAI,EACrB,oBAAkB,IAAI,2BAAsB,CAAC,wBAAqB;aAC9D,8BAA4B,IAAI,MAAG,CAAA,CAAC,CAAC;IAC/C,CAAC,CAAC,CAAC;IAEH,IAAI,CAAC,MAAM,CACP,IAAI,IAAI,CAAC,IAAI,IAAI,GAAG,IAAI,EACxB,oBAAkB,IAAI,uCAAiC,IAAI,GAAG,CAAC,OAAG,CAAC,CAAC;IAExE,IAAM,UAAU,GAAG,MAAM,CAAC,CAAC,CAAC,CAAC;IAC7B,MAAM,CAAC,OAAO,CAAC,UAAC,KAAK,EAAE,CAAC;QACtB,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,IAAI,EAAE,CAAC,EAAE,EAAE;YAC7B,IAAI,CAAC,MAAM,CACP,CAAC,CAAC,KAAK,IAAI,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,UAAU,CAAC,CAAC,CAAC,CAAC,EAC5C,oBAAkB,IAAI,4BAAuB,CAAC,WAAM,KAAK,OAAI;iBACzD,2CAAyC,UAAU,OAAI,CAAA;iBACvD,qCAAmC,CAAC,MAAG,CAAA,CAAC,CAAC;SAClD;IACH,CAAC,CAAC,CAAC;AACL,CAAC;AAvBD,wDAuBC;AAED,yBAAgC,MAAkB,EAAE,IAAY;IAC9D,IAAM,WAAW,GAAG,MAAM,CAAC,CAAC,CAAC,CAAC,KAAK,EAAE,CAAC;IACtC,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,MAAM,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;QACtC,WAAW,CAAC,IAAI,CAAC,IAAI,MAAM,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC;KACtC;IACD,OAAO,WAAW,CAAC;AACrB,CAAC;AAND,0CAMC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util_test.js
new file mode 100644
index 0000000..a52cc4c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util_test.js
@@ -0,0 +1,55 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var concat_util = require("./concat_util");
+describe('concat_util.assertConcatShapesMatch rank=3D', function () {
+    it('Non-3D tensor x1', function () {
+        var assertFn = function () {
+            concat_util.assertParamsConsistent([[1], [1, 2, 3]], 1);
+        };
+        expect(assertFn).toThrow();
+    });
+    it('Non-3D tensor x2', function () {
+        var assertFn = function () {
+            concat_util.assertParamsConsistent([[1, 2, 3], [2, 3]], 1);
+        };
+        expect(assertFn).toThrow();
+    });
+    it('axis out of bound', function () {
+        var assertFn = function () {
+            concat_util.assertParamsConsistent([[1, 2, 3], [1, 2, 3]], 4);
+        };
+        expect(assertFn).toThrow();
+    });
+    it('non-axis shape mismatch', function () {
+        var assertFn = function () {
+            concat_util.assertParamsConsistent([[2, 3, 3], [2, 2, 4]], 2);
+        };
+        expect(assertFn).toThrow();
+    });
+    it('shapes line up', function () {
+        var assertFn = function () {
+            concat_util.assertParamsConsistent([[2, 3, 3], [2, 3, 4]], 2);
+        };
+        expect(assertFn).not.toThrow();
+    });
+    it('3 shapes, all line up', function () {
+        var assertFn = function () {
+            concat_util.assertParamsConsistent([[2, 3, 3], [2, 3, 4], [2, 3, 8]], 2);
+        };
+        expect(assertFn).not.toThrow();
+    });
+    it('3 shapes, 3rd shape does not line up', function () {
+        var assertFn = function () {
+            concat_util.assertParamsConsistent([[2, 5, 3], [2, 1, 3], [2, 1, 5]], 1);
+        };
+        expect(assertFn).toThrow();
+    });
+});
+describe('concat_util.computeConcatOutputShape', function () {
+    it('compute output shape, axis=0', function () {
+        expect(concat_util.computeOutShape([[2, 2, 3], [1, 2, 3]], 0)).toEqual([
+            3, 2, 3
+        ]);
+    });
+});
+//# sourceMappingURL=concat_util_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util_test.js.map
new file mode 100644
index 0000000..20c3d9d
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/concat_util_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"concat_util_test.js","sourceRoot":"","sources":["../../src/ops/concat_util_test.ts"],"names":[],"mappings":";;AAiBA,2CAA6C;AAE7C,QAAQ,CAAC,6CAA6C,EAAE;IACtD,EAAE,CAAC,kBAAkB,EAAE;QACrB,IAAM,QAAQ,GAAG;YACf,WAAW,CAAC,sBAAsB,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QAC1D,CAAC,CAAC;QAEF,MAAM,CAAC,QAAQ,CAAC,CAAC,OAAO,EAAE,CAAC;IAC7B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,kBAAkB,EAAE;QACrB,IAAM,QAAQ,GAAG;YACf,WAAW,CAAC,sBAAsB,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QAC7D,CAAC,CAAC;QAEF,MAAM,CAAC,QAAQ,CAAC,CAAC,OAAO,EAAE,CAAC;IAC7B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,mBAAmB,EAAE;QACtB,IAAM,QAAQ,GAAG;YACf,WAAW,CAAC,sBAAsB,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QAChE,CAAC,CAAC;QAEF,MAAM,CAAC,QAAQ,CAAC,CAAC,OAAO,EAAE,CAAC;IAC7B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,yBAAyB,EAAE;QAC5B,IAAM,QAAQ,GAAG;YACf,WAAW,CAAC,sBAAsB,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QAChE,CAAC,CAAC;QAEF,MAAM,CAAC,QAAQ,CAAC,CAAC,OAAO,EAAE,CAAC;IAC7B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,gBAAgB,EAAE;QACnB,IAAM,QAAQ,GAAG;YACf,WAAW,CAAC,sBAAsB,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QAChE,CAAC,CAAC;QAEF,MAAM,CAAC,QAAQ,CAAC,CAAC,GAAG,CAAC,OAAO,EAAE,CAAC;IACjC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,uBAAuB,EAAE;QAC1B,IAAM,QAAQ,GAAG;YACf,WAAW,CAAC,sBAAsB,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QAC3E,CAAC,CAAC;QACF,MAAM,CAAC,QAAQ,CAAC,CAAC,GAAG,CAAC,OAAO,EAAE,CAAC;IACjC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,sCAAsC,EAAE;QACzC,IAAM,QAAQ,GAAG;YACf,WAAW,CAAC,sBAAsB,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QAC3E,CAAC,CAAC;QACF,MAAM,CAAC,QAAQ,CAAC,CAAC,OAAO,EAAE,CAAC;IAC7B,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC;AAEH,QAAQ,CAAC,sCAAsC,EAAE;IAC/C,EAAE,CAAC,8BAA8B,EAAE;QACjC,MAAM,CAAC,WAAW,CAAC,eAAe,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC;YACrE,CAAC,EAAE,CAAC,EAAE,CAAC;SACR,CAAC,CAAC;IACL,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix.d.ts
new file mode 100644
index 0000000..ae1a4d7
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix.d.ts
@@ -0,0 +1,4 @@
+import { Tensor1D, Tensor2D } from '../tensor';
+import { TensorLike } from '../types';
+export declare function confusionMatrix_(labels: Tensor1D | TensorLike, predictions: Tensor1D | TensorLike, numClasses: number): Tensor2D;
+export declare const confusionMatrix: typeof confusionMatrix_;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix.js b/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix.js
new file mode 100644
index 0000000..1a571d2
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix.js
@@ -0,0 +1,25 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tensor_util_env_1 = require("../tensor_util_env");
+var util = require("../util");
+var array_ops_1 = require("./array_ops");
+var operation_1 = require("./operation");
+function confusionMatrix_(labels, predictions, numClasses) {
+    var $labels = tensor_util_env_1.convertToTensor(labels, 'labels', 'confusionMatrix');
+    var $predictions = tensor_util_env_1.convertToTensor(predictions, 'predictions', 'confusionMatrix');
+    util.assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), "If provided, numClasses must be a positive integer, " +
+        ("but got " + numClasses));
+    util.assert($labels.rank === 1, "Expected the rank of labels to be 1, but got " + $labels.rank);
+    util.assert($predictions.rank === 1, "Expected the rank of predictions to be 1, " +
+        ("but got " + $predictions.rank));
+    util.assert($labels.shape[0] === $predictions.shape[0], "Mismatch in the number of examples: " +
+        ($labels.shape[0] + " vs. " + $predictions.shape[0] + ". ") +
+        "Labels and predictions should have the same number of elements.");
+    util.assert(numClasses > 0 && Number.isInteger(numClasses), "numClasses is required to be a positive integer, but got " + numClasses);
+    var oneHotLabels = array_ops_1.oneHot($labels.asType('int32'), numClasses);
+    var oneHotPredictions = array_ops_1.oneHot($predictions.asType('int32'), numClasses);
+    return oneHotLabels.transpose().matMul(oneHotPredictions).asType('int32');
+}
+exports.confusionMatrix_ = confusionMatrix_;
+exports.confusionMatrix = operation_1.op({ confusionMatrix_: confusionMatrix_ });
+//# sourceMappingURL=confusion_matrix.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix.js.map
new file mode 100644
index 0000000..c858fa5
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"confusion_matrix.js","sourceRoot":"","sources":["../../src/ops/confusion_matrix.ts"],"names":[],"mappings":";;AAkBA,sDAAmD;AAEnD,8BAAgC;AAEhC,yCAAmC;AACnC,yCAA+B;AA8B/B,0BACI,MAA2B,EAAE,WAAgC,EAC7D,UAAkB;IACpB,IAAM,OAAO,GAAG,iCAAe,CAAC,MAAM,EAAE,QAAQ,EAAE,iBAAiB,CAAC,CAAC;IACrE,IAAM,YAAY,GACd,iCAAe,CAAC,WAAW,EAAE,aAAa,EAAE,iBAAiB,CAAC,CAAC;IAEnE,IAAI,CAAC,MAAM,CACP,UAAU,IAAI,IAAI,IAAI,UAAU,GAAG,CAAC,IAAI,MAAM,CAAC,SAAS,CAAC,UAAU,CAAC,EACpE,sDAAsD;SAClD,aAAW,UAAY,CAAA,CAAC,CAAC;IACjC,IAAI,CAAC,MAAM,CACP,OAAO,CAAC,IAAI,KAAK,CAAC,EAClB,kDAAgD,OAAO,CAAC,IAAM,CAAC,CAAC;IACpE,IAAI,CAAC,MAAM,CACP,YAAY,CAAC,IAAI,KAAK,CAAC,EACvB,4CAA4C;SACxC,aAAW,YAAY,CAAC,IAAM,CAAA,CAAC,CAAC;IACxC,IAAI,CAAC,MAAM,CACP,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,YAAY,CAAC,KAAK,CAAC,CAAC,CAAC,EAC1C,sCAAsC;SAC/B,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,aAAQ,YAAY,CAAC,KAAK,CAAC,CAAC,CAAC,OAAI,CAAA;QACpD,iEAAiE,CAAC,CAAC;IAC3E,IAAI,CAAC,MAAM,CACP,UAAU,GAAG,CAAC,IAAI,MAAM,CAAC,SAAS,CAAC,UAAU,CAAC,EAC9C,8DAA4D,UAAY,CAAC,CAAC;IAI9E,IAAM,YAAY,GAAG,kBAAM,CAAC,OAAO,CAAC,MAAM,CAAC,OAAO,CAAC,EAAE,UAAU,CAAC,CAAC;IACjE,IAAM,iBAAiB,GAAG,kBAAM,CAAC,YAAY,CAAC,MAAM,CAAC,OAAO,CAAC,EAAE,UAAU,CAAC,CAAC;IAC3E,OAAO,YAAY,CAAC,SAAS,EAAE,CAAC,MAAM,CAAC,iBAAiB,CAAC,CAAC,MAAM,CAAC,OAAO,CAAC,CAAC;AAC5E,CAAC;AAhCD,4CAgCC;AAEY,QAAA,eAAe,GAAG,cAAE,CAAC,EAAC,gBAAgB,kBAAA,EAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix_test.js
new file mode 100644
index 0000000..a81c153
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix_test.js
@@ -0,0 +1,80 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('confusionMatrix', test_util_1.ALL_ENVS, function () {
+    it('3x3 all cases present in both labels and predictions', function () {
+        var labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');
+        var predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');
+        var numClasses = 3;
+        var out = tf.math.confusionMatrix(labels, predictions, numClasses);
+        test_util_1.expectArraysEqual(out, tf.tensor2d([[2, 0, 0], [0, 1, 1], [0, 0, 1]], [3, 3], 'int32'));
+    });
+    it('float32 arguments are accepted', function () {
+        var labels = tf.tensor1d([0, 1, 2, 1, 0], 'float32');
+        var predictions = tf.tensor1d([0, 2, 2, 1, 0], 'float32');
+        var numClasses = 3;
+        var out = tf.math.confusionMatrix(labels, predictions, numClasses);
+        test_util_1.expectArraysEqual(out, tf.tensor2d([[2, 0, 0], [0, 1, 1], [0, 0, 1]], [3, 3], 'int32'));
+    });
+    it('4x4 all cases present in labels, but not predictions', function () {
+        var labels = tf.tensor1d([3, 3, 2, 2, 1, 1, 0, 0], 'int32');
+        var predictions = tf.tensor1d([2, 2, 2, 2, 0, 0, 0, 0], 'int32');
+        var numClasses = 4;
+        var out = tf.math.confusionMatrix(labels, predictions, numClasses);
+        test_util_1.expectArraysEqual(out, tf.tensor2d([[2, 0, 0, 0], [2, 0, 0, 0], [0, 0, 2, 0], [0, 0, 2, 0]], [4, 4], 'int32'));
+    });
+    it('4x4 all cases present in predictions, but not labels', function () {
+        var labels = tf.tensor1d([2, 2, 2, 2, 0, 0, 0, 0], 'int32');
+        var predictions = tf.tensor1d([3, 3, 2, 2, 1, 1, 0, 0], 'int32');
+        var numClasses = 4;
+        var out = tf.math.confusionMatrix(labels, predictions, numClasses);
+        test_util_1.expectArraysEqual(out, tf.tensor2d([[2, 2, 0, 0], [0, 0, 0, 0], [0, 0, 2, 2], [0, 0, 0, 0]], [4, 4], 'int32'));
+    });
+    it('Plain arrays as inputs', function () {
+        var labels = [3, 3, 2, 2, 1, 1, 0, 0];
+        var predictions = [2, 2, 2, 2, 0, 0, 0, 0];
+        var numClasses = 4;
+        var out = tf.math.confusionMatrix(labels, predictions, numClasses);
+        test_util_1.expectArraysEqual(out, tf.tensor2d([[2, 0, 0, 0], [2, 0, 0, 0], [0, 0, 2, 0], [0, 0, 2, 0]], [4, 4], 'int32'));
+    });
+    it('Int32Arrays as inputs', function () {
+        var labels = new Int32Array([3, 3, 2, 2, 1, 1, 0, 0]);
+        var predictions = new Int32Array([2, 2, 2, 2, 0, 0, 0, 0]);
+        var numClasses = 4;
+        var out = tf.math.confusionMatrix(labels, predictions, numClasses);
+        test_util_1.expectArraysEqual(out, tf.tensor2d([[2, 0, 0, 0], [2, 0, 0, 0], [0, 0, 2, 0], [0, 0, 2, 0]], [4, 4], 'int32'));
+    });
+    it('5x5 predictions and labels both missing some cases', function () {
+        var labels = tf.tensor1d([0, 4], 'int32');
+        var predictions = tf.tensor1d([4, 0], 'int32');
+        var numClasses = 5;
+        var out = tf.math.confusionMatrix(labels, predictions, numClasses);
+        test_util_1.expectArraysEqual(out, tf.tensor2d([
+            [0, 0, 0, 0, 1], [0, 0, 0, 0, 0], [0, 0, 0, 0, 0],
+            [0, 0, 0, 0, 0], [1, 0, 0, 0, 0]
+        ], [5, 5], 'int32'));
+    });
+    it('Invalid numClasses leads to Error', function () {
+        expect(function () { return tf.math.confusionMatrix(tf.tensor1d([0, 1]), tf.tensor1d([1, 0]), 2.5); })
+            .toThrowError(/numClasses .* positive integer.* got 2\.5/);
+    });
+    it('Incorrect tensor rank leads to Error', function () {
+        expect(function () { return tf.math.confusionMatrix(tf.scalar(0), tf.scalar(0), 1); })
+            .toThrowError(/rank .* 1.*got 0/);
+        expect(function () {
+            return tf.math.confusionMatrix(tf.zeros([3, 3]), tf.zeros([9]), 2);
+        })
+            .toThrowError(/rank .* 1.*got 2/);
+        expect(function () {
+            return tf.math.confusionMatrix(tf.zeros([9]), tf.zeros([3, 3]), 2);
+        })
+            .toThrowError(/rank .* 1.*got 2/);
+    });
+    it('Mismatch in lengths leads to Error', function () {
+        expect(function () { return tf.math.confusionMatrix(tf.zeros([3]), tf.zeros([9]), 2); })
+            .toThrowError(/Mismatch .* 3 vs.* 9/);
+    });
+});
+//# sourceMappingURL=confusion_matrix_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix_test.js.map
new file mode 100644
index 0000000..e31fdb0
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/confusion_matrix_test.js.map
@@ -0,0 +1 @@
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diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/conv.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/conv.d.ts
new file mode 100644
index 0000000..5f08839
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/conv.d.ts
@@ -0,0 +1,17 @@
+import { Tensor2D, Tensor3D, Tensor4D, Tensor5D } from '../tensor';
+import { TensorLike } from '../types';
+declare function conv1d_<T extends Tensor2D | Tensor3D>(x: T | TensorLike, filter: Tensor3D | TensorLike, stride: number, pad: 'valid' | 'same' | number, dataFormat?: 'NWC' | 'NCW', dilation?: number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
+declare function conv2d_<T extends Tensor3D | Tensor4D>(x: T | TensorLike, filter: Tensor4D | TensorLike, strides: [number, number] | number, pad: 'valid' | 'same' | number, dataFormat?: 'NHWC' | 'NCHW', dilations?: [number, number] | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
+declare function conv2dDerFilter_<T extends Tensor3D | Tensor4D>(x: T, dy: T, filterShape: [number, number, number, number], strides: [number, number] | number, pad: 'valid' | 'same' | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): Tensor4D;
+declare function conv2dTranspose_<T extends Tensor3D | Tensor4D>(x: T | TensorLike, filter: Tensor4D | TensorLike, outputShape: [number, number, number, number] | [number, number, number], strides: [number, number] | number, pad: 'valid' | 'same' | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
+declare function depthwiseConv2d_<T extends Tensor3D | Tensor4D>(x: T | TensorLike, filter: Tensor4D | TensorLike, strides: [number, number] | number, pad: 'valid' | 'same' | number, dataFormat?: 'NHWC' | 'NCHW', dilations?: [number, number] | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
+declare function separableConv2d_<T extends Tensor3D | Tensor4D>(x: T | TensorLike, depthwiseFilter: Tensor4D | TensorLike, pointwiseFilter: Tensor4D | TensorLike, strides: [number, number] | number, pad: 'valid' | 'same', dilation?: [number, number] | number, dataFormat?: 'NHWC' | 'NCHW'): T;
+declare function conv3d_<T extends Tensor4D | Tensor5D>(x: T | TensorLike, filter: Tensor5D | TensorLike, strides: [number, number, number] | number, pad: 'valid' | 'same', dataFormat?: 'NHWC' | 'NCHW', dilations?: [number, number, number] | number): T;
+export declare const conv1d: typeof conv1d_;
+export declare const conv2d: typeof conv2d_;
+export declare const conv3d: typeof conv3d_;
+export declare const conv2dDerFilter: typeof conv2dDerFilter_;
+export declare const depthwiseConv2d: typeof depthwiseConv2d_;
+export declare const separableConv2d: typeof separableConv2d_;
+export declare const conv2dTranspose: typeof conv2dTranspose_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/conv.js b/node_modules/@tensorflow/tfjs-core/dist/ops/conv.js
new file mode 100644
index 0000000..6cfc267
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/conv.js
@@ -0,0 +1,382 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var tensor_util_env_1 = require("../tensor_util_env");
+var util = require("../util");
+var conv_util = require("./conv_util");
+var matmul_1 = require("./matmul");
+var operation_1 = require("./operation");
+function conv1d_(x, filter, stride, pad, dataFormat, dilation, dimRoundingMode) {
+    if (dataFormat === void 0) { dataFormat = 'NWC'; }
+    if (dilation === void 0) { dilation = 1; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'conv1d');
+    var $filter = tensor_util_env_1.convertToTensor(filter, 'filter', 'conv1d');
+    var x3D = $x;
+    var reshapedTo3D = false;
+    if ($x.rank === 2) {
+        reshapedTo3D = true;
+        x3D = $x.as3D(1, $x.shape[0], $x.shape[1]);
+    }
+    util.assert(x3D.rank === 3, "Error in conv1d: input must be rank 3, but got rank " + x3D.rank + ".");
+    util.assert($filter.rank === 3, "Error in conv1d: filter must be rank 3, but got rank " +
+        ($filter.rank + "."));
+    if (dimRoundingMode != null) {
+        util.assert(util.isInt(pad), "Error in conv1d: pad must be an integer when using, " +
+            ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."));
+    }
+    util.assert(x3D.shape[2] === $filter.shape[1], "Error in conv1d: depth of input (" + x3D.shape[2] + ") must match " +
+        ("input depth for filter " + $filter.shape[1] + "."));
+    util.assert(conv_util.eitherStridesOrDilationsAreOne(stride, dilation), 'Error in conv1D: Either stride or dilation must be 1. ' +
+        ("Got stride " + stride + " and dilation '" + dilation + "'"));
+    util.assert(dataFormat === 'NWC', "Error in conv1d: got dataFormat of " + dataFormat + " but only NWC is currently supported.");
+    var filter4D = $filter.as4D(1, $filter.shape[0], $filter.shape[1], $filter.shape[2]);
+    var input4D = x3D.as4D(x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]);
+    var strides = [1, stride];
+    var dilations = [1, dilation];
+    var conv2dDataFormat = 'NHWC';
+    var res = exports.conv2d(input4D, filter4D, strides, pad, conv2dDataFormat, dilations, dimRoundingMode);
+    if (reshapedTo3D) {
+        return res.as2D(res.shape[2], res.shape[3]);
+    }
+    return res.as3D(res.shape[0], res.shape[2], res.shape[3]);
+}
+function conv2d_(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode) {
+    if (dataFormat === void 0) { dataFormat = 'NHWC'; }
+    if (dilations === void 0) { dilations = [1, 1]; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'conv2d');
+    var $filter = tensor_util_env_1.convertToTensor(filter, 'filter', 'conv2d');
+    var x4D = $x;
+    var reshapedTo4D = false;
+    if ($x.rank === 3) {
+        reshapedTo4D = true;
+        x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
+    }
+    util.assert(x4D.rank === 4, "Error in conv2d: input must be rank 4, but got rank " + x4D.rank + ".");
+    util.assert($filter.rank === 4, "Error in conv2d: filter must be rank 4, but got rank " +
+        ($filter.rank + "."));
+    if (dimRoundingMode != null) {
+        util.assert(util.isInt(pad), "Error in conv2d: pad must be an integer when using, " +
+            ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."));
+    }
+    util.assert(x4D.shape[3] === $filter.shape[2], "Error in conv2d: depth of input (" + x4D.shape[3] + ") must match " +
+        ("input depth for filter " + $filter.shape[2] + "."));
+    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), 'Error in conv2D: Either strides or dilations must be 1. ' +
+        ("Got strides " + strides + " and dilations '" + dilations + "'"));
+    util.assert(dataFormat === 'NHWC', "Error in conv2d: got dataFormat of " + dataFormat + " but only NHWC is currently supported.");
+    var convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);
+    var res;
+    if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&
+        convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&
+        convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&
+        (convInfo.padInfo.type === 'SAME' || convInfo.padInfo.type === 'VALID')) {
+        var x2d = x4D.reshape([-1, convInfo.inChannels]);
+        var w2d = $filter.reshape([convInfo.inChannels, convInfo.outChannels]);
+        res = matmul_1.matMul(x2d, w2d).reshape(convInfo.outShape);
+    }
+    else {
+        var grad = function (dy) {
+            util.assert(conv_util.tupleValuesAreOne(dilations), 'Error in gradient of conv2D: dilation rates greater than 1 are not' +
+                ("yet supported in gradients. Got dilations '" + dilations + "'"));
+            return {
+                x: function () { return conv2dDerInput_(x4D.shape, dy, $filter, strides, pad); },
+                $filter: function () { return conv2dDerFilter_(x4D, dy, $filter.shape, strides, pad); }
+            };
+        };
+        res = environment_1.ENV.engine.runKernel(function (backend) { return backend.conv2d(x4D, $filter, convInfo); }, { x: x4D, $filter: $filter }, grad);
+    }
+    if (reshapedTo4D) {
+        return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
+    }
+    return res;
+}
+function conv2dDerInput_(xShape, dy, filter, strides, pad, dimRoundingMode) {
+    util.assert(xShape.length === dy.rank, "Length of inShape " +
+        ("(" + xShape.length + ") and rank of dy (" + dy.rank + ") must match"));
+    var xShape4D = xShape;
+    var dy4D = dy;
+    var reshapedTo4D = false;
+    if (dy.rank === 3) {
+        reshapedTo4D = true;
+        dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);
+        xShape4D = [1, xShape[0], xShape[1], xShape[2]];
+    }
+    var inDepth = xShape4D[3];
+    var outDepth = dy4D.shape[3];
+    util.assert(xShape4D.length === 4, "Error in conv2dDerInput: inShape must be length 4, but got length " +
+        (xShape4D.length + "."));
+    util.assert(dy4D.rank === 4, "Error in conv2dDerInput: dy must be rank 4, but got " +
+        ("rank " + dy4D.rank));
+    util.assert(filter.rank === 4, "Error in conv2dDerInput: filter must be rank 4, but got " +
+        ("rank " + filter.rank));
+    util.assert(inDepth === filter.shape[2], "Error in conv2dDerInput: depth of input (" + inDepth + ") must " +
+        ("match input depth for filter " + filter.shape[2] + "."));
+    util.assert(outDepth === filter.shape[3], "Error in conv2dDerInput: depth of output (" + outDepth + ") must " +
+        ("match output depth for filter " + filter.shape[3] + "."));
+    if (dimRoundingMode != null) {
+        util.assert(util.isInt(pad), "Error in conv2dDerInput: pad must be an integer when using, " +
+            ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."));
+    }
+    var dilations = 1;
+    var grad = function (ddx) {
+        var dataFormat = 'NHWC';
+        return {
+            dy4D: function () { return exports.conv2d(ddx, filter, strides, pad, dataFormat, dilations, dimRoundingMode); },
+            filter: function () { return exports.conv2dDerFilter(ddx, dy4D, filter.shape, strides, pad, dimRoundingMode); }
+        };
+    };
+    var convInfo = conv_util.computeConv2DInfo(xShape4D, filter.shape, strides, dilations, pad, dimRoundingMode);
+    var res = environment_1.ENV.engine.runKernel(function (backend) { return backend.conv2dDerInput(dy4D, filter, convInfo); }, { dy4D: dy4D, filter: filter }, grad);
+    if (reshapedTo4D) {
+        return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
+    }
+    return res;
+}
+function conv2dDerFilter_(x, dy, filterShape, strides, pad, dimRoundingMode) {
+    var x4D = x;
+    if (x.rank === 3) {
+        x4D = x.as4D(1, x.shape[0], x.shape[1], x.shape[2]);
+    }
+    var dy4D = dy;
+    if (dy4D.rank === 3) {
+        dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);
+    }
+    util.assert(x4D.rank === 4, "Error in conv2dDerFilter: input must be rank 4, but got shape " +
+        (x4D.shape + "."));
+    util.assert(dy4D.rank === 4, "Error in conv2dDerFilter: dy must be rank 4, but got shape " +
+        (dy4D.shape + "."));
+    util.assert(filterShape.length === 4, "Error in conv2dDerFilter: filterShape must be length 4, but got " +
+        (filterShape + "."));
+    util.assert(x4D.shape[3] === filterShape[2], "Error in conv2dDerFilter: depth of input " + x4D.shape[3] + ") must " +
+        ("match input depth in filter (" + filterShape[2] + "."));
+    util.assert(dy4D.shape[3] === filterShape[3], "Error in conv2dDerFilter: depth of dy (" + dy4D.shape[3] + ") must " +
+        ("match output depth for filter (" + filterShape[3] + ")."));
+    if (dimRoundingMode != null) {
+        util.assert(util.isInt(pad), "Error in conv2dDerFilter: pad must be an integer when using, " +
+            ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."));
+    }
+    var dilations = 1;
+    var convInfo = conv_util.computeConv2DInfo(x4D.shape, filterShape, strides, dilations, pad, dimRoundingMode);
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.conv2dDerFilter(x4D, dy4D, convInfo); }, { x4D: x4D, dy4D: dy4D });
+}
+function conv2dTranspose_(x, filter, outputShape, strides, pad, dimRoundingMode) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'conv2dTranspose');
+    var $filter = tensor_util_env_1.convertToTensor(filter, 'filter', 'conv2dTranspose');
+    return conv2dDerInput_(outputShape, $x, $filter, strides, pad, dimRoundingMode);
+}
+function depthwiseConv2d_(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode) {
+    if (dataFormat === void 0) { dataFormat = 'NHWC'; }
+    if (dilations === void 0) { dilations = [1, 1]; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'depthwiseConv2d');
+    var $filter = tensor_util_env_1.convertToTensor(filter, 'filter', 'depthwiseConv2d');
+    var x4D = $x;
+    var reshapedTo4D = false;
+    if ($x.rank === 3) {
+        reshapedTo4D = true;
+        x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
+    }
+    util.assert(x4D.rank === 4, "Error in depthwiseConv2d: input must be rank 4, but got " +
+        ("rank " + x4D.rank + "."));
+    util.assert($filter.rank === 4, "Error in depthwiseConv2d: filter must be rank 4, but got rank " +
+        ($filter.rank + "."));
+    util.assert(x4D.shape[3] === $filter.shape[2], "Error in depthwiseConv2d: number of input channels " +
+        ("(" + x4D.shape[3] + ") must match the inChannels dimension in ") +
+        ("filter " + $filter.shape[2] + "."));
+    if (dilations == null) {
+        dilations = [1, 1];
+    }
+    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), 'Error in depthwiseConv2d: Either strides or dilations must be 1. ' +
+        ("Got strides " + strides + " and dilations '" + dilations + "'"));
+    if (dimRoundingMode != null) {
+        util.assert(util.isInt(pad), "Error in depthwiseConv2d: pad must be an integer when using, " +
+            ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."));
+    }
+    var convInfo = conv_util.computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true);
+    var grad = function (dy) {
+        util.assert(conv_util.tupleValuesAreOne(dilations), 'Error in gradient of depthwiseConv2d: dilation rates greater than ' +
+            ("1 are not yet supported. Got dilations '" + dilations + "'"));
+        return {
+            x: function () { return depthwiseConv2dDerInput(x4D.shape, dy, $filter, convInfo); },
+            $filter: function () { return depthwiseConv2dDerFilter(x4D, dy, $filter.shape, convInfo); },
+        };
+    };
+    var res = environment_1.ENV.engine.runKernel(function (backend) { return backend.depthwiseConv2D(x4D, $filter, convInfo); }, { x: x4D, $filter: $filter }, grad);
+    if (reshapedTo4D) {
+        return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
+    }
+    return res;
+}
+function separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat) {
+    if (dilation === void 0) { dilation = [1, 1]; }
+    if (dataFormat === void 0) { dataFormat = 'NHWC'; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'separableConv2d');
+    var $depthwiseFilter = tensor_util_env_1.convertToTensor(depthwiseFilter, 'depthwiseFilter', 'separableConv2d');
+    var $pointwiseFilter = tensor_util_env_1.convertToTensor(pointwiseFilter, 'pointwiseFilter', 'separableConv2d');
+    var x4D = $x;
+    var reshapedTo4D = false;
+    if ($x.rank === 3) {
+        reshapedTo4D = true;
+        x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
+    }
+    if (dataFormat === 'NCHW') {
+        throw new Error('separableConv2d currently does not support dataFormat NCHW; only ' +
+            'NHWC is supported');
+    }
+    util.assert(x4D.rank === 4, "Error in separableConv2d: input must be rank 4, but got " +
+        ("rank " + x4D.rank + "."));
+    util.assert($depthwiseFilter.rank === 4, "Error in separableConv2d: depthwise filter must be rank 4, but got " +
+        ("rank " + $depthwiseFilter.rank + "."));
+    util.assert($pointwiseFilter.rank === 4, "Error in separableConv2d: pointwise filter must be rank 4, but got " +
+        ("rank " + $depthwiseFilter.rank + "."));
+    util.assert($pointwiseFilter.shape[0] === 1, "Error in separableConv2d: the first dimension of pointwise filter " +
+        (" must be 1, but got " + $pointwiseFilter.shape[0] + "."));
+    util.assert($pointwiseFilter.shape[1] === 1, "Error in separableConv2d: the second dimension of pointwise filter " +
+        (" must be 1, but got " + $pointwiseFilter.shape[1] + "."));
+    var inChannels = $depthwiseFilter.shape[2];
+    var channelMultiplier = $depthwiseFilter.shape[3];
+    util.assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, "Error in separableConv2d: the third dimension of pointwise filter " +
+        ("must be " + inChannels * channelMultiplier + ", ") +
+        ("but got " + $pointwiseFilter.shape[2] + "."));
+    var depthwise = exports.depthwiseConv2d(x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);
+    var pointwiseStride = 1;
+    var res = exports.conv2d(depthwise, $pointwiseFilter, pointwiseStride, 'valid', dataFormat);
+    if (reshapedTo4D) {
+        return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
+    }
+    return res;
+}
+function parseTupleParam(param) {
+    if (typeof param === 'number') {
+        return [param, param, param];
+    }
+    if (param.length === 2) {
+        return [param[0], param[1], 1];
+    }
+    return param;
+}
+function tupleValuesAreOne(param) {
+    var _a = parseTupleParam(param), dimA = _a[0], dimB = _a[1], dimC = _a[2];
+    return dimA === 1 && dimB === 1 && dimC === 1;
+}
+function eitherStridesOrDilationsAreOne(strides, dilations) {
+    return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);
+}
+function depthwiseConv2dDerInput(xShape, dy, filter, convInfo) {
+    var dy4D = dy;
+    var reshapedTo4D = false;
+    if (dy.rank === 3) {
+        reshapedTo4D = true;
+        dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);
+    }
+    var res = environment_1.ENV.engine.runKernel(function (backend) { return backend.depthwiseConv2DDerInput(dy4D, filter, convInfo); }, { dy4D: dy4D });
+    if (reshapedTo4D) {
+        return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
+    }
+    return res;
+}
+function depthwiseConv2dDerFilter(x, dy, filterShape, convInfo) {
+    var x4D = x;
+    if (x.rank === 3) {
+        x4D = x.as4D(1, x.shape[0], x.shape[1], x.shape[2]);
+    }
+    var dy4D = dy;
+    if (dy4D.rank === 3) {
+        dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);
+    }
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.depthwiseConv2DDerFilter(x4D, dy4D, convInfo); }, { x4D: x4D, dy4D: dy4D });
+}
+function conv3d_(x, filter, strides, pad, dataFormat, dilations) {
+    if (dataFormat === void 0) { dataFormat = 'NHWC'; }
+    if (dilations === void 0) { dilations = [1, 1, 1]; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'conv3d');
+    var $filter = tensor_util_env_1.convertToTensor(filter, 'filter', 'conv3d');
+    var x5D = $x;
+    var reshapedTo5D = false;
+    if ($x.rank === 4) {
+        reshapedTo5D = true;
+        x5D = $x.as5D(1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]);
+    }
+    util.assert(x5D.rank === 5, "Error in conv3d: input must be rank 5, but got rank " + x5D.rank + ".");
+    util.assert($filter.rank === 5, "Error in conv3d: filter must be rank 5, but got rank " +
+        ($filter.rank + "."));
+    util.assert(x5D.shape[4] === $filter.shape[3], "Error in conv3d: depth of input (" + x5D.shape[4] + ") must match " +
+        ("input depth for filter " + $filter.shape[3] + "."));
+    util.assert(eitherStridesOrDilationsAreOne(strides, dilations), 'Error in conv3D: Either strides or dilations must be 1. ' +
+        ("Got strides " + strides + " and dilations '" + dilations + "'"));
+    util.assert(dataFormat === 'NHWC', "Error in conv3d: got dataFormat of " + dataFormat + " but only NHWC is currently supported.");
+    var convInfo = conv_util.computeConv3DInfo(x5D.shape, $filter.shape, strides, dilations, pad);
+    var grad = function (dy) {
+        util.assert(tupleValuesAreOne(dilations), 'Error in gradient of conv3D: dilation rates greater than 1 are not' +
+            ("yet supported in gradients. Got dilations '" + dilations + "'"));
+        return {
+            x: function () { return conv3dDerInput_(x5D.shape, dy, $filter, strides, pad); },
+            $filter: function () { return conv3dDerFilter_(x5D, dy, $filter.shape, strides, pad); }
+        };
+    };
+    var res = environment_1.ENV.engine.runKernel(function (backend) { return backend.conv3d(x5D, $filter, convInfo); }, { x: x5D, $filter: $filter }, grad);
+    if (reshapedTo5D) {
+        return res.as4D(res.shape[1], res.shape[2], res.shape[3], res.shape[4]);
+    }
+    return res;
+}
+function conv3dDerInput_(xShape, dy, filter, strides, pad) {
+    util.assert(xShape.length === dy.rank, "Length of inShape " +
+        ("(" + xShape.length + ") and rank of dy (" + dy.rank + ") must match"));
+    var xShape5D = xShape;
+    var dy5D = dy;
+    var reshapedTo5D = false;
+    if (dy.rank === 4) {
+        reshapedTo5D = true;
+        dy5D = dy.as5D(1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]);
+        xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];
+    }
+    var inDepth = xShape5D[4];
+    var outDepth = dy5D.shape[4];
+    util.assert(xShape5D.length === 5, "Error in conv3dDerInput: inShape must be length 5, but got length " +
+        (xShape5D.length + "."));
+    util.assert(dy5D.rank === 5, "Error in conv3dDerInput: dy must be rank 5, but got " +
+        ("rank " + dy5D.rank));
+    util.assert(filter.rank === 5, "Error in conv3dDerInput: filter must be rank 5, but got " +
+        ("rank " + filter.rank));
+    util.assert(inDepth === filter.shape[3], "Error in conv3dDerInput: depth of input (" + inDepth + ") must " +
+        ("match input depth for filter " + filter.shape[3] + "."));
+    util.assert(outDepth === filter.shape[4], "Error in conv3dDerInput: depth of output (" + outDepth + ") must " +
+        ("match output depth for filter " + filter.shape[4] + "."));
+    var dilations = 1;
+    var convInfo = conv_util.computeConv3DInfo(xShape5D, filter.shape, strides, dilations, pad);
+    var res = environment_1.ENV.engine.runKernel(function (backend) { return backend.conv3dDerInput(dy5D, filter, convInfo); }, { dy5D: dy5D });
+    if (reshapedTo5D) {
+        return res.as4D(res.shape[1], res.shape[2], res.shape[3], res.shape[4]);
+    }
+    return res;
+}
+function conv3dDerFilter_(x, dy, filterShape, strides, pad) {
+    var x5D = x;
+    if (x.rank === 4) {
+        x5D = x.as5D(1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]);
+    }
+    var dy5D = dy;
+    if (dy5D.rank === 4) {
+        dy5D = dy.as5D(1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]);
+    }
+    util.assert(x5D.rank === 5, "Error in conv3dDerFilter: input must be rank 5, but got shape " +
+        (x5D.shape + "."));
+    util.assert(dy5D.rank === 5, "Error in conv3dDerFilter: dy must be rank 5, but got shape " +
+        (dy5D.shape + "."));
+    util.assert(filterShape.length === 5, "Error in conv3dDerFilter: filterShape must be length 5, but got " +
+        (filterShape + "."));
+    util.assert(x5D.shape[4] === filterShape[3], "Error in conv3dDerFilter: depth of input " + x5D.shape[4] + ") must " +
+        ("match input depth in filter (" + filterShape[3] + "."));
+    util.assert(dy5D.shape[4] === filterShape[4], "Error in conv3dDerFilter: depth of dy (" + dy5D.shape[4] + ") must " +
+        ("match output depth for filter (" + filterShape[4] + ")."));
+    var dilations = 1;
+    var convInfo = conv_util.computeConv3DInfo(x5D.shape, filterShape, strides, dilations, pad);
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.conv3dDerFilter(x5D, dy5D, convInfo); }, { x5D: x5D, dy5D: dy5D });
+}
+exports.conv1d = operation_1.op({ conv1d_: conv1d_ });
+exports.conv2d = operation_1.op({ conv2d_: conv2d_ });
+exports.conv3d = operation_1.op({ conv3d_: conv3d_ });
+exports.conv2dDerFilter = operation_1.op({ conv2dDerFilter_: conv2dDerFilter_ });
+exports.depthwiseConv2d = operation_1.op({ depthwiseConv2d_: depthwiseConv2d_ });
+exports.separableConv2d = operation_1.op({ separableConv2d_: separableConv2d_ });
+exports.conv2dTranspose = operation_1.op({ conv2dTranspose_: conv2dTranspose_ });
+//# sourceMappingURL=conv.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/conv.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/conv.js.map
new file mode 100644
index 0000000..0191ac4
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/conv.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/conv1d_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/conv1d_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/conv1d_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/conv1d_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/conv1d_test.js
new file mode 100644
index 0000000..a823663
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/conv1d_test.js
@@ -0,0 +1,234 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('conv1d', test_util_1.ALL_ENVS, function () {
+    it('conv1d input=2x2x1,d2=1,f=1,s=1,d=1,p=same', function () {
+        var inputDepth = 1;
+        var inputShape = [2, 2, inputDepth];
+        var outputDepth = 1;
+        var fSize = 1;
+        var pad = 'same';
+        var stride = 1;
+        var dataFormat = 'NWC';
+        var dilation = 1;
+        var x = tf.tensor3d([1, 2, 3, 4], inputShape);
+        var w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);
+        var result = tf.conv1d(x, w, stride, pad, dataFormat, dilation);
+        expect(result.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(result, [3, 6, 9, 12]);
+    });
+    it('conv1d input=4x1,d2=1,f=2x1x1,s=1,d=1,p=valid', function () {
+        var inputDepth = 1;
+        var inputShape = [4, inputDepth];
+        var outputDepth = 1;
+        var fSize = 2;
+        var pad = 'valid';
+        var stride = 1;
+        var dataFormat = 'NWC';
+        var dilation = 1;
+        var x = tf.tensor2d([1, 2, 3, 4], inputShape);
+        var w = tf.tensor3d([2, 1], [fSize, inputDepth, outputDepth]);
+        var result = tf.conv1d(x, w, stride, pad, dataFormat, dilation);
+        expect(result.shape).toEqual([3, 1]);
+        test_util_1.expectArraysClose(result, [4, 7, 10]);
+    });
+    it('conv1d input=4x1,d2=1,f=2x1x1,s=1,d=2,p=valid', function () {
+        var inputDepth = 1;
+        var inputShape = [4, inputDepth];
+        var outputDepth = 1;
+        var fSize = 2;
+        var fSizeDilated = 3;
+        var pad = 'valid';
+        var stride = 1;
+        var dataFormat = 'NWC';
+        var dilation = 2;
+        var dilationWEffective = 1;
+        var x = tf.tensor2d([1, 2, 3, 4], inputShape);
+        var w = tf.tensor3d([2, 1], [fSize, inputDepth, outputDepth]);
+        var wDilated = tf.tensor3d([2, 0, 1], [fSizeDilated, inputDepth, outputDepth]);
+        var result = tf.conv1d(x, w, stride, pad, dataFormat, dilation);
+        var expectedResult = tf.conv1d(x, wDilated, stride, pad, dataFormat, dilationWEffective);
+        expect(result.shape).toEqual(expectedResult.shape);
+        test_util_1.expectArraysClose(result, expectedResult);
+    });
+    it('conv1d input=14x1,d2=1,f=3x1x1,s=1,d=3,p=valid', function () {
+        var inputDepth = 1;
+        var inputShape = [14, inputDepth];
+        var outputDepth = 1;
+        var fSize = 3;
+        var fSizeDilated = 7;
+        var pad = 'valid';
+        var stride = 1;
+        var dataFormat = 'NWC';
+        var dilation = 3;
+        var dilationWEffective = 1;
+        var x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], inputShape);
+        var w = tf.tensor3d([3, 2, 1], [fSize, inputDepth, outputDepth]);
+        var wDilated = tf.tensor3d([3, 0, 0, 2, 0, 0, 1], [fSizeDilated, inputDepth, outputDepth]);
+        var result = tf.conv1d(x, w, stride, pad, dataFormat, dilation);
+        var expectedResult = tf.conv1d(x, wDilated, stride, pad, dataFormat, dilationWEffective);
+        expect(result.shape).toEqual(expectedResult.shape);
+        test_util_1.expectArraysClose(result, expectedResult);
+    });
+    it('TensorLike', function () {
+        var pad = 'same';
+        var stride = 1;
+        var dataFormat = 'NWC';
+        var dilation = 1;
+        var x = [[[1], [2]], [[3], [4]]];
+        var w = [[[3]]];
+        var result = tf.conv1d(x, w, stride, pad, dataFormat, dilation);
+        expect(result.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(result, [3, 6, 9, 12]);
+    });
+    it('TensorLike Chained', function () {
+        var inputDepth = 1;
+        var inputShape = [2, 2, inputDepth];
+        var pad = 'same';
+        var stride = 1;
+        var dataFormat = 'NWC';
+        var dilation = 1;
+        var x = tf.tensor3d([1, 2, 3, 4], inputShape);
+        var w = [[[3]]];
+        var result = x.conv1d(w, stride, pad, dataFormat, dilation);
+        expect(result.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(result, [3, 6, 9, 12]);
+    });
+    it('throws when x is not rank 3', function () {
+        var inputDepth = 1;
+        var outputDepth = 1;
+        var fSize = 2;
+        var pad = 0;
+        var stride = 1;
+        var dataFormat = 'NWC';
+        var dilation = 1;
+        var x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var w = tf.tensor3d([3, 1], [fSize, inputDepth, outputDepth]);
+        expect(function () { return tf.conv1d(x, w, stride, pad, dataFormat, dilation); })
+            .toThrowError();
+    });
+    it('throws when weights is not rank 3', function () {
+        var inputDepth = 1;
+        var inputShape = [2, 2, inputDepth];
+        var pad = 0;
+        var stride = 1;
+        var dataFormat = 'NWC';
+        var dilation = 1;
+        var x = tf.tensor3d([1, 2, 3, 4], inputShape);
+        var w = tf.tensor4d([3, 1, 5, 0], [2, 2, 1, 1]);
+        expect(function () { return tf.conv1d(x, w, stride, pad, dataFormat, dilation); })
+            .toThrowError();
+    });
+    it('throws when x depth does not match weight depth', function () {
+        var inputDepth = 1;
+        var wrongInputDepth = 5;
+        var inputShape = [2, 2, inputDepth];
+        var outputDepth = 1;
+        var fSize = 2;
+        var pad = 0;
+        var stride = 1;
+        var dataFormat = 'NWC';
+        var dilation = 1;
+        var x = tf.tensor3d([1, 2, 3, 4], inputShape);
+        var w = tf.randomNormal([fSize, wrongInputDepth, outputDepth]);
+        expect(function () { return tf.conv1d(x, w, stride, pad, dataFormat, dilation); })
+            .toThrowError();
+    });
+    it('throws when both stride and dilation are greater than 1', function () {
+        var inputDepth = 1;
+        var inputShape = [2, 2, inputDepth];
+        var outputDepth = 1;
+        var fSize = 1;
+        var pad = 'same';
+        var stride = 2;
+        var dataFormat = 'NWC';
+        var dilation = 2;
+        var x = tf.tensor3d([1, 2, 3, 4], inputShape);
+        var w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);
+        expect(function () { return tf.conv1d(x, w, stride, pad, dataFormat, dilation); })
+            .toThrowError();
+    });
+    it('throws when passed x as a non-tensor', function () {
+        var inputDepth = 1;
+        var outputDepth = 1;
+        var fSize = 1;
+        var pad = 'same';
+        var stride = 2;
+        var dataFormat = 'NWC';
+        var dilation = 2;
+        var w = tf.tensor3d([3], [fSize, inputDepth, outputDepth]);
+        expect(function () {
+            return tf.conv1d({}, w, stride, pad, dataFormat, dilation);
+        })
+            .toThrowError(/Argument 'x' passed to 'conv1d' must be a Tensor/);
+    });
+    it('throws when passed filter as a non-tensor', function () {
+        var inputDepth = 1;
+        var inputShape = [2, 2, inputDepth];
+        var pad = 'same';
+        var stride = 2;
+        var dataFormat = 'NWC';
+        var dilation = 2;
+        var x = tf.tensor3d([1, 2, 3, 4], inputShape);
+        expect(function () {
+            return tf.conv1d(x, {}, stride, pad, dataFormat, dilation);
+        })
+            .toThrowError(/Argument 'filter' passed to 'conv1d' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var pad = 'same';
+        var stride = 1;
+        var dataFormat = 'NWC';
+        var dilation = 1;
+        var x = [[[1], [2]], [[3], [4]]];
+        var w = [[[3]]];
+        var result = tf.conv1d(x, w, stride, pad, dataFormat, dilation);
+        expect(result.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(result, [3, 6, 9, 12]);
+    });
+    it('conv1d gradients, input=2x2x1,d2=1,f=1,s=1,d=1,p=same', function () {
+        var inputDepth = 1;
+        var inputShape = [2, 2, inputDepth];
+        var outputDepth = 1;
+        var fSize = 1;
+        var filterShape = [fSize, inputDepth, outputDepth];
+        var pad = 'same';
+        var stride = 1;
+        var dataFormat = 'NWC';
+        var dilation = 1;
+        var x = tf.tensor3d([1, 2, 3, 4], inputShape);
+        var w = tf.tensor3d([3], filterShape);
+        var dy = tf.tensor3d([3, 2, 1, 0], inputShape);
+        var grads = tf.grads(function (x, w) {
+            return tf.conv1d(x, w, stride, pad, dataFormat, dilation);
+        });
+        var _a = grads([x, w], dy), dx = _a[0], dw = _a[1];
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, [9, 6, 3, 0]);
+        expect(dw.shape).toEqual(w.shape);
+        test_util_1.expectArraysClose(dw, [10]);
+    });
+    it('conv1d gradients input=14x1,d2=1,f=3x1x1,s=1,p=valid', function () {
+        var inputDepth = 1;
+        var inputShape = [14, inputDepth];
+        var outputDepth = 1;
+        var fSize = 3;
+        var pad = 'valid';
+        var stride = 1;
+        var dataFormat = 'NWC';
+        var x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14], inputShape);
+        var w = tf.tensor3d([3, 2, 1], [fSize, inputDepth, outputDepth]);
+        var dy = tf.tensor2d([3, 2, 1, 0, 3, 2, 1, 0, 3, 2, 1, 0], [12, inputDepth]);
+        var grads = tf.grads(function (x, w) {
+            return tf.conv1d(x, w, stride, pad, dataFormat);
+        });
+        var _a = grads([x, w], dy), dx = _a[0], dw = _a[1];
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, [9, 12, 10, 4, 10, 12, 10, 4, 10, 12, 10, 4, 1, 0]);
+        expect(dw.shape).toEqual(w.shape);
+        test_util_1.expectArraysClose(dw, [102, 120, 138]);
+    });
+});
+//# sourceMappingURL=conv1d_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/conv1d_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/conv1d_test.js.map
new file mode 100644
index 0000000..b94e9f9
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/conv1d_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_depthwise_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_depthwise_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_depthwise_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_depthwise_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_depthwise_test.js
new file mode 100644
index 0000000..996c37b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_depthwise_test.js
@@ -0,0 +1,510 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('packed depthwiseConv2D', test_util_1.WEBGL_ENVS, function () {
+    var webglPackedBatchNormalizationSavedFlag = tf.ENV.get('WEBGL_PACK_DEPTHWISECONV');
+    beforeAll(function () {
+        tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', true);
+    });
+    afterAll(function () {
+        tf.ENV.set('WEBGL_PACK_DEPTHWISECONV', webglPackedBatchNormalizationSavedFlag);
+    });
+    it('should not leak memory', function () {
+        var x = tf.tensor4d([
+            0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
+            0.0741907, 0.409265, 0.351377
+        ], [1, 3, 3, 1]);
+        var w = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [2, 2, 1, 1]);
+        var startNumBytes = tf.memory().numBytes;
+        var startNumTensors = tf.memory().numTensors;
+        tf.depthwiseConv2d(x, w, 1, 'valid');
+        var endNumBytes = tf.memory().numBytes;
+        var endNumTensors = tf.memory().numTensors;
+        expect(endNumBytes - startNumBytes).toEqual(16);
+        expect(endNumTensors - startNumTensors).toEqual(1);
+    });
+    it('input=1x3x3x1,f=2,s=1,d=1,p=valid,chMul=1', function () {
+        var fSize = 2;
+        var pad = 'valid';
+        var stride = 1;
+        var chMul = 1;
+        var inDepth = 1;
+        var x = tf.tensor4d([
+            0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
+            0.0741907, 0.409265, 0.351377
+        ], [1, 3, 3, inDepth]);
+        var w = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
+        var result = tf.depthwiseConv2d(x, w, stride, pad);
+        expect(result.shape).toEqual([1, 2, 2, 1]);
+        var expected = [1.07022, 1.03167, 0.67041, 0.778863];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('input=1x4x4x1,f=2,s=2,d=1,p=valid,chMul=1', function () {
+        var fSize = 2;
+        var pad = 'valid';
+        var stride = 2;
+        var chMul = 1;
+        var inDepth = 1;
+        var x = tf.tensor4d([
+            0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
+            0.0741907, 0.409265, 0.351377, 0.488833, 0.733151, 0.518603, 0.271681,
+            0.388339, 0.361921, 0.586260
+        ], [1, 4, 4, inDepth]);
+        var w = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
+        var result = tf.depthwiseConv2d(x, w, stride, pad);
+        expect(result.shape).toEqual([1, 2, 2, 1]);
+        var expected = [1.0123528, 0.456418, 0.570019, 0.864883];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('input=1x3x3x2,f=2,s=1,d=1,p=same,chMul=1', function () {
+        var fSize = 2;
+        var pad = 'same';
+        var stride = 1;
+        var chMul = 1;
+        var inDepth = 2;
+        var x = tf.tensor4d([
+            0.111057, 0.661818, 0.701979, 0.424362, 0.992854, 0.417599, 0.423036,
+            0.500499, 0.368484, 0.714135, 0.456693, 0.531058, 0.636636, 0.345024,
+            0.0506303, 0.789682, 0.177473, 0.793569
+        ], [1, 3, 3, inDepth]);
+        var w = tf.tensor4d([
+            0.614293, 0.0648011, 0.101113, 0.452887, 0.0582746, 0.426481,
+            0.872743, 0.765767
+        ], [fSize, fSize, inDepth, chMul]);
+        var result = tf.depthwiseConv2d(x, w, stride, pad);
+        expect(result.shape).toEqual([1, 3, 3, 2]);
+        var expected = [
+            0.485445, 0.995389, 0.95166, 0.927856, 0.636516, 0.253547, 0.378414,
+            1.10771, 0.430373, 1.23126, 0.290885, 0.372855, 0.3962, 0.379995,
+            0.0490466, 0.410569, 0.10902, 0.0514242
+        ];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('input=1x4x4x2,f=3,s=2,d=1,p=same,chMul=1', function () {
+        var fSize = 3;
+        var pad = 'same';
+        var stride = 2;
+        var chMul = 1;
+        var inDepth = 2;
+        var x = tf.tensor4d([
+            0.111057, 0.661818, 0.701979, 0.424362, 0.992854, 0.417599, 0.423036,
+            0.500499, 0.368484, 0.714135, 0.456693, 0.531058, 0.636636, 0.345024,
+            0.050630, 0.789682, 0.177473, 0.793569, 0.680834, 0.662993, 0.628252,
+            0.016079, 0.974360, 0.663527, 0.827026, 0.699203, 0.768811, 0.303914,
+            0.739766, 0.808709, 0.580810, 0.869389
+        ], [1, 4, 4, inDepth]);
+        var w = tf.tensor4d([
+            0.614293, 0.0648011, 0.101113, 0.452887, 0.0582746, 0.426481,
+            0.872743, 0.765767, 0.8248714, 0.308840, 0.4714178, 0.200848,
+            0.391879, 0.518833, 0.3020929, 0.411819, 0.2635254, 0.584538
+        ], [fSize, fSize, inDepth, chMul]);
+        var result = tf.depthwiseConv2d(x, w, stride, pad);
+        expect(result.shape).toEqual([1, 2, 2, 2]);
+        var expected = [
+            1.6362686, 1.8875048, 1.7906065, 1.0434191, 1.919163, 1.1502573,
+            1.6091704, 1.1893295
+        ];
+        test_util_1.expectArraysClose(result, expected);
+    });
+});
+jasmine_util_1.describeWithFlags('depthwiseConv2D', test_util_1.ALL_ENVS, function () {
+    it('input=1x3x3x1,f=2,s=1,d=1,p=valid,chMul=1', function () {
+        var fSize = 2;
+        var pad = 'valid';
+        var stride = 1;
+        var chMul = 1;
+        var inDepth = 1;
+        var x = tf.tensor4d([
+            0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
+            0.0741907, 0.409265, 0.351377
+        ], [1, 3, 3, inDepth]);
+        var w = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
+        var result = tf.depthwiseConv2d(x, w, stride, pad);
+        expect(result.shape).toEqual([1, 2, 2, 1]);
+        var expected = [1.07022, 1.03167, 0.67041, 0.778863];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('input=1x3x3x1,f=2,s=1,d=2,p=valid,chMul=1', function () {
+        var fSize = 2;
+        var pad = 'valid';
+        var stride = 1;
+        var dilation = 2;
+        var chMul = 1;
+        var inDepth = 1;
+        var x = tf.tensor4d([
+            0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
+            0.0741907, 0.409265, 0.351377
+        ], [1, 3, 3, inDepth]);
+        var w = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
+        var fSizeDilated = fSize + (fSize - 1) * (dilation - 1);
+        var wDilated = tf.tensor4d([0.303873, 0, 0.229223, 0, 0, 0, 0.144333, 0, 0.803373], [fSizeDilated, fSizeDilated, inDepth, chMul]);
+        var result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilation);
+        var expectedResult = tf.depthwiseConv2d(x, wDilated, stride, pad);
+        expect(result.shape).toEqual(expectedResult.shape);
+        test_util_1.expectArraysClose(result, expectedResult);
+    });
+    it('input=1x3x3x2,f=2,s=1,d=1,p=same,chMul=1', function () {
+        var fSize = 2;
+        var pad = 'same';
+        var stride = 1;
+        var chMul = 1;
+        var inDepth = 2;
+        var x = tf.tensor4d([
+            0.111057, 0.661818, 0.701979, 0.424362, 0.992854, 0.417599, 0.423036,
+            0.500499, 0.368484, 0.714135, 0.456693, 0.531058, 0.636636, 0.345024,
+            0.0506303, 0.789682, 0.177473, 0.793569
+        ], [1, 3, 3, inDepth]);
+        var w = tf.tensor4d([
+            0.614293, 0.0648011, 0.101113, 0.452887, 0.0582746, 0.426481,
+            0.872743, 0.765767
+        ], [fSize, fSize, inDepth, chMul]);
+        var result = tf.depthwiseConv2d(x, w, stride, pad);
+        expect(result.shape).toEqual([1, 3, 3, 2]);
+        var expected = [
+            0.485445, 0.995389, 0.95166, 0.927856, 0.636516, 0.253547, 0.378414,
+            1.10771, 0.430373, 1.23126, 0.290885, 0.372855, 0.3962, 0.379995,
+            0.0490466, 0.410569, 0.10902, 0.0514242
+        ];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('input=1x3x3x2,f=2,s=1,d=2,p=same,chMul=1', function () {
+        var fSize = 2;
+        var pad = 'same';
+        var stride = 1;
+        var dilation = 2;
+        var inDepth = 2;
+        var x = tf.tensor4d([
+            0.111057, 0.661818, 0.701979, 0.424362, 0.992854, 0.417599, 0.423036,
+            0.500499, 0.368484, 0.714135, 0.456693, 0.531058, 0.636636, 0.345024,
+            0.0506303, 0.789682, 0.177473, 0.793569
+        ], [1, 3, 3, inDepth]);
+        var w = tf.stack([
+            tf.tensor2d([0.614293, 0.0648011, 0.101113, 0.452887], [fSize, fSize]),
+            tf.tensor2d([0.0582746, 0.426481, 0.872743, 0.765767], [fSize, fSize])
+        ], 2)
+            .expandDims(3);
+        var fSizeDilated = fSize + (fSize - 1) * (dilation - 1);
+        var wDilated = tf.stack([
+            tf.tensor2d([0.614293, 0, 0.0648011, 0, 0, 0, 0.101113, 0, 0.452887], [fSizeDilated, fSizeDilated]),
+            tf.tensor2d([0.0582746, 0, 0.426481, 0, 0, 0, 0.872743, 0, 0.765767], [fSizeDilated, fSizeDilated])
+        ], 2)
+            .expandDims(3);
+        expect(wDilated.shape).toEqual([fSizeDilated, fSizeDilated, inDepth, 1]);
+        var result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilation);
+        var expectedResult = tf.depthwiseConv2d(x, wDilated, stride, pad);
+        expect(result.shape).toEqual(expectedResult.shape);
+        test_util_1.expectArraysClose(result, expectedResult);
+    });
+    it('input=1x3x3x2,f=2,s=1,p=same,chMul=2', function () {
+        var fSize = 2;
+        var pad = 'same';
+        var stride = 1;
+        var chMul = 2;
+        var inDepth = 2;
+        var x = tf.tensor4d([
+            0.675707, 0.758567, 0.413529, 0.963967, 0.217291, 0.101335, 0.804231,
+            0.329673, 0.924503, 0.728742, 0.180217, 0.210459, 0.133869, 0.650827,
+            0.047613, 0.554795, 0.653365, 0.442196
+        ], [1, 3, 3, inDepth]);
+        var w = tf.tensor4d([
+            0.347154, 0.386692, 0.327191, 0.483784, 0.591807, 0.24263, 0.95182,
+            0.174353, 0.592136, 0.623469, 0.988244, 0.660731, 0.946534, 0.0801365,
+            0.864889, 0.874602
+        ], [fSize, fSize, inDepth, chMul]);
+        var result = tf.depthwiseConv2d(x, w, stride, pad);
+        expect(result.shape).toEqual([1, 3, 3, 4]);
+        var expected = [
+            1.83059, 0.937125, 2.1218, 1.39024, 0.990167, 0.803472,
+            1.31405, 1.14959, 0.182147, 0.196385, 0.241141, 0.188081,
+            0.950656, 0.622581, 1.92451, 1.20179, 1.07422, 0.483268,
+            1.36948, 1.14256, 0.449444, 0.477042, 0.505857, 0.393989,
+            0.0746509, 0.0633184, 0.74101, 0.41159, 0.403195, 0.176938,
+            0.602415, 0.345499, 0.226819, 0.252651, 0.144682, 0.213927
+        ];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('input=2x3x3x2,f=2,s=1,p=same,chMul=2', function () {
+        var fSize = 2;
+        var pad = 'same';
+        var stride = 1;
+        var chMul = 2;
+        var inDepth = 2;
+        var x = tf.tensor4d([
+            0.261945, 0.0528113, 0.656698, 0.127345, 0.610039, 0.169131,
+            0.458647, 0.0988288, 0.966109, 0.0421747, 0.82035, 0.274711,
+            0.359377, 0.512113, 0.689682, 0.941571, 0.31961, 0.743826,
+            0.858147, 0.984766, 0.926973, 0.579597, 0.444104, 0.505969,
+            0.241437, 0.937999, 0.0957074, 0.773611, 0.46023, 0.469379,
+            0.363789, 0.269745, 0.486136, 0.894215, 0.794299, 0.724615
+        ], [2, 3, 3, inDepth]);
+        var w = tf.tensor4d([
+            0.240347, 0.906352, 0.478657, 0.825918, 0.380769, 0.184705, 0.238241,
+            0.201907, 0.294087, 0.181165, 0.191303, 0.7225, 0.430064, 0.900622,
+            0.670338, 0.33478
+        ], [fSize, fSize, inDepth, chMul]);
+        var result = tf.depthwiseConv2d(x, w, stride, pad);
+        expect(result.shape).toEqual([2, 3, 3, 4]);
+        var expected = [
+            0.863379, 1.3119, 0.102795, 0.154853, 1.02704, 1.62173, 0.293466,
+            0.261764, 0.387876, 0.701529, 0.133508, 0.338167, 0.880395, 1.28039,
+            0.786492, 0.775361, 0.884845, 1.43995, 0.764374, 1.0196, 0.291162,
+            0.801428, 0.273788, 0.764303, 0.348985, 0.45311, 0.469447, 0.613073,
+            0.287461, 0.684128, 0.627899, 0.927844, 0.0768174, 0.28968, 0.356037,
+            0.614339, 0.67138, 1.07894, 1.30747, 1.86705, 0.617971, 1.35402,
+            0.860607, 1.29693, 0.242087, 0.485892, 0.331979, 0.757015, 0.410527,
+            0.740235, 1.28431, 1.42516, 0.68281, 0.975185, 1.13892, 1.62237,
+            0.344208, 0.561029, 0.363292, 0.911203, 0.272541, 0.419513, 0.342154,
+            0.403335, 0.419286, 0.587321, 0.600655, 0.884853, 0.190907, 0.719914,
+            0.346842, 0.598472
+        ];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('input=2x3x3x2,f=2,s=1,d=2,p=same,chMul=2', function () {
+        var fSize = 2;
+        var pad = 'same';
+        var stride = 1;
+        var inDepth = 2;
+        var dilation = 2;
+        var noDilation = 1;
+        var x = tf.tensor4d([
+            0.261945, 0.0528113, 0.656698, 0.127345, 0.610039, 0.169131,
+            0.458647, 0.0988288, 0.966109, 0.0421747, 0.82035, 0.274711,
+            0.359377, 0.512113, 0.689682, 0.941571, 0.31961, 0.743826,
+            0.858147, 0.984766, 0.926973, 0.579597, 0.444104, 0.505969,
+            0.241437, 0.937999, 0.0957074, 0.773611, 0.46023, 0.469379,
+            0.363789, 0.269745, 0.486136, 0.894215, 0.794299, 0.724615
+        ], [2, 3, 3, inDepth]);
+        var w = tf.stack([
+            tf.stack([
+                tf.tensor2d([0.240347, 0.906352, 0.478657, 0.825918], [fSize, fSize]),
+                tf.tensor2d([0.380769, 0.184705, 0.238241, 0.201907], [fSize, fSize])
+            ], 2),
+            tf.stack([
+                tf.tensor2d([0.294087, 0.181165, 0.191303, 0.7225], [fSize, fSize]),
+                tf.tensor2d([0.430064, 0.900622, 0.670338, 0.33478], [fSize, fSize])
+            ], 2)
+        ], 3);
+        var fSizeDilated = fSize + (fSize - 1) * (dilation - 1);
+        var wDilated = tf.stack([
+            tf.stack([
+                tf.tensor2d([0.240347, 0, 0.906352, 0, 0, 0, 0.478657, 0, 0.825918], [fSizeDilated, fSizeDilated]),
+                tf.tensor2d([0.380769, 0, 0.184705, 0, 0, 0, 0.238241, 0, 0.201907], [fSizeDilated, fSizeDilated])
+            ], 2),
+            tf.stack([
+                tf.tensor2d([0.294087, 0, 0.181165, 0, 0, 0, 0.191303, 0, 0.7225], [fSizeDilated, fSizeDilated]),
+                tf.tensor2d([0.430064, 0, 0.900622, 0, 0, 0, 0.670338, 0, 0.33478], [fSizeDilated, fSizeDilated])
+            ], 2)
+        ], 3);
+        var result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilation);
+        var expectedResult = tf.depthwiseConv2d(x, wDilated, stride, pad, 'NHWC', noDilation);
+        expect(result.shape).toEqual(expectedResult.shape);
+        test_util_1.expectArraysClose(result, expectedResult);
+    });
+    it('Tensor3D is allowed', function () {
+        var fSize = 2;
+        var pad = 'same';
+        var stride = 1;
+        var chMul = 3;
+        var inDepth = 2;
+        var x = tf.zeros([3, 3, inDepth]);
+        var w = tf.zeros([fSize, fSize, inDepth, chMul]);
+        var result = tf.depthwiseConv2d(x, w, stride, pad);
+        expect(result.shape).toEqual([3, 3, inDepth * chMul]);
+    });
+    it('Pass null for dilations, which defaults to [1, 1]', function () {
+        var fSize = 2;
+        var pad = 'same';
+        var stride = 1;
+        var chMul = 3;
+        var inDepth = 2;
+        var dilations = null;
+        var x = tf.zeros([3, 3, inDepth]);
+        var w = tf.zeros([fSize, fSize, inDepth, chMul]);
+        var result = tf.depthwiseConv2d(x, w, stride, pad, 'NHWC', dilations);
+        expect(result.shape).toEqual([3, 3, inDepth * chMul]);
+    });
+    it('TensorLike', function () {
+        var pad = 'valid';
+        var stride = 1;
+        var x = [[
+                [[0.230664], [0.987388], [0.0685208]],
+                [[0.419224], [0.887861], [0.731641]],
+                [[0.0741907], [0.409265], [0.351377]]
+            ]];
+        var w = [[[[0.303873]], [[0.229223]]], [[[0.144333]], [[0.803373]]]];
+        var result = tf.depthwiseConv2d(x, w, stride, pad);
+        var expected = [1.07022, 1.03167, 0.67041, 0.778863];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('TensorLike Chained', function () {
+        var pad = 'valid';
+        var stride = 1;
+        var inDepth = 1;
+        var x = tf.tensor4d([
+            0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
+            0.0741907, 0.409265, 0.351377
+        ], [1, 3, 3, inDepth]);
+        var w = [[[[0.303873]], [[0.229223]]], [[[0.144333]], [[0.803373]]]];
+        var result = x.depthwiseConv2D(w, stride, pad);
+        expect(result.shape).toEqual([1, 2, 2, 1]);
+        var expected = [1.07022, 1.03167, 0.67041, 0.778863];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('throws when passed x as a non-tensor', function () {
+        var inputDepth = 1;
+        var outputDepth = 1;
+        var fSize = 1;
+        var pad = 'same';
+        var stride = 2;
+        var dataFormat = 'NHWC';
+        var dilation = 2;
+        var w = tf.tensor4d([3], [fSize, fSize, inputDepth, outputDepth]);
+        var e = /Argument 'x' passed to 'depthwiseConv2d' must be a Tensor/;
+        expect(function () { return tf.depthwiseConv2d({}, w, stride, pad, dataFormat, dilation); })
+            .toThrowError(e);
+    });
+    it('throws when passed filter as a non-tensor', function () {
+        var inputDepth = 1;
+        var inputShape = [2, 2, inputDepth];
+        var pad = 'same';
+        var stride = 2;
+        var dataFormat = 'NHWC';
+        var dilation = 2;
+        var x = tf.tensor3d([1, 2, 3, 4], inputShape);
+        var e = /Argument 'filter' passed to 'depthwiseConv2d' must be a Tensor/;
+        expect(function () { return tf.depthwiseConv2d(x, {}, stride, pad, dataFormat, dilation); })
+            .toThrowError(e);
+    });
+    it('accepts a tensor-like object', function () {
+        var pad = 'valid';
+        var stride = 1;
+        var x = [[
+                [[0.230664], [0.987388], [0.0685208]],
+                [[0.419224], [0.887861], [0.731641]],
+                [[0.0741907], [0.409265], [0.351377]]
+            ]];
+        var w = [[[[0.303873]], [[0.229223]]], [[[0.144333]], [[0.803373]]]];
+        var result = tf.depthwiseConv2d(x, w, stride, pad);
+        expect(result.shape).toEqual([1, 2, 2, 1]);
+        var expected = [1.07022, 1.03167, 0.67041, 0.778863];
+        test_util_1.expectArraysClose(result, expected);
+    });
+});
+jasmine_util_1.describeWithFlags('depthwiseConv2d gradients', test_util_1.ALL_ENVS, function () {
+    var images;
+    var filter;
+    var result;
+    var stride = 1;
+    var pad = 'same';
+    beforeEach(function () {
+        images = tf.tensor4d([
+            [[[2, 3, 1], [3, 0, 2]], [[0, 4, 1], [3, 1, 3]]],
+            [[[2, 1, 0], [0, 3, 3]], [[4, 0, 1], [1, 4, 1]]]
+        ]);
+        filter = tf.tensor4d([
+            [[[1, 1], [1, 1], [0, 0]], [[0, 1], [1, 1], [1, 1]]],
+            [[[1, 0], [1, 1], [0, 0]], [[0, 1], [1, 0], [0, 0]]]
+        ]);
+        result = tf.tensor4d([
+            [
+                [[2, 8, 8, 7, 2, 2], [6, 3, 1, 1, 0, 0]],
+                [[0, 3, 5, 5, 3, 3], [3, 3, 1, 1, 0, 0]]
+            ],
+            [
+                [[6, 3, 8, 4, 3, 3], [1, 0, 7, 7, 0, 0]],
+                [[4, 5, 4, 4, 1, 1], [1, 1, 4, 4, 0, 0]]
+            ]
+        ]);
+    });
+    it('wrt input', function () {
+        var _a = tf.valueAndGrad(function (x) { return tf.depthwiseConv2d(x, filter, stride, pad); })(images), value = _a.value, grad = _a.grad;
+        test_util_1.expectArraysClose(value, result);
+        var expectedGrad = tf.tensor4d([
+            [[[2., 2., 0.], [3., 4., 2.]], [[3., 4., 0.], [5., 7., 2.]]],
+            [[[2., 2., 0.], [3., 4., 2.]], [[3., 4., 0.], [5., 7., 2.]]]
+        ]);
+        test_util_1.expectArraysClose(grad, expectedGrad);
+    });
+    it('wrt input, squared output', function () {
+        var grad = tf.grad(function (x) {
+            return tf.square(tf.depthwiseConv2d(x, filter, stride, pad));
+        })(images);
+        var expectedGrad = tf.tensor4d([
+            [[[20., 30., 0.], [34., 34., 8.]], [[10., 50., 0.], [46., 44., 12.]]],
+            [[[18., 24., 0.], [8., 52., 12.]], [[30., 40., 0.], [22., 76., 4.]]]
+        ]);
+        test_util_1.expectArraysClose(grad, expectedGrad);
+    });
+    it('wrt filter', function () {
+        var _a = tf.valueAndGrad(function (f) { return tf.depthwiseConv2d(images, f, stride, pad); })(filter), value = _a.value, grad = _a.grad;
+        test_util_1.expectArraysClose(value, result);
+        var expectedGrad = tf.tensor4d([
+            [[[15., 15.], [16., 16.], [12., 12.]], [[7., 7.], [8., 8.], [9., 9.]]],
+            [[[8., 8.], [9., 9.], [6., 6.]], [[4., 4.], [5., 5.], [4., 4.]]]
+        ]);
+        test_util_1.expectArraysClose(grad, expectedGrad);
+    });
+    it('wrt filter, squared output', function () {
+        var grad = tf.grad(function (f) {
+            return tf.square(tf.depthwiseConv2d(images, f, stride, pad));
+        })(filter);
+        var expectedGrad = tf.tensor4d([
+            [
+                [[120., 122.], [180., 166.], [12., 12.]],
+                [[20., 76.], [90., 66.], [46., 46.]]
+            ],
+            [
+                [[86., 42.], [122., 114.], [10., 10.]],
+                [[24., 54.], [80., 46.], [18., 18.]]
+            ]
+        ]);
+        test_util_1.expectArraysClose(grad, expectedGrad);
+    });
+    it('throws error on dilations > 1', function () {
+        var grad = tf.grad(function (x) {
+            return tf.depthwiseConv2d(x, filter, stride, pad, 'NHWC', 2);
+        });
+        expect(function () { return grad(images); })
+            .toThrowError(/dilation rates greater than 1 are not yet supported/);
+    });
+    it('wrt input, stride=2, pad=valid', function () {
+        var dx = tf.grad(function (x) { return tf.depthwiseConv2d(x, filter, 2, 'valid'); })(images);
+        test_util_1.expectArraysClose(dx, tf.tensor4d([
+            [[[2., 2., 0.], [1., 2., 2.]], [[1., 2., 0.], [1., 1., 0.]]],
+            [[[2., 2., 0.], [1., 2., 2.]], [[1., 2., 0.], [1., 1., 0.]]]
+        ]));
+    });
+    it('wrt filter, stride=2, pad=valid', function () {
+        var df = tf.grad(function (f) { return tf.depthwiseConv2d(images, f, 2, 'valid'); })(filter);
+        test_util_1.expectArraysClose(df, tf.tensor4d([
+            [[[4., 4.], [4., 4.], [1., 1.]], [[3., 3.], [3., 3.], [5., 5.]]],
+            [[[4., 4.], [4., 4.], [2., 2.]], [[4., 4.], [5., 5.], [4., 4.]]]
+        ]));
+    });
+    it('wrt input and filter, 1x3x3x1 and 2x2x1x1', function () {
+        var fSize = 2;
+        var pad = 'valid';
+        var stride = 1;
+        var chMul = 1;
+        var inDepth = 1;
+        var x = tf.tensor4d([
+            0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
+            0.0741907, 0.409265, 0.351377
+        ], [1, 3, 3, inDepth]);
+        var f = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
+        var _a = tf.grads(function (x, f) {
+            return tf.depthwiseConv2d(x, f, stride, pad);
+        })([x, f]), dx = _a[0], df = _a[1];
+        test_util_1.expectArraysClose(dx, tf.tensor4d([[
+                [[0.303873], [0.533096], [0.229223]],
+                [[0.448206], [1.480802], [1.032596]],
+                [[0.144333], [0.947706], [0.803373]]
+            ]]));
+        test_util_1.expectArraysClose(df, tf.tensor4d([
+            [[[2.525137]], [[2.6754108]]], [[[1.7905407]], [[2.380144]]]
+        ]));
+    });
+});
+//# sourceMappingURL=conv2d_depthwise_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_depthwise_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_depthwise_test.js.map
new file mode 100644
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+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_depthwise_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_separable_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_separable_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_separable_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_separable_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_separable_test.js
new file mode 100644
index 0000000..33356d2
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_separable_test.js
@@ -0,0 +1,349 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('separableConv2d', test_util_1.ALL_ENVS, function () {
+    it('input=1x3x3x1,f=2,s=1,d=1,p=valid,chMul=1,outDepth=2', function () {
+        var fSize = 2;
+        var pad = 'valid';
+        var stride = 1;
+        var chMul = 1;
+        var inDepth = 1;
+        var outDepth = 2;
+        var x = tf.tensor4d([
+            0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
+            0.0741907, 0.409265, 0.351377
+        ], [1, 3, 3, inDepth]);
+        var depthwiseFilter = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
+        var pointwiseFilter = tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
+        var result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
+        test_util_1.expectArraysClose(result, tf.tensor4d([
+            0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096,
+            -0.13408193, 0.07788632, -0.15577264
+        ], [1, 2, 2, outDepth]));
+    });
+    it('input=1x3x3x1,f=2,s=1,d=1,p=valid,chMul=1,outDepth=2 in tensor', function () {
+        var fSize = 2;
+        var pad = 'valid';
+        var stride = 1;
+        var chMul = 1;
+        var inDepth = 1;
+        var outDepth = 2;
+        var x = tf.tensor4d([
+            0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
+            0.0741907, 0.409265, 0.351377
+        ], [1, 3, 3, inDepth]);
+        var depthwiseFilter = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
+        var pointwiseFilter = tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
+        var result = x.separableConv2d(depthwiseFilter, pointwiseFilter, stride, pad);
+        test_util_1.expectArraysClose(result, tf.tensor4d([
+            0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096,
+            -0.13408193, 0.07788632, -0.15577264
+        ], [1, 2, 2, outDepth]));
+    });
+    it('input=1x3x3x1,f=2,s=1,d=1,p=valid,chMul=2,outDepth=2', function () {
+        var fSize = 2;
+        var pad = 'valid';
+        var stride = 1;
+        var chMul = 2;
+        var inDepth = 1;
+        var outDepth = 3;
+        var x = tf.tensor4d([
+            0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
+            0.0741907, 0.409265, 0.351377
+        ], [1, 3, 3, inDepth]);
+        var depthwiseFilter = tf.tensor4d([
+            0.303873, 0.229223, 0.144333, 0.803373, -0.303873, -0.229223,
+            -0.144333, -0.803373
+        ], [fSize, fSize, inDepth, chMul]);
+        var pointwiseFilter = tf.tensor4d([0.1, -0.2, -0.1, 0.2, 0.15, 0.15], [1, 1, inDepth * chMul, outDepth]);
+        var result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
+        test_util_1.expectArraysClose(result, tf.tensor4d([
+            0.00305368, 0.0140969, 0.00980358, -0.10853045, -0.06339455,
+            -0.0699412, 0.11010849, 0.0347524, 0.05214475, 0.10307151,
+            0.02221644, 0.04224815
+        ], [1, 2, 2, outDepth]));
+    });
+    it('input=1x3x3x1,f=2,s=1,d=1,p=valid,chMul=1,outDepth=2,3D input', function () {
+        var fSize = 2;
+        var pad = 'valid';
+        var stride = 1;
+        var chMul = 1;
+        var inDepth = 1;
+        var outDepth = 2;
+        var x = tf.tensor3d([
+            0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
+            0.0741907, 0.409265, 0.351377
+        ], [3, 3, inDepth]);
+        var depthwiseFilter = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
+        var pointwiseFilter = tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
+        var result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
+        test_util_1.expectArraysClose(result, tf.tensor3d([
+            0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096,
+            -0.13408193, 0.07788632, -0.15577264
+        ], [2, 2, outDepth]));
+    });
+    it('input=1x4x4x1,f=2,s=2,d=1,p=valid,chMul=1,outDepth=2', function () {
+        var fSize = 2;
+        var pad = 'valid';
+        var stride = [2, 2];
+        var chMul = 1;
+        var inDepth = 1;
+        var outDepth = 2;
+        var x = tf.tensor4d([
+            0.675707, 0.758567, 0.413529, 0.963967, 0.217291, 0.101335, 0.804231,
+            0.329673, 0.924503, 0.728742, 0.180217, 0.210459, 0.133869, 0.650827,
+            0.047613, 0.554795
+        ], [1, 4, 4, inDepth]);
+        var depthwiseFilter = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
+        var pointwiseFilter = tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
+        var result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
+        test_util_1.expectArraysClose(result, tf.tensor4d([
+            0.04919822, -0.09839644, 0.07275512, -0.14551024, 0.09901544,
+            -0.19803089, 0.05555845, -0.11111691
+        ], [1, 2, 2, outDepth]));
+    });
+    it('input=2x4x4x1,f=2,s=2,d=1,p=valid,chMul=1,outDepth=2', function () {
+        var fSize = 2;
+        var pad = 'valid';
+        var stride = [2, 2];
+        var chMul = 1;
+        var inDepth = 1;
+        var outDepth = 2;
+        var x = tf.tensor4d([
+            0.675707, 0.758567, 0.413529, 0.963967, 0.217291, 0.101335,
+            0.804231, 0.329673, 0.924503, 0.728742, 0.180217, 0.210459,
+            0.133869, 0.650827, 0.047613, 0.554795, -0.675707, -0.758567,
+            -0.413529, -0.963967, -0.217291, -0.101335, -0.804231, -0.329673,
+            -0.924503, -0.728742, -0.180217, -0.210459, -0.133869, -0.650827,
+            -0.047613, -0.554795
+        ], [2, 4, 4, inDepth]);
+        var depthwiseFilter = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
+        var pointwiseFilter = tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
+        var result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
+        test_util_1.expectArraysClose(result, tf.tensor4d([
+            0.04919822, -0.09839644, 0.07275512, -0.14551024, 0.09901544,
+            -0.19803089, 0.05555845, -0.11111691, -0.04919822, 0.09839644,
+            -0.07275512, 0.14551024, -0.09901544, 0.19803089, -0.05555845,
+            0.11111691
+        ], [2, 2, 2, outDepth]));
+    });
+    it('input=1x4x4x2,f=2,s=2,d=1,p=valid,chMul=1,outDepth=2', function () {
+        var fSize = 2;
+        var pad = 'valid';
+        var stride = [2, 2];
+        var chMul = 1;
+        var inDepth = 2;
+        var outDepth = 2;
+        var x = tf.tensor4d([
+            0.675707, 0.758567, 0.413529, 0.963967, 0.217291, 0.101335,
+            0.804231, 0.329673, 0.924503, 0.728742, 0.180217, 0.210459,
+            0.133869, 0.650827, 0.047613, 0.554795, -0.675707, -0.758567,
+            -0.413529, -0.963967, -0.217291, -0.101335, -0.804231, -0.329673,
+            -0.924503, -0.728742, -0.180217, -0.210459, -0.133869, -0.650827,
+            -0.047613, -0.554795
+        ], [1, 4, 4, inDepth]);
+        var depthwiseFilter = tf.tensor4d([
+            0.303873, 0.229223, 0.144333, 0.803373, 0.98976838, 0.56597068,
+            0.42654137, 0.66445535
+        ], [fSize, fSize, inDepth, chMul]);
+        var pointwiseFilter = tf.tensor4d([0.1, -0.2, 0.05, -0.05], [1, 1, inDepth * chMul, outDepth]);
+        var result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
+        test_util_1.expectArraysClose(result, tf.tensor4d([
+            0.20072255, -0.32641545, 0.08474462, -0.11823604, -0.20072255,
+            0.32641545, -0.08474462, 0.11823604
+        ], [1, 2, 2, outDepth]));
+    });
+    it('input=1x4x4x1,f=2,s=1,d=2,p=valid,chMul=1,outDepth=2', function () {
+        var fSize = 2;
+        var pad = 'valid';
+        var stride = 1;
+        var chMul = 1;
+        var inDepth = 1;
+        var outDepth = 2;
+        var dilationRate = 2;
+        var x = tf.tensor4d([
+            0.675707, 0.758567, 0.413529, 0.963967, 0.217291, 0.101335, 0.804231,
+            0.329673, 0.924503, 0.728742, 0.180217, 0.210459, 0.133869, 0.650827,
+            0.047613, 0.554795
+        ], [1, 4, 4, inDepth]);
+        var depthwiseFilter = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
+        var pointwiseFilter = tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
+        var result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad, dilationRate);
+        test_util_1.expectArraysClose(result, tf.tensor4d([
+            0.05783373, -0.11566745, 0.07257301, -0.14514601, 0.03079498,
+            -0.06158997, 0.06460048, -0.12920095
+        ], [1, 2, 2, outDepth]));
+    });
+    it('input=1x4x4x1,f=2,s=1,d=1,p=same,chMul=1,outDepth=2', function () {
+        var fSize = 2;
+        var pad = 'same';
+        var stride = 1;
+        var chMul = 1;
+        var inDepth = 1;
+        var outDepth = 2;
+        var x = tf.tensor4d([
+            0.675707, 0.758567, 0.413529, 0.963967, 0.217291, 0.101335, 0.804231,
+            0.329673, 0.924503, 0.728742, 0.180217, 0.210459, 0.133869, 0.650827,
+            0.047613, 0.554795
+        ], [1, 4, 4, inDepth]);
+        var depthwiseFilter = tf.tensor4d([0.303873, 0.229223, 0.144333, 0.803373], [fSize, fSize, inDepth, chMul]);
+        var pointwiseFilter = tf.tensor4d([0.1, -0.2], [1, 1, inDepth * chMul, outDepth]);
+        var result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
+        test_util_1.expectArraysClose(result, tf.tensor4d([
+            0.04919822, -0.09839644, 0.09860218, -0.19720435, 0.07275512,
+            -0.14551024, 0.03405062, -0.06810125, 0.08081452, -0.16162904,
+            0.04651042, -0.09302084, 0.05150411, -0.10300821, 0.01305549,
+            -0.02611098, 0.09901544, -0.19803089, 0.03949417, -0.07898834,
+            0.05555845, -0.11111691, 0.0144028, -0.02880561, 0.01898637,
+            -0.03797274, 0.02086828, -0.04173655, 0.01416401, -0.02832802,
+            0.01685872, -0.03371745
+        ], [1, 4, 4, outDepth]));
+    });
+    it('TensorLike', function () {
+        var pad = 'valid';
+        var stride = 1;
+        var outDepth = 2;
+        var x = [[
+                [[0.230664], [0.987388], [0.0685208]],
+                [[0.419224], [0.887861], [0.731641]],
+                [[0.0741907], [0.409265], [0.351377]]
+            ]];
+        var depthwiseFilter = [[[[0.303873]], [[0.229223]]], [[[0.144333]], [[0.803373]]]];
+        var pointwiseFilter = [[[[0.1, -0.2]]]];
+        var result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
+        test_util_1.expectArraysClose(result, tf.tensor4d([
+            0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096,
+            -0.13408193, 0.07788632, -0.15577264
+        ], [1, 2, 2, outDepth]));
+    });
+    it('TensorLike Chained', function () {
+        var pad = 'valid';
+        var stride = 1;
+        var outDepth = 2;
+        var inDepth = 1;
+        var x = tf.tensor4d([
+            0.230664, 0.987388, 0.0685208, 0.419224, 0.887861, 0.731641,
+            0.0741907, 0.409265, 0.351377
+        ], [1, 3, 3, inDepth]);
+        var depthwiseFilter = [[[[0.303873]], [[0.229223]]], [[[0.144333]], [[0.803373]]]];
+        var pointwiseFilter = [[[[0.1, -0.2]]]];
+        var result = x.separableConv2d(depthwiseFilter, pointwiseFilter, stride, pad);
+        test_util_1.expectArraysClose(result, tf.tensor4d([
+            0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096,
+            -0.13408193, 0.07788632, -0.15577264
+        ], [1, 2, 2, outDepth]));
+    });
+    it('Incorrect input rank raises error', function () {
+        var x = tf.zeros([4, 4]);
+        var depthwiseFilter = tf.zeros([2, 2, 1, 3]);
+        var pointwiseFilter = tf.zeros([1, 1, 2, 4]);
+        expect(function () {
+            return tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid');
+        })
+            .toThrowError(/rank 4/);
+    });
+    it('Incorrect depthwise filter rank raises error', function () {
+        var x = tf.zeros([1, 4, 4, 1]);
+        var depthwiseFilter = tf.zeros([2, 2, 1]);
+        var pointwiseFilter = tf.zeros([1, 1, 2, 4]);
+        expect(function () {
+            return tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid');
+        })
+            .toThrowError(/rank 4/);
+    });
+    it('Incorrect depthwise filter rank raises error', function () {
+        var x = tf.zeros([1, 4, 4, 1]);
+        var depthwiseFilter = tf.zeros([2, 2, 1, 3]);
+        var pointwiseFilter = tf.zeros([1, 1, 2]);
+        expect(function () {
+            return tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid');
+        })
+            .toThrowError(/rank 4/);
+    });
+    it('Incorrect point filter 1st dimension raises error', function () {
+        var x = tf.zeros([1, 4, 4, 1]);
+        var depthwiseFilter = tf.zeros([2, 2, 1, 3]);
+        var pointwiseFilter = tf.zeros([2, 1, 3, 6]);
+        expect(function () {
+            return tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid');
+        })
+            .toThrowError(/must be 1, but got 2/);
+    });
+    it('Incorrect point filter 2nd dimension raises error', function () {
+        var x = tf.zeros([1, 4, 4, 1]);
+        var depthwiseFilter = tf.zeros([2, 2, 1, 3]);
+        var pointwiseFilter = tf.zeros([1, 5, 3, 6]);
+        expect(function () {
+            return tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid');
+        })
+            .toThrowError(/must be 1, but got 5/);
+    });
+    it('Incorrect pointwise filter 3rd dimension raises error', function () {
+        var x = tf.zeros([1, 4, 4, 1]);
+        var depthwiseFilter = tf.zeros([2, 2, 1, 3]);
+        var pointwiseFilter = tf.zeros([1, 1, 4, 6]);
+        expect(function () {
+            return tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, 1, 'valid');
+        })
+            .toThrowError(/must be 3, but got 4/);
+    });
+    it('throws when passed x as a non-tensor', function () {
+        var fSize = 2;
+        var pad = 'valid';
+        var stride = 1;
+        var chMul = 1;
+        var inDepth = 1;
+        var outDepth = 2;
+        var depthwiseFilter = tf.zeros([fSize, fSize, inDepth, chMul]);
+        var pointwiseFilter = tf.zeros([1, 1, inDepth * chMul, outDepth]);
+        var e = /Argument 'x' passed to 'separableConv2d' must be a Tensor/;
+        expect(function () { return tf.separableConv2d({}, depthwiseFilter, pointwiseFilter, stride, pad); })
+            .toThrowError(e);
+    });
+    it('throws when passed depthwiseFilter as a non-tensor', function () {
+        var pad = 'valid';
+        var stride = 1;
+        var chMul = 1;
+        var inDepth = 1;
+        var outDepth = 2;
+        var x = tf.zeros([1, 3, 3, inDepth]);
+        var pointwiseFilter = tf.zeros([1, 1, inDepth * chMul, outDepth]);
+        var e = new RegExp('Argument \'depthwiseFilter\' passed to \'separableConv2d\'' +
+            ' must be a Tensor');
+        expect(function () { return tf.separableConv2d(x, {}, pointwiseFilter, stride, pad); })
+            .toThrowError(e);
+    });
+    it('throws when passed pointwiseFilter as a non-tensor', function () {
+        var fSize = 2;
+        var pad = 'valid';
+        var stride = 1;
+        var chMul = 1;
+        var inDepth = 1;
+        var x = tf.zeros([1, 3, 3, inDepth]);
+        var depthwiseFilter = tf.zeros([fSize, fSize, inDepth, chMul]);
+        var e = new RegExp('Argument \'pointwiseFilter\' passed to \'separableConv2d\'' +
+            ' must be a Tensor');
+        expect(function () { return tf.separableConv2d(x, depthwiseFilter, {}, stride, pad); })
+            .toThrowError(e);
+    });
+    it('accepts a tensor-like object', function () {
+        var pad = 'valid';
+        var stride = 1;
+        var outDepth = 2;
+        var x = [
+            [[0.230664], [0.987388], [0.0685208]],
+            [[0.419224], [0.887861], [0.731641]],
+            [[0.0741907], [0.409265], [0.351377]]
+        ];
+        var depthwiseFilter = [[[[0.303873]], [[0.229223]]], [[[0.144333]], [[0.803373]]]];
+        var pointwiseFilter = [[[[0.1, -0.2]]]];
+        var result = tf.separableConv2d(x, depthwiseFilter, pointwiseFilter, stride, pad);
+        test_util_1.expectArraysClose(result, tf.tensor3d([
+            0.10702161, -0.21404321, 0.10316753, -0.20633507, 0.06704096,
+            -0.13408193, 0.07788632, -0.15577264
+        ], [2, 2, outDepth]));
+    });
+});
+//# sourceMappingURL=conv2d_separable_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_separable_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_separable_test.js.map
new file mode 100644
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+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_separable_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_test.js
new file mode 100644
index 0000000..67fada4
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_test.js
@@ -0,0 +1,308 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+function generateCaseInputs(totalSizeTensor, totalSizeFilter) {
+    var inp = new Array(totalSizeTensor);
+    var filt = new Array(totalSizeFilter);
+    for (var i = 0; i < totalSizeTensor; i++) {
+        inp[i] = i + 1;
+    }
+    for (var i = 0; i < totalSizeFilter; i++) {
+        filt[i] = i + 1;
+    }
+    return { input: inp, filter: filt };
+}
+jasmine_util_1.describeWithFlags('conv im2row', test_util_1.WEBGL_ENVS, function () {
+    var webglConvIm2colSavedFlag = tf.ENV.get('WEBGL_CONV_IM2COL');
+    beforeAll(function () {
+        tf.ENV.set('WEBGL_CONV_IM2COL', true);
+    });
+    afterAll(function () {
+        tf.ENV.set('WEBGL_CONV_IM2COL', webglConvIm2colSavedFlag);
+    });
+    it('should not leak memory', function () {
+        var inputDepth = 1;
+        var inputShape = [2, 2, inputDepth];
+        var outputDepth = 1;
+        var fSize = 2;
+        var pad = 0;
+        var stride = 1;
+        var dataFormat = 'NHWC';
+        var dilation = 1;
+        var x = tf.tensor3d([1, 2, 3, 4], inputShape);
+        var w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, inputDepth, outputDepth]);
+        var startNumBytes = tf.memory().numBytes;
+        tf.conv2d(x, w, stride, pad, dataFormat, dilation);
+        var endNumBytes = tf.memory().numBytes;
+        expect(endNumBytes - startNumBytes).toEqual(4);
+    });
+    it('x=[3,3,1] f=[2,2,1,1] s=1 d=1 p=0', function () {
+        var inputDepth = 1;
+        var inputShape = [3, 3, inputDepth];
+        var outputDepth = 1;
+        var fSize = 2;
+        var pad = 0;
+        var stride = 1;
+        var dataFormat = 'NHWC';
+        var dilation = 1;
+        var x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
+        var w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, inputDepth, outputDepth]);
+        var result = tf.conv2d(x, w, stride, pad, dataFormat, dilation);
+        test_util_1.expectArraysClose(result, [25, 34, 52, 61]);
+    });
+    it('x=[2,2,1] f=[2,2,1,1] s=1 d=1 p=0', function () {
+        var inputDepth = 1;
+        var inputShape = [2, 2, inputDepth];
+        var outputDepth = 1;
+        var fSize = 2;
+        var pad = 0;
+        var stride = 1;
+        var dataFormat = 'NHWC';
+        var dilation = 1;
+        var x = tf.tensor3d([1, 2, 3, 4], inputShape);
+        var w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, inputDepth, outputDepth]);
+        var result = tf.conv2d(x, w, stride, pad, dataFormat, dilation);
+        test_util_1.expectArraysClose(result, [20]);
+    });
+    it('should work when output texture shape does not equal logical shape', function () {
+        var inputDepth = 3;
+        var inputSize = 300;
+        var filterSize = 3;
+        var outputDepth = 24;
+        var xData = new Float32Array(1 * inputSize * inputSize * inputDepth);
+        var wData = new Float32Array(filterSize * filterSize * inputDepth * outputDepth);
+        xData[0] = 1;
+        xData[100] = 1;
+        wData[0] = 1;
+        wData[100] = 1;
+        var x = tf.tensor4d(xData, [1, inputSize, inputSize, inputDepth]);
+        var w = tf.tensor4d(wData, [filterSize, filterSize, inputDepth, outputDepth]);
+        var result = tf.conv2d(x, w, 2, 'same');
+        var resultData = result.dataSync();
+        expect(resultData[0]).toEqual(1);
+        expect(resultData[388]).toEqual(1);
+    });
+    it('should work when input texture shapes do not equal logical shapes', function () {
+        var webglMaxTextureSize = tf.ENV.get('WEBGL_MAX_TEXTURE_SIZE');
+        tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', 10);
+        var inputDepth = 1;
+        var inputSize = 5;
+        var filterSize = 2;
+        var outputDepth = 1;
+        var x = tf.tensor3d([
+            0.4, 0.75, 0.65, 0.98, 0.1, 0.41, 0.01, 0.46, 0.49,
+            0.4, 0.11, 0.76, 0.73, 0.86, 0.34, 0.34, 0.71, 0.68,
+            0.62, 0.87, 0.64, 0.38, 0.29, 0.55, 0.95
+        ], [inputSize, inputSize, inputDepth]);
+        var w = tf.tensor4d([0.57, 0.64, 0.18, 0.18], [filterSize, filterSize, inputDepth, outputDepth]);
+        var result = tf.conv2d(x, w, 1, 'same');
+        tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', webglMaxTextureSize);
+        test_util_1.expectArraysClose(result, [
+            0.7836, 0.9281, 1.1687, 0.7828, 0.129, 0.3967, 0.5683, 0.862, 0.7513,
+            0.2892, 0.7381, 1.1506, 1.2005, 0.976, 0.3504, 0.8318, 0.9605, 0.9356,
+            1.1802, 0.6669, 0.608, 0.4022, 0.5173, 0.9215, 0.5415
+        ]);
+    });
+});
+jasmine_util_1.describeWithFlags('conv2d', test_util_1.ALL_ENVS, function () {
+    it('x=[2,2,1] f=[1,1,1,2] s=1 d=1 p=0', function () {
+        var inputDepth = 1;
+        var inputShape = [2, 2, inputDepth];
+        var outputDepth = 1;
+        var fSize = 1;
+        var pad = 0;
+        var stride = 1;
+        var x = tf.tensor3d([1, 2, 3, 4], inputShape);
+        var w = tf.tensor4d([2], [fSize, fSize, inputDepth, outputDepth]);
+        var result = tf.conv2d(x, w, stride, pad);
+        test_util_1.expectArraysClose(result, [2, 4, 6, 8]);
+    });
+    it('x=[2,2,2,1] f=[1,1,1,1] s=1 d=1 p=0', function () {
+        var inputDepth = 1;
+        var inShape = [2, 2, 2, inputDepth];
+        var outputDepth = 1;
+        var fSize = 1;
+        var pad = 0;
+        var stride = 1;
+        var x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], inShape);
+        var w = tf.tensor4d([2], [fSize, fSize, inputDepth, outputDepth]);
+        var result = tf.conv2d(x, w, stride, pad);
+        expect(result.shape).toEqual([2, 2, 2, 1]);
+        var expected = [2, 4, 6, 8, 10, 12, 14, 16];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('x=[2,2,1] f=[2,2,1,1] s=1 d=1 p=0', function () {
+        var inputDepth = 1;
+        var inputShape = [2, 2, inputDepth];
+        var outputDepth = 1;
+        var fSize = 2;
+        var pad = 0;
+        var stride = 1;
+        var dataFormat = 'NHWC';
+        var dilation = 1;
+        var x = tf.tensor3d([1, 2, 3, 4], inputShape);
+        var w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, inputDepth, outputDepth]);
+        var result = tf.conv2d(x, w, stride, pad, dataFormat, dilation);
+        test_util_1.expectArraysClose(result, [20]);
+    });
+    it('x=[4,4,1] f=[2,2,1,1] s=1 d=2 p=0', function () {
+        var inputDepth = 1;
+        var inputShape = [4, 4, inputDepth];
+        var outputDepth = 1;
+        var fSize = 2;
+        var fSizeDilated = 3;
+        var pad = 0;
+        var stride = 1;
+        var dataFormat = 'NHWC';
+        var dilation = 2;
+        var noDilation = 1;
+        var x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], inputShape);
+        var w = tf.tensor4d([3, 1, 5, 2], [fSize, fSize, inputDepth, outputDepth]);
+        var wDilated = tf.tensor4d([3, 0, 1, 0, 0, 0, 5, 0, 2], [fSizeDilated, fSizeDilated, inputDepth, outputDepth]);
+        var result = tf.conv2d(x, w, stride, pad, dataFormat, dilation);
+        var expectedResult = tf.conv2d(x, wDilated, stride, pad, dataFormat, noDilation);
+        expect(result.shape).toEqual(expectedResult.shape);
+        test_util_1.expectArraysClose(result, expectedResult);
+    });
+    it('x=[1,3,6,1] f=[2,2,1,1] s=[1,2] d=1 p=valid', function () {
+        var inputDepth = 1;
+        var inputShape = [1, 3, 6, inputDepth];
+        var outputDepth = 1;
+        var fSize = 2;
+        var pad = 'valid';
+        var stride = [1, 2];
+        var inputs = generateCaseInputs(1 * 3 * 6 * inputDepth, fSize * fSize);
+        var x = tf.tensor4d(inputs.input, inputShape);
+        var w = tf.tensor4d(inputs.filter, [fSize, fSize, inputDepth, outputDepth]);
+        var result = tf.conv2d(x, w, stride, pad);
+        test_util_1.expectArraysClose(result, [58.0, 78.0, 98.0, 118.0, 138.0, 158.0]);
+    });
+    it('throws when x is not rank 3', function () {
+        var inputDepth = 1;
+        var outputDepth = 1;
+        var fSize = 2;
+        var pad = 0;
+        var stride = 1;
+        var x = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, inputDepth, outputDepth]);
+        expect(function () { return tf.conv2d(x, w, stride, pad); }).toThrowError();
+    });
+    it('throws when weights is not rank 4', function () {
+        var inputDepth = 1;
+        var inputShape = [2, 2, inputDepth];
+        var pad = 0;
+        var stride = 1;
+        var x = tf.tensor3d([1, 2, 3, 4], inputShape);
+        var w = tf.tensor3d([3, 1, 5, 0], [2, 2, 1]);
+        expect(function () { return tf.conv2d(x, w, stride, pad); }).toThrowError();
+    });
+    it('throws when x depth does not match weight depth', function () {
+        var inputDepth = 1;
+        var wrongInputDepth = 5;
+        var inputShape = [2, 2, inputDepth];
+        var outputDepth = 1;
+        var fSize = 2;
+        var pad = 0;
+        var stride = 1;
+        var x = tf.tensor3d([1, 2, 3, 4], inputShape);
+        var w = tf.randomNormal([fSize, fSize, wrongInputDepth, outputDepth]);
+        expect(function () { return tf.conv2d(x, w, stride, pad); }).toThrowError();
+    });
+    it('throws when dimRoundingMode is set and pad is not a number', function () {
+        var inputDepth = 1;
+        var inputShape = [2, 2, inputDepth];
+        var outputDepth = 1;
+        var fSize = 2;
+        var pad = 'valid';
+        var stride = 1;
+        var dataFormat = 'NHWC';
+        var dilation = 1;
+        var dimRoundingMode = 'round';
+        var x = tf.tensor3d([1, 2, 3, 4], inputShape);
+        var w = tf.randomNormal([fSize, fSize, inputDepth, outputDepth]);
+        expect(function () {
+            return tf.conv2d(x, w, stride, pad, dataFormat, dilation, dimRoundingMode);
+        })
+            .toThrowError();
+    });
+    it('throws when both stride and dilation are greater than 1', function () {
+        var inputDepth = 1;
+        var inputShape = [2, 2, inputDepth];
+        var outputDepth = 1;
+        var fSize = 2;
+        var pad = 0;
+        var stride = [2, 1];
+        var dataFormat = 'NHWC';
+        var dilation = [1, 2];
+        var x = tf.tensor3d([1, 2, 3, 4], inputShape);
+        var w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, inputDepth, outputDepth]);
+        expect(function () { return tf.conv2d(x, w, stride, pad, dataFormat, dilation); })
+            .toThrowError();
+    });
+    it('gradient input=[3,3,1] f=[2,2,1,1] s=1 p=0', function () {
+        var inputDepth = 1;
+        var outputDepth = 1;
+        var inputShape = [3, 3, inputDepth];
+        var filterSize = 2;
+        var stride = 1;
+        var pad = 0;
+        var filterShape = [filterSize, filterSize, inputDepth, outputDepth];
+        var filter = tf.ones(filterShape);
+        var x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
+        var dy = tf.tensor3d([3, 1, 2, 0], [2, 2, 1]);
+        var grads = tf.grads(function (x, filter) { return x.conv2d(filter, stride, pad); });
+        var _a = grads([x, filter], dy), dx = _a[0], dfilter = _a[1];
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, [3, 4, 1, 5, 6, 1, 2, 2, 0]);
+        expect(dfilter.shape).toEqual(filterShape);
+        test_util_1.expectArraysClose(dfilter, [13, 19, 31, 37]);
+    });
+    it('gradient x=[2,3,3,1] f=[2,2,1,1] s=1 p=0', function () {
+        var inputDepth = 1;
+        var outputDepth = 1;
+        var inputShape = [2, 3, 3, inputDepth];
+        var filterSize = 2;
+        var stride = 1;
+        var pad = 0;
+        var filterShape = [filterSize, filterSize, inputDepth, outputDepth];
+        var filter = tf.ones(filterShape);
+        var x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9], inputShape);
+        var dy = tf.tensor4d([3, 1, 2, 0, 3, 1, 2, 0], [2, 2, 2, 1]);
+        var grads = tf.grads(function (x, filter) { return x.conv2d(filter, stride, pad); });
+        var _a = grads([x, filter], dy), dx = _a[0], dfilter = _a[1];
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, [3, 4, 1, 5, 6, 1, 2, 2, 0, 3, 4, 1, 5, 6, 1, 2, 2, 0]);
+        expect(dfilter.shape).toEqual(filterShape);
+        test_util_1.expectArraysClose(dfilter, [13 * 2, 19 * 2, 31 * 2, 37 * 2]);
+    });
+    it('throws when passed x as a non-tensor', function () {
+        var inputDepth = 1;
+        var outputDepth = 1;
+        var fSize = 1;
+        var pad = 0;
+        var stride = 1;
+        var w = tf.tensor4d([2], [fSize, fSize, inputDepth, outputDepth]);
+        expect(function () { return tf.conv2d({}, w, stride, pad); })
+            .toThrowError(/Argument 'x' passed to 'conv2d' must be a Tensor/);
+    });
+    it('throws when passed filter as a non-tensor', function () {
+        var inputDepth = 1;
+        var inputShape = [2, 2, inputDepth];
+        var pad = 0;
+        var stride = 1;
+        var x = tf.tensor3d([1, 2, 3, 4], inputShape);
+        expect(function () { return tf.conv2d(x, {}, stride, pad); })
+            .toThrowError(/Argument 'filter' passed to 'conv2d' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var pad = 0;
+        var stride = 1;
+        var x = [[[1], [2]], [[3], [4]]];
+        var w = [[[[2]]]];
+        var result = tf.conv2d(x, w, stride, pad);
+        test_util_1.expectArraysClose(result, [2, 4, 6, 8]);
+    });
+});
+//# sourceMappingURL=conv2d_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_test.js.map
new file mode 100644
index 0000000..8d931c0
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_test.js.map
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_transpose_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_transpose_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_transpose_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_transpose_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_transpose_test.js
new file mode 100644
index 0000000..c7b0718
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_transpose_test.js
@@ -0,0 +1,221 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('conv2dTranspose', test_util_1.ALL_ENVS, function () {
+    it('input=2x2x1,d2=1,f=2,s=1,p=0', function () {
+        var origInputDepth = 1;
+        var origOutputDepth = 1;
+        var inputShape = [1, 1, origOutputDepth];
+        var fSize = 2;
+        var origPad = 0;
+        var origStride = 1;
+        var x = tf.tensor3d([2], inputShape);
+        var w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);
+        var result = tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad);
+        var expected = [6, 2, 10, 0];
+        expect(result.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('input=2x2x1,d2=1,f=2,s=1,p=0, batch=2', function () {
+        var origInputDepth = 1;
+        var origOutputDepth = 1;
+        var inputShape = [2, 1, 1, origOutputDepth];
+        var fSize = 2;
+        var origPad = 0;
+        var origStride = 1;
+        var x = tf.tensor4d([2, 3], inputShape);
+        var w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);
+        var result = tf.conv2dTranspose(x, w, [2, 2, 2, 1], origStride, origPad);
+        var expected = [6, 2, 10, 0, 9, 3, 15, 0];
+        expect(result.shape).toEqual([2, 2, 2, 1]);
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('gradient input=[1,3,3,1] f=[2,2,2,1] s=1 padding=valid', function () {
+        var inputDepth = 1;
+        var outputDepth = 2;
+        var inputShape = [1, 3, 3, inputDepth];
+        var filterSize = 2;
+        var stride = 1;
+        var pad = 'valid';
+        var filterShape = [filterSize, filterSize, outputDepth, inputDepth];
+        var x = tf.tensor4d([[
+                [[-0.14656299], [0.32942239], [-1.90302866]],
+                [[-0.06487813], [-2.02637842], [-1.83669377]],
+                [[0.82650784], [-0.89249092], [0.01207666]]
+            ]], inputShape);
+        var filt = tf.tensor4d([
+            [[[-0.48280062], [1.26770487]], [[-0.83083738], [0.54341856]]],
+            [[[-0.274904], [0.73111374]], [[2.01885189], [-2.68975237]]]
+        ], filterShape);
+        var grads = tf.grads(function (x, filter) {
+            return tf.conv2dTranspose(x, filter, [1, 4, 4, outputDepth], stride, pad);
+        });
+        var dy = tf.ones([1, 4, 4, outputDepth]);
+        var _a = grads([x, filt], dy), xGrad = _a[0], filtGrad = _a[1];
+        test_util_1.expectArraysClose(xGrad, tf.ones([1, 3, 3, 1]).mul(tf.scalar(0.2827947)));
+        test_util_1.expectArraysClose(filtGrad, tf.ones([2, 2, 2, 1]).mul(tf.scalar(-5.70202599)));
+    });
+    it('gradient input=[1,2,2,1] f=[2,2,2,1] s=[2,2] padding=valid', function () {
+        var inputDepth = 1;
+        var outputDepth = 2;
+        var inputShape = [1, 2, 2, inputDepth];
+        var filterSize = 2;
+        var stride = [2, 2];
+        var pad = 'valid';
+        var filterShape = [filterSize, filterSize, outputDepth, inputDepth];
+        var x = tf.tensor4d([[[[-0.36541713], [-0.53973116]], [[0.01731674], [0.90227772]]]], inputShape);
+        var filt = tf.tensor4d([
+            [[[-0.01423461], [-1.00267384]], [[1.61163029], [0.66302646]]],
+            [[[-0.46900087], [-0.78649444]], [[0.87780536], [-0.84551637]]]
+        ], filterShape);
+        var grads = tf.grads(function (x, filter) {
+            return tf.conv2dTranspose(x, filter, [1, 4, 4, outputDepth], stride, pad);
+        });
+        var dy = tf.ones([1, 4, 4, outputDepth]).mul(tf.scalar(-1));
+        var _a = grads([x, filt], dy), xGrad = _a[0], filtGrad = _a[1];
+        test_util_1.expectArraysClose(xGrad, tf.ones([1, 2, 2, 1]).mul(tf.scalar(-0.03454196)));
+        test_util_1.expectArraysClose(filtGrad, tf.ones([2, 2, 2, 1]).mul(tf.scalar(-0.01444618)));
+    });
+    it('gradient input=[1,3,3,1] f=[2,2,2,1] s=[1,1] padding=same', function () {
+        var inputDepth = 1;
+        var outputDepth = 2;
+        var inputShape = [1, 3, 3, inputDepth];
+        var filterSize = 2;
+        var stride = [1, 1];
+        var pad = 'same';
+        var filterShape = [filterSize, filterSize, outputDepth, inputDepth];
+        var x = tf.tensor4d([[
+                [[1.52433065], [-0.77053435], [-0.64562341]],
+                [[0.77962889], [1.58413887], [-0.25581856]],
+                [[-0.58966221], [0.05411662], [0.70749138]]
+            ]], inputShape);
+        var filt = tf.tensor4d([
+            [[[0.11178388], [-0.96654977]], [[1.21021296], [0.84121729]]],
+            [[[0.34968338], [-0.42306114]], [[1.27395733], [-1.09014535]]]
+        ], filterShape);
+        var grads = tf.grads(function (x, filter) {
+            return tf.conv2dTranspose(x, filter, [1, 3, 3, outputDepth], stride, pad);
+        });
+        var dy = tf.ones([1, 3, 3, outputDepth]);
+        var _a = grads([x, filt], dy), xGrad = _a[0], filtGrad = _a[1];
+        test_util_1.expectArraysClose(xGrad, tf.tensor4d([[
+                [[1.30709858], [1.30709858], [-0.92814366]],
+                [[1.30709858], [1.30709858], [-0.92814366]],
+                [[1.19666437], [1.19666437], [-0.85476589]]
+            ]]));
+        test_util_1.expectArraysClose(filtGrad, tf.tensor4d([
+            [[[2.38806788], [2.38806788]], [[2.58201847], [2.58201847]]],
+            [[[2.2161221], [2.2161221]], [[3.11756406], [3.11756406]]]
+        ]));
+    });
+    it('gradient input=[1,2,2,2] f=[2,2,2,1] s=[2,2] padding=same', function () {
+        var inputDepth = 2;
+        var outputDepth = 2;
+        var inputShape = [1, 2, 2, inputDepth];
+        var filterSize = 2;
+        var stride = [2, 2];
+        var pad = 'same';
+        var filterShape = [filterSize, filterSize, outputDepth, inputDepth];
+        var x = tf.tensor4d([[
+                [[-1.81506593, 1.00900095], [-0.05199118, 0.26311377]],
+                [[-1.18469792, -0.34780521], [2.04971242, -0.65154692]]
+            ]], inputShape);
+        var filt = tf.tensor4d([
+            [
+                [[0.19529686, -0.79594708], [0.70314057, -0.06081263]],
+                [[0.28724744, 0.88522715], [-0.51824096, -0.97120989]]
+            ],
+            [
+                [[0.51872197, -1.17569193], [1.28316791, -0.81225092]],
+                [[-0.44221532, 0.70058174], [-0.4849217, 0.03806348]]
+            ]
+        ], filterShape);
+        var grads = tf.grads(function (x, filter) {
+            return tf.conv2dTranspose(x, filter, [1, 3, 3, outputDepth], stride, pad);
+        });
+        var dy = tf.ones([1, 3, 3, outputDepth]);
+        var _a = grads([x, filt], dy), xGrad = _a[0], filtGrad = _a[1];
+        test_util_1.expectArraysClose(xGrad, tf.tensor4d([[
+                [[1.54219678, -2.19204008], [2.70032732, -2.84470257]],
+                [[0.66744391, -0.94274245], [0.89843743, -0.85675972]]
+            ]]));
+        test_util_1.expectArraysClose(filtGrad, tf.tensor4d([
+            [
+                [[-1.00204261, 0.27276259], [-1.00204261, 0.27276259]],
+                [[-2.99976385, 0.66119574], [-2.99976385, 0.66119574]]
+            ],
+            [
+                [[-1.86705711, 1.27211472], [-1.86705711, 1.27211472]],
+                [[-1.81506593, 1.00900095], [-1.81506593, 1.00900095]]
+            ]
+        ]));
+    });
+    it('throws when x is not rank 3', function () {
+        var origInputDepth = 1;
+        var origOutputDepth = 1;
+        var fSize = 2;
+        var origPad = 0;
+        var origStride = 1;
+        var x = tf.tensor2d([2, 2], [2, 1]);
+        var w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);
+        expect(function () { return tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad); })
+            .toThrowError();
+    });
+    it('throws when weights is not rank 4', function () {
+        var origInputDepth = 1;
+        var origOutputDepth = 1;
+        var inputShape = [1, 1, origOutputDepth];
+        var fSize = 2;
+        var origPad = 0;
+        var origStride = 1;
+        var x = tf.tensor3d([2], inputShape);
+        var w = tf.tensor3d([3, 1, 5, 0], [fSize, fSize, origInputDepth]);
+        expect(function () { return tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad); })
+            .toThrowError();
+    });
+    it('throws when x depth does not match weights original output depth', function () {
+        var origInputDepth = 1;
+        var origOutputDepth = 2;
+        var wrongOrigOutputDepth = 3;
+        var inputShape = [1, 1, origOutputDepth];
+        var fSize = 2;
+        var origPad = 0;
+        var origStride = 1;
+        var x = tf.tensor3d([2, 2], inputShape);
+        var w = tf.randomNormal([fSize, fSize, origInputDepth, wrongOrigOutputDepth]);
+        expect(function () { return tf.conv2dTranspose(x, w, [2, 2, 2], origStride, origPad); })
+            .toThrowError();
+    });
+    it('throws when passed x as a non-tensor', function () {
+        var origInputDepth = 1;
+        var origOutputDepth = 1;
+        var fSize = 2;
+        var origPad = 0;
+        var origStride = 1;
+        var w = tf.tensor4d([3, 1, 5, 0], [fSize, fSize, origInputDepth, origOutputDepth]);
+        expect(function () { return tf.conv2dTranspose({}, w, [2, 2, 1], origStride, origPad); })
+            .toThrowError(/Argument 'x' passed to 'conv2dTranspose' must be a Tensor/);
+    });
+    it('throws when passed filter as a non-tensor', function () {
+        var origOutputDepth = 1;
+        var inputShape = [1, 1, origOutputDepth];
+        var origPad = 0;
+        var origStride = 1;
+        var x = tf.tensor3d([2], inputShape);
+        expect(function () { return tf.conv2dTranspose(x, {}, [2, 2, 1], origStride, origPad); })
+            .toThrowError(/Argument 'filter' passed to 'conv2dTranspose' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var origPad = 0;
+        var origStride = 1;
+        var x = [[[2]]];
+        var w = [[[[3]], [[1]]], [[[5]], [[0]]]];
+        var result = tf.conv2dTranspose(x, w, [2, 2, 1], origStride, origPad);
+        var expected = [6, 2, 10, 0];
+        expect(result.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(result, expected);
+    });
+});
+//# sourceMappingURL=conv2d_transpose_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_transpose_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_transpose_test.js.map
new file mode 100644
index 0000000..1a4e4ea
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/conv2d_transpose_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_test.js
new file mode 100644
index 0000000..38d6039
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_test.js
@@ -0,0 +1,347 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+var util_1 = require("../util");
+function generateCaseInputs(totalSizeTensor, totalSizeFilter) {
+    var inp = new Array(totalSizeTensor);
+    var filt = new Array(totalSizeFilter);
+    for (var i = 0; i < totalSizeTensor; i++) {
+        inp[i] = (i + 1) / totalSizeTensor;
+    }
+    for (var i = 0; i < totalSizeFilter; i++) {
+        filt[i] = (i + 1) / totalSizeFilter;
+    }
+    return { input: inp, filter: filt };
+}
+function generateGradientCaseInputs(totalSizeTensor, totalSizeFilter) {
+    var inp = new Array(totalSizeTensor);
+    var filt = new Array(totalSizeFilter);
+    for (var i = 0; i < totalSizeTensor; i++) {
+        inp[i] = i + 1;
+    }
+    for (var i = 0; i < totalSizeFilter; i++) {
+        filt[i] = i + 1;
+    }
+    return { input: inp, filter: filt };
+}
+function runConv3DTestCase(batch, inDepth, inHeight, inWidth, inChannels, outChannels, fDepth, fHeight, fWidth, pad, stride) {
+    var inputShape = [batch, inDepth, inHeight, inWidth, inChannels];
+    var filterShape = [fDepth, fHeight, fWidth, inChannels, outChannels];
+    var totalSizeTensor = util_1.sizeFromShape(inputShape);
+    var totalSizeFilter = util_1.sizeFromShape(filterShape);
+    var inputs = generateCaseInputs(totalSizeTensor, totalSizeFilter);
+    var x = tf.tensor5d(inputs.input, inputShape);
+    var w = tf.tensor5d(inputs.filter, filterShape);
+    var result = tf.conv3d(x, w, stride, pad);
+    return result;
+}
+function runGradientConv3DTestCase(batch, inDepth, inHeight, inWidth, inChannels, outChannels, fDepth, fHeight, fWidth, pad, stride) {
+    var inputShape = [batch, inDepth, inHeight, inWidth, inChannels];
+    var filterShape = [fDepth, fHeight, fWidth, inChannels, outChannels];
+    var totalSizeTensor = util_1.sizeFromShape(inputShape);
+    var totalSizeFilter = util_1.sizeFromShape(filterShape);
+    var inputs = generateGradientCaseInputs(totalSizeTensor, totalSizeFilter);
+    var x = tf.tensor5d(inputs.input, inputShape);
+    var w = tf.tensor5d(inputs.filter, filterShape);
+    var grads = tf.grads(function (x, filter) { return tf.conv3d(x, filter, stride, pad); });
+    var _a = grads([x, w]), dx = _a[0], dfilter = _a[1];
+    expect(dx.shape).toEqual(x.shape);
+    expect(dfilter.shape).toEqual(w.shape);
+    return [dx, dfilter];
+}
+jasmine_util_1.describeWithFlags('conv3d', test_util_1.ALL_ENVS, function () {
+    it('x=[1, 2, 3, 1, 3] f=[1, 1, 1, 3, 3] s=1 d=1 p=valid', function () {
+        var batch = 1;
+        var inDepth = 2;
+        var inHeight = 3;
+        var inWidth = 1;
+        var inChannels = 3;
+        var outChannels = 3;
+        var fSize = 1;
+        var pad = 'valid';
+        var stride = 1;
+        var result = runConv3DTestCase(batch, inDepth, inHeight, inWidth, inChannels, outChannels, fSize, fSize, fSize, pad, stride);
+        var expectedOutput = [
+            0.18518519, 0.22222222, 0.25925926, 0.40740741, 0.5, 0.59259259,
+            0.62962963, 0.77777778, 0.92592593, 0.85185185, 1.05555556, 1.25925926,
+            1.07407407, 1.33333333, 1.59259259, 1.2962963, 1.61111111, 1.92592593
+        ];
+        test_util_1.expectArraysClose(result, expectedOutput);
+    });
+    it('x=[1, 2, 1, 3, 3] f=[1, 1, 1, 3, 3] s=1 d=1 p=valid', function () {
+        var batch = 1;
+        var inDepth = 2;
+        var inHeight = 1;
+        var inWidth = 3;
+        var inChannels = 3;
+        var outChannels = 3;
+        var fSize = 1;
+        var pad = 'valid';
+        var stride = 1;
+        var result = runConv3DTestCase(batch, inDepth, inHeight, inWidth, inChannels, outChannels, fSize, fSize, fSize, pad, stride);
+        var expectedOutput = [
+            0.18518519, 0.22222222, 0.25925926, 0.40740741, 0.5, 0.59259259,
+            0.62962963, 0.77777778, 0.92592593, 0.85185185, 1.05555556, 1.25925926,
+            1.07407407, 1.33333333, 1.59259259, 1.2962963, 1.61111111, 1.92592593
+        ];
+        test_util_1.expectArraysClose(result, expectedOutput);
+    });
+    it('x=[1, 1, 2, 3, 3] f=[1, 1, 1, 3, 3] s=1 d=1 p=valid', function () {
+        var batch = 1;
+        var inDepth = 1;
+        var inHeight = 2;
+        var inWidth = 3;
+        var inChannels = 3;
+        var outChannels = 3;
+        var fSize = 1;
+        var pad = 'valid';
+        var stride = 1;
+        var result = runConv3DTestCase(batch, inDepth, inHeight, inWidth, inChannels, outChannels, fSize, fSize, fSize, pad, stride);
+        var expectedOutput = [
+            0.18518519, 0.22222222, 0.25925926, 0.40740741, 0.5, 0.59259259,
+            0.62962963, 0.77777778, 0.92592593, 0.85185185, 1.05555556, 1.25925926,
+            1.07407407, 1.33333333, 1.59259259, 1.2962963, 1.61111111, 1.92592593
+        ];
+        test_util_1.expectArraysClose(result, expectedOutput);
+    });
+    it('x=[1, 4, 2, 3, 3] f=[2, 2, 2, 3, 3] s=1 d=1 p=valid', function () {
+        var batch = 1;
+        var inDepth = 4;
+        var inHeight = 2;
+        var inWidth = 3;
+        var inChannels = 3;
+        var outChannels = 3;
+        var fSize = 2;
+        var pad = 'valid';
+        var stride = 1;
+        var result = runConv3DTestCase(batch, inDepth, inHeight, inWidth, inChannels, outChannels, fSize, fSize, fSize, pad, stride);
+        var expectedOutput = [
+            3.77199074, 3.85069444, 3.92939815, 4.2650463, 4.35763889, 4.45023148,
+            6.73032407, 6.89236111, 7.05439815, 7.22337963, 7.39930556, 7.57523148,
+            9.68865741, 9.93402778, 10.17939815, 10.18171296, 10.44097222, 10.70023148
+        ];
+        test_util_1.expectArraysClose(result, expectedOutput);
+    });
+    it('x=[1, 5, 8, 7, 1] f=[1, 2, 3, 1, 1] s=[2, 3, 1] d=1 p=same', function () {
+        var batch = 1;
+        var inDepth = 5;
+        var inHeight = 8;
+        var inWidth = 7;
+        var inChannels = 1;
+        var outChannels = 1;
+        var fDepth = 1;
+        var fHeight = 2;
+        var fWidth = 3;
+        var pad = 'same';
+        var stride = [2, 3, 1];
+        var result = runConv3DTestCase(batch, inDepth, inHeight, inWidth, inChannels, outChannels, fDepth, fHeight, fWidth, pad, stride);
+        var expectedOutput = [
+            0.06071429, 0.08988095, 0.10238095, 0.11488095, 0.12738095, 0.13988095,
+            0.08452381, 0.26071429, 0.35238095, 0.36488095, 0.37738095, 0.38988095,
+            0.40238095, 0.23452381, 0.46071429, 0.61488095, 0.62738095, 0.63988095,
+            0.65238095, 0.66488095, 0.38452381, 1.12738095, 1.48988095, 1.50238095,
+            1.51488095, 1.52738095, 1.53988095, 0.88452381, 1.32738095, 1.75238095,
+            1.76488095, 1.77738095, 1.78988095, 1.80238095, 1.03452381, 1.52738095,
+            2.01488095, 2.02738095, 2.03988095, 2.05238095, 2.06488095, 1.18452381,
+            2.19404762, 2.88988095, 2.90238095, 2.91488095, 2.92738095, 2.93988095,
+            1.68452381, 2.39404762, 3.15238095, 3.16488095, 3.17738095, 3.18988095,
+            3.20238095, 1.83452381, 2.59404762, 3.41488095, 3.42738095, 3.43988095,
+            3.45238095, 3.46488095, 1.98452381
+        ];
+        test_util_1.expectArraysClose(result, expectedOutput);
+    });
+    it('x=[1, 4, 2, 3, 3] f=[2, 2, 2, 3, 3] s=2 d=1 p=valid', function () {
+        var batch = 1;
+        var inDepth = 4;
+        var inHeight = 2;
+        var inWidth = 3;
+        var inChannels = 3;
+        var outChannels = 3;
+        var fSize = 2;
+        var pad = 'valid';
+        var stride = 2;
+        var result = runConv3DTestCase(batch, inDepth, inHeight, inWidth, inChannels, outChannels, fSize, fSize, fSize, pad, stride);
+        var expectedOutput = [
+            3.77199074, 3.85069444, 3.92939815, 9.68865741, 9.93402778, 10.17939815
+        ];
+        test_util_1.expectArraysClose(result, expectedOutput);
+    });
+    it('x=[1, 6, 7, 8, 2] f=[3, 2, 1, 2, 3] s=3 d=1 p=valid', function () {
+        var batch = 1;
+        var inDepth = 6;
+        var inHeight = 7;
+        var inWidth = 8;
+        var inChannels = 2;
+        var outChannels = 3;
+        var fDepth = 3;
+        var fHeight = 2;
+        var fWidth = 1;
+        var pad = 'valid';
+        var stride = 3;
+        var result = runConv3DTestCase(batch, inDepth, inHeight, inWidth, inChannels, outChannels, fDepth, fHeight, fWidth, pad, stride);
+        var expectedOutput = [
+            1.51140873, 1.57167659, 1.63194444, 1.56349206, 1.62673611, 1.68998016,
+            1.6155754, 1.68179563, 1.74801587, 1.9280754, 2.01215278, 2.09623016,
+            1.98015873, 2.0672123, 2.15426587, 2.03224206, 2.12227183, 2.21230159,
+            4.4280754, 4.65500992, 4.88194444, 4.48015873, 4.71006944, 4.93998016,
+            4.53224206, 4.76512897, 4.99801587, 4.84474206, 5.09548611, 5.34623016,
+            4.8968254, 5.15054563, 5.40426587, 4.94890873, 5.20560516, 5.46230159
+        ];
+        test_util_1.expectArraysClose(result, expectedOutput);
+    });
+    it('x=[1, 4, 2, 3, 3] f=[2, 2, 2, 3, 3] s=2 d=1 p=same', function () {
+        var batch = 1;
+        var inDepth = 4;
+        var inHeight = 2;
+        var inWidth = 3;
+        var inChannels = 3;
+        var outChannels = 3;
+        var fSize = 2;
+        var pad = 'same';
+        var stride = 2;
+        var result = runConv3DTestCase(batch, inDepth, inHeight, inWidth, inChannels, outChannels, fSize, fSize, fSize, pad, stride);
+        var expectedOutput = [
+            3.77199074, 3.85069444, 3.92939815, 2.0162037, 2.06597222, 2.11574074,
+            9.68865741, 9.93402778, 10.17939815, 4.59953704, 4.73263889, 4.86574074
+        ];
+        test_util_1.expectArraysClose(result, expectedOutput);
+    });
+    it('x=[1, 3, 3, 3, 1] f=[1, 1, 1, 1, 1] s=2 d=1 p=same', function () {
+        var batch = 1;
+        var inDepth = 3;
+        var inHeight = 3;
+        var inWidth = 3;
+        var inChannels = 1;
+        var outChannels = 1;
+        var fSize = 1;
+        var pad = 'same';
+        var stride = 2;
+        var result = runConv3DTestCase(batch, inDepth, inHeight, inWidth, inChannels, outChannels, fSize, fSize, fSize, pad, stride);
+        var expectedOutput = [
+            0.03703704, 0.11111111, 0.25925926, 0.33333333, 0.7037037, 0.77777778,
+            0.92592593, 1.
+        ];
+        test_util_1.expectArraysClose(result, expectedOutput);
+    });
+    it('x=[1, 3, 3, 3, 1] f=[1, 1, 1, 1, 1] s=2 d=1 p=valid', function () {
+        var batch = 1;
+        var inDepth = 3;
+        var inHeight = 3;
+        var inWidth = 3;
+        var inChannels = 1;
+        var outChannels = 1;
+        var fSize = 1;
+        var pad = 'valid';
+        var stride = 2;
+        var result = runConv3DTestCase(batch, inDepth, inHeight, inWidth, inChannels, outChannels, fSize, fSize, fSize, pad, stride);
+        var expectedOutput = [
+            0.03703704, 0.11111111, 0.25925926, 0.33333333, 0.7037037, 0.77777778,
+            0.92592593, 1.
+        ];
+        test_util_1.expectArraysClose(result, expectedOutput);
+    });
+    it('x=[1, 7, 7, 7, 1] f=[2, 2, 2, 1, 1] s=3 d=1 p=same', function () {
+        var batch = 1;
+        var inDepth = 7;
+        var inHeight = 7;
+        var inWidth = 7;
+        var inChannels = 1;
+        var outChannels = 1;
+        var fSize = 2;
+        var pad = 'same';
+        var stride = 3;
+        var result = runConv3DTestCase(batch, inDepth, inHeight, inWidth, inChannels, outChannels, fSize, fSize, fSize, pad, stride);
+        var expectedOutput = [
+            0.54081633, 0.58017493, 0.28061224, 0.81632653, 0.85568513, 0.40306122,
+            0.41873178, 0.4340379, 0.19642857, 2.46938776, 2.50874636, 1.1377551,
+            2.74489796, 2.78425656, 1.26020408, 1.16873178, 1.1840379, 0.51785714,
+            1.09511662, 1.10604956, 0.44642857, 1.17164723, 1.18258017, 0.47704082,
+            0.3691691, 0.37244898, 0.125
+        ];
+        test_util_1.expectArraysClose(result, expectedOutput);
+    });
+    it('x=[1, 7, 7, 7, 1] f=[2, 2, 2, 1, 1] s=3 d=1 p=valid', function () {
+        var batch = 1;
+        var inDepth = 7;
+        var inHeight = 7;
+        var inWidth = 7;
+        var inChannels = 1;
+        var outChannels = 1;
+        var fSize = 2;
+        var pad = 'valid';
+        var stride = 3;
+        var result = runConv3DTestCase(batch, inDepth, inHeight, inWidth, inChannels, outChannels, fSize, fSize, fSize, pad, stride);
+        var expectedOutput = [
+            0.540816, 0.580175, 0.816327, 0.855685, 2.469388, 2.508746, 2.744898,
+            2.784257
+        ];
+        test_util_1.expectArraysClose(result, expectedOutput);
+    });
+    it('x=[1, 2, 1, 2, 1] f=[2, 1, 2, 1, 2] s=1 d=1 p=valid', function () {
+        var batch = 1;
+        var inDepth = 2;
+        var inHeight = 1;
+        var inWidth = 2;
+        var inChannels = 1;
+        var outChannels = 2;
+        var fDepth = 2;
+        var fHeight = 1;
+        var fWidth = 2;
+        var pad = 'valid';
+        var stride = 1;
+        var result = runConv3DTestCase(batch, inDepth, inHeight, inWidth, inChannels, outChannels, fDepth, fHeight, fWidth, pad, stride);
+        var expectedOutput = [1.5625, 1.875];
+        test_util_1.expectArraysClose(result, expectedOutput);
+    });
+    it('gradient check, x=[1,3,6,1,1] filter=[2,2,1,1,1] s=1 d=1 p=valid', function () {
+        var batch = 1;
+        var inDepth = 3;
+        var inHeight = 6;
+        var inWidth = 1;
+        var inChannels = 1;
+        var outChannels = 1;
+        var fDepth = 2;
+        var fHeight = 2;
+        var fWidth = 1;
+        var pad = 'valid';
+        var stride = 1;
+        var _a = runGradientConv3DTestCase(batch, inDepth, inHeight, inWidth, inChannels, outChannels, fDepth, fHeight, fWidth, pad, stride), dx = _a[0], dfilter = _a[1];
+        var expectedFilterOutput = [60.0, 70.0, 120.0, 130.0];
+        var expectedOutput = [
+            1.0, 3.0, 3.0, 3.0, 3.0, 2.0, 4.0, 10.0, 10.0, 10.0, 10.0, 6.0, 3.0, 7.0,
+            7.0, 7.0, 7.0, 4.0
+        ];
+        test_util_1.expectArraysClose(dx, expectedOutput);
+        test_util_1.expectArraysClose(dfilter, expectedFilterOutput);
+    });
+    it('throws when passed x as a non-tensor', function () {
+        var inputDepth = 1;
+        var outputDepth = 1;
+        var fSize = 1;
+        var pad = 'valid';
+        var stride = 1;
+        var w = tf.tensor5d([2], [fSize, fSize, fSize, inputDepth, outputDepth]);
+        expect(function () { return tf.conv3d({}, w, stride, pad); })
+            .toThrowError(/Argument 'x' passed to 'conv3d' must be a Tensor/);
+    });
+    it('throws when passed filter as a non-tensor', function () {
+        var inputDepth = 1;
+        var inputShape = [2, 2, 1, inputDepth];
+        var pad = 'valid';
+        var stride = 1;
+        var x = tf.tensor4d([1, 2, 3, 4], inputShape);
+        expect(function () { return tf.conv3d(x, {}, stride, pad); })
+            .toThrowError(/Argument 'filter' passed to 'conv3d' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var pad = 'valid';
+        var stride = 1;
+        var x = [[[[1], [2]], [[3], [4]]]];
+        var w = [[[[[2]]]]];
+        var result = tf.conv3d(x, w, stride, pad);
+        test_util_1.expectArraysClose(result, [2, 4, 6, 8]);
+    });
+});
+//# sourceMappingURL=conv3d_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_test.js.map
new file mode 100644
index 0000000..9df64aa
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/conv3d_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/conv_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/conv_util.d.ts
new file mode 100644
index 0000000..57c04f5
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/conv_util.d.ts
@@ -0,0 +1,69 @@
+export declare type PadInfo = {
+    top: number;
+    left: number;
+    right: number;
+    bottom: number;
+    type: string;
+};
+export declare type PadInfo3D = {
+    top: number;
+    left: number;
+    right: number;
+    bottom: number;
+    front: number;
+    back: number;
+    type: string;
+};
+export declare type Conv2DInfo = {
+    batchSize: number;
+    inHeight: number;
+    inWidth: number;
+    inChannels: number;
+    outHeight: number;
+    outWidth: number;
+    outChannels: number;
+    dataFormat: 'channelsFirst' | 'channelsLast';
+    strideHeight: number;
+    strideWidth: number;
+    dilationHeight: number;
+    dilationWidth: number;
+    filterHeight: number;
+    filterWidth: number;
+    effectiveFilterHeight: number;
+    effectiveFilterWidth: number;
+    padInfo: PadInfo;
+    inShape: [number, number, number, number];
+    outShape: [number, number, number, number];
+    filterShape: [number, number, number, number];
+};
+export declare function computePool2DInfo(inShape: [number, number, number, number], filterSize: [number, number] | number, strides: number | [number, number], dilations: number | [number, number], pad: 'same' | 'valid' | number, roundingMode?: 'floor' | 'round' | 'ceil', dataFormat?: 'channelsFirst' | 'channelsLast'): Conv2DInfo;
+export declare function computeConv2DInfo(inShape: [number, number, number, number], filterShape: [number, number, number, number], strides: number | [number, number], dilations: number | [number, number], pad: 'same' | 'valid' | number, roundingMode?: 'floor' | 'round' | 'ceil', depthwise?: boolean, dataFormat?: 'channelsFirst' | 'channelsLast'): Conv2DInfo;
+export declare type Conv3DInfo = {
+    batchSize: number;
+    inDepth: number;
+    inHeight: number;
+    inWidth: number;
+    inChannels: number;
+    outDepth: number;
+    outHeight: number;
+    outWidth: number;
+    outChannels: number;
+    dataFormat: 'channelsFirst' | 'channelsLast';
+    strideDepth: number;
+    strideHeight: number;
+    strideWidth: number;
+    dilationDepth: number;
+    dilationHeight: number;
+    dilationWidth: number;
+    filterDepth: number;
+    filterHeight: number;
+    filterWidth: number;
+    padInfo: PadInfo3D;
+    inShape: [number, number, number, number, number];
+    outShape: [number, number, number, number, number];
+    filterShape: [number, number, number, number, number];
+};
+export declare function computeConv3DInfo(inShape: [number, number, number, number, number], filterShape: [number, number, number, number, number], strides: number | [number, number, number], dilations: number | [number, number, number], pad: 'same' | 'valid', depthwise?: boolean, dataFormat?: 'channelsFirst' | 'channelsLast'): Conv3DInfo;
+export declare function computeDefaultPad(inputShape: [number, number, number], fieldSize: number, stride: number, dilation?: number): number;
+export declare function tupleValuesAreOne(param: number | [number, number]): boolean;
+export declare function eitherStridesOrDilationsAreOne(strides: number | [number, number], dilations: number | [number, number]): boolean;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/conv_util.js b/node_modules/@tensorflow/tfjs-core/dist/ops/conv_util.js
new file mode 100644
index 0000000..4a57fe6
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/conv_util.js
@@ -0,0 +1,253 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var util = require("../util");
+function computePool2DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat) {
+    if (dataFormat === void 0) { dataFormat = 'channelsLast'; }
+    var _a = parseTupleParam(filterSize), filterHeight = _a[0], filterWidth = _a[1];
+    var filterShape;
+    if (dataFormat === 'channelsLast') {
+        filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];
+    }
+    else if (dataFormat === 'channelsFirst') {
+        filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];
+    }
+    else {
+        throw new Error("Unknown dataFormat " + dataFormat);
+    }
+    return computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, false, dataFormat);
+}
+exports.computePool2DInfo = computePool2DInfo;
+function computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, depthwise, dataFormat) {
+    if (depthwise === void 0) { depthwise = false; }
+    if (dataFormat === void 0) { dataFormat = 'channelsLast'; }
+    var _a = [-1, -1, -1, -1], batchSize = _a[0], inHeight = _a[1], inWidth = _a[2], inChannels = _a[3];
+    if (dataFormat === 'channelsLast') {
+        batchSize = inShape[0], inHeight = inShape[1], inWidth = inShape[2], inChannels = inShape[3];
+    }
+    else if (dataFormat === 'channelsFirst') {
+        batchSize = inShape[0], inChannels = inShape[1], inHeight = inShape[2], inWidth = inShape[3];
+    }
+    else {
+        throw new Error("Unknown dataFormat " + dataFormat);
+    }
+    var filterHeight = filterShape[0], filterWidth = filterShape[1], filterChannels = filterShape[3];
+    var _b = parseTupleParam(strides), strideHeight = _b[0], strideWidth = _b[1];
+    var _c = parseTupleParam(dilations), dilationHeight = _c[0], dilationWidth = _c[1];
+    var effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
+    var effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
+    var _d = getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode), padInfo = _d.padInfo, outHeight = _d.outHeight, outWidth = _d.outWidth;
+    var outChannels = depthwise ? filterChannels * inChannels : filterChannels;
+    var outShape;
+    if (dataFormat === 'channelsFirst') {
+        outShape = [batchSize, outChannels, outHeight, outWidth];
+    }
+    else if (dataFormat === 'channelsLast') {
+        outShape = [batchSize, outHeight, outWidth, outChannels];
+    }
+    return {
+        batchSize: batchSize,
+        dataFormat: dataFormat,
+        inHeight: inHeight,
+        inWidth: inWidth,
+        inChannels: inChannels,
+        outHeight: outHeight,
+        outWidth: outWidth,
+        outChannels: outChannels,
+        padInfo: padInfo,
+        strideHeight: strideHeight,
+        strideWidth: strideWidth,
+        filterHeight: filterHeight,
+        filterWidth: filterWidth,
+        effectiveFilterHeight: effectiveFilterHeight,
+        effectiveFilterWidth: effectiveFilterWidth,
+        dilationHeight: dilationHeight,
+        dilationWidth: dilationWidth,
+        inShape: inShape,
+        outShape: outShape,
+        filterShape: filterShape
+    };
+}
+exports.computeConv2DInfo = computeConv2DInfo;
+function computeConv3DInfo(inShape, filterShape, strides, dilations, pad, depthwise, dataFormat) {
+    if (depthwise === void 0) { depthwise = false; }
+    if (dataFormat === void 0) { dataFormat = 'channelsLast'; }
+    var _a = [-1, -1, -1, -1, -1], batchSize = _a[0], inDepth = _a[1], inHeight = _a[2], inWidth = _a[3], inChannels = _a[4];
+    if (dataFormat === 'channelsLast') {
+        batchSize = inShape[0], inDepth = inShape[1], inHeight = inShape[2], inWidth = inShape[3], inChannels = inShape[4];
+    }
+    else if (dataFormat === 'channelsFirst') {
+        batchSize = inShape[0], inChannels = inShape[1], inDepth = inShape[2], inHeight = inShape[3], inWidth = inShape[4];
+    }
+    else {
+        throw new Error("Unknown dataFormat " + dataFormat);
+    }
+    var filterDepth = filterShape[0], filterHeight = filterShape[1], filterWidth = filterShape[2], filterChannels = filterShape[4];
+    var _b = parse3TupleParam(strides), strideDepth = _b[0], strideHeight = _b[1], strideWidth = _b[2];
+    var _c = parse3TupleParam(dilations), dilationDepth = _c[0], dilationHeight = _c[1], dilationWidth = _c[2];
+    var effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);
+    var effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
+    var effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
+    var _d = get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth), padInfo = _d.padInfo, outDepth = _d.outDepth, outHeight = _d.outHeight, outWidth = _d.outWidth;
+    var outChannels = depthwise ? filterChannels * inChannels : filterChannels;
+    var outShape;
+    if (dataFormat === 'channelsFirst') {
+        outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];
+    }
+    else if (dataFormat === 'channelsLast') {
+        outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];
+    }
+    return {
+        batchSize: batchSize,
+        dataFormat: dataFormat,
+        inDepth: inDepth,
+        inHeight: inHeight,
+        inWidth: inWidth,
+        inChannels: inChannels,
+        outDepth: outDepth,
+        outHeight: outHeight,
+        outWidth: outWidth,
+        outChannels: outChannels,
+        padInfo: padInfo,
+        strideDepth: strideDepth,
+        strideHeight: strideHeight,
+        strideWidth: strideWidth,
+        filterDepth: filterDepth,
+        filterHeight: filterHeight,
+        filterWidth: filterWidth,
+        dilationDepth: dilationDepth,
+        dilationHeight: dilationHeight,
+        dilationWidth: dilationWidth,
+        inShape: inShape,
+        outShape: outShape,
+        filterShape: filterShape
+    };
+}
+exports.computeConv3DInfo = computeConv3DInfo;
+function computeOutputShape3D(inShape, fieldSize, outDepth, stride, zeroPad, roundingMode) {
+    if (zeroPad == null) {
+        zeroPad = computeDefaultPad(inShape, fieldSize, stride);
+    }
+    var inputRows = inShape[0];
+    var inputCols = inShape[1];
+    var outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
+    util.assert(util.isInt(outputRows), "The output # of rows (" + outputRows + ") must be an integer. Change the " +
+        "stride and/or zero pad parameters");
+    var outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
+    util.assert(util.isInt(outputCols), "The output # of columns (" + outputCols + ") must be an integer. Change " +
+        "the stride and/or zero pad parameters");
+    return [outputRows, outputCols, outDepth];
+}
+function computeDefaultPad(inputShape, fieldSize, stride, dilation) {
+    if (dilation === void 0) { dilation = 1; }
+    var effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);
+    return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);
+}
+exports.computeDefaultPad = computeDefaultPad;
+function parseTupleParam(param) {
+    return typeof param === 'number' ? [param, param] : param;
+}
+function parse3TupleParam(param) {
+    return typeof param === 'number' ? [param, param, param] : param;
+}
+function getEffectiveFilterSize(filterSize, dilation) {
+    if (dilation <= 1) {
+        return filterSize;
+    }
+    return filterSize + (filterSize - 1) * (dilation - 1);
+}
+function getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode) {
+    var padInfo;
+    var outHeight;
+    var outWidth;
+    if (typeof pad === 'number') {
+        var padType = (pad === 0) ? 'VALID' : 'NUMBER';
+        padInfo = { top: pad, bottom: pad, left: pad, right: pad, type: padType };
+        var outShape = computeOutputShape3D([inHeight, inWidth, 1], filterHeight, 1, strideHeight, pad, roundingMode);
+        outHeight = outShape[0];
+        outWidth = outShape[1];
+    }
+    else if (pad === 'same') {
+        outHeight = Math.ceil(inHeight / strideHeight);
+        outWidth = Math.ceil(inWidth / strideWidth);
+        var padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;
+        var padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;
+        var top_1 = Math.floor(padAlongHeight / 2);
+        var bottom = padAlongHeight - top_1;
+        var left = Math.floor(padAlongWidth / 2);
+        var right = padAlongWidth - left;
+        padInfo = { top: top_1, bottom: bottom, left: left, right: right, type: 'SAME' };
+    }
+    else if (pad === 'valid') {
+        padInfo = { top: 0, bottom: 0, left: 0, right: 0, type: 'VALID' };
+        outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);
+        outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);
+    }
+    else {
+        throw Error("Unknown padding parameter: " + pad);
+    }
+    return { padInfo: padInfo, outHeight: outHeight, outWidth: outWidth };
+}
+function get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth) {
+    var padInfo;
+    var outDepth;
+    var outHeight;
+    var outWidth;
+    if (pad === 'same') {
+        outDepth = Math.ceil(inDepth / strideDepth);
+        outHeight = Math.ceil(inHeight / strideHeight);
+        outWidth = Math.ceil(inWidth / strideWidth);
+        var padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;
+        var padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;
+        var padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;
+        var front = Math.floor(padAlongDepth / 2);
+        var back = padAlongDepth - front;
+        var top_2 = Math.floor(padAlongHeight / 2);
+        var bottom = padAlongHeight - top_2;
+        var left = Math.floor(padAlongWidth / 2);
+        var right = padAlongWidth - left;
+        padInfo = { top: top_2, bottom: bottom, left: left, right: right, front: front, back: back, type: 'SAME' };
+    }
+    else if (pad === 'valid') {
+        padInfo = {
+            top: 0,
+            bottom: 0,
+            left: 0,
+            right: 0,
+            front: 0,
+            back: 0,
+            type: 'VALID'
+        };
+        outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);
+        outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);
+        outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);
+    }
+    else {
+        throw Error("Unknown padding parameter: " + pad);
+    }
+    return { padInfo: padInfo, outDepth: outDepth, outHeight: outHeight, outWidth: outWidth };
+}
+function conditionalRound(value, roundingMode) {
+    if (!roundingMode) {
+        return value;
+    }
+    switch (roundingMode) {
+        case 'round':
+            return Math.round(value);
+        case 'ceil':
+            return Math.ceil(value);
+        case 'floor':
+            return Math.floor(value);
+        default:
+            throw new Error("Unknown roundingMode " + roundingMode);
+    }
+}
+function tupleValuesAreOne(param) {
+    var _a = parseTupleParam(param), dimA = _a[0], dimB = _a[1];
+    return dimA === 1 && dimB === 1;
+}
+exports.tupleValuesAreOne = tupleValuesAreOne;
+function eitherStridesOrDilationsAreOne(strides, dilations) {
+    return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);
+}
+exports.eitherStridesOrDilationsAreOne = eitherStridesOrDilationsAreOne;
+//# sourceMappingURL=conv_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/conv_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/conv_util.js.map
new file mode 100644
index 0000000..1798856
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/conv_util.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/conv_util_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/conv_util_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/conv_util_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/conv_util_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/conv_util_test.js
new file mode 100644
index 0000000..13ecc30
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/conv_util_test.js
@@ -0,0 +1,666 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var conv_util = require("./conv_util");
+describe('conv_util computeConv2DInfo', function () {
+    it('1x1 conv over 1x1 array with same pad', function () {
+        var inShape = [1, 1, 1, 1];
+        var stride = 1;
+        var dilation = 1;
+        var convInfo = conv_util.computeConv2DInfo(inShape, [1, 1, 1, 1], stride, dilation, 'same');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outHeight).toEqual(1);
+        expect(convInfo.outWidth).toEqual(1);
+        expect(convInfo.outChannels).toEqual(1);
+        expect(convInfo.effectiveFilterWidth).toEqual(1);
+        expect(convInfo.effectiveFilterHeight).toEqual(1);
+    });
+    it('2x2 conv over 3x3 array with same pad', function () {
+        var inShape = [1, 3, 3, 1];
+        var stride = 1;
+        var dilation = 1;
+        var convInfo = conv_util.computeConv2DInfo(inShape, [2, 2, 1, 1], stride, dilation, 'same');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outHeight).toEqual(3);
+        expect(convInfo.outWidth).toEqual(3);
+        expect(convInfo.outChannels).toEqual(1);
+        expect(convInfo.effectiveFilterWidth).toEqual(2);
+        expect(convInfo.effectiveFilterHeight).toEqual(2);
+        expect(convInfo.padInfo.left).toBe(0);
+        expect(convInfo.padInfo.right).toBe(1);
+        expect(convInfo.padInfo.top).toBe(0);
+        expect(convInfo.padInfo.bottom).toBe(1);
+    });
+    it('2x2 conv over 3x3 array with same pad', function () {
+        var inShape = [1, 3, 3, 1];
+        var stride = 1;
+        var dilation = 1;
+        var convInfo = conv_util.computeConv2DInfo(inShape, [2, 2, 1, 1], stride, dilation, 'same');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outHeight).toEqual(3);
+        expect(convInfo.outWidth).toEqual(3);
+        expect(convInfo.outChannels).toEqual(1);
+        expect(convInfo.effectiveFilterWidth).toEqual(2);
+        expect(convInfo.effectiveFilterHeight).toEqual(2);
+    });
+    it('2x2 conv over 3x3 array with valid pad', function () {
+        var inShape = [1, 3, 3, 1];
+        var stride = 1;
+        var dilation = 1;
+        var convInfo = conv_util.computeConv2DInfo(inShape, [2, 2, 1, 1], stride, dilation, 'valid');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outHeight).toEqual(2);
+        expect(convInfo.outWidth).toEqual(2);
+        expect(convInfo.outChannels).toEqual(1);
+        expect(convInfo.effectiveFilterWidth).toEqual(2);
+        expect(convInfo.effectiveFilterHeight).toEqual(2);
+    });
+    it('3x3 conv over 5x5 array with same pad with stride 2', function () {
+        var inShape = [1, 5, 5, 1];
+        var stride = 2;
+        var dilation = 1;
+        var convInfo = conv_util.computeConv2DInfo(inShape, [3, 3, 1, 1], stride, dilation, 'same');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outHeight).toEqual(3);
+        expect(convInfo.outWidth).toEqual(3);
+        expect(convInfo.outChannels).toEqual(1);
+        expect(convInfo.effectiveFilterWidth).toEqual(3);
+        expect(convInfo.effectiveFilterHeight).toEqual(3);
+        expect(convInfo.padInfo.left).toBe(1);
+        expect(convInfo.padInfo.right).toBe(1);
+        expect(convInfo.padInfo.top).toBe(1);
+        expect(convInfo.padInfo.bottom).toBe(1);
+    });
+    it('2x2 conv over 3x3 array with valid pad with stride 2', function () {
+        var inShape = [1, 3, 3, 1];
+        var stride = 2;
+        var dilation = 1;
+        var convInfo = conv_util.computeConv2DInfo(inShape, [2, 2, 1, 1], stride, dilation, 'valid');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outHeight).toEqual(1);
+        expect(convInfo.outWidth).toEqual(1);
+        expect(convInfo.outChannels).toEqual(1);
+        expect(convInfo.effectiveFilterWidth).toEqual(2);
+        expect(convInfo.effectiveFilterHeight).toEqual(2);
+    });
+    it('2x1 conv over 3x3 array with valid pad with stride 1', function () {
+        var inShape = [1, 3, 3, 1];
+        var stride = 1;
+        var dilation = 1;
+        var convInfo = conv_util.computeConv2DInfo(inShape, [2, 1, 1, 1], stride, dilation, 'valid');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outHeight).toEqual(2);
+        expect(convInfo.outWidth).toEqual(3);
+        expect(convInfo.outChannels).toEqual(1);
+        expect(convInfo.effectiveFilterWidth).toEqual(1);
+        expect(convInfo.effectiveFilterHeight).toEqual(2);
+    });
+    it('2x1 conv over 3x3 array with valid pad with strides h=2, w=1', function () {
+        var inShape = [1, 3, 3, 1];
+        var strides = [2, 1];
+        var dilation = 1;
+        var convInfo = conv_util.computeConv2DInfo(inShape, [2, 1, 1, 1], strides, dilation, 'valid');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outHeight).toEqual(1);
+        expect(convInfo.outWidth).toEqual(3);
+        expect(convInfo.outChannels).toEqual(1);
+        expect(convInfo.effectiveFilterWidth).toEqual(1);
+        expect(convInfo.effectiveFilterHeight).toEqual(2);
+    });
+    it('1x2 conv over 3x3 array with valid pad with stride 1', function () {
+        var inShape = [1, 3, 3, 1];
+        var stride = 1;
+        var dilation = 1;
+        var convInfo = conv_util.computeConv2DInfo(inShape, [1, 2, 1, 1], stride, dilation, 'valid');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outHeight).toEqual(3);
+        expect(convInfo.outWidth).toEqual(2);
+        expect(convInfo.outChannels).toEqual(1);
+        expect(convInfo.effectiveFilterWidth).toEqual(2);
+        expect(convInfo.effectiveFilterHeight).toEqual(1);
+    });
+    it('1x2 conv over 3x3 array with valid pad with stride 1, batch=5', function () {
+        var inShape = [5, 3, 3, 1];
+        var stride = 1;
+        var dilation = 1;
+        var convInfo = conv_util.computeConv2DInfo(inShape, [1, 2, 1, 1], stride, dilation, 'valid');
+        expect(convInfo.batchSize).toEqual(5);
+        expect(convInfo.outHeight).toEqual(3);
+        expect(convInfo.outWidth).toEqual(2);
+        expect(convInfo.outChannels).toEqual(1);
+        expect(convInfo.effectiveFilterWidth).toEqual(2);
+        expect(convInfo.effectiveFilterHeight).toEqual(1);
+    });
+    it('2x2 conv over 3x3 array with same pad with dilations 2', function () {
+        var inShape = [1, 3, 3, 1];
+        var stride = 1;
+        var dilations = 2;
+        var convInfo = conv_util.computeConv2DInfo(inShape, [2, 2, 1, 1], stride, dilations, 'same');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outHeight).toEqual(3);
+        expect(convInfo.outWidth).toEqual(3);
+        expect(convInfo.outChannels).toEqual(1);
+        expect(convInfo.padInfo.left).toBe(1);
+        expect(convInfo.padInfo.right).toBe(1);
+        expect(convInfo.padInfo.top).toBe(1);
+        expect(convInfo.padInfo.bottom).toBe(1);
+        expect(convInfo.effectiveFilterWidth).toEqual(3);
+        expect(convInfo.effectiveFilterHeight).toEqual(3);
+    });
+    it('2x1 conv over 3x3 array with same pad with dilations 2', function () {
+        var inShape = [1, 3, 3, 1];
+        var stride = 1;
+        var dilations = 2;
+        var convInfo = conv_util.computeConv2DInfo(inShape, [2, 1, 1, 1], stride, dilations, 'same');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outHeight).toEqual(3);
+        expect(convInfo.outWidth).toEqual(3);
+        expect(convInfo.outChannels).toEqual(1);
+        expect(convInfo.padInfo.left).toBe(0);
+        expect(convInfo.padInfo.right).toBe(0);
+        expect(convInfo.padInfo.top).toBe(1);
+        expect(convInfo.padInfo.bottom).toBe(1);
+        expect(convInfo.effectiveFilterWidth).toEqual(1);
+        expect(convInfo.effectiveFilterHeight).toEqual(3);
+    });
+    it('3x4 conv over 8x8 array with same pad with dilations h=4 w=3', function () {
+        var inShape = [1, 8, 8, 1];
+        var stride = 1;
+        var dilations = [4, 3];
+        var convInfo = conv_util.computeConv2DInfo(inShape, [3, 4, 1, 1], stride, dilations, 'same');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outHeight).toEqual(8);
+        expect(convInfo.outWidth).toEqual(8);
+        expect(convInfo.outChannels).toEqual(1);
+        expect(convInfo.effectiveFilterWidth).toEqual(10);
+        expect(convInfo.effectiveFilterHeight).toEqual(9);
+        expect(convInfo.padInfo.left).toBe(4);
+        expect(convInfo.padInfo.right).toBe(5);
+        expect(convInfo.padInfo.top).toBe(4);
+        expect(convInfo.padInfo.bottom).toBe(4);
+    });
+    it('2x1 conv over 3x3 array with valid pad with dilations 2', function () {
+        var inShape = [1, 3, 3, 1];
+        var stride = 1;
+        var dilations = 2;
+        var convInfo = conv_util.computeConv2DInfo(inShape, [2, 1, 1, 1], stride, dilations, 'valid');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outHeight).toEqual(1);
+        expect(convInfo.outWidth).toEqual(3);
+        expect(convInfo.outChannels).toEqual(1);
+        expect(convInfo.effectiveFilterWidth).toEqual(1);
+        expect(convInfo.effectiveFilterHeight).toEqual(3);
+    });
+    it('2x2 conv over 3x3 array with valid pad with dilations 2', function () {
+        var inShape = [1, 3, 3, 1];
+        var stride = 1;
+        var dilations = 2;
+        var convInfo = conv_util.computeConv2DInfo(inShape, [2, 2, 1, 1], stride, dilations, 'valid');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outHeight).toEqual(1);
+        expect(convInfo.outWidth).toEqual(1);
+        expect(convInfo.outChannels).toEqual(1);
+        expect(convInfo.effectiveFilterWidth).toEqual(3);
+        expect(convInfo.effectiveFilterHeight).toEqual(3);
+    });
+    it('2x2 conv over 4x4 array with valid pad with dilations 2', function () {
+        var inShape = [1, 4, 4, 1];
+        var stride = 1;
+        var dilations = 2;
+        var convInfo = conv_util.computeConv2DInfo(inShape, [2, 2, 1, 1], stride, dilations, 'valid');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outHeight).toEqual(2);
+        expect(convInfo.outWidth).toEqual(2);
+        expect(convInfo.outChannels).toEqual(1);
+        expect(convInfo.effectiveFilterWidth).toEqual(3);
+        expect(convInfo.effectiveFilterHeight).toEqual(3);
+    });
+});
+describe('conv_util computeConv3DInfo', function () {
+    it('1x1x1 conv over 1x1x1 array with same pad', function () {
+        var inShape = [1, 1, 1, 1, 1];
+        var stride = 1;
+        var dilation = 1;
+        var convInfo = conv_util.computeConv3DInfo(inShape, [1, 1, 1, 1, 1], stride, dilation, 'same');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outDepth).toEqual(1);
+        expect(convInfo.outHeight).toEqual(1);
+        expect(convInfo.outWidth).toEqual(1);
+        expect(convInfo.outChannels).toEqual(1);
+    });
+    it('2x2x2 conv over 3x3x3 array with same pad', function () {
+        var inShape = [1, 3, 3, 3, 1];
+        var stride = 1;
+        var dilation = 1;
+        var convInfo = conv_util.computeConv3DInfo(inShape, [2, 2, 2, 1, 1], stride, dilation, 'same');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outDepth).toEqual(3);
+        expect(convInfo.outHeight).toEqual(3);
+        expect(convInfo.outWidth).toEqual(3);
+        expect(convInfo.outChannels).toEqual(1);
+        expect(convInfo.padInfo.front).toBe(0);
+        expect(convInfo.padInfo.back).toBe(1);
+        expect(convInfo.padInfo.left).toBe(0);
+        expect(convInfo.padInfo.right).toBe(1);
+        expect(convInfo.padInfo.top).toBe(0);
+        expect(convInfo.padInfo.bottom).toBe(1);
+    });
+    it('2x2x2 conv over 3x3x3 array with same pad', function () {
+        var inShape = [1, 3, 3, 3, 1];
+        var stride = 1;
+        var dilation = 1;
+        var convInfo = conv_util.computeConv3DInfo(inShape, [2, 2, 2, 1, 1], stride, dilation, 'same');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outDepth).toEqual(3);
+        expect(convInfo.outHeight).toEqual(3);
+        expect(convInfo.outWidth).toEqual(3);
+        expect(convInfo.outChannels).toEqual(1);
+    });
+    it('2x2x2 conv over 3x3x3 array with valid pad', function () {
+        var inShape = [1, 3, 3, 3, 1];
+        var stride = 1;
+        var dilation = 1;
+        var convInfo = conv_util.computeConv3DInfo(inShape, [2, 2, 2, 1, 1], stride, dilation, 'valid');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outDepth).toEqual(2);
+        expect(convInfo.outHeight).toEqual(2);
+        expect(convInfo.outWidth).toEqual(2);
+        expect(convInfo.outChannels).toEqual(1);
+    });
+    it('3x3x3 conv over 5x5x5 array with same pad with stride 2', function () {
+        var inShape = [1, 5, 5, 5, 1];
+        var stride = 2;
+        var dilation = 1;
+        var convInfo = conv_util.computeConv3DInfo(inShape, [3, 3, 3, 1, 1], stride, dilation, 'same');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outDepth).toEqual(3);
+        expect(convInfo.outHeight).toEqual(3);
+        expect(convInfo.outWidth).toEqual(3);
+        expect(convInfo.outChannels).toEqual(1);
+        expect(convInfo.padInfo.front).toBe(1);
+        expect(convInfo.padInfo.back).toBe(1);
+        expect(convInfo.padInfo.left).toBe(1);
+        expect(convInfo.padInfo.right).toBe(1);
+        expect(convInfo.padInfo.top).toBe(1);
+        expect(convInfo.padInfo.bottom).toBe(1);
+    });
+    it('2x2x2 conv over 3x3x3 array with valid pad with stride 2', function () {
+        var inShape = [1, 3, 3, 3, 1];
+        var stride = 2;
+        var dilation = 1;
+        var convInfo = conv_util.computeConv3DInfo(inShape, [2, 2, 2, 1, 1], stride, dilation, 'valid');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outDepth).toEqual(1);
+        expect(convInfo.outHeight).toEqual(1);
+        expect(convInfo.outWidth).toEqual(1);
+        expect(convInfo.outChannels).toEqual(1);
+    });
+    it('2x1x1 conv over 3x3x3 array with valid pad with stride 1', function () {
+        var inShape = [1, 3, 3, 3, 1];
+        var stride = 1;
+        var dilation = 1;
+        var convInfo = conv_util.computeConv3DInfo(inShape, [2, 1, 1, 1, 1], stride, dilation, 'valid');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outDepth).toEqual(2);
+        expect(convInfo.outHeight).toEqual(3);
+        expect(convInfo.outWidth).toEqual(3);
+        expect(convInfo.outChannels).toEqual(1);
+    });
+    it('2x1x1 conv over 3x3x3 array with valid pad with strides d=2, h=1, w=1', function () {
+        var inShape = [1, 3, 3, 3, 1];
+        var strides = [2, 1, 1];
+        var dilation = 1;
+        var convInfo = conv_util.computeConv3DInfo(inShape, [2, 1, 1, 1, 1], strides, dilation, 'valid');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outDepth).toEqual(1);
+        expect(convInfo.outHeight).toEqual(3);
+        expect(convInfo.outWidth).toEqual(3);
+        expect(convInfo.outChannels).toEqual(1);
+    });
+    it('1x2x2 conv over 3x3x3 array with valid pad with stride 1', function () {
+        var inShape = [1, 3, 3, 3, 1];
+        var stride = 1;
+        var dilation = 1;
+        var convInfo = conv_util.computeConv3DInfo(inShape, [1, 2, 2, 1, 1], stride, dilation, 'valid');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outDepth).toEqual(3);
+        expect(convInfo.outHeight).toEqual(2);
+        expect(convInfo.outWidth).toEqual(2);
+        expect(convInfo.outChannels).toEqual(1);
+    });
+    it('1x2x2 conv over 3x3x3 array with valid pad with stride 1, batch=5', function () {
+        var inShape = [5, 3, 3, 3, 1];
+        var stride = 1;
+        var dilation = 1;
+        var convInfo = conv_util.computeConv3DInfo(inShape, [1, 2, 2, 1, 1], stride, dilation, 'valid');
+        expect(convInfo.batchSize).toEqual(5);
+        expect(convInfo.outDepth).toEqual(3);
+        expect(convInfo.outHeight).toEqual(2);
+        expect(convInfo.outWidth).toEqual(2);
+        expect(convInfo.outChannels).toEqual(1);
+    });
+    it('2x2x2 conv over 3x3x3 array with same pad with dilations 2', function () {
+        var inShape = [1, 3, 3, 3, 1];
+        var stride = 1;
+        var dilations = 2;
+        var convInfo = conv_util.computeConv3DInfo(inShape, [2, 2, 2, 1, 1], stride, dilations, 'same');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outDepth).toEqual(3);
+        expect(convInfo.outHeight).toEqual(3);
+        expect(convInfo.outWidth).toEqual(3);
+        expect(convInfo.outChannels).toEqual(1);
+        expect(convInfo.padInfo.front).toBe(1);
+        expect(convInfo.padInfo.back).toBe(1);
+        expect(convInfo.padInfo.left).toBe(1);
+        expect(convInfo.padInfo.right).toBe(1);
+        expect(convInfo.padInfo.top).toBe(1);
+        expect(convInfo.padInfo.bottom).toBe(1);
+    });
+    it('2x1x1 conv over 3x3x3 array with same pad with dilations 2', function () {
+        var inShape = [1, 3, 3, 3, 1];
+        var stride = 1;
+        var dilations = 2;
+        var convInfo = conv_util.computeConv3DInfo(inShape, [2, 1, 1, 1, 1], stride, dilations, 'same');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outDepth).toEqual(3);
+        expect(convInfo.outHeight).toEqual(3);
+        expect(convInfo.outWidth).toEqual(3);
+        expect(convInfo.outChannels).toEqual(1);
+        expect(convInfo.padInfo.front).toBe(1);
+        expect(convInfo.padInfo.back).toBe(1);
+        expect(convInfo.padInfo.left).toBe(0);
+        expect(convInfo.padInfo.right).toBe(0);
+        expect(convInfo.padInfo.top).toBe(0);
+        expect(convInfo.padInfo.bottom).toBe(0);
+    });
+    it('3x4x4 conv over 8x8 array with same pad with dilations d=4 h=3 w=3', function () {
+        var inShape = [1, 8, 8, 8, 1];
+        var stride = 1;
+        var dilations = [4, 3, 3];
+        var convInfo = conv_util.computeConv3DInfo(inShape, [3, 4, 4, 1, 1], stride, dilations, 'same');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outDepth).toEqual(8);
+        expect(convInfo.outHeight).toEqual(8);
+        expect(convInfo.outWidth).toEqual(8);
+        expect(convInfo.outChannels).toEqual(1);
+        expect(convInfo.padInfo.front).toBe(4);
+        expect(convInfo.padInfo.back).toBe(4);
+        expect(convInfo.padInfo.left).toBe(4);
+        expect(convInfo.padInfo.right).toBe(5);
+        expect(convInfo.padInfo.top).toBe(4);
+        expect(convInfo.padInfo.bottom).toBe(5);
+    });
+    it('2x1x1 conv over 3x3x3 array with valid pad with dilations 2', function () {
+        var inShape = [1, 3, 3, 3, 1];
+        var stride = 1;
+        var dilations = 2;
+        var convInfo = conv_util.computeConv3DInfo(inShape, [2, 1, 1, 1, 1], stride, dilations, 'valid');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outDepth).toEqual(1);
+        expect(convInfo.outHeight).toEqual(3);
+        expect(convInfo.outWidth).toEqual(3);
+        expect(convInfo.outChannels).toEqual(1);
+    });
+    it('2x2x2 conv over 3x3x3 array with valid pad with dilations 2', function () {
+        var inShape = [1, 3, 3, 3, 1];
+        var stride = 1;
+        var dilations = 2;
+        var convInfo = conv_util.computeConv3DInfo(inShape, [2, 2, 2, 1, 1], stride, dilations, 'valid');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outDepth).toEqual(1);
+        expect(convInfo.outHeight).toEqual(1);
+        expect(convInfo.outWidth).toEqual(1);
+        expect(convInfo.outChannels).toEqual(1);
+    });
+    it('2x2x2 conv over 4x4x4 array with valid pad with dilations 2', function () {
+        var inShape = [1, 4, 4, 4, 1];
+        var stride = 1;
+        var dilations = 2;
+        var convInfo = conv_util.computeConv3DInfo(inShape, [2, 2, 2, 1, 1], stride, dilations, 'valid');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outDepth).toEqual(2);
+        expect(convInfo.outHeight).toEqual(2);
+        expect(convInfo.outWidth).toEqual(2);
+        expect(convInfo.outChannels).toEqual(1);
+    });
+});
+describe('conv_util computeConv2DInfo with depthwise=true', function () {
+    it('1x1 filter over 1x1 array with same pad', function () {
+        var inChannels = 1;
+        var inShape = [1, 1, 1, inChannels];
+        var fSize = 1;
+        var chMul = 1;
+        var stride = 1;
+        var dilation = 1;
+        var pad = 'same';
+        var convInfo = conv_util.computeConv2DInfo(inShape, [fSize, fSize, inChannels, chMul], stride, dilation, pad, null, true);
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outHeight).toEqual(1);
+        expect(convInfo.outWidth).toEqual(1);
+        expect(convInfo.outChannels).toEqual(1);
+        expect(convInfo.effectiveFilterWidth).toEqual(1);
+        expect(convInfo.effectiveFilterHeight).toEqual(1);
+    });
+    it('2x2 filter over 3x3 array with same pad, chMul=3, depth=2', function () {
+        var inChannels = 2;
+        var batchSize = 1;
+        var inSize = 3;
+        var inShape = [batchSize, inSize, inSize, inChannels];
+        var fSize = 2;
+        var chMul = 3;
+        var stride = 1;
+        var dilation = 1;
+        var pad = 'same';
+        var convInfo = conv_util.computeConv2DInfo(inShape, [fSize, fSize, inChannels, chMul], stride, dilation, pad, null, true);
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outHeight).toEqual(3);
+        expect(convInfo.outWidth).toEqual(3);
+        expect(convInfo.outChannels).toEqual(6);
+        expect(convInfo.effectiveFilterWidth).toEqual(2);
+        expect(convInfo.effectiveFilterHeight).toEqual(2);
+    });
+    it('2x2 filter over 3x3 array with valid pad, chMul=3, depth=2', function () {
+        var inChannels = 2;
+        var batchSize = 1;
+        var inSize = 3;
+        var inShape = [batchSize, inSize, inSize, inChannels];
+        var fSize = 2;
+        var chMul = 3;
+        var stride = 1;
+        var dilation = 1;
+        var pad = 'valid';
+        var convInfo = conv_util.computeConv2DInfo(inShape, [fSize, fSize, inChannels, chMul], stride, dilation, pad, null, true);
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outHeight).toEqual(2);
+        expect(convInfo.outWidth).toEqual(2);
+        expect(convInfo.outChannels).toEqual(6);
+        expect(convInfo.effectiveFilterWidth).toEqual(2);
+        expect(convInfo.effectiveFilterHeight).toEqual(2);
+    });
+});
+describe('conv_util computeConv3DInfo with depthwise=true', function () {
+    it('1x1x1 filter over 1x1x1 array with same pad', function () {
+        var inChannels = 1;
+        var inShape = [1, 1, 1, 1, inChannels];
+        var fSize = 1;
+        var chMul = 1;
+        var stride = 1;
+        var dilation = 1;
+        var pad = 'same';
+        var convInfo = conv_util.computeConv3DInfo(inShape, [fSize, fSize, fSize, inChannels, chMul], stride, dilation, pad, true);
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outDepth).toEqual(1);
+        expect(convInfo.outHeight).toEqual(1);
+        expect(convInfo.outWidth).toEqual(1);
+        expect(convInfo.outChannels).toEqual(1);
+    });
+    it('2x2x2 filter over 3x3x3 array with same pad, chMul=3, depth=2', function () {
+        var inChannels = 2;
+        var batchSize = 1;
+        var inSize = 3;
+        var inShape = [batchSize, inSize, inSize, inSize, inChannels];
+        var fSize = 2;
+        var chMul = 3;
+        var stride = 1;
+        var dilation = 1;
+        var pad = 'same';
+        var convInfo = conv_util.computeConv3DInfo(inShape, [fSize, fSize, fSize, inChannels, chMul], stride, dilation, pad, true);
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outDepth).toEqual(3);
+        expect(convInfo.outHeight).toEqual(3);
+        expect(convInfo.outWidth).toEqual(3);
+        expect(convInfo.outChannels).toEqual(6);
+    });
+    it('2x2x2 filter over 3x3x3 array with valid pad, chMul=3, depth=2', function () {
+        var inChannels = 2;
+        var batchSize = 1;
+        var inSize = 3;
+        var inShape = [batchSize, inSize, inSize, inSize, inChannels];
+        var fSize = 2;
+        var chMul = 3;
+        var stride = 1;
+        var dilation = 1;
+        var pad = 'valid';
+        var convInfo = conv_util.computeConv3DInfo(inShape, [fSize, fSize, fSize, inChannels, chMul], stride, dilation, pad, true);
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outDepth).toEqual(2);
+        expect(convInfo.outHeight).toEqual(2);
+        expect(convInfo.outWidth).toEqual(2);
+        expect(convInfo.outChannels).toEqual(6);
+    });
+});
+describe('conv_util computeConv2DInfo channelsFirst', function () {
+    it('2x2 conv over 3x3 array with same pad', function () {
+        var inDepth = 2;
+        var outDepth = 4;
+        var inShape = [1, inDepth, 3, 3];
+        var stride = 1;
+        var dilation = 1;
+        var convInfo = conv_util.computeConv2DInfo(inShape, [2, 2, inDepth, outDepth], stride, dilation, 'same', null, false, 'channelsFirst');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outHeight).toEqual(3);
+        expect(convInfo.outWidth).toEqual(3);
+        expect(convInfo.outChannels).toEqual(4);
+        expect(convInfo.outShape).toEqual([1, 4, 3, 3]);
+        expect(convInfo.effectiveFilterWidth).toEqual(2);
+        expect(convInfo.effectiveFilterHeight).toEqual(2);
+        expect(convInfo.padInfo.left).toBe(0);
+        expect(convInfo.padInfo.right).toBe(1);
+        expect(convInfo.padInfo.top).toBe(0);
+        expect(convInfo.padInfo.bottom).toBe(1);
+    });
+    it('2x2 conv over 3x3 array with valid pad', function () {
+        var inDepth = 6;
+        var outDepth = 16;
+        var inShape = [1, inDepth, 3, 3];
+        var stride = 1;
+        var dilation = 1;
+        var convInfo = conv_util.computeConv2DInfo(inShape, [2, 2, inDepth, outDepth], stride, dilation, 'valid', null, false, 'channelsFirst');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outHeight).toEqual(2);
+        expect(convInfo.outWidth).toEqual(2);
+        expect(convInfo.outChannels).toEqual(16);
+        expect(convInfo.outShape).toEqual([1, 16, 2, 2]);
+        expect(convInfo.effectiveFilterWidth).toEqual(2);
+        expect(convInfo.effectiveFilterHeight).toEqual(2);
+        expect(convInfo.padInfo.left).toBe(0);
+        expect(convInfo.padInfo.right).toBe(0);
+        expect(convInfo.padInfo.top).toBe(0);
+        expect(convInfo.padInfo.bottom).toBe(0);
+    });
+});
+describe('conv_util computeConv3DInfo channelsFirst', function () {
+    it('2x2x2 conv over 3x3x3 array with same pad', function () {
+        var inDepth = 2;
+        var outDepth = 4;
+        var inShape = [1, inDepth, 3, 3, 3];
+        var stride = 1;
+        var dilation = 1;
+        var convInfo = conv_util.computeConv3DInfo(inShape, [2, 2, 2, inDepth, outDepth], stride, dilation, 'same', false, 'channelsFirst');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outDepth).toEqual(3);
+        expect(convInfo.outHeight).toEqual(3);
+        expect(convInfo.outWidth).toEqual(3);
+        expect(convInfo.outChannels).toEqual(4);
+        expect(convInfo.outShape).toEqual([1, 4, 3, 3, 3]);
+        expect(convInfo.padInfo.front).toBe(0);
+        expect(convInfo.padInfo.back).toBe(1);
+        expect(convInfo.padInfo.left).toBe(0);
+        expect(convInfo.padInfo.right).toBe(1);
+        expect(convInfo.padInfo.top).toBe(0);
+        expect(convInfo.padInfo.bottom).toBe(1);
+    });
+    it('2x2x2 conv over 3x3x3 array with valid pad', function () {
+        var inDepth = 6;
+        var outDepth = 16;
+        var inShape = [1, inDepth, 3, 3, 3];
+        var stride = 1;
+        var dilation = 1;
+        var convInfo = conv_util.computeConv3DInfo(inShape, [2, 2, 2, inDepth, outDepth], stride, dilation, 'valid', false, 'channelsFirst');
+        expect(convInfo.batchSize).toEqual(1);
+        expect(convInfo.outDepth).toEqual(2);
+        expect(convInfo.outHeight).toEqual(2);
+        expect(convInfo.outWidth).toEqual(2);
+        expect(convInfo.outChannels).toEqual(16);
+        expect(convInfo.outShape).toEqual([1, 16, 2, 2, 2]);
+        expect(convInfo.padInfo.front).toBe(0);
+        expect(convInfo.padInfo.back).toBe(0);
+        expect(convInfo.padInfo.left).toBe(0);
+        expect(convInfo.padInfo.right).toBe(0);
+        expect(convInfo.padInfo.top).toBe(0);
+        expect(convInfo.padInfo.bottom).toBe(0);
+    });
+});
+describe('conv_util computeConv2DInfo roundingMode', function () {
+    var inChannels = 6;
+    var batchSize = 1;
+    var inSize = 5;
+    var inShape = [batchSize, inSize, inSize, inChannels];
+    var fSize = 2;
+    var chMul = 12;
+    var stride = 2;
+    var dilation = 1;
+    var pad = 1;
+    it('should fail computing the output dimension of Conv Layer', function () {
+        expect(function () { return conv_util.computeConv2DInfo(inShape, [fSize, fSize, inChannels, chMul], stride, dilation, pad); })
+            .toThrowError();
+    });
+    it('Floor the output dimension of Conv Layer', function () {
+        var convInfo = conv_util.computeConv2DInfo(inShape, [fSize, fSize, inChannels, chMul], stride, dilation, pad, 'floor');
+        expect(convInfo.outShape).toEqual([batchSize, 3, 3, chMul]);
+    });
+    it('Round the output dimension of Conv Layer', function () {
+        var convInfo = conv_util.computeConv2DInfo(inShape, [fSize, fSize, inChannels, chMul], stride, dilation, pad, 'round');
+        expect(convInfo.outShape).toEqual([batchSize, 4, 4, chMul]);
+    });
+    it('Ceil the output dimension of Conv Layer', function () {
+        var convInfo = conv_util.computeConv2DInfo(inShape, [fSize, fSize, inChannels, chMul], stride, dilation, pad, 'ceil');
+        expect(convInfo.outShape).toEqual([batchSize, 4, 4, chMul]);
+    });
+});
+describe('conv_util computePoolInfo roundingMode', function () {
+    var inChannels = 6;
+    var batchSize = 1;
+    var inSize = 5;
+    var inShape = [batchSize, inSize, inSize, inChannels];
+    var fSize = 2;
+    var stride = 2;
+    var dilation = 1;
+    var pad = 1;
+    it('should fail computing the output dimension of Pool Layer', function () {
+        expect(function () { return conv_util.computePool2DInfo(inShape, [fSize, fSize], stride, dilation, pad); })
+            .toThrowError();
+    });
+    it('Floor the output dimension of Pool Layer', function () {
+        var poolInfo = conv_util.computePool2DInfo(inShape, [fSize, fSize], stride, pad, dilation, 'floor');
+        expect(poolInfo.outShape).toEqual([batchSize, 3, 3, inChannels]);
+    });
+    it('Round the output dimension of Pool Layer', function () {
+        var poolInfo = conv_util.computePool2DInfo(inShape, [fSize, fSize], stride, pad, dilation, 'round');
+        expect(poolInfo.outShape).toEqual([batchSize, 4, 4, inChannels]);
+    });
+    it('Ceil the output dimension of Pool Layer', function () {
+        var poolInfo = conv_util.computePool2DInfo(inShape, [fSize, fSize], stride, pad, dilation, 'ceil');
+        expect(poolInfo.outShape).toEqual([batchSize, 4, 4, inChannels]);
+    });
+});
+//# sourceMappingURL=conv_util_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/conv_util_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/conv_util_test.js.map
new file mode 100644
index 0000000..51d4e21
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/conv_util_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/erf_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/erf_util.d.ts
new file mode 100644
index 0000000..d05d1e0
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/erf_util.d.ts
@@ -0,0 +1,6 @@
+export declare const ERF_P = 0.3275911;
+export declare const ERF_A1 = 0.254829592;
+export declare const ERF_A2 = -0.284496736;
+export declare const ERF_A3 = 1.421413741;
+export declare const ERF_A4 = -1.453152027;
+export declare const ERF_A5 = 1.061405429;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/erf_util.js b/node_modules/@tensorflow/tfjs-core/dist/ops/erf_util.js
new file mode 100644
index 0000000..0987a81
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/erf_util.js
@@ -0,0 +1,9 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+exports.ERF_P = 0.3275911;
+exports.ERF_A1 = 0.254829592;
+exports.ERF_A2 = -0.284496736;
+exports.ERF_A3 = 1.421413741;
+exports.ERF_A4 = -1.453152027;
+exports.ERF_A5 = 1.061405429;
+//# sourceMappingURL=erf_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/erf_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/erf_util.js.map
new file mode 100644
index 0000000..d67cb7d
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/erf_util.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"erf_util.js","sourceRoot":"","sources":["../../src/ops/erf_util.ts"],"names":[],"mappings":";;AAiBa,QAAA,KAAK,GAAG,SAAS,CAAC;AAClB,QAAA,MAAM,GAAG,WAAW,CAAC;AACrB,QAAA,MAAM,GAAG,CAAC,WAAW,CAAC;AACtB,QAAA,MAAM,GAAG,WAAW,CAAC;AACrB,QAAA,MAAM,GAAG,CAAC,WAAW,CAAC;AACtB,QAAA,MAAM,GAAG,WAAW,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd.d.ts
new file mode 100644
index 0000000..d9eadf1
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd.d.ts
@@ -0,0 +1,5 @@
+import { Tensor } from '../tensor';
+import { Rank, TensorLike } from '../types';
+declare function gatherND_(x: Tensor | TensorLike, indices: Tensor | TensorLike): Tensor<Rank>;
+export declare const gatherND: typeof gatherND_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd.js b/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd.js
new file mode 100644
index 0000000..705c46f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd.js
@@ -0,0 +1,12 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var tensor_util_env_1 = require("../tensor_util_env");
+var operation_1 = require("./operation");
+function gatherND_(x, indices) {
+    var $indices = tensor_util_env_1.convertToTensor(indices, 'indices', 'gatherND', 'int32');
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'gatherND');
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.gatherND($x, $indices); }, { $x: $x, $indices: $indices });
+}
+exports.gatherND = operation_1.op({ gatherND_: gatherND_ });
+//# sourceMappingURL=gather_nd.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd.js.map
new file mode 100644
index 0000000..a6a377a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"gather_nd.js","sourceRoot":"","sources":["../../src/ops/gather_nd.ts"],"names":[],"mappings":";;AAgBA,8CAAmC;AAEnC,sDAAmD;AAEnD,yCAA+B;AAuC/B,mBACI,CAAoB,EAAE,OAA0B;IAClD,IAAM,QAAQ,GAAG,iCAAe,CAAC,OAAO,EAAE,SAAS,EAAE,UAAU,EAAE,OAAO,CAAC,CAAC;IAC1E,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,UAAU,CAAC,CAAC;IAC/C,OAAO,iBAAG,CAAC,MAAM,CAAC,SAAS,CAChB,UAAA,OAAO,IAAI,OAAA,OAAO,CAAC,QAAQ,CAAC,EAAE,EAAE,QAAQ,CAAC,EAA9B,CAA8B,EAAE,EAAC,EAAE,IAAA,EAAE,QAAQ,UAAA,EAAC,CACpD,CAAC;AACnB,CAAC;AACY,QAAA,QAAQ,GAAG,cAAE,CAAC,EAAC,SAAS,WAAA,EAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_test.js
new file mode 100644
index 0000000..991fb08
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_test.js
@@ -0,0 +1,111 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+var gather_nd_1 = require("./gather_nd");
+var tensor_ops_1 = require("./tensor_ops");
+jasmine_util_1.describeWithFlags('gatherND', test_util_1.ALL_ENVS, function () {
+    it('should work for simple slice', function () {
+        var indices = tensor_ops_1.tensor2d([0, 4, 8], [3, 1], 'int32');
+        var input = tensor_ops_1.tensor1d([100, 101, 102, 777, 778, 779, 1000, 1001, 1002], 'int32');
+        var shape = [3];
+        var result = gather_nd_1.gatherND(input, indices);
+        expect(result.shape).toEqual(shape);
+        expect(result.dtype).toEqual(input.dtype);
+        test_util_1.expectArraysClose(result, [100, 778, 1002]);
+    });
+    it('should work for indexing 2d', function () {
+        var indices = tensor_ops_1.tensor2d([0, 2], [2, 1], 'int32');
+        var input = tensor_ops_1.tensor2d([
+            5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8,
+            5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8
+        ], [8, 4], 'float32');
+        var shape = [2, 4];
+        var result = gather_nd_1.gatherND(input, indices);
+        expect(result.shape).toEqual(shape);
+        expect(result.dtype).toEqual(input.dtype);
+        test_util_1.expectArraysClose(result, [5, 5, 5, 5, 7, 7, 7, 7]);
+    });
+    it('should work for indexing 3d', function () {
+        var indices = tensor_ops_1.tensor2d([0, 2, 1, 1], [2, 2], 'int32');
+        var input = tensor_ops_1.tensor3d([
+            5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8,
+            5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8
+        ], [2, 4, 4], 'float32');
+        var shape = [2, 4];
+        var result = gather_nd_1.gatherND(input, indices);
+        expect(result.shape).toEqual(shape);
+        expect(result.dtype).toEqual(input.dtype);
+        test_util_1.expectArraysClose(result, [7, 7, 7, 7, 6, 6, 6, 6]);
+    });
+    it('should work for batch slice', function () {
+        var indices = tensor_ops_1.tensor3d([0, 4, 2], [3, 1, 1], 'int32');
+        var input = tensor_ops_1.tensor1d([100, 101, 102, 777, 778, 779, 10000, 10001, 10002], 'int32');
+        var shape = [3, 1];
+        var result = gather_nd_1.gatherND(input, indices);
+        expect(result.shape).toEqual(shape);
+        expect(result.dtype).toEqual(input.dtype);
+        test_util_1.expectArraysClose(result, [100, 778, 102]);
+    });
+    it('should work for batch indexing 2d', function () {
+        var indices = tensor_ops_1.tensor3d([0, 2], [2, 1, 1], 'int32');
+        var input = tensor_ops_1.tensor2d([
+            5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8,
+            5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8
+        ], [8, 4], 'float32');
+        var shape = [2, 1, 4];
+        var result = gather_nd_1.gatherND(input, indices);
+        expect(result.shape).toEqual(shape);
+        expect(result.dtype).toEqual(input.dtype);
+        test_util_1.expectArraysClose(result, [5, 5, 5, 5, 7, 7, 7, 7]);
+    });
+    it('should work for batch indexing 3d', function () {
+        var indices = tensor_ops_1.tensor3d([0, 2, 1, 1], [2, 1, 2], 'int32');
+        var input = tensor_ops_1.tensor3d([
+            5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8,
+            5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8
+        ], [2, 4, 4], 'float32');
+        var shape = [2, 1, 4];
+        var result = gather_nd_1.gatherND(input, indices);
+        expect(result.shape).toEqual(shape);
+        expect(result.dtype).toEqual(input.dtype);
+        test_util_1.expectArraysClose(result, [7, 7, 7, 7, 6, 6, 6, 6]);
+    });
+    it('should work for TensorLike inputs', function () {
+        var indices = [[0], [4], [8]];
+        var input = [100, 101, 102, 777, 778, 779, 1000, 1001, 1002];
+        var shape = [3];
+        var result = gather_nd_1.gatherND(input, indices);
+        expect(result.shape).toEqual(shape);
+        expect(result.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(result, [100, 778, 1002]);
+    });
+    it('should throw error when indices are not int32', function () {
+        var indices = tensor_ops_1.tensor1d([1], 'float32');
+        var input = tensor_ops_1.tensor2d([100, 101, 102, 103, 777, 778, 779, 780, 10000, 10001, 10002, 10004], [3, 4], 'float32');
+        expect(function () { return gather_nd_1.gatherND(input, indices); }).toThrow();
+    });
+    it('should throw error when indices are scalar', function () {
+        var indices = tensor_ops_1.scalar(1, 'int32');
+        var input = tensor_ops_1.tensor2d([100, 101, 102, 103, 777, 778, 779, 780, 10000, 10001, 10002, 10004], [3, 4], 'float32');
+        expect(function () { return gather_nd_1.gatherND(input, indices); }).toThrow();
+    });
+    it('should throw error when x is scalar', function () {
+        var indices = tensor_ops_1.tensor2d([0, 4, 2], [3, 1], 'int32');
+        var input = tensor_ops_1.scalar(1.0, 'float32');
+        expect(function () { return gather_nd_1.gatherND(input, indices); }).toThrow();
+    });
+    it('should throw error when indices inner dim > x shape length', function () {
+        var indices = tensor_ops_1.tensor2d([0, 4, 2], [1, 3], 'int32');
+        var input = tensor_ops_1.tensor2d([100, 101, 102, 10000, 10001, 10002], [3, 2], 'float32');
+        expect(function () { return gather_nd_1.gatherND(input, indices); }).toThrow();
+    });
+});
+jasmine_util_1.describeWithFlags('gatherND CPU', test_util_1.CPU_ENVS, function () {
+    it('should throw error when index out of range', function () {
+        var indices = tensor_ops_1.tensor2d([0, 2, 99], [3, 1], 'int32');
+        var input = tensor_ops_1.tensor2d([100, 101, 102, 777, 778, 779, 10000, 10001, 10002], [3, 3], 'float32');
+        expect(function () { return gather_nd_1.gatherND(input, indices); }).toThrow();
+    });
+});
+//# sourceMappingURL=gather_nd_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_test.js.map
new file mode 100644
index 0000000..177ea72
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"gather_nd_test.js","sourceRoot":"","sources":["../../src/ops/gather_nd_test.ts"],"names":[],"mappings":";;AAiBA,gDAAkD;AAClD,0CAAmE;AAEnE,yCAAqC;AACrC,2CAAkE;AAElE,gCAAiB,CAAC,UAAU,EAAE,oBAAQ,EAAE;IACtC,EAAE,CAAC,8BAA8B,EAAE;QACjC,IAAM,OAAO,GAAG,qBAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;QACrD,IAAM,KAAK,GACP,qBAAQ,CAAC,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,IAAI,EAAE,IAAI,EAAE,IAAI,CAAC,EAAE,OAAO,CAAC,CAAC;QACxE,IAAM,KAAK,GAAG,CAAC,CAAC,CAAC,CAAC;QAClB,IAAM,MAAM,GAAG,oBAAQ,CAAC,KAAK,EAAE,OAAO,CAAC,CAAC;QACxC,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC;QACpC,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC;QAC1C,6BAAiB,CAAC,MAAM,EAAE,CAAC,GAAG,EAAE,GAAG,EAAE,IAAI,CAAC,CAAC,CAAC;IAC9C,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,6BAA6B,EAAE;QAChC,IAAM,OAAO,GAAG,qBAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;QAClD,IAAM,KAAK,GAAG,qBAAQ,CAClB;YACE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC;YAC9C,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC;SAC/C,EACD,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,SAAS,CAAC,CAAC;QACvB,IAAM,KAAK,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACrB,IAAM,MAAM,GAAG,oBAAQ,CAAC,KAAK,EAAE,OAAO,CAAC,CAAC;QACxC,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC;QACpC,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC;QAC1C,6BAAiB,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IACtD,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,6BAA6B,EAAE;QAChC,IAAM,OAAO,GAAG,qBAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;QACxD,IAAM,KAAK,GAAG,qBAAQ,CAClB;YACE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC;YAC9C,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC;SAC/C,EACD,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,SAAS,CAAC,CAAC;QAC1B,IAAM,KAAK,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACrB,IAAM,MAAM,GAAG,oBAAQ,CAAC,KAAK,EAAE,OAAO,CAAC,CAAC;QACxC,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC;QACpC,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC;QAC1C,6BAAiB,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IACtD,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,6BAA6B,EAAE;QAChC,IAAM,OAAO,GAAG,qBAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;QACxD,IAAM,KAAK,GACP,qBAAQ,CAAC,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,KAAK,EAAE,KAAK,EAAE,KAAK,CAAC,EAAE,OAAO,CAAC,CAAC;QAC3E,IAAM,KAAK,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACrB,IAAM,MAAM,GAAG,oBAAQ,CAAC,KAAK,EAAE,OAAO,CAAC,CAAC;QACxC,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC;QACpC,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC;QAC1C,6BAAiB,CAAC,MAAM,EAAE,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC,CAAC,CAAC;IAC7C,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,mCAAmC,EAAE;QACtC,IAAM,OAAO,GAAG,qBAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;QACrD,IAAM,KAAK,GAAG,qBAAQ,CAClB;YACE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC;YAC9C,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC;SAC/C,EACD,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,SAAS,CAAC,CAAC;QACvB,IAAM,KAAK,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC;QACxB,IAAM,MAAM,GAAG,oBAAQ,CAAC,KAAK,EAAE,OAAO,CAAC,CAAC;QACxC,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC;QACpC,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC;QAC1C,6BAAiB,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IACtD,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,mCAAmC,EAAE;QACtC,IAAM,OAAO,GAAG,qBAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;QAC3D,IAAM,KAAK,GAAG,qBAAQ,CAClB;YACE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC;YAC9C,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC;SAC/C,EACD,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,SAAS,CAAC,CAAC;QAC1B,IAAM,KAAK,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC;Q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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_util.d.ts
new file mode 100644
index 0000000..f46df8c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_util.d.ts
@@ -0,0 +1,2 @@
+import { Tensor } from '../tensor';
+export declare function prepareAndValidate(tensor: Tensor, indices: Tensor): [number[], number, number, number[]];
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_util.js b/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_util.js
new file mode 100644
index 0000000..9f51f81
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_util.js
@@ -0,0 +1,43 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var util_1 = require("../util");
+function prepareAndValidate(tensor, indices) {
+    if (tensor.rank < 1) {
+        throw new Error('tf.gatherND() expects the input to be rank 1 or higher,' +
+            (" but the rank was " + tensor.rank + "."));
+    }
+    if (indices.rank < 1) {
+        throw new Error('tf.gatherND() expects the indices to be rank 1 or higher,' +
+            (" but the rank was " + indices.rank + "."));
+    }
+    if (indices.dtype !== 'int32') {
+        throw new Error('tf.gatherND() expects the indices to be int32 type,' +
+            (" but the dtype was " + indices.dtype + "."));
+    }
+    if (indices.shape[indices.rank - 1] > tensor.rank) {
+        throw new Error('index innermost dimension length must be <= tensor rank; saw: ' +
+            (indices.shape[indices.rank - 1] + " vs. " + tensor.rank));
+    }
+    if (tensor.size === 0) {
+        throw new Error('Requested more than 0 entries, but input is empty.' +
+            (" Input shape: " + tensor.shape + "."));
+    }
+    var indicesShape = indices.shape;
+    var sliceRank = indicesShape[indicesShape.length - 1];
+    var nResult = 1;
+    for (var i = 0; i < indicesShape.length - 1; ++i) {
+        nResult *= indicesShape[i];
+    }
+    var inputShape = tensor.shape;
+    var resultShape = indicesShape.slice();
+    resultShape.pop();
+    var sliceSize = 1;
+    for (var i = sliceRank; i < tensor.rank; ++i) {
+        sliceSize *= inputShape[i];
+        resultShape.push(inputShape[i]);
+    }
+    var strides = util_1.computeStrides(tensor.shape).map(function (stride) { return stride / sliceSize; }).concat([1]).slice(0, sliceRank);
+    return [resultShape, nResult, sliceSize, strides];
+}
+exports.prepareAndValidate = prepareAndValidate;
+//# sourceMappingURL=gather_nd_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_util.js.map
new file mode 100644
index 0000000..fc38d27
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/gather_nd_util.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"gather_nd_util.js","sourceRoot":"","sources":["../../src/ops/gather_nd_util.ts"],"names":[],"mappings":";;AAiBA,gCAAuC;AAUvC,4BACI,MAAc,EAAE,OAAe;IACjC,IAAI,MAAM,CAAC,IAAI,GAAG,CAAC,EAAE;QACnB,MAAM,IAAI,KAAK,CACX,yDAAyD;aACzD,uBAAqB,MAAM,CAAC,IAAI,MAAG,CAAA,CAAC,CAAC;KAC1C;IACD,IAAI,OAAO,CAAC,IAAI,GAAG,CAAC,EAAE;QACpB,MAAM,IAAI,KAAK,CACX,2DAA2D;aAC3D,uBAAqB,OAAO,CAAC,IAAI,MAAG,CAAA,CAAC,CAAC;KAC3C;IACD,IAAI,OAAO,CAAC,KAAK,KAAK,OAAO,EAAE;QAC7B,MAAM,IAAI,KAAK,CACX,qDAAqD;aACrD,wBAAsB,OAAO,CAAC,KAAK,MAAG,CAAA,CAAC,CAAC;KAC7C;IACD,IAAI,OAAO,CAAC,KAAK,CAAC,OAAO,CAAC,IAAI,GAAG,CAAC,CAAC,GAAG,MAAM,CAAC,IAAI,EAAE;QACjD,MAAM,IAAI,KAAK,CACX,gEAAgE;aAC7D,OAAO,CAAC,KAAK,CAAC,OAAO,CAAC,IAAI,GAAG,CAAC,CAAC,aAAQ,MAAM,CAAC,IAAM,CAAA,CAAC,CAAC;KAC9D;IAED,IAAI,MAAM,CAAC,IAAI,KAAK,CAAC,EAAE;QACrB,MAAM,IAAI,KAAK,CACX,oDAAoD;aACpD,mBAAiB,MAAM,CAAC,KAAK,MAAG,CAAA,CAAC,CAAC;KACvC;IAED,IAAM,YAAY,GAAG,OAAO,CAAC,KAAK,CAAC;IACnC,IAAM,SAAS,GAAG,YAAY,CAAC,YAAY,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC;IAIxD,IAAI,OAAO,GAAG,CAAC,CAAC;IAChB,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,YAAY,CAAC,MAAM,GAAG,CAAC,EAAE,EAAE,CAAC,EAAE;QAChD,OAAO,IAAI,YAAY,CAAC,CAAC,CAAC,CAAC;KAC5B;IAED,IAAM,UAAU,GAAG,MAAM,CAAC,KAAK,CAAC;IAEhC,IAAM,WAAW,GAAG,YAAY,CAAC,KAAK,EAAE,CAAC;IACzC,WAAW,CAAC,GAAG,EAAE,CAAC;IAElB,IAAI,SAAS,GAAG,CAAC,CAAC;IAClB,KAAK,IAAI,CAAC,GAAG,SAAS,EAAE,CAAC,GAAG,MAAM,CAAC,IAAI,EAAE,EAAE,CAAC,EAAE;QAC5C,SAAS,IAAI,UAAU,CAAC,CAAC,CAAC,CAAC;QAC3B,WAAW,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC,CAAC,CAAC,CAAC;KACjC;IAED,IAAM,OAAO,GACL,qBAAc,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,GAAG,CAAC,UAAA,MAAM,IAAI,OAAA,MAAM,GAAG,SAAS,EAAlB,CAAkB,CAAC,SACjE,CAAC,GAAE,KAAK,CAAC,CAAC,EAAE,SAAS,CAAC,CAAC;IAE5B,OAAO,CAAC,WAAW,EAAE,OAAO,EAAE,SAAS,EAAE,OAAO,CAAC,CAAC;AACpD,CAAC;AAvDD,gDAuDC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/image_ops.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/image_ops.d.ts
new file mode 100644
index 0000000..97cf279
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/image_ops.d.ts
@@ -0,0 +1,13 @@
+import { Tensor1D, Tensor2D, Tensor3D, Tensor4D } from '../tensor';
+import { TensorLike } from '../types';
+declare function resizeBilinear_<T extends Tensor3D | Tensor4D>(images: T | TensorLike, size: [number, number], alignCorners?: boolean): T;
+declare function resizeNearestNeighbor_<T extends Tensor3D | Tensor4D>(images: T | TensorLike, size: [number, number], alignCorners?: boolean): T;
+declare function nonMaxSuppression_(boxes: Tensor2D | TensorLike, scores: Tensor1D | TensorLike, maxOutputSize: number, iouThreshold?: number, scoreThreshold?: number): Tensor1D;
+declare function nonMaxSuppressionAsync_(boxes: Tensor2D | TensorLike, scores: Tensor1D | TensorLike, maxOutputSize: number, iouThreshold?: number, scoreThreshold?: number): Promise<Tensor1D>;
+declare function cropAndResize_(image: Tensor4D | TensorLike, boxes: Tensor2D | TensorLike, boxInd: Tensor1D | TensorLike, cropSize: [number, number], method?: 'bilinear' | 'nearest', extrapolationValue?: number): Tensor4D;
+export declare const resizeBilinear: typeof resizeBilinear_;
+export declare const resizeNearestNeighbor: typeof resizeNearestNeighbor_;
+export declare const nonMaxSuppression: typeof nonMaxSuppression_;
+export declare const nonMaxSuppressionAsync: typeof nonMaxSuppressionAsync_;
+export declare const cropAndResize: typeof cropAndResize_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/image_ops.js b/node_modules/@tensorflow/tfjs-core/dist/ops/image_ops.js
new file mode 100644
index 0000000..458e2d1
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/image_ops.js
@@ -0,0 +1,192 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var non_max_suppression_impl_1 = require("../kernels/non_max_suppression_impl");
+var tensor_util_env_1 = require("../tensor_util_env");
+var util = require("../util");
+var operation_1 = require("./operation");
+function resizeBilinear_(images, size, alignCorners) {
+    if (alignCorners === void 0) { alignCorners = false; }
+    var $images = tensor_util_env_1.convertToTensor(images, 'images', 'resizeBilinear');
+    util.assert($images.rank === 3 || $images.rank === 4, "Error in resizeBilinear: x must be rank 3 or 4, but got " +
+        ("rank " + $images.rank + "."));
+    util.assert(size.length === 2, "Error in resizeBilinear: new shape must 2D, but got shape " +
+        (size + "."));
+    var batchImages = $images;
+    var reshapedTo4D = false;
+    if ($images.rank === 3) {
+        reshapedTo4D = true;
+        batchImages =
+            $images.as4D(1, $images.shape[0], $images.shape[1], $images.shape[2]);
+    }
+    var newHeight = size[0], newWidth = size[1];
+    var forward = function (backend, save) {
+        return backend.resizeBilinear(batchImages, newHeight, newWidth, alignCorners);
+    };
+    var backward = function (dy, saved) {
+        return {
+            batchImages: function () { return environment_1.ENV.engine.runKernel(function (backend) {
+                return backend.resizeBilinearBackprop(dy, batchImages, alignCorners);
+            }, {}); }
+        };
+    };
+    var res = environment_1.ENV.engine.runKernel(forward, { batchImages: batchImages }, backward);
+    if (reshapedTo4D) {
+        return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
+    }
+    return res;
+}
+function resizeNearestNeighbor_(images, size, alignCorners) {
+    if (alignCorners === void 0) { alignCorners = false; }
+    var $images = tensor_util_env_1.convertToTensor(images, 'images', 'resizeNearestNeighbor');
+    util.assert($images.rank === 3 || $images.rank === 4, "Error in resizeNearestNeighbor: x must be rank 3 or 4, but got " +
+        ("rank " + $images.rank + "."));
+    util.assert(size.length === 2, "Error in resizeNearestNeighbor: new shape must 2D, but got shape " +
+        (size + "."));
+    util.assert($images.dtype === 'float32' || $images.dtype === 'int32', '`images` must have `int32` or `float32` as dtype');
+    var batchImages = $images;
+    var reshapedTo4D = false;
+    if ($images.rank === 3) {
+        reshapedTo4D = true;
+        batchImages =
+            $images.as4D(1, $images.shape[0], $images.shape[1], $images.shape[2]);
+    }
+    var newHeight = size[0], newWidth = size[1];
+    var forward = function (backend, save) {
+        return backend.resizeNearestNeighbor(batchImages, newHeight, newWidth, alignCorners);
+    };
+    var backward = function (dy, saved) {
+        return {
+            batchImages: function () { return environment_1.ENV.engine.runKernel(function (backend) { return backend.resizeNearestNeighborBackprop(dy, batchImages, alignCorners); }, {}); }
+        };
+    };
+    var res = environment_1.ENV.engine.runKernel(forward, { batchImages: batchImages }, backward);
+    if (reshapedTo4D) {
+        return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
+    }
+    return res;
+}
+function nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+    if (iouThreshold === void 0) { iouThreshold = 0.5; }
+    if (scoreThreshold === void 0) { scoreThreshold = Number.NEGATIVE_INFINITY; }
+    var $boxes = tensor_util_env_1.convertToTensor(boxes, 'boxes', 'nonMaxSuppression');
+    var $scores = tensor_util_env_1.convertToTensor(scores, 'scores', 'nonMaxSuppression');
+    var inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
+    maxOutputSize = inputs.maxOutputSize;
+    iouThreshold = inputs.iouThreshold;
+    scoreThreshold = inputs.scoreThreshold;
+    return environment_1.ENV.engine.runKernel(function (b) { return b.nonMaxSuppression($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold); }, { $boxes: $boxes });
+}
+function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+    if (iouThreshold === void 0) { iouThreshold = 0.5; }
+    if (scoreThreshold === void 0) { scoreThreshold = Number.NEGATIVE_INFINITY; }
+    return __awaiter(this, void 0, void 0, function () {
+        var $boxes, $scores, inputs, boxesVals, scoresVals, res;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    $boxes = tensor_util_env_1.convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');
+                    $scores = tensor_util_env_1.convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');
+                    inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
+                    maxOutputSize = inputs.maxOutputSize;
+                    iouThreshold = inputs.iouThreshold;
+                    scoreThreshold = inputs.scoreThreshold;
+                    return [4, $boxes.data()];
+                case 1:
+                    boxesVals = _a.sent();
+                    return [4, $scores.data()];
+                case 2:
+                    scoresVals = _a.sent();
+                    res = non_max_suppression_impl_1.nonMaxSuppressionImpl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
+                    if ($boxes !== boxes) {
+                        $boxes.dispose();
+                    }
+                    if ($scores !== scores) {
+                        $scores.dispose();
+                    }
+                    return [2, res];
+            }
+        });
+    });
+}
+function nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+    if (iouThreshold == null) {
+        iouThreshold = 0.5;
+    }
+    if (scoreThreshold == null) {
+        scoreThreshold = Number.NEGATIVE_INFINITY;
+    }
+    var numBoxes = boxes.shape[0];
+    maxOutputSize = Math.min(maxOutputSize, numBoxes);
+    util.assert(0 <= iouThreshold && iouThreshold <= 1, "iouThreshold must be in [0, 1], but was '" + iouThreshold + "'");
+    util.assert(boxes.rank === 2, "boxes must be a 2D tensor, but was of rank '" + boxes.rank + "'");
+    util.assert(boxes.shape[1] === 4, "boxes must have 4 columns, but 2nd dimension was " + boxes.shape[1]);
+    util.assert(scores.rank === 1, 'scores must be a 1D tensor');
+    util.assert(scores.shape[0] === numBoxes, "scores has incompatible shape with boxes. Expected " + numBoxes + ", " +
+        ("but was " + scores.shape[0]));
+    return { maxOutputSize: maxOutputSize, iouThreshold: iouThreshold, scoreThreshold: scoreThreshold };
+}
+function cropAndResize_(image, boxes, boxInd, cropSize, method, extrapolationValue) {
+    var $image = tensor_util_env_1.convertToTensor(image, 'image', 'cropAndResize', 'float32');
+    var $boxes = tensor_util_env_1.convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');
+    var $boxInd = tensor_util_env_1.convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');
+    method = method || 'bilinear';
+    extrapolationValue = extrapolationValue || 0;
+    var numBoxes = $boxes.shape[0];
+    util.assert($image.rank === 4, 'Error in cropAndResize: image must be rank 4,' +
+        ("but got rank " + $image.rank + "."));
+    util.assert($boxes.rank === 2 && $boxes.shape[1] === 4, "Error in cropAndResize: boxes must be have size [" + numBoxes + ",4] " +
+        ("but had shape " + $boxes.shape + "."));
+    util.assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, "Error in cropAndResize: boxInd must be have size [" + numBoxes + "] " +
+        ("but had shape " + $boxes.shape + "."));
+    util.assert(cropSize.length === 2, "Error in cropAndResize: cropSize must be of length 2, but got length " +
+        (cropSize.length + "."));
+    util.assert(cropSize[0] >= 1 && cropSize[1] >= 1, "cropSize must be atleast [1,1], but was " + cropSize);
+    util.assert(method === 'bilinear' || method === 'nearest', "method must be bilinear or nearest, but was " + method);
+    var forward = function (backend, save) {
+        return backend.cropAndResize($image, $boxes, $boxInd, cropSize, method, extrapolationValue);
+    };
+    var res = environment_1.ENV.engine.runKernel(forward, { $image: $image, $boxes: $boxes });
+    return res;
+}
+exports.resizeBilinear = operation_1.op({ resizeBilinear_: resizeBilinear_ });
+exports.resizeNearestNeighbor = operation_1.op({ resizeNearestNeighbor_: resizeNearestNeighbor_ });
+exports.nonMaxSuppression = operation_1.op({ nonMaxSuppression_: nonMaxSuppression_ });
+exports.nonMaxSuppressionAsync = nonMaxSuppressionAsync_;
+exports.cropAndResize = cropAndResize_;
+//# sourceMappingURL=image_ops.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/image_ops.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/image_ops.js.map
new file mode 100644
index 0000000..f414740
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/image_ops.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/image_ops_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/image_ops_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/image_ops_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/image_ops_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/image_ops_test.js
new file mode 100644
index 0000000..fe72a7e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/image_ops_test.js
@@ -0,0 +1,368 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+var _this = this;
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('nonMaxSuppression', test_util_1.ALL_ENVS, function () {
+    it('select from three clusters', function () {
+        var boxes = tf.tensor2d([
+            0, 0, 1, 1, 0, 0.1, 1, 1.1, 0, -0.1, 1, 0.9,
+            0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100, 1, 101
+        ], [6, 4]);
+        var scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
+        var maxOutputSize = 3;
+        var iouThreshold = 0.5;
+        var scoreThreshold = 0;
+        var indices = tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
+        expect(indices.shape).toEqual([3]);
+        test_util_1.expectArraysEqual(indices, [3, 0, 5]);
+    });
+    it('select from three clusters flipped coordinates', function () {
+        var boxes = tf.tensor2d([
+            1, 1, 0, 0, 0, 0.1, 1, 1.1, 0, .9, 1, -0.1,
+            0, 10, 1, 11, 1, 10.1, 0, 11.1, 1, 101, 0, 100
+        ], [6, 4]);
+        var scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
+        var maxOutputSize = 3;
+        var iouThreshold = 0.5;
+        var scoreThreshold = 0;
+        var indices = tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
+        expect(indices.shape).toEqual([3]);
+        test_util_1.expectArraysEqual(indices, [3, 0, 5]);
+    });
+    it('select at most two boxes from three clusters', function () {
+        var boxes = tf.tensor2d([
+            0, 0, 1, 1, 0, 0.1, 1, 1.1, 0, -0.1, 1, 0.9,
+            0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100, 1, 101
+        ], [6, 4]);
+        var scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
+        var maxOutputSize = 2;
+        var iouThreshold = 0.5;
+        var scoreThreshold = 0;
+        var indices = tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
+        expect(indices.shape).toEqual([2]);
+        test_util_1.expectArraysEqual(indices, [3, 0]);
+    });
+    it('select at most thirty boxes from three clusters', function () {
+        var boxes = tf.tensor2d([
+            0, 0, 1, 1, 0, 0.1, 1, 1.1, 0, -0.1, 1, 0.9,
+            0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100, 1, 101
+        ], [6, 4]);
+        var scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
+        var maxOutputSize = 30;
+        var iouThreshold = 0.5;
+        var scoreThreshold = 0;
+        var indices = tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
+        expect(indices.shape).toEqual([3]);
+        test_util_1.expectArraysEqual(indices, [3, 0, 5]);
+    });
+    it('select single box', function () {
+        var boxes = tf.tensor2d([0, 0, 1, 1], [1, 4]);
+        var scores = tf.tensor1d([0.9]);
+        var maxOutputSize = 3;
+        var iouThreshold = 0.5;
+        var scoreThreshold = 0;
+        var indices = tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
+        expect(indices.shape).toEqual([1]);
+        test_util_1.expectArraysEqual(indices, [0]);
+    });
+    it('select from ten identical boxes', function () {
+        var boxes = tf.tensor2d([0, 0, 1, 1], [1, 4]);
+        var scores = tf.tensor1d([0.9]);
+        var maxOutputSize = 3;
+        var iouThreshold = 0.5;
+        var scoreThreshold = 0;
+        var indices = tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
+        expect(indices.shape).toEqual([1]);
+        test_util_1.expectArraysEqual(indices, [0]);
+    });
+    it('select from ten identical boxes', function () {
+        var numBoxes = 10;
+        var corners = Array(numBoxes)
+            .fill(0)
+            .map(function (_) { return [0, 0, 1, 1]; })
+            .reduce(function (arr, curr) { return arr.concat(curr); });
+        var boxes = tf.tensor2d(corners, [numBoxes, 4]);
+        var scores = tf.tensor1d(Array(numBoxes).fill(0.9));
+        var maxOutputSize = 3;
+        var iouThreshold = 0.5;
+        var scoreThreshold = 0;
+        var indices = tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
+        expect(indices.shape).toEqual([1]);
+        test_util_1.expectArraysEqual(indices, [0]);
+    });
+    it('inconsistent box and score shapes', function () {
+        var boxes = tf.tensor2d([
+            0, 0, 1, 1, 0, 0.1, 1, 1.1, 0, -0.1, 1, 0.9,
+            0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100, 1, 101
+        ], [6, 4]);
+        var scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5]);
+        var maxOutputSize = 30;
+        var iouThreshold = 0.5;
+        var scoreThreshold = 0;
+        expect(function () { return tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold); })
+            .toThrowError(/scores has incompatible shape with boxes/);
+    });
+    it('invalid iou threshold', function () {
+        var boxes = tf.tensor2d([0, 0, 1, 1], [1, 4]);
+        var scores = tf.tensor1d([0.9]);
+        var maxOutputSize = 3;
+        var iouThreshold = 1.2;
+        var scoreThreshold = 0;
+        expect(function () { return tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold); })
+            .toThrowError(/iouThreshold must be in \[0, 1\]/);
+    });
+    it('empty input', function () {
+        var boxes = tf.tensor2d([], [0, 4]);
+        var scores = tf.tensor1d([]);
+        var maxOutputSize = 3;
+        var iouThreshold = 0.5;
+        var scoreThreshold = 0;
+        var indices = tf.image.nonMaxSuppression(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold);
+        expect(indices.shape).toEqual([0]);
+        test_util_1.expectArraysEqual(indices, []);
+    });
+    it('accepts a tensor-like object', function () {
+        var boxes = [[0, 0, 1, 1], [0, 1, 1, 2]];
+        var scores = [1, 2];
+        var indices = tf.image.nonMaxSuppression(boxes, scores, 10);
+        expect(indices.shape).toEqual([2]);
+        expect(indices.dtype).toEqual('int32');
+        test_util_1.expectArraysEqual(indices, [1, 0]);
+    });
+});
+jasmine_util_1.describeWithFlags('nonMaxSuppressionAsync', test_util_1.ALL_ENVS, function () {
+    it('select from three clusters', function () { return __awaiter(_this, void 0, void 0, function () {
+        var boxes, scores, maxOutputSize, iouThreshold, scoreThreshold, indices;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    boxes = tf.tensor2d([
+                        0, 0, 1, 1, 0, 0.1, 1, 1.1, 0, -0.1, 1, 0.9,
+                        0, 10, 1, 11, 0, 10.1, 1, 11.1, 0, 100, 1, 101
+                    ], [6, 4]);
+                    scores = tf.tensor1d([0.9, 0.75, 0.6, 0.95, 0.5, 0.3]);
+                    maxOutputSize = 3;
+                    iouThreshold = 0.5;
+                    scoreThreshold = 0;
+                    return [4, tf.image.nonMaxSuppressionAsync(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold)];
+                case 1:
+                    indices = _a.sent();
+                    expect(indices.shape).toEqual([3]);
+                    test_util_1.expectArraysEqual(indices, [3, 0, 5]);
+                    return [2];
+            }
+        });
+    }); });
+    it('accepts a tensor-like object', function () { return __awaiter(_this, void 0, void 0, function () {
+        var boxes, scores, indices;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    boxes = [[0, 0, 1, 1], [0, 1, 1, 2]];
+                    scores = [1, 2];
+                    return [4, tf.image.nonMaxSuppressionAsync(boxes, scores, 10)];
+                case 1:
+                    indices = _a.sent();
+                    expect(indices.shape).toEqual([2]);
+                    expect(indices.dtype).toEqual('int32');
+                    test_util_1.expectArraysEqual(indices, [1, 0]);
+                    return [2];
+            }
+        });
+    }); });
+});
+jasmine_util_1.describeWithFlags('cropAndResize', test_util_1.ALL_ENVS, function () {
+    it('1x1-bilinear', function () {
+        var image = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
+        var boxes = tf.tensor2d([0, 0, 1, 1], [1, 4]);
+        var boxInd = tf.tensor1d([0], 'int32');
+        var output = tf.image.cropAndResize(image, boxes, boxInd, [1, 1], 'bilinear', 0);
+        expect(output.shape).toEqual([1, 1, 1, 1]);
+        test_util_1.expectArraysClose(output, [2.5]);
+    });
+    it('1x1-nearest', function () {
+        var image = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
+        var boxes = tf.tensor2d([0, 0, 1, 1], [1, 4]);
+        var boxInd = tf.tensor1d([0], 'int32');
+        var output = tf.image.cropAndResize(image, boxes, boxInd, [1, 1], 'nearest', 0);
+        expect(output.shape).toEqual([1, 1, 1, 1]);
+        test_util_1.expectArraysClose(output, [4.0]);
+    });
+    it('1x1Flipped-bilinear', function () {
+        var image = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
+        var boxes = tf.tensor2d([1, 1, 0, 0], [1, 4]);
+        var boxInd = tf.tensor1d([0], 'int32');
+        var output = tf.image.cropAndResize(image, boxes, boxInd, [1, 1], 'bilinear', 0);
+        expect(output.shape).toEqual([1, 1, 1, 1]);
+        test_util_1.expectArraysClose(output, [2.5]);
+    });
+    it('1x1Flipped-nearest', function () {
+        var image = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
+        var boxes = tf.tensor2d([1, 1, 0, 0], [1, 4]);
+        var boxInd = tf.tensor1d([0], 'int32');
+        var output = tf.image.cropAndResize(image, boxes, boxInd, [1, 1], 'nearest', 0);
+        expect(output.shape).toEqual([1, 1, 1, 1]);
+        test_util_1.expectArraysClose(output, [4.0]);
+    });
+    it('3x3-bilinear', function () {
+        var image = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
+        var boxes = tf.tensor2d([0, 0, 1, 1], [1, 4]);
+        var boxInd = tf.tensor1d([0], 'int32');
+        var output = tf.image.cropAndResize(image, boxes, boxInd, [3, 3], 'bilinear', 0);
+        expect(output.shape).toEqual([1, 3, 3, 1]);
+        test_util_1.expectArraysClose(output, [1, 1.5, 2, 2, 2.5, 3, 3, 3.5, 4]);
+    });
+    it('3x3-nearest', function () {
+        var image = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
+        var boxes = tf.tensor2d([0, 0, 1, 1], [1, 4]);
+        var boxInd = tf.tensor1d([0], 'int32');
+        var output = tf.image.cropAndResize(image, boxes, boxInd, [3, 3], 'nearest', 0);
+        expect(output.shape).toEqual([1, 3, 3, 1]);
+        test_util_1.expectArraysClose(output, [1, 2, 2, 3, 4, 4, 3, 4, 4]);
+    });
+    it('3x3Flipped-bilinear', function () {
+        var image = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
+        var boxes = tf.tensor2d([1, 1, 0, 0], [1, 4]);
+        var boxInd = tf.tensor1d([0], 'int32');
+        var output = tf.image.cropAndResize(image, boxes, boxInd, [3, 3], 'bilinear', 0);
+        expect(output.shape).toEqual([1, 3, 3, 1]);
+        test_util_1.expectArraysClose(output, [4, 3.5, 3, 3, 2.5, 2, 2, 1.5, 1]);
+    });
+    it('3x3Flipped-nearest', function () {
+        var image = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
+        var boxes = tf.tensor2d([1, 1, 0, 0], [1, 4]);
+        var boxInd = tf.tensor1d([0], 'int32');
+        var output = tf.image.cropAndResize(image, boxes, boxInd, [3, 3], 'nearest', 0);
+        expect(output.shape).toEqual([1, 3, 3, 1]);
+        test_util_1.expectArraysClose(output, [4, 4, 3, 4, 4, 3, 2, 2, 1]);
+    });
+    it('3x3to2x2-bilinear', function () {
+        var image = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 3, 3, 1]);
+        var boxes = tf.tensor2d([0, 0, 1, 1, 0, 0, 0.5, 0.5], [2, 4]);
+        var boxInd = tf.tensor1d([0, 0], 'int32');
+        var output = tf.image.cropAndResize(image, boxes, boxInd, [2, 2], 'bilinear', 0);
+        expect(output.shape).toEqual([2, 2, 2, 1]);
+        test_util_1.expectArraysClose(output, [1, 3, 7, 9, 1, 2, 4, 5]);
+    });
+    it('3x3to2x2-nearest', function () {
+        var image = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 3, 3, 1]);
+        var boxes = tf.tensor2d([0, 0, 1, 1, 0, 0, 0.5, 0.5], [2, 4]);
+        var boxInd = tf.tensor1d([0, 0], 'int32');
+        var output = tf.image.cropAndResize(image, boxes, boxInd, [2, 2], 'nearest', 0);
+        expect(output.shape).toEqual([2, 2, 2, 1]);
+        test_util_1.expectArraysClose(output, [1, 3, 7, 9, 1, 2, 4, 5]);
+    });
+    it('3x3to2x2Flipped-bilinear', function () {
+        var image = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 3, 3, 1]);
+        var boxes = tf.tensor2d([1, 1, 0, 0, 0.5, 0.5, 0, 0], [2, 4]);
+        var boxInd = tf.tensor1d([0, 0], 'int32');
+        var output = tf.image.cropAndResize(image, boxes, boxInd, [2, 2], 'bilinear', 0);
+        expect(output.shape).toEqual([2, 2, 2, 1]);
+        test_util_1.expectArraysClose(output, [9, 7, 3, 1, 5, 4, 2, 1]);
+    });
+    it('3x3to2x2Flipped-nearest', function () {
+        var image = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9], [1, 3, 3, 1]);
+        var boxes = tf.tensor2d([1, 1, 0, 0, 0.5, 0.5, 0, 0], [2, 4]);
+        var boxInd = tf.tensor1d([0, 0], 'int32');
+        var output = tf.image.cropAndResize(image, boxes, boxInd, [2, 2], 'nearest', 0);
+        expect(output.shape).toEqual([2, 2, 2, 1]);
+        test_util_1.expectArraysClose(output, [9, 7, 3, 1, 5, 4, 2, 1]);
+    });
+    it('3x3-BoxisRectangular', function () {
+        var image = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
+        var boxes = tf.tensor2d([0, 0, 1, 1.5], [1, 4]);
+        var boxInd = tf.tensor1d([0], 'int32');
+        var output = tf.image.cropAndResize(image, boxes, boxInd, [3, 3], 'bilinear', 0);
+        expect(output.shape).toEqual([1, 3, 3, 1]);
+        test_util_1.expectArraysClose(output, [1, 1.75, 0, 2, 2.75, 0, 3, 3.75, 0]);
+    });
+    it('3x3-BoxisRectangular-nearest', function () {
+        var image = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
+        var boxes = tf.tensor2d([0, 0, 1, 1.5], [1, 4]);
+        var boxInd = tf.tensor1d([0], 'int32');
+        var output = tf.image.cropAndResize(image, boxes, boxInd, [3, 3], 'nearest', 0);
+        expect(output.shape).toEqual([1, 3, 3, 1]);
+        test_util_1.expectArraysClose(output, [1, 2, 0, 3, 4, 0, 3, 4, 0]);
+    });
+    it('2x2to3x3-Extrapolated', function () {
+        var val = -1;
+        var image = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
+        var boxes = tf.tensor2d([-1, -1, 1, 1], [1, 4]);
+        var boxInd = tf.tensor1d([0], 'int32');
+        var output = tf.image.cropAndResize(image, boxes, boxInd, [3, 3], 'bilinear', val);
+        expect(output.shape).toEqual([1, 3, 3, 1]);
+        test_util_1.expectArraysClose(output, [val, val, val, val, 1, 2, val, 3, 4]);
+    });
+    it('2x2to3x3-Extrapolated-Float', function () {
+        var val = -1.5;
+        var image = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
+        var boxes = tf.tensor2d([-1, -1, 1, 1], [1, 4]);
+        var boxInd = tf.tensor1d([0], 'int32');
+        var output = tf.image.cropAndResize(image, boxes, boxInd, [3, 3], 'bilinear', val);
+        expect(output.shape).toEqual([1, 3, 3, 1]);
+        test_util_1.expectArraysClose(output, [val, val, val, val, 1, 2, val, 3, 4]);
+    });
+    it('2x2to3x3-NoCrop', function () {
+        var val = -1.0;
+        var image = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);
+        var boxes = tf.tensor2d([], [0, 4]);
+        var boxInd = tf.tensor1d([], 'int32');
+        var output = tf.image.cropAndResize(image, boxes, boxInd, [3, 3], 'bilinear', val);
+        expect(output.shape).toEqual([0, 3, 3, 1]);
+        test_util_1.expectArraysClose(output, []);
+    });
+    it('MultipleBoxes-DifferentBoxes', function () {
+        var image = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
+        var boxes = tf.tensor2d([0, 0, 1, 1.5, 0, 0, 1.5, 1], [2, 4]);
+        var boxInd = tf.tensor1d([0, 1], 'int32');
+        var output = tf.image.cropAndResize(image, boxes, boxInd, [3, 3], 'bilinear', 0);
+        expect(output.shape).toEqual([2, 3, 3, 1]);
+        test_util_1.expectArraysClose(output, [1, 1.75, 0, 2, 2.75, 0, 3, 3.75, 0, 5, 5.5, 6, 6.5, 7, 7.5, 0, 0, 0]);
+    });
+    it('MultipleBoxes-DifferentBoxes-Nearest', function () {
+        var image = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
+        var boxes = tf.tensor2d([0, 0, 1, 1.5, 0, 0, 2, 1], [2, 4]);
+        var boxInd = tf.tensor1d([0, 1], 'int32');
+        var output = tf.image.cropAndResize(image, boxes, boxInd, [3, 3], 'nearest', 0);
+        expect(output.shape).toEqual([2, 3, 3, 1]);
+        test_util_1.expectArraysClose(output, [1, 2, 0, 3, 4, 0, 3, 4, 0, 5, 6, 6, 7, 8, 8, 0, 0, 0]);
+    });
+});
+//# sourceMappingURL=image_ops_test.js.map
\ No newline at end of file
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+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/image_ops_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/linalg_ops.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/linalg_ops.d.ts
new file mode 100644
index 0000000..e101b74
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/linalg_ops.d.ts
@@ -0,0 +1,6 @@
+import { Tensor, Tensor1D, Tensor2D } from '../tensor';
+declare function gramSchmidt_(xs: Tensor1D[] | Tensor2D): Tensor1D[] | Tensor2D;
+declare function qr_(x: Tensor, fullMatrices?: boolean): [Tensor, Tensor];
+export declare const gramSchmidt: typeof gramSchmidt_;
+export declare const qr: typeof qr_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/linalg_ops.js b/node_modules/@tensorflow/tfjs-core/dist/ops/linalg_ops.js
new file mode 100644
index 0000000..146998e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/linalg_ops.js
@@ -0,0 +1,145 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var globals_1 = require("../globals");
+var util_1 = require("../util");
+var array_ops_1 = require("./array_ops");
+var concat_split_1 = require("./concat_split");
+var norm_1 = require("./norm");
+var operation_1 = require("./operation");
+var reduction_ops_1 = require("./reduction_ops");
+var tensor_ops_1 = require("./tensor_ops");
+function gramSchmidt_(xs) {
+    var inputIsTensor2D;
+    if (Array.isArray(xs)) {
+        inputIsTensor2D = false;
+        util_1.assert(xs != null && xs.length > 0, 'Gram-Schmidt process: input must not be null, undefined, or empty');
+        var dim = xs[0].shape[0];
+        for (var i = 1; i < xs.length; ++i) {
+            util_1.assert(xs[i].shape[0] === dim, 'Gram-Schmidt: Non-unique lengths found in the input vectors: ' +
+                ("(" + xs[i].shape[0] + " vs. " + dim + ")"));
+        }
+    }
+    else {
+        inputIsTensor2D = true;
+        xs = concat_split_1.split(xs, xs.shape[0], 0).map(function (x) { return array_ops_1.squeeze(x, [0]); });
+    }
+    util_1.assert(xs.length <= xs[0].shape[0], "Gram-Schmidt: Number of vectors (" + xs.length + ") exceeds " +
+        ("number of dimensions (" + xs[0].shape[0] + ")."));
+    var ys = [];
+    var xs1d = xs;
+    var _loop_1 = function (i) {
+        ys.push(environment_1.ENV.engine.tidy(function () {
+            var x = xs1d[i];
+            if (i > 0) {
+                for (var j = 0; j < i; ++j) {
+                    var proj = reduction_ops_1.sum(ys[j].mulStrict(x)).mul(ys[j]);
+                    x = x.sub(proj);
+                }
+            }
+            return x.div(norm_1.norm(x, 'euclidean'));
+        }));
+    };
+    for (var i = 0; i < xs.length; ++i) {
+        _loop_1(i);
+    }
+    if (inputIsTensor2D) {
+        return array_ops_1.stack(ys, 0);
+    }
+    else {
+        return ys;
+    }
+}
+function qr_(x, fullMatrices) {
+    if (fullMatrices === void 0) { fullMatrices = false; }
+    if (x.rank < 2) {
+        throw new Error("qr() requires input tensor to have a rank >= 2, but got rank " + x.rank);
+    }
+    else if (x.rank === 2) {
+        return qr2d(x, fullMatrices);
+    }
+    else {
+        var outerDimsProd = x.shape.slice(0, x.shape.length - 2)
+            .reduce(function (value, prev) { return value * prev; });
+        var x2ds = array_ops_1.unstack(x.reshape([
+            outerDimsProd, x.shape[x.shape.length - 2],
+            x.shape[x.shape.length - 1]
+        ]), 0);
+        var q2ds_1 = [];
+        var r2ds_1 = [];
+        x2ds.forEach(function (x2d) {
+            var _a = qr2d(x2d, fullMatrices), q2d = _a[0], r2d = _a[1];
+            q2ds_1.push(q2d);
+            r2ds_1.push(r2d);
+        });
+        var q = array_ops_1.stack(q2ds_1, 0).reshape(x.shape);
+        var r = array_ops_1.stack(r2ds_1, 0).reshape(x.shape);
+        return [q, r];
+    }
+}
+function qr2d(x, fullMatrices) {
+    if (fullMatrices === void 0) { fullMatrices = false; }
+    return environment_1.ENV.engine.tidy(function () {
+        if (x.shape.length !== 2) {
+            throw new Error("qr2d() requires a 2D Tensor, but got a " + x.shape.length + "D Tensor.");
+        }
+        var m = x.shape[0];
+        var n = x.shape[1];
+        var q = array_ops_1.eye(m);
+        var r = x.clone();
+        var one2D = tensor_ops_1.tensor2d([[1]], [1, 1]);
+        var w = one2D.clone();
+        var iters = m >= n ? n : m;
+        var _loop_2 = function (j) {
+            var _a;
+            var rTemp = r;
+            var wTemp = w;
+            var qTemp = q;
+            _a = environment_1.ENV.engine.tidy(function () {
+                var rjEnd1 = r.slice([j, j], [m - j, 1]);
+                var normX = rjEnd1.norm();
+                var rjj = r.slice([j, j], [1, 1]);
+                var s = rjj.sign().neg();
+                var u1 = rjj.sub(s.mul(normX));
+                var wPre = rjEnd1.div(u1);
+                if (wPre.shape[0] === 1) {
+                    w = one2D.clone();
+                }
+                else {
+                    w = one2D.concat(wPre.slice([1, 0], [wPre.shape[0] - 1, wPre.shape[1]]), 0);
+                }
+                var tau = s.matMul(u1).div(normX).neg();
+                var rjEndAll = r.slice([j, 0], [m - j, n]);
+                var tauTimesW = tau.mul(w);
+                if (j === 0) {
+                    r = rjEndAll.sub(tauTimesW.matMul(w.transpose().matMul(rjEndAll)));
+                }
+                else {
+                    r = r.slice([0, 0], [j, n])
+                        .concat(rjEndAll.sub(tauTimesW.matMul(w.transpose().matMul(rjEndAll))), 0);
+                }
+                var qAllJEnd = q.slice([0, j], [m, q.shape[1] - j]);
+                if (j === 0) {
+                    q = qAllJEnd.sub(qAllJEnd.matMul(w).matMul(tauTimesW.transpose()));
+                }
+                else {
+                    q = q.slice([0, 0], [m, j])
+                        .concat(qAllJEnd.sub(qAllJEnd.matMul(w).matMul(tauTimesW.transpose())), 1);
+                }
+                return [w, r, q];
+            }), w = _a[0], r = _a[1], q = _a[2];
+            globals_1.dispose([rTemp, wTemp, qTemp]);
+        };
+        for (var j = 0; j < iters; ++j) {
+            _loop_2(j);
+        }
+        if (!fullMatrices && m > n) {
+            q = q.slice([0, 0], [m, n]);
+            r = r.slice([0, 0], [n, n]);
+        }
+        return [q, r];
+    });
+}
+exports.gramSchmidt = operation_1.op({ gramSchmidt_: gramSchmidt_ });
+exports.qr = operation_1.op({ qr_: qr_ });
+//# sourceMappingURL=linalg_ops.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/linalg_ops.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/linalg_ops.js.map
new file mode 100644
index 0000000..a93f6fd
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/linalg_ops.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/linalg_ops_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/linalg_ops_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/linalg_ops_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/linalg_ops_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/linalg_ops_test.js
new file mode 100644
index 0000000..b3a555e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/linalg_ops_test.js
@@ -0,0 +1,146 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+var ops_1 = require("./ops");
+jasmine_util_1.describeWithFlags('gramSchmidt-tiny', test_util_1.ALL_ENVS, function () {
+    it('2x2, Array of Tensor1D', function () {
+        var xs = [
+            tf.randomNormal([2], 0, 1, 'float32', 1),
+            tf.randomNormal([2], 0, 1, 'float32', 2)
+        ];
+        var ys = tf.linalg.gramSchmidt(xs);
+        var y = tf.stack(ys);
+        test_util_1.expectArraysClose(y.transpose().matMul(y), tf.eye(2));
+        test_util_1.expectArraysClose(tf.sum(xs[0].mul(ys[0])), tf.norm(xs[0]).mul(tf.norm(ys[0])));
+    });
+    it('3x3, Array of Tensor1D', function () {
+        var xs = [
+            tf.randomNormal([3], 0, 1, 'float32', 1),
+            tf.randomNormal([3], 0, 1, 'float32', 2),
+            tf.randomNormal([3], 0, 1, 'float32', 3)
+        ];
+        var ys = tf.linalg.gramSchmidt(xs);
+        var y = tf.stack(ys);
+        test_util_1.expectArraysClose(y.transpose().matMul(y), tf.eye(3));
+        test_util_1.expectArraysClose(tf.sum(xs[0].mul(ys[0])), tf.norm(xs[0]).mul(tf.norm(ys[0])));
+    });
+    it('3x3, Matrix', function () {
+        var xs = tf.randomNormal([3, 3], 0, 1, 'float32', 1);
+        var y = tf.linalg.gramSchmidt(xs);
+        test_util_1.expectArraysClose(y.transpose().matMul(y), tf.eye(3));
+    });
+    it('2x3, Matrix', function () {
+        var xs = tf.randomNormal([2, 3], 0, 1, 'float32', 1);
+        var y = tf.linalg.gramSchmidt(xs);
+        test_util_1.expectArraysClose(y.matMul(y.transpose()), tf.eye(2));
+    });
+    it('3x2 Matrix throws Error', function () {
+        var xs = tf.tensor2d([[1, 2], [3, -1], [5, 1]]);
+        expect(function () { return tf.linalg.gramSchmidt(xs); })
+            .toThrowError(/Number of vectors \(3\) exceeds number of dimensions \(2\)/);
+    });
+    it('Mismatching dimensions input throws Error', function () {
+        var xs = [tf.tensor1d([1, 2, 3]), tf.tensor1d([-1, 5, 1]), tf.tensor1d([0, 0])];
+        expect(function () { return tf.linalg.gramSchmidt(xs); }).toThrowError(/Non-unique/);
+    });
+    it('Empty input throws Error', function () {
+        expect(function () { return tf.linalg.gramSchmidt([]); }).toThrowError(/empty/);
+    });
+});
+jasmine_util_1.describeWithFlags('gramSchmidt-non-tiny', test_util_1.WEBGL_ENVS, function () {
+    it('32x512', function () {
+        var xs = tf.randomUniform([32, 512]);
+        var y = tf.linalg.gramSchmidt(xs);
+        test_util_1.expectArraysClose(y.matMul(y.transpose()), tf.eye(32));
+    });
+});
+jasmine_util_1.describeWithFlags('qr', test_util_1.ALL_ENVS, function () {
+    it('1x1', function () {
+        var x = ops_1.tensor2d([[10]], [1, 1]);
+        var _a = tf.linalg.qr(x), q = _a[0], r = _a[1];
+        test_util_1.expectArraysClose(q, ops_1.tensor2d([[-1]], [1, 1]));
+        test_util_1.expectArraysClose(r, ops_1.tensor2d([[-10]], [1, 1]));
+    });
+    it('2x2', function () {
+        var x = ops_1.tensor2d([[1, 3], [-2, -4]], [2, 2]);
+        var _a = tf.linalg.qr(x), q = _a[0], r = _a[1];
+        test_util_1.expectArraysClose(q, ops_1.tensor2d([[-0.4472, -0.8944], [0.8944, -0.4472]], [2, 2]));
+        test_util_1.expectArraysClose(r, ops_1.tensor2d([[-2.2361, -4.9193], [0, -0.8944]], [2, 2]));
+    });
+    it('2x2x2', function () {
+        var x = ops_1.tensor3d([[[-1, -3], [2, 4]], [[1, 3], [-2, -4]]], [2, 2, 2]);
+        var _a = tf.linalg.qr(x), q = _a[0], r = _a[1];
+        test_util_1.expectArraysClose(q, ops_1.tensor3d([
+            [[-0.4472, -0.8944], [0.8944, -0.4472]],
+            [[-0.4472, -0.8944], [0.8944, -0.4472]]
+        ], [2, 2, 2]));
+        test_util_1.expectArraysClose(r, ops_1.tensor3d([
+            [[2.2361, 4.9193], [0, 0.8944]],
+            [[-2.2361, -4.9193], [0, -0.8944]]
+        ], [2, 2, 2]));
+    });
+    it('2x1x2x2', function () {
+        var x = ops_1.tensor4d([[[[-1, -3], [2, 4]]], [[[1, 3], [-2, -4]]]], [2, 1, 2, 2]);
+        var _a = tf.linalg.qr(x), q = _a[0], r = _a[1];
+        test_util_1.expectArraysClose(q, ops_1.tensor4d([
+            [[[-0.4472, -0.8944], [0.8944, -0.4472]]],
+            [[[-0.4472, -0.8944], [0.8944, -0.4472]]],
+        ], [2, 1, 2, 2]));
+        test_util_1.expectArraysClose(r, ops_1.tensor4d([
+            [[[2.2361, 4.9193], [0, 0.8944]]],
+            [[[-2.2361, -4.9193], [0, -0.8944]]]
+        ], [2, 1, 2, 2]));
+    });
+    it('3x3', function () {
+        var x = ops_1.tensor2d([[1, 3, 2], [-2, 0, 7], [8, -9, 4]], [3, 3]);
+        var _a = tf.linalg.qr(x), q = _a[0], r = _a[1];
+        test_util_1.expectArraysClose(q, ops_1.tensor2d([
+            [-0.1204, 0.8729, 0.4729], [0.2408, -0.4364, 0.8669],
+            [-0.9631, -0.2182, 0.1576]
+        ], [3, 3]));
+        test_util_1.expectArraysClose(r, ops_1.tensor2d([[-8.3066, 8.3066, -2.4077], [0, 4.5826, -2.1822], [0, 0, 7.6447]], [3, 3]));
+    });
+    it('3x2, fullMatrices = default false', function () {
+        var x = ops_1.tensor2d([[1, 2], [3, -3], [-2, 1]], [3, 2]);
+        var _a = tf.linalg.qr(x), q = _a[0], r = _a[1];
+        test_util_1.expectArraysClose(q, ops_1.tensor2d([[-0.2673, 0.9221], [-0.8018, -0.3738], [0.5345, -0.0997]], [3, 2]));
+        test_util_1.expectArraysClose(r, ops_1.tensor2d([[-3.7417, 2.4054], [0, 2.8661]], [2, 2]));
+    });
+    it('3x2, fullMatrices = true', function () {
+        var x = ops_1.tensor2d([[1, 2], [3, -3], [-2, 1]], [3, 2]);
+        var _a = tf.linalg.qr(x, true), q = _a[0], r = _a[1];
+        test_util_1.expectArraysClose(q, ops_1.tensor2d([
+            [-0.2673, 0.9221, 0.2798], [-0.8018, -0.3738, 0.4663],
+            [0.5345, -0.0997, 0.8393]
+        ], [3, 3]));
+        test_util_1.expectArraysClose(r, ops_1.tensor2d([[-3.7417, 2.4054], [0, 2.8661], [0, 0]], [3, 2]));
+    });
+    it('2x3, fullMatrices = default false', function () {
+        var x = ops_1.tensor2d([[1, 2, 3], [-3, -2, 1]], [2, 3]);
+        var _a = tf.linalg.qr(x), q = _a[0], r = _a[1];
+        test_util_1.expectArraysClose(q, ops_1.tensor2d([[-0.3162278, -0.9486833], [0.9486833, -0.31622773]], [2, 2]));
+        test_util_1.expectArraysClose(r, ops_1.tensor2d([[-3.162, -2.5298, -2.3842e-07], [0, -1.2649, -3.162]], [2, 3]));
+    });
+    it('2x3, fullMatrices = true', function () {
+        var x = ops_1.tensor2d([[1, 2, 3], [-3, -2, 1]], [2, 3]);
+        var _a = tf.linalg.qr(x, true), q = _a[0], r = _a[1];
+        test_util_1.expectArraysClose(q, ops_1.tensor2d([[-0.3162278, -0.9486833], [0.9486833, -0.31622773]], [2, 2]));
+        test_util_1.expectArraysClose(r, ops_1.tensor2d([[-3.162, -2.5298, -2.3842e-07], [0, -1.2649, -3.162]], [2, 3]));
+    });
+    it('Does not leak memory', function () {
+        var x = ops_1.tensor2d([[1, 3], [-2, -4]], [2, 2]);
+        tf.linalg.qr(x);
+        var numTensors = tf.memory().numTensors;
+        tf.linalg.qr(x);
+        expect(tf.memory().numTensors).toEqual(numTensors + 2);
+    });
+    it('Insuffient input tensor rank leads to error', function () {
+        var x1 = ops_1.scalar(12);
+        expect(function () { return tf.linalg.qr(x1); }).toThrowError(/rank >= 2.*got rank 0/);
+        var x2 = ops_1.tensor1d([12]);
+        expect(function () { return tf.linalg.qr(x2); }).toThrowError(/rank >= 2.*got rank 1/);
+    });
+});
+//# sourceMappingURL=linalg_ops_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/linalg_ops_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/linalg_ops_test.js.map
new file mode 100644
index 0000000..d9bc3f8
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/linalg_ops_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/logical_ops.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/logical_ops.d.ts
new file mode 100644
index 0000000..7a48345
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/logical_ops.d.ts
@@ -0,0 +1,15 @@
+import { Tensor, Tensor2D } from '../tensor';
+import { TensorLike } from '../types';
+declare function logicalNot_<T extends Tensor>(x: T | TensorLike): T;
+declare function logicalAnd_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
+declare function logicalOr_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
+declare function logicalXor_<T extends Tensor>(a: Tensor | TensorLike, b: Tensor | TensorLike): T;
+declare function where_<T extends Tensor>(condition: Tensor | TensorLike, a: T | TensorLike, b: T | TensorLike): T;
+declare function whereAsync_(condition: Tensor | TensorLike): Promise<Tensor2D>;
+export declare const logicalAnd: typeof logicalAnd_;
+export declare const logicalNot: typeof logicalNot_;
+export declare const logicalOr: typeof logicalOr_;
+export declare const logicalXor: typeof logicalXor_;
+export declare const where: typeof where_;
+export declare const whereAsync: typeof whereAsync_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/logical_ops.js b/node_modules/@tensorflow/tfjs-core/dist/ops/logical_ops.js
new file mode 100644
index 0000000..c49eb5b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/logical_ops.js
@@ -0,0 +1,110 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var where_impl_1 = require("../kernels/where_impl");
+var tensor_util_env_1 = require("../tensor_util_env");
+var util_1 = require("../util");
+var broadcast_util_1 = require("./broadcast_util");
+var operation_1 = require("./operation");
+var tensor_ops_1 = require("./tensor_ops");
+function logicalNot_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'logicalNot', 'bool');
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.logicalNot($x); }, { $x: $x });
+}
+function logicalAnd_(a, b) {
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'logicalAnd', 'bool');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'logicalAnd', 'bool');
+    broadcast_util_1.assertAndGetBroadcastShape($a.shape, $b.shape);
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.logicalAnd($a, $b); }, { $a: $a, $b: $b });
+}
+function logicalOr_(a, b) {
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'logicalOr', 'bool');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'logicalOr', 'bool');
+    broadcast_util_1.assertAndGetBroadcastShape($a.shape, $b.shape);
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.logicalOr($a, $b); }, { $a: $a, $b: $b });
+}
+function logicalXor_(a, b) {
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'logicalXor', 'bool');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'logicalXor', 'bool');
+    broadcast_util_1.assertAndGetBroadcastShape($a.shape, $b.shape);
+    return exports.logicalOr(a, b).logicalAnd(exports.logicalAnd(a, b).logicalNot());
+}
+function where_(condition, a, b) {
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'where');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'where');
+    var $condition = tensor_util_env_1.convertToTensor(condition, 'condition', 'where', 'bool');
+    util_1.assertShapesMatch($a.shape, $b.shape, 'Error in where: ');
+    if ($condition.rank === 1) {
+        util_1.assert($condition.shape[0] === $a.shape[0], 'The first dimension of `a` must match the size of `condition`.');
+    }
+    else {
+        util_1.assertShapesMatch($condition.shape, $b.shape, 'Error in where: ');
+    }
+    var grad = function (dy) { return ({
+        $condition: function () { return tensor_ops_1.zerosLike($condition).toFloat(); },
+        $a: function () { return dy.mul($condition.cast(dy.dtype)); },
+        $b: function () { return dy.mul($condition.logicalNot().cast(dy.dtype)); }
+    }); };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.select($condition, $a, $b); }, { $condition: $condition, $a: $a, $b: $b }, grad);
+}
+function whereAsync_(condition) {
+    return __awaiter(this, void 0, void 0, function () {
+        var $condition, vals, res;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    $condition = tensor_util_env_1.convertToTensor(condition, 'condition', 'whereAsync', 'bool');
+                    return [4, $condition.data()];
+                case 1:
+                    vals = _a.sent();
+                    res = where_impl_1.whereImpl($condition.shape, vals);
+                    if (condition !== $condition) {
+                        $condition.dispose();
+                    }
+                    return [2, res];
+            }
+        });
+    });
+}
+exports.logicalAnd = operation_1.op({ logicalAnd_: logicalAnd_ });
+exports.logicalNot = operation_1.op({ logicalNot_: logicalNot_ });
+exports.logicalOr = operation_1.op({ logicalOr_: logicalOr_ });
+exports.logicalXor = operation_1.op({ logicalXor_: logicalXor_ });
+exports.where = operation_1.op({ where_: where_ });
+exports.whereAsync = whereAsync_;
+//# sourceMappingURL=logical_ops.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/logical_ops.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/logical_ops.js.map
new file mode 100644
index 0000000..f814d2f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/logical_ops.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/logical_ops_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/logical_ops_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/logical_ops_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/logical_ops_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/logical_ops_test.js
new file mode 100644
index 0000000..24829ab
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/logical_ops_test.js
@@ -0,0 +1,728 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+var _this = this;
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('logicalNot', test_util_1.ALL_ENVS, function () {
+    it('Tensor1D.', function () {
+        var a = tf.tensor1d([1, 0, 0], 'bool');
+        test_util_1.expectArraysClose(tf.logicalNot(a), [0, 1, 1]);
+        a = tf.tensor1d([0, 0, 0], 'bool');
+        test_util_1.expectArraysClose(tf.logicalNot(a), [1, 1, 1]);
+        a = tf.tensor1d([1, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalNot(a), [0, 0]);
+    });
+    it('Tests chaining in Tensor1D', function () {
+        var a = tf.tensor1d([1, 0, 0], 'bool');
+        test_util_1.expectArraysClose(a.logicalNot(), [0, 1, 1]);
+        a = tf.tensor1d([0, 0, 0], 'bool');
+        test_util_1.expectArraysClose(a.logicalNot(), [1, 1, 1]);
+        a = tf.tensor1d([1, 1], 'bool');
+        test_util_1.expectArraysClose(a.logicalNot(), [0, 0]);
+    });
+    it('Tensor2D', function () {
+        var a = tf.tensor2d([[1, 0, 1], [0, 0, 0]], [2, 3], 'bool');
+        test_util_1.expectArraysClose(tf.logicalNot(a), [0, 1, 0, 1, 1, 1]);
+        a = tf.tensor2d([[0, 0, 0], [1, 1, 1]], [2, 3], 'bool');
+        test_util_1.expectArraysClose(tf.logicalNot(a), [1, 1, 1, 0, 0, 0]);
+    });
+    it('Tensor3D', function () {
+        var a = tf.tensor3d([[[1], [0], [1]], [[0], [0], [0]]], [2, 3, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalNot(a), [0, 1, 0, 1, 1, 1]);
+        a = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalNot(a), [1, 1, 1, 0, 0, 0]);
+    });
+    it('Tensor4D', function () {
+        var a = tf.tensor4d([1, 0, 1, 0], [2, 2, 1, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalNot(a), [0, 1, 0, 1]);
+        a = tf.tensor4d([0, 0, 0, 0], [2, 2, 1, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalNot(a), [1, 1, 1, 1]);
+        a = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalNot(a), [0, 0, 0, 0]);
+    });
+    it('Tensor6D', function () {
+        var a = tf.tensor6d([1, 0, 1, 0], [2, 2, 1, 1, 1, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalNot(a), [0, 1, 0, 1]);
+        a = tf.zeros([2, 2, 2, 2, 2, 2]).cast('bool');
+        var expectedResult = new Int32Array(64);
+        expectedResult = expectedResult.fill(1);
+        test_util_1.expectArraysClose(tf.logicalNot(a), expectedResult);
+        a = tf.ones([2, 2, 2, 2, 2, 2]).cast('bool');
+        expectedResult = expectedResult.fill(0);
+        test_util_1.expectArraysClose(tf.logicalNot(a), expectedResult);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.logicalNot({}); })
+            .toThrowError(/Argument 'x' passed to 'logicalNot' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [1, 0, 0];
+        test_util_1.expectArraysClose(tf.logicalNot(a), [0, 1, 1]);
+    });
+});
+jasmine_util_1.describeWithFlags('logicalAnd', test_util_1.ALL_ENVS, function () {
+    it('Tensor1D.', function () {
+        var a = tf.tensor1d([1, 0, 0], 'bool');
+        var b = tf.tensor1d([0, 1, 0], 'bool');
+        test_util_1.expectArraysClose(tf.logicalAnd(a, b), [0, 0, 0]);
+        a = tf.tensor1d([0, 0, 0], 'bool');
+        b = tf.tensor1d([0, 0, 0], 'bool');
+        test_util_1.expectArraysClose(tf.logicalAnd(a, b), [0, 0, 0]);
+        a = tf.tensor1d([1, 1], 'bool');
+        b = tf.tensor1d([1, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalAnd(a, b), [1, 1]);
+    });
+    it('mismatched Tensor1D shapes', function () {
+        var a = tf.tensor1d([1, 0], 'bool');
+        var b = tf.tensor1d([0, 1, 0], 'bool');
+        var f = function () {
+            tf.logicalAnd(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('Tensor2D', function () {
+        var a = tf.tensor2d([[1, 0, 1], [0, 0, 0]], [2, 3], 'bool');
+        var b = tf.tensor2d([[0, 0, 0], [0, 1, 0]], [2, 3], 'bool');
+        test_util_1.expectArraysClose(tf.logicalAnd(a, b), [0, 0, 0, 0, 0, 0]);
+        a = tf.tensor2d([[0, 0, 0], [1, 1, 1]], [2, 3], 'bool');
+        b = tf.tensor2d([[0, 0, 0], [1, 1, 1]], [2, 3], 'bool');
+        test_util_1.expectArraysClose(tf.logicalAnd(a, b), [0, 0, 0, 1, 1, 1]);
+    });
+    it('broadcasting Tensor2D shapes', function () {
+        var a = tf.tensor2d([[1], [0]], [2, 1], 'bool');
+        var b = tf.tensor2d([[0, 1, 0], [0, 1, 0]], [2, 3], 'bool');
+        test_util_1.expectArraysClose(tf.logicalAnd(a, b), [0, 1, 0, 0, 0, 0]);
+    });
+    it('Tensor3D', function () {
+        var a = tf.tensor3d([[[1], [0], [1]], [[0], [0], [1]]], [2, 3, 1], 'bool');
+        var b = tf.tensor3d([[[0], [0], [1]], [[1], [0], [0]]], [2, 3, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalAnd(a, b), [0, 0, 1, 0, 0, 0]);
+        a = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'bool');
+        b = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalAnd(a, b), [0, 0, 0, 1, 1, 1]);
+    });
+    it('broadcasting Tensor3D shapes', function () {
+        var a = tf.tensor3d([[[1, 0], [0, 0], [1, 1]], [[0, 0], [0, 1], [0, 0]]], [2, 3, 2], 'bool');
+        var b = tf.tensor3d([[[0], [0], [1]], [[1], [0], [0]]], [2, 3, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalAnd(a, b), [0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0]);
+    });
+    it('Tensor4D', function () {
+        var a = tf.tensor4d([1, 0, 1, 0], [2, 2, 1, 1], 'bool');
+        var b = tf.tensor4d([0, 1, 1, 0], [2, 2, 1, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalAnd(a, b), [0, 0, 1, 0]);
+        a = tf.tensor4d([0, 0, 0, 0], [2, 2, 1, 1], 'bool');
+        b = tf.tensor4d([0, 0, 0, 0], [2, 2, 1, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalAnd(a, b), [0, 0, 0, 0]);
+        a = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'bool');
+        b = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalAnd(a, b), [1, 1, 1, 1]);
+    });
+    it('broadcasting Tensor4D shapes', function () {
+        var a = tf.tensor4d([1, 0, 1, 0], [2, 2, 1, 1], 'bool');
+        var b = tf.tensor4d([[[[1, 0]], [[0, 0]]], [[[0, 0]], [[1, 1]]]], [2, 2, 1, 2], 'bool');
+        test_util_1.expectArraysClose(tf.logicalAnd(a, b), [1, 0, 0, 0, 0, 0, 0, 0]);
+    });
+    it('TensorLike', function () {
+        var a = [true, false, false];
+        var b = [false, true, false];
+        test_util_1.expectArraysClose(tf.logicalAnd(a, b), [0, 0, 0]);
+    });
+    it('TensorLike Chained', function () {
+        var a = tf.tensor1d([1, 0, 0], 'bool');
+        var b = [false, true, false];
+        test_util_1.expectArraysClose(a.logicalAnd(b), [0, 0, 0]);
+    });
+    it('throws when passed a as a non-tensor', function () {
+        expect(function () { return tf.logicalAnd({}, tf.scalar(1, 'bool')); })
+            .toThrowError(/Argument 'a' passed to 'logicalAnd' must be a Tensor/);
+    });
+    it('throws when passed b as a non-tensor', function () {
+        expect(function () { return tf.logicalAnd(tf.scalar(1, 'bool'), {}); })
+            .toThrowError(/Argument 'b' passed to 'logicalAnd' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [1, 0, 0, 1];
+        var b = [0, 1, 0, 1];
+        test_util_1.expectArraysClose(tf.logicalAnd(a, b), [0, 0, 0, 1]);
+    });
+});
+jasmine_util_1.describeWithFlags('logicalOr', test_util_1.ALL_ENVS, function () {
+    it('Tensor1D.', function () {
+        var a = tf.tensor1d([1, 0, 0], 'bool');
+        var b = tf.tensor1d([0, 1, 0], 'bool');
+        test_util_1.expectArraysClose(tf.logicalOr(a, b), [1, 1, 0]);
+        a = tf.tensor1d([0, 0, 0], 'bool');
+        b = tf.tensor1d([0, 0, 0], 'bool');
+        test_util_1.expectArraysClose(tf.logicalOr(a, b), [0, 0, 0]);
+        a = tf.tensor1d([1, 1], 'bool');
+        b = tf.tensor1d([1, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalOr(a, b), [1, 1]);
+    });
+    it('mismatched Tensor1D shapes', function () {
+        var a = tf.tensor1d([1, 0], 'bool');
+        var b = tf.tensor1d([0, 1, 0], 'bool');
+        var f = function () {
+            tf.logicalOr(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('Tensor2D', function () {
+        var a = tf.tensor2d([[1, 0, 1], [0, 0, 0]], [2, 3], 'bool');
+        var b = tf.tensor2d([[0, 0, 0], [0, 1, 0]], [2, 3], 'bool');
+        test_util_1.expectArraysClose(tf.logicalOr(a, b), [1, 0, 1, 0, 1, 0]);
+        a = tf.tensor2d([[0, 0, 0], [1, 1, 1]], [2, 3], 'bool');
+        b = tf.tensor2d([[0, 0, 0], [1, 1, 1]], [2, 3], 'bool');
+        test_util_1.expectArraysClose(tf.logicalOr(a, b), [0, 0, 0, 1, 1, 1]);
+    });
+    it('broadcasting Tensor2D shapes', function () {
+        var a = tf.tensor2d([[1], [0]], [2, 1], 'bool');
+        var b = tf.tensor2d([[0, 0, 0], [0, 1, 0]], [2, 3], 'bool');
+        test_util_1.expectArraysClose(tf.logicalOr(a, b), [1, 1, 1, 0, 1, 0]);
+    });
+    it('Tensor3D', function () {
+        var a = tf.tensor3d([[[1], [0], [1]], [[0], [0], [0]]], [2, 3, 1], 'bool');
+        var b = tf.tensor3d([[[0], [0], [1]], [[1], [0], [0]]], [2, 3, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalOr(a, b), [1, 0, 1, 1, 0, 0]);
+        a = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'bool');
+        b = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalOr(a, b), [0, 0, 0, 1, 1, 1]);
+    });
+    it('broadcasting Tensor3D shapes', function () {
+        var a = tf.tensor3d([[[1, 0], [0, 0], [1, 1]], [[0, 0], [0, 1], [0, 0]]], [2, 3, 2], 'bool');
+        var b = tf.tensor3d([[[0], [0], [1]], [[1], [0], [0]]], [2, 3, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalOr(a, b), [1, 0, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0]);
+    });
+    it('Tensor4D', function () {
+        var a = tf.tensor4d([1, 0, 1, 0], [2, 2, 1, 1], 'bool');
+        var b = tf.tensor4d([0, 1, 0, 0], [2, 2, 1, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalOr(a, b), [1, 1, 1, 0]);
+        a = tf.tensor4d([0, 0, 0, 0], [2, 2, 1, 1], 'bool');
+        b = tf.tensor4d([0, 0, 0, 0], [2, 2, 1, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalOr(a, b), [0, 0, 0, 0]);
+        a = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'bool');
+        b = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalOr(a, b), [1, 1, 1, 1]);
+    });
+    it('broadcasting Tensor4D shapes', function () {
+        var a = tf.tensor4d([1, 0, 1, 0], [2, 2, 1, 1], 'bool');
+        var b = tf.tensor4d([[[[1, 0]], [[0, 0]]], [[[0, 0]], [[1, 1]]]], [2, 2, 1, 2], 'bool');
+        test_util_1.expectArraysClose(tf.logicalOr(a, b), [1, 1, 0, 0, 1, 1, 1, 1]);
+    });
+    it('TensorLike', function () {
+        var a = [true, false, false];
+        var b = [false, true, false];
+        test_util_1.expectArraysClose(tf.logicalOr(a, b), [1, 1, 0]);
+    });
+    it('TensorLike Chained', function () {
+        var a = tf.tensor1d([1, 0, 0], 'bool');
+        var b = [false, true, false];
+        test_util_1.expectArraysClose(a.logicalOr(b), [1, 1, 0]);
+    });
+    it('throws when passed a as a non-tensor', function () {
+        expect(function () { return tf.logicalOr({}, tf.scalar(1, 'bool')); })
+            .toThrowError(/Argument 'a' passed to 'logicalOr' must be a Tensor/);
+    });
+    it('throws when passed b as a non-tensor', function () {
+        expect(function () { return tf.logicalOr(tf.scalar(1, 'bool'), {}); })
+            .toThrowError(/Argument 'b' passed to 'logicalOr' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [1, 0, 0, 1];
+        var b = [0, 1, 0, 1];
+        test_util_1.expectArraysClose(tf.logicalOr(a, b), [1, 1, 0, 1]);
+    });
+});
+jasmine_util_1.describeWithFlags('logicalXor', test_util_1.ALL_ENVS, function () {
+    it('Tensor1D.', function () {
+        var a = tf.tensor1d([1, 0, 0], 'bool');
+        var b = tf.tensor1d([0, 1, 0], 'bool');
+        test_util_1.expectArraysClose(tf.logicalXor(a, b), [1, 1, 0]);
+        a = tf.tensor1d([0, 0, 0], 'bool');
+        b = tf.tensor1d([0, 0, 0], 'bool');
+        test_util_1.expectArraysClose(tf.logicalXor(a, b), [0, 0, 0]);
+        a = tf.tensor1d([1, 1], 'bool');
+        b = tf.tensor1d([1, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalXor(a, b), [0, 0]);
+    });
+    it('mismatched Tensor1D shapes', function () {
+        var a = tf.tensor1d([1, 0], 'bool');
+        var b = tf.tensor1d([0, 1, 0], 'bool');
+        var f = function () {
+            tf.logicalXor(a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('Tensor2D', function () {
+        var a = tf.tensor2d([[1, 0, 1], [0, 0, 0]], [2, 3], 'bool');
+        var b = tf.tensor2d([[0, 0, 0], [0, 1, 0]], [2, 3], 'bool');
+        test_util_1.expectArraysClose(tf.logicalXor(a, b), [1, 0, 1, 0, 1, 0]);
+        a = tf.tensor2d([[0, 0, 0], [1, 1, 1]], [2, 3], 'bool');
+        b = tf.tensor2d([[0, 0, 0], [1, 1, 1]], [2, 3], 'bool');
+        test_util_1.expectArraysClose(tf.logicalXor(a, b), [0, 0, 0, 0, 0, 0]);
+    });
+    it('broadcasting Tensor2D shapes', function () {
+        var a = tf.tensor2d([[1], [0]], [2, 1], 'bool');
+        var b = tf.tensor2d([[0, 0, 0], [0, 1, 0]], [2, 3], 'bool');
+        test_util_1.expectArraysClose(tf.logicalXor(a, b), [1, 1, 1, 0, 1, 0]);
+    });
+    it('Tensor3D', function () {
+        var a = tf.tensor3d([[[1], [0], [1]], [[0], [0], [0]]], [2, 3, 1], 'bool');
+        var b = tf.tensor3d([[[0], [0], [1]], [[1], [0], [0]]], [2, 3, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalXor(a, b), [1, 0, 0, 1, 0, 0]);
+        a = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'bool');
+        b = tf.tensor3d([[[0], [0], [0]], [[1], [1], [1]]], [2, 3, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalXor(a, b), [0, 0, 0, 0, 0, 0]);
+    });
+    it('broadcasting Tensor3D shapes', function () {
+        var a = tf.tensor3d([[[1, 0], [0, 0], [1, 1]], [[0, 0], [0, 1], [0, 0]]], [2, 3, 2], 'bool');
+        var b = tf.tensor3d([[[0], [0], [1]], [[1], [0], [0]]], [2, 3, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalXor(a, b), [1, 0, 0, 0, 0, 0, 1, 1, 0, 1, 0, 0]);
+    });
+    it('Tensor4D', function () {
+        var a = tf.tensor4d([1, 0, 1, 0], [2, 2, 1, 1], 'bool');
+        var b = tf.tensor4d([0, 1, 1, 0], [2, 2, 1, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalXor(a, b), [1, 1, 0, 0]);
+        a = tf.tensor4d([0, 0, 0, 0], [2, 2, 1, 1], 'bool');
+        b = tf.tensor4d([0, 0, 0, 0], [2, 2, 1, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalXor(a, b), [0, 0, 0, 0]);
+        a = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'bool');
+        b = tf.tensor4d([1, 1, 1, 1], [2, 2, 1, 1], 'bool');
+        test_util_1.expectArraysClose(tf.logicalXor(a, b), [0, 0, 0, 0]);
+    });
+    it('broadcasting Tensor4D shapes', function () {
+        var a = tf.tensor4d([1, 0, 1, 0], [2, 2, 1, 1], 'bool');
+        var b = tf.tensor4d([[[[1, 0]], [[0, 0]]], [[[0, 0]], [[1, 1]]]], [2, 2, 1, 2], 'bool');
+        test_util_1.expectArraysClose(tf.logicalXor(a, b), [0, 1, 0, 0, 1, 1, 1, 1]);
+    });
+    it('TensorLike', function () {
+        var a = [true, false, false];
+        var b = [false, true, false];
+        test_util_1.expectArraysClose(tf.logicalXor(a, b), [1, 1, 0]);
+    });
+    it('TensorLike Chained', function () {
+        var a = tf.tensor1d([1, 0, 0], 'bool');
+        var b = [false, true, false];
+        test_util_1.expectArraysClose(a.logicalXor(b), [1, 1, 0]);
+    });
+    it('throws when passed a as a non-tensor', function () {
+        expect(function () { return tf.logicalXor({}, tf.scalar(1, 'bool')); })
+            .toThrowError(/Argument 'a' passed to 'logicalXor' must be a Tensor/);
+    });
+    it('throws when passed b as a non-tensor', function () {
+        expect(function () { return tf.logicalXor(tf.scalar(1, 'bool'), {}); })
+            .toThrowError(/Argument 'b' passed to 'logicalXor' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [1, 0, 0, 1];
+        var b = [0, 1, 0, 1];
+        test_util_1.expectArraysClose(tf.logicalXor(a, b), [1, 1, 0, 0]);
+    });
+});
+jasmine_util_1.describeWithFlags('where', test_util_1.ALL_ENVS, function () {
+    it('Scalars.', function () {
+        var a = tf.scalar(10);
+        var b = tf.scalar(20);
+        var c = tf.scalar(1, 'bool');
+        test_util_1.expectArraysClose(tf.where(c, a, b), [10]);
+    });
+    it('Invalid condition type', function () {
+        var c = tf.tensor1d([1, 0, 1, 0], 'int32');
+        var a = tf.tensor1d([10, 10, 10, 10], 'bool');
+        var b = tf.tensor1d([20, 20, 20, 20], 'bool');
+        var f = function () {
+            tf.where(c, a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('Tensor1D', function () {
+        var c = tf.tensor1d([1, 0, 1, 0], 'bool');
+        var a = tf.tensor1d([10, 10, 10, 10]);
+        var b = tf.tensor1d([20, 20, 20, 20]);
+        test_util_1.expectArraysClose(tf.where(c, a, b), [10, 20, 10, 20]);
+    });
+    it('Tensor1D different a/b shapes', function () {
+        var c = tf.tensor1d([1, 0, 1, 0], 'bool');
+        var a = tf.tensor1d([10, 10, 10]);
+        var b = tf.tensor1d([20, 20, 20, 20]);
+        var f = function () {
+            tf.where(c, a, b);
+        };
+        expect(f).toThrowError();
+        c = tf.tensor1d([1, 0, 1, 0], 'bool');
+        a = tf.tensor1d([10, 10, 10, 10]);
+        b = tf.tensor1d([20, 20, 20]);
+        f = function () {
+            tf.where(c, a, b);
+        };
+    });
+    it('Tensor1D different condition/a shapes', function () {
+        var c = tf.tensor1d([1, 0, 1, 0], 'bool');
+        var a = tf.tensor1d([10, 10, 10]);
+        var b = tf.tensor1d([20, 20, 20]);
+        var f = function () {
+            tf.where(c, a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('Tensor2D', function () {
+        var c = tf.tensor2d([[1, 0], [0, 1]], [2, 2], 'bool');
+        var a = tf.tensor2d([[10, 10], [10, 10]], [2, 2]);
+        var b = tf.tensor2d([[5, 5], [5, 5]], [2, 2]);
+        test_util_1.expectArraysClose(tf.where(c, a, b), [10, 5, 5, 10]);
+    });
+    it('Tensor2D different a/b shapes', function () {
+        var c = tf.tensor2d([[1, 1], [0, 0]], [2, 2], 'bool');
+        var a = tf.tensor2d([[5, 5, 5], [5, 5, 5]], [2, 3]);
+        var b = tf.tensor2d([[4, 4], [4, 4]], [2, 2]);
+        var f = function () {
+            tf.where(c, a, b);
+        };
+        expect(f).toThrowError();
+        c = tf.tensor2d([[1, 1], [0, 0]], [2, 2], 'bool');
+        a = tf.tensor2d([[5, 5], [5, 5]], [2, 2]);
+        b = tf.tensor2d([[4, 4, 4], [4, 4, 4]], [2, 3]);
+        f = function () {
+            tf.where(c, a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('Tensor2D different condition/a shapes', function () {
+        var c = tf.tensor2d([[1, 0], [0, 1]], [2, 2], 'bool');
+        var a = tf.tensor2d([[10, 10, 10], [10, 10, 10]], [2, 3]);
+        var b = tf.tensor2d([[5, 5, 5], [5, 5, 5]], [2, 3]);
+        var f = function () {
+            tf.where(c, a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('Tensor2D different `a` dimension w/ condition rank=1', function () {
+        var c = tf.tensor1d([1, 0, 1, 0], 'bool');
+        var a = tf.tensor2d([[10, 10], [10, 10]], [2, 2]);
+        var b = tf.tensor2d([[5, 5], [5, 5]], [2, 2]);
+        var f = function () {
+            tf.where(c, a, b);
+        };
+        expect(f).toThrowError();
+        a = tf.tensor2d([[10], [10], [10], [10]], [4, 1]);
+        b = tf.tensor2d([[5], [5], [5], [5]], [4, 1]);
+        test_util_1.expectArraysClose(tf.where(c, a, b), [10, 5, 10, 5]);
+        a = tf.tensor2d([[10, 10], [10, 10], [10, 10], [10, 10]], [4, 2]);
+        b = tf.tensor2d([[5, 5], [5, 5], [5, 5], [5, 5]], [4, 2]);
+        test_util_1.expectArraysClose(tf.where(c, a, b), [10, 10, 5, 5, 10, 10, 5, 5]);
+    });
+    it('Tensor3D', function () {
+        var c = tf.tensor3d([[[1], [0], [1]], [[0], [0], [0]]], [2, 3, 1], 'bool');
+        var a = tf.tensor3d([[[5], [5], [5]], [[5], [5], [5]]], [2, 3, 1]);
+        var b = tf.tensor3d([[[3], [3], [3]], [[3], [3], [3]]], [2, 3, 1]);
+        test_util_1.expectArraysClose(tf.where(c, a, b), [5, 3, 5, 3, 3, 3]);
+    });
+    it('Tensor3D different a/b shapes', function () {
+        var c = tf.tensor3d([[[1], [0], [1]], [[0], [0], [0]]], [2, 3, 1], 'bool');
+        var a = tf.tensor3d([[[5], [5]], [[5], [5]]], [2, 2, 1]);
+        var b = tf.tensor3d([[[3], [3], [3]], [[3], [3], [3]]], [2, 3, 1]);
+        var f = function () {
+            tf.where(c, a, b);
+        };
+        expect(f).toThrowError();
+        a = tf.tensor3d([[[5], [5], [5]], [[5], [5], [5]]], [2, 3, 1]);
+        b = tf.tensor3d([[[3], [3]], [[3], [3]]], [2, 2, 1]);
+        f = function () {
+            tf.where(c, a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('Tensor3D different condition/a shapes', function () {
+        var c = tf.tensor3d([[[1], [0]], [[0], [0]]], [2, 2, 1], 'bool');
+        var a = tf.tensor3d([[[5], [5], [5]], [[5], [5], [5]]], [2, 3, 1]);
+        var b = tf.tensor3d([[[3], [3], [3]], [[3], [3], [3]]], [2, 3, 1]);
+        var f = function () {
+            tf.where(c, a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('Tensor3D different `a` dimension w/ condition rank=1', function () {
+        var c = tf.tensor1d([1, 0, 1, 0], 'bool');
+        var a = tf.tensor3d([[[9, 9], [9, 9]], [[9, 9], [9, 9]]], [2, 2, 2]);
+        var b = tf.tensor3d([[[8, 8], [8, 8]], [[8, 8], [8, 8]]], [2, 2, 2]);
+        var f = function () {
+            tf.where(c, a, b);
+        };
+        expect(f).toThrowError();
+        a = tf.tensor3d([[[9]], [[9]], [[9]], [[9]]], [4, 1, 1]);
+        b = tf.tensor3d([[[8]], [[8]], [[8]], [[8]]], [4, 1, 1]);
+        test_util_1.expectArraysClose(tf.where(c, a, b), [9, 8, 9, 8]);
+        a = tf.tensor3d([[[9], [9]], [[9], [9]], [[9], [9]], [[9], [9]]], [4, 2, 1]);
+        b = tf.tensor3d([[[8], [8]], [[8], [8]], [[8], [8]], [[8], [8]]], [4, 2, 1]);
+        test_util_1.expectArraysClose(tf.where(c, a, b), [9, 9, 8, 8, 9, 9, 8, 8]);
+    });
+    it('Tensor4D', function () {
+        var c = tf.tensor4d([1, 0, 1, 1], [2, 2, 1, 1], 'bool');
+        var a = tf.tensor4d([7, 7, 7, 7], [2, 2, 1, 1]);
+        var b = tf.tensor4d([3, 3, 3, 3], [2, 2, 1, 1]);
+        test_util_1.expectArraysClose(tf.where(c, a, b), [7, 3, 7, 7]);
+    });
+    it('Tensor4D different a/b shapes', function () {
+        var c = tf.tensor4d([1, 0, 1, 1], [2, 2, 1, 1], 'bool');
+        var a = tf.tensor4d([7, 7, 7, 7, 7, 7, 7, 7], [2, 2, 2, 1]);
+        var b = tf.tensor4d([3, 3, 3, 3], [2, 2, 1, 1]);
+        var f = function () {
+            tf.where(c, a, b);
+        };
+        expect(f).toThrowError();
+        a = tf.tensor4d([7, 7, 7, 7], [2, 2, 1, 1]);
+        b = tf.tensor4d([3, 3, 3, 3, 3, 3, 3, 3], [2, 2, 2, 1]);
+        f = function () {
+            tf.where(c, a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('Tensor4D different condition/a shapes', function () {
+        var c = tf.tensor4d([1, 0, 1, 1, 1, 0, 1, 1], [2, 2, 2, 1], 'bool');
+        var a = tf.tensor4d([7, 7, 7, 7], [2, 2, 1, 1]);
+        var b = tf.tensor4d([3, 3, 3, 3], [2, 2, 1, 1]);
+        var f = function () {
+            tf.where(c, a, b);
+        };
+        expect(f).toThrowError();
+    });
+    it('Tensor4D different `a` dimension w/ condition rank=1', function () {
+        var c = tf.tensor1d([1, 0, 1, 0], 'bool');
+        var a = tf.tensor4d([7, 7, 7, 7, 7, 7, 7, 7], [2, 2, 2, 1]);
+        var b = tf.tensor4d([3, 3, 3, 3, 3, 3, 3, 3], [2, 2, 2, 1]);
+        var f = function () {
+            tf.where(c, a, b);
+        };
+        expect(f).toThrowError();
+        a = tf.tensor4d([7, 7, 7, 7], [4, 1, 1, 1]);
+        b = tf.tensor4d([3, 3, 3, 3], [4, 1, 1, 1]);
+        test_util_1.expectArraysClose(tf.where(c, a, b), [7, 3, 7, 3]);
+        a = tf.tensor4d([7, 7, 7, 7, 7, 7, 7, 7], [4, 2, 1, 1]);
+        b = tf.tensor4d([3, 3, 3, 3, 3, 3, 3, 3], [4, 2, 1, 1]);
+        test_util_1.expectArraysClose(tf.where(c, a, b), [7, 7, 3, 3, 7, 7, 3, 3]);
+    });
+    it('TensorLike', function () {
+        test_util_1.expectArraysClose(tf.where(true, 10, 20), [10]);
+    });
+    it('TensorLike Chained', function () {
+        var a = tf.scalar(10);
+        test_util_1.expectArraysClose(a.where(true, 20), [10]);
+    });
+    it('throws when passed condition as a non-tensor', function () {
+        expect(function () { return tf.where({}, tf.scalar(1, 'bool'), tf.scalar(1, 'bool')); })
+            .toThrowError(/Argument 'condition' passed to 'where' must be a Tensor/);
+    });
+    it('throws when passed a as a non-tensor', function () {
+        expect(function () { return tf.where(tf.scalar(1, 'bool'), {}, tf.scalar(1, 'bool')); })
+            .toThrowError(/Argument 'a' passed to 'where' must be a Tensor/);
+    });
+    it('throws when passed b as a non-tensor', function () {
+        expect(function () { return tf.where(tf.scalar(1, 'bool'), tf.scalar(1, 'bool'), {}); })
+            .toThrowError(/Argument 'b' passed to 'where' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = 10;
+        var b = 20;
+        var c = 1;
+        test_util_1.expectArraysClose(tf.where(c, a, b), [10]);
+    });
+    it('1D gradient', function () {
+        var c = tf.tensor1d([1, 0, 1], 'bool');
+        var a = tf.tensor1d([1, 2, 3]);
+        var b = tf.tensor1d([4, 5, 6]);
+        var dy = tf.tensor1d([1, 2, 3]);
+        var grads = tf.grads(function (c, a, b) { return tf.where(c, a, b); });
+        var _a = grads([c, a, b], dy), dc = _a[0], da = _a[1], db = _a[2];
+        test_util_1.expectArraysClose(dc, [0, 0, 0]);
+        test_util_1.expectArraysClose(da, [1, 0, 3]);
+        test_util_1.expectArraysClose(db, [0, 2, 0]);
+        expect(dc.shape).toEqual(c.shape);
+        expect(da.shape).toEqual(a.shape);
+        expect(db.shape).toEqual(b.shape);
+    });
+    it('2D gradient', function () {
+        var c = tf.tensor2d([1, 0, 1, 1, 1, 0], [2, 3], 'bool');
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var b = tf.tensor2d([7, 8, 9, 10, 11, 12], [2, 3]);
+        var dy = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var grads = tf.grads(function (c, a, b) { return tf.where(c, a, b); });
+        var _a = grads([c, a, b], dy), dc = _a[0], da = _a[1], db = _a[2];
+        test_util_1.expectArraysClose(dc, [0, 0, 0, 0, 0, 0]);
+        test_util_1.expectArraysClose(da, [1, 0, 3, 4, 5, 0]);
+        test_util_1.expectArraysClose(db, [0, 2, 0, 0, 0, 6]);
+        expect(dc.shape).toEqual(c.shape);
+        expect(da.shape).toEqual(a.shape);
+        expect(db.shape).toEqual(b.shape);
+    });
+    it('3D gradient', function () {
+        var c = tf.tensor3d([1, 1, 0, 1, 1, 0], [2, 3, 1], 'bool');
+        var a = tf.tensor3d([1, 2, 3, 4, 5, 6], [2, 3, 1]);
+        var b = tf.tensor3d([7, 8, 9, 10, 11, 12], [2, 3, 1]);
+        var dy = tf.tensor3d([1, 2, 3, 4, 5, 6], [2, 3, 1]);
+        var grads = tf.grads(function (c, a, b) { return tf.where(c, a, b); });
+        var _a = grads([c, a, b], dy), dc = _a[0], da = _a[1], db = _a[2];
+        test_util_1.expectArraysClose(dc, [0, 0, 0, 0, 0, 0]);
+        test_util_1.expectArraysClose(da, [1, 2, 0, 4, 5, 0]);
+        test_util_1.expectArraysClose(db, [0, 0, 3, 0, 0, 6]);
+        expect(dc.shape).toEqual(c.shape);
+        expect(da.shape).toEqual(a.shape);
+        expect(db.shape).toEqual(b.shape);
+    });
+    it('4D gradient', function () {
+        var c = tf.tensor4d([1, 1, 0, 1], [2, 2, 1, 1], 'bool');
+        var a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1]);
+        var b = tf.tensor4d([5, 6, 7, 8], [2, 2, 1, 1]);
+        var dy = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1]);
+        var grads = tf.grads(function (c, a, b) { return tf.where(c, a, b); });
+        var _a = grads([c, a, b], dy), dc = _a[0], da = _a[1], db = _a[2];
+        test_util_1.expectArraysClose(dc, [0, 0, 0, 0]);
+        test_util_1.expectArraysClose(da, [1, 2, 0, 4]);
+        test_util_1.expectArraysClose(db, [0, 0, 3, 0]);
+        expect(dc.shape).toEqual(c.shape);
+        expect(da.shape).toEqual(a.shape);
+        expect(db.shape).toEqual(b.shape);
+    });
+});
+jasmine_util_1.describeWithFlags('whereAsync', test_util_1.ALL_ENVS, function () {
+    it('1d tensor', function () { return __awaiter(_this, void 0, void 0, function () {
+        var condition, res;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    condition = tf.tensor1d([true, false, true, true], 'bool');
+                    return [4, tf.whereAsync(condition)];
+                case 1:
+                    res = _a.sent();
+                    expect(res.dtype).toBe('int32');
+                    expect(res.shape).toEqual([3, 1]);
+                    test_util_1.expectArraysClose(res, [0, 2, 3]);
+                    return [2];
+            }
+        });
+    }); });
+    it('2d tensor', function () { return __awaiter(_this, void 0, void 0, function () {
+        var condition, res;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    condition = tf.tensor2d([[true, false, false], [false, true, true]], [2, 3], 'bool');
+                    return [4, tf.whereAsync(condition)];
+                case 1:
+                    res = _a.sent();
+                    expect(res.dtype).toBe('int32');
+                    expect(res.shape).toEqual([3, 2]);
+                    test_util_1.expectArraysClose(res, [0, 0, 1, 1, 1, 2]);
+                    return [2];
+            }
+        });
+    }); });
+    it('3d tensor', function () { return __awaiter(_this, void 0, void 0, function () {
+        var condition, res;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    condition = tf.tensor3d([
+                        [[true, false, false], [false, true, true]],
+                        [[false, false, false], [true, true, false]]
+                    ], [2, 2, 3], 'bool');
+                    return [4, tf.whereAsync(condition)];
+                case 1:
+                    res = _a.sent();
+                    expect(res.dtype).toBe('int32');
+                    expect(res.shape).toEqual([5, 3]);
+                    test_util_1.expectArraysClose(res, [0, 0, 0, 0, 1, 1, 0, 1, 2, 1, 1, 0, 1, 1, 1]);
+                    return [2];
+            }
+        });
+    }); });
+    it('accepts a tensor-like object', function () { return __awaiter(_this, void 0, void 0, function () {
+        var condition, res;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    condition = [true, false, true];
+                    return [4, tf.whereAsync(condition)];
+                case 1:
+                    res = _a.sent();
+                    expect(res.dtype).toBe('int32');
+                    expect(res.shape).toEqual([2, 1]);
+                    test_util_1.expectArraysClose(res, [0, 2]);
+                    return [2];
+            }
+        });
+    }); });
+    it('throws error if condition is not of type bool', function () { return __awaiter(_this, void 0, void 0, function () {
+        var condition, ex_1;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    condition = tf.tensor1d([1, 0, 1]);
+                    _a.label = 1;
+                case 1:
+                    _a.trys.push([1, 3, , 4]);
+                    return [4, tf.whereAsync(condition)];
+                case 2:
+                    _a.sent();
+                    throw new Error('The line above should have thrown an error');
+                case 3:
+                    ex_1 = _a.sent();
+                    expect(ex_1.message)
+                        .toMatch(/Argument 'condition' passed to 'whereAsync' must be bool/);
+                    return [3, 4];
+                case 4: return [2];
+            }
+        });
+    }); });
+    it('returns tensor with 0 in shape when no values are true', function () { return __awaiter(_this, void 0, void 0, function () {
+        var condition, res;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    condition = [[[false]], [[false]], [[false]]];
+                    return [4, tf.whereAsync(condition)];
+                case 1:
+                    res = _a.sent();
+                    expect(res.dtype).toBe('int32');
+                    expect(res.shape).toEqual([0, 3]);
+                    test_util_1.expectArraysClose(res, []);
+                    return [2];
+            }
+        });
+    }); });
+});
+//# sourceMappingURL=logical_ops_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/logical_ops_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/logical_ops_test.js.map
new file mode 100644
index 0000000..859d5ac
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/logical_ops_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops.d.ts
new file mode 100644
index 0000000..73688d6
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops.d.ts
@@ -0,0 +1,27 @@
+import { Tensor } from '../tensor';
+import { TensorLike } from '../types';
+export declare enum Reduction {
+    NONE = 0,
+    MEAN = 1,
+    SUM = 2,
+    SUM_BY_NONZERO_WEIGHTS = 3
+}
+declare function computeWeightedLoss_<T extends Tensor, O extends Tensor>(losses: T | TensorLike, weights?: Tensor | TensorLike, reduction?: Reduction): O;
+declare function absoluteDifference_<T extends Tensor, O extends Tensor>(labels: T | TensorLike, predictions: T | TensorLike, weights?: Tensor | TensorLike, reduction?: Reduction): O;
+declare function meanSquaredError_<T extends Tensor, O extends Tensor>(labels: T | TensorLike, predictions: T | TensorLike, weights?: Tensor | TensorLike, reduction?: Reduction): O;
+declare function cosineDistance_<T extends Tensor, O extends Tensor>(labels: T | TensorLike, predictions: T | TensorLike, axis: number, weights?: Tensor | TensorLike, reduction?: Reduction): O;
+declare function hingeLoss_<T extends Tensor, O extends Tensor>(labels: T | TensorLike, predictions: T | TensorLike, weights?: Tensor | TensorLike, reduction?: Reduction): O;
+declare function logLoss_<T extends Tensor, O extends Tensor>(labels: T | TensorLike, predictions: T | TensorLike, weights?: Tensor | TensorLike, epsilon?: number, reduction?: Reduction): O;
+declare function sigmoidCrossEntropy_<T extends Tensor, O extends Tensor>(multiClassLabels: T | TensorLike, logits: T | TensorLike, weights?: Tensor | TensorLike, labelSmoothing?: number, reduction?: Reduction): O;
+declare function huberLoss_<T extends Tensor, O extends Tensor>(labels: T | TensorLike, predictions: T | TensorLike, weights?: Tensor | TensorLike, delta?: number, reduction?: Reduction): O;
+declare function softmaxCrossEntropy_<T extends Tensor, O extends Tensor>(onehotLabels: T | TensorLike, logits: T | TensorLike, weights?: Tensor | TensorLike, labelSmoothing?: number, reduction?: Reduction): O;
+export declare const absoluteDifference: typeof absoluteDifference_;
+export declare const computeWeightedLoss: typeof computeWeightedLoss_;
+export declare const cosineDistance: typeof cosineDistance_;
+export declare const hingeLoss: typeof hingeLoss_;
+export declare const huberLoss: typeof huberLoss_;
+export declare const logLoss: typeof logLoss_;
+export declare const meanSquaredError: typeof meanSquaredError_;
+export declare const sigmoidCrossEntropy: typeof sigmoidCrossEntropy_;
+export declare const softmaxCrossEntropy: typeof softmaxCrossEntropy_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops.js b/node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops.js
new file mode 100644
index 0000000..f17f8e0
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops.js
@@ -0,0 +1,224 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var globals_1 = require("../globals");
+var tensor_util_env_1 = require("../tensor_util_env");
+var util_1 = require("../util");
+var axis_util_1 = require("./axis_util");
+var binary_ops_1 = require("./binary_ops");
+var operation_1 = require("./operation");
+var tensor_ops_1 = require("./tensor_ops");
+var Reduction;
+(function (Reduction) {
+    Reduction[Reduction["NONE"] = 0] = "NONE";
+    Reduction[Reduction["MEAN"] = 1] = "MEAN";
+    Reduction[Reduction["SUM"] = 2] = "SUM";
+    Reduction[Reduction["SUM_BY_NONZERO_WEIGHTS"] = 3] = "SUM_BY_NONZERO_WEIGHTS";
+})(Reduction = exports.Reduction || (exports.Reduction = {}));
+function computeWeightedLoss_(losses, weights, reduction) {
+    if (reduction === void 0) { reduction = Reduction.SUM_BY_NONZERO_WEIGHTS; }
+    var $losses = tensor_util_env_1.convertToTensor(losses, 'losses', 'computeWeightedLoss');
+    var $weights = null;
+    if (weights != null) {
+        $weights = tensor_util_env_1.convertToTensor(weights, 'weights', 'computeWeightedLoss');
+    }
+    var weightedLoss = ($weights == null) ? $losses : $losses.mul($weights);
+    if (reduction === Reduction.NONE) {
+        return weightedLoss;
+    }
+    if (reduction === Reduction.SUM) {
+        return weightedLoss.sum();
+    }
+    if (reduction === Reduction.MEAN) {
+        if ($weights == null) {
+            return weightedLoss.mean();
+        }
+        else {
+            var broadcastFactor = util_1.sizeFromShape($losses.shape) / util_1.sizeFromShape($weights.shape);
+            var result = weightedLoss.sum().div($weights.sum());
+            return broadcastFactor > 1 ? result.div(tensor_ops_1.scalar(broadcastFactor)) :
+                result;
+        }
+    }
+    if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {
+        if ($weights == null) {
+            return weightedLoss.sum().div(tensor_ops_1.scalar($losses.size));
+        }
+        else {
+            var broadcastedWeights = $weights.mul(tensor_ops_1.ones($losses.shape));
+            var numNonZeros = broadcastedWeights.notEqual(tensor_ops_1.scalar(0)).sum().toFloat();
+            return weightedLoss.sum().div(numNonZeros);
+        }
+    }
+    throw Error("Unknown reduction: " + reduction);
+}
+function absoluteDifference_(labels, predictions, weights, reduction) {
+    if (reduction === void 0) { reduction = Reduction.SUM_BY_NONZERO_WEIGHTS; }
+    var $labels = tensor_util_env_1.convertToTensor(labels, 'labels', 'absoluteDifference');
+    var $predictions = tensor_util_env_1.convertToTensor(predictions, 'predictions', 'absoluteDifference');
+    var $weights = null;
+    if (weights != null) {
+        $weights = tensor_util_env_1.convertToTensor(weights, 'weights', 'absoluteDifference');
+    }
+    util_1.assertShapesMatch($labels.shape, $predictions.shape, 'Error in absoluteDifference: ');
+    var losses = $labels.sub($predictions).abs();
+    return exports.computeWeightedLoss(losses, $weights, reduction);
+}
+function meanSquaredError_(labels, predictions, weights, reduction) {
+    if (reduction === void 0) { reduction = Reduction.SUM_BY_NONZERO_WEIGHTS; }
+    var $labels = tensor_util_env_1.convertToTensor(labels, 'labels', 'meanSquaredError');
+    var $predictions = tensor_util_env_1.convertToTensor(predictions, 'predictions', 'meanSquaredError');
+    var $weights = null;
+    if (weights != null) {
+        $weights = tensor_util_env_1.convertToTensor(weights, 'weights', 'meanSquaredError');
+    }
+    util_1.assertShapesMatch($labels.shape, $predictions.shape, 'Error in meanSquaredError: ');
+    var losses = $labels.squaredDifference($predictions);
+    return exports.computeWeightedLoss(losses, $weights, reduction);
+}
+function cosineDistance_(labels, predictions, axis, weights, reduction) {
+    if (reduction === void 0) { reduction = Reduction.SUM_BY_NONZERO_WEIGHTS; }
+    var $labels = tensor_util_env_1.convertToTensor(labels, 'labels', 'cosineDistance');
+    var $predictions = tensor_util_env_1.convertToTensor(predictions, 'predictions', 'cosineDistance');
+    var $weights = null;
+    if (weights != null) {
+        $weights = tensor_util_env_1.convertToTensor(weights, 'weights', 'cosineDistance');
+    }
+    util_1.assertShapesMatch($labels.shape, $predictions.shape, 'Error in cosineDistance: ');
+    var one = tensor_ops_1.scalar(1);
+    var losses = one.sub($labels.mul($predictions).sum(axis, true));
+    return exports.computeWeightedLoss(losses, $weights, reduction);
+}
+function hingeLoss_(labels, predictions, weights, reduction) {
+    if (reduction === void 0) { reduction = Reduction.SUM_BY_NONZERO_WEIGHTS; }
+    var $labels = tensor_util_env_1.convertToTensor(labels, 'labels', 'hingeLoss');
+    var $predictions = tensor_util_env_1.convertToTensor(predictions, 'predictions', 'hingeLoss');
+    var $weights = null;
+    if (weights != null) {
+        $weights = tensor_util_env_1.convertToTensor(weights, 'weights', 'hingeLoss');
+    }
+    util_1.assertShapesMatch($labels.shape, $predictions.shape, 'Error in hingeLoss: ');
+    var one = tensor_ops_1.scalar(1);
+    $labels = tensor_ops_1.scalar(2).mul($labels).sub(one);
+    var losses = one.sub($labels.mul($predictions)).relu();
+    return exports.computeWeightedLoss(losses, $weights, reduction);
+}
+function logLoss_(labels, predictions, weights, epsilon, reduction) {
+    if (epsilon === void 0) { epsilon = 1e-7; }
+    if (reduction === void 0) { reduction = Reduction.SUM_BY_NONZERO_WEIGHTS; }
+    var $labels = tensor_util_env_1.convertToTensor(labels, 'labels', 'logLoss');
+    var $predictions = tensor_util_env_1.convertToTensor(predictions, 'predictions', 'logLoss');
+    var $weights = null;
+    if (weights != null) {
+        $weights = tensor_util_env_1.convertToTensor(weights, 'weights', 'logLoss');
+    }
+    util_1.assertShapesMatch($labels.shape, $predictions.shape, 'Error in logLoss: ');
+    var one = tensor_ops_1.scalar(1);
+    var epsilonScalar = tensor_ops_1.scalar(epsilon);
+    var losses = $labels.mul($predictions.add(epsilonScalar).log())
+        .neg()
+        .sub(one.sub($labels).mul(one.sub($predictions).add(epsilonScalar).log()));
+    return exports.computeWeightedLoss(losses, $weights, reduction);
+}
+function sigmoidCrossEntropyWithLogits_(labels, logits) {
+    var $labels = tensor_util_env_1.convertToTensor(labels, 'labels', 'sigmoidCrossEntropyWithLogits');
+    var $logits = tensor_util_env_1.convertToTensor(logits, 'logits', 'sigmoidCrossEntropyWithLogits');
+    util_1.assertShapesMatch($labels.shape, $logits.shape, 'Error in sigmoidCrossEntropyWithLogits: ');
+    var maxOutput = $logits.relu();
+    var outputXTarget = $logits.mul($labels);
+    var sigmoidOutput = $logits.abs().neg().exp().log1p();
+    return maxOutput.sub(outputXTarget).add(sigmoidOutput);
+}
+function sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing, reduction) {
+    if (labelSmoothing === void 0) { labelSmoothing = 0; }
+    if (reduction === void 0) { reduction = Reduction.SUM_BY_NONZERO_WEIGHTS; }
+    var $multiClassLabels = tensor_util_env_1.convertToTensor(multiClassLabels, 'multiClassLabels', 'sigmoidCrossEntropy');
+    var $logits = tensor_util_env_1.convertToTensor(logits, 'logits', 'sigmoidCrossEntropy');
+    var $weights = null;
+    if (weights != null) {
+        $weights = tensor_util_env_1.convertToTensor(weights, 'weights', 'sigmoidCrossEntropy');
+    }
+    util_1.assertShapesMatch($multiClassLabels.shape, $logits.shape, 'Error in sigmoidCrossEntropy: ');
+    if (labelSmoothing > 0) {
+        var labelSmoothingScalar = tensor_ops_1.scalar(labelSmoothing);
+        var one = tensor_ops_1.scalar(1);
+        var half = tensor_ops_1.scalar(0.5);
+        $multiClassLabels = $multiClassLabels.mul(one.sub(labelSmoothingScalar))
+            .add(half.mul(labelSmoothingScalar));
+    }
+    var losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);
+    return exports.computeWeightedLoss(losses, $weights, reduction);
+}
+function huberLoss_(labels, predictions, weights, delta, reduction) {
+    if (delta === void 0) { delta = 1.0; }
+    if (reduction === void 0) { reduction = Reduction.SUM_BY_NONZERO_WEIGHTS; }
+    var $labels = tensor_util_env_1.convertToTensor(labels, 'labels', 'huberLoss');
+    var $predictions = tensor_util_env_1.convertToTensor(predictions, 'predictions', 'huberLoss');
+    var $weights = null;
+    if (weights != null) {
+        $weights = tensor_util_env_1.convertToTensor(weights, 'weights', 'huberLoss');
+    }
+    util_1.assertShapesMatch($labels.shape, $predictions.shape, 'Error in huberLoss: ');
+    var deltaScalar = tensor_ops_1.scalar(delta);
+    var error = $predictions.sub($labels).abs();
+    var quadratic = binary_ops_1.minimum(error, deltaScalar);
+    var linear = error.sub(quadratic);
+    var losses = tensor_ops_1.scalar(0.5).mul(quadratic.square()).add(deltaScalar.mul(linear));
+    return exports.computeWeightedLoss(losses, $weights, reduction);
+}
+function softmaxCrossEntropyWithLogits_(labels, logits, dim) {
+    if (dim === void 0) { dim = -1; }
+    if (dim === -1) {
+        dim = logits.rank - 1;
+    }
+    if (dim !== logits.rank - 1) {
+        throw Error("Softmax cross entropy along a non-last dimension is not yet " +
+            ("supported. Labels / logits was rank " + logits.rank + " ") +
+            ("and dim was " + dim));
+    }
+    var customOp = globals_1.customGrad(function (labels, logits) {
+        var keepDims = true;
+        var lse = logits.logSumExp([dim], keepDims);
+        var logResult = logits.toFloat().sub(lse);
+        var costVector = logResult.mul(labels).neg();
+        var value = costVector.sum([dim]);
+        var gradFunc = function (dy) {
+            var dyShape = axis_util_1.expandShapeToKeepDim(dy.shape, [dim]);
+            return [
+                dy.reshape(dyShape).mul(labels.toFloat().sub(logResult.exp())),
+                dy.reshape(dyShape).mul(logResult.exp().sub(labels.toFloat())),
+            ];
+        };
+        return { value: value, gradFunc: gradFunc };
+    });
+    return customOp(labels, logits);
+}
+function softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing, reduction) {
+    if (labelSmoothing === void 0) { labelSmoothing = 0; }
+    if (reduction === void 0) { reduction = Reduction.SUM_BY_NONZERO_WEIGHTS; }
+    var $onehotLabels = tensor_util_env_1.convertToTensor(onehotLabels, 'onehotLabels', 'softmaxCrossEntropy');
+    var $logits = tensor_util_env_1.convertToTensor(logits, 'logits', 'softmaxCrossEntropy');
+    var $weights = null;
+    if (weights != null) {
+        $weights = tensor_util_env_1.convertToTensor(weights, 'weights', 'softmaxCrossEntropy');
+    }
+    util_1.assertShapesMatch($onehotLabels.shape, $logits.shape, 'Error in softmaxCrossEntropy: ');
+    if (labelSmoothing > 0) {
+        var labelSmoothingScalar = tensor_ops_1.scalar(labelSmoothing);
+        var one = tensor_ops_1.scalar(1);
+        var numClasses = tensor_ops_1.scalar($onehotLabels.shape[1]);
+        $onehotLabels = $onehotLabels.mul(one.sub(labelSmoothingScalar))
+            .add(labelSmoothingScalar.div(numClasses));
+    }
+    var losses = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);
+    return exports.computeWeightedLoss(losses, $weights, reduction);
+}
+exports.absoluteDifference = operation_1.op({ absoluteDifference_: absoluteDifference_ });
+exports.computeWeightedLoss = operation_1.op({ computeWeightedLoss_: computeWeightedLoss_ });
+exports.cosineDistance = operation_1.op({ cosineDistance_: cosineDistance_ });
+exports.hingeLoss = operation_1.op({ hingeLoss_: hingeLoss_ });
+exports.huberLoss = operation_1.op({ huberLoss_: huberLoss_ });
+exports.logLoss = operation_1.op({ logLoss_: logLoss_ });
+exports.meanSquaredError = operation_1.op({ meanSquaredError_: meanSquaredError_ });
+exports.sigmoidCrossEntropy = operation_1.op({ sigmoidCrossEntropy_: sigmoidCrossEntropy_ });
+exports.softmaxCrossEntropy = operation_1.op({ softmaxCrossEntropy_: softmaxCrossEntropy_ });
+//# sourceMappingURL=loss_ops.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops.js.map
new file mode 100644
index 0000000..50300be
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops_test.js
new file mode 100644
index 0000000..646d289
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops_test.js
@@ -0,0 +1,1049 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('computeWeightedLoss', test_util_1.ALL_ENVS, function () {
+    it('1D - no weights', function () {
+        var losses = tf.tensor1d([1, 2, 3]);
+        var y = tf.losses.computeWeightedLoss(losses);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), (1 + 2 + 3) / 3);
+    });
+    it('1D - no weights - Reduction.NONE', function () {
+        var losses = tf.tensor1d([1, 2, 3]);
+        var y = tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.NONE);
+        expect(y.shape).toEqual([3]);
+        test_util_1.expectArraysClose(y, [1, 2, 3]);
+    });
+    it('1D - no weights - Reduction.MEAN', function () {
+        var losses = tf.tensor1d([1, 2, 3]);
+        var y = tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), (1 + 2 + 3) / 3);
+    });
+    it('1D - no weights - Reduction.SUM', function () {
+        var losses = tf.tensor1d([1, 2, 3]);
+        var y = tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.SUM);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), (1 + 2 + 3));
+    });
+    it('1D - weights', function () {
+        var losses = tf.tensor1d([1, 2, 3]);
+        var weights = tf.tensor1d([0.1, 0, 0.3]);
+        var y = tf.losses.computeWeightedLoss(losses, weights);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), (1 * 0.1 + 2 * 0 + 3 * 0.3) / 2);
+    });
+    it('2D - weights - broadcast', function () {
+        var losses = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
+        var weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
+        var y = tf.losses.computeWeightedLoss(losses, weights);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 0.06666667);
+    });
+    it('1D - weights - Reduction.NONE', function () {
+        var losses = tf.tensor1d([1, 2, 3]);
+        var weights = tf.tensor1d([0.1, 0.2, 0.3]);
+        var y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.NONE);
+        expect(y.shape).toEqual([3]);
+        test_util_1.expectArraysClose(y, [1 * 0.1, 2 * 0.2, 3 * 0.3]);
+    });
+    it('1D - weights - Reduction.MEAN', function () {
+        var losses = tf.tensor1d([1, 2, 3]);
+        var weights = tf.tensor1d([0.1, 0.2, 0.3]);
+        var y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), (1 * 0.1 + 2 * 0.2 + 3 * 0.3) / 0.6);
+    });
+    it('1D - weights - Reduction.SUM', function () {
+        var losses = tf.tensor1d([1, 2, 3]);
+        var weights = tf.tensor1d([0.1, 0.2, 0.3]);
+        var y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.SUM);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), (1 * 0.1 + 2 * 0.2 + 3 * 0.3));
+    });
+    it('2D - no weights', function () {
+        var losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var y = tf.losses.computeWeightedLoss(losses);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), (4 + 8 + 12 + 8 + 1 + 3) / 6);
+    });
+    it('2D - weights', function () {
+        var losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var weights = tf.tensor2d([1, 0, 2, -5, 0, 6], [2, 3]);
+        var y = tf.losses.computeWeightedLoss(losses, weights);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), (4 * 1 + 8 * 0 + 12 * 2 + (8 * -5) + 1 * 0 + 3 * 6) / 4);
+    });
+    it('2D - no weights - Reduction.MEAN', function () {
+        var losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var y = tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), (4 + 8 + 12 + 8 + 1 + 3) / 6);
+    });
+    it('2D - weights - Reduction.MEAN', function () {
+        var losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var weights = tf.tensor2d([1, 0, 2, -5, 0, 6], [2, 3]);
+        var y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), (4 * 1 + 8 * 0 + 12 * 2 + (8 * -5) + 1 * 0 + 3 * 6) / 4);
+    });
+    it('2D - weights - broadcast - MEAN', function () {
+        var losses = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
+        var weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
+        var y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), (0.3 + 0.1 + 0.2) / (3 * 0.6));
+    });
+    it('2D - no weights - Reduction.SUM', function () {
+        var losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var y = tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.SUM);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), (4 + 8 + 12 + 8 + 1 + 3));
+    });
+    it('2D - weights - Reduction.SUM', function () {
+        var losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var weights = tf.tensor2d([1, 0, 2, -5, 0, 6], [2, 3]);
+        var y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.SUM);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), (4 * 1 + 8 * 0 + 12 * 2 + (8 * -5) + 1 * 0 + 3 * 6));
+    });
+    it('2D - no weights - Reduction.NONE', function () {
+        var losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var y = tf.losses.computeWeightedLoss(losses, undefined, tf.Reduction.NONE);
+        expect(y.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(y, [4, 8, 12, 8, 1, 3]);
+    });
+    it('2D - weights - Reduction.NONE', function () {
+        var losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var weights = tf.tensor2d([1, 0, 2, -5, 0, 6], [2, 3]);
+        var y = tf.losses.computeWeightedLoss(losses, weights, tf.Reduction.NONE);
+        expect(y.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(y, [4 * 1, 8 * 0, 12 * 2, (8 * -5), 1 * 0, 3 * 6]);
+    });
+    it('throws when passed losses as a non-tensor', function () {
+        var weights = tf.tensor2d([1, 0, 2, -5, 0, 6], [2, 3]);
+        var e = /Argument 'losses' passed to 'computeWeightedLoss' must be a Tensor/;
+        expect(function () { return tf.losses.computeWeightedLoss({}, weights, tf.Reduction.NONE); })
+            .toThrowError(e);
+    });
+    it('throws when passed weights as a non-tensor', function () {
+        var losses = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var e = /Argument 'weights' passed to 'computeWeightedLoss' must be a Tensor/;
+        expect(function () { return tf.losses.computeWeightedLoss(losses, {}, tf.Reduction.NONE); })
+            .toThrowError(e);
+    });
+    it('accepts a tensor-like object', function () {
+        var losses = [1, 2, 3];
+        var weights = [0.1, 0, 0.3];
+        var y = tf.losses.computeWeightedLoss(losses, weights);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), (1 * 0.1 + 2 * 0 + 3 * 0.3) / 2);
+    });
+});
+jasmine_util_1.describeWithFlags('absoluteDifference', test_util_1.ALL_ENVS, function () {
+    it('1D', function () {
+        var predictions = tf.tensor1d([1, 2, 3]);
+        var label = tf.tensor1d([0.3, -0.6, -0.1]);
+        var y = tf.losses.absoluteDifference(label, predictions);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), (Math.abs(1 - 0.3) + Math.abs(2 - (-0.6)) + Math.abs(3 - (-0.1))) / 3);
+    });
+    it('1D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', function () {
+        var predictions = tf.tensor1d([1, 2, 3]);
+        var label = tf.tensor1d([0.3, -0.6, -0.1]);
+        var weights = tf.tensor1d([0.1, 0.2, 0.3]);
+        var y = tf.losses.absoluteDifference(label, predictions, weights);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), (Math.abs(1 - 0.3) * 0.1 + Math.abs(2 - (-0.6)) * 0.2 +
+            Math.abs(3 - (-0.1)) * 0.3) /
+            3);
+    });
+    it('1D - weighted - Reduction.NONE', function () {
+        var predictions = tf.tensor1d([1, 2, 3]);
+        var label = tf.tensor1d([0.3, -0.6, -0.1]);
+        var weights = tf.tensor1d([0.1, 0.2, 0.3]);
+        var y = tf.losses.absoluteDifference(label, predictions, weights, tf.Reduction.NONE);
+        expect(y.shape).toEqual([3]);
+        test_util_1.expectArraysClose(y, [
+            Math.abs(1 - 0.3) * 0.1, Math.abs(2 - (-0.6)) * 0.2,
+            Math.abs(3 - (-0.1)) * 0.3
+        ]);
+    });
+    it('1D - Reduction.MEAN', function () {
+        var predictions = tf.tensor1d([1, 2, 3]);
+        var label = tf.tensor1d([0.3, -0.6, -0.1]);
+        var y = tf.losses.absoluteDifference(label, predictions, undefined, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), (Math.abs(1 - 0.3) + Math.abs(2 - (-0.6)) + Math.abs(3 - (-0.1))) / 3);
+    });
+    it('1D - weighted - Reduction.MEAN', function () {
+        var predictions = tf.tensor1d([1, 2, 3]);
+        var label = tf.tensor1d([0.3, -0.6, -0.1]);
+        var weights = tf.tensor1d([0.1, 0.2, 0.3]);
+        var y = tf.losses.absoluteDifference(label, predictions, weights, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), ((Math.abs(1 - 0.3) * 0.1) + (Math.abs(2 - (-0.6)) * 0.2) +
+            (Math.abs(3 - (-0.1)) * 0.3)) /
+            0.6);
+    });
+    it('2D', function () {
+        var predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
+        var y = tf.losses.absoluteDifference(label, predictions);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), (Math.abs(4 - 1) + Math.abs(8 - 9) + Math.abs(12 - 2) +
+            Math.abs(8 - (-5)) + Math.abs(1 - (-2)) + Math.abs(3 - 6)) /
+            6);
+    });
+    it('2D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', function () {
+        var predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
+        var weights = tf.tensor2d([3, 0, 5, 0, 4, 2], [2, 3]);
+        var y = tf.losses.absoluteDifference(label, predictions, weights);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), (Math.abs(4 - 1) * 3 + Math.abs(8 - 9) * 0 + Math.abs(12 - 2) * 5 +
+            Math.abs(8 - (-5)) * 0 + Math.abs(1 - (-2)) * 4 +
+            Math.abs(3 - 6) * 2) /
+            4);
+    });
+    it('2D - weighted - Reduction.NONE', function () {
+        var predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
+        var weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
+        var y = tf.losses.absoluteDifference(label, predictions, weights, tf.Reduction.NONE);
+        expect(y.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(y, [
+            Math.abs(4 - 1) * 3, Math.abs(8 - 9) * 6, Math.abs(12 - 2) * 5,
+            Math.abs(8 - (-5)) * 0, Math.abs(1 - (-2)) * 4, Math.abs(3 - 6) * 2
+        ]);
+    });
+    it('2D - Reduction.MEAN', function () {
+        var predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
+        var y = tf.losses.absoluteDifference(label, predictions, undefined, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), (Math.abs(4 - 1) + Math.abs(8 - 9) + Math.abs(12 - 2) +
+            Math.abs(8 - (-5)) + Math.abs(1 - (-2)) + Math.abs(3 - 6)) /
+            6);
+    });
+    it('2D - weighted - Reduction.MEAN', function () {
+        var predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
+        var weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
+        var y = tf.losses.absoluteDifference(label, predictions, weights, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), (Math.abs(4 - 1) * 3 + Math.abs(8 - 9) * 6 + Math.abs(12 - 2) * 5 +
+            Math.abs(8 - (-5)) * 0 + Math.abs(1 - (-2)) * 4 +
+            Math.abs(3 - 6) * 2) /
+            20);
+    });
+    it('throws when passed label as a non-tensor', function () {
+        var predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
+        var e = /Argument 'labels' passed to 'absoluteDifference' must be a Tensor/;
+        expect(function () { return tf.losses.absoluteDifference({}, predictions, weights, tf.Reduction.MEAN); })
+            .toThrowError(e);
+    });
+    it('throws when passed label as a non-tensor', function () {
+        var label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
+        var weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
+        var e = new RegExp('Argument \'predictions\' passed to \'absoluteDifference\' ' +
+            'must be a Tensor');
+        expect(function () { return tf.losses.absoluteDifference(label, {}, weights, tf.Reduction.MEAN); })
+            .toThrowError(e);
+    });
+    it('throws when passed weights as a non-tensor', function () {
+        var predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
+        var e = /Argument 'weights' passed to 'absoluteDifference' must be a Tensor/;
+        expect(function () { return tf.losses.absoluteDifference(label, predictions, {}, tf.Reduction.MEAN); })
+            .toThrowError(e);
+    });
+    it('accepts a tensor-like object', function () {
+        var predictions = [1, 2, 3];
+        var label = [0.3, -0.6, -0.1];
+        var weights = [0.1, 0.2, 0.3];
+        var y = tf.losses.absoluteDifference(label, predictions, weights, tf.Reduction.NONE);
+        expect(y.shape).toEqual([3]);
+        test_util_1.expectArraysClose(y, [
+            Math.abs(1 - 0.3) * 0.1, Math.abs(2 - (-0.6)) * 0.2,
+            Math.abs(3 - (-0.1)) * 0.3
+        ]);
+    });
+});
+jasmine_util_1.describeWithFlags('meanSquaredError', test_util_1.ALL_ENVS, function () {
+    it('1D', function () {
+        var predictions = tf.tensor1d([1, 2, 3]);
+        var label = tf.tensor1d([0.3, -0.6, -0.1]);
+        var y = tf.losses.meanSquaredError(label, predictions);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), ((1 - 0.3) * (1 - 0.3) + (2 - (-0.6)) * (2 - (-0.6)) +
+            (3 - (-0.1)) * (3 - (-0.1))) /
+            3);
+    });
+    it('1D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', function () {
+        var predictions = tf.tensor1d([1, 2, 3]);
+        var label = tf.tensor1d([0.3, -0.6, -0.1]);
+        var weights = tf.tensor1d([0.1, 0.2, 0.3]);
+        var y = tf.losses.meanSquaredError(label, predictions, weights);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), ((1 - 0.3) * (1 - 0.3) * 0.1 + (2 - (-0.6)) * (2 - (-0.6)) * 0.2 +
+            (3 - (-0.1)) * (3 - (-0.1)) * 0.3) /
+            3);
+    });
+    it('1D - weighted - Reduction.NONE', function () {
+        var predictions = tf.tensor1d([1, 2, 3]);
+        var label = tf.tensor1d([0.3, -0.6, -0.1]);
+        var weights = tf.tensor1d([0.1, 0.2, 0.3]);
+        var y = tf.losses.meanSquaredError(label, predictions, weights, tf.Reduction.NONE);
+        expect(y.shape).toEqual([3]);
+        test_util_1.expectArraysClose(y, [
+            (1 - 0.3) * (1 - 0.3) * 0.1, (2 - (-0.6)) * (2 - (-0.6)) * 0.2,
+            (3 - (-0.1)) * (3 - (-0.1)) * 0.3
+        ]);
+    });
+    it('1D - Reduction.MEAN', function () {
+        var predictions = tf.tensor1d([1, 2, 3]);
+        var label = tf.tensor1d([0.3, -0.6, -0.1]);
+        var y = tf.losses.meanSquaredError(label, predictions, undefined, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), ((1 - 0.3) * (1 - 0.3) + (2 - (-0.6)) * (2 - (-0.6)) +
+            (3 - (-0.1)) * (3 - (-0.1))) /
+            3);
+    });
+    it('1D - weighted - Reduction.MEAN', function () {
+        var predictions = tf.tensor1d([1, 2, 3]);
+        var label = tf.tensor1d([0.3, -0.6, -0.1]);
+        var weights = tf.tensor1d([0.1, 0.2, 0.3]);
+        var y = tf.losses.meanSquaredError(label, predictions, weights, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), (((1 - 0.3) * (1 - 0.3) * 0.1) + ((2 - (-0.6)) * (2 - (-0.6)) * 0.2) +
+            ((3 - (-0.1)) * (3 - (-0.1)) * 0.3)) /
+            0.6);
+    });
+    it('2D', function () {
+        var predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
+        var y = tf.losses.meanSquaredError(label, predictions);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), ((4 - 1) * (4 - 1) + (8 - 9) * (8 - 9) + (12 - 2) * (12 - 2) +
+            (8 - (-5)) * (8 - (-5)) + (1 - (-2)) * (1 - (-2)) +
+            (3 - 6) * (3 - 6)) /
+            6);
+    });
+    it('2D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', function () {
+        var predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
+        var weights = tf.tensor2d([3, 0, 5, 0, 4, 2], [2, 3]);
+        var y = tf.losses.meanSquaredError(label, predictions, weights);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), ((4 - 1) * (4 - 1) * 3 + (8 - 9) * (8 - 9) * 0 +
+            (12 - 2) * (12 - 2) * 5 + (8 - (-5)) * (8 - (-5)) * 0 +
+            (1 - (-2)) * (1 - (-2)) * 4 + (3 - 6) * (3 - 6) * 2) /
+            4);
+    });
+    it('2D - weighted - Reduction.NONE', function () {
+        var predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
+        var weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
+        var y = tf.losses.meanSquaredError(label, predictions, weights, tf.Reduction.NONE);
+        expect(y.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(y, [
+            (4 - 1) * (4 - 1) * 3, (8 - 9) * (8 - 9) * 6, (12 - 2) * (12 - 2) * 5,
+            (8 - (-5)) * (8 - (-5)) * 0, (1 - (-2)) * (1 - (-2)) * 4,
+            (3 - 6) * (3 - 6) * 2
+        ]);
+    });
+    it('2D - Reduction.MEAN', function () {
+        var predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
+        var y = tf.losses.meanSquaredError(label, predictions, undefined, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), ((4 - 1) * (4 - 1) + (8 - 9) * (8 - 9) + (12 - 2) * (12 - 2) +
+            (8 - (-5)) * (8 - (-5)) + (1 - (-2)) * (1 - (-2)) +
+            (3 - 6) * (3 - 6)) /
+            6);
+    });
+    it('2D - weighted - Reduction.MEAN', function () {
+        var predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
+        var weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
+        var y = tf.losses.meanSquaredError(label, predictions, weights, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), ((4 - 1) * (4 - 1) * 3 + (8 - 9) * (8 - 9) * 6 +
+            (12 - 2) * (12 - 2) * 5 + (8 - (-5)) * (8 - (-5)) * 0 +
+            (1 - (-2)) * (1 - (-2)) * 4 + (3 - 6) * (3 - 6) * 2) /
+            20);
+    });
+    it('throws when passed label as a non-tensor', function () {
+        var predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
+        var e = /Argument 'labels' passed to 'meanSquaredError' must be a Tensor/;
+        expect(function () { return tf.losses.meanSquaredError({}, predictions, weights, tf.Reduction.MEAN); })
+            .toThrowError(e);
+    });
+    it('throws when passed label as a non-tensor', function () {
+        var label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
+        var weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
+        var e = new RegExp('Argument \'predictions\' passed to \'meanSquaredError\' ' +
+            'must be a Tensor');
+        expect(function () { return tf.losses.meanSquaredError(label, {}, weights, tf.Reduction.MEAN); })
+            .toThrowError(e);
+    });
+    it('throws when passed weights as a non-tensor', function () {
+        var predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
+        var e = /Argument 'weights' passed to 'meanSquaredError' must be a Tensor/;
+        expect(function () { return tf.losses.meanSquaredError(label, predictions, {}, tf.Reduction.MEAN); })
+            .toThrowError(e);
+    });
+    it('accepts a tensor-like object', function () {
+        var predictions = [1, 2, 3];
+        var label = [0.3, -0.6, -0.1];
+        var weights = [0.1, 0.2, 0.3];
+        var y = tf.losses.meanSquaredError(label, predictions, weights, tf.Reduction.NONE);
+        expect(y.shape).toEqual([3]);
+        test_util_1.expectArraysClose(y, [
+            (1 - 0.3) * (1 - 0.3) * 0.1, (2 - (-0.6)) * (2 - (-0.6)) * 0.2,
+            (3 - (-0.1)) * (3 - (-0.1)) * 0.3
+        ]);
+    });
+});
+jasmine_util_1.describeWithFlags('cosineDistance', test_util_1.ALL_ENVS, function () {
+    it('1D', function () {
+        var predictions = tf.tensor1d([1, 2, 3]);
+        var label = tf.tensor1d([0.3, -0.6, -0.1]);
+        var y = tf.losses.cosineDistance(label, predictions, 0);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1));
+    });
+    it('1D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', function () {
+        var predictions = tf.tensor1d([1, 2, 3]);
+        var label = tf.tensor1d([0.3, -0.6, -0.1]);
+        var weights = tf.scalar(0.1);
+        var y = tf.losses.cosineDistance(label, predictions, 0, weights);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), (1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1)) * 0.1);
+    });
+    it('1D - weighted - Reduction.NONE', function () {
+        var predictions = tf.tensor1d([1, 2, 3]);
+        var label = tf.tensor1d([0.3, -0.6, -0.1]);
+        var weights = tf.scalar(0.1);
+        var y = tf.losses.cosineDistance(label, predictions, 0, weights, tf.Reduction.NONE);
+        expect(y.shape).toEqual([1]);
+        test_util_1.expectArraysClose(y, [(1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1)) * 0.1]);
+    });
+    it('1D - Reduction.MEAN', function () {
+        var predictions = tf.tensor1d([1, 2, 3]);
+        var label = tf.tensor1d([0.3, -0.6, -0.1]);
+        var y = tf.losses.cosineDistance(label, predictions, 0, undefined, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), (1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1)));
+    });
+    it('1D - weighted - Reduction.MEAN', function () {
+        var predictions = tf.tensor1d([1, 2, 3]);
+        var label = tf.tensor1d([0.3, -0.6, -0.1]);
+        var weights = tf.scalar(0.1);
+        var y = tf.losses.cosineDistance(label, predictions, 0, weights, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), ((1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1)) * 0.1) / 0.1);
+    });
+    it('2D', function () {
+        var predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
+        var y = tf.losses.cosineDistance(label, predictions, 1);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), ((1 - (4 * 1 + 8 * 9 + 12 * 2)) + (1 - (8 * -5 + 1 * -2 + 3 * 6))) / 2);
+    });
+    it('2D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', function () {
+        var predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
+        var weights = tf.tensor2d([3, 0], [2, 1]);
+        var y = tf.losses.cosineDistance(label, predictions, 1, weights);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), ((1 - (4 * 1 + 8 * 9 + 12 * 2)) * 3 +
+            (1 - (8 * -5 + 1 * -2 + 3 * 6)) * 0) /
+            1);
+    });
+    it('2D - weighted - Reduction.NONE', function () {
+        var predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
+        var weights = tf.tensor2d([3, 0], [2, 1]);
+        var y = tf.losses.cosineDistance(label, predictions, 1, weights, tf.Reduction.NONE);
+        expect(y.shape).toEqual([2, 1]);
+        test_util_1.expectArraysClose(y, [
+            (1 - (4 * 1 + 8 * 9 + 12 * 2)) * 3, (1 - (8 * -5 + 1 * -2 + 3 * 6)) * 0
+        ]);
+    });
+    it('2D - Reduction.MEAN', function () {
+        var predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
+        var y = tf.losses.cosineDistance(label, predictions, 1, undefined, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), ((1 - (4 * 1 + 8 * 9 + 12 * 2)) + (1 - (8 * -5 + 1 * -2 + 3 * 6))) / 2);
+    });
+    it('2D - weighted - Reduction.MEAN', function () {
+        var predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
+        var weights = tf.tensor2d([3, 0], [2, 1]);
+        var y = tf.losses.cosineDistance(label, predictions, 1, weights, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), ((1 - (4 * 1 + 8 * 9 + 12 * 2)) * 3 +
+            (1 - (8 * -5 + 1 * -2 + 3 * 6)) * 0) /
+            3);
+    });
+    it('throws when passed label as a non-tensor', function () {
+        var predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
+        var e = /Argument 'labels' passed to 'cosineDistance' must be a Tensor/;
+        expect(function () { return tf.losses.cosineDistance({}, predictions, 0, weights, tf.Reduction.MEAN); })
+            .toThrowError(e);
+    });
+    it('throws when passed label as a non-tensor', function () {
+        var label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
+        var weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
+        var e = new RegExp('Argument \'predictions\' passed to \'cosineDistance\' ' +
+            'must be a Tensor');
+        expect(function () { return tf.losses.cosineDistance(label, {}, 0, weights, tf.Reduction.MEAN); })
+            .toThrowError(e);
+    });
+    it('throws when passed weights as a non-tensor', function () {
+        var predictions = tf.tensor2d([4, 8, 12, 8, 1, 3], [2, 3]);
+        var label = tf.tensor2d([1, 9, 2, -5, -2, 6], [2, 3]);
+        var e = /Argument 'weights' passed to 'cosineDistance' must be a Tensor/;
+        expect(function () { return tf.losses.cosineDistance(label, predictions, 0, {}, tf.Reduction.MEAN); })
+            .toThrowError(e);
+    });
+    it('accepts a tensor-like object', function () {
+        var predictions = [1, 2, 3];
+        var label = [0.3, -0.6, -0.1];
+        var weights = 0.1;
+        var y = tf.losses.cosineDistance(label, predictions, 0, weights, tf.Reduction.NONE);
+        expect(y.shape).toEqual([1]);
+        test_util_1.expectArraysClose(y, [(1 - (1 * 0.3 + 2 * -0.6 + 3 * -0.1)) * 0.1]);
+    });
+});
+jasmine_util_1.describeWithFlags('hingeLoss', test_util_1.ALL_ENVS, function () {
+    it('1D', function () {
+        var predictions = tf.tensor1d([0, 0, 1, 1]);
+        var label = tf.tensor1d([0, 1, 0, 1]);
+        var y = tf.losses.hingeLoss(label, predictions);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 1.0);
+    });
+    it('1D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', function () {
+        var predictions = tf.tensor1d([0, 0, 1, 1]);
+        var label = tf.tensor1d([0, 1, 0, 1]);
+        var weights = tf.tensor1d([0.1, 0.2, 0.3, 0.4]);
+        var y = tf.losses.hingeLoss(label, predictions, weights);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 0.225);
+    });
+    it('1D - weighted - Reduction.NONE', function () {
+        var predictions = tf.tensor1d([0, 0, 1, 1]);
+        var label = tf.tensor1d([0, 1, 0, 1]);
+        var weights = tf.tensor1d([0.1, 0.2, 0.3, 0.4]);
+        var y = tf.losses.hingeLoss(label, predictions, weights, tf.Reduction.NONE);
+        expect(y.shape).toEqual([4]);
+        test_util_1.expectArraysClose(y, [0.1, 0.2, 0.6, 0.0]);
+    });
+    it('1D - Reduction.MEAN', function () {
+        var predictions = tf.tensor1d([0, 0, 1, 1]);
+        var label = tf.tensor1d([0, 1, 0, 1]);
+        var y = tf.losses.hingeLoss(label, predictions, undefined, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 1.0);
+    });
+    it('1D - weighted - Reduction.MEAN', function () {
+        var predictions = tf.tensor1d([0, 0, 1, 1]);
+        var label = tf.tensor1d([0, 1, 0, 1]);
+        var weights = tf.tensor1d([0.1, 0.2, 0.3, 0.4]);
+        var y = tf.losses.hingeLoss(label, predictions, weights, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 0.9);
+    });
+    it('2D', function () {
+        var predictions = tf.tensor2d([0, 0, 0, 1, 1, 1], [2, 3]);
+        var label = tf.tensor2d([0, 1, 0, 1, 0, 1], [2, 3]);
+        var y = tf.losses.hingeLoss(label, predictions);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 0.8333333);
+    });
+    it('2D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', function () {
+        var predictions = tf.tensor2d([0, 0, 0, 1, 1, 1], [2, 3]);
+        var label = tf.tensor2d([0, 1, 0, 1, 0, 1], [2, 3]);
+        var weights = tf.tensor2d([0.1, 0.2, 0.3, 0.4, 0.5, 0.6], [2, 3]);
+        var y = tf.losses.hingeLoss(label, predictions, weights);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 0.26666668);
+    });
+    it('2D - weighted - Reduction.NONE', function () {
+        var predictions = tf.tensor2d([0, 0, 0, 1, 1, 1], [2, 3]);
+        var label = tf.tensor2d([0, 1, 0, 1, 0, 1], [2, 3]);
+        var weights = tf.tensor2d([0.1, 0.2, 0.3, 0.4, 0.5, 0.6], [2, 3]);
+        var y = tf.losses.hingeLoss(label, predictions, weights, tf.Reduction.NONE);
+        expect(y.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(y, [0.1, 0.2, 0.3, 0, 1, 0]);
+    });
+    it('2D - Reduction.MEAN', function () {
+        var predictions = tf.tensor2d([0, 0, 0, 1, 1, 1], [2, 3]);
+        var label = tf.tensor2d([0, 1, 0, 1, 0, 1], [2, 3]);
+        var y = tf.losses.hingeLoss(label, predictions, undefined, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 0.8333333);
+    });
+    it('2D - weighted - Reduction.MEAN', function () {
+        var predictions = tf.tensor2d([0, 0, 0, 1, 1, 1], [2, 3]);
+        var label = tf.tensor2d([0, 1, 0, 1, 0, 1], [2, 3]);
+        var weights = tf.tensor2d([0.1, 0.2, 0.3, 0.4, 0.5, 0.6], [2, 3]);
+        var y = tf.losses.hingeLoss(label, predictions, weights, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 0.76190484);
+    });
+    it('throws when passed label as a non-tensor', function () {
+        var predictions = tf.tensor2d([1, 0, 1, 0, 1, 0], [2, 3]);
+        var weights = tf.tensor2d([1, 0, 1, 0, 1, 0], [2, 3]);
+        var e = /Argument 'labels' passed to 'hingeLoss' must be a Tensor/;
+        expect(function () { return tf.losses.hingeLoss({}, predictions, weights, tf.Reduction.MEAN); })
+            .toThrowError(e);
+    });
+    it('throws when passed label as a non-tensor', function () {
+        var label = tf.tensor2d([1, 0, 1, 0, 1, 0], [2, 3]);
+        var weights = tf.tensor2d([1, 0, 1, 0, 1, 0], [2, 3]);
+        var e = new RegExp('Argument \'predictions\' passed to \'hingeLoss\' ' +
+            'must be a Tensor');
+        expect(function () { return tf.losses.hingeLoss(label, {}, weights, tf.Reduction.MEAN); })
+            .toThrowError(e);
+    });
+    it('throws when passed weights as a non-tensor', function () {
+        var predictions = tf.tensor2d([1, 0, 1, 0, 1, 0], [2, 3]);
+        var label = tf.tensor2d([1, 0, 1, 0, 1, 0], [2, 3]);
+        var e = /Argument 'weights' passed to 'hingeLoss' must be a Tensor/;
+        expect(function () { return tf.losses.hingeLoss(label, predictions, {}, tf.Reduction.MEAN); })
+            .toThrowError(e);
+    });
+    it('accepts a tensor-like object', function () {
+        var predictions = [0, 0, 1, 1];
+        var label = [0, 1, 0, 1];
+        var weights = [0.1, 0.2, 0.3, 0.4];
+        var y = tf.losses.hingeLoss(label, predictions, weights, tf.Reduction.NONE);
+        expect(y.shape).toEqual([4]);
+        test_util_1.expectArraysClose(y, [0.1, 0.2, 0.6, 0.0]);
+    });
+});
+jasmine_util_1.describeWithFlags('logLoss', test_util_1.ALL_ENVS, function () {
+    it('1D', function () {
+        var labels = tf.tensor1d([1, 2, 3]);
+        var predictions = tf.tensor1d([0.3, 0.6, 0.1]);
+        var y = tf.losses.logLoss(labels, predictions);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 2.668788);
+    });
+    it('1D - Check for negative values', function () {
+        var labels = tf.tensor1d([1, 2, 3]);
+        var predictions = tf.tensor1d([0.3, -0.6, -0.1]);
+        var y = tf.losses.logLoss(labels, predictions);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), NaN);
+    });
+    it('1D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', function () {
+        var labels = tf.tensor1d([1, 2, 3]);
+        var predictions = tf.tensor1d([0.3, 0.6, 0.1]);
+        var weights = tf.tensor1d([0.1, 0.2, 0.3]);
+        var y = tf.losses.logLoss(labels, predictions, weights);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 0.7168596);
+    });
+    it('1D - weighted - Reduction.NONE', function () {
+        var labels = tf.tensor1d([1, 2, 3]);
+        var predictions = tf.tensor1d([0.3, 0.6, 0.1]);
+        var weights = tf.tensor1d([0.1, 0.2, 0.3]);
+        var y = tf.losses.logLoss(labels, predictions, weights, undefined, tf.Reduction.NONE);
+        expect(y.shape).toEqual([3]);
+        test_util_1.expectArraysClose(y, [0.12039725, 0.02107204, 2.0091095]);
+    });
+    it('1D - Reduction.MEAN', function () {
+        var labels = tf.tensor1d([1, 2, 3]);
+        var predictions = tf.tensor1d([0.3, 0.6, 0.1]);
+        var y = tf.losses.logLoss(labels, predictions, undefined, undefined, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 2.668788);
+    });
+    it('1D - weighted - Reduction.MEAN', function () {
+        var labels = tf.tensor1d([1, 2, 3]);
+        var predictions = tf.tensor1d([0.3, 0.6, 0.1]);
+        var weights = tf.tensor1d([0.1, 0.2, 0.3]);
+        var y = tf.losses.logLoss(labels, predictions, weights, undefined, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 3.5842977);
+    });
+    it('2D', function () {
+        var labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
+        var predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
+        var y = tf.losses.logLoss(labels, predictions);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 0.60019904);
+    });
+    it('2D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', function () {
+        var labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
+        var predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
+        var weights = tf.tensor2d([3, 0, 5, 0, 4, 2], [2, 3]);
+        var y = tf.losses.logLoss(labels, predictions, weights);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 1.8866577);
+    });
+    it('2D - weighted - Reduction.NONE', function () {
+        var labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
+        var predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
+        var weights = tf.tensor2d([3, 0, 5, 0, 4, 2], [2, 3]);
+        var y = tf.losses.logLoss(labels, predictions, weights, undefined, tf.Reduction.NONE);
+        expect(y.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(y, [2.9527497, 0., 1.8451363, 0., 1.3829476, 1.3657978]);
+    });
+    it('2D - Reduction.MEAN', function () {
+        var labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
+        var predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
+        var y = tf.losses.logLoss(labels, predictions, undefined, undefined, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 0.60019904);
+    });
+    it('2D - weighted - Reduction.MEAN', function () {
+        var labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
+        var predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
+        var weights = tf.tensor2d([3, 0, 5, 0, 4, 2], [2, 3]);
+        var y = tf.losses.logLoss(labels, predictions, weights, undefined, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 0.53904504);
+    });
+    it('throws when passed label as a non-tensor', function () {
+        var predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
+        var weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
+        var e = /Argument 'labels' passed to 'logLoss' must be a Tensor/;
+        expect(function () { return tf.losses.logLoss({}, predictions, weights, tf.Reduction.MEAN); })
+            .toThrowError(e);
+    });
+    it('throws when passed label as a non-tensor', function () {
+        var labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
+        var weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
+        var e = new RegExp('Argument \'predictions\' passed to \'logLoss\' ' +
+            'must be a Tensor');
+        expect(function () { return tf.losses.logLoss(labels, {}, weights, tf.Reduction.MEAN); })
+            .toThrowError(e);
+    });
+    it('throws when passed weights as a non-tensor', function () {
+        var labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
+        var predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
+        var e = /Argument 'weights' passed to 'logLoss' must be a Tensor/;
+        expect(function () { return tf.losses.logLoss(labels, predictions, {}, tf.Reduction.MEAN); })
+            .toThrowError(e);
+    });
+    it('accepts a tensor-like object', function () {
+        var labels = [1, 2, 3];
+        var predictions = [0.3, 0.6, 0.1];
+        var weights = [0.1, 0.2, 0.3];
+        var y = tf.losses.logLoss(labels, predictions, weights, undefined, tf.Reduction.NONE);
+        expect(y.shape).toEqual([3]);
+        test_util_1.expectArraysClose(y, [0.12039725, 0.02107204, 2.0091095]);
+    });
+});
+jasmine_util_1.describeWithFlags('huberLoss', test_util_1.ALL_ENVS, function () {
+    it('1D', function () {
+        var labels = tf.tensor1d([1, 2, 3]);
+        var predictions = tf.tensor1d([0.3, 0.6, 0.1]);
+        var y = tf.losses.huberLoss(labels, predictions);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 1.1816667);
+    });
+    it('1D - delta', function () {
+        var labels = tf.tensor1d([1, 2, 3]);
+        var predictions = tf.tensor1d([0.3, 0.6, 0.1]);
+        var delta = 0.4;
+        var y = tf.losses.huberLoss(labels, predictions, undefined, delta);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 0.58666664);
+    });
+    it('1D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', function () {
+        var labels = tf.tensor1d([1, 2, 3]);
+        var predictions = tf.tensor1d([0.3, 0.6, 0.1]);
+        var weights = tf.tensor1d([0.1, 0.2, 0.3]);
+        var y = tf.losses.huberLoss(labels, predictions, weights);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 0.30816665);
+    });
+    it('1D - weighted - Reduction.NONE', function () {
+        var labels = tf.tensor1d([1, 2, 3]);
+        var predictions = tf.tensor1d([0.3, 0.6, 0.1]);
+        var weights = tf.tensor1d([0.1, 0.2, 0.3]);
+        var y = tf.losses.huberLoss(labels, predictions, weights, undefined, tf.Reduction.NONE);
+        expect(y.shape).toEqual([3]);
+        test_util_1.expectArraysClose(y, [0.0245, 0.17999999, 0.72]);
+    });
+    it('1D - Reduction.MEAN', function () {
+        var labels = tf.tensor1d([1, 2, 3]);
+        var predictions = tf.tensor1d([0.3, 0.6, 0.1]);
+        var y = tf.losses.huberLoss(labels, predictions, undefined, undefined, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 1.1816667);
+    });
+    it('1D - weighted - Reduction.MEAN', function () {
+        var labels = tf.tensor1d([1, 2, 3]);
+        var predictions = tf.tensor1d([0.3, 0.6, 0.1]);
+        var weights = tf.tensor1d([0.1, 0.2, 0.3]);
+        var y = tf.losses.huberLoss(labels, predictions, weights, undefined, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 1.5408332);
+    });
+    it('2D', function () {
+        var labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
+        var predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
+        var y = tf.losses.huberLoss(labels, predictions);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 0.01795);
+    });
+    it('2D - weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', function () {
+        var labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
+        var predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
+        var weights = tf.tensor2d([3, 0, 5, 0, 4, 2], [2, 3]);
+        var y = tf.losses.huberLoss(labels, predictions, weights);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 0.040875003);
+    });
+    it('2D - weighted - Reduction.NONE', function () {
+        var labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
+        var predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
+        var weights = tf.tensor2d([3, 0, 5, 0, 4, 2], [2, 3]);
+        var y = tf.losses.huberLoss(labels, predictions, weights, undefined, tf.Reduction.NONE);
+        expect(y.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(y, [0.135, 0., 0.001, 0., 0.005, 0.0225]);
+    });
+    it('2D - Reduction.MEAN', function () {
+        var labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
+        var predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
+        var y = tf.losses.huberLoss(labels, predictions, undefined, undefined, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 0.01795);
+    });
+    it('2D - weighted - Reduction.MEAN', function () {
+        var labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
+        var predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
+        var weights = tf.tensor2d([3, 0, 5, 0, 4, 2], [2, 3]);
+        var y = tf.losses.huberLoss(labels, predictions, weights, undefined, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 0.011678572);
+    });
+    it('throws when passed label as a non-tensor', function () {
+        var predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
+        var weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
+        var e = /Argument 'labels' passed to 'huberLoss' must be a Tensor/;
+        expect(function () { return tf.losses.huberLoss({}, predictions, weights, tf.Reduction.MEAN); })
+            .toThrowError(e);
+    });
+    it('throws when passed label as a non-tensor', function () {
+        var labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
+        var weights = tf.tensor2d([3, 6, 5, 0, 4, 2], [2, 3]);
+        var e = new RegExp('Argument \'predictions\' passed to \'huberLoss\' ' +
+            'must be a Tensor');
+        expect(function () { return tf.losses.huberLoss(labels, {}, weights, tf.Reduction.MEAN); })
+            .toThrowError(e);
+    });
+    it('throws when passed weights as a non-tensor', function () {
+        var labels = tf.tensor2d([0.4, 0.8, 0.12, 0.8, 0.1, 0.3], [2, 3]);
+        var predictions = tf.tensor2d([0.1, 0.7, 0.1, 0.5, 0.05, 0.15], [2, 3]);
+        var e = /Argument 'weights' passed to 'huberLoss' must be a Tensor/;
+        expect(function () { return tf.losses.huberLoss(labels, predictions, {}, tf.Reduction.MEAN); })
+            .toThrowError(e);
+    });
+    it('accepts a tensor-like object', function () {
+        var labels = [1, 2, 3];
+        var predictions = [0.3, 0.6, 0.1];
+        var weights = [0.1, 0.2, 0.3];
+        var y = tf.losses.huberLoss(labels, predictions, weights, undefined, tf.Reduction.NONE);
+        expect(y.shape).toEqual([3]);
+        test_util_1.expectArraysClose(y, [0.0245, 0.17999999, 0.72]);
+    });
+});
+jasmine_util_1.describeWithFlags('sigmoidCrossEntropy', test_util_1.ALL_ENVS, function () {
+    it('All wrong', function () {
+        var label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
+        var predictions = tf.tensor2d([[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]], [3, 3]);
+        var y = tf.losses.sigmoidCrossEntropy(label, predictions);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 6.6667123);
+    });
+    it('All right', function () {
+        var label = tf.tensor2d([[1, 0, 0], [0, 1, 0], [0, 0, 1]], [3, 3]);
+        var predictions = tf.tensor2d([[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]], [3, 3]);
+        var y = tf.losses.sigmoidCrossEntropy(label, predictions);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 0);
+    });
+    it('Weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', function () {
+        var label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
+        var predictions = tf.tensor2d([[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]], [3, 3]);
+        var weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
+        var y = tf.losses.sigmoidCrossEntropy(label, predictions, weights);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 1.3333424);
+    });
+    it('Weighted - Reduction.NONE', function () {
+        var label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
+        var predictions = tf.tensor2d([[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]], [3, 3]);
+        var weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
+        var y = tf.losses.sigmoidCrossEntropy(label, predictions, weights, undefined, tf.Reduction.NONE);
+        expect(y.shape).toEqual([3, 3]);
+        test_util_1.expectArraysClose(y, [
+            1.0000046, 9.0797803e-06, 3.0000138e+00, 1.0000046e+00, 2.0000093e+00,
+            1.3619671e-05, 4.5398901e-06, 2.0000093e+00, 3.0000138e+00
+        ]);
+    });
+    it('Reduction.MEAN', function () {
+        var label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
+        var predictions = tf.tensor2d([[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]], [3, 3]);
+        var y = tf.losses.sigmoidCrossEntropy(label, predictions, undefined, undefined, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 6.6667123);
+    });
+    it('Weighted - Reduction.MEAN', function () {
+        var label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
+        var predictions = tf.tensor2d([[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]], [3, 3]);
+        var weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
+        var y = tf.losses.sigmoidCrossEntropy(label, predictions, weights, undefined, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 6.666712284088135);
+    });
+    it('Label Smoothing - Weighted - Reduction.MEAN', function () {
+        var label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
+        var predictions = tf.tensor2d([[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]], [3, 3]);
+        var weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
+        var labelSmoothing = 0.3;
+        var y = tf.losses.sigmoidCrossEntropy(label, predictions, weights, labelSmoothing, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 6.1667128);
+    });
+    it('throws when multiClassLabels and logits are of different shapes', function () {
+        var multiClassLabels = tf.tensor2d([10, 10, 10, 10, 10, 10, 10, 10, 10], [3, 3]);
+        var logits = tf.tensor2d([10, 10, 10, 10, 10, 10], [2, 3]);
+        var e = new RegExp('Error in sigmoidCrossEntropy:  Shapes 3,3 and 2,3 must match');
+        expect(function () { return tf.losses.sigmoidCrossEntropy(multiClassLabels, logits); })
+            .toThrowError(e);
+    });
+    it('throws when passed multiClassLabels as a non-tensor', function () {
+        var predictions = tf.tensor2d([[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]], [3, 3]);
+        var weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
+        var e = new RegExp('Argument \'multiClassLabels\' passed to \'sigmoidCrossEntropy\' ' +
+            'must be a Tensor');
+        expect(function () { return tf.losses.sigmoidCrossEntropy({}, predictions, weights, tf.Reduction.MEAN); })
+            .toThrowError(e);
+    });
+    it('throws when passed logits as a non-tensor', function () {
+        var label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
+        var weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
+        var e = new RegExp('Argument \'logits\' passed to \'sigmoidCrossEntropy\' ' +
+            'must be a Tensor');
+        expect(function () { return tf.losses.sigmoidCrossEntropy(label, {}, weights, tf.Reduction.MEAN); })
+            .toThrowError(e);
+    });
+    it('throws when passed weights as a non-tensor', function () {
+        var label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
+        var predictions = tf.tensor2d([[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]], [3, 3]);
+        var e = /Argument 'weights' passed to 'sigmoidCrossEntropy' must be a Tensor/;
+        expect(function () { return tf.losses.sigmoidCrossEntropy(label, predictions, {}, tf.Reduction.MEAN); })
+            .toThrowError(e);
+    });
+});
+jasmine_util_1.describeWithFlags('softmaxCrossEntropy', test_util_1.ALL_ENVS, function () {
+    it('All wrong', function () {
+        var label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
+        var predictions = tf.tensor2d([[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]], [3, 3]);
+        var y = tf.losses.softmaxCrossEntropy(label, predictions);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 20);
+    });
+    it('All right', function () {
+        var label = tf.tensor2d([[1, 0, 0], [0, 1, 0], [0, 0, 1]], [3, 3]);
+        var predictions = tf.tensor2d([[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]], [3, 3]);
+        var y = tf.losses.softmaxCrossEntropy(label, predictions);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 0);
+    });
+    it('Weighted - Reduction.SUM_BY_NONZERO_WEIGHTS', function () {
+        var label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
+        var predictions = tf.tensor2d([[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]], [3, 3]);
+        var weights = tf.tensor2d([[0.1, 0.2, 0.3], [0.1, 0.2, 0.3], [0.1, 0.2, 0.3]]);
+        var y = tf.losses.softmaxCrossEntropy(label, predictions, weights);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 4);
+    });
+    it('Weighted - Reduction.NONE', function () {
+        var label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
+        var predictions = tf.tensor2d([[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]], [3, 3]);
+        var weights = tf.tensor1d([0.1, 0.2, 0.3]);
+        var y = tf.losses.softmaxCrossEntropy(label, predictions, weights, undefined, tf.Reduction.NONE);
+        expect(y.shape).toEqual([3]);
+        test_util_1.expectArraysClose(y, [2, 4, 6]);
+    });
+    it('Reduction.MEAN', function () {
+        var label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
+        var predictions = tf.tensor2d([[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]], [3, 3]);
+        var y = tf.losses.softmaxCrossEntropy(label, predictions, undefined, undefined, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 20);
+    });
+    it('Weighted - Reduction.MEAN', function () {
+        var label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
+        var predictions = tf.tensor2d([[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]], [3, 3]);
+        var weights = tf.tensor1d([0.1, 0.2, 0.3]);
+        var y = tf.losses.softmaxCrossEntropy(label, predictions, weights, undefined, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 20);
+    });
+    it('Label Smoothing - Weighted - Reduction.MEAN', function () {
+        var label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
+        var predictions = tf.tensor2d([[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]], [3, 3]);
+        var weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
+        var labelSmoothing = 0.3;
+        var y = tf.losses.softmaxCrossEntropy(label, predictions, weights, labelSmoothing, tf.Reduction.MEAN);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 18);
+    });
+    it('throws when multiClassLabels and logits are of different shapes', function () {
+        var multiClassLabels = tf.tensor2d([10, 10, 10, 10, 10, 10, 10, 10, 10], [3, 3]);
+        var logits = tf.tensor2d([10, 10, 10, 10, 10, 10], [2, 3]);
+        var e = new RegExp('Error in softmaxCrossEntropy:  Shapes 3,3 and 2,3 must match');
+        expect(function () { return tf.losses.softmaxCrossEntropy(multiClassLabels, logits); })
+            .toThrowError(e);
+    });
+    it('throws when passed multiClassLabels as a non-tensor', function () {
+        var predictions = tf.tensor2d([[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]], [3, 3]);
+        var weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
+        var e = new RegExp('Argument \'onehotLabels\' passed to \'softmaxCrossEntropy\' ' +
+            'must be a Tensor');
+        expect(function () { return tf.losses.softmaxCrossEntropy({}, predictions, weights, tf.Reduction.MEAN); })
+            .toThrowError(e);
+    });
+    it('throws when passed logits as a non-tensor', function () {
+        var label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
+        var weights = tf.tensor2d([[0.1, 0.2, 0.3]]);
+        var e = new RegExp('Argument \'logits\' passed to \'softmaxCrossEntropy\' ' +
+            'must be a Tensor');
+        expect(function () { return tf.losses.softmaxCrossEntropy(label, {}, weights, tf.Reduction.MEAN); })
+            .toThrowError(e);
+    });
+    it('throws when passed weights as a non-tensor', function () {
+        var label = tf.tensor2d([[0, 0, 1], [1, 0, 0], [0, 1, 0]], [3, 3]);
+        var predictions = tf.tensor2d([[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]], [3, 3]);
+        var e = /Argument 'weights' passed to 'softmaxCrossEntropy' must be a Tensor/;
+        expect(function () { return tf.losses.softmaxCrossEntropy(label, predictions, {}, tf.Reduction.MEAN); })
+            .toThrowError(e);
+    });
+    it('accepts a tensor-like object', function () {
+        var label = [[0, 0, 1], [1, 0, 0], [0, 1, 0]];
+        var predictions = [[10.0, -10.0, -10.0], [-10.0, 10.0, -10.0], [-10.0, -10.0, 10.0]];
+        var y = tf.losses.softmaxCrossEntropy(label, predictions);
+        expect(y.shape).toEqual([]);
+        test_util_1.expectNumbersClose(y.get(), 20);
+    });
+});
+//# sourceMappingURL=loss_ops_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops_test.js.map
new file mode 100644
index 0000000..5171c75
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+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/loss_ops_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/lrn.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/lrn.d.ts
new file mode 100644
index 0000000..7fa343f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/lrn.d.ts
@@ -0,0 +1,5 @@
+import { Tensor3D, Tensor4D } from '../tensor';
+import { TensorLike } from '../types';
+declare function localResponseNormalization_<T extends Tensor3D | Tensor4D>(x: T | TensorLike, depthRadius?: number, bias?: number, alpha?: number, beta?: number): T;
+export declare const localResponseNormalization: typeof localResponseNormalization_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/lrn.js b/node_modules/@tensorflow/tfjs-core/dist/ops/lrn.js
new file mode 100644
index 0000000..40c3403
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/lrn.js
@@ -0,0 +1,36 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var tensor_util_env_1 = require("../tensor_util_env");
+var util = require("../util");
+var operation_1 = require("./operation");
+function localResponseNormalization_(x, depthRadius, bias, alpha, beta) {
+    if (depthRadius === void 0) { depthRadius = 5; }
+    if (bias === void 0) { bias = 1; }
+    if (alpha === void 0) { alpha = 1; }
+    if (beta === void 0) { beta = 0.5; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'localResponseNormalization');
+    util.assert($x.rank === 4 || $x.rank === 3, "Error in localResponseNormalization: x must be rank 3 or 4 but got\n               rank " + $x.rank + ".");
+    util.assert(util.isInt(depthRadius), "Error in localResponseNormalization: depthRadius must be an integer\n                     but got depthRadius " + depthRadius + ".");
+    var x4D = $x;
+    var reshapedTo4D = false;
+    if ($x.rank === 3) {
+        reshapedTo4D = true;
+        x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
+    }
+    var backward = function (dy, saved) {
+        var outputImage = saved[0];
+        return {
+            x4D: function () { return environment_1.ENV.engine.runKernel(function (backend) { return backend.LRNGrad(dy, x4D, outputImage, depthRadius, bias, alpha, beta); }, {}); }
+        };
+    };
+    var res = environment_1.ENV.engine.runKernel(function (backend, save) { return save(backend.localResponseNormalization4D(x4D, depthRadius, bias, alpha, beta)); }, { x4D: x4D }, backward);
+    if (reshapedTo4D) {
+        return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
+    }
+    else {
+        return res;
+    }
+}
+exports.localResponseNormalization = operation_1.op({ localResponseNormalization_: localResponseNormalization_ });
+//# sourceMappingURL=lrn.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/lrn.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/lrn.js.map
new file mode 100644
index 0000000..4736bbf
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/lrn.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"lrn.js","sourceRoot":"","sources":["../../src/ops/lrn.ts"],"names":[],"mappings":";;AAiBA,8CAAmC;AAEnC,sDAAmD;AAEnD,8BAAgC;AAChC,yCAA+B;AAgB/B,qCACI,CAAe,EAAE,WAAe,EAAE,IAAQ,EAAE,KAAS,EAAE,IAAU;IAAhD,4BAAA,EAAA,eAAe;IAAE,qBAAA,EAAA,QAAQ;IAAE,sBAAA,EAAA,SAAS;IAAE,qBAAA,EAAA,UAAU;IACnE,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,4BAA4B,CAAC,CAAC;IACjE,IAAI,CAAC,MAAM,CACP,EAAE,CAAC,IAAI,KAAK,CAAC,IAAI,EAAE,CAAC,IAAI,KAAK,CAAC,EAC9B,6FACgB,EAAE,CAAC,IAAI,MAAG,CAAC,CAAC;IAChC,IAAI,CAAC,MAAM,CACP,IAAI,CAAC,KAAK,CAAC,WAAW,CAAC,EACvB,mHACqC,WAAW,MAAG,CAAC,CAAC;IAEzD,IAAI,GAAG,GAAG,EAAc,CAAC;IACzB,IAAI,YAAY,GAAG,KAAK,CAAC;IACzB,IAAI,EAAE,CAAC,IAAI,KAAK,CAAC,EAAE;QACjB,YAAY,GAAG,IAAI,CAAC;QACpB,GAAG,GAAG,EAAE,CAAC,IAAI,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC;KACzD;IACD,IAAM,QAAQ,GAAG,UAAC,EAAY,EAAE,KAAe;QACtC,IAAA,sBAAW,CAAU;QAC5B,OAAO;YACL,GAAG,EAAE,cAAM,OAAA,iBAAG,CAAC,MAAM,CAAC,SAAS,CAC3B,UAAA,OAAO,IAAI,OAAA,OAAO,CAAC,OAAO,CACtB,EAAE,EAAE,GAAG,EAAE,WAAuB,EAAE,WAAW,EAAE,IAAI,EAAE,KAAK,EAAE,IAAI,CAAC,EAD1D,CAC0D,EACrE,EAAE,CAAC,EAHI,CAGJ;SACR,CAAC;IACJ,CAAC,CAAC;IACF,IAAM,GAAG,GAAG,iBAAG,CAAC,MAAM,CAAC,SAAS,CAC5B,UAAC,OAAO,EAAE,IAAI,IAAK,OAAA,IAAI,CAAC,OAAO,CAAC,4BAA4B,CACxD,GAAG,EAAE,WAAW,EAAE,IAAI,EAAE,KAAK,EAAE,IAAI,CAAC,CAAC,EADtB,CACsB,EACzC,EAAC,GAAG,KAAA,EAAC,EAAE,QAAQ,CAAC,CAAC;IACrB,IAAI,YAAY,EAAE;QAChB,OAAO,GAAG,CAAC,IAAI,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAM,CAAC;KAChE;SAAM;QACL,OAAO,GAAQ,CAAC;KACjB;AACH,CAAC;AAEY,QAAA,0BAA0B,GAAG,cAAE,CAAC,EAAC,2BAA2B,6BAAA,EAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/lrn_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/lrn_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/lrn_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/lrn_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/lrn_test.js
new file mode 100644
index 0000000..b52eda9
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/lrn_test.js
@@ -0,0 +1,854 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+var sqArr = function (arr) { return arr.map(function (d) { return d * d; }); };
+var sumArr = function (arr) { return arr.reduce(function (prev, curr) { return prev + curr; }, 0); };
+var flatten = function (arr) {
+    return arr.reduce(function (prev, curr) {
+        return prev.concat(Array.isArray(curr) ? flatten(curr) : curr);
+    }, []);
+};
+jasmine_util_1.describeWithFlags('localResponseNormalization with Tensor3D', test_util_1.ALL_ENVS, function () {
+    it('throws error with invalid input', function () {
+        var x = tf.tensor2d([1, 20, 300, 4], [1, 4]);
+        var radius = 3;
+        expect(function () { return x.localResponseNormalization(radius); }).toThrowError();
+    });
+    it('throws error with invalid radius', function () {
+        var x = tf.tensor3d([1, 20, 300, 4], [1, 1, 4]);
+        var radius = 0.5;
+        expect(function () { return x.localResponseNormalization(radius); }).toThrowError();
+    });
+    it('computes simple normalization across channels', function () {
+        var x = tf.tensor3d([1, 20, 300, 4], [1, 1, 4]);
+        var radius = 1;
+        var bias = 1;
+        var alpha = 1;
+        var beta = 0.5;
+        var result = x.localResponseNormalization(radius, bias, alpha, beta);
+        var f = function () {
+            var vals = [];
+            for (var _i = 0; _i < arguments.length; _i++) {
+                vals[_i] = arguments[_i];
+            }
+            return Math.pow(bias + alpha * sumArr(sqArr(vals)), -beta);
+        };
+        test_util_1.expectArraysClose(result, [
+            x.get(0, 0, 0) * f(x.get(0, 0, 0), x.get(0, 0, 1)),
+            x.get(0, 0, 1) * f(x.get(0, 0, 0), x.get(0, 0, 1), x.get(0, 0, 2)),
+            x.get(0, 0, 2) * f(x.get(0, 0, 1), x.get(0, 0, 2), x.get(0, 0, 3)),
+            x.get(0, 0, 3) * f(x.get(0, 0, 2), x.get(0, 0, 3)),
+        ]);
+    });
+    it('uses beta = 1.0 to test GPU optimization', function () {
+        var x = tf.tensor3d([1, 20, 300, 4], [1, 1, 4]);
+        var radius = 1;
+        var bias = 1;
+        var alpha = 1;
+        var beta = 1.0;
+        var result = x.localResponseNormalization(radius, bias, alpha, beta);
+        var f = function () {
+            var vals = [];
+            for (var _i = 0; _i < arguments.length; _i++) {
+                vals[_i] = arguments[_i];
+            }
+            return Math.pow(bias + alpha * sumArr(sqArr(vals)), -beta);
+        };
+        test_util_1.expectArraysClose(result, [
+            x.get(0, 0, 0) * f(x.get(0, 0, 0), x.get(0, 0, 1)),
+            x.get(0, 0, 1) * f(x.get(0, 0, 0), x.get(0, 0, 1), x.get(0, 0, 2)),
+            x.get(0, 0, 2) * f(x.get(0, 0, 1), x.get(0, 0, 2), x.get(0, 0, 3)),
+            x.get(0, 0, 3) * f(x.get(0, 0, 2), x.get(0, 0, 3)),
+        ]);
+    });
+    it('uses beta = 0.75 to test GPU optimization', function () {
+        var x = tf.tensor3d([1, 20, 300, 4], [1, 1, 4]);
+        var radius = 1;
+        var bias = 1;
+        var alpha = 1;
+        var beta = 0.75;
+        var result = x.localResponseNormalization(radius, bias, alpha, beta);
+        var f = function () {
+            var vals = [];
+            for (var _i = 0; _i < arguments.length; _i++) {
+                vals[_i] = arguments[_i];
+            }
+            return Math.pow(bias + alpha * sumArr(sqArr(vals)), -beta);
+        };
+        test_util_1.expectArraysClose(result, [
+            x.get(0, 0, 0) * f(x.get(0, 0, 0), x.get(0, 0, 1)),
+            x.get(0, 0, 1) * f(x.get(0, 0, 0), x.get(0, 0, 1), x.get(0, 0, 2)),
+            x.get(0, 0, 2) * f(x.get(0, 0, 1), x.get(0, 0, 2), x.get(0, 0, 3)),
+            x.get(0, 0, 3) * f(x.get(0, 0, 2), x.get(0, 0, 3)),
+        ]);
+    });
+    it('computes complex normalization across channels', function () {
+        var x = tf.tensor3d([1, 20, 300, 4, 5, 15, 24, 200, 1, 20, 300, 4, 5, 15, 24, 200], [2, 2, 4]);
+        var radius = 1;
+        var bias = 1;
+        var alpha = 1;
+        var beta = 0.5;
+        var result = x.localResponseNormalization(radius, bias, alpha, beta);
+        var f = function () {
+            var vals = [];
+            for (var _i = 0; _i < arguments.length; _i++) {
+                vals[_i] = arguments[_i];
+            }
+            return Math.pow(bias + alpha * sumArr(sqArr(vals)), -beta);
+        };
+        test_util_1.expectArraysClose(result, [
+            x.get(0, 0, 0) * f(x.get(0, 0, 0), x.get(0, 0, 1)),
+            x.get(0, 0, 1) * f(x.get(0, 0, 0), x.get(0, 0, 1), x.get(0, 0, 2)),
+            x.get(0, 0, 2) * f(x.get(0, 0, 1), x.get(0, 0, 2), x.get(0, 0, 3)),
+            x.get(0, 0, 3) * f(x.get(0, 0, 2), x.get(0, 0, 3)),
+            x.get(0, 1, 0) * f(x.get(0, 1, 0), x.get(0, 1, 1)),
+            x.get(0, 1, 1) * f(x.get(0, 1, 0), x.get(0, 1, 1), x.get(0, 1, 2)),
+            x.get(0, 1, 2) * f(x.get(0, 1, 1), x.get(0, 1, 2), x.get(0, 1, 3)),
+            x.get(0, 1, 3) * f(x.get(0, 1, 2), x.get(0, 1, 3)),
+            x.get(1, 0, 0) * f(x.get(1, 0, 0), x.get(1, 0, 1)),
+            x.get(1, 0, 1) * f(x.get(1, 0, 0), x.get(1, 0, 1), x.get(1, 0, 2)),
+            x.get(1, 0, 2) * f(x.get(1, 0, 1), x.get(1, 0, 2), x.get(1, 0, 3)),
+            x.get(1, 0, 3) * f(x.get(1, 0, 2), x.get(1, 0, 3)),
+            x.get(1, 1, 0) * f(x.get(1, 1, 0), x.get(1, 1, 1)),
+            x.get(1, 1, 1) * f(x.get(1, 1, 0), x.get(1, 1, 1), x.get(1, 1, 2)),
+            x.get(1, 1, 2) * f(x.get(1, 1, 1), x.get(1, 1, 2), x.get(1, 1, 3)),
+            x.get(1, 1, 3) * f(x.get(1, 1, 2), x.get(1, 1, 3)),
+        ]);
+    });
+    it('yields same result as tensorflow', function () {
+        var input = [
+            [
+                [
+                    0.95782757, 0.12892687, 0.63624668, 0.70160735, 0.77376258,
+                    0.54166114, 0.71172535, 0.65087497
+                ],
+                [
+                    0.91872108, 0.38846886, 0.37847793, 0.50477624, 0.42154622,
+                    0.43310916, 0.36253822, 0.07576156
+                ],
+                [
+                    0.48662257, 0.4154036, 0.81704032, 0.91660416, 0.87671542, 0.64215934,
+                    0.29933751, 0.90671134
+                ]
+            ],
+            [
+                [
+                    0.6208992, 0.60847163, 0.41475761, 0.2127713, 0.65306914, 0.13923979,
+                    0.32003641, 0.28183973
+                ],
+                [
+                    0.04751575, 0.26870155, 0.45150304, 0.58678186, 0.99118924,
+                    0.58878231, 0.30913198, 0.18836617
+                ],
+                [
+                    0.16166461, 0.56322742, 0.67908955, 0.2269547, 0.38491273, 0.97113752,
+                    0.51210916, 0.69430435
+                ]
+            ],
+            [
+                [
+                    0.06625497, 0.13011181, 0.59202921, 0.88871598, 0.6366322, 0.47911358,
+                    0.96530843, 0.74259472
+                ],
+                [
+                    0.62660718, 0.0445286, 0.18430257, 0.76863647, 0.87511849, 0.53588808,
+                    0.27980685, 0.30281997
+                ],
+                [
+                    0.73987067, 0.91034842, 0.26241004, 0.72832751, 0.78974342,
+                    0.50751543, 0.05434644, 0.8231523
+                ]
+            ]
+        ];
+        var expected = [
+            [
+                [
+                    0.62630326, 0.07662392, 0.34354961, 0.41885775, 0.42621866,
+                    0.29751951, 0.42365381, 0.4364861
+                ],
+                [
+                    0.62828875, 0.251122, 0.23605582, 0.36483878, 0.30624411, 0.32672295,
+                    0.29576892, 0.06582346
+                ],
+                [
+                    0.3376624, 0.24321821, 0.42558169, 0.46646208, 0.45103404, 0.32380751,
+                    0.17021206, 0.59476018
+                ]
+            ],
+            [
+                [
+                    0.44719055, 0.43318295, 0.26775005, 0.14921051, 0.49148726,
+                    0.10764983, 0.25084552, 0.25714993
+                ],
+                [
+                    0.04202608, 0.21094096, 0.27973703, 0.34166718, 0.57487047,
+                    0.35158369, 0.19708875, 0.15495601
+                ],
+                [
+                    0.12034657, 0.41341963, 0.47968671, 0.13278878, 0.22735766,
+                    0.57154536, 0.30411762, 0.42352781
+                ]
+            ],
+            [
+                [
+                    0.05656794, 0.08849642, 0.36951816, 0.53186077, 0.33065733,
+                    0.24236222, 0.54666328, 0.45085984
+                ],
+                [
+                    0.52425432, 0.03133496, 0.11043368, 0.46954039, 0.5271349, 0.31946796,
+                    0.1876673, 0.25085902
+                ],
+                [
+                    0.47316891, 0.5277527, 0.13831842, 0.40036613, 0.50113004, 0.28860986,
+                    0.03395459, 0.59127772
+                ]
+            ]
+        ];
+        var x = tf.tensor3d(flatten(input), [3, 3, 8]);
+        var radius = 2;
+        var bias = 1;
+        var alpha = 1;
+        var beta = 0.5;
+        var result = x.localResponseNormalization(radius, bias, alpha, beta);
+        test_util_1.expectArraysClose(result, flatten(expected));
+    });
+    it('accepts a tensor-like object', function () {
+        var x = [[[1, 20, 300, 4]]];
+        var radius = 1;
+        var bias = 1;
+        var alpha = 1;
+        var beta = 0.5;
+        var result = tf.localResponseNormalization(x, radius, bias, alpha, beta);
+        var f = function () {
+            var vals = [];
+            for (var _i = 0; _i < arguments.length; _i++) {
+                vals[_i] = arguments[_i];
+            }
+            return Math.pow(bias + alpha * sumArr(sqArr(vals)), -beta);
+        };
+        test_util_1.expectArraysClose(result, [
+            x[0][0][0] * f(x[0][0][0], x[0][0][1]),
+            x[0][0][1] * f(x[0][0][0], x[0][0][1], x[0][0][2]),
+            x[0][0][2] * f(x[0][0][1], x[0][0][2], x[0][0][3]),
+            x[0][0][3] * f(x[0][0][2], x[0][0][3]),
+        ]);
+    });
+});
+jasmine_util_1.describeWithFlags('localResponseNormalization with Tensor4D', test_util_1.ALL_ENVS, function () {
+    it('throws error with invalid input', function () {
+        var x = tf.tensor2d([1, 20, 300, 4], [1, 4]);
+        var radius = 3;
+        expect(function () { return x.localResponseNormalization(radius); }).toThrowError();
+    });
+    it('throws error with invalid radius', function () {
+        var x = tf.tensor4d([1, 20, 300, 4], [1, 1, 1, 4]);
+        var radius = 0.5;
+        expect(function () { return x.localResponseNormalization(radius); }).toThrowError();
+    });
+    it('computes simple normalization across channels', function () {
+        var x = tf.tensor4d([1, 20, 300, 4, 1, 20, 300, 4], [2, 1, 1, 4]);
+        var radius = 1;
+        var bias = 1;
+        var alpha = 1;
+        var beta = 0.5;
+        var result = x.localResponseNormalization(radius, bias, alpha, beta);
+        var f = function () {
+            var vals = [];
+            for (var _i = 0; _i < arguments.length; _i++) {
+                vals[_i] = arguments[_i];
+            }
+            return Math.pow(bias + alpha * sumArr(sqArr(vals)), -beta);
+        };
+        var b0 = 0;
+        var b1 = 1;
+        test_util_1.expectArraysClose(result, [
+            x.get(b0, 0, 0, 0) * f(x.get(b0, 0, 0, 0), x.get(b0, 0, 0, 1)),
+            x.get(b0, 0, 0, 1) *
+                f(x.get(b0, 0, 0, 0), x.get(b0, 0, 0, 1), x.get(b0, 0, 0, 2)),
+            x.get(b0, 0, 0, 2) *
+                f(x.get(b0, 0, 0, 1), x.get(b0, 0, 0, 2), x.get(b0, 0, 0, 3)),
+            x.get(b0, 0, 0, 3) * f(x.get(b0, 0, 0, 2), x.get(b0, 0, 0, 3)),
+            x.get(b1, 0, 0, 0) * f(x.get(b1, 0, 0, 0), x.get(b1, 0, 0, 1)),
+            x.get(b1, 0, 0, 1) *
+                f(x.get(b1, 0, 0, 0), x.get(b1, 0, 0, 1), x.get(b1, 0, 0, 2)),
+            x.get(b1, 0, 0, 2) *
+                f(x.get(b1, 0, 0, 1), x.get(b1, 0, 0, 2), x.get(b1, 0, 0, 3)),
+            x.get(b1, 0, 0, 3) * f(x.get(b1, 0, 0, 2), x.get(b1, 0, 0, 3)),
+        ]);
+    });
+    it('yields same result as tensorflow', function () {
+        var input = [
+            [
+                [
+                    [
+                        0.5659827, 0.57000327, 0.75555623, 0.89843333, 0.55120194,
+                        0.53531718, 0.56402838, 0.95481384
+                    ],
+                    [
+                        0.57334661, 0.65172958, 0.75794137, 0.80764937, 0.376616,
+                        0.92726362, 0.36422753, 0.60535395
+                    ],
+                    [
+                        0.82404268, 0.01054764, 0.4649173, 0.91637003, 0.82287347, 0.043468,
+                        0.44953859, 0.92056584
+                    ]
+                ],
+                [
+                    [
+                        0.68583369, 0.52534163, 0.53325927, 0.39608097, 0.9337523,
+                        0.37397444, 0.81212556, 0.5697
+                    ],
+                    [
+                        0.34278774, 0.57656682, 0.2356832, 0.02636456, 0.49111438,
+                        0.17981696, 0.65398049, 0.70132935
+                    ],
+                    [
+                        0.14241767, 0.68376505, 0.65419888, 0.69369483, 0.21489143,
+                        0.46235347, 0.0559243, 0.60612857
+                    ]
+                ],
+                [
+                    [
+                        0.59678483, 0.09368539, 0.3017447, 0.36870825, 0.68145788,
+                        0.52048779, 0.46136606, 0.94114387
+                    ],
+                    [
+                        0.3156569, 0.75275254, 0.31970251, 0.3154043, 0.61088014,
+                        0.13359487, 0.99048364, 0.33625424
+                    ],
+                    [
+                        0.82103574, 0.52066624, 0.63629258, 0.42294252, 0.93214262,
+                        0.57041013, 0.66087878, 0.7019999
+                    ]
+                ]
+            ],
+            [
+                [
+                    [
+                        0.21894431, 0.43085241, 0.79883206, 0.19462204, 0.68623316,
+                        0.08703053, 0.82380795, 0.85634673
+                    ],
+                    [
+                        0.45011401, 0.70312083, 0.86319792, 0.83205295, 0.67109787,
+                        0.82081223, 0.46556532, 0.46408331
+                    ],
+                    [
+                        0.07028461, 0.0038743, 0.44619524, 0.0611403, 0.96373355,
+                        0.80561554, 0.42428243, 0.46897113
+                    ]
+                ],
+                [
+                    [
+                        0.21006894, 0.48764861, 0.36842632, 0.23030031, 0.69685507,
+                        0.31707478, 0.68662715, 0.0639503
+                    ],
+                    [
+                        0.53940296, 0.50777435, 0.12625301, 0.12324154, 0.89205229,
+                        0.69380629, 0.33191144, 0.81000078
+                    ],
+                    [
+                        0.52650976, 0.71220326, 0.07246161, 0.08874547, 0.42528927,
+                        0.36320579, 0.54055619, 0.79342318
+                    ]
+                ],
+                [
+                    [
+                        0.75916636, 0.74499428, 0.76877356, 0.87210917, 0.93040991,
+                        0.49491942, 0.70801985, 0.14901721
+                    ],
+                    [
+                        0.27037835, 0.89302075, 0.69147241, 0.23044991, 0.98916364,
+                        0.60161841, 0.63691151, 0.56759977
+                    ],
+                    [
+                        0.56307781, 0.92782414, 0.25880754, 0.98518133, 0.04097319,
+                        0.24640906, 0.54566145, 0.99261606
+                    ]
+                ]
+            ]
+        ];
+        var expected = [
+            [
+                [
+                    [
+                        0.38019636, 0.32782161, 0.414222, 0.49507114, 0.3040463, 0.28107059,
+                        0.33586296, 0.60191077
+                    ],
+                    [
+                        0.37577698, 0.37752095, 0.42895618, 0.4225589, 0.2054275,
+                        0.52219951, 0.23032214, 0.39414096
+                    ],
+                    [
+                        0.59856331, 0.00637784, 0.25168711, 0.5541048, 0.48015645,
+                        0.02301128, 0.27214608, 0.6427291
+                    ]
+                ],
+                [
+                    [
+                        0.48127589, 0.35518789, 0.30486941, 0.23976389, 0.52926594,
+                        0.21061926, 0.46920502, 0.39090639
+                    ],
+                    [
+                        0.27937523, 0.46979892, 0.17829391, 0.02044933, 0.37045884,
+                        0.12140442, 0.44160855, 0.50198948
+                    ],
+                    [
+                        0.10289387, 0.44164398, 0.41853485, 0.42720893, 0.14580171,
+                        0.31817055, 0.043797, 0.48155668
+                    ]
+                ],
+                [
+                    [
+                        0.49458414, 0.07425242, 0.21042404, 0.26262277, 0.46205613,
+                        0.30202535, 0.27406475, 0.61140078
+                    ],
+                    [
+                        0.23736385, 0.55076694, 0.2135559, 0.21463785, 0.38077739,
+                        0.08309806, 0.62830603, 0.23137885
+                    ],
+                    [
+                        0.5355776, 0.32740855, 0.3451882, 0.24221195, 0.51988536,
+                        0.31387195, 0.37391993, 0.46748781
+                    ]
+                ]
+            ],
+            [
+                [
+                    [
+                        0.16003507, 0.31178808, 0.51775187, 0.12722474, 0.40769571,
+                        0.05085804, 0.48455271, 0.5505302
+                    ],
+                    [
+                        0.2880325, 0.39714804, 0.45591024, 0.4131493, 0.34525412, 0.4554069,
+                        0.29119283, 0.31980222
+                    ],
+                    [
+                        0.0640529, 0.00352532, 0.3052578, 0.03666528, 0.56009793,
+                        0.46656418, 0.24587312, 0.32762629
+                    ]
+                ],
+                [
+                    [
+                        0.17643087, 0.40210918, 0.2634095, 0.16233148, 0.4649446,
+                        0.21803913, 0.47819966, 0.05093931
+                    ],
+                    [
+                        0.43121469, 0.403974, 0.08191212, 0.07693455, 0.57362044,
+                        0.39671475, 0.19025819, 0.54028469
+                    ],
+                    [
+                        0.39356521, 0.53120333, 0.05151648, 0.06554616, 0.33433318,
+                        0.2425479, 0.36161765, 0.5536595
+                    ]
+                ],
+                [
+                    [
+                        0.46011236, 0.39919043, 0.36865807, 0.43511948, 0.46734285,
+                        0.26861796, 0.43624333, 0.11205748
+                    ],
+                    [
+                        0.17642327, 0.57622254, 0.37609601, 0.12030836, 0.54640025,
+                        0.34052721, 0.36361033, 0.3926385
+                    ],
+                    [
+                        0.37581176, 0.51741964, 0.14429154, 0.57254595, 0.02646073,
+                        0.13531584, 0.35629693, 0.64837402
+                    ]
+                ]
+            ]
+        ];
+        var x = tf.tensor4d(flatten(input), [2, 3, 3, 8]);
+        var radius = 2;
+        var result = x.localResponseNormalization(radius);
+        test_util_1.expectArraysClose(result, flatten(expected));
+    });
+    it('throws when passed a non-tensor', function () {
+        var e = /Argument 'x' passed to 'localResponseNormalization' must be a Tensor/;
+        expect(function () { return tf.localResponseNormalization({}); })
+            .toThrowError(e);
+    });
+    it('gradient with 3D input', function () {
+        var input = [
+            [
+                [
+                    0.95782757, 0.12892687, 0.63624668, 0.70160735, 0.77376258,
+                    0.54166114, 0.71172535, 0.65087497
+                ],
+                [
+                    0.91872108, 0.38846886, 0.37847793, 0.50477624, 0.42154622,
+                    0.43310916, 0.36253822, 0.07576156
+                ],
+                [
+                    0.48662257, 0.4154036, 0.81704032, 0.91660416, 0.87671542, 0.64215934,
+                    0.29933751, 0.90671134
+                ]
+            ],
+            [
+                [
+                    0.6208992, 0.60847163, 0.41475761, 0.2127713, 0.65306914, 0.13923979,
+                    0.32003641, 0.28183973
+                ],
+                [
+                    0.04751575, 0.26870155, 0.45150304, 0.58678186, 0.99118924,
+                    0.58878231, 0.30913198, 0.18836617
+                ],
+                [
+                    0.16166461, 0.56322742, 0.67908955, 0.2269547, 0.38491273, 0.97113752,
+                    0.51210916, 0.69430435
+                ]
+            ],
+            [
+                [
+                    0.06625497, 0.13011181, 0.59202921, 0.88871598, 0.6366322, 0.47911358,
+                    0.96530843, 0.74259472
+                ],
+                [
+                    0.62660718, 0.0445286, 0.18430257, 0.76863647, 0.87511849, 0.53588808,
+                    0.27980685, 0.30281997
+                ],
+                [
+                    0.73987067, 0.91034842, 0.26241004, 0.72832751, 0.78974342,
+                    0.50751543, 0.05434644, 0.8231523
+                ]
+            ]
+        ];
+        var expected = [[
+                [
+                    [
+                        0.27552658, 0.52414668, 0.11137494, 0.24928074, 0.07215497,
+                        0.16210511, 0.19277242, 0.38672262
+                    ],
+                    [
+                        0.23314378, 0.38181645, 0.30470729, 0.35180706, 0.37793165,
+                        0.41450983, 0.60044503, 0.83605933
+                    ],
+                    [
+                        0.51801264, 0.38517883, 0.02934788, 0.03102355, 0.08222333,
+                        0.09746625, 0.4151727, 0.29936206
+                    ]
+                ],
+                [
+                    [
+                        0.37059873, 0.32463685, 0.26611608, 0.54228389, 0.30733055,
+                        0.66392428, 0.55629295, 0.79049641
+                    ],
+                    [
+                        0.87162501, 0.68129337, 0.35793597, 0.18797961, -0.03660985,
+                        0.23235559, 0.48184156, 0.76417446
+                    ],
+                    [
+                        0.65893668, 0.41059417, 0.26254228, 0.40696776, 0.3330358, 0.01789692,
+                        0.3162199, 0.28867012
+                    ]
+                ],
+                [
+                    [
+                        0.83880937, 0.62594998, 0.324698, 0.13046435, 0.09858654, 0.17851587,
+                        0.09067203, 0.30748016
+                    ],
+                    [
+                        0.57213897, 0.67710453, 0.45385274, 0.19951296, 0.07371041,
+                        0.20141563, 0.51362634, 0.7163325
+                    ],
+                    [
+                        0.33668244, 0.09696329, 0.33500126, 0.08948036, 0.26512182,
+                        0.19593786, 0.59144169, 0.379444
+                    ]
+                ]
+            ]];
+        var radius = 2.0;
+        var bias = 1.0;
+        var alpha = 1.0;
+        var beta = 0.5;
+        var t = tf.tensor3d(input);
+        var dy = tf.onesLike(t);
+        var gradients = tf.grad(function (t) {
+            return tf.localResponseNormalization(t, radius, bias, alpha, beta);
+        })(t, dy);
+        test_util_1.expectArraysEqual(gradients.shape, t.shape);
+        test_util_1.expectArraysClose(gradients, flatten(expected));
+    });
+    it('gradient with 4D input', function () {
+        var input = [
+            [
+                [
+                    [
+                        0.5659827, 0.57000327, 0.75555623, 0.89843333, 0.55120194,
+                        0.53531718, 0.56402838, 0.95481384
+                    ],
+                    [
+                        0.57334661, 0.65172958, 0.75794137, 0.80764937, 0.376616,
+                        0.92726362, 0.36422753, 0.60535395
+                    ],
+                    [
+                        0.82404268, 0.01054764, 0.4649173, 0.91637003, 0.82287347, 0.043468,
+                        0.44953859, 0.92056584
+                    ]
+                ],
+                [
+                    [
+                        0.68583369, 0.52534163, 0.53325927, 0.39608097, 0.9337523,
+                        0.37397444, 0.81212556, 0.5697
+                    ],
+                    [
+                        0.34278774, 0.57656682, 0.2356832, 0.02636456, 0.49111438,
+                        0.17981696, 0.65398049, 0.70132935
+                    ],
+                    [
+                        0.14241767, 0.68376505, 0.65419888, 0.69369483, 0.21489143,
+                        0.46235347, 0.0559243, 0.60612857
+                    ]
+                ],
+                [
+                    [
+                        0.59678483, 0.09368539, 0.3017447, 0.36870825, 0.68145788,
+                        0.52048779, 0.46136606, 0.94114387
+                    ],
+                    [
+                        0.3156569, 0.75275254, 0.31970251, 0.3154043, 0.61088014,
+                        0.13359487, 0.99048364, 0.33625424
+                    ],
+                    [
+                        0.82103574, 0.52066624, 0.63629258, 0.42294252, 0.93214262,
+                        0.57041013, 0.66087878, 0.7019999
+                    ]
+                ]
+            ],
+            [
+                [
+                    [
+                        0.21894431, 0.43085241, 0.79883206, 0.19462204, 0.68623316,
+                        0.08703053, 0.82380795, 0.85634673
+                    ],
+                    [
+                        0.45011401, 0.70312083, 0.86319792, 0.83205295, 0.67109787,
+                        0.82081223, 0.46556532, 0.46408331
+                    ],
+                    [
+                        0.07028461, 0.0038743, 0.44619524, 0.0611403, 0.96373355,
+                        0.80561554, 0.42428243, 0.46897113
+                    ]
+                ],
+                [
+                    [
+                        0.21006894, 0.48764861, 0.36842632, 0.23030031, 0.69685507,
+                        0.31707478, 0.68662715, 0.0639503
+                    ],
+                    [
+                        0.53940296, 0.50777435, 0.12625301, 0.12324154, 0.89205229,
+                        0.69380629, 0.33191144, 0.81000078
+                    ],
+                    [
+                        0.52650976, 0.71220326, 0.07246161, 0.08874547, 0.42528927,
+                        0.36320579, 0.54055619, 0.79342318
+                    ]
+                ],
+                [
+                    [
+                        0.75916636, 0.74499428, 0.76877356, 0.87210917, 0.93040991,
+                        0.49491942, 0.70801985, 0.14901721
+                    ],
+                    [
+                        0.27037835, 0.89302075, 0.69147241, 0.23044991, 0.98916364,
+                        0.60161841, 0.63691151, 0.56759977
+                    ],
+                    [
+                        0.56307781, 0.92782414, 0.25880754, 0.98518133, 0.04097319,
+                        0.24640906, 0.54566145, 0.99261606
+                    ]
+                ]
+            ]
+        ];
+        var dyVals = [
+            [
+                [
+                    [
+                        1.40394282, -1.68962789, -0.21134049, 1.15015793, 1.51244378,
+                        0.42844626, -2.70123291, 0.06449971
+                    ],
+                    [
+                        -0.29038581, 0.67567694, 0.95617437, -1.07383668, 0.20920482,
+                        0.39050213, -0.81124371, 2.42158198
+                    ],
+                    [
+                        -1.01235235, -0.63514435, -1.49017262, -0.01205151, 0.78492945,
+                        -0.20330679, -2.31419802, -0.31220308
+                    ]
+                ],
+                [
+                    [
+                        0.07061944, -0.46716127, 0.91232526, -1.30444264, -0.07080109,
+                        0.13207501, 0.26701283, -0.48946589
+                    ],
+                    [
+                        -0.74995744, -0.79466617, -1.03790498, -0.32234526, 1.33345711,
+                        0.11863081, 1.93010819, 0.47857195
+                    ],
+                    [
+                        0.37702683, -0.7804451, 0.45868117, 1.06967258, -0.65336537,
+                        0.3594887, 0.62512684, 0.77009726
+                    ]
+                ],
+                [
+                    [
+                        0.76865023, 1.00893021, -0.24408816, -0.3943336, 0.47094285,
+                        -2.61926222, 1.52929449, 0.7862013
+                    ],
+                    [
+                        -1.20878386, -0.26222935, -0.9076528, 0.03079577, -0.01467486,
+                        -0.06949636, 0.05466342, 1.44880533
+                    ],
+                    [
+                        0.05611863, 0.15142779, 0.7802065, -1.2623471, 0.09119794,
+                        -0.20110528, 0.17715968, -0.48476508
+                    ]
+                ]
+            ],
+            [
+                [
+                    [
+                        0.1549256, 0.94472402, -0.70033115, -1.05752802, -0.63035947,
+                        -1.35643113, -0.27211693, 2.33576941
+                    ],
+                    [
+                        0.81070906, -0.58353454, -0.3253817, 2.53953528, -1.40062141,
+                        1.7728076, -0.59849483, 1.49650824
+                    ],
+                    [
+                        -0.00610052, -2.29434419, -1.77995121, -0.66354084, -0.70676774,
+                        -0.81570011, -1.30821037, 0.40997007
+                    ]
+                ],
+                [
+                    [
+                        -1.02013469, -0.74198806, -0.82677251, -0.00890179, -1.62196338,
+                        -0.5095427, 1.26501179, 0.12931485
+                    ],
+                    [
+                        -1.14763546, 0.11011696, -0.23312508, 0.29730096, -0.49138394,
+                        -0.27012363, -0.15987533, -1.84277928
+                    ],
+                    [
+                        -0.03816459, -0.73517877, -2.00476885, 0.47192496, -0.27395752,
+                        0.99806124, 1.54439747, -1.02016675
+                    ]
+                ],
+                [
+                    [
+                        -1.27831209, -0.6961385, -0.73713994, -1.97954738, 0.39108652,
+                        -0.46152538, 1.8255372, 2.18119025
+                    ],
+                    [
+                        0.56322283, -1.59858179, 1.54127491, -0.57665956, -1.0098567,
+                        0.93239671, 0.25231698, -0.7346009
+                    ],
+                    [
+                        0.41614994, -1.20103085, 0.4330301, -1.23348403, -0.46117213,
+                        -0.3780126, 0.35449561, -0.60129249
+                    ]
+                ]
+            ]
+        ];
+        var depthRadius = 1;
+        var bias = 1;
+        var alpha = 1;
+        var beta = 0.75;
+        var expected = [
+            [
+                [
+                    [
+                        0.88732064, -0.98597342, -0.00569269, 0.09561057, 0.42255375,
+                        0.30286378, -1.17104781, 0.44769961
+                    ],
+                    [
+                        -0.22329885, 0.19271846, 0.41454071, -0.50674957, 0.14660946,
+                        0.1591837, -0.83707076, 1.19177234
+                    ],
+                    [
+                        -0.26728818, -0.3847312, -0.72818488, 0.09040837, 0.24023688,
+                        -0.11545581, -1.09341288, 0.33930668
+                    ]
+                ],
+                [
+                    [
+                        0.10079086, -0.38184536, 0.60918945, -0.7267822, 0.13867335,
+                        0.03526202, 0.17270499, -0.2705338
+                    ],
+                    [
+                        -0.38344458, -0.15589149, -0.68160093, -0.27644777, 0.79392856,
+                        -0.14384332, 0.67121017, -0.23130262
+                    ],
+                    [
+                        0.31069142, -0.39895257, 0.11755499, 0.39481708, -0.5234766,
+                        0.2511853, 0.40955079, 0.3492966
+                    ]
+                ],
+                [
+                    [
+                        0.32660595, 0.7240563, -0.18117335, -0.2649861, 0.67781603,
+                        -1.46250272, 0.8465963, -0.05466701
+                    ],
+                    [
+                        -0.71582067, 0.20831716, -0.50778204, 0.07256755, -0.00893679,
+                        -0.03798783, -0.18604305, 0.75747406
+                    ],
+                    [
+                        -0.00540833, -0.07677216, 0.41930205, -0.69235319, 0.20631291,
+                        -0.11946303, 0.19601521, -0.21237698
+                    ]
+                ]
+            ],
+            [
+                [
+                    [
+                        0.0800111, 0.60922205, -0.31155977, -0.46448132, -0.15912701,
+                        -0.72455585, -0.5727275, 0.71780092
+                    ],
+                    [
+                        0.50568235, -0.31544152, -0.40618286, 0.97909468, -1.15286613,
+                        0.8145386, -0.77758539, 0.93794745
+                    ],
+                    [
+                        -0.00535599, -1.99269259, -1.15343952, -0.31053686, 0.01680636,
+                        0.10109296, -0.66026396, 0.35474917
+                    ]
+                ],
+                [
+                    [
+                        -0.74113333, -0.20625943, -0.4339568, 0.21517368, -0.5734458,
+                        -0.23481363, 0.53855389, 0.05860626
+                    ],
+                    [
+                        -0.61435795, 0.29290834, -0.19639145, 0.20930134, -0.08880179,
+                        0.02209887, 0.21427482, -0.51696646
+                    ],
+                    [
+                        0.13036536, -0.19079237, -1.43941545, 0.42789665, -0.29732707,
+                        0.52354813, 0.78893, -0.59992862
+                    ]
+                ],
+                [
+                    [
+                        -0.328383, 0.15830949, 0.13110149, -0.492423, 0.46827313,
+                        -0.58950633, 0.56422544, 1.44929576
+                    ],
+                    [
+                        0.46141064, -0.80682266, 0.92562175, -0.28897452, -0.30567497,
+                        0.50646484, 0.16439518, -0.38878182
+                    ],
+                    [
+                        0.41004074, -0.38593128, 0.42881966, -0.22443436, -0.24573228,
+                        -0.2941249, 0.31119603, -0.17903978
+                    ]
+                ]
+            ]
+        ];
+        var t = tf.tensor(input);
+        var dy = tf.tensor(dyVals);
+        var gradients = tf.grad(function (t) { return tf.localResponseNormalization(t, depthRadius, bias, alpha, beta); })(t, dy);
+        test_util_1.expectArraysClose(gradients, flatten(expected));
+    });
+});
+//# sourceMappingURL=lrn_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/lrn_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/lrn_test.js.map
new file mode 100644
index 0000000..6680bf8
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/lrn_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/lstm.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/lstm.d.ts
new file mode 100644
index 0000000..a3d15a8
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/lstm.d.ts
@@ -0,0 +1,10 @@
+import { Scalar, Tensor1D, Tensor2D } from '../tensor';
+import { TensorLike } from '../types';
+export declare type LSTMCellFunc = {
+    (data: Tensor2D, c: Tensor2D, h: Tensor2D): [Tensor2D, Tensor2D];
+};
+declare function multiRNNCell_(lstmCells: LSTMCellFunc[], data: Tensor2D | TensorLike, c: Array<Tensor2D | TensorLike>, h: Array<Tensor2D | TensorLike>): [Tensor2D[], Tensor2D[]];
+declare function basicLSTMCell_(forgetBias: Scalar | TensorLike, lstmKernel: Tensor2D | TensorLike, lstmBias: Tensor1D | TensorLike, data: Tensor2D | TensorLike, c: Tensor2D | TensorLike, h: Tensor2D | TensorLike): [Tensor2D, Tensor2D];
+export declare const basicLSTMCell: typeof basicLSTMCell_;
+export declare const multiRNNCell: typeof multiRNNCell_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/lstm.js b/node_modules/@tensorflow/tfjs-core/dist/ops/lstm.js
new file mode 100644
index 0000000..3dcc92d
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/lstm.js
@@ -0,0 +1,48 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tensor_util_env_1 = require("../tensor_util_env");
+var operation_1 = require("./operation");
+function multiRNNCell_(lstmCells, data, c, h) {
+    var $data = tensor_util_env_1.convertToTensor(data, 'data', 'multiRNNCell');
+    var $c = tensor_util_env_1.convertToTensorArray(c, 'c', 'multiRNNCell');
+    var $h = tensor_util_env_1.convertToTensorArray(h, 'h', 'multiRNNCell');
+    var input = $data;
+    var newStates = [];
+    for (var i = 0; i < lstmCells.length; i++) {
+        var output = lstmCells[i](input, $c[i], $h[i]);
+        newStates.push(output[0]);
+        newStates.push(output[1]);
+        input = output[1];
+    }
+    var newC = [];
+    var newH = [];
+    for (var i = 0; i < newStates.length; i += 2) {
+        newC.push(newStates[i]);
+        newH.push(newStates[i + 1]);
+    }
+    return [newC, newH];
+}
+function basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {
+    var $forgetBias = tensor_util_env_1.convertToTensor(forgetBias, 'forgetBias', 'basicLSTMCell');
+    var $lstmKernel = tensor_util_env_1.convertToTensor(lstmKernel, 'lstmKernel', 'basicLSTMCell');
+    var $lstmBias = tensor_util_env_1.convertToTensor(lstmBias, 'lstmBias', 'basicLSTMCell');
+    var $data = tensor_util_env_1.convertToTensor(data, 'data', 'basicLSTMCell');
+    var $c = tensor_util_env_1.convertToTensor(c, 'c', 'basicLSTMCell');
+    var $h = tensor_util_env_1.convertToTensor(h, 'h', 'basicLSTMCell');
+    var combined = $data.concat($h, 1);
+    var weighted = combined.matMul($lstmKernel);
+    var res = weighted.add($lstmBias);
+    var batchSize = res.shape[0];
+    var sliceCols = res.shape[1] / 4;
+    var sliceSize = [batchSize, sliceCols];
+    var i = res.slice([0, 0], sliceSize);
+    var j = res.slice([0, sliceCols], sliceSize);
+    var f = res.slice([0, sliceCols * 2], sliceSize);
+    var o = res.slice([0, sliceCols * 3], sliceSize);
+    var newC = i.sigmoid().mulStrict(j.tanh()).addStrict($c.mulStrict($forgetBias.add(f).sigmoid()));
+    var newH = newC.tanh().mulStrict(o.sigmoid());
+    return [newC, newH];
+}
+exports.basicLSTMCell = operation_1.op({ basicLSTMCell_: basicLSTMCell_ });
+exports.multiRNNCell = operation_1.op({ multiRNNCell_: multiRNNCell_ });
+//# sourceMappingURL=lstm.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/lstm.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/lstm.js.map
new file mode 100644
index 0000000..df96380
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/lstm.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"lstm.js","sourceRoot":"","sources":["../../src/ops/lstm.ts"],"names":[],"mappings":";;AAkBA,sDAAyE;AAEzE,yCAA+B;AAwB/B,uBACI,SAAyB,EAAE,IAAyB,EACpD,CAA6B,EAC7B,CAA6B;IAC/B,IAAM,KAAK,GAAG,iCAAe,CAAC,IAAI,EAAE,MAAM,EAAE,cAAc,CAAC,CAAC;IAC5D,IAAM,EAAE,GAAG,sCAAoB,CAAC,CAAC,EAAE,GAAG,EAAE,cAAc,CAAC,CAAC;IACxD,IAAM,EAAE,GAAG,sCAAoB,CAAC,CAAC,EAAE,GAAG,EAAE,cAAc,CAAC,CAAC;IAExD,IAAI,KAAK,GAAG,KAAK,CAAC;IAClB,IAAM,SAAS,GAAG,EAAE,CAAC;IACrB,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,SAAS,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;QACzC,IAAM,MAAM,GAAG,SAAS,CAAC,CAAC,CAAC,CAAC,KAAK,EAAE,EAAE,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QACjD,SAAS,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC;QAC1B,SAAS,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC;QAC1B,KAAK,GAAG,MAAM,CAAC,CAAC,CAAC,CAAC;KACnB;IACD,IAAM,IAAI,GAAe,EAAE,CAAC;IAC5B,IAAM,IAAI,GAAe,EAAE,CAAC;IAC5B,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,SAAS,CAAC,MAAM,EAAE,CAAC,IAAI,CAAC,EAAE;QAC5C,IAAI,CAAC,IAAI,CAAC,SAAS,CAAC,CAAC,CAAC,CAAC,CAAC;QACxB,IAAI,CAAC,IAAI,CAAC,SAAS,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC;KAC7B;IACD,OAAO,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC;AACtB,CAAC;AAiBD,wBACI,UAA6B,EAAE,UAA+B,EAC9D,QAA6B,EAAE,IAAyB,EACxD,CAAsB,EAAE,CAAsB;IAChD,IAAM,WAAW,GACb,iCAAe,CAAC,UAAU,EAAE,YAAY,EAAE,eAAe,CAAC,CAAC;IAC/D,IAAM,WAAW,GACb,iCAAe,CAAC,UAAU,EAAE,YAAY,EAAE,eAAe,CAAC,CAAC;IAC/D,IAAM,SAAS,GAAG,iCAAe,CAAC,QAAQ,EAAE,UAAU,EAAE,eAAe,CAAC,CAAC;IACzE,IAAM,KAAK,GAAG,iCAAe,CAAC,IAAI,EAAE,MAAM,EAAE,eAAe,CAAC,CAAC;IAC7D,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,eAAe,CAAC,CAAC;IACpD,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,eAAe,CAAC,CAAC;IAEpD,IAAM,QAAQ,GAAG,KAAK,CAAC,MAAM,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC;IACrC,IAAM,QAAQ,GAAG,QAAQ,CAAC,MAAM,CAAC,WAAW,CAAC,CAAC;IAC9C,IAAM,GAAG,GAAG,QAAQ,CAAC,GAAG,CAAC,SAAS,CAAa,CAAC;IAGhD,IAAM,SAAS,GAAG,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC;IAC/B,IAAM,SAAS,GAAG,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC;IACnC,IAAM,SAAS,GAAqB,CAAC,SAAS,EAAE,SAAS,CAAC,CAAC;IAC3D,IAAM,CAAC,GAAG,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,SAAS,CAAC,CAAC;IACvC,IAAM,CAAC,GAAG,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,SAAS,CAAC,EAAE,SAAS,CAAC,CAAC;IAC/C,IAAM,CAAC,GAAG,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,SAAS,GAAG,CAAC,CAAC,EAAE,SAAS,CAAC,CAAC;IACnD,IAAM,CAAC,GAAG,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,SAAS,GAAG,CAAC,CAAC,EAAE,SAAS,CAAC,CAAC;IAEnD,IAAM,IAAI,GAAG,CAAC,CAAC,OAAO,EAAE,CAAC,SAAS,CAAC,CAAC,CAAC,IAAI,EAAE,CAAC,CAAC,SAAS,CAClD,EAAE,CAAC,SAAS,CAAC,WAAW,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,OAAO,EAAc,CAAC,CAAC,CAAC;IAC5D,IAAM,IAAI,GAAG,IAAI,CAAC,IAAI,EAAE,CAAC,SAAS,CAAC,CAAC,CAAC,OAAO,EAAE,CAAC,CAAC;IAChD,OAAO,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC;AACtB,CAAC;AAEY,QAAA,aAAa,GAAG,cAAE,CAAC,EAAC,cAAc,gBAAA,EAAC,CAAC,CAAC;AACrC,QAAA,YAAY,GAAG,cAAE,CAAC,EAAC,aAAa,eAAA,EAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/lstm_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/lstm_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/lstm_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/lstm_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/lstm_test.js
new file mode 100644
index 0000000..1d7e5b6
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/lstm_test.js
@@ -0,0 +1,209 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('lstm', test_util_1.ALL_ENVS, function () {
+    it('MultiRNNCell with 2 BasicLSTMCells', function () {
+        var lstmKernel1 = tf.tensor2d([
+            0.26242125034332275, -0.8787832260131836, 0.781475305557251,
+            1.337337851524353, 0.6180247068405151, -0.2760246992111206,
+            -0.11299663782119751, -0.46332040429115295, -0.1765323281288147,
+            0.6807947158813477, -0.8326982855796814, 0.6732975244522095
+        ], [3, 4]);
+        var lstmBias1 = tf.tensor1d([1.090713620185852, -0.8282332420349121, 0, 1.0889357328414917]);
+        var lstmKernel2 = tf.tensor2d([
+            -1.893059492111206, -1.0185645818710327, -0.6270437240600586,
+            -2.1829540729522705, -0.4583775997161865, -0.5454602241516113,
+            -0.3114445209503174, 0.8450229167938232
+        ], [2, 4]);
+        var lstmBias2 = tf.tensor1d([0.9906240105628967, 0.6248329877853394, 0, 1.0224634408950806]);
+        var forgetBias = tf.scalar(1.0);
+        var lstm1 = function (data, c, h) {
+            return tf.basicLSTMCell(forgetBias, lstmKernel1, lstmBias1, data, c, h);
+        };
+        var lstm2 = function (data, c, h) {
+            return tf.basicLSTMCell(forgetBias, lstmKernel2, lstmBias2, data, c, h);
+        };
+        var c = [
+            tf.zeros([1, lstmBias1.shape[0] / 4]),
+            tf.zeros([1, lstmBias2.shape[0] / 4])
+        ];
+        var h = [
+            tf.zeros([1, lstmBias1.shape[0] / 4]),
+            tf.zeros([1, lstmBias2.shape[0] / 4])
+        ];
+        var onehot = tf.buffer([1, 2], 'float32');
+        onehot.set(1.0, 0, 0);
+        var output = tf.multiRNNCell([lstm1, lstm2], onehot.toTensor(), c, h);
+        test_util_1.expectArraysClose(output[0][0], [-0.7440074682235718]);
+        test_util_1.expectArraysClose(output[0][1], [0.7460772395133972]);
+        test_util_1.expectArraysClose(output[1][0], [-0.5802832245826721]);
+        test_util_1.expectArraysClose(output[1][1], [0.5745711922645569]);
+    });
+    it('basicLSTMCell with batch=2', function () {
+        var lstmKernel = tf.randomNormal([3, 4]);
+        var lstmBias = tf.randomNormal([4]);
+        var forgetBias = tf.scalar(1.0);
+        var data = tf.randomNormal([1, 2]);
+        var batchedData = tf.concat2d([data, data], 0);
+        var c = tf.randomNormal([1, 1]);
+        var batchedC = tf.concat2d([c, c], 0);
+        var h = tf.randomNormal([1, 1]);
+        var batchedH = tf.concat2d([h, h], 0);
+        var _a = tf.basicLSTMCell(forgetBias, lstmKernel, lstmBias, batchedData, batchedC, batchedH), newC = _a[0], newH = _a[1];
+        expect(newC.get(0, 0)).toEqual(newC.get(1, 0));
+        expect(newH.get(0, 0)).toEqual(newH.get(1, 0));
+    });
+    it('basicLSTMCell accepts a tensor-like object', function () {
+        var lstmKernel = tf.randomNormal([3, 4]);
+        var lstmBias = [0, 0, 0, 0];
+        var forgetBias = 1;
+        var data = [[0, 0]];
+        var batchedData = tf.concat2d([data, data], 0);
+        var c = [[0]];
+        var batchedC = tf.concat2d([c, c], 0);
+        var h = [[0]];
+        var batchedH = tf.concat2d([h, h], 0);
+        var _a = tf.basicLSTMCell(forgetBias, lstmKernel, lstmBias, batchedData, batchedC, batchedH), newC = _a[0], newH = _a[1];
+        expect(newC.get(0, 0)).toEqual(newC.get(1, 0));
+        expect(newH.get(0, 0)).toEqual(newH.get(1, 0));
+    });
+});
+jasmine_util_1.describeWithFlags('multiRNN throws when passed non-tensor', test_util_1.ALL_ENVS, function () {
+    it('input: data', function () {
+        var lstmKernel1 = tf.zeros([3, 4]);
+        var lstmBias1 = tf.zeros([4]);
+        var lstmKernel2 = tf.zeros([2, 4]);
+        var lstmBias2 = tf.zeros([4]);
+        var forgetBias = tf.scalar(1.0);
+        var lstm1 = function (data, c, h) {
+            return tf.basicLSTMCell(forgetBias, lstmKernel1, lstmBias1, data, c, h);
+        };
+        var lstm2 = function (data, c, h) {
+            return tf.basicLSTMCell(forgetBias, lstmKernel2, lstmBias2, data, c, h);
+        };
+        var c = [
+            tf.zeros([1, lstmBias1.shape[0] / 4]),
+            tf.zeros([1, lstmBias2.shape[0] / 4])
+        ];
+        var h = [
+            tf.zeros([1, lstmBias1.shape[0] / 4]),
+            tf.zeros([1, lstmBias2.shape[0] / 4])
+        ];
+        expect(function () { return tf.multiRNNCell([lstm1, lstm2], {}, c, h); })
+            .toThrowError(/Argument 'data' passed to 'multiRNNCell' must be a Tensor/);
+    });
+    it('input: c', function () {
+        var lstmKernel1 = tf.zeros([3, 4]);
+        var lstmBias1 = tf.zeros([4]);
+        var lstmKernel2 = tf.zeros([2, 4]);
+        var lstmBias2 = tf.zeros([4]);
+        var forgetBias = tf.scalar(1.0);
+        var lstm1 = function (data, c, h) {
+            return tf.basicLSTMCell(forgetBias, lstmKernel1, lstmBias1, data, c, h);
+        };
+        var lstm2 = function (data, c, h) {
+            return tf.basicLSTMCell(forgetBias, lstmKernel2, lstmBias2, data, c, h);
+        };
+        var h = [
+            tf.zeros([1, lstmBias1.shape[0] / 4]),
+            tf.zeros([1, lstmBias2.shape[0] / 4])
+        ];
+        var data = tf.zeros([1, 2]);
+        expect(function () { return tf.multiRNNCell([lstm1, lstm2], data, [{}], h); })
+            .toThrowError(/Argument 'c\[0\]' passed to 'multiRNNCell' must be a Tensor/);
+    });
+    it('input: h', function () {
+        var lstmKernel1 = tf.zeros([3, 4]);
+        var lstmBias1 = tf.zeros([4]);
+        var lstmKernel2 = tf.zeros([2, 4]);
+        var lstmBias2 = tf.zeros([4]);
+        var forgetBias = tf.scalar(1.0);
+        var lstm1 = function (data, c, h) {
+            return tf.basicLSTMCell(forgetBias, lstmKernel1, lstmBias1, data, c, h);
+        };
+        var lstm2 = function (data, c, h) {
+            return tf.basicLSTMCell(forgetBias, lstmKernel2, lstmBias2, data, c, h);
+        };
+        var c = [
+            tf.zeros([1, lstmBias1.shape[0] / 4]),
+            tf.zeros([1, lstmBias2.shape[0] / 4])
+        ];
+        var data = tf.zeros([1, 2]);
+        expect(function () { return tf.multiRNNCell([lstm1, lstm2], data, c, [{}]); })
+            .toThrowError(/Argument 'h\[0\]' passed to 'multiRNNCell' must be a Tensor/);
+    });
+});
+jasmine_util_1.describeWithFlags('basicLSTMCell throws with non-tensor', test_util_1.ALL_ENVS, function () {
+    it('input: forgetBias', function () {
+        var lstmKernel = tf.randomNormal([3, 4]);
+        var lstmBias = tf.randomNormal([4]);
+        var data = tf.randomNormal([1, 2]);
+        var batchedData = tf.concat2d([data, data], 0);
+        var c = tf.randomNormal([1, 1]);
+        var batchedC = tf.concat2d([c, c], 0);
+        var h = tf.randomNormal([1, 1]);
+        var batchedH = tf.concat2d([h, h], 0);
+        expect(function () { return tf.basicLSTMCell({}, lstmKernel, lstmBias, batchedData, batchedC, batchedH); })
+            .toThrowError(/Argument 'forgetBias' passed to 'basicLSTMCell' must be a Tensor/);
+    });
+    it('input: lstmKernel', function () {
+        var lstmBias = tf.randomNormal([4]);
+        var forgetBias = tf.scalar(1.0);
+        var data = tf.randomNormal([1, 2]);
+        var batchedData = tf.concat2d([data, data], 0);
+        var c = tf.randomNormal([1, 1]);
+        var batchedC = tf.concat2d([c, c], 0);
+        var h = tf.randomNormal([1, 1]);
+        var batchedH = tf.concat2d([h, h], 0);
+        expect(function () { return tf.basicLSTMCell(forgetBias, {}, lstmBias, batchedData, batchedC, batchedH); })
+            .toThrowError(/Argument 'lstmKernel' passed to 'basicLSTMCell' must be a Tensor/);
+    });
+    it('input: lstmBias', function () {
+        var lstmKernel = tf.randomNormal([3, 4]);
+        var forgetBias = tf.scalar(1.0);
+        var data = tf.randomNormal([1, 2]);
+        var batchedData = tf.concat2d([data, data], 0);
+        var c = tf.randomNormal([1, 1]);
+        var batchedC = tf.concat2d([c, c], 0);
+        var h = tf.randomNormal([1, 1]);
+        var batchedH = tf.concat2d([h, h], 0);
+        expect(function () { return tf.basicLSTMCell(forgetBias, lstmKernel, {}, batchedData, batchedC, batchedH); })
+            .toThrowError(/Argument 'lstmBias' passed to 'basicLSTMCell' must be a Tensor/);
+    });
+    it('input: data', function () {
+        var lstmKernel = tf.randomNormal([3, 4]);
+        var lstmBias = tf.randomNormal([4]);
+        var forgetBias = tf.scalar(1.0);
+        var c = tf.randomNormal([1, 1]);
+        var batchedC = tf.concat2d([c, c], 0);
+        var h = tf.randomNormal([1, 1]);
+        var batchedH = tf.concat2d([h, h], 0);
+        expect(function () { return tf.basicLSTMCell(forgetBias, lstmKernel, lstmBias, {}, batchedC, batchedH); })
+            .toThrowError(/Argument 'data' passed to 'basicLSTMCell' must be a Tensor/);
+    });
+    it('input: c', function () {
+        var lstmKernel = tf.randomNormal([3, 4]);
+        var lstmBias = tf.randomNormal([4]);
+        var forgetBias = tf.scalar(1.0);
+        var data = tf.randomNormal([1, 2]);
+        var batchedData = tf.concat2d([data, data], 0);
+        var h = tf.randomNormal([1, 1]);
+        var batchedH = tf.concat2d([h, h], 0);
+        expect(function () { return tf.basicLSTMCell(forgetBias, lstmKernel, lstmBias, batchedData, {}, batchedH); })
+            .toThrowError(/Argument 'c' passed to 'basicLSTMCell' must be a Tensor/);
+    });
+    it('input: h', function () {
+        var lstmKernel = tf.randomNormal([3, 4]);
+        var lstmBias = tf.randomNormal([4]);
+        var forgetBias = tf.scalar(1.0);
+        var data = tf.randomNormal([1, 2]);
+        var batchedData = tf.concat2d([data, data], 0);
+        var c = tf.randomNormal([1, 1]);
+        var batchedC = tf.concat2d([c, c], 0);
+        expect(function () { return tf.basicLSTMCell(forgetBias, lstmKernel, lstmBias, batchedData, batchedC, {}); })
+            .toThrowError(/Argument 'h' passed to 'basicLSTMCell' must be a Tensor/);
+    });
+});
+//# sourceMappingURL=lstm_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/lstm_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/lstm_test.js.map
new file mode 100644
index 0000000..6a997a1
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/lstm_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/matmul.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/matmul.d.ts
new file mode 100644
index 0000000..10fdc64
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/matmul.d.ts
@@ -0,0 +1,9 @@
+import { Tensor, Tensor1D, Tensor2D } from '../tensor';
+import { TensorLike } from '../types';
+declare function matMul_<T extends Tensor>(a: T | TensorLike, b: T | TensorLike, transposeA?: boolean, transposeB?: boolean): T;
+declare function outerProduct_(v1: Tensor1D | TensorLike, v2: Tensor1D | TensorLike): Tensor2D;
+declare function dot_(t1: Tensor | TensorLike, t2: Tensor | TensorLike): Tensor;
+export declare const matMul: typeof matMul_;
+export declare const dot: typeof dot_;
+export declare const outerProduct: typeof outerProduct_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/matmul.js b/node_modules/@tensorflow/tfjs-core/dist/ops/matmul.js
new file mode 100644
index 0000000..74b7f9f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/matmul.js
@@ -0,0 +1,98 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var tensor_util_1 = require("../tensor_util");
+var tensor_util_env_1 = require("../tensor_util_env");
+var util = require("../util");
+var operation_1 = require("./operation");
+function matMul_(a, b, transposeA, transposeB) {
+    if (transposeA === void 0) { transposeA = false; }
+    if (transposeB === void 0) { transposeB = false; }
+    var _a;
+    var $a = tensor_util_env_1.convertToTensor(a, 'a', 'matMul');
+    var $b = tensor_util_env_1.convertToTensor(b, 'b', 'matMul');
+    _a = tensor_util_1.makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+    var innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];
+    var innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];
+    var outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];
+    var outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];
+    var outerDimsA = $a.shape.slice(0, -2);
+    var outerDimsB = $b.shape.slice(0, -2);
+    var batchDimA = util.sizeFromShape(outerDimsA);
+    var batchDimB = util.sizeFromShape(outerDimsB);
+    util.assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, "Error in matMul: inputs must have the same rank of at least 2, " +
+        ("got ranks " + $a.rank + " and " + $b.rank + "."));
+    util.assert(util.arraysEqual(outerDimsA, outerDimsB), "Error in matMul: outer dimensions (" + outerDimsA + ") and (" +
+        (outerDimsB + ") of Tensors with shapes " + $a.shape + " and ") +
+        ($b.shape + " must match."));
+    util.assert(innerShapeA === innerShapeB, "Error in matMul: inner shapes (" + innerShapeA + ") and (" +
+        (innerShapeB + ") of Tensors with shapes " + $a.shape + " and ") +
+        ($b.shape + " and transposeA=" + transposeA) +
+        (" and transposeB=" + transposeB + " must match."));
+    var outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);
+    var a3D = transposeA ? $a.as3D(batchDimA, innerShapeA, outerShapeA) :
+        $a.as3D(batchDimA, outerShapeA, innerShapeA);
+    var b3D = transposeB ? $b.as3D(batchDimB, outerShapeB, innerShapeB) :
+        $b.as3D(batchDimB, innerShapeB, outerShapeB);
+    var grad = function (dy) {
+        if (!transposeA && !transposeB) {
+            return {
+                $a: function () { return dy.matMul(b3D, false, true); },
+                $b: function () { return a3D.matMul(dy, true, false); }
+            };
+        }
+        else if (!transposeA && transposeB) {
+            return {
+                $a: function () { return dy.matMul(b3D, false, false); },
+                $b: function () { return dy.matMul(a3D, true, false); }
+            };
+        }
+        else if (transposeA && !transposeB) {
+            return {
+                $a: function () { return b3D.matMul(dy, false, true); },
+                $b: function () { return a3D.matMul(dy, false, false); }
+            };
+        }
+        else {
+            return {
+                $a: function () { return b3D.matMul(dy, true, true); },
+                $b: function () { return dy.matMul(a3D, true, true); }
+            };
+        }
+    };
+    var res = environment_1.ENV.engine.runKernel(function (backend) { return backend.batchMatMul(a3D, b3D, transposeA, transposeB); }, { $a: a3D, $b: b3D }, grad);
+    return res.reshape(outShape);
+}
+function outerProduct_(v1, v2) {
+    var $v1 = tensor_util_env_1.convertToTensor(v1, 'v1', 'outerProduct');
+    var $v2 = tensor_util_env_1.convertToTensor(v2, 'v2', 'outerProduct');
+    util.assert($v1.rank === 1 && $v2.rank === 1, "Error in outerProduct: inputs must be rank 1, but got ranks " +
+        ($v1.rank + " and " + $v2.rank + "."));
+    return $v1.as2D(-1, 1).matMul($v2.as2D(1, -1));
+}
+function dot_(t1, t2) {
+    var $t1 = tensor_util_env_1.convertToTensor(t1, 't1', 'dot');
+    var $t2 = tensor_util_env_1.convertToTensor(t2, 't2', 'dot');
+    util.assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), "Error in dot: inputs must all be rank 1 or 2, but got ranks " +
+        ($t1.rank + " and " + $t2.rank + "."));
+    var t1Inner = ($t1.rank === 1 ? $t1.size : $t1.shape[1]);
+    var t2Inner = ($t2.rank === 1 ? $t2.size : $t2.shape[0]);
+    util.assert(t1Inner === t2Inner, "Error in dot: inner dimensions of inputs must match, but got " +
+        (t1Inner + " and " + t2Inner + "."));
+    if ($t1.rank === 1 && $t2.rank === 1) {
+        return $t1.as2D(1, -1).matMul($t2.as2D(-1, 1)).asScalar();
+    }
+    else if ($t1.rank === 1 && $t2.rank === 2) {
+        return $t1.as2D(1, -1).matMul($t2.as2D($t2.shape[0], $t2.shape[1])).as1D();
+    }
+    else if ($t1.rank === 2 && $t2.rank === 1) {
+        return $t1.matMul($t2.as2D(-1, 1)).as1D();
+    }
+    else {
+        return $t1.matMul($t2.as2D($t2.shape[0], $t2.shape[1]));
+    }
+}
+exports.matMul = operation_1.op({ matMul_: matMul_ });
+exports.dot = operation_1.op({ dot_: dot_ });
+exports.outerProduct = operation_1.op({ outerProduct_: outerProduct_ });
+//# sourceMappingURL=matmul.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/matmul.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/matmul.js.map
new file mode 100644
index 0000000..cbb80bd
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/matmul.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"matmul.js","sourceRoot":"","sources":["../../src/ops/matmul.ts"],"names":[],"mappings":";;AAiBA,8CAAmC;AAEnC,8CAA8C;AAC9C,sDAAmD;AAEnD,8BAAgC;AAChC,yCAA+B;AAiB/B,iBACI,CAAe,EAAE,CAAe,EAAE,UAAkB,EACpD,UAAkB;IADgB,2BAAA,EAAA,kBAAkB;IACpD,2BAAA,EAAA,kBAAkB;;IACpB,IAAI,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,QAAQ,CAAC,CAAC;IAC3C,IAAI,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,QAAQ,CAAC,CAAC;IAC3C,yCAAiC,EAAhC,UAAE,EAAE,UAAE,CAA2B;IAElC,IAAM,WAAW,GACb,UAAU,CAAC,CAAC,CAAC,EAAE,CAAC,KAAK,CAAC,EAAE,CAAC,IAAI,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,KAAK,CAAC,EAAE,CAAC,IAAI,GAAG,CAAC,CAAC,CAAC;IAC/D,IAAM,WAAW,GACb,UAAU,CAAC,CAAC,CAAC,EAAE,CAAC,KAAK,CAAC,EAAE,CAAC,IAAI,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,KAAK,CAAC,EAAE,CAAC,IAAI,GAAG,CAAC,CAAC,CAAC;IAE/D,IAAM,WAAW,GACb,UAAU,CAAC,CAAC,CAAC,EAAE,CAAC,KAAK,CAAC,EAAE,CAAC,IAAI,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,KAAK,CAAC,EAAE,CAAC,IAAI,GAAG,CAAC,CAAC,CAAC;IAC/D,IAAM,WAAW,GACb,UAAU,CAAC,CAAC,CAAC,EAAE,CAAC,KAAK,CAAC,EAAE,CAAC,IAAI,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,KAAK,CAAC,EAAE,CAAC,IAAI,GAAG,CAAC,CAAC,CAAC;IAE/D,IAAM,UAAU,GAAG,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IACzC,IAAM,UAAU,GAAG,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IACzC,IAAM,SAAS,GAAG,IAAI,CAAC,aAAa,CAAC,UAAU,CAAC,CAAC;IACjD,IAAM,SAAS,GAAG,IAAI,CAAC,aAAa,CAAC,UAAU,CAAC,CAAC;IAEjD,IAAI,CAAC,MAAM,CACP,EAAE,CAAC,IAAI,IAAI,CAAC,IAAI,EAAE,CAAC,IAAI,IAAI,CAAC,IAAI,EAAE,CAAC,IAAI,KAAK,EAAE,CAAC,IAAI,EACnD,iEAAiE;SAC7D,eAAa,EAAE,CAAC,IAAI,aAAQ,EAAE,CAAC,IAAI,MAAG,CAAA,CAAC,CAAC;IAEhD,IAAI,CAAC,MAAM,CACP,IAAI,CAAC,WAAW,CAAC,UAAU,EAAE,UAAU,CAAC,EACxC,wCAAsC,UAAU,YAAS;SAClD,UAAU,iCAA4B,EAAE,CAAC,KAAK,UAAO,CAAA;SACrD,EAAE,CAAC,KAAK,iBAAc,CAAA,CAAC,CAAC;IAEnC,IAAI,CAAC,MAAM,CACP,WAAW,KAAK,WAAW,EAC3B,oCAAkC,WAAW,YAAS;SAC/C,WAAW,iCAA4B,EAAE,CAAC,KAAK,UAAO,CAAA;SACtD,EAAE,CAAC,KAAK,wBAAmB,UAAY,CAAA;SAC1C,qBAAmB,UAAU,iBAAc,CAAA,CAAC,CAAC;IAErD,IAAM,QAAQ,GAAG,EAAE,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,WAAW,EAAE,WAAW,CAAC,CAAC,CAAC;IAE1E,IAAM,GAAG,GAAG,UAAU,CAAC,CAAC,CAAC,EAAE,CAAC,IAAI,CAAC,SAAS,EAAE,WAAW,EAAE,WAAW,CAAC,CAAC,CAAC;QAC9C,EAAE,CAAC,IAAI,CAAC,SAAS,EAAE,WAAW,EAAE,WAAW,CAAC,CAAC;IACtE,IAAM,GAAG,GAAG,UAAU,CAAC,CAAC,CAAC,EAAE,CAAC,IAAI,CAAC,SAAS,EAAE,WAAW,EAAE,WAAW,CAAC,CAAC,CAAC;QAC9C,EAAE,CAAC,IAAI,CAAC,SAAS,EAAE,WAAW,EAAE,WAAW,CAAC,CAAC;IAEtE,IAAM,IAAI,GAAG,UAAC,EAAY;QACxB,IAAI,CAAC,UAAU,IAAI,CAAC,UAAU,EAAE;YAC9B,OAAO;gBACL,EAAE,EAAE,cAAM,OAAA,EAAE,CAAC,MAAM,CAAC,GAAG,EAAE,KAAK,EAAE,IAAI,CAAC,EAA3B,CAA2B;gBACrC,EAAE,EAAE,cAAM,OAAA,GAAG,CAAC,MAAM,CAAC,EAAE,EAAE,IAAI,EAAE,KAAK,CAAC,EAA3B,CAA2B;aACtC,CAAC;SACH;aAAM,IAAI,CAAC,UAAU,IAAI,UAAU,EAAE;YACpC,OAAO;gBACL,EAAE,EAAE,cAAM,OAAA,EAAE,CAAC,MAAM,CAAC,GAAG,EAAE,KAAK,EAAE,KAAK,CAAC,EAA5B,CAA4B;gBACtC,EAAE,EAAE,cAAM,OAAA,EAAE,CAAC,MAAM,CAAC,GAAG,EAAE,IAAI,EAAE,KAAK,CAAC,EAA3B,CAA2B;aACtC,CAAC;SACH;aAAM,IAAI,UAAU,IAAI,CAAC,UAAU,EAAE;YACpC,OAAO;gBACL,EAAE,EAAE,cAAM,OAAA,GAAG,CAAC,MAAM,CAAC,EAAE,EAAE,KAAK,EAAE,IAAI,CAAC,EAA3B,CAA2B;gBACrC,EAAE,EAAE,cAAM,OAAA,GAAG,CAAC,MAAM,CAAC,EAAE,EAAE,KAAK,EAAE,KAAK,CAAC,EAA5B,CAA4B;aACvC,CAAC;SACH;aAAM;YACL,OAAO;gBACL,EAAE,EAAE,cAAM,OAAA,GAAG,CAAC,MAAM,CAAC,EAAE,EAAE,IAAI,EAAE,IAAI,CAAC,EAA1B,CAA0B;gBACpC,EAAE,EAAE,cAAM,OAAA,EAAE,CAAC,MAAM,CAAC,GAAG,EAAE,IAAI,EAAE,IAAI,CAAC,EAA1B,CAA0B;aACrC,CAAC;SACH;IACH,CAAC,CAAC;IAEF,IAAM,GAAG,GAAG,iBAAG,CAAC,MAAM,CAAC,SAAS,CAC5B,UAAA,OAAO,IAAI,OAAA,OAAO,CAAC,WAAW,CAAC,GAAG,EAAE,GAAG,EAAE,UAAU,EAAE,UAAU,CAAC,EAArD,CAAqD,EAChE,EAAC,EAAE,EAAE,GAAG,EAAE,EAAE,EAAE,GAAG,EAAC,EAAE,IAAI,CAAC,CAAC;IAC9B,OAAO,GAAG,CAAC,OAAO,CAAC,QAAQ,CAAM,CAAC;AACpC,CAAC;AAeD,uBACI,EAAuB,EAAE,EAAuB;IAClD,IAAM,GAAG,GAAG,iCAAe,CAAC,EAAE,EAAE,IAAI,EAAE,cAAc,CAAC,CAAC;IACtD,IAAM,GAAG,GAAG,iCAAe,CAAC,EAAE,EAAE,IAAI,EAAE,cAAc,CAAC,CAAC;IAEtD,IAAI,CAAC,MAAM,CACP,GAAG,CAAC,IAAI,KAAK,CAAC,IAAI,GAAG,CAAC,IAAI,KAAK,CAAC,EAChC,8DAA8D;SACvD,GAAG,CAAC,IAAI,aAAQ,GAAG,CAAC,IAAI,MAAG,CAAA,CAAC,CAAC;IAExC,OAAO,GAAG,CAAC,IAAI,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,MAAM,CAAC,GAAG,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;AACjD,CAAC;AAkBD,cAAc,EAAqB,EAAE,EAAqB;IACxD,IAAM,GAAG,GAAG,iCAAe,CAAC,EAAE,EAAE,IAAI,EAAE,KAAK,CAAC,CAAC;IAC7C,IAAM,GAAG,GAAG,iCAAe,CAAC,EAAE,EAAE,IAAI,EAAE,KAAK,CAAC,CAAC;IAC7C,IAAI,CAAC,MAAM,CACP,CAAC,GAAG,CAAC,IAAI,KAAK,CAAC,IAAI,GAAG,CAAC,IAAI,KAAK,CAAC,CAAC,IAAI,CAAC,GAAG,CAAC,IAAI,KAAK,CAAC,IAAI,GAAG,CAAC,IAAI,KAAK,CAAC,CAAC,EACxE,8DAA8D;SACvD,GAAG,CAAC,IAAI,aAAQ,GAAG,CAAC,IAAI,MAAG,CAAA,CAAC,CAAC;IAExC,IAAM,OAAO,GAAG,CAAC,GAAG,CAAC,IAAI,KAAK,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,IAAI,CAAC,CAAC,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC;IAC3D,IAAM,OAAO,GAAG,CAAC,GAAG,CAAC,IAAI,KAAK,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,IAAI,CAAC,CAAC,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC;IAE3D,IAAI,CAAC,MAAM,CACP,OAAO,KAAK,OAAO,EACnB,+DAA+D;SACxD,OAAO,aAAQ,OAAO,MAAG,CAAA,CAAC,CAAC;IAEtC,IAAI,GAAG,CAAC,IAAI,KAAK,CAAC,IAAI,GAAG,CAAC,IAAI,KAAK,CAAC,EAAE;QACpC,OAAO,GAAG,CAAC,IAAI,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,GAAG,CAAC,IAAI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diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/matmul_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/matmul_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/matmul_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/matmul_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/matmul_test.js
new file mode 100644
index 0000000..1844115
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/matmul_test.js
@@ -0,0 +1,992 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var backend_webgl_1 = require("../kernels/backend_webgl");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('packed matmul', test_util_1.WEBGL_ENVS, function () {
+    it('should not leak memory', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9], [3, 3]);
+        var b = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [3, 5]);
+        var startNumBytes = tf.memory().numBytes;
+        tf.matMul(a, b);
+        var endNumBytes = tf.memory().numBytes;
+        expect(endNumBytes - startNumBytes).toEqual(60);
+    });
+    it('should work when input matrix dimensions are not divisible by 2', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9], [3, 3]);
+        var b = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [3, 5]);
+        var c = tf.matMul(a, b);
+        expect(c.shape).toEqual([3, 5]);
+        test_util_1.expectArraysClose(c, [46, 52, 58, 64, 70, 100, 115, 130, 145, 160, 154, 178, 202, 226, 250]);
+    });
+    it('should work when output texture shape != physical shape', function () {
+        var sharedDim = 16000;
+        var a = tf.buffer([2, sharedDim], 'float32');
+        var b = tf.buffer([sharedDim, 2], 'float32');
+        a.set(1, 0, sharedDim - 1);
+        a.set(1, 0, sharedDim - 2);
+        a.set(1, 1, sharedDim - 1);
+        b.set(1, sharedDim - 1, 0);
+        b.set(1, sharedDim - 2, 0);
+        var c = tf.matMul(a.toTensor(), b.toTensor());
+        var expected = [2, 0, 1, 0];
+        test_util_1.expectArraysClose(c, expected);
+    });
+    it('should work when input texture shapes != physical shape', function () {
+        var maxTextureSize = tf.ENV.get('WEBGL_MAX_TEXTURE_SIZE');
+        tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', 5);
+        var a = tf.tensor2d([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], [1, 12]);
+        var b = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [12, 1]);
+        var c = tf.matMul(a, b);
+        tf.ENV.set('WEBGL_MAX_TEXTURE_SIZE', maxTextureSize);
+        test_util_1.expectArraysClose(c, [572]);
+    });
+    it('A x B', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
+        var c = tf.matMul(a, b);
+        expect(c.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(c, [0, 8, -3, 20]);
+    });
+    it('A x B^t', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var b = tf.tensor2d([1, 0, 2, 4, 3, 0], [2, 3]);
+        var transposeA = false;
+        var transposeB = true;
+        var c = tf.matMul(a, b, transposeA, transposeB);
+        var expected = [7, 10, 16, 31];
+        test_util_1.expectArraysClose(c, expected);
+    });
+    it('A^t x B', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var b = tf.tensor2d([1, 0, 2, 4, 3, 0], [2, 3]);
+        var transposeA = true;
+        var transposeB = false;
+        var c = tf.matMul(a, b, transposeA, transposeB);
+        var expected = [17, 12, 2, 22, 15, 4, 27, 18, 6];
+        test_util_1.expectArraysClose(c, expected);
+    });
+    it('A^t x B^t', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
+        var b = tf.tensor2d([1, 0, 2, 4, 3, 0], [2, 3]);
+        var transposeA = true;
+        var transposeB = true;
+        var c = tf.matMul(a, b, transposeA, transposeB);
+        var expected = [11, 13, 14, 20];
+        test_util_1.expectArraysClose(c, expected);
+    });
+    it('works when followed by an op that requires unpacked inputs', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
+        var c = tf.matMul(a, b);
+        var d = tf.add(c, 1);
+        test_util_1.expectArraysClose(d, [1, 9, -2, 21]);
+    });
+    it('works when followed by a reshape that changes texture layout, and then an unpacked op', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9], [9, 1]);
+        var b = tf.tensor2d([1], [1, 1]);
+        var c = tf.matMul(a, b);
+        var d = tf.reshape(c, [1, 3, 3, 1]);
+        var e = tf.add(d, 1);
+        test_util_1.expectArraysClose(e, [2, 3, 4, 5, 6, 7, 8, 9, 10]);
+    });
+    it('works when preceded by an op that requires packed inputs', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
+        var c = tf.add(a, 1);
+        var d = tf.matMul(b, c);
+        test_util_1.expectArraysClose(d, [5, 6, 7, 4, 3, 2, 9, 12, 15]);
+    });
+});
+jasmine_util_1.describeWithFlags('matmul', test_util_1.ALL_ENVS, function () {
+    it('A x B', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var b = tf.tensor2d([0, 1, -3, 2, 2, 1], [3, 2]);
+        var c = tf.matMul(a, b);
+        expect(c.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(c, [0, 8, -3, 20]);
+    });
+    it('upcasts when dtypes dont match', function () {
+        var a = [1, 2, 3, 4, 5, 6];
+        var b = [0, 1, -3, 2, 2, 1];
+        var c = tf.matMul(tf.tensor(a, [2, 3], 'float32'), tf.tensor(b, [3, 2], 'int32'));
+        expect(c.shape).toEqual([2, 2]);
+        expect(c.dtype).toBe('float32');
+        test_util_1.expectArraysClose(c, [0, 8, -3, 20]);
+        c = tf.matMul(tf.tensor(a, [2, 3], 'int32'), tf.tensor(b, [3, 2], 'bool'));
+        expect(c.shape).toEqual([2, 2]);
+        expect(c.dtype).toBe('int32');
+        test_util_1.expectArraysClose(c, [5, 6, 11, 15]);
+    });
+    it('A x B^t', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var b = tf.tensor2d([1, 0, 2, 4, 3, 0], [2, 3]);
+        var transposeA = false;
+        var transposeB = true;
+        var c = tf.matMul(a, b, transposeA, transposeB);
+        var expected = [7, 10, 16, 31];
+        test_util_1.expectArraysClose(c, expected);
+    });
+    it('A^t x B', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var b = tf.tensor2d([1, 0, 2, 4, 3, 0], [2, 3]);
+        var transposeA = true;
+        var transposeB = false;
+        var c = tf.matMul(a, b, transposeA, transposeB);
+        var expected = [17, 12, 2, 22, 15, 4, 27, 18, 6];
+        test_util_1.expectArraysClose(c, expected);
+    });
+    it('A^t x B^t', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
+        var b = tf.tensor2d([1, 0, 2, 4, 3, 0], [2, 3]);
+        var transposeA = true;
+        var transposeB = true;
+        var c = tf.matMul(a, b, transposeA, transposeB);
+        var expected = [11, 13, 14, 20];
+        test_util_1.expectArraysClose(c, expected);
+    });
+    it('A x B^t shapes do not match', function () {
+        var a = tf.zeros([2, 3]);
+        var b = tf.zeros([3, 2]);
+        var f = function () {
+            var transposeA = false;
+            var transposeB = true;
+            tf.matMul(a, b, transposeA, transposeB);
+        };
+        expect(f).toThrowError();
+    });
+    it('A^t x B shapes do not match', function () {
+        var a = tf.zeros([2, 3]);
+        var b = tf.zeros([3, 2]);
+        var f = function () {
+            var transposeA = true;
+            var transposeB = false;
+            tf.matMul(a, b, transposeA, transposeB);
+        };
+        expect(f).toThrowError();
+    });
+    it('A^t x B^t shapes do not match', function () {
+        var a = tf.zeros([3, 2]);
+        var b = tf.zeros([3, 2]);
+        var f = function () {
+            var transposeA = true;
+            var transposeB = true;
+            tf.matMul(a, b, transposeA, transposeB);
+        };
+        expect(f).toThrowError();
+    });
+    it('matmul throws when inner dimensions dont match', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var b = tf.tensor2d([0, 1, -3, 2, 2, 1, 2, 2], [4, 2]);
+        expect(function () { return tf.matMul(a, b); }).toThrowError();
+    });
+    it('matmul throws when passed non matrices', function () {
+        var a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
+        var b = tf.tensor2d([0, 1, -3, 2, 2, 1, 2, 2], [4, 2]);
+        expect(function () { return tf.matMul(a, b); }).toThrowError();
+        expect(function () { return tf.matMul(b, a); }).toThrowError();
+    });
+    it('matmul throws when passed a vector', function () {
+        var v = tf.tensor1d([2, 3]);
+        var matrix = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        expect(function () { return tf.matMul(matrix, v); }).toThrowError();
+    });
+    it('Vector times matrix', function () {
+        var v = tf.tensor1d([2, 3]);
+        var matrix = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var result = tf.dot(v, matrix);
+        var expected = [11, 16];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('Vector times matrix with implicit reshape', function () {
+        var v = tf.tensor1d([2, 3]);
+        var matrix = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var result = tf.dot(v, matrix);
+        var expected = [11, 16];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('Matrix times vector', function () {
+        var matrix = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var v = tf.tensor1d([2, 3]);
+        var result = tf.dot(matrix, v);
+        var expected = [8, 18];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('batched matmul with the matrices being vectors', function () {
+        var batch = 3;
+        var sharedDim = backend_webgl_1.MATMUL_SHARED_DIM_THRESHOLD + 1;
+        var values = new Float32Array(batch * sharedDim);
+        values[10] = 2;
+        var a = tf.tensor(values, [batch, 1, sharedDim]);
+        var b = tf.tensor(values, [batch, sharedDim, 1]);
+        var result = tf.matMul(a, b);
+        expect(result.shape).toEqual([batch, 1, 1]);
+        test_util_1.expectArraysClose(result, [4, 0, 0]);
+    });
+    it('batched matmul with the matrices being vectors transposedA', function () {
+        var batch = 3;
+        var sharedDim = backend_webgl_1.MATMUL_SHARED_DIM_THRESHOLD + 1;
+        var values = new Float32Array(batch * sharedDim);
+        values[10] = 2;
+        var a = tf.tensor(values, [batch, sharedDim, 1]);
+        var b = tf.tensor(values, [batch, sharedDim, 1]);
+        var transposeA = true;
+        var transposeB = false;
+        var result = tf.matMul(a, b, transposeA, transposeB);
+        expect(result.shape).toEqual([batch, 1, 1]);
+        test_util_1.expectArraysClose(result, [4, 0, 0]);
+    });
+    it('batched matmul with the matrices being vectors transposedB', function () {
+        var batch = 3;
+        var sharedDim = backend_webgl_1.MATMUL_SHARED_DIM_THRESHOLD + 1;
+        var values = new Float32Array(batch * sharedDim);
+        values[10] = 2;
+        var a = tf.tensor(values, [batch, 1, sharedDim]);
+        var b = tf.tensor(values, [batch, 1, sharedDim]);
+        var transposeA = false;
+        var transposeB = true;
+        var result = tf.matMul(a, b, transposeA, transposeB);
+        expect(result.shape).toEqual([batch, 1, 1]);
+        test_util_1.expectArraysClose(result, [4, 0, 0]);
+    });
+    it('batched matmul with matrix x vector', function () {
+        var batch = 3;
+        var sharedDim = backend_webgl_1.MATMUL_SHARED_DIM_THRESHOLD + 1;
+        var values = new Float32Array(batch * sharedDim);
+        values[10] = 2;
+        var a = tf.ones([batch, 2, sharedDim]);
+        var b = tf.tensor(values, [batch, sharedDim, 1]);
+        var result = tf.matMul(a, b);
+        expect(result.shape).toEqual([batch, 2, 1]);
+        test_util_1.expectArraysClose(result, [2, 2, 0, 0, 0, 0]);
+    });
+    it('batched matmul with matrix x vector transposedA', function () {
+        var batch = 3;
+        var sharedDim = backend_webgl_1.MATMUL_SHARED_DIM_THRESHOLD + 1;
+        var values = new Float32Array(batch * sharedDim);
+        values[10] = 2;
+        var a = tf.ones([batch, sharedDim, 2]);
+        var b = tf.tensor(values, [batch, sharedDim, 1]);
+        var transposeA = true;
+        var transposeB = false;
+        var result = tf.matMul(a, b, transposeA, transposeB);
+        expect(result.shape).toEqual([batch, 2, 1]);
+        test_util_1.expectArraysClose(result, [2, 2, 0, 0, 0, 0]);
+    });
+    it('batched matmul with matrix x vector transposedB', function () {
+        var batch = 3;
+        var sharedDim = backend_webgl_1.MATMUL_SHARED_DIM_THRESHOLD + 1;
+        var values = new Float32Array(batch * sharedDim);
+        values[10] = 2;
+        var a = tf.ones([batch, 2, sharedDim]);
+        var b = tf.tensor(values, [batch, 1, sharedDim]);
+        var transposeA = false;
+        var transposeB = true;
+        var result = tf.matMul(a, b, transposeA, transposeB);
+        expect(result.shape).toEqual([batch, 2, 1]);
+        test_util_1.expectArraysClose(result, [2, 2, 0, 0, 0, 0]);
+    });
+    it('batched matmul with vector x matrix', function () {
+        var batch = 3;
+        var sharedDim = backend_webgl_1.MATMUL_SHARED_DIM_THRESHOLD + 1;
+        var values = new Float32Array(batch * sharedDim);
+        values[10] = 2;
+        var a = tf.tensor(values, [batch, 1, sharedDim]);
+        var b = tf.ones([batch, sharedDim, 2]);
+        var result = tf.matMul(a, b);
+        expect(result.shape).toEqual([batch, 1, 2]);
+        test_util_1.expectArraysClose(result, [2, 2, 0, 0, 0, 0]);
+    });
+    it('batched matmul with vector x matrix transposedA', function () {
+        var batch = 3;
+        var sharedDim = backend_webgl_1.MATMUL_SHARED_DIM_THRESHOLD + 1;
+        var values = new Float32Array(batch * sharedDim);
+        values[10] = 2;
+        var a = tf.tensor(values, [batch, sharedDim, 1]);
+        var b = tf.ones([batch, sharedDim, 2]);
+        var transposeA = true;
+        var transposeB = false;
+        var result = tf.matMul(a, b, transposeA, transposeB);
+        expect(result.shape).toEqual([batch, 1, 2]);
+        test_util_1.expectArraysClose(result, [2, 2, 0, 0, 0, 0]);
+    });
+    it('batched matmul with vector x matrix transposedB', function () {
+        var batch = 3;
+        var sharedDim = backend_webgl_1.MATMUL_SHARED_DIM_THRESHOLD + 1;
+        var values = new Float32Array(batch * sharedDim);
+        values[10] = 2;
+        var a = tf.tensor(values, [batch, 1, sharedDim]);
+        var b = tf.ones([batch, 2, sharedDim]);
+        var transposeA = false;
+        var transposeB = true;
+        var result = tf.matMul(a, b, transposeA, transposeB);
+        expect(result.shape).toEqual([batch, 1, 2]);
+        test_util_1.expectArraysClose(result, [2, 2, 0, 0, 0, 0]);
+    });
+    it('Matrix * vector propagates NaNs', function () {
+        var matrix = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var v = tf.tensor1d([2, NaN]);
+        var result = tf.dot(matrix, v);
+        var expected = [NaN, NaN];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('matrix times vector throws when not passed a matrix', function () {
+        var v = tf.tensor1d([2, 3]);
+        var matrix = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
+        expect(function () { return tf.dot(matrix, v); }).toThrowError();
+    });
+    it('Dot product', function () {
+        var v1 = tf.tensor1d([2, 3]);
+        var v2 = tf.tensor1d([2, 1]);
+        var result = tf.dot(v1, v2);
+        test_util_1.expectNumbersClose(result.get(), 7);
+    });
+    it('Dot product propagates NaNs', function () {
+        var v1 = tf.tensor1d([2, NaN]);
+        var v2 = tf.tensor1d([2, 1]);
+        var result = tf.dot(v1, v2);
+        expect(result.get()).toEqual(NaN);
+    });
+    it('Dot product throws when vectors are different size', function () {
+        var v1 = tf.tensor1d([2, 3, 3]);
+        var v2 = tf.tensor1d([2, 1]);
+        expect(function () { return tf.dot(v1, v2); }).toThrowError();
+        expect(function () { return tf.dot(v2, v1); }).toThrowError();
+    });
+    it('Outer product', function () {
+        var v1 = tf.tensor1d([2, 3]);
+        var v2 = tf.tensor1d([2, 1]);
+        var result = tf.outerProduct(v1, v2);
+        var expected = [4, 2, 6, 3];
+        expect(result.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('outer product accepts a tensor-like object', function () {
+        var v1 = [2, 3];
+        var v2 = [2, 1];
+        var result = tf.outerProduct(v1, v2);
+        var expected = [4, 2, 6, 3];
+        expect(result.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('gradients: A * B', function () {
+        var a = tf.tensor2d([1, 2, 3, 10, 20, 30], [2, 3]);
+        var b = tf.tensor2d([2, 3, 4, 1, 2, 3], [3, 2]);
+        var dy = tf.tensor2d([1, 10, 20, 30], [2, 2]);
+        var transposeA = false;
+        var transposeB = false;
+        var grads = tf.grads(function (a, b) {
+            return tf.matMul(a, b, transposeA, transposeB);
+        });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(da, [
+            dy.get(0, 0) * b.get(0, 0) + dy.get(0, 1) * b.get(0, 1),
+            dy.get(0, 0) * b.get(1, 0) + dy.get(0, 1) * b.get(1, 1),
+            dy.get(0, 0) * b.get(2, 0) + dy.get(0, 1) * b.get(2, 1),
+            dy.get(1, 0) * b.get(0, 0) + dy.get(1, 1) * b.get(0, 1),
+            dy.get(1, 0) * b.get(1, 0) + dy.get(1, 1) * b.get(1, 1),
+            dy.get(1, 0) * b.get(2, 0) + dy.get(1, 1) * b.get(2, 1)
+        ], 1e-1);
+        expect(db.shape).toEqual(b.shape);
+        test_util_1.expectArraysClose(db, [
+            a.get(0, 0) * dy.get(0, 0) + a.get(1, 0) * dy.get(1, 0),
+            a.get(0, 0) * dy.get(0, 1) + a.get(1, 0) * dy.get(1, 1),
+            a.get(0, 1) * dy.get(0, 0) + a.get(1, 1) * dy.get(1, 0),
+            a.get(0, 1) * dy.get(0, 1) + a.get(1, 1) * dy.get(1, 1),
+            a.get(0, 2) * dy.get(0, 0) + a.get(1, 2) * dy.get(1, 0),
+            a.get(0, 2) * dy.get(0, 1) + a.get(1, 2) * dy.get(1, 1)
+        ]);
+    });
+    it('gradients: a * bT', function () {
+        var a = tf.tensor2d([1, 2, 3, 10, 20, 30], [3, 2]);
+        var b = tf.tensor2d([2, 3, 4, 1, 2, 3], [3, 2]);
+        var dy = tf.tensor2d([1, 10, 20, 30, 40, 50, 60, 70, 80], [3, 3]);
+        var transposeA = false;
+        var transposeB = true;
+        var grads = tf.grads(function (a, b) {
+            return tf.matMul(a, b, transposeA, transposeB);
+        });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(da, [
+            dy.get(0, 0) * b.get(0, 0) + dy.get(0, 1) * b.get(1, 0) +
+                dy.get(0, 2) * b.get(2, 0),
+            dy.get(0, 0) * b.get(0, 1) + dy.get(0, 1) * b.get(1, 1) +
+                dy.get(0, 2) * b.get(2, 1),
+            dy.get(1, 0) * b.get(0, 0) + dy.get(1, 1) * b.get(1, 0) +
+                dy.get(1, 2) * b.get(2, 0),
+            dy.get(1, 0) * b.get(0, 1) + dy.get(1, 1) * b.get(1, 1) +
+                dy.get(1, 2) * b.get(2, 1),
+            dy.get(2, 0) * b.get(0, 0) + dy.get(2, 1) * b.get(1, 0) +
+                dy.get(2, 2) * b.get(2, 0),
+            dy.get(2, 0) * b.get(0, 1) + dy.get(2, 1) * b.get(1, 1) +
+                dy.get(2, 2) * b.get(2, 1)
+        ]);
+        expect(db.shape).toEqual(b.shape);
+        test_util_1.expectArraysClose(db, [
+            dy.get(0, 0) * a.get(0, 0) + dy.get(1, 0) * a.get(1, 0) +
+                dy.get(2, 0) * a.get(2, 0),
+            dy.get(0, 0) * a.get(0, 1) + dy.get(1, 0) * a.get(1, 1) +
+                dy.get(2, 0) * a.get(2, 1),
+            dy.get(0, 1) * a.get(0, 0) + dy.get(1, 1) * a.get(1, 0) +
+                dy.get(2, 1) * a.get(2, 0),
+            dy.get(0, 1) * a.get(0, 1) + dy.get(1, 1) * a.get(1, 1) +
+                dy.get(2, 1) * a.get(2, 1),
+            dy.get(0, 2) * a.get(0, 0) + dy.get(1, 2) * a.get(1, 0) +
+                dy.get(2, 2) * a.get(2, 0),
+            dy.get(0, 2) * a.get(0, 1) + dy.get(1, 2) * a.get(1, 1) +
+                dy.get(2, 2) * a.get(2, 1)
+        ]);
+    });
+    it('gradients: aT * b', function () {
+        var a = tf.tensor2d([1, 2, 3, 10, 20, 30], [3, 2]);
+        var b = tf.tensor2d([2, 3, 4, 1, 2, 3], [3, 2]);
+        var dy = tf.tensor2d([1, 10, 20, 30], [2, 2]);
+        var transposeA = true;
+        var transposeB = false;
+        var grads = tf.grads(function (a, b) {
+            return tf.matMul(a, b, transposeA, transposeB);
+        });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(da, [
+            dy.get(0, 0) * b.get(0, 0) + dy.get(0, 1) * b.get(0, 1),
+            dy.get(1, 0) * b.get(0, 0) + dy.get(1, 1) * b.get(0, 1),
+            dy.get(0, 0) * b.get(1, 0) + dy.get(0, 1) * b.get(1, 1),
+            dy.get(1, 0) * b.get(1, 0) + dy.get(1, 1) * b.get(1, 1),
+            dy.get(0, 0) * b.get(2, 0) + dy.get(0, 1) * b.get(2, 1),
+            dy.get(1, 0) * b.get(2, 0) + dy.get(1, 1) * b.get(2, 1)
+        ]);
+        expect(db.shape).toEqual(b.shape);
+        test_util_1.expectArraysClose(db, [
+            dy.get(0, 0) * a.get(0, 0) + dy.get(1, 0) * a.get(0, 1),
+            dy.get(0, 1) * a.get(0, 0) + dy.get(1, 1) * a.get(0, 1),
+            dy.get(0, 0) * a.get(1, 0) + dy.get(1, 0) * a.get(1, 1),
+            dy.get(0, 1) * a.get(1, 0) + dy.get(1, 1) * a.get(1, 1),
+            dy.get(0, 0) * a.get(2, 0) + dy.get(1, 0) * a.get(2, 1),
+            dy.get(0, 1) * a.get(2, 0) + dy.get(1, 1) * a.get(2, 1)
+        ]);
+    });
+    it('gradients: aT * bT', function () {
+        var a = tf.tensor2d([1, 2, 3, 10, 20, 30], [3, 2]);
+        var b = tf.tensor2d([2, 3, 4, 1, 2, 3], [2, 3]);
+        var dy = tf.tensor2d([1, 10, 20, 30], [2, 2]);
+        var transposeA = true;
+        var transposeB = true;
+        var grads = tf.grads(function (a, b) {
+            return tf.matMul(a, b, transposeA, transposeB);
+        });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(da, [
+            dy.get(0, 0) * b.get(0, 0) + dy.get(0, 1) * b.get(1, 0),
+            dy.get(1, 0) * b.get(0, 0) + dy.get(1, 1) * b.get(1, 0),
+            dy.get(0, 0) * b.get(0, 1) + dy.get(0, 1) * b.get(1, 1),
+            dy.get(1, 0) * b.get(0, 1) + dy.get(1, 1) * b.get(1, 1),
+            dy.get(0, 0) * b.get(0, 2) + dy.get(0, 1) * b.get(1, 2),
+            dy.get(1, 0) * b.get(0, 2) + dy.get(1, 1) * b.get(1, 2)
+        ]);
+        expect(db.shape).toEqual(b.shape);
+        test_util_1.expectArraysClose(db, [
+            dy.get(0, 0) * a.get(0, 0) + dy.get(1, 0) * a.get(0, 1),
+            dy.get(0, 0) * a.get(1, 0) + dy.get(1, 0) * a.get(1, 1),
+            dy.get(0, 0) * a.get(2, 0) + dy.get(1, 0) * a.get(2, 1),
+            dy.get(0, 1) * a.get(0, 0) + dy.get(1, 1) * a.get(0, 1),
+            dy.get(0, 1) * a.get(1, 0) + dy.get(1, 1) * a.get(1, 1),
+            dy.get(0, 1) * a.get(2, 0) + dy.get(1, 1) * a.get(2, 1)
+        ]);
+    });
+    it('throws when passed a as a non-tensor', function () {
+        expect(function () { return tf.matMul({}, tf.tensor2d([2], [1, 1])); })
+            .toThrowError(/Argument 'a' passed to 'matMul' must be a Tensor/);
+    });
+    it('throws when passed b as a non-tensor', function () {
+        expect(function () { return tf.matMul(tf.tensor2d([2], [1, 1]), {}); })
+            .toThrowError(/Argument 'b' passed to 'matMul' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [[1, 2, 3], [4, 5, 6]];
+        var b = [[0, 1], [-3, 2], [2, 1]];
+        var c = tf.matMul(a, b);
+        expect(c.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(c, [0, 8, -3, 20]);
+    });
+    it('accepts a tensor-like object chained', function () {
+        var a = tf.tensor2d([[1, 2, 3], [4, 5, 6]], [2, 3]);
+        var b = [[0, 1], [-3, 2], [2, 1]];
+        var c = a.matMul(b);
+        expect(c.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(c, [0, 8, -3, 20]);
+    });
+    it('a * b where a has zero in its shape', function () {
+        var a = tf.tensor2d([], [0, 3]);
+        var b = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
+        var c = tf.matMul(a, b);
+        expect(c.shape).toEqual([0, 2]);
+        expect(c.rank).toBe(2);
+        expect(c.size).toBe(0);
+        test_util_1.expectArraysClose(c, []);
+    });
+    it('(a * b) * c where a has zero in its shape, so a*b does also', function () {
+        var a = tf.tensor2d([], [0, 3]);
+        var b = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
+        var ab = tf.matMul(a, b);
+        expect(ab.shape).toEqual([0, 2]);
+        test_util_1.expectArraysClose(ab, []);
+        var c = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var res = tf.matMul(ab, c);
+        expect(res.shape).toEqual([0, 3]);
+        test_util_1.expectArraysClose(res, []);
+    });
+    it('throws error for string tensor', function () {
+        expect(function () { return tf.matMul([['a']], [['b']]); })
+            .toThrowError(/Argument 'a' passed to 'matMul' must be numeric tensor/);
+    });
+});
+jasmine_util_1.describeWithFlags('matmulBatch', test_util_1.ALL_ENVS, function () {
+    it('A x B', function () {
+        var a = tf.tensor3d([
+            -5, -5, -6, 8, -2, -8, 4, -7, -6, -9, -1, 3, 7, -2, 5,
+            -6, 3, 8, 7, -8, 1, 4, -4, 6, 4, -4, -9, -5, 2, -2
+        ], [5, 2, 3]);
+        var b = tf.tensor3d([
+            -8, -4, -1, 0, -7, 0, 3, 3, 6, 2, -1, 8, -4, 9, -6,
+            5, 8, 9, -9, 7, 0, -1, -1, -10, -7, 3, 4, 6, 3, -4
+        ], [5, 3, 2]);
+        var c = tf.matMul(a, b);
+        expect(c.shape).toEqual([5, 2, 2]);
+        test_util_1.expectArraysClose(c, [
+            87, 20, -6, -32, -24, -50, -36, -5, 24, 98,
+            70, 33, -64, 47, -42, -28, -71, 24, 37, 5
+        ]);
+    });
+    it('A x B in 4D', function () {
+        var a = tf.tensor4d([
+            -2, 3, 5, -5, 3, 9, -3, -5, 1, 1, -9, 9, -6, 6, -8,
+            -7, -1, 3, 9, -7, -7, 2, 10, -6, -8, -6, 9, -6, 4, -1,
+            9, -6, 10, 8, -9, 5, -8, -7, 0, 2, -5, -1, -9, -4, 3,
+            -2, 6, -4, 7, 1, -5, -4, 9, -8, -6, -8, 4, -1, 4, 3,
+            -7, 8, -7, 5, -3, -2, -4, 9, 2, -1, 1, -10, -3, 5, -4,
+            6, -8, -8, 9, -3, -5, 10, 3, -3, -3, 9, 3, -3, 2, -8,
+            10, 1, 9, -2, -2, -3, -4, 6, -10, -1, 8, -8, 7, 3, -2,
+            3, 6, -2, -2, -4, 1, -5, -4, 0, 5, 1, 9, -8, -2, -1
+        ], [4, 5, 2, 3]);
+        var b = tf.tensor4d([
+            -4, -3, -2, -6, 6, -1, -4, -1, 7, -4, 8, -9, -9, 0, -1,
+            -4, -6, -7, -3, -4, -7, 6, -8, 1, -2, 1, -1, -3, 8, -5,
+            9, -2, 5, 9, -2, 2, -5, -5, -8, -1, -2, -3, -2, -10, 6,
+            -3, 0, 1, 6, 7, 1, 2, -4, -5, 2, -5, -7, 9, 3, -6,
+            6, 4, -4, 6, 10, -3, -2, 8, 10, -8, 10, -1, -9, -7, -8,
+            -3, 1, 1, -2, -9, -7, -6, -1, 0, 7, -9, -7, -5, 0, -4,
+            -4, -7, 2, 4, 6, 6, -4, -6, -8, 3, -8, -9, 6, 9, -4,
+            1, -1, 0, 8, 9, 0, -5, 3, -1, 5, 0, -10, 7, -2, 6
+        ], [4, 5, 3, 2]);
+        var transposeA = false;
+        var transposeB = false;
+        var c = tf.matMul(a, b, transposeA, transposeB);
+        test_util_1.expectArraysClose(c, [
+            32, -17, 68, -12, -15, 14, 5, -46, 96, 32, 46, -17, 78, -85,
+            -28, 46, 94, -35, 0, -13, 31, -52, 17, -87, 96, 47, 32, -2,
+            -6, 105, 40, -2, 63, 76, 17, 30, 56, -66, -21, 23, -144, 41,
+            22, 8, 118, -106, -88, -6, -17, 2, 2, -26, 8, -63, -38, -108,
+            -84, -30, -35, 49, 16, -12, -14, -12, 48, 132, 4, 102, 32, 66,
+            -4, 33, -13, 1, -40, -25, -3, 61, -18, -20
+        ]);
+    });
+    it('A x B^t', function () {
+        var a = tf.tensor3d([
+            -5, -5, -6, 8, -2, -8, 4, -7, -6, -9, -1, 3, 7, -2, 5,
+            -6, 3, 8, 7, -8, 1, 4, -4, 6, 4, -4, -9, -5, 2, -2
+        ], [5, 2, 3]);
+        var b = tf.tensor3d([
+            -8, -4, -1, 0, -7, 0, 3, 3, 6, 2, -1, 8, -4, 9, -6,
+            5, 8, 9, -9, 7, 0, -1, -1, -10, -7, 3, 4, 6, 3, -4
+        ], [5, 2, 3]);
+        var transposeA = false;
+        var transposeB = true;
+        var c = tf.matMul(a, b, transposeA, transposeB);
+        expect(c.shape).toEqual([5, 2, 2]);
+        test_util_1.expectArraysClose(c, [
+            66, 35, -48, 14, -45, -33, -12, 7, -76, 64,
+            3, 66, -119, -9, -64, -60, -76, 48, 33, -16
+        ]);
+    });
+    it('A^t x B', function () {
+        var a = tf.tensor3d([
+            -5, -5, -6, 8, -2, -8, 4, -7, -6, -9, -1, 3, 7, -2, 5,
+            -6, 3, 8, 7, -8, 1, 4, -4, 6, 4, -4, -9, -5, 2, -2
+        ], [5, 2, 3]);
+        var b = tf.tensor3d([
+            -8, -4, -1, 0, -7, 0, 3, 3, 6, 2, -1, 8, -4, 9, -6,
+            5, 8, 9, -9, 7, 0, -1, -1, -10, -7, 3, 4, 6, 3, -4
+        ], [5, 2, 3]);
+        var transposeA = true;
+        var transposeB = false;
+        var c = tf.matMul(a, b, transposeA, transposeB);
+        test_util_1.expectArraysClose(c, [
+            40, -36, 5, 40, 34, 5, 48, 80, 6, -6, 21, -48, -23, -20, -50,
+            -12, -21, -12, -58, 15, -96, 23, 6, 39, 20, 109, 42, -67, 45, -40,
+            76, -52, 40, -15, 1, -60, -58, -3, 36, 40, -6, -24, 51, -33, -28
+        ]);
+    });
+    it('A^t x B in 4D', function () {
+        var a = tf.tensor4d([
+            -2, 3, 5, -5, 3, 9, -3, -5, 1, 1, -9, 9, -6, 6, -8,
+            -7, -1, 3, 9, -7, -7, 2, 10, -6, -8, -6, 9, -6, 4, -1,
+            9, -6, 10, 8, -9, 5, -8, -7, 0, 2, -5, -1, -9, -4, 3,
+            -2, 6, -4, 7, 1, -5, -4, 9, -8, -6, -8, 4, -1, 4, 3,
+            -7, 8, -7, 5, -3, -2, -4, 9, 2, -1, 1, -10, -3, 5, -4,
+            6, -8, -8, 9, -3, -5, 10, 3, -3, -3, 9, 3, -3, 2, -8,
+            10, 1, 9, -2, -2, -3, -4, 6, -10, -1, 8, -8, 7, 3, -2,
+            3, 6, -2, -2, -4, 1, -5, -4, 0, 5, 1, 9, -8, -2, -1
+        ], [4, 5, 2, 3]);
+        var b = tf.tensor4d([
+            -4, -3, -2, -6, 6, -1, -4, -1, 7, -4, 8, -9, -9, 0, -1,
+            -4, -6, -7, -3, -4, -7, 6, -8, 1, -2, 1, -1, -3, 8, -5,
+            9, -2, 5, 9, -2, 2, -5, -5, -8, -1, -2, -3, -2, -10, 6,
+            -3, 0, 1, 6, 7, 1, 2, -4, -5, 2, -5, -7, 9, 3, -6,
+            6, 4, -4, 6, 10, -3, -2, 8, 10, -8, 10, -1, -9, -7, -8,
+            -3, 1, 1, -2, -9, -7, -6, -1, 0, 7, -9, -7, -5, 0, -4,
+            -4, -7, 2, 4, 6, 6, -4, -6, -8, 3, -8, -9, 6, 9, -4,
+            1, -1, 0, 8, 9, 0, -5, 3, -1, 5, 0, -10, 7, -2, 6
+        ], [4, 5, 2, 3]);
+        var transposeA = true;
+        var transposeB = false;
+        var c = tf.matMul(a, b, transposeA, transposeB);
+        test_util_1.expectArraysClose(c, [
+            38, -24, 9, -30, 9, -9, -74, 39, -19, 8, 11, -30, 56, -67,
+            46, -40, 71, -74, 82, 42, 55, -50, 6, 1, 60, -18, -13, -15,
+            -52, -61, 81, -52, 59, -15, 76, 43, 34, -56, 38, 0, 26, -14,
+            -15, 1, -4, 153, -34, 61, -135, 30, -48, 135, -30, 60, 38, 36,
+            58, 40, 45, 71, 1, 2, 3, 24, 90, -56, -10, 40, -18, 6,
+            -30, 14, 34, 65, 27, 24, -29, -44, -46, -3, 35, -21, 27, 48,
+            20, 52, 32, 35, -11, -46, -12, 22, 13, 30, 2, -23, -54, -48,
+            34, 16, -42, -39, -26, 82, 89, 76, -84, 30, 9, 27, 30, -21,
+            -43, -48, 60, 20, 24, -78, -91, -63, -12, 24, 21, 28, 48, 35,
+            -6, 27, 33, 53, -81, -71, 61, -27, 11, -48, -82, 8, -12, -19,
+            -10, -48, -81, 0, 13, 32, 41, 0, -100, -120, 16, 124, 152, 45,
+            60, -28, 24, 21, -12, -14, -16, 8, 9, -33, 5, -12, -48, 4,
+            8, 9, 0, -31, 16, -98, -9, 4, -22, 38, 2, -96
+        ]);
+    });
+    it('A^t x B^t', function () {
+        var a = tf.tensor3d([
+            -5, -5, -6, 8, -2, -8, 4, -7, -6, -9, -1, 3, 7, -2, 5,
+            -6, 3, 8, 7, -8, 1, 4, -4, 6, 4, -4, -9, -5, 2, -2
+        ], [5, 3, 2]);
+        var b = tf.tensor3d([
+            -8, -4, -1, 0, -7, 0, 3, 3, 6, 2, -1, 8, -4, 9, -6,
+            5, 8, 9, -9, 7, 0, -1, -1, -10, -7, 3, 4, 6, 3, -4
+        ], [5, 2, 3]);
+        var transposeA = true;
+        var transposeB = true;
+        var c = tf.matMul(a, b, transposeA, transposeB);
+        test_util_1.expectArraysClose(c, [
+            66, 42, 16, -56, -12, 6, -30, 19, -1, 102,
+            -94, 14, -56, 32, 100, -56, -47, -11, 5, -31
+        ]);
+    });
+    it('batch dimensions do not match', function () {
+        var a = tf.tensor3d([
+            -5, -5, -6, 8, -2, -8, 4, -7, -6, -9, -1, 3,
+            7, -2, 5, -6, 3, 8, 7, -8, 1, 4, -4, 6
+        ], [4, 3, 2]);
+        var b = tf.tensor3d([
+            -8, -4, -1, 0, -7, 0, 3, 3, 6, 2, -1, 8, -4, 9, -6,
+            5, 8, 9, -9, 7, 0, -1, -1, -10, -7, 3, 4, 6, 3, -4
+        ], [5, 2, 3]);
+        var f = function () {
+            tf.matMul(a, b, false, false);
+        };
+        expect(f).toThrowError();
+    });
+    it('gradients: A x B', function () {
+        var a = tf.tensor3d([
+            -5, -5, -6, 8, -2, -8, 4, -7, -6, -9, -1, 3, 7, -2, 5,
+            -6, 3, 8, 7, -8, 1, 4, -4, 6, 4, -4, -9, -5, 2, -2
+        ], [5, 2, 3]);
+        var b = tf.tensor3d([
+            -8, -4, -1, 0, -7, 0, 3, 3, 6, 2, -1, 8, -4, 9, -6,
+            5, 8, 9, -9, 7, 0, -1, -1, -10, -7, 3, 4, 6, 3, -4
+        ], [5, 3, 2]);
+        var dy = tf.tensor3d([8, 2, -3, -2, -8, 4, 5, 7, 4, -4, -4, 5, 8, 10, 1, 0, 6, 6, -4, 7], [5, 2, 2]);
+        var grads = tf.grads(function (a, b) { return tf.matMul(a, b, false, false); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(da, [
+            -72, -8, -56, 32, 3, 21, -12, -40, 40, 36, 44, 51, -52, -44, -4,
+            61, 49, 13, -2, -10, -108, -9, 0, -1, -24, 60, -6, 49, 26, -40
+        ]);
+        expect(db.shape).toEqual(b.shape);
+        test_util_1.expectArraysClose(db, [
+            -64, -26, -34, -6, -24, 4, -77, -47, 51, -35, 63, -3, 52, -58, -20,
+            23, -12, 20, 60, 70, -68, -80, 14, 10, 44, -11, -32, -10, -46, -68
+        ]);
+    });
+    it('4d gradients: A x B', function () {
+        var a = tf.tensor4d([
+            -2, 3, 5, -5, 3, 9, -3, -5, 1, 1, -9, 9, -6, 6, -8,
+            -7, -1, 3, 9, -7, -7, 2, 10, -6, -8, -6, 9, -6, 4, -1,
+            9, -6, 10, 8, -9, 5, -8, -7, 0, 2, -5, -1, -9, -4, 3,
+            -2, 6, -4, 7, 1, -5, -4, 9, -8, -6, -8, 4, -1, 4, 3,
+            -7, 8, -7, 5, -3, -2, -4, 9, 2, -1, 1, -10, -3, 5, -4,
+            6, -8, -8, 9, -3, -5, 10, 3, -3, -3, 9, 3, -3, 2, -8,
+            10, 1, 9, -2, -2, -3, -4, 6, -10, -1, 8, -8, 7, 3, -2,
+            3, 6, -2, -2, -4, 1, -5, -4, 0, 5, 1, 9, -8, -2, -1
+        ], [4, 5, 2, 3]);
+        var b = tf.tensor4d([
+            -4, -3, -2, -6, 6, -1, -4, -1, 7, -4, 8, -9, -9, 0, -1,
+            -4, -6, -7, -3, -4, -7, 6, -8, 1, -2, 1, -1, -3, 8, -5,
+            9, -2, 5, 9, -2, 2, -5, -5, -8, -1, -2, -3, -2, -10, 6,
+            -3, 0, 1, 6, 7, 1, 2, -4, -5, 2, -5, -7, 9, 3, -6,
+            6, 4, -4, 6, 10, -3, -2, 8, 10, -8, 10, -1, -9, -7, -8,
+            -3, 1, 1, -2, -9, -7, -6, -1, 0, 7, -9, -7, -5, 0, -4,
+            -4, -7, 2, 4, 6, 6, -4, -6, -8, 3, -8, -9, 6, 9, -4,
+            1, -1, 0, 8, 9, 0, -5, 3, -1, 5, 0, -10, 7, -2, 6
+        ], [4, 5, 3, 2]);
+        var dy = tf.tensor4d([
+            8, -7, 0, -9, -5, -5, 0, 3, 7, -4, 6, -8, -8, 0, -1, -8,
+            -9, -7, -4, -9, 2, 3, 5, 8, -5, -7, 3, -10, -5, -9, -5, 1,
+            7, 1, -9, -10, 8, 5, 0, 8, -6, 4, 0, -5, 8, -7, -2, 1,
+            -8, 9, 9, -7, 1, 7, -2, 5, -2, 9, 1, -5, 7, 5, -7, -6,
+            6, 7, -8, 7, 4, -5, 4, -5, 3, -4, -5, 4, -6, 3, -8, 10
+        ], [4, 5, 2, 2]);
+        var grads = tf.grads(function (a, b) { return tf.matMul(a, b, false, false); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(da, [
+            -11, 26, 55, 27, 54, 9, 25, -15, 5, -3, -12, -27, -63, 9,
+            -14, -54, 26, 20, 24, 56, 64, 35, -41, 0, 11, 30, -37, -1,
+            31, 13, 12, 37, 2, 29, 97, 6, 60, 47, 31, 35, -14, 24,
+            100, -3, -9, 0, -33, 1, 49, 9, -33, -124, -29, 86, -9, -11,
+            -6, -40, 72, -48, -20, 48, -72, -20, -30, 15, -72, 136, 87, 12,
+            -28, -21, 9, 37, 1, -32, -51, 2, -65, -49, -1, -41, -16, 2,
+            -95, -31, -36, 52, 18, 20, -63, 34, 72, 70, -38, -78, -66, -27,
+            -111, -10, 85, 1, -21, -21, -4, -21, -21, -4, -12, 20, 13, -4,
+            -20, -19, -30, 81, 30, -40, 150, 76
+        ]);
+        expect(db.shape).toEqual(b.shape);
+        test_util_1.expectArraysClose(db, [
+            -16, 59, 24, -48, 40, -116, 15, 18, 25, -2, -5, 22, -84, 80,
+            36, -16, -38, 8, -74, -16, 46, -80, 62, 48, 96, 110, 38, 6,
+            -77, -54, 58, 91, -57, -90, 45, 70, 46, 36, 20, 99, -3, 10,
+            55, 79, -10, 42, 5, -31, 85, 47, -74, -89, 37, 75, -48, -38,
+            -64, -8, 32, 44, 42, -53, -48, 47, 42, -18, -30, 27, 70, -62,
+            36, -24, 78, -69, -112, 101, -40, 20, -11, 113, -9, -6, 1, -50,
+            3, -12, -16, 71, -14, 67, 84, 62, 21, 17, 84, 63, -16, -35,
+            -28, 98, 4, -126, 40, -50, 36, -45, -16, 20, 19, -12, 8, 0,
+            3, -4, 34, -65, 10, -17, -46, 17
+        ]);
+    });
+    it('gradients: A x B^t', function () {
+        var a = tf.tensor3d([
+            -5, -5, -6, 8, -2, -8, 4, -7, -6, -9, -1, 3, 7, -2, 5,
+            -6, 3, 8, 7, -8, 1, 4, -4, 6, 4, -4, -9, -5, 2, -2
+        ], [5, 3, 2]);
+        var b = tf.tensor3d([
+            -8, -4, -1, 0, -7, 0, 3, 3, 6, 2, -1, 8, -4, 9, -6,
+            5, 8, 9, -9, 7, 0, -1, -1, -10, -7, 3, 4, 6, 3, -4
+        ], [5, 3, 2]);
+        var dy = tf.tensor3d([
+            -0, 7, 5, 0, -9, 5, -7, 6, -5, -3, -2, -2, -4, 10, -3,
+            5, -1, 3, -2, -9, 4, -5, 7, 9, -10, -8, -8, -5, -0, -1,
+            3, 3, 4, 9, -7, 6, -2, -9, 5, 1, -5, -3, -1, 9, 4
+        ], [5, 3, 3]);
+        var grads = tf.grads(function (a, b) { return tf.matMul(a, b, false, true); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(da, [
+            -42, 0, -26, 0, 85, 28, -19, -29, 51, -16, 6, 37, 94, -27, 50,
+            71, 24, -202, 46, -25, -31, -22, -87, 10, -7, -80, -36, -15, 55, 35
+        ]);
+        expect(db.shape).toEqual(b.shape);
+        test_util_1.expectArraysClose(db, [
+            14, 56, 7, -155, -45, 55, 7, 72, -67, -79, 7, 50, -69, -46, -52,
+            -88, 49, -126, -68, 106, 31, -30, -27, 60, -19, 5, 27, 43, 55, -13
+        ]);
+    });
+    it('4d gradients: A x B^t', function () {
+        var a = tf.tensor4d([
+            -2, 3, 5, -5, 3, 9, -3, -5, 1, 1, -9, 9, -6, 6, -8,
+            -7, -1, 3, 9, -7, -7, 2, 10, -6, -8, -6, 9, -6, 4, -1,
+            9, -6, 10, 8, -9, 5, -8, -7, 0, 2, -5, -1, -9, -4, 3,
+            -2, 6, -4, 7, 1, -5, -4, 9, -8, -6, -8, 4, -1, 4, 3,
+            -7, 8, -7, 5, -3, -2, -4, 9, 2, -1, 1, -10, -3, 5, -4,
+            6, -8, -8, 9, -3, -5, 10, 3, -3, -3, 9, 3, -3, 2, -8,
+            10, 1, 9, -2, -2, -3, -4, 6, -10, -1, 8, -8, 7, 3, -2,
+            3, 6, -2, -2, -4, 1, -5, -4, 0, 5, 1, 9, -8, -2, -1
+        ], [4, 5, 3, 2]);
+        var b = tf.tensor4d([
+            -4, -3, -2, -6, 6, -1, -4, -1, 7, -4, 8, -9, -9, 0, -1,
+            -4, -6, -7, -3, -4, -7, 6, -8, 1, -2, 1, -1, -3, 8, -5,
+            9, -2, 5, 9, -2, 2, -5, -5, -8, -1, -2, -3, -2, -10, 6,
+            -3, 0, 1, 6, 7, 1, 2, -4, -5, 2, -5, -7, 9, 3, -6,
+            6, 4, -4, 6, 10, -3, -2, 8, 10, -8, 10, -1, -9, -7, -8,
+            -3, 1, 1, -2, -9, -7, -6, -1, 0, 7, -9, -7, -5, 0, -4,
+            -4, -7, 2, 4, 6, 6, -4, -6, -8, 3, -8, -9, 6, 9, -4,
+            1, -1, 0, 8, 9, 0, -5, 3, -1, 5, 0, -10, 7, -2, 6
+        ], [4, 5, 3, 2]);
+        var dy = tf.tensor4d([
+            5, -1, -5, -4, -1, 9, 1, -2, 10, 7, -1, 6, -8, 8, -3,
+            9, -4, 2, -4, -8, 8, 4, 8, -10, -8, -8, 6, 6, -5, 9,
+            -1, -7, -5, -3, -3, 2, -6, 5, 8, -9, 5, -8, -3, 8, 6,
+            2, 8, 5, 9, 7, 6, 2, -3, 10, 7, 7, -3, 4, -3, -6,
+            -8, -8, 9, 0, -8, -3, -2, -2, 8, 2, 3, -6, 3, 6, -3,
+            7, 7, -9, -3, 8, 7, 7, -1, -6, 5, 2, -1, -1, 1, 5,
+            0, -4, 3, -4, -10, 1, -2, -8, -9, -6, 4, 4, -7, -1, -1,
+            -9, 7, 1, -1, 8, 0, -2, -7, 5, 7, 8, 9, -3, -8, -6,
+            -7, -8, -1, 8, -4, 7, 5, -9, 9, 3, 0, -10, 7, -9, 4,
+            -7, 5, -2, -2, 3, 3, -6, 2, 0, 8, -5, -10, 3, -7, 0,
+            -6, 2, 3, -1, 3, 3, -10, 1, 3, -7, -1, 8, -2, -1, -1,
+            -3, -9, 7, 4, -6, 3, 0, -7, -4, -5, -8, -6, 10, -6, 4
+        ], [4, 5, 3, 3]);
+        var grads = tf.grads(function (a, b) { return tf.matMul(a, b, false, true); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(da, [
+            -48, -4, 72, 9, 60, -1, 13, -57, 64, 3, -48, -11, -4, -24,
+            16, 38, 44, -10, -55, -45, 92, -43, 14, -4, 71, -61, -51, 16,
+            46, -57, 48, 78, 104, 57, -17, -11, -85, -33, 16, 1, 86, 21,
+            -48, 21, -8, 34, 14, -35, 36, 48, 85, 108, -38, -40, 3, -8,
+            -7, -1, 6, -16, 46, -33, 26, -79, -70, -29, 92, -84, -6, -47,
+            98, -129, -55, -17, 79, 40, -118, -64, 68, 75, 71, 111, 5, -48,
+            98, -36, 21, 13, 112, -34, 26, 57, 32, 44, 28, 50, 88, 27,
+            44, -39, -16, 15, -21, -6, -67, -89, -46, -64, -19, -12, -3, 11,
+            41, 63, 78, -73, 67, -92, 102, -18
+        ]);
+        expect(db.shape).toEqual(b.shape);
+        test_util_1.expectArraysClose(db, [
+            -27, 44, -9, -16, 85, 30, -110, 38, 47, -23, -39, -15, 0, -76,
+            -8, -128, 26, 136, 31, -26, -26, 39, 136, -85, -45, 93, 37, -68,
+            -112, -6, 90, 70, 169, -7, 15, 68, -16, -33, -16, -47, -21, 0,
+            6, -4, 84, 24, 15, 20, -41, -1, 79, -86, 87, -23, -26, -64,
+            18, 9, 52, 64, 34, -16, 122, -66, -1, 47, 1, 43, -11, -33,
+            -17, 27, -45, -73, -60, -66, -92, -42, 32, -85, -44, -44, -28, -13,
+            8, -20, 9, -9, -49, 79, -76, 15, 73, -7, 7, -8, -110, 93,
+            106, -39, 64, -84, -29, -19, 13, 14, 63, 2, -15, 23, 17, 49,
+            -3, -31, -65, 30, -95, 63, -82, 40
+        ]);
+    });
+    it('gradients: A^t x B', function () {
+        var a = tf.tensor3d([
+            -5, -5, -6, 8, -2, -8, 4, -7, -6, -9, -1, 3, 7, -2, 5,
+            -6, 3, 8, 7, -8, 1, 4, -4, 6, 4, -4, -9, -5, 2, -2
+        ], [5, 3, 2]);
+        var b = tf.tensor3d([
+            -8, -4, -1, 0, -7, 0, 3, 3, 6, 2, -1, 8, -4, 9, -6,
+            5, 8, 9, -9, 7, 0, -1, -1, -10, -7, 3, 4, 6, 3, -4
+        ], [5, 3, 2]);
+        var dy = tf.tensor3d([8, 2, -3, -2, -8, 4, 5, 7, 4, -4, -4, 5, 8, 10, 1, 0, 6, 6, -4, 7], [5, 2, 2]);
+        var grads = tf.grads(function (a, b) { return tf.matMul(a, b, true, false); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(da, [
+            -72, 32, -8, 3, -56, 21, -12, 36, -40, 44, 40, 51, -52, 61, -44,
+            49, -4, 13, -2, -9, -10, 0, -108, -1, -24, 49, 60, 26, -6, -40
+        ]);
+        expect(db.shape).toEqual(b.shape);
+        test_util_1.expectArraysClose(db, [
+            -25, 0, -72, -28, 8, 12, -67, -33, 3, -87, 23, 17, 36, -38, 44,
+            -50, -20, 28, 48, 70, 12, 10, -26, -40, 40, -4, -34, -89, 20, -2
+        ]);
+    });
+    it('gradients: A^t x B^t', function () {
+        var a = tf.tensor3d([
+            -5, -5, -6, 8, -2, -8, 4, -7, -6, -9, -1, 3, 7, -2, 5,
+            -6, 3, 8, 7, -8, 1, 4, -4, 6, 4, -4, -9, -5, 2, -2
+        ], [5, 3, 2]);
+        var b = tf.tensor3d([
+            -8, -4, -1, 0, -7, 0, 3, 3, 6, 2, -1, 8, -4, 9, -6,
+            5, 8, 9, -9, 7, 0, -1, -1, -10, -7, 3, 4, 6, 3, -4
+        ], [5, 2, 3]);
+        var dy = tf.tensor3d([8, 2, -3, -2, -8, 4, 5, 7, 4, -4, -4, 5, 8, 10, 1, 0, 6, 6, -4, 7], [5, 2, 2]);
+        var grads = tf.grads(function (a, b) { return tf.matMul(a, b, true, true); });
+        var _a = grads([a, b], dy), da = _a[0], db = _a[1];
+        expect(da.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(da, [
+            -64, 24, -46, 26, -8, 3, -16, 29, -28, 8, -16, 86, -36, 41, 4,
+            4, -60, 69, -82, -9, 46, 7, -100, 0, -6, 70, 36, 9, 0, -44
+        ]);
+        expect(db.shape).toEqual(b.shape);
+        test_util_1.expectArraysClose(db, [
+            -25, -72, 8, 0, -28, 12, -67, 3, 23, -33, -87, 17, 36, 44, -20,
+            -38, -50, 28, 48, 12, -26, 70, 10, -40, 40, -34, 20, -4, -89, -2
+        ]);
+    });
+});
+jasmine_util_1.describeWithFlags('matmul webgl-only', test_util_1.WEBGL_ENVS, function () {
+    it('Matrix times vector, large matrix', function () {
+        var maxTexSize = 16000;
+        var sharedDim = maxTexSize + 4;
+        var matrix = tf.buffer([2, sharedDim], 'float32');
+        matrix.set(1, 0, sharedDim - 3);
+        matrix.set(1, 0, sharedDim - 2);
+        var v = tf.buffer([sharedDim], 'float32');
+        v.set(1, sharedDim - 3);
+        v.set(1, sharedDim - 2);
+        var result = tf.dot(matrix.toTensor(), v.toTensor());
+        var expected = [2, 0];
+        test_util_1.expectArraysClose(result, expected);
+    });
+});
+jasmine_util_1.describeWithFlags('dot', test_util_1.ALL_ENVS, function () {
+    var a;
+    var b;
+    var c;
+    var d;
+    var e;
+    beforeEach(function () {
+        a = tf.tensor1d([1, 2]);
+        b = tf.tensor2d([[1, 2], [3, 4]]);
+        c = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);
+        d = tf.tensor3d([1, 2], [1, 1, 2]);
+        e = tf.scalar(1);
+    });
+    it('vector-vector', function () {
+        var aa = tf.dot(a, a);
+        test_util_1.expectArraysClose(aa, [5]);
+        expect(aa.shape).toEqual([]);
+    });
+    it('vector-matrix', function () {
+        var ab = tf.dot(a, b);
+        var ac = tf.dot(a, c);
+        expect(ab.shape).toEqual([2]);
+        expect(ac.shape).toEqual([3]);
+        test_util_1.expectArraysClose(ab, [7, 10]);
+        test_util_1.expectArraysClose(ac, [9, 12, 15]);
+    });
+    it('matrix-vector', function () {
+        var ba = b.dot(a);
+        expect(ba.shape).toEqual([2]);
+        test_util_1.expectArraysClose(ba, [5, 11]);
+    });
+    it('matrix-matrix', function () {
+        var bb = tf.dot(b, b);
+        var bc = tf.dot(b, c);
+        expect(bb.shape).toEqual([2, 2]);
+        expect(bc.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(bb, [7, 10, 15, 22]);
+        test_util_1.expectArraysClose(bc, [9, 12, 15, 19, 26, 33]);
+    });
+    it('throws error on incompatible dimensions', function () {
+        expect(function () { return tf.dot(c, a); }).toThrowError();
+        expect(function () { return tf.dot(c, b); }).toThrowError();
+    });
+    it('throws error when inputs are not rank 1 or 2', function () {
+        expect(function () { return tf.dot(a, d); }).toThrowError();
+        expect(function () { return tf.dot(a, e); }).toThrowError();
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [1, 2, 3];
+        var res = tf.dot(a, a);
+        test_util_1.expectArraysClose(res, [14]);
+        expect(res.shape).toEqual([]);
+    });
+    it('throws error for string tensors', function () {
+        expect(function () { return tf.dot('a', 'b'); })
+            .toThrowError(/Argument 't1' passed to 'dot' must be numeric tensor/);
+    });
+});
+//# sourceMappingURL=matmul_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/matmul_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/matmul_test.js.map
new file mode 100644
index 0000000..6e00aaa
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/matmul_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/moving_average.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/moving_average.d.ts
new file mode 100644
index 0000000..94a8265
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/moving_average.d.ts
@@ -0,0 +1,5 @@
+import { Scalar, Tensor } from '../tensor';
+import { TensorLike } from '../types';
+declare function movingAverage_<T extends Tensor>(v: T | TensorLike, x: T | TensorLike, decay: number | Scalar, step?: number | Scalar, zeroDebias?: boolean): T;
+export declare const movingAverage: typeof movingAverage_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/moving_average.js b/node_modules/@tensorflow/tfjs-core/dist/ops/moving_average.js
new file mode 100644
index 0000000..4ea2c06
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/moving_average.js
@@ -0,0 +1,27 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tensor_util_1 = require("../tensor_util");
+var tensor_util_env_1 = require("../tensor_util_env");
+var util = require("../util");
+var binary_ops_1 = require("./binary_ops");
+var operation_1 = require("./operation");
+var tensor_ops_1 = require("./tensor_ops");
+function movingAverage_(v, x, decay, step, zeroDebias) {
+    if (zeroDebias === void 0) { zeroDebias = true; }
+    var $v = tensor_util_env_1.convertToTensor(v, 'v', 'movingAverage');
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'movingAverage');
+    var $decay = tensor_util_env_1.convertToTensor(decay, 'decay', 'movingAverage');
+    tensor_util_1.assertTypesMatch($v, $x);
+    util.assert(util.arraysEqual($v.shape, $x.shape), 'Shape mismatch in v and x');
+    var one = tensor_ops_1.scalar(1);
+    var oneMinusDecay = one.sub($decay);
+    var update = $x.sub($v).mul(oneMinusDecay);
+    if (zeroDebias) {
+        util.assert(step != null, 'When using zeroDebias: true, step is required.');
+        var $step = tensor_util_env_1.convertToTensor(step, 'step', 'movingAverage');
+        update = update.div(one.sub(binary_ops_1.pow($decay, $step)));
+    }
+    return $v.add(update);
+}
+exports.movingAverage = operation_1.op({ movingAverage_: movingAverage_ });
+//# sourceMappingURL=moving_average.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/moving_average.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/moving_average.js.map
new file mode 100644
index 0000000..1681b29
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/moving_average.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"moving_average.js","sourceRoot":"","sources":["../../src/ops/moving_average.ts"],"names":[],"mappings":";;AAkBA,8CAAgD;AAChD,sDAAmD;AAEnD,8BAAgC;AAChC,2CAAiC;AACjC,yCAA+B;AAC/B,2CAAoC;AA6BpC,wBACI,CAAe,EAAE,CAAe,EAAE,KAAoB,EACtD,IAAoB,EAAE,UAAiB;IAAjB,2BAAA,EAAA,iBAAiB;IACzC,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,eAAe,CAAC,CAAC;IACpD,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,eAAe,CAAC,CAAC;IACpD,IAAM,MAAM,GAAG,iCAAe,CAAC,KAAK,EAAE,OAAO,EAAE,eAAe,CAAC,CAAC;IAEhE,8BAAgB,CAAC,EAAE,EAAE,EAAE,CAAC,CAAC;IACzB,IAAI,CAAC,MAAM,CACP,IAAI,CAAC,WAAW,CAAC,EAAE,CAAC,KAAK,EAAE,EAAE,CAAC,KAAK,CAAC,EAAE,2BAA2B,CAAC,CAAC;IAEvE,IAAM,GAAG,GAAG,mBAAM,CAAC,CAAC,CAAC,CAAC;IACtB,IAAM,aAAa,GAAG,GAAG,CAAC,GAAG,CAAC,MAAM,CAAC,CAAC;IAEtC,IAAI,MAAM,GAAG,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,CAAC,GAAG,CAAC,aAAa,CAAC,CAAC;IAC3C,IAAI,UAAU,EAAE;QACd,IAAI,CAAC,MAAM,CAAC,IAAI,IAAI,IAAI,EAAE,gDAAgD,CAAC,CAAC;QAC5E,IAAM,KAAK,GAAG,iCAAe,CAAC,IAAI,EAAE,MAAM,EAAE,eAAe,CAAC,CAAC;QAC7D,MAAM,GAAG,MAAM,CAAC,GAAG,CAAC,GAAG,CAAC,GAAG,CAAC,gBAAG,CAAC,MAAM,EAAE,KAAK,CAAC,CAAC,CAAC,CAAC;KAClD;IACD,OAAO,EAAE,CAAC,GAAG,CAAC,MAAM,CAAC,CAAC;AACxB,CAAC;AAEY,QAAA,aAAa,GAAG,cAAE,CAAC,EAAC,cAAc,gBAAA,EAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/moving_average_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/moving_average_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/moving_average_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/moving_average_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/moving_average_test.js
new file mode 100644
index 0000000..85df968
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/moving_average_test.js
@@ -0,0 +1,77 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('movingAverage', test_util_1.ALL_ENVS, function () {
+    it('zeroDebias=true, decay and step are numbers', function () {
+        var v0 = tf.tensor2d([[0, 0], [0, 0]], [2, 2]);
+        var x = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
+        var decay = 0.6;
+        var v1 = tf.movingAverage(v0, x, decay, 1);
+        test_util_1.expectArraysClose(v1, tf.tensor2d([[1, 2], [3, 4]], [2, 2]));
+        var y = tf.tensor2d([[11, 12], [13, 14]], [2, 2]);
+        var v2 = tf.movingAverage(v1, y, decay, 2);
+        test_util_1.expectArraysClose(v2, tf.tensor2d([[7.25, 8.25], [9.25, 10.25]], [2, 2]));
+    });
+    it('zeroDebias=true, decay and step are scalars', function () {
+        var v0 = tf.tensor2d([[0, 0], [0, 0]], [2, 2]);
+        var x = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
+        var decay = tf.scalar(0.6);
+        var v1 = tf.movingAverage(v0, x, decay, tf.scalar(1));
+        test_util_1.expectArraysClose(v1, tf.tensor2d([[1, 2], [3, 4]], [2, 2]));
+        var y = tf.tensor2d([[11, 12], [13, 14]], [2, 2]);
+        var v2 = tf.movingAverage(v1, y, decay, tf.scalar(2));
+        test_util_1.expectArraysClose(v2, tf.tensor2d([[7.25, 8.25], [9.25, 10.25]], [2, 2]));
+    });
+    it('zeroDebias=false, decay and step are numbers', function () {
+        var v0 = tf.tensor2d([[0, 0], [0, 0]], [2, 2]);
+        var x = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
+        var decay = 0.6;
+        var v1 = tf.movingAverage(v0, x, decay, null, false);
+        test_util_1.expectArraysClose(v1, tf.tensor2d([[0.4, 0.8], [1.2, 1.6]], [2, 2]));
+        var y = tf.tensor2d([[11, 12], [13, 14]], [2, 2]);
+        var v2 = tf.movingAverage(v1, y, decay, null, false);
+        test_util_1.expectArraysClose(v2, tf.tensor2d([[4.64, 5.28], [5.92, 6.56]], [2, 2]));
+    });
+    it('zeroDebias=false, decay is scalar', function () {
+        var v0 = tf.tensor2d([[0, 0], [0, 0]], [2, 2]);
+        var x = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
+        var decay = tf.scalar(0.6);
+        var v1 = tf.movingAverage(v0, x, decay, null, false);
+        test_util_1.expectArraysClose(v1, tf.tensor2d([[0.4, 0.8], [1.2, 1.6]], [2, 2]));
+        var y = tf.tensor2d([[11, 12], [13, 14]], [2, 2]);
+        var v2 = tf.movingAverage(v1, y, decay, null, false);
+        test_util_1.expectArraysClose(v2, tf.tensor2d([[4.64, 5.28], [5.92, 6.56]], [2, 2]));
+    });
+    it('zeroDebias=true, no step throws error', function () {
+        var v0 = tf.tensor2d([[0, 0], [0, 0]], [2, 2]);
+        var x = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
+        var decay = tf.scalar(0.6);
+        expect(function () { return tf.movingAverage(v0, x, decay, null); }).toThrowError();
+    });
+    it('shape mismatch in v and x throws error', function () {
+        var v0 = tf.tensor2d([[0, 0], [0, 0]], [2, 2]);
+        var x = tf.tensor2d([[1, 2]], [1, 2]);
+        var decay = tf.scalar(0.6);
+        expect(function () { return tf.movingAverage(v0, x, decay, null); }).toThrowError();
+    });
+    it('throws when passed v as a non-tensor', function () {
+        var x = tf.tensor2d([[1, 2], [3, 4]], [2, 2]);
+        expect(function () { return tf.movingAverage({}, x, 1); })
+            .toThrowError(/Argument 'v' passed to 'movingAverage' must be a Tensor/);
+    });
+    it('throws when passed v as a non-tensor', function () {
+        var v = tf.tensor2d([[0, 0], [0, 0]], [2, 2]);
+        expect(function () { return tf.movingAverage(v, {}, 1); })
+            .toThrowError(/Argument 'x' passed to 'movingAverage' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var v0 = [[0, 0], [0, 0]];
+        var x = [[1, 2], [3, 4]];
+        var decay = 0.6;
+        var v1 = tf.movingAverage(v0, x, decay, 1);
+        test_util_1.expectArraysClose(v1, tf.tensor2d([[1, 2], [3, 4]], [2, 2]));
+    });
+});
+//# sourceMappingURL=moving_average_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/moving_average_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/moving_average_test.js.map
new file mode 100644
index 0000000..96e95e7
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/moving_average_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"moving_average_test.js","sourceRoot":"","sources":["../../src/ops/moving_average_test.ts"],"names":[],"mappings":";;AAiBA,6BAA+B;AAC/B,gDAAkD;AAClD,0CAAyD;AAEzD,gCAAiB,CAAC,eAAe,EAAE,oBAAQ,EAAE;IA0B3C,EAAE,CAAC,6CAA6C,EAAE;QAChD,IAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACjD,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAChD,IAAM,KAAK,GAAG,GAAG,CAAC;QAElB,IAAM,EAAE,GAAG,EAAE,CAAC,aAAa,CAAC,EAAE,EAAE,CAAC,EAAE,KAAK,EAAE,CAAC,CAAC,CAAC;QAC7C,6BAAiB,CAAC,EAAE,EAAE,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QAE7D,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,EAAE,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACpD,IAAM,EAAE,GAAG,EAAE,CAAC,aAAa,CAAC,EAAE,EAAE,CAAC,EAAE,KAAK,EAAE,CAAC,CAAC,CAAC;QAC7C,6BAAiB,CAAC,EAAE,EAAE,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,IAAI,EAAE,IAAI,CAAC,EAAE,CAAC,IAAI,EAAE,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;IAC5E,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,6CAA6C,EAAE;QAChD,IAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACjD,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAChD,IAAM,KAAK,GAAG,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAC;QAE7B,IAAM,EAAE,GAAG,EAAE,CAAC,aAAa,CAAC,EAAE,EAAE,CAAC,EAAE,KAAK,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC;QACxD,6BAAiB,CAAC,EAAE,EAAE,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QAE7D,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,EAAE,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACpD,IAAM,EAAE,GAAG,EAAE,CAAC,aAAa,CAAC,EAAE,EAAE,CAAC,EAAE,KAAK,EAAE,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC;QACxD,6BAAiB,CAAC,EAAE,EAAE,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,IAAI,EAAE,IAAI,CAAC,EAAE,CAAC,IAAI,EAAE,KAAK,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;IAC5E,CAAC,CAAC,CAAC;IA2BH,EAAE,CAAC,8CAA8C,EAAE;QACjD,IAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACjD,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAChD,IAAM,KAAK,GAAG,GAAG,CAAC;QAElB,IAAM,EAAE,GAAG,EAAE,CAAC,aAAa,CAAC,EAAE,EAAE,CAAC,EAAE,KAAK,EAAE,IAAI,EAAE,KAAK,CAAC,CAAC;QACvD,6BAAiB,CAAC,EAAE,EAAE,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,EAAE,GAAG,CAAC,EAAE,CAAC,GAAG,EAAE,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QAErE,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,EAAE,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACpD,IAAM,EAAE,GAAG,EAAE,CAAC,aAAa,CAAC,EAAE,EAAE,CAAC,EAAE,KAAK,EAAE,IAAI,EAAE,KAAK,CAAC,CAAC;QACvD,6BAAiB,CAAC,EAAE,EAAE,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,IAAI,EAAE,IAAI,CAAC,EAAE,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;IAC3E,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,mCAAmC,EAAE;QACtC,IAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACjD,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAChD,IAAM,KAAK,GAAG,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAC;QAE7B,IAAM,EAAE,GAAG,EAAE,CAAC,aAAa,CAAC,EAAE,EAAE,CAAC,EAAE,KAAK,EAAE,IAAI,EAAE,KAAK,CAAC,CAAC;QACvD,6BAAiB,CAAC,EAAE,EAAE,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,EAAE,GAAG,CAAC,EAAE,CAAC,GAAG,EAAE,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QAErE,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,EAAE,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACpD,IAAM,EAAE,GAAG,EAAE,CAAC,aAAa,CAAC,EAAE,EAAE,CAAC,EAAE,KAAK,EAAE,IAAI,EAAE,KAAK,CAAC,CAAC;QACvD,6BAAiB,CAAC,EAAE,EAAE,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,IAAI,EAAE,IAAI,CAAC,EAAE,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;IAC3E,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,uCAAuC,EAAE;QAC1C,IAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACjD,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAChD,IAAM,KAAK,GAAG,EAAE,CAAC,MAAM,CAAC,GAAG,CAAC,CAAC;QAE7B,MAAM,CAAC,cAAM,OAAA,EAAE,CAAC,aAAa,CAAC,EAAE,EAAE,CAAC,EAAE,KAAK,EAAE,IAAI,CAAC,EAApC,CAAoC,CAAC,CAAC,YAAY,EAAE,CAAC;IACpE,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,wCAAwC,EAAE;QAC3C,IAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/multinomial_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/multinomial_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/multinomial_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/multinomial_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/multinomial_test.js
new file mode 100644
index 0000000..2700b6b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/multinomial_test.js
@@ -0,0 +1,93 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('multinomial', test_util_1.ALL_ENVS, function () {
+    var NUM_SAMPLES = 1000;
+    var EPSILON = 0.05;
+    var SEED = 3.14;
+    it('Flip a fair coin and check bounds', function () {
+        var probs = tf.tensor1d([1, 1]);
+        var result = tf.multinomial(probs, NUM_SAMPLES, SEED);
+        expect(result.dtype).toBe('int32');
+        expect(result.shape).toEqual([NUM_SAMPLES]);
+        var outcomeProbs = computeProbs(result.dataSync(), 2);
+        test_util_1.expectArraysClose(outcomeProbs, [0.5, 0.5], EPSILON);
+    });
+    it('Flip a two-sided coin with 100% of heads', function () {
+        var logits = tf.tensor1d([1, -100]);
+        var result = tf.multinomial(logits, NUM_SAMPLES, SEED);
+        expect(result.dtype).toBe('int32');
+        expect(result.shape).toEqual([NUM_SAMPLES]);
+        var outcomeProbs = computeProbs(result.dataSync(), 2);
+        test_util_1.expectArraysClose(outcomeProbs, [1, 0], EPSILON);
+    });
+    it('Flip a two-sided coin with 100% of tails', function () {
+        var logits = tf.tensor1d([-100, 1]);
+        var result = tf.multinomial(logits, NUM_SAMPLES, SEED);
+        expect(result.dtype).toBe('int32');
+        expect(result.shape).toEqual([NUM_SAMPLES]);
+        var outcomeProbs = computeProbs(result.dataSync(), 2);
+        test_util_1.expectArraysClose(outcomeProbs, [0, 1], EPSILON);
+    });
+    it('Flip a single-sided coin throws error', function () {
+        var probs = tf.tensor1d([1]);
+        expect(function () { return tf.multinomial(probs, NUM_SAMPLES, SEED); }).toThrowError();
+    });
+    it('Flip a ten-sided coin and check bounds', function () {
+        var numOutcomes = 10;
+        var logits = tf.fill([numOutcomes], 1).as1D();
+        var result = tf.multinomial(logits, NUM_SAMPLES, SEED);
+        expect(result.dtype).toBe('int32');
+        expect(result.shape).toEqual([NUM_SAMPLES]);
+        var outcomeProbs = computeProbs(result.dataSync(), numOutcomes);
+        expect(outcomeProbs.length).toBeLessThanOrEqual(numOutcomes);
+    });
+    it('Flip 3 three-sided coins, each coin is 100% biases', function () {
+        var numOutcomes = 3;
+        var logits = tf.tensor2d([[-100, -100, 1], [-100, 1, -100], [1, -100, -100]], [3, numOutcomes]);
+        var result = tf.multinomial(logits, NUM_SAMPLES, SEED);
+        expect(result.dtype).toBe('int32');
+        expect(result.shape).toEqual([3, NUM_SAMPLES]);
+        var outcomeProbs = computeProbs(result.dataSync().slice(0, NUM_SAMPLES), numOutcomes);
+        test_util_1.expectArraysClose(outcomeProbs, [0, 0, 1], EPSILON);
+        outcomeProbs = computeProbs(result.dataSync().slice(NUM_SAMPLES, 2 * NUM_SAMPLES), numOutcomes);
+        test_util_1.expectArraysClose(outcomeProbs, [0, 1, 0], EPSILON);
+        outcomeProbs =
+            computeProbs(result.dataSync().slice(2 * NUM_SAMPLES), numOutcomes);
+        test_util_1.expectArraysClose(outcomeProbs, [1, 0, 0], EPSILON);
+    });
+    it('passing Tensor3D throws error', function () {
+        var probs = tf.zeros([3, 2, 2]);
+        var normalized = true;
+        expect(function () { return tf.multinomial(probs, 3, SEED, normalized); })
+            .toThrowError();
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.multinomial({}, NUM_SAMPLES, SEED); })
+            .toThrowError(/Argument 'logits' passed to 'multinomial' must be a Tensor/);
+    });
+    it('accepts a tensor-like object for logits (biased coin)', function () {
+        var res = tf.multinomial([-100, 1], NUM_SAMPLES, SEED);
+        expect(res.dtype).toBe('int32');
+        expect(res.shape).toEqual([NUM_SAMPLES]);
+        var outcomeProbs = computeProbs(res.dataSync(), 2);
+        test_util_1.expectArraysClose(outcomeProbs, [0, 1], EPSILON);
+    });
+    function computeProbs(events, numOutcomes) {
+        var counts = [];
+        for (var i = 0; i < numOutcomes; ++i) {
+            counts[i] = 0;
+        }
+        var numSamples = events.length;
+        for (var i = 0; i < events.length; ++i) {
+            counts[events[i]]++;
+        }
+        for (var i = 0; i < counts.length; i++) {
+            counts[i] /= numSamples;
+        }
+        return counts;
+    }
+});
+//# sourceMappingURL=multinomial_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/multinomial_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/multinomial_test.js.map
new file mode 100644
index 0000000..5d54a66
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/multinomial_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"multinomial_test.js","sourceRoot":"","sources":["../../src/ops/multinomial_test.ts"],"names":[],"mappings":";;AAiBA,6BAA+B;AAC/B,gDAAkD;AAElD,0CAAyD;AAEzD,gCAAiB,CAAC,aAAa,EAAE,oBAAQ,EAAE;IACzC,IAAM,WAAW,GAAG,IAAI,CAAC;IAEzB,IAAM,OAAO,GAAG,IAAI,CAAC;IACrB,IAAM,IAAI,GAAG,IAAI,CAAC;IAElB,EAAE,CAAC,mCAAmC,EAAE;QACtC,IAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAClC,IAAM,MAAM,GAAG,EAAE,CAAC,WAAW,CAAC,KAAK,EAAE,WAAW,EAAE,IAAI,CAAC,CAAC;QACxD,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC;QACnC,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC;QAC5C,IAAM,YAAY,GAAG,YAAY,CAAC,MAAM,CAAC,QAAQ,EAAE,EAAE,CAAC,CAAC,CAAC;QACxD,6BAAiB,CAAC,YAAY,EAAE,CAAC,GAAG,EAAE,GAAG,CAAC,EAAE,OAAO,CAAC,CAAC;IACvD,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,0CAA0C,EAAE;QAC7C,IAAM,MAAM,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,CAAC;QACtC,IAAM,MAAM,GAAG,EAAE,CAAC,WAAW,CAAC,MAAM,EAAE,WAAW,EAAE,IAAI,CAAC,CAAC;QACzD,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC;QACnC,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC;QAC5C,IAAM,YAAY,GAAG,YAAY,CAAC,MAAM,CAAC,QAAQ,EAAE,EAAE,CAAC,CAAC,CAAC;QACxD,6BAAiB,CAAC,YAAY,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;IACnD,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,0CAA0C,EAAE;QAC7C,IAAM,MAAM,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,EAAE,CAAC,CAAC,CAAC,CAAC;QACtC,IAAM,MAAM,GAAG,EAAE,CAAC,WAAW,CAAC,MAAM,EAAE,WAAW,EAAE,IAAI,CAAC,CAAC;QACzD,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC;QACnC,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC;QAC5C,IAAM,YAAY,GAAG,YAAY,CAAC,MAAM,CAAC,QAAQ,EAAE,EAAE,CAAC,CAAC,CAAC;QACxD,6BAAiB,CAAC,YAAY,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;IACnD,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,uCAAuC,EAAE;QAC1C,IAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QAC/B,MAAM,CAAC,cAAM,OAAA,EAAE,CAAC,WAAW,CAAC,KAAK,EAAE,WAAW,EAAE,IAAI,CAAC,EAAxC,CAAwC,CAAC,CAAC,YAAY,EAAE,CAAC;IACxE,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,wCAAwC,EAAE;QAC3C,IAAM,WAAW,GAAG,EAAE,CAAC;QACvB,IAAM,MAAM,GAAG,EAAE,CAAC,IAAI,CAAC,CAAC,WAAW,CAAC,EAAE,CAAC,CAAC,CAAC,IAAI,EAAE,CAAC;QAChD,IAAM,MAAM,GAAG,EAAE,CAAC,WAAW,CAAC,MAAM,EAAE,WAAW,EAAE,IAAI,CAAC,CAAC;QACzD,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC;QACnC,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC;QAC5C,IAAM,YAAY,GAAG,YAAY,CAAC,MAAM,CAAC,QAAQ,EAAE,EAAE,WAAW,CAAC,CAAC;QAClE,MAAM,CAAC,YAAY,CAAC,MAAM,CAAC,CAAC,mBAAmB,CAAC,WAAW,CAAC,CAAC;IAC/D,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,oDAAoD,EAAE;QACvD,IAAM,WAAW,GAAG,CAAC,CAAC;QACtB,IAAM,MAAM,GAAG,EAAE,CAAC,QAAQ,CACtB,CAAC,CAAC,CAAC,GAAG,EAAE,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,GAAG,EAAE,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,GAAG,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,WAAW,CAAC,CAAC,CAAC;QAC3E,IAAM,MAAM,GAAG,EAAE,CAAC,WAAW,CAAC,MAAM,EAAE,WAAW,EAAE,IAAI,CAAC,CAAC;QACzD,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC;QACnC,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE,WAAW,CAAC,CAAC,CAAC;QAG/C,IAAI,YAAY,GACZ,YAAY,CAAC,MAAM,CAAC,QAAQ,EAAE,CAAC,KAAK,CAAC,CAAC,EAAE,WAAW,CAAC,EAAE,WAAW,CAAC,CAAC;QACvE,6BAAiB,CAAC,YAAY,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;QAGpD,YAAY,GAAG,YAAY,CACvB,MAAM,CAAC,QAAQ,EAAE,CAAC,KAAK,CAAC,WAAW,EAAE,CAAC,GAAG,WAAW,CAAC,EAAE,WAAW,CAAC,CAAC;QACxE,6BAAiB,CAAC,YAAY,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;QAGpD,YAAY;YACR,YAAY,CAAC,MAAM,CAAC,QAAQ,EAAE,CAAC,KAAK,CAAC,CAAC,GAAG,WAAW,CAAC,EAAE,WAAW,CAAC,CAAC;QACxE,6BAAiB,CAAC,YAAY,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;IACtD,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,+BAA+B,EAAE;QAClC,IAAM,KAAK,GAAG,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAClC,IAAM,UAAU,GAAG,IAAI,CAAC;QACxB,MAAM,CAAC,cAAM,OAAA,EAAE,CAAC,WAAW,CAAC,KAAiB,EAAE,CAAC,EAAE,IAAI,EAAE,UAAU,CAAC,EAAtD,CAAsD,CAAC;aAC/D,YAAY,EAAE,CAAC;IACtB,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,iCAAiC,EAAE;QAEpC,MAAM,CAAC,cAAM,OAAA,EAAE,CAAC,WAAW,CAAC,EAAS,EAAE,WAAW,EAAE,IAAI,CAAC,EAA5C,CAA4C,CAAC;aACrD,YAAY,CACT,4DAA4D,CAAC,CAAC;IACxE,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,uDAAuD,EAAE;QAC1D,IAAM,GAAG,GAAG,EAAE,CAAC,WAAW,CAAC,CAAC,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,WAAW,EAAE,IAAI,CAAC,CAAC;QACzD,MAAM,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC;QAChC,MAAM,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC;QACzC,IAAM,YAAY,GAAG,YAAY,CAAC,GAAG,CAAC,QAAQ,EAAE,EAAE,CAAC,CAAC,CAAC;QACrD,6BAAiB,CAAC,YAAY,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;IACnD,CAAC,CAAC,CAAC;IAEH,sBACI,MAA0C,EAAE,WAAmB;QACjE,IAAM,MAAM,GAAG,EAAE,CAAC;QAClB,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,WAAW,EAAE,EAAE,CAAC,EAAE;YACpC,MAAM,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC;SACf;QACD,IAAM,UAAU,GAAG,MAAM,CAAC,MAAM,CAAC;QACjC,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,MAAM,CAAC,MAAM,EAAE,EAAE,CAAC,EAAE;YACtC,MAAM,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC;SACrB;QAED,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,MAAM,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;YACtC,MAAM,CAAC,CAAC,CAAC,IAA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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/norm.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/norm.d.ts
new file mode 100644
index 0000000..9b3f1d9
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/norm.d.ts
@@ -0,0 +1,5 @@
+import { Tensor } from '../tensor';
+import { TensorLike } from '../types';
+declare function norm_(x: Tensor | TensorLike, ord?: number | 'euclidean' | 'fro', axis?: number | number[], keepDims?: boolean): Tensor;
+export declare const norm: typeof norm_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/norm.js b/node_modules/@tensorflow/tfjs-core/dist/ops/norm.js
new file mode 100644
index 0000000..230a84a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/norm.js
@@ -0,0 +1,62 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tensor_util_env_1 = require("../tensor_util_env");
+var axis_util = require("./axis_util");
+var operation_1 = require("./operation");
+var tensor_ops_1 = require("./tensor_ops");
+function norm_(x, ord, axis, keepDims) {
+    if (ord === void 0) { ord = 'euclidean'; }
+    if (axis === void 0) { axis = null; }
+    if (keepDims === void 0) { keepDims = false; }
+    x = tensor_util_env_1.convertToTensor(x, 'x', 'norm');
+    var norm = normImpl(x, ord, axis);
+    var keepDimsShape = norm.shape;
+    if (keepDims) {
+        var axes = axis_util.parseAxisParam(axis, x.shape);
+        keepDimsShape = axis_util.expandShapeToKeepDim(norm.shape, axes);
+    }
+    return norm.reshape(keepDimsShape);
+}
+function normImpl(x, p, axis) {
+    if (axis === void 0) { axis = null; }
+    if (x.rank === 0) {
+        return x.abs();
+    }
+    if (x.rank !== 1 && axis === null) {
+        return normImpl(x.reshape([-1]), p, axis);
+    }
+    if (x.rank === 1 || typeof axis === 'number' ||
+        axis instanceof Array && axis.length === 1) {
+        if (p === 1) {
+            return x.abs().sum(axis);
+        }
+        if (p === Infinity) {
+            return x.abs().max(axis);
+        }
+        if (p === -Infinity) {
+            return x.abs().min(axis);
+        }
+        if (p === 'euclidean' || p === 2) {
+            return x.abs().pow(tensor_ops_1.scalar(2, 'int32')).sum(axis).sqrt();
+        }
+        throw new Error("Error in norm: invalid ord value: " + p);
+    }
+    if (axis instanceof Array && axis.length === 2) {
+        if (p === 1) {
+            return x.abs().sum(axis[0]).max(axis[1] - 1);
+        }
+        if (p === Infinity) {
+            return x.abs().sum(axis[1]).max(axis[0]);
+        }
+        if (p === -Infinity) {
+            return x.abs().sum(axis[1]).min(axis[0]);
+        }
+        if (p === 'fro' || p === 'euclidean') {
+            return x.square().sum(axis).sqrt();
+        }
+        throw new Error("Error in norm: invalid ord value: " + p);
+    }
+    throw new Error("Error in norm: invalid axis: " + axis);
+}
+exports.norm = operation_1.op({ norm_: norm_ });
+//# sourceMappingURL=norm.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/norm.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/norm.js.map
new file mode 100644
index 0000000..d9d23e7
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/norm.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"norm.js","sourceRoot":"","sources":["../../src/ops/norm.ts"],"names":[],"mappings":";;AAkBA,sDAAmD;AAEnD,uCAAyC;AACzC,yCAA+B;AAC/B,2CAAoC;AAuCpC,eACI,CAAoB,EAAE,GAA2C,EACjE,IAA4B,EAAE,QAAgB;IADxB,oBAAA,EAAA,iBAA2C;IACjE,qBAAA,EAAA,WAA4B;IAAE,yBAAA,EAAA,gBAAgB;IAChD,CAAC,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,MAAM,CAAC,CAAC;IAEpC,IAAM,IAAI,GAAG,QAAQ,CAAC,CAAC,EAAE,GAAG,EAAE,IAAI,CAAC,CAAC;IACpC,IAAI,aAAa,GAAG,IAAI,CAAC,KAAK,CAAC;IAC/B,IAAI,QAAQ,EAAE;QACZ,IAAM,IAAI,GAAG,SAAS,CAAC,cAAc,CAAC,IAAI,EAAE,CAAC,CAAC,KAAK,CAAC,CAAC;QACrD,aAAa,GAAG,SAAS,CAAC,oBAAoB,CAAC,IAAI,CAAC,KAAK,EAAE,IAAI,CAAC,CAAC;KAClE;IACD,OAAO,IAAI,CAAC,OAAO,CAAC,aAAa,CAAC,CAAC;AACrC,CAAC;AAED,kBACI,CAAS,EAAE,CAAgB,EAAE,IAA4B;IAA5B,qBAAA,EAAA,WAA4B;IAC3D,IAAI,CAAC,CAAC,IAAI,KAAK,CAAC,EAAE;QAChB,OAAO,CAAC,CAAC,GAAG,EAAE,CAAC;KAChB;IAGD,IAAI,CAAC,CAAC,IAAI,KAAK,CAAC,IAAI,IAAI,KAAK,IAAI,EAAE;QACjC,OAAO,QAAQ,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,IAAI,CAAC,CAAC;KAC3C;IAGD,IAAI,CAAC,CAAC,IAAI,KAAK,CAAC,IAAI,OAAO,IAAI,KAAK,QAAQ;QACxC,IAAI,YAAY,KAAK,IAAI,IAAI,CAAC,MAAM,KAAK,CAAC,EAAE;QAC9C,IAAI,CAAC,KAAK,CAAC,EAAE;YACX,OAAO,CAAC,CAAC,GAAG,EAAE,CAAC,GAAG,CAAC,IAAI,CAAC,CAAC;SAC1B;QACD,IAAI,CAAC,KAAK,QAAQ,EAAE;YAClB,OAAO,CAAC,CAAC,GAAG,EAAE,CAAC,GAAG,CAAC,IAAI,CAAC,CAAC;SAC1B;QACD,IAAI,CAAC,KAAK,CAAC,QAAQ,EAAE;YACnB,OAAO,CAAC,CAAC,GAAG,EAAE,CAAC,GAAG,CAAC,IAAI,CAAC,CAAC;SAC1B;QACD,IAAI,CAAC,KAAK,WAAW,IAAI,CAAC,KAAK,CAAC,EAAE;YAEhC,OAAO,CAAC,CAAC,GAAG,EAAE,CAAC,GAAG,CAAC,mBAAM,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC,CAAC,GAAG,CAAC,IAAI,CAAC,CAAC,IAAI,EAAY,CAAC;SACnE;QAED,MAAM,IAAI,KAAK,CAAC,uCAAqC,CAAG,CAAC,CAAC;KAC3D;IAGD,IAAI,IAAI,YAAY,KAAK,IAAI,IAAI,CAAC,MAAM,KAAK,CAAC,EAAE;QAC9C,IAAI,CAAC,KAAK,CAAC,EAAE;YACX,OAAO,CAAC,CAAC,GAAG,EAAE,CAAC,GAAG,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,IAAI,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC;SAC9C;QACD,IAAI,CAAC,KAAK,QAAQ,EAAE;YAClB,OAAO,CAAC,CAAC,GAAG,EAAE,CAAC,GAAG,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC;SAC1C;QACD,IAAI,CAAC,KAAK,CAAC,QAAQ,EAAE;YACnB,OAAO,CAAC,CAAC,GAAG,EAAE,CAAC,GAAG,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC;SAC1C;QACD,IAAI,CAAC,KAAK,KAAK,IAAI,CAAC,KAAK,WAAW,EAAE;YAEpC,OAAO,CAAC,CAAC,MAAM,EAAE,CAAC,GAAG,CAAC,IAAI,CAAC,CAAC,IAAI,EAAE,CAAC;SACpC;QAED,MAAM,IAAI,KAAK,CAAC,uCAAqC,CAAG,CAAC,CAAC;KAC3D;IAED,MAAM,IAAI,KAAK,CAAC,kCAAgC,IAAM,CAAC,CAAC;AAC1D,CAAC;AAEY,QAAA,IAAI,GAAG,cAAE,CAAC,EAAC,KAAK,OAAA,EAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/operation.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/operation.d.ts
new file mode 100644
index 0000000..488bdf5
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/operation.d.ts
@@ -0,0 +1,3 @@
+export declare function op<T extends Function>(f: {
+    [name: string]: T;
+}): T;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/operation.js b/node_modules/@tensorflow/tfjs-core/dist/ops/operation.js
new file mode 100644
index 0000000..67d3472
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/operation.js
@@ -0,0 +1,39 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+function op(f) {
+    var keys = Object.keys(f);
+    if (keys.length !== 1) {
+        throw new Error("Please provide an object with a single key " +
+            "(operation name) mapping to a function. Got an object with " +
+            (keys.length + " keys."));
+    }
+    var opName = keys[0];
+    var fn = f[opName];
+    if (opName.endsWith('_')) {
+        opName = opName.substring(0, opName.length - 1);
+    }
+    var f2 = function () {
+        var args = [];
+        for (var _i = 0; _i < arguments.length; _i++) {
+            args[_i] = arguments[_i];
+        }
+        environment_1.ENV.engine.startScope(opName);
+        try {
+            var result = fn.apply(void 0, args);
+            if (result instanceof Promise) {
+                console.error('Cannot return a Promise inside of tidy.');
+            }
+            environment_1.ENV.engine.endScope(result);
+            return result;
+        }
+        catch (ex) {
+            environment_1.ENV.engine.endScope(null);
+            throw ex;
+        }
+    };
+    Object.defineProperty(f2, 'name', { value: opName, configurable: true });
+    return f2;
+}
+exports.op = op;
+//# sourceMappingURL=operation.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/operation.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/operation.js.map
new file mode 100644
index 0000000..0e020d6
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/operation.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"operation.js","sourceRoot":"","sources":["../../src/ops/operation.ts"],"names":[],"mappings":";;AAiBA,8CAAmC;AAOnC,YAAuC,CAAsB;IAC3D,IAAM,IAAI,GAAG,MAAM,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;IAC5B,IAAI,IAAI,CAAC,MAAM,KAAK,CAAC,EAAE;QACrB,MAAM,IAAI,KAAK,CACX,6CAA6C;YAC7C,6DAA6D;aAC1D,IAAI,CAAC,MAAM,WAAQ,CAAA,CAAC,CAAC;KAC7B;IAED,IAAI,MAAM,GAAG,IAAI,CAAC,CAAC,CAAC,CAAC;IACrB,IAAM,EAAE,GAAG,CAAC,CAAC,MAAM,CAAC,CAAC;IAGrB,IAAI,MAAM,CAAC,QAAQ,CAAC,GAAG,CAAC,EAAE;QACxB,MAAM,GAAG,MAAM,CAAC,SAAS,CAAC,CAAC,EAAE,MAAM,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC;KACjD;IAGD,IAAM,EAAE,GAAG;QAAC,cAAc;aAAd,UAAc,EAAd,qBAAc,EAAd,IAAc;YAAd,yBAAc;;QACxB,iBAAG,CAAC,MAAM,CAAC,UAAU,CAAC,MAAM,CAAC,CAAC;QAC9B,IAAI;YACF,IAAM,MAAM,GAAG,EAAE,eAAI,IAAI,CAAC,CAAC;YAC3B,IAAI,MAAM,YAAY,OAAO,EAAE;gBAC7B,OAAO,CAAC,KAAK,CAAC,yCAAyC,CAAC,CAAC;aAC1D;YACD,iBAAG,CAAC,MAAM,CAAC,QAAQ,CAAC,MAAM,CAAC,CAAC;YAC5B,OAAO,MAAM,CAAC;SACf;QAAC,OAAO,EAAE,EAAE;YACX,iBAAG,CAAC,MAAM,CAAC,QAAQ,CAAC,IAAI,CAAC,CAAC;YAC1B,MAAM,EAAE,CAAC;SACV;IACH,CAAC,CAAC;IACF,MAAM,CAAC,cAAc,CAAC,EAAE,EAAE,MAAM,EAAE,EAAC,KAAK,EAAE,MAAM,EAAE,YAAY,EAAE,IAAI,EAAC,CAAC,CAAC;IAGvE,OAAO,EAAc,CAAC;AACxB,CAAC;AApCD,gBAoCC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/operation_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/operation_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/operation_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/operation_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/operation_test.js
new file mode 100644
index 0000000..95f9f30
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/operation_test.js
@@ -0,0 +1,25 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+var operation_1 = require("./operation");
+jasmine_util_1.describeWithFlags('operation', test_util_1.ALL_ENVS, function () {
+    it('executes and preserves function name', function () {
+        var f = function () { return 2; };
+        var opfn = operation_1.op({ 'opName': f });
+        expect(opfn.name).toBe('opName');
+        expect(opfn()).toBe(2);
+    });
+    it('executes, preserves function name, strips underscore', function () {
+        var f = function () { return 2; };
+        var opfn = operation_1.op({ 'opName_': f });
+        expect(opfn.name).toBe('opName');
+        expect(opfn()).toBe(2);
+    });
+    it('throws when passing an object with multiple keys', function () {
+        var f = function () { return 2; };
+        expect(function () { return operation_1.op({ 'opName_': f, 'opName2_': f }); })
+            .toThrowError(/Please provide an object with a single key/);
+    });
+});
+//# sourceMappingURL=operation_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/operation_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/operation_test.js.map
new file mode 100644
index 0000000..34a26af
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/operation_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"operation_test.js","sourceRoot":"","sources":["../../src/ops/operation_test.ts"],"names":[],"mappings":";;AAgBA,gDAAkD;AAClD,0CAAsC;AACtC,yCAA+B;AAE/B,gCAAiB,CAAC,WAAW,EAAE,oBAAQ,EAAE;IACvC,EAAE,CAAC,sCAAsC,EAAE;QACzC,IAAM,CAAC,GAAG,cAAM,OAAA,CAAC,EAAD,CAAC,CAAC;QAClB,IAAM,IAAI,GAAG,cAAE,CAAC,EAAC,QAAQ,EAAE,CAAC,EAAC,CAAC,CAAC;QAE/B,MAAM,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC;QACjC,MAAM,CAAC,IAAI,EAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;IACzB,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,sDAAsD,EAAE;QACzD,IAAM,CAAC,GAAG,cAAM,OAAA,CAAC,EAAD,CAAC,CAAC;QAClB,IAAM,IAAI,GAAG,cAAE,CAAC,EAAC,SAAS,EAAE,CAAC,EAAC,CAAC,CAAC;QAEhC,MAAM,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC;QACjC,MAAM,CAAC,IAAI,EAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;IACzB,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,kDAAkD,EAAE;QACrD,IAAM,CAAC,GAAG,cAAM,OAAA,CAAC,EAAD,CAAC,CAAC;QAClB,MAAM,CAAC,cAAM,OAAA,cAAE,CAAC,EAAC,SAAS,EAAE,CAAC,EAAE,UAAU,EAAE,CAAC,EAAC,CAAC,EAAjC,CAAiC,CAAC;aAC1C,YAAY,CAAC,4CAA4C,CAAC,CAAC;IAClE,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/ops.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/ops.d.ts
new file mode 100644
index 0000000..af1f491
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/ops.d.ts
@@ -0,0 +1,35 @@
+export * from './batchnorm';
+export * from './complex_ops';
+export * from './concat_split';
+export * from './conv';
+export * from './matmul';
+export * from './reverse';
+export * from './pool';
+export * from './slice';
+export * from './unary_ops';
+export * from './reduction_ops';
+export * from './compare';
+export * from './binary_ops';
+export * from './relu_ops';
+export * from './logical_ops';
+export * from './array_ops';
+export * from './tensor_ops';
+export * from './transpose';
+export * from './softmax';
+export * from './lrn';
+export * from './norm';
+export * from './segment_ops';
+export * from './lstm';
+export * from './moving_average';
+export * from './strided_slice';
+export * from './topk';
+export * from './scatter_nd';
+export * from './spectral_ops';
+export * from './sparse_to_dense';
+export * from './gather_nd';
+export { op } from './operation';
+import * as losses from './loss_ops';
+import * as linalg from './linalg_ops';
+import * as image from './image_ops';
+import * as spectral from './spectral_ops';
+export { image, linalg, losses, spectral };
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/ops.js b/node_modules/@tensorflow/tfjs-core/dist/ops/ops.js
new file mode 100644
index 0000000..5e26fe5
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/ops.js
@@ -0,0 +1,45 @@
+"use strict";
+function __export(m) {
+    for (var p in m) if (!exports.hasOwnProperty(p)) exports[p] = m[p];
+}
+Object.defineProperty(exports, "__esModule", { value: true });
+__export(require("./batchnorm"));
+__export(require("./complex_ops"));
+__export(require("./concat_split"));
+__export(require("./conv"));
+__export(require("./matmul"));
+__export(require("./reverse"));
+__export(require("./pool"));
+__export(require("./slice"));
+__export(require("./unary_ops"));
+__export(require("./reduction_ops"));
+__export(require("./compare"));
+__export(require("./binary_ops"));
+__export(require("./relu_ops"));
+__export(require("./logical_ops"));
+__export(require("./array_ops"));
+__export(require("./tensor_ops"));
+__export(require("./transpose"));
+__export(require("./softmax"));
+__export(require("./lrn"));
+__export(require("./norm"));
+__export(require("./segment_ops"));
+__export(require("./lstm"));
+__export(require("./moving_average"));
+__export(require("./strided_slice"));
+__export(require("./topk"));
+__export(require("./scatter_nd"));
+__export(require("./spectral_ops"));
+__export(require("./sparse_to_dense"));
+__export(require("./gather_nd"));
+var operation_1 = require("./operation");
+exports.op = operation_1.op;
+var losses = require("./loss_ops");
+exports.losses = losses;
+var linalg = require("./linalg_ops");
+exports.linalg = linalg;
+var image = require("./image_ops");
+exports.image = image;
+var spectral = require("./spectral_ops");
+exports.spectral = spectral;
+//# sourceMappingURL=ops.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/ops.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/ops.js.map
new file mode 100644
index 0000000..209c9ac
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/ops.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"ops.js","sourceRoot":"","sources":["../../src/ops/ops.ts"],"names":[],"mappings":";;;;;AAiBA,iCAA4B;AAC5B,mCAA8B;AAC9B,oCAA+B;AAC/B,4BAAuB;AACvB,8BAAyB;AACzB,+BAA0B;AAC1B,4BAAuB;AACvB,6BAAwB;AACxB,iCAA4B;AAC5B,qCAAgC;AAChC,+BAA0B;AAC1B,kCAA6B;AAC7B,gCAA2B;AAC3B,mCAA8B;AAC9B,iCAA4B;AAC5B,kCAA6B;AAC7B,iCAA4B;AAC5B,+BAA0B;AAC1B,2BAAsB;AACtB,4BAAuB;AACvB,mCAA8B;AAC9B,4BAAuB;AACvB,sCAAiC;AACjC,qCAAgC;AAChC,4BAAuB;AACvB,kCAA6B;AAC7B,oCAA+B;AAC/B,uCAAkC;AAClC,iCAA4B;AAE5B,yCAA+B;AAAvB,yBAAA,EAAE,CAAA;AAGV,mCAAqC;AAKd,wBAAM;AAJ7B,qCAAuC;AAIxB,wBAAM;AAHrB,mCAAqC;AAG7B,sBAAK;AAFb,yCAA2C;AAEZ,4BAAQ"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/pad_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/pad_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/pad_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/pad_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/pad_test.js
new file mode 100644
index 0000000..8e5a46d
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/pad_test.js
@@ -0,0 +1,153 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('pad1d', test_util_1.ALL_ENVS, function () {
+    it('Should pad 1D arrays', function () {
+        var a = tf.tensor1d([1, 2, 3, 4, 5, 6], 'int32');
+        var b = tf.pad1d(a, [2, 3]);
+        test_util_1.expectArraysClose(b, [0, 0, 1, 2, 3, 4, 5, 6, 0, 0, 0]);
+    });
+    it('Should not pad 1D arrays with 0s', function () {
+        var a = tf.tensor1d([1, 2, 3, 4], 'int32');
+        var b = tf.pad1d(a, [0, 0]);
+        test_util_1.expectArraysClose(b, [1, 2, 3, 4]);
+    });
+    it('Should handle padding with custom value', function () {
+        var a = tf.tensor1d([1, 2, 3, 4], 'int32');
+        var b = tf.pad1d(a, [2, 3], 9);
+        test_util_1.expectArraysClose(b, [9, 9, 1, 2, 3, 4, 9, 9, 9]);
+        a = tf.tensor1d([1, 2, 3, 4]);
+        b = tf.pad1d(a, [2, 1], 1.1);
+        test_util_1.expectArraysClose(b, [1.1, 1.1, 1, 2, 3, 4, 1.1]);
+        a = tf.tensor1d([1, 2, 3, 4]);
+        b = tf.pad1d(a, [2, 1], 1);
+        test_util_1.expectArraysClose(b, [1, 1, 1, 2, 3, 4, 1]);
+    });
+    it('Should handle NaNs with 1D arrays', function () {
+        var a = tf.tensor1d([1, NaN, 2, NaN]);
+        var b = tf.pad1d(a, [1, 1]);
+        test_util_1.expectArraysClose(b, [0, 1, NaN, 2, NaN, 0]);
+    });
+    it('Should handle invalid paddings', function () {
+        var a = tf.tensor1d([1, 2, 3, 4], 'int32');
+        var f = function () {
+            tf.pad1d(a, [2, 2, 2]);
+        };
+        expect(f).toThrowError();
+    });
+    it('grad', function () {
+        var a = tf.tensor1d([1, 2, 3]);
+        var dy = tf.tensor1d([10, 20, 30, 40, 50, 60]);
+        var da = tf.grad(function (a) { return tf.pad1d(a, [2, 1]); })(a, dy);
+        expect(da.shape).toEqual([3]);
+        test_util_1.expectArraysClose(da, [30, 40, 50]);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [1, 2, 3, 4, 5, 6];
+        var b = tf.pad1d(a, [2, 3]);
+        test_util_1.expectArraysClose(b, [0, 0, 1, 2, 3, 4, 5, 6, 0, 0, 0]);
+    });
+});
+jasmine_util_1.describeWithFlags('pad2d', test_util_1.ALL_ENVS, function () {
+    it('Should pad 2D arrays', function () {
+        var a = tf.tensor2d([[1], [2]], [2, 1], 'int32');
+        var b = tf.pad2d(a, [[1, 1], [1, 1]]);
+        test_util_1.expectArraysClose(b, [0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0]);
+        a = tf.tensor2d([[1, 2, 3], [4, 5, 6]], [2, 3], 'int32');
+        b = tf.pad2d(a, [[2, 2], [1, 1]]);
+        test_util_1.expectArraysClose(b, [
+            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 0,
+            0, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
+        ]);
+    });
+    it('Should not pad 2D arrays with 0s', function () {
+        var a = tf.tensor2d([[1, 2, 3], [4, 5, 6]], [2, 3], 'int32');
+        var b = tf.pad2d(a, [[0, 0], [0, 0]]);
+        test_util_1.expectArraysClose(b, [1, 2, 3, 4, 5, 6]);
+    });
+    it('Should handle padding with custom value', function () {
+        var a = tf.tensor2d([[1, 2, 3], [4, 5, 6]], [2, 3], 'int32');
+        var b = tf.pad2d(a, [[1, 1], [1, 1]], 10);
+        test_util_1.expectArraysClose(b, [
+            10, 10, 10, 10, 10, 10, 1, 2, 3, 10,
+            10, 4, 5, 6, 10, 10, 10, 10, 10, 10
+        ]);
+        a = tf.tensor2d([[1], [1]], [2, 1]);
+        b = tf.pad2d(a, [[1, 1], [1, 1]], -2.1);
+        test_util_1.expectArraysClose(b, [-2.1, -2.1, -2.1, -2.1, 1, -2.1, -2.1, 1, -2.1, -2.1, -2.1, -2.1]);
+        a = tf.tensor2d([[1], [1]], [2, 1]);
+        b = tf.pad2d(a, [[1, 1], [1, 1]], -2);
+        test_util_1.expectArraysClose(b, [-2, -2, -2, -2, 1, -2, -2, 1, -2, -2, -2, -2]);
+    });
+    it('Should handle NaNs with 2D arrays', function () {
+        var a = tf.tensor2d([[1, NaN], [1, NaN]], [2, 2]);
+        var b = tf.pad2d(a, [[1, 1], [1, 1]]);
+        test_util_1.expectArraysClose(b, [0, 0, 0, 0, 0, 1, NaN, 0, 0, 1, NaN, 0, 0, 0, 0, 0]);
+    });
+    it('Should handle invalid paddings', function () {
+        var a = tf.tensor2d([[1], [2]], [2, 1], 'int32');
+        var f = function () {
+            tf.pad2d(a, [[2, 2, 2], [1, 1, 1]]);
+        };
+        expect(f).toThrowError();
+    });
+    it('grad', function () {
+        var a = tf.tensor2d([[1, 2], [3, 4]]);
+        var dy = tf.tensor2d([[0, 0, 0], [10, 20, 0], [30, 40, 0]], [3, 3]);
+        var da = tf.grad(function (a) { return tf.pad2d(a, [[1, 0], [0, 1]]); })(a, dy);
+        expect(da.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(da, [10, 20, 30, 40]);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [[1, 2, 3], [4, 5, 6]];
+        var b = tf.pad2d(a, [[0, 0], [0, 0]]);
+        test_util_1.expectArraysClose(b, [1, 2, 3, 4, 5, 6]);
+    });
+});
+jasmine_util_1.describeWithFlags('pad4d', test_util_1.ALL_ENVS, function () {
+    it('Should pad 4D arrays', function () {
+        var a = tf.tensor4d([[[[9]]]], [1, 1, 1, 1], 'int32');
+        var b = tf.pad4d(a, [[0, 0], [1, 1], [1, 1], [0, 0]]);
+        var expected = tf.tensor4d([[[[0], [0], [0]], [[0], [9], [0]], [[0], [0], [0]]]], [1, 3, 3, 1], 'int32');
+        test_util_1.expectArraysClose(b, expected);
+    });
+    it('does not leak memory', function () {
+        var a = tf.tensor4d([[[[9]]]], [1, 1, 1, 1], 'int32');
+        tf.pad4d(a, [[0, 0], [1, 1], [1, 1], [0, 0]]);
+        var numTensors = environment_1.Environment.memory().numTensors;
+        tf.pad4d(a, [[0, 0], [1, 1], [1, 1], [0, 0]]);
+        expect(environment_1.Environment.memory().numTensors).toEqual(numTensors + 1);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [[[[9]]]];
+        var b = tf.pad4d(a, [[0, 0], [1, 1], [1, 1], [0, 0]]);
+        var expected = tf.tensor4d([[[[0], [0], [0]], [[0], [9], [0]], [[0], [0], [0]]]], [1, 3, 3, 1], 'float32');
+        test_util_1.expectArraysClose(b, expected);
+    });
+});
+jasmine_util_1.describeWithFlags('pad', test_util_1.ALL_ENVS, function () {
+    it('Pad tensor2d', function () {
+        var a = tf.tensor2d([[1], [2]], [2, 1], 'int32');
+        var b = tf.pad(a, [[1, 1], [1, 1]]);
+        test_util_1.expectArraysClose(b, [0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0]);
+        a = tf.tensor2d([[1, 2, 3], [4, 5, 6]], [2, 3], 'int32');
+        b = tf.pad(a, [[2, 2], [1, 1]]);
+        test_util_1.expectArraysClose(b, [
+            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 3, 0,
+            0, 4, 5, 6, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
+        ]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.pad({}, [[0, 0]]); })
+            .toThrowError(/Argument 'x' passed to 'pad' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var x = [[1], [2]];
+        var res = tf.pad(x, [[1, 1], [1, 1]]);
+        test_util_1.expectArraysClose(res, [0, 0, 0, 0, 1, 0, 0, 2, 0, 0, 0, 0]);
+    });
+});
+//# sourceMappingURL=pad_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/pad_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/pad_test.js.map
new file mode 100644
index 0000000..60b21fc
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/pad_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/pool.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/pool.d.ts
new file mode 100644
index 0000000..c4f50a3
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/pool.d.ts
@@ -0,0 +1,9 @@
+import { Tensor3D, Tensor4D } from '../tensor';
+import { TensorLike } from '../types';
+declare function maxPool_<T extends Tensor3D | Tensor4D>(x: T | TensorLike, filterSize: [number, number] | number, strides: [number, number] | number, pad: 'valid' | 'same' | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
+declare function avgPool_<T extends Tensor3D | Tensor4D>(x: T | TensorLike, filterSize: [number, number] | number, strides: [number, number] | number, pad: 'valid' | 'same' | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
+declare function pool_<T extends Tensor3D | Tensor4D>(input: T | TensorLike, windowShape: [number, number] | number, poolingType: 'avg' | 'max', pad: 'valid' | 'same' | number, dilations?: [number, number] | number, strides?: [number, number] | number): T;
+export declare const maxPool: typeof maxPool_;
+export declare const avgPool: typeof avgPool_;
+export declare const pool: typeof pool_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/pool.js b/node_modules/@tensorflow/tfjs-core/dist/ops/pool.js
new file mode 100644
index 0000000..6dd8bf2
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/pool.js
@@ -0,0 +1,193 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var tensor_util_env_1 = require("../tensor_util_env");
+var util = require("../util");
+var array_ops_1 = require("./array_ops");
+var conv_util = require("./conv_util");
+var operation_1 = require("./operation");
+function maxPoolImpl_(x, filterSize, strides, dilations, pad, dimRoundingMode) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'maxPool');
+    var x4D = $x;
+    var reshapedTo4D = false;
+    if ($x.rank === 3) {
+        reshapedTo4D = true;
+        x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
+    }
+    if (dilations == null) {
+        dilations = [1, 1];
+    }
+    util.assert(x4D.rank === 4, "Error in maxPool: input must be rank 4 but got rank " + x4D.rank + ".");
+    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), 'Error in maxPool: Either strides or dilations must be 1. ' +
+        ("Got strides " + strides + " and dilations '" + dilations + "'"));
+    if (dimRoundingMode != null) {
+        util.assert(util.isInt(pad), "Error in maxPool: pad must be an integer when using, " +
+            ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."));
+    }
+    var convInfo = conv_util.computePool2DInfo(x4D.shape, filterSize, strides, dilations, pad, dimRoundingMode);
+    var grad = function (dy, saved) {
+        var y4D = saved[0];
+        return {
+            x: function () { return maxPoolBackprop(dy, x4D, y4D, filterSize, strides, dilations, pad); }
+        };
+    };
+    var res = environment_1.ENV.engine.runKernel(function (backend, save) { return save(backend.maxPool(x4D, convInfo)); }, { x: x4D }, grad);
+    if (reshapedTo4D) {
+        return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
+    }
+    return res;
+}
+function maxPool_(x, filterSize, strides, pad, dimRoundingMode) {
+    return maxPoolImpl_(x, filterSize, strides, 1, pad, dimRoundingMode);
+}
+function avgPoolImpl_(x, filterSize, strides, dilations, pad, dimRoundingMode) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'avgPool', 'float32');
+    if (dilations == null) {
+        dilations = [1, 1];
+    }
+    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), 'Error in avgPool: Either strides or dilations must be 1. ' +
+        ("Got strides " + strides + " and dilations '" + dilations + "'"));
+    var x4D = $x;
+    var reshapedTo4D = false;
+    if ($x.rank === 3) {
+        reshapedTo4D = true;
+        x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
+    }
+    util.assert(x4D.rank === 4, "Error in avgPool: x must be rank 4 but got rank " + x4D.rank + ".");
+    if (dimRoundingMode != null) {
+        util.assert(util.isInt(pad), "Error in avgPool: pad must be an integer when using, " +
+            ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."));
+    }
+    var convInfo = conv_util.computePool2DInfo(x4D.shape, filterSize, strides, dilations, pad);
+    var grad = function (dy) {
+        return {
+            x: function () { return avgPoolBackprop(dy, x4D, filterSize, strides, dilations, pad); }
+        };
+    };
+    var res = environment_1.ENV.engine.runKernel(function (backend) { return backend.avgPool(x4D, convInfo); }, { x: x4D }, grad);
+    res = res.cast($x.dtype);
+    if (reshapedTo4D) {
+        return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
+    }
+    return res;
+}
+function avgPool_(x, filterSize, strides, pad, dimRoundingMode) {
+    return avgPoolImpl_(x, filterSize, strides, 1, pad, dimRoundingMode);
+}
+function pool_(input, windowShape, poolingType, pad, dilations, strides) {
+    if (dilations == null) {
+        dilations = [1, 1];
+    }
+    if (strides == null) {
+        strides = 1;
+    }
+    if (pad === 0) {
+        pad = 'valid';
+    }
+    var $x = tensor_util_env_1.convertToTensor(input, 'x', 'maxPool');
+    var x4D = $x;
+    var reshapedTo4D = false;
+    if ($x.rank === 3) {
+        reshapedTo4D = true;
+        x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
+    }
+    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), 'Error in pool: Either strides or dilations must be 1. ' +
+        ("Got strides " + strides + " and dilations '" + dilations + "'"));
+    var convInfo = conv_util.computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad);
+    var dilation = [convInfo.dilationHeight, convInfo.dilationWidth];
+    var basePadding;
+    if (pad === 'same') {
+        basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation);
+    }
+    else {
+        basePadding = [[0, 0], [0, 0]];
+    }
+    var isDilationOne = dilation[0] === 1 && dilation[1] === 1;
+    var _a = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding), adjustedPadding = _a[0], adjustedCrops = _a[1];
+    var convertedPad = isDilationOne ? pad : 'valid';
+    var convertedX = isDilationOne ? x4D : array_ops_1.spaceToBatchND(x4D, dilation, adjustedPadding);
+    var forwardOp = poolingType === 'avg' ?
+        function () { return avgPoolImpl_(convertedX, windowShape, strides, 1, convertedPad); } :
+        function () { return maxPoolImpl_(convertedX, windowShape, strides, 1, convertedPad); };
+    var y = forwardOp();
+    var res = isDilationOne ? y : array_ops_1.batchToSpaceND(y, dilation, adjustedCrops);
+    if (reshapedTo4D) {
+        return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
+    }
+    return res;
+}
+function maxPoolBackprop(dy, input, output, filterSize, strides, dilations, pad, dimRoundingMode) {
+    var $dy = tensor_util_env_1.convertToTensor(dy, 'dy', 'maxPoolBackprop');
+    var $input = tensor_util_env_1.convertToTensor(input, 'input', 'maxPoolBackprop');
+    var $output = tensor_util_env_1.convertToTensor(output, 'output', 'maxPoolBackprop');
+    util.assert($input.rank === $dy.rank, "Rank of input (" + $input.rank + ") does not match rank of dy (" + $dy.rank + ")");
+    if (dilations == null) {
+        dilations = [1, 1];
+    }
+    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), 'Error in maxPoolBackProp: Either strides or dilations must be 1. ' +
+        ("Got strides " + strides + " and dilations '" + dilations + "'"));
+    util.assert($dy.rank === 4, "Error in maxPoolBackprop: dy must be rank 4 but got rank " +
+        ($dy.rank + "."));
+    util.assert($input.rank === 4, "Error in maxPoolBackprop: input must be rank 4 but got rank " +
+        ($input.rank + "."));
+    if (dimRoundingMode != null) {
+        util.assert(util.isInt(pad), "Error in maxPoolBackprop: pad must be an integer when using, " +
+            ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."));
+    }
+    var convInfo = conv_util.computePool2DInfo($input.shape, filterSize, strides, dilations, pad, dimRoundingMode);
+    var res = environment_1.ENV.engine.runKernel(function (backend) { return backend.maxPoolBackprop($dy, $input, $output, convInfo); }, { $dy: $dy, $input: $input });
+    return res;
+}
+function avgPoolBackprop(dy, input, filterSize, strides, dilations, pad) {
+    var $dy = tensor_util_env_1.convertToTensor(dy, 'dy', 'avgPoolBackprop');
+    var $input = tensor_util_env_1.convertToTensor(input, 'input', 'avgPoolBackprop');
+    util.assert($input.rank === $dy.rank, "Rank of input (" + $input.rank + ") does not match rank of dy (" + $dy.rank + ")");
+    if (dilations == null) {
+        dilations = [1, 1];
+    }
+    util.assert(conv_util.eitherStridesOrDilationsAreOne(strides, dilations), 'Error in avgPoolBackprop: Either strides or dilations must be 1. ' +
+        ("Got strides " + strides + " and dilations '" + dilations + "'"));
+    var input4D = $input;
+    var dy4D = $dy;
+    var reshapedTo4D = false;
+    if ($input.rank === 3) {
+        reshapedTo4D = true;
+        input4D = $input.as4D(1, $input.shape[0], $input.shape[1], $input.shape[2]);
+        dy4D = $dy.as4D(1, $dy.shape[0], $dy.shape[1], $dy.shape[2]);
+    }
+    util.assert(dy4D.rank === 4, "Error in avgPoolBackprop: dy must be rank 4 but got rank " +
+        (dy4D.rank + "."));
+    util.assert(input4D.rank === 4, "Error in avgPoolBackprop: input must be rank 4 but got rank " +
+        (input4D.rank + "."));
+    var convInfo = conv_util.computePool2DInfo(input4D.shape, filterSize, strides, dilations, pad);
+    var res = environment_1.ENV.engine.runKernel(function (backend) { return backend.avgPoolBackprop(dy4D, input4D, convInfo); }, { dy4D: dy4D, input4D: input4D });
+    if (reshapedTo4D) {
+        return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
+    }
+    return res;
+}
+function requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {
+    var padStart = basePadding.map(function (b) { return b[0]; });
+    var origPadEnd = basePadding.map(function (b) { return b[1]; });
+    var fullInputShape = inputShape.concat(padStart, origPadEnd);
+    var padEndExtra = blockShape.map(function (b, i) { return (b - fullInputShape[i] % b) % b; });
+    var padEnd = origPadEnd.map(function (s, i) { return s + padEndExtra[i]; });
+    var paddings = blockShape.map(function (_, i) { return [padStart[i], padEnd[i]]; });
+    var crops = blockShape.map(function (_, i) { return [0, padEndExtra[i]]; });
+    return [paddings, crops];
+}
+function withSpaceToBatchBasePaddings(filterShape, dilation) {
+    var dilatedFilterShape = filterShape.map(function (s, i) {
+        return s + (s - 1) * (dilation[i] - 1);
+    });
+    var padExtraShape = dilatedFilterShape.map(function (s) { return s - 1; });
+    var padExtraStart = padExtraShape.map(function (s) { return Math.floor(s / 2); });
+    var padExtraEnd = padExtraShape.map(function (s, i) { return s - padExtraStart[i]; });
+    return padExtraShape.map(function (_, i) {
+        return [padExtraStart[i], padExtraEnd[i]];
+    });
+}
+exports.maxPool = operation_1.op({ maxPool_: maxPool_ });
+exports.avgPool = operation_1.op({ avgPool_: avgPool_ });
+exports.pool = operation_1.op({ pool_: pool_ });
+//# sourceMappingURL=pool.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/pool.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/pool.js.map
new file mode 100644
index 0000000..abfd12c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/pool.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/pool_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/pool_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/pool_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/pool_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/pool_test.js
new file mode 100644
index 0000000..23de58d
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/pool_test.js
@@ -0,0 +1,519 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('maxPool', test_util_1.ALL_ENVS, function () {
+    it('x=[1,1,1] f=[1,1] s=1 [0] => [0]', function () {
+        var x = tf.tensor3d([0], [1, 1, 1]);
+        var result = tf.maxPool(x, 1, 1, 0);
+        test_util_1.expectArraysClose(result, [0]);
+    });
+    it('x=[3,3,1] f=[2,2] s=1', function () {
+        var x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 9, 8], [3, 3, 1]);
+        var result = tf.maxPool(x, 2, 1, 0);
+        expect(result.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(result, [5, 6, 9, 9]);
+    });
+    it('x=[2,3,3,1] f=[2,2] s=1', function () {
+        var x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 9, 8, 1, 2, 3, 4, 5, 6, 7, 8, 9], [2, 3, 3, 1]);
+        var result = tf.maxPool(x, 2, 1, 0);
+        expect(result.shape).toEqual([2, 2, 2, 1]);
+        test_util_1.expectArraysClose(result, [5, 6, 9, 9, 5, 6, 8, 9]);
+    });
+    it('[x=[3,3,1] f=[2,2] s=1 ignores NaNs', function () {
+        var x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, NaN, 9], [3, 3, 1]);
+        var result = tf.maxPool(x, 2, 1, 0);
+        expect(result.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(result, [5, 6, 7, 9]);
+    });
+    it('x=[3,3,2] f=[2,2] s=1', function () {
+        var x = tf.tensor3d([1, 99, 2, 88, 3, 77, 4, 66, 5, 55, 6, 44, 7, 33, 9, 22, 8, 11], [3, 3, 2]);
+        var result = tf.maxPool(x, 2, 1, 0);
+        expect(result.shape).toEqual([2, 2, 2]);
+        test_util_1.expectArraysClose(result, [5, 99, 6, 88, 9, 66, 9, 55]);
+    });
+    it('x=[4,4,1] f=[2,2] s=2', function () {
+        var x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [4, 4, 1]);
+        var result = tf.maxPool(x, 2, 2, 0);
+        expect(result.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(result, [5, 7, 13, 15]);
+    });
+    it('x=[2,2,1] f=[2,2] s=1 p=same', function () {
+        var x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
+        var fSize = 2;
+        var strides = 1;
+        var result = tf.maxPool(x, fSize, strides, 'same');
+        expect(result.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(result, [4, 4, 4, 4]);
+    });
+    it('throws when x is not rank 3', function () {
+        var x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9], [3, 3]);
+        expect(function () { return tf.maxPool(x, 2, 1, 0); }).toThrowError();
+    });
+    it('throws when dimRoundingMode is set and pad is not a number', function () {
+        var x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
+        var pad = 'valid';
+        var dimRoundingMode = 'round';
+        expect(function () { return tf.maxPool(x, 2, 1, pad, dimRoundingMode); }).toThrowError();
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.maxPool({}, 2, 1, 'valid'); })
+            .toThrowError(/Argument 'x' passed to 'maxPool' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var x = [[[0]]];
+        var result = tf.maxPool(x, 1, 1, 0);
+        test_util_1.expectArraysClose(result, [0]);
+    });
+});
+jasmine_util_1.describeWithFlags('maxPoolBackprop', test_util_1.ALL_ENVS, function () {
+    it('gradients x=[3,3,1] f=[2,2] s=1 no dup max value, test #1', function () {
+        var dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
+        var x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9], [3, 3, 1]);
+        var expected = [0, 0, 0, 0, 1, 2, 0, 3, 4];
+        var dx = tf.grad(function (x) { return x.maxPool(2, 1, 0); })(x, dy);
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, expected);
+    });
+    it('gradients x=[3,3,1] f=[2,2] s=1 no dup max value, test #2', function () {
+        var dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
+        var x = tf.tensor3d([9, 5, 6, 6, 8, 4, 9, 5, 10], [3, 3, 1]);
+        var expected = [1, 0, 0, 0, 2, 0, 3, 0, 4];
+        var dx = tf.grad(function (x) { return x.maxPool(2, 1, 0); })(x, dy);
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, expected);
+    });
+    it('gradients x=[2,3,3,1] f=[2,2] s=1 no duplicate max value', function () {
+        var dy = tf.tensor4d([1, 2, 3, 4, 1, 2, 3, 4], [2, 2, 2, 1]);
+        var x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 9, 5, 6, 6, 8, 4, 9, 5, 10], [2, 3, 3, 1]);
+        var expected = [0, 0, 0, 0, 1, 2, 0, 3, 4, 1, 0, 0, 0, 2, 0, 3, 0, 4];
+        var dx = tf.grad(function (x) { return x.maxPool(2, 1, 0); })(x, dy);
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, expected);
+    });
+    it('gradient x=[3,3,1] f=[2,2] s=1 dup max value, test 1', function () {
+        var dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
+        var x = tf.tensor3d([0, 0, 0, 0, 5, 0, 0, 0, 0], [3, 3, 1]);
+        var expected = [0, 0, 0, 0, 10, 0, 0, 0, 0];
+        var dx = tf.grad(function (x) { return x.maxPool(2, 1, 0); })(x, dy);
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, expected);
+    });
+    it('gradient x=[3,3,1] f=[2,2] s=1 dup max value, test 2', function () {
+        var dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
+        var x = tf.tensor3d([1, 3, 2, 1, 2, 1, 1, 1, 5], [3, 3, 1]);
+        var expected = [0, 3, 0, 0, 3, 0, 0, 0, 4];
+        var dx = tf.grad(function (x) { return x.maxPool(2, 1, 0); })(x, dy);
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, expected);
+    });
+    it('gradient x=[2,3,3,1] f=[2,2] s=1 dup max value in 2nd input', function () {
+        var dy = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2, 1]);
+        var x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 9, 8], [2, 3, 3, 1]);
+        var expected = new Float32Array([0, 0, 0, 0, 1, 2, 0, 3, 4, 0, 0, 0, 0, 5, 6, 0, 15, 0]);
+        var dx = tf.grad(function (x) { return x.maxPool(2, 1, 0); })(x, dy);
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, expected);
+    });
+    it('gradient x=[4,4,1] f=[2,2] s=2 test #1', function () {
+        var dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
+        var x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [4, 4, 1]);
+        var expected = [0, 0, 0, 0, 0, 1, 0, 2, 0, 0, 0, 0, 0, 3, 0, 4];
+        var dx = tf.grad(function (x) { return x.maxPool(2, 2, 0); })(x, dy);
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, expected);
+    });
+    it('gradient x=[4,4,1] f=[2,2] s=2 test #2', function () {
+        var dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
+        var x = tf.tensor3d([1, 2, 2, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 1], [4, 4, 1]);
+        var expected = [0, 1, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 4, 0];
+        var dx = tf.grad(function (x) { return x.maxPool(2, 2, 0); })(x, dy);
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, expected);
+    });
+    it('gradient x=[5,5,1] f=[3,3] s=2 no duplicate max value', function () {
+        var dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
+        var x = tf.tensor3d([
+            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
+            13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24
+        ], [5, 5, 1]);
+        var expected = [
+            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
+            0, 2, 0, 0, 0, 0, 0, 0, 0, 3, 0, 4
+        ];
+        var dx = tf.grad(function (x) { return x.maxPool(3, 2, 0); })(x, dy);
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, expected);
+    });
+    it('gradient x=[5,5,1] f=[3,3] s=2 duplicate max value', function () {
+        var dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
+        var x = tf.tensor3d([
+            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 24,
+            13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 12
+        ], [5, 5, 1]);
+        var expected = [
+            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 10,
+            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
+        ];
+        var dx = tf.grad(function (x) { return x.maxPool(3, 2, 0); })(x, dy);
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, expected);
+    });
+    it('gradient x=[3,3,2] f=[2,2] s=1, no duplicate max value', function () {
+        var dy = tf.tensor3d([1, 44, 2, 33, 3, 22, 4, 11], [2, 2, 2]);
+        var x = tf.tensor3d([1, 99, 2, 55, 3, 66, 4, 66, 5, 88, 6, 44, 7, 99, 8, 55, 9, 100], [3, 3, 2]);
+        var expected = [0, 44, 0, 0, 0, 0, 0, 0, 1, 33, 2, 0, 0, 22, 3, 0, 4, 11];
+        var dx = tf.grad(function (x) { return x.maxPool(2, 1, 0); })(x, dy);
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, expected);
+    });
+    it('gradient x=[3,3,2] f=[2,2] s=1 duplicate max value', function () {
+        var dy = tf.tensor3d([1, 44, 2, 33, 3, 22, 4, 11], [2, 2, 2]);
+        var x = tf.tensor3d([0, 1, 0, 3, 0, 2, 0, 1, 5, 2, 0, 1, 0, 1, 0, 1, 0, 5], [3, 3, 2]);
+        var expected = new Float32Array([0, 0, 0, 77, 0, 0, 0, 0, 10, 22, 0, 0, 0, 0, 0, 0, 0, 11]);
+        var dx = tf.grad(function (x) { return x.maxPool(2, 1, 0); })(x, dy);
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, expected);
+    });
+    it('gradient x=[4,4,2] f=[2,2] s=1', function () {
+        var dy = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [2, 2, 2]);
+        var x = tf.tensor3d([
+            0, 1, 1, 2, 2, 2, 3, 1, 4, 1, 5, 1, 6, 1, 7, 1,
+            8, 1, 9, 1, 10, 1, 11, 1, 12, 1, 13, 2, 14, 2, 15, 1
+        ], [4, 4, 2]);
+        var expected = [
+            0, 0, 0, 11, 0, 22, 0, 0, 0, 0, 1, 0, 0, 0, 2, 0,
+            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 33, 0, 44, 4, 0
+        ];
+        var dx = tf.grad(function (x) { return x.maxPool(2, 2, 0); })(x, dy);
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, expected);
+    });
+    it('gradient x=5x5x2, f=3, s=2 no duplicate max value', function () {
+        var dy = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [2, 2, 2]);
+        var x = tf.tensor3d([
+            0, 0, 1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6, 7, 7, 8,
+            8, 9, 9, 10, 10, 11, 11, 12, 24, 13, 13, 14, 14, 15, 15, 16, 16,
+            17, 17, 18, 18, 19, 19, 20, 20, 21, 21, 22, 22, 23, 23, 24, 12
+        ], [5, 5, 2]);
+        var expected = [
+            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
+            0, 0, 0, 0, 0, 0, 0, 1, 110, 0, 0, 2, 0, 0, 0, 0, 0,
+            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 0, 4, 0
+        ];
+        var dx = tf.grad(function (x) { return x.maxPool(3, 2, 0); })(x, dy);
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, expected);
+    });
+});
+jasmine_util_1.describeWithFlags('avgPool', test_util_1.ALL_ENVS, function () {
+    it('x=[1,1,1] f=[1,1] s=1 [0] => [0]', function () {
+        var a = tf.tensor3d([0], [1, 1, 1]);
+        var result = tf.avgPool(a, 1, 1, 0);
+        test_util_1.expectArraysClose(result, [0]);
+    });
+    it('x=[3,3,1] f=[2,2] s=1', function () {
+        var a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 9, 8], [3, 3, 1]);
+        var result = tf.avgPool(a, 2, 1, 0);
+        expect(result.shape).toEqual([2, 2, 1]);
+        expect(result.dtype).toBe('float32');
+        test_util_1.expectArraysClose(result, [3, 4, 6.25, 7]);
+    });
+    it('input int32 throws error', function () {
+        var a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 9, 8], [3, 3, 1], 'int32');
+        expect(function () { return tf.avgPool(a, 2, 1, 0); }).toThrowError();
+    });
+    it('x=[2,3,3,1] f=[2,2], s=1', function () {
+        var a = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 9, 8, 1, 2, 3, 4, 5, 6, 7, 8, 9], [2, 3, 3, 1]);
+        var result = tf.avgPool(a, 2, 1, 0);
+        expect(result.shape).toEqual([2, 2, 2, 1]);
+        test_util_1.expectArraysClose(result, [3, 4, 6.25, 7, 3, 4, 6, 7]);
+    });
+    it('x=[3,3,1] f=[2,2] s=1 propagates NaNs', function () {
+        var a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, NaN, 8], [3, 3, 1]);
+        var result = tf.avgPool(a, 2, 1, 0);
+        expect(result.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(result, [3, 4, NaN, NaN]);
+    });
+    it('x=[3,3,2] f=[2,2] s=1', function () {
+        var a = tf.tensor3d([1, 99, 2, 88, 3, 77, 4, 66, 5, 55, 6, 44, 7, 33, 9, 22, 8, 11], [3, 3, 2]);
+        var result = tf.avgPool(a, 2, 1, 0);
+        expect(result.shape).toEqual([2, 2, 2]);
+        test_util_1.expectArraysClose(result, [3, 77, 4, 66, 6.25, 44, 7, 33]);
+    });
+    it('x=[4,4,1] f=[2,2] s=2', function () {
+        var a = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [4, 4, 1]);
+        var result = tf.avgPool(a, 2, 2, 0);
+        expect(result.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(result, [2.5, 4.5, 10.5, 12.5]);
+    });
+    it('x=[2,2,1] f=[2,2] s=1 p=same', function () {
+        var a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
+        var fSize = 2;
+        var strides = 1;
+        var result = tf.avgPool(a, fSize, strides, 'same');
+        expect(result.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(result, [2.5, 3, 3.5, 4]);
+    });
+    it('gradient x=[1,1,1] f=[1,1] s=1 [0] => [0]', function () {
+        var x = tf.tensor3d([0], [1, 1, 1]);
+        var dy = tf.tensor3d([0], [1, 1, 1]);
+        var dx = tf.grad(function (x) { return x.avgPool(1, 1, 0); })(x, dy);
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, [0]);
+    });
+    it('gradient x=[3,3,1] f=[2,2] s=1', function () {
+        var x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 9, 8], [3, 3, 1]);
+        var dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
+        var avgMultiplier = 1 / (2 * 2);
+        var dx = tf.grad(function (x) { return x.avgPool(2, 1, 0); })(x, dy);
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, [
+            1 * avgMultiplier, 3 * avgMultiplier, 2 * avgMultiplier,
+            4 * avgMultiplier, 10 * avgMultiplier, 6 * avgMultiplier,
+            3 * avgMultiplier, 7 * avgMultiplier, 4 * avgMultiplier
+        ]);
+    });
+    it('gradient x=[2,3,3,1] f=[2,2], s=1', function () {
+        var x = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 9, 8, 1, 2, 3, 4, 5, 6, 7, 8, 9], [2, 3, 3, 1]);
+        var dy = tf.tensor4d([1, 2, 3, 4, 1, 2, 3, 4], [2, 2, 2, 1]);
+        var avgMultiplier = 1 / (2 * 2);
+        var dx = tf.grad(function (x) { return x.avgPool(2, 1, 0); })(x, dy);
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, [
+            1 * avgMultiplier, 3 * avgMultiplier, 2 * avgMultiplier,
+            4 * avgMultiplier, 10 * avgMultiplier, 6 * avgMultiplier,
+            3 * avgMultiplier, 7 * avgMultiplier, 4 * avgMultiplier,
+            1 * avgMultiplier, 3 * avgMultiplier, 2 * avgMultiplier,
+            4 * avgMultiplier, 10 * avgMultiplier, 6 * avgMultiplier,
+            3 * avgMultiplier, 7 * avgMultiplier, 4 * avgMultiplier
+        ]);
+    });
+    it('throws when dimRoundingMode is set and pad is not a number', function () {
+        var x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
+        var pad = 'valid';
+        var dimRoundingMode = 'round';
+        expect(function () { return tf.avgPool(x, 2, 1, pad, dimRoundingMode); }).toThrowError();
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.avgPool({}, 2, 1, 'valid'); })
+            .toThrowError(/Argument 'x' passed to 'avgPool' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [[[0]]];
+        var result = tf.avgPool(a, 1, 1, 0);
+        test_util_1.expectArraysClose(result, [0]);
+    });
+});
+jasmine_util_1.describeWithFlags('pool', test_util_1.ALL_ENVS, function () {
+    it('max x=[1,1,1] f=[1,1] s=1 d=1 [0] => [0]', function () {
+        var x = tf.tensor3d([0], [1, 1, 1]);
+        var windowShape = 1;
+        var padding = 0;
+        var result = tf.pool(x, windowShape, 'max', padding);
+        test_util_1.expectArraysClose(result, [0]);
+    });
+    it('max x=[3,3,1] f=[2,2] s=1 d=1', function () {
+        var x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 9, 8], [3, 3, 1]);
+        var windowShape = 2;
+        var padding = 0;
+        var dilationRate = undefined;
+        var strides = undefined;
+        var result = tf.pool(x, windowShape, 'max', padding, dilationRate, strides);
+        expect(result.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(result, [5, 6, 9, 9]);
+    });
+    it('max x=[4,4,1] f=[2,2] s=2 d=1', function () {
+        var x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [4, 4, 1]);
+        var windowShape = 2;
+        var padding = 0;
+        var dilationRate = undefined;
+        var strides = 2;
+        var result = tf.pool(x, windowShape, 'max', padding, dilationRate, strides);
+        expect(result.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(result, [5, 7, 13, 15]);
+    });
+    it('max x=[2,2,1] f=[2,2] s=1 d=1 p=same', function () {
+        var x = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
+        var windowShape = 2;
+        var padding = 'same';
+        var dilationRate = undefined;
+        var strides = 1;
+        var result = tf.pool(x, windowShape, 'max', padding, dilationRate, strides);
+        expect(result.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(result, [4, 4, 4, 4]);
+    });
+    it('avg x=[1,1,1] f=[1,1] s=1 d=1 [0] => [0]', function () {
+        var a = tf.tensor3d([0], [1, 1, 1]);
+        var windowShape = 1;
+        var padding = 0;
+        var result = tf.pool(a, windowShape, 'avg', padding);
+        test_util_1.expectArraysClose(result, [0]);
+    });
+    it('avg x=[3,3,1] f=[2,2] s=1 d=1', function () {
+        var a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 9, 8], [3, 3, 1]);
+        var windowShape = 2;
+        var padding = 0;
+        var dilationRate = undefined;
+        var strides = undefined;
+        var result = tf.pool(a, windowShape, 'avg', padding, dilationRate, strides);
+        expect(result.shape).toEqual([2, 2, 1]);
+        expect(result.dtype).toBe('float32');
+        test_util_1.expectArraysClose(result, [3, 4, 6.25, 7]);
+    });
+    it('avg x=[4,4,1] f=[2,2] s=2 d=1', function () {
+        var a = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [4, 4, 1]);
+        var windowShape = 2;
+        var padding = 0;
+        var dilationRate = undefined;
+        var strides = 2;
+        var result = tf.pool(a, windowShape, 'avg', padding, dilationRate, strides);
+        expect(result.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(result, [2.5, 4.5, 10.5, 12.5]);
+    });
+    it('avg x=[2,2,1] f=[2,2] s=1 p=same', function () {
+        var a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
+        var windowShape = 2;
+        var padding = 'same';
+        var dilationRate = undefined;
+        var strides = 1;
+        var result = tf.pool(a, windowShape, 'avg', padding, dilationRate, strides);
+        expect(result.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(result, [2.5, 3, 3.5, 4]);
+    });
+    it('max x=[4,3,1] f=[2,2] s=1 d=2', function () {
+        var x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [4, 4, 1]);
+        var windowShape = 2;
+        var padding = 0;
+        var dilationRate = 2;
+        var strides = undefined;
+        var result = tf.pool(x, windowShape, 'max', padding, dilationRate, strides);
+        expect(result.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(result, [11, 12, 15, 16]);
+    });
+    it('max x=[2,4,4,1] f=[2,2] s=1 d=2', function () {
+        var x = tf.tensor4d([
+            1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
+            1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 12, 11, 13, 14, 16, 15
+        ], [2, 4, 4, 1]);
+        var windowShape = 2;
+        var padding = 0;
+        var dilationRate = 2;
+        var strides = undefined;
+        var result = tf.pool(x, windowShape, 'max', padding, dilationRate, strides);
+        expect(result.shape).toEqual([2, 2, 2, 1]);
+        test_util_1.expectArraysClose(result, [11, 12, 15, 16, 12, 11, 16, 15]);
+    });
+    it('avg x=[4,4,1] f=[2,2] s=1 d=2', function () {
+        var a = tf.tensor3d([1, 3, 2, 4, 6, 5, 8, 7, 9, 10, 12, 11, 16, 15, 14, 13], [4, 4, 1]);
+        var windowShape = 2;
+        var padding = 0;
+        var dilationRate = 2;
+        var strides = undefined;
+        var result = tf.pool(a, windowShape, 'avg', padding, dilationRate, strides);
+        expect(result.shape).toEqual([2, 2, 1]);
+        expect(result.dtype).toBe('float32');
+        test_util_1.expectArraysClose(result, [6, 7, 11, 10]);
+    });
+    it('max throws when neither s=1 nor d=1', function () {
+        var x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [4, 4, 1]);
+        var windowShape = 2;
+        var padding = 0;
+        var dilationRate = 2;
+        var strides = 2;
+        expect(function () { return tf.pool(x, windowShape, 'max', padding, dilationRate, strides); })
+            .toThrowError();
+    });
+    it('avg throws when neither s=1 nor d=1', function () {
+        var x = tf.tensor3d([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15], [4, 4, 1]);
+        var windowShape = 2;
+        var padding = 0;
+        var dilationRate = 2;
+        var strides = 2;
+        expect(function () { return tf.pool(x, windowShape, 'avg', padding, dilationRate, strides); })
+            .toThrowError();
+    });
+});
+jasmine_util_1.describeWithFlags('poolBackprop', test_util_1.ALL_ENVS, function () {
+    it('max gradients x=[3,3,1] f=[2,2] s=1 d=1 no dup max value', function () {
+        var dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
+        var x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9], [3, 3, 1]);
+        var expected = [0, 0, 0, 0, 1, 2, 0, 3, 4];
+        var windowShape = 2;
+        var padding = 0;
+        var dilationRate = undefined;
+        var strides = undefined;
+        var dx = tf.grad(function (x) {
+            return x.pool(windowShape, 'max', padding, dilationRate, strides);
+        })(x, dy);
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, expected);
+    });
+    it('max gradients x=[3,3,1] f=[2,2] s=1 d=2 no dup max value, test #1', function () {
+        var dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
+        var x = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16], [4, 4, 1]);
+        var expected = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0, 3, 4];
+        var windowShape = 2;
+        var padding = 0;
+        var dilationRate = 2;
+        var strides = undefined;
+        var dx = tf.grad(function (x) { return x.pool(windowShape, 'max', padding, dilationRate, strides); })(x, dy);
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, expected);
+    });
+    it('max gradients x=[3,3,1] f=[2,2] s=1 d=2 no dup max value, test #2', function () {
+        var dy = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
+        var x = tf.tensor3d([9, 5, 8, 6, 3, 1, 2, 4, 7, 3, 6, 4, 11, 15, 10, 16], [4, 4, 1]);
+        var expected = [1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 4];
+        var windowShape = 2;
+        var padding = 0;
+        var dilationRate = 2;
+        var strides = undefined;
+        var dx = tf.grad(function (x) { return x.pool(windowShape, 'max', padding, dilationRate, strides); })(x, dy);
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, expected);
+    });
+    it('max gradient x=[3,3,1] f=[2,2] s=1 d=2 dup max value', function () {
+        var dy = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9], [3, 3, 1]);
+        var x = tf.tensor3d([
+            0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 1,
+            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
+        ], [5, 5, 1]);
+        var expected = [
+            0, 0, 0, 0, 0, 0, 5, 10, 0, 0, 0, 10, 20,
+            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
+        ];
+        var windowShape = 2;
+        var padding = 0;
+        var dilationRate = 2;
+        var strides = undefined;
+        var dx = tf.grad(function (x) {
+            return x.pool(windowShape, 'max', padding, dilationRate, strides);
+        })(x, dy);
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, expected);
+    });
+    it('avg gradient x=[4,4,1] f=[2,2] s=1 d=2', function () {
+        var x = tf.tensor3d([
+            1, 3, 2, 4, 6, 5, 8, 7, 9, 10, 12, 11, 16,
+            15, 14, 13, 17, 18, 19, 20, 21, 22, 23, 24, 25
+        ], [5, 5, 1]);
+        var dy = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9], [3, 3, 1]);
+        var f = 1 / (2 * 2);
+        var windowShape = 2;
+        var padding = 0;
+        var dilationRate = 2;
+        var strides = undefined;
+        var dx = tf.grad(function (x) {
+            return x.pool(windowShape, 'avg', padding, dilationRate, strides);
+        })(x, dy);
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, [
+            1 * f, 2 * f, 4 * f, 2 * f, 3 * f, 4 * f, 5 * f, 10 * f, 5 * f,
+            6 * f, 8 * f, 10 * f, 20 * f, 10 * f, 12 * f, 4 * f, 5 * f, 10 * f,
+            5 * f, 6 * f, 7 * f, 8 * f, 16 * f, 8 * f, 9 * f
+        ]);
+    });
+});
+//# sourceMappingURL=pool_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/pool_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/pool_test.js.map
new file mode 100644
index 0000000..94b710f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/pool_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/rand.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/rand.d.ts
new file mode 100644
index 0000000..b5bad02
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/rand.d.ts
@@ -0,0 +1,21 @@
+export interface RandGauss {
+    nextValue(): number;
+}
+export interface RandNormalDataTypes {
+    float32: Float32Array;
+    int32: Int32Array;
+}
+export declare class MPRandGauss implements RandGauss {
+    private mean;
+    private stdDev;
+    private nextVal;
+    private dtype?;
+    private truncated?;
+    private upper?;
+    private lower?;
+    private random;
+    constructor(mean: number, stdDeviation: number, dtype?: keyof RandNormalDataTypes, truncated?: boolean, seed?: number);
+    nextValue(): number;
+    private convertValue;
+    private isValidTruncated;
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/rand.js b/node_modules/@tensorflow/tfjs-core/dist/ops/rand.js
new file mode 100644
index 0000000..defaee1
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/rand.js
@@ -0,0 +1,57 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var seedrandom = require("seedrandom");
+var MPRandGauss = (function () {
+    function MPRandGauss(mean, stdDeviation, dtype, truncated, seed) {
+        this.mean = mean;
+        this.stdDev = stdDeviation;
+        this.dtype = dtype;
+        this.nextVal = NaN;
+        this.truncated = truncated;
+        if (this.truncated) {
+            this.upper = this.mean + this.stdDev * 2;
+            this.lower = this.mean - this.stdDev * 2;
+        }
+        var seedValue = seed ? seed : Math.random();
+        this.random = seedrandom.alea(seedValue.toString());
+    }
+    MPRandGauss.prototype.nextValue = function () {
+        if (!isNaN(this.nextVal)) {
+            var value = this.nextVal;
+            this.nextVal = NaN;
+            return value;
+        }
+        var resultX, resultY;
+        var isValid = false;
+        while (!isValid) {
+            var v1 = void 0, v2 = void 0, s = void 0;
+            do {
+                v1 = 2 * this.random() - 1;
+                v2 = 2 * this.random() - 1;
+                s = v1 * v1 + v2 * v2;
+            } while (s >= 1 || s === 0);
+            var mul = Math.sqrt(-2.0 * Math.log(s) / s);
+            resultX = this.mean + this.stdDev * v1 * mul;
+            resultY = this.mean + this.stdDev * v2 * mul;
+            if (!this.truncated || this.isValidTruncated(resultX)) {
+                isValid = true;
+            }
+        }
+        if (!this.truncated || this.isValidTruncated(resultY)) {
+            this.nextVal = this.convertValue(resultY);
+        }
+        return this.convertValue(resultX);
+    };
+    MPRandGauss.prototype.convertValue = function (value) {
+        if (this.dtype == null || this.dtype === 'float32') {
+            return value;
+        }
+        return Math.round(value);
+    };
+    MPRandGauss.prototype.isValidTruncated = function (value) {
+        return value <= this.upper && value >= this.lower;
+    };
+    return MPRandGauss;
+}());
+exports.MPRandGauss = MPRandGauss;
+//# sourceMappingURL=rand.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/rand.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/rand.js.map
new file mode 100644
index 0000000..163c61e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/rand.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"rand.js","sourceRoot":"","sources":["../../src/ops/rand.ts"],"names":[],"mappings":";;AAiBA,uCAAyC;AAYzC;IAUE,qBACI,IAAY,EAAE,YAAoB,EAAE,KAAiC,EACrE,SAAmB,EAAE,IAAa;QACpC,IAAI,CAAC,IAAI,GAAG,IAAI,CAAC;QACjB,IAAI,CAAC,MAAM,GAAG,YAAY,CAAC;QAC3B,IAAI,CAAC,KAAK,GAAG,KAAK,CAAC;QACnB,IAAI,CAAC,OAAO,GAAG,GAAG,CAAC;QACnB,IAAI,CAAC,SAAS,GAAG,SAAS,CAAC;QAC3B,IAAI,IAAI,CAAC,SAAS,EAAE;YAClB,IAAI,CAAC,KAAK,GAAG,IAAI,CAAC,IAAI,GAAG,IAAI,CAAC,MAAM,GAAG,CAAC,CAAC;YACzC,IAAI,CAAC,KAAK,GAAG,IAAI,CAAC,IAAI,GAAG,IAAI,CAAC,MAAM,GAAG,CAAC,CAAC;SAC1C;QACD,IAAM,SAAS,GAAG,IAAI,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,IAAI,CAAC,MAAM,EAAE,CAAC;QAC9C,IAAI,CAAC,MAAM,GAAG,UAAU,CAAC,IAAI,CAAC,SAAS,CAAC,QAAQ,EAAE,CAAC,CAAC;IACtD,CAAC;IAGM,+BAAS,GAAhB;QACE,IAAI,CAAC,KAAK,CAAC,IAAI,CAAC,OAAO,CAAC,EAAE;YACxB,IAAM,KAAK,GAAG,IAAI,CAAC,OAAO,CAAC;YAC3B,IAAI,CAAC,OAAO,GAAG,GAAG,CAAC;YACnB,OAAO,KAAK,CAAC;SACd;QAED,IAAI,OAAe,EAAE,OAAe,CAAC;QACrC,IAAI,OAAO,GAAG,KAAK,CAAC;QACpB,OAAO,CAAC,OAAO,EAAE;YACf,IAAI,EAAE,SAAQ,EAAE,EAAE,SAAQ,EAAE,CAAC,SAAQ,CAAC;YACtC,GAAG;gBACD,EAAE,GAAG,CAAC,GAAG,IAAI,CAAC,MAAM,EAAE,GAAG,CAAC,CAAC;gBAC3B,EAAE,GAAG,CAAC,GAAG,IAAI,CAAC,MAAM,EAAE,GAAG,CAAC,CAAC;gBAC3B,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,CAAC;aACvB,QAAQ,CAAC,IAAI,CAAC,IAAI,CAAC,KAAK,CAAC,EAAE;YAE5B,IAAM,GAAG,GAAG,IAAI,CAAC,IAAI,CAAC,CAAC,GAAG,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC;YAC9C,OAAO,GAAG,IAAI,CAAC,IAAI,GAAG,IAAI,CAAC,MAAM,GAAG,EAAE,GAAG,GAAG,CAAC;YAC7C,OAAO,GAAG,IAAI,CAAC,IAAI,GAAG,IAAI,CAAC,MAAM,GAAG,EAAE,GAAG,GAAG,CAAC;YAE7C,IAAI,CAAC,IAAI,CAAC,SAAS,IAAI,IAAI,CAAC,gBAAgB,CAAC,OAAO,CAAC,EAAE;gBACrD,OAAO,GAAG,IAAI,CAAC;aAChB;SACF;QAED,IAAI,CAAC,IAAI,CAAC,SAAS,IAAI,IAAI,CAAC,gBAAgB,CAAC,OAAO,CAAC,EAAE;YACrD,IAAI,CAAC,OAAO,GAAG,IAAI,CAAC,YAAY,CAAC,OAAO,CAAC,CAAC;SAC3C;QACD,OAAO,IAAI,CAAC,YAAY,CAAC,OAAO,CAAC,CAAC;IACpC,CAAC;IAGO,kCAAY,GAApB,UAAqB,KAAa;QAChC,IAAI,IAAI,CAAC,KAAK,IAAI,IAAI,IAAI,IAAI,CAAC,KAAK,KAAK,SAAS,EAAE;YAClD,OAAO,KAAK,CAAC;SACd;QACD,OAAO,IAAI,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC;IAC3B,CAAC;IAGO,sCAAgB,GAAxB,UAAyB,KAAa;QACpC,OAAO,KAAK,IAAI,IAAI,CAAC,KAAK,IAAI,KAAK,IAAI,IAAI,CAAC,KAAK,CAAC;IACpD,CAAC;IACH,kBAAC;AAAD,CAAC,AAvED,IAuEC;AAvEY,kCAAW"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/rand_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/rand_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/rand_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/rand_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/rand_test.js
new file mode 100644
index 0000000..b08eb75
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/rand_test.js
@@ -0,0 +1,47 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var rand_1 = require("./rand");
+var rand_util_1 = require("./rand_util");
+function isFloat(n) {
+    return Number(n) === n && n % 1 !== 0;
+}
+describe('MPRandGauss', function () {
+    var EPSILON = 0.05;
+    var SEED = 2002;
+    it('should default to float32 numbers', function () {
+        var rand = new rand_1.MPRandGauss(0, 1.5);
+        expect(isFloat(rand.nextValue())).toBe(true);
+    });
+    it('should handle create a mean/stdv of float32 numbers', function () {
+        var rand = new rand_1.MPRandGauss(0, 1.5, 'float32', false, SEED);
+        var values = [];
+        var size = 10000;
+        for (var i = 0; i < size; i++) {
+            values.push(rand.nextValue());
+        }
+        rand_util_1.expectArrayInMeanStdRange(values, 0, 1.5, EPSILON);
+        rand_util_1.jarqueBeraNormalityTest(values);
+    });
+    it('should handle int32 numbers', function () {
+        var rand = new rand_1.MPRandGauss(0, 1, 'int32');
+        expect(isFloat(rand.nextValue())).toBe(false);
+    });
+    it('should handle create a mean/stdv of int32 numbers', function () {
+        var rand = new rand_1.MPRandGauss(0, 2, 'int32', false, SEED);
+        var values = [];
+        var size = 10000;
+        for (var i = 0; i < size; i++) {
+            values.push(rand.nextValue());
+        }
+        rand_util_1.expectArrayInMeanStdRange(values, 0, 2, EPSILON);
+        rand_util_1.jarqueBeraNormalityTest(values);
+    });
+    it('Should not have a more than 2x std-d from mean for truncated values', function () {
+        var stdv = 1.5;
+        var rand = new rand_1.MPRandGauss(0, stdv, 'float32', true);
+        for (var i = 0; i < 1000; i++) {
+            expect(Math.abs(rand.nextValue())).toBeLessThan(stdv * 2);
+        }
+    });
+});
+//# sourceMappingURL=rand_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/rand_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/rand_test.js.map
new file mode 100644
index 0000000..47539fb
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/rand_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"rand_test.js","sourceRoot":"","sources":["../../src/ops/rand_test.ts"],"names":[],"mappings":";;AAiBA,+BAAmC;AACnC,yCAA+E;AAE/E,iBAAiB,CAAS;IACxB,OAAO,MAAM,CAAC,CAAC,CAAC,KAAK,CAAC,IAAI,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC;AACxC,CAAC;AAED,QAAQ,CAAC,aAAa,EAAE;IACtB,IAAM,OAAO,GAAG,IAAI,CAAC;IACrB,IAAM,IAAI,GAAG,IAAI,CAAC;IAElB,EAAE,CAAC,mCAAmC,EAAE;QACtC,IAAM,IAAI,GAAG,IAAI,kBAAW,CAAC,CAAC,EAAE,GAAG,CAAC,CAAC;QACrC,MAAM,CAAC,OAAO,CAAC,IAAI,CAAC,SAAS,EAAE,CAAC,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;IAC/C,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,qDAAqD,EAAE;QACxD,IAAM,IAAI,GACN,IAAI,kBAAW,CAAC,CAAC,EAAE,GAAG,EAAE,SAAS,EAAE,KAAK,EAAkB,IAAI,CAAC,CAAC;QACpE,IAAM,MAAM,GAAG,EAAE,CAAC;QAClB,IAAM,IAAI,GAAG,KAAK,CAAC;QACnB,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,IAAI,EAAE,CAAC,EAAE,EAAE;YAC7B,MAAM,CAAC,IAAI,CAAC,IAAI,CAAC,SAAS,EAAE,CAAC,CAAC;SAC/B;QACD,qCAAyB,CAAC,MAAM,EAAE,CAAC,EAAE,GAAG,EAAE,OAAO,CAAC,CAAC;QACnD,mCAAuB,CAAC,MAAM,CAAC,CAAC;IAClC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,6BAA6B,EAAE;QAChC,IAAM,IAAI,GAAG,IAAI,kBAAW,CAAC,CAAC,EAAE,CAAC,EAAE,OAAO,CAAC,CAAC;QAC5C,MAAM,CAAC,OAAO,CAAC,IAAI,CAAC,SAAS,EAAE,CAAC,CAAC,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC;IAChD,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,mDAAmD,EAAE;QACtD,IAAM,IAAI,GAAG,IAAI,kBAAW,CAAC,CAAC,EAAE,CAAC,EAAE,OAAO,EAAE,KAAK,EAAkB,IAAI,CAAC,CAAC;QACzE,IAAM,MAAM,GAAG,EAAE,CAAC;QAClB,IAAM,IAAI,GAAG,KAAK,CAAC;QACnB,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,IAAI,EAAE,CAAC,EAAE,EAAE;YAC7B,MAAM,CAAC,IAAI,CAAC,IAAI,CAAC,SAAS,EAAE,CAAC,CAAC;SAC/B;QACD,qCAAyB,CAAC,MAAM,EAAE,CAAC,EAAE,CAAC,EAAE,OAAO,CAAC,CAAC;QACjD,mCAAuB,CAAC,MAAM,CAAC,CAAC;IAClC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,qEAAqE,EACrE;QACE,IAAM,IAAI,GAAG,GAAG,CAAC;QACjB,IAAM,IAAI,GAAG,IAAI,kBAAW,CAAC,CAAC,EAAE,IAAI,EAAE,SAAS,EAAE,IAAI,CAAiB,CAAC;QACvE,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,IAAI,EAAE,CAAC,EAAE,EAAE;YAC7B,MAAM,CAAC,IAAI,CAAC,GAAG,CAAC,IAAI,CAAC,SAAS,EAAE,CAAC,CAAC,CAAC,YAAY,CAAC,IAAI,GAAG,CAAC,CAAC,CAAC;SAC3D;IACH,CAAC,CAAC,CAAC;AACR,CAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/rand_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/rand_util.d.ts
new file mode 100644
index 0000000..9520f38
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/rand_util.d.ts
@@ -0,0 +1,4 @@
+import { Tensor } from '../tensor';
+import { TypedArray } from '../types';
+export declare function jarqueBeraNormalityTest(a: Tensor | TypedArray | number[]): void;
+export declare function expectArrayInMeanStdRange(actual: Tensor | TypedArray | number[], expectedMean: number, expectedStdDev: number, epsilon?: number): void;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/rand_util.js b/node_modules/@tensorflow/tfjs-core/dist/ops/rand_util.js
new file mode 100644
index 0000000..bc8315c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/rand_util.js
@@ -0,0 +1,79 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var tensor_1 = require("../tensor");
+var test_util_1 = require("../test_util");
+function jarqueBeraNormalityTest(a) {
+    var values;
+    if (a instanceof tensor_1.Tensor) {
+        values = a.dataSync();
+    }
+    else {
+        values = a;
+    }
+    var n = values.length;
+    var s = skewness(values);
+    var k = kurtosis(values);
+    var jb = n / 6 * (Math.pow(s, 2) + 0.25 * Math.pow(k - 3, 2));
+    var CHI_SQUARE_2DEG = 5.991;
+    if (jb > CHI_SQUARE_2DEG) {
+        throw new Error("Invalid p-value for JB: " + jb);
+    }
+}
+exports.jarqueBeraNormalityTest = jarqueBeraNormalityTest;
+function expectArrayInMeanStdRange(actual, expectedMean, expectedStdDev, epsilon) {
+    if (epsilon == null) {
+        epsilon = environment_1.ENV.get('TEST_EPSILON');
+    }
+    var actualValues;
+    if (actual instanceof tensor_1.Tensor) {
+        actualValues = actual.dataSync();
+    }
+    else {
+        actualValues = actual;
+    }
+    var actualMean = mean(actualValues);
+    test_util_1.expectNumbersClose(actualMean, expectedMean, epsilon);
+    test_util_1.expectNumbersClose(standardDeviation(actualValues, actualMean), expectedStdDev, epsilon);
+}
+exports.expectArrayInMeanStdRange = expectArrayInMeanStdRange;
+function mean(values) {
+    var sum = 0;
+    for (var i = 0; i < values.length; i++) {
+        sum += values[i];
+    }
+    return sum / values.length;
+}
+function standardDeviation(values, mean) {
+    var squareDiffSum = 0;
+    for (var i = 0; i < values.length; i++) {
+        var diff = values[i] - mean;
+        squareDiffSum += diff * diff;
+    }
+    return Math.sqrt(squareDiffSum / values.length);
+}
+function kurtosis(values) {
+    var valuesMean = mean(values);
+    var n = values.length;
+    var sum2 = 0;
+    var sum4 = 0;
+    for (var i = 0; i < n; i++) {
+        var v = values[i] - valuesMean;
+        sum2 += Math.pow(v, 2);
+        sum4 += Math.pow(v, 4);
+    }
+    return (1 / n) * sum4 / Math.pow((1 / n) * sum2, 2);
+}
+function skewness(values) {
+    var valuesMean = mean(values);
+    var n = values.length;
+    var sum2 = 0;
+    var sum3 = 0;
+    for (var i = 0; i < n; i++) {
+        var v = values[i] - valuesMean;
+        sum2 += Math.pow(v, 2);
+        sum3 += Math.pow(v, 3);
+    }
+    return (1 / n) * sum3 / Math.pow((1 / (n - 1)) * sum2, 3 / 2);
+}
+//# sourceMappingURL=rand_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/rand_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/rand_util.js.map
new file mode 100644
index 0000000..0ee864e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/rand_util.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"rand_util.js","sourceRoot":"","sources":["../../src/ops/rand_util.ts"],"names":[],"mappings":";;AAiBA,8CAAmC;AACnC,oCAAiC;AACjC,0CAAgD;AAGhD,iCAAwC,CAA6B;IACnE,IAAI,MAA2B,CAAC;IAChC,IAAI,CAAC,YAAY,eAAM,EAAE;QACvB,MAAM,GAAG,CAAC,CAAC,QAAQ,EAAE,CAAC;KACvB;SAAM;QACL,MAAM,GAAG,CAAC,CAAC;KACZ;IAED,IAAM,CAAC,GAAG,MAAM,CAAC,MAAM,CAAC;IACxB,IAAM,CAAC,GAAG,QAAQ,CAAC,MAAM,CAAC,CAAC;IAC3B,IAAM,CAAC,GAAG,QAAQ,CAAC,MAAM,CAAC,CAAC;IAC3B,IAAM,EAAE,GAAG,CAAC,GAAG,CAAC,GAAG,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,GAAG,IAAI,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IAGhE,IAAM,eAAe,GAAG,KAAK,CAAC;IAC9B,IAAI,EAAE,GAAG,eAAe,EAAE;QACxB,MAAM,IAAI,KAAK,CAAC,6BAA2B,EAAI,CAAC,CAAC;KAClD;AACH,CAAC;AAlBD,0DAkBC;AAED,mCACI,MAAkC,EAAE,YAAoB,EACxD,cAAsB,EAAE,OAAgB;IAC1C,IAAI,OAAO,IAAI,IAAI,EAAE;QACnB,OAAO,GAAG,iBAAG,CAAC,GAAG,CAAC,cAAc,CAAC,CAAC;KACnC;IACD,IAAI,YAAiC,CAAC;IACtC,IAAI,MAAM,YAAY,eAAM,EAAE;QAC5B,YAAY,GAAG,MAAM,CAAC,QAAQ,EAAE,CAAC;KAClC;SAAM;QACL,YAAY,GAAG,MAAM,CAAC;KACvB;IACD,IAAM,UAAU,GAAG,IAAI,CAAC,YAAY,CAAC,CAAC;IACtC,8BAAkB,CAAC,UAAU,EAAE,YAAY,EAAE,OAAO,CAAC,CAAC;IACtD,8BAAkB,CACd,iBAAiB,CAAC,YAAY,EAAE,UAAU,CAAC,EAAE,cAAc,EAAE,OAAO,CAAC,CAAC;AAC5E,CAAC;AAhBD,8DAgBC;AAED,cAAc,MAA2B;IACvC,IAAI,GAAG,GAAG,CAAC,CAAC;IACZ,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,MAAM,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;QACtC,GAAG,IAAI,MAAM,CAAC,CAAC,CAAC,CAAC;KAClB;IACD,OAAO,GAAG,GAAG,MAAM,CAAC,MAAM,CAAC;AAC7B,CAAC;AAED,2BAA2B,MAA2B,EAAE,IAAY;IAClE,IAAI,aAAa,GAAG,CAAC,CAAC;IACtB,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,MAAM,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;QACtC,IAAM,IAAI,GAAG,MAAM,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC;QAC9B,aAAa,IAAI,IAAI,GAAG,IAAI,CAAC;KAC9B;IACD,OAAO,IAAI,CAAC,IAAI,CAAC,aAAa,GAAG,MAAM,CAAC,MAAM,CAAC,CAAC;AAClD,CAAC;AAED,kBAAkB,MAA2B;IAE3C,IAAM,UAAU,GAAG,IAAI,CAAC,MAAM,CAAC,CAAC;IAChC,IAAM,CAAC,GAAG,MAAM,CAAC,MAAM,CAAC;IACxB,IAAI,IAAI,GAAG,CAAC,CAAC;IACb,IAAI,IAAI,GAAG,CAAC,CAAC;IACb,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,EAAE,EAAE;QAC1B,IAAM,CAAC,GAAG,MAAM,CAAC,CAAC,CAAC,GAAG,UAAU,CAAC;QACjC,IAAI,IAAI,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACvB,IAAI,IAAI,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;KACxB;IACD,OAAO,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,IAAI,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,IAAI,EAAE,CAAC,CAAC,CAAC;AACtD,CAAC;AAED,kBAAkB,MAA2B;IAE3C,IAAM,UAAU,GAAG,IAAI,CAAC,MAAM,CAAC,CAAC;IAChC,IAAM,CAAC,GAAG,MAAM,CAAC,MAAM,CAAC;IACxB,IAAI,IAAI,GAAG,CAAC,CAAC;IACb,IAAI,IAAI,GAAG,CAAC,CAAC;IACb,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,EAAE,EAAE;QAC1B,IAAM,CAAC,GAAG,MAAM,CAAC,CAAC,CAAC,GAAG,UAAU,CAAC;QACjC,IAAI,IAAI,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACvB,IAAI,IAAI,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;KACxB;IACD,OAAO,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,IAAI,GAAG,IAAI,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,IAAI,EAAE,CAAC,GAAG,CAAC,CAAC,CAAC;AAChE,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/reduce_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/reduce_util.d.ts
new file mode 100644
index 0000000..7d453a2
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/reduce_util.d.ts
@@ -0,0 +1,7 @@
+export declare const PARALLELIZE_THRESHOLD = 30;
+export interface ReduceInfo {
+    windowSize: number;
+    batchSize: number;
+    inSize: number;
+}
+export declare function computeOptimalWindowSize(inSize: number): number;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/reduce_util.js b/node_modules/@tensorflow/tfjs-core/dist/ops/reduce_util.js
new file mode 100644
index 0000000..cfbd2be
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/reduce_util.js
@@ -0,0 +1,12 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var util_1 = require("../util");
+exports.PARALLELIZE_THRESHOLD = 30;
+function computeOptimalWindowSize(inSize) {
+    if (inSize <= exports.PARALLELIZE_THRESHOLD) {
+        return inSize;
+    }
+    return util_1.nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
+}
+exports.computeOptimalWindowSize = computeOptimalWindowSize;
+//# sourceMappingURL=reduce_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/reduce_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/reduce_util.js.map
new file mode 100644
index 0000000..9654271
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/reduce_util.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"reduce_util.js","sourceRoot":"","sources":["../../src/ops/reduce_util.ts"],"names":[],"mappings":";;AAqBA,gCAAuC;AAE1B,QAAA,qBAAqB,GAAG,EAAE,CAAC;AAQxC,kCAAyC,MAAc;IACrD,IAAI,MAAM,IAAI,6BAAqB,EAAE;QACnC,OAAO,MAAM,CAAC;KACf;IACD,OAAO,qBAAc,CAAC,MAAM,EAAE,IAAI,CAAC,KAAK,CAAC,IAAI,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC;AAC/D,CAAC;AALD,4DAKC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/reduction_ops.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/reduction_ops.d.ts
new file mode 100644
index 0000000..b9f53be
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/reduction_ops.d.ts
@@ -0,0 +1,28 @@
+import { Tensor } from '../tensor';
+import { TensorLike } from '../types';
+declare function logSumExp_<T extends Tensor>(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): T;
+declare function sum_<T extends Tensor>(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): T;
+declare function prod_<T extends Tensor>(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): T;
+declare function mean_<T extends Tensor>(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): T;
+declare function min_<T extends Tensor>(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): T;
+declare function max_<T extends Tensor>(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): T;
+declare function argMin_<T extends Tensor>(x: Tensor | TensorLike, axis?: number): T;
+declare function argMax_<T extends Tensor>(x: Tensor | TensorLike, axis?: number): T;
+declare function all_<T extends Tensor>(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): T;
+declare function any_<T extends Tensor>(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): T;
+declare function moments_(x: Tensor | TensorLike, axis?: number | number[], keepDims?: boolean): {
+    mean: Tensor;
+    variance: Tensor;
+};
+export declare const all: typeof all_;
+export declare const any: typeof any_;
+export declare const argMax: typeof argMax_;
+export declare const argMin: typeof argMin_;
+export declare const logSumExp: typeof logSumExp_;
+export declare const max: typeof max_;
+export declare const mean: typeof mean_;
+export declare const min: typeof min_;
+export declare const moments: typeof moments_;
+export declare const sum: typeof sum_;
+export declare const prod: typeof prod_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/reduction_ops.js b/node_modules/@tensorflow/tfjs-core/dist/ops/reduction_ops.js
new file mode 100644
index 0000000..e88036e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/reduction_ops.js
@@ -0,0 +1,263 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var globals_1 = require("../globals");
+var tensor_util_env_1 = require("../tensor_util_env");
+var util = require("../util");
+var axis_util = require("./axis_util");
+var operation_1 = require("./operation");
+var tensor_ops_1 = require("./tensor_ops");
+function logSumExp_(x, axis, keepDims) {
+    if (axis === void 0) { axis = null; }
+    if (keepDims === void 0) { keepDims = false; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'logSumExp');
+    var axes = axis_util.parseAxisParam(axis, $x.shape);
+    var xMax = $x.max(axes, true);
+    var a = $x.sub(xMax);
+    var b = a.exp();
+    var c = b.sum(axes);
+    var d = c.log();
+    var res = xMax.reshape(d.shape).add(d);
+    if (keepDims) {
+        var newShape = axis_util.expandShapeToKeepDim(res.shape, axes);
+        return res.reshape(newShape);
+    }
+    return res;
+}
+function sum_(x, axis, keepDims) {
+    if (axis === void 0) { axis = null; }
+    if (keepDims === void 0) { keepDims = false; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'sum');
+    if ($x.dtype === 'bool') {
+        $x = $x.toInt();
+    }
+    var axes = axis_util.parseAxisParam(axis, $x.shape);
+    var customOp = globals_1.customGrad(function (x) {
+        var permutation = axis_util.getAxesPermutation(axes, x.rank);
+        var reductionAxes = axes;
+        var permutedX = x;
+        if (permutation != null) {
+            permutedX = x.transpose(permutation);
+            reductionAxes = axis_util.getInnerMostAxes(reductionAxes.length, x.rank);
+        }
+        var value = environment_1.ENV.engine.runKernel(function (backend) { return backend.sum(permutedX, reductionAxes); }, { permutedX: permutedX });
+        if (keepDims) {
+            var newShape = axis_util.expandShapeToKeepDim(value.shape, axes);
+            value = value.reshape(newShape);
+        }
+        var gradFunc = function (dy) {
+            var expandedDyShape = x.shape.slice();
+            axes.forEach(function (axis) {
+                expandedDyShape[axis] = 1;
+            });
+            var expandedDy = dy.reshape(expandedDyShape);
+            var derX = expandedDy.mul(tensor_ops_1.ones(x.shape, 'float32'));
+            return derX;
+        };
+        return { value: value, gradFunc: gradFunc };
+    });
+    return customOp($x);
+}
+function prod_(x, axis, keepDims) {
+    if (axis === void 0) { axis = null; }
+    if (keepDims === void 0) { keepDims = false; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'prod');
+    if ($x.dtype === 'bool') {
+        $x = $x.toInt();
+    }
+    var axes = axis_util.parseAxisParam(axis, $x.shape);
+    var permutation = axis_util.getAxesPermutation(axes, $x.rank);
+    var reductionAxes = axes;
+    var permutedX = $x;
+    if (permutation != null) {
+        permutedX = $x.transpose(permutation);
+        reductionAxes = axis_util.getInnerMostAxes(reductionAxes.length, $x.rank);
+    }
+    var value = environment_1.ENV.engine.runKernel(function (backend) { return backend.prod(permutedX, reductionAxes); }, { permutedX: permutedX });
+    if (keepDims) {
+        var newShape = axis_util.expandShapeToKeepDim(value.shape, axes);
+        value = value.reshape(newShape);
+    }
+    return value;
+}
+function mean_(x, axis, keepDims) {
+    if (axis === void 0) { axis = null; }
+    if (keepDims === void 0) { keepDims = false; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'mean');
+    var axes = axis_util.parseAxisParam(axis, $x.shape);
+    var shapes = axis_util.computeOutAndReduceShapes($x.shape, axes);
+    var reduceShape = shapes[1];
+    var reduceSize = util.sizeFromShape(reduceShape);
+    var customOp = globals_1.customGrad(function (x) {
+        var reduceSizeScalar = tensor_ops_1.scalar(reduceSize);
+        var xReduce = reduceSizeScalar.dtype === x.dtype ? x : x.cast(reduceSizeScalar.dtype);
+        var res = xReduce.div(reduceSizeScalar);
+        var value = res.sum(axis, keepDims);
+        var gradFunc = function (dy) {
+            var expandedDyShape = x.shape.slice();
+            axes.forEach(function (axis) {
+                expandedDyShape[axis] = 1;
+            });
+            var expandedDy = dy.reshape(expandedDyShape);
+            var derX = expandedDy.mul(tensor_ops_1.ones(x.shape, 'float32')).div(reduceSizeScalar);
+            return derX;
+        };
+        return { value: value, gradFunc: gradFunc };
+    });
+    return customOp($x);
+}
+function gradForMinAndMax(dy, saved, xOrig, origAxes, permutedAxes) {
+    var y = saved[0];
+    if (y.rank < xOrig.rank) {
+        y = y.reshape(axis_util.expandShapeToKeepDim(y.shape, origAxes));
+    }
+    if (dy.rank < xOrig.rank) {
+        dy = dy.reshape(axis_util.expandShapeToKeepDim(dy.shape, origAxes));
+    }
+    return {
+        $x: function () {
+            var dx = dy.mul(xOrig.equal(y).cast(dy.dtype));
+            return permutedAxes == null ? dx : dx.transpose(permutedAxes);
+        }
+    };
+}
+function min_(x, axis, keepDims) {
+    if (axis === void 0) { axis = null; }
+    if (keepDims === void 0) { keepDims = false; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'min');
+    var xOrig = $x;
+    var origAxes = axis_util.parseAxisParam(axis, $x.shape);
+    var axes = origAxes;
+    var permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);
+    if (permutedAxes != null) {
+        $x = $x.transpose(permutedAxes);
+        axes = axis_util.getInnerMostAxes(axes.length, $x.rank);
+    }
+    var grad = function (dy, saved) {
+        return gradForMinAndMax(dy, saved, xOrig, origAxes, permutedAxes);
+    };
+    var res = environment_1.ENV.engine.runKernel(function (backend, save) { return save(backend.min($x, axes)); }, { $x: $x }, grad);
+    if (keepDims) {
+        var newShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);
+        res = res.reshape(newShape);
+    }
+    return res;
+}
+function max_(x, axis, keepDims) {
+    if (axis === void 0) { axis = null; }
+    if (keepDims === void 0) { keepDims = false; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'max');
+    var xOrig = $x;
+    var origAxes = axis_util.parseAxisParam(axis, $x.shape);
+    var axes = origAxes;
+    var permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);
+    if (permutedAxes != null) {
+        $x = $x.transpose(permutedAxes);
+        axes = axis_util.getInnerMostAxes(axes.length, $x.rank);
+    }
+    var grad = function (dy, saved) {
+        return gradForMinAndMax(dy, saved, xOrig, origAxes, permutedAxes);
+    };
+    var res = environment_1.ENV.engine.runKernel(function (backend, save) { return save(backend.max($x, axes)); }, { $x: $x }, grad);
+    if (keepDims) {
+        var newShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);
+        res = res.reshape(newShape);
+    }
+    return res;
+}
+function argMin_(x, axis) {
+    if (axis === void 0) { axis = 0; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'argMin');
+    if (axis == null) {
+        axis = 0;
+    }
+    var axes = axis_util.parseAxisParam(axis, $x.shape);
+    var permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);
+    if (permutedAxes != null) {
+        $x = $x.transpose(permutedAxes);
+        axes = axis_util.getInnerMostAxes(axes.length, $x.rank);
+    }
+    var grad = function (dy) {
+        return { $x: function () { return tensor_ops_1.zerosLike($x); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.argMin($x, axes[0]); }, { $x: $x }, grad);
+}
+function argMax_(x, axis) {
+    if (axis === void 0) { axis = 0; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'argMax');
+    if (axis == null) {
+        axis = 0;
+    }
+    var axes = axis_util.parseAxisParam(axis, $x.shape);
+    var permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);
+    if (permutedAxes != null) {
+        $x = $x.transpose(permutedAxes);
+        axes = axis_util.getInnerMostAxes(axes.length, $x.rank);
+    }
+    var grad = function (dy) {
+        return { $x: function () { return tensor_ops_1.zerosLike($x); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.argMax($x, axes[0]); }, { $x: $x }, grad);
+}
+function all_(x, axis, keepDims) {
+    if (axis === void 0) { axis = null; }
+    if (keepDims === void 0) { keepDims = false; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'all', 'bool');
+    var origAxes = axis_util.parseAxisParam(axis, $x.shape);
+    var axes = origAxes;
+    var permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);
+    if (permutedAxes != null) {
+        $x = $x.transpose(permutedAxes);
+        axes = axis_util.getInnerMostAxes(axes.length, $x.rank);
+    }
+    var res = environment_1.ENV.engine.runKernel(function (backend) { return backend.all($x, axes); }, { $x: $x });
+    if (keepDims) {
+        var newShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);
+        return res.reshape(newShape);
+    }
+    return res;
+}
+function any_(x, axis, keepDims) {
+    if (axis === void 0) { axis = null; }
+    if (keepDims === void 0) { keepDims = false; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'any', 'bool');
+    var origAxes = axis_util.parseAxisParam(axis, $x.shape);
+    var axes = origAxes;
+    var permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);
+    if (permutedAxes != null) {
+        $x = $x.transpose(permutedAxes);
+        axes = axis_util.getInnerMostAxes(axes.length, $x.rank);
+    }
+    var res = environment_1.ENV.engine.runKernel(function (backend) { return backend.any($x, axes); }, { $x: $x });
+    if (keepDims) {
+        var newShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);
+        return res.reshape(newShape);
+    }
+    return res;
+}
+function moments_(x, axis, keepDims) {
+    if (axis === void 0) { axis = null; }
+    if (keepDims === void 0) { keepDims = false; }
+    x = tensor_util_env_1.convertToTensor(x, 'x', 'moments');
+    var axes = axis_util.parseAxisParam(axis, x.shape);
+    var mean = x.mean(axes, keepDims);
+    var keepDimsShape = mean.shape;
+    if (!keepDims) {
+        keepDimsShape = axis_util.expandShapeToKeepDim(mean.shape, axes);
+    }
+    var devSquared = x.toFloat().sub(mean.reshape(keepDimsShape)).square();
+    var variance = devSquared.mean(axes, keepDims);
+    return { mean: mean, variance: variance };
+}
+exports.all = operation_1.op({ all_: all_ });
+exports.any = operation_1.op({ any_: any_ });
+exports.argMax = operation_1.op({ argMax_: argMax_ });
+exports.argMin = operation_1.op({ argMin_: argMin_ });
+exports.logSumExp = operation_1.op({ logSumExp_: logSumExp_ });
+exports.max = operation_1.op({ max_: max_ });
+exports.mean = operation_1.op({ mean_: mean_ });
+exports.min = operation_1.op({ min_: min_ });
+exports.moments = operation_1.op({ moments_: moments_ });
+exports.sum = operation_1.op({ sum_: sum_ });
+exports.prod = operation_1.op({ prod_: prod_ });
+//# sourceMappingURL=reduction_ops.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/reduction_ops.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/reduction_ops.js.map
new file mode 100644
index 0000000..e091ddd
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/reduction_ops.js.map
@@ -0,0 +1 @@
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diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/reduction_ops_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/reduction_ops_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/reduction_ops_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/reduction_ops_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/reduction_ops_test.js
new file mode 100644
index 0000000..4873ec0
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/reduction_ops_test.js
@@ -0,0 +1,1351 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+var reduce_util = require("./reduce_util");
+jasmine_util_1.describeWithFlags('Reduction: min', test_util_1.ALL_ENVS, function () {
+    it('Tensor1D', function () {
+        var a = tf.tensor1d([3, -1, 0, 100, -7, 2]);
+        test_util_1.expectNumbersClose(tf.min(a).get(), -7);
+    });
+    it('ignores NaNs', function () {
+        var a = tf.tensor1d([3, NaN, 2]);
+        expect(tf.min(a).get()).toEqual(2);
+    });
+    it('2D', function () {
+        var a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
+        test_util_1.expectNumbersClose(tf.min(a).get(), -7);
+    });
+    it('2D axis=[0,1]', function () {
+        var a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
+        test_util_1.expectNumbersClose(tf.min(a, [0, 1]).get(), -7);
+    });
+    it('2D, axis=0', function () {
+        var a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
+        var r = tf.min(a, 0);
+        expect(r.shape).toEqual([3]);
+        test_util_1.expectArraysClose(r, [3, -7, 0]);
+    });
+    it('2D, axis=0, keepDims', function () {
+        var a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
+        var r = tf.min(a, 0, true);
+        expect(r.shape).toEqual([1, 3]);
+        test_util_1.expectArraysClose(r, [3, -7, 0]);
+    });
+    it('2D, axis=1 provided as a number', function () {
+        var a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
+        var r = tf.min(a, 1);
+        test_util_1.expectArraysClose(r, [2, -7]);
+    });
+    it('2D, axis = -1 provided as a number', function () {
+        var a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
+        var r = tf.min(a, -1);
+        test_util_1.expectArraysClose(r, [2, -7]);
+    });
+    it('2D, axis=[1]', function () {
+        var a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
+        var r = tf.min(a, [1]);
+        test_util_1.expectArraysClose(r, [2, -7]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.min({}); })
+            .toThrowError(/Argument 'x' passed to 'min' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        test_util_1.expectNumbersClose(tf.min([3, -1, 0, 100, -7, 2]).get(), -7);
+    });
+    it('min gradient: Scalar', function () {
+        var x = tf.scalar(42);
+        var dy = tf.scalar(-1);
+        var gradients = tf.grad(function (v) { return tf.min(v); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.scalar(-1));
+    });
+    it('min gradient: 1D, ties', function () {
+        var x = tf.tensor1d([-1, -3, -7, -7]);
+        var dy = tf.scalar(-1);
+        var gradients = tf.grad(function (v) { return tf.min(v); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor1d([0, 0, -1, -1]));
+    });
+    it('min gradient: 2D, axes=-1, keepDims=false', function () {
+        var x = tf.tensor2d([[-0, -20, -10], [10, 30, 20]]);
+        var dy = tf.tensor1d([-1, -1]);
+        var axis = -1;
+        var gradients = tf.grad(function (v) { return tf.min(v, axis); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor2d([[0, -1, 0], [-1, 0, 0]]));
+    });
+    it('min gradient: ties, 2D, axes=-1, keepDims=false', function () {
+        var x = tf.tensor2d([[0, -20, -20], [10, 30, 10]]);
+        var dy = tf.tensor1d([-1, -1]);
+        var axis = -1;
+        var gradients = tf.grad(function (v) { return tf.min(v, axis); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor2d([[0, -1, -1], [-1, 0, -1]]));
+    });
+    it('min gradient: 2D, axes=0, keepDims=false', function () {
+        var x = tf.tensor2d([[0, 20, 10], [-10, -30, 20]]);
+        var dy = tf.tensor1d([-1, -1, -1]);
+        var axis = 0;
+        var gradients = tf.grad(function (v) { return tf.max(v, axis); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor2d([[-1, -1, 0], [0, 0, -1]]));
+    });
+    it('min gradient: 2D, axes=-1, keepDims=true', function () {
+        var x = tf.tensor2d([[0, -20, -10], [10, 30, 20]]);
+        var dy = tf.tensor2d([[-1], [-1]]);
+        var axis = -1;
+        var keepDims = true;
+        var gradients = tf.grad(function (v) { return tf.min(v, axis, keepDims); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor2d([[0, -1, 0], [-1, 0, 0]]));
+    });
+    it('min gradient: 2D, axes=0, keepDims=true', function () {
+        var x = tf.tensor2d([[0, -20, -10], [10, 30, -20]]);
+        var dy = tf.tensor2d([[-1, -1, -1]]);
+        var axis = 0;
+        var keepDims = true;
+        var gradients = tf.grad(function (v) { return tf.min(v, axis, keepDims); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor2d([[-1, -1, 0], [0, 0, -1]]));
+    });
+    it('min gradient: 3D, axes=[1, 2], keepDims=false', function () {
+        var x = tf.tensor3d([[[0, -20], [-10, -15]], [[10, 30], [20, 15]]]);
+        var dy = tf.tensor1d([-1, -1]);
+        var axis = [1, 2];
+        var gradients = tf.grad(function (v) { return tf.min(v, axis); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor3d([[[0, -1], [0, 0]], [[-1, 0], [0, 0]]]));
+    });
+    it('min gradient: ties, 3D, axes=[1, 2], keepDims=false', function () {
+        var x = tf.tensor3d([[[0, -20], [-20, -20]], [[10, 30], [10, 15]]]);
+        var dy = tf.tensor1d([-1, -1]);
+        var axis = [1, 2];
+        var gradients = tf.grad(function (v) { return tf.min(v, axis); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor3d([[[0, -1], [-1, -1]], [[-1, 0], [-1, 0]]]));
+    });
+    it('min gradient: 3D, axes=2, keepDims=false', function () {
+        var x = tf.tensor3d([[[0, -20], [-10, -15]], [[10, 30], [20, 15]]]);
+        var dy = tf.tensor2d([[-1, -1], [-1, -1]]);
+        var axis = 2;
+        var gradients = tf.grad(function (v) { return tf.min(v, axis); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor3d([[[0, -1], [0, -1]], [[-1, 0], [0, -1]]]));
+    });
+    it('min gradient: 3D, axes=2, keepDims=true', function () {
+        var x = tf.tensor3d([[[0, -20], [-10, -15]], [[10, 30], [20, 15]]]);
+        var dy = tf.tensor3d([[[-1], [-1]], [[-1], [-1]]]);
+        var axis = 2;
+        var keepDims = true;
+        var gradients = tf.grad(function (v) { return tf.min(v, axis, keepDims); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor3d([[[0, -1], [0, -1]], [[-1, 0], [0, -1]]]));
+    });
+    it('min gradient: ties, 4D, axes=[1, 2, 3], keepDims=false', function () {
+        var x = tf.tensor4d([
+            [[[0, -20], [-20, -20]], [[10, 30], [10, 30]]],
+            [[[0, 20], [20, 20]], [[-10, -30], [-10, -30]]]
+        ]);
+        var dy = tf.tensor1d([-1, -1]);
+        var axis = [1, 2, 3];
+        var gradients = tf.grad(function (v) { return tf.min(v, axis); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor4d([
+            [[[0, -1], [-1, -1]], [[0, 0], [0, 0]]],
+            [[[0, 0], [0, 0]], [[0, -1], [0, -1]]]
+        ]));
+    });
+    it('min gradient: ties, 4D, axes=[2, 3], keepDims=true', function () {
+        var x = tf.tensor4d([
+            [[[0, -20], [-20, -20]], [[10, 30], [10, 30]]],
+            [[[0, 20], [20, 20]], [[-10, -30], [-10, -30]]]
+        ]);
+        var dy = tf.tensor4d([[[[-1]], [[-2]]], [[[-3]], [[-4]]]]);
+        var axis = [2, 3];
+        var keepDims = true;
+        var gradients = tf.grad(function (v) { return tf.min(v, axis, keepDims); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor4d([
+            [[[0, -1], [-1, -1]], [[-2, 0], [-2, 0]]],
+            [[[-3, 0], [0, 0]], [[0, -4], [0, -4]]]
+        ]));
+    });
+    it('throws error for string tensor', function () {
+        expect(function () { return tf.min(['a']); })
+            .toThrowError(/Argument 'x' passed to 'min' must be numeric tensor/);
+    });
+});
+jasmine_util_1.describeWithFlags('Reduction: max', test_util_1.ALL_ENVS, function () {
+    it('with one element dominating', function () {
+        var a = tf.tensor1d([3, -1, 0, 100, -7, 2]);
+        var r = tf.max(a);
+        test_util_1.expectNumbersClose(r.get(), 100);
+    });
+    it('with all elements being the same', function () {
+        var a = tf.tensor1d([3, 3, 3]);
+        var r = tf.max(a);
+        test_util_1.expectNumbersClose(r.get(), 3);
+    });
+    it('ignores NaNs', function () {
+        test_util_1.expectNumbersClose(tf.max(tf.tensor1d([3, NaN, 2])).get(), 3);
+    });
+    it('2D', function () {
+        var a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
+        test_util_1.expectNumbersClose(tf.max(a).get(), 100);
+    });
+    it('2D axis=[0,1]', function () {
+        var a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
+        test_util_1.expectNumbersClose(tf.max(a, [0, 1]).get(), 100);
+    });
+    it('2D, axis=0', function () {
+        var a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
+        var r = tf.max(a, [0]);
+        expect(r.shape).toEqual([3]);
+        test_util_1.expectArraysClose(r, [100, -1, 2]);
+    });
+    it('2D, axis=0, keepDims', function () {
+        var a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
+        var r = tf.max(a, [0], true);
+        expect(r.shape).toEqual([1, 3]);
+        test_util_1.expectArraysClose(r, [100, -1, 2]);
+    });
+    it('2D, axis=1 provided as a number', function () {
+        var a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
+        var r = tf.max(a, 1);
+        test_util_1.expectArraysClose(r, [5, 100]);
+    });
+    it('2D, axis = -1 provided as a number', function () {
+        var a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
+        var r = tf.max(a, -1);
+        test_util_1.expectArraysClose(r, [5, 100]);
+    });
+    it('2D, axis=[1]', function () {
+        var a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
+        var r = tf.max(a, [1]);
+        test_util_1.expectArraysClose(r, [5, 100]);
+    });
+    it('6D, axis=[5]', function () {
+        var a = tf.range(0, 64).reshape([2, 2, 2, 2, 2, 2]);
+        var r = tf.max(a, [5]);
+        var expectedResult = [
+            1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31,
+            33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63
+        ];
+        test_util_1.expectArraysClose(r, expectedResult);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.max({}); })
+            .toThrowError(/Argument 'x' passed to 'max' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var r = tf.max([3, -1, 0, 100, -7, 2]);
+        test_util_1.expectNumbersClose(r.get(), 100);
+    });
+    it('max gradient: Scalar', function () {
+        var x = tf.scalar(42);
+        var dy = tf.scalar(-1);
+        var gradients = tf.grad(function (v) { return tf.max(v); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.scalar(-1));
+    });
+    it('max gradient: 1D, ties', function () {
+        var x = tf.tensor1d([1, 3, 7, 7]);
+        var dy = tf.scalar(-1);
+        var gradients = tf.grad(function (v) { return tf.max(v); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor1d([0, 0, -1, -1]));
+    });
+    it('max gradient: 2D, axes=-1, keepDims=false', function () {
+        var x = tf.tensor2d([[0, 20, 10], [-10, -30, -20]]);
+        var dy = tf.tensor1d([-1, -1]);
+        var axis = -1;
+        var gradients = tf.grad(function (v) { return tf.max(v, axis); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor2d([[0, -1, 0], [-1, 0, 0]]));
+    });
+    it('max gradient: ties, 2D, axes=-1, keepDims=false', function () {
+        var x = tf.tensor2d([[0, 20, 20], [-10, -30, -10]]);
+        var dy = tf.tensor1d([-1, -1]);
+        var axis = -1;
+        var gradients = tf.grad(function (v) { return tf.max(v, axis); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor2d([[0, -1, -1], [-1, 0, -1]]));
+    });
+    it('max gradient: 2D, axes=0, keepDims=false', function () {
+        var x = tf.tensor2d([[0, 20, 10], [-10, -30, 20]]);
+        var dy = tf.tensor1d([-1, -1, -1]);
+        var axis = 0;
+        var gradients = tf.grad(function (v) { return tf.max(v, axis); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor2d([[-1, -1, 0], [0, 0, -1]]));
+    });
+    it('max gradient: 2D, axes=-1, keepDims=true', function () {
+        var x = tf.tensor2d([[0, 20, 10], [-10, -30, -20]]);
+        var dy = tf.tensor2d([[-1], [-1]]);
+        var axis = -1;
+        var keepDims = true;
+        var gradients = tf.grad(function (v) { return tf.max(v, axis, keepDims); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor2d([[0, -1, 0], [-1, 0, 0]]));
+    });
+    it('max gradient: 2D, axes=0, keepDims=true', function () {
+        var x = tf.tensor2d([[0, 20, 10], [-10, -30, 20]]);
+        var dy = tf.tensor2d([[-1, -1, -1]]);
+        var axis = 0;
+        var keepDims = true;
+        var gradients = tf.grad(function (v) { return tf.max(v, axis, keepDims); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor2d([[-1, -1, 0], [0, 0, -1]]));
+    });
+    it('max gradient: 3D, axes=[1, 2], keepDims=false', function () {
+        var x = tf.tensor3d([[[0, 20], [10, 15]], [[-10, -30], [-20, -15]]]);
+        var dy = tf.tensor1d([-1, -1]);
+        var axis = [1, 2];
+        var gradients = tf.grad(function (v) { return tf.max(v, axis); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor3d([[[0, -1], [0, 0]], [[-1, 0], [0, 0]]]));
+    });
+    it('max gradient: ties, 3D, axes=[1, 2], keepDims=false', function () {
+        var x = tf.tensor3d([[[0, 20], [20, 20]], [[-10, -30], [-10, -15]]]);
+        var dy = tf.tensor1d([-1, -1]);
+        var axis = [1, 2];
+        var gradients = tf.grad(function (v) { return tf.max(v, axis); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor3d([[[0, -1], [-1, -1]], [[-1, 0], [-1, 0]]]));
+    });
+    it('max gradient: 3D, axes=2, keepDims=false', function () {
+        var x = tf.tensor3d([[[0, 20], [10, 15]], [[-10, -30], [-20, -15]]]);
+        var dy = tf.tensor2d([[-1, -1], [-1, -1]]);
+        var axis = 2;
+        var gradients = tf.grad(function (v) { return tf.max(v, axis); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor3d([[[0, -1], [0, -1]], [[-1, 0], [0, -1]]]));
+    });
+    it('max gradient: 3D, axes=2, keepDims=true', function () {
+        var x = tf.tensor3d([[[0, 20], [10, 15]], [[-10, -30], [-20, -15]]]);
+        var dy = tf.tensor3d([[[-1], [-1]], [[-1], [-1]]]);
+        var axis = 2;
+        var keepDims = true;
+        var gradients = tf.grad(function (v) { return tf.max(v, axis, keepDims); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor3d([[[0, -1], [0, -1]], [[-1, 0], [0, -1]]]));
+    });
+    it('max gradient: ties, 4D, axes=[1, 2, 3], keepDims=false', function () {
+        var x = tf.tensor4d([
+            [[[0, 20], [20, 20]], [[-10, -30], [-10, -30]]],
+            [[[0, -20], [-20, -20]], [[10, 30], [10, 30]]]
+        ]);
+        var dy = tf.tensor1d([-1, -1]);
+        var axis = [1, 2, 3];
+        var gradients = tf.grad(function (v) { return tf.max(v, axis); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor4d([
+            [[[0, -1], [-1, -1]], [[0, 0], [0, 0]]],
+            [[[0, 0], [0, 0]], [[0, -1], [0, -1]]]
+        ]));
+    });
+    it('max gradient: ties, 4D, axes=[2, 3], keepDims=true', function () {
+        var x = tf.tensor4d([
+            [[[0, 20], [20, 20]], [[-10, -30], [-10, -30]]],
+            [[[0, -20], [-20, -20]], [[10, 30], [10, 30]]]
+        ]);
+        var dy = tf.tensor4d([[[[-1]], [[-2]]], [[[-3]], [[-4]]]]);
+        var axis = [2, 3];
+        var keepDims = true;
+        var gradients = tf.grad(function (v) { return tf.max(v, axis, keepDims); })(x, dy);
+        test_util_1.expectArraysClose(gradients, tf.tensor4d([
+            [[[0, -1], [-1, -1]], [[-2, 0], [-2, 0]]],
+            [[[-3, 0], [0, 0]], [[0, -4], [0, -4]]]
+        ]));
+    });
+    it('throws error for string tensor', function () {
+        expect(function () { return tf.max(['a']); })
+            .toThrowError(/Argument 'x' passed to 'max' must be numeric tensor/);
+    });
+});
+jasmine_util_1.describeWithFlags('Reduction: argmax', test_util_1.ALL_ENVS, function () {
+    it('Tensor1D', function () {
+        var a = tf.tensor1d([1, 0, 3, 2]);
+        var result = tf.argMax(a);
+        expect(result.dtype).toBe('int32');
+        expect(result.get()).toBe(2);
+    });
+    it('one value', function () {
+        var a = tf.tensor1d([10]);
+        var result = tf.argMax(a);
+        expect(result.dtype).toBe('int32');
+        expect(result.get()).toBe(0);
+    });
+    it('N > than parallelization threshold', function () {
+        var n = reduce_util.PARALLELIZE_THRESHOLD * 2;
+        var values = new Float32Array(n);
+        for (var i = 0; i < n; i++) {
+            values[i] = i;
+        }
+        var a = tf.tensor1d(values);
+        var result = tf.argMax(a);
+        expect(result.dtype).toBe('int32');
+        expect(result.get()).toBe(n - 1);
+    });
+    it('max index corresponds to start of a non-initial window', function () {
+        var n = reduce_util.PARALLELIZE_THRESHOLD * 2;
+        var windowSize = reduce_util.computeOptimalWindowSize(n);
+        var values = new Float32Array(n);
+        var index = windowSize * 2;
+        values[index] = 1;
+        var a = tf.tensor1d(values);
+        var result = tf.argMax(a);
+        expect(result.dtype).toBe('int32');
+        expect(result.get()).toBe(index);
+    });
+    it('ignores NaNs', function () {
+        var a = tf.tensor1d([0, 3, 5, NaN, 3]);
+        var res = tf.argMax(a);
+        expect(res.dtype).toBe('int32');
+        expect(res.get()).toBe(2);
+    });
+    it('2D, no axis specified', function () {
+        var a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
+        test_util_1.expectArraysEqual(tf.argMax(a), [1, 0, 1]);
+    });
+    it('2D, axis=0', function () {
+        var a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
+        var r = tf.argMax(a, 0);
+        expect(r.shape).toEqual([3]);
+        expect(r.dtype).toBe('int32');
+        test_util_1.expectArraysEqual(r, [1, 0, 1]);
+    });
+    it('2D, axis=1', function () {
+        var a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
+        var r = tf.argMax(a, 1);
+        expect(r.dtype).toBe('int32');
+        test_util_1.expectArraysEqual(r, [2, 0]);
+    });
+    it('2D, axis = -1', function () {
+        var a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
+        var r = tf.argMax(a, -1);
+        expect(r.dtype).toBe('int32');
+        test_util_1.expectArraysEqual(r, [2, 0]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.argMax({}); })
+            .toThrowError(/Argument 'x' passed to 'argMax' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var result = tf.argMax([1, 0, 3, 2]);
+        expect(result.dtype).toBe('int32');
+        expect(result.get()).toBe(2);
+    });
+    it('accepts tensor with bool values', function () {
+        var t = tf.tensor1d([0, 1], 'bool');
+        var result = tf.argMax(t);
+        expect(result.dtype).toBe('int32');
+        expect(result.get()).toBe(1);
+    });
+    it('has gradient', function () {
+        var a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
+        var dy = tf.ones([3], 'float32');
+        var da = tf.grad(function (x) { return tf.argMax(x); })(a, dy);
+        expect(da.dtype).toBe('float32');
+        expect(da.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(da, [0, 0, 0, 0, 0, 0]);
+    });
+    it('throws error for string tensor', function () {
+        expect(function () { return tf.argMax(['a']); })
+            .toThrowError(/Argument 'x' passed to 'argMax' must be numeric tensor/);
+    });
+});
+jasmine_util_1.describeWithFlags('Reduction: argmin', test_util_1.ALL_ENVS, function () {
+    it('Tensor1D', function () {
+        var a = tf.tensor1d([1, 0, 3, 2]);
+        var result = tf.argMin(a);
+        expect(result.get()).toBe(1);
+    });
+    it('one value', function () {
+        var a = tf.tensor1d([10]);
+        var result = tf.argMin(a);
+        expect(result.get()).toBe(0);
+    });
+    it('N > than parallelization threshold', function () {
+        var n = reduce_util.PARALLELIZE_THRESHOLD * 2;
+        var values = new Float32Array(n);
+        for (var i = 0; i < n; i++) {
+            values[i] = n - i;
+        }
+        var a = tf.tensor1d(values);
+        var result = tf.argMin(a);
+        expect(result.dtype).toBe('int32');
+        expect(result.get()).toBe(n - 1);
+    });
+    it('min index corresponds to start of a non-initial window', function () {
+        var n = reduce_util.PARALLELIZE_THRESHOLD * 2;
+        var windowSize = reduce_util.computeOptimalWindowSize(n);
+        var values = new Float32Array(n);
+        var index = windowSize * 2;
+        values[index] = -1;
+        var a = tf.tensor1d(values);
+        var result = tf.argMin(a);
+        expect(result.dtype).toBe('int32');
+        expect(result.get()).toBe(index);
+    });
+    it('ignores NaNs', function () {
+        var a = tf.tensor1d([5, 0, NaN, -1, 3]);
+        var res = tf.argMin(a);
+        expect(res.get()).toBe(3);
+    });
+    it('2D, no axis specified', function () {
+        var a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
+        test_util_1.expectArraysEqual(tf.argMin(a), [0, 1, 0]);
+    });
+    it('2D, axis=0', function () {
+        var a = tf.tensor2d([3, -1, 0, 100, -7, 2], [2, 3]);
+        var r = tf.argMin(a, 0);
+        expect(r.shape).toEqual([3]);
+        expect(r.dtype).toBe('int32');
+        test_util_1.expectArraysEqual(r, [0, 1, 0]);
+    });
+    it('2D, axis=1', function () {
+        var a = tf.tensor2d([3, 2, 5, 100, -7, -8], [2, 3]);
+        var r = tf.argMin(a, 1);
+        test_util_1.expectArraysEqual(r, [1, 2]);
+    });
+    it('2D, axis = -1', function () {
+        var a = tf.tensor2d([3, 2, 5, 100, -7, -8], [2, 3]);
+        var r = tf.argMin(a, -1);
+        test_util_1.expectArraysEqual(r, [1, 2]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.argMin({}); })
+            .toThrowError(/Argument 'x' passed to 'argMin' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var result = tf.argMin([1, 0, 3, 2]);
+        expect(result.get()).toBe(1);
+    });
+    it('accepts tensor with bool values', function () {
+        var t = tf.tensor1d([0, 1], 'bool');
+        var result = tf.argMin(t);
+        expect(result.dtype).toBe('int32');
+        expect(result.get()).toBe(0);
+    });
+    it('has gradient', function () {
+        var a = tf.tensor2d([3, 2, 5, 100, -7, 2], [2, 3]);
+        var dy = tf.ones([3], 'float32');
+        var da = tf.grad(function (x) { return tf.argMin(x); })(a, dy);
+        expect(da.dtype).toBe('float32');
+        expect(da.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(da, [0, 0, 0, 0, 0, 0]);
+    });
+    it('throws error for string tensor', function () {
+        expect(function () { return tf.argMin(['a']); })
+            .toThrowError(/Argument 'x' passed to 'argMin' must be numeric tensor/);
+    });
+});
+jasmine_util_1.describeWithFlags('Reduction: logSumExp', test_util_1.ALL_ENVS, function () {
+    it('0', function () {
+        var a = tf.scalar(0);
+        var result = tf.logSumExp(a);
+        test_util_1.expectNumbersClose(result.get(), 0);
+    });
+    it('basic', function () {
+        var a = tf.tensor1d([1, 2, -3]);
+        var result = tf.logSumExp(a);
+        test_util_1.expectNumbersClose(result.get(), Math.log(Math.exp(1) + Math.exp(2) + Math.exp(-3)));
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor1d([1, 2, NaN]);
+        var result = tf.logSumExp(a);
+        expect(result.get()).toEqual(NaN);
+    });
+    it('axes=0 in 2D array', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var r = tf.logSumExp(a, [0]);
+        expect(r.shape).toEqual([2]);
+        var expected = [
+            Math.log(Math.exp(1) + Math.exp(3) + Math.exp(0)),
+            Math.log(Math.exp(2) + Math.exp(0) + Math.exp(1))
+        ];
+        test_util_1.expectArraysClose(r, expected);
+    });
+    it('axes=0 in 2D array, keepDims', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var r = tf.logSumExp(a, [0], true);
+        expect(r.shape).toEqual([1, 2]);
+        var expected = [
+            Math.log(Math.exp(1) + Math.exp(3) + Math.exp(0)),
+            Math.log(Math.exp(2) + Math.exp(0) + Math.exp(1))
+        ];
+        test_util_1.expectArraysClose(r, expected);
+    });
+    it('axes=1 in 2D array', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var res = tf.logSumExp(a, [1]);
+        expect(res.shape).toEqual([3]);
+        var expected = [
+            Math.log(Math.exp(1) + Math.exp(2)),
+            Math.log(Math.exp(3) + Math.exp(0)),
+            Math.log(Math.exp(0) + Math.exp(1)),
+        ];
+        test_util_1.expectArraysClose(res, expected);
+    });
+    it('axes = -1 in 2D array', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var res = tf.logSumExp(a, -1);
+        expect(res.shape).toEqual([3]);
+        var expected = [
+            Math.log(Math.exp(1) + Math.exp(2)),
+            Math.log(Math.exp(3) + Math.exp(0)),
+            Math.log(Math.exp(0) + Math.exp(1)),
+        ];
+        test_util_1.expectArraysClose(res, expected);
+    });
+    it('2D, axes=1 provided as a single digit', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [2, 3]);
+        var res = tf.logSumExp(a, 1);
+        expect(res.shape).toEqual([2]);
+        var expected = [
+            Math.log(Math.exp(1) + Math.exp(2) + Math.exp(3)),
+            Math.log(Math.exp(0) + Math.exp(0) + Math.exp(1))
+        ];
+        test_util_1.expectArraysClose(res, expected);
+    });
+    it('axes=0,1 in 2D array', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var res = tf.logSumExp(a, [0, 1]);
+        expect(res.shape).toEqual([]);
+        var expected = [Math.log(Math.exp(1) + Math.exp(2) + Math.exp(3) + Math.exp(0) + Math.exp(0) +
+                Math.exp(1))];
+        test_util_1.expectArraysClose(res, expected);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.logSumExp({}); })
+            .toThrowError(/Argument 'x' passed to 'logSumExp' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var result = tf.logSumExp([1, 2, -3]);
+        test_util_1.expectNumbersClose(result.get(), Math.log(Math.exp(1) + Math.exp(2) + Math.exp(-3)));
+    });
+    it('throws error for string tensor', function () {
+        expect(function () { return tf.logSumExp(['a']); })
+            .toThrowError(/Argument 'x' passed to 'logSumExp' must be numeric tensor/);
+    });
+});
+jasmine_util_1.describeWithFlags('Reduction: sum', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var result = tf.sum(a);
+        test_util_1.expectNumbersClose(result.get(), 7);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor2d([1, 2, 3, NaN, 0, 1], [3, 2]);
+        expect(tf.sum(a).get()).toEqual(NaN);
+    });
+    it('sum over dtype int32', function () {
+        var a = tf.tensor1d([1, 5, 7, 3], 'int32');
+        var sum = tf.sum(a);
+        expect(sum.get()).toBe(16);
+    });
+    it('sum over dtype bool', function () {
+        var a = tf.tensor1d([true, false, false, true, true], 'bool');
+        var sum = tf.sum(a);
+        expect(sum.get()).toBe(3);
+    });
+    it('sums all values in 2D array with keep dim', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var res = tf.sum(a, null, true);
+        expect(res.shape).toEqual([1, 1]);
+        test_util_1.expectArraysClose(res, [7]);
+    });
+    it('sums across axis=0 in 2D array', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var res = tf.sum(a, [0]);
+        expect(res.shape).toEqual([2]);
+        test_util_1.expectArraysClose(res, [4, 3]);
+    });
+    it('sums across axis=0 in 2D array, keepDims', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var res = tf.sum(a, [0], true);
+        expect(res.shape).toEqual([1, 2]);
+        test_util_1.expectArraysClose(res, [4, 3]);
+    });
+    it('sums across axis=1 in 2D array', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var res = tf.sum(a, [1]);
+        expect(res.shape).toEqual([3]);
+        test_util_1.expectArraysClose(res, [3, 3, 1]);
+    });
+    it('2D, axis=1 provided as number', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [2, 3]);
+        var res = tf.sum(a, 1);
+        expect(res.shape).toEqual([2]);
+        test_util_1.expectArraysClose(res, [6, 1]);
+    });
+    it('2D, axis = -1 provided as number', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [2, 3]);
+        var res = tf.sum(a, -1);
+        expect(res.shape).toEqual([2]);
+        test_util_1.expectArraysClose(res, [6, 1]);
+    });
+    it('sums across axis=0,1 in 2D array', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var res = tf.sum(a, [0, 1]);
+        expect(res.shape).toEqual([]);
+        test_util_1.expectArraysClose(res, [7]);
+    });
+    it('2D, axis=[-1,-2] in 2D array', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var res = tf.sum(a, [-1, -2]);
+        expect(res.shape).toEqual([]);
+        test_util_1.expectArraysClose(res, [7]);
+    });
+    it('gradients: sum(2d)', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var dy = tf.scalar(10);
+        var gradients = tf.grad(function (a) { return a.sum(); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [10, 10, 10, 10, 10, 10]);
+    });
+    it('gradients: sum(2d, axis=0)', function () {
+        var a = tf.tensor2d([[1, 2], [3, 0], [0, 1]], [3, 2]);
+        var dy = tf.tensor1d([10, 20]);
+        var axis = 0;
+        var gradients = tf.grad(function (a) { return a.sum(axis); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [10, 20, 10, 20, 10, 20]);
+    });
+    it('gradients: sum(2d, axis=1)', function () {
+        var a = tf.tensor2d([[1, 2], [3, 0], [0, 1]], [3, 2]);
+        var dy = tf.tensor1d([10, 20, 30]);
+        var axis = 1;
+        var gradients = tf.grad(function (a) { return a.sum(axis); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [10, 10, 20, 20, 30, 30]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.sum({}); })
+            .toThrowError(/Argument 'x' passed to 'sum' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var result = tf.sum([[1, 2], [3, 0], [0, 1]]);
+        test_util_1.expectNumbersClose(result.get(), 7);
+    });
+    it('throws error for string tensor', function () {
+        expect(function () { return tf.sum(['a']); })
+            .toThrowError(/Argument 'x' passed to 'sum' must be numeric tensor/);
+    });
+});
+jasmine_util_1.describeWithFlags('Reduction: prod', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var result = tf.prod(a);
+        test_util_1.expectNumbersClose(result.get(), 0);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor2d([1, 2, 3, NaN, 0, 1], [3, 2]);
+        expect(tf.prod(a).get()).toEqual(NaN);
+    });
+    it('prod over dtype int32', function () {
+        var a = tf.tensor1d([1, 5, 7, 3], 'int32');
+        var prod = tf.prod(a);
+        expect(prod.get()).toBe(105);
+    });
+    it('prod over dtype bool', function () {
+        var a = tf.tensor1d([true, false, false, true, true], 'bool');
+        var prod = tf.prod(a);
+        expect(prod.get()).toBe(0);
+    });
+    it('prods all values in 2D array with keep dim', function () {
+        var a = tf.tensor2d([1, 2, 3, 1, 0, 1], [3, 2]);
+        var res = tf.prod(a, null, true);
+        expect(res.shape).toEqual([1, 1]);
+        test_util_1.expectArraysClose(res, [0]);
+    });
+    it('prods across axis=0 in 2D array', function () {
+        var a = tf.tensor2d([1, 2, 3, 1, 0, 1], [3, 2]);
+        var res = tf.prod(a, [0]);
+        expect(res.shape).toEqual([2]);
+        test_util_1.expectArraysClose(res, [0, 2]);
+    });
+    it('prods across axis=0 in 2D array, keepDims', function () {
+        var a = tf.tensor2d([1, 2, 3, 1, 0, 1], [3, 2]);
+        var res = tf.prod(a, [0], true);
+        expect(res.shape).toEqual([1, 2]);
+        test_util_1.expectArraysClose(res, [0, 2]);
+    });
+    it('prods across axis=1 in 2D array', function () {
+        var a = tf.tensor2d([1, 2, 3, 1, 1, 1], [3, 2]);
+        var res = tf.prod(a, [1]);
+        expect(res.shape).toEqual([3]);
+        test_util_1.expectArraysClose(res, [2, 3, 1]);
+    });
+    it('2D, axis=1 provided as number', function () {
+        var a = tf.tensor2d([1, 2, 3, 1, 1, 1], [2, 3]);
+        var res = tf.prod(a, 1);
+        expect(res.shape).toEqual([2]);
+        test_util_1.expectArraysClose(res, [6, 1]);
+    });
+    it('2D, axis = -1 provided as number', function () {
+        var a = tf.tensor2d([1, 2, 3, 1, 1, 1], [2, 3]);
+        var res = tf.prod(a, -1);
+        expect(res.shape).toEqual([2]);
+        test_util_1.expectArraysClose(res, [6, 1]);
+    });
+    it('prods across axis=0,1 in 2D array', function () {
+        var a = tf.tensor2d([1, 2, 3, 1, 1, 1], [3, 2]);
+        var res = tf.prod(a, [0, 1]);
+        expect(res.shape).toEqual([]);
+        test_util_1.expectArraysClose(res, [6]);
+    });
+    it('2D, axis=[-1,-2] in 2D array', function () {
+        var a = tf.tensor2d([1, 2, 3, 1, 1, 1], [3, 2]);
+        var res = tf.prod(a, [-1, -2]);
+        expect(res.shape).toEqual([]);
+        test_util_1.expectArraysClose(res, [6]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.prod({}); })
+            .toThrowError(/Argument 'x' passed to 'prod' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var result = tf.prod([[1, 2], [3, 1], [1, 1]]);
+        test_util_1.expectNumbersClose(result.get(), 6);
+    });
+    it('throws error for string tensor', function () {
+        expect(function () { return tf.prod(['a']); })
+            .toThrowError(/Argument 'x' passed to 'prod' must be numeric tensor/);
+    });
+});
+jasmine_util_1.describeWithFlags('Reduction: mean', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var r = tf.mean(a);
+        expect(r.dtype).toBe('float32');
+        test_util_1.expectNumbersClose(r.get(), 7 / 6);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor2d([1, 2, 3, NaN, 0, 1], [3, 2]);
+        var r = tf.mean(a);
+        expect(r.dtype).toBe('float32');
+        expect(r.get()).toEqual(NaN);
+    });
+    it('mean(int32) => float32', function () {
+        var a = tf.tensor1d([1, 5, 7, 3], 'int32');
+        var r = tf.mean(a);
+        expect(r.dtype).toBe('float32');
+        test_util_1.expectNumbersClose(r.get(), 4);
+    });
+    it('mean(bool) => float32', function () {
+        var a = tf.tensor1d([true, false, false, true, true], 'bool');
+        var r = tf.mean(a);
+        expect(r.dtype).toBe('float32');
+        test_util_1.expectNumbersClose(r.get(), 3 / 5);
+    });
+    it('2D array with keep dim', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var res = tf.mean(a, null, true);
+        expect(res.shape).toEqual([1, 1]);
+        expect(res.dtype).toBe('float32');
+        test_util_1.expectArraysClose(res, [7 / 6]);
+    });
+    it('axis=0 in 2D array', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var res = tf.mean(a, [0]);
+        expect(res.shape).toEqual([2]);
+        expect(res.dtype).toBe('float32');
+        test_util_1.expectArraysClose(res, [4 / 3, 1]);
+    });
+    it('axis=0 in 2D array, keepDims', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var res = tf.mean(a, [0], true);
+        expect(res.shape).toEqual([1, 2]);
+        expect(res.dtype).toBe('float32');
+        test_util_1.expectArraysClose(res, [4 / 3, 1]);
+    });
+    it('axis=1 in 2D array', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var res = tf.mean(a, [1]);
+        expect(res.dtype).toBe('float32');
+        expect(res.shape).toEqual([3]);
+        test_util_1.expectArraysClose(res, [1.5, 1.5, 0.5]);
+    });
+    it('axis = -1 in 2D array', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var res = tf.mean(a, [-1]);
+        expect(res.dtype).toBe('float32');
+        expect(res.shape).toEqual([3]);
+        test_util_1.expectArraysClose(res, [1.5, 1.5, 0.5]);
+    });
+    it('2D, axis=1 provided as number', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [2, 3]);
+        var res = tf.mean(a, 1);
+        expect(res.shape).toEqual([2]);
+        expect(res.dtype).toBe('float32');
+        test_util_1.expectArraysClose(res, [2, 1 / 3]);
+    });
+    it('axis=0,1 in 2D array', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var res = tf.mean(a, [0, 1]);
+        expect(res.shape).toEqual([]);
+        expect(res.dtype).toBe('float32');
+        test_util_1.expectArraysClose(res, [7 / 6]);
+    });
+    it('gradients', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var dy = tf.scalar(1.5);
+        var da = tf.grad(function (a) { return a.mean(); })(a, dy);
+        expect(da.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(da, [
+            dy.get() / a.size, dy.get() / a.size, dy.get() / a.size,
+            dy.get() / a.size, dy.get() / a.size, dy.get() / a.size
+        ]);
+    });
+    it('gradients throws for defined axis', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var dy = tf.scalar(1.5);
+        expect(function () { return tf.grad(function (a) { return a.mean(1); })(a, dy); }).toThrowError();
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.mean({}); })
+            .toThrowError(/Argument 'x' passed to 'mean' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var r = tf.mean([[1, 2, 3], [0, 0, 1]]);
+        expect(r.dtype).toBe('float32');
+        test_util_1.expectNumbersClose(r.get(), 7 / 6);
+    });
+    it('throws error for string tensor', function () {
+        expect(function () { return tf.mean(['a']); })
+            .toThrowError(/Argument 'x' passed to 'mean' must be numeric tensor/);
+    });
+});
+jasmine_util_1.describeWithFlags('Reduction: moments', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var _a = tf.moments(a), mean = _a.mean, variance = _a.variance;
+        expect(mean.dtype).toBe('float32');
+        expect(variance.dtype).toBe('float32');
+        test_util_1.expectNumbersClose(mean.get(), 7 / 6);
+        test_util_1.expectNumbersClose(variance.get(), 1.1389);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor2d([1, 2, 3, NaN, 0, 1], [3, 2]);
+        var _a = tf.moments(a), mean = _a.mean, variance = _a.variance;
+        expect(mean.dtype).toBe('float32');
+        expect(variance.dtype).toBe('float32');
+        expect(mean.get()).toEqual(NaN);
+        expect(variance.get()).toEqual(NaN);
+    });
+    it('moments(int32) => float32', function () {
+        var a = tf.tensor1d([1, 5, 7, 3], 'int32');
+        var _a = tf.moments(a), mean = _a.mean, variance = _a.variance;
+        expect(mean.dtype).toBe('float32');
+        expect(variance.dtype).toBe('float32');
+        test_util_1.expectNumbersClose(mean.get(), 4);
+        test_util_1.expectNumbersClose(variance.get(), 5);
+    });
+    it('moments(bool) => float32', function () {
+        var a = tf.tensor1d([true, false, false, true, true], 'bool');
+        var _a = tf.moments(a), mean = _a.mean, variance = _a.variance;
+        expect(mean.dtype).toBe('float32');
+        expect(variance.dtype).toBe('float32');
+        test_util_1.expectNumbersClose(mean.get(), 3 / 5);
+        test_util_1.expectNumbersClose(variance.get(), 0.23999998);
+    });
+    it('2D array with keep dim', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var _a = tf.moments(a, null, true), mean = _a.mean, variance = _a.variance;
+        expect(mean.shape).toEqual([1, 1]);
+        expect(mean.dtype).toBe('float32');
+        expect(variance.shape).toEqual([1, 1]);
+        expect(variance.dtype).toBe('float32');
+        test_util_1.expectArraysClose(mean, [7 / 6]);
+        test_util_1.expectArraysClose(variance, [1.138889]);
+    });
+    it('axis=0 in 2D array', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var _a = tf.moments(a, [0]), mean = _a.mean, variance = _a.variance;
+        expect(mean.shape).toEqual([2]);
+        expect(mean.dtype).toBe('float32');
+        expect(variance.shape).toEqual([2]);
+        expect(variance.dtype).toBe('float32');
+        test_util_1.expectArraysClose(mean, [4 / 3, 1]);
+        test_util_1.expectArraysClose(variance, [1.556, 2 / 3]);
+    });
+    it('axis=1 in 2D array', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var _a = tf.moments(a, [1]), mean = _a.mean, variance = _a.variance;
+        expect(mean.dtype).toBe('float32');
+        expect(mean.shape).toEqual([3]);
+        expect(variance.dtype).toBe('float32');
+        expect(variance.shape).toEqual([3]);
+        test_util_1.expectArraysClose(mean, [1.5, 1.5, 0.5]);
+        test_util_1.expectArraysClose(variance, [0.25, 2.25, 0.25]);
+    });
+    it('2D, axis=1 provided as number', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [2, 3]);
+        var _a = tf.moments(a, 1), mean = _a.mean, variance = _a.variance;
+        expect(mean.shape).toEqual([2]);
+        expect(mean.dtype).toBe('float32');
+        expect(variance.shape).toEqual([2]);
+        expect(variance.dtype).toBe('float32');
+        test_util_1.expectArraysClose(mean, [2, 1 / 3]);
+        test_util_1.expectArraysClose(variance, [2 / 3, 0.222]);
+    });
+    it('2D, axis=-1 provided as number', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [2, 3]);
+        var _a = tf.moments(a, -1), mean = _a.mean, variance = _a.variance;
+        expect(mean.shape).toEqual([2]);
+        expect(mean.dtype).toBe('float32');
+        expect(variance.shape).toEqual([2]);
+        expect(variance.dtype).toBe('float32');
+        test_util_1.expectArraysClose(mean, [2, 1 / 3]);
+        test_util_1.expectArraysClose(variance, [2 / 3, 0.222]);
+    });
+    it('axis=0,1 in 2D array', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var _a = tf.moments(a, [0, 1]), mean = _a.mean, variance = _a.variance;
+        expect(mean.shape).toEqual([]);
+        expect(mean.dtype).toBe('float32');
+        expect(variance.shape).toEqual([]);
+        expect(variance.dtype).toBe('float32');
+        test_util_1.expectArraysClose(mean, [7 / 6]);
+        test_util_1.expectArraysClose(variance, [1.1389]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.moments({}); })
+            .toThrowError(/Argument 'x' passed to 'moments' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var _a = tf.moments([1, 2, 3, 0, 0, 1]), mean = _a.mean, variance = _a.variance;
+        expect(mean.dtype).toBe('float32');
+        expect(variance.dtype).toBe('float32');
+        test_util_1.expectNumbersClose(mean.get(), 7 / 6);
+        test_util_1.expectNumbersClose(variance.get(), 1.1389);
+    });
+});
+jasmine_util_1.describeWithFlags('Reduction: norm', test_util_1.ALL_ENVS, function () {
+    it('scalar norm', function () {
+        var a = tf.scalar(-22.0);
+        var norm = tf.norm(a);
+        expect(norm.dtype).toBe('float32');
+        test_util_1.expectNumbersClose(norm.get(), 22);
+    });
+    it('vector inf norm', function () {
+        var a = tf.tensor1d([1, -2, 3, -4]);
+        var norm = tf.norm(a, Infinity);
+        expect(norm.dtype).toBe('float32');
+        test_util_1.expectNumbersClose(norm.get(), 4);
+    });
+    it('vector -inf norm', function () {
+        var a = tf.tensor1d([1, -2, 3, -4]);
+        var norm = tf.norm(a, -Infinity);
+        expect(norm.dtype).toBe('float32');
+        test_util_1.expectNumbersClose(norm.get(), 1);
+    });
+    it('vector 1 norm', function () {
+        var a = tf.tensor1d([1, -2, 3, -4]);
+        var norm = tf.norm(a, 1);
+        expect(norm.dtype).toBe('float32');
+        test_util_1.expectNumbersClose(norm.get(), 10);
+    });
+    it('vector euclidean norm', function () {
+        var a = tf.tensor1d([1, -2, 3, -4]);
+        var norm = tf.norm(a, 'euclidean');
+        expect(norm.dtype).toBe('float32');
+        test_util_1.expectNumbersClose(norm.get(), 5.4772);
+    });
+    it('vector 2-norm', function () {
+        var a = tf.tensor1d([1, -2, 3, -4]);
+        var norm = tf.norm(a, 2);
+        expect(norm.dtype).toBe('float32');
+        test_util_1.expectNumbersClose(norm.get(), 5.4772);
+    });
+    it('vector >2-norm to throw error', function () {
+        var a = tf.tensor1d([1, -2, 3, -4]);
+        expect(function () { return tf.norm(a, 3); }).toThrowError();
+    });
+    it('matrix inf norm', function () {
+        var a = tf.tensor2d([1, 2, -3, 1, 0, 1], [3, 2]);
+        var norm = tf.norm(a, Infinity, [0, 1]);
+        expect(norm.dtype).toBe('float32');
+        test_util_1.expectNumbersClose(norm.get(), 4);
+    });
+    it('matrix -inf norm', function () {
+        var a = tf.tensor2d([1, 2, -3, 1, 0, 1], [3, 2]);
+        var norm = tf.norm(a, -Infinity, [0, 1]);
+        expect(norm.dtype).toBe('float32');
+        test_util_1.expectNumbersClose(norm.get(), 1);
+    });
+    it('matrix 1 norm', function () {
+        var a = tf.tensor2d([1, 2, -3, 1, 1, 1], [3, 2]);
+        var norm = tf.norm(a, 1, [0, 1]);
+        expect(norm.dtype).toBe('float32');
+        test_util_1.expectNumbersClose(norm.get(), 5);
+    });
+    it('matrix euclidean norm', function () {
+        var a = tf.tensor2d([1, 2, -3, 1, 1, 1], [3, 2]);
+        var norm = tf.norm(a, 'euclidean', [0, 1]);
+        expect(norm.dtype).toBe('float32');
+        test_util_1.expectNumbersClose(norm.get(), 4.123);
+    });
+    it('matrix fro norm', function () {
+        var a = tf.tensor2d([1, 2, -3, 1, 1, 1], [3, 2]);
+        var norm = tf.norm(a, 'fro', [0, 1]);
+        expect(norm.dtype).toBe('float32');
+        test_util_1.expectNumbersClose(norm.get(), 4.123);
+    });
+    it('matrix other norm to throw error', function () {
+        var a = tf.tensor2d([1, 2, -3, 1, 1, 1], [3, 2]);
+        expect(function () { return tf.norm(a, 2, [0, 1]); }).toThrowError();
+    });
+    it('propagates NaNs for norm', function () {
+        var a = tf.tensor2d([1, 2, 3, NaN, 0, 1], [3, 2]);
+        var norm = tf.norm(a);
+        expect(norm.dtype).toBe('float32');
+        expect(norm.get()).toEqual(NaN);
+    });
+    it('axis=null in 2D array norm', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var norm = tf.norm(a, Infinity);
+        expect(norm.shape).toEqual([]);
+        expect(norm.dtype).toBe('float32');
+        test_util_1.expectArraysClose(norm, [3]);
+    });
+    it('2D array norm with keep dim', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var norm = tf.norm(a, Infinity, null, true);
+        expect(norm.shape).toEqual([1, 1]);
+        expect(norm.dtype).toBe('float32');
+        test_util_1.expectArraysClose(norm, [3]);
+    });
+    it('axis=0 in 2D array norm', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var norm = tf.norm(a, Infinity, [0]);
+        expect(norm.shape).toEqual([2]);
+        expect(norm.dtype).toBe('float32');
+        test_util_1.expectArraysClose(norm, [3, 2]);
+    });
+    it('axis=1 in 2D array norm', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var norm = tf.norm(a, Infinity, [1]);
+        expect(norm.dtype).toBe('float32');
+        expect(norm.shape).toEqual([3]);
+        test_util_1.expectArraysClose(norm, [2, 3, 1]);
+    });
+    it('axis=1 keepDims in 2D array norm', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var norm = tf.norm(a, Infinity, [1], true);
+        expect(norm.dtype).toBe('float32');
+        expect(norm.shape).toEqual([3, 1]);
+        test_util_1.expectArraysClose(norm, [2, 3, 1]);
+    });
+    it('2D norm with axis=1 provided as number', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [2, 3]);
+        var norm = tf.norm(a, Infinity, 1);
+        expect(norm.shape).toEqual([2]);
+        expect(norm.dtype).toBe('float32');
+        test_util_1.expectArraysClose(norm, [3, 1]);
+    });
+    it('axis=0,1 in 2D array norm', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var norm = tf.norm(a, Infinity, [0, 1]);
+        expect(norm.shape).toEqual([]);
+        expect(norm.dtype).toBe('float32');
+        test_util_1.expectArraysClose(norm, [3]);
+    });
+    it('axis=0,1 keepDims in 2D array norm', function () {
+        var a = tf.tensor2d([1, 2, 3, 0, 0, 1], [3, 2]);
+        var norm = tf.norm(a, Infinity, [0, 1], true);
+        expect(norm.shape).toEqual([1, 1]);
+        expect(norm.dtype).toBe('float32');
+        test_util_1.expectArraysClose(norm, [3]);
+    });
+    it('3D norm axis=0,1, matrix inf norm', function () {
+        var a = tf.tensor3d([1, 2, -3, 1, 0, 1], [3, 2, 1]);
+        var norm = tf.norm(a, Infinity, [0, 1]);
+        expect(norm.shape).toEqual([1]);
+        expect(norm.dtype).toBe('float32');
+        test_util_1.expectArraysClose(norm, [4]);
+    });
+    it('axis=0,1 keepDims in 3D array norm', function () {
+        var a = tf.tensor3d([1, 2, 3, 0, 0, 1], [3, 2, 1]);
+        var norm = tf.norm(a, Infinity, [0, 1], true);
+        expect(norm.shape).toEqual([1, 1, 1]);
+        expect(norm.dtype).toBe('float32');
+        test_util_1.expectArraysClose(norm, [3]);
+    });
+    it('axis=0,1 keepDims in 3D array norm', function () {
+        var a = tf.tensor3d([1, 2, 3, 0, 0, 1, 1, 2, 3, 0, 0, 1], [3, 2, 2]);
+        var norm = tf.norm(a, Infinity, [0, 1], true);
+        expect(norm.shape).toEqual([1, 1, 2]);
+        expect(norm.dtype).toBe('float32');
+        test_util_1.expectArraysClose(norm, [4, 3]);
+    });
+    it('axis=null in 3D array norm', function () {
+        var a = tf.tensor3d([1, 2, 3, 0, 0, 1], [3, 2, 1]);
+        var norm = tf.norm(a, Infinity);
+        expect(norm.shape).toEqual([]);
+        expect(norm.dtype).toBe('float32');
+        test_util_1.expectArraysClose(norm, [3]);
+    });
+    it('axis=null in 4D array norm', function () {
+        var a = tf.tensor4d([1, 2, 3, 0, 0, 1], [3, 2, 1, 1]);
+        var norm = tf.norm(a, Infinity);
+        expect(norm.shape).toEqual([]);
+        expect(norm.dtype).toBe('float32');
+        test_util_1.expectArraysClose(norm, [3]);
+    });
+    it('axis=0,1 in 4D array norm', function () {
+        var a = tf.tensor4d([
+            1, 2, 3, 0, 0, 1, 1, 2, 3, 0, 0, 1,
+            1, 2, 3, 0, 0, 1, 1, 2, 3, 0, 0, 1
+        ], [3, 2, 2, 2]);
+        var norm = tf.norm(a, Infinity, [0, 1]);
+        expect(norm.shape).toEqual([2, 2]);
+        expect(norm.dtype).toBe('float32');
+        test_util_1.expectArraysClose(norm, [4, 3, 4, 3]);
+    });
+    it('axis=0,1 in 4D array norm', function () {
+        var a = tf.tensor4d([
+            1, 2, 3, 0, 0, 1, 1, 2, 3, 0, 0, 1,
+            1, 2, 3, 0, 0, 1, 1, 2, 3, 0, 0, 1
+        ], [3, 2, 2, 2]);
+        var norm = tf.norm(a, Infinity, [0, 1], true);
+        expect(norm.shape).toEqual([1, 1, 2, 2]);
+        expect(norm.dtype).toBe('float32');
+        test_util_1.expectArraysClose(norm, [4, 3, 4, 3]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.norm({}); })
+            .toThrowError(/Argument 'x' passed to 'norm' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var norm = tf.norm([1, -2, 3, -4], 1);
+        expect(norm.dtype).toBe('float32');
+        test_util_1.expectNumbersClose(norm.get(), 10);
+    });
+    it('throws error for string tensors', function () {
+        expect(function () { return tf.norm(['a', 'b']); })
+            .toThrowError(/Argument 'x' passed to 'norm' must be numeric tensor/);
+    });
+});
+jasmine_util_1.describeWithFlags('Reduction: all', test_util_1.ALL_ENVS, function () {
+    it('Tensor1D', function () {
+        var a = tf.tensor1d([0, 0, 0], 'bool');
+        test_util_1.expectNumbersClose(tf.all(a).get(), 0);
+        a = tf.tensor1d([1, 0, 1], 'bool');
+        test_util_1.expectNumbersClose(tf.all(a).get(), 0);
+        a = tf.tensor1d([1, 1, 1], 'bool');
+        test_util_1.expectNumbersClose(tf.all(a).get(), 1);
+    });
+    it('ignores NaNs', function () {
+        var a = tf.tensor1d([1, NaN, 1], 'bool');
+        expect(tf.all(a).get()).toEqual(1);
+    });
+    it('2D', function () {
+        var a = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');
+        test_util_1.expectNumbersClose(tf.all(a).get(), 0);
+    });
+    it('2D axis=[0,1]', function () {
+        var a = tf.tensor2d([1, 1, 0, 0, 1, 0], [2, 3], 'bool');
+        test_util_1.expectNumbersClose(tf.all(a, [0, 1]).get(), 0);
+    });
+    it('2D, axis=0', function () {
+        var a = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');
+        var r = tf.all(a, 0);
+        expect(r.shape).toEqual([2]);
+        test_util_1.expectArraysClose(r, [0, 0]);
+        r = tf.all(a, 1);
+        expect(r.shape).toEqual([2]);
+        test_util_1.expectArraysClose(r, [1, 0]);
+    });
+    it('2D, axis=0, keepDims', function () {
+        var a = tf.tensor2d([1, 1, 0, 0, 1, 0], [2, 3], 'bool');
+        var r = a.all(0, true);
+        expect(r.shape).toEqual([1, 3]);
+        test_util_1.expectArraysClose(r, [0, 1, 0]);
+    });
+    it('2D, axis=1 provided as a number', function () {
+        var a = tf.tensor2d([1, 1, 0, 0, 1, 0], [2, 3], 'bool');
+        var r = tf.all(a, 1);
+        test_util_1.expectArraysClose(r, [0, 0]);
+    });
+    it('2D, axis = -1 provided as a number', function () {
+        var a = tf.tensor2d([1, 1, 0, 0, 1, 0], [2, 3], 'bool');
+        var r = tf.all(a, -1);
+        test_util_1.expectArraysClose(r, [0, 0]);
+    });
+    it('2D, axis=[1]', function () {
+        var a = tf.tensor2d([1, 1, 0, 0, 1, 0], [2, 3], 'bool');
+        var r = tf.all(a, [1]);
+        test_util_1.expectArraysClose(r, [0, 0]);
+    });
+    it('throws when dtype is not boolean', function () {
+        var a = tf.tensor2d([1, 1, 0, 0], [2, 2]);
+        expect(function () { return tf.all(a); })
+            .toThrowError(/Argument 'x' passed to 'all' must be bool tensor, but got float/);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.all({}); })
+            .toThrowError(/Argument 'x' passed to 'all' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [0, 0, 0];
+        test_util_1.expectNumbersClose(tf.all(a).get(), 0);
+    });
+    it('throws error for string tensor', function () {
+        expect(function () { return tf.all(['a']); })
+            .toThrowError(/Argument 'x' passed to 'all' must be bool tensor, but got string/);
+    });
+});
+jasmine_util_1.describeWithFlags('Reduction: any', test_util_1.ALL_ENVS, function () {
+    it('Tensor1D', function () {
+        var a = tf.tensor1d([0, 0, 0], 'bool');
+        test_util_1.expectNumbersClose(tf.any(a).get(), 0);
+        a = tf.tensor1d([1, 0, 1], 'bool');
+        test_util_1.expectNumbersClose(tf.any(a).get(), 1);
+        a = tf.tensor1d([1, 1, 1], 'bool');
+        test_util_1.expectNumbersClose(tf.any(a).get(), 1);
+    });
+    it('ignores NaNs', function () {
+        var a = tf.tensor1d([1, NaN, 0], 'bool');
+        expect(tf.any(a).get()).toEqual(1);
+    });
+    it('2D', function () {
+        var a = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');
+        test_util_1.expectNumbersClose(tf.any(a).get(), 1);
+    });
+    it('2D axis=[0,1]', function () {
+        var a = tf.tensor2d([1, 1, 0, 0, 1, 0], [2, 3], 'bool');
+        test_util_1.expectNumbersClose(tf.any(a, [0, 1]).get(), 1);
+    });
+    it('2D, axis=0', function () {
+        var a = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');
+        var r = tf.any(a, 0);
+        expect(r.shape).toEqual([2]);
+        test_util_1.expectArraysClose(r, [1, 1]);
+        r = tf.any(a, 1);
+        expect(r.shape).toEqual([2]);
+        test_util_1.expectArraysClose(r, [1, 0]);
+    });
+    it('2D, axis=0, keepDims', function () {
+        var a = tf.tensor2d([1, 1, 0, 0, 1, 0], [2, 3], 'bool');
+        var r = a.any(0, true);
+        expect(r.shape).toEqual([1, 3]);
+        test_util_1.expectArraysClose(r, [1, 1, 0]);
+    });
+    it('2D, axis=1 provided as a number', function () {
+        var a = tf.tensor2d([1, 1, 0, 0, 1, 0], [2, 3], 'bool');
+        var r = tf.any(a, 1);
+        test_util_1.expectArraysClose(r, [1, 1]);
+    });
+    it('2D, axis = -1 provided as a number', function () {
+        var a = tf.tensor2d([1, 1, 0, 0, 1, 0], [2, 3], 'bool');
+        var r = tf.any(a, -1);
+        test_util_1.expectArraysClose(r, [1, 1]);
+    });
+    it('2D, axis=[1]', function () {
+        var a = tf.tensor2d([1, 1, 0, 0, 1, 0], [2, 3], 'bool');
+        var r = tf.any(a, [1]);
+        test_util_1.expectArraysClose(r, [1, 1]);
+    });
+    it('throws when dtype is not boolean', function () {
+        var a = tf.tensor2d([1, 1, 0, 0], [2, 2]);
+        expect(function () { return tf.any(a); })
+            .toThrowError(/Argument 'x' passed to 'any' must be bool tensor, but got float/);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.any({}); })
+            .toThrowError(/Argument 'x' passed to 'any' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [0, 0, 0];
+        test_util_1.expectNumbersClose(tf.any(a).get(), 0);
+    });
+    it('throws error for string tensor', function () {
+        expect(function () { return tf.any(['a']); })
+            .toThrowError(/Argument 'x' passed to 'any' must be bool tensor/);
+    });
+});
+//# sourceMappingURL=reduction_ops_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/reduction_ops_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/reduction_ops_test.js.map
new file mode 100644
index 0000000..fe26239
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+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/reduction_ops_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/relu_ops.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/relu_ops.d.ts
new file mode 100644
index 0000000..16d3050
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/relu_ops.d.ts
@@ -0,0 +1,13 @@
+import { Tensor } from '../tensor';
+import { TensorLike } from '../types';
+declare function relu_<T extends Tensor>(x: T | TensorLike): T;
+declare function elu_<T extends Tensor>(x: T | TensorLike): T;
+declare function selu_<T extends Tensor>(x: T | TensorLike): T;
+declare function leakyRelu_<T extends Tensor>(x: T | TensorLike, alpha?: number): T;
+declare function prelu_<T extends Tensor>(x: T | TensorLike, alpha: T | TensorLike): T;
+export declare const elu: typeof elu_;
+export declare const leakyRelu: typeof leakyRelu_;
+export declare const prelu: typeof prelu_;
+export declare const relu: typeof relu_;
+export declare const selu: typeof selu_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/relu_ops.js b/node_modules/@tensorflow/tfjs-core/dist/ops/relu_ops.js
new file mode 100644
index 0000000..d33deef
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/relu_ops.js
@@ -0,0 +1,65 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var tensor_util_env_1 = require("../tensor_util_env");
+var binary_ops_1 = require("./binary_ops");
+var logical_ops_1 = require("./logical_ops");
+var operation_1 = require("./operation");
+var selu_util_1 = require("./selu_util");
+var tensor_ops_1 = require("./tensor_ops");
+function relu_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'relu');
+    if ($x.dtype === 'bool') {
+        return $x.toInt();
+    }
+    var grad = function (dy) {
+        var stepRes = $x.step();
+        return { $x: function () { return dy.mulStrict(stepRes.toFloat()); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.relu($x); }, { $x: $x }, grad);
+}
+function elu_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'elu');
+    var grad = function (dy, saved) {
+        var y = saved[0];
+        return {
+            $x: function () {
+                return environment_1.ENV.engine.runKernel(function (backend) { return backend.eluDer(dy, y); }, { dy: dy, y: y });
+            }
+        };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend, save) { return save(backend.elu($x)); }, { $x: $x }, grad);
+}
+function selu_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'selu');
+    var grad = function (dy) {
+        return {
+            $x: function () {
+                var mask = $x.greater(tensor_ops_1.scalar(0));
+                var scaleAlpha = tensor_ops_1.scalar(selu_util_1.SELU_SCALEALPHA);
+                var scale = tensor_ops_1.scalar(selu_util_1.SELU_SCALE);
+                var greaterThanZeroDer = dy.mul(scale);
+                var lessEqualZeroDer = dy.mul(scaleAlpha).mul($x.toFloat().exp());
+                return logical_ops_1.where(mask, greaterThanZeroDer, lessEqualZeroDer);
+            }
+        };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.selu($x); }, { $x: $x }, grad);
+}
+function leakyRelu_(x, alpha) {
+    if (alpha === void 0) { alpha = 0.2; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'leakyRelu');
+    return binary_ops_1.maximum(tensor_ops_1.scalar(alpha).mul($x), $x);
+}
+function prelu_(x, alpha) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'prelu');
+    var $alpha = tensor_util_env_1.convertToTensor(alpha, 'alpha', 'prelu');
+    var zero = tensor_ops_1.scalar(0);
+    return binary_ops_1.maximum(zero, $x).add($alpha.mul(binary_ops_1.minimum(zero, $x)));
+}
+exports.elu = operation_1.op({ elu_: elu_ });
+exports.leakyRelu = operation_1.op({ leakyRelu_: leakyRelu_ });
+exports.prelu = operation_1.op({ prelu_: prelu_ });
+exports.relu = operation_1.op({ relu_: relu_ });
+exports.selu = operation_1.op({ selu_: selu_ });
+//# sourceMappingURL=relu_ops.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/relu_ops.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/relu_ops.js.map
new file mode 100644
index 0000000..4041d22
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/relu_ops.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"relu_ops.js","sourceRoot":"","sources":["../../src/ops/relu_ops.ts"],"names":[],"mappings":";;AAiBA,8CAAmC;AAEnC,sDAAmD;AAEnD,2CAA8C;AAC9C,6CAAoC;AACpC,yCAA+B;AAC/B,yCAAwD;AACxD,2CAAoC;AAcpC,eAAiC,CAAe;IAC9C,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,MAAM,CAAC,CAAC;IAE3C,IAAI,EAAE,CAAC,KAAK,KAAK,MAAM,EAAE;QACvB,OAAO,EAAE,CAAC,KAAK,EAAE,CAAC;KACnB;IACD,IAAM,IAAI,GAAG,UAAC,EAAK;QACjB,IAAM,OAAO,GAAG,EAAE,CAAC,IAAI,EAAE,CAAC;QAC1B,OAAO,EAAC,EAAE,EAAE,cAAM,OAAA,EAAE,CAAC,SAAS,CAAC,OAAO,CAAC,OAAO,EAAE,CAAC,EAA/B,CAA+B,EAAC,CAAC;IACrD,CAAC,CAAC;IACF,OAAO,iBAAG,CAAC,MAAM,CAAC,SAAS,CAAC,UAAA,OAAO,IAAI,OAAA,OAAO,CAAC,IAAI,CAAC,EAAE,CAAC,EAAhB,CAAgB,EAAE,EAAC,EAAE,IAAA,EAAC,EAAE,IAAI,CAAC,CAAC;AACvE,CAAC;AAaD,cAAgC,CAAe;IAC7C,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,KAAK,CAAC,CAAC;IAE1C,IAAM,IAAI,GAAG,UAAC,EAAK,EAAE,KAAe;QAC3B,IAAA,YAAC,CAAU;QAClB,OAAO;YACL,EAAE,EAAE;gBACA,OAAA,iBAAG,CAAC,MAAM,CAAC,SAAS,CAAC,UAAA,OAAO,IAAI,OAAA,OAAO,CAAC,MAAM,CAAC,EAAE,EAAE,CAAC,CAAC,EAArB,CAAqB,EAAE,EAAC,EAAE,IAAA,EAAE,CAAC,GAAA,EAAC,CAAM;YAApE,CAAoE;SACzE,CAAC;IACJ,CAAC,CAAC;IACF,OAAO,iBAAG,CAAC,MAAM,CAAC,SAAS,CACvB,UAAC,OAAO,EAAE,IAAI,IAAK,OAAA,IAAI,CAAC,OAAO,CAAC,GAAG,CAAC,EAAE,CAAC,CAAC,EAArB,CAAqB,EAAE,EAAC,EAAE,IAAA,EAAC,EAAE,IAAI,CAAC,CAAC;AAC5D,CAAC;AAeD,eAAiC,CAAe;IAC9C,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,MAAM,CAAC,CAAC;IAE3C,IAAM,IAAI,GAAG,UAAC,EAAK;QACjB,OAAO;YACL,EAAE,EAAE;gBACF,IAAM,IAAI,GAAG,EAAE,CAAC,OAAO,CAAC,mBAAM,CAAC,CAAC,CAAC,CAAC,CAAC;gBAEnC,IAAM,UAAU,GAAG,mBAAM,CAAC,2BAAe,CAAC,CAAC;gBAC3C,IAAM,KAAK,GAAG,mBAAM,CAAC,sBAAU,CAAC,CAAC;gBAEjC,IAAM,kBAAkB,GAAG,EAAE,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC;gBACzC,IAAM,gBAAgB,GAAG,EAAE,CAAC,GAAG,CAAC,UAAU,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,OAAO,EAAE,CAAC,GAAG,EAAE,CAAC,CAAC;gBAEpE,OAAO,mBAAK,CAAC,IAAI,EAAE,kBAAkB,EAAE,gBAAgB,CAAM,CAAC;YAChE,CAAC;SACF,CAAC;IACJ,CAAC,CAAC;IACF,OAAO,iBAAG,CAAC,MAAM,CAAC,SAAS,CAAC,UAAA,OAAO,IAAI,OAAA,OAAO,CAAC,IAAI,CAAC,EAAE,CAAC,EAAhB,CAAgB,EAAE,EAAC,EAAE,IAAA,EAAC,EAAE,IAAI,CAAC,CAAC;AACvE,CAAC;AAkBD,oBAAsC,CAAe,EAAE,KAAW;IAAX,sBAAA,EAAA,WAAW;IAChE,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,WAAW,CAAC,CAAC;IAChD,OAAO,oBAAO,CAAC,mBAAM,CAAC,KAAK,CAAC,CAAC,GAAG,CAAC,EAAE,CAAC,EAAE,EAAE,CAAC,CAAC;AAC5C,CAAC;AAiBD,gBAAkC,CAAe,EAAE,KAAmB;IACpE,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,OAAO,CAAC,CAAC;IAC5C,IAAM,MAAM,GAAG,iCAAe,CAAC,KAAK,EAAE,OAAO,EAAE,OAAO,CAAC,CAAC;IAExD,IAAM,IAAI,GAAG,mBAAM,CAAC,CAAC,CAAC,CAAC;IACvB,OAAO,oBAAO,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC,GAAG,CAAC,MAAM,CAAC,GAAG,CAAC,oBAAO,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC,CAAC,CAAC;AAC9D,CAAC;AAEY,QAAA,GAAG,GAAG,cAAE,CAAC,EAAC,IAAI,MAAA,EAAC,CAAC,CAAC;AACjB,QAAA,SAAS,GAAG,cAAE,CAAC,EAAC,UAAU,YAAA,EAAC,CAAC,CAAC;AAC7B,QAAA,KAAK,GAAG,cAAE,CAAC,EAAC,MAAM,QAAA,EAAC,CAAC,CAAC;AACrB,QAAA,IAAI,GAAG,cAAE,CAAC,EAAC,KAAK,OAAA,EAAC,CAAC,CAAC;AACnB,QAAA,IAAI,GAAG,cAAE,CAAC,EAAC,KAAK,OAAA,EAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/resize_bilinear_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/resize_bilinear_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/resize_bilinear_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/resize_bilinear_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/resize_bilinear_test.js
new file mode 100644
index 0000000..440ac9d
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/resize_bilinear_test.js
@@ -0,0 +1,678 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('resizeBilinear', test_util_1.ALL_ENVS, function () {
+    it('simple alignCorners=false', function () {
+        var input = tf.tensor3d([2, 2, 4, 4], [2, 2, 1]);
+        var output = input.resizeBilinear([3, 3], false);
+        test_util_1.expectArraysClose(output, [2, 2, 2, 10 / 3, 10 / 3, 10 / 3, 4, 4, 4]);
+    });
+    it('simple alignCorners=true', function () {
+        var input = tf.tensor3d([2, 2, 4, 4], [2, 2, 1]);
+        var output = input.resizeBilinear([3, 3], true);
+        test_util_1.expectArraysClose(output, [2, 2, 2, 3, 3, 3, 4, 4, 4]);
+    });
+    it('matches tensorflow w/ random numbers alignCorners=false', function () {
+        var input = tf.tensor3d([
+            1.19074044, 0.91373104, 2.01611669, -0.52270832, 0.38725395,
+            1.30809779, 0.61835143, 3.49600659, 2.09230986, 0.56473997,
+            0.03823943, 1.19864896
+        ], [2, 3, 2]);
+        var output = input.resizeBilinear([4, 5], false);
+        test_util_1.expectArraysClose(output, [
+            1.19074047, 0.91373104, 1.68596613, 0.05186744, 1.69034398, -0.15654698,
+            0.7130264, 0.94193673, 0.38725394, 1.30809784, 0.9045459, 2.20486879,
+            1.59434628, 0.89455694, 1.68591988, 0.26748738, 0.58103991, 1.00690198,
+            0.21274668, 1.25337338, 0.6183514, 3.49600649, 1.50272655, 1.73724651,
+            1.68149579, 0.69152176, 0.44905344, 1.07186723, 0.03823943, 1.19864893,
+            0.6183514, 3.49600649, 1.50272655, 1.73724651, 1.68149579, 0.69152176,
+            0.44905344, 1.07186723, 0.03823943, 1.19864893
+        ]);
+    });
+    it('matches tensorflow w/ random numbers alignCorners=true', function () {
+        var input = tf.tensor3d([
+            1.56324531, 2.13817752, 1.44398421, 1.07632684, 0.59306785,
+            -0.36970865, 1.62451879, 1.8367334, 1.13944798, 2.01993218,
+            2.01919952, 2.67524054
+        ], [2, 3, 2]);
+        var output = input.resizeBilinear([4, 5], true);
+        test_util_1.expectArraysClose(output, [
+            1.5632453, 2.13817763, 1.50361478, 1.60725224, 1.44398427, 1.07632685,
+            1.01852608, 0.35330909, 0.59306782, -0.36970866, 1.58366978, 2.03769612,
+            1.46307099, 1.71427906, 1.3424722, 1.39086199, 1.20545864, 1.01806819,
+            1.06844509, 0.6452744, 1.60409427, 1.93721485, 1.42252707, 1.82130599,
+            1.24096, 1.70539713, 1.3923912, 1.68282723, 1.54382229, 1.66025746,
+            1.62451875, 1.83673346, 1.38198328, 1.92833281, 1.13944793, 2.01993227,
+            1.57932377, 2.34758639, 2.01919961, 2.67524052
+        ]);
+    });
+    it('batch of 2, simple, alignCorners=true', function () {
+        var input = tf.tensor4d([2, 2, 4, 4, 3, 3, 5, 5], [2, 2, 2, 1]);
+        var output = input.resizeBilinear([3, 3], true);
+        test_util_1.expectArraysClose(output, [2, 2, 2, 3, 3, 3, 4, 4, 4, 3, 3, 3, 4, 4, 4, 5, 5, 5]);
+    });
+    it('target width = 1, alignCorners=true', function () {
+        var input = tf.tensor3d([
+            [
+                [120.68856811523438, 134.51638793945312, 83.03671264648438],
+                [121.58008575439453, 113.28836059570312, 136.3172149658203],
+                [79.38370513916016, 101.87127685546875, 104.54979705810547],
+                [96.31678771972656, 111.77168273925781, 83.73509979248047]
+            ],
+            [
+                [119.45088195800781, 88.98846435546875, 97.47553253173828],
+                [117.5562973022461, 108.26356506347656, 99.62212371826172],
+                [136.62701416015625, 94.10433197021484, 80.97366333007812],
+                [83.61205291748047, 90.60148620605469, 81.82512664794922]
+            ],
+            [
+                [103.0362777709961, 123.1098403930664, 125.62944030761719],
+                [92.2915267944336, 103.15729522705078, 119.18060302734375],
+                [102.93293762207031, 117.821044921875, 99.40152740478516],
+                [96.32952117919922, 105.80963134765625, 104.8491439819336]
+            ],
+            [
+                [104.87507629394531, 134.0189208984375, 111.02627563476562],
+                [85.4534683227539, 107.68426513671875, 103.03722381591797],
+                [89.70533752441406, 98.25298309326172, 78.42916870117188],
+                [113.6744613647461, 95.8189697265625, 122.75005340576172]
+            ]
+        ]);
+        var output = input.resizeBilinear([3, 1], true);
+        var expected = tf.tensor3d([
+            [[120.68857, 134.51639, 83.03671]],
+            [[111.243576, 106.04915, 111.55249]],
+            [[104.87508, 134.01892, 111.026276]]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('target height = 1, alignCorners=true', function () {
+        var input = tf.tensor3d([
+            [
+                [120.68856811523438, 134.51638793945312, 83.03671264648438],
+                [121.58008575439453, 113.28836059570312, 136.3172149658203],
+                [79.38370513916016, 101.87127685546875, 104.54979705810547],
+                [96.31678771972656, 111.77168273925781, 83.73509979248047]
+            ],
+            [
+                [119.45088195800781, 88.98846435546875, 97.47553253173828],
+                [117.5562973022461, 108.26356506347656, 99.62212371826172],
+                [136.62701416015625, 94.10433197021484, 80.97366333007812],
+                [83.61205291748047, 90.60148620605469, 81.82512664794922]
+            ],
+            [
+                [103.0362777709961, 123.1098403930664, 125.62944030761719],
+                [92.2915267944336, 103.15729522705078, 119.18060302734375],
+                [102.93293762207031, 117.821044921875, 99.40152740478516],
+                [96.32952117919922, 105.80963134765625, 104.8491439819336]
+            ],
+            [
+                [104.87507629394531, 134.0189208984375, 111.02627563476562],
+                [85.4534683227539, 107.68426513671875, 103.03722381591797],
+                [89.70533752441406, 98.25298309326172, 78.42916870117188],
+                [113.6744613647461, 95.8189697265625, 122.75005340576172]
+            ]
+        ]);
+        var output = input.resizeBilinear([1, 3], true);
+        var expected = tf.tensor3d([[
+                [120.68857, 134.51639, 83.03671],
+                [100.481895, 107.57982, 120.4335],
+                [96.31679, 111.77168, 83.7351]
+            ]]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('throws when passed a non-tensor', function () {
+        var e = /Argument 'images' passed to 'resizeBilinear' must be a Tensor/;
+        expect(function () { return tf.image.resizeBilinear({}, [
+            1, 1
+        ]); }).toThrowError(e);
+    });
+    it('accepts a tensor-like object', function () {
+        var input = [[[2], [2]], [[4], [4]]];
+        var output = tf.image.resizeBilinear(input, [3, 3], false);
+        test_util_1.expectArraysClose(output, [2, 2, 2, 10 / 3, 10 / 3, 10 / 3, 4, 4, 4]);
+    });
+});
+jasmine_util_1.describeWithFlags('resizeBilinear gradients', test_util_1.ALL_ENVS, function () {
+    it('greyscale: upscale, same aspect ratio', function () {
+        var input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
+        var dy = tf.tensor3d([
+            [[1.0], [2.0], [3.0], [4.0]], [[5.0], [6.0], [7.0], [8.0]],
+            [[9.0], [10.0], [11.0], [12.0]], [[13.0], [14.0], [15.0], [16.0]]
+        ]);
+        var size = [4, 4];
+        var alignCorners = false;
+        var g = tf.grad(function (i) { return tf.image.resizeBilinear(i, size, alignCorners); });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([[[6.0], [17.0]], [[38.0], [75.0]]]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('greyscale: upscale, same aspect ratio, align corners', function () {
+        var input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
+        var dy = tf.tensor3d([
+            [[1.0], [2.0], [3.0], [4.0]], [[5.0], [6.0], [7.0], [8.0]],
+            [[9.0], [10.0], [11.0], [12.0]], [[13.0], [14.0], [15.0], [16.0]]
+        ]);
+        var size = [4, 4];
+        var alignCorners = true;
+        var g = tf.grad(function (i) { return tf.image.resizeBilinear(i, size, alignCorners); });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [[17.333330154418945], [23.999998092651367]],
+            [[44.0], [50.66666793823242]]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('greyscale: upscale, taller than wider', function () {
+        var input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
+        var dy = tf.tensor3d([
+            [[1.0], [2.0], [3.0], [4.0]], [[5.0], [6.0], [7.0], [8.0]],
+            [[9.0], [10.0], [11.0], [12.0]], [[13.0], [14.0], [15.0], [16.0]],
+            [[17.0], [18.0], [19.0], [20.0]], [[21.0], [22.0], [23.0], [24.0]],
+            [[25.0], [26.0], [27.0], [28.0]], [[29.0], [30.0], [31.0], [32.0]],
+            [[33.0], [34.0], [35.0], [36.0]]
+        ]);
+        var size = [9, 4];
+        var alignCorners = false;
+        var g = tf.grad(function (i) { return tf.image.resizeBilinear(i, size, alignCorners); });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [[25.55555534362793], [55.5555534362793]],
+            [[208.44444274902344], [376.4444274902344]]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('greyscale: upscale, taller than wider, align corners', function () {
+        var input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
+        var dy = tf.tensor3d([
+            [[1.0], [2.0], [3.0], [4.0]], [[5.0], [6.0], [7.0], [8.0]],
+            [[9.0], [10.0], [11.0], [12.0]], [[13.0], [14.0], [15.0], [16.0]],
+            [[17.0], [18.0], [19.0], [20.0]], [[21.0], [22.0], [23.0], [24.0]],
+            [[25.0], [26.0], [27.0], [28.0]], [[29.0], [30.0], [31.0], [32.0]],
+            [[33.0], [34.0], [35.0], [36.0]]
+        ]);
+        var size = [9, 4];
+        var alignCorners = true;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeBilinear(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([[[99.0], [114.0]], [[219.00001525878906], [233.99998474121094]]]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('greyscale: upscale, wider than taller', function () {
+        var input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
+        var dy = tf.tensor3d([
+            [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0], [7.0]],
+            [[8.0], [9.0], [10.0], [11.0], [12.0], [13.0], [14.0]],
+            [[15.0], [16.0], [17.0], [18.0], [19.0], [20.0], [21.0]],
+            [[22.0], [23.0], [24.0], [25.0], [26.0], [27.0], [28.0]]
+        ]);
+        var size = [4, 7];
+        var alignCorners = false;
+        var g = tf.grad(function (i) { return tf.image.resizeBilinear(i, size, alignCorners); });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [[14.428570747375488], [52.07142639160156]],
+            [[98.71427917480469], [240.78573608398438]]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('greyscale: upscale, wider than taller, align corners', function () {
+        var input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
+        var dy = tf.tensor3d([
+            [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0], [7.0]],
+            [[8.0], [9.0], [10.0], [11.0], [12.0], [13.0], [14.0]],
+            [[15.0], [16.0], [17.0], [18.0], [19.0], [20.0], [21.0]],
+            [[22.0], [23.0], [24.0], [25.0], [26.0], [27.0], [28.0]]
+        ]);
+        var size = [4, 7];
+        var alignCorners = true;
+        var g = tf.grad(function (i) { return tf.image.resizeBilinear(i, size, alignCorners); });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([[[51.33332824707031], [70.0]], [[133.0], [151.66668701171875]]]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('greyscale: downscale, same aspect ratio', function () {
+        var input = tf.tensor3d([
+            [[100.0], [50.0], [25.0], [10.0]], [[60.0], [20.0], [80.0], [20.0]],
+            [[40.0], [15.0], [200.0], [203.0]], [[40.0], [10.0], [230.0], [200.0]]
+        ]);
+        var dy = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]]]);
+        var size = [2, 2];
+        var alignCorners = false;
+        var g = tf.grad(function (i) { return tf.image.resizeBilinear(i, size, alignCorners); });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [[1.0], [0.0], [2.0], [0.0]], [[0.0], [0.0], [0.0], [0.0]],
+            [[3.0], [0.0], [4.0], [0.0]], [[0.0], [0.0], [0.0], [0.0]]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('greyscale: downscale, same aspect ratio, align corners', function () {
+        var input = tf.tensor3d([
+            [[100.0], [50.0], [25.0], [10.0]], [[60.0], [20.0], [80.0], [20.0]],
+            [[40.0], [15.0], [200.0], [203.0]], [[40.0], [10.0], [230.0], [200.0]]
+        ]);
+        var dy = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]]]);
+        var size = [2, 2];
+        var alignCorners = true;
+        var g = tf.grad(function (i) { return tf.image.resizeBilinear(i, size, alignCorners); });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [[1.0], [0.0], [0.0], [2.0]], [[0.0], [0.0], [0.0], [0.0]],
+            [[0.0], [0.0], [0.0], [0.0]], [[3.0], [0.0], [0.0], [4.0]]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('greyscale: downscale, taller than wider', function () {
+        var input = tf.tensor3d([
+            [[100.0], [50.0], [25.0], [10.0]], [[60.0], [20.0], [80.0], [20.0]],
+            [[40.0], [15.0], [200.0], [203.0]], [[40.0], [10.0], [230.0], [200.0]]
+        ]);
+        var dy = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]], [[5.0], [6.0]]]);
+        var size = [3, 2];
+        var alignCorners = false;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeBilinear(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [[1.0], [0.0], [2.0], [0.0]],
+            [[1.9999998807907104], [0.0], [2.6666665077209473], [0.0]],
+            [[2.6666665077209473], [0.0], [3.3333330154418945], [0.0]],
+            [[3.333333730697632], [0.0], [4.000000476837158], [0.0]]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('greyscale: downscale, taller than wider, align corners', function () {
+        var input = tf.tensor3d([
+            [[100.0], [50.0], [25.0], [10.0]], [[60.0], [20.0], [80.0], [20.0]],
+            [[40.0], [15.0], [200.0], [203.0]], [[40.0], [10.0], [230.0], [200.0]]
+        ]);
+        var dy = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]], [[5.0], [6.0]]]);
+        var size = [3, 2];
+        var alignCorners = true;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeBilinear(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [[1.0], [0.0], [0.0], [2.0]], [[1.5], [0.0], [0.0], [2.0]],
+            [[1.5], [0.0], [0.0], [2.0]], [[5.0], [0.0], [0.0], [6.0]]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('greyscale: downscale, wider than taller', function () {
+        var input = tf.tensor3d([
+            [[100.0], [50.0], [25.0], [10.0]], [[60.0], [20.0], [80.0], [20.0]],
+            [[40.0], [15.0], [200.0], [203.0]], [[40.0], [10.0], [230.0], [200.0]]
+        ]);
+        var dy = tf.tensor3d([[[1.0], [2.0], [3.0]], [[4.0], [5.0], [6.0]]]);
+        var size = [2, 3];
+        var alignCorners = false;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeBilinear(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [[1.0], [1.3333332538604736], [1.6666665077209473], [2.000000238418579]],
+            [[0.0], [0.0], [0.0], [0.0]],
+            [[4.0], [3.3333330154418945], [3.6666665077209473], [4.000000476837158]],
+            [[0.0], [0.0], [0.0], [0.0]]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('greyscale: downscale, wider than taller, align corners', function () {
+        var input = tf.tensor3d([
+            [[100.0], [50.0], [25.0], [10.0]], [[60.0], [20.0], [80.0], [20.0]],
+            [[40.0], [15.0], [200.0], [203.0]], [[40.0], [10.0], [230.0], [200.0]]
+        ]);
+        var dy = tf.tensor3d([[[1.0], [2.0], [3.0]], [[4.0], [5.0], [6.0]]]);
+        var size = [2, 3];
+        var alignCorners = true;
+        var g = tf.grad(function (i) { return tf.image.resizeBilinear(i, size, alignCorners); });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [[1.0], [1.0], [1.0], [3.0]], [[0.0], [0.0], [0.0], [0.0]],
+            [[0.0], [0.0], [0.0], [0.0]], [[4.0], [2.5], [2.5], [6.0]]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('greyscale: same size', function () {
+        var input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
+        var dy = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]]]);
+        var size = [2, 2];
+        var alignCorners = false;
+        var g = tf.grad(function (i) { return tf.image.resizeBilinear(i, size, alignCorners); });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]]]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('greyscale: same size, align corners', function () {
+        var input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
+        var dy = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]]]);
+        var size = [2, 2];
+        var alignCorners = true;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeBilinear(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]]]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('color: upscale, wider than taller', function () {
+        var input = tf.tensor3d([
+            [
+                [115.11029815673828, 111.90936279296875, 66.87433624267578],
+                [72.03849029541016, 81.86637878417969, 119.53585815429688]
+            ],
+            [
+                [68.555419921875, 97.49642181396484, 116.90741729736328],
+                [128.69467163085938, 86.78314208984375, 104.3116683959961]
+            ]
+        ]);
+        var dy = tf.tensor3d([
+            [
+                [1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0], [10.0, 11.0, 12.0],
+                [13.0, 14.0, 15.0]
+            ],
+            [
+                [16.0, 17.0, 18.0], [19.0, 20.0, 21.0], [22.0, 23.0, 24.0],
+                [25.0, 26.0, 27.0], [28.0, 29.0, 30.0]
+            ],
+            [
+                [31.0, 32.0, 33.0], [34.0, 35.0, 36.0], [37.0, 38.0, 39.0],
+                [40.0, 41.0, 42.0], [43.0, 44.0, 45.0]
+            ]
+        ]);
+        var size = [3, 5];
+        var alignCorners = false;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeBilinear(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [
+                [15.399999618530273, 17.799999237060547, 20.19999885559082],
+                [56.26666259765625, 60.533329010009766, 64.79999542236328]
+            ],
+            [
+                [80.00000762939453, 83.0, 86.0],
+                [178.33334350585938, 183.66668701171875, 189.00001525878906]
+            ]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('color: upscale, wider than taller, align corners', function () {
+        var input = tf.tensor3d([
+            [
+                [115.11029815673828, 111.90936279296875, 66.87433624267578],
+                [72.03849029541016, 81.86637878417969, 119.53585815429688]
+            ],
+            [
+                [68.555419921875, 97.49642181396484, 116.90741729736328],
+                [128.69467163085938, 86.78314208984375, 104.3116683959961]
+            ]
+        ]);
+        var dy = tf.tensor3d([
+            [
+                [1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0], [10.0, 11.0, 12.0],
+                [13.0, 14.0, 15.0]
+            ],
+            [
+                [16.0, 17.0, 18.0], [19.0, 20.0, 21.0], [22.0, 23.0, 24.0],
+                [25.0, 26.0, 27.0], [28.0, 29.0, 30.0]
+            ],
+            [
+                [31.0, 32.0, 33.0], [34.0, 35.0, 36.0], [37.0, 38.0, 39.0],
+                [40.0, 41.0, 42.0], [43.0, 44.0, 45.0]
+            ]
+        ]);
+        var size = [3, 5];
+        var alignCorners = true;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeBilinear(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [[33.75, 37.5, 41.25], [56.25, 60.0, 63.75]],
+            [[108.75, 112.5, 116.25], [131.25, 135.0, 138.75]]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('color: downscale, taller than wider', function () {
+        var input = tf.tensor3d([
+            [
+                [120.68856811523438, 134.51638793945312, 83.03671264648438],
+                [121.58008575439453, 113.28836059570312, 136.3172149658203],
+                [79.38370513916016, 101.87127685546875, 104.54979705810547],
+                [96.31678771972656, 111.77168273925781, 83.73509979248047]
+            ],
+            [
+                [119.45088195800781, 88.98846435546875, 97.47553253173828],
+                [117.5562973022461, 108.26356506347656, 99.62212371826172],
+                [136.62701416015625, 94.10433197021484, 80.97366333007812],
+                [83.61205291748047, 90.60148620605469, 81.82512664794922]
+            ],
+            [
+                [103.0362777709961, 123.1098403930664, 125.62944030761719],
+                [92.2915267944336, 103.15729522705078, 119.18060302734375],
+                [102.93293762207031, 117.821044921875, 99.40152740478516],
+                [96.32952117919922, 105.80963134765625, 104.8491439819336]
+            ],
+            [
+                [104.87507629394531, 134.0189208984375, 111.02627563476562],
+                [85.4534683227539, 107.68426513671875, 103.03722381591797],
+                [89.70533752441406, 98.25298309326172, 78.42916870117188],
+                [113.6744613647461, 95.8189697265625, 122.75005340576172]
+            ]
+        ]);
+        var dy = tf.tensor3d([[[1.0, 2.0, 3.0]], [[4.0, 5.0, 6.0]], [[7.0, 8.0, 9.0]]]);
+        var size = [3, 1];
+        var alignCorners = false;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeBilinear(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [[1.0, 2.0, 3.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
+            [
+                [2.6666665077209473, 3.3333330154418945, 3.999999761581421],
+                [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]
+            ],
+            [
+                [3.666666269302368, 4.3333330154418945, 4.999999523162842],
+                [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]
+            ],
+            [
+                [4.6666669845581055, 5.333333969116211, 6.000000953674316],
+                [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]
+            ]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('color: downscale, width = 1, align corners', function () {
+        var input = tf.tensor3d([
+            [
+                [120.68856811523438, 134.51638793945312, 83.03671264648438],
+                [121.58008575439453, 113.28836059570312, 136.3172149658203],
+                [79.38370513916016, 101.87127685546875, 104.54979705810547],
+                [96.31678771972656, 111.77168273925781, 83.73509979248047]
+            ],
+            [
+                [119.45088195800781, 88.98846435546875, 97.47553253173828],
+                [117.5562973022461, 108.26356506347656, 99.62212371826172],
+                [136.62701416015625, 94.10433197021484, 80.97366333007812],
+                [83.61205291748047, 90.60148620605469, 81.82512664794922]
+            ],
+            [
+                [103.0362777709961, 123.1098403930664, 125.62944030761719],
+                [92.2915267944336, 103.15729522705078, 119.18060302734375],
+                [102.93293762207031, 117.821044921875, 99.40152740478516],
+                [96.32952117919922, 105.80963134765625, 104.8491439819336]
+            ],
+            [
+                [104.87507629394531, 134.0189208984375, 111.02627563476562],
+                [85.4534683227539, 107.68426513671875, 103.03722381591797],
+                [89.70533752441406, 98.25298309326172, 78.42916870117188],
+                [113.6744613647461, 95.8189697265625, 122.75005340576172]
+            ]
+        ]);
+        var dy = tf.tensor3d([[[1.0, 2.0, 3.0]], [[4.0, 5.0, 6.0]], [[7.0, 8.0, 9.0]]]);
+        var size = [3, 1];
+        var alignCorners = true;
+        var g = tf.grad(function (i) { return tf.image.resizeBilinear(i, size, alignCorners); });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [[1.0, 2.0, 3.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
+            [[2.0, 2.5, 3.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
+            [[2.0, 2.5, 3.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
+            [[7.0, 8.0, 9.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('color: downscale, height = 1, align corners', function () {
+        var input = tf.tensor3d([
+            [
+                [120.68856811523438, 134.51638793945312, 83.03671264648438],
+                [121.58008575439453, 113.28836059570312, 136.3172149658203],
+                [79.38370513916016, 101.87127685546875, 104.54979705810547],
+                [96.31678771972656, 111.77168273925781, 83.73509979248047]
+            ],
+            [
+                [119.45088195800781, 88.98846435546875, 97.47553253173828],
+                [117.5562973022461, 108.26356506347656, 99.62212371826172],
+                [136.62701416015625, 94.10433197021484, 80.97366333007812],
+                [83.61205291748047, 90.60148620605469, 81.82512664794922]
+            ],
+            [
+                [103.0362777709961, 123.1098403930664, 125.62944030761719],
+                [92.2915267944336, 103.15729522705078, 119.18060302734375],
+                [102.93293762207031, 117.821044921875, 99.40152740478516],
+                [96.32952117919922, 105.80963134765625, 104.8491439819336]
+            ],
+            [
+                [104.87507629394531, 134.0189208984375, 111.02627563476562],
+                [85.4534683227539, 107.68426513671875, 103.03722381591797],
+                [89.70533752441406, 98.25298309326172, 78.42916870117188],
+                [113.6744613647461, 95.8189697265625, 122.75005340576172]
+            ]
+        ]);
+        var dy = tf.tensor3d([[[1., 2., 3.], [4., 5., 6.], [7., 8., 9.]]]);
+        var size = [1, 3];
+        var alignCorners = true;
+        var g = tf.grad(function (i) { return tf.image.resizeBilinear(i, size, alignCorners); });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [[1., 2., 3.], [2., 2.5, 3.], [2., 2.5, 3.], [7., 8., 9.]],
+            [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
+            [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.]],
+            [[0., 0., 0.], [0., 0., 0.], [0., 0., 0.], [0., 0., 0.]]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('color: downscale, taller than wider, align corners', function () {
+        var input = tf.tensor3d([
+            [
+                [120.68856811523438, 134.51638793945312, 83.03671264648438],
+                [121.58008575439453, 113.28836059570312, 136.3172149658203],
+                [79.38370513916016, 101.87127685546875, 104.54979705810547],
+                [96.31678771972656, 111.77168273925781, 83.73509979248047]
+            ],
+            [
+                [119.45088195800781, 88.98846435546875, 97.47553253173828],
+                [117.5562973022461, 108.26356506347656, 99.62212371826172],
+                [136.62701416015625, 94.10433197021484, 80.97366333007812],
+                [83.61205291748047, 90.60148620605469, 81.82512664794922]
+            ],
+            [
+                [103.0362777709961, 123.1098403930664, 125.62944030761719],
+                [92.2915267944336, 103.15729522705078, 119.18060302734375],
+                [102.93293762207031, 117.821044921875, 99.40152740478516],
+                [96.32952117919922, 105.80963134765625, 104.8491439819336]
+            ],
+            [
+                [104.87507629394531, 134.0189208984375, 111.02627563476562],
+                [85.4534683227539, 107.68426513671875, 103.03722381591797],
+                [89.70533752441406, 98.25298309326172, 78.42916870117188],
+                [113.6744613647461, 95.8189697265625, 122.75005340576172]
+            ]
+        ]);
+        var dy = tf.tensor3d([
+            [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]], [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]],
+            [[13.0, 14.0, 15.0], [16.0, 17.0, 18.0]]
+        ]);
+        var size = [3, 2];
+        var alignCorners = true;
+        var g = tf.grad(function (i) { return tf.image.resizeBilinear(i, size, alignCorners); });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [[1.0, 2.0, 3.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [4.0, 5.0, 6.0]],
+            [[3.5, 4.0, 4.5], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [5.0, 5.5, 6.0]],
+            [[3.5, 4.0, 4.5], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [5.0, 5.5, 6.0]],
+            [
+                [13.0, 14.0, 15.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0],
+                [16.0, 17.0, 18.0]
+            ]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('color: same size', function () {
+        var input = tf.tensor3d([
+            [
+                [115.11029815673828, 111.90936279296875, 66.87433624267578],
+                [72.03849029541016, 81.86637878417969, 119.53585815429688]
+            ],
+            [
+                [68.555419921875, 97.49642181396484, 116.90741729736328],
+                [128.69467163085938, 86.78314208984375, 104.3116683959961]
+            ]
+        ]);
+        var dy = tf.tensor3d([
+            [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],
+            [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]]
+        ]);
+        var size = [2, 2];
+        var alignCorners = false;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeBilinear(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],
+            [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('color: same size, align corners', function () {
+        var input = tf.tensor3d([
+            [
+                [115.11029815673828, 111.90936279296875, 66.87433624267578],
+                [72.03849029541016, 81.86637878417969, 119.53585815429688]
+            ],
+            [
+                [68.555419921875, 97.49642181396484, 116.90741729736328],
+                [128.69467163085938, 86.78314208984375, 104.3116683959961]
+            ]
+        ]);
+        var dy = tf.tensor3d([
+            [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],
+            [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]]
+        ]);
+        var size = [2, 2];
+        var alignCorners = true;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeBilinear(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],
+            [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+});
+//# sourceMappingURL=resize_bilinear_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/resize_bilinear_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/resize_bilinear_test.js.map
new file mode 100644
index 0000000..50acb86
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/resize_bilinear_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/resize_nearest_neighbor_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/resize_nearest_neighbor_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/resize_nearest_neighbor_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/resize_nearest_neighbor_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/resize_nearest_neighbor_test.js
new file mode 100644
index 0000000..8b3f4d2
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/resize_nearest_neighbor_test.js
@@ -0,0 +1,500 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('resizeNearestNeighbor', test_util_1.ALL_ENVS, function () {
+    it('simple alignCorners=false', function () {
+        var input = tf.tensor3d([2, 2, 4, 4], [2, 2, 1]);
+        var output = input.resizeNearestNeighbor([3, 3], false);
+        test_util_1.expectArraysClose(output, [2, 2, 2, 2, 2, 2, 4, 4, 4]);
+    });
+    it('simple alignCorners=true', function () {
+        var input = tf.tensor3d([2, 2, 4, 4], [2, 2, 1]);
+        var output = input.resizeNearestNeighbor([3, 3], true);
+        test_util_1.expectArraysClose(output, [2, 2, 2, 4, 4, 4, 4, 4, 4]);
+    });
+    it('matches tensorflow w/ random numbers alignCorners=false', function () {
+        var input = tf.tensor3d([
+            1.19074044, 0.91373104, 2.01611669, -0.52270832, 0.38725395,
+            1.30809779, 0.61835143, 3.49600659, 2.09230986, 0.56473997,
+            0.03823943, 1.19864896
+        ], [2, 3, 2]);
+        var output = input.resizeNearestNeighbor([4, 5], false);
+        test_util_1.expectArraysClose(output, [
+            1.19074047, 0.913731039, 1.19074047, 0.913731039, 2.01611662,
+            -0.522708297, 2.01611662, -0.522708297, 0.38725394, 1.30809784,
+            1.19074047, 0.913731039, 1.19074047, 0.913731039, 2.01611662,
+            -0.522708297, 2.01611662, -0.522708297, 0.38725394, 1.30809784,
+            0.61835146, 3.49600649, 0.61835146, 3.49600649, 2.09230995,
+            0.564739943, 2.09230995, 0.564739943, 0.0382394306, 1.19864893,
+            0.61835146, 3.49600649, 0.61835146, 3.49600649, 2.09230995,
+            0.564739943, 2.09230995, 0.564739943, 0.0382394306, 1.19864893
+        ]);
+    });
+    it('matches tensorflow w/ random numbers alignCorners=true', function () {
+        var input = tf.tensor3d([
+            1.19074044, 0.91373104, 2.01611669, -0.52270832, 0.38725395,
+            1.30809779, 0.61835143, 3.49600659, 2.09230986, 0.56473997,
+            0.03823943, 1.19864896
+        ], [2, 3, 2]);
+        var output = input.resizeNearestNeighbor([4, 5], true);
+        test_util_1.expectArraysClose(output, [
+            1.19074044, 0.91373104, 2.01611669, -0.52270832, 2.01611669, -0.52270832,
+            0.38725395, 1.30809779, 0.38725395, 1.30809779, 1.19074044, 0.91373104,
+            2.01611669, -0.52270832, 2.01611669, -0.52270832, 0.38725395, 1.30809779,
+            0.38725395, 1.30809779, 0.61835143, 3.49600659, 2.09230986, 0.56473997,
+            2.09230986, 0.56473997, 0.03823943, 1.19864896, 0.03823943, 1.19864896,
+            0.61835143, 3.49600659, 2.09230986, 0.56473997, 2.09230986, 0.56473997,
+            0.03823943, 1.19864896, 0.03823943, 1.19864896
+        ]);
+    });
+    it('batch of 2, simple, alignCorners=true', function () {
+        var input = tf.tensor4d([2, 2, 4, 4, 3, 3, 5, 5], [2, 2, 2, 1]);
+        var output = input.resizeNearestNeighbor([3, 3], true);
+        test_util_1.expectArraysClose(output, [2, 2, 2, 4, 4, 4, 4, 4, 4, 3, 3, 3, 5, 5, 5, 5, 5, 5]);
+    });
+    it('throws when passed a non-tensor', function () {
+        var e = /Argument 'images' passed to 'resizeNearestNeighbor' must be a Tensor/;
+        expect(function () { return tf.image.resizeNearestNeighbor({}, [
+            1, 1
+        ]); }).toThrowError(e);
+    });
+    it('accepts a tensor-like object', function () {
+        var input = [[[2], [2]], [[4], [4]]];
+        var output = tf.image.resizeNearestNeighbor(input, [3, 3], false);
+        test_util_1.expectArraysClose(output, [2, 2, 2, 2, 2, 2, 4, 4, 4]);
+    });
+    it('does not throw when some output dim is 1 and alignCorners=true', function () {
+        var input = tf.tensor3d([2, 2, 4, 4], [2, 2, 1]);
+        expect(function () { return input.resizeNearestNeighbor([1, 3], true); }).not.toThrow();
+    });
+});
+jasmine_util_1.describeWithFlags('resizeNearestNeighbor gradients', test_util_1.ALL_ENVS, function () {
+    it('greyscale: upscale, same aspect ratio', function () {
+        var input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
+        var dy = tf.tensor3d([
+            [[1.0], [2.0], [3.0], [4.0]], [[5.0], [6.0], [7.0], [8.0]],
+            [[9.0], [10.0], [11.0], [12.0]], [[13.0], [14.0], [15.0], [16.0]]
+        ]);
+        var size = [4, 4];
+        var alignCorners = false;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeNearestNeighbor(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([[[14.0], [22.0]], [[46.0], [54.0]]]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('greyscale: upscale, same aspect ratio, align corners', function () {
+        var input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
+        var dy = tf.tensor3d([
+            [[1.0], [2.0], [3.0], [4.0]], [[5.0], [6.0], [7.0], [8.0]],
+            [[9.0], [10.0], [11.0], [12.0]], [[13.0], [14.0], [15.0], [16.0]]
+        ]);
+        var size = [4, 4];
+        var alignCorners = true;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeNearestNeighbor(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([[[14.0], [22.0]], [[46.0], [54.0]]]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('greyscale: upscale, taller than wider', function () {
+        var input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
+        var dy = tf.tensor3d([
+            [[1.0], [2.0], [3.0], [4.0]], [[5.0], [6.0], [7.0], [8.0]],
+            [[9.0], [10.0], [11.0], [12.0]], [[13.0], [14.0], [15.0], [16.0]],
+            [[17.0], [18.0], [19.0], [20.0]], [[21.0], [22.0], [23.0], [24.0]],
+            [[25.0], [26.0], [27.0], [28.0]], [[29.0], [30.0], [31.0], [32.0]],
+            [[33.0], [34.0], [35.0], [36.0]]
+        ]);
+        var size = [9, 4];
+        var alignCorners = false;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeNearestNeighbor(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([[[95.0], [115.0]], [[220.0], [236.0]]]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('greyscale: upscale, taller than wider, align corners', function () {
+        var input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
+        var dy = tf.tensor3d([
+            [[1.0], [2.0], [3.0], [4.0]], [[5.0], [6.0], [7.0], [8.0]],
+            [[9.0], [10.0], [11.0], [12.0]], [[13.0], [14.0], [15.0], [16.0]],
+            [[17.0], [18.0], [19.0], [20.0]], [[21.0], [22.0], [23.0], [24.0]],
+            [[25.0], [26.0], [27.0], [28.0]], [[29.0], [30.0], [31.0], [32.0]],
+            [[33.0], [34.0], [35.0], [36.0]]
+        ]);
+        var size = [9, 4];
+        var alignCorners = true;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeNearestNeighbor(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([[[60.0], [76.0]], [[255.0], [275.0]]]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('greyscale: upscale, wider than taller', function () {
+        var input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
+        var dy = tf.tensor3d([
+            [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0], [7.0]],
+            [[8.0], [9.0], [10.0], [11.0], [12.0], [13.0], [14.0]],
+            [[15.0], [16.0], [17.0], [18.0], [19.0], [20.0], [21.0]],
+            [[22.0], [23.0], [24.0], [25.0], [26.0], [27.0], [28.0]]
+        ]);
+        var size = [4, 7];
+        var alignCorners = false;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeNearestNeighbor(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([[[48.0], [57.0]], [[160.0], [141.0]]]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('greyscale: upscale, wider than taller, align corners', function () {
+        var input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
+        var dy = tf.tensor3d([
+            [[1.0], [2.0], [3.0], [4.0], [5.0], [6.0], [7.0]],
+            [[8.0], [9.0], [10.0], [11.0], [12.0], [13.0], [14.0]],
+            [[15.0], [16.0], [17.0], [18.0], [19.0], [20.0], [21.0]],
+            [[22.0], [23.0], [24.0], [25.0], [26.0], [27.0], [28.0]]
+        ]);
+        var size = [4, 7];
+        var alignCorners = true;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeNearestNeighbor(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([[[33.0], [72.0]], [[117.0], [184.0]]]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('greyscale: downscale, same aspect ratio', function () {
+        var input = tf.tensor3d([
+            [[100.0], [50.0], [25.0], [10.0]], [[60.0], [20.0], [80.0], [20.0]],
+            [[40.0], [15.0], [200.0], [203.0]], [[40.0], [10.0], [230.0], [200.0]]
+        ]);
+        var dy = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]]]);
+        var size = [2, 2];
+        var alignCorners = false;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeNearestNeighbor(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [[1.0], [0.0], [2.0], [0.0]], [[0.0], [0.0], [0.0], [0.0]],
+            [[3.0], [0.0], [4.0], [0.0]], [[0.0], [0.0], [0.0], [0.0]]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('greyscale: downscale, same aspect ratio, align corners', function () {
+        var input = tf.tensor3d([
+            [[100.0], [50.0], [25.0], [10.0]], [[60.0], [20.0], [80.0], [20.0]],
+            [[40.0], [15.0], [200.0], [203.0]], [[40.0], [10.0], [230.0], [200.0]]
+        ]);
+        var dy = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]]]);
+        var size = [2, 2];
+        var alignCorners = true;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeNearestNeighbor(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [[1.0], [0.0], [0.0], [2.0]], [[0.0], [0.0], [0.0], [0.0]],
+            [[0.0], [0.0], [0.0], [0.0]], [[3.0], [0.0], [0.0], [4.0]]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('greyscale: downscale, taller than wider', function () {
+        var input = tf.tensor3d([
+            [[100.0], [50.0], [25.0], [10.0]], [[60.0], [20.0], [80.0], [20.0]],
+            [[40.0], [15.0], [200.0], [203.0]], [[40.0], [10.0], [230.0], [200.0]]
+        ]);
+        var dy = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]], [[5.0], [6.0]]]);
+        var size = [3, 2];
+        var alignCorners = false;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeNearestNeighbor(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [[1.0], [0.0], [2.0], [0.0]],
+            [[3.0], [0.0], [4.0], [0.0]],
+            [[5.0], [0.0], [6.0], [0.0]],
+            [[0.0], [0.0], [0.0], [0.0]]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('greyscale: downscale, taller than wider, align corners', function () {
+        var input = tf.tensor3d([
+            [[100.0], [50.0], [25.0], [10.0]], [[60.0], [20.0], [80.0], [20.0]],
+            [[40.0], [15.0], [200.0], [203.0]], [[40.0], [10.0], [230.0], [200.0]]
+        ]);
+        var dy = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]], [[5.0], [6.0]]]);
+        var size = [3, 2];
+        var alignCorners = true;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeNearestNeighbor(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [[1.0], [0.0], [0.0], [2.0]],
+            [[0.0], [0.0], [0.0], [0.0]],
+            [[3.0], [0.0], [0.0], [4.0]],
+            [[5.0], [0.0], [0.0], [6.0]]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('greyscale: downscale, taller than wider', function () {
+        var input = tf.tensor3d([
+            [[100.0], [50.0], [25.0], [10.0]], [[60.0], [20.0], [80.0], [20.0]],
+            [[40.0], [15.0], [200.0], [203.0]], [[40.0], [10.0], [230.0], [200.0]]
+        ]);
+        var dy = tf.tensor3d([[[1.0], [2.0], [3.0]], [[4.0], [5.0], [6.0]]]);
+        var size = [2, 3];
+        var alignCorners = false;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeNearestNeighbor(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [[1.0], [2.0], [3.0], [0.0]],
+            [[0.0], [0.0], [0.0], [0.0]],
+            [[4.0], [5.0], [6.0], [0.0]],
+            [[0.0], [0.0], [0.0], [0.0]]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('greyscale: downscale, taller than wider, align corners', function () {
+        var input = tf.tensor3d([
+            [[100.0], [50.0], [25.0], [10.0]], [[60.0], [20.0], [80.0], [20.0]],
+            [[40.0], [15.0], [200.0], [203.0]], [[40.0], [10.0], [230.0], [200.0]]
+        ]);
+        var dy = tf.tensor3d([[[1.0], [2.0], [3.0]], [[4.0], [5.0], [6.0]]]);
+        var size = [2, 3];
+        var alignCorners = true;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeNearestNeighbor(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [[1.0], [0.0], [2.0], [3.0]],
+            [[0.0], [0.0], [0.0], [0.0]],
+            [[0.0], [0.0], [0.0], [0.0]],
+            [[4.0], [0.0], [5.0], [6.0]]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('greyscale: downscale, same size', function () {
+        var input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
+        var dy = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]]]);
+        var size = [2, 2];
+        var alignCorners = false;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeNearestNeighbor(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]]]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('greyscale: downscale, same size, align corners', function () {
+        var input = tf.tensor3d([[[100.0], [50.0]], [[60.0], [20.0]]]);
+        var dy = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]]]);
+        var size = [2, 2];
+        var alignCorners = true;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeNearestNeighbor(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([[[1.0], [2.0]], [[3.0], [4.0]]]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('color: upscale, wider than taller', function () {
+        var input = tf.tensor3d([
+            [
+                [100.26818084716797, 74.61857604980469, 81.62117767333984],
+                [127.86964416503906, 85.0583267211914, 102.95439147949219]
+            ],
+            [
+                [104.3798828125, 96.70733642578125, 92.60601043701172],
+                [77.63021850585938, 68.55794525146484, 96.17212677001953]
+            ]
+        ]);
+        var dy = tf.tensor3d([
+            [
+                [1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0], [10.0, 11.0, 12.0],
+                [13.0, 14.0, 15.0]
+            ],
+            [
+                [16.0, 17.0, 18.0], [19.0, 20.0, 21.0], [22.0, 23.0, 24.0],
+                [25.0, 26.0, 27.0], [28.0, 29.0, 30.0]
+            ],
+            [
+                [31.0, 32.0, 33.0], [34.0, 35.0, 36.0], [37.0, 38.0, 39.0],
+                [40.0, 41.0, 42.0], [43.0, 44.0, 45.0]
+            ]
+        ]);
+        var size = [3, 5];
+        var alignCorners = false;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeNearestNeighbor(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [[69.0, 75.0, 81.0], [76.0, 80.0, 84.0]],
+            [[102.0, 105.0, 108.0], [83.0, 85.0, 87.0]]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('color: upscale, wider than taller, align corners', function () {
+        var input = tf.tensor3d([
+            [
+                [100.26818084716797, 74.61857604980469, 81.62117767333984],
+                [127.86964416503906, 85.0583267211914, 102.95439147949219]
+            ],
+            [
+                [104.3798828125, 96.70733642578125, 92.60601043701172],
+                [77.63021850585938, 68.55794525146484, 96.17212677001953]
+            ]
+        ]);
+        var dy = tf.tensor3d([
+            [
+                [1.0, 2.0, 3.0], [4.0, 5.0, 6.0], [7.0, 8.0, 9.0], [10.0, 11.0, 12.0],
+                [13.0, 14.0, 15.0]
+            ],
+            [
+                [16.0, 17.0, 18.0], [19.0, 20.0, 21.0], [22.0, 23.0, 24.0],
+                [25.0, 26.0, 27.0], [28.0, 29.0, 30.0]
+            ],
+            [
+                [31.0, 32.0, 33.0], [34.0, 35.0, 36.0], [37.0, 38.0, 39.0],
+                [40.0, 41.0, 42.0], [43.0, 44.0, 45.0]
+            ]
+        ]);
+        var size = [3, 5];
+        var alignCorners = true;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeNearestNeighbor(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [[5.0, 7.0, 9.0], [30.0, 33.0, 36.0]],
+            [[100.0, 104.0, 108.0], [195.0, 201.0, 207.0]]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('color: downscale, taller than wider', function () {
+        var input = tf.tensor3d([
+            [
+                [97.98934936523438, 77.24969482421875, 113.70111846923828],
+                [111.34081268310547, 113.15758514404297, 157.90521240234375],
+                [105.77980041503906, 85.75989532470703, 69.62374114990234],
+                [125.94231414794922, 73.11385345458984, 87.03099822998047]
+            ],
+            [
+                [62.25117111206055, 90.23927307128906, 119.1966552734375],
+                [93.55166625976562, 95.9106674194336, 115.56237030029297],
+                [102.98121643066406, 98.1983413696289, 97.55982971191406],
+                [86.47753143310547, 97.04051208496094, 121.50492095947266]
+            ],
+            [
+                [92.4140853881836, 118.45619201660156, 108.0341796875],
+                [126.43061065673828, 123.28077697753906, 121.03379821777344],
+                [128.6694793701172, 98.47042846679688, 114.47464752197266],
+                [93.31566619873047, 95.2713623046875, 102.51188659667969]
+            ],
+            [
+                [101.55884552001953, 83.31947326660156, 119.08016204833984],
+                [128.28546142578125, 92.56212615966797, 74.85054779052734],
+                [88.9786148071289, 119.43685913085938, 73.06110382080078],
+                [98.17908477783203, 105.54570007324219, 93.45832061767578]
+            ]
+        ]);
+        var dy = tf.tensor3d([[[1.0, 2.0, 3.0]], [[4.0, 5.0, 6.0]], [[7.0, 8.0, 9.0]]]);
+        var size = [3, 1];
+        var alignCorners = false;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeNearestNeighbor(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [[1.0, 2.0, 3.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
+            [[4.0, 5.0, 6.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
+            [[7.0, 8.0, 9.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
+            [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('color: downscale, taller than wider, align corners', function () {
+        var input = tf.tensor3d([
+            [
+                [97.98934936523438, 77.24969482421875, 113.70111846923828],
+                [111.34081268310547, 113.15758514404297, 157.90521240234375],
+                [105.77980041503906, 85.75989532470703, 69.62374114990234],
+                [125.94231414794922, 73.11385345458984, 87.03099822998047]
+            ],
+            [
+                [62.25117111206055, 90.23927307128906, 119.1966552734375],
+                [93.55166625976562, 95.9106674194336, 115.56237030029297],
+                [102.98121643066406, 98.1983413696289, 97.55982971191406],
+                [86.47753143310547, 97.04051208496094, 121.50492095947266]
+            ],
+            [
+                [92.4140853881836, 118.45619201660156, 108.0341796875],
+                [126.43061065673828, 123.28077697753906, 121.03379821777344],
+                [128.6694793701172, 98.47042846679688, 114.47464752197266],
+                [93.31566619873047, 95.2713623046875, 102.51188659667969]
+            ],
+            [
+                [101.55884552001953, 83.31947326660156, 119.08016204833984],
+                [128.28546142578125, 92.56212615966797, 74.85054779052734],
+                [88.9786148071289, 119.43685913085938, 73.06110382080078],
+                [98.17908477783203, 105.54570007324219, 93.45832061767578]
+            ]
+        ]);
+        var dy = tf.tensor3d([[[1.0, 2.0, 3.0]], [[4.0, 5.0, 6.0]], [[7.0, 8.0, 9.0]]]);
+        var size = [3, 1];
+        var alignCorners = true;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeNearestNeighbor(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [[1.0, 2.0, 3.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
+            [[0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
+            [[4.0, 5.0, 6.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]],
+            [[7.0, 8.0, 9.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0], [0.0, 0.0, 0.0]]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+    it('color: same size', function () {
+        var input = tf.tensor3d([
+            [
+                [100.26818084716797, 74.61857604980469, 81.62117767333984],
+                [127.86964416503906, 85.0583267211914, 102.95439147949219]
+            ],
+            [
+                [104.3798828125, 96.70733642578125, 92.60601043701172],
+                [77.63021850585938, 68.55794525146484, 96.17212677001953]
+            ]
+        ]);
+        var dy = tf.tensor3d([
+            [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],
+            [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]]
+        ]);
+        var size = [2, 2];
+        var alignCorners = false;
+        var g = tf.grad(function (i) {
+            return tf.image.resizeNearestNeighbor(i, size, alignCorners);
+        });
+        var output = g(input, dy);
+        var expected = tf.tensor3d([
+            [[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]],
+            [[7.0, 8.0, 9.0], [10.0, 11.0, 12.0]]
+        ]);
+        test_util_1.expectArraysClose(output, expected);
+    });
+});
+//# sourceMappingURL=resize_nearest_neighbor_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/resize_nearest_neighbor_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/resize_nearest_neighbor_test.js.map
new file mode 100644
index 0000000..c9bba8a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/resize_nearest_neighbor_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/reverse.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/reverse.d.ts
new file mode 100644
index 0000000..bc821de
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/reverse.d.ts
@@ -0,0 +1,13 @@
+import { Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D } from '../tensor';
+import { TensorLike } from '../types';
+declare function reverse1d_(x: Tensor1D | TensorLike): Tensor1D;
+declare function reverse2d_(x: Tensor2D | TensorLike, axis?: number | number[]): Tensor2D;
+declare function reverse3d_(x: Tensor3D | TensorLike, axis?: number | number[]): Tensor3D;
+declare function reverse4d_(x: Tensor4D | TensorLike, axis?: number | number[]): Tensor4D;
+declare function reverse_<T extends Tensor>(x: T | TensorLike, axis?: number | number[]): T;
+export declare const reverse: typeof reverse_;
+export declare const reverse1d: typeof reverse1d_;
+export declare const reverse2d: typeof reverse2d_;
+export declare const reverse3d: typeof reverse3d_;
+export declare const reverse4d: typeof reverse4d_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/reverse.js b/node_modules/@tensorflow/tfjs-core/dist/ops/reverse.js
new file mode 100644
index 0000000..097c5b4
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/reverse.js
@@ -0,0 +1,45 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var tensor_util_env_1 = require("../tensor_util_env");
+var util = require("../util");
+var axis_util_1 = require("./axis_util");
+var operation_1 = require("./operation");
+function reverse1d_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'reverse');
+    util.assert($x.rank === 1, "Error in reverse1D: x must be rank 1 but got\n             rank " + $x.rank + ".");
+    return exports.reverse($x, 0);
+}
+function reverse2d_(x, axis) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'reverse');
+    util.assert($x.rank === 2, "Error in reverse2D: x must be rank 2 but got\n             rank " + $x.rank + ".");
+    return exports.reverse($x, axis);
+}
+function reverse3d_(x, axis) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'reverse');
+    util.assert($x.rank === 3, "Error in reverse3D: x must be rank 3 but got\n             rank " + $x.rank + ".");
+    return exports.reverse($x, axis);
+}
+function reverse4d_(x, axis) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'reverse');
+    util.assert($x.rank === 4, "Error in reverse4D: x must be rank 4 but got\n             rank " + $x.rank + ".");
+    return exports.reverse($x, axis);
+}
+function reverse_(x, axis) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'reverse');
+    if ($x.rank === 0) {
+        return $x.clone();
+    }
+    var axes = axis_util_1.parseAxisParam(axis, $x.shape);
+    var grad = function (dy) {
+        return { $x: function () { return dy.reverse(axes); } };
+    };
+    var res = environment_1.ENV.engine.runKernel(function (backend) { return backend.reverse($x, axes); }, { $x: $x }, grad);
+    return res.reshapeAs($x);
+}
+exports.reverse = operation_1.op({ reverse_: reverse_ });
+exports.reverse1d = operation_1.op({ reverse1d_: reverse1d_ });
+exports.reverse2d = operation_1.op({ reverse2d_: reverse2d_ });
+exports.reverse3d = operation_1.op({ reverse3d_: reverse3d_ });
+exports.reverse4d = operation_1.op({ reverse4d_: reverse4d_ });
+//# sourceMappingURL=reverse.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/reverse.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/reverse.js.map
new file mode 100644
index 0000000..6193294
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/reverse.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"reverse.js","sourceRoot":"","sources":["../../src/ops/reverse.ts"],"names":[],"mappings":";;AAiBA,8CAAmC;AAEnC,sDAAmD;AAEnD,8BAAgC;AAChC,yCAA2C;AAC3C,yCAA+B;AAO/B,oBAAoB,CAAsB;IACxC,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,SAAS,CAAC,CAAC;IAC9C,IAAI,CAAC,MAAM,CAAC,EAAE,CAAC,IAAI,KAAK,CAAC,EAAE,qEACT,EAAE,CAAC,IAAI,MAAG,CAAC,CAAC;IAC9B,OAAO,eAAO,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC;AACxB,CAAC;AASD,oBAAoB,CAAsB,EAAE,IAAsB;IAChE,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,SAAS,CAAC,CAAC;IAC9C,IAAI,CAAC,MAAM,CAAC,EAAE,CAAC,IAAI,KAAK,CAAC,EAAE,qEACT,EAAE,CAAC,IAAI,MAAG,CAAC,CAAC;IAC9B,OAAO,eAAO,CAAC,EAAE,EAAE,IAAI,CAAC,CAAC;AAC3B,CAAC;AASD,oBAAoB,CAAsB,EAAE,IAAsB;IAChE,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,SAAS,CAAC,CAAC;IAC9C,IAAI,CAAC,MAAM,CAAC,EAAE,CAAC,IAAI,KAAK,CAAC,EAAE,qEACT,EAAE,CAAC,IAAI,MAAG,CAAC,CAAC;IAC9B,OAAO,eAAO,CAAC,EAAE,EAAE,IAAI,CAAC,CAAC;AAC3B,CAAC;AASD,oBAAoB,CAAsB,EAAE,IAAsB;IAChE,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,SAAS,CAAC,CAAC;IAC9C,IAAI,CAAC,MAAM,CAAC,EAAE,CAAC,IAAI,KAAK,CAAC,EAAE,qEACT,EAAE,CAAC,IAAI,MAAG,CAAC,CAAC;IAC9B,OAAO,eAAO,CAAC,EAAE,EAAE,IAAI,CAAC,CAAC;AAC3B,CAAC;AAgCD,kBACI,CAAe,EAAE,IAAsB;IACzC,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,SAAS,CAAC,CAAC;IAE9C,IAAI,EAAE,CAAC,IAAI,KAAK,CAAC,EAAE;QACjB,OAAO,EAAE,CAAC,KAAK,EAAE,CAAC;KACnB;IACD,IAAM,IAAI,GAAG,0BAAc,CAAC,IAAI,EAAE,EAAE,CAAC,KAAK,CAAC,CAAC;IAC5C,IAAM,IAAI,GAAG,UAAC,EAAK;QACjB,OAAO,EAAC,EAAE,EAAE,cAAM,OAAA,EAAE,CAAC,OAAO,CAAC,IAAI,CAAC,EAAhB,CAAgB,EAAC,CAAC;IACtC,CAAC,CAAC;IACF,IAAM,GAAG,GACL,iBAAG,CAAC,MAAM,CAAC,SAAS,CAAC,UAAA,OAAO,IAAI,OAAA,OAAO,CAAC,OAAO,CAAC,EAAE,EAAE,IAAI,CAAC,EAAzB,CAAyB,EAAE,EAAC,EAAE,IAAA,EAAC,EAAE,IAAI,CAAC,CAAC;IAC3E,OAAO,GAAG,CAAC,SAAS,CAAC,EAAE,CAAC,CAAC;AAC3B,CAAC;AAEY,QAAA,OAAO,GAAG,cAAE,CAAC,EAAC,QAAQ,UAAA,EAAC,CAAC,CAAC;AACzB,QAAA,SAAS,GAAG,cAAE,CAAC,EAAC,UAAU,YAAA,EAAC,CAAC,CAAC;AAC7B,QAAA,SAAS,GAAG,cAAE,CAAC,EAAC,UAAU,YAAA,EAAC,CAAC,CAAC;AAC7B,QAAA,SAAS,GAAG,cAAE,CAAC,EAAC,UAAU,YAAA,EAAC,CAAC,CAAC;AAC7B,QAAA,SAAS,GAAG,cAAE,CAAC,EAAC,UAAU,YAAA,EAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_test.js
new file mode 100644
index 0000000..4d201fd
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_test.js
@@ -0,0 +1,270 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('reverse1d', test_util_1.ALL_ENVS, function () {
+    it('reverse a 1D array', function () {
+        var input = tf.tensor1d([1, 2, 3, 4, 5]);
+        var result = tf.reverse1d(input);
+        expect(result.shape).toEqual(input.shape);
+        test_util_1.expectArraysClose(result, [5, 4, 3, 2, 1]);
+    });
+    it('grad', function () {
+        var a = tf.tensor1d([1, 2, 3]);
+        var dy = tf.tensor1d([10, 20, 30]);
+        var da = tf.grad(function (a) { return tf.reverse1d(a); })(a, dy);
+        expect(da.shape).toEqual([3]);
+        test_util_1.expectArraysClose(da, [30, 20, 10]);
+    });
+    it('accepts a tensor-like object', function () {
+        var input = [1, 2, 3, 4, 5];
+        var result = tf.reverse1d(input);
+        expect(result.shape).toEqual([5]);
+        test_util_1.expectArraysClose(result, [5, 4, 3, 2, 1]);
+    });
+});
+jasmine_util_1.describeWithFlags('reverse2d', test_util_1.ALL_ENVS, function () {
+    it('reverse a 2D array at axis [0]', function () {
+        var axis = [0];
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var result = tf.reverse2d(a, axis);
+        expect(result.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(result, [4, 5, 6, 1, 2, 3]);
+    });
+    it('reverse a 2D array at axis [1]', function () {
+        var axis = [1];
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var result = tf.reverse2d(a, axis);
+        expect(result.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(result, [3, 2, 1, 6, 5, 4]);
+    });
+    it('throws error with invalid input', function () {
+        var x = tf.tensor1d([1, 20, 300, 4]);
+        expect(function () { return tf.reverse2d(x, [0]); }).toThrowError();
+    });
+    it('throws error with invalid axis param', function () {
+        var x = tf.tensor2d([1, 20, 300, 4], [1, 4]);
+        expect(function () { return tf.reverse2d(x, [2]); }).toThrowError();
+        expect(function () { return tf.reverse2d(x, [-3]); }).toThrowError();
+    });
+    it('throws error with non integer axis param', function () {
+        var x = tf.tensor2d([1, 20, 300, 4], [1, 4]);
+        expect(function () { return tf.reverse2d(x, [0.5]); }).toThrowError();
+    });
+    it('grad', function () {
+        var a = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);
+        var dy = tf.tensor2d([[10, 20, 30], [40, 50, 60]]);
+        var da = tf.grad(function (a) { return tf.reverse2d(a); })(a, dy);
+        expect(da.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(da, [60, 50, 40, 30, 20, 10]);
+    });
+    it('grad with reverse(axis=0)', function () {
+        var a = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);
+        var dy = tf.tensor2d([[10, 20, 30], [40, 50, 60]]);
+        var da = tf.grad(function (a) { return tf.reverse2d(a, 0); })(a, dy);
+        expect(da.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(da, [40, 50, 60, 10, 20, 30]);
+    });
+    it('grad with reverse(axis=1)', function () {
+        var a = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);
+        var dy = tf.tensor2d([[10, 20, 30], [40, 50, 60]]);
+        var da = tf.grad(function (a) { return tf.reverse2d(a, 1); })(a, dy);
+        expect(da.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(da, [30, 20, 10, 60, 50, 40]);
+    });
+    it('accepts a tensor-like object', function () {
+        var axis = [0];
+        var a = [[1, 2, 3], [4, 5, 6]];
+        var result = tf.reverse2d(a, axis);
+        expect(result.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(result, [4, 5, 6, 1, 2, 3]);
+    });
+});
+jasmine_util_1.describeWithFlags('reverse3d', test_util_1.ALL_ENVS, function () {
+    var shape = [2, 3, 4];
+    var data = [
+        0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11,
+        12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23
+    ];
+    it('reverse a 3D array at axis [0]', function () {
+        var input = tf.tensor3d(data, shape);
+        var result = tf.reverse3d(input, [0]);
+        expect(result.shape).toEqual(input.shape);
+        test_util_1.expectArraysClose(result, [
+            12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,
+            0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11
+        ]);
+    });
+    it('reverse a 3D array at axis [1]', function () {
+        var input = tf.tensor3d(data, shape);
+        var result = tf.reverse3d(input, [1]);
+        expect(result.shape).toEqual(input.shape);
+        test_util_1.expectArraysClose(result, [
+            8, 9, 10, 11, 4, 5, 6, 7, 0, 1, 2, 3,
+            20, 21, 22, 23, 16, 17, 18, 19, 12, 13, 14, 15
+        ]);
+    });
+    it('reverse a 3D array at axis [2]', function () {
+        var input = tf.tensor3d(data, shape);
+        var result = tf.reverse3d(input, [2]);
+        expect(result.shape).toEqual(input.shape);
+        test_util_1.expectArraysClose(result, [
+            3, 2, 1, 0, 7, 6, 5, 4, 11, 10, 9, 8,
+            15, 14, 13, 12, 19, 18, 17, 16, 23, 22, 21, 20
+        ]);
+    });
+    it('reverse a 3D array at axis [0, 1]', function () {
+        var input = tf.tensor3d(data, shape);
+        var result = tf.reverse3d(input, [0, 1]);
+        expect(result.shape).toEqual(input.shape);
+        test_util_1.expectArraysClose(result, [
+            20, 21, 22, 23, 16, 17, 18, 19, 12, 13, 14, 15,
+            8, 9, 10, 11, 4, 5, 6, 7, 0, 1, 2, 3
+        ]);
+    });
+    it('reverse a 3D array at axis [0, 2]', function () {
+        var input = tf.tensor3d(data, shape);
+        var result = tf.reverse3d(input, [0, 2]);
+        expect(result.shape).toEqual(input.shape);
+        test_util_1.expectArraysClose(result, [
+            15, 14, 13, 12, 19, 18, 17, 16, 23, 22, 21, 20,
+            3, 2, 1, 0, 7, 6, 5, 4, 11, 10, 9, 8
+        ]);
+    });
+    it('reverse a 3D array at axis [1, 2]', function () {
+        var input = tf.tensor3d(data, shape);
+        var result = tf.reverse3d(input, [1, 2]);
+        expect(result.shape).toEqual(input.shape);
+        test_util_1.expectArraysClose(result, [
+            11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1, 0,
+            23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12
+        ]);
+    });
+    it('throws error with invalid input', function () {
+        var x = tf.tensor2d([1, 20, 300, 4], [1, 4]);
+        expect(function () { return tf.reverse3d(x, [1]); }).toThrowError();
+    });
+    it('throws error with invalid axis param', function () {
+        var x = tf.tensor3d([1, 20, 300, 4], [1, 1, 4]);
+        expect(function () { return tf.reverse3d(x, [3]); }).toThrowError();
+        expect(function () { return tf.reverse3d(x, [-4]); }).toThrowError();
+    });
+    it('throws error with non integer axis param', function () {
+        var x = tf.tensor3d([1, 20, 300, 4], [1, 1, 4]);
+        expect(function () { return tf.reverse3d(x, [0.5]); }).toThrowError();
+    });
+    it('accepts a tensor-like object', function () {
+        var input = [[[1], [2], [3]], [[4], [5], [6]]];
+        var result = tf.reverse3d(input, [0]);
+        expect(result.shape).toEqual([2, 3, 1]);
+        test_util_1.expectArraysClose(result, [4, 5, 6, 1, 2, 3]);
+    });
+});
+jasmine_util_1.describeWithFlags('reverse4d', test_util_1.ALL_ENVS, function () {
+    var shape = [3, 2, 3, 4];
+    var data = [
+        0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
+        18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
+        36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,
+        54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71
+    ];
+    it('reverse a 4D array at axis [0]', function () {
+        var input = tf.tensor4d(data, shape);
+        var result = tf.reverse4d(input, [0]);
+        expect(result.shape).toEqual(input.shape);
+        test_util_1.expectArraysClose(result, [
+            48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65,
+            66, 67, 68, 69, 70, 71, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,
+            36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 0, 1, 2, 3, 4, 5,
+            6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23
+        ]);
+    });
+    it('reverse a 4D array at axis [1]', function () {
+        var input = tf.tensor4d(data, shape);
+        var result = tf.reverse4d(input, [1]);
+        expect(result.shape).toEqual(input.shape);
+        test_util_1.expectArraysClose(result, [
+            12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 0, 1, 2, 3, 4, 5,
+            6, 7, 8, 9, 10, 11, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47,
+            24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 60, 61, 62, 63, 64, 65,
+            66, 67, 68, 69, 70, 71, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59
+        ]);
+    });
+    it('reverse a 4D array at axis [2]', function () {
+        var input = tf.tensor4d(data, shape);
+        var result = tf.reverse4d(input, [2]);
+        expect(result.shape).toEqual(input.shape);
+        test_util_1.expectArraysClose(result, [
+            8, 9, 10, 11, 4, 5, 6, 7, 0, 1, 2, 3, 20, 21, 22, 23, 16, 17,
+            18, 19, 12, 13, 14, 15, 32, 33, 34, 35, 28, 29, 30, 31, 24, 25, 26, 27,
+            44, 45, 46, 47, 40, 41, 42, 43, 36, 37, 38, 39, 56, 57, 58, 59, 52, 53,
+            54, 55, 48, 49, 50, 51, 68, 69, 70, 71, 64, 65, 66, 67, 60, 61, 62, 63
+        ]);
+    });
+    it('reverse a 4D array at axis [3]', function () {
+        var input = tf.tensor4d(data, shape);
+        var result = tf.reverse4d(input, [3]);
+        expect(result.shape).toEqual(input.shape);
+        test_util_1.expectArraysClose(result, [
+            3, 2, 1, 0, 7, 6, 5, 4, 11, 10, 9, 8, 15, 14, 13, 12, 19, 18,
+            17, 16, 23, 22, 21, 20, 27, 26, 25, 24, 31, 30, 29, 28, 35, 34, 33, 32,
+            39, 38, 37, 36, 43, 42, 41, 40, 47, 46, 45, 44, 51, 50, 49, 48, 55, 54,
+            53, 52, 59, 58, 57, 56, 63, 62, 61, 60, 67, 66, 65, 64, 71, 70, 69, 68
+        ]);
+    });
+    it('reverse a 4D array at axis [0, 2]', function () {
+        var input = tf.tensor4d(data, shape);
+        var result = tf.reverse4d(input, [0, 2]);
+        expect(result.shape).toEqual(input.shape);
+        test_util_1.expectArraysClose(result, [
+            56, 57, 58, 59, 52, 53, 54, 55, 48, 49, 50, 51, 68, 69, 70, 71, 64, 65,
+            66, 67, 60, 61, 62, 63, 32, 33, 34, 35, 28, 29, 30, 31, 24, 25, 26, 27,
+            44, 45, 46, 47, 40, 41, 42, 43, 36, 37, 38, 39, 8, 9, 10, 11, 4, 5,
+            6, 7, 0, 1, 2, 3, 20, 21, 22, 23, 16, 17, 18, 19, 12, 13, 14, 15
+        ]);
+    });
+    it('reverse a 4D array at axis [1, 3]', function () {
+        var input = tf.tensor4d(data, shape);
+        var result = tf.reverse4d(input, [1, 3]);
+        expect(result.shape).toEqual(input.shape);
+        test_util_1.expectArraysClose(result, [
+            15, 14, 13, 12, 19, 18, 17, 16, 23, 22, 21, 20, 3, 2, 1, 0, 7, 6,
+            5, 4, 11, 10, 9, 8, 39, 38, 37, 36, 43, 42, 41, 40, 47, 46, 45, 44,
+            27, 26, 25, 24, 31, 30, 29, 28, 35, 34, 33, 32, 63, 62, 61, 60, 67, 66,
+            65, 64, 71, 70, 69, 68, 51, 50, 49, 48, 55, 54, 53, 52, 59, 58, 57, 56
+        ]);
+    });
+    it('throws error with invalid input', function () {
+        var x = tf.tensor3d([1, 20, 300, 4], [1, 1, 4]);
+        expect(function () { return tf.reverse4d(x, [1]); }).toThrowError();
+    });
+    it('throws error with invalid axis param', function () {
+        var x = tf.tensor4d([1, 20, 300, 4], [1, 1, 1, 4]);
+        expect(function () { return tf.reverse4d(x, [4]); }).toThrowError();
+        expect(function () { return tf.reverse4d(x, [-5]); }).toThrowError();
+    });
+    it('throws error with non integer axis param', function () {
+        var x = tf.tensor4d([1, 20, 300, 4], [1, 1, 1, 4]);
+        expect(function () { return tf.reverse4d(x, [0.5]); }).toThrowError();
+    });
+    it('accepts a tensor-like object', function () {
+        var input = [[[[1]], [[2]], [[3]]], [[[4]], [[5]], [[6]]]];
+        var result = tf.reverse4d(input, [0]);
+        expect(result.shape).toEqual([2, 3, 1, 1]);
+        test_util_1.expectArraysClose(result, [4, 5, 6, 1, 2, 3]);
+    });
+});
+jasmine_util_1.describeWithFlags('reverse', test_util_1.ALL_ENVS, function () {
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.reverse({}); })
+            .toThrowError(/Argument 'x' passed to 'reverse' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var input = [1, 2, 3];
+        var result = tf.reverse(input);
+        expect(result.shape).toEqual([3]);
+        test_util_1.expectArraysClose(result, [3, 2, 1]);
+    });
+});
+//# sourceMappingURL=reverse_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_test.js.map
new file mode 100644
index 0000000..988fcd3
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/reverse_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd.d.ts
new file mode 100644
index 0000000..c8b7dff
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd.d.ts
@@ -0,0 +1,5 @@
+import { Tensor } from '../tensor';
+import { Rank, ShapeMap, TensorLike } from '../types';
+declare function scatterND_<R extends Rank>(indices: Tensor | TensorLike, updates: Tensor | TensorLike, shape: ShapeMap[R]): Tensor<R>;
+export declare const scatterND: typeof scatterND_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd.js b/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd.js
new file mode 100644
index 0000000..e17a690
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd.js
@@ -0,0 +1,14 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var tensor_util_env_1 = require("../tensor_util_env");
+var operation_1 = require("./operation");
+var scatter_nd_util = require("./scatter_nd_util");
+function scatterND_(indices, updates, shape) {
+    var $indices = tensor_util_env_1.convertToTensor(indices, 'indices', 'scatterND', 'int32');
+    var $updates = tensor_util_env_1.convertToTensor(updates, 'updates', 'scatterND');
+    scatter_nd_util.validateInput($updates, $indices, shape);
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.scatterND($indices, $updates, shape); }, { $indices: $indices, $updates: $updates });
+}
+exports.scatterND = operation_1.op({ scatterND_: scatterND_ });
+//# sourceMappingURL=scatter_nd.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd.js.map
new file mode 100644
index 0000000..aeb1ca1
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"scatter_nd.js","sourceRoot":"","sources":["../../src/ops/scatter_nd.ts"],"names":[],"mappings":";;AAiBA,8CAAmC;AAEnC,sDAAmD;AAGnD,yCAA+B;AAC/B,mDAAqD;AAoBrD,oBACI,OAA0B,EAAE,OAA0B,EACtD,KAAkB;IACpB,IAAM,QAAQ,GAAG,iCAAe,CAAC,OAAO,EAAE,SAAS,EAAE,WAAW,EAAE,OAAO,CAAC,CAAC;IAC3E,IAAM,QAAQ,GAAG,iCAAe,CAAC,OAAO,EAAE,SAAS,EAAE,WAAW,CAAC,CAAC;IAClE,eAAe,CAAC,aAAa,CAAC,QAAQ,EAAE,QAAQ,EAAE,KAAK,CAAC,CAAC;IAEzD,OAAO,iBAAG,CAAC,MAAM,CAAC,SAAS,CAChB,UAAA,OAAO,IAAI,OAAA,OAAO,CAAC,SAAS,CAAC,QAAQ,EAAE,QAAQ,EAAE,KAAK,CAAC,EAA5C,CAA4C,EACvD,EAAC,QAAQ,UAAA,EAAE,QAAQ,UAAA,EAAC,CAAc,CAAC;AAChD,CAAC;AAEY,QAAA,SAAS,GAAG,cAAE,CAAC,EAAC,UAAU,YAAA,EAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_test.js
new file mode 100644
index 0000000..24ecc32
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_test.js
@@ -0,0 +1,122 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('scatterND', test_util_1.ALL_ENVS, function () {
+    it('should work for 2d', function () {
+        var indices = tf.tensor1d([0, 4, 2], 'int32');
+        var updates = tf.tensor2d([100, 101, 102, 777, 778, 779, 1000, 1001, 1002], [3, 3], 'int32');
+        var shape = [5, 3];
+        var result = tf.scatterND(indices, updates, shape);
+        expect(result.shape).toEqual(shape);
+        expect(result.dtype).toEqual(updates.dtype);
+        test_util_1.expectArraysClose(result, [100, 101, 102, 0, 0, 0, 1000, 1001, 1002, 0, 0, 0, 777, 778, 779]);
+    });
+    it('should work for simple 1d', function () {
+        var indices = tf.tensor1d([3], 'int32');
+        var updates = tf.tensor1d([101], 'float32');
+        var shape = [5];
+        var result = tf.scatterND(indices, updates, shape);
+        expect(result.shape).toEqual(shape);
+        expect(result.dtype).toEqual(updates.dtype);
+        test_util_1.expectArraysClose(result, [0, 0, 0, 101, 0]);
+    });
+    it('should work for multiple 1d', function () {
+        var indices = tf.tensor1d([0, 4, 2], 'int32');
+        var updates = tf.tensor1d([100, 101, 102], 'float32');
+        var shape = [5];
+        var result = tf.scatterND(indices, updates, shape);
+        expect(result.shape).toEqual(shape);
+        expect(result.dtype).toEqual(updates.dtype);
+        test_util_1.expectArraysClose(result, [100, 0, 102, 0, 101]);
+    });
+    it('should work for high rank updates', function () {
+        var indices = tf.tensor2d([0, 2], [2, 1], 'int32');
+        var updates = tf.tensor3d([
+            5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8,
+            5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8
+        ], [2, 4, 4], 'float32');
+        var shape = [4, 4, 4];
+        var result = tf.scatterND(indices, updates, shape);
+        expect(result.shape).toEqual(shape);
+        expect(result.dtype).toEqual(updates.dtype);
+        test_util_1.expectArraysClose(result, [
+            5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7, 8, 8, 8, 8, 0, 0, 0, 0, 0, 0,
+            0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 5, 5, 5, 5, 6, 6, 6, 6, 7, 7, 7, 7,
+            8, 8, 8, 8, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0
+        ]);
+    });
+    it('should work for high rank indices', function () {
+        var indices = tf.tensor2d([0, 2, 0, 1], [2, 2], 'int32');
+        var updates = tf.tensor1d([10, 20], 'float32');
+        var shape = [3, 3];
+        var result = tf.scatterND(indices, updates, shape);
+        expect(result.shape).toEqual(shape);
+        expect(result.dtype).toEqual(updates.dtype);
+        test_util_1.expectArraysClose(result, [0, 20, 10, 0, 0, 0, 0, 0, 0]);
+    });
+    it('should sum the duplicated indices', function () {
+        var indices = tf.tensor1d([0, 4, 2, 1, 3, 0], 'int32');
+        var updates = tf.tensor1d([10, 20, 30, 40, 50, 60], 'float32');
+        var shape = [8];
+        var result = tf.scatterND(indices, updates, shape);
+        expect(result.shape).toEqual(shape);
+        expect(result.dtype).toEqual(updates.dtype);
+        test_util_1.expectArraysClose(result, [70, 40, 30, 50, 20, 0, 0, 0]);
+    });
+    it('should work for tensorLike input', function () {
+        var indices = [0, 4, 2];
+        var updates = [100, 101, 102];
+        var shape = [5];
+        var result = tf.scatterND(indices, updates, shape);
+        expect(result.shape).toEqual(shape);
+        expect(result.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(result, [100, 0, 102, 0, 101]);
+    });
+    it('should throw error when indices type is not int32', function () {
+        var indices = tf.tensor2d([0, 2, 0, 1], [2, 2], 'float32');
+        var updates = tf.tensor1d([10, 20], 'float32');
+        var shape = [3, 3];
+        expect(function () { return tf.scatterND(indices, updates, shape); }).toThrow();
+    });
+    it('should throw error when indices and update mismatch', function () {
+        var indices = tf.tensor2d([0, 4, 2], [3, 1], 'int32');
+        var updates = tf.tensor2d([100, 101, 102, 103, 777, 778, 779, 780, 10000, 10001, 10002, 10004], [3, 4], 'float32');
+        var shape = [5, 3];
+        expect(function () { return tf.scatterND(indices, updates, shape); }).toThrow();
+    });
+    it('should throw error when indices and update count mismatch', function () {
+        var indices = tf.tensor2d([0, 4, 2], [3, 1], 'int32');
+        var updates = tf.tensor2d([100, 101, 102, 10000, 10001, 10002], [2, 3], 'float32');
+        var shape = [5, 3];
+        expect(function () { return tf.scatterND(indices, updates, shape); }).toThrow();
+    });
+    it('should throw error when indices are scalar', function () {
+        var indices = tf.scalar(1, 'int32');
+        var updates = tf.tensor2d([100, 101, 102, 10000, 10001, 10002], [2, 3], 'float32');
+        var shape = [5, 3];
+        expect(function () { return tf.scatterND(indices, updates, shape); }).toThrow();
+    });
+    it('should throw error when update is scalar', function () {
+        var indices = tf.tensor2d([0, 4, 2], [3, 1], 'int32');
+        var updates = tf.scalar(1, 'float32');
+        var shape = [5, 3];
+        expect(function () { return tf.scatterND(indices, updates, shape); }).toThrow();
+    });
+});
+jasmine_util_1.describeWithFlags('scatterND CPU', test_util_1.CPU_ENVS, function () {
+    it('should throw error when index out of range', function () {
+        var indices = tf.tensor2d([0, 4, 99], [3, 1], 'int32');
+        var updates = tf.tensor2d([100, 101, 102, 777, 778, 779, 10000, 10001, 10002], [3, 3], 'float32');
+        var shape = [5, 3];
+        expect(function () { return tf.scatterND(indices, updates, shape); }).toThrow();
+    });
+    it('should throw error when indices has wrong dimension', function () {
+        var indices = tf.tensor2d([0, 4, 99], [3, 1], 'int32');
+        var updates = tf.tensor2d([100, 101, 102, 777, 778, 779, 10000, 10001, 10002], [3, 3], 'float32');
+        var shape = [2, 3];
+        expect(function () { return tf.scatterND(indices, updates, shape); }).toThrow();
+    });
+});
+//# sourceMappingURL=scatter_nd_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_test.js.map
new file mode 100644
index 0000000..8debc83
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"scatter_nd_test.js","sourceRoot":"","sources":["../../src/ops/scatter_nd_test.ts"],"names":[],"mappings":";;AAiBA,6BAA+B;AAC/B,gDAAkD;AAClD,0CAAmE;AAEnE,gCAAiB,CAAC,WAAW,EAAE,oBAAQ,EAAE;IACvC,EAAE,CAAC,oBAAoB,EAAE;QACvB,IAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;QAChD,IAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CACvB,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,IAAI,EAAE,IAAI,EAAE,IAAI,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;QACvE,IAAM,KAAK,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACrB,IAAM,MAAM,GAAG,EAAE,CAAC,SAAS,CAAC,OAAO,EAAE,OAAO,EAAE,KAAK,CAAC,CAAC;QACrD,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC;QACpC,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC;QAC5C,6BAAiB,CACb,MAAM,EACN,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,IAAI,EAAE,IAAI,EAAE,IAAI,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC,CAAC,CAAC;IAC1E,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,2BAA2B,EAAE;QAC9B,IAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;QAC1C,IAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,GAAG,CAAC,EAAE,SAAS,CAAC,CAAC;QAC9C,IAAM,KAAK,GAAG,CAAC,CAAC,CAAC,CAAC;QAClB,IAAM,MAAM,GAAG,EAAE,CAAC,SAAS,CAAC,OAAO,EAAE,OAAO,EAAE,KAAK,CAAC,CAAC;QACrD,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC;QACpC,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC;QAC5C,6BAAiB,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,GAAG,EAAE,CAAC,CAAC,CAAC,CAAC;IAC/C,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,6BAA6B,EAAE;QAChC,IAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;QAChD,IAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC,EAAE,SAAS,CAAC,CAAC;QACxD,IAAM,KAAK,GAAG,CAAC,CAAC,CAAC,CAAC;QAClB,IAAM,MAAM,GAAG,EAAE,CAAC,SAAS,CAAC,OAAO,EAAE,OAAO,EAAE,KAAK,CAAC,CAAC;QACrD,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC;QACpC,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC;QAC5C,6BAAiB,CAAC,MAAM,EAAE,CAAC,GAAG,EAAE,CAAC,EAAE,GAAG,EAAE,CAAC,EAAE,GAAG,CAAC,CAAC,CAAC;IACnD,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,mCAAmC,EAAE;QACtC,IAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;QACrD,IAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CACvB;YACE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC;YAC9C,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC;SAC/C,EACD,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,SAAS,CAAC,CAAC;QAC1B,IAAM,KAAK,GAAG,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC;QACxB,IAAM,MAAM,GAAG,EAAE,CAAC,SAAS,CAAC,OAAO,EAAE,OAAO,EAAE,KAAK,CAAC,CAAC;QACrD,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC;QACpC,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC;QAC5C,6BAAiB,CAAC,MAAM,EAAE;YACxB,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC;YAChE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC;YAChE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC;SAC3D,CAAC,CAAC;IACL,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,mCAAmC,EAAE;QACtC,IAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;QAC3D,IAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,EAAE,EAAE,EAAE,CAAC,EAAE,SAAS,CAAC,CAAC;QACjD,IAAM,KAAK,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACrB,IAAM,MAAM,GAAG,EAAE,CAAC,SAAS,CAAC,OAAO,EAAE,OAAO,EAAE,KAAK,CAAC,CAAC;QACrD,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC;QACpC,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC;QAC5C,6BAAiB,CAAC,MAAM,EAAE,CAAC,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IAC3D,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,mCAAmC,EAAE;QACtC,IAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;QACzD,IAAM,OAAO,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,SAAS,CAAC,CAAC;QACjE,IAAM,KAAK,GAAG,CAAC,CAAC,CAAC,CAAC;QAClB,IAAM,MAAM,GAAG,EAAE,CAAC,SAAS,CAAC,OAAO,EAAE,OAAO,EAAE,KAAK,CAAC,CAAC;QACrD,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC;QACpC,MAAM,CAAC,MAAM,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,OAAO,CAAC,KAAK,CAAC,CAAC;QAC5C,6BAAiB,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,CAA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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_util.d.ts
new file mode 100644
index 0000000..119f35a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_util.d.ts
@@ -0,0 +1,11 @@
+import { Tensor } from '../tensor';
+export declare function validateUpdateShape(shape: number[], indices: Tensor, updates: Tensor): void;
+export interface ScatterShapeInfo {
+    sliceRank: number;
+    numUpdates: number;
+    sliceSize: number;
+    strides: number[];
+    outputSize: number;
+}
+export declare function validateInput(updates: Tensor, indices: Tensor, shape: number[]): void;
+export declare function calculateShapes(updates: Tensor, indices: Tensor, shape: number[]): ScatterShapeInfo;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_util.js b/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_util.js
new file mode 100644
index 0000000..a5edc0b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_util.js
@@ -0,0 +1,76 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var util_1 = require("../util");
+function validateUpdateShape(shape, indices, updates) {
+    var sliceDim = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;
+    var batchDim = (indices.rank > 1) ? indices.rank - 1 : 1;
+    var shapeError = 'Must have updates.shape = indices.shape[:batchDim] + ' +
+        ("shape[sliceDim:], got updates.shape: " + updates.shape) +
+        (", indices.shape: " + indices.shape + ", shape: " + shape) +
+        (", sliceDim: " + sliceDim + ", and batchDim: " + batchDim + ".");
+    if (updates.rank < batchDim) {
+        throw new Error(shapeError + (" update.rank < " + batchDim + ". "));
+    }
+    if (shape.length < sliceDim + (updates.rank - batchDim)) {
+        throw new Error(shapeError +
+            (" Output shape length < " + (sliceDim + (updates.rank - batchDim))));
+    }
+    if (updates.rank !== batchDim + shape.length - sliceDim) {
+        throw new Error(shapeError + (" update.rank != " + (batchDim + shape.length - sliceDim)));
+    }
+    for (var d = 0; d < batchDim; ++d) {
+        if (updates.shape[d] !== indices.shape[d]) {
+            throw new Error(shapeError +
+                (" updates.shape[" + d + "] (" + updates.shape[d] + ") != indices.shape[" + d + "] (" + indices.shape[d] + ")."));
+        }
+    }
+    for (var d = 0; d < updates.rank - batchDim; ++d) {
+        if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {
+            throw new Error(shapeError +
+                (" updates.shape[" + (d + batchDim) + "] (" + updates.shape[d + batchDim] + ") != shape[" + (d + batchDim) + "] (" + shape[d + batchDim] + ")"));
+        }
+    }
+}
+exports.validateUpdateShape = validateUpdateShape;
+function validateInput(updates, indices, shape) {
+    if (indices.rank < 1) {
+        throw new Error('tf.scatterND() expects the indices to be rank 1 or higher,' +
+            (" but the rank was " + indices.rank + "."));
+    }
+    if (updates.rank < 1) {
+        throw new Error('tf.scatterND() expects the updates to be rank 1 or higher,' +
+            (" but the rank was " + updates.rank + "."));
+    }
+    if (indices.dtype !== 'int32') {
+        throw new Error("The dtype of 'indices' should be int32, but got dtype: " + indices.dtype);
+    }
+    if (shape.length < 1) {
+        throw new Error("Output rank must be greater or equal to 1, but got shape: " + shape);
+    }
+    if (shape.length === 0) {
+        if (indices.size === 0) {
+            throw new Error("Indices specified for empty output. indices shape: " + indices.shape);
+        }
+        if (updates.size === 0) {
+            throw new Error("Updates specified for empty output. updates shape: " + updates.shape);
+        }
+    }
+    validateUpdateShape(shape, indices, updates);
+}
+exports.validateInput = validateInput;
+function calculateShapes(updates, indices, shape) {
+    var sliceRank = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;
+    var totalNd = shape.length;
+    var sliceSize = 1;
+    for (var i = sliceRank; i < totalNd; ++i) {
+        sliceSize *= shape[i];
+    }
+    var safeSliceDim = (sliceRank < 1) ? 1 : sliceRank;
+    var numUpdates = indices.size / safeSliceDim;
+    var outputStrides = util_1.computeStrides(shape).concat([1]);
+    var strides = outputStrides.slice(outputStrides.length - sliceRank, outputStrides.length);
+    var outputSize = util_1.sizeFromShape(shape);
+    return { sliceRank: sliceRank, numUpdates: numUpdates, sliceSize: sliceSize, strides: strides, outputSize: outputSize };
+}
+exports.calculateShapes = calculateShapes;
+//# sourceMappingURL=scatter_nd_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_util.js.map
new file mode 100644
index 0000000..a3ed50c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/scatter_nd_util.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"scatter_nd_util.js","sourceRoot":"","sources":["../../src/ops/scatter_nd_util.ts"],"names":[],"mappings":";;AAiBA,gCAAsD;AAQtD,6BACI,KAAe,EAAE,OAAe,EAAE,OAAe;IACnD,IAAM,QAAQ,GAAG,CAAC,OAAO,CAAC,IAAI,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,KAAK,CAAC,OAAO,CAAC,IAAI,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;IAC1E,IAAM,QAAQ,GAAG,CAAC,OAAO,CAAC,IAAI,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,IAAI,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;IAE3D,IAAM,UAAU,GAAG,uDAAuD;SACtE,0CAAwC,OAAO,CAAC,KAAO,CAAA;SACvD,sBAAoB,OAAO,CAAC,KAAK,iBAAY,KAAO,CAAA;SACpD,iBAAe,QAAQ,wBAAmB,QAAQ,MAAG,CAAA,CAAC;IAE1D,IAAI,OAAO,CAAC,IAAI,GAAG,QAAQ,EAAE;QAC3B,MAAM,IAAI,KAAK,CAAC,UAAU,IAAG,oBAAkB,QAAQ,OAAI,CAAA,CAAC,CAAC;KAC9D;IACD,IAAI,KAAK,CAAC,MAAM,GAAG,QAAQ,GAAG,CAAC,OAAO,CAAC,IAAI,GAAG,QAAQ,CAAC,EAAE;QACvD,MAAM,IAAI,KAAK,CACX,UAAU;aACV,6BAA0B,QAAQ,GAAG,CAAC,OAAO,CAAC,IAAI,GAAG,QAAQ,CAAC,CAAE,CAAA,CAAC,CAAC;KACvE;IACD,IAAI,OAAO,CAAC,IAAI,KAAK,QAAQ,GAAG,KAAK,CAAC,MAAM,GAAG,QAAQ,EAAE;QACvD,MAAM,IAAI,KAAK,CACX,UAAU,IAAG,sBAAmB,QAAQ,GAAG,KAAK,CAAC,MAAM,GAAG,QAAQ,CAAE,CAAA,CAAC,CAAC;KAC3E;IACD,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,QAAQ,EAAE,EAAE,CAAC,EAAE;QACjC,IAAI,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,KAAK,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE;YACzC,MAAM,IAAI,KAAK,CACX,UAAU;iBACV,oBAAkB,CAAC,WAAM,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,2BAAsB,CAAC,WAC5D,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,OAAI,CAAA,CAAC,CAAC;SAC/B;KACF;IACD,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,OAAO,CAAC,IAAI,GAAG,QAAQ,EAAE,EAAE,CAAC,EAAE;QAChD,IAAI,OAAO,CAAC,KAAK,CAAC,CAAC,GAAG,QAAQ,CAAC,KAAK,KAAK,CAAC,CAAC,GAAG,QAAQ,CAAC,EAAE;YACvD,MAAM,IAAI,KAAK,CACX,UAAU;iBACV,qBAAkB,CAAC,GAAG,QAAQ,YAC1B,OAAO,CAAC,KAAK,CAAC,CAAC,GAAG,QAAQ,CAAC,oBAAc,CAAC,GAAG,QAAQ,YACrD,KAAK,CAAC,CAAC,GAAG,QAAQ,CAAC,MAAG,CAAA,CAAC,CAAC;SACjC;KACF;AACH,CAAC;AAvCD,kDAuCC;AAgBD,uBACI,OAAe,EAAE,OAAe,EAAE,KAAe;IACnD,IAAI,OAAO,CAAC,IAAI,GAAG,CAAC,EAAE;QACpB,MAAM,IAAI,KAAK,CACX,4DAA4D;aAC5D,uBAAqB,OAAO,CAAC,IAAI,MAAG,CAAA,CAAC,CAAC;KAC3C;IACD,IAAI,OAAO,CAAC,IAAI,GAAG,CAAC,EAAE;QACpB,MAAM,IAAI,KAAK,CACX,4DAA4D;aAC5D,uBAAqB,OAAO,CAAC,IAAI,MAAG,CAAA,CAAC,CAAC;KAC3C;IACD,IAAI,OAAO,CAAC,KAAK,KAAK,OAAO,EAAE;QAC7B,MAAM,IAAI,KAAK,CAAC,4DACZ,OAAO,CAAC,KAAO,CAAC,CAAC;KACtB;IACD,IAAI,KAAK,CAAC,MAAM,GAAG,CAAC,EAAE;QACpB,MAAM,IAAI,KAAK,CACX,+DAA6D,KAAO,CAAC,CAAC;KAC3E;IAED,IAAI,KAAK,CAAC,MAAM,KAAK,CAAC,EAAE;QACtB,IAAI,OAAO,CAAC,IAAI,KAAK,CAAC,EAAE;YACtB,MAAM,IAAI,KAAK,CAAC,wDACZ,OAAO,CAAC,KAAO,CAAC,CAAC;SACtB;QACD,IAAI,OAAO,CAAC,IAAI,KAAK,CAAC,EAAE;YACtB,MAAM,IAAI,KAAK,CAAC,wDACZ,OAAO,CAAC,KAAO,CAAC,CAAC;SACtB;KACF;IAED,mBAAmB,CAAC,KAAK,EAAE,OAAO,EAAE,OAAO,CAAC,CAAC;AAC/C,CAAC;AAjCD,sCAiCC;AAWD,yBACI,OAAe,EAAE,OAAe,EAAE,KAAe;IAEnD,IAAM,SAAS,GAAG,CAAC,OAAO,CAAC,IAAI,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,KAAK,CAAC,OAAO,CAAC,IAAI,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;IAK3E,IAAM,OAAO,GAAG,KAAK,CAAC,MAAM,CAAC;IAE7B,IAAI,SAAS,GAAG,CAAC,CAAC;IAClB,KAAK,IAAI,CAAC,GAAG,SAAS,EAAE,CAAC,GAAG,OAAO,EAAE,EAAE,CAAC,EAAE;QACxC,SAAS,IAAI,KAAK,CAAC,CAAC,CAAC,CAAC;KACvB;IAED,IAAM,YAAY,GAAG,CAAC,SAAS,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,SAAS,CAAC;IACrD,IAAM,UAAU,GAAG,OAAO,CAAC,IAAI,GAAG,YAAY,CAAC;IAE/C,IAAM,aAAa,GAAO,qBAAc,CAAC,KAAK,CAAC,SAAE,CAAC,EAAC,CAAC;IACpD,IAAM,OAAO,GAAG,aAAa,CAAC,KAAK,CAC/B,aAAa,CAAC,MAAM,GAAG,SAAS,EAAE,aAAa,CAAC,MAAM,CAAC,CAAC;IAC5D,IAAM,UAAU,GAAG,oBAAa,CAAC,KAAK,CAAC,CAAC;IACxC,OAAO,EAAC,SAAS,WAAA,EAAE,UAAU,YAAA,EAAE,SAAS,WAAA,EAAE,OAAO,SAAA,EAAE,UAAU,YAAA,EAAC,CAAC;AACjE,CAAC;AAvBD,0CAuBC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/segment_ops.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/segment_ops.d.ts
new file mode 100644
index 0000000..c2f0532
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/segment_ops.d.ts
@@ -0,0 +1,7 @@
+import { Tensor, Tensor1D } from '../tensor';
+import { TensorLike } from '../types';
+declare function unsortedSegmentSum_<T extends Tensor>(x: T | TensorLike, segmentIds: Tensor1D | TensorLike, numSegments: number): T;
+declare function gather_<T extends Tensor>(x: T | TensorLike, indices: Tensor1D | TensorLike, axis?: number): T;
+export declare const gather: typeof gather_;
+export declare const unsortedSegmentSum: typeof unsortedSegmentSum_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/segment_ops.js b/node_modules/@tensorflow/tfjs-core/dist/ops/segment_ops.js
new file mode 100644
index 0000000..db48726
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/segment_ops.js
@@ -0,0 +1,89 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var tensor_util_env_1 = require("../tensor_util_env");
+var util_1 = require("../util");
+var array_ops_1 = require("./array_ops");
+var axis_util_1 = require("./axis_util");
+var binary_ops_1 = require("./binary_ops");
+var compare_1 = require("./compare");
+var logical_ops_1 = require("./logical_ops");
+var operation_1 = require("./operation");
+var tensor_ops_1 = require("./tensor_ops");
+function unsortedSegmentSum_(x, segmentIds, numSegments) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'unsortedSegmentSum');
+    var $segmentIds = tensor_util_env_1.convertToTensor(segmentIds, 'segmentIds', 'unsortedSegmentSum', 'int32');
+    util_1.assert(util_1.isInt(numSegments), 'numSegments must be of dtype int');
+    var gradFunc = function (dy) {
+        var derX = function () {
+            return gatherDropNegatives(dy, $segmentIds);
+        };
+        return { $x: derX };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) {
+        return backend.unsortedSegmentSum($x, $segmentIds, numSegments);
+    }, { $x: $x }, gradFunc);
+}
+function gather_(x, indices, axis) {
+    if (axis === void 0) { axis = 0; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'gather');
+    var $indices = tensor_util_env_1.convertToTensor(indices, 'indices', 'gather', 'int32');
+    axis = axis_util_1.parseAxisParam(axis, $x.shape)[0];
+    var grad = function (dy) {
+        var derX = function () {
+            if (axis === 0) {
+                return exports.unsortedSegmentSum(dy, $indices, $x.shape[axis]);
+            }
+            var paramsShape = $x.shape;
+            var indicesSize = $indices.size;
+            var outerShape = paramsShape.slice(0, axis);
+            var outerDims = outerShape.length;
+            var innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);
+            var innerDims = innerShape.length;
+            var outerAxesIndices = arrayRange(0, outerDims);
+            var innerAxesIndices = arrayRange(outerDims + 1, outerDims + 1 + innerDims);
+            var valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);
+            var values = dy.reshape(valuesShape);
+            var reshapedIndices = $indices.reshape([indicesSize]);
+            var transposeDims = arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);
+            var valuesTranspose = values.transpose(transposeDims);
+            var paramsGrad = exports.unsortedSegmentSum(valuesTranspose, reshapedIndices, $x.shape[axis]);
+            var invertTransposeDims = axis_util_1.getUndoAxesPermutation(transposeDims);
+            paramsGrad = paramsGrad.transpose(invertTransposeDims);
+            return paramsGrad;
+        };
+        return { $x: derX };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.gather($x, $indices, axis); }, { $x: $x }, grad);
+}
+function arrayRange(start, stop) {
+    var result = [];
+    for (var i = start; i < stop; ++i) {
+        result.push(i);
+    }
+    return result;
+}
+function arrayConcat(arrays) {
+    var result = [];
+    for (var i = 0; i < arrays.length; ++i) {
+        for (var j = 0; j < arrays[i].length; ++j) {
+            result.push(arrays[i][j]);
+        }
+    }
+    return result;
+}
+function gatherDropNegatives(x, indices) {
+    var zeroClippedIndices = binary_ops_1.maximum(indices, tensor_ops_1.zerosLike(indices));
+    var gathered = exports.gather(x, zeroClippedIndices);
+    var isPositive = compare_1.greaterEqual(indices, tensor_ops_1.scalar(0, 'int32'));
+    var numIters = gathered.rank - isPositive.rank;
+    for (var i = 0; i < numIters; ++i) {
+        isPositive = array_ops_1.expandDims(isPositive, i + 1);
+    }
+    isPositive = logical_ops_1.logicalAnd(isPositive, tensor_ops_1.ones(gathered.shape, 'bool'));
+    var zeroSlice = tensor_ops_1.zerosLike(gathered);
+    return logical_ops_1.where(isPositive, gathered, zeroSlice);
+}
+exports.gather = operation_1.op({ gather_: gather_ });
+exports.unsortedSegmentSum = operation_1.op({ unsortedSegmentSum_: unsortedSegmentSum_ });
+//# sourceMappingURL=segment_ops.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/segment_ops.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/segment_ops.js.map
new file mode 100644
index 0000000..d8fb09d
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/segment_ops.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/segment_ops_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/segment_ops_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/segment_ops_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/segment_ops_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/segment_ops_test.js
new file mode 100644
index 0000000..ea53684
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/segment_ops_test.js
@@ -0,0 +1,110 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+var reduce_util_1 = require("./reduce_util");
+jasmine_util_1.describeWithFlags('unsortedSegmentSum', test_util_1.ALL_ENVS, function () {
+    it('tensor1D', function () {
+        var t = tf.tensor1d([1, 2, 3, 4]);
+        var segmentIds = tf.tensor1d([0, 2, 0, 1], 'int32');
+        var numSegments = 3;
+        var res = tf.unsortedSegmentSum(t, segmentIds, numSegments);
+        expect(res.shape).toEqual([numSegments]);
+        test_util_1.expectArraysClose(res, [4, 4, 2]);
+    });
+    it('tensor2D', function () {
+        var t = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var segmentIds = tf.tensor1d([0, 0], 'int32');
+        var numSegments = 2;
+        var res = tf.unsortedSegmentSum(t, segmentIds, numSegments);
+        expect(res.shape).toEqual([numSegments, 2]);
+        test_util_1.expectArraysClose(res, [4, 6, 0, 0]);
+    });
+    it('tensor3D', function () {
+        var t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [3, 2, 2]);
+        var segmentIds = tf.tensor1d([2, 1, 2], 'int32');
+        var numSegments = 3;
+        var res = tf.unsortedSegmentSum(t, segmentIds, numSegments);
+        expect(res.shape).toEqual([numSegments, 2, 2]);
+        test_util_1.expectArraysClose(res, [0, 0, 0, 0, 5, 6, 7, 8, 10, 12, 14, 16]);
+    });
+    it('N > than parallelization threshold, tensor1D', function () {
+        var n = reduce_util_1.PARALLELIZE_THRESHOLD * 2;
+        var values = new Float32Array(n);
+        var numSegments = 5;
+        var segmentIdValues = new Float32Array(n);
+        var vals = new Float32Array(numSegments);
+        for (var i = 0; i < n; i++) {
+            values[i] = i;
+            segmentIdValues[i] = i % numSegments;
+            vals[i % numSegments] += i;
+        }
+        var t = tf.tensor1d(values);
+        var segmentIds = tf.tensor1d(segmentIdValues, 'int32');
+        var res = tf.unsortedSegmentSum(t, segmentIds, numSegments);
+        expect(res.shape).toEqual([numSegments]);
+        test_util_1.expectArraysClose(res, vals);
+    });
+    it('ignores negative segmentIds', function () {
+        var t = tf.tensor1d([1, 2, 3, 4]);
+        var segmentIds = tf.tensor1d([0, 2, -1, 1], 'int32');
+        var numSegments = 3;
+        var res = tf.unsortedSegmentSum(t, segmentIds, numSegments);
+        expect(res.shape).toEqual([numSegments]);
+        test_util_1.expectArraysClose(res, [1, 4, 2]);
+    });
+    it('gradient ignores negative segmentIds', function () {
+        var t = tf.tensor1d([1, 2, 3, 4]);
+        var segmentIds = tf.tensor1d([0, 2, -1, 1], 'int32');
+        var numSegments = 3;
+        var dy = tf.tensor1d([11, 2, 7]);
+        var gradient = tf.grad(function (a) { return tf.unsortedSegmentSum(a, segmentIds, numSegments); })(t, dy);
+        expect(gradient.shape).toEqual(t.shape);
+        test_util_1.expectArraysClose(gradient, [11, 7, 0, 2]);
+    });
+    it('tensor1D gradient', function () {
+        var t = tf.tensor1d([1, 2, 3, 4]);
+        var segmentIds = tf.tensor1d([0, 2, 0, 1], 'int32');
+        var numSegments = 3;
+        var dy = tf.tensor1d([11, 2, 7]);
+        var gradient = tf.grad(function (a) { return tf.unsortedSegmentSum(a, segmentIds, numSegments); })(t, dy);
+        expect(gradient.shape).toEqual(t.shape);
+        test_util_1.expectArraysClose(gradient, [11, 7, 11, 2]);
+    });
+    it('tensor2D gradient', function () {
+        var t = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var segmentIds = tf.tensor1d([0, 0], 'int32');
+        var numSegments = 2;
+        var dy = tf.tensor2d([11, 2, 4, 5], [2, 2]);
+        var gradient = tf.grad(function (a) { return tf.unsortedSegmentSum(a, segmentIds, numSegments); })(t, dy);
+        expect(gradient.shape).toEqual(t.shape);
+        test_util_1.expectArraysClose(gradient, [11, 2, 11, 2]);
+    });
+    it('tensor3D gradient', function () {
+        var t = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [3, 2, 2]);
+        var segmentIds = tf.tensor1d([2, 1, 2], 'int32');
+        var numSegments = 3;
+        var dy = tf.tensor3d([11, 2, 4, 5, 17, 31, 1, 0, -1, 14, 3, 28], [3, 2, 2]);
+        var gradient = tf.grad(function (a) { return tf.unsortedSegmentSum(a, segmentIds, numSegments); })(t, dy);
+        expect(gradient.shape).toEqual(t.shape);
+        test_util_1.expectArraysClose(gradient, [-1, 14, 3, 28, 17, 31, 1, 0, -1, 14, 3, 28]);
+    });
+    it('accepts a tensor-like object', function () {
+        var x = [1, 2, 3, 4];
+        var segmentIds = [0, 2, 0, 1];
+        var numSegments = 3;
+        var res = tf.unsortedSegmentSum(x, segmentIds, numSegments);
+        expect(res.shape).toEqual([3]);
+        test_util_1.expectArraysClose(res, [4, 4, 2]);
+    });
+    it('accepts a tensor-like object chained', function () {
+        var x = tf.tensor1d([1, 2, 3, 4]);
+        var segmentIds = [0, 2, 0, 1];
+        var numSegments = 3;
+        var res = x.unsortedSegmentSum(segmentIds, numSegments);
+        expect(res.shape).toEqual([3]);
+        test_util_1.expectArraysClose(res, [4, 4, 2]);
+    });
+});
+//# sourceMappingURL=segment_ops_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/segment_ops_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/segment_ops_test.js.map
new file mode 100644
index 0000000..52bf43e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/segment_ops_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"segment_ops_test.js","sourceRoot":"","sources":["../../src/ops/segment_ops_test.ts"],"names":[],"mappings":";;AAiBA,6BAA+B;AAC/B,gDAAkD;AAClD,0CAAyD;AACzD,6CAAoD;AAEpD,gCAAiB,CAAC,oBAAoB,EAAE,oBAAQ,EAAE;IAChD,EAAE,CAAC,UAAU,EAAE;QACb,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACpC,IAAM,UAAU,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;QACtD,IAAM,WAAW,GAAG,CAAC,CAAC;QACtB,IAAM,GAAG,GAAG,EAAE,CAAC,kBAAkB,CAAC,CAAC,EAAE,UAAU,EAAE,WAAW,CAAC,CAAC;QAE9D,MAAM,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC;QACzC,6BAAiB,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IACpC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,UAAU,EAAE;QACb,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC5C,IAAM,UAAU,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;QAChD,IAAM,WAAW,GAAG,CAAC,CAAC;QACtB,IAAM,GAAG,GAAG,EAAE,CAAC,kBAAkB,CAAC,CAAC,EAAE,UAAU,EAAE,WAAW,CAAC,CAAC;QAE9D,MAAM,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,WAAW,EAAE,CAAC,CAAC,CAAC,CAAC;QAC5C,6BAAiB,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IACvC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,UAAU,EAAE;QACb,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC1E,IAAM,UAAU,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;QACnD,IAAM,WAAW,GAAG,CAAC,CAAC;QACtB,IAAM,GAAG,GAAG,EAAE,CAAC,kBAAkB,CAAC,CAAC,EAAE,UAAU,EAAE,WAAW,CAAC,CAAC;QAE9D,MAAM,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,WAAW,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC/C,6BAAiB,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,EAAE,CAAC,CAAC,CAAC;IACnE,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,8CAA8C,EAAE;QACjD,IAAM,CAAC,GAAG,mCAAqB,GAAG,CAAC,CAAC;QACpC,IAAM,MAAM,GAAG,IAAI,YAAY,CAAC,CAAC,CAAC,CAAC;QACnC,IAAM,WAAW,GAAG,CAAC,CAAC;QACtB,IAAM,eAAe,GAAG,IAAI,YAAY,CAAC,CAAC,CAAC,CAAC;QAC5C,IAAM,IAAI,GAAG,IAAI,YAAY,CAAC,WAAW,CAAC,CAAC;QAC3C,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,EAAE,EAAE;YAC1B,MAAM,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC;YACd,eAAe,CAAC,CAAC,CAAC,GAAG,CAAC,GAAG,WAAW,CAAC;YACrC,IAAI,CAAC,CAAC,GAAG,WAAW,CAAC,IAAI,CAAC,CAAC;SAC5B;QACD,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,MAAM,CAAC,CAAC;QAC9B,IAAM,UAAU,GAAG,EAAE,CAAC,QAAQ,CAAC,eAAe,EAAE,OAAO,CAAC,CAAC;QACzD,IAAM,GAAG,GAAG,EAAE,CAAC,kBAAkB,CAAC,CAAC,EAAE,UAAU,EAAE,WAAW,CAAC,CAAC;QAE9D,MAAM,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC;QACzC,6BAAiB,CAAC,GAAG,EAAE,IAAI,CAAC,CAAC;IAC/B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,6BAA6B,EAAE;QAChC,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACpC,IAAM,UAAU,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;QACvD,IAAM,WAAW,GAAG,CAAC,CAAC;QAEtB,IAAM,GAAG,GAAG,EAAE,CAAC,kBAAkB,CAAC,CAAC,EAAE,UAAU,EAAE,WAAW,CAAC,CAAC;QAE9D,MAAM,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,WAAW,CAAC,CAAC,CAAC;QACzC,6BAAiB,CAAC,GAAG,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IACpC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,sCAAsC,EAAE;QACzC,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACpC,IAAM,UAAU,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;QACvD,IAAM,WAAW,GAAG,CAAC,CAAC;QAEtB,IAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACnC,IAAM,QAAQ,GACV,EAAE,CAAC,IAAI,CAAC,UAAA,CAAC,IAAI,OAAA,EAAE,CAAC,kBAAkB,CAAC,CAAC,EAAE,UAAU,EAAE,WAAW,CAAC,EAAjD,CAAiD,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC;QAE3E,MAAM,CAAC,QAAQ,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC;QACxC,6BAAiB,CAAC,QAAQ,EAAE,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IAC7C,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,mBAAmB,EAAE;QACtB,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACpC,IAAM,UAAU,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;QACtD,IAAM,WAAW,GAAG,CAAC,CAAC;QAEtB,IAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACnC,IAAM,QAAQ,GACV,EAAE,CAAC,IAAI,CAAC,UAAA,CAAC,IAAI,OAAA,EAAE,CAAC,kBAAkB,CAAC,CAAC,EAAE,UAAU,EAAE,WAAW,CAAC,EAAjD,CAAiD,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC;QAE3E,MAAM,CAAC,QAAQ,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC;QACxC,6BAAiB,CAAC,QAAQ,EAAE,CAAC,EAAE,EAAE,CAAC,EAAE,EAAE,EAAE,CAAC,CAAC,CAAC,CAAC;IAC9C,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,mBAAmB,EAAE;QACtB,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC5C,IAAM,UAAU,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;QAChD,IAAM,WAAW,GAAG,CAAC,CAAC;QAEtB,IAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,C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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/segment_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/segment_util.d.ts
new file mode 100644
index 0000000..68d3397
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/segment_util.d.ts
@@ -0,0 +1,8 @@
+export interface SegOpInfo {
+    windowSize: number;
+    batchSize: number;
+    inSize: number;
+    numSegments: number;
+}
+export declare function segOpComputeOptimalWindowSize(inSize: number, numSegments: number): number;
+export declare function computeOutShape(aShape: number[], axis: number, numSegments: number): number[];
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/segment_util.js b/node_modules/@tensorflow/tfjs-core/dist/ops/segment_util.js
new file mode 100644
index 0000000..fd67cab
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/segment_util.js
@@ -0,0 +1,41 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var util_1 = require("../util");
+var reduce_util_1 = require("./reduce_util");
+function segOpComputeOptimalWindowSize(inSize, numSegments) {
+    var done = false;
+    var res;
+    if (inSize <= reduce_util_1.PARALLELIZE_THRESHOLD) {
+        res = inSize;
+        done = true;
+    }
+    else {
+        res = util_1.nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
+    }
+    while (!done) {
+        if (res > numSegments || res === inSize) {
+            done = true;
+            break;
+        }
+        else {
+            res = util_1.nearestDivisor(inSize, res + 1);
+        }
+    }
+    return res;
+}
+exports.segOpComputeOptimalWindowSize = segOpComputeOptimalWindowSize;
+function computeOutShape(aShape, axis, numSegments) {
+    var outShape = [];
+    var rank = aShape.length;
+    for (var dim = 0; dim < rank; dim++) {
+        if (dim !== axis) {
+            outShape.push(aShape[dim]);
+        }
+        else {
+            outShape.push(numSegments);
+        }
+    }
+    return outShape;
+}
+exports.computeOutShape = computeOutShape;
+//# sourceMappingURL=segment_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/segment_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/segment_util.js.map
new file mode 100644
index 0000000..7755ad4
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/segment_util.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"segment_util.js","sourceRoot":"","sources":["../../src/ops/segment_util.ts"],"names":[],"mappings":";;AAiBA,gCAAuC;AACvC,6CAAoD;AASpD,uCACI,MAAc,EAAE,WAAmB;IACrC,IAAI,IAAI,GAAG,KAAK,CAAC;IACjB,IAAI,GAAG,CAAC;IAER,IAAI,MAAM,IAAI,mCAAqB,EAAE;QACnC,GAAG,GAAG,MAAM,CAAC;QACb,IAAI,GAAG,IAAI,CAAC;KACb;SAAM;QACL,GAAG,GAAG,qBAAc,CAAC,MAAM,EAAE,IAAI,CAAC,KAAK,CAAC,IAAI,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC;KAC7D;IAED,OAAO,CAAC,IAAI,EAAE;QACZ,IAAI,GAAG,GAAG,WAAW,IAAI,GAAG,KAAK,MAAM,EAAE;YACvC,IAAI,GAAG,IAAI,CAAC;YACZ,MAAM;SACP;aAAM;YACL,GAAG,GAAG,qBAAc,CAAC,MAAM,EAAE,GAAG,GAAG,CAAC,CAAC,CAAC;SACvC;KACF;IACD,OAAO,GAAG,CAAC;AACb,CAAC;AArBD,sEAqBC;AAED,yBACI,MAAgB,EAAE,IAAY,EAAE,WAAmB;IACrD,IAAM,QAAQ,GAAG,EAAE,CAAC;IACpB,IAAM,IAAI,GAAG,MAAM,CAAC,MAAM,CAAC;IAC3B,KAAK,IAAI,GAAG,GAAG,CAAC,EAAE,GAAG,GAAG,IAAI,EAAE,GAAG,EAAE,EAAE;QACnC,IAAI,GAAG,KAAK,IAAI,EAAE;YAChB,QAAQ,CAAC,IAAI,CAAC,MAAM,CAAC,GAAG,CAAC,CAAC,CAAC;SAC5B;aAAM;YACL,QAAQ,CAAC,IAAI,CAAC,WAAW,CAAC,CAAC;SAC5B;KACF;IACD,OAAO,QAAQ,CAAC;AAClB,CAAC;AAZD,0CAYC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/selu_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/selu_util.d.ts
new file mode 100644
index 0000000..939c32c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/selu_util.d.ts
@@ -0,0 +1,2 @@
+export declare const SELU_SCALEALPHA = 1.7580993408473768;
+export declare const SELU_SCALE = 1.0507009873554805;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/selu_util.js b/node_modules/@tensorflow/tfjs-core/dist/ops/selu_util.js
new file mode 100644
index 0000000..356c45c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/selu_util.js
@@ -0,0 +1,5 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+exports.SELU_SCALEALPHA = 1.7580993408473768599402175208123;
+exports.SELU_SCALE = 1.0507009873554804934193349852946;
+//# sourceMappingURL=selu_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/selu_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/selu_util.js.map
new file mode 100644
index 0000000..d9acdcf
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/selu_util.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"selu_util.js","sourceRoot":"","sources":["../../src/ops/selu_util.ts"],"names":[],"mappings":";;AAiBa,QAAA,eAAe,GAAG,iCAAiC,CAAC;AACpD,QAAA,UAAU,GAAG,iCAAiC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/slice.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/slice.d.ts
new file mode 100644
index 0000000..52a0b5d
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/slice.d.ts
@@ -0,0 +1,13 @@
+import { Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D } from '../tensor';
+import { Rank, TensorLike } from '../types';
+declare function slice1d_(x: Tensor1D | TensorLike, begin: number, size: number): Tensor1D;
+declare function slice2d_(x: Tensor2D | TensorLike, begin: [number, number], size: [number, number]): Tensor2D;
+declare function slice3d_(x: Tensor3D | TensorLike, begin: [number, number, number], size: [number, number, number]): Tensor3D;
+declare function slice4d_(x: Tensor4D | TensorLike, begin: [number, number, number, number], size: [number, number, number, number]): Tensor4D;
+declare function slice_<R extends Rank, T extends Tensor<R>>(x: T | TensorLike, begin: number | number[], size?: number | number[]): T;
+export declare const slice: typeof slice_;
+export declare const slice1d: typeof slice1d_;
+export declare const slice2d: typeof slice2d_;
+export declare const slice3d: typeof slice3d_;
+export declare const slice4d: typeof slice4d_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/slice.js b/node_modules/@tensorflow/tfjs-core/dist/ops/slice.js
new file mode 100644
index 0000000..5df4b0b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/slice.js
@@ -0,0 +1,81 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var tensor_util_env_1 = require("../tensor_util_env");
+var util = require("../util");
+var operation_1 = require("./operation");
+var slice_util = require("./slice_util");
+function slice1d_(x, begin, size) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'slice1d');
+    util.assert($x.rank === 1, "slice1d expects a rank-1 tensor, but got a rank-" + $x.rank + " tensor");
+    return exports.slice($x, [begin], [size]);
+}
+function slice2d_(x, begin, size) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'slice2d');
+    util.assert($x.rank === 2, "slice2d expects a rank-2 tensor, but got a rank-" + $x.rank + " tensor");
+    return exports.slice($x, begin, size);
+}
+function slice3d_(x, begin, size) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'slice3d');
+    util.assert($x.rank === 3, "slice3d expects a rank-3 tensor, but got a rank-" + $x.rank + " tensor");
+    return exports.slice($x, begin, size);
+}
+function slice4d_(x, begin, size) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'slice4d');
+    util.assert($x.rank === 4, "slice4d expects a rank-4 tensor, but got a rank-" + $x.rank + " tensor");
+    return exports.slice($x, begin, size);
+}
+function slice_(x, begin, size) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'slice');
+    if ($x.rank === 0) {
+        throw new Error('Slicing scalar is not possible');
+    }
+    var begin_;
+    if (typeof begin === 'number') {
+        begin_ = [begin].concat(new Array($x.rank - 1).fill(0));
+    }
+    else if (begin.length < $x.rank) {
+        begin_ = begin.concat(new Array($x.rank - begin.length).fill(0));
+    }
+    else {
+        begin_ = begin.slice();
+    }
+    var size_;
+    if (size == null) {
+        size_ = new Array($x.rank).fill(-1);
+    }
+    else if (typeof size === 'number') {
+        size_ = [size].concat(new Array($x.rank - 1).fill(-1));
+    }
+    else if (size.length < $x.rank) {
+        size_ = size.concat(new Array($x.rank - size.length).fill(-1));
+    }
+    else {
+        size_ = size;
+    }
+    size_ = size_.map(function (d, i) {
+        if (d >= 0) {
+            return d;
+        }
+        else {
+            util.assert(d === -1, 'Bad value in size');
+            return $x.shape[i] - begin_[i];
+        }
+    });
+    slice_util.assertParamsValid($x, begin_, size_);
+    var inputShape = $x.shape;
+    var grad = function (dy) {
+        var paddings = [];
+        for (var i = 0; i < dy.rank; i++) {
+            paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);
+        }
+        return { $x: function () { return dy.pad(paddings); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.slice($x, begin_, size_); }, { $x: $x }, grad);
+}
+exports.slice = operation_1.op({ slice_: slice_ });
+exports.slice1d = operation_1.op({ slice1d_: slice1d_ });
+exports.slice2d = operation_1.op({ slice2d_: slice2d_ });
+exports.slice3d = operation_1.op({ slice3d_: slice3d_ });
+exports.slice4d = operation_1.op({ slice4d_: slice4d_ });
+//# sourceMappingURL=slice.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/slice.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/slice.js.map
new file mode 100644
index 0000000..bbca63e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/slice.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"slice.js","sourceRoot":"","sources":["../../src/ops/slice.ts"],"names":[],"mappings":";;AAiBA,8CAAmC;AAEnC,sDAAmD;AAEnD,8BAAgC;AAChC,yCAA+B;AAC/B,yCAA2C;AAM3C,kBACI,CAAsB,EAAE,KAAa,EAAE,IAAY;IACrD,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,SAAS,CAAC,CAAC;IAC9C,IAAI,CAAC,MAAM,CACP,EAAE,CAAC,IAAI,KAAK,CAAC,EACb,qDAAmD,EAAE,CAAC,IAAI,YAAS,CAAC,CAAC;IACzE,OAAO,aAAK,CAAC,EAAE,EAAE,CAAC,KAAK,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC;AACpC,CAAC;AAMD,kBACI,CAAsB,EAAE,KAAuB,EAC/C,IAAsB;IACxB,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,SAAS,CAAC,CAAC;IAC9C,IAAI,CAAC,MAAM,CACP,EAAE,CAAC,IAAI,KAAK,CAAC,EACb,qDAAmD,EAAE,CAAC,IAAI,YAAS,CAAC,CAAC;IACzE,OAAO,aAAK,CAAC,EAAE,EAAE,KAAK,EAAE,IAAI,CAAC,CAAC;AAChC,CAAC;AAMD,kBACI,CAAsB,EAAE,KAA+B,EACvD,IAA8B;IAChC,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,SAAS,CAAC,CAAC;IAC9C,IAAI,CAAC,MAAM,CACP,EAAE,CAAC,IAAI,KAAK,CAAC,EACb,qDAAmD,EAAE,CAAC,IAAI,YAAS,CAAC,CAAC;IACzE,OAAO,aAAK,CAAC,EAAE,EAAE,KAAK,EAAE,IAAI,CAAC,CAAC;AAChC,CAAC;AAMD,kBACI,CAAsB,EAAE,KAAuC,EAC/D,IAAsC;IACxC,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,SAAS,CAAC,CAAC;IAC9C,IAAI,CAAC,MAAM,CACP,EAAE,CAAC,IAAI,KAAK,CAAC,EACb,qDAAmD,EAAE,CAAC,IAAI,YAAS,CAAC,CAAC;IACzE,OAAO,aAAK,CAAC,EAAE,EAAE,KAAK,EAAE,IAAI,CAAC,CAAC;AAChC,CAAC;AAmCD,gBACI,CAAe,EAAE,KAAsB,EAAE,IAAsB;IACjE,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,OAAO,CAAC,CAAC;IAE5C,IAAI,EAAE,CAAC,IAAI,KAAK,CAAC,EAAE;QACjB,MAAM,IAAI,KAAK,CAAC,gCAAgC,CAAC,CAAC;KACnD;IAED,IAAI,MAAgB,CAAC;IACrB,IAAI,OAAO,KAAK,KAAK,QAAQ,EAAE;QAC7B,MAAM,IAAI,KAAK,SAAK,IAAI,KAAK,CAAC,EAAE,CAAC,IAAI,GAAG,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC;KACrD;SAAM,IAAI,KAAK,CAAC,MAAM,GAAG,EAAE,CAAC,IAAI,EAAE;QACjC,MAAM,GAAG,KAAK,CAAC,MAAM,CAAC,IAAI,KAAK,CAAC,EAAE,CAAC,IAAI,GAAG,KAAK,CAAC,MAAM,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC;KAClE;SAAM;QACL,MAAM,GAAG,KAAK,CAAC,KAAK,EAAE,CAAC;KACxB;IACD,IAAI,KAAe,CAAC;IACpB,IAAI,IAAI,IAAI,IAAI,EAAE;QAChB,KAAK,GAAG,IAAI,KAAK,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC;KACrC;SAAM,IAAI,OAAO,IAAI,KAAK,QAAQ,EAAE;QACnC,KAAK,IAAI,IAAI,SAAK,IAAI,KAAK,CAAC,EAAE,CAAC,IAAI,GAAG,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;KACpD;SAAM,IAAI,IAAI,CAAC,MAAM,GAAG,EAAE,CAAC,IAAI,EAAE;QAChC,KAAK,GAAG,IAAI,CAAC,MAAM,CAAC,IAAI,KAAK,CAAC,EAAE,CAAC,IAAI,GAAG,IAAI,CAAC,MAAM,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;KAChE;SAAM;QACL,KAAK,GAAG,IAAI,CAAC;KACd;IACD,KAAK,GAAG,KAAK,CAAC,GAAG,CAAC,UAAC,CAAC,EAAE,CAAC;QACrB,IAAI,CAAC,IAAI,CAAC,EAAE;YACV,OAAO,CAAC,CAAC;SACV;aAAM;YACL,IAAI,CAAC,MAAM,CAAC,CAAC,KAAK,CAAC,CAAC,EAAE,mBAAmB,CAAC,CAAC;YAC3C,OAAO,EAAE,CAAC,KAAK,CAAC,CAAC,CAAC,GAAG,MAAM,CAAC,CAAC,CAAC,CAAC;SAChC;IACH,CAAC,CAAC,CAAC;IACH,UAAU,CAAC,iBAAiB,CAAC,EAAE,EAAE,MAAM,EAAE,KAAK,CAAC,CAAC;IAChD,IAAM,UAAU,GAAG,EAAE,CAAC,KAAK,CAAC;IAC5B,IAAM,IAAI,GAAG,UAAC,EAAK;QAOjB,IAAM,QAAQ,GAA4B,EAAE,CAAC;QAC7C,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,EAAE,CAAC,IAAI,EAAE,CAAC,EAAE,EAAE;YAChC,QAAQ,CAAC,IAAI,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,EAAE,UAAU,CAAC,CAAC,CAAC,GAAG,MAAM,CAAC,CAAC,CAAC,GAAG,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;SAClE;QACD,OAAO,EAAC,EAAE,EAAE,cAAM,OAAA,EAAE,CAAC,GAAG,CAAC,QAAQ,CAAC,EAAhB,CAAgB,EAAC,CAAC;IACtC,CAAC,CAAC;IACF,OAAO,iBAAG,CAAC,MAAM,CAAC,SAAS,CAChB,UAAA,OAAO,IAAI,OAAA,OAAO,CAAC,KAAK,CAAC,EAAE,EAAE,MAAM,EAAE,KAAK,CAAC,EAAhC,CAAgC,EAAE,EAAC,EAAE,IAAA,EAAC,EAAE,IAAI,CAAM,CAAC;AAC3E,CAAC;AAEY,QAAA,KAAK,GAAG,cAAE,CAAC,EAAC,MAAM,QAAA,EAAC,CAAC,CAAC;AACrB,QAAA,OAAO,GAAG,cAAE,CAAC,EAAC,QAAQ,UAAA,EAAC,CAAC,CAAC;AACzB,QAAA,OAAO,GAAG,cAAE,CAAC,EAAC,QAAQ,UAAA,EAAC,CAAC,CAAC;AACzB,QAAA,OAAO,GAAG,cAAE,CAAC,EAAC,QAAQ,UAAA,EAAC,CAAC,CAAC;AACzB,QAAA,OAAO,GAAG,cAAE,CAAC,EAAC,QAAQ,UAAA,EAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/slice_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/slice_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/slice_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/slice_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/slice_test.js
new file mode 100644
index 0000000..12fbba8
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/slice_test.js
@@ -0,0 +1,172 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('slice1d', test_util_1.ALL_ENVS, function () {
+    it('slices 1x1 into 1x1 (effectively a copy)', function () {
+        var a = tf.tensor1d([5]);
+        var result = tf.slice1d(a, 0, 1);
+        expect(result.shape).toEqual([1]);
+        test_util_1.expectNumbersClose(result.get(0), 5);
+    });
+    it('slices 5x1 into shape 2x1 starting at 3', function () {
+        var a = tf.tensor1d([1, 2, 3, 4, 5]);
+        var result = tf.slice1d(a, 3, 2);
+        expect(result.shape).toEqual([2]);
+        test_util_1.expectArraysClose(result, [4, 5]);
+    });
+    it('slices 5x1 into shape 3x1 starting at 1', function () {
+        var a = tf.tensor1d([1, 2, 3, 4, 5]);
+        var result = tf.slice1d(a, 1, 3);
+        expect(result.shape).toEqual([3]);
+        test_util_1.expectArraysClose(result, [2, 3, 4]);
+    });
+    it('grad', function () {
+        var a = tf.tensor1d([1, 2, 3, 4, 5]);
+        var dy = tf.tensor1d([10, 100]);
+        var da = tf.grad(function (x) { return tf.slice1d(a, 1, 2); })(a, dy);
+        expect(da.shape).toEqual([5]);
+        test_util_1.expectArraysClose(da, [0, 10, 100, 0, 0]);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [5];
+        var result = tf.slice1d(a, 0, 1);
+        expect(result.shape).toEqual([1]);
+        test_util_1.expectNumbersClose(result.get(0), 5);
+    });
+});
+jasmine_util_1.describeWithFlags('slice2d', test_util_1.ALL_ENVS, function () {
+    it('slicing a 1x1 from a 1x1 returns a 1x1', function () {
+        var a = tf.tensor2d([0], [1, 1]);
+        var b = tf.slice2d(a, [0, 0], [1, 1]);
+        expect(b.shape).toEqual([1, 1]);
+    });
+    it('returns a tensor of slice size', function () {
+        var a = tf.zeros([100, 100]);
+        var b = tf.slice2d(a, [0, 0], [12, 34]);
+        expect(b.shape).toEqual([12, 34]);
+    });
+    it('returns the upper-left submatrix when begin is [0, 0]', function () {
+        var a = tf.randomUniform([10, 10], -1, 1);
+        var b = tf.slice2d(a, [0, 0], [2, 2]);
+        var aValues = a.dataSync();
+        test_util_1.expectArraysClose(b, [aValues[0], aValues[1], aValues[10], aValues[11]]);
+    });
+    it('returns the rectangle specified', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [4, 3]);
+        var b = tf.slice2d(a, [1, 1], [3, 2]);
+        test_util_1.expectArraysClose(b, [5, 6, 8, 9, 11, 12]);
+    });
+    it('throws when requesting out of bounds slice', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [4, 3]);
+        expect(function () { return tf.slice2d(a, [1, 1], [10, 10]); }).toThrowError();
+    });
+    it('grad', function () {
+        var a = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);
+        var dy = tf.tensor2d([[20], [50]]);
+        var da = tf.grad(function (x) { return tf.slice2d(a, [0, 1], [2, 1]); })(a, dy);
+        expect(da.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(da, [0, 20, 0, 0, 50, 0]);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [[0]];
+        var b = tf.slice2d(a, [0, 0], [1, 1]);
+        expect(b.shape).toEqual([1, 1]);
+    });
+});
+jasmine_util_1.describeWithFlags('slice3d', test_util_1.ALL_ENVS, function () {
+    it('slices 1x1x1 into shape 1x1x1 (effectively a copy)', function () {
+        var a = tf.tensor3d([[[5]]], [1, 1, 1]);
+        var result = tf.slice3d(a, [0, 0, 0], [1, 1, 1]);
+        expect(result.shape).toEqual([1, 1, 1]);
+        test_util_1.expectArraysClose(result, [5]);
+    });
+    it('slices 2x2x2 array into 1x2x2 starting at [1, 0, 0]', function () {
+        var a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
+        var result = tf.slice3d(a, [1, 0, 0], [1, 2, 2]);
+        expect(result.shape).toEqual([1, 2, 2]);
+        test_util_1.expectArraysClose(result, [5, 6, 7, 8]);
+    });
+    it('slices 2x2x2 array into 2x1x1 starting at [0, 1, 1]', function () {
+        var a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
+        var result = tf.slice3d(a, [0, 1, 1], [2, 1, 1]);
+        expect(result.shape).toEqual([2, 1, 1]);
+        test_util_1.expectArraysClose(result, [4, 8]);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [[[5]]];
+        var result = tf.slice3d(a, [0, 0, 0], [1, 1, 1]);
+        expect(result.shape).toEqual([1, 1, 1]);
+        test_util_1.expectArraysClose(result, [5]);
+    });
+});
+jasmine_util_1.describeWithFlags('slice4d', test_util_1.ALL_ENVS, function () {
+    it('slices 1x1x1x1 into shape 1x1x1x1 (effectively a copy)', function () {
+        var a = tf.tensor4d([[[[5]]]], [1, 1, 1, 1]);
+        var result = tf.slice4d(a, [0, 0, 0, 0], [1, 1, 1, 1]);
+        expect(result.shape).toEqual([1, 1, 1, 1]);
+        test_util_1.expectArraysClose(result, [5]);
+    });
+    it('slices 2x2x2x2 array into 1x2x2x2 starting at [1, 0, 0, 0]', function () {
+        var a = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 11, 22, 33, 44, 55, 66, 77, 88], [2, 2, 2, 2]);
+        var result = tf.slice4d(a, [1, 0, 0, 0], [1, 2, 2, 2]);
+        expect(result.shape).toEqual([1, 2, 2, 2]);
+        test_util_1.expectArraysClose(result, [11, 22, 33, 44, 55, 66, 77, 88]);
+    });
+    it('slices 2x2x2x2 array into 2x1x1x1 starting at [0, 1, 1, 1]', function () {
+        var a = tf.tensor4d([1, 2, 3, 4, 5, 6, 7, 8, 11, 22, 33, 44, 55, 66, 77, 88], [2, 2, 2, 2]);
+        var result = tf.slice4d(a, [0, 1, 1, 1], [2, 1, 1, 1]);
+        expect(result.shape).toEqual([2, 1, 1, 1]);
+        test_util_1.expectArraysClose(result, [8, 88]);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [[[[5]]]];
+        var result = tf.slice4d(a, [0, 0, 0, 0], [1, 1, 1, 1]);
+        expect(result.shape).toEqual([1, 1, 1, 1]);
+        test_util_1.expectArraysClose(result, [5]);
+    });
+});
+jasmine_util_1.describeWithFlags('slice ergonomics', test_util_1.ALL_ENVS, function () {
+    it('slices 2x2x2 array into 2x1x1 no size', function () {
+        var a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
+        var result = a.slice([0, 1, 1]);
+        expect(result.shape).toEqual([2, 1, 1]);
+        test_util_1.expectArraysClose(result, [4, 8]);
+    });
+    it('slices 2x2x2 array into 1x2x2 with scalar begin no size', function () {
+        var a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
+        var result = a.slice(1);
+        expect(result.shape).toEqual([1, 2, 2]);
+        test_util_1.expectArraysClose(result, [5, 6, 7, 8]);
+    });
+    it('slices 2x2x2 array using 2d size and 2d size', function () {
+        var a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
+        var result = a.slice([0, 1]);
+        expect(result.shape).toEqual([2, 1, 2]);
+        test_util_1.expectArraysClose(result, [3, 4, 7, 8]);
+    });
+    it('slices 2x2x2 array using negative size', function () {
+        var a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
+        var result = a.slice([0, 1], [-1, 1]);
+        expect(result.shape).toEqual([2, 1, 2]);
+        test_util_1.expectArraysClose(result, [3, 4, 7, 8]);
+    });
+    it('slices 2x2x2 array using 1d size', function () {
+        var a = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
+        var result = a.slice(0, 1);
+        expect(result.shape).toEqual([1, 2, 2]);
+        test_util_1.expectArraysClose(result, [1, 2, 3, 4]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.slice({}, 0, 0); })
+            .toThrowError(/Argument 'x' passed to 'slice' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var a = [[[1, 2], [3, 4]], [[5, 6], [7, 8]]];
+        var result = tf.slice(a, [0, 1, 1]);
+        expect(result.shape).toEqual([2, 1, 1]);
+        test_util_1.expectArraysClose(result, [4, 8]);
+    });
+});
+//# sourceMappingURL=slice_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/slice_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/slice_test.js.map
new file mode 100644
index 0000000..dcc9999
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/slice_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/slice_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/slice_util.d.ts
new file mode 100644
index 0000000..44ae02a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/slice_util.d.ts
@@ -0,0 +1,5 @@
+import { Tensor } from '../tensor';
+export declare function assertParamsValid(input: Tensor, begin: number[], size: number[]): void;
+export declare function getStridedSlicedInfo(shape: number[], begin: number[], end: number[], strides: number[], beginMask?: number, endMask?: number, ellipsisMask?: number, newAxisMask?: number, shrinkAxisMask?: number): [number[], number[], number[]];
+export declare function startForAxis(beginMask: number, startIndices: number[], strides: number[], inputShape: number[], axis: number): number;
+export declare function stopForAxis(endMask: number, stopIndices: number[], strides: number[], inputShape: number[], axis: number): number;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/slice_util.js b/node_modules/@tensorflow/tfjs-core/dist/ops/slice_util.js
new file mode 100644
index 0000000..991e193
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/slice_util.js
@@ -0,0 +1,90 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var util = require("../util");
+function assertParamsValid(input, begin, size) {
+    util.assert(input.rank === begin.length, "Error in slice" + input.rank + "D: Length of begin " + begin + " must " +
+        ("match the rank of the array (" + input.rank + ")."));
+    util.assert(input.rank === size.length, "Error in slice" + input.rank + "D: Length of size " + size + " must " +
+        ("match the rank of the array (" + input.rank + ")."));
+    for (var i = 0; i < input.rank; ++i) {
+        util.assert(begin[i] + size[i] <= input.shape[i], "Error in slice" + input.rank + "D: begin[" + i + "] + size[" + i + "] " +
+            ("(" + (begin[i] + size[i]) + ") would overflow input.shape[" + i + "] (" + input.shape[i] + ")"));
+    }
+}
+exports.assertParamsValid = assertParamsValid;
+function getStridedSlicedInfo(shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {
+    if (beginMask === void 0) { beginMask = 0; }
+    if (endMask === void 0) { endMask = 0; }
+    if (ellipsisMask === void 0) { ellipsisMask = 0; }
+    if (newAxisMask === void 0) { newAxisMask = 0; }
+    if (shrinkAxisMask === void 0) { shrinkAxisMask = 0; }
+    if (ellipsisMask !== 0) {
+        throw new Error('ellipsis mask is not yet supported');
+    }
+    if (newAxisMask !== 0) {
+        throw new Error('new axis mask is not yet supported');
+    }
+    var startIndex = [];
+    var endIndex = [];
+    var shrinkAxis = [];
+    for (var i = 0; i < shape.length; i++) {
+        startIndex[i] = startForAxis(beginMask, begin, strides, shape, i);
+        endIndex[i] = stopForAxis(endMask, end, strides, shape, i);
+        if (shrinkAxisMask & 1 << i) {
+            endIndex[i] = startIndex[i] + 1;
+            shrinkAxis.push(i);
+        }
+    }
+    var size = new Array(shape.length).fill(0);
+    size = size.map(function (d, i) {
+        var count = 0;
+        for (var start = startIndex[i]; !(strides[i] > 0 ? start >= endIndex[i] : start <= endIndex[i]); start += strides[i]) {
+            count += 1;
+        }
+        return count;
+    });
+    return [startIndex, size, shrinkAxis];
+}
+exports.getStridedSlicedInfo = getStridedSlicedInfo;
+function startForAxis(beginMask, startIndices, strides, inputShape, axis) {
+    var start = startIndices[axis];
+    if (beginMask & 1 << axis) {
+        if (strides[axis] > 0) {
+            start = Number.MIN_SAFE_INTEGER;
+        }
+        else {
+            start = Number.MAX_SAFE_INTEGER;
+        }
+    }
+    var axisSize = inputShape[axis];
+    if (start < 0) {
+        start += axisSize;
+    }
+    start = util.clamp(0, start, axisSize - 1);
+    return start;
+}
+exports.startForAxis = startForAxis;
+function stopForAxis(endMask, stopIndices, strides, inputShape, axis) {
+    var stop = stopIndices[axis];
+    if (endMask & (1 << axis)) {
+        if (strides[axis] > 0) {
+            stop = Number.MAX_SAFE_INTEGER;
+        }
+        else {
+            stop = Number.MIN_SAFE_INTEGER;
+        }
+    }
+    var axisSize = inputShape[axis];
+    if (stop < 0) {
+        stop += axisSize;
+    }
+    if (strides[axis] > 0) {
+        stop = util.clamp(0, stop, axisSize);
+    }
+    else {
+        stop = util.clamp(-1, stop, axisSize - 1);
+    }
+    return stop;
+}
+exports.stopForAxis = stopForAxis;
+//# sourceMappingURL=slice_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/slice_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/slice_util.js.map
new file mode 100644
index 0000000..2bc5800
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/slice_util.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/softmax.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/softmax.d.ts
new file mode 100644
index 0000000..8456e85
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/softmax.d.ts
@@ -0,0 +1,7 @@
+import { Tensor } from '../tensor';
+import { TensorLike } from '../types';
+declare function softmax_<T extends Tensor>(logits: T | TensorLike, dim?: number): T;
+declare function logSoftmax_<T extends Tensor>(logits: T | TensorLike, axis?: number): T;
+export declare const softmax: typeof softmax_;
+export declare const logSoftmax: typeof logSoftmax_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/softmax.js b/node_modules/@tensorflow/tfjs-core/dist/ops/softmax.js
new file mode 100644
index 0000000..cde558c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/softmax.js
@@ -0,0 +1,55 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var gradients_1 = require("../gradients");
+var tensor_util_env_1 = require("../tensor_util_env");
+var operation_1 = require("./operation");
+function softmax_(logits, dim) {
+    if (dim === void 0) { dim = -1; }
+    var $logits = tensor_util_env_1.convertToTensor(logits, 'logits', 'softmax');
+    if (dim === -1) {
+        dim = $logits.rank - 1;
+    }
+    if (dim !== $logits.rank - 1) {
+        throw Error('Softmax along a non-last dimension is not yet supported. ' +
+            ("Logits was rank " + $logits.rank + " and dim was " + dim));
+    }
+    var customOp = gradients_1.customGrad(function (logits) {
+        var keepDims = true;
+        var lse = logits.logSumExp([dim], keepDims);
+        var logResult = logits.toFloat().sub(lse);
+        var y = logResult.exp();
+        var gradFunc = function (dy) {
+            var dyTimesY = dy.mul(y);
+            var keepDims = true;
+            return dyTimesY.sub(dyTimesY.sum([dim], keepDims).mul(y));
+        };
+        return { value: y, gradFunc: gradFunc };
+    });
+    return customOp($logits);
+}
+function logSoftmax_(logits, axis) {
+    if (axis === void 0) { axis = -1; }
+    var $logits = tensor_util_env_1.convertToTensor(logits, 'logits', 'logSoftmax');
+    if (axis === -1) {
+        axis = $logits.rank - 1;
+    }
+    if (axis !== $logits.rank - 1) {
+        throw Error('Log Softmax along a non-last dimension is not yet supported. ' +
+            ("Logits was rank " + $logits.rank + " and axis was " + axis));
+    }
+    var customOp = gradients_1.customGrad(function (logits) {
+        var keepDims = true;
+        var xMax = logits.max(axis, true);
+        var shifted = logits.sub(xMax);
+        var value = shifted.toFloat().sub(shifted.exp().sum(axis, keepDims).log());
+        var gradFunc = function (dy) {
+            var softmax = value.exp();
+            return dy.sub(dy.sum(axis, keepDims).mul(softmax));
+        };
+        return { value: value, gradFunc: gradFunc };
+    });
+    return customOp($logits);
+}
+exports.softmax = operation_1.op({ softmax_: softmax_ });
+exports.logSoftmax = operation_1.op({ logSoftmax_: logSoftmax_ });
+//# sourceMappingURL=softmax.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/softmax.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/softmax.js.map
new file mode 100644
index 0000000..f609489
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/softmax.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"softmax.js","sourceRoot":"","sources":["../../src/ops/softmax.ts"],"names":[],"mappings":";;AAiBA,0CAAwC;AAExC,sDAAmD;AAEnD,yCAA+B;AAsB/B,kBAAoC,MAAoB,EAAE,GAAQ;IAAR,oBAAA,EAAA,OAAO,CAAC;IAChE,IAAM,OAAO,GAAG,iCAAe,CAAC,MAAM,EAAE,QAAQ,EAAE,SAAS,CAAC,CAAC;IAE7D,IAAI,GAAG,KAAK,CAAC,CAAC,EAAE;QACd,GAAG,GAAG,OAAO,CAAC,IAAI,GAAG,CAAC,CAAC;KACxB;IACD,IAAI,GAAG,KAAK,OAAO,CAAC,IAAI,GAAG,CAAC,EAAE;QAC5B,MAAM,KAAK,CACP,2DAA2D;aAC3D,qBAAmB,OAAO,CAAC,IAAI,qBAAgB,GAAK,CAAA,CAAC,CAAC;KAC3D;IAED,IAAM,QAAQ,GAAG,sBAAU,CAAC,UAAA,MAAM;QAGhC,IAAM,QAAQ,GAAG,IAAI,CAAC;QACtB,IAAM,GAAG,GAAG,MAAM,CAAC,SAAS,CAAC,CAAC,GAAG,CAAC,EAAE,QAAQ,CAAC,CAAC;QAC9C,IAAM,SAAS,GAAG,MAAM,CAAC,OAAO,EAAE,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC;QAC5C,IAAM,CAAC,GAAG,SAAS,CAAC,GAAG,EAAO,CAAC;QAE/B,IAAM,QAAQ,GAAG,UAAC,EAAK;YACrB,IAAM,QAAQ,GAAG,EAAE,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC;YAC3B,IAAM,QAAQ,GAAG,IAAI,CAAC;YACtB,OAAO,QAAQ,CAAC,GAAG,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC,GAAG,CAAC,EAAE,QAAQ,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,CAAC;QAC5D,CAAC,CAAC;QAEF,OAAO,EAAC,KAAK,EAAE,CAAC,EAAE,QAAQ,UAAA,EAAC,CAAC;IAC9B,CAAC,CAAC,CAAC;IAEH,OAAO,QAAQ,CAAC,OAAO,CAAC,CAAC;AAC3B,CAAC;AAsBD,qBAAuC,MAAoB,EAAE,IAAS;IAAT,qBAAA,EAAA,QAAQ,CAAC;IACpE,IAAM,OAAO,GAAG,iCAAe,CAAC,MAAM,EAAE,QAAQ,EAAE,YAAY,CAAC,CAAC;IAEhE,IAAI,IAAI,KAAK,CAAC,CAAC,EAAE;QACf,IAAI,GAAG,OAAO,CAAC,IAAI,GAAG,CAAC,CAAC;KACzB;IACD,IAAI,IAAI,KAAK,OAAO,CAAC,IAAI,GAAG,CAAC,EAAE;QAC7B,MAAM,KAAK,CACP,+DAA+D;aAC/D,qBAAmB,OAAO,CAAC,IAAI,sBAAiB,IAAM,CAAA,CAAC,CAAC;KAC7D;IAED,IAAM,QAAQ,GAAG,sBAAU,CAAC,UAAA,MAAM;QAChC,IAAM,QAAQ,GAAG,IAAI,CAAC;QACtB,IAAM,IAAI,GAAG,MAAM,CAAC,GAAG,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC;QACpC,IAAM,OAAO,GAAG,MAAM,CAAC,GAAG,CAAC,IAAI,CAAC,CAAC;QACjC,IAAM,KAAK,GACP,OAAO,CAAC,OAAO,EAAE,CAAC,GAAG,CAAC,OAAO,CAAC,GAAG,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,QAAQ,CAAC,CAAC,GAAG,EAAE,CAAM,CAAC;QAExE,IAAM,QAAQ,GAAG,UAAC,EAAK;YACrB,IAAM,OAAO,GAAG,KAAK,CAAC,GAAG,EAAE,CAAC;YAC5B,OAAO,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,IAAI,EAAE,QAAQ,CAAC,CAAC,GAAG,CAAC,OAAO,CAAC,CAAC,CAAC;QACrD,CAAC,CAAC;QAEF,OAAO,EAAC,KAAK,OAAA,EAAE,QAAQ,UAAA,EAAC,CAAC;IAC3B,CAAC,CAAC,CAAC;IAEH,OAAO,QAAQ,CAAC,OAAO,CAAC,CAAC;AAC3B,CAAC;AAEY,QAAA,OAAO,GAAG,cAAE,CAAC,EAAC,QAAQ,UAAA,EAAC,CAAC,CAAC;AACzB,QAAA,UAAU,GAAG,cAAE,CAAC,EAAC,WAAW,aAAA,EAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/softmax_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/softmax_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/softmax_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/softmax_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/softmax_test.js
new file mode 100644
index 0000000..ed85004
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/softmax_test.js
@@ -0,0 +1,139 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('softmax', test_util_1.ALL_ENVS, function () {
+    it('regular test', function () {
+        var y = tf.softmax(tf.tensor1d([2, 1, 3]));
+        test_util_1.expectArraysClose(y, [0.24472847, 0.09003057, 0.66524095]);
+        test_util_1.expectNumbersClose(y.get(0) + y.get(1) + y.get(2), 1);
+    });
+    it('overflow', function () {
+        var y = tf.softmax(tf.tensor1d([100, 100]));
+        test_util_1.expectArraysClose(y, [0.5, 0.5]);
+    });
+    it('underflow', function () {
+        var y = tf.softmax(tf.tensor1d([-100, -100]));
+        test_util_1.expectArraysClose(y, [0.5, 0.5]);
+    });
+    it('Huge difference between probabilities', function () {
+        var y = tf.softmax(tf.tensor1d([-1000, +1000]));
+        test_util_1.expectArraysClose(y, [0, 1]);
+    });
+    it('Propagates NaNs', function () {
+        var a = tf.tensor1d([2, 1, NaN]);
+        var y = tf.softmax(a);
+        test_util_1.expectArraysClose(y, [NaN, NaN, NaN]);
+    });
+    it('2D, dim=1', function () {
+        var y = tf.softmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]), 1);
+        var expected = [
+            0.24472847, 0.09003057, 0.66524095, 0.09003057, 0.66524095, 0.24472847
+        ];
+        expect(y.rank).toBe(2);
+        test_util_1.expectArraysClose(y, expected);
+    });
+    it('2D, implicit dim=1', function () {
+        var y = tf.softmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]));
+        var expected = [
+            0.24472847, 0.09003057, 0.66524095, 0.09003057, 0.66524095, 0.24472847
+        ];
+        expect(y.rank).toBe(2);
+        test_util_1.expectArraysClose(y, expected);
+    });
+    it('2D, dim=0 throws error', function () {
+        var f = function () {
+            tf.softmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]), 0);
+        };
+        expect(f).toThrowError();
+    });
+    it('1D gradient', function () {
+        var x = tf.tensor1d([10, 0, -1]);
+        var y = tf.softmax(x);
+        var dy = tf.tensor1d([1, 2, 3]);
+        var dx = tf.grad(function (x) { return x.softmax(); })(x, dy);
+        var totalSum = tf.sum(tf.mul(dy, y));
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, [
+            (dy.get(0) - totalSum.get()) * y.get(0),
+            (dy.get(1) - totalSum.get()) * y.get(1),
+            (dy.get(2) - totalSum.get()) * y.get(2)
+        ]);
+    });
+    it('2D gradient', function () {
+        var x = tf.tensor2d([10, 0, -1, 5, 4, 3], [2, 3]);
+        var y = tf.softmax(x);
+        var dy = tf.tensor2d([3, 2, 1, 1, 2, 3], [2, 3]);
+        var dx = tf.grad(function (x) { return x.softmax(); })(x, dy);
+        var axis = -1;
+        var totalSum = tf.sum(tf.mulStrict(dy, y), axis);
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, [
+            (dy.get(0, 0) - totalSum.get(0)) * y.get(0, 0),
+            (dy.get(0, 1) - totalSum.get(0)) * y.get(0, 1),
+            (dy.get(0, 2) - totalSum.get(0)) * y.get(0, 2),
+            (dy.get(1, 0) - totalSum.get(1)) * y.get(1, 0),
+            (dy.get(1, 1) - totalSum.get(1)) * y.get(1, 1),
+            (dy.get(1, 2) - totalSum.get(1)) * y.get(1, 2)
+        ]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.softmax({}); })
+            .toThrowError(/Argument 'logits' passed to 'softmax' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var y = tf.softmax([2, 1, 3]);
+        test_util_1.expectArraysClose(y, [0.24472847, 0.09003057, 0.66524095]);
+        test_util_1.expectNumbersClose(y.get(0) + y.get(1) + y.get(2), 1);
+    });
+});
+jasmine_util_1.describeWithFlags('logSoftmax', test_util_1.ALL_ENVS, function () {
+    it('regular test', function () {
+        var y = tf.logSoftmax(tf.tensor1d([2, 1, 3]));
+        test_util_1.expectArraysClose(y, [-1.407606, -2.4076061, -0.407606]);
+    });
+    it('Huge difference', function () {
+        var y = tf.logSoftmax(tf.tensor1d([-1000, +1000]));
+        test_util_1.expectArraysClose(y, [-2000, 0]);
+    });
+    it('Propagates NaNs', function () {
+        var a = tf.tensor1d([2, 1, NaN]);
+        var y = tf.logSoftmax(a);
+        test_util_1.expectArraysClose(y, [NaN, NaN, NaN]);
+    });
+    it('2D, axis=1', function () {
+        var y = tf.logSoftmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]), 1);
+        var expected = [-1.407606, -2.4076061, -0.407606, -2.4076061, -0.4076061, -1.4076061];
+        expect(y.rank).toBe(2);
+        test_util_1.expectArraysClose(y, expected);
+    });
+    it('2D, implicit axis=1', function () {
+        var y = tf.logSoftmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]));
+        var expected = [-1.407606, -2.4076061, -0.407606, -2.4076061, -0.4076061, -1.4076061];
+        expect(y.rank).toBe(2);
+        test_util_1.expectArraysClose(y, expected);
+    });
+    it('1D gradient', function () {
+        var x = tf.tensor1d([1, 2, 10]);
+        var dy = tf.tensor1d([1, 2, 3]);
+        var dx = tf.grad(function (x) { return x.logSoftmax(); })(x, dy);
+        expect(dx.shape).toEqual(x.shape);
+        test_util_1.expectArraysClose(dx, [0.9992599, 1.9979881, -2.9972477]);
+    });
+    it('2D, axis=0 throws error', function () {
+        var f = function () {
+            tf.logSoftmax(tf.tensor2d([[2, 1, 3], [1, 3, 2]], [2, 3]), 0);
+        };
+        expect(f).toThrowError();
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.logSoftmax({}); })
+            .toThrowError(/Argument 'logits' passed to 'logSoftmax' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var y = tf.logSoftmax([2, 1, 3]);
+        test_util_1.expectArraysClose(y, [-1.407606, -2.4076061, -0.407606]);
+    });
+});
+//# sourceMappingURL=softmax_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/softmax_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/softmax_test.js.map
new file mode 100644
index 0000000..b9dde3c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/softmax_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"softmax_test.js","sourceRoot":"","sources":["../../src/ops/softmax_test.ts"],"names":[],"mappings":";;AAiBA,6BAA+B;AAC/B,gDAAkD;AAClD,0CAA6E;AAE7E,gCAAiB,CAAC,SAAS,EAAE,oBAAQ,EAAE;IACrC,EAAE,CAAC,cAAc,EAAE;QACjB,IAAM,CAAC,GAAG,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QAE7C,6BAAiB,CAAC,CAAC,EAAE,CAAC,UAAU,EAAE,UAAU,EAAE,UAAU,CAAC,CAAC,CAAC;QAC3D,8BAAkB,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;IACxD,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,UAAU,EAAE;QACb,IAAM,CAAC,GAAG,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,QAAQ,CAAC,CAAC,GAAG,EAAE,GAAG,CAAC,CAAC,CAAC,CAAC;QAE9C,6BAAiB,CAAC,CAAC,EAAE,CAAC,GAAG,EAAE,GAAG,CAAC,CAAC,CAAC;IACnC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,WAAW,EAAE;QACd,IAAM,CAAC,GAAG,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,GAAG,EAAE,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC;QAEhD,6BAAiB,CAAC,CAAC,EAAE,CAAC,GAAG,EAAE,GAAG,CAAC,CAAC,CAAC;IACnC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,uCAAuC,EAAE;QAC1C,IAAM,CAAC,GAAG,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,IAAI,EAAE,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;QAElD,6BAAiB,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IAC/B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,iBAAiB,EAAE;QACpB,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,GAAG,CAAC,CAAC,CAAC;QACnC,IAAM,CAAC,GAAG,EAAE,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC;QACxB,6BAAiB,CAAC,CAAC,EAAE,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,CAAC,CAAC,CAAC;IACxC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,WAAW,EAAE;QACd,IAAM,CAAC,GAAG,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QACrE,IAAM,QAAQ,GAAG;YACf,UAAU,EAAE,UAAU,EAAE,UAAU,EAAE,UAAU,EAAE,UAAU,EAAE,UAAU;SACvE,CAAC;QACF,MAAM,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;QACvB,6BAAiB,CAAC,CAAC,EAAE,QAAQ,CAAC,CAAC;IACjC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,oBAAoB,EAAE;QACvB,IAAM,CAAC,GAAG,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QAClE,IAAM,QAAQ,GAAG;YACf,UAAU,EAAE,UAAU,EAAE,UAAU,EAAE,UAAU,EAAE,UAAU,EAAE,UAAU;SACvE,CAAC;QACF,MAAM,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;QACvB,6BAAiB,CAAC,CAAC,EAAE,QAAQ,CAAC,CAAC;IACjC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,wBAAwB,EAAE;QAC3B,IAAM,CAAC,GAAG;YACR,EAAE,CAAC,OAAO,CAAC,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;QAC7D,CAAC,CAAC;QACF,MAAM,CAAC,CAAC,CAAC,CAAC,YAAY,EAAE,CAAC;IAC3B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,aAAa,EAAE;QAChB,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QACnC,IAAM,CAAC,GAAG,EAAE,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC;QACxB,IAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAClC,IAAM,EAAE,GAAG,EAAE,CAAC,IAAI,CAAC,UAAC,CAAC,IAAK,OAAA,CAAC,CAAC,OAAO,EAAE,EAAX,CAAW,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC;QAE9C,IAAM,QAAQ,GAAG,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,CAAC,EAAE,EAAE,CAAC,CAAC,CAAC,CAAC;QAEvC,MAAM,CAAC,EAAE,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC;QAClC,6BAAiB,CAAC,EAAE,EAAE;YACpB,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,QAAQ,CAAC,GAAG,EAAE,CAAC,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC;YACvC,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,QAAQ,CAAC,GAAG,EAAE,CAAC,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC;YACvC,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,CAAC,GAAG,QAAQ,CAAC,GAAG,EAAE,CAAC,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC;SACxC,CAAC,CAAC;IACL,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,aAAa,EAAE;QAChB,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,EAAE,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACpD,IAAM,CAAC,GAAG,EAAE,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC;QACxB,IAAM,EAAE,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACnD,IAAM,EAAE,GAAG,EAAE,CAAC,IAAI,CAAC,UAAC,CAAC,IAAK,OAAA,CAAC,CAAC,OAAO,EAAE,EAAX,CAAW,CAAC,CAAC,CAAC,EAAE,EAAE,CAAC,CAAC;QAE9C,IAAM,IAAI,GAAG,CAAC,CAAC,CAAC;QAChB,IAAM,QAAQ,GAAG,EAAE,CAAC,GAAG,CAAC,EAAE,CAAC,SAAS,CAAC,EAAE,EAAE,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC;QAEnD,MAAM,CAAC,EAAE,CAAC,KAAK,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,KAAK,CAAC,CAAC;QAClC,6BAAiB,CAAC,EAAE,EAAE;YACpB,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,GAAG,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC;YAC9C,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,GAAG,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC;YAC9C,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,GAAG,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC;YAC9C,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,GAAG,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC;YAC9C,CAAC,EAAE,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,GAAG,QAAQ,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,GAA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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense.d.ts
new file mode 100644
index 0000000..73c6aba
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense.d.ts
@@ -0,0 +1,5 @@
+import { Scalar, Tensor } from '../tensor';
+import { Rank, ShapeMap, TensorLike } from '../types';
+declare function sparseToDense_<R extends Rank>(sparseIndices: Tensor | TensorLike, sparseValues: Tensor | TensorLike, outputShape: ShapeMap[R], defaultValue: Scalar | TensorLike): Tensor<R>;
+export declare const sparseToDense: typeof sparseToDense_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense.js b/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense.js
new file mode 100644
index 0000000..87c6e5f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense.js
@@ -0,0 +1,15 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var sparse_to_dense = require("../ops/sparse_to_dense_util");
+var tensor_util_env_1 = require("../tensor_util_env");
+var operation_1 = require("./operation");
+function sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue) {
+    var $sparseIndices = tensor_util_env_1.convertToTensor(sparseIndices, 'sparseIndices', 'sparseToDense', 'int32');
+    var $sparseValues = tensor_util_env_1.convertToTensor(sparseValues, 'sparseValues', 'sparseToDense');
+    var $defaultValue = tensor_util_env_1.convertToTensor(defaultValue, 'defaultValue', 'sparseToDense', $sparseValues.dtype);
+    sparse_to_dense.validateInput($sparseIndices, $sparseValues, outputShape, $defaultValue);
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.sparseToDense($sparseIndices, $sparseValues, outputShape, $defaultValue); }, { $sparseIndices: $sparseIndices, $sparseValues: $sparseValues, $defaultValue: $defaultValue });
+}
+exports.sparseToDense = operation_1.op({ sparseToDense_: sparseToDense_ });
+//# sourceMappingURL=sparse_to_dense.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense.js.map
new file mode 100644
index 0000000..1d15f0b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"sparse_to_dense.js","sourceRoot":"","sources":["../../src/ops/sparse_to_dense.ts"],"names":[],"mappings":";;AAiBA,8CAAmC;AACnC,6DAA+D;AAE/D,sDAAmD;AAGnD,yCAA+B;AAoC/B,wBACI,aAAgC,EAAE,YAA+B,EACjE,WAAwB,EAAE,YAA+B;IAC3D,IAAM,cAAc,GAChB,iCAAe,CAAC,aAAa,EAAE,eAAe,EAAE,eAAe,EAAE,OAAO,CAAC,CAAC;IAC9E,IAAM,aAAa,GACf,iCAAe,CAAC,YAAY,EAAE,cAAc,EAAE,eAAe,CAAC,CAAC;IACnE,IAAM,aAAa,GAAG,iCAAe,CACjC,YAAY,EAAE,cAAc,EAAE,eAAe,EAAE,aAAa,CAAC,KAAK,CAAC,CAAC;IAExE,eAAe,CAAC,aAAa,CACzB,cAAc,EAAE,aAAa,EAAE,WAAW,EAAE,aAAa,CAAC,CAAC;IAE/D,OAAO,iBAAG,CAAC,MAAM,CAAC,SAAS,CACvB,UAAA,OAAO,IAAI,OAAA,OAAO,CAAC,aAAa,CAC5B,cAAc,EAAE,aAAa,EAAE,WAAW,EAAE,aAAa,CAAC,EADnD,CACmD,EAC9D,EAAC,cAAc,gBAAA,EAAE,aAAa,eAAA,EAAE,aAAa,eAAA,EAAC,CAAC,CAAC;AACtD,CAAC;AAEY,QAAA,aAAa,GAAG,cAAE,CAAC,EAAC,cAAc,gBAAA,EAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_test.js
new file mode 100644
index 0000000..f4dd383
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_test.js
@@ -0,0 +1,104 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+var sparse_to_dense_1 = require("./sparse_to_dense");
+var tensor_ops_1 = require("./tensor_ops");
+var defaultValue;
+jasmine_util_1.describeWithFlags('sparseToDense', test_util_1.ALL_ENVS, function () {
+    beforeEach(function () { return defaultValue = tensor_ops_1.scalar(0, 'int32'); });
+    it('should work for scalar indices', function () {
+        var indices = tensor_ops_1.scalar(2, 'int32');
+        var values = tensor_ops_1.scalar(100, 'int32');
+        var shape = [6];
+        var result = sparse_to_dense_1.sparseToDense(indices, values, shape, defaultValue);
+        expect(result.shape).toEqual(shape);
+        expect(result.dtype).toEqual(values.dtype);
+        test_util_1.expectArraysClose(result, [0, 0, 100, 0, 0, 0]);
+    });
+    it('should work for vector', function () {
+        var indices = tensor_ops_1.tensor1d([0, 2, 4], 'int32');
+        var values = tensor_ops_1.tensor1d([100, 101, 102], 'int32');
+        var shape = [6];
+        var result = sparse_to_dense_1.sparseToDense(indices, values, shape, defaultValue);
+        expect(result.shape).toEqual(shape);
+        expect(result.dtype).toEqual(values.dtype);
+        test_util_1.expectArraysClose(result, [100, 0, 101, 0, 102, 0]);
+    });
+    it('should work for scalar value', function () {
+        var indices = tensor_ops_1.tensor1d([0, 2, 4], 'int32');
+        var values = tensor_ops_1.scalar(10, 'int32');
+        var shape = [6];
+        var result = sparse_to_dense_1.sparseToDense(indices, values, shape, defaultValue);
+        expect(result.shape).toEqual(shape);
+        expect(result.dtype).toEqual(values.dtype);
+        test_util_1.expectArraysClose(result, [10, 0, 10, 0, 10, 0]);
+    });
+    it('should work for matrix', function () {
+        var indices = tensor_ops_1.tensor2d([0, 1, 1, 1], [2, 2], 'int32');
+        var values = tensor_ops_1.tensor1d([5, 6], 'float32');
+        var shape = [2, 2];
+        var result = sparse_to_dense_1.sparseToDense(indices, values, shape, defaultValue.toFloat());
+        expect(result.shape).toEqual(shape);
+        expect(result.dtype).toEqual(values.dtype);
+        test_util_1.expectArraysClose(result, [0, 5, 0, 6]);
+    });
+    it('should throw exception if default value does not match dtype', function () {
+        var indices = tensor_ops_1.tensor2d([0, 1, 1, 1], [2, 2], 'int32');
+        var values = tensor_ops_1.tensor1d([5, 6], 'float32');
+        var shape = [2, 2];
+        expect(function () { return sparse_to_dense_1.sparseToDense(indices, values, shape, tensor_ops_1.scalar(1, 'int32')); })
+            .toThrowError();
+    });
+    it('should allow setting default value', function () {
+        var indices = tensor_ops_1.tensor2d([0, 1, 1, 1], [2, 2], 'int32');
+        var values = tensor_ops_1.tensor1d([5, 6], 'float32');
+        var shape = [2, 2];
+        var result = sparse_to_dense_1.sparseToDense(indices, values, shape, tensor_ops_1.scalar(1));
+        expect(result.shape).toEqual(shape);
+        expect(result.dtype).toEqual(values.dtype);
+        test_util_1.expectArraysClose(result, [1, 5, 1, 6]);
+    });
+    it('should support TensorLike inputs', function () {
+        var indices = [[0, 1], [1, 1]];
+        var values = [5, 6];
+        var shape = [2, 2];
+        var result = sparse_to_dense_1.sparseToDense(indices, values, shape, defaultValue.toFloat());
+        expect(result.shape).toEqual(shape);
+        expect(result.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(result, [0, 5, 0, 6]);
+    });
+    it('should throw error when indices are not int32', function () {
+        var indices = tensor_ops_1.scalar(2, 'float32');
+        var values = tensor_ops_1.scalar(100, 'int32');
+        var shape = [6];
+        expect(function () { return sparse_to_dense_1.sparseToDense(indices, values, shape, defaultValue); }).toThrow();
+    });
+    it('should throw error when indices rank > 2', function () {
+        var indices = tensor_ops_1.tensor3d([1], [1, 1, 1], 'int32');
+        var values = tensor_ops_1.tensor1d([100], 'float32');
+        var shape = [6];
+        expect(function () { return sparse_to_dense_1.sparseToDense(indices, values, shape, defaultValue); }).toThrow();
+    });
+    it('should throw error when values has rank > 1', function () {
+        var indices = tensor_ops_1.tensor1d([0, 4, 2], 'int32');
+        var values = tensor_ops_1.tensor2d([1.0, 2.0, 3.0], [3, 1], 'float32');
+        var shape = [6];
+        expect(function () { return sparse_to_dense_1.sparseToDense(indices, values, shape, defaultValue); }).toThrow();
+    });
+    it('should throw error when values has wrong size', function () {
+        var indices = tensor_ops_1.tensor1d([0, 4, 2], 'int32');
+        var values = tensor_ops_1.tensor1d([1.0, 2.0, 3.0, 4.0], 'float32');
+        var shape = [6];
+        expect(function () { return sparse_to_dense_1.sparseToDense(indices, values, shape, defaultValue); }).toThrow();
+    });
+});
+jasmine_util_1.describeWithFlags('sparseToDense CPU', test_util_1.CPU_ENVS, function () {
+    it('should throw error when index out of range', function () {
+        var indices = tensor_ops_1.tensor1d([0, 2, 6], 'int32');
+        var values = tensor_ops_1.tensor1d([100, 101, 102], 'int32');
+        var shape = [6];
+        expect(function () { return sparse_to_dense_1.sparseToDense(indices, values, shape, defaultValue); }).toThrow();
+    });
+});
+//# sourceMappingURL=sparse_to_dense_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_test.js.map
new file mode 100644
index 0000000..e1c972a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_util.d.ts
new file mode 100644
index 0000000..da9ef66
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_util.d.ts
@@ -0,0 +1,2 @@
+import { Tensor } from '../tensor';
+export declare function validateInput(sparseIndices: Tensor, sparseValues: Tensor, outputShape: number[], defaultValues: Tensor): void;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_util.js b/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_util.js
new file mode 100644
index 0000000..7be7c42
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_util.js
@@ -0,0 +1,29 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+function validateInput(sparseIndices, sparseValues, outputShape, defaultValues) {
+    if (sparseIndices.dtype !== 'int32') {
+        throw new Error('tf.sparseToDense() expects the indices to be int32 type,' +
+            (" but the dtype was " + sparseIndices.dtype + "."));
+    }
+    if (sparseIndices.rank > 2) {
+        throw new Error('sparseIndices should be a scalar, vector, or matrix,' +
+            (" but got shape " + sparseIndices.shape + "."));
+    }
+    var numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;
+    var numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;
+    if (outputShape.length !== numDims) {
+        throw new Error('outputShape has incorrect number of elements:,' +
+            (" " + outputShape.length + ", should be: " + numDims + "."));
+    }
+    var numValues = sparseValues.size;
+    if (!(sparseValues.rank === 0 ||
+        sparseValues.rank === 1 && numValues === numElems)) {
+        throw new Error('sparseValues has incorrect shape ' +
+            (sparseValues.shape + ", should be [] or [" + numElems + "]"));
+    }
+    if (sparseValues.dtype !== defaultValues.dtype) {
+        throw new Error('sparseValues.dtype must match defaultValues.dtype');
+    }
+}
+exports.validateInput = validateInput;
+//# sourceMappingURL=sparse_to_dense_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_util.js.map
new file mode 100644
index 0000000..d617959
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/sparse_to_dense_util.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"sparse_to_dense_util.js","sourceRoot":"","sources":["../../src/ops/sparse_to_dense_util.ts"],"names":[],"mappings":";;AA+BA,uBACI,aAAqB,EAAE,YAAoB,EAAE,WAAqB,EAClE,aAAqB;IACvB,IAAI,aAAa,CAAC,KAAK,KAAK,OAAO,EAAE;QACnC,MAAM,IAAI,KAAK,CACX,0DAA0D;aAC1D,wBAAsB,aAAa,CAAC,KAAK,MAAG,CAAA,CAAC,CAAC;KACnD;IACD,IAAI,aAAa,CAAC,IAAI,GAAG,CAAC,EAAE;QAC1B,MAAM,IAAI,KAAK,CACX,sDAAsD;aACtD,oBAAkB,aAAa,CAAC,KAAK,MAAG,CAAA,CAAC,CAAC;KAC/C;IAED,IAAM,QAAQ,GAAG,aAAa,CAAC,IAAI,GAAG,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;IACrE,IAAM,OAAO,GAAG,aAAa,CAAC,IAAI,GAAG,CAAC,CAAC,CAAC,CAAC,aAAa,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;IAEpE,IAAI,WAAW,CAAC,MAAM,KAAK,OAAO,EAAE;QAClC,MAAM,IAAI,KAAK,CACX,gDAAgD;aAChD,MAAI,WAAW,CAAC,MAAM,qBAAgB,OAAO,MAAG,CAAA,CAAC,CAAC;KACvD;IAED,IAAM,SAAS,GAAG,YAAY,CAAC,IAAI,CAAC;IACpC,IAAI,CAAC,CAAC,YAAY,CAAC,IAAI,KAAK,CAAC;QACvB,YAAY,CAAC,IAAI,KAAK,CAAC,IAAI,SAAS,KAAK,QAAQ,CAAC,EAAE;QACxD,MAAM,IAAI,KAAK,CACX,mCAAmC;aAChC,YAAY,CAAC,KAAK,2BAAsB,QAAQ,MAAG,CAAA,CAAC,CAAC;KAC7D;IAED,IAAI,YAAY,CAAC,KAAK,KAAK,aAAa,CAAC,KAAK,EAAE;QAC9C,MAAM,IAAI,KAAK,CAAC,mDAAmD,CAAC,CAAC;KACtE;AACH,CAAC;AAlCD,sCAkCC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/spectral_ops.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/spectral_ops.d.ts
new file mode 100644
index 0000000..2bcae4e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/spectral_ops.d.ts
@@ -0,0 +1,10 @@
+import { Tensor } from '../tensor';
+declare function fft_(input: Tensor): Tensor;
+declare function ifft_(input: Tensor): Tensor;
+declare function rfft_(input: Tensor): Tensor;
+declare function irfft_(input: Tensor): Tensor;
+export declare const fft: typeof fft_;
+export declare const ifft: typeof ifft_;
+export declare const rfft: typeof rfft_;
+export declare const irfft: typeof irfft_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/spectral_ops.js b/node_modules/@tensorflow/tfjs-core/dist/ops/spectral_ops.js
new file mode 100644
index 0000000..6e972e3
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/spectral_ops.js
@@ -0,0 +1,70 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var complex_ops_1 = require("../ops/complex_ops");
+var operation_1 = require("../ops/operation");
+var util_1 = require("../util");
+var tensor_ops_1 = require("./tensor_ops");
+function fft_(input) {
+    util_1.assert(input.dtype === 'complex64', "The dtype for tf.spectral.fft() must be complex64 " +
+        ("but got " + input.dtype + "."));
+    var innerDimensionSize = input.shape[input.shape.length - 1];
+    var batch = input.size / innerDimensionSize;
+    var input2D = input.as2D(batch, innerDimensionSize);
+    var ret = environment_1.ENV.engine.runKernel(function (backend) { return backend.fft(input2D); }, { input: input });
+    return ret.reshape(input.shape);
+}
+function ifft_(input) {
+    util_1.assert(input.dtype === 'complex64', "The dtype for tf.spectral.ifft() must be complex64 " +
+        ("but got " + input.dtype + "."));
+    var innerDimensionSize = input.shape[input.shape.length - 1];
+    var batch = input.size / innerDimensionSize;
+    var input2D = input.as2D(batch, innerDimensionSize);
+    var ret = environment_1.ENV.engine.runKernel(function (backend) { return backend.ifft(input2D); }, { input: input });
+    return ret.reshape(input.shape);
+}
+function rfft_(input) {
+    util_1.assert(input.dtype === 'float32', "The dtype for rfft() must be real value but\n    got " + input.dtype);
+    var innerDimensionSize = input.shape[input.shape.length - 1];
+    var batch = input.size / innerDimensionSize;
+    var zeros = input.zerosLike();
+    var complexInput = complex_ops_1.complex(input, zeros).as2D(batch, innerDimensionSize);
+    var ret = exports.fft(complexInput);
+    var half = Math.floor(innerDimensionSize / 2) + 1;
+    var realValues = complex_ops_1.real(ret);
+    var imagValues = complex_ops_1.imag(ret);
+    var realComplexConjugate = realValues.split([half, innerDimensionSize - half], realValues.shape.length - 1);
+    var imagComplexConjugate = imagValues.split([half, innerDimensionSize - half], imagValues.shape.length - 1);
+    var outputShape = input.shape.slice();
+    outputShape[input.shape.length - 1] = half;
+    return complex_ops_1.complex(realComplexConjugate[0], imagComplexConjugate[0])
+        .reshape(outputShape);
+}
+function irfft_(input) {
+    var innerDimensionSize = input.shape[input.shape.length - 1];
+    var batch = input.size / innerDimensionSize;
+    if (innerDimensionSize <= 2) {
+        var complexInput = input.as2D(batch, innerDimensionSize);
+        var ret = exports.ifft(complexInput);
+        return complex_ops_1.real(ret);
+    }
+    else {
+        var outputShape = [batch, 2 * (innerDimensionSize - 1)];
+        var realInput = complex_ops_1.real(input).as2D(batch, innerDimensionSize);
+        var imagInput = complex_ops_1.imag(input).as2D(batch, innerDimensionSize);
+        var realConjugate = realInput.slice([0, 1], [batch, innerDimensionSize - 2]).reverse(1);
+        var imagConjugate = imagInput.slice([0, 1], [batch, innerDimensionSize - 2])
+            .reverse(1)
+            .mul(tensor_ops_1.scalar(-1));
+        var r = realInput.concat(realConjugate, 1);
+        var i = imagInput.concat(imagConjugate, 1);
+        var complexInput = complex_ops_1.complex(r, i).as2D(outputShape[0], outputShape[1]);
+        var ret = exports.ifft(complexInput);
+        return complex_ops_1.real(ret);
+    }
+}
+exports.fft = operation_1.op({ fft_: fft_ });
+exports.ifft = operation_1.op({ ifft_: ifft_ });
+exports.rfft = operation_1.op({ rfft_: rfft_ });
+exports.irfft = operation_1.op({ irfft_: irfft_ });
+//# sourceMappingURL=spectral_ops.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/spectral_ops.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/spectral_ops.js.map
new file mode 100644
index 0000000..f68f245
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/spectral_ops.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"spectral_ops.js","sourceRoot":"","sources":["../../src/ops/spectral_ops.ts"],"names":[],"mappings":";;AAiBA,8CAAmC;AACnC,kDAAuD;AACvD,8CAAoC;AAEpC,gCAA+B;AAC/B,2CAAoC;AAoBpC,cAAc,KAAa;IACzB,aAAM,CACF,KAAK,CAAC,KAAK,KAAK,WAAW,EAC3B,oDAAoD;SAChD,aAAW,KAAK,CAAC,KAAK,MAAG,CAAA,CAAC,CAAC;IAGnC,IAAM,kBAAkB,GAAG,KAAK,CAAC,KAAK,CAAC,KAAK,CAAC,KAAK,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC;IAC/D,IAAM,KAAK,GAAG,KAAK,CAAC,IAAI,GAAG,kBAAkB,CAAC;IAC9C,IAAM,OAAO,GAAG,KAAK,CAAC,IAAI,CAAC,KAAK,EAAE,kBAAkB,CAAC,CAAC;IAEtD,IAAM,GAAG,GAAG,iBAAG,CAAC,MAAM,CAAC,SAAS,CAAC,UAAA,OAAO,IAAI,OAAA,OAAO,CAAC,GAAG,CAAC,OAAO,CAAC,EAApB,CAAoB,EAAE,EAAC,KAAK,OAAA,EAAC,CAAC,CAAC;IAE3E,OAAO,GAAG,CAAC,OAAO,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC;AAClC,CAAC;AAoBD,eAAe,KAAa;IAC1B,aAAM,CACF,KAAK,CAAC,KAAK,KAAK,WAAW,EAC3B,qDAAqD;SACjD,aAAW,KAAK,CAAC,KAAK,MAAG,CAAA,CAAC,CAAC;IAGnC,IAAM,kBAAkB,GAAG,KAAK,CAAC,KAAK,CAAC,KAAK,CAAC,KAAK,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC;IAC/D,IAAM,KAAK,GAAG,KAAK,CAAC,IAAI,GAAG,kBAAkB,CAAC;IAC9C,IAAM,OAAO,GAAG,KAAK,CAAC,IAAI,CAAC,KAAK,EAAE,kBAAkB,CAAC,CAAC;IAEtD,IAAM,GAAG,GAAG,iBAAG,CAAC,MAAM,CAAC,SAAS,CAAC,UAAA,OAAO,IAAI,OAAA,OAAO,CAAC,IAAI,CAAC,OAAO,CAAC,EAArB,CAAqB,EAAE,EAAC,KAAK,OAAA,EAAC,CAAC,CAAC;IAE5E,OAAO,GAAG,CAAC,OAAO,CAAC,KAAK,CAAC,KAAK,CAAC,CAAC;AAClC,CAAC;AAkBD,eAAe,KAAa;IAC1B,aAAM,CAAC,KAAK,CAAC,KAAK,KAAK,SAAS,EAAE,0DAC1B,KAAK,CAAC,KAAO,CAAC,CAAC;IAEvB,IAAM,kBAAkB,GAAG,KAAK,CAAC,KAAK,CAAC,KAAK,CAAC,KAAK,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC;IAC/D,IAAM,KAAK,GAAG,KAAK,CAAC,IAAI,GAAG,kBAAkB,CAAC;IAG9C,IAAM,KAAK,GAAG,KAAK,CAAC,SAAS,EAAE,CAAC;IAChC,IAAM,YAAY,GAAG,qBAAO,CAAC,KAAK,EAAE,KAAK,CAAC,CAAC,IAAI,CAAC,KAAK,EAAE,kBAAkB,CAAC,CAAC;IAE3E,IAAM,GAAG,GAAG,WAAG,CAAC,YAAY,CAAC,CAAC;IAG9B,IAAM,IAAI,GAAG,IAAI,CAAC,KAAK,CAAC,kBAAkB,GAAG,CAAC,CAAC,GAAG,CAAC,CAAC;IACpD,IAAM,UAAU,GAAG,kBAAI,CAAC,GAAG,CAAC,CAAC;IAC7B,IAAM,UAAU,GAAG,kBAAI,CAAC,GAAG,CAAC,CAAC;IAC7B,IAAM,oBAAoB,GAAG,UAAU,CAAC,KAAK,CACzC,CAAC,IAAI,EAAE,kBAAkB,GAAG,IAAI,CAAC,EAAE,UAAU,CAAC,KAAK,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC;IACpE,IAAM,oBAAoB,GAAG,UAAU,CAAC,KAAK,CACzC,CAAC,IAAI,EAAE,kBAAkB,GAAG,IAAI,CAAC,EAAE,UAAU,CAAC,KAAK,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC;IAEpE,IAAM,WAAW,GAAG,KAAK,CAAC,KAAK,CAAC,KAAK,EAAE,CAAC;IACxC,WAAW,CAAC,KAAK,CAAC,KAAK,CAAC,MAAM,GAAG,CAAC,CAAC,GAAG,IAAI,CAAC;IAE3C,OAAO,qBAAO,CAAC,oBAAoB,CAAC,CAAC,CAAC,EAAE,oBAAoB,CAAC,CAAC,CAAC,CAAC;SAC3D,OAAO,CAAC,WAAW,CAAC,CAAC;AAC5B,CAAC;AAkBD,gBAAgB,KAAa;IAC3B,IAAM,kBAAkB,GAAG,KAAK,CAAC,KAAK,CAAC,KAAK,CAAC,KAAK,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC;IAC/D,IAAM,KAAK,GAAG,KAAK,CAAC,IAAI,GAAG,kBAAkB,CAAC;IAE9C,IAAI,kBAAkB,IAAI,CAAC,EAAE;QAC3B,IAAM,YAAY,GAAG,KAAK,CAAC,IAAI,CAAC,KAAK,EAAE,kBAAkB,CAAC,CAAC;QAC3D,IAAM,GAAG,GAAG,YAAI,CAAC,YAAY,CAAC,CAAC;QAC/B,OAAO,kBAAI,CAAC,GAAG,CAAC,CAAC;KAClB;SAAM;QAGL,IAAM,WAAW,GAAG,CAAC,KAAK,EAAE,CAAC,GAAG,CAAC,kBAAkB,GAAG,CAAC,CAAC,CAAC,CAAC;QAC1D,IAAM,SAAS,GAAG,kBAAI,CAAC,KAAK,CAAC,CAAC,IAAI,CAAC,KAAK,EAAE,kBAAkB,CAAC,CAAC;QAC9D,IAAM,SAAS,GAAG,kBAAI,CAAC,KAAK,CAAC,CAAC,IAAI,CAAC,KAAK,EAAE,kBAAkB,CAAC,CAAC;QAE9D,IAAM,aAAa,GACf,SAAS,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,KAAK,EAAE,kBAAkB,GAAG,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC;QACxE,IAAM,aAAa,GACf,SAAS,CAAC,KAAK,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,KAAK,EAAE,kBAAkB,GAAG,CAAC,CAAC,CAAC;aACnD,OAAO,CAAC,CAAC,CAAC;aACV,GAAG,CAAC,mBAAM,CAAC,CAAC,CAAC,CAAC,CAAa,CAAC;QAErC,IAAM,CAAC,GAAG,SAAS,CAAC,MAAM,CAAC,aAAa,EAAE,CAAC,CAAC,CAAC;QAC7C,IAAM,CAAC,GAAG,SAAS,CAAC,MAAM,CAAC,aAAa,EAAE,CAAC,CAAC,CAAC;QAC7C,IAAM,YAAY,GAAG,qBAAO,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,IAAI,CAAC,WAAW,CAAC,CAAC,CAAC,EAAE,WAAW,CAAC,CAAC,CAAC,CAAC,CAAC;QACxE,IAAM,GAAG,GAAG,YAAI,CAAC,YAAY,CAAC,CAAC;QAC/B,OAAO,kBAAI,CAAC,GAAG,CAAC,CAAC;KAClB;AACH,CAAC;AAEY,QAAA,GAAG,GAAG,cAAE,CAAC,EAAC,IAAI,MAAA,EAAC,CAAC,CAAC;AACjB,QAAA,IAAI,GAAG,cAAE,CAAC,EAAC,KAAK,OAAA,EAAC,CAAC,CAAC;AACnB,QAAA,IAAI,GAAG,cAAE,CAAC,EAAC,KAAK,OAAA,EAAC,CAAC,CAAC;AACnB,QAAA,KAAK,GAAG,cAAE,CAAC,EAAC,MAAM,QAAA,EAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/spectral_ops_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/spectral_ops_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/spectral_ops_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/spectral_ops_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/spectral_ops_test.js
new file mode 100644
index 0000000..5ef8afd
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/spectral_ops_test.js
@@ -0,0 +1,227 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('1D FFT', test_util_1.ALL_ENVS, function () {
+    it('should return the same value with TensorFlow (2 elements)', function () {
+        var t1Real = tf.tensor1d([1, 2]);
+        var t1Imag = tf.tensor1d([1, 1]);
+        var t1 = tf.complex(t1Real, t1Imag);
+        test_util_1.expectArraysClose(tf.spectral.fft(t1), [3, 2, -1, 0]);
+    });
+    it('should calculate FFT from Tensor directly', function () {
+        var t1Real = tf.tensor1d([1, 2]);
+        var t1Imag = tf.tensor1d([1, 1]);
+        var t1 = tf.complex(t1Real, t1Imag);
+        test_util_1.expectArraysClose(t1.fft(), [3, 2, -1, 0]);
+    });
+    it('should return the same value as TensorFlow (3 elements)', function () {
+        var t1Real = tf.tensor1d([1, 2, 3]);
+        var t1Imag = tf.tensor1d([0, 0, 0]);
+        var t1 = tf.complex(t1Real, t1Imag);
+        test_util_1.expectArraysClose(tf.spectral.fft(t1), [6, 0, -1.5, 0.866025, -1.5, -0.866025]);
+    });
+    it('should return the same value as TensorFlow with imaginary (3 elements)', function () {
+        var t1Real = tf.tensor1d([1, 2, 3]);
+        var t1Imag = tf.tensor1d([1, 2, 3]);
+        var t1 = tf.complex(t1Real, t1Imag);
+        test_util_1.expectArraysClose(tf.spectral.fft(t1), [6, 6, -2.3660252, -0.63397473, -0.6339747, -2.3660254]);
+    });
+    it('should return the same value as TensorFlow (negative 3 elements)', function () {
+        var t1Real = tf.tensor1d([-1, -2, -3]);
+        var t1Imag = tf.tensor1d([-1, -2, -3]);
+        var t1 = tf.complex(t1Real, t1Imag);
+        test_util_1.expectArraysClose(tf.spectral.fft(t1), [-5.9999995, -6, 2.3660252, 0.63397473, 0.6339747, 2.3660254]);
+    });
+    it('should return the same value with TensorFlow (4 elements)', function () {
+        var t1Real = tf.tensor1d([1, 2, 3, 4]);
+        var t1Imag = tf.tensor1d([0, 0, 0, 0]);
+        var t1 = tf.complex(t1Real, t1Imag);
+        test_util_1.expectArraysClose(tf.spectral.fft(t1), [10, 0, -2, 2, -2, 0, -2, -2]);
+    });
+    it('should return the same value as TensorFlow with imaginary (4 elements)', function () {
+        var t1Real = tf.tensor1d([1, 2, 3, 4]);
+        var t1Imag = tf.tensor1d([1, 2, 3, 4]);
+        var t1 = tf.complex(t1Real, t1Imag);
+        test_util_1.expectArraysClose(tf.spectral.fft(t1), [10, 10, -4, 0, -2, -2, 0, -4]);
+    });
+});
+jasmine_util_1.describeWithFlags('2D FFT', test_util_1.ALL_ENVS, function () {
+    it('2D: should return the same value as TensorFlow', function () {
+        var t1Real = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var t1Imag = tf.tensor2d([5, 6, 7, 8], [2, 2]);
+        var t1 = tf.complex(t1Real, t1Imag);
+        var y = tf.spectral.fft(t1);
+        test_util_1.expectArraysClose(y, [3, 11, -1, -1, 7, 15, -1, -1]);
+        expect(y.shape).toEqual(t1Real.shape);
+    });
+    it('3D: should return the same value as TensorFlow', function () {
+        var t1Real = tf.tensor3d([1, 2, 3, 4, -1, -2, -3, -4], [2, 2, 2]);
+        var t1Imag = tf.tensor3d([5, 6, 7, 8, -5, -6, -7, -8], [2, 2, 2]);
+        var t1 = tf.complex(t1Real, t1Imag);
+        var y = tf.spectral.fft(t1);
+        test_util_1.expectArraysClose(y, [3, 11, -1, -1, 7, 15, -1, -1, -3, -11, 1, 1, -7, -15, 1, 1]);
+        expect(y.shape).toEqual(t1Real.shape);
+    });
+});
+jasmine_util_1.describeWithFlags('1D IFFT', test_util_1.ALL_ENVS, function () {
+    it('should return the same value with TensorFlow (2 elements)', function () {
+        var t1Real = tf.tensor1d([1, 2]);
+        var t1Imag = tf.tensor1d([1, 1]);
+        var t1 = tf.complex(t1Real, t1Imag);
+        test_util_1.expectArraysClose(tf.spectral.ifft(t1), [1.5, 1, -0.5, 0]);
+    });
+    it('should calculate FFT from Tensor directly', function () {
+        var t1Real = tf.tensor1d([1, 2]);
+        var t1Imag = tf.tensor1d([1, 1]);
+        var t1 = tf.complex(t1Real, t1Imag);
+        test_util_1.expectArraysClose(t1.ifft(), [1.5, 1, -0.5, 0]);
+    });
+    it('should return the same value as TensorFlow (3 elements)', function () {
+        var t1Real = tf.tensor1d([1, 2, 3]);
+        var t1Imag = tf.tensor1d([0, 0, 0]);
+        var t1 = tf.complex(t1Real, t1Imag);
+        test_util_1.expectArraysClose(tf.spectral.ifft(t1), [
+            2, -3.9736431e-08, -0.49999997, -.28867507, -0.49999994, 2.8867519e-01
+        ]);
+    });
+    it('should return the same value as TensorFlow with imaginary (3 elements)', function () {
+        var t1Real = tf.tensor1d([1, 2, 3]);
+        var t1Imag = tf.tensor1d([1, 2, 3]);
+        var t1 = tf.complex(t1Real, t1Imag);
+        test_util_1.expectArraysClose(tf.spectral.ifft(t1), [2, 1.9999999, -0.21132492, -0.78867507, -0.7886752, -0.2113249]);
+    });
+    it('should return the same value as TensorFlow (negative 3 elements)', function () {
+        var t1Real = tf.tensor1d([-1, -2, -3]);
+        var t1Imag = tf.tensor1d([-1, -2, -3]);
+        var t1 = tf.complex(t1Real, t1Imag);
+        test_util_1.expectArraysClose(tf.spectral.ifft(t1), [-2, -1.9999999, 0.21132492, 0.78867507, 0.7886752, 0.2113249]);
+    });
+    it('should return the same value with TensorFlow (4 elements)', function () {
+        var t1Real = tf.tensor1d([1, 2, 3, 4]);
+        var t1Imag = tf.tensor1d([0, 0, 0, 0]);
+        var t1 = tf.complex(t1Real, t1Imag);
+        test_util_1.expectArraysClose(tf.spectral.ifft(t1), [2.5, 0, -0.5, -0.5, -0.5, 0, -0.5, 0.5]);
+    });
+    it('should return the same value as TensorFlow with imaginary (4 elements)', function () {
+        var t1Real = tf.tensor1d([1, 2, 3, 4]);
+        var t1Imag = tf.tensor1d([1, 2, 3, 4]);
+        var t1 = tf.complex(t1Real, t1Imag);
+        test_util_1.expectArraysClose(tf.spectral.ifft(t1), [2.5, 2.5, 0, -1, -0.5, -0.5, -1, 0]);
+    });
+});
+jasmine_util_1.describeWithFlags('2D IFFT', test_util_1.ALL_ENVS, function () {
+    it('2D: should return the same value as TensorFlow', function () {
+        var t1Real = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var t1Imag = tf.tensor2d([5, 6, 7, 8], [2, 2]);
+        var t1 = tf.complex(t1Real, t1Imag);
+        var y = tf.spectral.ifft(t1);
+        test_util_1.expectArraysClose(y, [1.5, 5.5, -0.5, -0.5, 3.5, 7.5, -0.5, -0.5]);
+        expect(y.shape).toEqual(t1Real.shape);
+    });
+    it('3D: should return the same value as TensorFlow', function () {
+        var t1Real = tf.tensor3d([1, 2, 3, 4, -1, -2, -3, -4], [2, 2, 2]);
+        var t1Imag = tf.tensor3d([5, 6, 7, 8, -5, -6, -7, -8], [2, 2, 2]);
+        var t1 = tf.complex(t1Real, t1Imag);
+        var y = tf.spectral.ifft(t1);
+        test_util_1.expectArraysClose(y, [
+            1.5, 5.5, -0.5, -0.5, 3.5, 7.5, -0.5, -0.5, -1.5, -5.5, 0.5, 0.5, -3.5,
+            -7.5, 0.5, 0.5
+        ]);
+        expect(y.shape).toEqual(t1Real.shape);
+    });
+});
+jasmine_util_1.describeWithFlags('1D RFFT', test_util_1.ALL_ENVS, function () {
+    it('should return the same value with TensorFlow (3 elements)', function () {
+        var t1Real = tf.tensor1d([1, 2, 3]);
+        test_util_1.expectArraysClose(tf.spectral.rfft(t1Real), [6, 1.1920929e-07, -1.4999999, 8.6602521e-01]);
+    });
+    it('should return the same value with TensorFlow (6 elements)', function () {
+        var t1Real = tf.tensor1d([-3, -2, -1, 1, 2, 3]);
+        test_util_1.expectArraysClose(tf.spectral.rfft(t1Real), [
+            -5.8859587e-07, 1.1920929e-07, -3.9999995, 6.9282026e+00, -2.9999998,
+            1.7320497, -4.0000000, -2.3841858e-07
+        ]);
+    });
+});
+jasmine_util_1.describeWithFlags('2D RFFT', test_util_1.WEBGL_ENVS, function () {
+    it('should return the same value with TensorFlow (2x2 elements)', function () {
+        var t1Real = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        test_util_1.expectArraysClose(tf.spectral.rfft(t1Real), [3, 0, -1, 0, 7, 0, -1, 0]);
+    });
+    it('should return the same value with TensorFlow (2x3 elements)', function () {
+        var t1Real = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        test_util_1.expectArraysClose(tf.spectral.rfft(t1Real), [
+            6, 1.1920929e-07, -1.4999999, 8.6602521e-01, 15, -5.9604645e-08,
+            -1.4999998, 8.6602545e-01
+        ]);
+    });
+    it('should return the same value with TensorFlow (2x2x2 elements)', function () {
+        var t1Real = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8], [2, 2, 2]);
+        test_util_1.expectArraysClose(tf.spectral.rfft(t1Real), [3, 0, -1, 0, 7, 0, -1, 0, 11, 0, -1, 0, 15, 0, -1, 0]);
+    });
+});
+jasmine_util_1.describeWithFlags('1D IRFFT', test_util_1.ALL_ENVS, function () {
+    it('should return the same value with TensorFlow (2 elements)', function () {
+        var t1Real = tf.tensor1d([1, 2]);
+        var t1Imag = tf.tensor1d([0, 0]);
+        var t1 = tf.complex(t1Real, t1Imag);
+        test_util_1.expectArraysClose(tf.spectral.irfft(t1), [1.5, -0.5]);
+    });
+    it('should return the same value with TensorFlow (5 elements)', function () {
+        var t1Real = tf.tensor1d([1, 2, 3, 4, 5]);
+        var t1Imag = tf.tensor1d([0, 0, 0, 0, 0]);
+        var t1 = tf.complex(t1Real, t1Imag);
+        test_util_1.expectArraysClose(tf.spectral.irfft(t1), [3, -0.8535534, 0, -0.14644662, 0, -0.14644662, 0, -0.8535534]);
+    });
+    it('should return the same value with TensorFlow (5 elements) with imag', function () {
+        var t1Real = tf.tensor1d([1, 2, 3, 4, 5]);
+        var t1Imag = tf.tensor1d([1, 2, 3, 4, 5]);
+        var t1 = tf.complex(t1Real, t1Imag);
+        test_util_1.expectArraysClose(tf.spectral.irfft(t1), [3, -2.6642137, 0.5, -0.45710677, 0, 0.16421354, -0.5, 0.95710677]);
+    });
+});
+jasmine_util_1.describeWithFlags('2D IRFFT', test_util_1.ALL_ENVS, function () {
+    it('should return the same value with TensorFlow (2x2 elements)', function () {
+        var t1Real = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var t1Imag = tf.tensor2d([0, 0, 0, 0], [2, 2]);
+        var t1 = tf.complex(t1Real, t1Imag);
+        test_util_1.expectArraysClose(tf.spectral.irfft(t1), [1.5, -0.5, 3.5, -0.5]);
+    });
+    it('should return the same value with TensorFlow (2x3 elements)', function () {
+        var t1Real = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var t1Imag = tf.tensor2d([0, 0, 0, 0, 0, 0], [2, 3]);
+        var t1 = tf.complex(t1Real, t1Imag);
+        test_util_1.expectArraysClose(tf.spectral.irfft(t1), [2, -0.5, 0, -0.5, 5, -0.5, 0, -0.5]);
+    });
+    it('should return the same value with TensorFlow (2x3 elements) with imag', function () {
+        var t1Real = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var t1Imag = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var t1 = tf.complex(t1Real, t1Imag);
+        test_util_1.expectArraysClose(tf.spectral.irfft(t1), [2, -1.5, 0, 0.5, 5, -3, 0, 2]);
+    });
+});
+jasmine_util_1.describeWithFlags('3D IRFFT', test_util_1.ALL_ENVS, function () {
+    it('should return the same value with TensorFlow (2x2x2 elements)', function () {
+        var t1Real = tf.tensor3d([1, 2, 3, 4, 1, 2, 3, 4], [2, 2, 2]);
+        var t1Imag = tf.tensor3d([0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 2]);
+        var t1 = tf.complex(t1Real, t1Imag);
+        test_util_1.expectArraysClose(tf.spectral.irfft(t1), [1.5, -0.5, 3.5, -0.5, 1.5, -0.5, 3.5, -0.5]);
+    });
+    it('should return the same value with TensorFlow (2x2x3 elements)', function () {
+        var t1Real = tf.tensor3d([1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6], [2, 2, 3]);
+        var t1Imag = tf.tensor3d([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [2, 2, 3]);
+        var t1 = tf.complex(t1Real, t1Imag);
+        test_util_1.expectArraysClose(tf.spectral.irfft(t1), [
+            2, -0.5, 0, -0.5, 5, -0.5, 0, -0.5, 2, -0.5, 0, -0.5, 5, -0.5, 0, -0.5
+        ]);
+    });
+    it('should return the same value with TensorFlow (2x2x3 elements) with imag', function () {
+        var t1Real = tf.tensor3d([1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6], [2, 2, 3]);
+        var t1Imag = tf.tensor3d([1, 2, 3, 4, 5, 6, 1, 2, 3, 4, 5, 6], [2, 2, 3]);
+        var t1 = tf.complex(t1Real, t1Imag);
+        test_util_1.expectArraysClose(tf.spectral.irfft(t1), [2, -1.5, 0, 0.5, 5, -3, 0, 2, 2, -1.5, 0, 0.5, 5, -3, 0, 2]);
+    });
+});
+//# sourceMappingURL=spectral_ops_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/spectral_ops_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/spectral_ops_test.js.map
new file mode 100644
index 0000000..b0250c2
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/spectral_ops_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice.d.ts
new file mode 100644
index 0000000..ebdba5a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice.d.ts
@@ -0,0 +1,5 @@
+import { Tensor } from '../tensor';
+import { TensorLike } from '../types';
+declare function stridedSlice_<T extends Tensor>(x: T | TensorLike, begin: number[], end: number[], strides: number[], beginMask?: number, endMask?: number, ellipsisMask?: number, newAxisMask?: number, shrinkAxisMask?: number): T;
+export declare const stridedSlice: typeof stridedSlice_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice.js b/node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice.js
new file mode 100644
index 0000000..3008c96
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice.js
@@ -0,0 +1,22 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var tensor_util_env_1 = require("../tensor_util_env");
+var operation_1 = require("./operation");
+function stridedSlice_(x, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {
+    if (beginMask === void 0) { beginMask = 0; }
+    if (endMask === void 0) { endMask = 0; }
+    if (ellipsisMask === void 0) { ellipsisMask = 0; }
+    if (newAxisMask === void 0) { newAxisMask = 0; }
+    if (shrinkAxisMask === void 0) { shrinkAxisMask = 0; }
+    if (ellipsisMask !== 0) {
+        throw new Error('ellipsis mask is not yet supported');
+    }
+    if (newAxisMask !== 0) {
+        throw new Error('new axis mask is not yet supported');
+    }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'stridedSlice');
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.stridedSlice($x, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask); }, { $x: $x });
+}
+exports.stridedSlice = operation_1.op({ stridedSlice_: stridedSlice_ });
+//# sourceMappingURL=strided_slice.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice.js.map
new file mode 100644
index 0000000..1c61fe4
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"strided_slice.js","sourceRoot":"","sources":["../../src/ops/strided_slice.ts"],"names":[],"mappings":";;AAiBA,8CAAmC;AAEnC,sDAAmD;AAEnD,yCAA+B;AAkC/B,uBACI,CAAe,EAAE,KAAe,EAAE,GAAa,EAAE,OAAiB,EAClE,SAAa,EAAE,OAAW,EAAE,YAAgB,EAAE,WAAe,EAC7D,cAAkB;IADlB,0BAAA,EAAA,aAAa;IAAE,wBAAA,EAAA,WAAW;IAAE,6BAAA,EAAA,gBAAgB;IAAE,4BAAA,EAAA,eAAe;IAC7D,+BAAA,EAAA,kBAAkB;IACpB,IAAI,YAAY,KAAK,CAAC,EAAE;QACtB,MAAM,IAAI,KAAK,CAAC,oCAAoC,CAAC,CAAC;KACvD;IACD,IAAI,WAAW,KAAK,CAAC,EAAE;QACrB,MAAM,IAAI,KAAK,CAAC,oCAAoC,CAAC,CAAC;KACvD;IACD,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,cAAc,CAAC,CAAC;IACnD,OAAO,iBAAG,CAAC,MAAM,CAAC,SAAS,CAChB,UAAA,OAAO,IAAI,OAAA,OAAO,CAAC,YAAY,CAC3B,EAAE,EAAE,KAAK,EAAE,GAAG,EAAE,OAAO,EAAE,SAAS,EAAE,OAAO,EAAE,YAAY,EACzD,WAAW,EAAE,cAAc,CAAC,EAFrB,CAEqB,EAChC,EAAC,EAAE,IAAA,EAAC,CAAM,CAAC;AACxB,CAAC;AAEY,QAAA,YAAY,GAAG,cAAE,CAAC,EAAC,aAAa,eAAA,EAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice_test.js
new file mode 100644
index 0000000..b7db6ff
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice_test.js
@@ -0,0 +1,222 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('stridedSlice', test_util_1.ALL_ENVS, function () {
+    it('stridedSlice should fail if new axis mask is set', function () {
+        var tensor = tf.tensor1d([0, 1, 2, 3]);
+        expect(function () { return tf.stridedSlice(tensor, [0], [3], [2], 0, 0, 0, 1); }).toThrow();
+    });
+    it('stridedSlice should fail if ellipsis mask is set', function () {
+        var tensor = tf.tensor1d([0, 1, 2, 3]);
+        expect(function () { return tf.stridedSlice(tensor, [0], [3], [2], 0, 0, 1); }).toThrow();
+    });
+    it('stridedSlice should support 1d tensor', function () {
+        var tensor = tf.tensor1d([0, 1, 2, 3]);
+        var output = tf.stridedSlice(tensor, [0], [3], [2]);
+        expect(output.shape).toEqual([2]);
+        test_util_1.expectArraysClose(output, [0, 2]);
+    });
+    it('stridedSlice should support 1d tensor', function () {
+        var tensor = tf.tensor1d([0, 1, 2, 3]);
+        var output = tf.stridedSlice(tensor, [0], [3], [2]);
+        expect(output.shape).toEqual([2]);
+        test_util_1.expectArraysClose(output, [0, 2]);
+    });
+    it('stridedSlice with 1d tensor should be used by tensor directly', function () {
+        var t = tf.tensor1d([0, 1, 2, 3]);
+        var output = t.stridedSlice([0], [3], [2]);
+        expect(output.shape).toEqual([2]);
+        test_util_1.expectArraysClose(output, [0, 2]);
+    });
+    it('stridedSlice should support 1d tensor empty result', function () {
+        var tensor = tf.tensor1d([0, 1, 2, 3]);
+        var output = tf.stridedSlice(tensor, [10], [3], [2]);
+        expect(output.shape).toEqual([0]);
+        test_util_1.expectArraysClose(output, []);
+    });
+    it('stridedSlice should support 1d tensor negative begin', function () {
+        var tensor = tf.tensor1d([0, 1, 2, 3]);
+        var output = tf.stridedSlice(tensor, [-3], [3], [1]);
+        expect(output.shape).toEqual([2]);
+        test_util_1.expectArraysClose(output, [1, 2]);
+    });
+    it('stridedSlice should support 1d tensor out of range begin', function () {
+        var tensor = tf.tensor1d([0, 1, 2, 3]);
+        var output = tf.stridedSlice(tensor, [-5], [3], [1]);
+        expect(output.shape).toEqual([3]);
+        test_util_1.expectArraysClose(output, [0, 1, 2]);
+    });
+    it('stridedSlice should support 1d tensor negative end', function () {
+        var tensor = tf.tensor1d([0, 1, 2, 3]);
+        var output = tf.stridedSlice(tensor, [1], [-2], [1]);
+        expect(output.shape).toEqual([1]);
+        test_util_1.expectArraysClose(output, [1]);
+    });
+    it('stridedSlice should support 1d tensor out of range end', function () {
+        var tensor = tf.tensor1d([0, 1, 2, 3]);
+        var output = tf.stridedSlice(tensor, [-3], [5], [1]);
+        expect(output.shape).toEqual([3]);
+        test_util_1.expectArraysClose(output, [1, 2, 3]);
+    });
+    it('stridedSlice should support 1d tensor begin mask', function () {
+        var tensor = tf.tensor1d([0, 1, 2, 3]);
+        var output = tf.stridedSlice(tensor, [1], [3], [1], 1);
+        expect(output.shape).toEqual([3]);
+        test_util_1.expectArraysClose(output, [0, 1, 2]);
+    });
+    it('stridedSlice should support 1d tensor nagtive begin and stride', function () {
+        var tensor = tf.tensor1d([0, 1, 2, 3]);
+        var output = tf.stridedSlice(tensor, [-2], [-3], [-1]);
+        expect(output.shape).toEqual([1]);
+        test_util_1.expectArraysClose(output, [2]);
+    });
+    it('stridedSlice should support 1d tensor' +
+        ' out of range begin and negative stride', function () {
+        var tensor = tf.tensor1d([0, 1, 2, 3]);
+        var output = tf.stridedSlice(tensor, [5], [-2], [-1]);
+        expect(output.shape).toEqual([1]);
+        test_util_1.expectArraysClose(output, [3]);
+    });
+    it('stridedSlice should support 1d tensor nagtive end and stride', function () {
+        var tensor = tf.tensor1d([0, 1, 2, 3]);
+        var output = tf.stridedSlice(tensor, [2], [-4], [-1]);
+        expect(output.shape).toEqual([2]);
+        test_util_1.expectArraysClose(output, [2, 1]);
+    });
+    it('stridedSlice should support 1d tensor' +
+        ' out of range end and negative stride', function () {
+        var tensor = tf.tensor1d([0, 1, 2, 3]);
+        var output = tf.stridedSlice(tensor, [-3], [-5], [-1]);
+        expect(output.shape).toEqual([2]);
+        test_util_1.expectArraysClose(output, [1, 0]);
+    });
+    it('stridedSlice should support 1d tensor end mask', function () {
+        var tensor = tf.tensor1d([0, 1, 2, 3]);
+        var output = tf.stridedSlice(tensor, [1], [3], [1], 0, 1);
+        expect(output.shape).toEqual([3]);
+        test_util_1.expectArraysClose(output, [1, 2, 3]);
+    });
+    it('stridedSlice should support 1d tensor shrink axis mask', function () {
+        var tensor = tf.tensor1d([0, 1, 2, 3]);
+        var output = tf.stridedSlice(tensor, [1], [3], [1], 0, 0, 0, 0, 1);
+        expect(output.shape).toEqual([]);
+        test_util_1.expectArraysClose(output, [1]);
+    });
+    it('stridedSlice should support 1d tensor negative stride', function () {
+        var tensor = tf.tensor1d([0, 1, 2, 3]);
+        var output = tf.stridedSlice(tensor, [-1], [-4], [-1]);
+        expect(output.shape).toEqual([3]);
+        test_util_1.expectArraysClose(output, [3, 2, 1]);
+    });
+    it('stridedSlice should support 1d tensor even length stride', function () {
+        var tensor = tf.tensor1d([0, 1, 2, 3]);
+        var output = tf.stridedSlice(tensor, [0], [2], [2]);
+        expect(output.shape).toEqual([1]);
+        test_util_1.expectArraysClose(output, [0]);
+    });
+    it('stridedSlice should support 1d tensor odd length stride', function () {
+        var tensor = tf.tensor1d([0, 1, 2, 3]);
+        var output = tf.stridedSlice(tensor, [0], [3], [2]);
+        expect(output.shape).toEqual([2]);
+        test_util_1.expectArraysClose(output, [0, 2]);
+    });
+    it('stridedSlice should support 2d tensor identity', function () {
+        var tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var output = tf.stridedSlice(tensor, [0, 0], [2, 3], [1, 1]);
+        expect(output.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(output, [1, 2, 3, 4, 5, 6]);
+    });
+    it('stridedSlice should support 2d tensor', function () {
+        var tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var output = tf.stridedSlice(tensor, [1, 0], [2, 2], [1, 1]);
+        expect(output.shape).toEqual([1, 2]);
+        test_util_1.expectArraysClose(output, [4, 5]);
+    });
+    it('stridedSlice should support 2d tensor strides', function () {
+        var tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var output = tf.stridedSlice(tensor, [0, 0], [2, 3], [2, 2]);
+        expect(output.shape).toEqual([1, 2]);
+        test_util_1.expectArraysClose(output, [1, 3]);
+    });
+    it('stridedSlice with 2d tensor should be used by tensor directly', function () {
+        var t = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var output = t.stridedSlice([1, 0], [2, 2], [1, 1]);
+        expect(output.shape).toEqual([1, 2]);
+        test_util_1.expectArraysClose(output, [4, 5]);
+    });
+    it('stridedSlice should support 2d tensor negative strides', function () {
+        var tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var output = tf.stridedSlice(tensor, [1, -1], [2, -4], [2, -1]);
+        expect(output.shape).toEqual([1, 3]);
+        test_util_1.expectArraysClose(output, [6, 5, 4]);
+    });
+    it('stridedSlice should support 2d tensor begin mask', function () {
+        var tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var output = tf.stridedSlice(tensor, [1, 0], [2, 2], [1, 1], 1);
+        expect(output.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(output, [1, 2, 4, 5]);
+    });
+    it('stridedSlice should support 2d tensor shrink mask', function () {
+        var tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var output = tf.stridedSlice(tensor, [1, 0], [2, 2], [1, 1], 0, 0, 0, 0, 1);
+        expect(output.shape).toEqual([2]);
+        test_util_1.expectArraysClose(output, [4, 5]);
+    });
+    it('stridedSlice should support 2d tensor end mask', function () {
+        var tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var output = tf.stridedSlice(tensor, [1, 0], [2, 2], [1, 1], 0, 2);
+        expect(output.shape).toEqual([1, 3]);
+        test_util_1.expectArraysClose(output, [4, 5, 6]);
+    });
+    it('stridedSlice should support 2d tensor' +
+        ' negative strides and begin mask', function () {
+        var tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var output = tf.stridedSlice(tensor, [1, -2], [2, -4], [1, -1], 2);
+        expect(output.shape).toEqual([1, 3]);
+        test_util_1.expectArraysClose(output, [6, 5, 4]);
+    });
+    it('stridedSlice should support 2d tensor' +
+        ' negative strides and end mask', function () {
+        var tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var output = tf.stridedSlice(tensor, [1, -2], [2, -3], [1, -1], 0, 2);
+        expect(output.shape).toEqual([1, 2]);
+        test_util_1.expectArraysClose(output, [5, 4]);
+    });
+    it('stridedSlice should support 3d tensor identity', function () {
+        var tensor = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
+        var output = tf.stridedSlice(tensor, [0, 0, 0], [2, 3, 2], [1, 1, 1]);
+        expect(output.shape).toEqual([2, 3, 2]);
+        test_util_1.expectArraysClose(output, [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]);
+    });
+    it('stridedSlice should support 3d tensor negative stride', function () {
+        var tensor = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
+        var output = tf.stridedSlice(tensor, [-1, -1, -1], [-3, -4, -3], [-1, -1, -1]);
+        expect(output.shape).toEqual([2, 3, 2]);
+        test_util_1.expectArraysClose(output, [12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, 1]);
+    });
+    it('stridedSlice should support 3d tensor strided 2', function () {
+        var tensor = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
+        var output = tf.stridedSlice(tensor, [0, 0, 0], [2, 3, 2], [2, 2, 2]);
+        expect(output.shape).toEqual([1, 2, 1]);
+        test_util_1.expectArraysClose(output, [1, 5]);
+    });
+    it('stridedSlice should support 3d tensor shrink mask', function () {
+        var tensor = tf.tensor3d([1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12], [2, 3, 2]);
+        var output = tf.stridedSlice(tensor, [0, 0, 0], [2, 3, 2], [1, 1, 1], 0, 0, 0, 0, 1);
+        expect(output.shape).toEqual([3, 2]);
+        test_util_1.expectArraysClose(output, [1, 2, 3, 4, 5, 6]);
+    });
+    it('stridedSlice should throw when passed a non-tensor', function () {
+        expect(function () { return tf.stridedSlice({}, [0], [0], [1]); })
+            .toThrowError(/Argument 'x' passed to 'stridedSlice' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var tensor = [0, 1, 2, 3];
+        var output = tf.stridedSlice(tensor, [0], [3], [2]);
+        expect(output.shape).toEqual([2]);
+        test_util_1.expectArraysClose(output, [0, 2]);
+    });
+});
+//# sourceMappingURL=strided_slice_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice_test.js.map
new file mode 100644
index 0000000..16cf109
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/strided_slice_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops.d.ts
new file mode 100644
index 0000000..ee407cb
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops.d.ts
@@ -0,0 +1,21 @@
+import { Scalar, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, Tensor5D, Tensor6D } from '../tensor';
+import { TensorLike, TensorLike1D, TensorLike2D, TensorLike3D, TensorLike4D, TensorLike5D, TensorLike6D } from '../types';
+import { DataType, Rank, ShapeMap } from '../types';
+declare function tensor<R extends Rank>(values: TensorLike, shape?: ShapeMap[R], dtype?: DataType): Tensor<R>;
+declare function scalar(value: number | boolean | string, dtype?: DataType): Scalar;
+declare function tensor1d(values: TensorLike1D, dtype?: DataType): Tensor1D;
+declare function tensor2d(values: TensorLike2D, shape?: [number, number], dtype?: DataType): Tensor2D;
+declare function tensor3d(values: TensorLike3D, shape?: [number, number, number], dtype?: DataType): Tensor3D;
+declare function tensor4d(values: TensorLike4D, shape?: [number, number, number, number], dtype?: DataType): Tensor4D;
+declare function tensor5d(values: TensorLike5D, shape?: [number, number, number, number, number], dtype?: DataType): Tensor5D;
+declare function tensor6d(values: TensorLike6D, shape?: [number, number, number, number, number, number], dtype?: DataType): Tensor6D;
+declare function ones<R extends Rank>(shape: ShapeMap[R], dtype?: DataType): Tensor<R>;
+declare function zeros<R extends Rank>(shape: ShapeMap[R], dtype?: DataType): Tensor<R>;
+declare function fill<R extends Rank>(shape: ShapeMap[R], value: number | string, dtype?: DataType): Tensor<R>;
+declare function onesLike_<T extends Tensor>(x: T | TensorLike): T;
+declare function zerosLike_<T extends Tensor>(x: T | TensorLike): T;
+declare function linspace(start: number, stop: number, num: number): Tensor1D;
+declare function range(start: number, stop: number, step?: number, dtype?: 'float32' | 'int32'): Tensor1D;
+export { fill, linspace, ones, range, scalar, tensor, tensor1d, tensor2d, tensor3d, tensor4d, tensor5d, tensor6d, zeros };
+export declare const onesLike: typeof onesLike_;
+export declare const zerosLike: typeof zerosLike_;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops.js b/node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops.js
new file mode 100644
index 0000000..89b8c42
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops.js
@@ -0,0 +1,231 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var tensor_1 = require("../tensor");
+var tensor_util_env_1 = require("../tensor_util_env");
+var util_1 = require("../util");
+var complex_ops_1 = require("./complex_ops");
+var operation_1 = require("./operation");
+function tensor(values, shape, dtype) {
+    if (dtype == null) {
+        dtype = util_1.inferDtype(values);
+    }
+    if (dtype === 'complex64') {
+        throw new Error("Cannot construct a complex64 tensor directly. " +
+            "Please use tf.complex(real, imag).");
+    }
+    if (!util_1.isTypedArray(values) && !Array.isArray(values) &&
+        typeof values !== 'number' && typeof values !== 'boolean' &&
+        typeof values !== 'string') {
+        throw new Error('values passed to tensor(values) must be a number/boolean/string or ' +
+            'an array of numbers/booleans/strings, or a TypedArray');
+    }
+    var inferredShape = tensor_util_env_1.inferShape(values);
+    if (shape != null) {
+        var providedSize_1 = util_1.sizeFromShape(shape);
+        var inferredSize_1 = util_1.sizeFromShape(inferredShape);
+        util_1.assert(providedSize_1 === inferredSize_1, function () {
+            return "Based on the provided shape, [" + shape + "], the tensor should have " +
+                (providedSize_1 + " values but has " + inferredSize_1);
+        });
+        for (var i = 0; i < inferredShape.length; ++i) {
+            var inferred = inferredShape[i];
+            var flatDimsDontMatch = i === inferredShape.length - 1 ?
+                inferred !== util_1.sizeFromShape(shape.slice(i)) :
+                true;
+            util_1.assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, function () { return "Error creating a new Tensor. Inferred shape " +
+                ("(" + inferredShape + ") does not match the provided ") +
+                ("shape (" + shape + "). "); });
+        }
+    }
+    if (!util_1.isTypedArray(values) && !Array.isArray(values)) {
+        values = [values];
+    }
+    shape = shape || inferredShape;
+    values = dtype !== 'string' ? util_1.toTypedArray(values, dtype, environment_1.ENV.get('DEBUG')) :
+        util_1.flatten(values);
+    return tensor_1.Tensor.make(shape, { values: values }, dtype);
+}
+exports.tensor = tensor;
+function scalar(value, dtype) {
+    if ((util_1.isTypedArray(value) || Array.isArray(value)) && dtype !== 'complex64') {
+        throw new Error('Error creating a new Scalar: value must be a primitive ' +
+            '(number|boolean|string)');
+    }
+    return tensor(value, [], dtype);
+}
+exports.scalar = scalar;
+function tensor1d(values, dtype) {
+    util_1.assertNonNull(values);
+    var inferredShape = tensor_util_env_1.inferShape(values);
+    if (inferredShape.length !== 1) {
+        throw new Error('tensor1d() requires values to be a flat/TypedArray');
+    }
+    return tensor(values, inferredShape, dtype);
+}
+exports.tensor1d = tensor1d;
+function tensor2d(values, shape, dtype) {
+    util_1.assertNonNull(values);
+    if (shape != null && shape.length !== 2) {
+        throw new Error('tensor2d() requires shape to have two numbers');
+    }
+    var inferredShape = tensor_util_env_1.inferShape(values);
+    if (inferredShape.length !== 2 && inferredShape.length !== 1) {
+        throw new Error('tensor2d() requires values to be number[][] or flat/TypedArray');
+    }
+    if (inferredShape.length === 1 && shape == null) {
+        throw new Error('tensor2d() requires shape to be provided when `values` ' +
+            'are a flat/TypedArray');
+    }
+    shape = shape || inferredShape;
+    return tensor(values, shape, dtype);
+}
+exports.tensor2d = tensor2d;
+function tensor3d(values, shape, dtype) {
+    util_1.assertNonNull(values);
+    if (shape != null && shape.length !== 3) {
+        throw new Error('tensor3d() requires shape to have three numbers');
+    }
+    var inferredShape = tensor_util_env_1.inferShape(values);
+    if (inferredShape.length !== 3 && inferredShape.length !== 1) {
+        throw new Error('tensor3d() requires values to be number[][][] or flat/TypedArray');
+    }
+    if (inferredShape.length === 1 && shape == null) {
+        throw new Error('tensor3d() requires shape to be provided when `values` ' +
+            'are a flat array');
+    }
+    shape = shape || inferredShape;
+    return tensor(values, shape, dtype);
+}
+exports.tensor3d = tensor3d;
+function tensor4d(values, shape, dtype) {
+    util_1.assertNonNull(values);
+    if (shape != null && shape.length !== 4) {
+        throw new Error('tensor4d() requires shape to have four numbers');
+    }
+    var inferredShape = tensor_util_env_1.inferShape(values);
+    if (inferredShape.length !== 4 && inferredShape.length !== 1) {
+        throw new Error('tensor4d() requires values to be number[][][][] or flat/TypedArray');
+    }
+    if (inferredShape.length === 1 && shape == null) {
+        throw new Error('tensor4d() requires shape to be provided when `values` ' +
+            'are a flat array');
+    }
+    shape = shape || inferredShape;
+    return tensor(values, shape, dtype);
+}
+exports.tensor4d = tensor4d;
+function tensor5d(values, shape, dtype) {
+    util_1.assertNonNull(values);
+    if (shape != null && shape.length !== 5) {
+        throw new Error('tensor5d() requires shape to have five numbers');
+    }
+    var inferredShape = tensor_util_env_1.inferShape(values);
+    if (inferredShape.length !== 5 && inferredShape.length !== 1) {
+        throw new Error('tensor5d() requires values to be ' +
+            'number[][][][][] or flat/TypedArray');
+    }
+    if (inferredShape.length === 1 && shape == null) {
+        throw new Error('tensor5d() requires shape to be provided when `values` ' +
+            'are a flat array');
+    }
+    shape = shape || inferredShape;
+    return tensor(values, shape, dtype);
+}
+exports.tensor5d = tensor5d;
+function tensor6d(values, shape, dtype) {
+    util_1.assertNonNull(values);
+    if (shape != null && shape.length !== 6) {
+        throw new Error('tensor6d() requires shape to have six numbers');
+    }
+    var inferredShape = tensor_util_env_1.inferShape(values);
+    if (inferredShape.length !== 6 && inferredShape.length !== 1) {
+        throw new Error('tensor6d() requires values to be number[][][][] or flat/TypedArray');
+    }
+    if (inferredShape.length === 1 && shape == null) {
+        throw new Error('tensor6d() requires shape to be provided when `values` ' +
+            'are a flat array');
+    }
+    shape = shape ||
+        inferredShape;
+    return tensor(values, shape, dtype);
+}
+exports.tensor6d = tensor6d;
+function ones(shape, dtype) {
+    if (dtype === void 0) { dtype = 'float32'; }
+    if (dtype === 'complex64') {
+        var real = ones(shape, 'float32');
+        var imag = ones(shape, 'float32');
+        return complex_ops_1.complex(real, imag);
+    }
+    var values = util_1.makeOnesTypedArray(util_1.sizeFromShape(shape), dtype);
+    return tensor_1.Tensor.make(shape, { values: values }, dtype);
+}
+exports.ones = ones;
+function zeros(shape, dtype) {
+    if (dtype === void 0) { dtype = 'float32'; }
+    if (dtype === 'complex64') {
+        var real = zeros(shape, 'float32');
+        var imag = zeros(shape, 'float32');
+        return complex_ops_1.complex(real, imag);
+    }
+    var values = util_1.makeZerosTypedArray(util_1.sizeFromShape(shape), dtype);
+    return tensor_1.Tensor.make(shape, { values: values }, dtype);
+}
+exports.zeros = zeros;
+function fill(shape, value, dtype) {
+    dtype = dtype || util_1.inferDtype(value);
+    var values = util_1.getArrayFromDType(dtype, util_1.sizeFromShape(shape));
+    values.fill(value);
+    return tensor_1.Tensor.make(shape, { values: values }, dtype);
+}
+exports.fill = fill;
+function onesLike_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'onesLike');
+    return ones($x.shape, $x.dtype);
+}
+function zerosLike_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'zerosLike');
+    return zeros($x.shape, $x.dtype);
+}
+function linspace(start, stop, num) {
+    if (num === 0) {
+        throw new Error('Cannot request zero samples');
+    }
+    var step = (stop - start) / (num - 1);
+    var values = util_1.makeZerosTypedArray(num, 'float32');
+    values[0] = start;
+    for (var i = 1; i < values.length; i++) {
+        values[i] = values[i - 1] + step;
+    }
+    return tensor1d(values, 'float32');
+}
+exports.linspace = linspace;
+function range(start, stop, step, dtype) {
+    if (step === void 0) { step = 1; }
+    if (dtype === void 0) { dtype = 'float32'; }
+    if (step === 0) {
+        throw new Error('Cannot have a step of zero');
+    }
+    var sameStartStop = start === stop;
+    var increasingRangeNegativeStep = start < stop && step < 0;
+    var decreasingRangePositiveStep = stop < start && step > 1;
+    if (sameStartStop || increasingRangeNegativeStep ||
+        decreasingRangePositiveStep) {
+        return zeros([0], dtype);
+    }
+    var numElements = Math.abs(Math.ceil((stop - start) / step));
+    var values = util_1.makeZerosTypedArray(numElements, dtype);
+    if (stop < start && step === 1) {
+        step = -1;
+    }
+    values[0] = start;
+    for (var i = 1; i < values.length; i++) {
+        values[i] = values[i - 1] + step;
+    }
+    return tensor1d(values, dtype);
+}
+exports.range = range;
+exports.onesLike = operation_1.op({ onesLike_: onesLike_ });
+exports.zerosLike = operation_1.op({ zerosLike_: zerosLike_ });
+//# sourceMappingURL=tensor_ops.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops.js.map
new file mode 100644
index 0000000..5553e62
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/tensor_ops.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/topk.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/topk.d.ts
new file mode 100644
index 0000000..cf1d332
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/topk.d.ts
@@ -0,0 +1,8 @@
+import { Tensor } from '../tensor';
+import { TensorLike } from '../types';
+declare function topk_<T extends Tensor>(x: T | TensorLike, k?: number, sorted?: boolean): {
+    values: T;
+    indices: T;
+};
+export declare const topk: typeof topk_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/topk.js b/node_modules/@tensorflow/tfjs-core/dist/ops/topk.js
new file mode 100644
index 0000000..20f3fde
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/topk.js
@@ -0,0 +1,22 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var tensor_util_env_1 = require("../tensor_util_env");
+var operation_1 = require("./operation");
+function topk_(x, k, sorted) {
+    if (k === void 0) { k = 1; }
+    if (sorted === void 0) { sorted = true; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'topk');
+    if ($x.rank === 0) {
+        throw new Error('topk() expects the input to be of rank 1 or higher');
+    }
+    var lastDim = $x.shape[$x.shape.length - 1];
+    if (k > lastDim) {
+        throw new Error("'k' passed to topk() must be <= the last dimension (" + lastDim + ") " +
+            ("but got " + k));
+    }
+    var _a = environment_1.ENV.engine.runKernel(function (b) { return b.topk($x, k, sorted); }, { $x: $x }), values = _a[0], indices = _a[1];
+    return { values: values, indices: indices };
+}
+exports.topk = operation_1.op({ topk_: topk_ });
+//# sourceMappingURL=topk.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/topk.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/topk.js.map
new file mode 100644
index 0000000..847ee46
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/topk.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"topk.js","sourceRoot":"","sources":["../../src/ops/topk.ts"],"names":[],"mappings":";;AAiBA,8CAAmC;AAEnC,sDAAmD;AAEnD,yCAA+B;AAyB/B,eACI,CAAe,EAAE,CAAK,EAAE,MAAa;IAApB,kBAAA,EAAA,KAAK;IAAE,uBAAA,EAAA,aAAa;IACvC,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,MAAM,CAAC,CAAC;IAC3C,IAAI,EAAE,CAAC,IAAI,KAAK,CAAC,EAAE;QACjB,MAAM,IAAI,KAAK,CAAC,oDAAoD,CAAC,CAAC;KACvE;IACD,IAAM,OAAO,GAAG,EAAE,CAAC,KAAK,CAAC,EAAE,CAAC,KAAK,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC;IAC9C,IAAI,CAAC,GAAG,OAAO,EAAE;QACf,MAAM,IAAI,KAAK,CACX,yDAAuD,OAAO,OAAI;aAClE,aAAW,CAAG,CAAA,CAAC,CAAC;KACrB;IAEK,IAAA,mGACqE,EADpE,cAAM,EAAE,eAAO,CACsD;IAC5E,OAAO,EAAC,MAAM,QAAA,EAAE,OAAO,SAAA,EAA4B,CAAC;AACtD,CAAC;AAEY,QAAA,IAAI,GAAG,cAAE,CAAC,EAAC,KAAK,OAAA,EAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/topk_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/topk_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/topk_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/topk_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/topk_test.js
new file mode 100644
index 0000000..3f6614f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/topk_test.js
@@ -0,0 +1,127 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+var tensor_ops_1 = require("./tensor_ops");
+jasmine_util_1.describeWithFlags('topk', test_util_1.ALL_ENVS, function () {
+    it('1d array with default k', function () {
+        var a = tensor_ops_1.tensor1d([20, 10, 40, 30]);
+        var _a = tf.topk(a), values = _a.values, indices = _a.indices;
+        expect(values.shape).toEqual([1]);
+        expect(indices.shape).toEqual([1]);
+        expect(values.dtype).toBe('float32');
+        expect(indices.dtype).toBe('int32');
+        test_util_1.expectArraysClose(values, [40]);
+        test_util_1.expectArraysClose(indices, [2]);
+    });
+    it('1d array with default k from tensor.topk', function () {
+        var a = tensor_ops_1.tensor1d([20, 10, 40, 30]);
+        var _a = a.topk(), values = _a.values, indices = _a.indices;
+        expect(values.shape).toEqual([1]);
+        expect(indices.shape).toEqual([1]);
+        expect(values.dtype).toBe('float32');
+        expect(indices.dtype).toBe('int32');
+        test_util_1.expectArraysClose(values, [40]);
+        test_util_1.expectArraysClose(indices, [2]);
+    });
+    it('2d array with default k', function () {
+        var a = tensor_ops_1.tensor2d([[10, 50], [40, 30]]);
+        var _a = tf.topk(a), values = _a.values, indices = _a.indices;
+        expect(values.shape).toEqual([2, 1]);
+        expect(indices.shape).toEqual([2, 1]);
+        expect(values.dtype).toBe('float32');
+        expect(indices.dtype).toBe('int32');
+        test_util_1.expectArraysClose(values, [50, 40]);
+        test_util_1.expectArraysClose(indices, [1, 0]);
+    });
+    it('2d array with k=2', function () {
+        var a = tensor_ops_1.tensor2d([
+            [1, 5, 2],
+            [4, 3, 6],
+            [3, 2, 1],
+            [1, 2, 3],
+        ]);
+        var k = 2;
+        var _a = tf.topk(a, k), values = _a.values, indices = _a.indices;
+        expect(values.shape).toEqual([4, 2]);
+        expect(indices.shape).toEqual([4, 2]);
+        expect(values.dtype).toBe('float32');
+        expect(indices.dtype).toBe('int32');
+        test_util_1.expectArraysClose(values, [5, 2, 6, 4, 3, 2, 3, 2]);
+        test_util_1.expectArraysClose(indices, [1, 2, 2, 0, 0, 1, 2, 1]);
+    });
+    it('2d array with k=2 from tensor.topk', function () {
+        var a = tensor_ops_1.tensor2d([
+            [1, 5, 2],
+            [4, 3, 6],
+            [3, 2, 1],
+            [1, 2, 3],
+        ]);
+        var k = 2;
+        var _a = a.topk(k), values = _a.values, indices = _a.indices;
+        expect(values.shape).toEqual([4, 2]);
+        expect(indices.shape).toEqual([4, 2]);
+        expect(values.dtype).toBe('float32');
+        expect(indices.dtype).toBe('int32');
+        test_util_1.expectArraysClose(values, [5, 2, 6, 4, 3, 2, 3, 2]);
+        test_util_1.expectArraysClose(indices, [1, 2, 2, 0, 0, 1, 2, 1]);
+    });
+    it('3d array with k=3', function () {
+        var a = tensor_ops_1.tensor3d([
+            [[1, 5, 2], [4, 3, 6]],
+            [[3, 2, 1], [1, 2, 3]],
+        ]);
+        var k = 3;
+        var _a = tf.topk(a, k), values = _a.values, indices = _a.indices;
+        expect(values.shape).toEqual([2, 2, 3]);
+        expect(indices.shape).toEqual([2, 2, 3]);
+        expect(values.dtype).toBe('float32');
+        expect(indices.dtype).toBe('int32');
+        test_util_1.expectArraysClose(values, [5, 2, 1, 6, 4, 3, 3, 2, 1, 3, 2, 1]);
+        test_util_1.expectArraysClose(indices, [1, 2, 0, 2, 0, 1, 0, 1, 2, 2, 1, 0]);
+    });
+    it('topk(int32) propagates int32 dtype', function () {
+        var a = tensor_ops_1.tensor1d([2, 3, 1, 4], 'int32');
+        var _a = tf.topk(a), values = _a.values, indices = _a.indices;
+        expect(values.shape).toEqual([1]);
+        expect(indices.shape).toEqual([1]);
+        expect(values.dtype).toBe('int32');
+        expect(indices.dtype).toBe('int32');
+        test_util_1.expectArraysClose(values, [4]);
+        test_util_1.expectArraysClose(indices, [3]);
+    });
+    it('lower-index element appears first, k=4', function () {
+        var a = tensor_ops_1.tensor1d([1, 2, 2, 1], 'int32');
+        var k = 4;
+        var _a = tf.topk(a, k), values = _a.values, indices = _a.indices;
+        expect(values.shape).toEqual([4]);
+        expect(indices.shape).toEqual([4]);
+        expect(values.dtype).toBe('int32');
+        expect(indices.dtype).toBe('int32');
+        test_util_1.expectArraysClose(values, [2, 2, 1, 1]);
+        test_util_1.expectArraysClose(indices, [1, 2, 0, 3]);
+    });
+    it('throws when k > size of array', function () {
+        var a = tensor_ops_1.tensor2d([[10, 50], [40, 30]]);
+        expect(function () { return tf.topk(a, 3); })
+            .toThrowError(/'k' passed to topk\(\) must be <= the last dimension/);
+    });
+    it('throws when passed a scalar', function () {
+        var a = tensor_ops_1.scalar(2);
+        expect(function () { return tf.topk(a); })
+            .toThrowError(/topk\(\) expects the input to be of rank 1 or higher/);
+    });
+    it('accepts a tensor-like object, k=2', function () {
+        var a = [20, 10, 40, 30];
+        var k = 2;
+        var _a = tf.topk(a, k), values = _a.values, indices = _a.indices;
+        expect(values.shape).toEqual([2]);
+        expect(indices.shape).toEqual([2]);
+        expect(values.dtype).toBe('float32');
+        expect(indices.dtype).toBe('int32');
+        test_util_1.expectArraysClose(values, [40, 30]);
+        test_util_1.expectArraysClose(indices, [2, 3]);
+    });
+});
+//# sourceMappingURL=topk_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/topk_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/topk_test.js.map
new file mode 100644
index 0000000..10cb8ca
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/topk_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/transpose.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/transpose.d.ts
new file mode 100644
index 0000000..7a1824e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/transpose.d.ts
@@ -0,0 +1,5 @@
+import { Tensor } from '../tensor';
+import { TensorLike } from '../types';
+declare function transpose_<T extends Tensor>(x: T | TensorLike, perm?: number[]): T;
+export declare const transpose: typeof transpose_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/transpose.js b/node_modules/@tensorflow/tfjs-core/dist/ops/transpose.js
new file mode 100644
index 0000000..09d49f6
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/transpose.js
@@ -0,0 +1,29 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var tensor_util_env_1 = require("../tensor_util_env");
+var util = require("../util");
+var axis_util = require("./axis_util");
+var operation_1 = require("./operation");
+function transpose_(x, perm) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'transpose');
+    if (perm == null) {
+        perm = $x.shape.map(function (s, i) { return i; }).reverse();
+    }
+    util.assert($x.rank === perm.length, "Error in transpose: rank of input " + $x.rank + " " +
+        ("must match length of perm " + perm + "."));
+    perm.forEach(function (axis) {
+        util.assert(axis >= 0 && axis < $x.rank, "All entries in 'perm' must be between 0 and " + ($x.rank - 1) +
+            (" but got " + perm));
+    });
+    if ($x.rank <= 1) {
+        return $x.clone();
+    }
+    var der = function (dy) {
+        var undoPerm = axis_util.getUndoAxesPermutation(perm);
+        return { $x: function () { return dy.transpose(undoPerm); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.transpose($x, perm); }, { $x: $x }, der);
+}
+exports.transpose = operation_1.op({ transpose_: transpose_ });
+//# sourceMappingURL=transpose.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/transpose.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/transpose.js.map
new file mode 100644
index 0000000..4bbc5f9
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/transpose.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"transpose.js","sourceRoot":"","sources":["../../src/ops/transpose.ts"],"names":[],"mappings":";;AAiBA,8CAAmC;AAEnC,sDAAmD;AAEnD,8BAAgC;AAChC,uCAAyC;AACzC,yCAA+B;AAoB/B,oBAAsC,CAAe,EAAE,IAAe;IACpE,IAAM,EAAE,GAAG,iCAAe,CAAC,CAAC,EAAE,GAAG,EAAE,WAAW,CAAC,CAAC;IAEhD,IAAI,IAAI,IAAI,IAAI,EAAE;QAChB,IAAI,GAAG,EAAE,CAAC,KAAK,CAAC,GAAG,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,CAAC,EAAD,CAAC,CAAC,CAAC,OAAO,EAAE,CAAC;KAC5C;IACD,IAAI,CAAC,MAAM,CACP,EAAE,CAAC,IAAI,KAAK,IAAI,CAAC,MAAM,EACvB,uCAAqC,EAAE,CAAC,IAAI,MAAG;SAC3C,+BAA6B,IAAI,MAAG,CAAA,CAAC,CAAC;IAC9C,IAAI,CAAC,OAAO,CAAC,UAAA,IAAI;QACf,IAAI,CAAC,MAAM,CACP,IAAI,IAAI,CAAC,IAAI,IAAI,GAAG,EAAE,CAAC,IAAI,EAC3B,kDAA+C,EAAE,CAAC,IAAI,GAAG,CAAC,CAAE;aACxD,cAAY,IAAM,CAAA,CAAC,CAAC;IAC9B,CAAC,CAAC,CAAC;IAEH,IAAI,EAAE,CAAC,IAAI,IAAI,CAAC,EAAE;QAChB,OAAO,EAAE,CAAC,KAAK,EAAE,CAAC;KACnB;IAED,IAAM,GAAG,GAAG,UAAC,EAAK;QAChB,IAAM,QAAQ,GAAG,SAAS,CAAC,sBAAsB,CAAC,IAAI,CAAC,CAAC;QACxD,OAAO,EAAC,EAAE,EAAE,cAAM,OAAA,EAAE,CAAC,SAAS,CAAC,QAAQ,CAAC,EAAtB,CAAsB,EAAC,CAAC;IAC5C,CAAC,CAAC;IACF,OAAO,iBAAG,CAAC,MAAM,CAAC,SAAS,CACvB,UAAA,OAAO,IAAI,OAAA,OAAO,CAAC,SAAS,CAAC,EAAE,EAAE,IAAI,CAAC,EAA3B,CAA2B,EAAE,EAAC,EAAE,IAAA,EAAC,EAAE,GAAG,CAAC,CAAC;AACzD,CAAC;AAEY,QAAA,SAAS,GAAG,cAAE,CAAC,EAAC,UAAU,YAAA,EAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/transpose_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/transpose_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/transpose_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/transpose_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/transpose_test.js
new file mode 100644
index 0000000..e7f8eba
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/transpose_test.js
@@ -0,0 +1,114 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('transpose', test_util_1.ALL_ENVS, function () {
+    it('of scalar is no-op', function () {
+        var a = tf.scalar(3);
+        test_util_1.expectArraysClose(tf.transpose(a), [3]);
+    });
+    it('of 1D is no-op', function () {
+        var a = tf.tensor1d([1, 2, 3]);
+        test_util_1.expectArraysClose(tf.transpose(a), [1, 2, 3]);
+    });
+    it('of scalar with perm of incorrect rank throws error', function () {
+        var a = tf.scalar(3);
+        var perm = [0];
+        expect(function () { return tf.transpose(a, perm); }).toThrowError();
+    });
+    it('of 1d with perm out of bounds throws error', function () {
+        var a = tf.tensor1d([1, 2, 3]);
+        var perm = [1];
+        expect(function () { return tf.transpose(a, perm); }).toThrowError();
+    });
+    it('of 1d with perm incorrect rank throws error', function () {
+        var a = tf.tensor1d([1, 2, 3]);
+        var perm = [0, 0];
+        expect(function () { return tf.transpose(a, perm); }).toThrowError();
+    });
+    it('2D (no change)', function () {
+        var t = tf.tensor2d([1, 11, 2, 22, 3, 33, 4, 44], [2, 4]);
+        var t2 = tf.transpose(t, [0, 1]);
+        expect(t2.shape).toEqual(t.shape);
+        test_util_1.expectArraysClose(t2, t);
+    });
+    it('2D (transpose)', function () {
+        var t = tf.tensor2d([1, 11, 2, 22, 3, 33, 4, 44], [2, 4]);
+        var t2 = tf.transpose(t, [1, 0]);
+        expect(t2.shape).toEqual([4, 2]);
+        test_util_1.expectArraysClose(t2, [1, 3, 11, 33, 2, 4, 22, 44]);
+    });
+    it('3D [r, c, d] => [d, r, c]', function () {
+        var t = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [2, 2, 2]);
+        var t2 = tf.transpose(t, [2, 0, 1]);
+        expect(t2.shape).toEqual([2, 2, 2]);
+        test_util_1.expectArraysClose(t2, [1, 2, 3, 4, 11, 22, 33, 44]);
+    });
+    it('3D [r, c, d] => [d, c, r]', function () {
+        var t = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [2, 2, 2]);
+        var t2 = tf.transpose(t, [2, 1, 0]);
+        expect(t2.shape).toEqual([2, 2, 2]);
+        test_util_1.expectArraysClose(t2, [1, 3, 2, 4, 11, 33, 22, 44]);
+    });
+    it('5D [r, c, d, e, f] => [r, c, d, f, e]', function () {
+        var t = tf.tensor5d(new Array(32).fill(0).map(function (x, i) { return i + 1; }), [2, 2, 2, 2, 2]);
+        var t2 = tf.transpose(t, [0, 1, 2, 4, 3]);
+        expect(t2.shape).toEqual([2, 2, 2, 2, 2]);
+        test_util_1.expectArraysClose(t2, [
+            1, 3, 2, 4, 5, 7, 6, 8, 9, 11, 10, 12, 13, 15, 14, 16,
+            17, 19, 18, 20, 21, 23, 22, 24, 25, 27, 26, 28, 29, 31, 30, 32
+        ]);
+    });
+    it('5D [r, c, d, e, f] => [c, r, d, e, f]', function () {
+        var t = tf.tensor5d(new Array(32).fill(0).map(function (x, i) { return i + 1; }), [2, 2, 2, 2, 2]);
+        var t2 = tf.transpose(t, [1, 0, 2, 3, 4]);
+        expect(t2.shape).toEqual([2, 2, 2, 2, 2]);
+        test_util_1.expectArraysClose(t2, [
+            1, 2, 3, 4, 5, 6, 7, 8, 17, 18, 19, 20, 21, 22, 23, 24,
+            9, 10, 11, 12, 13, 14, 15, 16, 25, 26, 27, 28, 29, 30, 31, 32
+        ]);
+    });
+    it('6D [r, c, d, e, f] => [r, c, d, f, e]', function () {
+        var t = tf.tensor6d(new Array(64).fill(0).map(function (x, i) { return i + 1; }), [2, 2, 2, 2, 2, 2]);
+        var t2 = tf.transpose(t, [0, 1, 2, 3, 5, 4]);
+        expect(t2.shape).toEqual([2, 2, 2, 2, 2, 2]);
+        test_util_1.expectArraysClose(t2, [
+            1, 3, 2, 4, 5, 7, 6, 8, 9, 11, 10, 12, 13, 15, 14, 16,
+            17, 19, 18, 20, 21, 23, 22, 24, 25, 27, 26, 28, 29, 31, 30, 32,
+            33, 35, 34, 36, 37, 39, 38, 40, 41, 43, 42, 44, 45, 47, 46, 48,
+            49, 51, 50, 52, 53, 55, 54, 56, 57, 59, 58, 60, 61, 63, 62, 64
+        ]);
+    });
+    it('6D [r, c, d, e, f, g] => [c, r, d, e, f, g]', function () {
+        var t = tf.tensor6d(new Array(64).fill(0).map(function (x, i) { return i + 1; }), [2, 2, 2, 2, 2, 2]);
+        var t2 = tf.transpose(t, [1, 0, 2, 3, 4, 5]);
+        expect(t2.shape).toEqual([2, 2, 2, 2, 2, 2]);
+        test_util_1.expectArraysClose(t2, [
+            1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
+            33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48,
+            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32,
+            49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64
+        ]);
+    });
+    it('gradient 3D [r, c, d] => [d, c, r]', function () {
+        var t = tf.tensor3d([1, 11, 2, 22, 3, 33, 4, 44], [2, 2, 2]);
+        var perm = [2, 1, 0];
+        var dy = tf.tensor3d([111, 211, 121, 221, 112, 212, 122, 222], [2, 2, 2]);
+        var dt = tf.grad(function (t) { return t.transpose(perm); })(t, dy);
+        expect(dt.shape).toEqual(t.shape);
+        expect(dt.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(dt, [111, 112, 121, 122, 211, 212, 221, 222]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.transpose({}); })
+            .toThrowError(/Argument 'x' passed to 'transpose' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var t = [[1, 11, 2, 22], [3, 33, 4, 44]];
+        var res = tf.transpose(t, [1, 0]);
+        expect(res.shape).toEqual([4, 2]);
+        test_util_1.expectArraysClose(res, [1, 3, 11, 33, 2, 4, 22, 44]);
+    });
+});
+//# sourceMappingURL=transpose_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/transpose_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/transpose_test.js.map
new file mode 100644
index 0000000..5aef6ac
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/transpose_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/unary_ops.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/unary_ops.d.ts
new file mode 100644
index 0000000..5a839a1
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/unary_ops.d.ts
@@ -0,0 +1,67 @@
+import { Tensor } from '../tensor';
+import { TensorLike } from '../types';
+declare function neg_<T extends Tensor>(x: T | TensorLike): T;
+declare function ceil_<T extends Tensor>(x: T | TensorLike): T;
+declare function floor_<T extends Tensor>(x: T | TensorLike): T;
+declare function sign_<T extends Tensor>(x: T | TensorLike): T;
+declare function round_<T extends Tensor>(x: T | TensorLike): T;
+declare function exp_<T extends Tensor>(x: T | TensorLike): T;
+declare function expm1_<T extends Tensor>(x: T | TensorLike): T;
+declare function log_<T extends Tensor>(x: T | TensorLike): T;
+declare function log1p_<T extends Tensor>(x: T | TensorLike): T;
+declare function sqrt_<T extends Tensor>(x: T | TensorLike): T;
+declare function rsqrt_<T extends Tensor>(x: T | TensorLike): T;
+declare function square_<T extends Tensor>(x: T | TensorLike): T;
+declare function reciprocal_<T extends Tensor>(x: T | TensorLike): T;
+declare function abs_<T extends Tensor>(x: T | TensorLike): T;
+declare function clipByValue_<T extends Tensor>(x: T | TensorLike, clipValueMin: number, clipValueMax: number): T;
+declare function sigmoid_<T extends Tensor>(x: T | TensorLike): T;
+declare function logSigmoid_<T extends Tensor>(x: T | TensorLike): T;
+declare function softplus_<T extends Tensor>(x: T | TensorLike): T;
+declare function sin_<T extends Tensor>(x: T | TensorLike): T;
+declare function cos_<T extends Tensor>(x: T | TensorLike): T;
+declare function tan_<T extends Tensor>(x: T | TensorLike): T;
+declare function asin_<T extends Tensor>(x: T | TensorLike): T;
+declare function acos_<T extends Tensor>(x: T | TensorLike): T;
+declare function atan_<T extends Tensor>(x: T | TensorLike): T;
+declare function sinh_<T extends Tensor>(x: T | TensorLike): T;
+declare function cosh_<T extends Tensor>(x: T | TensorLike): T;
+declare function tanh_<T extends Tensor>(x: T | TensorLike): T;
+declare function asinh_<T extends Tensor>(x: T | TensorLike): T;
+declare function acosh_<T extends Tensor>(x: T | TensorLike): T;
+declare function atanh_<T extends Tensor>(x: T | TensorLike): T;
+declare function erf_<T extends Tensor>(x: T | TensorLike): T;
+declare function step_<T extends Tensor>(x: T | TensorLike, alpha?: number): T;
+export declare const abs: typeof abs_;
+export declare const acos: typeof acos_;
+export declare const acosh: typeof acosh_;
+export declare const asin: typeof asin_;
+export declare const asinh: typeof asinh_;
+export declare const atan: typeof atan_;
+export declare const atanh: typeof atanh_;
+export declare const ceil: typeof ceil_;
+export declare const clipByValue: typeof clipByValue_;
+export declare const cos: typeof cos_;
+export declare const cosh: typeof cosh_;
+export declare const erf: typeof erf_;
+export declare const exp: typeof exp_;
+export declare const expm1: typeof expm1_;
+export declare const floor: typeof floor_;
+export declare const log: typeof log_;
+export declare const log1p: typeof log1p_;
+export declare const logSigmoid: typeof logSigmoid_;
+export declare const neg: typeof neg_;
+export declare const reciprocal: typeof reciprocal_;
+export declare const round: typeof round_;
+export declare const rsqrt: typeof rsqrt_;
+export declare const sigmoid: typeof sigmoid_;
+export declare const sign: typeof sign_;
+export declare const sin: typeof sin_;
+export declare const sinh: typeof sinh_;
+export declare const softplus: typeof softplus_;
+export declare const sqrt: typeof sqrt_;
+export declare const square: typeof square_;
+export declare const step: typeof step_;
+export declare const tan: typeof tan_;
+export declare const tanh: typeof tanh_;
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/unary_ops.js b/node_modules/@tensorflow/tfjs-core/dist/ops/unary_ops.js
new file mode 100644
index 0000000..626b8c8
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/unary_ops.js
@@ -0,0 +1,290 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var tensor_util_env_1 = require("../tensor_util_env");
+var util = require("../util");
+var operation_1 = require("./operation");
+var tensor_ops_1 = require("./tensor_ops");
+function neg_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'neg');
+    var grad = function (dy) {
+        return { $x: function () { return dy.neg(); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.neg($x); }, { $x: $x }, grad);
+}
+function ceil_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'ceil');
+    var grad = function (dy) {
+        return { $x: function () { return tensor_ops_1.zerosLike(dy); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.ceil($x); }, { $x: $x }, grad);
+}
+function floor_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'floor');
+    var grad = function (dy) {
+        return { $x: function () { return tensor_ops_1.zerosLike(dy); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.floor($x); }, { $x: $x }, grad);
+}
+function sign_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'sign');
+    var grad = function (dy) {
+        return { $x: function () { return tensor_ops_1.zerosLike(dy); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.sign($x); }, { $x: $x }, grad);
+}
+function round_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'round');
+    var grad = function (dy) {
+        return { $x: function () { return tensor_ops_1.zerosLike(dy); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.round($x); }, { $x: $x }, grad);
+}
+function exp_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'exp');
+    var bck = function (dy, saved) {
+        var y = saved[0];
+        return { $x: function () { return dy.mulStrict(y); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend, save) { return save(backend.exp($x)); }, { $x: $x }, bck);
+}
+function expm1_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'expm1');
+    var grad = function (dy) {
+        return { $x: function () { return dy.mulStrict($x.exp()); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.expm1($x); }, { $x: $x }, grad);
+}
+function log_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'log');
+    var grad = function (dy) {
+        return { $x: function () { return dy.divStrict($x.toFloat()); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.log($x); }, { $x: $x }, grad);
+}
+function log1p_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'log1p');
+    var grad = function (dy) {
+        return { $x: function () { return dy.div($x.add(1)); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.log1p($x); }, { $x: $x }, grad);
+}
+function sqrt_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'sqrt');
+    var grad = function (dy) {
+        return { $x: function () { return dy.div($x.toFloat().sqrt().mul(2)); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.sqrt($x); }, { $x: $x }, grad);
+}
+function rsqrt_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'rsqrt');
+    var grad = function (dy) {
+        return { $x: function () { return dy.div($x.pow(1.5).mul(2)).neg(); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.rsqrt($x); }, { $x: $x }, grad);
+}
+function square_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'square');
+    var grad = function (dy) {
+        return { $x: function () { return dy.mul($x.toFloat().mul(2)); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.square($x); }, { $x: $x }, grad);
+}
+function reciprocal_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'reciprocal');
+    var grad = function (dy) {
+        return { $x: function () { return dy.divStrict($x.square().neg()); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.reciprocal($x); }, { $x: $x }, grad);
+}
+function abs_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'abs');
+    if ($x.dtype === 'complex64') {
+        return environment_1.ENV.engine.runKernel(function (backend) { return backend.complexAbs($x); }, { $x: $x });
+    }
+    var grad = function (dy) {
+        return { $x: function () { return dy.mulStrict($x.toFloat().step(-1)); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.abs($x); }, { $x: $x }, grad);
+}
+function clipByValue_(x, clipValueMin, clipValueMax) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'clipByValue');
+    util.assert((clipValueMin <= clipValueMax), "Error in clip: min (" + clipValueMin + ") must be " +
+        ("less than or equal to max (" + clipValueMax + ")."));
+    var grad = function (dy) {
+        return {
+            $x: function () { return dy.where($x.greaterEqual(clipValueMin)
+                .logicalAnd($x.lessEqual(clipValueMax)), tensor_ops_1.zerosLike(dy)); },
+        };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.clip($x, clipValueMin, clipValueMax); }, { $x: $x }, grad);
+}
+function sigmoid_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'sigmoid');
+    var grad = function (dy, saved) {
+        var y = saved[0];
+        return { $x: function () { return dy.mul(y.mul(tensor_ops_1.scalar(1).sub(y))); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend, save) { return save(backend.sigmoid($x)); }, { $x: $x }, grad);
+}
+function logSigmoid_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'logSigmoid');
+    var grad = function (dy) {
+        return { $x: function () { return dy.mulStrict($x.neg().sigmoid()); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.softplus($x.neg()).neg(); }, { $x: $x }, grad);
+}
+function softplus_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'softplus');
+    var grad = function (dy) {
+        return { $x: function () { return dy.mulStrict($x.sigmoid()); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.softplus($x); }, { $x: $x }, grad);
+}
+function sin_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'sin');
+    var grad = function (dy) {
+        return { $x: function () { return $x.toFloat().cos().mulStrict(dy); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.sin($x); }, { $x: $x }, grad);
+}
+function cos_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'cos');
+    var grad = function (dy) {
+        return { $x: function () { return $x.toFloat().sin().neg().mulStrict(dy); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.cos($x); }, { $x: $x }, grad);
+}
+function tan_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'tan');
+    var grad = function (dy) {
+        return { $x: function () { return dy.divStrict($x.cos().square()); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.tan($x); }, { $x: $x }, grad);
+}
+function asin_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'asin');
+    var grad = function (dy) {
+        return {
+            $x: function () { return dy.divStrict(tensor_ops_1.scalar(1).sub($x.toFloat().square()).sqrt()); }
+        };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.asin($x); }, { $x: $x }, grad);
+}
+function acos_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'acos');
+    var grad = function (dy) {
+        return {
+            $x: function () {
+                return dy.divStrict(tensor_ops_1.scalar(1).sub($x.toFloat().square()).sqrt()).neg();
+            }
+        };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.acos($x); }, { $x: $x }, grad);
+}
+function atan_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'atan');
+    var grad = function (dy) {
+        return { $x: function () { return dy.div($x.toFloat().square().add(1)); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.atan($x); }, { $x: $x }, grad);
+}
+function sinh_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'sinh');
+    var grad = function (dy) {
+        return { $x: function () { return $x.toFloat().cosh().mulStrict(dy); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.sinh($x); }, { $x: $x }, grad);
+}
+function cosh_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'cosh');
+    var grad = function (dy) {
+        return { $x: function () { return $x.toFloat().sinh().mulStrict(dy); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.cosh($x); }, { $x: $x }, grad);
+}
+function tanh_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'tanh');
+    var grad = function (dy, saved) {
+        var y = saved[0];
+        return { $x: function () { return tensor_ops_1.scalar(1).sub(y.square()).mulStrict(dy); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend, save) { return save(backend.tanh($x)); }, { $x: $x }, grad);
+}
+function asinh_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'asinh');
+    var grad = function (dy) {
+        return {
+            $x: function () { return dy.divStrict(tensor_ops_1.scalar(1).add($x.toFloat().square()).sqrt()); }
+        };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.asinh($x); }, { $x: $x }, grad);
+}
+function acosh_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'acosh');
+    var grad = function (dy) {
+        return { $x: function () { return dy.divStrict($x.toFloat().square().sub(1).sqrt()); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.acosh($x); }, { $x: $x }, grad);
+}
+function atanh_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'atanh');
+    var grad = function (dy) {
+        return { $x: function () { return dy.div(tensor_ops_1.scalar(1).sub($x.toFloat().square())); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.atanh($x); }, { $x: $x }, grad);
+}
+function erf_(x) {
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'erf');
+    util.assert($x.dtype === 'int32' || $x.dtype === 'float32', 'Input dtype must be `int32` or `float32`.');
+    if ($x.dtype === 'int32') {
+        $x = $x.toFloat();
+    }
+    var grad = function (dy) {
+        return {
+            $x: function () { return dy.mul($x.square().neg().exp().mul(2 / Math.sqrt(Math.PI))); }
+        };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.erf($x); }, { $x: $x }, grad);
+}
+function step_(x, alpha) {
+    if (alpha === void 0) { alpha = 0.0; }
+    var $x = tensor_util_env_1.convertToTensor(x, 'x', 'step');
+    var grad = function (dy) {
+        return { $x: function () { return tensor_ops_1.zerosLike(dy); } };
+    };
+    return environment_1.ENV.engine.runKernel(function (backend) { return backend.step($x, alpha); }, { $x: $x }, grad);
+}
+exports.abs = operation_1.op({ abs_: abs_ });
+exports.acos = operation_1.op({ acos_: acos_ });
+exports.acosh = operation_1.op({ acosh_: acosh_ });
+exports.asin = operation_1.op({ asin_: asin_ });
+exports.asinh = operation_1.op({ asinh_: asinh_ });
+exports.atan = operation_1.op({ atan_: atan_ });
+exports.atanh = operation_1.op({ atanh_: atanh_ });
+exports.ceil = operation_1.op({ ceil_: ceil_ });
+exports.clipByValue = operation_1.op({ clipByValue_: clipByValue_ });
+exports.cos = operation_1.op({ cos_: cos_ });
+exports.cosh = operation_1.op({ cosh_: cosh_ });
+exports.erf = operation_1.op({ erf_: erf_ });
+exports.exp = operation_1.op({ exp_: exp_ });
+exports.expm1 = operation_1.op({ expm1_: expm1_ });
+exports.floor = operation_1.op({ floor_: floor_ });
+exports.log = operation_1.op({ log_: log_ });
+exports.log1p = operation_1.op({ log1p_: log1p_ });
+exports.logSigmoid = operation_1.op({ logSigmoid_: logSigmoid_ });
+exports.neg = operation_1.op({ neg_: neg_ });
+exports.reciprocal = operation_1.op({ reciprocal_: reciprocal_ });
+exports.round = operation_1.op({ round_: round_ });
+exports.rsqrt = operation_1.op({ rsqrt_: rsqrt_ });
+exports.sigmoid = operation_1.op({ sigmoid_: sigmoid_ });
+exports.sign = operation_1.op({ sign_: sign_ });
+exports.sin = operation_1.op({ sin_: sin_ });
+exports.sinh = operation_1.op({ sinh_: sinh_ });
+exports.softplus = operation_1.op({ softplus_: softplus_ });
+exports.sqrt = operation_1.op({ sqrt_: sqrt_ });
+exports.square = operation_1.op({ square_: square_ });
+exports.step = operation_1.op({ step_: step_ });
+exports.tan = operation_1.op({ tan_: tan_ });
+exports.tanh = operation_1.op({ tanh_: tanh_ });
+//# sourceMappingURL=unary_ops.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/unary_ops.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/unary_ops.js.map
new file mode 100644
index 0000000..165b830
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/unary_ops.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/unary_ops_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/ops/unary_ops_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/unary_ops_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/unary_ops_test.js b/node_modules/@tensorflow/tfjs-core/dist/ops/unary_ops_test.js
new file mode 100644
index 0000000..b693412
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/unary_ops_test.js
@@ -0,0 +1,2489 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+var util = require("../util");
+var selu_util = require("./selu_util");
+jasmine_util_1.describeWithFlags('relu', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var a = tf.tensor1d([1, -2, 0, 3, -0.1]);
+        var result = tf.relu(a);
+        test_util_1.expectArraysClose(result, [1, 0, 0, 3, 0]);
+    });
+    it('5D', function () {
+        var a = tf.tensor5d([1, -2, 5, -3], [1, 2, 2, 1, 1]);
+        var result = tf.relu(a);
+        test_util_1.expectArraysClose(result, [1, 0, 5, 0]);
+    });
+    it('6D', function () {
+        var a = tf.tensor6d([1, -2, 5, -3, -1, 4, 7, 8], [1, 2, 2, 2, 1, 1]);
+        var result = tf.relu(a);
+        test_util_1.expectArraysClose(result, [1, 0, 5, 0, 0, 4, 7, 8]);
+    });
+    it('does nothing to positive values', function () {
+        var a = tf.scalar(1);
+        var result = tf.relu(a);
+        test_util_1.expectNumbersClose(result.get(), 1);
+    });
+    it('sets negative values to 0', function () {
+        var a = tf.scalar(-1);
+        var result = tf.relu(a);
+        test_util_1.expectNumbersClose(result.get(), 0);
+    });
+    it('preserves zero values', function () {
+        var a = tf.scalar(0);
+        var result = tf.relu(a);
+        test_util_1.expectNumbersClose(result.get(), 0);
+    });
+    it('propagates NaNs, float32', function () {
+        var a = tf.tensor1d([1, -2, 0, 3, -0.1, NaN]);
+        var result = tf.relu(a);
+        expect(result.dtype).toBe('float32');
+        test_util_1.expectArraysClose(result, [1, 0, 0, 3, 0, NaN]);
+    });
+    it('gradients: positive scalar', function () {
+        var a = tf.scalar(3);
+        var dy = tf.scalar(5);
+        var grad = tf.grad(function (a) { return tf.relu(a); });
+        var da = grad(a, dy);
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [5]);
+    });
+    it('gradients: negative scalar', function () {
+        var a = tf.scalar(-3);
+        var dy = tf.scalar(5);
+        var grad = tf.grad(function (a) { return tf.relu(a); });
+        var da = grad(a, dy);
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [0]);
+    });
+    it('gradients: array', function () {
+        var a = tf.tensor2d([1, -1, 0, .1], [2, 2]);
+        var dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var grad = tf.grad(function (a) { return tf.relu(a); });
+        var da = grad(a, dy);
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [1, 0, 0, 4]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.relu({}); })
+            .toThrowError(/Argument 'x' passed to 'relu' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var result = tf.relu([1, -2, 0, 3, -0.1]);
+        test_util_1.expectArraysClose(result, [1, 0, 0, 3, 0]);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.relu('q'); })
+            .toThrowError(/Argument 'x' passed to 'relu' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('abs', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var a = tf.tensor1d([1, -2, 0, 3, -0.1]);
+        var result = tf.abs(a);
+        test_util_1.expectArraysClose(result, [1, 2, 0, 3, 0.1]);
+    });
+    it('5D', function () {
+        var a = tf.tensor5d([1, -2, 0, -3], [1, 2, 2, 1, 1]);
+        var result = tf.abs(a);
+        test_util_1.expectArraysClose(result, [1, 2, 0, 3]);
+    });
+    it('6D', function () {
+        var a = tf.tensor6d([1, -2, 5, -3, -1, 4, 7, 8], [1, 2, 2, 2, 1, 1]);
+        var result = tf.abs(a);
+        test_util_1.expectArraysClose(result, [1, 2, 5, 3, 1, 4, 7, 8]);
+    });
+    it('complex64 rank-1', function () {
+        var a = tf.complex([-2, -1, 0, 1, 2], [1, 2, 3, 0, -1]);
+        var result = tf.abs(a);
+        test_util_1.expectArraysClose(result, [
+            Math.sqrt(-2 * -2 + 1 * 1), Math.sqrt(-1 * -1 + 2 * 2),
+            Math.sqrt(0 * 0 + 3 * 3), Math.sqrt(1 * 1 + 0 * 0),
+            Math.sqrt(2 * 2 + -1 * -1)
+        ]);
+        expect(result.shape).toEqual([5]);
+    });
+    it('complex64 rank-2', function () {
+        var a = tf.complex([[-3, -2, -1], [0, 1, 2]], [[4, 1, 2], [3, 0, -1]]);
+        var result = tf.abs(a);
+        test_util_1.expectArraysClose(result, [
+            Math.sqrt(-3 * -3 + 4 * 4), Math.sqrt(-2 * -2 + 1 * 1),
+            Math.sqrt(-1 * -1 + 2 * 2), Math.sqrt(0 * 0 + 3 * 3),
+            Math.sqrt(1 * 1 + 0 * 0), Math.sqrt(2 * 2 + -1 * -1)
+        ]);
+        expect(result.shape).toEqual([2, 3]);
+    });
+    it('complex64 rank-3', function () {
+        var a = tf.complex([[[-3, -2], [-1, 0]], [[1, 2], [3, 4]]], [[[4, 1], [2, 3]], [[0, -1], [-3, -4]]]);
+        var result = tf.abs(a);
+        test_util_1.expectArraysClose(result, [
+            Math.sqrt(-3 * -3 + 4 * 4), Math.sqrt(-2 * -2 + 1 * 1),
+            Math.sqrt(-1 * -1 + 2 * 2), Math.sqrt(0 * 0 + 3 * 3),
+            Math.sqrt(1 * 1 + 0 * 0), Math.sqrt(2 * 2 + -1 * -1),
+            Math.sqrt(3 * 3 + -3 * -3), Math.sqrt(4 * 4 + -4 * -4)
+        ]);
+        expect(result.shape).toEqual([2, 2, 2]);
+    });
+    it('is underflow-safe for complex64', function () {
+        var floatBits = environment_1.ENV.backend.floatPrecision();
+        var small;
+        switch (floatBits) {
+            case 32:
+                small = 1e-30;
+                break;
+            case 16:
+                small = 1e-4;
+                break;
+            default:
+                throw new Error("Test not implemented for ENV.engine.floatPrecision()=" + floatBits + ".");
+        }
+        var a = tf.complex([small, 0, small, 0], [small, small, 0, 0]);
+        var result = tf.abs(a);
+        test_util_1.expectArraysClose(result, [
+            Math.hypot(small, small), Math.hypot(0, small), Math.hypot(small, 0),
+            Math.hypot(0, 0)
+        ], small / 100);
+        expect(result.shape).toEqual([4]);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor1d([1, -2, 0, 3, -0.1, NaN]);
+        var result = tf.abs(a);
+        test_util_1.expectArraysClose(result, [1, 2, 0, 3, 0.1, NaN]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(4);
+        var dy = tf.scalar(8);
+        var da = tf.grad(function (a) { return tf.abs(a); })(a, dy);
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [8 * 1]);
+    });
+    it('gradients: Tensor1D', function () {
+        var a = tf.tensor1d([1, 2, -3, 5]);
+        var dy = tf.tensor1d([1, 2, 3, 4]);
+        var da = tf.grad(function (a) { return tf.abs(a); })(a, dy);
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [1 * 1, 2 * 1, 3 * -1, 4 * 1]);
+    });
+    it('gradients: Tensor2D', function () {
+        var a = tf.tensor2d([3, -1, -2, 3], [2, 2]);
+        var dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var da = tf.grad(function (a) { return tf.abs(a); })(a, dy);
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [1 * 1, 2 * -1, 3 * -1, 4 * 1]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.abs({}); })
+            .toThrowError(/Argument 'x' passed to 'abs' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var result = tf.abs([1, -2, 0, 3, -0.1]);
+        test_util_1.expectArraysClose(result, [1, 2, 0, 3, 0.1]);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.abs('q'); })
+            .toThrowError(/Argument 'x' passed to 'abs' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('step', test_util_1.ALL_ENVS, function () {
+    it('with 1d tensor', function () {
+        var a = tf.tensor1d([1, -2, -.01, 3, -0.1]);
+        var result = tf.step(a);
+        test_util_1.expectArraysClose(result, [1, 0, 0, 1, 0]);
+    });
+    it('with 1d tensor and alpha', function () {
+        var a = tf.tensor1d([1, -2, -.01, 3, NaN]);
+        var result = tf.step(a, 0.1);
+        test_util_1.expectArraysClose(result, [1, 0.1, 0.1, 1, NaN]);
+    });
+    it('with 2d tensor', function () {
+        var a = tf.tensor2d([1, -5, -3, 4], [2, 2]);
+        var result = tf.step(a);
+        expect(result.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(result, [1, 0, 0, 1]);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor1d([1, -2, -.01, 3, NaN]);
+        var result = tf.step(a);
+        test_util_1.expectArraysClose(result, [1, 0, 0, 1, NaN]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(-4);
+        var dy = tf.scalar(8);
+        var gradients = tf.grad(function (a) { return tf.step(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [0]);
+    });
+    it('gradients: Tensor1D', function () {
+        var a = tf.tensor1d([1, 2, -3, 5]);
+        var dy = tf.tensor1d([1, 2, 3, 4]);
+        var gradients = tf.grad(function (a) { return tf.step(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [0, 0, 0, 0]);
+    });
+    it('gradients: Tensor2D', function () {
+        var a = tf.tensor2d([3, -1, -2, 3], [2, 2]);
+        var dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.step(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [0, 0, 0, 0]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.step({}); })
+            .toThrowError(/Argument 'x' passed to 'step' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var result = tf.step([1, -2, -.01, 3, -0.1]);
+        test_util_1.expectArraysClose(result, [1, 0, 0, 1, 0]);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.step('q'); })
+            .toThrowError(/Argument 'x' passed to 'step' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('neg', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var a = tf.tensor1d([1, -3, 2, 7, -4]);
+        var result = tf.neg(a);
+        test_util_1.expectArraysClose(result, [-1, 3, -2, -7, 4]);
+    });
+    it('propagate NaNs', function () {
+        var a = tf.tensor1d([1, -3, 2, 7, NaN]);
+        var result = tf.neg(a);
+        var expected = [-1, 3, -2, -7, NaN];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(4);
+        var dy = tf.scalar(8);
+        var da = tf.grad(function (a) { return tf.neg(a); })(a, dy);
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [8 * -1]);
+    });
+    it('gradients: Tensor1D', function () {
+        var a = tf.tensor1d([1, 2, -3, 5]);
+        var dy = tf.tensor1d([1, 2, 3, 4]);
+        var da = tf.grad(function (a) { return tf.neg(a); })(a, dy);
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [1 * -1, 2 * -1, 3 * -1, 4 * -1]);
+    });
+    it('gradients: Tensor2D', function () {
+        var a = tf.tensor2d([3, -1, -2, 3], [2, 2]);
+        var dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var da = tf.grad(function (a) { return tf.neg(a); })(a, dy);
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [1 * -1, 2 * -1, 3 * -1, 4 * -1]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.neg({}); })
+            .toThrowError(/Argument 'x' passed to 'neg' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var result = tf.neg([1, -3, 2, 7, -4]);
+        test_util_1.expectArraysClose(result, [-1, 3, -2, -7, 4]);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.neg('q'); })
+            .toThrowError(/Argument 'x' passed to 'neg' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('sigmoid', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var values = [1, -3, 2, 7, -4];
+        var a = tf.tensor1d(values);
+        var result = tf.sigmoid(a);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = 1 / (1 + Math.exp(-values[i]));
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('6D', function () {
+        var a = tf.ones([2, 2, 2, 2, 2, 2]);
+        var result = tf.sigmoid(a);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = 1 / (1 + Math.exp(-1.0));
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor1d([3, NaN]);
+        var res = tf.sigmoid(a);
+        test_util_1.expectArraysClose(res, [1 / (1 + Math.exp(-3)), NaN]);
+    });
+    it('gradients: Tensor1D', function () {
+        var a = tf.tensor1d([1, 2, -3, 5]);
+        var dy = tf.tensor1d([1, 2, 3, 4]);
+        var da = tf.grad(function (a) { return tf.sigmoid(a); })(a, dy);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            var y = 1 / (1 + Math.exp(-a.get(i)));
+            expected[i] = dy.get(i) * y * (1 - y);
+        }
+        test_util_1.expectArraysClose(da, expected);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.sigmoid({}); })
+            .toThrowError(/Argument 'x' passed to 'sigmoid' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var values = [1, -3, 2, 7, -4];
+        var result = tf.sigmoid(values);
+        var expected = [];
+        for (var i = 0; i < values.length; i++) {
+            expected[i] = 1 / (1 + Math.exp(-values[i]));
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.sigmoid('q'); })
+            .toThrowError(/Argument 'x' passed to 'sigmoid' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('logSigmoid', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var values = [1, -3, 2, 7, -4];
+        var a = tf.tensor1d(values);
+        var result = tf.logSigmoid(a);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = Math.log(1 / (1 + Math.exp(-values[i])));
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('scalar', function () {
+        var a = tf.scalar(-2);
+        var result = tf.logSigmoid(a);
+        var expected = [Math.log(1 / (1 + Math.exp(2)))];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('tensor2D', function () {
+        var values = [1, 2, -3, 5];
+        var a = tf.tensor2d(values, [2, 2]);
+        var result = tf.logSigmoid(a);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = Math.log(1 / (1 + Math.exp(-values[i])));
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('larger magnitude negative inputs', function () {
+        var values = [-100, -200, -3000];
+        var a = tf.tensor1d(values);
+        var result = tf.logSigmoid(a);
+        var expected = [-100, -200, -3000];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('larger magnitude positive inputs', function () {
+        var values = [100, 200, 3000, 50000];
+        var a = tf.tensor1d(values);
+        var result = tf.logSigmoid(a);
+        var expected = [0, 0, 0, 0];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor1d([3, NaN]);
+        var res = tf.logSigmoid(a);
+        test_util_1.expectArraysClose(res, [Math.log(1 / (1 + Math.exp(-3))), NaN]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(3);
+        var dy = tf.scalar(4);
+        var da = tf.grad(function (a) { return tf.logSigmoid(a); })(a, dy).get();
+        var y = 1 / (1 + Math.exp(a.get()));
+        test_util_1.expectNumbersClose(da, dy.get() * y);
+    });
+    it('gradients: Tensor1D', function () {
+        var a = tf.tensor1d([1, 2, -3, 5]);
+        var dy = tf.tensor1d([1, 2, 3, 4]);
+        var da = tf.grad(function (a) { return tf.logSigmoid(a); })(a, dy);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            var y = 1 / (1 + Math.exp(a.get(i)));
+            expected[i] = dy.get(i) * y;
+        }
+        test_util_1.expectArraysClose(da, expected);
+    });
+    it('gradients: Tensor2D', function () {
+        var a = tf.tensor2d([1, 2, -3, 5], [2, 2]);
+        var dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var da = tf.grad(function (a) { return tf.logSigmoid(a); })(a, dy);
+        var expected = [];
+        var aVals = a.dataSync();
+        var dyVals = dy.dataSync();
+        for (var i = 0; i < a.size; i++) {
+            var y = 1 / (1 + Math.exp(aVals[i]));
+            expected[i] = dyVals[i] * y;
+        }
+        test_util_1.expectArraysClose(da, expected);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.logSigmoid({}); })
+            .toThrowError(/Argument 'x' passed to 'logSigmoid' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var result = tf.logSigmoid(-2);
+        var expected = [Math.log(1 / (1 + Math.exp(2)))];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.logSigmoid('q'); })
+            .toThrowError(/Argument 'x' passed to 'logSigmoid' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('softplus', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var values = [1, -3, 2, 7, -4];
+        var a = tf.tensor1d(values);
+        var result = tf.softplus(a);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = Math.log((1 + Math.exp(values[i])));
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('scalar', function () {
+        var a = tf.scalar(-2);
+        var result = tf.softplus(a);
+        var expected = [Math.log((1 + Math.exp(-2)))];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('tensor2D', function () {
+        var values = [1, 2, -3, 5];
+        var a = tf.tensor2d(values, [2, 2]);
+        var result = tf.softplus(a);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = Math.log((1 + Math.exp(values[i])));
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('larger magnitude negative inputs', function () {
+        var values = [-100, -200, -3000, -50000];
+        var a = tf.tensor1d(values);
+        var result = tf.softplus(a);
+        var expected = [0, 0, 0, 0];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('larger magnitude positive inputs', function () {
+        var values = [100, 200, 3000];
+        var a = tf.tensor1d(values);
+        var result = tf.softplus(a);
+        var expected = [100, 200, 3000];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor1d([3, NaN]);
+        var res = tf.softplus(a);
+        test_util_1.expectArraysClose(res, [Math.log((1 + Math.exp(3))), NaN]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(3);
+        var dy = tf.scalar(4);
+        var da = tf.grad(function (a) { return tf.softplus(a); })(a, dy);
+        var y = 1 / (1 + Math.exp(-a.get()));
+        test_util_1.expectNumbersClose(da.get(), dy.get() * y);
+    });
+    it('gradients: Tensor1D', function () {
+        var a = tf.tensor1d([1, 2, -3, 5]);
+        var dy = tf.tensor1d([1, 2, 3, 4]);
+        var da = tf.grad(function (a) { return tf.softplus(a); })(a, dy);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            var y = 1 / (1 + Math.exp(-a.get(i)));
+            expected[i] = dy.get(i) * y;
+        }
+        test_util_1.expectArraysClose(da, expected);
+    });
+    it('gradients: Tensor2D', function () {
+        var a = tf.tensor2d([1, 2, -3, 5], [2, 2]);
+        var dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var da = tf.grad(function (a) { return tf.softplus(a); })(a, dy);
+        var expected = [];
+        var aVals = a.dataSync();
+        var dyVals = dy.dataSync();
+        for (var i = 0; i < a.size; i++) {
+            var y = 1 / (1 + Math.exp(-aVals[i]));
+            expected[i] = dyVals[i] * y;
+        }
+        test_util_1.expectArraysClose(da, expected);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.softplus({}); })
+            .toThrowError(/Argument 'x' passed to 'softplus' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var result = tf.softplus(-2);
+        var expected = [Math.log((1 + Math.exp(-2)))];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.softplus('q'); })
+            .toThrowError(/Argument 'x' passed to 'softplus' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('sqrt', test_util_1.ALL_ENVS, function () {
+    it('sqrt', function () {
+        var a = tf.tensor1d([2, 4]);
+        var r = tf.sqrt(a);
+        test_util_1.expectNumbersClose(r.get(0), Math.sqrt(2));
+        test_util_1.expectNumbersClose(r.get(1), Math.sqrt(4));
+    });
+    it('sqrt propagates NaNs', function () {
+        var a = tf.tensor1d([1, NaN]);
+        var r = tf.sqrt(a);
+        test_util_1.expectArraysClose(r, [Math.sqrt(1), NaN]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(4);
+        var dy = tf.scalar(8);
+        var da = tf.grad(function (a) { return tf.sqrt(a); })(a, dy);
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [8 / (2 * Math.sqrt(4))]);
+    });
+    it('gradients: Tensor1D', function () {
+        var a = tf.tensor1d([1, 2, 3, 5]);
+        var dy = tf.tensor1d([1, 2, 3, 4]);
+        var gradients = tf.grad(function (a) { return tf.sqrt(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [
+            1 / (2 * Math.sqrt(1)), 2 / (2 * Math.sqrt(2)),
+            3 / (2 * Math.sqrt(3)), 4 / (2 * Math.sqrt(5))
+        ], 1e-1);
+    });
+    it('gradients: Tensor2D', function () {
+        var a = tf.tensor2d([3, 1, 2, 3], [2, 2]);
+        var dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.sqrt(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [
+            1 / (2 * Math.sqrt(3)), 2 / (2 * Math.sqrt(1)),
+            3 / (2 * Math.sqrt(2)), 4 / (2 * Math.sqrt(3))
+        ], 1e-1);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.sqrt({}); })
+            .toThrowError(/Argument 'x' passed to 'sqrt' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var r = tf.sqrt([2, 4]);
+        test_util_1.expectNumbersClose(r.get(0), Math.sqrt(2));
+        test_util_1.expectNumbersClose(r.get(1), Math.sqrt(4));
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.sqrt('q'); })
+            .toThrowError(/Argument 'x' passed to 'sqrt' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('rsqrt', test_util_1.ALL_ENVS, function () {
+    it('rsqrt', function () {
+        var a = tf.tensor1d([2, 4]);
+        var r = tf.rsqrt(a);
+        test_util_1.expectNumbersClose(r.get(0), 1 / Math.sqrt(2));
+        test_util_1.expectNumbersClose(r.get(1), 1 / Math.sqrt(4));
+    });
+    it('rsqrt propagates NaNs', function () {
+        var a = tf.tensor1d([1, NaN]);
+        var r = tf.rsqrt(a);
+        test_util_1.expectArraysClose(r, [1 / Math.sqrt(1), NaN]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(4);
+        var dy = tf.scalar(8);
+        var da = tf.grad(function (a) { return tf.rsqrt(a); })(a, dy);
+        expect(da.shape).toEqual(a.shape);
+        expect(da.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(da, [(-1 * 8) / (2 * Math.pow(4, 1.5))]);
+    });
+    it('gradients: Tensor1D', function () {
+        var a = tf.tensor1d([1, 2, 3, 5]);
+        var dy = tf.tensor1d([1, 2, 3, 4]);
+        var gradients = tf.grad(function (a) { return tf.rsqrt(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [
+            -1 * 1 / (2 * Math.pow(1, 1.5)), -1 * 2 / (2 * Math.pow(2, 1.5)),
+            -1 * 3 / (2 * Math.pow(3, 1.5)), -1 * 4 / (2 * Math.pow(5, 1.5))
+        ], 1e-1);
+    });
+    it('gradients: Tensor2D', function () {
+        var a = tf.tensor2d([3, 1, 2, 3], [2, 2]);
+        var dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.rsqrt(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [
+            -1 * 1 / (2 * Math.pow(3, 1.5)), -1 * 2 / (2 * Math.pow(1, 1.5)),
+            -1 * 3 / (2 * Math.pow(2, 1.5)), -1 * 4 / (2 * Math.pow(3, 1.5))
+        ], 1e-1);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.rsqrt({}); })
+            .toThrowError(/Argument 'x' passed to 'rsqrt' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var r = tf.rsqrt([2, 4]);
+        test_util_1.expectNumbersClose(r.get(0), 1 / Math.sqrt(2));
+        test_util_1.expectNumbersClose(r.get(1), 1 / Math.sqrt(4));
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.rsqrt('q'); })
+            .toThrowError(/Argument 'x' passed to 'rsqrt' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('square', test_util_1.ALL_ENVS, function () {
+    it('1D array', function () {
+        var a = tf.tensor1d([2, 4, Math.sqrt(2)]);
+        var r = tf.square(a);
+        test_util_1.expectArraysClose(r, [4, 16, 2]);
+    });
+    it('2D array', function () {
+        var a = tf.tensor2d([1, 2, Math.sqrt(2), Math.sqrt(3)], [2, 2]);
+        var r = tf.square(a);
+        expect(r.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(r, [1, 4, 2, 3]);
+    });
+    it('5D array', function () {
+        var a = tf.tensor5d([1, 2, Math.sqrt(2), Math.sqrt(3)], [1, 1, 2, 2, 1]);
+        var r = tf.square(a);
+        expect(r.shape).toEqual([1, 1, 2, 2, 1]);
+        test_util_1.expectArraysClose(r, [1, 4, 2, 3]);
+    });
+    it('6D array', function () {
+        var a = tf.tensor6d([1, 2, Math.sqrt(2), Math.sqrt(3), 3, 4, Math.sqrt(7), Math.sqrt(13)], [1, 1, 2, 2, 2, 1]);
+        var r = tf.square(a);
+        expect(r.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(r, [1, 4, 2, 3, 9, 16, 7, 13]);
+    });
+    it('square propagates NaNs', function () {
+        var a = tf.tensor1d([1.5, NaN]);
+        var r = tf.square(a);
+        test_util_1.expectArraysClose(r, [2.25, NaN]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(5);
+        var dy = tf.scalar(8);
+        var gradients = tf.grad(function (a) { return tf.square(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [2 * 5 * 8]);
+    });
+    it('gradients: Tensor1D', function () {
+        var a = tf.tensor1d([-1, 2, 3, -5]);
+        var dy = tf.tensor1d([1, 2, 3, 4]);
+        var gradients = tf.grad(function (a) { return tf.square(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [-2, 4 * 2, 6 * 3, -10 * 4]);
+    });
+    it('gradients: Tensor2D', function () {
+        var a = tf.tensor2d([-3, 1, 2, 3], [2, 2]);
+        var dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.square(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [-6 * 1, 2 * 2, 4 * 3, 6 * 4]);
+    });
+    it('gradients: Tensor5D', function () {
+        var a = tf.tensor5d([-3, 1, 2, 3], [1, 1, 1, 2, 2]);
+        var dy = tf.tensor5d([1, 2, 3, 4], [1, 1, 1, 2, 2]);
+        var gradients = tf.grad(function (a) { return tf.square(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [-6 * 1, 2 * 2, 4 * 3, 6 * 4]);
+    });
+    it('gradients: Tensor6D', function () {
+        var a = tf.tensor6d([-3, 1, 2, 3, -4, 5, 12, 3], [1, 1, 1, 2, 2, 2]);
+        var dy = tf.tensor6d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 1, 2, 2, 2]);
+        var gradients = tf.grad(function (a) { return tf.square(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [-6 * 1, 2 * 2, 4 * 3, 6 * 4, -8 * 5, 10 * 6, 24 * 7, 6 * 8]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.square({}); })
+            .toThrowError(/Argument 'x' passed to 'square' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var r = tf.square([2, 4, Math.sqrt(2)]);
+        test_util_1.expectArraysClose(r, [4, 16, 2]);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.square('q'); })
+            .toThrowError(/Argument 'x' passed to 'square' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('reciprocal', test_util_1.ALL_ENVS, function () {
+    it('1D array', function () {
+        var a = tf.tensor1d([2, 3, 0, NaN]);
+        var r = tf.reciprocal(a);
+        test_util_1.expectArraysClose(r, [1 / 2, 1 / 3, Infinity, NaN]);
+    });
+    it('2D array', function () {
+        var a = tf.tensor2d([1, Infinity, 0, NaN], [2, 2]);
+        var r = tf.reciprocal(a);
+        expect(r.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(r, [1 / 1, 0, Infinity, NaN]);
+    });
+    it('reciprocal propagates NaNs', function () {
+        var a = tf.tensor1d([1.5, NaN]);
+        var r = tf.reciprocal(a);
+        test_util_1.expectArraysClose(r, [1 / 1.5, NaN]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(5);
+        var dy = tf.scalar(8);
+        var gradients = tf.grad(function (a) { return tf.reciprocal(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [-1 * 8 * (1 / (5 * 5))]);
+    });
+    it('gradients: Tensor1D', function () {
+        var a = tf.tensor1d([-1, 2, 3, -5]);
+        var dy = tf.tensor1d([1, 2, 3, 4]);
+        var gradients = tf.grad(function (a) { return tf.reciprocal(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [
+            -1 * 1 * (1 / (-1 * -1)), -1 * 2 * (1 / (2 * 2)), -1 * 3 * (1 / (3 * 3)),
+            -1 * 4 * (1 / (-5 * -5))
+        ]);
+    });
+    it('gradients: Tensor2D', function () {
+        var a = tf.tensor2d([-1, 2, 3, -5], [2, 2]);
+        var dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.reciprocal(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [
+            -1 * 1 * (1 / (-1 * -1)), -1 * 2 * (1 / (2 * 2)), -1 * 3 * (1 / (3 * 3)),
+            -1 * 4 * (1 / (-5 * -5))
+        ]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.reciprocal({}); })
+            .toThrowError(/Argument 'x' passed to 'reciprocal' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var r = tf.reciprocal([2, 3, 0, NaN]);
+        test_util_1.expectArraysClose(r, [1 / 2, 1 / 3, Infinity, NaN]);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.reciprocal('q'); })
+            .toThrowError(/Argument 'x' passed to 'reciprocal' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('log', test_util_1.ALL_ENVS, function () {
+    it('log', function () {
+        var a = tf.tensor1d([1, 2]);
+        var r = tf.log(a);
+        test_util_1.expectNumbersClose(r.get(0), Math.log(1));
+        test_util_1.expectNumbersClose(r.get(1), Math.log(2));
+    });
+    it('log 6D', function () {
+        var a = tf.range(1, 65).reshape([2, 2, 2, 2, 2, 2]);
+        var r = tf.log(a);
+        var expectedResult = [];
+        for (var i = 1; i < 65; i++) {
+            expectedResult[i - 1] = Math.log(i);
+        }
+        test_util_1.expectArraysClose(r, expectedResult);
+    });
+    it('log propagates NaNs', function () {
+        var a = tf.tensor1d([1, NaN]);
+        var r = tf.log(a);
+        test_util_1.expectArraysClose(r, [Math.log(1), NaN]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(5);
+        var dy = tf.scalar(3);
+        var gradients = tf.grad(function (a) { return tf.log(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [3 / 5]);
+    });
+    it('gradients: Tensor1D', function () {
+        var a = tf.tensor1d([-1, 2, 3, -5]);
+        var dy = tf.tensor1d([1, 2, 3, 4]);
+        var gradients = tf.grad(function (a) { return tf.log(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [1 / -1, 2 / 2, 3 / 3, 4 / -5]);
+    });
+    it('gradients: Tensor2D', function () {
+        var a = tf.tensor2d([-3, 1, 2, 3], [2, 2]);
+        var dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.log(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [1 / -3, 2 / 1, 3 / 2, 4 / 3]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.log({}); })
+            .toThrowError(/Argument 'x' passed to 'log' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var r = tf.log([1, 2]);
+        test_util_1.expectNumbersClose(r.get(0), Math.log(1));
+        test_util_1.expectNumbersClose(r.get(1), Math.log(2));
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.log('q'); })
+            .toThrowError(/Argument 'x' passed to 'log' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('log1p', test_util_1.ALL_ENVS, function () {
+    it('log1p', function () {
+        var a = tf.tensor1d([1, 2]);
+        var r = tf.log1p(a);
+        test_util_1.expectNumbersClose(r.get(0), Math.log1p(1));
+        test_util_1.expectNumbersClose(r.get(1), Math.log1p(2));
+    });
+    it('log1p propagates NaNs', function () {
+        var a = tf.tensor1d([1, NaN]);
+        var r = tf.log1p(a);
+        test_util_1.expectArraysClose(r, [Math.log1p(1), NaN]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(5);
+        var dy = tf.scalar(3);
+        var gradients = tf.grad(function (a) { return tf.log1p(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [3 / (1 + 5)]);
+    });
+    it('gradients: Tensor1D', function () {
+        var a = tf.tensor1d([-1, 2, 3, -5]);
+        var dy = tf.tensor1d([1, 2, 3, 4]);
+        var gradients = tf.grad(function (a) { return tf.log1p(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [Infinity, 2 / (1 + 2), 3 / (1 + 3), 4 / (1 + -5)]);
+    });
+    it('gradients: Tensor2D', function () {
+        var a = tf.tensor2d([-3, 1, 2, 3], [2, 2]);
+        var dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.log1p(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [1 / (1 + -3), 2 / (1 + 1), 3 / (1 + 2), 4 / (1 + 3)]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.log1p({}); })
+            .toThrowError(/Argument 'x' passed to 'log1p' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var r = tf.log1p([1, 2]);
+        test_util_1.expectNumbersClose(r.get(0), Math.log1p(1));
+        test_util_1.expectNumbersClose(r.get(1), Math.log1p(2));
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.log1p('q'); })
+            .toThrowError(/Argument 'x' passed to 'log1p' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('ceil', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var a = tf.tensor1d([1.5, 2.1, -1.4]);
+        var r = tf.ceil(a);
+        test_util_1.expectNumbersClose(r.get(0), 2);
+        test_util_1.expectNumbersClose(r.get(1), 3);
+        test_util_1.expectNumbersClose(r.get(2), -1);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor1d([1.5, NaN, -1.4]);
+        var r = tf.ceil(a);
+        test_util_1.expectArraysClose(r, [2, NaN, -1]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(5.2);
+        var dy = tf.scalar(3);
+        var gradients = tf.grad(function (a) { return tf.ceil(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [0]);
+    });
+    it('gradients: Tensor1D', function () {
+        var a = tf.tensor1d([-1.1, 2.6, 3, -5.9]);
+        var dy = tf.tensor1d([1, 2, 3, 4]);
+        var gradients = tf.grad(function (a) { return tf.ceil(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [0, 0, 0, 0]);
+    });
+    it('gradients: Tensor2D', function () {
+        var a = tf.tensor2d([-3, 1, 2.2, 3], [2, 2]);
+        var dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.ceil(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [0, 0, 0, 0]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.ceil({}); })
+            .toThrowError(/Argument 'x' passed to 'ceil' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var r = tf.ceil([1.5, 2.1, -1.4]);
+        test_util_1.expectNumbersClose(r.get(0), 2);
+        test_util_1.expectNumbersClose(r.get(1), 3);
+        test_util_1.expectNumbersClose(r.get(2), -1);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.ceil('q'); })
+            .toThrowError(/Argument 'x' passed to 'ceil' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('floor', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var a = tf.tensor1d([1.5, 2.1, -1.4]);
+        var r = tf.floor(a);
+        test_util_1.expectNumbersClose(r.get(0), 1);
+        test_util_1.expectNumbersClose(r.get(1), 2);
+        test_util_1.expectNumbersClose(r.get(2), -2);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor1d([1.5, NaN, -1.4]);
+        var r = tf.floor(a);
+        test_util_1.expectArraysClose(r, [1, NaN, -2]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(5.2);
+        var dy = tf.scalar(3);
+        var gradients = tf.grad(function (a) { return tf.floor(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [0]);
+    });
+    it('gradients: Tensor1D', function () {
+        var a = tf.tensor1d([-1.1, 2.6, 3, -5.9]);
+        var dy = tf.tensor1d([1, 2, 3, 4]);
+        var gradients = tf.grad(function (a) { return tf.floor(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [0, 0, 0, 0]);
+    });
+    it('gradients: Tensor2D', function () {
+        var a = tf.tensor2d([-3, 1, 2.2, 3], [2, 2]);
+        var dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.floor(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [0, 0, 0, 0]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.floor({}); })
+            .toThrowError(/Argument 'x' passed to 'floor' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var r = tf.floor([1.5, 2.1, -1.4]);
+        test_util_1.expectNumbersClose(r.get(0), 1);
+        test_util_1.expectNumbersClose(r.get(1), 2);
+        test_util_1.expectNumbersClose(r.get(2), -2);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.floor('q'); })
+            .toThrowError(/Argument 'x' passed to 'floor' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('sign', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var a = tf.tensor1d([1.5, 0, NaN, -1.4]);
+        var r = tf.sign(a);
+        test_util_1.expectNumbersClose(r.get(0), 1);
+        test_util_1.expectNumbersClose(r.get(1), 0);
+        test_util_1.expectNumbersClose(r.get(2), 0);
+        test_util_1.expectNumbersClose(r.get(3), -1);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor1d([1.5, NaN, -1.4]);
+        var r = tf.sign(a);
+        test_util_1.expectArraysClose(r, [1, 0, -1]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(5.2);
+        var dy = tf.scalar(3);
+        var gradients = tf.grad(function (a) { return tf.sign(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [0]);
+    });
+    it('gradients: Tensor1D', function () {
+        var a = tf.tensor1d([-1.1, 2.6, 3, -5.9]);
+        var dy = tf.tensor1d([-1, 1, 1, -1]);
+        var gradients = tf.grad(function (a) { return tf.sign(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [0, 0, 0, 0]);
+    });
+    it('gradients: Tensor2D', function () {
+        var a = tf.tensor2d([-3, 1, 2.2, 3], [2, 2]);
+        var dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.sign(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [0, 0, 0, 0]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.sign({}); })
+            .toThrowError(/Argument 'x' passed to 'sign' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var r = tf.sign([1.5, 0, NaN, -1.4]);
+        test_util_1.expectNumbersClose(r.get(0), 1);
+        test_util_1.expectNumbersClose(r.get(1), 0);
+        test_util_1.expectNumbersClose(r.get(2), 0);
+        test_util_1.expectNumbersClose(r.get(3), -1);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.sign('q'); })
+            .toThrowError(/Argument 'x' passed to 'sign' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('exp', test_util_1.ALL_ENVS, function () {
+    it('exp', function () {
+        var a = tf.tensor1d([1, 2, 0]);
+        var r = tf.exp(a);
+        test_util_1.expectNumbersClose(r.get(0), Math.exp(1));
+        test_util_1.expectNumbersClose(r.get(1), Math.exp(2));
+        test_util_1.expectNumbersClose(r.get(2), 1);
+    });
+    it('exp propagates NaNs', function () {
+        var a = tf.tensor1d([1, NaN, 0]);
+        var r = tf.exp(a);
+        test_util_1.expectArraysClose(r, [Math.exp(1), NaN, 1]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(0.5);
+        var dy = tf.scalar(3);
+        var gradients = tf.grad(function (a) { return tf.exp(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [3 * Math.exp(0.5)]);
+    });
+    it('gradients: Tensor1D', function () {
+        var a = tf.tensor1d([-1, 2, 3, -5]);
+        var dy = tf.tensor1d([1, 2, 3, 4]);
+        var gradients = tf.grad(function (a) { return tf.exp(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [1 * Math.exp(-1), 2 * Math.exp(2), 3 * Math.exp(3), 4 * Math.exp(-5)], 1e-1);
+    });
+    it('gradients: Tensor2D', function () {
+        var a = tf.tensor2d([-3, 1, 2, 3], [2, 2]);
+        var dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.exp(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [1 * Math.exp(-3), 2 * Math.exp(1), 3 * Math.exp(2), 4 * Math.exp(3)], 1e-1);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.exp({}); })
+            .toThrowError(/Argument 'x' passed to 'exp' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var r = tf.exp([1, 2, 0]);
+        test_util_1.expectNumbersClose(r.get(0), Math.exp(1));
+        test_util_1.expectNumbersClose(r.get(1), Math.exp(2));
+        test_util_1.expectNumbersClose(r.get(2), 1);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.exp('q'); })
+            .toThrowError(/Argument 'x' passed to 'exp' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('expm1', test_util_1.ALL_ENVS, function () {
+    it('expm1', function () {
+        var a = tf.tensor1d([1, 2, 0]);
+        var r = tf.expm1(a);
+        test_util_1.expectNumbersClose(r.get(0), Math.expm1(1));
+        test_util_1.expectNumbersClose(r.get(1), Math.expm1(2));
+        test_util_1.expectNumbersClose(r.get(2), Math.expm1(0));
+    });
+    it('expm1 propagates NaNs', function () {
+        var a = tf.tensor1d([1, NaN, 0]);
+        var r = tf.expm1(a);
+        test_util_1.expectArraysClose(r, [Math.expm1(1), NaN, Math.expm1(0)]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(0.5);
+        var dy = tf.scalar(3);
+        var gradients = tf.grad(function (a) { return tf.expm1(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [3 * Math.exp(0.5)]);
+    });
+    it('gradients: Tensor1D', function () {
+        var a = tf.tensor1d([-1, 2, 3, -5]);
+        var dy = tf.tensor1d([1, 2, 3, 4]);
+        var gradients = tf.grad(function (a) { return tf.expm1(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [1 * Math.exp(-1), 2 * Math.exp(2), 3 * Math.exp(3), 4 * Math.exp(-5)], 1e-1);
+    });
+    it('gradients: Tensor2D', function () {
+        var a = tf.tensor2d([-3, 1, 2, 3], [2, 2]);
+        var dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.expm1(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [1 * Math.exp(-3), 2 * Math.exp(1), 3 * Math.exp(2), 4 * Math.exp(3)], 1e-1);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.expm1({}); })
+            .toThrowError(/Argument 'x' passed to 'expm1' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var r = tf.expm1([1, 2, 0]);
+        test_util_1.expectNumbersClose(r.get(0), Math.expm1(1));
+        test_util_1.expectNumbersClose(r.get(1), Math.expm1(2));
+        test_util_1.expectNumbersClose(r.get(2), Math.expm1(0));
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.expm1('q'); })
+            .toThrowError(/Argument 'x' passed to 'expm1' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('sin', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var values = [1, -3, 2, 7, -4];
+        var a = tf.tensor1d(values);
+        var result = tf.sin(a);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = Math.sin(values[i]);
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor1d([4, NaN, 0]);
+        var res = tf.sin(a);
+        test_util_1.expectArraysClose(res, [Math.sin(4), NaN, Math.sin(0)]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(5);
+        var dy = tf.scalar(8);
+        var gradients = tf.grad(function (a) { return tf.sin(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [8 * Math.cos(5)]);
+    });
+    it('gradients: Tensor1D', function () {
+        var a = tf.tensor1d([-1, 2, 3, -5]);
+        var dy = tf.tensor1d([1, 2, 3, 4]);
+        var gradients = tf.grad(function (a) { return tf.sin(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [1 * Math.cos(-1), 2 * Math.cos(2), 3 * Math.cos(3), 4 * Math.cos(-5)], 1e-1);
+    });
+    it('gradients: Tensor2D', function () {
+        var a = tf.tensor2d([-3, 1, 2, 3], [2, 2]);
+        var dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.sin(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [1 * Math.cos(-3), 2 * Math.cos(1), 3 * Math.cos(2), 4 * Math.cos(3)], 1e-1);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.sin({}); })
+            .toThrowError(/Argument 'x' passed to 'sin' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var values = [1, -3, 2, 7, -4];
+        var result = tf.sin(values);
+        var expected = [];
+        for (var i = 0; i < values.length; i++) {
+            expected[i] = Math.sin(values[i]);
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.sin('q'); })
+            .toThrowError(/Argument 'x' passed to 'sin' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('cos', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var values = [1, -3, 2, 7, -4];
+        var a = tf.tensor1d(values);
+        var result = tf.cos(a);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = Math.cos(values[i]);
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor1d([4, NaN, 0]);
+        var res = tf.cos(a);
+        test_util_1.expectArraysClose(res, [Math.cos(4), NaN, Math.cos(0)]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(5);
+        var dy = tf.scalar(8);
+        var gradients = tf.grad(function (a) { return tf.cos(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [8 * Math.sin(5) * -1]);
+    });
+    it('gradients: Tensor1D', function () {
+        var a = tf.tensor1d([-1, 2, 3, -5]);
+        var dy = tf.tensor1d([1, 2, 3, 4]);
+        var gradients = tf.grad(function (a) { return tf.cos(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [
+            1 * Math.sin(-1) * -1, 2 * Math.sin(2) * -1, 3 * Math.sin(3) * -1,
+            4 * Math.sin(-5) * -1
+        ], 1e-1);
+    });
+    it('gradients: Tensor2D', function () {
+        var a = tf.tensor2d([-3, 1, 2, 3], [2, 2]);
+        var dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.cos(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [
+            1 * Math.sin(-3) * -1, 2 * Math.sin(1) * -1, 3 * Math.sin(2) * -1,
+            4 * Math.sin(3) * -1
+        ], 1e-1);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.cos({}); })
+            .toThrowError(/Argument 'x' passed to 'cos' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var values = [1, -3, 2, 7, -4];
+        var result = tf.cos(values);
+        var expected = [];
+        for (var i = 0; i < values.length; i++) {
+            expected[i] = Math.cos(values[i]);
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.cos('q'); })
+            .toThrowError(/Argument 'x' passed to 'cos' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('tan', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var values = [1, -3, 2, 7, -4];
+        var a = tf.tensor1d(values);
+        var result = tf.tan(a);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = Math.tan(values[i]);
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor1d([4, NaN, 0]);
+        var res = tf.tan(a);
+        test_util_1.expectArraysClose(res, [Math.tan(4), NaN, Math.tan(0)]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(0.5);
+        var dy = tf.scalar(8);
+        var gradients = tf.grad(function (a) { return tf.tan(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [8 / (Math.cos(0.5) * Math.cos(0.5))]);
+    });
+    it('gradients: Tensor1D', function () {
+        var aValues = [-1, 2, 3, -5];
+        var dyValues = [1, 2, 3, 4];
+        var a = tf.tensor1d(aValues);
+        var dy = tf.tensor1d(dyValues);
+        var gradients = tf.grad(function (a) { return tf.tan(a); })(a, dy);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = dyValues[i] / (Math.cos(aValues[i]) * Math.cos(aValues[i]));
+        }
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, expected);
+    });
+    it('gradients: Tensor2D', function () {
+        var aValues = [-3, 1, 2, 3];
+        var dyValues = [1, 2, 3, 4];
+        var a = tf.tensor2d(aValues, [2, 2]);
+        var dy = tf.tensor2d(dyValues, [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.tan(a); })(a, dy);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = dyValues[i] / (Math.cos(aValues[i]) * Math.cos(aValues[i]));
+        }
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, expected);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.tan({}); })
+            .toThrowError(/Argument 'x' passed to 'tan' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var values = [1, -3, 2, 7, -4];
+        var result = tf.tan(values);
+        var expected = [];
+        for (var i = 0; i < values.length; i++) {
+            expected[i] = Math.tan(values[i]);
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.tan('q'); })
+            .toThrowError(/Argument 'x' passed to 'tan' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('asin', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var values = [.1, -3, 2, 7, -4];
+        var a = tf.tensor1d(values);
+        var result = tf.asin(a);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = Math.asin(values[i]);
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor1d([4, NaN, 0]);
+        var res = tf.asin(a);
+        test_util_1.expectArraysClose(res, [Math.asin(4), NaN, Math.asin(0)]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(0.5);
+        var dy = tf.scalar(8);
+        var gradients = tf.grad(function (a) { return tf.asin(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [8 / Math.sqrt(1 - (0.5 * 0.5))]);
+    });
+    it('gradients: Tensor1D', function () {
+        var aValues = [-0.1, 0.2, 0.3, -0.5];
+        var dyValues = [1, 2, 3, 4];
+        var a = tf.tensor1d(aValues);
+        var dy = tf.tensor1d(dyValues);
+        var gradients = tf.grad(function (a) { return tf.asin(a); })(a, dy);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = dyValues[i] / Math.sqrt(1 - (aValues[i] * aValues[i]));
+        }
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, expected);
+    });
+    it('gradients: Tensor2D', function () {
+        var aValues = [-0.3, 0.1, 0.2, 0.3];
+        var dyValues = [1, 2, 3, 4];
+        var a = tf.tensor2d(aValues, [2, 2]);
+        var dy = tf.tensor2d(dyValues, [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.asin(a); })(a, dy);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = dyValues[i] / Math.sqrt(1 - (aValues[i] * aValues[i]));
+        }
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, expected);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.asin({}); })
+            .toThrowError(/Argument 'x' passed to 'asin' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var values = [.1, -3, 2, 7, -4];
+        var result = tf.asin(values);
+        var expected = [];
+        for (var i = 0; i < values.length; i++) {
+            expected[i] = Math.asin(values[i]);
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.asin('q'); })
+            .toThrowError(/Argument 'x' passed to 'asin' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('acos', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var values = [.1, -3, 2, 7, -4];
+        var a = tf.tensor1d(values);
+        var result = tf.acos(a);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = Math.acos(values[i]);
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor1d([4, NaN, 0]);
+        var res = tf.acos(a);
+        test_util_1.expectArraysClose(res, [Math.acos(4), NaN, Math.acos(0)]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(0.5);
+        var dy = tf.scalar(8);
+        var gradients = tf.grad(function (a) { return tf.acos(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [(-1 * 8) / Math.sqrt(1 - (0.5 * 0.5))]);
+    });
+    it('gradients: Tensor1D', function () {
+        var aValues = [-0.1, 0.2, 0.3, -0.5];
+        var dyValues = [1, 2, 3, 4];
+        var a = tf.tensor1d(aValues);
+        var dy = tf.tensor1d(dyValues);
+        var gradients = tf.grad(function (a) { return tf.acos(a); })(a, dy);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] =
+                (-1 * dyValues[i]) / Math.sqrt(1 - (aValues[i] * aValues[i]));
+        }
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, expected);
+    });
+    it('gradients: Tensor2D', function () {
+        var aValues = [-0.3, 0.1, 0.2, 0.3];
+        var dyValues = [1, 2, 3, 4];
+        var a = tf.tensor2d(aValues, [2, 2]);
+        var dy = tf.tensor2d(dyValues, [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.acos(a); })(a, dy);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] =
+                (-1 * dyValues[i]) / Math.sqrt(1 - (aValues[i] * aValues[i]));
+        }
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, expected);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.acos({}); })
+            .toThrowError(/Argument 'x' passed to 'acos' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var values = [.1, -3, 2, 7, -4];
+        var result = tf.acos(values);
+        var expected = [];
+        for (var i = 0; i < values.length; i++) {
+            expected[i] = Math.acos(values[i]);
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.acos('q'); })
+            .toThrowError(/Argument 'x' passed to 'acos' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('atan', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var values = [1, -3, 2, 7, -4];
+        var a = tf.tensor1d(values);
+        var result = tf.atan(a);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = Math.atan(values[i]);
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('6D atan', function () {
+        var a = tf.range(1, 65).reshape([2, 2, 2, 2, 2, 2]);
+        var result = tf.atan(a);
+        var expected = [];
+        for (var i = 1; i < 65; ++i) {
+            expected[i - 1] = Math.atan(i);
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor1d([4, NaN, 0]);
+        var res = tf.atan(a);
+        test_util_1.expectArraysClose(res, [Math.atan(4), NaN, Math.atan(0)]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(0.5);
+        var dy = tf.scalar(8);
+        var gradients = tf.grad(function (a) { return tf.atan(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [8 / (1 + (0.5 * 0.5))]);
+    });
+    it('gradients: Tensor1D', function () {
+        var aValues = [-0.1, 0.2, 0.3, -0.5];
+        var dyValues = [1, 2, 3, 4];
+        var a = tf.tensor1d(aValues);
+        var dy = tf.tensor1d(dyValues);
+        var gradients = tf.grad(function (a) { return tf.atan(a); })(a, dy);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = dyValues[i] / (1 + (aValues[i] * aValues[i]));
+        }
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, expected);
+    });
+    it('gradients: Tensor2D', function () {
+        var aValues = [-0.3, 0.1, 0.2, 0.3];
+        var dyValues = [1, 2, 3, 4];
+        var a = tf.tensor2d(aValues, [2, 2]);
+        var dy = tf.tensor2d(dyValues, [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.atan(a); })(a, dy);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = dyValues[i] / (1 + (aValues[i] * aValues[i]));
+        }
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, expected);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.atan({}); })
+            .toThrowError(/Argument 'x' passed to 'atan' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var values = [1, -3, 2, 7, -4];
+        var result = tf.atan(values);
+        var expected = [];
+        for (var i = 0; i < values.length; i++) {
+            expected[i] = Math.atan(values[i]);
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.atan('q'); })
+            .toThrowError(/Argument 'x' passed to 'atan' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('sinh', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var values = [1, -3, 2, -1, -4];
+        var a = tf.tensor1d(values);
+        var result = tf.sinh(a);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = Math.sinh(values[i]);
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor1d([4, NaN, 0]);
+        var res = tf.sinh(a);
+        test_util_1.expectArraysClose(res, [Math.sinh(4), NaN, Math.sinh(0)]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(0.5);
+        var dy = tf.scalar(8);
+        var gradients = tf.grad(function (a) { return tf.sinh(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [8 * Math.cosh(0.5)]);
+    });
+    it('gradients: Tensor1D', function () {
+        var aValues = [-1, 2, 3, -5];
+        var dyValues = [1, 2, 3, 4];
+        var a = tf.tensor1d(aValues);
+        var dy = tf.tensor1d(dyValues);
+        var gradients = tf.grad(function (a) { return tf.sinh(a); })(a, dy);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = dyValues[i] * Math.cosh(aValues[i]);
+        }
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, expected);
+    });
+    it('gradients: Tensor2D', function () {
+        var aValues = [-3, 1, 2, 3];
+        var dyValues = [1, 2, 3, 4];
+        var a = tf.tensor2d(aValues, [2, 2]);
+        var dy = tf.tensor2d(dyValues, [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.sinh(a); })(a, dy);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = dyValues[i] * Math.cosh(aValues[i]);
+        }
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, expected);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.sinh({}); })
+            .toThrowError(/Argument 'x' passed to 'sinh' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var values = [1, -3, 2, -1, -4];
+        var result = tf.sinh(values);
+        var expected = [];
+        for (var i = 0; i < values.length; i++) {
+            expected[i] = Math.sinh(values[i]);
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.sinh('q'); })
+            .toThrowError(/Argument 'x' passed to 'sinh' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('cosh', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var values = [1, -3, 2, -1, -4];
+        var a = tf.tensor1d(values);
+        var result = tf.cosh(a);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = Math.cosh(values[i]);
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor1d([4, NaN, 0]);
+        var res = tf.cosh(a);
+        test_util_1.expectArraysClose(res, [Math.cosh(4), NaN, Math.cosh(0)]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(0.5);
+        var dy = tf.scalar(8);
+        var gradients = tf.grad(function (a) { return tf.cosh(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [8 * Math.sinh(0.5)]);
+    });
+    it('gradients: Tensor1D', function () {
+        var aValues = [-1, 2, 3, -5];
+        var dyValues = [1, 2, 3, 4];
+        var a = tf.tensor1d(aValues);
+        var dy = tf.tensor1d(dyValues);
+        var gradients = tf.grad(function (a) { return tf.cosh(a); })(a, dy);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = dyValues[i] * Math.sinh(aValues[i]);
+        }
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, expected);
+    });
+    it('gradients: Tensor2D', function () {
+        var aValues = [-3, 1, 2, 3];
+        var dyValues = [1, 2, 3, 4];
+        var a = tf.tensor2d(aValues, [2, 2]);
+        var dy = tf.tensor2d(dyValues, [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.cosh(a); })(a, dy);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = dyValues[i] * Math.sinh(aValues[i]);
+        }
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, expected);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.cosh({}); })
+            .toThrowError(/Argument 'x' passed to 'cosh' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var values = [1, -3, 2, -1, -4];
+        var result = tf.cosh(values);
+        var expected = [];
+        for (var i = 0; i < values.length; i++) {
+            expected[i] = Math.cosh(values[i]);
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.cosh('q'); })
+            .toThrowError(/Argument 'x' passed to 'cosh' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('tanh', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var values = [1, -3, 2, 7, -4];
+        var a = tf.tensor1d(values);
+        var result = tf.tanh(a);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = util.tanh(values[i]);
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor1d([4, NaN, 0]);
+        var res = tf.tanh(a);
+        test_util_1.expectArraysClose(res, [util.tanh(4), NaN, util.tanh(0)]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(0.5);
+        var dy = tf.scalar(8);
+        var gradients = tf.grad(function (a) { return tf.tanh(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [8 * (1 - (Math.tanh(0.5) * Math.tanh(0.5)))]);
+    });
+    it('gradients: Tensor1D', function () {
+        var aValues = [-1, 2, 3, -5];
+        var dyValues = [1, 2, 3, 4];
+        var a = tf.tensor1d(aValues);
+        var dy = tf.tensor1d(dyValues);
+        var gradients = tf.grad(function (a) { return tf.tanh(a); })(a, dy);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] =
+                dyValues[i] * (1 - (Math.tanh(aValues[i]) * Math.tanh(aValues[i])));
+        }
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, expected);
+    });
+    it('gradients: Tensor2D', function () {
+        var aValues = [-3, 1, 2, 3];
+        var dyValues = [1, 2, 3, 4];
+        var a = tf.tensor2d(aValues, [2, 2]);
+        var dy = tf.tensor2d(dyValues, [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.tanh(a); })(a, dy);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] =
+                dyValues[i] * (1 - (Math.tanh(aValues[i]) * Math.tanh(aValues[i])));
+        }
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, expected);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.tanh({}); })
+            .toThrowError(/Argument 'x' passed to 'tanh' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var values = [1, -3, 2, 7, -4];
+        var result = tf.tanh(values);
+        var expected = [];
+        for (var i = 0; i < values.length; i++) {
+            expected[i] = util.tanh(values[i]);
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.tanh('q'); })
+            .toThrowError(/Argument 'x' passed to 'tanh' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('leakyRelu', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var a = tf.tensor1d([0, 1, -2]);
+        var result = tf.leakyRelu(a);
+        expect(result.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(result, [0, 1, -0.4]);
+    });
+    it('propagates NaN', function () {
+        var a = tf.tensor1d([0, 1, NaN]);
+        var result = tf.leakyRelu(a);
+        expect(result.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(result, [0, 1, NaN]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(-4);
+        var dy = tf.scalar(8);
+        var alpha = 0.1;
+        var gradients = tf.grad(function (a) { return tf.leakyRelu(a, alpha); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [8 * alpha]);
+    });
+    it('gradients: Tensor1D', function () {
+        var aValues = [1, -1, 0.1];
+        var dyValues = [1, 2, 3];
+        var alpha = 0.1;
+        var a = tf.tensor1d(aValues);
+        var dy = tf.tensor1d(dyValues);
+        var gradients = tf.grad(function (a) { return tf.leakyRelu(a, alpha); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [1, 2 * alpha, 3]);
+    });
+    it('gradients: Tensor2D', function () {
+        var aValues = [1, -1, 0.1, 0.5];
+        var dyValues = [1, 2, 3, 4];
+        var alpha = 0.1;
+        var a = tf.tensor2d(aValues, [2, 2]);
+        var dy = tf.tensor2d(dyValues, [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.leakyRelu(a, alpha); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [1, 2 * alpha, 3, 4]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.leakyRelu({}); })
+            .toThrowError(/Argument 'x' passed to 'leakyRelu' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var result = tf.leakyRelu([0, 1, -2]);
+        expect(result.shape).toEqual([3]);
+        test_util_1.expectArraysClose(result, [0, 1, -0.4]);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.leakyRelu('q'); })
+            .toThrowError(/Argument 'x' passed to 'leakyRelu' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('elu', test_util_1.ALL_ENVS, function () {
+    it('calculate elu', function () {
+        var a = tf.tensor1d([1, -1, 0]);
+        var result = tf.elu(a);
+        expect(result.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(result, [1, -0.6321, 0]);
+    });
+    it('elu propagates NaN', function () {
+        var a = tf.tensor1d([1, NaN]);
+        var result = tf.elu(a);
+        expect(result.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(result, [1, NaN]);
+    });
+    it('derivative', function () {
+        var x = tf.tensor1d([1, 3, -2]);
+        var dy = tf.tensor1d([5, 50, 500]);
+        var gradients = tf.grad(function (a) { return tf.elu(a); })(x, dy);
+        expect(gradients.shape).toEqual(x.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [5, 50, 500 * Math.exp(-2)]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.elu({}); })
+            .toThrowError(/Argument 'x' passed to 'elu' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var result = tf.elu([1, -1, 0]);
+        expect(result.shape).toEqual(result.shape);
+        test_util_1.expectArraysClose(result, [1, -0.6321, 0]);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.elu('q'); })
+            .toThrowError(/Argument 'x' passed to 'elu' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('selu', test_util_1.ALL_ENVS, function () {
+    var scaleAlpha = selu_util.SELU_SCALEALPHA;
+    var scale = selu_util.SELU_SCALE;
+    it('calculate selu', function () {
+        var a = tf.tensor1d([1, -1, 0]);
+        var result = tf.selu(a);
+        expect(result.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(result, [1.0507, -1.1113, 0]);
+    });
+    it('selu propagates NaN', function () {
+        var a = tf.tensor1d([1, NaN]);
+        var result = tf.selu(a);
+        expect(result.shape).toEqual(a.shape);
+        test_util_1.expectArraysClose(result, [1.0507, NaN]);
+    });
+    it('gradients: Scalar', function () {
+        var aValue = 1;
+        var dyValue = 1;
+        var a = tf.scalar(aValue);
+        var dy = tf.scalar(dyValue);
+        var gradients = tf.grad(function (a) { return tf.selu(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [dyValue * scale]);
+        aValue = -1;
+        dyValue = 2;
+        a = tf.scalar(aValue);
+        dy = tf.scalar(dyValue);
+        gradients = tf.grad(function (a) { return tf.selu(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [dyValue * scaleAlpha * Math.exp(aValue)]);
+    });
+    it('gradients: Tensor1D', function () {
+        var aValues = [1, -1, 0];
+        var dyValues = [1, 2, 3];
+        var a = tf.tensor1d(aValues);
+        var dy = tf.tensor1d(dyValues);
+        var gradients = tf.grad(function (a) { return tf.selu(a); })(a, dy);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            if (aValues[i] > 0) {
+                expected[i] = dyValues[i] * scale;
+            }
+            else {
+                expected[i] = dyValues[i] * scaleAlpha * Math.exp(aValues[i]);
+            }
+        }
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, expected);
+    });
+    it('gradients: Tensor2D', function () {
+        var aValues = [1, -1, 0, 0.5];
+        var dyValues = [1, 2, 3, 4];
+        var a = tf.tensor2d(aValues, [2, 2]);
+        var dy = tf.tensor2d(dyValues, [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.selu(a); })(a, dy);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            if (aValues[i] > 0) {
+                expected[i] = dyValues[i] * scale;
+            }
+            else {
+                expected[i] = dyValues[i] * scaleAlpha * Math.exp(aValues[i]);
+            }
+        }
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, expected);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.selu({}); })
+            .toThrowError(/Argument 'x' passed to 'selu' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var result = tf.selu([1, -1, 0]);
+        expect(result.shape).toEqual([3]);
+        test_util_1.expectArraysClose(result, [1.0507, -1.1113, 0]);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.selu('q'); })
+            .toThrowError(/Argument 'x' passed to 'selu' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('packed clip', test_util_1.WEBGL_ENVS, function () {
+    var webglPackedClipSavedFlag = tf.ENV.get('WEBGL_PACK_CLIP');
+    beforeAll(function () {
+        tf.ENV.set('WEBGL_PACK_CLIP', true);
+    });
+    afterAll(function () {
+        tf.ENV.set('WEBGL_PACK_CLIP', webglPackedClipSavedFlag);
+    });
+    it('should not leak memory', function () {
+        var a = tf.tensor1d([3, -1, 0, 100, -7, 2]);
+        var min = -1;
+        var max = 50;
+        var startNumBytes = tf.memory().numBytes;
+        var startNumTensors = tf.memory().numTensors;
+        tf.clipByValue(a, min, max);
+        var endNumBytes = tf.memory().numBytes;
+        var endNumTensors = tf.memory().numTensors;
+        expect(endNumBytes - startNumBytes).toEqual(24);
+        expect(endNumTensors - startNumTensors).toEqual(1);
+    });
+    it('basic', function () {
+        var a = tf.tensor1d([3, -1, 0, 100, -7, 2]);
+        var min = -1;
+        var max = 50;
+        var result = tf.clipByValue(a, min, max);
+        test_util_1.expectArraysClose(result, [3, -1, 0, 50, -1, 2]);
+    });
+    it('should work for scalars', function () {
+        var a = tf.scalar(-4);
+        var min = -1;
+        var max = 50;
+        var result = tf.clipByValue(a, min, max);
+        test_util_1.expectArraysClose(result, [min]);
+    });
+    it('derivative: 1D tensor with max or min value', function () {
+        var min = -1;
+        var max = 2;
+        var x = tf.tensor1d([-1, 1, 2, 3]);
+        var dy = tf.tensor1d([1, 10, 100, 1000]);
+        var gradients = tf.grad(function (x) { return x.clipByValue(min, max); })(x, dy);
+        expect(gradients.shape).toEqual(x.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [1, 10, 100, 0]);
+    });
+});
+jasmine_util_1.describeWithFlags('clip', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var a = tf.tensor1d([3, -1, 0, 100, -7, 2]);
+        var min = -1;
+        var max = 50;
+        var result = tf.clipByValue(a, min, max);
+        test_util_1.expectArraysClose(result, [3, -1, 0, 50, -1, 2]);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor1d([3, -1, 0, 100, -7, 2, NaN]);
+        var min = -1;
+        var max = 50;
+        var result = tf.clipByValue(a, min, max);
+        test_util_1.expectArraysClose(result, [3, -1, 0, 50, -1, 2, NaN]);
+    });
+    it('min greater than max', function () {
+        var a = tf.tensor1d([3, -1, 0, 100, -7, 2]);
+        var min = 1;
+        var max = -1;
+        var f = function () {
+            tf.clipByValue(a, min, max);
+        };
+        expect(f).toThrowError();
+    });
+    it('derivative: 1D tensor', function () {
+        var min = -1;
+        var max = 2;
+        var x = tf.tensor1d([3, -2, 1]);
+        var dy = tf.tensor1d([5, 50, 500]);
+        var gradients = tf.grad(function (x) { return x.clipByValue(min, max); })(x, dy);
+        expect(gradients.shape).toEqual(x.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [0, 0, 500]);
+    });
+    it('derivative: 1D tensor with max or min value', function () {
+        var min = -1;
+        var max = 2;
+        var x = tf.tensor1d([-1, 1, 2, 3]);
+        var dy = tf.tensor1d([1, 10, 100, 1000]);
+        var gradients = tf.grad(function (x) { return x.clipByValue(min, max); })(x, dy);
+        expect(gradients.shape).toEqual(x.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [1, 10, 100, 0]);
+    });
+    it('derivative: scalar', function () {
+        var min = -1;
+        var max = 2;
+        var x = tf.scalar(-10);
+        var dy = tf.scalar(5);
+        var gradients = tf.grad(function (x) { return x.clipByValue(min, max); })(x, dy);
+        expect(gradients.shape).toEqual(x.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [0]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.clipByValue({}, 0, 1); })
+            .toThrowError(/Argument 'x' passed to 'clipByValue' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var min = -1;
+        var max = 50;
+        var result = tf.clipByValue([3, -1, 0, 100, -7, 2], min, max);
+        test_util_1.expectArraysClose(result, [3, -1, 0, 50, -1, 2]);
+    });
+    it('clip(x, eps, 1-eps) never returns 0 or 1', function () {
+        var min = tf.ENV.get('EPSILON');
+        var max = 0.5;
+        var res = tf.clipByValue([0, 1], min, max).dataSync();
+        expect(res[0]).toBeGreaterThan(0);
+        expect(res[1]).toBeCloseTo(max);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.clipByValue('q', 0, 1); })
+            .toThrowError(/Argument 'x' passed to 'clipByValue' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('round', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var a = tf.tensor1d([0.9, 2.5, 2.3, 1.5, -4.5]);
+        var r = a.round();
+        test_util_1.expectNumbersClose(r.get(0), 1.0);
+        test_util_1.expectNumbersClose(r.get(1), 2.0);
+        test_util_1.expectNumbersClose(r.get(2), 2.0);
+        test_util_1.expectNumbersClose(r.get(3), 2.0);
+        test_util_1.expectNumbersClose(r.get(4), -4.0);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor1d([1.5, NaN, -1.4]);
+        var r = tf.round(a);
+        test_util_1.expectArraysClose(r, [2, NaN, -1]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(5.2);
+        var dy = tf.scalar(3);
+        var gradients = tf.grad(function (a) { return tf.round(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [0]);
+    });
+    it('gradients: Tensor1D', function () {
+        var a = tf.tensor1d([-1.1, 2.6, 3, -5.9]);
+        var dy = tf.tensor1d([1, 2, 3, 4]);
+        var gradients = tf.grad(function (a) { return tf.round(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [0, 0, 0, 0]);
+    });
+    it('gradients: Tensor2D', function () {
+        var a = tf.tensor2d([-3, 1, 2.2, 3], [2, 2]);
+        var dy = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.round(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [0, 0, 0, 0]);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.round({}); })
+            .toThrowError(/Argument 'x' passed to 'round' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var r = tf.round([0.9, 2.5, 2.3, 1.5, -4.5]);
+        test_util_1.expectArraysClose(r, [1, 2, 2, 2, -4]);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.round('q'); })
+            .toThrowError(/Argument 'x' passed to 'round' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('asinh', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var values = [1, -3, 2, 7, -4];
+        var a = tf.tensor1d(values);
+        var result = tf.asinh(a);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = Math.asinh(values[i]);
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('scalar', function () {
+        var a = tf.scalar(1);
+        var result = tf.asinh(a);
+        var expected = [Math.asinh(1)];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('tensor2D', function () {
+        var values = [1, -3, 2, 7];
+        var a = tf.tensor2d(values, [2, 2]);
+        var result = tf.asinh(a);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = Math.asinh(values[i]);
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor1d([4, NaN, 0]);
+        var res = tf.asinh(a);
+        test_util_1.expectArraysClose(res, [Math.asinh(4), NaN, Math.asinh(0)]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(0.5);
+        var dy = tf.scalar(8);
+        var gradients = tf.grad(function (a) { return tf.asinh(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [8 / Math.sqrt(1.0 + 0.5 * 0.5)]);
+    });
+    it('gradients: Tensor1D', function () {
+        var aValues = [-1, 2, 3, -5];
+        var dyValues = [1, 2, 3, 4];
+        var a = tf.tensor1d(aValues);
+        var dy = tf.tensor1d(dyValues);
+        var gradients = tf.grad(function (a) { return tf.asinh(a); })(a, dy);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = dyValues[i] / Math.sqrt(1 + aValues[i] * aValues[i]);
+        }
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, expected);
+    });
+    it('gradients: Tensor2D', function () {
+        var aValues = [-3, 1, 2, 3];
+        var dyValues = [1, 2, 3, 4];
+        var a = tf.tensor2d(aValues, [2, 2]);
+        var dy = tf.tensor2d(dyValues, [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.asinh(a); })(a, dy);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = dyValues[i] / Math.sqrt(1 + aValues[i] * aValues[i]);
+        }
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, expected);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.asinh({}); })
+            .toThrowError(/Argument 'x' passed to 'asinh' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var values = [1, -3, 2, 7, -4];
+        var result = tf.asinh(values);
+        var expected = [];
+        for (var i = 0; i < values.length; i++) {
+            expected[i] = Math.asinh(values[i]);
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.asinh('q'); })
+            .toThrowError(/Argument 'x' passed to 'asinh' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('acosh', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var values = [2, 3, 4, 5, 6];
+        var a = tf.tensor1d(values);
+        var result = tf.acosh(a);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = Math.acosh(values[i]);
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('scalar', function () {
+        var value = 2;
+        var a = tf.scalar(value);
+        var result = tf.acosh(a);
+        var expected = [Math.acosh(value)];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('tensor2d', function () {
+        var values = [2, 3, 4, 5];
+        var a = tf.tensor2d(values, [2, 2]);
+        var result = tf.acosh(a);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = Math.acosh(values[i]);
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor1d([4, NaN, 2]);
+        var res = tf.acosh(a);
+        test_util_1.expectArraysClose(res, [Math.acosh(4), NaN, Math.acosh(2)]);
+    });
+    it('NaN outside function domain', function () {
+        var a = tf.tensor1d([4, -1, 2]);
+        var res = tf.acosh(a);
+        test_util_1.expectArraysClose(res, [Math.acosh(4), NaN, Math.acosh(2)]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(1.5);
+        var dy = tf.scalar(8);
+        var gradients = tf.grad(function (a) { return tf.acosh(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [8.0 / Math.sqrt(1.5 * 1.5 - 1.0)]);
+    });
+    it('gradients: Tensor1D', function () {
+        var aValues = [2, 3, 5, 10];
+        var dyValues = [1, 2, 3, 4];
+        var a = tf.tensor1d(aValues);
+        var dy = tf.tensor1d(dyValues);
+        var gradients = tf.grad(function (a) { return tf.acosh(a); })(a, dy);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = dyValues[i] / Math.sqrt(Math.pow(aValues[i], 2) - 1.0);
+        }
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, expected);
+    });
+    it('gradients: Tensor2D', function () {
+        var aValues = [2, 3, 5, 7];
+        var dyValues = [1, 2, 3, 4];
+        var a = tf.tensor2d(aValues, [2, 2]);
+        var dy = tf.tensor2d(dyValues, [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.acosh(a); })(a, dy);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = dyValues[i] / Math.sqrt(Math.pow(aValues[i], 2) - 1.0);
+        }
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, expected);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.acosh({}); })
+            .toThrowError(/Argument 'x' passed to 'acosh' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var values = [2, 3, 4, 5, 6];
+        var result = tf.acosh(values);
+        var expected = [];
+        for (var i = 0; i < values.length; i++) {
+            expected[i] = Math.acosh(values[i]);
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.acosh('q'); })
+            .toThrowError(/Argument 'x' passed to 'acosh' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('atanh', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var values = [-0.25, 0.25, 0.5, .75, -0.4];
+        var a = tf.tensor1d(values);
+        var result = tf.atanh(a);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = Math.atanh(values[i]);
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('scalar', function () {
+        var value = 0.2;
+        var a = tf.scalar(value);
+        var result = tf.atanh(a);
+        var expected = [Math.atanh(value)];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('tensor2d', function () {
+        var values = [0.2, 0.3, 0.4, 0.5];
+        var a = tf.tensor2d(values, [2, 2]);
+        var result = tf.atanh(a);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = Math.atanh(values[i]);
+        }
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor1d([0.5, NaN, 0]);
+        var res = tf.atanh(a);
+        test_util_1.expectArraysClose(res, [Math.atanh(0.5), NaN, Math.atanh(0)]);
+    });
+    it('NaN outside function domain', function () {
+        var a = tf.tensor1d([-2, 0, 2]);
+        var res = tf.atanh(a);
+        test_util_1.expectArraysClose(res, [NaN, Math.atanh(0), NaN]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(0.5);
+        var dy = tf.scalar(8);
+        var gradients = tf.grad(function (a) { return tf.atanh(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [8 / (1 - 0.5 * 0.5)]);
+    });
+    it('gradients: Tensor1D', function () {
+        var aValues = [-0.1, 0.2, 0.3, -0.5];
+        var dyValues = [1, 2, 3, 4];
+        var a = tf.tensor1d(aValues);
+        var dy = tf.tensor1d(dyValues);
+        var gradients = tf.grad(function (a) { return tf.atanh(a); })(a, dy);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = dyValues[i] / (1 - Math.pow(aValues[i], 2));
+        }
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, expected);
+    });
+    it('gradients: Tensor2D', function () {
+        var aValues = [-0.3, 0.1, 0.2, 0.3];
+        var dyValues = [1, 2, 3, 4];
+        var a = tf.tensor2d(aValues, [2, 2]);
+        var dy = tf.tensor2d(dyValues, [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.atanh(a); })(a, dy);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = dyValues[i] / (1 - Math.pow(aValues[i], 2));
+        }
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, expected);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.atanh({}); })
+            .toThrowError(/Argument 'x' passed to 'atanh' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var result = tf.atanh(0.2);
+        test_util_1.expectNumbersClose(result.get(), Math.atanh(0.2));
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.atanh('q'); })
+            .toThrowError(/Argument 'x' passed to 'atanh' must be numeric/);
+    });
+});
+jasmine_util_1.describeWithFlags('erf', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var values = [-0.25, 0.25, 0.5, .75, -0.4];
+        var a = tf.tensor1d(values);
+        var result = tf.erf(a);
+        var expected = [-0.2763264, 0.2763264, 0.5204999, 0.7111556, -0.4283924];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('scalar', function () {
+        var a = tf.scalar(1);
+        var result = tf.erf(a);
+        var expected = [0.8427008];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('scalar in int32', function () {
+        var a = tf.scalar(1, 'int32');
+        var result = tf.erf(a);
+        var expected = [0.8427008];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('tensor2d', function () {
+        var values = [0.2, 0.3, 0.4, 0.5];
+        var a = tf.tensor2d(values, [2, 2]);
+        var result = tf.erf(a);
+        var expected = [0.2227026, 0.32862678, 0.42839235, 0.5204999];
+        test_util_1.expectArraysClose(result, expected);
+    });
+    it('propagates NaNs', function () {
+        var a = tf.tensor1d([0.5, NaN, 0]);
+        var res = tf.erf(a);
+        test_util_1.expectArraysClose(res, [0.5204999, NaN, 0.0]);
+    });
+    it('gradients: Scalar', function () {
+        var a = tf.scalar(0.5);
+        var dy = tf.scalar(8);
+        var gradients = tf.grad(function (a) { return tf.erf(a); })(a, dy);
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, [8 * 2 * Math.exp(-0.5 * 0.5) / Math.sqrt(Math.PI)]);
+    });
+    it('gradients: Tensor1D', function () {
+        var aValues = [-0.1, 0.2, 0.3, -0.5];
+        var dyValues = [1, 2, 3, 4];
+        var a = tf.tensor1d(aValues);
+        var dy = tf.tensor1d(dyValues);
+        var gradients = tf.grad(function (a) { return tf.erf(a); })(a, dy);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = dyValues[i] * 2 * Math.exp(-aValues[i] * aValues[i]) /
+                Math.sqrt(Math.PI);
+        }
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, expected);
+    });
+    it('gradients: Tensor2D', function () {
+        var aValues = [-0.3, 0.1, 0.2, 0.3];
+        var dyValues = [1, 2, 3, 4];
+        var a = tf.tensor2d(aValues, [2, 2]);
+        var dy = tf.tensor2d(dyValues, [2, 2]);
+        var gradients = tf.grad(function (a) { return tf.erf(a); })(a, dy);
+        var expected = [];
+        for (var i = 0; i < a.size; i++) {
+            expected[i] = dyValues[i] * 2 * Math.exp(-aValues[i] * aValues[i]) /
+                Math.sqrt(Math.PI);
+        }
+        expect(gradients.shape).toEqual(a.shape);
+        expect(gradients.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(gradients, expected);
+    });
+    it('throws when passed a non-tensor', function () {
+        expect(function () { return tf.erf({}); })
+            .toThrowError(/Argument 'x' passed to 'erf' must be a Tensor/);
+    });
+    it('accepts a tensor-like object', function () {
+        var result = tf.erf(1);
+        test_util_1.expectNumbersClose(result.get(), 0.8427008);
+    });
+    it('throws for string tensor', function () {
+        expect(function () { return tf.erf('q'); })
+            .toThrowError(/Argument 'x' passed to 'erf' must be numeric/);
+    });
+});
+//# sourceMappingURL=unary_ops_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/ops/unary_ops_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/ops/unary_ops_test.js.map
new file mode 100644
index 0000000..faa3729
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/ops/unary_ops_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer.d.ts b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer.d.ts
new file mode 100644
index 0000000..c11bdc5
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer.d.ts
@@ -0,0 +1,20 @@
+import { ConfigDict, Serializable, SerializableConstructor } from '../serialization';
+import { NamedVariableMap } from '../tensor_types';
+import { Optimizer } from './optimizer';
+export declare class AdadeltaOptimizer extends Optimizer {
+    protected learningRate: number;
+    protected rho: number;
+    protected epsilon: number;
+    static className: string;
+    private c;
+    private epsilonScalar;
+    private rhoScalar;
+    private oneMinusRho;
+    private accumulatedGrads;
+    private accumulatedUpdates;
+    constructor(learningRate: number, rho: number, epsilon?: number);
+    applyGradients(variableGradients: NamedVariableMap): void;
+    dispose(): void;
+    getConfig(): ConfigDict;
+    static fromConfig<T extends Serializable>(cls: SerializableConstructor<T>, config: ConfigDict): T;
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer.js b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer.js
new file mode 100644
index 0000000..120d1f2
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer.js
@@ -0,0 +1,106 @@
+"use strict";
+var __extends = (this && this.__extends) || (function () {
+    var extendStatics = Object.setPrototypeOf ||
+        ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
+        function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
+    return function (d, b) {
+        extendStatics(d, b);
+        function __() { this.constructor = d; }
+        d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
+    };
+})();
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var globals_1 = require("../globals");
+var ops_1 = require("../ops/ops");
+var serialization_1 = require("../serialization");
+var optimizer_1 = require("./optimizer");
+var AdadeltaOptimizer = (function (_super) {
+    __extends(AdadeltaOptimizer, _super);
+    function AdadeltaOptimizer(learningRate, rho, epsilon) {
+        if (epsilon === void 0) { epsilon = null; }
+        var _this = _super.call(this) || this;
+        _this.learningRate = learningRate;
+        _this.rho = rho;
+        _this.epsilon = epsilon;
+        _this.accumulatedGrads = {};
+        _this.accumulatedUpdates = {};
+        _this.c = globals_1.keep(ops_1.scalar(-learningRate));
+        _this.rhoScalar = globals_1.keep(ops_1.scalar(rho));
+        _this.oneMinusRho = globals_1.keep(ops_1.scalar(1 - rho));
+        if (epsilon === null) {
+            epsilon = environment_1.ENV.get('EPSILON');
+        }
+        _this.epsilonScalar = globals_1.keep(ops_1.scalar(epsilon));
+        return _this;
+    }
+    AdadeltaOptimizer.prototype.applyGradients = function (variableGradients) {
+        var _this = this;
+        var _loop_1 = function (variableName) {
+            var value = environment_1.ENV.engine.registeredVariables[variableName];
+            if (this_1.accumulatedGrads[variableName] == null) {
+                var trainable_1 = false;
+                globals_1.tidy(function () {
+                    _this.accumulatedGrads[variableName] =
+                        ops_1.zerosLike(value).variable(trainable_1);
+                });
+            }
+            if (this_1.accumulatedUpdates[variableName] == null) {
+                var trainable_2 = false;
+                globals_1.tidy(function () {
+                    _this.accumulatedUpdates[variableName] =
+                        ops_1.zerosLike(value).variable(trainable_2);
+                });
+            }
+            var gradient = variableGradients[variableName];
+            var accumulatedGrad = this_1.accumulatedGrads[variableName];
+            var accumulatedUpdate = this_1.accumulatedUpdates[variableName];
+            globals_1.tidy(function () {
+                var newAccumulatedGrad = _this.rhoScalar.mul(accumulatedGrad)
+                    .add(_this.oneMinusRho.mul(gradient.square()));
+                var updates = accumulatedUpdate.add(_this.epsilonScalar)
+                    .sqrt()
+                    .div(accumulatedGrad.add(_this.epsilonScalar).sqrt())
+                    .mul(gradient);
+                var newAccumulatedUpdate = _this.rhoScalar.mul(accumulatedUpdate)
+                    .add(_this.oneMinusRho.mul(updates.square()));
+                _this.accumulatedGrads[variableName].assign(newAccumulatedGrad);
+                _this.accumulatedUpdates[variableName].assign(newAccumulatedUpdate);
+                var newValue = _this.c.mul(updates).add(value);
+                value.assign(newValue);
+            });
+        };
+        var this_1 = this;
+        for (var variableName in variableGradients) {
+            _loop_1(variableName);
+        }
+    };
+    AdadeltaOptimizer.prototype.dispose = function () {
+        var _this = this;
+        this.c.dispose();
+        this.epsilonScalar.dispose();
+        this.rhoScalar.dispose();
+        this.oneMinusRho.dispose();
+        if (this.accumulatedUpdates != null) {
+            Object.keys(this.accumulatedUpdates)
+                .forEach(function (name) { return _this.accumulatedUpdates[name].dispose(); });
+            Object.keys(this.accumulatedGrads)
+                .forEach(function (name) { return _this.accumulatedGrads[name].dispose(); });
+        }
+    };
+    AdadeltaOptimizer.prototype.getConfig = function () {
+        return {
+            learningRate: this.learningRate,
+            rho: this.rho,
+            epsilon: this.epsilon
+        };
+    };
+    AdadeltaOptimizer.fromConfig = function (cls, config) {
+        return new cls(config.learningRate, config.rho, config.epsilon);
+    };
+    AdadeltaOptimizer.className = 'AdadeltaOptimizer';
+    return AdadeltaOptimizer;
+}(optimizer_1.Optimizer));
+exports.AdadeltaOptimizer = AdadeltaOptimizer;
+serialization_1.registerClass(AdadeltaOptimizer);
+//# sourceMappingURL=adadelta_optimizer.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer.js.map b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer.js.map
new file mode 100644
index 0000000..170f1eb
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"adadelta_optimizer.js","sourceRoot":"","sources":["../../src/optimizers/adadelta_optimizer.ts"],"names":[],"mappings":";;;;;;;;;;;;AAiBA,8CAAmC;AACnC,sCAAsC;AACtC,kCAA6C;AAC7C,kDAAkG;AAGlG,yCAAsC;AAGtC;IAAuC,qCAAS;IAW9C,2BACc,YAAoB,EAAY,GAAW,EAC3C,OAAsB;QAAtB,wBAAA,EAAA,cAAsB;QAFpC,YAGE,iBAAO,SAWR;QAba,kBAAY,GAAZ,YAAY,CAAQ;QAAY,SAAG,GAAH,GAAG,CAAQ;QAC3C,aAAO,GAAP,OAAO,CAAe;QAL5B,sBAAgB,GAAqB,EAAE,CAAC;QACxC,wBAAkB,GAAqB,EAAE,CAAC;QAOhD,KAAI,CAAC,CAAC,GAAG,cAAI,CAAC,YAAM,CAAC,CAAC,YAAY,CAAC,CAAC,CAAC;QACrC,KAAI,CAAC,SAAS,GAAG,cAAI,CAAC,YAAM,CAAC,GAAG,CAAC,CAAC,CAAC;QACnC,KAAI,CAAC,WAAW,GAAG,cAAI,CAAC,YAAM,CAAC,CAAC,GAAG,GAAG,CAAC,CAAC,CAAC;QAEzC,IAAI,OAAO,KAAK,IAAI,EAAE;YACpB,OAAO,GAAG,iBAAG,CAAC,GAAG,CAAC,SAAS,CAAC,CAAC;SAC9B;QAED,KAAI,CAAC,aAAa,GAAG,cAAI,CAAC,YAAM,CAAC,OAAO,CAAC,CAAC,CAAC;;IAC7C,CAAC;IAED,0CAAc,GAAd,UAAe,iBAAmC;QAAlD,iBA2CC;gCA1CY,YAAY;YACrB,IAAM,KAAK,GAAG,iBAAG,CAAC,MAAM,CAAC,mBAAmB,CAAC,YAAY,CAAC,CAAC;YAC3D,IAAI,OAAK,gBAAgB,CAAC,YAAY,CAAC,IAAI,IAAI,EAAE;gBAC/C,IAAM,WAAS,GAAG,KAAK,CAAC;gBACxB,cAAI,CAAC;oBACH,KAAI,CAAC,gBAAgB,CAAC,YAAY,CAAC;wBAC/B,eAAS,CAAC,KAAK,CAAC,CAAC,QAAQ,CAAC,WAAS,CAAC,CAAC;gBAC3C,CAAC,CAAC,CAAC;aACJ;YACD,IAAI,OAAK,kBAAkB,CAAC,YAAY,CAAC,IAAI,IAAI,EAAE;gBACjD,IAAM,WAAS,GAAG,KAAK,CAAC;gBACxB,cAAI,CAAC;oBACH,KAAI,CAAC,kBAAkB,CAAC,YAAY,CAAC;wBACjC,eAAS,CAAC,KAAK,CAAC,CAAC,QAAQ,CAAC,WAAS,CAAC,CAAC;gBAC3C,CAAC,CAAC,CAAC;aACJ;YAED,IAAM,QAAQ,GAAG,iBAAiB,CAAC,YAAY,CAAC,CAAC;YACjD,IAAM,eAAe,GAAG,OAAK,gBAAgB,CAAC,YAAY,CAAC,CAAC;YAC5D,IAAM,iBAAiB,GAAG,OAAK,kBAAkB,CAAC,YAAY,CAAC,CAAC;YAEhE,cAAI,CAAC;gBACH,IAAM,kBAAkB,GACpB,KAAI,CAAC,SAAS,CAAC,GAAG,CAAC,eAAe,CAAC;qBAC9B,GAAG,CAAC,KAAI,CAAC,WAAW,CAAC,GAAG,CAAC,QAAQ,CAAC,MAAM,EAAE,CAAC,CAAC,CAAC;gBAEtD,IAAM,OAAO,GAAG,iBAAiB,CAAC,GAAG,CAAC,KAAI,CAAC,aAAa,CAAC;qBACpC,IAAI,EAAE;qBACN,GAAG,CAAC,eAAe,CAAC,GAAG,CAAC,KAAI,CAAC,aAAa,CAAC,CAAC,IAAI,EAAE,CAAC;qBACnD,GAAG,CAAC,QAAQ,CAAC,CAAC;gBAEnC,IAAM,oBAAoB,GACtB,KAAI,CAAC,SAAS,CAAC,GAAG,CAAC,iBAAiB,CAAC;qBAChC,GAAG,CAAC,KAAI,CAAC,WAAW,CAAC,GAAG,CAAC,OAAO,CAAC,MAAM,EAAE,CAAC,CAAC,CAAC;gBAErD,KAAI,CAAC,gBAAgB,CAAC,YAAY,CAAC,CAAC,MAAM,CAAC,kBAAkB,CAAC,CAAC;gBAC/D,KAAI,CAAC,kBAAkB,CAAC,YAAY,CAAC,CAAC,MAAM,CAAC,oBAAoB,CAAC,CAAC;gBAEnE,IAAM,QAAQ,GAAG,KAAI,CAAC,CAAC,CAAC,GAAG,CAAC,OAAO,CAAC,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC;gBAChD,KAAK,CAAC,MAAM,CAAC,QAAQ,CAAC,CAAC;YACzB,CAAC,CAAC,CAAC;QACL,CAAC;;QAzCD,KAAK,IAAM,YAAY,IAAI,iBAAiB;oBAAjC,YAAY;SAyCtB;IACH,CAAC;IAED,mCAAO,GAAP;QAAA,iBAWC;QAVC,IAAI,CAAC,CAAC,CAAC,OAAO,EAAE,CAAC;QACjB,IAAI,CAAC,aAAa,CAAC,OAAO,EAAE,CAAC;QAC7B,IAAI,CAAC,SAAS,CAAC,OAAO,EAAE,CAAC;QACzB,IAAI,CAAC,WAAW,CAAC,OAAO,EAAE,CAAC;QAC3B,IAAI,IAAI,CAAC,kBAAkB,IAAI,IAAI,EAAE;YACnC,MAAM,CAAC,IAAI,CAAC,IAAI,CAAC,kBAAkB,CAAC;iBAC/B,OAAO,CAAC,UAAA,IAAI,IAAI,OAAA,KAAI,CAAC,kBAAkB,CAAC,IAAI,CAAC,CAAC,OAAO,EAAE,EAAvC,CAAuC,CAAC,CAAC;YAC9D,MAAM,CAAC,IAAI,CAAC,IAAI,CAAC,gBAAgB,CAAC;iBAC7B,OAAO,CAAC,UAAA,IAAI,IAAI,OAAA,KAAI,CAAC,gBAAgB,CAAC,IAAI,CAAC,CAAC,OAAO,EAAE,EAArC,CAAqC,CAAC,CAAC;SAC7D;IACH,CAAC;IACD,qCAAS,GAAT;QACE,OAAO;YACL,YAAY,EAAE,IAAI,CAAC,YAAY;YAC/B,GAAG,EAAE,IAAI,CAAC,GAAG;YACb,OAAO,EAAE,IAAI,CAAC,OAAO;SACtB,CAAC;IACJ,CAAC;IACM,4BAAU,GAAjB,UACI,GAA+B,EAAE,MAAkB;QACrD,OAAO,IAAI,GAAG,CAAC,MAAM,CAAC,YAAY,EAAE,MAAM,CAAC,GAAG,EAAE,MAAM,CAAC,OAAO,CAAC,CAAC;IAClE,CAAC;IA5FM,2BAAS,GAAG,mBAAmB,CAAC;IA6FzC,wBAAC;CAAA,AA/FD,CAAuC,qBAAS,GA+F/C;AA/FY,8CAAiB;AAgG9B,6BAAa,CAAC,iBAAiB,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer_test.js b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer_test.js
new file mode 100644
index 0000000..3afad4c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer_test.js
@@ -0,0 +1,33 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('AdadeltaOptimizer', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var learningRate = .1;
+        var rho = .95;
+        var optimizer = tf.train.adadelta(learningRate, rho);
+        var x = tf.tensor1d([1, 2]).variable();
+        var f = function () { return x.square().sum(); };
+        var numTensors = tf.memory().numTensors;
+        var cost = optimizer.minimize(f, true);
+        expect(tf.memory().numTensors).toBe(numTensors + 3);
+        test_util_1.expectArraysClose(x, [0.8, 1.6]);
+        cost.dispose();
+        numTensors = tf.memory().numTensors;
+        cost = optimizer.minimize(f, false);
+        test_util_1.expectArraysClose(x, [0.64, 1.28]);
+        expect(tf.memory().numTensors).toBe(numTensors);
+        expect(cost).toBe(null);
+        x.dispose();
+        optimizer.dispose();
+        expect(tf.memory().numTensors).toBe(1);
+    });
+    it('serialization round-trip', function () {
+        var originalOpt = tf.train.adadelta(0.1, 0.2, 2e-8);
+        var reserialized = tf.AdadeltaOptimizer.fromConfig(tf.AdadeltaOptimizer, originalOpt.getConfig());
+        expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());
+    });
+});
+//# sourceMappingURL=adadelta_optimizer_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer_test.js.map
new file mode 100644
index 0000000..29749f7
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adadelta_optimizer_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"adadelta_optimizer_test.js","sourceRoot":"","sources":["../../src/optimizers/adadelta_optimizer_test.ts"],"names":[],"mappings":";;AAiBA,6BAA+B;AAC/B,gDAAkD;AAClD,0CAAyD;AAEzD,gCAAiB,CAAC,mBAAmB,EAAE,oBAAQ,EAAE;IAC/C,EAAE,CAAC,OAAO,EAAE;QACV,IAAM,YAAY,GAAG,EAAE,CAAC;QACxB,IAAM,GAAG,GAAG,GAAG,CAAC;QAChB,IAAM,SAAS,GAAG,EAAE,CAAC,KAAK,CAAC,QAAQ,CAAC,YAAY,EAAE,GAAG,CAAC,CAAC;QAEvD,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC;QAEzC,IAAM,CAAC,GAAG,cAAM,OAAA,CAAC,CAAC,MAAM,EAAE,CAAC,GAAG,EAAe,EAA7B,CAA6B,CAAC;QAE9C,IAAI,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC;QAExC,IAAI,IAAI,GAAG,SAAS,CAAC,QAAQ,CAAC,CAAC,EAAmB,IAAI,CAAC,CAAC;QAGxD,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,UAAU,GAAG,CAAC,CAAC,CAAC;QAepD,6BAAiB,CAAC,CAAC,EAAE,CAAC,GAAG,EAAE,GAAG,CAAC,CAAC,CAAC;QAEjC,IAAI,CAAC,OAAO,EAAE,CAAC;QACf,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC;QAEpC,IAAI,GAAG,SAAS,CAAC,QAAQ,CAAC,CAAC,EAAmB,KAAK,CAAC,CAAC;QAQrD,6BAAiB,CAAC,CAAC,EAAE,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC,CAAC;QAGnC,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC;QAEhD,MAAM,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;QAExB,CAAC,CAAC,OAAO,EAAE,CAAC;QACZ,SAAS,CAAC,OAAO,EAAE,CAAC;QAGpB,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;IACzC,CAAC,CAAC,CAAC;IACH,EAAE,CAAC,0BAA0B,EAAE;QAC7B,IAAM,WAAW,GAAG,EAAE,CAAC,KAAK,CAAC,QAAQ,CAAC,GAAG,EAAE,GAAG,EAAE,IAAI,CAAC,CAAC;QACtD,IAAM,YAAY,GAAG,EAAE,CAAC,iBAAiB,CAAC,UAAU,CAChD,EAAE,CAAC,iBAAiB,EAAE,WAAW,CAAC,SAAS,EAAE,CAAC,CAAC;QACnD,MAAM,CAAC,YAAY,CAAC,SAAS,EAAE,CAAC,CAAC,OAAO,CAAC,WAAW,CAAC,SAAS,EAAE,CAAC,CAAC;IACpE,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer.d.ts b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer.d.ts
new file mode 100644
index 0000000..88f51c5
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer.d.ts
@@ -0,0 +1,16 @@
+import { ConfigDict, Serializable, SerializableConstructor } from '../serialization';
+import { NamedVariableMap } from '../tensor_types';
+import { Optimizer } from './optimizer';
+export declare class AdagradOptimizer extends Optimizer {
+    protected learningRate: number;
+    private initialAccumulatorValue;
+    static className: string;
+    private c;
+    private epsilon;
+    private accumulatedGrads;
+    constructor(learningRate: number, initialAccumulatorValue?: number);
+    applyGradients(variableGradients: NamedVariableMap): void;
+    dispose(): void;
+    getConfig(): ConfigDict;
+    static fromConfig<T extends Serializable>(cls: SerializableConstructor<T>, config: ConfigDict): T;
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer.js b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer.js
new file mode 100644
index 0000000..f75c02d
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer.js
@@ -0,0 +1,81 @@
+"use strict";
+var __extends = (this && this.__extends) || (function () {
+    var extendStatics = Object.setPrototypeOf ||
+        ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
+        function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
+    return function (d, b) {
+        extendStatics(d, b);
+        function __() { this.constructor = d; }
+        d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
+    };
+})();
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var globals_1 = require("../globals");
+var ops_1 = require("../ops/ops");
+var serialization_1 = require("../serialization");
+var optimizer_1 = require("./optimizer");
+var AdagradOptimizer = (function (_super) {
+    __extends(AdagradOptimizer, _super);
+    function AdagradOptimizer(learningRate, initialAccumulatorValue) {
+        if (initialAccumulatorValue === void 0) { initialAccumulatorValue = 0.1; }
+        var _this = _super.call(this) || this;
+        _this.learningRate = learningRate;
+        _this.initialAccumulatorValue = initialAccumulatorValue;
+        _this.accumulatedGrads = {};
+        _this.c = globals_1.keep(ops_1.scalar(-learningRate));
+        _this.epsilon = globals_1.keep(ops_1.scalar(environment_1.ENV.get('EPSILON')));
+        return _this;
+    }
+    AdagradOptimizer.prototype.applyGradients = function (variableGradients) {
+        var _this = this;
+        var _loop_1 = function (variableName) {
+            var value = environment_1.ENV.engine.registeredVariables[variableName];
+            if (this_1.accumulatedGrads[variableName] == null) {
+                var trainable_1 = false;
+                globals_1.tidy(function () {
+                    _this.accumulatedGrads[variableName] =
+                        ops_1.fill(value.shape, _this.initialAccumulatorValue)
+                            .variable(trainable_1);
+                });
+            }
+            var gradient = variableGradients[variableName];
+            var accumulatedGrad = this_1.accumulatedGrads[variableName];
+            globals_1.tidy(function () {
+                var newAccumulatedGrad = accumulatedGrad.add(gradient.square());
+                _this.accumulatedGrads[variableName].assign(newAccumulatedGrad);
+                var newValue = _this.c
+                    .mul(gradient.div(newAccumulatedGrad.add(_this.epsilon).sqrt()))
+                    .add(value);
+                value.assign(newValue);
+            });
+        };
+        var this_1 = this;
+        for (var variableName in variableGradients) {
+            _loop_1(variableName);
+        }
+    };
+    AdagradOptimizer.prototype.dispose = function () {
+        var _this = this;
+        this.epsilon.dispose();
+        this.c.dispose();
+        if (this.accumulatedGrads != null) {
+            Object.keys(this.accumulatedGrads)
+                .forEach(function (name) { return _this.accumulatedGrads[name].dispose(); });
+        }
+    };
+    AdagradOptimizer.prototype.getConfig = function () {
+        return {
+            learningRate: this.learningRate,
+            initialAccumulatorValue: this.initialAccumulatorValue,
+        };
+    };
+    AdagradOptimizer.fromConfig = function (cls, config) {
+        return new cls(config.learningRate, config.initialAccumulatorValue);
+    };
+    AdagradOptimizer.className = 'AdagradOptimizer';
+    return AdagradOptimizer;
+}(optimizer_1.Optimizer));
+exports.AdagradOptimizer = AdagradOptimizer;
+serialization_1.registerClass(AdagradOptimizer);
+//# sourceMappingURL=adagrad_optimizer.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer.js.map b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer.js.map
new file mode 100644
index 0000000..4738800
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer_test.js b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer_test.js
new file mode 100644
index 0000000..86c4491
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer_test.js
@@ -0,0 +1,33 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('AdagradOptimizer', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var learningRate = .1;
+        var initialAccumulatorValue = .1;
+        var optimizer = tf.train.adagrad(learningRate, initialAccumulatorValue);
+        var x = tf.tensor1d([1, 2]).variable();
+        var f = function () { return x.square().sum(); };
+        var numTensors = tf.memory().numTensors;
+        var cost = optimizer.minimize(f, true);
+        expect(tf.memory().numTensors).toBe(numTensors + 2);
+        test_util_1.expectArraysClose(x, [0.9012270405, 1.9003110428]);
+        cost.dispose();
+        numTensors = tf.memory().numTensors;
+        cost = optimizer.minimize(f, false);
+        test_util_1.expectArraysClose(x, [0.8347372764, 1.83015597828], 1e-2);
+        expect(tf.memory().numTensors).toBe(numTensors);
+        expect(cost).toBe(null);
+        x.dispose();
+        optimizer.dispose();
+        expect(tf.memory().numTensors).toBe(1);
+    });
+    it('serialization round-trip', function () {
+        var originalOpt = tf.train.adagrad(0.1, 0.2);
+        var reserialized = tf.AdagradOptimizer.fromConfig(tf.AdagradOptimizer, originalOpt.getConfig());
+        expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());
+    });
+});
+//# sourceMappingURL=adagrad_optimizer_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer_test.js.map
new file mode 100644
index 0000000..83d25c6
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adagrad_optimizer_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"adagrad_optimizer_test.js","sourceRoot":"","sources":["../../src/optimizers/adagrad_optimizer_test.ts"],"names":[],"mappings":";;AAiBA,6BAA+B;AAC/B,gDAAkD;AAClD,0CAAyD;AAEzD,gCAAiB,CAAC,kBAAkB,EAAE,oBAAQ,EAAE;IAC9C,EAAE,CAAC,OAAO,EAAE;QACV,IAAM,YAAY,GAAG,EAAE,CAAC;QACxB,IAAM,uBAAuB,GAAG,EAAE,CAAC;QACnC,IAAM,SAAS,GAAG,EAAE,CAAC,KAAK,CAAC,OAAO,CAAC,YAAY,EAAE,uBAAuB,CAAC,CAAC;QAE1E,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC;QAEzC,IAAM,CAAC,GAAG,cAAM,OAAA,CAAC,CAAC,MAAM,EAAE,CAAC,GAAG,EAAe,EAA7B,CAA6B,CAAC;QAE9C,IAAI,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC;QAExC,IAAI,IAAI,GAAG,SAAS,CAAC,QAAQ,CAAC,CAAC,EAAmB,IAAI,CAAC,CAAC;QAGxD,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,UAAU,GAAG,CAAC,CAAC,CAAC;QASpD,6BAAiB,CAAC,CAAC,EAAE,CAAC,YAAY,EAAE,YAAY,CAAC,CAAC,CAAC;QAEnD,IAAI,CAAC,OAAO,EAAE,CAAC;QACf,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC;QAEpC,IAAI,GAAG,SAAS,CAAC,QAAQ,CAAC,CAAC,EAAmB,KAAK,CAAC,CAAC;QAQrD,6BAAiB,CAAC,CAAC,EAAE,CAAC,YAAY,EAAE,aAAa,CAAC,EAAE,IAAI,CAAC,CAAC;QAG1D,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC;QAEhD,MAAM,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;QAExB,CAAC,CAAC,OAAO,EAAE,CAAC;QACZ,SAAS,CAAC,OAAO,EAAE,CAAC;QAGpB,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;IACzC,CAAC,CAAC,CAAC;IACH,EAAE,CAAC,0BAA0B,EAAE;QAC7B,IAAM,WAAW,GAAG,EAAE,CAAC,KAAK,CAAC,OAAO,CAAC,GAAG,EAAE,GAAG,CAAC,CAAC;QAC/C,IAAM,YAAY,GAAG,EAAE,CAAC,gBAAgB,CAAC,UAAU,CAC/C,EAAE,CAAC,gBAAgB,EAAE,WAAW,CAAC,SAAS,EAAE,CAAC,CAAC;QAClD,MAAM,CAAC,YAAY,CAAC,SAAS,EAAE,CAAC,CAAC,OAAO,CAAC,WAAW,CAAC,SAAS,EAAE,CAAC,CAAC;IACpE,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer.d.ts b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer.d.ts
new file mode 100644
index 0000000..3afc76f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer.d.ts
@@ -0,0 +1,26 @@
+import { ConfigDict, Serializable, SerializableConstructor } from '../serialization';
+import { NamedVariableMap } from '../tensor_types';
+import { Optimizer } from './optimizer';
+export declare class AdamOptimizer extends Optimizer {
+    protected learningRate: number;
+    protected beta1: number;
+    protected beta2: number;
+    protected epsilon: number;
+    static className: string;
+    private c;
+    private epsScalar;
+    private beta1Scalar;
+    private beta2Scalar;
+    private accBeta1;
+    private accBeta2;
+    private oneMinusBeta1;
+    private oneMinusBeta2;
+    private one;
+    private accumulatedFirstMoment;
+    private accumulatedSecondMoment;
+    constructor(learningRate: number, beta1: number, beta2: number, epsilon?: number);
+    applyGradients(variableGradients: NamedVariableMap): void;
+    dispose(): void;
+    getConfig(): ConfigDict;
+    static fromConfig<T extends Serializable>(cls: SerializableConstructor<T>, config: ConfigDict): T;
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer.js b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer.js
new file mode 100644
index 0000000..216af03
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer.js
@@ -0,0 +1,118 @@
+"use strict";
+var __extends = (this && this.__extends) || (function () {
+    var extendStatics = Object.setPrototypeOf ||
+        ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
+        function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
+    return function (d, b) {
+        extendStatics(d, b);
+        function __() { this.constructor = d; }
+        d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
+    };
+})();
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var globals_1 = require("../globals");
+var ops_1 = require("../ops/ops");
+var serialization_1 = require("../serialization");
+var optimizer_1 = require("./optimizer");
+var AdamOptimizer = (function (_super) {
+    __extends(AdamOptimizer, _super);
+    function AdamOptimizer(learningRate, beta1, beta2, epsilon) {
+        if (epsilon === void 0) { epsilon = null; }
+        var _this = _super.call(this) || this;
+        _this.learningRate = learningRate;
+        _this.beta1 = beta1;
+        _this.beta2 = beta2;
+        _this.epsilon = epsilon;
+        _this.accumulatedFirstMoment = {};
+        _this.accumulatedSecondMoment = {};
+        _this.c = globals_1.keep(ops_1.scalar(-learningRate));
+        _this.beta1Scalar = globals_1.keep(ops_1.scalar(beta1));
+        _this.beta2Scalar = globals_1.keep(ops_1.scalar(beta2));
+        globals_1.tidy(function () {
+            _this.accBeta1 = ops_1.scalar(beta1).variable();
+            _this.accBeta2 = ops_1.scalar(beta2).variable();
+        });
+        _this.oneMinusBeta1 = globals_1.keep(ops_1.scalar(1 - beta1));
+        _this.oneMinusBeta2 = globals_1.keep(ops_1.scalar(1 - beta2));
+        _this.one = globals_1.keep(ops_1.scalar(1));
+        if (epsilon === null) {
+            epsilon = environment_1.ENV.get('EPSILON');
+        }
+        _this.epsScalar = globals_1.keep(ops_1.scalar(epsilon));
+        return _this;
+    }
+    AdamOptimizer.prototype.applyGradients = function (variableGradients) {
+        var _this = this;
+        globals_1.tidy(function () {
+            var oneMinusAccBeta1 = _this.one.sub(_this.accBeta1);
+            var oneMinusAccBeta2 = _this.one.sub(_this.accBeta2);
+            for (var variableName in variableGradients) {
+                var value = environment_1.ENV.engine.registeredVariables[variableName];
+                if (_this.accumulatedFirstMoment[variableName] == null) {
+                    var trainable = false;
+                    _this.accumulatedFirstMoment[variableName] =
+                        ops_1.zerosLike(value).variable(trainable);
+                }
+                if (_this.accumulatedSecondMoment[variableName] == null) {
+                    var trainable = false;
+                    _this.accumulatedSecondMoment[variableName] =
+                        ops_1.zerosLike(value).variable(trainable);
+                }
+                var gradient = variableGradients[variableName];
+                var firstMoment = _this.accumulatedFirstMoment[variableName];
+                var secondMoment = _this.accumulatedSecondMoment[variableName];
+                var newFirstMoment = _this.beta1Scalar.mul(firstMoment)
+                    .add(_this.oneMinusBeta1.mul(gradient));
+                var newSecondMoment = _this.beta2Scalar.mul(secondMoment)
+                    .add(_this.oneMinusBeta2.mul(gradient.square()));
+                var biasCorrectedFirstMoment = newFirstMoment.div(oneMinusAccBeta1);
+                var biasCorrectedSecondMoment = newSecondMoment.div(oneMinusAccBeta2);
+                _this.accumulatedFirstMoment[variableName].assign(newFirstMoment);
+                _this.accumulatedSecondMoment[variableName].assign(newSecondMoment);
+                var newValue = _this.c
+                    .mul(biasCorrectedFirstMoment.div(_this.epsScalar.add(biasCorrectedSecondMoment.sqrt())))
+                    .add(value);
+                value.assign(newValue);
+            }
+            _this.accBeta1.assign(_this.accBeta1.mul(_this.beta1Scalar));
+            _this.accBeta2.assign(_this.accBeta2.mul(_this.beta2Scalar));
+        });
+    };
+    AdamOptimizer.prototype.dispose = function () {
+        var _this = this;
+        this.c.dispose();
+        this.epsScalar.dispose();
+        this.beta1Scalar.dispose();
+        this.beta2Scalar.dispose();
+        this.accBeta1.dispose();
+        this.accBeta2.dispose();
+        this.oneMinusBeta1.dispose();
+        this.oneMinusBeta2.dispose();
+        this.one.dispose();
+        if (this.accumulatedFirstMoment != null) {
+            Object.keys(this.accumulatedFirstMoment)
+                .forEach(function (name) { return _this.accumulatedFirstMoment[name].dispose(); });
+        }
+        if (this.accumulatedSecondMoment != null) {
+            Object.keys(this.accumulatedSecondMoment)
+                .forEach(function (name) { return _this.accumulatedSecondMoment[name].dispose(); });
+        }
+    };
+    AdamOptimizer.prototype.getConfig = function () {
+        return {
+            learningRate: this.learningRate,
+            beta1: this.beta1,
+            beta2: this.beta2,
+            epsilon: this.epsilon,
+        };
+    };
+    AdamOptimizer.fromConfig = function (cls, config) {
+        return new cls(config.learningRate, config.beta1, config.beta2, config.epsilon);
+    };
+    AdamOptimizer.className = 'AdamOptimizer';
+    return AdamOptimizer;
+}(optimizer_1.Optimizer));
+exports.AdamOptimizer = AdamOptimizer;
+serialization_1.registerClass(AdamOptimizer);
+//# sourceMappingURL=adam_optimizer.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer.js.map b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer.js.map
new file mode 100644
index 0000000..7c6f2f1
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer_test.js b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer_test.js
new file mode 100644
index 0000000..dd884b2
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer_test.js
@@ -0,0 +1,34 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('AdamOptimizer', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var learningRate = .1;
+        var beta1 = .8;
+        var beta2 = .9;
+        var optimizer = tf.train.adam(learningRate, beta1, beta2);
+        var x = tf.tensor1d([2, 4]).variable();
+        var f = function () { return x.square().sum(); };
+        var numTensors = tf.memory().numTensors;
+        var cost = optimizer.minimize(f, true);
+        expect(tf.memory().numTensors).toBe(numTensors + 3);
+        test_util_1.expectArraysClose(x, [1.9, 3.9]);
+        cost.dispose();
+        numTensors = tf.memory().numTensors;
+        cost = optimizer.minimize(f, false);
+        test_util_1.expectArraysClose(x, [1.8000001, 3.8002]);
+        expect(tf.memory().numTensors).toBe(numTensors);
+        expect(cost).toBe(null);
+        x.dispose();
+        optimizer.dispose();
+        expect(tf.memory().numTensors).toBe(1);
+    });
+    it('serialization round-trip', function () {
+        var originalOpt = tf.train.adam(0.1, 0.2, 0.3, 2e-8);
+        var reserialized = tf.AdamOptimizer.fromConfig(tf.AdamOptimizer, originalOpt.getConfig());
+        expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());
+    });
+});
+//# sourceMappingURL=adam_optimizer_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer_test.js.map
new file mode 100644
index 0000000..91ab4ee
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adam_optimizer_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"adam_optimizer_test.js","sourceRoot":"","sources":["../../src/optimizers/adam_optimizer_test.ts"],"names":[],"mappings":";;AAiBA,6BAA+B;AAC/B,gDAAkD;AAClD,0CAAyD;AAEzD,gCAAiB,CAAC,eAAe,EAAE,oBAAQ,EAAE;IAC3C,EAAE,CAAC,OAAO,EAAE;QACV,IAAM,YAAY,GAAG,EAAE,CAAC;QACxB,IAAM,KAAK,GAAG,EAAE,CAAC;QACjB,IAAM,KAAK,GAAG,EAAE,CAAC;QACjB,IAAM,SAAS,GAAG,EAAE,CAAC,KAAK,CAAC,IAAI,CAAC,YAAY,EAAE,KAAK,EAAE,KAAK,CAAC,CAAC;QAE5D,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC;QAEzC,IAAM,CAAC,GAAG,cAAM,OAAA,CAAC,CAAC,MAAM,EAAE,CAAC,GAAG,EAAe,EAA7B,CAA6B,CAAC;QAE9C,IAAI,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC;QAExC,IAAI,IAAI,GAAG,SAAS,CAAC,QAAQ,CAAC,CAAC,EAAmB,IAAI,CAAC,CAAC;QAGxD,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,UAAU,GAAG,CAAC,CAAC,CAAC;QAapD,6BAAiB,CAAC,CAAC,EAAE,CAAC,GAAG,EAAE,GAAG,CAAC,CAAC,CAAC;QAEjC,IAAI,CAAC,OAAO,EAAE,CAAC;QACf,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC;QAEpC,IAAI,GAAG,SAAS,CAAC,QAAQ,CAAC,CAAC,EAAmB,KAAK,CAAC,CAAC;QAcrD,6BAAiB,CAAC,CAAC,EAAE,CAAC,SAAS,EAAE,MAAM,CAAC,CAAC,CAAC;QAE1C,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC;QAEhD,MAAM,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;QAExB,CAAC,CAAC,OAAO,EAAE,CAAC;QACZ,SAAS,CAAC,OAAO,EAAE,CAAC;QAGpB,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;IACzC,CAAC,CAAC,CAAC;IACH,EAAE,CAAC,0BAA0B,EAAE;QAC7B,IAAM,WAAW,GAAG,EAAE,CAAC,KAAK,CAAC,IAAI,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,IAAI,CAAC,CAAC;QACvD,IAAM,YAAY,GACd,EAAE,CAAC,aAAa,CAAC,UAAU,CAAC,EAAE,CAAC,aAAa,EAAE,WAAW,CAAC,SAAS,EAAE,CAAC,CAAC;QAC3E,MAAM,CAAC,YAAY,CAAC,SAAS,EAAE,CAAC,CAAC,OAAO,CAAC,WAAW,CAAC,SAAS,EAAE,CAAC,CAAC;IACpE,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer.d.ts b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer.d.ts
new file mode 100644
index 0000000..99d347a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer.d.ts
@@ -0,0 +1,27 @@
+import { ConfigDict, Serializable, SerializableConstructor } from '../serialization';
+import { NamedVariableMap } from '../tensor_types';
+import { Optimizer } from './optimizer';
+export declare class AdamaxOptimizer extends Optimizer {
+    protected learningRate: number;
+    protected beta1: number;
+    protected beta2: number;
+    protected epsilon: number;
+    protected decay: number;
+    static className: string;
+    private c;
+    private epsScalar;
+    private accBeta1;
+    private beta1Scalar;
+    private beta2Scalar;
+    private decayScalar;
+    private oneMinusBeta1;
+    private one;
+    private iteration;
+    private accumulatedFirstMoment;
+    private accumulatedWeightedInfNorm;
+    constructor(learningRate: number, beta1: number, beta2: number, epsilon?: number, decay?: number);
+    applyGradients(variableGradients: NamedVariableMap): void;
+    dispose(): void;
+    getConfig(): ConfigDict;
+    static fromConfig<T extends Serializable>(cls: SerializableConstructor<T>, config: ConfigDict): T;
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer.js b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer.js
new file mode 100644
index 0000000..7c7f964
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer.js
@@ -0,0 +1,120 @@
+"use strict";
+var __extends = (this && this.__extends) || (function () {
+    var extendStatics = Object.setPrototypeOf ||
+        ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
+        function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
+    return function (d, b) {
+        extendStatics(d, b);
+        function __() { this.constructor = d; }
+        d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
+    };
+})();
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var globals_1 = require("../globals");
+var ops_1 = require("../ops/ops");
+var serialization_1 = require("../serialization");
+var optimizer_1 = require("./optimizer");
+var AdamaxOptimizer = (function (_super) {
+    __extends(AdamaxOptimizer, _super);
+    function AdamaxOptimizer(learningRate, beta1, beta2, epsilon, decay) {
+        if (epsilon === void 0) { epsilon = null; }
+        if (decay === void 0) { decay = 0.0; }
+        var _this = _super.call(this) || this;
+        _this.learningRate = learningRate;
+        _this.beta1 = beta1;
+        _this.beta2 = beta2;
+        _this.epsilon = epsilon;
+        _this.decay = decay;
+        _this.accumulatedFirstMoment = {};
+        _this.accumulatedWeightedInfNorm = {};
+        _this.c = globals_1.keep(ops_1.scalar(-learningRate));
+        _this.beta1Scalar = globals_1.keep(ops_1.scalar(beta1));
+        _this.beta2Scalar = globals_1.keep(ops_1.scalar(beta2));
+        _this.decayScalar = globals_1.keep(ops_1.scalar(decay));
+        globals_1.tidy(function () {
+            _this.iteration = ops_1.scalar(0).variable();
+            _this.accBeta1 = ops_1.scalar(beta1).variable();
+        });
+        _this.oneMinusBeta1 = globals_1.keep(ops_1.scalar(1 - beta1));
+        _this.one = globals_1.keep(ops_1.scalar(1));
+        if (epsilon === null) {
+            epsilon = environment_1.ENV.get('EPSILON');
+        }
+        _this.epsScalar = globals_1.keep(ops_1.scalar(epsilon));
+        return _this;
+    }
+    AdamaxOptimizer.prototype.applyGradients = function (variableGradients) {
+        var _this = this;
+        globals_1.tidy(function () {
+            var oneMinusAccBeta1 = _this.one.sub(_this.accBeta1);
+            var lr = _this.c.div(_this.one.add(_this.decayScalar.mul(_this.iteration)));
+            for (var variableName in variableGradients) {
+                var value = environment_1.ENV.engine.registeredVariables[variableName];
+                if (_this.accumulatedFirstMoment[variableName] == null) {
+                    var trainable = false;
+                    _this.accumulatedFirstMoment[variableName] =
+                        ops_1.zerosLike(value).variable(trainable);
+                }
+                if (_this.accumulatedWeightedInfNorm[variableName] == null) {
+                    var trainable = false;
+                    _this.accumulatedWeightedInfNorm[variableName] =
+                        ops_1.zerosLike(value).variable(trainable);
+                }
+                var gradient = variableGradients[variableName];
+                var firstMoment = _this.accumulatedFirstMoment[variableName];
+                var weightedInfNorm = _this.accumulatedWeightedInfNorm[variableName];
+                var newFirstMoment = _this.beta1Scalar.mul(firstMoment)
+                    .add(_this.oneMinusBeta1.mul(gradient));
+                var ut0 = _this.beta2Scalar.mul(weightedInfNorm);
+                var ut1 = gradient.abs();
+                var newWeightedInfNorm = ut0.maximum(ut1);
+                _this.accumulatedFirstMoment[variableName].assign(newFirstMoment);
+                _this.accumulatedWeightedInfNorm[variableName].assign(newWeightedInfNorm);
+                var newValue = lr.div(oneMinusAccBeta1)
+                    .mul(newFirstMoment.div(_this.epsScalar.add(newWeightedInfNorm)))
+                    .add(value);
+                value.assign(newValue);
+            }
+            _this.iteration.assign(_this.iteration.add(_this.one));
+            _this.accBeta1.assign(_this.accBeta1.mul(_this.beta1Scalar));
+        });
+    };
+    AdamaxOptimizer.prototype.dispose = function () {
+        var _this = this;
+        this.c.dispose();
+        this.epsScalar.dispose();
+        this.accBeta1.dispose();
+        this.beta1Scalar.dispose();
+        this.beta2Scalar.dispose();
+        this.oneMinusBeta1.dispose();
+        this.decayScalar.dispose();
+        this.iteration.dispose();
+        this.one.dispose();
+        if (this.accumulatedFirstMoment != null) {
+            Object.keys(this.accumulatedFirstMoment)
+                .forEach(function (name) { return _this.accumulatedFirstMoment[name].dispose(); });
+        }
+        if (this.accumulatedWeightedInfNorm != null) {
+            Object.keys(this.accumulatedWeightedInfNorm)
+                .forEach(function (name) { return _this.accumulatedWeightedInfNorm[name].dispose(); });
+        }
+    };
+    AdamaxOptimizer.prototype.getConfig = function () {
+        return {
+            learningRate: this.learningRate,
+            beta1: this.beta1,
+            beta2: this.beta2,
+            epsilon: this.epsilon,
+            decay: this.decay
+        };
+    };
+    AdamaxOptimizer.fromConfig = function (cls, config) {
+        return new cls(config.learningRate, config.beta1, config.beta2, config.epsilon, config.decay);
+    };
+    AdamaxOptimizer.className = 'AdamaxOptimizer';
+    return AdamaxOptimizer;
+}(optimizer_1.Optimizer));
+exports.AdamaxOptimizer = AdamaxOptimizer;
+serialization_1.registerClass(AdamaxOptimizer);
+//# sourceMappingURL=adamax_optimizer.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer.js.map b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer.js.map
new file mode 100644
index 0000000..4611b90
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer_test.js b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer_test.js
new file mode 100644
index 0000000..9b7270f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer_test.js
@@ -0,0 +1,35 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('AdamaxOptimizer', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var learningRate = 0.1;
+        var beta1 = 0.8;
+        var beta2 = 0.9;
+        var decay = 0.1;
+        var optimizer = tf.train.adamax(learningRate, beta1, beta2, undefined, decay);
+        var x = tf.tensor1d([2, 4]).variable();
+        var f = function () { return x.square().sum(); };
+        var numTensors = tf.memory().numTensors;
+        var cost = optimizer.minimize(f, true);
+        expect(tf.memory().numTensors).toBe(numTensors + 3);
+        test_util_1.expectArraysClose(x, [1.9, 3.9]);
+        cost.dispose();
+        numTensors = tf.memory().numTensors;
+        cost = optimizer.minimize(f, false);
+        test_util_1.expectArraysClose(x, [1.80697, 3.8086]);
+        expect(tf.memory().numTensors).toBe(numTensors);
+        expect(cost).toBe(null);
+        x.dispose();
+        optimizer.dispose();
+        expect(tf.memory().numTensors).toBe(1);
+    });
+    it('serialization round-trip', function () {
+        var originalOpt = tf.train.adamax(0.1, 0.2, 0.3, 2e-8, 0.1);
+        var reserialized = tf.AdamaxOptimizer.fromConfig(tf.AdamaxOptimizer, originalOpt.getConfig());
+        expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());
+    });
+});
+//# sourceMappingURL=adamax_optimizer_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer_test.js.map
new file mode 100644
index 0000000..1408fe6
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/adamax_optimizer_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"adamax_optimizer_test.js","sourceRoot":"","sources":["../../src/optimizers/adamax_optimizer_test.ts"],"names":[],"mappings":";;AAiBA,6BAA+B;AAC/B,gDAAkD;AAClD,0CAAyD;AAEzD,gCAAiB,CAAC,iBAAiB,EAAE,oBAAQ,EAAE;IAC7C,EAAE,CAAC,OAAO,EAAE;QACV,IAAM,YAAY,GAAG,GAAG,CAAC;QACzB,IAAM,KAAK,GAAG,GAAG,CAAC;QAClB,IAAM,KAAK,GAAG,GAAG,CAAC;QAClB,IAAM,KAAK,GAAG,GAAG,CAAC;QAClB,IAAM,SAAS,GACX,EAAE,CAAC,KAAK,CAAC,MAAM,CAAC,YAAY,EAAE,KAAK,EAAE,KAAK,EAAE,SAAS,EAAE,KAAK,CAAC,CAAC;QAElE,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC;QAEzC,IAAM,CAAC,GAAG,cAAM,OAAA,CAAC,CAAC,MAAM,EAAE,CAAC,GAAG,EAAe,EAA7B,CAA6B,CAAC;QAE9C,IAAI,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC;QAExC,IAAI,IAAI,GAAG,SAAS,CAAC,QAAQ,CAAC,CAAC,EAAmB,IAAI,CAAC,CAAC;QAGxD,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,UAAU,GAAG,CAAC,CAAC,CAAC;QAuBpD,6BAAiB,CAAC,CAAC,EAAE,CAAC,GAAG,EAAE,GAAG,CAAC,CAAC,CAAC;QAEjC,IAAI,CAAC,OAAO,EAAE,CAAC;QACf,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC;QAEpC,IAAI,GAAG,SAAS,CAAC,QAAQ,CAAC,CAAC,EAAmB,KAAK,CAAC,CAAC;QAiCrD,6BAAiB,CAAC,CAAC,EAAE,CAAC,OAAO,EAAE,MAAM,CAAC,CAAC,CAAC;QAExC,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC;QAEhD,MAAM,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;QAExB,CAAC,CAAC,OAAO,EAAE,CAAC;QACZ,SAAS,CAAC,OAAO,EAAE,CAAC;QAGpB,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;IACzC,CAAC,CAAC,CAAC;IACH,EAAE,CAAC,0BAA0B,EAAE;QAC7B,IAAM,WAAW,GAAG,EAAE,CAAC,KAAK,CAAC,MAAM,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,IAAI,EAAE,GAAG,CAAC,CAAC;QAC9D,IAAM,YAAY,GAAG,EAAE,CAAC,eAAe,CAAC,UAAU,CAC9C,EAAE,CAAC,eAAe,EAAE,WAAW,CAAC,SAAS,EAAE,CAAC,CAAC;QACjD,MAAM,CAAC,YAAY,CAAC,SAAS,EAAE,CAAC,CAAC,OAAO,CAAC,WAAW,CAAC,SAAS,EAAE,CAAC,CAAC;IACpE,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer.d.ts b/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer.d.ts
new file mode 100644
index 0000000..421f63a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer.d.ts
@@ -0,0 +1,17 @@
+import { ConfigDict, Serializable, SerializableConstructor } from '../serialization';
+import { NamedVariableMap } from '../tensor_types';
+import { SGDOptimizer } from './sgd_optimizer';
+export declare class MomentumOptimizer extends SGDOptimizer {
+    protected learningRate: number;
+    private momentum;
+    private useNesterov;
+    static className: string;
+    private m;
+    private accumulations;
+    constructor(learningRate: number, momentum: number, useNesterov?: boolean);
+    applyGradients(variableGradients: NamedVariableMap): void;
+    dispose(): void;
+    setMomentum(momentum: number): void;
+    getConfig(): ConfigDict;
+    static fromConfig<T extends Serializable>(cls: SerializableConstructor<T>, config: ConfigDict): T;
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer.js b/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer.js
new file mode 100644
index 0000000..e4bdc32
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer.js
@@ -0,0 +1,89 @@
+"use strict";
+var __extends = (this && this.__extends) || (function () {
+    var extendStatics = Object.setPrototypeOf ||
+        ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
+        function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
+    return function (d, b) {
+        extendStatics(d, b);
+        function __() { this.constructor = d; }
+        d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
+    };
+})();
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var globals_1 = require("../globals");
+var ops_1 = require("../ops/ops");
+var serialization_1 = require("../serialization");
+var sgd_optimizer_1 = require("./sgd_optimizer");
+var MomentumOptimizer = (function (_super) {
+    __extends(MomentumOptimizer, _super);
+    function MomentumOptimizer(learningRate, momentum, useNesterov) {
+        if (useNesterov === void 0) { useNesterov = false; }
+        var _this = _super.call(this, learningRate) || this;
+        _this.learningRate = learningRate;
+        _this.momentum = momentum;
+        _this.useNesterov = useNesterov;
+        _this.m = ops_1.scalar(_this.momentum);
+        _this.accumulations = {};
+        return _this;
+    }
+    MomentumOptimizer.prototype.applyGradients = function (variableGradients) {
+        var _this = this;
+        var _loop_1 = function (variableName) {
+            var value = environment_1.ENV.engine.registeredVariables[variableName];
+            if (this_1.accumulations[variableName] == null) {
+                var trainable_1 = false;
+                globals_1.tidy(function () {
+                    _this.accumulations[variableName] =
+                        ops_1.zerosLike(value).variable(trainable_1);
+                });
+            }
+            var accumulation = this_1.accumulations[variableName];
+            var gradient = variableGradients[variableName];
+            globals_1.tidy(function () {
+                var newValue;
+                var newAccumulation = _this.m.mul(accumulation).add(gradient);
+                if (_this.useNesterov) {
+                    newValue =
+                        _this.c.mul(gradient.add(newAccumulation.mul(_this.m))).add(value);
+                }
+                else {
+                    newValue = _this.c.mul(newAccumulation).add(value);
+                }
+                _this.accumulations[variableName].assign(newAccumulation);
+                value.assign(newValue);
+            });
+        };
+        var this_1 = this;
+        for (var variableName in variableGradients) {
+            _loop_1(variableName);
+        }
+    };
+    MomentumOptimizer.prototype.dispose = function () {
+        _super.prototype.dispose.call(this);
+        this.m.dispose();
+        if (this.accumulations != null) {
+            for (var variableName in this.accumulations) {
+                this.accumulations[variableName].dispose();
+            }
+        }
+    };
+    MomentumOptimizer.prototype.setMomentum = function (momentum) {
+        this.momentum = momentum;
+    };
+    MomentumOptimizer.prototype.getConfig = function () {
+        return {
+            learningRate: this.learningRate,
+            momentum: this.momentum,
+            useNesterov: this.useNesterov
+        };
+    };
+    MomentumOptimizer.fromConfig = function (cls, config) {
+        return new cls(config.learningRate, config.momentum, config.useNesterov);
+    };
+    MomentumOptimizer.className = 'MomentumOptimizer';
+    return MomentumOptimizer;
+}(sgd_optimizer_1.SGDOptimizer));
+exports.MomentumOptimizer = MomentumOptimizer;
+serialization_1.registerClass(MomentumOptimizer);
+//# sourceMappingURL=momentum_optimizer.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer.js.map b/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer.js.map
new file mode 100644
index 0000000..26ac814
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"momentum_optimizer.js","sourceRoot":"","sources":["../../src/optimizers/momentum_optimizer.ts"],"names":[],"mappings":";;;;;;;;;;;;AAiBA,8CAAmC;AACnC,sCAAgC;AAChC,kCAA6C;AAC7C,kDAAkG;AAGlG,iDAA6C;AAG7C;IAAuC,qCAAY;IAMjD,2BACc,YAAoB,EAAU,QAAgB,EAChD,WAAmB;QAAnB,4BAAA,EAAA,mBAAmB;QAF/B,YAGE,kBAAM,YAAY,CAAC,SAGpB;QALa,kBAAY,GAAZ,YAAY,CAAQ;QAAU,cAAQ,GAAR,QAAQ,CAAQ;QAChD,iBAAW,GAAX,WAAW,CAAQ;QAE7B,KAAI,CAAC,CAAC,GAAG,YAAM,CAAC,KAAI,CAAC,QAAQ,CAAC,CAAC;QAC/B,KAAI,CAAC,aAAa,GAAG,EAAE,CAAC;;IAC1B,CAAC;IAED,0CAAc,GAAd,UAAe,iBAAmC;QAAlD,iBA2BC;gCA1BY,YAAY;YACrB,IAAM,KAAK,GAAG,iBAAG,CAAC,MAAM,CAAC,mBAAmB,CAAC,YAAY,CAAC,CAAC;YAC3D,IAAI,OAAK,aAAa,CAAC,YAAY,CAAC,IAAI,IAAI,EAAE;gBAC5C,IAAM,WAAS,GAAG,KAAK,CAAC;gBACxB,cAAI,CAAC;oBACH,KAAI,CAAC,aAAa,CAAC,YAAY,CAAC;wBAC5B,eAAS,CAAC,KAAK,CAAC,CAAC,QAAQ,CAAC,WAAS,CAAC,CAAC;gBAC3C,CAAC,CAAC,CAAC;aACJ;YAED,IAAM,YAAY,GAAG,OAAK,aAAa,CAAC,YAAY,CAAC,CAAC;YACtD,IAAM,QAAQ,GAAG,iBAAiB,CAAC,YAAY,CAAC,CAAC;YAEjD,cAAI,CAAC;gBACH,IAAI,QAAgB,CAAC;gBACrB,IAAM,eAAe,GAAG,KAAI,CAAC,CAAC,CAAC,GAAG,CAAC,YAAY,CAAC,CAAC,GAAG,CAAC,QAAQ,CAAC,CAAC;gBAC/D,IAAI,KAAI,CAAC,WAAW,EAAE;oBACpB,QAAQ;wBACJ,KAAI,CAAC,CAAC,CAAC,GAAG,CAAC,QAAQ,CAAC,GAAG,CAAC,eAAe,CAAC,GAAG,CAAC,KAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC;iBACtE;qBAAM;oBACL,QAAQ,GAAG,KAAI,CAAC,CAAC,CAAC,GAAG,CAAC,eAAe,CAAC,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC;iBACnD;gBACD,KAAI,CAAC,aAAa,CAAC,YAAY,CAAC,CAAC,MAAM,CAAC,eAAe,CAAC,CAAC;gBACzD,KAAK,CAAC,MAAM,CAAC,QAAQ,CAAC,CAAC;YACzB,CAAC,CAAC,CAAC;QACL,CAAC;;QAzBD,KAAK,IAAM,YAAY,IAAI,iBAAiB;oBAAjC,YAAY;SAyBtB;IACH,CAAC;IAED,mCAAO,GAAP;QACE,iBAAM,OAAO,WAAE,CAAC;QAChB,IAAI,CAAC,CAAC,CAAC,OAAO,EAAE,CAAC;QACjB,IAAI,IAAI,CAAC,aAAa,IAAI,IAAI,EAAE;YAC9B,KAAK,IAAM,YAAY,IAAI,IAAI,CAAC,aAAa,EAAE;gBAC7C,IAAI,CAAC,aAAa,CAAC,YAAY,CAAC,CAAC,OAAO,EAAE,CAAC;aAC5C;SACF;IACH,CAAC;IAOD,uCAAW,GAAX,UAAY,QAAgB;QAC1B,IAAI,CAAC,QAAQ,GAAG,QAAQ,CAAC;IAC3B,CAAC;IAED,qCAAS,GAAT;QACE,OAAO;YACL,YAAY,EAAE,IAAI,CAAC,YAAY;YAC/B,QAAQ,EAAE,IAAI,CAAC,QAAQ;YACvB,WAAW,EAAE,IAAI,CAAC,WAAW;SAC9B,CAAC;IACJ,CAAC;IACM,4BAAU,GAAjB,UACI,GAA+B,EAAE,MAAkB;QACrD,OAAO,IAAI,GAAG,CAAC,MAAM,CAAC,YAAY,EAAE,MAAM,CAAC,QAAQ,EAAE,MAAM,CAAC,WAAW,CAAC,CAAC;IAC3E,CAAC;IAtEM,2BAAS,GAAG,mBAAmB,CAAC;IAuEzC,wBAAC;CAAA,AAzED,CAAuC,4BAAY,GAyElD;AAzEY,8CAAiB;AA0E9B,6BAAa,CAAC,iBAAiB,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer_test.js b/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer_test.js
new file mode 100644
index 0000000..6364dea
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer_test.js
@@ -0,0 +1,54 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('MomentumOptimizer', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var learningRate = .1;
+        var momentum = .5;
+        var optimizer = tf.train.momentum(learningRate, momentum);
+        var x = tf.tensor1d([1, 2]).variable();
+        var f = function () { return x.square().sum(); };
+        var numTensors = tf.memory().numTensors;
+        var cost = optimizer.minimize(f, true);
+        expect(tf.memory().numTensors).toBe(numTensors + 2);
+        test_util_1.expectArraysClose(x, [.8, 1.6]);
+        cost.dispose();
+        numTensors = tf.memory().numTensors;
+        cost = optimizer.minimize(f, false);
+        test_util_1.expectArraysClose(x, [0.54, 1.08]);
+        expect(tf.memory().numTensors).toBe(numTensors);
+        expect(cost).toBe(null);
+        x.dispose();
+        optimizer.dispose();
+        expect(tf.memory().numTensors).toBe(1);
+    });
+    it('basic - with Nesterov', function () {
+        var learningRate = .1;
+        var momentum = .5;
+        var useNesterov = true;
+        var optimizer = tf.train.momentum(learningRate, momentum, useNesterov);
+        var x = tf.tensor1d([1, 2]).variable();
+        var f = function () { return x.square().sum(); };
+        var numTensors = tf.memory().numTensors;
+        var cost = optimizer.minimize(f, true);
+        expect(tf.memory().numTensors).toBe(numTensors + 2);
+        test_util_1.expectArraysClose(x, [.7, 1.4]);
+        cost.dispose();
+        numTensors = tf.memory().numTensors;
+        cost = optimizer.minimize(f, false);
+        test_util_1.expectArraysClose(x, [0.44, 0.88]);
+        expect(tf.memory().numTensors).toBe(numTensors);
+        expect(cost).toBe(null);
+        x.dispose();
+        optimizer.dispose();
+        expect(tf.memory().numTensors).toBe(1);
+    });
+    it('serialization round-trip', function () {
+        var originalOpt = tf.train.momentum(0.1, 0.2, true);
+        var reserialized = tf.MomentumOptimizer.fromConfig(tf.MomentumOptimizer, originalOpt.getConfig());
+        expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());
+    });
+});
+//# sourceMappingURL=momentum_optimizer_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer_test.js.map
new file mode 100644
index 0000000..e492c00
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/momentum_optimizer_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"momentum_optimizer_test.js","sourceRoot":"","sources":["../../src/optimizers/momentum_optimizer_test.ts"],"names":[],"mappings":";;AAiBA,6BAA+B;AAC/B,gDAAkD;AAClD,0CAAyD;AAEzD,gCAAiB,CAAC,mBAAmB,EAAE,oBAAQ,EAAE;IAC/C,EAAE,CAAC,OAAO,EAAE;QACV,IAAM,YAAY,GAAG,EAAE,CAAC;QACxB,IAAM,QAAQ,GAAG,EAAE,CAAC;QACpB,IAAM,SAAS,GAAG,EAAE,CAAC,KAAK,CAAC,QAAQ,CAAC,YAAY,EAAE,QAAQ,CAAC,CAAC;QAE5D,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC;QAEzC,IAAM,CAAC,GAAG,cAAM,OAAA,CAAC,CAAC,MAAM,EAAE,CAAC,GAAG,EAAe,EAA7B,CAA6B,CAAC;QAE9C,IAAI,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC;QAExC,IAAI,IAAI,GAAG,SAAS,CAAC,QAAQ,CAAC,CAAC,EAAmB,IAAI,CAAC,CAAC;QAGxD,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,UAAU,GAAG,CAAC,CAAC,CAAC;QAQpD,6BAAiB,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,GAAG,CAAC,CAAC,CAAC;QAEhC,IAAI,CAAC,OAAO,EAAE,CAAC;QACf,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC;QAEpC,IAAI,GAAG,SAAS,CAAC,QAAQ,CAAC,CAAC,EAAmB,KAAK,CAAC,CAAC;QAMrD,6BAAiB,CAAC,CAAC,EAAE,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC,CAAC;QAGnC,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC;QAEhD,MAAM,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;QAExB,CAAC,CAAC,OAAO,EAAE,CAAC;QACZ,SAAS,CAAC,OAAO,EAAE,CAAC;QAGpB,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;IACzC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,uBAAuB,EAAE;QAC1B,IAAM,YAAY,GAAG,EAAE,CAAC;QACxB,IAAM,QAAQ,GAAG,EAAE,CAAC;QACpB,IAAM,WAAW,GAAG,IAAI,CAAC;QACzB,IAAM,SAAS,GAAG,EAAE,CAAC,KAAK,CAAC,QAAQ,CAAC,YAAY,EAAE,QAAQ,EAAE,WAAW,CAAC,CAAC;QAEzE,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC;QAEzC,IAAM,CAAC,GAAG,cAAM,OAAA,CAAC,CAAC,MAAM,EAAE,CAAC,GAAG,EAAe,EAA7B,CAA6B,CAAC;QAE9C,IAAI,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC;QAExC,IAAI,IAAI,GAAG,SAAS,CAAC,QAAQ,CAAC,CAAC,EAAmB,IAAI,CAAC,CAAC;QAGxD,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,UAAU,GAAG,CAAC,CAAC,CAAC;QAUpD,6BAAiB,CAAC,CAAC,EAAE,CAAC,EAAE,EAAE,GAAG,CAAC,CAAC,CAAC;QAEhC,IAAI,CAAC,OAAO,EAAE,CAAC;QACf,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC;QAEpC,IAAI,GAAG,SAAS,CAAC,QAAQ,CAAC,CAAC,EAAmB,KAAK,CAAC,CAAC;QAOrD,6BAAiB,CAAC,CAAC,EAAE,CAAC,IAAI,EAAE,IAAI,CAAC,CAAC,CAAC;QAGnC,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC;QAEhD,MAAM,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;QAExB,CAAC,CAAC,OAAO,EAAE,CAAC;QACZ,SAAS,CAAC,OAAO,EAAE,CAAC;QAGpB,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;IACzC,CAAC,CAAC,CAAC;IACH,EAAE,CAAC,0BAA0B,EAAE;QAC7B,IAAM,WAAW,GAAG,EAAE,CAAC,KAAK,CAAC,QAAQ,CAAC,GAAG,EAAE,GAAG,EAAE,IAAI,CAAC,CAAC;QACtD,IAAM,YAAY,GAAG,EAAE,CAAC,iBAAiB,CAAC,UAAU,CAChD,EAAE,CAAC,iBAAiB,EAAE,WAAW,CAAC,SAAS,EAAE,CAAC,CAAC;QACnD,MAAM,CAAC,YAAY,CAAC,SAAS,EAAE,CAAC,CAAC,OAAO,CAAC,WAAW,CAAC,SAAS,EAAE,CAAC,CAAC;IACpE,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer.d.ts b/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer.d.ts
new file mode 100644
index 0000000..8a28238
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer.d.ts
@@ -0,0 +1,11 @@
+import { Serializable } from '../serialization';
+import { Scalar, Variable } from '../tensor';
+import { NamedTensorMap } from '../tensor_types';
+export declare abstract class Optimizer extends Serializable {
+    minimize(f: () => Scalar, returnCost?: boolean, varList?: Variable[]): Scalar | null;
+    computeGradients(f: () => Scalar, varList?: Variable[]): {
+        value: Scalar;
+        grads: NamedTensorMap;
+    };
+    abstract applyGradients(variableGradients: NamedTensorMap): void;
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer.js b/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer.js
new file mode 100644
index 0000000..c96a875
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer.js
@@ -0,0 +1,40 @@
+"use strict";
+var __extends = (this && this.__extends) || (function () {
+    var extendStatics = Object.setPrototypeOf ||
+        ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
+        function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
+    return function (d, b) {
+        extendStatics(d, b);
+        function __() { this.constructor = d; }
+        d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
+    };
+})();
+Object.defineProperty(exports, "__esModule", { value: true });
+var globals_1 = require("../globals");
+var serialization_1 = require("../serialization");
+var Optimizer = (function (_super) {
+    __extends(Optimizer, _super);
+    function Optimizer() {
+        return _super !== null && _super.apply(this, arguments) || this;
+    }
+    Optimizer.prototype.minimize = function (f, returnCost, varList) {
+        if (returnCost === void 0) { returnCost = false; }
+        var _a = this.computeGradients(f, varList), value = _a.value, grads = _a.grads;
+        this.applyGradients(grads);
+        var varNames = Object.keys(grads);
+        varNames.forEach(function (varName) { return grads[varName].dispose(); });
+        if (returnCost) {
+            return value;
+        }
+        else {
+            value.dispose();
+            return null;
+        }
+    };
+    Optimizer.prototype.computeGradients = function (f, varList) {
+        return globals_1.variableGrads(f, varList);
+    };
+    return Optimizer;
+}(serialization_1.Serializable));
+exports.Optimizer = Optimizer;
+//# sourceMappingURL=optimizer.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer.js.map b/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer.js.map
new file mode 100644
index 0000000..23ebd5c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"optimizer.js","sourceRoot":"","sources":["../../src/optimizers/optimizer.ts"],"names":[],"mappings":";;;;;;;;;;;;AAiBA,sCAAyC;AACzC,kDAA8C;AAK9C;IAAwC,6BAAY;IAApD;;IAsDA,CAAC;IAxCC,4BAAQ,GAAR,UAAS,CAAe,EAAE,UAAkB,EAAE,OAAoB;QAAxC,2BAAA,EAAA,kBAAkB;QAEpC,IAAA,sCAAkD,EAAjD,gBAAK,EAAE,gBAAK,CAAsC;QAEzD,IAAI,CAAC,cAAc,CAAC,KAAK,CAAC,CAAC;QAG3B,IAAM,QAAQ,GAAG,MAAM,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC;QACpC,QAAQ,CAAC,OAAO,CAAC,UAAA,OAAO,IAAI,OAAA,KAAK,CAAC,OAAO,CAAC,CAAC,OAAO,EAAE,EAAxB,CAAwB,CAAC,CAAC;QAEtD,IAAI,UAAU,EAAE;YACd,OAAO,KAAe,CAAC;SACxB;aAAM;YACL,KAAK,CAAC,OAAO,EAAE,CAAC;YAChB,OAAO,IAAI,CAAC;SACb;IACH,CAAC;IAaD,oCAAgB,GAAhB,UAAiB,CAAe,EAAE,OAAoB;QAEpD,OAAO,uBAAa,CAAC,CAAC,EAAE,OAAO,CAAC,CAAC;IACnC,CAAC;IAQH,gBAAC;AAAD,CAAC,AAtDD,CAAwC,4BAAY,GAsDnD;AAtDqB,8BAAS"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_constructors.d.ts b/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_constructors.d.ts
new file mode 100644
index 0000000..953c4ae
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_constructors.d.ts
@@ -0,0 +1,16 @@
+import { AdadeltaOptimizer } from './adadelta_optimizer';
+import { AdagradOptimizer } from './adagrad_optimizer';
+import { AdamOptimizer } from './adam_optimizer';
+import { AdamaxOptimizer } from './adamax_optimizer';
+import { MomentumOptimizer } from './momentum_optimizer';
+import { RMSPropOptimizer } from './rmsprop_optimizer';
+import { SGDOptimizer } from './sgd_optimizer';
+export declare class OptimizerConstructors {
+    static sgd(learningRate: number): SGDOptimizer;
+    static momentum(learningRate: number, momentum: number, useNesterov?: boolean): MomentumOptimizer;
+    static rmsprop(learningRate: number, decay?: number, momentum?: number, epsilon?: number, centered?: boolean): RMSPropOptimizer;
+    static adam(learningRate?: number, beta1?: number, beta2?: number, epsilon?: number): AdamOptimizer;
+    static adadelta(learningRate?: number, rho?: number, epsilon?: number): AdadeltaOptimizer;
+    static adamax(learningRate?: number, beta1?: number, beta2?: number, epsilon?: number, decay?: number): AdamaxOptimizer;
+    static adagrad(learningRate: number, initialAccumulatorValue?: number): AdagradOptimizer;
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_constructors.js b/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_constructors.js
new file mode 100644
index 0000000..37d3b07
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_constructors.js
@@ -0,0 +1,55 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var adadelta_optimizer_1 = require("./adadelta_optimizer");
+var adagrad_optimizer_1 = require("./adagrad_optimizer");
+var adam_optimizer_1 = require("./adam_optimizer");
+var adamax_optimizer_1 = require("./adamax_optimizer");
+var momentum_optimizer_1 = require("./momentum_optimizer");
+var rmsprop_optimizer_1 = require("./rmsprop_optimizer");
+var sgd_optimizer_1 = require("./sgd_optimizer");
+var OptimizerConstructors = (function () {
+    function OptimizerConstructors() {
+    }
+    OptimizerConstructors.sgd = function (learningRate) {
+        return new sgd_optimizer_1.SGDOptimizer(learningRate);
+    };
+    OptimizerConstructors.momentum = function (learningRate, momentum, useNesterov) {
+        if (useNesterov === void 0) { useNesterov = false; }
+        return new momentum_optimizer_1.MomentumOptimizer(learningRate, momentum, useNesterov);
+    };
+    OptimizerConstructors.rmsprop = function (learningRate, decay, momentum, epsilon, centered) {
+        if (decay === void 0) { decay = .9; }
+        if (momentum === void 0) { momentum = 0.0; }
+        if (epsilon === void 0) { epsilon = null; }
+        if (centered === void 0) { centered = false; }
+        return new rmsprop_optimizer_1.RMSPropOptimizer(learningRate, decay, momentum, epsilon, centered);
+    };
+    OptimizerConstructors.adam = function (learningRate, beta1, beta2, epsilon) {
+        if (learningRate === void 0) { learningRate = 0.001; }
+        if (beta1 === void 0) { beta1 = 0.9; }
+        if (beta2 === void 0) { beta2 = 0.999; }
+        if (epsilon === void 0) { epsilon = null; }
+        return new adam_optimizer_1.AdamOptimizer(learningRate, beta1, beta2, epsilon);
+    };
+    OptimizerConstructors.adadelta = function (learningRate, rho, epsilon) {
+        if (learningRate === void 0) { learningRate = .001; }
+        if (rho === void 0) { rho = .95; }
+        if (epsilon === void 0) { epsilon = null; }
+        return new adadelta_optimizer_1.AdadeltaOptimizer(learningRate, rho, epsilon);
+    };
+    OptimizerConstructors.adamax = function (learningRate, beta1, beta2, epsilon, decay) {
+        if (learningRate === void 0) { learningRate = 0.002; }
+        if (beta1 === void 0) { beta1 = 0.9; }
+        if (beta2 === void 0) { beta2 = 0.999; }
+        if (epsilon === void 0) { epsilon = null; }
+        if (decay === void 0) { decay = 0.0; }
+        return new adamax_optimizer_1.AdamaxOptimizer(learningRate, beta1, beta2, epsilon, decay);
+    };
+    OptimizerConstructors.adagrad = function (learningRate, initialAccumulatorValue) {
+        if (initialAccumulatorValue === void 0) { initialAccumulatorValue = 0.1; }
+        return new adagrad_optimizer_1.AdagradOptimizer(learningRate, initialAccumulatorValue);
+    };
+    return OptimizerConstructors;
+}());
+exports.OptimizerConstructors = OptimizerConstructors;
+//# sourceMappingURL=optimizer_constructors.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_constructors.js.map b/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_constructors.js.map
new file mode 100644
index 0000000..50300fd
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_constructors.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"optimizer_constructors.js","sourceRoot":"","sources":["../../src/optimizers/optimizer_constructors.ts"],"names":[],"mappings":";;AAiBA,2DAAuD;AACvD,yDAAqD;AACrD,mDAA+C;AAC/C,uDAAmD;AACnD,2DAAuD;AACvD,yDAAqD;AACrD,iDAA6C;AAE7C;IAAA;IA0KA,CAAC;IAnIQ,yBAAG,GAAV,UAAW,YAAoB;QAC7B,OAAO,IAAI,4BAAY,CAAC,YAAY,CAAC,CAAC;IACxC,CAAC;IAkBM,8BAAQ,GAAf,UAAgB,YAAoB,EAAE,QAAgB,EAAE,WAAmB;QAAnB,4BAAA,EAAA,mBAAmB;QAEzE,OAAO,IAAI,sCAAiB,CAAC,YAAY,EAAE,QAAQ,EAAE,WAAW,CAAC,CAAC;IACpE,CAAC;IAuBM,6BAAO,GAAd,UACI,YAAoB,EAAE,KAAU,EAAE,QAAc,EAAE,OAAsB,EACxE,QAAgB;QADM,sBAAA,EAAA,UAAU;QAAE,yBAAA,EAAA,cAAc;QAAE,wBAAA,EAAA,cAAsB;QACxE,yBAAA,EAAA,gBAAgB;QAClB,OAAO,IAAI,oCAAgB,CACvB,YAAY,EAAE,KAAK,EAAE,QAAQ,EAAE,OAAO,EAAE,QAAQ,CAAC,CAAC;IACxD,CAAC;IAeM,0BAAI,GAAX,UACI,YAAoB,EAAE,KAAW,EAAE,KAAa,EAChD,OAAsB;QADtB,6BAAA,EAAA,oBAAoB;QAAE,sBAAA,EAAA,WAAW;QAAE,sBAAA,EAAA,aAAa;QAChD,wBAAA,EAAA,cAAsB;QACxB,OAAO,IAAI,8BAAa,CAAC,YAAY,EAAE,KAAK,EAAE,KAAK,EAAE,OAAO,CAAC,CAAC;IAChE,CAAC;IAeM,8BAAQ,GAAf,UAAgB,YAAmB,EAAE,GAAS,EAAE,OAAsB;QAAtD,6BAAA,EAAA,mBAAmB;QAAE,oBAAA,EAAA,SAAS;QAAE,wBAAA,EAAA,cAAsB;QAEpE,OAAO,IAAI,sCAAiB,CAAC,YAAY,EAAE,GAAG,EAAE,OAAO,CAAC,CAAC;IAC3D,CAAC;IAgBM,4BAAM,GAAb,UACI,YAAoB,EAAE,KAAW,EAAE,KAAa,EAAE,OAAsB,EACxE,KAAW;QADX,6BAAA,EAAA,oBAAoB;QAAE,sBAAA,EAAA,WAAW;QAAE,sBAAA,EAAA,aAAa;QAAE,wBAAA,EAAA,cAAsB;QACxE,sBAAA,EAAA,WAAW;QACb,OAAO,IAAI,kCAAe,CAAC,YAAY,EAAE,KAAK,EAAE,KAAK,EAAE,OAAO,EAAE,KAAK,CAAC,CAAC;IACzE,CAAC;IAmBM,6BAAO,GAAd,UAAe,YAAoB,EAAE,uBAA6B;QAA7B,wCAAA,EAAA,6BAA6B;QAEhE,OAAO,IAAI,oCAAgB,CAAC,YAAY,EAAE,uBAAuB,CAAC,CAAC;IACrE,CAAC;IACH,4BAAC;AAAD,CAAC,AA1KD,IA0KC;AA1KY,sDAAqB"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_test.js b/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_test.js
new file mode 100644
index 0000000..a3b900f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_test.js
@@ -0,0 +1,136 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+var sgd_optimizer_1 = require("./sgd_optimizer");
+jasmine_util_1.describeWithFlags('optimizer', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var learningRate = .1;
+        var optimizer = tf.train.sgd(learningRate);
+        var x = tf.scalar(4).variable();
+        var bias = tf.scalar(1).variable();
+        var strayVariable = tf.scalar(-1).variable();
+        var numTensors = tf.memory().numTensors;
+        var f = function () { return x.square().addStrict(bias); };
+        var cost = optimizer.minimize(f, true);
+        expect(tf.memory().numTensors).toBe(numTensors + 1);
+        var expectedX1 = -2 * 4 * learningRate + 4;
+        var expectedBias1 = -1 * learningRate + 1;
+        test_util_1.expectArraysClose(x, [expectedX1]);
+        test_util_1.expectArraysClose(bias, [expectedBias1]);
+        test_util_1.expectArraysClose(cost, [Math.pow(4, 2) + 1]);
+        test_util_1.expectArraysClose(strayVariable, [-1]);
+        cost.dispose();
+        numTensors = tf.memory().numTensors;
+        cost = optimizer.minimize(f, false);
+        expect(tf.memory().numTensors).toBe(numTensors);
+        var expectedX2 = -2 * expectedX1 * learningRate + expectedX1;
+        var expectedBias2 = -learningRate + expectedBias1;
+        test_util_1.expectArraysClose(x, [expectedX2]);
+        test_util_1.expectArraysClose(bias, [expectedBias2]);
+        expect(cost).toBe(null);
+        test_util_1.expectArraysClose(strayVariable, [-1]);
+        optimizer.dispose();
+        x.dispose();
+        bias.dispose();
+        strayVariable.dispose();
+        expect(tf.memory().numTensors).toBe(3);
+    });
+    it('varList array of all variables', function () {
+        var learningRate = .1;
+        var optimizer = new sgd_optimizer_1.SGDOptimizer(learningRate);
+        var x = tf.scalar(4).variable();
+        var bias = tf.scalar(1).variable();
+        var strayVariable = tf.scalar(-1).variable();
+        var varList = [x, bias];
+        var f = function () { return x.square().addStrict(bias); };
+        var cost = optimizer.minimize(f, true, varList);
+        var expectedX1 = -2 * 4 * learningRate + 4;
+        var expectedBias1 = -1 * learningRate + 1;
+        test_util_1.expectArraysClose(x, [expectedX1]);
+        test_util_1.expectArraysClose(bias, [expectedBias1]);
+        test_util_1.expectArraysClose(cost, [Math.pow(4, 2) + 1]);
+        test_util_1.expectArraysClose(strayVariable, [-1]);
+        cost = optimizer.minimize(f, false, varList);
+        var expectedX2 = -2 * expectedX1 * learningRate + expectedX1;
+        var expectedBias2 = -learningRate + expectedBias1;
+        test_util_1.expectArraysClose(x, [expectedX2]);
+        test_util_1.expectArraysClose(bias, [expectedBias2]);
+        test_util_1.expectArraysClose(strayVariable, [-1]);
+        expect(cost).toBe(null);
+    });
+    it('varList empty array of variables throws error', function () {
+        var learningRate = .1;
+        var optimizer = new sgd_optimizer_1.SGDOptimizer(learningRate);
+        var x = tf.scalar(4).variable();
+        var bias = tf.scalar(1).variable();
+        tf.scalar(-1).variable();
+        var varList = [];
+        var f = function () { return x.square().addStrict(bias); };
+        expect(function () { return optimizer.minimize(f, true, varList); })
+            .toThrowError();
+    });
+    it('varList subset of variables update', function () {
+        var learningRate = .1;
+        var optimizer = new sgd_optimizer_1.SGDOptimizer(learningRate);
+        var x = tf.scalar(4).variable();
+        var bias = tf.scalar(1).variable();
+        var strayVariable = tf.scalar(-1).variable();
+        var varList = [x];
+        var f = function () { return x.square().addStrict(bias); };
+        var cost = optimizer.minimize(f, true, varList);
+        var expectedValue1 = -2 * 4 * learningRate + 4;
+        test_util_1.expectArraysClose(x, [expectedValue1]);
+        test_util_1.expectArraysClose(bias, [1]);
+        test_util_1.expectArraysClose(cost, [Math.pow(4, 2) + 1]);
+        test_util_1.expectArraysClose(strayVariable, [-1]);
+        cost = optimizer.minimize(f, false, varList);
+        var expectedValue2 = -2 * expectedValue1 * learningRate + expectedValue1;
+        test_util_1.expectArraysClose(x, [expectedValue2]);
+        test_util_1.expectArraysClose(bias, [1]);
+        expect(cost).toBe(null);
+        test_util_1.expectArraysClose(strayVariable, [-1]);
+    });
+    it('only bias trainable', function () {
+        var learningRate = .1;
+        var optimizer = new sgd_optimizer_1.SGDOptimizer(learningRate);
+        var trainable = false;
+        var x = tf.scalar(4).variable(trainable);
+        var bias = tf.scalar(1).variable();
+        var strayVariable = tf.scalar(-1).variable();
+        var f = function () { return x.square().addStrict(bias); };
+        var cost = optimizer.minimize(f, true);
+        test_util_1.expectArraysClose(x, [4]);
+        var expectedBias1 = -1 * learningRate + 1;
+        test_util_1.expectArraysClose(bias, [expectedBias1]);
+        test_util_1.expectArraysClose(cost, [Math.pow(4, 2) + 1]);
+        test_util_1.expectArraysClose(strayVariable, [-1]);
+        cost = optimizer.minimize(f, false);
+        test_util_1.expectArraysClose(x, [4]);
+        var expectedBias2 = -learningRate + expectedBias1;
+        test_util_1.expectArraysClose(bias, [expectedBias2]);
+        expect(cost).toBe(null);
+        test_util_1.expectArraysClose(strayVariable, [-1]);
+    });
+    it('only bias trainable, only x in varList throws error', function () {
+        var learningRate = .1;
+        var optimizer = new sgd_optimizer_1.SGDOptimizer(learningRate);
+        var trainable = false;
+        var x = tf.scalar(4).variable(trainable);
+        var bias = tf.scalar(1).variable();
+        tf.scalar(-1).variable();
+        var varList = [x];
+        var f = function () { return x.square().addStrict(bias); };
+        expect(function () { return optimizer.minimize(f, true, varList); })
+            .toThrowError();
+    });
+    it('throws error when f returns a non-scalar', function () {
+        var learningRate = .1;
+        var optimizer = new sgd_optimizer_1.SGDOptimizer(learningRate);
+        var x = tf.tensor1d([1, 2]).variable();
+        var f = function () { return x.square(); };
+        expect(function () { return optimizer.minimize(f); }).toThrowError();
+    });
+});
+//# sourceMappingURL=optimizer_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_test.js.map
new file mode 100644
index 0000000..2967761
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/optimizer_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"optimizer_test.js","sourceRoot":"","sources":["../../src/optimizers/optimizer_test.ts"],"names":[],"mappings":";;AAiBA,6BAA+B;AAC/B,gDAAkD;AAElD,0CAAyD;AACzD,iDAA6C;AAE7C,gCAAiB,CAAC,WAAW,EAAE,oBAAQ,EAAE;IACvC,EAAE,CAAC,OAAO,EAAE;QACV,IAAM,YAAY,GAAG,EAAE,CAAC;QACxB,IAAM,SAAS,GAAG,EAAE,CAAC,KAAK,CAAC,GAAG,CAAC,YAAY,CAAC,CAAC;QAE7C,IAAM,CAAC,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC;QAClC,IAAM,IAAI,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC;QACrC,IAAM,aAAa,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC;QAE/C,IAAI,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC;QAExC,IAAM,CAAC,GAAG,cAAM,OAAA,CAAC,CAAC,MAAM,EAAE,CAAC,SAAS,CAAC,IAAI,CAAC,EAA1B,CAA0B,CAAC;QAE3C,IAAI,IAAI,GAAG,SAAS,CAAC,QAAQ,CAAC,CAAC,EAAmB,IAAI,CAAC,CAAC;QAGxD,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,UAAU,GAAG,CAAC,CAAC,CAAC;QAGpD,IAAM,UAAU,GAAG,CAAC,CAAC,GAAG,CAAC,GAAG,YAAY,GAAG,CAAC,CAAC;QAE7C,IAAM,aAAa,GAAG,CAAC,CAAC,GAAG,YAAY,GAAG,CAAC,CAAC;QAC5C,6BAAiB,CAAC,CAAC,EAAE,CAAC,UAAU,CAAC,CAAC,CAAC;QACnC,6BAAiB,CAAC,IAAI,EAAE,CAAC,aAAa,CAAC,CAAC,CAAC;QACzC,6BAAiB,CAAC,IAAI,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC;QAE9C,6BAAiB,CAAC,aAAa,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QAEvC,IAAI,CAAC,OAAO,EAAE,CAAC;QACf,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC;QAEpC,IAAI,GAAG,SAAS,CAAC,QAAQ,CAAC,CAAC,EAAmB,KAAK,CAAC,CAAC;QAErD,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC;QAEhD,IAAM,UAAU,GAAG,CAAC,CAAC,GAAG,UAAU,GAAG,YAAY,GAAG,UAAU,CAAC;QAC/D,IAAM,aAAa,GAAG,CAAC,YAAY,GAAG,aAAa,CAAC;QACpD,6BAAiB,CAAC,CAAC,EAAE,CAAC,UAAU,CAAC,CAAC,CAAC;QACnC,6BAAiB,CAAC,IAAI,EAAE,CAAC,aAAa,CAAC,CAAC,CAAC;QACzC,MAAM,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;QAExB,6BAAiB,CAAC,aAAa,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QAEvC,SAAS,CAAC,OAAO,EAAE,CAAC;QACpB,CAAC,CAAC,OAAO,EAAE,CAAC;QACZ,IAAI,CAAC,OAAO,EAAE,CAAC;QACf,aAAa,CAAC,OAAO,EAAE,CAAC;QAExB,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;IACzC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,gCAAgC,EAAE;QACnC,IAAM,YAAY,GAAG,EAAE,CAAC;QACxB,IAAM,SAAS,GAAG,IAAI,4BAAY,CAAC,YAAY,CAAC,CAAC;QAEjD,IAAM,CAAC,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC;QAClC,IAAM,IAAI,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC;QACrC,IAAM,aAAa,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC;QAC/C,IAAM,OAAO,GAAG,CAAC,CAAC,EAAE,IAAI,CAAC,CAAC;QAE1B,IAAM,CAAC,GAAG,cAAM,OAAA,CAAC,CAAC,MAAM,EAAE,CAAC,SAAS,CAAC,IAAI,CAAC,EAA1B,CAA0B,CAAC;QAE3C,IAAI,IAAI,GAAG,SAAS,CAAC,QAAQ,CAAC,CAAC,EAAmB,IAAI,EAAE,OAAO,CAAC,CAAC;QAGjE,IAAM,UAAU,GAAG,CAAC,CAAC,GAAG,CAAC,GAAG,YAAY,GAAG,CAAC,CAAC;QAE7C,IAAM,aAAa,GAAG,CAAC,CAAC,GAAG,YAAY,GAAG,CAAC,CAAC;QAC5C,6BAAiB,CAAC,CAAC,EAAE,CAAC,UAAU,CAAC,CAAC,CAAC;QACnC,6BAAiB,CAAC,IAAI,EAAE,CAAC,aAAa,CAAC,CAAC,CAAC;QACzC,6BAAiB,CAAC,IAAI,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC;QAE9C,6BAAiB,CAAC,aAAa,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QAEvC,IAAI,GAAG,SAAS,CAAC,QAAQ,CAAC,CAAC,EAAmB,KAAK,EAAE,OAAO,CAAC,CAAC;QAE9D,IAAM,UAAU,GAAG,CAAC,CAAC,GAAG,UAAU,GAAG,YAAY,GAAG,UAAU,CAAC;QAC/D,IAAM,aAAa,GAAG,CAAC,YAAY,GAAG,aAAa,CAAC;QACpD,6BAAiB,CAAC,CAAC,EAAE,CAAC,UAAU,CAAC,CAAC,CAAC;QACnC,6BAAiB,CAAC,IAAI,EAAE,CAAC,aAAa,CAAC,CAAC,CAAC;QAEzC,6BAAiB,CAAC,aAAa,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QACvC,MAAM,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;IAC1B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,+CAA+C,EAAE;QAClD,IAAM,YAAY,GAAG,EAAE,CAAC;QACxB,IAAM,SAAS,GAAG,IAAI,4BAAY,CAAC,YAAY,CAAC,CAAC;QAEjD,IAAM,CAAC,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC;QAClC,IAAM,IAAI,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC;QAErC,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC;QACzB,IAAM,OAAO,GAAe,EAAE,CAAC;QAE/B,IAAM,CAAC,GAAG,cAAM,OAAA,CAAC,CAAC,MAAM,EAAE,CAAC,SAAS,CAAC,IAAI,CAAC,EAA1B,CAA0B,CAAC;QAE3C,MAAM,CAAC,cAAM,OAAA,SAAS,CAAC,QAAQ,CAAC,CAAC,EAAmB,IAAI,EAAE,OAAO,CAAC,EAArD,CAAqD,CAAC;aAC9D,YAAY,EAAE,CAAC;IACtB,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,oCAAoC,EAAE;QACvC,IAAM,YAAY,GAAG,EAAE,CAAC;QACxB,IAAM,SAAS,GAAG,IAAI,4BAAY,CAAC,YAAY,CAAC,CAAC;QAEjD,IAAM,CAAC,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC;QAClC,IAAM,IAAI,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC;QACrC,IAAM,aAAa,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC;QAC/C,IAAM,OAAO,GAAG,CAAC,CAAC,CAAC,CAAC;QAEpB,IAAM,CAAC,GAAG,cAAM,OAAA,CAAC,CAAC,MAAM,EAAE,CAAC,SAAS,CAAC,IAAI,CAAC,EAA1B,CAA0B,CAAC;QAE3C,IAAI,IAAI,GAAG,SAAS,CAAC,QAAQ,CAAC,CAAC,EAAmB,IAAI,EAAE,OAAO,CAAC,CAAC;QAGjE,IAAM,cAAc,GAAG,CAAC,CAAC,GAAG,CAAC,GAAG,YAAY,GAAG,CAAC,CAAC;QACjD,6BAAiB,CAAC,CAAC,EAAE,CAAC,cAAc,CAAC,CAAC,CAAC;QAEvC,6BAAiB,CAAC,IAAI,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QAC7B,6BAAiB,CAAC,IAAI,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC;QAE9C,6BAAiB,CAAC,aAAa,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;QAEvC,IAAI,GAAG,SAAS,CAAC,QAAQ,CAAC,CAAC,EAAmB,KAAK,EAAE,OAAO,CAAC,CAAC;QAE9D,IAAM,cAAc,GAAG,CAAC,CAAC,GAAG,cAAc,GAAG,YAAY,GAAG,cAAc,CAAC;QAC3E,6BAAiB,CAAC,CAAC,EAAE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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer.d.ts b/node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer.d.ts
new file mode 100644
index 0000000..bd90b74
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer.d.ts
@@ -0,0 +1,24 @@
+import { ConfigDict, Serializable, SerializableConstructor } from '../serialization';
+import { NamedVariableMap } from '../tensor_types';
+import { Optimizer } from './optimizer';
+export declare class RMSPropOptimizer extends Optimizer {
+    protected learningRate: number;
+    protected decay: number;
+    protected momentum: number;
+    protected epsilon: number;
+    static className: string;
+    private c;
+    private epsilonScalar;
+    private decayScalar;
+    private momentumScalar;
+    private oneMinusDecay;
+    private centered;
+    private accumulatedMeanSquares;
+    private accumulatedMeanGrads;
+    private accumulatedMoments;
+    constructor(learningRate: number, decay?: number, momentum?: number, epsilon?: number, centered?: boolean);
+    applyGradients(variableGradients: NamedVariableMap): void;
+    dispose(): void;
+    getConfig(): ConfigDict;
+    static fromConfig<T extends Serializable>(cls: SerializableConstructor<T>, config: ConfigDict): T;
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer.js b/node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer.js
new file mode 100644
index 0000000..973ed25
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer.js
@@ -0,0 +1,143 @@
+"use strict";
+var __extends = (this && this.__extends) || (function () {
+    var extendStatics = Object.setPrototypeOf ||
+        ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
+        function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
+    return function (d, b) {
+        extendStatics(d, b);
+        function __() { this.constructor = d; }
+        d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
+    };
+})();
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var globals_1 = require("../globals");
+var ops_1 = require("../ops/ops");
+var serialization_1 = require("../serialization");
+var optimizer_1 = require("./optimizer");
+var RMSPropOptimizer = (function (_super) {
+    __extends(RMSPropOptimizer, _super);
+    function RMSPropOptimizer(learningRate, decay, momentum, epsilon, centered) {
+        if (decay === void 0) { decay = 0.9; }
+        if (momentum === void 0) { momentum = 0.0; }
+        if (epsilon === void 0) { epsilon = null; }
+        if (centered === void 0) { centered = false; }
+        var _this = _super.call(this) || this;
+        _this.learningRate = learningRate;
+        _this.decay = decay;
+        _this.momentum = momentum;
+        _this.epsilon = epsilon;
+        _this.accumulatedMeanSquares = {};
+        _this.accumulatedMeanGrads = {};
+        _this.accumulatedMoments = {};
+        _this.c = globals_1.keep(ops_1.scalar(learningRate));
+        _this.decayScalar = globals_1.keep(ops_1.scalar(decay));
+        _this.momentumScalar = globals_1.keep(ops_1.scalar(momentum));
+        _this.oneMinusDecay = globals_1.keep(ops_1.scalar(1 - decay));
+        _this.centered = centered;
+        if (epsilon === null) {
+            epsilon = environment_1.ENV.get('EPSILON');
+        }
+        _this.epsilonScalar = globals_1.keep(ops_1.scalar(epsilon));
+        return _this;
+    }
+    RMSPropOptimizer.prototype.applyGradients = function (variableGradients) {
+        var _this = this;
+        var _loop_1 = function (variableName) {
+            var value = environment_1.ENV.engine.registeredVariables[variableName];
+            if (this_1.accumulatedMeanSquares[variableName] == null) {
+                var trainable_1 = false;
+                globals_1.tidy(function () {
+                    _this.accumulatedMeanSquares[variableName] =
+                        ops_1.zerosLike(value).variable(trainable_1);
+                });
+            }
+            if (this_1.accumulatedMeanGrads[variableName] == null && this_1.centered) {
+                var trainable_2 = false;
+                globals_1.tidy(function () {
+                    _this.accumulatedMeanGrads[variableName] =
+                        ops_1.zerosLike(value).variable(trainable_2);
+                });
+            }
+            if (this_1.accumulatedMoments[variableName] == null) {
+                var trainable_3 = false;
+                globals_1.tidy(function () {
+                    _this.accumulatedMoments[variableName] =
+                        ops_1.zerosLike(value).variable(trainable_3);
+                });
+            }
+            var accumulatedMeanSquare = this_1.accumulatedMeanSquares[variableName];
+            var accumulatedMeanGrad = this_1.accumulatedMeanGrads[variableName];
+            var accumulatedMoments = this_1.accumulatedMoments[variableName];
+            var gradient = variableGradients[variableName];
+            globals_1.tidy(function () {
+                var newAccumulatedMeanSquare = _this.decayScalar.mul(accumulatedMeanSquare)
+                    .add(_this.oneMinusDecay.mul(gradient.square()));
+                if (_this.centered) {
+                    var newAccumulatedMeanGrad = _this.decayScalar.mul(accumulatedMeanGrad)
+                        .add(_this.oneMinusDecay.mul(gradient));
+                    var newAccumulatedMoments = _this.momentumScalar.mul(accumulatedMoments)
+                        .add(_this.c.mul(gradient).div(newAccumulatedMeanSquare
+                        .sub(newAccumulatedMeanGrad.square().add(_this.epsilonScalar))
+                        .sqrt()));
+                    _this.accumulatedMeanSquares[variableName].assign(newAccumulatedMeanSquare);
+                    _this.accumulatedMeanGrads[variableName].assign(newAccumulatedMeanGrad);
+                    _this.accumulatedMoments[variableName].assign(newAccumulatedMoments);
+                    var newValue = value.sub(newAccumulatedMoments);
+                    value.assign(newValue);
+                }
+                else {
+                    var newAccumulatedMeanSquare_1 = _this.decayScalar.mul(accumulatedMeanSquare)
+                        .add(_this.oneMinusDecay.mul(gradient.square()));
+                    var newAccumulatedMoments = _this.momentumScalar.mul(accumulatedMoments)
+                        .add(_this.c.mul(gradient).div(newAccumulatedMeanSquare_1.add(_this.epsilonScalar).sqrt()));
+                    _this.accumulatedMeanSquares[variableName].assign(newAccumulatedMeanSquare_1);
+                    _this.accumulatedMoments[variableName].assign(newAccumulatedMoments);
+                    var newValue = value.sub(newAccumulatedMoments);
+                    value.assign(newValue);
+                }
+            });
+        };
+        var this_1 = this;
+        for (var variableName in variableGradients) {
+            _loop_1(variableName);
+        }
+    };
+    RMSPropOptimizer.prototype.dispose = function () {
+        var _this = this;
+        this.c.dispose();
+        this.epsilonScalar.dispose();
+        this.decayScalar.dispose();
+        this.momentumScalar.dispose();
+        this.oneMinusDecay.dispose();
+        if (this.accumulatedMeanSquares != null) {
+            Object.keys(this.accumulatedMeanSquares)
+                .forEach(function (name) { return _this.accumulatedMeanSquares[name].dispose(); });
+        }
+        if (this.accumulatedMeanGrads != null && this.centered) {
+            Object.keys(this.accumulatedMeanGrads)
+                .forEach(function (name) { return _this.accumulatedMeanGrads[name].dispose(); });
+        }
+        if (this.accumulatedMoments != null) {
+            Object.keys(this.accumulatedMoments)
+                .forEach(function (name) { return _this.accumulatedMoments[name].dispose(); });
+        }
+    };
+    RMSPropOptimizer.prototype.getConfig = function () {
+        return {
+            learningRate: this.learningRate,
+            decay: this.decay,
+            momentum: this.momentum,
+            epsilon: this.epsilon,
+            centered: this.centered
+        };
+    };
+    RMSPropOptimizer.fromConfig = function (cls, config) {
+        return new cls(config.learningRate, config.decay, config.momentum, config.epsilon, config.centered);
+    };
+    RMSPropOptimizer.className = 'RMSPropOptimizer';
+    return RMSPropOptimizer;
+}(optimizer_1.Optimizer));
+exports.RMSPropOptimizer = RMSPropOptimizer;
+serialization_1.registerClass(RMSPropOptimizer);
+//# sourceMappingURL=rmsprop_optimizer.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer.js.map b/node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer.js.map
new file mode 100644
index 0000000..b1354e1
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer_test.js b/node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer_test.js
new file mode 100644
index 0000000..eef2b44
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer_test.js
@@ -0,0 +1,56 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('RMSPropOptimizer', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var learningRate = 0.1;
+        var moment = 0.1;
+        var rho = 0.95;
+        var optimizer = tf.train.rmsprop(learningRate, rho, moment);
+        var x = tf.tensor1d([1, 2]).variable();
+        var f = function () { return x.square().sum(); };
+        var numTensors = tf.memory().numTensors;
+        var cost = optimizer.minimize(f, true);
+        expect(tf.memory().numTensors).toBe(numTensors + 3);
+        test_util_1.expectArraysClose(x, [0.55279, 1.55279]);
+        cost.dispose();
+        numTensors = tf.memory().numTensors;
+        cost = optimizer.minimize(f, false);
+        test_util_1.expectArraysClose(x, [0.28745, 1.222943], 1e-2);
+        expect(tf.memory().numTensors).toBe(numTensors);
+        expect(cost).toBe(null);
+        x.dispose();
+        optimizer.dispose();
+        expect(tf.memory().numTensors).toBe(1);
+    });
+    it('gradient with centered momentum', function () {
+        var learningRate = 0.1;
+        var moment = 0.1;
+        var rho = 0.95;
+        var eps = 1e-8;
+        var optimizer = tf.train.rmsprop(learningRate, rho, moment, eps, true);
+        var x = tf.tensor1d([1, 2]).variable();
+        var f = function () { return x.square().sum(); };
+        var numTensors = tf.memory().numTensors;
+        var cost = optimizer.minimize(f, true);
+        expect(tf.memory().numTensors).toBe(numTensors + 4);
+        test_util_1.expectArraysClose(x, [0.54117, 1.541169]);
+        cost.dispose();
+        numTensors = tf.memory().numTensors;
+        cost = optimizer.minimize(f, false);
+        test_util_1.expectArraysClose(x, [0.267785, 1.2035924], 1e-2);
+        expect(tf.memory().numTensors).toBe(numTensors);
+        expect(cost).toBe(null);
+        x.dispose();
+        optimizer.dispose();
+        expect(tf.memory().numTensors).toBe(1);
+    });
+    it('serialization round-trip', function () {
+        var originalOpt = tf.train.rmsprop(0.1, 0.5, 0.1, 1e-7, true);
+        var reserialized = tf.RMSPropOptimizer.fromConfig(tf.RMSPropOptimizer, originalOpt.getConfig());
+        expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());
+    });
+});
+//# sourceMappingURL=rmsprop_optimizer_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer_test.js.map
new file mode 100644
index 0000000..0f92610
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/rmsprop_optimizer_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"rmsprop_optimizer_test.js","sourceRoot":"","sources":["../../src/optimizers/rmsprop_optimizer_test.ts"],"names":[],"mappings":";;AAiBA,6BAA+B;AAC/B,gDAAkD;AAClD,0CAAyD;AAEzD,gCAAiB,CAAC,kBAAkB,EAAE,oBAAQ,EAAE;IAC9C,EAAE,CAAC,OAAO,EAAE;QACV,IAAM,YAAY,GAAG,GAAG,CAAC;QACzB,IAAM,MAAM,GAAG,GAAG,CAAC;QACnB,IAAM,GAAG,GAAG,IAAI,CAAC;QACjB,IAAM,SAAS,GAAG,EAAE,CAAC,KAAK,CAAC,OAAO,CAAC,YAAY,EAAE,GAAG,EAAE,MAAM,CAAC,CAAC;QAE9D,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC;QAEzC,IAAM,CAAC,GAAG,cAAM,OAAA,CAAC,CAAC,MAAM,EAAE,CAAC,GAAG,EAAe,EAA7B,CAA6B,CAAC;QAE9C,IAAI,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC;QAExC,IAAI,IAAI,GAAG,SAAS,CAAC,QAAQ,CAAC,CAAC,EAAmB,IAAI,CAAC,CAAC;QAGxD,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,UAAU,GAAG,CAAC,CAAC,CAAC;QAgBpD,6BAAiB,CAAC,CAAC,EAAE,CAAC,OAAO,EAAE,OAAO,CAAC,CAAC,CAAC;QAEzC,IAAI,CAAC,OAAO,EAAE,CAAC;QACf,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC;QAEpC,IAAI,GAAG,SAAS,CAAC,QAAQ,CAAC,CAAC,EAAmB,KAAK,CAAC,CAAC;QAWrD,6BAAiB,CAAC,CAAC,EAAE,CAAC,OAAO,EAAE,QAAQ,CAAC,EAAE,IAAI,CAAC,CAAC;QAGhD,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC;QAEhD,MAAM,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;QAExB,CAAC,CAAC,OAAO,EAAE,CAAC;QACZ,SAAS,CAAC,OAAO,EAAE,CAAC;QAEpB,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;IACzC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,iCAAiC,EAAE;QACpC,IAAM,YAAY,GAAG,GAAG,CAAC;QACzB,IAAM,MAAM,GAAG,GAAG,CAAC;QACnB,IAAM,GAAG,GAAG,IAAI,CAAC;QACjB,IAAM,GAAG,GAAG,IAAI,CAAC;QACjB,IAAM,SAAS,GAAG,EAAE,CAAC,KAAK,CAAC,OAAO,CAAC,YAAY,EAAE,GAAG,EAAE,MAAM,EAAE,GAAG,EAAE,IAAI,CAAC,CAAC;QAEzE,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC;QAEzC,IAAM,CAAC,GAAG,cAAM,OAAA,CAAC,CAAC,MAAM,EAAE,CAAC,GAAG,EAAe,EAA7B,CAA6B,CAAC;QAE9C,IAAI,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC;QAExC,IAAI,IAAI,GAAG,SAAS,CAAC,QAAQ,CAAC,CAAC,EAAmB,IAAI,CAAC,CAAC;QAGxD,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,UAAU,GAAG,CAAC,CAAC,CAAC;QAqBpD,6BAAiB,CAAC,CAAC,EAAE,CAAC,OAAO,EAAE,QAAQ,CAAC,CAAC,CAAC;QAE1C,IAAI,CAAC,OAAO,EAAE,CAAC;QACf,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC;QAEpC,IAAI,GAAG,SAAS,CAAC,QAAQ,CAAC,CAAC,EAAmB,KAAK,CAAC,CAAC;QAarD,6BAAiB,CAAC,CAAC,EAAE,CAAC,QAAQ,EAAE,SAAS,CAAC,EAAE,IAAI,CAAC,CAAC;QAGlD,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC;QAEhD,MAAM,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;QAExB,CAAC,CAAC,OAAO,EAAE,CAAC;QACZ,SAAS,CAAC,OAAO,EAAE,CAAC;QAEpB,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;IACzC,CAAC,CAAC,CAAC;IACH,EAAE,CAAC,0BAA0B,EAAE;QAC7B,IAAM,WAAW,GAAG,EAAE,CAAC,KAAK,CAAC,OAAO,CAAC,GAAG,EAAE,GAAG,EAAE,GAAG,EAAE,IAAI,EAAE,IAAI,CAAC,CAAC;QAChE,IAAM,YAAY,GAAG,EAAE,CAAC,gBAAgB,CAAC,UAAU,CAC/C,EAAE,CAAC,gBAAgB,EAAE,WAAW,CAAC,SAAS,EAAE,CAAC,CAAC;QAClD,MAAM,CAAC,YAAY,CAAC,SAAS,EAAE,CAAC,CAAC,OAAO,CAAC,WAAW,CAAC,SAAS,EAAE,CAAC,CAAC;IACpE,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer.d.ts b/node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer.d.ts
new file mode 100644
index 0000000..de644b2
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer.d.ts
@@ -0,0 +1,15 @@
+import { ConfigDict, Serializable, SerializableConstructor } from '../serialization';
+import { Scalar } from '../tensor';
+import { NamedTensorMap } from '../tensor_types';
+import { Optimizer } from './optimizer';
+export declare class SGDOptimizer extends Optimizer {
+    protected learningRate: number;
+    static className: string;
+    protected c: Scalar;
+    constructor(learningRate: number);
+    applyGradients(variableGradients: NamedTensorMap): void;
+    setLearningRate(learningRate: number): void;
+    dispose(): void;
+    getConfig(): ConfigDict;
+    static fromConfig<T extends Serializable>(cls: SerializableConstructor<T>, config: ConfigDict): T;
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer.js b/node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer.js
new file mode 100644
index 0000000..15778cd
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer.js
@@ -0,0 +1,59 @@
+"use strict";
+var __extends = (this && this.__extends) || (function () {
+    var extendStatics = Object.setPrototypeOf ||
+        ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
+        function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
+    return function (d, b) {
+        extendStatics(d, b);
+        function __() { this.constructor = d; }
+        d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
+    };
+})();
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("../environment");
+var globals_1 = require("../globals");
+var ops_1 = require("../ops/ops");
+var serialization_1 = require("../serialization");
+var optimizer_1 = require("./optimizer");
+var SGDOptimizer = (function (_super) {
+    __extends(SGDOptimizer, _super);
+    function SGDOptimizer(learningRate) {
+        var _this = _super.call(this) || this;
+        _this.learningRate = learningRate;
+        _this.setLearningRate(learningRate);
+        return _this;
+    }
+    SGDOptimizer.prototype.applyGradients = function (variableGradients) {
+        var _this = this;
+        var varNames = Object.keys(variableGradients);
+        varNames.forEach(function (varName) {
+            var gradient = variableGradients[varName];
+            var value = environment_1.ENV.engine.registeredVariables[varName];
+            globals_1.tidy(function () {
+                var newValue = _this.c.mul(gradient).add(value);
+                value.assign(newValue);
+            });
+        });
+    };
+    SGDOptimizer.prototype.setLearningRate = function (learningRate) {
+        this.learningRate = learningRate;
+        if (this.c != null) {
+            this.c.dispose();
+        }
+        this.c = globals_1.keep(ops_1.scalar(-learningRate));
+    };
+    SGDOptimizer.prototype.dispose = function () {
+        this.c.dispose();
+    };
+    SGDOptimizer.prototype.getConfig = function () {
+        return { learningRate: this.learningRate };
+    };
+    SGDOptimizer.fromConfig = function (cls, config) {
+        return new cls(config.learningRate);
+    };
+    SGDOptimizer.className = 'SGDOptimizer';
+    return SGDOptimizer;
+}(optimizer_1.Optimizer));
+exports.SGDOptimizer = SGDOptimizer;
+serialization_1.registerClass(SGDOptimizer);
+//# sourceMappingURL=sgd_optimizer.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer.js.map b/node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer.js.map
new file mode 100644
index 0000000..5fb7cc1
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"sgd_optimizer.js","sourceRoot":"","sources":["../../src/optimizers/sgd_optimizer.ts"],"names":[],"mappings":";;;;;;;;;;;;AAiBA,8CAAmC;AACnC,sCAAsC;AACtC,kCAAkC;AAClC,kDAAkG;AAGlG,yCAAsC;AAGtC;IAAkC,gCAAS;IAKzC,sBAAsB,YAAoB;QAA1C,YACE,iBAAO,SAER;QAHqB,kBAAY,GAAZ,YAAY,CAAQ;QAExC,KAAI,CAAC,eAAe,CAAC,YAAY,CAAC,CAAC;;IACrC,CAAC;IAED,qCAAc,GAAd,UAAe,iBAAiC;QAAhD,iBAWC;QAVC,IAAM,QAAQ,GAAG,MAAM,CAAC,IAAI,CAAC,iBAAiB,CAAC,CAAC;QAChD,QAAQ,CAAC,OAAO,CAAC,UAAA,OAAO;YACtB,IAAM,QAAQ,GAAG,iBAAiB,CAAC,OAAO,CAAC,CAAC;YAC5C,IAAM,KAAK,GAAG,iBAAG,CAAC,MAAM,CAAC,mBAAmB,CAAC,OAAO,CAAC,CAAC;YAEtD,cAAI,CAAC;gBACH,IAAM,QAAQ,GAAG,KAAI,CAAC,CAAC,CAAC,GAAG,CAAC,QAAQ,CAAC,CAAC,GAAG,CAAC,KAAK,CAAC,CAAC;gBACjD,KAAK,CAAC,MAAM,CAAC,QAAQ,CAAC,CAAC;YACzB,CAAC,CAAC,CAAC;QACL,CAAC,CAAC,CAAC;IACL,CAAC;IAKD,sCAAe,GAAf,UAAgB,YAAoB;QAClC,IAAI,CAAC,YAAY,GAAG,YAAY,CAAC;QACjC,IAAI,IAAI,CAAC,CAAC,IAAI,IAAI,EAAE;YAClB,IAAI,CAAC,CAAC,CAAC,OAAO,EAAE,CAAC;SAClB;QACD,IAAI,CAAC,CAAC,GAAG,cAAI,CAAC,YAAM,CAAC,CAAC,YAAY,CAAC,CAAC,CAAC;IACvC,CAAC;IAED,8BAAO,GAAP;QACE,IAAI,CAAC,CAAC,CAAC,OAAO,EAAE,CAAC;IACnB,CAAC;IAED,gCAAS,GAAT;QACE,OAAO,EAAC,YAAY,EAAE,IAAI,CAAC,YAAY,EAAC,CAAC;IAC3C,CAAC;IACM,uBAAU,GAAjB,UACI,GAA+B,EAAE,MAAkB;QACrD,OAAO,IAAI,GAAG,CAAC,MAAM,CAAC,YAAY,CAAC,CAAC;IACtC,CAAC;IA1CM,sBAAS,GAAG,cAAc,CAAC;IA2CpC,mBAAC;CAAA,AA7CD,CAAkC,qBAAS,GA6C1C;AA7CY,oCAAY;AA8CzB,6BAAa,CAAC,YAAY,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer_test.js b/node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer_test.js
new file mode 100644
index 0000000..b7fcb69
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer_test.js
@@ -0,0 +1,35 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("../index");
+var jasmine_util_1 = require("../jasmine_util");
+var test_util_1 = require("../test_util");
+jasmine_util_1.describeWithFlags('SGDOptimizer', test_util_1.ALL_ENVS, function () {
+    it('basic', function () {
+        var learningRate = .1;
+        var optimizer = tf.train.sgd(learningRate);
+        var x = tf.scalar(4).variable();
+        var numTensors = tf.memory().numTensors;
+        var cost = optimizer.minimize(function () { return x.square(); }, true);
+        expect(tf.memory().numTensors).toBe(numTensors + 1);
+        var expectedValue1 = -2 * 4 * learningRate + 4;
+        test_util_1.expectArraysClose(x, [expectedValue1]);
+        test_util_1.expectArraysClose(cost, [Math.pow(4, 2)]);
+        cost.dispose();
+        numTensors = tf.memory().numTensors;
+        cost = optimizer.minimize(function () { return x.square(); }, false);
+        expect(tf.memory().numTensors).toBe(numTensors);
+        var expectedValue2 = -2 * expectedValue1 * learningRate + expectedValue1;
+        test_util_1.expectArraysClose(x, [expectedValue2]);
+        expect(cost).toBe(null);
+        optimizer.dispose();
+        x.dispose();
+        expect(tf.memory().numTensors).toBe(1);
+    });
+    it('serialization round-trip', function () {
+        var learningRate = .1;
+        var originalOpt = tf.train.sgd(learningRate);
+        var reserialized = tf.SGDOptimizer.fromConfig(tf.SGDOptimizer, originalOpt.getConfig());
+        expect(reserialized.getConfig()).toEqual(originalOpt.getConfig());
+    });
+});
+//# sourceMappingURL=sgd_optimizer_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer_test.js.map
new file mode 100644
index 0000000..0d768c1
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/optimizers/sgd_optimizer_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"sgd_optimizer_test.js","sourceRoot":"","sources":["../../src/optimizers/sgd_optimizer_test.ts"],"names":[],"mappings":";;AAiBA,6BAA+B;AAC/B,gDAAkD;AAClD,0CAAyD;AAEzD,gCAAiB,CAAC,cAAc,EAAE,oBAAQ,EAAE;IAC1C,EAAE,CAAC,OAAO,EAAE;QACV,IAAM,YAAY,GAAG,EAAE,CAAC;QACxB,IAAM,SAAS,GAAG,EAAE,CAAC,KAAK,CAAC,GAAG,CAAC,YAAY,CAAC,CAAC;QAE7C,IAAM,CAAC,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC;QAElC,IAAI,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC;QAExC,IAAI,IAAI,GAAG,SAAS,CAAC,QAAQ,CAAC,cAAM,OAAA,CAAC,CAAC,MAAM,EAAE,EAAV,CAAU,EAAmB,IAAI,CAAC,CAAC;QAGvE,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,UAAU,GAAG,CAAC,CAAC,CAAC;QAGpD,IAAM,cAAc,GAAG,CAAC,CAAC,GAAG,CAAC,GAAG,YAAY,GAAG,CAAC,CAAC;QACjD,6BAAiB,CAAC,CAAC,EAAE,CAAC,cAAc,CAAC,CAAC,CAAC;QACvC,6BAAiB,CAAC,IAAI,EAAE,CAAC,IAAI,CAAC,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QAE1C,IAAI,CAAC,OAAO,EAAE,CAAC;QACf,UAAU,GAAG,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC;QAEpC,IAAI,GAAG,SAAS,CAAC,QAAQ,CAAC,cAAM,OAAA,CAAC,CAAC,MAAM,EAAE,EAAV,CAAU,EAAmB,KAAK,CAAC,CAAC;QAEpE,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,UAAU,CAAC,CAAC;QAEhD,IAAM,cAAc,GAAG,CAAC,CAAC,GAAG,cAAc,GAAG,YAAY,GAAG,cAAc,CAAC;QAC3E,6BAAiB,CAAC,CAAC,EAAE,CAAC,cAAc,CAAC,CAAC,CAAC;QACvC,MAAM,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;QAExB,SAAS,CAAC,OAAO,EAAE,CAAC;QACpB,CAAC,CAAC,OAAO,EAAE,CAAC;QAEZ,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;IACzC,CAAC,CAAC,CAAC;IACH,EAAE,CAAC,0BAA0B,EAAE;QAC7B,IAAM,YAAY,GAAG,EAAE,CAAC;QACxB,IAAM,WAAW,GAAG,EAAE,CAAC,KAAK,CAAC,GAAG,CAAC,YAAY,CAAC,CAAC;QAC/C,IAAM,YAAY,GACd,EAAE,CAAC,YAAY,CAAC,UAAU,CAAC,EAAE,CAAC,YAAY,EAAE,WAAW,CAAC,SAAS,EAAE,CAAC,CAAC;QACzE,MAAM,CAAC,YAAY,CAAC,SAAS,EAAE,CAAC,CAAC,OAAO,CAAC,WAAW,CAAC,SAAS,EAAE,CAAC,CAAC;IACpE,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/profiler.d.ts b/node_modules/@tensorflow/tfjs-core/dist/profiler.d.ts
new file mode 100644
index 0000000..adb2100
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/profiler.d.ts
@@ -0,0 +1,12 @@
+import { BackendTimer } from './kernels/backend';
+import { Tensor } from './tensor';
+import { TypedArray } from './types';
+export declare class Profiler {
+    private backendTimer;
+    private logger?;
+    constructor(backendTimer: BackendTimer, logger?: Logger);
+    profileKernel<T extends Tensor | Tensor[]>(name: string, f: () => T | Tensor[]): T;
+}
+export declare class Logger {
+    logKernelProfile(name: string, result: Tensor, vals: TypedArray, timeMs: number, extraInfo?: string): void;
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/profiler.js b/node_modules/@tensorflow/tfjs-core/dist/profiler.js
new file mode 100644
index 0000000..704964c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/profiler.js
@@ -0,0 +1,50 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var util = require("./util");
+var Profiler = (function () {
+    function Profiler(backendTimer, logger) {
+        this.backendTimer = backendTimer;
+        this.logger = logger;
+        if (logger == null) {
+            this.logger = new Logger();
+        }
+    }
+    Profiler.prototype.profileKernel = function (name, f) {
+        var _this = this;
+        var result;
+        var holdResultWrapperFn = function () {
+            result = f();
+        };
+        var timer = this.backendTimer.time(holdResultWrapperFn);
+        var results = Array.isArray(result) ? result : [result];
+        results.forEach(function (r) {
+            var vals = r.dataSync();
+            util.checkComputationForNaN(vals, r.dtype, name);
+            timer.then(function (timing) {
+                var extraInfo = '';
+                if (timing.getExtraProfileInfo != null) {
+                    extraInfo = timing.getExtraProfileInfo();
+                }
+                _this.logger.logKernelProfile(name, r, vals, timing.kernelMs, extraInfo);
+            });
+        });
+        return result;
+    };
+    return Profiler;
+}());
+exports.Profiler = Profiler;
+var Logger = (function () {
+    function Logger() {
+    }
+    Logger.prototype.logKernelProfile = function (name, result, vals, timeMs, extraInfo) {
+        var time = util.rightPad(timeMs + "ms", 9);
+        var paddedName = util.rightPad(name, 25);
+        var rank = result.rank;
+        var size = result.size;
+        var shape = util.rightPad(result.shape.toString(), 14);
+        console.log("%c" + paddedName + "\t%c" + time + "\t%c" + rank + "D " + shape + "\t%c" + size + "\t%c" + extraInfo, 'font-weight:bold', 'color:red', 'color:blue', 'color: orange', 'color: green');
+    };
+    return Logger;
+}());
+exports.Logger = Logger;
+//# sourceMappingURL=profiler.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/profiler.js.map b/node_modules/@tensorflow/tfjs-core/dist/profiler.js.map
new file mode 100644
index 0000000..f14f7d7
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/profiler.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"profiler.js","sourceRoot":"","sources":["../src/profiler.ts"],"names":[],"mappings":";;AAoBA,6BAA+B;AAE/B;IACE,kBAAoB,YAA0B,EAAU,MAAe;QAAnD,iBAAY,GAAZ,YAAY,CAAc;QAAU,WAAM,GAAN,MAAM,CAAS;QACrE,IAAI,MAAM,IAAI,IAAI,EAAE;YAClB,IAAI,CAAC,MAAM,GAAG,IAAI,MAAM,EAAE,CAAC;SAC5B;IACH,CAAC;IAED,gCAAa,GAAb,UAAyC,IAAY,EAAE,CAAqB;QAA5E,iBAyBC;QAvBC,IAAI,MAAkB,CAAC;QACvB,IAAM,mBAAmB,GAAG;YAC1B,MAAM,GAAG,CAAC,EAAE,CAAC;QACf,CAAC,CAAC;QACF,IAAM,KAAK,GAAG,IAAI,CAAC,YAAY,CAAC,IAAI,CAAC,mBAAmB,CAAC,CAAC;QAE1D,IAAM,OAAO,GACT,KAAK,CAAC,OAAO,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,MAAM,CAAa,CAAC;QAC1D,OAAO,CAAC,OAAO,CAAC,UAAA,CAAC;YACf,IAAM,IAAI,GAAG,CAAC,CAAC,QAAQ,EAAE,CAAC;YAC1B,IAAI,CAAC,sBAAsB,CAAC,IAAI,EAAE,CAAC,CAAC,KAAK,EAAE,IAAI,CAAC,CAAC;YAEjD,KAAK,CAAC,IAAI,CAAC,UAAA,MAAM;gBACf,IAAI,SAAS,GAAG,EAAE,CAAC;gBACnB,IAAI,MAAM,CAAC,mBAAmB,IAAI,IAAI,EAAE;oBACtC,SAAS,GAAG,MAAM,CAAC,mBAAmB,EAAE,CAAC;iBAC1C;gBAED,KAAI,CAAC,MAAM,CAAC,gBAAgB,CAAC,IAAI,EAAE,CAAC,EAAE,IAAI,EAAE,MAAM,CAAC,QAAQ,EAAE,SAAS,CAAC,CAAC;YAC1E,CAAC,CAAC,CAAC;QACL,CAAC,CAAC,CAAC;QAEH,OAAO,MAAW,CAAC;IACrB,CAAC;IACH,eAAC;AAAD,CAAC,AAjCD,IAiCC;AAjCY,4BAAQ;AAmCrB;IAAA;IAgBA,CAAC;IAfC,iCAAgB,GAAhB,UACI,IAAY,EAAE,MAAc,EAAE,IAAgB,EAAE,MAAc,EAC9D,SAAkB;QACpB,IAAM,IAAI,GAAG,IAAI,CAAC,QAAQ,CAAI,MAAM,OAAI,EAAE,CAAC,CAAC,CAAC;QAC7C,IAAM,UAAU,GAAG,IAAI,CAAC,QAAQ,CAAC,IAAI,EAAE,EAAE,CAAC,CAAC;QAC3C,IAAM,IAAI,GAAG,MAAM,CAAC,IAAI,CAAC;QACzB,IAAM,IAAI,GAAG,MAAM,CAAC,IAAI,CAAC;QACzB,IAAM,KAAK,GAAG,IAAI,CAAC,QAAQ,CAAC,MAAM,CAAC,KAAK,CAAC,QAAQ,EAAE,EAAE,EAAE,CAAC,CAAC;QAEzD,OAAO,CAAC,GAAG,CACP,OAAK,UAAU,YAAO,IAAI,YAAO,IAAI,UAAK,KAAK,YAAO,IAAI,YACtD,SAAW,EACf,kBAAkB,EAAE,WAAW,EAAE,YAAY,EAAE,eAAe,EAC9D,cAAc,CAAC,CAAC;IACtB,CAAC;IACH,aAAC;AAAD,CAAC,AAhBD,IAgBC;AAhBY,wBAAM"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/profiler_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/profiler_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/profiler_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/profiler_test.js b/node_modules/@tensorflow/tfjs-core/dist/profiler_test.js
new file mode 100644
index 0000000..70fff48
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/profiler_test.js
@@ -0,0 +1,152 @@
+"use strict";
+var __extends = (this && this.__extends) || (function () {
+    var extendStatics = Object.setPrototypeOf ||
+        ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
+        function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
+    return function (d, b) {
+        extendStatics(d, b);
+        function __() { this.constructor = d; }
+        d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
+    };
+})();
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("./index");
+var profiler_1 = require("./profiler");
+var TestBackendTimer = (function () {
+    function TestBackendTimer(delayMs, queryTimeMs, extraInfo) {
+        this.delayMs = delayMs;
+        this.queryTimeMs = queryTimeMs;
+        this.extraInfo = extraInfo;
+        this.counter = 1;
+    }
+    TestBackendTimer.prototype.time = function (query) {
+        return __awaiter(this, void 0, void 0, function () {
+            var kernelMs;
+            var _this = this;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        query();
+                        return [4, new Promise(function (resolve) { return setTimeout(resolve(_this.queryTimeMs * _this.counter++), _this.delayMs); })];
+                    case 1:
+                        kernelMs = _a.sent();
+                        return [2, { kernelMs: kernelMs, getExtraProfileInfo: function () { return _this.extraInfo; } }];
+                }
+            });
+        });
+    };
+    return TestBackendTimer;
+}());
+var TestLogger = (function (_super) {
+    __extends(TestLogger, _super);
+    function TestLogger() {
+        return _super !== null && _super.apply(this, arguments) || this;
+    }
+    TestLogger.prototype.logKernelProfile = function (name, result, vals, timeMs) { };
+    return TestLogger;
+}(profiler_1.Logger));
+describe('profiler.Profiler', function () {
+    it('profiles simple function', function (doneFn) {
+        var delayMs = 5;
+        var queryTimeMs = 10;
+        var extraInfo = '';
+        var timer = new TestBackendTimer(delayMs, queryTimeMs, extraInfo);
+        var logger = new TestLogger();
+        var profiler = new profiler_1.Profiler(timer, logger);
+        spyOn(timer, 'time').and.callThrough();
+        spyOn(logger, 'logKernelProfile').and.callThrough();
+        var timeSpy = timer.time;
+        var logKernelProfileSpy = logger.logKernelProfile;
+        var kernelCalled = false;
+        var result = 1;
+        var resultScalar = tf.scalar(result);
+        profiler.profileKernel('MatMul', function () {
+            kernelCalled = true;
+            return resultScalar;
+        });
+        setTimeout(function () {
+            expect(timeSpy.calls.count()).toBe(1);
+            expect(logKernelProfileSpy.calls.count()).toBe(1);
+            expect(logKernelProfileSpy.calls.first().args).toEqual([
+                'MatMul', resultScalar, new Float32Array([result]), queryTimeMs,
+                extraInfo
+            ]);
+            expect(kernelCalled).toBe(true);
+            doneFn();
+        }, delayMs * 2);
+    });
+    it('profiles nested kernel', function (doneFn) {
+        var delayMs = 5;
+        var queryTimeMs = 10;
+        var extraInfo = '';
+        var timer = new TestBackendTimer(delayMs, queryTimeMs, extraInfo);
+        var logger = new TestLogger();
+        var profiler = new profiler_1.Profiler(timer, logger);
+        spyOn(timer, 'time').and.callThrough();
+        spyOn(logger, 'logKernelProfile').and.callThrough();
+        var timeSpy = timer.time;
+        var logKernelProfileSpy = logger.logKernelProfile;
+        var matmulKernelCalled = false;
+        var maxKernelCalled = false;
+        var result = 1;
+        var resultScalar = tf.scalar(result);
+        profiler.profileKernel('MatMul', function () {
+            var result = profiler.profileKernel('Max', function () {
+                maxKernelCalled = true;
+                return resultScalar;
+            });
+            matmulKernelCalled = true;
+            return result;
+        });
+        setTimeout(function () {
+            expect(timeSpy.calls.count()).toBe(2);
+            expect(logKernelProfileSpy.calls.count()).toBe(2);
+            expect(logKernelProfileSpy.calls.first().args).toEqual([
+                'Max', resultScalar, new Float32Array([result]), queryTimeMs, extraInfo
+            ]);
+            expect(logKernelProfileSpy.calls.argsFor(1)).toEqual([
+                'MatMul', resultScalar, new Float32Array([result]), queryTimeMs * 2,
+                extraInfo
+            ]);
+            expect(matmulKernelCalled).toBe(true);
+            expect(maxKernelCalled).toBe(true);
+            doneFn();
+        }, delayMs * 2);
+    });
+});
+//# sourceMappingURL=profiler_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/profiler_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/profiler_test.js.map
new file mode 100644
index 0000000..9c0914a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/profiler_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"profiler_test.js","sourceRoot":"","sources":["../src/profiler_test.ts"],"names":[],"mappings":";;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;;AAiBA,4BAA8B;AAE9B,uCAA4C;AAI5C;IAEE,0BACY,OAAe,EAAU,WAAmB,EAC5C,SAAiB;QADjB,YAAO,GAAP,OAAO,CAAQ;QAAU,gBAAW,GAAX,WAAW,CAAQ;QAC5C,cAAS,GAAT,SAAS,CAAQ;QAHrB,YAAO,GAAG,CAAC,CAAC;IAGY,CAAC;IAE3B,+BAAI,GAAV,UAAW,KAAiB;;;;;;;wBAC1B,KAAK,EAAE,CAAC;wBACS,WAAM,IAAI,OAAO,CAC9B,UAAA,OAAO,IAAI,OAAA,UAAU,CACjB,OAAO,CAAC,KAAI,CAAC,WAAW,GAAG,KAAI,CAAC,OAAO,EAAE,CAAC,EAAE,KAAI,CAAC,OAAO,CAAC,EADlD,CACkD,CAAC,EAAA;;wBAF5D,QAAQ,GAAG,SAEiD;wBAClE,WAAO,EAAC,QAAQ,UAAA,EAAE,mBAAmB,EAAE,cAAM,OAAA,KAAI,CAAC,SAAS,EAAd,CAAc,EAAC,EAAC;;;;KAC9D;IACH,uBAAC;AAAD,CAAC,AAbD,IAaC;AAED;IAAyB,8BAAM;IAA/B;;IAGA,CAAC;IAFC,qCAAgB,GAAhB,UACI,IAAY,EAAE,MAAc,EAAE,IAAgB,EAAE,MAAc,IAAG,CAAC;IACxE,iBAAC;AAAD,CAAC,AAHD,CAAyB,iBAAM,GAG9B;AAED,QAAQ,CAAC,mBAAmB,EAAE;IAC5B,EAAE,CAAC,0BAA0B,EAAE,UAAA,MAAM;QACnC,IAAM,OAAO,GAAG,CAAC,CAAC;QAClB,IAAM,WAAW,GAAG,EAAE,CAAC;QACvB,IAAM,SAAS,GAAG,EAAE,CAAC;QACrB,IAAM,KAAK,GAAG,IAAI,gBAAgB,CAAC,OAAO,EAAE,WAAW,EAAE,SAAS,CAAC,CAAC;QACpE,IAAM,MAAM,GAAG,IAAI,UAAU,EAAE,CAAC;QAChC,IAAM,QAAQ,GAAG,IAAI,mBAAQ,CAAC,KAAK,EAAE,MAAM,CAAC,CAAC;QAE7C,KAAK,CAAC,KAAK,EAAE,MAAM,CAAC,CAAC,GAAG,CAAC,WAAW,EAAE,CAAC;QACvC,KAAK,CAAC,MAAM,EAAE,kBAAkB,CAAC,CAAC,GAAG,CAAC,WAAW,EAAE,CAAC;QAEpD,IAAM,OAAO,GAAG,KAAK,CAAC,IAAmB,CAAC;QAC1C,IAAM,mBAAmB,GAAG,MAAM,CAAC,gBAA+B,CAAC;QAEnE,IAAI,YAAY,GAAG,KAAK,CAAC;QACzB,IAAM,MAAM,GAAG,CAAC,CAAC;QACjB,IAAM,YAAY,GAAG,EAAE,CAAC,MAAM,CAAC,MAAM,CAAC,CAAC;QAEvC,QAAQ,CAAC,aAAa,CAAC,QAAQ,EAAE;YAC/B,YAAY,GAAG,IAAI,CAAC;YACpB,OAAO,YAAY,CAAC;QACtB,CAAC,CAAC,CAAC;QAEH,UAAU,CAAC;YACT,MAAM,CAAC,OAAO,CAAC,KAAK,CAAC,KAAK,EAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;YAEtC,MAAM,CAAC,mBAAmB,CAAC,KAAK,CAAC,KAAK,EAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;YAElD,MAAM,CAAC,mBAAmB,CAAC,KAAK,CAAC,KAAK,EAAE,CAAC,IAAI,CAAC,CAAC,OAAO,CAAC;gBACrD,QAAQ,EAAE,YAAY,EAAE,IAAI,YAAY,CAAC,CAAC,MAAM,CAAC,CAAC,EAAE,WAAW;gBAC/D,SAAS;aACV,CAAC,CAAC;YAEH,MAAM,CAAC,YAAY,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;YAChC,MAAM,EAAE,CAAC;QACX,CAAC,EAAE,OAAO,GAAG,CAAC,CAAC,CAAC;IAClB,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,wBAAwB,EAAE,UAAA,MAAM;QACjC,IAAM,OAAO,GAAG,CAAC,CAAC;QAClB,IAAM,WAAW,GAAG,EAAE,CAAC;QACvB,IAAM,SAAS,GAAG,EAAE,CAAC;QACrB,IAAM,KAAK,GAAG,IAAI,gBAAgB,CAAC,OAAO,EAAE,WAAW,EAAE,SAAS,CAAC,CAAC;QACpE,IAAM,MAAM,GAAG,IAAI,UAAU,EAAE,CAAC;QAChC,IAAM,QAAQ,GAAG,IAAI,mBAAQ,CAAC,KAAK,EAAE,MAAM,CAAC,CAAC;QAE7C,KAAK,CAAC,KAAK,EAAE,MAAM,CAAC,CAAC,GAAG,CAAC,WAAW,EAAE,CAAC;QACvC,KAAK,CAAC,MAAM,EAAE,kBAAkB,CAAC,CAAC,GAAG,CAAC,WAAW,EAAE,CAAC;QACpD,IAAM,OAAO,GAAG,KAAK,CAAC,IAAmB,CAAC;QAC1C,IAAM,mBAAmB,GAAG,MAAM,CAAC,gBAA+B,CAAC;QAEnE,IAAI,kBAAkB,GAAG,KAAK,CAAC;QAC/B,IAAI,eAAe,GAAG,KAAK,CAAC;QAC5B,IAAM,MAAM,GAAG,CAAC,CAAC;QACjB,IAAM,YAAY,GAAG,EAAE,CAAC,MAAM,CAAC,MAAM,CAAC,CAAC;QAEvC,QAAQ,CAAC,aAAa,CAAC,QAAQ,EAAE;YAC/B,IAAM,MAAM,GAAG,QAAQ,CAAC,aAAa,CAAC,KAAK,EAAE;gBAC3C,eAAe,GAAG,IAAI,CAAC;gBACvB,OAAO,YAAY,CAAC;YACtB,CAAC,CAAC,CAAC;YACH,kBAAkB,GAAG,IAAI,CAAC;YAC1B,OAAO,MAAM,CAAC;QAChB,CAAC,CAAC,CAAC;QAEH,UAAU,CAAC;YACT,MAAM,CAAC,OAAO,CAAC,KAAK,CAAC,KAAK,EAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;YAEtC,MAAM,CAAC,mBAAmB,CAAC,KAAK,CAAC,KAAK,EAAE,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;YAClD,MAAM,CAAC,mBAAmB,CAAC,KAAK,CAAC,KAAK,EAAE,CAAC,IAAI,CAAC,CAAC,OAAO,CAAC;gBACrD,KAAK,EAAE,YAAY,EAAE,IAAI,YAAY,CAAC,CAAC,MAAM,CAAC,CAAC,EAAE,WAAW,EAAE,SAAS;aACxE,CAAC,CAAC;YACH,MAAM,CAAC,mBAAmB,CAAC,KAAK,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC;gBACnD,QAAQ,EAAE,YAAY,EAAE,IAAI,YAAY,CAAC,CAAC,MAAM,CAAC,CAAC,EAAE,WAAW,GAAG,CAAC;gBACnE,SAAS;aACV,CAAC,CAAC;YAEH,MAAM,CAAC,kBAAkB,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;YACtC,MAAM,CAAC,eAAe,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;YACnC,MAAM,EAAE,CAAC;QACX,CAAC,EAAE,OAAO,GAAG,CAAC,CAAC,CAAC;IAClB,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/serialization.d.ts b/node_modules/@tensorflow/tfjs-core/dist/serialization.d.ts
new file mode 100644
index 0000000..1ad7cf6
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/serialization.d.ts
@@ -0,0 +1,27 @@
+export declare type ConfigDictValue = boolean | number | string | null | ConfigDictArray | ConfigDict;
+export interface ConfigDict {
+    [key: string]: ConfigDictValue;
+}
+export interface ConfigDictArray extends Array<ConfigDictValue> {
+}
+export declare type SerializableConstructor<T extends Serializable> = {
+    new (...args: any[]): T;
+    className: string;
+    fromConfig: FromConfigMethod<T>;
+};
+export declare type FromConfigMethod<T extends Serializable> = (cls: SerializableConstructor<T>, config: ConfigDict) => T;
+export declare abstract class Serializable {
+    getClassName(): string;
+    abstract getConfig(): ConfigDict;
+    static fromConfig<T extends Serializable>(cls: SerializableConstructor<T>, config: ConfigDict): T;
+}
+export declare class SerializationMap {
+    private static instance;
+    classNameMap: {
+        [className: string]: [SerializableConstructor<Serializable>, FromConfigMethod<Serializable>];
+    };
+    private constructor();
+    static getMap(): SerializationMap;
+    static register<T extends Serializable>(cls: SerializableConstructor<T>): void;
+}
+export declare function registerClass<T extends Serializable>(cls: SerializableConstructor<T>): void;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/serialization.js b/node_modules/@tensorflow/tfjs-core/dist/serialization.js
new file mode 100644
index 0000000..dfeeac3
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/serialization.js
@@ -0,0 +1,44 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var util_1 = require("./util");
+var Serializable = (function () {
+    function Serializable() {
+    }
+    Serializable.prototype.getClassName = function () {
+        return this.constructor
+            .className;
+    };
+    Serializable.fromConfig = function (cls, config) {
+        return new cls(config);
+    };
+    return Serializable;
+}());
+exports.Serializable = Serializable;
+var SerializationMap = (function () {
+    function SerializationMap() {
+        this.classNameMap = {};
+    }
+    SerializationMap.getMap = function () {
+        if (SerializationMap.instance == null) {
+            SerializationMap.instance = new SerializationMap();
+        }
+        return SerializationMap.instance;
+    };
+    SerializationMap.register = function (cls) {
+        SerializationMap.getMap().classNameMap[cls.className] =
+            [cls, cls.fromConfig];
+    };
+    return SerializationMap;
+}());
+exports.SerializationMap = SerializationMap;
+function registerClass(cls) {
+    util_1.assert(cls.className != null, "Class being registered does not have the static className property " +
+        "defined.");
+    util_1.assert(typeof cls.className === 'string', "className is required to be a string, but got type " +
+        typeof cls.className);
+    util_1.assert(cls.className.length > 0, "Class being registered has an empty-string as its className, which " +
+        "is disallowed.");
+    SerializationMap.register(cls);
+}
+exports.registerClass = registerClass;
+//# sourceMappingURL=serialization.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/serialization.js.map b/node_modules/@tensorflow/tfjs-core/dist/serialization.js.map
new file mode 100644
index 0000000..7b51587
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/serialization.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"serialization.js","sourceRoot":"","sources":["../src/serialization.ts"],"names":[],"mappings":";;AAiBA,+BAA8B;AA6C9B;IAAA;IAkCA,CAAC;IAtBC,mCAAY,GAAZ;QACE,OAAQ,IAAI,CAAC,WAAqD;aAC7D,SAAS,CAAC;IACjB,CAAC;IAeM,uBAAU,GAAjB,UACI,GAA+B,EAAE,MAAkB;QACrD,OAAO,IAAI,GAAG,CAAC,MAAM,CAAC,CAAC;IACzB,CAAC;IACH,mBAAC;AAAD,CAAC,AAlCD,IAkCC;AAlCqB,oCAAY;AA2ClC;IAOE;QACE,IAAI,CAAC,YAAY,GAAG,EAAE,CAAC;IACzB,CAAC;IAKM,uBAAM,GAAb;QACE,IAAI,gBAAgB,CAAC,QAAQ,IAAI,IAAI,EAAE;YACrC,gBAAgB,CAAC,QAAQ,GAAG,IAAI,gBAAgB,EAAE,CAAC;SACpD;QACD,OAAO,gBAAgB,CAAC,QAAQ,CAAC;IACnC,CAAC;IAKM,yBAAQ,GAAf,UAAwC,GAA+B;QACrE,gBAAgB,CAAC,MAAM,EAAE,CAAC,YAAY,CAAC,GAAG,CAAC,SAAS,CAAC;YACjD,CAAC,GAAG,EAAE,GAAG,CAAC,UAAU,CAAC,CAAC;IAC5B,CAAC;IACH,uBAAC;AAAD,CAAC,AA5BD,IA4BC;AA5BY,4CAAgB;AAqD7B,uBACI,GAA+B;IACjC,aAAM,CACF,GAAG,CAAC,SAAS,IAAI,IAAI,EACrB,qEAAqE;QACjE,UAAU,CAAC,CAAC;IACpB,aAAM,CACF,OAAO,GAAG,CAAC,SAAS,KAAK,QAAQ,EACjC,qDAAqD;QACjD,OAAO,GAAG,CAAC,SAAS,CAAC,CAAC;IAC9B,aAAM,CACF,GAAG,CAAC,SAAS,CAAC,MAAM,GAAG,CAAC,EACxB,qEAAqE;QACjE,gBAAgB,CAAC,CAAC;IAE1B,gBAAgB,CAAC,QAAQ,CAAC,GAAG,CAAC,CAAC;AACjC,CAAC;AAhBD,sCAgBC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/serialization_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/serialization_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/serialization_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/serialization_test.js b/node_modules/@tensorflow/tfjs-core/dist/serialization_test.js
new file mode 100644
index 0000000..7189069
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/serialization_test.js
@@ -0,0 +1,82 @@
+"use strict";
+var __extends = (this && this.__extends) || (function () {
+    var extendStatics = Object.setPrototypeOf ||
+        ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
+        function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
+    return function (d, b) {
+        extendStatics(d, b);
+        function __() { this.constructor = d; }
+        d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
+    };
+})();
+Object.defineProperty(exports, "__esModule", { value: true });
+var optimizer_1 = require("./optimizers/optimizer");
+var serialization_1 = require("./serialization");
+describe('registerClass', function () {
+    var randomClassName = "OptimizerForTest" + Math.random();
+    var OptimizerForTest = (function (_super) {
+        __extends(OptimizerForTest, _super);
+        function OptimizerForTest() {
+            return _super.call(this) || this;
+        }
+        OptimizerForTest.prototype.applyGradients = function (variableGradients) { };
+        OptimizerForTest.prototype.getConfig = function () {
+            return {};
+        };
+        OptimizerForTest.className = randomClassName;
+        return OptimizerForTest;
+    }(optimizer_1.Optimizer));
+    it('registerClass succeeds', function () {
+        serialization_1.registerClass(OptimizerForTest);
+        expect(serialization_1.SerializationMap.getMap().classNameMap[randomClassName] != null)
+            .toEqual(true);
+    });
+    var OptimizerWithoutClassName = (function (_super) {
+        __extends(OptimizerWithoutClassName, _super);
+        function OptimizerWithoutClassName() {
+            return _super.call(this) || this;
+        }
+        OptimizerWithoutClassName.prototype.applyGradients = function (variableGradients) { };
+        OptimizerWithoutClassName.prototype.getConfig = function () {
+            return {};
+        };
+        return OptimizerWithoutClassName;
+    }(optimizer_1.Optimizer));
+    it('registerClass fails on missing className', function () {
+        expect(function () { return serialization_1.registerClass(OptimizerWithoutClassName); })
+            .toThrowError(/does not have the static className property/);
+    });
+    var OptimizerWithEmptyClassName = (function (_super) {
+        __extends(OptimizerWithEmptyClassName, _super);
+        function OptimizerWithEmptyClassName() {
+            return _super.call(this) || this;
+        }
+        OptimizerWithEmptyClassName.prototype.applyGradients = function (variableGradients) { };
+        OptimizerWithEmptyClassName.prototype.getConfig = function () {
+            return {};
+        };
+        OptimizerWithEmptyClassName.className = '';
+        return OptimizerWithEmptyClassName;
+    }(optimizer_1.Optimizer));
+    it('registerClass fails on missing className', function () {
+        expect(function () { return serialization_1.registerClass(OptimizerWithEmptyClassName); })
+            .toThrowError(/has an empty-string as its className/);
+    });
+    var OptimizerWithNonStringClassName = (function (_super) {
+        __extends(OptimizerWithNonStringClassName, _super);
+        function OptimizerWithNonStringClassName() {
+            return _super.call(this) || this;
+        }
+        OptimizerWithNonStringClassName.prototype.applyGradients = function (variableGradients) { };
+        OptimizerWithNonStringClassName.prototype.getConfig = function () {
+            return {};
+        };
+        OptimizerWithNonStringClassName.className = 42;
+        return OptimizerWithNonStringClassName;
+    }(optimizer_1.Optimizer));
+    it('registerClass fails on missing className', function () {
+        expect(function () { return serialization_1.registerClass(OptimizerWithNonStringClassName); })
+            .toThrowError(/is required to be a string, but got type number/);
+    });
+});
+//# sourceMappingURL=serialization_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/serialization_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/serialization_test.js.map
new file mode 100644
index 0000000..e1154e1
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/serialization_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"serialization_test.js","sourceRoot":"","sources":["../src/serialization_test.ts"],"names":[],"mappings":";;;;;;;;;;;;AAiBA,oDAAiD;AACjD,iDAA4E;AAG5E,QAAQ,CAAC,eAAe,EAAE;IACxB,IAAM,eAAe,GAAG,qBAAmB,IAAI,CAAC,MAAM,EAAI,CAAC;IAC3D;QAA+B,oCAAS;QAEtC;mBACE,iBAAO;QACT,CAAC;QACD,yCAAc,GAAd,UAAe,iBAAmC,IAAG,CAAC;QAEtD,oCAAS,GAAT;YACE,OAAO,EAAE,CAAC;QACZ,CAAC;QARM,0BAAS,GAAG,eAAe,CAAC;QASrC,uBAAC;KAAA,AAVD,CAA+B,qBAAS,GAUvC;IACD,EAAE,CAAC,wBAAwB,EAAE;QAC3B,6BAAa,CAAC,gBAAgB,CAAC,CAAC;QAChC,MAAM,CAAC,gCAAgB,CAAC,MAAM,EAAE,CAAC,YAAY,CAAC,eAAe,CAAC,IAAI,IAAI,CAAC;aAClE,OAAO,CAAC,IAAI,CAAC,CAAC;IACrB,CAAC,CAAC,CAAC;IAEH;QAAwC,6CAAS;QAC/C;mBACE,iBAAO;QACT,CAAC;QACD,kDAAc,GAAd,UAAe,iBAAmC,IAAG,CAAC;QAEtD,6CAAS,GAAT;YACE,OAAO,EAAE,CAAC;QACZ,CAAC;QACH,gCAAC;IAAD,CAAC,AATD,CAAwC,qBAAS,GAShD;IACD,EAAE,CAAC,0CAA0C,EAAE;QAE7C,MAAM,CAAC,cAAM,OAAA,6BAAa,CAAC,yBAAgC,CAAC,EAA/C,CAA+C,CAAC;aACxD,YAAY,CAAC,6CAA6C,CAAC,CAAC;IACnE,CAAC,CAAC,CAAC;IAEH;QAA0C,+CAAS;QAEjD;mBACE,iBAAO;QACT,CAAC;QACD,oDAAc,GAAd,UAAe,iBAAmC,IAAG,CAAC;QAEtD,+CAAS,GAAT;YACE,OAAO,EAAE,CAAC;QACZ,CAAC;QARM,qCAAS,GAAG,EAAE,CAAC;QASxB,kCAAC;KAAA,AAVD,CAA0C,qBAAS,GAUlD;IACD,EAAE,CAAC,0CAA0C,EAAE;QAC7C,MAAM,CAAC,cAAM,OAAA,6BAAa,CAAC,2BAA2B,CAAC,EAA1C,CAA0C,CAAC;aACnD,YAAY,CAAC,sCAAsC,CAAC,CAAC;IAC5D,CAAC,CAAC,CAAC;IAEH;QAA8C,mDAAS;QAErD;mBACE,iBAAO;QACT,CAAC;QACD,wDAAc,GAAd,UAAe,iBAAmC,IAAG,CAAC;QAEtD,mDAAS,GAAT;YACE,OAAO,EAAE,CAAC;QACZ,CAAC;QARM,yCAAS,GAAG,EAAE,CAAC;QASxB,sCAAC;KAAA,AAVD,CAA8C,qBAAS,GAUtD;IACD,EAAE,CAAC,0CAA0C,EAAE;QAE7C,MAAM,CAAC,cAAM,OAAA,6BAAa,CAAC,+BAAsC,CAAC,EAArD,CAAqD,CAAC;aAC9D,YAAY,CAAC,iDAAiD,CAAC,CAAC;IACvE,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tape.d.ts b/node_modules/@tensorflow/tfjs-core/dist/tape.d.ts
new file mode 100644
index 0000000..14a94ea
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tape.d.ts
@@ -0,0 +1,16 @@
+import { Tensor } from './tensor';
+import { NamedTensorMap } from './tensor_types';
+export interface TapeNode {
+    id: number;
+    name: string;
+    outputs: Tensor[];
+    inputs: NamedTensorMap;
+    gradient?: (dy: Tensor | Tensor[]) => NamedGradientMap;
+}
+export declare type NamedGradientMap = {
+    [inputName: string]: () => Tensor;
+};
+export declare function getFilteredNodesXToY(tape: TapeNode[], xs: Tensor[], y: Tensor): TapeNode[];
+export declare function backpropagateGradients(tensorAccumulatedGradientMap: {
+    [tensorId: number]: Tensor;
+}, filteredTape: TapeNode[]): void;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tape.js b/node_modules/@tensorflow/tfjs-core/dist/tape.js
new file mode 100644
index 0000000..91d4601
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tape.js
@@ -0,0 +1,116 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tensor_1 = require("./tensor");
+var util = require("./util");
+function getFilteredNodesXToY(tape, xs, y) {
+    var tensorsFromX = {};
+    var nodesFromX = {};
+    for (var i = 0; i < xs.length; i++) {
+        tensorsFromX[xs[i].id] = true;
+    }
+    for (var i = 0; i < tape.length; i++) {
+        var node = tape[i];
+        var nodeInputs = node.inputs;
+        for (var inputName in nodeInputs) {
+            var input = nodeInputs[inputName];
+            var anyInputFromX = false;
+            for (var j = 0; j < xs.length; j++) {
+                if (tensorsFromX[input.id]) {
+                    node.outputs.forEach(function (output) { return tensorsFromX[output.id] = true; });
+                    anyInputFromX = true;
+                    nodesFromX[node.id] = true;
+                    break;
+                }
+            }
+            if (anyInputFromX) {
+                break;
+            }
+        }
+    }
+    var tensorsLeadToY = {};
+    tensorsLeadToY[y.id] = true;
+    var nodesToY = {};
+    for (var i = tape.length - 1; i >= 0; i--) {
+        var node = tape[i];
+        var nodeInputs = node.inputs;
+        for (var j = 0; j < node.outputs.length; j++) {
+            if (tensorsLeadToY[node.outputs[j].id]) {
+                for (var inputName in nodeInputs) {
+                    tensorsLeadToY[nodeInputs[inputName].id] = true;
+                    nodesToY[node.id] = true;
+                }
+                break;
+            }
+        }
+    }
+    var filteredTape = [];
+    for (var i = 0; i < tape.length; i++) {
+        var node = tape[i];
+        if (nodesFromX[node.id] && nodesToY[node.id]) {
+            var prunedInputs = {};
+            for (var inputName in node.inputs) {
+                var nodeInput = node.inputs[inputName];
+                if (tensorsFromX[nodeInput.id]) {
+                    prunedInputs[inputName] = nodeInput;
+                }
+            }
+            var prunedNode = Object.assign({}, node);
+            prunedNode.inputs = prunedInputs;
+            prunedNode.outputs = node.outputs;
+            filteredTape.push(prunedNode);
+        }
+    }
+    return filteredTape;
+}
+exports.getFilteredNodesXToY = getFilteredNodesXToY;
+function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape) {
+    var _loop_1 = function (i) {
+        var node = filteredTape[i];
+        var dys = [];
+        node.outputs.forEach(function (o) {
+            var gradTensor = tensorAccumulatedGradientMap[o.id];
+            if (gradTensor != null) {
+                dys.push(gradTensor);
+            }
+            else {
+                var dy = tensor_1.Tensor.make(o.shape, { values: util.makeZerosTypedArray(o.size, o.dtype) }, o.dtype);
+                dys.push(dy);
+            }
+        });
+        if (node.gradient == null) {
+            throw new Error("Cannot compute gradient: gradient function not found " +
+                ("for " + node.name + "."));
+        }
+        var inputGradients = node.gradient(node.outputs.length === 1 ? dys[0] : dys);
+        for (var inputName in node.inputs) {
+            if (!(inputName in inputGradients)) {
+                throw new Error("Cannot backprop through input " + inputName + ". " +
+                    ("Available gradients found: " + Object.keys(inputGradients) + "."));
+            }
+            var dx = inputGradients[inputName]();
+            if (dx.dtype !== 'float32') {
+                throw new Error("Error in gradient for op " + node.name + ". The gradient of input " +
+                    (inputName + " must have 'float32' dtype, but has '" + dx.dtype + "'"));
+            }
+            var x = node.inputs[inputName];
+            if (!util.arraysEqual(dx.shape, x.shape)) {
+                throw new Error("Error in gradient for op " + node.name + ". The gradient of input " +
+                    ("'" + inputName + "' has shape '" + dx.shape + "', which does not match ") +
+                    ("the shape of the input '" + x.shape + "'"));
+            }
+            if (tensorAccumulatedGradientMap[x.id] == null) {
+                tensorAccumulatedGradientMap[x.id] = dx;
+            }
+            else {
+                var curGradient = tensorAccumulatedGradientMap[x.id];
+                tensorAccumulatedGradientMap[x.id] = curGradient.add(dx);
+                curGradient.dispose();
+            }
+        }
+    };
+    for (var i = filteredTape.length - 1; i >= 0; i--) {
+        _loop_1(i);
+    }
+}
+exports.backpropagateGradients = backpropagateGradients;
+//# sourceMappingURL=tape.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tape.js.map b/node_modules/@tensorflow/tfjs-core/dist/tape.js.map
new file mode 100644
index 0000000..e67cfaa
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tape.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tape_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/tape_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tape_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tape_test.js b/node_modules/@tensorflow/tfjs-core/dist/tape_test.js
new file mode 100644
index 0000000..c9f693b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tape_test.js
@@ -0,0 +1,294 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("./index");
+var jasmine_util_1 = require("./jasmine_util");
+var tape_1 = require("./tape");
+var test_util_1 = require("./test_util");
+jasmine_util_1.describeWithFlags('getFilteredNodesXToY', test_util_1.ALL_ENVS, function () {
+    it('no paths from x to y', function () {
+        var x = tf.scalar(1);
+        var intermediate1 = tf.scalar(0);
+        var intermediate2 = tf.scalar(0);
+        var y = tf.scalar(2);
+        var tape = [
+            {
+                id: 0,
+                name: 'node0',
+                inputs: { x: x },
+                outputs: [intermediate1],
+                gradient: null
+            },
+            {
+                id: 1,
+                name: 'node1',
+                inputs: { intermediate2: intermediate2 },
+                outputs: [y],
+                gradient: null
+            }
+        ];
+        var filteredTapeNodes = tape_1.getFilteredNodesXToY(tape, [x], y);
+        expect(filteredTapeNodes.length).toBe(0);
+        expect(filteredTapeNodes).toEqual([]);
+    });
+    it('one operation x => y', function () {
+        var x = tf.scalar(1);
+        var y = tf.scalar(2);
+        var tape = [{ id: 0, name: 'node0', inputs: { x: x }, outputs: [y], gradient: null }];
+        var filteredTapeNodes = tape_1.getFilteredNodesXToY(tape, [x], y);
+        expect(filteredTapeNodes.length).toBe(1);
+        expect(filteredTapeNodes).toEqual(tape);
+    });
+    it('1 operation [x0, x1] => y, all input paths', function () {
+        var x0 = tf.scalar(0);
+        var x1 = tf.scalar(1);
+        var y = tf.scalar(2);
+        var tape = [
+            { id: 0, name: 'node0', inputs: { x0: x0, x1: x1 }, outputs: [y], gradient: null }
+        ];
+        var filteredTapeNodes = tape_1.getFilteredNodesXToY(tape, [x0, x1], y);
+        expect(filteredTapeNodes.length).toBe(1);
+        expect(filteredTapeNodes).toEqual(tape);
+    });
+    it('one operation [x0, x1] => y, one input paths', function () {
+        var x0 = tf.scalar(0);
+        var x1 = tf.scalar(1);
+        var y = tf.scalar(2);
+        var tape = [
+            { id: 0, name: 'node0', inputs: { x0: x0, x1: x1 }, outputs: [y], gradient: null }
+        ];
+        var filteredTapeNodes = tape_1.getFilteredNodesXToY(tape, [x0], y);
+        expect(filteredTapeNodes.length).toBe(1);
+        expect(filteredTapeNodes[0])
+            .toEqual({ id: 0, name: 'node0', inputs: { x0: x0 }, outputs: [y], gradient: null });
+    });
+    it('two operations x => intermediate => y', function () {
+        var x = tf.scalar(1);
+        var intermediate = tf.scalar(0);
+        var y = tf.scalar(2);
+        var tape = [
+            {
+                id: 0,
+                name: 'node0',
+                inputs: { x: x },
+                outputs: [intermediate],
+                gradient: null
+            },
+            {
+                id: 1,
+                name: 'node1',
+                inputs: { intermediate: intermediate },
+                outputs: [y],
+                gradient: null
+            }
+        ];
+        var filteredTapeNodes = tape_1.getFilteredNodesXToY(tape, [x], y);
+        expect(filteredTapeNodes.length).toBe(2);
+        expect(filteredTapeNodes).toEqual(tape);
+    });
+    it('two operations [x0, x1], [x2] => ' +
+        'intermediate => y', function () {
+        var x0 = tf.scalar(1);
+        var x1 = tf.scalar(2);
+        var x2 = tf.scalar(3);
+        var intermediate = tf.scalar(4);
+        var y = tf.scalar(2);
+        var tape = [
+            {
+                id: 0,
+                name: 'node0',
+                inputs: { x0: x0, x1: x1 },
+                outputs: [intermediate],
+                gradient: null
+            },
+            {
+                id: 1,
+                name: 'node1',
+                inputs: { x2: x2, intermediate: intermediate },
+                outputs: [y],
+                gradient: null
+            }
+        ];
+        var filteredTapeNodes = tape_1.getFilteredNodesXToY(tape, [x0, x1, x2], y);
+        expect(filteredTapeNodes.length).toBe(2);
+        expect(filteredTapeNodes).toEqual(tape);
+    });
+    it('x => y and x => orphan', function () {
+        var x = tf.scalar(1);
+        var orphan = tf.scalar(0);
+        var y = tf.scalar(2);
+        var tape = [
+            { id: 0, name: 'node0', inputs: { x: x }, outputs: [orphan], gradient: null },
+            { id: 1, name: 'node1', inputs: { x: x }, outputs: [y], gradient: null }
+        ];
+        var filteredTapeNodes = tape_1.getFilteredNodesXToY(tape, [x], y);
+        expect(filteredTapeNodes.length).toBe(1);
+        expect(filteredTapeNodes[0]).toEqual(tape[1]);
+    });
+    it('x => y and orphan => y', function () {
+        var x = tf.scalar(1);
+        var orphan = tf.scalar(0);
+        var y = tf.scalar(2);
+        var tape = [{
+                id: 0,
+                name: 'node0',
+                inputs: { x: x, orphan: orphan },
+                outputs: [y],
+                gradient: null
+            }];
+        var filteredTapeNodes = tape_1.getFilteredNodesXToY(tape, [x], y);
+        expect(filteredTapeNodes.length).toBe(1);
+        expect(filteredTapeNodes[0])
+            .toEqual({ id: 0, name: 'node0', inputs: { x: x }, outputs: [y], gradient: null });
+    });
+    it('1 op with 3 outputs x => y1, y2, y3', function () {
+        var x = tf.scalar(1);
+        var y1 = tf.scalar(2);
+        var y2 = tf.scalar(2);
+        var y3 = tf.scalar(2);
+        var tape = [{
+                id: 0,
+                name: 'node0',
+                inputs: { x: x },
+                outputs: [y1, y2, y3],
+                gradient: null
+            }];
+        var filteredNodes1 = tape_1.getFilteredNodesXToY(tape, [x], y1);
+        expect(filteredNodes1.length).toBe(1);
+        expect(filteredNodes1).toEqual(tape);
+        var filteredNodes2 = tape_1.getFilteredNodesXToY(tape, [x], y2);
+        expect(filteredNodes2.length).toBe(1);
+        expect(filteredNodes2).toEqual(tape);
+        var filteredNodes3 = tape_1.getFilteredNodesXToY(tape, [x], y3);
+        expect(filteredNodes3.length).toBe(1);
+        expect(filteredNodes3).toEqual(tape);
+    });
+});
+jasmine_util_1.describeWithFlags('backpropagateGradients', test_util_1.ALL_ENVS, function () {
+    it('Throws if gradient is not defined', function () {
+        var x = tf.scalar(0);
+        var y = tf.scalar(1);
+        var dy = tf.scalar(1);
+        var accumulatedGradientsMap = {};
+        accumulatedGradientsMap[y.id] = dy;
+        var tape = [{ id: 0, name: 'node0', inputs: { x: x }, outputs: [y], gradient: null }];
+        expect(function () { return tape_1.backpropagateGradients(accumulatedGradientsMap, tape); })
+            .toThrowError();
+    });
+    it('basic backprop with 1 node', function () {
+        var x = tf.scalar(0);
+        var y = tf.scalar(1);
+        var dy = tf.scalar(1);
+        var accumulatedGradientsMap = {};
+        accumulatedGradientsMap[y.id] = dy;
+        var tape = [{
+                id: 0,
+                name: 'node0',
+                inputs: { x: x },
+                outputs: [y],
+                gradient: function (dy) {
+                    return { x: function () { return dy.add(tf.scalar(1)); } };
+                }
+            }];
+        tape_1.backpropagateGradients(accumulatedGradientsMap, tape);
+        test_util_1.expectArraysClose(accumulatedGradientsMap[x.id], [2]);
+    });
+    it('basic backprop with 2 nodes', function () {
+        var x = tf.scalar(0);
+        var intermediate = tf.scalar(1);
+        var y = tf.scalar(2);
+        var dy = tf.scalar(1);
+        var accumulatedGradientsMap = {};
+        accumulatedGradientsMap[y.id] = dy;
+        var tape = [
+            {
+                id: 0,
+                name: 'node0',
+                inputs: { x: x },
+                outputs: [intermediate],
+                gradient: function (dy) {
+                    return { x: function () { return dy.add(tf.scalar(1)); } };
+                }
+            },
+            {
+                id: 1,
+                name: 'node1',
+                inputs: { intermediate: intermediate },
+                outputs: [y],
+                gradient: function (dy) {
+                    return { intermediate: function () { return dy.add(tf.scalar(1)); } };
+                }
+            }
+        ];
+        tape_1.backpropagateGradients(accumulatedGradientsMap, tape);
+        test_util_1.expectArraysClose(accumulatedGradientsMap[x.id], [3]);
+    });
+    it('basic backprop with a split node accumulates gradients', function () {
+        var x = tf.scalar(0);
+        var intermediate1 = tf.scalar(1);
+        var intermediate2 = tf.scalar(2);
+        var y = tf.scalar(3);
+        var dy = tf.scalar(1);
+        var accumulatedGradientsMap = {};
+        accumulatedGradientsMap[y.id] = dy;
+        var tape = [
+            {
+                id: 0,
+                name: 'node0',
+                inputs: { x: x },
+                outputs: [intermediate1],
+                gradient: function (dy) {
+                    return { x: function () { return dy.add(tf.scalar(1)); } };
+                }
+            },
+            {
+                id: 1,
+                name: 'node1',
+                inputs: { x: x },
+                outputs: [intermediate2],
+                gradient: function (dy) {
+                    return { x: function () { return dy.add(tf.scalar(1)); } };
+                }
+            },
+            {
+                id: 2,
+                name: 'node2',
+                inputs: { intermediate1: intermediate1, intermediate2: intermediate2 },
+                outputs: [y],
+                gradient: function (dy) {
+                    return {
+                        intermediate1: function () { return dy.add(tf.scalar(1)); },
+                        intermediate2: function () { return dy.add(tf.scalar(1)); }
+                    };
+                }
+            }
+        ];
+        tape_1.backpropagateGradients(accumulatedGradientsMap, tape);
+        test_util_1.expectArraysClose(accumulatedGradientsMap[x.id], [dy.dataSync()[0] + 5]);
+    });
+    it('backprop over 1 node with 3 outputs, w.r.t to the 2nd output', function () {
+        var x = tf.tensor1d([1, 1, 1]);
+        var y1 = tf.scalar(1);
+        var y2 = tf.scalar(1);
+        var y3 = tf.scalar(1);
+        var accumulatedGradientsMap = {};
+        var dy2 = tf.scalar(5);
+        accumulatedGradientsMap[y2.id] = dy2;
+        var dys;
+        var tape = [{
+                id: 0,
+                name: 'node0',
+                inputs: { x: x },
+                outputs: [y1, y2, y3],
+                gradient: function (dys_) {
+                    dys = dys_;
+                    return { x: function () { return tf.stack(dys_); } };
+                }
+            }];
+        tape_1.backpropagateGradients(accumulatedGradientsMap, tape);
+        test_util_1.expectArraysClose(accumulatedGradientsMap[x.id], [0, 5, 0]);
+        test_util_1.expectArraysClose(dys[0], [0]);
+        test_util_1.expectArraysClose(dys[1], [5]);
+        test_util_1.expectArraysClose(dys[2], [0]);
+    });
+});
+//# sourceMappingURL=tape_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tape_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/tape_test.js.map
new file mode 100644
index 0000000..ee3ce69
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tape_test.js.map
@@ -0,0 +1 @@
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diff --git a/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts b/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts
new file mode 100644
index 0000000..4535e14
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tensor.d.ts
@@ -0,0 +1,361 @@
+import { DataType, DataTypeMap, DataValues, NumericDataType, Rank, ShapeMap, SingleValueMap, TensorLike, TensorLike1D, TensorLike3D, TensorLike4D, TypedArray } from './types';
+export interface TensorData<D extends DataType> {
+    dataId?: DataId;
+    values?: DataTypeMap[D];
+}
+export declare class TensorBuffer<R extends Rank, D extends DataType = 'float32'> {
+    dtype: D;
+    size: number;
+    shape: ShapeMap[R];
+    strides: number[];
+    values: DataTypeMap[D];
+    constructor(shape: ShapeMap[R], dtype: D, values?: DataTypeMap[D]);
+    set(value: SingleValueMap[D], ...locs: number[]): void;
+    get(...locs: number[]): SingleValueMap[D];
+    locToIndex(locs: number[]): number;
+    indexToLoc(index: number): number[];
+    readonly rank: number;
+    toTensor(): Tensor<R>;
+}
+export interface TensorTracker {
+    registerTensor(t: Tensor): void;
+    disposeTensor(t: Tensor): void;
+    write(dataId: DataId, values: DataValues): void;
+    read(dataId: DataId): Promise<DataValues>;
+    readSync(dataId: DataId): DataValues;
+    registerVariable(v: Variable): void;
+    nextTensorId(): number;
+    nextVariableId(): number;
+}
+export interface OpHandler {
+    cast<T extends Tensor>(x: T, dtype: DataType): T;
+    buffer<R extends Rank, D extends DataType>(shape: ShapeMap[R], dtype: D, values?: DataTypeMap[D]): TensorBuffer<R, D>;
+    print<T extends Tensor>(x: T, verbose: boolean): void;
+    reshape<R2 extends Rank>(x: Tensor, shape: ShapeMap[R2]): Tensor<R2>;
+    expandDims<R2 extends Rank>(x: Tensor, axis: number): Tensor<R2>;
+    cumsum<T extends Tensor>(x: Tensor, axis: number, exclusive: boolean, reverse: boolean): T;
+    squeeze<T extends Tensor>(x: Tensor, axis?: number[]): T;
+    clone<T extends Tensor>(x: T): T;
+    tile<T extends Tensor>(x: T, reps: number[]): T;
+    gather<T extends Tensor>(x: T, indices: Tensor1D | TensorLike1D, axis: number): T;
+    matMul<T extends Tensor>(a: T, b: T | TensorLike, transposeA: boolean, transposeB: boolean): T;
+    dot(t1: Tensor, t2: Tensor | TensorLike): Tensor;
+    norm(x: Tensor, ord: number | 'euclidean' | 'fro', axis: number | number[], keepDims: boolean): Tensor;
+    slice<R extends Rank, T extends Tensor<R>>(x: T, begin: number | number[], size?: number | number[]): T;
+    split<T extends Tensor>(x: T, numOrSizeSplits: number[] | number, axis?: number): T[];
+    reverse<T extends Tensor>(x: T, axis?: number | number[]): T;
+    concat<T extends Tensor>(tensors: Array<T | TensorLike>, axis: number): T;
+    stack<T extends Tensor>(tensors: Array<T | TensorLike>, axis: number): Tensor;
+    unstack<T extends Tensor>(value: T, axis: number): Tensor[];
+    pad<T extends Tensor>(x: T, paddings: Array<[number, number]>, constantValue: number): T;
+    batchNormalization<R extends Rank>(x: Tensor<R>, mean: Tensor<R> | Tensor1D | TensorLike, variance: Tensor<R> | Tensor1D | TensorLike, varianceEpsilon: number, scale?: Tensor<R> | Tensor1D | TensorLike, offset?: Tensor<R> | Tensor1D | TensorLike): Tensor<R>;
+    all<T extends Tensor>(x: Tensor, axis: number | number[], keepDims: boolean): T;
+    any<T extends Tensor>(x: Tensor, axis: number | number[], keepDims: boolean): T;
+    logSumExp<T extends Tensor>(x: Tensor, axis: number | number[], keepDims: boolean): T;
+    sum<T extends Tensor>(x: Tensor, axis: number | number[], keepDims: boolean): T;
+    prod<T extends Tensor>(x: Tensor, axis: number | number[], keepDims: boolean): T;
+    mean<T extends Tensor>(x: Tensor, axis: number | number[], keepDims: boolean): T;
+    min<T extends Tensor>(x: Tensor, axis: number | number[], keepDims: boolean): T;
+    max<T extends Tensor>(x: Tensor, axis: number | number[], keepDims: boolean): T;
+    argMin<T extends Tensor>(x: Tensor, axis: number): T;
+    argMax<T extends Tensor>(x: Tensor, axis: number): T;
+    add<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
+    addStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
+    atan2<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
+    sub<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
+    subStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
+    pow<T extends Tensor>(base: T, exp: Tensor | TensorLike): T;
+    powStrict<T extends Tensor>(base: T, exp: Tensor | TensorLike): T;
+    mul<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
+    mulStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
+    div<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
+    floorDiv<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
+    divStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
+    mod<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
+    modStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
+    minimum<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
+    minimumStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
+    maximum<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
+    maximumStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
+    squaredDifference<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
+    squaredDifferenceStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
+    transpose<T extends Tensor>(x: T, perm?: number[]): T;
+    logicalNot<T extends Tensor>(x: T): T;
+    logicalAnd<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
+    logicalOr<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
+    logicalXor<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
+    where<T extends Tensor>(condition: Tensor | TensorLike, a: T, b: T | TensorLike): T;
+    notEqual<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
+    notEqualStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
+    less<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
+    lessStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
+    equal<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
+    equalStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
+    lessEqual<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
+    lessEqualStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
+    greater<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
+    greaterStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
+    greaterEqual<T extends Tensor>(a: Tensor, b: Tensor | TensorLike): T;
+    greaterEqualStrict<T extends Tensor>(a: T, b: T | TensorLike): T;
+    neg<T extends Tensor>(x: T): T;
+    ceil<T extends Tensor>(x: T): T;
+    floor<T extends Tensor>(x: T): T;
+    sign<T extends Tensor>(x: T): T;
+    round<T extends Tensor>(x: T): T;
+    exp<T extends Tensor>(x: T): T;
+    expm1<T extends Tensor>(x: T): T;
+    log<T extends Tensor>(x: T): T;
+    log1p<T extends Tensor>(x: T): T;
+    sqrt<T extends Tensor>(x: T): T;
+    rsqrt<T extends Tensor>(x: T): T;
+    square<T extends Tensor>(x: T): T;
+    reciprocal<T extends Tensor>(x: T): T;
+    abs<T extends Tensor>(x: T): T;
+    clipByValue<T extends Tensor>(x: T, clipValueMin: number, clipValueMax: number): T;
+    sigmoid<T extends Tensor>(x: T): T;
+    logSigmoid<T extends Tensor>(x: T): T;
+    softplus<T extends Tensor>(x: T): T;
+    zerosLike<T extends Tensor>(x: T): T;
+    onesLike<T extends Tensor>(x: T): T;
+    sin<T extends Tensor>(x: T): T;
+    cos<T extends Tensor>(x: T): T;
+    tan<T extends Tensor>(x: T): T;
+    asin<T extends Tensor>(x: T): T;
+    acos<T extends Tensor>(x: T): T;
+    atan<T extends Tensor>(x: T): T;
+    sinh<T extends Tensor>(x: T): T;
+    cosh<T extends Tensor>(x: T): T;
+    tanh<T extends Tensor>(x: T): T;
+    asinh<T extends Tensor>(x: T): T;
+    acosh<T extends Tensor>(x: T): T;
+    atanh<T extends Tensor>(x: T): T;
+    erf<T extends Tensor>(x: T): T;
+    step<T extends Tensor>(x: T, alpha: number): T;
+    relu<T extends Tensor>(x: T): T;
+    elu<T extends Tensor>(x: T): T;
+    selu<T extends Tensor>(x: T): T;
+    leakyRelu<T extends Tensor>(x: T, alpha: number): T;
+    prelu<T extends Tensor>(x: T, alpha: T | TensorLike): T;
+    softmax<T extends Tensor>(logits: T, dim: number): T;
+    logSoftmax<T extends Tensor>(logits: T, axis: number): T;
+    image: {
+        resizeBilinear<T extends Tensor3D | Tensor4D>(images: T, size: [number, number], alignCorners: boolean): T;
+        resizeNearestNeighbor<T extends Tensor3D | Tensor4D>(images: T, size: [number, number], alignCorners: boolean): T;
+    };
+    conv1d<T extends Tensor2D | Tensor3D>(x: T, filter: Tensor3D | TensorLike3D, stride: number, pad: 'valid' | 'same' | number, dataFormat: 'NWC' | 'NCW', dilation: number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
+    conv2d<T extends Tensor3D | Tensor4D>(x: T, filter: Tensor4D | TensorLike4D, strides: [number, number] | number, pad: 'valid' | 'same' | number, dataFormat: 'NHWC' | 'NCHW', dilations: [number, number] | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
+    conv2dTranspose<T extends Tensor3D | Tensor4D>(x: T, filter: Tensor4D | TensorLike4D, outputShape: [number, number, number, number] | [number, number, number], strides: [number, number] | number, pad: 'valid' | 'same' | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
+    depthwiseConv2d<T extends Tensor3D | Tensor4D>(x: T, filter: Tensor4D | TensorLike4D, strides: [number, number] | number, pad: 'valid' | 'same' | number, dataFormat: 'NHWC' | 'NCHW', dilations: [number, number] | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
+    separableConv2d<T extends Tensor3D | Tensor4D>(x: T | TensorLike, depthwiseFilter: Tensor4D | TensorLike4D, pointwiseFilter: Tensor4D | TensorLike, strides: [number, number] | number, pad: 'valid' | 'same', dilation: [number, number] | number, dataFormat: 'NHWC' | 'NCHW'): T;
+    maxPool<T extends Tensor3D | Tensor4D>(x: T, filterSize: [number, number] | number, strides: [number, number] | number, pad: 'valid' | 'same' | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
+    avgPool<T extends Tensor3D | Tensor4D>(x: T, filterSize: [number, number] | number, strides: [number, number] | number, pad: 'valid' | 'same' | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
+    pool<T extends Tensor3D | Tensor4D>(input: T, windowShape: [number, number] | number, poolingType: 'avg' | 'max', padding: 'valid' | 'same' | number, diationRate?: [number, number] | number, strides?: [number, number] | number): T;
+    localResponseNormalization<T extends Tensor3D | Tensor4D>(x: T, depthRadius: number, bias: number, alpha: number, beta: number): T;
+    unsortedSegmentSum<T extends Tensor>(x: T, segmentIds: Tensor1D | TensorLike1D, numSegments: number): T;
+    batchToSpaceND<T extends Tensor>(x: T, blockShape: number[], crops: number[][]): T;
+    spaceToBatchND<T extends Tensor>(x: T, blockShape: number[], paddings: number[][]): T;
+    topk<T extends Tensor>(x: T, k: number, sorted: boolean): {
+        values: T;
+        indices: T;
+    };
+    stridedSlice<T extends Tensor>(x: T, begin: number[], end: number[], strides: number[], beginMask: number, endMask: number): T;
+    depthToSpace(x: Tensor4D, blockSize: number, dataFormat: string): Tensor4D;
+    spectral: {
+        fft(x: Tensor): Tensor;
+        ifft(x: Tensor): Tensor;
+    };
+}
+export declare function setTensorTracker(fn: () => TensorTracker): void;
+export declare function setOpHandler(handler: OpHandler): void;
+export declare type DataId = object;
+export declare class Tensor<R extends Rank = Rank> {
+    readonly id: number;
+    dataId: DataId;
+    readonly shape: ShapeMap[R];
+    readonly size: number;
+    readonly dtype: DataType;
+    readonly rankType: R;
+    readonly strides: number[];
+    protected constructor(shape: ShapeMap[R], dtype: DataType, values?: DataValues, dataId?: DataId);
+    static make<T extends Tensor<R>, D extends DataType = 'float32', R extends Rank = Rank>(shape: ShapeMap[R], data: TensorData<D>, dtype?: D): T;
+    flatten(): Tensor1D;
+    asScalar(): Scalar;
+    as1D(): Tensor1D;
+    as2D(rows: number, columns: number): Tensor2D;
+    as3D(rows: number, columns: number, depth: number): Tensor3D;
+    as4D(rows: number, columns: number, depth: number, depth2: number): Tensor4D;
+    as5D(rows: number, columns: number, depth: number, depth2: number, depth3: number): Tensor5D;
+    asType<T extends this>(this: T, dtype: DataType): T;
+    readonly rank: number;
+    get(...locs: number[]): number;
+    buffer<D extends DataType>(): TensorBuffer<R, D>;
+    data<D extends DataType = NumericDataType>(): Promise<DataTypeMap[D]>;
+    dataSync<D extends DataType = NumericDataType>(): DataTypeMap[D];
+    dispose(): void;
+    private isDisposedInternal;
+    readonly isDisposed: boolean;
+    private throwIfDisposed;
+    toFloat<T extends this>(this: T): T;
+    toInt(): this;
+    toBool(): this;
+    print(verbose?: boolean): void;
+    reshape<R2 extends Rank>(newShape: ShapeMap[R2]): Tensor<R2>;
+    reshapeAs<T extends Tensor>(x: T): T;
+    expandDims<R2 extends Rank>(axis?: number): Tensor<R2>;
+    cumsum<T extends Tensor>(axis?: number, exclusive?: boolean, reverse?: boolean): T;
+    squeeze<T extends Tensor>(axis?: number[]): T;
+    clone<T extends Tensor>(this: T): T;
+    toString(verbose?: boolean): string;
+    tile<T extends this>(this: T, reps: number[]): T;
+    gather<T extends this>(this: T, indices: Tensor1D | TensorLike1D, axis?: number): T;
+    matMul<T extends Tensor>(this: T, b: T | TensorLike, transposeA?: boolean, transposeB?: boolean): T;
+    dot(b: Tensor | TensorLike): Tensor;
+    norm(ord?: number | 'euclidean' | 'fro', axis?: number | number[], keepDims?: boolean): Tensor;
+    slice<T extends Tensor<R>>(this: T, begin: number | number[], size?: number | number[]): T;
+    reverse<T extends Tensor>(this: T, axis?: number | number[]): T;
+    concat<T extends Tensor>(this: T, x: T | Array<T | TensorLike>, axis?: number): T;
+    split<T extends Tensor>(this: T, numOrSizeSplits: number[] | number, axis?: number): T[];
+    stack(x: Tensor, axis?: number): Tensor;
+    unstack(x: Tensor, axis?: number): Tensor[];
+    pad<T extends Tensor>(this: T, paddings: Array<[number, number]>, constantValue?: number): T;
+    batchNormalization(mean: Tensor<R> | Tensor1D | TensorLike, variance: Tensor<R> | Tensor1D | TensorLike, varianceEpsilon?: number, scale?: Tensor<R> | Tensor1D | TensorLike, offset?: Tensor<R> | Tensor1D | TensorLike): Tensor<R>;
+    all<T extends Tensor>(axis?: number | number[], keepDims?: boolean): T;
+    any<T extends Tensor>(axis?: number | number[], keepDims?: boolean): T;
+    logSumExp<T extends Tensor>(axis?: number | number[], keepDims?: boolean): T;
+    sum<T extends Tensor>(axis?: number | number[], keepDims?: boolean): T;
+    prod<T extends Tensor>(axis?: number | number[], keepDims?: boolean): T;
+    mean<T extends Tensor>(axis?: number | number[], keepDims?: boolean): T;
+    min<T extends Tensor>(axis?: number | number[], keepDims?: boolean): T;
+    max<T extends Tensor>(axis?: number | number[], keepDims?: boolean): T;
+    argMin<T extends Tensor>(axis?: number): T;
+    argMax<T extends Tensor>(axis?: number): T;
+    cast<T extends this>(dtype: DataType): T;
+    add<T extends Tensor>(x: Tensor | TensorLike): T;
+    addStrict<T extends this>(this: T, x: T | TensorLike): T;
+    atan2<T extends this>(this: T, x: T | TensorLike): T;
+    sub<T extends Tensor>(x: Tensor | TensorLike): T;
+    subStrict<T extends this>(this: T, x: T | TensorLike): T;
+    pow<T extends Tensor>(this: T, exp: Tensor | TensorLike): T;
+    powStrict(exp: Tensor | TensorLike): Tensor<R>;
+    mul<T extends Tensor>(x: Tensor | TensorLike): T;
+    mulStrict<T extends this>(this: T, x: T | TensorLike): T;
+    div<T extends Tensor>(x: Tensor | TensorLike): T;
+    floorDiv<T extends Tensor>(x: Tensor | TensorLike): T;
+    divStrict<T extends this>(this: T, x: T | TensorLike): T;
+    minimum<T extends Tensor>(x: Tensor | TensorLike): T;
+    minimumStrict<T extends this>(this: T, x: T | TensorLike): T;
+    maximum<T extends Tensor>(x: Tensor | TensorLike): T;
+    maximumStrict<T extends this>(this: T, x: T | TensorLike): T;
+    mod<T extends Tensor>(x: Tensor | TensorLike): T;
+    modStrict<T extends this>(this: T, x: T | TensorLike): T;
+    squaredDifference<T extends Tensor>(x: Tensor | TensorLike): T;
+    squaredDifferenceStrict<T extends this>(this: T, x: T | TensorLike): T;
+    transpose<T extends Tensor>(this: T, perm?: number[]): T;
+    notEqual<T extends Tensor>(x: Tensor | TensorLike): T;
+    notEqualStrict<T extends this>(this: T, x: T | TensorLike): T;
+    less<T extends Tensor>(x: Tensor | TensorLike): T;
+    lessStrict<T extends this>(this: T, x: T | TensorLike): T;
+    equal<T extends Tensor>(x: Tensor | TensorLike): T;
+    equalStrict<T extends this>(this: T, x: T | TensorLike): T;
+    lessEqual<T extends Tensor>(x: Tensor | TensorLike): T;
+    lessEqualStrict<T extends this>(this: T, x: T | TensorLike): T;
+    greater<T extends Tensor>(x: Tensor | TensorLike): T;
+    greaterStrict<T extends this>(this: T, x: T | TensorLike): T;
+    greaterEqual<T extends Tensor>(x: Tensor | TensorLike): T;
+    greaterEqualStrict<T extends this>(this: T, x: T | TensorLike): T;
+    logicalAnd(x: Tensor | TensorLike): Tensor;
+    logicalOr(x: Tensor | TensorLike): Tensor;
+    logicalNot<T extends Tensor>(this: T): T;
+    logicalXor(x: Tensor | TensorLike): Tensor;
+    where(condition: Tensor | TensorLike, x: Tensor | TensorLike): Tensor;
+    neg<T extends Tensor>(this: T): T;
+    ceil<T extends Tensor>(this: T): T;
+    floor<T extends Tensor>(this: T): T;
+    sign<T extends Tensor>(this: T): T;
+    exp<T extends Tensor>(this: T): T;
+    expm1<T extends Tensor>(this: T): T;
+    log<T extends Tensor>(this: T): T;
+    log1p<T extends Tensor>(this: T): T;
+    sqrt<T extends Tensor>(this: T): T;
+    rsqrt<T extends Tensor>(this: T): T;
+    square<T extends Tensor>(this: T): T;
+    reciprocal<T extends Tensor>(this: T): T;
+    abs<T extends Tensor>(this: T): T;
+    clipByValue(min: number, max: number): Tensor<R>;
+    relu<T extends Tensor>(this: T): T;
+    elu<T extends Tensor>(this: T): T;
+    selu<T extends Tensor>(this: T): T;
+    leakyRelu(alpha?: number): Tensor<R>;
+    prelu(alpha: Tensor<R> | TensorLike): Tensor<R>;
+    sigmoid<T extends Tensor>(this: T): T;
+    logSigmoid<T extends Tensor>(this: T): T;
+    softplus<T extends Tensor>(this: T): T;
+    zerosLike<T extends Tensor>(this: T): T;
+    onesLike<T extends Tensor>(this: T): T;
+    sin<T extends Tensor>(this: T): T;
+    cos<T extends Tensor>(this: T): T;
+    tan<T extends Tensor>(this: T): T;
+    asin<T extends Tensor>(this: T): T;
+    acos<T extends Tensor>(this: T): T;
+    atan<T extends Tensor>(this: T): T;
+    sinh<T extends Tensor>(this: T): T;
+    cosh<T extends Tensor>(this: T): T;
+    tanh<T extends Tensor>(this: T): T;
+    asinh<T extends Tensor>(this: T): T;
+    acosh<T extends Tensor>(this: T): T;
+    atanh<T extends Tensor>(this: T): T;
+    erf<T extends Tensor>(this: T): T;
+    round<T extends Tensor>(this: T): T;
+    step<T extends Tensor>(this: T, alpha?: number): T;
+    softmax<T extends this>(this: T, dim?: number): T;
+    logSoftmax<T extends this>(this: T, axis?: number): T;
+    resizeBilinear<T extends Tensor3D | Tensor4D>(this: T, newShape2D: [number, number], alignCorners?: boolean): T;
+    resizeNearestNeighbor<T extends Tensor3D | Tensor4D>(this: T, newShape2D: [number, number], alignCorners?: boolean): T;
+    conv1d<T extends Tensor2D | Tensor3D>(this: T, filter: Tensor3D | TensorLike3D, stride: number, pad: 'valid' | 'same' | number, dataFormat?: 'NWC' | 'NCW', dilation?: number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
+    conv2d<T extends Tensor3D | Tensor4D>(this: T, filter: Tensor4D | TensorLike4D, strides: [number, number] | number, pad: 'valid' | 'same' | number, dataFormat?: 'NHWC' | 'NCHW', dilations?: [number, number] | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
+    conv2dTranspose<T extends Tensor3D | Tensor4D>(this: T, filter: Tensor4D | TensorLike4D, outputShape: [number, number, number, number] | [number, number, number], strides: [number, number] | number, pad: 'valid' | 'same' | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
+    depthwiseConv2D<T extends Tensor3D | Tensor4D>(this: T, filter: Tensor4D | TensorLike4D, strides: [number, number] | number, pad: 'valid' | 'same' | number, dataFormat?: 'NHWC' | 'NCHW', dilations?: [number, number] | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
+    separableConv2d<T extends Tensor3D | Tensor4D>(this: T | TensorLike, depthwiseFilter: Tensor4D | TensorLike4D, pointwiseFilter: Tensor4D | TensorLike, strides: [number, number] | number, pad: 'valid' | 'same', dilation?: [number, number] | number, dataFormat?: 'NHWC' | 'NCHW'): T;
+    avgPool<T extends Tensor3D | Tensor4D>(this: T, filterSize: [number, number] | number, strides: [number, number] | number, pad: 'valid' | 'same' | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
+    maxPool<T extends Tensor3D | Tensor4D>(this: T, filterSize: [number, number] | number, strides: [number, number] | number, pad: 'valid' | 'same' | number, dimRoundingMode?: 'floor' | 'round' | 'ceil'): T;
+    localResponseNormalization<T extends Tensor3D | Tensor4D>(this: T, radius?: number, bias?: number, alpha?: number, beta?: number): T;
+    pool<T extends Tensor3D | Tensor4D>(this: T, windowShape: [number, number] | number, poolingType: 'max' | 'avg', padding: 'valid' | 'same' | number, dilationRate?: [number, number] | number, strides?: [number, number] | number): T;
+    variable(trainable?: boolean, name?: string, dtype?: DataType): Variable<R>;
+    unsortedSegmentSum<T extends Tensor>(this: T, segmentIds: Tensor1D | TensorLike1D, numSegments: number): T;
+    batchToSpaceND<T extends Tensor>(this: T, blockShape: number[], crops: number[][]): T;
+    spaceToBatchND<T extends Tensor>(this: T, blockShape: number[], paddings: number[][]): T;
+    topk<T extends Tensor>(this: T, k?: number, sorted?: boolean): {
+        values: T;
+        indices: T;
+    };
+    stridedSlice<T extends Tensor>(this: T, begin: number[], end: number[], strides: number[], beginMask?: number, endMask?: number): T;
+    depthToSpace(this: Tensor4D, blockSize: number, dataFormat: 'NHWC' | 'NCHW'): Tensor4D;
+    fft(this: Tensor): Tensor;
+    ifft(this: Tensor): Tensor;
+}
+export interface NumericTensor<R extends Rank = Rank> extends Tensor<R> {
+    dtype: NumericDataType;
+    data(): Promise<TypedArray>;
+    dataSync(): TypedArray;
+}
+export interface StringTensor<R extends Rank = Rank> extends Tensor<R> {
+    dtype: 'string';
+    dataSync(): string[];
+    data(): Promise<string[]>;
+}
+export declare type Scalar = Tensor<Rank.R0>;
+export declare type Tensor1D = Tensor<Rank.R1>;
+export declare type Tensor2D = Tensor<Rank.R2>;
+export declare type Tensor3D = Tensor<Rank.R3>;
+export declare type Tensor4D = Tensor<Rank.R4>;
+export declare type Tensor5D = Tensor<Rank.R5>;
+export declare type Tensor6D = Tensor<Rank.R6>;
+export declare class Variable<R extends Rank = Rank> extends Tensor<R> {
+    trainable: boolean;
+    name: string;
+    private constructor();
+    static variable<R extends Rank>(initialValue: Tensor<R>, trainable?: boolean, name?: string, dtype?: DataType): Variable<R>;
+    assign(newValue: Tensor<R>): void;
+}
+declare const variable: typeof Variable.variable;
+export { variable };
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tensor.js b/node_modules/@tensorflow/tfjs-core/dist/tensor.js
new file mode 100644
index 0000000..292039c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tensor.js
@@ -0,0 +1,903 @@
+"use strict";
+var __extends = (this && this.__extends) || (function () {
+    var extendStatics = Object.setPrototypeOf ||
+        ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
+        function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
+    return function (d, b) {
+        extendStatics(d, b);
+        function __() { this.constructor = d; }
+        d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
+    };
+})();
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+Object.defineProperty(exports, "__esModule", { value: true });
+var tensor_format_1 = require("./tensor_format");
+var util = require("./util");
+var util_1 = require("./util");
+var TensorBuffer = (function () {
+    function TensorBuffer(shape, dtype, values) {
+        this.dtype = dtype;
+        this.shape = shape.slice();
+        this.size = util.sizeFromShape(shape);
+        if (values != null) {
+            var n = values.length;
+            util.assert(n === this.size, "Length of values '" + n + "' does not match the size " +
+                ("inferred by the shape '" + this.size + "'."));
+        }
+        if (dtype === 'complex64') {
+            throw new Error("complex64 dtype TensorBuffers are not supported. Please create " +
+                "a TensorBuffer for the real and imaginary parts separately and " +
+                "call tf.complex(real, imag).");
+        }
+        this.values =
+            values || util.getArrayFromDType(dtype, util.sizeFromShape(this.shape));
+        this.strides = util_1.computeStrides(shape);
+    }
+    TensorBuffer.prototype.set = function (value) {
+        var locs = [];
+        for (var _i = 1; _i < arguments.length; _i++) {
+            locs[_i - 1] = arguments[_i];
+        }
+        if (locs.length === 0) {
+            locs = [0];
+        }
+        util.assert(locs.length === this.rank, "The number of provided coordinates (" + locs.length + ") must " +
+            ("match the rank (" + this.rank + ")"));
+        var index = this.locToIndex(locs);
+        this.values[index] = value;
+    };
+    TensorBuffer.prototype.get = function () {
+        var locs = [];
+        for (var _i = 0; _i < arguments.length; _i++) {
+            locs[_i] = arguments[_i];
+        }
+        if (locs.length === 0) {
+            locs = [0];
+        }
+        var index = locs[locs.length - 1];
+        for (var i = 0; i < locs.length - 1; ++i) {
+            index += this.strides[i] * locs[i];
+        }
+        return this.values[index];
+    };
+    TensorBuffer.prototype.locToIndex = function (locs) {
+        if (this.rank === 0) {
+            return 0;
+        }
+        else if (this.rank === 1) {
+            return locs[0];
+        }
+        var index = locs[locs.length - 1];
+        for (var i = 0; i < locs.length - 1; ++i) {
+            index += this.strides[i] * locs[i];
+        }
+        return index;
+    };
+    TensorBuffer.prototype.indexToLoc = function (index) {
+        if (this.rank === 0) {
+            return [];
+        }
+        else if (this.rank === 1) {
+            return [index];
+        }
+        var locs = new Array(this.shape.length);
+        for (var i = 0; i < locs.length - 1; ++i) {
+            locs[i] = Math.floor(index / this.strides[i]);
+            index -= locs[i] * this.strides[i];
+        }
+        locs[locs.length - 1] = index;
+        return locs;
+    };
+    Object.defineProperty(TensorBuffer.prototype, "rank", {
+        get: function () {
+            return this.shape.length;
+        },
+        enumerable: true,
+        configurable: true
+    });
+    TensorBuffer.prototype.toTensor = function () {
+        return Tensor.make(this.shape, { values: this.values }, this.dtype);
+    };
+    return TensorBuffer;
+}());
+exports.TensorBuffer = TensorBuffer;
+var trackerFn = null;
+var opHandler = null;
+function setTensorTracker(fn) {
+    trackerFn = fn;
+}
+exports.setTensorTracker = setTensorTracker;
+function setOpHandler(handler) {
+    opHandler = handler;
+}
+exports.setOpHandler = setOpHandler;
+var Tensor = (function () {
+    function Tensor(shape, dtype, values, dataId) {
+        this.isDisposedInternal = false;
+        this.shape = shape.slice();
+        this.dtype = dtype || 'float32';
+        this.size = util.sizeFromShape(shape);
+        this.strides = util_1.computeStrides(shape);
+        this.dataId = dataId != null ? dataId : {};
+        this.id = trackerFn().nextTensorId();
+        this.rankType = (this.rank < 5 ? this.rank.toString() : 'higher');
+        trackerFn().registerTensor(this);
+        if (values != null) {
+            trackerFn().write(this.dataId, values);
+        }
+    }
+    Tensor.make = function (shape, data, dtype) {
+        return new Tensor(shape, dtype, data.values, data.dataId);
+    };
+    Tensor.prototype.flatten = function () {
+        this.throwIfDisposed();
+        return this.as1D();
+    };
+    Tensor.prototype.asScalar = function () {
+        this.throwIfDisposed();
+        util.assert(this.size === 1, 'The array must have only 1 element.');
+        return this.reshape([]);
+    };
+    Tensor.prototype.as1D = function () {
+        this.throwIfDisposed();
+        return this.reshape([this.size]);
+    };
+    Tensor.prototype.as2D = function (rows, columns) {
+        this.throwIfDisposed();
+        return this.reshape([rows, columns]);
+    };
+    Tensor.prototype.as3D = function (rows, columns, depth) {
+        this.throwIfDisposed();
+        return this.reshape([rows, columns, depth]);
+    };
+    Tensor.prototype.as4D = function (rows, columns, depth, depth2) {
+        this.throwIfDisposed();
+        return this.reshape([rows, columns, depth, depth2]);
+    };
+    Tensor.prototype.as5D = function (rows, columns, depth, depth2, depth3) {
+        this.throwIfDisposed();
+        return this.reshape([rows, columns, depth, depth2, depth3]);
+    };
+    Tensor.prototype.asType = function (dtype) {
+        this.throwIfDisposed();
+        return opHandler.cast(this, dtype);
+    };
+    Object.defineProperty(Tensor.prototype, "rank", {
+        get: function () {
+            return this.shape.length;
+        },
+        enumerable: true,
+        configurable: true
+    });
+    Tensor.prototype.get = function () {
+        var locs = [];
+        for (var _i = 0; _i < arguments.length; _i++) {
+            locs[_i] = arguments[_i];
+        }
+        util.assert(locs.length === this.rank, 'Number of coordinates in get() must match the rank of the tensor');
+        util.assert(this.dtype !== 'complex64', 'Tensor.get() is not supported for complex64 tensors yet.');
+        this.throwIfDisposed();
+        if (locs.length === 0) {
+            locs = [0];
+        }
+        var index = locs[locs.length - 1];
+        for (var i = 0; i < locs.length - 1; ++i) {
+            index += this.strides[i] * locs[i];
+        }
+        return this.dataSync()[index];
+    };
+    Tensor.prototype.buffer = function () {
+        return opHandler.buffer(this.shape, this.dtype, this.dataSync());
+    };
+    Tensor.prototype.data = function () {
+        return __awaiter(this, void 0, void 0, function () {
+            return __generator(this, function (_a) {
+                this.throwIfDisposed();
+                return [2, trackerFn().read(this.dataId)];
+            });
+        });
+    };
+    Tensor.prototype.dataSync = function () {
+        this.throwIfDisposed();
+        return trackerFn().readSync(this.dataId);
+    };
+    Tensor.prototype.dispose = function () {
+        if (this.isDisposed) {
+            return;
+        }
+        trackerFn().disposeTensor(this);
+        this.isDisposedInternal = true;
+    };
+    Object.defineProperty(Tensor.prototype, "isDisposed", {
+        get: function () {
+            return this.isDisposedInternal;
+        },
+        enumerable: true,
+        configurable: true
+    });
+    Tensor.prototype.throwIfDisposed = function () {
+        if (this.isDisposed) {
+            throw new Error("Tensor is disposed.");
+        }
+    };
+    Tensor.prototype.toFloat = function () {
+        return this.asType('float32');
+    };
+    Tensor.prototype.toInt = function () {
+        return this.asType('int32');
+    };
+    Tensor.prototype.toBool = function () {
+        return this.asType('bool');
+    };
+    Tensor.prototype.print = function (verbose) {
+        if (verbose === void 0) { verbose = false; }
+        return opHandler.print(this, verbose);
+    };
+    Tensor.prototype.reshape = function (newShape) {
+        this.throwIfDisposed();
+        return opHandler.reshape(this, newShape);
+    };
+    Tensor.prototype.reshapeAs = function (x) {
+        this.throwIfDisposed();
+        return this.reshape(x.shape);
+    };
+    Tensor.prototype.expandDims = function (axis) {
+        if (axis === void 0) { axis = 0; }
+        return opHandler.expandDims(this, axis);
+    };
+    Tensor.prototype.cumsum = function (axis, exclusive, reverse) {
+        if (axis === void 0) { axis = 0; }
+        if (exclusive === void 0) { exclusive = false; }
+        if (reverse === void 0) { reverse = false; }
+        return opHandler.cumsum(this, axis, exclusive, reverse);
+    };
+    Tensor.prototype.squeeze = function (axis) {
+        this.throwIfDisposed();
+        return opHandler.squeeze(this, axis);
+    };
+    Tensor.prototype.clone = function () {
+        this.throwIfDisposed();
+        return opHandler.clone(this);
+    };
+    Tensor.prototype.toString = function (verbose) {
+        if (verbose === void 0) { verbose = false; }
+        var vals = this.dataSync();
+        return tensor_format_1.tensorToString(vals, this.shape, this.dtype, verbose);
+    };
+    Tensor.prototype.tile = function (reps) {
+        this.throwIfDisposed();
+        return opHandler.tile(this, reps);
+    };
+    Tensor.prototype.gather = function (indices, axis) {
+        if (axis === void 0) { axis = 0; }
+        this.throwIfDisposed();
+        return opHandler.gather(this, indices, axis);
+    };
+    Tensor.prototype.matMul = function (b, transposeA, transposeB) {
+        if (transposeA === void 0) { transposeA = false; }
+        if (transposeB === void 0) { transposeB = false; }
+        this.throwIfDisposed();
+        return opHandler.matMul(this, b, transposeA, transposeB);
+    };
+    Tensor.prototype.dot = function (b) {
+        this.throwIfDisposed();
+        return opHandler.dot(this, b);
+    };
+    Tensor.prototype.norm = function (ord, axis, keepDims) {
+        if (ord === void 0) { ord = 'euclidean'; }
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        this.throwIfDisposed();
+        return opHandler.norm(this, ord, axis, keepDims);
+    };
+    Tensor.prototype.slice = function (begin, size) {
+        this.throwIfDisposed();
+        return opHandler.slice(this, begin, size);
+    };
+    Tensor.prototype.reverse = function (axis) {
+        this.throwIfDisposed();
+        return opHandler.reverse(this, axis);
+    };
+    Tensor.prototype.concat = function (x, axis) {
+        if (axis === void 0) { axis = 0; }
+        this.throwIfDisposed();
+        if (x instanceof Tensor) {
+            x = [x];
+        }
+        return opHandler.concat([this].concat(x), axis);
+    };
+    Tensor.prototype.split = function (numOrSizeSplits, axis) {
+        if (axis === void 0) { axis = 0; }
+        this.throwIfDisposed();
+        return opHandler.split(this, numOrSizeSplits, axis);
+    };
+    Tensor.prototype.stack = function (x, axis) {
+        if (axis === void 0) { axis = 0; }
+        return opHandler.stack([this, x], axis);
+    };
+    Tensor.prototype.unstack = function (x, axis) {
+        if (axis === void 0) { axis = 0; }
+        return opHandler.unstack(this, axis);
+    };
+    Tensor.prototype.pad = function (paddings, constantValue) {
+        if (constantValue === void 0) { constantValue = 0; }
+        return opHandler.pad(this, paddings, constantValue);
+    };
+    Tensor.prototype.batchNormalization = function (mean, variance, varianceEpsilon, scale, offset) {
+        if (varianceEpsilon === void 0) { varianceEpsilon = .001; }
+        this.throwIfDisposed();
+        return opHandler.batchNormalization(this, mean, variance, varianceEpsilon, scale, offset);
+    };
+    Tensor.prototype.all = function (axis, keepDims) {
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        this.throwIfDisposed();
+        return opHandler.all(this, axis, keepDims);
+    };
+    Tensor.prototype.any = function (axis, keepDims) {
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        this.throwIfDisposed();
+        return opHandler.any(this, axis, keepDims);
+    };
+    Tensor.prototype.logSumExp = function (axis, keepDims) {
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        this.throwIfDisposed();
+        return opHandler.logSumExp(this, axis, keepDims);
+    };
+    Tensor.prototype.sum = function (axis, keepDims) {
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        this.throwIfDisposed();
+        return opHandler.sum(this, axis, keepDims);
+    };
+    Tensor.prototype.prod = function (axis, keepDims) {
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        this.throwIfDisposed();
+        return opHandler.prod(this, axis, keepDims);
+    };
+    Tensor.prototype.mean = function (axis, keepDims) {
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        this.throwIfDisposed();
+        return opHandler.mean(this, axis, keepDims);
+    };
+    Tensor.prototype.min = function (axis, keepDims) {
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        this.throwIfDisposed();
+        return opHandler.min(this, axis, keepDims);
+    };
+    Tensor.prototype.max = function (axis, keepDims) {
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        this.throwIfDisposed();
+        return opHandler.max(this, axis, keepDims);
+    };
+    Tensor.prototype.argMin = function (axis) {
+        if (axis === void 0) { axis = null; }
+        this.throwIfDisposed();
+        return opHandler.argMin(this, axis);
+    };
+    Tensor.prototype.argMax = function (axis) {
+        if (axis === void 0) { axis = null; }
+        this.throwIfDisposed();
+        return opHandler.argMax(this, axis);
+    };
+    Tensor.prototype.cast = function (dtype) {
+        this.throwIfDisposed();
+        return opHandler.cast(this, dtype);
+    };
+    Tensor.prototype.add = function (x) {
+        this.throwIfDisposed();
+        return opHandler.add(this, x);
+    };
+    Tensor.prototype.addStrict = function (x) {
+        this.throwIfDisposed();
+        return opHandler.addStrict(this, x);
+    };
+    Tensor.prototype.atan2 = function (x) {
+        this.throwIfDisposed();
+        return opHandler.atan2(this, x);
+    };
+    Tensor.prototype.sub = function (x) {
+        this.throwIfDisposed();
+        return opHandler.sub(this, x);
+    };
+    Tensor.prototype.subStrict = function (x) {
+        this.throwIfDisposed();
+        return opHandler.subStrict(this, x);
+    };
+    Tensor.prototype.pow = function (exp) {
+        this.throwIfDisposed();
+        return opHandler.pow(this, exp);
+    };
+    Tensor.prototype.powStrict = function (exp) {
+        this.throwIfDisposed();
+        return opHandler.powStrict(this, exp);
+    };
+    Tensor.prototype.mul = function (x) {
+        this.throwIfDisposed();
+        return opHandler.mul(this, x);
+    };
+    Tensor.prototype.mulStrict = function (x) {
+        this.throwIfDisposed();
+        return opHandler.mulStrict(this, x);
+    };
+    Tensor.prototype.div = function (x) {
+        this.throwIfDisposed();
+        return opHandler.div(this, x);
+    };
+    Tensor.prototype.floorDiv = function (x) {
+        this.throwIfDisposed();
+        return opHandler.floorDiv(this, x);
+    };
+    Tensor.prototype.divStrict = function (x) {
+        this.throwIfDisposed();
+        return opHandler.divStrict(this, x);
+    };
+    Tensor.prototype.minimum = function (x) {
+        this.throwIfDisposed();
+        return opHandler.minimum(this, x);
+    };
+    Tensor.prototype.minimumStrict = function (x) {
+        this.throwIfDisposed();
+        return opHandler.minimumStrict(this, x);
+    };
+    Tensor.prototype.maximum = function (x) {
+        this.throwIfDisposed();
+        return opHandler.maximum(this, x);
+    };
+    Tensor.prototype.maximumStrict = function (x) {
+        this.throwIfDisposed();
+        return opHandler.maximumStrict(this, x);
+    };
+    Tensor.prototype.mod = function (x) {
+        this.throwIfDisposed();
+        return opHandler.mod(this, x);
+    };
+    Tensor.prototype.modStrict = function (x) {
+        this.throwIfDisposed();
+        return opHandler.modStrict(this, x);
+    };
+    Tensor.prototype.squaredDifference = function (x) {
+        this.throwIfDisposed();
+        return opHandler.squaredDifference(this, x);
+    };
+    Tensor.prototype.squaredDifferenceStrict = function (x) {
+        this.throwIfDisposed();
+        return opHandler.squaredDifferenceStrict(this, x);
+    };
+    Tensor.prototype.transpose = function (perm) {
+        this.throwIfDisposed();
+        return opHandler.transpose(this, perm);
+    };
+    Tensor.prototype.notEqual = function (x) {
+        this.throwIfDisposed();
+        return opHandler.notEqual(this, x);
+    };
+    Tensor.prototype.notEqualStrict = function (x) {
+        this.throwIfDisposed();
+        return opHandler.notEqualStrict(this, x);
+    };
+    Tensor.prototype.less = function (x) {
+        this.throwIfDisposed();
+        return opHandler.less(this, x);
+    };
+    Tensor.prototype.lessStrict = function (x) {
+        this.throwIfDisposed();
+        return opHandler.lessStrict(this, x);
+    };
+    Tensor.prototype.equal = function (x) {
+        this.throwIfDisposed();
+        return opHandler.equal(this, x);
+    };
+    Tensor.prototype.equalStrict = function (x) {
+        this.throwIfDisposed();
+        return opHandler.equalStrict(this, x);
+    };
+    Tensor.prototype.lessEqual = function (x) {
+        this.throwIfDisposed();
+        return opHandler.lessEqual(this, x);
+    };
+    Tensor.prototype.lessEqualStrict = function (x) {
+        this.throwIfDisposed();
+        return opHandler.lessEqualStrict(this, x);
+    };
+    Tensor.prototype.greater = function (x) {
+        this.throwIfDisposed();
+        return opHandler.greater(this, x);
+    };
+    Tensor.prototype.greaterStrict = function (x) {
+        this.throwIfDisposed();
+        return opHandler.greaterStrict(this, x);
+    };
+    Tensor.prototype.greaterEqual = function (x) {
+        this.throwIfDisposed();
+        return opHandler.greaterEqual(this, x);
+    };
+    Tensor.prototype.greaterEqualStrict = function (x) {
+        this.throwIfDisposed();
+        return opHandler.greaterEqualStrict(this, x);
+    };
+    Tensor.prototype.logicalAnd = function (x) {
+        this.throwIfDisposed();
+        return opHandler.logicalAnd(this, x);
+    };
+    Tensor.prototype.logicalOr = function (x) {
+        this.throwIfDisposed();
+        return opHandler.logicalOr(this, x);
+    };
+    Tensor.prototype.logicalNot = function () {
+        this.throwIfDisposed();
+        return opHandler.logicalNot(this);
+    };
+    Tensor.prototype.logicalXor = function (x) {
+        this.throwIfDisposed();
+        return opHandler.logicalXor(this, x);
+    };
+    Tensor.prototype.where = function (condition, x) {
+        this.throwIfDisposed();
+        return opHandler.where(condition, this, x);
+    };
+    Tensor.prototype.neg = function () {
+        this.throwIfDisposed();
+        return opHandler.neg(this);
+    };
+    Tensor.prototype.ceil = function () {
+        this.throwIfDisposed();
+        return opHandler.ceil(this);
+    };
+    Tensor.prototype.floor = function () {
+        this.throwIfDisposed();
+        return opHandler.floor(this);
+    };
+    Tensor.prototype.sign = function () {
+        this.throwIfDisposed();
+        return opHandler.sign(this);
+    };
+    Tensor.prototype.exp = function () {
+        this.throwIfDisposed();
+        return opHandler.exp(this);
+    };
+    Tensor.prototype.expm1 = function () {
+        this.throwIfDisposed();
+        return opHandler.expm1(this);
+    };
+    Tensor.prototype.log = function () {
+        this.throwIfDisposed();
+        return opHandler.log(this);
+    };
+    Tensor.prototype.log1p = function () {
+        this.throwIfDisposed();
+        return opHandler.log1p(this);
+    };
+    Tensor.prototype.sqrt = function () {
+        this.throwIfDisposed();
+        return opHandler.sqrt(this);
+    };
+    Tensor.prototype.rsqrt = function () {
+        this.throwIfDisposed();
+        return opHandler.rsqrt(this);
+    };
+    Tensor.prototype.square = function () {
+        this.throwIfDisposed();
+        return opHandler.square(this);
+    };
+    Tensor.prototype.reciprocal = function () {
+        this.throwIfDisposed();
+        return opHandler.reciprocal(this);
+    };
+    Tensor.prototype.abs = function () {
+        this.throwIfDisposed();
+        return opHandler.abs(this);
+    };
+    Tensor.prototype.clipByValue = function (min, max) {
+        this.throwIfDisposed();
+        return opHandler.clipByValue(this, min, max);
+    };
+    Tensor.prototype.relu = function () {
+        this.throwIfDisposed();
+        return opHandler.relu(this);
+    };
+    Tensor.prototype.elu = function () {
+        this.throwIfDisposed();
+        return opHandler.elu(this);
+    };
+    Tensor.prototype.selu = function () {
+        this.throwIfDisposed();
+        return opHandler.selu(this);
+    };
+    Tensor.prototype.leakyRelu = function (alpha) {
+        if (alpha === void 0) { alpha = 0.2; }
+        this.throwIfDisposed();
+        return opHandler.leakyRelu(this, alpha);
+    };
+    Tensor.prototype.prelu = function (alpha) {
+        this.throwIfDisposed();
+        return opHandler.prelu(this, alpha);
+    };
+    Tensor.prototype.sigmoid = function () {
+        this.throwIfDisposed();
+        return opHandler.sigmoid(this);
+    };
+    Tensor.prototype.logSigmoid = function () {
+        this.throwIfDisposed();
+        return opHandler.logSigmoid(this);
+    };
+    Tensor.prototype.softplus = function () {
+        this.throwIfDisposed();
+        return opHandler.softplus(this);
+    };
+    Tensor.prototype.zerosLike = function () {
+        this.throwIfDisposed();
+        return opHandler.zerosLike(this);
+    };
+    Tensor.prototype.onesLike = function () {
+        this.throwIfDisposed();
+        return opHandler.onesLike(this);
+    };
+    Tensor.prototype.sin = function () {
+        this.throwIfDisposed();
+        return opHandler.sin(this);
+    };
+    Tensor.prototype.cos = function () {
+        this.throwIfDisposed();
+        return opHandler.cos(this);
+    };
+    Tensor.prototype.tan = function () {
+        this.throwIfDisposed();
+        return opHandler.tan(this);
+    };
+    Tensor.prototype.asin = function () {
+        this.throwIfDisposed();
+        return opHandler.asin(this);
+    };
+    Tensor.prototype.acos = function () {
+        this.throwIfDisposed();
+        return opHandler.acos(this);
+    };
+    Tensor.prototype.atan = function () {
+        this.throwIfDisposed();
+        return opHandler.atan(this);
+    };
+    Tensor.prototype.sinh = function () {
+        this.throwIfDisposed();
+        return opHandler.sinh(this);
+    };
+    Tensor.prototype.cosh = function () {
+        this.throwIfDisposed();
+        return opHandler.cosh(this);
+    };
+    Tensor.prototype.tanh = function () {
+        this.throwIfDisposed();
+        return opHandler.tanh(this);
+    };
+    Tensor.prototype.asinh = function () {
+        this.throwIfDisposed();
+        return opHandler.asinh(this);
+    };
+    Tensor.prototype.acosh = function () {
+        this.throwIfDisposed();
+        return opHandler.acosh(this);
+    };
+    Tensor.prototype.atanh = function () {
+        this.throwIfDisposed();
+        return opHandler.atanh(this);
+    };
+    Tensor.prototype.erf = function () {
+        this.throwIfDisposed();
+        return opHandler.erf(this);
+    };
+    Tensor.prototype.round = function () {
+        this.throwIfDisposed();
+        return opHandler.round(this);
+    };
+    Tensor.prototype.step = function (alpha) {
+        if (alpha === void 0) { alpha = 0.0; }
+        this.throwIfDisposed();
+        return opHandler.step(this, alpha);
+    };
+    Tensor.prototype.softmax = function (dim) {
+        if (dim === void 0) { dim = -1; }
+        this.throwIfDisposed();
+        return opHandler.softmax(this, dim);
+    };
+    Tensor.prototype.logSoftmax = function (axis) {
+        if (axis === void 0) { axis = -1; }
+        this.throwIfDisposed();
+        return opHandler.logSoftmax(this, axis);
+    };
+    Tensor.prototype.resizeBilinear = function (newShape2D, alignCorners) {
+        if (alignCorners === void 0) { alignCorners = false; }
+        this.throwIfDisposed();
+        return opHandler.image.resizeBilinear(this, newShape2D, alignCorners);
+    };
+    Tensor.prototype.resizeNearestNeighbor = function (newShape2D, alignCorners) {
+        if (alignCorners === void 0) { alignCorners = false; }
+        this.throwIfDisposed();
+        return opHandler.image.resizeNearestNeighbor(this, newShape2D, alignCorners);
+    };
+    Tensor.prototype.conv1d = function (filter, stride, pad, dataFormat, dilation, dimRoundingMode) {
+        if (dataFormat === void 0) { dataFormat = 'NWC'; }
+        if (dilation === void 0) { dilation = 1; }
+        this.throwIfDisposed();
+        return opHandler.conv1d(this, filter, stride, pad, dataFormat, dilation, dimRoundingMode);
+    };
+    Tensor.prototype.conv2d = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {
+        if (dataFormat === void 0) { dataFormat = 'NHWC'; }
+        if (dilations === void 0) { dilations = [1, 1]; }
+        this.throwIfDisposed();
+        return opHandler.conv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);
+    };
+    Tensor.prototype.conv2dTranspose = function (filter, outputShape, strides, pad, dimRoundingMode) {
+        this.throwIfDisposed();
+        return opHandler.conv2dTranspose(this, filter, outputShape, strides, pad, dimRoundingMode);
+    };
+    Tensor.prototype.depthwiseConv2D = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {
+        if (dataFormat === void 0) { dataFormat = 'NHWC'; }
+        if (dilations === void 0) { dilations = [1, 1]; }
+        this.throwIfDisposed();
+        return opHandler.depthwiseConv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);
+    };
+    Tensor.prototype.separableConv2d = function (depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat) {
+        if (dilation === void 0) { dilation = [1, 1]; }
+        if (dataFormat === void 0) { dataFormat = 'NHWC'; }
+        this.throwIfDisposed();
+        return opHandler.separableConv2d(this, depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat);
+    };
+    Tensor.prototype.avgPool = function (filterSize, strides, pad, dimRoundingMode) {
+        this.throwIfDisposed();
+        return opHandler.avgPool(this, filterSize, strides, pad, dimRoundingMode);
+    };
+    Tensor.prototype.maxPool = function (filterSize, strides, pad, dimRoundingMode) {
+        this.throwIfDisposed();
+        return opHandler.maxPool(this, filterSize, strides, pad, dimRoundingMode);
+    };
+    Tensor.prototype.localResponseNormalization = function (radius, bias, alpha, beta) {
+        if (radius === void 0) { radius = 5; }
+        if (bias === void 0) { bias = 1; }
+        if (alpha === void 0) { alpha = 1; }
+        if (beta === void 0) { beta = 0.5; }
+        return opHandler.localResponseNormalization(this, radius, bias, alpha, beta);
+    };
+    Tensor.prototype.pool = function (windowShape, poolingType, padding, dilationRate, strides) {
+        this.throwIfDisposed();
+        return opHandler.pool(this, windowShape, poolingType, padding, dilationRate, strides);
+    };
+    Tensor.prototype.variable = function (trainable, name, dtype) {
+        if (trainable === void 0) { trainable = true; }
+        this.throwIfDisposed();
+        return Variable.variable(this, trainable, name, dtype);
+    };
+    Tensor.prototype.unsortedSegmentSum = function (segmentIds, numSegments) {
+        this.throwIfDisposed();
+        return opHandler.unsortedSegmentSum(this, segmentIds, numSegments);
+    };
+    Tensor.prototype.batchToSpaceND = function (blockShape, crops) {
+        this.throwIfDisposed();
+        return opHandler.batchToSpaceND(this, blockShape, crops);
+    };
+    Tensor.prototype.spaceToBatchND = function (blockShape, paddings) {
+        this.throwIfDisposed();
+        return opHandler.spaceToBatchND(this, blockShape, paddings);
+    };
+    Tensor.prototype.topk = function (k, sorted) {
+        if (k === void 0) { k = 1; }
+        if (sorted === void 0) { sorted = true; }
+        this.throwIfDisposed();
+        return opHandler.topk(this, k, sorted);
+    };
+    Tensor.prototype.stridedSlice = function (begin, end, strides, beginMask, endMask) {
+        if (beginMask === void 0) { beginMask = 0; }
+        if (endMask === void 0) { endMask = 0; }
+        this.throwIfDisposed();
+        return opHandler.stridedSlice(this, begin, end, strides, beginMask, endMask);
+    };
+    Tensor.prototype.depthToSpace = function (blockSize, dataFormat) {
+        this.throwIfDisposed();
+        return opHandler.depthToSpace(this, blockSize, dataFormat);
+    };
+    Tensor.prototype.fft = function () {
+        this.throwIfDisposed();
+        return opHandler.spectral.fft(this);
+    };
+    Tensor.prototype.ifft = function () {
+        this.throwIfDisposed();
+        return opHandler.spectral.ifft(this);
+    };
+    return Tensor;
+}());
+exports.Tensor = Tensor;
+Object.defineProperty(Tensor, Symbol.hasInstance, {
+    value: function (instance) {
+        return !!instance && instance.shape != null && instance.dtype != null;
+    }
+});
+var Variable = (function (_super) {
+    __extends(Variable, _super);
+    function Variable(initialValue, trainable, name) {
+        if (trainable === void 0) { trainable = true; }
+        var _this = _super.call(this, initialValue.shape, initialValue.dtype, null, initialValue.dataId) || this;
+        _this.trainable = trainable;
+        _this.name = name;
+        if (_this.name == null) {
+            _this.name = trackerFn().nextVariableId().toString();
+        }
+        try {
+            trackerFn().registerVariable(_this);
+        }
+        catch (ex) {
+            trackerFn().disposeTensor(_this);
+            throw ex;
+        }
+        return _this;
+    }
+    Variable.variable = function (initialValue, trainable, name, dtype) {
+        if (trainable === void 0) { trainable = true; }
+        if (dtype != null && dtype !== initialValue.dtype) {
+            initialValue = initialValue.asType(dtype);
+        }
+        return new Variable(initialValue, trainable, name);
+    };
+    Variable.prototype.assign = function (newValue) {
+        if (newValue.dtype !== this.dtype) {
+            throw new Error("dtype of the new value (" + newValue.dtype + ") and " +
+                ("previous value (" + this.dtype + ") must match"));
+        }
+        if (!util.arraysEqual(newValue.shape, this.shape)) {
+            throw new Error("shape of the new value (" + newValue.shape + ") and " +
+                ("previous value (" + this.shape + ") must match"));
+        }
+        trackerFn().disposeTensor(this);
+        this.dataId = newValue.dataId;
+        trackerFn().registerTensor(this);
+    };
+    return Variable;
+}(Tensor));
+exports.Variable = Variable;
+Object.defineProperty(Variable, Symbol.hasInstance, {
+    value: function (instance) {
+        return instance instanceof Tensor && instance.assign != null &&
+            instance.assign instanceof Function;
+    }
+});
+var variable = Variable.variable;
+exports.variable = variable;
+//# sourceMappingURL=tensor.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tensor.js.map b/node_modules/@tensorflow/tfjs-core/dist/tensor.js.map
new file mode 100644
index 0000000..17f98c5
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tensor.js.map
@@ -0,0 +1 @@
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diff --git a/node_modules/@tensorflow/tfjs-core/dist/tensor_format.d.ts b/node_modules/@tensorflow/tfjs-core/dist/tensor_format.d.ts
new file mode 100644
index 0000000..279bd83
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tensor_format.d.ts
@@ -0,0 +1,2 @@
+import { DataType, TypedArray } from './types';
+export declare function tensorToString(vals: TypedArray | string[], shape: number[], dtype: DataType, verbose: boolean): string;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tensor_format.js b/node_modules/@tensorflow/tfjs-core/dist/tensor_format.js
new file mode 100644
index 0000000..b5d088d
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tensor_format.js
@@ -0,0 +1,134 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var util_1 = require("./util");
+var FORMAT_LIMIT_NUM_VALS = 20;
+var FORMAT_NUM_FIRST_LAST_VALS = 3;
+var FORMAT_NUM_SIG_DIGITS = 7;
+function tensorToString(vals, shape, dtype, verbose) {
+    var strides = util_1.computeStrides(shape);
+    var padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);
+    var rank = shape.length;
+    var valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);
+    var lines = ['Tensor'];
+    if (verbose) {
+        lines.push("  dtype: " + dtype);
+        lines.push("  rank: " + rank);
+        lines.push("  shape: [" + shape + "]");
+        lines.push("  values:");
+    }
+    lines.push(valsLines.map(function (l) { return '    ' + l; }).join('\n'));
+    return lines.join('\n');
+}
+exports.tensorToString = tensorToString;
+function computeMaxSizePerColumn(vals, shape, dtype, strides) {
+    var n = util_1.sizeFromShape(shape);
+    var numCols = strides[strides.length - 1];
+    var padPerCol = new Array(numCols).fill(0);
+    var rank = shape.length;
+    var valuesOrTuples = dtype === 'complex64' ? createComplexTuples(vals) : vals;
+    if (rank > 1) {
+        for (var row = 0; row < n / numCols; row++) {
+            var offset = row * numCols;
+            for (var j = 0; j < numCols; j++) {
+                padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0).length);
+            }
+        }
+    }
+    return padPerCol;
+}
+function valToString(val, pad) {
+    var valStr;
+    if (Array.isArray(val)) {
+        valStr = parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS)) + " + " +
+            (parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS)) + "j");
+    }
+    else if (util_1.isString(val)) {
+        valStr = "'" + val + "'";
+    }
+    else {
+        valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();
+    }
+    return util_1.rightPad(valStr, pad);
+}
+function subTensorToString(vals, shape, dtype, strides, padPerCol, isLast) {
+    if (isLast === void 0) { isLast = true; }
+    var storagePerElement = dtype === 'complex64' ? 2 : 1;
+    var size = shape[0];
+    var rank = shape.length;
+    if (rank === 0) {
+        if (dtype === 'complex64') {
+            var complexTuple = createComplexTuples(vals);
+            return [valToString(complexTuple[0], 0)];
+        }
+        return [vals[0].toString()];
+    }
+    if (rank === 1) {
+        if (size > FORMAT_LIMIT_NUM_VALS) {
+            var firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;
+            var firstVals = Array.from(vals.slice(0, firstValsSize));
+            var lastVals = Array.from(vals.slice(size - FORMAT_NUM_FIRST_LAST_VALS * storagePerElement, size));
+            if (dtype === 'complex64') {
+                firstVals = createComplexTuples(firstVals);
+                lastVals = createComplexTuples(lastVals);
+            }
+            return [
+                '[' + firstVals.map(function (x, i) { return valToString(x, padPerCol[i]); }).join(', ') +
+                    ', ..., ' +
+                    lastVals
+                        .map(function (x, i) { return valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i]); })
+                        .join(', ') +
+                    ']'
+            ];
+        }
+        var displayVals = dtype === 'complex64' ? createComplexTuples(vals) :
+            Array.from(vals);
+        return [
+            '[' + displayVals.map(function (x, i) { return valToString(x, padPerCol[i]); }).join(', ') +
+                ']'
+        ];
+    }
+    var subshape = shape.slice(1);
+    var substrides = strides.slice(1);
+    var stride = strides[0] * storagePerElement;
+    var lines = [];
+    if (size > FORMAT_LIMIT_NUM_VALS) {
+        for (var i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {
+            var start = i * stride;
+            var end = start + stride;
+            lines.push.apply(lines, subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false));
+        }
+        lines.push('...');
+        for (var i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {
+            var start = i * stride;
+            var end = start + stride;
+            lines.push.apply(lines, subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1));
+        }
+    }
+    else {
+        for (var i = 0; i < size; i++) {
+            var start = i * stride;
+            var end = start + stride;
+            lines.push.apply(lines, subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1));
+        }
+    }
+    var sep = rank === 2 ? ',' : '';
+    lines[0] = '[' + lines[0] + sep;
+    for (var i = 1; i < lines.length - 1; i++) {
+        lines[i] = ' ' + lines[i] + sep;
+    }
+    var newLineSep = ',\n';
+    for (var i = 2; i < rank; i++) {
+        newLineSep += '\n';
+    }
+    lines[lines.length - 1] =
+        ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep);
+    return lines;
+}
+function createComplexTuples(vals) {
+    var complexTuples = [];
+    for (var i = 0; i < vals.length; i += 2) {
+        complexTuples.push([vals[i], vals[i + 1]]);
+    }
+    return complexTuples;
+}
+//# sourceMappingURL=tensor_format.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tensor_format.js.map b/node_modules/@tensorflow/tfjs-core/dist/tensor_format.js.map
new file mode 100644
index 0000000..677490e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tensor_format.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"tensor_format.js","sourceRoot":"","sources":["../src/tensor_format.ts"],"names":[],"mappings":";;AAkBA,+BAAyE;AAGzE,IAAM,qBAAqB,GAAG,EAAE,CAAC;AAEjC,IAAM,0BAA0B,GAAG,CAAC,CAAC;AAErC,IAAM,qBAAqB,GAAG,CAAC,CAAC;AAEhC,wBACI,IAAyB,EAAE,KAAe,EAAE,KAAe,EAC3D,OAAgB;IAClB,IAAM,OAAO,GAAG,qBAAc,CAAC,KAAK,CAAC,CAAC;IACtC,IAAM,SAAS,GAAG,uBAAuB,CAAC,IAAI,EAAE,KAAK,EAAE,KAAK,EAAE,OAAO,CAAC,CAAC;IACvE,IAAM,IAAI,GAAG,KAAK,CAAC,MAAM,CAAC;IAC1B,IAAM,SAAS,GAAG,iBAAiB,CAAC,IAAI,EAAE,KAAK,EAAE,KAAK,EAAE,OAAO,EAAE,SAAS,CAAC,CAAC;IAC5E,IAAM,KAAK,GAAG,CAAC,QAAQ,CAAC,CAAC;IACzB,IAAI,OAAO,EAAE;QACX,KAAK,CAAC,IAAI,CAAC,cAAY,KAAO,CAAC,CAAC;QAChC,KAAK,CAAC,IAAI,CAAC,aAAW,IAAM,CAAC,CAAC;QAC9B,KAAK,CAAC,IAAI,CAAC,eAAa,KAAK,MAAG,CAAC,CAAC;QAClC,KAAK,CAAC,IAAI,CAAC,WAAW,CAAC,CAAC;KACzB;IACD,KAAK,CAAC,IAAI,CAAC,SAAS,CAAC,GAAG,CAAC,UAAA,CAAC,IAAI,OAAA,MAAM,GAAG,CAAC,EAAV,CAAU,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC,CAAC;IACtD,OAAO,KAAK,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC;AAC1B,CAAC;AAhBD,wCAgBC;AAED,iCACI,IAAyB,EAAE,KAAe,EAAE,KAAe,EAC3D,OAAiB;IACnB,IAAM,CAAC,GAAG,oBAAa,CAAC,KAAK,CAAC,CAAC;IAC/B,IAAM,OAAO,GAAG,OAAO,CAAC,OAAO,CAAC,MAAM,GAAG,CAAC,CAAC,CAAC;IAC5C,IAAM,SAAS,GAAG,IAAI,KAAK,CAAC,OAAO,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;IAC7C,IAAM,IAAI,GAAG,KAAK,CAAC,MAAM,CAAC;IAC1B,IAAM,cAAc,GAChB,KAAK,KAAK,WAAW,CAAC,CAAC,CAAC,mBAAmB,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,IAAI,CAAC;IAE7D,IAAI,IAAI,GAAG,CAAC,EAAE;QACZ,KAAK,IAAI,GAAG,GAAG,CAAC,EAAE,GAAG,GAAG,CAAC,GAAG,OAAO,EAAE,GAAG,EAAE,EAAE;YAC1C,IAAM,MAAM,GAAG,GAAG,GAAG,OAAO,CAAC;YAC7B,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,OAAO,EAAE,CAAC,EAAE,EAAE;gBAChC,SAAS,CAAC,CAAC,CAAC,GAAG,IAAI,CAAC,GAAG,CACnB,SAAS,CAAC,CAAC,CAAC,EAAE,WAAW,CAAC,cAAc,CAAC,MAAM,GAAG,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,MAAM,CAAC,CAAC;aACtE;SACF;KACF;IACD,OAAO,SAAS,CAAC;AACnB,CAAC;AAED,qBAAqB,GAAmC,EAAE,GAAW;IACnE,IAAI,MAAc,CAAC;IACnB,IAAI,KAAK,CAAC,OAAO,CAAC,GAAG,CAAC,EAAE;QACtB,MAAM,GAAM,UAAU,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,qBAAqB,CAAC,CAAC,QAAK;aAC3D,UAAU,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC,OAAO,CAAC,qBAAqB,CAAC,CAAC,MAAG,CAAA,CAAC;KAC7D;SAAM,IAAI,eAAQ,CAAC,GAAG,CAAC,EAAE;QACxB,MAAM,GAAG,MAAI,GAAG,MAAG,CAAC;KACrB;SAAM;QACL,MAAM,GAAG,UAAU,CAAC,GAAG,CAAC,OAAO,CAAC,qBAAqB,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC;KACpE;IAED,OAAO,eAAQ,CAAC,MAAM,EAAE,GAAG,CAAC,CAAC;AAC/B,CAAC;AAED,2BACI,IAAyB,EAAE,KAAe,EAAE,KAAe,EAC3D,OAAiB,EAAE,SAAmB,EAAE,MAAa;IAAb,uBAAA,EAAA,aAAa;IACvD,IAAM,iBAAiB,GAAG,KAAK,KAAK,WAAW,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;IAExD,IAAM,IAAI,GAAG,KAAK,CAAC,CAAC,CAAC,CAAC;IACtB,IAAM,IAAI,GAAG,KAAK,CAAC,MAAM,CAAC;IAC1B,IAAI,IAAI,KAAK,CAAC,EAAE;QACd,IAAI,KAAK,KAAK,WAAW,EAAE;YACzB,IAAM,YAAY,GAAG,mBAAmB,CAAC,IAAI,CAAC,CAAC;YAC/C,OAAO,CAAC,WAAW,CAAC,YAAY,CAAC,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;SAC1C;QACD,OAAO,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC,CAAC;KAC7B;IAED,IAAI,IAAI,KAAK,CAAC,EAAE;QACd,IAAI,IAAI,GAAG,qBAAqB,EAAE;YAChC,IAAM,aAAa,GAAG,0BAA0B,GAAG,iBAAiB,CAAC;YAErE,IAAI,SAAS,GAAG,KAAK,CAAC,IAAI,CACtB,IAAI,CAAC,KAAK,CAAC,CAAC,EAAE,aAAa,CAAC,CAAC,CAAC;YAClC,IAAI,QAAQ,GAAG,KAAK,CAAC,IAAI,CAAiC,IAAI,CAAC,KAAK,CAChE,IAAI,GAAG,0BAA0B,GAAG,iBAAiB,EAAE,IAAI,CAAC,CAAC,CAAC;YAClE,IAAI,KAAK,KAAK,WAAW,EAAE;gBACzB,SAAS,GAAG,mBAAmB,CAAC,SAAS,CAAC,CAAC;gBAC3C,QAAQ,GAAG,mBAAmB,CAAC,QAAQ,CAAC,CAAC;aAC1C;YACD,OAAO;gBACL,GAAG,GAAG,SAAS,CAAC,GAAG,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,WAAW,CAAC,CAAC,EAAE,SAAS,CAAC,CAAC,CAAC,CAAC,EAA5B,CAA4B,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC;oBACtE,SAAS;oBACT,QAAQ;yBACH,GAAG,CACA,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,WAAW,CACjB,CAAC,EAAE,SAAS,CAAC,IAAI,GAAG,0BAA0B,GAAG,CAAC,CAAC,CAAC,EAD9C,CAC8C,CAAC;yBAC5D,IAAI,CAAC,IAAI,CAAC;oBACf,GAAG;aACJ,CAAC;SACH;QACD,IAAM,WAAW,GACb,KAAK,KAAK,WAAW,CAAC,CAAC,CAAC,mBAAmB,CAAC,IAAI,CAAC,CAAC,CAAC;YAC3B,KAAK,CAAC,IAAI,CAAgB,IAAI,CAAC,CAAC;QAE5D,OAAO;YACL,GAAG,GAAG,WAAW,CAAC,GAAG,CAAC,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,WAAW,CAAC,CAAC,EAAE,SAAS,CAAC,CAAC,CAAC,CAAC,EAA5B,CAA4B,CAAC,CAAC,IAAI,CAAC,IAAI,CAAC;gBACxE,GAAG;SACJ,CAAC;KACH;IAGD,IAAM,QAAQ,GAAG,KAAK,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC;IAChC,IAAM,UAAU,GAAG,OAAO,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC;IACpC,IAAM,MAAM,GAAG,OAAO,CAAC,CAAC,CAAC,GAAG,iBAAiB,CAAC;IAC9C,IAAM,KAAK,GAAa,EAAE,CAAC;IAC3B,IAAI,IAAI,GAAG,qBAAqB,EAAE;QAChC,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,0BAA0B,EAAE,CAAC,EAAE,EAAE;YACnD,IAAM,KAAK,GAAG,CAAC,GAAG,MAAM,CAAC;YACzB,IAAM,GAAG,GAAG,KAAK,GAAG,MAAM,CAAC;YAC3B,KAAK,CAAC,IAAI,OAAV,KAAK,EAAS,iBAAiB,CAC3B,IAAI,CAAC,KAAK,CAAC,KAAK,EAAE,GAAG,CAAC,EAAE,QAAQ,EAAE,KAAK,EAAE,UAAU,EAAE,SAAS,EAC9D,KAAK,CAAc,EAAE;SAC1B;QACD,KAAK,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC;QAClB,KAAK,IAAI,CAAC,GAAG,IAAI,GAAG,0BAA0B,EAAE,CAAC,GAAG,IAAI,EAAE,CAAC,EAAE,EAAE;YAC7D,IAAM,KAAK,GAAG,CAAC,GAAG,MAAM,CAAC;YACzB,IAAM,GAAG,GAAG,KAAK,GAAG,MAAM,CAAC;YAC3B,KAAK,CAAC,IAAI,OAAV,KAAK,EAAS,iBAAiB,CAC3B,IAAI,CAAC,KAAK,CAAC,KAAK,EAAE,GAAG,CAAC,EAAE,QAAQ,EAAE,KAAK,EAAE,UAAU,EAAE,SAAS,EAC9D,CAAC,KAAK,IAAI,GAAG,CAAC,CAAc,EAAE;SACnC;KACF;SAAM;QACL,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,IAAI,EAAE,CAAC,EAAE,EAAE;YAC7B,IAAM,KAAK,GAAG,CAAC,GAAG,MAAM,CAAC;YACzB,IAAM,GAAG,GAAG,KAAK,GAAG,MAAM,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diff --git a/node_modules/@tensorflow/tfjs-core/dist/tensor_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/tensor_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tensor_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tensor_test.js b/node_modules/@tensorflow/tfjs-core/dist/tensor_test.js
new file mode 100644
index 0000000..de617c0
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tensor_test.js
@@ -0,0 +1,1786 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+var _this = this;
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("./index");
+var jasmine_util_1 = require("./jasmine_util");
+var tensor_1 = require("./tensor");
+var test_util_1 = require("./test_util");
+jasmine_util_1.describeWithFlags('tensor', test_util_1.ALL_ENVS, function () {
+    it('Tensors of arbitrary size', function () {
+        var t = tf.tensor1d([1, 2, 3]);
+        expect(t.rank).toBe(1);
+        expect(t.size).toBe(3);
+        test_util_1.expectArraysClose(t, [1, 2, 3]);
+        expect(t.get(4)).toBeUndefined();
+        t = tf.tensor2d([1, 2, 3], [1, 3]);
+        expect(t.rank).toBe(2);
+        expect(t.size).toBe(3);
+        test_util_1.expectArraysClose(t, [1, 2, 3]);
+        expect(t.get(0, 4)).toBeUndefined();
+        t = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        expect(t.rank).toBe(2);
+        expect(t.size).toBe(6);
+        test_util_1.expectArraysClose(t, [1, 2, 3, 4, 5, 6]);
+        expect(t.get(5, 3)).toBeUndefined();
+        expect(function () { return tf.tensor2d([1], [1, 2]); }).toThrowError();
+    });
+    it('Tensors of explicit size', function () {
+        var t = tf.tensor1d([5, 3, 2]);
+        expect(t.rank).toBe(1);
+        expect(t.shape).toEqual([3]);
+        test_util_1.expectNumbersClose(t.get(1), 3);
+        expect(function () { return tf.tensor3d([1, 2], [1, 2, 3, 5]); }).toThrowError();
+        var t4 = tf.tensor4d([1, 2, 3, 4], [1, 2, 1, 2]);
+        test_util_1.expectNumbersClose(t4.get(0, 0, 0, 0), 1);
+        test_util_1.expectNumbersClose(t4.get(0, 0, 0, 1), 2);
+        test_util_1.expectNumbersClose(t4.get(0, 1, 0, 0), 3);
+        test_util_1.expectNumbersClose(t4.get(0, 1, 0, 1), 4);
+        var x = tf.ones([3, 4, 2]);
+        expect(x.rank).toBe(3);
+        expect(x.size).toBe(24);
+        for (var i = 0; i < 3; i++) {
+            for (var j = 0; j < 4; j++) {
+                for (var k = 0; k < 2; k++) {
+                    test_util_1.expectNumbersClose(x.get(i, j, k), 1);
+                }
+            }
+        }
+        var z = tf.zeros([3, 4, 2]);
+        expect(z.rank).toBe(3);
+        expect(z.size).toBe(24);
+        for (var i = 0; i < 3; i++) {
+            for (var j = 0; j < 4; j++) {
+                for (var k = 0; k < 2; k++) {
+                    test_util_1.expectNumbersClose(z.get(i, j, k), 0);
+                }
+            }
+        }
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        test_util_1.expectNumbersClose(a.get(1, 2), 6);
+    });
+    it('Tensor dataSync CPU --> GPU', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
+        test_util_1.expectArraysClose(a.dataSync(), new Float32Array([1, 2, 3, 4, 5, 6]));
+    });
+    it('Tensor.data() CPU --> GPU', function () { return __awaiter(_this, void 0, void 0, function () {
+        var a, _a;
+        return __generator(this, function (_b) {
+            switch (_b.label) {
+                case 0:
+                    a = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
+                    _a = test_util_1.expectArraysClose;
+                    return [4, a.data()];
+                case 1:
+                    _a.apply(void 0, [_b.sent(), new Float32Array([1, 2, 3, 4, 5, 6])]);
+                    return [2];
+            }
+        });
+    }); });
+    it('Tensor.data() packed CPU --> GPU', function () { return __awaiter(_this, void 0, void 0, function () {
+        var a, _a;
+        return __generator(this, function (_b) {
+            switch (_b.label) {
+                case 0:
+                    a = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
+                    tf.matMul(a, tf.tensor2d([1, 2], [2, 1]));
+                    _a = test_util_1.expectArraysClose;
+                    return [4, a.data()];
+                case 1:
+                    _a.apply(void 0, [_b.sent(), new Float32Array([1, 2, 3, 4, 5, 6])]);
+                    return [2];
+            }
+        });
+    }); });
+    it('Scalar basic methods', function () {
+        var a = tf.scalar(5);
+        test_util_1.expectNumbersClose(a.get(), 5);
+        test_util_1.expectArraysClose(a, [5]);
+        expect(a.rank).toBe(0);
+        expect(a.size).toBe(1);
+        expect(a.shape).toEqual([]);
+    });
+    it('indexToLoc Scalar', function () {
+        var a = tf.scalar(0).buffer();
+        expect(a.indexToLoc(0)).toEqual([]);
+        var b = tf.zeros([]).buffer();
+        expect(b.indexToLoc(0)).toEqual([]);
+    });
+    it('indexToLoc Tensor1D', function () {
+        var a = tf.zeros([3]).buffer();
+        expect(a.indexToLoc(0)).toEqual([0]);
+        expect(a.indexToLoc(1)).toEqual([1]);
+        expect(a.indexToLoc(2)).toEqual([2]);
+        var b = tf.zeros([3]).buffer();
+        expect(b.indexToLoc(0)).toEqual([0]);
+        expect(b.indexToLoc(1)).toEqual([1]);
+        expect(b.indexToLoc(2)).toEqual([2]);
+    });
+    it('indexToLoc Tensor2D', function () {
+        var a = tf.zeros([3, 2]).buffer();
+        expect(a.indexToLoc(0)).toEqual([0, 0]);
+        expect(a.indexToLoc(1)).toEqual([0, 1]);
+        expect(a.indexToLoc(2)).toEqual([1, 0]);
+        expect(a.indexToLoc(3)).toEqual([1, 1]);
+        expect(a.indexToLoc(4)).toEqual([2, 0]);
+        expect(a.indexToLoc(5)).toEqual([2, 1]);
+        var b = tf.zeros([3, 2]).buffer();
+        expect(b.indexToLoc(0)).toEqual([0, 0]);
+        expect(b.indexToLoc(1)).toEqual([0, 1]);
+        expect(b.indexToLoc(2)).toEqual([1, 0]);
+        expect(b.indexToLoc(3)).toEqual([1, 1]);
+        expect(b.indexToLoc(4)).toEqual([2, 0]);
+        expect(b.indexToLoc(5)).toEqual([2, 1]);
+    });
+    it('indexToLoc Tensor3D', function () {
+        var a = tf.zeros([3, 2, 2]).buffer();
+        expect(a.indexToLoc(0)).toEqual([0, 0, 0]);
+        expect(a.indexToLoc(1)).toEqual([0, 0, 1]);
+        expect(a.indexToLoc(2)).toEqual([0, 1, 0]);
+        expect(a.indexToLoc(3)).toEqual([0, 1, 1]);
+        expect(a.indexToLoc(4)).toEqual([1, 0, 0]);
+        expect(a.indexToLoc(5)).toEqual([1, 0, 1]);
+        expect(a.indexToLoc(11)).toEqual([2, 1, 1]);
+        var b = tf.zeros([3, 2, 2]).buffer();
+        expect(b.indexToLoc(0)).toEqual([0, 0, 0]);
+        expect(b.indexToLoc(1)).toEqual([0, 0, 1]);
+        expect(b.indexToLoc(2)).toEqual([0, 1, 0]);
+        expect(b.indexToLoc(3)).toEqual([0, 1, 1]);
+        expect(b.indexToLoc(4)).toEqual([1, 0, 0]);
+        expect(b.indexToLoc(5)).toEqual([1, 0, 1]);
+        expect(b.indexToLoc(11)).toEqual([2, 1, 1]);
+    });
+    it('indexToLoc Tensor 5D', function () {
+        var values = new Float32Array([1, 2, 3, 4]);
+        var a = tensor_1.Tensor.make([2, 1, 1, 1, 2], { values: values }).buffer();
+        expect(a.indexToLoc(0)).toEqual([0, 0, 0, 0, 0]);
+        expect(a.indexToLoc(1)).toEqual([0, 0, 0, 0, 1]);
+        expect(a.indexToLoc(2)).toEqual([1, 0, 0, 0, 0]);
+        expect(a.indexToLoc(3)).toEqual([1, 0, 0, 0, 1]);
+    });
+    it('locToIndex Scalar', function () {
+        var a = tf.scalar(0).buffer();
+        expect(a.locToIndex([])).toEqual(0);
+        var b = tf.zeros([]).buffer();
+        expect(b.locToIndex([])).toEqual(0);
+    });
+    it('locToIndex Tensor1D', function () {
+        var a = tf.zeros([3]).buffer();
+        expect(a.locToIndex([0])).toEqual(0);
+        expect(a.locToIndex([1])).toEqual(1);
+        expect(a.locToIndex([2])).toEqual(2);
+        var b = tf.zeros([3]).buffer();
+        expect(b.locToIndex([0])).toEqual(0);
+        expect(b.locToIndex([1])).toEqual(1);
+        expect(b.locToIndex([2])).toEqual(2);
+    });
+    it('locToIndex Tensor2D', function () {
+        var a = tf.zeros([3, 2]).buffer();
+        expect(a.locToIndex([0, 0])).toEqual(0);
+        expect(a.locToIndex([0, 1])).toEqual(1);
+        expect(a.locToIndex([1, 0])).toEqual(2);
+        expect(a.locToIndex([1, 1])).toEqual(3);
+        expect(a.locToIndex([2, 0])).toEqual(4);
+        expect(a.locToIndex([2, 1])).toEqual(5);
+        var b = tf.zeros([3, 2]).buffer();
+        expect(b.locToIndex([0, 0])).toEqual(0);
+        expect(b.locToIndex([0, 1])).toEqual(1);
+        expect(b.locToIndex([1, 0])).toEqual(2);
+        expect(b.locToIndex([1, 1])).toEqual(3);
+        expect(b.locToIndex([2, 0])).toEqual(4);
+        expect(b.locToIndex([2, 1])).toEqual(5);
+    });
+    it('locToIndex Tensor3D', function () {
+        var a = tf.zeros([3, 2, 2]).buffer();
+        expect(a.locToIndex([0, 0, 0])).toEqual(0);
+        expect(a.locToIndex([0, 0, 1])).toEqual(1);
+        expect(a.locToIndex([0, 1, 0])).toEqual(2);
+        expect(a.locToIndex([0, 1, 1])).toEqual(3);
+        expect(a.locToIndex([1, 0, 0])).toEqual(4);
+        expect(a.locToIndex([1, 0, 1])).toEqual(5);
+        expect(a.locToIndex([2, 1, 1])).toEqual(11);
+        var b = tf.zeros([3, 2, 2]).buffer();
+        expect(b.locToIndex([0, 0, 0])).toEqual(0);
+        expect(b.locToIndex([0, 0, 1])).toEqual(1);
+        expect(b.locToIndex([0, 1, 0])).toEqual(2);
+        expect(b.locToIndex([0, 1, 1])).toEqual(3);
+        expect(b.locToIndex([1, 0, 0])).toEqual(4);
+        expect(b.locToIndex([1, 0, 1])).toEqual(5);
+        expect(b.locToIndex([2, 1, 1])).toEqual(11);
+    });
+    it('Tensor assignability (asserts compiler)', function () {
+        var a = null;
+        var b = a;
+        expect(b).toBeNull();
+        var a1 = null;
+        var b1 = a1;
+        expect(b1).toBeNull();
+        var a2 = null;
+        var b2 = a2;
+        expect(b2).toBeNull();
+        var a3 = null;
+        var b3 = a3;
+        expect(b3).toBeNull();
+        var a4 = null;
+        var b4 = a4;
+        expect(b4).toBeNull();
+    });
+    it('tf.tensor1d() from number[]', function () {
+        var a = tf.tensor1d([1, 2, 3]);
+        test_util_1.expectArraysClose(a, [1, 2, 3]);
+    });
+    it('tf.tensor1d() throw error with null input value', function () {
+        expect(function () { return tf.tensor1d(null); })
+            .toThrowError('The input to the tensor constructor ' +
+            'must be a non-null value.');
+    });
+    it('tf.tensor1d() from string[]', function () {
+        var a = tf.tensor1d(['aa', 'bb', 'cc']);
+        expect(a.dtype).toBe('string');
+        expect(a.shape).toEqual([3]);
+        test_util_1.expectArraysEqual(a, ['aa', 'bb', 'cc']);
+    });
+    it('tf.tensor1d() from number[][], shape mismatch', function () {
+        expect(function () { return tf.tensor1d([[1], [2], [3]]); }).toThrowError();
+    });
+    it('tf.tensor1d() from string[][], shape mismatch', function () {
+        expect(function () { return tf.tensor1d([['a'], ['b'], ['c']]); }).toThrowError();
+    });
+    it('tf.tensor2d() from number[][]', function () {
+        var a = tf.tensor2d([[1, 2, 3], [4, 5, 6]], [2, 3]);
+        test_util_1.expectArraysClose(a, [1, 2, 3, 4, 5, 6]);
+    });
+    it('tf.tensor2d() from string[][]', function () {
+        var a = tf.tensor2d([['aa', 'bb'], ['cc', 'dd']]);
+        expect(a.dtype).toBe('string');
+        expect(a.shape).toEqual([2, 2]);
+        test_util_1.expectArraysEqual(a, ['aa', 'bb', 'cc', 'dd']);
+    });
+    it('tf.tensor2d() requires shape to be of length 2', function () {
+        var shape = [4];
+        expect(function () { return tf.tensor2d([1, 2, 3, 4], shape); }).toThrowError();
+    });
+    it('tf.tensor2d() from number[][], but shape does not match', function () {
+        expect(function () { return tf.tensor2d([[1, 2, 3], [4, 5, 6]], [3, 2]); }).toThrowError();
+    });
+    it('tf.tensor2d() from string[][], but shape does not match', function () {
+        var vals = [['a', 'b', 'c'], ['d', 'e', 'f']];
+        expect(function () { return tf.tensor2d(vals, [3, 2]); }).toThrowError();
+    });
+    it('tf.tensor2d() from number[], but no shape throws error', function () {
+        expect(function () { return tf.tensor2d([1, 2, 3, 4]); }).toThrowError();
+    });
+    it('tf.tensor2d() from string[], but no shape throws error', function () {
+        expect(function () { return tf.tensor2d(['a', 'b', 'c', 'd']); }).toThrowError();
+    });
+    it('tf.tensor2d() throw error with null input value', function () {
+        expect(function () { return tf.tensor2d(null); })
+            .toThrowError('The input to the tensor constructor ' +
+            'must be a non-null value.');
+    });
+    it('tensor3d() from number[][][]', function () {
+        var a = tf.tensor3d([[[1], [2], [3]], [[4], [5], [6]]], [2, 3, 1]);
+        test_util_1.expectArraysClose(a, [1, 2, 3, 4, 5, 6]);
+    });
+    it('tensor3d() from string[][][]', function () {
+        var vals = [[['a'], ['b'], ['c']], [['d'], ['e'], ['f']]];
+        var a = tf.tensor3d(vals, [2, 3, 1]);
+        expect(a.dtype).toBe('string');
+        expect(a.shape).toEqual([2, 3, 1]);
+        test_util_1.expectArraysEqual(a, ['a', 'b', 'c', 'd', 'e', 'f']);
+    });
+    it('tensor3d() from number[][][], but shape does not match', function () {
+        var values = [[[1], [2], [3]], [[4], [5], [6]]];
+        expect(function () { return tf.tensor3d(values, [3, 2, 1]); }).toThrowError();
+    });
+    it('tf.tensor3d() from number[], but no shape throws error', function () {
+        expect(function () { return tf.tensor3d([1, 2, 3, 4]); }).toThrowError();
+    });
+    it('tf.tensor3d() requires shape to be of length 3', function () {
+        var shape = [4, 1];
+        expect(function () { return tf.tensor3d([1, 2, 3, 4], shape); }).toThrowError();
+    });
+    it('tf.tensor3d() throw error with null input value', function () {
+        expect(function () { return tf.tensor3d(null); })
+            .toThrowError('The input to the tensor constructor ' +
+            'must be a non-null value.');
+    });
+    it('tensor4d() from number[][][][]', function () {
+        var a = tf.tensor4d([[[[1]], [[2]]], [[[4]], [[5]]]], [2, 2, 1, 1]);
+        test_util_1.expectArraysClose(a, [1, 2, 4, 5]);
+    });
+    it('tensor4d() from string[][][][]', function () {
+        var vals = [[[['a']], [['b']]], [[['c']], [['d']]]];
+        var a = tf.tensor4d(vals, [2, 2, 1, 1]);
+        expect(a.dtype).toBe('string');
+        expect(a.shape).toEqual([2, 2, 1, 1]);
+        test_util_1.expectArraysEqual(a, ['a', 'b', 'c', 'd']);
+    });
+    it('tensor4d() from string[][][][] infer shape', function () {
+        var vals = [[[['a']], [['b']]], [[['c']], [['d']]]];
+        var a = tf.tensor4d(vals);
+        expect(a.dtype).toBe('string');
+        expect(a.shape).toEqual([2, 2, 1, 1]);
+        test_util_1.expectArraysEqual(a, ['a', 'b', 'c', 'd']);
+    });
+    it('tensor4d() from number[][][][], but shape does not match', function () {
+        var f = function () {
+            tf.tensor4d([[[[1]], [[2]]], [[[4]], [[5]]]], [2, 1, 2, 1]);
+        };
+        expect(f).toThrowError();
+    });
+    it('tf.tensor4d() from number[], but no shape throws error', function () {
+        expect(function () { return tf.tensor4d([1, 2, 3, 4]); }).toThrowError();
+    });
+    it('tf.tensor4d() requires shape to be of length 4', function () {
+        var shape = [4, 1];
+        expect(function () { return tf.tensor4d([1, 2, 3, 4], shape); }).toThrowError();
+    });
+    it('tf.tensor4d() throw error with null input value', function () {
+        expect(function () { return tf.tensor4d(null); })
+            .toThrowError('The input to the tensor constructor ' +
+            'must be a non-null value.');
+    });
+    it('tf.tensor5d() throw error with null input value', function () {
+        expect(function () { return tf.tensor5d(null); })
+            .toThrowError('The input to the tensor constructor ' +
+            'must be a non-null value.');
+    });
+    it('tf.tensor6d() throw error with null input value', function () {
+        expect(function () { return tf.tensor6d(null); })
+            .toThrowError('The input to the tensor constructor ' +
+            'must be a non-null value.');
+    });
+    it('default dtype', function () {
+        var a = tf.scalar(3);
+        expect(a.dtype).toBe('float32');
+        test_util_1.expectArraysClose(a, [3]);
+    });
+    it('float32 dtype', function () {
+        var a = tf.scalar(3, 'float32');
+        expect(a.dtype).toBe('float32');
+        test_util_1.expectArraysClose(a, [3]);
+    });
+    it('int32 dtype', function () {
+        var a = tf.scalar(3, 'int32');
+        expect(a.dtype).toBe('int32');
+        test_util_1.expectArraysEqual(a, [3]);
+    });
+    it('int32 dtype, 3.9 => 3, like numpy', function () {
+        var a = tf.scalar(3.9, 'int32');
+        expect(a.dtype).toBe('int32');
+        test_util_1.expectArraysEqual(a, [3]);
+    });
+    it('int32 dtype, -3.9 => -3, like numpy', function () {
+        var a = tf.scalar(-3.9, 'int32');
+        expect(a.dtype).toBe('int32');
+        test_util_1.expectArraysEqual(a, [-3]);
+    });
+    it('bool dtype, 3 => true, like numpy', function () {
+        var a = tf.scalar(3, 'bool');
+        expect(a.dtype).toBe('bool');
+        expect(a.get()).toBe(1);
+    });
+    it('bool dtype, -2 => true, like numpy', function () {
+        var a = tf.scalar(-2, 'bool');
+        expect(a.dtype).toBe('bool');
+        expect(a.get()).toBe(1);
+    });
+    it('bool dtype, 0 => false, like numpy', function () {
+        var a = tf.scalar(0, 'bool');
+        expect(a.dtype).toBe('bool');
+        expect(a.get()).toBe(0);
+    });
+    it('bool dtype from boolean', function () {
+        var a = tf.scalar(false, 'bool');
+        expect(a.get()).toBe(0);
+        expect(a.dtype).toBe('bool');
+        var b = tf.scalar(true, 'bool');
+        expect(b.get()).toBe(1);
+        expect(b.dtype).toBe('bool');
+    });
+    it('int32 dtype from boolean', function () {
+        var a = tf.scalar(true, 'int32');
+        expect(a.get()).toBe(1);
+        expect(a.dtype).toBe('int32');
+    });
+    it('default dtype from boolean', function () {
+        var a = tf.scalar(false);
+        test_util_1.expectNumbersClose(a.get(), 0);
+        expect(a.dtype).toBe('bool');
+    });
+    it('default dtype', function () {
+        var a = tf.tensor1d([1, 2, 3]);
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([3]);
+        test_util_1.expectArraysClose(a, [1, 2, 3]);
+    });
+    it('float32 dtype', function () {
+        var a = tf.tensor1d([1, 2, 3], 'float32');
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([3]);
+        test_util_1.expectArraysClose(a, [1, 2, 3]);
+    });
+    it('int32 dtype', function () {
+        var a = tf.tensor1d([1, 2, 3], 'int32');
+        expect(a.dtype).toBe('int32');
+        expect(a.shape).toEqual([3]);
+        test_util_1.expectArraysEqual(a, [1, 2, 3]);
+    });
+    it('int32 dtype, non-ints get floored, like numpy', function () {
+        var a = tf.tensor1d([1.1, 2.5, 3.9], 'int32');
+        expect(a.dtype).toBe('int32');
+        expect(a.shape).toEqual([3]);
+        test_util_1.expectArraysEqual(a, [1, 2, 3]);
+    });
+    it('int32 dtype, negative non-ints get ceiled, like numpy', function () {
+        var a = tf.tensor1d([-1.1, -2.5, -3.9], 'int32');
+        expect(a.dtype).toBe('int32');
+        expect(a.shape).toEqual([3]);
+        test_util_1.expectArraysEqual(a, [-1, -2, -3]);
+    });
+    it('bool dtype, !=0 is truthy, 0 is falsy, like numpy', function () {
+        var a = tf.tensor1d([1, -2, 0, 3], 'bool');
+        expect(a.dtype).toBe('bool');
+        expect(a.shape).toEqual([4]);
+        expect(a.get(0)).toBe(1);
+        expect(a.get(1)).toBe(1);
+        expect(a.get(2)).toBe(0);
+        expect(a.get(3)).toBe(1);
+    });
+    it('default dtype from boolean[]', function () {
+        var a = tf.tensor1d([false, false, true]);
+        expect(a.dtype).toBe('bool');
+        test_util_1.expectArraysClose(a, [0, 0, 1]);
+    });
+    it('default dtype from UInt8Array', function () {
+        var a = tf.tensor1d(new Uint8Array([1, 5, 2]));
+        expect(a.dtype).toBe('int32');
+        expect(a.shape).toEqual([3]);
+        test_util_1.expectArraysClose(a, [1, 5, 2]);
+    });
+    it('default dtype from Int32Array', function () {
+        var a = tf.tensor1d(new Int32Array([1, 5, 2]));
+        expect(a.dtype).toBe('int32');
+        expect(a.shape).toEqual([3]);
+        test_util_1.expectArraysClose(a, [1, 5, 2]);
+    });
+    it('tf.tensor() from Float32Array and number[]', function () {
+        var a = tf.tensor([
+            new Float32Array([1, 2]), new Float32Array([3, 4]),
+            new Float32Array([5, 6]), [7, 8]
+        ]);
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([4, 2]);
+        test_util_1.expectArraysClose(a, [1, 2, 3, 4, 5, 6, 7, 8]);
+    });
+    it('tf.tensor() from Int32Array and number[]', function () {
+        var a = tf.tensor([
+            new Int32Array([1, 2]), new Int32Array([3, 4]), new Int32Array([5, 6]),
+            [7, 8]
+        ]);
+        expect(a.dtype).toBe('int32');
+        expect(a.shape).toEqual([4, 2]);
+        test_util_1.expectArraysClose(a, [1, 2, 3, 4, 5, 6, 7, 8]);
+    });
+    it('tf.tensor() from mixed TypedArray', function () {
+        var a = tf.tensor([
+            new Float32Array([1, 2]), new Int32Array([3, 4]), new Uint8Array([5, 6]),
+            [7, 8]
+        ]);
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([4, 2]);
+        test_util_1.expectArraysClose(a, [1, 2, 3, 4, 5, 6, 7, 8]);
+    });
+    it('tf.tensor() from TypedArrays which are themselves 3D', function () {
+        var img1 = new Float32Array(20 * 20 * 3);
+        var img2 = new Float32Array(20 * 20 * 3);
+        var t = tf.tensor([img1, img2], [2, 20, 20, 3]);
+        expect(t.dtype).toBe('float32');
+        expect(t.shape).toEqual([2, 20, 20, 3]);
+    });
+    it('tf.tensor() from TypeedArrays which are themselves 3D, wrong shape', function () {
+        var img1 = new Float32Array(20 * 20 * 3);
+        var img2 = new Float32Array(20 * 20 * 3);
+        expect(function () { return tf.tensor([img1, img2], [3, 20, 20, 3]); }).toThrowError();
+    });
+    it('tf.tensor() from TypedArray + number[] fails due to wrong shape', function () {
+        expect(function () { return tf.tensor([
+            new Float32Array([1, 2]),
+            new Float32Array([3, 4]),
+            new Float32Array([5, 6]),
+            [7, 8, 9, 10],
+        ]); })
+            .toThrowError(/Element arr\[3\] should have 2 elements, but has 4 elements/);
+    });
+    it('default dtype from ascii string', function () {
+        var a = tf.tensor('hello');
+        expect(a.dtype).toBe('string');
+        expect(a.shape).toEqual([]);
+        test_util_1.expectArraysEqual(a, ['hello']);
+    });
+    it('default dtype from utf-8 string', function () {
+        var a = tf.tensor('даниел');
+        expect(a.dtype).toBe('string');
+        expect(a.shape).toEqual([]);
+        test_util_1.expectArraysEqual(a, ['даниел']);
+    });
+    it('default dtype from empty string', function () {
+        var a = tf.tensor('');
+        expect(a.dtype).toBe('string');
+        expect(a.shape).toEqual([]);
+        test_util_1.expectArraysEqual(a, ['']);
+    });
+    it('default dtype from unicode escaped string', function () {
+        var a = tf.tensor('\u0434\u0430\u043d\u0438\u0435\u043b');
+        expect(a.dtype).toBe('string');
+        expect(a.shape).toEqual([]);
+        test_util_1.expectArraysEqual(a, ['даниел']);
+    });
+    it('default dtype from string[]', function () {
+        var a = tf.tensor(['a', 'b']);
+        expect(a.dtype).toBe('string');
+        expect(a.shape).toEqual([2]);
+        test_util_1.expectArraysEqual(a, ['a', 'b']);
+    });
+    it('float32 dtype from boolean[]', function () {
+        var a = tf.tensor1d([false, false, true], 'float32');
+        expect(a.dtype).toBe('float32');
+        test_util_1.expectArraysClose(a, [0, 0, 1]);
+    });
+    it('int32 dtype from boolean[]', function () {
+        var a = tf.tensor1d([false, false, true], 'int32');
+        expect(a.dtype).toBe('int32');
+        test_util_1.expectArraysEqual(a, [0, 0, 1]);
+    });
+    it('bool dtype from boolean[]', function () {
+        var a = tf.tensor1d([false, false, true], 'bool');
+        expect(a.dtype).toBe('bool');
+        test_util_1.expectArraysEqual(a, [0, 0, 1]);
+    });
+    it('default dtype', function () {
+        var a = tf.tensor2d([1, 2, 3, 4], [2, 2]);
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(a, [1, 2, 3, 4]);
+    });
+    it('float32 dtype', function () {
+        var a = tf.tensor2d([1, 2, 3, 4], [2, 2], 'float32');
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(a, [1, 2, 3, 4]);
+    });
+    it('int32 dtype', function () {
+        var a = tf.tensor2d([[1, 2], [3, 4]], [2, 2], 'int32');
+        expect(a.dtype).toBe('int32');
+        expect(a.shape).toEqual([2, 2]);
+        test_util_1.expectArraysEqual(a, [1, 2, 3, 4]);
+    });
+    it('int32 dtype, non-ints get floored, like numpy', function () {
+        var a = tf.tensor2d([1.1, 2.5, 3.9, 4.0], [2, 2], 'int32');
+        expect(a.dtype).toBe('int32');
+        expect(a.shape).toEqual([2, 2]);
+        test_util_1.expectArraysEqual(a, [1, 2, 3, 4]);
+    });
+    it('int32 dtype, negative non-ints get ceiled, like numpy', function () {
+        var a = tf.tensor2d([-1.1, -2.5, -3.9, -4.0], [2, 2], 'int32');
+        expect(a.dtype).toBe('int32');
+        expect(a.shape).toEqual([2, 2]);
+        test_util_1.expectArraysEqual(a, [-1, -2, -3, -4]);
+    });
+    it('bool dtype, !=0 is truthy, 0 is falsy, like numpy', function () {
+        var a = tf.tensor2d([1, -2, 0, 3], [2, 2], 'bool');
+        expect(a.dtype).toBe('bool');
+        expect(a.shape).toEqual([2, 2]);
+        expect(a.get(0, 0)).toBe(1);
+        expect(a.get(0, 1)).toBe(1);
+        expect(a.get(1, 0)).toBe(0);
+        expect(a.get(1, 1)).toBe(1);
+    });
+    it('default dtype from boolean[]', function () {
+        var a = tf.tensor2d([[false, false], [true, false]], [2, 2]);
+        expect(a.dtype).toBe('bool');
+        test_util_1.expectArraysClose(a, [0, 0, 1, 0]);
+    });
+    it('float32 dtype from boolean[]', function () {
+        var a = tf.tensor2d([[false, false], [true, false]], [2, 2], 'float32');
+        expect(a.dtype).toBe('float32');
+        test_util_1.expectArraysEqual(a, [0, 0, 1, 0]);
+    });
+    it('int32 dtype from boolean[]', function () {
+        var a = tf.tensor2d([[false, false], [true, false]], [2, 2], 'int32');
+        expect(a.dtype).toBe('int32');
+        test_util_1.expectArraysEqual(a, [0, 0, 1, 0]);
+    });
+    it('bool dtype from boolean[]', function () {
+        var a = tf.tensor2d([[false, false], [true, false]], [2, 2], 'bool');
+        expect(a.dtype).toBe('bool');
+        test_util_1.expectArraysEqual(a, [0, 0, 1, 0]);
+    });
+    it('default dtype', function () {
+        var a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1]);
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(a, [1, 2, 3, 4]);
+    });
+    it('float32 dtype', function () {
+        var a = tf.tensor3d([1, 2, 3, 4], [2, 2, 1], 'float32');
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysClose(a, [1, 2, 3, 4]);
+    });
+    it('int32 dtype', function () {
+        var a = tf.tensor3d([[[1], [2]], [[3], [4]]], [2, 2, 1], 'int32');
+        expect(a.dtype).toBe('int32');
+        expect(a.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysEqual(a, [1, 2, 3, 4]);
+    });
+    it('int32 dtype, non-ints get floored, like numpy', function () {
+        var a = tf.tensor3d([1.1, 2.5, 3.9, 4.0], [2, 2, 1], 'int32');
+        expect(a.dtype).toBe('int32');
+        expect(a.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysEqual(a, [1, 2, 3, 4]);
+    });
+    it('int32 dtype, negative non-ints get ceiled, like numpy', function () {
+        var a = tf.tensor3d([-1.1, -2.5, -3.9, -4.0], [2, 2, 1], 'int32');
+        expect(a.dtype).toBe('int32');
+        expect(a.shape).toEqual([2, 2, 1]);
+        test_util_1.expectArraysEqual(a, [-1, -2, -3, -4]);
+    });
+    it('bool dtype, !=0 is truthy, 0 is falsy, like numpy', function () {
+        var a = tf.tensor3d([1, -2, 0, 3], [2, 2, 1], 'bool');
+        expect(a.dtype).toBe('bool');
+        expect(a.shape).toEqual([2, 2, 1]);
+        expect(a.get(0, 0, 0)).toBe(1);
+        expect(a.get(0, 1, 0)).toBe(1);
+        expect(a.get(1, 0, 0)).toBe(0);
+        expect(a.get(1, 1, 0)).toBe(1);
+    });
+    it('default dtype from boolean[]', function () {
+        var a = tf.tensor3d([[[false], [false]], [[true], [false]]], [2, 2, 1]);
+        expect(a.dtype).toBe('bool');
+        test_util_1.expectArraysClose(a, [0, 0, 1, 0]);
+    });
+    it('float32 dtype from boolean[]', function () {
+        var a = tf.tensor3d([[[false], [false]], [[true], [false]]], [2, 2, 1], 'float32');
+        expect(a.dtype).toBe('float32');
+        test_util_1.expectArraysClose(a, [0, 0, 1, 0]);
+    });
+    it('int32 dtype from boolean[]', function () {
+        var a = tf.tensor3d([[[false], [false]], [[true], [false]]], [2, 2, 1], 'int32');
+        expect(a.dtype).toBe('int32');
+        test_util_1.expectArraysEqual(a, [0, 0, 1, 0]);
+    });
+    it('bool dtype from boolean[]', function () {
+        var a = tf.tensor3d([[[false], [false]], [[true], [false]]], [2, 2, 1], 'bool');
+        expect(a.dtype).toBe('bool');
+        test_util_1.expectArraysEqual(a, [0, 0, 1, 0]);
+    });
+    it('default dtype', function () {
+        var a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1]);
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([2, 2, 1, 1]);
+        test_util_1.expectArraysClose(a, [1, 2, 3, 4]);
+    });
+    it('float32 dtype', function () {
+        var a = tf.tensor4d([1, 2, 3, 4], [2, 2, 1, 1], 'float32');
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([2, 2, 1, 1]);
+        test_util_1.expectArraysClose(a, [1, 2, 3, 4]);
+    });
+    it('int32 dtype', function () {
+        var a = tf.tensor4d([[[[1]], [[2]]], [[[3]], [[4]]]], [2, 2, 1, 1], 'int32');
+        expect(a.dtype).toBe('int32');
+        expect(a.shape).toEqual([2, 2, 1, 1]);
+        test_util_1.expectArraysEqual(a, [1, 2, 3, 4]);
+    });
+    it('int32 dtype, non-ints get floored, like numpy', function () {
+        var a = tf.tensor4d([1.1, 2.5, 3.9, 4.0], [2, 2, 1, 1], 'int32');
+        expect(a.dtype).toBe('int32');
+        expect(a.shape).toEqual([2, 2, 1, 1]);
+        test_util_1.expectArraysEqual(a, [1, 2, 3, 4]);
+    });
+    it('int32 dtype, negative non-ints get ceiled, like numpy', function () {
+        var a = tf.tensor4d([-1.1, -2.5, -3.9, -4.0], [2, 2, 1, 1], 'int32');
+        expect(a.dtype).toBe('int32');
+        expect(a.shape).toEqual([2, 2, 1, 1]);
+        test_util_1.expectArraysEqual(a, [-1, -2, -3, -4]);
+    });
+    it('bool dtype, !=0 is truthy, 0 is falsy, like numpy', function () {
+        var a = tf.tensor4d([1, -2, 0, 3], [2, 2, 1, 1], 'bool');
+        expect(a.dtype).toBe('bool');
+        expect(a.shape).toEqual([2, 2, 1, 1]);
+        expect(a.get(0, 0, 0, 0)).toBe(1);
+        expect(a.get(0, 1, 0, 0)).toBe(1);
+        expect(a.get(1, 0, 0, 0)).toBe(0);
+        expect(a.get(1, 1, 0, 0)).toBe(1);
+    });
+    it('default dtype from boolean[]', function () {
+        var a = tf.tensor4d([[[[false], [false]], [[true], [false]]]], [1, 2, 2, 1]);
+        expect(a.dtype).toBe('bool');
+        test_util_1.expectArraysClose(a, [0, 0, 1, 0]);
+    });
+    it('float32 dtype from boolean[]', function () {
+        var a = tf.tensor4d([[[[false], [false]], [[true], [false]]]], [1, 2, 2, 1], 'float32');
+        expect(a.dtype).toBe('float32');
+        test_util_1.expectArraysClose(a, [0, 0, 1, 0]);
+    });
+    it('int32 dtype from boolean[]', function () {
+        var a = tf.tensor4d([[[[false], [false]], [[true], [false]]]], [1, 2, 2, 1], 'int32');
+        expect(a.dtype).toBe('int32');
+        test_util_1.expectArraysEqual(a, [0, 0, 1, 0]);
+    });
+    it('bool dtype from boolean[]', function () {
+        var a = tf.tensor4d([[[[false], [false]], [[true], [false]]]], [1, 2, 2, 1], 'bool');
+        expect(a.dtype).toBe('bool');
+        test_util_1.expectArraysEqual(a, [0, 0, 1, 0]);
+    });
+    it('Scalar default dtype', function () {
+        var a = tf.scalar(4);
+        var b = a.reshape([1, 1]);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([1, 1]);
+        test_util_1.expectArraysClose(a.dataSync(), b.dataSync());
+    });
+    it('Scalar float32 dtype', function () {
+        var a = tf.scalar(4, 'float32');
+        var b = a.reshape([1, 1]);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([1, 1]);
+    });
+    it('Scalar string dtype', function () {
+        var a = tf.scalar('test', 'string');
+        var b = a.reshape([1, 1]);
+        expect(b.dtype).toBe('string');
+        expect(b.shape).toEqual([1, 1]);
+    });
+    it('Scalar inferred dtype from bool', function () {
+        var a = tf.scalar(true);
+        expect(a.dtype).toBe('bool');
+        expect(a.shape).toEqual([]);
+        test_util_1.expectArraysClose(a, [1]);
+    });
+    it('Scalar inferred dtype from string', function () {
+        var a = tf.scalar('hello');
+        expect(a.dtype).toBe('string');
+        expect(a.shape).toEqual([]);
+        test_util_1.expectArraysEqual(a, ['hello']);
+    });
+    it('Scalar int32 dtype', function () {
+        var a = tf.scalar(4, 'int32');
+        var b = a.reshape([1, 1]);
+        expect(b.dtype).toBe('int32');
+        expect(b.shape).toEqual([1, 1]);
+    });
+    it('Scalar bool dtype', function () {
+        var a = tf.scalar(4, 'bool');
+        var b = a.reshape([1, 1, 1]);
+        expect(b.dtype).toBe('bool');
+        expect(b.shape).toEqual([1, 1, 1]);
+        test_util_1.expectArraysClose(a.dataSync(), b.dataSync());
+    });
+    it('Scalar complex64 dtype', function () {
+        var a = tf.complex(4, 5);
+        var b = a.reshape([1, 1]);
+        test_util_1.expectArraysClose(a, [4, 5]);
+        expect(b.dtype).toBe('complex64');
+        expect(b.shape).toEqual([1, 1]);
+        test_util_1.expectArraysClose(a.dataSync(), b.dataSync());
+    });
+    it('Tensor1D default dtype', function () {
+        var a = tf.tensor1d([1, 2, 3, 4]);
+        var b = a.reshape([2, 2]);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(a.dataSync(), b.dataSync());
+    });
+    it('Tensor1D inferred dtype from bools', function () {
+        var a = tf.tensor1d([true, false, false, true]);
+        expect(a.dtype).toBe('bool');
+        expect(a.shape).toEqual([4]);
+        test_util_1.expectArraysClose(a, [1, 0, 0, 1]);
+    });
+    it('Tensor1D inferred dtype from strings', function () {
+        var a = tf.tensor1d(['a', 'b', 'c']);
+        expect(a.dtype).toBe('string');
+        expect(a.shape).toEqual([3]);
+        test_util_1.expectArraysEqual(a, ['a', 'b', 'c']);
+    });
+    it('Tensor1D float32 dtype', function () {
+        var a = tf.tensor1d([1, 2, 3, 4], 'float32');
+        var b = a.reshape([2, 2]);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([2, 2]);
+    });
+    it('Tensor1D int32 dtype', function () {
+        var a = tf.tensor1d([1, 2, 3, 4], 'int32');
+        var b = a.reshape([2, 2]);
+        expect(b.dtype).toBe('int32');
+        expect(b.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(a.dataSync(), b.dataSync());
+    });
+    it('Tensor1D complex64 dtype', function () {
+        var a = tf.complex([1, 3, 5, 7], [2, 4, 6, 8]);
+        var b = a.reshape([2, 2]);
+        expect(b.dtype).toBe('complex64');
+        expect(b.shape).toEqual([2, 2]);
+        test_util_1.expectArraysClose(a.dataSync(), b.dataSync());
+    });
+    it('Tensor2D default dtype', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);
+        var b = a.reshape([6]);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([6]);
+        test_util_1.expectArraysClose(a.dataSync(), b.dataSync());
+    });
+    it('Tensor2D float32 dtype', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3], 'float32');
+        var b = a.reshape([6]);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([6]);
+    });
+    it('Tensor2D int32 dtype', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3], 'int32');
+        var b = a.reshape([6]);
+        expect(b.dtype).toBe('int32');
+        expect(b.shape).toEqual([6]);
+    });
+    it('Tensor2D bool dtype', function () {
+        var a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3], 'bool');
+        var b = a.reshape([6]);
+        expect(b.dtype).toBe('bool');
+        expect(b.shape).toEqual([6]);
+        test_util_1.expectArraysClose(a.dataSync(), b.dataSync());
+    });
+    it('Tensor2D complex64 dtype', function () {
+        var a = tf.complex([[1, 3, 5], [7, 9, 11]], [[2, 4, 6], [8, 10, 12]]);
+        var b = a.reshape([6]);
+        expect(b.dtype).toBe('complex64');
+        expect(b.shape).toEqual([6]);
+        test_util_1.expectArraysClose(a.dataSync(), b.dataSync());
+    });
+    it('Tensor3D default dtype', function () {
+        var a = tf.tensor3d([1, 2, 3, 4, 5, 6], [2, 3, 1]);
+        var b = a.reshape([6]);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([6]);
+        test_util_1.expectArraysClose(a.dataSync(), b.dataSync());
+    });
+    it('Tensor3D float32 dtype', function () {
+        var a = tf.tensor3d([1, 2, 3, 4, 5, 6], [2, 3, 1], 'float32');
+        var b = a.reshape([6]);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([6]);
+    });
+    it('Tensor3D int32 dtype', function () {
+        var a = tf.tensor3d([1, 2, 3, 4, 5, 6], [2, 3, 1], 'int32');
+        var b = a.reshape([6]);
+        expect(b.dtype).toBe('int32');
+        expect(b.shape).toEqual([6]);
+    });
+    it('Tensor3D bool dtype', function () {
+        var a = tf.tensor3d([1, 2, 3, 4, 5, 6], [2, 3, 1], 'bool');
+        var b = a.reshape([6]);
+        expect(b.dtype).toBe('bool');
+        expect(b.shape).toEqual([6]);
+        test_util_1.expectArraysClose(a.dataSync(), b.dataSync());
+    });
+    it('Tensor3D complex64 dtype', function () {
+        var a = tf.complex([[[1], [3], [5]], [[7], [9], [11]]], [[[2], [4], [6]], [[8], [10], [12]]]);
+        var b = a.reshape([6]);
+        expect(b.dtype).toBe('complex64');
+        expect(b.shape).toEqual([6]);
+        test_util_1.expectArraysClose(a.dataSync(), b.dataSync());
+    });
+    it('Tensor4D default dtype', function () {
+        var a = tf.tensor4d([1, 2, 3, 4, 5, 6], [2, 3, 1, 1]);
+        var b = a.reshape([2, 3]);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([2, 3]);
+        test_util_1.expectArraysClose(a.dataSync(), b.dataSync());
+    });
+    it('Tensor4D float32 dtype', function () {
+        var a = tf.tensor4d([1, 2, 3, 4, 5, 6], [2, 3, 1, 1], 'float32');
+        var b = a.reshape([2, 3]);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([2, 3]);
+    });
+    it('Tensor4D int32 dtype', function () {
+        var a = tf.tensor4d([1, 2, 3, 4, 5, 6], [2, 3, 1, 1], 'int32');
+        var b = a.reshape([3, 2]);
+        expect(b.dtype).toBe('int32');
+        expect(b.shape).toEqual([3, 2]);
+        test_util_1.expectArraysClose(a.dataSync(), b.dataSync());
+    });
+    it('Tensor4D complex64 dtype', function () {
+        var a = tf.complex([[[[1]], [[3]], [[5]]], [[[7]], [[9]], [[11]]]], [[[[2]], [[4]], [[6]]], [[[8]], [[10]], [[12]]]]);
+        var b = a.reshape([3, 2]);
+        expect(b.dtype).toBe('complex64');
+        expect(b.shape).toEqual([3, 2]);
+        test_util_1.expectArraysClose(a.dataSync(), b.dataSync());
+    });
+    it('Tensor4D bool dtype', function () {
+        var a = tf.tensor4d([1, 2, 3, 4, 5, 6], [2, 3, 1, 1], 'bool');
+        var b = a.reshape([3, 2]);
+        expect(b.dtype).toBe('bool');
+        expect(b.shape).toEqual([3, 2]);
+    });
+    it('.dataSync() with casting, string tensor', function () {
+        var a = tf.tensor(['a', 'b']);
+        var data = a.dataSync();
+        expect(data).toEqual(['a', 'b']);
+    });
+    it('.data() with casting, string tensor', function () { return __awaiter(_this, void 0, void 0, function () {
+        var a, data;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    a = tf.tensor(['a', 'b']);
+                    return [4, a.data()];
+                case 1:
+                    data = _a.sent();
+                    expect(data).toEqual(['a', 'b']);
+                    return [2];
+            }
+        });
+    }); });
+    it('reshape is functional', function () {
+        var a = tf.scalar(2.4);
+        var b = a.reshape([]);
+        expect(a.id).not.toBe(b.id);
+        b.dispose();
+        test_util_1.expectArraysClose(a, [2.4]);
+    });
+    it('reshape a string tensor', function () {
+        var a = tf.tensor(['a', 'b']);
+        var b = a.reshape([2, 1, 1]);
+        expect(b.dtype).toBe('string');
+        expect(b.shape).toEqual([2, 1, 1]);
+        test_util_1.expectArraysEqual(b, ['a', 'b']);
+    });
+    it('reshape throws when passed a non-tensor', function () {
+        expect(function () { return tf.reshape({}, []); })
+            .toThrowError(/Argument 'x' passed to 'reshape' must be a Tensor/);
+    });
+    it('reshape accepts a tensor-like object', function () {
+        var res = tf.reshape([[1, 2, 3], [4, 5, 6]], [3, 2]);
+        expect(res.dtype).toBe('float32');
+        expect(res.shape).toEqual([3, 2]);
+        test_util_1.expectArraysClose(res, [1, 2, 3, 4, 5, 6]);
+    });
+    it('cast bool -> bool', function () {
+        var a = tf.tensor1d([1, 0], 'bool');
+        expect(a.cast('bool').dtype).toEqual('bool');
+    });
+    it('cast bool -> int32', function () {
+        var a = tf.tensor1d([1, 0], 'bool');
+        expect(a.cast('int32').dtype).toEqual('int32');
+    });
+    it('cast bool -> float32', function () {
+        var a = tf.tensor1d([1, 0], 'bool');
+        expect(a.cast('float32').dtype).toEqual('float32');
+    });
+    it('cast int32 -> bool', function () {
+        var a = tf.tensor1d([1, 0], 'int32');
+        expect(a.cast('bool').dtype).toEqual('bool');
+    });
+    it('cast int32 -> int32', function () {
+        var a = tf.tensor1d([1, 2], 'int32');
+        expect(a.cast('int32').dtype).toEqual('int32');
+    });
+    it('cast int32 -> float32', function () {
+        var a = tf.tensor1d([1, 2], 'int32');
+        expect(a.cast('float32').dtype).toEqual('float32');
+    });
+    it('cast float32 -> bool', function () {
+        var a = tf.tensor1d([1.0, 0.0]);
+        expect(a.cast('bool').dtype).toEqual('bool');
+    });
+    it('cast float32 -> int32', function () {
+        var a = tf.tensor1d([1.0, 2.0]);
+        expect(a.cast('int32').dtype).toEqual('int32');
+    });
+    it('cast float32 -> int32. async download', function () { return __awaiter(_this, void 0, void 0, function () {
+        var a, aInt, asyncData;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    a = tf.tensor1d([1, 2]);
+                    aInt = a.cast('int32');
+                    expect(aInt.dtype).toEqual('int32');
+                    return [4, aInt.data()];
+                case 1:
+                    asyncData = _a.sent();
+                    expect(asyncData instanceof Int32Array).toEqual(true);
+                    return [2];
+            }
+        });
+    }); });
+    it('cast float32 -> int32. queued async download', function () { return __awaiter(_this, void 0, void 0, function () {
+        var a, aInt, _a, first, second;
+        return __generator(this, function (_b) {
+            switch (_b.label) {
+                case 0:
+                    a = tf.tensor1d([1, 2]);
+                    aInt = a.cast('int32');
+                    expect(aInt.dtype).toEqual('int32');
+                    return [4, Promise.all([aInt.data(), aInt.data()])];
+                case 1:
+                    _a = _b.sent(), first = _a[0], second = _a[1];
+                    expect(first instanceof Int32Array).toEqual(true);
+                    expect(second instanceof Int32Array).toEqual(true);
+                    return [2];
+            }
+        });
+    }); });
+    it('cast float32 -> int32. sync download', function () {
+        var a = tf.tensor1d([1, 2]).cast('int32');
+        expect(a.dtype).toEqual('int32');
+        var data = a.dataSync();
+        expect(data instanceof Int32Array).toEqual(true);
+    });
+    it('cast float32 -> float32', function () {
+        var a = tf.tensor1d([1.0, 2.0]);
+        expect(a.cast('float32').dtype).toEqual('float32');
+    });
+    it('cast complex64 -> float32', function () {
+        var a = tf.complex([1.0, 2.0], [3.0, 4.0]);
+        var result = a.cast('float32');
+        expect(result.dtype).toEqual('float32');
+        test_util_1.expectArraysClose(result, [1.0, 2.0]);
+    });
+    it('cast complex64 -> int32', function () {
+        var a = tf.complex([1.0, 2.0], [3.0, 4.0]);
+        var result = a.cast('int32');
+        expect(result.dtype).toEqual('int32');
+        test_util_1.expectArraysEqual(result, [1, 2]);
+    });
+    it('cast complex64 -> bool', function () {
+        var a = tf.complex([1.0, 0.0], [1.0, 1.0]);
+        var result = a.cast('bool');
+        expect(result.dtype).toEqual('bool');
+        test_util_1.expectArraysEqual(result, [true, false]);
+    });
+    it('cast throws when passed a non-tensor', function () {
+        expect(function () { return tf.cast({}, 'float32'); })
+            .toThrowError(/Argument 'x' passed to 'cast' must be a Tensor/);
+    });
+    it('cast accepts a tensor-like object', function () {
+        var a = [1.0, 2.0];
+        var res = tf.cast(a, 'int32');
+        expect(res.dtype).toEqual('int32');
+        test_util_1.expectArraysClose(res, [1, 2]);
+    });
+    it('cast string -> !string throws error', function () {
+        var a = ['a', 'b'];
+        expect(function () { return tf.cast(a, 'int32'); }).toThrowError();
+        expect(function () { return tf.cast(a, 'float32'); }).toThrowError();
+        expect(function () { return tf.cast(a, 'bool'); }).toThrowError();
+        expect(function () { return tf.cast(a, 'complex64'); }).toThrowError();
+    });
+    it('cast !string -> string throws error', function () {
+        expect(function () { return tf.cast(tf.tensor(1, [], 'float32'), 'string'); }).toThrowError();
+        expect(function () { return tf.cast(tf.tensor(1, [], 'int32'), 'string'); }).toThrowError();
+        expect(function () { return tf.cast(tf.tensor(1, [], 'bool'), 'string'); }).toThrowError();
+        expect(function () { return tf.cast(tf.tensor(1, [], 'complex64'), 'string'); })
+            .toThrowError();
+    });
+    it('scalar bool -> int32', function () {
+        var a = tf.scalar(true, 'bool').toInt();
+        expect(a.dtype).toBe('int32');
+        expect(a.get()).toBe(1);
+    });
+    it('Tensor1D float32 -> int32', function () {
+        var a = tf.tensor1d([1.1, 3.9, -2.9, 0]).toInt();
+        expect(a.dtype).toBe('int32');
+        test_util_1.expectArraysEqual(a, [1, 3, -2, 0]);
+    });
+    it('Tensor2D float32 -> bool', function () {
+        var a = tf.tensor2d([1.1, 3.9, -2.9, 0], [2, 2]).asType('bool');
+        expect(a.dtype).toBe('bool');
+        expect(a.get(0, 0)).toBe(1);
+        expect(a.get(0, 1)).toBe(1);
+        expect(a.get(1, 0)).toBe(1);
+        expect(a.get(1, 1)).toBe(0);
+    });
+    it('Tensor2D int32 -> bool', function () {
+        var a = tf.tensor2d([1, 3, 0, -1], [2, 2], 'int32').toBool();
+        expect(a.dtype).toBe('bool');
+        expect(a.get(0, 0)).toBe(1);
+        expect(a.get(0, 1)).toBe(1);
+        expect(a.get(1, 0)).toBe(0);
+        expect(a.get(1, 1)).toBe(1);
+    });
+    it('Tensor3D bool -> float32', function () {
+        var a = tf.tensor3d([true, false, false, true], [2, 2, 1], 'bool').toFloat();
+        expect(a.dtype).toBe('float32');
+        test_util_1.expectArraysClose(a, [1, 0, 0, 1]);
+    });
+    it('bool CPU -> GPU -> CPU', function () {
+        var a = tf.tensor1d([1, 2, 0, 0, 5], 'bool');
+        test_util_1.expectArraysEqual(a, [1, 1, 0, 0, 1]);
+    });
+    it('int32 CPU -> GPU -> CPU', function () {
+        var a = tf.tensor1d([1, 2, 0, 0, 5], 'int32');
+        test_util_1.expectArraysEqual(a, [1, 2, 0, 0, 5]);
+    });
+    it('asType is functional', function () {
+        var a = tf.scalar(2.4, 'float32');
+        var b = a.toFloat();
+        expect(a.id).not.toBe(b.id);
+        b.dispose();
+        test_util_1.expectArraysClose(a, [2.4]);
+    });
+    it('squeeze no axis', function () {
+        var a = tf.tensor2d([4, 2, 1], [3, 1], 'bool');
+        var b = a.squeeze();
+        expect(b.shape).toEqual([3]);
+    });
+    it('squeeze with axis', function () {
+        var a = tf.tensor3d([4, 2, 1], [3, 1, 1], 'bool');
+        var b = a.squeeze([1]);
+        expect(b.shape).toEqual([3, 1]);
+    });
+    it('squeeze wrong axis', function () {
+        var a = tf.tensor3d([4, 2, 1], [3, 1, 1], 'bool');
+        expect(function () { return a.squeeze([0, 1]); }).toThrowError();
+    });
+    it('squeeze throws when passed a non-tensor', function () {
+        expect(function () { return tf.squeeze({}); })
+            .toThrowError(/Argument 'x' passed to 'squeeze' must be a Tensor/);
+    });
+    it('squeeze accepts a tensor-like object', function () {
+        var res = tf.squeeze([[[4]], [[2]], [[1]]]);
+        expect(res.shape).toEqual([3]);
+        test_util_1.expectArraysClose(res, [4, 2, 1]);
+    });
+    it('squeeze a zero-sized tensor', function () {
+        var a = tf.tensor3d([], [0, 1, 0]);
+        var res = tf.squeeze(a);
+        expect(res.shape).toEqual([0, 0]);
+    });
+    it('squeeze a complex64 tensor', function () {
+        var a = tf.complex([[4], [1], [5]], [[2], [3], [6]]);
+        var b = a.squeeze();
+        expect(b.shape).toEqual([3]);
+        test_util_1.expectArraysClose(b, [4, 2, 1, 3, 5, 6]);
+    });
+    it('scalar -> 2d', function () {
+        var a = tf.scalar(4, 'int32');
+        var b = a.as2D(1, 1);
+        expect(b.dtype).toBe('int32');
+        expect(b.shape).toEqual([1, 1]);
+    });
+    it('1d -> 2d', function () {
+        var a = tf.tensor1d([4, 2, 1], 'bool');
+        var b = a.as2D(3, 1);
+        expect(b.dtype).toBe('bool');
+        expect(b.shape).toEqual([3, 1]);
+    });
+    it('2d -> 4d', function () {
+        var a = tf.tensor2d([4, 2, 1, 3], [2, 2]);
+        var b = a.as4D(1, 1, 2, 2);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([1, 1, 2, 2]);
+    });
+    it('3d -> 2d', function () {
+        var a = tf.tensor3d([4, 2, 1, 3], [2, 2, 1], 'float32');
+        var b = a.as2D(2, 2);
+        expect(b.dtype).toBe('float32');
+        expect(b.shape).toEqual([2, 2]);
+    });
+    it('4d -> 1d', function () {
+        var a = tf.tensor4d([4, 2, 1, 3], [2, 2, 1, 1], 'bool');
+        var b = a.as1D();
+        expect(b.dtype).toBe('bool');
+        expect(b.shape).toEqual([4]);
+    });
+});
+describe('tensor.toString', function () {
+    it('scalar verbose', function () {
+        var verbose = true;
+        var str = tf.scalar(5).toString(verbose);
+        expect(str).toEqual('Tensor\n' +
+            '  dtype: float32\n' +
+            '  rank: 0\n' +
+            '  shape: []\n' +
+            '  values:\n' +
+            '    5');
+    });
+    it('string scalar verbose', function () {
+        var verbose = true;
+        var str = tf.scalar('test').toString(verbose);
+        expect(str).toEqual('Tensor\n' +
+            '  dtype: string\n' +
+            '  rank: 0\n' +
+            '  shape: []\n' +
+            '  values:\n' +
+            '    test');
+    });
+    it('1d tensor verbose', function () {
+        var verbose = true;
+        var str = tf.zeros([4]).toString(verbose);
+        expect(str).toEqual('Tensor\n' +
+            '  dtype: float32\n' +
+            '  rank: 1\n' +
+            '  shape: [4]\n' +
+            '  values:\n' +
+            '    [0, 0, 0, 0]');
+    });
+    it('1d string tensor verbose', function () {
+        var verbose = true;
+        var str = tf.tensor(['a', 'bb', 'ccc']).toString(verbose);
+        expect(str).toEqual('Tensor\n' +
+            '  dtype: string\n' +
+            '  rank: 1\n' +
+            '  shape: [3]\n' +
+            '  values:\n' +
+            '    [\'a\', \'bb\', \'ccc\']');
+    });
+    it('2d tensor verbose', function () {
+        var verbose = true;
+        var str = tf.zeros([3, 3]).toString(verbose);
+        expect(str).toEqual('Tensor\n' +
+            '  dtype: float32\n' +
+            '  rank: 2\n' +
+            '  shape: [3,3]\n' +
+            '  values:\n' +
+            '    [[0, 0, 0],\n' +
+            '     [0, 0, 0],\n' +
+            '     [0, 0, 0]]');
+    });
+    it('2d string tensor verbose', function () {
+        var verbose = true;
+        var vals = [
+            ['a', 'bb', 'ccc'],
+            ['d', 'e', 'f'],
+            ['g', 'h', 'i'],
+        ];
+        var str = tf.tensor(vals).toString(verbose);
+        expect(str).toEqual('Tensor\n' +
+            '  dtype: string\n' +
+            '  rank: 2\n' +
+            '  shape: [3,3]\n' +
+            '  values:\n' +
+            '    [[\'a\', \'bb\', \'ccc\'],\n' +
+            '     [\'d\', \'e\' , \'f\'  ],\n' +
+            '     [\'g\', \'h\' , \'i\'  ]]');
+    });
+    it('3d tensor verbose', function () {
+        var verbose = true;
+        var str = tf.zeros([3, 3, 2]).toString(verbose);
+        expect(str).toEqual('Tensor\n' +
+            '  dtype: float32\n' +
+            '  rank: 3\n' +
+            '  shape: [3,3,2]\n' +
+            '  values:\n' +
+            '    [[[0, 0],\n' +
+            '      [0, 0],\n' +
+            '      [0, 0]],\n\n' +
+            '     [[0, 0],\n' +
+            '      [0, 0],\n' +
+            '      [0, 0]],\n\n' +
+            '     [[0, 0],\n' +
+            '      [0, 0],\n' +
+            '      [0, 0]]]');
+    });
+    it('3d string tensor verbose', function () {
+        var verbose = true;
+        var vals = [
+            [['a', 'bb'], ['ccc', 'dddd']],
+            [['e', 'ff'], ['ggg', 'hhhh']],
+            [['i', 'jj'], ['kkk', 'llll']],
+        ];
+        var str = tf.tensor(vals).toString(verbose);
+        expect(str).toEqual('Tensor\n' +
+            '  dtype: string\n' +
+            '  rank: 3\n' +
+            '  shape: [3,2,2]\n' +
+            '  values:\n' +
+            '    [[[\'a\'  , \'bb\'  ],\n' +
+            '      [\'ccc\', \'dddd\']],\n\n' +
+            '     [[\'e\'  , \'ff\'  ],\n' +
+            '      [\'ggg\', \'hhhh\']],\n\n' +
+            '     [[\'i\'  , \'jj\'  ],\n' +
+            '      [\'kkk\', \'llll\']]]');
+    });
+    it('1d long tensor verbose', function () {
+        var verbose = true;
+        var str = tf.zeros([100]).toString(verbose);
+        expect(str).toEqual('Tensor\n' +
+            '  dtype: float32\n' +
+            '  rank: 1\n' +
+            '  shape: [100]\n' +
+            '  values:\n' +
+            '    [0, 0, 0, ..., 0, 0, 0]');
+    });
+    it('1d long string tensor verbose', function () {
+        var verbose = true;
+        var str = tf.fill([100], 'hi').toString(verbose);
+        expect(str).toEqual('Tensor\n' +
+            '  dtype: string\n' +
+            '  rank: 1\n' +
+            '  shape: [100]\n' +
+            '  values:\n' +
+            '    [\'hi\', \'hi\', \'hi\', ..., \'hi\', \'hi\', \'hi\']');
+    });
+    it('2d long tensor verbose', function () {
+        var verbose = true;
+        var str = tf.zeros([100, 100]).toString(verbose);
+        expect(str).toEqual('Tensor\n' +
+            '  dtype: float32\n' +
+            '  rank: 2\n' +
+            '  shape: [100,100]\n' +
+            '  values:\n' +
+            '    [[0, 0, 0, ..., 0, 0, 0],\n' +
+            '     [0, 0, 0, ..., 0, 0, 0],\n' +
+            '     [0, 0, 0, ..., 0, 0, 0],\n' +
+            '     ...,\n' +
+            '     [0, 0, 0, ..., 0, 0, 0],\n' +
+            '     [0, 0, 0, ..., 0, 0, 0],\n' +
+            '     [0, 0, 0, ..., 0, 0, 0]]');
+    });
+    it('2d long string tensor verbose', function () {
+        var verbose = true;
+        var str = tf.fill([100, 100], 'a').toString(verbose);
+        expect(str).toEqual('Tensor\n' +
+            '  dtype: string\n' +
+            '  rank: 2\n' +
+            '  shape: [100,100]\n' +
+            '  values:\n' +
+            '    [[\'a\', \'a\', \'a\', ..., \'a\', \'a\', \'a\'],\n' +
+            '     [\'a\', \'a\', \'a\', ..., \'a\', \'a\', \'a\'],\n' +
+            '     [\'a\', \'a\', \'a\', ..., \'a\', \'a\', \'a\'],\n' +
+            '     ...,\n' +
+            '     [\'a\', \'a\', \'a\', ..., \'a\', \'a\', \'a\'],\n' +
+            '     [\'a\', \'a\', \'a\', ..., \'a\', \'a\', \'a\'],\n' +
+            '     [\'a\', \'a\', \'a\', ..., \'a\', \'a\', \'a\']]');
+    });
+    it('2d with padding to align columns verbose', function () {
+        var verbose = true;
+        var str = tf.tensor([
+            [0.8597712, 3, 0.2740789], [0.6696132, 0.4825962, 2.75],
+            [1.991, 0.0640865, 0.2983858]
+        ]).toString(verbose);
+        expect(str).toEqual('Tensor\n' +
+            '  dtype: float32\n' +
+            '  rank: 2\n' +
+            '  shape: [3,3]\n' +
+            '  values:\n' +
+            '    [[0.8597712, 3        , 0.2740789],\n' +
+            '     [0.6696132, 0.4825962, 2.75     ],\n' +
+            '     [1.9910001, 0.0640865, 0.2983858]]');
+    });
+    it('2d string tensor with padding verbose', function () {
+        var verbose = true;
+        var str = tf.tensor([
+            ['abcdef', 'a', 'abcdef'],
+            ['abcdef', 'abcdef', 'abc'],
+            ['abcd', 'abcdef', 'abcdef'],
+        ]).toString(verbose);
+        expect(str).toEqual('Tensor\n' +
+            '  dtype: string\n' +
+            '  rank: 2\n' +
+            '  shape: [3,3]\n' +
+            '  values:\n' +
+            '    [[\'abcdef\', \'a\'     , \'abcdef\'],\n' +
+            '     [\'abcdef\', \'abcdef\', \'abc\'   ],\n' +
+            '     [\'abcd\'  , \'abcdef\', \'abcdef\']]');
+    });
+    it('scalar', function () {
+        var str = tf.scalar(5).toString();
+        expect(str).toEqual('Tensor\n' +
+            '    5');
+    });
+    it('scalar string', function () {
+        var str = tf.scalar('hello').toString();
+        expect(str).toEqual('Tensor\n' +
+            '    hello');
+    });
+    it('1d tensor', function () {
+        var str = tf.zeros([4]).toString();
+        expect(str).toEqual('Tensor\n' +
+            '    [0, 0, 0, 0]');
+    });
+    it('2d tensor', function () {
+        var str = tf.zeros([3, 3]).toString();
+        expect(str).toEqual('Tensor\n' +
+            '    [[0, 0, 0],\n' +
+            '     [0, 0, 0],\n' +
+            '     [0, 0, 0]]');
+    });
+    it('3d tensor', function () {
+        var str = tf.zeros([3, 3, 2]).toString();
+        expect(str).toEqual('Tensor\n' +
+            '    [[[0, 0],\n' +
+            '      [0, 0],\n' +
+            '      [0, 0]],\n\n' +
+            '     [[0, 0],\n' +
+            '      [0, 0],\n' +
+            '      [0, 0]],\n\n' +
+            '     [[0, 0],\n' +
+            '      [0, 0],\n' +
+            '      [0, 0]]]');
+    });
+    it('1d long tensor', function () {
+        var str = tf.zeros([100]).toString();
+        expect(str).toEqual('Tensor\n' +
+            '    [0, 0, 0, ..., 0, 0, 0]');
+    });
+    it('2d long tensor', function () {
+        var str = tf.zeros([100, 100]).toString();
+        expect(str).toEqual('Tensor\n' +
+            '    [[0, 0, 0, ..., 0, 0, 0],\n' +
+            '     [0, 0, 0, ..., 0, 0, 0],\n' +
+            '     [0, 0, 0, ..., 0, 0, 0],\n' +
+            '     ...,\n' +
+            '     [0, 0, 0, ..., 0, 0, 0],\n' +
+            '     [0, 0, 0, ..., 0, 0, 0],\n' +
+            '     [0, 0, 0, ..., 0, 0, 0]]');
+    });
+    it('2d with padding to align columns', function () {
+        var str = tf.tensor([
+            [0.8597712, 3, 0.2740789], [0.6696132, 0.4825962, 2.75],
+            [1.991, 0.0640865, 0.2983858]
+        ]).toString();
+        expect(str).toEqual('Tensor\n' +
+            '    [[0.8597712, 3        , 0.2740789],\n' +
+            '     [0.6696132, 0.4825962, 2.75     ],\n' +
+            '     [1.9910001, 0.0640865, 0.2983858]]');
+    });
+    it('scalar complex64 verbose', function () {
+        var verbose = true;
+        var str = tf.complex(5, 6).toString(verbose);
+        expect(str).toEqual('Tensor\n' +
+            '  dtype: complex64\n' +
+            '  rank: 0\n' +
+            '  shape: []\n' +
+            '  values:\n' +
+            '    5 + 6j');
+    });
+    it('1d complex64 tensor verbose', function () {
+        var verbose = true;
+        var str = tf.complex([3, 5], [4, 6]).toString(verbose);
+        expect(str).toEqual('Tensor\n' +
+            '  dtype: complex64\n' +
+            '  rank: 1\n' +
+            '  shape: [2]\n' +
+            '  values:\n' +
+            '    [3 + 4j, 5 + 6j]');
+    });
+    it('2d complex64 tensor verbose', function () {
+        var verbose = true;
+        var str = tf.zeros([3, 3], 'complex64').toString(verbose);
+        expect(str).toEqual('Tensor\n' +
+            '  dtype: complex64\n' +
+            '  rank: 2\n' +
+            '  shape: [3,3]\n' +
+            '  values:\n' +
+            '    [[0 + 0j, 0 + 0j, 0 + 0j],\n' +
+            '     [0 + 0j, 0 + 0j, 0 + 0j],\n' +
+            '     [0 + 0j, 0 + 0j, 0 + 0j]]');
+    });
+    it('3d complex64 tensor verbose', function () {
+        var verbose = true;
+        var str = tf.zeros([3, 3, 2], 'complex64').toString(verbose);
+        expect(str).toEqual('Tensor\n' +
+            '  dtype: complex64\n' +
+            '  rank: 3\n' +
+            '  shape: [3,3,2]\n' +
+            '  values:\n' +
+            '    [[[0 + 0j, 0 + 0j],\n' +
+            '      [0 + 0j, 0 + 0j],\n' +
+            '      [0 + 0j, 0 + 0j]],\n\n' +
+            '     [[0 + 0j, 0 + 0j],\n' +
+            '      [0 + 0j, 0 + 0j],\n' +
+            '      [0 + 0j, 0 + 0j]],\n\n' +
+            '     [[0 + 0j, 0 + 0j],\n' +
+            '      [0 + 0j, 0 + 0j],\n' +
+            '      [0 + 0j, 0 + 0j]]]');
+    });
+    it('1d long tensor verbose', function () {
+        var verbose = true;
+        var str = tf.zeros([100], 'complex64').toString(verbose);
+        expect(str).toEqual('Tensor\n' +
+            '  dtype: complex64\n' +
+            '  rank: 1\n' +
+            '  shape: [100]\n' +
+            '  values:\n' +
+            '    [0 + 0j, 0 + 0j, 0 + 0j, ..., 0 + 0j, 0 + 0j, 0 + 0j]');
+    });
+    it('2d long tensor verbose', function () {
+        var verbose = true;
+        var str = tf.zeros([100, 100], 'complex64').toString(verbose);
+        expect(str).toEqual('Tensor\n' +
+            '  dtype: complex64\n' +
+            '  rank: 2\n' +
+            '  shape: [100,100]\n' +
+            '  values:\n' +
+            '    [[0 + 0j, 0 + 0j, 0 + 0j, ..., 0 + 0j, 0 + 0j, 0 + 0j],\n' +
+            '     [0 + 0j, 0 + 0j, 0 + 0j, ..., 0 + 0j, 0 + 0j, 0 + 0j],\n' +
+            '     [0 + 0j, 0 + 0j, 0 + 0j, ..., 0 + 0j, 0 + 0j, 0 + 0j],\n' +
+            '     ...,\n' +
+            '     [0 + 0j, 0 + 0j, 0 + 0j, ..., 0 + 0j, 0 + 0j, 0 + 0j],\n' +
+            '     [0 + 0j, 0 + 0j, 0 + 0j, ..., 0 + 0j, 0 + 0j, 0 + 0j],\n' +
+            '     [0 + 0j, 0 + 0j, 0 + 0j, ..., 0 + 0j, 0 + 0j, 0 + 0j]]');
+    });
+    it('2d complex64 with padding to align columns verbose', function () {
+        var verbose = true;
+        var str = tf.complex([
+            [0.8597712, 3, 0.2740789], [0.6696132, 0.4825962, 2.75],
+            [1.991, 0.0640865, 0.2983858]
+        ], [[1, 1.0102332, 3], [2, 5, 2.34424], [1.23, 2, 0.123]])
+            .toString(verbose);
+        expect(str).toEqual('Tensor\n' +
+            '  dtype: complex64\n' +
+            '  rank: 2\n' +
+            '  shape: [3,3]\n' +
+            '  values:\n' +
+            '    [[0.8597712 + 1j   , 3 + 1.0102332j, 0.2740789 + 3j    ],\n' +
+            '     [0.6696132 + 2j   , 0.4825962 + 5j, 2.75 + 2.34424j   ],\n' +
+            '     [1.9910001 + 1.23j, 0.0640865 + 2j, 0.2983858 + 0.123j]]');
+    });
+    it('scalar complex64', function () {
+        var str = tf.complex(5, 4).toString();
+        expect(str).toEqual('Tensor\n' +
+            '    5 + 4j');
+    });
+    it('1d complex64 tensor', function () {
+        var str = tf.zeros([4], 'complex64').toString();
+        expect(str).toEqual('Tensor\n' +
+            '    [0 + 0j, 0 + 0j, 0 + 0j, 0 + 0j]');
+    });
+    it('2d complex64 tensor', function () {
+        var str = tf.zeros([3, 3], 'complex64').toString();
+        expect(str).toEqual('Tensor\n' +
+            '    [[0 + 0j, 0 + 0j, 0 + 0j],\n' +
+            '     [0 + 0j, 0 + 0j, 0 + 0j],\n' +
+            '     [0 + 0j, 0 + 0j, 0 + 0j]]');
+    });
+    it('3d complex64 tensor', function () {
+        var str = tf.zeros([3, 3, 2], 'complex64').toString();
+        expect(str).toEqual('Tensor\n' +
+            '    [[[0 + 0j, 0 + 0j],\n' +
+            '      [0 + 0j, 0 + 0j],\n' +
+            '      [0 + 0j, 0 + 0j]],\n\n' +
+            '     [[0 + 0j, 0 + 0j],\n' +
+            '      [0 + 0j, 0 + 0j],\n' +
+            '      [0 + 0j, 0 + 0j]],\n\n' +
+            '     [[0 + 0j, 0 + 0j],\n' +
+            '      [0 + 0j, 0 + 0j],\n' +
+            '      [0 + 0j, 0 + 0j]]]');
+    });
+    it('1d long complex64 tensor', function () {
+        var str = tf.zeros([100], 'complex64').toString();
+        expect(str).toEqual('Tensor\n' +
+            '    [0 + 0j, 0 + 0j, 0 + 0j, ..., 0 + 0j, 0 + 0j, 0 + 0j]');
+    });
+    it('2d long complex64 tensor', function () {
+        var str = tf.zeros([100, 100], 'complex64').toString();
+        expect(str).toEqual('Tensor\n' +
+            '    [[0 + 0j, 0 + 0j, 0 + 0j, ..., 0 + 0j, 0 + 0j, 0 + 0j],\n' +
+            '     [0 + 0j, 0 + 0j, 0 + 0j, ..., 0 + 0j, 0 + 0j, 0 + 0j],\n' +
+            '     [0 + 0j, 0 + 0j, 0 + 0j, ..., 0 + 0j, 0 + 0j, 0 + 0j],\n' +
+            '     ...,\n' +
+            '     [0 + 0j, 0 + 0j, 0 + 0j, ..., 0 + 0j, 0 + 0j, 0 + 0j],\n' +
+            '     [0 + 0j, 0 + 0j, 0 + 0j, ..., 0 + 0j, 0 + 0j, 0 + 0j],\n' +
+            '     [0 + 0j, 0 + 0j, 0 + 0j, ..., 0 + 0j, 0 + 0j, 0 + 0j]]');
+    });
+    it('2d complex64 with padding to align columns', function () {
+        var str = tf.complex([
+            [0.8597712, 3, 0.2740789], [0.6696132, 0.4825962, 2.75],
+            [1.991, 0.0640865, 0.2983858]
+        ], [[1, 1.0102332, 3], [2, 5, 2.34424], [1.23, 2, 0.123]])
+            .toString();
+        expect(str).toEqual('Tensor\n' +
+            '    [[0.8597712 + 1j   , 3 + 1.0102332j, 0.2740789 + 3j    ],\n' +
+            '     [0.6696132 + 2j   , 0.4825962 + 5j, 2.75 + 2.34424j   ],\n' +
+            '     [1.9910001 + 1.23j, 0.0640865 + 2j, 0.2983858 + 0.123j]]');
+    });
+});
+jasmine_util_1.describeWithFlags('tensor grad', test_util_1.ALL_ENVS, function () {
+    it('grad with second derivative', function () {
+        var f = function (x) { return x.pow(tf.scalar(3, 'int32')); };
+        var g = tf.grad(f);
+        var gg = tf.grad(g);
+        var x = tf.tensor1d([2, 3]);
+        var data = gg(x);
+        test_util_1.expectArraysClose(data, [12, 18]);
+    });
+});
+jasmine_util_1.describeWithFlags('tensor.data', test_util_1.ALL_ENVS, function () {
+    it('interleaving .data() and .dataSync()', function () { return __awaiter(_this, void 0, void 0, function () {
+        var a, b, ra, rb, _a, _b, _c;
+        return __generator(this, function (_d) {
+            switch (_d.label) {
+                case 0:
+                    a = tf.tensor1d([1, 2, 3]);
+                    b = tf.tensor1d([4, 5, 6]);
+                    ra = a.square().data();
+                    rb = b.square().dataSync();
+                    test_util_1.expectArraysClose(a, [1, 2, 3]);
+                    test_util_1.expectArraysClose(b, [4, 5, 6]);
+                    test_util_1.expectArraysClose(Array.from(rb), [16, 25, 36]);
+                    _a = test_util_1.expectArraysClose;
+                    _c = (_b = Array).from;
+                    return [4, ra];
+                case 1:
+                    _a.apply(void 0, [_c.apply(_b, [_d.sent()]), [1, 4, 9]]);
+                    return [2];
+            }
+        });
+    }); });
+    it('.data() postpones disposal of tensor', function (done) {
+        expect(tf.memory().numTensors).toBe(0);
+        tf.tidy(function () {
+            var a = tf.scalar(5);
+            expect(tf.memory().numTensors).toBe(1);
+            a.square();
+            a.data().then(function (vals) {
+                test_util_1.expectNumbersClose(vals[0], 5);
+            });
+        });
+        setTimeout(function () {
+            expect(tf.memory().numTensors).toBe(0);
+            done();
+        });
+    });
+    it('calling .data() twice works (2 subscribers to a single read)', function (done) {
+        tf.tidy(function () {
+            var a = tf.scalar(5);
+            a.square();
+            a.data().then(function (vals) {
+                test_util_1.expectNumbersClose(vals[0], 5);
+            });
+            a.data()
+                .then(function (vals) {
+                test_util_1.expectNumbersClose(vals[0], 5);
+            })
+                .then(done);
+        });
+    });
+});
+jasmine_util_1.describeWithFlags('x instanceof Tensor', test_util_1.ALL_ENVS, function () {
+    it('x: Tensor', function () {
+        var t = tf.scalar(1);
+        expect(t instanceof tensor_1.Tensor).toBe(true);
+    });
+    it('x: Tensor-like', function () {
+        var t = { shape: [2], dtype: 'float32' };
+        expect(t instanceof tensor_1.Tensor).toBe(true);
+    });
+    it('x: other object, fails', function () {
+        var t = { something: 'else' };
+        expect(t instanceof tensor_1.Tensor).toBe(false);
+    });
+    it('x: undefined or null, fails', function () {
+        expect(undefined instanceof tensor_1.Tensor).toBe(false);
+        expect(null instanceof tensor_1.Tensor).toBe(false);
+    });
+});
+jasmine_util_1.describeWithFlags('tensor with 0 in shape', test_util_1.ALL_ENVS, function () {
+    it('1d of shape [0]', function () {
+        var a = tf.tensor1d([]);
+        expect(a.dtype).toBe('float32');
+        expect(a.rank).toBe(1);
+        expect(a.shape).toEqual([0]);
+        test_util_1.expectArraysEqual(a, []);
+    });
+    it('1d string tensor of shape [0]', function () {
+        var a = tf.tensor1d([], 'string');
+        expect(a.dtype).toBe('string');
+        expect(a.rank).toBe(1);
+        expect(a.shape).toEqual([0]);
+        test_util_1.expectArraysEqual(a, []);
+    });
+    it('2d of shape [0, 5]', function () {
+        var a = tf.tensor2d([], [0, 5]);
+        expect(a.dtype).toBe('float32');
+        expect(a.rank).toBe(2);
+        expect(a.shape).toEqual([0, 5]);
+        test_util_1.expectArraysEqual(a, []);
+    });
+    it('2d string tensor of shape [0, 5]', function () {
+        var a = tf.tensor2d([], [0, 5], 'string');
+        expect(a.dtype).toBe('string');
+        expect(a.rank).toBe(2);
+        expect(a.shape).toEqual([0, 5]);
+        test_util_1.expectArraysEqual(a, []);
+    });
+    it('2d throws when values are not empty', function () {
+        var values = [1, 2, 3, 4];
+        expect(function () { return tf.tensor2d(values, [0, 5], 'float32'); })
+            .toThrowError('Based on the provided shape, [0,5], the ' +
+            'tensor should have 0 values but has 4');
+    });
+    it('3d of shape [0, 3, 0]', function () {
+        var a = tf.tensor3d([], [0, 3, 0]);
+        expect(a.dtype).toBe('float32');
+        expect(a.rank).toBe(3);
+        expect(a.shape).toEqual([0, 3, 0]);
+        test_util_1.expectArraysEqual(a, []);
+    });
+    it('3d throws when values are not empty', function () {
+        var values = [1, 2, 3];
+        expect(function () { return tf.tensor3d(values, [0, 3, 0], 'float32'); })
+            .toThrowError('Based on the provided shape, [0,3,0], the ' +
+            'tensor should have 0 values but has 3');
+    });
+    it('4d of shape [1, 3, 0, 5]', function () {
+        var a = tf.tensor4d([], [1, 3, 0, 5]);
+        expect(a.dtype).toBe('float32');
+        expect(a.rank).toBe(4);
+        expect(a.shape).toEqual([1, 3, 0, 5]);
+        test_util_1.expectArraysEqual(a, []);
+    });
+    it('4d throws when values are not empty', function () {
+        var values = [1, 2, 3];
+        expect(function () { return tf.tensor4d(values, [1, 3, 0, 5], 'float32'); })
+            .toThrowError('Based on the provided shape, [1,3,0,5], the ' +
+            'tensor should have 0 values but has 3');
+    });
+    it('complex64 with 0 in shape', function () {
+        var areal = tf.tensor2d([], [0, 5]);
+        var breal = tf.tensor2d([], [0, 5]);
+        var a = tf.complex(areal, breal);
+        expect(a.dtype).toBe('complex64');
+        expect(a.rank).toBe(2);
+        expect(a.shape).toEqual([0, 5]);
+        test_util_1.expectArraysEqual(a, []);
+    });
+});
+//# sourceMappingURL=tensor_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tensor_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/tensor_test.js.map
new file mode 100644
index 0000000..c716f9f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tensor_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tensor_types.d.ts b/node_modules/@tensorflow/tfjs-core/dist/tensor_types.d.ts
new file mode 100644
index 0000000..277822f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tensor_types.d.ts
@@ -0,0 +1,19 @@
+import { Tensor, Variable } from './tensor';
+import { DataType } from './types';
+export declare type NamedTensorMap = {
+    [name: string]: Tensor;
+};
+export declare type NamedVariableMap = {
+    [name: string]: Variable;
+};
+export declare type TensorContainer = void | Tensor | string | number | boolean | TensorContainerObject | TensorContainerArray;
+export interface TensorContainerObject {
+    [x: string]: TensorContainer;
+}
+export interface TensorContainerArray extends Array<TensorContainer> {
+}
+export interface TensorInfo {
+    name: string;
+    shape?: number[];
+    dtype: DataType;
+}
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tensor_types.js b/node_modules/@tensorflow/tfjs-core/dist/tensor_types.js
new file mode 100644
index 0000000..6c7dba7
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tensor_types.js
@@ -0,0 +1,3 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+//# sourceMappingURL=tensor_types.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tensor_types.js.map b/node_modules/@tensorflow/tfjs-core/dist/tensor_types.js.map
new file mode 100644
index 0000000..f832df6
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tensor_types.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"tensor_types.js","sourceRoot":"","sources":["../src/tensor_types.ts"],"names":[],"mappings":""}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tensor_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/tensor_util.d.ts
new file mode 100644
index 0000000..3a80f7e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tensor_util.d.ts
@@ -0,0 +1,8 @@
+import { Tensor } from './tensor';
+import { NamedTensorMap, TensorContainer } from './tensor_types';
+export declare function makeTypesMatch<T extends Tensor>(a: T, b: T): [T, T];
+export declare function assertTypesMatch(a: Tensor, b: Tensor): void;
+export declare function isTensorInList(tensor: Tensor, tensorList: Tensor[]): boolean;
+export declare function flattenNameArrayMap(nameArrayMap: Tensor | NamedTensorMap, keys?: string[]): Tensor[];
+export declare function unflattenToNameArrayMap(keys: string[], flatArrays: Tensor[]): NamedTensorMap;
+export declare function getTensorsInContainer(result: TensorContainer): Tensor[];
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tensor_util.js b/node_modules/@tensorflow/tfjs-core/dist/tensor_util.js
new file mode 100644
index 0000000..b6c2387
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tensor_util.js
@@ -0,0 +1,83 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tensor_1 = require("./tensor");
+var types_1 = require("./types");
+var util_1 = require("./util");
+function makeTypesMatch(a, b) {
+    if (a.dtype === b.dtype) {
+        return [a, b];
+    }
+    var dtype = types_1.upcastType(a.dtype, b.dtype);
+    return [a.cast(dtype), b.cast(dtype)];
+}
+exports.makeTypesMatch = makeTypesMatch;
+function assertTypesMatch(a, b) {
+    util_1.assert(a.dtype === b.dtype, "The dtypes of the first(" + a.dtype + ") and" +
+        (" second(" + b.dtype + ") input must match"));
+}
+exports.assertTypesMatch = assertTypesMatch;
+function isTensorInList(tensor, tensorList) {
+    for (var i = 0; i < tensorList.length; i++) {
+        if (tensorList[i].id === tensor.id) {
+            return true;
+        }
+    }
+    return false;
+}
+exports.isTensorInList = isTensorInList;
+function flattenNameArrayMap(nameArrayMap, keys) {
+    var xs = [];
+    if (nameArrayMap instanceof tensor_1.Tensor) {
+        xs.push(nameArrayMap);
+    }
+    else {
+        var xMap = nameArrayMap;
+        for (var i = 0; i < keys.length; i++) {
+            xs.push(xMap[keys[i]]);
+        }
+    }
+    return xs;
+}
+exports.flattenNameArrayMap = flattenNameArrayMap;
+function unflattenToNameArrayMap(keys, flatArrays) {
+    if (keys.length !== flatArrays.length) {
+        throw new Error("Cannot unflatten Tensor[], keys and arrays are not of same length.");
+    }
+    var result = {};
+    for (var i = 0; i < keys.length; i++) {
+        result[keys[i]] = flatArrays[i];
+    }
+    return result;
+}
+exports.unflattenToNameArrayMap = unflattenToNameArrayMap;
+function getTensorsInContainer(result) {
+    var list = [];
+    var seen = new Set();
+    walkTensorContainer(result, list, seen);
+    return list;
+}
+exports.getTensorsInContainer = getTensorsInContainer;
+function walkTensorContainer(container, list, seen) {
+    if (container == null) {
+        return;
+    }
+    if (container instanceof tensor_1.Tensor) {
+        list.push(container);
+        return;
+    }
+    if (!isIterable(container)) {
+        return;
+    }
+    var iterable = container;
+    for (var k in iterable) {
+        var val = iterable[k];
+        if (!seen.has(val)) {
+            seen.add(val);
+            walkTensorContainer(val, list, seen);
+        }
+    }
+}
+function isIterable(obj) {
+    return Array.isArray(obj) || typeof obj === 'object';
+}
+//# sourceMappingURL=tensor_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tensor_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/tensor_util.js.map
new file mode 100644
index 0000000..f365e7f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tensor_util.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"tensor_util.js","sourceRoot":"","sources":["../src/tensor_util.ts"],"names":[],"mappings":";;AAiBA,mCAAgC;AAEhC,iCAAmC;AACnC,+BAA8B;AAE9B,wBAAiD,CAAI,EAAE,CAAI;IACzD,IAAI,CAAC,CAAC,KAAK,KAAK,CAAC,CAAC,KAAK,EAAE;QACvB,OAAO,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC;KACf;IACD,IAAM,KAAK,GAAG,kBAAU,CAAC,CAAC,CAAC,KAAK,EAAE,CAAC,CAAC,KAAK,CAAC,CAAC;IAC3C,OAAO,CAAC,CAAC,CAAC,IAAI,CAAC,KAAK,CAAC,EAAE,CAAC,CAAC,IAAI,CAAC,KAAK,CAAC,CAAC,CAAC;AACxC,CAAC;AAND,wCAMC;AAED,0BAAiC,CAAS,EAAE,CAAS;IACnD,aAAM,CACF,CAAC,CAAC,KAAK,KAAK,CAAC,CAAC,KAAK,EACnB,6BAA2B,CAAC,CAAC,KAAK,UAAO;SACrC,aAAW,CAAC,CAAC,KAAK,uBAAoB,CAAA,CAAC,CAAC;AAClD,CAAC;AALD,4CAKC;AAED,wBAA+B,MAAc,EAAE,UAAoB;IACjE,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,UAAU,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;QAC1C,IAAI,UAAU,CAAC,CAAC,CAAC,CAAC,EAAE,KAAK,MAAM,CAAC,EAAE,EAAE;YAClC,OAAO,IAAI,CAAC;SACb;KACF;IACD,OAAO,KAAK,CAAC;AACf,CAAC;AAPD,wCAOC;AAED,6BACI,YAAmC,EAAE,IAAe;IACtD,IAAM,EAAE,GAAa,EAAE,CAAC;IACxB,IAAI,YAAY,YAAY,eAAM,EAAE;QAClC,EAAE,CAAC,IAAI,CAAC,YAAY,CAAC,CAAC;KACvB;SAAM;QACL,IAAM,IAAI,GAAG,YAAyC,CAAC;QACvD,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,IAAI,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;YACpC,EAAE,CAAC,IAAI,CAAC,IAAI,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,CAAC,CAAC;SACxB;KACF;IACD,OAAO,EAAE,CAAC;AACZ,CAAC;AAZD,kDAYC;AAED,iCACI,IAAc,EAAE,UAAoB;IACtC,IAAI,IAAI,CAAC,MAAM,KAAK,UAAU,CAAC,MAAM,EAAE;QACrC,MAAM,IAAI,KAAK,CACX,oEAAoE,CAAC,CAAC;KAC3E;IACD,IAAM,MAAM,GAAmB,EAAE,CAAC;IAClC,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,IAAI,CAAC,MAAM,EAAE,CAAC,EAAE,EAAE;QACpC,MAAM,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC,GAAG,UAAU,CAAC,CAAC,CAAC,CAAC;KACjC;IACD,OAAO,MAAM,CAAC;AAChB,CAAC;AAXD,0DAWC;AAcD,+BAAsC,MAAuB;IAC3D,IAAM,IAAI,GAAa,EAAE,CAAC;IAC1B,IAAM,IAAI,GAAG,IAAI,GAAG,EAAW,CAAC;IAChC,mBAAmB,CAAC,MAAM,EAAE,IAAI,EAAE,IAAI,CAAC,CAAC;IACxC,OAAO,IAAI,CAAC;AACd,CAAC;AALD,sDAKC;AAED,6BACI,SAA0B,EAAE,IAAc,EAAE,IAAkB;IAChE,IAAI,SAAS,IAAI,IAAI,EAAE;QACrB,OAAO;KACR;IACD,IAAI,SAAS,YAAY,eAAM,EAAE;QAC/B,IAAI,CAAC,IAAI,CAAC,SAAS,CAAC,CAAC;QACrB,OAAO;KACR;IACD,IAAI,CAAC,UAAU,CAAC,SAAS,CAAC,EAAE;QAC1B,OAAO;KACR;IAED,IAAM,QAAQ,GAAG,SAAiC,CAAC;IACnD,KAAK,IAAM,CAAC,IAAI,QAAQ,EAAE;QACxB,IAAM,GAAG,GAAG,QAAQ,CAAC,CAAC,CAAC,CAAC;QACxB,IAAI,CAAC,IAAI,CAAC,GAAG,CAAC,GAAG,CAAC,EAAE;YAClB,IAAI,CAAC,GAAG,CAAC,GAAG,CAAC,CAAC;YACd,mBAAmB,CAAC,GAAG,EAAE,IAAI,EAAE,IAAI,CAAC,CAAC;SACtC;KACF;AACH,CAAC;AAGD,oBAAoB,GAAQ;IAC1B,OAAO,KAAK,CAAC,OAAO,CAAC,GAAG,CAAC,IAAI,OAAO,GAAG,KAAK,QAAQ,CAAC;AACvD,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.d.ts b/node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.d.ts
new file mode 100644
index 0000000..3fcc714
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.d.ts
@@ -0,0 +1,5 @@
+import { Tensor } from './tensor';
+import { DataType, TensorLike } from './types';
+export declare function inferShape(val: TensorLike): number[];
+export declare function convertToTensor<T extends Tensor>(x: T | TensorLike, argName: string, functionName: string, parseAsDtype?: DataType | 'numeric'): T;
+export declare function convertToTensorArray<T extends Tensor>(arg: Array<T | TensorLike>, argName: string, functionName: string): T[];
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.js b/node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.js
new file mode 100644
index 0000000..050778c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.js
@@ -0,0 +1,87 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("./environment");
+var tensor_1 = require("./tensor");
+var util_1 = require("./util");
+function inferShape(val) {
+    var firstElem = val;
+    if (util_1.isTypedArray(val)) {
+        return [val.length];
+    }
+    if (!Array.isArray(val)) {
+        return [];
+    }
+    var shape = [];
+    while (firstElem instanceof Array || util_1.isTypedArray(firstElem)) {
+        shape.push(firstElem.length);
+        firstElem = firstElem[0];
+    }
+    if (val instanceof Array && environment_1.ENV.get('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')) {
+        deepAssertShapeConsistency(val, shape, []);
+    }
+    return shape;
+}
+exports.inferShape = inferShape;
+function deepAssertShapeConsistency(val, shape, indices) {
+    indices = indices || [];
+    if (!(val instanceof Array) && !util_1.isTypedArray(val)) {
+        util_1.assert(shape.length === 0, function () { return "Element arr[" + indices.join('][') + "] is a primitive, " +
+            ("but should be an array/TypedArray of " + shape[0] + " elements"); });
+        return;
+    }
+    util_1.assert(shape.length > 0, function () { return "Element arr[" + indices.join('][') + "] should be a primitive, " +
+        ("but is an array of " + val.length + " elements"); });
+    util_1.assert(val.length === shape[0], function () { return "Element arr[" + indices.join('][') + "] should have " + shape[0] + " " +
+        ("elements, but has " + val.length + " elements"); });
+    var subShape = shape.slice(1);
+    for (var i = 0; i < val.length; ++i) {
+        deepAssertShapeConsistency(val[i], subShape, indices.concat(i));
+    }
+}
+function assertDtype(expectedDtype, actualDType, argName, functionName) {
+    if (expectedDtype == null) {
+        return;
+    }
+    if (expectedDtype !== 'numeric' && expectedDtype !== actualDType ||
+        expectedDtype === 'numeric' && actualDType === 'string') {
+        throw new Error("Argument '" + argName + "' passed to '" + functionName + "' must " +
+            ("be " + expectedDtype + " tensor, but got " + actualDType + " tensor"));
+    }
+}
+function convertToTensor(x, argName, functionName, parseAsDtype) {
+    if (parseAsDtype === void 0) { parseAsDtype = 'numeric'; }
+    if (x instanceof tensor_1.Tensor) {
+        assertDtype(parseAsDtype, x.dtype, argName, functionName);
+        return x;
+    }
+    var inferredDtype = util_1.inferDtype(x);
+    if (inferredDtype !== 'string' &&
+        ['bool', 'int32', 'float32'].indexOf(parseAsDtype) >= 0) {
+        inferredDtype = parseAsDtype;
+    }
+    assertDtype(parseAsDtype, inferredDtype, argName, functionName);
+    if (!util_1.isTypedArray(x) && !Array.isArray(x) && typeof x !== 'number' &&
+        typeof x !== 'boolean' && typeof x !== 'string') {
+        throw new Error("Argument '" + argName + "' passed to '" + functionName + "' must be a " +
+            ("Tensor or TensorLike, but got '" + x.constructor.name + "'"));
+    }
+    var inferredShape = inferShape(x);
+    if (!util_1.isTypedArray(x) && !Array.isArray(x)) {
+        x = [x];
+    }
+    var values = inferredDtype !== 'string' ?
+        util_1.toTypedArray(x, inferredDtype, environment_1.ENV.get('DEBUG')) :
+        util_1.flatten(x);
+    return tensor_1.Tensor.make(inferredShape, { values: values }, inferredDtype);
+}
+exports.convertToTensor = convertToTensor;
+function convertToTensorArray(arg, argName, functionName) {
+    if (!Array.isArray(arg)) {
+        throw new Error("Argument " + argName + " passed to " + functionName + " must be a " +
+            '`Tensor[]` or `TensorLike[]`');
+    }
+    var tensors = arg;
+    return tensors.map(function (t, i) { return convertToTensor(t, argName + "[" + i + "]", functionName); });
+}
+exports.convertToTensorArray = convertToTensorArray;
+//# sourceMappingURL=tensor_util_env.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.js.map b/node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.js.map
new file mode 100644
index 0000000..ce539c0
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tensor_util_env.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"tensor_util_env.js","sourceRoot":"","sources":["../src/tensor_util_env.ts"],"names":[],"mappings":";;AAiBA,6CAAkC;AAClC,mCAAgC;AAEhC,+BAA+E;AAE/E,oBAA2B,GAAe;IACxC,IAAI,SAAS,GAAe,GAAG,CAAC;IAEhC,IAAI,mBAAY,CAAC,GAAG,CAAC,EAAE;QACrB,OAAO,CAAE,GAAkB,CAAC,MAAM,CAAC,CAAC;KACrC;IACD,IAAI,CAAC,KAAK,CAAC,OAAO,CAAC,GAAG,CAAC,EAAE;QACvB,OAAO,EAAE,CAAC;KACX;IACD,IAAM,KAAK,GAAa,EAAE,CAAC;IAE3B,OAAO,SAAS,YAAY,KAAK,IAAI,mBAAY,CAAC,SAAS,CAAC,EAAE;QAC5D,KAAK,CAAC,IAAI,CAAC,SAAS,CAAC,MAAM,CAAC,CAAC;QAC7B,SAAS,GAAG,SAAS,CAAC,CAAC,CAAC,CAAC;KAC1B;IACD,IAAI,GAAG,YAAY,KAAK,IAAI,iBAAG,CAAC,GAAG,CAAC,oCAAoC,CAAC,EAAE;QACzE,0BAA0B,CAAC,GAAG,EAAE,KAAK,EAAE,EAAE,CAAC,CAAC;KAC5C;IAED,OAAO,KAAK,CAAC;AACf,CAAC;AApBD,gCAoBC;AAED,oCACI,GAAe,EAAE,KAAe,EAAE,OAAiB;IACrD,OAAO,GAAG,OAAO,IAAI,EAAE,CAAC;IACxB,IAAI,CAAC,CAAC,GAAG,YAAY,KAAK,CAAC,IAAI,CAAC,mBAAY,CAAC,GAAG,CAAC,EAAE;QACjD,aAAM,CACF,KAAK,CAAC,MAAM,KAAK,CAAC,EAClB,cAAM,OAAA,iBAAe,OAAO,CAAC,IAAI,CAAC,IAAI,CAAC,uBAAoB;aACvD,0CAAwC,KAAK,CAAC,CAAC,CAAC,cAAW,CAAA,EADzD,CACyD,CAAC,CAAC;QACrE,OAAO;KACR;IACD,aAAM,CACF,KAAK,CAAC,MAAM,GAAG,CAAC,EAChB,cAAM,OAAA,iBAAe,OAAO,CAAC,IAAI,CAAC,IAAI,CAAC,8BAA2B;SAC9D,wBAAsB,GAAG,CAAC,MAAM,cAAW,CAAA,EADzC,CACyC,CAAC,CAAC;IACrD,aAAM,CACF,GAAG,CAAC,MAAM,KAAK,KAAK,CAAC,CAAC,CAAC,EACvB,cAAM,OAAA,iBAAe,OAAO,CAAC,IAAI,CAAC,IAAI,CAAC,sBAAiB,KAAK,CAAC,CAAC,CAAC,MAAG;SAC/D,uBAAqB,GAAG,CAAC,MAAM,cAAW,CAAA,EADxC,CACwC,CAAC,CAAC;IACpD,IAAM,QAAQ,GAAG,KAAK,CAAC,KAAK,CAAC,CAAC,CAAC,CAAC;IAChC,KAAK,IAAI,CAAC,GAAG,CAAC,EAAE,CAAC,GAAG,GAAG,CAAC,MAAM,EAAE,EAAE,CAAC,EAAE;QACnC,0BAA0B,CAAC,GAAG,CAAC,CAAC,CAAC,EAAE,QAAQ,EAAE,OAAO,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC,CAAC;KACjE;AACH,CAAC;AAED,qBACI,aAAiC,EAAE,WAAqB,EAAE,OAAe,EACzE,YAAoB;IACtB,IAAI,aAAa,IAAI,IAAI,EAAE;QACzB,OAAO;KACR;IACD,IAAI,aAAa,KAAK,SAAS,IAAI,aAAa,KAAK,WAAW;QAC5D,aAAa,KAAK,SAAS,IAAI,WAAW,KAAK,QAAQ,EAAE;QAC3D,MAAM,IAAI,KAAK,CACX,eAAa,OAAO,qBAAgB,YAAY,YAAS;aACzD,QAAM,aAAa,yBAAoB,WAAW,YAAS,CAAA,CAAC,CAAC;KAClE;AACH,CAAC;AAED,yBACI,CAAe,EAAE,OAAe,EAAE,YAAoB,EACtD,YAA4C;IAA5C,6BAAA,EAAA,wBAA4C;IAC9C,IAAI,CAAC,YAAY,eAAM,EAAE;QACvB,WAAW,CAAC,YAAY,EAAE,CAAC,CAAC,KAAK,EAAE,OAAO,EAAE,YAAY,CAAC,CAAC;QAC1D,OAAO,CAAC,CAAC;KACV;IACD,IAAI,aAAa,GAAG,iBAAU,CAAC,CAAC,CAAC,CAAC;IAGlC,IAAI,aAAa,KAAK,QAAQ;QAC1B,CAAC,MAAM,EAAE,OAAO,EAAE,SAAS,CAAC,CAAC,OAAO,CAAC,YAAY,CAAC,IAAI,CAAC,EAAE;QAC3D,aAAa,GAAG,YAAwB,CAAC;KAC1C;IACD,WAAW,CAAC,YAAY,EAAE,aAAa,EAAE,OAAO,EAAE,YAAY,CAAC,CAAC;IAEhE,IAAI,CAAC,mBAAY,CAAC,CAAC,CAAC,IAAI,CAAC,KAAK,CAAC,OAAO,CAAC,CAAC,CAAC,IAAI,OAAO,CAAC,KAAK,QAAQ;QAC9D,OAAO,CAAC,KAAK,SAAS,IAAI,OAAO,CAAC,KAAK,QAAQ,EAAE;QACnD,MAAM,IAAI,KAAK,CACX,eAAa,OAAO,qBAAgB,YAAY,iBAAc;aAC9D,oCAAmC,CAAQ,CAAC,WAAW,CAAC,IAAI,MAAG,CAAA,CAAC,CAAC;KACtE;IACD,IAAM,aAAa,GAAG,UAAU,CAAC,CAAC,CAAC,CAAC;IACpC,IAAI,CAAC,mBAAY,CAAC,CAAC,CAAC,IAAI,CAAC,KAAK,CAAC,OAAO,CAAC,CAAC,CAAC,EAAE;QACzC,CAAC,GAAG,CAAC,CAAC,CAAa,CAAC;KACrB;IACD,IAAM,MAAM,GAAG,aAAa,KAAK,QAAQ,CAAC,CAAC;QACvC,mBAAY,CAAC,CAAC,EAAE,aAAyB,EAAE,iBAAG,CAAC,GAAG,CAAC,OAAO,CAAC,CAAC,CAAC,CAAC;QAC9D,cAAO,CAAC,CAAa,CAAa,CAAC;IACvC,OAAO,eAAM,CAAC,IAAI,CAAC,aAAa,EAAE,EAAC,MAAM,QAAA,EAAC,EAAE,aAAa,CAAC,CAAC;AAC7D,CAAC;AA9BD,0CA8BC;AAED,8BACI,GAAwB,EAAE,OAAe,EAAE,YAAoB;IACjE,IAAI,CAAC,KAAK,CAAC,OAAO,CAAC,GAAG,CAAC,EAAE;QACvB,MAAM,IAAI,KAAK,CACX,cAAY,OAAO,mBAAc,YAAY,gBAAa;YAC1D,8BAA8B,CAAC,CAAC;KACrC;IACD,IAAM,OAAO,GAAG,GAAU,CAAC;IAC3B,OAAO,OAAO,CAAC,GAAG,CACd,UAAC,CAAC,EAAE,CAAC,IAAK,OAAA,eAAe,CAAC,CAAC,EAAK,OAAO,SAAI,CAAC,MAAG,EAAE,YAAY,CAAC,EAApD,CAAoD,CAAC,CAAC;AACtE,CAAC;AAVD,oDAUC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tensor_util_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/tensor_util_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tensor_util_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tensor_util_test.js b/node_modules/@tensorflow/tfjs-core/dist/tensor_util_test.js
new file mode 100644
index 0000000..3506adf
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tensor_util_test.js
@@ -0,0 +1,181 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("./index");
+var jasmine_util_1 = require("./jasmine_util");
+var tensor_util_1 = require("./tensor_util");
+var tensor_util_env_1 = require("./tensor_util_env");
+var test_util_1 = require("./test_util");
+describe('tensor_util.isTensorInList', function () {
+    it('not in list', function () {
+        var a = tf.scalar(1);
+        var list = [tf.scalar(1), tf.tensor1d([1, 2, 3])];
+        expect(tensor_util_1.isTensorInList(a, list)).toBe(false);
+    });
+    it('in list', function () {
+        var a = tf.scalar(1);
+        var list = [tf.scalar(2), tf.tensor1d([1, 2, 3]), a];
+        expect(tensor_util_1.isTensorInList(a, list)).toBe(true);
+    });
+});
+describe('tensor_util.flattenNameArrayMap', function () {
+    it('basic', function () {
+        var a = tf.scalar(1);
+        var b = tf.scalar(3);
+        var c = tf.tensor1d([1, 2, 3]);
+        var map = { a: a, b: b, c: c };
+        expect(tensor_util_1.flattenNameArrayMap(map, Object.keys(map))).toEqual([a, b, c]);
+    });
+});
+describe('tensor_util.unflattenToNameArrayMap', function () {
+    it('basic', function () {
+        var a = tf.scalar(1);
+        var b = tf.scalar(3);
+        var c = tf.tensor1d([1, 2, 3]);
+        expect(tensor_util_1.unflattenToNameArrayMap(['a', 'b', 'c'], [
+            a, b, c
+        ])).toEqual({ a: a, b: b, c: c });
+    });
+});
+describe('getTensorsInContainer', function () {
+    it('null input returns empty tensor', function () {
+        var results = tensor_util_1.getTensorsInContainer(null);
+        expect(results).toEqual([]);
+    });
+    it('tensor input returns one element tensor', function () {
+        var x = tf.scalar(1);
+        var results = tensor_util_1.getTensorsInContainer(x);
+        expect(results).toEqual([x]);
+    });
+    it('name tensor map returns flattened tensor', function () {
+        var x1 = tf.scalar(1);
+        var x2 = tf.scalar(3);
+        var x3 = tf.scalar(4);
+        var results = tensor_util_1.getTensorsInContainer({ x1: x1, x2: x2, x3: x3 });
+        expect(results).toEqual([x1, x2, x3]);
+    });
+    it('can extract from arbitrary depth', function () {
+        var container = [
+            { x: tf.scalar(1), y: tf.scalar(2) }, [[[tf.scalar(3)]], { z: tf.scalar(4) }]
+        ];
+        var results = tensor_util_1.getTensorsInContainer(container);
+        expect(results.length).toBe(4);
+    });
+    it('works with loops in container', function () {
+        var container = [tf.scalar(1), tf.scalar(2), [tf.scalar(3)]];
+        var innerContainer = [container];
+        container.push(innerContainer);
+        var results = tensor_util_1.getTensorsInContainer(container);
+        expect(results.length).toBe(3);
+    });
+});
+jasmine_util_1.describeWithFlags('convertToTensor', test_util_1.ALL_ENVS, function () {
+    it('primitive integer, NaN converts to zero, no error thrown', function () {
+        var a = function () { return tensor_util_env_1.convertToTensor(NaN, 'a', 'test', 'int32'); };
+        expect(a).not.toThrowError();
+        var b = tensor_util_env_1.convertToTensor(NaN, 'b', 'test', 'int32');
+        expect(b.rank).toBe(0);
+        expect(b.dtype).toBe('int32');
+        test_util_1.expectNumbersClose(b.get(), 0);
+    });
+    it('primitive number', function () {
+        var a = tensor_util_env_1.convertToTensor(3, 'a', 'test');
+        expect(a.rank).toBe(0);
+        expect(a.dtype).toBe('float32');
+        test_util_1.expectNumbersClose(a.get(), 3);
+    });
+    it('primitive integer, NaN converts to zero', function () {
+        var a = tensor_util_env_1.convertToTensor(NaN, 'a', 'test', 'int32');
+        expect(a.rank).toBe(0);
+        expect(a.dtype).toBe('int32');
+        test_util_1.expectNumbersClose(a.get(), 0);
+    });
+    it('primitive boolean, parsed as bool tensor', function () {
+        var a = tensor_util_env_1.convertToTensor(true, 'a', 'test');
+        expect(a.rank).toBe(0);
+        expect(a.dtype).toBe('bool');
+        test_util_1.expectNumbersClose(a.get(), 1);
+    });
+    it('primitive boolean, forced to be parsed as bool tensor', function () {
+        var a = tensor_util_env_1.convertToTensor(true, 'a', 'test', 'bool');
+        expect(a.rank).toBe(0);
+        expect(a.dtype).toBe('bool');
+        expect(a.get()).toBe(1);
+    });
+    it('array1d', function () {
+        var a = tensor_util_env_1.convertToTensor([1, 2, 3], 'a', 'test');
+        expect(a.rank).toBe(1);
+        expect(a.dtype).toBe('float32');
+        expect(a.shape).toEqual([3]);
+        test_util_1.expectArraysClose(a, [1, 2, 3]);
+    });
+    it('array2d', function () {
+        var a = tensor_util_env_1.convertToTensor([[1], [2], [3]], 'a', 'test');
+        expect(a.rank).toBe(2);
+        expect(a.shape).toEqual([3, 1]);
+        expect(a.dtype).toBe('float32');
+        test_util_1.expectArraysClose(a, [1, 2, 3]);
+    });
+    it('array3d', function () {
+        var a = tensor_util_env_1.convertToTensor([[[1], [2]], [[3], [4]]], 'a', 'test');
+        expect(a.rank).toBe(3);
+        expect(a.shape).toEqual([2, 2, 1]);
+        expect(a.dtype).toBe('float32');
+        test_util_1.expectArraysClose(a, [1, 2, 3, 4]);
+    });
+    it('array4d', function () {
+        var a = tensor_util_env_1.convertToTensor([[[[1]], [[2]]], [[[3]], [[4]]]], 'a', 'test');
+        expect(a.rank).toBe(4);
+        expect(a.shape).toEqual([2, 2, 1, 1]);
+        expect(a.dtype).toBe('float32');
+        test_util_1.expectArraysClose(a, [1, 2, 3, 4]);
+    });
+    it('passing a tensor returns the tensor itself', function () {
+        var s = tf.scalar(3);
+        var res = tensor_util_env_1.convertToTensor(s, 'a', 'test');
+        expect(res).toBe(s);
+    });
+    it('passing a tensor with wrong type errors', function () {
+        var s = tf.scalar(3);
+        expect(function () { return tensor_util_env_1.convertToTensor(s, 'p', 'f', 'bool'); })
+            .toThrowError(/Argument 'p' passed to 'f' must be bool tensor, but got float32/);
+    });
+    it('fails when passed a string and force numeric is true', function () {
+        var expectedDtype = 'numeric';
+        expect(function () { return tensor_util_env_1.convertToTensor('hello', 'p', 'test', expectedDtype); })
+            .toThrowError();
+    });
+    it('force numeric is true by default', function () {
+        expect(function () { return tensor_util_env_1.convertToTensor('hello', 'p', 'test'); }).toThrowError();
+    });
+    it('primitive string, do not force numeric', function () {
+        var t = tensor_util_env_1.convertToTensor('hello', 'p', 'test', null);
+        expect(t.dtype).toBe('string');
+        expect(t.shape).toEqual([]);
+    });
+    it('string[], do not force numeric', function () {
+        var t = tensor_util_env_1.convertToTensor(['a', 'b', 'c'], 'p', 'test', null);
+        expect(t.dtype).toBe('string');
+        expect(t.shape).toEqual([3]);
+    });
+    it('string, explicitly parse as bool', function () {
+        expect(function () { return tensor_util_env_1.convertToTensor('a', 'argName', 'func', 'bool'); })
+            .toThrowError('Argument \'argName\' passed to \'func\' must be bool tensor' +
+            ', but got string tensor');
+    });
+    it('fails to convert a dict to tensor', function () {
+        expect(function () { return tensor_util_env_1.convertToTensor({}, 'a', 'test'); })
+            .toThrowError('Argument \'a\' passed to \'test\' must be a Tensor ' +
+            'or TensorLike, but got \'Object\'');
+    });
+    it('fails to convert a string to tensor', function () {
+        expect(function () { return tensor_util_env_1.convertToTensor('asdf', 'a', 'test'); })
+            .toThrowError('Argument \'a\' passed to \'test\' must be numeric tensor, ' +
+            'but got string tensor');
+    });
+    it('fails to convert a non-valid shape array to tensor', function () {
+        var a = [[1, 2], [3], [4, 5, 6]];
+        expect(function () { return tensor_util_env_1.convertToTensor(a, 'a', 'test'); })
+            .toThrowError('Element arr[1] should have 2 elements, but has 1 elements');
+    });
+});
+//# sourceMappingURL=tensor_util_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tensor_util_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/tensor_util_test.js.map
new file mode 100644
index 0000000..3e0f3a6
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tensor_util_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/test_env.d.ts b/node_modules/@tensorflow/tfjs-core/dist/test_env.d.ts
new file mode 100644
index 0000000..099af5b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/test_env.d.ts
@@ -0,0 +1,2 @@
+import './kernels/backend_cpu';
+import './kernels/backend_webgl';
diff --git a/node_modules/@tensorflow/tfjs-core/dist/test_env.js b/node_modules/@tensorflow/tfjs-core/dist/test_env.js
new file mode 100644
index 0000000..f9cf81f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/test_env.js
@@ -0,0 +1,5 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+require("./kernels/backend_cpu");
+require("./kernels/backend_webgl");
+//# sourceMappingURL=test_env.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/test_env.js.map b/node_modules/@tensorflow/tfjs-core/dist/test_env.js.map
new file mode 100644
index 0000000..ba69991
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/test_env.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"test_env.js","sourceRoot":"","sources":["../src/test_env.ts"],"names":[],"mappings":";;AAiBA,iCAA+B;AAC/B,mCAAiC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/test_node.d.ts b/node_modules/@tensorflow/tfjs-core/dist/test_node.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/test_node.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/test_node.js b/node_modules/@tensorflow/tfjs-core/dist/test_node.js
new file mode 100644
index 0000000..a2e6850
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/test_node.js
@@ -0,0 +1,13 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var jasmine_util_1 = require("./jasmine_util");
+var backend_cpu_1 = require("./kernels/backend_cpu");
+var jasmine = require('jasmine');
+process.on('unhandledRejection', function (e) {
+    throw e;
+});
+jasmine_util_1.setTestEnvs([{ name: 'node', factory: function () { return new backend_cpu_1.MathBackendCPU(); }, features: {} }]);
+var runner = new jasmine();
+runner.loadConfig({ spec_files: ['src/**/**_test.ts'], random: false });
+runner.execute();
+//# sourceMappingURL=test_node.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/test_node.js.map b/node_modules/@tensorflow/tfjs-core/dist/test_node.js.map
new file mode 100644
index 0000000..eeb126b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/test_node.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"test_node.js","sourceRoot":"","sources":["../src/test_node.ts"],"names":[],"mappings":";;AAiBA,+CAA2C;AAC3C,qDAAqD;AAErD,IAAM,OAAO,GAAG,OAAO,CAAC,SAAS,CAAC,CAAC;AAEnC,OAAO,CAAC,EAAE,CAAC,oBAAoB,EAAE,UAAA,CAAC;IAChC,MAAM,CAAC,CAAC;AACV,CAAC,CAAC,CAAC;AAEH,0BAAW,CACP,CAAC,EAAC,IAAI,EAAE,MAAM,EAAE,OAAO,EAAE,cAAM,OAAA,IAAI,4BAAc,EAAE,EAApB,CAAoB,EAAE,QAAQ,EAAE,EAAE,EAAC,CAAC,CAAC,CAAC;AAEzE,IAAM,MAAM,GAAG,IAAI,OAAO,EAAE,CAAC;AAC7B,MAAM,CAAC,UAAU,CAAC,EAAC,UAAU,EAAE,CAAC,mBAAmB,CAAC,EAAE,MAAM,EAAE,KAAK,EAAC,CAAC,CAAC;AACtE,MAAM,CAAC,OAAO,EAAE,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/test_util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/test_util.d.ts
new file mode 100644
index 0000000..6996f0a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/test_util.d.ts
@@ -0,0 +1,19 @@
+import { Features } from './environment_util';
+import { Tensor } from './tensor';
+import { TypedArray } from './types';
+export declare const WEBGL_ENVS: Features;
+export declare const NODE_ENVS: Features;
+export declare const CHROME_ENVS: Features;
+export declare const BROWSER_ENVS: Features;
+export declare const CPU_ENVS: Features;
+export declare const ALL_ENVS: Features;
+export declare function expectArraysClose(actual: Tensor | TypedArray | number[], expected: Tensor | TypedArray | number[] | boolean[], epsilon?: number): void;
+export interface DoneFn {
+    (): void;
+    fail: (message?: Error | string) => void;
+}
+export declare function expectPromiseToFail(fn: () => Promise<{}>, done: DoneFn): void;
+export declare function expectArraysEqual(actual: Tensor | TypedArray | number[] | string[], expected: Tensor | TypedArray | number[] | boolean[] | string[]): void;
+export declare function expectNumbersClose(a: number, e: number, epsilon?: number): void;
+export declare function expectValuesInRange(actual: Tensor | TypedArray | number[], low: number, high: number): void;
+export declare function expectArrayBuffersEqual(actual: ArrayBuffer, expected: ArrayBuffer): void;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/test_util.js b/node_modules/@tensorflow/tfjs-core/dist/test_util.js
new file mode 100644
index 0000000..1e3ab0c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/test_util.js
@@ -0,0 +1,130 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var environment_1 = require("./environment");
+var tensor_1 = require("./tensor");
+var util = require("./util");
+var util_1 = require("./util");
+exports.WEBGL_ENVS = {
+    'HAS_WEBGL': true
+};
+exports.NODE_ENVS = {
+    'IS_NODE': true
+};
+exports.CHROME_ENVS = {
+    'IS_CHROME': true
+};
+exports.BROWSER_ENVS = {
+    'IS_BROWSER': true
+};
+exports.CPU_ENVS = {
+    'HAS_WEBGL': false
+};
+exports.ALL_ENVS = {};
+function expectArraysClose(actual, expected, epsilon) {
+    if (epsilon == null) {
+        epsilon = environment_1.ENV.get('TEST_EPSILON');
+    }
+    return expectArraysPredicate(actual, expected, function (a, b) { return areClose(a, Number(b), epsilon); });
+}
+exports.expectArraysClose = expectArraysClose;
+function expectArraysPredicate(actual, expected, predicate) {
+    if (!(actual instanceof tensor_1.Tensor) && !(expected instanceof tensor_1.Tensor)) {
+        var aType = actual.constructor.name;
+        var bType = expected.constructor.name;
+        if (aType !== bType) {
+            throw new Error("Arrays are of different type actual: " + aType + " " +
+                ("vs expected: " + bType));
+        }
+    }
+    else if (actual instanceof tensor_1.Tensor && expected instanceof tensor_1.Tensor) {
+        if (actual.dtype !== expected.dtype) {
+            throw new Error("Arrays are of different type actual: " + actual.dtype + " " +
+                ("vs expected: " + expected.dtype + "."));
+        }
+        if (!util.arraysEqual(actual.shape, expected.shape)) {
+            throw new Error("Arrays are of different shape actual: " + actual.shape + " " +
+                ("vs expected: " + expected.shape + "."));
+        }
+    }
+    var actualValues;
+    var expectedValues;
+    if (actual instanceof tensor_1.Tensor) {
+        actualValues = actual.dataSync();
+    }
+    else {
+        actualValues = actual;
+    }
+    if (expected instanceof tensor_1.Tensor) {
+        expectedValues = expected.dataSync();
+    }
+    else {
+        expectedValues = expected;
+    }
+    if (actualValues.length !== expectedValues.length) {
+        throw new Error("Arrays have different lengths actual: " + actualValues.length + " vs " +
+            ("expected: " + expectedValues.length + ".\n") +
+            ("Actual:   " + actualValues + ".\n") +
+            ("Expected: " + expectedValues + "."));
+    }
+    for (var i = 0; i < expectedValues.length; ++i) {
+        var a = actualValues[i];
+        var e = expectedValues[i];
+        if (!predicate(a, e)) {
+            throw new Error("Arrays differ: actual[" + i + "] = " + a + ", expected[" + i + "] = " + e + ".\n" +
+                ("Actual:   " + actualValues + ".\n") +
+                ("Expected: " + expectedValues + "."));
+        }
+    }
+}
+function expectPromiseToFail(fn, done) {
+    fn().then(function () { return done.fail(); }, function () { return done(); });
+}
+exports.expectPromiseToFail = expectPromiseToFail;
+function expectArraysEqual(actual, expected) {
+    if (actual instanceof tensor_1.Tensor && actual.dtype === 'string' ||
+        expected instanceof tensor_1.Tensor && expected.dtype === 'string' ||
+        actual instanceof Array && util_1.isString(actual[0]) ||
+        expected instanceof Array && util_1.isString(expected[0])) {
+        return expectArraysPredicate(actual, expected, function (a, b) { return a == b; });
+    }
+    return expectArraysClose(actual, expected, 0);
+}
+exports.expectArraysEqual = expectArraysEqual;
+function expectNumbersClose(a, e, epsilon) {
+    if (epsilon == null) {
+        epsilon = environment_1.ENV.get('TEST_EPSILON');
+    }
+    if (!areClose(a, e, epsilon)) {
+        throw new Error("Numbers differ: actual === " + a + ", expected === " + e);
+    }
+}
+exports.expectNumbersClose = expectNumbersClose;
+function areClose(a, e, epsilon) {
+    if (isNaN(a) && isNaN(e)) {
+        return true;
+    }
+    if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon) {
+        return false;
+    }
+    return true;
+}
+function expectValuesInRange(actual, low, high) {
+    var actualVals;
+    if (actual instanceof tensor_1.Tensor) {
+        actualVals = actual.dataSync();
+    }
+    else {
+        actualVals = actual;
+    }
+    for (var i = 0; i < actualVals.length; i++) {
+        if (actualVals[i] < low || actualVals[i] > high) {
+            throw new Error("Value out of range:" + actualVals[i] + " low: " + low + ", high: " + high);
+        }
+    }
+}
+exports.expectValuesInRange = expectValuesInRange;
+function expectArrayBuffersEqual(actual, expected) {
+    expect(new Float32Array(actual)).toEqual(new Float32Array(expected));
+}
+exports.expectArrayBuffersEqual = expectArrayBuffersEqual;
+//# sourceMappingURL=test_util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/test_util.js.map b/node_modules/@tensorflow/tfjs-core/dist/test_util.js.map
new file mode 100644
index 0000000..cd555c7
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/test_util.js.map
@@ -0,0 +1 @@
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diff --git a/node_modules/@tensorflow/tfjs-core/dist/tf-core.esm.js b/node_modules/@tensorflow/tfjs-core/dist/tf-core.esm.js
new file mode 100644
index 0000000..14f7027
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tf-core.esm.js
@@ -0,0 +1,18 @@
+/**
+ * @license
+ * Copyright 2018 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+var extendStatics=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var n in t)t.hasOwnProperty(n)&&(e[n]=t[n])};function __extends(e,t){function n(){this.constructor=e}extendStatics(e,t),e.prototype=null===t?Object.create(t):(n.prototype=t.prototype,new n)}var __assign=Object.assign||function(e){for(var t,n=1,r=arguments.length;n<r;n++)for(var o in t=arguments[n])Object.prototype.hasOwnProperty.call(t,o)&&(e[o]=t[o]);return e};function __awaiter(e,t,n,r){return new(n||(n=Promise))(function(o,a){function i(e){try{u(r.next(e))}catch(e){a(e)}}function s(e){try{u(r.throw(e))}catch(e){a(e)}}function u(e){e.done?o(e.value):new n(function(t){t(e.value)}).then(i,s)}u((r=r.apply(e,t||[])).next())})}function __generator(e,t){var n,r,o,a,i={label:0,sent:function(){if(1&o[0])throw o[1];return o[1]},trys:[],ops:[]};return a={next:s(0),throw:s(1),return:s(2)},"function"==typeof Symbol&&(a[Symbol.iterator]=function(){return this}),a;function 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|nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(e.substr(0,4))}function shuffle(e){for(var t=e.length,n=0,r=0;t>0;)r=Math.random()*t|0,n=e[--t],e[t]=e[r],e[r]=n}function clamp(e,t,n){return Math.max(e,Math.min(t,n))}function nearestLargerEven(e){return e%2==0?e:e+1}function sum(e){for(var t=0,n=0;n<e.length;n++)t+=e[n];return t}function randUniform(e,t){var n=Math.random();return t*n+(1-n)*e}function distSquared(e,t){for(var n=0,r=0;r<e.length;r++){var o=Number(e[r])-Number(t[r]);n+=o*o}return n}function assert(e,t){if(!e)throw new Error("string"==typeof t?t:t())}function assertShapesMatch(e,t,n){void 0===n&&(n=""),assert(arraysEqual(e,t),n+" Shapes "+e+" and "+t+" must match")}function assertNonNull(e){assert(null!=e,"The input to the tensor constructor must be a non-null value.")}function flatten(e,t){if(void 0===t&&(t=[]),Array.isArray(e)||isTypedArray(e))for(var n=0;n<e.length;++n)flatten(e[n],t);else t.push(e);return t}function sizeFromShape(e){if(0===e.length)return 1;for(var t=e[0],n=1;n<e.length;n++)t*=e[n];return t}function isScalarShape(e){return 0===e.length}function arraysEqual(e,t){if(e===t)return!0;if(null==e||null==t)return!1;if(e.length!==t.length)return!1;for(var n=0;n<e.length;n++)if(e[n]!==t[n])return!1;return!0}function isInt(e){return e%1==0}function tanh(e){if(null!=Math.tanh)return Math.tanh(e);if(e===1/0)return 1;if(e===-1/0)return-1;var t=Math.exp(2*e);return(t-1)/(t+1)}function sizeToSquarishShape(e){for(var t=Math.floor(Math.sqrt(e));t>1;--t)if(e%t==0)return[t,e/t];return[1,e]}function createShuffledIndices(e){for(var t=new Uint32Array(e),n=0;n<e;++n)t[n]=n;return shuffle(t),t}function rightPad(e,t){return t<=e.length?e:e+" ".repeat(t-e.length)}function repeatedTry(e,t,n){return void 0===t&&(t=function(e){return 0}),new Promise(function(r,o){var a=0,i=function(){if(e())r();else{var s=t(++a);null!=n&&a>=n?o():setTimeout(i,s)}};i()})}function inferFromImplicitShape(e,t){for(var n=1,r=-1,o=0;o<e.length;++o)if(e[o]>=0)n*=e[o];else if(-1===e[o]){if(-1!==r)throw Error("Shapes can only have 1 implicit size. Found -1 at dim "+r+" and dim "+o);r=o}else if(e[o]<0)throw Error("Shapes can not be < 0. Found "+e[o]+" at dim "+o);if(-1===r){if(t>0&&t!==n)throw Error("Size("+t+") must match the product of shape "+e);return e}if(0===n)throw Error("Cannot infer the missing size in ["+e+"] when there are 0 elements");if(t%n!=0)throw Error("The implicit shape can't be a fractional number. Got "+t+" / "+n);var a=e.slice();return a[r]=t/n,a}function squeezeShape(e,t){for(var n=[],r=[],o=0,a=0;a<e.length;++a){if(null!=t){if(t[o]===a&&1!==e[a])throw new Error("Can't squeeze axis "+a+" since its dim '"+e[a]+"' is not 1");(null==t[o]||t[o]>a)&&1===e[a]&&(n.push(e[a]),r.push(a)),t[o]<=a&&o++}1!==e[a]&&(n.push(e[a]),r.push(a))}return{newShape:n,keptDims:r}}function getTypedArrayFromDType(e,t){var n=null;if(null==e||"float32"===e)n=new Float32Array(t);else if("int32"===e)n=new Int32Array(t);else{if("bool"!==e)throw new Error("Unknown data type "+e);n=new Uint8Array(t)}return n}function getArrayFromDType(e,t){var n=null;if(null==e||"float32"===e)n=new Float32Array(t);else if("int32"===e)n=new Int32Array(t);else if("bool"===e)n=new Uint8Array(t);else{if("string"!==e)throw new Error("Unknown data type "+e);n=new Array(t)}return n}function checkComputationForNaN(e,t,n){if("float32"===t)for(var r=0;r<e.length;r++)if(isNaN(e[r]))throw Error("The result of the '"+n+"' has NaNs.")}function checkConversionForNaN(e,t){if("float32"!==t)for(var n=0;n<e.length;n++)if(isNaN(e[n]))throw Error("NaN is not a valid value for dtype: '"+t+"'.")}function hasEncodingLoss(e,t){return"complex64"!==t&&(("float32"!==t||"complex64"===e)&&(("int32"!==t||"float32"===e||"complex64"===e)&&("bool"!==t||"bool"!==e)))}function isTypedArray(e){return e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array}function bytesPerElement(e){if("float32"===e||"int32"===e)return 4;if("complex64"===e)return 8;if("bool"===e)return 1;throw new Error("Unknown dtype "+e)}function bytesFromStringArray(e){if(null==e)return 0;var t=0;return e.forEach(function(e){return t+=2*e.length}),t}function isString(e){return"string"==typeof e||e instanceof String}function isBoolean(e){return"boolean"==typeof e}function isNumber(e){return"number"==typeof e}function inferDtype(e){return e instanceof Array?inferDtype(e[0]):e instanceof Float32Array?"float32":e instanceof Int32Array||e instanceof Uint8Array?"int32":isNumber(e)?"float32":isString(e)?"string":isBoolean(e)?"bool":"float32"}function isFunction(e){return!!(e&&e.constructor&&e.call&&e.apply)}function nearestDivisor(e,t){for(var n=t;n<e;++n)if(e%n==0)return n;return e}function computeStrides(e){var t=e.length;if(t<2)return[];var n=new Array(t-1);n[t-2]=e[t-1];for(var r=t-3;r>=0;--r)n[r]=n[r+1]*e[r+1];return n}function toTypedArray(e,t,n){if("string"===t)throw new Error("Cannot convert a string[] to a TypedArray");if(noConversionNeeded(e,t))return e;if(Array.isArray(e)&&(e=flatten(e)),null==t||"float32"===t||"complex64"===t)return new Float32Array(e);if("int32"===t)return n&&checkConversionForNaN(e,t),new Int32Array(e);if("bool"===t){for(var r=new Uint8Array(e.length),o=0;o<r.length;++o)0!==Math.round(e[o])&&(r[o]=1);return r}throw new Error("Unknown data type "+t)}function noConversionNeeded(e,t){return e instanceof Float32Array&&"float32"===t||e instanceof Int32Array&&"int32"===t||e instanceof Uint8Array&&"bool"===t}function makeOnesTypedArray(e,t){for(var n=makeZerosTypedArray(e,t),r=0;r<n.length;r++)n[r]=1;return n}function makeZerosTypedArray(e,t){if(null==t||"float32"===t||"complex64"===t)return new Float32Array(e);if("int32"===t)return new Int32Array(e);if("bool"===t)return new Uint8Array(e);throw new Error("Unknown data type "+t)}function now(){if("undefined"!=typeof performance)return performance.now();if("undefined"!=typeof process){var e=process.hrtime();return 1e3*e[0]+e[1]/1e6}throw new Error("Cannot measure time in this environment. You should run tf.js in the browser or in Node.js")}var util=Object.freeze({shuffle:shuffle,clamp:clamp,nearestLargerEven:nearestLargerEven,sum:sum,randUniform:randUniform,distSquared:distSquared,assert:assert,assertShapesMatch:assertShapesMatch,assertNonNull:assertNonNull,flatten:flatten,sizeFromShape:sizeFromShape,isScalarShape:isScalarShape,arraysEqual:arraysEqual,isInt:isInt,tanh:tanh,sizeToSquarishShape:sizeToSquarishShape,createShuffledIndices:createShuffledIndices,rightPad:rightPad,repeatedTry:repeatedTry,inferFromImplicitShape:inferFromImplicitShape,squeezeShape:squeezeShape,getTypedArrayFromDType:getTypedArrayFromDType,getArrayFromDType:getArrayFromDType,checkComputationForNaN:checkComputationForNaN,checkConversionForNaN:checkConversionForNaN,hasEncodingLoss:hasEncodingLoss,isTypedArray:isTypedArray,bytesPerElement:bytesPerElement,bytesFromStringArray:bytesFromStringArray,isString:isString,isBoolean:isBoolean,isNumber:isNumber,inferDtype:inferDtype,isFunction:isFunction,nearestDivisor:nearestDivisor,computeStrides:computeStrides,toTypedArray:toTypedArray,makeOnesTypedArray:makeOnesTypedArray,makeZerosTypedArray:makeZerosTypedArray,now:now}),Profiler=function(){function e(e,t){this.backendTimer=e,this.logger=t,null==t&&(this.logger=new Logger)}return e.prototype.profileKernel=function(e,t){var n,r=this,o=this.backendTimer.time(function(){n=t()});return(Array.isArray(n)?n:[n]).forEach(function(t){var n=t.dataSync();checkComputationForNaN(n,t.dtype,e),o.then(function(o){var a="";null!=o.getExtraProfileInfo&&(a=o.getExtraProfileInfo()),r.logger.logKernelProfile(e,t,n,o.kernelMs,a)})}),n},e}(),Logger=function(){function e(){}return e.prototype.logKernelProfile=function(e,t,n,r,o){var a=rightPad(r+"ms",9),i=rightPad(e,25),s=t.rank,u=t.size,l=rightPad(t.shape.toString(),14);console.log("%c"+i+"\t%c"+a+"\t%c"+s+"D "+l+"\t%c"+u+"\t%c"+o,"font-weight:bold","color:red","color:blue","color: orange","color: green")},e}(),FORMAT_LIMIT_NUM_VALS=20,FORMAT_NUM_FIRST_LAST_VALS=3,FORMAT_NUM_SIG_DIGITS=7;function tensorToString(e,t,n,r){var o=computeStrides(t),a=computeMaxSizePerColumn(e,t,n,o),i=t.length,s=subTensorToString(e,t,n,o,a),u=["Tensor"];return r&&(u.push("  dtype: "+n),u.push("  rank: "+i),u.push("  shape: ["+t+"]"),u.push("  values:")),u.push(s.map(function(e){return"    "+e}).join("\n")),u.join("\n")}function computeMaxSizePerColumn(e,t,n,r){var o=sizeFromShape(t),a=r[r.length-1],i=new Array(a).fill(0),s=t.length,u="complex64"===n?createComplexTuples(e):e;if(s>1)for(var l=0;l<o/a;l++)for(var c=l*a,p=0;p<a;p++)i[p]=Math.max(i[p],valToString(u[c+p],0).length);return i}function valToString(e,t){return rightPad(Array.isArray(e)?parseFloat(e[0].toFixed(FORMAT_NUM_SIG_DIGITS))+" + "+parseFloat(e[1].toFixed(FORMAT_NUM_SIG_DIGITS))+"j":isString(e)?"'"+e+"'":parseFloat(e.toFixed(FORMAT_NUM_SIG_DIGITS)).toString(),t)}function subTensorToString(e,t,n,r,o,a){void 0===a&&(a=!0);var i="complex64"===n?2:1,s=t[0],u=t.length;if(0===u)return"complex64"===n?[valToString(createComplexTuples(e)[0],0)]:[e[0].toString()];if(1===u){if(s>FORMAT_LIMIT_NUM_VALS){var l=FORMAT_NUM_FIRST_LAST_VALS*i,c=Array.from(e.slice(0,l)),p=Array.from(e.slice(s-FORMAT_NUM_FIRST_LAST_VALS*i,s));return"complex64"===n&&(c=createComplexTuples(c),p=createComplexTuples(p)),["["+c.map(function(e,t){return valToString(e,o[t])}).join(", ")+", ..., "+p.map(function(e,t){return valToString(e,o[s-FORMAT_NUM_FIRST_LAST_VALS+t])}).join(", ")+"]"]}return["["+("complex64"===n?createComplexTuples(e):Array.from(e)).map(function(e,t){return valToString(e,o[t])}).join(", ")+"]"]}var d=t.slice(1),h=r.slice(1),f=r[0]*i,m=[];if(s>FORMAT_LIMIT_NUM_VALS){for(var g=0;g<FORMAT_NUM_FIRST_LAST_VALS;g++){var v=(y=g*f)+f;m.push.apply(m,subTensorToString(e.slice(y,v),d,n,h,o,!1))}m.push("...");for(g=s-FORMAT_NUM_FIRST_LAST_VALS;g<s;g++){v=(y=g*f)+f;m.push.apply(m,subTensorToString(e.slice(y,v),d,n,h,o,g===s-1))}}else for(g=0;g<s;g++){var y;v=(y=g*f)+f;m.push.apply(m,subTensorToString(e.slice(y,v),d,n,h,o,g===s-1))}var x=2===u?",":"";m[0]="["+m[0]+x;for(g=1;g<m.length-1;g++)m[g]=" "+m[g]+x;var T=",\n";for(g=2;g<u;g++)T+="\n";return m[m.length-1]=" "+m[m.length-1]+"]"+(a?"":T),m}function createComplexTuples(e){for(var t=[],n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var TensorBuffer=function(){function e(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=sizeFromShape(e),null!=n){var r=n.length;assert(r===this.size,"Length of values '"+r+"' does not match the size inferred by the shape '"+this.size+"'.")}if("complex64"===t)throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||getArrayFromDType(t,sizeFromShape(this.shape)),this.strides=computeStrides(e)}return e.prototype.set=function(e){for(var t=[],n=1;n<arguments.length;n++)t[n-1]=arguments[n];0===t.length&&(t=[0]),assert(t.length===this.rank,"The number of provided coordinates ("+t.length+") must match the rank ("+this.rank+")");var r=this.locToIndex(t);this.values[r]=e},e.prototype.get=function(){for(var e=[],t=0;t<arguments.length;t++)e[t]=arguments[t];0===e.length&&(e=[0]);for(var n=e[e.length-1],r=0;r<e.length-1;++r)n+=this.strides[r]*e[r];return this.values[n]},e.prototype.locToIndex=function(e){if(0===this.rank)return 0;if(1===this.rank)return e[0];for(var t=e[e.length-1],n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t},e.prototype.indexToLoc=function(e){if(0===this.rank)return[];if(1===this.rank)return[e];for(var t=new Array(this.shape.length),n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t},Object.defineProperty(e.prototype,"rank",{get:function(){return this.shape.length},enumerable:!0,configurable:!0}),e.prototype.toTensor=function(){return Tensor.make(this.shape,{values:this.values},this.dtype)},e}(),trackerFn=null,opHandler=null;function setTensorTracker(e){trackerFn=e}function setOpHandler(e){opHandler=e}var Tensor=function(){function e(e,t,n,r){this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=sizeFromShape(e),this.strides=computeStrides(e),this.dataId=null!=r?r:{},this.id=trackerFn().nextTensorId(),this.rankType=this.rank<5?this.rank.toString():"higher",trackerFn().registerTensor(this),null!=n&&trackerFn().write(this.dataId,n)}return e.make=function(t,n,r){return new e(t,r,n.values,n.dataId)},e.prototype.flatten=function(){return this.throwIfDisposed(),this.as1D()},e.prototype.asScalar=function(){return this.throwIfDisposed(),assert(1===this.size,"The array must have only 1 element."),this.reshape([])},e.prototype.as1D=function(){return this.throwIfDisposed(),this.reshape([this.size])},e.prototype.as2D=function(e,t){return this.throwIfDisposed(),this.reshape([e,t])},e.prototype.as3D=function(e,t,n){return this.throwIfDisposed(),this.reshape([e,t,n])},e.prototype.as4D=function(e,t,n,r){return this.throwIfDisposed(),this.reshape([e,t,n,r])},e.prototype.as5D=function(e,t,n,r,o){return this.throwIfDisposed(),this.reshape([e,t,n,r,o])},e.prototype.asType=function(e){return this.throwIfDisposed(),opHandler.cast(this,e)},Object.defineProperty(e.prototype,"rank",{get:function(){return this.shape.length},enumerable:!0,configurable:!0}),e.prototype.get=function(){for(var e=[],t=0;t<arguments.length;t++)e[t]=arguments[t];assert(e.length===this.rank,"Number of coordinates in get() must match the rank of the tensor"),assert("complex64"!==this.dtype,"Tensor.get() is not supported for complex64 tensors yet."),this.throwIfDisposed(),0===e.length&&(e=[0]);for(var n=e[e.length-1],r=0;r<e.length-1;++r)n+=this.strides[r]*e[r];return this.dataSync()[n]},e.prototype.buffer=function(){return opHandler.buffer(this.shape,this.dtype,this.dataSync())},e.prototype.data=function(){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(e){return this.throwIfDisposed(),[2,trackerFn().read(this.dataId)]})})},e.prototype.dataSync=function(){return this.throwIfDisposed(),trackerFn().readSync(this.dataId)},e.prototype.dispose=function(){this.isDisposed||(trackerFn().disposeTensor(this),this.isDisposedInternal=!0)},Object.defineProperty(e.prototype,"isDisposed",{get:function(){return this.isDisposedInternal},enumerable:!0,configurable:!0}),e.prototype.throwIfDisposed=function(){if(this.isDisposed)throw new Error("Tensor is disposed.")},e.prototype.toFloat=function(){return this.asType("float32")},e.prototype.toInt=function(){return this.asType("int32")},e.prototype.toBool=function(){return this.asType("bool")},e.prototype.print=function(e){return void 0===e&&(e=!1),opHandler.print(this,e)},e.prototype.reshape=function(e){return this.throwIfDisposed(),opHandler.reshape(this,e)},e.prototype.reshapeAs=function(e){return this.throwIfDisposed(),this.reshape(e.shape)},e.prototype.expandDims=function(e){return void 0===e&&(e=0),opHandler.expandDims(this,e)},e.prototype.cumsum=function(e,t,n){return void 0===e&&(e=0),void 0===t&&(t=!1),void 0===n&&(n=!1),opHandler.cumsum(this,e,t,n)},e.prototype.squeeze=function(e){return this.throwIfDisposed(),opHandler.squeeze(this,e)},e.prototype.clone=function(){return this.throwIfDisposed(),opHandler.clone(this)},e.prototype.toString=function(e){return void 0===e&&(e=!1),tensorToString(this.dataSync(),this.shape,this.dtype,e)},e.prototype.tile=function(e){return this.throwIfDisposed(),opHandler.tile(this,e)},e.prototype.gather=function(e,t){return void 0===t&&(t=0),this.throwIfDisposed(),opHandler.gather(this,e,t)},e.prototype.matMul=function(e,t,n){return void 0===t&&(t=!1),void 0===n&&(n=!1),this.throwIfDisposed(),opHandler.matMul(this,e,t,n)},e.prototype.dot=function(e){return this.throwIfDisposed(),opHandler.dot(this,e)},e.prototype.norm=function(e,t,n){return void 0===e&&(e="euclidean"),void 0===t&&(t=null),void 0===n&&(n=!1),this.throwIfDisposed(),opHandler.norm(this,e,t,n)},e.prototype.slice=function(e,t){return this.throwIfDisposed(),opHandler.slice(this,e,t)},e.prototype.reverse=function(e){return this.throwIfDisposed(),opHandler.reverse(this,e)},e.prototype.concat=function(t,n){return void 0===n&&(n=0),this.throwIfDisposed(),t instanceof e&&(t=[t]),opHandler.concat([this].concat(t),n)},e.prototype.split=function(e,t){return void 0===t&&(t=0),this.throwIfDisposed(),opHandler.split(this,e,t)},e.prototype.stack=function(e,t){return void 0===t&&(t=0),opHandler.stack([this,e],t)},e.prototype.unstack=function(e,t){return void 0===t&&(t=0),opHandler.unstack(this,t)},e.prototype.pad=function(e,t){return void 0===t&&(t=0),opHandler.pad(this,e,t)},e.prototype.batchNormalization=function(e,t,n,r,o){return void 0===n&&(n=.001),this.throwIfDisposed(),opHandler.batchNormalization(this,e,t,n,r,o)},e.prototype.all=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.all(this,e,t)},e.prototype.any=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.any(this,e,t)},e.prototype.logSumExp=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.logSumExp(this,e,t)},e.prototype.sum=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.sum(this,e,t)},e.prototype.prod=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.prod(this,e,t)},e.prototype.mean=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.mean(this,e,t)},e.prototype.min=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.min(this,e,t)},e.prototype.max=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.max(this,e,t)},e.prototype.argMin=function(e){return void 0===e&&(e=null),this.throwIfDisposed(),opHandler.argMin(this,e)},e.prototype.argMax=function(e){return void 0===e&&(e=null),this.throwIfDisposed(),opHandler.argMax(this,e)},e.prototype.cast=function(e){return this.throwIfDisposed(),opHandler.cast(this,e)},e.prototype.add=function(e){return this.throwIfDisposed(),opHandler.add(this,e)},e.prototype.addStrict=function(e){return this.throwIfDisposed(),opHandler.addStrict(this,e)},e.prototype.atan2=function(e){return this.throwIfDisposed(),opHandler.atan2(this,e)},e.prototype.sub=function(e){return this.throwIfDisposed(),opHandler.sub(this,e)},e.prototype.subStrict=function(e){return this.throwIfDisposed(),opHandler.subStrict(this,e)},e.prototype.pow=function(e){return this.throwIfDisposed(),opHandler.pow(this,e)},e.prototype.powStrict=function(e){return this.throwIfDisposed(),opHandler.powStrict(this,e)},e.prototype.mul=function(e){return this.throwIfDisposed(),opHandler.mul(this,e)},e.prototype.mulStrict=function(e){return this.throwIfDisposed(),opHandler.mulStrict(this,e)},e.prototype.div=function(e){return this.throwIfDisposed(),opHandler.div(this,e)},e.prototype.floorDiv=function(e){return this.throwIfDisposed(),opHandler.floorDiv(this,e)},e.prototype.divStrict=function(e){return this.throwIfDisposed(),opHandler.divStrict(this,e)},e.prototype.minimum=function(e){return this.throwIfDisposed(),opHandler.minimum(this,e)},e.prototype.minimumStrict=function(e){return this.throwIfDisposed(),opHandler.minimumStrict(this,e)},e.prototype.maximum=function(e){return this.throwIfDisposed(),opHandler.maximum(this,e)},e.prototype.maximumStrict=function(e){return this.throwIfDisposed(),opHandler.maximumStrict(this,e)},e.prototype.mod=function(e){return this.throwIfDisposed(),opHandler.mod(this,e)},e.prototype.modStrict=function(e){return this.throwIfDisposed(),opHandler.modStrict(this,e)},e.prototype.squaredDifference=function(e){return this.throwIfDisposed(),opHandler.squaredDifference(this,e)},e.prototype.squaredDifferenceStrict=function(e){return this.throwIfDisposed(),opHandler.squaredDifferenceStrict(this,e)},e.prototype.transpose=function(e){return this.throwIfDisposed(),opHandler.transpose(this,e)},e.prototype.notEqual=function(e){return this.throwIfDisposed(),opHandler.notEqual(this,e)},e.prototype.notEqualStrict=function(e){return this.throwIfDisposed(),opHandler.notEqualStrict(this,e)},e.prototype.less=function(e){return this.throwIfDisposed(),opHandler.less(this,e)},e.prototype.lessStrict=function(e){return this.throwIfDisposed(),opHandler.lessStrict(this,e)},e.prototype.equal=function(e){return this.throwIfDisposed(),opHandler.equal(this,e)},e.prototype.equalStrict=function(e){return this.throwIfDisposed(),opHandler.equalStrict(this,e)},e.prototype.lessEqual=function(e){return this.throwIfDisposed(),opHandler.lessEqual(this,e)},e.prototype.lessEqualStrict=function(e){return this.throwIfDisposed(),opHandler.lessEqualStrict(this,e)},e.prototype.greater=function(e){return this.throwIfDisposed(),opHandler.greater(this,e)},e.prototype.greaterStrict=function(e){return this.throwIfDisposed(),opHandler.greaterStrict(this,e)},e.prototype.greaterEqual=function(e){return this.throwIfDisposed(),opHandler.greaterEqual(this,e)},e.prototype.greaterEqualStrict=function(e){return this.throwIfDisposed(),opHandler.greaterEqualStrict(this,e)},e.prototype.logicalAnd=function(e){return this.throwIfDisposed(),opHandler.logicalAnd(this,e)},e.prototype.logicalOr=function(e){return this.throwIfDisposed(),opHandler.logicalOr(this,e)},e.prototype.logicalNot=function(){return this.throwIfDisposed(),opHandler.logicalNot(this)},e.prototype.logicalXor=function(e){return this.throwIfDisposed(),opHandler.logicalXor(this,e)},e.prototype.where=function(e,t){return this.throwIfDisposed(),opHandler.where(e,this,t)},e.prototype.neg=function(){return this.throwIfDisposed(),opHandler.neg(this)},e.prototype.ceil=function(){return this.throwIfDisposed(),opHandler.ceil(this)},e.prototype.floor=function(){return this.throwIfDisposed(),opHandler.floor(this)},e.prototype.sign=function(){return this.throwIfDisposed(),opHandler.sign(this)},e.prototype.exp=function(){return this.throwIfDisposed(),opHandler.exp(this)},e.prototype.expm1=function(){return this.throwIfDisposed(),opHandler.expm1(this)},e.prototype.log=function(){return this.throwIfDisposed(),opHandler.log(this)},e.prototype.log1p=function(){return this.throwIfDisposed(),opHandler.log1p(this)},e.prototype.sqrt=function(){return this.throwIfDisposed(),opHandler.sqrt(this)},e.prototype.rsqrt=function(){return this.throwIfDisposed(),opHandler.rsqrt(this)},e.prototype.square=function(){return this.throwIfDisposed(),opHandler.square(this)},e.prototype.reciprocal=function(){return this.throwIfDisposed(),opHandler.reciprocal(this)},e.prototype.abs=function(){return this.throwIfDisposed(),opHandler.abs(this)},e.prototype.clipByValue=function(e,t){return this.throwIfDisposed(),opHandler.clipByValue(this,e,t)},e.prototype.relu=function(){return this.throwIfDisposed(),opHandler.relu(this)},e.prototype.elu=function(){return this.throwIfDisposed(),opHandler.elu(this)},e.prototype.selu=function(){return this.throwIfDisposed(),opHandler.selu(this)},e.prototype.leakyRelu=function(e){return void 0===e&&(e=.2),this.throwIfDisposed(),opHandler.leakyRelu(this,e)},e.prototype.prelu=function(e){return this.throwIfDisposed(),opHandler.prelu(this,e)},e.prototype.sigmoid=function(){return this.throwIfDisposed(),opHandler.sigmoid(this)},e.prototype.logSigmoid=function(){return this.throwIfDisposed(),opHandler.logSigmoid(this)},e.prototype.softplus=function(){return this.throwIfDisposed(),opHandler.softplus(this)},e.prototype.zerosLike=function(){return this.throwIfDisposed(),opHandler.zerosLike(this)},e.prototype.onesLike=function(){return this.throwIfDisposed(),opHandler.onesLike(this)},e.prototype.sin=function(){return this.throwIfDisposed(),opHandler.sin(this)},e.prototype.cos=function(){return this.throwIfDisposed(),opHandler.cos(this)},e.prototype.tan=function(){return this.throwIfDisposed(),opHandler.tan(this)},e.prototype.asin=function(){return this.throwIfDisposed(),opHandler.asin(this)},e.prototype.acos=function(){return this.throwIfDisposed(),opHandler.acos(this)},e.prototype.atan=function(){return this.throwIfDisposed(),opHandler.atan(this)},e.prototype.sinh=function(){return this.throwIfDisposed(),opHandler.sinh(this)},e.prototype.cosh=function(){return this.throwIfDisposed(),opHandler.cosh(this)},e.prototype.tanh=function(){return this.throwIfDisposed(),opHandler.tanh(this)},e.prototype.asinh=function(){return this.throwIfDisposed(),opHandler.asinh(this)},e.prototype.acosh=function(){return this.throwIfDisposed(),opHandler.acosh(this)},e.prototype.atanh=function(){return this.throwIfDisposed(),opHandler.atanh(this)},e.prototype.erf=function(){return this.throwIfDisposed(),opHandler.erf(this)},e.prototype.round=function(){return this.throwIfDisposed(),opHandler.round(this)},e.prototype.step=function(e){return void 0===e&&(e=0),this.throwIfDisposed(),opHandler.step(this,e)},e.prototype.softmax=function(e){return void 0===e&&(e=-1),this.throwIfDisposed(),opHandler.softmax(this,e)},e.prototype.logSoftmax=function(e){return void 0===e&&(e=-1),this.throwIfDisposed(),opHandler.logSoftmax(this,e)},e.prototype.resizeBilinear=function(e,t){return void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.image.resizeBilinear(this,e,t)},e.prototype.resizeNearestNeighbor=function(e,t){return void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.image.resizeNearestNeighbor(this,e,t)},e.prototype.conv1d=function(e,t,n,r,o,a){return void 0===r&&(r="NWC"),void 0===o&&(o=1),this.throwIfDisposed(),opHandler.conv1d(this,e,t,n,r,o,a)},e.prototype.conv2d=function(e,t,n,r,o,a){return void 0===r&&(r="NHWC"),void 0===o&&(o=[1,1]),this.throwIfDisposed(),opHandler.conv2d(this,e,t,n,r,o,a)},e.prototype.conv2dTranspose=function(e,t,n,r,o){return this.throwIfDisposed(),opHandler.conv2dTranspose(this,e,t,n,r,o)},e.prototype.depthwiseConv2D=function(e,t,n,r,o,a){return void 0===r&&(r="NHWC"),void 0===o&&(o=[1,1]),this.throwIfDisposed(),opHandler.depthwiseConv2d(this,e,t,n,r,o,a)},e.prototype.separableConv2d=function(e,t,n,r,o,a){return void 0===o&&(o=[1,1]),void 0===a&&(a="NHWC"),this.throwIfDisposed(),opHandler.separableConv2d(this,e,t,n,r,o,a)},e.prototype.avgPool=function(e,t,n,r){return this.throwIfDisposed(),opHandler.avgPool(this,e,t,n,r)},e.prototype.maxPool=function(e,t,n,r){return this.throwIfDisposed(),opHandler.maxPool(this,e,t,n,r)},e.prototype.localResponseNormalization=function(e,t,n,r){return void 0===e&&(e=5),void 0===t&&(t=1),void 0===n&&(n=1),void 0===r&&(r=.5),opHandler.localResponseNormalization(this,e,t,n,r)},e.prototype.pool=function(e,t,n,r,o){return this.throwIfDisposed(),opHandler.pool(this,e,t,n,r,o)},e.prototype.variable=function(e,t,n){return void 0===e&&(e=!0),this.throwIfDisposed(),Variable.variable(this,e,t,n)},e.prototype.unsortedSegmentSum=function(e,t){return this.throwIfDisposed(),opHandler.unsortedSegmentSum(this,e,t)},e.prototype.batchToSpaceND=function(e,t){return this.throwIfDisposed(),opHandler.batchToSpaceND(this,e,t)},e.prototype.spaceToBatchND=function(e,t){return this.throwIfDisposed(),opHandler.spaceToBatchND(this,e,t)},e.prototype.topk=function(e,t){return void 0===e&&(e=1),void 0===t&&(t=!0),this.throwIfDisposed(),opHandler.topk(this,e,t)},e.prototype.stridedSlice=function(e,t,n,r,o){return void 0===r&&(r=0),void 0===o&&(o=0),this.throwIfDisposed(),opHandler.stridedSlice(this,e,t,n,r,o)},e.prototype.depthToSpace=function(e,t){return this.throwIfDisposed(),opHandler.depthToSpace(this,e,t)},e.prototype.fft=function(){return this.throwIfDisposed(),opHandler.spectral.fft(this)},e.prototype.ifft=function(){return this.throwIfDisposed(),opHandler.spectral.ifft(this)},e}();Object.defineProperty(Tensor,Symbol.hasInstance,{value:function(e){return!!e&&null!=e.shape&&null!=e.dtype}});var Variable=function(e){function t(t,n,r){void 0===n&&(n=!0);var o=e.call(this,t.shape,t.dtype,null,t.dataId)||this;o.trainable=n,o.name=r,null==o.name&&(o.name=trackerFn().nextVariableId().toString());try{trackerFn().registerVariable(o)}catch(e){throw trackerFn().disposeTensor(o),e}return o}return __extends(t,e),t.variable=function(e,n,r,o){return void 0===n&&(n=!0),null!=o&&o!==e.dtype&&(e=e.asType(o)),new t(e,n,r)},t.prototype.assign=function(e){if(e.dtype!==this.dtype)throw new Error("dtype of the new value ("+e.dtype+") and previous value ("+this.dtype+") must match");if(!arraysEqual(e.shape,this.shape))throw new Error("shape of the new value ("+e.shape+") and previous value ("+this.shape+") must match");trackerFn().disposeTensor(this),this.dataId=e.dataId,trackerFn().registerTensor(this)},t}(Tensor);Object.defineProperty(Variable,Symbol.hasInstance,{value:function(e){return e instanceof Tensor&&null!=e.assign&&e.assign instanceof Function}});var Rank,UpcastInt32AndMap,UpcastBoolAndMap,UpcastFloat32AndMap,UpcastComplex64AndMap,variable=Variable.variable;function getFilteredNodesXToY(e,t,n){for(var r={},o={},a=0;a<t.length;a++)r[t[a].id]=!0;for(a=0;a<e.length;a++){var i=(f=e[a]).inputs;for(var s in i){for(var u=i[s],l=!1,c=0;c<t.length;c++)if(r[u.id]){f.outputs.forEach(function(e){return r[e.id]=!0}),l=!0,o[f.id]=!0;break}if(l)break}}var p={};p[n.id]=!0;var d={};for(a=e.length-1;a>=0;a--)for(i=(f=e[a]).inputs,c=0;c<f.outputs.length;c++)if(p[f.outputs[c].id]){for(var s in i)p[i[s].id]=!0,d[f.id]=!0;break}var h=[];for(a=0;a<e.length;a++){var f;if(o[(f=e[a]).id]&&d[f.id]){var m={};for(var s in f.inputs){var g=f.inputs[s];r[g.id]&&(m[s]=g)}var v=Object.assign({},f);v.inputs=m,v.outputs=f.outputs,h.push(v)}}return h}function backpropagateGradients(e,t){for(var n=function(n){var r=t[n],o=[];if(r.outputs.forEach(function(t){var n=e[t.id];if(null!=n)o.push(n);else{var r=Tensor.make(t.shape,{values:makeZerosTypedArray(t.size,t.dtype)},t.dtype);o.push(r)}}),null==r.gradient)throw new Error("Cannot compute gradient: gradient function not found for "+r.name+".");var a=r.gradient(1===r.outputs.length?o[0]:o);for(var i in r.inputs){if(!(i in a))throw new Error("Cannot backprop through input "+i+". Available gradients found: "+Object.keys(a)+".");var s=a[i]();if("float32"!==s.dtype)throw new Error("Error in gradient for op "+r.name+". The gradient of input "+i+" must have 'float32' dtype, but has '"+s.dtype+"'");var u=r.inputs[i];if(!arraysEqual(s.shape,u.shape))throw new Error("Error in gradient for op "+r.name+". The gradient of input '"+i+"' has shape '"+s.shape+"', which does not match the shape of the input '"+u.shape+"'");if(null==e[u.id])e[u.id]=s;else{var l=e[u.id];e[u.id]=l.add(s),l.dispose()}}},r=t.length-1;r>=0;r--)n(r)}!function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6"}(Rank||(Rank={})),function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64"}(UpcastInt32AndMap||(UpcastInt32AndMap={})),function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64"}(UpcastBoolAndMap||(UpcastBoolAndMap={})),function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64"}(UpcastFloat32AndMap||(UpcastFloat32AndMap={})),function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64"}(UpcastComplex64AndMap||(UpcastComplex64AndMap={}));var upcastTypeMap={float32:UpcastFloat32AndMap,int32:UpcastInt32AndMap,bool:UpcastBoolAndMap,complex64:UpcastComplex64AndMap};function upcastType(e,t){if("string"===e||"string"===t){if("string"===e&&"string"===t)return"string";throw new Error("Can not upcast "+e+" with "+t)}return upcastTypeMap[e][t]}function sumOutType(e){return upcastType(e,"int32")}function makeTypesMatch(e,t){if(e.dtype===t.dtype)return[e,t];var n=upcastType(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function assertTypesMatch(e,t){assert(e.dtype===t.dtype,"The dtypes of the first("+e.dtype+") and second("+t.dtype+") input must match")}function isTensorInList(e,t){for(var n=0;n<t.length;n++)if(t[n].id===e.id)return!0;return!1}function getTensorsInContainer(e){var t=[];return walkTensorContainer(e,t,new Set),t}function walkTensorContainer(e,t,n){if(null!=e)if(e instanceof Tensor)t.push(e);else if(isIterable(e)){var r=e;for(var o in r){var a=r[o];n.has(a)||(n.add(a),walkTensorContainer(a,t,n))}}}function isIterable(e){return Array.isArray(e)||"object"==typeof e}var Type,Engine=function(){function e(e,t,n){this.backend=e,this.safeMode=t,this.debugMode=n,this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.profiling=!1,this.gradientScopeCount=0,this.customGradientDepth=0,this.scopeStack=[],this.keepTensors=new Set,this.tensorInfo=new WeakMap,this.profiler=new Profiler(e),this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}return e.prototype.moveData=function(e){this.write(e,this.readSync(e))},e.prototype.tidy=function(e,t,n){var r=this;void 0===n&&(n=!1);var o,a=null;if(null==t){if("function"!=typeof e)throw new Error("Please provide a function to tidy()");t=e}else{if("string"!=typeof e&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if("function"!=typeof t)throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");a=e}return this.scopedRun(function(){return r.startScope(a,n)},function(){return r.endScope(o,n)},function(){return(o=t())instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),o})},e.prototype.scopedRun=function(e,t,n){e();try{var r=n();return t(),r}catch(e){throw t(),e}},e.prototype.nextTensorId=function(){return e.nextTensorId++},e.prototype.nextVariableId=function(){return e.nextVariableId++},e.prototype.runKernel=function(e,t,n){var r,o=this,a=[],i=function(e){return a.push(e),e},s=this.activeScope.name,u=this.numBytes,l=this.numTensors;if(this.scopedRun(function(){return o.customGradientDepth++},function(){return o.customGradientDepth--},function(){r=o.debugMode()?o.profiler.profileKernel(s,function(){return e(o.backend,i)}):e(o.backend,i)}),this.shouldRecord()){var c={id:this.nextTapeNodeId++,name:s,inputs:t,outputs:Array.isArray(r)?r:[r]};null!=n&&(c.gradient=function(e){return n(e,a)}),this.activeTape.push(c)}return this.profiling&&this.activeProfile.kernels.push({name:s,bytesAdded:this.numBytes-u,totalBytesSnapshot:this.numBytes,tensorsAdded:this.numTensors-l,totalTensorsSnapshot:this.numTensors,inputShapes:Object.keys(t).map(function(e){return t[e].shape}),outputShape:Array.isArray(r)?r.map(function(e){return e.shape}):r.shape}),r},e.prototype.registerTensor=function(e){var t=this.tensorInfo.has(e.dataId)?this.tensorInfo.get(e.dataId).refCount:0;if(this.numTensors++,"string"===e.dtype&&this.numStringTensors++,0===t){this.numDataBuffers++;var n=0;"complex64"!==e.dtype&&"string"!==e.dtype&&(n=sizeFromShape(e.shape)*bytesPerElement(e.dtype)),this.tensorInfo.set(e.dataId,{backend:this.backend,dtype:e.dtype,shape:e.shape,bytes:n,refCount:0}),this.numBytes+=n,this.backend.register(e.dataId,e.shape,e.dtype)}this.tensorInfo.get(e.dataId).refCount++,e instanceof Variable||this.track(e)},e.prototype.registerVariable=function(e){if(null!=this.registeredVariables[e.name])throw new Error("Variable with name "+e.name+" was already registered");this.registeredVariables[e.name]=e},e.prototype.disposeTensor=function(e){if(this.tensorInfo.has(e.dataId)){this.keepTensors.has(e.id)&&this.keepTensors.delete(e.id),this.numTensors--,"string"===e.dtype&&this.numStringTensors--;var t=this.tensorInfo.get(e.dataId);t.refCount<=1?("complex64"!==e.dtype&&(this.numBytes-=t.bytes),this.numDataBuffers--,t.backend.disposeData(e.dataId),this.tensorInfo.delete(e.dataId)):this.tensorInfo.get(e.dataId).refCount--}},e.prototype.disposeVariables=function(){for(var e in this.registeredVariables){var t=this.registeredVariables[e];this.disposeTensor(t),delete this.registeredVariables[e]}},e.prototype.memory=function(){var e=this.backend.memory();return e.numTensors=this.numTensors,e.numDataBuffers=this.numDataBuffers,e.numBytes=this.numBytes,this.numStringTensors>0&&(e.unreliable=!0,null==e.reasons&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e},e.prototype.profile=function(e){return __awaiter(this,void 0,void 0,function(){var t,n;return __generator(this,function(r){return this.profiling=!0,t=this.numBytes,n=this.numTensors,this.activeProfile.kernels=[],this.activeProfile.result=e(),this.profiling=!1,this.activeProfile.peakBytes=Math.max.apply(Math,this.activeProfile.kernels.map(function(e){return e.totalBytesSnapshot})),this.activeProfile.newBytes=this.numBytes-t,this.activeProfile.newTensors=this.numTensors-n,[2,this.activeProfile]})})},e.prototype.shouldRecord=function(){return null!=this.activeTape&&0===this.customGradientDepth},e.prototype.addTapeNode=function(e,t,n){var r={};e.forEach(function(e,t){r[t]=e});var o={id:this.nextTapeNodeId++,name:this.activeScope.name,inputs:r,outputs:[t],gradient:function(e){var t={};return n(e).forEach(function(e,n){t[n]=function(){return e}}),t}};this.activeTape.push(o)},e.prototype.keep=function(e){if(1===this.scopeStack.length&&this.safeMode)throw new Error("Safe mode is ON. Enclose all tensor operations inside tf.tidy(): tf.tidy(() => {...}) to avoid memory leaks.");return this.keepTensors.add(e.id),e},e.prototype.startScope=function(e,t){void 0===t&&(t=!1),t&&0===this.gradientScopeCount&&(this.activeTape=[]),t&&this.gradientScopeCount++;var n={track:[],name:"unnamed scope"};e&&(n.name=e),this.scopeStack.push(n),this.activeScope=n},e.prototype.endScope=function(e,t){var n=this;void 0===t&&(t=!1),t&&(this.gradientScopeCount--,0===this.gradientScopeCount&&(this.activeTape=null));var r=new Set(this.keepTensors),o=getTensorsInContainer(e);o.forEach(function(e){return r.add(e.id)});for(var a=0;a<this.activeScope.track.length;a++){var i=this.activeScope.track[a];r.has(i.id)||(null!=this.activeTape?o.push(i):i.dispose())}var s=this.scopeStack.pop();this.activeScope=0===this.scopeStack.length?null:this.scopeStack[this.scopeStack.length-1],o.forEach(function(e){!n.keepTensors.has(e.id)&&isTensorInList(e,s.track)&&n.track(e)})},e.prototype.gradients=function(e,t,n,r){var o=this;if(void 0===r&&(r=!1),assert(t.length>0,"gradients() received an empty list of xs."),null!=n&&"float32"!==n.dtype)throw new Error("dy must have 'float32' dtype, but has '"+n.dtype+"'");return this.tidy("gradients",function(){var a=e();assert(a instanceof Tensor,"The result y returned by f() must be a tensor.");var i=getFilteredNodesXToY(o.activeTape,t,a);if(!r&&0===i.length&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");var s={};return s[a.id]=null==n?ones(a.shape):n,backpropagateGradients(s,i),{value:a,grads:t.map(function(e){return s[e.id]})}},!0)},e.prototype.customGrad=function(e){var t=this;return assert(isFunction(e),"The f passed in customGrad(f) must be a function."),function(){for(var n,r,o=[],a=0;a<arguments.length;a++)o[a]=arguments[a];if(assert(o.every(function(e){return e instanceof Tensor}),"The args passed in customGrad(f)(x1, x2,...) must all be tensors"),t.scopedRun(function(){return t.customGradientDepth++},function(){return t.customGradientDepth--},function(){r=t.tidy(e.name,function(){var t=e.apply(void 0,o),r=t.value,a=t.gradFunc;return assert(r instanceof Tensor,"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),assert(isFunction(a),"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n=a,r},!0)}),t.shouldRecord()){t.addTapeNode(o,r,function(e){var t=n(e),r=Array.isArray(t)?t:[t];return assert(r.length===o.length,"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),assert(r.every(function(e){return e instanceof Tensor}),"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors."),r})}return r}},e.prototype.write=function(e,t){var n=this.tensorInfo.get(e);if("string"===n.dtype){var r=bytesFromStringArray(t);this.numBytes+=r-n.bytes,n.bytes=r}this.backend!==n.backend&&(n.backend.disposeData(e),n.backend=this.backend,this.backend.register(e,n.shape,n.dtype)),this.backend.write(e,t)},e.prototype.readSync=function(e){return this.tensorInfo.get(e).backend.readSync(e)},e.prototype.read=function(e){return this.tensorInfo.get(e).backend.read(e)},e.prototype.fromPixels=function(e,t){return this.backend.fromPixels(e,t)},e.prototype.time=function(e){return __awaiter(this,void 0,void 0,function(){var t,n;return __generator(this,function(r){switch(r.label){case 0:return t=now(),[4,this.backend.time(e)];case 1:return(n=r.sent()).wallMs=now()-t,[2,n]}})})},e.prototype.track=function(e){if(1===this.scopeStack.length&&this.safeMode)throw new Error("Safe mode is ON. Enclose all tensor operations inside tf.tidy(): tf.tidy(() => {op();...}); to avoid memory leaks.");return null!=this.activeScope&&this.activeScope.track.push(e),e},e.nextTensorId=0,e.nextVariableId=0,e}();function ones(e){var t=makeOnesTypedArray(sizeFromShape(e),"float32");return Tensor.make(e,{values:t})}!function(e){e[e.NUMBER=0]="NUMBER",e[e.BOOLEAN=1]="BOOLEAN",e[e.STRING=2]="STRING"}(Type||(Type={}));var MAX_TEXTURE_SIZE,URL_PROPERTIES=[{name:"DEBUG",type:Type.BOOLEAN},{name:"IS_BROWSER",type:Type.BOOLEAN},{name:"WEBGL_LAZILY_UNPACK",type:Type.BOOLEAN},{name:"WEBGL_CPU_FORWARD",type:Type.BOOLEAN},{name:"WEBGL_PACK",type:Type.BOOLEAN},{name:"WEBGL_PACK_BATCHNORMALIZATION",type:Type.BOOLEAN},{name:"WEBGL_PACK_CLIP",type:Type.BOOLEAN},{name:"WEBGL_PACK_DEPTHWISECONV",type:Type.BOOLEAN},{name:"WEBGL_CONV_IM2COL",type:Type.BOOLEAN},{name:"WEBGL_MAX_TEXTURE_SIZE",type:Type.NUMBER},{name:"WEBGL_PAGING_ENABLED",type:Type.BOOLEAN},{name:"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",type:Type.NUMBER},{name:"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",type:Type.BOOLEAN},{name:"WEBGL_VERSION",type:Type.NUMBER},{name:"WEBGL_RENDER_FLOAT32_ENABLED",type:Type.BOOLEAN},{name:"WEBGL_DOWNLOAD_FLOAT_ENABLED",type:Type.BOOLEAN},{name:"WEBGL_FENCE_API_ENABLED",type:Type.BOOLEAN},{name:"WEBGL_SIZE_UPLOAD_UNIFORM",type:Type.NUMBER},{name:"BACKEND",type:Type.STRING},{name:"EPSILON",type:Type.NUMBER},{name:"PROD",type:Type.BOOLEAN},{name:"TENSORLIKE_CHECK_SHAPE_CONSISTENCY",type:Type.BOOLEAN}];function isWebGLVersionEnabled(e){try{if(null!=getWebGLContext(e))return!0}catch(e){return!1}return!1}function getWebGLMaxTextureSize(e){if(null==MAX_TEXTURE_SIZE){var t=getWebGLContext(e);MAX_TEXTURE_SIZE=t.getParameter(t.MAX_TEXTURE_SIZE)}return MAX_TEXTURE_SIZE}function getWebGLDisjointQueryTimerVersion(e){if(0===e)return 0;var t=getWebGLContext(e);return hasExtension(t,"EXT_disjoint_timer_query_webgl2")&&2===e?2:hasExtension(t,"EXT_disjoint_timer_query")?1:0}function isRenderToFloatTextureEnabled(e){if(0===e)return!1;var t=getWebGLContext(e);if(1===e){if(!hasExtension(t,"OES_texture_float"))return!1}else if(!hasExtension(t,"EXT_color_buffer_float"))return!1;return createFloatTextureAndBindToFramebuffer(t,e)}function isDownloadFloatTextureEnabled(e){if(0===e)return!1;var t=getWebGLContext(e);if(1===e){if(!hasExtension(t,"OES_texture_float"))return!1;if(!hasExtension(t,"WEBGL_color_buffer_float"))return!1}else if(!hasExtension(t,"EXT_color_buffer_float"))return!1;return createFloatTextureAndBindToFramebuffer(t,e)}function isWebGLFenceEnabled(e){return 2===e&&null!=getWebGLContext(e).fenceSync}function isChrome(){return"undefined"!=typeof navigator&&null!=navigator&&null!=navigator.userAgent&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor)}var TENSORFLOWJS_FLAGS_PREFIX="tfjsflags";function getFeaturesFromURL(){var e={};if("undefined"==typeof window||void 0===window.location||void 0===window.location.search)return e;var t=getQueryParams(window.location.search);if(TENSORFLOWJS_FLAGS_PREFIX in t){var n={};t[TENSORFLOWJS_FLAGS_PREFIX].split(",").forEach(function(e){var t=e.split(":"),r=t[0],o=t[1];n[r]=o}),URL_PROPERTIES.forEach(function(t){t.name in n&&(console.log("Setting feature override from URL "+t.name+": "+n[t.name]),t.type===Type.NUMBER?e[t.name]=+n[t.name]:t.type===Type.BOOLEAN?e[t.name]="true"===n[t.name]:t.type===Type.STRING?e[t.name]=n[t.name]:console.warn("Unknown URL param: "+t.name+"."))})}return e}function hasExtension(e,t){return null!=e.getExtension(t)}function createFloatTextureAndBindToFramebuffer(e,t){var n=e.createFramebuffer(),r=e.createTexture();e.bindTexture(e.TEXTURE_2D,r);var o=2===t?e.RGBA32F:e.RGBA;e.texImage2D(e.TEXTURE_2D,0,o,1,1,0,e.RGBA,e.FLOAT,null),e.bindFramebuffer(e.FRAMEBUFFER,n),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,r,0);var a=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(r),e.deleteFramebuffer(n),a}function getQueryParams(e){var t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,function(e){for(var n=[],r=1;r<arguments.length;r++)n[r-1]=arguments[r];return decodeParam(t,n[0],n[1]),n.join("=")}),t}function decodeParam(e,t,n){e[decodeURIComponent(t)]=decodeURIComponent(n||"")}var EPSILON_FLOAT16=1e-4,TEST_EPSILON_FLOAT16=.1,EPSILON_FLOAT32=1e-7,TEST_EPSILON_FLOAT32=.001,Environment=function(){function e(e){this.features={},this.registry={},null!=e&&(this.features=e),this.get("DEBUG")&&console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.")}return e.setBackend=function(e,t){if(void 0===t&&(t=!1),!(e in ENV.registry))throw new Error("Backend name '"+e+"' not found in registry");ENV.engine.backend=ENV.findBackend(e),ENV.backendName=e},e.getBackend=function(){return ENV.initEngine(),ENV.backendName},e.disposeVariables=function(){ENV.engine.disposeVariables()},e.memory=function(){return ENV.engine.memory()},e.profile=function(e){return ENV.engine.profile(e)},e.tidy=function(e,t){return ENV.engine.tidy(e,t)},e.dispose=function(e){getTensorsInContainer(e).forEach(function(e){return e.dispose()})},e.keep=function(e){return ENV.engine.keep(e)},e.time=function(e){return ENV.engine.time(e)},e.prototype.get=function(e){return e in this.features?this.features[e]:(this.features[e]=this.evaluateFeature(e),this.features[e])},e.prototype.getFeatures=function(){return this.features},e.prototype.set=function(e,t){this.features[e]=t},e.prototype.getBestBackendName=function(){var e=this;if(0===Object.keys(this.registry).length)throw new Error("No backend found in registry.");return Object.keys(this.registry).map(function(t){return{name:t,entry:e.registry[t]}}).sort(function(e,t){return t.entry.priority-e.entry.priority})[0].name},e.prototype.evaluateFeature=function(e){if("DEBUG"===e)return!1;if("IS_BROWSER"===e)return"undefined"!=typeof window;if("IS_NODE"===e)return"undefined"!=typeof process&&void 0!==process.versions&&void 0!==process.versions.node;if("IS_CHROME"===e)return isChrome();if("WEBGL_CPU_FORWARD"===e)return!0;if("WEBGL_PACK"===e)return!1;if("WEBGL_PACK_BATCHNORMALIZATION"===e)return this.get("WEBGL_PACK");if("WEBGL_PACK_CLIP"===e)return this.get("WEBGL_PACK");if("WEBGL_PACK_DEPTHWISECONV"===e)return this.get("WEBGL_PACK");if("WEBGL_LAZILY_UNPACK"===e)return this.get("WEBGL_PACK");if("WEBGL_CONV_IM2COL"===e)return this.get("WEBGL_PACK");if("WEBGL_PAGING_ENABLED"===e)return this.get("IS_BROWSER")&&!this.get("PROD");if("WEBGL_MAX_TEXTURE_SIZE"===e)return getWebGLMaxTextureSize(this.get("WEBGL_VERSION"));if("IS_TEST"===e)return!1;if("BACKEND"===e)return this.getBestBackendName();if("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"===e){var t=this.get("WEBGL_VERSION");return 0===t?0:getWebGLDisjointQueryTimerVersion(t)}if("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE"===e)return this.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!isMobile();if("HAS_WEBGL"===e)return this.get("WEBGL_VERSION")>0;if("WEBGL_VERSION"===e)return isWebGLVersionEnabled(2)?2:isWebGLVersionEnabled(1)?1:0;if("WEBGL_RENDER_FLOAT32_ENABLED"===e)return isRenderToFloatTextureEnabled(this.get("WEBGL_VERSION"));if("WEBGL_DOWNLOAD_FLOAT_ENABLED"===e)return isDownloadFloatTextureEnabled(this.get("WEBGL_VERSION"));if("WEBGL_FENCE_API_ENABLED"===e)return isWebGLFenceEnabled(this.get("WEBGL_VERSION"));if("WEBGL_SIZE_UPLOAD_UNIFORM"===e)return this.get("WEBGL_RENDER_FLOAT32_ENABLED")?4:0;if("TEST_EPSILON"===e)return 32===this.backend.floatPrecision()?TEST_EPSILON_FLOAT32:TEST_EPSILON_FLOAT16;if("EPSILON"===e)return 32===this.backend.floatPrecision()?EPSILON_FLOAT32:EPSILON_FLOAT16;if("PROD"===e)return!1;if("TENSORLIKE_CHECK_SHAPE_CONSISTENCY"===e)return!this.get("PROD");throw new Error("Unknown feature "+e+".")},e.prototype.setFeatures=function(e){this.features=Object.assign({},e)},e.prototype.reset=function(){this.features=getFeaturesFromURL(),null!=this.globalEngine&&(this.globalEngine=null)},Object.defineProperty(e.prototype,"backend",{get:function(){return this.engine.backend},enumerable:!0,configurable:!0}),e.prototype.findBackend=function(e){return e in this.registry?this.registry[e].backend:null},e.prototype.registerBackend=function(e,t,n,r){var o=this;if(void 0===n&&(n=1),e in this.registry)return console.warn(e+" backend was already registered. Reusing existing backend"),null!=r&&r(function(){return o.engine}),!1;try{var a=t();return a.setDataMover({moveData:function(e){return o.engine.moveData(e)}}),this.registry[e]={backend:a,priority:n},!0}catch(t){return console.warn("Registration of backend "+e+" failed"),console.warn(t.stack||t.message),!1}},e.prototype.removeBackend=function(e){if(!(e in this.registry))throw new Error(e+" backend not found in registry");this.registry[e].backend.dispose(),delete this.registry[e]},Object.defineProperty(e.prototype,"engine",{get:function(){return this.initEngine(),this.globalEngine},enumerable:!0,configurable:!0}),e.prototype.initEngine=function(){var e=this;if(null==this.globalEngine){this.backendName=this.get("BACKEND");var t=this.findBackend(this.backendName);this.globalEngine=new Engine(t,!1,function(){return e.get("DEBUG")})}},e}();function getGlobalNamespace(){var e;if("undefined"!=typeof window)e=window;else{if("undefined"==typeof process)throw new Error("Could not find a global object");e=process}return e}function getOrMakeEnvironment(){var e=getGlobalNamespace();return null==e.ENV&&(e.ENV=new Environment(getFeaturesFromURL()),setTensorTracker(function(){return e.ENV.engine})),e.ENV}var ENV=getOrMakeEnvironment(),environment=Object.freeze({EPSILON_FLOAT16:EPSILON_FLOAT16,EPSILON_FLOAT32:EPSILON_FLOAT32,Environment:Environment,ENV:ENV});function grad(e){return assert(isFunction(e),"The f passed in grad(f) must be a function"),function(t,n){return assert(t instanceof Tensor,"The x passed in grad(f)(x) must be a tensor"),assert(null==n||n instanceof Tensor,"The dy passed in grad(f)(x, dy) must be a tensor"),ENV.engine.tidy(function(){var r=ENV.engine.gradients(function(){return e(t)},[t],n),o=r.value,a=r.grads;return null!=n&&assertShapesMatch(o.shape,n.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),checkGrads(a),a[0]})}}function grads(e){return assert(isFunction(e),"The f passed in grads(f) must be a function"),function(t,n){return assert(Array.isArray(t)&&t.every(function(e){return e instanceof Tensor}),"The args passed in grads(f)(args) must be an array of tensors"),assert(null==n||n instanceof Tensor,"The dy passed in grads(f)(args, dy) must be a tensor"),ENV.engine.tidy(function(){var r=ENV.engine.gradients(function(){return e.apply(void 0,t)},t,n),o=r.value,a=r.grads;return null!=n&&assertShapesMatch(o.shape,n.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(a),a})}}function valueAndGrad(e){return assert(isFunction(e),"The f passed in valueAndGrad(f) must be a function"),function(t,n){assert(t instanceof Tensor,"The x passed in valueAndGrad(f)(x) must be a tensor"),assert(null==n||n instanceof Tensor,"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");var r=ENV.engine.gradients(function(){return e(t)},[t],n),o=r.grads,a=r.value;return checkGrads(o),{grad:o[0],value:a}}}function valueAndGrads(e){return assert(isFunction(e),"The f passed in valueAndGrads(f) must be a function"),function(t,n){assert(Array.isArray(t)&&t.every(function(e){return e instanceof Tensor}),"The args passed in valueAndGrads(f)(args) must be array of tensors"),assert(null==n||n instanceof Tensor,"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");var r=ENV.engine.gradients(function(){return e.apply(void 0,t)},t,n);return null!=n&&assertShapesMatch(r.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(r.grads),r}}function variableGrads(e,t){if(assert(isFunction(e),"The f passed in variableGrads(f) must be a function"),assert(null==t||Array.isArray(t)&&t.every(function(e){return e instanceof Variable}),"The varList passed in variableGrads(f, varList) must be an array of variables"),null==t)for(var n in t=[],ENV.engine.registeredVariables)t.push(ENV.engine.registeredVariables[n]);var r=t.length;assert((t=t.filter(function(e){return e.trainable})).length>0,"variableGrads() expects at least one of the input variables to be trainable, but none of the "+r+" variables is trainable.");var o=ENV.engine.gradients(e,t,null,!0),a=o.value,i=o.grads;assert(i.some(function(e){return null!=e}),"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),assert(0===a.rank,"The f passed in variableGrads(f) must return a scalar, but it returned a rank-"+a.rank+" tensor");var s={};return t.forEach(function(e,t){null!=i[t]&&(s[e.name]=i[t])}),{value:a,grads:s}}function customGrad(e){return ENV.engine.customGrad(e)}function checkGrads(e){if(e.filter(function(e){return null==e}).length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that\n    the f you passed encloses all operations that lead from x to y.")}var tidy=Environment.tidy,keep=Environment.keep,dispose=Environment.dispose,time=Environment.time,profile=Environment.profile;function warn(){for(var e=[],t=0;t<arguments.length;t++)e[t]=arguments[t];ENV.get("IS_TEST")||console.warn.apply(console,e)}function getReshaped(e,t,n,r){void 0===r&&(r=!0);var o=[];if(r)(o=o.concat(t.slice(0))).push(e[0]/n),o=o.concat(e.slice(1));else{o=o.concat(e[0]);for(var a=t.length,i=0;i<a;++i)o=o.concat([e[i+1]/t[i],t[i]]);o=o.concat(e.slice(a+1))}return o}function getPermuted(e,t,n){void 0===n&&(n=!0);var r=[];if(n){r.push(t);for(var o=t+1;o<e;++o)o<=2*t?(r.push(o),r.push(o-(t+1))):r.push(o)}else{var a=[],i=[];for(o=1;o<e;++o)o>=2*t+1||o%2==1?i.push(o):a.push(o);r.push.apply(r,a),r.push(0),r.push.apply(r,i)}return r}function getReshapedPermuted(e,t,n,r){void 0===r&&(r=!0);var o=[];r?o.push(e[0]/n):o.push(e[0]*n);for(var a=1;a<e.length;++a)a<=t.length?r?o.push(t[a-1]*e[a]):o.push(e[a]/t[a-1]):o.push(e[a]);return o}function getSliceBeginCoords(e,t){for(var n=[0],r=0;r<t;++r)n.push(e[r][0]);return n}function getSliceSize(e,t,n){for(var r=e.slice(0,1),o=0;o<n;++o)r.push(e[o+1]-t[o][0]-t[o][1]);return r}function axesAreInnerMostDims(e,t){for(var n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return!1;return!0}function combineLocations(e,t,n){for(var r=e.length+t.length,o=[],a=0,i=0,s=0;s<r;s++)-1===n.indexOf(s)?o.push(e[a++]):o.push(t[i++]);return o}function computeOutAndReduceShapes(e,t){for(var n=[],r=e.length,o=0;o<r;o++)-1===t.indexOf(o)&&n.push(e[o]);return[n,t.map(function(t){return e[t]})]}function expandShapeToKeepDim(e,t){return combineLocations(e,t.map(function(e){return 1}),t)}function parseAxisParam(e,t){var n=t.length;return assert((e=null==e?t.map(function(e,t){return t}):[].concat(e)).every(function(e){return e>=-n&&e<n}),"All values in axis param must be in range [-"+n+", "+n+") but got axis "+e),assert(e.every(function(e){return isInt(e)}),"All values in axis param must be integers but got axis "+e),e.map(function(e){return e<0?n+e:e})}function assertAxesAreInnerMostDims(e,t,n){assert(axesAreInnerMostDims(t,n),e+" supports only inner-most axes for now. Got axes "+t+" and rank-"+n+" input.")}function getAxesPermutation(e,t){if(axesAreInnerMostDims(e,t))return null;for(var n=[],r=0;r<t;++r)-1===e.indexOf(r)&&n.push(r);return e.forEach(function(e){return n.push(e)}),n}function getUndoAxesPermutation(e){return e.map(function(e,t){return[t,e]}).sort(function(e,t){return e[1]-t[1]}).map(function(e){return e[0]})}function getInnerMostAxes(e,t){for(var n=[],r=t-e;r<t;++r)n.push(r);return n}function assertParamsConsistent(e,t){var n=e[0].length;e.forEach(function(e,t){assert(e.length===n,"Error in concat"+n+"D: rank of tensors["+t+"] must be the same as the rank of the rest ("+n+")")}),assert(t>=0&&t<n,"Error in concat"+n+"D: axis must be between 0 and "+(n-1)+".");var r=e[0];e.forEach(function(e,o){for(var a=0;a<n;a++)assert(a===t||e[a]===r[a],"Error in concat"+n+"D: Shape of tensors["+o+"] ("+e+") does not match the shape of the rest ("+r+") along the non-concatenated axis "+o+".")})}function computeOutShape(e,t){for(var n=e[0].slice(),r=1;r<e.length;r++)n[t]+=e[r][t];return n}function prepareAndValidate(e,t){if(e.rank<1)throw new Error("tf.gatherND() expects the input to be rank 1 or higher, but the rank was "+e.rank+".");if(t.rank<1)throw new Error("tf.gatherND() expects the indices to be rank 1 or higher, but the rank was "+t.rank+".");if("int32"!==t.dtype)throw new Error("tf.gatherND() expects the indices to be int32 type, but the dtype was "+t.dtype+".");if(t.shape[t.rank-1]>e.rank)throw new Error("index innermost dimension length must be <= tensor rank; saw: "+t.shape[t.rank-1]+" vs. "+e.rank);if(0===e.size)throw new Error("Requested more than 0 entries, but input is empty. Input shape: "+e.shape+".");for(var n=t.shape,r=n[n.length-1],o=1,a=0;a<n.length-1;++a)o*=n[a];var i=e.shape,s=n.slice();s.pop();var u=1;for(a=r;a<e.rank;++a)u*=i[a],s.push(i[a]);var l=computeStrides(e.shape).map(function(e){return e/u}).concat([1]).slice(0,r);return[s,o,u,l]}var PARALLELIZE_THRESHOLD=30;function computeOptimalWindowSize(e){return e<=PARALLELIZE_THRESHOLD?e:nearestDivisor(e,Math.floor(Math.sqrt(e)))}function validateUpdateShape(e,t,n){var r=t.rank>1?t.shape[t.rank-1]:1,o=t.rank>1?t.rank-1:1,a="Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: "+n.shape+", indices.shape: "+t.shape+", shape: "+e+", sliceDim: "+r+", and batchDim: "+o+".";if(n.rank<o)throw new Error(a+" update.rank < "+o+". ");if(e.length<r+(n.rank-o))throw new Error(a+" Output shape length < "+(r+(n.rank-o)));if(n.rank!==o+e.length-r)throw new Error(a+" update.rank != "+(o+e.length-r));for(var i=0;i<o;++i)if(n.shape[i]!==t.shape[i])throw new Error(a+" updates.shape["+i+"] ("+n.shape[i]+") != indices.shape["+i+"] ("+t.shape[i]+").");for(i=0;i<n.rank-o;++i)if(n.shape[i+o]!==e[i+r])throw new Error(a+" updates.shape["+(i+o)+"] ("+n.shape[i+o]+") != shape["+(i+o)+"] ("+e[i+o]+")")}function validateInput(e,t,n){if(t.rank<1)throw new Error("tf.scatterND() expects the indices to be rank 1 or higher, but the rank was "+t.rank+".");if(e.rank<1)throw new Error("tf.scatterND() expects the updates to be rank 1 or higher, but the rank was "+e.rank+".");if("int32"!==t.dtype)throw new Error("The dtype of 'indices' should be int32, but got dtype: "+t.dtype);if(n.length<1)throw new Error("Output rank must be greater or equal to 1, but got shape: "+n);if(0===n.length){if(0===t.size)throw new Error("Indices specified for empty output. indices shape: "+t.shape);if(0===e.size)throw new Error("Updates specified for empty output. updates shape: "+e.shape)}validateUpdateShape(n,t,e)}function calculateShapes(e,t,n){for(var r=t.rank>1?t.shape[t.rank-1]:1,o=n.length,a=1,i=r;i<o;++i)a*=n[i];var s=r<1?1:r,u=t.size/s,l=computeStrides(n).concat([1]);return{sliceRank:r,numUpdates:u,sliceSize:a,strides:l.slice(l.length-r,l.length),outputSize:sizeFromShape(n)}}function segOpComputeOptimalWindowSize(e,t){var n,r=!1;for(e<=PARALLELIZE_THRESHOLD?(n=e,r=!0):n=nearestDivisor(e,Math.floor(Math.sqrt(e)));!r;){if(n>t||n===e){r=!0;break}n=nearestDivisor(e,n+1)}return n}function computeOutShape$1(e,t,n){for(var r=[],o=e.length,a=0;a<o;a++)a!==t?r.push(e[a]):r.push(n);return r}function assertParamsValid(e,t,n){assert(e.rank===t.length,"Error in slice"+e.rank+"D: Length of begin "+t+" must match the rank of the array ("+e.rank+")."),assert(e.rank===n.length,"Error in slice"+e.rank+"D: Length of size "+n+" must match the rank of the array ("+e.rank+").");for(var r=0;r<e.rank;++r)assert(t[r]+n[r]<=e.shape[r],"Error in slice"+e.rank+"D: begin["+r+"] + size["+r+"] ("+(t[r]+n[r])+") would overflow input.shape["+r+"] ("+e.shape[r]+")")}function getStridedSlicedInfo(e,t,n,r,o,a,i,s,u){if(void 0===o&&(o=0),void 0===a&&(a=0),void 0===i&&(i=0),void 0===s&&(s=0),void 0===u&&(u=0),0!==i)throw new Error("ellipsis mask is not yet supported");if(0!==s)throw new Error("new axis mask is not yet supported");for(var l=[],c=[],p=[],d=0;d<e.length;d++)l[d]=startForAxis(o,t,r,e,d),c[d]=stopForAxis(a,n,r,e,d),u&1<<d&&(c[d]=l[d]+1,p.push(d));var h=new Array(e.length).fill(0);return h=h.map(function(e,t){for(var n=0,o=l[t];!(r[t]>0?o>=c[t]:o<=c[t]);o+=r[t])n+=1;return n}),[l,h,p]}function startForAxis(e,t,n,r,o){var a=t[o];e&1<<o&&(a=n[o]>0?Number.MIN_SAFE_INTEGER:Number.MAX_SAFE_INTEGER);var i=r[o];return a<0&&(a+=i),a=clamp(0,a,i-1)}function stopForAxis(e,t,n,r,o){var a=t[o];e&1<<o&&(a=n[o]>0?Number.MAX_SAFE_INTEGER:Number.MIN_SAFE_INTEGER);var i=r[o];return a<0&&(a+=i),a=n[o]>0?clamp(0,a,i):clamp(-1,a,i-1)}function inferShape(e){var t=e;if(isTypedArray(e))return[e.length];if(!Array.isArray(e))return[];for(var n=[];t instanceof Array||isTypedArray(t);)n.push(t.length),t=t[0];return e instanceof Array&&ENV.get("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&deepAssertShapeConsistency(e,n,[]),n}function deepAssertShapeConsistency(e,t,n){if(n=n||[],e instanceof Array||isTypedArray(e)){assert(t.length>0,function(){return"Element arr["+n.join("][")+"] should be a primitive, but is an array of "+e.length+" elements"}),assert(e.length===t[0],function(){return"Element arr["+n.join("][")+"] should have "+t[0]+" elements, but has "+e.length+" elements"});for(var r=t.slice(1),o=0;o<e.length;++o)deepAssertShapeConsistency(e[o],r,n.concat(o))}else assert(0===t.length,function(){return"Element arr["+n.join("][")+"] is a primitive, but should be an array/TypedArray of "+t[0]+" elements"})}function assertDtype(e,t,n,r){if(null!=e&&("numeric"!==e&&e!==t||"numeric"===e&&"string"===t))throw new Error("Argument '"+n+"' passed to '"+r+"' must be "+e+" tensor, but got "+t+" tensor")}function convertToTensor(e,t,n,r){if(void 0===r&&(r="numeric"),e instanceof Tensor)return assertDtype(r,e.dtype,t,n),e;var o=inferDtype(e);if("string"!==o&&["bool","int32","float32"].indexOf(r)>=0&&(o=r),assertDtype(r,o,t,n),!isTypedArray(e)&&!Array.isArray(e)&&"number"!=typeof e&&"boolean"!=typeof e&&"string"!=typeof e)throw new Error("Argument '"+t+"' passed to '"+n+"' must be a Tensor or TensorLike, but got '"+e.constructor.name+"'");var a=inferShape(e);isTypedArray(e)||Array.isArray(e)||(e=[e]);var i="string"!==o?toTypedArray(e,o,ENV.get("DEBUG")):flatten(e);return Tensor.make(a,{values:i},o)}function convertToTensorArray(e,t,n){if(!Array.isArray(e))throw new Error("Argument "+t+" passed to "+n+" must be a `Tensor[]` or `TensorLike[]`");return e.map(function(e,r){return convertToTensor(e,t+"["+r+"]",n)})}function op(e){var t=Object.keys(e);if(1!==t.length)throw new Error("Please provide an object with a single key (operation name) mapping to a function. Got an object with "+t.length+" keys.");var n=t[0],r=e[n];n.endsWith("_")&&(n=n.substring(0,n.length-1));var o=function(){for(var e=[],t=0;t<arguments.length;t++)e[t]=arguments[t];ENV.engine.startScope(n);try{var o=r.apply(void 0,e);return o instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),ENV.engine.endScope(o),o}catch(e){throw ENV.engine.endScope(null),e}};return Object.defineProperty(o,"name",{value:n,configurable:!0}),o}function softmax_(e,t){void 0===t&&(t=-1);var n=convertToTensor(e,"logits","softmax");if(-1===t&&(t=n.rank-1),t!==n.rank-1)throw Error("Softmax along a non-last dimension is not yet supported. Logits was rank "+n.rank+" and dim was "+t);return customGrad(function(e){var n=e.logSumExp([t],!0),r=e.toFloat().sub(n).exp();return{value:r,gradFunc:function(e){var n=e.mul(r);return n.sub(n.sum([t],!0).mul(r))}}})(n)}function logSoftmax_(e,t){void 0===t&&(t=-1);var n=convertToTensor(e,"logits","logSoftmax");if(-1===t&&(t=n.rank-1),t!==n.rank-1)throw Error("Log Softmax along a non-last dimension is not yet supported. Logits was rank "+n.rank+" and axis was "+t);return customGrad(function(e){var n=e.max(t,!0),r=e.sub(n),o=r.toFloat().sub(r.exp().sum(t,!0).log());return{value:o,gradFunc:function(e){var n=o.exp();return e.sub(e.sum(t,!0).mul(n))}}})(n)}var softmax=op({softmax_:softmax_}),logSoftmax=op({logSoftmax_:logSoftmax_});function complex_(e,t){var n=convertToTensor(e,"real","complex"),r=convertToTensor(t,"imag","complex");return assertShapesMatch(n.shape,r.shape,"real and imag shapes, "+n.shape+" and "+r.shape+", must match in call to tf.complex()."),ENV.engine.runKernel(function(e){return e.complex(n,r)},{$real:n,$imag:r})}function real_(e){var t=convertToTensor(e,"input","real");return ENV.engine.runKernel(function(e){return e.real(t)},{$input:t})}function imag_(e){var t=convertToTensor(e,"input","imag");return ENV.engine.runKernel(function(e){return e.imag(t)},{$input:t})}var complex=op({complex_:complex_}),real=op({real_:real_}),imag=op({imag_:imag_});function tensor(e,t,n){if(null==n&&(n=inferDtype(e)),"complex64"===n)throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!isTypedArray(e)&&!Array.isArray(e)&&"number"!=typeof e&&"boolean"!=typeof e&&"string"!=typeof e)throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");var r=inferShape(e);if(null!=t){var o=sizeFromShape(t),a=sizeFromShape(r);assert(o===a,function(){return"Based on the provided shape, ["+t+"], the tensor should have "+o+" values but has "+a});for(var i=0;i<r.length;++i){var s=r[i],u=i!==r.length-1||s!==sizeFromShape(t.slice(i));assert(r[i]===t[i]||!u,function(){return"Error creating a new Tensor. Inferred shape ("+r+") does not match the provided shape ("+t+"). "})}}return isTypedArray(e)||Array.isArray(e)||(e=[e]),t=t||r,e="string"!==n?toTypedArray(e,n,ENV.get("DEBUG")):flatten(e),Tensor.make(t,{values:e},n)}function scalar(e,t){if((isTypedArray(e)||Array.isArray(e))&&"complex64"!==t)throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");return tensor(e,[],t)}function tensor1d(e,t){assertNonNull(e);var n=inferShape(e);if(1!==n.length)throw new Error("tensor1d() requires values to be a flat/TypedArray");return tensor(e,n,t)}function tensor2d(e,t,n){if(assertNonNull(e),null!=t&&2!==t.length)throw new Error("tensor2d() requires shape to have two numbers");var r=inferShape(e);if(2!==r.length&&1!==r.length)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return tensor(e,t=t||r,n)}function tensor3d(e,t,n){if(assertNonNull(e),null!=t&&3!==t.length)throw new Error("tensor3d() requires shape to have three numbers");var r=inferShape(e);if(3!==r.length&&1!==r.length)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return tensor(e,t=t||r,n)}function tensor4d(e,t,n){if(assertNonNull(e),null!=t&&4!==t.length)throw new Error("tensor4d() requires shape to have four numbers");var r=inferShape(e);if(4!==r.length&&1!==r.length)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return tensor(e,t=t||r,n)}function tensor5d(e,t,n){if(assertNonNull(e),null!=t&&5!==t.length)throw new Error("tensor5d() requires shape to have five numbers");var r=inferShape(e);if(5!==r.length&&1!==r.length)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return tensor(e,t=t||r,n)}function tensor6d(e,t,n){if(assertNonNull(e),null!=t&&6!==t.length)throw new Error("tensor6d() requires shape to have six numbers");var r=inferShape(e);if(6!==r.length&&1!==r.length)throw new Error("tensor6d() requires values to be number[][][][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return tensor(e,t=t||r,n)}function ones$1(e,t){if(void 0===t&&(t="float32"),"complex64"===t){var n=ones$1(e,"float32"),r=ones$1(e,"float32");return complex(n,r)}var o=makeOnesTypedArray(sizeFromShape(e),t);return Tensor.make(e,{values:o},t)}function zeros(e,t){if(void 0===t&&(t="float32"),"complex64"===t){var n=zeros(e,"float32"),r=zeros(e,"float32");return complex(n,r)}var o=makeZerosTypedArray(sizeFromShape(e),t);return Tensor.make(e,{values:o},t)}function fill(e,t,n){var r=getArrayFromDType(n=n||inferDtype(t),sizeFromShape(e));return r.fill(t),Tensor.make(e,{values:r},n)}function onesLike_(e){var t=convertToTensor(e,"x","onesLike");return ones$1(t.shape,t.dtype)}function zerosLike_(e){var t=convertToTensor(e,"x","zerosLike");return zeros(t.shape,t.dtype)}function linspace(e,t,n){if(0===n)throw new Error("Cannot request zero samples");var r=(t-e)/(n-1),o=makeZerosTypedArray(n,"float32");o[0]=e;for(var a=1;a<o.length;a++)o[a]=o[a-1]+r;return tensor1d(o,"float32")}function range(e,t,n,r){if(void 0===n&&(n=1),void 0===r&&(r="float32"),0===n)throw new Error("Cannot have a step of zero");if(e===t||e<t&&n<0||t<e&&n>1)return zeros([0],r);var o=makeZerosTypedArray(Math.abs(Math.ceil((t-e)/n)),r);t<e&&1===n&&(n=-1),o[0]=e;for(var a=1;a<o.length;a++)o[a]=o[a-1]+n;return tensor1d(o,r)}var onesLike=op({onesLike_:onesLike_}),zerosLike=op({zerosLike_:zerosLike_}),DataStorage=function(){function e(e){this.dataMover=e,this.data=new WeakMap}return e.prototype.get=function(e){return this.data.has(e)||this.dataMover.moveData(e),this.data.get(e)},e.prototype.set=function(e,t){this.data.set(e,t)},e.prototype.has=function(e){return this.data.has(e)},e.prototype.delete=function(e){return this.data.delete(e)},e}(),KernelBackend=function(){function e(){}return e.prototype.time=function(e){throw new Error("Not yet implemented.")},e.prototype.read=function(e){throw new Error("Not yet implemented.")},e.prototype.readSync=function(e){throw new Error("Not yet implemented.")},e.prototype.disposeData=function(e){throw new Error("Not yet implemented.")},e.prototype.write=function(e,t){throw new Error("Not yet implemented.")},e.prototype.fromPixels=function(e,t){throw new Error("Not yet implemented.")},e.prototype.register=function(e,t,n){throw new Error("Not yet implemented.")},e.prototype.memory=function(){throw new Error("Not yet implemented.")},e.prototype.floatPrecision=function(){throw new Error("Not yet implemented")},e.prototype.batchMatMul=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.slice=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.stridedSlice=function(e,t,n,r,o,a,i,s,u){throw new Error("Not yet implemented")},e.prototype.reverse=function(e,t){throw new Error("Not yet implemented")},e.prototype.concat=function(e,t){throw new Error("Not yet implemented")},e.prototype.neg=function(e){throw new Error("Not yet implemented")},e.prototype.add=function(e,t){throw new Error("Not yet implemented")},e.prototype.addN=function(e){throw new Error("Not yet implemented")},e.prototype.subtract=function(e,t){throw new Error("Not yet implemented")},e.prototype.multiply=function(e,t){throw new Error("Not yet implemented")},e.prototype.realDivide=function(e,t){throw new Error("Not yet implemented")},e.prototype.floorDiv=function(e,t){throw new Error("Not yet implemented")},e.prototype.sum=function(e,t){throw new Error("Not yet implemented")},e.prototype.prod=function(e,t){throw new Error("Not yet implemented")},e.prototype.unsortedSegmentSum=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.argMin=function(e,t){throw new Error("Not yet implemented")},e.prototype.argMax=function(e,t){throw new Error("Not yet implemented")},e.prototype.equal=function(e,t){throw new Error("Not yet implemented")},e.prototype.notEqual=function(e,t){throw new Error("Not yet implemented")},e.prototype.less=function(e,t){throw new Error("Not yet implemented")},e.prototype.lessEqual=function(e,t){throw new Error("Not yet implemented")},e.prototype.greater=function(e,t){throw new Error("Not yet implemented")},e.prototype.greaterEqual=function(e,t){throw new Error("Not yet implemented")},e.prototype.logicalNot=function(e){throw new Error("Not yet implemented")},e.prototype.logicalAnd=function(e,t){throw new Error("Not yet implemented")},e.prototype.logicalOr=function(e,t){throw new Error("Not yet implemented")},e.prototype.where=function(e){throw new Error("Not yet implemented")},e.prototype.select=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.topk=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.min=function(e,t){throw new Error("Not yet implemented")},e.prototype.minimum=function(e,t){throw new Error("Not yet implemented")},e.prototype.mod=function(e,t){throw new Error("Not yet implemented")},e.prototype.max=function(e,t){throw new Error("Not yet implemented")},e.prototype.maximum=function(e,t){throw new Error("Not yet implemented")},e.prototype.all=function(e,t){throw new Error("Not yet implemented")},e.prototype.any=function(e,t){throw new Error("Not yet implemented")},e.prototype.squaredDifference=function(e,t){throw new Error("Not yet implemented")},e.prototype.ceil=function(e){throw new Error("Not yet implemented")},e.prototype.floor=function(e){throw new Error("Not yet implemented")},e.prototype.round=function(e){throw new Error("Not yet implemented")},e.prototype.sign=function(e){throw new Error("Not yet implemented")},e.prototype.pow=function(e,t){throw new Error("Not yet implemented")},e.prototype.exp=function(e){throw new Error("Not yet implemented")},e.prototype.expm1=function(e){throw new Error("Not yet implemented")},e.prototype.log=function(e){throw new Error("Not yet implemented")},e.prototype.log1p=function(e){throw new Error("Not yet implemented")},e.prototype.sqrt=function(e){throw new Error("Not yet implemented")},e.prototype.rsqrt=function(e){throw new Error("Not yet implemented")},e.prototype.square=function(e){throw new Error("Not yet implemented")},e.prototype.reciprocal=function(e){throw new Error("Not yet implemented")},e.prototype.relu=function(e){throw new Error("Not yet implemented")},e.prototype.elu=function(e){throw new Error("Not yet implemented")},e.prototype.eluDer=function(e,t){throw new Error("Not yet implemented")},e.prototype.selu=function(e){throw new Error("Not yet implemented")},e.prototype.int=function(e){throw new Error("Not yet implemented")},e.prototype.clip=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.abs=function(e){throw new Error("Not yet implemented")},e.prototype.complexAbs=function(e){throw new Error("Not yet implemented")},e.prototype.sigmoid=function(e){throw new Error("Not yet implemented")},e.prototype.softplus=function(e){throw new Error("Not yet implemented")},e.prototype.sin=function(e){throw new Error("Not yet implemented")},e.prototype.cos=function(e){throw new Error("Not yet implemented")},e.prototype.tan=function(e){throw new Error("Not yet implemented")},e.prototype.asin=function(e){throw new Error("Not yet implemented")},e.prototype.acos=function(e){throw new Error("Not yet implemented")},e.prototype.atan=function(e){throw new Error("Not yet implemented")},e.prototype.atan2=function(e,t){throw new Error("Not yet implemented")},e.prototype.sinh=function(e){throw new Error("Not yet implemented")},e.prototype.cosh=function(e){throw new Error("Not yet implemented")},e.prototype.tanh=function(e){throw new Error("Not yet implemented")},e.prototype.asinh=function(e){throw new Error("Not yet implemented")},e.prototype.acosh=function(e){throw new Error("Not yet implemented")},e.prototype.atanh=function(e){throw new Error("Not yet implemented")},e.prototype.erf=function(e){throw new Error("Not yet implemented")},e.prototype.step=function(e,t){throw new Error("Not yet implemented")},e.prototype.conv2d=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.conv2dDerInput=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.conv2dDerFilter=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.depthwiseConv2D=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.depthwiseConv2DDerInput=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.depthwiseConv2DDerFilter=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.conv3d=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.conv3dDerInput=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.conv3dDerFilter=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.maxPool=function(e,t){throw new Error("Not yet implemented")},e.prototype.maxPoolBackprop=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.avgPool=function(e,t){throw new Error("Not yet implemented")},e.prototype.avgPoolBackprop=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.reshape=function(e,t){throw new Error("Not yet implemented")},e.prototype.cast=function(e,t){throw new Error("Not yet implemented")},e.prototype.tile=function(e,t){throw new Error("Not yet implemented")},e.prototype.pad=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.transpose=function(e,t){throw new Error("Not yet implemented")},e.prototype.gather=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.gatherND=function(e,t){throw new Error("Not yet implemented")},e.prototype.scatterND=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.batchToSpaceND=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.spaceToBatchND=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.resizeBilinear=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.resizeBilinearBackprop=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.resizeNearestNeighbor=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.resizeNearestNeighborBackprop=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.batchNormalization=function(e,t,n,r,o,a){throw new Error("Not yet implemented")},e.prototype.localResponseNormalization4D=function(e,t,n,r,o){throw new Error("Not yet implemented")},e.prototype.LRNGrad=function(e,t,n,r,o,a,i){throw new Error("Not yet implemented")},e.prototype.multinomial=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.oneHot=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.cumsum=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.nonMaxSuppression=function(e,t,n,r,o){throw new Error("Not yet implemented")},e.prototype.fft=function(e){throw new Error("Not yet implemented")},e.prototype.ifft=function(e){throw new Error("Not yet implemented")},e.prototype.complex=function(e,t){throw new Error("Not yet implemented")},e.prototype.real=function(e){throw new Error("Not yet implemented")},e.prototype.imag=function(e){throw new Error("Not yet implemented")},e.prototype.cropAndResize=function(e,t,n,r,o,a){throw new Error("Not yet implemented")},e.prototype.depthToSpace=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.split=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.sparseToDense=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.setDataMover=function(e){throw new Error("Not yet implemented")},e.prototype.dispose=function(){throw new Error("Not yet implemented")},e}();function castTensor(e,t,n){if("complex64"===t){if("complex64"===e.dtype)return e.clone();var r=zeros(e.shape),o=e.toFloat(),a=n.complex(o,r);return r.dispose(),o.dispose(),a}if(!hasEncodingLoss(e.dtype,t))return Tensor.make(e.shape,{dataId:e.dataId},t);if("complex64"===e.dtype){var i=n.real(e);a=i.cast(t);return i.dispose(),a}if("int32"===t)return n.int(e);if("bool"===t){var s=scalar(0,e.dtype);a=n.notEqual(e,s);return s.dispose(),a}throw new Error("Error in Cast: unknown dtype argument ("+t+")")}function reshapeTensor(e,t){return Tensor.make(t,{dataId:e.dataId},e.dtype)}function mergeRealAndImagArrays(e,t){if(e.length!==t.length)throw new Error("Cannot merge real and imag arrays of different lengths. real:"+e.length+", imag: "+t.length+".");for(var n=new Float32Array(2*e.length),r=0;r<n.length;r+=2)n[r]=e[r/2],n[r+1]=t[r/2];return n}function splitRealAndImagArrays(e){for(var t=new Float32Array(e.length/2),n=new Float32Array(e.length/2),r=0;r<e.length;r+=2)t[r/2]=e[r],n[r/2]=e[r+1];return{real:t,imag:n}}function complexWithEvenIndex(e){for(var t=Math.ceil(e.length/4),n=new Float32Array(t),r=new Float32Array(t),o=0;o<e.length;o+=4)n[Math.floor(o/4)]=e[o],r[Math.floor(o/4)]=e[o+1];return{real:n,imag:r}}function complexWithOddIndex(e){for(var t=Math.floor(e.length/4),n=new Float32Array(t),r=new Float32Array(t),o=2;o<e.length;o+=4)n[Math.floor(o/4)]=e[o],r[Math.floor(o/4)]=e[o+1];return{real:n,imag:r}}function getComplexWithIndex(e,t){return{real:e[2*t],imag:e[2*t+1]}}function assignToTypedArray(e,t,n,r){e[2*r]=t,e[2*r+1]=n}function exponents(e,t){for(var n=new Float32Array(e/2),r=new Float32Array(e/2),o=0;o<Math.ceil(e/2);o++){var a=(t?2:-2)*Math.PI*(o/e);n[o]=Math.cos(a),r[o]=Math.sin(a)}return{real:n,imag:r}}function exponent(e,t,n){var r=(n?2:-2)*Math.PI*(e/t);return{real:Math.cos(r),imag:Math.sin(r)}}function nonMaxSuppressionImpl(e,t,n,r,o){for(var a=Array.from(t).map(function(e,t){return{score:e,boxIndex:t}}).filter(function(e){return e.score>o}).sort(function(e,t){return t.score-e.score}),i=[],s=0;s<a.length;s++){var u=a[s],l=u.score,c=u.boxIndex;if(l<o)break;for(var p=!1,d=i.length-1;d>=0;--d){if(intersectionOverUnion(e,c,i[d])>=r){p=!0;break}}if(!p&&(i.push(c),i.length>=n))break}return tensor1d(i,"int32")}function intersectionOverUnion(e,t,n){var r=e.subarray(4*t,4*t+4),o=e.subarray(4*n,4*n+4),a=Math.min(r[0],r[2]),i=Math.min(r[1],r[3]),s=Math.max(r[0],r[2]),u=Math.max(r[1],r[3]),l=Math.min(o[0],o[2]),c=Math.min(o[1],o[3]),p=Math.max(o[0],o[2]),d=Math.max(o[1],o[3]),h=(s-a)*(u-i),f=(p-l)*(d-c);if(h<=0||f<=0)return 0;var m=Math.max(a,l),g=Math.max(i,c),v=Math.min(s,p),y=Math.min(u,d),x=Math.max(v-m,0)*Math.max(y-g,0);return x/(h+f-x)}function split(e,t,n){var r=Array(e.rank).fill(0),o=e.shape.slice();return t.map(function(t){o[n]=t;var a=e.slice(r,o);return r[n]+=t,a})}function topkImpl(e,t,n,r,o){for(var a=t[t.length-1],i=[e.length/a,a],s=i[0],u=i[1],l=getTypedArrayFromDType(n,s*r),c=getTypedArrayFromDType("int32",s*r),p=0;p<s;p++){for(var d=p*u,h=e.subarray(d,d+u),f=[],m=0;m<h.length;m++)f.push({value:h[m],index:m});f.sort(function(e,t){return t.value-e.value});var g=p*r,v=l.subarray(g,g+r),y=c.subarray(g,g+r);for(m=0;m<r;m++)v[m]=f[m].value,y[m]=f[m].index}var x=t.slice();return x[x.length-1]=r,[tensor(l,x,n),tensor(c,x,"int32")]}var ArgMinMaxProgram=function(){return function(e,t,n){this.variableNames=["A"];var r=e.windowSize,o=e.batchSize,a=e.inSize,i=Math.ceil(a/r);n||this.variableNames.push("bestIndicesA"),this.outputShape=[o,i];var s="max"===t?">":"<",u=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode="\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * "+r+";\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < "+r+"; i++) {\n          int inIdx = "+u+";\n          float candidate = getA(batch, inIdx);\n          if (candidate "+s+" bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    "}}(),AvgPool2DBackpropProgram=function(){return function(e){this.variableNames=["dy"],this.outputShape=e.inShape;var t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,o=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,s=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=s-1-e.padInfo.top,c=u-1-e.padInfo.left,p=1/(t*n);this.userCode="\n      const ivec2 pads = ivec2("+l+", "+c+");\n      const float avgMultiplier = float("+p+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+s+";\n            wR += "+a+") {\n          float dyR = float(dyRCorner + wR) / "+r+".0;\n\n          if (dyR < 0.0 || dyR >= "+e.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < "+u+";\n            wC+= "+i+") {\n            float dyC = float(dyCCorner + wC) / "+o+".0;\n\n            if (dyC < 0.0 || dyC >= "+e.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    "}}();function getBroadcastDims(e,t){for(var n=e.length,r=[],o=0;o<n;o++){var a=n-1-o,i=e[a]||1;(t[t.length-1-o]||1)>1&&1===i&&r.unshift(a)}return r}function getReductionAxes(e,t){for(var n=[],r=0;r<t.length;r++){var o=e[e.length-r-1],a=t.length-r-1,i=t[a];(null==o||1===o&&i>1)&&n.unshift(a)}return n}function broadcastDimsAreOuter(e){for(var t=0;t<e.length;t++)if(e[t]!==t)return!1;return!0}function assertAndGetBroadcastShape(e,t){for(var n=[],r=Math.max(e.length,t.length),o=0;o<r;o++){var a=e[e.length-o-1];null==a&&(a=1);var i=t[t.length-o-1];if(null==i&&(i=1),1===a)n.unshift(i);else if(1===i)n.unshift(a);else{if(a!==i)throw Error("Operands could not be broadcast together with shapes "+e+" and "+t+".");n.unshift(a)}}return n}var BatchNormProgram=function(){return function(e,t,n,r,o,a){this.outputShape=[],this.supportsBroadcasting=!0,this.variableNames=["x","mean","variance"],assertAndGetBroadcastShape(e,t),assertAndGetBroadcastShape(e,n);var i="0.0";null!=r&&(assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");var s="1.0";null!=o&&(assertAndGetBroadcastShape(e,o),this.variableNames.push("scale"),s="getScaleAtOutCoords()"),this.outputShape=e,this.userCode="\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = "+i+";\n        float scale = "+s+";\n        float inv = scale * inversesqrt(variance + float("+a+"));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    "}}(),BatchNormPackedProgram=function(){return function(e,t,n,r,o,a){this.supportsBroadcasting=!0,this.usesPackedTextures=!0,this.variableNames=["x","mean","variance"],assertAndGetBroadcastShape(e,t),assertAndGetBroadcastShape(e,n);var i="vec4(0.0)";null!=r&&(assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");var s="vec4(1.0)";null!=o&&(assertAndGetBroadcastShape(e,o),this.variableNames.push("scale"),s="getScaleAtOutCoords()"),this.outputShape=e,this.userCode="\n      void main() {\n        vec4 offset = "+i+";\n        vec4 scale = "+s+";\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4("+a+"));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    "}}(),COMPLEX_MULTIPLY={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},BinaryOpComplexProgram=function(){return function(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.supportsBroadcasting=!0,this.outputShape=assertAndGetBroadcastShape(t,n),this.userCode="\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        "+e+"\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    "}}(),CHECK_NAN_SNIPPET="\n  if (isNaN(a)) return a;\n  if (isNaN(b)) return b;\n",ADD="return a + b;",SUB="return a - b;",MUL="return a * b;",DIV="if (a == b) return 1.0;\n  return a / b;",INT_DIV="\n  float resultSign = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  int result = ia / ib;\n  int amodb = ia - ib * result;\n\n  if (resultSign < 0.0 && amodb != 0) {\n    result -= 1;\n  }\n  return float(result);\n",POW="\nif(a < 0.0 && floor(b) < b){\n  return NAN;\n}\nreturn (round(mod(b, 2.0)) == 0 || round(mod(b, 2.0)) == 2) ?\n    pow(abs(a), b) : sign(a) * pow(abs(a), b);\n",SQUARED_DIFFERENCE="return (a - b) * (a - b);",EQUAL="return float(a == b);",NOT_EQUAL="return float(a != b);",LESS="return float(a < b);",LESS_EQUAL="return float(a <= b);",GREATER="return float(a > b);",GREATER_EQUAL="return float(a >= b);",LOGICAL_AND="return float(a >= 1.0 && b >= 1.0);",LOGICAL_OR="return float(a >= 1.0 || b >= 1.0);",MAX=CHECK_NAN_SNIPPET+"\n  return max(a, b);\n",MIN=CHECK_NAN_SNIPPET+"\n  return min(a, b);\n",MOD="if (b == 0.0) return NAN;\n  return mod(a, b);",ATAN2=CHECK_NAN_SNIPPET+"\n  return atan(a, b);\n",ELU_DER="return (b >= 1.0) ? a : a * (b + 1.0);",BinaryOpProgram=function(){function e(e,t,n){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.outputShape=assertAndGetBroadcastShape(t,n),this.userCode="\n      uniform float NAN;\n      float binaryOperation(float a, float b) {\n        "+e+"\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    "}return e.prototype.getCustomSetupFunc=function(){var e=this;return function(t,n){null==e.startLoc&&(e.startLoc=t.getUniformLocationNoThrow(n,"NAN"),null==e.startLoc)||t.gl.uniform1f(e.startLoc,NaN)}},e}(),ClipProgram=function(){return function(e,t,n){this.variableNames=["A"],this.outputShape=e,this.userCode="\n      void main() {\n        float value = getAAtOutCoords();\n        if (isNaN(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, float("+t+"), float("+n+")));\n      }\n    "}}(),ClipPackedProgram=function(){return function(e,t,n){this.variableNames=["A"],this.usesPackedTextures=!0,this.outputShape=e,this.userCode="\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (hasNaN(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4("+t+"), vec4("+n+")));\n      }\n    "}}(),ComplexAbsProgram=function(){return function(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode="\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    "}}(),ConcatProgram=function(){return function(e,t){this.variableNames=["A","B"],this.outputShape=[],this.outputShape=computeOutShape([e,t],1),this.userCode="\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        float value = 0.0;\n        if (yC < "+e[1]+") {\n          value = getA(yR, yC);\n        } else {\n          yC -= "+e[1]+";\n          value = getB(yR, yC);\n        }\n\n        setOutput(value);\n      }\n    "}}(),Conv2DDerFilterProgram=function(){return function(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;var t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,o=e.padInfo.left;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < "+e.batchSize+"; b++) {\n          for (int yR = 0; yR < "+e.outHeight+"; yR++) {\n            int xR = wR + yR * "+t+" - "+r+";\n\n            if (xR < 0 || xR >= "+e.inHeight+") {\n              continue;\n            }\n\n            for (int yC = 0; yC < "+e.outWidth+"; yC++) {\n              int xC = wC + yC * "+n+" - "+o+";\n\n              if (xC < 0 || xC >= "+e.inWidth+") {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "}}(),Conv2DDerInputProgram=function(){return function(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;var t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,o=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left;this.userCode="\n      const ivec2 pads = ivec2("+a+", "+i+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+t+"; wR++) {\n          float dyR = float(dyRCorner + wR) / "+r+".0;\n\n          if (dyR < 0.0 || dyR >= "+e.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = "+t+" - 1 - wR;\n\n          for (int wC = 0; wC < "+n+"; wC++) {\n            float dyC = float(dyCCorner + wC) / "+o+".0;\n\n            if (dyC < 0.0 || dyC >= "+e.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = "+n+" - 1 - wC;\n\n            for (int d2 = 0; d2 < "+e.outChannels+"; d2++) {\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, d2);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "}}(),Conv3DDerFilterProgram=function(){return function(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;var t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,o=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode="\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < "+e.batchSize+"; b++) {\n          for (int yF = 0; yF < "+e.outDepth+"; yF++) {\n            int xF = wF + yF * "+t+" - "+o+";\n\n            if (xF < 0 || xF >= "+e.inDepth+") {\n              continue;\n            }\n\n            for (int yR = 0; yR < "+e.outHeight+"; yR++) {\n              int xR = wR + yR * "+n+" - "+a+";\n\n              if (xR < 0 || xR >= "+e.inHeight+") {\n                continue;\n              }\n\n              for (int yC = 0; yC < "+e.outWidth+"; yC++) {\n                int xC = wC + yC * "+r+" - "+i+";\n\n                if (xC < 0 || xC >= "+e.inWidth+") {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "}}(),Conv3DDerInputProgram=function(){return function(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;var t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,o=e.strideDepth,a=e.strideHeight,i=e.strideWidth,s=t-1-e.padInfo.front,u=n-1-e.padInfo.top,l=r-1-e.padInfo.left;this.userCode="\n      const ivec3 pads = ivec3("+s+", "+u+", "+l+");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < "+t+"; wF++) {\n          float dyF = float(dyFCorner + wF) / "+o+".0;\n\n          if (dyF < 0.0 || dyF >= "+e.outDepth+".0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = "+t+" - 1 - wF;\n\n          for (int wR = 0; wR < "+n+"; wR++) {\n            float dyR = float(dyRCorner + wR) / "+a+".0;\n\n            if (dyR < 0.0 || dyR >= "+e.outHeight+".0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = "+n+" - 1 - wR;\n\n            for (int wC = 0; wC < "+r+"; wC++) {\n              float dyC = float(dyCCorner + wC) / "+i+".0;\n\n              if (dyC < 0.0 || dyC >= "+e.outWidth+".0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = "+r+" - 1 - wC;\n\n              for (int d2 = 0; d2 < "+e.outChannels+"; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "}}(),DepthwiseConv2DDerFilterProgram=function(){return function(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;var t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,o=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * "+a+" + dm;\n\n        float dotProd = 0.0;\n\n        // TODO: Vec4 over the batch size\n        for (int b = 0; b < "+e.batchSize+"; b++) {\n          for (int yR = 0; yR < "+e.outHeight+"; yR++) {\n            int xR = wR + yR * "+t+" - "+r+";\n\n            if (xR < 0 || xR >= "+e.inHeight+") {\n              continue;\n            }\n\n            for (int yC = 0; yC < "+e.outWidth+"; yC++) {\n              int xC = wC + yC * "+n+" - "+o+";\n\n              if (xC < 0 || xC >= "+e.inWidth+") {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "}}(),DepthwiseConv2DDerInputProgram=function(){return function(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;var t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,o=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode="\n      const ivec2 pads = ivec2("+a+", "+i+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < "+t+"; wR++) {\n          float dyR = float(dyRCorner + wR) / "+r+".0;\n\n          if (dyR < 0.0 || dyR >= "+e.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = "+t+" - 1 - wR;\n\n          for (int wC = 0; wC < "+n+"; wC++) {\n            float dyC = float(dyCCorner + wC) / "+o+".0;\n\n            if (dyC < 0.0 || dyC >= "+e.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = "+n+" - 1 - wC;\n\n            // TODO: Vec4 over the channelMul\n            for (int dm = 0; dm < "+s+"; dm++) {\n              int d2 = d1 * "+s+" + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "}}(),Conv2DProgram=function(){return function(e){this.variableNames=["x","W"],this.outputShape=e.outShape;var t=e.padInfo.top,n=e.padInfo.left,r=e.strideHeight,o=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,s=e.filterHeight,u=e.filterWidth,l=4*Math.floor(e.inChannels/4),c=e.inChannels%4;this.userCode="\n      const ivec2 strides = ivec2("+r+", "+o+");\n      const ivec2 pads = ivec2("+t+", "+n+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+s+"; wR++) {\n          int xR = xRCorner + wR * "+a+";\n\n          if (xR < 0 || xR >= "+e.inHeight+") {\n            continue;\n          }\n\n          for (int wC = 0; wC < "+u+"; wC++) {\n            int xC = xCCorner + wC * "+i+";\n\n            if (xC < 0 || xC >= "+e.inWidth+") {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < "+l+"; d1 += 4) {\n              vec4 xValues = vec4(\n                getX(batch, xR, xC, d1),\n                getX(batch, xR, xC, d1 + 1),\n                getX(batch, xR, xC, d1 + 2),\n                getX(batch, xR, xC, d1 + 3)\n              );\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              dotProd += dot(xValues, wValues);\n            }\n\n            if ("+(1===c)+") {\n              dotProd +=\n                getX(batch, xR, xC, "+l+") *\n                getW(wR, wC, "+l+", d2);\n            } else if ("+(2===c)+") {\n              vec2 xValues = vec2(\n                getX(batch, xR, xC, "+l+"),\n                getX(batch, xR, xC, "+l+" + 1)\n              );\n              vec2 wValues = vec2(\n                getW(wR, wC, "+l+", d2),\n                getW(wR, wC, "+l+" + 1, d2)\n              );\n              dotProd += dot(xValues, wValues);\n            } else if ("+(3===c)+") {\n              vec3 xValues = vec3(\n                getX(batch, xR, xC, "+l+"),\n                getX(batch, xR, xC, "+l+" + 1),\n                getX(batch, xR, xC, "+l+" + 2)\n              );\n              vec3 wValues = vec3(\n                getW(wR, wC, "+l+", d2),\n                getW(wR, wC, "+l+" + 1, d2),\n                getW(wR, wC, "+l+" + 2, d2)\n              );\n              dotProd += dot(xValues, wValues);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "}}(),Conv3DProgram=function(){return function(e){this.variableNames=["x","W"],this.outputShape=e.outShape;var t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,o=e.strideDepth,a=e.strideHeight,i=e.strideWidth,s=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=4*Math.floor(e.inChannels/4),f=e.inChannels%4;this.userCode="\n      const ivec3 strides = ivec3("+o+", "+a+", "+i+");\n      const ivec3 pads = ivec3("+t+", "+n+", "+r+");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < "+c+"; wF++) {\n          int xF = xFCorner + wF * "+s+";\n\n          if (xF < 0 || xF >= "+e.inDepth+") {\n            continue;\n          }\n\n          for (int wR = 0; wR < "+p+"; wR++) {\n            int xR = xRCorner + wR * "+u+";\n\n            if (xR < 0 || xR >= "+e.inHeight+") {\n              continue;\n            }\n\n            for (int wC = 0; wC < "+d+"; wC++) {\n              int xC = xCCorner + wC * "+l+";\n\n              if (xC < 0 || xC >= "+e.inWidth+") {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < "+h+"; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if ("+(1===f)+") {\n                dotProd +=\n                  getX(batch, xF, xR, xC, "+h+") *\n                  getW(wF, wR, wC, "+h+", d2);\n              } else if ("+(2===f)+") {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, "+h+"),\n                  getX(batch, xF, xR, xC, "+h+" + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, "+h+", d2),\n                  getW(wF, wR, wC, "+h+" + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if ("+(3===f)+") {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, "+h+"),\n                  getX(batch, xF, xR, xC, "+h+" + 1),\n                  getX(batch, xF, xR, xC, "+h+" + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, "+h+", d2),\n                  getW(wF, wR, wC, "+h+" + 1, d2),\n                  getW(wF, wR, wC, "+h+" + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "}}(),DepthwiseConv2DProgram=function(){return function(e){this.variableNames=["x","W"],this.outputShape=e.outShape;var t=e.inHeight,n=e.inWidth,r=e.padInfo.top,o=e.padInfo.left,a=e.strideHeight,i=e.strideWidth,s=e.dilationHeight,u=e.dilationWidth,l=e.filterHeight,c=e.filterWidth,p=e.outChannels/e.inChannels;this.userCode="\n      const ivec2 strides = ivec2("+a+", "+i+");\n      const ivec2 pads = ivec2("+r+", "+o+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / "+p+";\n        int q = d2 - d1 * "+p+";\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TODO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < "+l+"; wR++) {\n          int xR = xRCorner + wR * "+s+";\n\n          if (xR < 0 || xR >= "+t+") {\n            continue;\n          }\n\n          for (int wC = 0; wC < "+c+"; wC++) {\n            int xC = xCCorner + wC * "+u+";\n\n            if (xC < 0 || xC >= "+n+") {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n        setOutput(dotProd);\n      }\n    "}}(),DepthwiseConvPacked2DProgram=function(){return function(e){this.variableNames=["x","W"],this.usesPackedTextures=!0,this.outputShape=e.outShape;for(var t=e.inHeight,n=e.inWidth,r=e.padInfo.top,o=e.padInfo.left,a=e.strideHeight,i=e.strideWidth,s=e.filterHeight,u=e.filterWidth,l=Math.ceil((u+1)/2),c="int xR; int xC;",p=0;p<s;p++){for(var d=-o;d<2*l;d++)c+="vec4 "+xTexelName(p,d)+" = vec4(0.);";for(d=0;d<u;d++)c+="\n          vec4 wR"+p+"C"+d+" = vec4(0.);\n          vec4 xR"+p+"C"+d+" = vec4(0.);"}for(p=0;p<s;p++)for(d=0;d<l;d++){var h=2*d,f=2*d+o;c+="\n          xR = xRCorner + "+p+";\n          xC = xCCorner + "+f+";\n\n          if(xR >= 0 && xR < "+t+" && xC >= 0 && xC < "+n+") {\n            "+xTexelName(p,f)+" = getX(batch, xR, xC, d1);\n          }",0===o?h<u&&d===l-1&&(i>1&&(c+="\n                vec4 "+xTexelName(p,f+2)+" = vec4(0.);\n\n                if(xR >= 0 && xR < "+t+" && xC + 2 < "+n+") {\n                  "+xTexelName(p,f+2)+" = getX(batch, xR, xC + 2, d1);\n                }"),c+="\n              xR"+p+"C"+f+" = "+constructTexel(p,f,i,o)+";\n            "):0===d&&(c+="\n            if(xR >= 0 && xR < "+t+" && xC - 2 >= 0) {\n              "+xTexelName(p,f-2)+" = getX(batch, xR, xC - 2, d1);\n            }"),h>0&&(c+="xR"+p+"C"+(f-2)+" =\n            "+constructTexel(p,f-2,i,o)+";"),f-1>=0&&f-1<u&&(c+="xR"+p+"C"+(f-1)+" =\n              "+constructTexel(p,f-1,i,o)+";"),h<u&&(c+="\n            vec4 wTexel"+p+"C"+h+" = getW("+p+", "+h+", d1, q);\n            wR"+p+"C"+h+" = vec4(wTexel"+p+"C"+h+".xz, wTexel"+p+"C"+h+".xz);\n          ",h+1<u&&(c+="\n              vec4 wTexelR"+p+"C"+(h+1)+" = getW("+p+", "+(h+1)+", d1, q);\n              wR"+p+"C"+(h+1)+" =\n                vec4(wTexelR"+p+"C"+(h+1)+".xz, wTexelR"+p+"C"+(h+1)+".xz);"))}for(p=0;p<s;p++)for(d=0;d<u;d++)c+="result += xR"+p+"C"+d+" * wR"+p+"C"+d+";";this.userCode="\n      const ivec2 strides = ivec2("+a+", "+i+");\n      const ivec2 pads = ivec2("+r+", "+o+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2;\n        int q = 0;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        vec4 result = vec4(0.);\n\n        "+c+"\n\n        setOutput(result);\n      }\n    "}}();function xTexelName(e,t){return"xTexelR"+e+"C"+(t<0?"minus"+Math.abs(t).toString():t)}function constructTexel(e,t,n,r){return 1===n?r%2==t%2?xTexelName(e,t):"vec4("+xTexelName(e,t-1)+".zw, "+xTexelName(e,t+1)+".xy)":r%2==t%2?"vec4("+xTexelName(e,t)+".xy, "+xTexelName(e,t+2)+".xy)":"vec4("+xTexelName(e,t-1)+".zw, "+xTexelName(e,t+1)+".zw)"}var CropAndResizeProgram=function(){return function(e,t,n,r,o){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];var a=e[0],i=e[1],s=e[2],u=e[3],l=t[0],c=n[0],p=n[1];this.outputShape=[l,c,p,u];var d="bilinear"===r?1:0,h=[i-1+".0",s-1+".0"],f=h[0],m=h[1],g=c>1?[""+(i-1)/(c-1),"(y2-y1) * height_ratio","y1*"+f+" + float(y)*(height_scale)"]:["0.0","0.0","0.5 * (y1+y2) * "+f],v=g[0],y=g[1],x=g[2],T=p>1?[""+(s-1)/(p-1),"(x2-x1) * width_ratio","x1*"+m+" + float(x)*(width_scale)"]:["0.0","0.0","0.5 * (x1+x2) * "+m],w=T[0],E=T[1],S=T[2];this.userCode="\n      const float height_ratio = float("+v+");\n      const float width_ratio = float("+w+");\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= "+a+") {\n          return;\n        }\n\n        float height_scale = "+y+";\n        float width_scale = "+E+";\n\n        float in_y = "+x+";\n        if( in_y < 0.0 || in_y > "+f+" ) {\n          setOutput(float("+o+"));\n          return;\n        }\n        float in_x = "+S+";\n        if( in_x < 0.0 || in_x > "+m+" ) {\n          setOutput(float("+o+"));\n          return;\n        }\n\n        vec2 sourceFracIndexRC = vec2(in_y,in_x);\n        if("+d+" == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n          ivec2 sourceCeilRC = ivec2(ceil(sourceFracIndexRC));\n\n          float topLeft = getImage(b, sourceFloorRC.x, sourceFloorRC.y, d);\n          float bottomLeft = getImage(b, sourceCeilRC.x, sourceFloorRC.y, d);\n          float topRight = getImage(b, sourceFloorRC.x, sourceCeilRC.y, d);\n          float bottomRight = getImage(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n          vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n          float top = topLeft + (topRight - topLeft) * fracRC.y;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n          float newValue = top + (bottom - top) * fracRC.x;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestRC = ivec2(floor(\n            sourceFracIndexRC + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestRC.x, sourceNearestRC.y, d);\n          setOutput(newValue);\n        }\n      }\n    "}}();function getLogicalCoordinatesFromFlatIndex(e,t,n){void 0===n&&(n="index");var r=computeStrides(t);return r.map(function(t,o){return"int "+e[o]+" = "+n+" / "+t+"; "+(o===r.length-1?"int "+e[o+1]+" = "+n+" - "+e[o]+" * "+t:"index -= "+e[o]+" * "+t)+";"}).join("")}function buildVec(e){return 1===e.length?""+e[0]:"vec"+e.length+"("+e.join(",")+")"}function dotify(e,t){if(e.length!==t.length)throw new Error("Vectors to be dotted must be of the same length -got "+e.length+" and "+t.length);for(var n=[],r=Math.floor(e.length/4),o=e.length%4,a=0;a<r;a++){var i=e.slice(4*a,4*a+4),s=t.slice(4*a,4*a+4);n.push(buildVec(i)+", "+buildVec(s))}if(0!==o){i=e.slice(4*r),s=t.slice(4*r);1===i.length&&(i=i.map(function(e){return"float("+e+")"}),s=s.map(function(e){return"float("+e+")"})),n.push(buildVec(i)+", "+buildVec(s))}return n.map(function(e,t){return"dot("+e+")"}).join("+")}function makeShader(e,t,n,r,o){var a=e.map(function(e){var t=sizeFromShape(e.shapeInfo.logicalShape);return e.shapeInfo.isUniform?"uniform float "+e.name+(t>1?"["+t+"]":"")+";":"uniform sampler2D "+e.name+";"});a=a.join("\n");var i,s,u=e.map(function(e){return getInputSamplingSnippet(e,t,r,o)}).join("\n"),l=t.texShape,c=SHADER_PREFIX;return t.isPacked?(i=getPackedOutputSamplingSnippet(t.logicalShape,l),s=FLOAT_TEXTURE_SET_RGBA_SNIPPET):(i=getOutputSamplingSnippet(t.logicalShape,l),s=FLOAT_TEXTURE_SET_R_SNIPPET),o&&(c+=SHADER_PACKED_PREFIX),[c,FLOAT_TEXTURE_SAMPLE_SNIPPET,s,a,i,u,n].join("\n")}function getSamplerFromInInfo(e){var t=e.shapeInfo.logicalShape;switch(t.length){case 0:return getSamplerScalar(e);case 1:return getSampler1D(e);case 2:return getSampler2D(e);case 3:return getSampler3D(e);case 4:return getSampler4D(e);case 5:return getSampler5D(e);case 6:return getSampler6D(e);default:throw new Error(t.length+"-D input sampling is not yet supported")}}function getPackedSamplerFromInInfo(e){var t=e.shapeInfo.logicalShape;switch(t.length){case 0:return getPackedSamplerScalar(e);case 1:return getPackedSampler1D(e);case 2:return getPackedSampler2D(e);case 3:return getPackedSampler3D(e);case 4:return getPackedSampler4D(e);default:throw new Error("Packed "+t.length+"-D input sampling is not yet supported")}}function getInputSamplingSnippet(e,t,n,r){void 0===r&&(r=!1);var o=getSamplerFlat(e);return o+=r?getPackedSamplerFromInInfo(e):getSamplerFromInInfo(e),(n||arraysEqual(e.shapeInfo.logicalShape,t.logicalShape))&&(o+=r?getPackedSamplerAtOutputCoords(e,t,n):getSamplerAtOutputCoords(e,t,n)),o}function getPackedOutputSamplingSnippet(e,t){switch(e.length){case 0:return getOutputScalarCoords();case 1:return getOutputPacked1DCoords(e,t);case 2:return getOutputPacked2DCoords(e,t);case 3:return getOutputPacked3DCoords(e,t);case 4:return getOutputPacked4DCoords(e,t);default:throw new Error(e.length+"-D packed output coordinate fetching is not yet supported")}}function getOutputSamplingSnippet(e,t){switch(e.length){case 0:return getOutputScalarCoords();case 1:return getOutput1DCoords(e,t);case 2:return getOutput2DCoords(e,t);case 3:return getOutput3DCoords(e,t);case 4:return getOutput4DCoords(e,t);case 5:return getOutput5DCoords(e,t);case 6:return getOutput6DCoords(e,t);default:throw new Error(e.length+"-D output sampling is not yet supported")}}var SAMPLE_1D_SNIPPET="\nvec2 UVfrom1D(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",SAMPLE_2D_SNIPPET="\nvec2 UVfrom2D(int texNumR, int texNumC, int numC, int row, int col) {\n  int index = row * numC + col;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",SAMPLE_3D_SNIPPET="\nvec2 UVfrom3D(int texNumR, int texNumC, int stride0,\n    int stride1, int row, int col, int depth) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",SAMPLE_4D_SNIPPET="\nvec2 UVfrom4D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int row, int col, int depth,\n    int depth2) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth * stride2 + depth2;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom4D(int texNumR, int texNumC, int texelsInBatch2,\n    int texelsInBatch, int texelsInLogicalRow, int b2, int b,\n    int row, int col) {\n  int index = b2 * texelsInBatch2 + b * texelsInBatch +\n    (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",SAMPLE_5D_SNIPPET="\nvec2 UVfrom5D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int stride3, int row, int col, int depth,\n    int depth2, int depth3) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 +\n              depth * stride2 + depth2 * stride3 + depth3;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",SAMPLE_6D_SNIPPET="\nvec2 UVfrom6D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int stride3, int stride4,\n    int row, int col, int depth, int depth2, int depth3, int depth4) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth * stride2 + depth2 *\n    stride3 + depth3 * stride4 + depth4;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",FLOAT_TEXTURE_SAMPLE_SNIPPET="\n  float sampleTexture(sampler2D textureSampler, vec2 uv) {\n    return texture2D(textureSampler, uv).r;\n  }\n",FLOAT_TEXTURE_SET_R_SNIPPET="\n  void setOutput(float val) {\n    gl_FragColor = vec4(val, 0, 0, 0);\n  }\n",FLOAT_TEXTURE_SET_RGBA_SNIPPET="\n  void setOutput(vec4 val) {\n    gl_FragColor = val;\n  }\n",NAN_CHECKS="",SHADER_PREFIX="\n  precision highp float;\n  precision highp int;\n  varying vec2 resultUV;\n  const vec2 halfCR = vec2(0.5, 0.5);\n\n  struct ivec5\n  {\n    int x;\n    int y;\n    int z;\n    int w;\n    int u;\n  };\n\n  struct ivec6\n  {\n    int x;\n    int y;\n    int z;\n    int w;\n    int u;\n    int v;\n  };\n\n  "+(NAN_CHECKS=ENV.get("PROD")?"\n    bool isNaN(float val) {\n      return false;\n    }\n\n    bool hasNaN(vec4 values) {\n      return false;\n    }\n  ":"\n    bool isNaN(float val) {\n      return (val < 1.0 || 0.0 < val || val == 0.0) ? false : true;\n    }\n\n    bool hasNaN(vec4 values) {\n      return any(bvec4(\n        isNaN(values.x),\n        isNaN(values.y),\n        isNaN(values.z),\n        isNaN(values.w)\n      ));\n    }\n  ")+"\n\n  float getNaN(vec4 values) {\n    return dot(vec4(1), values);\n  }\n\n  int round(float value) {\n    return int(floor(value + 0.5));\n  }\n\n  int imod(int x, int y) {\n    return x - y * (x / y);\n  }\n\n  //Based on the work of Dave Hoskins\n  //https://www.shadertoy.com/view/4djSRW\n  #define HASHSCALE1 443.8975\n  float random(float seed){\n    vec2 p = resultUV * seed;\n    vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n    p3 += dot(p3, p3.yzx + 19.19);\n    return fract((p3.x + p3.y) * p3.z);\n  }\n\n  "+SAMPLE_1D_SNIPPET+"\n  "+SAMPLE_2D_SNIPPET+"\n  "+SAMPLE_3D_SNIPPET+"\n  "+SAMPLE_4D_SNIPPET+"\n  "+SAMPLE_5D_SNIPPET+"\n  "+SAMPLE_6D_SNIPPET+"\n",SHADER_PACKED_PREFIX="\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n";function getOutputScalarCoords(){return"\n    int getOutputCoords() {\n      return 0;\n    }\n  "}function getOutputPacked1DCoords(e,t){var n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return 1===t[0]?"\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * "+n[1]+".0);\n      }\n    ":1===t[1]?"\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * "+n[0]+".0);\n      }\n    ":"\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+n[0]+", "+n[1]+"));\n      return resTexRC.x * "+n[1]+" + resTexRC.y;\n    }\n  "}function getOutput1DCoords(e,t){return 1===t[0]?"\n      int getOutputCoords() {\n        return int(resultUV.x * "+t[1]+".0);\n      }\n    ":1===t[1]?"\n      int getOutputCoords() {\n        return int(resultUV.y * "+t[0]+".0);\n      }\n    ":"\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+t[0]+", "+t[1]+"));\n      return resTexRC.x * "+t[1]+" + resTexRC.y;\n    }\n  "}function getOutputPacked3DCoords(e,t){var n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),o=r*Math.ceil(e[1]/2);return"\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+n[0]+", "+n[1]+"));\n      int index = resTexRC.x * "+n[1]+" + resTexRC.y;\n\n      int b = index / "+o+";\n      index -= b * "+o+";\n\n      int r = 2 * (index / "+r+");\n      int c = imod(index, "+r+") * 2;\n\n      return ivec3(b, r, c);\n    }\n  "}function getOutput3DCoords(e,t){var n=getLogicalCoordinatesFromFlatIndex(["r","c","d"],e);return"\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+t[0]+", "+t[1]+"));\n      int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n      "+n+"\n      return ivec3(r, c, d);\n    }\n  "}function getOutputPacked4DCoords(e,t){var n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[3]/2),o=r*Math.ceil(e[2]/2),a=o*e[1];return"\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+n[0]+", "+n[1]+"));\n      int index = resTexRC.x * "+n[1]+" + resTexRC.y;\n\n      int b2 = index / "+a+";\n      index -= b2 * "+a+";\n\n      int b = index / "+o+";\n      index -= b * "+o+";\n\n      int r = 2 * (index / "+r+");\n      int c = imod(index, "+r+") * 2;\n\n      return ivec4(b2, b, r, c);\n    }\n  "}function getOutput4DCoords(e,t){var n=getLogicalCoordinatesFromFlatIndex(["r","c","d","d2"],e);return"\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2("+t[0]+", "+t[1]+"));\n      int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n      "+n+"\n      return ivec4(r, c, d, d2);\n    }\n  "}function getOutput5DCoords(e,t){var n=getLogicalCoordinatesFromFlatIndex(["r","c","d","d2","d3"],e);return"\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2("+t[0]+",\n                             "+t[1]+"));\n\n      int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n\n      "+n+"\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  "}function getOutput6DCoords(e,t){var n=getLogicalCoordinatesFromFlatIndex(["r","c","d","d2","d3","d4"],e);return"\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2("+t[0]+", "+t[1]+"));\n      int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n\n      "+n+"\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  "}function getOutputPacked2DCoords(e,t){var n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(arraysEqual(e,t))return"\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2("+n[0]+", "+n[1]+"));\n      }\n    ";var r=Math.ceil(e[1]/2);return"\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+n[0]+", "+n[1]+"));\n\n      int index = resTexRC.x * "+n[1]+" + resTexRC.y;\n      int r = 2 * (index / "+r+");\n      int c = imod(index, "+r+") * 2;\n\n      return ivec2(r, c);\n    }\n  "}function getOutput2DCoords(e,t){return arraysEqual(e,t)?"\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2("+t[0]+", "+t[1]+"));\n      }\n    ":1===e[1]?"\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2("+t[0]+", "+t[1]+"));\n        int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    ":1===e[0]?"\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2("+t[0]+", "+t[1]+"));\n        int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n        return ivec2(0, index);\n      }\n    ":"\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+t[0]+", "+t[1]+"));\n      int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n      int r = index / "+e[1]+";\n      int c = index - r * "+e[1]+";\n      return ivec2(r, c);\n    }\n  "}function getPackedSamplerScalar(e){var t=e.name;return"\n    vec4 "+("get"+t.charAt(0).toUpperCase()+t.slice(1))+"() {\n      return texture2D("+t+", halfCR);\n    }\n  "}function getSamplerScalar(e){var t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);return e.shapeInfo.isUniform?"float "+n+"() {return "+t+";}":"\n    float "+n+"() {\n      return sampleTexture("+t+", halfCR);\n    }\n  "}function getPackedSampler1D(e){var t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=e.shapeInfo.texShape,o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return"\n    vec4 "+n+"(int index) {\n      vec2 uv = packedUVfrom1D(\n        "+o[0]+", "+o[1]+", index);\n      return texture2D("+t+", uv);\n    }\n  "}function getSampler1D(e){var t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);return"\n    float "+n+"(int index) {\n      return "+n+"Flat(index);\n    }\n  "}function getPackedSampler2D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e.shapeInfo.texShape,a=o[0],i=o[1];if(null!=o&&arraysEqual(t,o))return"\n      vec4 "+r+"(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2("+i+".0, "+a+".0);\n\n        return texture2D("+n+", uv);\n      }\n    ";var s=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];return"\n    vec4 "+r+"(int row, int col) {\n      vec2 uv = packedUVfrom2D("+Math.ceil(t[1]/2)+", "+s[0]+", "+s[1]+", row, col);\n      return texture2D("+n+", uv);\n    }\n  "}function getSampler2D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e.shapeInfo.texShape;if(null!=o&&arraysEqual(t,o)){var a=o[0];return"\n    float "+r+"(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2("+o[1]+".0, "+a+".0);\n      return sampleTexture("+n+", uv);\n    }\n  "}var i=squeezeShape(t),s=i.newShape,u=i.keptDims,l=s;if(l.length<t.length){return"\n      "+getSamplerFromInInfo(squeezeInputInfo(e,l))+"\n      float "+r+"(int row, int col) {\n        return "+r+"("+getSqueezedParams(["row","col"],u)+");\n      }\n    "}if(e.shapeInfo.isUniform)return"\n      float "+r+"(int row, int col) {\n        float index = dot(vec2(row, col), vec2("+t[1]+", 1));\n        return "+r+"Flat(round(index));\n      }\n    ";var c=o[0],p=o[1];return 1===p?"\n    float "+r+"(int row, int col) {\n      float index = dot(vec2(row, col), vec2("+t[1]+", 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / "+c+".0);\n      return sampleTexture("+n+", uv);\n    }\n  ":1===c?"\n    float "+r+"(int row, int col) {\n      float index = dot(vec2(row, col), vec2("+t[1]+", 1));\n      vec2 uv = vec2((index + 0.5) / "+p+".0, 0.5);\n      return sampleTexture("+n+", uv);\n    }\n  ":"\n  float "+r+"(int row, int col) {\n    vec2 uv = UVfrom2D("+c+", "+p+", "+t[1]+", row, col);\n    return sampleTexture("+n+", uv);\n  }\n"}function getPackedSampler3D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e.shapeInfo.texShape,a=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];if(1===t[0]){return"\n        "+getPackedSamplerFromInInfo(squeezeInputInfo(e,t.slice(1)))+"\n        vec4 "+r+"(int b, int row, int col) {\n          return "+r+"("+getSqueezedParams(["b","row","col"],[1,2])+");\n        }\n      "}var i=a[0],s=a[1],u=Math.ceil(t[2]/2);return"\n    vec4 "+r+"(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        "+i+", "+s+", "+u*Math.ceil(t[1]/2)+", "+u+", b, row, col);\n      return texture2D("+n+", uv);\n    }\n  "}function getSampler3D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=t[1]*t[2],a=t[2],i=squeezeShape(t),s=i.newShape,u=i.keptDims,l=s;if(l.length<t.length){return"\n        "+getSamplerFromInInfo(squeezeInputInfo(e,l))+"\n        float "+r+"(int row, int col, int depth) {\n          return "+r+"("+getSqueezedParams(["row","col","depth"],u)+");\n        }\n      "}if(e.shapeInfo.isUniform)return"\n      float "+r+"(int row, int col, int depth) {\n        float index = dot(vec3(row, col, depth),\n                          vec3("+o+", "+a+", 1));\n        return "+r+"Flat(round(index));\n      }\n    ";var c=e.shapeInfo.texShape,p=c[0],d=c[1];return d===o?"\n        float "+r+"(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2("+a+", 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2("+d+".0, "+p+".0);\n          return sampleTexture("+n+", uv);\n        }\n      ":d===a?"\n    float "+r+"(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2("+t[1]+", 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2("+d+".0, "+p+".0);\n      return sampleTexture("+n+", uv);\n    }\n  ":"\n      float "+r+"(int row, int col, int depth) {\n        vec2 uv = UVfrom3D(\n            "+p+", "+d+", "+o+", "+a+", row, col, depth);\n        return sampleTexture("+n+", uv);\n      }\n  "}function getPackedSampler4D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e.shapeInfo.texShape,a=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],i=a[0],s=a[1],u=Math.ceil(t[3]/2),l=u*Math.ceil(t[2]/2);return"\n    vec4 "+r+"(int b2, int b, int row, int col) {\n      vec2 uv = packedUVfrom4D(\n        "+i+", "+s+", "+l*t[1]+",\n        "+l+", "+u+", b2, b, row, col);\n      return texture2D("+n+", uv);\n    }\n  "}function getSampler4D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=t[3],a=t[2]*o,i=t[1]*a,s=squeezeShape(t),u=s.newShape,l=s.keptDims;if(u.length<t.length){return"\n      "+getSamplerFromInInfo(squeezeInputInfo(e,u))+"\n      float "+r+"(int row, int col, int depth, int depth2) {\n        return "+r+"("+getSqueezedParams(["row","col","depth","depth2"],l)+");\n      }\n    "}if(e.shapeInfo.isUniform)return"\n      float "+r+"(int row, int col, int depth, int depth2) {\n        float index = dot(vec4(row, col, depth, depth2),\n                          vec4("+i+", "+a+", "+o+", 1));\n        return "+r+"Flat(round(index));\n      }\n    ";var c=e.shapeInfo.texShape,p=c[0],d=c[1];return d===i?"\n      float "+r+"(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2), vec3("+a+", "+o+", 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2("+d+".0, "+p+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":d===o?"\n      float "+r+"(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3("+t[1]*t[2]+", "+t[2]+", 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2("+d+".0, "+p+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":"\n    float "+r+"(int row, int col, int depth, int depth2) {\n      vec2 uv = UVfrom4D("+p+", "+d+", "+i+", "+a+",\n          "+o+", row, col, depth, depth2);\n      return sampleTexture("+n+", uv);\n    }\n  "}function getSampler5D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=t[4],a=t[3]*o,i=t[2]*a,s=t[1]*i,u=squeezeShape(t),l=u.newShape,c=u.keptDims;if(l.length<t.length){return"\n      "+getSamplerFromInInfo(squeezeInputInfo(e,l))+"\n      float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n        return "+r+"("+getSqueezedParams(["row","col","depth","depth2","depth3"],c)+");\n      }\n    "}if(e.shapeInfo.isUniform)return"\n      float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4("+s+", "+i+", "+a+", "+o+")) +\n          depth3;\n        return "+r+"Flat(index);\n      }\n    ";var p=e.shapeInfo.texShape,d=p[0],h=p[1];return h===s?"\n      float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(\n          vec4(col, depth, depth2, depth3),\n          vec4("+i+", "+a+", "+o+", 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2("+h+".0, "+d+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":h===o?"\n      float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4("+t[1]*t[2]*t[3]+", "+t[2]*t[3]+",\n            "+t[3]+", 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2("+h+".0, "+d+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":"\n    float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n      vec2 uv = UVfrom5D("+d+", "+h+", "+s+", "+i+",\n          "+a+", "+o+", row, col, depth, depth2, depth3);\n      return sampleTexture("+n+", uv);\n    }\n  "}function getSampler6D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=t[5],a=t[4]*o,i=t[3]*a,s=t[2]*i,u=t[1]*s,l=squeezeShape(t),c=l.newShape,p=l.keptDims;if(c.length<t.length){return"\n      "+getSamplerFromInInfo(squeezeInputInfo(e,c))+"\n      float "+r+"(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return "+r+"("+getSqueezedParams(["row","col","depth","depth2","depth3","depth4"],p)+");\n      }\n    "}if(e.shapeInfo.isUniform)return"\n      float "+r+"(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4("+u+", "+s+", "+i+", "+a+")) +\n          dot(\n            vec2(depth3, depth4),\n            vec2("+o+", 1));\n        return "+r+"Flat(index);\n      }\n    ";var d=e.shapeInfo.texShape,h=d[0],f=d[1];return f===u?"\n      float "+r+"(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(\n          vec4(col, depth, depth2, depth3),\n          vec4("+s+", "+i+", "+a+", "+o+")) + depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2("+f+".0, "+h+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":f===o?"\n      float "+r+"(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4("+t[1]*t[2]*t[3]*t[4]+",\n               "+t[2]*t[3]*t[4]+",\n               "+t[3]*t[4]+",\n               "+t[4]+")) + depth3;\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2("+f+".0, "+h+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":"\n    float "+r+"(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      vec2 uv = UVfrom6D("+h+", "+f+", "+u+", "+s+",\n          "+i+", "+a+", "+o+"\n          ,row, col, depth, depth2, depth3, depth4);\n      return sampleTexture("+n+", uv);\n    }\n  "}function getSamplerFlat(e){var t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1)+"Flat",r=sizeFromShape(e.shapeInfo.logicalShape);if(e.shapeInfo.isUniform)return 1===r?"float "+n+"(int index) {return "+t+";}":"\n      float "+n+"(int index) {\n        for (int i = 0; i < "+r+"; i++) {\n          if (i == index) {\n            return "+t+"[i];\n          }\n        }\n      }\n    ";var o=e.shapeInfo.texShape,a=o[0],i=o[1];return 1===i&&1===a?"\n      float "+n+"(int index) {\n        return sampleTexture("+t+", halfCR);\n      }\n    ":1===i?"\n      float "+n+"(int index) {\n        vec2 uv = vec2(0.5, (float(index) + 0.5) / "+a+".0);\n        return sampleTexture("+t+", uv);\n      }\n    ":1===a?"\n      float "+n+"(int index) {\n        vec2 uv = vec2((float(index) + 0.5) / "+i+".0, 0.5);\n        return sampleTexture("+t+", uv);\n      }\n    ":"\n    float "+n+"(int index) {\n      vec2 uv = UVfrom1D("+a+", "+i+", index);\n      return sampleTexture("+t+", uv);\n    }\n  "}function getBroadcastOutputCoordsSampler(e,t,n,r){var o=e.shapeInfo.logicalShape.length,a=t.logicalShape.length,i="int";2===a?i="ivec2":3===a?i="ivec3":4===a&&(i="ivec4");var s=getBroadcastDims(e.shapeInfo.logicalShape,t.logicalShape),u=a-o;return"\n    float "+r+"() {\n      "+i+" coords = getOutputCoords();\n      "+(0===o?"":a<2&&s.length>=1?"coords = 0;":s.map(function(e){return"coords["+(e+u)+"] = 0;"}).join("\n"))+"\n      return get"+n+"("+(a<2&&o>0?"coords":e.shapeInfo.logicalShape.map(function(e,t){return"coords["+(t+u)+"]"}).join(", "))+");\n    }\n  "}function getPackedSamplerAtOutputCoords(e,t,n){var r=e.name,o=r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape,i="get"+o+"AtOutCoords",s=t.texShape,u=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],l=u[0],c=u[1],p=getBroadcastDims(e.shapeInfo.logicalShape,t.logicalShape),d=e.shapeInfo.logicalShape.length,h=t.logicalShape.length;if(p.length)throw Error("Packed broadcast sampling is not implemented yet.");if(arraysEqual(e.shapeInfo.texShape,s))return"\n      vec4 "+i+"() {\n        return texture2D("+r+", resultUV);\n      }\n    ";var f="return texture2D("+r+", uv)";return 1===d&&h>1?f="\n      vec4 sample = texture2D("+r+", uv);\n      return vec4(sample.xy, sample.xy);\n    ":0===d&&h>0&&(f=1===h?"\n        vec4 sample = texture2D("+r+", uv);\n        return vec4(sample.x, sample.x, 0., 0.);\n      ":"\n        vec4 sample = texture2D("+r+", uv);\n        return vec4(sample.x);\n      "),"\n    vec4 "+i+"() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+u[0]+", "+u[1]+"));\n      int index = resTexRC.x * "+u[1]+" + resTexRC.y;\n\n      int texR = index / "+c+";\n      int texC = index - texR * "+c+";\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2("+c+", "+l+");\n\n      "+f+";\n    }\n  "}function getSamplerAtOutputCoords(e,t,n){var r=e.name,o=r.charAt(0).toUpperCase()+r.slice(1),a="get"+o+"AtOutCoords",i=getBroadcastDims(e.shapeInfo.logicalShape,t.logicalShape),s=e.shapeInfo.logicalShape.length,u=t.logicalShape.length,l=n&&(u>s||i.length>0),c=broadcastDimsAreOuter(i),p=e.shapeInfo.isUniform;if(l&&!c)return getBroadcastOutputCoordsSampler(e,t,o,a);var d=sizeFromShape(e.shapeInfo.logicalShape),h="";l&&c&&(h="\n        int mainPart = index / "+d+";\n        index -= mainPart * "+d+";\n      ");var f=t.texShape;if(p)return 1===d?"float "+a+"() {return "+r+";}":"\n      float "+a+"() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                              vec2("+f[0]+", "+f[1]+"));\n        int index = resTexRC.x * "+f[1]+" + resTexRC.y;\n        "+h+"\n        return get"+o+"Flat(index);\n      }\n    ";var m=e.shapeInfo.texShape;return arraysEqual(m,f)?"\n      float "+a+"() {\n        return sampleTexture("+r+", resultUV);\n      }\n    ":"\n    float "+a+"() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+f[0]+", "+f[1]+"));\n      int index = resTexRC.x * "+f[1]+" + resTexRC.y;\n      "+h+"\n      int texR = index / "+m[1]+";\n      int texC = index - texR * "+m[1]+";\n      vec2 uv = (vec2(texC, texR) + halfCR) /\n                 vec2("+m[1]+".0, "+m[0]+".0);\n\n      return sampleTexture("+r+", uv);\n    }\n  "}function getCoordsDataType(e){if(e<=1)return"int";if(2===e)return"ivec2";if(3===e)return"ivec3";if(4===e)return"ivec4";if(5===e)return"ivec5";if(6===e)return"ivec6";throw Error("GPU for rank "+e+" is not yet supported")}function squeezeInputInfo(e,t){var n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function getSqueezedParams(e,t){return t.map(function(t){return e[t]}).join(", ")}var CumSumProgram=function(){return function(e,t,n){this.variableNames=["x"],this.outputShape=e;var r=e.length,o=e[e.length-1],a=n?"<":">";this.userCode="\n      int getIndex(int i) {\n        "+(n?"return "+o+" -i - 1;":"return i;")+"\n      }\n\n      void main() {\n        "+getCoordsDataType(r)+" coords = getOutputCoords();\n        int end = "+getFinalCoord(r,"coords")+";\n        float val = 0.0;\n        for (int i = "+o+" - 1; i >= 0; i -= 1) {\n          int idx = getIndex(i);\n          if (idx "+a+" end) {\n            continue;\n          }\n          if (idx == end && "+t+") {\n            continue;\n          }\n          "+getFinalCoord(r,"coords")+" = idx;\n          val += getX("+getCoords(r,"coords")+");\n        }\n        setOutput(val);\n      }\n    "}}();function getCoords(e,t){if(1===e)return""+t;if(2===e)return t+".x, "+t+".y";if(3===e)return t+".x, "+t+".y, "+t+".z";if(4===e)return t+".x, "+t+".y, "+t+".z, "+t+".w";throw Error("Cumulative sum for rank "+e+" is not yet supported")}function getFinalCoord(e,t){if(1===e)return""+t;if(2===e)return t+".y";if(3===e)return t+".z";if(4===e)return t+".w";throw Error("Cumulative sum for rank "+e+" is not yet supported")}var DepthToSpaceProgram=function(){function e(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode="\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = "+this.getHeightCoordString()+";\n      int w = "+this.getWidthCoordString()+";\n      int d = "+this.getDepthCoordString()+";\n\n      int in_h = h / "+t+";\n      int offset_h = imod(h, "+t+");\n      int in_w = w / "+t+";\n      int offset_w = imod(w, "+t+");\n      int offset_d = (offset_h * "+t+" + offset_w) *\n        "+this.getOutputDepthSize()+";\n      int in_d = d + offset_d;\n\n      float result = "+this.getInputSamplingString()+";\n      setOutput(result);\n    }\n  "}return e.prototype.getHeightCoordString=function(){return"NHWC"===this.dataFormat?"coords[1]":"coords[2]"},e.prototype.getWidthCoordString=function(){return"NHWC"===this.dataFormat?"coords[2]":"coords[3]"},e.prototype.getDepthCoordString=function(){return"NHWC"===this.dataFormat?"coords[3]":"coords[1]"},e.prototype.getOutputDepthSize=function(){return"NHWC"===this.dataFormat?this.outputShape[3]:this.outputShape[1]},e.prototype.getInputSamplingString=function(){return"NHWC"===this.dataFormat?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"},e}(),EncodeFloatProgram=function(){return function(e){this.variableNames=["A"],this.outputShape=e,this.userCode="\n      const float FLOAT_MAX = 1.70141184e38;\n      const float FLOAT_MIN = 1.17549435e-38;\n\n      lowp vec4 encode_float(highp float v) {\n        if (isNaN(v)) {\n          return vec4(255, 255, 255, 255);\n        }\n\n        highp float av = abs(v);\n\n        if(av < FLOAT_MIN) {\n          return vec4(0.0, 0.0, 0.0, 0.0);\n        } else if(v > FLOAT_MAX) {\n          return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n        } else if(v < -FLOAT_MAX) {\n          return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n        }\n\n        highp vec4 c = vec4(0,0,0,0);\n\n        highp float e = floor(log2(av));\n        highp float m = exp2(fract(log2(av))) - 1.0;\n\n        c[2] = floor(128.0 * m);\n        m -= c[2] / 128.0;\n        c[1] = floor(32768.0 * m);\n        m -= c[1] / 32768.0;\n        c[0] = floor(8388608.0 * m);\n\n        highp float ebias = e + 127.0;\n        c[3] = floor(ebias / 2.0);\n        ebias -= c[3] * 2.0;\n        c[2] += floor(ebias) * 128.0;\n\n        c[3] += 128.0 * step(0.0, -v);\n\n        return c / 255.0;\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        gl_FragColor = encode_float(x);\n      }\n    "}}(),COMPLEX_FFT={REAL:"return real * expR - imag * expI;",IMAG:"return real * expI + imag * expR;"},FFTProgram=function(){return function(e,t,n){this.variableNames=["real","imag"];var r=t[1];this.outputShape=t;var o=n?"2.0 * "+Math.PI:"-2.0 * "+Math.PI,a=n?r+".0":"1.0";this.userCode="\n      const float exponentMultiplier = "+o+";\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        "+e+"\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float("+r+");\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < "+r+"; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / "+a+";\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    "}}(),FromPixelsProgram=function(){return function(e){this.variableNames=["A"];var t=e[0],n=e[1];this.outputShape=e,this.userCode="\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2("+n+".0, "+t+".0);\n\n        vec4 values = texture2D(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    "}}(),GatherProgram=function(){return function(e,t,n){this.variableNames=["A","indices"];var r=e.slice();r[n]=t,this.outputShape=r,this.rank=r.length;var o=getCoordsDataType(this.rank),a=getSourceCoords(e,n);this.userCode="\n      void main() {\n        "+o+" resRC = getOutputCoords();\n        setOutput(getA("+a+"));\n      }\n    "}}();function getSourceCoords(e,t){var n=e.length;if(n>4)throw Error("Gather for rank "+n+" is not yet supported");if(1===n)return"int(getIndices(resRC))";for(var r=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],a=0;a<e.length;a++)a===t?o.push("int(getIndices("+r[a]+"))"):o.push(""+r[a]);return o.join()}var TextureUsage,PhysicalTextureType,GatherNDProgram=function(){return function(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;var r=getCoordsDataType(t.length),o=getCoordsDataType(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode="\n        "+r+" strides = "+r+"("+this.strides+");\n         void main() {\n          "+o+" coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < "+this.sliceDim+"; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * "+a+";\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      "}}();function getUnpackedMatrixTextureShapeWidthHeight(e,t){return[t,e]}function getUnpackedArraySizeFromMatrixSize(e,t){return e*t}function getMatrixSizeFromUnpackedArraySize(e,t){if(e%t!=0)throw new Error("unpackedSize ("+e+") must be a multiple of "+t);return e/t}function encodeMatrixToUnpackedArray(e,t,n){var r=getUnpackedArraySizeFromMatrixSize(e.length,n);if(t.length<r)throw new Error("unpackedArray length ("+t.length+") must be >= "+r);for(var o=0,a=0;a<e.length;++a)t[o]=e[a],o+=n}function decodeMatrixFromUnpackedArray(e,t,n){var r=getMatrixSizeFromUnpackedArraySize(e.length,n);if(t.length<r)throw new Error("matrix length ("+t.length+") must be >= "+r);for(var o=0,a=0;a<e.length;a+=n)t[o++]=e[a]}function getPackedMatrixTextureShapeWidthHeight(e,t){return[Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function getPackedRGBAArraySizeFromMatrixShape(e,t){var n=getPackedMatrixTextureShapeWidthHeight(e,t);return n[0]*n[1]*4}function encodeMatrixToPackedRGBA(e,t,n,r,o){var a=getPackedRGBAArraySizeFromMatrixShape(n,r);if(o.length<a)throw new Error("packedRGBA length ("+o.length+") must be >=\n        "+a);for(var i=r%2==1,s=n%2==1,u=Math.floor(r/2),l=Math.floor(n/2),c=Math.ceil(r/2),p=c*Math.ceil(n/2),d=nearestLargerEven(n)*nearestLargerEven(r),h=0;h<t;h++){for(var f=h*n*r,m=h*d,g=i?4:0,v=r,y=m,x=0;x<l;++x){for(var T=2*x*r,w=0;w<u;++w){var E=f+T+2*w;o[y]=e[E],o[y+1]=e[E+1],o[y+2]=e[E+v],o[y+3]=e[E+v+1],y+=4}y+=g}if(i){E=f+r-1,y=m+4*(c-1);var S=2*r;for(g=4*c,x=0;x<l;++x)o[y]=e[E],o[y+2]=e[E+r],E+=S,y+=g}if(s){for(E=f+(n-1)*r,y=m+4*(p-c),w=0;w<u;++w)o[y++]=e[E++],o[y++]=e[E++],y+=2;i&&s&&(o[m+d-4]=e[E])}}return o}function decodeMatrixFromPackedRGBA(e,t,n,r,o){var a=n*r;if(o.length<a)throw new Error("matrix length ("+o.length+") must be >= "+a);for(var i=r%2==1,s=n%2==1,u=Math.floor(r/2),l=Math.floor(n/2),c=Math.ceil(r/2),p=c*Math.ceil(n/2),d=nearestLargerEven(n)*nearestLargerEven(r),h=0;h<t;h++){for(var f=h*n*r,m=h*d,g=i?4:0,v=r+(i?1:0),y=m,x=f,T=f+r,w=0;w<l;++w){for(var E=0;E<u;++E)o[x++]=e[y++],o[x++]=e[y++],o[T++]=e[y++],o[T++]=e[y++];y+=g,x+=v,T+=v}if(i){y=m+4*(c-1);var S=f+r-1;for(g=4*c,v=2*r,w=0;w<l;++w)o[S]=e[y],o[S+r]=e[y+2],y+=g,S+=v}if(s){for(y=m+4*(p-c),S=f+(n-1)*r,E=0;E<u;++E)o[S++]=e[y++],o[S++]=e[y++],y+=2;i&&(o[f+n*r-1]=e[y])}}return o}function callAndCheck(e,t){var n=t();return checkWebGLError(e),n}!function(e){e[e.RENDER=0]="RENDER",e[e.UPLOAD=1]="UPLOAD",e[e.PIXELS=2]="PIXELS",e[e.DOWNLOAD=3]="DOWNLOAD"}(TextureUsage||(TextureUsage={})),function(e){e[e.UNPACKED_FLOAT16=0]="UNPACKED_FLOAT16",e[e.UNPACKED_FLOAT32=1]="UNPACKED_FLOAT32",e[e.PACKED_4X1_UNSIGNED_BYTE=2]="PACKED_4X1_UNSIGNED_BYTE",e[e.PACKED_2X2_FLOAT32=3]="PACKED_2X2_FLOAT32",e[e.PACKED_2X2_FLOAT16=4]="PACKED_2X2_FLOAT16"}(PhysicalTextureType||(PhysicalTextureType={}));var webGLDebugErrorCheckingEnabled=!1;function enableDebugWebGLErrorChecking(e){webGLDebugErrorCheckingEnabled=e}function checkWebGLError(e){if(webGLDebugErrorCheckingEnabled){var t=e.getError();if(t!==e.NO_ERROR)throw new Error("WebGL Error: "+getWebGLErrorMessage(e,t))}}function getWebGLErrorMessage(e,t){switch(t){case e.NO_ERROR:return"NO_ERROR";case e.INVALID_ENUM:return"INVALID_ENUM";case e.INVALID_VALUE:return"INVALID_VALUE";case e.INVALID_OPERATION:return"INVALID_OPERATION";case e.INVALID_FRAMEBUFFER_OPERATION:return"INVALID_FRAMEBUFFER_OPERATION";case e.OUT_OF_MEMORY:return"OUT_OF_MEMORY";case e.CONTEXT_LOST_WEBGL:return"CONTEXT_LOST_WEBGL";default:return"Unknown error code "+t}}function getExtensionOrThrow(e,t){return throwIfNull(e,function(){return e.getExtension(t)},'Extension "'+t+'" not supported on this browser.')}function createVertexShader(e,t){var n=throwIfNull(e,function(){return e.createShader(e.VERTEX_SHADER)},"Unable to create vertex WebGLShader.");if(callAndCheck(e,function(){return e.shaderSource(n,t)}),callAndCheck(e,function(){return e.compileShader(n)}),!1===e.getShaderParameter(n,e.COMPILE_STATUS))throw console.log(e.getShaderInfoLog(n)),new Error("Failed to compile vertex shader.");return n}function createFragmentShader(e,t){var n=throwIfNull(e,function(){return e.createShader(e.FRAGMENT_SHADER)},"Unable to create fragment WebGLShader.");if(callAndCheck(e,function(){return e.shaderSource(n,t)}),callAndCheck(e,function(){return e.compileShader(n)}),!1===e.getShaderParameter(n,e.COMPILE_STATUS))throw logShaderSourceAndInfoLog(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}var lineNumberRegex=/ERROR: [0-9]+:([0-9]+):/g;function logShaderSourceAndInfoLog(e,t){var n=lineNumberRegex.exec(t);if(null==n)return console.log("Couldn't parse line number in error: "+t),void console.log(e);for(var r=+n[1],o=e.split("\n"),a=o.length.toString().length+2,i=o.map(function(e,t){return rightPad((t+1).toString(),a)+e}),s=0,u=0;u<i.length;u++)s=Math.max(i[u].length,s);var l=i.slice(0,r-1),c=i.slice(r-1,r),p=i.slice(r);console.log(l.join("\n")),console.log(t.split("\n")[0]),console.log("%c "+rightPad(c[0],s),"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(p.join("\n"))}function createProgram(e){return throwIfNull(e,function(){return e.createProgram()},"Unable to create WebGLProgram.")}function linkProgram(e,t){if(callAndCheck(e,function(){return e.linkProgram(t)}),!1===e.getProgramParameter(t,e.LINK_STATUS))throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function validateProgram(e,t){if(callAndCheck(e,function(){return e.validateProgram(t)}),!1===e.getProgramParameter(t,e.VALIDATE_STATUS))throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function createStaticVertexBuffer(e,t){var n=throwIfNull(e,function(){return e.createBuffer()},"Unable to create WebGLBuffer");return callAndCheck(e,function(){return e.bindBuffer(e.ARRAY_BUFFER,n)}),callAndCheck(e,function(){return e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)}),n}function createStaticIndexBuffer(e,t){var n=throwIfNull(e,function(){return e.createBuffer()},"Unable to create WebGLBuffer");return callAndCheck(e,function(){return e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)}),callAndCheck(e,function(){return e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)}),n}function getNumChannels(){return 2===ENV.get("WEBGL_VERSION")?1:4}function createTexture(e){return throwIfNull(e,function(){return e.createTexture()},"Unable to create WebGLTexture.")}function validateTextureSize(e,t){var n=ENV.get("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){var r="["+e+"x"+t+"]";throw new Error("Requested texture size "+r+" is invalid.")}if(e>n||t>n){r="["+e+"x"+t+"]";throw new Error("Requested texture size "+r+" greater than WebGL maximum on this browser / GPU "+("["+n+"x"+n+"]")+".")}}function createFramebuffer(e){return throwIfNull(e,function(){return e.createFramebuffer()},"Unable to create WebGLFramebuffer.")}function bindVertexBufferToProgramAttribute(e,t,n,r,o,a,i){var s=e.getAttribLocation(t,n);return-1!==s&&(callAndCheck(e,function(){return e.bindBuffer(e.ARRAY_BUFFER,r)}),callAndCheck(e,function(){return e.vertexAttribPointer(s,o,e.FLOAT,!1,a,i)}),callAndCheck(e,function(){return e.enableVertexAttribArray(s)}),!0)}function bindTextureUnit(e,t,n){validateTextureUnit(e,n),callAndCheck(e,function(){return e.activeTexture(e.TEXTURE0+n)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,t)})}function unbindTextureUnit(e,t){validateTextureUnit(e,t),callAndCheck(e,function(){return e.activeTexture(e.TEXTURE0+t)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)})}function getProgramUniformLocationOrThrow(e,t,n){return throwIfNull(e,function(){return e.getUniformLocation(t,n)},'uniform "'+n+'" not present in program.')}function getProgramUniformLocation(e,t,n){return e.getUniformLocation(t,n)}function bindTextureToProgramUniformSampler(e,t,n,r,o){callAndCheck(e,function(){return bindTextureUnit(e,n,o)}),callAndCheck(e,function(){return e.uniform1i(r,o)})}function bindCanvasToFramebuffer(e){callAndCheck(e,function(){return e.bindFramebuffer(e.FRAMEBUFFER,null)}),callAndCheck(e,function(){return e.viewport(0,0,e.canvas.width,e.canvas.height)}),callAndCheck(e,function(){return e.scissor(0,0,e.canvas.width,e.canvas.height)})}function bindColorTextureToFramebuffer(e,t,n){callAndCheck(e,function(){return e.bindFramebuffer(e.FRAMEBUFFER,n)}),callAndCheck(e,function(){return e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0)})}function unbindColorTextureFromFramebuffer(e,t){callAndCheck(e,function(){return e.bindFramebuffer(e.FRAMEBUFFER,t)}),callAndCheck(e,function(){return e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0)})}function validateFramebuffer(e){var t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+getFramebufferErrorMessage(e,t))}function getFramebufferErrorMessage(e,t){switch(t){case e.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return"FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case e.FRAMEBUFFER_UNSUPPORTED:return"FRAMEBUFFER_UNSUPPORTED";default:return"unknown error "+t}}function throwIfNull(e,t,n){var r=callAndCheck(e,function(){return t()});if(null==r)throw new Error(n);return r}function validateTextureUnit(e,t){var n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,r=t+e.TEXTURE0;if(r<e.TEXTURE0||r>n)throw new Error("textureUnit must be in "+("[gl.TEXTURE0, gl.TEXTURE"+n+"]")+".")}function getTextureShapeFromLogicalShape(e,t){void 0===t&&(t=!1);var n=ENV.get("WEBGL_MAX_TEXTURE_SIZE");if(t&&(n*=2,e=e.map(function(t,n){return n>=e.length-2?nearestLargerEven(e[n]):e[n]})),2!==e.length){var r=squeezeShape(e);e=r.newShape}var o=sizeFromShape(e);return e.length<=1&&o<=n?[1,o]:2===e.length&&e[0]<=n&&e[1]<=n?e:3===e.length&&e[0]*e[1]<=n&&e[2]<=n?[e[0]*e[1],e[2]]:3===e.length&&e[0]<=n&&e[1]*e[2]<=n?[e[0],e[1]*e[2]]:4===e.length&&e[0]*e[1]*e[2]<=n&&e[3]<=n?[e[0]*e[1]*e[2],e[3]]:4===e.length&&e[0]<=n&&e[1]*e[2]*e[3]<=n?[e[0],e[1]*e[2]*e[3]]:sizeToSquarishShape(o)}function isEven(e){return e%2==0}function isReshapeFree(e,t){if(arraysEqual(e=e.slice(-2),t=t.slice(-2)))return!0;if(!e.length||!t.length)return!0;if(0===e[0]||0===e[1]||0===t[0]||0===t[1])return!0;if(e.length!==t.length){var n=e.slice(-1)[0],r=t.slice(-1)[0];if(n===r)return!0;if(isEven(n)&&isEven(r)&&(1===e[0]||1===t[0]))return!0}else if(isEven(e[0])&&isEven(t[0])){if(isEven(e[1])&&isEven(t[1]))return!0;if(e[1]===t[1])return!0}return!1}var webgl_util=Object.freeze({callAndCheck:callAndCheck,enableDebugWebGLErrorChecking:enableDebugWebGLErrorChecking,checkWebGLError:checkWebGLError,getWebGLErrorMessage:getWebGLErrorMessage,getExtensionOrThrow:getExtensionOrThrow,createVertexShader:createVertexShader,createFragmentShader:createFragmentShader,createProgram:createProgram,linkProgram:linkProgram,validateProgram:validateProgram,createStaticVertexBuffer:createStaticVertexBuffer,createStaticIndexBuffer:createStaticIndexBuffer,getNumChannels:getNumChannels,createTexture:createTexture,validateTextureSize:validateTextureSize,createFramebuffer:createFramebuffer,bindVertexBufferToProgramAttribute:bindVertexBufferToProgramAttribute,bindTextureUnit:bindTextureUnit,unbindTextureUnit:unbindTextureUnit,getProgramUniformLocationOrThrow:getProgramUniformLocationOrThrow,getProgramUniformLocation:getProgramUniformLocation,bindTextureToProgramUniformSampler:bindTextureToProgramUniformSampler,bindCanvasToFramebuffer:bindCanvasToFramebuffer,bindColorTextureToFramebuffer:bindColorTextureToFramebuffer,unbindColorTextureFromFramebuffer:unbindColorTextureFromFramebuffer,validateFramebuffer:validateFramebuffer,getFramebufferErrorMessage:getFramebufferErrorMessage,getTextureShapeFromLogicalShape:getTextureShapeFromLogicalShape,isReshapeFree:isReshapeFree});function createVertexShader$1(e){return createVertexShader(e,"\n    precision highp float;\n    attribute vec3 clipSpacePos;\n    attribute vec2 uv;\n    varying vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }")}function createVertexBuffer(e){return createStaticVertexBuffer(e,new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]))}function createIndexBuffer(e){return createStaticIndexBuffer(e,new Uint16Array([0,1,2,2,1,3]))}function getTextureConfig(e,t){var n,r,o,a,i,s,u,l=e;return 2===ENV.get("WEBGL_VERSION")?(n=l.R32F,r=l.R16F,o=l.RGBA32F,a=l.RED,i=4,s=1,u=l.HALF_FLOAT):(n=e.RGBA,r=e.RGBA,o=l.RGBA,a=e.RGBA,i=4,s=4,u=null!=t?t.HALF_FLOAT_OES:null),{internalFormatFloat:n,internalFormatHalfFloat:r,internalFormatPackedFloat:o,textureFormatFloat:a,downloadTextureFormat:e.RGBA,downloadUnpackNumChannels:i,defaultNumChannels:s,textureTypeHalfFloat:u}}function createAndConfigureTexture(e,t,n,r,o,a){validateTextureSize(t,n);var i=createTexture(e),s=e.TEXTURE_2D;return callAndCheck(e,function(){return e.bindTexture(s,i)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_MIN_FILTER,e.NEAREST)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_MAG_FILTER,e.NEAREST)}),callAndCheck(e,function(){return e.texImage2D(s,0,r,t,n,0,o,a,null)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)}),i}function createFloat32MatrixTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],r.internalFormatFloat,r.textureFormatFloat,e.FLOAT)}function createFloat16MatrixTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],r.internalFormatFloat,r.textureFormatFloat,r.textureTypeHalfFloat)}function createUnsignedBytesMatrixTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],e.RGBA,e.RGBA,e.UNSIGNED_BYTE)}function createPackedMatrixTexture(e,t,n,r){var o=getPackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],r.internalFormatPackedFloat,e.RGBA,e.FLOAT)}function createFloat16PackedMatrixTexture(e,t,n,r){var o=getPackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],r.internalFormatHalfFloat,e.RGBA,r.textureTypeHalfFloat)}function bindVertexProgramAttributeStreams(e,t,n){return callAndCheck(e,function(){return e.bindBuffer(e.ARRAY_BUFFER,n)}),bindVertexBufferToProgramAttribute(e,t,"clipSpacePos",n,3,20,0)&&bindVertexBufferToProgramAttribute(e,t,"uv",n,2,20,12)}function uploadPixelDataToTexture(e,t,n){callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,t)}),callAndCheck(e,function(){return e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)})}function uploadDataToTexture(e,t,n,r,o,a){validateTextureSize(n,r),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,t)}),callAndCheck(e,function(){return e.texSubImage2D(e.TEXTURE_2D,0,0,0,n,r,a,e.FLOAT,o)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)})}function uploadMatrixToTexture(e,t,n,r,o,a,i){var s,u=getUnpackedMatrixTextureShapeWidthHeight(n,r),l=u[0],c=u[1];1===i.defaultNumChannels?s=o:encodeMatrixToUnpackedArray(o,s=new Float32Array(getUnpackedArraySizeFromMatrixSize(o.length,a)),a),uploadDataToTexture(e,t,l,c,s,i.textureFormatFloat)}function uploadMatrixToPackedTexture(e,t,n,r,o,a,i,s,u){var l=getPackedMatrixTextureShapeWidthHeight(a,i),c=l[0],p=l[1],d=new Float32Array(n*getPackedRGBAArraySizeFromMatrixShape(r,o));encodeMatrixToPackedRGBA(s,n,r,o,d),uploadDataToTexture(e,t,c,p,d,e.RGBA)}function maybeCreateBufferFromOutputTexture(e,t,n,r,o){var a=t;if(2===ENV.get("WEBGL_VERSION")){var i=e,s=i.createBuffer();callAndCheck(e,function(){return e.bindBuffer(i.PIXEL_PACK_BUFFER,s)});var u=4*getUnpackedArraySizeFromMatrixSize(n*r,o.downloadUnpackNumChannels);callAndCheck(e,function(){return e.bufferData(i.PIXEL_PACK_BUFFER,u,e.STATIC_DRAW)}),callAndCheck(e,function(){return i.readPixels(0,0,r,n,e.RGBA,e.FLOAT,0)}),callAndCheck(e,function(){return e.bindBuffer(i.PIXEL_PACK_BUFFER,null)}),a=s}return a}function downloadFloat32MatrixFromBuffer(e,t,n,r,o){var a=e,i=new Float32Array(getUnpackedArraySizeFromMatrixSize(n*r,o.downloadUnpackNumChannels));a.bindBuffer(e.ARRAY_BUFFER,t),a.getBufferSubData(e.ARRAY_BUFFER,0,i),a.bindBuffer(e.ARRAY_BUFFER,null);var s=new Float32Array(n*r);return decodeMatrixFromUnpackedArray(i,s,o.downloadUnpackNumChannels),s}function downloadFloat32MatrixFromOutputTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n),a=o[0],i=o[1],s=new Float32Array(getUnpackedArraySizeFromMatrixSize(t*n,r.downloadUnpackNumChannels));callAndCheck(e,function(){return e.readPixels(0,0,a,i,r.downloadTextureFormat,e.FLOAT,s)});var u=new Float32Array(t*n);return decodeMatrixFromUnpackedArray(s,u,r.downloadUnpackNumChannels),u}function downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n),a=o[0],i=o[1],s=new Uint8Array(getUnpackedArraySizeFromMatrixSize(t*n,4));return callAndCheck(e,function(){return e.readPixels(0,0,a,i,r.downloadTextureFormat,e.UNSIGNED_BYTE,s)}),new Float32Array(s.buffer)}function downloadPackedMatrixFromBuffer(e,t,n,r,o,a,i,s){var u=e,l=new Float32Array(getPackedRGBAArraySizeFromMatrixShape(a,i));u.bindBuffer(e.ARRAY_BUFFER,t),u.getBufferSubData(e.ARRAY_BUFFER,0,l),u.bindBuffer(e.ARRAY_BUFFER,null);var c=new Float32Array(sizeFromShape([n,r,o]));return decodeMatrixFromPackedRGBA(l,n,r,o,c),c}function downloadMatrixFromPackedOutputTexture(e,t,n,r,o,a,i){var s=getPackedMatrixTextureShapeWidthHeight(o,a),u=s[0],l=s[1],c=new Float32Array(getPackedRGBAArraySizeFromMatrixShape(o,a));callAndCheck(e,function(){return e.readPixels(0,0,u,l,e.RGBA,e.FLOAT,c)});var p=new Float32Array(sizeFromShape([t,n,r]));return decodeMatrixFromPackedRGBA(c,t,n,r,p)}var gpgpu_util=Object.freeze({createVertexShader:createVertexShader$1,createVertexBuffer:createVertexBuffer,createIndexBuffer:createIndexBuffer,getTextureConfig:getTextureConfig,createFloat32MatrixTexture:createFloat32MatrixTexture,createFloat16MatrixTexture:createFloat16MatrixTexture,createUnsignedBytesMatrixTexture:createUnsignedBytesMatrixTexture,createPackedMatrixTexture:createPackedMatrixTexture,createFloat16PackedMatrixTexture:createFloat16PackedMatrixTexture,bindVertexProgramAttributeStreams:bindVertexProgramAttributeStreams,uploadPixelDataToTexture:uploadPixelDataToTexture,uploadMatrixToTexture:uploadMatrixToTexture,uploadMatrixToPackedTexture:uploadMatrixToPackedTexture,maybeCreateBufferFromOutputTexture:maybeCreateBufferFromOutputTexture,downloadFloat32MatrixFromBuffer:downloadFloat32MatrixFromBuffer,downloadFloat32MatrixFromOutputTexture:downloadFloat32MatrixFromOutputTexture,downloadByteEncodedFloatMatrixFromOutputTexture:downloadByteEncodedFloatMatrixFromOutputTexture,downloadPackedMatrixFromBuffer:downloadPackedMatrixFromBuffer,downloadMatrixFromPackedOutputTexture:downloadMatrixFromPackedOutputTexture}),GPGPUContext=function(){function e(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.autoDebugValidate=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[],this.gl=null!=e?e:getWebGLContext(ENV.get("WEBGL_VERSION")),1===ENV.get("WEBGL_VERSION")?(this.textureFloatExtension=getExtensionOrThrow(this.gl,"OES_texture_float"),this.colorBufferFloatExtension=this.gl.getExtension("WEBGL_color_buffer_float"),ENV.get("WEBGL_RENDER_FLOAT32_ENABLED")||(this.textureHalfFloatExtension=getExtensionOrThrow(this.gl,"OES_texture_half_float"),this.colorBufferHalfFloatExtension=this.gl.getExtension("EXT_color_buffer_half_float"))):this.colorBufferFloatExtension=getExtensionOrThrow(this.gl,"EXT_color_buffer_float"),this.vertexBuffer=createVertexBuffer(this.gl),this.indexBuffer=createIndexBuffer(this.gl),this.framebuffer=createFramebuffer(this.gl),this.textureConfig=getTextureConfig(this.gl,this.textureHalfFloatExtension)}return e.prototype.dispose=function(){var e=this;if(!this.disposed){null!=this.program&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),null!=this.outputTexture&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");var t=this.gl;callAndCheck(t,function(){return t.finish()}),callAndCheck(t,function(){return t.bindFramebuffer(t.FRAMEBUFFER,null)}),callAndCheck(t,function(){return t.deleteFramebuffer(e.framebuffer)}),callAndCheck(t,function(){return t.bindBuffer(t.ARRAY_BUFFER,null)}),callAndCheck(t,function(){return t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)}),callAndCheck(t,function(){return t.deleteBuffer(e.indexBuffer)}),this.disposed=!0}},e.prototype.enableAutomaticDebugValidation=function(e){this.autoDebugValidate=e,enableDebugWebGLErrorChecking(e)},e.prototype.createFloat32MatrixTexture=function(e,t){return this.throwIfDisposed(),createFloat32MatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.createFloat16MatrixTexture=function(e,t){return this.throwIfDisposed(),createFloat16MatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.createUnsignedBytesMatrixTexture=function(e,t){return this.throwIfDisposed(),createUnsignedBytesMatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.uploadPixelDataToTexture=function(e,t){this.throwIfDisposed(),uploadPixelDataToTexture(this.gl,e,t)},e.prototype.createFloat16PackedMatrixTexture=function(e,t){return this.throwIfDisposed(),createFloat16PackedMatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.createPackedMatrixTexture=function(e,t){return this.throwIfDisposed(),createPackedMatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.deleteMatrixTexture=function(e){var t=this;this.throwIfDisposed(),this.outputTexture===e&&(unbindColorTextureFromFramebuffer(this.gl,this.framebuffer),this.outputTexture=null),callAndCheck(this.gl,function(){return t.gl.deleteTexture(e)})},e.prototype.uploadMatrixToTexture=function(e,t,n,r){this.throwIfDisposed();var o=getNumChannels();return uploadMatrixToTexture(this.gl,e,t,n,r,o,this.textureConfig)},e.prototype.uploadMatrixToPackedTexture=function(e,t,n,r,o,a,i){return this.throwIfDisposed(),uploadMatrixToPackedTexture(this.gl,e,t,n,r,o,a,i,this.textureConfig)},e.prototype.downloadFloat32MatrixFromOutputTexture=function(e,t,n){var r=this;return this.downloadMatrixDriver(e,function(){return downloadFloat32MatrixFromOutputTexture(r.gl,t,n,r.textureConfig)})},e.prototype.downloadByteEncodedFloatMatrixFromOutputTexture=function(e,t,n){var r=this;return this.downloadMatrixDriver(e,function(){return downloadByteEncodedFloatMatrixFromOutputTexture(r.gl,t,n,r.textureConfig)})},e.prototype.downloadPackedMatrixFromBuffer=function(e,t,n,r,o,a){return downloadPackedMatrixFromBuffer(this.gl,e,t,n,r,o,a,this.textureConfig)},e.prototype.downloadFloat32MatrixFromBuffer=function(e,t,n){return downloadFloat32MatrixFromBuffer(this.gl,e,t,n,this.textureConfig)},e.prototype.maybeCreateBufferFromTexture=function(e,t,n){this.bindTextureToFrameBuffer(e);var r=maybeCreateBufferFromOutputTexture(this.gl,e,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r},e.prototype.createAndWaitForFence=function(){var e=this.createFence(this.gl);return this.pollFence(e)},e.prototype.createFence=function(e){var t,n,r=this;if(ENV.get("WEBGL_FENCE_API_ENABLED")){var o=e,a=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=function(){var e=o.clientWaitSync(a,0,0);return e===o.ALREADY_SIGNALED||e===o.CONDITION_SATISFIED},t=a}else ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=function(){return r.isQueryAvailable(t,ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}):n=function(){return!0};return{query:t,isFencePassed:n}},e.prototype.downloadMatrixFromPackedTexture=function(e,t,n,r,o,a){var i=this;return this.downloadMatrixDriver(e,function(){return downloadMatrixFromPackedOutputTexture(i.gl,t,n,r,o,a,i.textureConfig)})},e.prototype.createProgram=function(e){this.throwIfDisposed();var t=this.gl,n=createFragmentShader(t,e),r=createVertexShader$1(t),o=createProgram(t);return callAndCheck(t,function(){return t.attachShader(o,r)}),callAndCheck(t,function(){return t.attachShader(o,n)}),linkProgram(t,o),this.autoDebugValidate&&validateProgram(t,o),this.vertexAttrsAreBound||(this.setProgram(o),this.vertexAttrsAreBound=bindVertexProgramAttributeStreams(t,this.program,this.vertexBuffer)),o},e.prototype.deleteProgram=function(e){var t=this;this.throwIfDisposed(),e===this.program&&(this.program=null),null!=e&&callAndCheck(this.gl,function(){return t.gl.deleteProgram(e)})},e.prototype.setProgram=function(e){var t=this;this.throwIfDisposed(),this.program=e,null!=this.program&&this.autoDebugValidate&&validateProgram(this.gl,this.program),callAndCheck(this.gl,function(){return t.gl.useProgram(e)})},e.prototype.getUniformLocation=function(e,t,n){return void 0===n&&(n=!0),this.throwIfDisposed(),n?getProgramUniformLocationOrThrow(this.gl,e,t):getProgramUniformLocation(this.gl,e,t)},e.prototype.getAttributeLocation=function(e,t){var n=this;return this.throwIfDisposed(),callAndCheck(this.gl,function(){return n.gl.getAttribLocation(e,t)})},e.prototype.getUniformLocationNoThrow=function(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)},e.prototype.setInputMatrixTexture=function(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),bindTextureToProgramUniformSampler(this.gl,this.program,e,t,n)},e.prototype.setOutputMatrixTexture=function(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)},e.prototype.setOutputPackedMatrixTexture=function(e,t,n){this.throwIfDisposed();var r=getPackedMatrixTextureShapeWidthHeight(t,n),o=r[0],a=r[1];this.setOutputMatrixTextureDriver(e,o,a)},e.prototype.setOutputMatrixWriteRegion=function(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)},e.prototype.setOutputPackedMatrixWriteRegion=function(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")},e.prototype.debugValidate=function(){null!=this.program&&validateProgram(this.gl,this.program),validateFramebuffer(this.gl)},e.prototype.executeProgram=function(){this.throwIfDisposed(),this.throwIfNoProgram();var e=this.gl;this.autoDebugValidate&&this.debugValidate(),callAndCheck(e,function(){return e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0)})},e.prototype.blockUntilAllProgramsCompleted=function(){var e=this;this.throwIfDisposed(),callAndCheck(this.gl,function(){return e.gl.finish()})},e.prototype.getQueryTimerExtension=function(){return null==this.disjointQueryTimerExtension&&(this.disjointQueryTimerExtension=getExtensionOrThrow(this.gl,2===ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension},e.prototype.getQueryTimerExtensionWebGL2=function(){return this.getQueryTimerExtension()},e.prototype.getQueryTimerExtensionWebGL1=function(){return this.getQueryTimerExtension()},e.prototype.beginQuery=function(){if(2===ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){var e=this.gl,t=this.getQueryTimerExtensionWebGL2(),n=e.createQuery();return e.beginQuery(t.TIME_ELAPSED_EXT,n),n}var r=this.getQueryTimerExtensionWebGL1(),o=r.createQueryEXT();return r.beginQueryEXT(r.TIME_ELAPSED_EXT,o),o},e.prototype.endQuery=function(){if(2!==ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){var e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}else{var t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT)}},e.prototype.waitForQueryAndGetTime=function(e){return __awaiter(this,void 0,void 0,function(){var t=this;return __generator(this,function(n){switch(n.label){case 0:return[4,repeatedTry(function(){return t.disposed||t.isQueryAvailable(e,ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))})];case 1:return n.sent(),[2,this.getQueryTime(e,ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))]}})})},e.prototype.getQueryTime=function(e,t){if(0===t)return null;if(2===t){var n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}var r=this.getQueryTimerExtensionWebGL1();return r.getQueryObjectEXT(e,r.QUERY_RESULT_EXT)/1e6},e.prototype.isQueryAvailable=function(e,t){if(0===t)return!0;if(2===t){var n=this.gl,r=this.getQueryTimerExtensionWebGL2(),o=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint}o=(r=this.getQueryTimerExtensionWebGL1()).getQueryObjectEXT(e,r.QUERY_RESULT_AVAILABLE_EXT);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint},e.prototype.pollFence=function(e){var t=this;return new Promise(function(n){t.addItemToPoll(function(){return e.isFencePassed()},function(){return n()})})},e.prototype.pollItems=function(){for(var e=binSearchLastTrue(this.itemsToPoll.map(function(e){return e.isDoneFn})),t=0;t<=e;++t){(0,this.itemsToPoll[t].resolveFn)()}this.itemsToPoll=this.itemsToPoll.slice(e+1)},e.prototype.addItemToPoll=function(e,t){var n=this;this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1||repeatedTry(function(){return n.pollItems(),0===n.itemsToPoll.length})},e.prototype.bindTextureToFrameBuffer=function(e){this.throwIfDisposed(),bindColorTextureToFramebuffer(this.gl,e,this.framebuffer),this.autoDebugValidate&&validateFramebuffer(this.gl)},e.prototype.unbindTextureToFrameBuffer=function(){null!=this.outputTexture?(bindColorTextureToFramebuffer(this.gl,this.outputTexture,this.framebuffer),this.autoDebugValidate&&validateFramebuffer(this.gl)):unbindColorTextureFromFramebuffer(this.gl,this.framebuffer)},e.prototype.downloadMatrixDriver=function(e,t){this.bindTextureToFrameBuffer(e);var n=t();return this.unbindTextureToFrameBuffer(),n},e.prototype.setOutputMatrixTextureDriver=function(e,t,n){this.throwIfDisposed();var r=this.gl;bindColorTextureToFramebuffer(r,e,this.framebuffer),this.autoDebugValidate&&validateFramebuffer(r),this.outputTexture=e,callAndCheck(r,function(){return r.viewport(0,0,t,n)}),callAndCheck(r,function(){return r.scissor(0,0,t,n)})},e.prototype.setOutputMatrixWriteRegionDriver=function(e,t,n,r){var o=this;this.throwIfDisposed(),callAndCheck(this.gl,function(){return o.gl.scissor(e,t,n,r)})},e.prototype.throwIfDisposed=function(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")},e.prototype.throwIfNoProgram=function(){if(null==this.program)throw new Error("No GPU program is currently set.")},e}();function binSearchLastTrue(e){for(var t=0,n=e.length-1,r=-1;t<=n;){var o=t+n>>1;e[o]()?(r=o,t=o+1):n=o-1}return r}function compileProgram(e,t,n,r){for(var o=t.userCode,a=n.map(function(e,n){var r={logicalShape:e.shape,texShape:e.isUniform?null:e.texData.texShape,isUniform:e.isUniform,isPacked:!e.isUniform&&e.texData.isPacked};return{name:t.variableNames[n],shapeInfo:r}}),i=a.map(function(e){return e.shapeInfo}),s={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked},u=makeShader(a,s,o,!0===t.supportsBroadcasting,t.usesPackedTextures),l=e.createProgram(u),c={},p=0;p<t.variableNames.length;p++){var d=t.variableNames[p];c[d]=e.getUniformLocation(l,d,!1)}return{program:t,source:u,webGLProgram:l,uniformLocations:c,gpgpu:e,inShapeInfos:i,outShapeInfo:s}}function validateBinaryAndProgram(e,t){if(e.length!==t.length)throw Error("Binary was compiled with "+e.length+" inputs, but was executed with "+t.length+" inputs");e.forEach(function(e,n){var r=e.logicalShape,o=t[n],a=o.shape;if(!arraysEqual(r,a))throw Error("Binary was compiled with different shapes than the current args. Shapes "+r+" and "+a+" must match");if(!e.isUniform||!o.isUniform){var i=e.texShape,s=o.isUniform?null:o.texData.texShape;if(!arraysEqual(i,s))throw Error("Binary was compiled with different texture shapes than the current args. Shape "+i+" and "+s+" must match")}})}function runProgram(e,t,n,r){validateBinaryAndProgram(e.inShapeInfos,t),validateBinaryAndProgram([e.outShapeInfo],[n]);var o=n.texData.texture,a=n.texData.texShape,i=e.gpgpu;n.texData.isPacked?i.setOutputPackedMatrixTexture(o,a[0],a[1]):i.setOutputMatrixTexture(o,a[0],a[1]),i.setProgram(e.webGLProgram),t.forEach(function(t,n){var r=e.program.variableNames[n],o=e.uniformLocations[r];if(null!=o){if(t.isUniform){if(1===sizeFromShape(t.shape))i.gl.uniform1f(o,t.uniformValues[0]);else{var a=t.uniformValues;a instanceof Float32Array||(a=new Float32Array(a)),i.gl.uniform1fv(o,a)}return}var s=t.texData.texture;i.setInputMatrixTexture(s,o,n)}}),null!=r&&r(i,e.webGLProgram),i.executeProgram()}function makeShaderKey(e,t,n){var r="";t.concat(n).forEach(function(e){r+=e.shape+"_"+(e.isUniform?"uniform":e.texData.texShape)});var o=e.userCode,a=(!0===e.supportsBroadcasting).toString(),i=e.constructor.name;return i+="_"+a+"_"+r+"_"+o}var Im2ColProgram=function(){return function(e,t,n){this.variableNames=["A"],this.outputShape=e;var r=n.filterWidth,o=n.inChannels,a=n.strideWidth,i=n.strideHeight,s=n.padInfo,u=n.outWidth,l=n.dilationWidth,c=n.dilationHeight,p=s.left,d=s.top,h=o*r;this.userCode="\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            int blockIndex = rc.y + col;\n            int pos = rc.x + row;\n\n            if(blockIndex >= "+e[1]+" || pos >= "+e[0]+") continue;\n\n            int offsetY = int(blockIndex / ("+u+")) * "+i+" - "+d+";\n            int d0 = offsetY + "+c+" * (pos / "+h+");\n\n            if(d0 >= "+t[0]+" || d0 < 0) continue;\n\n            int offsetX = int(mod(float(blockIndex), "+u+".) * "+a+". - "+p+".);\n            int d1 = offsetX + "+l+" * (int(mod(float(pos), "+h+".) / "+o+".));\n\n            if(d1 >= "+t[1]+" || d1 < 0) continue;\n\n            result[row * 2 + col] = getA(d0, d1, int(mod(float(pos), "+o+".)));\n          }\n        }\n\n        gl_FragColor = result;\n      }\n    "}}(),LRNProgram=function(){return function(e,t,n,r,o){this.variableNames=["x"],this.outputShape=[];var a,i=t,s=e[3]-1;this.outputShape=e;var u="float("+n+") + float("+r+") * sum";a=.5===o?"inversesqrt("+u+")":1===o?"1.0/("+u+")":"exp(log("+u+") * float(-"+o+"));",this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -"+i+"; j <= "+i+"; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  "+s+") {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * "+a+";\n        setOutput(val);\n      }\n    "}}(),LRNGradProgram=function(){return function(e,t,n,r,o){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=o,this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < "+this.depth+"; ++d) {\n          int depthBegin = int(max(0.0, float(d - "+t+")));\n          int depthEnd = int(min(float("+this.depth+"),\n              float(d + "+t+" + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = "+this.depth+";\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float("+r+") * norm + float("+n+");\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float("+r+")\n                * float("+o+")\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * "+o+");\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    "}}(),MaxPool2DBackpropProgram=function(){return function(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;var t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,o=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=o-1-e.padInfo.top,s=a-1-e.padInfo.left,u=o*a-1;this.userCode="\n      const ivec2 pads = ivec2("+i+", "+s+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+o+";\n          wR += "+r+") {\n          float dyR = float(dyRCorner + wR) / "+t+".0;\n\n          if (dyR < 0.0 || dyR >= "+e.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < "+a+"; wC++) {\n            float dyC = float(dyCCorner + wC) / "+n+".0;\n\n            if (dyC < 0.0 || dyC >= "+e.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = "+u+" - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * "+a+" + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    "}}(),MatMulProgram=function(){return function(e,t,n,r){void 0===n&&(n=!1),void 0===r&&(r=!1),this.variableNames=["matrixA","matrixB"];var o=e[0],a=n?e[2]:e[1],i=r?t[1]:t[2],s=n?e[1]:e[2];this.outputShape=[o,a,i];var u=function(e,t){return n?"batch, "+t+" + "+e+", aRow":"batch, aRow, "+t+" + "+e},l=function(e,t){return r?"batch, bCol, "+t+" + "+e:"batch, "+t+" + "+e+", bCol"},c=4*Math.floor(s/4),p=s%4;this.userCode=" float dotARowBCol(int batch, int aRow, int bCol) {\n      float result = 0.0;\n      for (int i = 0; i < "+c+"; i += 4) {\n        vec4 a = vec4(\n          getMatrixA("+u(0,"i")+"),\n          getMatrixA("+u(1,"i")+"),\n          getMatrixA("+u(2,"i")+"),\n          getMatrixA("+u(3,"i")+")\n        );\n        vec4 b = vec4(\n          getMatrixB("+l(0,"i")+"),\n          getMatrixB("+l(1,"i")+"),\n          getMatrixB("+l(2,"i")+"),\n          getMatrixB("+l(3,"i")+")\n        );\n\n        result += dot(a, b);\n      }\n\n      if ("+(1===p)+") {\n        result += getMatrixA("+u(0,c)+") *\n          getMatrixB("+l(0,c)+");\n      } else if ("+(2===p)+") {\n        vec2 a = vec2(\n          getMatrixA("+u(0,c)+"),\n          getMatrixA("+u(1,c)+")\n        );\n        vec2 b = vec2(\n          getMatrixB("+l(0,c)+"),\n          getMatrixB("+l(1,c)+")\n        );\n        result += dot(a, b);\n      } else if ("+(3===p)+") {\n        vec3 a = vec3(\n          getMatrixA("+u(0,c)+"),\n          getMatrixA("+u(1,c)+"),\n          getMatrixA("+u(2,c)+")\n        );\n        vec3 b = vec3(\n          getMatrixB("+l(0,c)+"),\n          getMatrixB("+l(1,c)+"),\n          getMatrixB("+l(2,c)+")\n        );\n        result += dot(a, b);\n      }\n\n      return result;\n    }\n\n    void main() {\n      ivec3 resBRC = getOutputCoords();\n      setOutput(dotARowBCol(resBRC.x, resBRC.y, resBRC.z));\n    }\n    "}}(),MatMulPackedProgram=function(){return function(e,t,n,r,o){void 0===r&&(r=!1),void 0===o&&(o=!1),this.variableNames=["matrixA","matrixB"],this.usesPackedTextures=!0,this.outputShape=n;var a=r?e[0]:e[1],i=Math.ceil(a/2),s=r?"i * 2, rc.x":"rc.x, i * 2",u=o?"rc.y, i * 2":"i * 2, rc.y",l=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],c=o?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"];this.userCode="\n      const float sharedDimension = "+i+".0;\n\n      vec4 dot2x2ARowBCol(ivec2 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < "+i+"; i++) {\n          vec4 a = getMatrixA("+s+");\n          vec4 b = getMatrixB("+u+");\n\n          result += ("+l[0]+" * "+c[0]+") + ("+l[1]+" * "+c[1]+");\n        }\n        return result;\n      }\n\n      void main() {\n        ivec2 rc = getOutputCoords();\n        setOutput(dot2x2ARowBCol(rc));\n      }\n    "}}(),MultinomialProgram=function(){function e(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode="\n      uniform float seed;\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < "+(t-1)+"; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float("+(t-1)+"));\n      }\n    "}return e.prototype.getCustomSetupFunc=function(e){var t=this;return function(n,r){null==t.seedLoc&&(t.seedLoc=n.getUniformLocation(r,"seed")),n.gl.uniform1f(t.seedLoc,e)}},e}(),OneHotProgram=function(){return function(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode="\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float("+r+"), float("+n+"),\n                      float(index == coords.y)));\n      }\n    "}}();function getVecChannels(e,t){return["x","y","z","w","u","v"].slice(0,t).map(function(t){return e+"."+t})}function getChannels(e,t){return 1===t?[e]:getVecChannels(e,t)}function getSourceCoords$1(e,t){if(1===e)return"rc";for(var n="",r=0;r<e;r++)n+=t[r],r<e-1&&(n+=",");return n}var PackProgram=function(){return function(e){this.variableNames=["A"],this.isPackShader=!0,this.outputShape=e;var t=e.length;if(0===t)this.userCode="\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      ";else{var n=getChannels("rc",t),r=getCoordsDataType(t),o=getOutOfBoundsCondition(t,e,n),a=getSetup(t,e[e.length-1],e[e.length-2],n),i=getOutput(e,n);this.userCode="\n        void main() {\n          "+r+" rc = getOutputCoords();\n\n          if("+o+") {\n            setOutput(vec4(0));\n          } else {\n            "+a+"\n\n            setOutput(vec4("+i+"));\n          }\n        }\n      "}}}();function getSourceCoordsArr(e,t){for(var n=[],r=0;r<=1;r++)for(var o=0;o<=1;o++){for(var a=(0===r?"r":"rp1")+", "+(0===o?"c":"cp1"),i=2;i<e;i++)a=t[t.length-1-i]+","+a;n.push(a)}return n}function getOutOfBoundsCondition(e,t,n){if(1===e)return"rc > "+t[0];for(var r="",o=e-2;o<e;o++)r+=n[o]+" >= "+t[o],o<e-1&&(r+="||");return r}function getSetup(e,t,n,r){if(1===e)return"";var o=r.slice(-2);return"\n    int r = "+o[0]+";\n    int c = "+o[1]+";\n    int rp1 = r + 1;\n    int cp1 = c + 1;\n\n    bool cEdge = cp1 >= "+t+";\n    bool rEdge = rp1 >= "+n+";\n  "}function getOutput(e,t){var n=e.length,r=getSourceCoordsArr(n,t);return 1===n?"getA(rc),\n            rc + 1 >= "+e[0]+" ? 0. : getA(rc + 1),\n            0, 0":"getA("+r[0]+"),\n          cEdge ? 0. : getA("+r[1]+"),\n          rEdge ? 0. : getA("+r[2]+"),\n          rEdge || cEdge ? 0. : getA("+r[3]+")"}var PadProgram=function(){return function(e,t,n){this.variableNames=["x"],this.outputShape=t.map(function(t,n){return t[0]+e[n]+t[1]});var r=e.length,o=getCoordsDataType(r),a=t.map(function(e){return e[0]}).join(","),i=t.map(function(t,n){return t[0]+e[n]}).join(","),s=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);this.userCode=1!==r?"\n      "+o+" start = "+o+"("+a+");\n      "+o+" end = "+o+"("+i+");\n\n      void main() {\n        "+o+" outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(float("+n+"));\n        } else {\n          "+o+" coords = outC - start;\n          setOutput(getX("+s+"));\n        }\n      }\n    ":"\n        int start = "+a+";\n        int end = "+i+";\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(float("+n+"));\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      "}}(),Pool2DProgram=function(){return function(e,t,n){if(this.variableNames=["x"],"avg"===t&&n)throw new Error("Cannot compute positions for average pool.");var r=e.filterWidth,o=e.strideHeight,a=e.strideWidth,i=e.dilationHeight,s=e.dilationWidth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;var d="avg"===t,h="0.0";if(d||(h="-1.0 / 0.0"),n)this.userCode="\n        const ivec2 strides = ivec2("+o+", "+a+");\n        const ivec2 pads = ivec2("+c+", "+p+");\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < "+u+";\n              wR += "+i+") {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= "+e.inHeight+") {\n              continue;\n            }\n\n            for (int wC = 0; wC < "+l+";\n                wC += "+s+") {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= "+e.inWidth+") {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value >= currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = wR * "+l+" + wC;\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      ";else{var f=t+"("+t+"("+t+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"avg"===t&&(f="avgValue / count");var m=4*Math.floor(r/4),g=r%4,v="\n      if ("+d+") {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = max(values, minMaxValue);\n      }\n    ";this.userCode="\n      const ivec2 strides = ivec2("+o+", "+a+");\n      const ivec2 pads = ivec2("+c+", "+p+");\n      const float initializationValue = "+h+";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= "+e.inWidth+") {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4("+h+");\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < "+u+";\n            wR += "+i+") {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= "+e.inHeight+") {\n            continue;\n          }\n\n          for (int wC = 0; wC < "+m+"; wC += 4) {\n            int xC = xCCorner + wC * "+s+";\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + "+s+", d),\n              getValue(batch, xR, xC + 2 * "+s+", d),\n              getValue(batch, xR, xC + 3 * "+s+", d)\n            );\n\n            "+v+"\n          }\n\n          int xC = xCCorner + "+m+";\n          if ("+(1===g)+") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            "+v+"\n          } else if ("+(2===g)+") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + "+s+", d),\n              initializationValue,\n              initializationValue\n            );\n\n            "+v+"\n          } else if ("+(3===g)+") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + "+s+", d),\n              getValue(batch, xR, xC + 2 * "+s+", d),\n              initializationValue\n            );\n\n            "+v+"\n          }\n        }\n        setOutput("+f+");\n      }\n    "}}}(),ReduceProgram=function(){return function(e,t){this.variableNames=["x"];var n=e.windowSize,r=e.batchSize,o=e.inSize,a=Math.ceil(o/n);this.outputShape=[r,a];var i="0.0",s="";"prod"===t?i="1.0":"min"===t?(i="1.0 / 0.0",s="min"):"max"===t&&(i="-1.0 / 0.0",s="max");var u=t+"("+t+"("+t+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"sum"===t?u="sumValue":"prod"===t?u="prodValue":"all"===t?u="allValue":"any"===t&&(u="anyValue");var l=4*Math.floor(n/4),c=n%4,p="\n      if ("+("sum"===t)+") {\n        sumValue += dot(values, ones);\n      } else if ("+("prod"===t)+") {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = "+s+"(values, minMaxValue);\n      }\n    ",d="vec4";"all"===t?(i="1.0",p="\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      ",d="bvec4"):"any"===t&&(i="0.0",p="\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      ",d="bvec4");var h="";o%n>0&&(h="\n        if (inIdx < 0 || inIdx >= "+o+") {\n          return initializationValue;\n        }\n      "),this.userCode="\n      const float initializationValue = "+i+";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        "+h+"\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * "+n+";\n\n        vec4 minMaxValue = vec4("+i+");\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < "+l+"; i += 4) {\n          int inIdx = inOffset + i;\n          "+d+" values = "+d+"(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          "+p+"\n        }\n\n        int inIdx = inOffset + "+l+";\n        if ("+(1===c)+") {\n          "+d+" values = "+d+"(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          "+p+"\n        } else if ("+(2===c)+") {\n          "+d+" values = "+d+"(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          "+p+"\n        } else if ("+(3===c)+") {\n          "+d+" values = "+d+"(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          "+p+"\n        }\n        setOutput("+u+");\n      }\n    "}}(),ReshapePackedProgram=function(){return function(e,t){this.variableNames=["A"],this.usesPackedTextures=!0,this.outputShape=e;for(var n="",r=0;r<4;r++){var o="thisRC = rc;";r%2==1&&(o+="thisRC.z += 1;"),r>1&&(o+="thisRC.y += 1;"),n+="\n        "+o+"\n        "+(r>0?"if(thisRC.y < rows && thisRC.z < cols){":"")+"\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result["+r+"] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        "+(r>0?"}":"")+"\n      "}this.userCode="\n      "+getReshapedInputCoords(t)+"\n      "+getFlatIndex(e)+"\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = "+e[1]+";\n        int cols = "+e[2]+";\n\n        "+n+"\n\n        setOutput(result);\n      }\n    "}}();function getFlatIndex(e){return"\n    int getFlatIndex(ivec3 coords) {\n      return round("+dotify(["coords.x","coords.y","coords.z"],computeStrides(e).map(function(e){return e.toString()}).concat(["1."]))+");\n    }\n  "}function getReshapedInputCoords(e){return"\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      "+getLogicalCoordinatesFromFlatIndex(["r","c","d"],e)+"\n      return ivec3(r, c, d);\n    }\n  "}var ResizeBilinearBackpropProgram=function(){return function(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t.shape;var r=t.shape,o=r[1],a=r[2],i=e.shape,s=i[1],u=i[2],l=[n&&s>1?o-1:o,n&&u>1?a-1:a],c=[n&&s>1?s-1:s,n&&u>1?u-1:u],p=l[0]/c[0],d=l[1]/c[1],h=1/p,f=1/d,m=2*Math.ceil(h)+2,g=2*Math.ceil(f)+2;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float("+p+");\n        const float widthScale = float("+d+");\n\n        const float invHeightScale = float("+h+");\n        const float invWidthScale = float("+f+");\n\n        const int winHeight = int("+m+");\n        const int winWidth = int("+g+");\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= "+s+") {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= "+u+") {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), "+(o-1)+".0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), "+(a-1)+".0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    "}}(),ResizeBilinearProgram=function(){return function(e,t,n,r){this.variableNames=["A"],this.outputShape=[];var o=e[0],a=e[1],i=e[2],s=e[3];this.outputShape=[o,t,n,s];var u=[r&&t>1?a-1:a,r&&n>1?i-1:i],l=[r&&t>1?t-1:t,r&&n>1?n-1:n];this.userCode="\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          "+u[0]/l[0]+",\n          "+u[1]/l[1]+");\n      const vec2 inputShapeRC = vec2("+a+".0, "+i+".0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    "}}(),ResizeNearestNeigborBackpropProgram=function(){return function(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t.shape;var r=t.shape,o=r[1],a=r[2],i=e.shape,s=i[1],u=i[2],l=[n&&s>1?o-1:o,n&&u>1?a-1:a],c=[n&&s>1?s-1:s,n&&u>1?u-1:u],p=l[0]/c[0],d=l[1]/c[1],h=1/p,f=1/d,m=2*Math.ceil(h)+2,g=2*Math.ceil(f)+2;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float("+p+");\n        const float widthScale = float("+d+");\n\n        const float invHeightScale = float("+h+");\n        const float invWidthScale = float("+f+");\n\n        const int winHeight = int("+m+");\n        const int winWidth = int("+g+");\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= "+s+") {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= "+u+") {\n              continue;\n            }\n\n            float sourceFracRow =\n              float("+l[0]+") *\n                (float(dyR) / float("+c[0]+"));\n\n            float sourceFracCol =\n                float("+l[1]+") *\n                  (float(dyC) / float("+c[1]+"));\n\n            int sourceNearestRow = int(min(\n                float(int("+o+") - 1),\n                "+n+" ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int("+a+") - 1),\n                "+n+" ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    "}}(),ResizeNearestNeighborProgram=function(){return function(e,t,n,r){this.variableNames=["A"],this.outputShape=[];var o=e[0],a=e[1],i=e[2],s=e[3];this.outputShape=[o,t,n,s];var u=[r&&t>1?a-1:a,r&&n>1?i-1:i],l=[r&&t>1?t-1:t,r&&n>1?n-1:n],c=r?"0.5":"0.0";this.userCode="\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          "+u[0]/l[0]+",\n          "+u[1]/l[1]+");\n      const vec2 inputShapeRC = vec2("+a+".0, "+i+".0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + "+c+")));\n\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    "}}(),ReverseProgram=function(){return function(e,t){this.variableNames=["x"];var n=e.length;if(n>4)throw new Error("WebGL backend: Reverse of rank-"+n+" tensor is not yet supported");if(this.outputShape=e,1!==n){var r=e.map(function(n,r){return function(n){return-1!==t.indexOf(n)&&1!==e[n]?e[n]+" - coords["+n+"] - 1":"coords["+n+"]"}(r)}).join(","),o=getCoordsDataType(n);this.userCode="\n      void main() {\n        "+o+" coords = getOutputCoords();\n        setOutput(getX("+r+"));\n      }\n    "}else this.userCode="\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX("+e[0]+" - coord - 1));\n        }\n      "}}(),ScatterProgram=function(){return function(e,t,n,r,o,a,i){void 0===i&&(i=!0),this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;var s=getCoordsDataType(o.length),u=getCoordsDataType(a.length),l="";1===n?l="i":2===n&&(l="i, j");var c="getIndices("+l+")",p="";1===r?p="i":2===r&&(p="i, coords[1]");var d="getUpdates("+p+")",h=t>1?"strides[j]":"strides";this.userCode="\n        "+s+" strides = "+s+"("+o+");\n\n        void main() {\n          "+u+" coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < "+e+"; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < "+t+"; j++) {\n              int index = round("+c+");\n              flattenedIndex += index * "+h+";\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += "+d+";\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      "}}(),SegmentOpProgram=function(){return function(e,t){this.variableNames=["x","segmentIds"];var n=e.windowSize,r=e.batchSize,o=e.inSize,a=e.numSegments,i=a*Math.ceil(o/n);this.outputShape=[r,i];var s=4*Math.floor(n/4),u=n%4,l="\n        sumValue += dot(values, filter);\n    ",c="";o%n>0&&(c="\n        if (inIdx < 0 || inIdx >= "+o+") {\n          return initializationValue;\n        }\n      ");var p="";o%n>0&&(p="\n        if (inIdx < 0 || inIdx >= "+o+") {\n          return -1.0;\n        }\n      "),this.userCode="\n      const float initializationValue = 0.0;\n\n      float getValue(int batch, int inIdx) {\n        "+c+"\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        "+p+"\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          "+a+")) * float("+n+"));\n        int currentSeg = int(mod(float(outIdx), float("+a+")));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < "+s+"; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          "+l+"\n        }\n\n        int inIdx = inOffset + "+s+";\n        if ("+(1===u)+") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          "+l+"\n        } else if ("+(2===u)+") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          "+l+"\n        } else if ("+(3===u)+") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          "+l+"\n        }\n        setOutput(sumValue);\n      }\n    "}}(),SelectProgram=function(){return function(e,t,n){var r,o;if(this.variableNames=["c","a","b"],this.outputShape=t,n>4)throw Error("Where for rank "+n+" is not yet supported");if(1===n)o="resRC",r="resRC";else{for(var a=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],s=[],u=0;u<t.length;u++)s.push(""+a[u]),u<e&&i.push(""+a[u]);r=i.join(),o=s.join()}var l=getCoordsDataType(n);this.userCode="\n      void main() {\n        "+l+" resRC = getOutputCoords();\n        float cVal = getC("+r+");\n        if (cVal >= 1.0) {\n          setOutput(getA("+o+"));\n        } else {\n          setOutput(getB("+o+"));\n        }\n      }\n    "}}(),SliceProgram=function(){function e(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;var t=getCoordsDataType(this.rank),n=getCoords$1(this.rank);this.userCode="\n      uniform "+t+" start;\n\n      void main() {\n        "+t+" sourceLoc = start + getOutputCoords();\n        setOutput(getSource("+n+"));\n      }\n    "}return e.prototype.getCustomSetupFunc=function(e){var t=this;if(e.length!==this.rank)throw Error("The rank ("+this.rank+") of the program must match the length of start ("+e.length+")");return function(n,r){if(null!=t.startLoc||(t.startLoc=n.getUniformLocationNoThrow(r,"start"),null!=t.startLoc))if(1===t.rank)n.gl.uniform1i(t.startLoc,e[0]);else if(2===t.rank)n.gl.uniform2i(t.startLoc,e[0],e[1]);else if(3===t.rank)n.gl.uniform3i(t.startLoc,e[0],e[1],e[2]);else{if(4!==t.rank)throw Error("Slicing for rank "+t.rank+" is not yet supported");n.gl.uniform4i(t.startLoc,e[0],e[1],e[2],e[3])}}},e}();function getCoords$1(e){if(1===e)return"sourceLoc";if(2===e)return"sourceLoc.x, sourceLoc.y";if(3===e)return"sourceLoc.x, sourceLoc.y, sourceLoc.z";if(4===e)return"sourceLoc.x, sourceLoc.y, sourceLoc.z, sourceLoc.w";throw Error("Slicing for rank "+e+" is not yet supported")}var StridedSliceProgram=function(){return function(e,t,n,r){this.variableNames=["x"];var o=n.filter(function(e,t){return-1===r.indexOf(t)});this.outputShape=o;var a=n.length,i=getCoordsDataType(n.length),s=getCoordsDataType(o.length),u="";if(1===a)u="coords * strides + begin";else{var l=0;u=n.map(function(e,t){return-1===r.indexOf(t)?(l++,1===o.length?"coords * strides["+t+"] + begin["+t+"]":"coords["+(l-1)+"] * strides["+t+"] + begin["+t+"]"):"begin["+t+"]"}).join(",")}this.userCode="\n      "+i+" begin = "+i+"("+e+");\n      "+i+" strides = "+i+"("+t+");\n\n      void main() {\n        "+s+" coords = getOutputCoords();\n        setOutput(getX("+u+"));\n      }\n    "}}(),TextureManager=function(){function e(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}return e.prototype.acquireTexture=function(e,t,n){var r,o=getPhysicalFromLogicalTextureType(t,n),a=getKeyFromTextureShape(e,o,n);if(a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]),this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this.log();var i=this.freeTextures[a].shift();return this.usedTextures[a].push(i),i}return this.numUsedTextures++,this.log(),o===PhysicalTextureType.PACKED_2X2_FLOAT32?r=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):o===PhysicalTextureType.PACKED_2X2_FLOAT16?r=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):o===PhysicalTextureType.UNPACKED_FLOAT32?r=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):o===PhysicalTextureType.UNPACKED_FLOAT16?r=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):o===PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE&&(r=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(r),r},e.prototype.releaseTexture=function(e,t,n,r){if(null!=this.freeTextures){var o=getKeyFromTextureShape(t,getPhysicalFromLogicalTextureType(n,r),r);o in this.freeTextures||(this.freeTextures[o]=[]),this.freeTextures[o].push(e),this.numFreeTextures++,this.numUsedTextures--;var a=this.usedTextures[o],i=a.indexOf(e);if(i<0)throw new Error("Cannot release a texture that was never provided by this texture manager");a.splice(i,1),this.log()}},e.prototype.log=function(){if(this.logEnabled){var e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",this.numFreeTextures+" / "+this.numUsedTextures,"("+e+")")}},e.prototype.getNumUsedTextures=function(){return this.numUsedTextures},e.prototype.getNumFreeTextures=function(){return this.numFreeTextures},e.prototype.dispose=function(){var e=this;if(null!=this.freeTextures){for(var t in this.freeTextures)this.freeTextures[t].forEach(function(t){e.gpgpu.deleteMatrixTexture(t)});for(var t in this.usedTextures)this.usedTextures[t].forEach(function(t){e.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0}},e}();function getPhysicalFromLogicalTextureType(e,t){if(e===TextureUsage.UPLOAD)return t?PhysicalTextureType.PACKED_2X2_FLOAT32:PhysicalTextureType.UNPACKED_FLOAT32;if(e===TextureUsage.RENDER||null==e)return t?ENV.get("WEBGL_RENDER_FLOAT32_ENABLED")?PhysicalTextureType.PACKED_2X2_FLOAT32:PhysicalTextureType.PACKED_2X2_FLOAT16:ENV.get("WEBGL_RENDER_FLOAT32_ENABLED")?PhysicalTextureType.UNPACKED_FLOAT32:PhysicalTextureType.UNPACKED_FLOAT16;if(e===TextureUsage.DOWNLOAD||e===TextureUsage.PIXELS)return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;throw new Error("Unknown logical texture type "+e)}function getKeyFromTextureShape(e,t,n){return e[0]+"_"+e[1]+"_"+t+"_"+n}var TileProgram=function(){return function(e,t){this.variableNames=["A"];for(var n=new Array(e.length),r=0;r<n.length;r++)n[r]=e[r]*t[r];this.outputShape=n,this.rank=n.length;var o=getCoordsDataType(this.rank),a=getSourceCoords$2(e);this.userCode="\n      void main() {\n        "+o+" resRC = getOutputCoords();\n        setOutput(getA("+a+"));\n      }\n    "}}();function getSourceCoords$2(e){var t=e.length;if(t>5)throw Error("Tile for rank "+t+" is not yet supported");if(1===t)return"imod(resRC, "+e[0]+")";for(var n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[],o=0;o<e.length;o++)r.push("imod("+n[o]+", "+e[o]+")");return r.join()}var TransposeProgram=function(){return function(e,t){this.variableNames=["A"];for(var n=new Array(e.length),r=0;r<n.length;r++)n[r]=e[t[r]];this.outputShape=n,this.rank=n.length;var o=getCoordsDataType(this.rank),a=getSwitchedCoords(t);this.userCode="\n    void main() {\n      "+o+" resRC = getOutputCoords();\n      setOutput(getA("+a+"));\n    }\n    "}}();function getSwitchedCoords(e){var t=e.length;if(t>6)throw Error("Transpose for rank "+t+" is not yet supported");for(var n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t),o=0;o<e.length;o++)r[e[o]]=n[o];return r.join()}var ERF_P=.3275911,ERF_A1=.254829592,ERF_A2=-.284496736,ERF_A3=1.421413741,ERF_A4=-1.453152027,ERF_A5=1.061405429,SELU_SCALEALPHA=1.7580993408473768,SELU_SCALE=1.0507009873554805,UnaryOpProgram=function(){function 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e(e,t,n,r,o){this.mean=e,this.stdDev=t,this.dtype=n,this.nextVal=NaN,this.truncated=r,this.truncated&&(this.upper=this.mean+2*this.stdDev,this.lower=this.mean-2*this.stdDev);var a=o||Math.random();this.random=seedrandom_1(a.toString())}return e.prototype.nextValue=function(){if(!isNaN(this.nextVal)){var e=this.nextVal;return this.nextVal=NaN,e}for(var t,n,r=!1;!r;){var o=void 0,a=void 0,i=void 0;do{i=(o=2*this.random()-1)*o+(a=2*this.random()-1)*a}while(i>=1||0===i);var s=Math.sqrt(-2*Math.log(i)/i);t=this.mean+this.stdDev*o*s,n=this.mean+this.stdDev*a*s,this.truncated&&!this.isValidTruncated(t)||(r=!0)}return this.truncated&&!this.isValidTruncated(n)||(this.nextVal=this.convertValue(n)),this.convertValue(t)},e.prototype.convertValue=function(e){return null==this.dtype||"float32"===this.dtype?e:Math.round(e)},e.prototype.isValidTruncated=function(e){return e<=this.upper&&e>=this.lower},e}();function clone_(e){var t=convertToTensor(e,"x","clone",null);return ENV.engine.runKernel(function(e){return Tensor.make(t.shape,{dataId:t.dataId},t.dtype)},{$x:t},function(e){return{$x:function(){return e.toFloat()}}})}function eye_(e,t,n,r){void 0===r&&(r="float32"),null==t&&(t=e);for(var o=buffer([e,t],r),a=e<=t?e:t,i=0;i<a;++i)o.set(1,i,i);var s=o.toTensor().as2D(e,t);if(null==n)return s;if(1===n.length)return tile(expandDims(s,0),[n[0],1,1]);if(2===n.length)return tile(expandDims(expandDims(s,0),0),[n[0],n[1],1,1]);if(3===n.length)return tile(expandDims(expandDims(expandDims(s,0),0),0),[n[0],n[1],n[2],1,1]);throw new Error("eye() currently supports only 1D and 2D batchShapes, but received "+n.length+"D.")}function randomNormal_(e,t,n,r,o){if(void 0===t&&(t=0),void 0===n&&(n=1),null!=r&&"bool"===r)throw new Error("Unsupported data type "+r);for(var a=new MPRandGauss(t,n,r,!1,o),i=buffer(e,r),s=0;s<i.values.length;s++)i.values[s]=a.nextValue();return i.toTensor()}function truncatedNormal_(e,t,n,r,o){if(void 0===t&&(t=0),void 0===n&&(n=1),null!=r&&"bool"===r)throw new Error("Unsupported data type "+r);for(var a=new MPRandGauss(t,n,r,!0,o),i=buffer(e,r),s=0;s<i.values.length;s++)i.values[s]=a.nextValue();return i.toTensor()}function randomUniform_(e,t,n,r){void 0===t&&(t=0),void 0===n&&(n=1),void 0===r&&(r="float32");for(var o=buffer(e,r),a=0;a<o.values.length;a++)o.values[a]=randUniform(t,n);return o.toTensor()}function rand_(e,t,n){var r=sizeFromShape(e),o=null;if(null==n||"float32"===n)o=new Float32Array(r);else if("int32"===n)o=new Int32Array(r);else{if("bool"!==n)throw new Error("Unknown data type "+n);o=new Uint8Array(r)}for(var a=0;a<r;a++)o[a]=t();return Tensor.make(e,{values:o},n)}function multinomial_(e,t,n,r){void 0===r&&(r=!1);var o=convertToTensor(e,"logits","multinomial"),a=o.size,i=o.rank;if(a<2)throw new Error("Error in multinomial: you need at least 2 outcomes, but got "+a+".");if(i>2)throw new Error("Rank of probabilities must be 1 or 2, but is "+i);n=n||Math.random();var s=1===i?o.as2D(1,-1):o,u=ENV.engine.runKernel(function(e){return e.multinomial(s,r,t,n)},{logits2D:s});return 1===i?u.as1D():u}function oneHot_(e,t,n,r){void 0===n&&(n=1),void 0===r&&(r=0);var o=convertToTensor(e,"indices","oneHot","int32");if(t<2)throw new Error("Error in oneHot: depth must be >=2, but it is "+t);return ENV.engine.runKernel(function(e){return e.oneHot(o,t,n,r)},{$indices:o},function(e){return{$indices:function(){return zeros(o.shape,"float32")}}})}function fromPixels_(e,t){if(void 0===t&&(t=3),t>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");return ENV.engine.fromPixels(e,t)}function toPixels(e,t){return __awaiter(this,void 0,void 0,function(){var n,r,o,a,i,s,u,l,c,p,d,h,f,m,g,v,y,x,T,w;return __generator(this,function(E){switch(E.label){case 0:if(n=convertToTensor(e,"img","toPixels"),e instanceof Tensor||(n=n.toInt()),2!==n.rank&&3!==n.rank)throw new Error("toPixels only supports rank 2 or 3 tensors, got rank "+n.rank+".");if(r=n.shape.slice(0,2),o=r[0],a=r[1],(i=2===n.rank?1:n.shape[2])>4||2===i)throw new Error("toPixels only supports depth of size 1, 3 or 4 but got "+i);return s=n.min(),u=n.max(),[4,s.data()];case 1:return l=E.sent()[0],[4,u.data()];case 2:if(c=E.sent()[0],s.dispose(),u.dispose(),"float32"===n.dtype){if(l<0||c>1)throw new Error("Tensor values for a float32 Tensor must be in the range [0 - 1] but got range ["+l+" - "+c+"].")}else{if("int32"!==n.dtype)throw new Error("Unsupported type for toPixels: "+n.dtype+". Please use float32 or int32 tensors.");if(l<0||c>255)throw new Error("Tensor values for a int32 Tensor must be in the range [0 - 255] but got range ["+l+" - "+c+"].")}return[4,n.data()];case 3:for(p=E.sent(),d="float32"===n.dtype?255:1,h=new Uint8ClampedArray(a*o*4),f=0;f<o*a;++f)m=void 0,g=void 0,v=void 0,y=void 0,1===i?(m=p[f]*d,g=p[f]*d,v=p[f]*d,y=255):3===i?(m=p[3*f]*d,g=p[3*f+1]*d,v=p[3*f+2]*d,y=255):4===i&&(m=p[4*f]*d,g=p[4*f+1]*d,v=p[4*f+2]*d,y=p[4*f+3]*d),h[(x=4*f)+0]=Math.round(m),h[x+1]=Math.round(g),h[x+2]=Math.round(v),h[x+3]=Math.round(y);return null!=t&&(t.width=a,t.height=o,T=t.getContext("2d"),w=new ImageData(h,a,o),T.putImageData(w,0,0)),n!==e&&n.dispose(),[2,h]}})})}function reshape_(e,t){var n=convertToTensor(e,"x","reshape",null);t=inferFromImplicitShape(t,n.size),assert(n.size===sizeFromShape(t),"new shape and old shape must have the same number of elements.");return ENV.engine.runKernel(function(e){return e.reshape(n,t)},{$x:n},function(e){return{$x:function(){return e.reshape(n.shape)}}})}function squeeze_(e,t){var n=convertToTensor(e,"x","squeeze");return reshape(n,squeezeShape(n.shape,t).newShape)}function cast_(e,t){var n=convertToTensor(e,"x","cast");return ENV.engine.runKernel(function(e){return e.cast(n,t)},{$x:n},function(e){return{$x:function(){return e.clone()}}})}function tile_(e,t){var n=convertToTensor(e,"x","tile");assert(n.rank===t.length,"Error in transpose: rank of input "+n.rank+" must match length of reps "+t+".");return ENV.engine.runKernel(function(e){return e.tile(n,t)},{$x:n},function(e){return{$x:function(){var r=zerosLike(n);if(1===n.rank)for(var o=0;o<t[0];++o)r=r.add(e.slice([o*n.shape[0]],[n.shape[0]]));else if(2===n.rank)for(o=0;o<t[0];++o)for(var a=0;a<t[1];++a)r=r.add(e.slice([o*n.shape[0],a*n.shape[1]],[n.shape[0],n.shape[1]]));else if(3===n.rank)for(o=0;o<t[0];++o)for(a=0;a<t[1];++a)for(var i=0;i<t[2];++i)r=r.add(e.slice([o*n.shape[0],a*n.shape[1],i*n.shape[2]],[n.shape[0],n.shape[1],n.shape[2]]));else{if(4!==n.rank)throw new Error("Gradient for tile operation is not implemented for rank-"+n.rank+" tensors yet.");for(o=0;o<t[0];++o)for(a=0;a<t[1];++a)for(i=0;i<t[2];++i)for(var s=0;s<t[3];++s)r=r.add(e.slice([o*n.shape[0],a*n.shape[1],i*n.shape[2],s*n.shape[3]],[n.shape[0],n.shape[1],n.shape[2],n.shape[3]]))}return r}}})}function pad1d_(e,t,n){return void 0===n&&(n=0),assert(2===t.length,"Invalid number of paddings. Must be length of 2."),pad(e,[t],n)}function pad2d_(e,t,n){return void 0===n&&(n=0),assert(2===t.length&&2===t[0].length&&2===t[1].length,"Invalid number of paddings. Must be length of 2 each."),pad(e,t,n)}function pad3d_(e,t,n){return void 0===n&&(n=0),assert(3===t.length&&2===t[0].length&&2===t[1].length&&2===t[2].length,"Invalid number of paddings. Must be length of 2 each."),pad(e,t,n)}function pad4d_(e,t,n){return void 0===n&&(n=0),assert(4===t.length&&2===t[0].length&&2===t[1].length&&2===t[2].length&&2===t[3].length,"Invalid number of paddings. Must be length of 2 each."),pad(e,t,n)}function pad_(e,t,n){void 0===n&&(n=0);var r=convertToTensor(e,"x","pad");if(0===r.rank)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");var o=t.map(function(e){return e[0]});return ENV.engine.runKernel(function(e){return e.pad(r,t,n)},{$x:r},function(e){return{$x:function(){return e.slice(o,r.shape)}}})}function stack_(e,t){void 0===t&&(t=0);var n=convertToTensorArray(e,"tensors","stack");if(assert(n.length>=1,"Pass at least one tensor to tf.stack"),1===n.length)return n[0].expandDims(t);var r=n[0].rank,o=n[0].shape,a=n[0].dtype;assert(t<=r,"Axis must be <= rank of the tensor"),n.forEach(function(e){assertShapesMatch(o,e.shape,"All tensors passed to stack must have matching shapes")}),n.forEach(function(e){assert(a===e.dtype,"All tensors passed to stack must have matching dtypes")});var i=n.map(function(e){return e.expandDims(t)});return concat(i,t)}function batchToSpaceND_(e,t,n){var r=convertToTensor(e,"x","batchToSpaceND"),o=t.reduce(function(e,t){return e*t});assert(r.rank>=1+t.length,"input rank is "+r.rank+" but should be > than blockShape.length "+t.length),assert(n.length===t.length,"crops.length is "+n.length+" but should be equal to blockShape.length  "+t.length),assert(r.shape[0]%o==0,"input tensor batch is "+r.shape[0]+" but is not divisible by the product of the elements of blockShape "+t.join(" * ")+" === "+o);return ENV.engine.runKernel(function(e){return e.batchToSpaceND(r,t,n)},{$x:r},function(e){return{$x:function(){return e.spaceToBatchND(t,n)}}})}function spaceToBatchND_(e,t,n){var r=convertToTensor(e,"x","spaceToBatchND");assert(r.rank>=1+t.length,"input rank "+r.rank+" should be > than [blockShape] "+t.length),assert(n.length===t.length,"paddings.shape[0] "+n.length+" must be equal to [blockShape] "+t.length),assert(r.shape.reduce(function(e,r,o){return o>0&&o<=t.length?e&&(r+n[o-1][0]+n[o-1][1])%t[o-1]==0:e},!0),"input spatial dimensions "+r.shape.slice(1)+" with paddings "+n.toString()+" must be divisible by blockShapes "+t.toString());return ENV.engine.runKernel(function(e){return e.spaceToBatchND(r,t,n)},{$x:r},function(e){return{$x:function(){return e.batchToSpaceND(t,n)}}})}function unstack_(e,t){void 0===t&&(t=0);for(var n,r=convertToTensor(e,"x","unstack"),o=r.shape[t],a=Array(r.rank-1).fill(0),i=0,s=0;s<r.rank;s++)s!==t&&(a[i]=r.shape[s],i++);n=Array(o).fill(1);var u=Array(r.rank).fill(0),l=r.shape.slice();return n.map(function(e){l[t]=e;var n=r.slice(u,l);return u[t]+=e,n.reshape(a)})}function cumsum_(e,t,n,r){void 0===t&&(t=0),void 0===n&&(n=!1),void 0===r&&(r=!1);var o=convertToTensor(e,"x","cumsum"),a=getAxesPermutation([t|=0],o.rank),i=o;null!=a&&(i=o.transpose(a));var s=getInnerMostAxes(1,o.rank)[0],u=ENV.engine.runKernel(function(e){return e.cumsum(i,s,n,r)},{permutedX:i},function(e){return{permutedX:function(){return e.cumsum(t,n,!r)}}});return null!=a&&(u=u.transpose(a)),u}function expandDims_(e,t){void 0===t&&(t=0);var n=convertToTensor(e,"x","expandDims");assert(t<=n.rank,"Axis must be <= rank of the tensor");var r=n.shape.slice();return t<0&&(assert(-(n.rank+1)<=t,"Axis must be in the interval ["+-(n.rank+1)+", "+n.rank+"]"),t=n.rank+t+1),r.splice(t,0,1),reshape(n,r)}function depthToSpace_(e,t,n){void 0===n&&(n="NHWC");var r=convertToTensor(e,"x","depthToSpace"),o="NHWC"===n?r.shape[1]:r.shape[2],a="NHWC"===n?r.shape[2]:r.shape[3],i="NHWC"===n?r.shape[3]:r.shape[1];return assert(o*t>=0,"Negative dimension size caused by overflow when multiplying\n      "+o+" and "+t+"  for depthToSpace with input shape\n      "+r.shape),assert(a*t>=0,"Negative dimension size caused by overflow when multiplying\n      "+a+" and "+t+" for depthToSpace with input shape\n          "+r.shape),assert(i%(t*t)==0,"Dimension size must be evenly divisible by "+t*t+" but is "+i+" for depthToSpace with input shape "+r.shape),ENV.engine.runKernel(function(e){return e.depthToSpace(r,t,n)},{$x:r})}function setdiff1dAsync_(e,t){return __awaiter(this,void 0,void 0,function(){var n,r,o,a,i,s,u,l,c,p;return __generator(this,function(d){switch(d.label){case 0:return n=convertToTensor(e,"x","setdiff1d"),r=convertToTensor(t,"y","setdiff1d"),assert(n.dtype===r.dtype,"x and y should have the same dtype, but got x ("+n.dtype+") and y ("+r.dtype+")."),assert(1===n.rank,"x should be 1D tensor, but got x ("+n.shape+")."),assert(1===r.rank,"y should be 1D tensor, but got y ("+r.shape+")."),[4,n.data()];case 1:return o=d.sent(),[4,r.data()];case 2:for(a=d.sent(),i=new Set(a),s=0,c=0;c<o.length;c++)i.has(o[c])||s++;for(u=new TensorBuffer([s],n.dtype),l=new TensorBuffer([s],"int32"),c=0,p=0;c<o.length;c++)i.has(o[c])||(u.values[p]=o[c],l.values[p]=c,p++);return[2,[u.toTensor(),l.toTensor()]]}})})}function buffer(e,t,n){return void 0===t&&(t="float32"),new TensorBuffer(e,t=t||"float32",n)}function print(e,t){void 0===t&&(t=!1),console.log(e.toString(t))}var batchToSpaceND=op({batchToSpaceND_:batchToSpaceND_}),cast=op({cast_:cast_}),clone=op({clone_:clone_}),cumsum=op({cumsum_:cumsum_}),depthToSpace=op({depthToSpace_:depthToSpace_}),expandDims=op({expandDims_:expandDims_}),eye=op({eye_:eye_}),fromPixels=op({fromPixels_:fromPixels_}),multinomial=op({multinomial_:multinomial_}),oneHot=op({oneHot_:oneHot_}),pad=op({pad_:pad_}),pad1d=op({pad1d_:pad1d_}),pad2d=op({pad2d_:pad2d_}),pad3d=op({pad3d_:pad3d_}),pad4d=op({pad4d_:pad4d_}),rand=op({rand_:rand_}),randomNormal=op({randomNormal_:randomNormal_}),randomUniform=op({randomUniform_:randomUniform_}),reshape=op({reshape_:reshape_}),spaceToBatchND=op({spaceToBatchND_:spaceToBatchND_}),squeeze=op({squeeze_:squeeze_}),stack=op({stack_:stack_}),tile=op({tile_:tile_}),truncatedNormal=op({truncatedNormal_:truncatedNormal_}),unstack=op({unstack_:unstack_}),setdiff1dAsync=setdiff1dAsync_;function whereImpl(e,t){for(var n=[],r=0;r<t.length;r++)t[r]&&n.push(r);var o=buffer(e,"int32"),a=buffer([n.length,e.length],"int32");for(r=0;r<n.length;r++){var i=o.indexToLoc(n[r]),s=r*e.length;a.values.set(i,s)}return a.toTensor()}var CPU_HANDOFF_SIZE_THRESHOLD=10,BEFORE_PAGING_CONSTANT=300,MATMUL_SHARED_DIM_THRESHOLD=1e3,MathBackendWebGL=function(){function e(e,t){if(void 0===t&&(t=!0),this.gpgpu=e,this.delayedStorage=t,this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.lruDataGPU=[],this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.binaryCache={},this.disposed=!1,ENV.get("WEBGL_VERSION")<1)throw new Error("WebGL is not supported on this device");if(null==e){var n=getWebGLContext(ENV.get("WEBGL_VERSION"));this.gpgpu=new GPGPUContext(n),this.canvas=n.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;ENV.get("WEBGL_PAGING_ENABLED")&&(this.NUM_BYTES_BEFORE_PAGING=window.screen.height*window.screen.width*window.devicePixelRatio*BEFORE_PAGING_CONSTANT),this.textureManager=new TextureManager(this.gpgpu)}return e.prototype.register=function(e,t,n){if(this.texData.has(e))throw new Error("Data buffer is already registered");this.texData.set(e,{shape:t,dtype:n})},e.prototype.setDataMover=function(e){this.texData=new DataStorage(e)},e.prototype.fromPixels=function(e,t){if(null==e)throw new Error("pixels passed to tf.fromPixels() can not be null");var n=[e.height,e.width],r=[e.height,e.width,t];if(!(e instanceof HTMLVideoElement||e instanceof HTMLImageElement||e instanceof HTMLCanvasElement||e instanceof ImageData))throw new Error("pixels passed to tf.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ImageData, but was "+e.constructor.name);if(e instanceof HTMLVideoElement){if(null==this.fromPixels2DContext){if(!ENV.get("IS_BROWSER"))throw new Error("Can't read pixels from HTMLImageElement outside the browser.");if("complete"!==document.readyState)throw new Error("The DOM is not ready yet. Please call tf.fromPixels() once the DOM is ready. One way to do that is to add an event listener for `DOMContentLoaded` on the document object");this.fromPixels2DContext=document.createElement("canvas").getContext("2d")}this.fromPixels2DContext.canvas.width=e.width,this.fromPixels2DContext.canvas.height=e.height,this.fromPixels2DContext.drawImage(e,0,0,e.width,e.height),e=this.fromPixels2DContext.canvas}var o=this.makeTensorHandle(n,"int32");this.texData.get(o.dataId).usage=TextureUsage.PIXELS,this.gpgpu.uploadPixelDataToTexture(this.getTexture(o.dataId),e);var a=new FromPixelsProgram(r),i=this.compileAndRun(a,[o]);return this.disposeData(o.dataId),i},e.prototype.makeTensorHandle=function(e,t){var n={};return this.register(n,e,t),{dataId:n,shape:e,dtype:t}},e.prototype.write=function(e,t){if(null==t)throw new Error("MathBackendWebGL.write(): values can not be null");var n=this.texData.get(e),r=n.texture,o=n.texShape,a=n.usage,i=n.dtype,s=n.isPacked;if("complex64"===i)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");null!=r&&(this.releaseTexture(e,r,o,a,s),n.texture=null,n.texShape=null),n.usage=TextureUsage.UPLOAD,n.values=t,this.delayedStorage||this.uploadToGPU(e)},e.prototype.readSync=function(e){var t=this.texData.get(e),n=t.values,r=t.dtype,o=t.complexTensors;if(null!=n)return this.convertAndCacheOnCPU(e);if("string"===r)return n;var a,i,s=null!=this.activeTimers;(s&&(a=performance.now()),"complex64"===r)?i=mergeRealAndImagArrays(o.real.dataSync(),o.imag.dataSync()):i=this.getValuesFromTexture(e);return s&&(this.downloadWaitMs+=performance.now()-a),this.convertAndCacheOnCPU(e,i)},e.prototype.read=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o,a,i,s,u,l,c,p,d,h,f,m,g,v,y;return __generator(this,function(x){switch(x.label){case 0:if(this.pendingRead.has(e))return r=this.pendingRead.get(e),[2,new Promise(function(e){return r.push(e)})];if(o=this.texData.get(e),a=o.texture,i=o.values,s=o.texShape,u=o.isPacked,l=o.shape,null!=i)return[2,this.convertAndCacheOnCPU(e)];if(this.pendingRead.set(e,[]),!ENV.get("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&2===ENV.get("WEBGL_VERSION"))throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");return c=s[1],p=s[0],u&&(t=getPackedMatrixTextureShapeWidthHeight(s[0],s[1]),c=t[0],p=t[1]),d=this.gpgpu.maybeCreateBufferFromTexture(a,p,c),[4,this.gpgpu.createAndWaitForFence()];case 1:return x.sent(),d instanceof WebGLTexture?h=this.getValuesFromTexture(e):u?(f=this.getBatchDim(l),m=1,g=1,l.length&&(n=this.getRowsCols(l),m=n[0],g=n[1]),h=this.gpgpu.downloadPackedMatrixFromBuffer(d,f,m,g,s[0],s[1])):h=this.gpgpu.downloadFloat32MatrixFromBuffer(d,s[0],s[1]),v=this.convertAndCacheOnCPU(e,h),y=this.pendingRead.get(e),this.pendingRead.delete(e),y.forEach(function(e){return e(v)}),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)),[2,v]}})})},e.prototype.getValuesFromTexture=function(e){var t,n=this.texData.get(e),r=n.shape,o=n.dtype,a=n.texture,i=n.texShape;if(ENV.get("WEBGL_DOWNLOAD_FLOAT_ENABLED")){if(this.texData.get(e).isPacked){var s=this.getBatchDim(r),u=1,l=1;return r.length&&(u=(t=this.getRowsCols(r))[0],l=t[1]),this.gpgpu.downloadMatrixFromPackedTexture(a,s,u,l,i[0],i[1])}return this.gpgpu.downloadFloat32MatrixFromOutputTexture(a,i[0],i[1])}var c=this.makeTensorHandle(r,"float32");c.size=sizeFromShape(r),this.texData.get(c.dataId).usage=TextureUsage.DOWNLOAD;var p=new EncodeFloatProgram(r);this.compileAndRun(p,[{shape:r,dtype:o,dataId:e}],c,null,!1);var d=this.texData.get(c.dataId),h=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(d.texture,d.texShape[0],d.texShape[1]);return this.disposeData(c.dataId),h},e.prototype.time=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o,a,i,s;return __generator(this,function(u){switch(u.label){case 0:return t=this.activeTimers,n=[],r=!1,null==this.programTimersStack?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e(),o=flatten(this.activeTimers.map(function(e){return e.query})).filter(function(e){return null!=e}),a=flatten(this.activeTimers.map(function(e){return e.name})).filter(function(e){return null!=e}),this.activeTimers=t,r&&(this.programTimersStack=null),[4,Promise.all(o)];case 1:return i=u.sent(),s={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:sum(i),getExtraProfileInfo:function(){return i.map(function(e,t){return{name:a[t],ms:e}}).map(function(e){return e.name+": "+e.ms}).join(", ")},wallMs:null},this.uploadWaitMs=0,this.downloadWaitMs=0,[2,s]}})})},e.prototype.memory=function(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU}},e.prototype.startTimer=function(){return ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?this.gpgpu.beginQuery():{startMs:performance.now(),endMs:null}},e.prototype.endTimer=function(e){return ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(this.gpgpu.endQuery(),e):(e.endMs=performance.now(),e)},e.prototype.getQueryTime=function(e){return __awaiter(this,void 0,void 0,function(){var t;return __generator(this,function(n){return ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?[2,this.gpgpu.waitForQueryAndGetTime(e)]:[2,(t=e).endMs-t.startMs]})})},e.prototype.disposeData=function(e){if(!this.pendingDisposal.has(e))if(this.pendingRead.has(e))this.pendingDisposal.add(e);else if(this.texData.has(e)){var t=this.texData.get(e),n=t.texture,r=t.texShape,o=t.usage,a=t.complexTensors,i=t.isPacked;null!=n&&this.releaseTexture(e,n,r,o,i),null!=a&&(a.real.dispose(),a.imag.dispose()),this.texData.delete(e)}},e.prototype.getTexture=function(e){return this.uploadToGPU(e),this.texData.get(e).texture},e.prototype.getCPUBackend=function(){return ENV.get("WEBGL_CPU_FORWARD")?(null==this.cpuBackend&&(this.cpuBackend=ENV.findBackend("cpu")),this.cpuBackend):null},e.prototype.shouldExecuteOnCPU=function(e,t){var n=this;return void 0===t&&(t=CPU_HANDOFF_SIZE_THRESHOLD),null!=this.getCPUBackend()&&e.every(function(e){return null==n.texData.get(e.dataId).texture&&e.size<t})},e.prototype.getGPGPUContext=function(){return this.gpgpu},e.prototype.getCanvas=function(){return this.canvas},e.prototype.complex=function(e,t){var n=this.makeOutputArray(e.shape,"complex64");return this.texData.get(n.dataId).complexTensors={real:ENV.engine.keep(e.clone()),imag:ENV.engine.keep(t.clone())},n},e.prototype.real=function(e){return this.texData.get(e.dataId).complexTensors.real.clone()},e.prototype.imag=function(e){return this.texData.get(e.dataId).complexTensors.imag.clone()},e.prototype.slice=function(e,t,n){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.slice(e,t,n);var r=new SliceProgram(n),o=r.getCustomSetupFunc(t);return this.compileAndRun(r,[e],null,o)},e.prototype.stridedSlice=function(e,t,n,r,o,a,i,s,u){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.stridedSlice(e,t,n,r,o,a,i,s,u);var l=getStridedSlicedInfo(e.shape,t,n,r,o,a,i,s,u),c=l[0],p=l[1],d=l[2],h=p.filter(function(e,t){return-1===d.indexOf(t)});if(h.some(function(e){return 0===e}))return tensor([],h);var f=new StridedSliceProgram(c,r,p,d);return this.compileAndRun(f,[e])},e.prototype.reverse=function(e,t){var n=new ReverseProgram(e.shape,t);return this.compileAndRun(n,[e])},e.prototype.concat2Tensors=function(e,t,n){var r=computeOutShape([e.shape,t.shape],n),o=e.as2D(-1,sizeFromShape(e.shape.slice(n))),a=t.as2D(-1,sizeFromShape(t.shape.slice(n))),i=new ConcatProgram(o.shape,a.shape);return this.compileAndRun(i,[o,a]).reshape(r)},e.prototype.concat=function(e,t){if(this.shouldExecuteOnCPU(e))return this.cpuBackend.concat(e,t);if(1===e.length)return e[0];for(var n=e[0],r=1;r<e.length;++r)n=this.concat2Tensors(n,e[r],t);return n},e.prototype.neg=function(e){var t=new UnaryOpProgram(e.shape,NEG);return this.compileAndRun(t,[e])},e.prototype.batchMatMul=function(e,t,n,r){var o=n?e.shape[2]:e.shape[1],a=r?t.shape[1]:t.shape[2],i=n?e.shape[1]:e.shape[2],s=e.shape[0];if((1===o||1===a)&&i>MATMUL_SHARED_DIM_THRESHOLD){n&&(e=e.transpose([0,2,1])),r&&(t=t.transpose([0,2,1]));var u=1===a?e:e.as3D(s,i,1),l=1===a?2:1,c=1===a?t.as3D(s,1,i):t;return this.multiply(u,c).sum(l,!0)}var p=upcastType(e.dtype,t.dtype);if(1===s){var d=e.as2D(e.shape[1],e.shape[2]),h=t.as2D(t.shape[1],t.shape[2]),f=new MatMulPackedProgram(d.shape,h.shape,[o,a],n,r),m=this.makePackedTensor(f.outputShape,p),g=this.compileAndRun(f,[d,h],m);return g.reshape([1,g.shape[0],g.shape[1]])}f=new MatMulProgram(e.shape,t.shape,n,r),m=this.makeOutputArray(f.outputShape,p);return this.compileAndRun(f,[e,t],m)},e.prototype.multiply=function(e,t){if("complex64"===e.dtype){var n=this.texData.get(e.dataId),r=this.texData.get(t.dataId),o=new BinaryOpComplexProgram(COMPLEX_MULTIPLY.REAL,e.shape,t.shape),a=new BinaryOpComplexProgram(COMPLEX_MULTIPLY.IMAG,e.shape,t.shape),i=[this.makeComplexComponentTensorHandle(e,n.complexTensors.real),this.makeComplexComponentTensorHandle(e,n.complexTensors.imag),this.makeComplexComponentTensorHandle(t,r.complexTensors.real),this.makeComplexComponentTensorHandle(t,r.complexTensors.imag)],s=this.compileAndRun(o,i),u=this.compileAndRun(a,i),l=this.complex(s,u);return s.dispose(),u.dispose(),l}if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.multiply(e,t);var c=new BinaryOpProgram(MUL,e.shape,t.shape),p=this.makeOutputArray(c.outputShape,e.dtype);return this.compileAndRun(c,[e,t],p)},e.prototype.batchNormalization=function(e,t,n,r,o,a){var i=[e,t,n],s=null;null!=a&&(s=a.shape,i.push(a));var u=null;if(null!=o&&(u=o.shape,i.push(o)),ENV.get("WEBGL_PACK_BATCHNORMALIZATION")){var l=new BatchNormPackedProgram(e.shape,t.shape,n.shape,s,u,r);return this.compileAndRun(l,i)}var c=new BatchNormProgram(e.shape,t.shape,n.shape,s,u,r);return this.compileAndRun(c,i)},e.prototype.localResponseNormalization4D=function(e,t,n,r,o){var a=new LRNProgram(e.shape,t,n,r,o);return this.compileAndRun(a,[e])},e.prototype.LRNGrad=function(e,t,n,r,o,a,i){var s=new LRNGradProgram(t.shape,r,o,a,i);return this.compileAndRun(s,[t,n,e])},e.prototype.tile=function(e,t){var n=new TileProgram(e.shape,t);return this.compileAndRun(n,[e])},e.prototype.pad=function(e,t,n){var r=new PadProgram(e.shape,t,n);return this.compileAndRun(r,[e])},e.prototype.transpose=function(e,t){var n=new TransposeProgram(e.shape,t);return this.compileAndRun(n,[e])},e.prototype.gather=function(e,t,n){var r=new GatherProgram(e.shape,t.size,n);return this.compileAndRun(r,[e,t])},e.prototype.batchToSpaceND=function(e,t,n){assert(e.rank<=4,"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");var r=t.reduce(function(e,t){return e*t}),o=getReshaped(e.shape,t,r),a=getPermuted(o.length,t.length),i=getReshapedPermuted(e.shape,t,r),s=getSliceBeginCoords(n,t.length),u=getSliceSize(i,n,t.length);return e.reshape(o).transpose(a).reshape(i).slice(s,u)},e.prototype.spaceToBatchND=function(e,t,n){assert(e.rank<=4,"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");var r=t.reduce(function(e,t){return e*t}),o=[[0,0]];o.push.apply(o,n);for(var a=1+t.length;a<e.shape.length;++a)o.push([0,0]);var i=e.pad(o),s=getReshaped(i.shape,t,r,!1),u=getPermuted(s.length,t.length,!1),l=getReshapedPermuted(i.shape,t,r,!1);return i.reshape(s).transpose(u).reshape(l)},e.prototype.reduce=function(e,t,n){var r=e.shape[0],o=e.shape[1],a=computeOptimalWindowSize(o),i=new ReduceProgram({windowSize:a,inSize:o,batchSize:r},t),s=i.outputShape,u=s[0],l=s[1],c=this.makeOutputArray([u,l],n);return this.compileAndRun(i,[e],c),1===c.shape[1]?c:this.reduce(c,t,n)},e.prototype.argReduce=function(e,t,n){void 0===n&&(n=null);var r=e.shape[0],o=e.shape[1];null!=n&&(r=n.shape[0],o=n.shape[1]);var a=computeOptimalWindowSize(o),i=new ArgMinMaxProgram({windowSize:a,inSize:o,batchSize:r},t,null==n),s=i.outputShape,u=s[0],l=s[1],c=this.makeOutputArray([u,l],"int32"),p=[e];return null!=n&&p.push(n),this.compileAndRun(i,p,c),1===c.shape[1]?c:this.argReduce(e,t,c)},e.prototype.sum=function(e,t){assertAxesAreInnerMostDims("sum",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o),i=sumOutType(e.dtype);return this.reduce(a,"sum",i).reshape(r)},e.prototype.prod=function(e,t){var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o),i=sumOutType(e.dtype);return this.reduce(a,"prod",i).reshape(r)},e.prototype.unsortedSegmentSum=function(e,t,n){var r=0,o=getAxesPermutation([r],e.rank),a=e;null!=o&&(a=e.transpose(o),r=getInnerMostAxes(1,e.rank)[0]);var i=computeOutShape$1(a.shape,r,n),s=sizeFromShape([a.shape[r]]),u=a.as2D(-1,s),l=sumOutType(e.dtype),c=this.segOpCompute(u,"unsortedSegmentSum",t,l,n).reshape(i);return null!=o&&(c=c.transpose(getUndoAxesPermutation(o))),c},e.prototype.segOpCompute=function(e,t,n,r,o){var a=e.shape[0],i=e.shape[1],s=segOpComputeOptimalWindowSize(i,o),u=new SegmentOpProgram({windowSize:s,inSize:i,batchSize:a,numSegments:o},t),l=u.outputShape,c=l[0],p=l[1],d=this.makeOutputArray([c,p],r);return this.compileAndRun(u,[e,n],d),d.shape[1]===o?d:(n=range(0,o).tile([i/s]),this.segOpCompute(d,t,n,r,o))},e.prototype.argMin=function(e,t){var n=[t];assertAxesAreInnerMostDims("argMin",n,e.rank);var r=computeOutAndReduceShapes(e.shape,n),o=r[0],a=sizeFromShape(r[1]),i=e.as2D(-1,a);return this.argReduce(i,"min").reshape(o)},e.prototype.argMax=function(e,t){var n=[t];assertAxesAreInnerMostDims("argMax",n,e.rank);var r=computeOutAndReduceShapes(e.shape,n),o=r[0],a=sizeFromShape(r[1]),i=e.as2D(-1,a);return this.argReduce(i,"max").reshape(o)},e.prototype.cumsum=function(e,t,n,r){if(t!==e.rank-1)throw new Error("WebGL cumsum shader expects an inner-most axis="+(e.rank-1)+" but got axis="+t);var o=new CumSumProgram(e.shape,n,r);return this.compileAndRun(o,[e])},e.prototype.equal=function(e,t){var n=new BinaryOpProgram(EQUAL,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.notEqual=function(e,t){var n=new BinaryOpProgram(NOT_EQUAL,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.less=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.less(e,t);var n=new BinaryOpProgram(LESS,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.lessEqual=function(e,t){var n=new BinaryOpProgram(LESS_EQUAL,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.greater=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.greater(e,t);var n=new BinaryOpProgram(GREATER,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.greaterEqual=function(e,t){var n=new BinaryOpProgram(GREATER_EQUAL,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.logicalNot=function(e){var t=new UnaryOpProgram(e.shape,LOGICAL_NOT);return this.compileAndRun(t,[e])},e.prototype.logicalAnd=function(e,t){var n=new BinaryOpProgram(LOGICAL_AND,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.logicalOr=function(e,t){var n=new BinaryOpProgram(LOGICAL_OR,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.select=function(e,t,n){var r=new SelectProgram(e.rank,t.shape,t.rank),o=this.makeOutputArray(r.outputShape,upcastType(t.dtype,n.dtype));return this.compileAndRun(r,[e,t,n],o)},e.prototype.where=function(e){warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");var t=e.dataSync();return whereImpl(e.shape,t)},e.prototype.topk=function(e,t,n){return topkImpl(e.dataSync(),e.shape,e.dtype,t,n)},e.prototype.min=function(e,t){assertAxesAreInnerMostDims("min",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o);return this.reduce(a,"min",a.dtype).reshape(r)},e.prototype.minimum=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.minimum(e,t);var n=new BinaryOpProgram(MIN,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.mod=function(e,t){var n=new BinaryOpProgram(MOD,e.shape,t.shape),r=n.getCustomSetupFunc();return this.compileAndRun(n,[e,t],null,r)},e.prototype.max=function(e,t){assertAxesAreInnerMostDims("max",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o);return this.reduce(a,"max",a.dtype).reshape(r)},e.prototype.maximum=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.maximum(e,t);var n=new BinaryOpProgram(MAX,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.all=function(e,t){assertAxesAreInnerMostDims("all",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o);return this.reduce(a,"all",a.dtype).reshape(r)},e.prototype.any=function(e,t){assertAxesAreInnerMostDims("any",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o);return this.reduce(a,"any",a.dtype).reshape(r)},e.prototype.squaredDifference=function(e,t){var n=new BinaryOpProgram(SQUARED_DIFFERENCE,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.realDivide=function(e,t){var n=new BinaryOpProgram(DIV,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"float32");return this.compileAndRun(n,[e,t],r)},e.prototype.floorDiv=function(e,t){var n=new BinaryOpProgram(INT_DIV,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"int32");return this.compileAndRun(n,[e,t],r)},e.prototype.add=function(e,t){if("complex64"===e.dtype&&"complex64"===t.dtype)return this.complexSeparableBinaryOp(e,t,ADD);var n=new BinaryOpProgram(ADD,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,upcastType(e.dtype,t.dtype));return this.compileAndRun(n,[e,t],r)},e.prototype.complexSeparableBinaryOp=function(e,t,n){var r=this,o=this.texData.get(e.dataId),a=this.texData.get(t.dataId),i=[[o.complexTensors.real,a.complexTensors.real],[o.complexTensors.imag,a.complexTensors.imag]].map(function(o){var a=o[0],i=o[1],s=new BinaryOpProgram(n,e.shape,t.shape),u=r.makeOutputArray(s.outputShape,upcastType(a.dtype,i.dtype)),l=r.makeComplexComponentTensorHandle(e,a),c=r.makeComplexComponentTensorHandle(t,i);return r.compileAndRun(s,[l,c],u)}),s=i[0],u=i[1],l=this.complex(s,u);return s.dispose(),u.dispose(),l},e.prototype.makeComplexComponentTensorHandle=function(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}},e.prototype.addN=function(e){for(var t=e[0],n=1;n<e.length;n++)t=this.add(t,e[n]);return t},e.prototype.subtract=function(e,t){if("complex64"===e.dtype&&"complex64"===t.dtype)return this.complexSeparableBinaryOp(e,t,SUB);if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.subtract(e,t);var n=new BinaryOpProgram(SUB,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,upcastType(e.dtype,t.dtype));return this.compileAndRun(n,[e,t],r)},e.prototype.pow=function(e,t){var n=new BinaryOpProgram(POW,e.shape,t.shape),r=n.getCustomSetupFunc(),o=this.makeOutputArray(n.outputShape,upcastType(e.dtype,t.dtype));return this.compileAndRun(n,[e,t],o,r)},e.prototype.ceil=function(e){var t=new UnaryOpProgram(e.shape,CEIL);return this.compileAndRun(t,[e])},e.prototype.floor=function(e){var t=new UnaryOpProgram(e.shape,FLOOR);return this.compileAndRun(t,[e])},e.prototype.sign=function(e){var t=new UnaryOpProgram(e.shape,SIGN);return this.compileAndRun(t,[e])},e.prototype.round=function(e){var t=new UnaryOpProgram(e.shape,ROUND);return this.compileAndRun(t,[e])},e.prototype.exp=function(e){var t=new UnaryOpProgram(e.shape,EXP);return this.compileAndRun(t,[e])},e.prototype.expm1=function(e){var t=new UnaryOpProgram(e.shape,EXPM1);return this.compileAndRun(t,[e])},e.prototype.log=function(e){var t=new UnaryOpProgram(e.shape,LOG),n=t.getCustomSetupFunc();return this.compileAndRun(t,[e],null,n)},e.prototype.log1p=function(e){var t=new UnaryOpProgram(e.shape,LOG1P);return this.compileAndRun(t,[e])},e.prototype.sqrt=function(e){var t=new UnaryOpProgram(e.shape,SQRT);return this.compileAndRun(t,[e])},e.prototype.rsqrt=function(e){var t=new UnaryOpProgram(e.shape,RSQRT);return this.compileAndRun(t,[e])},e.prototype.square=function(e){var t=new UnaryOpProgram(e.shape,SQUARE);return this.compileAndRun(t,[e])},e.prototype.reciprocal=function(e){var t=new UnaryOpProgram(e.shape,RECIPROCAL);return this.compileAndRun(t,[e])},e.prototype.relu=function(e){var t=new UnaryOpProgram(e.shape,RELU);return this.compileAndRun(t,[e])},e.prototype.elu=function(e){var t=new UnaryOpProgram(e.shape,ELU);return this.compileAndRun(t,[e])},e.prototype.eluDer=function(e,t){var n=new BinaryOpProgram(ELU_DER,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.selu=function(e){var t=new UnaryOpProgram(e.shape,SELU);return this.compileAndRun(t,[e])},e.prototype.int=function(e){var t=new UnaryOpProgram(e.shape,TO_INT),n=this.makeOutputArray(t.outputShape,"int32");return this.compileAndRun(t,[e],n)},e.prototype.clip=function(e,t,n){var r;return r=ENV.get("WEBGL_PACK_CLIP")?new ClipPackedProgram(e.shape,t,n):new ClipProgram(e.shape,t,n),this.compileAndRun(r,[e])},e.prototype.abs=function(e){var t=new UnaryOpProgram(e.shape,ABS);return this.compileAndRun(t,[e])},e.prototype.complexAbs=function(e){var t=this.texData.get(e.dataId),n=new ComplexAbsProgram(e.shape),r=[this.makeComplexComponentTensorHandle(e,t.complexTensors.real),this.makeComplexComponentTensorHandle(e,t.complexTensors.imag)];return this.compileAndRun(n,r)},e.prototype.sigmoid=function(e){var t=new UnaryOpProgram(e.shape,SIGMOID);return this.compileAndRun(t,[e])},e.prototype.softplus=function(e){var t=new UnaryOpProgram(e.shape,SOFTPLUS);return this.compileAndRun(t,[e])},e.prototype.sin=function(e){var t=new UnaryOpProgram(e.shape,SIN);return this.compileAndRun(t,[e])},e.prototype.cos=function(e){var t=new UnaryOpProgram(e.shape,COS);return this.compileAndRun(t,[e])},e.prototype.tan=function(e){var t=new UnaryOpProgram(e.shape,TAN);return this.compileAndRun(t,[e])},e.prototype.asin=function(e){var t=new UnaryOpProgram(e.shape,ASIN);return this.compileAndRun(t,[e])},e.prototype.acos=function(e){var t=new UnaryOpProgram(e.shape,ACOS);return this.compileAndRun(t,[e])},e.prototype.atan=function(e){var t=new UnaryOpProgram(e.shape,ATAN);return this.compileAndRun(t,[e])},e.prototype.atan2=function(e,t){var n=new BinaryOpProgram(ATAN2,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.sinh=function(e){var t=new UnaryOpProgram(e.shape,SINH);return this.compileAndRun(t,[e])},e.prototype.cosh=function(e){var t=new UnaryOpProgram(e.shape,COSH);return this.compileAndRun(t,[e])},e.prototype.tanh=function(e){var t=new UnaryOpProgram(e.shape,TANH);return this.compileAndRun(t,[e])},e.prototype.asinh=function(e){var t=new UnaryOpProgram(e.shape,ASINH);return this.compileAndRun(t,[e])},e.prototype.acosh=function(e){var t=new UnaryOpProgram(e.shape,ACOSH),n=t.getCustomSetupFunc();return this.compileAndRun(t,[e],null,n)},e.prototype.atanh=function(e){var t=new UnaryOpProgram(e.shape,ATANH),n=t.getCustomSetupFunc();return this.compileAndRun(t,[e],null,n)},e.prototype.erf=function(e){var t=new UnaryOpProgram(e.shape,ERF);return this.compileAndRun(t,[e])},e.prototype.step=function(e,t){var n=new UnaryOpProgram(e.shape,STEP(t));return this.compileAndRun(n,[e])},e.prototype.conv2dWithIm2Row=function(e,t,n){var r=n.filterWidth,o=n.filterHeight,a=n.inChannels,i=n.outWidth,s=n.outHeight,u=r*o*a,l=s*i,c=[u,l],p=e.squeeze([0]),d=t.reshape([u,-1]),h=new Im2ColProgram(c,p.shape,n),f=this.compileAndRun(h,[p]),m=new MatMulPackedProgram(f.shape,d.shape,[l,n.outChannels],!0,!1);return this.compileAndRun(m,[f,d]).reshape([1,s,i,n.outChannels])},e.prototype.conv2d=function(e,t,n){if(ENV.get("WEBGL_CONV_IM2COL")&&1===e.shape[0])return this.conv2dWithIm2Row(e,t,n);var r=new Conv2DProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv2dDerInput=function(e,t,n){var r=new Conv2DDerInputProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv2dDerFilter=function(e,t,n){var r=new Conv2DDerFilterProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.depthwiseConv2D=function(e,t,n){var r;return ENV.get("WEBGL_PACK_DEPTHWISECONV")&&1===n.dilationWidth&&1===n.dilationHeight&&n.padInfo.left<=1&&n.strideWidth<=2&&n.outChannels/n.inChannels==1?(r=new DepthwiseConvPacked2DProgram(n),this.compileAndRun(r,[e,t],this.makePackedTensor(n.outShape,e.dtype))):(r=new DepthwiseConv2DProgram(n),this.compileAndRun(r,[e,t]))},e.prototype.depthwiseConv2DDerInput=function(e,t,n){var r=new DepthwiseConv2DDerInputProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.depthwiseConv2DDerFilter=function(e,t,n){var r=new DepthwiseConv2DDerFilterProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv3d=function(e,t,n){var r=new Conv3DProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv3dDerInput=function(e,t,n){var r=new Conv3DDerInputProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv3dDerFilter=function(e,t,n){var r=new Conv3DDerFilterProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.maxPool=function(e,t){var n=new Pool2DProgram(t,"max",!1),r=this.makeOutputArray(n.outputShape,e.dtype);return this.compileAndRun(n,[e],r)},e.prototype.avgPool=function(e,t){var n=new Pool2DProgram(t,"avg",!1),r=this.makeOutputArray(n.outputShape,"float32");return this.compileAndRun(n,[e],r)},e.prototype.maxPoolBackprop=function(e,t,n,r){var o=new Pool2DProgram(r,"max",!0),a=this.compileAndRun(o,[t]),i=new MaxPool2DBackpropProgram(r),s=this.makeOutputArray(i.outputShape,t.dtype),u=this.compileAndRun(i,[e,a],s);return a.dispose(),u},e.prototype.avgPoolBackprop=function(e,t,n){var r=new AvgPool2DBackpropProgram(n),o=this.makeOutputArray(r.outputShape,t.dtype);return this.compileAndRun(r,[e],o)},e.prototype.cast=function(e,t){return castTensor(e,t,this)},e.prototype.reshape=function(e,t){return this.texData.get(e.dataId).isPacked&&!isReshapeFree(e.shape,t)?this.packedReshape(e,t):reshapeTensor(e,t)},e.prototype.resizeBilinear=function(e,t,n,r){var o=new ResizeBilinearProgram(e.shape,t,n,r);return this.compileAndRun(o,[e])},e.prototype.resizeBilinearBackprop=function(e,t,n){var r=new ResizeBilinearBackpropProgram(e,t,n);return this.compileAndRun(r,[e])},e.prototype.resizeNearestNeighbor=function(e,t,n,r){var o=new ResizeNearestNeighborProgram(e.shape,t,n,r);return this.compileAndRun(o,[e])},e.prototype.resizeNearestNeighborBackprop=function(e,t,n){var r=new ResizeNearestNeigborBackpropProgram(e,t,n);return this.compileAndRun(r,[e])},e.prototype.multinomial=function(e,t,n,r){var o=t?e:softmax(e),a=o.shape[0],i=o.shape[1],s=new MultinomialProgram(a,i,n),u=this.makeOutputArray(s.outputShape,"int32"),l=s.getCustomSetupFunc(r);return this.compileAndRun(s,[o],u,l)},e.prototype.oneHot=function(e,t,n,r){var o=new OneHotProgram(e.size,t,n,r);return this.compileAndRun(o,[e])},e.prototype.nonMaxSuppression=function(e,t,n,r,o){return warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"),nonMaxSuppressionImpl(e.dataSync(),t.dataSync(),n,r,o)},e.prototype.cropAndResize=function(e,t,n,r,o,a){var i=new CropAndResizeProgram(e.shape,t.shape,r,o,a);return this.compileAndRun(i,[e,t,n])},e.prototype.depthToSpace=function(e,t,n){assert(t>1,"blockSize should be > 1 for depthToSpace, but was: "+t);var r=e.shape[0],o=("NHWC"===n?e.shape[1]:e.shape[2])*t,a=("NHWC"===n?e.shape[2]:e.shape[3])*t,i=("NHWC"===n?e.shape[3]:e.shape[1])/(t*t),s=new DepthToSpaceProgram("NHWC"===n?[r,o,a,i]:[r,i,o,a],t,n);return this.compileAndRun(s,[e])},e.prototype.split=function(e,t,n){return split(e,t,n)},e.prototype.scatterND=function(e,t,n){var r=calculateShapes(t,e,n),o=r.sliceRank,a=r.numUpdates,i=r.sliceSize,s=r.strides,u=r.outputSize,l=[u/i,i],c=e.reshape([a,o]),p=t.reshape([a,i]);if(0===u)return reshapeTensor(tensor([]),n);var d=scalar(0),h=new ScatterProgram(a,o,c.rank,p.rank,s,l);return this.compileAndRun(h,[p,c,d]).reshape(n)},e.prototype.sparseToDense=function(e,t,n,r){var o=calculateShapes(t,e,n),a=o.sliceRank,i=o.numUpdates,s=o.strides,u=o.outputSize,l=new ScatterProgram(i,a,e.rank,t.rank,s,[u,1],!1);return this.compileAndRun(l,[t,e,r]).reshape(n)},e.prototype.fft=function(e){return this.fftImpl(e,!1)},e.prototype.ifft=function(e){return this.fftImpl(e,!0)},e.prototype.fftImpl=function(e,t){var n=this.texData.get(e.dataId),r=new FFTProgram(COMPLEX_FFT.REAL,e.shape,t),o=new FFTProgram(COMPLEX_FFT.IMAG,e.shape,t),a=[this.makeComplexComponentTensorHandle(e,n.complexTensors.real),this.makeComplexComponentTensorHandle(e,n.complexTensors.imag)],i=this.compileAndRun(r,a),s=this.compileAndRun(o,a),u=this.complex(i,s).as2D(e.shape[0],e.shape[1]);return i.dispose(),s.dispose(),u},e.prototype.gatherND=function(e,t){var n=t.shape,r=n[n.length-1],o=prepareAndValidate(e,t),a=o[0],i=o[1],s=o[2],u=o[3],l=t.reshape([i,r]),c=e.reshape([e.size/s,s]),p=new GatherNDProgram(r,u,[i,s]);return this.compileAndRun(p,[c,l]).reshape(a)},e.prototype.makeOutputArray=function(e,t){return Tensor.make(e,{},t)},e.prototype.makePackedTensor=function(e,t){var n=Tensor.make(e,{},t);return this.texData.get(n.dataId).isPacked=!0,n},e.prototype.unpackTensor=function(e){var t=new UnpackProgram(e.shape);return this.compileAndRun(t,[e],Tensor.make(t.outputShape,{},e.dtype))},e.prototype.getBatchDim=function(e,t){return void 0===t&&(t=2),sizeFromShape(e.slice(0,e.length-t))},e.prototype.getRowsCols=function(e){if(0===e.length)throw Error("Cannot get rows and columns of an empty shape array.");return[e.length>1?e[e.length-2]:1,e[e.length-1]]},e.prototype.packedReshape=function(e,t){var n=e.reshape([this.getBatchDim(e.shape)].concat(this.getRowsCols(e.shape))),r=[this.getBatchDim(t)].concat(this.getRowsCols(t)),o=new ReshapePackedProgram(r,n.shape);return this.compileAndRun(o,[n]).reshape(t)},e.prototype.compileAndRun=function(e,t,n,r,o){var a=this;if(void 0===o&&(o=!0),null==n&&(n=e.usesPackedTextures?this.makePackedTensor(e.outputShape,t[0].dtype):this.makeOutputArray(e.outputShape,t[0].dtype)),0===n.size)return this.texData.get(n.dataId).values=getTypedArrayFromDType(n.dtype,0),n;var i=t.map(function(t){if("complex64"===t.dtype)throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");var n=a.texData.get(t.dataId);if(null==n.texture){if(!e.usesPackedTextures&&sizeFromShape(t.shape)<=ENV.get("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:t.shape,texData:null,isUniform:!0,uniformValues:a.readSync(t.dataId)};e.usesPackedTextures&&(n.isPacked=!0,n.shape=t.shape)}else if(!!n.isPacked!=!!e.usesPackedTextures){var r=void 0,o=void 0;n.isPacked?(r=new UnpackProgram(t.shape),o=a.compileAndRun(r,[t],Tensor.make(r.outputShape,{},t.dtype))):(r=new PackProgram(t.shape),o=a.compileAndRun(r,[t],a.makePackedTensor(t.shape,t.dtype))),n=a.texData.get(o.dataId),t=o}else if(n.isPacked&&!isReshapeFree(n.shape,t.shape)){a.delayedStorage=!1;var i=t.dataSync();a.delayedStorage=!0,t=Tensor.make(t.shape,{values:i},t.dtype),(n=a.texData.get(t.dataId)).isPacked=!0}return a.uploadToGPU(t.dataId),{shape:t.shape,texData:n,isUniform:!1}});this.uploadToGPU(n.dataId);var s,u={shape:n.shape,texData:this.texData.get(n.dataId),isUniform:!1},l=makeShaderKey(e,i,u),c=this.getAndSaveBinary(l,function(){return compileProgram(a.gpgpu,e,i,u)}),p=null!=this.activeTimers;if(p&&(s=this.startTimer()),runProgram(c,i,u,r),ENV.get("WEBGL_PAGING_ENABLED")&&o&&this.numBytesInGPU>this.NUM_BYTES_BEFORE_PAGING)for(var d=this.numBytesInGPU-this.NUM_BYTES_BEFORE_PAGING;d>0&&this.lruDataGPU.length>0;){var h=this.lruDataGPU.shift(),f=this.texData.get(h),m=f.shape,g=f.dtype;d-=this.computeBytes(m,g),this.read(h)}return p&&(s=this.endTimer(s),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(s)})),ENV.get("WEBGL_LAZILY_UNPACK")||!this.texData.get(n.dataId).isPacked||e.isPackShader?n:this.unpackTensor(n)},e.prototype.getAndSaveBinary=function(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]},e.prototype.getTextureManager=function(){return this.textureManager},e.prototype.dispose=function(){if(!this.disposed){for(var e in this.binaryCache)this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram);this.textureManager.dispose(),this.canvas.remove(),null!=this.fromPixels2DContext&&this.fromPixels2DContext.canvas.remove(),this.gpgpuCreatedLocally&&this.gpgpu.dispose(),this.disposed=!0}},e.prototype.floatPrecision=function(){var e=this;return tidy(function(){return e.abs(scalar(1e-8)).get()>0?32:16})},e.prototype.uploadToGPU=function(e){var t,n=this.texData.get(e),r=n.shape,o=n.values,a=n.texture,i=n.usage,s=n.isPacked;if(null==a){var u,l=null!=this.activeTimers;l&&(u=performance.now());var c=getTextureShapeFromLogicalShape(r,s);n.texShape=c;var p=this.acquireTexture(e,c,i,s);if(n.texture=p,null!=o){if(s){var d=this.getBatchDim(r),h=1,f=1;r.length&&(h=(t=this.getRowsCols(r))[0],f=t[1]),this.gpgpu.uploadMatrixToPackedTexture(p,d,h,f,c[0],c[1],typedArrayToFloat32(o))}else this.gpgpu.uploadMatrixToTexture(p,c[0],c[1],typedArrayToFloat32(o));n.values=null,l&&(this.uploadWaitMs+=performance.now()-u)}}else{ENV.get("WEBGL_PAGING_ENABLED")&&this.lruDataGPU.indexOf(e)>=0&&(this.lruDataGPU.splice(this.lruDataGPU.indexOf(e),1),this.lruDataGPU.push(e))}},e.prototype.convertAndCacheOnCPU=function(e,t){var n=this.delayedStorage,r=this.texData.get(e),o=r.texture,a=r.texShape,i=r.dtype,s=r.usage,u=r.isPacked;return n&&null!=o&&(this.releaseTexture(e,o,a,s,u),r.texture=null,r.texShape=null),r.usage=TextureUsage.UPLOAD,null!=t&&(r.values=float32ToTypedArray(t,i)),r.values},e.prototype.releaseTexture=function(e,t,n,r,o){var a=this.texData.get(e),i=a.shape,s=a.dtype;if(ENV.get("WEBGL_PAGING_ENABLED")){var u=this.lruDataGPU.indexOf(e);u>=0&&this.lruDataGPU.splice(u,1)}this.numBytesInGPU-=this.computeBytes(i,s),this.textureManager.releaseTexture(t,n,r,o)},e.prototype.acquireTexture=function(e,t,n,r){var o=this.texData.get(e),a=o.shape,i=o.dtype;return ENV.get("WEBGL_PAGING_ENABLED")&&this.lruDataGPU.push(e),this.numBytesInGPU+=this.computeBytes(a,i),this.textureManager.acquireTexture(t,n,r)},e.prototype.computeBytes=function(e,t){return sizeFromShape(e)*bytesPerElement(t)},e}();function float32ToTypedArray(e,t){if("float32"===t||"complex64"===t)return e;if("int32"===t||"bool"===t){for(var n="int32"===t?new Int32Array(e.length):new Uint8Array(e.length),r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}throw new Error("Unknown dtype "+t)}function typedArrayToFloat32(e){return e instanceof Float32Array?e:new Float32Array(e)}function neg_(e){var t=convertToTensor(e,"x","neg");return ENV.engine.runKernel(function(e){return e.neg(t)},{$x:t},function(e){return{$x:function(){return e.neg()}}})}function ceil_(e){var t=convertToTensor(e,"x","ceil");return ENV.engine.runKernel(function(e){return e.ceil(t)},{$x:t},function(e){return{$x:function(){return zerosLike(e)}}})}function floor_(e){var t=convertToTensor(e,"x","floor");return ENV.engine.runKernel(function(e){return e.floor(t)},{$x:t},function(e){return{$x:function(){return zerosLike(e)}}})}function sign_(e){var t=convertToTensor(e,"x","sign");return ENV.engine.runKernel(function(e){return e.sign(t)},{$x:t},function(e){return{$x:function(){return zerosLike(e)}}})}function round_(e){var t=convertToTensor(e,"x","round");return ENV.engine.runKernel(function(e){return e.round(t)},{$x:t},function(e){return{$x:function(){return zerosLike(e)}}})}function exp_(e){var t=convertToTensor(e,"x","exp");return ENV.engine.runKernel(function(e,n){return n(e.exp(t))},{$x:t},function(e,t){var n=t[0];return{$x:function(){return e.mulStrict(n)}}})}function expm1_(e){var t=convertToTensor(e,"x","expm1");return ENV.engine.runKernel(function(e){return e.expm1(t)},{$x:t},function(e){return{$x:function(){return e.mulStrict(t.exp())}}})}function log_(e){var t=convertToTensor(e,"x","log");return ENV.engine.runKernel(function(e){return e.log(t)},{$x:t},function(e){return{$x:function(){return e.divStrict(t.toFloat())}}})}function log1p_(e){var t=convertToTensor(e,"x","log1p");return ENV.engine.runKernel(function(e){return e.log1p(t)},{$x:t},function(e){return{$x:function(){return e.div(t.add(1))}}})}function sqrt_(e){var t=convertToTensor(e,"x","sqrt");return ENV.engine.runKernel(function(e){return e.sqrt(t)},{$x:t},function(e){return{$x:function(){return e.div(t.toFloat().sqrt().mul(2))}}})}function rsqrt_(e){var t=convertToTensor(e,"x","rsqrt");return ENV.engine.runKernel(function(e){return e.rsqrt(t)},{$x:t},function(e){return{$x:function(){return e.div(t.pow(1.5).mul(2)).neg()}}})}function square_(e){var t=convertToTensor(e,"x","square");return ENV.engine.runKernel(function(e){return e.square(t)},{$x:t},function(e){return{$x:function(){return e.mul(t.toFloat().mul(2))}}})}function reciprocal_(e){var t=convertToTensor(e,"x","reciprocal");return ENV.engine.runKernel(function(e){return e.reciprocal(t)},{$x:t},function(e){return{$x:function(){return e.divStrict(t.square().neg())}}})}function abs_(e){var t=convertToTensor(e,"x","abs");if("complex64"===t.dtype)return ENV.engine.runKernel(function(e){return e.complexAbs(t)},{$x:t});return ENV.engine.runKernel(function(e){return e.abs(t)},{$x:t},function(e){return{$x:function(){return e.mulStrict(t.toFloat().step(-1))}}})}function clipByValue_(e,t,n){var r=convertToTensor(e,"x","clipByValue");assert(t<=n,"Error in clip: min ("+t+") must be less than or equal to max ("+n+").");return ENV.engine.runKernel(function(e){return e.clip(r,t,n)},{$x:r},function(e){return{$x:function(){return e.where(r.greaterEqual(t).logicalAnd(r.lessEqual(n)),zerosLike(e))}}})}function sigmoid_(e){var t=convertToTensor(e,"x","sigmoid");return ENV.engine.runKernel(function(e,n){return n(e.sigmoid(t))},{$x:t},function(e,t){var n=t[0];return{$x:function(){return e.mul(n.mul(scalar(1).sub(n)))}}})}function logSigmoid_(e){var t=convertToTensor(e,"x","logSigmoid");return ENV.engine.runKernel(function(e){return e.softplus(t.neg()).neg()},{$x:t},function(e){return{$x:function(){return e.mulStrict(t.neg().sigmoid())}}})}function softplus_(e){var t=convertToTensor(e,"x","softplus");return ENV.engine.runKernel(function(e){return e.softplus(t)},{$x:t},function(e){return{$x:function(){return e.mulStrict(t.sigmoid())}}})}function sin_(e){var t=convertToTensor(e,"x","sin");return ENV.engine.runKernel(function(e){return e.sin(t)},{$x:t},function(e){return{$x:function(){return t.toFloat().cos().mulStrict(e)}}})}function cos_(e){var t=convertToTensor(e,"x","cos");return ENV.engine.runKernel(function(e){return e.cos(t)},{$x:t},function(e){return{$x:function(){return t.toFloat().sin().neg().mulStrict(e)}}})}function tan_(e){var t=convertToTensor(e,"x","tan");return ENV.engine.runKernel(function(e){return e.tan(t)},{$x:t},function(e){return{$x:function(){return e.divStrict(t.cos().square())}}})}function asin_(e){var t=convertToTensor(e,"x","asin");return ENV.engine.runKernel(function(e){return e.asin(t)},{$x:t},function(e){return{$x:function(){return e.divStrict(scalar(1).sub(t.toFloat().square()).sqrt())}}})}function acos_(e){var t=convertToTensor(e,"x","acos");return ENV.engine.runKernel(function(e){return e.acos(t)},{$x:t},function(e){return{$x:function(){return e.divStrict(scalar(1).sub(t.toFloat().square()).sqrt()).neg()}}})}function atan_(e){var t=convertToTensor(e,"x","atan");return ENV.engine.runKernel(function(e){return e.atan(t)},{$x:t},function(e){return{$x:function(){return e.div(t.toFloat().square().add(1))}}})}function sinh_(e){var t=convertToTensor(e,"x","sinh");return ENV.engine.runKernel(function(e){return e.sinh(t)},{$x:t},function(e){return{$x:function(){return t.toFloat().cosh().mulStrict(e)}}})}function cosh_(e){var t=convertToTensor(e,"x","cosh");return ENV.engine.runKernel(function(e){return e.cosh(t)},{$x:t},function(e){return{$x:function(){return t.toFloat().sinh().mulStrict(e)}}})}function tanh_(e){var t=convertToTensor(e,"x","tanh");return ENV.engine.runKernel(function(e,n){return n(e.tanh(t))},{$x:t},function(e,t){var n=t[0];return{$x:function(){return scalar(1).sub(n.square()).mulStrict(e)}}})}function asinh_(e){var t=convertToTensor(e,"x","asinh");return ENV.engine.runKernel(function(e){return e.asinh(t)},{$x:t},function(e){return{$x:function(){return e.divStrict(scalar(1).add(t.toFloat().square()).sqrt())}}})}function acosh_(e){var t=convertToTensor(e,"x","acosh");return ENV.engine.runKernel(function(e){return e.acosh(t)},{$x:t},function(e){return{$x:function(){return e.divStrict(t.toFloat().square().sub(1).sqrt())}}})}function atanh_(e){var t=convertToTensor(e,"x","atanh");return ENV.engine.runKernel(function(e){return e.atanh(t)},{$x:t},function(e){return{$x:function(){return e.div(scalar(1).sub(t.toFloat().square()))}}})}function erf_(e){var t=convertToTensor(e,"x","erf");assert("int32"===t.dtype||"float32"===t.dtype,"Input dtype must be `int32` or `float32`."),"int32"===t.dtype&&(t=t.toFloat());return ENV.engine.runKernel(function(e){return e.erf(t)},{$x:t},function(e){return{$x:function(){return e.mul(t.square().neg().exp().mul(2/Math.sqrt(Math.PI)))}}})}function step_(e,t){void 0===t&&(t=0);var n=convertToTensor(e,"x","step");return ENV.engine.runKernel(function(e){return e.step(n,t)},{$x:n},function(e){return{$x:function(){return zerosLike(e)}}})}ENV.get("IS_BROWSER")&&ENV.registerBackend("webgl",function(){return new MathBackendWebGL},2,setTensorTracker);var abs=op({abs_:abs_}),acos=op({acos_:acos_}),acosh=op({acosh_:acosh_}),asin=op({asin_:asin_}),asinh=op({asinh_:asinh_}),atan=op({atan_:atan_}),atanh=op({atanh_:atanh_}),ceil=op({ceil_:ceil_}),clipByValue=op({clipByValue_:clipByValue_}),cos=op({cos_:cos_}),cosh=op({cosh_:cosh_}),erf=op({erf_:erf_}),exp=op({exp_:exp_}),expm1=op({expm1_:expm1_}),floor=op({floor_:floor_}),log$1=op({log_:log_}),log1p=op({log1p_:log1p_}),logSigmoid=op({logSigmoid_:logSigmoid_}),neg=op({neg_:neg_}),reciprocal=op({reciprocal_:reciprocal_}),round=op({round_:round_}),rsqrt=op({rsqrt_:rsqrt_}),sigmoid=op({sigmoid_:sigmoid_}),sign=op({sign_:sign_}),sin=op({sin_:sin_}),sinh=op({sinh_:sinh_}),softplus=op({softplus_:softplus_}),sqrt=op({sqrt_:sqrt_}),square=op({square_:square_}),step=op({step_:step_}),tan=op({tan_:tan_}),tanh$1=op({tanh_:tanh_});function batchNormalization2d_(e,t,n,r,o,a){void 0===r&&(r=.001);var i,s,u=convertToTensor(e,"x","batchNormalization"),l=convertToTensor(t,"mean","batchNormalization"),c=convertToTensor(n,"variance","batchNormalization");return null!=o&&(i=convertToTensor(o,"scale","batchNormalization")),null!=a&&(s=convertToTensor(a,"offset","batchNormalization")),assert(2===u.rank,"Error in batchNormalization3D: x must be rank 3 but got rank "+u.rank+"."),assert(2===l.rank||1===l.rank,"Error in batchNormalization2D: mean must be rank 2 or rank 1 but got rank "+l.rank+"."),assert(2===c.rank||1===c.rank,"Error in batchNormalization2D: variance must be rank 2 or rank 1 but got rank "+c.rank+"."),null!=i&&assert(2===i.rank||1===i.rank,"Error in batchNormalization2D: scale must be rank 2 or rank 1 but got rank "+i.rank+"."),null!=s&&assert(2===s.rank||1===s.rank,"Error in batchNormalization2D: offset must be rank 2 or rank 1 but got rank "+s.rank+"."),batchNormalization(u,l,c,r,i,s)}function batchNormalization3d_(e,t,n,r,o,a){void 0===r&&(r=.001);var i,s,u=convertToTensor(e,"x","batchNormalization"),l=convertToTensor(t,"mean","batchNormalization"),c=convertToTensor(n,"variance","batchNormalization");return null!=o&&(i=convertToTensor(o,"scale","batchNormalization")),null!=a&&(s=convertToTensor(a,"offset","batchNormalization")),assert(3===u.rank,"Error in batchNormalization3D: x must be rank 3 but got rank "+u.rank+"."),assert(3===l.rank||1===l.rank,"Error in batchNormalization3D: mean must be rank 3 or rank 1 but got rank "+l.rank+"."),assert(3===c.rank||1===c.rank,"Error in batchNormalization3D: variance must be rank 3 or rank 1 but got rank "+c.rank+"."),null!=i&&assert(3===i.rank||1===i.rank,"Error in batchNormalization3D: scale must be rank 3 or rank 1 but got rank "+i.rank+"."),null!=s&&assert(3===s.rank||1===s.rank,"Error in batchNormalization3D: offset must be rank 3 or rank 1 but got rank "+s.rank+"."),batchNormalization(u,l,c,r,i,s)}function batchNormalization4d_(e,t,n,r,o,a){void 0===r&&(r=.001);var i,s,u=convertToTensor(e,"x","batchNormalization"),l=convertToTensor(t,"mean","batchNormalization"),c=convertToTensor(n,"variance","batchNormalization");return null!=o&&(i=convertToTensor(o,"scale","batchNormalization")),null!=a&&(s=convertToTensor(a,"offset","batchNormalization")),assert(4===u.rank,"Error in batchNormalization4D: x must be rank 4 but got rank "+u.rank+"."),assert(4===l.rank||1===l.rank,"Error in batchNormalization4D: mean must be rank 4 or rank 1 but got rank "+l.rank+"."),assert(4===c.rank||1===c.rank,"Error in batchNormalization4D: variance must be rank 4 or rank 1 but got rank "+c.rank+"."),null!=i&&assert(4===i.rank||1===i.rank,"Error in batchNormalization4D: scale must be rank 4 or rank 1 but got rank "+i.rank+"."),null!=s&&assert(4===s.rank||1===s.rank,"Error in batchNormalization4D: offset must be rank 4 or rank 1 but got rank "+s.rank+"."),batchNormalization(u,l,c,r,i,s)}function batchNormalization_(e,t,n,r,o,a){void 0===r&&(r=.001);var i,s,u,l=convertToTensor(e,"x","batchNormalization"),c=convertToTensor(t,"mean","batchNormalization"),p=convertToTensor(n,"variance","batchNormalization");null!=o&&(i=convertToTensor(o,"scale","batchNormalization")),null!=a&&(s=convertToTensor(a,"offset","batchNormalization")),assert(c.rank===p.rank,"Batch normalization gradient requires mean and variance to have equal ranks."),assert(null==s||c.rank===s.rank,"Batch normalization gradient requires mean and offset to have equal ranks."),assert(null==i||c.rank===i.rank,"Batch normalization gradient requires mean and scale to have equal ranks."),u=0===l.rank||1===l.rank?l.as4D(1,1,1,l.size):2===l.rank?l.as4D(1,1,l.shape[0],l.shape[1]):3===l.rank?l.as4D(1,l.shape[0],l.shape[1],l.shape[2]):l;return ENV.engine.runKernel(function(e){return e.batchNormalization(u,batchnormReshape4D(c),batchnormReshape4D(p),r,batchnormReshape4D(i),batchnormReshape4D(s))},{$x:l,$mean:c,$variance:p,$scale:i,$offset:s},function(e){var t=null==i?scalar(1):i,n=getReductionAxes(c.shape,u.shape),o=[];if(1===c.rank){for(var a=0;a<u.shape.length-1;++a)o.push(u.shape[a]);o.push(1)}var s=l.sub(c),d=e.mul(t),h=rsqrt(p.add(scalar(r))),f=h.mul(h).mul(h).mul(scalar(-.5));return{$x:function(){return 1===c.rank?e.mul(tile(h.as4D(1,1,1,c.shape[0]),o)).mul(t).reshape(l.shape):e.mul(h).mul(t).reshape(l.shape)},$mean:function(){var e=h.mul(scalar(-1)).mul(d);return 1===c.rank&&(e=e.sum(n)),e.reshape(c.shape)},$variance:function(){var e=f.mul(s).mul(d);return 1===c.rank&&(e=e.sum(n)),e.reshape(c.shape)},$scale:function(){var t=s.mul(h),r=e.mul(t);return 1===c.rank&&(r=r.sum(n)),r.reshape(c.shape)},$offset:function(){var t=e;return 1===c.rank&&(t=t.sum(n)),t.reshape(c.shape)}}}).reshape(l.shape)}function batchnormReshape4D(e){return null==e?null:0===e.rank?e.as1D():1===e.rank?e:2===e.rank?e.as4D(1,1,e.shape[0],e.shape[1]):3===e.rank?e.as4D(1,e.shape[0],e.shape[1],e.shape[2]):e}var batchNormalization2d=op({batchNormalization2d_:batchNormalization2d_}),batchNormalization3d=op({batchNormalization3d_:batchNormalization3d_}),batchNormalization4d=op({batchNormalization4d_:batchNormalization4d_}),batchNormalization=op({batchNormalization_:batchNormalization_});function computePool2DInfo(e,t,n,r,o,a,i){void 0===i&&(i="channelsLast");var s,u=parseTupleParam(t),l=u[0],c=u[1];if("channelsLast"===i)s=[l,c,e[3],e[3]];else{if("channelsFirst"!==i)throw new Error("Unknown dataFormat "+i);s=[l,c,e[1],e[1]]}return computeConv2DInfo(e,s,n,r,o,a,!1,i)}function computeConv2DInfo(e,t,n,r,o,a,i,s){void 0===i&&(i=!1),void 0===s&&(s="channelsLast");var u=[-1,-1,-1,-1],l=u[0],c=u[1],p=u[2],d=u[3];if("channelsLast"===s)l=e[0],c=e[1],p=e[2],d=e[3];else{if("channelsFirst"!==s)throw new Error("Unknown dataFormat "+s);l=e[0],d=e[1],c=e[2],p=e[3]}var h,f=t[0],m=t[1],g=t[3],v=parseTupleParam(n),y=v[0],x=v[1],T=parseTupleParam(r),w=T[0],E=T[1],S=getEffectiveFilterSize(f,w),b=getEffectiveFilterSize(m,E),C=getPadAndOutInfo(o,c,p,y,x,S,b,a),A=C.padInfo,_=C.outHeight,N=C.outWidth,R=i?g*d:g;return"channelsFirst"===s?h=[l,R,_,N]:"channelsLast"===s&&(h=[l,_,N,R]),{batchSize:l,dataFormat:s,inHeight:c,inWidth:p,inChannels:d,outHeight:_,outWidth:N,outChannels:R,padInfo:A,strideHeight:y,strideWidth:x,filterHeight:f,filterWidth:m,effectiveFilterHeight:S,effectiveFilterWidth:b,dilationHeight:w,dilationWidth:E,inShape:e,outShape:h,filterShape:t}}function computeConv3DInfo(e,t,n,r,o,a,i){void 0===a&&(a=!1),void 0===i&&(i="channelsLast");var s=[-1,-1,-1,-1,-1],u=s[0],l=s[1],c=s[2],p=s[3],d=s[4];if("channelsLast"===i)u=e[0],l=e[1],c=e[2],p=e[3],d=e[4];else{if("channelsFirst"!==i)throw new Error("Unknown dataFormat "+i);u=e[0],d=e[1],l=e[2],c=e[3],p=e[4]}var h,f=t[0],m=t[1],g=t[2],v=t[4],y=parse3TupleParam(n),x=y[0],T=y[1],w=y[2],E=parse3TupleParam(r),S=E[0],b=E[1],C=E[2],A=get3DPadAndOutInfo(o,l,c,p,x,T,w,getEffectiveFilterSize(f,S),getEffectiveFilterSize(m,b),getEffectiveFilterSize(g,C)),_=A.padInfo,N=A.outDepth,R=A.outHeight,k=A.outWidth,I=a?v*d:v;return"channelsFirst"===i?h=[u,I,N,R,k]:"channelsLast"===i&&(h=[u,N,R,k,I]),{batchSize:u,dataFormat:i,inDepth:l,inHeight:c,inWidth:p,inChannels:d,outDepth:N,outHeight:R,outWidth:k,outChannels:I,padInfo:_,strideDepth:x,strideHeight:T,strideWidth:w,filterDepth:f,filterHeight:m,filterWidth:g,dilationDepth:S,dilationHeight:b,dilationWidth:C,inShape:e,outShape:h,filterShape:t}}function computeOutputShape3D(e,t,n,r,o,a){null==o&&(o=computeDefaultPad(e,t,r));var i=e[0],s=e[1],u=conditionalRound((i-t+2*o)/r+1,a);assert(isInt(u),"The output # of rows ("+u+") must be an integer. Change the stride and/or zero pad parameters");var l=conditionalRound((s-t+2*o)/r+1,a);return assert(isInt(l),"The output # of columns ("+l+") must be an integer. Change the stride and/or zero pad parameters"),[u,l,n]}function computeDefaultPad(e,t,n,r){void 0===r&&(r=1);var o=getEffectiveFilterSize(t,r);return Math.floor((e[0]*(n-1)-n+o)/2)}function parseTupleParam(e){return"number"==typeof e?[e,e]:e}function parse3TupleParam(e){return"number"==typeof e?[e,e,e]:e}function getEffectiveFilterSize(e,t){return t<=1?e:e+(e-1)*(t-1)}function getPadAndOutInfo(e,t,n,r,o,a,i,s){var u,l,c;if("number"==typeof e){u={top:e,bottom:e,left:e,right:e,type:0===e?"VALID":"NUMBER"};var p=computeOutputShape3D([t,n,1],a,1,r,e,s);l=p[0],c=p[1]}else if("same"===e){var d=((l=Math.ceil(t/r))-1)*r+a-t,h=((c=Math.ceil(n/o))-1)*o+i-n,f=Math.floor(d/2),m=d-f,g=Math.floor(h/2);u={top:f,bottom:m,left:g,right:h-g,type:"SAME"}}else{if("valid"!==e)throw Error("Unknown padding parameter: "+e);u={top:0,bottom:0,left:0,right:0,type:"VALID"},l=Math.ceil((t-a+1)/r),c=Math.ceil((n-i+1)/o)}return{padInfo:u,outHeight:l,outWidth:c}}function get3DPadAndOutInfo(e,t,n,r,o,a,i,s,u,l){var c,p,d,h;if("same"===e){var f=((p=Math.ceil(t/o))-1)*o+s-t,m=((d=Math.ceil(n/a))-1)*a+u-n,g=((h=Math.ceil(r/i))-1)*i+l-r,v=Math.floor(f/2),y=f-v,x=Math.floor(m/2),T=m-x,w=Math.floor(g/2);c={top:x,bottom:T,left:w,right:g-w,front:v,back:y,type:"SAME"}}else{if("valid"!==e)throw Error("Unknown padding parameter: "+e);c={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},p=Math.ceil((t-s+1)/o),d=Math.ceil((n-u+1)/a),h=Math.ceil((r-l+1)/i)}return{padInfo:c,outDepth:p,outHeight:d,outWidth:h}}function conditionalRound(e,t){if(!t)return e;switch(t){case"round":return Math.round(e);case"ceil":return Math.ceil(e);case"floor":return Math.floor(e);default:throw new Error("Unknown roundingMode "+t)}}function tupleValuesAreOne(e){var t=parseTupleParam(e),n=t[0],r=t[1];return 1===n&&1===r}function eitherStridesOrDilationsAreOne(e,t){return tupleValuesAreOne(e)||tupleValuesAreOne(t)}function matMul_(e,t,n,r){var o;void 0===n&&(n=!1),void 0===r&&(r=!1);var a=convertToTensor(e,"a","matMul"),i=convertToTensor(t,"b","matMul");a=(o=makeTypesMatch(a,i))[0],i=o[1];var s=n?a.shape[a.rank-2]:a.shape[a.rank-1],u=r?i.shape[i.rank-1]:i.shape[i.rank-2],l=n?a.shape[a.rank-1]:a.shape[a.rank-2],c=r?i.shape[i.rank-2]:i.shape[i.rank-1],p=a.shape.slice(0,-2),d=i.shape.slice(0,-2),h=sizeFromShape(p),f=sizeFromShape(d);assert(a.rank>=2&&i.rank>=2&&a.rank===i.rank,"Error in matMul: inputs must have the same rank of at least 2, got ranks "+a.rank+" and "+i.rank+"."),assert(arraysEqual(p,d),"Error in matMul: outer dimensions ("+p+") and ("+d+") of Tensors with shapes "+a.shape+" and "+i.shape+" must match."),assert(s===u,"Error in matMul: inner shapes ("+s+") and ("+u+") of Tensors with shapes "+a.shape+" and "+i.shape+" and transposeA="+n+" and transposeB="+r+" must match.");var m=a.shape.slice(0,-2).concat([l,c]),g=n?a.as3D(h,s,l):a.as3D(h,l,s),v=r?i.as3D(f,c,u):i.as3D(f,u,c);return ENV.engine.runKernel(function(e){return e.batchMatMul(g,v,n,r)},{$a:g,$b:v},function(e){return n||r?!n&&r?{$a:function(){return e.matMul(v,!1,!1)},$b:function(){return e.matMul(g,!0,!1)}}:n&&!r?{$a:function(){return v.matMul(e,!1,!0)},$b:function(){return g.matMul(e,!1,!1)}}:{$a:function(){return v.matMul(e,!0,!0)},$b:function(){return e.matMul(g,!0,!0)}}:{$a:function(){return e.matMul(v,!1,!0)},$b:function(){return g.matMul(e,!0,!1)}}}).reshape(m)}function outerProduct_(e,t){var n=convertToTensor(e,"v1","outerProduct"),r=convertToTensor(t,"v2","outerProduct");return assert(1===n.rank&&1===r.rank,"Error in outerProduct: inputs must be rank 1, but got ranks "+n.rank+" and "+r.rank+"."),n.as2D(-1,1).matMul(r.as2D(1,-1))}function dot_(e,t){var n=convertToTensor(e,"t1","dot"),r=convertToTensor(t,"t2","dot");assert(!(1!==n.rank&&2!==n.rank||1!==r.rank&&2!==r.rank),"Error in dot: inputs must all be rank 1 or 2, but got ranks "+n.rank+" and "+r.rank+".");var o=1===n.rank?n.size:n.shape[1],a=1===r.rank?r.size:r.shape[0];return assert(o===a,"Error in dot: inner dimensions of inputs must match, but got "+o+" and "+a+"."),1===n.rank&&1===r.rank?n.as2D(1,-1).matMul(r.as2D(-1,1)).asScalar():1===n.rank&&2===r.rank?n.as2D(1,-1).matMul(r.as2D(r.shape[0],r.shape[1])).as1D():2===n.rank&&1===r.rank?n.matMul(r.as2D(-1,1)).as1D():n.matMul(r.as2D(r.shape[0],r.shape[1]))}var matMul=op({matMul_:matMul_}),dot=op({dot_:dot_}),outerProduct=op({outerProduct_:outerProduct_});function conv1d_(e,t,n,r,o,a,i){void 0===o&&(o="NWC"),void 0===a&&(a=1);var s=convertToTensor(e,"x","conv1d"),u=convertToTensor(t,"filter","conv1d"),l=s,c=!1;2===s.rank&&(c=!0,l=s.as3D(1,s.shape[0],s.shape[1])),assert(3===l.rank,"Error in conv1d: input must be rank 3, but got rank "+l.rank+"."),assert(3===u.rank,"Error in conv1d: filter must be rank 3, but got rank "+u.rank+"."),null!=i&&assert(isInt(r),"Error in conv1d: pad must be an integer when using, dimRoundingMode "+i+" but got pad "+r+"."),assert(l.shape[2]===u.shape[1],"Error in conv1d: depth of input ("+l.shape[2]+") must match input depth for filter "+u.shape[1]+"."),assert(eitherStridesOrDilationsAreOne(n,a),"Error in conv1D: Either stride or dilation must be 1. Got stride "+n+" and dilation '"+a+"'"),assert("NWC"===o,"Error in conv1d: got dataFormat of "+o+" but only NWC is currently supported.");var p=u.as4D(1,u.shape[0],u.shape[1],u.shape[2]),d=l.as4D(l.shape[0],1,l.shape[1],l.shape[2]),h=conv2d(d,p,[1,n],r,"NHWC",[1,a],i);return c?h.as2D(h.shape[2],h.shape[3]):h.as3D(h.shape[0],h.shape[2],h.shape[3])}function conv2d_(e,t,n,r,o,a,i){void 0===o&&(o="NHWC"),void 0===a&&(a=[1,1]);var s=convertToTensor(e,"x","conv2d"),u=convertToTensor(t,"filter","conv2d"),l=s,c=!1;3===s.rank&&(c=!0,l=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),assert(4===l.rank,"Error in conv2d: input must be rank 4, but got rank "+l.rank+"."),assert(4===u.rank,"Error in conv2d: filter must be rank 4, but got rank "+u.rank+"."),null!=i&&assert(isInt(r),"Error in conv2d: pad must be an integer when using, dimRoundingMode "+i+" but got pad "+r+"."),assert(l.shape[3]===u.shape[2],"Error in conv2d: depth of input ("+l.shape[3]+") must match input depth for filter "+u.shape[2]+"."),assert(eitherStridesOrDilationsAreOne(n,a),"Error in conv2D: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+a+"'"),assert("NHWC"===o,"Error in conv2d: got dataFormat of "+o+" but only NHWC is currently supported.");var p,d=computeConv2DInfo(l.shape,u.shape,n,a,r,i);if(1!==d.filterHeight||1!==d.filterWidth||1!==d.dilationHeight||1!==d.dilationWidth||1!==d.strideHeight||1!==d.strideWidth||"SAME"!==d.padInfo.type&&"VALID"!==d.padInfo.type){p=ENV.engine.runKernel(function(e){return e.conv2d(l,u,d)},{x:l,$filter:u},function(e){return assert(tupleValuesAreOne(a),"Error in gradient of conv2D: dilation rates greater than 1 are notyet supported in gradients. Got dilations '"+a+"'"),{x:function(){return conv2dDerInput_(l.shape,e,u,n,r)},$filter:function(){return conv2dDerFilter_(l,e,u.shape,n,r)}}})}else{var h=l.reshape([-1,d.inChannels]),f=u.reshape([d.inChannels,d.outChannels]);p=matMul(h,f).reshape(d.outShape)}return c?p.as3D(p.shape[1],p.shape[2],p.shape[3]):p}function conv2dDerInput_(e,t,n,r,o,a){assert(e.length===t.rank,"Length of inShape ("+e.length+") and rank of dy ("+t.rank+") must match");var i=e,s=t,u=!1;3===t.rank&&(u=!0,s=t.as4D(1,t.shape[0],t.shape[1],t.shape[2]),i=[1,e[0],e[1],e[2]]);var l=i[3],c=s.shape[3];assert(4===i.length,"Error in conv2dDerInput: inShape must be length 4, but got length "+i.length+"."),assert(4===s.rank,"Error in conv2dDerInput: dy must be rank 4, but got rank "+s.rank),assert(4===n.rank,"Error in conv2dDerInput: filter must be rank 4, but got rank "+n.rank),assert(l===n.shape[2],"Error in conv2dDerInput: depth of input ("+l+") must match input depth for filter "+n.shape[2]+"."),assert(c===n.shape[3],"Error in conv2dDerInput: depth of output ("+c+") must match output depth for filter "+n.shape[3]+"."),null!=a&&assert(isInt(o),"Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode "+a+" but got pad "+o+".");var p=computeConv2DInfo(i,n.shape,r,1,o,a),d=ENV.engine.runKernel(function(e){return e.conv2dDerInput(s,n,p)},{dy4D:s,filter:n},function(e){return{dy4D:function(){return conv2d(e,n,r,o,"NHWC",1,a)},filter:function(){return conv2dDerFilter(e,s,n.shape,r,o,a)}}});return u?d.as3D(d.shape[1],d.shape[2],d.shape[3]):d}function conv2dDerFilter_(e,t,n,r,o,a){var i=e;3===e.rank&&(i=e.as4D(1,e.shape[0],e.shape[1],e.shape[2]));var s=t;3===s.rank&&(s=t.as4D(1,t.shape[0],t.shape[1],t.shape[2])),assert(4===i.rank,"Error in conv2dDerFilter: input must be rank 4, but got shape "+i.shape+"."),assert(4===s.rank,"Error in conv2dDerFilter: dy must be rank 4, but got shape "+s.shape+"."),assert(4===n.length,"Error in conv2dDerFilter: filterShape must be length 4, but got "+n+"."),assert(i.shape[3]===n[2],"Error in conv2dDerFilter: depth of input "+i.shape[3]+") must match input depth in filter ("+n[2]+"."),assert(s.shape[3]===n[3],"Error in conv2dDerFilter: depth of dy ("+s.shape[3]+") must match output depth for filter ("+n[3]+")."),null!=a&&assert(isInt(o),"Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode "+a+" but got pad "+o+".");var u=computeConv2DInfo(i.shape,n,r,1,o,a);return ENV.engine.runKernel(function(e){return e.conv2dDerFilter(i,s,u)},{x4D:i,dy4D:s})}function conv2dTranspose_(e,t,n,r,o,a){return conv2dDerInput_(n,convertToTensor(e,"x","conv2dTranspose"),convertToTensor(t,"filter","conv2dTranspose"),r,o,a)}function depthwiseConv2d_(e,t,n,r,o,a,i){void 0===o&&(o="NHWC"),void 0===a&&(a=[1,1]);var s=convertToTensor(e,"x","depthwiseConv2d"),u=convertToTensor(t,"filter","depthwiseConv2d"),l=s,c=!1;3===s.rank&&(c=!0,l=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),assert(4===l.rank,"Error in depthwiseConv2d: input must be rank 4, but got rank "+l.rank+"."),assert(4===u.rank,"Error in depthwiseConv2d: filter must be rank 4, but got rank "+u.rank+"."),assert(l.shape[3]===u.shape[2],"Error in depthwiseConv2d: number of input channels ("+l.shape[3]+") must match the inChannels dimension in filter "+u.shape[2]+"."),null==a&&(a=[1,1]),assert(eitherStridesOrDilationsAreOne(n,a),"Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+a+"'"),null!=i&&assert(isInt(r),"Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode "+i+" but got pad "+r+".");var p=computeConv2DInfo(l.shape,u.shape,n,a,r,i,!0),d=ENV.engine.runKernel(function(e){return e.depthwiseConv2D(l,u,p)},{x:l,$filter:u},function(e){return assert(tupleValuesAreOne(a),"Error in gradient of depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '"+a+"'"),{x:function(){return depthwiseConv2dDerInput(l.shape,e,u,p)},$filter:function(){return depthwiseConv2dDerFilter(l,e,u.shape,p)}}});return c?d.as3D(d.shape[1],d.shape[2],d.shape[3]):d}function separableConv2d_(e,t,n,r,o,a,i){void 0===a&&(a=[1,1]),void 0===i&&(i="NHWC");var s=convertToTensor(e,"x","separableConv2d"),u=convertToTensor(t,"depthwiseFilter","separableConv2d"),l=convertToTensor(n,"pointwiseFilter","separableConv2d"),c=s,p=!1;if(3===s.rank&&(p=!0,c=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),"NCHW"===i)throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");assert(4===c.rank,"Error in separableConv2d: input must be rank 4, but got rank "+c.rank+"."),assert(4===u.rank,"Error in separableConv2d: depthwise filter must be rank 4, but got rank "+u.rank+"."),assert(4===l.rank,"Error in separableConv2d: pointwise filter must be rank 4, but got rank "+u.rank+"."),assert(1===l.shape[0],"Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got "+l.shape[0]+"."),assert(1===l.shape[1],"Error in separableConv2d: the second dimension of pointwise filter  must be 1, but got "+l.shape[1]+".");var d=u.shape[2],h=u.shape[3];assert(l.shape[2]===d*h,"Error in separableConv2d: the third dimension of pointwise filter must be "+d*h+", but got "+l.shape[2]+".");var f=depthwiseConv2d(c,u,r,o,i,a),m=conv2d(f,l,1,"valid",i);return p?m.as3D(m.shape[1],m.shape[2],m.shape[3]):m}function parseTupleParam$1(e){return"number"==typeof e?[e,e,e]:2===e.length?[e[0],e[1],1]:e}function tupleValuesAreOne$1(e){var t=parseTupleParam$1(e),n=t[0],r=t[1],o=t[2];return 1===n&&1===r&&1===o}function eitherStridesOrDilationsAreOne$1(e,t){return tupleValuesAreOne$1(e)||tupleValuesAreOne$1(t)}function depthwiseConv2dDerInput(e,t,n,r){var o=t,a=!1;3===t.rank&&(a=!0,o=t.as4D(1,t.shape[0],t.shape[1],t.shape[2]));var i=ENV.engine.runKernel(function(e){return e.depthwiseConv2DDerInput(o,n,r)},{dy4D:o});return a?i.as3D(i.shape[1],i.shape[2],i.shape[3]):i}function depthwiseConv2dDerFilter(e,t,n,r){var o=e;3===e.rank&&(o=e.as4D(1,e.shape[0],e.shape[1],e.shape[2]));var a=t;return 3===a.rank&&(a=t.as4D(1,t.shape[0],t.shape[1],t.shape[2])),ENV.engine.runKernel(function(e){return e.depthwiseConv2DDerFilter(o,a,r)},{x4D:o,dy4D:a})}function conv3d_(e,t,n,r,o,a){void 0===o&&(o="NHWC"),void 0===a&&(a=[1,1,1]);var i=convertToTensor(e,"x","conv3d"),s=convertToTensor(t,"filter","conv3d"),u=i,l=!1;4===i.rank&&(l=!0,u=i.as5D(1,i.shape[0],i.shape[1],i.shape[2],i.shape[3])),assert(5===u.rank,"Error in conv3d: input must be rank 5, but got rank "+u.rank+"."),assert(5===s.rank,"Error in conv3d: filter must be rank 5, but got rank "+s.rank+"."),assert(u.shape[4]===s.shape[3],"Error in conv3d: depth of input ("+u.shape[4]+") must match input depth for filter "+s.shape[3]+"."),assert(eitherStridesOrDilationsAreOne$1(n,a),"Error in conv3D: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+a+"'"),assert("NHWC"===o,"Error in conv3d: got dataFormat of "+o+" but only NHWC is currently supported.");var c=computeConv3DInfo(u.shape,s.shape,n,a,r),p=ENV.engine.runKernel(function(e){return e.conv3d(u,s,c)},{x:u,$filter:s},function(e){return assert(tupleValuesAreOne$1(a),"Error in gradient of conv3D: dilation rates greater than 1 are notyet supported in gradients. Got dilations '"+a+"'"),{x:function(){return conv3dDerInput_(u.shape,e,s,n,r)},$filter:function(){return conv3dDerFilter_(u,e,s.shape,n,r)}}});return l?p.as4D(p.shape[1],p.shape[2],p.shape[3],p.shape[4]):p}function conv3dDerInput_(e,t,n,r,o){assert(e.length===t.rank,"Length of inShape ("+e.length+") and rank of dy ("+t.rank+") must match");var a=e,i=t,s=!1;4===t.rank&&(s=!0,i=t.as5D(1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]),a=[1,e[0],e[1],e[2],e[3]]);var u=a[4],l=i.shape[4];assert(5===a.length,"Error in conv3dDerInput: inShape must be length 5, but got length "+a.length+"."),assert(5===i.rank,"Error in conv3dDerInput: dy must be rank 5, but got rank "+i.rank),assert(5===n.rank,"Error in conv3dDerInput: filter must be rank 5, but got rank "+n.rank),assert(u===n.shape[3],"Error in conv3dDerInput: depth of input ("+u+") must match input depth for filter "+n.shape[3]+"."),assert(l===n.shape[4],"Error in conv3dDerInput: depth of output ("+l+") must match output depth for filter "+n.shape[4]+".");var c=computeConv3DInfo(a,n.shape,r,1,o),p=ENV.engine.runKernel(function(e){return e.conv3dDerInput(i,n,c)},{dy5D:i});return s?p.as4D(p.shape[1],p.shape[2],p.shape[3],p.shape[4]):p}function conv3dDerFilter_(e,t,n,r,o){var a=e;4===e.rank&&(a=e.as5D(1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]));var i=t;4===i.rank&&(i=t.as5D(1,t.shape[0],t.shape[1],t.shape[2],t.shape[3])),assert(5===a.rank,"Error in conv3dDerFilter: input must be rank 5, but got shape "+a.shape+"."),assert(5===i.rank,"Error in conv3dDerFilter: dy must be rank 5, but got shape "+i.shape+"."),assert(5===n.length,"Error in conv3dDerFilter: filterShape must be length 5, but got "+n+"."),assert(a.shape[4]===n[3],"Error in conv3dDerFilter: depth of input "+a.shape[4]+") must match input depth in filter ("+n[3]+"."),assert(i.shape[4]===n[4],"Error in conv3dDerFilter: depth of dy ("+i.shape[4]+") must match output depth for filter ("+n[4]+").");var s=computeConv3DInfo(a.shape,n,r,1,o);return ENV.engine.runKernel(function(e){return e.conv3dDerFilter(a,i,s)},{x5D:a,dy5D:i})}var conv1d=op({conv1d_:conv1d_}),conv2d=op({conv2d_:conv2d_}),conv3d=op({conv3d_:conv3d_}),conv2dDerFilter=op({conv2dDerFilter_:conv2dDerFilter_}),depthwiseConv2d=op({depthwiseConv2d_:depthwiseConv2d_}),separableConv2d=op({separableConv2d_:separableConv2d_}),conv2dTranspose=op({conv2dTranspose_:conv2dTranspose_});function reverse1d_(e){var t=convertToTensor(e,"x","reverse");return assert(1===t.rank,"Error in reverse1D: x must be rank 1 but got\n             rank "+t.rank+"."),reverse(t,0)}function reverse2d_(e,t){var n=convertToTensor(e,"x","reverse");return assert(2===n.rank,"Error in reverse2D: x must be rank 2 but got\n             rank "+n.rank+"."),reverse(n,t)}function reverse3d_(e,t){var n=convertToTensor(e,"x","reverse");return assert(3===n.rank,"Error in reverse3D: x must be rank 3 but got\n             rank "+n.rank+"."),reverse(n,t)}function reverse4d_(e,t){var n=convertToTensor(e,"x","reverse");return assert(4===n.rank,"Error in reverse4D: x must be rank 4 but got\n             rank "+n.rank+"."),reverse(n,t)}function reverse_(e,t){var n=convertToTensor(e,"x","reverse");if(0===n.rank)return n.clone();var r=parseAxisParam(t,n.shape);return ENV.engine.runKernel(function(e){return e.reverse(n,r)},{$x:n},function(e){return{$x:function(){return e.reverse(r)}}}).reshapeAs(n)}var reverse=op({reverse_:reverse_}),reverse1d=op({reverse1d_:reverse1d_}),reverse2d=op({reverse2d_:reverse2d_}),reverse3d=op({reverse3d_:reverse3d_}),reverse4d=op({reverse4d_:reverse4d_});function maxPoolImpl_(e,t,n,r,o,a){var i=convertToTensor(e,"x","maxPool"),s=i,u=!1;3===i.rank&&(u=!0,s=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),null==r&&(r=[1,1]),assert(4===s.rank,"Error in maxPool: input must be rank 4 but got rank "+s.rank+"."),assert(eitherStridesOrDilationsAreOne(n,r),"Error in maxPool: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+r+"'"),null!=a&&assert(isInt(o),"Error in maxPool: pad must be an integer when using, dimRoundingMode "+a+" but got pad "+o+".");var l=computePool2DInfo(s.shape,t,n,r,o,a),c=ENV.engine.runKernel(function(e,t){return t(e.maxPool(s,l))},{x:s},function(e,a){var i=a[0];return{x:function(){return maxPoolBackprop(e,s,i,t,n,r,o)}}});return u?c.as3D(c.shape[1],c.shape[2],c.shape[3]):c}function maxPool_(e,t,n,r,o){return maxPoolImpl_(e,t,n,1,r,o)}function avgPoolImpl_(e,t,n,r,o,a){var i=convertToTensor(e,"x","avgPool","float32");null==r&&(r=[1,1]),assert(eitherStridesOrDilationsAreOne(n,r),"Error in avgPool: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+r+"'");var s=i,u=!1;3===i.rank&&(u=!0,s=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),assert(4===s.rank,"Error in avgPool: x must be rank 4 but got rank "+s.rank+"."),null!=a&&assert(isInt(o),"Error in avgPool: pad must be an integer when using, dimRoundingMode "+a+" but got pad "+o+".");var l=computePool2DInfo(s.shape,t,n,r,o),c=ENV.engine.runKernel(function(e){return e.avgPool(s,l)},{x:s},function(e){return{x:function(){return avgPoolBackprop(e,s,t,n,r,o)}}});return c=c.cast(i.dtype),u?c.as3D(c.shape[1],c.shape[2],c.shape[3]):c}function avgPool_(e,t,n,r,o){return avgPoolImpl_(e,t,n,1,r,o)}function pool_(e,t,n,r,o,a){null==o&&(o=[1,1]),null==a&&(a=1),0===r&&(r="valid");var i=convertToTensor(e,"x","maxPool"),s=i,u=!1;3===i.rank&&(u=!0,s=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),assert(eitherStridesOrDilationsAreOne(a,o),"Error in pool: Either strides or dilations must be 1. Got strides "+a+" and dilations '"+o+"'");var l,c=computePool2DInfo(s.shape,t,a,o,r),p=[c.dilationHeight,c.dilationWidth];l="same"===r?withSpaceToBatchBasePaddings([c.filterHeight,c.filterWidth],p):[[0,0],[0,0]];var d=1===p[0]&&1===p[1],h=requiredSpaceToBatchPaddings([c.inHeight,c.inWidth],p,l),f=h[0],m=h[1],g=d?r:"valid",v=d?s:spaceToBatchND(s,p,f),y=("avg"===n?function(){return avgPoolImpl_(v,t,a,1,g)}:function(){return maxPoolImpl_(v,t,a,1,g)})(),x=d?y:batchToSpaceND(y,p,m);return u?x.as3D(x.shape[1],x.shape[2],x.shape[3]):x}function maxPoolBackprop(e,t,n,r,o,a,i,s){var u=convertToTensor(e,"dy","maxPoolBackprop"),l=convertToTensor(t,"input","maxPoolBackprop"),c=convertToTensor(n,"output","maxPoolBackprop");assert(l.rank===u.rank,"Rank of input ("+l.rank+") does not match rank of dy ("+u.rank+")"),null==a&&(a=[1,1]),assert(eitherStridesOrDilationsAreOne(o,a),"Error in maxPoolBackProp: Either strides or dilations must be 1. Got strides "+o+" and dilations '"+a+"'"),assert(4===u.rank,"Error in maxPoolBackprop: dy must be rank 4 but got rank "+u.rank+"."),assert(4===l.rank,"Error in maxPoolBackprop: input must be rank 4 but got rank "+l.rank+"."),null!=s&&assert(isInt(i),"Error in maxPoolBackprop: pad must be an integer when using, dimRoundingMode "+s+" but got pad "+i+".");var p=computePool2DInfo(l.shape,r,o,a,i,s);return ENV.engine.runKernel(function(e){return e.maxPoolBackprop(u,l,c,p)},{$dy:u,$input:l})}function avgPoolBackprop(e,t,n,r,o,a){var i=convertToTensor(e,"dy","avgPoolBackprop"),s=convertToTensor(t,"input","avgPoolBackprop");assert(s.rank===i.rank,"Rank of input ("+s.rank+") does not match rank of dy ("+i.rank+")"),null==o&&(o=[1,1]),assert(eitherStridesOrDilationsAreOne(r,o),"Error in avgPoolBackprop: Either strides or dilations must be 1. Got strides "+r+" and dilations '"+o+"'");var u=s,l=i,c=!1;3===s.rank&&(c=!0,u=s.as4D(1,s.shape[0],s.shape[1],s.shape[2]),l=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),assert(4===l.rank,"Error in avgPoolBackprop: dy must be rank 4 but got rank "+l.rank+"."),assert(4===u.rank,"Error in avgPoolBackprop: input must be rank 4 but got rank "+u.rank+".");var p=computePool2DInfo(u.shape,n,r,o,a),d=ENV.engine.runKernel(function(e){return e.avgPoolBackprop(l,u,p)},{dy4D:l,input4D:u});return c?d.as3D(d.shape[1],d.shape[2],d.shape[3]):d}function requiredSpaceToBatchPaddings(e,t,n){var r=n.map(function(e){return e[0]}),o=n.map(function(e){return e[1]}),a=e.concat(r,o),i=t.map(function(e,t){return(e-a[t]%e)%e}),s=o.map(function(e,t){return e+i[t]});return[t.map(function(e,t){return[r[t],s[t]]}),t.map(function(e,t){return[0,i[t]]})]}function withSpaceToBatchBasePaddings(e,t){var n=e.map(function(e,n){return e+(e-1)*(t[n]-1)}).map(function(e){return e-1}),r=n.map(function(e){return Math.floor(e/2)}),o=n.map(function(e,t){return e-r[t]});return n.map(function(e,t){return[r[t],o[t]]})}var maxPool=op({maxPool_:maxPool_}),avgPool=op({avgPool_:avgPool_}),pool=op({pool_:pool_});function slice1d_(e,t,n){var r=convertToTensor(e,"x","slice1d");return assert(1===r.rank,"slice1d expects a rank-1 tensor, but got a rank-"+r.rank+" tensor"),slice(r,[t],[n])}function slice2d_(e,t,n){var r=convertToTensor(e,"x","slice2d");return assert(2===r.rank,"slice2d expects a rank-2 tensor, but got a rank-"+r.rank+" tensor"),slice(r,t,n)}function slice3d_(e,t,n){var r=convertToTensor(e,"x","slice3d");return assert(3===r.rank,"slice3d expects a rank-3 tensor, but got a rank-"+r.rank+" tensor"),slice(r,t,n)}function slice4d_(e,t,n){var r=convertToTensor(e,"x","slice4d");return assert(4===r.rank,"slice4d expects a rank-4 tensor, but got a rank-"+r.rank+" tensor"),slice(r,t,n)}function slice_(e,t,n){var r,o,a=convertToTensor(e,"x","slice");if(0===a.rank)throw new Error("Slicing scalar is not possible");r="number"==typeof t?[t].concat(new Array(a.rank-1).fill(0)):t.length<a.rank?t.concat(new Array(a.rank-t.length).fill(0)):t.slice(),o=(o=null==n?new Array(a.rank).fill(-1):"number"==typeof n?[n].concat(new Array(a.rank-1).fill(-1)):n.length<a.rank?n.concat(new Array(a.rank-n.length).fill(-1)):n).map(function(e,t){return e>=0?e:(assert(-1===e,"Bad value in size"),a.shape[t]-r[t])}),assertParamsValid(a,r,o);var i=a.shape;return ENV.engine.runKernel(function(e){return e.slice(a,r,o)},{$x:a},function(e){for(var t=[],n=0;n<e.rank;n++)t.push([r[n],i[n]-r[n]-o[n]]);return{$x:function(){return e.pad(t)}}})}var slice=op({slice_:slice_}),slice1d=op({slice1d_:slice1d_}),slice2d=op({slice2d_:slice2d_}),slice3d=op({slice3d_:slice3d_}),slice4d=op({slice4d_:slice4d_});function logSumExp_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","logSumExp"),o=parseAxisParam(t,r.shape),a=r.max(o,!0),i=r.sub(a).exp().sum(o).log(),s=a.reshape(i.shape).add(i);if(n){var u=expandShapeToKeepDim(s.shape,o);return s.reshape(u)}return s}function sum_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","sum");"bool"===r.dtype&&(r=r.toInt());var o=parseAxisParam(t,r.shape);return customGrad(function(e){var t=getAxesPermutation(o,e.rank),r=o,a=e;null!=t&&(a=e.transpose(t),r=getInnerMostAxes(r.length,e.rank));var i=ENV.engine.runKernel(function(e){return e.sum(a,r)},{permutedX:a});if(n){var s=expandShapeToKeepDim(i.shape,o);i=i.reshape(s)}return{value:i,gradFunc:function(t){var n=e.shape.slice();return o.forEach(function(e){n[e]=1}),t.reshape(n).mul(ones$1(e.shape,"float32"))}}})(r)}function prod_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","prod");"bool"===r.dtype&&(r=r.toInt());var o=parseAxisParam(t,r.shape),a=getAxesPermutation(o,r.rank),i=o,s=r;null!=a&&(s=r.transpose(a),i=getInnerMostAxes(i.length,r.rank));var u=ENV.engine.runKernel(function(e){return e.prod(s,i)},{permutedX:s});if(n){var l=expandShapeToKeepDim(u.shape,o);u=u.reshape(l)}return u}function mean_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","mean"),o=parseAxisParam(t,r.shape),a=sizeFromShape(computeOutAndReduceShapes(r.shape,o)[1]);return customGrad(function(e){var r=scalar(a);return{value:(r.dtype===e.dtype?e:e.cast(r.dtype)).div(r).sum(t,n),gradFunc:function(t){var n=e.shape.slice();return o.forEach(function(e){n[e]=1}),t.reshape(n).mul(ones$1(e.shape,"float32")).div(r)}}})(r)}function gradForMinAndMax(e,t,n,r,o){var a=t[0];return a.rank<n.rank&&(a=a.reshape(expandShapeToKeepDim(a.shape,r))),e.rank<n.rank&&(e=e.reshape(expandShapeToKeepDim(e.shape,r))),{$x:function(){var t=e.mul(n.equal(a).cast(e.dtype));return null==o?t:t.transpose(o)}}}function min_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","min"),o=r,a=parseAxisParam(t,r.shape),i=a,s=getAxesPermutation(i,r.rank);null!=s&&(r=r.transpose(s),i=getInnerMostAxes(i.length,r.rank));var u=ENV.engine.runKernel(function(e,t){return t(e.min(r,i))},{$x:r},function(e,t){return gradForMinAndMax(e,t,o,a,s)});if(n){var l=expandShapeToKeepDim(u.shape,a);u=u.reshape(l)}return u}function max_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","max"),o=r,a=parseAxisParam(t,r.shape),i=a,s=getAxesPermutation(i,r.rank);null!=s&&(r=r.transpose(s),i=getInnerMostAxes(i.length,r.rank));var u=ENV.engine.runKernel(function(e,t){return t(e.max(r,i))},{$x:r},function(e,t){return gradForMinAndMax(e,t,o,a,s)});if(n){var l=expandShapeToKeepDim(u.shape,a);u=u.reshape(l)}return u}function argMin_(e,t){void 0===t&&(t=0);var n=convertToTensor(e,"x","argMin");null==t&&(t=0);var r=parseAxisParam(t,n.shape),o=getAxesPermutation(r,n.rank);null!=o&&(n=n.transpose(o),r=getInnerMostAxes(r.length,n.rank));return ENV.engine.runKernel(function(e){return e.argMin(n,r[0])},{$x:n},function(e){return{$x:function(){return zerosLike(n)}}})}function argMax_(e,t){void 0===t&&(t=0);var n=convertToTensor(e,"x","argMax");null==t&&(t=0);var r=parseAxisParam(t,n.shape),o=getAxesPermutation(r,n.rank);null!=o&&(n=n.transpose(o),r=getInnerMostAxes(r.length,n.rank));return ENV.engine.runKernel(function(e){return e.argMax(n,r[0])},{$x:n},function(e){return{$x:function(){return zerosLike(n)}}})}function all_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","all","bool"),o=parseAxisParam(t,r.shape),a=o,i=getAxesPermutation(a,r.rank);null!=i&&(r=r.transpose(i),a=getInnerMostAxes(a.length,r.rank));var s=ENV.engine.runKernel(function(e){return e.all(r,a)},{$x:r});if(n){var u=expandShapeToKeepDim(s.shape,o);return s.reshape(u)}return s}function any_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","any","bool"),o=parseAxisParam(t,r.shape),a=o,i=getAxesPermutation(a,r.rank);null!=i&&(r=r.transpose(i),a=getInnerMostAxes(a.length,r.rank));var s=ENV.engine.runKernel(function(e){return e.any(r,a)},{$x:r});if(n){var u=expandShapeToKeepDim(s.shape,o);return s.reshape(u)}return s}function moments_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=parseAxisParam(t,(e=convertToTensor(e,"x","moments")).shape),o=e.mean(r,n),a=o.shape;return n||(a=expandShapeToKeepDim(o.shape,r)),{mean:o,variance:e.toFloat().sub(o.reshape(a)).square().mean(r,n)}}var all=op({all_:all_}),any=op({any_:any_}),argMax=op({argMax_:argMax_}),argMin=op({argMin_:argMin_}),logSumExp=op({logSumExp_:logSumExp_}),max=op({max_:max_}),mean=op({mean_:mean_}),min=op({min_:min_}),moments=op({moments_:moments_}),sum$1=op({sum_:sum_}),prod=op({prod_:prod_});function notEqual_(e,t){var n,r=convertToTensor(e,"a","notEqual"),o=convertToTensor(t,"b","notEqual");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.notEqual(r,o)},{$a:r,$b:o})}function notEqualStrict_(e,t){var n=convertToTensor(e,"a","notEqualStrict"),r=convertToTensor(t,"b","notEqualStrict");return assertShapesMatch(n.shape,r.shape,"Error in notEqualStrict: "),n.notEqual(r)}function less_(e,t){var n,r=convertToTensor(e,"a","less"),o=convertToTensor(t,"b","less");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.less(r,o)},{$a:r,$b:o})}function lessStrict_(e,t){var n=convertToTensor(e,"a","lessStrict"),r=convertToTensor(t,"b","lessStrict");return assertShapesMatch(n.shape,r.shape,"Error in lessStrict: "),n.less(r)}function equal_(e,t){var n,r=convertToTensor(e,"a","equal"),o=convertToTensor(t,"b","equal");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.equal(r,o)},{$a:r,$b:o})}function equalStrict_(e,t){var n=convertToTensor(e,"a","equalStrict"),r=convertToTensor(t,"b","equalStrict");return assertShapesMatch(n.shape,r.shape,"Error in equalStrict: "),n.equal(r)}function lessEqual_(e,t){var n,r=convertToTensor(e,"a","lessEqual"),o=convertToTensor(t,"b","lessEqual");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.lessEqual(r,o)},{$a:r,$b:o})}function lessEqualStrict_(e,t){var n=convertToTensor(e,"a","lessEqualStrict"),r=convertToTensor(t,"b","lessEqualStrict");return assertShapesMatch(n.shape,r.shape,"Error in lessEqualStrict: "),n.lessEqual(r)}function greater_(e,t){var n,r=convertToTensor(e,"a","greater"),o=convertToTensor(t,"b","greater");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.greater(r,o)},{$a:r,$b:o})}function greaterStrict_(e,t){var n=convertToTensor(e,"a","greaterStrict"),r=convertToTensor(t,"b","greaterStrict");return assertShapesMatch(n.shape,r.shape,"Error in greaterStrict: "),n.greater(r)}function greaterEqual_(e,t){var n,r=convertToTensor(e,"a","greaterEqual"),o=convertToTensor(t,"b","greaterEqual");n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.greaterEqual(r,o)},{$a:r,$b:o},function(e){return{$a:function(){return zerosLike(r)},$b:function(){return zerosLike(o)}}})}function greaterEqualStrict_(e,t){var n=convertToTensor(e,"a","greaterEqualStrict"),r=convertToTensor(t,"b","greaterEqualStrict");return assertShapesMatch(n.shape,r.shape,"Error in greaterEqualStrict: "),n.greaterEqual(r)}var equal=op({equal_:equal_}),equalStrict=op({equalStrict_:equalStrict_}),greater=op({greater_:greater_}),greaterEqual=op({greaterEqual_:greaterEqual_}),greaterEqualStrict=op({greaterEqualStrict_:greaterEqualStrict_}),greaterStrict=op({greaterStrict_:greaterStrict_}),less=op({less_:less_}),lessEqual=op({lessEqual_:lessEqual_}),lessEqualStrict=op({lessEqualStrict_:lessEqualStrict_}),lessStrict=op({lessStrict_:lessStrict_}),notEqual=op({notEqual_:notEqual_}),notEqualStrict=op({notEqualStrict_:notEqualStrict_});function add_(e,t){var n,r=convertToTensor(e,"a","add"),o=convertToTensor(t,"b","add");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.add(r,o)},{$a:r,$b:o},function(e){return{$a:function(){var t=e,n=getReductionAxes(r.shape,a);return n.length>0&&(t=t.sum(n)),t.reshape(r.shape)},$b:function(){var t=e,n=getReductionAxes(o.shape,a);return n.length>0&&(t=t.sum(n)),t.reshape(o.shape)}}})}function addN_(e){assert(Array.isArray(e),function(){return"The argument passed to tf.addN() must be a list of tensors"}),assert(e.length>=1,function(){return"Must pass at least one tensor to tf.addN(), but got "+e.length});var t=e.map(function(e,t){return convertToTensor(e,"tensors"+t,"addN")}),n=t[0];t.forEach(function(e){if(e.dtype!==n.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),t.forEach(function(e){if(!arraysEqual(e.shape,n.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});var r=t;return ENV.engine.runKernel(function(e){return e.addN(t)},r,function(e){var n={};return t.forEach(function(t,r){n[r]=function(){return e.clone()}}),n})}function addStrict_(e,t){var n=convertToTensor(e,"a","addStrict"),r=convertToTensor(t,"b","addStrict");return assertShapesMatch(n.shape,r.shape,"Error in addStrict: "),n.add(r)}function sub_(e,t){var n,r=convertToTensor(e,"a","sub"),o=convertToTensor(t,"b","sub");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.subtract(r,o)},{$a:r,$b:o},function(e){return{$a:function(){var t=e,n=getReductionAxes(r.shape,a);return n.length>0&&(t=t.sum(n)),t.reshape(r.shape)},$b:function(){var t=e,n=getReductionAxes(o.shape,a);return n.length>0&&(t=t.sum(n)),t.neg().reshape(o.shape)}}})}function subStrict_(e,t){var n=convertToTensor(e,"a","subStrict"),r=convertToTensor(t,"b","subStrict");return assertShapesMatch(n.shape,r.shape,"Error in subStrict: "),n.sub(r)}function pow_(e,t){var n=convertToTensor(e,"base","pow"),r=convertToTensor(t,"exp","pow"),o=assertAndGetBroadcastShape(n.shape,r.shape);e=n.cast(upcastType(n.dtype,r.dtype)),t=r.cast(upcastType(n.dtype,r.dtype));return ENV.engine.runKernel(function(e,t){return t(e.pow(n,r))},{$base:n,$exp:r},function(e,t){var a=t[0];return{$base:function(){var t=r.toFloat(),a=e.mul(t.mul(n.pow(t.sub(scalar(1))))),i=getReductionAxes(n.shape,o);return i.length>0&&(a=a.sum(i)),a.reshape(n.shape)},$exp:function(){var t=e.mul(a.mul(n.log()).toFloat()),i=getReductionAxes(r.shape,o);return i.length>0&&(t=t.sum(i)),t.reshape(r.shape)}}})}function powStrict_(e,t){return assertShapesMatch(e.shape,t.shape,"Error in powStrict: "),e.pow(t)}function mul_(e,t){var n,r=convertToTensor(e,"a","mul"),o=convertToTensor(t,"b","mul");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.multiply(r,o)},{$a:r,$b:o},function(e){return{$a:function(){var t=e.mul(o.toFloat()),n=getReductionAxes(r.shape,a);return n.length>0?t.sum(n).reshape(r.shape):t},$b:function(){var t=e.mul(r.toFloat()),n=getReductionAxes(o.shape,a);return n.length>0?t.sum(n).reshape(o.shape):t}}})}function mulStrict_(e,t){var n=convertToTensor(e,"a","mul"),r=convertToTensor(t,"b","mul");return assertShapesMatch(n.shape,r.shape,"Error in multiplyStrict: "),n.mul(r)}function div_(e,t){var n,r,o=convertToTensor(e,"a","div"),a=convertToTensor(t,"b","div");if(n=makeTypesMatch(o,a),o=n[0],a=n[1],"int32"===o.dtype&&"int32"===a.dtype)return floorDiv(o,a);r=function(e){return e.realDivide(o,a)};var i=assertAndGetBroadcastShape(o.shape,a.shape);return ENV.engine.runKernel(r,{$a:o,$b:a},function(e){return{$a:function(){var t=e.div(a.toFloat()),n=getReductionAxes(o.shape,i);return n.length>0?t.sum(n).reshape(o.shape):t},$b:function(){var t=e.mul(o.toFloat()),n=getReductionAxes(a.shape,i);n.length>0&&(t=t.sum(n).reshape(a.shape));var r=a.square();return t.div(r.toFloat()).neg()}}})}function floorDiv_(e,t){var n,r=convertToTensor(e,"a","floorDiv"),o=convertToTensor(t,"b","floorDiv");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.floorDiv(r,o)},{$a:r,$b:o},function(e){return{$a:function(){var t=e.div(o.toFloat()),n=getReductionAxes(r.shape,a);return n.length>0?t.sum(n).reshape(r.shape):t},$b:function(){var t=e.mul(r.toFloat()),n=getReductionAxes(o.shape,a);n.length>0&&(t=t.sum(n).reshape(o.shape));var i=o.square();return t.div(i.toFloat()).neg()}}})}function divStrict_(e,t){var n=convertToTensor(e,"a","div"),r=convertToTensor(t,"b","div");return assertShapesMatch(n.shape,r.shape,"Error in divideStrict: "),n.div(r)}function mod_(e,t){var n,r=convertToTensor(e,"a","mod"),o=convertToTensor(t,"b","mod");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.mod(r,o)},{$a:r,$b:o},function(e){return{$a:function(){var t=getReductionAxes(r.shape,a);return t.length>0?e.sum(t).reshape(r.shape):e},$b:function(){var t=e.mul(r.div(o).floor().neg()),n=getReductionAxes(o.shape,a);return n.length>0?t.sum(n).reshape(o.shape):t}}})}function modStrict_(e,t){var n=convertToTensor(e,"a","modStrict"),r=convertToTensor(t,"b","modStrict");return assertShapesMatch(n.shape,r.shape,"Error in modStrict: "),n.mod(r)}function minimum_(e,t){var n,r=convertToTensor(e,"a","minimum"),o=convertToTensor(t,"b","minimum");n=makeTypesMatch(r,o),r=n[0],o=n[1],"bool"===r.dtype&&(r=r.toInt(),o=o.toInt()),assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.minimum(r,o)},{$a:r,$b:o},function(e){return{$a:function(){return e.mul(r.lessEqual(o).toFloat())},$b:function(){return e.mul(r.greater(o).toFloat())}}})}function minimumStrict_(e,t){var n=convertToTensor(e,"a","minimumStrict"),r=convertToTensor(t,"b","minimumStrict");return assertShapesMatch(n.shape,r.shape,"Error in minimumStrict: "),n.minimum(r)}function maximum_(e,t){var n,r=convertToTensor(e,"a","maximum"),o=convertToTensor(t,"b","maximum");n=makeTypesMatch(r,o),r=n[0],o=n[1],"bool"===r.dtype&&(r=r.toInt(),o=o.toInt()),assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.maximum(r,o)},{$a:r,$b:o},function(e){return{$a:function(){return e.mul(r.greaterEqual(o).toFloat())},$b:function(){return e.mul(r.less(o).toFloat())}}})}function maximumStrict_(e,t){var n=convertToTensor(e,"a","maximumStrict"),r=convertToTensor(t,"b","maximumStrict");return assertShapesMatch(n.shape,r.shape,"Error in maximumStrict: "),n.maximum(r)}function squaredDifference_(e,t){var n,r=convertToTensor(e,"a","squaredDifference"),o=convertToTensor(t,"b","squaredDifference");n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.squaredDifference(r,o)},{$a:r,$b:o},function(e){var t=scalar(2);return{$a:function(){return e.mul(r.sub(o).mul(t))},$b:function(){return e.mul(o.sub(r).mul(t))}}})}function squaredDifferenceStrict_(e,t){var n=convertToTensor(e,"a","squaredDifferenceStrict"),r=convertToTensor(t,"b","squaredDifferenceStrict");return assertShapesMatch(n.shape,r.shape,"Error in squaredDifferenceStrict: "),n.squaredDifference(r)}function atan2_(e,t){var n,r=convertToTensor(e,"a","atan2"),o=convertToTensor(t,"b","atan2");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.atan2(r,o)},{$a:r,$b:o},function(e){return{$a:function(){var t=add(r.square(),o.square()),n=e.mul(o.div(t)),i=getReductionAxes(r.shape,a);return i.length>0&&(n=n.sum(i)),n.reshape(r.shape)},$b:function(){var t=add(r.square(),o.square()),n=neg(e.mul(r.div(t))),i=getReductionAxes(o.shape,a);return i.length>0&&(n=n.sum(i)),n.reshape(o.shape)}}})}var add=op({add_:add_}),addN=op({addN_:addN_}),addStrict=op({addStrict_:addStrict_}),atan2=op({atan2_:atan2_}),div=op({div_:div_}),divStrict=op({divStrict_:divStrict_}),floorDiv=op({floorDiv_:floorDiv_}),maximum=op({maximum_:maximum_}),maximumStrict=op({maximumStrict_:maximumStrict_}),minimum=op({minimum_:minimum_}),minimumStrict=op({minimumStrict_:minimumStrict_}),mod=op({mod_:mod_}),modStrict=op({modStrict_:modStrict_}),mul=op({mul_:mul_}),mulStrict=op({mulStrict_:mulStrict_}),pow=op({pow_:pow_}),powStrict=op({powStrict_:powStrict_}),squaredDifference=op({squaredDifference_:squaredDifference_}),squaredDifferenceStrict=op({squaredDifferenceStrict_:squaredDifferenceStrict_}),sub=op({sub_:sub_}),subStrict=op({subStrict_:subStrict_});function logicalNot_(e){var t=convertToTensor(e,"x","logicalNot","bool");return ENV.engine.runKernel(function(e){return e.logicalNot(t)},{$x:t})}function logicalAnd_(e,t){var n=convertToTensor(e,"a","logicalAnd","bool"),r=convertToTensor(t,"b","logicalAnd","bool");return assertAndGetBroadcastShape(n.shape,r.shape),ENV.engine.runKernel(function(e){return e.logicalAnd(n,r)},{$a:n,$b:r})}function logicalOr_(e,t){var n=convertToTensor(e,"a","logicalOr","bool"),r=convertToTensor(t,"b","logicalOr","bool");return assertAndGetBroadcastShape(n.shape,r.shape),ENV.engine.runKernel(function(e){return e.logicalOr(n,r)},{$a:n,$b:r})}function logicalXor_(e,t){var n=convertToTensor(e,"a","logicalXor","bool"),r=convertToTensor(t,"b","logicalXor","bool");return assertAndGetBroadcastShape(n.shape,r.shape),logicalOr(e,t).logicalAnd(logicalAnd(e,t).logicalNot())}function where_(e,t,n){var r=convertToTensor(t,"a","where"),o=convertToTensor(n,"b","where"),a=convertToTensor(e,"condition","where","bool");assertShapesMatch(r.shape,o.shape,"Error in where: "),1===a.rank?assert(a.shape[0]===r.shape[0],"The first dimension of `a` must match the size of `condition`."):assertShapesMatch(a.shape,o.shape,"Error in where: ");return ENV.engine.runKernel(function(e){return e.select(a,r,o)},{$condition:a,$a:r,$b:o},function(e){return{$condition:function(){return zerosLike(a).toFloat()},$a:function(){return e.mul(a.cast(e.dtype))},$b:function(){return e.mul(a.logicalNot().cast(e.dtype))}}})}function whereAsync_(e){return __awaiter(this,void 0,void 0,function(){var t,n,r;return __generator(this,function(o){switch(o.label){case 0:return[4,(t=convertToTensor(e,"condition","whereAsync","bool")).data()];case 1:return n=o.sent(),r=whereImpl(t.shape,n),e!==t&&t.dispose(),[2,r]}})})}var logicalAnd=op({logicalAnd_:logicalAnd_}),logicalNot=op({logicalNot_:logicalNot_}),logicalOr=op({logicalOr_:logicalOr_}),logicalXor=op({logicalXor_:logicalXor_}),where=op({where_:where_}),whereAsync=whereAsync_;function relu_(e){var t=convertToTensor(e,"x","relu");if("bool"===t.dtype)return t.toInt();return ENV.engine.runKernel(function(e){return e.relu(t)},{$x:t},function(e){var n=t.step();return{$x:function(){return e.mulStrict(n.toFloat())}}})}function elu_(e){var t=convertToTensor(e,"x","elu");return ENV.engine.runKernel(function(e,n){return n(e.elu(t))},{$x:t},function(e,t){var n=t[0];return{$x:function(){return ENV.engine.runKernel(function(t){return t.eluDer(e,n)},{dy:e,y:n})}}})}function selu_(e){var t=convertToTensor(e,"x","selu");return ENV.engine.runKernel(function(e){return e.selu(t)},{$x:t},function(e){return{$x:function(){var n=t.greater(scalar(0)),r=scalar(SELU_SCALEALPHA),o=scalar(SELU_SCALE),a=e.mul(o),i=e.mul(r).mul(t.toFloat().exp());return where(n,a,i)}}})}function leakyRelu_(e,t){void 0===t&&(t=.2);var n=convertToTensor(e,"x","leakyRelu");return maximum(scalar(t).mul(n),n)}function prelu_(e,t){var n=convertToTensor(e,"x","prelu"),r=convertToTensor(t,"alpha","prelu"),o=scalar(0);return maximum(o,n).add(r.mul(minimum(o,n)))}var elu=op({elu_:elu_}),leakyRelu=op({leakyRelu_:leakyRelu_}),prelu=op({prelu_:prelu_}),relu=op({relu_:relu_}),selu=op({selu_:selu_});function transpose_(e,t){var n=convertToTensor(e,"x","transpose");if(null==t&&(t=n.shape.map(function(e,t){return t}).reverse()),assert(n.rank===t.length,"Error in transpose: rank of input "+n.rank+" must match length of perm "+t+"."),t.forEach(function(e){assert(e>=0&&e<n.rank,"All entries in 'perm' must be between 0 and "+(n.rank-1)+" but got "+t)}),n.rank<=1)return n.clone();return ENV.engine.runKernel(function(e){return e.transpose(n,t)},{$x:n},function(e){var n=getUndoAxesPermutation(t);return{$x:function(){return e.transpose(n)}}})}var transpose=op({transpose_:transpose_});function localResponseNormalization_(e,t,n,r,o){void 0===t&&(t=5),void 0===n&&(n=1),void 0===r&&(r=1),void 0===o&&(o=.5);var a=convertToTensor(e,"x","localResponseNormalization");assert(4===a.rank||3===a.rank,"Error in localResponseNormalization: x must be rank 3 or 4 but got\n               rank "+a.rank+"."),assert(isInt(t),"Error in localResponseNormalization: depthRadius must be an integer\n                     but got depthRadius "+t+".");var i=a,s=!1;3===a.rank&&(s=!0,i=a.as4D(1,a.shape[0],a.shape[1],a.shape[2]));var u=ENV.engine.runKernel(function(e,a){return a(e.localResponseNormalization4D(i,t,n,r,o))},{x4D:i},function(e,a){var s=a[0];return{x4D:function(){return ENV.engine.runKernel(function(a){return a.LRNGrad(e,i,s,t,n,r,o)},{})}}});return s?u.as3D(u.shape[1],u.shape[2],u.shape[3]):u}var localResponseNormalization=op({localResponseNormalization_:localResponseNormalization_});function norm_(e,t,n,r){void 0===t&&(t="euclidean"),void 0===n&&(n=null),void 0===r&&(r=!1);var o=normImpl(e=convertToTensor(e,"x","norm"),t,n),a=o.shape;if(r){var i=parseAxisParam(n,e.shape);a=expandShapeToKeepDim(o.shape,i)}return o.reshape(a)}function normImpl(e,t,n){if(void 0===n&&(n=null),0===e.rank)return e.abs();if(1!==e.rank&&null===n)return normImpl(e.reshape([-1]),t,n);if(1===e.rank||"number"==typeof n||n instanceof Array&&1===n.length){if(1===t)return e.abs().sum(n);if(t===1/0)return e.abs().max(n);if(t===-1/0)return e.abs().min(n);if("euclidean"===t||2===t)return e.abs().pow(scalar(2,"int32")).sum(n).sqrt();throw new Error("Error in norm: invalid ord value: "+t)}if(n instanceof Array&&2===n.length){if(1===t)return e.abs().sum(n[0]).max(n[1]-1);if(t===1/0)return e.abs().sum(n[1]).max(n[0]);if(t===-1/0)return e.abs().sum(n[1]).min(n[0]);if("fro"===t||"euclidean"===t)return e.square().sum(n).sqrt();throw new Error("Error in norm: invalid ord value: "+t)}throw new Error("Error in norm: invalid axis: "+n)}var norm=op({norm_:norm_});function unsortedSegmentSum_(e,t,n){var r=convertToTensor(e,"x","unsortedSegmentSum"),o=convertToTensor(t,"segmentIds","unsortedSegmentSum","int32");assert(isInt(n),"numSegments must be of dtype int");return ENV.engine.runKernel(function(e){return e.unsortedSegmentSum(r,o,n)},{$x:r},function(e){return{$x:function(){return gatherDropNegatives(e,o)}}})}function gather_(e,t,n){void 0===n&&(n=0);var r=convertToTensor(e,"x","gather"),o=convertToTensor(t,"indices","gather","int32");n=parseAxisParam(n,r.shape)[0];return ENV.engine.runKernel(function(e){return e.gather(r,o,n)},{$x:r},function(e){return{$x:function(){if(0===n)return unsortedSegmentSum(e,o,r.shape[n]);var t=r.shape,a=o.size,i=t.slice(0,n),s=i.length,u=t.slice(n,t.length).slice(1),l=u.length,c=arrayRange(0,s),p=arrayRange(s+1,s+1+l),d=arrayConcat([i,[a],u]),h=e.reshape(d),f=o.reshape([a]),m=arrayConcat([[s],c,p]),g=h.transpose(m),v=unsortedSegmentSum(g,f,r.shape[n]),y=getUndoAxesPermutation(m);return v=v.transpose(y)}}})}function arrayRange(e,t){for(var n=[],r=e;r<t;++r)n.push(r);return n}function arrayConcat(e){for(var t=[],n=0;n<e.length;++n)for(var r=0;r<e[n].length;++r)t.push(e[n][r]);return t}function gatherDropNegatives(e,t){for(var n=maximum(t,zerosLike(t)),r=gather(e,n),o=greaterEqual(t,scalar(0,"int32")),a=r.rank-o.rank,i=0;i<a;++i)o=expandDims(o,i+1);o=logicalAnd(o,ones$1(r.shape,"bool"));var s=zerosLike(r);return where(o,r,s)}var gather=op({gather_:gather_}),unsortedSegmentSum=op({unsortedSegmentSum_:unsortedSegmentSum_});function multiRNNCell_(e,t,n,r){for(var o=convertToTensor(t,"data","multiRNNCell"),a=convertToTensorArray(n,"c","multiRNNCell"),i=convertToTensorArray(r,"h","multiRNNCell"),s=o,u=[],l=0;l<e.length;l++){var c=e[l](s,a[l],i[l]);u.push(c[0]),u.push(c[1]),s=c[1]}var p=[],d=[];for(l=0;l<u.length;l+=2)p.push(u[l]),d.push(u[l+1]);return[p,d]}function basicLSTMCell_(e,t,n,r,o,a){var i=convertToTensor(e,"forgetBias","basicLSTMCell"),s=convertToTensor(t,"lstmKernel","basicLSTMCell"),u=convertToTensor(n,"lstmBias","basicLSTMCell"),l=convertToTensor(r,"data","basicLSTMCell"),c=convertToTensor(o,"c","basicLSTMCell"),p=convertToTensor(a,"h","basicLSTMCell"),d=l.concat(p,1).matMul(s).add(u),h=d.shape[0],f=d.shape[1]/4,m=[h,f],g=d.slice([0,0],m),v=d.slice([0,f],m),y=d.slice([0,2*f],m),x=d.slice([0,3*f],m),T=g.sigmoid().mulStrict(v.tanh()).addStrict(c.mulStrict(i.add(y).sigmoid()));return[T,T.tanh().mulStrict(x.sigmoid())]}var basicLSTMCell=op({basicLSTMCell_:basicLSTMCell_}),multiRNNCell=op({multiRNNCell_:multiRNNCell_});function movingAverage_(e,t,n,r,o){void 0===o&&(o=!0);var a=convertToTensor(e,"v","movingAverage"),i=convertToTensor(t,"x","movingAverage"),s=convertToTensor(n,"decay","movingAverage");assertTypesMatch(a,i),assert(arraysEqual(a.shape,i.shape),"Shape mismatch in v and x");var u=scalar(1),l=u.sub(s),c=i.sub(a).mul(l);if(o){assert(null!=r,"When using zeroDebias: true, step is required.");var p=convertToTensor(r,"step","movingAverage");c=c.div(u.sub(pow(s,p)))}return a.add(c)}var movingAverage=op({movingAverage_:movingAverage_});function stridedSlice_(e,t,n,r,o,a,i,s,u){if(void 0===o&&(o=0),void 0===a&&(a=0),void 0===i&&(i=0),void 0===s&&(s=0),void 0===u&&(u=0),0!==i)throw new Error("ellipsis mask is not yet supported");if(0!==s)throw new Error("new axis mask is not yet supported");var l=convertToTensor(e,"x","stridedSlice");return ENV.engine.runKernel(function(e){return e.stridedSlice(l,t,n,r,o,a,i,s,u)},{$x:l})}var stridedSlice=op({stridedSlice_:stridedSlice_});function topk_(e,t,n){void 0===t&&(t=1),void 0===n&&(n=!0);var r=convertToTensor(e,"x","topk");if(0===r.rank)throw new Error("topk() expects the input to be of rank 1 or higher");var o=r.shape[r.shape.length-1];if(t>o)throw new Error("'k' passed to topk() must be <= the last dimension ("+o+") but got "+t);var a=ENV.engine.runKernel(function(e){return e.topk(r,t,n)},{$x:r});return{values:a[0],indices:a[1]}}var topk=op({topk_:topk_});function scatterND_(e,t,n){var r=convertToTensor(e,"indices","scatterND","int32"),o=convertToTensor(t,"updates","scatterND");return validateInput(o,r,n),ENV.engine.runKernel(function(e){return e.scatterND(r,o,n)},{$indices:r,$updates:o})}var scatterND=op({scatterND_:scatterND_});function fft_(e){assert("complex64"===e.dtype,"The dtype for tf.spectral.fft() must be complex64 but got "+e.dtype+".");var t=e.shape[e.shape.length-1],n=e.size/t,r=e.as2D(n,t);return ENV.engine.runKernel(function(e){return e.fft(r)},{input:e}).reshape(e.shape)}function ifft_(e){assert("complex64"===e.dtype,"The dtype for tf.spectral.ifft() must be complex64 but got "+e.dtype+".");var t=e.shape[e.shape.length-1],n=e.size/t,r=e.as2D(n,t);return ENV.engine.runKernel(function(e){return e.ifft(r)},{input:e}).reshape(e.shape)}function rfft_(e){assert("float32"===e.dtype,"The dtype for rfft() must be real value but\n    got "+e.dtype);var t=e.shape[e.shape.length-1],n=e.size/t,r=e.zerosLike(),o=complex(e,r).as2D(n,t),a=fft(o),i=Math.floor(t/2)+1,s=real(a),u=imag(a),l=s.split([i,t-i],s.shape.length-1),c=u.split([i,t-i],u.shape.length-1),p=e.shape.slice();return p[e.shape.length-1]=i,complex(l[0],c[0]).reshape(p)}function irfft_(e){var t=e.shape[e.shape.length-1],n=e.size/t;if(t<=2){var r=e.as2D(n,t),o=ifft(r);return real(o)}var a=[n,2*(t-1)],i=real(e).as2D(n,t),s=imag(e).as2D(n,t),u=i.slice([0,1],[n,t-2]).reverse(1),l=s.slice([0,1],[n,t-2]).reverse(1).mul(scalar(-1)),c=i.concat(u,1),p=s.concat(l,1);r=complex(c,p).as2D(a[0],a[1]),o=ifft(r);return real(o)}var fft=op({fft_:fft_}),ifft=op({ifft_:ifft_}),rfft=op({rfft_:rfft_}),irfft=op({irfft_:irfft_}),spectral_ops=Object.freeze({fft:fft,ifft:ifft,rfft:rfft,irfft:irfft});function validateInput$1(e,t,n,r){if("int32"!==e.dtype)throw new Error("tf.sparseToDense() expects the indices to be int32 type, but the dtype was "+e.dtype+".");if(e.rank>2)throw new Error("sparseIndices should be a scalar, vector, or matrix, but got shape "+e.shape+".");var o=e.rank>0?e.shape[0]:1,a=e.rank>1?e.shape[1]:1;if(n.length!==a)throw new Error("outputShape has incorrect number of elements:, "+n.length+", should be: "+a+".");var i=t.size;if(0!==t.rank&&(1!==t.rank||i!==o))throw new Error("sparseValues has incorrect shape "+t.shape+", should be [] or ["+o+"]");if(t.dtype!==r.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function sparseToDense_(e,t,n,r){var o=convertToTensor(e,"sparseIndices","sparseToDense","int32"),a=convertToTensor(t,"sparseValues","sparseToDense"),i=convertToTensor(r,"defaultValue","sparseToDense",a.dtype);return validateInput$1(o,a,n,i),ENV.engine.runKernel(function(e){return e.sparseToDense(o,a,n,i)},{$sparseIndices:o,$sparseValues:a,$defaultValue:i})}var sparseToDense=op({sparseToDense_:sparseToDense_});function gatherND_(e,t){var n=convertToTensor(t,"indices","gatherND","int32"),r=convertToTensor(e,"x","gatherND");return ENV.engine.runKernel(function(e){return e.gatherND(r,n)},{$x:r,$indices:n})}var Reduction,gatherND=op({gatherND_:gatherND_});function computeWeightedLoss_(e,t,n){void 0===n&&(n=Reduction.SUM_BY_NONZERO_WEIGHTS);var r=convertToTensor(e,"losses","computeWeightedLoss"),o=null;null!=t&&(o=convertToTensor(t,"weights","computeWeightedLoss"));var a=null==o?r:r.mul(o);if(n===Reduction.NONE)return a;if(n===Reduction.SUM)return a.sum();if(n===Reduction.MEAN){if(null==o)return a.mean();var i=sizeFromShape(r.shape)/sizeFromShape(o.shape),s=a.sum().div(o.sum());return i>1?s.div(scalar(i)):s}if(n===Reduction.SUM_BY_NONZERO_WEIGHTS){if(null==o)return a.sum().div(scalar(r.size));var u=o.mul(ones$1(r.shape)).notEqual(scalar(0)).sum().toFloat();return a.sum().div(u)}throw Error("Unknown reduction: "+n)}function absoluteDifference_(e,t,n,r){void 0===r&&(r=Reduction.SUM_BY_NONZERO_WEIGHTS);var o=convertToTensor(e,"labels","absoluteDifference"),a=convertToTensor(t,"predictions","absoluteDifference"),i=null;null!=n&&(i=convertToTensor(n,"weights","absoluteDifference")),assertShapesMatch(o.shape,a.shape,"Error in absoluteDifference: ");var s=o.sub(a).abs();return computeWeightedLoss(s,i,r)}function meanSquaredError_(e,t,n,r){void 0===r&&(r=Reduction.SUM_BY_NONZERO_WEIGHTS);var o=convertToTensor(e,"labels","meanSquaredError"),a=convertToTensor(t,"predictions","meanSquaredError"),i=null;null!=n&&(i=convertToTensor(n,"weights","meanSquaredError")),assertShapesMatch(o.shape,a.shape,"Error in meanSquaredError: ");var s=o.squaredDifference(a);return computeWeightedLoss(s,i,r)}function cosineDistance_(e,t,n,r,o){void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,"labels","cosineDistance"),i=convertToTensor(t,"predictions","cosineDistance"),s=null;null!=r&&(s=convertToTensor(r,"weights","cosineDistance")),assertShapesMatch(a.shape,i.shape,"Error in cosineDistance: ");var u=scalar(1).sub(a.mul(i).sum(n,!0));return computeWeightedLoss(u,s,o)}function hingeLoss_(e,t,n,r){void 0===r&&(r=Reduction.SUM_BY_NONZERO_WEIGHTS);var o=convertToTensor(e,"labels","hingeLoss"),a=convertToTensor(t,"predictions","hingeLoss"),i=null;null!=n&&(i=convertToTensor(n,"weights","hingeLoss")),assertShapesMatch(o.shape,a.shape,"Error in hingeLoss: ");var s=scalar(1);o=scalar(2).mul(o).sub(s);var u=s.sub(o.mul(a)).relu();return computeWeightedLoss(u,i,r)}function logLoss_(e,t,n,r,o){void 0===r&&(r=1e-7),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,"labels","logLoss"),i=convertToTensor(t,"predictions","logLoss"),s=null;null!=n&&(s=convertToTensor(n,"weights","logLoss")),assertShapesMatch(a.shape,i.shape,"Error in logLoss: ");var u=scalar(1),l=scalar(r),c=a.mul(i.add(l).log()).neg().sub(u.sub(a).mul(u.sub(i).add(l).log()));return computeWeightedLoss(c,s,o)}function sigmoidCrossEntropyWithLogits_(e,t){var n=convertToTensor(e,"labels","sigmoidCrossEntropyWithLogits"),r=convertToTensor(t,"logits","sigmoidCrossEntropyWithLogits");assertShapesMatch(n.shape,r.shape,"Error in sigmoidCrossEntropyWithLogits: ");var o=r.relu(),a=r.mul(n),i=r.abs().neg().exp().log1p();return o.sub(a).add(i)}function sigmoidCrossEntropy_(e,t,n,r,o){void 0===r&&(r=0),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,"multiClassLabels","sigmoidCrossEntropy"),i=convertToTensor(t,"logits","sigmoidCrossEntropy"),s=null;if(null!=n&&(s=convertToTensor(n,"weights","sigmoidCrossEntropy")),assertShapesMatch(a.shape,i.shape,"Error in sigmoidCrossEntropy: "),r>0){var u=scalar(r),l=scalar(1),c=scalar(.5);a=a.mul(l.sub(u)).add(c.mul(u))}var p=sigmoidCrossEntropyWithLogits_(a,i);return computeWeightedLoss(p,s,o)}function huberLoss_(e,t,n,r,o){void 0===r&&(r=1),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,"labels","huberLoss"),i=convertToTensor(t,"predictions","huberLoss"),s=null;null!=n&&(s=convertToTensor(n,"weights","huberLoss")),assertShapesMatch(a.shape,i.shape,"Error in huberLoss: ");var u=scalar(r),l=i.sub(a).abs(),c=minimum(l,u),p=l.sub(c),d=scalar(.5).mul(c.square()).add(u.mul(p));return computeWeightedLoss(d,s,o)}function softmaxCrossEntropyWithLogits_(e,t,n){if(void 0===n&&(n=-1),-1===n&&(n=t.rank-1),n!==t.rank-1)throw Error("Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank "+t.rank+" and dim was "+n);return customGrad(function(e,t){var r=t.logSumExp([n],!0),o=t.toFloat().sub(r);return{value:o.mul(e).neg().sum([n]),gradFunc:function(t){var r=expandShapeToKeepDim(t.shape,[n]);return[t.reshape(r).mul(e.toFloat().sub(o.exp())),t.reshape(r).mul(o.exp().sub(e.toFloat()))]}}})(e,t)}function softmaxCrossEntropy_(e,t,n,r,o){void 0===r&&(r=0),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,"onehotLabels","softmaxCrossEntropy"),i=convertToTensor(t,"logits","softmaxCrossEntropy"),s=null;if(null!=n&&(s=convertToTensor(n,"weights","softmaxCrossEntropy")),assertShapesMatch(a.shape,i.shape,"Error in softmaxCrossEntropy: "),r>0){var u=scalar(r),l=scalar(1),c=scalar(a.shape[1]);a=a.mul(l.sub(u)).add(u.div(c))}var p=softmaxCrossEntropyWithLogits_(a,i);return computeWeightedLoss(p,s,o)}!function(e){e[e.NONE=0]="NONE",e[e.MEAN=1]="MEAN",e[e.SUM=2]="SUM",e[e.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS"}(Reduction||(Reduction={}));var absoluteDifference=op({absoluteDifference_:absoluteDifference_}),computeWeightedLoss=op({computeWeightedLoss_:computeWeightedLoss_}),cosineDistance=op({cosineDistance_:cosineDistance_}),hingeLoss=op({hingeLoss_:hingeLoss_}),huberLoss=op({huberLoss_:huberLoss_}),logLoss=op({logLoss_:logLoss_}),meanSquaredError=op({meanSquaredError_:meanSquaredError_}),sigmoidCrossEntropy=op({sigmoidCrossEntropy_:sigmoidCrossEntropy_}),softmaxCrossEntropy=op({softmaxCrossEntropy_:softmaxCrossEntropy_}),loss_ops=Object.freeze({get Reduction(){return Reduction},absoluteDifference:absoluteDifference,computeWeightedLoss:computeWeightedLoss,cosineDistance:cosineDistance,hingeLoss:hingeLoss,huberLoss:huberLoss,logLoss:logLoss,meanSquaredError:meanSquaredError,sigmoidCrossEntropy:sigmoidCrossEntropy,softmaxCrossEntropy:softmaxCrossEntropy});function gramSchmidt_(e){var t;if(Array.isArray(e)){t=!1,assert(null!=e&&e.length>0,"Gram-Schmidt process: input must not be null, undefined, or empty");for(var n=e[0].shape[0],r=1;r<e.length;++r)assert(e[r].shape[0]===n,"Gram-Schmidt: Non-unique lengths found in the input vectors: ("+e[r].shape[0]+" vs. "+n+")")}else t=!0,e=split$1(e,e.shape[0],0).map(function(e){return squeeze(e,[0])});assert(e.length<=e[0].shape[0],"Gram-Schmidt: Number of vectors ("+e.length+") exceeds number of dimensions ("+e[0].shape[0]+").");var o=[],a=e,i=function(e){o.push(ENV.engine.tidy(function(){var t=a[e];if(e>0)for(var n=0;n<e;++n){var r=sum$1(o[n].mulStrict(t)).mul(o[n]);t=t.sub(r)}return t.div(norm(t,"euclidean"))}))};for(r=0;r<e.length;++r)i(r);return t?stack(o,0):o}function qr_(e,t){if(void 0===t&&(t=!1),e.rank<2)throw new Error("qr() requires input tensor to have a rank >= 2, but got rank "+e.rank);if(2===e.rank)return qr2d(e,t);var n=e.shape.slice(0,e.shape.length-2).reduce(function(e,t){return e*t}),r=[],o=[];return unstack(e.reshape([n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0).forEach(function(e){var n=qr2d(e,t),a=n[0],i=n[1];r.push(a),o.push(i)}),[stack(r,0).reshape(e.shape),stack(o,0).reshape(e.shape)]}function qr2d(e,t){return void 0===t&&(t=!1),ENV.engine.tidy(function(){if(2!==e.shape.length)throw new Error("qr2d() requires a 2D Tensor, but got a "+e.shape.length+"D Tensor.");for(var n=e.shape[0],r=e.shape[1],o=eye(n),a=e.clone(),i=tensor2d([[1]],[1,1]),s=i.clone(),u=n>=r?r:n,l=function(e){var t,u=a,l=s,c=o;t=ENV.engine.tidy(function(){var t=a.slice([e,e],[n-e,1]),u=t.norm(),l=a.slice([e,e],[1,1]),c=l.sign().neg(),p=l.sub(c.mul(u)),d=t.div(p);s=1===d.shape[0]?i.clone():i.concat(d.slice([1,0],[d.shape[0]-1,d.shape[1]]),0);var h=c.matMul(p).div(u).neg(),f=a.slice([e,0],[n-e,r]),m=h.mul(s);a=0===e?f.sub(m.matMul(s.transpose().matMul(f))):a.slice([0,0],[e,r]).concat(f.sub(m.matMul(s.transpose().matMul(f))),0);var g=o.slice([0,e],[n,o.shape[1]-e]);return o=0===e?g.sub(g.matMul(s).matMul(m.transpose())):o.slice([0,0],[n,e]).concat(g.sub(g.matMul(s).matMul(m.transpose())),1),[s,a,o]}),s=t[0],a=t[1],o=t[2],dispose([u,l,c])},c=0;c<u;++c)l(c);return!t&&n>r&&(o=o.slice([0,0],[n,r]),a=a.slice([0,0],[r,r])),[o,a]})}var gramSchmidt=op({gramSchmidt_:gramSchmidt_}),qr=op({qr_:qr_}),linalg_ops=Object.freeze({gramSchmidt:gramSchmidt,qr:qr});function resizeBilinear_(e,t,n){void 0===n&&(n=!1);var r=convertToTensor(e,"images","resizeBilinear");assert(3===r.rank||4===r.rank,"Error in resizeBilinear: x must be rank 3 or 4, but got rank "+r.rank+"."),assert(2===t.length,"Error in resizeBilinear: new shape must 2D, but got shape "+t+".");var o=r,a=!1;3===r.rank&&(a=!0,o=r.as4D(1,r.shape[0],r.shape[1],r.shape[2]));var i=t[0],s=t[1],u=ENV.engine.runKernel(function(e,t){return e.resizeBilinear(o,i,s,n)},{batchImages:o},function(e,t){return{batchImages:function(){return ENV.engine.runKernel(function(t){return t.resizeBilinearBackprop(e,o,n)},{})}}});return a?u.as3D(u.shape[1],u.shape[2],u.shape[3]):u}function resizeNearestNeighbor_(e,t,n){void 0===n&&(n=!1);var r=convertToTensor(e,"images","resizeNearestNeighbor");assert(3===r.rank||4===r.rank,"Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank "+r.rank+"."),assert(2===t.length,"Error in resizeNearestNeighbor: new shape must 2D, but got shape "+t+"."),assert("float32"===r.dtype||"int32"===r.dtype,"`images` must have `int32` or `float32` as dtype");var o=r,a=!1;3===r.rank&&(a=!0,o=r.as4D(1,r.shape[0],r.shape[1],r.shape[2]));var i=t[0],s=t[1],u=ENV.engine.runKernel(function(e,t){return e.resizeNearestNeighbor(o,i,s,n)},{batchImages:o},function(e,t){return{batchImages:function(){return ENV.engine.runKernel(function(t){return t.resizeNearestNeighborBackprop(e,o,n)},{})}}});return a?u.as3D(u.shape[1],u.shape[2],u.shape[3]):u}function nonMaxSuppression_(e,t,n,r,o){void 0===r&&(r=.5),void 0===o&&(o=Number.NEGATIVE_INFINITY);var a=convertToTensor(e,"boxes","nonMaxSuppression"),i=convertToTensor(t,"scores","nonMaxSuppression"),s=nonMaxSuppSanityCheck(a,i,n,r,o);return n=s.maxOutputSize,r=s.iouThreshold,o=s.scoreThreshold,ENV.engine.runKernel(function(e){return e.nonMaxSuppression(a,i,n,r,o)},{$boxes:a})}function nonMaxSuppressionAsync_(e,t,n,r,o){return void 0===r&&(r=.5),void 0===o&&(o=Number.NEGATIVE_INFINITY),__awaiter(this,void 0,void 0,function(){var a,i,s,u,l,c;return __generator(this,function(p){switch(p.label){case 0:return a=convertToTensor(e,"boxes","nonMaxSuppressionAsync"),i=convertToTensor(t,"scores","nonMaxSuppressionAsync"),s=nonMaxSuppSanityCheck(a,i,n,r,o),n=s.maxOutputSize,r=s.iouThreshold,o=s.scoreThreshold,[4,a.data()];case 1:return u=p.sent(),[4,i.data()];case 2:return l=p.sent(),c=nonMaxSuppressionImpl(u,l,n,r,o),a!==e&&a.dispose(),i!==t&&i.dispose(),[2,c]}})})}function nonMaxSuppSanityCheck(e,t,n,r,o){null==r&&(r=.5),null==o&&(o=Number.NEGATIVE_INFINITY);var a=e.shape[0];return n=Math.min(n,a),assert(0<=r&&r<=1,"iouThreshold must be in [0, 1], but was '"+r+"'"),assert(2===e.rank,"boxes must be a 2D tensor, but was of rank '"+e.rank+"'"),assert(4===e.shape[1],"boxes must have 4 columns, but 2nd dimension was "+e.shape[1]),assert(1===t.rank,"scores must be a 1D tensor"),assert(t.shape[0]===a,"scores has incompatible shape with boxes. Expected "+a+", but was "+t.shape[0]),{maxOutputSize:n,iouThreshold:r,scoreThreshold:o}}function cropAndResize_(e,t,n,r,o,a){var i=convertToTensor(e,"image","cropAndResize","float32"),s=convertToTensor(t,"boxes","cropAndResize","float32"),u=convertToTensor(n,"boxInd","cropAndResize","int32");o=o||"bilinear",a=a||0;var l=s.shape[0];assert(4===i.rank,"Error in cropAndResize: image must be rank 4,but got rank "+i.rank+"."),assert(2===s.rank&&4===s.shape[1],"Error in cropAndResize: boxes must be have size ["+l+",4] but had shape "+s.shape+"."),assert(1===u.rank&&u.shape[0]===l,"Error in cropAndResize: boxInd must be have size ["+l+"] but had shape "+s.shape+"."),assert(2===r.length,"Error in cropAndResize: cropSize must be of length 2, but got length "+r.length+"."),assert(r[0]>=1&&r[1]>=1,"cropSize must be atleast [1,1], but was "+r),assert("bilinear"===o||"nearest"===o,"method must be bilinear or nearest, but was "+o);return ENV.engine.runKernel(function(e,t){return e.cropAndResize(i,s,u,r,o,a)},{$image:i,$boxes:s})}var resizeBilinear=op({resizeBilinear_:resizeBilinear_}),resizeNearestNeighbor=op({resizeNearestNeighbor_:resizeNearestNeighbor_}),nonMaxSuppression=op({nonMaxSuppression_:nonMaxSuppression_}),nonMaxSuppressionAsync=nonMaxSuppressionAsync_,cropAndResize=cropAndResize_,image_ops=Object.freeze({resizeBilinear:resizeBilinear,resizeNearestNeighbor:resizeNearestNeighbor,nonMaxSuppression:nonMaxSuppression,nonMaxSuppressionAsync:nonMaxSuppressionAsync,cropAndResize:cropAndResize}),ops=Object.freeze({image:image_ops,linalg:linalg_ops,losses:loss_ops,spectral:spectral_ops,op:op,batchNormalization2d:batchNormalization2d,batchNormalization3d:batchNormalization3d,batchNormalization4d:batchNormalization4d,batchNormalization:batchNormalization,complex:complex,real:real,imag:imag,concat:concat,concat1d:concat1d,concat2d:concat2d,concat3d:concat3d,concat4d:concat4d,split:split$1,conv1d:conv1d,conv2d:conv2d,conv3d:conv3d,conv2dDerFilter:conv2dDerFilter,depthwiseConv2d:depthwiseConv2d,separableConv2d:separableConv2d,conv2dTranspose:conv2dTranspose,matMul:matMul,dot:dot,outerProduct:outerProduct,reverse:reverse,reverse1d:reverse1d,reverse2d:reverse2d,reverse3d:reverse3d,reverse4d:reverse4d,maxPool:maxPool,avgPool:avgPool,pool:pool,slice:slice,slice1d:slice1d,slice2d:slice2d,slice3d:slice3d,slice4d:slice4d,abs:abs,acos:acos,acosh:acosh,asin:asin,asinh:asinh,atan:atan,atanh:atanh,ceil:ceil,clipByValue:clipByValue,cos:cos,cosh:cosh,erf:erf,exp:exp,expm1:expm1,floor:floor,log:log$1,log1p:log1p,logSigmoid:logSigmoid,neg:neg,reciprocal:reciprocal,round:round,rsqrt:rsqrt,sigmoid:sigmoid,sign:sign,sin:sin,sinh:sinh,softplus:softplus,sqrt:sqrt,square:square,step:step,tan:tan,tanh:tanh$1,all:all,any:any,argMax:argMax,argMin:argMin,logSumExp:logSumExp,max:max,mean:mean,min:min,moments:moments,sum:sum$1,prod:prod,equal:equal,equalStrict:equalStrict,greater:greater,greaterEqual:greaterEqual,greaterEqualStrict:greaterEqualStrict,greaterStrict:greaterStrict,less:less,lessEqual:lessEqual,lessEqualStrict:lessEqualStrict,lessStrict:lessStrict,notEqual:notEqual,notEqualStrict:notEqualStrict,add:add,addN:addN,addStrict:addStrict,atan2:atan2,div:div,divStrict:divStrict,floorDiv:floorDiv,maximum:maximum,maximumStrict:maximumStrict,minimum:minimum,minimumStrict:minimumStrict,mod:mod,modStrict:modStrict,mul:mul,mulStrict:mulStrict,pow:pow,powStrict:powStrict,squaredDifference:squaredDifference,squaredDifferenceStrict:squaredDifferenceStrict,sub:sub,subStrict:subStrict,elu:elu,leakyRelu:leakyRelu,prelu:prelu,relu:relu,selu:selu,logicalAnd:logicalAnd,logicalNot:logicalNot,logicalOr:logicalOr,logicalXor:logicalXor,where:where,whereAsync:whereAsync,buffer:buffer,toPixels:toPixels,print:print,batchToSpaceND:batchToSpaceND,cast:cast,clone:clone,cumsum:cumsum,depthToSpace:depthToSpace,expandDims:expandDims,eye:eye,fromPixels:fromPixels,multinomial:multinomial,oneHot:oneHot,pad:pad,pad1d:pad1d,pad2d:pad2d,pad3d:pad3d,pad4d:pad4d,rand:rand,randomNormal:randomNormal,randomUniform:randomUniform,reshape:reshape,spaceToBatchND:spaceToBatchND,squeeze:squeeze,stack:stack,tile:tile,truncatedNormal:truncatedNormal,unstack:unstack,setdiff1dAsync:setdiff1dAsync,fill:fill,linspace:linspace,ones:ones$1,range:range,scalar:scalar,tensor:tensor,tensor1d:tensor1d,tensor2d:tensor2d,tensor3d:tensor3d,tensor4d:tensor4d,tensor5d:tensor5d,tensor6d:tensor6d,zeros:zeros,onesLike:onesLike,zerosLike:zerosLike,transpose:transpose,softmax:softmax,logSoftmax:logSoftmax,localResponseNormalization:localResponseNormalization,norm:norm,gather:gather,unsortedSegmentSum:unsortedSegmentSum,basicLSTMCell:basicLSTMCell,multiRNNCell:multiRNNCell,movingAverage:movingAverage,stridedSlice:stridedSlice,topk:topk,scatterND:scatterND,fft:fft,ifft:ifft,rfft:rfft,irfft:irfft,sparseToDense:sparseToDense,gatherND:gatherND}),MathBackendCPU=function(){function e(){this.blockSize=48,this.firstUse=!0,ENV.get("IS_BROWSER")&&(this.fromPixels2DContext=document.createElement("canvas").getContext("2d"))}return e.prototype.setDataMover=function(e){this.data=new DataStorage(e)},e.prototype.register=function(e,t,n){if(this.firstUse&&(this.firstUse=!1,ENV.get("IS_NODE")&&warn("\n============================\nHi there 👋. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.\n============================\n")),this.data.has(e))throw new Error("Data buffer is already registered");this.data.set(e,{dtype:n})},e.prototype.write=function(e,t){if(null==t)throw new Error("MathBackendCPU.write(): values can not be null");this.data.get(e).values=t},e.prototype.fromPixels=function(e,t){if(null==e)throw new Error("pixels passed to tf.fromPixels() can not be null");var n,r;if(ENV.get("IS_NODE")&&null==e.getContext)throw new Error("When running in node, pixels must be an HTMLCanvasElement like the one returned by the `canvas` npm package");if(null!=e.getContext)n=e.getContext("2d").getImageData(0,0,e.width,e.height).data;else if(e instanceof ImageData)n=e.data;else{if(!(e instanceof HTMLImageElement||e instanceof HTMLVideoElement))throw new Error("pixels passed to tf.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ImageData, but was "+e.constructor.name);if(null==this.fromPixels2DContext)throw new Error("Can't read pixels from HTMLImageElement outside the browser.");this.fromPixels2DContext.canvas.width=e.width,this.fromPixels2DContext.canvas.height=e.height,this.fromPixels2DContext.drawImage(e,0,0,e.width,e.height),n=this.fromPixels2DContext.getImageData(0,0,e.width,e.height).data}if(4===t)r=new Int32Array(n);else{var o=e.width*e.height;r=new Int32Array(o*t);for(var a=0;a<o;a++)for(var i=0;i<t;++i)r[a*t+i]=n[4*a+i]}return tensor3d(r,[e.height,e.width,t],"int32")},e.prototype.read=function(e){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(t){return[2,this.readSync(e)]})})},e.prototype.readSync=function(e){var t=this.data.get(e),n=t.dtype,r=t.complexTensors;return"complex64"===n?mergeRealAndImagArrays(r.real.dataSync(),r.imag.dataSync()):this.data.get(e).values},e.prototype.disposeData=function(e){if(this.data.has(e)){var t=this.data.get(e).complexTensors;null!=t&&(t.real.dispose(),t.imag.dispose()),this.data.delete(e)}},e.prototype.time=function(e){return __awaiter(this,void 0,void 0,function(){var t;return __generator(this,function(n){return t=now(),e(),[2,{kernelMs:now()-t}]})})},e.prototype.memory=function(){return{unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}},e.prototype.complex=function(e,t){var n=Tensor.make(e.shape,{},"complex64");return this.data.get(n.dataId).complexTensors={real:ENV.engine.keep(e.clone()),imag:ENV.engine.keep(t.clone())},n},e.prototype.real=function(e){return this.data.get(e.dataId).complexTensors.real.clone()},e.prototype.imag=function(e){return this.data.get(e.dataId).complexTensors.imag.clone()},e.prototype.assertNotComplex=function(e,t){Array.isArray(e)||(e=[e]),e.forEach(function(e){null!=e&&assert("complex64"!==e.dtype,t+" does not support complex64 tensors.")})},e.prototype.slice=function(e,t,n){this.assertNotComplex(e,"slice");for(var r=buffer(n,e.dtype),o=0;o<r.size;++o){var a=r.indexToLoc(o),i=a.map(function(e,n){return e+t[n]});r.set.apply(r,[e.get.apply(e,i)].concat(a))}return r.toTensor()},e.prototype.stridedSlice=function(e,t,n,r,o,a,i,s,u){this.assertNotComplex(e,"stridedSlice");var l=getStridedSlicedInfo(e.shape,t,n,r,o,a,i,s,u),c=l[0],p=l[1],d=l[2],h=p.filter(function(e,t){return-1===d.indexOf(t)});if(h.some(function(e){return 0===e}))return tensor([],h);for(var f=buffer(p,e.dtype),m=0;m<f.size;m++){for(var g=f.indexToLoc(m),v=new Array(g.length),y=0;y<v.length;y++)v[y]=g[y]*r[y]+c[y];f.set.apply(f,[e.get.apply(e,v)].concat(g))}return f.toTensor().reshape(h)},e.prototype.reverse=function(e,t){this.assertNotComplex(e,"reverse");for(var n=buffer(e.shape,e.dtype),r=e.buffer(),o=function(o){var a=n.indexToLoc(o),i=a.slice();t.forEach(function(t){return i[t]=e.shape[t]-1-i[t]}),n.set.apply(n,[r.get.apply(r,i)].concat(a))},a=0;a<n.size;a++)o(a);return n.toTensor()},e.prototype.concat=function(e,t){this.assertNotComplex(e,"concat");var n=e.map(function(e){var n=sizeFromShape(e.shape.slice(t));return e.as2D(-1,n)}),r=computeOutShape(n.map(function(e){return e.shape}),1),o=buffer(r,e[0].dtype).values;if(1===n[0].shape[0]){var a=0;n.forEach(function(e){o.set(e.dataSync(),a),a+=e.size})}else{var i=0;n.forEach(function(e){for(var t=e.dataSync(),n=0,a=0;a<e.shape[0];++a)for(var s=a*r[1]+i,u=0;u<e.shape[1];++u)o[s+u]=t[n++];i+=e.shape[1]})}var s=computeOutShape(e.map(function(e){return e.shape}),t);return tensor(o,s,e[0].dtype)},e.prototype.neg=function(e){return this.assertNotComplex(e,"neg"),this.multiply(scalar(-1),e)},e.prototype.add=function(e,t){return"complex64"===e.dtype||"complex64"===t.dtype?this.broadcastedBinaryComplexOp(e.cast("complex64"),t.cast("complex64"),function(e,t,n,r){return{real:e+n,imag:t+r}}):this.broadcastedBinaryOp(e,t,upcastType(e.dtype,t.dtype),function(e,t){return e+t})},e.prototype.addN=function(e){this.assertNotComplex(e,"addN");for(var t=e.map(function(e){return e.dataSync()}),n=buffer(e[0].shape,e[0].dtype),r=n.values,o=0;o<e.length;o++)for(var a=t[o],i=0;i<r.length;i++)r[i]+=a[i];return n.toTensor()},e.prototype.subtract=function(e,t){return"complex64"===e.dtype||"complex64"===t.dtype?this.broadcastedBinaryComplexOp(e.cast("complex64"),t.cast("complex64"),function(e,t,n,r){return{real:e-n,imag:t-r}}):this.broadcastedBinaryOp(e,t,upcastType(e.dtype,t.dtype),function(e,t){return e-t})},e.prototype.pow=function(e,t){return this.assertNotComplex([e,t],"pow"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){return Math.pow(e,t)})},e.prototype.batchMatMul=function(e,t,n,r){this.assertNotComplex([e,t],"matMul");for(var o=n?e.shape[1]:e.shape[2],a=n?e.shape[2]:e.shape[1],i=r?t.shape[1]:t.shape[2],s=e.shape[0],u=e.dataSync(),l=t.dataSync(),c=n?[e.strides[0],1,e.strides[1]]:[e.strides[0],e.strides[1],1],p=c[0],d=c[1],h=c[2],f=r?[1,t.strides[1],t.strides[0]]:[t.strides[1],1,t.strides[0]],m=f[0],g=f[1],v=f[2],y=a*i,x=buffer([s,a,i],e.dtype),T=x.values,w=this.blockSize,E=0;E<s;E++)for(var S=0;S<a;S+=w)for(var b=0;b<i;b+=w)for(var C=0;C<o;C+=w)for(var A=Math.min(S+w,a),_=Math.min(b+w,i),N=Math.min(C+w,o),R=S;R<A;R++)for(var k=b;k<_;k++){for(var I=0,D=C;D<N;D++)I+=u[E*p+R*d+D*h]*l[D*m+k*g+E*v];T[E*y+(R*i+k)]+=I}return x.toTensor()},e.prototype.multiply=function(e,t){return"complex64"===e.dtype||"complex64"===t.dtype?this.broadcastedBinaryComplexOp(e.cast("complex64"),t.cast("complex64"),function(e,t,n,r){return{real:e*n-t*r,imag:e*r+t*n}}):this.broadcastedBinaryOp(e,t,upcastType(e.dtype,t.dtype),function(e,t){return e*t})},e.prototype.realDivide=function(e,t){this.assertNotComplex([e,t],"realDivide");return this.broadcastedBinaryOp(e,t,"float32",function(e,t){return e/t})},e.prototype.floorDiv=function(e,t){this.assertNotComplex([e,t],"floorDiv");return this.broadcastedBinaryOp(e,t,"int32",function(e,t){return Math.floor(e/t)})},e.prototype.sum=function(e,t){this.assertNotComplex(e,"sum"),assertAxesAreInnerMostDims("sum",t,e.rank);for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,upcastType(e.dtype,"int32")),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),l=0;l<s.length;++l){for(var c=l*i,p=0,d=0;d<i;++d)p+=u[c+d];s[l]=p}return a},e.prototype.prod=function(e,t){this.assertNotComplex(e,"sum");for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,upcastType(e.dtype,"int32")),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),l=0;l<s.length;++l){for(var c=l*i,p=1,d=0;d<i;++d)p*=u[c+d];s[l]=p}return a},e.prototype.unsortedSegmentSum=function(e,t,n){this.assertNotComplex(e,"unsortedSegmentSum");for(var r=[],o=e.rank-t.rank,a=0;a<o;++a)t=t.expandDims(a+1);for(a=0;a<n;++a){var i=scalar(a,"int32"),s=equal(i,t).asType("float32").mul(e).sum(0);r.push(s)}return stack(r)},e.prototype.argMin=function(e,t){this.assertNotComplex(e,"argMin");var n=[t];assertAxesAreInnerMostDims("argMin",n,e.rank);for(var r=computeOutAndReduceShapes(e.shape,n),o=r[0],a=r[1],i=zeros(o,"int32"),s=sizeFromShape(a),u=i.dataSync(),l=e.dataSync(),c=0;c<u.length;++c){for(var p=c*s,d=l[p],h=0,f=0;f<s;++f){var m=l[p+f];m<d&&(d=m,h=f)}u[c]=h}return i},e.prototype.argMax=function(e,t){this.assertNotComplex(e,"argMax");var n=[t];assertAxesAreInnerMostDims("argMax",n,e.rank);for(var r=computeOutAndReduceShapes(e.shape,n),o=r[0],a=r[1],i=zeros(o,"int32"),s=sizeFromShape(a),u=i.dataSync(),l=e.dataSync(),c=0;c<u.length;++c){for(var p=c*s,d=l[p],h=0,f=0;f<s;++f){var m=l[p+f];m>d&&(d=m,h=f)}u[c]=h}return i},e.prototype.cumsum=function(e,t,n,r){if(this.assertNotComplex(e,"cumsum"),t!==e.rank-1)throw new Error("backend.cumsum in CPU expects an inner-most axis="+(e.rank-1)+" but got axis="+t);for(var o=upcastType(e.dtype,"int32"),a=zeros(e.shape,o),i=a.dataSync(),s=e.dataSync(),u=e.shape[e.rank-1],l=r?function(e,t){return e+u-t-1}:function(e,t){return e+t},c=0;c<s.length;c+=u)for(var p=0;p<u;p++){var d=l(c,p);if(0===p)i[d]=n?0:s[d];else{var h=l(c,p-1);i[d]=n?s[h]+i[h]:s[d]+i[h]}}return a},e.prototype.equal=function(e,t){return this.assertNotComplex([e,t],"equal"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e===t?1:0})},e.prototype.notEqual=function(e,t){return this.assertNotComplex([e,t],"notEqual"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e!==t?1:0})},e.prototype.less=function(e,t){return this.assertNotComplex([e,t],"less"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e<t?1:0})},e.prototype.lessEqual=function(e,t){return this.assertNotComplex([e,t],"lessEqual"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e<=t?1:0})},e.prototype.greater=function(e,t){return this.assertNotComplex([e,t],"greater"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e>t?1:0})},e.prototype.greaterEqual=function(e,t){return this.assertNotComplex([e,t],"greaterEqual"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e>=t?1:0})},e.prototype.logicalNot=function(e){this.assertNotComplex(e,"logicalNot");for(var t=e.dataSync(),n=new Uint8Array(t.length),r=0;r<t.length;++r)n[r]=t[r]?0:1;return Tensor.make(e.shape,{values:n},"bool")},e.prototype.logicalAnd=function(e,t){return this.assertNotComplex([e,t],"logicalAnd"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e&&t})},e.prototype.logicalOr=function(e,t){return this.assertNotComplex([e,t],"logicalOr"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e||t})},e.prototype.select=function(e,t,n){this.assertNotComplex([e,t,n],"select");for(var r=e.dataSync(),o=t.dataSync(),a=n.dataSync(),i=zeros(t.shape,upcastType(t.dtype,n.dtype)),s=i.dataSync(),u=0,l=0===e.rank||e.rank>1||1===t.rank?1:t.shape[1],c=0;c<r.length;c++)for(var p=0;p<l;p++)1===r[c]?s[u++]=o[c]:s[u++]=a[c];return i},e.prototype.where=function(e){this.assertNotComplex([e],"where");var t=e.dataSync();return whereImpl(e.shape,t)},e.prototype.topk=function(e,t,n){return this.assertNotComplex(e,"topk"),topkImpl(e.dataSync(),e.shape,e.dtype,t,n)},e.prototype.min=function(e,t){this.assertNotComplex(e,"min"),assertAxesAreInnerMostDims("min",t,e.rank);for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,e.dtype),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),l=0;l<s.length;++l){for(var c=l*i,p=u[c],d=0;d<i;++d){var h=u[c+d];h<p&&(p=h)}s[l]=p}return a},e.prototype.minimum=function(e,t){return this.assertNotComplex([e,t],"minimum"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){return Math.min(e,t)})},e.prototype.mod=function(e,t){return this.assertNotComplex([e,t],"mod"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){var n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t})},e.prototype.max=function(e,t){this.assertNotComplex(e,"max"),assertAxesAreInnerMostDims("max",t,e.rank);for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,e.dtype),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),l=0;l<s.length;++l){for(var c=l*i,p=u[c],d=0;d<i;++d){var h=u[c+d];h>p&&(p=h)}s[l]=p}return a},e.prototype.maximum=function(e,t){return this.assertNotComplex([e,t],"maximum"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){return Math.max(e,t)})},e.prototype.all=function(e,t){this.assertNotComplex(e,"all"),assertAxesAreInnerMostDims("all",t,e.rank);for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,e.dtype),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),l=0;l<s.length;++l){for(var c=l*i,p=u[c],d=0;d<i;++d){var h=u[c+d];p=p&&h}s[l]=p}return a},e.prototype.any=function(e,t){this.assertNotComplex(e,"any"),assertAxesAreInnerMostDims("any",t,e.rank);for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,e.dtype),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),l=0;l<s.length;++l){for(var c=l*i,p=u[c],d=0;d<i;++d){var h=u[c+d];p=p||h}s[l]=p}return a},e.prototype.squaredDifference=function(e,t){return this.assertNotComplex([e,t],"squaredDifference"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){var n=e-t;return n*n})},e.prototype.ceil=function(e){this.assertNotComplex(e,"ceil");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)n[r]=Math.ceil(t[r]);return Tensor.make(e.shape,{values:n})},e.prototype.floor=function(e){this.assertNotComplex(e,"floor");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)n[r]=Math.floor(t[r]);return Tensor.make(e.shape,{values:n})},e.prototype.sign=function(e){this.assertNotComplex(e,"x");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)t[r]<0?n[r]=-1:t[r]>0?n[r]=1:n[r]=0;return Tensor.make(e.shape,{values:n})},e.prototype.round=function(e){this.assertNotComplex(e,"round");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=Math.floor(t[r]);t[r]-o<.5?n[r]=Math.floor(t[r]):t[r]-o>.5?n[r]=Math.ceil(t[r]):n[r]=o%2==0?o:o+1}return Tensor.make(e.shape,{values:n})},e.prototype.exp=function(e){this.assertNotComplex(e,"exp");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)n[r]=Math.exp(t[r]);return Tensor.make(e.shape,{values:n})},e.prototype.expm1=function(e){this.assertNotComplex(e,"expm1");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)n[r]=Math.expm1(t[r]);return Tensor.make(e.shape,{values:n})},e.prototype.log=function(e){this.assertNotComplex(e,"log");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=Math.log(o)}return Tensor.make(e.shape,{values:n})},e.prototype.log1p=function(e){this.assertNotComplex(e,"log1p");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=Math.log1p(o)}return Tensor.make(e.shape,{values:n})},e.prototype.sqrt=function(e){this.assertNotComplex(e,"sqrt");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=Math.sqrt(o)}return Tensor.make(e.shape,{values:n})},e.prototype.rsqrt=function(e){this.assertNotComplex(e,"rsqrt");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=1/Math.sqrt(o)}return Tensor.make(e.shape,{values:n})},e.prototype.square=function(e){this.assertNotComplex(e,"square");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=o*o}return Tensor.make(e.shape,{values:n})},e.prototype.reciprocal=function(e){this.assertNotComplex(e,"reciprocal");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)n[r]=1/t[r];return Tensor.make(e.shape,{values:n})},e.prototype.relu=function(e){this.assertNotComplex(e,"relu");for(var t=zeros(e.shape,e.dtype),n=t.dataSync(),r=e.dataSync(),o=0;o<r.length;++o)n[o]=Math.max(0,r[o]);return t},e.prototype.elu=function(e){this.assertNotComplex(e,"elu");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r){var o=n[r];t[r]=o>=0?o:Math.exp(o)-1}return Tensor.make(e.shape,{values:t})},e.prototype.eluDer=function(e,t){this.assertNotComplex([e,t],"eluDer");for(var n=new Float32Array(t.size),r=t.dataSync(),o=e.dataSync(),a=0;a<r.length;++a){var i=r[a];n[a]=i>=1?o[a]:o[a]*(i+1)}return Tensor.make(t.shape,{values:n})},e.prototype.selu=function(e){this.assertNotComplex(e,"selu");for(var t=SELU_SCALEALPHA,n=SELU_SCALE,r=new Float32Array(e.size),o=e.dataSync(),a=0;a<o.length;++a){var i=o[a];r[a]=i>=0?n*i:t*(Math.exp(i)-1)}return Tensor.make(e.shape,{values:r})},e.prototype.clip=function(e,t,n){this.assertNotComplex(e,"clip");for(var r=new Float32Array(e.size),o=e.dataSync(),a=0;a<o.length;++a){var i=o[a];r[a]=i>n?n:i<t?t:i}return Tensor.make(e.shape,{values:r})},e.prototype.abs=function(e){for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.abs(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.complexAbs=function(e){for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<e.size;++r){var o=n[2*r],a=n[2*r+1];t[r]=Math.hypot(o,a)}return Tensor.make(e.shape,{values:t})},e.prototype.int=function(e){this.assertNotComplex(e,"int");for(var t=new Int32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=n[r];return Tensor.make(e.shape,{values:t},"int32")},e.prototype.sigmoid=function(e){this.assertNotComplex(e,"sigmoid");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=1/(1+Math.exp(-n[r]));return Tensor.make(e.shape,{values:t})},e.prototype.softplus=function(e){this.assertNotComplex(e,"softplus");for(var t=Math.log(1.1920928955078125e-7)+2,n=new Float32Array(e.size),r=e.dataSync(),o=0;o<r.length;++o){var a=r[o]>-t,i=r[o]<t,s=Math.exp(r[o]),u=void 0;u=i?s:a?r[o]:Math.log(1+s),n[o]=u}return Tensor.make(e.shape,{values:n})},e.prototype.sin=function(e){this.assertNotComplex(e,"sin");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.sin(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.cos=function(e){this.assertNotComplex(e,"cos");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.cos(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.tan=function(e){this.assertNotComplex(e,"tan");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.tan(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.asin=function(e){this.assertNotComplex(e,"asin");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.asin(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.acos=function(e){this.assertNotComplex(e,"acos");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.acos(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.atan=function(e){this.assertNotComplex(e,"atan");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.atan(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.atan2=function(e,t){return this.assertNotComplex([e,t],"atan2"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){return Math.atan2(e,t)})},e.prototype.sinh=function(e){this.assertNotComplex(e,"sinh");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.sinh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.cosh=function(e){this.assertNotComplex(e,"cosh");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.cosh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.tanh=function(e){this.assertNotComplex(e,"tanh");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=tanh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.asinh=function(e){this.assertNotComplex(e,"asinh");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.asinh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.acosh=function(e){this.assertNotComplex(e,"acosh");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.acosh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.atanh=function(e){this.assertNotComplex(e,"atanh");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.atanh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.erf=function(e){this.assertNotComplex(e,"erf");for(var t=new Float32Array(e.size),n=e.dataSync(),r=ERF_P,o=ERF_A1,a=ERF_A2,i=ERF_A3,s=ERF_A4,u=ERF_A5,l=0;l<n.length;++l){var c=n[l],p=1/(1+r*c);t[l]=1-((((u*p+s)*p+i)*p+a)*p+o)*p*Math.exp(-c*c)}return Tensor.make(e.shape,{values:t})},e.prototype.step=function(e,t){void 0===t&&(t=0),this.assertNotComplex(e,"step");for(var n=new Float32Array(e.size),r=e.dataSync(),o=0;o<r.length;++o){var a=r[o];isNaN(a)?n[o]=NaN:n[o]=a>0?1:t}return Tensor.make(e.shape,{values:n})},e.prototype.conv2d=function(e,t,n){this.assertNotComplex([e,t],"conv2d");for(var r=n.filterHeight,o=n.filterWidth,a=n.dilationHeight,i=n.dilationWidth,s=n.padInfo.left,u=n.padInfo.top,l=buffer(n.outShape,e.dtype),c=e.dataSync(),p=t.dataSync(),d=l.values,h=0;h<n.batchSize;++h)for(var f=h*e.strides[0],m=h*l.strides[0],g=0;g<n.outHeight;++g)for(var v=m+g*l.strides[1],y=g*n.strideHeight-s,x=0;x<r;x++){var T=y+x*a;if(!(T<0||T>=n.inHeight))for(var w=x*t.strides[0],E=f+T*e.strides[1],S=0;S<n.outWidth;++S)for(var b=v+S*n.outChannels,C=S*n.strideWidth-u,A=0;A<o;A++){var _=C+A*i;if(!(_<0||_>=n.inWidth))for(var N=w+A*t.strides[1],R=E+_*n.inChannels,k=N,I=0;I<n.inChannels;++I){for(var D=c[R+I],M=0;M<n.outChannels;++M)d[b+M]+=D*p[k+M];k+=n.outChannels}}}return l.toTensor()},e.prototype.conv3d=function(e,t,n){for(var r=n.filterDepth,o=n.filterHeight,a=n.filterWidth,i=n.dilationDepth,s=n.dilationHeight,u=n.dilationWidth,l=n.padInfo.front,c=n.padInfo.left,p=n.padInfo.top,d=buffer(n.outShape,e.dtype),h=e.dataSync(),f=t.dataSync(),m=d.values,g=0;g<n.batchSize;++g)for(var v=g*e.strides[0],y=g*d.strides[0],x=0;x<n.outDepth;++x)for(var T=y+x*d.strides[1],w=x*n.strideDepth-l,E=0;E<r;E++){var S=w+E*i;if(!(S<0||S>=n.inDepth))for(var b=E*t.strides[0],C=v+S*e.strides[1],A=0;A<n.outHeight;++A)for(var _=T+A*d.strides[2],N=A*n.strideHeight-p,R=0;R<o;R++){var k=N+R*s;if(!(k<0||k>=n.inHeight))for(var I=b+R*t.strides[1],D=C+k*e.strides[2],M=0;M<n.outWidth;++M)for(var P=_+M*n.outChannels,O=M*n.strideWidth-c,F=0;F<a;F++){var L=O+F*u;if(!(L<0||L>=n.inWidth))for(var B=I+F*t.strides[2],U=D+L*n.inChannels,V=B,z=0;z<n.inChannels;++z){for(var H=h[U+z],W=0;W<n.outChannels;++W)m[P+W]+=H*f[V+W];V+=n.outChannels}}}}return d.toTensor()},e.prototype.conv2dDerInput=function(e,t,n){this.assertNotComplex([e,t],"conv2dDerInput");for(var r=buffer(n.inShape,"float32"),o=r.values,a=r.strides,i=a[0],s=a[1],u=a[2],l=e.dataSync(),c=e.strides,p=c[0],d=c[1],h=c[2],f=t.dataSync(),m=t.strides,g=m[0],v=m[1],y=m[2],x=n.batchSize,T=n.filterHeight,w=n.filterWidth,E=n.inChannels,S=n.inHeight,b=n.inWidth,C=n.outChannels,A=n.outHeight,_=n.outWidth,N=n.strideHeight,R=n.strideWidth,k=T-1-n.padInfo.top,I=w-1-n.padInfo.left,D=0;D<x;++D)for(var M=0;M<E;++M)for(var P=0;P<S;++P)for(var O=P-k,F=Math.max(0,Math.ceil(O/N)),L=Math.min(A,(T+O)/N),B=0;B<b;++B){for(var U=B-I,V=Math.max(0,Math.ceil(U/R)),z=Math.min(_,(w+U)/R),H=0,W=F;W<L;++W)for(var G=W*N-O,q=V;q<z;++q)for(var $=p*D+d*W+h*q,X=g*(T-1-G)+v*(w-1-(q*R-U))+y*M,K=0;K<C;++K){H+=l[$+K]*f[X+K]}o[i*D+s*P+u*B+M]=H}return r.toTensor()},e.prototype.conv3dDerInput=function(e,t,n){for(var r=buffer(n.inShape,"float32"),o=r.values,a=r.strides,i=a[0],s=a[1],u=a[2],l=a[3],c=e.dataSync(),p=e.strides,d=p[0],h=p[1],f=p[2],m=p[3],g=t.dataSync(),v=t.strides,y=v[0],x=v[1],T=v[2],w=v[3],E=n.batchSize,S=n.filterDepth,b=n.filterHeight,C=n.filterWidth,A=n.inChannels,_=n.inDepth,N=n.inHeight,R=n.inWidth,k=n.outChannels,I=n.outDepth,D=n.outHeight,M=n.outWidth,P=n.strideDepth,O=n.strideHeight,F=n.strideWidth,L=S-1-n.padInfo.front,B=b-1-n.padInfo.top,U=C-1-n.padInfo.left,V=0;V<E;++V)for(var z=0;z<A;++z)for(var H=0;H<_;++H)for(var W=H-L,G=Math.max(0,Math.ceil(W/P)),q=Math.min(I,(S+W)/P),$=0;$<N;++$)for(var X=$-B,K=Math.max(0,Math.ceil(X/O)),j=Math.min(D,(b+X)/O),Y=0;Y<R;++Y){for(var Q=Y-U,J=Math.max(0,Math.ceil(Q/F)),Z=Math.min(M,(C+Q)/F),ee=0,te=G;te<q;++te)for(var ne=te*P-W,re=K;re<j;++re)for(var oe=re*O-X,ae=J;ae<Z;++ae)for(var ie=d*V+h*te+f*re+m*ae,se=y*(S-1-ne)+x*(b-1-oe)+T*(C-1-(ae*F-Q))+w*z,ue=0;ue<k;++ue){ee+=c[ie+ue]*g[se+ue]}o[i*V+s*H+u*$+l*Y+z]=ee}return r.toTensor()},e.prototype.conv2dDerFilter=function(e,t,n){this.assertNotComplex([e,t],"conv2dDerFilter");for(var r=n.strideHeight,o=n.strideWidth,a=n.filterHeight,i=n.filterWidth,s=buffer(n.filterShape,"float32"),u=n.padInfo.left,l=n.padInfo.top,c=0;c<a;++c)for(var p=Math.max(0,Math.ceil((l-c)/r)),d=Math.min(n.outHeight,(n.inHeight+l-c)/r),h=0;h<i;++h)for(var f=Math.max(0,Math.ceil((u-h)/o)),m=Math.min(n.outWidth,(n.inWidth+u-h)/o),g=0;g<n.inChannels;++g)for(var v=0;v<n.outChannels;++v){for(var y=0,x=0;x<n.batchSize;++x)for(var T=p;T<d;++T)for(var w=c+T*r-l,E=f;E<m;++E){var S=h+E*o-u;y+=e.get(x,w,S,g)*t.get(x,T,E,v)}s.set(y,c,h,g,v)}return s.toTensor()},e.prototype.conv3dDerFilter=function(e,t,n){for(var r=n.strideDepth,o=n.strideHeight,a=n.strideWidth,i=n.filterDepth,s=n.filterHeight,u=n.filterWidth,l=buffer(n.filterShape,"float32"),c=l.values,p=l.strides,d=p[0],h=p[1],f=p[2],m=p[3],g=t.dataSync(),v=t.strides,y=v[0],x=v[1],T=v[2],w=v[3],E=e.dataSync(),S=e.strides,b=S[0],C=S[1],A=S[2],_=S[3],N=n.padInfo.front,R=n.padInfo.left,k=n.padInfo.top,I=0;I<i;++I)for(var D=Math.max(0,Math.ceil((N-I)/r)),M=Math.min(n.outDepth,(n.inDepth+N-I)/r),P=I*d,O=0;O<s;++O)for(var F=Math.max(0,Math.ceil((k-O)/o)),L=Math.min(n.outHeight,(n.inHeight+k-O)/o),B=O*h+P,U=0;U<u;++U)for(var V=Math.max(0,Math.ceil((R-U)/a)),z=Math.min(n.outWidth,(n.inWidth+R-U)/a),H=U*f+B,W=0;W<n.inChannels;++W)for(var G=W*m+H,q=0;q<n.outChannels;++q){for(var $=0,X=0;X<n.batchSize;++X)for(var K=X*b,j=X*y,Y=D;Y<M;++Y)for(var Q=(I+Y*r-N)*C+K,J=Y*x+j,Z=F;Z<L;++Z)for(var ee=(O+Z*o-k)*A+Q,te=Z*T+J,ne=V;ne<z;++ne){var re=ne*w+te;$+=E[(U+ne*a-R)*_+ee+W]*g[re+q]}c[G+q]=$}return l.toTensor()},e.prototype.depthwiseConv2D=function(e,t,n){this.assertNotComplex([e,t],"depthwiseConv2D");for(var r=n.filterHeight,o=n.filterWidth,a=n.dilationHeight,i=n.dilationWidth,s=n.padInfo.left,u=n.padInfo.top,l=n.outChannels/n.inChannels,c=buffer(n.outShape,e.dtype),p=e.dataSync(),d=t.dataSync(),h=c.values,f=0;f<n.batchSize;++f)for(var m=f*e.strides[0],g=f*c.strides[0],v=0;v<n.outHeight;++v)for(var y=g+v*c.strides[1],x=v*n.strideHeight-s,T=0;T<r;++T){var w=x+T*a;if(!(w<0||w>=n.inHeight))for(var E=T*t.strides[0],S=m+w*e.strides[1],b=0;b<n.outWidth;++b)for(var C=y+b*c.strides[2],A=b*n.strideWidth-u,_=0;_<o;++_){var N=A+_*i;if(!(N<0||N>=n.inWidth))for(var R=E+_*t.strides[1],k=S+N*n.inChannels,I=C,D=R,M=0;M<n.inChannels;++M){for(var P=p[k+M],O=0;O<l;++O)h[I+O]+=P*d[D+O];I+=l,D+=l}}}return c.toTensor()},e.prototype.depthwiseConv2DDerInput=function(e,t,n){this.assertNotComplex([e,t],"depthwiseConv2DDerInput");for(var r=buffer(n.inShape,"float32"),o=r.values,a=r.strides,i=a[0],s=a[1],u=a[2],l=e.dataSync(),c=e.strides,p=c[0],d=c[1],h=c[2],f=t.dataSync(),m=t.strides,g=m[0],v=m[1],y=m[2],x=n.batchSize,T=n.filterHeight,w=n.filterWidth,E=n.inChannels,S=n.inHeight,b=n.inWidth,C=n.outChannels,A=n.outHeight,_=n.outWidth,N=n.strideHeight,R=n.strideWidth,k=T-1-n.padInfo.top,I=w-1-n.padInfo.left,D=C/E,M=0;M<x;++M)for(var P=0;P<E;++P)for(var O=0;O<S;++O)for(var F=O-k,L=Math.max(0,Math.ceil(F/N)),B=Math.min(A,(T+F)/N),U=0;U<b;++U){for(var V=U-I,z=Math.max(0,Math.ceil(V/R)),H=Math.min(_,(w+V)/R),W=0,G=L;G<B;++G)for(var q=G*N-F,$=z;$<H;++$)for(var X=p*M+d*G+h*$,K=g*(T-1-q)+v*(w-1-($*R-V))+y*P,j=0;j<D;++j){W+=l[X+(P*D+j)]*f[K+j]}o[i*M+s*O+u*U+P]=W}return r.toTensor()},e.prototype.depthwiseConv2DDerFilter=function(e,t,n){this.assertNotComplex([e,t],"depthwiseConv2DDerFilter");for(var r=n.strideHeight,o=n.strideWidth,a=n.filterHeight,i=n.filterWidth,s=buffer(n.filterShape,"float32"),u=n.padInfo.left,l=n.padInfo.top,c=n.outChannels/n.inChannels,p=0;p<a;++p)for(var d=Math.max(0,Math.ceil((l-p)/r)),h=Math.min(n.outHeight,(n.inHeight+l-p)/r),f=0;f<i;++f)for(var m=Math.max(0,Math.ceil((u-f)/o)),g=Math.min(n.outWidth,(n.inWidth+u-f)/o),v=0;v<n.outChannels;++v){for(var y=Math.trunc(v/c),x=v%c,T=0,w=0;w<n.batchSize;++w)for(var E=d;E<h;++E)for(var S=p+E*r-l,b=m;b<g;++b){var C=f+b*o-u;T+=e.get(w,S,C,y)*t.get(w,E,b,v)}s.set(T,p,f,y,x)}return s.toTensor()},e.prototype.tile=function(e,t){this.assertNotComplex(e,"tile");for(var n=new Array(e.rank),r=0;r<n.length;r++)n[r]=e.shape[r]*t[r];var o=buffer(n,e.dtype),a=e.buffer();for(r=0;r<o.values.length;++r){for(var i=o.indexToLoc(r),s=new Array(e.rank),u=0;u<s.length;u++)s[u]=i[u]%e.shape[u];var l=a.locToIndex(s);o.values[r]=a.values[l]}return o.toTensor()},e.prototype.pad=function(e,t,n){this.assertNotComplex(e,"pad");var r=t.map(function(t,n){return t[0]+e.shape[n]+t[1]}),o=t.map(function(e){return e[0]}),a=e.buffer(),i=buffer(r,e.dtype);0!==n&&i.values.fill(n);for(var s=0;s<e.size;s++){var u=a.indexToLoc(s),l=u.map(function(e,t){return e+o[t]});i.set.apply(i,[e.get.apply(e,u)].concat(l))}return i.toTensor()},e.prototype.transpose=function(e,t){this.assertNotComplex(e,"transpose");for(var n=new Array(e.rank),r=0;r<n.length;r++)n[r]=e.shape[t[r]];var o=e.dataSync(),a=buffer(n,e.dtype),i=e.buffer();for(r=0;r<e.size;++r){for(var s=i.indexToLoc(r),u=new Array(s.length),l=0;l<u.length;l++)u[l]=s[t[l]];var c=a.locToIndex(u);a.values[c]=o[r]}return a.toTensor()},e.prototype.gather=function(e,t,n){this.assertNotComplex([e,t],"gather");var r=e.shape.slice(),o=t.dataSync();r[n]=o.length;for(var a=buffer(r,e.dtype),i=e.buffer(),s=0;s<a.size;++s){var u=a.indexToLoc(s),l=u.slice();l[n]=o[u[n]];var c=i.locToIndex(l);a.values[s]=i.values[c]}return a.toTensor()},e.prototype.batchToSpaceND=function(e,t,n){this.assertNotComplex([e],"batchToSpaceND");var r=t.reduce(function(e,t){return e*t}),o=getReshaped(e.shape,t,r),a=getPermuted(o.length,t.length),i=getReshapedPermuted(e.shape,t,r),s=getSliceBeginCoords(n,t.length),u=getSliceSize(i,n,t.length);return e.reshape(o).transpose(a).reshape(i).slice(s,u)},e.prototype.spaceToBatchND=function(e,t,n){this.assertNotComplex([e],"spaceToBatchND");var r=t.reduce(function(e,t){return e*t}),o=[[0,0]];o.push.apply(o,n);for(var a=1+t.length;a<e.shape.length;++a)o.push([0,0]);var i=e.pad(o),s=getReshaped(i.shape,t,r,!1),u=getPermuted(s.length,t.length,!1),l=getReshapedPermuted(i.shape,t,r,!1);return i.reshape(s).transpose(u).reshape(l)},e.prototype.pool=function(e,t,n){this.assertNotComplex(e,"pool");for(var r=t.strideHeight,o=t.strideWidth,a=t.dilationHeight,i=t.dilationWidth,s=t.effectiveFilterHeight,u=t.effectiveFilterWidth,l=t.padInfo.top,c=t.padInfo.left,p="max"===n?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,d=e.dataSync(),h=buffer(t.outShape,e.dtype),f=h.values,m=t.outShape[1]*t.outShape[2]*t.outShape[3],g=t.outShape[2]*t.outShape[3],v=t.outShape[3],y=0;y<t.batchSize;++y)for(var x=y*m,T=y*e.strides[0],w=0;w<t.inChannels;++w)for(var E=0;E<t.outHeight;++E)for(var S=E*r-l,b=Math.max(0,S),C=Math.min(t.inHeight,s+S),A=x+E*g,_=0;_<t.outWidth;++_){for(var N=_*o-c,R=Math.max(0,N),k=Math.min(t.inWidth,u+N),I=p,D=0,M=0,P=b;P<C;P+=a){for(var O=T+P*e.strides[1],F=R;F<k;F+=i){var L=d[O+F*e.strides[2]+w];"max"===n&&L>I?I=L:"avg"===n&&(D+=L,M++)}if(isNaN(I))break}f[A+_*v+w]="avg"===n?D/M:I}return h.toTensor()},e.prototype.maxPool=function(e,t){return this.pool(e,t,"max")},e.prototype.maxPoolPositions=function(e,t){for(var n=buffer(t.outShape,"int32"),r=t.strideHeight,o=t.strideWidth,a=t.dilationHeight,i=t.dilationWidth,s=t.effectiveFilterHeight,u=t.effectiveFilterWidth,l=t.padInfo.top,c=t.padInfo.left,p=0;p<t.batchSize;++p)for(var d=0;d<t.inChannels;++d)for(var h=0;h<t.outHeight;++h){for(var f=h*r-l,m=f;m<0;)m+=a;for(var g=Math.min(t.inHeight,s+f),v=0;v<t.outWidth;++v){for(var y=v*o-c,x=y;x<0;)x+=i;for(var T=Math.min(t.inWidth,u+y),w=Number.NEGATIVE_INFINITY,E=-1,S=m;S<g;S+=a)for(var b=S-f,C=x;C<T;C+=i){var A=C-y,_=e.get(p,S,C,d);_>w&&(w=_,E=b*u+A)}n.set(E,p,h,v,d)}}return n.toTensor()},e.prototype.maxPoolBackprop=function(e,t,n,r){this.assertNotComplex([t,n],"maxPoolBackprop");for(var o=this.maxPoolPositions(t,r),a=r.strideHeight,i=r.strideWidth,s=r.dilationHeight,u=r.dilationWidth,l=r.effectiveFilterHeight,c=r.effectiveFilterWidth,p=c-1-r.padInfo.left,d=l-1-r.padInfo.top,h=buffer(t.shape,"float32"),f=0;f<r.batchSize;++f)for(var m=0;m<r.inChannels;++m)for(var g=0;g<r.inHeight;++g)for(var v=0;v<r.inWidth;++v){for(var y=g-d,x=v-p,T=0,w=0;w<l;w+=s){var E=(y+w)/a;if(!(E<0||E>=r.outHeight||Math.floor(E)!==E))for(var S=0;S<c;S+=u){var b=(x+S)/i;if(!(b<0||b>=r.outWidth||Math.floor(b)!==b)){var C=l*c-1-o.get(f,E,b,m)===w*c+S?1:0;if(0!==C)T+=e.get(f,E,b,m)*C}}}h.set(T,f,g,v,m)}return h.toTensor()},e.prototype.avgPoolBackprop=function(e,t,n){this.assertNotComplex([e,t],"avgPoolBackprop");for(var r=n.strideHeight,o=n.strideWidth,a=n.filterHeight,i=n.filterWidth,s=n.dilationHeight,u=n.dilationWidth,l=n.effectiveFilterHeight,c=n.effectiveFilterWidth,p=c-1-n.padInfo.left,d=l-1-n.padInfo.top,h=buffer(t.shape,"float32"),f=1/(a*i),m=0;m<n.batchSize;++m)for(var g=0;g<n.inChannels;++g)for(var v=0;v<n.inHeight;++v)for(var y=0;y<n.inWidth;++y){for(var x=v-d,T=y-p,w=0,E=0;E<l;E+=s){var S=(x+E)/r;if(!(S<0||S>=n.outHeight||Math.floor(S)!==S))for(var b=0;b<c;b+=u){var C=(T+b)/o;if(!(C<0||C>=n.outWidth||Math.floor(C)!==C))w+=e.get(m,S,C,g)}}h.set(w*f,m,v,y,g)}return h.toTensor()},e.prototype.cast=function(e,t){return castTensor(e,t,this)},e.prototype.reshape=function(e,t){return reshapeTensor(e,t)},e.prototype.avgPool=function(e,t){return this.assertNotComplex(e,"avgPool"),this.pool(e,t,"avg").toFloat()},e.prototype.resizeBilinear=function(e,t,n,r){this.assertNotComplex(e,"resizeBilinear");for(var o=e.shape,a=o[0],i=o[1],s=o[2],u=o[3],l=e.dataSync(),c=new Float32Array(sizeFromShape([a,t,n,u])),p=[r&&t>1?i-1:i,r&&n>1?s-1:s],d=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=0,f=p[0]/d[0],m=p[1]/d[1],g=0;g<a;g++)for(var v=0;v<t;v++)for(var y=f*v,x=Math.floor(y),T=y-x,w=Math.min(i-1,Math.ceil(y)),E=g*e.strides[0]+x*e.strides[1],S=g*e.strides[0]+w*e.strides[1],b=0;b<n;b++)for(var C=m*b,A=Math.floor(C),_=C-A,N=Math.min(s-1,Math.ceil(C)),R=E+A*e.strides[2],k=S+A*e.strides[2],I=E+ +N*e.strides[2],D=S+N*e.strides[2],M=0;M<u;M++){var P=l[R+M],O=l[k+M],F=P+(l[I+M]-P)*_,L=F+(O+(l[D+M]-O)*_-F)*T;c[h++]=L}return tensor(c,[a,t,n,u])},e.prototype.resizeBilinearBackprop=function(e,t,n){this.assertNotComplex([e,t],"resizeBilinearBackprop");for(var r=t.shape,o=r[0],a=r[1],i=r[2],s=r[3],u=e.shape,l=u[1],c=u[2],p=new Float32Array(o*a*i*s),d=[n&&l>1?a-1:a,n&&c>1?i-1:i],h=[n&&l>1?l-1:l,n&&c>1?c-1:c],f=d[0]/h[0],m=d[1]/h[1],g=e.dataSync(),v=0,y=0;y<o;y++)for(var x=y*t.strides[0],T=0;T<l;T++)for(var w=T*f,E=Math.floor(w),S=Math.min(Math.ceil(w),a-1),b=x+E*t.strides[1],C=x+S*t.strides[1],A=w-E,_=1-A,N=0;N<c;N++)for(var R=N*m,k=Math.floor(R),I=Math.min(Math.ceil(R),i-1),D=R-k,M=1-D,P=b+k*t.strides[2],O=b+I*t.strides[2],F=C+k*t.strides[2],L=C+I*t.strides[2],B=_*M,U=_*D,V=A*M,z=A*D,H=0;H<s;H++){var W=g[v++];p[P+H]+=W*B,p[O+H]+=W*U,p[F+H]+=W*V,p[L+H]+=W*z}return tensor4d(p,[o,i,a,s],t.dtype)},e.prototype.resizeNearestNeighbor=function(e,t,n,r){this.assertNotComplex(e,"resizeNearestNeighbor");for(var o=e.shape,a=o[0],i=o[1],s=o[2],u=o[3],l=e.dataSync(),c=new Float32Array(a*t*n*u),p=[r&&t>1?i-1:i,r&&n>1?s-1:s],d=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=p[0]/d[0],f=p[1]/d[1],m=0,g=0;g<a;g++)for(var v=g*e.strides[0],y=0;y<t;y++)for(var x=h*y,T=v+Math.min(i-1,r?Math.round(x):Math.floor(x))*e.strides[1],w=0;w<n;w++)for(var E=f*w,S=T+Math.min(s-1,r?Math.round(E):Math.floor(E))*e.strides[2],b=0;b<u;b++){var C=l[S+b];c[m++]=C}return tensor(c,[a,t,n,u],e.dtype)},e.prototype.resizeNearestNeighborBackprop=function(e,t,n){this.assertNotComplex([e,t],"resizeNearestNeighborBackprop");for(var r=t.shape,o=r[0],a=r[1],i=r[2],s=r[3],u=e.shape,l=u[1],c=u[2],p=new Float32Array(o*a*i*s),d=e.dataSync(),h=[n&&l>1?a-1:a,n&&c>1?i-1:i],f=[n&&l>1?l-1:l,n&&c>1?c-1:c],m=h[0]/f[0],g=h[1]/f[1],v=1/m,y=1/g,x=2*Math.ceil(v)+2,T=2*Math.ceil(y)+2,w=0;w<o;w++)for(var E=w*t.strides[0],S=0;S<a;S++)for(var b=E+S*t.strides[1],C=Math.floor(S*v),A=Math.floor(C-x/2),_=0;_<i;_++)for(var N=b+_*t.strides[2],R=Math.floor(_*y),k=Math.floor(R-T/2),I=0;I<s;I++){for(var D=0,M=0;M<x;M++){var P=M+A;if(!(P<0||P>=l)){var O=E+P*e.strides[1],F=P*m;if(S===Math.min(a-1,n?Math.round(F):Math.floor(F)))for(var L=0;L<T;L++){var B=L+k;if(!(B<0||B>=c)){var U=O+B*e.strides[2],V=B*g;_===Math.min(i-1,n?Math.round(V):Math.floor(V))&&(D+=d[U+I])}}}}p[N+I]=D}return tensor4d(p,t.shape,t.dtype)},e.prototype.batchNormalization=function(e,t,n,r,o,a){this.assertNotComplex([e,t,n,o,a],"batchNormalization");for(var i=e.dataSync(),s=t.dataSync(),u=n.dataSync(),l=o?o.dataSync():new Float32Array([1]),c=a?a.dataSync():new Float32Array([0]),p=new Float32Array(i.length),d=c.length,h=l.length,f=u.length,m=s.length,g=0,v=0,y=0,x=0,T=0;T<i.length;++T)p[T]=c[g++]+(i[T]-s[v++])*l[y++]/Math.sqrt(u[x++]+r),g>=d&&(g=0),v>=m&&(v=0),y>=h&&(y=0),x>=f&&(x=0);return tensor4d(p,e.shape)},e.prototype.localResponseNormalization4D=function(e,t,n,r,o){this.assertNotComplex(e,"localResponseNormalization4D");var a=e.shape[3],i=a-1,s=e.dataSync(),u=sizeFromShape(e.shape),l=new Float32Array(u);function c(e){for(var n=e%a,r=e-n+Math.max(0,n-t),o=e-n+Math.min(n+t,i),u=0;r<=o;r++){var l=s[r];u+=l*l}return u}for(var p=0;p<u;p++){var d=c(p),h=s[p]*Math.pow(n+r*d,-o);l[p]=h}return tensor4d(l,e.shape)},e.prototype.LRNGrad=function(e,t,n,r,o,a,i){this.assertNotComplex(e,"LRNGrad");for(var s=e.shape[3],u=e.dataSync(),l=t.dataSync(),c=n.dataSync(),p=new Float32Array(sizeFromShape(e.shape)),d=sizeFromShape(e.shape),h=0;h<d;h++){for(var f=h%s,m=h-f+Math.max(0,f-r),g=h-f+Math.min(s,f+r+1),v=0,y=m;y<g;y++)v+=Math.pow(l[y],2);v=a*v+o;for(y=m;y<g;y++){var x=-2*a*i*l[y]*c[h]/v;h===y&&(x+=Math.pow(v,-i)),x*=u[h],p[y]+=x}}return tensor4d(p,e.shape)},e.prototype.multinomial=function(e,t,n,r){this.assertNotComplex(e,"multinomial");for(var o=t?e:softmax(e),a=o.shape[0],i=o.shape[1],s=zeros([a,n],"int32"),u=s.dataSync(),l=o.dataSync(),c=0;c<a;++c){var p=c*i,d=new Float32Array(i-1);d[0]=l[p];for(var h=1;h<d.length;++h)d[h]=d[h-1]+l[p+h];for(var f=seedrandom_1(r.toString()),m=c*n,g=0;g<n;++g){var v=f();u[m+g]=d.length;for(var y=0;y<d.length;y++)if(v<d[y]){u[m+g]=y;break}}}return s},e.prototype.oneHot=function(e,t,n,r){this.assertNotComplex(e,"oneHot");var o=new Float32Array(e.size*t);o.fill(r);for(var a=0;a<e.size;++a)e.get(a)>=0&&e.get(a)<t&&(o[a*t+e.get(a)]=n);return tensor2d(o,[e.size,t],"int32")},e.prototype.nonMaxSuppression=function(e,t,n,r,o){return this.assertNotComplex(e,"nonMaxSuppression"),nonMaxSuppressionImpl(e.dataSync(),t.dataSync(),n,r,o)},e.prototype.fft=function(e){return this.fftBatch(e,!1)},e.prototype.ifft=function(e){return this.fftBatch(e,!0)},e.prototype.fftBatch=function(e,t){for(var n=e.shape[0],r=e.shape[1],o=buffer(e.shape,"float32"),a=buffer(e.shape,"float32"),i=real(e).as2D(n,r),s=imag(e).as2D(n,r),u=0;u<n;u++)for(var l=i.slice([u,0],[1,r]),c=s.slice([u,0],[1,r]),p=complex(l,c),d=this.fftImpl(p,t).dataSync(),h=0;h<r;h++){var f=getComplexWithIndex(d,h);o.values[u*r+h]=f.real,a.values[u*r+h]=f.imag}return complex(o.toTensor(),a.toTensor()).as2D(n,r)},e.prototype.fftImpl=function(e,t){var n=e.as1D(),r=n.size;if(this.isExponentOf2(r)){var o=this.fftRadix2(n,r,t).as2D(e.shape[0],e.shape[1]);return t&&(o=complex(real(o).div(scalar(r)),imag(o).div(scalar(r)))),o}var a=e.dataSync(),i=splitRealAndImagArrays(this.fourierTransformByMatmul(a,r,t));return complex(i.real,i.imag).as2D(e.shape[0],e.shape[1])},e.prototype.isExponentOf2=function(e){return 0==(e&e-1)},e.prototype.fftRadix2=function(e,t,n){if(1===t)return e;var r=e.dataSync(),o=t/2,a=complexWithEvenIndex(r),i=complex(a.real,a.imag).as1D(),s=complexWithOddIndex(r),u=complex(s.real,s.imag).as1D();i=this.fftRadix2(i,o,n),u=this.fftRadix2(u,o,n);var l=exponents(t,n),c=complex(l.real,l.imag).mul(u),p=i.add(c),d=i.sub(c),h=real(p).concat(real(d)),f=imag(p).concat(imag(d));return complex(h,f).as1D()},e.prototype.fourierTransformByMatmul=function(e,t,n){for(var r=new Float32Array(2*t),o=0;o<t;o++){for(var a=0,i=0,s=0;s<t;s++){var u=exponent(o*s,t,n),l=getComplexWithIndex(e,s);a+=l.real*u.real-l.imag*u.imag,i+=l.real*u.imag+l.imag*u.real}n&&(a/=t,i/=t),assignToTypedArray(r,a,i,o)}return r},e.prototype.depthToSpace=function(e,t,n){assert("NHWC"===n,"Only NHWC dataFormat supported on CPU for depthToSpace. Got "+n),assert(t>1,"blockSize should be > 1 for depthToSpace, but was: "+t);for(var r=e.shape[0],o=e.shape[1],a=e.shape[2],i=e.shape[3],s=o*t,u=a*t,l=i/(t*t),c=e.dataSync(),p=new Float32Array(r*s*u*l),d=0,h=0;h<r;++h)for(var f=0;f<s;++f)for(var m=Math.floor(f/t),g=f%t,v=0;v<u;++v)for(var y=Math.floor(v/t),x=(g*t+v%t)*l,T=0;T<l;++T){var w=T+x+i*(y+a*(m+o*h));p[d++]=c[w]}return tensor4d(p,[r,s,u,l])},e.prototype.broadcastedBinaryOp=function(e,t,n,r){var o=assertAndGetBroadcastShape(e.shape,t.shape),a=buffer(o,n),i=e.dataSync(),s=t.dataSync(),u=getBroadcastDims(e.shape,o),l=getBroadcastDims(t.shape,o),c=a.values;if(u.length+l.length===0)for(var p=0;p<c.length;++p)c[p]=r(i[p%i.length],s[p%s.length]);else{var d=e.buffer(),h=t.buffer(),f=function(n){var o=a.indexToLoc(n),p=o.slice(-e.rank);u.forEach(function(e){return p[e]=0});var f=d.locToIndex(p),m=o.slice(-t.rank);l.forEach(function(e){return m[e]=0});var g=h.locToIndex(m);c[n]=r(i[f],s[g])};for(p=0;p<c.length;++p)f(p)}return a.toTensor()},e.prototype.broadcastedBinaryComplexOp=function(e,t,n){var r=assertAndGetBroadcastShape(e.shape,t.shape),o=buffer(r,"float32"),a=buffer(r,"float32"),i=e.dataSync(),s=t.dataSync(),u=getBroadcastDims(e.shape,r),l=getBroadcastDims(t.shape,r),c=o.values,p=a.values;if(u.length+l.length===0)for(var d=0;d<c.length;d++){var h=d%i.length,f=d%s.length,m=n(i[2*h],i[2*h+1],s[2*f],s[2*f+1]);c[d]=m.real,p[d]=m.imag}else{var g=this.data.get(e.dataId).complexTensors.real.buffer(),v=this.data.get(t.dataId).complexTensors.real.buffer(),y=function(r){var a=o.indexToLoc(r),d=a.slice(-e.rank);u.forEach(function(e){return d[e]=0});var h=g.locToIndex(d),f=a.slice(-t.rank);l.forEach(function(e){return f[e]=0});var m=v.locToIndex(f),y=n(i[2*h],i[2*h+1],s[2*m],s[2*m+1]);c[r]=y.real,p[r]=y.imag};for(d=0;d<c.length;d++)y(d)}return this.complex(o.toTensor(),a.toTensor())},e.prototype.split=function(e,t,n){return split(e,t,n)},e.prototype.dispose=function(){},e.prototype.floatPrecision=function(){return 32},e.prototype.cropAndResize=function(e,t,n,r,o,a){for(var i=e.shape,s=i[0],u=i[1],l=i[2],c=i[3],p=t.shape[0],d=r[0],h=r[1],f=buffer([p,d,h,c],e.dtype),m=t.dataSync(),g=n.dataSync(),v=e.dataSync(),y=e.strides,x=f.strides,T=0;T<p;T++){var w=4*T,E=m[w],S=m[w+1],b=m[w+2],C=m[w+3],A=g[T];if(!(A>=s))for(var _=d>1?(b-E)*(u-1)/(d-1):0,N=h>1?(C-S)*(l-1)/(h-1):0,R=0;R<d;R++){var k=d>1?E*(u-1)+R*_:.5*(E+b)*(u-1);if(k<0||k>u-1)for(var I=0;I<h;I++)for(var D=0;D<c;D++){var M=D+I*x[2]+R*x[1]+T*x[0];f.values[M]=a}else if("bilinear"===o){var P=Math.floor(k),O=Math.ceil(k),F=k-P;for(I=0;I<h;I++){if((q=h>1?S*(l-1)+I*N:.5*(S+C)*(l-1))<0||q>l-1)for(D=0;D<c;D++){M=D+I*x[2]+R*x[1]+T*x[0];f.values[M]=a}else{var L=Math.floor(q),B=Math.ceil(q),U=q-L;for(D=0;D<c;D++){var V=v[M=D+L*y[2]+P*y[1]+A*y[0]],z=v[M=D+B*y[2]+P*y[1]+A*y[0]],H=v[M=D+L*y[2]+O*y[1]+A*y[0]],W=V+(z-V)*U,G=H+(v[M=D+B*y[2]+O*y[1]+A*y[0]]-H)*U;M=D+I*x[2]+R*x[1]+T*x[0],f.values[M]=W+(G-W)*F}}}}else for(I=0;I<h;++I){var q;if((q=h>1?S*(l-1)+I*N:.5*(S+C)*(l-1))<0||q>l-1)for(D=0;D<c;D++){M=D+I*x[2]+R*x[1]+T*x[0];f.values[M]=a}else{var $=Math.round(q),X=Math.round(k);for(D=0;D<c;D++){var K=D+$*y[2]+X*y[1]+A*y[0],j=D+I*x[2]+R*x[1]+T*x[0];f.values[j]=v[K]}}}}}return f.toTensor()},e.prototype.sparseToDense=function(e,t,n,r){var o=calculateShapes(t,e,n),a=o.sliceRank,i=o.numUpdates,s=o.sliceSize,u=o.strides,l=o.outputSize;return this.scatter(e,t,n,l,s,i,a,u,r,!1)},e.prototype.gatherND=function(e,t){var n=t.shape,r=n[n.length-1],o=prepareAndValidate(e,t),a=o[0],i=o[1],s=o[2],u=o[3];if(0===i)return tensor([],a,e.dtype);for(var l=new TensorBuffer([i,s],e.dtype),c=t.dataSync(),p=e.dataSync(),d=0;d<i;d++){for(var h=[],f=0,m=0;m<r;m++){var g=c[d*r+m];f+=g*u[m],h.push(g)}if(f<0||f>=e.size/s)throw new Error("Invalid indices: "+h+" does not index into "+e.shape);for(var v=0;v<s;v++)l.values[d*s+v]=p[f*s+v]}return l.toTensor().reshape(a)},e.prototype.scatterND=function(e,t,n){var r=calculateShapes(t,e,n),o=r.sliceRank,a=r.numUpdates,i=r.sliceSize,s=r.strides,u=r.outputSize,l=scalar(0);return this.scatter(e,t,n,u,i,a,o,s,l,!0)},e.prototype.scatter=function(e,t,n,r,o,a,i,s,u,l){var c=[r/o,o],p=e.dataSync(),d=t.dataSync();if(0===r)return tensor([],n,t.dtype);var h=new TensorBuffer(c,t.dtype);h.values.fill(u.dataSync()[0]);for(var f=0;f<a;f++){for(var m=[],g=0,v=0;v<i;v++){var y=p[f*i+v];m.push(y),g+=y*s[v]}if(g<0||g>=r/o)throw new Error("Invalid indices: "+m+" does not index into "+n);for(var x=0;x<o;x++)l?h.values[g*o+x]+=d[f*o+x]:h.values[g*o+x]=0===t.rank?d[0]:d[f*o+x]}return h.toTensor().reshape(n)},e}();ENV.registerBackend("cpu",function(){return new MathBackendCPU},1,setTensorTracker);var delayCallback="undefined"!=typeof requestAnimationFrame?requestAnimationFrame:setImmediate;function nextFrame(){return new Promise(function(e){return delayCallback(function(){return e()})})}var DTYPE_VALUE_SIZE_MAP={float32:4,int32:4,uint16:2,uint8:1,bool:1};function encodeWeights(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o;return __generator(this,function(a){switch(a.label){case 0:for(r in t=[],n=[],e){if("float32"!==(o=e[r]).dtype&&"int32"!==o.dtype&&"bool"!==o.dtype)throw new Error("Unsupported dtype in weight '"+r+"': "+o.dtype);t.push({name:r,shape:o.shape,dtype:o.dtype}),n.push(o.data())}return[4,Promise.all(n)];case 1:return[2,{data:concatenateTypedArrays(a.sent()),specs:t}]}})})}function decodeWeights(e,t){for(var n={},r=0,o=function(t){var o=t.name,a=t.dtype,i=t.shape,s=sizeFromShape(i),u=void 0;if("quantization"in t){var l=t.quantization;if("uint8"!==l.dtype&&"uint16"!==l.dtype)throw new Error("Weight "+t.name+" has unknown quantization dtype "+l.dtype+". Supported quantization dtypes are: 'uint8' and 'uint16'.");var c=DTYPE_VALUE_SIZE_MAP[l.dtype],p=e.slice(r,r+s*c),d="uint8"===l.dtype?new Uint8Array(p):new Uint16Array(p);if("float32"===a)u=Float32Array.from(d,function(e){return e*l.scale+l.min});else{if("int32"!==a)throw new Error("Unsupported dtype in weight '"+o+"': "+a);u=Int32Array.from(d,function(e){return Math.round(e*l.scale+l.min)})}r+=s*c}else{var h=DTYPE_VALUE_SIZE_MAP[a];p=e.slice(r,r+s*h);if("float32"===a)u=new Float32Array(p);else if("int32"===a)u=new Int32Array(p);else{if("bool"!==a)throw new Error("Unsupported dtype in weight '"+o+"': "+a);u=new Uint8Array(p)}r+=s*h}var f=void 0;if("float32"===a)f=tensor(u,i,"float32");else if("int32"===a)f=tensor(u,i,"int32");else{if("bool"!==a)throw new Error("Unsupported dtype in weight '"+o+"': "+a);f=tensor(u,i,"bool")}n[o]=f},a=0,i=t;a<i.length;a++){o(i[a])}return n}function concatenateTypedArrays(e){if(null===e)throw new Error("Invalid input value: "+JSON.stringify(e));var t=0,n=[];e.forEach(function(e){if(t+=e.byteLength,n.push(e.byteLength===e.buffer.byteLength?e:new e.constructor(e)),!(e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array))throw new Error("Unsupported TypedArray subtype: "+e.constructor.name)});var r=new Uint8Array(t),o=0;return n.forEach(function(e){r.set(new Uint8Array(e.buffer),o),o+=e.byteLength}),r.buffer}var useNodeBuffer="undefined"!=typeof Buffer&&("undefined"==typeof Blob||"undefined"==typeof atob||"undefined"==typeof btoa);function stringByteLength(e){return useNodeBuffer?Buffer.byteLength(e):new Blob([e]).size}function arrayBufferToBase64String(e){return useNodeBuffer?Buffer.from(e).toString("base64"):btoa(String.fromCharCode.apply(null,new Uint8Array(e)))}function base64StringToArrayBuffer(e){if(useNodeBuffer){var t=Buffer.from(e,"base64");return t.buffer.slice(t.byteOffset,t.byteOffset+t.byteLength)}for(var n=atob(e),r=new Uint8Array(n.length),o=0;o<n.length;++o)r.set([n.charCodeAt(o)],o);return r.buffer}function concatenateArrayBuffers(e){var t=0;e.forEach(function(e){t+=e.byteLength});var n=new Uint8Array(t),r=0;return e.forEach(function(e){n.set(new Uint8Array(e),r),r+=e.byteLength}),n.buffer}function basename(e){for(e=e.trim();e.endsWith("/");)e=e.slice(0,e.length-1);var t=e.split("/");return t[t.length-1]}function getModelArtifactsInfoForJSON(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return{dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:null==e.modelTopology?0:stringByteLength(JSON.stringify(e.modelTopology)),weightSpecsBytes:null==e.weightSpecs?0:stringByteLength(JSON.stringify(e.weightSpecs)),weightDataBytes:null==e.weightData?0:e.weightData.byteLength}}var IORouterRegistry=function(){function e(){this.saveRouters=[],this.loadRouters=[]}return e.getInstance=function(){return null==e.instance&&(e.instance=new e),e.instance},e.registerSaveRouter=function(t){e.getInstance().saveRouters.push(t)},e.registerLoadRouter=function(t){e.getInstance().loadRouters.push(t)},e.getSaveHandlers=function(t){return e.getHandlers(t,"save")},e.getLoadHandlers=function(t){return e.getHandlers(t,"load")},e.getHandlers=function(e,t){var n=[];return("load"===t?this.getInstance().loadRouters:this.getInstance().saveRouters).forEach(function(t){var r=t(e);null!==r&&n.push(r)}),n},e}(),URL_SCHEME_SUFFIX="://",ModelStoreManagerRegistry=function(){function e(){this.managers={}}return e.getInstance=function(){return null==e.instance&&(e.instance=new e),e.instance},e.registerManager=function(t,n){assert(null!=t,"scheme must not be undefined or null."),t.endsWith(URL_SCHEME_SUFFIX)&&(t=t.slice(0,t.indexOf(URL_SCHEME_SUFFIX))),assert(t.length>0,"scheme must not be an empty string.");var r=e.getInstance();assert(null==r.managers[t],"A model store manager is already registered for scheme '"+t+"'."),r.managers[t]=n},e.getManager=function(e){var t=this.getInstance().managers[e];if(null==t)throw new Error("Cannot find model manager for scheme '"+e+"'");return t},e.getSchemes=function(){return Object.keys(this.getInstance().managers)},e}();function parseURL(e){if(-1===e.indexOf(URL_SCHEME_SUFFIX))throw new Error("The url string provided does not contain a scheme. Supported schemes are: "+ModelStoreManagerRegistry.getSchemes().join(","));return{scheme:e.split(URL_SCHEME_SUFFIX)[0],path:e.split(URL_SCHEME_SUFFIX)[1]}}function cloneModelInternal(e,t,n){return void 0===n&&(n=!1),__awaiter(this,void 0,void 0,function(){var r,o,a,i,s,u,l,c,p;return __generator(this,function(d){switch(d.label){case 0:return assert(e!==t,"Old path and new path are the same: '"+e+"'"),assert((r=IORouterRegistry.getLoadHandlers(e)).length>0,"Copying failed because no load handler is found for source URL "+e+"."),assert(r.length<2,"Copying failed because more than one ("+r.length+") load handlers for source URL "+e+"."),o=r[0],assert((a=IORouterRegistry.getSaveHandlers(t)).length>0,"Copying failed because no save handler is found for destination URL "+t+"."),assert(a.length<2,"Copying failed because more than one ("+r.length+") save handlers for destination URL "+t+"."),i=a[0],s=parseURL(e).scheme,u=parseURL(e).path,l=s===parseURL(e).scheme,[4,o.load()];case 1:return c=d.sent(),n&&l?[4,ModelStoreManagerRegistry.getManager(s).removeModel(u)]:[3,3];case 2:d.sent(),d.label=3;case 3:return[4,i.save(c)];case 4:return p=d.sent(),!n||l?[3,6]:[4,ModelStoreManagerRegistry.getManager(s).removeModel(u)];case 5:d.sent(),d.label=6;case 6:return[2,p.modelArtifactsInfo]}})})}function listModels(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o,a,i;return __generator(this,function(s){switch(s.label){case 0:e=ModelStoreManagerRegistry.getSchemes(),t={},n=0,r=e,s.label=1;case 1:return n<r.length?(o=r[n],[4,ModelStoreManagerRegistry.getManager(o).listModels()]):[3,4];case 2:for(i in a=s.sent())t[o+URL_SCHEME_SUFFIX+i]=a[i];s.label=3;case 3:return n++,[3,1];case 4:return[2,t]}})})}function removeModel(e){return __awaiter(this,void 0,void 0,function(){var t;return __generator(this,function(n){switch(n.label){case 0:return t=parseURL(e),[4,ModelStoreManagerRegistry.getManager(t.scheme).removeModel(t.path)];case 1:return[2,n.sent()]}})})}function copyModel(e,t){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(n){switch(n.label){case 0:return[4,cloneModelInternal(e,t,!1)];case 1:return[2,n.sent()]}})})}function moveModel(e,t){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(n){switch(n.label){case 0:return[4,cloneModelInternal(e,t,!0)];case 1:return[2,n.sent()]}})})}var DATABASE_NAME="tensorflowjs",DATABASE_VERSION=1,MODEL_STORE_NAME="models_store",INFO_STORE_NAME="model_info_store";function getIndexedDBFactory(){if(!ENV.get("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");var e=window,t=e.indexedDB||e.mozIndexedDB||e.webkitIndexedDB||e.msIndexedDB||e.shimIndexedDB;if(null==t)throw new Error("The current browser does not appear to support IndexedDB.");return t}function setUpDatabase(e){var t=e.result;t.createObjectStore(MODEL_STORE_NAME,{keyPath:"modelPath"}),t.createObjectStore(INFO_STORE_NAME,{keyPath:"modelPath"})}var BrowserIndexedDB=function(){function e(e){if(this.indexedDB=getIndexedDBFactory(),null==e||!e)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=e}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(t){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return[2,this.databaseAction(this.modelPath,e)]})})},e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(e){return[2,this.databaseAction(this.modelPath)]})})},e.prototype.databaseAction=function(e,t){var n=this;return new Promise(function(e,r){var o=n.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);o.onupgradeneeded=function(){return setUpDatabase(o)},o.onsuccess=function(){var a=o.result;if(null==t){var i=a.transaction(MODEL_STORE_NAME,"readonly"),s=i.objectStore(MODEL_STORE_NAME).get(n.modelPath);s.onsuccess=function(){if(null==s.result)return a.close(),r(new Error("Cannot find model with path '"+n.modelPath+"' in IndexedDB."));e(s.result.modelArtifacts)},s.onerror=function(e){return a.close(),r(s.error)},i.oncomplete=function(){return a.close()}}else{var u,l=getModelArtifactsInfoForJSON(t),c=a.transaction(INFO_STORE_NAME,"readwrite"),p=c.objectStore(INFO_STORE_NAME),d=p.put({modelPath:n.modelPath,modelArtifactsInfo:l});d.onsuccess=function(){var o=(u=a.transaction(MODEL_STORE_NAME,"readwrite")).objectStore(MODEL_STORE_NAME).put({modelPath:n.modelPath,modelArtifacts:t,modelArtifactsInfo:l});o.onsuccess=function(){return e({modelArtifactsInfo:l})},o.onerror=function(e){var t=(p=c.objectStore(INFO_STORE_NAME)).delete(n.modelPath);t.onsuccess=function(){return a.close(),r(o.error)},t.onerror=function(e){return a.close(),r(o.error)}}},d.onerror=function(e){return a.close(),r(d.error)},c.oncomplete=function(){null==u?a.close():u.oncomplete=function(){return a.close()}}}},o.onerror=function(e){return r(o.error)}})},e.URL_SCHEME="indexeddb://",e}(),indexedDBRouter=function(e){return ENV.get("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(BrowserIndexedDB.URL_SCHEME)?browserIndexedDB(e.slice(BrowserIndexedDB.URL_SCHEME.length)):null};function browserIndexedDB(e){return new BrowserIndexedDB(e)}function maybeStripScheme(e){return e.startsWith(BrowserIndexedDB.URL_SCHEME)?e.slice(BrowserIndexedDB.URL_SCHEME.length):e}IORouterRegistry.registerSaveRouter(indexedDBRouter),IORouterRegistry.registerLoadRouter(indexedDBRouter);var BrowserIndexedDBManager=function(){function e(){this.indexedDB=getIndexedDBFactory()}return e.prototype.listModels=function(){return __awaiter(this,void 0,void 0,function(){var e=this;return __generator(this,function(t){return[2,new Promise(function(t,n){var r=e.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);r.onupgradeneeded=function(){return setUpDatabase(r)},r.onsuccess=function(){var e=r.result,o=e.transaction(INFO_STORE_NAME,"readonly"),a=o.objectStore(INFO_STORE_NAME).getAll();a.onsuccess=function(){for(var e={},n=0,r=a.result;n<r.length;n++){var o=r[n];e[o.modelPath]=o.modelArtifactsInfo}t(e)},a.onerror=function(t){return e.close(),n(a.error)},o.oncomplete=function(){return e.close()}},r.onerror=function(e){return n(r.error)}})]})})},e.prototype.removeModel=function(e){return __awaiter(this,void 0,void 0,function(){var t=this;return __generator(this,function(n){return e=maybeStripScheme(e),[2,new Promise(function(n,r){var o=t.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);o.onupgradeneeded=function(){return setUpDatabase(o)},o.onsuccess=function(){var t,a=o.result,i=a.transaction(INFO_STORE_NAME,"readwrite"),s=i.objectStore(INFO_STORE_NAME),u=s.get(e);u.onsuccess=function(){if(null==u.result)return a.close(),r(new Error("Cannot find model with path '"+e+"' in IndexedDB."));var o=s.delete(e),i=function(){var o=(t=a.transaction(MODEL_STORE_NAME,"readwrite")).objectStore(MODEL_STORE_NAME).delete(e);o.onsuccess=function(){return n(u.result.modelArtifactsInfo)},o.onerror=function(e){return r(u.error)}};o.onsuccess=i,o.onerror=function(e){return i(),a.close(),r(u.error)}},u.onerror=function(e){return a.close(),r(u.error)},i.oncomplete=function(){null==t?a.close():t.oncomplete=function(){return a.close()}}},o.onerror=function(e){return r(o.error)}})]})})},e}();if(ENV.get("IS_BROWSER"))try{ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME,new BrowserIndexedDBManager)}catch(e){}var PATH_SEPARATOR="/",PATH_PREFIX="tensorflowjs_models",INFO_SUFFIX="info",MODEL_TOPOLOGY_SUFFIX="model_topology",WEIGHT_SPECS_SUFFIX="weight_specs",WEIGHT_DATA_SUFFIX="weight_data";function getModelKeys(e){return{info:[PATH_PREFIX,e,INFO_SUFFIX].join(PATH_SEPARATOR),topology:[PATH_PREFIX,e,MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),weightSpecs:[PATH_PREFIX,e,WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),weightData:[PATH_PREFIX,e,WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR)}}function getModelPathFromKey(e){var t=e.split(PATH_SEPARATOR);if(t.length<3)throw new Error("Invalid key format: "+e);return t.slice(1,t.length-1).join(PATH_SEPARATOR)}function maybeStripScheme$1(e){return e.startsWith(BrowserLocalStorage.URL_SCHEME)?e.slice(BrowserLocalStorage.URL_SCHEME.length):e}var BrowserLocalStorage=function(){function e(e){if(!ENV.get("IS_BROWSER")||void 0===window.localStorage)throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,null==e||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=getModelKeys(this.modelPath)}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o;return __generator(this,function(a){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),r=getModelArtifactsInfoForJSON(e);try{return this.LS.setItem(this.keys.info,JSON.stringify(r)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,arrayBufferToBase64String(e.weightData)),[2,{modelArtifactsInfo:r}]}catch(e){for(o in this.keys)this.LS.removeItem(this.keys[o]);throw new Error("Failed to save model '"+this.modelPath+"' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes="+r.modelTopologyBytes+", weightSpecsBytes="+r.weightSpecsBytes+", weightDataBytes="+r.weightDataBytes+".")}return[2]})})},e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o;return __generator(this,function(a){if(null==(e=JSON.parse(this.LS.getItem(this.keys.info))))throw new Error("In local storage, there is no model with name '"+this.modelPath+"'");if("JSON"!==e.modelTopologyType)throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");if(t={},null==(n=JSON.parse(this.LS.getItem(this.keys.topology))))throw new Error("In local storage, the topology of model '"+this.modelPath+"' is missing.");if(t.modelTopology=n,null==(r=JSON.parse(this.LS.getItem(this.keys.weightSpecs))))throw new Error("In local storage, the weight specs of model '"+this.modelPath+"' are missing.");if(t.weightSpecs=r,null==(o=this.LS.getItem(this.keys.weightData)))throw new Error("In local storage, the binary weight values of model '"+this.modelPath+"' are missing.");return t.weightData=base64StringToArrayBuffer(o),[2,t]})})},e.URL_SCHEME="localstorage://",e}(),localStorageRouter=function(e){return ENV.get("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(BrowserLocalStorage.URL_SCHEME)?browserLocalStorage(e.slice(BrowserLocalStorage.URL_SCHEME.length)):null};function browserLocalStorage(e){return new BrowserLocalStorage(e)}IORouterRegistry.registerSaveRouter(localStorageRouter),IORouterRegistry.registerLoadRouter(localStorageRouter);var BrowserLocalStorageManager=function(){function e(){assert(ENV.get("IS_BROWSER"),"Current environment is not a web browser"),assert(void 0!==window.localStorage,"Current browser does not appear to support localStorage"),this.LS=window.localStorage}return e.prototype.listModels=function(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o,a;return __generator(this,function(i){for(e={},t=PATH_PREFIX+PATH_SEPARATOR,n=PATH_SEPARATOR+INFO_SUFFIX,r=0;r<this.LS.length;++r)(o=this.LS.key(r)).startsWith(t)&&o.endsWith(n)&&(a=getModelPathFromKey(o),e[a]=JSON.parse(this.LS.getItem(o)));return[2,e]})})},e.prototype.removeModel=function(e){return __awaiter(this,void 0,void 0,function(){var t,n;return __generator(this,function(r){if(e=maybeStripScheme$1(e),t=getModelKeys(e),null==this.LS.getItem(t.info))throw new Error("Cannot find model at path '"+e+"'");return n=JSON.parse(this.LS.getItem(t.info)),this.LS.removeItem(t.info),this.LS.removeItem(t.topology),this.LS.removeItem(t.weightSpecs),this.LS.removeItem(t.weightData),[2,n]})})},e}();if(ENV.get("IS_BROWSER"))try{ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME,new BrowserLocalStorageManager)}catch(e){}var DEFAULT_FILE_NAME_PREFIX="model",DEFAULT_JSON_EXTENSION_NAME=".json",DEFAULT_WEIGHT_DATA_EXTENSION_NAME=".weights.bin",BrowserDownloads=function(){function e(t){if(!ENV.get("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");t.startsWith(e.URL_SCHEME)&&(t=t.slice(e.URL_SCHEME.length)),null!=t&&0!==t.length||(t=DEFAULT_FILE_NAME_PREFIX),this.modelTopologyFileName=t+DEFAULT_JSON_EXTENSION_NAME,this.weightDataFileName=t+DEFAULT_WEIGHT_DATA_EXTENSION_NAME}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o,a,i;return __generator(this,function(s){if(t=window.URL.createObjectURL(new Blob([e.weightData],{type:"application/octet-stream"})),e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");return n=[{paths:["./"+this.weightDataFileName],weights:e.weightSpecs}],r={modelTopology:e.modelTopology,weightsManifest:n},o=window.URL.createObjectURL(new Blob([JSON.stringify(r)],{type:"application/json"})),(a=null==this.jsonAnchor?document.createElement("a"):this.jsonAnchor).download=this.modelTopologyFileName,a.href=o,a.click(),null!=e.weightData&&((i=null==this.weightDataAnchor?document.createElement("a"):this.weightDataAnchor).download=this.weightDataFileName,i.href=t,i.click()),[2,{modelArtifactsInfo:getModelArtifactsInfoForJSON(e)}]})})},e.URL_SCHEME="downloads://",e}(),BrowserFiles=function(){function e(e){if(null==e||e.length<1)throw new Error("When calling browserFiles, at least 1 file is required, but received "+e);this.files=e}return e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){var e,t,n=this;return __generator(this,function(r){return e=this.files[0],t=this.files.slice(1),[2,new Promise(function(r,o){var a=new FileReader;a.onload=function(a){var i=JSON.parse(a.target.result),s=i.modelTopology;if(null!=s){0===t.length&&r({modelTopology:s});var u=i.weightsManifest;if(null!=u){var l;try{l=n.checkManifestAndWeightFiles(u,t)}catch(e){return void o(e)}var c=[],p=[],d=[];u.forEach(function(e){e.paths.forEach(function(e){p.push(e),d.push(null)}),c.push.apply(c,e.weights)}),u.forEach(function(e){e.paths.forEach(function(e){var t=new FileReader;t.onload=function(t){var n=t.target.result,o=p.indexOf(e);d[o]=n,-1===d.indexOf(null)&&r({modelTopology:s,weightSpecs:c,weightData:concatenateArrayBuffers(d)})},t.onerror=function(t){return o("Failed to weights data from file of path '"+e+"'.")},t.readAsArrayBuffer(l[e])})})}else o(new Error("weightManifest field is missing from file "+e.name))}else o(new Error("modelTopology field is missing from file "+e.name))},a.onerror=function(t){return o("Failed to read model topology and weights manifest JSON from file '"+e.name+"'. BrowserFiles supports loading Keras-style tf.Model artifacts only.")},a.readAsText(e)})]})})},e.prototype.checkManifestAndWeightFiles=function(e,t){for(var n=[],r=t.map(function(e){return basename(e.name)}),o={},a=0,i=e;a<i.length;a++){i[a].paths.forEach(function(e){var a=basename(e);if(-1!==n.indexOf(a))throw new Error("Duplicate file basename found in weights manifest: '"+a+"'");if(n.push(a),-1===r.indexOf(a))throw new Error("Weight file with basename '"+a+"' is not provided.");o[e]=t[r.indexOf(a)]})}if(n.length!==t.length)throw new Error("Mismatch in the number of files in weights manifest ("+n.length+") and the number of weight files provided ("+t.length+").");return o},e}(),browserDownloadsRouter=function(e){return ENV.get("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(BrowserDownloads.URL_SCHEME)?browserDownloads(e.slice(BrowserDownloads.URL_SCHEME.length)):null};function browserDownloads(e){return void 0===e&&(e="model"),new BrowserDownloads(e)}function browserFiles(e){return new BrowserFiles(e)}function loadWeightsAsArrayBuffer(e,t,n){return __awaiter(this,void 0,void 0,function(){var r,o;return __generator(this,function(a){switch(a.label){case 0:return null==n&&(n=fetch),r=e.map(function(e){return n(e,t)}),[4,Promise.all(r)];case 1:return o=a.sent(),[4,Promise.all(o.map(function(e){return e.arrayBuffer()}))];case 2:return[2,a.sent()]}})})}function loadWeights(e,t,n,r){return void 0===t&&(t=""),__awaiter(this,void 0,void 0,function(){return __generator(this,function(o){return[2,weightsLoaderFactory(function(e){return loadWeightsAsArrayBuffer(e,r)})(e,t,n)]})})}function weightsLoaderFactory(e){var t=this;return function(n,r,o){return void 0===r&&(r=""),__awaiter(t,void 0,void 0,function(){var t,a,i,s,u,l,c,p,d,h;return __generator(this,function(f){switch(f.label){case 0:if(t=n.map(function(){return!1}),a={},i=null!=o?o.map(function(){return!1}):[],s=[],n.forEach(function(e,n){var r=0;e.weights.forEach(function(e){var u="quantization"in e?e.quantization.dtype:e.dtype,l=DTYPE_VALUE_SIZE_MAP[u]*sizeFromShape(e.shape),c=function(){t[n]=!0,null==a[n]&&(a[n]=[]),a[n].push({manifestEntry:e,groupOffset:r,sizeBytes:l})};null!=o?o.forEach(function(t,n){t===e.name&&(c(),i[n]=!0)}):c(),s.push(e.name),r+=l})}),!i.every(function(e){return e}))throw u=o.filter(function(e,t){return!i[t]}),new Error("Could not find weights in manifest with names: "+u.join(", ")+". \nManifest JSON has weights with names: "+s.join(", ")+".");return l=t.reduce(function(e,t,n){return t&&e.push(n),e},[]),c=[],l.forEach(function(e){n[e].paths.forEach(function(e){var t=r+(r.endsWith("/")?"":"/")+e;c.push(t)})}),[4,e(c)];case 1:return p=f.sent(),d={},h=0,l.forEach(function(e){for(var t=n[e].paths.length,r=0,o=0;o<t;o++)r+=p[h+o].byteLength;for(var i=new ArrayBuffer(r),s=new Uint8Array(i),u=0,l=0;l<t;l++){var c=new Uint8Array(p[h+l]);s.set(c,u),u+=c.byteLength}a[e].forEach(function(e){var t=decodeWeights(i.slice(e.groupOffset,e.groupOffset+e.sizeBytes),[e.manifestEntry]);for(var n in t)d[n]=t[n]}),h+=t}),[2,d]}})})}}IORouterRegistry.registerSaveRouter(browserDownloadsRouter);var BrowserHTTPRequest=function(){function e(e,t,n,r){if(this.weightPathPrefix=n,this.DEFAULT_METHOD="POST",null==r){if("undefined"==typeof fetch)throw new Error("browserHTTPRequest is not supported outside the web browser without a fetch polyfill.");this.fetchFunc=fetch.bind("undefined"==typeof window?null:window)}else assert("function"==typeof r,"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetchFunc=r;if(assert(null!=e&&e.length>0,"URL path for browserHTTPRequest must not be null, undefined or empty."),Array.isArray(e)&&assert(2===e.length,"URL paths for browserHTTPRequest must have a length of 2, (actual length is "+e.length+")."),this.path=e,null!=t&&null!=t.body)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=t||{}}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o;return __generator(this,function(a){switch(a.label){case 0:if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");return(t=Object.assign({method:this.DEFAULT_METHOD},this.requestInit)).body=new FormData,n=[{paths:["./model.weights.bin"],weights:e.weightSpecs}],r={modelTopology:e.modelTopology,weightsManifest:n},t.body.append("model.json",new Blob([JSON.stringify(r)],{type:"application/json"}),"model.json"),null!=e.weightData&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:"application/octet-stream"}),"model.weights.bin"),[4,this.getFetchFunc()(this.path,t)];case 1:if((o=a.sent()).ok)return[2,{modelArtifactsInfo:getModelArtifactsInfoForJSON(e),responses:[o]}];throw new Error("BrowserHTTPRequest.save() failed due to HTTP response status "+o.status+".")}})})},e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(e){return[2,Array.isArray(this.path)?this.loadBinaryModel():this.loadJSONModel()]})})},e.prototype.loadBinaryTopology=function(){return __awaiter(this,void 0,void 0,function(){var e,t;return __generator(this,function(n){switch(n.label){case 0:return n.trys.push([0,3,,4]),[4,this.getFetchFunc()(this.path[0],this.requestInit)];case 1:if(!(e=n.sent()).ok)throw new Error("BrowserHTTPRequest.load() failed due to HTTP response: "+e.statusText);return[4,e.arrayBuffer()];case 2:return[2,n.sent()];case 3:throw t=n.sent(),new Error(this.path[0]+" not found. "+t);case 4:return[2]}})})},e.prototype.loadBinaryModel=function(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o,a,i,s;return __generator(this,function(u){switch(u.label){case 0:return e=this.loadBinaryTopology(),[4,this.getFetchFunc()(this.path[1],this.requestInit)];case 1:if(!(t=u.sent()).ok)throw new Error("BrowserHTTPRequest.load() failed due to HTTP response: "+t.statusText);return[4,Promise.all([e,t])];case 2:return n=u.sent(),r=n[0],[4,n[1].json()];case 3:return null==(o=u.sent())?[3,5]:[4,this.loadWeights(o)];case 4:s=u.sent(),a=s[0],i=s[1],u.label=5;case 5:return[2,{modelTopology:r,weightSpecs:a,weightData:i}]}})})},e.prototype.loadJSONModel=function(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o,a,i,s;return __generator(this,function(u){switch(u.label){case 0:return[4,this.getFetchFunc()(this.path,this.requestInit)];case 1:if(!(e=u.sent()).ok)throw new Error("BrowserHTTPRequest.load() failed due to HTTP response: "+e.statusText);return[4,e.json()];case 2:if(t=u.sent(),n=t.modelTopology,r=t.weightsManifest,null==n&&null==r)throw new Error("The JSON from HTTP path "+this.path+" contains neither model topology or manifest for weights.");return null==r?[3,4]:(i=t.weightsManifest,[4,this.loadWeights(i)]);case 3:s=u.sent(),o=s[0],a=s[1],u.label=4;case 4:return[2,{modelTopology:n,weightSpecs:o,weightData:a}]}})})},e.prototype.loadWeights=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o,a,i,s,u,l,c,p,d;return __generator(this,function(h){switch(h.label){case 0:for(t=Array.isArray(this.path)?this.path[1]:this.path,n=parseUrl(t),r=n[0],o=n[1],a=this.weightPathPrefix||r,i=[],s=0,u=e;s<u.length;s++)l=u[s],i.push.apply(i,l.weights);return c=[],e.forEach(function(e){e.paths.forEach(function(e){c.push(a+e+o)})}),p=[i],d=concatenateArrayBuffers,[4,loadWeightsAsArrayBuffer(c,this.requestInit,this.getFetchFunc())];case 1:return[2,p.concat([d.apply(void 0,[h.sent()])])]}})})},e.prototype.getFetchFunc=function(){return this.fetchFunc},e.URL_SCHEME_REGEX=/^https?:\/\//,e}();function parseUrl(e){var t=e.lastIndexOf("/"),n=e.lastIndexOf("?");return[e.substring(0,t)+"/",n>t?e.substring(n):""]}function isHTTPScheme(e){return null!=e.match(BrowserHTTPRequest.URL_SCHEME_REGEX)}var httpRequestRouter=function(e){if("undefined"==typeof fetch)return null;return(Array.isArray(e)?e.every(function(e){return isHTTPScheme(e)}):isHTTPScheme(e))?browserHTTPRequest(e):null};function browserHTTPRequest(e,t,n,r){return new BrowserHTTPRequest(e,t,n,r)}IORouterRegistry.registerSaveRouter(httpRequestRouter),IORouterRegistry.registerLoadRouter(httpRequestRouter);var PassthroughLoader=function(){function e(e,t,n){this.modelTopology=e,this.weightSpecs=t,this.weightData=n}return e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){var e;return __generator(this,function(t){return e={},null!=this.modelTopology&&(e=__assign({modelTopology:this.modelTopology},e)),null!=this.weightSpecs&&this.weightSpecs.length>0&&(e=__assign({weightSpecs:this.weightSpecs},e)),null!=this.weightData&&this.weightData.byteLength>0&&(e=__assign({weightData:this.weightData},e)),[2,e]})})},e}(),PassthroughSaver=function(){function e(e){this.saveHandler=e}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(t){return[2,this.saveHandler(e)]})})},e}();function fromMemory(e,t,n){return new PassthroughLoader(e,t,n)}function withSaveHandler(e){return new PassthroughSaver(e)}var registerSaveRouter=IORouterRegistry.registerSaveRouter,registerLoadRouter=IORouterRegistry.registerLoadRouter,getSaveHandlers=IORouterRegistry.getSaveHandlers,getLoadHandlers=IORouterRegistry.getLoadHandlers,io=Object.freeze({browserFiles:browserFiles,browserHTTPRequest:browserHTTPRequest,concatenateArrayBuffers:concatenateArrayBuffers,decodeWeights:decodeWeights,encodeWeights:encodeWeights,fromMemory:fromMemory,getLoadHandlers:getLoadHandlers,getModelArtifactsInfoForJSON:getModelArtifactsInfoForJSON,getSaveHandlers:getSaveHandlers,isHTTPScheme:isHTTPScheme,loadWeights:loadWeights,registerLoadRouter:registerLoadRouter,registerSaveRouter:registerSaveRouter,weightsLoaderFactory:weightsLoaderFactory,withSaveHandler:withSaveHandler,copyModel:copyModel,listModels:listModels,moveModel:moveModel,removeModel:removeModel});function confusionMatrix_(e,t,n){var r=convertToTensor(e,"labels","confusionMatrix"),o=convertToTensor(t,"predictions","confusionMatrix");assert(null==n||n>0&&Number.isInteger(n),"If provided, numClasses must be a positive integer, but got "+n),assert(1===r.rank,"Expected the rank of labels to be 1, but got "+r.rank),assert(1===o.rank,"Expected the rank of predictions to be 1, but got "+o.rank),assert(r.shape[0]===o.shape[0],"Mismatch in the number of examples: "+r.shape[0]+" vs. "+o.shape[0]+". Labels and predictions should have the same number of elements."),assert(n>0&&Number.isInteger(n),"numClasses is required to be a positive integer, but got "+n);var a=oneHot(r.asType("int32"),n),i=oneHot(o.asType("int32"),n);return a.transpose().matMul(i).asType("int32")}var confusionMatrix=op({confusionMatrix_:confusionMatrix_}),math=Object.freeze({confusionMatrix:confusionMatrix}),Serializable=function(){function e(){}return e.prototype.getClassName=function(){return this.constructor.className},e.fromConfig=function(e,t){return new e(t)},e}(),SerializationMap=function(){function e(){this.classNameMap={}}return e.getMap=function(){return null==e.instance&&(e.instance=new e),e.instance},e.register=function(t){e.getMap().classNameMap[t.className]=[t,t.fromConfig]},e}();function registerClass(e){assert(null!=e.className,"Class being registered does not have the static className property defined."),assert("string"==typeof e.className,"className is required to be a string, but got type "+typeof e.className),assert(e.className.length>0,"Class being registered has an empty-string as its className, which is disallowed."),SerializationMap.register(e)}var serialization=Object.freeze({Serializable:Serializable,SerializationMap:SerializationMap,registerClass:registerClass}),WEBGL_ENVS={HAS_WEBGL:!0},NODE_ENVS={IS_NODE:!0},CHROME_ENVS={IS_CHROME:!0},BROWSER_ENVS={IS_BROWSER:!0},CPU_ENVS={HAS_WEBGL:!1},ALL_ENVS={};function expectArraysClose(e,t,n){return null==n&&(n=ENV.get("TEST_EPSILON")),expectArraysPredicate(e,t,function(e,t){return areClose(e,Number(t),n)})}function expectArraysPredicate(e,t,n){if(e instanceof Tensor||t instanceof Tensor){if(e instanceof Tensor&&t instanceof Tensor){if(e.dtype!==t.dtype)throw new Error("Arrays are of different type actual: "+e.dtype+" vs expected: "+t.dtype+".");if(!arraysEqual(e.shape,t.shape))throw new Error("Arrays are of different shape actual: "+e.shape+" vs expected: "+t.shape+".")}}else{var r=e.constructor.name,o=t.constructor.name;if(r!==o)throw new Error("Arrays are of different type actual: "+r+" vs expected: "+o)}var a,i;if(a=e instanceof Tensor?e.dataSync():e,i=t instanceof Tensor?t.dataSync():t,a.length!==i.length)throw new Error("Arrays have different lengths actual: "+a.length+" vs expected: "+i.length+".\nActual:   "+a+".\nExpected: "+i+".");for(var s=0;s<i.length;++s){var u=a[s],l=i[s];if(!n(u,l))throw new Error("Arrays differ: actual["+s+"] = "+u+", expected["+s+"] = "+l+".\nActual:   "+a+".\nExpected: "+i+".")}}function expectPromiseToFail(e,t){e().then(function(){return t.fail()},function(){return t()})}function expectArraysEqual(e,t){return e instanceof Tensor&&"string"===e.dtype||t instanceof Tensor&&"string"===t.dtype||e instanceof Array&&isString(e[0])||t instanceof Array&&isString(t[0])?expectArraysPredicate(e,t,function(e,t){return e==t}):expectArraysClose(e,t,0)}function expectNumbersClose(e,t,n){if(null==n&&(n=ENV.get("TEST_EPSILON")),!areClose(e,t,n))throw new Error("Numbers differ: actual === "+e+", expected === "+t)}function areClose(e,t,n){return!(!isNaN(e)||!isNaN(t))||!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function expectValuesInRange(e,t,n){var r;r=e instanceof Tensor?e.dataSync():e;for(var o=0;o<r.length;o++)if(r[o]<t||r[o]>n)throw new Error("Value out of range:"+r[o]+" low: "+t+", high: "+n)}function expectArrayBuffersEqual(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t))}var test_util=Object.freeze({WEBGL_ENVS:WEBGL_ENVS,NODE_ENVS:NODE_ENVS,CHROME_ENVS:CHROME_ENVS,BROWSER_ENVS:BROWSER_ENVS,CPU_ENVS:CPU_ENVS,ALL_ENVS:ALL_ENVS,expectArraysClose:expectArraysClose,expectPromiseToFail:expectPromiseToFail,expectArraysEqual:expectArraysEqual,expectNumbersClose:expectNumbersClose,expectValuesInRange:expectValuesInRange,expectArrayBuffersEqual:expectArrayBuffersEqual}),version="0.14.2",webgl=Object.freeze({gpgpu_util:gpgpu_util,webgl_util:webgl_util,MathBackendWebGL:MathBackendWebGL,GPGPUContext:GPGPUContext}),Optimizer=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends(t,e),t.prototype.minimize=function(e,t,n){void 0===t&&(t=!1);var r=this.computeGradients(e,n),o=r.value,a=r.grads;return this.applyGradients(a),Object.keys(a).forEach(function(e){return a[e].dispose()}),t?o:(o.dispose(),null)},t.prototype.computeGradients=function(e,t){return variableGrads(e,t)},t}(Serializable),AdadeltaOptimizer=function(e){function t(t,n,r){void 0===r&&(r=null);var o=e.call(this)||this;return o.learningRate=t,o.rho=n,o.epsilon=r,o.accumulatedGrads={},o.accumulatedUpdates={},o.c=keep(scalar(-t)),o.rhoScalar=keep(scalar(n)),o.oneMinusRho=keep(scalar(1-n)),null===r&&(r=ENV.get("EPSILON")),o.epsilonScalar=keep(scalar(r)),o}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulatedGrads[n]){tidy(function(){t.accumulatedGrads[n]=zerosLike(o).variable(!1)})}if(null==r.accumulatedUpdates[n]){tidy(function(){t.accumulatedUpdates[n]=zerosLike(o).variable(!1)})}var a=e[n],i=r.accumulatedGrads[n],s=r.accumulatedUpdates[n];tidy(function(){var e=t.rhoScalar.mul(i).add(t.oneMinusRho.mul(a.square())),r=s.add(t.epsilonScalar).sqrt().div(i.add(t.epsilonScalar).sqrt()).mul(a),u=t.rhoScalar.mul(s).add(t.oneMinusRho.mul(r.square()));t.accumulatedGrads[n].assign(e),t.accumulatedUpdates[n].assign(u);var l=t.c.mul(r).add(o);o.assign(l)})},r=this;for(var o in e)n(o)},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsilonScalar.dispose(),this.rhoScalar.dispose(),this.oneMinusRho.dispose(),null!=this.accumulatedUpdates&&(Object.keys(this.accumulatedUpdates).forEach(function(t){return e.accumulatedUpdates[t].dispose()}),Object.keys(this.accumulatedGrads).forEach(function(t){return e.accumulatedGrads[t].dispose()}))},t.prototype.getConfig=function(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}},t.fromConfig=function(e,t){return new e(t.learningRate,t.rho,t.epsilon)},t.className="AdadeltaOptimizer",t}(Optimizer);registerClass(AdadeltaOptimizer);var AdagradOptimizer=function(e){function t(t,n){void 0===n&&(n=.1);var r=e.call(this)||this;return r.learningRate=t,r.initialAccumulatorValue=n,r.accumulatedGrads={},r.c=keep(scalar(-t)),r.epsilon=keep(scalar(ENV.get("EPSILON"))),r}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulatedGrads[n]){tidy(function(){t.accumulatedGrads[n]=fill(o.shape,t.initialAccumulatorValue).variable(!1)})}var a=e[n],i=r.accumulatedGrads[n];tidy(function(){var e=i.add(a.square());t.accumulatedGrads[n].assign(e);var r=t.c.mul(a.div(e.add(t.epsilon).sqrt())).add(o);o.assign(r)})},r=this;for(var o in e)n(o)},t.prototype.dispose=function(){var e=this;this.epsilon.dispose(),this.c.dispose(),null!=this.accumulatedGrads&&Object.keys(this.accumulatedGrads).forEach(function(t){return e.accumulatedGrads[t].dispose()})},t.prototype.getConfig=function(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}},t.fromConfig=function(e,t){return new e(t.learningRate,t.initialAccumulatorValue)},t.className="AdagradOptimizer",t}(Optimizer);registerClass(AdagradOptimizer);var AdamOptimizer=function(e){function t(t,n,r,o){void 0===o&&(o=null);var a=e.call(this)||this;return a.learningRate=t,a.beta1=n,a.beta2=r,a.epsilon=o,a.accumulatedFirstMoment={},a.accumulatedSecondMoment={},a.c=keep(scalar(-t)),a.beta1Scalar=keep(scalar(n)),a.beta2Scalar=keep(scalar(r)),tidy(function(){a.accBeta1=scalar(n).variable(),a.accBeta2=scalar(r).variable()}),a.oneMinusBeta1=keep(scalar(1-n)),a.oneMinusBeta2=keep(scalar(1-r)),a.one=keep(scalar(1)),null===o&&(o=ENV.get("EPSILON")),a.epsScalar=keep(scalar(o)),a}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this;tidy(function(){var n=t.one.sub(t.accBeta1),r=t.one.sub(t.accBeta2);for(var o in e){var a=ENV.engine.registeredVariables[o];if(null==t.accumulatedFirstMoment[o]){var i=!1;t.accumulatedFirstMoment[o]=zerosLike(a).variable(i)}if(null==t.accumulatedSecondMoment[o]){i=!1;t.accumulatedSecondMoment[o]=zerosLike(a).variable(i)}var s=e[o],u=t.accumulatedFirstMoment[o],l=t.accumulatedSecondMoment[o],c=t.beta1Scalar.mul(u).add(t.oneMinusBeta1.mul(s)),p=t.beta2Scalar.mul(l).add(t.oneMinusBeta2.mul(s.square())),d=c.div(n),h=p.div(r);t.accumulatedFirstMoment[o].assign(c),t.accumulatedSecondMoment[o].assign(p);var f=t.c.mul(d.div(t.epsScalar.add(h.sqrt()))).add(a);a.assign(f)}t.accBeta1.assign(t.accBeta1.mul(t.beta1Scalar)),t.accBeta2.assign(t.accBeta2.mul(t.beta2Scalar))})},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsScalar.dispose(),this.beta1Scalar.dispose(),this.beta2Scalar.dispose(),this.accBeta1.dispose(),this.accBeta2.dispose(),this.oneMinusBeta1.dispose(),this.oneMinusBeta2.dispose(),this.one.dispose(),null!=this.accumulatedFirstMoment&&Object.keys(this.accumulatedFirstMoment).forEach(function(t){return e.accumulatedFirstMoment[t].dispose()}),null!=this.accumulatedSecondMoment&&Object.keys(this.accumulatedSecondMoment).forEach(function(t){return e.accumulatedSecondMoment[t].dispose()})},t.prototype.getConfig=function(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}},t.fromConfig=function(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)},t.className="AdamOptimizer",t}(Optimizer);registerClass(AdamOptimizer);var AdamaxOptimizer=function(e){function t(t,n,r,o,a){void 0===o&&(o=null),void 0===a&&(a=0);var i=e.call(this)||this;return i.learningRate=t,i.beta1=n,i.beta2=r,i.epsilon=o,i.decay=a,i.accumulatedFirstMoment={},i.accumulatedWeightedInfNorm={},i.c=keep(scalar(-t)),i.beta1Scalar=keep(scalar(n)),i.beta2Scalar=keep(scalar(r)),i.decayScalar=keep(scalar(a)),tidy(function(){i.iteration=scalar(0).variable(),i.accBeta1=scalar(n).variable()}),i.oneMinusBeta1=keep(scalar(1-n)),i.one=keep(scalar(1)),null===o&&(o=ENV.get("EPSILON")),i.epsScalar=keep(scalar(o)),i}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this;tidy(function(){var n=t.one.sub(t.accBeta1),r=t.c.div(t.one.add(t.decayScalar.mul(t.iteration)));for(var o in e){var a=ENV.engine.registeredVariables[o];if(null==t.accumulatedFirstMoment[o]){var i=!1;t.accumulatedFirstMoment[o]=zerosLike(a).variable(i)}if(null==t.accumulatedWeightedInfNorm[o]){i=!1;t.accumulatedWeightedInfNorm[o]=zerosLike(a).variable(i)}var s=e[o],u=t.accumulatedFirstMoment[o],l=t.accumulatedWeightedInfNorm[o],c=t.beta1Scalar.mul(u).add(t.oneMinusBeta1.mul(s)),p=t.beta2Scalar.mul(l),d=s.abs(),h=p.maximum(d);t.accumulatedFirstMoment[o].assign(c),t.accumulatedWeightedInfNorm[o].assign(h);var f=r.div(n).mul(c.div(t.epsScalar.add(h))).add(a);a.assign(f)}t.iteration.assign(t.iteration.add(t.one)),t.accBeta1.assign(t.accBeta1.mul(t.beta1Scalar))})},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsScalar.dispose(),this.accBeta1.dispose(),this.beta1Scalar.dispose(),this.beta2Scalar.dispose(),this.oneMinusBeta1.dispose(),this.decayScalar.dispose(),this.iteration.dispose(),this.one.dispose(),null!=this.accumulatedFirstMoment&&Object.keys(this.accumulatedFirstMoment).forEach(function(t){return e.accumulatedFirstMoment[t].dispose()}),null!=this.accumulatedWeightedInfNorm&&Object.keys(this.accumulatedWeightedInfNorm).forEach(function(t){return e.accumulatedWeightedInfNorm[t].dispose()})},t.prototype.getConfig=function(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}},t.fromConfig=function(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)},t.className="AdamaxOptimizer",t}(Optimizer);registerClass(AdamaxOptimizer);var SGDOptimizer=function(e){function t(t){var n=e.call(this)||this;return n.learningRate=t,n.setLearningRate(t),n}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this;Object.keys(e).forEach(function(n){var r=e[n],o=ENV.engine.registeredVariables[n];tidy(function(){var e=t.c.mul(r).add(o);o.assign(e)})})},t.prototype.setLearningRate=function(e){this.learningRate=e,null!=this.c&&this.c.dispose(),this.c=keep(scalar(-e))},t.prototype.dispose=function(){this.c.dispose()},t.prototype.getConfig=function(){return{learningRate:this.learningRate}},t.fromConfig=function(e,t){return new e(t.learningRate)},t.className="SGDOptimizer",t}(Optimizer);registerClass(SGDOptimizer);var MomentumOptimizer=function(e){function t(t,n,r){void 0===r&&(r=!1);var o=e.call(this,t)||this;return o.learningRate=t,o.momentum=n,o.useNesterov=r,o.m=scalar(o.momentum),o.accumulations={},o}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulations[n]){tidy(function(){t.accumulations[n]=zerosLike(o).variable(!1)})}var a=r.accumulations[n],i=e[n];tidy(function(){var e,r=t.m.mul(a).add(i);e=t.useNesterov?t.c.mul(i.add(r.mul(t.m))).add(o):t.c.mul(r).add(o),t.accumulations[n].assign(r),o.assign(e)})},r=this;for(var o in e)n(o)},t.prototype.dispose=function(){if(e.prototype.dispose.call(this),this.m.dispose(),null!=this.accumulations)for(var t in this.accumulations)this.accumulations[t].dispose()},t.prototype.setMomentum=function(e){this.momentum=e},t.prototype.getConfig=function(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}},t.fromConfig=function(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)},t.className="MomentumOptimizer",t}(SGDOptimizer);registerClass(MomentumOptimizer);var RMSPropOptimizer=function(e){function t(t,n,r,o,a){void 0===n&&(n=.9),void 0===r&&(r=0),void 0===o&&(o=null),void 0===a&&(a=!1);var i=e.call(this)||this;return i.learningRate=t,i.decay=n,i.momentum=r,i.epsilon=o,i.accumulatedMeanSquares={},i.accumulatedMeanGrads={},i.accumulatedMoments={},i.c=keep(scalar(t)),i.decayScalar=keep(scalar(n)),i.momentumScalar=keep(scalar(r)),i.oneMinusDecay=keep(scalar(1-n)),i.centered=a,null===o&&(o=ENV.get("EPSILON")),i.epsilonScalar=keep(scalar(o)),i}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulatedMeanSquares[n]){tidy(function(){t.accumulatedMeanSquares[n]=zerosLike(o).variable(!1)})}if(null==r.accumulatedMeanGrads[n]&&r.centered){tidy(function(){t.accumulatedMeanGrads[n]=zerosLike(o).variable(!1)})}if(null==r.accumulatedMoments[n]){tidy(function(){t.accumulatedMoments[n]=zerosLike(o).variable(!1)})}var a=r.accumulatedMeanSquares[n],i=r.accumulatedMeanGrads[n],s=r.accumulatedMoments[n],u=e[n];tidy(function(){var e=t.decayScalar.mul(a).add(t.oneMinusDecay.mul(u.square()));if(t.centered){var r=t.decayScalar.mul(i).add(t.oneMinusDecay.mul(u)),l=t.momentumScalar.mul(s).add(t.c.mul(u).div(e.sub(r.square().add(t.epsilonScalar)).sqrt()));t.accumulatedMeanSquares[n].assign(e),t.accumulatedMeanGrads[n].assign(r),t.accumulatedMoments[n].assign(l);var c=o.sub(l);o.assign(c)}else{var p=t.decayScalar.mul(a).add(t.oneMinusDecay.mul(u.square()));l=t.momentumScalar.mul(s).add(t.c.mul(u).div(p.add(t.epsilonScalar).sqrt()));t.accumulatedMeanSquares[n].assign(p),t.accumulatedMoments[n].assign(l);c=o.sub(l);o.assign(c)}})},r=this;for(var o in e)n(o)},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsilonScalar.dispose(),this.decayScalar.dispose(),this.momentumScalar.dispose(),this.oneMinusDecay.dispose(),null!=this.accumulatedMeanSquares&&Object.keys(this.accumulatedMeanSquares).forEach(function(t){return e.accumulatedMeanSquares[t].dispose()}),null!=this.accumulatedMeanGrads&&this.centered&&Object.keys(this.accumulatedMeanGrads).forEach(function(t){return e.accumulatedMeanGrads[t].dispose()}),null!=this.accumulatedMoments&&Object.keys(this.accumulatedMoments).forEach(function(t){return e.accumulatedMoments[t].dispose()})},t.prototype.getConfig=function(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}},t.fromConfig=function(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)},t.className="RMSPropOptimizer",t}(Optimizer);registerClass(RMSPropOptimizer);var OptimizerConstructors=function(){function e(){}return e.sgd=function(e){return new SGDOptimizer(e)},e.momentum=function(e,t,n){return void 0===n&&(n=!1),new MomentumOptimizer(e,t,n)},e.rmsprop=function(e,t,n,r,o){return void 0===t&&(t=.9),void 0===n&&(n=0),void 0===r&&(r=null),void 0===o&&(o=!1),new RMSPropOptimizer(e,t,n,r,o)},e.adam=function(e,t,n,r){return void 0===e&&(e=.001),void 0===t&&(t=.9),void 0===n&&(n=.999),void 0===r&&(r=null),new AdamOptimizer(e,t,n,r)},e.adadelta=function(e,t,n){return void 0===e&&(e=.001),void 0===t&&(t=.95),void 0===n&&(n=null),new AdadeltaOptimizer(e,t,n)},e.adamax=function(e,t,n,r,o){return void 0===e&&(e=.002),void 0===t&&(t=.9),void 0===n&&(n=.999),void 0===r&&(r=null),void 0===o&&(o=0),new AdamaxOptimizer(e,t,n,r,o)},e.adagrad=function(e,t){return void 0===t&&(t=.1),new AdagradOptimizer(e,t)},e}(),train={sgd:OptimizerConstructors.sgd,momentum:OptimizerConstructors.momentum,adadelta:OptimizerConstructors.adadelta,adagrad:OptimizerConstructors.adagrad,rmsprop:OptimizerConstructors.rmsprop,adamax:OptimizerConstructors.adamax,adam:OptimizerConstructors.adam},setBackend=Environment.setBackend,getBackend=Environment.getBackend,disposeVariables=Environment.disposeVariables,memory=Environment.memory;setOpHandler(ops);export{setBackend,getBackend,disposeVariables,memory,version as version_core,nextFrame,environment,io,math,serialization,test_util,util,webgl,AdadeltaOptimizer,AdagradOptimizer,AdamOptimizer,AdamaxOptimizer,MomentumOptimizer,Optimizer,RMSPropOptimizer,SGDOptimizer,Tensor,TensorBuffer,variable,Variable,Rank,Reduction,ENV,Environment,KernelBackend,DataStorage,image_ops as image,linalg_ops as linalg,loss_ops as losses,spectral_ops as spectral,op,batchNormalization2d,batchNormalization3d,batchNormalization4d,batchNormalization,complex,real,imag,concat,concat1d,concat2d,concat3d,concat4d,split$1 as split,conv1d,conv2d,conv3d,conv2dDerFilter,depthwiseConv2d,separableConv2d,conv2dTranspose,matMul,dot,outerProduct,reverse,reverse1d,reverse2d,reverse3d,reverse4d,maxPool,avgPool,pool,slice,slice1d,slice2d,slice3d,slice4d,abs,acos,acosh,asin,asinh,atan,atanh,ceil,clipByValue,cos,cosh,erf,exp,expm1,floor,log$1 as log,log1p,logSigmoid,neg,reciprocal,round,rsqrt,sigmoid,sign,sin,sinh,softplus,sqrt,square,step,tan,tanh$1 as tanh,all,any,argMax,argMin,logSumExp,max,mean,min,moments,sum$1 as sum,prod,equal,equalStrict,greater,greaterEqual,greaterEqualStrict,greaterStrict,less,lessEqual,lessEqualStrict,lessStrict,notEqual,notEqualStrict,add,addN,addStrict,atan2,div,divStrict,floorDiv,maximum,maximumStrict,minimum,minimumStrict,mod,modStrict,mul,mulStrict,pow,powStrict,squaredDifference,squaredDifferenceStrict,sub,subStrict,elu,leakyRelu,prelu,relu,selu,logicalAnd,logicalNot,logicalOr,logicalXor,where,whereAsync,buffer,toPixels,print,batchToSpaceND,cast,clone,cumsum,depthToSpace,expandDims,eye,fromPixels,multinomial,oneHot,pad,pad1d,pad2d,pad3d,pad4d,rand,randomNormal,randomUniform,reshape,spaceToBatchND,squeeze,stack,tile,truncatedNormal,unstack,setdiff1dAsync,fill,linspace,ones$1 as ones,range,scalar,tensor,tensor1d,tensor2d,tensor3d,tensor4d,tensor5d,tensor6d,zeros,onesLike,zerosLike,transpose,softmax,logSoftmax,localResponseNormalization,norm,gather,unsortedSegmentSum,basicLSTMCell,multiRNNCell,movingAverage,stridedSlice,topk,scatterND,fft,ifft,rfft,irfft,sparseToDense,gatherND,train,tidy,keep,dispose,time,profile,customGrad,grad,grads,valueAndGrad,valueAndGrads,variableGrads};
+//# sourceMappingURL=tf-core.esm.js.map
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tf-core.esm.js.map b/node_modules/@tensorflow/tfjs-core/dist/tf-core.esm.js.map
new file mode 100644
index 0000000..5db1988
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tf-core.esm.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"tf-core.esm.js","sources":["../src/canvas_util.ts","../src/device_util.ts","../src/util.ts","../src/profiler.ts","../src/tensor_format.ts","../src/tensor.ts","../src/types.ts","../src/tape.ts","../src/tensor_util.ts","../src/engine.ts","../src/environment_util.ts","../src/environment.ts","../src/gradients.ts","../src/globals.ts","../src/log.ts","../src/ops/array_ops_util.ts","../src/ops/axis_util.ts","../src/ops/concat_util.ts","../src/ops/gather_nd_util.ts","../src/ops/reduce_util.ts","../src/ops/scatter_nd_util.ts","../src/ops/segment_util.ts","../src/ops/slice_util.ts","../src/tensor_util_env.ts","../src/ops/operation.ts","../src/ops/softmax.ts","../src/ops/complex_ops.ts","../src/ops/tensor_ops.ts","../src/kernels/backend.ts","../src/kernels/backend_util.ts","../src/kernels/complex_util.ts","../src/kernels/non_max_suppression_impl.ts","../src/kernels/split_shared.ts","../src/kernels/topk_impl.ts","../src/kernels/webgl/argminmax_gpu.ts","../src/kernels/webgl/avg_pool_backprop_gpu.ts","../src/ops/broadcast_util.ts","../src/kernels/webgl/batchnorm_gpu.ts","../src/kernels/webgl/batchnorm_packed_gpu.ts","../src/kernels/webgl/binaryop_complex_gpu.ts","../src/kernels/webgl/binaryop_gpu.ts","../src/kernels/webgl/clip_gpu.ts","../src/kernels/webgl/clip_packed_gpu.ts","../src/kernels/webgl/complex_abs_gpu.ts","../src/kernels/webgl/concat_gpu.ts","../src/kernels/webgl/conv_backprop_gpu.ts","../src/kernels/webgl/conv_backprop_gpu_depthwise.ts","../src/kernels/webgl/conv_gpu.ts","../src/kernels/webgl/conv_gpu_depthwise.ts","../src/kernels/webgl/conv_packed_gpu_depthwise.ts","../src/kernels/webgl/crop_and_resize_gpu.ts","../src/kernels/webgl/shader_compiler_util.ts","../src/kernels/webgl/shader_compiler.ts","../src/kernels/webgl/cumsum_gpu.ts","../src/kernels/webgl/depth_to_space_gpu.ts","../src/kernels/webgl/encode_float_gpu.ts","../src/kernels/webgl/fft_gpu.ts","../src/kernels/webgl/from_pixels_gpu.ts","../src/kernels/webgl/gather_gpu.ts","../src/kernels/webgl/gather_nd_gpu.ts","../src/kernels/webgl/tex_util.ts","../src/kernels/webgl/webgl_util.ts","../src/kernels/webgl/gpgpu_util.ts","../src/kernels/webgl/gpgpu_context.ts","../src/kernels/webgl/gpgpu_math.ts","../src/kernels/webgl/im2col_gpu.ts","../src/kernels/webgl/lrn_gpu.ts","../src/kernels/webgl/lrn_grad_gpu.ts","../src/kernels/webgl/max_pool_backprop_gpu.ts","../src/kernels/webgl/mulmat_gpu.ts","../src/kernels/webgl/mulmat_packed_gpu.ts","../src/kernels/webgl/multinomial_gpu.ts","../src/kernels/webgl/onehot_gpu.ts","../src/kernels/packing_util.ts","../src/kernels/webgl/pack_gpu.ts","../src/kernels/webgl/pad_gpu.ts","../src/kernels/webgl/pool_gpu.ts","../src/kernels/webgl/reduce_gpu.ts","../src/kernels/webgl/reshape_packed_gpu.ts","../src/kernels/webgl/resize_bilinear_backprop_gpu.ts","../src/kernels/webgl/resize_bilinear_gpu.ts","../src/kernels/webgl/resize_nearest_neighbor_backprop_gpu.ts","../src/kernels/webgl/resize_nearest_neighbor_gpu.ts","../src/kernels/webgl/reverse_gpu.ts","../src/kernels/webgl/scatter_gpu.ts","../src/kernels/webgl/segment_gpu.ts","../src/kernels/webgl/select_gpu.ts","../src/kernels/webgl/slice_gpu.ts","../src/kernels/webgl/strided_slice_gpu.ts","../src/kernels/webgl/texture_manager.ts","../src/kernels/webgl/tile_gpu.ts","../src/kernels/webgl/transpose_gpu.ts","../src/ops/erf_util.ts","../src/ops/selu_util.ts","../src/kernels/webgl/unaryop_gpu.ts","../src/kernels/webgl/unpack_gpu.ts","../src/ops/concat_split.ts","../node_modules/seedrandom/lib/alea.js","../node_modules/seedrandom/lib/xor128.js","../node_modules/seedrandom/lib/xorwow.js","../node_modules/seedrandom/lib/xorshift7.js","../node_modules/seedrandom/lib/xor4096.js","../node_modules/seedrandom/lib/tychei.js","../node_modules/seedrandom/seedrandom.js","../node_modules/seedrandom/index.js","../src/ops/rand.ts","../src/ops/array_ops.ts","../src/kernels/where_impl.ts","../src/kernels/backend_webgl.ts","../src/ops/unary_ops.ts","../src/ops/batchnorm.ts","../src/ops/conv_util.ts","../src/ops/matmul.ts","../src/ops/conv.ts","../src/ops/reverse.ts","../src/ops/pool.ts","../src/ops/slice.ts","../src/ops/reduction_ops.ts","../src/ops/compare.ts","../src/ops/binary_ops.ts","../src/ops/logical_ops.ts","../src/ops/relu_ops.ts","../src/ops/transpose.ts","../src/ops/lrn.ts","../src/ops/norm.ts","../src/ops/segment_ops.ts","../src/ops/lstm.ts","../src/ops/moving_average.ts","../src/ops/strided_slice.ts","../src/ops/topk.ts","../src/ops/scatter_nd.ts","../src/ops/spectral_ops.ts","../src/ops/sparse_to_dense_util.ts","../src/ops/sparse_to_dense.ts","../src/ops/gather_nd.ts","../src/ops/loss_ops.ts","../src/ops/linalg_ops.ts","../src/ops/image_ops.ts","../src/kernels/backend_cpu.ts","../src/browser_util.ts","../src/io/types.ts","../src/io/io_utils.ts","../src/io/router_registry.ts","../src/io/model_management.ts","../src/io/indexed_db.ts","../src/io/local_storage.ts","../src/io/browser_files.ts","../src/io/weights_loader.ts","../src/io/browser_http.ts","../src/io/passthro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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nconst contexts: {[key: string]: WebGLRenderingContext} = {};\n\nconst WEBGL_ATTRIBUTES: WebGLContextAttributes = {\n  alpha: false,\n  antialias: false,\n  premultipliedAlpha: false,\n  preserveDrawingBuffer: false,\n  depth: false,\n  stencil: false,\n  failIfMajorPerformanceCaveat: true\n};\n\nexport function getWebGLContext(webGLVersion: number): WebGLRenderingContext {\n  if (!(webGLVersion in contexts)) {\n    const canvas = document.createElement('canvas');\n    canvas.addEventListener('webglcontextlost', ev => {\n      ev.preventDefault();\n      delete contexts[webGLVersion];\n    }, false);\n    contexts[webGLVersion] = getWebGLRenderingContext(webGLVersion);\n  }\n  const gl = contexts[webGLVersion];\n  if (gl.isContextLost()) {\n    delete contexts[webGLVersion];\n    return getWebGLContext(webGLVersion);\n  }\n\n  gl.disable(gl.DEPTH_TEST);\n  gl.disable(gl.STENCIL_TEST);\n  gl.disable(gl.BLEND);\n  gl.disable(gl.DITHER);\n  gl.disable(gl.POLYGON_OFFSET_FILL);\n  gl.disable(gl.SAMPLE_COVERAGE);\n  gl.enable(gl.SCISSOR_TEST);\n  gl.enable(gl.CULL_FACE);\n  gl.cullFace(gl.BACK);\n\n  return contexts[webGLVersion];\n}\n\nfunction getWebGLRenderingContext(webGLVersion: number): WebGLRenderingContext {\n  if (webGLVersion !== 1 && webGLVersion !== 2) {\n    throw new Error('Cannot get WebGL rendering context, WebGL is disabled.');\n  }\n\n  const canvas = document.createElement('canvas');\n  if (webGLVersion === 1) {\n    return (canvas.getContext('webgl', WEBGL_ATTRIBUTES) ||\n            canvas.getContext('experimental-webgl', WEBGL_ATTRIBUTES)) as\n        WebGLRenderingContext;\n  }\n  return canvas.getContext('webgl2', WEBGL_ATTRIBUTES) as WebGLRenderingContext;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport function isMobile(): boolean {\n  // tslint:disable-next-line:no-any\n  const a = navigator.userAgent || navigator.vendor || (window as any).opera;\n  // tslint:disable-next-line:max-line-length\n  return /(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i\n             .test(a) ||\n      // tslint:disable-next-line:max-line-length\n      /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\\-(n|u)|c55\\/|capi|ccwa|cdm\\-|cell|chtm|cldc|cmd\\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\\-s|devi|dica|dmob|do(c|p)o|ds(12|\\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\\-|_)|g1 u|g560|gene|gf\\-5|g\\-mo|go(\\.w|od)|gr(ad|un)|haie|hcit|hd\\-(m|p|t)|hei\\-|hi(pt|ta)|hp( i|ip)|hs\\-c|ht(c(\\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\\-(20|go|ma)|i230|iac( |\\-|\\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\\/)|klon|kpt |kwc\\-|kyo(c|k)|le(no|xi)|lg( g|\\/(k|l|u)|50|54|\\-[a-w])|libw|lynx|m1\\-w|m3ga|m50\\/|ma(te|ui|xo)|mc(01|21|ca)|m\\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\\-2|po(ck|rt|se)|prox|psio|pt\\-g|qa\\-a|qc(07|12|21|32|60|\\-[2-7]|i\\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\\-|oo|p\\-)|sdk\\/|se(c(\\-|0|1)|47|mc|nd|ri)|sgh\\-|shar|sie(\\-|m)|sk\\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\\-|v\\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\\-|tdg\\-|tel(i|m)|tim\\-|t\\-mo|to(pl|sh)|ts(70|m\\-|m3|m5)|tx\\-9|up(\\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\\-|your|zeto|zte\\-/i\n          .test(a.substr(0, 4));\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, DataTypeMap, FlatVector, NumericDataType, RecursiveArray, TensorLike, TypedArray} from './types';\n\n/** Shuffles the array using Fisher-Yates algorithm. */\n// tslint:disable-next-line:no-any\nexport function shuffle(array: any[]|Uint32Array|Int32Array|\n                        Float32Array): void {\n  let counter = array.length;\n  let temp = 0;\n  let index = 0;\n  // While there are elements in the array\n  while (counter > 0) {\n    // Pick a random index\n    index = (Math.random() * counter) | 0;\n    // Decrease counter by 1\n    counter--;\n    // And swap the last element with it\n    temp = array[counter];\n    array[counter] = array[index];\n    array[index] = temp;\n  }\n}\n\n/** Clamps a value to a specified range. */\nexport function clamp(min: number, x: number, max: number): number {\n  return Math.max(min, Math.min(x, max));\n}\n\nexport function nearestLargerEven(val: number): number {\n  return val % 2 === 0 ? val : val + 1;\n}\n\nexport function sum(arr: number[]): number {\n  let sum = 0;\n  for (let i = 0; i < arr.length; i++) {\n    sum += arr[i];\n  }\n  return sum;\n}\n\n/**\n * Returns a sample from a uniform [a, b) distribution.\n *\n * @param a The minimum support (inclusive).\n * @param b The maximum support (exclusive).\n * @return A pseudorandom number on the half-open interval [a,b).\n */\nexport function randUniform(a: number, b: number) {\n  const r = Math.random();\n  return (b * r) + (1 - r) * a;\n}\n\n/** Returns the squared Euclidean distance between two vectors. */\nexport function distSquared(a: FlatVector, b: FlatVector): number {\n  let result = 0;\n  for (let i = 0; i < a.length; i++) {\n    const diff = Number(a[i]) - Number(b[i]);\n    result += diff * diff;\n  }\n  return result;\n}\n\nexport function assert(expr: boolean, msg: string|(() => string)) {\n  if (!expr) {\n    throw new Error(typeof msg === 'string' ? msg : msg());\n  }\n}\n\nexport function assertShapesMatch(\n    shapeA: number[], shapeB: number[], errorMessagePrefix = ''): void {\n  assert(\n      arraysEqual(shapeA, shapeB),\n      errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\n\nexport function assertNonNull(a: TensorLike): void {\n  assert(\n      a != null,\n      `The input to the tensor constructor must be a non-null value.`);\n}\n\n// NOTE: We explicitly type out what T extends instead of any so that\n// util.flatten on a nested array of number doesn't try to infer T as a\n// number[][], causing us to explicitly type util.flatten<number>().\nexport function\nflatten<T extends number|boolean|string|Promise<number>|TypedArray>(\n    arr: T|RecursiveArray<T>, ret: T[] = []): T[] {\n  if (Array.isArray(arr) || isTypedArray(arr)) {\n    for (let i = 0; i < arr.length; ++i) {\n      flatten(arr[i], ret);\n    }\n  } else {\n    ret.push(arr as T);\n  }\n  return ret;\n}\n\nexport function sizeFromShape(shape: number[]): number {\n  if (shape.length === 0) {\n    // Scalar.\n    return 1;\n  }\n  let size = shape[0];\n  for (let i = 1; i < shape.length; i++) {\n    size *= shape[i];\n  }\n  return size;\n}\n\nexport function isScalarShape(shape: number[]): boolean {\n  return shape.length === 0;\n}\n\nexport function arraysEqual(n1: FlatVector, n2: FlatVector) {\n  if (n1 === n2) {\n    return true;\n  }\n  if (n1 == null || n2 == null) {\n    return false;\n  }\n\n  if (n1.length !== n2.length) {\n    return false;\n  }\n  for (let i = 0; i < n1.length; i++) {\n    if (n1[i] !== n2[i]) {\n      return false;\n    }\n  }\n  return true;\n}\n\nexport function isInt(a: number): boolean {\n  return a % 1 === 0;\n}\n\nexport function tanh(x: number): number {\n  // tslint:disable-next-line:no-any\n  if ((Math as any).tanh != null) {\n    // tslint:disable-next-line:no-any\n    return (Math as any).tanh(x);\n  }\n  if (x === Infinity) {\n    return 1;\n  } else if (x === -Infinity) {\n    return -1;\n  } else {\n    const e2x = Math.exp(2 * x);\n    return (e2x - 1) / (e2x + 1);\n  }\n}\n\nexport function sizeToSquarishShape(size: number): [number, number] {\n  for (let a = Math.floor(Math.sqrt(size)); a > 1; --a) {\n    if (size % a === 0) {\n      return [a, size / a];\n    }\n  }\n  return [1, size];\n}\n\nexport function createShuffledIndices(n: number): Uint32Array {\n  const shuffledIndices = new Uint32Array(n);\n  for (let i = 0; i < n; ++i) {\n    shuffledIndices[i] = i;\n  }\n  shuffle(shuffledIndices);\n  return shuffledIndices;\n}\n\nexport function rightPad(a: string, size: number): string {\n  if (size <= a.length) {\n    return a;\n  }\n  return a + ' '.repeat(size - a.length);\n}\n\nexport function repeatedTry(\n    checkFn: () => boolean, delayFn = (counter: number) => 0,\n    maxCounter?: number): Promise<void> {\n  return new Promise<void>((resolve, reject) => {\n    let tryCount = 0;\n\n    const tryFn = () => {\n      if (checkFn()) {\n        resolve();\n        return;\n      }\n\n      tryCount++;\n\n      const nextBackoff = delayFn(tryCount);\n\n      if (maxCounter != null && tryCount >= maxCounter) {\n        reject();\n        return;\n      }\n      setTimeout(tryFn, nextBackoff);\n    };\n\n    tryFn();\n  });\n}\n\n/**\n * Given the full size of the array and a shape that may contain -1 as the\n * implicit dimension, returns the inferred shape where -1 is replaced.\n * E.g. For shape=[2, -1, 3] and size=24, it will return [2, 4, 3].\n *\n * @param shape The shape, which may contain -1 in some dimension.\n * @param size The full size (number of elements) of the array.\n * @return The inferred shape where -1 is replaced with the inferred size.\n */\nexport function inferFromImplicitShape(\n    shape: number[], size: number): number[] {\n  let shapeProd = 1;\n  let implicitIdx = -1;\n\n  for (let i = 0; i < shape.length; ++i) {\n    if (shape[i] >= 0) {\n      shapeProd *= shape[i];\n    } else if (shape[i] === -1) {\n      if (implicitIdx !== -1) {\n        throw Error(\n            `Shapes can only have 1 implicit size. ` +\n            `Found -1 at dim ${implicitIdx} and dim ${i}`);\n      }\n      implicitIdx = i;\n    } else if (shape[i] < 0) {\n      throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);\n    }\n  }\n\n  if (implicitIdx === -1) {\n    if (size > 0 && size !== shapeProd) {\n      throw Error(`Size(${size}) must match the product of shape ${shape}`);\n    }\n    return shape;\n  }\n\n  if (shapeProd === 0) {\n    throw Error(\n        `Cannot infer the missing size in [${shape}] when ` +\n        `there are 0 elements`);\n  }\n  if (size % shapeProd !== 0) {\n    throw Error(\n        `The implicit shape can't be a fractional number. ` +\n        `Got ${size} / ${shapeProd}`);\n  }\n\n  const newShape = shape.slice();\n  newShape[implicitIdx] = size / shapeProd;\n  return newShape;\n}\n\n/** Reduces the shape by removing all dimensions of shape 1. */\nexport function squeezeShape(shape: number[], axis?: number[]):\n    {newShape: number[], keptDims: number[]} {\n  const newShape: number[] = [];\n  const keptDims: number[] = [];\n  let j = 0;\n  for (let i = 0; i < shape.length; ++i) {\n    if (axis != null) {\n      if (axis[j] === i && shape[i] !== 1) {\n        throw new Error(\n            `Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);\n      }\n      if ((axis[j] == null || axis[j] > i) && shape[i] === 1) {\n        newShape.push(shape[i]);\n        keptDims.push(i);\n      }\n      if (axis[j] <= i) {\n        j++;\n      }\n    }\n    if (shape[i] !== 1) {\n      newShape.push(shape[i]);\n      keptDims.push(i);\n    }\n  }\n  return {newShape, keptDims};\n}\n\nexport function getTypedArrayFromDType<D extends NumericDataType>(\n    dtype: D, size: number): DataTypeMap[D] {\n  let values = null;\n  if (dtype == null || dtype === 'float32') {\n    values = new Float32Array(size);\n  } else if (dtype === 'int32') {\n    values = new Int32Array(size);\n  } else if (dtype === 'bool') {\n    values = new Uint8Array(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n  return values;\n}\n\nexport function getArrayFromDType<D extends DataType>(\n    dtype: D, size: number): DataTypeMap[D] {\n  let values = null;\n  if (dtype == null || dtype === 'float32') {\n    values = new Float32Array(size);\n  } else if (dtype === 'int32') {\n    values = new Int32Array(size);\n  } else if (dtype === 'bool') {\n    values = new Uint8Array(size);\n  } else if (dtype === 'string') {\n    values = new Array<'string'>(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n  return values;\n}\n\nexport function checkComputationForNaN<D extends DataType>(\n    vals: DataTypeMap[D], dtype: D, name: string): void {\n  if (dtype !== 'float32') {\n    // Only floating point computations will generate NaN values\n    return;\n  }\n  for (let i = 0; i < vals.length; i++) {\n    if (isNaN(vals[i] as number)) {\n      throw Error(`The result of the '${name}' has NaNs.`);\n    }\n  }\n}\n\nexport function checkConversionForNaN<D extends DataType>(\n    vals: DataTypeMap[D]|number[], dtype: D): void {\n  if (dtype === 'float32') {\n    // NaN is valid for floating point conversions\n    return;\n  }\n\n  for (let i = 0; i < vals.length; i++) {\n    if (isNaN(vals[i] as number)) {\n      throw Error(`NaN is not a valid value for dtype: '${dtype}'.`);\n    }\n  }\n}\n\n/**\n * Returns true if the new type can't encode the old type without loss of\n * precision.\n */\nexport function hasEncodingLoss(oldType: DataType, newType: DataType): boolean {\n  if (newType === 'complex64') {\n    return false;\n  }\n  if (newType === 'float32' && oldType !== 'complex64') {\n    return false;\n  }\n  if (newType === 'int32' && oldType !== 'float32' && oldType !== 'complex64') {\n    return false;\n  }\n  if (newType === 'bool' && oldType === 'bool') {\n    return false;\n  }\n  return true;\n}\n\nexport function isTypedArray(a: {}): a is Float32Array|Int32Array|Uint8Array {\n  return a instanceof Float32Array || a instanceof Int32Array ||\n      a instanceof Uint8Array;\n}\n\nexport function bytesPerElement(dtype: DataType): number {\n  if (dtype === 'float32' || dtype === 'int32') {\n    return 4;\n  } else if (dtype === 'complex64') {\n    return 8;\n  } else if (dtype === 'bool') {\n    return 1;\n  } else {\n    throw new Error(`Unknown dtype ${dtype}`);\n  }\n}\n\n/**\n * Returns the approximate number of bytes allocated in the string array - 2\n * bytes per character. Computing the exact bytes for a native string in JS is\n * not possible since it depends on the encoding of the html page that serves\n * the website.\n */\nexport function bytesFromStringArray(arr: string[]): number {\n  if (arr == null) {\n    return 0;\n  }\n  let bytes = 0;\n  arr.forEach(x => bytes += x.length * 2);\n  return bytes;\n}\n\n/** Returns true if the value is a string. */\nexport function isString(value: {}): value is string {\n  return typeof value === 'string' || value instanceof String;\n}\n\nexport function isBoolean(value: {}): boolean {\n  return typeof value === 'boolean';\n}\n\nexport function isNumber(value: {}): boolean {\n  return typeof value === 'number';\n}\n\nexport function inferDtype(values: TensorLike): DataType {\n  if (values instanceof Array) {\n    return inferDtype(values[0]);\n  }\n  if (values instanceof Float32Array) {\n    return 'float32';\n  } else if (values instanceof Int32Array || values instanceof Uint8Array) {\n    return 'int32';\n  } else if (isNumber(values)) {\n    return 'float32';\n  } else if (isString(values)) {\n    return 'string';\n  } else if (isBoolean(values)) {\n    return 'bool';\n  }\n  return 'float32';\n}\n\nexport function isFunction(f: Function) {\n  return !!(f && f.constructor && f.call && f.apply);\n}\n\nexport function nearestDivisor(size: number, start: number): number {\n  for (let i = start; i < size; ++i) {\n    if (size % i === 0) {\n      return i;\n    }\n  }\n  return size;\n}\n\nexport function computeStrides(shape: number[]): number[] {\n  const rank = shape.length;\n  if (rank < 2) {\n    return [];\n  }\n\n  // Last dimension has implicit stride of 1, thus having D-1 (instead of D)\n  // strides.\n  const strides = new Array(rank - 1);\n  strides[rank - 2] = shape[rank - 1];\n  for (let i = rank - 3; i >= 0; --i) {\n    strides[i] = strides[i + 1] * shape[i + 1];\n  }\n  return strides;\n}\n\nexport function toTypedArray(\n    a: TensorLike, dtype: DataType, debugMode: boolean): TypedArray {\n  if (dtype === 'string') {\n    throw new Error('Cannot convert a string[] to a TypedArray');\n  }\n  if (noConversionNeeded(a, dtype)) {\n    return a as TypedArray;\n  }\n  if (Array.isArray(a)) {\n    a = flatten(a as number[]);\n  }\n  if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n    return new Float32Array(a as number[]);\n  } else if (dtype === 'int32') {\n    if (debugMode) {\n      checkConversionForNaN(a as number[], dtype);\n    }\n    return new Int32Array(a as number[]);\n  } else if (dtype === 'bool') {\n    const bool = new Uint8Array((a as number[]).length);\n    for (let i = 0; i < bool.length; ++i) {\n      if (Math.round((a as number[])[i] as number) !== 0) {\n        bool[i] = 1;\n      }\n    }\n    return bool;\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n}\n\nfunction noConversionNeeded(a: TensorLike, dtype: DataType): boolean {\n  return (a instanceof Float32Array && dtype === 'float32') ||\n      (a instanceof Int32Array && dtype === 'int32') ||\n      (a instanceof Uint8Array && dtype === 'bool');\n}\n\nexport function makeOnesTypedArray<D extends DataType>(\n    size: number, dtype: D): DataTypeMap[D] {\n  const array = makeZerosTypedArray(size, dtype);\n  for (let i = 0; i < array.length; i++) {\n    array[i] = 1;\n  }\n  return array;\n}\n\nexport function makeZerosTypedArray<D extends DataType>(\n    size: number, dtype: D): DataTypeMap[D] {\n  if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n    return new Float32Array(size);\n  } else if (dtype === 'int32') {\n    return new Int32Array(size);\n  } else if (dtype === 'bool') {\n    return new Uint8Array(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n}\n\n/**\n * Returns the current high-resolution real time in milliseconds. It is\n * relative to an arbitrary time in the past.\n */\nexport function now(): number {\n  if (typeof performance !== 'undefined') {\n    return performance.now();\n  } else if (typeof process !== 'undefined') {\n    const time = process.hrtime();\n    return time[0] * 1000 + time[1] / 1000000;\n  } else {\n    throw new Error(\n        'Cannot measure time in this environment. You should run tf.js ' +\n        'in the browser or in Node.js');\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {BackendTimer} from './kernels/backend';\nimport {Tensor} from './tensor';\nimport {TypedArray} from './types';\nimport * as util from './util';\n\nexport class Profiler {\n  constructor(private backendTimer: BackendTimer, private logger?: Logger) {\n    if (logger == null) {\n      this.logger = new Logger();\n    }\n  }\n\n  profileKernel<T extends Tensor|Tensor[]>(name: string, f: () => T | Tensor[]):\n      T {\n    let result: T|Tensor[];\n    const holdResultWrapperFn = () => {\n      result = f();\n    };\n    const timer = this.backendTimer.time(holdResultWrapperFn);\n\n    const results: Tensor[] =\n        Array.isArray(result) ? result : [result] as Tensor[];\n    results.forEach(r => {\n      const vals = r.dataSync();\n      util.checkComputationForNaN(vals, r.dtype, name);\n\n      timer.then(timing => {\n        let extraInfo = '';\n        if (timing.getExtraProfileInfo != null) {\n          extraInfo = timing.getExtraProfileInfo();\n        }\n\n        this.logger.logKernelProfile(name, r, vals, timing.kernelMs, extraInfo);\n      });\n    });\n\n    return result as T;\n  }\n}\n\nexport class Logger {\n  logKernelProfile(\n      name: string, result: Tensor, vals: TypedArray, timeMs: number,\n      extraInfo?: string) {\n    const time = util.rightPad(`${timeMs}ms`, 9);\n    const paddedName = util.rightPad(name, 25);\n    const rank = result.rank;\n    const size = result.size;\n    const shape = util.rightPad(result.shape.toString(), 14);\n\n    console.log(\n        `%c${paddedName}\\t%c${time}\\t%c${rank}D ${shape}\\t%c${size}\\t%c${\n            extraInfo}`,\n        'font-weight:bold', 'color:red', 'color:blue', 'color: orange',\n        'color: green');\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, TypedArray} from './types';\nimport {computeStrides, isString, rightPad, sizeFromShape} from './util';\n\n// Maximum number of values before we decide to show ellipsis.\nconst FORMAT_LIMIT_NUM_VALS = 20;\n// Number of first and last values to show when displaying a, b,...,y, z.\nconst FORMAT_NUM_FIRST_LAST_VALS = 3;\n// Number of significant digits to show.\nconst FORMAT_NUM_SIG_DIGITS = 7;\n\nexport function tensorToString(\n    vals: TypedArray|string[], shape: number[], dtype: DataType,\n    verbose: boolean) {\n  const strides = computeStrides(shape);\n  const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);\n  const rank = shape.length;\n  const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);\n  const lines = ['Tensor'];\n  if (verbose) {\n    lines.push(`  dtype: ${dtype}`);\n    lines.push(`  rank: ${rank}`);\n    lines.push(`  shape: [${shape}]`);\n    lines.push(`  values:`);\n  }\n  lines.push(valsLines.map(l => '    ' + l).join('\\n'));\n  return lines.join('\\n');\n}\n\nfunction computeMaxSizePerColumn(\n    vals: TypedArray|string[], shape: number[], dtype: DataType,\n    strides: number[]): number[] {\n  const n = sizeFromShape(shape);\n  const numCols = strides[strides.length - 1];\n  const padPerCol = new Array(numCols).fill(0);\n  const rank = shape.length;\n  const valuesOrTuples =\n      dtype === 'complex64' ? createComplexTuples(vals) : vals;\n\n  if (rank > 1) {\n    for (let row = 0; row < n / numCols; row++) {\n      const offset = row * numCols;\n      for (let j = 0; j < numCols; j++) {\n        padPerCol[j] = Math.max(\n            padPerCol[j], valToString(valuesOrTuples[offset + j], 0).length);\n      }\n    }\n  }\n  return padPerCol;\n}\n\nfunction valToString(val: number|string|[number, number], pad: number) {\n  let valStr: string;\n  if (Array.isArray(val)) {\n    valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ` +\n        `${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;\n  } else if (isString(val)) {\n    valStr = `'${val}'`;\n  } else {\n    valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();\n  }\n\n  return rightPad(valStr, pad);\n}\n\nfunction subTensorToString(\n    vals: TypedArray|string[], shape: number[], dtype: DataType,\n    strides: number[], padPerCol: number[], isLast = true): string[] {\n  const storagePerElement = dtype === 'complex64' ? 2 : 1;\n\n  const size = shape[0];\n  const rank = shape.length;\n  if (rank === 0) {\n    if (dtype === 'complex64') {\n      const complexTuple = createComplexTuples(vals);\n      return [valToString(complexTuple[0], 0)];\n    }\n    return [vals[0].toString()];\n  }\n\n  if (rank === 1) {\n    if (size > FORMAT_LIMIT_NUM_VALS) {\n      const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;\n\n      let firstVals = Array.from<number|string|[number, number]>(\n          vals.slice(0, firstValsSize));\n      let lastVals = Array.from<number|string|[number, number]>(vals.slice(\n          size - FORMAT_NUM_FIRST_LAST_VALS * storagePerElement, size));\n      if (dtype === 'complex64') {\n        firstVals = createComplexTuples(firstVals);\n        lastVals = createComplexTuples(lastVals);\n      }\n      return [\n        '[' + firstVals.map((x, i) => valToString(x, padPerCol[i])).join(', ') +\n        ', ..., ' +\n        lastVals\n            .map(\n                (x, i) => valToString(\n                    x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i]))\n            .join(', ') +\n        ']'\n      ];\n    }\n    const displayVals: Array<number|string|[number, number]> =\n        dtype === 'complex64' ? createComplexTuples(vals) :\n                                Array.from<number|string>(vals);\n\n    return [\n      '[' + displayVals.map((x, i) => valToString(x, padPerCol[i])).join(', ') +\n      ']'\n    ];\n  }\n\n  // The array is rank 2 or more.\n  const subshape = shape.slice(1);\n  const substrides = strides.slice(1);\n  const stride = strides[0] * storagePerElement;\n  const lines: string[] = [];\n  if (size > FORMAT_LIMIT_NUM_VALS) {\n    for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {\n      const start = i * stride;\n      const end = start + stride;\n      lines.push(...subTensorToString(\n          vals.slice(start, end), subshape, dtype, substrides, padPerCol,\n          false /* isLast */));\n    }\n    lines.push('...');\n    for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {\n      const start = i * stride;\n      const end = start + stride;\n      lines.push(...subTensorToString(\n          vals.slice(start, end), subshape, dtype, substrides, padPerCol,\n          i === size - 1 /* isLast */));\n    }\n  } else {\n    for (let i = 0; i < size; i++) {\n      const start = i * stride;\n      const end = start + stride;\n      lines.push(...subTensorToString(\n          vals.slice(start, end), subshape, dtype, substrides, padPerCol,\n          i === size - 1 /* isLast */));\n    }\n  }\n  const sep = rank === 2 ? ',' : '';\n  lines[0] = '[' + lines[0] + sep;\n  for (let i = 1; i < lines.length - 1; i++) {\n    lines[i] = ' ' + lines[i] + sep;\n  }\n  let newLineSep = ',\\n';\n  for (let i = 2; i < rank; i++) {\n    newLineSep += '\\n';\n  }\n  lines[lines.length - 1] =\n      ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep);\n  return lines;\n}\n\nfunction createComplexTuples(vals: Array<{}>|\n                             TypedArray): Array<[number, number]> {\n  const complexTuples: Array<[number, number]> = [];\n  for (let i = 0; i < vals.length; i += 2) {\n    complexTuples.push([vals[i], vals[i + 1]] as [number, number]);\n  }\n  return complexTuples;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {tensorToString} from './tensor_format';\nimport {DataType, DataTypeMap, DataValues, NumericDataType, Rank, ShapeMap, SingleValueMap, TensorLike, TensorLike1D, TensorLike3D, TensorLike4D, TypedArray} from './types';\nimport * as util from './util';\nimport {computeStrides} from './util';\n\nexport interface TensorData<D extends DataType> {\n  dataId?: DataId;\n  values?: DataTypeMap[D];\n}\n\n/**\n * A mutable object, similar to `tf.Tensor`, that allows users to set values\n * at locations before converting to an immutable `tf.Tensor`.\n *\n * See `tf.buffer` for creating a tensor buffer.\n */\n/** @doc {heading: 'Tensors', subheading: 'Classes'} */\nexport class TensorBuffer<R extends Rank, D extends DataType = 'float32'> {\n  size: number;\n  shape: ShapeMap[R];\n  strides: number[];\n  values: DataTypeMap[D];\n\n  constructor(shape: ShapeMap[R], public dtype: D, values?: DataTypeMap[D]) {\n    this.shape = shape.slice();\n    this.size = util.sizeFromShape(shape);\n\n    if (values != null) {\n      const n = values.length;\n      util.assert(\n          n === this.size,\n          `Length of values '${n}' does not match the size ` +\n              `inferred by the shape '${this.size}'.`);\n    }\n    if (dtype === 'complex64') {\n      throw new Error(\n          `complex64 dtype TensorBuffers are not supported. Please create ` +\n          `a TensorBuffer for the real and imaginary parts separately and ` +\n          `call tf.complex(real, imag).`);\n    }\n    this.values =\n        values || util.getArrayFromDType(dtype, util.sizeFromShape(this.shape));\n    this.strides = computeStrides(shape);\n  }\n\n  /**\n   * Sets a value in the buffer at a given location.\n   *\n   * @param value The value to set.\n   * @param locs  The location indices.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Creation'} */\n  set(value: SingleValueMap[D], ...locs: number[]): void {\n    if (locs.length === 0) {\n      locs = [0];\n    }\n    util.assert(\n        locs.length === this.rank,\n        `The number of provided coordinates (${locs.length}) must ` +\n            `match the rank (${this.rank})`);\n\n    const index = this.locToIndex(locs);\n    this.values[index] = value as number;\n  }\n\n  /**\n   * Returns the value in the buffer at the provided location.\n   *\n   * @param locs The location indices.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Creation'} */\n  get(...locs: number[]): SingleValueMap[D] {\n    if (locs.length === 0) {\n      locs = [0];\n    }\n    let index = locs[locs.length - 1];\n    for (let i = 0; i < locs.length - 1; ++i) {\n      index += this.strides[i] * locs[i];\n    }\n    return this.values[index];\n  }\n\n  locToIndex(locs: number[]): number {\n    if (this.rank === 0) {\n      return 0;\n    } else if (this.rank === 1) {\n      return locs[0];\n    }\n    let index = locs[locs.length - 1];\n    for (let i = 0; i < locs.length - 1; ++i) {\n      index += this.strides[i] * locs[i];\n    }\n    return index;\n  }\n\n  indexToLoc(index: number): number[] {\n    if (this.rank === 0) {\n      return [];\n    } else if (this.rank === 1) {\n      return [index];\n    }\n    const locs: number[] = new Array(this.shape.length);\n    for (let i = 0; i < locs.length - 1; ++i) {\n      locs[i] = Math.floor(index / this.strides[i]);\n      index -= locs[i] * this.strides[i];\n    }\n    locs[locs.length - 1] = index;\n    return locs;\n  }\n\n  get rank() {\n    return this.shape.length;\n  }\n\n  /**\n   * Creates an immutable `tf.Tensor` object from the buffer.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Creation'} */\n  toTensor(): Tensor<R> {\n    return Tensor.make(this.shape, {values: this.values}, this.dtype);\n  }\n}\n\nexport interface TensorTracker {\n  registerTensor(t: Tensor): void;\n  disposeTensor(t: Tensor): void;\n  write(dataId: DataId, values: DataValues): void;\n  read(dataId: DataId): Promise<DataValues>;\n  readSync(dataId: DataId): DataValues;\n  registerVariable(v: Variable): void;\n  nextTensorId(): number;\n  nextVariableId(): number;\n}\n\n/**\n * The Tensor class calls into this handler to delegate chaining operations.\n */\nexport interface OpHandler {\n  cast<T extends Tensor>(x: T, dtype: DataType): T;\n  buffer<R extends Rank, D extends DataType>(\n      shape: ShapeMap[R], dtype: D,\n      values?: DataTypeMap[D]): TensorBuffer<R, D>;\n  print<T extends Tensor>(x: T, verbose: boolean): void;\n  reshape<R2 extends Rank>(x: Tensor, shape: ShapeMap[R2]): Tensor<R2>;\n  expandDims<R2 extends Rank>(x: Tensor, axis: number): Tensor<R2>;\n  cumsum<T extends Tensor>(\n      x: Tensor, axis: number, exclusive: boolean, reverse: boolean): T;\n  squeeze<T extends Tensor>(x: Tensor, axis?: number[]): T;\n  clone<T extends Tensor>(x: T): T;\n  tile<T extends Tensor>(x: T, reps: number[]): T;\n  gather<T extends Tensor>(x: T, indices: Tensor1D|TensorLike1D, axis: number):\n      T;\n  matMul<T extends Tensor>(\n      a: T, b: T|TensorLike, transposeA: boolean, transposeB: boolean): T;\n  dot(t1: Tensor, t2: Tensor|TensorLike): Tensor;\n  norm(\n      x: Tensor, ord: number|'euclidean'|'fro', axis: number|number[],\n      keepDims: boolean): Tensor;\n  slice<R extends Rank, T extends Tensor<R>>(\n      x: T, begin: number|number[], size?: number|number[]): T;\n  split<T extends Tensor>(\n      x: T, numOrSizeSplits: number[]|number, axis?: number): T[];\n  reverse<T extends Tensor>(x: T, axis?: number|number[]): T;\n  concat<T extends Tensor>(tensors: Array<T|TensorLike>, axis: number): T;\n  stack<T extends Tensor>(tensors: Array<T|TensorLike>, axis: number): Tensor;\n  unstack<T extends Tensor>(value: T, axis: number): Tensor[];\n  pad<T extends Tensor>(\n      x: T, paddings: Array<[number, number]>, constantValue: number): T;\n  batchNormalization<R extends Rank>(\n      x: Tensor<R>, mean: Tensor<R>|Tensor1D|TensorLike,\n      variance: Tensor<R>|Tensor1D|TensorLike, varianceEpsilon: number,\n      scale?: Tensor<R>|Tensor1D|TensorLike,\n      offset?: Tensor<R>|Tensor1D|TensorLike): Tensor<R>;\n  all<T extends Tensor>(x: Tensor, axis: number|number[], keepDims: boolean): T;\n  any<T extends Tensor>(x: Tensor, axis: number|number[], keepDims: boolean): T;\n  logSumExp<T extends Tensor>(\n      x: Tensor, axis: number|number[], keepDims: boolean): T;\n  sum<T extends Tensor>(x: Tensor, axis: number|number[], keepDims: boolean): T;\n  prod<T extends Tensor>(x: Tensor, axis: number|number[], keepDims: boolean):\n      T;\n  mean<T extends Tensor>(x: Tensor, axis: number|number[], keepDims: boolean):\n      T;\n  min<T extends Tensor>(x: Tensor, axis: number|number[], keepDims: boolean): T;\n  max<T extends Tensor>(x: Tensor, axis: number|number[], keepDims: boolean): T;\n  argMin<T extends Tensor>(x: Tensor, axis: number): T;\n  argMax<T extends Tensor>(x: Tensor, axis: number): T;\n  add<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  addStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  atan2<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  sub<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  subStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  pow<T extends Tensor>(base: T, exp: Tensor|TensorLike): T;\n  powStrict<T extends Tensor>(base: T, exp: Tensor|TensorLike): T;\n  mul<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  mulStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  div<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  floorDiv<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  divStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  mod<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  modStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  minimum<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  minimumStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  maximum<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  maximumStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  squaredDifference<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  squaredDifferenceStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  transpose<T extends Tensor>(x: T, perm?: number[]): T;\n  logicalNot<T extends Tensor>(x: T): T;\n  logicalAnd<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  logicalOr<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  logicalXor<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  where<T extends Tensor>(condition: Tensor|TensorLike, a: T, b: T|TensorLike):\n      T;\n  notEqual<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  notEqualStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  less<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  lessStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  equal<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  equalStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  lessEqual<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  lessEqualStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  greater<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  greaterStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  greaterEqual<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  greaterEqualStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  neg<T extends Tensor>(x: T): T;\n  ceil<T extends Tensor>(x: T): T;\n  floor<T extends Tensor>(x: T): T;\n  sign<T extends Tensor>(x: T): T;\n  round<T extends Tensor>(x: T): T;\n  exp<T extends Tensor>(x: T): T;\n  expm1<T extends Tensor>(x: T): T;\n  log<T extends Tensor>(x: T): T;\n  log1p<T extends Tensor>(x: T): T;\n  sqrt<T extends Tensor>(x: T): T;\n  rsqrt<T extends Tensor>(x: T): T;\n  square<T extends Tensor>(x: T): T;\n  reciprocal<T extends Tensor>(x: T): T;\n  abs<T extends Tensor>(x: T): T;\n  clipByValue<T extends Tensor>(\n      x: T, clipValueMin: number, clipValueMax: number): T;\n  sigmoid<T extends Tensor>(x: T): T;\n  logSigmoid<T extends Tensor>(x: T): T;\n  softplus<T extends Tensor>(x: T): T;\n  zerosLike<T extends Tensor>(x: T): T;\n  onesLike<T extends Tensor>(x: T): T;\n  sin<T extends Tensor>(x: T): T;\n  cos<T extends Tensor>(x: T): T;\n  tan<T extends Tensor>(x: T): T;\n  asin<T extends Tensor>(x: T): T;\n  acos<T extends Tensor>(x: T): T;\n  atan<T extends Tensor>(x: T): T;\n  sinh<T extends Tensor>(x: T): T;\n  cosh<T extends Tensor>(x: T): T;\n  tanh<T extends Tensor>(x: T): T;\n  asinh<T extends Tensor>(x: T): T;\n  acosh<T extends Tensor>(x: T): T;\n  atanh<T extends Tensor>(x: T): T;\n  erf<T extends Tensor>(x: T): T;\n  step<T extends Tensor>(x: T, alpha: number): T;\n  relu<T extends Tensor>(x: T): T;\n  elu<T extends Tensor>(x: T): T;\n  selu<T extends Tensor>(x: T): T;\n  leakyRelu<T extends Tensor>(x: T, alpha: number): T;\n  prelu<T extends Tensor>(x: T, alpha: T|TensorLike): T;\n  softmax<T extends Tensor>(logits: T, dim: number): T;\n  logSoftmax<T extends Tensor>(logits: T, axis: number): T;\n  image: {\n    resizeBilinear<T extends Tensor3D|Tensor4D>(\n        images: T, size: [number, number], alignCorners: boolean): T;\n    resizeNearestNeighbor<T extends Tensor3D|Tensor4D>(\n        images: T, size: [number, number], alignCorners: boolean): T;\n  };\n  conv1d<T extends Tensor2D|Tensor3D>(\n      x: T, filter: Tensor3D|TensorLike3D, stride: number,\n      pad: 'valid'|'same'|number, dataFormat: 'NWC'|'NCW', dilation: number,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n  conv2d<T extends Tensor3D|Tensor4D>(\n      x: T, filter: Tensor4D|TensorLike4D, strides: [number, number]|number,\n      pad: 'valid'|'same'|number, dataFormat: 'NHWC'|'NCHW',\n      dilations: [number, number]|number,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n  conv2dTranspose<T extends Tensor3D|Tensor4D>(\n      x: T, filter: Tensor4D|TensorLike4D,\n      outputShape: [number, number, number, number]|[number, number, number],\n      strides: [number, number]|number, pad: 'valid'|'same'|number,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n  depthwiseConv2d<T extends Tensor3D|Tensor4D>(\n      x: T, filter: Tensor4D|TensorLike4D, strides: [number, number]|number,\n      pad: 'valid'|'same'|number, dataFormat: 'NHWC'|'NCHW',\n      dilations: [number, number]|number,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n  separableConv2d<T extends Tensor3D|Tensor4D>(\n      x: T|TensorLike, depthwiseFilter: Tensor4D|TensorLike4D,\n      pointwiseFilter: Tensor4D|TensorLike, strides: [number, number]|number,\n      pad: 'valid'|'same', dilation: [number, number]|number,\n      dataFormat: 'NHWC'|'NCHW'): T;\n  maxPool<T extends Tensor3D|Tensor4D>(\n      x: T, filterSize: [number, number]|number,\n      strides: [number, number]|number, pad: 'valid'|'same'|number,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n  avgPool<T extends Tensor3D|Tensor4D>(\n      x: T, filterSize: [number, number]|number,\n      strides: [number, number]|number, pad: 'valid'|'same'|number,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n  pool<T extends Tensor3D|Tensor4D>(\n      input: T, windowShape: [number, number]|number, poolingType: 'avg'|'max',\n      padding: 'valid'|'same'|number, diationRate?: [number, number]|number,\n      strides?: [number, number]|number): T;\n  localResponseNormalization<T extends Tensor3D|Tensor4D>(\n      x: T, depthRadius: number, bias: number, alpha: number, beta: number): T;\n  unsortedSegmentSum<T extends Tensor>(\n      x: T, segmentIds: Tensor1D|TensorLike1D, numSegments: number): T;\n  batchToSpaceND<T extends Tensor>(\n      x: T, blockShape: number[], crops: number[][]): T;\n  spaceToBatchND<T extends Tensor>(\n      x: T, blockShape: number[], paddings: number[][]): T;\n  topk<T extends Tensor>(x: T, k: number, sorted: boolean):\n      {values: T, indices: T};\n  stridedSlice<T extends Tensor>(\n      x: T, begin: number[], end: number[], strides: number[],\n      beginMask: number, endMask: number): T;\n  depthToSpace(x: Tensor4D, blockSize: number, dataFormat: string): Tensor4D;\n  spectral: {fft(x: Tensor): Tensor; ifft(x: Tensor): Tensor;};\n}\n\n// For tracking tensor creation and disposal.\nlet trackerFn: () => TensorTracker = null;\n// Used by chaining methods to call into ops.\nlet opHandler: OpHandler = null;\n\n/**\n * An external consumer can register itself as the tensor tracker. This way\n * the Tensor class can notify the tracker for every tensor created and\n * disposed.\n */\nexport function setTensorTracker(fn: () => TensorTracker) {\n  trackerFn = fn;\n}\n\n/**\n * An external consumer can register itself as the op handler. This way the\n * Tensor class can have chaining methods that call into ops via the op handler.\n */\nexport function setOpHandler(handler: OpHandler) {\n  opHandler = handler;\n}\n\n/**\n * We wrap data id since we use weak map to avoid memory leaks.\n * Since we have our own memory management, we have a reference counter\n * mapping a tensor to its data, so there is always a pointer (even if that\n * data is otherwise garbage collectable).\n * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/\n * Global_Objects/WeakMap\n */\nexport type DataId = object;  // object instead of {} to force non-primitive.\n\n/**\n * A `tf.Tensor` object represents an immutable, multidimensional array of\n * numbers that has a shape and a data type.\n *\n * See `tf.tensor` for details on how to create a `tf.Tensor`.\n */\n/** @doc {heading: 'Tensors', subheading: 'Classes'} */\nexport class Tensor<R extends Rank = Rank> {\n  /** Unique id of this tensor. */\n  readonly id: number;\n  /**\n   * Id of the bucket holding the data for this tensor. Multiple arrays can\n   * point to the same bucket (e.g. when calling array.reshape()).\n   */\n  dataId: DataId;\n  /** The shape of the tensor. */\n  readonly shape: ShapeMap[R];\n  /** Number of elements in the tensor. */\n  readonly size: number;\n  /** The data type for the array. */\n  readonly dtype: DataType;\n  /** The rank type for the array (see `Rank` enum). */\n  readonly rankType: R;\n\n  /**\n   * Number of elements to skip in each dimension when indexing. See\n   * https://docs.scipy.org/doc/numpy/reference/generated/\\\n   * numpy.ndarray.strides.html\n   */\n  readonly strides: number[];\n\n  protected constructor(\n      shape: ShapeMap[R], dtype: DataType, values?: DataValues,\n      dataId?: DataId) {\n    this.shape = shape.slice();\n    this.dtype = dtype || 'float32';\n    this.size = util.sizeFromShape(shape);\n    this.strides = computeStrides(shape);\n    this.dataId = dataId != null ? dataId : {};\n    this.id = trackerFn().nextTensorId();\n    this.rankType = (this.rank < 5 ? this.rank.toString() : 'higher') as R;\n    trackerFn().registerTensor(this);\n    if (values != null) {\n      trackerFn().write(this.dataId, values);\n    }\n  }\n\n  /**\n   * Makes a new tensor with the provided shape and values. Values should be in\n   * a flat array.\n   */\n  static make<T extends Tensor<R>, D extends DataType = 'float32',\n                                             R extends Rank = Rank>(\n      shape: ShapeMap[R], data: TensorData<D>, dtype?: D): T {\n    return new Tensor(shape, dtype, data.values, data.dataId) as T;\n  }\n\n  /** Flatten a Tensor to a 1D array. */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  flatten(): Tensor1D {\n    this.throwIfDisposed();\n    return this.as1D();\n  }\n\n  /** Converts a size-1 `tf.Tensor` to a `tf.Scalar`. */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  asScalar(): Scalar {\n    this.throwIfDisposed();\n    util.assert(this.size === 1, 'The array must have only 1 element.');\n    return this.reshape<Rank.R0>([]);\n  }\n\n  /** Converts a `tf.Tensor` to a `tf.Tensor1D`. */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  as1D(): Tensor1D {\n    this.throwIfDisposed();\n    return this.reshape<Rank.R1>([this.size]);\n  }\n\n  /**\n   * Converts a `tf.Tensor` to a `tf.Tensor2D`.\n   *\n   * @param rows Number of rows in `tf.Tensor2D`.\n   * @param columns Number of columns in `tf.Tensor2D`.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  as2D(rows: number, columns: number): Tensor2D {\n    this.throwIfDisposed();\n    return this.reshape<Rank.R2>([rows, columns]);\n  }\n\n  /**\n   * Converts a `tf.Tensor` to a `tf.Tensor3D`.\n   *\n   * @param rows Number of rows in `tf.Tensor3D`.\n   * @param columns Number of columns in `tf.Tensor3D`.\n   * @param depth Depth of `tf.Tensor3D`.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  as3D(rows: number, columns: number, depth: number): Tensor3D {\n    this.throwIfDisposed();\n    return this.reshape<Rank.R3>([rows, columns, depth]);\n  }\n\n  /**\n   * Converts a `tf.Tensor` to a `tf.Tensor4D`.\n   *\n   * @param rows Number of rows in `tf.Tensor4D`.\n   * @param columns Number of columns in `tf.Tensor4D`.\n   * @param depth Depth of `tf.Tensor4D`.\n   * @param depth2 4th dimension of `tf.Tensor4D`.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  as4D(rows: number, columns: number, depth: number, depth2: number): Tensor4D {\n    this.throwIfDisposed();\n    return this.reshape<Rank.R4>([rows, columns, depth, depth2]);\n  }\n\n  /**\n   * Converts a `tf.Tensor` to a `tf.Tensor5D`.\n   *\n   * @param rows Number of rows in `tf.Tensor5D`.\n   * @param columns Number of columns in `tf.Tensor5D`.\n   * @param depth Depth of `tf.Tensor5D`.\n   * @param depth2 4th dimension of `tf.Tensor5D`.\n   * @param depth3 5th dimension of 'tf.Tensor5D'\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  as5D(\n      rows: number, columns: number, depth: number, depth2: number,\n      depth3: number): Tensor5D {\n    this.throwIfDisposed();\n    return this.reshape<Rank.R5>([rows, columns, depth, depth2, depth3]);\n  }\n\n  /**\n   * Casts a `tf.Tensor` to a specified dtype.\n   *\n   * @param dtype Data-type to cast the tensor to.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  asType<T extends this>(this: T, dtype: DataType): T {\n    this.throwIfDisposed();\n    return opHandler.cast(this, dtype) as T;\n  }\n\n  get rank(): number {\n    return this.shape.length;\n  }\n\n  /**\n   * Returns the value in the tensor at the provided location.\n   * If using WebGL backend, this is a blocking call.\n   * Prefer calling the `async data()[flatIndex]` method instead.\n   *\n   * @param locs The location indices.\n   */\n  get(...locs: number[]) {\n    util.assert(\n        locs.length === this.rank,\n        'Number of coordinates in get() must match the rank of the tensor');\n    util.assert(\n        this.dtype !== 'complex64',\n        'Tensor.get() is not supported for complex64 tensors yet.');\n    this.throwIfDisposed();\n    if (locs.length === 0) {\n      locs = [0];\n    }\n    let index = locs[locs.length - 1];\n    for (let i = 0; i < locs.length - 1; ++i) {\n      index += this.strides[i] * locs[i];\n    }\n    return this.dataSync()[index];\n  }\n\n  /** Returns a `tf.TensorBuffer` that holds the underlying data. */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  buffer<D extends DataType>(): TensorBuffer<R, D> {\n    return opHandler.buffer(this.shape, this.dtype as D, this.dataSync());\n  }\n\n  /**\n   * Asynchronously downloads the values from the `tf.Tensor`. Returns a promise\n   * of `TypedArray` that resolves when the computation has finished.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  async data<D extends DataType = NumericDataType>(): Promise<DataTypeMap[D]> {\n    this.throwIfDisposed();\n    return trackerFn().read(this.dataId);\n  }\n\n  /**\n   * Synchronously downloads the values from the `tf.Tensor`. This blocks the UI\n   * thread until the values are ready, which can cause performance issues.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  dataSync<D extends DataType = NumericDataType>(): DataTypeMap[D] {\n    this.throwIfDisposed();\n    return trackerFn().readSync(this.dataId);\n  }\n\n  /**\n   * Disposes `tf.Tensor` from memory.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  dispose(): void {\n    if (this.isDisposed) {\n      return;\n    }\n    trackerFn().disposeTensor(this);\n    this.isDisposedInternal = true;\n  }\n\n  private isDisposedInternal = false;\n  get isDisposed(): boolean {\n    return this.isDisposedInternal;\n  }\n\n  private throwIfDisposed() {\n    if (this.isDisposed) {\n      throw new Error(`Tensor is disposed.`);\n    }\n  }\n\n  /** Casts the array to type `float32` */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  toFloat<T extends this>(this: T): T {\n    return this.asType('float32');\n  }\n\n  /** Casts the array to type `int32` */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  toInt() {\n    return this.asType('int32');\n  }\n\n  /** Casts the array to type `bool` */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  toBool() {\n    return this.asType('bool');\n  }\n\n  /**\n   * Prints the `tf.Tensor`. See `tf.print` for details.\n   *\n   * @param verbose Whether to print verbose information about the tensor,\n   *    including dtype and size.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  print(verbose = false): void {\n    return opHandler.print(this, verbose);\n  }\n\n  /**\n   * Reshapes the tensor into the provided shape.\n   * See `tf.reshape` for more details.\n   *\n   * @param newShape An array of integers defining the output tensor shape.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  reshape<R2 extends Rank>(newShape: ShapeMap[R2]): Tensor<R2> {\n    this.throwIfDisposed();\n    return opHandler.reshape(this, newShape);\n  }\n\n  /**\n   * Reshapes the tensor into the shape of the provided tensor.\n   *\n   * @param x The tensor of required shape.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  reshapeAs<T extends Tensor>(x: T): T {\n    this.throwIfDisposed();\n    return this.reshape(x.shape) as T;\n  }\n\n  /**\n   * Returns a `tf.Tensor` that has expanded rank, by inserting a dimension\n   * into the tensor's shape. See `tf.expandDims` for details.\n   *\n   * @param axis The dimension index at which to insert shape of 1. Defaults to\n   *    0 (the first dimension).\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  expandDims<R2 extends Rank>(axis = 0): Tensor<R2> {\n    return opHandler.expandDims(this, axis);\n  }\n\n  /**\n   * Returns the cumulative sum of the `tf.Tensor` along `axis`.\n   *\n   * @param axis The axis along which to sum. Optional. Defaults to 0.\n   * @param exclusive Whether to perform exclusive cumulative sum. Defaults to\n   *    false. If set to true then the sum of each tensor entry does not include\n   *    its own value, but only the values previous to it along the specified\n   *    axis.\n   * @param reverse Whether to sum in the opposite direction. Defaults to\n   *    false.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  cumsum<T extends Tensor>(axis = 0, exclusive = false, reverse = false): T {\n    return opHandler.cumsum(this, axis, exclusive, reverse);\n  }\n\n  /**\n   * Returns a `tf.Tensor` with dimensions of size 1 removed from the shape.\n   * See `tf.squeeze` for more details.\n   *\n   * @param axis A list of numbers. If specified, only squeezes the\n   *    dimensions listed. The dimension index starts at 0. It is an error to\n   *    squeeze a dimension that is not 1.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  squeeze<T extends Tensor>(axis?: number[]): T {\n    this.throwIfDisposed();\n    return opHandler.squeeze(this, axis);\n  }\n\n  /** Returns a copy of the tensor. See `tf.clone` for details. */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  clone<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.clone(this);\n  }\n\n  /** Returns a human-readable description of the tensor. Useful for logging. */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  toString(verbose = false): string {\n    const vals = this.dataSync();\n    return tensorToString(vals, this.shape, this.dtype, verbose);\n  }\n\n  // Below is chain API that is not exposed to docs to avoid repetition. To\n  // expose a method, move it above this comment and add @doc and jsdoc.\n\n  tile<T extends this>(this: T, reps: number[]): T {\n    this.throwIfDisposed();\n    return opHandler.tile(this, reps) as T;\n  }\n\n  gather<T extends this>(this: T, indices: Tensor1D|TensorLike1D, axis = 0): T {\n    this.throwIfDisposed();\n    return opHandler.gather(this, indices, axis) as T;\n  }\n\n  matMul<T extends Tensor>(\n      this: T, b: T|TensorLike, transposeA = false, transposeB = false): T {\n    this.throwIfDisposed();\n    return opHandler.matMul(this, b, transposeA, transposeB);\n  }\n  dot(b: Tensor|TensorLike): Tensor {\n    this.throwIfDisposed();\n    return opHandler.dot(this, b);\n  }\n  norm(\n      ord: number|'euclidean'|'fro' = 'euclidean', axis: number|number[] = null,\n      keepDims = false): Tensor {\n    this.throwIfDisposed();\n    return opHandler.norm(this, ord, axis, keepDims);\n  }\n  slice<T extends Tensor<R>>(\n      this: T, begin: number|number[], size?: number|number[]): T {\n    this.throwIfDisposed();\n    return opHandler.slice(this, begin, size);\n  }\n  reverse<T extends Tensor>(this: T, axis?: number|number[]): T {\n    this.throwIfDisposed();\n    return opHandler.reverse(this, axis);\n  }\n  concat<T extends Tensor>(this: T, x: T|Array<T|TensorLike>, axis = 0): T {\n    this.throwIfDisposed();\n    if (x instanceof Tensor) {\n      x = [x];\n    }\n    return opHandler.concat([this, ...x], axis);\n  }\n  split<T extends Tensor>(this: T, numOrSizeSplits: number[]|number, axis = 0):\n      T[] {\n    this.throwIfDisposed();\n    return opHandler.split(this, numOrSizeSplits, axis);\n  }\n  stack(x: Tensor, axis = 0): Tensor {\n    return opHandler.stack([this, x], axis);\n  }\n  unstack(x: Tensor, axis = 0): Tensor[] {\n    return opHandler.unstack(this, axis);\n  }\n  pad<T extends Tensor>(\n      this: T, paddings: Array<[number, number]>, constantValue = 0): T {\n    return opHandler.pad(this, paddings, constantValue);\n  }\n  batchNormalization(\n      mean: Tensor<R>|Tensor1D|TensorLike,\n      variance: Tensor<R>|Tensor1D|TensorLike, varianceEpsilon = .001,\n      scale?: Tensor<R>|Tensor1D|TensorLike,\n      offset?: Tensor<R>|Tensor1D|TensorLike): Tensor<R> {\n    this.throwIfDisposed();\n    return opHandler.batchNormalization(\n        this, mean, variance, varianceEpsilon, scale, offset);\n  }\n\n  // Reduction ops.\n  all<T extends Tensor>(axis: number|number[] = null, keepDims = false): T {\n    this.throwIfDisposed();\n    return opHandler.all(this, axis, keepDims);\n  }\n  any<T extends Tensor>(axis: number|number[] = null, keepDims = false): T {\n    this.throwIfDisposed();\n    return opHandler.any(this, axis, keepDims);\n  }\n  logSumExp<T extends Tensor>(axis: number|number[] = null, keepDims = false):\n      T {\n    this.throwIfDisposed();\n    return opHandler.logSumExp(this, axis, keepDims);\n  }\n  sum<T extends Tensor>(axis: number|number[] = null, keepDims = false): T {\n    this.throwIfDisposed();\n    return opHandler.sum(this, axis, keepDims);\n  }\n  prod<T extends Tensor>(axis: number|number[] = null, keepDims = false): T {\n    this.throwIfDisposed();\n    return opHandler.prod(this, axis, keepDims);\n  }\n  mean<T extends Tensor>(axis: number|number[] = null, keepDims = false): T {\n    this.throwIfDisposed();\n    return opHandler.mean(this, axis, keepDims);\n  }\n  min<T extends Tensor>(axis: number|number[] = null, keepDims = false): T {\n    this.throwIfDisposed();\n    return opHandler.min(this, axis, keepDims);\n  }\n  max<T extends Tensor>(axis: number|number[] = null, keepDims = false): T {\n    this.throwIfDisposed();\n    return opHandler.max(this, axis, keepDims);\n  }\n  argMin<T extends Tensor>(axis: number = null): T {\n    this.throwIfDisposed();\n    return opHandler.argMin(this, axis);\n  }\n  argMax<T extends Tensor>(axis: number = null): T {\n    this.throwIfDisposed();\n    return opHandler.argMax(this, axis);\n  }\n\n  // Transformations\n  cast<T extends this>(dtype: DataType): T {\n    this.throwIfDisposed();\n    return opHandler.cast(this as T, dtype) as T;\n  }\n\n  // Binary ops.\n\n  add<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.add(this, x);\n  }\n  addStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.addStrict(this, x) as T;\n  }\n  atan2<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.atan2(this, x) as T;\n  }\n  sub<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.sub(this, x);\n  }\n  subStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.subStrict(this, x) as T;\n  }\n  pow<T extends Tensor>(this: T, exp: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.pow(this, exp);\n  }\n  powStrict(exp: Tensor|TensorLike): Tensor<R> {\n    this.throwIfDisposed();\n    return opHandler.powStrict(this, exp);\n  }\n  mul<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.mul(this, x);\n  }\n  mulStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.mulStrict(this, x) as T;\n  }\n  div<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.div(this, x);\n  }\n  floorDiv<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.floorDiv(this, x);\n  }\n  divStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.divStrict(this, x) as T;\n  }\n  minimum<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.minimum(this, x);\n  }\n  minimumStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.minimumStrict(this, x) as T;\n  }\n  maximum<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.maximum(this, x);\n  }\n  maximumStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.maximumStrict(this, x) as T;\n  }\n  mod<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.mod(this, x);\n  }\n  modStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.modStrict(this, x) as T;\n  }\n  squaredDifference<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.squaredDifference(this, x);\n  }\n  squaredDifferenceStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.squaredDifferenceStrict(this, x) as T;\n  }\n  transpose<T extends Tensor>(this: T, perm?: number[]): T {\n    this.throwIfDisposed();\n    return opHandler.transpose(this, perm);\n  }\n\n  // Compare ops.\n\n  notEqual<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.notEqual(this, x);\n  }\n  notEqualStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.notEqualStrict(this, x) as T;\n  }\n  less<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.less(this, x);\n  }\n  lessStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.lessStrict(this, x) as T;\n  }\n  equal<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.equal(this, x);\n  }\n  equalStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.equalStrict(this, x) as T;\n  }\n  lessEqual<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.lessEqual(this, x);\n  }\n  lessEqualStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.lessEqualStrict(this, x) as T;\n  }\n  greater<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.greater(this, x);\n  }\n  greaterStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.greaterStrict(this, x) as T;\n  }\n  greaterEqual<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.greaterEqual(this, x);\n  }\n  greaterEqualStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.greaterEqualStrict(this, x) as T;\n  }\n\n  // Compare ops.\n  logicalAnd(x: Tensor|TensorLike): Tensor {\n    this.throwIfDisposed();\n    return opHandler.logicalAnd(this, x);\n  }\n  logicalOr(x: Tensor|TensorLike): Tensor {\n    this.throwIfDisposed();\n    return opHandler.logicalOr(this, x);\n  }\n  logicalNot<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.logicalNot(this);\n  }\n  logicalXor(x: Tensor|TensorLike): Tensor {\n    this.throwIfDisposed();\n    return opHandler.logicalXor(this, x);\n  }\n  where(condition: Tensor|TensorLike, x: Tensor|TensorLike): Tensor {\n    this.throwIfDisposed();\n    return opHandler.where(condition, this, x);\n  }\n\n  // Unary ops.\n  neg<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.neg(this);\n  }\n  ceil<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.ceil(this);\n  }\n  floor<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.floor(this);\n  }\n  sign<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.sign(this);\n  }\n  exp<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.exp(this);\n  }\n  expm1<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.expm1(this);\n  }\n  log<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.log(this);\n  }\n  log1p<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.log1p(this);\n  }\n  sqrt<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.sqrt(this);\n  }\n  rsqrt<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.rsqrt(this);\n  }\n  square<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.square(this);\n  }\n  reciprocal<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.reciprocal(this);\n  }\n  abs<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.abs(this);\n  }\n  clipByValue(min: number, max: number): Tensor<R> {\n    this.throwIfDisposed();\n    return opHandler.clipByValue(this, min, max);\n  }\n  relu<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.relu(this);\n  }\n  elu<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.elu(this);\n  }\n  selu<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.selu(this);\n  }\n  leakyRelu(alpha = 0.2): Tensor<R> {\n    this.throwIfDisposed();\n    return opHandler.leakyRelu(this, alpha);\n  }\n  prelu(alpha: Tensor<R>|TensorLike): Tensor<R> {\n    this.throwIfDisposed();\n    return opHandler.prelu(this, alpha);\n  }\n  sigmoid<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.sigmoid(this);\n  }\n  logSigmoid<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.logSigmoid(this);\n  }\n  softplus<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.softplus(this);\n  }\n  zerosLike<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.zerosLike(this);\n  }\n  onesLike<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.onesLike(this);\n  }\n  sin<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.sin(this);\n  }\n  cos<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.cos(this);\n  }\n  tan<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.tan(this);\n  }\n  asin<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.asin(this);\n  }\n  acos<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.acos(this);\n  }\n  atan<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.atan(this);\n  }\n  sinh<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.sinh(this);\n  }\n  cosh<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.cosh(this);\n  }\n  tanh<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.tanh(this);\n  }\n  asinh<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.asinh(this);\n  }\n  acosh<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.acosh(this);\n  }\n  atanh<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.atanh(this);\n  }\n  erf<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.erf(this);\n  }\n  round<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.round(this);\n  }\n  step<T extends Tensor>(this: T, alpha = 0.0): T {\n    this.throwIfDisposed();\n    return opHandler.step(this, alpha);\n  }\n  softmax<T extends this>(this: T, dim = -1): T {\n    this.throwIfDisposed();\n    return opHandler.softmax(this, dim) as T;\n  }\n  logSoftmax<T extends this>(this: T, axis = -1): T {\n    this.throwIfDisposed();\n    return opHandler.logSoftmax(this, axis) as T;\n  }\n\n  // Image ops.\n  resizeBilinear<T extends Tensor3D|Tensor4D>(\n      this: T, newShape2D: [number, number], alignCorners = false): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.image.resizeBilinear(this, newShape2D, alignCorners);\n  }\n\n  resizeNearestNeighbor<T extends Tensor3D|Tensor4D>(\n      this: T, newShape2D: [number, number], alignCorners = false): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.image.resizeNearestNeighbor(\n        this, newShape2D, alignCorners);\n  }\n\n  // Convolutions.\n  conv1d<T extends Tensor2D|Tensor3D>(\n      this: T, filter: Tensor3D|TensorLike3D, stride: number,\n      pad: 'valid'|'same'|number, dataFormat: 'NWC'|'NCW' = 'NWC', dilation = 1,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.conv1d(\n        this, filter, stride, pad, dataFormat, dilation, dimRoundingMode);\n  }\n  conv2d<T extends Tensor3D|Tensor4D>(\n      this: T, filter: Tensor4D|TensorLike4D, strides: [number, number]|number,\n      pad: 'valid'|'same'|number, dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n      dilations: [number, number]|number = [1, 1],\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.conv2d(\n        this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n  }\n  conv2dTranspose<T extends Tensor3D|Tensor4D>(\n      this: T, filter: Tensor4D|TensorLike4D,\n      outputShape: [number, number, number, number]|[number, number, number],\n      strides: [number, number]|number, pad: 'valid'|'same'|number,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.conv2dTranspose(\n        this, filter, outputShape, strides, pad, dimRoundingMode);\n  }\n  depthwiseConv2D<T extends Tensor3D|Tensor4D>(\n      this: T, filter: Tensor4D|TensorLike4D, strides: [number, number]|number,\n      pad: 'valid'|'same'|number, dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n      dilations: [number, number]|number = [1, 1],\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.depthwiseConv2d(\n        this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n  }\n\n  separableConv2d<T extends Tensor3D|Tensor4D>(\n      this: T|TensorLike, depthwiseFilter: Tensor4D|TensorLike4D,\n      pointwiseFilter: Tensor4D|TensorLike, strides: [number, number]|number,\n      pad: 'valid'|'same', dilation: [number, number]|number = [1, 1],\n      dataFormat: 'NHWC'|'NCHW' = 'NHWC'): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.separableConv2d(\n        this, depthwiseFilter, pointwiseFilter, strides, pad, dilation,\n        dataFormat);\n  }\n\n  // Pooling.\n  avgPool<T extends Tensor3D|Tensor4D>(\n      this: T, filterSize: [number, number]|number,\n      strides: [number, number]|number, pad: 'valid'|'same'|number,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.avgPool(this, filterSize, strides, pad, dimRoundingMode);\n  }\n  maxPool<T extends Tensor3D|Tensor4D>(\n      this: T, filterSize: [number, number]|number,\n      strides: [number, number]|number, pad: 'valid'|'same'|number,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.maxPool(this, filterSize, strides, pad, dimRoundingMode);\n  }\n  localResponseNormalization<T extends Tensor3D|Tensor4D>(\n      this: T, radius = 5, bias = 1, alpha = 1, beta = 0.5): T {\n    return opHandler.localResponseNormalization(\n        this, radius, bias, alpha, beta);\n  }\n  pool<T extends Tensor3D|Tensor4D>(\n      this: T, windowShape: [number, number]|number, poolingType: 'max'|'avg',\n      padding: 'valid'|'same'|number, dilationRate?: [number, number]|number,\n      strides?: [number, number]|number): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.pool(\n        this, windowShape, poolingType, padding, dilationRate, strides);\n  }\n\n  variable(trainable = true, name?: string, dtype?: DataType): Variable<R> {\n    this.throwIfDisposed();\n    return Variable.variable(this, trainable, name, dtype);\n  }\n\n  unsortedSegmentSum<T extends Tensor>(\n      this: T, segmentIds: Tensor1D|TensorLike1D, numSegments: number): T {\n    this.throwIfDisposed();\n    return opHandler.unsortedSegmentSum(this, segmentIds, numSegments);\n  }\n\n  batchToSpaceND<T extends Tensor>(\n      this: T, blockShape: number[], crops: number[][]): T {\n    this.throwIfDisposed();\n    return opHandler.batchToSpaceND(this, blockShape, crops);\n  }\n\n  spaceToBatchND<T extends Tensor>(\n      this: T, blockShape: number[], paddings: number[][]): T {\n    this.throwIfDisposed();\n    return opHandler.spaceToBatchND(this, blockShape, paddings);\n  }\n\n  topk<T extends Tensor>(this: T, k = 1, sorted = true):\n      {values: T, indices: T} {\n    this.throwIfDisposed();\n    return opHandler.topk(this, k, sorted);\n  }\n\n  stridedSlice<T extends Tensor>(\n      this: T, begin: number[], end: number[], strides: number[], beginMask = 0,\n      endMask = 0): T {\n    this.throwIfDisposed();\n    return opHandler.stridedSlice(\n        this, begin, end, strides, beginMask, endMask);\n  }\n\n  depthToSpace(this: Tensor4D, blockSize: number, dataFormat: 'NHWC'|'NCHW'):\n      Tensor4D {\n    this.throwIfDisposed();\n    return opHandler.depthToSpace(this, blockSize, dataFormat);\n  }\n\n  fft(this: Tensor): Tensor {\n    this.throwIfDisposed();\n    return opHandler.spectral.fft(this);\n  }\n\n  ifft(this: Tensor): Tensor {\n    this.throwIfDisposed();\n    return opHandler.spectral.ifft(this);\n  }\n}\nObject.defineProperty(Tensor, Symbol.hasInstance, {\n  value: (instance: Tensor) => {\n    return !!instance && instance.shape != null && instance.dtype != null;\n  }\n});\n\nexport interface NumericTensor<R extends Rank = Rank> extends Tensor<R> {\n  dtype: NumericDataType;\n  data(): Promise<TypedArray>;\n  dataSync(): TypedArray;\n}\n\nexport interface StringTensor<R extends Rank = Rank> extends Tensor<R> {\n  dtype: 'string';\n  dataSync(): string[];\n  data(): Promise<string[]>;\n}\n\n/** @doclink Tensor */\nexport type Scalar = Tensor<Rank.R0>;\n/** @doclink Tensor */\nexport type Tensor1D = Tensor<Rank.R1>;\n/** @doclink Tensor */\nexport type Tensor2D = Tensor<Rank.R2>;\n/** @doclink Tensor */\nexport type Tensor3D = Tensor<Rank.R3>;\n/** @doclink Tensor */\nexport type Tensor4D = Tensor<Rank.R4>;\n/** @doclink Tensor */\nexport type Tensor5D = Tensor<Rank.R5>;\n/** @doclink Tensor */\nexport type Tensor6D = Tensor<Rank.R6>;\n\n/**\n * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.\n */\n/** @doc {heading: 'Tensors', subheading: 'Classes'} */\nexport class Variable<R extends Rank = Rank> extends Tensor<R> {\n  name: string;\n\n  /**\n   * Private constructor since we cannot add logic before calling `super()`.\n   * Instead, we expose static `Variable.variable` method below, which will be\n   * added to global namespace.\n   */\n  private constructor(\n      initialValue: Tensor<R>, public trainable = true, name?: string) {\n    super(\n        initialValue.shape, initialValue.dtype, null /* values */,\n        initialValue.dataId);\n    this.name = name;\n    if (this.name == null) {\n      this.name = trackerFn().nextVariableId().toString();\n    }\n    try {\n      trackerFn().registerVariable(this);\n    } catch (ex) {\n      trackerFn().disposeTensor(this);\n      throw ex;\n    }\n  }\n\n  /**\n   * Creates a new variable with the provided initial value.\n   * ```js\n   * const x = tf.variable(tf.tensor([1, 2, 3]));\n   * x.assign(tf.tensor([4, 5, 6]));\n   *\n   * x.print();\n   * ```\n   *\n   * @param initialValue Initial value for the tensor.\n   * @param trainable If true, optimizers are allowed to update it.\n   * @param name Name of the variable. Defaults to a unique id.\n   * @param dtype If set, initialValue will be converted to the given type.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Creation'} */\n  static variable<R extends Rank>(\n      initialValue: Tensor<R>, trainable = true, name?: string,\n      dtype?: DataType): Variable<R> {\n    if (dtype != null && dtype !== initialValue.dtype) {\n      initialValue = initialValue.asType(dtype) as Tensor<R>;\n    }\n    return new Variable(initialValue, trainable, name);\n  }\n\n  /**\n   * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have\n   * the same shape and dtype as the old `tf.Tensor`.\n   *\n   * @param newValue New tensor to be assigned to this variable.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  assign(newValue: Tensor<R>): void {\n    if (newValue.dtype !== this.dtype) {\n      throw new Error(\n          `dtype of the new value (${newValue.dtype}) and ` +\n          `previous value (${this.dtype}) must match`);\n    }\n    if (!util.arraysEqual(newValue.shape, this.shape)) {\n      throw new Error(\n          `shape of the new value (${newValue.shape}) and ` +\n          `previous value (${this.shape}) must match`);\n    }\n    trackerFn().disposeTensor(this);\n    this.dataId = newValue.dataId;\n    trackerFn().registerTensor(this);\n  }\n}\nObject.defineProperty(Variable, Symbol.hasInstance, {\n  value: (instance: Variable) => {\n    return instance instanceof Tensor && instance.assign != null &&\n        instance.assign instanceof Function;\n  }\n});\n\nconst variable = Variable.variable;\nexport {variable};\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/** @docalias number[] */\nexport interface ShapeMap {\n  R0: number[];\n  R1: [number];\n  R2: [number, number];\n  R3: [number, number, number];\n  R4: [number, number, number, number];\n  R5: [number, number, number, number, number];\n  R6: [number, number, number, number, number, number];\n}\n\nexport interface DataTypeMap {\n  float32: Float32Array;\n  int32: Int32Array;\n  bool: Uint8Array;\n  complex64: Float32Array;\n  string: string[];\n}\n\nexport interface SingleValueMap {\n  bool: boolean;\n  int32: number;\n  float32: number;\n  complex64: number;\n  string: string;\n}\n\n/** @docalias 'float32'|'int32'|'bool'|'complex64'|'string' */\nexport type DataType = keyof DataTypeMap;\nexport type NumericDataType = 'float32'|'int32'|'bool'|'complex64';\nexport type TypedArray = Float32Array|Int32Array|Uint8Array;\nexport type DataValues = DataTypeMap[DataType];\n\nexport enum Rank {\n  R0 = 'R0',\n  R1 = 'R1',\n  R2 = 'R2',\n  R3 = 'R3',\n  R4 = 'R4',\n  R5 = 'R5',\n  R6 = 'R6'\n}\n\nexport type FlatVector = boolean[]|number[]|TypedArray;\nexport type RegularArray<T> =\n    T[]|T[][]|T[][][]|T[][][][]|T[][][][][]|T[][][][][][];\n\n// tslint:disable-next-line:no-any\nexport interface RecursiveArray<T extends any> {\n  [index: number]: T|RecursiveArray<T>;\n}\n\n// Looks for upcasting types. Used, for example, in operations with mixed dtype\n// inputs.\nenum UpcastInt32AndMap {\n  'float32' = 'float32',\n  'int32' = 'int32',\n  'bool' = 'int32',\n  'complex64' = 'complex64'\n}\n\nenum UpcastBoolAndMap {\n  'float32' = 'float32',\n  'int32' = 'int32',\n  'bool' = 'bool',\n  'complex64' = 'complex64'\n}\n\nenum UpcastFloat32AndMap {\n  'float32' = 'float32',\n  'int32' = 'float32',\n  'bool' = 'float32',\n  'complex64' = 'complex64'\n}\n\nenum UpcastComplex64AndMap {\n  'float32' = 'complex64',\n  'int32' = 'complex64',\n  'bool' = 'complex64',\n  'complex64' = 'complex64'\n}\n\nconst upcastTypeMap = {\n  'float32': UpcastFloat32AndMap,\n  'int32': UpcastInt32AndMap,\n  'bool': UpcastBoolAndMap,\n  'complex64': UpcastComplex64AndMap\n};\n\nexport function upcastType(typeA: DataType, typeB: DataType): DataType {\n  if (typeA === 'string' || typeB === 'string') {\n    if (typeA === 'string' && typeB === 'string') {\n      return 'string';\n    }\n    throw new Error(`Can not upcast ${typeA} with ${typeB}`);\n  }\n  return upcastTypeMap[typeA][typeB];\n}\n\n/** Returns the output type after summation. */\nexport function sumOutType(type: DataType): DataType {\n  return upcastType(type, 'int32');\n}\n\n/** @docalias TypedArray|Array */\nexport type TensorLike =\n    TypedArray|number|boolean|string|RegularArray<number|number[]|TypedArray>|\n    RegularArray<boolean>|RegularArray<string>;\n/** @docalias TypedArray|Array */\nexport type TensorLike1D = TypedArray|number[]|boolean[]|string[];\n/** @docalias TypedArray|Array */\nexport type TensorLike2D =\n    TypedArray|number[]|number[][]|boolean[]|boolean[][]|string[]|string[][];\n/** @docalias TypedArray|Array */\nexport type TensorLike3D = TypedArray|number[]|number[][][]|boolean[]|\n    boolean[][][]|string[]|string[][][];\n/** @docalias TypedArray|Array */\nexport type TensorLike4D = TypedArray|number[]|number[][][][]|boolean[]|\n    boolean[][][][]|string[]|string[][][][];\n/** @docalias TypedArray|Array */\nexport type TensorLike5D = TypedArray|number[]|number[][][][][]|boolean[]|\n    boolean[][][][][]|string[]|string[][][][][];\n/** @docalias TypedArray|Array */\nexport type TensorLike6D = TypedArray|number[]|number[][][][][][]|boolean[]|\n    boolean[][][][][][]|string[]|string[][][][][][];\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from './tensor';\nimport {NamedTensorMap} from './tensor_types';\nimport * as util from './util';\n\nexport interface TapeNode {\n  id: number;\n  name: string;\n  outputs: Tensor[];\n  inputs: NamedTensorMap;\n  // Optional params, defined only for ops with gradient impl.\n  gradient?: (dy: Tensor|Tensor[]) => NamedGradientMap;\n}\n\nexport type NamedGradientMap = {\n  [inputName: string]: () => Tensor;\n};\n\n/**\n * Computes a list of TapeNodes that connect x to y, filtering everything else\n * out and preserving the order of the original tape elements.\n *\n * @param tape The tape elements to filter.\n * @param xs The input Tensors.\n * @param y The output Tensor.\n */\nexport function getFilteredNodesXToY(\n    tape: TapeNode[], xs: Tensor[], y: Tensor): TapeNode[] {\n  // Forward pass to compute all the nodes and Tensors that are transitively a\n  // function of x.\n  const tensorsFromX: {[tensorId: number]: boolean} = {};\n  const nodesFromX: {[nodeId: number]: boolean} = {};\n  for (let i = 0; i < xs.length; i++) {\n    tensorsFromX[xs[i].id] = true;\n  }\n\n  for (let i = 0; i < tape.length; i++) {\n    const node = tape[i];\n    const nodeInputs = node.inputs;\n    for (const inputName in nodeInputs) {\n      const input = nodeInputs[inputName];\n\n      let anyInputFromX = false;\n      for (let j = 0; j < xs.length; j++) {\n        if (tensorsFromX[input.id]) {\n          node.outputs.forEach(output => tensorsFromX[output.id] = true);\n          anyInputFromX = true;\n          nodesFromX[node.id] = true;\n          break;\n        }\n      }\n\n      if (anyInputFromX) {\n        break;\n      }\n    }\n  }\n\n  // Backward pass to find all of the nodes and Tensors that lead to y.\n  const tensorsLeadToY: {[tensorId: number]: boolean} = {};\n  tensorsLeadToY[y.id] = true;\n  const nodesToY: {[nodeId: number]: boolean} = {};\n\n  for (let i = tape.length - 1; i >= 0; i--) {\n    const node = tape[i];\n    const nodeInputs = node.inputs;\n\n    // If any of the outputs lead to y, mark all of the inputs as leading to y.\n    for (let j = 0; j < node.outputs.length; j++) {\n      if (tensorsLeadToY[node.outputs[j].id]) {\n        for (const inputName in nodeInputs) {\n          tensorsLeadToY[nodeInputs[inputName].id] = true;\n          nodesToY[node.id] = true;\n        }\n        break;\n      }\n    }\n  }\n\n  // Return the paths that come from x and lead to y.\n  const filteredTape: TapeNode[] = [];\n  for (let i = 0; i < tape.length; i++) {\n    const node = tape[i];\n\n    if (nodesFromX[node.id] && nodesToY[node.id]) {\n      // Prune the inputs from the node that aren't a function of x.\n      const prunedInputs: {[inputName: string]: Tensor} = {};\n      for (const inputName in node.inputs) {\n        const nodeInput = node.inputs[inputName];\n        if (tensorsFromX[nodeInput.id]) {\n          prunedInputs[inputName] = nodeInput;\n        }\n      }\n\n      // Copy the node and overwrite inputsAndArgs to the pruned version.\n      const prunedNode = Object.assign({}, node) as TapeNode;\n      prunedNode.inputs = prunedInputs;\n      prunedNode.outputs = node.outputs;\n\n      filteredTape.push(prunedNode);\n    }\n  }\n\n  return filteredTape;\n}\n\n/**\n * Backpropagate gradients through the filtered TapeNodes.\n *\n * @param tensorAccumulatedGradientMap A map of Tensor to its gradient. This map\n * is mutated by this method.\n * @param filteredTape The filtered TapeNodes to backprop through.\n */\nexport function backpropagateGradients(\n    tensorAccumulatedGradientMap: {[tensorId: number]: Tensor},\n    filteredTape: TapeNode[]) {\n  // Walk the tape backward and keep a map of Tensor to its gradient.\n  for (let i = filteredTape.length - 1; i >= 0; i--) {\n    const node = filteredTape[i];\n\n    const dys: Tensor[] = [];\n    node.outputs.forEach(o => {\n      const gradTensor = tensorAccumulatedGradientMap[o.id];\n      if (gradTensor != null) {\n        dys.push(gradTensor);\n      } else {\n        // This particular output is not in the back-propagation subgraph, so it\n        // does not affect the final output, thus we put zeros for its dy.\n        const dy = Tensor.make(\n            o.shape, {values: util.makeZerosTypedArray(o.size, o.dtype)},\n            o.dtype);\n        dys.push(dy);\n      }\n    });\n\n    if (node.gradient == null) {\n      throw new Error(\n          `Cannot compute gradient: gradient function not found ` +\n          `for ${node.name}.`);\n    }\n\n    // Backprop dy through this node and accumulate gradients over the inputs.\n    const inputGradients =\n        // Grad functions of ops with single outputs expect a dy, while ops\n        // with multiple outputs expect dys (array of dy).\n        node.gradient(node.outputs.length === 1 ? dys[0] : dys);\n    for (const inputName in node.inputs) {\n      if (!(inputName in inputGradients)) {\n        throw new Error(\n            `Cannot backprop through input ${inputName}. ` +\n            `Available gradients found: ${Object.keys(inputGradients)}.`);\n      }\n\n      // Call the gradient function.\n      const dx = inputGradients[inputName]();\n      if (dx.dtype !== 'float32') {\n        throw new Error(\n            `Error in gradient for op ${node.name}. The gradient of input ` +\n            `${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);\n      }\n      const x = node.inputs[inputName];\n      if (!util.arraysEqual(dx.shape, x.shape)) {\n        throw new Error(\n            `Error in gradient for op ${node.name}. The gradient of input ` +\n            `'${inputName}' has shape '${dx.shape}', which does not match ` +\n            `the shape of the input '${x.shape}'`);\n      }\n\n      if (tensorAccumulatedGradientMap[x.id] == null) {\n        tensorAccumulatedGradientMap[x.id] = dx;\n      } else {\n        const curGradient = tensorAccumulatedGradientMap[x.id];\n        tensorAccumulatedGradientMap[x.id] = curGradient.add(dx);\n        curGradient.dispose();\n      }\n    }\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from './tensor';\nimport {NamedTensorMap, TensorContainer, TensorContainerArray} from './tensor_types';\nimport {upcastType} from './types';\nimport {assert} from './util';\n\nexport function makeTypesMatch<T extends Tensor>(a: T, b: T): [T, T] {\n  if (a.dtype === b.dtype) {\n    return [a, b];\n  }\n  const dtype = upcastType(a.dtype, b.dtype);\n  return [a.cast(dtype), b.cast(dtype)];\n}\n\nexport function assertTypesMatch(a: Tensor, b: Tensor): void {\n  assert(\n      a.dtype === b.dtype,\n      `The dtypes of the first(${a.dtype}) and` +\n          ` second(${b.dtype}) input must match`);\n}\n\nexport function isTensorInList(tensor: Tensor, tensorList: Tensor[]): boolean {\n  for (let i = 0; i < tensorList.length; i++) {\n    if (tensorList[i].id === tensor.id) {\n      return true;\n    }\n  }\n  return false;\n}\n\nexport function flattenNameArrayMap(\n    nameArrayMap: Tensor|NamedTensorMap, keys?: string[]): Tensor[] {\n  const xs: Tensor[] = [];\n  if (nameArrayMap instanceof Tensor) {\n    xs.push(nameArrayMap);\n  } else {\n    const xMap = nameArrayMap as {[xName: string]: Tensor};\n    for (let i = 0; i < keys.length; i++) {\n      xs.push(xMap[keys[i]]);\n    }\n  }\n  return xs;\n}\n\nexport function unflattenToNameArrayMap(\n    keys: string[], flatArrays: Tensor[]): NamedTensorMap {\n  if (keys.length !== flatArrays.length) {\n    throw new Error(\n        `Cannot unflatten Tensor[], keys and arrays are not of same length.`);\n  }\n  const result: NamedTensorMap = {};\n  for (let i = 0; i < keys.length; i++) {\n    result[keys[i]] = flatArrays[i];\n  }\n  return result;\n}\n\n/**\n * Extracts any `Tensor`s found within the provided object.\n *\n * @param container an object that may be a `Tensor` or may directly contain\n *   `Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. In general it\n *   is safe to pass any object here, except that `Promise`s are not\n *   supported.\n * @returns An array of `Tensors` found within the passed object. If the\n *   argument is simply a `Tensor', a list containing that `Tensor` is\n *   returned. If the object is not a `Tensor` or does not\n *   contain `Tensors`, an empty list is returned.\n */\nexport function getTensorsInContainer(result: TensorContainer): Tensor[] {\n  const list: Tensor[] = [];\n  const seen = new Set<{}|void>();\n  walkTensorContainer(result, list, seen);\n  return list;\n}\n\nfunction walkTensorContainer(\n    container: TensorContainer, list: Tensor[], seen: Set<{}|void>): void {\n  if (container == null) {\n    return;\n  }\n  if (container instanceof Tensor) {\n    list.push(container);\n    return;\n  }\n  if (!isIterable(container)) {\n    return;\n  }\n  // Iteration over keys works also for arrays.\n  const iterable = container as TensorContainerArray;\n  for (const k in iterable) {\n    const val = iterable[k];\n    if (!seen.has(val)) {\n      seen.add(val);\n      walkTensorContainer(val, list, seen);\n    }\n  }\n}\n\n// tslint:disable-next-line:no-any\nfunction isIterable(obj: any): boolean {\n  return Array.isArray(obj) || typeof obj === 'object';\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {BackendTimingInfo, DataMover, KernelBackend} from './kernels/backend';\nimport {Profiler} from './profiler';\nimport {backpropagateGradients, getFilteredNodesXToY, NamedGradientMap, TapeNode} from './tape';\nimport {DataId, Tensor, Tensor3D, Variable} from './tensor';\nimport {NamedTensorMap, NamedVariableMap, TensorContainer} from './tensor_types';\nimport {getTensorsInContainer, isTensorInList} from './tensor_util';\nimport {DataType, DataValues} from './types';\nimport * as util from './util';\nimport {bytesFromStringArray, makeOnesTypedArray, now, sizeFromShape} from './util';\n\n/**\n * A function that computes an output. The save function is for saving tensors\n * computed in the forward pass, that we need in the backward pass.\n */\nexport type ForwardFunc<T> =\n    (backend: KernelBackend, save?: <S extends Tensor>(tensor: S) => S) => T;\n\n/**\n * @docalias (a: Tensor, b: Tensor,...) => {\n * value: Tensor, * gradFunc: (dy: Tensor) => Tensor | Tensor[] * }\n */\nexport type CustomGradientFunc<T extends Tensor> = (...args: Tensor[]) => {\n  value: T, gradFunc: (dy: T) => Tensor | Tensor[];\n};\n\nexport type MemoryInfo = {\n  numTensors: number; numDataBuffers: number; numBytes: number;\n  unreliable?: boolean; reasons: string[];\n};\n\ntype KernelProfile = {\n  name: string; bytesAdded: number; totalBytesSnapshot: number;\n  tensorsAdded: number;\n  totalTensorsSnapshot: number;\n  inputShapes: number[][];\n  outputShape: number[] | number[][];\n};\n\nexport type ProfileInfo = {\n  newBytes: number; newTensors: number; peakBytes: number;\n  kernels: KernelProfile[];\n  result: TensorContainer;\n};\n\nexport interface TimingInfo extends BackendTimingInfo {\n  wallMs: number;\n}\n\n/** @docalias Function */\nexport type ScopeFn<T extends TensorContainer> = () => T;\n\nexport interface TensorManager {\n  registerTensor(a: Tensor): void;\n  registerVariable(v: Variable): void;\n  disposeTensor(a: Tensor): void;\n  memory(): {numDataBuffers: number; numBytes: number;};\n}\n\ninterface ScopeState {\n  track: Tensor[];\n  name: string;\n}\n\nexport class Engine implements TensorManager, DataMover {\n  // Public since optimizers will use it.\n  registeredVariables: NamedVariableMap = {};\n\n  private nextTapeNodeId = 0;\n  private numBytes = 0;\n  private numTensors = 0;\n  private numStringTensors = 0;\n  private numDataBuffers = 0;\n\n  private profiling = false;\n  private activeProfile: ProfileInfo;\n\n  private activeTape: TapeNode[];\n  private gradientScopeCount = 0;\n  private customGradientDepth = 0;\n\n  // Keep Tensors that parallel the tapes.\n  private activeScope: ScopeState;\n  private scopeStack: ScopeState[] = [];\n  private keepTensors: Set<number> = new Set();\n  private profiler: Profiler;\n\n  private tensorInfo = new WeakMap<DataId, {\n    backend: KernelBackend,\n    bytes: number,\n    dtype: DataType,\n    shape: number[],\n    refCount: number\n  }>();\n\n  constructor(\n      public backend: KernelBackend, public safeMode: boolean,\n      private debugMode: () => boolean) {\n    this.profiler = new Profiler(backend);\n    this.activeProfile =\n        {newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null};\n  }\n\n  moveData(dataId: DataId) {\n    this.write(dataId, this.readSync(dataId));\n  }\n\n  tidy<T extends TensorContainer>(\n      nameOrFn: string|ScopeFn<T>, fn?: ScopeFn<T>, gradMode = false): T {\n    // gradMode Primarily for internal use during backprop\n    //          If true, will start a tape if it is the outermost tidy.\n\n    let name: string = null;\n    if (fn == null) {\n      // Called with only 1 argument.\n      if (typeof nameOrFn !== 'function') {\n        throw new Error('Please provide a function to tidy()');\n      }\n      fn = nameOrFn;\n    } else {\n      // Called with 2 arguments.\n      if (typeof nameOrFn !== 'string' && !(nameOrFn instanceof String)) {\n        throw new Error(\n            'When calling with two arguments, the first argument ' +\n            'to tidy() must be a string');\n      }\n      if (typeof fn !== 'function') {\n        throw new Error(\n            'When calling with two arguments, the 2nd argument ' +\n            'to tidy() must be a function');\n      }\n      name = nameOrFn as string;\n      // TODO(nsthorat,smilkov): Do operation logging and performance\n      // profiling.\n    }\n    let result: T;\n    return this.scopedRun(\n        () => this.startScope(name, gradMode),\n        () => this.endScope(result, gradMode), () => {\n          result = fn();\n          if (result instanceof Promise) {\n            console.error('Cannot return a Promise inside of tidy.');\n          }\n          return result;\n        });\n  }\n\n  private scopedRun<T>(start: () => void, end: () => void, f: () => T): T {\n    start();\n    try {\n      const res = f();\n      end();\n      return res;\n    } catch (ex) {\n      end();\n      throw ex;\n    }\n  }\n\n  private static nextTensorId = 0;\n  nextTensorId(): number {\n    return Engine.nextTensorId++;\n  }\n\n  private static nextVariableId = 0;\n  nextVariableId(): number {\n    return Engine.nextVariableId++;\n  }\n\n  runKernel<T extends Tensor|Tensor[], I extends NamedTensorMap>(\n      forwardFunc: ForwardFunc<T>,\n      inputs: I,\n      backwardsFunc?: (dy: T, saved: Tensor[]) => {[P in keyof I]: () => I[P]},\n      ): T {\n    let result: T;\n    const saved: Tensor[] = [];\n    const saveFunc = <T extends Tensor>(x: T): T => {\n      saved.push(x);\n      return x;\n    };\n    const scopeName = this.activeScope.name;\n    const startingBytecount = this.numBytes;\n    const startingNumTensors = this.numTensors;\n\n    // Stop recording to a tape when running a kernel.\n    this.scopedRun(\n        () => this.customGradientDepth++, () => this.customGradientDepth--,\n        () => {\n          if (!this.debugMode()) {\n            result = forwardFunc(this.backend, saveFunc);\n          } else {\n            result = this.profiler.profileKernel(\n                scopeName, () => forwardFunc(this.backend, saveFunc));\n          }\n        });\n\n    if (this.shouldRecord()) {\n      const tapeNode: TapeNode = {\n        id: this.nextTapeNodeId++,\n        name: scopeName,\n        inputs,\n        outputs: Array.isArray(result) ? result : [result] as Tensor[]\n      };\n      if (backwardsFunc != null) {\n        tapeNode.gradient =\n            ((dy: T) => backwardsFunc(dy, saved)) as (dy: Tensor) =>\n                NamedGradientMap;\n      }\n      this.activeTape.push(tapeNode);\n    }\n\n    if (this.profiling) {\n      this.activeProfile.kernels.push({\n        name: scopeName,\n        bytesAdded: this.numBytes - startingBytecount,\n        totalBytesSnapshot: this.numBytes,\n        tensorsAdded: this.numTensors - startingNumTensors,\n        totalTensorsSnapshot: this.numTensors,\n        inputShapes: Object.keys(inputs).map(key => inputs[key].shape),\n        outputShape: Array.isArray(result) ?\n            (result as Tensor[]).map(item => (item as Tensor).shape) :\n            (result as Tensor).shape\n      });\n    }\n\n    return result;\n  }\n\n  // TensorManager implementation.\n\n  registerTensor(a: Tensor|Variable): void {\n    const refCount = this.tensorInfo.has(a.dataId) ?\n        this.tensorInfo.get(a.dataId).refCount :\n        0;\n    this.numTensors++;\n    if (a.dtype === 'string') {\n      this.numStringTensors++;\n    }\n    if (refCount === 0) {\n      this.numDataBuffers++;\n\n      // Bytes for complex numbers are counted by their components. Bytes for\n      // string tensors are counted when writing values.\n      let bytes = 0;\n      if (a.dtype !== 'complex64' && a.dtype !== 'string') {\n        bytes = util.sizeFromShape(a.shape) * util.bytesPerElement(a.dtype);\n      }\n      this.tensorInfo.set(a.dataId, {\n        backend: this.backend,\n        dtype: a.dtype,\n        shape: a.shape,\n        bytes,\n        refCount: 0\n      });\n      this.numBytes += bytes;\n      this.backend.register(a.dataId, a.shape, a.dtype);\n    }\n    this.tensorInfo.get(a.dataId).refCount++;\n    if (!(a instanceof Variable)) {\n      this.track(a);\n    }\n  }\n\n  registerVariable(v: Variable) {\n    if (this.registeredVariables[v.name] != null) {\n      throw new Error(`Variable with name ${v.name} was already registered`);\n    }\n    this.registeredVariables[v.name] = v;\n  }\n\n  disposeTensor(a: Tensor): void {\n    if (!this.tensorInfo.has(a.dataId)) {\n      return;\n    }\n    if (this.keepTensors.has(a.id)) {\n      this.keepTensors.delete(a.id);\n    }\n    this.numTensors--;\n    if (a.dtype === 'string') {\n      this.numStringTensors--;\n    }\n    const info = this.tensorInfo.get(a.dataId);\n    const refCount = info.refCount;\n    if (refCount <= 1) {\n      // Don't count bytes for complex numbers as they are counted by their\n      // components.\n      if (a.dtype !== 'complex64') {\n        this.numBytes -= info.bytes;\n      }\n      this.numDataBuffers--;\n      info.backend.disposeData(a.dataId);\n      this.tensorInfo.delete(a.dataId);\n    } else {\n      this.tensorInfo.get(a.dataId).refCount--;\n    }\n    // TODO(nsthorat): Construct an error and save the stack trace for\n    // debugging when in debug mode. Creating a stack trace is too expensive\n    // to do unconditionally.\n  }\n\n  disposeVariables(): void {\n    for (const varName in this.registeredVariables) {\n      const v = this.registeredVariables[varName];\n      this.disposeTensor(v);\n      delete this.registeredVariables[varName];\n    }\n  }\n\n  memory(): MemoryInfo {\n    const info = this.backend.memory() as MemoryInfo;\n    info.numTensors = this.numTensors;\n    info.numDataBuffers = this.numDataBuffers;\n    info.numBytes = this.numBytes;\n    if (this.numStringTensors > 0) {\n      info.unreliable = true;\n      if (info.reasons == null) {\n        info.reasons = [];\n      }\n      info.reasons.push(\n          'Memory usage by string tensors is approximate ' +\n          '(2 bytes per character)');\n    }\n    return info;\n  }\n\n  async profile(query: () => TensorContainer): Promise<ProfileInfo> {\n    this.profiling = true;\n\n    const startBytes = this.numBytes;\n    const startNumTensors = this.numTensors;\n\n    this.activeProfile.kernels = [];\n    this.activeProfile.result = query();\n\n    this.profiling = false;\n\n    this.activeProfile.peakBytes =\n        Math.max(...this.activeProfile.kernels.map(d => d.totalBytesSnapshot));\n    this.activeProfile.newBytes = this.numBytes - startBytes;\n    this.activeProfile.newTensors = this.numTensors - startNumTensors;\n    return this.activeProfile;\n  }\n\n  private shouldRecord(): boolean {\n    return this.activeTape != null && this.customGradientDepth === 0;\n  }\n\n  private addTapeNode(\n      inputs: Tensor[], result: Tensor,\n      gradientsFunc: (dy: Tensor) => Tensor[]): void {\n    const inputsMap: NamedTensorMap = {};\n    inputs.forEach((input, idx) => {\n      inputsMap[idx] = input;\n    });\n\n    const gradient = (dy: Tensor) => {\n      const res = gradientsFunc(dy);\n      const resMap: NamedGradientMap = {};\n      res.forEach((r, idx) => {\n        resMap[idx] = () => r;\n      });\n      return resMap;\n    };\n\n    const tapeNode: TapeNode = {\n      id: this.nextTapeNodeId++,\n      name: this.activeScope.name,\n      inputs: inputsMap,\n      outputs: [result],\n      gradient\n    };\n    this.activeTape.push(tapeNode);\n  }\n\n  keep<T extends Tensor>(result: T): T {\n    if (this.scopeStack.length === 1 && this.safeMode) {\n      throw new Error(\n          'Safe mode is ON. Enclose all tensor operations inside tf.tidy(): ' +\n          'tf.tidy(() => {...}) to avoid memory leaks.');\n    }\n    this.keepTensors.add(result.id);\n    return result;\n  }\n\n  /**\n   * Start a scope. Use this with endScope() to achieve the same functionality\n   * as scope() without the need for a function closure.\n   */\n  startScope(name?: string, gradientsMode = false) {\n    if (gradientsMode && this.gradientScopeCount === 0) {\n      this.activeTape = [];\n    }\n    if (gradientsMode) {\n      this.gradientScopeCount++;\n    }\n\n    const scopeInfo: ScopeState = {track: [], name: 'unnamed scope'};\n    if (name) {\n      scopeInfo.name = name;\n    }\n    this.scopeStack.push(scopeInfo);\n    this.activeScope = scopeInfo;\n  }\n\n  /**\n   * End a scope. Use this with startScope() to achieve the same functionality\n   * as scope() without the need for a function closure.\n   */\n  endScope(result?: TensorContainer, gradientsMode = false) {\n    if (gradientsMode) {\n      this.gradientScopeCount--;\n      if (this.gradientScopeCount === 0) {\n        this.activeTape = null;\n      }\n    }\n\n    const tensorsToKeep = new Set(this.keepTensors);\n\n    const tensorsToTrackInParent = getTensorsInContainer(result);\n    tensorsToTrackInParent.forEach(tensor => tensorsToKeep.add(tensor.id));\n\n    // Dispose the arrays tracked in this scope.\n    for (let i = 0; i < this.activeScope.track.length; i++) {\n      const tensor = this.activeScope.track[i];\n      if (tensorsToKeep.has(tensor.id)) {\n        continue;\n      }\n\n      if (this.activeTape != null) {\n        tensorsToTrackInParent.push(tensor);\n      } else {\n        tensor.dispose();\n      }\n    }\n\n    const oldScope = this.scopeStack.pop();\n    this.activeScope = this.scopeStack.length === 0 ?\n        null :\n        this.scopeStack[this.scopeStack.length - 1];\n\n    // Track the current result in the parent scope.\n    tensorsToTrackInParent.forEach(tensor => {\n      // Only track the tensor if was allocated in the inner scope and is not\n      // globally kept.\n      if (!this.keepTensors.has(tensor.id) &&\n          isTensorInList(tensor, oldScope.track)) {\n        this.track(tensor);\n      }\n    });\n  }\n\n  /**\n   * Returns gradients of `f` with respect to each of the `xs`. The gradients\n   * returned are of the same length as `xs`, but some might be null if `f` was\n   * not a function of that `x`. It also takes optional dy to multiply the\n   * gradient, which defaults to `1`.\n   */\n  gradients<T extends Tensor>(\n      f: () => T, xs: Tensor[], dy?: T,\n      allowNoGradients = false): {value: T, grads: Tensor[]} {\n    util.assert(xs.length > 0, 'gradients() received an empty list of xs.');\n    if (dy != null && dy.dtype !== 'float32') {\n      throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);\n    }\n\n    return this.tidy('gradients', () => {\n      const y = f();\n      util.assert(\n          y instanceof Tensor,\n          'The result y returned by f() must be a tensor.');\n      // Filter out the nodes that don't connect x => y.\n      const filteredTape = getFilteredNodesXToY(this.activeTape, xs, y);\n      if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {\n        throw new Error(\n            'Cannot compute gradient of y=f(x) with respect to x. Make sure ' +\n            'that the f you passed encloses all operations that lead from x ' +\n            'to y.');\n      }\n\n      const accumulatedGradientMap: {[tensorId: number]: Tensor} = {};\n      accumulatedGradientMap[y.id] = (dy == null) ? ones(y.shape) : dy;\n\n      // Backprop gradients through the filtered nodes.\n      backpropagateGradients(accumulatedGradientMap, filteredTape);\n\n      const grads = xs.map(x => accumulatedGradientMap[x.id]);\n      return {value: y, grads};\n    }, true /* gradientsMode */);\n  }\n\n  customGrad<T extends Tensor>(f: CustomGradientFunc<T>):\n      (...args: Tensor[]) => T {\n    util.assert(\n        util.isFunction(f),\n        'The f passed in customGrad(f) must be a function.');\n    return (...inputs: Tensor[]): T => {\n      util.assert(\n          inputs.every(t => t instanceof Tensor),\n          'The args passed in customGrad(f)(x1, x2,...) must all be tensors');\n\n      let gradientsFunc: (dy: T) => Tensor | Tensor[];\n      let result: T;\n      this.scopedRun(\n          () => this.customGradientDepth++, () => this.customGradientDepth--,\n          () => {\n            const gradientsMode = true;\n            result = this.tidy(f.name, () => {\n              const {value, gradFunc} = f(...inputs);\n              util.assert(\n                  value instanceof Tensor,\n                  'The function f passed in customGrad(f) must return an ' +\n                      'object where `obj.value` is a tensor');\n              util.assert(\n                  util.isFunction(gradFunc),\n                  'The function f passed in customGrad(f) must return an ' +\n                      'object where `obj.gradFunc` is a function.');\n              gradientsFunc = gradFunc;\n              return value;\n            }, gradientsMode);\n          });\n\n      if (this.shouldRecord()) {\n        const gradFunc = (dy: T): Tensor[] => {\n          const res = gradientsFunc(dy);\n          const grads: Tensor[] = Array.isArray(res) ? res : [res];\n          util.assert(\n              grads.length === inputs.length,\n              'The function f passed in customGrad(f) must return an object ' +\n                  'where `obj.gradFunc` is a function that returns the same ' +\n                  'number of tensors as inputs passed to f(...).');\n          util.assert(\n              grads.every(t => t instanceof Tensor),\n              'The function f passed in customGrad(f) must return an object ' +\n                  'where `obj.gradFunc` is a function that returns a list of ' +\n                  'only tensors.');\n          return grads;\n        };\n        this.addTapeNode(inputs, result, gradFunc);\n      }\n      return result;\n    };\n  }\n\n  // Forwarding to backend.\n  write(dataId: DataId, values: DataValues): void {\n    const info = this.tensorInfo.get(dataId);\n    // Bytes for string tensors are counted when writing.\n    if (info.dtype === 'string') {\n      const newBytes = bytesFromStringArray(values as string[]);\n      this.numBytes += newBytes - info.bytes;\n      info.bytes = newBytes;\n    }\n\n    if (this.backend !== info.backend) {\n      // Delete the tensor from the old backend and move it to the new backend.\n      info.backend.disposeData(dataId);\n      info.backend = this.backend;\n      this.backend.register(dataId, info.shape, info.dtype);\n    }\n    this.backend.write(dataId, values);\n  }\n  readSync(dataId: DataId): DataValues {\n    // Route the read to the correct backend.\n    const info = this.tensorInfo.get(dataId);\n    return info.backend.readSync(dataId);\n  }\n  read(dataId: DataId): Promise<DataValues> {\n    // Route the read to the correct backend.\n    const info = this.tensorInfo.get(dataId);\n    return info.backend.read(dataId);\n  }\n  fromPixels(\n      pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement,\n      numChannels: number): Tensor3D {\n    return this.backend.fromPixels(pixels, numChannels);\n  }\n  async time(query: () => void): Promise<TimingInfo> {\n    const start = now();\n    const timingInfo = await this.backend.time(query) as TimingInfo;\n    timingInfo.wallMs = now() - start;\n    return timingInfo;\n  }\n\n  /**\n   * Tracks a Tensor in the current scope to be automatically cleaned up\n   * when the current scope ends, and returns the value.\n   *\n   * @param result The Tensor to track in the current scope.\n   */\n  private track<T extends Tensor>(result: T): T {\n    if (this.scopeStack.length === 1 && this.safeMode) {\n      throw new Error(\n          'Safe mode is ON. Enclose all tensor operations inside tf.tidy(): ' +\n          'tf.tidy(() => {op();...}); to avoid memory leaks.');\n    }\n    if (this.activeScope != null) {\n      this.activeScope.track.push(result);\n    }\n    return result;\n  }\n}\n\nfunction ones(shape: number[]): Tensor {\n  const values = makeOnesTypedArray(sizeFromShape(shape), 'float32');\n  return Tensor.make(shape, {values});\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getWebGLContext} from './canvas_util';\n\nexport interface Features {\n  // Whether to enable debug mode.\n  'DEBUG'?: boolean;\n  // Whether we are in a browser (as versus, say, node.js) environment.\n  'IS_BROWSER'?: boolean;\n  // Whether we are in the Node.js environment.\n  'IS_NODE'?: boolean;\n  // Whether packed WebGL kernels lazily unpack their outputs.\n  'WEBGL_LAZILY_UNPACK'?: boolean;\n  // Whether the WebGL backend will sometimes forward ops to the CPU.\n  'WEBGL_CPU_FORWARD'?: boolean;\n  // Whether to turn all packing related flags on.\n  'WEBGL_PACK'?: boolean;\n  // Whether we will pack the batchnormalization op.\n  'WEBGL_PACK_BATCHNORMALIZATION'?: boolean;\n  // Whether we will pack the clipping op.\n  'WEBGL_PACK_CLIP'?: boolean;\n  // Whether we pack the depthwise convolution op.\n  'WEBGL_PACK_DEPTHWISECONV'?: boolean;\n  // Whether we will use the im2col algorithm to speed up convolutions.\n  'WEBGL_CONV_IM2COL'?: boolean;\n  // Whether we will perform memory paging.\n  'WEBGL_PAGING_ENABLED'?: boolean;\n  // The maximum texture dimension.\n  'WEBGL_MAX_TEXTURE_SIZE'?: number;\n  // The disjoint_query_timer extension version.\n  // 0: disabled, 1: EXT_disjoint_timer_query, 2:\n  // EXT_disjoint_timer_query_webgl2.\n  // In Firefox with WebGL 2.0,\n  // EXT_disjoint_timer_query_webgl2 is not available, so we must use the\n  // WebGL 1.0 extension.\n  'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'?: number;\n  // Whether the timer object from the disjoint_query_timer extension gives\n  // timing information that is reliable.\n  'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE'?: boolean;\n  // 0: No WebGL, 1: WebGL 1.0, 2: WebGL 2.0.\n  'WEBGL_VERSION'?: number;\n  // True if WebGL is supported.\n  'HAS_WEBGL'?: boolean;\n  // Whether rendering to float32 textures is enabled. If disabled, renders to\n  // float16 textures.\n  'WEBGL_RENDER_FLOAT32_ENABLED'?: boolean;\n  // Whether downloading float textures is enabled. If disabled, uses IEEE 754\n  // encoding of the float32 values to 4 uint8 when downloading.\n  'WEBGL_DOWNLOAD_FLOAT_ENABLED'?: boolean;\n  // Whether the fence API is available.\n  'WEBGL_FENCE_API_ENABLED'?: boolean;\n  // Tensors with size <= than this will be uploaded as uniforms, not textures.\n  'WEBGL_SIZE_UPLOAD_UNIFORM'?: number;\n  'BACKEND'?: string;\n  // Test precision for unit tests. This is decreased when we can't render\n  // float32 textures.\n  'TEST_EPSILON'?: number;\n  'IS_CHROME'?: boolean;\n  // True if running unit tests.\n  'IS_TEST'?: boolean;\n  // Smallest positive value used to make ops like division and log numerically\n  // stable.\n  'EPSILON'?: number;\n  // True when the environment is \"production\" where we disable safety checks\n  // to gain performance.\n  'PROD'?: boolean;\n  // Whether to do sanity checks when inferring a shape from user-provided\n  // values, used when creating a new tensor.\n  'TENSORLIKE_CHECK_SHAPE_CONSISTENCY'?: boolean;\n}\n\nexport enum Type {\n  NUMBER,\n  BOOLEAN,\n  STRING\n}\n\nexport const URL_PROPERTIES: URLProperty[] = [\n  {name: 'DEBUG', type: Type.BOOLEAN},\n  {name: 'IS_BROWSER', type: Type.BOOLEAN},\n  {name: 'WEBGL_LAZILY_UNPACK', type: Type.BOOLEAN},\n  {name: 'WEBGL_CPU_FORWARD', type: Type.BOOLEAN},\n  {name: 'WEBGL_PACK', type: Type.BOOLEAN},\n  {name: 'WEBGL_PACK_BATCHNORMALIZATION', type: Type.BOOLEAN},\n  {name: 'WEBGL_PACK_CLIP', type: Type.BOOLEAN},\n  {name: 'WEBGL_PACK_DEPTHWISECONV', type: Type.BOOLEAN},\n  {name: 'WEBGL_CONV_IM2COL', type: Type.BOOLEAN},\n  {name: 'WEBGL_MAX_TEXTURE_SIZE', type: Type.NUMBER},\n  {name: 'WEBGL_PAGING_ENABLED', type: Type.BOOLEAN},\n  {name: 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION', type: Type.NUMBER},\n  {name: 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE', type: Type.BOOLEAN},\n  {name: 'WEBGL_VERSION', type: Type.NUMBER},\n  {name: 'WEBGL_RENDER_FLOAT32_ENABLED', type: Type.BOOLEAN},\n  {name: 'WEBGL_DOWNLOAD_FLOAT_ENABLED', type: Type.BOOLEAN},\n  {name: 'WEBGL_FENCE_API_ENABLED', type: Type.BOOLEAN},\n  {name: 'WEBGL_SIZE_UPLOAD_UNIFORM', type: Type.NUMBER},\n  {name: 'BACKEND', type: Type.STRING},\n  {name: 'EPSILON', type: Type.NUMBER},\n  {name: 'PROD', type: Type.BOOLEAN},\n  {name: 'TENSORLIKE_CHECK_SHAPE_CONSISTENCY', type: Type.BOOLEAN},\n];\n\nexport interface URLProperty {\n  name: keyof Features;\n  type: Type;\n}\n\nexport function isWebGLVersionEnabled(webGLVersion: 1|2) {\n  try {\n    const gl = getWebGLContext(webGLVersion);\n    if (gl != null) {\n      return true;\n    }\n  } catch (e) {\n    return false;\n  }\n  return false;\n}\n\nlet MAX_TEXTURE_SIZE: number;\n// Caching MAX_TEXTURE_SIZE here because the environment gets reset between\n// unit tests and we don't want to constantly query the WebGLContext for\n// MAX_TEXTURE_SIZE.\nexport function getWebGLMaxTextureSize(webGLVersion: number): number {\n  if (MAX_TEXTURE_SIZE == null) {\n    const gl = getWebGLContext(webGLVersion);\n    MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);\n  }\n  return MAX_TEXTURE_SIZE;\n}\n\nexport function getWebGLDisjointQueryTimerVersion(webGLVersion: number):\n    number {\n  if (webGLVersion === 0) {\n    return 0;\n  }\n\n  let queryTimerVersion: number;\n  const gl = getWebGLContext(webGLVersion);\n\n  if (hasExtension(gl, 'EXT_disjoint_timer_query_webgl2') &&\n      webGLVersion === 2) {\n    queryTimerVersion = 2;\n  } else if (hasExtension(gl, 'EXT_disjoint_timer_query')) {\n    queryTimerVersion = 1;\n  } else {\n    queryTimerVersion = 0;\n  }\n  return queryTimerVersion;\n}\n\nexport function isRenderToFloatTextureEnabled(webGLVersion: number): boolean {\n  if (webGLVersion === 0) {\n    return false;\n  }\n\n  const gl = getWebGLContext(webGLVersion);\n\n  if (webGLVersion === 1) {\n    if (!hasExtension(gl, 'OES_texture_float')) {\n      return false;\n    }\n  } else {\n    if (!hasExtension(gl, 'EXT_color_buffer_float')) {\n      return false;\n    }\n  }\n\n  const isFrameBufferComplete =\n      createFloatTextureAndBindToFramebuffer(gl, webGLVersion);\n  return isFrameBufferComplete;\n}\n\nexport function isDownloadFloatTextureEnabled(webGLVersion: number): boolean {\n  if (webGLVersion === 0) {\n    return false;\n  }\n\n  const gl = getWebGLContext(webGLVersion);\n\n  if (webGLVersion === 1) {\n    if (!hasExtension(gl, 'OES_texture_float')) {\n      return false;\n    }\n    if (!hasExtension(gl, 'WEBGL_color_buffer_float')) {\n      return false;\n    }\n  } else {\n    if (!hasExtension(gl, 'EXT_color_buffer_float')) {\n      return false;\n    }\n  }\n\n  const isFrameBufferComplete =\n      createFloatTextureAndBindToFramebuffer(gl, webGLVersion);\n  return isFrameBufferComplete;\n}\n\nexport function isWebGLFenceEnabled(webGLVersion: number) {\n  if (webGLVersion !== 2) {\n    return false;\n  }\n  const gl = getWebGLContext(webGLVersion);\n\n  // tslint:disable-next-line:no-any\n  const isEnabled = (gl as any).fenceSync != null;\n  return isEnabled;\n}\n\nexport function isChrome() {\n  return typeof navigator !== 'undefined' && navigator != null &&\n      navigator.userAgent != null && /Chrome/.test(navigator.userAgent) &&\n      /Google Inc/.test(navigator.vendor);\n}\n\n// Expects flags from URL in the format ?tfjsflags=FLAG1:1,FLAG2:true.\nconst TENSORFLOWJS_FLAGS_PREFIX = 'tfjsflags';\nexport function getFeaturesFromURL(): Features {\n  const features: Features = {};\n\n  if (typeof window === 'undefined' || typeof window.location === 'undefined' ||\n      typeof window.location.search === 'undefined') {\n    return features;\n  }\n\n  const urlParams = getQueryParams(window.location.search);\n  if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {\n    const urlFlags: {[key: string]: string} = {};\n\n    const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(',');\n    keyValues.forEach(keyValue => {\n      const [key, value] = keyValue.split(':') as [string, string];\n      urlFlags[key] = value;\n    });\n\n    URL_PROPERTIES.forEach(urlProperty => {\n      if (urlProperty.name in urlFlags) {\n        console.log(\n            `Setting feature override from URL ${urlProperty.name}: ` +\n            `${urlFlags[urlProperty.name]}`);\n        if (urlProperty.type === Type.NUMBER) {\n          features[urlProperty.name] = +urlFlags[urlProperty.name];\n        } else if (urlProperty.type === Type.BOOLEAN) {\n          features[urlProperty.name] = urlFlags[urlProperty.name] === 'true';\n        } else if (urlProperty.type === Type.STRING) {\n          // tslint:disable-next-line:no-any\n          features[urlProperty.name] = urlFlags[urlProperty.name] as any;\n        } else {\n          console.warn(`Unknown URL param: ${urlProperty.name}.`);\n        }\n      }\n    });\n  }\n\n  return features;\n}\n\nfunction hasExtension(gl: WebGLRenderingContext, extensionName: string) {\n  const ext = gl.getExtension(extensionName);\n  return ext != null;\n}\n\nfunction createFloatTextureAndBindToFramebuffer(\n    gl: WebGLRenderingContext, webGLVersion: number): boolean {\n  const frameBuffer = gl.createFramebuffer();\n  const texture = gl.createTexture();\n\n  gl.bindTexture(gl.TEXTURE_2D, texture);\n\n  // tslint:disable-next-line:no-any\n  const internalFormat = webGLVersion === 2 ? (gl as any).RGBA32F : gl.RGBA;\n  gl.texImage2D(\n      gl.TEXTURE_2D, 0, internalFormat, 1, 1, 0, gl.RGBA, gl.FLOAT, null);\n\n  gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n  gl.framebufferTexture2D(\n      gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n\n  const isFrameBufferComplete =\n      gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n\n  gl.bindTexture(gl.TEXTURE_2D, null);\n  gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n  gl.deleteTexture(texture);\n  gl.deleteFramebuffer(frameBuffer);\n\n  return isFrameBufferComplete;\n}\n\nexport function getQueryParams(queryString: string): {[key: string]: string} {\n  const params = {};\n  queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {\n    decodeParam(params, t[0], t[1]);\n    return t.join('=');\n  });\n  return params;\n}\n\nfunction decodeParam(\n    params: {[key: string]: string}, name: string, value?: string) {\n  params[decodeURIComponent(name)] = decodeURIComponent(value || '');\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as device_util from './device_util';\nimport {Engine, MemoryInfo, ProfileInfo, ScopeFn, TimingInfo} from './engine';\nimport {Features, getFeaturesFromURL, getWebGLDisjointQueryTimerVersion, getWebGLMaxTextureSize, isChrome, isDownloadFloatTextureEnabled, isRenderToFloatTextureEnabled, isWebGLFenceEnabled, isWebGLVersionEnabled} from './environment_util';\nimport {KernelBackend} from './kernels/backend';\nimport {DataId, setTensorTracker, Tensor, TensorTracker} from './tensor';\nimport {TensorContainer} from './tensor_types';\nimport {getTensorsInContainer} from './tensor_util';\n\nexport const EPSILON_FLOAT16 = 1e-4;\nconst TEST_EPSILON_FLOAT16 = 1e-1;\n\nexport const EPSILON_FLOAT32 = 1e-7;\nconst TEST_EPSILON_FLOAT32 = 1e-3;\n\nexport class Environment {\n  private features: Features = {};\n  private globalEngine: Engine;\n  private registry:\n      {[id: string]: {backend: KernelBackend, priority: number}} = {};\n  backendName: string;\n\n  constructor(features?: Features) {\n    if (features != null) {\n      this.features = features;\n    }\n\n    if (this.get('DEBUG')) {\n      console.warn(\n          'Debugging mode is ON. The output of every math call will ' +\n          'be downloaded to CPU and checked for NaNs. ' +\n          'This significantly impacts performance.');\n    }\n  }\n\n  /**\n   * Sets the backend (cpu, webgl, etc) responsible for creating tensors and\n   * executing operations on those tensors.\n   *\n   * Note this disposes the current backend, if any, as well as any tensors\n   * associated with it. A new backend is initialized, even if it is of the\n   * same type as the previous one.\n   *\n   * @param backendName The name of the backend. Currently supports\n   *     `'webgl'|'cpu'` in the browser, and `'tensorflow'` under node.js\n   *     (requires tfjs-node).\n   * @param safeMode Defaults to false. In safe mode, you are forced to\n   *     construct tensors and call math operations inside a `tidy()` which\n   *     will automatically clean up intermediate tensors.\n   */\n  /** @doc {heading: 'Environment'} */\n  static setBackend(backendName: string, safeMode = false) {\n    if (!(backendName in ENV.registry)) {\n      throw new Error(`Backend name '${backendName}' not found in registry`);\n    }\n    ENV.engine.backend = ENV.findBackend(backendName);\n    ENV.backendName = backendName;\n  }\n\n  /**\n   * Returns the current backend name (cpu, webgl, etc). The backend is\n   * responsible for creating tensors and executing operations on those tensors.\n   */\n  /** @doc {heading: 'Environment'} */\n  static getBackend(): string {\n    ENV.initEngine();\n    return ENV.backendName;\n  }\n\n  /**\n   * Dispose all variables kept in backend engine.\n   */\n  /** @doc {heading: 'Environment'} */\n  static disposeVariables(): void {\n    ENV.engine.disposeVariables();\n  }\n\n  /**\n   * Returns memory info at the current time in the program. The result is an\n   * object with the following properties:\n   *\n   * - `numBytes`: Number of bytes allocated (undisposed) at this time.\n   * - `numTensors`: Number of unique tensors allocated.\n   * - `numDataBuffers`: Number of unique data buffers allocated\n   *   (undisposed) at this time, which is ≤ the number of tensors\n   *   (e.g. `a.reshape(newShape)` makes a new Tensor that shares the same\n   *   data buffer with `a`).\n   * - `unreliable`: True if the memory usage is unreliable. See `reasons` when\n   *    `unrealible` is true.\n   * - `reasons`: `string[]`, reasons why the memory is unreliable, present if\n   *    `unreliable` is true.\n   */\n  /** @doc {heading: 'Performance', subheading: 'Memory'} */\n  static memory(): MemoryInfo {\n    return ENV.engine.memory();\n  }\n\n  /**\n   * Executes the provided function `f()` and returns a promise that resolves\n   * with information about the function's memory use:\n   * - `newBytes`: tne number of new bytes allocated\n   * - `newTensors`: the number of new tensors created\n   * - `peakBytes`: the peak number of bytes allocated\n   * - `kernels`: an array of objects for each kernel involved that reports\n   * their input and output shapes, number of bytes used, and number of new\n   * tensors created.\n   *\n   * ```js\n   * const profile = await tf.profile(() => {\n   *   const x = tf.tensor1d([1, 2, 3]);\n   *   let x2 = x.square();\n   *   x2.dispose();\n   *   x2 = x.square();\n   *   x2.dispose();\n   *   return x;\n   * });\n   *\n   * console.log(`newBytes: ${profile.newBytes}`);\n   * console.log(`newTensors: ${profile.newTensors}`);\n   * console.log(`byte usage over all kernels: ${profile.kernels.map(k =>\n   * k.totalBytesSnapshot)}`);\n   * ```\n   *\n   */\n  /** @doc {heading: 'Performance', subheading: 'Profile'} */\n  static profile(f: () => TensorContainer): Promise<ProfileInfo> {\n    return ENV.engine.profile(f);\n  }\n\n  /**\n   * Executes the provided function `fn` and after it is executed, cleans up all\n   * intermediate tensors allocated by `fn` except those returned by `fn`.\n   * `fn` must not return a Promise (async functions not allowed). The returned\n   * result can be a complex object.\n   *\n   * Using this method helps avoid memory leaks. In general, wrap calls to\n   * operations in `tf.tidy` for automatic memory cleanup.\n   *\n   * When in safe mode, you must enclose all `tf.Tensor` creation and ops\n   * inside a `tf.tidy` to prevent memory leaks.\n   *\n   * ```js\n   * // y = 2 ^ 2 + 1\n   * const y = tf.tidy(() => {\n   *   // a, b, and one will be cleaned up when the tidy ends.\n   *   const one = tf.scalar(1);\n   *   const a = tf.scalar(2);\n   *   const b = a.square();\n   *\n   *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n   *\n   *   // The value returned inside the tidy function will return\n   *   // through the tidy, in this case to the variable y.\n   *   return b.add(one);\n   * });\n   *\n   * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n   * y.print();\n   * ```\n   *\n   * @param nameOrFn The name of the closure, or the function to execute.\n   *     If a name is provided, the 2nd argument should be the function.\n   *     If debug mode is on, the timing and the memory usage of the function\n   *     will be tracked and displayed on the console using the provided name.\n   * @param fn The function to execute.\n   */\n  /** @doc {heading: 'Performance', subheading: 'Memory'} */\n  static tidy<T extends TensorContainer>(\n      nameOrFn: string|ScopeFn<T>, fn?: ScopeFn<T>): T {\n    return ENV.engine.tidy(nameOrFn, fn);\n  }\n\n  /**\n   * Disposes any `tf.Tensor`s found within the provided object.\n   *\n   * @param container an object that may be a `tf.Tensor` or may directly\n   *     contain `tf.Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. If\n   *     the object is not a `tf.Tensor` or does not contain `Tensors`, nothing\n   *     happens. In general it is safe to pass any object here, except that\n   *     `Promise`s are not supported.\n   */\n  /** @doc {heading: 'Performance', subheading: 'Memory'} */\n  static dispose(container: TensorContainer) {\n    const tensors = getTensorsInContainer(container);\n    tensors.forEach(tensor => tensor.dispose());\n  }\n\n  /**\n   * Keeps a `tf.Tensor` generated inside a `tf.tidy` from being disposed\n   * automatically.\n   *\n   * ```js\n   * let b;\n   * const y = tf.tidy(() => {\n   *   const one = tf.scalar(1);\n   *   const a = tf.scalar(2);\n   *\n   *   // b will not be cleaned up by the tidy. a and one will be cleaned up\n   *   // when the tidy ends.\n   *   b = tf.keep(a.square());\n   *\n   *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n   *\n   *   // The value returned inside the tidy function will return\n   *   // through the tidy, in this case to the variable y.\n   *   return b.add(one);\n   * });\n   *\n   * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n   * console.log('y:');\n   * y.print();\n   * console.log('b:');\n   * b.print();\n   * ```\n   *\n   * @param result The tensor to keep from being disposed.\n   */\n  /** @doc {heading: 'Performance', subheading: 'Memory'} */\n  static keep<T extends Tensor>(result: T): T {\n    return ENV.engine.keep(result);\n  }\n\n  /**\n   * Executes `f()` and returns a promise that resolves with timing\n   * information.\n   *\n   * The result is an object with the following properties:\n   *\n   * - `wallMs`: Wall execution time.\n   * - `kernelMs`: Kernel execution time, ignoring data transfer.\n   * - On `WebGL` The following additional properties exist:\n   *   - `uploadWaitMs`: CPU blocking time on texture uploads.\n   *   - `downloadWaitMs`: CPU blocking time on texture downloads (readPixels).\n   *\n   * ```js\n   * const x = tf.randomNormal([20, 20]);\n   * const time = await tf.time(() => x.matMul(x));\n   *\n   * console.log(`kernelMs: ${time.kernelMs}, wallTimeMs: ${time.wallMs}`);\n   * ```\n   *\n   * @param f The function to execute and time.\n   */\n  /** @doc {heading: 'Performance', subheading: 'Timing'} */\n  static time(f: () => void): Promise<TimingInfo> {\n    return ENV.engine.time(f);\n  }\n\n  get<K extends keyof Features>(feature: K): Features[K] {\n    if (feature in this.features) {\n      return this.features[feature];\n    }\n\n    this.features[feature] = this.evaluateFeature(feature);\n\n    return this.features[feature];\n  }\n\n  getFeatures(): Features {\n    return this.features;\n  }\n\n  set<K extends keyof Features>(feature: K, value: Features[K]): void {\n    this.features[feature] = value;\n  }\n\n  private getBestBackendName(): string {\n    if (Object.keys(this.registry).length === 0) {\n      throw new Error('No backend found in registry.');\n    }\n    const sortedBackends = Object.keys(this.registry)\n                               .map(name => {\n                                 return {name, entry: this.registry[name]};\n                               })\n                               .sort((a, b) => {\n                                 // Highest priority comes first.\n                                 return b.entry.priority - a.entry.priority;\n                               });\n    return sortedBackends[0].name;\n  }\n\n  private evaluateFeature<K extends keyof Features>(feature: K): Features[K] {\n    if (feature === 'DEBUG') {\n      return false;\n    } else if (feature === 'IS_BROWSER') {\n      return typeof window !== 'undefined';\n    } else if (feature === 'IS_NODE') {\n      return (typeof process !== 'undefined') &&\n          (typeof process.versions !== 'undefined') &&\n          (typeof process.versions.node !== 'undefined');\n    } else if (feature === 'IS_CHROME') {\n      return isChrome();\n    } else if (feature === 'WEBGL_CPU_FORWARD') {\n      return true;\n    } else if (feature === 'WEBGL_PACK') {\n      return false;\n    } else if (feature === 'WEBGL_PACK_BATCHNORMALIZATION') {\n      return this.get('WEBGL_PACK');\n    } else if (feature === 'WEBGL_PACK_CLIP') {\n      return this.get('WEBGL_PACK');\n    } else if (feature === 'WEBGL_PACK_DEPTHWISECONV') {\n      return this.get('WEBGL_PACK');\n    } else if (feature === 'WEBGL_LAZILY_UNPACK') {\n      return this.get('WEBGL_PACK');\n    } else if (feature === 'WEBGL_CONV_IM2COL') {\n      return this.get('WEBGL_PACK');\n    } else if (feature === 'WEBGL_PAGING_ENABLED') {\n      return this.get('IS_BROWSER') && !this.get('PROD');\n    } else if (feature === 'WEBGL_MAX_TEXTURE_SIZE') {\n      return getWebGLMaxTextureSize(this.get('WEBGL_VERSION'));\n    } else if (feature === 'IS_TEST') {\n      return false;\n    } else if (feature === 'BACKEND') {\n      return this.getBestBackendName();\n    } else if (feature === 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') {\n      const webGLVersion = this.get('WEBGL_VERSION');\n\n      if (webGLVersion === 0) {\n        return 0;\n      }\n      return getWebGLDisjointQueryTimerVersion(webGLVersion);\n    } else if (feature === 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') {\n      return this.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0 &&\n          !device_util.isMobile();\n    } else if (feature === 'HAS_WEBGL') {\n      return this.get('WEBGL_VERSION') > 0;\n    } else if (feature === 'WEBGL_VERSION') {\n      if (isWebGLVersionEnabled(2)) {\n        return 2;\n      } else if (isWebGLVersionEnabled(1)) {\n        return 1;\n      }\n      return 0;\n    } else if (feature === 'WEBGL_RENDER_FLOAT32_ENABLED') {\n      return isRenderToFloatTextureEnabled(this.get('WEBGL_VERSION'));\n    } else if (feature === 'WEBGL_DOWNLOAD_FLOAT_ENABLED') {\n      return isDownloadFloatTextureEnabled(this.get('WEBGL_VERSION'));\n    } else if (feature === 'WEBGL_FENCE_API_ENABLED') {\n      return isWebGLFenceEnabled(this.get('WEBGL_VERSION'));\n    } else if (feature === 'WEBGL_SIZE_UPLOAD_UNIFORM') {\n      // Use uniform uploads only when 32bit floats are supported. In 16bit\n      // environments there are problems with comparing a 16bit texture value\n      // with a 32bit uniform value.\n      const useUniforms = this.get('WEBGL_RENDER_FLOAT32_ENABLED');\n      return useUniforms ? 4 : 0;\n    } else if (feature === 'TEST_EPSILON') {\n      return this.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 :\n                                                    TEST_EPSILON_FLOAT16;\n    } else if (feature === 'EPSILON') {\n      return this.backend.floatPrecision() === 32 ? EPSILON_FLOAT32 :\n                                                    EPSILON_FLOAT16;\n    } else if (feature === 'PROD') {\n      return false;\n    } else if (feature === 'TENSORLIKE_CHECK_SHAPE_CONSISTENCY') {\n      return !this.get('PROD');\n    }\n    throw new Error(`Unknown feature ${feature}.`);\n  }\n\n  setFeatures(features: Features) {\n    this.features = Object.assign({}, features);\n  }\n\n  reset() {\n    this.features = getFeaturesFromURL();\n    if (this.globalEngine != null) {\n      this.globalEngine = null;\n    }\n  }\n\n  get backend(): KernelBackend {\n    return this.engine.backend;\n  }\n\n  findBackend(name: string): KernelBackend {\n    if (!(name in this.registry)) {\n      return null;\n    }\n    return this.registry[name].backend;\n  }\n\n  /**\n   * Registers a global backend. The registration should happen when importing\n   * a module file (e.g. when importing `backend_webgl.ts`), and is used for\n   * modular builds (e.g. custom tfjs bundle with only webgl support).\n   *\n   * @param factory The backend factory function. When called, it should\n   * return an instance of the backend.\n   * @param priority The priority of the backend (higher = more important).\n   *     In case multiple backends are registered, the priority is used to find\n   *     the best backend. Defaults to 1.\n   * @return False if the creation/registration failed. True otherwise.\n   */\n  registerBackend(\n      name: string, factory: () => KernelBackend, priority = 1,\n      setTensorTrackerFn?: (f: () => TensorTracker) => void): boolean {\n    if (name in this.registry) {\n      console.warn(\n          `${name} backend was already registered. Reusing existing backend`);\n      if (setTensorTrackerFn != null) {\n        setTensorTrackerFn(() => this.engine);\n      }\n      return false;\n    }\n    try {\n      const backend = factory();\n      backend.setDataMover(\n          {moveData: (dataId: DataId) => this.engine.moveData(dataId)});\n      this.registry[name] = {backend, priority};\n      return true;\n    } catch (err) {\n      console.warn(`Registration of backend ${name} failed`);\n      console.warn(err.stack || err.message);\n      return false;\n    }\n  }\n\n  removeBackend(name: string): void {\n    if (!(name in this.registry)) {\n      throw new Error(`${name} backend not found in registry`);\n    }\n    this.registry[name].backend.dispose();\n    delete this.registry[name];\n  }\n\n  get engine(): Engine {\n    this.initEngine();\n    return this.globalEngine;\n  }\n\n  private initEngine() {\n    if (this.globalEngine == null) {\n      this.backendName = this.get('BACKEND');\n      const backend = this.findBackend(this.backendName);\n      this.globalEngine =\n          new Engine(backend, false /* safeMode */, () => this.get('DEBUG'));\n    }\n  }\n}\n\nfunction getGlobalNamespace(): {ENV: Environment} {\n  // tslint:disable-next-line:no-any\n  let ns: any;\n  if (typeof (window) !== 'undefined') {\n    ns = window;\n  } else if (typeof (process) !== 'undefined') {\n    ns = process;\n  } else {\n    throw new Error('Could not find a global object');\n  }\n  return ns;\n}\n\nfunction getOrMakeEnvironment(): Environment {\n  const ns = getGlobalNamespace();\n  if (ns.ENV == null) {\n    ns.ENV = new Environment(getFeaturesFromURL());\n    setTensorTracker(() => ns.ENV.engine);\n  }\n  return ns.ENV;\n}\n\nexport let ENV = getOrMakeEnvironment();\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {CustomGradientFunc, ScopeFn} from './engine';\nimport {ENV} from './environment';\nimport {Scalar, Tensor, Variable} from './tensor';\nimport {NamedTensorMap, TensorContainer} from './tensor_types';\nimport * as util from './util';\n\n/**\n * Create a new gradient scope. Similar to scope, but forces all inner scopes\n * to not clean up so that gradient operations can be used inside of this\n * scope.\n * @param nameOrScopeFn The name of the scope, or the function to execute.\n *     If a name is provided, the 2nd argument should be the function.\n *     If a name is provided, and debug mode is on, the timing and the memory\n *     usage of the function will be tracked and displayed on the console\n *     using the provided name.\n * @param scopeFn The function to execute.\n */\nfunction gradScope<T extends TensorContainer>(\n    nameOrScopeFn: string|ScopeFn<T>, scopeFn?: ScopeFn<T>): T {\n  return ENV.engine.tidy(nameOrScopeFn, scopeFn, true /* gradScope */);\n}\n\n/**\n * Provided `f(x)`, returns another function `g(x, dy?)`, which gives the\n * gradient of `f(x)` with respect to `x`.\n *\n * If `dy` is provided, the gradient of `f(x).mul(dy).sum()` with respect to\n * `x` is computed instead. `f(x)` must take a single tensor `x` and return a\n * single tensor `y`. If `f()` takes multiple inputs, use `tf.grads` instead.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.grad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * g(x).print();\n * ```\n *\n * ```js\n * // f(x) = x ^ 3\n * const f = x => x.pow(tf.scalar(3, 'int32'));\n * // f'(x) = 3x ^ 2\n * const g = tf.grad(f);\n * // f''(x) = 6x\n * const gg = tf.grad(g);\n *\n * const x = tf.tensor1d([2, 3]);\n * gg(x).print();\n * ```\n *\n * @param f The function f(x), to compute gradient for.\n */\n/** @doc {heading: 'Training', subheading: 'Gradients'} */\nfunction grad<I extends Tensor, O extends Tensor>(f: (x: I) => O): (\n    x: I, dy?: O) => I {\n  util.assert(util.isFunction(f), 'The f passed in grad(f) must be a function');\n  return (x: I, dy?: O): I => {\n    util.assert(\n        x instanceof Tensor, 'The x passed in grad(f)(x) must be a tensor');\n    util.assert(\n        dy == null || dy instanceof Tensor,\n        'The dy passed in grad(f)(x, dy) must be a tensor');\n    return ENV.engine.tidy(() => {\n      const {value, grads} = ENV.engine.gradients(() => f(x), [x], dy);\n      if (dy != null) {\n        util.assertShapesMatch(\n            value.shape, dy.shape,\n            'The shape of dy passed in grad(f)(x, dy) must match the shape ' +\n                'returned by f(x)');\n      }\n      checkGrads(grads);\n      return grads[0] as I;\n    });\n  };\n}\n\n/**\n * Provided `f(x1, x2,...)`, returns another function `g([x1, x2,...], dy?)`,\n * which gives an array of gradients of `f()` with respect to each input\n * [`x1`,`x2`,...].\n *\n * If `dy` is passed when calling `g()`, the gradient of\n * `f(x1,...).mul(dy).sum()` with respect to each input is computed instead.\n * The provided `f` must take one or more tensors and return a single tensor\n * `y`. If `f()` takes a single input, we recommend using `tf.grad` instead.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df / da = b, df / db = a\n * const g = tf.grads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const [da, db] = g([a, b]);\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @param f The function `f(x1, x2,...)` to compute gradients for.\n */\n/** @doc {heading: 'Training', subheading: 'Gradients'} */\nfunction grads<O extends Tensor>(f: (...args: Tensor[]) => O): (\n    args: Tensor[], dy?: O) => Tensor[] {\n  util.assert(\n      util.isFunction(f), 'The f passed in grads(f) must be a function');\n  return (args: Tensor[], dy?: O): Tensor[] => {\n    util.assert(\n        Array.isArray(args) && args.every(arg => arg instanceof Tensor),\n        'The args passed in grads(f)(args) must be an array of tensors');\n    util.assert(\n        dy == null || dy instanceof Tensor,\n        'The dy passed in grads(f)(args, dy) must be a tensor');\n    return ENV.engine.tidy(() => {\n      const {value, grads} = ENV.engine.gradients(() => f(...args), args, dy);\n      if (dy != null) {\n        util.assertShapesMatch(\n            value.shape, dy.shape,\n            'The shape of dy passed in grads(f)([x1,...], dy) must ' +\n                'match the shape returned by f([x1,...])');\n      }\n      checkGrads(grads);\n      return grads;\n    });\n  };\n}\n\n/**\n * Like `tf.grad`, but also returns the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grad: The gradient of `f(x)` w.r.t `x` (result of `tf.grad`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.valueAndGrad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * const {value, grad} = g(x);\n *\n * console.log('value');\n * value.print();\n * console.log('grad');\n * grad.print();\n * ```\n */\n/** @doc {heading: 'Training', subheading: 'Gradients'} */\nfunction valueAndGrad<I extends Tensor, O extends Tensor>(f: (x: I) => O): (\n    x: I, dy?: O) => {\n  value: O;\n  grad: I;\n} {\n  util.assert(\n      util.isFunction(f), 'The f passed in valueAndGrad(f) must be a function');\n  return (x: I, dy?: O) => {\n    util.assert(\n        x instanceof Tensor,\n        'The x passed in valueAndGrad(f)(x) must be a tensor');\n    util.assert(\n        dy == null || dy instanceof Tensor,\n        'The dy passed in valueAndGrad(f)(x, dy) must be a tensor');\n    const {grads, value} = ENV.engine.gradients(() => f(x), [x], dy);\n    checkGrads(grads);\n    return {grad: grads[0] as I, value: value as O};\n  };\n}\n\n/**\n * Like `tf.grads`, but returns also the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grads: The gradients of `f()` w.r.t each input (result of `tf.grads`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df/da = b, df/db = a\n * const g = tf.valueAndGrads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const {value, grads} = g([a, b]);\n *\n * const [da, db] = grads;\n *\n * console.log('value');\n * value.print();\n *\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n */\n/** @doc {heading: 'Training', subheading: 'Gradients'} */\nfunction valueAndGrads<O extends Tensor>(f: (...args: Tensor[]) => O): (\n    args: Tensor[], dy?: O) => {\n  grads: Tensor[];\n  value: O;\n} {\n  util.assert(\n      util.isFunction(f),\n      'The f passed in valueAndGrads(f) must be a function');\n  return (args: Tensor[], dy?: O) => {\n    util.assert(\n        Array.isArray(args) && args.every(arg => arg instanceof Tensor),\n        'The args passed in valueAndGrads(f)(args) must be array of tensors');\n    util.assert(\n        dy == null || dy instanceof Tensor,\n        'The dy passed in valueAndGrads(f)(args, dy) must be a tensor');\n    const res = ENV.engine.gradients(() => f(...args), args, dy);\n    if (dy != null) {\n      util.assertShapesMatch(\n          res.value.shape, dy.shape,\n          'The shape of dy passed in valueAndGrads(f)([x1,...], dy) must ' +\n              'match the shape returned by f([x1,...])');\n    }\n    checkGrads(res.grads);\n    return res;\n  };\n}\n\n/**\n * Computes and returns the gradient of f(x) with respect to the list of\n * trainable variables provided by `varList`. If no list is provided, it\n * defaults to all trainable variables.\n *\n * ```js\n * const a = tf.variable(tf.tensor1d([3, 4]));\n * const b = tf.variable(tf.tensor1d([5, 6]));\n * const x = tf.tensor1d([1, 2]);\n *\n * // f(a, b) = a * x ^ 2 + b * x\n * const f = () => a.mul(x.square()).add(b.mul(x)).sum();\n * // df/da = x ^ 2, df/db = x\n * const {value, grads} = tf.variableGrads(f);\n *\n * Object.keys(grads).forEach(varName => grads[varName].print());\n * ```\n *\n * @param f The function to execute. f() should return a scalar.\n * @param varList The list of trainable variables. Defaults to all variables.\n */\n/** @doc {heading: 'Training', subheading: 'Gradients'} */\nfunction variableGrads(f: () => Scalar, varList?: Variable[]):\n    {value: Scalar, grads: NamedTensorMap} {\n  util.assert(\n      util.isFunction(f),\n      'The f passed in variableGrads(f) must be a function');\n  util.assert(\n      varList == null ||\n          Array.isArray(varList) && varList.every(v => v instanceof Variable),\n      'The varList passed in variableGrads(f, varList) must be an array ' +\n          'of variables');\n  if (varList == null) {\n    // Get all of the trainable variables.\n    varList = [];\n    for (const varName in ENV.engine.registeredVariables) {\n      varList.push(ENV.engine.registeredVariables[varName]);\n    }\n  }\n  // Prune non-trainable variables.\n  const originalVarCount = varList.length;\n  varList = varList.filter(variable => variable.trainable);\n  util.assert(\n      varList.length > 0,\n      `variableGrads() expects at least one of the input variables to be ` +\n          `trainable, but none of the ${originalVarCount} variables is ` +\n          `trainable.`);\n\n  const allowNoGradients = true;\n  const {value, grads} =\n      ENV.engine.gradients(f, varList, null, allowNoGradients);\n\n  util.assert(\n      grads.some(g => g != null),\n      'Cannot find a connection between any variable and the result of the ' +\n          'loss function y=f(x). Please make sure the operations that use ' +\n          'variables are inside the function f passed to minimize().');\n  util.assert(\n      value.rank === 0,\n      `The f passed in variableGrads(f) must return a scalar, but it ` +\n          `returned a rank-${value.rank} tensor`);\n\n  const namedGrads: NamedTensorMap = {};\n  varList.forEach((v, i) => {\n    if (grads[i] != null) {\n      namedGrads[v.name] = grads[i];\n    }\n  });\n  return {value, grads: namedGrads};\n}\n\n/**\n * Overrides the gradient computation of a function `f`.\n *\n * Takes a function\n * `f(...inputs) => {value: Tensor, gradFunc: dy => Tensor[]}` and returns\n * another function `g(...inputs)` which takes the same inputs as `f`. When\n * called, `g` returns `f().value`. In backward mode, custom gradients with\n * respect to each input of `f` are computed using `f().gradFunc`.\n *\n * ```js\n * const customOp = tf.customGrad(x => {\n *   // Override gradient of our custom x ^ 2 op to be dy * abs(x);\n *   return {value: x.square(), gradFunc: dy => [dy.mul(x.abs())]};\n * });\n *\n * const x = tf.tensor1d([-1, -2, 3]);\n * const dx = tf.grad(x => customOp(x));\n *\n * console.log(`f(x):`);\n * customOp(x).print();\n * console.log(`f'(x):`);\n * dx(x).print();\n * ```\n *\n * @param f The function to evaluate in forward mode, which should return\n *     `{value: Tensor, gradFunc: (dy) => Tensor[]}`, where `gradFunc` returns\n *     the custom gradients of `f` with respect to its inputs.\n */\n/** @doc {heading: 'Training', subheading: 'Gradients'} */\nfunction customGrad<T extends Tensor>(f: CustomGradientFunc<T>):\n    (...args: Tensor[]) => T {\n  return ENV.engine.customGrad(f);\n}\n\nfunction checkGrads(grads: Tensor[]) {\n  const numNullGradients = grads.filter(g => g == null).length;\n  if (numNullGradients > 0) {\n    throw new Error(\n        `Cannot compute gradient of y=f(x) with respect to x. Make sure that\n    the f you passed encloses all operations that lead from x to y.`);\n  }\n}\n\nexport {\n  gradScope,\n  customGrad,\n  variableGrads,\n  valueAndGrad,\n  valueAndGrads,\n  grad,\n  grads,\n};\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Environment} from './environment';\nexport {customGrad, grad, grads, valueAndGrad, valueAndGrads, variableGrads} from './gradients';\n\nexport const tidy = Environment.tidy;\nexport const keep = Environment.keep;\nexport const dispose = Environment.dispose;\nexport const time = Environment.time;\nexport const profile = Environment.profile;","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from './environment';\n\nexport function warn(...msg: Array<{}>): void {\n  if (!ENV.get('IS_TEST')) {\n    console.warn(...msg);\n  }\n}\n\nexport function log(...msg: Array<{}>): void {\n  if (!ENV.get('IS_TEST')) {\n    console.log(...msg);\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Gets the new shape of the input Tensor after it's been reshaped\n * to:\n * [blockShape[0], ..., blockShape[M-1], batch / prod(blockShape),\n * inputShape[1], ..., inputShape[N-1]]\n *\n * See step 1: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshaped(\n    inputShape: number[], blockShape: number[], prod: number,\n    batchToSpace = true): number[] {\n  let reshaped: number[] = [];\n  if (batchToSpace) {\n    reshaped = reshaped.concat(blockShape.slice(0));\n    reshaped.push(inputShape[0] / prod);\n    reshaped = reshaped.concat(inputShape.slice(1));\n  } else {\n    reshaped = reshaped.concat(inputShape[0]);\n    const spatialLength = blockShape.length;\n    for (let i = 0; i < spatialLength; ++i) {\n      reshaped =\n          reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);\n    }\n    reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));\n  }\n  return reshaped;\n}\n\n/**\n * Gets the permutation that will transpose the dimensions of the\n * reshaped tensor to shape:\n *\n * [batch / prod(block_shape),inputShape[1], blockShape[0], ...,\n * inputShape[M], blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * see step 2: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getPermuted(\n    reshapedRank: number, blockShapeRank: number,\n    batchToSpace = true): number[] {\n  const permuted = [];\n  if (batchToSpace) {\n    permuted.push(blockShapeRank);\n    for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {\n      if (i <= 2 * blockShapeRank) {\n        permuted.push(i);\n        permuted.push(i - (blockShapeRank + 1));\n      } else {\n        permuted.push(i);\n      }\n    }\n  } else {\n    const permutedBeforeBatch = [];\n    const permutedAfterBatch = [];\n    for (let i = 1; i < reshapedRank; ++i) {\n      if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {\n        permutedAfterBatch.push(i);\n      } else {\n        permutedBeforeBatch.push(i);\n      }\n    }\n    permuted.push(...permutedBeforeBatch);\n    permuted.push(0);\n    permuted.push(...permutedAfterBatch);\n  }\n  return permuted;\n}\n\n/**\n * Gets the shape of the reshaped and permuted input Tensor before any cropping\n * is applied.  The new shape will be:\n *\n * [batch / prod(blockShape),inputShape[1] * blockShape[0], ...,\n * inputShape[M] * blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 3: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshapedPermuted(\n    inputShape: number[], blockShape: number[], prod: number,\n    batchToSpace = true): number[] {\n  const reshapedPermuted = [];\n\n  if (batchToSpace) {\n    reshapedPermuted.push(inputShape[0] / prod);\n  } else {\n    reshapedPermuted.push(inputShape[0] * prod);\n  }\n\n  for (let i = 1; i < inputShape.length; ++i) {\n    if (i <= blockShape.length) {\n      if (batchToSpace) {\n        reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);\n      } else {\n        reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);\n      }\n    } else {\n      reshapedPermuted.push(inputShape[i]);\n    }\n  }\n\n  return reshapedPermuted;\n}\n\n/**\n * Converts the crops argument into the beginning coordinates of a slice\n * operation.\n */\nexport function getSliceBeginCoords(\n    crops: number[][], blockShape: number): number[] {\n  const sliceBeginCoords = [0];\n  for (let i = 0; i < blockShape; ++i) {\n    sliceBeginCoords.push(crops[i][0]);\n  }\n  return sliceBeginCoords;\n}\n\n/**\n * Converts the crops argument into the size of a slice operation.  When\n * combined with getSliceBeginCoords this function allows the reshaped and\n * permuted Tensor to be cropped to its final output shape of:\n *\n * inputShape[1] * blockShape[0] - crops[0,0] - crops[0,1], ...,\n * inputShape[M] * blockShape[M-1] -crops[M-1,0] -\n * crops[M-1,1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 4: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getSliceSize(\n    uncroppedShape: number[], crops: number[][], blockShape: number): number[] {\n  const sliceSize = uncroppedShape.slice(0, 1);\n  for (let i = 0; i < blockShape; ++i) {\n    sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);\n  }\n\n  return sliceSize;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as util from '../util';\n\n/**\n * Returns true if the axis specifies the inner most dimensions of the\n * array.\n */\nexport function axesAreInnerMostDims(axes: number[], rank: number): boolean {\n  for (let i = 0; i < axes.length; ++i) {\n    if (axes[axes.length - i - 1] !== rank - 1 - i) {\n      return false;\n    }\n  }\n  return true;\n}\n\nexport function combineLocations(\n    outputLoc: number[], reduceLoc: number[], axes: number[]): number[] {\n  const rank = outputLoc.length + reduceLoc.length;\n  const loc = [];\n  let outIdx = 0;\n  let reduceIdx = 0;\n    for (let dim = 0; dim < rank; dim++) {\n    if (axes.indexOf(dim) === -1) {\n      loc.push(outputLoc[outIdx++]);\n    } else {\n      loc.push(reduceLoc[reduceIdx++]);\n    }\n  }\n  return loc;\n}\n\nexport function computeOutAndReduceShapes(\n    aShape: number[], axes: number[]): [number[], number[]] {\n  const outShape = [];\n  const rank = aShape.length;\n  for (let dim = 0; dim < rank; dim++) {\n    if (axes.indexOf(dim) === -1) {\n      outShape.push(aShape[dim]);\n    }\n  }\n  const reduceShape = axes.map(dim => aShape[dim]);\n  return [outShape, reduceShape];\n}\n\nexport function expandShapeToKeepDim(\n    shape: number[], axes: number[]): number[] {\n  const reduceSubShape = axes.map(x => 1);\n  return combineLocations(shape, reduceSubShape, axes);\n}\n\nexport function parseAxisParam(\n    axis: number|number[], shape: number[]): number[] {\n  const rank = shape.length;\n\n  // Normalize input\n  axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);\n\n  // Check for valid range\n  util.assert(\n      axis.every(ax => ax >= -rank && ax < rank),\n      `All values in axis param must be in range [-${rank}, ${rank}) but ` +\n          `got axis ${axis}`);\n\n  // Check for only integers\n  util.assert(\n      axis.every(ax => util.isInt(ax)),\n      `All values in axis param must be integers but ` +\n          `got axis ${axis}`);\n\n  // Handle negative axis.\n  return axis.map(a => a < 0 ? rank + a : a);\n}\n\nexport function assertAxesAreInnerMostDims(\n    msg: string, axes: number[], rank: number): void {\n  util.assert(\n      axesAreInnerMostDims(axes, rank),\n      `${msg} supports only inner-most axes for now. ` +\n          `Got axes ${axes} and rank-${rank} input.`);\n}\n\n/**\n * Returns the axes permutation to be used with `tf.transpose`, if such\n * permutation is necessary. Otherwise it returns null. This method is used by\n * operations that operate only on inner-most axes.\n */\nexport function getAxesPermutation(axes: number[], rank: number): number[]|\n    null {\n  if (axesAreInnerMostDims(axes, rank)) {\n    return null;\n  }\n  const result: number[] = [];\n  for (let i = 0; i < rank; ++i) {\n    if (axes.indexOf(i) === -1) {\n      result.push(i);\n    }\n  }\n  axes.forEach(axis => result.push(axis));\n  return result;\n}\n\n/** Returns the axes permutation that undoes the original permutation. */\nexport function getUndoAxesPermutation(axes: number[]): number[] {\n  return axes.map((axis, i) => [i, axis])\n      .sort((a, b) => a[1] - b[1])\n      .map(x => x[0]);\n}\n\nexport function getInnerMostAxes(numAxes: number, rank: number): number[] {\n  const res: number[] = [];\n  for (let i = rank - numAxes; i < rank; ++i) {\n    res.push(i);\n  }\n  return res;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as util from '../util';\n\nexport function assertParamsConsistent(shapes: number[][], axis: number) {\n  const rank = shapes[0].length;\n  shapes.forEach((shape, i) => {\n    util.assert(\n        shape.length === rank,\n        `Error in concat${rank}D: rank of tensors[${i}] must be the same ` +\n            `as the rank of the rest (${rank})`);\n  });\n\n  util.assert(\n      axis >= 0 && axis < rank,\n      `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);\n\n  const firstShape = shapes[0];\n  shapes.forEach((shape, i) => {\n    for (let r = 0; r < rank; r++) {\n      util.assert(\n          (r === axis) || (shape[r] === firstShape[r]),\n          `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) ` +\n              `does not match the shape of the rest (${firstShape}) ` +\n              `along the non-concatenated axis ${i}.`);\n    }\n  });\n}\n\nexport function computeOutShape(shapes: number[][], axis: number): number[] {\n  const outputShape = shapes[0].slice();\n  for (let i = 1; i < shapes.length; i++) {\n    outputShape[axis] += shapes[i][axis];\n  }\n  return outputShape;\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor} from '../tensor';\nimport {computeStrides} from '../util';\n\n/**\n * Validate gather nd inputs.\n *\n * @param tensor The tensor contains the source values.\n * @param indices The tensor contains the indices to slice the source.\n *\n * @returns [resultShape, numUpdates, sliceSize, strides]\n */\nexport function prepareAndValidate(\n    tensor: Tensor, indices: Tensor): [number[], number, number, number[]] {\n  if (tensor.rank < 1) {\n    throw new Error(\n        'tf.gatherND() expects the input to be rank 1 or higher,' +\n        ` but the rank was ${tensor.rank}.`);\n  }\n  if (indices.rank < 1) {\n    throw new Error(\n        'tf.gatherND() expects the indices to be rank 1 or higher,' +\n        ` but the rank was ${indices.rank}.`);\n  }\n  if (indices.dtype !== 'int32') {\n    throw new Error(\n        'tf.gatherND() expects the indices to be int32 type,' +\n        ` but the dtype was ${indices.dtype}.`);\n  }\n  if (indices.shape[indices.rank - 1] > tensor.rank) {\n    throw new Error(\n        'index innermost dimension length must be <= tensor rank; saw: ' +\n        `${indices.shape[indices.rank - 1]} vs. ${tensor.rank}`);\n  }\n\n  if (tensor.size === 0) {\n    throw new Error(\n        'Requested more than 0 entries, but input is empty.' +\n        ` Input shape: ${tensor.shape}.`);\n  }\n\n  const indicesShape = indices.shape;\n  const sliceRank = indicesShape[indicesShape.length - 1];\n\n  // The result shape is\n  //   indices.shape[:-1] + params.shape[indices.shape[-1]:]\n  let nResult = 1;\n  for (let i = 0; i < indicesShape.length - 1; ++i) {\n    nResult *= indicesShape[i];\n  }\n\n  const inputShape = tensor.shape;\n\n  const resultShape = indicesShape.slice();\n  resultShape.pop();\n\n  let sliceSize = 1;\n  for (let i = sliceRank; i < tensor.rank; ++i) {\n    sliceSize *= inputShape[i];\n    resultShape.push(inputShape[i]);\n  }\n\n  const strides =\n      [...computeStrides(tensor.shape).map(stride => stride / sliceSize),\n       1].slice(0, sliceRank);\n\n  return [resultShape, nResult, sliceSize, strides];\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Inputs of size above this threshold will be parallelized by calling multiple\n * shader programs.\n */\nimport {nearestDivisor} from '../util';\n\nexport const PARALLELIZE_THRESHOLD = 30;\n\nexport interface ReduceInfo {\n  windowSize: number;\n  batchSize: number;\n  inSize: number;\n}\n\nexport function computeOptimalWindowSize(inSize: number): number {\n  if (inSize <= PARALLELIZE_THRESHOLD) {\n    return inSize;\n  }\n  return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor} from '../tensor';\nimport {computeStrides, sizeFromShape} from '../util';\n\n/**\n * Check whether updates.shape = indices.shape[:batchDim] +\n * shape[sliceDim:]\n *\n * @param x The input tensor.\n */\nexport function validateUpdateShape(\n    shape: number[], indices: Tensor, updates: Tensor) {\n  const sliceDim = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;\n  const batchDim = (indices.rank > 1) ? indices.rank - 1 : 1;\n\n  const shapeError = 'Must have updates.shape = indices.shape[:batchDim] + ' +\n      `shape[sliceDim:], got updates.shape: ${updates.shape}` +\n      `, indices.shape: ${indices.shape}, shape: ${shape}` +\n      `, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;\n\n  if (updates.rank < batchDim) {\n    throw new Error(shapeError + ` update.rank < ${batchDim}. `);\n  }\n  if (shape.length < sliceDim + (updates.rank - batchDim)) {\n    throw new Error(\n        shapeError +\n        ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);\n  }\n  if (updates.rank !== batchDim + shape.length - sliceDim) {\n    throw new Error(\n        shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);\n  }\n  for (let d = 0; d < batchDim; ++d) {\n    if (updates.shape[d] !== indices.shape[d]) {\n      throw new Error(\n          shapeError +\n          ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${\n              indices.shape[d]}).`);\n    }\n  }\n  for (let d = 0; d < updates.rank - batchDim; ++d) {\n    if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {\n      throw new Error(\n          shapeError +\n          ` updates.shape[${d + batchDim}] (${\n              updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${\n              shape[d + batchDim]})`);\n    }\n  }\n}\n\nexport interface ScatterShapeInfo {\n  sliceRank: number;\n  numUpdates: number;\n  sliceSize: number;\n  strides: number[];\n  outputSize: number;\n}\n/**\n * Validate scatter nd inputs.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n */\nexport function validateInput(\n    updates: Tensor, indices: Tensor, shape: number[]) {\n  if (indices.rank < 1) {\n    throw new Error(\n        'tf.scatterND() expects the indices to be rank 1 or higher,' +\n        ` but the rank was ${indices.rank}.`);\n  }\n  if (updates.rank < 1) {\n    throw new Error(\n        'tf.scatterND() expects the updates to be rank 1 or higher,' +\n        ` but the rank was ${updates.rank}.`);\n  }\n  if (indices.dtype !== 'int32') {\n    throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${\n        indices.dtype}`);\n  }\n  if (shape.length < 1) {\n    throw new Error(\n        `Output rank must be greater or equal to 1, but got shape: ${shape}`);\n  }\n\n  if (shape.length === 0) {\n    if (indices.size === 0) {\n      throw new Error(`Indices specified for empty output. indices shape: ${\n          indices.shape}`);\n    }\n    if (updates.size === 0) {\n      throw new Error(`Updates specified for empty output. updates shape: ${\n          updates.shape}`);\n    }\n  }\n\n  validateUpdateShape(shape, indices, updates);\n}\n\n/**\n * Calculate the shape information for the output.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n *\n * @returns ScatterShapeInfo\n */\nexport function calculateShapes(\n    updates: Tensor, indices: Tensor, shape: number[]): ScatterShapeInfo {\n  // Calculate the number of dimensions in indices\n  const sliceRank = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;\n\n  // Calculate the number of elements that make up each slice of our updated\n  // tensor. This allows us to work with flattened tensors and copy over whole\n  // slices at a time.\n  const totalNd = shape.length;\n\n  let sliceSize = 1;\n  for (let i = sliceRank; i < totalNd; ++i) {\n    sliceSize *= shape[i];\n  }\n\n  const safeSliceDim = (sliceRank < 1) ? 1 : sliceRank;\n  const numUpdates = indices.size / safeSliceDim;\n\n  const outputStrides = [...computeStrides(shape), 1];\n  const strides = outputStrides.slice(\n      outputStrides.length - sliceRank, outputStrides.length);\n  const outputSize = sizeFromShape(shape);\n  return {sliceRank, numUpdates, sliceSize, strides, outputSize};\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {nearestDivisor} from '../util';\nimport {PARALLELIZE_THRESHOLD} from './reduce_util';\n\nexport interface SegOpInfo {\n  windowSize: number;\n  batchSize: number;\n  inSize: number;\n  numSegments: number;\n}\n\nexport function segOpComputeOptimalWindowSize(\n    inSize: number, numSegments: number): number {\n  let done = false;\n  let res;\n\n  if (inSize <= PARALLELIZE_THRESHOLD) {\n    res = inSize;\n    done = true;\n  } else {\n    res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n  }\n\n  while (!done) {\n    if (res > numSegments || res === inSize) {\n      done = true;\n      break;\n    } else {\n      res = nearestDivisor(inSize, res + 1);\n    }\n  }\n  return res;\n}\n\nexport function computeOutShape(\n    aShape: number[], axis: number, numSegments: number): number[] {\n  const outShape = [];\n  const rank = aShape.length;\n  for (let dim = 0; dim < rank; dim++) {\n    if (dim !== axis) {\n      outShape.push(aShape[dim]);\n    } else {\n      outShape.push(numSegments);\n    }\n  }\n  return outShape;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport * as util from '../util';\n\nexport function assertParamsValid(\n    input: Tensor, begin: number[], size: number[]): void {\n  util.assert(\n      input.rank === begin.length,\n      `Error in slice${input.rank}D: Length of begin ${begin} must ` +\n          `match the rank of the array (${input.rank}).`);\n  util.assert(\n      input.rank === size.length,\n      `Error in slice${input.rank}D: Length of size ${size} must ` +\n          `match the rank of the array (${input.rank}).`);\n\n  for (let i = 0; i < input.rank; ++i) {\n    util.assert(\n        begin[i] + size[i] <= input.shape[i],\n        `Error in slice${input.rank}D: begin[${i}] + size[${i}] ` +\n            `(${begin[i] + size[i]}) would overflow input.shape[${i}] (${\n                input.shape[i]})`);\n  }\n}\n\n/**\n * Calculate the start index and output tensor shape for strided slice op.\n * @returns array of [startIndex, size, shrinkAxis]\n */\nexport function getStridedSlicedInfo(\n    shape: number[], begin: number[], end: number[], strides: number[],\n    beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0,\n    shrinkAxisMask = 0): [number[], number[], number[]] {\n  if (ellipsisMask !== 0) {\n    throw new Error('ellipsis mask is not yet supported');\n  }\n  if (newAxisMask !== 0) {\n    throw new Error('new axis mask is not yet supported');\n  }\n  // Note that the axis orders are reversed for runtime ops, so the indices,\n  // strides and masks must be as well too.\n  const startIndex: number[] = [];\n  const endIndex: number[] = [];\n  const shrinkAxis: number[] = [];\n  for (let i = 0; i < shape.length; i++) {\n    startIndex[i] = startForAxis(beginMask, begin, strides, shape, i);\n    endIndex[i] = stopForAxis(endMask, end, strides, shape, i);\n    // When shrinking an axis, use startIndex + 1 for endIndex.\n    // Check the axis bit from right of shrinkAxisMask\n    if (shrinkAxisMask & 1 << i) {\n      endIndex[i] = startIndex[i] + 1;\n      shrinkAxis.push(i);\n    }\n  }\n\n  let size = new Array(shape.length).fill(0);\n  size = size.map((d, i) => {\n    let count = 0;\n    for (let start = startIndex[i];\n         !(strides[i] > 0 ? start >= endIndex[i] : start <= endIndex[i]);\n         start += strides[i]) {\n      count += 1;\n    }\n    return count;\n  });\n\n  return [startIndex, size, shrinkAxis];\n}\n\nexport function startForAxis(\n    beginMask: number, startIndices: number[], strides: number[],\n    inputShape: number[], axis: number): number {\n  // Begin with the specified index\n  let start = startIndices[axis];\n\n  // Check the axis bit from right of beginMask\n  if (beginMask & 1 << axis) {\n    if (strides[axis] > 0) {\n      // Forward iteration - use the first element. These values will get\n      // clamped below (Note: We could have set them to 0 and axis_size-1, but\n      // use lowest() and max() to maintain symmetry with StopForAxis())\n      start = Number.MIN_SAFE_INTEGER;\n    } else {\n      // Backward iteration - use the last element.\n      start = Number.MAX_SAFE_INTEGER;\n    }\n  }\n\n  // Handle negative indices\n  const axisSize = inputShape[axis];\n  if (start < 0) {\n    start += axisSize;\n  }\n\n  // Clamping\n  start = util.clamp(0, start, axisSize - 1);\n\n  return start;\n}\n\nexport function stopForAxis(\n    endMask: number, stopIndices: number[], strides: number[],\n    inputShape: number[], axis: number): number {\n  // Begin with the specified index\n  let stop = stopIndices[axis];\n\n  // Check the axis bit from right of endMask\n  if (endMask & (1 << axis)) {\n    if (strides[axis] > 0) {\n      // Forward iteration - use the last element. These values will get\n      // clamped below\n      stop = Number.MAX_SAFE_INTEGER;\n    } else {\n      // Backward iteration - use the first element.\n      stop = Number.MIN_SAFE_INTEGER;\n    }\n  }\n\n  // Handle negative indices\n  const axisSize = inputShape[axis];\n  if (stop < 0) {\n    stop += axisSize;\n  }\n\n  // Clamping\n  // Because the end index points one past the last element, we need slightly\n  // different clamping ranges depending on the direction.\n  if (strides[axis] > 0) {\n    // Forward iteration\n    stop = util.clamp(0, stop, axisSize);\n  } else {\n    // Backward iteration\n    stop = util.clamp(-1, stop, axisSize - 1);\n  }\n\n  return stop;\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from './environment';\nimport {Tensor} from './tensor';\nimport {DataType, TensorLike, TypedArray} from './types';\nimport {assert, flatten, inferDtype, isTypedArray, toTypedArray} from './util';\n\nexport function inferShape(val: TensorLike): number[] {\n  let firstElem: typeof val = val;\n\n  if (isTypedArray(val)) {\n    return [(val as TypedArray).length];\n  }\n  if (!Array.isArray(val)) {\n    return [];  // Scalar.\n  }\n  const shape: number[] = [];\n\n  while (firstElem instanceof Array || isTypedArray(firstElem)) {\n    shape.push(firstElem.length);\n    firstElem = firstElem[0];\n  }\n  if (val instanceof Array && ENV.get('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')) {\n    deepAssertShapeConsistency(val, shape, []);\n  }\n\n  return shape;\n}\n\nfunction deepAssertShapeConsistency(\n    val: TensorLike, shape: number[], indices: number[]) {\n  indices = indices || [];\n  if (!(val instanceof Array) && !isTypedArray(val)) {\n    assert(\n        shape.length === 0,\n        () => `Element arr[${indices.join('][')}] is a primitive, ` +\n            `but should be an array/TypedArray of ${shape[0]} elements`);\n    return;\n  }\n  assert(\n      shape.length > 0,\n      () => `Element arr[${indices.join('][')}] should be a primitive, ` +\n          `but is an array of ${val.length} elements`);\n  assert(\n      val.length === shape[0],\n      () => `Element arr[${indices.join('][')}] should have ${shape[0]} ` +\n          `elements, but has ${val.length} elements`);\n  const subShape = shape.slice(1);\n  for (let i = 0; i < val.length; ++i) {\n    deepAssertShapeConsistency(val[i], subShape, indices.concat(i));\n  }\n}\n\nfunction assertDtype(\n    expectedDtype: DataType|'numeric', actualDType: DataType, argName: string,\n    functionName: string) {\n  if (expectedDtype == null) {\n    return;\n  }\n  if (expectedDtype !== 'numeric' && expectedDtype !== actualDType ||\n      expectedDtype === 'numeric' && actualDType === 'string') {\n    throw new Error(\n        `Argument '${argName}' passed to '${functionName}' must ` +\n        `be ${expectedDtype} tensor, but got ${actualDType} tensor`);\n  }\n}\n\nexport function convertToTensor<T extends Tensor>(\n    x: T|TensorLike, argName: string, functionName: string,\n    parseAsDtype: DataType|'numeric' = 'numeric'): T {\n  if (x instanceof Tensor) {\n    assertDtype(parseAsDtype, x.dtype, argName, functionName);\n    return x;\n  }\n  let inferredDtype = inferDtype(x);\n  // If the user expects a bool/int/float, use that info to update the\n  // inferredDtype when it is not a string.\n  if (inferredDtype !== 'string' &&\n      ['bool', 'int32', 'float32'].indexOf(parseAsDtype) >= 0) {\n    inferredDtype = parseAsDtype as DataType;\n  }\n  assertDtype(parseAsDtype, inferredDtype, argName, functionName);\n\n  if (!isTypedArray(x) && !Array.isArray(x) && typeof x !== 'number' &&\n      typeof x !== 'boolean' && typeof x !== 'string') {\n    throw new Error(\n        `Argument '${argName}' passed to '${functionName}' must be a ` +\n        `Tensor or TensorLike, but got '${(x as {}).constructor.name}'`);\n  }\n  const inferredShape = inferShape(x);\n  if (!isTypedArray(x) && !Array.isArray(x)) {\n    x = [x] as number[];\n  }\n  const values = inferredDtype !== 'string' ?\n      toTypedArray(x, inferredDtype as DataType, ENV.get('DEBUG')) :\n      flatten(x as string[]) as string[];\n  return Tensor.make(inferredShape, {values}, inferredDtype);\n}\n\nexport function convertToTensorArray<T extends Tensor>(\n    arg: Array<T|TensorLike>, argName: string, functionName: string): T[] {\n  if (!Array.isArray(arg)) {\n    throw new Error(\n        `Argument ${argName} passed to ${functionName} must be a ` +\n        '`Tensor[]` or `TensorLike[]`');\n  }\n  const tensors = arg as T[];\n  return tensors.map(\n      (t, i) => convertToTensor(t, `${argName}[${i}]`, functionName));\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\n\n/**\n * Used for wrapping functions that perform math operations on\n * Tensors. The function will be wrapped in a named scope that cleans all\n * memory usage after the function is done.\n */\nexport function op<T extends Function>(f: {[name: string]: T}): T {\n  const keys = Object.keys(f);\n  if (keys.length !== 1) {\n    throw new Error(\n        `Please provide an object with a single key ` +\n        `(operation name) mapping to a function. Got an object with ` +\n        `${keys.length} keys.`);\n  }\n\n  let opName = keys[0];\n  const fn = f[opName];\n\n  // Strip the underscore from the end of the function name.\n  if (opName.endsWith('_')) {\n    opName = opName.substring(0, opName.length - 1);\n  }\n\n  // tslint:disable-next-line:no-any\n  const f2 = (...args: any[]) => {\n    ENV.engine.startScope(opName);\n    try {\n      const result = fn(...args);\n      if (result instanceof Promise) {\n        console.error('Cannot return a Promise inside of tidy.');\n      }\n      ENV.engine.endScope(result);\n      return result;\n    } catch (ex) {\n      ENV.engine.endScope(null);\n      throw ex;\n    }\n  };\n  Object.defineProperty(f2, 'name', {value: opName, configurable: true});\n\n  // tslint:disable-next-line:no-any\n  return f2 as any as T;\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {customGrad} from '../gradients';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * Computes the softmax normalized vector given the logits.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n */\n/** @doc {heading: 'Operations', subheading: 'Normalization'} */\nfunction softmax_<T extends Tensor>(logits: T|TensorLike, dim = -1): T {\n  const $logits = convertToTensor(logits, 'logits', 'softmax');\n\n  if (dim === -1) {\n    dim = $logits.rank - 1;\n  }\n  if (dim !== $logits.rank - 1) {\n    throw Error(\n        'Softmax along a non-last dimension is not yet supported. ' +\n        `Logits was rank ${$logits.rank} and dim was ${dim}`);\n  }\n\n  const customOp = customGrad(logits => {\n    // Do it in log space for numerical stability.\n    // exp(X - logSumExp(X))\n    const keepDims = true;\n    const lse = logits.logSumExp([dim], keepDims);\n    const logResult = logits.toFloat().sub(lse);\n    const y = logResult.exp() as T;\n\n    const gradFunc = (dy: T) => {\n      const dyTimesY = dy.mul(y);\n      const keepDims = true;\n      return dyTimesY.sub(dyTimesY.sum([dim], keepDims).mul(y));\n    };\n\n    return {value: y, gradFunc};\n  });\n\n  return customOp($logits);\n}\n\n/**\n * Computes the log softmax.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param axis The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n */\n/** @doc {heading: 'Operations', subheading: 'Normalization'} */\nfunction logSoftmax_<T extends Tensor>(logits: T|TensorLike, axis = -1): T {\n  const $logits = convertToTensor(logits, 'logits', 'logSoftmax');\n\n  if (axis === -1) {\n    axis = $logits.rank - 1;\n  }\n  if (axis !== $logits.rank - 1) {\n    throw Error(\n        'Log Softmax along a non-last dimension is not yet supported. ' +\n        `Logits was rank ${$logits.rank} and axis was ${axis}`);\n  }\n\n  const customOp = customGrad(logits => {\n    const keepDims = true;\n    const xMax = logits.max(axis, true);\n    const shifted = logits.sub(xMax);\n    const value =\n        shifted.toFloat().sub(shifted.exp().sum(axis, keepDims).log()) as T;\n\n    const gradFunc = (dy: T) => {\n      const softmax = value.exp();\n      return dy.sub(dy.sum(axis, keepDims).mul(softmax));\n    };\n\n    return {value, gradFunc};\n  });\n\n  return customOp($logits);\n}\n\nexport const softmax = op({softmax_});\nexport const logSoftmax = op({logSoftmax_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\n\n/**\n * Converts two real numbers to a complex number.\n *\n * Given a tensor `real` representing the real part of a complex number, and a\n * tensor `imag` representing the imaginary part of a complex number, this\n * operation returns complex numbers elementwise of the form [r0, i0, r1, i1],\n * where r represents the real part and i represents the imag part.\n *\n * The input tensors real and imag must have the same shape.\n *\n * ```js\n * const real = tf.tensor1d([2.25, 3.25]);\n * const imag = tf.tensor1d([4.75, 5.75]);\n * const complex = tf.complex(real, imag);\n *\n * complex.print();\n * ```\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction complex_<T extends Tensor>(real: T|TensorLike, imag: T|TensorLike): T {\n  const $real = convertToTensor(real, 'real', 'complex');\n  const $imag = convertToTensor(imag, 'imag', 'complex');\n  util.assertShapesMatch(\n      $real.shape, $imag.shape,\n      `real and imag shapes, ${$real.shape} and ${$imag.shape}, ` +\n          `must match in call to tf.complex().`);\n\n  return ENV.engine.runKernel(\n             backend => backend.complex($real, $imag), {$real, $imag}) as T;\n}\n\n/**\n * Returns the real part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the real part of each element in input considered as a complex number.\n *\n * If the input is real, it simply makes a clone.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.real(x).print();\n * ```\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction real_<T extends Tensor>(input: T|TensorLike): T {\n  const $input = convertToTensor(input, 'input', 'real');\n\n  return ENV.engine.runKernel(backend => backend.real($input), {$input}) as T;\n}\n\n/**\n * Returns the imaginary part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the imaginary part of each element in input considered as a complex number.\n * If input is real, a tensor of all zeros is returned.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.imag(x).print();\n * ```\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction imag_<T extends Tensor>(input: T|TensorLike): T {\n  const $input = convertToTensor(input, 'input', 'imag');\n\n  return ENV.engine.runKernel(backend => backend.imag($input), {$input}) as T;\n}\n\nexport const complex = op({complex_});\nexport const real = op({real_});\nexport const imag = op({imag_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Scalar, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, Tensor5D, Tensor6D} from '../tensor';\nimport {convertToTensor, inferShape} from '../tensor_util_env';\nimport {TensorLike, TensorLike1D, TensorLike2D, TensorLike3D, TensorLike4D, TensorLike5D, TensorLike6D, TypedArray} from '../types';\nimport {DataType, Rank, ShapeMap} from '../types';\nimport {assert, assertNonNull, flatten, getArrayFromDType, inferDtype, isTypedArray, makeOnesTypedArray, makeZerosTypedArray, sizeFromShape, toTypedArray} from '../util';\n\nimport {complex} from './complex_ops';\nimport {op} from './operation';\n\n/**\n * Creates a `tf.Tensor` with the provided values, shape and dtype.\n *\n * ```js\n * // Pass an array of values to create a vector.\n * tf.tensor([1, 2, 3, 4]).print();\n * ```\n *\n * ```js\n * // Pass a nested array of values to make a matrix or a higher\n * // dimensional tensor.\n * tf.tensor([[1, 2], [3, 4]]).print();\n * ```\n *\n * ```js\n * // Pass a flat array and specify a shape yourself.\n * tf.tensor([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor<R extends Rank>(\n    values: TensorLike, shape?: ShapeMap[R], dtype?: DataType): Tensor<R> {\n  if (dtype == null) {\n    dtype = inferDtype(values);\n  }\n  if (dtype === 'complex64') {\n    throw new Error(\n        `Cannot construct a complex64 tensor directly. ` +\n        `Please use tf.complex(real, imag).`);\n  }\n  if (!isTypedArray(values) && !Array.isArray(values) &&\n      typeof values !== 'number' && typeof values !== 'boolean' &&\n      typeof values !== 'string') {\n    throw new Error(\n        'values passed to tensor(values) must be a number/boolean/string or ' +\n        'an array of numbers/booleans/strings, or a TypedArray');\n  }\n  const inferredShape = inferShape(values);\n  if (shape != null) {\n    const providedSize = sizeFromShape(shape);\n    const inferredSize = sizeFromShape(inferredShape);\n    assert(\n        providedSize === inferredSize,\n        () =>\n            `Based on the provided shape, [${shape}], the tensor should have ` +\n            `${providedSize} values but has ${inferredSize}`);\n\n    for (let i = 0; i < inferredShape.length; ++i) {\n      const inferred = inferredShape[i];\n      const flatDimsDontMatch = i === inferredShape.length - 1 ?\n          inferred !== sizeFromShape(shape.slice(i)) :\n          true;\n      assert(\n          inferredShape[i] === shape[i] || !flatDimsDontMatch,\n          () => `Error creating a new Tensor. Inferred shape ` +\n              `(${inferredShape}) does not match the provided ` +\n              `shape (${shape}). `);\n    }\n  }\n  if (!isTypedArray(values) && !Array.isArray(values)) {\n    values = [values] as number[];\n  }\n  shape = shape || inferredShape;\n  values = dtype !== 'string' ? toTypedArray(values, dtype, ENV.get('DEBUG')) :\n                                flatten(values as string[]) as string[];\n  return Tensor.make(shape, {values}, dtype);\n}\n\n/**\n * Creates rank-0 `tf.Tensor` (scalar) with the provided value and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.scalar` as it makes the code more readable.\n *\n * ```js\n * tf.scalar(3.14).print();\n * ```\n *\n * @param value The value of the scalar.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction scalar(value: number|boolean|string, dtype?: DataType): Scalar {\n  if ((isTypedArray(value) || Array.isArray(value)) && dtype !== 'complex64') {\n    throw new Error(\n        'Error creating a new Scalar: value must be a primitive ' +\n        '(number|boolean|string)');\n  }\n  return tensor(value, [], dtype);\n}\n\n/**\n * Creates rank-1 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor1d` as it makes the code more readable.\n *\n * ```js\n * tf.tensor1d([1, 2, 3]).print();\n * ```\n *\n * @param values The values of the tensor. Can be array of numbers,\n *     or a `TypedArray`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor1d(values: TensorLike1D, dtype?: DataType): Tensor1D {\n  assertNonNull(values);\n  const inferredShape = inferShape(values);\n  if (inferredShape.length !== 1) {\n    throw new Error('tensor1d() requires values to be a flat/TypedArray');\n  }\n  return tensor(values, inferredShape as [number], dtype);\n}\n\n/**\n * Creates rank-2 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor2d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor2d([[1, 2], [3, 4]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided, it is inferred from\n *     `values`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor2d(\n    values: TensorLike2D, shape?: [number, number],\n    dtype?: DataType): Tensor2D {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 2) {\n    throw new Error('tensor2d() requires shape to have two numbers');\n  }\n  const inferredShape = inferShape(values);\n  if (inferredShape.length !== 2 && inferredShape.length !== 1) {\n    throw new Error(\n        'tensor2d() requires values to be number[][] or flat/TypedArray');\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\n        'tensor2d() requires shape to be provided when `values` ' +\n        'are a flat/TypedArray');\n  }\n  shape = shape || inferredShape as [number, number];\n  return tensor(values, shape, dtype);\n}\n\n/**\n * Creates rank-3 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor3d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor3d([[[1], [2]], [[3], [4]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided,  it is inferred from\n *     `values`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor3d(\n    values: TensorLike3D, shape?: [number, number, number],\n    dtype?: DataType): Tensor3D {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 3) {\n    throw new Error('tensor3d() requires shape to have three numbers');\n  }\n  const inferredShape = inferShape(values);\n  if (inferredShape.length !== 3 && inferredShape.length !== 1) {\n    throw new Error(\n        'tensor3d() requires values to be number[][][] or flat/TypedArray');\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\n        'tensor3d() requires shape to be provided when `values` ' +\n        'are a flat array');\n  }\n  shape = shape || inferredShape as [number, number, number];\n  return tensor(values, shape, dtype);\n}\n\n/**\n * Creates rank-4 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor4d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor4d(\n    values: TensorLike4D, shape?: [number, number, number, number],\n    dtype?: DataType): Tensor4D {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 4) {\n    throw new Error('tensor4d() requires shape to have four numbers');\n  }\n  const inferredShape = inferShape(values);\n  if (inferredShape.length !== 4 && inferredShape.length !== 1) {\n    throw new Error(\n        'tensor4d() requires values to be number[][][][] or flat/TypedArray');\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\n        'tensor4d() requires shape to be provided when `values` ' +\n        'are a flat array');\n  }\n  shape = shape || inferredShape as [number, number, number, number];\n  return tensor(values, shape, dtype);\n}\n\n/**\n * Creates rank-5 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor5d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor5d([[[[[1], [2]], [[3], [4]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor5d(\n    values: TensorLike5D, shape?: [number, number, number, number, number],\n    dtype?: DataType): Tensor5D {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 5) {\n    throw new Error('tensor5d() requires shape to have five numbers');\n  }\n  const inferredShape = inferShape(values);\n  if (inferredShape.length !== 5 && inferredShape.length !== 1) {\n    throw new Error(\n        'tensor5d() requires values to be ' +\n        'number[][][][][] or flat/TypedArray');\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\n        'tensor5d() requires shape to be provided when `values` ' +\n        'are a flat array');\n  }\n  shape = shape || inferredShape as [number, number, number, number, number];\n  return tensor(values, shape, dtype);\n}\n\n/**\n * Creates rank-6 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor6d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor6d([[[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor6d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor6d(\n    values: TensorLike6D,\n    shape?: [number, number, number, number, number, number],\n    dtype?: DataType): Tensor6D {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 6) {\n    throw new Error('tensor6d() requires shape to have six numbers');\n  }\n  const inferredShape = inferShape(values);\n  if (inferredShape.length !== 6 && inferredShape.length !== 1) {\n    throw new Error(\n        'tensor6d() requires values to be number[][][][] or flat/TypedArray');\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\n        'tensor6d() requires shape to be provided when `values` ' +\n        'are a flat array');\n  }\n  shape = shape ||\n      inferredShape as [number, number, number, number, number, number];\n  return tensor(values, shape, dtype);\n}\n\n/**\n * Creates a `tf.Tensor` with all elements set to 1.\n *\n * ```js\n * tf.ones([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n *     'float'.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction ones<R extends Rank>(\n    shape: ShapeMap[R], dtype: DataType = 'float32'): Tensor<R> {\n  if (dtype === 'complex64') {\n    const real = ones(shape, 'float32');\n    const imag = ones(shape, 'float32');\n    return complex(real, imag);\n  }\n  const values = makeOnesTypedArray(sizeFromShape(shape), dtype);\n  return Tensor.make(shape, {values}, dtype);\n}\n\n/**\n * Creates a `tf.Tensor` with all elements set to 0.\n *\n * ```js\n * tf.zeros([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Can\n *     be 'float32', 'int32' or 'bool'. Defaults to 'float'.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction zeros<R extends Rank>(\n    shape: ShapeMap[R], dtype: DataType = 'float32'): Tensor<R> {\n  if (dtype === 'complex64') {\n    const real = zeros(shape, 'float32');\n    const imag = zeros(shape, 'float32');\n    return complex(real, imag);\n  }\n  const values = makeZerosTypedArray(sizeFromShape(shape), dtype);\n  return Tensor.make(shape, {values}, dtype);\n}\n\n/**\n * Creates a `tf.Tensor` filled with a scalar value.\n *\n * ```js\n * tf.fill([2, 2], 4).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param value The scalar value to fill the tensor with.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n * 'float'.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction fill<R extends Rank>(\n    shape: ShapeMap[R], value: number|string, dtype?: DataType): Tensor<R> {\n  dtype = dtype || inferDtype(value);\n  const values = getArrayFromDType(dtype, sizeFromShape(shape)) as TypedArray;\n  values.fill(value as number);\n  return Tensor.make(shape, {values}, dtype);\n}\n\n/**\n * Creates a `tf.Tensor` with all elements set to 1 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.onesLike(x).print();\n * ```\n * @param x A tensor.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction onesLike_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'onesLike');\n  return ones($x.shape, $x.dtype) as T;\n}\n\n/**\n * Creates a `tf.Tensor` with all elements set to 0 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.zerosLike(x).print();\n * ```\n *\n * @param x The tensor of required shape.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction zerosLike_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'zerosLike');\n  return zeros($x.shape, $x.dtype) as T;\n}\n\n/**\n * Return an evenly spaced sequence of numbers over the given interval.\n *\n * ```js\n * tf.linspace(0, 9, 10).print();\n * ```\n * @param start The start value of the sequence.\n * @param stop The end value of the sequence.\n * @param num The number of values to generate.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction linspace(start: number, stop: number, num: number): Tensor1D {\n  if (num === 0) {\n    throw new Error('Cannot request zero samples');\n  }\n\n  const step = (stop - start) / (num - 1);\n\n  const values = makeZerosTypedArray(num, 'float32');\n  values[0] = start;\n  for (let i = 1; i < values.length; i++) {\n    values[i] = values[i - 1] + step;\n  }\n\n  return tensor1d(values, 'float32');\n}\n\n/**\n * Creates a new `tf.Tensor1D` filled with the numbers in the range provided.\n *\n * The tensor is a is half-open interval meaning it includes start, but\n * excludes stop. Decrementing ranges and negative step values are also\n * supported.\n *\n * ```js\n * tf.range(0, 9, 2).print();\n * ```\n *\n * @param start An integer start value\n * @param stop An integer stop value\n * @param step An integer increment (will default to 1 or -1)\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction range(\n    start: number, stop: number, step = 1,\n    dtype: 'float32'|'int32' = 'float32'): Tensor1D {\n  if (step === 0) {\n    throw new Error('Cannot have a step of zero');\n  }\n\n  const sameStartStop = start === stop;\n  const increasingRangeNegativeStep = start < stop && step < 0;\n  const decreasingRangePositiveStep = stop < start && step > 1;\n\n  if (sameStartStop || increasingRangeNegativeStep ||\n      decreasingRangePositiveStep) {\n    return zeros([0], dtype);\n  }\n\n  const numElements = Math.abs(Math.ceil((stop - start) / step));\n  const values = makeZerosTypedArray(numElements, dtype);\n\n  if (stop < start && step === 1) {\n    // Auto adjust the step's sign if it hasn't been set\n    // (or was set to 1)\n    step = -1;\n  }\n\n  values[0] = start;\n  for (let i = 1; i < values.length; i++) {\n    values[i] = values[i - 1] + step;\n  }\n\n  return tensor1d(values, dtype);\n}\n\nexport {\n  fill,\n  linspace,\n  ones,\n  range,\n  scalar,\n  tensor,\n  tensor1d,\n  tensor2d,\n  tensor3d,\n  tensor4d,\n  tensor5d,\n  tensor6d,\n  zeros\n};\n\nexport const onesLike = op({onesLike_});\nexport const zerosLike = op({zerosLike_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo, Conv3DInfo} from '../ops/conv_util';\nimport {DataId, Scalar, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, Tensor5D} from '../tensor';\nimport {DataType, DataValues, Rank, ShapeMap} from '../types';\n\n// Required information for all backends.\nexport interface BackendTimingInfo {\n  kernelMs: number;\n  getExtraProfileInfo?(): string;  // a field for additional timing information\n                                   // e.g. packing / unpacking for WebGL backend\n}\n\nexport interface TensorStorage {\n  read(dataId: DataId): Promise<DataValues>;\n  readSync(dataId: DataId): DataValues;\n  disposeData(dataId: DataId): void;\n  write(dataId: DataId, values: DataValues): void;\n  fromPixels(\n      pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement,\n      numChannels: number): Tensor3D;\n  register(dataId: DataId, shape: number[], dtype: DataType): void;\n  memory(): {unreliable: boolean;};  // Backend-specific information.\n}\n\n/** Convenient class for storing tensor-related data. */\nexport class DataStorage<T> {\n  private data = new WeakMap<DataId, T>();\n\n  constructor(private dataMover: DataMover) {}\n\n  get(dataId: DataId) {\n    if (!this.data.has(dataId)) {\n      this.dataMover.moveData(dataId);\n    }\n    return this.data.get(dataId);\n  }\n\n  set(dataId: DataId, value: T): void {\n    this.data.set(dataId, value);\n  }\n\n  has(dataId: DataId): boolean {\n    return this.data.has(dataId);\n  }\n\n  delete(dataId: DataId): boolean {\n    return this.data.delete(dataId);\n  }\n}\n\nexport interface DataMover {\n  /**\n   * To be called by backends whenever they see a dataId that they don't own.\n   * Upon calling this method, the mover will fetch the tensor from another\n   * backend and register it with the current active backend.\n   */\n  moveData(dataId: DataId): void;\n}\n\nexport interface BackendTimer {\n  time(f: () => void): Promise<BackendTimingInfo>;\n}\n\n/**\n * The interface that defines the kernels that should be implemented when\n * adding a new backend. New backends don't need to implement every one of the\n * methods, this can be done gradually (throw an error for unimplemented\n * methods).\n */\nexport class KernelBackend implements TensorStorage, BackendTimer {\n  time(f: () => void): Promise<BackendTimingInfo> {\n    throw new Error('Not yet implemented.');\n  }\n  read(dataId: object): Promise<DataValues> {\n    throw new Error('Not yet implemented.');\n  }\n  readSync(dataId: object): DataValues {\n    throw new Error('Not yet implemented.');\n  }\n  disposeData(dataId: object): void {\n    throw new Error('Not yet implemented.');\n  }\n  write(dataId: object, values: DataValues): void {\n    throw new Error('Not yet implemented.');\n  }\n  fromPixels(\n      pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement,\n      numChannels: number): Tensor<Rank.R3> {\n    throw new Error('Not yet implemented.');\n  }\n  register(dataId: object, shape: number[], dtype: DataType): void {\n    throw new Error('Not yet implemented.');\n  }\n  memory(): {unreliable: boolean; reasons?: string[]} {\n    throw new Error('Not yet implemented.');\n  }\n  /** Returns the highest precision for floats in bits (e.g. 16 or 32) */\n  floatPrecision(): number {\n    throw new Error('Not yet implemented');\n  }\n\n  batchMatMul(\n      a: Tensor3D, b: Tensor3D, transposeA: boolean,\n      transposeB: boolean): Tensor3D {\n    throw new Error('Not yet implemented');\n  }\n\n  slice<T extends Tensor>(x: T, begin: number[], size: number[]): T {\n    throw new Error('Not yet implemented');\n  }\n  stridedSlice<T extends Tensor>(\n      x: T, begin: number[], end: number[], strides: number[],\n      beginMask: number, endMask: number, ellipsisMask: number,\n      newAxisMask: number, shrinkAxisMask: number): T {\n    throw new Error('Not yet implemented');\n  }\n  reverse<T extends Tensor>(a: T, axis: number[]): T {\n    throw new Error('Not yet implemented');\n  }\n\n  concat(tensors: Tensor[], axis: number): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  neg<T extends Tensor>(a: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  add(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  addN<T extends Tensor>(tensors: T[]): T {\n    throw new Error('Not yet implemented');\n  }\n  subtract(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  multiply(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  realDivide(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  floorDiv(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  sum(x: Tensor, axes: number[]): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  prod(x: Tensor, axes: number[]): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  unsortedSegmentSum<T extends Tensor>(\n      x: T, segmentIds: Tensor1D, numSegments: number): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  argMin(x: Tensor, axis: number): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  argMax(x: Tensor, axis: number): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  equal(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  notEqual(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  less(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  lessEqual(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  greater(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  greaterEqual(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  logicalNot<T extends Tensor>(a: T): T {\n    throw new Error('Not yet implemented');\n  }\n  logicalAnd(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  logicalOr(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  where(condition: Tensor): Tensor2D {\n    throw new Error('Not yet implemented');\n  }\n  select(condition: Tensor, a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  topk<T extends Tensor>(x: T, k: number, sorted: boolean): [T, T] {\n    throw new Error('Not yet implemented');\n  }\n\n  min(x: Tensor, axes: number[]): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  minimum(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  mod(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  max(x: Tensor, axes: number[]): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  maximum(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  all(x: Tensor, axes: number[]): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  any(x: Tensor, axes: number[]): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  squaredDifference(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  ceil<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  floor<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  round<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  sign<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  pow<T extends Tensor>(a: T, b: Tensor): T {\n    throw new Error('Not yet implemented');\n  }\n  exp<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  expm1<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  log<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  log1p<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  sqrt<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  rsqrt<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  square<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  reciprocal<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  relu<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  elu<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  eluDer<T extends Tensor>(dy: T, y: T): T {\n    throw new Error('Not yet implemented');\n  }\n  selu<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  int<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  clip<T extends Tensor>(x: T, min: number, max: number): T {\n    throw new Error('Not yet implemented');\n  }\n\n  abs<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  complexAbs<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  sigmoid<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  softplus<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  sin<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  cos<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  tan<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  asin<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  acos<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  atan<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  atan2<T extends Tensor>(a: T, b: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  sinh<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  cosh<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  tanh<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  asinh<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  acosh<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  atanh<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  erf<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  step<T extends Tensor>(x: T, alpha: number): T {\n    throw new Error('Not yet implemented');\n  }\n\n  conv2d(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n  conv2dDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n  conv2dDerFilter(x: Tensor4D, dY: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  depthwiseConv2D(input: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n  depthwiseConv2DDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n  depthwiseConv2DDerFilter(x: Tensor4D, dY: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n  conv3d(x: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n    throw new Error('Not yet implemented');\n  }\n  conv3dDerInput(dy: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo):\n      Tensor5D {\n    throw new Error('Not yet implemented');\n  }\n  conv3dDerFilter(x: Tensor5D, dY: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n    throw new Error('Not yet implemented');\n  }\n  maxPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n  maxPoolBackprop(dy: Tensor4D, x: Tensor4D, y: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n  avgPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n  avgPoolBackprop(dy: Tensor4D, x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  reshape<T extends Tensor, R extends Rank>(x: T, shape: ShapeMap[R]):\n      Tensor<R> {\n    throw new Error('Not yet implemented');\n  }\n  cast<T extends Tensor>(x: T, dtype: DataType): T {\n    throw new Error('Not yet implemented');\n  }\n\n  tile<T extends Tensor>(x: T, reps: number[]): T {\n    throw new Error('Not yet implemented');\n  }\n\n  pad<T extends Tensor>(\n      x: T, paddings: Array<[number, number]>, constantValue: number): T {\n    throw new Error('Not yet implemented');\n  }\n\n  transpose<T extends Tensor>(x: T, perm: number[]): T {\n    throw new Error('Not yet implemented');\n  }\n\n  gather<T extends Tensor>(x: T, indices: Tensor1D, axis: number): T {\n    throw new Error('Not yet implemented');\n  }\n\n  gatherND(x: Tensor, indices: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  scatterND<R extends Rank>(\n      indices: Tensor, updates: Tensor, shape: ShapeMap[R]): Tensor<R> {\n    throw new Error('Not yet implemented');\n  }\n\n  batchToSpaceND<T extends Tensor>(\n      x: T, blockShape: number[], crops: number[][]): T {\n    throw new Error('Not yet implemented');\n  }\n\n  spaceToBatchND<T extends Tensor>(\n      x: T, blockShape: number[], paddings: number[][]): T {\n    throw new Error('Not yet implemented');\n  }\n\n  resizeBilinear(\n      x: Tensor4D, newHeight: number, newWidth: number,\n      alignCorners: boolean): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  resizeBilinearBackprop(dy: Tensor4D, x: Tensor4D, alignCorners: boolean):\n      Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  resizeNearestNeighbor(\n      x: Tensor4D, newHEight: number, newWidth: number,\n      alignCorners: boolean): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  resizeNearestNeighborBackprop(\n      dy: Tensor4D, x: Tensor4D, alignCorners: boolean): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  batchNormalization(\n      x: Tensor4D, mean: Tensor4D|Tensor1D, variance: Tensor4D|Tensor1D,\n      varianceEpsilon: number, scale?: Tensor4D|Tensor1D,\n      offset?: Tensor4D|Tensor1D): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  localResponseNormalization4D(\n      x: Tensor4D, radius: number, bias: number, alpha: number,\n      beta: number): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  LRNGrad(\n      dy: Tensor4D, inputImage: Tensor4D, outputImage: Tensor4D, radius: number,\n      bias: number, alpha: number, beta: number): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  multinomial(\n      logits: Tensor2D, normalized: boolean, numSamples: number,\n      seed: number): Tensor2D {\n    throw new Error('Not yet implemented');\n  }\n\n  oneHot(indices: Tensor1D, depth: number, onValue: number, offValue: number):\n      Tensor2D {\n    throw new Error('Not yet implemented');\n  }\n\n  cumsum(x: Tensor, axis: number, exclusive: boolean, reverse: boolean):\n      Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  nonMaxSuppression(\n      boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number,\n      iouThreshold: number, scoreThreshold?: number): Tensor1D {\n    throw new Error('Not yet implemented');\n  }\n\n  fft(x: Tensor2D): Tensor2D {\n    throw new Error('Not yet implemented');\n  }\n  ifft(x: Tensor2D): Tensor2D {\n    throw new Error('Not yet implemented');\n  }\n  complex<T extends Tensor>(real: T, imag: T): T {\n    throw new Error('Not yet implemented');\n  }\n  real<T extends Tensor>(input: T): T {\n    throw new Error('Not yet implemented');\n  }\n  imag<T extends Tensor>(input: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  cropAndResize(\n      image: Tensor4D, boxes: Tensor2D, boxIndex: Tensor1D,\n      cropSize: [number, number], method: 'bilinear'|'nearest',\n      extrapolationValue: number): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  depthToSpace(x: Tensor4D, blockSize: number, dataFormat: string): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  // Aligns with the \"SplitV\" kernel in TensorFlow.\n  split<T extends Tensor>(value: T, sizeSplits: number[], axis: number): T[] {\n    throw new Error('Not yet implemented');\n  }\n\n  sparseToDense<R extends Rank>(\n      sparseIndices: Tensor, sparseValues: Tensor, outputShape: ShapeMap[R],\n      defaultValue: Scalar): Tensor<R> {\n    throw new Error('Not yet implemented');\n  }\n  /**\n   * Sets the data mover for this backend. Backends should use the mover to\n   * move data from other backends to this backend.\n   */\n  setDataMover(dataMover: DataMover): void {\n    throw new Error('Not yet implemented');\n  }\n\n  dispose(): void {\n    throw new Error('Not yet implemented');\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {scalar, zeros} from '../ops/tensor_ops';\nimport {Tensor} from '../tensor';\nimport {Rank} from '../types';\nimport {DataType, ShapeMap} from '../types';\nimport {hasEncodingLoss} from '../util';\nimport {KernelBackend} from './backend';\n\nexport function castTensor<T extends Tensor>(\n    x: T, dtype: DataType, backend: KernelBackend): T {\n  if (dtype === 'complex64') {\n    if (x.dtype === 'complex64') {\n      return x.clone();\n    }\n    const zerosTensor = zeros(x.shape);\n    const floatX = x.toFloat();\n    const result = backend.complex(floatX, zerosTensor);\n    zerosTensor.dispose();\n    floatX.dispose();\n    return result as T;\n  }\n\n  if (!hasEncodingLoss(x.dtype, dtype)) {\n    // We don't change the underlying data, since we cast to higher\n    // precision.\n    return Tensor.make(x.shape, {dataId: x.dataId}, dtype) as T;\n  }\n  if (x.dtype === 'complex64') {\n    const real = backend.real(x);\n    const result = real.cast(dtype);\n    real.dispose();\n    return result;\n  }\n  if (dtype === 'int32') {\n    return backend.int(x);\n  } else if (dtype === 'bool') {\n    const zero = scalar(0, x.dtype);\n    const result = backend.notEqual(x, zero) as T;\n    zero.dispose();\n    return result;\n  } else {\n    throw new Error(`Error in Cast: unknown dtype argument (${dtype})`);\n  }\n}\n\nexport function reshapeTensor<T extends Tensor, R extends Rank>(\n    x: T, shape: ShapeMap[R]): Tensor<R> {\n  return Tensor.make(shape, {dataId: x.dataId}, x.dtype);\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {TypedArray} from '../types';\n/**\n * Merges real and imaginary Float32Arrays into a single complex Float32Array.\n *\n * The memory layout is interleaved as follows:\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n * complex: [r0, i0, r1, i1, r2, i2]\n *\n * This is the inverse of splitRealAndImagArrays.\n *\n * @param real The real values of the complex tensor values.\n * @param imag The imag values of the complex tensor values.\n * @returns A complex tensor as a Float32Array with merged values.\n */\nexport function mergeRealAndImagArrays(\n    real: Float32Array, imag: Float32Array): Float32Array {\n  if (real.length !== imag.length) {\n    throw new Error(\n        `Cannot merge real and imag arrays of different lengths. real:` +\n        `${real.length}, imag: ${imag.length}.`);\n  }\n  const result = new Float32Array(real.length * 2);\n  for (let i = 0; i < result.length; i += 2) {\n    result[i] = real[i / 2];\n    result[i + 1] = imag[i / 2];\n  }\n  return result;\n}\n\n/**\n * Splits a complex Float32Array into real and imag parts.\n *\n * The memory layout is interleaved as follows:\n * complex: [r0, i0, r1, i1, r2, i2]\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n *\n * This is the inverse of mergeRealAndImagArrays.\n *\n * @param complex The complex tensor values.\n * @returns An object with real and imag Float32Array components of the complex\n *     tensor.\n */\nexport function splitRealAndImagArrays(complex: Float32Array):\n    {real: Float32Array, imag: Float32Array} {\n  const real = new Float32Array(complex.length / 2);\n  const imag = new Float32Array(complex.length / 2);\n  for (let i = 0; i < complex.length; i += 2) {\n    real[i / 2] = complex[i];\n    imag[i / 2] = complex[i + 1];\n  }\n  return {real, imag};\n}\n\n/**\n * Extracts even indexed complex values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithEvenIndex(complex: Float32Array):\n    {real: Float32Array, imag: Float32Array} {\n  const len = Math.ceil(complex.length / 4);\n  const real = new Float32Array(len);\n  const imag = new Float32Array(len);\n  for (let i = 0; i < complex.length; i += 4) {\n    real[Math.floor(i / 4)] = complex[i];\n    imag[Math.floor(i / 4)] = complex[i + 1];\n  }\n  return {real, imag};\n}\n\n/**\n * Extracts odd indexed comple values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithOddIndex(complex: Float32Array):\n    {real: Float32Array, imag: Float32Array} {\n  const len = Math.floor(complex.length / 4);\n  const real = new Float32Array(len);\n  const imag = new Float32Array(len);\n  for (let i = 2; i < complex.length; i += 4) {\n    real[Math.floor(i / 4)] = complex[i];\n    imag[Math.floor(i / 4)] = complex[i + 1];\n  }\n  return {real, imag};\n}\n\n/**\n * Get the map representing a complex value in the given array.\n * @param complex The complex tensor values.\n * @param index An index of the target complex value.\n */\nexport function getComplexWithIndex(\n    complex: Float32Array, index: number): {real: number, imag: number} {\n  const real = complex[index * 2];\n  const imag = complex[index * 2 + 1];\n  return {real, imag};\n}\n\n/**\n * Insert a given complex value into the TypedArray.\n * @param data The array in which the complex value is inserted.\n * @param c The complex value to be inserted.\n * @param index An index of the target complex value.\n */\nexport function assignToTypedArray(\n    data: TypedArray, real: number, imag: number, index: number) {\n  data[index * 2] = real;\n  data[index * 2 + 1] = imag;\n}\n\n/**\n * Make the list of exponent terms used by FFT.\n */\nexport function exponents(\n    n: number, inverse: boolean): {real: Float32Array, imag: Float32Array} {\n  const real = new Float32Array(n / 2);\n  const imag = new Float32Array(n / 2);\n  for (let i = 0; i < Math.ceil(n / 2); i++) {\n    const x = (inverse ? 2 : -2) * Math.PI * (i / n);\n    real[i] = Math.cos(x);\n    imag[i] = Math.sin(x);\n  }\n  return {real, imag};\n}\n\n/**\n * Make the exponent term used by FFT.\n */\nexport function exponent(\n    k: number, n: number, inverse: boolean): {real: number, imag: number} {\n  const x = (inverse ? 2 : -2) * Math.PI * (k / n);\n  const real = Math.cos(x);\n  const imag = Math.sin(x);\n  return {real, imag};\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Implementation of the NonMaxSuppression kernel shared between webgl and cpu.\n */\n\nimport {tensor1d} from '../ops/tensor_ops';\nimport {Tensor1D} from '../tensor';\nimport {TypedArray} from '../types';\n\nexport function nonMaxSuppressionImpl(\n    boxes: TypedArray, scores: TypedArray, maxOutputSize: number,\n    iouThreshold: number, scoreThreshold: number): Tensor1D {\n  const candidates = Array.from(scores)\n                         .map((score, boxIndex) => ({score, boxIndex}))\n                         .filter(c => c.score > scoreThreshold)\n                         .sort((c1, c2) => c2.score - c1.score);\n\n  const selected: number[] = [];\n\n  for (let i = 0; i < candidates.length; i++) {\n    const {score, boxIndex} = candidates[i];\n    if (score < scoreThreshold) {\n      break;\n    }\n\n    let ignoreCandidate = false;\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = intersectionOverUnion(boxes, boxIndex, selected[j]);\n      if (iou >= iouThreshold) {\n        ignoreCandidate = true;\n        break;\n      }\n    }\n\n    if (!ignoreCandidate) {\n      selected.push(boxIndex);\n      if (selected.length >= maxOutputSize) {\n        break;\n      }\n    }\n  }\n\n  return tensor1d(selected, 'int32');\n}\n\nfunction intersectionOverUnion(boxes: TypedArray, i: number, j: number) {\n  const iCoord = boxes.subarray(i * 4, i * 4 + 4);\n  const jCoord = boxes.subarray(j * 4, j * 4 + 4);\n  const yminI = Math.min(iCoord[0], iCoord[2]);\n  const xminI = Math.min(iCoord[1], iCoord[3]);\n  const ymaxI = Math.max(iCoord[0], iCoord[2]);\n  const xmaxI = Math.max(iCoord[1], iCoord[3]);\n  const yminJ = Math.min(jCoord[0], jCoord[2]);\n  const xminJ = Math.min(jCoord[1], jCoord[3]);\n  const ymaxJ = Math.max(jCoord[0], jCoord[2]);\n  const xmaxJ = Math.max(jCoord[1], jCoord[3]);\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0;\n  }\n  const intersectionYmin = Math.max(yminI, yminJ);\n  const intersectionXmin = Math.max(xminI, xminJ);\n  const intersectionYmax = Math.min(ymaxI, ymaxJ);\n  const intersectionXmax = Math.min(xmaxI, xmaxJ);\n  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *\n      Math.max(intersectionXmax - intersectionXmin, 0.0);\n  return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\n\n/** Shared implementation of the split kernel across WebGL and CPU. */\nexport function split<T extends Tensor>(\n    x: T, sizeSplits: number[], axis: number): T[] {\n  const begin = Array(x.rank).fill(0);\n  const size = x.shape.slice();\n  return sizeSplits.map(s => {\n    size[axis] = s;\n    const slice = x.slice(begin, size);\n    begin[axis] += s;\n    return slice;\n  });\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/** An implementation of the TopK kernel shared between webgl and cpu. */\n\nimport {tensor} from '../ops/tensor_ops';\nimport {Tensor} from '../tensor';\nimport {NumericDataType, TypedArray} from '../types';\nimport {getTypedArrayFromDType} from '../util';\n\nexport function topkImpl<T extends Tensor>(\n    x: TypedArray, xShape: number[], xDtype: NumericDataType, k: number,\n    sorted: boolean): [T, T] {\n  // Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim.\n  const lastDim = xShape[xShape.length - 1];\n  const [batch, size] = [x.length / lastDim, lastDim];\n  const allTopKVals = getTypedArrayFromDType(xDtype, batch * k);\n  const allTopKIndices = getTypedArrayFromDType('int32', batch * k);\n\n  for (let b = 0; b < batch; b++) {\n    const offset = b * size;\n    const vals = x.subarray(offset, offset + size);\n    const valAndInd: Array<{value: number, index: number}> = [];\n    for (let i = 0; i < vals.length; i++) {\n      valAndInd.push({value: vals[i], index: i});\n    }\n    valAndInd.sort((a, b) => b.value - a.value);\n\n    const outOffset = b * k;\n    const topKVals = allTopKVals.subarray(outOffset, outOffset + k);\n    const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);\n    for (let i = 0; i < k; i++) {\n      topKVals[i] = valAndInd[i].value;\n      topKIndices[i] = valAndInd[i].index;\n    }\n  }\n  // Reshape back to the original input shape, except that the last\n  // dimension is k.\n  const outputShape = xShape.slice();\n  outputShape[outputShape.length - 1] = k;\n  return [\n    tensor(allTopKVals, outputShape, xDtype) as T,\n    tensor(allTopKIndices, outputShape, 'int32') as T\n  ];\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ReduceInfo} from '../../ops/reduce_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ArgMinMaxProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(reduceInfo: ReduceInfo, op: 'max'|'min', firstPass: boolean) {\n    const windowSize = reduceInfo.windowSize;\n    const batchSize = reduceInfo.batchSize;\n    const inSize = reduceInfo.inSize;\n    const outSize = Math.ceil(inSize / windowSize);\n    if (!firstPass) {\n      this.variableNames.push('bestIndicesA');\n    }\n    this.outputShape = [batchSize, outSize];\n    const compOp = (op === 'max') ? '>' : '<';\n    const indexSnippet = firstPass ?\n        'inOffset + i;' :\n        'round(getBestIndicesA(batch, inOffset + i));';\n\n    this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          int inIdx = ${indexSnippet};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${compOp} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class AvgPool2DBackpropProgram implements GPGPUProgram {\n  variableNames = ['dy'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv2DInfo) {\n    this.outputShape = convInfo.inShape;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n\n    const avgMultiplier = 1 / (filterHeight * filterWidth);\n\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth};\n            wC+= ${dilationWidth}) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Returns the dimensions in the input shape that are broadcasted to\n * produce the provided output shape.\n *\n * The returned dimensions are 0-indexed and sorted. An example:\n * inShape = [4, 1, 3]\n * outShape = [5, 4, 3, 3]\n * result = [1]. Dimension 1 (2nd dimension of input) gets broadcasted 1 => 3.\n */\nexport function getBroadcastDims(\n    inShape: number[], outShape: number[]): number[] {\n  const inRank = inShape.length;\n  const dims: number[] = [];\n  for (let i = 0; i < inRank; i++) {\n    const dim = inRank - 1 - i;\n    const a = inShape[dim] || 1;\n    const b = outShape[outShape.length - 1 - i] || 1;\n    if (b > 1 && a === 1) {\n      dims.unshift(dim);\n    }\n  }\n  return dims;\n}\n\n/**\n * Returns the axes in the output space that should be reduced to produce\n * the input space.\n */\nexport function getReductionAxes(\n    inShape: number[], outShape: number[]): number[] {\n  const result: number[] = [];\n  for (let i = 0; i < outShape.length; i++) {\n    const inDim = inShape[inShape.length - i - 1];\n    const outAxis = outShape.length - i - 1;\n    const outDim = outShape[outAxis];\n    if (inDim == null || (inDim === 1 && outDim > 1)) {\n      result.unshift(outAxis);\n    }\n  }\n  return result;\n}\n\n/**\n * Given the output of `getBroadcastDims()`, returns true if the broadcasting\n * is along the outer-most dimensions of the input.\n */\nexport function broadcastDimsAreOuter(dims: number[]): boolean {\n  for (let i = 0; i < dims.length; i++) {\n    if (dims[i] !== i) {\n      return false;\n    }\n  }\n  return true;\n}\n\nexport function assertAndGetBroadcastShape(\n    shapeA: number[], shapeB: number[]): number[] {\n  const result: number[] = [];\n  const l = Math.max(shapeA.length, shapeB.length);\n\n  for (let i = 0; i < l; i++) {\n    let a = shapeA[shapeA.length - i - 1];\n    if (a == null) {\n      a = 1;\n    }\n    let b = shapeB[shapeB.length - i - 1];\n    if (b == null) {\n      b = 1;\n    }\n    if (a === 1) {\n      result.unshift(b);\n    } else if (b === 1) {\n      result.unshift(a);\n    } else if (a !== b) {\n      const errMsg = `Operands could not be broadcast together with shapes ` +\n          `${shapeA} and ${shapeB}.`;\n      throw Error(errMsg);\n    } else {\n      result.unshift(a);\n    }\n  }\n  return result;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as broadcast_util from '../../ops/broadcast_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class BatchNormProgram implements GPGPUProgram {\n  variableNames: string[];\n  outputShape: number[] = [];\n  userCode: string;\n  supportsBroadcasting = true;\n\n  constructor(\n      xShape: number[], meanShape: number[], varianceShape: number[],\n      offsetShape: number[]|null, scaleShape: number[]|null,\n      varianceEpsilon: number) {\n    this.variableNames = ['x', 'mean', 'variance'];\n    broadcast_util.assertAndGetBroadcastShape(xShape, meanShape);\n    broadcast_util.assertAndGetBroadcastShape(xShape, varianceShape);\n\n    let offsetSnippet = '0.0';\n    if (offsetShape != null) {\n      broadcast_util.assertAndGetBroadcastShape(xShape, offsetShape);\n      this.variableNames.push('offset');\n      offsetSnippet = 'getOffsetAtOutCoords()';\n    }\n\n    let scaleSnippet = '1.0';\n    if (scaleShape != null) {\n      broadcast_util.assertAndGetBroadcastShape(xShape, scaleShape);\n      this.variableNames.push('scale');\n      scaleSnippet = 'getScaleAtOutCoords()';\n    }\n\n    this.outputShape = xShape;\n    this.userCode = `\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = ${offsetSnippet};\n        float scale = ${scaleSnippet};\n        float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as broadcast_util from '../../ops/broadcast_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class BatchNormPackedProgram implements GPGPUProgram {\n  variableNames: string[];\n  outputShape: number[];\n  userCode: string;\n  supportsBroadcasting = true;\n  usesPackedTextures = true;\n\n  constructor(\n      xShape: number[], meanShape: number[], varianceShape: number[],\n      offsetShape: number[]|null, scaleShape: number[]|null,\n      varianceEpsilon: number) {\n    this.variableNames = ['x', 'mean', 'variance'];\n    broadcast_util.assertAndGetBroadcastShape(xShape, meanShape);\n    broadcast_util.assertAndGetBroadcastShape(xShape, varianceShape);\n\n    let offsetSnippet = 'vec4(0.0)';\n    if (offsetShape != null) {\n      broadcast_util.assertAndGetBroadcastShape(xShape, offsetShape);\n      this.variableNames.push('offset');\n      offsetSnippet = 'getOffsetAtOutCoords()';\n    }\n\n    let scaleSnippet = 'vec4(1.0)';\n    if (scaleShape != null) {\n      broadcast_util.assertAndGetBroadcastShape(xShape, scaleShape);\n      this.variableNames.push('scale');\n      scaleSnippet = 'getScaleAtOutCoords()';\n    }\n\n    this.outputShape = xShape;\n    this.userCode = `\n      void main() {\n        vec4 offset = ${offsetSnippet};\n        vec4 scale = ${scaleSnippet};\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as broadcast_util from '../../ops/broadcast_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\n// (Ar + Ai)(Br + Bi) =\n// ArBr + ArBi + AiBr + AiBi = ArBr - AB + ArBi + AiBr\n// Yr = ArBr - AB\n// Yi = ArBi + AiBr\nexport const COMPLEX_MULTIPLY = {\n  REAL: 'return areal * breal - aimag * bimag;',\n  IMAG: 'return areal * bimag + aimag * breal;'\n};\n\nexport class BinaryOpComplexProgram implements GPGPUProgram {\n  variableNames = ['AReal', 'AImag', 'BReal', 'BImag'];\n  userCode: string;\n  outputShape: number[];\n  supportsBroadcasting = true;\n\n  constructor(op: string, aShape: number[], bShape: number[]) {\n    this.outputShape =\n        broadcast_util.assertAndGetBroadcastShape(aShape, bShape);\n\n    this.userCode = `\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ${op}\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as broadcast_util from '../../ops/broadcast_util';\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\n\nconst CHECK_NAN_SNIPPET = `\n  if (isNaN(a)) return a;\n  if (isNaN(b)) return b;\n`;\n\nexport const ADD = 'return a + b;';\nexport const SUB = 'return a - b;';\nexport const MUL = 'return a * b;';\nexport const DIV = `if (a == b) return 1.0;\n  return a / b;`;\n\n// We use native integer division to deal with floating point imprecision. Since\n// we implement floor division and glsl implements truncated division, we\n// correct for this by subtracting 1 from result when the result is negative and\n// there is a remainder.\nexport const INT_DIV = `\n  float resultSign = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  int result = ia / ib;\n  int amodb = ia - ib * result;\n\n  if (resultSign < 0.0 && amodb != 0) {\n    result -= 1;\n  }\n  return float(result);\n`;\n\nexport const POW = `\nif(a < 0.0 && floor(b) < b){\n  return NAN;\n}\nreturn (round(mod(b, 2.0)) == 0 || round(mod(b, 2.0)) == 2) ?\n    pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`;\nexport const SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';\n\nexport const EQUAL = `return float(a == b);`;\n\nexport const NOT_EQUAL = `return float(a != b);`;\n\nexport const LESS = `return float(a < b);`;\n\nexport const LESS_EQUAL = `return float(a <= b);`;\n\nexport const GREATER = `return float(a > b);`;\n\nexport const GREATER_EQUAL = `return float(a >= b);`;\n\nexport const LOGICAL_AND = `return float(a >= 1.0 && b >= 1.0);`;\n\nexport const LOGICAL_OR = `return float(a >= 1.0 || b >= 1.0);`;\n\nexport const MAX = CHECK_NAN_SNIPPET + `\n  return max(a, b);\n`;\nexport const MIN = CHECK_NAN_SNIPPET + `\n  return min(a, b);\n`;\nexport const MOD = `if (b == 0.0) return NAN;\n  return mod(a, b);`;\n\nexport const ATAN2 = CHECK_NAN_SNIPPET + `\n  return atan(a, b);\n`;\n\nexport const ELU_DER = `return (b >= 1.0) ? a : a * (b + 1.0);`;\n\nexport class BinaryOpProgram implements GPGPUProgram {\n  variableNames = ['A', 'B'];\n  outputShape: number[];\n  userCode: string;\n  supportsBroadcasting = true;\n\n  // Caching uniform location for speed.\n  startLoc: WebGLUniformLocation;\n\n  constructor(op: string, aShape: number[], bShape: number[]) {\n    this.outputShape =\n        broadcast_util.assertAndGetBroadcastShape(aShape, bShape);\n    this.userCode = `\n      uniform float NAN;\n      float binaryOperation(float a, float b) {\n        ${op}\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    `;\n  }\n\n  getCustomSetupFunc() {\n    return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n      if (this.startLoc == null) {\n        this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'NAN');\n        if (this.startLoc == null) {\n          // This means the compiler has optimized and realized it doesn't need\n          // the uniform.\n          return;\n        }\n      }\n      gpgpu.gl.uniform1f(this.startLoc, NaN);\n    };\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ClipProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  userCode: string;\n  outputShape: number[];\n\n  constructor(aShape: number[], min: number, max: number) {\n    this.outputShape = aShape;\n    this.userCode = `\n      void main() {\n        float value = getAAtOutCoords();\n        if (isNaN(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, float(${min}), float(${max})));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ClipPackedProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  usesPackedTextures = true;\n  userCode: string;\n  outputShape: number[];\n\n  constructor(aShape: number[], min: number, max: number) {\n    this.outputShape = aShape;\n    this.userCode = `\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (hasNaN(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(${min}), vec4(${max})));\n      }\n    `;\n  }\n}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ComplexAbsProgram implements GPGPUProgram {\n  variableNames = ['real', 'imag'];\n  userCode: string;\n  outputShape: number[];\n\n  constructor(shape: number[]) {\n    this.outputShape = shape;\n    this.userCode = `\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as concat_util from '../../ops/concat_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ConcatProgram implements GPGPUProgram {\n  variableNames = ['A', 'B'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  // Concats 2d tensors along axis=1. See comments in MathBackendWebGL.concat().\n  constructor(aShape: [number, number], bShape: [number, number]) {\n    this.outputShape =\n        concat_util.computeOutShape([aShape, bShape], 1 /* axis */);\n\n    this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        float value = 0.0;\n        if (yC < ${aShape[1]}) {\n          value = getA(yR, yC);\n        } else {\n          yC -= ${aShape[1]};\n          value = getB(yR, yC);\n        }\n\n        setOutput(value);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo, Conv3DInfo} from '../../ops/conv_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class Conv2DDerFilterProgram implements GPGPUProgram {\n  variableNames = ['x', 'dy'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv2DInfo) {\n    this.outputShape = convInfo.filterShape;\n\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n\nexport class Conv2DDerInputProgram implements GPGPUProgram {\n  variableNames = ['dy', 'W'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv2DInfo) {\n    this.outputShape = convInfo.inShape;\n\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n\n    const padTop = filterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, d2);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n\nexport class Conv3DDerFilterProgram implements GPGPUProgram {\n  variableNames = ['x', 'dy'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv3DInfo) {\n    this.outputShape = convInfo.filterShape;\n\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const padFront = convInfo.padInfo.front;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n\n    this.userCode = `\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {\n            int xF = wF + yF * ${strideDepth} - ${padFront};\n\n            if (xF < 0 || xF >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n              int xR = wR + yR * ${strideHeight} - ${padTop};\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n                int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n\nexport class Conv3DDerInputProgram implements GPGPUProgram {\n  variableNames = ['dy', 'W'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv3DInfo) {\n    this.outputShape = convInfo.inShape;\n\n    const filterDepth = convInfo.filterDepth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n\n    const padFront = filterDepth - 1 - convInfo.padInfo.front;\n    const padTop = filterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n\n    this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          float dyF = float(dyFCorner + wF) / ${strideDepth}.0;\n\n          if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${filterDepth} - 1 - wF;\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ${filterHeight} - 1 - wR;\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ${filterWidth} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class DepthwiseConv2DDerFilterProgram implements GPGPUProgram {\n  variableNames = ['x', 'dy'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv2DInfo) {\n    this.outputShape = convInfo.filterShape;\n\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const channelMul = convInfo.outChannels / convInfo.inChannels;\n\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ${channelMul} + dm;\n\n        float dotProd = 0.0;\n\n        // TODO: Vec4 over the batch size\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n\nexport class DepthwiseConv2DDerInputProgram implements GPGPUProgram {\n  variableNames = ['dy', 'W'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv2DInfo) {\n    this.outputShape = convInfo.inShape;\n\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n\n    const padTop = filterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n    const channelMul = convInfo.outChannels / convInfo.inChannels;\n\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            // TODO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${channelMul}; dm++) {\n              int d2 = d1 * ${channelMul} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo, Conv3DInfo} from '../../ops/conv_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class Conv2DProgram implements GPGPUProgram {\n  variableNames = ['x', 'W'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv2DInfo) {\n    this.outputShape = convInfo.outShape;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n\n    const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n    const inputDepthVec4Remainder = convInfo.inChannels % 4;\n\n    this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${convInfo.inWidth}) {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n              vec4 xValues = vec4(\n                getX(batch, xR, xC, d1),\n                getX(batch, xR, xC, d1 + 1),\n                getX(batch, xR, xC, d1 + 2),\n                getX(batch, xR, xC, d1 + 3)\n              );\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              dotProd += dot(xValues, wValues);\n            }\n\n            if (${inputDepthVec4Remainder === 1}) {\n              dotProd +=\n                getX(batch, xR, xC, ${inputDepthNearestVec4}) *\n                getW(wR, wC, ${inputDepthNearestVec4}, d2);\n            } else if (${inputDepthVec4Remainder === 2}) {\n              vec2 xValues = vec2(\n                getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)\n              );\n              vec2 wValues = vec2(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)\n              );\n              dotProd += dot(xValues, wValues);\n            } else if (${inputDepthVec4Remainder === 3}) {\n              vec3 xValues = vec3(\n                getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),\n                getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)\n              );\n              vec3 wValues = vec3(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)\n              );\n              dotProd += dot(xValues, wValues);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n\nexport class Conv3DProgram implements GPGPUProgram {\n  variableNames = ['x', 'W'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv3DInfo) {\n    this.outputShape = convInfo.outShape;\n    const padFront = convInfo.padInfo.front;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationDepth = convInfo.dilationDepth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const filterDepth = convInfo.filterDepth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n\n    const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n    const inputDepthVec4Remainder = convInfo.inChannels % 4;\n\n    this.userCode = `\n      const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${\n        strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          int xF = xFCorner + wF * ${dilationDepth};\n\n          if (xF < 0 || xF >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            int xR = xRCorner + wR * ${dilationHeight};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${inputDepthVec4Remainder === 1}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);\n              } else if (${inputDepthVec4Remainder === 2}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${inputDepthVec4Remainder === 3}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class DepthwiseConv2DProgram implements GPGPUProgram {\n  variableNames = ['x', 'W'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv2DInfo) {\n    this.outputShape = convInfo.outShape;\n\n    const xNumRows = convInfo.inHeight;\n    const xNumCols = convInfo.inWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const channelMul = convInfo.outChannels / convInfo.inChannels;\n\n    this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${channelMul};\n        int q = d2 - d1 * ${channelMul};\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TODO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${xNumRows}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${xNumCols}) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class DepthwiseConvPacked2DProgram implements GPGPUProgram {\n  variableNames = ['x', 'W'];\n  usesPackedTextures = true;\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv2DInfo) {\n    this.outputShape = convInfo.outShape;\n\n    const xNumRows = convInfo.inHeight;\n    const xNumCols = convInfo.inWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const texelsAcross = Math.ceil((filterWidth + 1) / 2);\n\n    let mainLoop = `int xR; int xC;`;\n\n    for (let r = 0; r < filterHeight; r++) {\n      for (let c = -padLeft; c < texelsAcross * 2; c++) {\n        mainLoop += `vec4 ${xTexelName(r, c)} = vec4(0.);`;\n      }\n\n      for (let c = 0; c < filterWidth; c++) {\n        mainLoop += `\n          vec4 wR${r}C${c} = vec4(0.);\n          vec4 xR${r}C${c} = vec4(0.);`;\n      }\n    }\n\n    /**\n     * This vectorized implementation of depthwiseConv works by gathering the\n     * values needed for each output channel's dot product into vec4's and then\n     * multiplying them all together (this happens in the final double for-loop\n     * below). Most of the main loop consists of constructing these vec4's with\n     * the minimum number of texture2D calls as possible, which entails logic\n     * for making use of all four returned values from a texture2D call at once.\n     */\n    for (let r = 0; r < filterHeight; r++) {\n      for (let c = 0; c < texelsAcross; c++) {\n        const col = c * 2;\n        const left = c * 2 + padLeft;\n\n        mainLoop += `\n          xR = xRCorner + ${r};\n          xC = xCCorner + ${left};\n\n          if(xR >= 0 && xR < ${xNumRows} && xC >= 0 && xC < ${xNumCols}) {\n            ${xTexelName(r, left)} = getX(batch, xR, xC, d1);\n          }`;\n\n        if (padLeft === 0) {\n          if (col < filterWidth && c === texelsAcross - 1) {\n            if (strideWidth > 1) {\n              mainLoop += `\n                vec4 ${xTexelName(r, left + 2)} = vec4(0.);\n\n                if(xR >= 0 && xR < ${xNumRows} && xC + 2 < ${xNumCols}) {\n                  ${xTexelName(r, left + 2)} = getX(batch, xR, xC + 2, d1);\n                }`;\n            }\n\n            mainLoop += `\n              xR${r}C${left} = ${constructTexel(r, left, strideWidth, padLeft)};\n            `;\n          }\n        } else if (c === 0) {\n          mainLoop += `\n            if(xR >= 0 && xR < ${xNumRows} && xC - 2 >= 0) {\n              ${xTexelName(r, left - 2)} = getX(batch, xR, xC - 2, d1);\n            }`;\n        }\n\n        if (col > 0) {\n          mainLoop += `xR${r}C${left - 2} =\n            ${constructTexel(r, left - 2, strideWidth, padLeft)};`;\n        }\n\n        if (left - 1 >= 0 && left - 1 < filterWidth) {\n          mainLoop += `xR${r}C${left - 1} =\n              ${constructTexel(r, left - 1, strideWidth, padLeft)};`;\n        }\n\n        if (col < filterWidth) {\n          mainLoop += `\n            vec4 wTexel${r}C${col} = getW(${r}, ${col}, d1, q);\n            wR${r}C${col} = vec4(wTexel${r}C${col}.xz, wTexel${r}C${col}.xz);\n          `;\n\n          if (col + 1 < filterWidth) {\n            mainLoop += `\n              vec4 wTexelR${r}C${col + 1} = getW(${r}, ${col + 1}, d1, q);\n              wR${r}C${col + 1} =\n                vec4(wTexelR${r}C${col + 1}.xz, wTexelR${r}C${col + 1}.xz);`;\n          }\n        }\n      }\n    }\n\n    for (let r = 0; r < filterHeight; r++) {\n      for (let c = 0; c < filterWidth; c++) {\n        mainLoop += `result += xR${r}C${c} * wR${r}C${c};`;\n      }\n    }\n\n    this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2;\n        int q = 0;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        vec4 result = vec4(0.);\n\n        ${mainLoop}\n\n        setOutput(result);\n      }\n    `;\n  }\n}\n\nfunction xTexelName(r: number, c: number): string {\n  return `xTexelR${r}C${c < 0 ? 'minus' + Math.abs(c).toString() : c}`;\n}\n\n/**\n * Given a 2x2 filter, we want to multiply xR0C0, wR0C0, xR0C1, wR0C1, xR1C0,\n * wR1C0, xR1C1, xR1C1. The xRC's are constructed out of xTexelRC's, which are\n * the vec4's returned from sampling calls. Sometimes this means mixing channels\n * from adjacent samples, which constructTexel handles.\n */\nfunction constructTexel(\n    r: number, c: number, stride: number, padLeft: number): string {\n  if (stride === 1) {\n    if (padLeft % 2 === c % 2) {\n      return xTexelName(r, c);\n    }\n    return `vec4(${xTexelName(r, c - 1)}.zw, ${xTexelName(r, c + 1)}.xy)`;\n  }\n\n  if (padLeft % 2 === c % 2) {\n    return `vec4(${xTexelName(r, c)}.xy, ${xTexelName(r, c + 2)}.xy)`;\n  }\n  return `vec4(${xTexelName(r, c - 1)}.zw, ${xTexelName(r, c + 1)}.zw)`;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport { GPGPUProgram } from './gpgpu_math';\n\nexport class CropAndResizeProgram implements GPGPUProgram {\n  variableNames = ['Image', 'Boxes', 'BoxInd'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(\n    imageShape: [number, number, number, number], boxShape: [number, number],\n    cropSize: [number, number], method: 'bilinear' | 'nearest',\n    extrapolationValue: number) {\n    const [batch, imageHeight, imageWidth, depth] = imageShape;\n    const [numBoxes,] = boxShape;\n    const [cropHeight, cropWidth] = cropSize;\n    this.outputShape = [numBoxes, cropHeight, cropWidth, depth];\n    const methodId = method === 'bilinear' ? 1 : 0;\n\n    const [inputHeightFloat, inputWidthFloat] =\n      [`${imageHeight - 1}.0`, `${imageWidth - 1}.0`];\n\n    const [heightRatio, heightScale, inY] = cropHeight > 1 ?\n      [\n        `${(imageHeight-1)/(cropHeight-1)}`,\n        '(y2-y1) * height_ratio',\n        `y1*${inputHeightFloat} + float(y)*(height_scale)`,\n      ] :\n      [\n        '0.0',\n        '0.0',\n        `0.5 * (y1+y2) * ${inputHeightFloat}`,\n      ];\n    const [widthRatio, widthScale, inX] = cropWidth > 1 ?\n      [\n        `${(imageWidth-1)/(cropWidth-1)}`,\n        '(x2-x1) * width_ratio',\n        `x1*${inputWidthFloat} + float(x)*(width_scale)`,\n      ] :\n      [\n        '0.0',\n        '0.0',\n        `0.5 * (x1+x2) * ${inputWidthFloat}`,\n      ];\n\n    // Reference implementation\n    // tslint:disable-next-line:max-line-length\n    // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op_gpu.cu.cc\n    this.userCode = `\n      const float height_ratio = float(${heightRatio});\n      const float width_ratio = float(${widthRatio});\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= ${batch}) {\n          return;\n        }\n\n        float height_scale = ${heightScale};\n        float width_scale = ${widthScale};\n\n        float in_y = ${inY};\n        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n        float in_x = ${inX};\n        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n\n        vec2 sourceFracIndexRC = vec2(in_y,in_x);\n        if(${methodId} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n          ivec2 sourceCeilRC = ivec2(ceil(sourceFracIndexRC));\n\n          float topLeft = getImage(b, sourceFloorRC.x, sourceFloorRC.y, d);\n          float bottomLeft = getImage(b, sourceCeilRC.x, sourceFloorRC.y, d);\n          float topRight = getImage(b, sourceFloorRC.x, sourceCeilRC.y, d);\n          float bottomRight = getImage(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n          vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n          float top = topLeft + (topRight - topLeft) * fracRC.y;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n          float newValue = top + (bottom - top) * fracRC.x;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestRC = ivec2(floor(\n            sourceFracIndexRC + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestRC.x, sourceNearestRC.y, d);\n          setOutput(newValue);\n        }\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as util from '../../util';\n\n/**\n * Produces GLSL code that derives logical coordinates from a flat\n * index. The code performs integer division with each stride and decrements\n * the index until the index equals the final dimension coordinate.\n */\nexport function getLogicalCoordinatesFromFlatIndex(\n    coords: string[], shape: number[], index = 'index'): string {\n  const strides = util.computeStrides(shape);\n  return strides\n      .map((stride, i) => {\n        const line1 = `int ${coords[i]} = ${index} / ${stride}`;\n        const line2 = i === strides.length - 1 ?\n            `int ${coords[i + 1]} = ${index} - ${coords[i]} * ${stride}` :\n            `index -= ${coords[i]} * ${stride}`;\n        return `${line1}; ${line2};`;\n      })\n      .join('');\n}\n\nfunction buildVec(x: string[]): string {\n  if (x.length === 1) {\n    return `${x[0]}`;\n  }\n  return `vec${x.length}(${x.join(',')})`;\n}\n\n/**\n * Produces GLSL code that computes the dot product of the input x and y\n * vectors. Handles splitting inputs into increments of vec4s when necessary.\n */\nexport function dotify(x: string[], y: string[]): string {\n  if (x.length !== y.length) {\n    throw new Error(\n        `Vectors to be dotted must be of the same length -` +\n        `got ${x.length} and ${y.length}`);\n  }\n\n  const slices: string[] = [];\n  const nearestVec4 = Math.floor(x.length / 4);\n  const nearestVec4Remainder = x.length % 4;\n\n  for (let i = 0; i < nearestVec4; i++) {\n    const xSlice = x.slice(i * 4, i * 4 + 4);\n    const ySlice = y.slice(i * 4, i * 4 + 4);\n    slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n  }\n\n  if (nearestVec4Remainder !== 0) {\n    let xSlice = x.slice(nearestVec4 * 4);\n    let ySlice = y.slice(nearestVec4 * 4);\n    if (xSlice.length === 1) {\n      xSlice = xSlice.map(d => `float(${d})`);\n      ySlice = ySlice.map(d => `float(${d})`);\n    }\n    slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n  }\n\n  return slices.map((d, i) => `dot(${d})`).join('+');\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENV} from '../../environment';\nimport * as broadcast_util from '../../ops/broadcast_util';\nimport * as util from '../../util';\n\nimport * as shader_util from './shader_compiler_util';\n\nexport type ShapeInfo = {\n  logicalShape: number[],\n  texShape: [number, number],\n  isUniform: boolean,\n  isPacked: boolean\n};\n\nexport type InputInfo = {\n  name: string,\n  shapeInfo: ShapeInfo\n};\n\nexport function makeShader(\n    inputsInfo: InputInfo[], outputShape: ShapeInfo, userCode: string,\n    broadcast: boolean, usesPackedTextures: boolean): string {\n  let inputPrefixSnippet: string[]|string = inputsInfo.map(x => {\n    const size = util.sizeFromShape(x.shapeInfo.logicalShape);\n    if (x.shapeInfo.isUniform) {\n      return `uniform float ${x.name}${size > 1 ? `[${size}]` : ''};`;\n    }\n    return `uniform sampler2D ${x.name};`;\n  });\n  inputPrefixSnippet = inputPrefixSnippet.join('\\n');\n\n  const inputSamplingSnippet =\n      inputsInfo\n          .map(\n              x => getInputSamplingSnippet(\n                  x, outputShape, broadcast, usesPackedTextures))\n          .join('\\n');\n  const outTexShape = outputShape.texShape;\n  let outputSamplingSnippet: string;\n  let floatTextureSetOutputSnippet: string;\n  let shaderPrefix = SHADER_PREFIX;\n\n  if (outputShape.isPacked) {\n    outputSamplingSnippet =\n        getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n    floatTextureSetOutputSnippet = FLOAT_TEXTURE_SET_RGBA_SNIPPET;\n  } else {\n    outputSamplingSnippet =\n        getOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n    floatTextureSetOutputSnippet = FLOAT_TEXTURE_SET_R_SNIPPET;\n  }\n\n  if (usesPackedTextures) {\n    shaderPrefix += SHADER_PACKED_PREFIX;\n  }\n\n  const source = [\n    shaderPrefix, FLOAT_TEXTURE_SAMPLE_SNIPPET, floatTextureSetOutputSnippet,\n    inputPrefixSnippet, outputSamplingSnippet, inputSamplingSnippet, userCode\n  ].join('\\n');\n  return source;\n}\n\nfunction getSamplerFromInInfo(inInfo: InputInfo): string {\n  const shape = inInfo.shapeInfo.logicalShape;\n  switch (shape.length) {\n    case 0:\n      return getSamplerScalar(inInfo);\n    case 1:\n      return getSampler1D(inInfo);\n    case 2:\n      return getSampler2D(inInfo);\n    case 3:\n      return getSampler3D(inInfo);\n    case 4:\n      return getSampler4D(inInfo);\n    case 5:\n      return getSampler5D(inInfo);\n    case 6:\n      return getSampler6D(inInfo);\n    default:\n      throw new Error(\n          `${shape.length}-D input sampling` +\n          ` is not yet supported`);\n  }\n}\n\nfunction getPackedSamplerFromInInfo(inInfo: InputInfo): string {\n  const shape = inInfo.shapeInfo.logicalShape;\n  switch (shape.length) {\n    case 0:\n      return getPackedSamplerScalar(inInfo);\n    case 1:\n      return getPackedSampler1D(inInfo);\n    case 2:\n      return getPackedSampler2D(inInfo);\n    case 3:\n      return getPackedSampler3D(inInfo);\n    case 4:\n      return getPackedSampler4D(inInfo);\n    default:\n      throw new Error(\n          `Packed ${shape.length}-D input sampling` +\n          ` is not yet supported`);\n  }\n}\n\nfunction getInputSamplingSnippet(\n    inInfo: InputInfo, outShapeInfo: ShapeInfo, broadcast: boolean,\n    usesPackedTextures = false): string {\n  let res = getSamplerFlat(inInfo);\n  if (usesPackedTextures) {\n    res += getPackedSamplerFromInInfo(inInfo);\n  } else {\n    res += getSamplerFromInInfo(inInfo);\n  }\n\n  // If input and output have matching logical shapes, add\n  // getTexNameAtOutCoord() method that samples the input textureSampler using\n  // the output coordinates.\n  if (broadcast ||\n      util.arraysEqual(\n          inInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape)) {\n    if (usesPackedTextures) {\n      res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo, broadcast);\n    } else {\n      res += getSamplerAtOutputCoords(inInfo, outShapeInfo, broadcast);\n    }\n  }\n  return res;\n}\n\nfunction getPackedOutputSamplingSnippet(\n    outShape: number[], outTexShape: [number, number]): string {\n  switch (outShape.length) {\n    case 0:\n      return getOutputScalarCoords();\n    case 1:\n      return getOutputPacked1DCoords(outShape as [number], outTexShape);\n    case 2:\n      return getOutputPacked2DCoords(outShape as [number, number], outTexShape);\n    case 3:\n      return getOutputPacked3DCoords(\n          outShape as [number, number, number], outTexShape);\n    case 4:\n      return getOutputPacked4DCoords(\n          outShape as [number, number, number, number], outTexShape);\n    default:\n      throw new Error(\n          `${outShape.length}-D packed output ` +\n          `coordinate fetching is not yet supported`);\n  }\n}\n\nfunction getOutputSamplingSnippet(\n    outShape: number[], outTexShape: [number, number]): string {\n  switch (outShape.length) {\n    case 0:\n      return getOutputScalarCoords();\n    case 1:\n      return getOutput1DCoords(outShape as [number], outTexShape);\n    case 2:\n      return getOutput2DCoords(outShape as [number, number], outTexShape);\n    case 3:\n      return getOutput3DCoords(\n          outShape as [number, number, number], outTexShape);\n    case 4:\n      return getOutput4DCoords(\n          outShape as [number, number, number, number], outTexShape);\n    case 5:\n      return getOutput5DCoords(\n          outShape as [number, number, number, number, number], outTexShape);\n    case 6:\n      return getOutput6DCoords(\n          outShape as [number, number, number, number, number, number],\n          outTexShape);\n    default:\n      throw new Error(\n          `${outShape.length}-D output sampling is not yet supported`);\n  }\n}\n\nconst SAMPLE_1D_SNIPPET = `\nvec2 UVfrom1D(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\n\nconst SAMPLE_2D_SNIPPET = `\nvec2 UVfrom2D(int texNumR, int texNumC, int numC, int row, int col) {\n  int index = row * numC + col;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\n\nconst SAMPLE_3D_SNIPPET = `\nvec2 UVfrom3D(int texNumR, int texNumC, int stride0,\n    int stride1, int row, int col, int depth) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\n\nconst SAMPLE_4D_SNIPPET = `\nvec2 UVfrom4D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int row, int col, int depth,\n    int depth2) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth * stride2 + depth2;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom4D(int texNumR, int texNumC, int texelsInBatch2,\n    int texelsInBatch, int texelsInLogicalRow, int b2, int b,\n    int row, int col) {\n  int index = b2 * texelsInBatch2 + b * texelsInBatch +\n    (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\n\nconst SAMPLE_5D_SNIPPET = `\nvec2 UVfrom5D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int stride3, int row, int col, int depth,\n    int depth2, int depth3) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 +\n              depth * stride2 + depth2 * stride3 + depth3;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\n\nconst SAMPLE_6D_SNIPPET = `\nvec2 UVfrom6D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int stride3, int stride4,\n    int row, int col, int depth, int depth2, int depth3, int depth4) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth * stride2 + depth2 *\n    stride3 + depth3 * stride4 + depth4;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\n\nconst FLOAT_TEXTURE_SAMPLE_SNIPPET = `\n  float sampleTexture(sampler2D textureSampler, vec2 uv) {\n    return texture2D(textureSampler, uv).r;\n  }\n`;\n\nconst FLOAT_TEXTURE_SET_R_SNIPPET = `\n  void setOutput(float val) {\n    gl_FragColor = vec4(val, 0, 0, 0);\n  }\n`;\n\nconst FLOAT_TEXTURE_SET_RGBA_SNIPPET = `\n  void setOutput(vec4 val) {\n    gl_FragColor = val;\n  }\n`;\n\nlet NAN_CHECKS = '';\nif (ENV.get('PROD')) {\n  NAN_CHECKS = `\n    bool isNaN(float val) {\n      return false;\n    }\n\n    bool hasNaN(vec4 values) {\n      return false;\n    }\n  `;\n} else {\n  /**\n   * Previous NaN check '(val < 0.0 || 0.0 < val || val == 0.0) ? false : true'\n   * does not work on iOS 12\n   */\n  NAN_CHECKS = `\n    bool isNaN(float val) {\n      return (val < 1.0 || 0.0 < val || val == 0.0) ? false : true;\n    }\n\n    bool hasNaN(vec4 values) {\n      return any(bvec4(\n        isNaN(values.x),\n        isNaN(values.y),\n        isNaN(values.z),\n        isNaN(values.w)\n      ));\n    }\n  `;\n}\n\nconst SHADER_PREFIX = `\n  precision highp float;\n  precision highp int;\n  varying vec2 resultUV;\n  const vec2 halfCR = vec2(0.5, 0.5);\n\n  struct ivec5\n  {\n    int x;\n    int y;\n    int z;\n    int w;\n    int u;\n  };\n\n  struct ivec6\n  {\n    int x;\n    int y;\n    int z;\n    int w;\n    int u;\n    int v;\n  };\n\n  ${NAN_CHECKS}\n\n  float getNaN(vec4 values) {\n    return dot(vec4(1), values);\n  }\n\n  int round(float value) {\n    return int(floor(value + 0.5));\n  }\n\n  int imod(int x, int y) {\n    return x - y * (x / y);\n  }\n\n  //Based on the work of Dave Hoskins\n  //https://www.shadertoy.com/view/4djSRW\n  #define HASHSCALE1 443.8975\n  float random(float seed){\n    vec2 p = resultUV * seed;\n    vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n    p3 += dot(p3, p3.yzx + 19.19);\n    return fract((p3.x + p3.y) * p3.z);\n  }\n\n  ${SAMPLE_1D_SNIPPET}\n  ${SAMPLE_2D_SNIPPET}\n  ${SAMPLE_3D_SNIPPET}\n  ${SAMPLE_4D_SNIPPET}\n  ${SAMPLE_5D_SNIPPET}\n  ${SAMPLE_6D_SNIPPET}\n`;\n\nconst SHADER_PACKED_PREFIX = `\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n`;\n\nfunction getOutputScalarCoords() {\n  return `\n    int getOutputCoords() {\n      return 0;\n    }\n  `;\n}\n\nfunction getOutputPacked1DCoords(\n    shape: [number], texShape: [number, number]): string {\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  if (texShape[0] === 1) {\n    return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ${packedTexShape[1]}.0);\n      }\n    `;\n  }\n\n  if (texShape[1] === 1) {\n    return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ${packedTexShape[0]}.0);\n      }\n    `;\n  }\n\n  return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      return resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n    }\n  `;\n}\n\nfunction getOutput1DCoords(\n    shape: [number], texShape: [number, number]): string {\n  if (texShape[0] === 1) {\n    return `\n      int getOutputCoords() {\n        return int(resultUV.x * ${texShape[1]}.0);\n      }\n    `;\n  }\n  if (texShape[1] === 1) {\n    return `\n      int getOutputCoords() {\n        return int(resultUV.y * ${texShape[0]}.0);\n      }\n    `;\n  }\n  return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      return resTexRC.x * ${texShape[1]} + resTexRC.y;\n    }\n  `;\n}\n\nfunction getOutputPacked3DCoords(\n    shape: [number, number, number], texShape: [number, number]): string {\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const texelsInLogicalRow = Math.ceil(shape[2] / 2);\n  const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);\n\n  return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;\n}\n\nfunction getOutput3DCoords(\n    shape: [number, number, number], texShape: [number, number]): string {\n  const coordsFromIndexSnippet =\n      shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n\n  return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\n\nfunction getOutputPacked4DCoords(\n    shape: [number, number, number, number],\n    texShape: [number, number]): string {\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n\n  const texelsInLogicalRow = Math.ceil(shape[3] / 2);\n  const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[2] / 2);\n  const texelsInBatch2 = texelsInBatch * shape[1];\n\n  return `\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      int b2 = index / ${texelsInBatch2};\n      index -= b2 * ${texelsInBatch2};\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec4(b2, b, r, c);\n    }\n  `;\n}\n\nfunction getOutput4DCoords(\n    shape: [number, number, number, number],\n    texShape: [number, number]): string {\n  const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(\n      ['r', 'c', 'd', 'd2'], shape);\n\n  return `\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec4(r, c, d, d2);\n    }\n  `;\n}\n\nfunction getOutput5DCoords(\n    shape: [number, number, number, number, number],\n    texShape: [number, number]): string {\n  const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(\n      ['r', 'c', 'd', 'd2', 'd3'], shape);\n\n  return `\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},\n                             ${texShape[1]}));\n\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `;\n}\n\nfunction getOutput6DCoords(\n    shape: [number, number, number, number, number, number],\n    texShape: [number, number]): string {\n  const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(\n      ['r', 'c', 'd', 'd2', 'd3', 'd4'], shape);\n\n  return `\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `;\n}\n\nfunction getOutputPacked2DCoords(\n    shape: [number, number], texShape: [number, number]): string {\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  if (util.arraysEqual(shape, texShape)) {\n    return `\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${\n        packedTexShape[1]}));\n      }\n    `;\n  }\n\n  // texels needed to accommodate a logical row\n  const texelsInLogicalRow = Math.ceil(shape[1] / 2);\n\n  /**\n   * getOutputCoords\n   *\n   * resTexRC: The rows and columns of the texels. If you move over one\n   * texel to the right in the packed texture, you are moving over one column\n   * (not two).\n   *\n   * index: The texel index\n   */\n  return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec2(r, c);\n    }\n  `;\n}\n\nfunction getOutput2DCoords(\n    shape: [number, number], texShape: [number, number]): string {\n  if (util.arraysEqual(shape, texShape)) {\n    return `\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));\n      }\n    `;\n  }\n  if (shape[1] === 1) {\n    return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `;\n  }\n  if (shape[0] === 1) {\n    return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `;\n  }\n  return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      int r = index / ${shape[1]};\n      int c = index - r * ${shape[1]};\n      return ivec2(r, c);\n    }\n  `;\n}\n\nfunction getPackedSamplerScalar(inputInfo: InputInfo): string {\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  return `\n    vec4 ${funcName}() {\n      return texture2D(${texName}, halfCR);\n    }\n  `;\n}\n\nfunction getSamplerScalar(inputInfo: InputInfo): string {\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  if (inputInfo.shapeInfo.isUniform) {\n    return `float ${funcName}() {return ${texName};}`;\n  }\n  return `\n    float ${funcName}() {\n      return sampleTexture(${texName}, halfCR);\n    }\n  `;\n}\n\nfunction getPackedSampler1D(inputInfo: InputInfo): string {\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n\n  return `\n    vec4 ${funcName}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${packedTexShape[0]}, ${packedTexShape[1]}, index);\n      return texture2D(${texName}, uv);\n    }\n  `;\n}\n\nfunction getSampler1D(inputInfo: InputInfo): string {\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n\n  return `\n    float ${funcName}(int index) {\n      return ${funcName}Flat(index);\n    }\n  `;\n}\n\nfunction getPackedSampler2D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  if (texShape != null && util.arraysEqual(shape, texShape)) {\n    return `\n      vec4 ${funcName}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n\n        return texture2D(${texName}, uv);\n      }\n    `;\n  }\n\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const valuesPerRow = Math.ceil(shape[1] / 2);\n\n  return `\n    vec4 ${funcName}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${\n      packedTexShape[1]}, row, col);\n      return texture2D(${texName}, uv);\n    }\n  `;\n}\n\nfunction getSampler2D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n\n  const texShape = inputInfo.shapeInfo.texShape;\n  if (texShape != null && util.arraysEqual(shape, texShape)) {\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    return `\n    float ${funcName}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n\n  const {newShape, keptDims} = util.squeezeShape(shape);\n  const squeezedShape = newShape;\n  if (squeezedShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n    const params = ['row', 'col'];\n    return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n\n  if (inputInfo.shapeInfo.isUniform) {\n    // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col) {\n        float index = dot(vec2(row, col), vec2(${shape[1]}, 1));\n        return ${funcName}Flat(round(index));\n      }\n    `;\n  }\n\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  if (texNumC === 1) {\n    // index is used directly as physical (no risk of float16 overflow).\n    return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec2(row, col), vec2(${shape[1]}, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  if (texNumR === 1) {\n    // index is used directly as physical (no risk of float16 overflow).\n    return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec2(row, col), vec2(${shape[1]}, 1));\n      vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  return `\n  float ${funcName}(int row, int col) {\n    vec2 uv = UVfrom2D(${texNumR}, ${texNumC}, ${shape[1]}, row, col);\n    return sampleTexture(${texName}, uv);\n  }\n`;\n}\n\nfunction getPackedSampler3D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n\n  if (shape[0] === 1) {\n    const squeezedShape = shape.slice(1);\n    const keptDims = [1, 2];\n    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n    const params = ['b', 'row', 'col'];\n    return `\n        ${getPackedSamplerFromInInfo(newInputInfo)}\n        vec4 ${funcName}(int b, int row, int col) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n  }\n\n  const texNumR = packedTexShape[0];\n  const texNumC = packedTexShape[1];\n\n  const valuesPerRow = Math.ceil(shape[2] / 2);\n  const texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);\n\n  return `\n    vec4 ${funcName}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);\n      return texture2D(${texName}, uv);\n    }\n  `;\n}\n\nfunction getSampler3D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const stride0 = shape[1] * shape[2];\n  const stride1 = shape[2];\n\n  const {newShape, keptDims} = util.squeezeShape(shape);\n  const squeezedShape = newShape;\n  if (squeezedShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n    const params = ['row', 'col', 'depth'];\n    return `\n        ${getSamplerFromInInfo(newInputInfo)}\n        float ${funcName}(int row, int col, int depth) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n  }\n\n  if (inputInfo.shapeInfo.isUniform) {\n    // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth) {\n        float index = dot(vec3(row, col, depth),\n                          vec3(${stride0}, ${stride1}, 1));\n        return ${funcName}Flat(round(index));\n      }\n    `;\n  }\n\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  if (texNumC === stride0) {\n    // texC is used directly as physical (no risk of float16 overflow).\n    return `\n        float ${funcName}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${stride1}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${texNumC}.0, ${texNumR}.0);\n          return sampleTexture(${texName}, uv);\n        }\n      `;\n  }\n\n  if (texNumC === stride1) {\n    // texR is used directly as physical (no risk of float16 overflow).\n    return `\n    float ${funcName}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n\n  return `\n      float ${funcName}(int row, int col, int depth) {\n        vec2 uv = UVfrom3D(\n            ${texNumR}, ${texNumC}, ${stride0}, ${stride1}, row, col, depth);\n        return sampleTexture(${texName}, uv);\n      }\n  `;\n}\n\nfunction getPackedSampler4D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const texNumR = packedTexShape[0];\n  const texNumC = packedTexShape[1];\n\n  const valuesPerRow = Math.ceil(shape[3] / 2);\n  const texelsInBatch = valuesPerRow * Math.ceil(shape[2] / 2);\n  const texelsInBatch2 = texelsInBatch * shape[1];\n\n  return `\n    vec4 ${funcName}(int b2, int b, int row, int col) {\n      vec2 uv = packedUVfrom4D(\n        ${texNumR}, ${texNumC}, ${texelsInBatch2},\n        ${texelsInBatch}, ${valuesPerRow}, b2, b, row, col);\n      return texture2D(${texName}, uv);\n    }\n  `;\n}\n\nfunction getSampler4D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const stride2 = shape[3];\n  const stride1 = shape[2] * stride2;\n  const stride0 = shape[1] * stride1;\n\n  const {newShape, keptDims} = util.squeezeShape(shape);\n  if (newShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n    const params = ['row', 'col', 'depth', 'depth2'];\n    return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n\n  if (inputInfo.shapeInfo.isUniform) {\n    // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float index = dot(vec4(row, col, depth, depth2),\n                          vec4(${stride0}, ${stride1}, ${stride2}, 1));\n        return ${funcName}Flat(round(index));\n      }\n    `;\n  }\n\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  if (texNumC === stride0) {\n    // texC is used directly as physical (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2), vec3(${stride1}, ${stride2}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  if (texNumC === stride2) {\n    // texR is used directly as physical (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${shape[1] * shape[2]}, ${shape[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  return `\n    float ${funcName}(int row, int col, int depth, int depth2) {\n      vec2 uv = UVfrom4D(${texNumR}, ${texNumC}, ${stride0}, ${stride1},\n          ${stride2}, row, col, depth, depth2);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\n\nfunction getSampler5D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const stride3 = shape[4];\n  const stride2 = shape[3] * stride3;\n  const stride1 = shape[2] * stride2;\n  const stride0 = shape[1] * stride1;\n\n  const {newShape, keptDims} = util.squeezeShape(shape);\n  if (newShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n    const params = ['row', 'col', 'depth', 'depth2', 'depth3'];\n    return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n\n  if (inputInfo.shapeInfo.isUniform) {\n    // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          depth3;\n        return ${funcName}Flat(index);\n      }\n    `;\n  }\n\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n\n  if (texNumC === stride0) {\n    // texC is used directly as physical (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(\n          vec4(col, depth, depth2, depth3),\n          vec4(${stride1}, ${stride2}, ${stride3}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n\n  if (texNumC === stride3) {\n    // texR is used directly as physical (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3]}, ${shape[2] * shape[3]},\n            ${shape[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n\n  return `\n    float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n      vec2 uv = UVfrom5D(${texNumR}, ${texNumC}, ${stride0}, ${stride1},\n          ${stride2}, ${stride3}, row, col, depth, depth2, depth3);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\n\nfunction getSampler6D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const stride4 = shape[5];\n  const stride3 = shape[4] * stride4;\n  const stride2 = shape[3] * stride3;\n  const stride1 = shape[2] * stride2;\n  const stride0 = shape[1] * stride1;\n  const {newShape, keptDims} = util.squeezeShape(shape);\n  if (newShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n    const params = ['row', 'col', 'depth', 'depth2', 'depth3', 'depth4'];\n    return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n\n  if (inputInfo.shapeInfo.isUniform) {\n    // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${stride4}, 1));\n        return ${funcName}Flat(index);\n      }\n    `;\n  }\n\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  if (texNumC === stride0) {\n    // texC is used directly as physical (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(\n          vec4(col, depth, depth2, depth3),\n          vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) + depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  if (texNumC === stride4) {\n    // texR is used directly as physical (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3] * shape[4]},\n               ${shape[2] * shape[3] * shape[4]},\n               ${shape[3] * shape[4]},\n               ${shape[4]})) + depth3;\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  return `\n    float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      vec2 uv = UVfrom6D(${texNumR}, ${texNumC}, ${stride0}, ${stride1},\n          ${stride2}, ${stride3}, ${stride4}\n          ,row, col, depth, depth2, depth3, depth4);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\n\nfunction getSamplerFlat(inputInfo: InputInfo): string {\n  const texName = inputInfo.name;\n  const funcName =\n      'get' + texName.charAt(0).toUpperCase() + texName.slice(1) + 'Flat';\n  const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n\n  if (inputInfo.shapeInfo.isUniform) {\n    if (inSize === 1) {\n      return `float ${funcName}(int index) {return ${texName};}`;\n    }\n    return `\n      float ${funcName}(int index) {\n        for (int i = 0; i < ${inSize}; i++) {\n          if (i == index) {\n            return ${texName}[i];\n          }\n        }\n      }\n    `;\n  }\n\n  const texShape = inputInfo.shapeInfo.texShape;\n  const tNumR = texShape[0];\n  const tNumC = texShape[1];\n  if (tNumC === 1 && tNumR === 1) {\n    return `\n      float ${funcName}(int index) {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n  }\n  if (tNumC === 1) {\n    return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2(0.5, (float(index) + 0.5) / ${tNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  if (tNumR === 1) {\n    return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2((float(index) + 0.5) / ${tNumC}.0, 0.5);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  return `\n    float ${funcName}(int index) {\n      vec2 uv = UVfrom1D(${tNumR}, ${tNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\n\nfunction getBroadcastOutputCoordsSampler(\n    inputInfo: InputInfo, outShapeInfo: ShapeInfo, texFuncSnippet: string,\n    funcName: string): string {\n  const inRank = inputInfo.shapeInfo.logicalShape.length;\n  const outRank = outShapeInfo.logicalShape.length;\n\n  let type = 'int';\n  if (outRank === 2) {\n    type = 'ivec2';\n  } else if (outRank === 3) {\n    type = 'ivec3';\n  } else if (outRank === 4) {\n    type = 'ivec4';\n  }\n  const broadcastDims = broadcast_util.getBroadcastDims(\n      inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n  const rankDiff = outRank - inRank;\n  let coordsSnippet: string;\n  if (inRank === 0) {\n    coordsSnippet = '';\n  } else if (outRank < 2 && broadcastDims.length >= 1) {\n    coordsSnippet = 'coords = 0;';\n  } else {\n    coordsSnippet =\n        broadcastDims.map(d => `coords[${d + rankDiff}] = 0;`).join('\\n');\n  }\n  let unpackedCoordsSnippet = '';\n  if (outRank < 2 && inRank > 0) {\n    unpackedCoordsSnippet = 'coords';\n  } else {\n    unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape\n                                .map((s, i) => `coords[${i + rankDiff}]`)\n                                .join(', ');\n  }\n  return `\n    float ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      return get${texFuncSnippet}(${unpackedCoordsSnippet});\n    }\n  `;\n}\n\nfunction getPackedSamplerAtOutputCoords(\n    inputInfo: InputInfo, outShapeInfo: ShapeInfo,\n    supportsBroadcasting: boolean) {\n  const texName = inputInfo.name;\n  const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n  const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n  const outTexShape = outShapeInfo.texShape;\n\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const texNumR = packedTexShape[0];\n  const texNumC = packedTexShape[1];\n\n  const broadcastDims = broadcast_util.getBroadcastDims(\n      inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n  const inRank = inputInfo.shapeInfo.logicalShape.length;\n  const outRank = outShapeInfo.logicalShape.length;\n  if (broadcastDims.length) {\n    throw Error('Packed broadcast sampling is not implemented yet.');\n  }\n\n  const inTexShape = inputInfo.shapeInfo.texShape;\n  if (util.arraysEqual(inTexShape, outTexShape)) {\n    return `\n      vec4 ${funcName}() {\n        return texture2D(${texName}, resultUV);\n      }\n    `;\n  }\n\n  let output = `return texture2D(${texName}, uv)`;\n\n  if (inRank === 1 && outRank > 1) {\n    output = `\n      vec4 sample = texture2D(${texName}, uv);\n      return vec4(sample.xy, sample.xy);\n    `;\n  } else if (inRank === 0 && outRank > 0) {\n    if (outRank === 1) {\n      output = `\n        vec4 sample = texture2D(${texName}, uv);\n        return vec4(sample.x, sample.x, 0., 0.);\n      `;\n    } else {\n      output = `\n        vec4 sample = texture2D(${texName}, uv);\n        return vec4(sample.x);\n      `;\n    }\n  }\n\n  return `\n    vec4 ${funcName}() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      int texR = index / ${texNumC};\n      int texC = index - texR * ${texNumC};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});\n\n      ${output};\n    }\n  `;\n}\n\nfunction getSamplerAtOutputCoords(\n    inputInfo: InputInfo, outShapeInfo: ShapeInfo,\n    supportsBroadcasting: boolean) {\n  const texName = inputInfo.name;\n  const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n  const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n\n  const broadcastDims = broadcast_util.getBroadcastDims(\n      inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n  const inRank = inputInfo.shapeInfo.logicalShape.length;\n  const outRank = outShapeInfo.logicalShape.length;\n  const doBroadcast =\n      supportsBroadcasting && ((outRank > inRank) || broadcastDims.length > 0);\n  const broadcastOverOuter =\n      broadcast_util.broadcastDimsAreOuter(broadcastDims);\n  const isUniform = inputInfo.shapeInfo.isUniform;\n\n  if (doBroadcast && !broadcastOverOuter) {\n    return getBroadcastOutputCoordsSampler(\n        inputInfo, outShapeInfo, texFuncSnippet, funcName);\n  }\n\n  const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n  let broadcastSnippet = '';\n  if (doBroadcast && broadcastOverOuter) {\n    broadcastSnippet = `\n        int mainPart = index / ${inSize};\n        index -= mainPart * ${inSize};\n      `;\n  }\n\n  const outTexShape = outShapeInfo.texShape;\n  if (isUniform) {\n    if (inSize === 1) {\n      return `float ${funcName}() {return ${texName};}`;\n    }\n    return `\n      float ${funcName}() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                              vec2(${outTexShape[0]}, ${outTexShape[1]}));\n        int index = resTexRC.x * ${outTexShape[1]} + resTexRC.y;\n        ${broadcastSnippet}\n        return get${texFuncSnippet}Flat(index);\n      }\n    `;\n  }\n\n  // At this point, the input is not a uniform.\n  const inTexShape = inputInfo.shapeInfo.texShape;\n  if (util.arraysEqual(inTexShape, outTexShape)) {\n    return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, resultUV);\n      }\n    `;\n  }\n\n  return `\n    float ${funcName}() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${outTexShape[0]}, ${outTexShape[1]}));\n      int index = resTexRC.x * ${outTexShape[1]} + resTexRC.y;\n      ${broadcastSnippet}\n      int texR = index / ${inTexShape[1]};\n      int texC = index - texR * ${inTexShape[1]};\n      vec2 uv = (vec2(texC, texR) + halfCR) /\n                 vec2(${inTexShape[1]}.0, ${inTexShape[0]}.0);\n\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\n\nexport function getCoordsDataType(rank: number): string {\n  if (rank <= 1) {\n    return 'int';\n  } else if (rank === 2) {\n    return 'ivec2';\n  } else if (rank === 3) {\n    return 'ivec3';\n  } else if (rank === 4) {\n    return 'ivec4';\n  } else if (rank === 5) {\n    return 'ivec5';\n  } else if (rank === 6) {\n    return 'ivec6';\n  } else {\n    throw Error(`GPU for rank ${rank} is not yet supported`);\n  }\n}\n\n/** Returns a new input info (a copy) that has a squeezed logical shape. */\nfunction squeezeInputInfo(\n    inInfo: InputInfo, squeezedShape: number[]): InputInfo {\n  // Deep copy.\n  const newInputInfo: InputInfo = JSON.parse(JSON.stringify(inInfo));\n  newInputInfo.shapeInfo.logicalShape = squeezedShape;\n  return newInputInfo;\n}\n\nfunction getSqueezedParams(params: string[], keptDims: number[]): string {\n  return keptDims.map(d => params[d]).join(', ');\n}","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class CumSumProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(shape: number[], exclusive: boolean, reverse: boolean) {\n    this.outputShape = shape;\n    const rank = shape.length;\n    const finalDim = shape[shape.length - 1];\n    const comparator = reverse ? '<' : '>';\n\n    this.userCode = `\n      int getIndex(int i) {\n        ${reverse ? `return ${finalDim} -i - 1;` : 'return i;'}\n      }\n\n      void main() {\n        ${getCoordsDataType(rank)} coords = getOutputCoords();\n        int end = ${getFinalCoord(rank, 'coords')};\n        float val = 0.0;\n        for (int i = ${finalDim} - 1; i >= 0; i -= 1) {\n          int idx = getIndex(i);\n          if (idx ${comparator} end) {\n            continue;\n          }\n          if (idx == end && ${exclusive}) {\n            continue;\n          }\n          ${getFinalCoord(rank, 'coords')} = idx;\n          val += getX(${getCoords(rank, 'coords')});\n        }\n        setOutput(val);\n      }\n    `;\n  }\n}\n\nfunction getCoords(rank: number, name: string): string {\n  if (rank === 1) {\n    return `${name}`;\n  } else if (rank === 2) {\n    return `${name}.x, ${name}.y`;\n  } else if (rank === 3) {\n    return `${name}.x, ${name}.y, ${name}.z`;\n  } else if (rank === 4) {\n    return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;\n  } else {\n    throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n  }\n}\n\nfunction getFinalCoord(rank: number, name: string): string {\n  if (rank === 1) {\n    return `${name}`;\n  } else if (rank === 2) {\n    return `${name}.y`;\n  } else if (rank === 3) {\n    return `${name}.z`;\n  } else if (rank === 4) {\n    return `${name}.w`;\n  } else {\n    throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class DepthToSpaceProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[] = [];\n  userCode: string;\n  blockSize: number;\n  dataFormat: string;\n\n  constructor(\n      outputShape: number[], blockSize: number, dataFormat: 'NHWC'|'NCHW') {\n    this.outputShape = outputShape;\n    this.blockSize = blockSize;\n    this.dataFormat = dataFormat;\n    this.userCode = `\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = ${this.getHeightCoordString()};\n      int w = ${this.getWidthCoordString()};\n      int d = ${this.getDepthCoordString()};\n\n      int in_h = h / ${blockSize};\n      int offset_h = imod(h, ${blockSize});\n      int in_w = w / ${blockSize};\n      int offset_w = imod(w, ${blockSize});\n      int offset_d = (offset_h * ${blockSize} + offset_w) *\n        ${this.getOutputDepthSize()};\n      int in_d = d + offset_d;\n\n      float result = ${this.getInputSamplingString()};\n      setOutput(result);\n    }\n  `;\n  }\n\n  private getHeightCoordString(): string {\n    if (this.dataFormat === 'NHWC') {\n      return `coords[1]`;\n    } else {\n      return `coords[2]`;\n    }\n  }\n\n  private getWidthCoordString(): string {\n    if (this.dataFormat === 'NHWC') {\n      return `coords[2]`;\n    } else {\n      return `coords[3]`;\n    }\n  }\n\n  private getDepthCoordString(): string {\n    if (this.dataFormat === 'NHWC') {\n      return `coords[3]`;\n    } else {\n      return `coords[1]`;\n    }\n  }\n\n  private getOutputDepthSize(): number {\n    if (this.dataFormat === 'NHWC') {\n      return this.outputShape[3];\n    } else {\n      return this.outputShape[1];\n    }\n  }\n\n  private getInputSamplingString(): string {\n    if (this.dataFormat === 'NHWC') {\n      return `getX(b, in_h, in_w, in_d)`;\n    } else {\n      return `getX(b, in_d, in_h, in_w)`;\n    }\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class EncodeFloatProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  userCode: string;\n  outputShape: number[];\n\n  constructor(outputShape: number[]) {\n    this.outputShape = outputShape;\n    this.userCode = `\n      const float FLOAT_MAX = 1.70141184e38;\n      const float FLOAT_MIN = 1.17549435e-38;\n\n      lowp vec4 encode_float(highp float v) {\n        if (isNaN(v)) {\n          return vec4(255, 255, 255, 255);\n        }\n\n        highp float av = abs(v);\n\n        if(av < FLOAT_MIN) {\n          return vec4(0.0, 0.0, 0.0, 0.0);\n        } else if(v > FLOAT_MAX) {\n          return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n        } else if(v < -FLOAT_MAX) {\n          return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n        }\n\n        highp vec4 c = vec4(0,0,0,0);\n\n        highp float e = floor(log2(av));\n        highp float m = exp2(fract(log2(av))) - 1.0;\n\n        c[2] = floor(128.0 * m);\n        m -= c[2] / 128.0;\n        c[1] = floor(32768.0 * m);\n        m -= c[1] / 32768.0;\n        c[0] = floor(8388608.0 * m);\n\n        highp float ebias = e + 127.0;\n        c[3] = floor(ebias / 2.0);\n        ebias -= c[3] * 2.0;\n        c[2] += floor(ebias) * 128.0;\n\n        c[3] += 128.0 * step(0.0, -v);\n\n        return c / 255.0;\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        gl_FragColor = encode_float(x);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport const COMPLEX_FFT = {\n  REAL: 'return real * expR - imag * expI;',\n  IMAG: 'return real * expI + imag * expR;'\n};\n\nexport class FFTProgram implements GPGPUProgram {\n  variableNames = ['real', 'imag'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(op: string, inputShape: [number, number], inverse: boolean) {\n    const innerDim = inputShape[1];\n    this.outputShape = inputShape;\n\n    const exponentMultiplierSnippet =\n        inverse ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`;\n    const resultDenominator = inverse ? `${innerDim}.0` : '1.0';\n\n    this.userCode = `\n      const float exponentMultiplier = ${exponentMultiplierSnippet};\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        ${op}\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float(${innerDim});\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < ${innerDim}; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / ${resultDenominator};\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class FromPixelsProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  userCode: string;\n  outputShape: number[];\n\n  constructor(outputShape: number[]) {\n    const [height, width, ] = outputShape;\n    this.outputShape = outputShape;\n    this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0);\n\n        vec4 values = texture2D(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class GatherProgram implements GPGPUProgram {\n  variableNames = ['A', 'indices'];\n  outputShape: number[];\n  userCode: string;\n  rank: number;\n\n  constructor(aShape: number[], indicesLength: number, axis: number) {\n    const outputShape: number[] = aShape.slice();\n    outputShape[axis] = indicesLength;\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    const dtype = getCoordsDataType(this.rank);\n    const sourceCoords = getSourceCoords(aShape, axis);\n\n    this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n  }\n}\n\nfunction getSourceCoords(aShape: number[], axis: number): string {\n  const rank = aShape.length;\n  if (rank > 4) {\n    throw Error(`Gather for rank ${rank} is not yet supported`);\n  }\n  if (rank === 1) {\n    return `int(getIndices(resRC))`;\n  }\n\n  const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n\n  const sourceCoords = [];\n  for (let i = 0; i < aShape.length; i++) {\n    if (i === axis) {\n      sourceCoords.push(`int(getIndices(${currentCoords[i]}))`);\n    } else {\n      sourceCoords.push(`${currentCoords[i]}`);\n    }\n  }\n  return sourceCoords.join();\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class GatherNDProgram implements GPGPUProgram {\n  variableNames = ['x', 'indices'];\n  outputShape: number[];\n  userCode: string;\n  constructor(\n      private sliceDim: number, private strides: number[], shape: number[]) {\n    this.outputShape = shape;\n    const stridesType = getCoordsDataType(strides.length);\n    const dtype = getCoordsDataType(shape.length);\n    const strideString = this.sliceDim > 1 ? 'strides[j]' : 'strides';\n    this.userCode = `\n        ${stridesType} strides = ${stridesType}(${this.strides});\n         void main() {\n          ${dtype} coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < ${this.sliceDim}; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * ${strideString};\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../../tensor';\nimport {DataType, DataValues} from '../../types';\nimport * as util from '../../util';\n\nexport enum TextureUsage {\n  RENDER,\n  UPLOAD,\n  PIXELS,\n  DOWNLOAD\n}\n\nexport enum PhysicalTextureType {\n  UNPACKED_FLOAT16,\n  UNPACKED_FLOAT32,\n  PACKED_4X1_UNSIGNED_BYTE,\n  PACKED_2X2_FLOAT32,\n  PACKED_2X2_FLOAT16\n}\n\nexport interface TextureData {\n  // Required.\n  shape: number[];\n  dtype: DataType;\n\n  // Optional.\n  values?: DataValues;\n  texture?: WebGLTexture;\n  // For complex numbers, the real and imaginary parts are stored as their own\n  // individual tensors, with a parent joining the two with the\n  // complexTensors field. When this is defined, texture will be null.\n  complexTensors?: {real: Tensor, imag: Tensor};\n  /** [rows, columns] shape of the texture. */\n  texShape?: [number, number];\n  usage?: TextureUsage;\n  isPacked?: boolean;\n}\n\nexport function getUnpackedMatrixTextureShapeWidthHeight(\n    rows: number, columns: number): [number, number] {\n  return [columns, rows];\n}\n\nexport function getUnpackedArraySizeFromMatrixSize(\n    matrixSize: number, channelsPerTexture: number): number {\n  return matrixSize * channelsPerTexture;\n}\n\nexport function getColorMatrixTextureShapeWidthHeight(\n    rows: number, columns: number): [number, number] {\n  return [columns * 4, rows];\n}\n\nexport function getMatrixSizeFromUnpackedArraySize(\n    unpackedSize: number, channelsPerTexture: number): number {\n  if (unpackedSize % channelsPerTexture !== 0) {\n    throw new Error(\n        `unpackedSize (${unpackedSize}) must be a multiple of ` +\n        `${channelsPerTexture}`);\n  }\n  return unpackedSize / channelsPerTexture;\n}\n\nexport function encodeMatrixToUnpackedArray(\n    matrix: Float32Array|Uint8Array, unpackedArray: Float32Array|Uint8Array,\n    channelsPerTexture: number) {\n  const requiredSize =\n      getUnpackedArraySizeFromMatrixSize(matrix.length, channelsPerTexture);\n  if (unpackedArray.length < requiredSize) {\n    throw new Error(\n        `unpackedArray length (${unpackedArray.length}) must be >= ` +\n        `${requiredSize}`);\n  }\n  let dst = 0;\n  for (let src = 0; src < matrix.length; ++src) {\n    unpackedArray[dst] = matrix[src];\n    dst += channelsPerTexture;\n  }\n}\n\nexport function decodeMatrixFromUnpackedArray(\n    unpackedArray: Float32Array, matrix: Float32Array,\n    channelsPerTexture: number) {\n  const requiredSize = getMatrixSizeFromUnpackedArraySize(\n      unpackedArray.length, channelsPerTexture);\n  if (matrix.length < requiredSize) {\n    throw new Error(\n        `matrix length (${matrix.length}) must be >= ${requiredSize}`);\n  }\n  let dst = 0;\n  for (let src = 0; src < unpackedArray.length; src += channelsPerTexture) {\n    matrix[dst++] = unpackedArray[src];\n  }\n}\n\nexport function decodeMatrixFromUnpackedColorRGBAArray(\n    unpackedArray: Float32Array, matrix: Float32Array, channels: number) {\n  const requiredSize = unpackedArray.length * channels / 4;\n  if (matrix.length < requiredSize) {\n    throw new Error(\n        `matrix length (${matrix.length}) must be >= ${requiredSize}`);\n  }\n  let dst = 0;\n  for (let src = 0; src < unpackedArray.length; src += 4) {\n    for (let c = 0; c < channels; c++) {\n      matrix[dst++] = unpackedArray[src + c];\n    }\n  }\n}\n\nexport function getPackedMatrixTextureShapeWidthHeight(\n    rows: number, columns: number): [number, number] {\n  return [\n    Math.max(1, Math.ceil(columns / 2)), Math.max(1, Math.ceil(rows / 2))\n  ];\n}\n\nexport function getPackedRGBAArraySizeFromMatrixShape(\n    rows: number, columns: number): number {\n  const [w, h] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n  return w * h * 4;\n}\n\n/*\nThis is how encodeMatrixToPackedRGBA encodes a tensor with shape = [2, 3, 5]\n(indices are [batch, row, col]).\n\n000|001   002|003   004|xxx   020|021   022|023   024|xxx\n-------   -------   -------   -------   -------   -------\n010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n\n100|101   102|103   104|xxx   120|121   122|123   124|xxx\n-------   -------   -------   -------   -------   -------\n110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n\nSingle texels contain only values from the same batch, and from adjacent rows\nand columns.\n\nNote the batch dimension is needed so xxx's are inserted below 020, 021, 022,\n023, and 024.\n */\n\nexport function encodeMatrixToPackedRGBA(\n    matrix: Float32Array, batches: number, rows: number, columns: number,\n    packedRGBA: Float32Array) {\n  const requiredSize = getPackedRGBAArraySizeFromMatrixShape(rows, columns);\n  if (packedRGBA.length < requiredSize) {\n    throw new Error(`packedRGBA length (${packedRGBA.length}) must be >=\n        ${requiredSize}`);\n  }\n\n  const oddWidth = (columns % 2) === 1;\n  const oddHeight = (rows % 2) === 1;\n  const widthInFullBlocks = Math.floor(columns / 2);\n  const heightInFullBlocks = Math.floor(rows / 2);\n\n  const texelsPerRow = Math.ceil(columns / 2);\n  const texelsPerBatch = texelsPerRow * Math.ceil(rows / 2);\n\n  const flattenedMatrixSize =\n      util.nearestLargerEven(rows) * util.nearestLargerEven(columns);\n\n  for (let batch = 0; batch < batches; batch++) {\n    const sourceOffset = batch * rows * columns;\n    const batchOffset = batch * flattenedMatrixSize;\n\n    // loop over full 2x2 blocks\n    {\n      const dstStride = (oddWidth ? 4 : 0);\n      const oneRow = columns;\n      let dst = batchOffset;\n      for (let blockY = 0; blockY < heightInFullBlocks; ++blockY) {\n        const matrixSrcRow = (blockY * 2 * columns);\n        for (let blockX = 0; blockX < widthInFullBlocks; ++blockX) {\n          const matrixSrcCol = blockX * 2;\n          const src = sourceOffset + matrixSrcRow + matrixSrcCol;\n          packedRGBA[dst] = matrix[src];\n          packedRGBA[dst + 1] = matrix[src + 1];\n          packedRGBA[dst + 2] = matrix[src + oneRow];\n          packedRGBA[dst + 3] = matrix[src + oneRow + 1];\n          dst += 4;\n        }\n        dst += dstStride;\n      }\n    }\n\n    // loop down final odd column\n    if (oddWidth) {\n      let src = sourceOffset + columns - 1;\n      let dst = batchOffset + (texelsPerRow - 1) * 4;\n      const srcStride = 2 * columns;\n      const dstStride = texelsPerRow * 4;\n      for (let blockY = 0; blockY < heightInFullBlocks; ++blockY) {\n        packedRGBA[dst] = matrix[src];\n        packedRGBA[dst + 2] = matrix[src + columns];\n        src += srcStride;\n        dst += dstStride;\n      }\n    }\n\n    // loop across final row\n    if (oddHeight) {\n      let src = sourceOffset + (rows - 1) * columns;\n      let dst = batchOffset + (texelsPerBatch - texelsPerRow) * 4;\n      for (let blockX = 0; blockX < widthInFullBlocks; ++blockX) {\n        packedRGBA[dst++] = matrix[src++];\n        packedRGBA[dst++] = matrix[src++];\n        dst += 2;\n      }\n\n      // fill in bottom-right texel\n      if (oddWidth && oddHeight) {\n        packedRGBA[batchOffset + flattenedMatrixSize - 4] = matrix[src];\n      }\n    }\n  }\n\n  return packedRGBA;\n}\n\nexport function decodeMatrixFromPackedRGBA(\n    packedRGBA: Float32Array, batches: number, rows: number, columns: number,\n    matrix: Float32Array): Float32Array {\n  const requiredSize = rows * columns;\n  if (matrix.length < requiredSize) {\n    throw new Error(\n        `matrix length (${matrix.length}) must be >= ${requiredSize}`);\n  }\n\n  const oddWidth = (columns % 2) === 1;\n  const oddHeight = (rows % 2) === 1;\n  const widthInFullBlocks = Math.floor(columns / 2);\n  const heightInFullBlocks = Math.floor(rows / 2);\n\n  const texelsPerRow = Math.ceil(columns / 2);\n  const texelsPerBatch = texelsPerRow * Math.ceil(rows / 2);\n\n  const flattenedMatrixSize =\n      util.nearestLargerEven(rows) * util.nearestLargerEven(columns);\n\n  for (let batch = 0; batch < batches; batch++) {\n    const batchOffset = batch * rows * columns;\n    const sourceOffset = batch * flattenedMatrixSize;\n\n    // loop over full 2x2 blocks\n    {\n      const srcStride = oddWidth ? 4 : 0;\n      const dstStride = columns + (oddWidth ? 1 : 0);\n      let src = sourceOffset;\n      let dstRow1 = batchOffset;\n      let dstRow2 = batchOffset + columns;\n      for (let blockY = 0; blockY < heightInFullBlocks; ++blockY) {\n        for (let blockX = 0; blockX < widthInFullBlocks; ++blockX) {\n          matrix[dstRow1++] = packedRGBA[src++];\n          matrix[dstRow1++] = packedRGBA[src++];\n          matrix[dstRow2++] = packedRGBA[src++];\n          matrix[dstRow2++] = packedRGBA[src++];\n        }\n        src += srcStride;\n        dstRow1 += dstStride;\n        dstRow2 += dstStride;\n      }\n    }\n\n    // loop down final column\n    if (oddWidth) {\n      let src = sourceOffset + (texelsPerRow - 1) * 4;\n      let dst = batchOffset + columns - 1;\n      const srcStride = texelsPerRow * 4;\n      const dstStride = 2 * columns;\n      for (let blockY = 0; blockY < heightInFullBlocks; ++blockY) {\n        matrix[dst] = packedRGBA[src];\n        matrix[dst + columns] = packedRGBA[src + 2];\n        src += srcStride;\n        dst += dstStride;\n      }\n    }\n\n    // loop across final row\n    if (oddHeight) {\n      let src = sourceOffset + (texelsPerBatch - texelsPerRow) * 4;\n      let dst = batchOffset + (rows - 1) * columns;\n      for (let blockX = 0; blockX < widthInFullBlocks; ++blockX) {\n        matrix[dst++] = packedRGBA[src++];\n        matrix[dst++] = packedRGBA[src++];\n        src += 2;\n      }\n\n      // fill in bottom-right cell\n      if (oddWidth) {\n        matrix[batchOffset + (rows * columns) - 1] = packedRGBA[src];\n      }\n    }\n  }\n\n  return matrix;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../../environment';\nimport * as util from '../../util';\n\nexport function callAndCheck<T>(gl: WebGLRenderingContext, func: () => T): T {\n  const returnValue = func();\n  checkWebGLError(gl);\n  return returnValue;\n}\n\nlet webGLDebugErrorCheckingEnabled = false;\n\nexport function enableDebugWebGLErrorChecking(enabled: boolean) {\n  webGLDebugErrorCheckingEnabled = enabled;\n}\n\nexport function checkWebGLError(gl: WebGLRenderingContext) {\n  if (webGLDebugErrorCheckingEnabled) {\n    const error = gl.getError();\n    if (error !== gl.NO_ERROR) {\n      throw new Error('WebGL Error: ' + getWebGLErrorMessage(gl, error));\n    }\n  }\n}\n\nexport function getWebGLErrorMessage(\n    gl: WebGLRenderingContext, status: number): string {\n  switch (status) {\n    case gl.NO_ERROR:\n      return 'NO_ERROR';\n    case gl.INVALID_ENUM:\n      return 'INVALID_ENUM';\n    case gl.INVALID_VALUE:\n      return 'INVALID_VALUE';\n    case gl.INVALID_OPERATION:\n      return 'INVALID_OPERATION';\n    case gl.INVALID_FRAMEBUFFER_OPERATION:\n      return 'INVALID_FRAMEBUFFER_OPERATION';\n    case gl.OUT_OF_MEMORY:\n      return 'OUT_OF_MEMORY';\n    case gl.CONTEXT_LOST_WEBGL:\n      return 'CONTEXT_LOST_WEBGL';\n    default:\n      return `Unknown error code ${status}`;\n  }\n}\n\nexport function getExtensionOrThrow(\n    gl: WebGLRenderingContext, extensionName: string): {} {\n  return throwIfNull<{}>(\n      gl, () => gl.getExtension(extensionName),\n      'Extension \"' + extensionName + '\" not supported on this browser.');\n}\n\nexport function createVertexShader(\n    gl: WebGLRenderingContext, vertexShaderSource: string): WebGLShader {\n  const vertexShader: WebGLShader = throwIfNull<WebGLShader>(\n      gl, () => gl.createShader(gl.VERTEX_SHADER),\n      'Unable to create vertex WebGLShader.');\n  callAndCheck(gl, () => gl.shaderSource(vertexShader, vertexShaderSource));\n  callAndCheck(gl, () => gl.compileShader(vertexShader));\n  if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {\n    console.log(gl.getShaderInfoLog(vertexShader));\n    throw new Error('Failed to compile vertex shader.');\n  }\n  return vertexShader;\n}\n\nexport function createFragmentShader(\n    gl: WebGLRenderingContext, fragmentShaderSource: string): WebGLShader {\n  const fragmentShader: WebGLShader = throwIfNull<WebGLShader>(\n      gl, () => gl.createShader(gl.FRAGMENT_SHADER),\n      'Unable to create fragment WebGLShader.');\n  callAndCheck(gl, () => gl.shaderSource(fragmentShader, fragmentShaderSource));\n  callAndCheck(gl, () => gl.compileShader(fragmentShader));\n  if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {\n    logShaderSourceAndInfoLog(\n        fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));\n    throw new Error('Failed to compile fragment shader.');\n  }\n  return fragmentShader;\n}\n\nconst lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;\nfunction logShaderSourceAndInfoLog(\n    shaderSource: string, shaderInfoLog: string) {\n  const lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);\n  if (lineNumberRegexResult == null) {\n    console.log(`Couldn't parse line number in error: ${shaderInfoLog}`);\n    console.log(shaderSource);\n    return;\n  }\n\n  const lineNumber = +lineNumberRegexResult[1];\n\n  const shaderLines = shaderSource.split('\\n');\n  const pad = shaderLines.length.toString().length + 2;\n  const linesWithLineNumbers = shaderLines.map(\n      (line, lineNumber) =>\n          util.rightPad((lineNumber + 1).toString(), pad) + line);\n  let maxLineLength = 0;\n  for (let i = 0; i < linesWithLineNumbers.length; i++) {\n    maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength);\n  }\n\n  const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);\n  const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);\n  const afterErrorLines = linesWithLineNumbers.slice(lineNumber);\n\n  console.log(beforeErrorLines.join('\\n'));\n  console.log(shaderInfoLog.split('\\n')[0]);\n  console.log(\n      `%c ${util.rightPad(errorLine[0], maxLineLength)}`,\n      'border:1px solid red; background-color:#e3d2d2; color:#a61717');\n  console.log(afterErrorLines.join('\\n'));\n}\n\nexport function createProgram(gl: WebGLRenderingContext): WebGLProgram {\n  return throwIfNull<WebGLProgram>(\n      gl, () => gl.createProgram(), 'Unable to create WebGLProgram.');\n}\n\nexport function linkProgram(gl: WebGLRenderingContext, program: WebGLProgram) {\n  callAndCheck(gl, () => gl.linkProgram(program));\n  if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {\n    console.log(gl.getProgramInfoLog(program));\n    throw new Error('Failed to link vertex and fragment shaders.');\n  }\n}\n\nexport function validateProgram(\n    gl: WebGLRenderingContext, program: WebGLProgram) {\n  callAndCheck(gl, () => gl.validateProgram(program));\n  if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {\n    console.log(gl.getProgramInfoLog(program));\n    throw new Error('Shader program validation failed.');\n  }\n}\n\nexport function createStaticVertexBuffer(\n    gl: WebGLRenderingContext, data: Float32Array): WebGLBuffer {\n  const buffer: WebGLBuffer = throwIfNull<WebGLBuffer>(\n      gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n  callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n  callAndCheck(gl, () => gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW));\n  return buffer;\n}\n\nexport function createStaticIndexBuffer(\n    gl: WebGLRenderingContext, data: Uint16Array): WebGLBuffer {\n  const buffer: WebGLBuffer = throwIfNull<WebGLBuffer>(\n      gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n  callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer));\n  callAndCheck(\n      gl, () => gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW));\n  return buffer;\n}\n\nexport function getNumChannels(): number {\n  if (ENV.get('WEBGL_VERSION') === 2) {\n    return 1;\n  }\n  return 4;\n}\n\nexport function createTexture(gl: WebGLRenderingContext): WebGLTexture {\n  return throwIfNull<WebGLTexture>(\n      gl, () => gl.createTexture(), 'Unable to create WebGLTexture.');\n}\n\nexport function validateTextureSize(width: number, height: number) {\n  const maxTextureSize = ENV.get('WEBGL_MAX_TEXTURE_SIZE');\n  if ((width <= 0) || (height <= 0)) {\n    const requested = `[${width}x${height}]`;\n    throw new Error('Requested texture size ' + requested + ' is invalid.');\n  }\n  if ((width > maxTextureSize) || (height > maxTextureSize)) {\n    const requested = `[${width}x${height}]`;\n    const max = `[${maxTextureSize}x${maxTextureSize}]`;\n    throw new Error(\n        'Requested texture size ' + requested +\n        ' greater than WebGL maximum on this browser / GPU ' + max + '.');\n  }\n}\n\nexport function createFramebuffer(gl: WebGLRenderingContext): WebGLFramebuffer {\n  return throwIfNull<WebGLFramebuffer>(\n      gl, () => gl.createFramebuffer(), 'Unable to create WebGLFramebuffer.');\n}\n\nexport function bindVertexBufferToProgramAttribute(\n    gl: WebGLRenderingContext, program: WebGLProgram, attribute: string,\n    buffer: WebGLBuffer, arrayEntriesPerItem: number, itemStrideInBytes: number,\n    itemOffsetInBytes: number): boolean {\n  const loc = gl.getAttribLocation(program, attribute);\n  if (loc === -1) {\n    // The GPU compiler decided to strip out this attribute because it's unused,\n    // thus no need to bind.\n    return false;\n  }\n  callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n  callAndCheck(\n      gl,\n      () => gl.vertexAttribPointer(\n          loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes,\n          itemOffsetInBytes));\n  callAndCheck(gl, () => gl.enableVertexAttribArray(loc));\n  return true;\n}\n\nexport function bindTextureUnit(\n    gl: WebGLRenderingContext, texture: WebGLTexture, textureUnit: number) {\n  validateTextureUnit(gl, textureUnit);\n  callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n}\n\nexport function unbindTextureUnit(\n    gl: WebGLRenderingContext, textureUnit: number) {\n  validateTextureUnit(gl, textureUnit);\n  callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\n\nexport function getProgramUniformLocationOrThrow(\n    gl: WebGLRenderingContext, program: WebGLProgram,\n    uniformName: string): WebGLUniformLocation {\n  return throwIfNull<WebGLUniformLocation>(\n      gl, () => gl.getUniformLocation(program, uniformName),\n      'uniform \"' + uniformName + '\" not present in program.');\n}\n\nexport function getProgramUniformLocation(\n    gl: WebGLRenderingContext, program: WebGLProgram,\n    uniformName: string): WebGLUniformLocation {\n  return gl.getUniformLocation(program, uniformName);\n}\n\nexport function bindTextureToProgramUniformSampler(\n    gl: WebGLRenderingContext, program: WebGLProgram, texture: WebGLTexture,\n    uniformSamplerLocation: WebGLUniformLocation, textureUnit: number) {\n  callAndCheck(gl, () => bindTextureUnit(gl, texture, textureUnit));\n  callAndCheck(gl, () => gl.uniform1i(uniformSamplerLocation, textureUnit));\n}\n\nexport function bindCanvasToFramebuffer(gl: WebGLRenderingContext) {\n  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n  callAndCheck(gl, () => gl.viewport(0, 0, gl.canvas.width, gl.canvas.height));\n  callAndCheck(gl, () => gl.scissor(0, 0, gl.canvas.width, gl.canvas.height));\n}\n\nexport function bindColorTextureToFramebuffer(\n    gl: WebGLRenderingContext, texture: WebGLTexture,\n    framebuffer: WebGLFramebuffer) {\n  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n  callAndCheck(\n      gl,\n      () => gl.framebufferTexture2D(\n          gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0));\n}\n\nexport function unbindColorTextureFromFramebuffer(\n    gl: WebGLRenderingContext, framebuffer: WebGLFramebuffer) {\n  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n  callAndCheck(\n      gl,\n      () => gl.framebufferTexture2D(\n          gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0));\n}\n\nexport function validateFramebuffer(gl: WebGLRenderingContext) {\n  const status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);\n  if (status !== gl.FRAMEBUFFER_COMPLETE) {\n    throw new Error(\n        'Error binding framebuffer: ' + getFramebufferErrorMessage(gl, status));\n  }\n}\n\nexport function getFramebufferErrorMessage(\n    gl: WebGLRenderingContext, status: number): string {\n  switch (status) {\n    case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:\n      return 'FRAMEBUFFER_INCOMPLETE_ATTACHMENT';\n    case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:\n      return 'FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT';\n    case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:\n      return 'FRAMEBUFFER_INCOMPLETE_DIMENSIONS';\n    case gl.FRAMEBUFFER_UNSUPPORTED:\n      return 'FRAMEBUFFER_UNSUPPORTED';\n    default:\n      return `unknown error ${status}`;\n  }\n}\n\nfunction throwIfNull<T>(\n    gl: WebGLRenderingContext, returnTOrNull: () => T | null,\n    failureMessage: string): T {\n  const tOrNull: T|null = callAndCheck(gl, () => returnTOrNull());\n  if (tOrNull == null) {\n    throw new Error(failureMessage);\n  }\n  return tOrNull as T;\n}\n\nfunction validateTextureUnit(gl: WebGLRenderingContext, textureUnit: number) {\n  const maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;\n  const glTextureUnit = textureUnit + gl.TEXTURE0;\n  if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {\n    const textureUnitRange = `[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;\n    throw new Error(`textureUnit must be in ${textureUnitRange}.`);\n  }\n}\n\nexport function getTextureShapeFromLogicalShape(\n    logShape: number[], isPacked = false): [number, number] {\n  let maxTexSize = ENV.get('WEBGL_MAX_TEXTURE_SIZE');\n  if (isPacked) {\n    maxTexSize = maxTexSize * 2;\n\n    // This logic ensures we accurately count the number of packed texels needed\n    // to accommodate the tensor. We can only pack values in the same texel if\n    // they are from adjacent pairs of rows/cols within the same batch. So if a\n    // tensor has 3 rows, we pretend it has 4 rows in order to account for the\n    // fact that the texels containing the third row are half empty.\n    logShape = logShape.map(\n        (d, i) => i >= logShape.length - 2 ?\n            util.nearestLargerEven(logShape[i]) :\n            logShape[i]);\n  }\n\n  // If logical shape is 2, we don't squeeze, since we want to match physical.\n  if (logShape.length !== 2) {\n    const squeezeResult = util.squeezeShape(logShape);\n    logShape = squeezeResult.newShape;\n  }\n\n  const size = util.sizeFromShape(logShape);\n  if (logShape.length <= 1 && size <= maxTexSize) {\n    return [1, size];\n  } else if (\n      logShape.length === 2 && logShape[0] <= maxTexSize &&\n      logShape[1] <= maxTexSize) {\n    return logShape as [number, number];\n  } else if (\n      logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize &&\n      logShape[2] <= maxTexSize) {\n    return [logShape[0] * logShape[1], logShape[2]];\n  } else if (\n      logShape.length === 3 && logShape[0] <= maxTexSize &&\n      logShape[1] * logShape[2] <= maxTexSize) {\n    return [logShape[0], logShape[1] * logShape[2]];\n  } else if (\n      logShape.length === 4 &&\n      logShape[0] * logShape[1] * logShape[2] <= maxTexSize &&\n      logShape[3] <= maxTexSize) {\n    return [logShape[0] * logShape[1] * logShape[2], logShape[3]];\n  } else if (\n      logShape.length === 4 && logShape[0] <= maxTexSize &&\n      logShape[1] * logShape[2] * logShape[3] <= maxTexSize) {\n    return [logShape[0], logShape[1] * logShape[2] * logShape[3]];\n  } else {\n    return util.sizeToSquarishShape(size);\n  }\n}\n\nfunction isEven(n: number): boolean {\n  return n % 2 === 0;\n}\n\n/**\n * This determines whether reshaping a packed texture requires rearranging\n * the data within the texture, assuming 2x2 packing.\n */\nexport function isReshapeFree(shape1: number[], shape2: number[]): boolean {\n  shape1 = shape1.slice(-2);\n  shape2 = shape2.slice(-2);\n\n  if (util.arraysEqual(shape1, shape2)) {\n    return true;\n  }\n\n  if (!shape1.length || !shape2.length) {  // One of the shapes is a scalar.\n    return true;\n  }\n\n  if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 ||\n      shape2[1] === 0) {\n    return true;\n  }\n\n  if (shape1.length !== shape2.length) {  // One of the shapes is a vector.\n    const shape1Cols = shape1.slice(-1)[0];\n    const shape2Cols = shape2.slice(-1)[0];\n    if (shape1Cols === shape2Cols) {\n      return true;\n    }\n\n    if (isEven(shape1Cols) && isEven(shape2Cols) &&\n        (shape1[0] === 1 || shape2[0] === 1)) {\n      return true;\n    }\n  } else {\n    if (isEven(shape1[0]) && isEven(shape2[0])) {\n      if (isEven(shape1[1]) && isEven(shape2[1])) {\n        return true;\n      }\n\n      if (shape1[1] === shape2[1]) {\n        return true;\n      }\n    }\n  }\n\n  return false;\n}","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../../environment';\nimport * as util from '../../util';\n\nimport * as tex_util from './tex_util';\nimport * as webgl_util from './webgl_util';\n\nexport interface TextureConfig {\n  internalFormatFloat: number;\n  textureFormatFloat: number;\n  internalFormatHalfFloat: number;\n  internalFormatPackedFloat: number;\n\n  // The format to use during a gl.readPixels call.\n  downloadTextureFormat: number;\n  // How many channels need to be unpacked after a gl.readPixels call.\n  downloadUnpackNumChannels: number;\n\n  defaultNumChannels: number;\n  textureTypeHalfFloat: number;\n}\n\nexport function createVertexShader(gl: WebGLRenderingContext): WebGLShader {\n  const vertexShaderSource = `\n    precision highp float;\n    attribute vec3 clipSpacePos;\n    attribute vec2 uv;\n    varying vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }`;\n  return webgl_util.createVertexShader(gl, vertexShaderSource);\n}\n\nexport function createVertexBuffer(gl: WebGLRenderingContext): WebGLBuffer {\n  // [x y z u v] * [upper-left, lower-left, upper-right, lower-right]\n  const vertexArray = new Float32Array(\n      [-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);\n  return webgl_util.createStaticVertexBuffer(gl, vertexArray);\n}\n\nexport function createIndexBuffer(gl: WebGLRenderingContext): WebGLBuffer {\n  // OpenGL (and WebGL) have \"CCW == front\" winding\n  const triangleVertexIndices = new Uint16Array([0, 1, 2, 2, 1, 3]);\n  return webgl_util.createStaticIndexBuffer(gl, triangleVertexIndices);\n}\n\nexport function getTextureConfig(\n    // tslint:disable-next-line:no-any\n    gl: WebGLRenderingContext, textureHalfFloatExtension?: any): TextureConfig {\n  // tslint:disable-next-line:no-any\n  const glany = gl as any;\n\n  let internalFormatFloat: number;\n  let internalFormatHalfFloat: number;\n  let internalFormatPackedFloat: number;\n  let textureFormatFloat: number;\n\n  let downloadTextureFormat: number;\n  let downloadUnpackNumChannels: number;\n\n  let defaultNumChannels: number;\n  let textureTypeHalfFloat: number;\n\n  if (ENV.get('WEBGL_VERSION') === 2) {\n    internalFormatFloat = glany.R32F;\n    internalFormatHalfFloat = glany.R16F;\n    internalFormatPackedFloat = glany.RGBA32F;\n    textureFormatFloat = glany.RED;\n    downloadUnpackNumChannels = 4;\n    defaultNumChannels = 1;\n    textureTypeHalfFloat = glany.HALF_FLOAT;\n  } else {\n    internalFormatFloat = gl.RGBA;\n    internalFormatHalfFloat = gl.RGBA;\n    internalFormatPackedFloat = glany.RGBA;\n    textureFormatFloat = gl.RGBA;\n    downloadUnpackNumChannels = 4;\n    defaultNumChannels = 4;\n    textureTypeHalfFloat = textureHalfFloatExtension != null ?\n        textureHalfFloatExtension.HALF_FLOAT_OES :\n        null;\n  }\n  downloadTextureFormat = gl.RGBA;\n\n  return {\n    internalFormatFloat,\n    internalFormatHalfFloat,\n    internalFormatPackedFloat,\n    textureFormatFloat,\n    downloadTextureFormat,\n    downloadUnpackNumChannels,\n    defaultNumChannels,\n    textureTypeHalfFloat\n  };\n}\n\nfunction createAndConfigureTexture(\n    gl: WebGLRenderingContext, width: number, height: number,\n    internalFormat: number, textureFormat: number,\n    textureType: number): WebGLTexture {\n  webgl_util.validateTextureSize(width, height);\n  const texture = webgl_util.createTexture(gl);\n\n  const tex2d = gl.TEXTURE_2D;\n  webgl_util.callAndCheck(gl, () => gl.bindTexture(tex2d, texture));\n  webgl_util.callAndCheck(\n      gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE));\n  webgl_util.callAndCheck(\n      gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE));\n  webgl_util.callAndCheck(\n      gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST));\n  webgl_util.callAndCheck(\n      gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST));\n  webgl_util.callAndCheck(\n      gl,\n      () => gl.texImage2D(\n          tex2d, 0, internalFormat, width, height, 0, textureFormat,\n          textureType, null));\n  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n  return texture;\n}\n\nexport function createFloat32MatrixTexture(\n    gl: WebGLRenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig): WebGLTexture {\n  const [width, height] =\n      tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(\n      gl, width, height, textureConfig.internalFormatFloat,\n      textureConfig.textureFormatFloat, gl.FLOAT);\n}\n\nexport function createFloat16MatrixTexture(\n    gl: WebGLRenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig): WebGLTexture {\n  const [width, height] =\n      tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(\n      gl, width, height, textureConfig.internalFormatFloat,\n      textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);\n}\n\nexport function createUnsignedBytesMatrixTexture(\n    gl: WebGLRenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig): WebGLTexture {\n  const [width, height] =\n      tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(\n      gl, width, height, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE);\n}\n\nexport function createPackedMatrixTexture(\n    gl: WebGLRenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig): WebGLTexture {\n  const [width, height] =\n      tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(\n      gl, width, height, textureConfig.internalFormatPackedFloat, gl.RGBA,\n      gl.FLOAT);\n}\n\nexport function createFloat16PackedMatrixTexture(\n    gl: WebGLRenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig): WebGLTexture {\n  const [width, height] =\n      tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(\n      gl, width, height, textureConfig.internalFormatHalfFloat, gl.RGBA,\n      textureConfig.textureTypeHalfFloat);\n}\n\nexport function bindVertexProgramAttributeStreams(\n    gl: WebGLRenderingContext, program: WebGLProgram,\n    vertexBuffer: WebGLBuffer): boolean {\n  const posOffset = 0;               // x is the first buffer element\n  const uvOffset = 3 * 4;            // uv comes after [x y z]\n  const stride = (3 * 4) + (2 * 4);  // xyz + uv, each entry is 4-byte float.\n  webgl_util.callAndCheck(\n      gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer));\n  const success = webgl_util.bindVertexBufferToProgramAttribute(\n      gl, program, 'clipSpacePos', vertexBuffer, 3, stride, posOffset);\n  return success &&\n      webgl_util.bindVertexBufferToProgramAttribute(\n          gl, program, 'uv', vertexBuffer, 2, stride, uvOffset);\n}\n\nexport function uploadPixelDataToTexture(\n    gl: WebGLRenderingContext, texture: WebGLTexture,\n    pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement) {\n  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n  webgl_util.callAndCheck(\n      gl,\n      () => gl.texImage2D(\n          gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels));\n  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\n\nfunction uploadDataToTexture(\n    gl: WebGLRenderingContext, texture: WebGLTexture, width: number,\n    height: number, data: Float32Array, textureFormat: number) {\n  webgl_util.validateTextureSize(width, height);\n  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n  webgl_util.callAndCheck(\n      gl,\n      () => gl.texSubImage2D(\n          gl.TEXTURE_2D, 0, 0, 0, width, height, textureFormat, gl.FLOAT,\n          data));\n\n  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\n\nexport function uploadMatrixToTexture(\n    gl: WebGLRenderingContext, texture: WebGLTexture, rows: number,\n    columns: number, matrix: Float32Array, numChannels: number,\n    textureConfig: TextureConfig) {\n  const [w, h] =\n      tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n\n  let unpackedArray: Float32Array;\n  if (textureConfig.defaultNumChannels === 1) {\n    // No need to allocate a temporary array.\n    unpackedArray = matrix;\n  } else {\n    unpackedArray =\n        new Float32Array(tex_util.getUnpackedArraySizeFromMatrixSize(\n            matrix.length, numChannels));\n    tex_util.encodeMatrixToUnpackedArray(matrix, unpackedArray, numChannels);\n  }\n\n  uploadDataToTexture(\n      gl, texture, w, h, unpackedArray, textureConfig.textureFormatFloat);\n}\n\n/**\n * This method writes a tensor to a packed texture in a way that respects how we\n * represent data using each texel's r,g,b,a channels. Specifically, we lay\n * out the four channels in two rows each containing two channels, so a single\n * texel can represent up to four values from the tensor. That means a texture\n * that has a channel width of 11 and channel height of 4 will have a texel\n * width of 6 and texel height of 2.\n *\n * rows, columns: Logical number of rows and columns in the tensor to be\n * uploaded.\n *\n * physicalRows, physicalCols: Channel dimensions of the texture that will hold\n * the tensor.\n *\n * width, height (internal parameters): Texel dimensions of the texture.\n */\nexport function uploadMatrixToPackedTexture(\n    gl: WebGLRenderingContext, texture: WebGLTexture, batch: number,\n    rows: number, columns: number, physicalRows: number, physicalCols: number,\n    matrix: Float32Array, textureConfig: TextureConfig) {\n  const [w, h] = tex_util.getPackedMatrixTextureShapeWidthHeight(\n      physicalRows, physicalCols);\n  const packedRGBA = new Float32Array(\n      batch * tex_util.getPackedRGBAArraySizeFromMatrixShape(rows, columns));\n  tex_util.encodeMatrixToPackedRGBA(matrix, batch, rows, columns, packedRGBA);\n  uploadDataToTexture(gl, texture, w, h, packedRGBA, gl.RGBA);\n}\n\nexport function maybeCreateBufferFromOutputTexture(\n    gl: WebGLRenderingContext, texture: WebGLTexture, rows: number,\n    columns: number, textureConfig: TextureConfig): WebGLBuffer|WebGLTexture {\n  let bufferOrTexture: WebGLBuffer|WebGLTexture = texture;\n\n  if (ENV.get('WEBGL_VERSION') === 2) {\n    const gl2 = gl as WebGL2RenderingContext;\n\n    // Create and bind the buffer.\n    const buffer = gl2.createBuffer();\n    webgl_util.callAndCheck(\n        gl, () => gl.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer));\n\n    // Initialize the buffer to the size of the texture in bytes.\n    const bytesPerFloat = 4;\n    const bufferSizeBytes = bytesPerFloat *\n        tex_util.getUnpackedArraySizeFromMatrixSize(\n            rows * columns, textureConfig.downloadUnpackNumChannels);\n\n    webgl_util.callAndCheck(\n        gl,\n        () => gl.bufferData(\n            gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl.STATIC_DRAW));\n\n    // Enqueue a command on the GPU command queue to copy of texture into the\n    // buffer.\n    webgl_util.callAndCheck(\n        gl, () => gl2.readPixels(0, 0, columns, rows, gl.RGBA, gl.FLOAT, 0));\n\n    webgl_util.callAndCheck(\n        gl, () => gl.bindBuffer(gl2.PIXEL_PACK_BUFFER, null));\n\n    bufferOrTexture = buffer;\n  }\n\n  return bufferOrTexture;\n}\n\nexport function downloadFloat32MatrixFromBuffer(\n    gl: WebGLRenderingContext, buffer: WebGLBuffer, rows: number,\n    columns: number, textureConfig: TextureConfig): Float32Array {\n  const gl2 = gl as WebGL2RenderingContext;\n\n  const downloadTarget =\n      new Float32Array(tex_util.getUnpackedArraySizeFromMatrixSize(\n          rows * columns, textureConfig.downloadUnpackNumChannels));\n\n  gl2.bindBuffer(gl.ARRAY_BUFFER, buffer);\n  gl2.getBufferSubData(gl.ARRAY_BUFFER, 0, downloadTarget);\n  gl2.bindBuffer(gl.ARRAY_BUFFER, null);\n\n  const matrix = new Float32Array(rows * columns);\n  tex_util.decodeMatrixFromUnpackedArray(\n      downloadTarget as Float32Array, matrix,\n      textureConfig.downloadUnpackNumChannels);\n\n  return matrix;\n}\n\nexport function downloadFloat32MatrixFromOutputTexture(\n    gl: WebGLRenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig): Float32Array {\n  const [w, h] =\n      tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n\n  const downloadTarget =\n      new Float32Array(tex_util.getUnpackedArraySizeFromMatrixSize(\n          rows * columns, textureConfig.downloadUnpackNumChannels));\n\n  webgl_util.callAndCheck(\n      gl,\n      () => gl.readPixels(\n          0, 0, w, h, textureConfig.downloadTextureFormat, gl.FLOAT,\n          downloadTarget));\n\n  const matrix = new Float32Array(rows * columns);\n  tex_util.decodeMatrixFromUnpackedArray(\n      downloadTarget as Float32Array, matrix,\n      textureConfig.downloadUnpackNumChannels);\n  return matrix;\n}\n\nexport function downloadByteEncodedFloatMatrixFromOutputTexture(\n    gl: WebGLRenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig) {\n  const [w, h] =\n      tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n\n  const numChannels = 4;\n  const downloadTarget = new Uint8Array(\n      tex_util.getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));\n\n  webgl_util.callAndCheck(\n      gl,\n      () => gl.readPixels(\n          0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE,\n          downloadTarget));\n\n  // By wrapping the buffer in a Float32Array, we use native browser IEEE 754\n  // decoding of the 4 bytes that back each 32 bit float.\n  return new Float32Array(downloadTarget.buffer);\n}\n\nexport function downloadPackedMatrixFromBuffer(\n    gl: WebGLRenderingContext, buffer: WebGLBuffer, batch: number, rows: number,\n    cols: number, physicalRows: number, physicalCols: number,\n    textureConfig: TextureConfig): Float32Array {\n  const gl2 = gl as WebGL2RenderingContext;\n\n  const downloadTarget =\n      new Float32Array(tex_util.getPackedRGBAArraySizeFromMatrixShape(\n          physicalRows, physicalCols));\n\n  gl2.bindBuffer(gl.ARRAY_BUFFER, buffer);\n  gl2.getBufferSubData(gl.ARRAY_BUFFER, 0, downloadTarget);\n  gl2.bindBuffer(gl.ARRAY_BUFFER, null);\n\n  const matrix = new Float32Array(util.sizeFromShape([batch, rows, cols]));\n  tex_util.decodeMatrixFromPackedRGBA(\n      downloadTarget, batch, rows, cols, matrix);\n  return matrix;\n}\n\nexport function downloadMatrixFromPackedOutputTexture(\n    gl: WebGLRenderingContext, batch: number, rows: number, cols: number,\n    physicalRows: number, physicalCols: number,\n    textureConfig: TextureConfig): Float32Array {\n  const [w, h] = tex_util.getPackedMatrixTextureShapeWidthHeight(\n      physicalRows, physicalCols);\n\n  const packedRGBA =\n      new Float32Array(tex_util.getPackedRGBAArraySizeFromMatrixShape(\n          physicalRows, physicalCols));\n  webgl_util.callAndCheck(\n      gl, () => gl.readPixels(0, 0, w, h, gl.RGBA, gl.FLOAT, packedRGBA));\n  const matrix = new Float32Array(util.sizeFromShape([batch, rows, cols]));\n  return tex_util.decodeMatrixFromPackedRGBA(\n      packedRGBA, batch, rows, cols, matrix);\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getWebGLContext} from '../../canvas_util';\nimport {ENV} from '../../environment';\nimport * as util from '../../util';\nimport * as gpgpu_util from './gpgpu_util';\nimport {TextureConfig} from './gpgpu_util';\nimport * as tex_util from './tex_util';\nimport {WebGL1DisjointQueryTimerExtension, WebGL2DisjointQueryTimerExtension} from './webgl_types';\nimport * as webgl_util from './webgl_util';\n\nexport interface FenceContext {\n  query: WebGLQuery|WebGLSync;\n  isFencePassed(): boolean;\n}\n\nexport class GPGPUContext {\n  gl: WebGLRenderingContext;\n  textureFloatExtension: {};\n  textureHalfFloatExtension: {};\n  colorBufferFloatExtension: {};\n  colorBufferHalfFloatExtension: {};\n  getBufferSubDataAsyncExtension: {};\n  disjointQueryTimerExtension: WebGL2DisjointQueryTimerExtension|\n      WebGL1DisjointQueryTimerExtension;\n  vertexBuffer: WebGLBuffer;\n  indexBuffer: WebGLBuffer;\n  framebuffer: WebGLFramebuffer;\n  outputTexture: WebGLTexture|null = null;\n  program: WebGLProgram|null = null;\n  private disposed = false;\n  private autoDebugValidate = false;\n  private disjoint: boolean;\n  private textureConfig: TextureConfig;\n\n  constructor(gl?: WebGLRenderingContext) {\n    if (gl != null) {\n      this.gl = gl;\n    } else {\n      this.gl = getWebGLContext(ENV.get('WEBGL_VERSION'));\n    }\n    // WebGL 2.0 enables texture floats without an extension.\n    if (ENV.get('WEBGL_VERSION') === 1) {\n      this.textureFloatExtension =\n          webgl_util.getExtensionOrThrow(this.gl, 'OES_texture_float');\n      this.colorBufferFloatExtension =\n          this.gl.getExtension('WEBGL_color_buffer_float');\n\n      if (!ENV.get('WEBGL_RENDER_FLOAT32_ENABLED')) {\n        this.textureHalfFloatExtension =\n            webgl_util.getExtensionOrThrow(this.gl, 'OES_texture_half_float');\n        this.colorBufferHalfFloatExtension =\n            this.gl.getExtension('EXT_color_buffer_half_float');\n      }\n    } else {\n      this.colorBufferFloatExtension =\n          webgl_util.getExtensionOrThrow(this.gl, 'EXT_color_buffer_float');\n    }\n\n    this.vertexBuffer = gpgpu_util.createVertexBuffer(this.gl);\n    this.indexBuffer = gpgpu_util.createIndexBuffer(this.gl);\n    this.framebuffer = webgl_util.createFramebuffer(this.gl);\n\n    this.textureConfig =\n        gpgpu_util.getTextureConfig(this.gl, this.textureHalfFloatExtension);\n  }\n\n  public dispose() {\n    if (this.disposed) {\n      return;\n    }\n    if (this.program != null) {\n      console.warn(\n          'Disposing a GPGPUContext that still has a bound WebGLProgram.' +\n          ' This is probably a resource leak, delete the program with ' +\n          'GPGPUContext.deleteProgram before disposing.');\n    }\n    if (this.outputTexture != null) {\n      console.warn(\n          'Disposing a GPGPUContext that still has a bound output matrix ' +\n          'texture.  This is probably a resource leak, delete the output ' +\n          'matrix texture with GPGPUContext.deleteMatrixTexture before ' +\n          'disposing.');\n    }\n    const gl = this.gl;\n    webgl_util.callAndCheck(gl, () => gl.finish());\n    webgl_util.callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n    webgl_util.callAndCheck(gl, () => gl.deleteFramebuffer(this.framebuffer));\n    webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, null));\n    webgl_util.callAndCheck(\n        gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null));\n    webgl_util.callAndCheck(gl, () => gl.deleteBuffer(this.indexBuffer));\n    this.disposed = true;\n  }\n\n  public enableAutomaticDebugValidation(enabled: boolean) {\n    this.autoDebugValidate = enabled;\n    webgl_util.enableDebugWebGLErrorChecking(enabled);\n  }\n\n  public createFloat32MatrixTexture(rows: number, columns: number):\n      WebGLTexture {\n    this.throwIfDisposed();\n    return gpgpu_util.createFloat32MatrixTexture(\n        this.gl, rows, columns, this.textureConfig);\n  }\n\n  public createFloat16MatrixTexture(rows: number, columns: number):\n      WebGLTexture {\n    this.throwIfDisposed();\n    return gpgpu_util.createFloat16MatrixTexture(\n        this.gl, rows, columns, this.textureConfig);\n  }\n\n  public createUnsignedBytesMatrixTexture(rows: number, columns: number):\n      WebGLTexture {\n    this.throwIfDisposed();\n    return gpgpu_util.createUnsignedBytesMatrixTexture(\n        this.gl, rows, columns, this.textureConfig);\n  }\n\n  public uploadPixelDataToTexture(\n      texture: WebGLTexture,\n      pixels: ImageData|HTMLImageElement|HTMLCanvasElement) {\n    this.throwIfDisposed();\n    gpgpu_util.uploadPixelDataToTexture(this.gl, texture, pixels);\n  }\n\n  public createFloat16PackedMatrixTexture(rows: number, columns: number):\n      WebGLTexture {\n    this.throwIfDisposed();\n    return gpgpu_util.createFloat16PackedMatrixTexture(\n        this.gl, rows, columns, this.textureConfig);\n  }\n\n  public createPackedMatrixTexture(rows: number, columns: number):\n      WebGLTexture {\n    this.throwIfDisposed();\n    return gpgpu_util.createPackedMatrixTexture(\n        this.gl, rows, columns, this.textureConfig);\n  }\n\n  public deleteMatrixTexture(texture: WebGLTexture) {\n    this.throwIfDisposed();\n    if (this.outputTexture === texture) {\n      webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n      this.outputTexture = null;\n    }\n    webgl_util.callAndCheck(this.gl, () => this.gl.deleteTexture(texture));\n  }\n\n  public uploadMatrixToTexture(\n      texture: WebGLTexture, rows: number, columns: number,\n      matrix: Float32Array) {\n    this.throwIfDisposed();\n    const numChannels = webgl_util.getNumChannels();\n    return gpgpu_util.uploadMatrixToTexture(\n        this.gl, texture, rows, columns, matrix, numChannels,\n        this.textureConfig);\n  }\n\n  public uploadMatrixToPackedTexture(\n      texture: WebGLTexture, batch: number, rows: number, columns: number,\n      physicalRows: number, physicalCols: number, matrix: Float32Array) {\n    this.throwIfDisposed();\n    return gpgpu_util.uploadMatrixToPackedTexture(\n        this.gl, texture, batch, rows, columns, physicalRows, physicalCols,\n        matrix, this.textureConfig);\n  }\n\n  public downloadFloat32MatrixFromOutputTexture(\n      texture: WebGLTexture, rows: number, columns: number): Float32Array {\n    return this.downloadMatrixDriver(\n        texture,\n        () => gpgpu_util.downloadFloat32MatrixFromOutputTexture(\n            this.gl, rows, columns, this.textureConfig));\n  }\n\n  public downloadByteEncodedFloatMatrixFromOutputTexture(\n      texture: WebGLTexture, rows: number, columns: number): Float32Array {\n    return this.downloadMatrixDriver(\n        texture,\n        () => gpgpu_util.downloadByteEncodedFloatMatrixFromOutputTexture(\n            this.gl, rows, columns, this.textureConfig));\n  }\n\n  public downloadPackedMatrixFromBuffer(\n      buffer: WebGLBuffer, batch: number, rows: number, columns: number,\n      physicalRows: number, physicalCols: number): Float32Array {\n    return gpgpu_util.downloadPackedMatrixFromBuffer(\n        this.gl, buffer, batch, rows, columns, physicalRows, physicalCols,\n        this.textureConfig);\n  }\n\n  public downloadFloat32MatrixFromBuffer(\n      buffer: WebGLBuffer, rows: number, columns: number): Float32Array {\n    return gpgpu_util.downloadFloat32MatrixFromBuffer(\n        this.gl, buffer, rows, columns, this.textureConfig);\n  }\n\n  public maybeCreateBufferFromTexture(\n      texture: WebGLTexture, rows: number, columns: number): WebGLBuffer\n      |WebGLTexture {\n    this.bindTextureToFrameBuffer(texture);\n    const result = gpgpu_util.maybeCreateBufferFromOutputTexture(\n        this.gl, texture, rows, columns, this.textureConfig);\n    this.unbindTextureToFrameBuffer();\n    return result;\n  }\n\n  public createAndWaitForFence(): Promise<void> {\n    const fenceContext = this.createFence(this.gl);\n    return this.pollFence(fenceContext);\n  }\n\n  private createFence(gl: WebGLRenderingContext): FenceContext {\n    let query: WebGLQuery|WebGLSync;\n    let isFencePassed: () => boolean;\n\n    if (ENV.get('WEBGL_FENCE_API_ENABLED')) {\n      const gl2 = gl as WebGL2RenderingContext;\n\n      const sync = gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE, 0);\n      gl.flush();\n\n      isFencePassed = () => {\n        const status = gl2.clientWaitSync(sync, 0, 0);\n        return status === gl2.ALREADY_SIGNALED ||\n            status === gl2.CONDITION_SATISFIED;\n      };\n\n      query = sync;\n    } else if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {\n      query = this.beginQuery();\n      this.endQuery();\n      isFencePassed = () => this.isQueryAvailable(\n          query, ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n    } else {\n      // If we have no way to fence, return true immediately. This will fire in\n      // WebGL 1.0 when there is no disjoint query timer. In this case, because\n      // the fence passes immediately, we'll immediately ask for a download of\n      // the texture, which will cause the UI thread to hang.\n      isFencePassed = () => true;\n    }\n\n    return {query, isFencePassed};\n  }\n\n  public downloadMatrixFromPackedTexture(\n      texture: WebGLTexture, batch: number, rows: number, columns: number,\n      physicalRows: number, physicalCols: number): Float32Array {\n    return this.downloadMatrixDriver(\n        texture,\n        () => gpgpu_util.downloadMatrixFromPackedOutputTexture(\n            this.gl, batch, rows, columns, physicalRows, physicalCols,\n            this.textureConfig));\n  }\n\n  private vertexAttrsAreBound = false;\n\n  public createProgram(fragmentShaderSource: string): WebGLProgram {\n    this.throwIfDisposed();\n    const gl = this.gl;\n    const fragmentShader: WebGLShader =\n        webgl_util.createFragmentShader(gl, fragmentShaderSource);\n    const vertexShader: WebGLShader = gpgpu_util.createVertexShader(gl);\n    const program: WebGLProgram = webgl_util.createProgram(gl);\n    webgl_util.callAndCheck(gl, () => gl.attachShader(program, vertexShader));\n    webgl_util.callAndCheck(gl, () => gl.attachShader(program, fragmentShader));\n    webgl_util.linkProgram(gl, program);\n    if (this.autoDebugValidate) {\n      webgl_util.validateProgram(gl, program);\n    }\n    if (!this.vertexAttrsAreBound) {\n      this.setProgram(program);\n      this.vertexAttrsAreBound = gpgpu_util.bindVertexProgramAttributeStreams(\n          gl, this.program, this.vertexBuffer);\n    }\n    return program;\n  }\n\n  public deleteProgram(program: WebGLProgram) {\n    this.throwIfDisposed();\n    if (program === this.program) {\n      this.program = null;\n    }\n    if (program != null) {\n      webgl_util.callAndCheck(this.gl, () => this.gl.deleteProgram(program));\n    }\n  }\n\n  public setProgram(program: WebGLProgram|null) {\n    this.throwIfDisposed();\n    this.program = program;\n    if ((this.program != null) && this.autoDebugValidate) {\n      webgl_util.validateProgram(this.gl, this.program);\n    }\n    webgl_util.callAndCheck(this.gl, () => this.gl.useProgram(program));\n  }\n\n  public getUniformLocation(\n      program: WebGLProgram, uniformName: string,\n      shouldThrow = true): WebGLUniformLocation {\n    this.throwIfDisposed();\n    if (shouldThrow) {\n      return webgl_util.getProgramUniformLocationOrThrow(\n          this.gl, program, uniformName);\n    } else {\n      return webgl_util.getProgramUniformLocation(\n          this.gl, program, uniformName);\n    }\n  }\n\n  public getAttributeLocation(program: WebGLProgram, attribute: string):\n      number {\n    this.throwIfDisposed();\n    return webgl_util.callAndCheck(\n        this.gl, () => this.gl.getAttribLocation(program, attribute));\n  }\n\n  public getUniformLocationNoThrow(program: WebGLProgram, uniformName: string):\n      WebGLUniformLocation {\n    this.throwIfDisposed();\n    return this.gl.getUniformLocation(program, uniformName);\n  }\n\n  public setInputMatrixTexture(\n      inputMatrixTexture: WebGLTexture, uniformLocation: WebGLUniformLocation,\n      textureUnit: number) {\n    this.throwIfDisposed();\n    this.throwIfNoProgram();\n    webgl_util.bindTextureToProgramUniformSampler(\n        this.gl, this.program, inputMatrixTexture, uniformLocation,\n        textureUnit);\n  }\n\n  public setOutputMatrixTexture(\n      outputMatrixTexture: WebGLTexture, rows: number, columns: number) {\n    this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);\n  }\n\n  public setOutputPackedMatrixTexture(\n      outputPackedMatrixTexture: WebGLTexture, rows: number, columns: number) {\n    this.throwIfDisposed();\n    const [width, height] =\n        tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);\n  }\n\n  public setOutputMatrixWriteRegion(\n      startRow: number, numRows: number, startColumn: number,\n      numColumns: number) {\n    this.setOutputMatrixWriteRegionDriver(\n        startColumn, startRow, numColumns, numRows);\n  }\n\n  public setOutputPackedMatrixWriteRegion(\n      startRow: number, numRows: number, startColumn: number,\n      numColumns: number) {\n    throw new Error('setOutputPackedMatrixWriteRegion not implemented.');\n  }\n\n  public debugValidate() {\n    if (this.program != null) {\n      webgl_util.validateProgram(this.gl, this.program);\n    }\n    webgl_util.validateFramebuffer(this.gl);\n  }\n\n  public executeProgram() {\n    this.throwIfDisposed();\n    this.throwIfNoProgram();\n    const gl = this.gl;\n    if (this.autoDebugValidate) {\n      this.debugValidate();\n    }\n    webgl_util.callAndCheck(\n        gl, () => gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0));\n  }\n\n  public blockUntilAllProgramsCompleted() {\n    this.throwIfDisposed();\n    webgl_util.callAndCheck(this.gl, () => this.gl.finish());\n  }\n\n  private getQueryTimerExtension(): WebGL1DisjointQueryTimerExtension\n      |WebGL2DisjointQueryTimerExtension {\n    if (this.disjointQueryTimerExtension == null) {\n      this.disjointQueryTimerExtension =\n          webgl_util.getExtensionOrThrow(\n              this.gl,\n              ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2 ?\n                  'EXT_disjoint_timer_query_webgl2' :\n                  'EXT_disjoint_timer_query') as\n              WebGL1DisjointQueryTimerExtension |\n          WebGL2DisjointQueryTimerExtension;\n    }\n    return this.disjointQueryTimerExtension;\n  }\n\n  private getQueryTimerExtensionWebGL2(): WebGL2DisjointQueryTimerExtension {\n    return this.getQueryTimerExtension();\n  }\n\n  private getQueryTimerExtensionWebGL1(): WebGL1DisjointQueryTimerExtension {\n    return this.getQueryTimerExtension() as WebGL1DisjointQueryTimerExtension;\n  }\n\n  beginQuery(): WebGLQuery {\n    if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n      const gl2 = this.gl as WebGL2RenderingContext;\n      const ext = this.getQueryTimerExtensionWebGL2();\n\n      const query = gl2.createQuery();\n      gl2.beginQuery(ext.TIME_ELAPSED_EXT, query);\n      return query;\n    }\n    const ext = this.getQueryTimerExtensionWebGL1();\n    const query = ext.createQueryEXT() as WebGLQuery;\n    ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);\n    return query;\n  }\n\n  endQuery() {\n    if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n      const gl2 = this.gl as WebGL2RenderingContext;\n      const ext = this.getQueryTimerExtensionWebGL2();\n      gl2.endQuery(ext.TIME_ELAPSED_EXT);\n      return;\n    }\n    const ext = this.getQueryTimerExtensionWebGL1();\n    ext.endQueryEXT(ext.TIME_ELAPSED_EXT);\n  }\n\n  public async waitForQueryAndGetTime(query: WebGLQuery): Promise<number> {\n    await util.repeatedTry(\n        () => this.disposed ||  // while testing contexts are created / disposed\n                                // in rapid succession, so without this check we\n                                // may poll for the query timer indefinitely\n            this.isQueryAvailable(\n                query,\n                ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION')));\n    return this.getQueryTime(\n        query, ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n  }\n\n  private getQueryTime(query: WebGLQuery, queryTimerVersion: number): number {\n    if (queryTimerVersion === 0) {\n      return null;\n    }\n\n    if (queryTimerVersion === 2) {\n      const gl2 = this.gl as WebGL2RenderingContext;\n\n      const timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);\n      // Return milliseconds.\n      return timeElapsedNanos / 1000000;\n    } else {\n      const ext = this.getQueryTimerExtensionWebGL1();\n\n      const timeElapsedNanos =\n          ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);\n      // Return milliseconds.\n      return timeElapsedNanos / 1000000;\n    }\n  }\n\n  private isQueryAvailable(query: WebGLQuery, queryTimerVersion: number):\n      boolean {\n    if (queryTimerVersion === 0) {\n      return true;\n    }\n\n    if (queryTimerVersion === 2) {\n      const gl2 = this.gl as WebGL2RenderingContext;\n      const ext = this.getQueryTimerExtensionWebGL2();\n\n      const available =\n          gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);\n      if (this.disjoint == null) {\n        this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n      }\n\n      return available && !this.disjoint;\n    } else {\n      const ext = this.getQueryTimerExtensionWebGL1();\n\n      const available =\n          ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);\n      if (this.disjoint == null) {\n        this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n      }\n\n      return available && !this.disjoint;\n    }\n  }\n\n  pollFence(fenceContext: FenceContext) {\n    return new Promise<void>(resolve => {\n      this.addItemToPoll(() => fenceContext.isFencePassed(), () => resolve());\n    });\n  }\n\n  private itemsToPoll: PollItem[] = [];\n\n  pollItems(): void {\n    // Find the last query that has finished using binary search.\n    // All other queries before it are also done.\n    const index = binSearchLastTrue(this.itemsToPoll.map(x => x.isDoneFn));\n    for (let i = 0; i <= index; ++i) {\n      const {resolveFn} = this.itemsToPoll[i];\n      resolveFn();\n    }\n    this.itemsToPoll = this.itemsToPoll.slice(index + 1);\n  }\n\n  private addItemToPoll(isDoneFn: () => boolean, resolveFn: () => void) {\n    this.itemsToPoll.push({isDoneFn, resolveFn});\n    if (this.itemsToPoll.length > 1) {\n      // We already have a running loop that polls.\n      return;\n    }\n    // Start a new loop that polls.\n    util.repeatedTry(() => {\n      this.pollItems();\n      // End the loop if no more items to poll.\n      return this.itemsToPoll.length === 0;\n    });\n  }\n\n  private bindTextureToFrameBuffer(texture: WebGLTexture) {\n    this.throwIfDisposed();\n    webgl_util.bindColorTextureToFramebuffer(\n        this.gl, texture, this.framebuffer);\n    if (this.autoDebugValidate) {\n      webgl_util.validateFramebuffer(this.gl);\n    }\n  }\n\n  private unbindTextureToFrameBuffer() {\n    if (this.outputTexture != null) {\n      webgl_util.bindColorTextureToFramebuffer(\n          this.gl, this.outputTexture, this.framebuffer);\n      if (this.autoDebugValidate) {\n        webgl_util.validateFramebuffer(this.gl);\n      }\n    } else {\n      webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n    }\n  }\n\n  private downloadMatrixDriver(\n      texture: WebGLTexture,\n      downloadAndDecode: () => Float32Array): Float32Array {\n    this.bindTextureToFrameBuffer(texture);\n    const result = downloadAndDecode();\n    this.unbindTextureToFrameBuffer();\n\n    return result;\n  }\n\n  private setOutputMatrixTextureDriver(\n      outputMatrixTextureMaybePacked: WebGLTexture, width: number,\n      height: number) {\n    this.throwIfDisposed();\n    const gl = this.gl;\n    webgl_util.bindColorTextureToFramebuffer(\n        gl, outputMatrixTextureMaybePacked, this.framebuffer);\n    if (this.autoDebugValidate) {\n      webgl_util.validateFramebuffer(gl);\n    }\n    this.outputTexture = outputMatrixTextureMaybePacked;\n    webgl_util.callAndCheck(gl, () => gl.viewport(0, 0, width, height));\n    webgl_util.callAndCheck(gl, () => gl.scissor(0, 0, width, height));\n  }\n\n  private setOutputMatrixWriteRegionDriver(\n      x: number, y: number, width: number, height: number) {\n    this.throwIfDisposed();\n    webgl_util.callAndCheck(\n        this.gl, () => this.gl.scissor(x, y, width, height));\n  }\n\n  private throwIfDisposed() {\n    if (this.disposed) {\n      throw new Error('Attempted to use disposed GPGPUContext.');\n    }\n  }\n\n  private throwIfNoProgram() {\n    if (this.program == null) {\n      throw new Error('No GPU program is currently set.');\n    }\n  }\n}\n\ntype PollItem = {\n  isDoneFn: () => boolean,\n  resolveFn: () => void\n};\n\n/**\n * Finds the index of the last true element using binary search where\n * evaluation of an entry is expensive.\n */\nexport function binSearchLastTrue(arr: Array<() => boolean>): number {\n  let start = 0;\n  let end = arr.length - 1;\n  let best = -1;\n  while (start <= end) {\n    const mid = (start + end) >> 1;\n    const isDone = arr[mid]();\n    if (isDone) {\n      best = mid;\n      start = mid + 1;\n    } else {\n      end = mid - 1;\n    }\n  }\n  return best;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../../tensor';\nimport {TypedArray} from '../../types';\nimport * as util from '../../util';\n\nimport {GPGPUContext} from './gpgpu_context';\nimport * as shader_compiler from './shader_compiler';\nimport {InputInfo, ShapeInfo} from './shader_compiler';\nimport {TextureData} from './tex_util';\n\nexport interface GPGPUProgram {\n  variableNames: string[];\n  outputShape: number[];\n  userCode: string;\n  usesPackedTextures?: boolean;\n  supportsBroadcasting?: boolean;\n  isPackShader?: boolean;  // This property is used to single out the packing\n                           // shader so its output does not get eagerly unpacked\n                           // by backend_webgl.compileAndRun.\n}\n\nexport interface GPGPUBinary {\n  webGLProgram: WebGLProgram;\n  program: GPGPUProgram;\n  uniformLocations: {[name: string]: WebGLUniformLocation};\n  gpgpu: GPGPUContext;\n  source: string;\n  inShapeInfos: ShapeInfo[];\n  outShapeInfo: ShapeInfo;\n}\n\nexport interface TensorData {\n  shape: number[];\n  texData: TextureData;\n  isUniform: boolean;\n  uniformValues?: TypedArray;\n}\n\nexport function compileProgram<T extends Tensor, K extends Tensor>(\n    gpgpu: GPGPUContext, program: GPGPUProgram, inputs: TensorData[],\n    output: TensorData): GPGPUBinary {\n  const userCode = program.userCode;\n  const inputInfos: InputInfo[] = inputs.map((input, i) => {\n    const shapeInfo = {\n      logicalShape: input.shape,\n      texShape: input.isUniform ? null : input.texData.texShape,\n      isUniform: input.isUniform,\n      isPacked: input.isUniform ? false : input.texData.isPacked\n    };\n    return {name: program.variableNames[i], shapeInfo};\n  });\n  const inShapeInfos = inputInfos.map(x => x.shapeInfo);\n  const outShapeInfo = {\n    logicalShape: output.shape,\n    texShape: output.texData.texShape,\n    isUniform: false,\n    isPacked: output.texData.isPacked\n  };\n  const source = shader_compiler.makeShader(\n      inputInfos, outShapeInfo, userCode, program.supportsBroadcasting === true,\n      program.usesPackedTextures);\n\n  const webGLProgram = gpgpu.createProgram(source);\n\n  const uniformLocations: {[name: string]: WebGLUniformLocation} = {};\n  for (let i = 0; i < program.variableNames.length; i++) {\n    const uniformName = program.variableNames[i];\n    const shouldThrow = false;\n    uniformLocations[uniformName] =\n        gpgpu.getUniformLocation(webGLProgram, uniformName, shouldThrow);\n  }\n\n  return {\n    program,\n    source,\n    webGLProgram,\n    uniformLocations,\n    gpgpu,\n    inShapeInfos,\n    outShapeInfo\n  };\n}\n\nfunction validateBinaryAndProgram(\n    shapeInfos: ShapeInfo[], inputs: TensorData[]) {\n  if (shapeInfos.length !== inputs.length) {\n    throw Error(\n        `Binary was compiled with ${shapeInfos.length} inputs, but ` +\n        `was executed with ${inputs.length} inputs`);\n  }\n\n  shapeInfos.forEach((s, i) => {\n    const shapeA = s.logicalShape;\n    const input = inputs[i];\n    const shapeB = input.shape;\n\n    if (!util.arraysEqual(shapeA, shapeB)) {\n      throw Error(\n          `Binary was compiled with different shapes than ` +\n          `the current args. Shapes ${shapeA} and ${shapeB} must match`);\n    }\n    // The input is uploaded as uniform.\n    if (s.isUniform && input.isUniform) {\n      return;\n    }\n\n    const texShapeA = s.texShape;\n    const texShapeB = input.isUniform ? null : input.texData.texShape;\n    if (!util.arraysEqual(texShapeA, texShapeB)) {\n      throw Error(\n          `Binary was compiled with different texture shapes than the` +\n          ` current args. Shape ${texShapeA} and ${texShapeB} must match`);\n    }\n  });\n}\n\nexport function runProgram<T extends Tensor, K extends Tensor>(\n    binary: GPGPUBinary, inputs: TensorData[], output: TensorData,\n    customSetup?: (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) =>\n        void): void {\n  validateBinaryAndProgram(binary.inShapeInfos, inputs);\n  validateBinaryAndProgram([binary.outShapeInfo], [output]);\n\n  const outTex = output.texData.texture;\n  const outTexShape = output.texData.texShape;\n  const gpgpu = binary.gpgpu;\n  if (output.texData.isPacked) {\n    gpgpu.setOutputPackedMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n  } else {\n    gpgpu.setOutputMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n  }\n  gpgpu.setProgram(binary.webGLProgram);\n  inputs.forEach((input, i) => {\n    const variableName = binary.program.variableNames[i];\n    const variableUniformLocation = binary.uniformLocations[variableName];\n    if (variableUniformLocation != null) {\n      if (input.isUniform) {\n        if (util.sizeFromShape(input.shape) === 1) {\n          gpgpu.gl.uniform1f(variableUniformLocation, input.uniformValues[0]);\n        } else {\n          let vals = input.uniformValues;\n          if (!(vals instanceof Float32Array)) {\n            vals = new Float32Array(vals);\n          }\n          gpgpu.gl.uniform1fv(variableUniformLocation, vals);\n        }\n        return;\n      }\n      const tex = input.texData.texture;\n      gpgpu.setInputMatrixTexture(tex, variableUniformLocation, i);\n    }\n  });\n\n  if (customSetup != null) {\n    customSetup(gpgpu, binary.webGLProgram);\n  }\n  gpgpu.executeProgram();\n}\n\nexport function makeShaderKey(\n    program: GPGPUProgram, inputs: TensorData[], output: TensorData): string {\n  let keyInputs = '';\n  inputs.concat(output).forEach(x => {\n    keyInputs += `${x.shape}_${x.isUniform ? 'uniform' : x.texData.texShape}`;\n  });\n  const keyUserCode = program.userCode;\n  const keyBroadcast = (program.supportsBroadcasting === true).toString();\n  let key = program.constructor.name;\n  // Fast string concat. See https://jsperf.com/string-concatenation/14.\n  key += '_' + keyBroadcast + '_' + keyInputs + '_' + keyUserCode;\n  return key;\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class Im2ColProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      outputShape: number[], inputShape: number[], convInfo: Conv2DInfo) {\n    this.outputShape = outputShape;\n\n    const {\n      filterWidth,\n      inChannels,\n      strideWidth,\n      strideHeight,\n      padInfo,\n      outWidth,\n      dilationWidth,\n      dilationHeight\n    } = convInfo;\n    const {left, top} = padInfo;\n    const itemsPerBlockRow = inChannels * filterWidth;\n\n    this.userCode = `\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            int blockIndex = rc.y + col;\n            int pos = rc.x + row;\n\n            if(blockIndex >= ${outputShape[1]} || pos >= ${\n        outputShape[0]}) continue;\n\n            int offsetY = int(blockIndex / (${outWidth})) * ${strideHeight} - ${\n        top};\n            int d0 = offsetY + ${dilationHeight} * (pos / ${itemsPerBlockRow});\n\n            if(d0 >= ${inputShape[0]} || d0 < 0) continue;\n\n            int offsetX = int(mod(float(blockIndex), ${outWidth}.) * ${\n        strideWidth}. - ${left}.);\n            int d1 = offsetX + ${dilationWidth} * (int(mod(float(pos), ${\n        itemsPerBlockRow}.) / ${inChannels}.));\n\n            if(d1 >= ${inputShape[1]} || d1 < 0) continue;\n\n            result[row * 2 + col] = getA(d0, d1, int(mod(float(pos), ${\n        inChannels}.)));\n          }\n        }\n\n        gl_FragColor = result;\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class LRNProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(\n      xShape: number[], radius: number, bias: number, alpha: number,\n      beta: number) {\n    const rad = radius;\n    const maxD = xShape[3] - 1;\n    this.outputShape = xShape;\n\n    // optimize pow(bias + alpha * sum, -beta)\n    // src: https://github.com/tensorflow/tensorflow/..\n    // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..\n    // tensorflow/core/kernels/mkl_lrn_op.cc#L320\n    let powOperator;\n    const basis = `float(${bias}) + float(${alpha}) * sum`;\n    if (beta === 0.5) {\n      powOperator = `inversesqrt(${basis})`;\n    } else if (beta === 1.0) {\n      powOperator = `1.0/(${basis})`;\n    } else {\n      powOperator = `exp(log(${basis}) * float(-${beta}));`;\n    }\n\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${rad}; j <= ${rad}; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  ${maxD}) {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ${powOperator};\n        setOutput(val);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class LRNGradProgram implements GPGPUProgram {\n  variableNames = ['inputImage', 'outputImage', 'dy'];\n  outputShape: number[] = [];\n  userCode: string;\n  depthRadius: number;\n  bias: number;\n  alpha: number;\n  beta: number;\n  depth: number;\n\n  constructor(\n      inputShape: number[], depthRadius: number, bias: number, alpha: number,\n      beta: number) {\n    this.outputShape = inputShape;\n    this.depth = inputShape[3];\n    this.depthRadius = depthRadius;\n    this.bias = bias;\n    this.alpha = alpha;\n    this.beta = beta;\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < ${this.depth}; ++d) {\n          int depthBegin = int(max(0.0, float(d - ${depthRadius})));\n          int depthEnd = int(min(float(${this.depth}),\n              float(d + ${depthRadius} + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${alpha}) * norm + float(${bias});\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(${alpha})\n                * float(${beta})\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${beta});\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class MaxPool2DBackpropProgram implements GPGPUProgram {\n  variableNames = ['dy', 'maxPos'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv2DInfo) {\n    this.outputShape = convInfo.inShape;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n\n    const lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n          wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * ${effectiveFilterWidth} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class MatMulProgram implements GPGPUProgram {\n  variableNames = ['matrixA', 'matrixB'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      aShape: [number, number, number], bShape: [number, number, number],\n      transposeA = false, transposeB = false) {\n    const batchSize = aShape[0];\n    const outerShapeA = transposeA ? aShape[2] : aShape[1];\n    const outerShapeB = transposeB ? bShape[1] : bShape[2];\n    const sharedDim = transposeA ? aShape[1] : aShape[2];\n    this.outputShape = [batchSize, outerShapeA, outerShapeB];\n\n    const aSnippetFromOffset = (vec4Offset: number, indexVar: string|number) =>\n        transposeA ? `batch, ${indexVar} + ${vec4Offset}, aRow` :\n                     `batch, aRow, ${indexVar} + ${vec4Offset}`;\n    const bSnippetFromOffset = (vec4Offset: number, indexVar: string|number) =>\n        transposeB ? `batch, bCol, ${indexVar} + ${vec4Offset}` :\n                     `batch, ${indexVar} + ${vec4Offset}, bCol`;\n\n    const sharedDimNearestVec4 = Math.floor(sharedDim / 4) * 4;\n    const sharedDimVec4Remainder = sharedDim % 4;\n\n    this.userCode = ` float dotARowBCol(int batch, int aRow, int bCol) {\n      float result = 0.0;\n      for (int i = 0; i < ${sharedDimNearestVec4}; i += 4) {\n        vec4 a = vec4(\n          getMatrixA(${aSnippetFromOffset(0, 'i')}),\n          getMatrixA(${aSnippetFromOffset(1, 'i')}),\n          getMatrixA(${aSnippetFromOffset(2, 'i')}),\n          getMatrixA(${aSnippetFromOffset(3, 'i')})\n        );\n        vec4 b = vec4(\n          getMatrixB(${bSnippetFromOffset(0, 'i')}),\n          getMatrixB(${bSnippetFromOffset(1, 'i')}),\n          getMatrixB(${bSnippetFromOffset(2, 'i')}),\n          getMatrixB(${bSnippetFromOffset(3, 'i')})\n        );\n\n        result += dot(a, b);\n      }\n\n      if (${sharedDimVec4Remainder === 1}) {\n        result += getMatrixA(${aSnippetFromOffset(0, sharedDimNearestVec4)}) *\n          getMatrixB(${bSnippetFromOffset(0, sharedDimNearestVec4)});\n      } else if (${sharedDimVec4Remainder === 2}) {\n        vec2 a = vec2(\n          getMatrixA(${aSnippetFromOffset(0, sharedDimNearestVec4)}),\n          getMatrixA(${aSnippetFromOffset(1, sharedDimNearestVec4)})\n        );\n        vec2 b = vec2(\n          getMatrixB(${bSnippetFromOffset(0, sharedDimNearestVec4)}),\n          getMatrixB(${bSnippetFromOffset(1, sharedDimNearestVec4)})\n        );\n        result += dot(a, b);\n      } else if (${sharedDimVec4Remainder === 3}) {\n        vec3 a = vec3(\n          getMatrixA(${aSnippetFromOffset(0, sharedDimNearestVec4)}),\n          getMatrixA(${aSnippetFromOffset(1, sharedDimNearestVec4)}),\n          getMatrixA(${aSnippetFromOffset(2, sharedDimNearestVec4)})\n        );\n        vec3 b = vec3(\n          getMatrixB(${bSnippetFromOffset(0, sharedDimNearestVec4)}),\n          getMatrixB(${bSnippetFromOffset(1, sharedDimNearestVec4)}),\n          getMatrixB(${bSnippetFromOffset(2, sharedDimNearestVec4)})\n        );\n        result += dot(a, b);\n      }\n\n      return result;\n    }\n\n    void main() {\n      ivec3 resBRC = getOutputCoords();\n      setOutput(dotARowBCol(resBRC.x, resBRC.y, resBRC.z));\n    }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class MatMulPackedProgram implements GPGPUProgram {\n  variableNames = ['matrixA', 'matrixB'];\n  usesPackedTextures = true;\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      aShape: [number, number], bShape: [number, number],\n      outputShape: [number, number], transposeA = false, transposeB = false) {\n    this.outputShape = outputShape;\n\n    const sharedDim = transposeA ? aShape[0] : aShape[1];\n    const sharedDimensionPacked = Math.ceil(sharedDim / 2);\n\n    const aSample = transposeA ? 'i * 2, rc.x' : 'rc.x, i * 2';\n    const bSample = transposeB ? 'rc.y, i * 2' : 'i * 2, rc.y';\n    const aSwizzle = transposeA ? ['a.xxyy', 'a.zzww'] : ['a.xxzz', 'a.yyww'];\n    const bSwizzle = transposeB ? ['b.xzxz', 'b.ywyw'] : ['b.xyxy', 'b.zwzw'];\n\n    this.userCode = `\n      const float sharedDimension = ${sharedDimensionPacked}.0;\n\n      vec4 dot2x2ARowBCol(ivec2 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < ${sharedDimensionPacked}; i++) {\n          vec4 a = getMatrixA(${aSample});\n          vec4 b = getMatrixB(${bSample});\n\n          result += (${aSwizzle[0]} * ${bSwizzle[0]}) + (${aSwizzle[1]} * ${\n        bSwizzle[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec2 rc = getOutputCoords();\n        setOutput(dot2x2ARowBCol(rc));\n      }\n    `;\n  }\n}","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class MultinomialProgram implements GPGPUProgram {\n  variableNames = ['probs'];\n  outputShape: number[];\n  userCode: string;\n\n  // Caching uniform location for speed.\n  seedLoc: WebGLUniformLocation;\n\n  constructor(batchSize: number, numOutcomes: number, numSamples: number) {\n    this.outputShape = [batchSize, numSamples];\n\n    this.userCode = `\n      uniform float seed;\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < ${numOutcomes - 1}; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float(${numOutcomes - 1}));\n      }\n    `;\n  }\n\n  getCustomSetupFunc(seed: number) {\n    return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n      if (this.seedLoc == null) {\n        this.seedLoc = gpgpu.getUniformLocation(webGLProgram, 'seed');\n      }\n      gpgpu.gl.uniform1f(this.seedLoc, seed);\n    };\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class OneHotProgram implements GPGPUProgram {\n  variableNames = ['indices'];\n  outputShape: number[];\n  userCode: string;\n\n  // Caching uniform location for speed.\n  seedLoc: WebGLUniformLocation;\n\n  constructor(\n      numIndices: number, depth: number, onValue: number, offValue: number) {\n    this.outputShape = [numIndices, depth];\n\n    this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float(${offValue}), float(${onValue}),\n                      float(index == coords.y)));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport function getVecChannels(name: string, rank: number): string[] {\n  return ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank).map(d => `${name}.${d}`);\n}\n\nexport function getChannels(name: string, rank: number): string[] {\n  if (rank === 1) {\n    return [name];\n  }\n  return getVecChannels(name, rank);\n}\n\nexport function getSourceCoords(rank: number, dims: string[]): string {\n  if (rank === 1) {\n    return 'rc';\n  }\n\n  let coords = '';\n  for (let i = 0; i < rank; i++) {\n    coords += dims[i];\n    if (i < rank - 1) {\n      coords += ',';\n    }\n  }\n  return coords;\n}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getChannels} from '../packing_util';\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class PackProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  isPackShader = true;\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      outputShape:\n          number[]) {  // TODO(https://github.com/tensorflow/tfjs/issues/893):\n                       // Only input / output 3D tensors.\n    this.outputShape = outputShape;\n    const rank = outputShape.length;\n\n    if (rank === 0) {\n      this.userCode = `\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      `;\n    } else {\n      const channels = getChannels('rc', rank);\n      const dtype = getCoordsDataType(rank);\n      const outOfBoundsCondition =\n          getOutOfBoundsCondition(rank, outputShape, channels);\n      const setup = getSetup(\n          rank, outputShape[outputShape.length - 1],\n          outputShape[outputShape.length - 2], channels);\n      const output = getOutput(outputShape, channels);\n\n      this.userCode = `\n        void main() {\n          ${dtype} rc = getOutputCoords();\n\n          if(${outOfBoundsCondition}) {\n            setOutput(vec4(0));\n          } else {\n            ${setup}\n\n            setOutput(vec4(${output}));\n          }\n        }\n      `;\n    }\n  }\n}\n\nfunction getSourceCoordsArr(rank: number, dims: string[]): string[] {\n  const coords = [];\n\n  for (let row = 0; row <= 1; row++) {\n    for (let col = 0; col <= 1; col++) {\n      let coord = `${row === 0 ? 'r' : 'rp1'}, ${col === 0 ? 'c' : 'cp1'}`;\n\n      for (let d = 2; d < rank; d++) {\n        coord = `${dims[dims.length - 1 - d]},` + coord;\n      }\n\n      coords.push(coord);\n    }\n  }\n  return coords;\n}\n\nfunction getOutOfBoundsCondition(\n    rank: number, shape: number[], dims: string[]): string {\n  if (rank === 1) {\n    return `rc > ${shape[0]}`;\n  }\n\n  let cond = '';\n  for (let i = rank - 2; i < rank; i++) {\n    cond += `${dims[i]} >= ${shape[i]}`;\n    if (i < rank - 1) {\n      cond += '||';\n    }\n  }\n\n  return cond;\n}\n\nfunction getSetup(\n    rank: number, cols: number, rows: number, dims: string[]): string {\n  if (rank === 1) {\n    return '';\n  }\n\n  const innerDims = dims.slice(-2);\n\n  return `\n    int r = ${innerDims[0]};\n    int c = ${innerDims[1]};\n    int rp1 = r + 1;\n    int cp1 = c + 1;\n\n    bool cEdge = cp1 >= ${cols};\n    bool rEdge = rp1 >= ${rows};\n  `;\n}\n\nfunction getOutput(shape: number[], dims: string[]): string {\n  const rank = shape.length;\n  const sourceCoords = getSourceCoordsArr(rank, dims);\n  if (rank === 1) {\n    return `getA(rc),\n            rc + 1 >= ${shape[0]} ? 0. : getA(rc + 1),\n            0, 0`;\n  }\n\n  return `getA(${sourceCoords[0]}),\n          cEdge ? 0. : getA(${sourceCoords[1]}),\n          rEdge ? 0. : getA(${sourceCoords[2]}),\n          rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`;\n}","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class PadProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      xShape: number[], paddings: Array<[number, number]>,\n      constantValue: number) {\n    this.outputShape = paddings.map(\n        (p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n    const rank = xShape.length;\n    const type = getCoordsDataType(rank);\n\n    const start = paddings.map(p => p[0]).join(',');\n    const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n    const unpackedCoords =\n         ['coords[0]', 'coords[1]', 'coords[2]','coords[3]'].slice(0, rank);\n\n    if (rank === 1) {\n      this.userCode = `\n        int start = ${start};\n        int end = ${end};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(float(${constantValue}));\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      `;\n      return;\n    }\n    this.userCode = `\n      ${type} start = ${type}(${start});\n      ${type} end = ${type}(${end});\n\n      void main() {\n        ${type} outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(float(${constantValue}));\n        } else {\n          ${type} coords = outC - start;\n          setOutput(getX(${unpackedCoords}));\n        }\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class Pool2DProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      convInfo: Conv2DInfo, poolType: 'max'|'avg', computePositions: boolean) {\n    if (poolType === 'avg' && computePositions) {\n      throw new Error('Cannot compute positions for average pool.');\n    }\n\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    this.outputShape = convInfo.outShape;\n\n    const isAvgPool = poolType === 'avg';\n\n    let initializationValue = '0.0';\n    if (!isAvgPool) {\n      initializationValue = '-1.0 / 0.0';\n    }\n\n    if (computePositions) {\n      const compareOp = '>=';\n\n      this.userCode = `\n        const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n        const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ${compareOp} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = wR * ${effectiveFilterWidth} + wC;\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n      return;\n    }\n\n    const compareOp = 'max';\n\n    let returnValue = `${poolType}(${poolType}(${poolType}(` +\n        'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n    if (poolType === 'avg') {\n      returnValue = `avgValue / count`;\n    }\n\n    const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n    const filterWidthVec4Remainder = filterWidth % 4;\n\n    const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n\n    this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              getValue(batch, xR, xC + 3 * ${dilationWidth}, d)\n            );\n\n            ${updateSnippet}\n          }\n\n          int xC = xCCorner + ${filterWidthNearestVec4};\n          if (${filterWidthVec4Remainder === 1}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 2}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 3}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              initializationValue\n            );\n\n            ${updateSnippet}\n          }\n        }\n        setOutput(${returnValue});\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ReduceInfo} from '../../ops/reduce_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ReduceProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      reduceInfo: ReduceInfo,\n      reduceType: 'all'|'any'|'max'|'min'|'sum'|'prod') {\n    const windowSize = reduceInfo.windowSize;\n    const batchSize = reduceInfo.batchSize;\n    const inSize = reduceInfo.inSize;\n    const outSize = Math.ceil(inSize / windowSize);\n    this.outputShape = [batchSize, outSize];\n\n    let initializationValue = '0.0';\n    let compareOp = ``;\n\n    if (reduceType === 'prod') {\n      initializationValue = '1.0';\n    } else if (reduceType === 'min') {\n      initializationValue = '1.0 / 0.0';\n      compareOp = `min`;\n    } else if (reduceType === 'max') {\n      initializationValue = '-1.0 / 0.0';\n      compareOp = `max`;\n    }\n\n    let returnValue = `${reduceType}(${reduceType}(${reduceType}(` +\n        'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n\n    if (reduceType === 'sum') {\n      returnValue = `sumValue`;\n    } else if (reduceType === 'prod') {\n      returnValue = `prodValue`;\n    } else if (reduceType === 'all') {\n      returnValue = `allValue`;\n    } else if (reduceType === 'any') {\n      returnValue = `anyValue`;\n    }\n\n    const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n    const windowSizeVec4Remainder = windowSize % 4;\n\n    let updateSnippet = `\n      if (${reduceType === 'sum'}) {\n        sumValue += dot(values, ones);\n      } else if (${reduceType === 'prod'}) {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n\n    let vecType = `vec4`;\n\n    if (reduceType === 'all') {\n      initializationValue = '1.0';\n      updateSnippet = `\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      `;\n      vecType = `bvec4`;\n    } else if (reduceType === 'any') {\n      initializationValue = '0.0';\n      updateSnippet = `\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      `;\n      vecType = `bvec4`;\n    }\n\n    let checkOutOfBounds = '';\n    if (inSize % windowSize > 0) {\n      checkOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n    }\n    this.userCode = `\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${checkOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        vec4 minMaxValue = vec4(${initializationValue});\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as util from '../../util';\nimport {GPGPUProgram} from './gpgpu_math';\nimport * as shader_util from './shader_compiler_util';\n\nexport class ReshapePackedProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  usesPackedTextures = true;\n  outputShape: number[];\n  userCode: string;\n\n  constructor(outputShape: [number, number, number], inputShape: [\n    number, number, number\n  ]) {\n    this.outputShape = outputShape;\n\n    let mainLoop = ``;\n    for (let i = 0; i < 4; i++) {\n      let thisRC = `thisRC = rc;`;\n      if (i % 2 === 1) {\n        thisRC += `thisRC.z += 1;`;\n      }\n      if (i > 1) {\n        thisRC += `thisRC.y += 1;`;\n      }\n\n      mainLoop += `\n        ${thisRC}\n        ${i > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : ''}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${i}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${i > 0 ? '}' : ''}\n      `;\n    }\n\n    this.userCode = `\n      ${getReshapedInputCoords(inputShape)}\n      ${getFlatIndex(outputShape)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${outputShape[1]};\n        int cols = ${outputShape[2]};\n\n        ${mainLoop}\n\n        setOutput(result);\n      }\n    `;\n  }\n}\n\nfunction getFlatIndex(shape: [number, number, number]): string {\n  const dotCoordsWithStrides = shader_util.dotify(\n      ['coords.x', 'coords.y', 'coords.z'],\n      util.computeStrides(shape).map(d => d.toString()).concat(['1.']));\n\n  return `\n    int getFlatIndex(ivec3 coords) {\n      return round(${dotCoordsWithStrides});\n    }\n  `;\n}\n\nfunction getReshapedInputCoords(shape: [number, number, number]): string {\n  const coordsFromIndexSnippet =\n      shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n\n  return `\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor4D} from '../../tensor';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ResizeBilinearBackpropProgram implements GPGPUProgram {\n  variableNames = ['dy'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(dy: Tensor4D, x: Tensor4D, alignCorners: boolean) {\n    this.outputShape = x.shape;\n    const [, xHeight, xWidth, ] = x.shape;\n    const [, yHeight, yWidth] = dy.shape;\n\n    // In the backwards pass, we want to find the pixels that were generated for\n    // each pixel in the input image the forward pass and add the corresponding\n    // coefficient from dy to the gradient (with some interpolation).\n\n    const effectiveXSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n      (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n    ];\n\n    const effectiveYSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n      (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n    ];\n\n    const heightScale = effectiveXSize[0] / effectiveYSize[0];\n    const widthScale = effectiveXSize[1] / effectiveYSize[1];\n\n    const invHeightScale = 1 / heightScale;\n    const invWidthScale = 1 / widthScale;\n\n    // This defines the size of the window of values around a particular\n    // index in dy that we want to search for contributions to dx.\n    const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n    const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ${xHeight - 1}.0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ${xWidth - 1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ResizeBilinearProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(\n      inputShape: [number, number, number, number], newHeight: number,\n      newWidth: number, alignCorners: boolean) {\n    const [batch, oldHeight, oldWidth, depth] = inputShape;\n    this.outputShape = [batch, newHeight, newWidth, depth];\n\n    const effectiveInSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n      (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n    ];\n\n    const effectiveOutSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n      (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n    ];\n\n    this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor4D} from '../../tensor';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ResizeNearestNeigborBackpropProgram implements GPGPUProgram {\n  variableNames = ['dy'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(dy: Tensor4D, x: Tensor4D, alignCorners: boolean) {\n    this.outputShape = x.shape;\n    const [, xHeight, xWidth, ] = x.shape;\n    const [, yHeight, yWidth] = dy.shape;\n\n    // In the backwards pass, we want to find the pixels that were generated for\n    // each pixel in the input image the forward pass and add the corresponding\n    // coefficient from dy to the gradient (with some interpolation).\n\n    const effectiveXSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n      (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n    ];\n\n    const effectiveYSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n      (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n    ];\n\n    const heightScale = effectiveXSize[0] / effectiveYSize[0];\n    const widthScale = effectiveXSize[1] / effectiveYSize[1];\n\n    const invHeightScale = 1 / heightScale;\n    const invWidthScale = 1 / widthScale;\n\n    // This defines the size of the window of values around a particular\n    // index in dy that we want to search for contributions to dx.\n    const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n    const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${effectiveXSize[0]}) *\n                (float(dyR) / float(${effectiveYSize[0]}));\n\n            float sourceFracCol =\n                float(${effectiveXSize[1]}) *\n                  (float(dyC) / float(${effectiveYSize[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${xHeight}) - 1),\n                ${alignCorners} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${xWidth}) - 1),\n                ${alignCorners} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ResizeNearestNeighborProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(\n      inputShape: [number, number, number, number], newHeight: number,\n      newWidth: number, alignCorners: boolean) {\n    const [batch, oldHeight, oldWidth, depth] = inputShape;\n    this.outputShape = [batch, newHeight, newWidth, depth];\n\n    const effectiveInSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n      (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n    ];\n\n    const effectiveOutSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n      (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n    ];\n\n    // When align corners is false, we rounds the value with floor.\n    const roundBase = alignCorners ? '0.5' : '0.0';\n\n    this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));\n\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class ReverseProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(xShape: number[], axis: number[]) {\n    const rank = xShape.length;\n    if (rank > 4) {\n      throw new Error(\n          `WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n    }\n    this.outputShape = xShape;\n\n    if (rank === 1) {\n      this.userCode = `\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(${xShape[0]} - coord - 1));\n        }\n      `;\n      return;\n    }\n    const getInCoord = (i: number) => {\n      if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n        return `${xShape[i]} - coords[${i}] - 1`;\n      }\n      return `coords[${i}]`;\n    };\n    const inCoords = xShape.map((_, i) => getInCoord(i)).join(',');\n    const type = getCoordsDataType(rank);\n\n    this.userCode = `\n      void main() {\n        ${type} coords = getOutputCoords();\n        setOutput(getX(${inCoords}));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class ScatterProgram implements GPGPUProgram {\n  variableNames = ['updates', 'indices', 'defaultValue'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      updateSize: number, sliceDim: number, indicesRank: number,\n      updatesRank: number, strides: number[], shape: number[],\n      summingDupeIndex = true) {\n    this.outputShape = shape;\n    const stridesType = getCoordsDataType(strides.length);\n    const dtype = getCoordsDataType(shape.length);\n    let indicesString = '';\n    if (indicesRank === 1) {\n      indicesString = 'i';\n    } else if (indicesRank === 2) {\n      indicesString = 'i, j';\n    }\n    const indicesSnippet = `getIndices(${indicesString})`;\n\n    let updatesString = '';\n    if (updatesRank === 1) {\n      updatesString = 'i';\n    } else if (updatesRank === 2) {\n      updatesString = 'i, coords[1]';\n    }\n    const updatesSnippet = `getUpdates(${updatesString})`;\n\n    const strideString = sliceDim > 1 ? 'strides[j]' : 'strides';\n    this.userCode = `\n        ${stridesType} strides = ${stridesType}(${strides});\n\n        void main() {\n          ${dtype} coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < ${updateSize}; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < ${sliceDim}; j++) {\n              int index = round(${indicesSnippet});\n              flattenedIndex += index * ${strideString};\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += ${updatesSnippet};\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {SegOpInfo} from '../../ops/segment_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class SegmentOpProgram implements GPGPUProgram {\n  variableNames = ['x', 'segmentIds'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(segOpInfo: SegOpInfo, segOpType: 'unsortedSegmentSum') {\n    const windowSize = segOpInfo.windowSize;\n    const batchSize = segOpInfo.batchSize;\n    const inSize = segOpInfo.inSize;\n    const numSegments = segOpInfo.numSegments;\n    const outSize = numSegments * Math.ceil(inSize / windowSize);\n    this.outputShape = [batchSize, outSize];\n\n    const initializationValue = '0.0';\n    const returnValue = `sumValue`;\n\n    const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n    const windowSizeVec4Remainder = windowSize % 4;\n\n    const updateSnippet = `\n        sumValue += dot(values, filter);\n    `;\n\n    let checkValueOutOfBounds = '';\n    if (inSize % windowSize > 0) {\n      checkValueOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n    }\n\n    let checkSegmentIdOutOfBounds = '';\n    if (inSize % windowSize > 0) {\n      checkSegmentIdOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return -1.0;\n        }\n      `;\n    }\n\n    this.userCode = `\n      const float initializationValue = ${initializationValue};\n\n      float getValue(int batch, int inIdx) {\n        ${checkValueOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${checkSegmentIdOutOfBounds}\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          ${numSegments})) * float(${windowSize}));\n        int currentSeg = int(mod(float(outIdx), float(${numSegments})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class SelectProgram implements GPGPUProgram {\n  variableNames = ['c', 'a', 'b'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(cRank: number, shape: number[], rank: number) {\n    this.outputShape = shape;\n\n    let cCoords;\n    let abCoords;\n    if (rank > 4) {\n      throw Error(`Where for rank ${rank} is not yet supported`);\n    }\n\n    if (rank === 1) {\n      abCoords = `resRC`;\n      cCoords = `resRC`;\n    } else {\n      const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n      const cCoordVars = [];\n      const abCoordVars = [];\n      for (let i = 0; i < shape.length; i++) {\n        abCoordVars.push(`${currentCoords[i]}`);\n        if (i < cRank) {\n          cCoordVars.push(`${currentCoords[i]}`);\n        }\n      }\n      cCoords = cCoordVars.join();\n      abCoords = abCoordVars.join();\n    }\n\n    const dtype = getCoordsDataType(rank);\n\n    this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        float cVal = getC(${cCoords});\n        if (cVal >= 1.0) {\n          setOutput(getA(${abCoords}));\n        } else {\n          setOutput(getB(${abCoords}));\n        }\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class SliceProgram implements GPGPUProgram {\n  variableNames = ['source'];\n  outputShape: number[];\n  userCode: string;\n  rank: number;\n\n  // Caching uniform location for speed.\n  startLoc: WebGLUniformLocation;\n\n  constructor(destSize: number[]) {\n    this.outputShape = destSize;\n    this.rank = destSize.length;\n\n    const dtype = getCoordsDataType(this.rank);\n    const sourceCoords = getCoords(this.rank);\n\n    this.userCode = `\n      uniform ${dtype} start;\n\n      void main() {\n        ${dtype} sourceLoc = start + getOutputCoords();\n        setOutput(getSource(${sourceCoords}));\n      }\n    `;\n  }\n\n  getCustomSetupFunc(start: number[]) {\n    if (start.length !== this.rank) {\n      throw Error(\n          `The rank (${this.rank}) of the program must match the ` +\n          `length of start (${start.length})`);\n    }\n    return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n      if (this.startLoc == null) {\n        this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');\n        if (this.startLoc == null) {\n          // This means the compiler has optimized and realized it doesn't need\n          // the uniform.\n          return;\n        }\n      }\n      if (this.rank === 1) {\n        gpgpu.gl.uniform1i(this.startLoc, start[0]);\n      } else if (this.rank === 2) {\n        gpgpu.gl.uniform2i(this.startLoc, start[0], start[1]);\n      } else if (this.rank === 3) {\n        gpgpu.gl.uniform3i(this.startLoc, start[0], start[1], start[2]);\n      } else if (this.rank === 4) {\n        gpgpu.gl.uniform4i(\n            this.startLoc, start[0], start[1], start[2], start[3]);\n      } else {\n        throw Error(`Slicing for rank ${this.rank} is not yet supported`);\n      }\n    };\n  }\n}\n\nfunction getCoords(rank: number): string {\n  if (rank === 1) {\n    return 'sourceLoc';\n  } else if (rank === 2) {\n    return 'sourceLoc.x, sourceLoc.y';\n  } else if (rank === 3) {\n    return 'sourceLoc.x, sourceLoc.y, sourceLoc.z';\n  } else if (rank === 4) {\n    return 'sourceLoc.x, sourceLoc.y, sourceLoc.z, sourceLoc.w';\n  } else {\n    throw Error(`Slicing for rank ${rank} is not yet supported`);\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class StridedSliceProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      begin: number[], strides: number[], size: number[],\n      shrinkAxis: number[]) {\n    const shape = size.filter((v, index) => shrinkAxis.indexOf(index) === -1);\n    this.outputShape = shape;\n    const rank = size.length;\n    const inputDtype = getCoordsDataType(size.length);\n    const dtype = getCoordsDataType(shape.length);\n\n    let newCoords = '';\n    if (rank === 1) {\n      newCoords = 'coords * strides + begin';\n    } else {\n      let outputAxis = 0;\n      newCoords =\n          size.map((_, i) => {\n                if (shrinkAxis.indexOf(i) === -1) {\n                  outputAxis++;\n                  return shape.length === 1 ?\n                      `coords * strides[${i}] + begin[${i}]` :\n                      `coords[${outputAxis - 1}] * strides[${i}] + begin[${i}]`;\n                } else {\n                  return `begin[${i}]`;\n                }\n              })\n              .join(',');\n    }\n\n    this.userCode = `\n      ${inputDtype} begin = ${inputDtype}(${begin});\n      ${inputDtype} strides = ${inputDtype}(${strides});\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        setOutput(getX(${newCoords}));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../../environment';\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {PhysicalTextureType, TextureUsage} from './tex_util';\n\nexport class TextureManager {\n  private numUsedTextures = 0;\n  private numFreeTextures = 0;\n  private freeTextures: {[shape: string]: WebGLTexture[]} = {};\n  private logEnabled = false;\n  private usedTextures: {[shape: string]: WebGLTexture[]} = {};\n\n  constructor(private gpgpu: GPGPUContext) {}\n\n  acquireTexture(\n      shapeRC: [number, number], usage: TextureUsage,\n      isPacked: boolean): WebGLTexture {\n    const physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);\n\n    const shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);\n    if (!(shapeKey in this.freeTextures)) {\n      this.freeTextures[shapeKey] = [];\n    }\n    if (!(shapeKey in this.usedTextures)) {\n      this.usedTextures[shapeKey] = [];\n    }\n\n    if (this.freeTextures[shapeKey].length > 0) {\n      this.numFreeTextures--;\n      this.numUsedTextures++;\n      this.log();\n      const newTexture = this.freeTextures[shapeKey].shift();\n      this.usedTextures[shapeKey].push(newTexture);\n      return newTexture;\n    }\n    this.numUsedTextures++;\n    this.log();\n\n    let newTexture: WebGLTexture;\n    if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) {\n      newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);\n    } else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) {\n      newTexture =\n          this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);\n    } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) {\n      newTexture =\n          this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);\n    } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) {\n      newTexture =\n          this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);\n\n    } else if (\n        physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) {\n      newTexture =\n          this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);\n    }\n    this.usedTextures[shapeKey].push(newTexture);\n\n    return newTexture;\n  }\n\n  releaseTexture(\n      texture: WebGLTexture, shape: [number, number],\n      logicalTexType: TextureUsage, isPacked: boolean): void {\n    if (this.freeTextures == null) {\n      // Already disposed.\n      return;\n    }\n    const physicalTexType =\n        getPhysicalFromLogicalTextureType(logicalTexType, isPacked);\n    const shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);\n    if (!(shapeKey in this.freeTextures)) {\n      this.freeTextures[shapeKey] = [];\n    }\n    this.freeTextures[shapeKey].push(texture);\n    this.numFreeTextures++;\n    this.numUsedTextures--;\n    const texList = this.usedTextures[shapeKey];\n    const texIndex = texList.indexOf(texture);\n    if (texIndex < 0) {\n      throw new Error(\n          'Cannot release a texture that was never provided by this ' +\n          'texture manager');\n    }\n    texList.splice(texIndex, 1);\n    this.log();\n  }\n\n  private log() {\n    if (!this.logEnabled) {\n      return;\n    }\n    const total = this.numFreeTextures + this.numUsedTextures;\n    console.log(\n        'Free/Used', `${this.numFreeTextures} / ${this.numUsedTextures}`,\n        `(${total})`);\n  }\n\n  getNumUsedTextures(): number {\n    return this.numUsedTextures;\n  }\n\n  getNumFreeTextures(): number {\n    return this.numFreeTextures;\n  }\n\n  dispose() {\n    if (this.freeTextures == null) {\n      // Already disposed.\n      return;\n    }\n    for (const texShape in this.freeTextures) {\n      this.freeTextures[texShape].forEach(tex => {\n        this.gpgpu.deleteMatrixTexture(tex);\n      });\n    }\n    for (const texShape in this.usedTextures) {\n      this.usedTextures[texShape].forEach(tex => {\n        this.gpgpu.deleteMatrixTexture(tex);\n      });\n    }\n    this.freeTextures = null;\n    this.usedTextures = null;\n    this.numUsedTextures = 0;\n    this.numFreeTextures = 0;\n  }\n}\n\nfunction getPhysicalFromLogicalTextureType(\n    logicalTexType: TextureUsage, isPacked: boolean): PhysicalTextureType {\n  if (logicalTexType === TextureUsage.UPLOAD) {\n    return isPacked ? PhysicalTextureType.PACKED_2X2_FLOAT32 :\n                      PhysicalTextureType.UNPACKED_FLOAT32;\n  } else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) {\n    if (isPacked) {\n      return ENV.get('WEBGL_RENDER_FLOAT32_ENABLED') ?\n          PhysicalTextureType.PACKED_2X2_FLOAT32 :\n          PhysicalTextureType.PACKED_2X2_FLOAT16;\n    }\n    return ENV.get('WEBGL_RENDER_FLOAT32_ENABLED') ?\n        PhysicalTextureType.UNPACKED_FLOAT32 :\n        PhysicalTextureType.UNPACKED_FLOAT16;\n  } else if (\n      logicalTexType === TextureUsage.DOWNLOAD ||\n      logicalTexType === TextureUsage.PIXELS) {\n    return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;\n  }\n  throw new Error(`Unknown logical texture type ${logicalTexType}`);\n}\n\nfunction getKeyFromTextureShape(\n    shapeRowsCol: [number, number], physicalTexType: PhysicalTextureType,\n    isPacked: boolean): string {\n  return `${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class TileProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  outputShape: number[];\n  userCode: string;\n  rank: number;\n\n  constructor(aShape: number[], reps: number[]) {\n    const outputShape: number[] = new Array(aShape.length);\n    for (let i = 0; i < outputShape.length; i++) {\n      outputShape[i] = aShape[i] * reps[i];\n    }\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    const dtype = getCoordsDataType(this.rank);\n    const sourceCoords = getSourceCoords(aShape);\n\n    this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n  }\n}\n\nfunction getSourceCoords(aShape: number[]): string {\n  const rank = aShape.length;\n  if (rank > 5) {\n    throw Error(`Tile for rank ${rank} is not yet supported`);\n  }\n  if (rank === 1) {\n    return `imod(resRC, ${aShape[0]})`;\n  }\n\n  const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u'];\n\n  const sourceCoords = [];\n  for (let i = 0; i < aShape.length; i++) {\n    sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);\n  }\n  return sourceCoords.join();\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class TransposeProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  outputShape: number[];\n  userCode: string;\n  rank: number;\n\n  constructor(aShape: number[], newDim: number[]) {\n    const outputShape: number[] = new Array(aShape.length);\n    for (let i = 0; i < outputShape.length; i++) {\n      outputShape[i] = aShape[newDim[i]];\n    }\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    const dtype = getCoordsDataType(this.rank);\n    const switched = getSwitchedCoords(newDim);\n\n    this.userCode = `\n    void main() {\n      ${dtype} resRC = getOutputCoords();\n      setOutput(getA(${switched}));\n    }\n    `;\n  }\n}\n\nfunction getSwitchedCoords(newDim: number[]): string {\n  const rank = newDim.length;\n  if (rank > 6) {\n    throw Error(`Transpose for rank ${rank} is not yet supported`);\n  }\n  const originalOrder =\n      ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u', 'resRC.v'];\n  const switchedCoords = new Array(rank);\n  for (let i = 0; i < newDim.length; i++) {\n    switchedCoords[newDim[i]] = originalOrder[i];\n  }\n  return switchedCoords.join();\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport const ERF_P = 0.3275911;\nexport const ERF_A1 = 0.254829592;\nexport const ERF_A2 = -0.284496736;\nexport const ERF_A3 = 1.421413741;\nexport const ERF_A4 = -1.453152027;\nexport const ERF_A5 = 1.061405429;\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport const SELU_SCALEALPHA = 1.7580993408473768599402175208123;\nexport const SELU_SCALE = 1.0507009873554804934193349852946;\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as erf_util from '../../ops/erf_util';\nimport * as selu_util from '../../ops/selu_util';\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {GPGPUContext} from './gpgpu_context';\n\nexport class UnaryOpProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  userCode: string;\n  outputShape: number[];\n\n  // Caching uniform location for speed.\n  startLoc: WebGLUniformLocation;\n\n  constructor(aShape: number[], opSnippet: string) {\n    this.outputShape = aShape;\n    this.userCode = `\n      uniform float NAN;\n      float unaryOperation(float x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n  }\n\n  getCustomSetupFunc() {\n    return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n      if (this.startLoc == null) {\n        this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'NAN');\n        if (this.startLoc == null) {\n          // This means the compiler has optimized and realized it doesn't need\n          // the uniform.\n          return;\n        }\n      }\n      gpgpu.gl.uniform1f(this.startLoc, NaN);\n    };\n  }\n}\n\nconst CHECK_NAN_SNIPPET = `if (isNaN(x)) return x;`;\n\nexport const ABS = `return abs(x);`;\n\nexport const RELU = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : x;\n`;\n\nexport const ELU = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;\n\nexport const SELU = `\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = ${selu_util.SELU_SCALEALPHA};\n  float scale = ${selu_util.SELU_SCALE};\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`;\n\nexport function STEP(alpha = 0.0) {\n  return CHECK_NAN_SNIPPET + `\n    return x > 0.0 ? 1.0 : float(${alpha});\n  `;\n}\n\nexport const NEG = `return -x;`;\n\nexport const CEIL = `return ceil(x);`;\n\nexport const FLOOR = `return floor(x);`;\n\nexport const SIGN = `\n  if (isNaN(x)) { return 0.0; }\n  return sign(x);\n`;\n\nexport const ROUND = `\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n`;\n\nexport const EXP = `return exp(x);`;\n\nexport const EXPM1 = `return exp(x) - 1.0;`;\n\nexport const LOG = `if (x < 0.0) return NAN;\n  return log(x);`;\n\nexport const LOG1P = `return log(1.0 + x);`;\n\nexport const SQRT = `return sqrt(x);`;\n\nexport const RSQRT = `return inversesqrt(x);`;\n\nexport const SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;\n\n/**\n * mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX\n *\n * epsilon is the difference between 1.0 and the next representable\n * float. For a single precision 32 bit float this should be 2^-23, see:\n * https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm\n *\n * too_large = (x > -threshold) is value above which exp(x) may overflow\n * but softplus(x) == x is within machine epsilon\n *\n * too_small = (x < threshold) is value below which exp(x) may underflow,\n * but softplus(x) == exp(x) is within machine epsilon.\n */\nexport const SOFTPLUS = `\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n`;\n\nexport const SIN = CHECK_NAN_SNIPPET + `\n  return sin(x);\n`;\n\nexport const COS = CHECK_NAN_SNIPPET + `\n  return cos(x);\n`;\n\nexport const TAN = `return tan(x);`;\n\nexport const ASIN = `return asin(x);`;\n\nexport const ACOS = `return acos(x);`;\n\nexport const ATAN = CHECK_NAN_SNIPPET + `\n  return atan(x);\n`;\n\nexport const SINH = `\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n`;\n\nexport const COSH = `\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n`;\n\nexport const TANH = `\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`;\n\nexport const ASINH = `return log(x + sqrt(x * x + 1.0));`;\n\nexport const ACOSH = CHECK_NAN_SNIPPET + `\n  if (x < 1.0) return NAN;\n  return log(x + sqrt(x * x - 1.0));`;\n\nexport const ATANH = CHECK_NAN_SNIPPET + `\n  if ((x < -1.0) || (x > 1.0)) return NAN;\n  return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;\n\nexport const ERF = `\n  // Error function is calculated approximately with elementary function.\n  // See \"Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n  float p = ${erf_util.ERF_P};\n  float a1 = ${erf_util.ERF_A1};\n  float a2 = ${erf_util.ERF_A2};\n  float a3 = ${erf_util.ERF_A3};\n  float a4 = ${erf_util.ERF_A4};\n  float a5 = ${erf_util.ERF_A5};\n\n  float t = 1.0 / (1.0 + p * x);\n  return 1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x);\n`;\n\nexport const SQUARE = `return x * x;`;\n\nexport const RECIPROCAL = `return 1.0 / x;`;\n\nexport const LOGICAL_NOT = `return float(!(x >= 1.0));`;\n\nexport const TO_INT = `return float(int(x));`;\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getChannels, getSourceCoords} from '../packing_util';\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class UnpackProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  usesPackedTextures = true;\n  outputShape: number[];\n  userCode: string;\n\n  constructor(outputShape: number[]) {\n    this.outputShape = outputShape;\n    const rank = outputShape.length;\n\n    const channels = getChannels('rc', rank);\n    const dtype = getCoordsDataType(rank);\n    const sourceCoords = getSourceCoords(rank, channels);\n    const innerDims = channels.slice(-2);\n    const coords = rank <= 1 ? 'rc' : `vec2(${innerDims.join(',')})`;\n\n    this.userCode = `\n      void main() {\n        ${dtype} rc = getOutputCoords();\n        vec4 packedInput = getA(${sourceCoords});\n\n        setOutput(getChannel(packedInput, ${coords}));\n      }\n    `;\n  }\n}","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor, convertToTensorArray} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assert, sizeFromShape} from '../util';\nimport {parseAxisParam} from './axis_util';\nimport {assertParamsConsistent, computeOutShape} from './concat_util';\nimport {op} from './operation';\nimport {tensor} from './tensor_ops';\n\n/**\n * Concatenates a list of`tf.Tensor1D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(3) = |r1, g1, b1|\n * B: shape(2) = |r2, g2|\n * C = tf.concat1d([A, B]) == |r1, g1, b1, r2, g2|\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @return The concatenated array.\n */\nfunction concat1d_(tensors: Array<Tensor1D|TensorLike>): Tensor1D {\n  return concat(tensors, 0 /* axis */);\n}\n\n/**\n * Concatenates a list of`tf.Tensor2D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *\n * B: shape(2, 3) = | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * C = tf.concat2d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *                  | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * if axis = 1:\n * C = shape(2, 6) = | r1, g1, b1, r3, g3, b3 |\n *                   | r2, g2, b2, r4, g4, b4 |\n *\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @param axis The axis to concatenate along.\n * @return The concatenated array.\n */\nfunction concat2d_(\n    tensors: Array<Tensor2D|TensorLike>, axis: number): Tensor2D {\n  return concat(tensors, axis);\n}\n\n/**\n * Concatenates a list of`tf.Tensor3D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *\n * B: shape(2, 1, 3) = | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * C = tf.concat3d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *                     | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * if axis = 1:\n * C: shape(2, 2, 3) = | r1, g1, b1, r3, g3, b3 |\n *                     | r2, g2, b2, r4, g4, b4 |\n *\n * if axis = 2:\n * C = shape(2, 1, 6) = | r1, g1, b1, r3, g3, b3 |\n *                      | r2, g2, b2, r4, g4, b4 |\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat3d_(\n    tensors: Array<Tensor3D|TensorLike>, axis: number): Tensor3D {\n  return concat(tensors, axis);\n}\n\n/**\n * Concatenates a list of`tf.Tensor4D`s along an axis. See `concat` for details.\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat4d_(\n    tensors: Array<Tensor4D|TensorLike>, axis: number): Tensor4D {\n  return concat(tensors, axis);\n}\n\n/**\n * Concatenates a list of`tf.Tensor`s along a given axis.\n *\n * The tensors ranks and types must match, and their sizes must match in all\n * dimensions except `axis`.\n *\n * Also available are stricter rank-specific methods that assert that\n * `tensors` are of the given rank:\n *   - `tf.concat1d`\n *   - `tf.concat2d`\n *   - `tf.concat3d`\n *   - `tf.concat4d`\n *\n * Except `tf.concat1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * a.concat(b).print();  // or a.concat(b)\n * ```\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.concat([a, b, c]).print();\n * ```\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [10, 20]]);\n * const b = tf.tensor2d([[3, 4], [30, 40]]);\n * const axis = 1;\n * tf.concat([a, b], axis).print();\n * ```\n * @param tensors A list of tensors to concatenate.\n * @param axis The axis to concate along. Defaults to 0 (the first dim).\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction concat_<T extends Tensor>(tensors: Array<T|TensorLike>, axis = 0): T {\n  assert(tensors.length >= 1, 'Pass at least one tensor to concat');\n  let $tensors = convertToTensorArray(tensors, 'tensors', 'concat');\n  axis = parseAxisParam(axis, $tensors[0].shape)[0];\n  const outShape = computeOutShape($tensors.map(t => t.shape), axis);\n  if (sizeFromShape(outShape) === 0) {\n    return tensor([], outShape) as T;\n  }\n  // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n  $tensors = $tensors.filter(t => t.size > 0);\n  if ($tensors.length === 1) {\n    return $tensors[0];\n  }\n\n  const shapes = $tensors.map(t => t.shape);\n  assertParamsConsistent(shapes, axis);\n  const der = (dy: T) => {\n    const sizeSplits = shapes.map(s => s[axis]);\n    const derTensors = split(dy, sizeSplits, axis);\n    return derTensors.map(t => () => t) as {};\n  };\n  const inputs = $tensors as {};\n  return ENV.engine.runKernel(\n      backend => backend.concat($tensors, axis) as T, inputs, der);\n}\n\n/**\n * Splits a`tf.Tensor` into sub tensors.\n *\n * If `numOrSizeSplits` is a number, splits `x` along dimension `axis`\n * into `numOrSizeSplits` smaller tensors.\n * Requires that `numOrSizeSplits` evenly divides `x.shape[axis]`.\n *\n * If `numOrSizeSplits` is a number array, splits `x` into\n * `(numOrSizeSplits.length` pieces. The shape of the `i`-th piece has the\n * same size as `x` except along dimension `axis` where the size is\n * `numOrSizeSplits[i]`.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);\n * const [a, b] = tf.split(x, 2, 1);\n * a.print();\n * b.print();\n *\n * const [c, d, e] = tf.split(x, [1, 2, 1], 1);\n * c.print();\n * d.print();\n * e.print();\n * ```\n *\n * @param x The input tensor to split.\n * @param numOrSizeSplits Either an integer indicating the number of\n * splits along the axis or an array of integers containing the sizes of\n * each output tensor along the axis. If a number then it must evenly divide\n * `x.shape[axis]`; otherwise the sum of sizes must match `x.shape[axis]`.\n * @param axis The dimension along which to split. Defaults to 0 (the first\n * dim).\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction split_<T extends Tensor>(\n    x: T|TensorLike, numOrSizeSplits: number[]|number, axis = 0): T[] {\n  const $x = convertToTensor(x, 'x', 'split');\n\n  axis = parseAxisParam(axis, $x.shape)[0];\n  let splitSizes: number[];\n  if (typeof (numOrSizeSplits) === 'number') {\n    assert(\n        $x.shape[axis] % numOrSizeSplits === 0,\n        'Number of splits must evenly divide the axis.');\n    splitSizes = Array(numOrSizeSplits).fill($x.shape[axis] / numOrSizeSplits);\n  } else {\n    assert(\n        $x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b),\n        'The sum of sizes must match the size of the axis dimension.');\n    splitSizes = numOrSizeSplits;\n  }\n  const der = (dy: T[]) => ({$x: () => concat(dy, axis)});\n  return ENV.engine.runKernel(\n      backend => backend.split($x, splitSizes, axis), {$x}, der);\n}\n\nexport const concat = op({concat_});\nexport const concat1d = op({concat1d_});\nexport const concat2d = op({concat2d_});\nexport const concat3d = op({concat3d_});\nexport const concat4d = op({concat4d_});\nexport const split = op({split_});\n","// A port of an algorithm by Johannes Baagøe <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baagøe <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n","// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n","// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n","// A Javascript implementaion of the \"xorshift7\" algorithm by\n// François Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n","// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n","// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n","/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n","// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baagøe.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by François Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as seedrandom from 'seedrandom';\n\nexport interface RandGauss {\n  nextValue(): number;\n}\n\nexport interface RandNormalDataTypes {\n  float32: Float32Array;\n  int32: Int32Array;\n}\n\n// https://en.wikipedia.org/wiki/Marsaglia_polar_method\nexport class MPRandGauss implements RandGauss {\n  private mean: number;\n  private stdDev: number;\n  private nextVal: number;\n  private dtype?: keyof RandNormalDataTypes;\n  private truncated?: boolean;\n  private upper?: number;\n  private lower?: number;\n  private random: seedrandom.prng;\n\n  constructor(\n      mean: number, stdDeviation: number, dtype?: keyof RandNormalDataTypes,\n      truncated?: boolean, seed?: number) {\n    this.mean = mean;\n    this.stdDev = stdDeviation;\n    this.dtype = dtype;\n    this.nextVal = NaN;\n    this.truncated = truncated;\n    if (this.truncated) {\n      this.upper = this.mean + this.stdDev * 2;\n      this.lower = this.mean - this.stdDev * 2;\n    }\n    const seedValue = seed ? seed : Math.random();\n    this.random = seedrandom.alea(seedValue.toString());\n  }\n\n  /** Returns next sample from a gaussian distribution. */\n  public nextValue(): number {\n    if (!isNaN(this.nextVal)) {\n      const value = this.nextVal;\n      this.nextVal = NaN;\n      return value;\n    }\n\n    let resultX: number, resultY: number;\n    let isValid = false;\n    while (!isValid) {\n      let v1: number, v2: number, s: number;\n      do {\n        v1 = 2 * this.random() - 1;\n        v2 = 2 * this.random() - 1;\n        s = v1 * v1 + v2 * v2;\n      } while (s >= 1 || s === 0);\n\n      const mul = Math.sqrt(-2.0 * Math.log(s) / s);\n      resultX = this.mean + this.stdDev * v1 * mul;\n      resultY = this.mean + this.stdDev * v2 * mul;\n\n      if (!this.truncated || this.isValidTruncated(resultX)) {\n        isValid = true;\n      }\n    }\n\n    if (!this.truncated || this.isValidTruncated(resultY)) {\n      this.nextVal = this.convertValue(resultY);\n    }\n    return this.convertValue(resultX);\n  }\n\n  /** Handles proper rounding for non floating point numbers. */\n  private convertValue(value: number): number {\n    if (this.dtype == null || this.dtype === 'float32') {\n      return value;\n    }\n    return Math.round(value);\n  }\n\n  /** Returns true if less than 2-standard-deviations from the mean. */\n  private isValidTruncated(value: number): boolean {\n    return value <= this.upper && value >= this.lower;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, TensorBuffer} from '../tensor';\nimport {convertToTensor, convertToTensorArray} from '../tensor_util_env';\nimport {DataType, DataTypeMap, Rank, ShapeMap, TensorLike, TensorLike1D, TensorLike4D} from '../types';\nimport * as util from '../util';\nimport {getAxesPermutation, getInnerMostAxes} from './axis_util';\nimport {concat} from './concat_split';\nimport {op} from './operation';\nimport {MPRandGauss} from './rand';\nimport {zeros, zerosLike} from './tensor_ops';\n\n/**\n * Creates a new tensor with the same values and shape as the specified\n * tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n *\n * x.clone().print();\n * ```\n *\n * @param x The tensor to clone.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction clone_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'clone', null);\n  const der = (dy: T) => {\n    return {$x: () => dy.toFloat()};\n  };\n\n  return ENV.engine.runKernel(\n             backend =>\n                 Tensor.make($x.shape, {dataId: $x.dataId}, $x.dtype) as T,\n             {$x}, der) as T;\n}\n\n/**\n * Create an identity matrix.\n *\n * @param numRows Number of rows.\n * @param numColumns Number of columns. Defaults to `numRows`.\n * @param batchShape If provided, will add the batch shape to the beginning\n *   of the shape of the returned `tf.Tensor` by repeating the identity\n *   matrix.\n * @param dtype Data type.\n * @returns Identity matrix of the specified size and data type, possibly\n *   with batch repetition if `batchShape` is specified.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction eye_(\n    numRows: number, numColumns?: number,\n    batchShape?:\n        [\n          number\n        ]|[number,\n           number]|[number, number, number]|[number, number, number, number],\n    dtype: DataType = 'float32'): Tensor2D {\n  if (numColumns == null) {\n    numColumns = numRows;\n  }\n  const buff = buffer([numRows, numColumns], dtype);\n  const n = numRows <= numColumns ? numRows : numColumns;\n  for (let i = 0; i < n; ++i) {\n    buff.set(1, i, i);\n  }\n  const out = buff.toTensor().as2D(numRows, numColumns);\n  if (batchShape == null) {\n    return out;\n  } else {\n    if (batchShape.length === 1) {\n      return tile(expandDims(out, 0), [batchShape[0], 1, 1]);\n    } else if (batchShape.length === 2) {\n      return tile(\n          expandDims(expandDims(out, 0), 0),\n          [batchShape[0], batchShape[1], 1, 1]);\n    } else if (batchShape.length === 3) {\n      return tile(\n          expandDims(expandDims(expandDims(out, 0), 0), 0),\n          [batchShape[0], batchShape[1], batchShape[2], 1, 1]);\n    } else {\n      throw new Error(\n          `eye() currently supports only 1D and 2D ` +\n          // tslint:disable-next-line:no-any\n          `batchShapes, but received ${(batchShape as any).length}D.`);\n    }\n  }\n}\n\n/**\n * Creates a `tf.Tensor` with values sampled from a normal distribution.\n *\n * ```js\n * tf.randomNormal([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output.\n * @param seed The seed for the random number generator.\n */\n/** @doc {heading: 'Tensors', subheading: 'Random'} */\nfunction randomNormal_<R extends Rank>(\n    shape: ShapeMap[R], mean = 0, stdDev = 1, dtype?: 'float32'|'int32',\n    seed?: number): Tensor<R> {\n  if (dtype != null && (dtype as DataType) === 'bool') {\n    throw new Error(`Unsupported data type ${dtype}`);\n  }\n  const randGauss =\n      new MPRandGauss(mean, stdDev, dtype, false /* truncated */, seed);\n  const res = buffer(shape, dtype);\n  for (let i = 0; i < res.values.length; i++) {\n    res.values[i] = randGauss.nextValue();\n  }\n  return res.toTensor();\n}\n\n/**\n * Creates a `tf.Tensor` with values sampled from a truncated normal\n * distribution.\n *\n * ```js\n * tf.truncatedNormal([2, 2]).print();\n * ```\n *\n * The generated values follow a normal distribution with specified mean and\n * standard deviation, except that values whose magnitude is more than 2\n * standard deviations from the mean are dropped and re-picked.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output tensor.\n * @param seed The seed for the random number generator.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction truncatedNormal_<R extends Rank>(\n    shape: ShapeMap[R], mean = 0, stdDev = 1, dtype?: 'float32'|'int32',\n    seed?: number): Tensor<R> {\n  if (dtype != null && (dtype as DataType) === 'bool') {\n    throw new Error(`Unsupported data type ${dtype}`);\n  }\n  const randGauss =\n      new MPRandGauss(mean, stdDev, dtype, true /* truncated */, seed);\n  const res = buffer(shape, dtype);\n  for (let i = 0; i < res.values.length; i++) {\n    res.values[i] = randGauss.nextValue();\n  }\n  return res.toTensor();\n}\n\n/**\n * Creates a `tf.Tensor` with values sampled from a uniform distribution.\n *\n * The generated values follow a uniform distribution in the range [minval,\n * maxval). The lower bound minval is included in the range, while the upper\n * bound maxval is excluded.\n *\n * ```js\n * tf.randomUniform([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param minval The lower bound on the range of random values to generate.\n *   Defaults to 0.\n * @param maxval The upper bound on the range of random values to generate.\n *   Defaults to 1.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n */\n/** @doc {heading: 'Tensors', subheading: 'Random'} */\nfunction randomUniform_<R extends Rank>(\n    shape: ShapeMap[R], minval = 0, maxval = 1,\n    dtype: DataType = 'float32'): Tensor<R> {\n  const res = buffer(shape, dtype);\n  for (let i = 0; i < res.values.length; i++) {\n    res.values[i] = util.randUniform(minval, maxval);\n  }\n  return res.toTensor();\n}\n\n/**\n * Creates a `tf.Tensor` with values sampled from a random number generator\n * function defined by the user.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param randFunction A random number generator function which is called\n * for each element in the output tensor.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n */\nfunction rand_<R extends Rank>(\n    shape: ShapeMap[R], randFunction: () => number,\n    dtype?: DataType): Tensor<R> {\n  const size = util.sizeFromShape(shape);\n\n  let values = null;\n  if (dtype == null || dtype === 'float32') {\n    values = new Float32Array(size);\n  } else if (dtype === 'int32') {\n    values = new Int32Array(size);\n  } else if (dtype === 'bool') {\n    values = new Uint8Array(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n\n  for (let i = 0; i < size; i++) {\n    values[i] = randFunction();\n  }\n  return Tensor.make(shape, {values}, dtype);\n}\n\n/**\n * Creates a `tf.Tensor` with values drawn from a multinomial distribution.\n *\n * ```js\n * const probs = tf.tensor([.75, .25]);\n * tf.multinomial(probs, 3).print();\n * ```\n *\n * @param logits 1D array with unnormalized log-probabilities, or\n *     2D array of shape `[batchSize, numOutcomes]`. See the `normalized`\n *     parameter.\n * @param numSamples Number of samples to draw for each row slice.\n * @param seed The seed number.\n * @param normalized Whether the provided `logits` are normalized true\n *     probabilities (sum to 1). Defaults to false.\n * @return 1D array of shape `[numSamples]`, or 2D array of shape\n *     `[batchSize, numSamples]`, depending on the rank of the input.\n */\n/** @doc {heading: 'Tensors', subheading: 'Random'} */\nfunction multinomial_(\n    logits: Tensor1D|Tensor2D|TensorLike, numSamples: number, seed?: number,\n    normalized = false): Tensor1D|Tensor2D {\n  const $logits = convertToTensor(logits, 'logits', 'multinomial');\n  const numOutcomes = $logits.size;\n  const origRank = $logits.rank;\n  if (numOutcomes < 2) {\n    throw new Error(\n        `Error in multinomial: you need at least 2 outcomes, but got ` +\n        `${numOutcomes}.`);\n  }\n  if (origRank > 2) {\n    throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);\n  }\n  seed = seed || Math.random();\n  const logits2D = origRank === 1 ? $logits.as2D(1, -1) : $logits as Tensor2D;\n  const res = ENV.engine.runKernel(\n      backend => backend.multinomial(logits2D, normalized, numSamples, seed),\n      {logits2D});\n\n  return origRank === 1 ? res.as1D() : res;\n}\n\n/**\n * Creates a one-hot `tf.Tensor`. The locations represented by `indices` take\n * value `onValue` (defaults to 1), while all other locations take value\n * `offValue` (defaults to 0).\n *\n * ```js\n * tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print();\n * ```\n *\n * @param indices `tf.Tensor1D` of indices with dtype `int32`.\n * @param depth The depth of the one hot dimension.\n * @param onValue A number used to fill in the output when the index matches\n * the location.\n * @param offValue A number used to fill in the output when the index does\n *     not match the location.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction oneHot_(\n    indices: Tensor1D|TensorLike1D, depth: number, onValue = 1,\n    offValue = 0): Tensor2D {\n  const $indices =\n      convertToTensor(indices, 'indices', 'oneHot', 'int32') as Tensor1D;\n\n  if (depth < 2) {\n    throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);\n  }\n  const grad = (dy: Tensor2D) => {\n    return {$indices: () => zeros($indices.shape, 'float32') as Tensor1D};\n  };\n  return ENV.engine.runKernel(\n      backend => backend.oneHot($indices, depth, onValue, offValue), {$indices},\n      grad);\n}\n\n/**\n * Creates a `tf.Tensor` from an image.\n *\n * ```js\n * const image = new ImageData(1, 1);\n * image.data[0] = 100;\n * image.data[1] = 150;\n * image.data[2] = 200;\n * image.data[3] = 255;\n *\n * tf.fromPixels(image).print();\n * ```\n *\n * @param pixels The input image to construct the tensor from. The\n * supported image types are all 4-channel.\n * @param numChannels The number of channels of the output tensor. A\n * numChannels value less than 4 allows you to ignore channels. Defaults to\n * 3 (ignores alpha channel of input image).\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction fromPixels_(\n    pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement,\n    numChannels = 3): Tensor3D {\n  if (numChannels > 4) {\n    throw new Error(\n        'Cannot construct Tensor with more than 4 channels from pixels.');\n  }\n  return ENV.engine.fromPixels(pixels, numChannels);\n}\n\n/**\n * Draws a `tf.Tensor` of pixel values to a byte array or optionally a\n * canvas.\n *\n * When the dtype of the input is 'float32', we assume values in the range\n * [0-1]. Otherwise, when input is 'int32', we assume values in the range\n * [0-255].\n *\n * Returns a promise that resolves when the canvas has been drawn to.\n *\n * @param img A rank-2 or rank-3 tensor. If rank-2, draws grayscale. If\n *     rank-3, must have depth of 1, 3 or 4. When depth of 1, draws\n * grayscale. When depth of 3, we draw with the first three components of\n * the depth dimension corresponding to r, g, b and alpha = 1. When depth of\n * 4, all four components of the depth dimension correspond to r, g, b, a.\n * @param canvas The canvas to draw to.\n */\n/** @doc {heading: 'Visualization'} */\nasync function toPixels(\n    img: Tensor2D|Tensor3D|TensorLike,\n    canvas?: HTMLCanvasElement): Promise<Uint8ClampedArray> {\n  let $img = convertToTensor(img, 'img', 'toPixels');\n  if (!(img instanceof Tensor)) {\n    // Assume int32 if user passed a native array.\n    $img = $img.toInt();\n  }\n  if ($img.rank !== 2 && $img.rank !== 3) {\n    throw new Error(\n        `toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);\n  }\n  const [height, width] = $img.shape.slice(0, 2);\n  const depth = $img.rank === 2 ? 1 : $img.shape[2];\n\n  if (depth > 4 || depth === 2) {\n    throw new Error(\n        `toPixels only supports depth of size ` +\n        `1, 3 or 4 but got ${depth}`);\n  }\n\n  const minTensor = $img.min();\n  const maxTensor = $img.max();\n  const min = (await minTensor.data())[0];\n  const max = (await maxTensor.data())[0];\n  minTensor.dispose();\n  maxTensor.dispose();\n  if ($img.dtype === 'float32') {\n    if (min < 0 || max > 1) {\n      throw new Error(\n          `Tensor values for a float32 Tensor must be in the ` +\n          `range [0 - 1] but got range [${min} - ${max}].`);\n    }\n  } else if ($img.dtype === 'int32') {\n    if (min < 0 || max > 255) {\n      throw new Error(\n          `Tensor values for a int32 Tensor must be in the ` +\n          `range [0 - 255] but got range [${min} - ${max}].`);\n    }\n  } else {\n    throw new Error(\n        `Unsupported type for toPixels: ${$img.dtype}.` +\n        ` Please use float32 or int32 tensors.`);\n  }\n\n  const data = await $img.data();\n  const multiplier = $img.dtype === 'float32' ? 255 : 1;\n  const bytes = new Uint8ClampedArray(width * height * 4);\n\n  for (let i = 0; i < height * width; ++i) {\n    let r, g, b, a;\n    if (depth === 1) {\n      r = data[i] * multiplier;\n      g = data[i] * multiplier;\n      b = data[i] * multiplier;\n      a = 255;\n    } else if (depth === 3) {\n      r = data[i * 3] * multiplier;\n      g = data[i * 3 + 1] * multiplier;\n      b = data[i * 3 + 2] * multiplier;\n      a = 255;\n    } else if (depth === 4) {\n      r = data[i * 4] * multiplier;\n      g = data[i * 4 + 1] * multiplier;\n      b = data[i * 4 + 2] * multiplier;\n      a = data[i * 4 + 3] * multiplier;\n    }\n\n    const j = i * 4;\n    bytes[j + 0] = Math.round(r);\n    bytes[j + 1] = Math.round(g);\n    bytes[j + 2] = Math.round(b);\n    bytes[j + 3] = Math.round(a);\n  }\n\n  if (canvas != null) {\n    canvas.width = width;\n    canvas.height = height;\n    const ctx = canvas.getContext('2d');\n    const imageData = new ImageData(bytes, width, height);\n    ctx.putImageData(imageData, 0, 0);\n  }\n  if ($img !== img) {\n    $img.dispose();\n  }\n  return bytes;\n}\n\n/**\n * Reshapes a `tf.Tensor` to a given shape.\n *\n * Given an input tensor, returns a new tensor with the same values as the\n * input tensor with shape `shape`.\n *\n * If one component of shape is the special value -1, the size of that\n * dimension is computed so that the total size remains constant. In\n * particular, a shape of [-1] flattens into 1-D. At most one component of\n * shape can be -1.\n *\n * If shape is 1-D or higher, then the operation returns a tensor with shape\n * shape filled with the values of tensor. In this case, the number of\n * elements implied by shape must be the same as the number of elements in\n * tensor.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.reshape([2, 2]).print();\n * ```\n *\n * @param x The input tensor to be reshaped.\n * @param shape An array of integers defining the output tensor shape.\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction reshape_<R2 extends Rank>(\n    x: Tensor|TensorLike, shape: ShapeMap[R2]): Tensor<R2> {\n  const $x = convertToTensor(x, 'x', 'reshape', null);\n  shape = util.inferFromImplicitShape(shape, $x.size);\n  util.assert(\n      $x.size === util.sizeFromShape(shape),\n      'new shape and old shape must have the same number of elements.');\n\n  const grad = (dy: Tensor<R2>) => {\n    return {$x: () => dy.reshape($x.shape)};\n  };\n  return ENV.engine.runKernel(\n      backend => backend.reshape($x, shape), {$x}, grad);\n}\n\n/**\n * Removes dimensions of size 1 from the shape of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4], [1, 1, 4]);\n * x.squeeze().print();\n * ```\n *\n * @param x The input tensor to be squeezed.\n * @param axis An optional list of numbers. If specified, only\n *     squeezes the dimensions listed. The dimension index starts at 0. It\n * is an error to squeeze a dimension that is not 1.\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction squeeze_<T extends Tensor>(x: Tensor|TensorLike, axis?: number[]): T {\n  const $x = convertToTensor(x, 'x', 'squeeze');\n  return reshape($x, util.squeezeShape($x.shape, axis).newShape) as T;\n}\n\n/**\n * Casts a `tf.Tensor` to a new dtype.\n *\n * ```js\n * const x = tf.tensor1d([1.5, 2.5, 3]);\n * tf.cast(x, 'int32').print();\n * ```\n * @param x The input tensor to be casted.\n * @param dtype The dtype to cast the input tensor to.\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction cast_<T extends Tensor>(x: T|TensorLike, dtype: DataType): T {\n  const $x = convertToTensor(x, 'x', 'cast');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.clone()};\n  };\n  return ENV.engine.runKernel(backend => backend.cast($x, dtype), {$x}, grad) as\n      T;\n}\n\n/**\n * Construct a tensor by repeating it the number of times given by reps.\n *\n * This operation creates a new tensor by replicating `input` `reps`\n * times. The output tensor's i'th dimension has `input.shape[i] *\n * reps[i]` elements, and the values of `input` are replicated\n * `reps[i]` times along the i'th dimension. For example, tiling\n * `[a, b, c, d]` by `[2]` produces `[a, b, c, d, a, b, c, d]`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n *\n * a.tile([2]).print();    // or a.tile([2])\n * ```\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.tile([1, 2]).print();  // or a.tile([1, 2])\n * ```\n * @param x The tensor to tile.\n * @param reps Determines the number of replications per dimension.\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction tile_<T extends Tensor>(x: T|TensorLike, reps: number[]): T {\n  const $x = convertToTensor(x, 'x', 'tile');\n\n  util.assert(\n      $x.rank === reps.length,\n      `Error in transpose: rank of input ${$x.rank} ` +\n          `must match length of reps ${reps}.`);\n  const grad = (dy: T) => {\n    const derX = () => {\n      let xGrad = zerosLike($x);\n      // TODO(cais): Maybe reduce memory footprint by avoiding repeated\n      // slicing.\n      if ($x.rank === 1) {\n        for (let i = 0; i < reps[0]; ++i) {\n          xGrad = xGrad.add(dy.slice([i * $x.shape[0]], [$x.shape[0]]));\n        }\n      } else if ($x.rank === 2) {\n        for (let i = 0; i < reps[0]; ++i) {\n          for (let j = 0; j < reps[1]; ++j) {\n            xGrad = xGrad.add(dy.slice(\n                [i * $x.shape[0], j * $x.shape[1]],\n                [$x.shape[0], $x.shape[1]]));\n          }\n        }\n      } else if ($x.rank === 3) {\n        for (let i = 0; i < reps[0]; ++i) {\n          for (let j = 0; j < reps[1]; ++j) {\n            for (let k = 0; k < reps[2]; ++k) {\n              xGrad = xGrad.add(dy.slice(\n                  [i * $x.shape[0], j * $x.shape[1], k * $x.shape[2]],\n                  [$x.shape[0], $x.shape[1], $x.shape[2]]));\n            }\n          }\n        }\n      } else if ($x.rank === 4) {\n        for (let i = 0; i < reps[0]; ++i) {\n          for (let j = 0; j < reps[1]; ++j) {\n            for (let k = 0; k < reps[2]; ++k) {\n              for (let l = 0; l < reps[3]; ++l) {\n                xGrad = xGrad.add(dy.slice(\n                    [\n                      i * $x.shape[0], j * $x.shape[1], k * $x.shape[2],\n                      l * $x.shape[3]\n                    ],\n                    [$x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]));\n              }\n            }\n          }\n        }\n      } else {\n        throw new Error(\n            `Gradient for tile operation is not implemented for rank-` +\n            `${$x.rank} tensors yet.`);\n      }\n      return xGrad;\n    };\n    return {$x: derX};\n  };\n  return ENV.engine.runKernel(backend => backend.tile($x, reps), {$x}, grad);\n}\n\n/**\n * Pads a `tf.Tensor1D` with a given value and paddings. See `pad` for details.\n */\nfunction pad1d_(\n    x: Tensor1D|TensorLike, paddings: [number, number],\n    constantValue = 0): Tensor1D {\n  util.assert(\n      paddings.length === 2,\n      'Invalid number of paddings. Must be length of 2.');\n  return pad(x, [paddings], constantValue);\n}\n\n/**\n * Pads a `tf.Tensor2D` with a given value and paddings. See `pad` for details.\n */\nfunction pad2d_(\n    x: Tensor2D|TensorLike, paddings: [[number, number], [number, number]],\n    constantValue = 0): Tensor2D {\n  util.assert(\n      paddings.length === 2 && paddings[0].length === 2 &&\n          paddings[1].length === 2,\n      'Invalid number of paddings. Must be length of 2 each.');\n  return pad(x, paddings, constantValue);\n}\n\n/**\n * Pads a `tf.Tensor3D` with a given value and paddings. See `pad` for details.\n */\nfunction pad3d_(\n    x: Tensor3D|TensorLike,\n    paddings: [[number, number], [number, number], [number, number]],\n    constantValue = 0): Tensor3D {\n  util.assert(\n      paddings.length === 3 && paddings[0].length === 2 &&\n          paddings[1].length === 2 && paddings[2].length === 2,\n      'Invalid number of paddings. Must be length of 2 each.');\n  return pad(x, paddings, constantValue);\n}\n\n/**\n * Pads a `tf.Tensor4D` with a given value and paddings. See `pad` for details.\n */\nfunction pad4d_(\n    x: Tensor4D|TensorLike,\n    paddings:\n        [\n          [number, number], [number, number], [number, number], [number, number]\n        ],\n    constantValue = 0): Tensor4D {\n  util.assert(\n      paddings.length === 4 && paddings[0].length === 2 &&\n          paddings[1].length === 2 && paddings[2].length === 2 &&\n          paddings[3].length === 2,\n      'Invalid number of paddings. Must be length of 2 each.');\n  return pad(x, paddings, constantValue);\n}\n\n/**\n * Pads a `tf.Tensor` with a given value and paddings.\n *\n * This operation currently only implements the `CONSTANT` mode.\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `paddings` is of given length.\n *   - `tf.pad1d`\n *   - `tf.pad2d`\n *   - `tf.pad3d`\n *   - `tf.pad4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.pad([[1, 2]]).print();\n * ```\n * @param x The tensor to pad.\n * @param paddings An array of length `R` (the rank of the tensor), where\n * each element is a length-2 tuple of ints `[padBefore, padAfter]`,\n * specifying how much to pad along each dimension of the tensor.\n * @param constantValue The pad value to use. Defaults to 0.\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction pad_<T extends Tensor>(\n    x: T|TensorLike, paddings: Array<[number, number]>, constantValue = 0): T {\n  const $x = convertToTensor(x, 'x', 'pad');\n\n  if ($x.rank === 0) {\n    throw new Error('pad(scalar) is not defined. Pass non-scalar to pad');\n  }\n  // Pad introduces values around the original tensor, so the gradient\n  // slices the original shape out of the gradient.\n  const begin = paddings.map(p => p[0]);\n  const grad = (dy: T) => {\n    return {$x: () => dy.slice(begin, $x.shape)};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.pad($x, paddings, constantValue), {$x}, grad) as\n      T;\n}\n\n/**\n * Stacks a list of rank-`R` `tf.Tensor`s into one rank-`(R+1)` `tf.Tensor`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.stack([a, b, c]).print();\n * ```\n *\n * @param tensors A list of tensor objects with the same shape and dtype.\n * @param axis The axis to stack along. Defaults to 0 (the first dim).\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction stack_<T extends Tensor>(\n    tensors: Array<T|TensorLike>, axis = 0): Tensor {\n  const $tensors = convertToTensorArray(tensors, 'tensors', 'stack');\n\n  util.assert($tensors.length >= 1, 'Pass at least one tensor to tf.stack');\n  if ($tensors.length === 1) {\n    return $tensors[0].expandDims(axis);\n  }\n  const rank = $tensors[0].rank;\n  const shape = $tensors[0].shape;\n  const dtype = $tensors[0].dtype;\n\n  util.assert(axis <= rank, 'Axis must be <= rank of the tensor');\n\n  $tensors.forEach(t => {\n    util.assertShapesMatch(\n        shape, t.shape,\n        'All tensors passed to stack must have matching shapes');\n  });\n\n  $tensors.forEach(t => {\n    util.assert(\n        dtype === t.dtype,\n        'All tensors passed to stack must have matching dtypes');\n  });\n  const expandedTensors = $tensors.map(t => t.expandDims(axis));\n  return concat(expandedTensors, axis);\n}\n\n/**\n * This operation reshapes the \"batch\" dimension 0 into `M + 1` dimensions of\n * shape `blockShape + [batch]`, interleaves these blocks back into the grid\n * defined by the spatial dimensions `[1, ..., M]`, to obtain a result with\n * the same rank as the input. The spatial dimensions of this intermediate\n * result are then optionally cropped according to `crops` to produce the\n * output. This is the reverse of `tf.spaceToBatchND`. See below for a precise\n * description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);\n * const blockShape = [2, 2];\n * const crops = [[0, 0], [0, 0]];\n *\n * x.batchToSpaceND(blockShape, crops).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param crops A 2-D array.  Must have shape `[M, 2]`, all values must be >= 0.\n * `crops[i] = [cropStart, cropEnd]` specifies the amount to crop from input\n * dimension `i + 1`, which corresponds to spatial dimension `i`. It is required\n * that `cropStart[i] + cropEnd[i] <= blockShape[i] * inputShape[i + 1]`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Reshape `x` to `reshaped` of shape: `[blockShape[0], ...,\n * blockShape[M-1], batch / prod(blockShape), x.shape[1], ...,\n * x.shape[N-1]]`\n *\n * 2. Permute dimensions of `reshaped`to produce `permuted` of shape `[batch /\n * prod(blockShape),x.shape[1], blockShape[0], ..., x.shape[M],\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 3. Reshape `permuted` to produce `reshapedPermuted` of shape `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0], ..., x.shape[M] *\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 4. Crop the start and end of dimensions `[1, ..., M]` of `reshapedPermuted`\n * according to `crops` to produce the output of shape: `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0] - crops[0,0] - crops[0,1],\n * ..., x.shape[M] * blockShape[M-1] - crops[M-1,0] -\n * crops[M-1,1],x.shape[M+1], ..., x.shape[N-1]]`\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction batchToSpaceND_<T extends Tensor>(\n    x: T|TensorLike, blockShape: number[], crops: number[][]): T {\n  const $x = convertToTensor(x, 'x', 'batchToSpaceND');\n  const prod = blockShape.reduce((a, b) => a * b);\n\n  util.assert(\n      $x.rank >= 1 + blockShape.length,\n      `input rank is ${$x.rank} but should be > than blockShape.length ${\n          blockShape.length}`);\n\n  util.assert(\n      crops.length === blockShape.length,\n      `crops.length is ${\n          crops.length} but should be equal to blockShape.length  ${\n          blockShape.length}`);\n\n  util.assert(\n      $x.shape[0] % prod === 0,\n      `input tensor batch is ${\n          $x.shape[0]} but is not divisible by the product of ` +\n          `the elements of blockShape ${blockShape.join(' * ')} === ${prod}`);\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.spaceToBatchND(blockShape, crops)};\n  };\n\n  return ENV.engine.runKernel(\n      backend => backend.batchToSpaceND($x, blockShape, crops), {$x}, grad);\n}\n\n/**\n * This operation divides \"spatial\" dimensions `[1, ..., M]` of the input into\n * a grid of blocks of shape `blockShape`, and interleaves these blocks with\n * the \"batch\" dimension (0) such that in the output, the spatial\n * dimensions `[1, ..., M]` correspond to the position within the grid,\n * and the batch dimension combines both the position within a spatial block\n * and the original batch position. Prior to division into blocks,\n * the spatial dimensions of the input are optionally zero padded\n * according to `paddings`. See below for a precise description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);\n * const blockShape = [2, 2];\n * const paddings = [[0, 0], [0, 0]];\n *\n * x.spaceToBatchND(blockShape, paddings).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param paddings A 2-D array. Must have shape `[M, 2]`, all values must be >=\n *     0. `paddings[i] = [padStart, padEnd]` specifies the amount to zero-pad\n * from input dimension `i + 1`, which corresponds to spatial dimension `i`. It\n * is required that\n * `(inputShape[i + 1] + padStart + padEnd) % blockShape[i] === 0`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the input\n * according to `paddings` to produce `padded` of shape paddedShape.\n *\n * 2. Reshape `padded` to `reshapedPadded` of shape:\n * `[batch] + [paddedShape[1] / blockShape[0], blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1], blockShape[M-1]] + remainingShape`\n *\n * 3. Permute dimensions of `reshapedPadded` to produce `permutedReshapedPadded`\n * of shape: `blockShape + [batch] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * 4. Reshape `permutedReshapedPadded` to flatten `blockShape` into the\n * batch dimension, producing an output tensor of shape:\n * `[batch * prod(blockShape)] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction spaceToBatchND_<T extends Tensor>(\n    x: T|TensorLike, blockShape: number[], paddings: number[][]): T {\n  const $x = convertToTensor(x, 'x', 'spaceToBatchND');\n\n  util.assert(\n      $x.rank >= 1 + blockShape.length,\n      `input rank ${$x.rank} should be > than [blockShape] ${\n          blockShape.length}`);\n\n  util.assert(\n      paddings.length === blockShape.length,\n      `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${\n          blockShape.length}`);\n\n  util.assert(\n      $x.shape.reduce(\n          (a, b, i) => {\n            if (i > 0 && i <= blockShape.length) {\n              return a &&\n                  ((b + paddings[i - 1][0] + paddings[i - 1][1]) %\n                       blockShape[i - 1] ===\n                   0);\n            }\n            return a;\n          },\n          true),\n      `input spatial dimensions ${$x.shape.slice(1)} with paddings ${\n          paddings.toString()} must be divisible by blockShapes ${\n          blockShape.toString()}`);\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.batchToSpaceND(blockShape, paddings)};\n  };\n\n  return ENV.engine.runKernel(\n      backend => backend.spaceToBatchND($x, blockShape, paddings), {$x}, grad);\n}\n\n/**\n * Unstacks a `tf.Tensor` of rank-`R` into a list of rank-`(R-1)` `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * tf.unstack(a).forEach(tensor => tensor.print());\n * ```\n *\n * @param x A tensor object.\n * @param axis The axis to unstack along. Defaults to 0 (the first dim).\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction unstack_<T extends Tensor>(x: T|TensorLike, axis = 0): Tensor[] {\n  const $x = convertToTensor(x, 'x', 'unstack');\n  const num = $x.shape[axis];\n  const outputShape: number[] = Array($x.rank - 1).fill(0);\n  let outIndex = 0;\n  for (let i = 0; i < $x.rank; i++) {\n    if (i !== axis) {\n      outputShape[outIndex] = $x.shape[i];\n      outIndex++;\n    }\n  }\n\n  let splitSizes: number[];\n  splitSizes = Array(num).fill(1);\n  const begin = Array($x.rank).fill(0);\n  const size = $x.shape.slice();\n  return splitSizes.map(s => {\n    size[axis] = s;\n    const slice = $x.slice(begin, size);\n    begin[axis] += s;\n    return slice.reshape(outputShape);\n  });\n}\n\n/**\n * Computes the cumulative sum of a `tf.Tensor` along `axis`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4]);\n * x.cumsum().print();\n * ```\n * ```js\n * const x = tf.tensor([[1, 2], [3, 4]]);\n * x.cumsum().print();\n * ```\n *\n * @param x The input tensor to be summed.\n * @param axis The axis along which to sum. Optional. Defaults to 0.\n * @param exclusive Whether to perform exclusive cumulative sum. Optional.\n *     Defaults to false. If set to true then the sum of each tensor entry\n *     does not include its own value, but only the values previous to it\n *     along the specified axis.\n * @param reverse Whether to sum in the opposite direction. Optional.\n *     Defaults to false.\n */\n/** @doc {heading: 'Operations', subheading: 'Scan'} */\nfunction cumsum_<T extends Tensor>(\n    x: Tensor|TensorLike, axis = 0, exclusive = false, reverse = false): T {\n  const $x = convertToTensor(x, 'x', 'cumsum');\n\n  axis = axis | 0;\n  const permutation = getAxesPermutation([axis], $x.rank);\n  let permutedX = $x;\n  if (permutation != null) {\n    permutedX = $x.transpose(permutation);\n  }\n  const permutedAxis = getInnerMostAxes(1, $x.rank)[0];\n\n  const grad = (dy: T) => {\n    return {permutedX: () => dy.cumsum(axis, exclusive, !reverse)};\n  };\n  let value = ENV.engine.runKernel(\n                  backend => backend.cumsum(\n                      permutedX, permutedAxis, exclusive, reverse),\n                  {permutedX}, grad) as T;\n\n  if (permutation != null) {\n    value = value.transpose(permutation);\n  }\n  return value;\n}\n\n/**\n * Returns a `tf.Tensor` that has expanded rank, by inserting a dimension\n * into the tensor's shape.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const axis = 1;\n * x.expandDims(axis).print();\n * ```\n *\n * @param x The input tensor whose dimensions to be expanded.\n * @param axis The dimension index at which to insert shape of `1`. Defaults\n *     to 0 (the first dimension).\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction expandDims_<R2 extends Rank>(\n    x: Tensor|TensorLike, axis = 0): Tensor<R2> {\n  const $x = convertToTensor(x, 'x', 'expandDims');\n\n  util.assert(axis <= $x.rank, 'Axis must be <= rank of the tensor');\n  const newShape = $x.shape.slice();\n  if (axis < 0) {\n    // Negative value is counted from the tail of rank.\n    util.assert(\n        -($x.rank + 1) <= axis,\n        `Axis must be in the interval [${- ($x.rank + 1)}, ${$x.rank}]`);\n    axis = $x.rank + axis + 1;\n  }\n  newShape.splice(axis, 0, 1);\n  return reshape($x, newShape);\n}\n\n/**\n * Rearranges data from depth into blocks of spatial data. More specifically,\n * this op outputs a copy of the input tensor where values from the `depth`\n * dimension are moved in spatial blocks to the `height` and `width` dimensions.\n * The attr `blockSize` indicates the input block size and how the data is\n * moved.\n *\n *  - Chunks of data of size `blockSize * blockSize` from depth are rearranged\n * into non-overlapping blocks of size `blockSize x blockSize`\n *\n *  - The width the output tensor is `inputWidth * blockSize`, whereas the\n * height is `inputHeight * blockSize`\n *\n *  - The Y, X coordinates within each block of the output image are determined\n * by the high order component of the input channel index\n *\n *  - The depth of the input tensor must be divisible by `blockSize *\n * blockSize`\n *\n * The `dataFormat` attr specifies the layout of the input and output tensors\n * with the following options: \"NHWC\": [ `batch, height, width, channels` ]\n * \"NCHW\": [ `batch, channels, height, width` ]\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]);\n * const blockSize = 2;\n * const dataFormat = \"NHWC\";\n *\n * tf.depthToSpace(x, blockSize, dataFormat).print();\n * ```\n *\n * @param x The input tensor of rank 4\n * @param blockSIze  An `int` that is `>= 2`. The size of the spatial block\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to \"NHWC\"\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction depthToSpace_(\n    x: Tensor4D|TensorLike4D, blockSize: number,\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC'): Tensor4D {\n  const $x = convertToTensor(x, 'x', 'depthToSpace') as Tensor4D;\n\n  const inputHeight = (dataFormat === 'NHWC') ? $x.shape[1] : $x.shape[2];\n  const inputWidth = (dataFormat === 'NHWC') ? $x.shape[2] : $x.shape[3];\n  const inputDepth = (dataFormat === 'NHWC') ? $x.shape[3] : $x.shape[1];\n\n  util.assert(\n      inputHeight * blockSize >= 0,\n      `Negative dimension size caused by overflow when multiplying\n      ${inputHeight} and ${blockSize}  for depthToSpace with input shape\n      ${$x.shape}`);\n\n  util.assert(\n      inputWidth * blockSize >= 0,\n      `Negative dimension size caused by overflow when multiplying\n      ${inputWidth} and ${blockSize} for depthToSpace with input shape\n          ${$x.shape}`);\n\n  util.assert(\n      (inputDepth % (blockSize * blockSize) === 0),\n      `Dimension size must be evenly divisible by ${\n          blockSize * blockSize} but is ${\n          inputDepth} for depthToSpace with input shape ${$x.shape}`);\n\n  return ENV.engine.runKernel(\n      backend => backend.depthToSpace($x, blockSize, dataFormat), {$x});\n}\n\n/**\n * Computes the difference between two lists of numbers.\n *\n * Given a Tensor `x` and a Tensor `y`, this operation returns a Tensor `out`\n * that represents all values that are in `x` but not in `y`. The returned\n * Tensor `out` is sorted in the same order that the numbers appear in `x`\n * (duplicates are preserved). This operation also returns a Tensor indices that\n * represents the position of each out element in `x`. In other words:\n *\n * `out[i] = x[idx[i]] for i in [0, 1, ..., out.length - 1]`\n *\n * ```js\n * const x = [1, 2, 3, 4, 5, 6];\n * const y = [1, 3, 5];\n *\n * const [out, indices] = await tf.setdiff1dAsync(x, y);\n * out.print(); // [2, 4, 6]\n * indices.print(); // [1, 3, 5]\n * ```\n *\n * @param x 1-D Tensor. Values to keep.\n * @param y 1-D Tensor. Must have the same type as x. Values to exclude in the\n *     output.\n * @returns Promise of Tensor tuple [out, indices].\n *  out: Tensor with the same type as x.\n *  indices: A Tensor of type int32.\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nasync function setdiff1dAsync_(\n    x: Tensor|TensorLike, y: Tensor|TensorLike): Promise<[Tensor, Tensor]> {\n  const $x = convertToTensor(x, 'x', 'setdiff1d');\n  const $y = convertToTensor(y, 'y', 'setdiff1d');\n\n  util.assert(\n      $x.dtype === $y.dtype,\n      `x and y should have the same dtype, but got x (${$x.dtype}) and y (${\n          $y.dtype}).`);\n\n  util.assert($x.rank === 1, `x should be 1D tensor, but got x (${$x.shape}).`);\n\n  util.assert($y.rank === 1, `y should be 1D tensor, but got y (${$y.shape}).`);\n\n  const xVals = await $x.data();\n  const yVals = await $y.data();\n  const ySet = new Set(yVals);\n\n  let outputSize = 0;\n  for (let i = 0; i < xVals.length; i++) {\n    if (!ySet.has(xVals[i])) {\n      outputSize++;\n    }\n  }\n\n  const buffer = new TensorBuffer([outputSize], $x.dtype);\n  const indices = new TensorBuffer([outputSize], 'int32');\n  for (let i = 0, p = 0; i < xVals.length; i++) {\n    if (!ySet.has(xVals[i])) {\n      buffer.values[p] = xVals[i];\n      indices.values[p] = i;\n      p++;\n    }\n  }\n  return [buffer.toTensor(), indices.toTensor()];\n}\n\n/**\n * Creates an empty `tf.TensorBuffer` with the specified `shape` and `dtype`.\n *\n * The values are stored in CPU as `TypedArray`. Fill the buffer using\n * `buffer.set()`, or by modifying directly `buffer.values`.\n *\n * When done, call `buffer.toTensor()` to get an immutable `tf.Tensor` with\n * those values.\n *\n * ```js\n * // Create a buffer and set values at particular indices.\n * const buffer = tf.buffer([2, 2]);\n * buffer.set(3, 0, 0);\n * buffer.set(5, 1, 0);\n *\n * // Convert the buffer back to a tensor.\n * buffer.toTensor().print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The dtype of the buffer. Defaults to 'float32'.\n * @param values The values of the buffer as `TypedArray`. Defaults to\n * zeros.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction buffer<R extends Rank, D extends DataType = 'float32'>(\n    shape: ShapeMap[R], dtype: D = 'float32' as D,\n    values?: DataTypeMap[D]): TensorBuffer<R, D> {\n  dtype = dtype || 'float32' as D;\n  return new TensorBuffer<R, D>(shape, dtype, values);\n}\n\n/**\n * Prints information about the `tf.Tensor` including its data.\n *\n * ```js\n * const verbose = true;\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose);\n * ```\n * @param x The tensor to be printed.\n * @param verbose Whether to print verbose information about the ` Tensor`,\n * including dtype and size.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction print<T extends Tensor>(x: T, verbose = false): void {\n  console.log(x.toString(verbose));\n}\n\nexport {\n  buffer,    // Not wrapped in op() since no tensors.\n  toPixels,  // Not wrapped in op() since async.\n  print      // Not wrapped in op() since no need to increase stack trace.\n};\n\nexport const batchToSpaceND = op({batchToSpaceND_});\nexport const cast = op({cast_});\nexport const clone = op({clone_});\nexport const cumsum = op({cumsum_});\nexport const depthToSpace = op({depthToSpace_});\nexport const expandDims = op({expandDims_});\nexport const eye = op({eye_});\nexport const fromPixels = op({fromPixels_});\nexport const multinomial = op({multinomial_});\nexport const oneHot = op({oneHot_});\nexport const pad = op({pad_});\nexport const pad1d = op({pad1d_});\nexport const pad2d = op({pad2d_});\nexport const pad3d = op({pad3d_});\nexport const pad4d = op({pad4d_});\nexport const rand = op({rand_});\nexport const randomNormal = op({randomNormal_});\nexport const randomUniform = op({randomUniform_});\nexport const reshape = op({reshape_});\nexport const spaceToBatchND = op({spaceToBatchND_});\nexport const squeeze = op({squeeze_});\nexport const stack = op({stack_});\nexport const tile = op({tile_});\nexport const truncatedNormal = op({truncatedNormal_});\nexport const unstack = op({unstack_});\nexport const setdiff1dAsync = setdiff1dAsync_;\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/** An implementation of the Where kernel shared between cpu and webgl */\n\nimport {buffer} from '../ops/array_ops';\nimport {Tensor2D} from '../tensor';\nimport {TypedArray} from '../types';\n\nexport function whereImpl(condShape: number[], condVals: TypedArray): Tensor2D {\n  const indices = [];\n  for (let i = 0; i < condVals.length; i++) {\n    if (condVals[i]) {\n      indices.push(i);\n    }\n  }\n\n  const inBuffer = buffer(condShape, 'int32');\n\n  const out = buffer([indices.length, condShape.length], 'int32');\n  for (let i = 0; i < indices.length; i++) {\n    const loc = inBuffer.indexToLoc(indices[i]);\n    const offset = i * condShape.length;\n    out.values.set(loc, offset);\n  }\n  return out.toTensor() as Tensor2D;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getWebGLContext} from '../canvas_util';\nimport {MemoryInfo, TimingInfo} from '../engine';\nimport {ENV} from '../environment';\nimport {tidy} from '../globals';\nimport {warn} from '../log';\nimport * as array_ops_util from '../ops/array_ops_util';\nimport * as axis_util from '../ops/axis_util';\nimport {computeOutShape} from '../ops/concat_util';\nimport {Conv2DInfo, Conv3DInfo} from '../ops/conv_util';\nimport * as gather_nd_util from '../ops/gather_nd_util';\nimport * as reduce_util from '../ops/reduce_util';\nimport * as scatter_nd_util from '../ops/scatter_nd_util';\nimport * as segment_util from '../ops/segment_util';\nimport {getStridedSlicedInfo} from '../ops/slice_util';\nimport {softmax} from '../ops/softmax';\nimport {range, scalar, tensor} from '../ops/tensor_ops';\nimport {DataId, Scalar, setTensorTracker, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, Tensor5D} from '../tensor';\nimport {DataType, DataTypeMap, DataValues, NumericDataType, Rank, RecursiveArray, ShapeMap, sumOutType, TypedArray, upcastType} from '../types';\nimport * as util from '../util';\nimport {getTypedArrayFromDType, sizeFromShape} from '../util';\nimport {DataMover, DataStorage, KernelBackend} from './backend';\nimport * as backend_util from './backend_util';\nimport {mergeRealAndImagArrays} from './complex_util';\nimport {nonMaxSuppressionImpl} from './non_max_suppression_impl';\nimport {split} from './split_shared';\nimport {topkImpl} from './topk_impl';\nimport {ArgMinMaxProgram} from './webgl/argminmax_gpu';\nimport {AvgPool2DBackpropProgram} from './webgl/avg_pool_backprop_gpu';\nimport {BatchNormProgram} from './webgl/batchnorm_gpu';\nimport {BatchNormPackedProgram} from './webgl/batchnorm_packed_gpu';\nimport * as binaryop_complex_gpu from './webgl/binaryop_complex_gpu';\nimport {BinaryOpComplexProgram} from './webgl/binaryop_complex_gpu';\nimport * as binaryop_gpu from './webgl/binaryop_gpu';\nimport {BinaryOpProgram} from './webgl/binaryop_gpu';\nimport {ClipProgram} from './webgl/clip_gpu';\nimport {ClipPackedProgram} from './webgl/clip_packed_gpu';\nimport {ComplexAbsProgram} from './webgl/complex_abs_gpu';\nimport {ConcatProgram} from './webgl/concat_gpu';\nimport {Conv2DDerFilterProgram, Conv2DDerInputProgram, Conv3DDerFilterProgram, Conv3DDerInputProgram} from './webgl/conv_backprop_gpu';\nimport {DepthwiseConv2DDerFilterProgram, DepthwiseConv2DDerInputProgram} from './webgl/conv_backprop_gpu_depthwise';\nimport {Conv2DProgram, Conv3DProgram} from './webgl/conv_gpu';\nimport {DepthwiseConv2DProgram} from './webgl/conv_gpu_depthwise';\nimport {DepthwiseConvPacked2DProgram} from './webgl/conv_packed_gpu_depthwise';\nimport {CropAndResizeProgram} from './webgl/crop_and_resize_gpu';\nimport {CumSumProgram} from './webgl/cumsum_gpu';\nimport {DepthToSpaceProgram} from './webgl/depth_to_space_gpu';\nimport {EncodeFloatProgram} from './webgl/encode_float_gpu';\nimport * as fft_gpu from './webgl/fft_gpu';\nimport {FFTProgram} from './webgl/fft_gpu';\nimport {FromPixelsProgram} from './webgl/from_pixels_gpu';\nimport {GatherProgram} from './webgl/gather_gpu';\nimport {GatherNDProgram} from './webgl/gather_nd_gpu';\nimport {GPGPUContext} from './webgl/gpgpu_context';\nimport * as gpgpu_math from './webgl/gpgpu_math';\nimport {GPGPUBinary, GPGPUProgram, TensorData} from './webgl/gpgpu_math';\nimport {Im2ColProgram} from './webgl/im2col_gpu';\nimport {LRNProgram} from './webgl/lrn_gpu';\nimport {LRNGradProgram} from './webgl/lrn_grad_gpu';\nimport {MaxPool2DBackpropProgram} from './webgl/max_pool_backprop_gpu';\nimport {MatMulProgram} from './webgl/mulmat_gpu';\nimport {MatMulPackedProgram} from './webgl/mulmat_packed_gpu';\nimport {MultinomialProgram} from './webgl/multinomial_gpu';\nimport {OneHotProgram} from './webgl/onehot_gpu';\nimport {PackProgram} from './webgl/pack_gpu';\nimport {PadProgram} from './webgl/pad_gpu';\nimport {Pool2DProgram} from './webgl/pool_gpu';\nimport {ReduceProgram} from './webgl/reduce_gpu';\nimport {ReshapePackedProgram} from './webgl/reshape_packed_gpu';\nimport {ResizeBilinearBackpropProgram} from './webgl/resize_bilinear_backprop_gpu';\nimport {ResizeBilinearProgram} from './webgl/resize_bilinear_gpu';\nimport {ResizeNearestNeigborBackpropProgram} from './webgl/resize_nearest_neighbor_backprop_gpu';\nimport {ResizeNearestNeighborProgram} from './webgl/resize_nearest_neighbor_gpu';\nimport {ReverseProgram} from './webgl/reverse_gpu';\nimport {ScatterProgram} from './webgl/scatter_gpu';\nimport {SegmentOpProgram} from './webgl/segment_gpu';\nimport {SelectProgram} from './webgl/select_gpu';\nimport {SliceProgram} from './webgl/slice_gpu';\nimport {StridedSliceProgram} from './webgl/strided_slice_gpu';\nimport * as tex_util from './webgl/tex_util';\nimport {TextureData, TextureUsage} from './webgl/tex_util';\nimport {TextureManager} from './webgl/texture_manager';\nimport {TileProgram} from './webgl/tile_gpu';\nimport {TransposeProgram} from './webgl/transpose_gpu';\nimport * as unary_op from './webgl/unaryop_gpu';\nimport {UnaryOpProgram} from './webgl/unaryop_gpu';\nimport {UnpackProgram} from './webgl/unpack_gpu';\nimport * as webgl_util from './webgl/webgl_util';\nimport {whereImpl} from './where_impl';\n\ntype KernelInfo = {\n  name: string; query: Promise<number>;\n};\n\nexport type TimerNode = RecursiveArray<KernelInfo>|KernelInfo;\nexport interface CPUTimerQuery {\n  startMs: number;\n  endMs?: number;\n}\n\nexport interface WebGLMemoryInfo extends MemoryInfo {\n  numBytesInGPU: number;\n  unreliable: boolean;\n}\n\nexport interface WebGLTimingInfo extends TimingInfo {\n  uploadWaitMs: number;\n  downloadWaitMs: number;\n}\n\n// Combines a dataId, a shape, and a dtype without a Tensor object so that\n// programs can be executed without a full Tensor object.\nexport interface TensorHandle {\n  dataId: DataId;\n  shape: number[];\n  dtype: DataType;\n}\n\n// Empirically determined constant used to determine size threshold for handing\n// off execution to the CPU.\nconst CPU_HANDOFF_SIZE_THRESHOLD = 10;\n// Empirically determined constant used to decide the number of bytes on GPU\n// before we start paging. The bytes are this constant * screen area * dpi.\nconst BEFORE_PAGING_CONSTANT = 300;\n// Tensors with size <= than this will be uploaded as uniforms, not textures.\nexport const SIZE_UPLOAD_UNIFORM = 4;\n// Empirically determined minimal shared dimension in matmul before we forward\n// to a.mul(b).sum() in order to take advantage of GPU parallelism. See\n// https://github.com/tensorflow/tfjs-core/pull/1379 for benchmarks.\nexport const MATMUL_SHARED_DIM_THRESHOLD = 1000;\n\nexport class MathBackendWebGL implements KernelBackend {\n  private texData: DataStorage<TextureData>;\n  // Maps data ids that have a pending read operation, to list of subscribers.\n  private pendingRead = new WeakMap<DataId, Array<(arr: TypedArray) => void>>();\n  // List of data ids that are scheduled for disposal, but are waiting on a\n  // pending read operation.\n  private pendingDisposal = new WeakSet<DataId>();\n  // List of data ids that are currently residing on gpu memory. Sorted with\n  // least recently used being first.\n  private lruDataGPU: DataId[] = [];\n  private numBytesInGPU = 0;\n  /**\n   * Number of bytes allocated on the GPU before we start moving data to cpu.\n   * Moving avoids gpu memory leaks and relies on JS's garbage collector.\n   */\n  private NUM_BYTES_BEFORE_PAGING: number;\n\n  private canvas: HTMLCanvasElement;\n  private fromPixels2DContext: CanvasRenderingContext2D;\n\n  private programTimersStack: TimerNode[];\n  private activeTimers: TimerNode[];\n  // Accumulated time spent (including blocking) in uploading data to webgl.\n  private uploadWaitMs = 0;\n  // Accumulated time spent (including blocking in downloading data from webgl.\n  private downloadWaitMs = 0;\n  private cpuBackend: KernelBackend;\n\n  register(dataId: DataId, shape: number[], dtype: DataType): void {\n    if (this.texData.has(dataId)) {\n      throw new Error('Data buffer is already registered');\n    }\n    this.texData.set(dataId, {shape, dtype});\n  }\n\n  setDataMover(dataMover: DataMover): void {\n    this.texData = new DataStorage(dataMover);\n  }\n\n  fromPixels(\n      pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement,\n      numChannels: number): Tensor3D {\n    if (pixels == null) {\n      throw new Error('pixels passed to tf.fromPixels() can not be null');\n    }\n    const texShape: [number, number] = [pixels.height, pixels.width];\n    const outShape = [pixels.height, pixels.width, numChannels];\n\n    if (!(pixels instanceof HTMLVideoElement) &&\n        !(pixels instanceof HTMLImageElement) &&\n        !(pixels instanceof HTMLCanvasElement) &&\n        !(pixels instanceof ImageData)) {\n      throw new Error(\n          'pixels passed to tf.fromPixels() must be either an ' +\n          `HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ` +\n          `ImageData, but was ${(pixels as {}).constructor.name}`);\n    }\n    if (pixels instanceof HTMLVideoElement) {\n      if (this.fromPixels2DContext == null) {\n        if (!ENV.get('IS_BROWSER')) {\n          throw new Error(\n              'Can\\'t read pixels from HTMLImageElement outside the browser.');\n        }\n        if (document.readyState !== 'complete') {\n          throw new Error(\n              'The DOM is not ready yet. Please call tf.fromPixels() ' +\n              'once the DOM is ready. One way to do that is to add an event ' +\n              'listener for `DOMContentLoaded` on the document object');\n        }\n        this.fromPixels2DContext =\n            document.createElement('canvas').getContext('2d');\n      }\n      this.fromPixels2DContext.canvas.width = pixels.width;\n      this.fromPixels2DContext.canvas.height = pixels.height;\n      this.fromPixels2DContext.drawImage(\n          pixels, 0, 0, pixels.width, pixels.height);\n      pixels = this.fromPixels2DContext.canvas;\n    }\n    const tempPixelHandle = this.makeTensorHandle(texShape, 'int32');\n    // This is a byte texture with pixels.\n    this.texData.get(tempPixelHandle.dataId).usage = TextureUsage.PIXELS;\n    this.gpgpu.uploadPixelDataToTexture(\n        this.getTexture(tempPixelHandle.dataId), pixels);\n    const program = new FromPixelsProgram(outShape);\n    const res = this.compileAndRun(program, [tempPixelHandle]);\n\n    this.disposeData(tempPixelHandle.dataId);\n\n    return res as Tensor3D;\n  }\n\n  private makeTensorHandle(shape: number[], dtype: DataType): TensorHandle {\n    const dataId = {};\n    this.register(dataId, shape, dtype);\n    return {dataId, shape, dtype};\n  }\n\n  write(dataId: DataId, values: DataValues): void {\n    if (values == null) {\n      throw new Error('MathBackendWebGL.write(): values can not be null');\n    }\n    const texData = this.texData.get(dataId);\n    const {texture, texShape, usage, dtype, isPacked} = texData;\n    if (dtype === 'complex64') {\n      throw new Error(\n          `Cannot write to a complex64 dtype. ` +\n          `Please use tf.complex(real, imag).`);\n    }\n\n    if (texture != null) {\n      // Release the old texture.\n      this.releaseTexture(dataId, texture, texShape, usage, isPacked);\n      texData.texture = null;\n      texData.texShape = null;\n    }\n    texData.usage = TextureUsage.UPLOAD;\n    texData.values = values;\n\n    if (!this.delayedStorage) {\n      this.uploadToGPU(dataId);\n    }\n  }\n  readSync(dataId: DataId): DataValues {\n    const texData = this.texData.get(dataId);\n    const {values, dtype, complexTensors} = texData;\n    if (values != null) {\n      return this.convertAndCacheOnCPU(dataId);\n    }\n    if (dtype === 'string') {\n      return values;\n    }\n    const shouldTimeProgram = this.activeTimers != null;\n    let start: number;\n    if (shouldTimeProgram) {\n      start = performance.now();\n    }\n\n    let result: Float32Array;\n    if (dtype === 'complex64') {\n      const realValues = complexTensors.real.dataSync() as Float32Array;\n      const imagValues = complexTensors.imag.dataSync() as Float32Array;\n      result = mergeRealAndImagArrays(realValues, imagValues);\n    } else {\n      result = this.getValuesFromTexture(dataId);\n    }\n\n    if (shouldTimeProgram) {\n      this.downloadWaitMs += performance.now() - start;\n    }\n    return this.convertAndCacheOnCPU(dataId, result);\n  }\n\n  async read(dataId: DataId): Promise<DataValues> {\n    if (this.pendingRead.has(dataId)) {\n      const subscribers = this.pendingRead.get(dataId);\n      return new Promise<TypedArray>(resolve => subscribers.push(resolve));\n    }\n    const texData = this.texData.get(dataId);\n    const {texture, values, texShape, isPacked, shape} = texData;\n    if (values != null) {\n      return this.convertAndCacheOnCPU(dataId);\n    }\n\n    this.pendingRead.set(dataId, []);\n\n    if (!ENV.get('WEBGL_DOWNLOAD_FLOAT_ENABLED') &&\n        ENV.get('WEBGL_VERSION') === 2) {\n      throw new Error(\n          `tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and ` +\n          `WEBGL_VERSION=2 not yet supported.`);\n    }\n\n    // Possibly copy the texture into a buffer before inserting a fence.\n    let width = texShape[1];\n    let height = texShape[0];\n    if (isPacked) {\n      [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(\n          texShape[0], texShape[1]);\n    }\n    const bufferOrTexture =\n        this.gpgpu.maybeCreateBufferFromTexture(texture, height, width);\n\n    // Create a fence and wait for it to resolve.\n    await this.gpgpu.createAndWaitForFence();\n\n    // Download the values from the GPU.\n    let vals: Float32Array;\n    if (bufferOrTexture instanceof WebGLTexture) {\n      vals = this.getValuesFromTexture(dataId);\n    } else {\n      if (isPacked) {\n        const batch = this.getBatchDim(shape);\n        let rows = 1, cols = 1;\n        if (shape.length) {\n          [rows, cols] = this.getRowsCols(shape);\n        }\n        vals = this.gpgpu.downloadPackedMatrixFromBuffer(\n            bufferOrTexture, batch, rows, cols, texShape[0], texShape[1]);\n      } else {\n        vals = this.gpgpu.downloadFloat32MatrixFromBuffer(\n            bufferOrTexture, texShape[0], texShape[1]);\n      }\n    }\n    const dTypeVals = this.convertAndCacheOnCPU(dataId, vals);\n\n    const subscribers = this.pendingRead.get(dataId);\n    this.pendingRead.delete(dataId);\n\n    // Notify all pending reads.\n    subscribers.forEach(resolve => resolve(dTypeVals));\n    if (this.pendingDisposal.has(dataId)) {\n      this.pendingDisposal.delete(dataId);\n      this.disposeData(dataId);\n    }\n    return dTypeVals;\n  }\n\n  private getValuesFromTexture(dataId: DataId): Float32Array {\n    const {shape, dtype, texture, texShape} = this.texData.get(dataId);\n    if (ENV.get('WEBGL_DOWNLOAD_FLOAT_ENABLED')) {\n      if (this.texData.get(dataId).isPacked) {\n        const batch = this.getBatchDim(shape);\n        let rows = 1, cols = 1;\n        if (shape.length) {\n          [rows, cols] = this.getRowsCols(shape);\n        }\n        return this.gpgpu.downloadMatrixFromPackedTexture(\n            texture, batch, rows, cols, texShape[0], texShape[1]);\n      } else {\n        return this.gpgpu.downloadFloat32MatrixFromOutputTexture(\n            texture, texShape[0], texShape[1]);\n      }\n    }\n\n    const tmpTarget = this.makeTensorHandle(shape, 'float32') as TensorHandle &\n        {size: number};\n    tmpTarget.size = sizeFromShape(shape);\n    this.texData.get(tmpTarget.dataId).usage = TextureUsage.DOWNLOAD;\n    const program = new EncodeFloatProgram(shape);\n    const pageToCpu = false;\n    this.compileAndRun(\n        program, [{shape, dtype, dataId}], tmpTarget, null, pageToCpu);\n    const tmpData = this.texData.get(tmpTarget.dataId);\n    const vals = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(\n        tmpData.texture, tmpData.texShape[0], tmpData.texShape[1]);\n    this.disposeData(tmpTarget.dataId);\n\n    return vals;\n  }\n\n  async time(f: () => void): Promise<WebGLTimingInfo> {\n    const oldActiveTimers = this.activeTimers;\n    const newActiveTimers: TimerNode[] = [];\n\n    let outerMostTime = false;\n    if (this.programTimersStack == null) {\n      this.programTimersStack = newActiveTimers;\n      outerMostTime = true;\n    } else {\n      this.activeTimers.push(newActiveTimers);\n    }\n    this.activeTimers = newActiveTimers;\n\n    f();\n\n    // needing to split these up because util.flatten only accepts certain types\n    const flattenedActiveTimerQueries =\n        util.flatten(this.activeTimers.map((d: KernelInfo) => d.query))\n            .filter(d => d != null);\n    const flattenedActiveTimerNames =\n        util.flatten(this.activeTimers.map((d: KernelInfo) => d.name))\n            .filter(d => d != null);\n\n    this.activeTimers = oldActiveTimers;\n\n    if (outerMostTime) {\n      this.programTimersStack = null;\n    }\n\n    const kernelMs = await Promise.all(flattenedActiveTimerQueries);\n\n    const res: WebGLTimingInfo = {\n      uploadWaitMs: this.uploadWaitMs,\n      downloadWaitMs: this.downloadWaitMs,\n      kernelMs: util.sum(kernelMs),\n      getExtraProfileInfo: () =>\n          kernelMs.map((d, i) => ({name: flattenedActiveTimerNames[i], ms: d}))\n              .map(d => `${d.name}: ${d.ms}`)\n              .join(', '),\n      wallMs: null  // will be filled by the engine\n    };\n    this.uploadWaitMs = 0;\n    this.downloadWaitMs = 0;\n    return res;\n  }\n  memory(): WebGLMemoryInfo {\n    return {unreliable: false, numBytesInGPU: this.numBytesInGPU} as\n        WebGLMemoryInfo;\n  }\n\n  private startTimer(): WebGLQuery|CPUTimerQuery {\n    if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {\n      return this.gpgpu.beginQuery();\n    }\n    return {startMs: performance.now(), endMs: null};\n  }\n\n  private endTimer(query: WebGLQuery|CPUTimerQuery): WebGLQuery|CPUTimerQuery {\n    if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {\n      this.gpgpu.endQuery();\n      return query;\n    }\n    (query as CPUTimerQuery).endMs = performance.now();\n    return query;\n  }\n\n  private async getQueryTime(query: WebGLQuery|CPUTimerQuery): Promise<number> {\n    if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {\n      return this.gpgpu.waitForQueryAndGetTime(query as WebGLQuery);\n    }\n    const timerQuery = query as CPUTimerQuery;\n    return timerQuery.endMs - timerQuery.startMs;\n  }\n\n  disposeData(dataId: DataId): void {\n    if (this.pendingDisposal.has(dataId)) {\n      return;\n    }\n    if (this.pendingRead.has(dataId)) {\n      this.pendingDisposal.add(dataId);\n      return;\n    }\n    if (this.texData.has(dataId)) {\n      const {texture, texShape, usage, complexTensors, isPacked} =\n          this.texData.get(dataId);\n      if (texture != null) {\n        this.releaseTexture(dataId, texture, texShape, usage, isPacked);\n      }\n      if (complexTensors != null) {\n        complexTensors.real.dispose();\n        complexTensors.imag.dispose();\n      }\n      this.texData.delete(dataId);\n    }\n  }\n\n  getTexture(dataId: DataId): WebGLTexture {\n    this.uploadToGPU(dataId);\n    return this.texData.get(dataId).texture;\n  }\n\n  private textureManager: TextureManager;\n  private binaryCache: {[key: string]: GPGPUBinary} = {};\n  private gpgpuCreatedLocally: boolean;\n\n  constructor(private gpgpu?: GPGPUContext, private delayedStorage = true) {\n    if (ENV.get('WEBGL_VERSION') < 1) {\n      throw new Error('WebGL is not supported on this device');\n    }\n\n    if (gpgpu == null) {\n      const gl = getWebGLContext(ENV.get('WEBGL_VERSION'));\n      this.gpgpu = new GPGPUContext(gl);\n      this.canvas = gl.canvas;\n      this.gpgpuCreatedLocally = true;\n    } else {\n      this.gpgpuCreatedLocally = false;\n      this.canvas = gpgpu.gl.canvas;\n    }\n    if (ENV.get('WEBGL_PAGING_ENABLED')) {\n      // Use the device screen's resolution as a heuristic to decide on the\n      // maximum memory allocated on the GPU before starting to page.\n      this.NUM_BYTES_BEFORE_PAGING =\n          (window.screen.height * window.screen.width *\n           window.devicePixelRatio) *\n          BEFORE_PAGING_CONSTANT;\n    }\n    this.textureManager = new TextureManager(this.gpgpu);\n  }\n\n  private getCPUBackend(): KernelBackend|null {\n    if (!ENV.get('WEBGL_CPU_FORWARD')) {\n      return null;\n    }\n\n    if (this.cpuBackend == null) {\n      this.cpuBackend = ENV.findBackend('cpu');\n    }\n\n    return this.cpuBackend;\n  }\n\n  /*\n  Tests whether all the inputs to an op are small and on the CPU. This heuristic\n  determines when it would be faster to execute a kernel on the CPU. WebGL\n  kernels opt into running this check and forwarding when appropriate.\n  TODO(https://github.com/tensorflow/tfjs/issues/872): Develop a more\n  sustainable strategy for optimizing backend execution of ops.\n   */\n  private shouldExecuteOnCPU(\n      inputs: Tensor[], sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD): boolean {\n    return this.getCPUBackend() != null &&\n        inputs.every(\n            input => this.texData.get(input.dataId).texture == null &&\n                input.size < sizeThreshold);\n  }\n\n  getGPGPUContext(): GPGPUContext {\n    return this.gpgpu;\n  }\n  getCanvas(): HTMLCanvasElement {\n    return this.canvas;\n  }\n\n  complex<T extends Tensor>(real: T, imag: T): T {\n    const result = this.makeOutputArray(real.shape, 'complex64') as T;\n    const resultData = this.texData.get(result.dataId);\n    // The backend owns the reference to the underlying real and imaginary\n    // clones. These will explicitly get disposed when the complex tensor is\n    // disposed.\n    resultData.complexTensors = {\n      real: ENV.engine.keep(real.clone()),\n      imag: ENV.engine.keep(imag.clone())\n    };\n\n    return result;\n  }\n  real<T extends Tensor>(input: T): T {\n    const resultData = this.texData.get(input.dataId);\n    return resultData.complexTensors.real.clone() as T;\n  }\n  imag<T extends Tensor>(input: T): T {\n    const resultData = this.texData.get(input.dataId);\n    return resultData.complexTensors.imag.clone() as T;\n  }\n\n  slice<T extends Tensor>(x: T, begin: number[], size: number[]): T {\n    if (this.shouldExecuteOnCPU([x])) {\n      return this.cpuBackend.slice(x, begin, size);\n    }\n\n    const program = new SliceProgram(size);\n    const customSetup = program.getCustomSetupFunc(begin);\n    return this.compileAndRun(program, [x], null, customSetup);\n  }\n\n  stridedSlice<T extends Tensor>(\n      x: T, begin: number[], end: number[], strides: number[],\n      beginMask: number, endMask: number, ellipsisMask: number,\n      newAxisMask: number, shrinkAxisMask: number): T {\n    if (this.shouldExecuteOnCPU([x])) {\n      return this.cpuBackend.stridedSlice(\n          x, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask,\n          shrinkAxisMask);\n    }\n\n    const [beginIndex, size, shrinkAxis] = getStridedSlicedInfo(\n        x.shape, begin, end, strides, beginMask, endMask, ellipsisMask,\n        newAxisMask, shrinkAxisMask);\n\n    const shape = size.filter((v, index) => shrinkAxis.indexOf(index) === -1);\n    if (shape.some(axis => axis === 0)) {\n      return tensor([], shape) as T;\n    }\n\n    const program =\n        new StridedSliceProgram(beginIndex, strides, size, shrinkAxis);\n    return this.compileAndRun(program, [x]);\n  }\n\n  reverse<T extends Tensor>(x: T, axis: number[]): T {\n    const program = new ReverseProgram(x.shape, axis);\n    return this.compileAndRun(program, [x]);\n  }\n\n  private concat2Tensors<T extends Tensor>(a: T, b: T, axis: number): T {\n    // Any concat of n-dimensional tensors across any axis can be reduced to\n    // a concatenation of two-dimensional tensors across the axis 1 by first\n    // partitioning the axes of the original tensors into those less than the\n    // axis to be concatenated and the rest. Then reshape the tensors\n    // into a two-dimensional tensor by collapsing these two sets of axes and\n    // concatenate the resulting matrices across the axis 1, finally reshaping\n    // the result to have the proper shape.\n    const outShape = computeOutShape([a.shape, b.shape], axis);\n    const a2D = a.as2D(-1, sizeFromShape(a.shape.slice(axis)));\n    const b2D = b.as2D(-1, sizeFromShape(b.shape.slice(axis)));\n    const program = new ConcatProgram(a2D.shape, b2D.shape);\n    const res = this.compileAndRun(program, [a2D, b2D]) as Tensor;\n    return res.reshape(outShape) as T;\n  }\n\n  concat(tensors: Tensor[], axis: number): Tensor {\n    if (this.shouldExecuteOnCPU(tensors)) {\n      return this.cpuBackend.concat(tensors, axis);\n    }\n\n    if (tensors.length === 1) {\n      return tensors[0];\n    }\n    let result = tensors[0];\n    for (let i = 1; i < tensors.length; ++i) {\n      result = this.concat2Tensors(result, tensors[i], axis);\n    }\n    return result;\n  }\n\n  neg<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.NEG);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  batchMatMul(\n      a: Tensor3D, b: Tensor3D, transposeA: boolean,\n      transposeB: boolean): Tensor3D {\n    const outerShapeA = transposeA ? a.shape[2] : a.shape[1];\n    const outerShapeB = transposeB ? b.shape[1] : b.shape[2];\n    const sharedDim = transposeA ? a.shape[1] : a.shape[2];\n    const [batch, , ] = a.shape;\n\n    // Since the matrices are vectors, it is faster to call mul().sum()\n    // because sum() is O(sqrt(N)) due to divide-and-conquer.\n    if ((outerShapeA === 1 || outerShapeB === 1) &&\n        sharedDim > MATMUL_SHARED_DIM_THRESHOLD) {\n      if (transposeA) {\n        a = a.transpose([0, 2, 1]);\n      }\n      if (transposeB) {\n        b = b.transpose([0, 2, 1]);\n      }\n\n      const a3D = outerShapeB === 1 ? a : a.as3D(batch, sharedDim, 1);\n      const axis = outerShapeB === 1 ? 2 : 1;\n      const b3D = outerShapeB === 1 ? b.as3D(batch, 1, sharedDim) : b;\n      return this.multiply(a3D, b3D).sum(axis, true /* keepDims */);\n    }\n\n    const dtype = upcastType(a.dtype, b.dtype);\n\n    // TODO(https://github.com/tensorflow/tfjs/issues/693): Support 3D tensors\n    if (batch === 1) {\n      const aSqueezed = a.as2D(a.shape[1], a.shape[2]);\n      const bSqueezed = b.as2D(b.shape[1], b.shape[2]);\n\n      const program = new MatMulPackedProgram(\n          aSqueezed.shape, bSqueezed.shape, [outerShapeA, outerShapeB],\n          transposeA, transposeB);\n      const output =\n          this.makePackedTensor(program.outputShape, dtype) as Tensor2D;\n      const result =\n          this.compileAndRun<Tensor2D>(program, [aSqueezed, bSqueezed], output);\n      return result.reshape([1, result.shape[0], result.shape[1]]);\n    } else {\n      const program =\n          new MatMulProgram(a.shape, b.shape, transposeA, transposeB);\n      const output =\n          this.makeOutputArray(program.outputShape, dtype) as Tensor3D;\n      return this.compileAndRun(program, [a, b], output);\n    }\n  }\n\n  multiply(a: Tensor, b: Tensor): Tensor {\n    if (a.dtype === 'complex64') {\n      const aData = this.texData.get(a.dataId);\n      const bData = this.texData.get(b.dataId);\n\n      const realProgram = new BinaryOpComplexProgram(\n          binaryop_complex_gpu.COMPLEX_MULTIPLY.REAL, a.shape, b.shape);\n      const imagProgram = new BinaryOpComplexProgram(\n          binaryop_complex_gpu.COMPLEX_MULTIPLY.IMAG, a.shape, b.shape);\n\n      const inputs = [\n        this.makeComplexComponentTensorHandle(a, aData.complexTensors.real),\n        this.makeComplexComponentTensorHandle(a, aData.complexTensors.imag),\n        this.makeComplexComponentTensorHandle(b, bData.complexTensors.real),\n        this.makeComplexComponentTensorHandle(b, bData.complexTensors.imag)\n      ];\n      const real = this.compileAndRun<Tensor>(realProgram, inputs);\n      const imag = this.compileAndRun<Tensor>(imagProgram, inputs);\n\n      const complex = this.complex(real, imag);\n      real.dispose();\n      imag.dispose();\n      return complex;\n    }\n\n    if (this.shouldExecuteOnCPU([a, b])) {\n      return this.cpuBackend.multiply(a, b);\n    }\n\n    const program = new BinaryOpProgram(binaryop_gpu.MUL, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, a.dtype) as Tensor;\n    return this.compileAndRun(program, [a, b], output) as Tensor;\n  }\n\n  batchNormalization(\n      x: Tensor4D, mean: Tensor4D|Tensor1D, variance: Tensor4D|Tensor1D,\n      varianceEpsilon: number, scale?: Tensor4D|Tensor1D,\n      offset?: Tensor4D|Tensor1D): Tensor4D {\n    const inputs = [x, mean, variance];\n\n    let offsetShape = null;\n    if (offset != null) {\n      offsetShape = offset.shape;\n      inputs.push(offset);\n    }\n\n    let scaleShape = null;\n    if (scale != null) {\n      scaleShape = scale.shape;\n      inputs.push(scale);\n    }\n\n    if (ENV.get('WEBGL_PACK_BATCHNORMALIZATION')) {\n      const batchNormPackedProgram = new BatchNormPackedProgram(\n          x.shape, mean.shape, variance.shape, offsetShape, scaleShape,\n          varianceEpsilon);\n      return this.compileAndRun<Tensor4D>(batchNormPackedProgram, inputs);\n    }\n\n    const batchNormProgram = new BatchNormProgram(\n        x.shape, mean.shape, variance.shape, offsetShape, scaleShape,\n        varianceEpsilon);\n    return this.compileAndRun(batchNormProgram, inputs);\n  }\n\n  localResponseNormalization4D(\n      x: Tensor4D, radius: number, bias: number, alpha: number,\n      beta: number): Tensor4D {\n    const program = new LRNProgram(x.shape, radius, bias, alpha, beta);\n    return this.compileAndRun(program, [x]);\n  }\n\n  LRNGrad(\n      dy: Tensor4D, inputImage: Tensor4D, outputImage: Tensor4D,\n      depthRadius: number, bias: number, alpha: number,\n      beta: number): Tensor4D {\n    const program =\n        new LRNGradProgram(inputImage.shape, depthRadius, bias, alpha, beta);\n    return this.compileAndRun(program, [inputImage, outputImage, dy]);\n  }\n\n  tile<T extends Tensor>(x: T, reps: number[]): T {\n    const program = new TileProgram(x.shape, reps);\n    return this.compileAndRun(program, [x]);\n  }\n\n  pad<T extends Tensor>(\n      x: T, paddings: Array<[number, number]>, constantValue: number): T {\n    const program = new PadProgram(x.shape, paddings, constantValue);\n    return this.compileAndRun(program, [x]);\n  }\n\n  transpose<T extends Tensor>(x: T, perm: number[]): T {\n    const program = new TransposeProgram(x.shape, perm);\n    return this.compileAndRun(program, [x]);\n  }\n\n  gather<T extends Tensor>(x: T, indices: Tensor1D, axis: number): T {\n    const program = new GatherProgram(x.shape, indices.size, axis);\n    return this.compileAndRun(program, [x, indices]);\n  }\n\n  batchToSpaceND<T extends Tensor>(\n      x: T, blockShape: number[], crops: number[][]): T {\n    util.assert(\n        x.rank <= 4,\n        'batchToSpaceND for rank > 4 with a WebGL backend not implemented yet');\n    const prod = blockShape.reduce((a, b) => a * b);\n\n    const reshaped = array_ops_util.getReshaped(x.shape, blockShape, prod);\n    const permuted =\n        array_ops_util.getPermuted(reshaped.length, blockShape.length);\n    const reshapedPermuted =\n        array_ops_util.getReshapedPermuted(x.shape, blockShape, prod);\n    const sliceBeginCoords =\n        array_ops_util.getSliceBeginCoords(crops, blockShape.length);\n    const sliceSize =\n        array_ops_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n\n    return x.reshape(reshaped)\n               .transpose(permuted)\n               .reshape(reshapedPermuted)\n               .slice(sliceBeginCoords, sliceSize) as T;\n  }\n\n  spaceToBatchND<T extends Tensor>(\n      x: T, blockShape: number[], paddings: Array<[number, number]>): T {\n    util.assert(\n        x.rank <= 4,\n        'spaceToBatchND for rank > 4 with a WebGL backend not implemented yet');\n\n    const prod = blockShape.reduce((a, b) => a * b);\n\n    const completePaddings: Array<[number, number]> = [[0, 0]];\n    completePaddings.push(...paddings);\n    for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n      completePaddings.push([0, 0]);\n    }\n\n    const paddedX = x.pad(completePaddings);\n\n    const reshapedPaddedShape =\n        array_ops_util.getReshaped(paddedX.shape, blockShape, prod, false);\n\n    const permutedReshapedPaddedPermutation = array_ops_util.getPermuted(\n        reshapedPaddedShape.length, blockShape.length, false);\n\n    const flattenShape = array_ops_util.getReshapedPermuted(\n        paddedX.shape, blockShape, prod, false);\n\n    return paddedX.reshape(reshapedPaddedShape)\n               .transpose(permutedReshapedPaddedPermutation)\n               .reshape(flattenShape) as T;\n  }\n\n  private reduce(\n      x: Tensor2D, reduceType: 'all'|'any'|'max'|'min'|'sum'|'prod',\n      dtype: DataType): Tensor2D {\n    const batchSize = x.shape[0];\n    const inSize = x.shape[1];\n    const windowSize = reduce_util.computeOptimalWindowSize(inSize);\n    const reduceInfo = {windowSize, inSize, batchSize};\n    const program = new ReduceProgram(reduceInfo, reduceType);\n    const [rows, cols] = program.outputShape;\n    const output = this.makeOutputArray<Tensor2D>([rows, cols], dtype);\n\n    this.compileAndRun(program, [x], output);\n    // No need to run another GPGPU program.\n    if (output.shape[1] === 1) {\n      return output;\n    }\n    return this.reduce(output, reduceType, dtype);\n  }\n\n  private argReduce(\n      x: Tensor2D, reduceType: 'max'|'min',\n      bestIndicesA: Tensor2D = null): Tensor2D {\n    let batchSize = x.shape[0];\n    let inSize = x.shape[1];\n    if (bestIndicesA != null) {\n      batchSize = bestIndicesA.shape[0];\n      inSize = bestIndicesA.shape[1];\n    }\n    const windowSize = reduce_util.computeOptimalWindowSize(inSize);\n    const reduceInfo = {windowSize, inSize, batchSize};\n    const program =\n        new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null);\n    const [rows, cols] = program.outputShape;\n    const output = this.makeOutputArray<Tensor2D>([rows, cols], 'int32');\n    const inputs = [x];\n    if (bestIndicesA != null) {\n      inputs.push(bestIndicesA);\n    }\n    this.compileAndRun(program, inputs, output);\n    // No need to run another GPGPU program.\n    if (output.shape[1] === 1) {\n      return output;\n    }\n    return this.argReduce(x, reduceType, output);\n  }\n\n  sum(x: Tensor, axes: number[]): Tensor {\n    axis_util.assertAxesAreInnerMostDims('sum', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const inSize = util.sizeFromShape(reduceShape);\n    const a2D = x.as2D(-1, inSize);\n    const outputDType = sumOutType(x.dtype);\n    return this.reduce(a2D, 'sum', outputDType).reshape(outShape);\n  }\n\n  prod(x: Tensor, axes: number[]): Tensor {\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const inSize = util.sizeFromShape(reduceShape);\n    const a2D = x.as2D(-1, inSize);\n    const outputDType = sumOutType(x.dtype);\n    return this.reduce(a2D, 'prod', outputDType).reshape(outShape);\n  }\n\n  unsortedSegmentSum<T extends Tensor>(\n      x: T, segmentIds: Tensor1D, numSegments: number): Tensor {\n    let axis = 0;\n    const permutation = axis_util.getAxesPermutation([axis], x.rank);\n    let permutedX = x;\n    if (permutation != null) {\n      permutedX = x.transpose(permutation);\n      axis = axis_util.getInnerMostAxes(1, x.rank)[0];\n    }\n\n    const outShape =\n        segment_util.computeOutShape(permutedX.shape, axis, numSegments);\n    const inSize = util.sizeFromShape([permutedX.shape[axis]]);\n    const a2D = permutedX.as2D(-1, inSize);\n    const outputDType = sumOutType(x.dtype);\n    let result =\n        this.segOpCompute(\n                a2D, 'unsortedSegmentSum', segmentIds, outputDType, numSegments)\n            .reshape(outShape);\n    if (permutation != null) {\n      result = result.transpose(axis_util.getUndoAxesPermutation(permutation));\n    }\n    return result;\n  }\n\n  private segOpCompute(\n      x: Tensor2D, segOpType: 'unsortedSegmentSum', segmentIds: Tensor1D,\n      dtype: DataType, numSegments: number): Tensor2D {\n    const batchSize = x.shape[0];\n    const inSize = x.shape[1];\n    const windowSize =\n        segment_util.segOpComputeOptimalWindowSize(inSize, numSegments);\n    const segOpInfo = {windowSize, inSize, batchSize, numSegments};\n    const program = new SegmentOpProgram(segOpInfo, segOpType);\n    const [rows, cols] = program.outputShape;\n    const output = this.makeOutputArray<Tensor2D>([rows, cols], dtype);\n    this.compileAndRun(program, [x, segmentIds], output);\n    // No need to run another GPGPU program.\n    if (output.shape[1] === numSegments) {\n      return output;\n    }\n    segmentIds = range(0, numSegments).tile([inSize / windowSize]);\n    return this.segOpCompute(output, segOpType, segmentIds, dtype, numSegments);\n  }\n\n  argMin(x: Tensor, axis: number): Tensor {\n    const axes = [axis];\n    axis_util.assertAxesAreInnerMostDims('argMin', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const inSize = util.sizeFromShape(reduceShape);\n    const a2D = x.as2D(-1, inSize);\n    return this.argReduce(a2D, 'min').reshape(outShape);\n  }\n\n  argMax(x: Tensor, axis: number): Tensor {\n    const axes = [axis];\n    axis_util.assertAxesAreInnerMostDims('argMax', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const inSize = util.sizeFromShape(reduceShape);\n    const a2D = x.as2D(-1, inSize);\n    return this.argReduce(a2D, 'max').reshape(outShape);\n  }\n\n  cumsum(x: Tensor, axis: number, exclusive: boolean, reverse: boolean):\n      Tensor {\n    if (axis !== x.rank - 1) {\n      throw new Error(\n          `WebGL cumsum shader expects an inner-most axis=${x.rank - 1} ` +\n          `but got axis=${axis}`);\n    }\n    const program = new CumSumProgram(x.shape, exclusive, reverse);\n    return this.compileAndRun(program, [x]);\n  }\n\n  equal(a: Tensor, b: Tensor): Tensor {\n    const program = new BinaryOpProgram(binaryop_gpu.EQUAL, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, 'bool');\n    return this.compileAndRun(program, [a, b], output);\n  }\n\n  notEqual(a: Tensor, b: Tensor): Tensor {\n    const program =\n        new BinaryOpProgram(binaryop_gpu.NOT_EQUAL, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, 'bool');\n    return this.compileAndRun(program, [a, b], output);\n  }\n\n  less(a: Tensor, b: Tensor): Tensor {\n    if (this.shouldExecuteOnCPU([a, b])) {\n      return this.cpuBackend.less(a, b);\n    }\n\n    const program = new BinaryOpProgram(binaryop_gpu.LESS, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, 'bool');\n    return this.compileAndRun(program, [a, b], output);\n  }\n\n  lessEqual(a: Tensor, b: Tensor): Tensor {\n    const program =\n        new BinaryOpProgram(binaryop_gpu.LESS_EQUAL, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, 'bool');\n    return this.compileAndRun(program, [a, b], output);\n  }\n\n  greater(a: Tensor, b: Tensor): Tensor {\n    if (this.shouldExecuteOnCPU([a, b])) {\n      return this.cpuBackend.greater(a, b);\n    }\n\n    const program = new BinaryOpProgram(binaryop_gpu.GREATER, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, 'bool');\n    return this.compileAndRun(program, [a, b], output);\n  }\n\n  greaterEqual(a: Tensor, b: Tensor): Tensor {\n    const program =\n        new BinaryOpProgram(binaryop_gpu.GREATER_EQUAL, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, 'bool');\n    return this.compileAndRun(program, [a, b], output);\n  }\n\n  logicalNot<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.LOGICAL_NOT);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  logicalAnd(a: Tensor, b: Tensor): Tensor {\n    const program =\n        new BinaryOpProgram(binaryop_gpu.LOGICAL_AND, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, 'bool');\n    return this.compileAndRun(program, [a, b], output);\n  }\n\n  logicalOr(a: Tensor, b: Tensor): Tensor {\n    const program =\n        new BinaryOpProgram(binaryop_gpu.LOGICAL_OR, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, 'bool');\n    return this.compileAndRun(program, [a, b], output);\n  }\n\n  select(condition: Tensor, a: Tensor, b: Tensor): Tensor {\n    const program = new SelectProgram(condition.rank, a.shape, a.rank);\n    const output =\n        this.makeOutputArray(program.outputShape, upcastType(a.dtype, b.dtype));\n    return this.compileAndRun(program, [condition, a, b], output);\n  }\n\n  where(condition: Tensor): Tensor2D {\n    warn(\n        'tf.where() in webgl locks the UI thread. ' +\n        'Call tf.whereAsync() instead');\n    const condVals = condition.dataSync();\n    return whereImpl(condition.shape, condVals);\n  }\n\n  topk<T extends Tensor>(x: T, k: number, sorted: boolean): [T, T] {\n    const xVals = x.dataSync();\n    return topkImpl(xVals, x.shape, x.dtype as NumericDataType, k, sorted);\n  }\n\n  min(x: Tensor, axes: number[]): Tensor {\n    axis_util.assertAxesAreInnerMostDims('min', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const inSize = util.sizeFromShape(reduceShape);\n    const a2D = x.as2D(-1, inSize);\n    return this.reduce(a2D, 'min', a2D.dtype).reshape(outShape);\n  }\n\n  minimum(a: Tensor, b: Tensor): Tensor {\n    if (this.shouldExecuteOnCPU([a, b])) {\n      return this.cpuBackend.minimum(a, b);\n    }\n\n    const program = new BinaryOpProgram(binaryop_gpu.MIN, a.shape, b.shape);\n    return this.compileAndRun(program, [a, b]);\n  }\n\n  mod(a: Tensor, b: Tensor): Tensor {\n    const program = new BinaryOpProgram(binaryop_gpu.MOD, a.shape, b.shape);\n    const customSetup = program.getCustomSetupFunc();\n    return this.compileAndRun(program, [a, b], null, customSetup);\n  }\n\n  max(x: Tensor, axes: number[]): Tensor {\n    axis_util.assertAxesAreInnerMostDims('max', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const inSize = util.sizeFromShape(reduceShape);\n    const a2D = x.as2D(-1, inSize);\n    return this.reduce(a2D, 'max', a2D.dtype).reshape(outShape);\n  }\n\n  maximum(a: Tensor, b: Tensor): Tensor {\n    if (this.shouldExecuteOnCPU([a, b])) {\n      return this.cpuBackend.maximum(a, b);\n    }\n\n    const program = new BinaryOpProgram(binaryop_gpu.MAX, a.shape, b.shape);\n    return this.compileAndRun(program, [a, b]);\n  }\n\n  all(x: Tensor, axes: number[]): Tensor {\n    axis_util.assertAxesAreInnerMostDims('all', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const inSize = util.sizeFromShape(reduceShape);\n    const a2D = x.as2D(-1, inSize);\n    return this.reduce(a2D, 'all', a2D.dtype).reshape(outShape);\n  }\n\n  any(x: Tensor, axes: number[]): Tensor {\n    axis_util.assertAxesAreInnerMostDims('any', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const inSize = util.sizeFromShape(reduceShape);\n    const a2D = x.as2D(-1, inSize);\n    return this.reduce(a2D, 'any', a2D.dtype).reshape(outShape);\n  }\n\n  squaredDifference(a: Tensor, b: Tensor): Tensor {\n    const program =\n        new BinaryOpProgram(binaryop_gpu.SQUARED_DIFFERENCE, a.shape, b.shape);\n    return this.compileAndRun(program, [a, b]);\n  }\n\n  realDivide(a: Tensor, b: Tensor): Tensor {\n    const op = binaryop_gpu.DIV;\n    const outputDtype = 'float32';\n    const program = new BinaryOpProgram(op, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, outputDtype);\n    return this.compileAndRun<Tensor>(program, [a, b], output);\n  }\n\n  floorDiv(a: Tensor, b: Tensor): Tensor {\n    const op = binaryop_gpu.INT_DIV;\n    const outputDtype = 'int32';\n    const program = new BinaryOpProgram(op, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, outputDtype);\n    return this.compileAndRun<Tensor>(program, [a, b], output);\n  }\n\n  add(a: Tensor, b: Tensor): Tensor {\n    if (a.dtype === 'complex64' && b.dtype === 'complex64') {\n      return this.complexSeparableBinaryOp(a, b, binaryop_gpu.ADD);\n    }\n\n    const program = new BinaryOpProgram(binaryop_gpu.ADD, a.shape, b.shape);\n    const output =\n        this.makeOutputArray(\n            program.outputShape, upcastType(a.dtype, b.dtype)) as Tensor;\n    return this.compileAndRun<Tensor>(program, [a, b], output);\n  }\n\n  /**\n   * Computes a complex binary operation that can be decomposed into a simple\n   * binary operation on both the real and imagary parts.\n   */\n  private complexSeparableBinaryOp(a: Tensor, b: Tensor, op: string): Tensor {\n    const aData = this.texData.get(a.dataId);\n    const bData = this.texData.get(b.dataId);\n\n    const [real, imag] = [\n      [aData.complexTensors.real, bData.complexTensors.real],\n      [aData.complexTensors.imag, bData.complexTensors.imag]\n    ].map(complexParts => {\n      const [aPart, bPart] = complexParts;\n\n      const program = new BinaryOpProgram(op, a.shape, b.shape);\n      const output = this.makeOutputArray(\n                         program.outputShape,\n                         upcastType(aPart.dtype, bPart.dtype)) as Tensor;\n\n      const aHandle = this.makeComplexComponentTensorHandle(a, aPart);\n      const bHandle = this.makeComplexComponentTensorHandle(b, bPart);\n\n      return this.compileAndRun<Tensor>(program, [aHandle, bHandle], output);\n    });\n\n    const complex = this.complex(real, imag);\n    real.dispose();\n    imag.dispose();\n    return complex;\n  }\n\n  // Returns a TensorHandle with the complex shape and the dataId of the\n  // underlying part. We need to do this because a reshaped complex tensor is\n  // not reflected in its parts.\n  private makeComplexComponentTensorHandle(\n      complexTensor: Tensor, complexPart: Tensor): TensorHandle {\n    return {\n      dataId: complexPart.dataId,\n      dtype: complexPart.dtype,\n      shape: complexTensor.shape\n    };\n  }\n\n  addN<T extends Tensor>(tensors: T[]): T {\n    let res = tensors[0];\n    for (let i = 1; i < tensors.length; i++) {\n      res = this.add(res, tensors[i]) as T;\n    }\n    return res;\n  }\n\n  subtract(a: Tensor, b: Tensor): Tensor {\n    if (a.dtype === 'complex64' && b.dtype === 'complex64') {\n      return this.complexSeparableBinaryOp(a, b, binaryop_gpu.SUB);\n    }\n\n    if (this.shouldExecuteOnCPU([a, b])) {\n      return this.cpuBackend.subtract(a, b);\n    }\n\n    const program = new BinaryOpProgram(binaryop_gpu.SUB, a.shape, b.shape);\n    const output =\n        this.makeOutputArray(\n            program.outputShape, upcastType(a.dtype, b.dtype)) as Tensor;\n    return this.compileAndRun<Tensor>(program, [a, b], output);\n  }\n\n  pow<T extends Tensor>(a: T, b: Tensor): T {\n    const program = new BinaryOpProgram(binaryop_gpu.POW, a.shape, b.shape);\n    const customSetup = program.getCustomSetupFunc();\n    const output = this.makeOutputArray(\n                       program.outputShape, upcastType(a.dtype, b.dtype)) as T;\n    return this.compileAndRun<T>(program, [a, b], output, customSetup);\n  }\n\n  ceil<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.CEIL);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  floor<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.FLOOR);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  sign<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.SIGN);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  round<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ROUND);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  exp<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.EXP);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  expm1<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.EXPM1);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  log<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.LOG);\n    const customSetup = program.getCustomSetupFunc();\n    return this.compileAndRun(program, [x], null, customSetup) as T;\n  }\n\n  log1p<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.LOG1P);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  sqrt<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.SQRT);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  rsqrt<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.RSQRT);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  square<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.SQUARE);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  reciprocal<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.RECIPROCAL);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  relu<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.RELU);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  elu<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ELU);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  eluDer<T extends Tensor>(dy: T, y: T): T {\n    const program =\n        new BinaryOpProgram(binaryop_gpu.ELU_DER, dy.shape, y.shape);\n    return this.compileAndRun(program, [dy, y]) as T;\n  }\n\n  selu<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.SELU);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  int<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.TO_INT);\n    const output = this.makeOutputArray(program.outputShape, 'int32');\n    return this.compileAndRun(program, [x], output) as T;\n  }\n\n  clip<T extends Tensor>(x: T, min: number, max: number): T {\n    let program;\n    if (ENV.get('WEBGL_PACK_CLIP')) {\n      program = new ClipPackedProgram(x.shape, min, max);\n    } else {\n      program = new ClipProgram(x.shape, min, max);\n    }\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  abs<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ABS);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  complexAbs<T extends Tensor>(x: T): T {\n    const xData = this.texData.get(x.dataId);\n\n    const program = new ComplexAbsProgram(x.shape);\n    const inputs = [\n      this.makeComplexComponentTensorHandle(x, xData.complexTensors.real),\n      this.makeComplexComponentTensorHandle(x, xData.complexTensors.imag),\n    ];\n\n    return this.compileAndRun<Tensor>(program, inputs) as T;\n  }\n\n  sigmoid<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.SIGMOID);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  softplus<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.SOFTPLUS);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  sin<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.SIN);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  cos<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.COS);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  tan<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.TAN);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  asin<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ASIN);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  acos<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ACOS);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  atan<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ATAN);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  atan2<T extends Tensor>(a: T, b: T): T {\n    const program = new BinaryOpProgram(binaryop_gpu.ATAN2, a.shape, b.shape);\n    return this.compileAndRun(program, [a, b]) as T;\n  }\n\n  sinh<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.SINH);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  cosh<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.COSH);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  tanh<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.TANH);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  asinh<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ASINH);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  acosh<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ACOSH);\n    const customSetup = program.getCustomSetupFunc();\n    return this.compileAndRun(program, [x], null, customSetup) as T;\n  }\n\n  atanh<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ATANH);\n    const customSetup = program.getCustomSetupFunc();\n    return this.compileAndRun(program, [x], null, customSetup) as T;\n  }\n\n  erf<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ERF);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  step<T extends Tensor>(x: T, alpha: number): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.STEP(alpha));\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  conv2dWithIm2Row(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    // Rearranges conv2d input so each block to be convolved over forms the\n    // column of a new matrix with shape [filterWidth * filterHeight *\n    // inChannels, outHeight * outWidth]. The filter is also rearranged so each\n    // output channel forms a row of a new matrix with shape [outChannels,\n    // filterWidth * filterHeight * inChannels]. The convolution is then\n    // computed by multiplying these matrices and reshaping the result.\n    const {\n      filterWidth,\n      filterHeight,\n      inChannels,\n      outWidth,\n      outHeight,\n    } = convInfo;\n\n    const sharedDim = filterWidth * filterHeight * inChannels;\n    const numCols = outHeight * outWidth;\n    const x2ColShape = [sharedDim, numCols];\n\n    const xSqueezed = x.squeeze([0]);\n    const w2Row = filter.reshape([sharedDim, -1]) as Tensor2D;\n\n    const im2ColProgram =\n        new Im2ColProgram(x2ColShape, xSqueezed.shape, convInfo);\n    const im2Col = this.compileAndRun<Tensor2D>(im2ColProgram, [xSqueezed]);\n\n    const matmulProgram = new MatMulPackedProgram(\n        im2Col.shape, w2Row.shape, [numCols, convInfo.outChannels], true,\n        false);\n    const product =\n        this.compileAndRun<Tensor4D>(matmulProgram, [im2Col, w2Row]);\n\n    return product.reshape([1, outHeight, outWidth, convInfo.outChannels]);\n  }\n\n  conv2d(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    if (ENV.get('WEBGL_CONV_IM2COL') && x.shape[0] === 1) {\n      return this.conv2dWithIm2Row(x, filter, convInfo);\n    }\n    const program = new Conv2DProgram(convInfo);\n    return this.compileAndRun(program, [x, filter]);\n  }\n\n  conv2dDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    const program = new Conv2DDerInputProgram(convInfo);\n    return this.compileAndRun(program, [dy, filter]);\n  }\n\n  conv2dDerFilter(x: Tensor4D, dy: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    const program = new Conv2DDerFilterProgram(convInfo);\n    return this.compileAndRun(program, [x, dy]);\n  }\n\n  depthwiseConv2D(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    let program: DepthwiseConv2DProgram|DepthwiseConvPacked2DProgram;\n    if (ENV.get('WEBGL_PACK_DEPTHWISECONV') && convInfo.dilationWidth === 1 &&\n        convInfo.dilationHeight === 1 && convInfo.padInfo.left <= 1 &&\n        convInfo.strideWidth <= 2 &&\n        convInfo.outChannels / convInfo.inChannels === 1) {\n      program = new DepthwiseConvPacked2DProgram(convInfo);\n      return this.compileAndRun(\n          program, [x, filter],\n          this.makePackedTensor(convInfo.outShape, x.dtype));\n    }\n\n    program = new DepthwiseConv2DProgram(convInfo);\n    return this.compileAndRun(program, [x, filter]);\n  }\n\n  depthwiseConv2DDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    const program = new DepthwiseConv2DDerInputProgram(convInfo);\n    return this.compileAndRun(program, [dy, filter]);\n  }\n\n  depthwiseConv2DDerFilter(x: Tensor4D, dy: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    const program = new DepthwiseConv2DDerFilterProgram(convInfo);\n    return this.compileAndRun(program, [x, dy]);\n  }\n\n  conv3d(x: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n    const program = new Conv3DProgram(convInfo);\n    return this.compileAndRun(program, [x, filter]);\n  }\n\n  conv3dDerInput(dy: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo):\n      Tensor5D {\n    const program = new Conv3DDerInputProgram(convInfo);\n    return this.compileAndRun(program, [dy, filter]);\n  }\n\n  conv3dDerFilter(x: Tensor5D, dy: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n    const program = new Conv3DDerFilterProgram(convInfo);\n    return this.compileAndRun(program, [x, dy]);\n  }\n\n  maxPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    const program = new Pool2DProgram(convInfo, 'max', false);\n    const output =\n        this.makeOutputArray(program.outputShape, x.dtype) as Tensor4D;\n    return this.compileAndRun(program, [x], output);\n  }\n\n  avgPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    const program = new Pool2DProgram(convInfo, 'avg', false);\n    const output = this.makeOutputArray(program.outputShape, 'float32');\n    return this.compileAndRun(program, [x], output) as Tensor4D;\n  }\n\n  maxPoolBackprop(dy: Tensor4D, x: Tensor4D, y: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    const getPositions = true;\n    const maxPoolPositionsProgram =\n        new Pool2DProgram(convInfo, 'max', getPositions);\n    const maxPoolPositions: Tensor4D =\n        this.compileAndRun(maxPoolPositionsProgram, [x]);\n\n    const maxPoolBackPropProgram = new MaxPool2DBackpropProgram(convInfo);\n    const output =\n        this.makeOutputArray(maxPoolBackPropProgram.outputShape, x.dtype);\n    const result = this.compileAndRun(\n        maxPoolBackPropProgram, [dy, maxPoolPositions], output);\n    maxPoolPositions.dispose();\n    return result as Tensor4D;\n  }\n\n  avgPoolBackprop(dy: Tensor4D, x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    const avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo);\n    const output =\n        this.makeOutputArray(avgPoolBackpropProgram.outputShape, x.dtype);\n    return this.compileAndRun(avgPoolBackpropProgram, [dy], output) as Tensor4D;\n  }\n\n  cast<T extends Tensor>(x: T, dtype: DataType): T {\n    return backend_util.castTensor(x, dtype, this);\n  }\n\n  reshape<R extends Rank>(x: Tensor, shape: ShapeMap[R]): Tensor<R> {\n    if (this.texData.get(x.dataId).isPacked &&\n        !webgl_util.isReshapeFree(x.shape, shape)) {\n      return this.packedReshape(x, shape);\n    }\n    return backend_util.reshapeTensor(x, shape);\n  }\n\n  resizeBilinear(\n      x: Tensor4D, newHeight: number, newWidth: number,\n      alignCorners: boolean): Tensor4D {\n    const program =\n        new ResizeBilinearProgram(x.shape, newHeight, newWidth, alignCorners);\n    return this.compileAndRun(program, [x]);\n  }\n\n  resizeBilinearBackprop(dy: Tensor4D, x: Tensor4D, alignCorners: boolean):\n      Tensor4D {\n    const program = new ResizeBilinearBackpropProgram(dy, x, alignCorners);\n\n    return this.compileAndRun(program, [dy]);\n  }\n\n  resizeNearestNeighbor(\n      x: Tensor4D, newHeight: number, newWidth: number,\n      alignCorners: boolean): Tensor4D {\n    const program = new ResizeNearestNeighborProgram(\n        x.shape, newHeight, newWidth, alignCorners);\n    return this.compileAndRun(program, [x]);\n  }\n\n  resizeNearestNeighborBackprop(\n      dy: Tensor4D, x: Tensor4D, alignCorners: boolean): Tensor4D {\n    const program =\n        new ResizeNearestNeigborBackpropProgram(dy, x, alignCorners);\n    return this.compileAndRun(program, [dy]);\n  }\n\n  multinomial(\n      logits: Tensor2D, normalized: boolean, numSamples: number,\n      seed: number): Tensor2D {\n    const probs = normalized ? logits : softmax(logits);\n    const batchSize = probs.shape[0];\n    const numOutcomes = probs.shape[1];\n    const program = new MultinomialProgram(batchSize, numOutcomes, numSamples);\n    const output =\n        this.makeOutputArray(program.outputShape, 'int32') as Tensor2D;\n    const customSetup = program.getCustomSetupFunc(seed);\n    return this.compileAndRun(program, [probs], output, customSetup);\n  }\n\n  oneHot(indices: Tensor1D, depth: number, onValue: number, offValue: number):\n      Tensor2D {\n    const program = new OneHotProgram(indices.size, depth, onValue, offValue);\n    return this.compileAndRun(program, [indices]);\n  }\n\n  nonMaxSuppression(\n      boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number,\n      iouThreshold: number, scoreThreshold: number): Tensor1D {\n    warn(\n        'tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n        'Call tf.nonMaxSuppressionAsync() instead');\n    const boxesVals = boxes.dataSync();\n    const scoresVals = scores.dataSync();\n    return nonMaxSuppressionImpl(\n        boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n  }\n\n  cropAndResize(\n      image: Tensor4D, boxes: Tensor2D, boxIndex: Tensor1D,\n      cropSize: [number, number], method: 'bilinear'|'nearest',\n      extrapolationValue: number): Tensor4D {\n    const program = new CropAndResizeProgram(\n        image.shape, boxes.shape, cropSize, method, extrapolationValue);\n    return this.compileAndRun(program, [image, boxes, boxIndex]);\n  }\n\n  depthToSpace(x: Tensor4D, blockSize: number, dataFormat: 'NHWC'|'NCHW'):\n      Tensor4D {\n    util.assert(\n        blockSize > 1,\n        `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n\n    const batchSize = x.shape[0];\n    const inputHeight = (dataFormat === 'NHWC') ? x.shape[1] : x.shape[2];\n    const inputWidth = (dataFormat === 'NHWC') ? x.shape[2] : x.shape[3];\n    const inputDepth = (dataFormat === 'NHWC') ? x.shape[3] : x.shape[1];\n\n    const outputHeight = inputHeight * blockSize;\n    const outputWidth = inputWidth * blockSize;\n    const outputDepth = inputDepth / (blockSize * blockSize);\n\n    const outputShape = (dataFormat === 'NHWC') ?\n        [batchSize, outputHeight, outputWidth, outputDepth] :\n        [batchSize, outputDepth, outputHeight, outputWidth];\n\n    const program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat);\n    return this.compileAndRun(program, [x]);\n  }\n\n  split<T extends Tensor>(x: T, sizeSplits: number[], axis: number): T[] {\n    return split(x, sizeSplits, axis);\n  }\n\n  scatterND<R extends Rank>(\n      indices: Tensor, updates: Tensor, shape: ShapeMap[R]): Tensor<R> {\n    const {sliceRank, numUpdates, sliceSize, strides, outputSize} =\n        scatter_nd_util.calculateShapes(updates, indices, shape);\n\n    const flattenShape = [outputSize / sliceSize, sliceSize];\n    const flattenIndices = indices.reshape([numUpdates, sliceRank]);\n    const flattenX = updates.reshape([numUpdates, sliceSize]);\n\n    if (outputSize === 0) {\n      return backend_util.reshapeTensor(tensor([]), shape);\n    }\n    const defaultValue = scalar(0);\n    const program = new ScatterProgram(\n        numUpdates, sliceRank, flattenIndices.rank, flattenX.rank, strides,\n        flattenShape);\n    return (this.compileAndRun(\n                program, [flattenX, flattenIndices, defaultValue]) as Tensor)\n        .reshape(shape);\n  }\n\n  sparseToDense<R extends Rank>(\n      sparseIndices: Tensor, sparseValues: Tensor, outputShape: ShapeMap[R],\n      defaultValue: Scalar): Tensor<R> {\n    const {sliceRank, numUpdates, strides, outputSize} =\n        scatter_nd_util.calculateShapes(\n            sparseValues, sparseIndices, outputShape);\n\n    const sumDupeIndices = false;\n    const program = new ScatterProgram(\n        numUpdates, sliceRank, sparseIndices.rank, sparseValues.rank, strides,\n        [outputSize, 1], sumDupeIndices);\n    return (this.compileAndRun(\n                program, [sparseValues, sparseIndices, defaultValue]) as Tensor)\n        .reshape(outputShape);\n  }\n\n  fft(x: Tensor2D): Tensor2D {\n    const inverse = false;\n    return this.fftImpl(x, inverse);\n  }\n\n  ifft(x: Tensor2D): Tensor2D {\n    const inverse = true;\n    return this.fftImpl(x, inverse);\n  }\n\n  private fftImpl(x: Tensor2D, inverse: boolean): Tensor2D {\n    const xData = this.texData.get(x.dataId);\n\n    const realProgram =\n        new FFTProgram(fft_gpu.COMPLEX_FFT.REAL, x.shape, inverse);\n    const imagProgram =\n        new FFTProgram(fft_gpu.COMPLEX_FFT.IMAG, x.shape, inverse);\n    const inputs = [\n      this.makeComplexComponentTensorHandle(x, xData.complexTensors.real),\n      this.makeComplexComponentTensorHandle(x, xData.complexTensors.imag),\n    ];\n\n    const real = this.compileAndRun<Tensor>(realProgram, inputs);\n    const imag = this.compileAndRun<Tensor>(imagProgram, inputs);\n    const complex = this.complex(real, imag).as2D(x.shape[0], x.shape[1]);\n    real.dispose();\n    imag.dispose();\n    return complex;\n  }\n\n  gatherND(x: Tensor, indices: Tensor): Tensor<Rank> {\n    const indicesShape = indices.shape;\n    const sliceRank = indicesShape[indicesShape.length - 1];\n\n    const [resultShape, numSlices, sliceSize, strides] =\n        gather_nd_util.prepareAndValidate(x, indices);\n\n    const flattenIndices = indices.reshape([numSlices, sliceRank]);\n    const flattenX = x.reshape([x.size / sliceSize, sliceSize]);\n    const program =\n        new GatherNDProgram(sliceRank, strides, [numSlices, sliceSize]);\n    return (this.compileAndRun(program, [flattenX, flattenIndices]) as Tensor)\n        .reshape(resultShape);\n  }\n\n  private makeOutputArray<T extends Tensor>(shape: number[], dtype: DataType):\n      T {\n    return Tensor.make(shape, {}, dtype) as T;\n  }\n\n  private makePackedTensor<T extends Tensor>(shape: number[], dtype: DataType):\n      T {\n    const packedTensor = Tensor.make(shape, {}, dtype);\n    this.texData.get(packedTensor.dataId).isPacked = true;\n    return packedTensor as T;\n  }\n\n  private unpackTensor<T extends Tensor>(input: T): T {\n    const program = new UnpackProgram(input.shape);\n    return this.compileAndRun(\n        program, [input], Tensor.make(program.outputShape, {}, input.dtype));\n  }\n\n  private getBatchDim(shape: number[], dimsToSkip = 2): number {\n    return util.sizeFromShape(shape.slice(0, shape.length - dimsToSkip));\n  }\n\n  private getRowsCols(shape: number[]): [number, number] {\n    if (shape.length === 0) {\n      throw Error('Cannot get rows and columns of an empty shape array.');\n    }\n\n    return [\n      shape.length > 1 ? shape[shape.length - 2] : 1, shape[shape.length - 1]\n    ];\n  }\n\n  private packedReshape<R extends Rank>(input: Tensor, afterShape: ShapeMap[R]):\n      Tensor<R> {\n    const inputAs3D = input.reshape(\n        [this.getBatchDim(input.shape), ...this.getRowsCols(input.shape)]);\n    const afterShapeAs3D =\n        [this.getBatchDim(afterShape), ...this.getRowsCols(afterShape)];\n    const program = new ReshapePackedProgram(\n        afterShapeAs3D as [number, number, number],\n        inputAs3D.shape as [number, number, number]);\n    return this.compileAndRun<Tensor<R>>(program, [inputAs3D])\n        .reshape(afterShape);\n  }\n\n  public compileAndRun<\n      K extends {dtype: DataType, size: number, dataId: {}, shape: number[]}>(\n      program: GPGPUProgram, inputs: TensorHandle[], output?: K,\n      customSetup?: (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => void,\n      pageToCpu = true): K {\n    if (output == null) {\n      if (program.usesPackedTextures) {\n        output = this.makePackedTensor(program.outputShape, inputs[0].dtype) as\n            {} as K;\n      } else {\n        output = this.makeOutputArray(program.outputShape, inputs[0].dtype) as\n            {} as K;\n      }\n    }\n    if (output.size === 0) {\n      // Short-circuit the computation since the result is empty (has 0 in its\n      // shape).\n      this.texData.get(output.dataId).values =\n          getTypedArrayFromDType(output.dtype as 'float32', 0);\n      return output;\n    }\n\n    const inputsData: TensorData[] = inputs.map(input => {\n      if (input.dtype === 'complex64') {\n        throw new Error(\n            `GPGPUProgram does not support complex64 input. For complex64 ` +\n            `dtypes, please separate the program into real and imaginary ` +\n            `parts.`);\n      }\n\n      let texData = this.texData.get(input.dataId);\n\n      if (texData.texture == null) {\n        if (!program.usesPackedTextures &&\n            util.sizeFromShape(input.shape) <=\n                ENV.get('WEBGL_SIZE_UPLOAD_UNIFORM')) {\n          // Upload small tensors that live on the CPU as uniforms, not as\n          // textures. Do this only when the environment supports 32bit floats\n          // due to problems when comparing 16bit floats with 32bit floats.\n          // TODO(https://github.com/tensorflow/tfjs/issues/821): Make it\n          // possible for packed shaders to sample from uniforms.\n          return {\n            shape: input.shape,\n            texData: null,\n            isUniform: true,\n            uniformValues: this.readSync(input.dataId) as TypedArray\n          };\n        }\n\n        // This ensures that if a packed program's inputs have not yet been\n        // uploaded to the GPU, they get uploaded as packed right off the bat.\n        if (program.usesPackedTextures) {\n          texData.isPacked = true;\n          texData.shape = input.shape;\n        }\n      } else if (!!texData.isPacked !== !!program.usesPackedTextures) {\n        let preProcessProgram: UnpackProgram|PackProgram;\n        let processedInput: Tensor;\n\n        // Explicitly specifying output tensors because compileAndRun assumes\n        // that programs that take packed inputs produce a packed output.\n        if (texData.isPacked) {\n          preProcessProgram = new UnpackProgram(input.shape);\n          processedInput = this.compileAndRun(\n              preProcessProgram, [input],\n              Tensor.make(preProcessProgram.outputShape, {}, input.dtype));\n        } else {\n          preProcessProgram = new PackProgram(input.shape);\n          processedInput = this.compileAndRun(\n              preProcessProgram, [input],\n              this.makePackedTensor(input.shape, input.dtype));\n        }\n\n        texData = this.texData.get(processedInput.dataId);\n        input = processedInput;\n      } else if (\n          texData.isPacked &&\n          !webgl_util.isReshapeFree(texData.shape, input.shape)) {\n        // This is a special, temporary case where a texture exists for a tensor\n        // but the shapes are incompatible (due to packing constraints) because\n        // the tensor did not have a chance to go through the packed reshape\n        // shader. This only happens when we reshape the *same* tensor to form\n        // *distinct* inputs to an op, e.g. dotting a vector with itself. This\n        // case will disappear once packed uploading is the default.\n\n        // Temporarily disable delayedStorage so the texture isn't removed from\n        // the original input\n        this.delayedStorage = false;\n        const inputValues = (input as Tensor).dataSync();\n        this.delayedStorage = true;\n\n        input = Tensor.make(input.shape, {values: inputValues}, input.dtype);\n        texData = this.texData.get(input.dataId);\n        texData.isPacked = true;\n      }\n\n      this.uploadToGPU(input.dataId);\n      return {shape: input.shape, texData, isUniform: false};\n    });\n\n    this.uploadToGPU(output.dataId);\n    const outputData = {\n      shape: output.shape,\n      texData: this.texData.get(output.dataId),\n      isUniform: false\n    };\n    const key = gpgpu_math.makeShaderKey(program, inputsData, outputData);\n    const binary = this.getAndSaveBinary(key, () => {\n      return gpgpu_math.compileProgram(\n          this.gpgpu, program, inputsData, outputData);\n    });\n    const shouldTimeProgram = this.activeTimers != null;\n    let query: WebGLQuery|CPUTimerQuery;\n    if (shouldTimeProgram) {\n      query = this.startTimer();\n    }\n\n    gpgpu_math.runProgram(binary, inputsData, outputData, customSetup);\n\n    if (ENV.get('WEBGL_PAGING_ENABLED') && pageToCpu &&\n        this.numBytesInGPU > this.NUM_BYTES_BEFORE_PAGING) {\n      let numBytesToPage = this.numBytesInGPU - this.NUM_BYTES_BEFORE_PAGING;\n      while (numBytesToPage > 0 && this.lruDataGPU.length > 0) {\n        const dataId = this.lruDataGPU.shift();\n        const {shape, dtype} = this.texData.get(dataId);\n        numBytesToPage -= this.computeBytes(shape, dtype);\n        this.read(dataId);\n      }\n    }\n\n    if (shouldTimeProgram) {\n      query = this.endTimer(query);\n      this.activeTimers.push(\n          {name: program.constructor.name, query: this.getQueryTime(query)});\n    }\n\n    if (!ENV.get('WEBGL_LAZILY_UNPACK') &&\n        this.texData.get(output.dataId).isPacked && !program.isPackShader) {\n      return this.unpackTensor(output as {} as Tensor) as {} as K;\n    }\n    return output;\n  }\n\n  private getAndSaveBinary(key: string, getBinary: () => GPGPUBinary):\n      GPGPUBinary {\n    if (!(key in this.binaryCache)) {\n      this.binaryCache[key] = getBinary();\n    }\n    return this.binaryCache[key];\n  }\n\n  getTextureManager(): TextureManager {\n    return this.textureManager;\n  }\n\n  private disposed = false;\n\n  dispose() {\n    if (this.disposed) {\n      return;\n    }\n    for (const key in this.binaryCache) {\n      this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram);\n    }\n    this.textureManager.dispose();\n    this.canvas.remove();\n    if (this.fromPixels2DContext != null) {\n      this.fromPixels2DContext.canvas.remove();\n    }\n    if (this.gpgpuCreatedLocally) {\n      this.gpgpu.dispose();\n    }\n    this.disposed = true;\n  }\n\n  floatPrecision(): number {\n    return tidy(() => {\n      if (this.abs(scalar(1e-8)).get() > 0) {\n        return 32;\n      }\n      return 16;\n    });\n  }\n\n  private uploadToGPU(dataId: DataId): void {\n    const texData = this.texData.get(dataId);\n    const {shape, values, texture, usage, isPacked} = texData;\n    if (texture != null) {\n      // Array is already on GPU. No-op.\n      // Touching the texture.\n      if (ENV.get('WEBGL_PAGING_ENABLED')) {\n        const index = this.lruDataGPU.indexOf(dataId);\n        if (index >= 0) {\n          this.lruDataGPU.splice(this.lruDataGPU.indexOf(dataId), 1);\n          this.lruDataGPU.push(dataId);\n        }\n      }\n      return;\n    }\n    const shouldTimeProgram = this.activeTimers != null;\n    let start: number;\n    if (shouldTimeProgram) {\n      start = performance.now();\n    }\n    const texShape =\n        webgl_util.getTextureShapeFromLogicalShape(shape, isPacked);\n    texData.texShape = texShape;\n    const newTexture = this.acquireTexture(dataId, texShape, usage, isPacked);\n    texData.texture = newTexture;\n    if (values != null) {\n      // TODO(smilkov): Propagate the original typed array to gpgpu.\n      if (isPacked) {\n        const batch = this.getBatchDim(shape);\n        let rows = 1, cols = 1;\n        if (shape.length) {\n          [rows, cols] = this.getRowsCols(shape);\n        }\n        this.gpgpu.uploadMatrixToPackedTexture(\n            newTexture, batch, rows, cols, texShape[0], texShape[1],\n            typedArrayToFloat32(values as Float32Array));\n      } else {\n        this.gpgpu.uploadMatrixToTexture(\n            newTexture, texShape[0], texShape[1],\n            typedArrayToFloat32(values as Float32Array));\n      }\n      // Once uploaded, don't store the values on cpu.\n      texData.values = null;\n      if (shouldTimeProgram) {\n        this.uploadWaitMs += performance.now() - start;\n      }\n    }\n  }\n\n  private convertAndCacheOnCPU(dataId: DataId, float32Values?: Float32Array):\n      TypedArray {\n    // In delayed storage mode, when the user reads data, we don't keep a\n    // copy on the gpu, to minimize likelihood of memory leak. We re-upload\n    // to gpu the next time a gpgpu program needs the texture.\n    const dontKeepCopyOnGPU = this.delayedStorage;\n    const texData = this.texData.get(dataId);\n    const {texture, texShape, dtype, usage, isPacked} = texData;\n    if (dontKeepCopyOnGPU && texture != null) {\n      this.releaseTexture(dataId, texture, texShape, usage, isPacked);\n      texData.texture = null;\n      texData.texShape = null;\n    }\n    texData.usage = TextureUsage.UPLOAD;\n    if (float32Values != null) {\n      texData.values = float32ToTypedArray(float32Values, dtype as 'float32');\n    }\n    return texData.values as TypedArray;\n  }\n\n  private releaseTexture(\n      dataId: DataId, texture: WebGLTexture, texShape: [number, number],\n      texType: TextureUsage, isPacked: boolean) {\n    const {shape, dtype} = this.texData.get(dataId);\n\n    if (ENV.get('WEBGL_PAGING_ENABLED')) {\n      const idx = this.lruDataGPU.indexOf(dataId);\n      if (idx >= 0) {\n        this.lruDataGPU.splice(idx, 1);\n      }\n    }\n    this.numBytesInGPU -= this.computeBytes(shape, dtype);\n    this.textureManager.releaseTexture(texture, texShape, texType, isPacked);\n  }\n\n  private acquireTexture(\n      dataId: DataId, texShape: [number, number], texType: TextureUsage,\n      isPacked: boolean): WebGLTexture {\n    const {shape, dtype} = this.texData.get(dataId);\n    if (ENV.get('WEBGL_PAGING_ENABLED')) {\n      this.lruDataGPU.push(dataId);\n    }\n    this.numBytesInGPU += this.computeBytes(shape, dtype);\n    return this.textureManager.acquireTexture(texShape, texType, isPacked);\n  }\n\n  private computeBytes(shape: number[], dtype: DataType) {\n    return util.sizeFromShape(shape) * util.bytesPerElement(dtype);\n  }\n}\n\nif (ENV.get('IS_BROWSER')) {\n  ENV.registerBackend(\n      'webgl', () => new MathBackendWebGL(), 2 /* priority */,\n      setTensorTracker);\n}\n\nfunction float32ToTypedArray<D extends NumericDataType>(\n    a: Float32Array, dtype: D): DataTypeMap[D] {\n  if (dtype === 'float32' || dtype === 'complex64') {\n    return a;\n  } else if (dtype === 'int32' || dtype === 'bool') {\n    const result = (dtype === 'int32') ? new Int32Array(a.length) :\n                                         new Uint8Array(a.length);\n    for (let i = 0; i < result.length; ++i) {\n      result[i] = Math.round(a[i]);\n    }\n    return result;\n  } else {\n    throw new Error(`Unknown dtype ${dtype}`);\n  }\n}\n\nfunction typedArrayToFloat32(a: TypedArray): Float32Array {\n  return (a instanceof Float32Array) ? a : new Float32Array(a);\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\nimport {scalar, zerosLike} from './tensor_ops';\n\n/**\n * Computes `-1 * x` element-wise.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, -2, 0], [2, 2]);\n *\n * x.neg().print();  // or tf.neg(x)\n * ```\n *\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction neg_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'neg');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.neg()};\n  };\n  return ENV.engine.runKernel(backend => backend.neg($x), {$x}, grad);\n}\n\n/**\n * Computes ceiling of input `tf.Tensor` element-wise: `ceil(x)`\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.ceil().print();  // or tf.ceil(x)\n * ```\n * @param x The input Tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction ceil_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'ceil');\n\n  // TODO(manrajgrover): Return null for gradients when backprop supports it.\n  const grad = (dy: T) => {\n    return {$x: () => zerosLike(dy)};\n  };\n  return ENV.engine.runKernel(backend => backend.ceil($x), {$x}, grad);\n}\n\n/**\n * Computes floor of input `tf.Tensor` element-wise: `floor(x)`.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.floor().print();  // or tf.floor(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction floor_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'floor');\n\n  // TODO(nsthorat): Let gradients be null for cases where we want to stop\n  // backpropgation.\n  const grad = (dy: T) => {\n    return {$x: () => zerosLike(dy)};\n  };\n  return ENV.engine.runKernel(backend => backend.floor($x), {$x}, grad);\n}\n\n/**\n * Returns an element-wise indication of the sign of a number.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3, NaN, 0]);\n *\n * x.sign().print();  // or tf.sign(x)\n * ```\n * @param x The input Tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction sign_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'sign');\n\n  const grad = (dy: T) => {\n    return {$x: () => zerosLike(dy)};\n  };\n  return ENV.engine.runKernel(backend => backend.sign($x), {$x}, grad);\n}\n\n/**\n * Computes round of input `tf.Tensor` element-wise: `round(x)`.\n * It implements banker's rounding.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.round().print();  // or tf.round(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction round_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'round');\n\n  // TODO(nsthorat): Let gradients be null for cases where we want to stop\n  // backpropgation.\n  const grad = (dy: T) => {\n    return {$x: () => zerosLike(dy)};\n  };\n  return ENV.engine.runKernel(backend => backend.round($x), {$x}, grad);\n}\n\n/**\n * Computes exponential of the input `tf.Tensor` element-wise. `e ^ x`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.exp().print();  // or tf.exp(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction exp_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'exp');\n\n  const bck = (dy: T, saved: Tensor[]) => {\n    const [y] = saved;\n    return {$x: () => dy.mulStrict(y as T)};\n  };\n  return ENV.engine.runKernel(\n      (backend, save) => save(backend.exp($x)), {$x}, bck);\n}\n\n/**\n * Computes exponential of the input `tf.Tensor` minus one element-wise.\n * `e ^ x - 1`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.expm1().print();  // or tf.expm1(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction expm1_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'expm1');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.mulStrict($x.exp())};\n  };\n  return ENV.engine.runKernel(backend => backend.expm1($x), {$x}, grad);\n}\n\n/**\n * Computes natural logarithm of the input `tf.Tensor` element-wise: `ln(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E]);\n *\n * x.log().print();  // or tf.log(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction log_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'log');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.divStrict($x.toFloat())};\n  };\n  return ENV.engine.runKernel(backend => backend.log($x), {$x}, grad);\n}\n\n/**\n * Computes natural logarithm of the input `tf.Tensor` plus one\n * element-wise: `ln(1 + x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E - 1]);\n *\n * x.log1p().print();  // or tf.log1p(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction log1p_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'log1p');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.div($x.add(1)) as T};\n  };\n  return ENV.engine.runKernel(backend => backend.log1p($x), {$x}, grad);\n}\n\n/**\n * Computes square root of the input `tf.Tensor` element-wise: `y = sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.sqrt().print();  // or tf.sqrt(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction sqrt_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'sqrt');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.div($x.toFloat().sqrt().mul(2)) as T};\n  };\n  return ENV.engine.runKernel(backend => backend.sqrt($x), {$x}, grad);\n}\n\n/**\n * Computes reciprocal of square root of the input `tf.Tensor` element-wise:\n * `y = 1 / sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.rsqrt().print();  // or tf.rsqrt(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction rsqrt_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'rsqrt');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.div($x.pow(1.5).mul(2)).neg() as T};\n  };\n  return ENV.engine.runKernel(backend => backend.rsqrt($x), {$x}, grad);\n}\n\n/**\n * Computes square of `x` element-wise: `x ^ 2`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.sqrt(2), -1]);\n *\n * x.square().print();  // or tf.square(x)\n * ```\n * @param x The input Tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction square_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'square');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.mul($x.toFloat().mul(2)) as T};\n  };\n  return ENV.engine.runKernel(backend => backend.square($x), {$x}, grad);\n}\n\n/**\n * Computes reciprocal of x element-wise: `1 / x`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, 2]);\n *\n * x.reciprocal().print();  // or tf.reciprocal(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction reciprocal_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'reciprocal');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.divStrict($x.square().neg())};\n  };\n  return ENV.engine.runKernel(backend => backend.reciprocal($x), {$x}, grad);\n}\n\n/**\n * Computes absolute value element-wise: `abs(x)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.abs().print();  // or tf.abs(x)\n * ```\n * @param x The input `tf.Tensor`.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction abs_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'abs');\n\n  if ($x.dtype === 'complex64') {\n    return ENV.engine.runKernel(backend => backend.complexAbs($x), {$x});\n  }\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.mulStrict($x.toFloat().step(-1))};\n  };\n  return ENV.engine.runKernel(backend => backend.abs($x), {$x}, grad);\n}\n\n/**\n * Clips values element-wise. `max(min(x, clipValueMax), clipValueMin)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.clipByValue(-2, 3).print();  // or tf.clipByValue(x, -2, 3)\n * ```\n * @param x The input tensor.\n * @param clipValueMin Lower-bound of range to be clipped to.\n * @param clipValueMax Upper-bound of range to be clipped to.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction clipByValue_<T extends Tensor>(\n    x: T|TensorLike, clipValueMin: number, clipValueMax: number): T {\n  const $x = convertToTensor(x, 'x', 'clipByValue');\n  util.assert(\n      (clipValueMin <= clipValueMax),\n      `Error in clip: min (${clipValueMin}) must be ` +\n          `less than or equal to max (${clipValueMax}).`);\n\n  const grad = (dy: T) => {\n    return {\n      $x: () => dy.where(\n                    $x.greaterEqual(clipValueMin)\n                        .logicalAnd($x.lessEqual(clipValueMax)),\n                    zerosLike(dy)) as T,\n    };\n  };\n  return ENV.engine.runKernel(\n      backend => backend.clip($x, clipValueMin, clipValueMax), {$x}, grad);\n}\n\n/**\n * Computes sigmoid element-wise, `1 / (1 + exp(-x))`\n *\n * ```js\n * const x = tf.tensor1d([0, -1, 2, -3]);\n *\n * x.sigmoid().print();  // or tf.sigmoid(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction sigmoid_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'sigmoid');\n\n  const grad = (dy: T, saved: Tensor[]) => {\n    const [y] = saved;\n    return {$x: () => dy.mul(y.mul(scalar(1).sub(y))) as T};\n  };\n  return ENV.engine.runKernel(\n      (backend, save) => save(backend.sigmoid($x)), {$x}, grad);\n}\n\n/**\n * Computes log sigmoid of the input `tf.Tensor` element-wise:\n * `logSigmoid(x)`. For numerical stability, we use `-tf.softplus(-x)`.\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.logSigmoid().print();  // or tf.logSigmoid(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction logSigmoid_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'logSigmoid');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.mulStrict($x.neg().sigmoid())};\n  };\n  return ENV.engine.runKernel(\n      backend => backend.softplus($x.neg()).neg(), {$x}, grad);\n}\n\n/**\n * Computes softplus of the input `tf.Tensor` element-wise: `log(exp(x) + 1)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.softplus().print();  // or tf.softplus(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction softplus_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'softplus');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.mulStrict($x.sigmoid())};\n  };\n  return ENV.engine.runKernel(backend => backend.softplus($x), {$x}, grad);\n}\n\n/**\n * Computes sin of the input Tensor element-wise: `sin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.sin().print();  // or tf.sin(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction sin_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'sin');\n\n  const grad = (dy: T) => {\n    return {$x: () => $x.toFloat().cos().mulStrict(dy)};\n  };\n  return ENV.engine.runKernel(backend => backend.sin($x), {$x}, grad);\n}\n\n/**\n * Computes cos of the input `tf.Tensor` element-wise: `cos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.cos().print();  // or tf.cos(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction cos_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'cos');\n\n  const grad = (dy: T) => {\n    return {$x: () => $x.toFloat().sin().neg().mulStrict(dy)};\n  };\n  return ENV.engine.runKernel(backend => backend.cos($x), {$x}, grad);\n}\n\n/**\n * Computes tan of the input `tf.Tensor` element-wise, `tan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.tan().print();  // or tf.tan(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction tan_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'tan');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.divStrict($x.cos().square())};\n  };\n  return ENV.engine.runKernel(backend => backend.tan($x), {$x}, grad);\n}\n\n/**\n * Computes asin of the input `tf.Tensor` element-wise: `asin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asin().print();  // or tf.asin(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction asin_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'asin');\n\n  const grad = (dy: T) => {\n    return {\n      $x: () => dy.divStrict(scalar(1).sub($x.toFloat().square()).sqrt() as T)\n    };\n  };\n  return ENV.engine.runKernel(backend => backend.asin($x), {$x}, grad);\n}\n\n/**\n * Computes acos of the input `tf.Tensor` element-wise: `acos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.acos().print();  // or tf.acos(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction acos_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'acos');\n\n  const grad = (dy: T) => {\n    return {\n      $x: () =>\n          dy.divStrict(scalar(1).sub($x.toFloat().square()).sqrt() as T).neg()\n    };\n  };\n  return ENV.engine.runKernel(backend => backend.acos($x), {$x}, grad);\n}\n\n/**\n * Computes atan of the input `tf.Tensor` element-wise: `atan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.atan().print();  // or tf.atan(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction atan_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'atan');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.div($x.toFloat().square().add(1)) as T};\n  };\n  return ENV.engine.runKernel(backend => backend.atan($x), {$x}, grad);\n}\n\n/**\n * Computes hyperbolic sin of the input `tf.Tensor` element-wise: `sinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.sinh().print();  // or tf.sinh(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction sinh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'sinh');\n\n  const grad = (dy: T) => {\n    return {$x: () => $x.toFloat().cosh().mulStrict(dy)};\n  };\n  return ENV.engine.runKernel(backend => backend.sinh($x), {$x}, grad);\n}\n\n/**\n * Computes hyperbolic cos of the input `tf.Tensor` element-wise: `cosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.cosh().print();  // or tf.cosh(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction cosh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'cosh');\n\n  const grad = (dy: T) => {\n    return {$x: () => $x.toFloat().sinh().mulStrict(dy)};\n  };\n  return ENV.engine.runKernel(backend => backend.cosh($x), {$x}, grad);\n}\n\n/**\n * Computes hyperbolic tangent of the input `tf.Tensor` element-wise: `tanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, 70]);\n *\n * x.tanh().print();  // or tf.tanh(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction tanh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'tanh');\n\n  const grad = (dy: T, saved: Tensor[]) => {\n    const [y] = saved;\n    return {$x: () => scalar(1).sub(y.square()).mulStrict(dy) as T};\n  };\n  return ENV.engine.runKernel(\n      (backend, save) => save(backend.tanh($x)), {$x}, grad);\n}\n\n/**\n * Computes inverse hyperbolic sin of the input `tf.Tensor` element-wise:\n * `asinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asinh().print();  // or tf.asinh(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction asinh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'asinh');\n\n  const grad = (dy: T) => {\n    return {\n      $x: () => dy.divStrict(scalar(1).add($x.toFloat().square()).sqrt() as T)\n    };\n  };\n  return ENV.engine.runKernel(backend => backend.asinh($x), {$x}, grad);\n}\n\n/**\n * Computes the inverse hyperbolic cos of the input `tf.Tensor` element-wise:\n * `acosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([10, 1, 3, 5.7]);\n *\n * x.acosh().print();  // or tf.acosh(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction acosh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'acosh');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.divStrict($x.toFloat().square().sub(1).sqrt() as T)};\n  };\n  return ENV.engine.runKernel(backend => backend.acosh($x), {$x}, grad);\n}\n\n/**\n * Computes inverse hyperbolic tan of the input `tf.Tensor` element-wise:\n * `atanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.atanh().print();  // or tf.atanh(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction atanh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'atanh');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.div(scalar(1).sub($x.toFloat().square())) as T};\n  };\n  return ENV.engine.runKernel(backend => backend.atanh($x), {$x}, grad);\n}\n\n/**\n * Computes gause error function of the input `tf.Tensor` element-wise:\n * `erf(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.erf().print(); // or tf.erf(x);\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction erf_<T extends Tensor>(x: T|TensorLike): T {\n  let $x = convertToTensor(x, 'x', 'erf');\n  util.assert(\n      $x.dtype === 'int32' || $x.dtype === 'float32',\n      'Input dtype must be `int32` or `float32`.');\n\n  if ($x.dtype === 'int32') {\n    $x = $x.toFloat();\n  }\n\n  const grad = (dy: T) => {\n    return {\n      $x: () => dy.mul($x.square().neg().exp().mul(2 / Math.sqrt(Math.PI))) as T\n    };\n  };\n  return ENV.engine.runKernel(backend => backend.erf($x), {$x}, grad);\n}\n\n/**\n * Computes step of the input `tf.Tensor` element-wise: `x > 0 ? 1 : alpha * x`\n *\n * ```js\n * const x = tf.tensor1d([0, 2, -1, -3]);\n *\n * x.step(.5).print();  // or tf.step(x, .5)\n * ```\n * @param x The input tensor.\n * @param alpha The gradient when input is negative.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction step_<T extends Tensor>(x: T|TensorLike, alpha = 0.0): T {\n  const $x = convertToTensor(x, 'x', 'step');\n\n  // TODO(manrajgrover): Return null for gradients when backprop supports\n  // it.\n  const grad = (dy: T) => {\n    return {$x: () => zerosLike(dy)};\n  };\n  return ENV.engine.runKernel(backend => backend.step($x, alpha), {$x}, grad);\n}\n\nexport const abs = op({abs_});\nexport const acos = op({acos_});\nexport const acosh = op({acosh_});\nexport const asin = op({asin_});\nexport const asinh = op({asinh_});\nexport const atan = op({atan_});\nexport const atanh = op({atanh_});\nexport const ceil = op({ceil_});\nexport const clipByValue = op({clipByValue_});\nexport const cos = op({cos_});\nexport const cosh = op({cosh_});\nexport const erf = op({erf_});\nexport const exp = op({exp_});\nexport const expm1 = op({expm1_});\nexport const floor = op({floor_});\nexport const log = op({log_});\nexport const log1p = op({log1p_});\nexport const logSigmoid = op({logSigmoid_});\nexport const neg = op({neg_});\nexport const reciprocal = op({reciprocal_});\nexport const round = op({round_});\nexport const rsqrt = op({rsqrt_});\nexport const sigmoid = op({sigmoid_});\nexport const sign = op({sign_});\nexport const sin = op({sin_});\nexport const sinh = op({sinh_});\nexport const softplus = op({softplus_});\nexport const sqrt = op({sqrt_});\nexport const square = op({square_});\nexport const step = op({step_});\nexport const tan = op({tan_});\nexport const tanh = op({tanh_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {tile} from './array_ops';\nimport {getReductionAxes} from './broadcast_util';\nimport {op} from './operation';\nimport {scalar} from './tensor_ops';\nimport {rsqrt} from './unary_ops';\n\n/**\n * Batch normalization, strictly for 2D. For the more relaxed version, see\n * `tf.batchNormalization`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n * @param scale A scale Tensor.\n * @param offset An offset Tensor.\n */\nfunction batchNormalization2d_(\n    x: Tensor2D|TensorLike, mean: Tensor2D|Tensor1D|TensorLike,\n    variance: Tensor2D|Tensor1D|TensorLike, varianceEpsilon = .001,\n    scale?: Tensor2D|Tensor1D|TensorLike,\n    offset?: Tensor2D|Tensor1D|TensorLike): Tensor2D {\n  const $x = convertToTensor(x, 'x', 'batchNormalization');\n  const $mean = convertToTensor(mean, 'mean', 'batchNormalization');\n  const $variance = convertToTensor(variance, 'variance', 'batchNormalization');\n  let $scale: Tensor2D|Tensor1D;\n  if (scale != null) {\n    $scale = convertToTensor(scale, 'scale', 'batchNormalization');\n  }\n  let $offset: Tensor2D|Tensor1D;\n  if (offset != null) {\n    $offset = convertToTensor(offset, 'offset', 'batchNormalization');\n  }\n  util.assert(\n      $x.rank === 2,\n      `Error in batchNormalization3D: x must be rank 3 but got rank ` +\n          `${$x.rank}.`);\n  util.assert(\n      $mean.rank === 2 || $mean.rank === 1,\n      `Error in batchNormalization2D: mean must be rank 2 or rank 1 but ` +\n          `got rank ${$mean.rank}.`);\n  util.assert(\n      $variance.rank === 2 || $variance.rank === 1,\n      `Error in batchNormalization2D: variance must be rank 2 or rank 1 ` +\n          `but got rank ${$variance.rank}.`);\n  if ($scale != null) {\n    util.assert(\n        $scale.rank === 2 || $scale.rank === 1,\n        `Error in batchNormalization2D: scale must be rank 2 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n  }\n  if ($offset != null) {\n    util.assert(\n        $offset.rank === 2 || $offset.rank === 1,\n        `Error in batchNormalization2D: offset must be rank 2 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n  }\n\n  return batchNormalization(\n      $x, $mean, $variance, varianceEpsilon, $scale, $offset);\n}\n\n/**\n * Batch normalization, strictly for 3D. For the more relaxed version, see\n * `tf.batchNormalization`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n * @param scale A scale Tensor.\n * @param offset An offset Tensor.\n */\nfunction batchNormalization3d_(\n    x: Tensor3D|TensorLike, mean: Tensor3D|Tensor1D|TensorLike,\n    variance: Tensor3D|Tensor1D|TensorLike, varianceEpsilon = .001,\n    scale?: Tensor3D|Tensor1D|TensorLike,\n    offset?: Tensor3D|Tensor1D|TensorLike): Tensor3D {\n  const $x = convertToTensor(x, 'x', 'batchNormalization');\n  const $mean = convertToTensor(mean, 'mean', 'batchNormalization');\n  const $variance = convertToTensor(variance, 'variance', 'batchNormalization');\n  let $scale: Tensor3D|Tensor1D;\n  if (scale != null) {\n    $scale = convertToTensor(scale, 'scale', 'batchNormalization');\n  }\n  let $offset: Tensor3D|Tensor1D;\n  if (offset != null) {\n    $offset = convertToTensor(offset, 'offset', 'batchNormalization');\n  }\n  util.assert(\n      $x.rank === 3,\n      `Error in batchNormalization3D: x must be rank 3 but got rank ` +\n          `${$x.rank}.`);\n  util.assert(\n      $mean.rank === 3 || $mean.rank === 1,\n      `Error in batchNormalization3D: mean must be rank 3 or rank 1 but ` +\n          `got rank ${$mean.rank}.`);\n  util.assert(\n      $variance.rank === 3 || $variance.rank === 1,\n      `Error in batchNormalization3D: variance must be rank 3 or rank 1 ` +\n          `but got rank ${$variance.rank}.`);\n  if ($scale != null) {\n    util.assert(\n        $scale.rank === 3 || $scale.rank === 1,\n        `Error in batchNormalization3D: scale must be rank 3 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n  }\n  if ($offset != null) {\n    util.assert(\n        $offset.rank === 3 || $offset.rank === 1,\n        `Error in batchNormalization3D: offset must be rank 3 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n  }\n\n  return batchNormalization(\n      $x, $mean, $variance, varianceEpsilon, $scale, $offset);\n}\n\n/**\n * Batch normalization, strictly for 4D. For the more relaxed version, see\n * `tf.batchNormalization`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n * @param scale A scale Tensor.\n * @param offset An offset Tensor.\n */\nfunction batchNormalization4d_(\n    x: Tensor4D|TensorLike, mean: Tensor4D|Tensor1D|TensorLike,\n    variance: Tensor4D|Tensor1D|TensorLike, varianceEpsilon = .001,\n    scale?: Tensor4D|Tensor1D|TensorLike,\n    offset?: Tensor4D|Tensor1D|TensorLike): Tensor4D {\n  const $x = convertToTensor(x, 'x', 'batchNormalization');\n  const $mean = convertToTensor(mean, 'mean', 'batchNormalization');\n  const $variance = convertToTensor(variance, 'variance', 'batchNormalization');\n  let $scale: Tensor4D|Tensor1D;\n  if (scale != null) {\n    $scale = convertToTensor(scale, 'scale', 'batchNormalization');\n  }\n  let $offset: Tensor4D|Tensor1D;\n  if (offset != null) {\n    $offset = convertToTensor(offset, 'offset', 'batchNormalization');\n  }\n  util.assert(\n      $x.rank === 4,\n      `Error in batchNormalization4D: x must be rank 4 but got rank ` +\n          `${$x.rank}.`);\n  util.assert(\n      $mean.rank === 4 || $mean.rank === 1,\n      `Error in batchNormalization4D: mean must be rank 4 or rank 1 but ` +\n          `got rank ${$mean.rank}.`);\n  util.assert(\n      $variance.rank === 4 || $variance.rank === 1,\n      `Error in batchNormalization4D: variance must be rank 4 or rank 1 ` +\n          `but got rank ${$variance.rank}.`);\n  if ($scale != null) {\n    util.assert(\n        $scale.rank === 4 || $scale.rank === 1,\n        `Error in batchNormalization4D: scale must be rank 4 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n  }\n  if ($offset != null) {\n    util.assert(\n        $offset.rank === 4 || $offset.rank === 1,\n        `Error in batchNormalization4D: offset must be rank 4 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n  }\n  return batchNormalization(\n      $x, $mean, $variance, varianceEpsilon, $scale, $offset);\n}\n\n/**\n * Batch normalization.\n *\n * As described in\n * [http://arxiv.org/abs/1502.03167](http://arxiv.org/abs/1502.03167).\n *\n * Mean, variance, scale, and offset can be of two shapes:\n *   - The same shape as the input.\n *   - In the common case, the depth dimension is the last dimension of x, so\n *     the values would be an `tf.Tensor1D` of shape [depth].\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that parameters passed are of given rank\n *   - `tf.batchNormalization2d`\n *   - `tf.batchNormalization3d`\n *   - `tf.batchNormalization4d`\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n * @param scale A scale Tensor.\n * @param offset An offset Tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Normalization'} */\nfunction batchNormalization_<R extends Rank>(\n    x: Tensor<R>|Tensor1D|TensorLike, mean: Tensor<R>|Tensor1D|TensorLike,\n    variance: Tensor<R>|Tensor1D|TensorLike, varianceEpsilon = .001,\n    scale?: Tensor<R>|Tensor1D|TensorLike,\n    offset?: Tensor<R>|Tensor1D|TensorLike): Tensor<R> {\n  const $x = convertToTensor(x, 'x', 'batchNormalization');\n  const $mean = convertToTensor(mean, 'mean', 'batchNormalization');\n  const $variance = convertToTensor(variance, 'variance', 'batchNormalization');\n  let $scale: Tensor<R>|Tensor1D;\n  if (scale != null) {\n    $scale = convertToTensor(scale, 'scale', 'batchNormalization');\n  }\n  let $offset: Tensor<R>|Tensor1D;\n  if (offset != null) {\n    $offset = convertToTensor(offset, 'offset', 'batchNormalization');\n  }\n\n  util.assert(\n      $mean.rank === $variance.rank,\n      'Batch normalization gradient requires mean and variance to have ' +\n          'equal ranks.');\n  util.assert(\n      $offset == null || $mean.rank === $offset.rank,\n      'Batch normalization gradient requires mean and offset to have ' +\n          'equal ranks.');\n  util.assert(\n      $scale == null || $mean.rank === $scale.rank,\n      'Batch normalization gradient requires mean and scale to have ' +\n          'equal ranks.');\n\n  let x4D: Tensor4D;\n  if ($x.rank === 0 || $x.rank === 1) {\n    x4D = $x.as4D(1, 1, 1, $x.size);\n  } else if ($x.rank === 2) {\n    x4D = $x.as4D(1, 1, $x.shape[0], $x.shape[1]);\n  } else if ($x.rank === 3) {\n    x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]) as Tensor4D;\n  } else {\n    x4D = $x as Tensor4D;\n  }\n\n  const der = (dy: Tensor) => {\n    const scaleValue = $scale == null ? scalar(1) : $scale;\n    const reductionAxes = getReductionAxes($mean.shape, x4D.shape);\n    const tileShape: number[] = [];\n    if ($mean.rank === 1) {\n      for (let i = 0; i < x4D.shape.length - 1; ++i) {\n        tileShape.push(x4D.shape[i]);\n      }\n      tileShape.push(1);\n    }\n\n    const xMinusMean = $x.sub($mean);\n    const dyTimesScaleValue = dy.mul(scaleValue);\n    const oneOverSqrtVariance = rsqrt($variance.add(scalar(varianceEpsilon)));\n    const minusHalfRCube = oneOverSqrtVariance.mul(oneOverSqrtVariance)\n                               .mul(oneOverSqrtVariance)\n                               .mul(scalar(-0.5));\n    const derX = () => {\n      if ($mean.rank === 1) {\n        return dy\n            .mul(tile(\n                oneOverSqrtVariance.as4D(1, 1, 1, $mean.shape[0]), tileShape))\n            .mul(scaleValue)\n            .reshape($x.shape);\n      } else {\n        return dy.mul(oneOverSqrtVariance).mul(scaleValue).reshape($x.shape);\n      }\n    };\n    const derMean = () => {\n      let meanDer = oneOverSqrtVariance.mul(scalar(-1)).mul(dyTimesScaleValue);\n      if ($mean.rank === 1) {\n        meanDer = meanDer.sum(reductionAxes);\n      }\n      return meanDer.reshape($mean.shape);\n    };\n    const derVariance = () => {\n      let varianceDer = minusHalfRCube.mul(xMinusMean).mul(dyTimesScaleValue);\n      if ($mean.rank === 1) {\n        varianceDer = varianceDer.sum(reductionAxes);\n      }\n      return varianceDer.reshape($mean.shape);\n    };\n    const derScale = () => {\n      const xMinusMean2TimesRsqrt = xMinusMean.mul(oneOverSqrtVariance);\n      let scaleDer = dy.mul(xMinusMean2TimesRsqrt);\n      if ($mean.rank === 1) {\n        scaleDer = scaleDer.sum(reductionAxes);\n      }\n      return scaleDer.reshape($mean.shape);\n    };\n    const derOffset = () => {\n      let offsetDer = dy;\n      if ($mean.rank === 1) {\n        offsetDer = offsetDer.sum(reductionAxes);\n      }\n      return offsetDer.reshape($mean.shape);\n    };\n    return {\n      $x: derX,\n      $mean: derMean,\n      $variance: derVariance,\n      $scale: derScale,\n      $offset: derOffset\n    };\n  };\n\n  const res = ENV.engine.runKernel(\n      backend => backend.batchNormalization(\n          x4D, batchnormReshape4D($mean), batchnormReshape4D($variance),\n          varianceEpsilon, batchnormReshape4D($scale),\n          batchnormReshape4D($offset)),\n      {$x, $mean, $variance, $scale, $offset}, der);\n  return res.reshape($x.shape);\n}\n\nfunction batchnormReshape4D(x: Tensor): Tensor4D|Tensor1D {\n  if (x == null) {\n    return null;\n  }\n  if (x.rank === 0) {\n    return x.as1D();\n  } else if (x.rank === 1) {\n    return x as Tensor1D;\n  } else if (x.rank === 2) {\n    return x.as4D(1, 1, x.shape[0], x.shape[1]);\n  } else if (x.rank === 3) {\n    return x.as4D(1, x.shape[0], x.shape[1], x.shape[2]);\n  }\n  return x as Tensor4D;\n}\n\nexport const batchNormalization2d = op({batchNormalization2d_});\nexport const batchNormalization3d = op({batchNormalization3d_});\nexport const batchNormalization4d = op({batchNormalization4d_});\nexport const batchNormalization = op({batchNormalization_});\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as util from '../util';\n\nexport type PadInfo = {\n  top: number,\n  left: number,\n  right: number,\n  bottom: number,\n  type: string\n};\n\nexport type PadInfo3D = {\n  top: number,\n  left: number,\n  right: number,\n  bottom: number,\n  front: number,\n  back: number,\n  type: string\n};\n\n/**\n * Information about the forward pass of a convolution/pooling operation.\n * It includes input and output shape, strides, filter size and padding\n * information.\n */\nexport type Conv2DInfo = {\n  batchSize: number,\n  inHeight: number,\n  inWidth: number,\n  inChannels: number,\n  outHeight: number,\n  outWidth: number,\n  outChannels: number,\n  dataFormat: 'channelsFirst'|'channelsLast',\n  strideHeight: number,\n  strideWidth: number,\n  dilationHeight: number,\n  dilationWidth: number,\n  filterHeight: number,\n  filterWidth: number,\n  effectiveFilterHeight: number,\n  effectiveFilterWidth: number,\n  padInfo: PadInfo,\n  inShape: [number, number, number, number],\n  outShape: [number, number, number, number],\n  filterShape: [number, number, number, number]\n};\n\nexport function computePool2DInfo(\n    inShape: [number, number, number, number],\n    filterSize: [number, number]|number, strides: number|[number, number],\n    dilations: number|[number, number], pad: 'same'|'valid'|number,\n    roundingMode?: 'floor'|'round'|'ceil',\n    dataFormat: 'channelsFirst'|'channelsLast' = 'channelsLast'): Conv2DInfo {\n  const [filterHeight, filterWidth] = parseTupleParam(filterSize);\n\n  let filterShape: [number, number, number, number];\n  if (dataFormat === 'channelsLast') {\n    filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];\n  } else if (dataFormat === 'channelsFirst') {\n    filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n\n  return computeConv2DInfo(\n      inShape, filterShape, strides, dilations, pad, roundingMode, false,\n      dataFormat);\n}\n\n/**\n * Computes the information for a forward pass of a convolution/pooling\n * operation.\n */\nexport function computeConv2DInfo(\n    inShape: [number, number, number, number],\n    filterShape: [number, number, number, number],\n    strides: number|[number, number], dilations: number|[number, number],\n    pad: 'same'|'valid'|number, roundingMode?: 'floor'|'round'|'ceil',\n    depthwise = false,\n    dataFormat: 'channelsFirst'|'channelsLast' = 'channelsLast'): Conv2DInfo {\n  let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];\n  if (dataFormat === 'channelsLast') {\n    [batchSize, inHeight, inWidth, inChannels] = inShape;\n  } else if (dataFormat === 'channelsFirst') {\n    [batchSize, inChannels, inHeight, inWidth] = inShape;\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n\n  const [filterHeight, filterWidth, , filterChannels] = filterShape;\n  const [strideHeight, strideWidth] = parseTupleParam(strides);\n  const [dilationHeight, dilationWidth] = parseTupleParam(dilations);\n\n  const effectiveFilterHeight =\n      getEffectiveFilterSize(filterHeight, dilationHeight);\n  const effectiveFilterWidth =\n      getEffectiveFilterSize(filterWidth, dilationWidth);\n  const {padInfo, outHeight, outWidth} = getPadAndOutInfo(\n      pad, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight,\n      effectiveFilterWidth, roundingMode);\n\n  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n\n  let outShape: [number, number, number, number];\n  if (dataFormat === 'channelsFirst') {\n    outShape = [batchSize, outChannels, outHeight, outWidth];\n  } else if (dataFormat === 'channelsLast') {\n    outShape = [batchSize, outHeight, outWidth, outChannels];\n  }\n\n  return {\n    batchSize,\n    dataFormat,\n    inHeight,\n    inWidth,\n    inChannels,\n    outHeight,\n    outWidth,\n    outChannels,\n    padInfo,\n    strideHeight,\n    strideWidth,\n    filterHeight,\n    filterWidth,\n    effectiveFilterHeight,\n    effectiveFilterWidth,\n    dilationHeight,\n    dilationWidth,\n    inShape,\n    outShape,\n    filterShape\n  };\n}\n\n/**\n * Information about the forward pass of a 3D convolution/pooling operation.\n * It includes input and output shape, strides, filter size and padding\n * information.\n */\nexport type Conv3DInfo = {\n  batchSize: number,\n  inDepth: number,\n  inHeight: number,\n  inWidth: number,\n  inChannels: number,\n  outDepth: number,\n  outHeight: number,\n  outWidth: number,\n  outChannels: number,\n  dataFormat: 'channelsFirst'|'channelsLast',\n  strideDepth: number,\n  strideHeight: number,\n  strideWidth: number,\n  dilationDepth: number,\n  dilationHeight: number,\n  dilationWidth: number,\n  filterDepth: number,\n  filterHeight: number,\n  filterWidth: number,\n  padInfo: PadInfo3D,\n  inShape: [number, number, number, number, number],\n  outShape: [number, number, number, number, number],\n  filterShape: [number, number, number, number, number]\n};\n\n/**\n * Computes the information for a forward pass of a 3D convolution/pooling\n * operation.\n */\nexport function computeConv3DInfo(\n    inShape: [number, number, number, number, number],\n    filterShape: [number, number, number, number, number],\n    strides: number|[number, number, number],\n    dilations: number|[number, number, number], pad: 'same'|'valid',\n    depthwise = false,\n    dataFormat: 'channelsFirst'|'channelsLast' = 'channelsLast'): Conv3DInfo {\n  let [batchSize, inDepth, inHeight, inWidth, inChannels] =\n      [-1, -1, -1, -1, -1];\n  if (dataFormat === 'channelsLast') {\n    [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;\n  } else if (dataFormat === 'channelsFirst') {\n    [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n\n  const [filterDepth, filterHeight, filterWidth, , filterChannels] =\n      filterShape;\n  const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);\n  const [dilationDepth, dilationHeight, dilationWidth] =\n      parse3TupleParam(dilations);\n\n  const effectiveFilterDepth =\n      getEffectiveFilterSize(filterDepth, dilationDepth);\n  const effectiveFilterHeight =\n      getEffectiveFilterSize(filterHeight, dilationHeight);\n  const effectiveFilterWidth =\n      getEffectiveFilterSize(filterWidth, dilationWidth);\n  const {padInfo, outDepth, outHeight, outWidth} = get3DPadAndOutInfo(\n      pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth,\n      effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth);\n\n  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n\n  let outShape: [number, number, number, number, number];\n  if (dataFormat === 'channelsFirst') {\n    outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];\n  } else if (dataFormat === 'channelsLast') {\n    outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];\n  }\n\n  return {\n    batchSize,\n    dataFormat,\n    inDepth,\n    inHeight,\n    inWidth,\n    inChannels,\n    outDepth,\n    outHeight,\n    outWidth,\n    outChannels,\n    padInfo,\n    strideDepth,\n    strideHeight,\n    strideWidth,\n    filterDepth,\n    filterHeight,\n    filterWidth,\n    dilationDepth,\n    dilationHeight,\n    dilationWidth,\n    inShape,\n    outShape,\n    filterShape\n  };\n}\n\nfunction computeOutputShape3D(\n    inShape: [number, number, number], fieldSize: number, outDepth: number,\n    stride: number, zeroPad?: number,\n    roundingMode?: 'floor'|'round'|'ceil'): [number, number, number] {\n  if (zeroPad == null) {\n    zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n  }\n  const inputRows = inShape[0];\n  const inputCols = inShape[1];\n\n  const outputRows = conditionalRound(\n      (inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  util.assert(\n      util.isInt(outputRows),\n      `The output # of rows (${outputRows}) must be an integer. Change the ` +\n          `stride and/or zero pad parameters`);\n\n  const outputCols = conditionalRound(\n      (inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  util.assert(\n      util.isInt(outputCols),\n      `The output # of columns (${outputCols}) must be an integer. Change ` +\n          `the stride and/or zero pad parameters`);\n\n  return [outputRows, outputCols, outDepth];\n}\n\nexport function computeDefaultPad(\n    inputShape: [number, number, number], fieldSize: number, stride: number,\n    dilation = 1): number {\n  const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);\n  return Math.floor(\n      (inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);\n}\n\nfunction parseTupleParam(param: number|[number, number]): [number, number] {\n  return typeof param === 'number' ? [param, param] : param;\n}\n\nfunction parse3TupleParam(param: number|[number, number, number]):\n    [number, number, number] {\n  return typeof param === 'number' ? [param, param, param] : param;\n}\n\n/* See https://www.tensorflow.org/api_docs/python/tf/nn/atrous_conv2d\n * Atrous convolution is equivalent to standard convolution with upsampled\n * filters with effective_filter_height =\n * filter_height + (filter_height - 1) * (dilation - 1)\n * and effective_filter_width =\n * filter_width + (filter_width - 1) * (dilation - 1),\n * produced by inserting dilation - 1 zeros along consecutive elements across\n * the filters' spatial dimensions.\n * When there is a dilation, this converts a filter dimension to the\n * effective filter dimension, so it can be used in a standard convolution.\n */\nfunction getEffectiveFilterSize(filterSize: number, dilation: number) {\n  if (dilation <= 1) {\n    return filterSize;\n  }\n\n  return filterSize + (filterSize - 1) * (dilation - 1);\n}\n\nfunction getPadAndOutInfo(\n    pad: 'same'|'valid'|number, inHeight: number, inWidth: number,\n    strideHeight: number, strideWidth: number, filterHeight: number,\n    filterWidth: number, roundingMode?: 'floor'|'round'|'ceil'):\n    {padInfo: PadInfo, outHeight: number, outWidth: number} {\n  let padInfo: PadInfo;\n  let outHeight: number;\n  let outWidth: number;\n\n  if (typeof pad === 'number') {\n    const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n    padInfo = {top: pad, bottom: pad, left: pad, right: pad, type: padType};\n    const outShape = computeOutputShape3D(\n        [inHeight, inWidth, 1], filterHeight, 1, strideHeight, pad,\n        roundingMode);\n    outHeight = outShape[0];\n    outWidth = outShape[1];\n  } else if (pad === 'same') {\n    outHeight = Math.ceil(inHeight / strideHeight);\n    outWidth = Math.ceil(inWidth / strideWidth);\n    const padAlongHeight =\n        (outHeight - 1) * strideHeight + filterHeight - inHeight;\n    const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;\n    const top = Math.floor(padAlongHeight / 2);\n    const bottom = padAlongHeight - top;\n    const left = Math.floor(padAlongWidth / 2);\n    const right = padAlongWidth - left;\n    padInfo = {top, bottom, left, right, type: 'SAME'};\n  } else if (pad === 'valid') {\n    padInfo = {top: 0, bottom: 0, left: 0, right: 0, type: 'VALID'};\n    outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n    outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n  } else {\n    throw Error(`Unknown padding parameter: ${pad}`);\n  }\n  return {padInfo, outHeight, outWidth};\n}\n\nfunction get3DPadAndOutInfo(\n    pad: 'same'|'valid', inDepth: number, inHeight: number, inWidth: number,\n    strideDepth: number, strideHeight: number, strideWidth: number,\n    filterDepth: number, filterHeight: number, filterWidth: number): {\n  padInfo: PadInfo3D,\n  outDepth: number,\n  outHeight: number,\n  outWidth: number\n} {\n  let padInfo: PadInfo3D;\n  let outDepth: number;\n  let outHeight: number;\n  let outWidth: number;\n\n  if (pad === 'same') {\n    outDepth = Math.ceil(inDepth / strideDepth);\n    outHeight = Math.ceil(inHeight / strideHeight);\n    outWidth = Math.ceil(inWidth / strideWidth);\n    const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;\n    const padAlongHeight =\n        (outHeight - 1) * strideHeight + filterHeight - inHeight;\n    const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;\n    const front = Math.floor(padAlongDepth / 2);\n    const back = padAlongDepth - front;\n    const top = Math.floor(padAlongHeight / 2);\n    const bottom = padAlongHeight - top;\n    const left = Math.floor(padAlongWidth / 2);\n    const right = padAlongWidth - left;\n\n    padInfo = {top, bottom, left, right, front, back, type: 'SAME'};\n  } else if (pad === 'valid') {\n    padInfo = {\n      top: 0,\n      bottom: 0,\n      left: 0,\n      right: 0,\n      front: 0,\n      back: 0,\n      type: 'VALID'\n    };\n    outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);\n    outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n    outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n  } else {\n    throw Error(`Unknown padding parameter: ${pad}`);\n  }\n  return {padInfo, outDepth, outHeight, outWidth};\n}\n\n/**\n * Rounds a value depending on the rounding mode\n * @param value\n * @param roundingMode\n */\nfunction conditionalRound(\n    value: number, roundingMode?: 'floor'|'round'|'ceil') {\n  if (!roundingMode) {\n    return value;\n  }\n  switch (roundingMode) {\n    case 'round':\n      // used for Caffe Conv\n      return Math.round(value);\n    case 'ceil':\n      // used for Caffe Pool\n      return Math.ceil(value);\n    case 'floor':\n      return Math.floor(value);\n    default:\n      throw new Error(`Unknown roundingMode ${roundingMode}`);\n  }\n}\n\nexport function tupleValuesAreOne(param: number|[number, number]): boolean {\n  const [dimA, dimB] = parseTupleParam(param);\n  return dimA === 1 && dimB === 1;\n}\n\nexport function eitherStridesOrDilationsAreOne(\n    strides: number|[number, number],\n    dilations: number|[number, number]): boolean {\n  return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D} from '../tensor';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\n\n/**\n * Computes the dot product of two matrices, A * B. These must be matrices.\n *\n * ```js\n * const a = tf.tensor2d([1, 2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.matMul(b).print();  // or tf.matMul(a, b)\n * ```\n * @param a First matrix in dot product operation.\n * @param b Second matrix in dot product operation.\n * @param transposeA If true, `a` is transposed before multiplication.\n * @param transposeB If true, `b` is transposed before multiplication.\n */\n/** @doc {heading: 'Operations', subheading: 'Matrices'} */\nfunction matMul_<T extends Tensor>(\n    a: T|TensorLike, b: T|TensorLike, transposeA = false,\n    transposeB = false): T {\n  let $a = convertToTensor(a, 'a', 'matMul');\n  let $b = convertToTensor(b, 'b', 'matMul');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const innerShapeA =\n      transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n  const innerShapeB =\n      transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n\n  const outerShapeA =\n      transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n  const outerShapeB =\n      transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n\n  const outerDimsA = $a.shape.slice(0, -2);\n  const outerDimsB = $b.shape.slice(0, -2);\n  const batchDimA = util.sizeFromShape(outerDimsA);\n  const batchDimB = util.sizeFromShape(outerDimsB);\n\n  util.assert(\n      $a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank,\n      `Error in matMul: inputs must have the same rank of at least 2, ` +\n          `got ranks ${$a.rank} and ${$b.rank}.`);\n\n  util.assert(\n      util.arraysEqual(outerDimsA, outerDimsB),\n      `Error in matMul: outer dimensions (${outerDimsA}) and (` +\n          `${outerDimsB}) of Tensors with shapes ${$a.shape} and ` +\n          `${$b.shape} must match.`);\n\n  util.assert(\n      innerShapeA === innerShapeB,\n      `Error in matMul: inner shapes (${innerShapeA}) and (` +\n          `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n          `${$b.shape} and transposeA=${transposeA}` +\n          ` and transposeB=${transposeB} must match.`);\n\n  const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);\n\n  const a3D = transposeA ? $a.as3D(batchDimA, innerShapeA, outerShapeA) :\n                           $a.as3D(batchDimA, outerShapeA, innerShapeA);\n  const b3D = transposeB ? $b.as3D(batchDimB, outerShapeB, innerShapeB) :\n                           $b.as3D(batchDimB, innerShapeB, outerShapeB);\n\n  const grad = (dy: Tensor3D) => {\n    if (!transposeA && !transposeB) {\n      return {\n        $a: () => dy.matMul(b3D, false, true),\n        $b: () => a3D.matMul(dy, true, false)\n      };\n    } else if (!transposeA && transposeB) {\n      return {\n        $a: () => dy.matMul(b3D, false, false),\n        $b: () => dy.matMul(a3D, true, false)\n      };\n    } else if (transposeA && !transposeB) {\n      return {\n        $a: () => b3D.matMul(dy, false, true),\n        $b: () => a3D.matMul(dy, false, false)\n      };\n    } else {\n      return {\n        $a: () => b3D.matMul(dy, true, true),\n        $b: () => dy.matMul(a3D, true, true)\n      };\n    }\n  };\n\n  const res = ENV.engine.runKernel(\n      backend => backend.batchMatMul(a3D, b3D, transposeA, transposeB),\n      {$a: a3D, $b: b3D}, grad);\n  return res.reshape(outShape) as T;\n}\n\n/**\n * Computes the outer product of two vectors, `v1` and `v2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([3, 4, 5]);\n *\n * tf.outerProduct(a, b).print();\n * ```\n * @param v1 The first vector in the outer product operation.\n * @param v2 The second vector in the outer product operation.\n */\n/** @doc {heading: 'Operations', subheading: 'Matrices'} */\nfunction outerProduct_(\n    v1: Tensor1D|TensorLike, v2: Tensor1D|TensorLike): Tensor2D {\n  const $v1 = convertToTensor(v1, 'v1', 'outerProduct');\n  const $v2 = convertToTensor(v2, 'v2', 'outerProduct');\n\n  util.assert(\n      $v1.rank === 1 && $v2.rank === 1,\n      `Error in outerProduct: inputs must be rank 1, but got ranks ` +\n          `${$v1.rank} and ${$v2.rank}.`);\n\n  return $v1.as2D(-1, 1).matMul($v2.as2D(1, -1));\n}\n\n/**\n * Computes the dot product of two matrices and/or vectors, `t1` and `t2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor2d([[1, 2], [3, 4]]);\n * const c = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);\n *\n * a.dot(b).print();  // or tf.dot(a, b)\n * b.dot(a).print();\n * b.dot(c).print();\n * ```\n * @param t1 The first tensor in the dot operation.\n * @param t2 The second tensor in the dot operation.\n */\n/** @doc {heading: 'Operations', subheading: 'Matrices'} */\nfunction dot_(t1: Tensor|TensorLike, t2: Tensor|TensorLike): Tensor {\n  const $t1 = convertToTensor(t1, 't1', 'dot');\n  const $t2 = convertToTensor(t2, 't2', 'dot');\n  util.assert(\n      ($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2),\n      `Error in dot: inputs must all be rank 1 or 2, but got ranks ` +\n          `${$t1.rank} and ${$t2.rank}.`);\n\n  const t1Inner = ($t1.rank === 1 ? $t1.size : $t1.shape[1]);\n  const t2Inner = ($t2.rank === 1 ? $t2.size : $t2.shape[0]);\n\n  util.assert(\n      t1Inner === t2Inner,\n      `Error in dot: inner dimensions of inputs must match, but got ` +\n          `${t1Inner} and ${t2Inner}.`);\n\n  if ($t1.rank === 1 && $t2.rank === 1) {\n    return $t1.as2D(1, -1).matMul($t2.as2D(-1, 1)).asScalar();\n  } else if ($t1.rank === 1 && $t2.rank === 2) {\n    return $t1.as2D(1, -1).matMul($t2.as2D($t2.shape[0], $t2.shape[1])).as1D();\n  } else if ($t1.rank === 2 && $t2.rank === 1) {\n    return $t1.matMul($t2.as2D(-1, 1)).as1D();\n  } else {\n    return $t1.matMul($t2.as2D($t2.shape[0], $t2.shape[1]));\n  }\n}\n\nexport const matMul = op({matMul_});\nexport const dot = op({dot_});\nexport const outerProduct = op({outerProduct_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor2D, Tensor3D, Tensor4D, Tensor5D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, TensorLike} from '../types';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport {matMul} from './matmul';\nimport {op} from './operation';\n\n/**\n * Computes a 1D convolution over the input x.\n *\n * @param x The input tensor, of rank 3 or rank 2, of shape\n *     `[batch, width, inChannels]`. If rank 2, batch of 1 is assumed.\n * @param filter The filter, rank 3, of shape\n *     `[filterWidth, inDepth, outDepth]`.\n * @param stride The number of entries by which the filter is moved right at\n *     each step.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat An optional string from \"NWC\", \"NCW\". Defaults to \"NWC\",\n *     the data is stored in the order of [batch, in_width, in_channels]. Only\n *     \"NWC\" is currently supported.\n * @param dilation The dilation rate in which we sample input values in\n *     atrous convolution. Defaults to `1`. If it is greater than 1, then\n *     stride must be `1`.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction conv1d_<T extends Tensor2D|Tensor3D>(\n    x: T|TensorLike, filter: Tensor3D|TensorLike, stride: number,\n    pad: 'valid'|'same'|number, dataFormat: 'NWC'|'NCW' = 'NWC', dilation = 1,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'conv1d');\n  const $filter = convertToTensor(filter, 'filter', 'conv1d');\n\n  let x3D = $x as Tensor3D;\n  let reshapedTo3D = false;\n  if ($x.rank === 2) {\n    reshapedTo3D = true;\n    x3D = $x.as3D(1, $x.shape[0], $x.shape[1]);\n  }\n\n  util.assert(\n      x3D.rank === 3,\n      `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);\n  util.assert(\n      $filter.rank === 3,\n      `Error in conv1d: filter must be rank 3, but got rank ` +\n          `${$filter.rank}.`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        `Error in conv1d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  util.assert(\n      x3D.shape[2] === $filter.shape[1],\n      `Error in conv1d: depth of input (${x3D.shape[2]}) must match ` +\n          `input depth for filter ${$filter.shape[1]}.`);\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(stride, dilation),\n      'Error in conv1D: Either stride or dilation must be 1. ' +\n          `Got stride ${stride} and dilation '${dilation}'`);\n  util.assert(\n      dataFormat === 'NWC',\n      `Error in conv1d: got dataFormat of ${\n          dataFormat} but only NWC is currently supported.`);\n\n  const filter4D =\n      $filter.as4D(1, $filter.shape[0], $filter.shape[1], $filter.shape[2]);\n  const input4D = x3D.as4D(x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]);\n  const strides: [number, number] = [1, stride];\n  const dilations: [number, number] = [1, dilation];\n\n  const conv2dDataFormat = 'NHWC';\n\n  const res = conv2d(\n      input4D, filter4D, strides, pad, conv2dDataFormat, dilations,\n      dimRoundingMode);\n\n  if (reshapedTo3D) {\n    return res.as2D(res.shape[2], res.shape[3]) as T;\n  }\n  return res.as3D(res.shape[0], res.shape[2], res.shape[3]) as T;\n}\n\n/**\n * Computes a 2D convolution over the input x.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction conv2d_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filter: Tensor4D|TensorLike,\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n    dilations: [number, number]|number = [1, 1],\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'conv2d');\n  const $filter = convertToTensor(filter, 'filter', 'conv2d');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n  }\n  util.assert(\n      x4D.rank === 4,\n      `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n  util.assert(\n      $filter.rank === 4,\n      `Error in conv2d: filter must be rank 4, but got rank ` +\n          `${$filter.rank}.`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        `Error in conv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  util.assert(\n      x4D.shape[3] === $filter.shape[2],\n      `Error in conv2d: depth of input (${x4D.shape[3]}) must match ` +\n          `input depth for filter ${$filter.shape[2]}.`);\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      'Error in conv2D: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n  util.assert(\n      dataFormat === 'NHWC',\n      `Error in conv2d: got dataFormat of ${\n          dataFormat} but only NHWC is currently supported.`);\n\n  const convInfo = conv_util.computeConv2DInfo(\n      x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);\n\n  let res: Tensor3D|Tensor4D;\n  if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&\n      convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&\n      convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&\n      (convInfo.padInfo.type === 'SAME' || convInfo.padInfo.type === 'VALID')) {\n    const x2d = x4D.reshape([-1, convInfo.inChannels]) as Tensor2D;\n    const w2d = $filter.reshape([convInfo.inChannels, convInfo.outChannels]) as\n        Tensor2D;\n\n    res = matMul(x2d, w2d).reshape<Rank.R4>(convInfo.outShape);\n  } else {\n    const grad = (dy: Tensor4D) => {\n      util.assert(\n          conv_util.tupleValuesAreOne(dilations),\n          'Error in gradient of conv2D: dilation rates greater than 1 are not' +\n              `yet supported in gradients. Got dilations '${dilations}'`);\n\n      return {\n        x: () => conv2dDerInput_(x4D.shape, dy, $filter, strides, pad),\n        $filter: () => conv2dDerFilter_(x4D, dy, $filter.shape, strides, pad)\n      };\n    };\n\n    res = ENV.engine.runKernel(\n        backend => backend.conv2d(x4D, $filter, convInfo), {x: x4D, $filter},\n        grad);\n  }\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the derivative of the input of a 2D convolution.\n *\n * @param xShape The shape of the input: [batch, height, width, inDepth].\n * If length of 3, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 4 or rank 3 of shape\n *   `[batch, outHeight, outWidth, outDepth]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction conv2dDerInput_<T extends Tensor3D|Tensor4D>(\n    xShape: [number, number, number, number]|[number, number, number], dy: T,\n    filter: Tensor4D, strides: [number, number]|number,\n    pad: 'valid'|'same'|number, dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  util.assert(\n      xShape.length === dy.rank,\n      `Length of inShape ` +\n          `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n\n  let xShape4D = xShape as [number, number, number, number];\n  let dy4D = dy as Tensor4D;\n  let reshapedTo4D = false;\n  if (dy.rank === 3) {\n    reshapedTo4D = true;\n    dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);\n    xShape4D = [1, xShape[0], xShape[1], xShape[2]];\n  }\n\n  const inDepth = xShape4D[3];\n  const outDepth = dy4D.shape[3];\n  util.assert(\n      xShape4D.length === 4,\n      `Error in conv2dDerInput: inShape must be length 4, but got length ` +\n          `${xShape4D.length}.`);\n  util.assert(\n      dy4D.rank === 4,\n      `Error in conv2dDerInput: dy must be rank 4, but got ` +\n          `rank ${dy4D.rank}`);\n  util.assert(\n      filter.rank === 4,\n      `Error in conv2dDerInput: filter must be rank 4, but got ` +\n          `rank ${filter.rank}`);\n  util.assert(\n      inDepth === filter.shape[2],\n      `Error in conv2dDerInput: depth of input (${inDepth}) must ` +\n          `match input depth for filter ${filter.shape[2]}.`);\n  util.assert(\n      outDepth === filter.shape[3],\n      `Error in conv2dDerInput: depth of output (${outDepth}) must ` +\n          `match output depth for filter ${filter.shape[3]}.`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        `Error in conv2dDerInput: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const dilations = 1;\n\n  const grad = (ddx: Tensor4D) => {\n    const dataFormat = 'NHWC';\n    return {\n      dy4D: () => conv2d(\n          ddx, filter, strides, pad, dataFormat, dilations, dimRoundingMode),\n      filter: () => conv2dDerFilter(\n          ddx, dy4D, filter.shape, strides, pad, dimRoundingMode)\n    };\n  };\n\n  const convInfo = conv_util.computeConv2DInfo(\n      xShape4D, filter.shape, strides, dilations, pad, dimRoundingMode);\n  const res = ENV.engine.runKernel(\n      backend => backend.conv2dDerInput(dy4D, filter, convInfo), {dy4D, filter},\n      grad);\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the derivative of the filter of a 2D convolution.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.\n * @param dy The dy image, of rank 4 or rank 3, of shape\n *     [batch, height, width, outDepth]. If rank 3, batch of 1 is assumed.\n * @param filterShape The shape of the filter, length 4,\n *     [filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction conv2dDerFilter_<T extends Tensor3D|Tensor4D>(\n    x: T, dy: T, filterShape: [number, number, number, number],\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): Tensor4D {\n  let x4D = x as Tensor4D;\n  if (x.rank === 3) {\n    x4D = x.as4D(1, x.shape[0], x.shape[1], x.shape[2]);\n  }\n  let dy4D = dy as Tensor4D;\n  if (dy4D.rank === 3) {\n    dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);\n  }\n  util.assert(\n      x4D.rank === 4,\n      `Error in conv2dDerFilter: input must be rank 4, but got shape ` +\n          `${x4D.shape}.`);\n  util.assert(\n      dy4D.rank === 4,\n      `Error in conv2dDerFilter: dy must be rank 4, but got shape ` +\n          `${dy4D.shape}.`);\n  util.assert(\n      filterShape.length === 4,\n      `Error in conv2dDerFilter: filterShape must be length 4, but got ` +\n          `${filterShape}.`);\n  util.assert(\n      x4D.shape[3] === filterShape[2],\n      `Error in conv2dDerFilter: depth of input ${x4D.shape[3]}) must ` +\n          `match input depth in filter (${filterShape[2]}.`);\n  util.assert(\n      dy4D.shape[3] === filterShape[3],\n      `Error in conv2dDerFilter: depth of dy (${dy4D.shape[3]}) must ` +\n          `match output depth for filter (${filterShape[3]}).`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        `Error in conv2dDerFilter: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const dilations = 1;\n\n  const convInfo = conv_util.computeConv2DInfo(\n      x4D.shape, filterShape, strides, dilations, pad, dimRoundingMode);\n  return ENV.engine.runKernel(\n      backend => backend.conv2dDerFilter(x4D, dy4D, convInfo), {x4D, dy4D});\n}\n\n/**\n * Computes the transposed 2D convolution of an image, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 4 or rank 3, of shape\n *   `[batch, height, width, inDepth]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, outDepth, inDepth]`.\n *     `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 4 or rank 3:\n *     `[batch, height, width, outDepth]`. If rank 3, batch of 1 is assumed.\n * @param strides The strides of the original convolution:\n *     `[strideHeight, strideWidth]`.\n * @param pad  The type of padding algorithm used in the non-transpose version\n *    of the op.\n * @param dimRoundingMode The rounding mode used when computing output\n *    dimensions if pad is a number. If none is provided, it will not round\n *    and error if the output is of fractional size.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction conv2dTranspose_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filter: Tensor4D|TensorLike,\n    outputShape: [number, number, number, number]|[number, number, number],\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'conv2dTranspose');\n  const $filter = convertToTensor(filter, 'filter', 'conv2dTranspose');\n\n  return conv2dDerInput_(\n      outputShape, $x, $filter, strides, pad, dimRoundingMode);\n}\n\n/**\n * Depthwise 2D convolution.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction depthwiseConv2d_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filter: Tensor4D|TensorLike,\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n    dilations: [number, number]|number = [1, 1],\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'depthwiseConv2d');\n  const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n  }\n  util.assert(\n      x4D.rank === 4,\n      `Error in depthwiseConv2d: input must be rank 4, but got ` +\n          `rank ${x4D.rank}.`);\n  util.assert(\n      $filter.rank === 4,\n      `Error in depthwiseConv2d: filter must be rank 4, but got rank ` +\n          `${$filter.rank}.`);\n  util.assert(\n      x4D.shape[3] === $filter.shape[2],\n      `Error in depthwiseConv2d: number of input channels ` +\n          `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n          `filter ${$filter.shape[2]}.`);\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      'Error in depthwiseConv2d: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        `Error in depthwiseConv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const convInfo = conv_util.computeConv2DInfo(\n      x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode,\n      true /* depthwise */);\n\n  const grad = (dy: Tensor4D) => {\n    util.assert(\n        conv_util.tupleValuesAreOne(dilations),\n        'Error in gradient of depthwiseConv2d: dilation rates greater than ' +\n            `1 are not yet supported. Got dilations '${dilations}'`);\n    return {\n      x: () => depthwiseConv2dDerInput(x4D.shape, dy, $filter, convInfo),\n      $filter: () => depthwiseConv2dDerFilter(x4D, dy, $filter.shape, convInfo),\n    };\n  };\n\n  const res = ENV.engine.runKernel(\n      backend => backend.depthwiseConv2D(x4D, $filter, convInfo),\n      {x: x4D, $filter}, grad);\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\n/**\n * 2-D convolution with separable filters.\n *\n * Performs a depthwise convolution that acts separately on channels followed\n * by a pointwise convolution that mixes channels. Note that this is\n * separability between dimensions [1, 2] and 3, not spatial separability\n * between dimensions 1 and 2.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param depthwiseFilter The depthwise filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`. This is\n *     the filter used in the first step.\n * @param pointwiseFilter The pointwise filter tensor, rank 4, of shape\n *     `[1, 1, inChannels * channelMultiplier, outChannels]`. This is\n *     the filter used in the second step.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction separableConv2d_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, depthwiseFilter: Tensor4D|TensorLike,\n    pointwiseFilter: Tensor4D|TensorLike, strides: [number, number]|number,\n    pad: 'valid'|'same', dilation: [number, number]|number = [1, 1],\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC'): T {\n  const $x = convertToTensor(x, 'x', 'separableConv2d');\n  const $depthwiseFilter =\n      convertToTensor(depthwiseFilter, 'depthwiseFilter', 'separableConv2d');\n  const $pointwiseFilter =\n      convertToTensor(pointwiseFilter, 'pointwiseFilter', 'separableConv2d');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n  }\n\n  if (dataFormat === 'NCHW') {\n    throw new Error(\n        'separableConv2d currently does not support dataFormat NCHW; only ' +\n        'NHWC is supported');\n  }\n\n  util.assert(\n      x4D.rank === 4,\n      `Error in separableConv2d: input must be rank 4, but got ` +\n          `rank ${x4D.rank}.`);\n  util.assert(\n      $depthwiseFilter.rank === 4,\n      `Error in separableConv2d: depthwise filter must be rank 4, but got ` +\n          `rank ${$depthwiseFilter.rank}.`);\n  util.assert(\n      $pointwiseFilter.rank === 4,\n      `Error in separableConv2d: pointwise filter must be rank 4, but got ` +\n          `rank ${$depthwiseFilter.rank}.`);\n  util.assert(\n      $pointwiseFilter.shape[0] === 1,\n      `Error in separableConv2d: the first dimension of pointwise filter ` +\n          ` must be 1, but got ${$pointwiseFilter.shape[0]}.`);\n  util.assert(\n      $pointwiseFilter.shape[1] === 1,\n      `Error in separableConv2d: the second dimension of pointwise filter ` +\n          ` must be 1, but got ${$pointwiseFilter.shape[1]}.`);\n\n  const inChannels = $depthwiseFilter.shape[2];\n  const channelMultiplier = $depthwiseFilter.shape[3];\n  util.assert(\n      $pointwiseFilter.shape[2] === inChannels * channelMultiplier,\n      `Error in separableConv2d: the third dimension of pointwise filter ` +\n          `must be ${inChannels * channelMultiplier}, ` +\n          `but got ${$pointwiseFilter.shape[2]}.`);\n\n  const depthwise = depthwiseConv2d(\n      x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);\n  const pointwiseStride = 1;\n  const res =\n      conv2d(depthwise, $pointwiseFilter, pointwiseStride, 'valid', dataFormat);\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\nfunction parseTupleParam(\n    param: number|[number, number]|[number, number, number]):\n    [number, number, number] {\n  if (typeof param === 'number') {\n    return [param, param, param];\n  }\n  if (param.length === 2) {\n    return [param[0], param[1], 1];\n  }\n  return param;\n}\n\nfunction tupleValuesAreOne(\n    param: number|[number, number]|[number, number, number]): boolean {\n  const [dimA, dimB, dimC] = parseTupleParam(param);\n  return dimA === 1 && dimB === 1 && dimC === 1;\n}\n\nfunction eitherStridesOrDilationsAreOne(\n    strides: number|[number, number]|[number, number, number],\n    dilations: number|[number, number]|[number, number, number]): boolean {\n  return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);\n}\n\nfunction depthwiseConv2dDerInput<T extends Tensor3D|Tensor4D>(\n    xShape: [number, number, number, number]|[number, number, number], dy: T,\n    filter: Tensor4D, convInfo: conv_util.Conv2DInfo): T {\n  let dy4D = dy as Tensor4D;\n  let reshapedTo4D = false;\n  if (dy.rank === 3) {\n    reshapedTo4D = true;\n    dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);\n  }\n  const res = ENV.engine.runKernel(\n      backend => backend.depthwiseConv2DDerInput(dy4D, filter, convInfo),\n      {dy4D});\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\nfunction depthwiseConv2dDerFilter<T extends Tensor3D|Tensor4D>(\n    x: T, dy: T, filterShape: [number, number, number, number],\n    convInfo: conv_util.Conv2DInfo): Tensor4D {\n  let x4D = x as Tensor4D;\n  if (x.rank === 3) {\n    x4D = x.as4D(1, x.shape[0], x.shape[1], x.shape[2]);\n  }\n  let dy4D = dy as Tensor4D;\n  if (dy4D.rank === 3) {\n    dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);\n  }\n  return ENV.engine.runKernel(\n      backend => backend.depthwiseConv2DDerFilter(x4D, dy4D, convInfo),\n      {x4D, dy4D});\n}\n\n/**\n * Computes a 3D convolution over the input x.\n *\n * @param x The input tensor, of rank 5 or rank 4, of shape\n *     `[batch, depth, height, width, channels]`. If rank 4,\n * batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`.\n *      inChannels must match between input and filter.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationDepth, dilationHeight,\n *     dilationWidth]` in which we sample input values across the height\n *     and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`.\n *     If `dilations` is a single number, then\n *     `dilationDepth == dilationHeight == dilationWidth`. If it is greater\n *     than 1, then all values of `strides` must be 1.\n */\n\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction conv3d_<T extends Tensor4D|Tensor5D>(\n    x: T|TensorLike, filter: Tensor5D|TensorLike,\n    strides: [number, number, number]|number, pad: 'valid'|'same',\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n    dilations: [number, number, number]|number = [1, 1, 1]): T {\n  const $x = convertToTensor(x, 'x', 'conv3d');\n  const $filter = convertToTensor(filter, 'filter', 'conv3d');\n\n  let x5D = $x as Tensor5D;\n  let reshapedTo5D = false;\n\n  if ($x.rank === 4) {\n    reshapedTo5D = true;\n    x5D = $x.as5D(1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]);\n  }\n  util.assert(\n      x5D.rank === 5,\n      `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);\n  util.assert(\n      $filter.rank === 5,\n      `Error in conv3d: filter must be rank 5, but got rank ` +\n          `${$filter.rank}.`);\n  util.assert(\n      x5D.shape[4] === $filter.shape[3],\n      `Error in conv3d: depth of input (${x5D.shape[4]}) must match ` +\n          `input depth for filter ${$filter.shape[3]}.`);\n  util.assert(\n      eitherStridesOrDilationsAreOne(strides, dilations),\n      'Error in conv3D: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n  util.assert(\n      dataFormat === 'NHWC',\n      `Error in conv3d: got dataFormat of ${\n          dataFormat} but only NHWC is currently supported.`);\n\n  const convInfo = conv_util.computeConv3DInfo(\n      x5D.shape, $filter.shape, strides, dilations, pad);\n\n  const grad = (dy: Tensor5D) => {\n    util.assert(\n        tupleValuesAreOne(dilations),\n        'Error in gradient of conv3D: dilation rates greater than 1 are not' +\n            `yet supported in gradients. Got dilations '${dilations}'`);\n\n    return {\n      x: () => conv3dDerInput_(x5D.shape, dy, $filter, strides, pad),\n      $filter: () => conv3dDerFilter_(x5D, dy, $filter.shape, strides, pad)\n    };\n  };\n\n  const res = ENV.engine.runKernel(\n      backend => backend.conv3d(x5D, $filter, convInfo), {x: x5D, $filter},\n      grad);\n  if (reshapedTo5D) {\n    return res.as4D(res.shape[1], res.shape[2], res.shape[3], res.shape[4]) as\n        T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the derivative of the input of a 3D convolution.\n *\n * @param xShape The shape of the input: [batch, depth, height, width,\n * in_channels]. If length of 4, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 5 or rank 4 of shape\n *   `[batch, outDepth, outHeight, outWidth, in_channels]`.\n * If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n */\nfunction conv3dDerInput_<T extends Tensor4D|Tensor5D>(\n    xShape:\n        [number, number, number, number,\n         number]|[number, number, number, number],\n    dy: T, filter: Tensor5D, strides: [number, number, number]|number,\n    pad: 'valid'|'same'): T {\n  util.assert(\n      xShape.length === dy.rank,\n      `Length of inShape ` +\n          `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n\n  let xShape5D = xShape as [number, number, number, number, number];\n  let dy5D = dy as Tensor5D;\n  let reshapedTo5D = false;\n  if (dy.rank === 4) {\n    reshapedTo5D = true;\n    dy5D = dy.as5D(1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]);\n    xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];\n  }\n\n  const inDepth = xShape5D[4];\n  const outDepth = dy5D.shape[4];\n  util.assert(\n      xShape5D.length === 5,\n      `Error in conv3dDerInput: inShape must be length 5, but got length ` +\n          `${xShape5D.length}.`);\n  util.assert(\n      dy5D.rank === 5,\n      `Error in conv3dDerInput: dy must be rank 5, but got ` +\n          `rank ${dy5D.rank}`);\n  util.assert(\n      filter.rank === 5,\n      `Error in conv3dDerInput: filter must be rank 5, but got ` +\n          `rank ${filter.rank}`);\n  util.assert(\n      inDepth === filter.shape[3],\n      `Error in conv3dDerInput: depth of input (${inDepth}) must ` +\n          `match input depth for filter ${filter.shape[3]}.`);\n  util.assert(\n      outDepth === filter.shape[4],\n      `Error in conv3dDerInput: depth of output (${outDepth}) must ` +\n          `match output depth for filter ${filter.shape[4]}.`);\n\n  const dilations = 1;\n\n  const convInfo = conv_util.computeConv3DInfo(\n      xShape5D, filter.shape, strides, dilations, pad);\n  const res = ENV.engine.runKernel(\n      backend => backend.conv3dDerInput(dy5D, filter, convInfo), {dy5D});\n  if (reshapedTo5D) {\n    return res.as4D(res.shape[1], res.shape[2], res.shape[3], res.shape[4]) as\n        T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the derivative of the filter of a 3D convolution.\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     [batch, depth, height, width, inChannels]. If rank 4, batch of 1 is\n *     assumed.\n * @param dy The dy image, of rank 5 or rank 4, of shape\n *     [batch, depth, height, width, outDepth]. If rank 4, batch of 1 is\n *     assumed.\n * @param filterShape The shape of the filter, length 5,\n *     [filterDepth, filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideDepth, strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n */\nfunction conv3dDerFilter_<T extends Tensor4D|Tensor5D>(\n    x: T, dy: T, filterShape: [number, number, number, number, number],\n    strides: [number, number, number]|number, pad: 'valid'|'same'): Tensor5D {\n  let x5D = x as Tensor5D;\n  if (x.rank === 4) {\n    x5D = x.as5D(1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]);\n  }\n  let dy5D = dy as Tensor5D;\n  if (dy5D.rank === 4) {\n    dy5D = dy.as5D(1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]);\n  }\n  util.assert(\n      x5D.rank === 5,\n      `Error in conv3dDerFilter: input must be rank 5, but got shape ` +\n          `${x5D.shape}.`);\n  util.assert(\n      dy5D.rank === 5,\n      `Error in conv3dDerFilter: dy must be rank 5, but got shape ` +\n          `${dy5D.shape}.`);\n  util.assert(\n      filterShape.length === 5,\n      `Error in conv3dDerFilter: filterShape must be length 5, but got ` +\n          `${filterShape}.`);\n  util.assert(\n      x5D.shape[4] === filterShape[3],\n      `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must ` +\n          `match input depth in filter (${filterShape[3]}.`);\n  util.assert(\n      dy5D.shape[4] === filterShape[4],\n      `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must ` +\n          `match output depth for filter (${filterShape[4]}).`);\n\n  const dilations = 1;\n\n  const convInfo = conv_util.computeConv3DInfo(\n      x5D.shape, filterShape, strides, dilations, pad);\n  return ENV.engine.runKernel(\n      backend => backend.conv3dDerFilter(x5D, dy5D, convInfo), {x5D, dy5D});\n}\n\nexport const conv1d = op({conv1d_});\nexport const conv2d = op({conv2d_});\nexport const conv3d = op({conv3d_});\nexport const conv2dDerFilter = op({conv2dDerFilter_});\nexport const depthwiseConv2d = op({depthwiseConv2d_});\nexport const separableConv2d = op({separableConv2d_});\nexport const conv2dTranspose = op({conv2dTranspose_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {parseAxisParam} from './axis_util';\nimport {op} from './operation';\n\n/**\n * Reverses a `tf.Tensor1D`.\n *\n * @param x The input tensor.\n */\nfunction reverse1d_(x: Tensor1D|TensorLike): Tensor1D {\n  const $x = convertToTensor(x, 'x', 'reverse');\n  util.assert($x.rank === 1, `Error in reverse1D: x must be rank 1 but got\n             rank ${$x.rank}.`);\n  return reverse($x, 0);\n}\n\n/**\n * Reverses a `tf.Tensor2D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse2d_(x: Tensor2D|TensorLike, axis?: number|number[]): Tensor2D {\n  const $x = convertToTensor(x, 'x', 'reverse');\n  util.assert($x.rank === 2, `Error in reverse2D: x must be rank 2 but got\n             rank ${$x.rank}.`);\n  return reverse($x, axis);\n}\n\n/**\n * Reverses a `tf.Tensor3D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse3d_(x: Tensor3D|TensorLike, axis?: number|number[]): Tensor3D {\n  const $x = convertToTensor(x, 'x', 'reverse');\n  util.assert($x.rank === 3, `Error in reverse3D: x must be rank 3 but got\n             rank ${$x.rank}.`);\n  return reverse($x, axis);\n}\n\n/**\n * Reverses a `tf.Tensor4D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse4d_(x: Tensor4D|TensorLike, axis?: number|number[]): Tensor4D {\n  const $x = convertToTensor(x, 'x', 'reverse');\n  util.assert($x.rank === 4, `Error in reverse4D: x must be rank 4 but got\n             rank ${$x.rank}.`);\n  return reverse($x, axis);\n}\n\n/**\n * Reverses a `tf.Tensor` along a specified axis.\n *\n * Also available are stricter rank-specific methods that assert that `x` is\n * of the given rank:\n *   - `tf.reverse1d`\n *   - `tf.reverse2d`\n *   - `tf.reverse3d`\n *   - `tf.reverse4d`\n *\n * Except `tf.reverse1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.reverse().print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.reverse(axis).print();\n * ```\n * @param x The input tensor to be reversed.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction reverse_<T extends Tensor>(\n    x: T|TensorLike, axis?: number|number[]): T {\n  const $x = convertToTensor(x, 'x', 'reverse');\n\n  if ($x.rank === 0) {\n    return $x.clone();\n  }\n  const axes = parseAxisParam(axis, $x.shape);\n  const grad = (dy: T) => {\n    return {$x: () => dy.reverse(axes)};\n  };\n  const res =\n      ENV.engine.runKernel(backend => backend.reverse($x, axes), {$x}, grad);\n  return res.reshapeAs($x);\n}\n\nexport const reverse = op({reverse_});\nexport const reverse1d = op({reverse1d_});\nexport const reverse2d = op({reverse2d_});\nexport const reverse3d = op({reverse3d_});\nexport const reverse4d = op({reverse4d_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {batchToSpaceND, spaceToBatchND} from './array_ops';\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\n\n/**\n * Computes the 2D max pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction maxPoolImpl_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filterSize: [number, number]|number,\n    strides: [number, number]|number, dilations: [number, number]|number,\n    pad: 'valid'|'same'|number, dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'maxPool');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n  }\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  util.assert(\n      x4D.rank === 4,\n      `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      'Error in maxPool: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        `Error in maxPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n  const convInfo = conv_util.computePool2DInfo(\n      x4D.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n\n  const grad = (dy: Tensor4D, saved: Tensor[]) => {\n    const [y4D] = saved;\n    return {\n      x: () => maxPoolBackprop(\n          dy, x4D, y4D as Tensor4D, filterSize, strides, dilations, pad)\n    };\n  };\n\n  const res = ENV.engine.runKernel(\n      (backend, save) => save(backend.maxPool(x4D, convInfo)), {x: x4D}, grad);\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the 2D max pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction maxPool_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filterSize: [number, number]|number,\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  return maxPoolImpl_(x, filterSize, strides, 1, pad, dimRoundingMode);\n}\n\n/**\n * Computes the 2D average pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction avgPoolImpl_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filterSize: [number, number]|number,\n    strides: [number, number]|number, dilations: [number, number]|number,\n    pad: 'valid'|'same'|number, dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'avgPool', 'float32');\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      'Error in avgPool: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n  }\n  util.assert(\n      x4D.rank === 4,\n      `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        `Error in avgPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const convInfo = conv_util.computePool2DInfo(\n      x4D.shape, filterSize, strides, dilations, pad);\n\n  const grad = (dy: Tensor4D) => {\n    return {\n      x: () => avgPoolBackprop(dy, x4D, filterSize, strides, dilations, pad)\n    };\n  };\n  let res = ENV.engine.runKernel(\n      backend => backend.avgPool(x4D, convInfo), {x: x4D}, grad);\n  res = res.cast($x.dtype);\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the 2D average pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction avgPool_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filterSize: [number, number]|number,\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  return avgPoolImpl_(x, filterSize, strides, 1, pad, dimRoundingMode);\n}\n\n/**\n * Performs an N-D pooling operation\n *\n * @param input The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param windowShape The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param poolingType The type of pooling, either 'max' or 'avg'.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilationRate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction pool_<T extends Tensor3D|Tensor4D>(\n    input: T|TensorLike, windowShape: [number, number]|number,\n    poolingType: 'avg'|'max', pad: 'valid'|'same'|number,\n    dilations?: [number, number]|number, strides?: [number, number]|number) {\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  if (strides == null) {\n    strides = 1;\n  }\n  if (pad === 0) {\n    pad = 'valid';\n  }\n  const $x = convertToTensor(input, 'x', 'maxPool');\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n  }\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      'Error in pool: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n  const convInfo = conv_util.computePool2DInfo(\n      x4D.shape, windowShape, strides, dilations, pad);\n  const dilation: [number, number] =\n      [convInfo.dilationHeight, convInfo.dilationWidth];\n\n  // The following implementation does batchToSpace(pool(spaceToBatch(x)))\n  // whenever dilation > 1 since the TF kernels do not support dilation > 1.\n  // tslint:disable-next-line:max-line-length\n  // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L1037\n\n  let basePadding: number[][];\n  if (pad === 'same') {\n    basePadding = withSpaceToBatchBasePaddings(\n        [convInfo.filterHeight, convInfo.filterWidth], dilation);\n  } else {\n    basePadding = [[0, 0], [0, 0]];\n  }\n  const isDilationOne = dilation[0] === 1 && dilation[1] === 1;\n  const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings(\n      [convInfo.inHeight, convInfo.inWidth], dilation, basePadding);\n  const convertedPad = isDilationOne ? pad : 'valid';\n  const convertedX =\n      isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);\n  const forwardOp = poolingType === 'avg' ?\n      () => avgPoolImpl_(\n          convertedX, windowShape, strides, 1 /* dilation */, convertedPad) :\n      () => maxPoolImpl_(\n          convertedX, windowShape, strides, 1 /* dilation */, convertedPad);\n  const y = forwardOp();\n  const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the backprop of a max pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The original input image, of rank 4, of shape\n *     [batchSize, height, width, channels].\n * @param output The original output image, of rank 4, of shape\n *     [batchSize, outHeight, outWidth, channels].\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction maxPoolBackprop(\n    dy: Tensor4D|TensorLike, input: Tensor4D|TensorLike,\n    output: Tensor4D|TensorLike, filterSize: [number, number]|number,\n    strides: [number, number]|number, dilations: [number, number]|number,\n    pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): Tensor4D {\n  const $dy = convertToTensor(dy, 'dy', 'maxPoolBackprop');\n  const $input = convertToTensor(input, 'input', 'maxPoolBackprop');\n  const $output = convertToTensor(output, 'output', 'maxPoolBackprop');\n  util.assert(\n      $input.rank === $dy.rank,\n      `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      'Error in maxPoolBackProp: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n\n  util.assert(\n      $dy.rank === 4,\n      `Error in maxPoolBackprop: dy must be rank 4 but got rank ` +\n          `${$dy.rank}.`);\n  util.assert(\n      $input.rank === 4,\n      `Error in maxPoolBackprop: input must be rank 4 but got rank ` +\n          `${$input.rank}.`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        `Error in maxPoolBackprop: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const convInfo = conv_util.computePool2DInfo(\n      $input.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n  const res = ENV.engine.runKernel(\n      backend => backend.maxPoolBackprop($dy, $input, $output, convInfo),\n      {$dy, $input});\n  return res;\n}\n\n/**\n * Computes the backprop of an avg pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The input image, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n */\nfunction avgPoolBackprop<T extends Tensor3D|Tensor4D>(\n    dy: T|TensorLike, input: T|TensorLike, filterSize: [number, number]|number,\n    strides: [number, number]|number, dilations: [number, number]|number,\n    pad: 'valid'|'same'|number): T {\n  const $dy = convertToTensor(dy, 'dy', 'avgPoolBackprop');\n  const $input = convertToTensor(input, 'input', 'avgPoolBackprop');\n  util.assert(\n      $input.rank === $dy.rank,\n      `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      'Error in avgPoolBackprop: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n\n  let input4D = $input as Tensor4D;\n  let dy4D = $dy as Tensor4D;\n  let reshapedTo4D = false;\n  if ($input.rank === 3) {\n    reshapedTo4D = true;\n    input4D = $input.as4D(1, $input.shape[0], $input.shape[1], $input.shape[2]);\n    dy4D = $dy.as4D(1, $dy.shape[0], $dy.shape[1], $dy.shape[2]);\n  }\n\n  util.assert(\n      dy4D.rank === 4,\n      `Error in avgPoolBackprop: dy must be rank 4 but got rank ` +\n          `${dy4D.rank}.`);\n  util.assert(\n      input4D.rank === 4,\n      `Error in avgPoolBackprop: input must be rank 4 but got rank ` +\n          `${input4D.rank}.`);\n\n  const convInfo = conv_util.computePool2DInfo(\n      input4D.shape, filterSize, strides, dilations, pad);\n  const res = ENV.engine.runKernel(\n      backend => backend.avgPoolBackprop(dy4D, input4D, convInfo),\n      {dy4D, input4D});\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\n// Helper function to compute crops and paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/array_ops.py#L2184\nfunction requiredSpaceToBatchPaddings(\n    inputShape: [number, number], blockShape: [number, number],\n    basePadding: number[][]) {\n  const padStart = basePadding.map(b => b[0]);\n  const origPadEnd = basePadding.map(b => b[1]);\n  const fullInputShape = inputShape.concat(padStart, origPadEnd);\n  const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);\n  const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);\n  const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);\n  const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);\n  return [paddings, crops];\n}\n\n// Helper function to compute base paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L524\nfunction withSpaceToBatchBasePaddings(\n    filterShape: [number, number], dilation: [number, number]) {\n  // Spatial dimensions of the filters and the upsampled filters in which we\n  // introduce (rate - 1) zeros between consecutive filter values.\n  const dilatedFilterShape = filterShape.map((s, i) => {\n    return s + (s - 1) * (dilation[i] - 1);\n  });\n  const padExtraShape = dilatedFilterShape.map(s => s - 1);\n\n  // When padding is odd, we pad more at end, following the same\n  // convention as conv2d.\n  const padExtraStart = padExtraShape.map(s => Math.floor(s / 2));\n  const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);\n  return padExtraShape.map((_, i) => {\n    return [padExtraStart[i], padExtraEnd[i]];\n  });\n}\n\nexport const maxPool = op({maxPool_});\nexport const avgPool = op({avgPool_});\nexport const pool = op({pool_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\nimport * as slice_util from './slice_util';\n\n/**\n * Extracts a 1D slice from 1D array starting at coordinates `begin` and is\n * of length `size`. See `slice` for details.\n */\nfunction slice1d_(\n    x: Tensor1D|TensorLike, begin: number, size: number): Tensor1D {\n  const $x = convertToTensor(x, 'x', 'slice1d');\n  util.assert(\n      $x.rank === 1,\n      `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, [begin], [size]);\n}\n\n/**\n * Extracts a 2D slice from a 2D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice2d_(\n    x: Tensor2D|TensorLike, begin: [number, number],\n    size: [number, number]): Tensor2D {\n  const $x = convertToTensor(x, 'x', 'slice2d');\n  util.assert(\n      $x.rank === 2,\n      `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, begin, size);\n}\n\n/**\n * Extracts a 3D slice from a 3D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice3d_(\n    x: Tensor3D|TensorLike, begin: [number, number, number],\n    size: [number, number, number]): Tensor3D {\n  const $x = convertToTensor(x, 'x', 'slice3d');\n  util.assert(\n      $x.rank === 3,\n      `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, begin, size);\n}\n\n/**\n * Extracts a 4D slice from a 4D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice4d_(\n    x: Tensor4D|TensorLike, begin: [number, number, number, number],\n    size: [number, number, number, number]): Tensor4D {\n  const $x = convertToTensor(x, 'x', 'slice4d');\n  util.assert(\n      $x.rank === 4,\n      `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, begin, size);\n}\n\n/**\n * Extracts a slice from a `tf.Tensor` starting at coordinates `begin`\n * and is of size `size`.\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `x` is of the given rank:\n *   - `tf.slice1d`\n *   - `tf.slice2d`\n *   - `tf.slice3d`\n *   - `tf.slice4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.slice([1], [2]).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * x.slice([1, 0], [1, 2]).print();\n * ```\n * @param x The input `tf.Tensor` to slice from.\n * @param begin The coordinates to start the slice from. The length can be\n *     less than the rank of x - the rest of the axes will have implicit 0 as\n *     start. Can also be a single number, in which case it specifies the\n *     first axis.\n * @param size The size of the slice. The length can be less than the rank of\n *     x - the rest of the axes will have implicit -1. A value of -1 requests\n *     the rest of the dimensions in the axis. Can also be a single number,\n *     in which case it specifies the size of the first axis.\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction slice_<R extends Rank, T extends Tensor<R>>(\n    x: T|TensorLike, begin: number|number[], size?: number|number[]): T {\n  const $x = convertToTensor(x, 'x', 'slice');\n\n  if ($x.rank === 0) {\n    throw new Error('Slicing scalar is not possible');\n  }\n  // The following logic allows for more ergonomic calls.\n  let begin_: number[];\n  if (typeof begin === 'number') {\n    begin_ = [begin, ...new Array($x.rank - 1).fill(0)];\n  } else if (begin.length < $x.rank) {\n    begin_ = begin.concat(new Array($x.rank - begin.length).fill(0));\n  } else {\n    begin_ = begin.slice();\n  }\n  let size_: number[];\n  if (size == null) {\n    size_ = new Array($x.rank).fill(-1);\n  } else if (typeof size === 'number') {\n    size_ = [size, ...new Array($x.rank - 1).fill(-1)];\n  } else if (size.length < $x.rank) {\n    size_ = size.concat(new Array($x.rank - size.length).fill(-1));\n  } else {\n    size_ = size;\n  }\n  size_ = size_.map((d, i) => {\n    if (d >= 0) {\n      return d;\n    } else {\n      util.assert(d === -1, 'Bad value in size');\n      return $x.shape[i] - begin_[i];\n    }\n  });\n  slice_util.assertParamsValid($x, begin_, size_);\n  const inputShape = $x.shape;\n  const grad = (dy: T) => {\n    // Create an Nx2 padding where the first column represents how many\n    // zeros are prepended (at start) for each dimension, and the second\n    // column indicates how many zeros are appended (at end).\n\n    // The number of zeros to append is the shape of the input\n    // elementwise-subtracted by both the begin vector and sizes vector.\n    const paddings: Array<[number, number]> = [];\n    for (let i = 0; i < dy.rank; i++) {\n      paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);\n    }\n    return {$x: () => dy.pad(paddings)};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.slice($x, begin_, size_), {$x}, grad) as T;\n}\n\nexport const slice = op({slice_});\nexport const slice1d = op({slice1d_});\nexport const slice2d = op({slice2d_});\nexport const slice3d = op({slice3d_});\nexport const slice4d = op({slice4d_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {customGrad} from '../globals';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport {op} from './operation';\nimport {ones, scalar, zerosLike} from './tensor_ops';\n\n/**\n * Computes the log(sum(exp(elements across the reduction dimensions)).\n *\n * Reduces the input along the dimensions given in `axis`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.logSumExp().print();  // or tf.logSumExp(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.logSumExp(axis).print();  // or tf.logSumExp(a, axis)\n * ```\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. If null (the default),\n *     reduces all dimensions.\n * @param keepDims If true, retains reduced dimensions with length\n *     of 1. Defaults to false.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction logSumExp_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  const $x = convertToTensor(x, 'x', 'logSumExp');\n\n  const axes = axis_util.parseAxisParam(axis, $x.shape);\n  const xMax = $x.max(axes, true /* keepDims */);\n  const a = $x.sub(xMax);\n  const b = a.exp();\n  const c = b.sum(axes);\n  const d = c.log();\n  const res = xMax.reshape(d.shape).add(d);\n\n  if (keepDims) {\n    const newShape = axis_util.expandShapeToKeepDim(res.shape, axes);\n    return res.reshape(newShape) as T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the sum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If axes has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.sum().print();  // or tf.sum(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.sum(axis).print();  // or tf.sum(x, axis)\n * ```\n *\n * @param x The input tensor to compute the sum over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction sum_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  let $x = convertToTensor(x, 'x', 'sum');\n\n  if ($x.dtype === 'bool') {\n    $x = $x.toInt();\n  }\n  const axes = axis_util.parseAxisParam(axis, $x.shape);\n\n  // Use a custom gradient to bypass 2 gradient backprops since sum is used\n  // extremely often.\n  const customOp = customGrad(x => {\n    const permutation = axis_util.getAxesPermutation(axes, x.rank);\n    let reductionAxes = axes;\n    let permutedX = x;\n    if (permutation != null) {\n      permutedX = x.transpose(permutation);\n      reductionAxes = axis_util.getInnerMostAxes(reductionAxes.length, x.rank);\n    }\n    let value = ENV.engine.runKernel(\n        backend => backend.sum(permutedX, reductionAxes), {permutedX});\n    if (keepDims) {\n      const newShape = axis_util.expandShapeToKeepDim(value.shape, axes);\n      value = value.reshape(newShape);\n    }\n\n    const gradFunc = (dy: Tensor) => {\n      const expandedDyShape = x.shape.slice();\n      axes.forEach(axis => {\n        expandedDyShape[axis] = 1;\n      });\n      const expandedDy = dy.reshape(expandedDyShape);\n      const derX = expandedDy.mul(ones(x.shape, 'float32'));\n      return derX;\n    };\n    return {value, gradFunc};\n  });\n\n  return customOp($x) as T;\n}\n\n/**\n * Computes the product of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.prod().print();  // or tf.prod(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.prod(axis).print();  // or tf.prod(x, axis)\n * ```\n *\n * @param x The input tensor to compute the product over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction prod_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  let $x = convertToTensor(x, 'x', 'prod');\n\n  if ($x.dtype === 'bool') {\n    $x = $x.toInt();\n  }\n  const axes = axis_util.parseAxisParam(axis, $x.shape);\n\n  const permutation = axis_util.getAxesPermutation(axes, $x.rank);\n  let reductionAxes = axes;\n  let permutedX = $x;\n  if (permutation != null) {\n    permutedX = $x.transpose(permutation);\n    reductionAxes = axis_util.getInnerMostAxes(reductionAxes.length, $x.rank);\n  }\n  let value = ENV.engine.runKernel(\n      backend => backend.prod(permutedX, reductionAxes), {permutedX});\n  if (keepDims) {\n    const newShape = axis_util.expandShapeToKeepDim(value.shape, axes);\n    value = value.reshape(newShape);\n  }\n\n  return value as T;\n}\n/**\n * Computes the mean of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces `x` along the dimensions given in `axis`. Unless `keepDims` is\n * true, the rank of the `tf.Tensor` is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and a `tf.Tensor` with\n * a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.mean().print();  // or tf.mean(a)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.mean(axis).print();  // or tf.mean(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction mean_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  const $x = convertToTensor(x, 'x', 'mean');\n\n  const axes = axis_util.parseAxisParam(axis, $x.shape);\n  const shapes = axis_util.computeOutAndReduceShapes($x.shape, axes);\n  const reduceShape = shapes[1];\n  const reduceSize = util.sizeFromShape(reduceShape);\n\n  // Use a custom gradient to bypass 2 gradient backprops since mean is used\n  // extremely often.\n  const customOp = customGrad(x => {\n    const reduceSizeScalar = scalar(reduceSize);\n    // Cast if needed.\n    const xReduce =\n        reduceSizeScalar.dtype === x.dtype ? x : x.cast(reduceSizeScalar.dtype);\n    const res = xReduce.div(reduceSizeScalar);\n    const value = res.sum(axis, keepDims);\n\n    const gradFunc = (dy: Tensor) => {\n      const expandedDyShape = x.shape.slice();\n      axes.forEach(axis => {\n        expandedDyShape[axis] = 1;\n      });\n      const expandedDy = dy.reshape(expandedDyShape);\n      const derX =\n          expandedDy.mul(ones(x.shape, 'float32')).div(reduceSizeScalar);\n      return derX;\n    };\n    return {value, gradFunc};\n  });\n\n  return customOp($x) as T;\n}\n\n/**\n * Gradient helper function for the min and max operations.\n */\nfunction gradForMinAndMax<T extends Tensor>(\n    dy: T, saved: Tensor[], xOrig: Tensor, origAxes: number[],\n    permutedAxes: number[]) {\n  let [y] = saved;\n  if (y.rank < xOrig.rank) {\n    y = y.reshape(axis_util.expandShapeToKeepDim(y.shape, origAxes)) as T;\n  }\n  if (dy.rank < xOrig.rank) {\n    dy = dy.reshape(axis_util.expandShapeToKeepDim(dy.shape, origAxes)) as T;\n  }\n  return {\n    $x: () => {\n      const dx = dy.mul(xOrig.equal(y).cast(dy.dtype));\n      return permutedAxes == null ? dx : dx.transpose(permutedAxes);\n    }\n  };\n}\n\n/**\n * Computes the minimum value from the input.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axes`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axes` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.min().print();  // or tf.min(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.min(axis).print();  // or tf.min(x, axis)\n * ```\n *\n * @param x The input Tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction min_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  let $x = convertToTensor(x, 'x', 'min');\n  const xOrig = $x;\n\n  const origAxes = axis_util.parseAxisParam(axis, $x.shape);\n  let axes = origAxes;\n  const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n  if (permutedAxes != null) {\n    $x = $x.transpose(permutedAxes);\n    axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n  }\n\n  const grad = (dy: T, saved: Tensor[]) =>\n      gradForMinAndMax(dy, saved, xOrig, origAxes, permutedAxes);\n  let res = ENV.engine.runKernel(\n      (backend, save) => save(backend.min($x, axes)), {$x}, grad);\n  if (keepDims) {\n    const newShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);\n    res = res.reshape(newShape) as T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the maximum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.max().print();  // or tf.max(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.max(axis).print();  // or tf.max(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction max_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  let $x = convertToTensor(x, 'x', 'max');\n  const xOrig = $x;\n\n  const origAxes = axis_util.parseAxisParam(axis, $x.shape);\n  let axes = origAxes;\n  const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n  if (permutedAxes != null) {\n    $x = $x.transpose(permutedAxes);\n    axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n  }\n\n  const grad = (dy: T, saved: Tensor[]) =>\n      gradForMinAndMax(dy, saved, xOrig, origAxes, permutedAxes);\n  let res = ENV.engine.runKernel(\n      (backend, save) => save(backend.max($x, axes)), {$x}, grad);\n  if (keepDims) {\n    const newShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);\n    res = res.reshape(newShape) as T;\n  }\n  return res as T;\n}\n\n/**\n * Returns the indices of the minimum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMin().print();  // or tf.argMin(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMin(axis).print();  // or tf.argMin(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction argMin_<T extends Tensor>(x: Tensor|TensorLike, axis = 0): T {\n  let $x = convertToTensor(x, 'x', 'argMin');\n\n  if (axis == null) {\n    axis = 0;\n  }\n  let axes = axis_util.parseAxisParam(axis, $x.shape);\n  const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n  if (permutedAxes != null) {\n    $x = $x.transpose(permutedAxes);\n    axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n  }\n  const grad = (dy: T) => {\n    return {$x: () => zerosLike($x)};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.argMin($x, axes[0]), {$x}, grad) as T;\n}\n\n/**\n * Returns the indices of the maximum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMax().print();  // or tf.argMax(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMax(axis).print();  // or tf.argMax(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction argMax_<T extends Tensor>(x: Tensor|TensorLike, axis = 0): T {\n  let $x = convertToTensor(x, 'x', 'argMax');\n\n  if (axis == null) {\n    axis = 0;\n  }\n  let axes = axis_util.parseAxisParam(axis, $x.shape);\n  const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n  if (permutedAxes != null) {\n    $x = $x.transpose(permutedAxes);\n    axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n  }\n  const grad = (dy: T) => {\n    return {$x: () => zerosLike($x)};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.argMax($x, axes[0]), {$x}, grad) as T;\n}\n\n/**\n * Computes the logical and of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1]);\n *\n * x.all().print();  // or tf.all(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.all(axis).print();  // or tf.all(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction all_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  let $x = convertToTensor(x, 'x', 'all', 'bool');\n\n  const origAxes = axis_util.parseAxisParam(axis, $x.shape);\n  let axes = origAxes;\n  const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n  if (permutedAxes != null) {\n    $x = $x.transpose(permutedAxes);\n    axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n  }\n  const res = ENV.engine.runKernel(backend => backend.all($x, axes), {$x});\n  if (keepDims) {\n    const newShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);\n    return res.reshape(newShape) as T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the logical or of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1]);\n *\n * x.any().print();  // or tf.any(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.any(axis).print();  // or tf.any(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction any_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  let $x = convertToTensor(x, 'x', 'any', 'bool');\n\n  const origAxes = axis_util.parseAxisParam(axis, $x.shape);\n  let axes = origAxes;\n  const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n  if (permutedAxes != null) {\n    $x = $x.transpose(permutedAxes);\n    axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n  }\n  const res = ENV.engine.runKernel(backend => backend.any($x, axes), {$x});\n  if (keepDims) {\n    const newShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);\n    return res.reshape(newShape) as T;\n  }\n  return res as T;\n}\n\n/**\n * Calculates the mean and variance of `x`. The mean and variance are\n * calculated by aggregating the contents of `x` across `axes`. If `x` is\n * 1-D and `axes = [0]` this is just the mean and variance of a vector.\n *\n * @param x The input tensor.\n * @param axis The dimension(s) along with to compute mean and\n *     variance. By default it reduces all dimensions.\n * @param keepDims If true, the moments have the same dimensionality as the\n *     input.\n * @return An object with two keys: `mean` and `variance`.\n */\n/** @doc {heading: 'Operations', subheading: 'Normalization'} */\nfunction moments_(\n    x: Tensor|TensorLike, axis: number|number[] = null,\n    keepDims = false): {mean: Tensor, variance: Tensor} {\n  x = convertToTensor(x, 'x', 'moments');\n  const axes = axis_util.parseAxisParam(axis, x.shape);\n  const mean = x.mean(axes, keepDims);\n  let keepDimsShape = mean.shape;\n  if (!keepDims) {\n    keepDimsShape = axis_util.expandShapeToKeepDim(mean.shape, axes);\n  }\n  const devSquared = x.toFloat().sub(mean.reshape(keepDimsShape)).square();\n  const variance = devSquared.mean(axes, keepDims);\n  return {mean, variance};\n}\n\nexport const all = op({all_});\n// tslint:disable-next-line:variable-name\nexport const any = op({any_});\nexport const argMax = op({argMax_});\nexport const argMin = op({argMin_});\nexport const logSumExp = op({logSumExp_});\nexport const max = op({max_});\nexport const mean = op({mean_});\nexport const min = op({min_});\nexport const moments = op({moments_});\nexport const sum = op({sum_});\nexport const prod = op({prod_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assertShapesMatch} from '../util';\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\nimport {zerosLike} from './tensor_ops';\n\n/**\n * Returns the truth value of (a != b) element-wise. Supports broadcasting.\n *\n * We also expose `tf.notEqualStrict` which has the same signature as this op\n * and asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([0, 2, 3]);\n *\n * a.notEqual(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction notEqual_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'notEqual');\n  let $b = convertToTensor(b, 'b', 'notEqual');\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  return ENV.engine.runKernel(backend => backend.notEqual($a, $b), {$a, $b}) as\n      T;\n}\n\n/**\n * Strict version of `tf.notEqual` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n *     `a`.\n */\nfunction notEqualStrict_<T extends Tensor>(\n    a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'notEqualStrict');\n  const $b = convertToTensor(b, 'b', 'notEqualStrict');\n  assertShapesMatch($a.shape, $b.shape, 'Error in notEqualStrict: ');\n  return $a.notEqual($b);\n}\n\n/**\n * Returns the truth value of (a < b) element-wise. Supports broadcasting.\n *\n * We also expose `tf.lessStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.less(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction less_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'less');\n  let $b = convertToTensor(b, 'b', 'less');\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  return ENV.engine.runKernel(backend => backend.less($a, $b), {$a, $b}) as T;\n}\n\n/**\n * Strict version of `tf.less` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n *     `a`.\n */\nfunction lessStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'lessStrict');\n  const $b = convertToTensor(b, 'b', 'lessStrict');\n  assertShapesMatch($a.shape, $b.shape, 'Error in lessStrict: ');\n  return $a.less($b);\n}\n\n/**\n * Returns the truth value of (a == b) element-wise. Supports broadcasting.\n *\n * We also expose `tf.equalStrict` which has the same signature as this op\n * and asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.equal(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction equal_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'equal');\n  let $b = convertToTensor(b, 'b', 'equal');\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  return ENV.engine.runKernel(backend => backend.equal($a, $b), {$a, $b}) as T;\n}\n\nfunction equalStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'equalStrict');\n  const $b = convertToTensor(b, 'b', 'equalStrict');\n  assertShapesMatch($a.shape, $b.shape, 'Error in equalStrict: ');\n  return $a.equal($b);\n}\n\n/**\n * Returns the truth value of (a <= b) element-wise. Supports broadcasting.\n *\n * We also expose `tf.lessEqualStrict` which has the same signature as this op\n * and asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.lessEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction lessEqual_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'lessEqual');\n  let $b = convertToTensor(b, 'b', 'lessEqual');\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  return ENV.engine.runKernel(backend => backend.lessEqual($a, $b), {$a, $b}) as\n      T;\n}\n\nfunction lessEqualStrict_<T extends Tensor>(\n    a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'lessEqualStrict');\n  const $b = convertToTensor(b, 'b', 'lessEqualStrict');\n  assertShapesMatch($a.shape, $b.shape, 'Error in lessEqualStrict: ');\n  return $a.lessEqual($b);\n}\n\n/**\n * Returns the truth value of (a > b) element-wise. Supports broadcasting.\n *\n * We also expose `tf.greaterStrict` which has the same signature as this\n * op and asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greater(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction greater_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'greater');\n  let $b = convertToTensor(b, 'b', 'greater');\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  return ENV.engine.runKernel(backend => backend.greater($a, $b), {$a, $b}) as\n      T;\n}\n\nfunction greaterStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'greaterStrict');\n  const $b = convertToTensor(b, 'b', 'greaterStrict');\n  assertShapesMatch($a.shape, $b.shape, 'Error in greaterStrict: ');\n  return $a.greater($b);\n}\n\n/**\n * Returns the truth value of (a >= b) element-wise. Supports broadcasting.\n *\n * We also expose `tf.greaterEqualStrict` which has the same signature as this\n * op and asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greaterEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction greaterEqual_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'greaterEqual');\n  let $b = convertToTensor(b, 'b', 'greaterEqual');\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const grad = (dy: T) => {\n    return {$a: () => zerosLike($a), $b: () => zerosLike($b)};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.greaterEqual($a, $b), {$a, $b}, grad) as T;\n}\n\nfunction greaterEqualStrict_<T extends Tensor>(\n    a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'greaterEqualStrict');\n  const $b = convertToTensor(b, 'b', 'greaterEqualStrict');\n  assertShapesMatch($a.shape, $b.shape, 'Error in greaterEqualStrict: ');\n  return $a.greaterEqual($b);\n}\n\nexport const equal = op({equal_});\nexport const equalStrict = op({equalStrict_});\nexport const greater = op({greater_});\nexport const greaterEqual = op({greaterEqual_});\nexport const greaterEqualStrict = op({greaterEqualStrict_});\nexport const greaterStrict = op({greaterStrict_});\nexport const less = op({less_});\nexport const lessEqual = op({lessEqual_});\nexport const lessEqualStrict = op({lessEqualStrict_});\nexport const lessStrict = op({lessStrict_});\nexport const notEqual = op({notEqual_});\nexport const notEqualStrict = op({notEqualStrict_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {KernelBackend} from '../kernels/backend';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike, upcastType} from '../types';\nimport * as util from '../util';\nimport * as broadcast_util from './broadcast_util';\nimport {op} from './operation';\nimport {scalar} from './tensor_ops';\nimport {neg} from './unary_ops';\n\n/**\n * Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting.\n *\n * We also expose `tf.addStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n *\n * ```js\n * // Broadcast add a with b.\n * const a = tf.scalar(5);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n * @param a The first `tf.Tensor` to add.\n * @param b The second `tf.Tensor` to add. Must have the same type as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction add_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'add');\n  let $b = convertToTensor(b, 'b', 'add');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const outShape =\n      broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const der = (dy: Tensor) => {\n    const derA = () => {\n      let res = dy;\n      const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes);\n      }\n      return res.reshape($a.shape);\n    };\n    const derB = () => {\n      let res = dy;\n      const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes);\n      }\n      return res.reshape($b.shape);\n    };\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(backend => backend.add($a, $b), {$a, $b}, der) as\n      T;\n}\n\n/**\n * Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n *\n * tf.addN([a, b, c]).print();\n * ```\n * @param tensors A list of tensors with the same shape and dtype.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction addN_<T extends Tensor>(tensors: Array<T|TensorLike>): T {\n  util.assert(\n      Array.isArray(tensors),\n      () => 'The argument passed to tf.addN() must be a list of tensors');\n  util.assert(\n      tensors.length >= 1,\n      () => `Must pass at least one tensor to tf.addN(), but got ` +\n          `${tensors.length}`);\n  const $tensors =\n      tensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'addN'));\n  const firstTensor = $tensors[0];\n  $tensors.forEach(t => {\n    if (t.dtype !== firstTensor.dtype) {\n      throw new Error(\n          'All tensors passed to tf.addN() must have the same dtype');\n    }\n  });\n  $tensors.forEach(t => {\n    if (!util.arraysEqual(t.shape, firstTensor.shape)) {\n      throw new Error(\n          'All tensors passed to tf.addN() must have the same shape');\n    }\n  });\n\n  const der = (dy: T) => {\n    const ders: {[key: string]: () => Tensor} = {};\n    $tensors.forEach((t, i) => {\n      ders[i] = () => dy.clone();\n    });\n    return ders;\n  };\n  const inputs: NamedTensorMap = $tensors as {} as NamedTensorMap;\n  return ENV.engine.runKernel(backend => backend.addN($tensors), inputs, der);\n}\n\n/**\n * Adds two `tf.Tensor`s element-wise, A + B.\n *\n * Inputs must be the same shape. For broadcasting support, use add() instead.\n *\n * @param a The first Tensor to add element-wise.\n * @param b The second Tensor to add element-wise.\n */\nfunction addStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'addStrict');\n  const $b = convertToTensor(b, 'b', 'addStrict');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in addStrict: ');\n  return $a.add($b);\n}\n\n/**\n * Subtracts two `tf.Tensor`s element-wise, A - B. Supports broadcasting.\n *\n * We also expose `tf.subStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n *\n * ```js\n * // Broadcast subtract a with b.\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.scalar(5);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n * @param a The first `tf.Tensor` to subtract from.\n * @param b The second `tf.Tensor` to be subtracted. Must have the same dtype as\n * `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction sub_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'sub');\n  let $b = convertToTensor(b, 'b', 'sub');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const outShape =\n      broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const der = (dy: Tensor) => {\n    const derA = () => {\n      let res = dy;\n      const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes);\n      }\n      return res.reshape($a.shape);\n    };\n    const derB = () => {\n      let res = dy;\n      const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes);\n      }\n      return res.neg().reshape($b.shape);\n    };\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.subtract($a, $b), {$a, $b}, der) as T;\n}\n\n/**\n * Subtracts two `tf.Tensor`s element-wise, A - B. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.sub` instead.\n *\n * @param a The first Tensor to subtract element-wise.\n * @param b The second Tensor to subtract element-wise.\n */\nfunction subStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'subStrict');\n  const $b = convertToTensor(b, 'b', 'subStrict');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in subStrict: ');\n  return $a.sub($b);\n}\n\n/**\n * Computes the power of one `tf.Tensor` to another. Supports broadcasting.\n *\n * Given a `tf.Tensor` x and a `tf.Tensor` y, this operation computes x^y for\n * corresponding elements in x and y. The result's dtype will be the upcasted\n * type of the `base` and `exp` dtypes.\n *\n * ```js\n * const a = tf.tensor([[2, 3], [4, 5]])\n * const b = tf.tensor([[1, 2], [3, 0]]).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n *\n * ```js\n * const a = tf.tensor([[1, 2], [3, 4]])\n * const b = tf.tensor(2).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n * We also expose `powStrict` which has the same signature as this op and\n * asserts that `base` and `exp` are the same shape (does not broadcast).\n *\n * @param base The base `tf.Tensor` to pow element-wise.\n * @param exp The exponent `tf.Tensor` to pow element-wise.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction pow_<T extends Tensor>(base: T|TensorLike, exp: Tensor|TensorLike): T {\n  const $base = convertToTensor(base, 'base', 'pow');\n  const $exp = convertToTensor(exp, 'exp', 'pow');\n\n  const outShape =\n      broadcast_util.assertAndGetBroadcastShape($base.shape, $exp.shape);\n  base = $base.cast(upcastType($base.dtype, $exp.dtype));\n  exp = $exp.cast(upcastType($base.dtype, $exp.dtype));\n  const grad = (dy: Tensor, saved: Tensor[]) => {\n    const [y] = saved;\n    const derBase = () => {\n      const expFloat = $exp.toFloat();\n      let res = dy.mul(expFloat.mul($base.pow(expFloat.sub(scalar(1)))));\n      const reduceAxes = broadcast_util.getReductionAxes($base.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes);\n      }\n      return res.reshape($base.shape) as T;\n    };\n    const derExp = () => {\n      let res = dy.mul(y.mul($base.log()).toFloat());\n      const reduceAxes = broadcast_util.getReductionAxes($exp.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes);\n      }\n      return res.reshape($exp.shape);\n    };\n    return {$base: derBase, $exp: derExp};\n  };\n  return ENV.engine.runKernel(\n             (backend, save) => save(backend.pow($base, $exp)), {$base, $exp},\n             grad) as T;\n}\n\n/**\n * Computes the power of one `tf.Tensor` to another. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.pow` instead.\n *\n * @param base The base tensor to pow element-wise.\n * @param exp The exponent tensor to pow element-wise.\n */\nfunction powStrict_<T extends Tensor>(base: T, exp: Tensor): T {\n  util.assertShapesMatch(base.shape, exp.shape, 'Error in powStrict: ');\n  return base.pow(exp);\n}\n\n/**\n * Multiplies two `tf.Tensor`s element-wise, A * B. Supports broadcasting.\n *\n * We also expose `tf.mulStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([2, 3, 4, 5]);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n *\n * ```js\n * // Broadcast mul a with b.\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.scalar(5);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n * @param a The first tensor to multiply.\n * @param b The second tensor to multiply. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction mul_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'mul');\n  let $b = convertToTensor(b, 'b', 'mul');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const outShape =\n      broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const der = (dy: Tensor) => {\n    const derA = () => {\n      const res = dy.mul($b.toFloat());\n      const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return res.sum(reduceAxes).reshape($a.shape);\n      }\n      return res;\n    };\n    const derB = () => {\n      const res = dy.mul($a.toFloat());\n      const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return res.sum(reduceAxes).reshape($b.shape);\n      }\n      return res;\n    };\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.multiply($a, $b), {$a, $b}, der) as T;\n}\n\n/**\n * Multiplies two `tf.Tensor`s element-wise, A * B.\n *\n * Inputs must be the same shape. For broadcasting support, use `tf.mul`.\n *\n * @param a The first tensor to multiply.\n * @param b The first tensor to multiply. Must have the same\n *    dtype as `a`.\n */\nfunction mulStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'mul');\n  const $b = convertToTensor(b, 'b', 'mul');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in multiplyStrict: ');\n  return $a.mul($b) as T;\n}\n\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n *\n * We also expose `tf.divStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction div_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'div');\n  let $b = convertToTensor(b, 'b', 'div');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  let forwardFunc: (backend: KernelBackend) => Tensor;\n  if ($a.dtype === 'int32' && $b.dtype === 'int32') {\n    return floorDiv($a, $b);\n  } else {\n    forwardFunc = (backend: KernelBackend) => backend.realDivide($a, $b);\n  }\n\n  const outShape =\n      broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n  const der = (dy: Tensor) => {\n    const derA = () => {\n      const res = dy.div($b.toFloat());\n      const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return res.sum(reduceAxes).reshape($a.shape);\n      }\n      return res;\n    };\n    const derB = () => {\n      let res = dy.mul($a.toFloat());\n      const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes).reshape($b.shape);\n      }\n      const tmp = $b.square() as Tensor;\n      return res.div(tmp.toFloat()).neg() as Tensor;\n    };\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(forwardFunc, {$a, $b}, der) as T;\n}\n\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n * The result is rounded with floor function.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.floorDiv(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.floorDiv(b).print();  // or tf.floorDiv(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction floorDiv_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'floorDiv');\n  let $b = convertToTensor(b, 'b', 'floorDiv');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const forwardFunc = (backend: KernelBackend) => backend.floorDiv($a, $b);\n  const outShape =\n      broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n  const der = (dy: Tensor) => {\n    const derA = () => {\n      const res = dy.div($b.toFloat());\n      const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return res.sum(reduceAxes).reshape($a.shape);\n      }\n      return res;\n    };\n    const derB = () => {\n      let res = dy.mul($a.toFloat());\n      const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes).reshape($b.shape);\n      }\n      const tmp = $b.square() as Tensor;\n      return res.div(tmp.toFloat()).neg() as Tensor;\n    };\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(forwardFunc, {$a, $b}, der) as T;\n}\n\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Inputs must\n * be the same shape.\n *\n * @param a The first tensor as the numerator for element-wise division.\n * @param b The second tensor as the denominator for element-wise division.\n */\nfunction divStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'div');\n  const $b = convertToTensor(b, 'b', 'div');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in divideStrict: ');\n  return $a.div($b) as T;\n}\n\n/**\n * Returns the mod of a and b element-wise.\n * `floor(x / y) * y + mod(x, y) = x`\n * Supports broadcasting.\n *\n * We also expose `tf.modStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * ```js\n * // Broadcast a mod b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction mod_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'mod');\n  let $b = convertToTensor(b, 'b', 'mod');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const outShape =\n      broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n  const der = (dy: Tensor) => {\n    const derA = () => {\n      const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return dy.sum(reduceAxes).reshape($a.shape);\n      }\n      return dy;\n    };\n    const derB = () => {\n      const res = dy.mul($a.div($b).floor().neg());\n      const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return res.sum(reduceAxes).reshape($b.shape);\n      }\n      return res;\n    };\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(backend => backend.mod($a, $b), {$a, $b}, der) as\n      T;\n}\n\n/**\n * Returns the mod of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use mod().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction modStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'modStrict');\n  const $b = convertToTensor(b, 'b', 'modStrict');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in modStrict: ');\n  return $a.mod($b);\n}\n\n/**\n * Returns the min of a and b (`a < b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `minimumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * ```js\n * // Broadcast minimum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction minimum_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'minimum');\n  let $b = convertToTensor(b, 'b', 'minimum');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  if ($a.dtype === 'bool') {\n    $a = $a.toInt();\n    $b = $b.toInt();\n  }\n\n  broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n  const der = (dy: Tensor) => {\n    const derA = () => dy.mul($a.lessEqual($b).toFloat());\n    const derB = () => dy.mul($a.greater($b).toFloat());\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.minimum($a, $b), {$a, $b}, der) as T;\n}\n\n/**\n * Returns the min of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use minimum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction minimumStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'minimumStrict');\n  const $b = convertToTensor(b, 'b', 'minimumStrict');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in minimumStrict: ');\n  return $a.minimum($b);\n}\n\n/**\n * Returns the max of a and b (`a > b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `tf.maximumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * ```js\n * // Broadcast maximum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction maximum_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'maximum');\n  let $b = convertToTensor(b, 'b', 'maximum');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  if ($a.dtype === 'bool') {\n    $a = $a.toInt();\n    $b = $b.toInt();\n  }\n\n  broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n  const der = (dy: Tensor) => {\n    const derA = () => dy.mul($a.greaterEqual($b).toFloat());\n    const derB = () => dy.mul($a.less($b).toFloat());\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.maximum($a, $b), {$a, $b}, der) as T;\n}\n\n/**\n * Returns the max of a and b (`a > b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use maximum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction maximumStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'maximumStrict');\n  const $b = convertToTensor(b, 'b', 'maximumStrict');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in maximumStrict: ');\n  return $a.maximum($b);\n}\n\n/**\n * Returns (a - b) * (a - b) element-wise.\n * Supports broadcasting.\n *\n * We also expose `tf.squaredDifferenceStrict` which has the same signature as\n * this op and asserts that `a` and `b` are the same shape (does not\n * broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * ```js\n * // Broadcast squared difference  a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction squaredDifference_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'squaredDifference');\n  let $b = convertToTensor(b, 'b', 'squaredDifference');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n  const der = (dy: Tensor) => {\n    const two = scalar(2);\n    const derA = () => dy.mul($a.sub($b).mul(two));\n    const derB = () => dy.mul($b.sub($a).mul(two));\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.squaredDifference($a, $b), {$a, $b}, der) as T;\n}\n\n/**\n * Returns (a - b) * (a - b) element-wise.\n *\n * Inputs must be the same shape. For broadcasting support, use\n * `tf.squaredDifference` instead.\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\nfunction squaredDifferenceStrict_<T extends Tensor>(\n    a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'squaredDifferenceStrict');\n  const $b = convertToTensor(b, 'b', 'squaredDifferenceStrict');\n  util.assertShapesMatch(\n      $a.shape, $b.shape, 'Error in squaredDifferenceStrict: ');\n  return $a.squaredDifference($b);\n}\n\n/**\n * Computes arctangent of `tf.Tensor`s a / b element-wise: `atan2(a, b)`.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1.0, 1.0, -1.0, .7]);\n * const b = tf.tensor1d([2.0, 13.0, 3.5, .21]);\n *\n * tf.atan2(a, b).print()\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n *\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction atan2_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'atan2');\n  let $b = convertToTensor(b, 'b', 'atan2');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const outShape =\n      broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const der = (dy: Tensor) => {\n    const derA = () => {\n      const d = add($a.square(), $b.square());\n      let res = dy.mul($b.div(d));\n      const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes);\n      }\n      return res.reshape($a.shape);\n    };\n    const derB = () => {\n      const d = add($a.square(), $b.square()) as T;\n      let res = neg(dy.mul($a.div(d)));\n      const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes);\n      }\n      return res.reshape($b.shape);\n    };\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.atan2($a, $b), {$a, $b}, der) as T;\n}\n\nexport const add = op({add_});\nexport const addN = op({addN_});\nexport const addStrict = op({addStrict_});\nexport const atan2 = op({atan2_});\nexport const div = op({div_});\nexport const divStrict = op({divStrict_});\nexport const floorDiv = op({floorDiv_});\nexport const maximum = op({maximum_});\nexport const maximumStrict = op({maximumStrict_});\nexport const minimum = op({minimum_});\nexport const minimumStrict = op({minimumStrict_});\nexport const mod = op({mod_});\nexport const modStrict = op({modStrict_});\nexport const mul = op({mul_});\nexport const mulStrict = op({mulStrict_});\nexport const pow = op({pow_});\nexport const powStrict = op({powStrict_});\nexport const squaredDifference = op({squaredDifference_});\nexport const squaredDifferenceStrict = op({squaredDifferenceStrict_});\nexport const sub = op({sub_});\nexport const subStrict = op({subStrict_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {whereImpl} from '../kernels/where_impl';\nimport {Tensor, Tensor2D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assert, assertShapesMatch} from '../util';\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\nimport {zerosLike} from './tensor_ops';\n\n/**\n * Returns the truth value of `NOT x` element-wise.\n *\n * ```js\n * const a = tf.tensor1d([false, true], 'bool');\n *\n * a.logicalNot().print();\n * ```\n *\n * @param x The input tensor. Must be of dtype 'bool'.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction logicalNot_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'logicalNot', 'bool');\n  return ENV.engine.runKernel(backend => backend.logicalNot($x), {$x});\n}\n\n/**\n * Returns the truth value of `a AND b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalAnd(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction logicalAnd_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'logicalAnd', 'bool');\n  const $b = convertToTensor(b, 'b', 'logicalAnd', 'bool');\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  return ENV.engine.runKernel(\n             backend => backend.logicalAnd($a, $b), {$a, $b}) as T;\n}\n\n/**\n * Returns the truth value of `a OR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalOr(b).print();\n * ```\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction logicalOr_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'logicalOr', 'bool');\n  const $b = convertToTensor(b, 'b', 'logicalOr', 'bool');\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  return ENV.engine.runKernel(backend => backend.logicalOr($a, $b), {$a, $b}) as\n      T;\n}\n\n/**\n * Returns the truth value of `a XOR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalXor(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction logicalXor_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'logicalXor', 'bool');\n  const $b = convertToTensor(b, 'b', 'logicalXor', 'bool');\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  // x ^ y = (x | y) & ~(x & y)\n  return logicalOr(a, b).logicalAnd(logicalAnd(a, b).logicalNot()) as T;\n}\n\n/**\n * Returns the elements, either `a` or `b` depending on the `condition`.\n *\n * If the condition is true, select from `a`, otherwise select from `b`.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const a = tf.tensor1d([1 , 2, 3]);\n * const b = tf.tensor1d([-1, -2, -3]);\n *\n * a.where(cond, b).print();\n * ```\n *\n * @param condition The input condition. Must be of dtype bool.\n * @param a If `condition` is rank 1, `a` may have a higher rank but\n *     its first dimension must match the size of `condition`.\n * @param b A tensor with the same shape and type as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction where_<T extends Tensor>(\n    condition: Tensor|TensorLike, a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'where');\n  const $b = convertToTensor(b, 'b', 'where');\n  const $condition = convertToTensor(condition, 'condition', 'where', 'bool');\n\n  assertShapesMatch($a.shape, $b.shape, 'Error in where: ');\n\n  if ($condition.rank === 1) {\n    // If condition rank is 1, then the first dimension must match the size of\n    // condition.\n    assert(\n        $condition.shape[0] === $a.shape[0],\n        'The first dimension of `a` must match the size of `condition`.');\n  } else {\n    // A must have the same shape as condition.\n    assertShapesMatch($condition.shape, $b.shape, 'Error in where: ');\n  }\n\n  // TODO(julianoks): Return null for condition gradient\n  // when backprop supports it.\n  const grad = (dy: T) => ({\n    $condition: () => zerosLike($condition).toFloat(),\n    $a: () => dy.mul($condition.cast(dy.dtype)) as T,\n    $b: () => dy.mul($condition.logicalNot().cast(dy.dtype)) as T\n  });\n\n  return ENV.engine.runKernel(\n             backend => backend.select($condition, $a, $b),\n             {$condition, $a, $b}, grad) as T;\n}\n\n/**\n * Returns the coordinates of true elements of condition.\n *\n * The coordinates are returned in a 2-D tensor where the first dimension (rows)\n * represents the number of true elements, and the second dimension (columns)\n * represents the coordinates of the true elements. Keep in mind, the shape of\n * the output tensor can vary depending on how many true values there are in\n * input. Indices are output in row-major order. The resulting tensor has the\n * shape `[numTrueElems, condition.rank]`.\n *\n * This is analogous to calling the python `tf.where(cond)` without an x or y.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * tf.whereAsync(cond).then(result => result.print());\n * ```\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nasync function whereAsync_(condition: Tensor|TensorLike): Promise<Tensor2D> {\n  const $condition =\n      convertToTensor(condition, 'condition', 'whereAsync', 'bool');\n  const vals = await $condition.data();\n  const res = whereImpl($condition.shape, vals);\n  if (condition !== $condition) {\n    $condition.dispose();\n  }\n  return res;\n}\n\nexport const logicalAnd = op({logicalAnd_});\nexport const logicalNot = op({logicalNot_});\nexport const logicalOr = op({logicalOr_});\nexport const logicalXor = op({logicalXor_});\nexport const where = op({where_});\nexport const whereAsync = whereAsync_;\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {maximum, minimum} from './binary_ops';\nimport {where} from './logical_ops';\nimport {op} from './operation';\nimport {SELU_SCALE, SELU_SCALEALPHA} from './selu_util';\nimport {scalar} from './tensor_ops';\n\n/**\n * Computes rectified linear element-wise: `max(x, 0)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.relu().print();  // or tf.relu(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n *     `int32'.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction relu_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'relu');\n\n  if ($x.dtype === 'bool') {\n    return $x.toInt();\n  }\n  const grad = (dy: T) => {\n    const stepRes = $x.step();\n    return {$x: () => dy.mulStrict(stepRes.toFloat())};\n  };\n  return ENV.engine.runKernel(backend => backend.relu($x), {$x}, grad);\n}\n\n/**\n * Computes exponential linear element-wise: `x > 0 ? e ^ x - 1 : 0`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 1, -3, 2]);\n *\n * x.elu().print();  // or tf.elu(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction elu_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'elu');\n\n  const grad = (dy: T, saved: Tensor[]) => {\n    const [y] = saved;\n    return {\n      $x: () =>\n          ENV.engine.runKernel(backend => backend.eluDer(dy, y), {dy, y}) as T\n    };\n  };\n  return ENV.engine.runKernel(\n      (backend, save) => save(backend.elu($x)), {$x}, grad);\n}\n\n/**\n * Computes scaled exponential linear element-wise.\n *\n * `x < 0 ? scale * alpha * (exp(x) - 1) : x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.selu().print();  // or tf.selu(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction selu_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'selu');\n\n  const grad = (dy: T) => {\n    return {\n      $x: () => {\n        const mask = $x.greater(scalar(0));\n\n        const scaleAlpha = scalar(SELU_SCALEALPHA);\n        const scale = scalar(SELU_SCALE);\n\n        const greaterThanZeroDer = dy.mul(scale);\n        const lessEqualZeroDer = dy.mul(scaleAlpha).mul($x.toFloat().exp());\n\n        return where(mask, greaterThanZeroDer, lessEqualZeroDer) as T;\n      }\n    };\n  };\n  return ENV.engine.runKernel(backend => backend.selu($x), {$x}, grad);\n}\n\n/**\n * Computes leaky rectified linear element-wise.\n *\n * See\n * [http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf](\n *     http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf)\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.leakyRelu(0.1).print();  // or tf.leakyRelu(x, 0.1)\n * ```\n * @param x The input tensor.\n * @param alpha The scaling factor for negative values, defaults to 0.2.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction leakyRelu_<T extends Tensor>(x: T|TensorLike, alpha = 0.2): T {\n  const $x = convertToTensor(x, 'x', 'leakyRelu');\n  return maximum(scalar(alpha).mul($x), $x);\n}\n\n/**\n * Computes leaky rectified linear element-wise with parametric alphas.\n *\n * `x < 0 ? alpha * x : f(x) = x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n * const alpha = tf.scalar(0.1);\n *\n * x.prelu(alpha).print();  // or tf.prelu(x, alpha)\n * ```\n * @param x The input tensor.\n * @param alpha Scaling factor for negative values.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction prelu_<T extends Tensor>(x: T|TensorLike, alpha: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'prelu');\n  const $alpha = convertToTensor(alpha, 'alpha', 'prelu');\n\n  const zero = scalar(0);\n  return maximum(zero, $x).add($alpha.mul(minimum(zero, $x)));\n}\n\nexport const elu = op({elu_});\nexport const leakyRelu = op({leakyRelu_});\nexport const prelu = op({prelu_});\nexport const relu = op({relu_});\nexport const selu = op({selu_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport {op} from './operation';\n\n/**\n * Transposes the `tf.Tensor`. Permutes the dimensions according to `perm`.\n *\n * The returned `tf.Tensor`'s dimension `i` will correspond to the input\n * dimension `perm[i]`. If `perm` is not given, it is set to `[n-1...0]`,\n * where `n` is the rank of the input `tf.Tensor`. Hence by default, this\n * operation performs a regular matrix transpose on 2-D input `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n *\n * a.transpose().print();  // or tf.transpose(a)\n * ```\n *\n * @param x The tensor to transpose.\n * @param perm The permutation of the dimensions of a.\n */\n/** @doc {heading: 'Operations', subheading: 'Matrices'} */\nfunction transpose_<T extends Tensor>(x: T|TensorLike, perm?: number[]): T {\n  const $x = convertToTensor(x, 'x', 'transpose');\n\n  if (perm == null) {\n    perm = $x.shape.map((s, i) => i).reverse();\n  }\n  util.assert(\n      $x.rank === perm.length,\n      `Error in transpose: rank of input ${$x.rank} ` +\n          `must match length of perm ${perm}.`);\n  perm.forEach(axis => {\n    util.assert(\n        axis >= 0 && axis < $x.rank,\n        `All entries in 'perm' must be between 0 and ${$x.rank - 1}` +\n            ` but got ${perm}`);\n  });\n\n  if ($x.rank <= 1) {\n    return $x.clone();\n  }\n\n  const der = (dy: T) => {\n    const undoPerm = axis_util.getUndoAxesPermutation(perm);\n    return {$x: () => dy.transpose(undoPerm)};\n  };\n  return ENV.engine.runKernel(\n      backend => backend.transpose($x, perm), {$x}, der);\n}\n\nexport const transpose = op({transpose_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\n\n/**\n * Normalizes the activation of a local neighborhood across or within\n * channels.\n *\n * @param x The input tensor. The 4-D input tensor is treated as a 3-D array\n *     of 1D vectors (along the last dimension), and each vector is\n *     normalized independently.\n * @param depthRadius The number of adjacent channels in the 1D normalization\n *     window.\n * @param bias A constant bias term for the basis.\n * @param alpha A scale factor, usually positive.\n * @param beta An exponent.\n */\n/** @doc {heading: 'Operations', subheading: 'Normalization'} */\nfunction localResponseNormalization_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5): T {\n  const $x = convertToTensor(x, 'x', 'localResponseNormalization');\n  util.assert(\n      $x.rank === 4 || $x.rank === 3,\n      `Error in localResponseNormalization: x must be rank 3 or 4 but got\n               rank ${$x.rank}.`);\n  util.assert(\n      util.isInt(depthRadius),\n      `Error in localResponseNormalization: depthRadius must be an integer\n                     but got depthRadius ${depthRadius}.`);\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n  }\n  const backward = (dy: Tensor4D, saved: Tensor[]) => {\n    const [outputImage] = saved;\n    return {\n      x4D: () => ENV.engine.runKernel(\n          backend => backend.LRNGrad(\n              dy, x4D, outputImage as Tensor4D, depthRadius, bias, alpha, beta),\n          {})\n    };\n  };\n  const res = ENV.engine.runKernel(\n      (backend, save) => save(backend.localResponseNormalization4D(\n          x4D, depthRadius, bias, alpha, beta)),\n      {x4D}, backward);\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  } else {\n    return res as T;\n  }\n}\n\nexport const localResponseNormalization = op({localResponseNormalization_});","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as axis_util from './axis_util';\nimport {op} from './operation';\nimport {scalar} from './tensor_ops';\n\n/**\n * Computes the norm of scalar, vectors, and matrices.\n * This function can compute several different vector norms (the 1-norm, the\n * Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0)\n * and matrix norms (Frobenius, 1-norm, and inf-norm).\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.norm().print();  // or tf.norm(x)\n * ```\n *\n * @param x The input array.\n * @param ord Optional. Order of the norm. Supported norm types are\n * following:\n *\n *  | ord        | norm for matrices         | norm for vectors\n *  |------------|---------------------------|---------------------\n *  |'euclidean' |Frobenius norm             |2-norm\n *  |'fro'       |Frobenius norm\t           |\n *  |Infinity    |max(sum(abs(x), axis=1))   |max(abs(x))\n *  |-Infinity   |min(sum(abs(x), axis=1))   |min(abs(x))\n *  |1           |max(sum(abs(x), axis=0))   |sum(abs(x))\n *  |2           |                           |sum(abs(x)^2)^1/2*\n *\n * @param axis Optional. If axis is null (the default), the input is\n * considered a vector and a single vector norm is computed over the entire\n * set of values in the Tensor, i.e. norm(x, ord) is equivalent\n * to norm(x.reshape([-1]), ord). If axis is a integer, the input\n * is considered a batch of vectors, and axis determines the axis in x\n * over which to compute vector norms. If axis is a 2-tuple of integer it is\n * considered a batch of matrices and axis determines the axes in NDArray\n * over which to compute a matrix norm.\n * @param keepDims Optional. If true, the norm have the same dimensionality\n * as the input.\n */\n/** @doc {heading: 'Operations', subheading: 'Matrices'} */\nfunction norm_(\n    x: Tensor|TensorLike, ord: number|'euclidean'|'fro' = 'euclidean',\n    axis: number|number[] = null, keepDims = false): Tensor {\n  x = convertToTensor(x, 'x', 'norm');\n\n  const norm = normImpl(x, ord, axis);\n  let keepDimsShape = norm.shape;\n  if (keepDims) {\n    const axes = axis_util.parseAxisParam(axis, x.shape);\n    keepDimsShape = axis_util.expandShapeToKeepDim(norm.shape, axes);\n  }\n  return norm.reshape(keepDimsShape);\n}\n\nfunction normImpl(\n    x: Tensor, p: number|string, axis: number|number[] = null): Tensor {\n  if (x.rank === 0) {\n    return x.abs();\n  }\n\n  // consider vector when no axis is specified\n  if (x.rank !== 1 && axis === null) {\n    return normImpl(x.reshape([-1]), p, axis);\n  }\n\n  // vector\n  if (x.rank === 1 || typeof axis === 'number' ||\n      axis instanceof Array && axis.length === 1) {\n    if (p === 1) {\n      return x.abs().sum(axis);\n    }\n    if (p === Infinity) {\n      return x.abs().max(axis);\n    }\n    if (p === -Infinity) {\n      return x.abs().min(axis);\n    }\n    if (p === 'euclidean' || p === 2) {\n      // norm(x, 2) = sum(abs(xi) ^ 2) ^ 1/2\n      return x.abs().pow(scalar(2, 'int32')).sum(axis).sqrt() as Tensor;\n    }\n\n    throw new Error(`Error in norm: invalid ord value: ${p}`);\n  }\n\n  // matrix (assumption axis[0] < axis[1])\n  if (axis instanceof Array && axis.length === 2) {\n    if (p === 1) {\n      return x.abs().sum(axis[0]).max(axis[1] - 1);\n    }\n    if (p === Infinity) {\n      return x.abs().sum(axis[1]).max(axis[0]);\n    }\n    if (p === -Infinity) {\n      return x.abs().sum(axis[1]).min(axis[0]);\n    }\n    if (p === 'fro' || p === 'euclidean') {\n      // norm(x) = sqrt(sum(pow(x, 2)))\n      return x.square().sum(axis).sqrt();\n    }\n\n    throw new Error(`Error in norm: invalid ord value: ${p}`);\n  }\n\n  throw new Error(`Error in norm: invalid axis: ${axis}`);\n}\n\nexport const norm = op({norm_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor1D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assert, isInt} from '../util';\nimport {expandDims} from './array_ops';\nimport {getUndoAxesPermutation, parseAxisParam} from './axis_util';\nimport {maximum} from './binary_ops';\nimport {greaterEqual} from './compare';\nimport {logicalAnd, where} from './logical_ops';\nimport {op} from './operation';\nimport {ones, scalar, zerosLike} from './tensor_ops';\n\n/**\n * Computes the sum along segments of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const segmentIds = tf.tensor1d([1, 2, 0, 1], 'int32');\n * const numSegments = 3;\n *\n * x.unsortedSegmentSum(segmentIds, numSegments).print()\n * //or tf.unsortedSegmentSum(x, segmentIds, numSegments)\n * ```\n * @param x The `tf.Tensor` that will be summed along its segments.\n * @param segmentIds A `tf.Tensor1D` whose rank is equal to the rank of `x`'s\n * dimension along the `axis`.  Maps each element of `x` to a segment.\n * @param numSegments The number of distinct `segmentIds`.\n */\n/** @doc {heading: 'Operations', subheading: 'Segment'} */\nfunction unsortedSegmentSum_<T extends Tensor>(\n    x: T|TensorLike, segmentIds: Tensor1D|TensorLike, numSegments: number): T {\n  const $x = convertToTensor(x, 'x', 'unsortedSegmentSum');\n  const $segmentIds =\n      convertToTensor(segmentIds, 'segmentIds', 'unsortedSegmentSum', 'int32');\n  assert(isInt(numSegments), 'numSegments must be of dtype int');\n\n  const gradFunc = (dy: T) => {\n    const derX = () => {\n      return gatherDropNegatives(dy, $segmentIds);\n    };\n    return {$x: derX};\n  };\n  return ENV.engine.runKernel(\n             backend =>\n                 backend.unsortedSegmentSum($x, $segmentIds, numSegments),\n             {$x}, gradFunc) as T;\n}\n\n/**\n * Gather slices from tensor `x`'s axis `axis` according to `indices`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const indices = tf.tensor1d([1, 3, 3], 'int32');\n *\n * x.gather(indices).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const indices = tf.tensor1d([1, 1, 0], 'int32');\n *\n * x.gather(indices).print();\n * ```\n * @param x The input tensor whose slices to be gathered.\n * @param indices The indices of the values to extract.\n * @param axis The axis over which to select values. Defaults to 0.\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction gather_<T extends Tensor>(\n    x: T|TensorLike, indices: Tensor1D|TensorLike, axis = 0): T {\n  const $x = convertToTensor(x, 'x', 'gather');\n  const $indices = convertToTensor(indices, 'indices', 'gather', 'int32');\n  axis = parseAxisParam(axis, $x.shape)[0];\n  const grad = (dy: T) => {\n    const derX = () => {\n      if (axis === 0) {\n        return unsortedSegmentSum(dy, $indices, $x.shape[axis]);\n      }\n      const paramsShape = $x.shape;\n      const indicesSize = $indices.size;\n\n      const outerShape = paramsShape.slice(0, axis);\n      const outerDims = outerShape.length;\n      const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);\n      const innerDims = innerShape.length;\n\n      const outerAxesIndices = arrayRange(0, outerDims);\n      const innerAxesIndices =\n          arrayRange(outerDims + 1, outerDims + 1 + innerDims);\n\n      const valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);\n\n      const values = dy.reshape(valuesShape);\n      const reshapedIndices = $indices.reshape([indicesSize]);\n\n      const transposeDims =\n          arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);\n      const valuesTranspose = values.transpose(transposeDims);\n\n      let paramsGrad = unsortedSegmentSum(\n          valuesTranspose, reshapedIndices as Tensor1D, $x.shape[axis]);\n\n      const invertTransposeDims = getUndoAxesPermutation(transposeDims);\n      paramsGrad = paramsGrad.transpose(invertTransposeDims);\n\n      return paramsGrad as T;\n    };\n    return {$x: derX};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.gather($x, $indices as Tensor1D, axis), {$x},\n             grad) as T;\n}\n\nfunction arrayRange(start: number, stop: number): number[] {\n  const result = [];\n  for (let i = start; i < stop; ++i) {\n    result.push(i);\n  }\n  return result;\n}\n\nfunction arrayConcat(arrays: number[][]): number[] {\n  const result = [];\n  for (let i = 0; i < arrays.length; ++i) {\n    for (let j = 0; j < arrays[i].length; ++j) {\n      result.push(arrays[i][j]);\n    }\n  }\n  return result;\n}\n\nfunction gatherDropNegatives<T extends Tensor>(x: T, indices: Tensor1D) {\n  // Helper function for unsorted segment ops. Gathers params for\n  // positive segment ids and gathers 0 for inputs with negative segment id.\n  // Mirrors _GatherDropNegatives from tensorflow/python/ops/math_grad.py\n  const zeroClippedIndices = maximum(indices, zerosLike(indices));\n  const gathered = gather(x, zeroClippedIndices as Tensor1D);\n  let isPositive = greaterEqual(indices, scalar(0, 'int32'));\n  const numIters = gathered.rank - isPositive.rank;\n  for (let i = 0; i < numIters; ++i) {\n    isPositive = expandDims(isPositive, i + 1);\n  }\n  isPositive = logicalAnd(isPositive, ones(gathered.shape, 'bool'));\n  const zeroSlice = zerosLike(gathered);\n  return where(isPositive, gathered, zeroSlice);\n}\n\nexport const gather = op({gather_});\nexport const unsortedSegmentSum = op({unsortedSegmentSum_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Scalar, Tensor1D, Tensor2D} from '../tensor';\nimport {convertToTensor, convertToTensorArray} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * @docalias (data: Tensor2D, c: Tensor2D, h: Tensor2D): [Tensor2D, Tensor2D]\n */\nexport type LSTMCellFunc = {\n  (data: Tensor2D, c: Tensor2D, h: Tensor2D): [Tensor2D, Tensor2D];\n};\n\n/**\n * Computes the next states and outputs of a stack of LSTMCells.\n *\n * Each cell output is used as input to the next cell.\n *\n * Returns `[cellState, cellOutput]`.\n *\n * Derived from tf.contrib.rn.MultiRNNCell.\n *\n * @param lstmCells Array of LSTMCell functions.\n * @param data The input to the cell.\n * @param c Array of previous cell states.\n * @param h Array of previous cell outputs.\n */\n/** @doc {heading: 'Operations', subheading: 'RNN'} */\nfunction multiRNNCell_(\n    lstmCells: LSTMCellFunc[], data: Tensor2D|TensorLike,\n    c: Array<Tensor2D|TensorLike>,\n    h: Array<Tensor2D|TensorLike>): [Tensor2D[], Tensor2D[]] {\n  const $data = convertToTensor(data, 'data', 'multiRNNCell');\n  const $c = convertToTensorArray(c, 'c', 'multiRNNCell');\n  const $h = convertToTensorArray(h, 'h', 'multiRNNCell');\n\n  let input = $data;\n  const newStates = [];\n  for (let i = 0; i < lstmCells.length; i++) {\n    const output = lstmCells[i](input, $c[i], $h[i]);\n    newStates.push(output[0]);\n    newStates.push(output[1]);\n    input = output[1];\n  }\n  const newC: Tensor2D[] = [];\n  const newH: Tensor2D[] = [];\n  for (let i = 0; i < newStates.length; i += 2) {\n    newC.push(newStates[i]);\n    newH.push(newStates[i + 1]);\n  }\n  return [newC, newH];\n}\n\n/**\n * Computes the next state and output of a BasicLSTMCell.\n *\n * Returns `[newC, newH]`.\n *\n * Derived from tf.contrib.rnn.BasicLSTMCell.\n *\n * @param forgetBias Forget bias for the cell.\n * @param lstmKernel The weights for the cell.\n * @param lstmBias The bias for the cell.\n * @param data The input to the cell.\n * @param c Previous cell state.\n * @param h Previous cell output.\n */\n/** @doc {heading: 'Operations', subheading: 'RNN'} */\nfunction basicLSTMCell_(\n    forgetBias: Scalar|TensorLike, lstmKernel: Tensor2D|TensorLike,\n    lstmBias: Tensor1D|TensorLike, data: Tensor2D|TensorLike,\n    c: Tensor2D|TensorLike, h: Tensor2D|TensorLike): [Tensor2D, Tensor2D] {\n  const $forgetBias =\n      convertToTensor(forgetBias, 'forgetBias', 'basicLSTMCell');\n  const $lstmKernel =\n      convertToTensor(lstmKernel, 'lstmKernel', 'basicLSTMCell');\n  const $lstmBias = convertToTensor(lstmBias, 'lstmBias', 'basicLSTMCell');\n  const $data = convertToTensor(data, 'data', 'basicLSTMCell');\n  const $c = convertToTensor(c, 'c', 'basicLSTMCell');\n  const $h = convertToTensor(h, 'h', 'basicLSTMCell');\n\n  const combined = $data.concat($h, 1);\n  const weighted = combined.matMul($lstmKernel);\n  const res = weighted.add($lstmBias) as Tensor2D;\n\n  // i = input_gate, j = new_input, f = forget_gate, o = output_gate\n  const batchSize = res.shape[0];\n  const sliceCols = res.shape[1] / 4;\n  const sliceSize: [number, number] = [batchSize, sliceCols];\n  const i = res.slice([0, 0], sliceSize);\n  const j = res.slice([0, sliceCols], sliceSize);\n  const f = res.slice([0, sliceCols * 2], sliceSize);\n  const o = res.slice([0, sliceCols * 3], sliceSize);\n\n  const newC = i.sigmoid().mulStrict(j.tanh()).addStrict(\n      $c.mulStrict($forgetBias.add(f).sigmoid() as Tensor2D));\n  const newH = newC.tanh().mulStrict(o.sigmoid());\n  return [newC, newH];\n}\n\nexport const basicLSTMCell = op({basicLSTMCell_});\nexport const multiRNNCell = op({multiRNNCell_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Scalar, Tensor} from '../tensor';\nimport {assertTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {pow} from './binary_ops';\nimport {op} from './operation';\nimport {scalar} from './tensor_ops';\n\n/**\n * Compute the moving average of a variable.\n *\n * Without zeroDebias, the moving average operation is defined by:\n *   `v += delta`\n * where\n *   `delta = (1 - decay) * (x - v)`\n *\n * With zeroDebias (default), the `delta` term is scaled to debias the\n * effect of the (assumed) zero-initialization of `v`.\n *   `delta /= (1 - decay ^ step)`\n *\n * For more details on the zero-debiasing algorithm, see:\n *   https://arxiv.org/abs/1412.6980\n *\n * Note that this function is completely stateless and does not keep track of\n * step count. The step count needs to be maintained by the caller and passed\n * in as `step`.\n *\n * @param v The current moving average value.\n * @param x New input value, must have the same shape and dtype as `v`.\n * @param decay The decay factor. Typical values are 0.95 and 0.99.\n * @param step Step count.\n * @param zeroDebias: Whether zeroDebias is to be performed (default: `true`).\n * @returns The new moving average value.\n */\n/** @doc {heading: 'Operations', subheading: 'Moving Average'} */\nfunction movingAverage_<T extends Tensor>(\n    v: T|TensorLike, x: T|TensorLike, decay: number|Scalar,\n    step?: number|Scalar, zeroDebias = true): T {\n  const $v = convertToTensor(v, 'v', 'movingAverage');\n  const $x = convertToTensor(x, 'x', 'movingAverage');\n  const $decay = convertToTensor(decay, 'decay', 'movingAverage');\n\n  assertTypesMatch($v, $x);\n  util.assert(\n      util.arraysEqual($v.shape, $x.shape), 'Shape mismatch in v and x');\n\n  const one = scalar(1);\n  const oneMinusDecay = one.sub($decay);\n\n  let update = $x.sub($v).mul(oneMinusDecay);\n  if (zeroDebias) {\n    util.assert(step != null, 'When using zeroDebias: true, step is required.');\n    const $step = convertToTensor(step, 'step', 'movingAverage');\n    update = update.div(one.sub(pow($decay, $step)));\n  }\n  return $v.add(update);\n}\n\nexport const movingAverage = op({movingAverage_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * Extracts a strided slice of a tensor.\n *\n * Roughly speaking, this op extracts a slice of size (end-begin)/stride from\n * the given input_ tensor. Starting at the location specified by begin the\n * slice continues by adding stride to the index until all dimensions are not\n * less than end. Note that a stride can be negative, which causes a reverse\n * slice.\n *\n * ```js\n * t = tf.tensor3d([1, 1, 1 ,2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],\n *    [3, 2, 3]);\n * t.stridedSlice([1, 0, 0], [2, 1, 3], [1, 1, 1]).print()  // [[[3, 3, 3]]]\n * t.stridedSlice([1, 0, 0], [2, 2, 3], [1, 1, 1]).print()  // [[[3, 3, 3],\n *                                                     // [4, 4, 4]]]\n * t.stridedSlice([1, -1, 0], [2, -3, 3], [1, -1, 1]).print() // [[[4, 4, 4],\n *                                                     // [3, 3, 3]]]\n * ```\n *\n * @param x The tensor to stride slice.\n * @param begin The coordinates to start the slice from.\n * @param end: The coordinates to end the slice at.\n * @param strides: The size of the slice.\n * @param beginMask: If the ith bit of begin_mask is set, begin[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param endMask: If the ith bit of end_mask is set, end[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param shrinkAxisMask: a bitmask where bit i implies that\n * the ith specification should shrink the dimensionality. begin and end must\n * imply a slice of size 1 in the dimension.\n */\n/** @doc {heading: 'Operations', subheading: 'Slicing and Joining'} */\nfunction stridedSlice_<T extends Tensor>(\n    x: T|TensorLike, begin: number[], end: number[], strides: number[],\n    beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0,\n    shrinkAxisMask = 0): T {\n  if (ellipsisMask !== 0) {\n    throw new Error('ellipsis mask is not yet supported');\n  }\n  if (newAxisMask !== 0) {\n    throw new Error('new axis mask is not yet supported');\n  }\n  const $x = convertToTensor(x, 'x', 'stridedSlice');\n  return ENV.engine.runKernel(\n             backend => backend.stridedSlice(\n                 $x, begin, end, strides, beginMask, endMask, ellipsisMask,\n                 newAxisMask, shrinkAxisMask),\n             {$x}) as T;\n}\n\nexport const stridedSlice = op({stridedSlice_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {NumericTensor, Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * Finds the values and indices of the `k` largest entries along the last\n * dimension.\n *\n * If the input is a vector (rank=1), finds the k largest entries in the vector\n * and outputs their values and indices as vectors. Thus values[j] is the j-th\n * largest entry in input, and its index is indices[j].\n * For higher rank inputs, computes the top k entries along the last dimension.\n *\n * If two elements are equal, the lower-index element appears first.\n *\n * ```js\n * const a = tf.tensor2d([[1, 5], [4, 3]]);\n * const {values, indices} = tf.topk(a);\n * values.print();\n * indices.print();\n * ```\n * @param x 1-D or higher `tf.Tensor` with last dimension being at least `k`.\n * @param k Number of top elements to look for along the last dimension.\n * @param sorted If true, the resulting `k` elements will be sorted by the\n *     values in descending order.\n */\n/** @doc {heading: 'Operations', subheading: 'Evaluation'} */\nfunction topk_<T extends Tensor>(\n    x: T|TensorLike, k = 1, sorted = true): {values: T, indices: T} {\n  const $x = convertToTensor(x, 'x', 'topk');\n  if ($x.rank === 0) {\n    throw new Error('topk() expects the input to be of rank 1 or higher');\n  }\n  const lastDim = $x.shape[$x.shape.length - 1];\n  if (k > lastDim) {\n    throw new Error(\n        `'k' passed to topk() must be <= the last dimension (${lastDim}) ` +\n        `but got ${k}`);\n  }\n\n  const [values, indices] =\n      ENV.engine.runKernel(b => b.topk($x as NumericTensor, k, sorted), {$x});\n  return {values, indices} as {values: T, indices: T};\n}\n\nexport const topk = op({topk_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, ShapeMap, TensorLike} from '../types';\n\nimport {op} from './operation';\nimport * as scatter_nd_util from './scatter_nd_util';\n\n/**\n * Creates a new tensor by applying sparse updates to individual\n * values or slices within a zero tensor of the given shape tensor according to\n * indices. This operator is the inverse of the `tf.gatherND` operator which\n * extracts values or slices from a given tensor.\n *\n * ```js\n * const indices = tf.tensor2d([[4], [3], [1], [7]]);\n * const updates = tf.tensor2d([9, 10, 11, 12]);\n * const shape = [8];\n * tf.scatterND(indices, updates, shape]).print() //[0, 11, 0, 10, 9, 0, 0, 12]\n * ```\n *\n * @param indices The tensor contains the indices into the output tensor.\n * @param updates The tensor contains the value for the indices.\n * @param shape: The shape of the output tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Slicing and Joining'} */\nfunction scatterND_<R extends Rank>(\n    indices: Tensor|TensorLike, updates: Tensor|TensorLike,\n    shape: ShapeMap[R]): Tensor<R> {\n  const $indices = convertToTensor(indices, 'indices', 'scatterND', 'int32');\n  const $updates = convertToTensor(updates, 'updates', 'scatterND');\n  scatter_nd_util.validateInput($updates, $indices, shape);\n\n  return ENV.engine.runKernel(\n             backend => backend.scatterND($indices, $updates, shape),\n             {$indices, $updates}) as Tensor<R>;\n}\n\nexport const scatterND = op({scatterND_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {complex, imag, real} from '../ops/complex_ops';\nimport {op} from '../ops/operation';\nimport {Tensor, Tensor2D} from '../tensor';\nimport {assert} from '../util';\nimport {scalar} from './tensor_ops';\n\n/**\n * Fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the inner-most\n * dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.fft().print();  // tf.spectral.fft(x).print();\n * ```\n * @param input The complex input to compute an fft over.\n */\n/**\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction fft_(input: Tensor): Tensor {\n  assert(\n      input.dtype === 'complex64',\n      `The dtype for tf.spectral.fft() must be complex64 ` +\n          `but got ${input.dtype}.`);\n\n  // Collapse all outer dimensions to a single batch dimension.\n  const innerDimensionSize = input.shape[input.shape.length - 1];\n  const batch = input.size / innerDimensionSize;\n  const input2D = input.as2D(batch, innerDimensionSize);\n\n  const ret = ENV.engine.runKernel(backend => backend.fft(input2D), {input});\n\n  return ret.reshape(input.shape);\n}\n\n/**\n * Inverse fast Fourier transform.\n *\n * Computes the inverse 1-dimensional discrete Fourier transform over the\n * inner-most dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.ifft().print();  // tf.spectral.ifft(x).print();\n * ```\n * @param input The complex input to compute an ifft over.\n */\n/**\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction ifft_(input: Tensor): Tensor {\n  assert(\n      input.dtype === 'complex64',\n      `The dtype for tf.spectral.ifft() must be complex64 ` +\n          `but got ${input.dtype}.`);\n\n  // Collapse all outer dimensions to a single batch dimension.\n  const innerDimensionSize = input.shape[input.shape.length - 1];\n  const batch = input.size / innerDimensionSize;\n  const input2D = input.as2D(batch, innerDimensionSize);\n\n  const ret = ENV.engine.runKernel(backend => backend.ifft(input2D), {input});\n\n  return ret.reshape(input.shape);\n}\n\n/**\n * Real value input fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n *\n * x.rfft().print();\n * ```\n * @param input The real value input to compute an rfft over.\n */\n/**\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction rfft_(input: Tensor): Tensor {\n  assert(input.dtype === 'float32', `The dtype for rfft() must be real value but\n    got ${input.dtype}`);\n\n  const innerDimensionSize = input.shape[input.shape.length - 1];\n  const batch = input.size / innerDimensionSize;\n\n  // Complement the input with zero imaginary numbers.\n  const zeros = input.zerosLike();\n  const complexInput = complex(input, zeros).as2D(batch, innerDimensionSize);\n\n  const ret = fft(complexInput);\n\n  // Exclude complex conjugations. These conjugations are put symmetrically.\n  const half = Math.floor(innerDimensionSize / 2) + 1;\n  const realValues = real(ret);\n  const imagValues = imag(ret);\n  const realComplexConjugate = realValues.split(\n      [half, innerDimensionSize - half], realValues.shape.length - 1);\n  const imagComplexConjugate = imagValues.split(\n      [half, innerDimensionSize - half], imagValues.shape.length - 1);\n\n  const outputShape = input.shape.slice();\n  outputShape[input.shape.length - 1] = half;\n\n  return complex(realComplexConjugate[0], imagComplexConjugate[0])\n      .reshape(outputShape);\n}\n\n/**\n * Inversed real value input fast Fourier transform.\n *\n * Computes the 1-dimensional inversed discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n *\n * x.irfft().print();\n * ```\n * @param input The real value input to compute an irfft over.\n */\n/**\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction irfft_(input: Tensor): Tensor {\n  const innerDimensionSize = input.shape[input.shape.length - 1];\n  const batch = input.size / innerDimensionSize;\n\n  if (innerDimensionSize <= 2) {\n    const complexInput = input.as2D(batch, innerDimensionSize);\n    const ret = ifft(complexInput);\n    return real(ret);\n  } else {\n    // The length of unique components of the DFT of a real-valued signal\n    // is 2 * (input_len - 1)\n    const outputShape = [batch, 2 * (innerDimensionSize - 1)];\n    const realInput = real(input).as2D(batch, innerDimensionSize);\n    const imagInput = imag(input).as2D(batch, innerDimensionSize);\n\n    const realConjugate =\n        realInput.slice([0, 1], [batch, innerDimensionSize - 2]).reverse(1);\n    const imagConjugate =\n        imagInput.slice([0, 1], [batch, innerDimensionSize - 2])\n            .reverse(1)\n            .mul(scalar(-1)) as Tensor2D;\n\n    const r = realInput.concat(realConjugate, 1);\n    const i = imagInput.concat(imagConjugate, 1);\n    const complexInput = complex(r, i).as2D(outputShape[0], outputShape[1]);\n    const ret = ifft(complexInput);\n    return real(ret);\n  }\n}\n\nexport const fft = op({fft_});\nexport const ifft = op({ifft_});\nexport const rfft = op({rfft_});\nexport const irfft = op({irfft_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor} from '../tensor';\n\n/**\n * Validate sparseToDense inputs.\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape number[]. Shape of the dense output tensor.\n * @param validateIndices boolean. indice validation is not supported, error\n * will be thrown if it is set.\n */\nexport function validateInput(\n    sparseIndices: Tensor, sparseValues: Tensor, outputShape: number[],\n    defaultValues: Tensor) {\n  if (sparseIndices.dtype !== 'int32') {\n    throw new Error(\n        'tf.sparseToDense() expects the indices to be int32 type,' +\n        ` but the dtype was ${sparseIndices.dtype}.`);\n  }\n  if (sparseIndices.rank > 2) {\n    throw new Error(\n        'sparseIndices should be a scalar, vector, or matrix,' +\n        ` but got shape ${sparseIndices.shape}.`);\n  }\n\n  const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;\n  const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;\n\n  if (outputShape.length !== numDims) {\n    throw new Error(\n        'outputShape has incorrect number of elements:,' +\n        ` ${outputShape.length}, should be: ${numDims}.`);\n  }\n\n  const numValues = sparseValues.size;\n  if (!(sparseValues.rank === 0 ||\n        sparseValues.rank === 1 && numValues === numElems)) {\n    throw new Error(\n        'sparseValues has incorrect shape ' +\n        `${sparseValues.shape}, should be [] or [${numElems}]`);\n  }\n\n  if (sparseValues.dtype !== defaultValues.dtype) {\n    throw new Error('sparseValues.dtype must match defaultValues.dtype');\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport * as sparse_to_dense from '../ops/sparse_to_dense_util';\nimport {Scalar, Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, ShapeMap, TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Converts a sparse representation into a dense tensor.\n *\n * Builds an array dense with shape outputShape such that:\n *\n * // If sparseIndices is scalar\n * dense[i] = (i == sparseIndices ? sparseValues : defaultValue)\n *\n * // If sparseIndices is a vector, then for each i\n * dense[sparseIndices[i]] = sparseValues[i]\n *\n * // If sparseIndices is an n by d matrix, then for each i in [0, n)\n * dense[sparseIndices[i][0], ..., sparseIndices[i][d-1]] = sparseValues[i]\n * All other values in dense are set to defaultValue. If sparseValues is a\n * scalar, all sparse indices are set to this single value.\n *\n * ```js\n * const indices = tf.tensor1d([4, 5, 6, 1, 2, 3], 'int32');\n * const values = tf.tensor1d([10, 11, 12, 13, 14, 15], 'float32');\n * const shape = [8];\n * tf.sparseToDense(indices, values, shape).print();\n * ```\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape Shape of the dense output tensor. the type is inferred.\n * @param defaultValue Scalar. Value to set for indices not specified in\n * sparseIndices. Defaults to zero.\n */\n/** @doc {heading: 'Operations', subheading: 'Normalization'} */\nfunction sparseToDense_<R extends Rank>(\n    sparseIndices: Tensor|TensorLike, sparseValues: Tensor|TensorLike,\n    outputShape: ShapeMap[R], defaultValue: Scalar|TensorLike): Tensor<R> {\n  const $sparseIndices =\n      convertToTensor(sparseIndices, 'sparseIndices', 'sparseToDense', 'int32');\n  const $sparseValues =\n      convertToTensor(sparseValues, 'sparseValues', 'sparseToDense');\n  const $defaultValue = convertToTensor(\n      defaultValue, 'defaultValue', 'sparseToDense', $sparseValues.dtype);\n\n  sparse_to_dense.validateInput(\n      $sparseIndices, $sparseValues, outputShape, $defaultValue);\n\n  return ENV.engine.runKernel(\n      backend => backend.sparseToDense(\n          $sparseIndices, $sparseValues, outputShape, $defaultValue),\n      {$sparseIndices, $sparseValues, $defaultValue});\n}\n\nexport const sparseToDense = op({sparseToDense_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * Gather slices from input tensor into a Tensor with shape specified by\n * `indices`.\n *\n * `indices` is an K-dimensional integer tensor, best thought of as a\n * (K-1)-dimensional tensor of indices into input, where each element defines a\n * slice of input:\n * output[\\\\(i_0, ..., i_{K-2}\\\\)] = input[indices[\\\\(i_0, ..., i_{K-2}\\\\)]]\n *\n * Whereas in `tf.gather`, `indices` defines slices into the first dimension of\n * input, in `tf.gatherND`, `indices` defines slices into the first N dimensions\n * of input, where N = indices.shape[-1].\n *\n * The last dimension of indices can be at most the rank of input:\n * indices.shape[-1] <= input.rank\n *\n * The last dimension of `indices` corresponds to elements\n * (if indices.shape[-1] == input.rank) or slices\n * (if indices.shape[-1] < input.rank) along dimension indices.shape[-1] of\n * input.\n * The output tensor has shape\n * indices.shape[:-1] + input.shape[indices.shape[-1]:]\n *\n * Note that on CPU, if an out of bound index is found, an error is returned. On\n * GPU, if an out of bound index is found, a 0 is stored in the corresponding\n * output value.\n *\n * ```js\n * const indices = tf.tensor2d([0, 1, 1, 0], [2,2], 'int32');\n * const input = tf.tensor2d([9, 10, 11, 12], [2, 2]);\n * tf.gatherND(input, indices).print() //[10, 11]\n * ```\n *\n * @param x The tensor from which to gather values.\n * @param indices Index tensor, must be of type int32.\n */\n/** @doc {heading: 'Operations', subheading: 'Slicing and Joining'} */\nfunction gatherND_(\n    x: Tensor|TensorLike, indices: Tensor|TensorLike): Tensor<Rank> {\n  const $indices = convertToTensor(indices, 'indices', 'gatherND', 'int32');\n  const $x = convertToTensor(x, 'x', 'gatherND');\n  return ENV.engine.runKernel(\n             backend => backend.gatherND($x, $indices), {$x, $indices}) as\n      Tensor<Rank>;\n}\nexport const gatherND = op({gatherND_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {customGrad} from '../globals';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assertShapesMatch, sizeFromShape} from '../util';\n\nimport {expandShapeToKeepDim} from './axis_util';\n\nimport {minimum} from './binary_ops';\nimport {op} from './operation';\nimport {ones, scalar} from './tensor_ops';\n\nexport enum Reduction {\n  NONE,\n  MEAN,\n  SUM,\n  SUM_BY_NONZERO_WEIGHTS\n}\n\n/**\n * Computes the weighted loss between two tensors.\n *\n * @param losses Tensor of shape `[batch_size, d1, ... dN]`.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `losses`, and must be broadcastable to `losses` (i.e., all\n *    dimensions must be either `1`, or the same as the corresponding\n *    `losses` dimension).\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction computeWeightedLoss_<T extends Tensor, O extends Tensor>(\n    losses: T|TensorLike, weights?: Tensor|TensorLike,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $losses = convertToTensor(losses, 'losses', 'computeWeightedLoss');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'computeWeightedLoss');\n  }\n\n  const weightedLoss = ($weights == null) ? $losses : $losses.mul($weights);\n\n  if (reduction === Reduction.NONE) {\n    return weightedLoss as O;\n  }\n  if (reduction === Reduction.SUM) {\n    return weightedLoss.sum();\n  }\n  if (reduction === Reduction.MEAN) {\n    if ($weights == null) {\n      return weightedLoss.mean();\n    } else {\n      const broadcastFactor =\n          sizeFromShape($losses.shape) / sizeFromShape($weights.shape);\n      const result = weightedLoss.sum().div($weights.sum());\n      return broadcastFactor > 1 ? result.div(scalar(broadcastFactor)) :\n                                   result as O;\n    }\n  }\n  if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    if ($weights == null) {\n      return weightedLoss.sum().div(scalar($losses.size));\n    } else {\n      const broadcastedWeights = $weights.mul(ones($losses.shape));\n\n      const numNonZeros =\n          broadcastedWeights.notEqual(scalar(0)).sum().toFloat();\n      return weightedLoss.sum().div(numNonZeros);\n    }\n  }\n\n  throw Error(`Unknown reduction: ${reduction}`);\n}\n\n/**\n * Computes the absolute difference loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction absoluteDifference_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike,\n    weights?: Tensor|TensorLike,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $labels = convertToTensor(labels, 'labels', 'absoluteDifference');\n  const $predictions =\n      convertToTensor(predictions, 'predictions', 'absoluteDifference');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'absoluteDifference');\n  }\n  assertShapesMatch(\n      $labels.shape, $predictions.shape, 'Error in absoluteDifference: ');\n\n  const losses = $labels.sub($predictions).abs();\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\n/**\n * Computes the mean squared error between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction meanSquaredError_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike,\n    weights?: Tensor|TensorLike,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $labels = convertToTensor(labels, 'labels', 'meanSquaredError');\n  const $predictions =\n      convertToTensor(predictions, 'predictions', 'meanSquaredError');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'meanSquaredError');\n  }\n  assertShapesMatch(\n      $labels.shape, $predictions.shape, 'Error in meanSquaredError: ');\n\n  const losses = $labels.squaredDifference($predictions);\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\n/**\n * Computes the cosine distance loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param axis The dimension along which the cosine distance is computed.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction cosineDistance_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike, axis: number,\n    weights?: Tensor|TensorLike,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $labels = convertToTensor(labels, 'labels', 'cosineDistance');\n  const $predictions =\n      convertToTensor(predictions, 'predictions', 'cosineDistance');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'cosineDistance');\n  }\n  assertShapesMatch(\n      $labels.shape, $predictions.shape, 'Error in cosineDistance: ');\n\n  const one = scalar(1);\n  const losses = one.sub($labels.mul($predictions).sum(axis, true));\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\n/**\n * Computes the Hinge loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction hingeLoss_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike,\n    weights?: Tensor|TensorLike,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  let $labels = convertToTensor(labels, 'labels', 'hingeLoss');\n  const $predictions = convertToTensor(predictions, 'predictions', 'hingeLoss');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'hingeLoss');\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, 'Error in hingeLoss: ');\n\n  const one = scalar(1);\n  // Convert binary labels to (-1, 1)\n  $labels = scalar(2).mul($labels).sub(one);\n  const losses = one.sub($labels.mul($predictions)).relu();\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\n/**\n * Computes the log loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param epsilon A small increment to avoid taking log of zero\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction logLoss_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike,\n    weights?: Tensor|TensorLike, epsilon = 1e-7,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $labels = convertToTensor(labels, 'labels', 'logLoss');\n  const $predictions = convertToTensor(predictions, 'predictions', 'logLoss');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'logLoss');\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, 'Error in logLoss: ');\n\n  const one = scalar(1);\n  const epsilonScalar = scalar(epsilon);\n  const losses = $labels.mul($predictions.add(epsilonScalar).log())\n                     .neg()\n                     .sub(one.sub($labels).mul(\n                         one.sub($predictions).add(epsilonScalar).log()));\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\nfunction sigmoidCrossEntropyWithLogits_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, logits: T|TensorLike): O {\n  const $labels =\n      convertToTensor(labels, 'labels', 'sigmoidCrossEntropyWithLogits');\n  const $logits =\n      convertToTensor(logits, 'logits', 'sigmoidCrossEntropyWithLogits');\n  assertShapesMatch(\n      $labels.shape, $logits.shape, 'Error in sigmoidCrossEntropyWithLogits: ');\n\n  /**\n   * Implementation Details:\n   *\n   * For brevity, let `x = logits`, `z = labels`.  The logistic loss is\n   *     z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n   *   = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n   *   = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n   *   = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n   *   = (1 - z) * x + log(1 + exp(-x))\n   *   = x - x * z + log(1 + exp(-x))\n   *\n   *   For x < 0, to avoid overflow in exp(-x), we reformulate the above\n   *     x - x * z + log(1 + exp(-x))\n   *   = log(exp(x)) - x * z + log(1 + exp(-x))\n   *   = - x * z + log(1 + exp(x))\n   *\n   * Hence, to ensure stability and avoid overflow, the implementation uses\n   * this equivalent formulation:\n   *     max(x, 0) - x * z + log(1 + exp(-abs(x)))\n   */\n  const maxOutput = $logits.relu();\n  const outputXTarget = $logits.mul($labels);\n  const sigmoidOutput = $logits.abs().neg().exp().log1p();\n\n  return maxOutput.sub(outputXTarget).add(sigmoidOutput);\n}\n\n/**\n * Computes the sigmoid cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newMulticlassLabels = multiclassLabels * (1 - labelSmoothing)\n *                         + 0.5 * labelSmoothing\n *\n * @param multiClassLabels The ground truth output tensor of shape\n * [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n */\n/** @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' } */\nfunction sigmoidCrossEntropy_<T extends Tensor, O extends Tensor>(\n    multiClassLabels: T|TensorLike, logits: T|TensorLike,\n    weights?: Tensor|TensorLike, labelSmoothing = 0,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  let $multiClassLabels = convertToTensor(\n      multiClassLabels, 'multiClassLabels', 'sigmoidCrossEntropy');\n  const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropy');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'sigmoidCrossEntropy');\n  }\n  assertShapesMatch(\n      $multiClassLabels.shape, $logits.shape, 'Error in sigmoidCrossEntropy: ');\n\n  if (labelSmoothing > 0) {\n    const labelSmoothingScalar = scalar(labelSmoothing);\n    const one = scalar(1);\n    const half = scalar(0.5);\n\n    $multiClassLabels = $multiClassLabels.mul(one.sub(labelSmoothingScalar))\n                            .add(half.mul(labelSmoothingScalar));\n  }\n  const losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);\n\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\n/**\n * Computes the huber loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param delta Point where huber loss changes from quadratic to linear.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`.\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction huberLoss_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike,\n    weights?: Tensor|TensorLike, delta = 1.0,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $labels = convertToTensor(labels, 'labels', 'huberLoss');\n  const $predictions = convertToTensor(predictions, 'predictions', 'huberLoss');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'huberLoss');\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, 'Error in huberLoss: ');\n\n  const deltaScalar = scalar(delta);\n  const error = $predictions.sub($labels).abs();\n  const quadratic = minimum(error, deltaScalar);\n  const linear = error.sub(quadratic);\n\n  const losses =\n      scalar(0.5).mul(quadratic.square()).add(deltaScalar.mul(linear));\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\n/**\n * Computes softmax cross entropy between logits and labels.\n *\n * Measures the probability error in discrete classification tasks in which\n * the classes are mutually exclusive (each entry is in exactly one class).\n * For example, each CIFAR-10 image is labeled with one and only one label: an\n * image can be a dog or a truck, but not both.\n *\n * `NOTE`: While the classes are mutually exclusive, their probabilities need\n * not be. All that is required is that each row of labels is a valid\n * probability distribution. If they are not, the computation of the gradient\n * will be incorrect.\n *\n * `WARNING`: This op expects unscaled logits, since it performs a softmax on\n * logits internally for efficiency. Do not call this op with the output of\n * softmax, as it will produce incorrect results.\n *\n * logits and labels must have the same shape, e.g. [batch_size, num_classes]\n * and the same dtype.\n * @param labels The labels array.\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n */\nfunction softmaxCrossEntropyWithLogits_<T extends Tensor, O extends Tensor>(\n    labels: T, logits: T, dim = -1): O {\n  if (dim === -1) {\n    dim = logits.rank - 1;\n  }\n\n  if (dim !== logits.rank - 1) {\n    throw Error(\n        `Softmax cross entropy along a non-last dimension is not yet ` +\n        `supported. Labels / logits was rank ${logits.rank} ` +\n        `and dim was ${dim}`);\n  }\n  // Use a custom gradient for numerical stability.\n  const customOp = customGrad((labels, logits) => {\n    // Reference:\n    //   1. http://cs231n.github.io/linear-classify/#softmax\n    //   2. https://blog.feedly.com/tricks-of-the-trade-logsumexp/\n    const keepDims = true;\n    const lse = logits.logSumExp([dim], keepDims);\n\n    const logResult = logits.toFloat().sub(lse);\n    const costVector = logResult.mul(labels).neg();\n\n    const value = costVector.sum([dim]) as O;\n\n    const gradFunc = (dy: O) => {\n      const dyShape = expandShapeToKeepDim(dy.shape, [dim]);\n      return [\n        dy.reshape(dyShape).mul(labels.toFloat().sub(logResult.exp())),\n        dy.reshape(dyShape).mul(logResult.exp().sub(labels.toFloat())),\n      ];\n    };\n    return {value, gradFunc};\n  });\n\n  return customOp(labels, logits);\n}\n\n/**\n * Computes the softmax cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newOnehotLabels = onehotLabels * (1 - labelSmoothing)\n *                         + labelSmoothing / numClasses\n *\n * @param onehotLabels One hot encoded labels\n *    [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or 1, and must be\n *    broadcastable to `loss`  of shape [batch_size]\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n */\n/** @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' } */\nfunction softmaxCrossEntropy_<T extends Tensor, O extends Tensor>(\n    onehotLabels: T|TensorLike, logits: T|TensorLike,\n    weights?: Tensor|TensorLike, labelSmoothing = 0,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  let $onehotLabels =\n      convertToTensor(onehotLabels, 'onehotLabels', 'softmaxCrossEntropy');\n  const $logits = convertToTensor(logits, 'logits', 'softmaxCrossEntropy');\n  let $weights: Tensor = null;\n\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'softmaxCrossEntropy');\n  }\n\n  assertShapesMatch(\n      $onehotLabels.shape, $logits.shape, 'Error in softmaxCrossEntropy: ');\n\n  if (labelSmoothing > 0) {\n    const labelSmoothingScalar = scalar(labelSmoothing);\n    const one = scalar(1);\n    const numClasses = scalar($onehotLabels.shape[1]);\n\n    $onehotLabels = $onehotLabels.mul(one.sub(labelSmoothingScalar))\n                        .add(labelSmoothingScalar.div(numClasses));\n  }\n\n  const losses = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);\n\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\nexport const absoluteDifference = op({absoluteDifference_});\nexport const computeWeightedLoss = op({computeWeightedLoss_});\nexport const cosineDistance = op({cosineDistance_});\nexport const hingeLoss = op({hingeLoss_});\nexport const huberLoss = op({huberLoss_});\nexport const logLoss = op({logLoss_});\nexport const meanSquaredError = op({meanSquaredError_});\nexport const sigmoidCrossEntropy = op({sigmoidCrossEntropy_});\nexport const softmaxCrossEntropy = op({softmaxCrossEntropy_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Linear algebra ops.\n */\n\nimport {ENV} from '../environment';\nimport {dispose} from '../globals';\nimport {Tensor, Tensor1D, Tensor2D} from '../tensor';\nimport {assert} from '../util';\nimport {eye, squeeze, stack, unstack} from './array_ops';\nimport {split} from './concat_split';\nimport {norm} from './norm';\nimport {op} from './operation';\nimport {sum} from './reduction_ops';\nimport {tensor2d} from './tensor_ops';\n\n/**\n * Gram-Schmidt orthogonalization.\n *\n * ```js\n * const x = tf.tensor2d([[1, 2], [3, 4]]);\n * let y = tf.linalg.gramSchmidt(x);\n * y.print();\n * console.log('Othogonalized:');\n * y.dot(y.transpose()).print();  // should be nearly the identity matrix.\n * console.log('First row direction maintained:');\n * console.log(y.get(0, 1) / y.get(0, 0));  // should be nearly 2.\n * ```\n *\n * @param xs The vectors to be orthogonalized, in one of the two following\n *   formats:\n *   - An Array of `tf.Tensor1D`.\n *   - A `tf.Tensor2D`, i.e., a matrix, in which case the vectors are the rows\n *     of `xs`.\n *   In each case, all the vectors must have the same length and the length\n *   must be greater than or equal to the number of vectors.\n * @returns The orthogonalized and normalized vectors or matrix.\n *   Orthogonalization means that the vectors or the rows of the matrix\n *   are orthogonal (zero inner products). Normalization means that each\n *   vector or each row of the matrix has an L2 norm that equals `1`.\n */\n/**\n * @doc {heading:'Operations',\n *       subheading:'Linear Algebra',\n *       namespace:'linalg'}\n */\nfunction gramSchmidt_(xs: Tensor1D[]|Tensor2D): Tensor1D[]|Tensor2D {\n  let inputIsTensor2D: boolean;\n  if (Array.isArray(xs)) {\n    inputIsTensor2D = false;\n    assert(\n        xs != null && xs.length > 0,\n        'Gram-Schmidt process: input must not be null, undefined, or empty');\n    const dim = xs[0].shape[0];\n    for (let i = 1; i < xs.length; ++i) {\n      assert(\n          xs[i].shape[0] === dim,\n          'Gram-Schmidt: Non-unique lengths found in the input vectors: ' +\n              `(${xs[i].shape[0]} vs. ${dim})`);\n    }\n  } else {\n    inputIsTensor2D = true;\n    xs = split(xs, xs.shape[0], 0).map(x => squeeze(x, [0]));\n  }\n\n  assert(\n      xs.length <= xs[0].shape[0],\n      `Gram-Schmidt: Number of vectors (${xs.length}) exceeds ` +\n          `number of dimensions (${xs[0].shape[0]}).`);\n\n  const ys: Tensor1D[] = [];\n  const xs1d = xs as Tensor1D[];\n  for (let i = 0; i < xs.length; ++i) {\n    ys.push(ENV.engine.tidy(() => {\n      let x = xs1d[i];\n      if (i > 0) {\n        for (let j = 0; j < i; ++j) {\n          const proj = sum(ys[j].mulStrict(x)).mul(ys[j]);\n          x = x.sub(proj);\n        }\n      }\n      return x.div(norm(x, 'euclidean'));\n    }));\n  }\n\n  if (inputIsTensor2D) {\n    return stack(ys, 0) as Tensor2D;\n  } else {\n    return ys;\n  }\n}\n\n/**\n * Compute QR decomposition of m-by-n matrix using Householder transformation.\n *\n * Implementation based on\n *   [http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf]\n * (http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf)\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [3, 4]]);\n * let [q, r] = tf.linalg.qr(a);\n * console.log('Q');\n * q.print();\n * console.log('R');\n * r.print();\n * console.log('Orthogonalized');\n * q.dot(q.transpose()).print()  // should be nearly the identity matrix.\n * console.log('Reconstructed');\n * q.dot(r).print(); // should be nearly [[1, 2], [3, 4]];\n * ```\n *\n * @param x The `tf.Tensor` to be QR-decomposed. Must have rank >= 2. Suppose\n *   it has the shape `[..., M, N]`.\n * @param fullMatrices An optional boolean parameter. Defaults to `false`.\n *   If `true`, compute full-sized `Q`. If `false` (the default),\n *   compute only the leading N columns of `Q` and `R`.\n * @returns An `Array` of two `tf.Tensor`s: `[Q, R]`. `Q` is a unitary matrix,\n *   i.e., its columns all have unit norm and are mutually orthogonal.\n *   If `M >= N`,\n *     If `fullMatrices` is `false` (default),\n *       - `Q` has a shape of `[..., M, N]`,\n *       - `R` has a shape of `[..., N, N]`.\n *     If `fullMatrices` is `true` (default),\n *       - `Q` has a shape of `[..., M, M]`,\n *       - `R` has a shape of `[..., M, N]`.\n *   If `M < N`,\n *     - `Q` has a shape of `[..., M, M]`,\n *     - `R` has a shape of `[..., M, N]`.\n * @throws If the rank of `x` is less than 2.\n */\n/**\n * @doc {heading:'Operations',\n *       subheading:'Linear Algebra',\n *       namespace:'linalg'}\n */\nfunction qr_(x: Tensor, fullMatrices = false): [Tensor, Tensor] {\n  if (x.rank < 2) {\n    throw new Error(\n        `qr() requires input tensor to have a rank >= 2, but got rank ${\n            x.rank}`);\n  } else if (x.rank === 2) {\n    return qr2d(x as Tensor2D, fullMatrices);\n  } else {\n    // Rank > 2.\n    // TODO(cais): Below we split the input into individual 2D tensors,\n    //   perform QR decomposition on them and then stack the results back\n    //   together. We should explore whether this can be parallelized.\n    const outerDimsProd = x.shape.slice(0, x.shape.length - 2)\n                              .reduce((value, prev) => value * prev);\n    const x2ds = unstack(\n        x.reshape([\n          outerDimsProd, x.shape[x.shape.length - 2],\n          x.shape[x.shape.length - 1]\n        ]),\n        0);\n    const q2ds: Tensor2D[] = [];\n    const r2ds: Tensor2D[] = [];\n    x2ds.forEach(x2d => {\n      const [q2d, r2d] = qr2d(x2d as Tensor2D, fullMatrices);\n      q2ds.push(q2d);\n      r2ds.push(r2d);\n    });\n    const q = stack(q2ds, 0).reshape(x.shape);\n    const r = stack(r2ds, 0).reshape(x.shape);\n    return [q, r];\n  }\n}\n\nfunction qr2d(x: Tensor2D, fullMatrices = false): [Tensor2D, Tensor2D] {\n  return ENV.engine.tidy(() => {\n    if (x.shape.length !== 2) {\n      throw new Error(\n          `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);\n    }\n\n    const m = x.shape[0];\n    const n = x.shape[1];\n\n    let q = eye(m) as Tensor2D;  // Orthogonal transform so far.\n    let r = x.clone();           // Transformed matrix so far.\n\n    const one2D = tensor2d([[1]], [1, 1]);\n    let w: Tensor2D = one2D.clone();\n\n    const iters = m >= n ? n : m;\n    for (let j = 0; j < iters; ++j) {\n      // This tidy within the for-loop ensures we clean up temporary\n      // tensors as soon as they are no longer needed.\n      const rTemp = r;\n      const wTemp = w;\n      const qTemp = q;\n      [w, r, q] = ENV.engine.tidy((): [Tensor2D, Tensor2D, Tensor2D] => {\n        // Find H = I - tau * w * w', to put zeros below R(j, j).\n        const rjEnd1 = r.slice([j, j], [m - j, 1]);\n        const normX = rjEnd1.norm();\n        const rjj = r.slice([j, j], [1, 1]);\n        const s = rjj.sign().neg() as Tensor2D;\n        const u1 = rjj.sub(s.mul(normX)) as Tensor2D;\n        const wPre = rjEnd1.div(u1);\n        if (wPre.shape[0] === 1) {\n          w = one2D.clone();\n        } else {\n          w = one2D.concat(\n              wPre.slice([1, 0], [wPre.shape[0] - 1, wPre.shape[1]]) as\n                  Tensor2D,\n              0);\n        }\n        const tau = s.matMul(u1).div(normX).neg() as Tensor2D;\n\n        // -- R := HR, Q := QH.\n        const rjEndAll = r.slice([j, 0], [m - j, n]);\n        const tauTimesW = tau.mul(w) as Tensor2D;\n        if (j === 0) {\n          r = rjEndAll.sub(tauTimesW.matMul(w.transpose().matMul(rjEndAll)));\n        } else {\n          r = r.slice([0, 0], [j, n])\n                  .concat(\n                      rjEndAll.sub(tauTimesW.matMul(\n                          w.transpose().matMul(rjEndAll))) as Tensor2D,\n                      0) as Tensor2D;\n        }\n        const qAllJEnd = q.slice([0, j], [m, q.shape[1] - j]);\n        if (j === 0) {\n          q = qAllJEnd.sub(qAllJEnd.matMul(w).matMul(tauTimesW.transpose()));\n        } else {\n          q = q.slice([0, 0], [m, j])\n                  .concat(\n                      qAllJEnd.sub(qAllJEnd.matMul(w).matMul(\n                          tauTimesW.transpose())) as Tensor2D,\n                      1) as Tensor2D;\n        }\n        return [w, r, q];\n      });\n      dispose([rTemp, wTemp, qTemp]);\n    }\n\n    if (!fullMatrices && m > n) {\n      q = q.slice([0, 0], [m, n]);\n      r = r.slice([0, 0], [n, n]);\n    }\n\n    return [q, r];\n  }) as [Tensor2D, Tensor2D];\n}\n\nexport const gramSchmidt = op({gramSchmidt_});\nexport const qr = op({qr_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ForwardFunc} from '../engine';\nimport {ENV} from '../environment';\nimport {nonMaxSuppressionImpl} from '../kernels/non_max_suppression_impl';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\n\n/**\n * Bilinear resize a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction resizeBilinear_<T extends Tensor3D|Tensor4D>(\n    images: T|TensorLike, size: [number, number], alignCorners = false): T {\n  const $images = convertToTensor(images, 'images', 'resizeBilinear');\n  util.assert(\n      $images.rank === 3 || $images.rank === 4,\n      `Error in resizeBilinear: x must be rank 3 or 4, but got ` +\n          `rank ${$images.rank}.`);\n  util.assert(\n      size.length === 2,\n      `Error in resizeBilinear: new shape must 2D, but got shape ` +\n          `${size}.`);\n\n  let batchImages = $images as Tensor4D;\n  let reshapedTo4D = false;\n  if ($images.rank === 3) {\n    reshapedTo4D = true;\n    batchImages =\n        $images.as4D(1, $images.shape[0], $images.shape[1], $images.shape[2]);\n  }\n\n  const [newHeight, newWidth] = size;\n  const forward: ForwardFunc<Tensor4D> = (backend, save) =>\n      backend.resizeBilinear(batchImages, newHeight, newWidth, alignCorners);\n\n  const backward = (dy: Tensor4D, saved: Tensor[]) => {\n    return {\n      batchImages: () => ENV.engine.runKernel(\n          backend =>\n              backend.resizeBilinearBackprop(dy, batchImages, alignCorners),\n          {})\n    };\n  };\n\n  const res = ENV.engine.runKernel(forward, {batchImages}, backward);\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\n/**\n * NearestNeighbor resize a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction resizeNearestNeighbor_<T extends Tensor3D|Tensor4D>(\n    images: T|TensorLike, size: [number, number], alignCorners = false): T {\n  const $images = convertToTensor(images, 'images', 'resizeNearestNeighbor');\n  util.assert(\n      $images.rank === 3 || $images.rank === 4,\n      `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got ` +\n          `rank ${$images.rank}.`);\n  util.assert(\n      size.length === 2,\n      `Error in resizeNearestNeighbor: new shape must 2D, but got shape ` +\n          `${size}.`);\n  util.assert(\n      $images.dtype === 'float32' || $images.dtype === 'int32',\n      '`images` must have `int32` or `float32` as dtype');\n\n  let batchImages = $images as Tensor4D;\n  let reshapedTo4D = false;\n  if ($images.rank === 3) {\n    reshapedTo4D = true;\n    batchImages =\n        $images.as4D(1, $images.shape[0], $images.shape[1], $images.shape[2]);\n  }\n  const [newHeight, newWidth] = size;\n\n  const forward: ForwardFunc<Tensor4D> = (backend, save) =>\n      backend.resizeNearestNeighbor(\n          batchImages, newHeight, newWidth, alignCorners);\n\n  const backward = (dy: Tensor4D, saved: Tensor[]) => {\n    return {\n      batchImages: () => ENV.engine.runKernel(\n          backend => backend.resizeNearestNeighborBackprop(\n              dy, batchImages, alignCorners),\n          {})\n    };\n  };\n\n  const res = ENV.engine.runKernel(forward, {batchImages}, backward);\n\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union)\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @return A 1D tensor with the selected box indices.\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction nonMaxSuppression_(\n    boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,\n    maxOutputSize: number, iouThreshold = 0.5,\n    scoreThreshold = Number.NEGATIVE_INFINITY): Tensor1D {\n  const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n  const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n\n  const inputs = nonMaxSuppSanityCheck(\n      $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n  maxOutputSize = inputs.maxOutputSize;\n  iouThreshold = inputs.iouThreshold;\n  scoreThreshold = inputs.scoreThreshold;\n\n  return ENV.engine.runKernel(\n      b => b.nonMaxSuppression(\n          $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold),\n      {$boxes});\n}\n\n/** This is the async version of `nonMaxSuppression` */\nasync function nonMaxSuppressionAsync_(\n    boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,\n    maxOutputSize: number, iouThreshold = 0.5,\n    scoreThreshold = Number.NEGATIVE_INFINITY): Promise<Tensor1D> {\n  const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n  const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n\n  const inputs = nonMaxSuppSanityCheck(\n      $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n  maxOutputSize = inputs.maxOutputSize;\n  iouThreshold = inputs.iouThreshold;\n  scoreThreshold = inputs.scoreThreshold;\n\n  const boxesVals = await $boxes.data();\n  const scoresVals = await $scores.data();\n  const res = nonMaxSuppressionImpl(\n      boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n  if ($boxes !== boxes) {\n    $boxes.dispose();\n  }\n  if ($scores !== scores) {\n    $scores.dispose();\n  }\n  return res;\n}\n\nfunction nonMaxSuppSanityCheck(\n    boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number,\n    iouThreshold: number, scoreThreshold: number):\n    {maxOutputSize: number, iouThreshold: number, scoreThreshold: number} {\n  if (iouThreshold == null) {\n    iouThreshold = 0.5;\n  }\n  if (scoreThreshold == null) {\n    scoreThreshold = Number.NEGATIVE_INFINITY;\n  }\n  const numBoxes = boxes.shape[0];\n  maxOutputSize = Math.min(maxOutputSize, numBoxes);\n\n  util.assert(\n      0 <= iouThreshold && iouThreshold <= 1,\n      `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);\n  util.assert(\n      boxes.rank === 2,\n      `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);\n  util.assert(\n      boxes.shape[1] === 4,\n      `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);\n  util.assert(scores.rank === 1, 'scores must be a 1D tensor');\n  util.assert(\n      scores.shape[0] === numBoxes,\n      `scores has incompatible shape with boxes. Expected ${numBoxes}, ` +\n          `but was ${scores.shape[0]}`);\n  return {maxOutputSize, iouThreshold, scoreThreshold};\n}\n\n/**\n * Extracts crops from the input image tensor and resizes them using bilinear\n * sampling or nearest neighbor sampling (possibly with aspect ratio change)\n * to a common output size specified by crop_size.\n *\n * @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`,\n *     where imageHeight and imageWidth must be positive, specifying the\n *     batch of images from which to take crops\n * @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized\n *     coordinates of the box in the boxInd[i]'th image in the batch\n * @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range\n *     `[0, batch)` that specifies the image that the `i`-th box refers to.\n * @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`\n *     specifying the size to which all crops are resized to.\n * @param method Optional string from `'bilinear' | 'nearest'`,\n *     defaults to bilinear, which specifies the sampling method for resizing\n * @param extrapolationValue A threshold for deciding when to remove boxes based\n *     on score. Defaults to 0.\n * @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]`\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction cropAndResize_(\n    image: Tensor4D|TensorLike,\n    boxes: Tensor2D|TensorLike,\n    boxInd: Tensor1D|TensorLike,\n    cropSize: [number, number],\n    method?: 'bilinear'|'nearest',\n    extrapolationValue?: number,\n    ): Tensor4D {\n  const $image = convertToTensor(image, 'image', 'cropAndResize', 'float32');\n  const $boxes = convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');\n  const $boxInd = convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');\n  method = method || 'bilinear';\n  extrapolationValue = extrapolationValue || 0;\n\n  const numBoxes = $boxes.shape[0];\n\n  util.assert(\n      $image.rank === 4,\n      'Error in cropAndResize: image must be rank 4,' +\n          `but got rank ${$image.rank}.`);\n  util.assert(\n      $boxes.rank === 2 && $boxes.shape[1] === 4,\n      `Error in cropAndResize: boxes must be have size [${numBoxes},4] ` +\n          `but had shape ${$boxes.shape}.`);\n  util.assert(\n      $boxInd.rank === 1 && $boxInd.shape[0] === numBoxes,\n      `Error in cropAndResize: boxInd must be have size [${numBoxes}] ` +\n          `but had shape ${$boxes.shape}.`);\n  util.assert(\n      cropSize.length === 2,\n      `Error in cropAndResize: cropSize must be of length 2, but got length ` +\n          `${cropSize.length}.`);\n  util.assert(\n      cropSize[0] >= 1 && cropSize[1] >= 1,\n      `cropSize must be atleast [1,1], but was ${cropSize}`);\n  util.assert(\n      method === 'bilinear' || method === 'nearest',\n      `method must be bilinear or nearest, but was ${method}`);\n\n  const forward: ForwardFunc<Tensor4D> = (backend, save) =>\n      backend.cropAndResize(\n          $image, $boxes, $boxInd, cropSize, method, extrapolationValue);\n\n  const res = ENV.engine.runKernel(forward, {$image, $boxes});\n  return res as Tensor4D;\n}\n\nexport const resizeBilinear = op({resizeBilinear_});\nexport const resizeNearestNeighbor = op({resizeNearestNeighbor_});\nexport const nonMaxSuppression = op({nonMaxSuppression_});\nexport const nonMaxSuppressionAsync = nonMaxSuppressionAsync_;\nexport const cropAndResize = cropAndResize_;\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as seedrandom from 'seedrandom';\nimport {ENV} from '../environment';\nimport {warn} from '../log';\nimport * as array_ops_util from '../ops/array_ops_util';\nimport * as axis_util from '../ops/axis_util';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport * as concat_util from '../ops/concat_util';\nimport {Conv2DInfo, Conv3DInfo} from '../ops/conv_util';\nimport * as erf_util from '../ops/erf_util';\nimport * as gather_nd_util from '../ops/gather_nd_util';\nimport * as ops from '../ops/ops';\nimport {buffer, scalar, tensor, tensor3d, tensor4d} from '../ops/ops';\nimport * as scatter_nd_util from '../ops/scatter_nd_util';\nimport * as selu_util from '../ops/selu_util';\nimport {getStridedSlicedInfo} from '../ops/slice_util';\nimport {DataId, Scalar, setTensorTracker, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, Tensor5D, TensorBuffer} from '../tensor';\nimport {DataType, DataTypeMap, DataValues, NumericDataType, Rank, ShapeMap, TypedArray, upcastType} from '../types';\nimport * as util from '../util';\nimport {now} from '../util';\nimport {BackendTimingInfo, DataMover, DataStorage, KernelBackend} from './backend';\nimport * as backend_util from './backend_util';\nimport * as complex_util from './complex_util';\nimport {nonMaxSuppressionImpl} from './non_max_suppression_impl';\nimport {split} from './split_shared';\nimport {topkImpl} from './topk_impl';\nimport {whereImpl} from './where_impl';\n\ninterface TensorData<D extends DataType> {\n  values?: DataTypeMap[D];\n  dtype: D;\n  // For complex numbers, the real and imaginary parts are stored as their own\n  // individual tensors, with a parent joining the two with the\n  // complexTensors field. When this is defined, texture will be null.\n  complexTensors?: {real: Tensor, imag: Tensor};\n}\n\nexport class MathBackendCPU implements KernelBackend {\n  public blockSize = 48;\n\n  private data: DataStorage<TensorData<DataType>>;\n  private fromPixels2DContext: CanvasRenderingContext2D;\n  private firstUse = true;\n\n  constructor() {\n    if (ENV.get('IS_BROWSER')) {\n      this.fromPixels2DContext =\n          document.createElement('canvas').getContext('2d');\n    }\n  }\n\n  setDataMover(dataMover: DataMover): void {\n    this.data = new DataStorage(dataMover);\n  }\n\n  register(dataId: DataId, shape: number[], dtype: DataType): void {\n    if (this.firstUse) {\n      this.firstUse = false;\n      if (ENV.get('IS_NODE')) {\n        warn(\n            '\\n============================\\n' +\n            'Hi there 👋. Looks like you are running TensorFlow.js in ' +\n            'Node.js. To speed things up dramatically, install our node ' +\n            'backend, which binds to TensorFlow C++, by running ' +\n            'npm i @tensorflow/tfjs-node, ' +\n            'or npm i @tensorflow/tfjs-node-gpu if you have CUDA. ' +\n            'Then call require(\\'@tensorflow/tfjs-node\\'); (-gpu ' +\n            'suffix for CUDA) at the start of your program. ' +\n            'Visit https://github.com/tensorflow/tfjs-node for more details.' +\n            '\\n============================\\n');\n      }\n    }\n    if (this.data.has(dataId)) {\n      throw new Error(`Data buffer is already registered`);\n    }\n    this.data.set(dataId, {dtype});\n  }\n  write(dataId: DataId, values: DataValues): void {\n    if (values == null) {\n      throw new Error('MathBackendCPU.write(): values can not be null');\n    }\n    this.data.get(dataId).values = values;\n  }\n  fromPixels(\n      pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement,\n      numChannels: number): Tensor3D {\n    if (pixels == null) {\n      throw new Error('pixels passed to tf.fromPixels() can not be null');\n    }\n    let vals: Uint8ClampedArray;\n    // tslint:disable-next-line:no-any\n    if (ENV.get('IS_NODE') && (pixels as any).getContext == null) {\n      throw new Error(\n          'When running in node, pixels must be an HTMLCanvasElement ' +\n          'like the one returned by the `canvas` npm package');\n    }\n    // tslint:disable-next-line:no-any\n    if ((pixels as any).getContext != null) {\n      // tslint:disable-next-line:no-any\n      vals = (pixels as any)\n                 .getContext('2d')\n                 .getImageData(0, 0, pixels.width, pixels.height)\n                 .data;\n    } else if (pixels instanceof ImageData) {\n      vals = pixels.data;\n    } else if (\n        pixels instanceof HTMLImageElement ||\n        pixels instanceof HTMLVideoElement) {\n      if (this.fromPixels2DContext == null) {\n        throw new Error(\n            'Can\\'t read pixels from HTMLImageElement outside ' +\n            'the browser.');\n      }\n      this.fromPixels2DContext.canvas.width = pixels.width;\n      this.fromPixels2DContext.canvas.height = pixels.height;\n      this.fromPixels2DContext.drawImage(\n          pixels, 0, 0, pixels.width, pixels.height);\n      vals = this.fromPixels2DContext\n                 .getImageData(0, 0, pixels.width, pixels.height)\n                 .data;\n    } else {\n      throw new Error(\n          'pixels passed to tf.fromPixels() must be either an ' +\n          `HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ` +\n          `ImageData, but was ${(pixels as {}).constructor.name}`);\n    }\n    let values: Int32Array;\n    if (numChannels === 4) {\n      values = new Int32Array(vals);\n    } else {\n      const numPixels = pixels.width * pixels.height;\n      values = new Int32Array(numPixels * numChannels);\n      for (let i = 0; i < numPixels; i++) {\n        for (let channel = 0; channel < numChannels; ++channel) {\n          values[i * numChannels + channel] = vals[i * 4 + channel];\n        }\n      }\n    }\n    const outShape: [number, number, number] =\n        [pixels.height, pixels.width, numChannels];\n    return tensor3d(values, outShape, 'int32');\n  }\n  async read(dataId: DataId): Promise<DataValues> {\n    return this.readSync(dataId);\n  }\n  readSync(dataId: DataId): DataValues {\n    const {dtype, complexTensors} = this.data.get(dataId);\n    if (dtype === 'complex64') {\n      const realValues = complexTensors.real.dataSync() as Float32Array;\n      const imagValues = complexTensors.imag.dataSync() as Float32Array;\n      return complex_util.mergeRealAndImagArrays(realValues, imagValues);\n    }\n    return this.data.get(dataId).values;\n  }\n\n  disposeData(dataId: DataId): void {\n    if (this.data.has(dataId)) {\n      const {complexTensors} = this.data.get(dataId);\n      if (complexTensors != null) {\n        complexTensors.real.dispose();\n        complexTensors.imag.dispose();\n      }\n      this.data.delete(dataId);\n    }\n  }\n\n  async time(f: () => void): Promise<BackendTimingInfo> {\n    const start = now();\n    f();\n    const kernelMs = now() - start;\n    return {kernelMs};\n  }\n\n  memory() {\n    return {\n      // Unreliable due to automatic gc. The numbers above are cumulative.\n      unreliable: true,\n      reasons:\n          ['The reported memory is an upper bound. Due to automatic garbage ' +\n           'collection, the true allocated memory may be less.']\n    };\n  }\n\n  complex<T extends Tensor>(real: T, imag: T): T {\n    const result = Tensor.make(real.shape, {}, 'complex64') as T;\n\n    const resultData = this.data.get(result.dataId);\n    // The backend owns the reference to the underlying real and imaginary\n    // clones. These will explicitly get disposed when the complex tensor is\n    // disposed.\n    resultData.complexTensors = {\n      real: ENV.engine.keep(real.clone()),\n      imag: ENV.engine.keep(imag.clone())\n    };\n\n    return result;\n  }\n  real<T extends Tensor>(input: T): T {\n    const resultData = this.data.get(input.dataId);\n    return resultData.complexTensors.real.clone() as T;\n  }\n  imag<T extends Tensor>(input: T): T {\n    const resultData = this.data.get(input.dataId);\n    return resultData.complexTensors.imag.clone() as T;\n  }\n\n  private assertNotComplex(tensor: Tensor|Tensor[], opName: string) {\n    if (!Array.isArray(tensor)) {\n      tensor = [tensor];\n    }\n    tensor.forEach(t => {\n      if (t != null) {\n        util.assert(\n            t.dtype !== 'complex64',\n            `${opName} does not support complex64 tensors.`);\n      }\n    });\n  }\n\n  slice<T extends Tensor>(x: T, begin: number[], size: number[]): T {\n    this.assertNotComplex(x, 'slice');\n\n    const buffer = ops.buffer(size, x.dtype);\n\n    for (let i = 0; i < buffer.size; ++i) {\n      const loc = buffer.indexToLoc(i);\n      const xLoc = loc.map((idx, j) => idx + begin[j]);\n      buffer.set(x.get(...xLoc), ...loc);\n    }\n    return buffer.toTensor() as T;\n  }\n\n  stridedSlice<T extends Tensor>(\n      x: T, begin: number[], end: number[], strides: number[],\n      beginMask: number, endMask: number, ellipsisMask: number,\n      newAxisMask: number, shrinkAxisMask: number): T {\n    this.assertNotComplex(x, 'stridedSlice');\n\n    const [beginIndex, size, shrinkAxis] = getStridedSlicedInfo(\n        x.shape, begin, end, strides, beginMask, endMask, ellipsisMask,\n        newAxisMask, shrinkAxisMask);\n\n    const shape = size.filter((v, index) => shrinkAxis.indexOf(index) === -1);\n\n    if (shape.some(axis => axis === 0)) {\n      return ops.tensor([], shape) as T;\n    }\n\n    const buffer = ops.buffer(size, x.dtype);\n\n    for (let i = 0; i < buffer.size; i++) {\n      const loc = buffer.indexToLoc(i);\n\n      const newLoc: number[] = new Array(loc.length);\n      for (let j = 0; j < newLoc.length; j++) {\n        newLoc[j] = loc[j] * strides[j] + beginIndex[j];\n      }\n      buffer.set(x.get(...newLoc), ...loc);\n    }\n\n    return buffer.toTensor().reshape(shape) as T;\n  }\n\n  reverse<T extends Tensor>(x: T, axis: number[]): T {\n    this.assertNotComplex(x, 'reverse');\n\n    const buffer = ops.buffer(x.shape, x.dtype);\n    const xBuffer = x.buffer();\n\n    for (let i = 0; i < buffer.size; i++) {\n      const outLoc = buffer.indexToLoc(i);\n      const inLoc = outLoc.slice();\n      axis.forEach(ax => inLoc[ax] = x.shape[ax] - 1 - inLoc[ax]);\n      buffer.set(xBuffer.get(...inLoc), ...outLoc);\n    }\n\n    return buffer.toTensor() as T;\n  }\n\n  concat(tensors: Tensor[], axis: number): Tensor {\n    this.assertNotComplex(tensors, 'concat');\n    const tensors2D = tensors.map(t => {\n      const innerSize = util.sizeFromShape(t.shape.slice(axis));\n      return t.as2D(-1, innerSize);\n    });\n    const outShape =\n        concat_util.computeOutShape(tensors2D.map(t => t.shape), 1 /* axis */);\n    const values =\n        ops.buffer(outShape as [number, number], tensors[0].dtype as 'float32')\n            .values;\n    if (tensors2D[0].shape[0] === 1) {\n      // Use built-in TypedArray.set() method for speed.\n      let offset = 0;\n      tensors2D.forEach(t => {\n        values.set(t.dataSync(), offset);\n        offset += t.size;\n      });\n    } else {\n      let colOffset = 0;\n      tensors2D.forEach(t => {\n        const tVals = t.dataSync();\n        let tIdx = 0;\n        for (let row = 0; row < t.shape[0]; ++row) {\n          const resIdx = row * outShape[1] + colOffset;\n          for (let col = 0; col < t.shape[1]; ++col) {\n            values[resIdx + col] = tVals[tIdx++];\n          }\n        }\n        colOffset += t.shape[1];\n      });\n    }\n    const finalOutShape =\n        concat_util.computeOutShape(tensors.map(t => t.shape), axis);\n    return tensor(values, finalOutShape, tensors[0].dtype);\n  }\n\n  neg<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'neg');\n\n    return this.multiply(ops.scalar(-1), x) as T;\n  }\n\n  add(a: Tensor, b: Tensor): Tensor {\n    if (a.dtype === 'complex64' || b.dtype === 'complex64') {\n      return this.broadcastedBinaryComplexOp(\n                 a.cast('complex64'), b.cast('complex64'),\n                 (aReal, aImag, bReal, bImag) => {\n                   return {real: aReal + bReal, imag: aImag + bImag};\n                 }) as Tensor;\n    }\n\n    return this.broadcastedBinaryOp(\n               a, b, upcastType(a.dtype, b.dtype),\n               (aValue, bValue) => aValue + bValue) as Tensor;\n  }\n\n  addN<T extends Tensor>(tensors: T[]): T {\n    this.assertNotComplex(tensors, 'addN');\n\n    const vals = tensors.map(t => t.dataSync());\n    const result = ops.buffer(tensors[0].shape, tensors[0].dtype as 'float32');\n    const resultVals = result.values;\n    for (let i = 0; i < tensors.length; i++) {\n      const currVals = vals[i];\n      for (let j = 0; j < resultVals.length; j++) {\n        resultVals[j] += currVals[j];\n      }\n    }\n    return result.toTensor() as T;\n  }\n\n  subtract(a: Tensor, b: Tensor): Tensor {\n    if (a.dtype === 'complex64' || b.dtype === 'complex64') {\n      return this.broadcastedBinaryComplexOp(\n                 a.cast('complex64'), b.cast('complex64'),\n                 (aReal, aImag, bReal, bImag) => {\n                   return {real: aReal - bReal, imag: aImag - bImag};\n                 }) as Tensor;\n    }\n\n    return this.broadcastedBinaryOp(\n               a, b, upcastType(a.dtype, b.dtype),\n               (aValue, bValue) => aValue - bValue) as Tensor;\n  }\n\n  pow<T extends Tensor>(a: T, b: Tensor): T {\n    this.assertNotComplex([a, b], 'pow');\n\n    return this.broadcastedBinaryOp(\n               a, b, a.dtype, (aValue, bValue) => Math.pow(aValue, bValue)) as\n        T;\n  }\n\n  batchMatMul(\n      a: Tensor3D, b: Tensor3D, transposeA: boolean,\n      transposeB: boolean): Tensor3D {\n    this.assertNotComplex([a, b], 'matMul');\n\n    const sharedDim = transposeA ? a.shape[1] : a.shape[2];\n    const leftDim = transposeA ? a.shape[2] : a.shape[1];\n    const rightDim = transposeB ? b.shape[1] : b.shape[2];\n    const batchDim = a.shape[0];\n\n    const aValues = a.dataSync();\n    const bValues = b.dataSync();\n    const [aBatch, aOuterStep, aInnerStep] = transposeA ?\n        [a.strides[0], 1, a.strides[1]] :\n        [a.strides[0], a.strides[1], 1];\n    const [bInnerStep, bOuterStep, bBatch] = transposeB ?\n        [1, b.strides[1], b.strides[0]] :\n        [b.strides[1], 1, b.strides[0]];\n\n    const size = leftDim * rightDim;\n    const result = buffer([batchDim, leftDim, rightDim], a.dtype);\n    const resVals = result.values as TypedArray;\n    const blockSize = this.blockSize;\n\n    for (let b = 0; b < batchDim; b++) {\n      for (let i0 = 0; i0 < leftDim; i0 += blockSize) {\n        for (let j0 = 0; j0 < rightDim; j0 += blockSize) {\n          for (let k0 = 0; k0 < sharedDim; k0 += blockSize) {\n            // for when blockSize doesn't evenly divide the input\n            const iBlock = Math.min(i0 + blockSize, leftDim);\n            const jBlock = Math.min(j0 + blockSize, rightDim);\n            const kBlock = Math.min(k0 + blockSize, sharedDim);\n\n            for (let i = i0; i < iBlock; i++) {\n              for (let j = j0; j < jBlock; j++) {\n                let sum = 0.0;\n\n                for (let k = k0; k < kBlock; k++) {\n                  sum += aValues[b * aBatch + i * aOuterStep + k * aInnerStep] *\n                      bValues[k * bInnerStep + j * bOuterStep + b * bBatch];\n                }\n                resVals[b * size + (i * rightDim + j)] += sum;\n              }\n            }\n          }\n        }\n      }\n    }\n    return result.toTensor() as Tensor3D;\n  }\n\n  multiply(a: Tensor, b: Tensor): Tensor {\n    if (a.dtype === 'complex64' || b.dtype === 'complex64') {\n      return this.broadcastedBinaryComplexOp(\n                 a.cast('complex64'), b.cast('complex64'),\n                 (aReal, aImag, bReal, bImag) => {\n                   return {\n                     real: aReal * bReal - aImag * bImag,\n                     imag: aReal * bImag + aImag * bReal\n                   };\n                 }) as Tensor;\n    }\n\n    return this.broadcastedBinaryOp(\n               a, b, upcastType(a.dtype, b.dtype),\n               (aValue, bValue) => aValue * bValue) as Tensor;\n  }\n\n  realDivide(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'realDivide');\n\n    const op = (a: number, b: number) => a / b;\n    const outputDtype = 'float32';\n    return this.broadcastedBinaryOp(a, b, outputDtype, op) as Tensor;\n  }\n\n  floorDiv(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'floorDiv');\n\n    const op = (a: number, b: number) => Math.floor(a / b);\n    const outputDtype = 'int32';\n    return this.broadcastedBinaryOp(a, b, outputDtype, op) as Tensor;\n  }\n\n  sum(x: Tensor, axes: number[]): Tensor {\n    this.assertNotComplex(x, 'sum');\n\n    axis_util.assertAxesAreInnerMostDims('sum', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const resultDtype = upcastType(x.dtype, 'int32');\n    const result = ops.zeros(outShape, resultDtype);\n    const reduceSize = util.sizeFromShape(reduceShape);\n    const vals = result.dataSync();\n\n    const aVals = x.dataSync();\n    for (let i = 0; i < vals.length; ++i) {\n      const offset = i * reduceSize;\n      let sum = 0;\n      for (let j = 0; j < reduceSize; ++j) {\n        sum += aVals[offset + j];\n      }\n      vals[i] = sum;\n    }\n    return result;\n  }\n\n  prod(x: Tensor, axes: number[]): Tensor {\n    this.assertNotComplex(x, 'sum');\n\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const resultDtype = upcastType(x.dtype, 'int32');\n    const result = ops.zeros(outShape, resultDtype);\n    const reduceSize = util.sizeFromShape(reduceShape);\n    const vals = result.dataSync();\n\n    const aVals = x.dataSync();\n    for (let i = 0; i < vals.length; ++i) {\n      const offset = i * reduceSize;\n      let prod = 1;\n      for (let j = 0; j < reduceSize; ++j) {\n        prod *= aVals[offset + j];\n      }\n      vals[i] = prod;\n    }\n    return result;\n  }\n\n  unsortedSegmentSum<T extends Tensor>(\n      x: T, segmentIds: Tensor1D, numSegments: number): Tensor {\n    this.assertNotComplex(x, 'unsortedSegmentSum');\n\n    const res = [];\n\n    // Reshape the segment id's so that they can be broadcast with\n    // x. The new shape should be [segmentIds.shape, 1, ..., 1]\n    const numIters = x.rank - segmentIds.rank;\n    for (let i = 0; i < numIters; ++i) {\n      segmentIds = segmentIds.expandDims(i + 1);\n    }\n\n    for (let i = 0; i < numSegments; ++i) {\n      const segmentId = ops.scalar(i, 'int32');\n      const mask = ops.equal(segmentId, segmentIds).asType('float32');\n      const sum = mask.mul(x).sum(0);\n      res.push(sum);\n    }\n\n    return ops.stack(res);\n  }\n\n  argMin(x: Tensor, axis: number): Tensor {\n    this.assertNotComplex(x, 'argMin');\n\n    const axes = [axis];\n    axis_util.assertAxesAreInnerMostDims('argMin', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const result = ops.zeros(outShape, 'int32');\n    const reduceSize = util.sizeFromShape(reduceShape);\n    const vals = result.dataSync();\n\n    const aVals = x.dataSync();\n    for (let i = 0; i < vals.length; ++i) {\n      const offset = i * reduceSize;\n      let min = aVals[offset];\n      let minIndex = 0;\n      for (let j = 0; j < reduceSize; ++j) {\n        const value = aVals[offset + j];\n        if (value < min) {\n          min = value;\n          minIndex = j;\n        }\n      }\n      vals[i] = minIndex;\n    }\n    return result;\n  }\n\n  argMax(x: Tensor, axis: number): Tensor {\n    this.assertNotComplex(x, 'argMax');\n\n    const axes = [axis];\n    axis_util.assertAxesAreInnerMostDims('argMax', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const result = ops.zeros(outShape, 'int32');\n    const reduceSize = util.sizeFromShape(reduceShape);\n    const vals = result.dataSync();\n\n    const aVals = x.dataSync();\n    for (let i = 0; i < vals.length; ++i) {\n      const offset = i * reduceSize;\n      let max = aVals[offset];\n      let maxIndex = 0;\n      for (let j = 0; j < reduceSize; ++j) {\n        const value = aVals[offset + j];\n        if (value > max) {\n          max = value;\n          maxIndex = j;\n        }\n      }\n      vals[i] = maxIndex;\n    }\n    return result;\n  }\n\n  cumsum(x: Tensor, axis: number, exclusive: boolean, reverse: boolean):\n      Tensor {\n    this.assertNotComplex(x, 'cumsum');\n\n    if (axis !== x.rank - 1) {\n      throw new Error(\n          `backend.cumsum in CPU expects an inner-most axis=${x.rank - 1} ` +\n          `but got axis=${axis}`);\n    }\n    const resultDtype = upcastType(x.dtype, 'int32');\n    const result = ops.zeros(x.shape, resultDtype);\n    const vals = result.dataSync();\n\n    const aVals = x.dataSync();\n    const finalDim = x.shape[x.rank - 1];\n    const indexAdjuster = reverse ?\n        (i: number, j: number) => i + finalDim - j - 1 :\n        (i: number, j: number) => i + j;\n    for (let i = 0; i < aVals.length; i += finalDim) {\n      for (let j = 0; j < finalDim; j++) {\n        const idx = indexAdjuster(i, j);\n        if (j === 0) {\n          vals[idx] = exclusive ? 0 : aVals[idx];\n        } else {\n          const prevIdx = indexAdjuster(i, j - 1);\n          vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] :\n                                  aVals[idx] + vals[prevIdx];\n        }\n      }\n    }\n    return result;\n  }\n\n  equal(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'equal');\n\n    return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n      return (aVal === bVal) ? 1 : 0;\n    });\n  }\n\n  notEqual(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'notEqual');\n\n    return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n      return (aVal !== bVal) ? 1 : 0;\n    });\n  }\n\n  less(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'less');\n\n    return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n      return (aVal < bVal) ? 1 : 0;\n    });\n  }\n\n  lessEqual(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'lessEqual');\n\n    return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n      return (aVal <= bVal) ? 1 : 0;\n    });\n  }\n\n  greater(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'greater');\n\n    return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n      return (aVal > bVal) ? 1 : 0;\n    });\n  }\n\n  greaterEqual(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'greaterEqual');\n\n    return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n      return (aVal >= bVal) ? 1 : 0;\n    });\n  }\n\n  logicalNot<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'logicalNot');\n\n    const values = x.dataSync();\n    const newValues = new Uint8Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      newValues[i] = values[i] ? 0 : 1;\n    }\n    return Tensor.make(x.shape, {values: newValues}, 'bool') as T;\n  }\n\n  logicalAnd(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'logicalAnd');\n\n    return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n      return aVal && bVal;\n    });\n  }\n\n  logicalOr(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'logicalOr');\n\n    return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n      return aVal || bVal;\n    });\n  }\n\n  select(condition: Tensor, a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([condition, a, b], 'select');\n\n    const values = condition.dataSync();\n    const aValues = a.dataSync();\n    const bValues = b.dataSync();\n    const result = ops.zeros(a.shape, upcastType(a.dtype, b.dtype));\n    const newValues = result.dataSync();\n    let index = 0;\n    const offset = condition.rank === 0 || condition.rank > 1 || a.rank === 1 ?\n        1 :\n        a.shape[1];\n\n    for (let i = 0; i < values.length; i++) {\n      for (let j = 0; j < offset; j++) {\n        if (values[i] === 1) {\n          newValues[index++] = aValues[i];\n        } else {\n          newValues[index++] = bValues[i];\n        }\n      }\n    }\n    return result;\n  }\n\n  where(condition: Tensor): Tensor2D {\n    this.assertNotComplex([condition], 'where');\n\n    const condVals = condition.dataSync();\n    return whereImpl(condition.shape, condVals);\n  }\n\n  topk<T extends Tensor>(x: T, k: number, sorted: boolean): [T, T] {\n    this.assertNotComplex(x, 'topk');\n\n    const xVals = x.dataSync();\n    return topkImpl(xVals, x.shape, x.dtype as NumericDataType, k, sorted);\n  }\n\n  min(x: Tensor, axes: number[]): Tensor {\n    this.assertNotComplex(x, 'min');\n\n    axis_util.assertAxesAreInnerMostDims('min', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const result = ops.zeros(outShape, x.dtype);\n    const reduceSize = util.sizeFromShape(reduceShape);\n    const vals = result.dataSync();\n\n    const aVals = x.dataSync();\n    for (let i = 0; i < vals.length; ++i) {\n      const offset = i * reduceSize;\n      let min = aVals[offset];\n      for (let j = 0; j < reduceSize; ++j) {\n        const value = aVals[offset + j];\n        if (value < min) {\n          min = value;\n        }\n      }\n      vals[i] = min;\n    }\n    return result;\n  }\n\n  minimum(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'minimum');\n\n    return this.broadcastedBinaryOp(\n        a, b, a.dtype, (aVal, bVal) => Math.min(aVal, bVal));\n  }\n\n  mod(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'mod');\n\n    return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => {\n      const rem = aVal % bVal;\n      if ((aVal < 0 && bVal < 0) || (aVal >= 0 && bVal >= 0)) {\n        return rem;\n      } else {\n        return (rem + bVal) % bVal;\n      }\n    });\n  }\n\n  max(x: Tensor, axes: number[]): Tensor {\n    this.assertNotComplex(x, 'max');\n\n    axis_util.assertAxesAreInnerMostDims('max', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const result = ops.zeros(outShape, x.dtype);\n    const reduceSize = util.sizeFromShape(reduceShape);\n    const vals = result.dataSync();\n\n    const aVals = x.dataSync();\n    for (let i = 0; i < vals.length; ++i) {\n      const offset = i * reduceSize;\n      let max = aVals[offset];\n      for (let j = 0; j < reduceSize; ++j) {\n        const value = aVals[offset + j];\n        if (value > max) {\n          max = value;\n        }\n      }\n      vals[i] = max;\n    }\n    return result;\n  }\n\n  maximum(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'maximum');\n\n    return this.broadcastedBinaryOp(\n        a, b, a.dtype, (aVal, bVal) => Math.max(aVal, bVal));\n  }\n\n  all(x: Tensor, axes: number[]): Tensor {\n    this.assertNotComplex(x, 'all');\n\n    axis_util.assertAxesAreInnerMostDims('all', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const result = ops.zeros(outShape, x.dtype);\n    const reduceSize = util.sizeFromShape(reduceShape);\n    const vals = result.dataSync();\n\n    const aVals = x.dataSync();\n    for (let i = 0; i < vals.length; ++i) {\n      const offset = i * reduceSize;\n      let all = aVals[offset];\n      for (let j = 0; j < reduceSize; ++j) {\n        const value = aVals[offset + j];\n        all = all && value;\n      }\n      vals[i] = all;\n    }\n    return result;\n  }\n\n  any(x: Tensor, axes: number[]): Tensor {\n    this.assertNotComplex(x, 'any');\n\n    axis_util.assertAxesAreInnerMostDims('any', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const result = ops.zeros(outShape, x.dtype);\n    const reduceSize = util.sizeFromShape(reduceShape);\n    const vals = result.dataSync();\n\n    const aVals = x.dataSync();\n    for (let i = 0; i < vals.length; ++i) {\n      const offset = i * reduceSize;\n      let anyVal = aVals[offset];\n      for (let j = 0; j < reduceSize; ++j) {\n        const value = aVals[offset + j];\n        anyVal = anyVal || value;\n      }\n      vals[i] = anyVal;\n    }\n    return result;\n  }\n\n  squaredDifference(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'squaredDifference');\n\n    return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => {\n      const diff = aVal - bVal;\n      return diff * diff;\n    });\n  }\n\n  ceil<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'ceil');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      newValues[i] = Math.ceil(values[i]);\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  floor<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'floor');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      newValues[i] = Math.floor(values[i]);\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  sign<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'x');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      if (values[i] < 0) {\n        newValues[i] = -1;\n      } else if (values[i] > 0) {\n        newValues[i] = 1;\n      } else {\n        newValues[i] = 0;\n      }\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  round<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'round');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      // The algorithm is based on banker's rounding.\n      const base = Math.floor(values[i]);\n      if (values[i] - base < 0.5) {\n        newValues[i] = Math.floor(values[i]);\n      } else if (values[i] - base > 0.5) {\n        newValues[i] = Math.ceil(values[i]);\n      } else {\n        if (base % 2.0 === 0.0) {\n          newValues[i] = base;\n        } else {\n          newValues[i] = base + 1.0;\n        }\n      }\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  exp<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'exp');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      newValues[i] = Math.exp(values[i]);\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  expm1<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'expm1');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      newValues[i] = Math.expm1(values[i]);\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  log<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'log');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      const value = values[i];\n      newValues[i] = Math.log(value);\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  log1p<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'log1p');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      const value = values[i];\n      newValues[i] = Math.log1p(value);\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  sqrt<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'sqrt');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      const value = values[i];\n      newValues[i] = Math.sqrt(value);\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  rsqrt<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'rsqrt');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      const value = values[i];\n      newValues[i] = 1 / Math.sqrt(value);\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  square<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'square');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      const value = values[i];\n      newValues[i] = value * value;\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  reciprocal<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'reciprocal');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      newValues[i] = 1 / values[i];\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  relu<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'relu');\n\n    const res = ops.zeros(x.shape, x.dtype);\n    const resVals = res.dataSync();\n    const inVals = x.dataSync();\n    for (let i = 0; i < inVals.length; ++i) {\n      resVals[i] = Math.max(0, inVals[i]);\n    }\n    return res as T;\n  }\n\n  elu<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'elu');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      const v = values[i];\n      if (v >= 0) {\n        resultValues[i] = v;\n      } else {\n        resultValues[i] = (Math.exp(v) - 1);\n      }\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  eluDer<T extends Tensor>(dy: T, y: T): T {\n    this.assertNotComplex([dy, y], 'eluDer');\n\n    const resultValues = new Float32Array(y.size);\n    const values = y.dataSync();\n    const dyValues = dy.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      const v = values[i];\n      if (v >= 1) {\n        resultValues[i] = dyValues[i];\n      } else {\n        resultValues[i] = dyValues[i] * (v + 1);\n      }\n    }\n    return Tensor.make(y.shape, {values: resultValues}) as T;\n  }\n\n  selu<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'selu');\n\n    // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n    // see: https://arxiv.org/abs/1706.02515\n    const scaleAlpha = selu_util.SELU_SCALEALPHA;\n    const scale = selu_util.SELU_SCALE;\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      const v = values[i];\n      if (v >= 0) {\n        resultValues[i] = scale * v;\n      } else {\n        resultValues[i] = scaleAlpha * (Math.exp(v) - 1);\n      }\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  clip<T extends Tensor>(x: T, min: number, max: number): T {\n    this.assertNotComplex(x, 'clip');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      const v = values[i];\n      resultValues[i] = v > max ? max : (v < min ? min : v);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  abs<T extends Tensor>(x: T): T {\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.abs(values[i]);\n    }\n\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  complexAbs<T extends Tensor>(x: T): T {\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n\n    for (let i = 0; i < x.size; ++i) {\n      const real = values[i * 2];\n      const imag = values[i * 2 + 1];\n      resultValues[i] = Math.hypot(real, imag);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  int<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'int');\n\n    const resultValues = new Int32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = values[i];\n    }\n    return Tensor.make(x.shape, {values: resultValues}, 'int32');\n  }\n\n  sigmoid<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'sigmoid');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = 1 / (1 + Math.exp(-values[i]));\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  softplus<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'softplus');\n\n    // mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX\n\n    // epsilon is the difference between 1.0 and the next representable float.\n    // For a single precision 32 bit float this should be 2^-23, see:\n    // https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm\n    const epsilon = 1.1920928955078125e-7;\n    const threshold = Math.log(epsilon) + 2.0;\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n\n    for (let i = 0; i < values.length; ++i) {\n      // Value above which exp(x) may overflow, but softplus(x) == x\n      // is within machine epsilon.\n      const tooLarge = values[i] > -threshold;\n\n      // Value below which exp(x) may underflow, but softplus(x) == exp(x)\n      // is within machine epsilon.\n      const tooSmall = values[i] < threshold;\n\n      const expX = Math.exp(values[i]);\n      let result;\n\n      if (tooSmall) {\n        result = expX;\n      } else if (tooLarge) {\n        result = values[i];\n      } else {\n        result = Math.log(1.0 + expX);\n      }\n      resultValues[i] = result;\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  sin<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'sin');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.sin(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  cos<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'cos');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.cos(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  tan<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'tan');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.tan(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  asin<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'asin');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.asin(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  acos<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'acos');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.acos(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  atan<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'atan');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.atan(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  atan2<T extends Tensor>(a: T, b: T): T {\n    this.assertNotComplex([a, b], 'atan2');\n\n    return this.broadcastedBinaryOp(\n               a, b, a.dtype, (aValue, bValue) => Math.atan2(aValue, bValue)) as\n        T;\n  }\n\n  sinh<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'sinh');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.sinh(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  cosh<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'cosh');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.cosh(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  tanh<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'tanh');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = util.tanh(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  asinh<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'asinh');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.asinh(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  acosh<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'acosh');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.acosh(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  atanh<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'atanh');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.atanh(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  erf<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'erf');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    const p = erf_util.ERF_P;\n    const a1 = erf_util.ERF_A1;\n    const a2 = erf_util.ERF_A2;\n    const a3 = erf_util.ERF_A3;\n    const a4 = erf_util.ERF_A4;\n    const a5 = erf_util.ERF_A5;\n    for (let i = 0; i < values.length; ++i) {\n      const v = values[i];\n      const t = 1.0 / (1.0 + p * v);\n      resultValues[i] = 1.0 -\n          (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t *\n              Math.exp(-v * v);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  step<T extends Tensor>(x: T, alpha = 0): T {\n    this.assertNotComplex(x, 'step');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      const value = values[i];\n      if (isNaN(value)) {\n        resultValues[i] = NaN;\n      } else {\n        resultValues[i] = value > 0 ? 1 : alpha;\n      }\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  conv2d(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    this.assertNotComplex([x, filter], 'conv2d');\n\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const padLeft = convInfo.padInfo.left;\n    const padTop = convInfo.padInfo.top;\n    const y = ops.buffer(convInfo.outShape, x.dtype as 'float32');\n\n    const xVals = x.dataSync();\n    const wVals = filter.dataSync();\n    const yVals = y.values;\n\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n      const xOffset1 = b * x.strides[0];\n      const yOffset1 = b * y.strides[0];\n      for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n        const yOffset2 = yOffset1 + yR * y.strides[1];\n        const xRCorner = yR * convInfo.strideHeight - padLeft;\n        for (let wR = 0; wR < filterHeight; wR++) {\n          const xR = xRCorner + wR * dilationHeight;\n          if (xR < 0 || xR >= convInfo.inHeight) {\n            continue;\n          }\n          const wOffset1 = wR * filter.strides[0];\n          const xOffset2 = xOffset1 + xR * x.strides[1];\n          for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n            const yOffset3 = yOffset2 + yC * convInfo.outChannels;\n            const xCCorner = yC * convInfo.strideWidth - padTop;\n            for (let wC = 0; wC < filterWidth; wC++) {\n              const xC = xCCorner + wC * dilationWidth;\n              if (xC < 0 || xC >= convInfo.inWidth) {\n                continue;\n              }\n              const wOffset2 = wOffset1 + wC * filter.strides[1];\n              const xOffset3 = xOffset2 + xC * convInfo.inChannels;\n              let wOffset3 = wOffset2;\n              for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                const xVal = xVals[xOffset3 + d1];\n                for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                  yVals[yOffset3 + d2] += xVal * wVals[wOffset3 + d2];\n                }\n                wOffset3 += convInfo.outChannels;\n              }\n            }\n          }\n        }\n      }\n    }\n    return y.toTensor() as Tensor4D;\n  }\n\n  conv3d(x: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n    const filterDepth = convInfo.filterDepth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dilationDepth = convInfo.dilationDepth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const padFront = convInfo.padInfo.front;\n    const padLeft = convInfo.padInfo.left;\n    const padTop = convInfo.padInfo.top;\n    const y = ops.buffer<Rank.R5>(convInfo.outShape, x.dtype as 'float32');\n\n    const xVals = x.dataSync();\n    const wVals = filter.dataSync();\n    const yVals = y.values;\n\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n      const xOffset1 = b * x.strides[0];\n      const yOffset1 = b * y.strides[0];\n      for (let yF = 0; yF < convInfo.outDepth; ++yF) {\n        const yOffset2 = yOffset1 + yF * y.strides[1];\n        const xFCorner = yF * convInfo.strideDepth - padFront;\n        for (let wF = 0; wF < filterDepth; wF++) {\n          const xF = xFCorner + wF * dilationDepth;\n          if (xF < 0 || xF >= convInfo.inDepth) {\n            continue;\n          }\n          const wOffset1 = wF * filter.strides[0];\n          const xOffset2 = xOffset1 + xF * x.strides[1];\n\n          for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n            const yOffset3 = yOffset2 + yR * y.strides[2];\n            const xRCorner = yR * convInfo.strideHeight - padTop;\n            for (let wR = 0; wR < filterHeight; wR++) {\n              const xR = xRCorner + wR * dilationHeight;\n              if (xR < 0 || xR >= convInfo.inHeight) {\n                continue;\n              }\n              const wOffset2 = wOffset1 + wR * filter.strides[1];\n              const xOffset3 = xOffset2 + xR * x.strides[2];\n              for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                const yOffset4 = yOffset3 + yC * convInfo.outChannels;\n                const xCCorner = yC * convInfo.strideWidth - padLeft;\n                for (let wC = 0; wC < filterWidth; wC++) {\n                  const xC = xCCorner + wC * dilationWidth;\n                  if (xC < 0 || xC >= convInfo.inWidth) {\n                    continue;\n                  }\n                  const wOffset3 = wOffset2 + wC * filter.strides[2];\n                  const xOffset4 = xOffset3 + xC * convInfo.inChannels;\n                  let wOffset4 = wOffset3;\n                  for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                    const xVal = xVals[xOffset4 + d1];\n                    for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                      yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];\n                    }\n                    wOffset4 += convInfo.outChannels;\n                  }\n                }\n              }\n            }\n          }\n        }\n      }\n    }\n    return y.toTensor();\n  }\n\n  conv2dDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    this.assertNotComplex([dy, filter], 'conv2dDerInput');\n\n    const dx = ops.buffer<Rank.R4>(convInfo.inShape, 'float32');\n    const dxValues = dx.values;\n    const [dxS0, dxS1, dxS2] = dx.strides;\n    const dyValues = dy.dataSync();\n    const [dyS0, dyS1, dyS2] = dy.strides;\n    const fltValues = filter.dataSync();\n    const [fltS0, fltS1, fltS2] = filter.strides;\n    const {\n      batchSize,\n      filterHeight,\n      filterWidth,\n      inChannels,\n      inHeight,\n      inWidth,\n      outChannels,\n      outHeight,\n      outWidth,\n      strideHeight,\n      strideWidth\n    } = convInfo;\n    const topPad = filterHeight - 1 - convInfo.padInfo.top;\n    const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n\n    for (let b = 0; b < batchSize; ++b) {\n      for (let d1 = 0; d1 < inChannels; ++d1) {\n        for (let xR = 0; xR < inHeight; ++xR) {\n          const xRCorner = xR - topPad;\n          const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n          const yRMax =\n              Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n\n          for (let xC = 0; xC < inWidth; ++xC) {\n            const xCCorner = xC - leftPad;\n            const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n            const yCMax =\n                Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n\n            let dotProd = 0;\n            for (let yR = xRMin; yR < yRMax; ++yR) {\n              const wR = yR * strideHeight - xRCorner;\n\n              for (let yC = xCMin; yC < yCMax; ++yC) {\n                const wC = yC * strideWidth - xCCorner;\n                const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;\n                const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n                    fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n\n                for (let d2 = 0; d2 < outChannels; ++d2) {\n                  const pixel = dyValues[dyOffset + d2];\n                  const weight = fltValues[fltOffset + d2];\n                  dotProd += pixel * weight;\n                }\n              }\n            }\n            dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;\n          }\n        }\n      }\n    }\n    return dx.toTensor();\n  }\n\n  conv3dDerInput(dy: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo):\n      Tensor5D {\n    const dx = ops.buffer<Rank.R5>(convInfo.inShape, 'float32');\n    const dxValues = dx.values;\n    const [dxS0, dxS1, dxS2, dxS3] = dx.strides;\n    const dyValues = dy.dataSync();\n    const [dyS0, dyS1, dyS2, dyS3] = dy.strides;\n    const fltValues = filter.dataSync();\n    const [fltS0, fltS1, fltS2, fltS3] = filter.strides;\n    const {\n      batchSize,\n      filterDepth,\n      filterHeight,\n      filterWidth,\n      inChannels,\n      inDepth,\n      inHeight,\n      inWidth,\n      outChannels,\n      outDepth,\n      outHeight,\n      outWidth,\n      strideDepth,\n      strideHeight,\n      strideWidth\n    } = convInfo;\n    const frontPad = filterDepth - 1 - convInfo.padInfo.front;\n    const topPad = filterHeight - 1 - convInfo.padInfo.top;\n    const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n\n    for (let b = 0; b < batchSize; ++b) {\n      for (let d1 = 0; d1 < inChannels; ++d1) {\n        // Frames of depth\n        for (let xF = 0; xF < inDepth; ++xF) {\n          const xFCorner = xF - frontPad;\n          const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));\n          const yFMax =\n              Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);\n\n          // Rows as per standard 2d matrix notation\n          for (let xR = 0; xR < inHeight; ++xR) {\n            const xRCorner = xR - topPad;\n            const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n            const yRMax =\n                Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n            // Columns as per standard 2d matrix notation\n            for (let xC = 0; xC < inWidth; ++xC) {\n              const xCCorner = xC - leftPad;\n              const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n              const yCMax =\n                  Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n\n              let dotProd = 0;\n              for (let yF = xFMin; yF < yFMax; ++yF) {\n                const wF = yF * strideDepth - xFCorner;\n\n                for (let yR = xRMin; yR < yRMax; ++yR) {\n                  const wR = yR * strideHeight - xRCorner;\n\n                  for (let yC = xCMin; yC < yCMax; ++yC) {\n                    const wC = yC * strideWidth - xCCorner;\n                    const dyOffset =\n                        dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;\n                    const fltOffset = fltS0 * (filterDepth - 1 - wF) +\n                        fltS1 * (filterHeight - 1 - wR) +\n                        fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;\n\n                    for (let d2 = 0; d2 < outChannels; ++d2) {\n                      const pixel = dyValues[dyOffset + d2];\n                      const weight = fltValues[fltOffset + d2];\n                      dotProd += pixel * weight;\n                    }\n                  }\n                }\n              }\n              dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] =\n                  dotProd;\n            }\n          }\n        }\n      }\n    }\n    return dx.toTensor();\n  }\n\n  conv2dDerFilter(x: Tensor4D, dy: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    this.assertNotComplex([x, dy], 'conv2dDerFilter');\n\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dW = ops.buffer<Rank.R4>(convInfo.filterShape, 'float32');\n\n    const leftPad = convInfo.padInfo.left;\n    const topPad = convInfo.padInfo.top;\n\n    for (let wR = 0; wR < filterHeight; ++wR) {\n      const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n      const yRMax = Math.min(\n          convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n\n      for (let wC = 0; wC < filterWidth; ++wC) {\n        const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n        const yCMax = Math.min(\n            convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n\n        for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n          for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n            // Need to convolve.\n            let dotProd = 0;\n            for (let b = 0; b < convInfo.batchSize; ++b) {\n              for (let yR = yRMin; yR < yRMax; ++yR) {\n                const xR = wR + yR * strideHeight - topPad;\n                for (let yC = yCMin; yC < yCMax; ++yC) {\n                  const xC = wC + yC * strideWidth - leftPad;\n                  dotProd += x.get(b, xR, xC, d1) * dy.get(b, yR, yC, d2);\n                }\n              }\n            }\n            dW.set(dotProd, wR, wC, d1, d2);\n          }\n        }\n      }\n    }\n    return dW.toTensor();\n  }\n\n  conv3dDerFilter(x: Tensor5D, dy: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const filterDepth = convInfo.filterDepth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n\n    const dw = ops.buffer<Rank.R5>(convInfo.filterShape, 'float32');\n    const dwValues = dw.values;\n    const [dwS0, dwS1, dwS2, dwS3] = dw.strides;\n    const dyValues = dy.dataSync();\n    const [dyS0, dyS1, dyS2, dyS3] = dy.strides;\n    const xValues = x.dataSync();\n    const [xS0, xS1, xS2, xS3] = x.strides;\n\n    const frontPad = convInfo.padInfo.front;\n    const leftPad = convInfo.padInfo.left;\n    const topPad = convInfo.padInfo.top;\n\n    for (let wF = 0; wF < filterDepth; ++wF) {\n      const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));\n      const yFMax = Math.min(\n          convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);\n      const wOffset1 = wF * dwS0;\n\n      for (let wR = 0; wR < filterHeight; ++wR) {\n        const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n        const yRMax = Math.min(\n            convInfo.outHeight,\n            (convInfo.inHeight + topPad - wR) / strideHeight);\n        const wOffset2 = wR * dwS1 + wOffset1;\n\n        for (let wC = 0; wC < filterWidth; ++wC) {\n          const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n          const yCMax = Math.min(\n              convInfo.outWidth,\n              (convInfo.inWidth + leftPad - wC) / strideWidth);\n          const wOffset3 = wC * dwS2 + wOffset2;\n\n          for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n            const wOffset4 = d1 * dwS3 + wOffset3;\n\n            for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n              let dotProd = 0;\n              for (let b = 0; b < convInfo.batchSize; ++b) {\n                const xOffset1 = b * xS0;\n                const yOffset1 = b * dyS0;\n\n                for (let yF = yFMin; yF < yFMax; ++yF) {\n                  const xF = wF + yF * strideDepth - frontPad;\n                  const xOffset2 = xF * xS1 + xOffset1;\n                  const yOffset2 = yF * dyS1 + yOffset1;\n\n                  for (let yR = yRMin; yR < yRMax; ++yR) {\n                    const xR = wR + yR * strideHeight - topPad;\n                    const xOffset3 = xR * xS2 + xOffset2;\n                    const yOffset3 = yR * dyS2 + yOffset2;\n\n                    for (let yC = yCMin; yC < yCMax; ++yC) {\n                      const xC = wC + yC * strideWidth - leftPad;\n                      const xOffset4 = xC * xS3 + xOffset3;\n                      const yOffset4 = yC * dyS3 + yOffset3;\n\n                      dotProd +=\n                          xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];\n                    }\n                  }\n                }\n              }\n              dwValues[wOffset4 + d2] = dotProd;\n            }\n          }\n        }\n      }\n    }\n    return dw.toTensor();\n  }\n\n  depthwiseConv2D(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    this.assertNotComplex([x, filter], 'depthwiseConv2D');\n\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const padLeft = convInfo.padInfo.left;\n    const padTop = convInfo.padInfo.top;\n    const chMul = convInfo.outChannels / convInfo.inChannels;\n    const y = ops.buffer(convInfo.outShape, x.dtype as 'float32');\n    const xVals = x.dataSync();\n    const wVals = filter.dataSync();\n    const yVals = y.values;\n\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n      const xOffset1 = b * x.strides[0];\n      const yOffset1 = b * y.strides[0];\n      for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n        const yOffset2 = yOffset1 + yR * y.strides[1];\n        const xRCorner = yR * convInfo.strideHeight - padLeft;\n        for (let wR = 0; wR < filterHeight; ++wR) {\n          const xR = xRCorner + wR * dilationHeight;\n          if (xR < 0 || xR >= convInfo.inHeight) {\n            continue;\n          }\n          const wOffset1 = wR * filter.strides[0];\n          const xOffset2 = xOffset1 + xR * x.strides[1];\n          for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n            const yOffset3 = yOffset2 + yC * y.strides[2];\n            const xCCorner = yC * convInfo.strideWidth - padTop;\n            for (let wC = 0; wC < filterWidth; ++wC) {\n              const xC = xCCorner + wC * dilationWidth;\n              if (xC < 0 || xC >= convInfo.inWidth) {\n                continue;\n              }\n              const wOffset2 = wOffset1 + wC * filter.strides[1];\n              const xOffset3 = xOffset2 + xC * convInfo.inChannels;\n              let yOffset4 = yOffset3;\n              let wOffset3 = wOffset2;\n              for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                const xVal = xVals[xOffset3 + d1];\n                for (let q = 0; q < chMul; ++q) {\n                  yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q];\n                }\n                yOffset4 += chMul;\n                wOffset3 += chMul;\n              }\n            }\n          }\n        }\n      }\n    }\n\n    return y.toTensor() as Tensor4D;\n  }\n\n  depthwiseConv2DDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    this.assertNotComplex([dy, filter], 'depthwiseConv2DDerInput');\n\n    const dx = ops.buffer<Rank.R4>(convInfo.inShape, 'float32');\n    const dxValues = dx.values;\n    const [dxS0, dxS1, dxS2] = dx.strides;\n    const dyValues = dy.dataSync();\n    const [dyS0, dyS1, dyS2] = dy.strides;\n    const fltValues = filter.dataSync();\n    const [fltS0, fltS1, fltS2] = filter.strides;\n    const {\n      batchSize,\n      filterHeight,\n      filterWidth,\n      inChannels,\n      inHeight,\n      inWidth,\n      outChannels,\n      outHeight,\n      outWidth,\n      strideHeight,\n      strideWidth\n    } = convInfo;\n    const topPad = filterHeight - 1 - convInfo.padInfo.top;\n    const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n    const chMul = outChannels / inChannels;\n\n    for (let b = 0; b < batchSize; ++b) {\n      for (let d1 = 0; d1 < inChannels; ++d1) {\n        for (let xR = 0; xR < inHeight; ++xR) {\n          const xRCorner = xR - topPad;\n          const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n          const yRMax =\n              Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n\n          for (let xC = 0; xC < inWidth; ++xC) {\n            const xCCorner = xC - leftPad;\n            const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n            const yCMax =\n                Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n\n            let dotProd = 0;\n            for (let yR = xRMin; yR < yRMax; ++yR) {\n              const wR = yR * strideHeight - xRCorner;\n\n              for (let yC = xCMin; yC < yCMax; ++yC) {\n                const wC = yC * strideWidth - xCCorner;\n                const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;\n                const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n                    fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n\n                for (let dm = 0; dm < chMul; ++dm) {\n                  const d2 = d1 * chMul + dm;\n                  const pixel = dyValues[dyOffset + d2];\n                  const weight = fltValues[fltOffset + dm];\n                  dotProd += pixel * weight;\n                }\n              }\n            }\n            dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;\n          }\n        }\n      }\n    }\n    return dx.toTensor();\n  }\n\n  depthwiseConv2DDerFilter(x: Tensor4D, dy: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    this.assertNotComplex([x, dy], 'depthwiseConv2DDerFilter');\n\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dW = ops.buffer<Rank.R4>(convInfo.filterShape, 'float32');\n\n    const leftPad = convInfo.padInfo.left;\n    const topPad = convInfo.padInfo.top;\n    const chMul = convInfo.outChannels / convInfo.inChannels;\n\n    for (let wR = 0; wR < filterHeight; ++wR) {\n      const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n      const yRMax = Math.min(\n          convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n\n      for (let wC = 0; wC < filterWidth; ++wC) {\n        const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n        const yCMax = Math.min(\n            convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n\n        for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n          const d1 = Math.trunc(d2 / chMul);\n          const dm = d2 % chMul;\n\n          let dotProd = 0;\n          for (let b = 0; b < convInfo.batchSize; ++b) {\n            for (let yR = yRMin; yR < yRMax; ++yR) {\n              const xR = wR + yR * strideHeight - topPad;\n              for (let yC = yCMin; yC < yCMax; ++yC) {\n                const xC = wC + yC * strideWidth - leftPad;\n                dotProd += x.get(b, xR, xC, d1) * dy.get(b, yR, yC, d2);\n              }\n            }\n          }\n          dW.set(dotProd, wR, wC, d1, dm);\n        }\n      }\n    }\n    return dW.toTensor();\n  }\n\n  tile<T extends Tensor>(x: T, reps: number[]): T {\n    this.assertNotComplex(x, 'tile');\n\n    const newShape: number[] = new Array(x.rank);\n    for (let i = 0; i < newShape.length; i++) {\n      newShape[i] = x.shape[i] * reps[i];\n    }\n    const result = ops.buffer(newShape, x.dtype);\n    const xBuf = x.buffer();\n    for (let i = 0; i < result.values.length; ++i) {\n      const newLoc = result.indexToLoc(i);\n\n      const originalLoc: number[] = new Array(x.rank);\n      for (let i = 0; i < originalLoc.length; i++) {\n        originalLoc[i] = newLoc[i] % x.shape[i];\n      }\n\n      const originalIndex = xBuf.locToIndex(originalLoc);\n\n      result.values[i] = xBuf.values[originalIndex];\n    }\n    return result.toTensor() as T;\n  }\n\n  pad<T extends Tensor>(\n      x: T, paddings: Array<[number, number]>, constantValue: number): T {\n    this.assertNotComplex(x, 'pad');\n\n    const outShape = paddings.map(\n        (p, i) => p[0] /* beforePad */ + x.shape[i] + p[1] /* afterPad */);\n    const start = paddings.map(p => p[0]);\n    const xBuffer = x.buffer();\n    const buffer = ops.buffer(outShape, x.dtype as 'float32');\n    if (constantValue !== 0) {\n      buffer.values.fill(constantValue);\n    }\n\n    for (let i = 0; i < x.size; i++) {\n      const coords = xBuffer.indexToLoc(i);\n      const outCoords = coords.map((c, i) => c + start[i]);\n      buffer.set(x.get(...coords), ...outCoords);\n    }\n    return buffer.toTensor() as T;\n  }\n\n  transpose<T extends Tensor>(x: T, perm: number[]): T {\n    this.assertNotComplex(x, 'transpose');\n\n    const newShape: number[] = new Array(x.rank);\n    for (let i = 0; i < newShape.length; i++) {\n      newShape[i] = x.shape[perm[i]];\n    }\n    const values = x.dataSync();\n    const result = buffer(newShape, x.dtype);\n\n    const xBuf = x.buffer();\n    for (let i = 0; i < x.size; ++i) {\n      const loc = xBuf.indexToLoc(i);\n\n      // Permute location.\n      const newLoc: number[] = new Array(loc.length);\n      for (let i = 0; i < newLoc.length; i++) {\n        newLoc[i] = loc[perm[i]];\n      }\n\n      const newIndex = result.locToIndex(newLoc);\n      result.values[newIndex] = values[i];\n    }\n    return result.toTensor() as T;\n  }\n\n  gather<T extends Tensor>(x: T, indices: Tensor1D, axis: number): T {\n    this.assertNotComplex([x, indices], 'gather');\n\n    const newShape: number[] = x.shape.slice();\n    const indicesValues = indices.dataSync();\n    newShape[axis] = indicesValues.length;\n    const result = buffer(newShape, x.dtype);\n    const xBuf = x.buffer();\n\n    for (let i = 0; i < result.size; ++i) {\n      const newLoc = result.indexToLoc(i);\n\n      const originalLoc: number[] = newLoc.slice();\n      originalLoc[axis] = indicesValues[newLoc[axis]];\n\n      const originalIndex = xBuf.locToIndex(originalLoc);\n      result.values[i] = xBuf.values[originalIndex];\n    }\n    return result.toTensor() as T;\n  }\n\n  batchToSpaceND<T extends Tensor>(\n      x: T, blockShape: number[], crops: number[][]): T {\n    this.assertNotComplex([x], 'batchToSpaceND');\n\n    const prod = blockShape.reduce((a, b) => a * b);\n\n    const reshaped = array_ops_util.getReshaped(x.shape, blockShape, prod);\n    const permuted =\n        array_ops_util.getPermuted(reshaped.length, blockShape.length);\n    const reshapedPermuted =\n        array_ops_util.getReshapedPermuted(x.shape, blockShape, prod);\n    const sliceBeginCoords =\n        array_ops_util.getSliceBeginCoords(crops, blockShape.length);\n    const sliceSize =\n        array_ops_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n\n    return x.reshape(reshaped)\n               .transpose(permuted)\n               .reshape(reshapedPermuted)\n               .slice(sliceBeginCoords, sliceSize) as T;\n  }\n\n  spaceToBatchND<T extends Tensor>(\n      x: T, blockShape: number[], paddings: Array<[number, number]>): T {\n    this.assertNotComplex([x], 'spaceToBatchND');\n\n    const prod = blockShape.reduce((a, b) => a * b);\n\n    const completePaddings: Array<[number, number]> = [[0, 0]];\n    completePaddings.push(...paddings);\n    for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n      completePaddings.push([0, 0]);\n    }\n\n    const paddedX = x.pad(completePaddings);\n\n    const reshapedPaddedShape =\n        array_ops_util.getReshaped(paddedX.shape, blockShape, prod, false);\n    const permutedReshapedPaddedPermutation = array_ops_util.getPermuted(\n        reshapedPaddedShape.length, blockShape.length, false);\n    const flattenShape = array_ops_util.getReshapedPermuted(\n        paddedX.shape, blockShape, prod, false);\n\n    return paddedX.reshape(reshapedPaddedShape)\n               .transpose(permutedReshapedPaddedPermutation)\n               .reshape(flattenShape) as T;\n  }\n\n  private pool(x: Tensor4D, convInfo: Conv2DInfo, poolType: 'max'|'avg'):\n      Tensor4D {\n    this.assertNotComplex(x, 'pool');\n\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n\n    const initialValue =\n        (poolType === 'max' ? Number.NEGATIVE_INFINITY :\n                              Number.POSITIVE_INFINITY);\n\n    const xValues = x.dataSync();\n    const output = ops.buffer(convInfo.outShape, x.dtype);\n    const outputVals = output.values;\n\n    const outputBatchStrides =\n        convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3];\n    const outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3];\n    const outputColStrides = convInfo.outShape[3];\n\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n      const outputBatchOffset = b * outputBatchStrides;\n      const inputBatchOffset = b * x.strides[0];\n      for (let d = 0; d < convInfo.inChannels; ++d) {\n        for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n          const xRCorner = yR * strideHeight - padTop;\n          const xRMin = Math.max(0, xRCorner);\n          const xRMax =\n              Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n          const outputRowOffset = outputBatchOffset + yR * outputRowStrides;\n          for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n            const xCCorner = yC * strideWidth - padLeft;\n            const xCMin = Math.max(0, xCCorner);\n            const xCMax =\n                Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n            let minMaxValue = initialValue;\n            let avgValue = 0;\n            let count = 0;\n            for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n              const xROffset = inputBatchOffset + xR * x.strides[1];\n              for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n                const xCOffset = xROffset + xC * x.strides[2];\n                const pixel = xValues[xCOffset + d];\n                if ((poolType === 'max' && pixel > minMaxValue)) {\n                  minMaxValue = pixel;\n                } else if (poolType === 'avg') {\n                  avgValue += pixel;\n                  count++;\n                }\n              }\n              if (isNaN(minMaxValue)) {\n                break;\n              }\n            }\n            const outputOffset = outputRowOffset + yC * outputColStrides + d;\n            outputVals[outputOffset] =\n                poolType === 'avg' ? avgValue / count : minMaxValue;\n          }\n        }\n      }\n    }\n    return output.toTensor() as Tensor4D;\n  }\n\n  maxPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    return this.pool(x, convInfo, 'max');\n  }\n\n  private maxPoolPositions(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    const maxPositions = ops.buffer(convInfo.outShape, 'int32');\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n      for (let d = 0; d < convInfo.inChannels; ++d) {\n        for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n          const xRCorner = yR * strideHeight - padTop;\n          let xRMin = xRCorner;\n          while (xRMin < 0) {\n            xRMin += dilationHeight;\n          }\n          // const xRMin = Math.max(0, xRCorner);\n          const xRMax =\n              Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n          for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n            const xCCorner = yC * strideWidth - padLeft;\n            let xCMin = xCCorner;\n            while (xCMin < 0) {\n              xCMin += dilationWidth;\n            }\n            const xCMax =\n                Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n            let maxValue = Number.NEGATIVE_INFINITY;\n            let maxPosition = -1;\n\n            for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n              const wR = xR - xRCorner;\n              for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n                const wC = xC - xCCorner;\n                const pixel = x.get(b, xR, xC, d);\n                if (pixel > maxValue) {\n                  maxValue = pixel;\n                  maxPosition = wR * effectiveFilterWidth + wC;\n                }\n              }\n            }\n            maxPositions.set(maxPosition, b, yR, yC, d);\n          }\n        }\n      }\n    }\n    return maxPositions.toTensor() as Tensor4D;\n  }\n\n  maxPoolBackprop(dy: Tensor4D, x: Tensor4D, y: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    this.assertNotComplex([x, y], 'maxPoolBackprop');\n\n    const maxPositions = this.maxPoolPositions(x, convInfo);\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const dx = ops.buffer<Rank.R4>(x.shape, 'float32');\n\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n      for (let d = 0; d < convInfo.inChannels; ++d) {\n        for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n          for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n            // Shader code begins.\n            const dyRCorner = dxR - padTop;\n            const dyCCorner = dxC - padLeft;\n            let dotProd = 0;\n            for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n              const dyR = (dyRCorner + wR) / strideHeight;\n              if (dyR < 0 || dyR >= convInfo.outHeight ||\n                  Math.floor(dyR) !== dyR) {\n                continue;\n              }\n              for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n                const dyC = (dyCCorner + wC) / strideWidth;\n                if (dyC < 0 || dyC >= convInfo.outWidth ||\n                    Math.floor(dyC) !== dyC) {\n                  continue;\n                }\n                const maxPos = effectiveFilterHeight * effectiveFilterWidth -\n                    1 - maxPositions.get(b, dyR, dyC, d);\n                const curPos = wR * effectiveFilterWidth + wC;\n\n                const mask = maxPos === curPos ? 1 : 0;\n                if (mask === 0) {\n                  continue;\n                }\n\n                const pixel = dy.get(b, dyR, dyC, d);\n                dotProd += pixel * mask;\n              }\n            }\n            dx.set(dotProd, b, dxR, dxC, d);\n          }\n        }\n      }\n    }\n    return dx.toTensor();\n  }\n\n  avgPoolBackprop(dy: Tensor4D, x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    this.assertNotComplex([dy, x], 'avgPoolBackprop');\n\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const dx = ops.buffer<Rank.R4>(x.shape, 'float32');\n\n    const avgMultiplier = 1 / (filterHeight * filterWidth);\n\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n      for (let d = 0; d < convInfo.inChannels; ++d) {\n        for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n          for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n            // Shader code begins.\n            const dyRCorner = dxR - padTop;\n            const dyCCorner = dxC - padLeft;\n            let dotProd = 0;\n            for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n              const dyR = (dyRCorner + wR) / strideHeight;\n              if (dyR < 0 || dyR >= convInfo.outHeight ||\n                  Math.floor(dyR) !== dyR) {\n                continue;\n              }\n              for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n                const dyC = (dyCCorner + wC) / strideWidth;\n                if (dyC < 0 || dyC >= convInfo.outWidth ||\n                    Math.floor(dyC) !== dyC) {\n                  continue;\n                }\n\n                const pixel = dy.get(b, dyR, dyC, d);\n                dotProd += pixel;\n              }\n            }\n            dx.set(dotProd * avgMultiplier, b, dxR, dxC, d);\n          }\n        }\n      }\n    }\n    return dx.toTensor();\n  }\n\n  cast<T extends Tensor>(x: T, dtype: DataType): T {\n    return backend_util.castTensor(x, dtype, this);\n  }\n\n  reshape<R extends Rank>(x: Tensor, shape: ShapeMap[R]): Tensor<R> {\n    return backend_util.reshapeTensor(x, shape);\n  }\n\n  avgPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    this.assertNotComplex(x, 'avgPool');\n\n    return this.pool(x, convInfo, 'avg').toFloat();\n  }\n\n  resizeBilinear(\n      x: Tensor4D, newHeight: number, newWidth: number,\n      alignCorners: boolean): Tensor4D {\n    this.assertNotComplex(x, 'resizeBilinear');\n\n    const [batch, oldHeight, oldWidth, numChannels] = x.shape;\n    const xValues = x.dataSync();\n    const result = new Float32Array(\n        util.sizeFromShape([batch, newHeight, newWidth, numChannels]));\n\n    const effectiveInputSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n      (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n    ];\n\n    const effectiveOutputSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n      (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n    ];\n    let outputIdx = 0;\n    const effectiveRowSizeRatio =\n        effectiveInputSize[0] / effectiveOutputSize[0];\n    const effectiveColSizeRatio =\n        effectiveInputSize[1] / effectiveOutputSize[1];\n    for (let b = 0; b < batch; b++) {\n      for (let r = 0; r < newHeight; r++) {\n        const sourceFracRow = effectiveRowSizeRatio * r;\n        const sourceRowFloor = Math.floor(sourceFracRow);\n        const rowFrac = sourceFracRow - sourceRowFloor;\n        const sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));\n        const topRowOffset = b * x.strides[0] + sourceRowFloor * x.strides[1];\n        const botRowOffset = b * x.strides[0] + sourceRowCeil * x.strides[1];\n        for (let c = 0; c < newWidth; c++) {\n          const sourceFracCol = effectiveColSizeRatio * c;\n          const sourceColFloor = Math.floor(sourceFracCol);\n          const colFrac = sourceFracCol - sourceColFloor;\n          const sourceColCeil =\n              Math.min(oldWidth - 1, Math.ceil(sourceFracCol));\n          const topLeftOffest = topRowOffset + sourceColFloor * x.strides[2];\n          const botLeftOffset = botRowOffset + sourceColFloor * x.strides[2];\n          const topRightOffset = topRowOffset + +sourceColCeil * x.strides[2];\n          const botRightOffest = botRowOffset + sourceColCeil * x.strides[2];\n          for (let d = 0; d < numChannels; d++) {\n            // Begin shader.\n\n            // Compute the fractional index of the source.\n            const topLeft = xValues[topLeftOffest + d];\n            const bottomLeft = xValues[botLeftOffset + d];\n            const topRight = xValues[topRightOffset + d];\n            const bottomRight = xValues[botRightOffest + d];\n\n            const top = topLeft + (topRight - topLeft) * colFrac;\n            const bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;\n            const newValue = top + (bottom - top) * rowFrac;\n\n            result[outputIdx++] = newValue;\n          }\n        }\n      }\n    }\n    return ops.tensor(result, [batch, newHeight, newWidth, numChannels]);\n  }\n\n  resizeBilinearBackprop(dy: Tensor4D, x: Tensor4D, alignCorners: boolean) {\n    this.assertNotComplex([dy, x], 'resizeBilinearBackprop');\n\n    const [batch, xHeight, xWidth, depth] = x.shape;\n    const [, yHeight, yWidth] = dy.shape;\n\n    const output = new Float32Array(batch * xHeight * xWidth * depth);\n\n    // In the backwards pass, we want to find the pixels that were generated\n    // for each pixel in the input image the forward pass and add the\n    // corresponding coefficient from dy to the gradient (with some\n    // interpolation).\n\n    const effectiveXSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n      (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n    ];\n\n    const effectiveYSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n      (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n    ];\n\n    const heightScale = effectiveXSize[0] / effectiveYSize[0];\n    const widthScale = effectiveXSize[1] / effectiveYSize[1];\n\n    // Reference implementation\n    // tslint:disable-next-line:max-line-length\n    // https://github.com/tensorflow/tensorflow/blob/3039375c86a5bbc9610c7725dcaa95d635f87ba2/tensorflow/core/kernels/resize_bilinear_op.cc#L275\n\n    const dyValues = dy.dataSync();\n    let offset = 0;\n    for (let b = 0; b < batch; b++) {\n      const bOffset = b * x.strides[0];\n      for (let r = 0; r < yHeight; r++) {\n        const dxR = r * heightScale;\n        const topDxRIndex = Math.floor(dxR);\n        const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);\n\n        const topDxROffset = bOffset + topDxRIndex * x.strides[1];\n        const bottomDxROffset = bOffset + bottomDxRIndex * x.strides[1];\n\n        const dxRLerp = dxR - topDxRIndex;\n        const inverseDxRLerp = 1.0 - dxRLerp;\n        for (let c = 0; c < yWidth; c++) {\n          const dxC = c * widthScale;\n          const leftDxCIndex = Math.floor(dxC);\n          const rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1);\n          const dxCLerp = dxC - leftDxCIndex;\n          const inverseDxCLerp = 1.0 - dxCLerp;\n\n          const topLeftRCOffset = topDxROffset + leftDxCIndex * x.strides[2];\n          const topRightRCOffset = topDxROffset + rightDxCIndex * x.strides[2];\n          const bottomLeftRCOffset =\n              bottomDxROffset + leftDxCIndex * x.strides[2];\n          const bottomRightRCOffset =\n              bottomDxROffset + rightDxCIndex * x.strides[2];\n\n          const inverseDxRLerpTimesInverseDxCLerp =\n              inverseDxRLerp * inverseDxCLerp;\n          const inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp;\n          const dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp;\n          const dxRLerpTimesDxCLerp = dxRLerp * dxCLerp;\n          for (let d = 0; d < depth; d++) {\n            const dyVal = dyValues[offset++];\n            output[topLeftRCOffset + d] +=\n                dyVal * inverseDxRLerpTimesInverseDxCLerp;\n            output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp;\n            output[bottomLeftRCOffset + d] +=\n                dyVal * dxRLerpTimesInverseDxCLerp;\n            output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp;\n          }\n        }\n      }\n    }\n    return ops.tensor4d(output, [batch, xWidth, xHeight, depth], x.dtype);\n  }\n\n  resizeNearestNeighbor(\n      x: Tensor4D, newHeight: number, newWidth: number,\n      alignCorners: boolean): Tensor4D {\n    this.assertNotComplex(x, 'resizeNearestNeighbor');\n\n    const [batch, oldHeight, oldWidth, numChannels] = x.shape;\n    const xValues = x.dataSync();\n    const output = new Float32Array(batch * newHeight * newWidth * numChannels);\n\n    const effectiveInputSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n      (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n    ];\n\n    const effectiveOutputSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n      (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n    ];\n\n    const effectiveRowSizeRatio =\n        effectiveInputSize[0] / effectiveOutputSize[0];\n    const effectiveColSizeRatio =\n        effectiveInputSize[1] / effectiveOutputSize[1];\n\n    let outputOffset = 0;\n    for (let b = 0; b < batch; b++) {\n      const batchOffset = b * x.strides[0];\n      for (let r = 0; r < newHeight; r++) {\n        const sourceFracRow = effectiveRowSizeRatio * r;\n        const sourceNearestRow = Math.min(\n            oldHeight - 1,\n            alignCorners ? Math.round(sourceFracRow) :\n                           Math.floor(sourceFracRow));\n        const rowOffset = batchOffset + sourceNearestRow * x.strides[1];\n        for (let c = 0; c < newWidth; c++) {\n          const sourceFracCol = effectiveColSizeRatio * c;\n          const sourceNearestCol = Math.min(\n              oldWidth - 1,\n              alignCorners ? Math.round(sourceFracCol) :\n                             Math.floor(sourceFracCol));\n          const colOffset = rowOffset + sourceNearestCol * x.strides[2];\n          for (let d = 0; d < numChannels; d++) {\n            // Begin shader.\n            // Compute the fractional index of the source.\n            const newVal = xValues[colOffset + d];\n            output[outputOffset++] = newVal;\n          }\n        }\n      }\n    }\n    return ops.tensor(\n        output, [batch, newHeight, newWidth, numChannels], x.dtype);\n  }\n\n  resizeNearestNeighborBackprop(\n      dy: Tensor4D, x: Tensor4D, alignCorners: boolean) {\n    this.assertNotComplex([dy, x], 'resizeNearestNeighborBackprop');\n\n    const [batch, xHeight, xWidth, depth] = x.shape;\n    const [, yHeight, yWidth] = dy.shape;\n\n    const output = new Float32Array(batch * xHeight * xWidth * depth);\n    const dyValues = dy.dataSync();\n\n    // In the backwards pass, we want to find the pixels that were generated\n    // for each pixel in the input image the forward pass\n\n    const effectiveXSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n      (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n    ];\n\n    const effectiveYSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n      (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n    ];\n\n    const heightScale = effectiveXSize[0] / effectiveYSize[0];\n    const widthScale = effectiveXSize[1] / effectiveYSize[1];\n\n    const invHeightScale = 1 / heightScale;\n    const invWidthScale = 1 / widthScale;\n\n    // This defines the size of the window of values around a particular\n    // index in dy that we want to search for contributions to dx.\n    const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n    const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n\n    // Loop over the output space.\n    for (let b = 0; b < batch; b++) {\n      const batchOffset = b * x.strides[0];\n      for (let r = 0; r < xHeight; r++) {\n        const rowOffset = batchOffset + r * x.strides[1];\n\n        // Compute bounds for where in dy we will look\n        const startRLerp = Math.floor(r * invHeightScale);\n        const startDyR = Math.floor(startRLerp - (winHeight / 2));\n        for (let c = 0; c < xWidth; c++) {\n          const colOffset = rowOffset + c * x.strides[2];\n\n          // Compute bounds for where in dy we will look\n          const startCLerp = Math.floor(c * invWidthScale);\n          const startDyC = Math.floor(startCLerp - (winWidth / 2));\n\n          for (let d = 0; d < depth; d++) {\n            let accum = 0;\n            // loop over dy\n\n            for (let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {\n              const dyR = dyRIndex + startDyR;\n              // Guard against the window exceeding the bounds of dy\n              if (dyR < 0 || dyR >= yHeight) {\n                continue;\n              }\n\n              const dyROffset = batchOffset + dyR * dy.strides[1];\n              const sourceFracRow = dyR * heightScale;\n              const sourceNearestRow = Math.min(\n                  xHeight - 1,\n                  alignCorners ? Math.round(sourceFracRow) :\n                                 Math.floor(sourceFracRow));\n              if (r !== sourceNearestRow) {\n                continue;\n              }\n              for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {\n                const dyC = dyCIndex + startDyC;\n                // Guard against the window exceeding the bounds of dy\n                if (dyC < 0 || dyC >= yWidth) {\n                  continue;\n                }\n\n                const dyCOffset = dyROffset + dyC * dy.strides[2];\n                const sourceFracCol = dyC * widthScale;\n                const sourceNearestCol = Math.min(\n                    xWidth - 1,\n                    alignCorners ? Math.round(sourceFracCol) :\n                                   Math.floor(sourceFracCol));\n\n                if (c === sourceNearestCol) {\n                  accum += dyValues[dyCOffset + d];\n                }\n              }\n            }\n            output[colOffset + d] = accum;\n          }\n        }\n      }\n    }\n    return ops.tensor4d(output, x.shape, x.dtype);\n  }\n\n  batchNormalization(\n      x: Tensor4D, mean: Tensor4D|Tensor1D, variance: Tensor4D|Tensor1D,\n      varianceEpsilon: number, scale?: Tensor4D|Tensor1D,\n      offset?: Tensor4D|Tensor1D): Tensor4D {\n    this.assertNotComplex(\n        [x, mean, variance, scale, offset], 'batchNormalization');\n\n    const xVals = x.dataSync();\n    const mVals = mean.dataSync();\n    const varVals = variance.dataSync();\n    const sVals = scale ? scale.dataSync() : new Float32Array([1]);\n    const offVals = offset ? offset.dataSync() : new Float32Array([0]);\n    const outVals = new Float32Array(xVals.length);\n\n    const offValsLength = offVals.length;\n    const sValsLength = sVals.length;\n    const varValsLength = varVals.length;\n    const mValsLength = mVals.length;\n\n    let offi = 0;\n    let mi = 0;\n    let si = 0;\n    let vi = 0;\n    for (let i = 0; i < xVals.length; ++i) {\n      outVals[i] = offVals[offi++] +\n          (xVals[i] - mVals[mi++]) * sVals[si++] /\n              Math.sqrt(varVals[vi++] + varianceEpsilon);\n      if (offi >= offValsLength) {\n        offi = 0;\n      }\n      if (mi >= mValsLength) {\n        mi = 0;\n      }\n      if (si >= sValsLength) {\n        si = 0;\n      }\n      if (vi >= varValsLength) {\n        vi = 0;\n      }\n    }\n    return tensor4d(outVals, x.shape);\n  }\n\n  localResponseNormalization4D(\n      x: Tensor4D, depthRadius: number, bias: number, alpha: number,\n      beta: number): Tensor4D {\n    this.assertNotComplex(x, 'localResponseNormalization4D');\n\n    const channels = x.shape[3];\n    const maxD = channels - 1;\n    const xValues = x.dataSync();\n    const size = util.sizeFromShape(x.shape);\n    const result = new Float32Array(size);\n\n    function sumAcrossChannels(offset: number) {\n      const currentChannel = offset % channels;\n      let beginSumOffset =\n          offset - currentChannel + Math.max(0, currentChannel - depthRadius);\n      const endSumOffset = offset - currentChannel +\n          Math.min(currentChannel + depthRadius, maxD);\n\n      let sum = 0.0;\n      for (; beginSumOffset <= endSumOffset; beginSumOffset++) {\n        const z = xValues[beginSumOffset];\n        sum += z * z;\n      }\n      return sum;\n    }\n\n    for (let offset = 0; offset < size; offset++) {\n      const sum = sumAcrossChannels(offset);\n      const val = xValues[offset] * Math.pow(bias + alpha * sum, -beta);\n      result[offset] = val;\n    }\n\n    return ops.tensor4d(result, x.shape);\n  }\n\n  LRNGrad(\n      dy: Tensor4D, inputImage: Tensor4D, outputImage: Tensor4D,\n      depthRadius: number, bias: number, alpha: number,\n      beta: number): Tensor4D {\n    this.assertNotComplex(dy, 'LRNGrad');\n    const channels = dy.shape[3];\n    const dyValues = dy.dataSync();\n    const inputImageValues = inputImage.dataSync();\n    const outputImageValues = outputImage.dataSync();\n    const result = new Float32Array(util.sizeFromShape(dy.shape));\n    const size = util.sizeFromShape(dy.shape);\n\n    for (let offset = 0; offset < size; offset++) {\n      const currentChannel = offset % channels;\n      const depthBegin =\n          (offset - currentChannel) + Math.max(0, currentChannel - depthRadius);\n      const depthEnd = (offset - currentChannel) +\n          Math.min(channels, currentChannel + depthRadius + 1);\n\n      let norm = 0;\n      for (let k = depthBegin; k < depthEnd; k++) {\n        norm += Math.pow(inputImageValues[k], 2);\n      }\n      norm = alpha * norm + bias;\n\n      for (let k = depthBegin; k < depthEnd; k++) {\n        let dyi = -2 * alpha * beta * inputImageValues[k] *\n            outputImageValues[offset] / norm;\n        if (offset === k) {\n          dyi += Math.pow(norm, -beta);\n        }\n        dyi *= dyValues[offset];\n        result[k] += dyi;\n      }\n    }\n    return ops.tensor4d(result, dy.shape);\n  }\n\n  multinomial(\n      logits: Tensor2D, normalized: boolean, numSamples: number,\n      seed: number): Tensor2D {\n    this.assertNotComplex(logits, 'multinomial');\n\n    const probabilities = normalized ? logits : ops.softmax(logits);\n    const batchSize = probabilities.shape[0];\n    const numEvents = probabilities.shape[1];\n    const res = ops.zeros<Rank.R2>([batchSize, numSamples], 'int32');\n    const resVals = res.dataSync();\n    const probVals = probabilities.dataSync();\n\n    for (let b = 0; b < batchSize; ++b) {\n      const offset = b * numEvents;\n      // The cdf won't include the last event. It will be implicit if no other\n      // event happened.\n      const cdf = new Float32Array(numEvents - 1);\n      cdf[0] = probVals[offset];\n      for (let event = 1; event < cdf.length; ++event) {\n        cdf[event] = cdf[event - 1] + probVals[offset + event];\n      }\n\n      const random = seedrandom.alea(seed.toString());\n      const outOffset = b * numSamples;\n      for (let sampleId = 0; sampleId < numSamples; ++sampleId) {\n        const r = random();\n\n        // Assume last event happened by default.\n        resVals[outOffset + sampleId] = cdf.length;\n\n        for (let event = 0; event < cdf.length; event++) {\n          if (r < cdf[event]) {\n            resVals[outOffset + sampleId] = event;\n            break;\n          }\n        }\n      }\n    }\n    return res;\n  }\n\n  oneHot(indices: Tensor1D, depth: number, onValue: number, offValue: number):\n      Tensor2D {\n    this.assertNotComplex(indices, 'oneHot');\n\n    const res = new Float32Array(indices.size * depth);\n    res.fill(offValue);\n\n    for (let event = 0; event < indices.size; ++event) {\n      if (indices.get(event) >= 0 && indices.get(event) < depth) {\n        res[event * depth + indices.get(event)] = onValue;\n      }\n    }\n    return ops.tensor2d(res, [indices.size, depth], 'int32');\n  }\n\n  nonMaxSuppression(\n      boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number,\n      iouThreshold: number, scoreThreshold: number): Tensor1D {\n    this.assertNotComplex(boxes, 'nonMaxSuppression');\n\n    const boxesVals = boxes.dataSync();\n    const scoresVals = scores.dataSync();\n    return nonMaxSuppressionImpl(\n        boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n  }\n\n  fft(x: Tensor2D): Tensor2D {\n    return this.fftBatch(x, false);\n  }\n\n  ifft(x: Tensor2D): Tensor2D {\n    return this.fftBatch(x, true);\n  }\n\n  /**\n   * Calculate FFT of inner most elements of batch tensor.\n   */\n  private fftBatch(x: Tensor2D, inverse: boolean): Tensor2D {\n    const batch = x.shape[0];\n    const innerDim = x.shape[1];\n    // Collects real and imaginary values separately.\n    const realResult = ops.buffer(x.shape, 'float32');\n    const imagResult = ops.buffer(x.shape, 'float32');\n\n    const real = ops.real(x).as2D(batch, innerDim);\n    const imag = ops.imag(x).as2D(batch, innerDim);\n\n    for (let b = 0; b < batch; b++) {\n      // TODO: Support slice ops for complex type.\n      const r = real.slice([b, 0], [1, innerDim]);\n      const i = imag.slice([b, 0], [1, innerDim]);\n      const input = ops.complex(r, i);\n      // Run FFT by batch element.\n      const res = this.fftImpl(input, inverse).dataSync() as Float32Array;\n      for (let d = 0; d < innerDim; d++) {\n        const c = complex_util.getComplexWithIndex(res, d);\n        realResult.values[b * innerDim + d] = c.real;\n        imagResult.values[b * innerDim + d] = c.imag;\n      }\n    }\n\n    const t = ops.complex(realResult.toTensor(), imagResult.toTensor());\n    return t.as2D(batch, innerDim);\n  }\n\n  private fftImpl(x: Tensor2D, inverse: boolean): Tensor2D {\n    const x1D = x.as1D();\n\n    const n = x1D.size;\n\n    if (this.isExponentOf2(n)) {\n      let result = this.fftRadix2(x1D, n, inverse).as2D(x.shape[0], x.shape[1]);\n      if (inverse) {\n        result = ops.complex(\n                     ops.real(result).div(scalar(n)),\n                     ops.imag(result).div(scalar(n))) as Tensor2D;\n      }\n      return result;\n    } else {\n      const data = x.dataSync();\n      const rawOutput =\n          this.fourierTransformByMatmul(data, n, inverse) as Float32Array;\n      const output = complex_util.splitRealAndImagArrays(rawOutput);\n      return ops.complex(output.real, output.imag).as2D(x.shape[0], x.shape[1]);\n    }\n  }\n\n  private isExponentOf2(size: number): boolean {\n    return (size & size - 1) === 0;\n  }\n\n  // FFT using Cooley-Tukey algorithm on radix 2 dimensional input.\n  private fftRadix2(input: Tensor1D, size: number, inverse: boolean): Tensor1D {\n    if (size === 1) {\n      return input;\n    }\n    const data = input.dataSync() as Float32Array;\n    const half = size / 2;\n    const evenComplex = complex_util.complexWithEvenIndex(data);\n    let evenTensor = ops.complex(evenComplex.real, evenComplex.imag).as1D();\n    const oddComplex = complex_util.complexWithOddIndex(data);\n    let oddTensor = ops.complex(oddComplex.real, oddComplex.imag).as1D();\n\n    // Recursive call for half part of original input.\n    evenTensor = this.fftRadix2(evenTensor, half, inverse);\n    oddTensor = this.fftRadix2(oddTensor, half, inverse);\n\n    const e = complex_util.exponents(size, inverse);\n    const exponent = ops.complex(e.real, e.imag).mul(oddTensor);\n\n    const addPart = evenTensor.add(exponent);\n    const subPart = evenTensor.sub(exponent);\n\n    const realTensor = ops.real(addPart).concat(ops.real(subPart));\n    const imagTensor = ops.imag(addPart).concat(ops.imag(subPart));\n\n    return ops.complex(realTensor, imagTensor).as1D();\n  }\n\n  // Calculate fourier transform by multplying sinusoid matrix.\n  private fourierTransformByMatmul(\n      data: TypedArray, size: number, inverse: boolean): TypedArray {\n    const ret = new Float32Array(size * 2);\n    // TODO: Use matmul instead once it supports complex64 type.\n    for (let r = 0; r < size; r++) {\n      let real = 0.0;\n      let imag = 0.0;\n      for (let c = 0; c < size; c++) {\n        const e = complex_util.exponent(r * c, size, inverse);\n        const term = complex_util.getComplexWithIndex(data as Float32Array, c);\n        real += term.real * e.real - term.imag * e.imag;\n        imag += term.real * e.imag + term.imag * e.real;\n      }\n      if (inverse) {\n        real /= size;\n        imag /= size;\n      }\n      complex_util.assignToTypedArray(ret, real, imag, r);\n    }\n    return ret;\n  }\n\n  depthToSpace(x: Tensor4D, blockSize: number, dataFormat: 'NHWC'|'NCHW'):\n      Tensor4D {\n    util.assert(\n        dataFormat === 'NHWC',\n        `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${\n            dataFormat}`);\n    util.assert(\n        blockSize > 1,\n        `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n\n    const batchSize = x.shape[0];\n    const inputHeight = x.shape[1];\n    const inputWidth = x.shape[2];\n    const inputDepth = x.shape[3];\n\n    const outputHeight = inputHeight * blockSize;\n    const outputWidth = inputWidth * blockSize;\n    const outputDepth = inputDepth / (blockSize * blockSize);\n\n    const xValues = x.dataSync();\n    const result =\n        new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);\n\n    let outputIdx = 0;\n    for (let b = 0; b < batchSize; ++b) {\n      for (let h = 0; h < outputHeight; ++h) {\n        const inH = Math.floor(h / blockSize);\n        const offsetH = (h % blockSize);\n        for (let w = 0; w < outputWidth; ++w) {\n          const inW = Math.floor(w / blockSize);\n          const offsetW = (w % blockSize);\n          const offsetD = (offsetH * blockSize + offsetW) * outputDepth;\n          for (let d = 0; d < outputDepth; ++d) {\n            const inD = d + offsetD;\n            const inputIdx =\n                inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));\n            result[outputIdx++] = xValues[inputIdx];\n          }\n        }\n      }\n    }\n    return ops.tensor4d(\n        result, [batchSize, outputHeight, outputWidth, outputDepth]);\n  }\n\n  private broadcastedBinaryOp(\n      a: Tensor, b: Tensor, dtype: DataType,\n      op: (a: number, b: number) => number): Tensor {\n    const newShape =\n        broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n    const result = ops.buffer(newShape, dtype);\n    const aVals = a.dataSync();\n    const bVals = b.dataSync();\n    const aBroadcastDims = broadcast_util.getBroadcastDims(a.shape, newShape);\n    const bBroadcastDims = broadcast_util.getBroadcastDims(b.shape, newShape);\n\n    const resVals = result.values;\n    if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n      for (let i = 0; i < resVals.length; ++i) {\n        resVals[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);\n      }\n    } else {\n      const aBuf = a.buffer();\n      const bBuf = b.buffer();\n      for (let i = 0; i < resVals.length; ++i) {\n        const loc = result.indexToLoc(i);\n\n        const aLoc = loc.slice(-a.rank);\n        aBroadcastDims.forEach(d => aLoc[d] = 0);\n        const aIndex = aBuf.locToIndex(aLoc);\n\n        const bLoc = loc.slice(-b.rank);\n        bBroadcastDims.forEach(d => bLoc[d] = 0);\n        const bIndex = bBuf.locToIndex(bLoc);\n\n        resVals[i] = op(aVals[aIndex], bVals[bIndex]);\n      }\n    }\n    return result.toTensor();\n  }\n\n  private broadcastedBinaryComplexOp(\n      a: Tensor, b: Tensor,\n      op:\n          (aReal: number, aImag: number, bReal: number,\n           bImag: number) => {real: number, imag: number}): Tensor {\n    const newShape =\n        broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n    const realResult = ops.buffer(newShape, 'float32');\n    const imagResult = ops.buffer(newShape, 'float32');\n\n    const aVals = a.dataSync();\n    const bVals = b.dataSync();\n    const aBroadcastDims = broadcast_util.getBroadcastDims(a.shape, newShape);\n    const bBroadcastDims = broadcast_util.getBroadcastDims(b.shape, newShape);\n\n    const realVals = realResult.values;\n    const imagVals = imagResult.values;\n\n    if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n      for (let i = 0; i < realVals.length; i++) {\n        const aIdx = i % aVals.length;\n        const bIdx = i % bVals.length;\n\n        const result =\n            op(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2],\n               bVals[bIdx * 2 + 1]);\n\n        realVals[i] = result.real;\n        imagVals[i] = result.imag;\n      }\n    } else {\n      const aRealBuf = this.data.get(a.dataId).complexTensors.real.buffer();\n      const bRealBuf = this.data.get(b.dataId).complexTensors.real.buffer();\n      for (let i = 0; i < realVals.length; i++) {\n        const loc = realResult.indexToLoc(i);\n\n        const aLoc = loc.slice(-a.rank);\n        aBroadcastDims.forEach(d => aLoc[d] = 0);\n        const aIndex = aRealBuf.locToIndex(aLoc);\n\n        const bLoc = loc.slice(-b.rank);\n        bBroadcastDims.forEach(d => bLoc[d] = 0);\n        const bIndex = bRealBuf.locToIndex(bLoc);\n\n        const opResult =\n            op(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2],\n               bVals[bIndex * 2 + 1]);\n\n        realVals[i] = opResult.real;\n        imagVals[i] = opResult.imag;\n      }\n    }\n    return this.complex(realResult.toTensor(), imagResult.toTensor());\n  }\n\n  split<T extends Tensor>(x: T, sizeSplits: number[], axis: number): T[] {\n    return split(x, sizeSplits, axis);\n  }\n\n  dispose() {}\n\n  floatPrecision() {\n    return 32;\n  }\n\n  cropAndResize(\n      images: Tensor4D,\n      boxes: Tensor2D,\n      boxIndex: Tensor1D,\n      cropSize: [number, number],\n      method: string,\n      extrapolationValue: number,\n  ) {\n    const [batch, imageHeight, imageWidth, numChannels] = images.shape;\n    const numBoxes = boxes.shape[0];\n\n    const [cropHeight, cropWidth] = cropSize;\n    const output = ops.buffer(\n        [numBoxes, cropHeight, cropWidth, numChannels], images.dtype);\n\n    const boxVals = boxes.dataSync();\n    const boxIndVals = boxIndex.dataSync();\n    const imageVals = images.dataSync();\n\n    const inStride = images.strides;   // to calculate flat indexes into image\n    const outStride = output.strides;  // to calculate flat indexes into output\n\n    // Reference implementation\n    // tslint:disable-next-line:max-line-length\n    // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op.cc\n    for (let b = 0; b < numBoxes; b++) {\n      const startInd = b * 4;\n      const y1 = boxVals[startInd];\n      const x1 = boxVals[startInd + 1];\n      const y2 = boxVals[startInd + 2];\n      const x2 = boxVals[startInd + 3];\n\n      const bInd: number = boxIndVals[b];\n      if (bInd >= batch) {\n        continue;\n      }\n\n      const heightScale = (cropHeight > 1) ?\n          (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) :\n          0;\n      const widthScale =\n          (cropWidth > 1) ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;\n\n      for (let y = 0; y < cropHeight; y++) {\n        const yInd: number = (cropHeight > 1) ?\n            y1 * (imageHeight - 1) + y * (heightScale) :\n            0.5 * (y1 + y2) * (imageHeight - 1);\n\n        if (yInd < 0 || yInd > imageHeight - 1) {\n          for (let x = 0; x < cropWidth; x++) {\n            for (let c = 0; c < numChannels; c++) {\n              const ind =\n                  c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n              output.values[ind] = extrapolationValue;\n            }\n          }\n          continue;\n        }\n\n        if (method === 'bilinear') {\n          const topInd = Math.floor(yInd);\n          const bottomInd = Math.ceil(yInd);\n          const yLerp = yInd - topInd;\n\n          for (let x = 0; x < cropWidth; x++) {\n            const xInd = (cropWidth > 1) ?\n                x1 * (imageWidth - 1) + x * widthScale :\n                0.5 * (x1 + x2) * (imageWidth - 1);\n\n            if (xInd < 0 || xInd > imageWidth - 1) {\n              for (let c = 0; c < numChannels; c++) {\n                const ind =\n                    c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                output.values[ind] = extrapolationValue;\n              }\n              continue;\n            }\n\n            const leftInd = Math.floor(xInd);\n            const rightInd = Math.ceil(xInd);\n            const xLerp = xInd - leftInd;\n\n            for (let c = 0; c < numChannels; c++) {\n              let ind = c + leftInd * inStride[2] + topInd * inStride[1] +\n                  bInd * inStride[0];\n              const topLeft = imageVals[ind];\n\n              ind = c + rightInd * inStride[2] + topInd * inStride[1] +\n                  bInd * inStride[0];\n              const topRight = imageVals[ind];\n\n              ind = c + leftInd * inStride[2] + bottomInd * inStride[1] +\n                  bInd * inStride[0];\n              const bottomLeft = imageVals[ind];\n\n              ind = c + rightInd * inStride[2] + bottomInd * inStride[1] +\n                  bInd * inStride[0];\n              const bottomRight = imageVals[ind];\n\n              const top = topLeft + (topRight - topLeft) * xLerp;\n              const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;\n\n              ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n              output.values[ind] = top + ((bottom - top) * yLerp);\n            }\n          }\n        } else {  // method == \"nearest\"\n          for (let x = 0; x < cropWidth; ++x) {\n            const xInd = (cropWidth > 1) ?\n                x1 * (imageWidth - 1) + x * widthScale :\n                0.5 * (x1 + x2) * (imageWidth - 1);\n\n            if (xInd < 0 || xInd > imageWidth - 1) {\n              for (let c = 0; c < numChannels; c++) {\n                const ind =\n                    c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                output.values[ind] = extrapolationValue;\n              }\n              continue;\n            }\n\n            const closestX = Math.round(xInd);\n            const closestY = Math.round(yInd);\n            for (let c = 0; c < numChannels; c++) {\n              const inInd = c + closestX * inStride[2] +\n                  closestY * inStride[1] + bInd * inStride[0];\n              const outInd =\n                  c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n              output.values[outInd] = imageVals[inInd];\n            }\n          }\n        }\n      }\n    }\n    return output.toTensor() as Tensor4D;\n  }\n\n  sparseToDense<R extends Rank>(\n      sparseIndices: Tensor, sparseValues: Tensor, outputShape: ShapeMap[R],\n      defaultValue: Scalar): Tensor<R> {\n    const {sliceRank, numUpdates, sliceSize, strides, outputSize} =\n        scatter_nd_util.calculateShapes(\n            sparseValues, sparseIndices, outputShape);\n    const sumDupeIndices = false;\n    return this.scatter(\n        sparseIndices, sparseValues, outputShape, outputSize, sliceSize,\n        numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);\n  }\n\n  gatherND(x: Tensor, indices: Tensor): Tensor<Rank> {\n    const indicesShape = indices.shape;\n    const sliceRank = indicesShape[indicesShape.length - 1];\n\n    const [resultShape, numSlices, sliceSize, strides] =\n        gather_nd_util.prepareAndValidate(x, indices);\n    if (numSlices === 0) {\n      return tensor([], resultShape, x.dtype);\n    }\n\n    const buffer = new TensorBuffer([numSlices, sliceSize], x.dtype);\n    const indicesData = indices.dataSync();\n    const xData = x.dataSync();\n\n    for (let i = 0; i < numSlices; i++) {\n      const index = [];\n      let flattenIndex = 0;\n      for (let j = 0; j < sliceRank; j++) {\n        const dim = indicesData[i * sliceRank + j];\n        flattenIndex += dim * strides[j];\n        index.push(dim);\n      }\n      if (flattenIndex < 0 || flattenIndex >= x.size / sliceSize) {\n        throw new Error(\n            `Invalid indices: ${index} does not index into ${x.shape}`);\n      }\n\n      for (let k = 0; k < sliceSize; k++) {\n        buffer.values[i * sliceSize + k] = xData[flattenIndex * sliceSize + k];\n      }\n    }\n    return buffer.toTensor().reshape(resultShape);\n  }\n\n  scatterND<R extends Rank>(\n      indices: Tensor, updates: Tensor, shape: ShapeMap[R]): Tensor<R> {\n    const {sliceRank, numUpdates, sliceSize, strides, outputSize} =\n        scatter_nd_util.calculateShapes(updates, indices, shape);\n    const defaultValue = scalar(0);\n    const sumDupeIndices = true;\n    return this.scatter(\n        indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank,\n        strides, defaultValue, sumDupeIndices);\n  }\n\n  private scatter<R extends Rank>(\n      indices: Tensor, updates: Tensor, shape: ShapeMap[R], outputSize: number,\n      sliceSize: number, numUpdates: number, sliceRank: number,\n      strides: number[], defaultValue: Scalar,\n      sumDupeIndices: boolean): Tensor<R> {\n    const flattenShape = [outputSize / sliceSize, sliceSize];\n    const indicesData = indices.dataSync();\n    const updatesData = updates.dataSync();\n\n    if (outputSize === 0) {\n      return tensor([], shape, updates.dtype);\n    }\n\n    const buffer = new TensorBuffer(flattenShape, updates.dtype as 'float32');\n    buffer.values.fill(defaultValue.dataSync()[0]);\n\n    for (let i = 0; i < numUpdates; i++) {\n      const index = [];\n      let flattenIndex = 0;\n      for (let j = 0; j < sliceRank; j++) {\n        const dim = indicesData[i * sliceRank + j];\n        index.push(dim);\n        flattenIndex += dim * strides[j];\n      }\n\n      if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) {\n        throw new Error(\n            `Invalid indices: ${index} does not index into ${shape}`);\n      }\n\n      for (let k = 0; k < sliceSize; k++) {\n        if (sumDupeIndices) {\n          buffer.values[flattenIndex * sliceSize + k] +=\n              updatesData[i * sliceSize + k];\n        } else {\n          buffer.values[flattenIndex * sliceSize + k] = updates.rank === 0 ?\n              updatesData[0] :\n              updatesData[i * sliceSize + k];\n        }\n      }\n    }\n    return buffer.toTensor().reshape(shape);\n  }\n}\n\nENV.registerBackend(\n    'cpu', () => new MathBackendCPU(), 1 /* priority */, setTensorTracker);\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nconst delayCallback: Function = typeof requestAnimationFrame !== 'undefined' ?\n    requestAnimationFrame :  // Browsers\n    setImmediate;            // Node.js\n\n/**\n * Returns a promise that resolve when a requestAnimationFrame has completed.\n *\n * On Node.js this uses setImmediate instead of requestAnimationFrame.\n *\n * This is simply a sugar method so that users can do the following:\n * `await tf.nextFrame();`\n */\n/** @doc {heading: 'Performance', subheading: 'Timing'} */\nfunction nextFrame(): Promise<void> {\n  return new Promise<void>(resolve => delayCallback(() => resolve()));\n}\n\nexport {nextFrame};\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/* Type definitions for exporting and importing of models. */\n\n/**\n * A map from Tensor dtype to number of bytes per element of the Tensor.\n */\nexport const DTYPE_VALUE_SIZE_MAP: {[dtype: string]: number} = {\n  'float32': 4,\n  'int32': 4,\n  'uint16': 2,\n  'uint8': 1,\n  'bool': 1,\n};\n\n/**\n * A weight manifest.\n *\n * The weight manifest consists of an ordered list of weight-manifest groups.\n * Each weight-manifest group (\"group\" for short hereafter) consists of a\n * number of weight values stored in a number of paths.\n * See the documentation of `WeightManifestGroupConfig` below for more details.\n */\nexport declare type WeightsManifestConfig = WeightsManifestGroupConfig[];\n\n/**\n * A weight-manifest group.\n *\n * Consists of an ordered list of weight values encoded in binary format,\n * stored in an ordered list of paths.\n */\nexport declare interface WeightsManifestGroupConfig {\n  /**\n   * An ordered list of paths.\n   *\n   * Paths are intentionally abstract in order to be general. For example, they\n   * can be relative URL paths or relative paths on the file system.\n   */\n  paths: string[];\n\n  /**\n   * Specifications of the weights stored in the paths.\n   */\n  weights: WeightsManifestEntry[];\n}\n\n/**\n * An entry in the weight manifest.\n *\n * The entry contains specification of a weight.\n */\nexport declare interface WeightsManifestEntry {\n  /**\n   * Name of the weight, e.g., 'Dense_1/bias'\n   */\n  name: string;\n\n  /**\n   * Shape of the weight.\n   */\n  shape: number[];\n\n  /**\n   * Data type of the weight.\n   */\n  dtype: 'float32'|'int32'|'bool';\n\n  /**\n   * Information for dequantization of the weight.\n   */\n  quantization?: {\n    scale: number,           // The scaling constant to multiply by.\n    min: number,             // The (possibly nudged) minimum weight to add.\n    dtype: 'uint16'|'uint8'  // The dtype of the quantized weights.\n  };\n}\n\n/**\n * Options for saving a model.\n */\nexport interface SaveConfig {\n  /**\n   * Whether to save only the trainable weights of the model, ignoring the\n   * untrainable ones.\n   */\n  trainableOnly?: boolean;\n}\n\n/**\n * Result of a saving operation.\n */\nexport interface SaveResult {\n  /**\n   * Information about the model artifacts saved.\n   */\n  modelArtifactsInfo: ModelArtifactsInfo;\n\n  /**\n   * HTTP responses from the server that handled the model-saving request (if\n   * any). This is applicable only to server-based saving routes.\n   */\n  responses?: Response[];\n\n  /**\n   * Error messages and related data (if any).\n   */\n  errors?: Array<{}|string>;\n}\n\nexport declare interface ModelArtifactsInfo {\n  /**\n   * Timestamp for when the model is saved.\n   */\n  dateSaved: Date;\n\n  /**\n   * Type of the model topology\n   *\n   * Possible values:\n   *   - JSON: JSON config (human-readable, e.g., Keras JSON).\n   *   - GraphDef: TensorFlow\n   *     [GraphDef](https://www.tensorflow.org/extend/tool_developers/#graphdef)\n   *     protocol buffer (binary).\n   */\n  modelTopologyType: 'JSON'|'GraphDef';\n\n  /**\n   * Size of model topology (Keras JSON or GraphDef), in bytes.\n   */\n  modelTopologyBytes?: number;\n\n  /**\n   * Size of weight specification or manifest, in bytes.\n   */\n  weightSpecsBytes?: number;\n\n  /**\n   * Size of weight value data, in bytes.\n   */\n  weightDataBytes?: number;\n}\n\n/**\n * The serialized artifacts of a model, including topology and weights.\n *\n * The `modelTopology`, `weightSpecs` and `weightData` fields of this interface\n * are optional, in order to support topology- or weights-only saving and\n * loading.\n */\nexport declare interface ModelArtifacts {\n  /**\n   * Model topology.\n   *\n   * For Keras-style `tf.Model`s, this is a JSON object.\n   * For TensorFlow-style models (e.g., `FrozenModel`), this is a binary buffer\n   * carrying the `GraphDef` protocol buffer.\n   */\n  modelTopology?: {}|ArrayBuffer;\n\n  /**\n   * Weight specifications.\n   *\n   * This corresponds to the weightsData below.\n   */\n  weightSpecs?: WeightsManifestEntry[];\n\n  /**\n   * Binary buffer for all weight values concatenated in the order specified\n   * by `weightSpecs`.\n   */\n  weightData?: ArrayBuffer;\n}\n\n/**\n * Type definition for handlers of loading operations.\n */\nexport type LoadHandler = () => Promise<ModelArtifacts>;\n\n/**\n * Type definition for handlers of saving operations.\n */\nexport type SaveHandler = (modelArtifact: ModelArtifacts) =>\n    Promise<SaveResult>;\n\n/**\n * Interface for a model import/export handler.\n *\n * The `save` and `load` handlers are both optional, in order to allow handlers\n * that support only saving or loading.\n */\n// tslint:disable-next-line:interface-name\nexport interface IOHandler {\n  save?: SaveHandler;\n  load?: LoadHandler;\n}\n\n/**\n * An interface for the manager of a model store.\n *\n * A model store is defined as a storage medium on which multiple models can\n * be stored. Each stored model has a unique `path` as its identifier.\n * A `ModelStoreManager` for the store allows actions including\n *\n * - Listing the models stored in the store.\n * - Deleting a model from the store.\n */\nexport interface ModelStoreManager {\n  /**\n   * List all models in the model store.\n   *\n   * @returns A dictionary mapping paths of existing models to their\n   *   model artifacts info. Model artifacts info include type of the model's\n   *   topology, byte sizes of the topology, weights, etc.\n   */\n  listModels(): Promise<{[path: string]: ModelArtifactsInfo}>;\n\n  /**\n   * Remove a model specified by `path`.\n   *\n   * @param path\n   * @returns ModelArtifactsInfo of the deleted model (if and only if deletion\n   *   is successful).\n   * @throws Error if deletion fails, e.g., if no model exists at `path`.\n   */\n  removeModel(path: string): Promise<ModelArtifactsInfo>;\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {tensor} from '../ops/tensor_ops';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {TypedArray} from '../types';\nimport {sizeFromShape} from '../util';\nimport {DTYPE_VALUE_SIZE_MAP, ModelArtifacts, ModelArtifactsInfo, WeightsManifestEntry} from './types';\n\n/**\n * Encode a map from names to weight values as an ArrayBuffer, along with an\n * `Array` of `WeightsManifestEntry` as specification of the encoded weights.\n *\n * This function does not perform sharding.\n *\n * This function is the reverse of `decodeWeights`.\n *\n * @param tensors A map (\"dict\") from names to tensors.\n * @returns A `Promise` of\n *   - A flat `ArrayBuffer` with all the binary values of the `Tensor`s\n *     concatenated.\n *   - An `Array` of `WeightManifestEntry`s, carrying information including\n *     tensor names, `dtype`s and shapes.\n * @throws Error: on unsupported tensor `dtype`.\n */\nexport async function encodeWeights(tensors: NamedTensorMap):\n    Promise<{data: ArrayBuffer, specs: WeightsManifestEntry[]}> {\n  // TODO(adarob, cais): Support quantization.\n  const specs: WeightsManifestEntry[] = [];\n  const dataPromises: Array<Promise<TypedArray>> = [];\n  for (const name in tensors) {\n    const t = tensors[name];\n\n    if (t.dtype !== 'float32' && t.dtype !== 'int32' && t.dtype !== 'bool') {\n      throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);\n    }\n    specs.push({name, shape: t.shape, dtype: t.dtype});\n    dataPromises.push(t.data());\n  }\n  const tensorValues = await Promise.all(dataPromises);\n  return {data: concatenateTypedArrays(tensorValues), specs};\n}\n\n/**\n * Decode flat ArrayBuffer as weights.\n *\n * This function does not handle sharding.\n *\n * This function is the reverse of `encodeWeights`.\n *\n * @param buffer A flat ArrayBuffer carrying the binary values of the tensors\n *   concatenated in the order specified in `specs`.\n * @param specs Specifications of the names, dtypes and shapes of the tensors\n *   whose value are encoded by `buffer`.\n * @return A map from tensor name to tensor value, with the names corresponding\n *   to names in `specs`.\n * @throws Error, if any of the tensors has unsupported dtype.\n */\nexport function decodeWeights(\n    buffer: ArrayBuffer, specs: WeightsManifestEntry[]): NamedTensorMap {\n  // TODO(adarob, cais): Support quantization.\n  const out: NamedTensorMap = {};\n  let offset = 0;\n  for (const spec of specs) {\n    const name = spec.name;\n    const dtype = spec.dtype;\n    const shape = spec.shape;\n    const size = sizeFromShape(shape);\n    let typedArray: TypedArray;\n\n    if ('quantization' in spec) {\n      const quantization = spec.quantization;\n      if (quantization.dtype !== 'uint8' && quantization.dtype !== 'uint16') {\n        throw new Error(\n            `Weight ${spec.name} has unknown ` +\n            `quantization dtype ${quantization.dtype}. ` +\n            `Supported quantization dtypes are: 'uint8' and 'uint16'.`);\n      }\n      const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];\n      const byteBuffer =\n          buffer.slice(offset, offset + size * quantizationSizeFactor);\n      const quantizedArray = (quantization.dtype === 'uint8') ?\n          new Uint8Array(byteBuffer) :\n          new Uint16Array(byteBuffer);\n      if (dtype === 'float32') {\n        typedArray = Float32Array.from(\n            quantizedArray, v => v * quantization.scale + quantization.min);\n      } else if (dtype === 'int32') {\n        typedArray = Int32Array.from(\n            quantizedArray,\n            v => Math.round(v * quantization.scale + quantization.min));\n      } else {\n        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n      }\n      offset += size * quantizationSizeFactor;\n    } else {\n      const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];\n      const byteBuffer = buffer.slice(offset, offset + size * dtypeFactor);\n\n      if (dtype === 'float32') {\n        typedArray = new Float32Array(byteBuffer);\n      } else if (dtype === 'int32') {\n        typedArray = new Int32Array(byteBuffer);\n      } else if (dtype === 'bool') {\n        typedArray = new Uint8Array(byteBuffer);\n      } else {\n        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n      }\n      offset += size * dtypeFactor;\n    }\n\n    let value: Tensor;\n    if (dtype === 'float32') {\n      value = tensor(typedArray, shape, 'float32');\n    } else if (dtype === 'int32') {\n      value = tensor(typedArray, shape, 'int32');\n    } else if (dtype === 'bool') {\n      value = tensor(typedArray, shape, 'bool');\n    } else {\n      throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n    }\n    out[name] = value;\n  }\n  return out;\n}\n\n/**\n * Concatenate TypedArrays into an ArrayBuffer.\n */\nexport function concatenateTypedArrays(xs: TypedArray[]): ArrayBuffer {\n  // TODO(adarob, cais): Support quantization.\n  if (xs === null) {\n    throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);\n  }\n\n  let totalByteLength = 0;\n\n  // `normalizedXs` is here for this reason: a `TypedArray`'s `buffer'\n  // can have a different byte length from that of the `TypedArray` itself,\n  // for example, when the `TypedArray` is created from an offset in an\n  // `ArrayBuffer`. `normliazedXs` holds `TypedArray`s whose `buffer`s match\n  // the `TypedArray` in byte length. If an element of `xs` does not show\n  // this property, a new `TypedArray` that satisfy this property will be\n  // constructed and pushed into `normalizedXs`.\n  const normalizedXs: TypedArray[] = [];\n  xs.forEach((x: TypedArray) => {\n    totalByteLength += x.byteLength;\n    // tslint:disable:no-any\n    normalizedXs.push(\n        x.byteLength === x.buffer.byteLength ? x :\n                                               new (x.constructor as any)(x));\n    if (!(x as any instanceof Float32Array || x as any instanceof Int32Array ||\n          x as any instanceof Uint8Array)) {\n      throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);\n    }\n    // tslint:enable:no-any\n  });\n\n  const y = new Uint8Array(totalByteLength);\n  let offset = 0;\n  normalizedXs.forEach((x: TypedArray) => {\n    y.set(new Uint8Array(x.buffer), offset);\n    offset += x.byteLength;\n  });\n\n  return y.buffer;\n}\n\n// Use Buffer on Node.js instead of Blob/atob/btoa\nconst useNodeBuffer = typeof Buffer !== 'undefined' &&\n    (typeof Blob === 'undefined' || typeof atob === 'undefined' ||\n     typeof btoa === 'undefined');\n\n/**\n * Calculate the byte length of a JavaScript string.\n *\n * Note that a JavaScript string can contain wide characters, therefore the\n * length of the string is not necessarily equal to the byte length.\n *\n * @param str Input string.\n * @returns Byte length.\n */\nexport function stringByteLength(str: string): number {\n  if (useNodeBuffer) {\n    return Buffer.byteLength(str);\n  }\n  return new Blob([str]).size;\n}\n\n/**\n * Encode an ArrayBuffer as a base64 encoded string.\n *\n * @param buffer `ArrayBuffer` to be converted.\n * @returns A string that base64-encodes `buffer`.\n */\nexport function arrayBufferToBase64String(buffer: ArrayBuffer): string {\n  if (useNodeBuffer) {\n    return Buffer.from(buffer).toString('base64');\n  }\n  return btoa(String.fromCharCode.apply(null, new Uint8Array(buffer)));\n}\n\n/**\n * Decode a base64 string as an ArrayBuffer.\n *\n * @param str Base64 string.\n * @returns Decoded `ArrayBuffer`.\n */\nexport function base64StringToArrayBuffer(str: string): ArrayBuffer {\n  if (useNodeBuffer) {\n    const buf = Buffer.from(str, 'base64');\n    return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);\n  }\n  const s = atob(str);\n  const buffer = new Uint8Array(s.length);\n  for (let i = 0; i < s.length; ++i) {\n    buffer.set([s.charCodeAt(i)], i);\n  }\n  return buffer.buffer;\n}\n\n/**\n * Concatenate a number of ArrayBuffers into one.\n *\n * @param buffers A number of array buffers to concatenate.\n * @returns Result of concatenating `buffers` in order.\n */\nexport function concatenateArrayBuffers(buffers: ArrayBuffer[]): ArrayBuffer {\n  let totalByteLength = 0;\n  buffers.forEach((buffer: ArrayBuffer) => {\n    totalByteLength += buffer.byteLength;\n  });\n\n  const temp = new Uint8Array(totalByteLength);\n  let offset = 0;\n  buffers.forEach((buffer: ArrayBuffer) => {\n    temp.set(new Uint8Array(buffer), offset);\n    offset += buffer.byteLength;\n  });\n  return temp.buffer;\n}\n\n/**\n * Get the basename of a path.\n *\n * Behaves in a way analogous to Linux's basename command.\n *\n * @param path\n */\nexport function basename(path: string): string {\n  const SEPARATOR = '/';\n  path = path.trim();\n  while (path.endsWith(SEPARATOR)) {\n    path = path.slice(0, path.length - 1);\n  }\n  const items = path.split(SEPARATOR);\n  return items[items.length - 1];\n}\n\n/**\n * Populate ModelArtifactsInfo fields for a model with JSON topology.\n * @param modelArtifacts\n * @returns A ModelArtifactsInfo object.\n */\nexport function getModelArtifactsInfoForJSON(modelArtifacts: ModelArtifacts):\n    ModelArtifactsInfo {\n  if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n    throw new Error('Expected JSON model topology, received ArrayBuffer.');\n  }\n\n  return {\n    dateSaved: new Date(),\n    modelTopologyType: 'JSON',\n    modelTopologyBytes: modelArtifacts.modelTopology == null ?\n        0 :\n        stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),\n    weightSpecsBytes: modelArtifacts.weightSpecs == null ?\n        0 :\n        stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),\n    weightDataBytes: modelArtifacts.weightData == null ?\n        0 :\n        modelArtifacts.weightData.byteLength,\n  };\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {IOHandler} from './types';\n\nexport type IORouter = (url: string|string[]) => IOHandler;\n\nexport class IORouterRegistry {\n  // Singleton instance.\n  private static instance: IORouterRegistry;\n\n  private saveRouters: IORouter[];\n  private loadRouters: IORouter[];\n\n  private constructor() {\n    this.saveRouters = [];\n    this.loadRouters = [];\n  }\n\n  private static getInstance(): IORouterRegistry {\n    if (IORouterRegistry.instance == null) {\n      IORouterRegistry.instance = new IORouterRegistry();\n    }\n    return IORouterRegistry.instance;\n  }\n\n  /**\n   * Register a save-handler router.\n   *\n   * @param saveRouter A function that maps a URL-like string onto an instance\n   * of `IOHandler` with the `save` method defined or `null`.\n   */\n  static registerSaveRouter(saveRouter: IORouter) {\n    IORouterRegistry.getInstance().saveRouters.push(saveRouter);\n  }\n\n  /**\n   * Register a load-handler router.\n   *\n   * @param loadRouter A function that maps a URL-like string onto an instance\n   * of `IOHandler` with the `load` method defined or `null`.\n   */\n  static registerLoadRouter(loadRouter: IORouter) {\n    IORouterRegistry.getInstance().loadRouters.push(loadRouter);\n  }\n\n  /**\n   * Look up IOHandler for saving, given a URL-like string.\n   *\n   * @param url\n   * @returns If only one match is found, an instance of IOHandler with the\n   * `save` method defined. If no match is found, `null`.\n   * @throws Error, if more than one match is found.\n   */\n  static getSaveHandlers(url: string|string[]): IOHandler[] {\n    return IORouterRegistry.getHandlers(url, 'save');\n  }\n\n  /**\n   * Look up IOHandler for loading, given a URL-like string.\n   *\n   * @param url\n   * @returns All valid handlers for `url`, given the currently registered\n   *   handler routers.\n   */\n  static getLoadHandlers(url: string|string[]): IOHandler[] {\n    return IORouterRegistry.getHandlers(url, 'load');\n  }\n\n  private static getHandlers(url: string|string[], handlerType: 'save'|'load'):\n      IOHandler[] {\n    const validHandlers: IOHandler[] = [];\n    const routers = handlerType === 'load' ? this.getInstance().loadRouters :\n                                             this.getInstance().saveRouters;\n    routers.forEach(router => {\n      const handler = router(url);\n      if (handler !== null) {\n        validHandlers.push(handler);\n      }\n    });\n    return validHandlers;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Classes and functions for model management across multiple storage mediums.\n *\n * Supported client actions:\n * - Listing models on all registered storage mediums.\n * - Remove model by URL from any registered storage mediums, by using URL\n *   string.\n * - Moving or copying model from one path to another in the same medium or from\n *   one medium to another, by using URL strings.\n */\n\nimport {assert} from '../util';\n\nimport {IORouterRegistry} from './router_registry';\nimport {ModelArtifactsInfo, ModelStoreManager} from './types';\n\nconst URL_SCHEME_SUFFIX = '://';\n\nexport class ModelStoreManagerRegistry {\n  // Singleton instance.\n  private static instance: ModelStoreManagerRegistry;\n\n  private managers: {[scheme: string]: ModelStoreManager};\n\n  private constructor() {\n    this.managers = {};\n  }\n\n  private static getInstance(): ModelStoreManagerRegistry {\n    if (ModelStoreManagerRegistry.instance == null) {\n      ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();\n    }\n    return ModelStoreManagerRegistry.instance;\n  }\n\n  /**\n   * Register a save-handler router.\n   *\n   * @param saveRouter A function that maps a URL-like string onto an instance\n   * of `IOHandler` with the `save` method defined or `null`.\n   */\n  static registerManager(scheme: string, manager: ModelStoreManager) {\n    assert(scheme != null, 'scheme must not be undefined or null.');\n    if (scheme.endsWith(URL_SCHEME_SUFFIX)) {\n      scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));\n    }\n    assert(scheme.length > 0, 'scheme must not be an empty string.');\n    const registry = ModelStoreManagerRegistry.getInstance();\n    assert(\n        registry.managers[scheme] == null,\n        `A model store manager is already registered for scheme '${scheme}'.`);\n    registry.managers[scheme] = manager;\n  }\n\n  static getManager(scheme: string): ModelStoreManager {\n    const manager = this.getInstance().managers[scheme];\n    if (manager == null) {\n      throw new Error(`Cannot find model manager for scheme '${scheme}'`);\n    }\n    return manager;\n  }\n\n  static getSchemes(): string[] {\n    return Object.keys(this.getInstance().managers);\n  }\n}\n\n/**\n * Helper method for parsing a URL string into a scheme and a path.\n *\n * @param url E.g., 'localstorage://my-model'\n * @returns A dictionary with two fields: scheme and path.\n *   Scheme: e.g., 'localstorage' in the example above.\n *   Path: e.g., 'my-model' in the example above.\n */\nfunction parseURL(url: string): {scheme: string, path: string} {\n  if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {\n    throw new Error(\n        `The url string provided does not contain a scheme. ` +\n        `Supported schemes are: ` +\n        `${ModelStoreManagerRegistry.getSchemes().join(',')}`);\n  }\n  return {\n    scheme: url.split(URL_SCHEME_SUFFIX)[0],\n    path: url.split(URL_SCHEME_SUFFIX)[1],\n  };\n}\n\nasync function cloneModelInternal(\n    sourceURL: string, destURL: string,\n    deleteSource = false): Promise<ModelArtifactsInfo> {\n  assert(\n      sourceURL !== destURL,\n      `Old path and new path are the same: '${sourceURL}'`);\n\n  const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);\n  assert(\n      loadHandlers.length > 0,\n      `Copying failed because no load handler is found for source URL ${\n          sourceURL}.`);\n  assert(\n      loadHandlers.length < 2,\n      `Copying failed because more than one (${loadHandlers.length}) ` +\n          `load handlers for source URL ${sourceURL}.`);\n  const loadHandler = loadHandlers[0];\n\n  const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);\n  assert(\n      saveHandlers.length > 0,\n      `Copying failed because no save handler is found for destination URL ` +\n          `${destURL}.`);\n  assert(\n      saveHandlers.length < 2,\n      `Copying failed because more than one (${loadHandlers.length}) ` +\n          `save handlers for destination URL ${destURL}.`);\n  const saveHandler = saveHandlers[0];\n\n  const sourceScheme = parseURL(sourceURL).scheme;\n  const sourcePath = parseURL(sourceURL).path;\n  const sameMedium = sourceScheme === parseURL(sourceURL).scheme;\n\n  const modelArtifacts = await loadHandler.load();\n\n  // If moving within the same storage medium, remove the old model as soon as\n  // the loading is done. Without doing this, it is possible that the combined\n  // size of the two models will cause the cloning to fail.\n  if (deleteSource && sameMedium) {\n    await ModelStoreManagerRegistry.getManager(sourceScheme)\n        .removeModel(sourcePath);\n  }\n\n  const saveResult = await saveHandler.save(modelArtifacts);\n\n  // If moving between mediums, the deletion is done after the save succeeds.\n  // This guards against the case in which saving to the destination medium\n  // fails.\n  if (deleteSource && !sameMedium) {\n    await ModelStoreManagerRegistry.getManager(sourceScheme)\n        .removeModel(sourcePath);\n  }\n\n  return saveResult.modelArtifactsInfo;\n}\n\n/**\n * List all models stored in registered storage mediums.\n *\n * For a web browser environment, the registered mediums are Local Storage and\n * IndexedDB.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @returns A `Promise` of a dictionary mapping URLs of existing models to\n * their model artifacts info. URLs include medium-specific schemes, e.g.,\n *   'indexeddb://my/model/1'. Model artifacts info include type of the\n * model's topology, byte sizes of the topology, weights, etc.\n */\n/** @doc {heading: 'Models', subheading: 'Management', namespace: 'io'} */\nasync function listModels(): Promise<{[url: string]: ModelArtifactsInfo}> {\n  const schemes = ModelStoreManagerRegistry.getSchemes();\n  const out: {[url: string]: ModelArtifactsInfo} = {};\n  for (const scheme of schemes) {\n    const schemeOut =\n        await ModelStoreManagerRegistry.getManager(scheme).listModels();\n    for (const path in schemeOut) {\n      const url = scheme + URL_SCHEME_SUFFIX + path;\n      out[url] = schemeOut[path];\n    }\n  }\n  return out;\n}\n\n/**\n * Remove a model specified by URL from a reigstered storage medium.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @param url A URL to a stored model, with a scheme prefix, e.g.,\n *   'localstorage://my-model-1', 'indexeddb://my/model/2'.\n * @returns ModelArtifactsInfo of the deleted model (if and only if deletion\n *   is successful).\n * @throws Error if deletion fails, e.g., if no model exists at `path`.\n */\n/** @doc {heading: 'Models', subheading: 'Management', namespace: 'io'} */\nasync function removeModel(url: string): Promise<ModelArtifactsInfo> {\n  const schemeAndPath = parseURL(url);\n  const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);\n  return await manager.removeModel(schemeAndPath.path);\n}\n\n/**\n * Copy a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Copying within a storage medium, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Copying between two storage mediums, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Copy the model, from Local Storage to IndexedDB.\n * await tf.io.copyModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove both models.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of copying.\n * @param destURL Destination URL of copying.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n */\n/** @doc {heading: 'Models', subheading: 'Management', namespace: 'io'} */\nasync function copyModel(\n    sourceURL: string, destURL: string): Promise<ModelArtifactsInfo> {\n  const deleteSource = false;\n  return await cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n\n/**\n * Move a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Moving within a storage medium, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Moving between two storage mediums, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Move the model, from Local Storage to IndexedDB.\n * await tf.io.moveModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove the moved model.\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of moving.\n * @param destURL Destination URL of moving.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n */\n/** @doc {heading: 'Models', subheading: 'Management', namespace: 'io'} */\nasync function moveModel(\n    sourceURL: string, destURL: string): Promise<ModelArtifactsInfo> {\n  const deleteSource = true;\n  return await cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n\nexport {moveModel, copyModel, removeModel, listModels};\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {getModelArtifactsInfoForJSON} from './io_utils';\nimport {ModelStoreManagerRegistry} from './model_management';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, ModelArtifactsInfo, ModelStoreManager, SaveResult} from './types';\n\nconst DATABASE_NAME = 'tensorflowjs';\nconst DATABASE_VERSION = 1;\n\n// Model data and ModelArtifactsInfo (metadata) are stored in two separate\n// stores for efficient access of the list of stored models and their metadata.\n// 1. The object store for model data: topology, weights and weight manifests.\nconst MODEL_STORE_NAME = 'models_store';\n// 2. The object store for ModelArtifactsInfo, including meta-information such\n//    as the type of topology (JSON vs binary), byte size of the topology, byte\n//    size of the weights, etc.\nconst INFO_STORE_NAME = 'model_info_store';\n\n/**\n * Delete the entire database for tensorflow.js, including the models store.\n */\nexport async function deleteDatabase(): Promise<void> {\n  const idbFactory = getIndexedDBFactory();\n\n  return new Promise<void>((resolve, reject) => {\n    const deleteRequest = idbFactory.deleteDatabase(DATABASE_NAME);\n    deleteRequest.onsuccess = () => resolve();\n    deleteRequest.onerror = error => reject(error);\n  });\n}\n\nfunction getIndexedDBFactory(): IDBFactory {\n  if (!ENV.get('IS_BROWSER')) {\n    // TODO(cais): Add more info about what IOHandler subtypes are available.\n    //   Maybe point to a doc page on the web and/or automatically determine\n    //   the available IOHandlers and print them in the error message.\n    throw new Error(\n        'Failed to obtain IndexedDB factory because the current environment' +\n        'is not a web browser.');\n  }\n  // tslint:disable-next-line:no-any\n  const theWindow: any = window;\n  const factory = theWindow.indexedDB || theWindow.mozIndexedDB ||\n      theWindow.webkitIndexedDB || theWindow.msIndexedDB ||\n      theWindow.shimIndexedDB;\n  if (factory == null) {\n    throw new Error(\n        'The current browser does not appear to support IndexedDB.');\n  }\n  return factory;\n}\n\nfunction setUpDatabase(openRequest: IDBRequest) {\n  const db = openRequest.result as IDBDatabase;\n  db.createObjectStore(MODEL_STORE_NAME, {keyPath: 'modelPath'});\n  db.createObjectStore(INFO_STORE_NAME, {keyPath: 'modelPath'});\n}\n\n/**\n * IOHandler subclass: Browser IndexedDB.\n *\n * See the doc string of `browserIndexedDB` for more details.\n */\nexport class BrowserIndexedDB implements IOHandler {\n  protected readonly indexedDB: IDBFactory;\n  protected readonly modelPath: string;\n\n  static readonly URL_SCHEME = 'indexeddb://';\n\n  constructor(modelPath: string) {\n    this.indexedDB = getIndexedDBFactory();\n\n    if (modelPath == null || !modelPath) {\n      throw new Error(\n          'For IndexedDB, modelPath must not be null, undefined or empty.');\n    }\n    this.modelPath = modelPath;\n  }\n\n  async save(modelArtifacts: ModelArtifacts): Promise<SaveResult> {\n    // TODO(cais): Support saving GraphDef models.\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\n          'BrowserLocalStorage.save() does not support saving model topology ' +\n          'in binary formats yet.');\n    }\n\n    return this.databaseAction(this.modelPath, modelArtifacts) as\n        Promise<SaveResult>;\n  }\n\n  async load(): Promise<ModelArtifacts> {\n    return this.databaseAction(this.modelPath) as Promise<ModelArtifacts>;\n  }\n\n  /**\n   * Perform database action to put model artifacts into or read model artifacts\n   * from IndexedDB object store.\n   *\n   * Whether the action is put or get depends on whether `modelArtifacts` is\n   * specified. If it is specified, the action will be put; otherwise the action\n   * will be get.\n   *\n   * @param modelPath A unique string path for the model.\n   * @param modelArtifacts If specified, it will be the model artifacts to be\n   *   stored in IndexedDB.\n   * @returns A `Promise` of `SaveResult`, if the action is put, or a `Promise`\n   *   of `ModelArtifacts`, if the action is get.\n   */\n  private databaseAction(modelPath: string, modelArtifacts?: ModelArtifacts):\n      Promise<ModelArtifacts|SaveResult> {\n    return new Promise<ModelArtifacts|SaveResult>((resolve, reject) => {\n      const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n      openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n\n      openRequest.onsuccess = () => {\n        const db = openRequest.result as IDBDatabase;\n\n        if (modelArtifacts == null) {\n          // Read model out from object store.\n          const modelTx = db.transaction(MODEL_STORE_NAME, 'readonly');\n          const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n          const getRequest = modelStore.get(this.modelPath);\n          getRequest.onsuccess = () => {\n            if (getRequest.result == null) {\n              db.close();\n              return reject(new Error(\n                  `Cannot find model with path '${this.modelPath}' ` +\n                  `in IndexedDB.`));\n            } else {\n              resolve(getRequest.result.modelArtifacts);\n            }\n          };\n          getRequest.onerror = error => {\n            db.close();\n            return reject(getRequest.error);\n          };\n          modelTx.oncomplete = () => db.close();\n        } else {\n          // Put model into object store.\n          const modelArtifactsInfo: ModelArtifactsInfo =\n              getModelArtifactsInfoForJSON(modelArtifacts);\n          // First, put ModelArtifactsInfo into info store.\n          const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n          let infoStore = infoTx.objectStore(INFO_STORE_NAME);\n          const putInfoRequest =\n              infoStore.put({modelPath: this.modelPath, modelArtifactsInfo});\n          let modelTx: IDBTransaction;\n          putInfoRequest.onsuccess = () => {\n            // Second, put model data into model store.\n            modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n            const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n            const putModelRequest = modelStore.put({\n              modelPath: this.modelPath,\n              modelArtifacts,\n              modelArtifactsInfo\n            });\n            putModelRequest.onsuccess = () => resolve({modelArtifactsInfo});\n            putModelRequest.onerror = error => {\n              // If the put-model request fails, roll back the info entry as\n              // well.\n              infoStore = infoTx.objectStore(INFO_STORE_NAME);\n              const deleteInfoRequest = infoStore.delete(this.modelPath);\n              deleteInfoRequest.onsuccess = () => {\n                db.close();\n                return reject(putModelRequest.error);\n              };\n              deleteInfoRequest.onerror = error => {\n                db.close();\n                return reject(putModelRequest.error);\n              };\n            };\n          };\n          putInfoRequest.onerror = error => {\n            db.close();\n            return reject(putInfoRequest.error);\n          };\n          infoTx.oncomplete = () => {\n            if (modelTx == null) {\n              db.close();\n            } else {\n              modelTx.oncomplete = () => db.close();\n            }\n          };\n        }\n      };\n      openRequest.onerror = error => reject(openRequest.error);\n    });\n  }\n}\n\nexport const indexedDBRouter: IORouter = (url: string|string[]) => {\n  if (!ENV.get('IS_BROWSER')) {\n    return null;\n  } else {\n    if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {\n      return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));\n    } else {\n      return null;\n    }\n  }\n};\nIORouterRegistry.registerSaveRouter(indexedDBRouter);\nIORouterRegistry.registerLoadRouter(indexedDBRouter);\n\n/**\n * Creates a browser IndexedDB IOHandler for saving and loading models.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save('indexeddb://MyModel'));\n * console.log(saveResult);\n * ```\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `BrowserIndexedDB` (sublcass of `IOHandler`),\n *   which can be used with, e.g., `tf.Model.save`.\n */\nexport function browserIndexedDB(modelPath: string): IOHandler {\n  return new BrowserIndexedDB(modelPath);\n}\n\nfunction maybeStripScheme(key: string) {\n  return key.startsWith(BrowserIndexedDB.URL_SCHEME) ?\n      key.slice(BrowserIndexedDB.URL_SCHEME.length) :\n      key;\n}\n\nexport class BrowserIndexedDBManager implements ModelStoreManager {\n  private indexedDB: IDBFactory;\n\n  constructor() {\n    this.indexedDB = getIndexedDBFactory();\n  }\n\n  async listModels(): Promise<{[path: string]: ModelArtifactsInfo}> {\n    return new Promise<{[path: string]: ModelArtifactsInfo}>(\n        (resolve, reject) => {\n          const openRequest =\n              this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n          openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n\n          openRequest.onsuccess = () => {\n            const db = openRequest.result as IDBDatabase;\n            const tx = db.transaction(INFO_STORE_NAME, 'readonly');\n            const store = tx.objectStore(INFO_STORE_NAME);\n            // tslint:disable:max-line-length\n            // Need to cast `store` as `any` here because TypeScript's DOM\n            // library does not have the `getAll()` method even though the\n            // method is supported in the latest version of most mainstream\n            // browsers:\n            // https://developer.mozilla.org/en-US/docs/Web/API/IDBObjectStore/getAll\n            // tslint:enable:max-line-length\n            // tslint:disable-next-line:no-any\n            const getAllInfoRequest = (store as any).getAll() as IDBRequest;\n            getAllInfoRequest.onsuccess = () => {\n              const out: {[path: string]: ModelArtifactsInfo} = {};\n              for (const item of getAllInfoRequest.result) {\n                out[item.modelPath] = item.modelArtifactsInfo;\n              }\n              resolve(out);\n            };\n            getAllInfoRequest.onerror = error => {\n              db.close();\n              return reject(getAllInfoRequest.error);\n            };\n            tx.oncomplete = () => db.close();\n          };\n          openRequest.onerror = error => reject(openRequest.error);\n        });\n  }\n\n  async removeModel(path: string): Promise<ModelArtifactsInfo> {\n    path = maybeStripScheme(path);\n    return new Promise<ModelArtifactsInfo>((resolve, reject) => {\n      const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n      openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n\n      openRequest.onsuccess = () => {\n        const db = openRequest.result as IDBDatabase;\n        const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n        const infoStore = infoTx.objectStore(INFO_STORE_NAME);\n\n        const getInfoRequest = infoStore.get(path);\n        let modelTx: IDBTransaction;\n        getInfoRequest.onsuccess = () => {\n          if (getInfoRequest.result == null) {\n            db.close();\n            return reject(new Error(\n                `Cannot find model with path '${path}' ` +\n                `in IndexedDB.`));\n          } else {\n            // First, delete the entry in the info store.\n            const deleteInfoRequest = infoStore.delete(path);\n            const deleteModelData = () => {\n              // Second, delete the entry in the model store.\n              modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n              const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n              const deleteModelRequest = modelStore.delete(path);\n              deleteModelRequest.onsuccess = () =>\n                  resolve(getInfoRequest.result.modelArtifactsInfo);\n              deleteModelRequest.onerror = error =>\n                  reject(getInfoRequest.error);\n            };\n            // Proceed with deleting model data regardless of whether deletion\n            // of info data succeeds or not.\n            deleteInfoRequest.onsuccess = deleteModelData;\n            deleteInfoRequest.onerror = error => {\n              deleteModelData();\n              db.close();\n              return reject(getInfoRequest.error);\n            };\n          }\n        };\n        getInfoRequest.onerror = error => {\n          db.close();\n          return reject(getInfoRequest.error);\n        };\n\n        infoTx.oncomplete = () => {\n          if (modelTx == null) {\n            db.close();\n          } else {\n            modelTx.oncomplete = () => db.close();\n          }\n        };\n      };\n      openRequest.onerror = error => reject(openRequest.error);\n    });\n  }\n}\n\nif (ENV.get('IS_BROWSER')) {\n  // Wrap the construction and registration, to guard against browsers that\n  // don't support Local Storage.\n  try {\n    ModelStoreManagerRegistry.registerManager(\n        BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());\n  } catch (err) {\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {assert} from '../util';\nimport {arrayBufferToBase64String, base64StringToArrayBuffer, getModelArtifactsInfoForJSON} from './io_utils';\nimport {ModelStoreManagerRegistry} from './model_management';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, ModelArtifactsInfo, ModelStoreManager, SaveResult} from './types';\n\nconst PATH_SEPARATOR = '/';\nconst PATH_PREFIX = 'tensorflowjs_models';\nconst INFO_SUFFIX = 'info';\nconst MODEL_TOPOLOGY_SUFFIX = 'model_topology';\nconst WEIGHT_SPECS_SUFFIX = 'weight_specs';\nconst WEIGHT_DATA_SUFFIX = 'weight_data';\n\n/**\n * Purge all tensorflow.js-saved model artifacts from local storage.\n *\n * @returns Paths of the models purged.\n */\nexport function purgeLocalStorageArtifacts(): string[] {\n  if (!ENV.get('IS_BROWSER') || typeof window.localStorage === 'undefined') {\n    throw new Error(\n        'purgeLocalStorageModels() cannot proceed because local storage is ' +\n        'unavailable in the current environment.');\n  }\n  const LS = window.localStorage;\n  const purgedModelPaths: string[] = [];\n  for (let i = 0; i < LS.length; ++i) {\n    const key = LS.key(i);\n    const prefix = PATH_PREFIX + PATH_SEPARATOR;\n    if (key.startsWith(prefix) && key.length > prefix.length) {\n      LS.removeItem(key);\n      const modelName = getModelPathFromKey(key);\n      if (purgedModelPaths.indexOf(modelName) === -1) {\n        purgedModelPaths.push(modelName);\n      }\n    }\n  }\n  return purgedModelPaths;\n}\n\nfunction getModelKeys(path: string):\n    {info: string, topology: string, weightSpecs: string, weightData: string} {\n  return {\n    info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),\n    topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),\n    weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),\n    weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR)\n  };\n}\n\n/**\n * Get model path from a local-storage key.\n *\n * E.g., 'tensorflowjs_models/my/model/1/info' --> 'my/model/1'\n *\n * @param key\n */\nfunction getModelPathFromKey(key: string) {\n  const items = key.split(PATH_SEPARATOR);\n  if (items.length < 3) {\n    throw new Error(`Invalid key format: ${key}`);\n  }\n  return items.slice(1, items.length - 1).join(PATH_SEPARATOR);\n}\n\nfunction maybeStripScheme(key: string) {\n  return key.startsWith(BrowserLocalStorage.URL_SCHEME) ?\n      key.slice(BrowserLocalStorage.URL_SCHEME.length) :\n      key;\n}\n\n/**\n * IOHandler subclass: Browser Local Storage.\n *\n * See the doc string to `browserLocalStorage` for more details.\n */\nexport class BrowserLocalStorage implements IOHandler {\n  protected readonly LS: Storage;\n  protected readonly modelPath: string;\n  protected readonly keys: {[key: string]: string};\n\n  static readonly URL_SCHEME = 'localstorage://';\n\n  constructor(modelPath: string) {\n    if (!ENV.get('IS_BROWSER') || typeof window.localStorage === 'undefined') {\n      // TODO(cais): Add more info about what IOHandler subtypes are\n      // available.\n      //   Maybe point to a doc page on the web and/or automatically determine\n      //   the available IOHandlers and print them in the error message.\n      throw new Error(\n          'The current environment does not support local storage.');\n    }\n    this.LS = window.localStorage;\n\n    if (modelPath == null || !modelPath) {\n      throw new Error(\n          'For local storage, modelPath must not be null, undefined or empty.');\n    }\n    this.modelPath = modelPath;\n    this.keys = getModelKeys(this.modelPath);\n  }\n\n  /**\n   * Save model artifacts to browser local storage.\n   *\n   * See the documentation to `browserLocalStorage` for details on the saved\n   * artifacts.\n   *\n   * @param modelArtifacts The model artifacts to be stored.\n   * @returns An instance of SaveResult.\n   */\n  async save(modelArtifacts: ModelArtifacts): Promise<SaveResult> {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\n          'BrowserLocalStorage.save() does not support saving model topology ' +\n          'in binary formats yet.');\n    } else {\n      const topology = JSON.stringify(modelArtifacts.modelTopology);\n      const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);\n\n      const modelArtifactsInfo: ModelArtifactsInfo =\n          getModelArtifactsInfoForJSON(modelArtifacts);\n\n      try {\n        this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));\n        this.LS.setItem(this.keys.topology, topology);\n        this.LS.setItem(this.keys.weightSpecs, weightSpecs);\n        this.LS.setItem(\n            this.keys.weightData,\n            arrayBufferToBase64String(modelArtifacts.weightData));\n\n        return {modelArtifactsInfo};\n      } catch (err) {\n        // If saving failed, clean up all items saved so far.\n        for (const key in this.keys) {\n          this.LS.removeItem(this.keys[key]);\n        }\n\n        throw new Error(\n            `Failed to save model '${this.modelPath}' to local storage: ` +\n            `size quota being exceeded is a possible cause of this failure: ` +\n            `modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, ` +\n            `weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, ` +\n            `weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);\n      }\n    }\n  }\n\n  /**\n   * Load a model from local storage.\n   *\n   * See the documentation to `browserLocalStorage` for details on the saved\n   * artifacts.\n   *\n   * @returns The loaded model (if loading succeeds).\n   */\n  async load(): Promise<ModelArtifacts> {\n    const info =\n        JSON.parse(this.LS.getItem(this.keys.info)) as ModelArtifactsInfo;\n    if (info == null) {\n      throw new Error(\n          `In local storage, there is no model with name '${this.modelPath}'`);\n    }\n\n    if (info.modelTopologyType !== 'JSON') {\n      throw new Error(\n          'BrowserLocalStorage does not support loading non-JSON model ' +\n          'topology yet.');\n    }\n\n    const out: ModelArtifacts = {};\n\n    // Load topology.\n    const topology = JSON.parse(this.LS.getItem(this.keys.topology));\n    if (topology == null) {\n      throw new Error(\n          `In local storage, the topology of model '${this.modelPath}' ` +\n          `is missing.`);\n    }\n    out.modelTopology = topology;\n\n    // Load weight specs.\n    const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));\n    if (weightSpecs == null) {\n      throw new Error(\n          `In local storage, the weight specs of model '${this.modelPath}' ` +\n          `are missing.`);\n    }\n    out.weightSpecs = weightSpecs;\n\n    // Load weight data.\n    const weightDataBase64 = this.LS.getItem(this.keys.weightData);\n    if (weightDataBase64 == null) {\n      throw new Error(\n          `In local storage, the binary weight values of model ` +\n          `'${this.modelPath}' are missing.`);\n    }\n    out.weightData = base64StringToArrayBuffer(weightDataBase64);\n\n    return out;\n  }\n}\n\nexport const localStorageRouter: IORouter = (url: string|string[]) => {\n  if (!ENV.get('IS_BROWSER')) {\n    return null;\n  } else {\n    if (!Array.isArray(url) &&\n        url.startsWith(BrowserLocalStorage.URL_SCHEME)) {\n      return browserLocalStorage(\n          url.slice(BrowserLocalStorage.URL_SCHEME.length));\n    } else {\n      return null;\n    }\n  }\n};\nIORouterRegistry.registerSaveRouter(localStorageRouter);\nIORouterRegistry.registerLoadRouter(localStorageRouter);\n\n/**\n * Factory function for local storage IOHandler.\n *\n * This `IOHandler` supports both `save` and `load`.\n *\n * For each model's saved artifacts, four items are saved to local storage.\n *   - `${PATH_SEPARATOR}/${modelPath}/info`: Contains meta-info about the\n *     model, such as date saved, type of the topology, size in bytes, etc.\n *   - `${PATH_SEPARATOR}/${modelPath}/topology`: Model topology. For Keras-\n *     style models, this is a stringized JSON.\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_specs`: Weight specs of the\n *     model, can be used to decode the saved binary weight values (see\n *     item below).\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_data`: Concatenated binary\n *     weight values, stored as a base64-encoded string.\n *\n * Saving may throw an `Error` if the total size of the artifacts exceed the\n * browser-specific quota.\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `IOHandler`, which can be used with, e.g.,\n *   `tf.Model.save`.\n */\nexport function browserLocalStorage(modelPath: string): IOHandler {\n  return new BrowserLocalStorage(modelPath);\n}\n\nexport class BrowserLocalStorageManager implements ModelStoreManager {\n  private readonly LS: Storage;\n\n  constructor() {\n    assert(ENV.get('IS_BROWSER'), 'Current environment is not a web browser');\n    assert(\n        typeof window.localStorage !== 'undefined',\n        'Current browser does not appear to support localStorage');\n    this.LS = window.localStorage;\n  }\n\n  async listModels(): Promise<{[path: string]: ModelArtifactsInfo}> {\n    const out: {[path: string]: ModelArtifactsInfo} = {};\n    const prefix = PATH_PREFIX + PATH_SEPARATOR;\n    const suffix = PATH_SEPARATOR + INFO_SUFFIX;\n    for (let i = 0; i < this.LS.length; ++i) {\n      const key = this.LS.key(i);\n      if (key.startsWith(prefix) && key.endsWith(suffix)) {\n        const modelPath = getModelPathFromKey(key);\n        out[modelPath] = JSON.parse(this.LS.getItem(key)) as ModelArtifactsInfo;\n      }\n    }\n    return out;\n  }\n\n  async removeModel(path: string): Promise<ModelArtifactsInfo> {\n    path = maybeStripScheme(path);\n    const keys = getModelKeys(path);\n    if (this.LS.getItem(keys.info) == null) {\n      throw new Error(`Cannot find model at path '${path}'`);\n    }\n    const info = JSON.parse(this.LS.getItem(keys.info)) as ModelArtifactsInfo;\n\n    this.LS.removeItem(keys.info);\n    this.LS.removeItem(keys.topology);\n    this.LS.removeItem(keys.weightSpecs);\n    this.LS.removeItem(keys.weightData);\n    return info;\n  }\n}\n\nif (ENV.get('IS_BROWSER')) {\n  // Wrap the construction and registration, to guard against browsers that\n  // don't support Local Storage.\n  try {\n    ModelStoreManagerRegistry.registerManager(\n        BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());\n  } catch (err) {\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * IOHandlers related to files, such as browser-triggered file downloads,\n * user-selected files in browser.\n */\n\nimport {ENV} from '../environment';\nimport {basename, concatenateArrayBuffers, getModelArtifactsInfoForJSON} from './io_utils';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, SaveResult, WeightsManifestConfig, WeightsManifestEntry} from './types';\n\nconst DEFAULT_FILE_NAME_PREFIX = 'model';\nconst DEFAULT_JSON_EXTENSION_NAME = '.json';\nconst DEFAULT_WEIGHT_DATA_EXTENSION_NAME = '.weights.bin';\n\nexport class BrowserDownloads implements IOHandler {\n  private readonly modelTopologyFileName: string;\n  private readonly weightDataFileName: string;\n  private readonly jsonAnchor: HTMLAnchorElement;\n  private readonly weightDataAnchor: HTMLAnchorElement;\n\n  static readonly URL_SCHEME = 'downloads://';\n\n  constructor(fileNamePrefix?: string) {\n    if (!ENV.get('IS_BROWSER')) {\n      // TODO(cais): Provide info on what IOHandlers are available under the\n      //   current environment.\n      throw new Error(\n          'browserDownloads() cannot proceed because the current environment ' +\n          'is not a browser.');\n    }\n\n    if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {\n      fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);\n    }\n    if (fileNamePrefix == null || fileNamePrefix.length === 0) {\n      fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;\n    }\n\n    this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;\n    this.weightDataFileName =\n        fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;\n  }\n\n  async save(modelArtifacts: ModelArtifacts): Promise<SaveResult> {\n    const weightsURL = window.URL.createObjectURL(new Blob(\n        [modelArtifacts.weightData], {type: 'application/octet-stream'}));\n\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\n          'BrowserDownloads.save() does not support saving model topology ' +\n          'in binary formats yet.');\n    } else {\n      const weightsManifest: WeightsManifestConfig = [{\n        paths: ['./' + this.weightDataFileName],\n        weights: modelArtifacts.weightSpecs\n      }];\n      const modelTopologyAndWeightManifest = {\n        modelTopology: modelArtifacts.modelTopology,\n        weightsManifest\n      };\n      const modelTopologyAndWeightManifestURL =\n          window.URL.createObjectURL(new Blob(\n              [JSON.stringify(modelTopologyAndWeightManifest)],\n              {type: 'application/json'}));\n\n      // If anchor elements are not provided, create them without attaching them\n      // to parents, so that the downloaded file names can be controlled.\n      const jsonAnchor = this.jsonAnchor == null ? document.createElement('a') :\n                                                   this.jsonAnchor;\n      jsonAnchor.download = this.modelTopologyFileName;\n      jsonAnchor.href = modelTopologyAndWeightManifestURL;\n      // Trigger downloads by calling the `click` methods on the download\n      // anchors.\n      jsonAnchor.click();\n\n      if (modelArtifacts.weightData != null) {\n        const weightDataAnchor = this.weightDataAnchor == null ?\n            document.createElement('a') :\n            this.weightDataAnchor;\n        weightDataAnchor.download = this.weightDataFileName;\n        weightDataAnchor.href = weightsURL;\n        weightDataAnchor.click();\n      }\n\n      return {modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts)};\n    }\n  }\n}\n\nclass BrowserFiles implements IOHandler {\n  private readonly files: File[];\n\n  constructor(files: File[]) {\n    if (files == null || files.length < 1) {\n      throw new Error(\n          `When calling browserFiles, at least 1 file is required, ` +\n          `but received ${files}`);\n    }\n    this.files = files;\n  }\n\n  async load(): Promise<ModelArtifacts> {\n    const jsonFile = this.files[0];\n    const weightFiles = this.files.slice(1);\n\n    return new Promise<ModelArtifacts>((resolve, reject) => {\n      const jsonReader = new FileReader();\n      jsonReader.onload = (event: Event) => {\n        // tslint:disable-next-line:no-any\n        const modelJSON = JSON.parse((event.target as any).result);\n        const modelTopology = modelJSON.modelTopology as {};\n        if (modelTopology == null) {\n          reject(new Error(\n              `modelTopology field is missing from file ${jsonFile.name}`));\n          return;\n        }\n\n        if (weightFiles.length === 0) {\n          resolve({modelTopology});\n        }\n\n        const weightsManifest =\n            modelJSON.weightsManifest as WeightsManifestConfig;\n        if (weightsManifest == null) {\n          reject(new Error(\n              `weightManifest field is missing from file ${jsonFile.name}`));\n          return;\n        }\n\n        let pathToFile: {[path: string]: File};\n        try {\n          pathToFile =\n              this.checkManifestAndWeightFiles(weightsManifest, weightFiles);\n        } catch (err) {\n          reject(err);\n          return;\n        }\n\n        const weightSpecs: WeightsManifestEntry[] = [];\n        const paths: string[] = [];\n        const perFileBuffers: ArrayBuffer[] = [];\n        weightsManifest.forEach(weightsGroup => {\n          weightsGroup.paths.forEach(path => {\n            paths.push(path);\n            perFileBuffers.push(null);\n          });\n          weightSpecs.push(...weightsGroup.weights);\n        });\n\n        weightsManifest.forEach(weightsGroup => {\n          weightsGroup.paths.forEach(path => {\n            const weightFileReader = new FileReader();\n            weightFileReader.onload = (event: Event) => {\n              // tslint:disable-next-line:no-any\n              const weightData = (event.target as any).result as ArrayBuffer;\n              const index = paths.indexOf(path);\n              perFileBuffers[index] = weightData;\n              if (perFileBuffers.indexOf(null) === -1) {\n                resolve({\n                  modelTopology,\n                  weightSpecs,\n                  weightData: concatenateArrayBuffers(perFileBuffers),\n                });\n              }\n            };\n            weightFileReader.onerror = error =>\n                reject(`Failed to weights data from file of path '${path}'.`);\n            weightFileReader.readAsArrayBuffer(pathToFile[path]);\n          });\n        });\n      };\n      jsonReader.onerror = error => reject(\n          `Failed to read model topology and weights manifest JSON ` +\n          `from file '${jsonFile.name}'. BrowserFiles supports loading ` +\n          `Keras-style tf.Model artifacts only.`);\n      jsonReader.readAsText(jsonFile);\n    });\n  }\n\n  /**\n   * Check the compatibility between weights manifest and weight files.\n   */\n  private checkManifestAndWeightFiles(\n      manifest: WeightsManifestConfig, files: File[]): {[path: string]: File} {\n    const basenames: string[] = [];\n    const fileNames = files.map(file => basename(file.name));\n    const pathToFile: {[path: string]: File} = {};\n    for (const group of manifest) {\n      group.paths.forEach(path => {\n        const pathBasename = basename(path);\n        if (basenames.indexOf(pathBasename) !== -1) {\n          throw new Error(\n              `Duplicate file basename found in weights manifest: ` +\n              `'${pathBasename}'`);\n        }\n        basenames.push(pathBasename);\n        if (fileNames.indexOf(pathBasename) === -1) {\n          throw new Error(\n              `Weight file with basename '${pathBasename}' is not provided.`);\n        } else {\n          pathToFile[path] = files[fileNames.indexOf(pathBasename)];\n        }\n      });\n    }\n\n    if (basenames.length !== files.length) {\n      throw new Error(\n          `Mismatch in the number of files in weights manifest ` +\n          `(${basenames.length}) and the number of weight files provided ` +\n          `(${files.length}).`);\n    }\n    return pathToFile;\n  }\n}\n\nexport const browserDownloadsRouter: IORouter = (url: string|string[]) => {\n  if (!ENV.get('IS_BROWSER')) {\n    return null;\n  } else {\n    if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {\n      return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));\n    } else {\n      return null;\n    }\n  }\n};\nIORouterRegistry.registerSaveRouter(browserDownloadsRouter);\n\n/**\n * Creates an IOHandler that triggers file downloads from the browser.\n *\n * The returned `IOHandler` instance can be used as model exporting methods such\n * as `tf.Model.save` and supports only saving.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * const saveResult = await model.save('downloads://mymodel'));\n * // This will trigger downloading of two files:\n * //   'mymodel.json' and 'mymodel.weights.bin'.\n * console.log(saveResult);\n * ```\n *\n * @param fileNamePrefix Prefix name of the files to be downloaded. For use with\n *   `tf.Model`, `fileNamePrefix` should follow either of the following two\n *   formats:\n *   1. `null` or `undefined`, in which case the default file\n *      names will be used:\n *      - 'model.json' for the JSON file containing the model topology and\n *        weights manifest.\n *      - 'model.weights.bin' for the binary file containing the binary weight\n *        values.\n *   2. A single string or an Array of a single string, as the file name prefix.\n *      For example, if `'foo'` is provided, the downloaded JSON\n *      file and binary weights file will be named 'foo.json' and\n *      'foo.weights.bin', respectively.\n * @param config Additional configuration for triggering downloads.\n * @returns An instance of `BrowserDownloads` `IOHandler`.\n */\n/** @doc {heading: 'Models', subheading: 'Loading', namespace: 'io'} */\nexport function browserDownloads(fileNamePrefix = 'model'): IOHandler {\n  return new BrowserDownloads(fileNamePrefix);\n}\n\n/**\n * Creates an IOHandler that loads model artifacts from user-selected files.\n *\n * This method can be used for loading from files such as user-selected files\n * in the browser.\n * When used in conjunction with `tf.loadModel`, an instance of `tf.Model`\n * (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * // Note: This code snippet won't run properly without the actual file input\n * //   elements in the HTML DOM.\n *\n * // Suppose there are two HTML file input (`<input type=\"file\" ...>`)\n * // elements.\n * const uploadJSONInput = document.getElementById('upload-json');\n * const uploadWeightsInput = document.getElementById('upload-weights');\n * const model = await tfl.loadModel(tf.io.browserFiles(\n *     [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));\n * ```\n *\n * @param files `File`s to load from. Currently, this function supports only\n *   loading from files that contain Keras-style models (i.e., `tf.Model`s), for\n *   which an `Array` of `File`s is expected (in that order):\n *   - A JSON file containing the model topology and weight manifest.\n *   - Optionally, One or more binary files containing the binary weights.\n *     These files must have names that match the paths in the `weightsManifest`\n *     contained by the aforementioned JSON file, or errors will be thrown\n *     during loading. These weights files have the same format as the ones\n *     generated by `tensorflowjs_converter` that comes with the `tensorflowjs`\n *     Python PIP package. If no weights files are provided, only the model\n *     topology will be loaded from the JSON file above.\n * @returns An instance of `Files` `IOHandler`.\n */\n/** @doc {heading: 'Models', subheading: 'Loading', namespace: 'io'} */\nexport function browserFiles(files: File[]): IOHandler {\n  return new BrowserFiles(files);\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {NamedTensorMap} from '../tensor_types';\nimport * as util from '../util';\n\nimport {decodeWeights} from './io_utils';\nimport {DTYPE_VALUE_SIZE_MAP, WeightsManifestConfig, WeightsManifestEntry} from './types';\n\n/**\n * Reads binary weights data from a number of URLs.\n *\n * @param fetchURLs URLs to send the HTTP requests at, using `fetch` calls.\n * @param requestOptions RequestInit (options) for the HTTP requests.\n * @param fetchFunc Optional overriding value for the `window.fetch` function.\n * @returns A `Promise` of an Array of `ArrayBuffer`. The Array has the same\n *   length as `fetchURLs`.\n */\nexport async function loadWeightsAsArrayBuffer(\n    fetchURLs: string[], requestOptions?: RequestInit, fetchFunc?: Function):\n  Promise<ArrayBuffer[]> {\n  if (fetchFunc == null) {\n    fetchFunc = fetch;\n  }\n\n  // Create the requests for all of the weights in parallel.\n  const requests = fetchURLs.map(\n      fetchURL => fetchFunc(fetchURL, requestOptions));\n  const responses = await Promise.all(requests);\n  const buffers =\n      await Promise.all(responses.map(response => response.arrayBuffer()));\n  return buffers;\n}\n\n/**\n * Reads a weights manifest JSON configuration, fetches the weights and\n * returns them as `Tensor`s.\n *\n * @param manifest The weights manifest JSON.\n * @param filePathPrefix The path prefix for filenames given in the manifest.\n *     Defaults to the empty string.\n * @param weightNames The names of the weights to be fetched.\n */\nexport async function loadWeights(\n    manifest: WeightsManifestConfig,\n    filePathPrefix = '',\n    weightNames?: string[],\n    requestOptions?: RequestInit): Promise<NamedTensorMap> {\n  // TODO(nsthorat): Groups are currently fetched atomically. If you need a\n  // single weight from a group, the whole group will be fetched. At a future\n  // date, we should support fetching only the individual shards within a\n  // group that are needed to reconstruct the requested weight.\n  // TODO(cais): Use `decodeWeights` for implementation.\n\n  const fetchWeights = (fetchUrls: string[]) =>\n    loadWeightsAsArrayBuffer(fetchUrls, requestOptions);\n  const loadWeights = weightsLoaderFactory(fetchWeights);\n\n  return loadWeights(manifest, filePathPrefix, weightNames);\n}\n\n/**\n * Creates a function, which reads a weights manifest JSON configuration,\n * fetches the weight files using the specified function and returns them as\n * `Tensor`s.\n *\n * ```js\n * // example for creating a nodejs weight loader, which reads the weight files\n * // from disk using fs.readFileSync\n *\n * import * as fs from 'fs'\n *\n * const fetchWeightsFromDisk = (filePaths: string[]) =>\n *   filePaths.map(filePath => fs.readFileSync(filePath).buffer)\n *\n * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n *\n * const manifest = JSON.parse(\n *   fs.readFileSync('./my_model-weights_manifest').toString()\n * )\n * const weightMap = await loadWeights(manifest, './')\n * ```\n * @param fetchWeightsFunction The function used for fetching the weight files.\n * @returns Weight loading function.\n */\nexport function weightsLoaderFactory(\n  fetchWeightsFunction: (fetchUrls: string[]) => Promise<ArrayBuffer[]>\n): (\n  manifest: WeightsManifestConfig,\n  filePathPrefix?: string,\n  weightNames?: string[]\n) => Promise<NamedTensorMap> {\n\n  return async (\n    manifest: WeightsManifestConfig,\n    filePathPrefix = '',\n    weightNames?: string[]\n  ): Promise<NamedTensorMap> => {\n\n    // Collect all the groups, weights, and their relative offsets to be\n    // fetched.\n    const groupIndicesToFetchMap = manifest.map(() => false);\n    const groupWeightsToFetch: {\n      [group: number]: Array<{\n        manifestEntry: WeightsManifestEntry; groupOffset: number;\n        sizeBytes: number;\n      }>\n    } = {};\n    const weightsFound = weightNames != null\n      ? weightNames.map(() => false)\n      : [];\n    const allManifestWeightNames: string[] = [];\n    manifest.forEach((manifestGroupConfig, groupIndex) => {\n      let groupOffset = 0;\n      manifestGroupConfig.weights.forEach(weightsEntry => {\n        const rawDtype = ('quantization' in weightsEntry) ?\n            weightsEntry.quantization.dtype :\n            weightsEntry.dtype;\n\n        const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] *\n            util.sizeFromShape(weightsEntry.shape);\n\n        const enqueueWeightsForFetchingFn = () => {\n          groupIndicesToFetchMap[groupIndex] = true;\n          if (groupWeightsToFetch[groupIndex] == null) {\n            groupWeightsToFetch[groupIndex] = [];\n          }\n\n          groupWeightsToFetch[groupIndex].push({\n            manifestEntry: weightsEntry,\n            groupOffset,\n            sizeBytes: weightsBytes\n          });\n        };\n\n        if (weightNames != null) {\n          weightNames.forEach((weightName, weightIndex) => {\n            if (weightName === weightsEntry.name) {\n              enqueueWeightsForFetchingFn();\n              weightsFound[weightIndex] = true;\n            }\n          });\n        } else {\n          enqueueWeightsForFetchingFn();\n        }\n\n        allManifestWeightNames.push(weightsEntry.name);\n        groupOffset += weightsBytes;\n      });\n    });\n\n    if (!weightsFound.every(found => found)) {\n      const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);\n      throw new Error(\n          `Could not find weights in manifest with names: ` +\n          `${weightsNotFound.join(', ')}. \\n` +\n          `Manifest JSON has weights with names: ` +\n          `${allManifestWeightNames.join(', ')}.`);\n    }\n\n    // Convert the one-hot boolean groupId => shouldFetch map to a list of group\n    // IDs.\n    const groupIndicesToFetch =\n        groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {\n          if (shouldFetch) {\n            accumulator.push(i);\n          }\n          return accumulator;\n        }, []);\n\n    const fetchUrls: string[] = [];\n    groupIndicesToFetch.forEach(i => {\n      manifest[i].paths.forEach(filepath => {\n        const fetchUrl = filePathPrefix +\n            (!filePathPrefix.endsWith('/') ? '/' : '') + filepath;\n        fetchUrls.push(fetchUrl);\n      });\n    });\n    const buffers = await fetchWeightsFunction(fetchUrls);\n\n    const weightsTensorMap: NamedTensorMap = {};\n    let bufferIndexOffset = 0;\n    groupIndicesToFetch.forEach(i => {\n      const numBuffers = manifest[i].paths.length;\n\n      let groupBytes = 0;\n      for (let i = 0; i < numBuffers; i++) {\n        groupBytes += buffers[bufferIndexOffset + i].byteLength;\n      }\n\n      // Create a buffer for the whole group.\n      const groupBuffer = new ArrayBuffer(groupBytes);\n      const groupByteBuffer = new Uint8Array(groupBuffer);\n      let groupBufferOffset = 0;\n      for (let i = 0; i < numBuffers; i++) {\n        const buffer = new Uint8Array(buffers[bufferIndexOffset + i]);\n        groupByteBuffer.set(buffer, groupBufferOffset);\n        groupBufferOffset += buffer.byteLength;\n      }\n\n      const weightsEntries = groupWeightsToFetch[i];\n      weightsEntries.forEach(weightsEntry => {\n        const byteBuffer = groupBuffer.slice(\n            weightsEntry.groupOffset,\n            weightsEntry.groupOffset + weightsEntry.sizeBytes);\n        const nameToTensorMap =\n            decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);\n        for (const name in nameToTensorMap) {\n          weightsTensorMap[name] = nameToTensorMap[name];\n        }\n      });\n\n      bufferIndexOffset += numBuffers;\n    });\n\n    return weightsTensorMap;\n  };\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * IOHandler implementations based on HTTP requests in the web browser.\n *\n * Uses [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n */\n\nimport {assert} from '../util';\nimport {concatenateArrayBuffers, getModelArtifactsInfoForJSON} from './io_utils';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, SaveResult, WeightsManifestConfig, WeightsManifestEntry} from './types';\nimport {loadWeightsAsArrayBuffer} from './weights_loader';\n\nexport class BrowserHTTPRequest implements IOHandler {\n  protected readonly path: string|string[];\n  protected readonly requestInit: RequestInit;\n\n  private readonly fetchFunc: Function;\n\n  readonly DEFAULT_METHOD = 'POST';\n\n  static readonly URL_SCHEME_REGEX = /^https?:\\/\\//;\n\n  constructor(\n      path: string|string[], requestInit?: RequestInit,\n      private readonly weightPathPrefix?: string, fetchFunc?: Function) {\n    if (fetchFunc == null) {\n      if (typeof fetch === 'undefined') {\n        throw new Error(\n            'browserHTTPRequest is not supported outside the web browser ' +\n            'without a fetch polyfill.');\n      }\n      // Make sure fetch is always bound to window (the\n      // original object) when available.\n      this.fetchFunc =\n          fetch.bind(typeof window === 'undefined' ? null : window);\n    } else {\n      assert(\n          typeof fetchFunc === 'function',\n          'Must pass a function that matches the signature of ' +\n              '`fetch` (see ' +\n              'https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)');\n      this.fetchFunc = fetchFunc;\n    }\n\n    assert(\n        path != null && path.length > 0,\n        'URL path for browserHTTPRequest must not be null, undefined or ' +\n            'empty.');\n\n    if (Array.isArray(path)) {\n      assert(\n          path.length === 2,\n          'URL paths for browserHTTPRequest must have a length of 2, ' +\n              `(actual length is ${path.length}).`);\n    }\n    this.path = path;\n\n    if (requestInit != null && requestInit.body != null) {\n      throw new Error(\n          'requestInit is expected to have no pre-existing body, but has one.');\n    }\n    this.requestInit = requestInit || {};\n  }\n\n  async save(modelArtifacts: ModelArtifacts): Promise<SaveResult> {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\n          'BrowserHTTPRequest.save() does not support saving model topology ' +\n          'in binary formats yet.');\n    }\n\n    const init = Object.assign({method: this.DEFAULT_METHOD}, this.requestInit);\n    init.body = new FormData();\n\n    const weightsManifest: WeightsManifestConfig = [{\n      paths: ['./model.weights.bin'],\n      weights: modelArtifacts.weightSpecs,\n    }];\n    const modelTopologyAndWeightManifest = {\n      modelTopology: modelArtifacts.modelTopology,\n      weightsManifest\n    };\n\n    init.body.append(\n        'model.json',\n        new Blob(\n            [JSON.stringify(modelTopologyAndWeightManifest)],\n            {type: 'application/json'}),\n        'model.json');\n\n    if (modelArtifacts.weightData != null) {\n      init.body.append(\n          'model.weights.bin',\n          new Blob(\n              [modelArtifacts.weightData], {type: 'application/octet-stream'}),\n          'model.weights.bin');\n    }\n\n    const response = await this.getFetchFunc()(this.path as string, init);\n\n    if (response.ok) {\n      return {\n        modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),\n        responses: [response],\n      };\n    } else {\n      throw new Error(\n          `BrowserHTTPRequest.save() failed due to HTTP response status ` +\n          `${response.status}.`);\n    }\n  }\n\n  /**\n   * Load model artifacts via HTTP request(s).\n   *\n   * See the documentation to `browserHTTPRequest` for details on the saved\n   * artifacts.\n   *\n   * @returns The loaded model artifacts (if loading succeeds).\n   */\n  async load(): Promise<ModelArtifacts> {\n    return Array.isArray(this.path) ? this.loadBinaryModel() :\n                                      this.loadJSONModel();\n  }\n\n  /**\n   * Loads the model topology file and build the in memory graph of the model.\n   */\n  private async loadBinaryTopology(): Promise<ArrayBuffer> {\n    try {\n      const response =\n          await this.getFetchFunc()(this.path[0], this.requestInit);\n      if (!response.ok) {\n        throw new Error(\n            `BrowserHTTPRequest.load() failed due to HTTP response: ${\n                response.statusText}`);\n      }\n      return await response.arrayBuffer();\n    } catch (error) {\n      throw new Error(`${this.path[0]} not found. ${error}`);\n    }\n  }\n\n  protected async loadBinaryModel(): Promise<ModelArtifacts> {\n    const graphPromise = this.loadBinaryTopology();\n    const manifestPromise =\n        await this.getFetchFunc()(this.path[1], this.requestInit);\n    if (!manifestPromise.ok) {\n      throw new Error(`BrowserHTTPRequest.load() failed due to HTTP response: ${\n          manifestPromise.statusText}`);\n    }\n\n    const results = await Promise.all([graphPromise, manifestPromise]);\n    const [modelTopology, weightsManifestResponse] = results;\n\n    const weightsManifest =\n        await weightsManifestResponse.json() as WeightsManifestConfig;\n\n    let weightSpecs: WeightsManifestEntry[];\n    let weightData: ArrayBuffer;\n    if (weightsManifest != null) {\n      const results = await this.loadWeights(weightsManifest);\n      [weightSpecs, weightData] = results;\n    }\n\n    return {modelTopology, weightSpecs, weightData};\n  }\n\n  protected async loadJSONModel(): Promise<ModelArtifacts> {\n    const modelConfigRequest =\n        await this.getFetchFunc()(this.path as string, this.requestInit);\n    if (!modelConfigRequest.ok) {\n      throw new Error(`BrowserHTTPRequest.load() failed due to HTTP response: ${\n          modelConfigRequest.statusText}`);\n    }\n    const modelConfig = await modelConfigRequest.json();\n    const modelTopology = modelConfig['modelTopology'];\n    const weightsManifest = modelConfig['weightsManifest'];\n\n    // We do not allow both modelTopology and weightsManifest to be missing.\n    if (modelTopology == null && weightsManifest == null) {\n      throw new Error(\n          `The JSON from HTTP path ${this.path} contains neither model ` +\n          `topology or manifest for weights.`);\n    }\n\n    let weightSpecs: WeightsManifestEntry[];\n    let weightData: ArrayBuffer;\n    if (weightsManifest != null) {\n      const weightsManifest =\n          modelConfig['weightsManifest'] as WeightsManifestConfig;\n      const results = await this.loadWeights(weightsManifest);\n      [weightSpecs, weightData] = results;\n    }\n\n    return {modelTopology, weightSpecs, weightData};\n  }\n\n  private async loadWeights(weightsManifest: WeightsManifestConfig):\n      Promise<[WeightsManifestEntry[], ArrayBuffer]> {\n    const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;\n    const [prefix, suffix] = parseUrl(weightPath);\n    const pathPrefix = this.weightPathPrefix || prefix;\n\n    const weightSpecs = [];\n    for (const entry of weightsManifest) {\n      weightSpecs.push(...entry.weights);\n    }\n\n    const fetchURLs: string[] = [];\n    weightsManifest.forEach(weightsGroup => {\n      weightsGroup.paths.forEach(path => {\n        fetchURLs.push(pathPrefix + path + suffix);\n      });\n    });\n\n    return [\n      weightSpecs,\n      concatenateArrayBuffers(await loadWeightsAsArrayBuffer(\n          fetchURLs, this.requestInit, this.getFetchFunc()))\n    ];\n  }\n\n  /**\n   * Helper method to get the `fetch`-like function set for this instance.\n   *\n   * This is mainly for avoiding confusion with regard to what context\n   * the `fetch`-like function is bound to. In the default (browser) case,\n   * the function will be bound to `window`, instead of `this`.\n   */\n  private getFetchFunc() {\n    return this.fetchFunc;\n  }\n}\n\n/**\n * Extract the prefix and suffix of the url, where the prefix is the path before\n * the last file, and suffix is the search params after the last file.\n * ```\n * const url = 'http://tfhub.dev/model/1/tensorflowjs_model.pb?tfjs-format=file'\n * [prefix, suffix] = parseUrl(url)\n * // prefix = 'http://tfhub.dev/model/1/'\n * // suffix = '?tfjs-format=file'\n * ```\n * @param url the model url to be parsed.\n */\nexport function parseUrl(url: string): [string, string] {\n  const lastSlash = url.lastIndexOf('/');\n  const lastSearchParam = url.lastIndexOf('?');\n  const prefix = url.substring(0, lastSlash);\n  const suffix =\n      lastSearchParam > lastSlash ? url.substring(lastSearchParam) : '';\n  return [prefix + '/', suffix];\n}\n\nexport function isHTTPScheme(url: string): boolean {\n  return url.match(BrowserHTTPRequest.URL_SCHEME_REGEX) != null;\n}\n\nexport const httpRequestRouter: IORouter = (url: string|string[]) => {\n  if (typeof fetch === 'undefined') {\n    // browserHTTPRequest uses `fetch`, if one wants to use it in node.js\n    // they have to setup a global fetch polyfill.\n    return null;\n  } else {\n    let isHTTP = true;\n    if (Array.isArray(url)) {\n      isHTTP = url.every(urlItem => isHTTPScheme(urlItem));\n    } else {\n      isHTTP = isHTTPScheme(url);\n    }\n    if (isHTTP) {\n      return browserHTTPRequest(url);\n    }\n  }\n  return null;\n};\nIORouterRegistry.registerSaveRouter(httpRequestRouter);\nIORouterRegistry.registerLoadRouter(httpRequestRouter);\n\n/**\n * Creates an IOHandler subtype that sends model artifacts to HTTP server.\n *\n * An HTTP request of the `multipart/form-data` mime type will be sent to the\n * `path` URL. The form data includes artifacts that represent the topology\n * and/or weights of the model. In the case of Keras-style `tf.Model`, two\n * blobs (files) exist in form-data:\n *   - A JSON file consisting of `modelTopology` and `weightsManifest`.\n *   - A binary weights file consisting of the concatenated weight values.\n * These files are in the same format as the one generated by\n * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).\n *\n * The following code snippet exemplifies the client-side code that uses this\n * function:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save(tf.io.browserHTTPRequest(\n *     'http://model-server:5000/upload', {method: 'PUT'}));\n * console.log(saveResult);\n * ```\n *\n * If the default `POST` method is to be used, without any custom parameters\n * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:\n *\n * ```js\n * const saveResult = await model.save('http://model-server:5000/upload');\n * ```\n *\n * The following Python code snippet based on the\n * [flask](https://github.com/pallets/flask) server framework implements a\n * server that can receive the request. Upon receiving the model artifacts\n * via the requst, this particular server reconsistutes instances of\n * [Keras Models](https://keras.io/models/model/) in memory.\n *\n * ```python\n * # pip install -U flask flask-cors tensorflow tensorflowjs\n *\n * from __future__ import absolute_import\n * from __future__ import division\n * from __future__ import print_function\n *\n * import io\n *\n * from flask import Flask, Response, request\n * from flask_cors import CORS, cross_origin\n * import tensorflow as tf\n * import tensorflowjs as tfjs\n * import werkzeug.formparser\n *\n *\n * class ModelReceiver(object):\n *\n *   def __init__(self):\n *     self._model = None\n *     self._model_json_bytes = None\n *     self._model_json_writer = None\n *     self._weight_bytes = None\n *     self._weight_writer = None\n *\n *   @property\n *   def model(self):\n *     self._model_json_writer.flush()\n *     self._weight_writer.flush()\n *     self._model_json_writer.seek(0)\n *     self._weight_writer.seek(0)\n *\n *     json_content = self._model_json_bytes.read()\n *     weights_content = self._weight_bytes.read()\n *     return tfjs.converters.deserialize_keras_model(\n *         json_content,\n *         weight_data=[weights_content],\n *         use_unique_name_scope=True)\n *\n *   def stream_factory(self,\n *                      total_content_length,\n *                      content_type,\n *                      filename,\n *                      content_length=None):\n *     # Note: this example code is *not* thread-safe.\n *     if filename == 'model.json':\n *       self._model_json_bytes = io.BytesIO()\n *       self._model_json_writer = io.BufferedWriter(self._model_json_bytes)\n *       return self._model_json_writer\n *     elif filename == 'model.weights.bin':\n *       self._weight_bytes = io.BytesIO()\n *       self._weight_writer = io.BufferedWriter(self._weight_bytes)\n *       return self._weight_writer\n *\n *\n * def main():\n *   app = Flask('model-server')\n *   CORS(app)\n *   app.config['CORS_HEADER'] = 'Content-Type'\n *\n *   model_receiver = ModelReceiver()\n *\n *   @app.route('/upload', methods=['POST'])\n *   @cross_origin()\n *   def upload():\n *     print('Handling request...')\n *     werkzeug.formparser.parse_form_data(\n *         request.environ, stream_factory=model_receiver.stream_factory)\n *     print('Received model:')\n *     with tf.Graph().as_default(), tf.Session():\n *       model = model_receiver.model\n *       model.summary()\n *       # You can perform `model.predict()`, `model.fit()`,\n *       # `model.evaluate()` etc. here.\n *     return Response(status=200)\n *\n *   app.run('localhost', 5000)\n *\n *\n * if __name__ == '__main__':\n *   main()\n * ```\n *\n * @param path A single URL path or an Array of URL  paths.\n *   Currently, only a single URL path is supported. Array input is reserved\n *   for future development.\n *   Can be an absolute HTTP path (e.g.,\n *   'http://localhost:8000/model-upload)') or a relative path (e.g.,\n *   './model-upload').\n * @param requestInit Request configurations to be used when sending\n *    HTTP request to server using `fetch`. It can contain fields such as\n *    `method`, `credentials`, `headers`, `mode`, etc. See\n *    https://developer.mozilla.org/en-US/docs/Web/API/Request/Request\n *    for more information. `requestInit` must not have a body, because the\n * body will be set by TensorFlow.js. File blobs representing the model\n * topology (filename: 'model.json') and the weights of the model (filename:\n * 'model.weights.bin') will be appended to the body. If `requestInit` has a\n * `body`, an Error will be thrown.\n * @param weightPathPrefix Optional, this specifies the path prefix for weight\n *   files, by default this is calculated from the path param.\n * @param fetchFunc Optional, custom `fetch` function. E.g., in Node.js,\n *   the `fetch` from node-fetch can be used here.\n * @returns An instance of `IOHandler`.\n */\nexport function browserHTTPRequest(\n    path: string|string[], requestInit?: RequestInit, weightPathPrefix?: string,\n    fetchFunc?: Function): IOHandler {\n  return new BrowserHTTPRequest(path, requestInit, weightPathPrefix, fetchFunc);\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * IOHandlers that pass through the in-memory ModelArtifacts format.\n */\n\nimport {IOHandler, ModelArtifacts, SaveResult, WeightsManifestEntry} from './types';\n\nclass PassthroughLoader implements IOHandler {\n  constructor(\n      private readonly modelTopology?: {}|ArrayBuffer,\n      private readonly weightSpecs?: WeightsManifestEntry[],\n      private readonly weightData?: ArrayBuffer) {}\n\n  async load(): Promise<ModelArtifacts> {\n    let result = {};\n    if (this.modelTopology != null) {\n      result = {modelTopology: this.modelTopology, ...result};\n    }\n    if (this.weightSpecs != null && this.weightSpecs.length > 0) {\n      result = {weightSpecs: this.weightSpecs, ...result};\n    }\n    if (this.weightData != null && this.weightData.byteLength > 0) {\n      result = {weightData: this.weightData, ...result};\n    }\n    return result;\n  }\n}\n\nclass PassthroughSaver implements IOHandler {\n  constructor(\n      private readonly saveHandler:\n          (artifacts: ModelArtifacts) => Promise<SaveResult>) {}\n\n  async save(modelArtifacts: ModelArtifacts) {\n    return this.saveHandler(modelArtifacts);\n  }\n}\n\n/**\n * Creates an IOHandler that loads model artifacts from memory.\n *\n * When used in conjunction with `tf.loadModel`, an instance of `tf.Model`\n * (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * const model = await tf.loadModel(tf.io.fromMemory(\n *     modelTopology, weightSpecs, weightData));\n * ```\n *\n * @param modelTopology a object containing model topology (i.e., parsed from\n *   the JSON format).\n * @param weightSpecs An array of `WeightsManifestEntry` objects describing the\n *   names, shapes, types, and quantization of the weight data.\n * @param weightData A single `ArrayBuffer` containing the weight data,\n *   concatenated in the order described by the weightSpecs.\n *\n * @returns A passthrough `IOHandler` that simply loads the provided data.\n */\nexport function fromMemory(\n    modelTopology: {}, weightSpecs?: WeightsManifestEntry[],\n    weightData?: ArrayBuffer): IOHandler {\n  return new PassthroughLoader(modelTopology, weightSpecs, weightData);\n}\n\n/**\n * Creates an IOHandler that passes saved model artifacts to a callback.\n *\n * ```js\n * function handleSave(artifacts) {\n *   // ... do something with the artifacts ...\n *   return {modelArtifactsInfo: {...}, ...};\n * }\n *\n * const saveResult = model.save(tf.io.withSaveHandler(handleSave));\n * ```\n *\n * @param saveHandler A function that accepts a `ModelArtifacts` and returns a\n *     `SaveResult`.\n */\nexport function withSaveHandler(\n    saveHandler: (artifacts: ModelArtifacts) =>\n        Promise<SaveResult>): IOHandler {\n  return new PassthroughSaver(saveHandler);\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// Importing local_storage and indexed_db is necessary for the routers to be\n// registered.\nimport './indexed_db';\nimport './local_storage';\n\nimport {browserFiles} from './browser_files';\nimport {browserHTTPRequest, isHTTPScheme} from './browser_http';\nimport {concatenateArrayBuffers, decodeWeights, encodeWeights, getModelArtifactsInfoForJSON} from './io_utils';\nimport {fromMemory, withSaveHandler} from './passthrough';\nimport {IORouterRegistry} from './router_registry';\nimport {IOHandler, LoadHandler, ModelArtifacts, ModelStoreManager, SaveConfig, SaveHandler, SaveResult, WeightsManifestConfig, WeightsManifestEntry} from './types';\nimport {loadWeights, weightsLoaderFactory} from './weights_loader';\n\nconst registerSaveRouter = IORouterRegistry.registerSaveRouter;\nconst registerLoadRouter = IORouterRegistry.registerLoadRouter;\nconst getSaveHandlers = IORouterRegistry.getSaveHandlers;\nconst getLoadHandlers = IORouterRegistry.getLoadHandlers;\n\nexport {copyModel, listModels, moveModel, removeModel} from './model_management';\n\nexport {\n  browserFiles,\n  browserHTTPRequest,\n  concatenateArrayBuffers,\n  decodeWeights,\n  encodeWeights,\n  fromMemory,\n  getLoadHandlers,\n  getModelArtifactsInfoForJSON,\n  getSaveHandlers,\n  IOHandler,\n  isHTTPScheme,\n  LoadHandler,\n  loadWeights,\n  ModelArtifacts,\n  ModelStoreManager,\n  registerLoadRouter,\n  registerSaveRouter,\n  SaveConfig,\n  SaveHandler,\n  SaveResult,\n  WeightsManifestConfig,\n  WeightsManifestEntry,\n  weightsLoaderFactory,\n  withSaveHandler\n};\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor1D, Tensor2D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {oneHot} from './array_ops';\nimport {op} from './operation';\n\n/**\n * Computes the confusion matrix from true labels and predicted labels.\n *\n * ```js\n * const labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');\n * const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');\n * const numClasses = 3;\n * const out = tf.math.confusionMatrix(labels, predictions, numClasses);\n * out.print();\n * // Expected output matrix:\n * // [[2, 0, 0],\n * //  [0, 1, 1],\n * //  [0, 0, 1]]\n * ```\n *\n * @param labels The target labels, assumed to be 0-based integers\n *   for the classes. The shape is `[numExamples]`, where\n *   `numExamples` is the number of examples included.\n * @param predictions The predicted classes, assumed to be\n *   0-based integers for the classes. Must have the same shape as `labels`.\n * @param numClasses Number of all classes, as an integer.\n *   Its value must be larger than the largest element in `labels` and\n *   `predictions`.\n * @returns The confusion matrix as a int32-type 2D tensor. The value at\n *   row `r` and column `c` is the number of times examples of actual class\n *   `r` were predicted as class `c`.\n */\n/** @doc {heading: 'Operations', subheading: 'Evaluation'} */\nexport function confusionMatrix_(\n    labels: Tensor1D|TensorLike, predictions: Tensor1D|TensorLike,\n    numClasses: number): Tensor2D {\n  const $labels = convertToTensor(labels, 'labels', 'confusionMatrix');\n  const $predictions =\n      convertToTensor(predictions, 'predictions', 'confusionMatrix');\n\n  util.assert(\n      numClasses == null || numClasses > 0 && Number.isInteger(numClasses),\n      `If provided, numClasses must be a positive integer, ` +\n          `but got ${numClasses}`);\n  util.assert(\n      $labels.rank === 1,\n      `Expected the rank of labels to be 1, but got ${$labels.rank}`);\n  util.assert(\n      $predictions.rank === 1,\n      `Expected the rank of predictions to be 1, ` +\n          `but got ${$predictions.rank}`);\n  util.assert(\n      $labels.shape[0] === $predictions.shape[0],\n      `Mismatch in the number of examples: ` +\n          `${$labels.shape[0]} vs. ${$predictions.shape[0]}. ` +\n          `Labels and predictions should have the same number of elements.`);\n  util.assert(\n      numClasses > 0 && Number.isInteger(numClasses),\n      `numClasses is required to be a positive integer, but got ${numClasses}`);\n  // TODO(cais): In the future, if oneHot supports tensors inputs for\n  //   `numClasses`, `confusionMatrix` can make `numClasses` optional.\n\n  const oneHotLabels = oneHot($labels.asType('int32'), numClasses);\n  const oneHotPredictions = oneHot($predictions.asType('int32'), numClasses);\n  return oneHotLabels.transpose().matMul(oneHotPredictions).asType('int32');\n}\n\nexport const confusionMatrix = op({confusionMatrix_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {assert} from './util';\n\n/**\n * Types to support JSON-esque data structures internally.\n *\n * Internally ConfigDict's use camelCase keys and values where the\n * values are class names to be instantiated.  On the python side, these\n * will be snake_case.  Internally we allow Enums into the values for better\n * type safety, but these need to be converted to raw primitives (usually\n * strings) for round-tripping with python.\n *\n * toConfig returns the TS-friendly representation. model.toJSON() returns\n * the pythonic version as that's the portable format.  If you need to\n * python-ify a non-model level toConfig output, you'll need to use a\n * convertTsToPythonic from serialization_utils in -Layers.\n *\n */\nexport type ConfigDictValue =\n    boolean|number|string|null|ConfigDictArray|ConfigDict;\nexport interface ConfigDict {\n  [key: string]: ConfigDictValue;\n}\nexport interface ConfigDictArray extends Array<ConfigDictValue> {}\n\n/**\n * Type to represent the class-type of Serializable objects.\n *\n * Ie the class prototype with access to the constructor and any\n * static members/methods. Instance methods are not listed here.\n *\n * Source for this idea: https://stackoverflow.com/a/43607255\n */\nexport type SerializableConstructor<T extends Serializable> = {\n  // tslint:disable-next-line:no-any\n  new (...args: any[]): T; className: string; fromConfig: FromConfigMethod<T>;\n};\nexport type FromConfigMethod<T extends Serializable> =\n    (cls: SerializableConstructor<T>, config: ConfigDict) => T;\n\n/**\n * Serializable defines the serialization contract.\n *\n * TFJS requires serializable classes to return their className when asked\n * to avoid issues with minification.\n */\nexport abstract class Serializable {\n  /**\n   * Return the class name for this class to use in serialization contexts.\n   *\n   * Generally speaking this will be the same thing that constructor.name\n   * would have returned.  However, the class name needs to be robust\n   * against minification for serialization/deserialization to work properly.\n   *\n   * There's also places such as initializers.VarianceScaling, where\n   * implementation details between different languages led to different\n   * class hierarchies and a non-leaf node is used for serialization purposes.\n   */\n  getClassName(): string {\n    return (this.constructor as SerializableConstructor<Serializable>)\n        .className;\n  }\n\n  /**\n   * Return all the non-weight state needed to serialize this object.\n   */\n  abstract getConfig(): ConfigDict;\n\n  /**\n   * Creates an instance of T from a ConfigDict.\n   *\n   * This works for most descendants of serializable.  A few need to\n   * provide special handling.\n   * @param cls A Constructor for the class to instantiate.\n   * @param config The Configuration for the object.\n   */\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(config);\n  }\n}\n\n/**\n * Maps string keys to class constructors.\n *\n * Used during (de)serialization from the cross-language JSON format, which\n * requires the class name in the serialization format matches the class\n * names as used in Python, should it exist.\n */\nexport class SerializationMap {\n  private static instance: SerializationMap;\n  classNameMap: {\n    [className: string]:\n        [SerializableConstructor<Serializable>, FromConfigMethod<Serializable>]\n  };\n\n  private constructor() {\n    this.classNameMap = {};\n  }\n\n  /**\n   * Returns the singleton instance of the map.\n   */\n  static getMap(): SerializationMap {\n    if (SerializationMap.instance == null) {\n      SerializationMap.instance = new SerializationMap();\n    }\n    return SerializationMap.instance;\n  }\n\n  /**\n   * Registers the class as serializable.\n   */\n  static register<T extends Serializable>(cls: SerializableConstructor<T>) {\n    SerializationMap.getMap().classNameMap[cls.className] =\n        [cls, cls.fromConfig];\n  }\n}\n\n/**\n * Register a class with the serialization map of TensorFlow.js.\n *\n * This is often used for registering custom Layers, so they can be\n * serialized and deserialized.\n *\n * Example:\n *\n * ```js\n * class MyCustomLayer extends tf.layers.Layer {\n *   static className = 'MyCustomLayer';\n *\n *   constructor(config) {\n *     super(config);\n *   }\n * }\n * tf.serialization.registerClass(MyCustomLayer);\n * ```\n *\n * @param cls The class to be registered. It must have a public static member\n *   called `className` defined and the value must be a non-empty string.\n */\n/** @doc {heading: 'Models', subheading: 'Serialization'} */\nexport function registerClass<T extends Serializable>(\n    cls: SerializableConstructor<T>) {\n  assert(\n      cls.className != null,\n      `Class being registered does not have the static className property ` +\n          `defined.`);\n  assert(\n      typeof cls.className === 'string',\n      `className is required to be a string, but got type ` +\n          typeof cls.className);\n  assert(\n      cls.className.length > 0,\n      `Class being registered has an empty-string as its className, which ` +\n          `is disallowed.`);\n\n  SerializationMap.register(cls);\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from './environment';\nimport {Features} from './environment_util';\nimport {Tensor} from './tensor';\nimport {TypedArray} from './types';\nimport * as util from './util';\nimport {isString} from './util';\n\n// TODO(smilkov): Move these constants to jasmine_util.\nexport const WEBGL_ENVS: Features = {\n  'HAS_WEBGL': true\n};\nexport const NODE_ENVS: Features = {\n  'IS_NODE': true\n};\nexport const CHROME_ENVS: Features = {\n  'IS_CHROME': true\n};\nexport const BROWSER_ENVS: Features = {\n  'IS_BROWSER': true\n};\nexport const CPU_ENVS: Features = {\n  'HAS_WEBGL': false\n};\n\nexport const ALL_ENVS: Features = {};\n\nexport function expectArraysClose(\n    actual: Tensor|TypedArray|number[],\n    expected: Tensor|TypedArray|number[]|boolean[], epsilon?: number) {\n  if (epsilon == null) {\n    epsilon = ENV.get('TEST_EPSILON');\n  }\n  return expectArraysPredicate(\n      actual, expected, (a, b) => areClose(a as number, Number(b), epsilon));\n}\n\nfunction expectArraysPredicate(\n    actual: Tensor|TypedArray|number[]|string[],\n    expected: Tensor|TypedArray|number[]|boolean[]|string[],\n    predicate: (a: {}, b: {}) => boolean) {\n  if (!(actual instanceof Tensor) && !(expected instanceof Tensor)) {\n    const aType = actual.constructor.name;\n    const bType = expected.constructor.name;\n\n    if (aType !== bType) {\n      throw new Error(\n          `Arrays are of different type actual: ${aType} ` +\n          `vs expected: ${bType}`);\n    }\n  } else if (actual instanceof Tensor && expected instanceof Tensor) {\n    if (actual.dtype !== expected.dtype) {\n      throw new Error(\n          `Arrays are of different type actual: ${actual.dtype} ` +\n          `vs expected: ${expected.dtype}.`);\n    }\n    if (!util.arraysEqual(actual.shape, expected.shape)) {\n      throw new Error(\n          `Arrays are of different shape actual: ${actual.shape} ` +\n          `vs expected: ${expected.shape}.`);\n    }\n  }\n\n  let actualValues: TypedArray|number[]|string[];\n  let expectedValues: TypedArray|number[]|boolean[]|string[];\n  if (actual instanceof Tensor) {\n    actualValues = actual.dataSync();\n  } else {\n    actualValues = actual;\n  }\n  if (expected instanceof Tensor) {\n    expectedValues = expected.dataSync();\n  } else {\n    expectedValues = expected;\n  }\n\n  if (actualValues.length !== expectedValues.length) {\n    throw new Error(\n        `Arrays have different lengths actual: ${actualValues.length} vs ` +\n        `expected: ${expectedValues.length}.\\n` +\n        `Actual:   ${actualValues}.\\n` +\n        `Expected: ${expectedValues}.`);\n  }\n  for (let i = 0; i < expectedValues.length; ++i) {\n    const a = actualValues[i];\n    const e = expectedValues[i];\n\n    if (!predicate(a, e)) {\n      throw new Error(\n          `Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.\\n` +\n          `Actual:   ${actualValues}.\\n` +\n          `Expected: ${expectedValues}.`);\n    }\n  }\n}\n\nexport interface DoneFn {\n  (): void;\n  fail: (message?: Error|string) => void;\n}\n\nexport function expectPromiseToFail(fn: () => Promise<{}>, done: DoneFn): void {\n  fn().then(() => done.fail(), () => done());\n}\n\nexport function expectArraysEqual(\n    actual: Tensor|TypedArray|number[]|string[],\n    expected: Tensor|TypedArray|number[]|boolean[]|string[]) {\n  if (actual instanceof Tensor && actual.dtype === 'string' ||\n      expected instanceof Tensor && expected.dtype === 'string' ||\n      actual instanceof Array && isString(actual[0]) ||\n      expected instanceof Array && isString(expected[0])) {\n    // tslint:disable-next-line:triple-equals\n    return expectArraysPredicate(actual, expected, (a, b) => a == b);\n  }\n  return expectArraysClose(actual as Tensor, expected as Tensor, 0);\n}\n\nexport function expectNumbersClose(a: number, e: number, epsilon?: number) {\n  if (epsilon == null) {\n    epsilon = ENV.get('TEST_EPSILON');\n  }\n  if (!areClose(a, e, epsilon)) {\n    throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);\n  }\n}\n\nfunction areClose(a: number, e: number, epsilon: number): boolean {\n  if (isNaN(a) && isNaN(e)) {\n    return true;\n  }\n  if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon) {\n    return false;\n  }\n  return true;\n}\n\nexport function expectValuesInRange(\n    actual: Tensor|TypedArray|number[], low: number, high: number) {\n  let actualVals: TypedArray|number[];\n  if (actual instanceof Tensor) {\n    actualVals = actual.dataSync();\n  } else {\n    actualVals = actual;\n  }\n  for (let i = 0; i < actualVals.length; i++) {\n    if (actualVals[i] < low || actualVals[i] > high) {\n      throw new Error(\n          `Value out of range:${actualVals[i]} low: ${low}, high: ${high}`);\n    }\n  }\n}\n\nexport function expectArrayBuffersEqual(\n    actual: ArrayBuffer, expected: ArrayBuffer) {\n  // Safari & Jasmine don't like comparing ArrayBuffers directly. Wrapping in\n  // a Float32Array solves this issue.\n  expect(new Float32Array(actual)).toEqual(new Float32Array(expected));\n}\n","/** @license See the LICENSE file. */\n\n// This code is auto-generated, do not modify this file!\nconst version = '0.14.2';\nexport {version};\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {variableGrads} from '../globals';\nimport {Serializable} from '../serialization';\nimport {Scalar, Variable} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\n\n/** @doc {heading: 'Training', subheading: 'Classes', namespace: 'train'} */\nexport abstract class Optimizer extends Serializable {\n  /**\n   * Executes `f()` and minimizes the scalar output of `f()` by computing\n   * gradients of y with respect to the list of trainable variables provided by\n   * `varList`. If no list is provided, it defaults to all trainable variables.\n   *\n   * @param f The function to execute and whose output to minimize.\n   * @param returnCost Whether to return the scalar cost value produced by\n   * executing `f()`.\n   * @param varList An optional list of variables to update. If specified, only\n   * the trainable variables in varList will be updated by minimize. Defaults to\n   * all trainable variables.\n   */\n  /** @doc {heading: 'Training', subheading: 'Optimizers'} */\n  minimize(f: () => Scalar, returnCost = false, varList?: Variable[]): Scalar\n      |null {\n    const {value, grads} = this.computeGradients(f, varList);\n\n    this.applyGradients(grads);\n\n    // Dispose gradients.\n    const varNames = Object.keys(grads);\n    varNames.forEach(varName => grads[varName].dispose());\n\n    if (returnCost) {\n      return value as Scalar;\n    } else {\n      value.dispose();\n      return null;\n    }\n  }\n\n  /**\n   * Executes f() and computes the gradient of the scalar output of f() with\n   * respect to the list of trainable variables provided by `varList`. If no\n   * list is provided, it defaults to all trainable variables.\n   *\n   * @param f The function to execute and whose output to use for computing\n   * gradients with respect to variables.\n   * @param varList An optional list of variables to compute gradients with\n   * respect to. If specified, only the trainable variables in varList will have\n   * gradients computed with respect to. Defaults to all trainable variables.\n   */\n  computeGradients(f: () => Scalar, varList?: Variable[]):\n      {value: Scalar, grads: NamedTensorMap} {\n    return variableGrads(f, varList);\n  }\n\n  /**\n   * Updates variables by using the computed gradients.\n   *\n   * @param variableGradients A mapping of variable name to its gradient value.\n   */\n  abstract applyGradients(variableGradients: NamedTensorMap): void;\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {keep, tidy} from '../globals';\nimport {scalar, zerosLike} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar} from '../tensor';\nimport {NamedVariableMap} from '../tensor_types';\nimport {Optimizer} from './optimizer';\n\n/** @doclink Optimizer */\nexport class AdadeltaOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'AdadeltaOptimizer';\n  private c: Scalar;\n  private epsilonScalar: Scalar;\n  private rhoScalar: Scalar;\n  private oneMinusRho: Scalar;\n\n  private accumulatedGrads: NamedVariableMap = {};\n  private accumulatedUpdates: NamedVariableMap = {};\n\n  constructor(\n      protected learningRate: number, protected rho: number,\n      protected epsilon: number = null) {\n    super();\n\n    this.c = keep(scalar(-learningRate));\n    this.rhoScalar = keep(scalar(rho));\n    this.oneMinusRho = keep(scalar(1 - rho));\n\n    if (epsilon === null) {\n      epsilon = ENV.get('EPSILON');\n    }\n\n    this.epsilonScalar = keep(scalar(epsilon));\n  }\n\n  applyGradients(variableGradients: NamedVariableMap) {\n    for (const variableName in variableGradients) {\n      const value = ENV.engine.registeredVariables[variableName];\n      if (this.accumulatedGrads[variableName] == null) {\n        const trainable = false;\n        tidy(() => {\n          this.accumulatedGrads[variableName] =\n              zerosLike(value).variable(trainable);\n        });\n      }\n      if (this.accumulatedUpdates[variableName] == null) {\n        const trainable = false;\n        tidy(() => {\n          this.accumulatedUpdates[variableName] =\n              zerosLike(value).variable(trainable);\n        });\n      }\n\n      const gradient = variableGradients[variableName];\n      const accumulatedGrad = this.accumulatedGrads[variableName];\n      const accumulatedUpdate = this.accumulatedUpdates[variableName];\n\n      tidy(() => {\n        const newAccumulatedGrad =\n            this.rhoScalar.mul(accumulatedGrad)\n                .add(this.oneMinusRho.mul(gradient.square()));\n\n        const updates = accumulatedUpdate.add(this.epsilonScalar)\n                            .sqrt()\n                            .div(accumulatedGrad.add(this.epsilonScalar).sqrt())\n                            .mul(gradient);\n\n        const newAccumulatedUpdate =\n            this.rhoScalar.mul(accumulatedUpdate)\n                .add(this.oneMinusRho.mul(updates.square()));\n\n        this.accumulatedGrads[variableName].assign(newAccumulatedGrad);\n        this.accumulatedUpdates[variableName].assign(newAccumulatedUpdate);\n\n        const newValue = this.c.mul(updates).add(value);\n        value.assign(newValue);\n      });\n    }\n  }\n\n  dispose() {\n    this.c.dispose();\n    this.epsilonScalar.dispose();\n    this.rhoScalar.dispose();\n    this.oneMinusRho.dispose();\n    if (this.accumulatedUpdates != null) {\n      Object.keys(this.accumulatedUpdates)\n          .forEach(name => this.accumulatedUpdates[name].dispose());\n      Object.keys(this.accumulatedGrads)\n          .forEach(name => this.accumulatedGrads[name].dispose());\n    }\n  }\n  getConfig(): ConfigDict {\n    return {\n      learningRate: this.learningRate,\n      rho: this.rho,\n      epsilon: this.epsilon\n    };\n  }\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(config.learningRate, config.rho, config.epsilon);\n  }\n}\nregisterClass(AdadeltaOptimizer);\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {keep, tidy} from '../globals';\nimport {fill, scalar} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar} from '../tensor';\nimport {NamedVariableMap} from '../tensor_types';\nimport {Optimizer} from './optimizer';\n\n/** @doclink Optimizer */\nexport class AdagradOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'AdagradOptimizer';\n  private c: Scalar;\n  private epsilon: Scalar;\n\n  private accumulatedGrads: NamedVariableMap = {};\n\n  constructor(\n      protected learningRate: number, private initialAccumulatorValue = 0.1) {\n    super();\n    this.c = keep(scalar(-learningRate));\n\n    this.epsilon = keep(scalar(ENV.get('EPSILON')));\n  }\n\n  applyGradients(variableGradients: NamedVariableMap) {\n    for (const variableName in variableGradients) {\n      const value = ENV.engine.registeredVariables[variableName];\n      if (this.accumulatedGrads[variableName] == null) {\n        const trainable = false;\n        tidy(() => {\n          this.accumulatedGrads[variableName] =\n              fill(value.shape, this.initialAccumulatorValue)\n                  .variable(trainable);\n        });\n      }\n\n      const gradient = variableGradients[variableName];\n      const accumulatedGrad = this.accumulatedGrads[variableName];\n\n      tidy(() => {\n        const newAccumulatedGrad = accumulatedGrad.add(gradient.square());\n        this.accumulatedGrads[variableName].assign(newAccumulatedGrad);\n\n        const newValue =\n            this.c\n                .mul(gradient.div(newAccumulatedGrad.add(this.epsilon).sqrt()))\n                .add(value);\n        value.assign(newValue);\n      });\n    }\n  }\n\n  dispose() {\n    this.epsilon.dispose();\n    this.c.dispose();\n    if (this.accumulatedGrads != null) {\n      Object.keys(this.accumulatedGrads)\n          .forEach(name => this.accumulatedGrads[name].dispose());\n    }\n  }\n  getConfig(): ConfigDict {\n    return {\n      learningRate: this.learningRate,\n      initialAccumulatorValue: this.initialAccumulatorValue,\n    };\n  }\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(config.learningRate, config.initialAccumulatorValue);\n  }\n}\nregisterClass(AdagradOptimizer);\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {keep, tidy} from '../globals';\nimport {scalar, zerosLike} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar, Variable} from '../tensor';\nimport {NamedVariableMap} from '../tensor_types';\nimport {Optimizer} from './optimizer';\n\nexport class AdamOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'AdamOptimizer';\n  private c: Scalar;\n  private epsScalar: Scalar;\n  private beta1Scalar: Scalar;\n  private beta2Scalar: Scalar;\n  private accBeta1: Variable;\n  private accBeta2: Variable;\n  private oneMinusBeta1: Scalar;\n  private oneMinusBeta2: Scalar;\n  private one: Scalar;\n\n  private accumulatedFirstMoment: NamedVariableMap = {};\n  private accumulatedSecondMoment: NamedVariableMap = {};\n\n  constructor(\n      protected learningRate: number, protected beta1: number,\n      protected beta2: number, protected epsilon: number = null) {\n    super();\n    this.c = keep(scalar(-learningRate));\n    // b1, b2 keep initial value of beta* hyperparameters.\n    this.beta1Scalar = keep(scalar(beta1));\n    this.beta2Scalar = keep(scalar(beta2));\n    tidy(() => {\n      // accB* will be updated by batch.\n      this.accBeta1 = scalar(beta1).variable();\n      this.accBeta2 = scalar(beta2).variable();\n    });\n    this.oneMinusBeta1 = keep(scalar(1 - beta1));\n    this.oneMinusBeta2 = keep(scalar(1 - beta2));\n    this.one = keep(scalar(1));\n\n    if (epsilon === null) {\n      epsilon = ENV.get('EPSILON');\n    }\n\n    this.epsScalar = keep(scalar(epsilon));\n  }\n\n  applyGradients(variableGradients: NamedVariableMap) {\n    tidy(() => {\n      const oneMinusAccBeta1 = this.one.sub(this.accBeta1);\n      const oneMinusAccBeta2 = this.one.sub(this.accBeta2);\n\n      for (const variableName in variableGradients) {\n        const value = ENV.engine.registeredVariables[variableName];\n        if (this.accumulatedFirstMoment[variableName] == null) {\n          const trainable = false;\n          this.accumulatedFirstMoment[variableName] =\n              zerosLike(value).variable(trainable);\n        }\n        if (this.accumulatedSecondMoment[variableName] == null) {\n          const trainable = false;\n          this.accumulatedSecondMoment[variableName] =\n              zerosLike(value).variable(trainable);\n        }\n\n        const gradient = variableGradients[variableName];\n        const firstMoment = this.accumulatedFirstMoment[variableName];\n        const secondMoment = this.accumulatedSecondMoment[variableName];\n\n        const newFirstMoment = this.beta1Scalar.mul(firstMoment)\n                                   .add(this.oneMinusBeta1.mul(gradient));\n        const newSecondMoment =\n            this.beta2Scalar.mul(secondMoment)\n                .add(this.oneMinusBeta2.mul(gradient.square()));\n\n        const biasCorrectedFirstMoment = newFirstMoment.div(oneMinusAccBeta1);\n        const biasCorrectedSecondMoment = newSecondMoment.div(oneMinusAccBeta2);\n\n        this.accumulatedFirstMoment[variableName].assign(newFirstMoment);\n        this.accumulatedSecondMoment[variableName].assign(newSecondMoment);\n\n        const newValue =\n            this.c\n                .mul(biasCorrectedFirstMoment.div(\n                    this.epsScalar.add(biasCorrectedSecondMoment.sqrt())))\n                .add(value);\n        value.assign(newValue);\n      }\n\n      this.accBeta1.assign(this.accBeta1.mul(this.beta1Scalar));\n      this.accBeta2.assign(this.accBeta2.mul(this.beta2Scalar));\n    });\n  }\n\n  dispose() {\n    this.c.dispose();\n    this.epsScalar.dispose();\n    this.beta1Scalar.dispose();\n    this.beta2Scalar.dispose();\n    this.accBeta1.dispose();\n    this.accBeta2.dispose();\n    this.oneMinusBeta1.dispose();\n    this.oneMinusBeta2.dispose();\n    this.one.dispose();\n\n    if (this.accumulatedFirstMoment != null) {\n      Object.keys(this.accumulatedFirstMoment)\n          .forEach(name => this.accumulatedFirstMoment[name].dispose());\n    }\n\n    if (this.accumulatedSecondMoment != null) {\n      Object.keys(this.accumulatedSecondMoment)\n          .forEach(name => this.accumulatedSecondMoment[name].dispose());\n    }\n  }\n  getConfig(): ConfigDict {\n    return {\n      learningRate: this.learningRate,\n      beta1: this.beta1,\n      beta2: this.beta2,\n      epsilon: this.epsilon,\n    };\n  }\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(\n        config.learningRate, config.beta1, config.beta2, config.epsilon);\n  }\n}\nregisterClass(AdamOptimizer);\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {keep, tidy} from '../globals';\nimport {scalar, zerosLike} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar, Variable} from '../tensor';\nimport {NamedVariableMap} from '../tensor_types';\nimport {Optimizer} from './optimizer';\n\nexport class AdamaxOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'AdamaxOptimizer';\n  private c: Scalar;\n  private epsScalar: Scalar;\n  private accBeta1: Variable;\n  private beta1Scalar: Scalar;\n  private beta2Scalar: Scalar;\n  private decayScalar: Scalar;\n  private oneMinusBeta1: Scalar;\n  private one: Scalar;\n  private iteration: Variable;\n\n  private accumulatedFirstMoment: NamedVariableMap = {};\n  private accumulatedWeightedInfNorm: NamedVariableMap = {};\n\n  constructor(\n      protected learningRate: number, protected beta1: number,\n      protected beta2: number, protected epsilon: number = null,\n      protected decay = 0.0) {\n    super();\n    this.c = keep(scalar(-learningRate));\n\n    // b1, b2 keep initial value of beta* hyperparameters.\n    this.beta1Scalar = keep(scalar(beta1));\n    this.beta2Scalar = keep(scalar(beta2));\n\n    this.decayScalar = keep(scalar(decay));\n\n    tidy(() => {\n      this.iteration = scalar(0).variable();\n      this.accBeta1 = scalar(beta1).variable();\n    });\n\n    this.oneMinusBeta1 = keep(scalar(1 - beta1));\n    this.one = keep(scalar(1));\n\n    if (epsilon === null) {\n      epsilon = ENV.get('EPSILON');\n    }\n\n    this.epsScalar = keep(scalar(epsilon));\n  }\n\n  applyGradients(variableGradients: NamedVariableMap) {\n    tidy(() => {\n      const oneMinusAccBeta1 = this.one.sub(this.accBeta1);\n      const lr = this.c.div(this.one.add(this.decayScalar.mul(this.iteration)));\n\n      for (const variableName in variableGradients) {\n        const value = ENV.engine.registeredVariables[variableName];\n        if (this.accumulatedFirstMoment[variableName] == null) {\n          const trainable = false;\n          this.accumulatedFirstMoment[variableName] =\n              zerosLike(value).variable(trainable);\n        }\n        if (this.accumulatedWeightedInfNorm[variableName] == null) {\n          const trainable = false;\n          this.accumulatedWeightedInfNorm[variableName] =\n              zerosLike(value).variable(trainable);\n        }\n\n        const gradient = variableGradients[variableName];\n        const firstMoment = this.accumulatedFirstMoment[variableName];\n        const weightedInfNorm = this.accumulatedWeightedInfNorm[variableName];\n\n        const newFirstMoment = this.beta1Scalar.mul(firstMoment)\n                                   .add(this.oneMinusBeta1.mul(gradient));\n\n        const ut0 = this.beta2Scalar.mul(weightedInfNorm);\n        const ut1 = gradient.abs();\n\n        const newWeightedInfNorm = ut0.maximum(ut1);\n\n        this.accumulatedFirstMoment[variableName].assign(newFirstMoment);\n        this.accumulatedWeightedInfNorm[variableName].assign(\n            newWeightedInfNorm);\n\n        const newValue =\n            lr.div(oneMinusAccBeta1)\n                .mul(newFirstMoment.div(this.epsScalar.add(newWeightedInfNorm)))\n                .add(value);\n\n        value.assign(newValue);\n      }\n\n      this.iteration.assign(this.iteration.add(this.one));\n      this.accBeta1.assign(this.accBeta1.mul(this.beta1Scalar));\n    });\n  }\n\n  dispose() {\n    this.c.dispose();\n    this.epsScalar.dispose();\n    this.accBeta1.dispose();\n    this.beta1Scalar.dispose();\n    this.beta2Scalar.dispose();\n    this.oneMinusBeta1.dispose();\n\n    this.decayScalar.dispose();\n    this.iteration.dispose();\n\n    this.one.dispose();\n\n    if (this.accumulatedFirstMoment != null) {\n      Object.keys(this.accumulatedFirstMoment)\n          .forEach(name => this.accumulatedFirstMoment[name].dispose());\n    }\n\n    if (this.accumulatedWeightedInfNorm != null) {\n      Object.keys(this.accumulatedWeightedInfNorm)\n          .forEach(name => this.accumulatedWeightedInfNorm[name].dispose());\n    }\n  }\n  getConfig(): ConfigDict {\n    return {\n      learningRate: this.learningRate,\n      beta1: this.beta1,\n      beta2: this.beta2,\n      epsilon: this.epsilon,\n      decay: this.decay\n    };\n  }\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(\n        config.learningRate, config.beta1, config.beta2, config.epsilon,\n        config.decay);\n  }\n}\nregisterClass(AdamaxOptimizer);\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {keep, tidy} from '../globals';\nimport {scalar} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {Optimizer} from './optimizer';\n\n/** @doclink Optimizer */\nexport class SGDOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'SGDOptimizer';\n  protected c: Scalar;\n\n  constructor(protected learningRate: number) {\n    super();\n    this.setLearningRate(learningRate);\n  }\n\n  applyGradients(variableGradients: NamedTensorMap) {\n    const varNames = Object.keys(variableGradients);\n    varNames.forEach(varName => {\n      const gradient = variableGradients[varName];\n      const value = ENV.engine.registeredVariables[varName];\n\n      tidy(() => {\n        const newValue = this.c.mul(gradient).add(value);\n        value.assign(newValue);\n      });\n    });\n  }\n\n  /**\n   * Sets the learning rate of the optimizer.\n   */\n  setLearningRate(learningRate: number) {\n    this.learningRate = learningRate;\n    if (this.c != null) {\n      this.c.dispose();\n    }\n    this.c = keep(scalar(-learningRate));\n  }\n\n  dispose() {\n    this.c.dispose();\n  }\n\n  getConfig(): ConfigDict {\n    return {learningRate: this.learningRate};\n  }\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(config.learningRate);\n  }\n}\nregisterClass(SGDOptimizer);\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {tidy} from '../globals';\nimport {scalar, zerosLike} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar, Tensor} from '../tensor';\nimport {NamedVariableMap} from '../tensor_types';\nimport {SGDOptimizer} from './sgd_optimizer';\n\n/** @doclink Optimizer */\nexport class MomentumOptimizer extends SGDOptimizer {\n  /** @nocollapse */\n  static className = 'MomentumOptimizer';\n  private m: Scalar;\n  private accumulations: NamedVariableMap;\n\n  constructor(\n      protected learningRate: number, private momentum: number,\n      private useNesterov = false) {\n    super(learningRate);\n    this.m = scalar(this.momentum);\n    this.accumulations = {};\n  }\n\n  applyGradients(variableGradients: NamedVariableMap) {\n    for (const variableName in variableGradients) {\n      const value = ENV.engine.registeredVariables[variableName];\n      if (this.accumulations[variableName] == null) {\n        const trainable = false;\n        tidy(() => {\n          this.accumulations[variableName] =\n              zerosLike(value).variable(trainable);\n        });\n      }\n\n      const accumulation = this.accumulations[variableName];\n      const gradient = variableGradients[variableName];\n\n      tidy(() => {\n        let newValue: Tensor;\n        const newAccumulation = this.m.mul(accumulation).add(gradient);\n        if (this.useNesterov) {\n          newValue =\n              this.c.mul(gradient.add(newAccumulation.mul(this.m))).add(value);\n        } else {\n          newValue = this.c.mul(newAccumulation).add(value);\n        }\n        this.accumulations[variableName].assign(newAccumulation);\n        value.assign(newValue);\n      });\n    }\n  }\n\n  dispose() {\n    super.dispose();\n    this.m.dispose();\n    if (this.accumulations != null) {\n      for (const variableName in this.accumulations) {\n        this.accumulations[variableName].dispose();\n      }\n    }\n  }\n\n  /**\n   * Sets the momentum of the optimizer.\n   *\n   * @param momentum\n   */\n  setMomentum(momentum: number) {\n    this.momentum = momentum;\n  }\n\n  getConfig(): ConfigDict {\n    return {\n      learningRate: this.learningRate,\n      momentum: this.momentum,\n      useNesterov: this.useNesterov\n    };\n  }\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(config.learningRate, config.momentum, config.useNesterov);\n  }\n}\nregisterClass(MomentumOptimizer);\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {keep, tidy} from '../globals';\nimport {scalar, zerosLike} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar} from '../tensor';\nimport {NamedVariableMap} from '../tensor_types';\nimport {Optimizer} from './optimizer';\n\n/** @doclink Optimizer */\nexport class RMSPropOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'RMSPropOptimizer';\n  private c: Scalar;\n  private epsilonScalar: Scalar;\n  private decayScalar: Scalar;\n  private momentumScalar: Scalar;\n  private oneMinusDecay: Scalar;\n  private centered: boolean;\n\n  private accumulatedMeanSquares: NamedVariableMap = {};\n  private accumulatedMeanGrads: NamedVariableMap = {};\n  private accumulatedMoments: NamedVariableMap = {};\n\n  constructor(\n      protected learningRate: number, protected decay = 0.9,\n      protected momentum = 0.0, protected epsilon: number = null,\n      centered = false) {\n    super();\n\n    this.c = keep(scalar(learningRate));\n    this.decayScalar = keep(scalar(decay));\n    this.momentumScalar = keep(scalar(momentum));\n    this.oneMinusDecay = keep(scalar(1 - decay));\n    this.centered = centered;\n\n    if (epsilon === null) {\n      epsilon = ENV.get('EPSILON');\n    }\n\n    this.epsilonScalar = keep(scalar(epsilon));\n  }\n\n  applyGradients(variableGradients: NamedVariableMap) {\n    for (const variableName in variableGradients) {\n      const value = ENV.engine.registeredVariables[variableName];\n      if (this.accumulatedMeanSquares[variableName] == null) {\n        const trainable = false;\n        tidy(() => {\n          this.accumulatedMeanSquares[variableName] =\n              zerosLike(value).variable(trainable);\n        });\n      }\n      if (this.accumulatedMeanGrads[variableName] == null && this.centered) {\n        const trainable = false;\n        tidy(() => {\n          this.accumulatedMeanGrads[variableName] =\n              zerosLike(value).variable(trainable);\n        });\n      }\n      if (this.accumulatedMoments[variableName] == null) {\n        const trainable = false;\n        tidy(() => {\n          this.accumulatedMoments[variableName] =\n              zerosLike(value).variable(trainable);\n        });\n      }\n\n      const accumulatedMeanSquare = this.accumulatedMeanSquares[variableName];\n      const accumulatedMeanGrad = this.accumulatedMeanGrads[variableName];\n      const accumulatedMoments = this.accumulatedMoments[variableName];\n      const gradient = variableGradients[variableName];\n\n      tidy(() => {\n        const newAccumulatedMeanSquare =\n            this.decayScalar.mul(accumulatedMeanSquare)\n                .add(this.oneMinusDecay.mul(gradient.square()));\n\n        if (this.centered) {\n          // Centered gradient\n          const newAccumulatedMeanGrad =\n              this.decayScalar.mul(accumulatedMeanGrad)\n                  .add(this.oneMinusDecay.mul(gradient));\n\n          const newAccumulatedMoments =\n              this.momentumScalar.mul(accumulatedMoments)\n                  .add(this.c.mul(gradient).div(\n                      newAccumulatedMeanSquare\n                          .sub(newAccumulatedMeanGrad.square().add(\n                              this.epsilonScalar))\n                          .sqrt()));\n\n          this.accumulatedMeanSquares[variableName].assign(\n              newAccumulatedMeanSquare);\n          this.accumulatedMeanGrads[variableName].assign(\n              newAccumulatedMeanGrad);\n          this.accumulatedMoments[variableName].assign(newAccumulatedMoments);\n\n          const newValue = value.sub(newAccumulatedMoments);\n          value.assign(newValue);\n        } else {\n          // Plain gradient\n          const newAccumulatedMeanSquare =\n              this.decayScalar.mul(accumulatedMeanSquare)\n                  .add(this.oneMinusDecay.mul(gradient.square()));\n\n          const newAccumulatedMoments =\n              this.momentumScalar.mul(accumulatedMoments)\n                  .add(this.c.mul(gradient).div(\n                      newAccumulatedMeanSquare.add(this.epsilonScalar).sqrt()));\n\n          this.accumulatedMeanSquares[variableName].assign(\n              newAccumulatedMeanSquare);\n          this.accumulatedMoments[variableName].assign(newAccumulatedMoments);\n\n          const newValue = value.sub(newAccumulatedMoments);\n          value.assign(newValue);\n        }\n      });\n    }\n  }\n\n  dispose() {\n    this.c.dispose();\n    this.epsilonScalar.dispose();\n    this.decayScalar.dispose();\n    this.momentumScalar.dispose();\n    this.oneMinusDecay.dispose();\n    if (this.accumulatedMeanSquares != null) {\n      Object.keys(this.accumulatedMeanSquares)\n          .forEach(name => this.accumulatedMeanSquares[name].dispose());\n    }\n    if (this.accumulatedMeanGrads != null && this.centered) {\n      Object.keys(this.accumulatedMeanGrads)\n          .forEach(name => this.accumulatedMeanGrads[name].dispose());\n    }\n    if (this.accumulatedMoments != null) {\n      Object.keys(this.accumulatedMoments)\n          .forEach(name => this.accumulatedMoments[name].dispose());\n    }\n  }\n\n  getConfig(): ConfigDict {\n    return {\n      learningRate: this.learningRate,\n      decay: this.decay,\n      momentum: this.momentum,\n      epsilon: this.epsilon,\n      centered: this.centered\n    };\n  }\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(\n        config.learningRate, config.decay, config.momentum, config.epsilon,\n        config.centered);\n  }\n}\nregisterClass(RMSPropOptimizer);\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {AdadeltaOptimizer} from './adadelta_optimizer';\nimport {AdagradOptimizer} from './adagrad_optimizer';\nimport {AdamOptimizer} from './adam_optimizer';\nimport {AdamaxOptimizer} from './adamax_optimizer';\nimport {MomentumOptimizer} from './momentum_optimizer';\nimport {RMSPropOptimizer} from './rmsprop_optimizer';\nimport {SGDOptimizer} from './sgd_optimizer';\n\nexport class OptimizerConstructors {\n  /**\n   * Constructs a `tf.SGDOptimizer` that uses stochastic gradient descent.\n   *\n   * ```js\n   * // Fit a quadratic function by learning the coefficients a, b, c.\n   * const xs = tf.tensor1d([0, 1, 2, 3]);\n   * const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]);\n   *\n   * const a = tf.scalar(Math.random()).variable();\n   * const b = tf.scalar(Math.random()).variable();\n   * const c = tf.scalar(Math.random()).variable();\n   *\n   * // y = a * x^2 + b * x + c.\n   * const f = x => a.mul(x.square()).add(b.mul(x)).add(c);\n   * const loss = (pred, label) => pred.sub(label).square().mean();\n   *\n   * const learningRate = 0.01;\n   * const optimizer = tf.train.sgd(learningRate);\n   *\n   * // Train the model.\n   * for (let i = 0; i < 10; i++) {\n   *   optimizer.minimize(() => loss(f(xs), ys));\n   * }\n   *\n   * // Make predictions.\n   * console.log(\n   *     `a: ${a.dataSync()}, b: ${b.dataSync()}, c: ${c.dataSync()}`);\n   * const preds = f(xs).dataSync();\n   * preds.forEach((pred, i) => {\n   *   console.log(`x: ${i}, pred: ${pred}`);\n   * });\n   * ```\n   *\n   * @param learningRate The learning rate to use for the SGD algorithm.\n   */\n  /**\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static sgd(learningRate: number): SGDOptimizer {\n    return new SGDOptimizer(learningRate);\n  }\n\n  /**\n   * Constructs a `tf.MomentumOptimizer` that uses momentum gradient\n   * descent.\n   *\n   * See\n   * [http://proceedings.mlr.press/v28/sutskever13.pdf](\n   * http://proceedings.mlr.press/v28/sutskever13.pdf)\n   *\n   * @param learningRate The learning rate to use for the Momentum gradient\n   * descent algorithm.\n   * @param momentum The momentum to use for the momentum gradient descent\n   * algorithm.\n   */\n  /**\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static momentum(learningRate: number, momentum: number, useNesterov = false):\n      MomentumOptimizer {\n    return new MomentumOptimizer(learningRate, momentum, useNesterov);\n  }\n\n  /**\n   * Constructs a `tf.RMSPropOptimizer` that uses RMSProp gradient\n   * descent. This implementation uses plain momentum and is not centered\n   * version of RMSProp.\n   *\n   * See\n   * [http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf](\n   * http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)\n   *\n   * @param learningRate The learning rate to use for the RMSProp gradient\n   * descent algorithm.\n   * @param decay The discounting factor for the history/coming gradient.\n   * @param momentum The momentum to use for the RMSProp gradient descent\n   * algorithm.\n   * @param epsilon Small value to avoid zero denominator.\n   * @param centered If true, gradients are normalized by the estimated\n   * variance of the gradient.\n   */\n  /**\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static rmsprop(\n      learningRate: number, decay = .9, momentum = 0.0, epsilon: number = null,\n      centered = false): RMSPropOptimizer {\n    return new RMSPropOptimizer(\n        learningRate, decay, momentum, epsilon, centered);\n  }\n\n  /**\n   * Constructs a `tf.AdamOptimizer` that uses the Adam algorithm.\n   * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n   *\n   * @param learningRate The learning rate to use for the Adam gradient\n   * descent algorithm.\n   * @param beta1 The exponential decay rate for the 1st moment estimates.\n   * @param beta2 The exponential decay rate for the 2nd moment estimates.\n   * @param epsilon A small constant for numerical stability.\n   */\n  /**\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static adam(\n      learningRate = 0.001, beta1 = 0.9, beta2 = 0.999,\n      epsilon: number = null): AdamOptimizer {\n    return new AdamOptimizer(learningRate, beta1, beta2, epsilon);\n  }\n\n  /**\n   * Constructs a `tf.AdadeltaOptimizer` that uses the Adadelta algorithm.\n   * See [https://arxiv.org/abs/1212.5701](https://arxiv.org/abs/1212.5701)\n   *\n   * @param learningRate The learning rate to use for the Adadelta gradient\n   * descent algorithm.\n   * @param rho The learning rate decay over each update.\n   * @param epsilon A constant epsilon used to better condition the grad\n   * update.\n   */\n  /**\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static adadelta(learningRate = .001, rho = .95, epsilon: number = null):\n      AdadeltaOptimizer {\n    return new AdadeltaOptimizer(learningRate, rho, epsilon);\n  }\n\n  /**\n   * Constructs a `tf.AdamaxOptimizer` that uses the Adamax algorithm.\n   * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n   *\n   * @param learningRate The learning rate to use for the Adamax gradient\n   * descent algorithm.\n   * @param beta1 The exponential decay rate for the 1st moment estimates.\n   * @param beta2 The exponential decay rate for the 2nd moment estimates.\n   * @param epsilon A small constant for numerical stability.\n   * @param decay The learning rate decay over each update.\n   */\n  /**\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static adamax(\n      learningRate = 0.002, beta1 = 0.9, beta2 = 0.999, epsilon: number = null,\n      decay = 0.0): AdamaxOptimizer {\n    return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon, decay);\n  }\n\n  /**\n   * Constructs a `tf.AdagradOptimizer` that uses the Adagrad algorithm.\n   * See\n   * [http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf](\n   * http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)\n   * or\n   * [http://ruder.io/optimizing-gradient-descent/index.html#adagrad](\n   * http://ruder.io/optimizing-gradient-descent/index.html#adagrad)\n   *\n   * @param learningRate The learning rate to use for the Adagrad gradient\n   * descent algorithm.\n   * @param initialAccumulatorValue Starting value for the accumulators, must be\n   * positive.\n   */\n  /**\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static adagrad(learningRate: number, initialAccumulatorValue = 0.1):\n      AdagradOptimizer {\n    return new AdagradOptimizer(learningRate, initialAccumulatorValue);\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// So typings can propagate.\nimport {AdadeltaOptimizer} from './optimizers/adadelta_optimizer';\nimport {AdagradOptimizer} from './optimizers/adagrad_optimizer';\nimport {AdamOptimizer} from './optimizers/adam_optimizer';\nimport {AdamaxOptimizer} from './optimizers/adamax_optimizer';\nimport {MomentumOptimizer} from './optimizers/momentum_optimizer';\nimport {OptimizerConstructors} from './optimizers/optimizer_constructors';\nimport {RMSPropOptimizer} from './optimizers/rmsprop_optimizer';\nimport {SGDOptimizer} from './optimizers/sgd_optimizer';\n\n// tslint:disable-next-line:no-unused-expression\n[MomentumOptimizer, SGDOptimizer, AdadeltaOptimizer, AdagradOptimizer,\n RMSPropOptimizer, AdamaxOptimizer, AdamOptimizer];\n\nexport const train = {\n  sgd: OptimizerConstructors.sgd,\n  momentum: OptimizerConstructors.momentum,\n  adadelta: OptimizerConstructors.adadelta,\n  adagrad: OptimizerConstructors.adagrad,\n  rmsprop: OptimizerConstructors.rmsprop,\n  adamax: OptimizerConstructors.adamax,\n  adam: OptimizerConstructors.adam\n};\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// backend_cpu.ts and backend_webgl.ts are standalone files and should be\n// explicitly included here. Below, there is an export from backend_webgl, but\n// that doesn't count since it's exporting a Typescript interface.\nimport './kernels/backend_webgl';\nimport './kernels/backend_cpu';\n\nimport {nextFrame} from './browser_util';\nimport * as environment from './environment';\nimport {Environment} from './environment';\n\n// Serialization.\nimport * as io from './io/io';\nimport * as math from './math';\nimport * as serialization from './serialization';\nimport {setOpHandler} from './tensor';\nimport * as test_util from './test_util';\nimport * as util from './util';\nimport {version} from './version';\nimport * as webgl from './webgl';\n\nexport {InferenceModel, ModelPredictConfig} from './model_types';\n\n// Optimizers.\nexport {AdadeltaOptimizer} from './optimizers/adadelta_optimizer';\nexport {AdagradOptimizer} from './optimizers/adagrad_optimizer';\nexport {AdamOptimizer} from './optimizers/adam_optimizer';\nexport {AdamaxOptimizer} from './optimizers/adamax_optimizer';\nexport {MomentumOptimizer} from './optimizers/momentum_optimizer';\nexport {Optimizer} from './optimizers/optimizer';\nexport {RMSPropOptimizer} from './optimizers/rmsprop_optimizer';\nexport {SGDOptimizer} from './optimizers/sgd_optimizer';\nexport {Scalar, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, TensorBuffer, variable, Variable} from './tensor';\nexport {NamedTensorMap} from './tensor_types';\nexport {DataType, DataTypeMap, DataValues, Rank, ShapeMap} from './types';\n\nexport * from './ops/ops';\nexport {LSTMCellFunc} from './ops/lstm';\nexport {Reduction} from './ops/loss_ops';\n\nexport * from './train';\nexport * from './globals';\n\nexport {Features} from './environment_util';\nexport {TimingInfo} from './engine';\nexport {ENV, Environment} from './environment';\n\nexport const setBackend = Environment.setBackend;\nexport const getBackend = Environment.getBackend;\nexport const disposeVariables = Environment.disposeVariables;\nexport const memory = Environment.memory;\nexport {version as version_core};\n\nexport {nextFrame};\n\n// Second level exports.\nexport {environment, io, math, serialization, test_util, util, webgl};\n\n// Backend specific.\nexport {KernelBackend, BackendTimingInfo, DataMover, DataStorage} from './kernels/backend';\n\nimport * as ops from 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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tf-core.js b/node_modules/@tensorflow/tfjs-core/dist/tf-core.js
new file mode 100644
index 0000000..17acd5d
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tf-core.js
@@ -0,0 +1,19534 @@
+/**
+ * @license
+ * Copyright 2018 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
+(function (global, factory) {
+    typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports) :
+    typeof define === 'function' && define.amd ? define(['exports'], factory) :
+    (factory((global.tf = global.tf || {})));
+}(this, (function (exports) { 'use strict';
+
+    /*! *****************************************************************************
+    Copyright (c) Microsoft Corporation. All rights reserved.
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use
+    this file except in compliance with the License. You may obtain a copy of the
+    License at http://www.apache.org/licenses/LICENSE-2.0
+
+    THIS CODE IS PROVIDED ON AN *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
+    WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
+    MERCHANTABLITY OR NON-INFRINGEMENT.
+
+    See the Apache Version 2.0 License for specific language governing permissions
+    and limitations under the License.
+    ***************************************************************************** */
+    /* global Reflect, Promise */
+
+    var extendStatics = Object.setPrototypeOf ||
+        ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
+        function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
+
+    function __extends(d, b) {
+        extendStatics(d, b);
+        function __() { this.constructor = d; }
+        d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
+    }
+
+    var __assign = Object.assign || function __assign(t) {
+        for (var s, i = 1, n = arguments.length; i < n; i++) {
+            s = arguments[i];
+            for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];
+        }
+        return t;
+    };
+
+    function __awaiter(thisArg, _arguments, P, generator) {
+        return new (P || (P = Promise))(function (resolve, reject) {
+            function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+            function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+            function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+            step((generator = generator.apply(thisArg, _arguments || [])).next());
+        });
+    }
+
+    function __generator(thisArg, body) {
+        var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+        return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+        function verb(n) { return function (v) { return step([n, v]); }; }
+        function step(op) {
+            if (f) throw new TypeError("Generator is already executing.");
+            while (_) try {
+                if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+                if (y = 0, t) op = [op[0] & 2, t.value];
+                switch (op[0]) {
+                    case 0: case 1: t = op; break;
+                    case 4: _.label++; return { value: op[1], done: false };
+                    case 5: _.label++; y = op[1]; op = [0]; continue;
+                    case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                    default:
+                        if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                        if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                        if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                        if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                        if (t[2]) _.ops.pop();
+                        _.trys.pop(); continue;
+                }
+                op = body.call(thisArg, _);
+            } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+            if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+        }
+    }
+
+    var contexts = {};
+    var WEBGL_ATTRIBUTES = {
+        alpha: false,
+        antialias: false,
+        premultipliedAlpha: false,
+        preserveDrawingBuffer: false,
+        depth: false,
+        stencil: false,
+        failIfMajorPerformanceCaveat: true
+    };
+    function getWebGLContext(webGLVersion) {
+        if (!(webGLVersion in contexts)) {
+            var canvas = document.createElement('canvas');
+            canvas.addEventListener('webglcontextlost', function (ev) {
+                ev.preventDefault();
+                delete contexts[webGLVersion];
+            }, false);
+            contexts[webGLVersion] = getWebGLRenderingContext(webGLVersion);
+        }
+        var gl = contexts[webGLVersion];
+        if (gl.isContextLost()) {
+            delete contexts[webGLVersion];
+            return getWebGLContext(webGLVersion);
+        }
+        gl.disable(gl.DEPTH_TEST);
+        gl.disable(gl.STENCIL_TEST);
+        gl.disable(gl.BLEND);
+        gl.disable(gl.DITHER);
+        gl.disable(gl.POLYGON_OFFSET_FILL);
+        gl.disable(gl.SAMPLE_COVERAGE);
+        gl.enable(gl.SCISSOR_TEST);
+        gl.enable(gl.CULL_FACE);
+        gl.cullFace(gl.BACK);
+        return contexts[webGLVersion];
+    }
+    function getWebGLRenderingContext(webGLVersion) {
+        if (webGLVersion !== 1 && webGLVersion !== 2) {
+            throw new Error('Cannot get WebGL rendering context, WebGL is disabled.');
+        }
+        var canvas = document.createElement('canvas');
+        if (webGLVersion === 1) {
+            return (canvas.getContext('webgl', WEBGL_ATTRIBUTES) ||
+                canvas.getContext('experimental-webgl', WEBGL_ATTRIBUTES));
+        }
+        return canvas.getContext('webgl2', WEBGL_ATTRIBUTES);
+    }
+
+    function isMobile() {
+        var a = navigator.userAgent || navigator.vendor || window.opera;
+        return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i
+            .test(a) ||
+            /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( |\-|\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\/)|klon|kpt |kwc\-|kyo(c|k)|le(no|xi)|lg( g|\/(k|l|u)|50|54|\-[a-w])|libw|lynx|m1\-w|m3ga|m50\/|ma(te|ui|xo)|mc(01|21|ca)|m\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\-2|po(ck|rt|se)|prox|psio|pt\-g|qa\-a|qc(07|12|21|32|60|\-[2-7]|i\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\-|oo|p\-)|sdk\/|se(c(\-|0|1)|47|mc|nd|ri)|sgh\-|shar|sie(\-|m)|sk\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\-|v\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\-|tdg\-|tel(i|m)|tim\-|t\-mo|to(pl|sh)|ts(70|m\-|m3|m5)|tx\-9|up(\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i
+                .test(a.substr(0, 4));
+    }
+
+    function shuffle(array) {
+        var counter = array.length;
+        var temp = 0;
+        var index = 0;
+        while (counter > 0) {
+            index = (Math.random() * counter) | 0;
+            counter--;
+            temp = array[counter];
+            array[counter] = array[index];
+            array[index] = temp;
+        }
+    }
+    function clamp(min, x, max) {
+        return Math.max(min, Math.min(x, max));
+    }
+    function nearestLargerEven(val) {
+        return val % 2 === 0 ? val : val + 1;
+    }
+    function sum(arr) {
+        var sum = 0;
+        for (var i = 0; i < arr.length; i++) {
+            sum += arr[i];
+        }
+        return sum;
+    }
+    function randUniform(a, b) {
+        var r = Math.random();
+        return (b * r) + (1 - r) * a;
+    }
+    function distSquared(a, b) {
+        var result = 0;
+        for (var i = 0; i < a.length; i++) {
+            var diff = Number(a[i]) - Number(b[i]);
+            result += diff * diff;
+        }
+        return result;
+    }
+    function assert(expr, msg) {
+        if (!expr) {
+            throw new Error(typeof msg === 'string' ? msg : msg());
+        }
+    }
+    function assertShapesMatch(shapeA, shapeB, errorMessagePrefix) {
+        if (errorMessagePrefix === void 0) { errorMessagePrefix = ''; }
+        assert(arraysEqual(shapeA, shapeB), errorMessagePrefix + (" Shapes " + shapeA + " and " + shapeB + " must match"));
+    }
+    function assertNonNull(a) {
+        assert(a != null, "The input to the tensor constructor must be a non-null value.");
+    }
+    function flatten(arr, ret) {
+        if (ret === void 0) { ret = []; }
+        if (Array.isArray(arr) || isTypedArray(arr)) {
+            for (var i = 0; i < arr.length; ++i) {
+                flatten(arr[i], ret);
+            }
+        }
+        else {
+            ret.push(arr);
+        }
+        return ret;
+    }
+    function sizeFromShape(shape) {
+        if (shape.length === 0) {
+            return 1;
+        }
+        var size = shape[0];
+        for (var i = 1; i < shape.length; i++) {
+            size *= shape[i];
+        }
+        return size;
+    }
+    function isScalarShape(shape) {
+        return shape.length === 0;
+    }
+    function arraysEqual(n1, n2) {
+        if (n1 === n2) {
+            return true;
+        }
+        if (n1 == null || n2 == null) {
+            return false;
+        }
+        if (n1.length !== n2.length) {
+            return false;
+        }
+        for (var i = 0; i < n1.length; i++) {
+            if (n1[i] !== n2[i]) {
+                return false;
+            }
+        }
+        return true;
+    }
+    function isInt(a) {
+        return a % 1 === 0;
+    }
+    function tanh(x) {
+        if (Math.tanh != null) {
+            return Math.tanh(x);
+        }
+        if (x === Infinity) {
+            return 1;
+        }
+        else if (x === -Infinity) {
+            return -1;
+        }
+        else {
+            var e2x = Math.exp(2 * x);
+            return (e2x - 1) / (e2x + 1);
+        }
+    }
+    function sizeToSquarishShape(size) {
+        for (var a = Math.floor(Math.sqrt(size)); a > 1; --a) {
+            if (size % a === 0) {
+                return [a, size / a];
+            }
+        }
+        return [1, size];
+    }
+    function createShuffledIndices(n) {
+        var shuffledIndices = new Uint32Array(n);
+        for (var i = 0; i < n; ++i) {
+            shuffledIndices[i] = i;
+        }
+        shuffle(shuffledIndices);
+        return shuffledIndices;
+    }
+    function rightPad(a, size) {
+        if (size <= a.length) {
+            return a;
+        }
+        return a + ' '.repeat(size - a.length);
+    }
+    function repeatedTry(checkFn, delayFn, maxCounter) {
+        if (delayFn === void 0) { delayFn = function (counter) { return 0; }; }
+        return new Promise(function (resolve, reject) {
+            var tryCount = 0;
+            var tryFn = function () {
+                if (checkFn()) {
+                    resolve();
+                    return;
+                }
+                tryCount++;
+                var nextBackoff = delayFn(tryCount);
+                if (maxCounter != null && tryCount >= maxCounter) {
+                    reject();
+                    return;
+                }
+                setTimeout(tryFn, nextBackoff);
+            };
+            tryFn();
+        });
+    }
+    function inferFromImplicitShape(shape, size) {
+        var shapeProd = 1;
+        var implicitIdx = -1;
+        for (var i = 0; i < shape.length; ++i) {
+            if (shape[i] >= 0) {
+                shapeProd *= shape[i];
+            }
+            else if (shape[i] === -1) {
+                if (implicitIdx !== -1) {
+                    throw Error("Shapes can only have 1 implicit size. " +
+                        ("Found -1 at dim " + implicitIdx + " and dim " + i));
+                }
+                implicitIdx = i;
+            }
+            else if (shape[i] < 0) {
+                throw Error("Shapes can not be < 0. Found " + shape[i] + " at dim " + i);
+            }
+        }
+        if (implicitIdx === -1) {
+            if (size > 0 && size !== shapeProd) {
+                throw Error("Size(" + size + ") must match the product of shape " + shape);
+            }
+            return shape;
+        }
+        if (shapeProd === 0) {
+            throw Error("Cannot infer the missing size in [" + shape + "] when " +
+                "there are 0 elements");
+        }
+        if (size % shapeProd !== 0) {
+            throw Error("The implicit shape can't be a fractional number. " +
+                ("Got " + size + " / " + shapeProd));
+        }
+        var newShape = shape.slice();
+        newShape[implicitIdx] = size / shapeProd;
+        return newShape;
+    }
+    function squeezeShape(shape, axis) {
+        var newShape = [];
+        var keptDims = [];
+        var j = 0;
+        for (var i = 0; i < shape.length; ++i) {
+            if (axis != null) {
+                if (axis[j] === i && shape[i] !== 1) {
+                    throw new Error("Can't squeeze axis " + i + " since its dim '" + shape[i] + "' is not 1");
+                }
+                if ((axis[j] == null || axis[j] > i) && shape[i] === 1) {
+                    newShape.push(shape[i]);
+                    keptDims.push(i);
+                }
+                if (axis[j] <= i) {
+                    j++;
+                }
+            }
+            if (shape[i] !== 1) {
+                newShape.push(shape[i]);
+                keptDims.push(i);
+            }
+        }
+        return { newShape: newShape, keptDims: keptDims };
+    }
+    function getTypedArrayFromDType(dtype, size) {
+        var values = null;
+        if (dtype == null || dtype === 'float32') {
+            values = new Float32Array(size);
+        }
+        else if (dtype === 'int32') {
+            values = new Int32Array(size);
+        }
+        else if (dtype === 'bool') {
+            values = new Uint8Array(size);
+        }
+        else {
+            throw new Error("Unknown data type " + dtype);
+        }
+        return values;
+    }
+    function getArrayFromDType(dtype, size) {
+        var values = null;
+        if (dtype == null || dtype === 'float32') {
+            values = new Float32Array(size);
+        }
+        else if (dtype === 'int32') {
+            values = new Int32Array(size);
+        }
+        else if (dtype === 'bool') {
+            values = new Uint8Array(size);
+        }
+        else if (dtype === 'string') {
+            values = new Array(size);
+        }
+        else {
+            throw new Error("Unknown data type " + dtype);
+        }
+        return values;
+    }
+    function checkComputationForNaN(vals, dtype, name) {
+        if (dtype !== 'float32') {
+            return;
+        }
+        for (var i = 0; i < vals.length; i++) {
+            if (isNaN(vals[i])) {
+                throw Error("The result of the '" + name + "' has NaNs.");
+            }
+        }
+    }
+    function checkConversionForNaN(vals, dtype) {
+        if (dtype === 'float32') {
+            return;
+        }
+        for (var i = 0; i < vals.length; i++) {
+            if (isNaN(vals[i])) {
+                throw Error("NaN is not a valid value for dtype: '" + dtype + "'.");
+            }
+        }
+    }
+    function hasEncodingLoss(oldType, newType) {
+        if (newType === 'complex64') {
+            return false;
+        }
+        if (newType === 'float32' && oldType !== 'complex64') {
+            return false;
+        }
+        if (newType === 'int32' && oldType !== 'float32' && oldType !== 'complex64') {
+            return false;
+        }
+        if (newType === 'bool' && oldType === 'bool') {
+            return false;
+        }
+        return true;
+    }
+    function isTypedArray(a) {
+        return a instanceof Float32Array || a instanceof Int32Array ||
+            a instanceof Uint8Array;
+    }
+    function bytesPerElement(dtype) {
+        if (dtype === 'float32' || dtype === 'int32') {
+            return 4;
+        }
+        else if (dtype === 'complex64') {
+            return 8;
+        }
+        else if (dtype === 'bool') {
+            return 1;
+        }
+        else {
+            throw new Error("Unknown dtype " + dtype);
+        }
+    }
+    function bytesFromStringArray(arr) {
+        if (arr == null) {
+            return 0;
+        }
+        var bytes = 0;
+        arr.forEach(function (x) { return bytes += x.length * 2; });
+        return bytes;
+    }
+    function isString(value) {
+        return typeof value === 'string' || value instanceof String;
+    }
+    function isBoolean(value) {
+        return typeof value === 'boolean';
+    }
+    function isNumber(value) {
+        return typeof value === 'number';
+    }
+    function inferDtype(values) {
+        if (values instanceof Array) {
+            return inferDtype(values[0]);
+        }
+        if (values instanceof Float32Array) {
+            return 'float32';
+        }
+        else if (values instanceof Int32Array || values instanceof Uint8Array) {
+            return 'int32';
+        }
+        else if (isNumber(values)) {
+            return 'float32';
+        }
+        else if (isString(values)) {
+            return 'string';
+        }
+        else if (isBoolean(values)) {
+            return 'bool';
+        }
+        return 'float32';
+    }
+    function isFunction(f) {
+        return !!(f && f.constructor && f.call && f.apply);
+    }
+    function nearestDivisor(size, start) {
+        for (var i = start; i < size; ++i) {
+            if (size % i === 0) {
+                return i;
+            }
+        }
+        return size;
+    }
+    function computeStrides(shape) {
+        var rank = shape.length;
+        if (rank < 2) {
+            return [];
+        }
+        var strides = new Array(rank - 1);
+        strides[rank - 2] = shape[rank - 1];
+        for (var i = rank - 3; i >= 0; --i) {
+            strides[i] = strides[i + 1] * shape[i + 1];
+        }
+        return strides;
+    }
+    function toTypedArray(a, dtype, debugMode) {
+        if (dtype === 'string') {
+            throw new Error('Cannot convert a string[] to a TypedArray');
+        }
+        if (noConversionNeeded(a, dtype)) {
+            return a;
+        }
+        if (Array.isArray(a)) {
+            a = flatten(a);
+        }
+        if (dtype == null || dtype === 'float32' || dtype === 'complex64') {
+            return new Float32Array(a);
+        }
+        else if (dtype === 'int32') {
+            if (debugMode) {
+                checkConversionForNaN(a, dtype);
+            }
+            return new Int32Array(a);
+        }
+        else if (dtype === 'bool') {
+            var bool = new Uint8Array(a.length);
+            for (var i = 0; i < bool.length; ++i) {
+                if (Math.round(a[i]) !== 0) {
+                    bool[i] = 1;
+                }
+            }
+            return bool;
+        }
+        else {
+            throw new Error("Unknown data type " + dtype);
+        }
+    }
+    function noConversionNeeded(a, dtype) {
+        return (a instanceof Float32Array && dtype === 'float32') ||
+            (a instanceof Int32Array && dtype === 'int32') ||
+            (a instanceof Uint8Array && dtype === 'bool');
+    }
+    function makeOnesTypedArray(size, dtype) {
+        var array = makeZerosTypedArray(size, dtype);
+        for (var i = 0; i < array.length; i++) {
+            array[i] = 1;
+        }
+        return array;
+    }
+    function makeZerosTypedArray(size, dtype) {
+        if (dtype == null || dtype === 'float32' || dtype === 'complex64') {
+            return new Float32Array(size);
+        }
+        else if (dtype === 'int32') {
+            return new Int32Array(size);
+        }
+        else if (dtype === 'bool') {
+            return new Uint8Array(size);
+        }
+        else {
+            throw new Error("Unknown data type " + dtype);
+        }
+    }
+    function now() {
+        if (typeof performance !== 'undefined') {
+            return performance.now();
+        }
+        else if (typeof process !== 'undefined') {
+            var time = process.hrtime();
+            return time[0] * 1000 + time[1] / 1000000;
+        }
+        else {
+            throw new Error('Cannot measure time in this environment. You should run tf.js ' +
+                'in the browser or in Node.js');
+        }
+    }
+
+    var util = /*#__PURE__*/Object.freeze({
+        shuffle: shuffle,
+        clamp: clamp,
+        nearestLargerEven: nearestLargerEven,
+        sum: sum,
+        randUniform: randUniform,
+        distSquared: distSquared,
+        assert: assert,
+        assertShapesMatch: assertShapesMatch,
+        assertNonNull: assertNonNull,
+        flatten: flatten,
+        sizeFromShape: sizeFromShape,
+        isScalarShape: isScalarShape,
+        arraysEqual: arraysEqual,
+        isInt: isInt,
+        tanh: tanh,
+        sizeToSquarishShape: sizeToSquarishShape,
+        createShuffledIndices: createShuffledIndices,
+        rightPad: rightPad,
+        repeatedTry: repeatedTry,
+        inferFromImplicitShape: inferFromImplicitShape,
+        squeezeShape: squeezeShape,
+        getTypedArrayFromDType: getTypedArrayFromDType,
+        getArrayFromDType: getArrayFromDType,
+        checkComputationForNaN: checkComputationForNaN,
+        checkConversionForNaN: checkConversionForNaN,
+        hasEncodingLoss: hasEncodingLoss,
+        isTypedArray: isTypedArray,
+        bytesPerElement: bytesPerElement,
+        bytesFromStringArray: bytesFromStringArray,
+        isString: isString,
+        isBoolean: isBoolean,
+        isNumber: isNumber,
+        inferDtype: inferDtype,
+        isFunction: isFunction,
+        nearestDivisor: nearestDivisor,
+        computeStrides: computeStrides,
+        toTypedArray: toTypedArray,
+        makeOnesTypedArray: makeOnesTypedArray,
+        makeZerosTypedArray: makeZerosTypedArray,
+        now: now
+    });
+
+    var Profiler = (function () {
+        function Profiler(backendTimer, logger) {
+            this.backendTimer = backendTimer;
+            this.logger = logger;
+            if (logger == null) {
+                this.logger = new Logger();
+            }
+        }
+        Profiler.prototype.profileKernel = function (name, f) {
+            var _this = this;
+            var result;
+            var holdResultWrapperFn = function () {
+                result = f();
+            };
+            var timer = this.backendTimer.time(holdResultWrapperFn);
+            var results = Array.isArray(result) ? result : [result];
+            results.forEach(function (r) {
+                var vals = r.dataSync();
+                checkComputationForNaN(vals, r.dtype, name);
+                timer.then(function (timing) {
+                    var extraInfo = '';
+                    if (timing.getExtraProfileInfo != null) {
+                        extraInfo = timing.getExtraProfileInfo();
+                    }
+                    _this.logger.logKernelProfile(name, r, vals, timing.kernelMs, extraInfo);
+                });
+            });
+            return result;
+        };
+        return Profiler;
+    }());
+    var Logger = (function () {
+        function Logger() {
+        }
+        Logger.prototype.logKernelProfile = function (name, result, vals, timeMs, extraInfo) {
+            var time = rightPad(timeMs + "ms", 9);
+            var paddedName = rightPad(name, 25);
+            var rank = result.rank;
+            var size = result.size;
+            var shape = rightPad(result.shape.toString(), 14);
+            console.log("%c" + paddedName + "\t%c" + time + "\t%c" + rank + "D " + shape + "\t%c" + size + "\t%c" + extraInfo, 'font-weight:bold', 'color:red', 'color:blue', 'color: orange', 'color: green');
+        };
+        return Logger;
+    }());
+
+    var FORMAT_LIMIT_NUM_VALS = 20;
+    var FORMAT_NUM_FIRST_LAST_VALS = 3;
+    var FORMAT_NUM_SIG_DIGITS = 7;
+    function tensorToString(vals, shape, dtype, verbose) {
+        var strides = computeStrides(shape);
+        var padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);
+        var rank = shape.length;
+        var valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);
+        var lines = ['Tensor'];
+        if (verbose) {
+            lines.push("  dtype: " + dtype);
+            lines.push("  rank: " + rank);
+            lines.push("  shape: [" + shape + "]");
+            lines.push("  values:");
+        }
+        lines.push(valsLines.map(function (l) { return '    ' + l; }).join('\n'));
+        return lines.join('\n');
+    }
+    function computeMaxSizePerColumn(vals, shape, dtype, strides) {
+        var n = sizeFromShape(shape);
+        var numCols = strides[strides.length - 1];
+        var padPerCol = new Array(numCols).fill(0);
+        var rank = shape.length;
+        var valuesOrTuples = dtype === 'complex64' ? createComplexTuples(vals) : vals;
+        if (rank > 1) {
+            for (var row = 0; row < n / numCols; row++) {
+                var offset = row * numCols;
+                for (var j = 0; j < numCols; j++) {
+                    padPerCol[j] = Math.max(padPerCol[j], valToString(valuesOrTuples[offset + j], 0).length);
+                }
+            }
+        }
+        return padPerCol;
+    }
+    function valToString(val, pad) {
+        var valStr;
+        if (Array.isArray(val)) {
+            valStr = parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS)) + " + " +
+                (parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS)) + "j");
+        }
+        else if (isString(val)) {
+            valStr = "'" + val + "'";
+        }
+        else {
+            valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();
+        }
+        return rightPad(valStr, pad);
+    }
+    function subTensorToString(vals, shape, dtype, strides, padPerCol, isLast) {
+        if (isLast === void 0) { isLast = true; }
+        var storagePerElement = dtype === 'complex64' ? 2 : 1;
+        var size = shape[0];
+        var rank = shape.length;
+        if (rank === 0) {
+            if (dtype === 'complex64') {
+                var complexTuple = createComplexTuples(vals);
+                return [valToString(complexTuple[0], 0)];
+            }
+            return [vals[0].toString()];
+        }
+        if (rank === 1) {
+            if (size > FORMAT_LIMIT_NUM_VALS) {
+                var firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;
+                var firstVals = Array.from(vals.slice(0, firstValsSize));
+                var lastVals = Array.from(vals.slice(size - FORMAT_NUM_FIRST_LAST_VALS * storagePerElement, size));
+                if (dtype === 'complex64') {
+                    firstVals = createComplexTuples(firstVals);
+                    lastVals = createComplexTuples(lastVals);
+                }
+                return [
+                    '[' + firstVals.map(function (x, i) { return valToString(x, padPerCol[i]); }).join(', ') +
+                        ', ..., ' +
+                        lastVals
+                            .map(function (x, i) { return valToString(x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i]); })
+                            .join(', ') +
+                        ']'
+                ];
+            }
+            var displayVals = dtype === 'complex64' ? createComplexTuples(vals) :
+                Array.from(vals);
+            return [
+                '[' + displayVals.map(function (x, i) { return valToString(x, padPerCol[i]); }).join(', ') +
+                    ']'
+            ];
+        }
+        var subshape = shape.slice(1);
+        var substrides = strides.slice(1);
+        var stride = strides[0] * storagePerElement;
+        var lines = [];
+        if (size > FORMAT_LIMIT_NUM_VALS) {
+            for (var i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {
+                var start = i * stride;
+                var end = start + stride;
+                lines.push.apply(lines, subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, false));
+            }
+            lines.push('...');
+            for (var i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {
+                var start = i * stride;
+                var end = start + stride;
+                lines.push.apply(lines, subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1));
+            }
+        }
+        else {
+            for (var i = 0; i < size; i++) {
+                var start = i * stride;
+                var end = start + stride;
+                lines.push.apply(lines, subTensorToString(vals.slice(start, end), subshape, dtype, substrides, padPerCol, i === size - 1));
+            }
+        }
+        var sep = rank === 2 ? ',' : '';
+        lines[0] = '[' + lines[0] + sep;
+        for (var i = 1; i < lines.length - 1; i++) {
+            lines[i] = ' ' + lines[i] + sep;
+        }
+        var newLineSep = ',\n';
+        for (var i = 2; i < rank; i++) {
+            newLineSep += '\n';
+        }
+        lines[lines.length - 1] =
+            ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep);
+        return lines;
+    }
+    function createComplexTuples(vals) {
+        var complexTuples = [];
+        for (var i = 0; i < vals.length; i += 2) {
+            complexTuples.push([vals[i], vals[i + 1]]);
+        }
+        return complexTuples;
+    }
+
+    var TensorBuffer = (function () {
+        function TensorBuffer(shape, dtype, values) {
+            this.dtype = dtype;
+            this.shape = shape.slice();
+            this.size = sizeFromShape(shape);
+            if (values != null) {
+                var n = values.length;
+                assert(n === this.size, "Length of values '" + n + "' does not match the size " +
+                    ("inferred by the shape '" + this.size + "'."));
+            }
+            if (dtype === 'complex64') {
+                throw new Error("complex64 dtype TensorBuffers are not supported. Please create " +
+                    "a TensorBuffer for the real and imaginary parts separately and " +
+                    "call tf.complex(real, imag).");
+            }
+            this.values =
+                values || getArrayFromDType(dtype, sizeFromShape(this.shape));
+            this.strides = computeStrides(shape);
+        }
+        TensorBuffer.prototype.set = function (value) {
+            var locs = [];
+            for (var _i = 1; _i < arguments.length; _i++) {
+                locs[_i - 1] = arguments[_i];
+            }
+            if (locs.length === 0) {
+                locs = [0];
+            }
+            assert(locs.length === this.rank, "The number of provided coordinates (" + locs.length + ") must " +
+                ("match the rank (" + this.rank + ")"));
+            var index = this.locToIndex(locs);
+            this.values[index] = value;
+        };
+        TensorBuffer.prototype.get = function () {
+            var locs = [];
+            for (var _i = 0; _i < arguments.length; _i++) {
+                locs[_i] = arguments[_i];
+            }
+            if (locs.length === 0) {
+                locs = [0];
+            }
+            var index = locs[locs.length - 1];
+            for (var i = 0; i < locs.length - 1; ++i) {
+                index += this.strides[i] * locs[i];
+            }
+            return this.values[index];
+        };
+        TensorBuffer.prototype.locToIndex = function (locs) {
+            if (this.rank === 0) {
+                return 0;
+            }
+            else if (this.rank === 1) {
+                return locs[0];
+            }
+            var index = locs[locs.length - 1];
+            for (var i = 0; i < locs.length - 1; ++i) {
+                index += this.strides[i] * locs[i];
+            }
+            return index;
+        };
+        TensorBuffer.prototype.indexToLoc = function (index) {
+            if (this.rank === 0) {
+                return [];
+            }
+            else if (this.rank === 1) {
+                return [index];
+            }
+            var locs = new Array(this.shape.length);
+            for (var i = 0; i < locs.length - 1; ++i) {
+                locs[i] = Math.floor(index / this.strides[i]);
+                index -= locs[i] * this.strides[i];
+            }
+            locs[locs.length - 1] = index;
+            return locs;
+        };
+        Object.defineProperty(TensorBuffer.prototype, "rank", {
+            get: function () {
+                return this.shape.length;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        TensorBuffer.prototype.toTensor = function () {
+            return Tensor.make(this.shape, { values: this.values }, this.dtype);
+        };
+        return TensorBuffer;
+    }());
+    var trackerFn = null;
+    var opHandler = null;
+    function setTensorTracker(fn) {
+        trackerFn = fn;
+    }
+    function setOpHandler(handler) {
+        opHandler = handler;
+    }
+    var Tensor = (function () {
+        function Tensor(shape, dtype, values, dataId) {
+            this.isDisposedInternal = false;
+            this.shape = shape.slice();
+            this.dtype = dtype || 'float32';
+            this.size = sizeFromShape(shape);
+            this.strides = computeStrides(shape);
+            this.dataId = dataId != null ? dataId : {};
+            this.id = trackerFn().nextTensorId();
+            this.rankType = (this.rank < 5 ? this.rank.toString() : 'higher');
+            trackerFn().registerTensor(this);
+            if (values != null) {
+                trackerFn().write(this.dataId, values);
+            }
+        }
+        Tensor.make = function (shape, data, dtype) {
+            return new Tensor(shape, dtype, data.values, data.dataId);
+        };
+        Tensor.prototype.flatten = function () {
+            this.throwIfDisposed();
+            return this.as1D();
+        };
+        Tensor.prototype.asScalar = function () {
+            this.throwIfDisposed();
+            assert(this.size === 1, 'The array must have only 1 element.');
+            return this.reshape([]);
+        };
+        Tensor.prototype.as1D = function () {
+            this.throwIfDisposed();
+            return this.reshape([this.size]);
+        };
+        Tensor.prototype.as2D = function (rows, columns) {
+            this.throwIfDisposed();
+            return this.reshape([rows, columns]);
+        };
+        Tensor.prototype.as3D = function (rows, columns, depth) {
+            this.throwIfDisposed();
+            return this.reshape([rows, columns, depth]);
+        };
+        Tensor.prototype.as4D = function (rows, columns, depth, depth2) {
+            this.throwIfDisposed();
+            return this.reshape([rows, columns, depth, depth2]);
+        };
+        Tensor.prototype.as5D = function (rows, columns, depth, depth2, depth3) {
+            this.throwIfDisposed();
+            return this.reshape([rows, columns, depth, depth2, depth3]);
+        };
+        Tensor.prototype.asType = function (dtype) {
+            this.throwIfDisposed();
+            return opHandler.cast(this, dtype);
+        };
+        Object.defineProperty(Tensor.prototype, "rank", {
+            get: function () {
+                return this.shape.length;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Tensor.prototype.get = function () {
+            var locs = [];
+            for (var _i = 0; _i < arguments.length; _i++) {
+                locs[_i] = arguments[_i];
+            }
+            assert(locs.length === this.rank, 'Number of coordinates in get() must match the rank of the tensor');
+            assert(this.dtype !== 'complex64', 'Tensor.get() is not supported for complex64 tensors yet.');
+            this.throwIfDisposed();
+            if (locs.length === 0) {
+                locs = [0];
+            }
+            var index = locs[locs.length - 1];
+            for (var i = 0; i < locs.length - 1; ++i) {
+                index += this.strides[i] * locs[i];
+            }
+            return this.dataSync()[index];
+        };
+        Tensor.prototype.buffer = function () {
+            return opHandler.buffer(this.shape, this.dtype, this.dataSync());
+        };
+        Tensor.prototype.data = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                return __generator(this, function (_a) {
+                    this.throwIfDisposed();
+                    return [2, trackerFn().read(this.dataId)];
+                });
+            });
+        };
+        Tensor.prototype.dataSync = function () {
+            this.throwIfDisposed();
+            return trackerFn().readSync(this.dataId);
+        };
+        Tensor.prototype.dispose = function () {
+            if (this.isDisposed) {
+                return;
+            }
+            trackerFn().disposeTensor(this);
+            this.isDisposedInternal = true;
+        };
+        Object.defineProperty(Tensor.prototype, "isDisposed", {
+            get: function () {
+                return this.isDisposedInternal;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Tensor.prototype.throwIfDisposed = function () {
+            if (this.isDisposed) {
+                throw new Error("Tensor is disposed.");
+            }
+        };
+        Tensor.prototype.toFloat = function () {
+            return this.asType('float32');
+        };
+        Tensor.prototype.toInt = function () {
+            return this.asType('int32');
+        };
+        Tensor.prototype.toBool = function () {
+            return this.asType('bool');
+        };
+        Tensor.prototype.print = function (verbose) {
+            if (verbose === void 0) { verbose = false; }
+            return opHandler.print(this, verbose);
+        };
+        Tensor.prototype.reshape = function (newShape) {
+            this.throwIfDisposed();
+            return opHandler.reshape(this, newShape);
+        };
+        Tensor.prototype.reshapeAs = function (x) {
+            this.throwIfDisposed();
+            return this.reshape(x.shape);
+        };
+        Tensor.prototype.expandDims = function (axis) {
+            if (axis === void 0) { axis = 0; }
+            return opHandler.expandDims(this, axis);
+        };
+        Tensor.prototype.cumsum = function (axis, exclusive, reverse) {
+            if (axis === void 0) { axis = 0; }
+            if (exclusive === void 0) { exclusive = false; }
+            if (reverse === void 0) { reverse = false; }
+            return opHandler.cumsum(this, axis, exclusive, reverse);
+        };
+        Tensor.prototype.squeeze = function (axis) {
+            this.throwIfDisposed();
+            return opHandler.squeeze(this, axis);
+        };
+        Tensor.prototype.clone = function () {
+            this.throwIfDisposed();
+            return opHandler.clone(this);
+        };
+        Tensor.prototype.toString = function (verbose) {
+            if (verbose === void 0) { verbose = false; }
+            var vals = this.dataSync();
+            return tensorToString(vals, this.shape, this.dtype, verbose);
+        };
+        Tensor.prototype.tile = function (reps) {
+            this.throwIfDisposed();
+            return opHandler.tile(this, reps);
+        };
+        Tensor.prototype.gather = function (indices, axis) {
+            if (axis === void 0) { axis = 0; }
+            this.throwIfDisposed();
+            return opHandler.gather(this, indices, axis);
+        };
+        Tensor.prototype.matMul = function (b, transposeA, transposeB) {
+            if (transposeA === void 0) { transposeA = false; }
+            if (transposeB === void 0) { transposeB = false; }
+            this.throwIfDisposed();
+            return opHandler.matMul(this, b, transposeA, transposeB);
+        };
+        Tensor.prototype.dot = function (b) {
+            this.throwIfDisposed();
+            return opHandler.dot(this, b);
+        };
+        Tensor.prototype.norm = function (ord, axis, keepDims) {
+            if (ord === void 0) { ord = 'euclidean'; }
+            if (axis === void 0) { axis = null; }
+            if (keepDims === void 0) { keepDims = false; }
+            this.throwIfDisposed();
+            return opHandler.norm(this, ord, axis, keepDims);
+        };
+        Tensor.prototype.slice = function (begin, size) {
+            this.throwIfDisposed();
+            return opHandler.slice(this, begin, size);
+        };
+        Tensor.prototype.reverse = function (axis) {
+            this.throwIfDisposed();
+            return opHandler.reverse(this, axis);
+        };
+        Tensor.prototype.concat = function (x, axis) {
+            if (axis === void 0) { axis = 0; }
+            this.throwIfDisposed();
+            if (x instanceof Tensor) {
+                x = [x];
+            }
+            return opHandler.concat([this].concat(x), axis);
+        };
+        Tensor.prototype.split = function (numOrSizeSplits, axis) {
+            if (axis === void 0) { axis = 0; }
+            this.throwIfDisposed();
+            return opHandler.split(this, numOrSizeSplits, axis);
+        };
+        Tensor.prototype.stack = function (x, axis) {
+            if (axis === void 0) { axis = 0; }
+            return opHandler.stack([this, x], axis);
+        };
+        Tensor.prototype.unstack = function (x, axis) {
+            if (axis === void 0) { axis = 0; }
+            return opHandler.unstack(this, axis);
+        };
+        Tensor.prototype.pad = function (paddings, constantValue) {
+            if (constantValue === void 0) { constantValue = 0; }
+            return opHandler.pad(this, paddings, constantValue);
+        };
+        Tensor.prototype.batchNormalization = function (mean, variance, varianceEpsilon, scale, offset) {
+            if (varianceEpsilon === void 0) { varianceEpsilon = .001; }
+            this.throwIfDisposed();
+            return opHandler.batchNormalization(this, mean, variance, varianceEpsilon, scale, offset);
+        };
+        Tensor.prototype.all = function (axis, keepDims) {
+            if (axis === void 0) { axis = null; }
+            if (keepDims === void 0) { keepDims = false; }
+            this.throwIfDisposed();
+            return opHandler.all(this, axis, keepDims);
+        };
+        Tensor.prototype.any = function (axis, keepDims) {
+            if (axis === void 0) { axis = null; }
+            if (keepDims === void 0) { keepDims = false; }
+            this.throwIfDisposed();
+            return opHandler.any(this, axis, keepDims);
+        };
+        Tensor.prototype.logSumExp = function (axis, keepDims) {
+            if (axis === void 0) { axis = null; }
+            if (keepDims === void 0) { keepDims = false; }
+            this.throwIfDisposed();
+            return opHandler.logSumExp(this, axis, keepDims);
+        };
+        Tensor.prototype.sum = function (axis, keepDims) {
+            if (axis === void 0) { axis = null; }
+            if (keepDims === void 0) { keepDims = false; }
+            this.throwIfDisposed();
+            return opHandler.sum(this, axis, keepDims);
+        };
+        Tensor.prototype.prod = function (axis, keepDims) {
+            if (axis === void 0) { axis = null; }
+            if (keepDims === void 0) { keepDims = false; }
+            this.throwIfDisposed();
+            return opHandler.prod(this, axis, keepDims);
+        };
+        Tensor.prototype.mean = function (axis, keepDims) {
+            if (axis === void 0) { axis = null; }
+            if (keepDims === void 0) { keepDims = false; }
+            this.throwIfDisposed();
+            return opHandler.mean(this, axis, keepDims);
+        };
+        Tensor.prototype.min = function (axis, keepDims) {
+            if (axis === void 0) { axis = null; }
+            if (keepDims === void 0) { keepDims = false; }
+            this.throwIfDisposed();
+            return opHandler.min(this, axis, keepDims);
+        };
+        Tensor.prototype.max = function (axis, keepDims) {
+            if (axis === void 0) { axis = null; }
+            if (keepDims === void 0) { keepDims = false; }
+            this.throwIfDisposed();
+            return opHandler.max(this, axis, keepDims);
+        };
+        Tensor.prototype.argMin = function (axis) {
+            if (axis === void 0) { axis = null; }
+            this.throwIfDisposed();
+            return opHandler.argMin(this, axis);
+        };
+        Tensor.prototype.argMax = function (axis) {
+            if (axis === void 0) { axis = null; }
+            this.throwIfDisposed();
+            return opHandler.argMax(this, axis);
+        };
+        Tensor.prototype.cast = function (dtype) {
+            this.throwIfDisposed();
+            return opHandler.cast(this, dtype);
+        };
+        Tensor.prototype.add = function (x) {
+            this.throwIfDisposed();
+            return opHandler.add(this, x);
+        };
+        Tensor.prototype.addStrict = function (x) {
+            this.throwIfDisposed();
+            return opHandler.addStrict(this, x);
+        };
+        Tensor.prototype.atan2 = function (x) {
+            this.throwIfDisposed();
+            return opHandler.atan2(this, x);
+        };
+        Tensor.prototype.sub = function (x) {
+            this.throwIfDisposed();
+            return opHandler.sub(this, x);
+        };
+        Tensor.prototype.subStrict = function (x) {
+            this.throwIfDisposed();
+            return opHandler.subStrict(this, x);
+        };
+        Tensor.prototype.pow = function (exp) {
+            this.throwIfDisposed();
+            return opHandler.pow(this, exp);
+        };
+        Tensor.prototype.powStrict = function (exp) {
+            this.throwIfDisposed();
+            return opHandler.powStrict(this, exp);
+        };
+        Tensor.prototype.mul = function (x) {
+            this.throwIfDisposed();
+            return opHandler.mul(this, x);
+        };
+        Tensor.prototype.mulStrict = function (x) {
+            this.throwIfDisposed();
+            return opHandler.mulStrict(this, x);
+        };
+        Tensor.prototype.div = function (x) {
+            this.throwIfDisposed();
+            return opHandler.div(this, x);
+        };
+        Tensor.prototype.floorDiv = function (x) {
+            this.throwIfDisposed();
+            return opHandler.floorDiv(this, x);
+        };
+        Tensor.prototype.divStrict = function (x) {
+            this.throwIfDisposed();
+            return opHandler.divStrict(this, x);
+        };
+        Tensor.prototype.minimum = function (x) {
+            this.throwIfDisposed();
+            return opHandler.minimum(this, x);
+        };
+        Tensor.prototype.minimumStrict = function (x) {
+            this.throwIfDisposed();
+            return opHandler.minimumStrict(this, x);
+        };
+        Tensor.prototype.maximum = function (x) {
+            this.throwIfDisposed();
+            return opHandler.maximum(this, x);
+        };
+        Tensor.prototype.maximumStrict = function (x) {
+            this.throwIfDisposed();
+            return opHandler.maximumStrict(this, x);
+        };
+        Tensor.prototype.mod = function (x) {
+            this.throwIfDisposed();
+            return opHandler.mod(this, x);
+        };
+        Tensor.prototype.modStrict = function (x) {
+            this.throwIfDisposed();
+            return opHandler.modStrict(this, x);
+        };
+        Tensor.prototype.squaredDifference = function (x) {
+            this.throwIfDisposed();
+            return opHandler.squaredDifference(this, x);
+        };
+        Tensor.prototype.squaredDifferenceStrict = function (x) {
+            this.throwIfDisposed();
+            return opHandler.squaredDifferenceStrict(this, x);
+        };
+        Tensor.prototype.transpose = function (perm) {
+            this.throwIfDisposed();
+            return opHandler.transpose(this, perm);
+        };
+        Tensor.prototype.notEqual = function (x) {
+            this.throwIfDisposed();
+            return opHandler.notEqual(this, x);
+        };
+        Tensor.prototype.notEqualStrict = function (x) {
+            this.throwIfDisposed();
+            return opHandler.notEqualStrict(this, x);
+        };
+        Tensor.prototype.less = function (x) {
+            this.throwIfDisposed();
+            return opHandler.less(this, x);
+        };
+        Tensor.prototype.lessStrict = function (x) {
+            this.throwIfDisposed();
+            return opHandler.lessStrict(this, x);
+        };
+        Tensor.prototype.equal = function (x) {
+            this.throwIfDisposed();
+            return opHandler.equal(this, x);
+        };
+        Tensor.prototype.equalStrict = function (x) {
+            this.throwIfDisposed();
+            return opHandler.equalStrict(this, x);
+        };
+        Tensor.prototype.lessEqual = function (x) {
+            this.throwIfDisposed();
+            return opHandler.lessEqual(this, x);
+        };
+        Tensor.prototype.lessEqualStrict = function (x) {
+            this.throwIfDisposed();
+            return opHandler.lessEqualStrict(this, x);
+        };
+        Tensor.prototype.greater = function (x) {
+            this.throwIfDisposed();
+            return opHandler.greater(this, x);
+        };
+        Tensor.prototype.greaterStrict = function (x) {
+            this.throwIfDisposed();
+            return opHandler.greaterStrict(this, x);
+        };
+        Tensor.prototype.greaterEqual = function (x) {
+            this.throwIfDisposed();
+            return opHandler.greaterEqual(this, x);
+        };
+        Tensor.prototype.greaterEqualStrict = function (x) {
+            this.throwIfDisposed();
+            return opHandler.greaterEqualStrict(this, x);
+        };
+        Tensor.prototype.logicalAnd = function (x) {
+            this.throwIfDisposed();
+            return opHandler.logicalAnd(this, x);
+        };
+        Tensor.prototype.logicalOr = function (x) {
+            this.throwIfDisposed();
+            return opHandler.logicalOr(this, x);
+        };
+        Tensor.prototype.logicalNot = function () {
+            this.throwIfDisposed();
+            return opHandler.logicalNot(this);
+        };
+        Tensor.prototype.logicalXor = function (x) {
+            this.throwIfDisposed();
+            return opHandler.logicalXor(this, x);
+        };
+        Tensor.prototype.where = function (condition, x) {
+            this.throwIfDisposed();
+            return opHandler.where(condition, this, x);
+        };
+        Tensor.prototype.neg = function () {
+            this.throwIfDisposed();
+            return opHandler.neg(this);
+        };
+        Tensor.prototype.ceil = function () {
+            this.throwIfDisposed();
+            return opHandler.ceil(this);
+        };
+        Tensor.prototype.floor = function () {
+            this.throwIfDisposed();
+            return opHandler.floor(this);
+        };
+        Tensor.prototype.sign = function () {
+            this.throwIfDisposed();
+            return opHandler.sign(this);
+        };
+        Tensor.prototype.exp = function () {
+            this.throwIfDisposed();
+            return opHandler.exp(this);
+        };
+        Tensor.prototype.expm1 = function () {
+            this.throwIfDisposed();
+            return opHandler.expm1(this);
+        };
+        Tensor.prototype.log = function () {
+            this.throwIfDisposed();
+            return opHandler.log(this);
+        };
+        Tensor.prototype.log1p = function () {
+            this.throwIfDisposed();
+            return opHandler.log1p(this);
+        };
+        Tensor.prototype.sqrt = function () {
+            this.throwIfDisposed();
+            return opHandler.sqrt(this);
+        };
+        Tensor.prototype.rsqrt = function () {
+            this.throwIfDisposed();
+            return opHandler.rsqrt(this);
+        };
+        Tensor.prototype.square = function () {
+            this.throwIfDisposed();
+            return opHandler.square(this);
+        };
+        Tensor.prototype.reciprocal = function () {
+            this.throwIfDisposed();
+            return opHandler.reciprocal(this);
+        };
+        Tensor.prototype.abs = function () {
+            this.throwIfDisposed();
+            return opHandler.abs(this);
+        };
+        Tensor.prototype.clipByValue = function (min, max) {
+            this.throwIfDisposed();
+            return opHandler.clipByValue(this, min, max);
+        };
+        Tensor.prototype.relu = function () {
+            this.throwIfDisposed();
+            return opHandler.relu(this);
+        };
+        Tensor.prototype.elu = function () {
+            this.throwIfDisposed();
+            return opHandler.elu(this);
+        };
+        Tensor.prototype.selu = function () {
+            this.throwIfDisposed();
+            return opHandler.selu(this);
+        };
+        Tensor.prototype.leakyRelu = function (alpha) {
+            if (alpha === void 0) { alpha = 0.2; }
+            this.throwIfDisposed();
+            return opHandler.leakyRelu(this, alpha);
+        };
+        Tensor.prototype.prelu = function (alpha) {
+            this.throwIfDisposed();
+            return opHandler.prelu(this, alpha);
+        };
+        Tensor.prototype.sigmoid = function () {
+            this.throwIfDisposed();
+            return opHandler.sigmoid(this);
+        };
+        Tensor.prototype.logSigmoid = function () {
+            this.throwIfDisposed();
+            return opHandler.logSigmoid(this);
+        };
+        Tensor.prototype.softplus = function () {
+            this.throwIfDisposed();
+            return opHandler.softplus(this);
+        };
+        Tensor.prototype.zerosLike = function () {
+            this.throwIfDisposed();
+            return opHandler.zerosLike(this);
+        };
+        Tensor.prototype.onesLike = function () {
+            this.throwIfDisposed();
+            return opHandler.onesLike(this);
+        };
+        Tensor.prototype.sin = function () {
+            this.throwIfDisposed();
+            return opHandler.sin(this);
+        };
+        Tensor.prototype.cos = function () {
+            this.throwIfDisposed();
+            return opHandler.cos(this);
+        };
+        Tensor.prototype.tan = function () {
+            this.throwIfDisposed();
+            return opHandler.tan(this);
+        };
+        Tensor.prototype.asin = function () {
+            this.throwIfDisposed();
+            return opHandler.asin(this);
+        };
+        Tensor.prototype.acos = function () {
+            this.throwIfDisposed();
+            return opHandler.acos(this);
+        };
+        Tensor.prototype.atan = function () {
+            this.throwIfDisposed();
+            return opHandler.atan(this);
+        };
+        Tensor.prototype.sinh = function () {
+            this.throwIfDisposed();
+            return opHandler.sinh(this);
+        };
+        Tensor.prototype.cosh = function () {
+            this.throwIfDisposed();
+            return opHandler.cosh(this);
+        };
+        Tensor.prototype.tanh = function () {
+            this.throwIfDisposed();
+            return opHandler.tanh(this);
+        };
+        Tensor.prototype.asinh = function () {
+            this.throwIfDisposed();
+            return opHandler.asinh(this);
+        };
+        Tensor.prototype.acosh = function () {
+            this.throwIfDisposed();
+            return opHandler.acosh(this);
+        };
+        Tensor.prototype.atanh = function () {
+            this.throwIfDisposed();
+            return opHandler.atanh(this);
+        };
+        Tensor.prototype.erf = function () {
+            this.throwIfDisposed();
+            return opHandler.erf(this);
+        };
+        Tensor.prototype.round = function () {
+            this.throwIfDisposed();
+            return opHandler.round(this);
+        };
+        Tensor.prototype.step = function (alpha) {
+            if (alpha === void 0) { alpha = 0.0; }
+            this.throwIfDisposed();
+            return opHandler.step(this, alpha);
+        };
+        Tensor.prototype.softmax = function (dim) {
+            if (dim === void 0) { dim = -1; }
+            this.throwIfDisposed();
+            return opHandler.softmax(this, dim);
+        };
+        Tensor.prototype.logSoftmax = function (axis) {
+            if (axis === void 0) { axis = -1; }
+            this.throwIfDisposed();
+            return opHandler.logSoftmax(this, axis);
+        };
+        Tensor.prototype.resizeBilinear = function (newShape2D, alignCorners) {
+            if (alignCorners === void 0) { alignCorners = false; }
+            this.throwIfDisposed();
+            return opHandler.image.resizeBilinear(this, newShape2D, alignCorners);
+        };
+        Tensor.prototype.resizeNearestNeighbor = function (newShape2D, alignCorners) {
+            if (alignCorners === void 0) { alignCorners = false; }
+            this.throwIfDisposed();
+            return opHandler.image.resizeNearestNeighbor(this, newShape2D, alignCorners);
+        };
+        Tensor.prototype.conv1d = function (filter, stride, pad, dataFormat, dilation, dimRoundingMode) {
+            if (dataFormat === void 0) { dataFormat = 'NWC'; }
+            if (dilation === void 0) { dilation = 1; }
+            this.throwIfDisposed();
+            return opHandler.conv1d(this, filter, stride, pad, dataFormat, dilation, dimRoundingMode);
+        };
+        Tensor.prototype.conv2d = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {
+            if (dataFormat === void 0) { dataFormat = 'NHWC'; }
+            if (dilations === void 0) { dilations = [1, 1]; }
+            this.throwIfDisposed();
+            return opHandler.conv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);
+        };
+        Tensor.prototype.conv2dTranspose = function (filter, outputShape, strides, pad, dimRoundingMode) {
+            this.throwIfDisposed();
+            return opHandler.conv2dTranspose(this, filter, outputShape, strides, pad, dimRoundingMode);
+        };
+        Tensor.prototype.depthwiseConv2D = function (filter, strides, pad, dataFormat, dilations, dimRoundingMode) {
+            if (dataFormat === void 0) { dataFormat = 'NHWC'; }
+            if (dilations === void 0) { dilations = [1, 1]; }
+            this.throwIfDisposed();
+            return opHandler.depthwiseConv2d(this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);
+        };
+        Tensor.prototype.separableConv2d = function (depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat) {
+            if (dilation === void 0) { dilation = [1, 1]; }
+            if (dataFormat === void 0) { dataFormat = 'NHWC'; }
+            this.throwIfDisposed();
+            return opHandler.separableConv2d(this, depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat);
+        };
+        Tensor.prototype.avgPool = function (filterSize, strides, pad, dimRoundingMode) {
+            this.throwIfDisposed();
+            return opHandler.avgPool(this, filterSize, strides, pad, dimRoundingMode);
+        };
+        Tensor.prototype.maxPool = function (filterSize, strides, pad, dimRoundingMode) {
+            this.throwIfDisposed();
+            return opHandler.maxPool(this, filterSize, strides, pad, dimRoundingMode);
+        };
+        Tensor.prototype.localResponseNormalization = function (radius, bias, alpha, beta) {
+            if (radius === void 0) { radius = 5; }
+            if (bias === void 0) { bias = 1; }
+            if (alpha === void 0) { alpha = 1; }
+            if (beta === void 0) { beta = 0.5; }
+            return opHandler.localResponseNormalization(this, radius, bias, alpha, beta);
+        };
+        Tensor.prototype.pool = function (windowShape, poolingType, padding, dilationRate, strides) {
+            this.throwIfDisposed();
+            return opHandler.pool(this, windowShape, poolingType, padding, dilationRate, strides);
+        };
+        Tensor.prototype.variable = function (trainable, name, dtype) {
+            if (trainable === void 0) { trainable = true; }
+            this.throwIfDisposed();
+            return Variable.variable(this, trainable, name, dtype);
+        };
+        Tensor.prototype.unsortedSegmentSum = function (segmentIds, numSegments) {
+            this.throwIfDisposed();
+            return opHandler.unsortedSegmentSum(this, segmentIds, numSegments);
+        };
+        Tensor.prototype.batchToSpaceND = function (blockShape, crops) {
+            this.throwIfDisposed();
+            return opHandler.batchToSpaceND(this, blockShape, crops);
+        };
+        Tensor.prototype.spaceToBatchND = function (blockShape, paddings) {
+            this.throwIfDisposed();
+            return opHandler.spaceToBatchND(this, blockShape, paddings);
+        };
+        Tensor.prototype.topk = function (k, sorted) {
+            if (k === void 0) { k = 1; }
+            if (sorted === void 0) { sorted = true; }
+            this.throwIfDisposed();
+            return opHandler.topk(this, k, sorted);
+        };
+        Tensor.prototype.stridedSlice = function (begin, end, strides, beginMask, endMask) {
+            if (beginMask === void 0) { beginMask = 0; }
+            if (endMask === void 0) { endMask = 0; }
+            this.throwIfDisposed();
+            return opHandler.stridedSlice(this, begin, end, strides, beginMask, endMask);
+        };
+        Tensor.prototype.depthToSpace = function (blockSize, dataFormat) {
+            this.throwIfDisposed();
+            return opHandler.depthToSpace(this, blockSize, dataFormat);
+        };
+        Tensor.prototype.fft = function () {
+            this.throwIfDisposed();
+            return opHandler.spectral.fft(this);
+        };
+        Tensor.prototype.ifft = function () {
+            this.throwIfDisposed();
+            return opHandler.spectral.ifft(this);
+        };
+        return Tensor;
+    }());
+    Object.defineProperty(Tensor, Symbol.hasInstance, {
+        value: function (instance) {
+            return !!instance && instance.shape != null && instance.dtype != null;
+        }
+    });
+    var Variable = (function (_super) {
+        __extends(Variable, _super);
+        function Variable(initialValue, trainable, name) {
+            if (trainable === void 0) { trainable = true; }
+            var _this = _super.call(this, initialValue.shape, initialValue.dtype, null, initialValue.dataId) || this;
+            _this.trainable = trainable;
+            _this.name = name;
+            if (_this.name == null) {
+                _this.name = trackerFn().nextVariableId().toString();
+            }
+            try {
+                trackerFn().registerVariable(_this);
+            }
+            catch (ex) {
+                trackerFn().disposeTensor(_this);
+                throw ex;
+            }
+            return _this;
+        }
+        Variable.variable = function (initialValue, trainable, name, dtype) {
+            if (trainable === void 0) { trainable = true; }
+            if (dtype != null && dtype !== initialValue.dtype) {
+                initialValue = initialValue.asType(dtype);
+            }
+            return new Variable(initialValue, trainable, name);
+        };
+        Variable.prototype.assign = function (newValue) {
+            if (newValue.dtype !== this.dtype) {
+                throw new Error("dtype of the new value (" + newValue.dtype + ") and " +
+                    ("previous value (" + this.dtype + ") must match"));
+            }
+            if (!arraysEqual(newValue.shape, this.shape)) {
+                throw new Error("shape of the new value (" + newValue.shape + ") and " +
+                    ("previous value (" + this.shape + ") must match"));
+            }
+            trackerFn().disposeTensor(this);
+            this.dataId = newValue.dataId;
+            trackerFn().registerTensor(this);
+        };
+        return Variable;
+    }(Tensor));
+    Object.defineProperty(Variable, Symbol.hasInstance, {
+        value: function (instance) {
+            return instance instanceof Tensor && instance.assign != null &&
+                instance.assign instanceof Function;
+        }
+    });
+    var variable = Variable.variable;
+
+    function getFilteredNodesXToY(tape, xs, y) {
+        var tensorsFromX = {};
+        var nodesFromX = {};
+        for (var i = 0; i < xs.length; i++) {
+            tensorsFromX[xs[i].id] = true;
+        }
+        for (var i = 0; i < tape.length; i++) {
+            var node = tape[i];
+            var nodeInputs = node.inputs;
+            for (var inputName in nodeInputs) {
+                var input = nodeInputs[inputName];
+                var anyInputFromX = false;
+                for (var j = 0; j < xs.length; j++) {
+                    if (tensorsFromX[input.id]) {
+                        node.outputs.forEach(function (output) { return tensorsFromX[output.id] = true; });
+                        anyInputFromX = true;
+                        nodesFromX[node.id] = true;
+                        break;
+                    }
+                }
+                if (anyInputFromX) {
+                    break;
+                }
+            }
+        }
+        var tensorsLeadToY = {};
+        tensorsLeadToY[y.id] = true;
+        var nodesToY = {};
+        for (var i = tape.length - 1; i >= 0; i--) {
+            var node = tape[i];
+            var nodeInputs = node.inputs;
+            for (var j = 0; j < node.outputs.length; j++) {
+                if (tensorsLeadToY[node.outputs[j].id]) {
+                    for (var inputName in nodeInputs) {
+                        tensorsLeadToY[nodeInputs[inputName].id] = true;
+                        nodesToY[node.id] = true;
+                    }
+                    break;
+                }
+            }
+        }
+        var filteredTape = [];
+        for (var i = 0; i < tape.length; i++) {
+            var node = tape[i];
+            if (nodesFromX[node.id] && nodesToY[node.id]) {
+                var prunedInputs = {};
+                for (var inputName in node.inputs) {
+                    var nodeInput = node.inputs[inputName];
+                    if (tensorsFromX[nodeInput.id]) {
+                        prunedInputs[inputName] = nodeInput;
+                    }
+                }
+                var prunedNode = Object.assign({}, node);
+                prunedNode.inputs = prunedInputs;
+                prunedNode.outputs = node.outputs;
+                filteredTape.push(prunedNode);
+            }
+        }
+        return filteredTape;
+    }
+    function backpropagateGradients(tensorAccumulatedGradientMap, filteredTape) {
+        var _loop_1 = function (i) {
+            var node = filteredTape[i];
+            var dys = [];
+            node.outputs.forEach(function (o) {
+                var gradTensor = tensorAccumulatedGradientMap[o.id];
+                if (gradTensor != null) {
+                    dys.push(gradTensor);
+                }
+                else {
+                    var dy = Tensor.make(o.shape, { values: makeZerosTypedArray(o.size, o.dtype) }, o.dtype);
+                    dys.push(dy);
+                }
+            });
+            if (node.gradient == null) {
+                throw new Error("Cannot compute gradient: gradient function not found " +
+                    ("for " + node.name + "."));
+            }
+            var inputGradients = node.gradient(node.outputs.length === 1 ? dys[0] : dys);
+            for (var inputName in node.inputs) {
+                if (!(inputName in inputGradients)) {
+                    throw new Error("Cannot backprop through input " + inputName + ". " +
+                        ("Available gradients found: " + Object.keys(inputGradients) + "."));
+                }
+                var dx = inputGradients[inputName]();
+                if (dx.dtype !== 'float32') {
+                    throw new Error("Error in gradient for op " + node.name + ". The gradient of input " +
+                        (inputName + " must have 'float32' dtype, but has '" + dx.dtype + "'"));
+                }
+                var x = node.inputs[inputName];
+                if (!arraysEqual(dx.shape, x.shape)) {
+                    throw new Error("Error in gradient for op " + node.name + ". The gradient of input " +
+                        ("'" + inputName + "' has shape '" + dx.shape + "', which does not match ") +
+                        ("the shape of the input '" + x.shape + "'"));
+                }
+                if (tensorAccumulatedGradientMap[x.id] == null) {
+                    tensorAccumulatedGradientMap[x.id] = dx;
+                }
+                else {
+                    var curGradient = tensorAccumulatedGradientMap[x.id];
+                    tensorAccumulatedGradientMap[x.id] = curGradient.add(dx);
+                    curGradient.dispose();
+                }
+            }
+        };
+        for (var i = filteredTape.length - 1; i >= 0; i--) {
+            _loop_1(i);
+        }
+    }
+
+    (function (Rank) {
+        Rank["R0"] = "R0";
+        Rank["R1"] = "R1";
+        Rank["R2"] = "R2";
+        Rank["R3"] = "R3";
+        Rank["R4"] = "R4";
+        Rank["R5"] = "R5";
+        Rank["R6"] = "R6";
+    })(exports.Rank || (exports.Rank = {}));
+    var UpcastInt32AndMap;
+    (function (UpcastInt32AndMap) {
+        UpcastInt32AndMap["float32"] = "float32";
+        UpcastInt32AndMap["int32"] = "int32";
+        UpcastInt32AndMap["bool"] = "int32";
+        UpcastInt32AndMap["complex64"] = "complex64";
+    })(UpcastInt32AndMap || (UpcastInt32AndMap = {}));
+    var UpcastBoolAndMap;
+    (function (UpcastBoolAndMap) {
+        UpcastBoolAndMap["float32"] = "float32";
+        UpcastBoolAndMap["int32"] = "int32";
+        UpcastBoolAndMap["bool"] = "bool";
+        UpcastBoolAndMap["complex64"] = "complex64";
+    })(UpcastBoolAndMap || (UpcastBoolAndMap = {}));
+    var UpcastFloat32AndMap;
+    (function (UpcastFloat32AndMap) {
+        UpcastFloat32AndMap["float32"] = "float32";
+        UpcastFloat32AndMap["int32"] = "float32";
+        UpcastFloat32AndMap["bool"] = "float32";
+        UpcastFloat32AndMap["complex64"] = "complex64";
+    })(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));
+    var UpcastComplex64AndMap;
+    (function (UpcastComplex64AndMap) {
+        UpcastComplex64AndMap["float32"] = "complex64";
+        UpcastComplex64AndMap["int32"] = "complex64";
+        UpcastComplex64AndMap["bool"] = "complex64";
+        UpcastComplex64AndMap["complex64"] = "complex64";
+    })(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));
+    var upcastTypeMap = {
+        'float32': UpcastFloat32AndMap,
+        'int32': UpcastInt32AndMap,
+        'bool': UpcastBoolAndMap,
+        'complex64': UpcastComplex64AndMap
+    };
+    function upcastType(typeA, typeB) {
+        if (typeA === 'string' || typeB === 'string') {
+            if (typeA === 'string' && typeB === 'string') {
+                return 'string';
+            }
+            throw new Error("Can not upcast " + typeA + " with " + typeB);
+        }
+        return upcastTypeMap[typeA][typeB];
+    }
+    function sumOutType(type) {
+        return upcastType(type, 'int32');
+    }
+
+    function makeTypesMatch(a, b) {
+        if (a.dtype === b.dtype) {
+            return [a, b];
+        }
+        var dtype = upcastType(a.dtype, b.dtype);
+        return [a.cast(dtype), b.cast(dtype)];
+    }
+    function assertTypesMatch(a, b) {
+        assert(a.dtype === b.dtype, "The dtypes of the first(" + a.dtype + ") and" +
+            (" second(" + b.dtype + ") input must match"));
+    }
+    function isTensorInList(tensor, tensorList) {
+        for (var i = 0; i < tensorList.length; i++) {
+            if (tensorList[i].id === tensor.id) {
+                return true;
+            }
+        }
+        return false;
+    }
+    function getTensorsInContainer(result) {
+        var list = [];
+        var seen = new Set();
+        walkTensorContainer(result, list, seen);
+        return list;
+    }
+    function walkTensorContainer(container, list, seen) {
+        if (container == null) {
+            return;
+        }
+        if (container instanceof Tensor) {
+            list.push(container);
+            return;
+        }
+        if (!isIterable(container)) {
+            return;
+        }
+        var iterable = container;
+        for (var k in iterable) {
+            var val = iterable[k];
+            if (!seen.has(val)) {
+                seen.add(val);
+                walkTensorContainer(val, list, seen);
+            }
+        }
+    }
+    function isIterable(obj) {
+        return Array.isArray(obj) || typeof obj === 'object';
+    }
+
+    var Engine = (function () {
+        function Engine(backend, safeMode, debugMode) {
+            this.backend = backend;
+            this.safeMode = safeMode;
+            this.debugMode = debugMode;
+            this.registeredVariables = {};
+            this.nextTapeNodeId = 0;
+            this.numBytes = 0;
+            this.numTensors = 0;
+            this.numStringTensors = 0;
+            this.numDataBuffers = 0;
+            this.profiling = false;
+            this.gradientScopeCount = 0;
+            this.customGradientDepth = 0;
+            this.scopeStack = [];
+            this.keepTensors = new Set();
+            this.tensorInfo = new WeakMap();
+            this.profiler = new Profiler(backend);
+            this.activeProfile =
+                { newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null };
+        }
+        Engine.prototype.moveData = function (dataId) {
+            this.write(dataId, this.readSync(dataId));
+        };
+        Engine.prototype.tidy = function (nameOrFn, fn, gradMode) {
+            var _this = this;
+            if (gradMode === void 0) { gradMode = false; }
+            var name = null;
+            if (fn == null) {
+                if (typeof nameOrFn !== 'function') {
+                    throw new Error('Please provide a function to tidy()');
+                }
+                fn = nameOrFn;
+            }
+            else {
+                if (typeof nameOrFn !== 'string' && !(nameOrFn instanceof String)) {
+                    throw new Error('When calling with two arguments, the first argument ' +
+                        'to tidy() must be a string');
+                }
+                if (typeof fn !== 'function') {
+                    throw new Error('When calling with two arguments, the 2nd argument ' +
+                        'to tidy() must be a function');
+                }
+                name = nameOrFn;
+            }
+            var result;
+            return this.scopedRun(function () { return _this.startScope(name, gradMode); }, function () { return _this.endScope(result, gradMode); }, function () {
+                result = fn();
+                if (result instanceof Promise) {
+                    console.error('Cannot return a Promise inside of tidy.');
+                }
+                return result;
+            });
+        };
+        Engine.prototype.scopedRun = function (start, end, f) {
+            start();
+            try {
+                var res = f();
+                end();
+                return res;
+            }
+            catch (ex) {
+                end();
+                throw ex;
+            }
+        };
+        Engine.prototype.nextTensorId = function () {
+            return Engine.nextTensorId++;
+        };
+        Engine.prototype.nextVariableId = function () {
+            return Engine.nextVariableId++;
+        };
+        Engine.prototype.runKernel = function (forwardFunc, inputs, backwardsFunc) {
+            var _this = this;
+            var result;
+            var saved = [];
+            var saveFunc = function (x) {
+                saved.push(x);
+                return x;
+            };
+            var scopeName = this.activeScope.name;
+            var startingBytecount = this.numBytes;
+            var startingNumTensors = this.numTensors;
+            this.scopedRun(function () { return _this.customGradientDepth++; }, function () { return _this.customGradientDepth--; }, function () {
+                if (!_this.debugMode()) {
+                    result = forwardFunc(_this.backend, saveFunc);
+                }
+                else {
+                    result = _this.profiler.profileKernel(scopeName, function () { return forwardFunc(_this.backend, saveFunc); });
+                }
+            });
+            if (this.shouldRecord()) {
+                var tapeNode = {
+                    id: this.nextTapeNodeId++,
+                    name: scopeName,
+                    inputs: inputs,
+                    outputs: Array.isArray(result) ? result : [result]
+                };
+                if (backwardsFunc != null) {
+                    tapeNode.gradient =
+                        (function (dy) { return backwardsFunc(dy, saved); });
+                }
+                this.activeTape.push(tapeNode);
+            }
+            if (this.profiling) {
+                this.activeProfile.kernels.push({
+                    name: scopeName,
+                    bytesAdded: this.numBytes - startingBytecount,
+                    totalBytesSnapshot: this.numBytes,
+                    tensorsAdded: this.numTensors - startingNumTensors,
+                    totalTensorsSnapshot: this.numTensors,
+                    inputShapes: Object.keys(inputs).map(function (key) { return inputs[key].shape; }),
+                    outputShape: Array.isArray(result) ?
+                        result.map(function (item) { return item.shape; }) :
+                        result.shape
+                });
+            }
+            return result;
+        };
+        Engine.prototype.registerTensor = function (a) {
+            var refCount = this.tensorInfo.has(a.dataId) ?
+                this.tensorInfo.get(a.dataId).refCount :
+                0;
+            this.numTensors++;
+            if (a.dtype === 'string') {
+                this.numStringTensors++;
+            }
+            if (refCount === 0) {
+                this.numDataBuffers++;
+                var bytes = 0;
+                if (a.dtype !== 'complex64' && a.dtype !== 'string') {
+                    bytes = sizeFromShape(a.shape) * bytesPerElement(a.dtype);
+                }
+                this.tensorInfo.set(a.dataId, {
+                    backend: this.backend,
+                    dtype: a.dtype,
+                    shape: a.shape,
+                    bytes: bytes,
+                    refCount: 0
+                });
+                this.numBytes += bytes;
+                this.backend.register(a.dataId, a.shape, a.dtype);
+            }
+            this.tensorInfo.get(a.dataId).refCount++;
+            if (!(a instanceof Variable)) {
+                this.track(a);
+            }
+        };
+        Engine.prototype.registerVariable = function (v) {
+            if (this.registeredVariables[v.name] != null) {
+                throw new Error("Variable with name " + v.name + " was already registered");
+            }
+            this.registeredVariables[v.name] = v;
+        };
+        Engine.prototype.disposeTensor = function (a) {
+            if (!this.tensorInfo.has(a.dataId)) {
+                return;
+            }
+            if (this.keepTensors.has(a.id)) {
+                this.keepTensors.delete(a.id);
+            }
+            this.numTensors--;
+            if (a.dtype === 'string') {
+                this.numStringTensors--;
+            }
+            var info = this.tensorInfo.get(a.dataId);
+            var refCount = info.refCount;
+            if (refCount <= 1) {
+                if (a.dtype !== 'complex64') {
+                    this.numBytes -= info.bytes;
+                }
+                this.numDataBuffers--;
+                info.backend.disposeData(a.dataId);
+                this.tensorInfo.delete(a.dataId);
+            }
+            else {
+                this.tensorInfo.get(a.dataId).refCount--;
+            }
+        };
+        Engine.prototype.disposeVariables = function () {
+            for (var varName in this.registeredVariables) {
+                var v = this.registeredVariables[varName];
+                this.disposeTensor(v);
+                delete this.registeredVariables[varName];
+            }
+        };
+        Engine.prototype.memory = function () {
+            var info = this.backend.memory();
+            info.numTensors = this.numTensors;
+            info.numDataBuffers = this.numDataBuffers;
+            info.numBytes = this.numBytes;
+            if (this.numStringTensors > 0) {
+                info.unreliable = true;
+                if (info.reasons == null) {
+                    info.reasons = [];
+                }
+                info.reasons.push('Memory usage by string tensors is approximate ' +
+                    '(2 bytes per character)');
+            }
+            return info;
+        };
+        Engine.prototype.profile = function (query) {
+            return __awaiter(this, void 0, void 0, function () {
+                var startBytes, startNumTensors;
+                return __generator(this, function (_a) {
+                    this.profiling = true;
+                    startBytes = this.numBytes;
+                    startNumTensors = this.numTensors;
+                    this.activeProfile.kernels = [];
+                    this.activeProfile.result = query();
+                    this.profiling = false;
+                    this.activeProfile.peakBytes = Math.max.apply(Math, this.activeProfile.kernels.map(function (d) { return d.totalBytesSnapshot; }));
+                    this.activeProfile.newBytes = this.numBytes - startBytes;
+                    this.activeProfile.newTensors = this.numTensors - startNumTensors;
+                    return [2, this.activeProfile];
+                });
+            });
+        };
+        Engine.prototype.shouldRecord = function () {
+            return this.activeTape != null && this.customGradientDepth === 0;
+        };
+        Engine.prototype.addTapeNode = function (inputs, result, gradientsFunc) {
+            var inputsMap = {};
+            inputs.forEach(function (input, idx) {
+                inputsMap[idx] = input;
+            });
+            var gradient = function (dy) {
+                var res = gradientsFunc(dy);
+                var resMap = {};
+                res.forEach(function (r, idx) {
+                    resMap[idx] = function () { return r; };
+                });
+                return resMap;
+            };
+            var tapeNode = {
+                id: this.nextTapeNodeId++,
+                name: this.activeScope.name,
+                inputs: inputsMap,
+                outputs: [result],
+                gradient: gradient
+            };
+            this.activeTape.push(tapeNode);
+        };
+        Engine.prototype.keep = function (result) {
+            if (this.scopeStack.length === 1 && this.safeMode) {
+                throw new Error('Safe mode is ON. Enclose all tensor operations inside tf.tidy(): ' +
+                    'tf.tidy(() => {...}) to avoid memory leaks.');
+            }
+            this.keepTensors.add(result.id);
+            return result;
+        };
+        Engine.prototype.startScope = function (name, gradientsMode) {
+            if (gradientsMode === void 0) { gradientsMode = false; }
+            if (gradientsMode && this.gradientScopeCount === 0) {
+                this.activeTape = [];
+            }
+            if (gradientsMode) {
+                this.gradientScopeCount++;
+            }
+            var scopeInfo = { track: [], name: 'unnamed scope' };
+            if (name) {
+                scopeInfo.name = name;
+            }
+            this.scopeStack.push(scopeInfo);
+            this.activeScope = scopeInfo;
+        };
+        Engine.prototype.endScope = function (result, gradientsMode) {
+            var _this = this;
+            if (gradientsMode === void 0) { gradientsMode = false; }
+            if (gradientsMode) {
+                this.gradientScopeCount--;
+                if (this.gradientScopeCount === 0) {
+                    this.activeTape = null;
+                }
+            }
+            var tensorsToKeep = new Set(this.keepTensors);
+            var tensorsToTrackInParent = getTensorsInContainer(result);
+            tensorsToTrackInParent.forEach(function (tensor) { return tensorsToKeep.add(tensor.id); });
+            for (var i = 0; i < this.activeScope.track.length; i++) {
+                var tensor = this.activeScope.track[i];
+                if (tensorsToKeep.has(tensor.id)) {
+                    continue;
+                }
+                if (this.activeTape != null) {
+                    tensorsToTrackInParent.push(tensor);
+                }
+                else {
+                    tensor.dispose();
+                }
+            }
+            var oldScope = this.scopeStack.pop();
+            this.activeScope = this.scopeStack.length === 0 ?
+                null :
+                this.scopeStack[this.scopeStack.length - 1];
+            tensorsToTrackInParent.forEach(function (tensor) {
+                if (!_this.keepTensors.has(tensor.id) &&
+                    isTensorInList(tensor, oldScope.track)) {
+                    _this.track(tensor);
+                }
+            });
+        };
+        Engine.prototype.gradients = function (f, xs, dy, allowNoGradients) {
+            var _this = this;
+            if (allowNoGradients === void 0) { allowNoGradients = false; }
+            assert(xs.length > 0, 'gradients() received an empty list of xs.');
+            if (dy != null && dy.dtype !== 'float32') {
+                throw new Error("dy must have 'float32' dtype, but has '" + dy.dtype + "'");
+            }
+            return this.tidy('gradients', function () {
+                var y = f();
+                assert(y instanceof Tensor, 'The result y returned by f() must be a tensor.');
+                var filteredTape = getFilteredNodesXToY(_this.activeTape, xs, y);
+                if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {
+                    throw new Error('Cannot compute gradient of y=f(x) with respect to x. Make sure ' +
+                        'that the f you passed encloses all operations that lead from x ' +
+                        'to y.');
+                }
+                var accumulatedGradientMap = {};
+                accumulatedGradientMap[y.id] = (dy == null) ? ones(y.shape) : dy;
+                backpropagateGradients(accumulatedGradientMap, filteredTape);
+                var grads = xs.map(function (x) { return accumulatedGradientMap[x.id]; });
+                return { value: y, grads: grads };
+            }, true);
+        };
+        Engine.prototype.customGrad = function (f) {
+            var _this = this;
+            assert(isFunction(f), 'The f passed in customGrad(f) must be a function.');
+            return function () {
+                var inputs = [];
+                for (var _i = 0; _i < arguments.length; _i++) {
+                    inputs[_i] = arguments[_i];
+                }
+                assert(inputs.every(function (t) { return t instanceof Tensor; }), 'The args passed in customGrad(f)(x1, x2,...) must all be tensors');
+                var gradientsFunc;
+                var result;
+                _this.scopedRun(function () { return _this.customGradientDepth++; }, function () { return _this.customGradientDepth--; }, function () {
+                    var gradientsMode = true;
+                    result = _this.tidy(f.name, function () {
+                        var _a = f.apply(void 0, inputs), value = _a.value, gradFunc = _a.gradFunc;
+                        assert(value instanceof Tensor, 'The function f passed in customGrad(f) must return an ' +
+                            'object where `obj.value` is a tensor');
+                        assert(isFunction(gradFunc), 'The function f passed in customGrad(f) must return an ' +
+                            'object where `obj.gradFunc` is a function.');
+                        gradientsFunc = gradFunc;
+                        return value;
+                    }, gradientsMode);
+                });
+                if (_this.shouldRecord()) {
+                    var gradFunc = function (dy) {
+                        var res = gradientsFunc(dy);
+                        var grads = Array.isArray(res) ? res : [res];
+                        assert(grads.length === inputs.length, 'The function f passed in customGrad(f) must return an object ' +
+                            'where `obj.gradFunc` is a function that returns the same ' +
+                            'number of tensors as inputs passed to f(...).');
+                        assert(grads.every(function (t) { return t instanceof Tensor; }), 'The function f passed in customGrad(f) must return an object ' +
+                            'where `obj.gradFunc` is a function that returns a list of ' +
+                            'only tensors.');
+                        return grads;
+                    };
+                    _this.addTapeNode(inputs, result, gradFunc);
+                }
+                return result;
+            };
+        };
+        Engine.prototype.write = function (dataId, values) {
+            var info = this.tensorInfo.get(dataId);
+            if (info.dtype === 'string') {
+                var newBytes = bytesFromStringArray(values);
+                this.numBytes += newBytes - info.bytes;
+                info.bytes = newBytes;
+            }
+            if (this.backend !== info.backend) {
+                info.backend.disposeData(dataId);
+                info.backend = this.backend;
+                this.backend.register(dataId, info.shape, info.dtype);
+            }
+            this.backend.write(dataId, values);
+        };
+        Engine.prototype.readSync = function (dataId) {
+            var info = this.tensorInfo.get(dataId);
+            return info.backend.readSync(dataId);
+        };
+        Engine.prototype.read = function (dataId) {
+            var info = this.tensorInfo.get(dataId);
+            return info.backend.read(dataId);
+        };
+        Engine.prototype.fromPixels = function (pixels, numChannels) {
+            return this.backend.fromPixels(pixels, numChannels);
+        };
+        Engine.prototype.time = function (query) {
+            return __awaiter(this, void 0, void 0, function () {
+                var start, timingInfo;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0:
+                            start = now();
+                            return [4, this.backend.time(query)];
+                        case 1:
+                            timingInfo = _a.sent();
+                            timingInfo.wallMs = now() - start;
+                            return [2, timingInfo];
+                    }
+                });
+            });
+        };
+        Engine.prototype.track = function (result) {
+            if (this.scopeStack.length === 1 && this.safeMode) {
+                throw new Error('Safe mode is ON. Enclose all tensor operations inside tf.tidy(): ' +
+                    'tf.tidy(() => {op();...}); to avoid memory leaks.');
+            }
+            if (this.activeScope != null) {
+                this.activeScope.track.push(result);
+            }
+            return result;
+        };
+        Engine.nextTensorId = 0;
+        Engine.nextVariableId = 0;
+        return Engine;
+    }());
+    function ones(shape) {
+        var values = makeOnesTypedArray(sizeFromShape(shape), 'float32');
+        return Tensor.make(shape, { values: values });
+    }
+
+    var Type;
+    (function (Type) {
+        Type[Type["NUMBER"] = 0] = "NUMBER";
+        Type[Type["BOOLEAN"] = 1] = "BOOLEAN";
+        Type[Type["STRING"] = 2] = "STRING";
+    })(Type || (Type = {}));
+    var URL_PROPERTIES = [
+        { name: 'DEBUG', type: Type.BOOLEAN },
+        { name: 'IS_BROWSER', type: Type.BOOLEAN },
+        { name: 'WEBGL_LAZILY_UNPACK', type: Type.BOOLEAN },
+        { name: 'WEBGL_CPU_FORWARD', type: Type.BOOLEAN },
+        { name: 'WEBGL_PACK', type: Type.BOOLEAN },
+        { name: 'WEBGL_PACK_BATCHNORMALIZATION', type: Type.BOOLEAN },
+        { name: 'WEBGL_PACK_CLIP', type: Type.BOOLEAN },
+        { name: 'WEBGL_PACK_DEPTHWISECONV', type: Type.BOOLEAN },
+        { name: 'WEBGL_CONV_IM2COL', type: Type.BOOLEAN },
+        { name: 'WEBGL_MAX_TEXTURE_SIZE', type: Type.NUMBER },
+        { name: 'WEBGL_PAGING_ENABLED', type: Type.BOOLEAN },
+        { name: 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION', type: Type.NUMBER },
+        { name: 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE', type: Type.BOOLEAN },
+        { name: 'WEBGL_VERSION', type: Type.NUMBER },
+        { name: 'WEBGL_RENDER_FLOAT32_ENABLED', type: Type.BOOLEAN },
+        { name: 'WEBGL_DOWNLOAD_FLOAT_ENABLED', type: Type.BOOLEAN },
+        { name: 'WEBGL_FENCE_API_ENABLED', type: Type.BOOLEAN },
+        { name: 'WEBGL_SIZE_UPLOAD_UNIFORM', type: Type.NUMBER },
+        { name: 'BACKEND', type: Type.STRING },
+        { name: 'EPSILON', type: Type.NUMBER },
+        { name: 'PROD', type: Type.BOOLEAN },
+        { name: 'TENSORLIKE_CHECK_SHAPE_CONSISTENCY', type: Type.BOOLEAN },
+    ];
+    function isWebGLVersionEnabled(webGLVersion) {
+        try {
+            var gl = getWebGLContext(webGLVersion);
+            if (gl != null) {
+                return true;
+            }
+        }
+        catch (e) {
+            return false;
+        }
+        return false;
+    }
+    var MAX_TEXTURE_SIZE;
+    function getWebGLMaxTextureSize(webGLVersion) {
+        if (MAX_TEXTURE_SIZE == null) {
+            var gl = getWebGLContext(webGLVersion);
+            MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);
+        }
+        return MAX_TEXTURE_SIZE;
+    }
+    function getWebGLDisjointQueryTimerVersion(webGLVersion) {
+        if (webGLVersion === 0) {
+            return 0;
+        }
+        var queryTimerVersion;
+        var gl = getWebGLContext(webGLVersion);
+        if (hasExtension(gl, 'EXT_disjoint_timer_query_webgl2') &&
+            webGLVersion === 2) {
+            queryTimerVersion = 2;
+        }
+        else if (hasExtension(gl, 'EXT_disjoint_timer_query')) {
+            queryTimerVersion = 1;
+        }
+        else {
+            queryTimerVersion = 0;
+        }
+        return queryTimerVersion;
+    }
+    function isRenderToFloatTextureEnabled(webGLVersion) {
+        if (webGLVersion === 0) {
+            return false;
+        }
+        var gl = getWebGLContext(webGLVersion);
+        if (webGLVersion === 1) {
+            if (!hasExtension(gl, 'OES_texture_float')) {
+                return false;
+            }
+        }
+        else {
+            if (!hasExtension(gl, 'EXT_color_buffer_float')) {
+                return false;
+            }
+        }
+        var isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl, webGLVersion);
+        return isFrameBufferComplete;
+    }
+    function isDownloadFloatTextureEnabled(webGLVersion) {
+        if (webGLVersion === 0) {
+            return false;
+        }
+        var gl = getWebGLContext(webGLVersion);
+        if (webGLVersion === 1) {
+            if (!hasExtension(gl, 'OES_texture_float')) {
+                return false;
+            }
+            if (!hasExtension(gl, 'WEBGL_color_buffer_float')) {
+                return false;
+            }
+        }
+        else {
+            if (!hasExtension(gl, 'EXT_color_buffer_float')) {
+                return false;
+            }
+        }
+        var isFrameBufferComplete = createFloatTextureAndBindToFramebuffer(gl, webGLVersion);
+        return isFrameBufferComplete;
+    }
+    function isWebGLFenceEnabled(webGLVersion) {
+        if (webGLVersion !== 2) {
+            return false;
+        }
+        var gl = getWebGLContext(webGLVersion);
+        var isEnabled = gl.fenceSync != null;
+        return isEnabled;
+    }
+    function isChrome() {
+        return typeof navigator !== 'undefined' && navigator != null &&
+            navigator.userAgent != null && /Chrome/.test(navigator.userAgent) &&
+            /Google Inc/.test(navigator.vendor);
+    }
+    var TENSORFLOWJS_FLAGS_PREFIX = 'tfjsflags';
+    function getFeaturesFromURL() {
+        var features = {};
+        if (typeof window === 'undefined' || typeof window.location === 'undefined' ||
+            typeof window.location.search === 'undefined') {
+            return features;
+        }
+        var urlParams = getQueryParams(window.location.search);
+        if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {
+            var urlFlags_1 = {};
+            var keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(',');
+            keyValues.forEach(function (keyValue) {
+                var _a = keyValue.split(':'), key = _a[0], value = _a[1];
+                urlFlags_1[key] = value;
+            });
+            URL_PROPERTIES.forEach(function (urlProperty) {
+                if (urlProperty.name in urlFlags_1) {
+                    console.log("Setting feature override from URL " + urlProperty.name + ": " +
+                        ("" + urlFlags_1[urlProperty.name]));
+                    if (urlProperty.type === Type.NUMBER) {
+                        features[urlProperty.name] = +urlFlags_1[urlProperty.name];
+                    }
+                    else if (urlProperty.type === Type.BOOLEAN) {
+                        features[urlProperty.name] = urlFlags_1[urlProperty.name] === 'true';
+                    }
+                    else if (urlProperty.type === Type.STRING) {
+                        features[urlProperty.name] = urlFlags_1[urlProperty.name];
+                    }
+                    else {
+                        console.warn("Unknown URL param: " + urlProperty.name + ".");
+                    }
+                }
+            });
+        }
+        return features;
+    }
+    function hasExtension(gl, extensionName) {
+        var ext = gl.getExtension(extensionName);
+        return ext != null;
+    }
+    function createFloatTextureAndBindToFramebuffer(gl, webGLVersion) {
+        var frameBuffer = gl.createFramebuffer();
+        var texture = gl.createTexture();
+        gl.bindTexture(gl.TEXTURE_2D, texture);
+        var internalFormat = webGLVersion === 2 ? gl.RGBA32F : gl.RGBA;
+        gl.texImage2D(gl.TEXTURE_2D, 0, internalFormat, 1, 1, 0, gl.RGBA, gl.FLOAT, null);
+        gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);
+        gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);
+        var isFrameBufferComplete = gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;
+        gl.bindTexture(gl.TEXTURE_2D, null);
+        gl.bindFramebuffer(gl.FRAMEBUFFER, null);
+        gl.deleteTexture(texture);
+        gl.deleteFramebuffer(frameBuffer);
+        return isFrameBufferComplete;
+    }
+    function getQueryParams(queryString) {
+        var params = {};
+        queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, function (s) {
+            var t = [];
+            for (var _i = 1; _i < arguments.length; _i++) {
+                t[_i - 1] = arguments[_i];
+            }
+            decodeParam(params, t[0], t[1]);
+            return t.join('=');
+        });
+        return params;
+    }
+    function decodeParam(params, name, value) {
+        params[decodeURIComponent(name)] = decodeURIComponent(value || '');
+    }
+
+    var EPSILON_FLOAT16 = 1e-4;
+    var TEST_EPSILON_FLOAT16 = 1e-1;
+    var EPSILON_FLOAT32 = 1e-7;
+    var TEST_EPSILON_FLOAT32 = 1e-3;
+    var Environment = (function () {
+        function Environment(features) {
+            this.features = {};
+            this.registry = {};
+            if (features != null) {
+                this.features = features;
+            }
+            if (this.get('DEBUG')) {
+                console.warn('Debugging mode is ON. The output of every math call will ' +
+                    'be downloaded to CPU and checked for NaNs. ' +
+                    'This significantly impacts performance.');
+            }
+        }
+        Environment.setBackend = function (backendName, safeMode) {
+            if (safeMode === void 0) { safeMode = false; }
+            if (!(backendName in ENV.registry)) {
+                throw new Error("Backend name '" + backendName + "' not found in registry");
+            }
+            ENV.engine.backend = ENV.findBackend(backendName);
+            ENV.backendName = backendName;
+        };
+        Environment.getBackend = function () {
+            ENV.initEngine();
+            return ENV.backendName;
+        };
+        Environment.disposeVariables = function () {
+            ENV.engine.disposeVariables();
+        };
+        Environment.memory = function () {
+            return ENV.engine.memory();
+        };
+        Environment.profile = function (f) {
+            return ENV.engine.profile(f);
+        };
+        Environment.tidy = function (nameOrFn, fn) {
+            return ENV.engine.tidy(nameOrFn, fn);
+        };
+        Environment.dispose = function (container) {
+            var tensors = getTensorsInContainer(container);
+            tensors.forEach(function (tensor) { return tensor.dispose(); });
+        };
+        Environment.keep = function (result) {
+            return ENV.engine.keep(result);
+        };
+        Environment.time = function (f) {
+            return ENV.engine.time(f);
+        };
+        Environment.prototype.get = function (feature) {
+            if (feature in this.features) {
+                return this.features[feature];
+            }
+            this.features[feature] = this.evaluateFeature(feature);
+            return this.features[feature];
+        };
+        Environment.prototype.getFeatures = function () {
+            return this.features;
+        };
+        Environment.prototype.set = function (feature, value) {
+            this.features[feature] = value;
+        };
+        Environment.prototype.getBestBackendName = function () {
+            var _this = this;
+            if (Object.keys(this.registry).length === 0) {
+                throw new Error('No backend found in registry.');
+            }
+            var sortedBackends = Object.keys(this.registry)
+                .map(function (name) {
+                return { name: name, entry: _this.registry[name] };
+            })
+                .sort(function (a, b) {
+                return b.entry.priority - a.entry.priority;
+            });
+            return sortedBackends[0].name;
+        };
+        Environment.prototype.evaluateFeature = function (feature) {
+            if (feature === 'DEBUG') {
+                return false;
+            }
+            else if (feature === 'IS_BROWSER') {
+                return typeof window !== 'undefined';
+            }
+            else if (feature === 'IS_NODE') {
+                return (typeof process !== 'undefined') &&
+                    (typeof process.versions !== 'undefined') &&
+                    (typeof process.versions.node !== 'undefined');
+            }
+            else if (feature === 'IS_CHROME') {
+                return isChrome();
+            }
+            else if (feature === 'WEBGL_CPU_FORWARD') {
+                return true;
+            }
+            else if (feature === 'WEBGL_PACK') {
+                return false;
+            }
+            else if (feature === 'WEBGL_PACK_BATCHNORMALIZATION') {
+                return this.get('WEBGL_PACK');
+            }
+            else if (feature === 'WEBGL_PACK_CLIP') {
+                return this.get('WEBGL_PACK');
+            }
+            else if (feature === 'WEBGL_PACK_DEPTHWISECONV') {
+                return this.get('WEBGL_PACK');
+            }
+            else if (feature === 'WEBGL_LAZILY_UNPACK') {
+                return this.get('WEBGL_PACK');
+            }
+            else if (feature === 'WEBGL_CONV_IM2COL') {
+                return this.get('WEBGL_PACK');
+            }
+            else if (feature === 'WEBGL_PAGING_ENABLED') {
+                return this.get('IS_BROWSER') && !this.get('PROD');
+            }
+            else if (feature === 'WEBGL_MAX_TEXTURE_SIZE') {
+                return getWebGLMaxTextureSize(this.get('WEBGL_VERSION'));
+            }
+            else if (feature === 'IS_TEST') {
+                return false;
+            }
+            else if (feature === 'BACKEND') {
+                return this.getBestBackendName();
+            }
+            else if (feature === 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') {
+                var webGLVersion = this.get('WEBGL_VERSION');
+                if (webGLVersion === 0) {
+                    return 0;
+                }
+                return getWebGLDisjointQueryTimerVersion(webGLVersion);
+            }
+            else if (feature === 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') {
+                return this.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0 &&
+                    !isMobile();
+            }
+            else if (feature === 'HAS_WEBGL') {
+                return this.get('WEBGL_VERSION') > 0;
+            }
+            else if (feature === 'WEBGL_VERSION') {
+                if (isWebGLVersionEnabled(2)) {
+                    return 2;
+                }
+                else if (isWebGLVersionEnabled(1)) {
+                    return 1;
+                }
+                return 0;
+            }
+            else if (feature === 'WEBGL_RENDER_FLOAT32_ENABLED') {
+                return isRenderToFloatTextureEnabled(this.get('WEBGL_VERSION'));
+            }
+            else if (feature === 'WEBGL_DOWNLOAD_FLOAT_ENABLED') {
+                return isDownloadFloatTextureEnabled(this.get('WEBGL_VERSION'));
+            }
+            else if (feature === 'WEBGL_FENCE_API_ENABLED') {
+                return isWebGLFenceEnabled(this.get('WEBGL_VERSION'));
+            }
+            else if (feature === 'WEBGL_SIZE_UPLOAD_UNIFORM') {
+                var useUniforms = this.get('WEBGL_RENDER_FLOAT32_ENABLED');
+                return useUniforms ? 4 : 0;
+            }
+            else if (feature === 'TEST_EPSILON') {
+                return this.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 :
+                    TEST_EPSILON_FLOAT16;
+            }
+            else if (feature === 'EPSILON') {
+                return this.backend.floatPrecision() === 32 ? EPSILON_FLOAT32 :
+                    EPSILON_FLOAT16;
+            }
+            else if (feature === 'PROD') {
+                return false;
+            }
+            else if (feature === 'TENSORLIKE_CHECK_SHAPE_CONSISTENCY') {
+                return !this.get('PROD');
+            }
+            throw new Error("Unknown feature " + feature + ".");
+        };
+        Environment.prototype.setFeatures = function (features) {
+            this.features = Object.assign({}, features);
+        };
+        Environment.prototype.reset = function () {
+            this.features = getFeaturesFromURL();
+            if (this.globalEngine != null) {
+                this.globalEngine = null;
+            }
+        };
+        Object.defineProperty(Environment.prototype, "backend", {
+            get: function () {
+                return this.engine.backend;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Environment.prototype.findBackend = function (name) {
+            if (!(name in this.registry)) {
+                return null;
+            }
+            return this.registry[name].backend;
+        };
+        Environment.prototype.registerBackend = function (name, factory, priority, setTensorTrackerFn) {
+            var _this = this;
+            if (priority === void 0) { priority = 1; }
+            if (name in this.registry) {
+                console.warn(name + " backend was already registered. Reusing existing backend");
+                if (setTensorTrackerFn != null) {
+                    setTensorTrackerFn(function () { return _this.engine; });
+                }
+                return false;
+            }
+            try {
+                var backend = factory();
+                backend.setDataMover({ moveData: function (dataId) { return _this.engine.moveData(dataId); } });
+                this.registry[name] = { backend: backend, priority: priority };
+                return true;
+            }
+            catch (err) {
+                console.warn("Registration of backend " + name + " failed");
+                console.warn(err.stack || err.message);
+                return false;
+            }
+        };
+        Environment.prototype.removeBackend = function (name) {
+            if (!(name in this.registry)) {
+                throw new Error(name + " backend not found in registry");
+            }
+            this.registry[name].backend.dispose();
+            delete this.registry[name];
+        };
+        Object.defineProperty(Environment.prototype, "engine", {
+            get: function () {
+                this.initEngine();
+                return this.globalEngine;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Environment.prototype.initEngine = function () {
+            var _this = this;
+            if (this.globalEngine == null) {
+                this.backendName = this.get('BACKEND');
+                var backend = this.findBackend(this.backendName);
+                this.globalEngine =
+                    new Engine(backend, false, function () { return _this.get('DEBUG'); });
+            }
+        };
+        return Environment;
+    }());
+    function getGlobalNamespace() {
+        var ns;
+        if (typeof (window) !== 'undefined') {
+            ns = window;
+        }
+        else if (typeof (process) !== 'undefined') {
+            ns = process;
+        }
+        else {
+            throw new Error('Could not find a global object');
+        }
+        return ns;
+    }
+    function getOrMakeEnvironment() {
+        var ns = getGlobalNamespace();
+        if (ns.ENV == null) {
+            ns.ENV = new Environment(getFeaturesFromURL());
+            setTensorTracker(function () { return ns.ENV.engine; });
+        }
+        return ns.ENV;
+    }
+    var ENV = getOrMakeEnvironment();
+
+    var environment = /*#__PURE__*/Object.freeze({
+        EPSILON_FLOAT16: EPSILON_FLOAT16,
+        EPSILON_FLOAT32: EPSILON_FLOAT32,
+        Environment: Environment,
+        ENV: ENV
+    });
+
+    function grad(f) {
+        assert(isFunction(f), 'The f passed in grad(f) must be a function');
+        return function (x, dy) {
+            assert(x instanceof Tensor, 'The x passed in grad(f)(x) must be a tensor');
+            assert(dy == null || dy instanceof Tensor, 'The dy passed in grad(f)(x, dy) must be a tensor');
+            return ENV.engine.tidy(function () {
+                var _a = ENV.engine.gradients(function () { return f(x); }, [x], dy), value = _a.value, grads = _a.grads;
+                if (dy != null) {
+                    assertShapesMatch(value.shape, dy.shape, 'The shape of dy passed in grad(f)(x, dy) must match the shape ' +
+                        'returned by f(x)');
+                }
+                checkGrads(grads);
+                return grads[0];
+            });
+        };
+    }
+    function grads(f) {
+        assert(isFunction(f), 'The f passed in grads(f) must be a function');
+        return function (args, dy) {
+            assert(Array.isArray(args) && args.every(function (arg) { return arg instanceof Tensor; }), 'The args passed in grads(f)(args) must be an array of tensors');
+            assert(dy == null || dy instanceof Tensor, 'The dy passed in grads(f)(args, dy) must be a tensor');
+            return ENV.engine.tidy(function () {
+                var _a = ENV.engine.gradients(function () { return f.apply(void 0, args); }, args, dy), value = _a.value, grads = _a.grads;
+                if (dy != null) {
+                    assertShapesMatch(value.shape, dy.shape, 'The shape of dy passed in grads(f)([x1,...], dy) must ' +
+                        'match the shape returned by f([x1,...])');
+                }
+                checkGrads(grads);
+                return grads;
+            });
+        };
+    }
+    function valueAndGrad(f) {
+        assert(isFunction(f), 'The f passed in valueAndGrad(f) must be a function');
+        return function (x, dy) {
+            assert(x instanceof Tensor, 'The x passed in valueAndGrad(f)(x) must be a tensor');
+            assert(dy == null || dy instanceof Tensor, 'The dy passed in valueAndGrad(f)(x, dy) must be a tensor');
+            var _a = ENV.engine.gradients(function () { return f(x); }, [x], dy), grads = _a.grads, value = _a.value;
+            checkGrads(grads);
+            return { grad: grads[0], value: value };
+        };
+    }
+    function valueAndGrads(f) {
+        assert(isFunction(f), 'The f passed in valueAndGrads(f) must be a function');
+        return function (args, dy) {
+            assert(Array.isArray(args) && args.every(function (arg) { return arg instanceof Tensor; }), 'The args passed in valueAndGrads(f)(args) must be array of tensors');
+            assert(dy == null || dy instanceof Tensor, 'The dy passed in valueAndGrads(f)(args, dy) must be a tensor');
+            var res = ENV.engine.gradients(function () { return f.apply(void 0, args); }, args, dy);
+            if (dy != null) {
+                assertShapesMatch(res.value.shape, dy.shape, 'The shape of dy passed in valueAndGrads(f)([x1,...], dy) must ' +
+                    'match the shape returned by f([x1,...])');
+            }
+            checkGrads(res.grads);
+            return res;
+        };
+    }
+    function variableGrads(f, varList) {
+        assert(isFunction(f), 'The f passed in variableGrads(f) must be a function');
+        assert(varList == null ||
+            Array.isArray(varList) && varList.every(function (v) { return v instanceof Variable; }), 'The varList passed in variableGrads(f, varList) must be an array ' +
+            'of variables');
+        if (varList == null) {
+            varList = [];
+            for (var varName in ENV.engine.registeredVariables) {
+                varList.push(ENV.engine.registeredVariables[varName]);
+            }
+        }
+        var originalVarCount = varList.length;
+        varList = varList.filter(function (variable$$1) { return variable$$1.trainable; });
+        assert(varList.length > 0, "variableGrads() expects at least one of the input variables to be " +
+            ("trainable, but none of the " + originalVarCount + " variables is ") +
+            "trainable.");
+        var allowNoGradients = true;
+        var _a = ENV.engine.gradients(f, varList, null, allowNoGradients), value = _a.value, grads = _a.grads;
+        assert(grads.some(function (g) { return g != null; }), 'Cannot find a connection between any variable and the result of the ' +
+            'loss function y=f(x). Please make sure the operations that use ' +
+            'variables are inside the function f passed to minimize().');
+        assert(value.rank === 0, "The f passed in variableGrads(f) must return a scalar, but it " +
+            ("returned a rank-" + value.rank + " tensor"));
+        var namedGrads = {};
+        varList.forEach(function (v, i) {
+            if (grads[i] != null) {
+                namedGrads[v.name] = grads[i];
+            }
+        });
+        return { value: value, grads: namedGrads };
+    }
+    function customGrad(f) {
+        return ENV.engine.customGrad(f);
+    }
+    function checkGrads(grads) {
+        var numNullGradients = grads.filter(function (g) { return g == null; }).length;
+        if (numNullGradients > 0) {
+            throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that\n    the f you passed encloses all operations that lead from x to y.");
+        }
+    }
+
+    var tidy = Environment.tidy;
+    var keep = Environment.keep;
+    var dispose = Environment.dispose;
+    var time = Environment.time;
+    var profile = Environment.profile;
+
+    function warn() {
+        var msg = [];
+        for (var _i = 0; _i < arguments.length; _i++) {
+            msg[_i] = arguments[_i];
+        }
+        if (!ENV.get('IS_TEST')) {
+            console.warn.apply(console, msg);
+        }
+    }
+
+    function getReshaped(inputShape, blockShape, prod, batchToSpace) {
+        if (batchToSpace === void 0) { batchToSpace = true; }
+        var reshaped = [];
+        if (batchToSpace) {
+            reshaped = reshaped.concat(blockShape.slice(0));
+            reshaped.push(inputShape[0] / prod);
+            reshaped = reshaped.concat(inputShape.slice(1));
+        }
+        else {
+            reshaped = reshaped.concat(inputShape[0]);
+            var spatialLength = blockShape.length;
+            for (var i = 0; i < spatialLength; ++i) {
+                reshaped =
+                    reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);
+            }
+            reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));
+        }
+        return reshaped;
+    }
+    function getPermuted(reshapedRank, blockShapeRank, batchToSpace) {
+        if (batchToSpace === void 0) { batchToSpace = true; }
+        var permuted = [];
+        if (batchToSpace) {
+            permuted.push(blockShapeRank);
+            for (var i = blockShapeRank + 1; i < reshapedRank; ++i) {
+                if (i <= 2 * blockShapeRank) {
+                    permuted.push(i);
+                    permuted.push(i - (blockShapeRank + 1));
+                }
+                else {
+                    permuted.push(i);
+                }
+            }
+        }
+        else {
+            var permutedBeforeBatch = [];
+            var permutedAfterBatch = [];
+            for (var i = 1; i < reshapedRank; ++i) {
+                if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {
+                    permutedAfterBatch.push(i);
+                }
+                else {
+                    permutedBeforeBatch.push(i);
+                }
+            }
+            permuted.push.apply(permuted, permutedBeforeBatch);
+            permuted.push(0);
+            permuted.push.apply(permuted, permutedAfterBatch);
+        }
+        return permuted;
+    }
+    function getReshapedPermuted(inputShape, blockShape, prod, batchToSpace) {
+        if (batchToSpace === void 0) { batchToSpace = true; }
+        var reshapedPermuted = [];
+        if (batchToSpace) {
+            reshapedPermuted.push(inputShape[0] / prod);
+        }
+        else {
+            reshapedPermuted.push(inputShape[0] * prod);
+        }
+        for (var i = 1; i < inputShape.length; ++i) {
+            if (i <= blockShape.length) {
+                if (batchToSpace) {
+                    reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);
+                }
+                else {
+                    reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);
+                }
+            }
+            else {
+                reshapedPermuted.push(inputShape[i]);
+            }
+        }
+        return reshapedPermuted;
+    }
+    function getSliceBeginCoords(crops, blockShape) {
+        var sliceBeginCoords = [0];
+        for (var i = 0; i < blockShape; ++i) {
+            sliceBeginCoords.push(crops[i][0]);
+        }
+        return sliceBeginCoords;
+    }
+    function getSliceSize(uncroppedShape, crops, blockShape) {
+        var sliceSize = uncroppedShape.slice(0, 1);
+        for (var i = 0; i < blockShape; ++i) {
+            sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);
+        }
+        return sliceSize;
+    }
+
+    function axesAreInnerMostDims(axes, rank) {
+        for (var i = 0; i < axes.length; ++i) {
+            if (axes[axes.length - i - 1] !== rank - 1 - i) {
+                return false;
+            }
+        }
+        return true;
+    }
+    function combineLocations(outputLoc, reduceLoc, axes) {
+        var rank = outputLoc.length + reduceLoc.length;
+        var loc = [];
+        var outIdx = 0;
+        var reduceIdx = 0;
+        for (var dim = 0; dim < rank; dim++) {
+            if (axes.indexOf(dim) === -1) {
+                loc.push(outputLoc[outIdx++]);
+            }
+            else {
+                loc.push(reduceLoc[reduceIdx++]);
+            }
+        }
+        return loc;
+    }
+    function computeOutAndReduceShapes(aShape, axes) {
+        var outShape = [];
+        var rank = aShape.length;
+        for (var dim = 0; dim < rank; dim++) {
+            if (axes.indexOf(dim) === -1) {
+                outShape.push(aShape[dim]);
+            }
+        }
+        var reduceShape = axes.map(function (dim) { return aShape[dim]; });
+        return [outShape, reduceShape];
+    }
+    function expandShapeToKeepDim(shape, axes) {
+        var reduceSubShape = axes.map(function (x) { return 1; });
+        return combineLocations(shape, reduceSubShape, axes);
+    }
+    function parseAxisParam(axis, shape) {
+        var rank = shape.length;
+        axis = axis == null ? shape.map(function (s, i) { return i; }) : [].concat(axis);
+        assert(axis.every(function (ax) { return ax >= -rank && ax < rank; }), "All values in axis param must be in range [-" + rank + ", " + rank + ") but " +
+            ("got axis " + axis));
+        assert(axis.every(function (ax) { return isInt(ax); }), "All values in axis param must be integers but " +
+            ("got axis " + axis));
+        return axis.map(function (a) { return a < 0 ? rank + a : a; });
+    }
+    function assertAxesAreInnerMostDims(msg, axes, rank) {
+        assert(axesAreInnerMostDims(axes, rank), msg + " supports only inner-most axes for now. " +
+            ("Got axes " + axes + " and rank-" + rank + " input."));
+    }
+    function getAxesPermutation(axes, rank) {
+        if (axesAreInnerMostDims(axes, rank)) {
+            return null;
+        }
+        var result = [];
+        for (var i = 0; i < rank; ++i) {
+            if (axes.indexOf(i) === -1) {
+                result.push(i);
+            }
+        }
+        axes.forEach(function (axis) { return result.push(axis); });
+        return result;
+    }
+    function getUndoAxesPermutation(axes) {
+        return axes.map(function (axis, i) { return [i, axis]; })
+            .sort(function (a, b) { return a[1] - b[1]; })
+            .map(function (x) { return x[0]; });
+    }
+    function getInnerMostAxes(numAxes, rank) {
+        var res = [];
+        for (var i = rank - numAxes; i < rank; ++i) {
+            res.push(i);
+        }
+        return res;
+    }
+
+    function assertParamsConsistent(shapes, axis) {
+        var rank = shapes[0].length;
+        shapes.forEach(function (shape, i) {
+            assert(shape.length === rank, "Error in concat" + rank + "D: rank of tensors[" + i + "] must be the same " +
+                ("as the rank of the rest (" + rank + ")"));
+        });
+        assert(axis >= 0 && axis < rank, "Error in concat" + rank + "D: axis must be between 0 and " + (rank - 1) + ".");
+        var firstShape = shapes[0];
+        shapes.forEach(function (shape, i) {
+            for (var r = 0; r < rank; r++) {
+                assert((r === axis) || (shape[r] === firstShape[r]), "Error in concat" + rank + "D: Shape of tensors[" + i + "] (" + shape + ") " +
+                    ("does not match the shape of the rest (" + firstShape + ") ") +
+                    ("along the non-concatenated axis " + i + "."));
+            }
+        });
+    }
+    function computeOutShape(shapes, axis) {
+        var outputShape = shapes[0].slice();
+        for (var i = 1; i < shapes.length; i++) {
+            outputShape[axis] += shapes[i][axis];
+        }
+        return outputShape;
+    }
+
+    function prepareAndValidate(tensor, indices) {
+        if (tensor.rank < 1) {
+            throw new Error('tf.gatherND() expects the input to be rank 1 or higher,' +
+                (" but the rank was " + tensor.rank + "."));
+        }
+        if (indices.rank < 1) {
+            throw new Error('tf.gatherND() expects the indices to be rank 1 or higher,' +
+                (" but the rank was " + indices.rank + "."));
+        }
+        if (indices.dtype !== 'int32') {
+            throw new Error('tf.gatherND() expects the indices to be int32 type,' +
+                (" but the dtype was " + indices.dtype + "."));
+        }
+        if (indices.shape[indices.rank - 1] > tensor.rank) {
+            throw new Error('index innermost dimension length must be <= tensor rank; saw: ' +
+                (indices.shape[indices.rank - 1] + " vs. " + tensor.rank));
+        }
+        if (tensor.size === 0) {
+            throw new Error('Requested more than 0 entries, but input is empty.' +
+                (" Input shape: " + tensor.shape + "."));
+        }
+        var indicesShape = indices.shape;
+        var sliceRank = indicesShape[indicesShape.length - 1];
+        var nResult = 1;
+        for (var i = 0; i < indicesShape.length - 1; ++i) {
+            nResult *= indicesShape[i];
+        }
+        var inputShape = tensor.shape;
+        var resultShape = indicesShape.slice();
+        resultShape.pop();
+        var sliceSize = 1;
+        for (var i = sliceRank; i < tensor.rank; ++i) {
+            sliceSize *= inputShape[i];
+            resultShape.push(inputShape[i]);
+        }
+        var strides = computeStrides(tensor.shape).map(function (stride) { return stride / sliceSize; }).concat([1]).slice(0, sliceRank);
+        return [resultShape, nResult, sliceSize, strides];
+    }
+
+    var PARALLELIZE_THRESHOLD = 30;
+    function computeOptimalWindowSize(inSize) {
+        if (inSize <= PARALLELIZE_THRESHOLD) {
+            return inSize;
+        }
+        return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
+    }
+
+    function validateUpdateShape(shape, indices, updates) {
+        var sliceDim = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;
+        var batchDim = (indices.rank > 1) ? indices.rank - 1 : 1;
+        var shapeError = 'Must have updates.shape = indices.shape[:batchDim] + ' +
+            ("shape[sliceDim:], got updates.shape: " + updates.shape) +
+            (", indices.shape: " + indices.shape + ", shape: " + shape) +
+            (", sliceDim: " + sliceDim + ", and batchDim: " + batchDim + ".");
+        if (updates.rank < batchDim) {
+            throw new Error(shapeError + (" update.rank < " + batchDim + ". "));
+        }
+        if (shape.length < sliceDim + (updates.rank - batchDim)) {
+            throw new Error(shapeError +
+                (" Output shape length < " + (sliceDim + (updates.rank - batchDim))));
+        }
+        if (updates.rank !== batchDim + shape.length - sliceDim) {
+            throw new Error(shapeError + (" update.rank != " + (batchDim + shape.length - sliceDim)));
+        }
+        for (var d = 0; d < batchDim; ++d) {
+            if (updates.shape[d] !== indices.shape[d]) {
+                throw new Error(shapeError +
+                    (" updates.shape[" + d + "] (" + updates.shape[d] + ") != indices.shape[" + d + "] (" + indices.shape[d] + ")."));
+            }
+        }
+        for (var d = 0; d < updates.rank - batchDim; ++d) {
+            if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {
+                throw new Error(shapeError +
+                    (" updates.shape[" + (d + batchDim) + "] (" + updates.shape[d + batchDim] + ") != shape[" + (d + batchDim) + "] (" + shape[d + batchDim] + ")"));
+            }
+        }
+    }
+    function validateInput(updates, indices, shape) {
+        if (indices.rank < 1) {
+            throw new Error('tf.scatterND() expects the indices to be rank 1 or higher,' +
+                (" but the rank was " + indices.rank + "."));
+        }
+        if (updates.rank < 1) {
+            throw new Error('tf.scatterND() expects the updates to be rank 1 or higher,' +
+                (" but the rank was " + updates.rank + "."));
+        }
+        if (indices.dtype !== 'int32') {
+            throw new Error("The dtype of 'indices' should be int32, but got dtype: " + indices.dtype);
+        }
+        if (shape.length < 1) {
+            throw new Error("Output rank must be greater or equal to 1, but got shape: " + shape);
+        }
+        if (shape.length === 0) {
+            if (indices.size === 0) {
+                throw new Error("Indices specified for empty output. indices shape: " + indices.shape);
+            }
+            if (updates.size === 0) {
+                throw new Error("Updates specified for empty output. updates shape: " + updates.shape);
+            }
+        }
+        validateUpdateShape(shape, indices, updates);
+    }
+    function calculateShapes(updates, indices, shape) {
+        var sliceRank = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;
+        var totalNd = shape.length;
+        var sliceSize = 1;
+        for (var i = sliceRank; i < totalNd; ++i) {
+            sliceSize *= shape[i];
+        }
+        var safeSliceDim = (sliceRank < 1) ? 1 : sliceRank;
+        var numUpdates = indices.size / safeSliceDim;
+        var outputStrides = computeStrides(shape).concat([1]);
+        var strides = outputStrides.slice(outputStrides.length - sliceRank, outputStrides.length);
+        var outputSize = sizeFromShape(shape);
+        return { sliceRank: sliceRank, numUpdates: numUpdates, sliceSize: sliceSize, strides: strides, outputSize: outputSize };
+    }
+
+    function segOpComputeOptimalWindowSize(inSize, numSegments) {
+        var done = false;
+        var res;
+        if (inSize <= PARALLELIZE_THRESHOLD) {
+            res = inSize;
+            done = true;
+        }
+        else {
+            res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));
+        }
+        while (!done) {
+            if (res > numSegments || res === inSize) {
+                done = true;
+                break;
+            }
+            else {
+                res = nearestDivisor(inSize, res + 1);
+            }
+        }
+        return res;
+    }
+    function computeOutShape$1(aShape, axis, numSegments) {
+        var outShape = [];
+        var rank = aShape.length;
+        for (var dim = 0; dim < rank; dim++) {
+            if (dim !== axis) {
+                outShape.push(aShape[dim]);
+            }
+            else {
+                outShape.push(numSegments);
+            }
+        }
+        return outShape;
+    }
+
+    function assertParamsValid(input, begin, size) {
+        assert(input.rank === begin.length, "Error in slice" + input.rank + "D: Length of begin " + begin + " must " +
+            ("match the rank of the array (" + input.rank + ")."));
+        assert(input.rank === size.length, "Error in slice" + input.rank + "D: Length of size " + size + " must " +
+            ("match the rank of the array (" + input.rank + ")."));
+        for (var i = 0; i < input.rank; ++i) {
+            assert(begin[i] + size[i] <= input.shape[i], "Error in slice" + input.rank + "D: begin[" + i + "] + size[" + i + "] " +
+                ("(" + (begin[i] + size[i]) + ") would overflow input.shape[" + i + "] (" + input.shape[i] + ")"));
+        }
+    }
+    function getStridedSlicedInfo(shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {
+        if (beginMask === void 0) { beginMask = 0; }
+        if (endMask === void 0) { endMask = 0; }
+        if (ellipsisMask === void 0) { ellipsisMask = 0; }
+        if (newAxisMask === void 0) { newAxisMask = 0; }
+        if (shrinkAxisMask === void 0) { shrinkAxisMask = 0; }
+        if (ellipsisMask !== 0) {
+            throw new Error('ellipsis mask is not yet supported');
+        }
+        if (newAxisMask !== 0) {
+            throw new Error('new axis mask is not yet supported');
+        }
+        var startIndex = [];
+        var endIndex = [];
+        var shrinkAxis = [];
+        for (var i = 0; i < shape.length; i++) {
+            startIndex[i] = startForAxis(beginMask, begin, strides, shape, i);
+            endIndex[i] = stopForAxis(endMask, end, strides, shape, i);
+            if (shrinkAxisMask & 1 << i) {
+                endIndex[i] = startIndex[i] + 1;
+                shrinkAxis.push(i);
+            }
+        }
+        var size = new Array(shape.length).fill(0);
+        size = size.map(function (d, i) {
+            var count = 0;
+            for (var start = startIndex[i]; !(strides[i] > 0 ? start >= endIndex[i] : start <= endIndex[i]); start += strides[i]) {
+                count += 1;
+            }
+            return count;
+        });
+        return [startIndex, size, shrinkAxis];
+    }
+    function startForAxis(beginMask, startIndices, strides, inputShape, axis) {
+        var start = startIndices[axis];
+        if (beginMask & 1 << axis) {
+            if (strides[axis] > 0) {
+                start = Number.MIN_SAFE_INTEGER;
+            }
+            else {
+                start = Number.MAX_SAFE_INTEGER;
+            }
+        }
+        var axisSize = inputShape[axis];
+        if (start < 0) {
+            start += axisSize;
+        }
+        start = clamp(0, start, axisSize - 1);
+        return start;
+    }
+    function stopForAxis(endMask, stopIndices, strides, inputShape, axis) {
+        var stop = stopIndices[axis];
+        if (endMask & (1 << axis)) {
+            if (strides[axis] > 0) {
+                stop = Number.MAX_SAFE_INTEGER;
+            }
+            else {
+                stop = Number.MIN_SAFE_INTEGER;
+            }
+        }
+        var axisSize = inputShape[axis];
+        if (stop < 0) {
+            stop += axisSize;
+        }
+        if (strides[axis] > 0) {
+            stop = clamp(0, stop, axisSize);
+        }
+        else {
+            stop = clamp(-1, stop, axisSize - 1);
+        }
+        return stop;
+    }
+
+    function inferShape(val) {
+        var firstElem = val;
+        if (isTypedArray(val)) {
+            return [val.length];
+        }
+        if (!Array.isArray(val)) {
+            return [];
+        }
+        var shape = [];
+        while (firstElem instanceof Array || isTypedArray(firstElem)) {
+            shape.push(firstElem.length);
+            firstElem = firstElem[0];
+        }
+        if (val instanceof Array && ENV.get('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')) {
+            deepAssertShapeConsistency(val, shape, []);
+        }
+        return shape;
+    }
+    function deepAssertShapeConsistency(val, shape, indices) {
+        indices = indices || [];
+        if (!(val instanceof Array) && !isTypedArray(val)) {
+            assert(shape.length === 0, function () { return "Element arr[" + indices.join('][') + "] is a primitive, " +
+                ("but should be an array/TypedArray of " + shape[0] + " elements"); });
+            return;
+        }
+        assert(shape.length > 0, function () { return "Element arr[" + indices.join('][') + "] should be a primitive, " +
+            ("but is an array of " + val.length + " elements"); });
+        assert(val.length === shape[0], function () { return "Element arr[" + indices.join('][') + "] should have " + shape[0] + " " +
+            ("elements, but has " + val.length + " elements"); });
+        var subShape = shape.slice(1);
+        for (var i = 0; i < val.length; ++i) {
+            deepAssertShapeConsistency(val[i], subShape, indices.concat(i));
+        }
+    }
+    function assertDtype(expectedDtype, actualDType, argName, functionName) {
+        if (expectedDtype == null) {
+            return;
+        }
+        if (expectedDtype !== 'numeric' && expectedDtype !== actualDType ||
+            expectedDtype === 'numeric' && actualDType === 'string') {
+            throw new Error("Argument '" + argName + "' passed to '" + functionName + "' must " +
+                ("be " + expectedDtype + " tensor, but got " + actualDType + " tensor"));
+        }
+    }
+    function convertToTensor(x, argName, functionName, parseAsDtype) {
+        if (parseAsDtype === void 0) { parseAsDtype = 'numeric'; }
+        if (x instanceof Tensor) {
+            assertDtype(parseAsDtype, x.dtype, argName, functionName);
+            return x;
+        }
+        var inferredDtype = inferDtype(x);
+        if (inferredDtype !== 'string' &&
+            ['bool', 'int32', 'float32'].indexOf(parseAsDtype) >= 0) {
+            inferredDtype = parseAsDtype;
+        }
+        assertDtype(parseAsDtype, inferredDtype, argName, functionName);
+        if (!isTypedArray(x) && !Array.isArray(x) && typeof x !== 'number' &&
+            typeof x !== 'boolean' && typeof x !== 'string') {
+            throw new Error("Argument '" + argName + "' passed to '" + functionName + "' must be a " +
+                ("Tensor or TensorLike, but got '" + x.constructor.name + "'"));
+        }
+        var inferredShape = inferShape(x);
+        if (!isTypedArray(x) && !Array.isArray(x)) {
+            x = [x];
+        }
+        var values = inferredDtype !== 'string' ?
+            toTypedArray(x, inferredDtype, ENV.get('DEBUG')) :
+            flatten(x);
+        return Tensor.make(inferredShape, { values: values }, inferredDtype);
+    }
+    function convertToTensorArray(arg, argName, functionName) {
+        if (!Array.isArray(arg)) {
+            throw new Error("Argument " + argName + " passed to " + functionName + " must be a " +
+                '`Tensor[]` or `TensorLike[]`');
+        }
+        var tensors = arg;
+        return tensors.map(function (t, i) { return convertToTensor(t, argName + "[" + i + "]", functionName); });
+    }
+
+    function op(f) {
+        var keys = Object.keys(f);
+        if (keys.length !== 1) {
+            throw new Error("Please provide an object with a single key " +
+                "(operation name) mapping to a function. Got an object with " +
+                (keys.length + " keys."));
+        }
+        var opName = keys[0];
+        var fn = f[opName];
+        if (opName.endsWith('_')) {
+            opName = opName.substring(0, opName.length - 1);
+        }
+        var f2 = function () {
+            var args = [];
+            for (var _i = 0; _i < arguments.length; _i++) {
+                args[_i] = arguments[_i];
+            }
+            ENV.engine.startScope(opName);
+            try {
+                var result = fn.apply(void 0, args);
+                if (result instanceof Promise) {
+                    console.error('Cannot return a Promise inside of tidy.');
+                }
+                ENV.engine.endScope(result);
+                return result;
+            }
+            catch (ex) {
+                ENV.engine.endScope(null);
+                throw ex;
+            }
+        };
+        Object.defineProperty(f2, 'name', { value: opName, configurable: true });
+        return f2;
+    }
+
+    function softmax_(logits, dim) {
+        if (dim === void 0) { dim = -1; }
+        var $logits = convertToTensor(logits, 'logits', 'softmax');
+        if (dim === -1) {
+            dim = $logits.rank - 1;
+        }
+        if (dim !== $logits.rank - 1) {
+            throw Error('Softmax along a non-last dimension is not yet supported. ' +
+                ("Logits was rank " + $logits.rank + " and dim was " + dim));
+        }
+        var customOp = customGrad(function (logits) {
+            var keepDims = true;
+            var lse = logits.logSumExp([dim], keepDims);
+            var logResult = logits.toFloat().sub(lse);
+            var y = logResult.exp();
+            var gradFunc = function (dy) {
+                var dyTimesY = dy.mul(y);
+                var keepDims = true;
+                return dyTimesY.sub(dyTimesY.sum([dim], keepDims).mul(y));
+            };
+            return { value: y, gradFunc: gradFunc };
+        });
+        return customOp($logits);
+    }
+    function logSoftmax_(logits, axis) {
+        if (axis === void 0) { axis = -1; }
+        var $logits = convertToTensor(logits, 'logits', 'logSoftmax');
+        if (axis === -1) {
+            axis = $logits.rank - 1;
+        }
+        if (axis !== $logits.rank - 1) {
+            throw Error('Log Softmax along a non-last dimension is not yet supported. ' +
+                ("Logits was rank " + $logits.rank + " and axis was " + axis));
+        }
+        var customOp = customGrad(function (logits) {
+            var keepDims = true;
+            var xMax = logits.max(axis, true);
+            var shifted = logits.sub(xMax);
+            var value = shifted.toFloat().sub(shifted.exp().sum(axis, keepDims).log());
+            var gradFunc = function (dy) {
+                var softmax = value.exp();
+                return dy.sub(dy.sum(axis, keepDims).mul(softmax));
+            };
+            return { value: value, gradFunc: gradFunc };
+        });
+        return customOp($logits);
+    }
+    var softmax = op({ softmax_: softmax_ });
+    var logSoftmax = op({ logSoftmax_: logSoftmax_ });
+
+    function complex_(real, imag) {
+        var $real = convertToTensor(real, 'real', 'complex');
+        var $imag = convertToTensor(imag, 'imag', 'complex');
+        assertShapesMatch($real.shape, $imag.shape, "real and imag shapes, " + $real.shape + " and " + $imag.shape + ", " +
+            "must match in call to tf.complex().");
+        return ENV.engine.runKernel(function (backend) { return backend.complex($real, $imag); }, { $real: $real, $imag: $imag });
+    }
+    function real_(input) {
+        var $input = convertToTensor(input, 'input', 'real');
+        return ENV.engine.runKernel(function (backend) { return backend.real($input); }, { $input: $input });
+    }
+    function imag_(input) {
+        var $input = convertToTensor(input, 'input', 'imag');
+        return ENV.engine.runKernel(function (backend) { return backend.imag($input); }, { $input: $input });
+    }
+    var complex = op({ complex_: complex_ });
+    var real = op({ real_: real_ });
+    var imag = op({ imag_: imag_ });
+
+    function tensor(values, shape, dtype) {
+        if (dtype == null) {
+            dtype = inferDtype(values);
+        }
+        if (dtype === 'complex64') {
+            throw new Error("Cannot construct a complex64 tensor directly. " +
+                "Please use tf.complex(real, imag).");
+        }
+        if (!isTypedArray(values) && !Array.isArray(values) &&
+            typeof values !== 'number' && typeof values !== 'boolean' &&
+            typeof values !== 'string') {
+            throw new Error('values passed to tensor(values) must be a number/boolean/string or ' +
+                'an array of numbers/booleans/strings, or a TypedArray');
+        }
+        var inferredShape = inferShape(values);
+        if (shape != null) {
+            var providedSize_1 = sizeFromShape(shape);
+            var inferredSize_1 = sizeFromShape(inferredShape);
+            assert(providedSize_1 === inferredSize_1, function () {
+                return "Based on the provided shape, [" + shape + "], the tensor should have " +
+                    (providedSize_1 + " values but has " + inferredSize_1);
+            });
+            for (var i = 0; i < inferredShape.length; ++i) {
+                var inferred = inferredShape[i];
+                var flatDimsDontMatch = i === inferredShape.length - 1 ?
+                    inferred !== sizeFromShape(shape.slice(i)) :
+                    true;
+                assert(inferredShape[i] === shape[i] || !flatDimsDontMatch, function () { return "Error creating a new Tensor. Inferred shape " +
+                    ("(" + inferredShape + ") does not match the provided ") +
+                    ("shape (" + shape + "). "); });
+            }
+        }
+        if (!isTypedArray(values) && !Array.isArray(values)) {
+            values = [values];
+        }
+        shape = shape || inferredShape;
+        values = dtype !== 'string' ? toTypedArray(values, dtype, ENV.get('DEBUG')) :
+            flatten(values);
+        return Tensor.make(shape, { values: values }, dtype);
+    }
+    function scalar(value, dtype) {
+        if ((isTypedArray(value) || Array.isArray(value)) && dtype !== 'complex64') {
+            throw new Error('Error creating a new Scalar: value must be a primitive ' +
+                '(number|boolean|string)');
+        }
+        return tensor(value, [], dtype);
+    }
+    function tensor1d(values, dtype) {
+        assertNonNull(values);
+        var inferredShape = inferShape(values);
+        if (inferredShape.length !== 1) {
+            throw new Error('tensor1d() requires values to be a flat/TypedArray');
+        }
+        return tensor(values, inferredShape, dtype);
+    }
+    function tensor2d(values, shape, dtype) {
+        assertNonNull(values);
+        if (shape != null && shape.length !== 2) {
+            throw new Error('tensor2d() requires shape to have two numbers');
+        }
+        var inferredShape = inferShape(values);
+        if (inferredShape.length !== 2 && inferredShape.length !== 1) {
+            throw new Error('tensor2d() requires values to be number[][] or flat/TypedArray');
+        }
+        if (inferredShape.length === 1 && shape == null) {
+            throw new Error('tensor2d() requires shape to be provided when `values` ' +
+                'are a flat/TypedArray');
+        }
+        shape = shape || inferredShape;
+        return tensor(values, shape, dtype);
+    }
+    function tensor3d(values, shape, dtype) {
+        assertNonNull(values);
+        if (shape != null && shape.length !== 3) {
+            throw new Error('tensor3d() requires shape to have three numbers');
+        }
+        var inferredShape = inferShape(values);
+        if (inferredShape.length !== 3 && inferredShape.length !== 1) {
+            throw new Error('tensor3d() requires values to be number[][][] or flat/TypedArray');
+        }
+        if (inferredShape.length === 1 && shape == null) {
+            throw new Error('tensor3d() requires shape to be provided when `values` ' +
+                'are a flat array');
+        }
+        shape = shape || inferredShape;
+        return tensor(values, shape, dtype);
+    }
+    function tensor4d(values, shape, dtype) {
+        assertNonNull(values);
+        if (shape != null && shape.length !== 4) {
+            throw new Error('tensor4d() requires shape to have four numbers');
+        }
+        var inferredShape = inferShape(values);
+        if (inferredShape.length !== 4 && inferredShape.length !== 1) {
+            throw new Error('tensor4d() requires values to be number[][][][] or flat/TypedArray');
+        }
+        if (inferredShape.length === 1 && shape == null) {
+            throw new Error('tensor4d() requires shape to be provided when `values` ' +
+                'are a flat array');
+        }
+        shape = shape || inferredShape;
+        return tensor(values, shape, dtype);
+    }
+    function tensor5d(values, shape, dtype) {
+        assertNonNull(values);
+        if (shape != null && shape.length !== 5) {
+            throw new Error('tensor5d() requires shape to have five numbers');
+        }
+        var inferredShape = inferShape(values);
+        if (inferredShape.length !== 5 && inferredShape.length !== 1) {
+            throw new Error('tensor5d() requires values to be ' +
+                'number[][][][][] or flat/TypedArray');
+        }
+        if (inferredShape.length === 1 && shape == null) {
+            throw new Error('tensor5d() requires shape to be provided when `values` ' +
+                'are a flat array');
+        }
+        shape = shape || inferredShape;
+        return tensor(values, shape, dtype);
+    }
+    function tensor6d(values, shape, dtype) {
+        assertNonNull(values);
+        if (shape != null && shape.length !== 6) {
+            throw new Error('tensor6d() requires shape to have six numbers');
+        }
+        var inferredShape = inferShape(values);
+        if (inferredShape.length !== 6 && inferredShape.length !== 1) {
+            throw new Error('tensor6d() requires values to be number[][][][] or flat/TypedArray');
+        }
+        if (inferredShape.length === 1 && shape == null) {
+            throw new Error('tensor6d() requires shape to be provided when `values` ' +
+                'are a flat array');
+        }
+        shape = shape ||
+            inferredShape;
+        return tensor(values, shape, dtype);
+    }
+    function ones$1(shape, dtype) {
+        if (dtype === void 0) { dtype = 'float32'; }
+        if (dtype === 'complex64') {
+            var real$$1 = ones$1(shape, 'float32');
+            var imag$$1 = ones$1(shape, 'float32');
+            return complex(real$$1, imag$$1);
+        }
+        var values = makeOnesTypedArray(sizeFromShape(shape), dtype);
+        return Tensor.make(shape, { values: values }, dtype);
+    }
+    function zeros(shape, dtype) {
+        if (dtype === void 0) { dtype = 'float32'; }
+        if (dtype === 'complex64') {
+            var real$$1 = zeros(shape, 'float32');
+            var imag$$1 = zeros(shape, 'float32');
+            return complex(real$$1, imag$$1);
+        }
+        var values = makeZerosTypedArray(sizeFromShape(shape), dtype);
+        return Tensor.make(shape, { values: values }, dtype);
+    }
+    function fill(shape, value, dtype) {
+        dtype = dtype || inferDtype(value);
+        var values = getArrayFromDType(dtype, sizeFromShape(shape));
+        values.fill(value);
+        return Tensor.make(shape, { values: values }, dtype);
+    }
+    function onesLike_(x) {
+        var $x = convertToTensor(x, 'x', 'onesLike');
+        return ones$1($x.shape, $x.dtype);
+    }
+    function zerosLike_(x) {
+        var $x = convertToTensor(x, 'x', 'zerosLike');
+        return zeros($x.shape, $x.dtype);
+    }
+    function linspace(start, stop, num) {
+        if (num === 0) {
+            throw new Error('Cannot request zero samples');
+        }
+        var step = (stop - start) / (num - 1);
+        var values = makeZerosTypedArray(num, 'float32');
+        values[0] = start;
+        for (var i = 1; i < values.length; i++) {
+            values[i] = values[i - 1] + step;
+        }
+        return tensor1d(values, 'float32');
+    }
+    function range(start, stop, step, dtype) {
+        if (step === void 0) { step = 1; }
+        if (dtype === void 0) { dtype = 'float32'; }
+        if (step === 0) {
+            throw new Error('Cannot have a step of zero');
+        }
+        var sameStartStop = start === stop;
+        var increasingRangeNegativeStep = start < stop && step < 0;
+        var decreasingRangePositiveStep = stop < start && step > 1;
+        if (sameStartStop || increasingRangeNegativeStep ||
+            decreasingRangePositiveStep) {
+            return zeros([0], dtype);
+        }
+        var numElements = Math.abs(Math.ceil((stop - start) / step));
+        var values = makeZerosTypedArray(numElements, dtype);
+        if (stop < start && step === 1) {
+            step = -1;
+        }
+        values[0] = start;
+        for (var i = 1; i < values.length; i++) {
+            values[i] = values[i - 1] + step;
+        }
+        return tensor1d(values, dtype);
+    }
+    var onesLike = op({ onesLike_: onesLike_ });
+    var zerosLike = op({ zerosLike_: zerosLike_ });
+
+    var DataStorage = (function () {
+        function DataStorage(dataMover) {
+            this.dataMover = dataMover;
+            this.data = new WeakMap();
+        }
+        DataStorage.prototype.get = function (dataId) {
+            if (!this.data.has(dataId)) {
+                this.dataMover.moveData(dataId);
+            }
+            return this.data.get(dataId);
+        };
+        DataStorage.prototype.set = function (dataId, value) {
+            this.data.set(dataId, value);
+        };
+        DataStorage.prototype.has = function (dataId) {
+            return this.data.has(dataId);
+        };
+        DataStorage.prototype.delete = function (dataId) {
+            return this.data.delete(dataId);
+        };
+        return DataStorage;
+    }());
+    var KernelBackend = (function () {
+        function KernelBackend() {
+        }
+        KernelBackend.prototype.time = function (f) {
+            throw new Error('Not yet implemented.');
+        };
+        KernelBackend.prototype.read = function (dataId) {
+            throw new Error('Not yet implemented.');
+        };
+        KernelBackend.prototype.readSync = function (dataId) {
+            throw new Error('Not yet implemented.');
+        };
+        KernelBackend.prototype.disposeData = function (dataId) {
+            throw new Error('Not yet implemented.');
+        };
+        KernelBackend.prototype.write = function (dataId, values) {
+            throw new Error('Not yet implemented.');
+        };
+        KernelBackend.prototype.fromPixels = function (pixels, numChannels) {
+            throw new Error('Not yet implemented.');
+        };
+        KernelBackend.prototype.register = function (dataId, shape, dtype) {
+            throw new Error('Not yet implemented.');
+        };
+        KernelBackend.prototype.memory = function () {
+            throw new Error('Not yet implemented.');
+        };
+        KernelBackend.prototype.floatPrecision = function () {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.batchMatMul = function (a, b, transposeA, transposeB) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.slice = function (x, begin, size) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.stridedSlice = function (x, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.reverse = function (a, axis) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.concat = function (tensors, axis) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.neg = function (a) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.add = function (a, b) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.addN = function (tensors) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.subtract = function (a, b) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.multiply = function (a, b) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.realDivide = function (a, b) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.floorDiv = function (a, b) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.sum = function (x, axes) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.prod = function (x, axes) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.unsortedSegmentSum = function (x, segmentIds, numSegments) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.argMin = function (x, axis) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.argMax = function (x, axis) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.equal = function (a, b) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.notEqual = function (a, b) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.less = function (a, b) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.lessEqual = function (a, b) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.greater = function (a, b) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.greaterEqual = function (a, b) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.logicalNot = function (a) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.logicalAnd = function (a, b) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.logicalOr = function (a, b) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.where = function (condition) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.select = function (condition, a, b) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.topk = function (x, k, sorted) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.min = function (x, axes) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.minimum = function (a, b) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.mod = function (a, b) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.max = function (x, axes) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.maximum = function (a, b) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.all = function (x, axes) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.any = function (x, axes) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.squaredDifference = function (a, b) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.ceil = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.floor = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.round = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.sign = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.pow = function (a, b) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.exp = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.expm1 = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.log = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.log1p = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.sqrt = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.rsqrt = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.square = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.reciprocal = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.relu = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.elu = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.eluDer = function (dy, y) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.selu = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.int = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.clip = function (x, min, max) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.abs = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.complexAbs = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.sigmoid = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.softplus = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.sin = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.cos = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.tan = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.asin = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.acos = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.atan = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.atan2 = function (a, b) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.sinh = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.cosh = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.tanh = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.asinh = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.acosh = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.atanh = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.erf = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.step = function (x, alpha) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.conv2d = function (x, filter, convInfo) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.conv2dDerInput = function (dy, filter, convInfo) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.conv2dDerFilter = function (x, dY, convInfo) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.depthwiseConv2D = function (input, filter, convInfo) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.depthwiseConv2DDerInput = function (dy, filter, convInfo) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.depthwiseConv2DDerFilter = function (x, dY, convInfo) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.conv3d = function (x, filter, convInfo) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.conv3dDerInput = function (dy, filter, convInfo) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.conv3dDerFilter = function (x, dY, convInfo) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.maxPool = function (x, convInfo) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.maxPoolBackprop = function (dy, x, y, convInfo) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.avgPool = function (x, convInfo) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.avgPoolBackprop = function (dy, x, convInfo) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.reshape = function (x, shape) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.cast = function (x, dtype) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.tile = function (x, reps) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.pad = function (x, paddings, constantValue) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.transpose = function (x, perm) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.gather = function (x, indices, axis) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.gatherND = function (x, indices) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.scatterND = function (indices, updates, shape) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.batchToSpaceND = function (x, blockShape, crops) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.spaceToBatchND = function (x, blockShape, paddings) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.resizeBilinear = function (x, newHeight, newWidth, alignCorners) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.resizeBilinearBackprop = function (dy, x, alignCorners) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.resizeNearestNeighbor = function (x, newHEight, newWidth, alignCorners) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.resizeNearestNeighborBackprop = function (dy, x, alignCorners) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.batchNormalization = function (x, mean, variance, varianceEpsilon, scale, offset) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.localResponseNormalization4D = function (x, radius, bias, alpha, beta) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.LRNGrad = function (dy, inputImage, outputImage, radius, bias, alpha, beta) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.multinomial = function (logits, normalized, numSamples, seed) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.oneHot = function (indices, depth, onValue, offValue) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.cumsum = function (x, axis, exclusive, reverse) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.nonMaxSuppression = function (boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.fft = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.ifft = function (x) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.complex = function (real, imag) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.real = function (input) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.imag = function (input) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.cropAndResize = function (image, boxes, boxIndex, cropSize, method, extrapolationValue) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.depthToSpace = function (x, blockSize, dataFormat) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.split = function (value, sizeSplits, axis) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.sparseToDense = function (sparseIndices, sparseValues, outputShape, defaultValue) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.setDataMover = function (dataMover) {
+            throw new Error('Not yet implemented');
+        };
+        KernelBackend.prototype.dispose = function () {
+            throw new Error('Not yet implemented');
+        };
+        return KernelBackend;
+    }());
+
+    function castTensor(x, dtype, backend) {
+        if (dtype === 'complex64') {
+            if (x.dtype === 'complex64') {
+                return x.clone();
+            }
+            var zerosTensor = zeros(x.shape);
+            var floatX = x.toFloat();
+            var result = backend.complex(floatX, zerosTensor);
+            zerosTensor.dispose();
+            floatX.dispose();
+            return result;
+        }
+        if (!hasEncodingLoss(x.dtype, dtype)) {
+            return Tensor.make(x.shape, { dataId: x.dataId }, dtype);
+        }
+        if (x.dtype === 'complex64') {
+            var real = backend.real(x);
+            var result = real.cast(dtype);
+            real.dispose();
+            return result;
+        }
+        if (dtype === 'int32') {
+            return backend.int(x);
+        }
+        else if (dtype === 'bool') {
+            var zero = scalar(0, x.dtype);
+            var result = backend.notEqual(x, zero);
+            zero.dispose();
+            return result;
+        }
+        else {
+            throw new Error("Error in Cast: unknown dtype argument (" + dtype + ")");
+        }
+    }
+    function reshapeTensor(x, shape) {
+        return Tensor.make(shape, { dataId: x.dataId }, x.dtype);
+    }
+
+    function mergeRealAndImagArrays(real, imag) {
+        if (real.length !== imag.length) {
+            throw new Error("Cannot merge real and imag arrays of different lengths. real:" +
+                (real.length + ", imag: " + imag.length + "."));
+        }
+        var result = new Float32Array(real.length * 2);
+        for (var i = 0; i < result.length; i += 2) {
+            result[i] = real[i / 2];
+            result[i + 1] = imag[i / 2];
+        }
+        return result;
+    }
+    function splitRealAndImagArrays(complex) {
+        var real = new Float32Array(complex.length / 2);
+        var imag = new Float32Array(complex.length / 2);
+        for (var i = 0; i < complex.length; i += 2) {
+            real[i / 2] = complex[i];
+            imag[i / 2] = complex[i + 1];
+        }
+        return { real: real, imag: imag };
+    }
+    function complexWithEvenIndex(complex) {
+        var len = Math.ceil(complex.length / 4);
+        var real = new Float32Array(len);
+        var imag = new Float32Array(len);
+        for (var i = 0; i < complex.length; i += 4) {
+            real[Math.floor(i / 4)] = complex[i];
+            imag[Math.floor(i / 4)] = complex[i + 1];
+        }
+        return { real: real, imag: imag };
+    }
+    function complexWithOddIndex(complex) {
+        var len = Math.floor(complex.length / 4);
+        var real = new Float32Array(len);
+        var imag = new Float32Array(len);
+        for (var i = 2; i < complex.length; i += 4) {
+            real[Math.floor(i / 4)] = complex[i];
+            imag[Math.floor(i / 4)] = complex[i + 1];
+        }
+        return { real: real, imag: imag };
+    }
+    function getComplexWithIndex(complex, index) {
+        var real = complex[index * 2];
+        var imag = complex[index * 2 + 1];
+        return { real: real, imag: imag };
+    }
+    function assignToTypedArray(data, real, imag, index) {
+        data[index * 2] = real;
+        data[index * 2 + 1] = imag;
+    }
+    function exponents(n, inverse) {
+        var real = new Float32Array(n / 2);
+        var imag = new Float32Array(n / 2);
+        for (var i = 0; i < Math.ceil(n / 2); i++) {
+            var x = (inverse ? 2 : -2) * Math.PI * (i / n);
+            real[i] = Math.cos(x);
+            imag[i] = Math.sin(x);
+        }
+        return { real: real, imag: imag };
+    }
+    function exponent(k, n, inverse) {
+        var x = (inverse ? 2 : -2) * Math.PI * (k / n);
+        var real = Math.cos(x);
+        var imag = Math.sin(x);
+        return { real: real, imag: imag };
+    }
+
+    function nonMaxSuppressionImpl(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+        var candidates = Array.from(scores)
+            .map(function (score, boxIndex) { return ({ score: score, boxIndex: boxIndex }); })
+            .filter(function (c) { return c.score > scoreThreshold; })
+            .sort(function (c1, c2) { return c2.score - c1.score; });
+        var selected = [];
+        for (var i = 0; i < candidates.length; i++) {
+            var _a = candidates[i], score = _a.score, boxIndex = _a.boxIndex;
+            if (score < scoreThreshold) {
+                break;
+            }
+            var ignoreCandidate = false;
+            for (var j = selected.length - 1; j >= 0; --j) {
+                var iou = intersectionOverUnion(boxes, boxIndex, selected[j]);
+                if (iou >= iouThreshold) {
+                    ignoreCandidate = true;
+                    break;
+                }
+            }
+            if (!ignoreCandidate) {
+                selected.push(boxIndex);
+                if (selected.length >= maxOutputSize) {
+                    break;
+                }
+            }
+        }
+        return tensor1d(selected, 'int32');
+    }
+    function intersectionOverUnion(boxes, i, j) {
+        var iCoord = boxes.subarray(i * 4, i * 4 + 4);
+        var jCoord = boxes.subarray(j * 4, j * 4 + 4);
+        var yminI = Math.min(iCoord[0], iCoord[2]);
+        var xminI = Math.min(iCoord[1], iCoord[3]);
+        var ymaxI = Math.max(iCoord[0], iCoord[2]);
+        var xmaxI = Math.max(iCoord[1], iCoord[3]);
+        var yminJ = Math.min(jCoord[0], jCoord[2]);
+        var xminJ = Math.min(jCoord[1], jCoord[3]);
+        var ymaxJ = Math.max(jCoord[0], jCoord[2]);
+        var xmaxJ = Math.max(jCoord[1], jCoord[3]);
+        var areaI = (ymaxI - yminI) * (xmaxI - xminI);
+        var areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
+        if (areaI <= 0 || areaJ <= 0) {
+            return 0.0;
+        }
+        var intersectionYmin = Math.max(yminI, yminJ);
+        var intersectionXmin = Math.max(xminI, xminJ);
+        var intersectionYmax = Math.min(ymaxI, ymaxJ);
+        var intersectionXmax = Math.min(xmaxI, xmaxJ);
+        var intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *
+            Math.max(intersectionXmax - intersectionXmin, 0.0);
+        return intersectionArea / (areaI + areaJ - intersectionArea);
+    }
+
+    function split(x, sizeSplits, axis) {
+        var begin = Array(x.rank).fill(0);
+        var size = x.shape.slice();
+        return sizeSplits.map(function (s) {
+            size[axis] = s;
+            var slice = x.slice(begin, size);
+            begin[axis] += s;
+            return slice;
+        });
+    }
+
+    function topkImpl(x, xShape, xDtype, k, sorted) {
+        var lastDim = xShape[xShape.length - 1];
+        var _a = [x.length / lastDim, lastDim], batch = _a[0], size = _a[1];
+        var allTopKVals = getTypedArrayFromDType(xDtype, batch * k);
+        var allTopKIndices = getTypedArrayFromDType('int32', batch * k);
+        for (var b = 0; b < batch; b++) {
+            var offset = b * size;
+            var vals = x.subarray(offset, offset + size);
+            var valAndInd = [];
+            for (var i = 0; i < vals.length; i++) {
+                valAndInd.push({ value: vals[i], index: i });
+            }
+            valAndInd.sort(function (a, b) { return b.value - a.value; });
+            var outOffset = b * k;
+            var topKVals = allTopKVals.subarray(outOffset, outOffset + k);
+            var topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);
+            for (var i = 0; i < k; i++) {
+                topKVals[i] = valAndInd[i].value;
+                topKIndices[i] = valAndInd[i].index;
+            }
+        }
+        var outputShape = xShape.slice();
+        outputShape[outputShape.length - 1] = k;
+        return [
+            tensor(allTopKVals, outputShape, xDtype),
+            tensor(allTopKIndices, outputShape, 'int32')
+        ];
+    }
+
+    var ArgMinMaxProgram = (function () {
+        function ArgMinMaxProgram(reduceInfo, op, firstPass) {
+            this.variableNames = ['A'];
+            var windowSize = reduceInfo.windowSize;
+            var batchSize = reduceInfo.batchSize;
+            var inSize = reduceInfo.inSize;
+            var outSize = Math.ceil(inSize / windowSize);
+            if (!firstPass) {
+                this.variableNames.push('bestIndicesA');
+            }
+            this.outputShape = [batchSize, outSize];
+            var compOp = (op === 'max') ? '>' : '<';
+            var indexSnippet = firstPass ?
+                'inOffset + i;' :
+                'round(getBestIndicesA(batch, inOffset + i));';
+            this.userCode = "\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * " + windowSize + ";\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < " + windowSize + "; i++) {\n          int inIdx = " + indexSnippet + ";\n          float candidate = getA(batch, inIdx);\n          if (candidate " + compOp + " bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    ";
+        }
+        return ArgMinMaxProgram;
+    }());
+
+    var AvgPool2DBackpropProgram = (function () {
+        function AvgPool2DBackpropProgram(convInfo) {
+            this.variableNames = ['dy'];
+            this.outputShape = convInfo.inShape;
+            var filterHeight = convInfo.filterHeight;
+            var filterWidth = convInfo.filterWidth;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var dilationHeight = convInfo.dilationHeight;
+            var dilationWidth = convInfo.dilationWidth;
+            var effectiveFilterHeight = convInfo.effectiveFilterHeight;
+            var effectiveFilterWidth = convInfo.effectiveFilterWidth;
+            var padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+            var padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+            var avgMultiplier = 1 / (filterHeight * filterWidth);
+            this.userCode = "\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n      const float avgMultiplier = float(" + avgMultiplier + ");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < " + effectiveFilterHeight + ";\n            wR += " + dilationHeight + ") {\n          float dyR = float(dyRCorner + wR) / " + strideHeight + ".0;\n\n          if (dyR < 0.0 || dyR >= " + convInfo.outHeight + ".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < " + effectiveFilterWidth + ";\n            wC+= " + dilationWidth + ") {\n            float dyC = float(dyCCorner + wC) / " + strideWidth + ".0;\n\n            if (dyC < 0.0 || dyC >= " + convInfo.outWidth + ".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+        }
+        return AvgPool2DBackpropProgram;
+    }());
+
+    function getBroadcastDims(inShape, outShape) {
+        var inRank = inShape.length;
+        var dims = [];
+        for (var i = 0; i < inRank; i++) {
+            var dim = inRank - 1 - i;
+            var a = inShape[dim] || 1;
+            var b = outShape[outShape.length - 1 - i] || 1;
+            if (b > 1 && a === 1) {
+                dims.unshift(dim);
+            }
+        }
+        return dims;
+    }
+    function getReductionAxes(inShape, outShape) {
+        var result = [];
+        for (var i = 0; i < outShape.length; i++) {
+            var inDim = inShape[inShape.length - i - 1];
+            var outAxis = outShape.length - i - 1;
+            var outDim = outShape[outAxis];
+            if (inDim == null || (inDim === 1 && outDim > 1)) {
+                result.unshift(outAxis);
+            }
+        }
+        return result;
+    }
+    function broadcastDimsAreOuter(dims) {
+        for (var i = 0; i < dims.length; i++) {
+            if (dims[i] !== i) {
+                return false;
+            }
+        }
+        return true;
+    }
+    function assertAndGetBroadcastShape(shapeA, shapeB) {
+        var result = [];
+        var l = Math.max(shapeA.length, shapeB.length);
+        for (var i = 0; i < l; i++) {
+            var a = shapeA[shapeA.length - i - 1];
+            if (a == null) {
+                a = 1;
+            }
+            var b = shapeB[shapeB.length - i - 1];
+            if (b == null) {
+                b = 1;
+            }
+            if (a === 1) {
+                result.unshift(b);
+            }
+            else if (b === 1) {
+                result.unshift(a);
+            }
+            else if (a !== b) {
+                var errMsg = "Operands could not be broadcast together with shapes " +
+                    (shapeA + " and " + shapeB + ".");
+                throw Error(errMsg);
+            }
+            else {
+                result.unshift(a);
+            }
+        }
+        return result;
+    }
+
+    var BatchNormProgram = (function () {
+        function BatchNormProgram(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {
+            this.outputShape = [];
+            this.supportsBroadcasting = true;
+            this.variableNames = ['x', 'mean', 'variance'];
+            assertAndGetBroadcastShape(xShape, meanShape);
+            assertAndGetBroadcastShape(xShape, varianceShape);
+            var offsetSnippet = '0.0';
+            if (offsetShape != null) {
+                assertAndGetBroadcastShape(xShape, offsetShape);
+                this.variableNames.push('offset');
+                offsetSnippet = 'getOffsetAtOutCoords()';
+            }
+            var scaleSnippet = '1.0';
+            if (scaleShape != null) {
+                assertAndGetBroadcastShape(xShape, scaleShape);
+                this.variableNames.push('scale');
+                scaleSnippet = 'getScaleAtOutCoords()';
+            }
+            this.outputShape = xShape;
+            this.userCode = "\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = " + offsetSnippet + ";\n        float scale = " + scaleSnippet + ";\n        float inv = scale * inversesqrt(variance + float(" + varianceEpsilon + "));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    ";
+        }
+        return BatchNormProgram;
+    }());
+
+    var BatchNormPackedProgram = (function () {
+        function BatchNormPackedProgram(xShape, meanShape, varianceShape, offsetShape, scaleShape, varianceEpsilon) {
+            this.supportsBroadcasting = true;
+            this.usesPackedTextures = true;
+            this.variableNames = ['x', 'mean', 'variance'];
+            assertAndGetBroadcastShape(xShape, meanShape);
+            assertAndGetBroadcastShape(xShape, varianceShape);
+            var offsetSnippet = 'vec4(0.0)';
+            if (offsetShape != null) {
+                assertAndGetBroadcastShape(xShape, offsetShape);
+                this.variableNames.push('offset');
+                offsetSnippet = 'getOffsetAtOutCoords()';
+            }
+            var scaleSnippet = 'vec4(1.0)';
+            if (scaleShape != null) {
+                assertAndGetBroadcastShape(xShape, scaleShape);
+                this.variableNames.push('scale');
+                scaleSnippet = 'getScaleAtOutCoords()';
+            }
+            this.outputShape = xShape;
+            this.userCode = "\n      void main() {\n        vec4 offset = " + offsetSnippet + ";\n        vec4 scale = " + scaleSnippet + ";\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(" + varianceEpsilon + "));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    ";
+        }
+        return BatchNormPackedProgram;
+    }());
+
+    var COMPLEX_MULTIPLY = {
+        REAL: 'return areal * breal - aimag * bimag;',
+        IMAG: 'return areal * bimag + aimag * breal;'
+    };
+    var BinaryOpComplexProgram = (function () {
+        function BinaryOpComplexProgram(op, aShape, bShape) {
+            this.variableNames = ['AReal', 'AImag', 'BReal', 'BImag'];
+            this.supportsBroadcasting = true;
+            this.outputShape =
+                assertAndGetBroadcastShape(aShape, bShape);
+            this.userCode = "\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        " + op + "\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    ";
+        }
+        return BinaryOpComplexProgram;
+    }());
+
+    var CHECK_NAN_SNIPPET = "\n  if (isNaN(a)) return a;\n  if (isNaN(b)) return b;\n";
+    var ADD = 'return a + b;';
+    var SUB = 'return a - b;';
+    var MUL = 'return a * b;';
+    var DIV = "if (a == b) return 1.0;\n  return a / b;";
+    var INT_DIV = "\n  float resultSign = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  int result = ia / ib;\n  int amodb = ia - ib * result;\n\n  if (resultSign < 0.0 && amodb != 0) {\n    result -= 1;\n  }\n  return float(result);\n";
+    var POW = "\nif(a < 0.0 && floor(b) < b){\n  return NAN;\n}\nreturn (round(mod(b, 2.0)) == 0 || round(mod(b, 2.0)) == 2) ?\n    pow(abs(a), b) : sign(a) * pow(abs(a), b);\n";
+    var SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';
+    var EQUAL = "return float(a == b);";
+    var NOT_EQUAL = "return float(a != b);";
+    var LESS = "return float(a < b);";
+    var LESS_EQUAL = "return float(a <= b);";
+    var GREATER = "return float(a > b);";
+    var GREATER_EQUAL = "return float(a >= b);";
+    var LOGICAL_AND = "return float(a >= 1.0 && b >= 1.0);";
+    var LOGICAL_OR = "return float(a >= 1.0 || b >= 1.0);";
+    var MAX = CHECK_NAN_SNIPPET + "\n  return max(a, b);\n";
+    var MIN = CHECK_NAN_SNIPPET + "\n  return min(a, b);\n";
+    var MOD = "if (b == 0.0) return NAN;\n  return mod(a, b);";
+    var ATAN2 = CHECK_NAN_SNIPPET + "\n  return atan(a, b);\n";
+    var ELU_DER = "return (b >= 1.0) ? a : a * (b + 1.0);";
+    var BinaryOpProgram = (function () {
+        function BinaryOpProgram(op, aShape, bShape) {
+            this.variableNames = ['A', 'B'];
+            this.supportsBroadcasting = true;
+            this.outputShape =
+                assertAndGetBroadcastShape(aShape, bShape);
+            this.userCode = "\n      uniform float NAN;\n      float binaryOperation(float a, float b) {\n        " + op + "\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    ";
+        }
+        BinaryOpProgram.prototype.getCustomSetupFunc = function () {
+            var _this = this;
+            return function (gpgpu, webGLProgram) {
+                if (_this.startLoc == null) {
+                    _this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'NAN');
+                    if (_this.startLoc == null) {
+                        return;
+                    }
+                }
+                gpgpu.gl.uniform1f(_this.startLoc, NaN);
+            };
+        };
+        return BinaryOpProgram;
+    }());
+
+    var ClipProgram = (function () {
+        function ClipProgram(aShape, min, max) {
+            this.variableNames = ['A'];
+            this.outputShape = aShape;
+            this.userCode = "\n      void main() {\n        float value = getAAtOutCoords();\n        if (isNaN(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, float(" + min + "), float(" + max + ")));\n      }\n    ";
+        }
+        return ClipProgram;
+    }());
+
+    var ClipPackedProgram = (function () {
+        function ClipPackedProgram(aShape, min, max) {
+            this.variableNames = ['A'];
+            this.usesPackedTextures = true;
+            this.outputShape = aShape;
+            this.userCode = "\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (hasNaN(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(" + min + "), vec4(" + max + ")));\n      }\n    ";
+        }
+        return ClipPackedProgram;
+    }());
+
+    var ComplexAbsProgram = (function () {
+        function ComplexAbsProgram(shape) {
+            this.variableNames = ['real', 'imag'];
+            this.outputShape = shape;
+            this.userCode = "\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    ";
+        }
+        return ComplexAbsProgram;
+    }());
+
+    var ConcatProgram = (function () {
+        function ConcatProgram(aShape, bShape) {
+            this.variableNames = ['A', 'B'];
+            this.outputShape = [];
+            this.outputShape =
+                computeOutShape([aShape, bShape], 1);
+            this.userCode = "\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        float value = 0.0;\n        if (yC < " + aShape[1] + ") {\n          value = getA(yR, yC);\n        } else {\n          yC -= " + aShape[1] + ";\n          value = getB(yR, yC);\n        }\n\n        setOutput(value);\n      }\n    ";
+        }
+        return ConcatProgram;
+    }());
+
+    var Conv2DDerFilterProgram = (function () {
+        function Conv2DDerFilterProgram(convInfo) {
+            this.variableNames = ['x', 'dy'];
+            this.outputShape = convInfo.filterShape;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var padTop = convInfo.padInfo.top;
+            var padLeft = convInfo.padInfo.left;
+            this.userCode = "\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < " + convInfo.batchSize + "; b++) {\n          for (int yR = 0; yR < " + convInfo.outHeight + "; yR++) {\n            int xR = wR + yR * " + strideHeight + " - " + padTop + ";\n\n            if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n              continue;\n            }\n\n            for (int yC = 0; yC < " + convInfo.outWidth + "; yC++) {\n              int xC = wC + yC * " + strideWidth + " - " + padLeft + ";\n\n              if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+        }
+        return Conv2DDerFilterProgram;
+    }());
+    var Conv2DDerInputProgram = (function () {
+        function Conv2DDerInputProgram(convInfo) {
+            this.variableNames = ['dy', 'W'];
+            this.outputShape = convInfo.inShape;
+            var filterHeight = convInfo.filterHeight;
+            var filterWidth = convInfo.filterWidth;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var padTop = filterHeight - 1 - convInfo.padInfo.top;
+            var padLeft = filterWidth - 1 - convInfo.padInfo.left;
+            this.userCode = "\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < " + filterHeight + "; wR++) {\n          float dyR = float(dyRCorner + wR) / " + strideHeight + ".0;\n\n          if (dyR < 0.0 || dyR >= " + convInfo.outHeight + ".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = " + filterHeight + " - 1 - wR;\n\n          for (int wC = 0; wC < " + filterWidth + "; wC++) {\n            float dyC = float(dyCCorner + wC) / " + strideWidth + ".0;\n\n            if (dyC < 0.0 || dyC >= " + convInfo.outWidth + ".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = " + filterWidth + " - 1 - wC;\n\n            for (int d2 = 0; d2 < " + convInfo.outChannels + "; d2++) {\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, d2);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+        }
+        return Conv2DDerInputProgram;
+    }());
+    var Conv3DDerFilterProgram = (function () {
+        function Conv3DDerFilterProgram(convInfo) {
+            this.variableNames = ['x', 'dy'];
+            this.outputShape = convInfo.filterShape;
+            var strideDepth = convInfo.strideDepth;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var padFront = convInfo.padInfo.front;
+            var padTop = convInfo.padInfo.top;
+            var padLeft = convInfo.padInfo.left;
+            this.userCode = "\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < " + convInfo.batchSize + "; b++) {\n          for (int yF = 0; yF < " + convInfo.outDepth + "; yF++) {\n            int xF = wF + yF * " + strideDepth + " - " + padFront + ";\n\n            if (xF < 0 || xF >= " + convInfo.inDepth + ") {\n              continue;\n            }\n\n            for (int yR = 0; yR < " + convInfo.outHeight + "; yR++) {\n              int xR = wR + yR * " + strideHeight + " - " + padTop + ";\n\n              if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n                continue;\n              }\n\n              for (int yC = 0; yC < " + convInfo.outWidth + "; yC++) {\n                int xC = wC + yC * " + strideWidth + " - " + padLeft + ";\n\n                if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+        }
+        return Conv3DDerFilterProgram;
+    }());
+    var Conv3DDerInputProgram = (function () {
+        function Conv3DDerInputProgram(convInfo) {
+            this.variableNames = ['dy', 'W'];
+            this.outputShape = convInfo.inShape;
+            var filterDepth = convInfo.filterDepth;
+            var filterHeight = convInfo.filterHeight;
+            var filterWidth = convInfo.filterWidth;
+            var strideDepth = convInfo.strideDepth;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var padFront = filterDepth - 1 - convInfo.padInfo.front;
+            var padTop = filterHeight - 1 - convInfo.padInfo.top;
+            var padLeft = filterWidth - 1 - convInfo.padInfo.left;
+            this.userCode = "\n      const ivec3 pads = ivec3(" + padFront + ", " + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < " + filterDepth + "; wF++) {\n          float dyF = float(dyFCorner + wF) / " + strideDepth + ".0;\n\n          if (dyF < 0.0 || dyF >= " + convInfo.outDepth + ".0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = " + filterDepth + " - 1 - wF;\n\n          for (int wR = 0; wR < " + filterHeight + "; wR++) {\n            float dyR = float(dyRCorner + wR) / " + strideHeight + ".0;\n\n            if (dyR < 0.0 || dyR >= " + convInfo.outHeight + ".0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = " + filterHeight + " - 1 - wR;\n\n            for (int wC = 0; wC < " + filterWidth + "; wC++) {\n              float dyC = float(dyCCorner + wC) / " + strideWidth + ".0;\n\n              if (dyC < 0.0 || dyC >= " + convInfo.outWidth + ".0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = " + filterWidth + " - 1 - wC;\n\n              for (int d2 = 0; d2 < " + convInfo.outChannels + "; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+        }
+        return Conv3DDerInputProgram;
+    }());
+
+    var DepthwiseConv2DDerFilterProgram = (function () {
+        function DepthwiseConv2DDerFilterProgram(convInfo) {
+            this.variableNames = ['x', 'dy'];
+            this.outputShape = convInfo.filterShape;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var padTop = convInfo.padInfo.top;
+            var padLeft = convInfo.padInfo.left;
+            var channelMul = convInfo.outChannels / convInfo.inChannels;
+            this.userCode = "\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * " + channelMul + " + dm;\n\n        float dotProd = 0.0;\n\n        // TODO: Vec4 over the batch size\n        for (int b = 0; b < " + convInfo.batchSize + "; b++) {\n          for (int yR = 0; yR < " + convInfo.outHeight + "; yR++) {\n            int xR = wR + yR * " + strideHeight + " - " + padTop + ";\n\n            if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n              continue;\n            }\n\n            for (int yC = 0; yC < " + convInfo.outWidth + "; yC++) {\n              int xC = wC + yC * " + strideWidth + " - " + padLeft + ";\n\n              if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+        }
+        return DepthwiseConv2DDerFilterProgram;
+    }());
+    var DepthwiseConv2DDerInputProgram = (function () {
+        function DepthwiseConv2DDerInputProgram(convInfo) {
+            this.variableNames = ['dy', 'W'];
+            this.outputShape = convInfo.inShape;
+            var filterHeight = convInfo.filterHeight;
+            var filterWidth = convInfo.filterWidth;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var padTop = filterHeight - 1 - convInfo.padInfo.top;
+            var padLeft = filterWidth - 1 - convInfo.padInfo.left;
+            var channelMul = convInfo.outChannels / convInfo.inChannels;
+            this.userCode = "\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < " + filterHeight + "; wR++) {\n          float dyR = float(dyRCorner + wR) / " + strideHeight + ".0;\n\n          if (dyR < 0.0 || dyR >= " + convInfo.outHeight + ".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = " + filterHeight + " - 1 - wR;\n\n          for (int wC = 0; wC < " + filterWidth + "; wC++) {\n            float dyC = float(dyCCorner + wC) / " + strideWidth + ".0;\n\n            if (dyC < 0.0 || dyC >= " + convInfo.outWidth + ".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = " + filterWidth + " - 1 - wC;\n\n            // TODO: Vec4 over the channelMul\n            for (int dm = 0; dm < " + channelMul + "; dm++) {\n              int d2 = d1 * " + channelMul + " + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+        }
+        return DepthwiseConv2DDerInputProgram;
+    }());
+
+    var Conv2DProgram = (function () {
+        function Conv2DProgram(convInfo) {
+            this.variableNames = ['x', 'W'];
+            this.outputShape = convInfo.outShape;
+            var padTop = convInfo.padInfo.top;
+            var padLeft = convInfo.padInfo.left;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var dilationHeight = convInfo.dilationHeight;
+            var dilationWidth = convInfo.dilationWidth;
+            var filterHeight = convInfo.filterHeight;
+            var filterWidth = convInfo.filterWidth;
+            var inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;
+            var inputDepthVec4Remainder = convInfo.inChannels % 4;
+            this.userCode = "\n      const ivec2 strides = ivec2(" + strideHeight + ", " + strideWidth + ");\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < " + filterHeight + "; wR++) {\n          int xR = xRCorner + wR * " + dilationHeight + ";\n\n          if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n            continue;\n          }\n\n          for (int wC = 0; wC < " + filterWidth + "; wC++) {\n            int xC = xCCorner + wC * " + dilationWidth + ";\n\n            if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < " + inputDepthNearestVec4 + "; d1 += 4) {\n              vec4 xValues = vec4(\n                getX(batch, xR, xC, d1),\n                getX(batch, xR, xC, d1 + 1),\n                getX(batch, xR, xC, d1 + 2),\n                getX(batch, xR, xC, d1 + 3)\n              );\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              dotProd += dot(xValues, wValues);\n            }\n\n            if (" + (inputDepthVec4Remainder === 1) + ") {\n              dotProd +=\n                getX(batch, xR, xC, " + inputDepthNearestVec4 + ") *\n                getW(wR, wC, " + inputDepthNearestVec4 + ", d2);\n            } else if (" + (inputDepthVec4Remainder === 2) + ") {\n              vec2 xValues = vec2(\n                getX(batch, xR, xC, " + inputDepthNearestVec4 + "),\n                getX(batch, xR, xC, " + inputDepthNearestVec4 + " + 1)\n              );\n              vec2 wValues = vec2(\n                getW(wR, wC, " + inputDepthNearestVec4 + ", d2),\n                getW(wR, wC, " + inputDepthNearestVec4 + " + 1, d2)\n              );\n              dotProd += dot(xValues, wValues);\n            } else if (" + (inputDepthVec4Remainder === 3) + ") {\n              vec3 xValues = vec3(\n                getX(batch, xR, xC, " + inputDepthNearestVec4 + "),\n                getX(batch, xR, xC, " + inputDepthNearestVec4 + " + 1),\n                getX(batch, xR, xC, " + inputDepthNearestVec4 + " + 2)\n              );\n              vec3 wValues = vec3(\n                getW(wR, wC, " + inputDepthNearestVec4 + ", d2),\n                getW(wR, wC, " + inputDepthNearestVec4 + " + 1, d2),\n                getW(wR, wC, " + inputDepthNearestVec4 + " + 2, d2)\n              );\n              dotProd += dot(xValues, wValues);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+        }
+        return Conv2DProgram;
+    }());
+    var Conv3DProgram = (function () {
+        function Conv3DProgram(convInfo) {
+            this.variableNames = ['x', 'W'];
+            this.outputShape = convInfo.outShape;
+            var padFront = convInfo.padInfo.front;
+            var padTop = convInfo.padInfo.top;
+            var padLeft = convInfo.padInfo.left;
+            var strideDepth = convInfo.strideDepth;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var dilationDepth = convInfo.dilationDepth;
+            var dilationHeight = convInfo.dilationHeight;
+            var dilationWidth = convInfo.dilationWidth;
+            var filterDepth = convInfo.filterDepth;
+            var filterHeight = convInfo.filterHeight;
+            var filterWidth = convInfo.filterWidth;
+            var inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;
+            var inputDepthVec4Remainder = convInfo.inChannels % 4;
+            this.userCode = "\n      const ivec3 strides = ivec3(" + strideDepth + ", " + strideHeight + ", " + strideWidth + ");\n      const ivec3 pads = ivec3(" + padFront + ", " + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < " + filterDepth + "; wF++) {\n          int xF = xFCorner + wF * " + dilationDepth + ";\n\n          if (xF < 0 || xF >= " + convInfo.inDepth + ") {\n            continue;\n          }\n\n          for (int wR = 0; wR < " + filterHeight + "; wR++) {\n            int xR = xRCorner + wR * " + dilationHeight + ";\n\n            if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n              continue;\n            }\n\n            for (int wC = 0; wC < " + filterWidth + "; wC++) {\n              int xC = xCCorner + wC * " + dilationWidth + ";\n\n              if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < " + inputDepthNearestVec4 + "; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (" + (inputDepthVec4Remainder === 1) + ") {\n                dotProd +=\n                  getX(batch, xF, xR, xC, " + inputDepthNearestVec4 + ") *\n                  getW(wF, wR, wC, " + inputDepthNearestVec4 + ", d2);\n              } else if (" + (inputDepthVec4Remainder === 2) + ") {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, " + inputDepthNearestVec4 + "),\n                  getX(batch, xF, xR, xC, " + inputDepthNearestVec4 + " + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, " + inputDepthNearestVec4 + ", d2),\n                  getW(wF, wR, wC, " + inputDepthNearestVec4 + " + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (" + (inputDepthVec4Remainder === 3) + ") {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, " + inputDepthNearestVec4 + "),\n                  getX(batch, xF, xR, xC, " + inputDepthNearestVec4 + " + 1),\n                  getX(batch, xF, xR, xC, " + inputDepthNearestVec4 + " + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, " + inputDepthNearestVec4 + ", d2),\n                  getW(wF, wR, wC, " + inputDepthNearestVec4 + " + 1, d2),\n                  getW(wF, wR, wC, " + inputDepthNearestVec4 + " + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+        }
+        return Conv3DProgram;
+    }());
+
+    var DepthwiseConv2DProgram = (function () {
+        function DepthwiseConv2DProgram(convInfo) {
+            this.variableNames = ['x', 'W'];
+            this.outputShape = convInfo.outShape;
+            var xNumRows = convInfo.inHeight;
+            var xNumCols = convInfo.inWidth;
+            var padTop = convInfo.padInfo.top;
+            var padLeft = convInfo.padInfo.left;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var dilationHeight = convInfo.dilationHeight;
+            var dilationWidth = convInfo.dilationWidth;
+            var filterHeight = convInfo.filterHeight;
+            var filterWidth = convInfo.filterWidth;
+            var channelMul = convInfo.outChannels / convInfo.inChannels;
+            this.userCode = "\n      const ivec2 strides = ivec2(" + strideHeight + ", " + strideWidth + ");\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / " + channelMul + ";\n        int q = d2 - d1 * " + channelMul + ";\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TODO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < " + filterHeight + "; wR++) {\n          int xR = xRCorner + wR * " + dilationHeight + ";\n\n          if (xR < 0 || xR >= " + xNumRows + ") {\n            continue;\n          }\n\n          for (int wC = 0; wC < " + filterWidth + "; wC++) {\n            int xC = xCCorner + wC * " + dilationWidth + ";\n\n            if (xC < 0 || xC >= " + xNumCols + ") {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+        }
+        return DepthwiseConv2DProgram;
+    }());
+
+    var DepthwiseConvPacked2DProgram = (function () {
+        function DepthwiseConvPacked2DProgram(convInfo) {
+            this.variableNames = ['x', 'W'];
+            this.usesPackedTextures = true;
+            this.outputShape = convInfo.outShape;
+            var xNumRows = convInfo.inHeight;
+            var xNumCols = convInfo.inWidth;
+            var padTop = convInfo.padInfo.top;
+            var padLeft = convInfo.padInfo.left;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var filterHeight = convInfo.filterHeight;
+            var filterWidth = convInfo.filterWidth;
+            var texelsAcross = Math.ceil((filterWidth + 1) / 2);
+            var mainLoop = "int xR; int xC;";
+            for (var r = 0; r < filterHeight; r++) {
+                for (var c = -padLeft; c < texelsAcross * 2; c++) {
+                    mainLoop += "vec4 " + xTexelName(r, c) + " = vec4(0.);";
+                }
+                for (var c = 0; c < filterWidth; c++) {
+                    mainLoop += "\n          vec4 wR" + r + "C" + c + " = vec4(0.);\n          vec4 xR" + r + "C" + c + " = vec4(0.);";
+                }
+            }
+            for (var r = 0; r < filterHeight; r++) {
+                for (var c = 0; c < texelsAcross; c++) {
+                    var col = c * 2;
+                    var left = c * 2 + padLeft;
+                    mainLoop += "\n          xR = xRCorner + " + r + ";\n          xC = xCCorner + " + left + ";\n\n          if(xR >= 0 && xR < " + xNumRows + " && xC >= 0 && xC < " + xNumCols + ") {\n            " + xTexelName(r, left) + " = getX(batch, xR, xC, d1);\n          }";
+                    if (padLeft === 0) {
+                        if (col < filterWidth && c === texelsAcross - 1) {
+                            if (strideWidth > 1) {
+                                mainLoop += "\n                vec4 " + xTexelName(r, left + 2) + " = vec4(0.);\n\n                if(xR >= 0 && xR < " + xNumRows + " && xC + 2 < " + xNumCols + ") {\n                  " + xTexelName(r, left + 2) + " = getX(batch, xR, xC + 2, d1);\n                }";
+                            }
+                            mainLoop += "\n              xR" + r + "C" + left + " = " + constructTexel(r, left, strideWidth, padLeft) + ";\n            ";
+                        }
+                    }
+                    else if (c === 0) {
+                        mainLoop += "\n            if(xR >= 0 && xR < " + xNumRows + " && xC - 2 >= 0) {\n              " + xTexelName(r, left - 2) + " = getX(batch, xR, xC - 2, d1);\n            }";
+                    }
+                    if (col > 0) {
+                        mainLoop += "xR" + r + "C" + (left - 2) + " =\n            " + constructTexel(r, left - 2, strideWidth, padLeft) + ";";
+                    }
+                    if (left - 1 >= 0 && left - 1 < filterWidth) {
+                        mainLoop += "xR" + r + "C" + (left - 1) + " =\n              " + constructTexel(r, left - 1, strideWidth, padLeft) + ";";
+                    }
+                    if (col < filterWidth) {
+                        mainLoop += "\n            vec4 wTexel" + r + "C" + col + " = getW(" + r + ", " + col + ", d1, q);\n            wR" + r + "C" + col + " = vec4(wTexel" + r + "C" + col + ".xz, wTexel" + r + "C" + col + ".xz);\n          ";
+                        if (col + 1 < filterWidth) {
+                            mainLoop += "\n              vec4 wTexelR" + r + "C" + (col + 1) + " = getW(" + r + ", " + (col + 1) + ", d1, q);\n              wR" + r + "C" + (col + 1) + " =\n                vec4(wTexelR" + r + "C" + (col + 1) + ".xz, wTexelR" + r + "C" + (col + 1) + ".xz);";
+                        }
+                    }
+                }
+            }
+            for (var r = 0; r < filterHeight; r++) {
+                for (var c = 0; c < filterWidth; c++) {
+                    mainLoop += "result += xR" + r + "C" + c + " * wR" + r + "C" + c + ";";
+                }
+            }
+            this.userCode = "\n      const ivec2 strides = ivec2(" + strideHeight + ", " + strideWidth + ");\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2;\n        int q = 0;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        vec4 result = vec4(0.);\n\n        " + mainLoop + "\n\n        setOutput(result);\n      }\n    ";
+        }
+        return DepthwiseConvPacked2DProgram;
+    }());
+    function xTexelName(r, c) {
+        return "xTexelR" + r + "C" + (c < 0 ? 'minus' + Math.abs(c).toString() : c);
+    }
+    function constructTexel(r, c, stride, padLeft) {
+        if (stride === 1) {
+            if (padLeft % 2 === c % 2) {
+                return xTexelName(r, c);
+            }
+            return "vec4(" + xTexelName(r, c - 1) + ".zw, " + xTexelName(r, c + 1) + ".xy)";
+        }
+        if (padLeft % 2 === c % 2) {
+            return "vec4(" + xTexelName(r, c) + ".xy, " + xTexelName(r, c + 2) + ".xy)";
+        }
+        return "vec4(" + xTexelName(r, c - 1) + ".zw, " + xTexelName(r, c + 1) + ".zw)";
+    }
+
+    var CropAndResizeProgram = (function () {
+        function CropAndResizeProgram(imageShape, boxShape, cropSize, method, extrapolationValue) {
+            this.variableNames = ['Image', 'Boxes', 'BoxInd'];
+            this.outputShape = [];
+            var batch = imageShape[0], imageHeight = imageShape[1], imageWidth = imageShape[2], depth = imageShape[3];
+            var numBoxes = boxShape[0];
+            var cropHeight = cropSize[0], cropWidth = cropSize[1];
+            this.outputShape = [numBoxes, cropHeight, cropWidth, depth];
+            var methodId = method === 'bilinear' ? 1 : 0;
+            var _a = [imageHeight - 1 + ".0", imageWidth - 1 + ".0"], inputHeightFloat = _a[0], inputWidthFloat = _a[1];
+            var _b = cropHeight > 1 ?
+                [
+                    "" + (imageHeight - 1) / (cropHeight - 1),
+                    '(y2-y1) * height_ratio',
+                    "y1*" + inputHeightFloat + " + float(y)*(height_scale)",
+                ] :
+                [
+                    '0.0',
+                    '0.0',
+                    "0.5 * (y1+y2) * " + inputHeightFloat,
+                ], heightRatio = _b[0], heightScale = _b[1], inY = _b[2];
+            var _c = cropWidth > 1 ?
+                [
+                    "" + (imageWidth - 1) / (cropWidth - 1),
+                    '(x2-x1) * width_ratio',
+                    "x1*" + inputWidthFloat + " + float(x)*(width_scale)",
+                ] :
+                [
+                    '0.0',
+                    '0.0',
+                    "0.5 * (x1+x2) * " + inputWidthFloat,
+                ], widthRatio = _c[0], widthScale = _c[1], inX = _c[2];
+            this.userCode = "\n      const float height_ratio = float(" + heightRatio + ");\n      const float width_ratio = float(" + widthRatio + ");\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= " + batch + ") {\n          return;\n        }\n\n        float height_scale = " + heightScale + ";\n        float width_scale = " + widthScale + ";\n\n        float in_y = " + inY + ";\n        if( in_y < 0.0 || in_y > " + inputHeightFloat + " ) {\n          setOutput(float(" + extrapolationValue + "));\n          return;\n        }\n        float in_x = " + inX + ";\n        if( in_x < 0.0 || in_x > " + inputWidthFloat + " ) {\n          setOutput(float(" + extrapolationValue + "));\n          return;\n        }\n\n        vec2 sourceFracIndexRC = vec2(in_y,in_x);\n        if(" + methodId + " == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n          ivec2 sourceCeilRC = ivec2(ceil(sourceFracIndexRC));\n\n          float topLeft = getImage(b, sourceFloorRC.x, sourceFloorRC.y, d);\n          float bottomLeft = getImage(b, sourceCeilRC.x, sourceFloorRC.y, d);\n          float topRight = getImage(b, sourceFloorRC.x, sourceCeilRC.y, d);\n          float bottomRight = getImage(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n          vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n          float top = topLeft + (topRight - topLeft) * fracRC.y;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n          float newValue = top + (bottom - top) * fracRC.x;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestRC = ivec2(floor(\n            sourceFracIndexRC + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestRC.x, sourceNearestRC.y, d);\n          setOutput(newValue);\n        }\n      }\n    ";
+        }
+        return CropAndResizeProgram;
+    }());
+
+    function getLogicalCoordinatesFromFlatIndex(coords, shape, index) {
+        if (index === void 0) { index = 'index'; }
+        var strides = computeStrides(shape);
+        return strides
+            .map(function (stride, i) {
+            var line1 = "int " + coords[i] + " = " + index + " / " + stride;
+            var line2 = i === strides.length - 1 ?
+                "int " + coords[i + 1] + " = " + index + " - " + coords[i] + " * " + stride :
+                "index -= " + coords[i] + " * " + stride;
+            return line1 + "; " + line2 + ";";
+        })
+            .join('');
+    }
+    function buildVec(x) {
+        if (x.length === 1) {
+            return "" + x[0];
+        }
+        return "vec" + x.length + "(" + x.join(',') + ")";
+    }
+    function dotify(x, y) {
+        if (x.length !== y.length) {
+            throw new Error("Vectors to be dotted must be of the same length -" +
+                ("got " + x.length + " and " + y.length));
+        }
+        var slices = [];
+        var nearestVec4 = Math.floor(x.length / 4);
+        var nearestVec4Remainder = x.length % 4;
+        for (var i = 0; i < nearestVec4; i++) {
+            var xSlice = x.slice(i * 4, i * 4 + 4);
+            var ySlice = y.slice(i * 4, i * 4 + 4);
+            slices.push(buildVec(xSlice) + ", " + buildVec(ySlice));
+        }
+        if (nearestVec4Remainder !== 0) {
+            var xSlice = x.slice(nearestVec4 * 4);
+            var ySlice = y.slice(nearestVec4 * 4);
+            if (xSlice.length === 1) {
+                xSlice = xSlice.map(function (d) { return "float(" + d + ")"; });
+                ySlice = ySlice.map(function (d) { return "float(" + d + ")"; });
+            }
+            slices.push(buildVec(xSlice) + ", " + buildVec(ySlice));
+        }
+        return slices.map(function (d, i) { return "dot(" + d + ")"; }).join('+');
+    }
+
+    function makeShader(inputsInfo, outputShape, userCode, broadcast, usesPackedTextures) {
+        var inputPrefixSnippet = inputsInfo.map(function (x) {
+            var size = sizeFromShape(x.shapeInfo.logicalShape);
+            if (x.shapeInfo.isUniform) {
+                return "uniform float " + x.name + (size > 1 ? "[" + size + "]" : '') + ";";
+            }
+            return "uniform sampler2D " + x.name + ";";
+        });
+        inputPrefixSnippet = inputPrefixSnippet.join('\n');
+        var inputSamplingSnippet = inputsInfo
+            .map(function (x) { return getInputSamplingSnippet(x, outputShape, broadcast, usesPackedTextures); })
+            .join('\n');
+        var outTexShape = outputShape.texShape;
+        var outputSamplingSnippet;
+        var floatTextureSetOutputSnippet;
+        var shaderPrefix = SHADER_PREFIX;
+        if (outputShape.isPacked) {
+            outputSamplingSnippet =
+                getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape);
+            floatTextureSetOutputSnippet = FLOAT_TEXTURE_SET_RGBA_SNIPPET;
+        }
+        else {
+            outputSamplingSnippet =
+                getOutputSamplingSnippet(outputShape.logicalShape, outTexShape);
+            floatTextureSetOutputSnippet = FLOAT_TEXTURE_SET_R_SNIPPET;
+        }
+        if (usesPackedTextures) {
+            shaderPrefix += SHADER_PACKED_PREFIX;
+        }
+        var source = [
+            shaderPrefix, FLOAT_TEXTURE_SAMPLE_SNIPPET, floatTextureSetOutputSnippet,
+            inputPrefixSnippet, outputSamplingSnippet, inputSamplingSnippet, userCode
+        ].join('\n');
+        return source;
+    }
+    function getSamplerFromInInfo(inInfo) {
+        var shape = inInfo.shapeInfo.logicalShape;
+        switch (shape.length) {
+            case 0:
+                return getSamplerScalar(inInfo);
+            case 1:
+                return getSampler1D(inInfo);
+            case 2:
+                return getSampler2D(inInfo);
+            case 3:
+                return getSampler3D(inInfo);
+            case 4:
+                return getSampler4D(inInfo);
+            case 5:
+                return getSampler5D(inInfo);
+            case 6:
+                return getSampler6D(inInfo);
+            default:
+                throw new Error(shape.length + "-D input sampling" +
+                    " is not yet supported");
+        }
+    }
+    function getPackedSamplerFromInInfo(inInfo) {
+        var shape = inInfo.shapeInfo.logicalShape;
+        switch (shape.length) {
+            case 0:
+                return getPackedSamplerScalar(inInfo);
+            case 1:
+                return getPackedSampler1D(inInfo);
+            case 2:
+                return getPackedSampler2D(inInfo);
+            case 3:
+                return getPackedSampler3D(inInfo);
+            case 4:
+                return getPackedSampler4D(inInfo);
+            default:
+                throw new Error("Packed " + shape.length + "-D input sampling" +
+                    " is not yet supported");
+        }
+    }
+    function getInputSamplingSnippet(inInfo, outShapeInfo, broadcast, usesPackedTextures) {
+        if (usesPackedTextures === void 0) { usesPackedTextures = false; }
+        var res = getSamplerFlat(inInfo);
+        if (usesPackedTextures) {
+            res += getPackedSamplerFromInInfo(inInfo);
+        }
+        else {
+            res += getSamplerFromInInfo(inInfo);
+        }
+        if (broadcast ||
+            arraysEqual(inInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape)) {
+            if (usesPackedTextures) {
+                res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo, broadcast);
+            }
+            else {
+                res += getSamplerAtOutputCoords(inInfo, outShapeInfo, broadcast);
+            }
+        }
+        return res;
+    }
+    function getPackedOutputSamplingSnippet(outShape, outTexShape) {
+        switch (outShape.length) {
+            case 0:
+                return getOutputScalarCoords();
+            case 1:
+                return getOutputPacked1DCoords(outShape, outTexShape);
+            case 2:
+                return getOutputPacked2DCoords(outShape, outTexShape);
+            case 3:
+                return getOutputPacked3DCoords(outShape, outTexShape);
+            case 4:
+                return getOutputPacked4DCoords(outShape, outTexShape);
+            default:
+                throw new Error(outShape.length + "-D packed output " +
+                    "coordinate fetching is not yet supported");
+        }
+    }
+    function getOutputSamplingSnippet(outShape, outTexShape) {
+        switch (outShape.length) {
+            case 0:
+                return getOutputScalarCoords();
+            case 1:
+                return getOutput1DCoords(outShape, outTexShape);
+            case 2:
+                return getOutput2DCoords(outShape, outTexShape);
+            case 3:
+                return getOutput3DCoords(outShape, outTexShape);
+            case 4:
+                return getOutput4DCoords(outShape, outTexShape);
+            case 5:
+                return getOutput5DCoords(outShape, outTexShape);
+            case 6:
+                return getOutput6DCoords(outShape, outTexShape);
+            default:
+                throw new Error(outShape.length + "-D output sampling is not yet supported");
+        }
+    }
+    var SAMPLE_1D_SNIPPET = "\nvec2 UVfrom1D(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n";
+    var SAMPLE_2D_SNIPPET = "\nvec2 UVfrom2D(int texNumR, int texNumC, int numC, int row, int col) {\n  int index = row * numC + col;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n";
+    var SAMPLE_3D_SNIPPET = "\nvec2 UVfrom3D(int texNumR, int texNumC, int stride0,\n    int stride1, int row, int col, int depth) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n";
+    var SAMPLE_4D_SNIPPET = "\nvec2 UVfrom4D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int row, int col, int depth,\n    int depth2) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth * stride2 + depth2;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom4D(int texNumR, int texNumC, int texelsInBatch2,\n    int texelsInBatch, int texelsInLogicalRow, int b2, int b,\n    int row, int col) {\n  int index = b2 * texelsInBatch2 + b * texelsInBatch +\n    (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n";
+    var SAMPLE_5D_SNIPPET = "\nvec2 UVfrom5D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int stride3, int row, int col, int depth,\n    int depth2, int depth3) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 +\n              depth * stride2 + depth2 * stride3 + depth3;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n";
+    var SAMPLE_6D_SNIPPET = "\nvec2 UVfrom6D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int stride3, int stride4,\n    int row, int col, int depth, int depth2, int depth3, int depth4) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth * stride2 + depth2 *\n    stride3 + depth3 * stride4 + depth4;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n";
+    var FLOAT_TEXTURE_SAMPLE_SNIPPET = "\n  float sampleTexture(sampler2D textureSampler, vec2 uv) {\n    return texture2D(textureSampler, uv).r;\n  }\n";
+    var FLOAT_TEXTURE_SET_R_SNIPPET = "\n  void setOutput(float val) {\n    gl_FragColor = vec4(val, 0, 0, 0);\n  }\n";
+    var FLOAT_TEXTURE_SET_RGBA_SNIPPET = "\n  void setOutput(vec4 val) {\n    gl_FragColor = val;\n  }\n";
+    var NAN_CHECKS = '';
+    if (ENV.get('PROD')) {
+        NAN_CHECKS = "\n    bool isNaN(float val) {\n      return false;\n    }\n\n    bool hasNaN(vec4 values) {\n      return false;\n    }\n  ";
+    }
+    else {
+        NAN_CHECKS = "\n    bool isNaN(float val) {\n      return (val < 1.0 || 0.0 < val || val == 0.0) ? false : true;\n    }\n\n    bool hasNaN(vec4 values) {\n      return any(bvec4(\n        isNaN(values.x),\n        isNaN(values.y),\n        isNaN(values.z),\n        isNaN(values.w)\n      ));\n    }\n  ";
+    }
+    var SHADER_PREFIX = "\n  precision highp float;\n  precision highp int;\n  varying vec2 resultUV;\n  const vec2 halfCR = vec2(0.5, 0.5);\n\n  struct ivec5\n  {\n    int x;\n    int y;\n    int z;\n    int w;\n    int u;\n  };\n\n  struct ivec6\n  {\n    int x;\n    int y;\n    int z;\n    int w;\n    int u;\n    int v;\n  };\n\n  " + NAN_CHECKS + "\n\n  float getNaN(vec4 values) {\n    return dot(vec4(1), values);\n  }\n\n  int round(float value) {\n    return int(floor(value + 0.5));\n  }\n\n  int imod(int x, int y) {\n    return x - y * (x / y);\n  }\n\n  //Based on the work of Dave Hoskins\n  //https://www.shadertoy.com/view/4djSRW\n  #define HASHSCALE1 443.8975\n  float random(float seed){\n    vec2 p = resultUV * seed;\n    vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n    p3 += dot(p3, p3.yzx + 19.19);\n    return fract((p3.x + p3.y) * p3.z);\n  }\n\n  " + SAMPLE_1D_SNIPPET + "\n  " + SAMPLE_2D_SNIPPET + "\n  " + SAMPLE_3D_SNIPPET + "\n  " + SAMPLE_4D_SNIPPET + "\n  " + SAMPLE_5D_SNIPPET + "\n  " + SAMPLE_6D_SNIPPET + "\n";
+    var SHADER_PACKED_PREFIX = "\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n";
+    function getOutputScalarCoords() {
+        return "\n    int getOutputCoords() {\n      return 0;\n    }\n  ";
+    }
+    function getOutputPacked1DCoords(shape, texShape) {
+        var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+        if (texShape[0] === 1) {
+            return "\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * " + packedTexShape[1] + ".0);\n      }\n    ";
+        }
+        if (texShape[1] === 1) {
+            return "\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * " + packedTexShape[0] + ".0);\n      }\n    ";
+        }
+        return "\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + packedTexShape[0] + ", " + packedTexShape[1] + "));\n      return resTexRC.x * " + packedTexShape[1] + " + resTexRC.y;\n    }\n  ";
+    }
+    function getOutput1DCoords(shape, texShape) {
+        if (texShape[0] === 1) {
+            return "\n      int getOutputCoords() {\n        return int(resultUV.x * " + texShape[1] + ".0);\n      }\n    ";
+        }
+        if (texShape[1] === 1) {
+            return "\n      int getOutputCoords() {\n        return int(resultUV.y * " + texShape[0] + ".0);\n      }\n    ";
+        }
+        return "\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + texShape[0] + ", " + texShape[1] + "));\n      return resTexRC.x * " + texShape[1] + " + resTexRC.y;\n    }\n  ";
+    }
+    function getOutputPacked3DCoords(shape, texShape) {
+        var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+        var texelsInLogicalRow = Math.ceil(shape[2] / 2);
+        var texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);
+        return "\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + packedTexShape[0] + ", " + packedTexShape[1] + "));\n      int index = resTexRC.x * " + packedTexShape[1] + " + resTexRC.y;\n\n      int b = index / " + texelsInBatch + ";\n      index -= b * " + texelsInBatch + ";\n\n      int r = 2 * (index / " + texelsInLogicalRow + ");\n      int c = imod(index, " + texelsInLogicalRow + ") * 2;\n\n      return ivec3(b, r, c);\n    }\n  ";
+    }
+    function getOutput3DCoords(shape, texShape) {
+        var coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);
+        return "\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + texShape[0] + ", " + texShape[1] + "));\n      int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n      " + coordsFromIndexSnippet + "\n      return ivec3(r, c, d);\n    }\n  ";
+    }
+    function getOutputPacked4DCoords(shape, texShape) {
+        var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+        var texelsInLogicalRow = Math.ceil(shape[3] / 2);
+        var texelsInBatch = texelsInLogicalRow * Math.ceil(shape[2] / 2);
+        var texelsInBatch2 = texelsInBatch * shape[1];
+        return "\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + packedTexShape[0] + ", " + packedTexShape[1] + "));\n      int index = resTexRC.x * " + packedTexShape[1] + " + resTexRC.y;\n\n      int b2 = index / " + texelsInBatch2 + ";\n      index -= b2 * " + texelsInBatch2 + ";\n\n      int b = index / " + texelsInBatch + ";\n      index -= b * " + texelsInBatch + ";\n\n      int r = 2 * (index / " + texelsInLogicalRow + ");\n      int c = imod(index, " + texelsInLogicalRow + ") * 2;\n\n      return ivec4(b2, b, r, c);\n    }\n  ";
+    }
+    function getOutput4DCoords(shape, texShape) {
+        var coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2'], shape);
+        return "\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(" + texShape[0] + ", " + texShape[1] + "));\n      int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n      " + coordsFromIndexSnippet + "\n      return ivec4(r, c, d, d2);\n    }\n  ";
+    }
+    function getOutput5DCoords(shape, texShape) {
+        var coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2', 'd3'], shape);
+        return "\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(" + texShape[0] + ",\n                             " + texShape[1] + "));\n\n      int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n\n      " + coordsFromIndexSnippet + "\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  ";
+    }
+    function getOutput6DCoords(shape, texShape) {
+        var coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd', 'd2', 'd3', 'd4'], shape);
+        return "\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(" + texShape[0] + ", " + texShape[1] + "));\n      int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n\n      " + coordsFromIndexSnippet + "\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  ";
+    }
+    function getOutputPacked2DCoords(shape, texShape) {
+        var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+        if (arraysEqual(shape, texShape)) {
+            return "\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(" + packedTexShape[0] + ", " + packedTexShape[1] + "));\n      }\n    ";
+        }
+        var texelsInLogicalRow = Math.ceil(shape[1] / 2);
+        return "\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + packedTexShape[0] + ", " + packedTexShape[1] + "));\n\n      int index = resTexRC.x * " + packedTexShape[1] + " + resTexRC.y;\n      int r = 2 * (index / " + texelsInLogicalRow + ");\n      int c = imod(index, " + texelsInLogicalRow + ") * 2;\n\n      return ivec2(r, c);\n    }\n  ";
+    }
+    function getOutput2DCoords(shape, texShape) {
+        if (arraysEqual(shape, texShape)) {
+            return "\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(" + texShape[0] + ", " + texShape[1] + "));\n      }\n    ";
+        }
+        if (shape[1] === 1) {
+            return "\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(" + texShape[0] + ", " + texShape[1] + "));\n        int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    ";
+        }
+        if (shape[0] === 1) {
+            return "\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(" + texShape[0] + ", " + texShape[1] + "));\n        int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n        return ivec2(0, index);\n      }\n    ";
+        }
+        return "\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + texShape[0] + ", " + texShape[1] + "));\n      int index = resTexRC.x * " + texShape[1] + " + resTexRC.y;\n      int r = index / " + shape[1] + ";\n      int c = index - r * " + shape[1] + ";\n      return ivec2(r, c);\n    }\n  ";
+    }
+    function getPackedSamplerScalar(inputInfo) {
+        var texName = inputInfo.name;
+        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+        return "\n    vec4 " + funcName + "() {\n      return texture2D(" + texName + ", halfCR);\n    }\n  ";
+    }
+    function getSamplerScalar(inputInfo) {
+        var texName = inputInfo.name;
+        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+        if (inputInfo.shapeInfo.isUniform) {
+            return "float " + funcName + "() {return " + texName + ";}";
+        }
+        return "\n    float " + funcName + "() {\n      return sampleTexture(" + texName + ", halfCR);\n    }\n  ";
+    }
+    function getPackedSampler1D(inputInfo) {
+        var texName = inputInfo.name;
+        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+        var texShape = inputInfo.shapeInfo.texShape;
+        var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+        return "\n    vec4 " + funcName + "(int index) {\n      vec2 uv = packedUVfrom1D(\n        " + packedTexShape[0] + ", " + packedTexShape[1] + ", index);\n      return texture2D(" + texName + ", uv);\n    }\n  ";
+    }
+    function getSampler1D(inputInfo) {
+        var texName = inputInfo.name;
+        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+        return "\n    float " + funcName + "(int index) {\n      return " + funcName + "Flat(index);\n    }\n  ";
+    }
+    function getPackedSampler2D(inputInfo) {
+        var shape = inputInfo.shapeInfo.logicalShape;
+        var texName = inputInfo.name;
+        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+        var texShape = inputInfo.shapeInfo.texShape;
+        var texNumR = texShape[0];
+        var texNumC = texShape[1];
+        if (texShape != null && arraysEqual(shape, texShape)) {
+            return "\n      vec4 " + funcName + "(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(" + texNumC + ".0, " + texNumR + ".0);\n\n        return texture2D(" + texName + ", uv);\n      }\n    ";
+        }
+        var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+        var valuesPerRow = Math.ceil(shape[1] / 2);
+        return "\n    vec4 " + funcName + "(int row, int col) {\n      vec2 uv = packedUVfrom2D(" + valuesPerRow + ", " + packedTexShape[0] + ", " + packedTexShape[1] + ", row, col);\n      return texture2D(" + texName + ", uv);\n    }\n  ";
+    }
+    function getSampler2D(inputInfo) {
+        var shape = inputInfo.shapeInfo.logicalShape;
+        var texName = inputInfo.name;
+        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+        var texShape = inputInfo.shapeInfo.texShape;
+        if (texShape != null && arraysEqual(shape, texShape)) {
+            var texNumR_1 = texShape[0];
+            var texNumC_1 = texShape[1];
+            return "\n    float " + funcName + "(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(" + texNumC_1 + ".0, " + texNumR_1 + ".0);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
+        }
+        var _a = squeezeShape(shape), newShape = _a.newShape, keptDims = _a.keptDims;
+        var squeezedShape = newShape;
+        if (squeezedShape.length < shape.length) {
+            var newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
+            var params = ['row', 'col'];
+            return "\n      " + getSamplerFromInInfo(newInputInfo) + "\n      float " + funcName + "(int row, int col) {\n        return " + funcName + "(" + getSqueezedParams(params, keptDims) + ");\n      }\n    ";
+        }
+        if (inputInfo.shapeInfo.isUniform) {
+            return "\n      float " + funcName + "(int row, int col) {\n        float index = dot(vec2(row, col), vec2(" + shape[1] + ", 1));\n        return " + funcName + "Flat(round(index));\n      }\n    ";
+        }
+        var texNumR = texShape[0];
+        var texNumC = texShape[1];
+        if (texNumC === 1) {
+            return "\n    float " + funcName + "(int row, int col) {\n      float index = dot(vec2(row, col), vec2(" + shape[1] + ", 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / " + texNumR + ".0);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
+        }
+        if (texNumR === 1) {
+            return "\n    float " + funcName + "(int row, int col) {\n      float index = dot(vec2(row, col), vec2(" + shape[1] + ", 1));\n      vec2 uv = vec2((index + 0.5) / " + texNumC + ".0, 0.5);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
+        }
+        return "\n  float " + funcName + "(int row, int col) {\n    vec2 uv = UVfrom2D(" + texNumR + ", " + texNumC + ", " + shape[1] + ", row, col);\n    return sampleTexture(" + texName + ", uv);\n  }\n";
+    }
+    function getPackedSampler3D(inputInfo) {
+        var shape = inputInfo.shapeInfo.logicalShape;
+        var texName = inputInfo.name;
+        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+        var texShape = inputInfo.shapeInfo.texShape;
+        var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+        if (shape[0] === 1) {
+            var squeezedShape = shape.slice(1);
+            var keptDims = [1, 2];
+            var newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
+            var params = ['b', 'row', 'col'];
+            return "\n        " + getPackedSamplerFromInInfo(newInputInfo) + "\n        vec4 " + funcName + "(int b, int row, int col) {\n          return " + funcName + "(" + getSqueezedParams(params, keptDims) + ");\n        }\n      ";
+        }
+        var texNumR = packedTexShape[0];
+        var texNumC = packedTexShape[1];
+        var valuesPerRow = Math.ceil(shape[2] / 2);
+        var texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);
+        return "\n    vec4 " + funcName + "(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        " + texNumR + ", " + texNumC + ", " + texelsInBatch + ", " + valuesPerRow + ", b, row, col);\n      return texture2D(" + texName + ", uv);\n    }\n  ";
+    }
+    function getSampler3D(inputInfo) {
+        var shape = inputInfo.shapeInfo.logicalShape;
+        var texName = inputInfo.name;
+        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+        var stride0 = shape[1] * shape[2];
+        var stride1 = shape[2];
+        var _a = squeezeShape(shape), newShape = _a.newShape, keptDims = _a.keptDims;
+        var squeezedShape = newShape;
+        if (squeezedShape.length < shape.length) {
+            var newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);
+            var params = ['row', 'col', 'depth'];
+            return "\n        " + getSamplerFromInInfo(newInputInfo) + "\n        float " + funcName + "(int row, int col, int depth) {\n          return " + funcName + "(" + getSqueezedParams(params, keptDims) + ");\n        }\n      ";
+        }
+        if (inputInfo.shapeInfo.isUniform) {
+            return "\n      float " + funcName + "(int row, int col, int depth) {\n        float index = dot(vec3(row, col, depth),\n                          vec3(" + stride0 + ", " + stride1 + ", 1));\n        return " + funcName + "Flat(round(index));\n      }\n    ";
+        }
+        var texShape = inputInfo.shapeInfo.texShape;
+        var texNumR = texShape[0];
+        var texNumC = texShape[1];
+        if (texNumC === stride0) {
+            return "\n        float " + funcName + "(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(" + stride1 + ", 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(" + texNumC + ".0, " + texNumR + ".0);\n          return sampleTexture(" + texName + ", uv);\n        }\n      ";
+        }
+        if (texNumC === stride1) {
+            return "\n    float " + funcName + "(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(" + shape[1] + ", 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(" + texNumC + ".0, " + texNumR + ".0);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
+        }
+        return "\n      float " + funcName + "(int row, int col, int depth) {\n        vec2 uv = UVfrom3D(\n            " + texNumR + ", " + texNumC + ", " + stride0 + ", " + stride1 + ", row, col, depth);\n        return sampleTexture(" + texName + ", uv);\n      }\n  ";
+    }
+    function getPackedSampler4D(inputInfo) {
+        var shape = inputInfo.shapeInfo.logicalShape;
+        var texName = inputInfo.name;
+        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+        var texShape = inputInfo.shapeInfo.texShape;
+        var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+        var texNumR = packedTexShape[0];
+        var texNumC = packedTexShape[1];
+        var valuesPerRow = Math.ceil(shape[3] / 2);
+        var texelsInBatch = valuesPerRow * Math.ceil(shape[2] / 2);
+        var texelsInBatch2 = texelsInBatch * shape[1];
+        return "\n    vec4 " + funcName + "(int b2, int b, int row, int col) {\n      vec2 uv = packedUVfrom4D(\n        " + texNumR + ", " + texNumC + ", " + texelsInBatch2 + ",\n        " + texelsInBatch + ", " + valuesPerRow + ", b2, b, row, col);\n      return texture2D(" + texName + ", uv);\n    }\n  ";
+    }
+    function getSampler4D(inputInfo) {
+        var shape = inputInfo.shapeInfo.logicalShape;
+        var texName = inputInfo.name;
+        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+        var stride2 = shape[3];
+        var stride1 = shape[2] * stride2;
+        var stride0 = shape[1] * stride1;
+        var _a = squeezeShape(shape), newShape = _a.newShape, keptDims = _a.keptDims;
+        if (newShape.length < shape.length) {
+            var newInputInfo = squeezeInputInfo(inputInfo, newShape);
+            var params = ['row', 'col', 'depth', 'depth2'];
+            return "\n      " + getSamplerFromInInfo(newInputInfo) + "\n      float " + funcName + "(int row, int col, int depth, int depth2) {\n        return " + funcName + "(" + getSqueezedParams(params, keptDims) + ");\n      }\n    ";
+        }
+        if (inputInfo.shapeInfo.isUniform) {
+            return "\n      float " + funcName + "(int row, int col, int depth, int depth2) {\n        float index = dot(vec4(row, col, depth, depth2),\n                          vec4(" + stride0 + ", " + stride1 + ", " + stride2 + ", 1));\n        return " + funcName + "Flat(round(index));\n      }\n    ";
+        }
+        var texShape = inputInfo.shapeInfo.texShape;
+        var texNumR = texShape[0];
+        var texNumC = texShape[1];
+        if (texNumC === stride0) {
+            return "\n      float " + funcName + "(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2), vec3(" + stride1 + ", " + stride2 + ", 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(" + texNumC + ".0, " + texNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
+        }
+        if (texNumC === stride2) {
+            return "\n      float " + funcName + "(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(" + shape[1] * shape[2] + ", " + shape[2] + ", 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(" + texNumC + ".0, " + texNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
+        }
+        return "\n    float " + funcName + "(int row, int col, int depth, int depth2) {\n      vec2 uv = UVfrom4D(" + texNumR + ", " + texNumC + ", " + stride0 + ", " + stride1 + ",\n          " + stride2 + ", row, col, depth, depth2);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
+    }
+    function getSampler5D(inputInfo) {
+        var shape = inputInfo.shapeInfo.logicalShape;
+        var texName = inputInfo.name;
+        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+        var stride3 = shape[4];
+        var stride2 = shape[3] * stride3;
+        var stride1 = shape[2] * stride2;
+        var stride0 = shape[1] * stride1;
+        var _a = squeezeShape(shape), newShape = _a.newShape, keptDims = _a.keptDims;
+        if (newShape.length < shape.length) {
+            var newInputInfo = squeezeInputInfo(inputInfo, newShape);
+            var params = ['row', 'col', 'depth', 'depth2', 'depth3'];
+            return "\n      " + getSamplerFromInInfo(newInputInfo) + "\n      float " + funcName + "(int row, int col, int depth, int depth2, int depth3) {\n        return " + funcName + "(" + getSqueezedParams(params, keptDims) + ");\n      }\n    ";
+        }
+        if (inputInfo.shapeInfo.isUniform) {
+            return "\n      float " + funcName + "(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(" + stride0 + ", " + stride1 + ", " + stride2 + ", " + stride3 + ")) +\n          depth3;\n        return " + funcName + "Flat(index);\n      }\n    ";
+        }
+        var texShape = inputInfo.shapeInfo.texShape;
+        var texNumR = texShape[0];
+        var texNumC = texShape[1];
+        if (texNumC === stride0) {
+            return "\n      float " + funcName + "(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(\n          vec4(col, depth, depth2, depth3),\n          vec4(" + stride1 + ", " + stride2 + ", " + stride3 + ", 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(" + texNumC + ".0, " + texNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
+        }
+        if (texNumC === stride3) {
+            return "\n      float " + funcName + "(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(" + shape[1] * shape[2] * shape[3] + ", " + shape[2] * shape[3] + ",\n            " + shape[3] + ", 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(" + texNumC + ".0, " + texNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
+        }
+        return "\n    float " + funcName + "(int row, int col, int depth, int depth2, int depth3) {\n      vec2 uv = UVfrom5D(" + texNumR + ", " + texNumC + ", " + stride0 + ", " + stride1 + ",\n          " + stride2 + ", " + stride3 + ", row, col, depth, depth2, depth3);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
+    }
+    function getSampler6D(inputInfo) {
+        var shape = inputInfo.shapeInfo.logicalShape;
+        var texName = inputInfo.name;
+        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);
+        var stride4 = shape[5];
+        var stride3 = shape[4] * stride4;
+        var stride2 = shape[3] * stride3;
+        var stride1 = shape[2] * stride2;
+        var stride0 = shape[1] * stride1;
+        var _a = squeezeShape(shape), newShape = _a.newShape, keptDims = _a.keptDims;
+        if (newShape.length < shape.length) {
+            var newInputInfo = squeezeInputInfo(inputInfo, newShape);
+            var params = ['row', 'col', 'depth', 'depth2', 'depth3', 'depth4'];
+            return "\n      " + getSamplerFromInInfo(newInputInfo) + "\n      float " + funcName + "(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return " + funcName + "(" + getSqueezedParams(params, keptDims) + ");\n      }\n    ";
+        }
+        if (inputInfo.shapeInfo.isUniform) {
+            return "\n      float " + funcName + "(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(" + stride0 + ", " + stride1 + ", " + stride2 + ", " + stride3 + ")) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(" + stride4 + ", 1));\n        return " + funcName + "Flat(index);\n      }\n    ";
+        }
+        var texShape = inputInfo.shapeInfo.texShape;
+        var texNumR = texShape[0];
+        var texNumC = texShape[1];
+        if (texNumC === stride0) {
+            return "\n      float " + funcName + "(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(\n          vec4(col, depth, depth2, depth3),\n          vec4(" + stride1 + ", " + stride2 + ", " + stride3 + ", " + stride4 + ")) + depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(" + texNumC + ".0, " + texNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
+        }
+        if (texNumC === stride4) {
+            return "\n      float " + funcName + "(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(" + shape[1] * shape[2] * shape[3] * shape[4] + ",\n               " + shape[2] * shape[3] * shape[4] + ",\n               " + shape[3] * shape[4] + ",\n               " + shape[4] + ")) + depth3;\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(" + texNumC + ".0, " + texNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
+        }
+        return "\n    float " + funcName + "(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      vec2 uv = UVfrom6D(" + texNumR + ", " + texNumC + ", " + stride0 + ", " + stride1 + ",\n          " + stride2 + ", " + stride3 + ", " + stride4 + "\n          ,row, col, depth, depth2, depth3, depth4);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
+    }
+    function getSamplerFlat(inputInfo) {
+        var texName = inputInfo.name;
+        var funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1) + 'Flat';
+        var inSize = sizeFromShape(inputInfo.shapeInfo.logicalShape);
+        if (inputInfo.shapeInfo.isUniform) {
+            if (inSize === 1) {
+                return "float " + funcName + "(int index) {return " + texName + ";}";
+            }
+            return "\n      float " + funcName + "(int index) {\n        for (int i = 0; i < " + inSize + "; i++) {\n          if (i == index) {\n            return " + texName + "[i];\n          }\n        }\n      }\n    ";
+        }
+        var texShape = inputInfo.shapeInfo.texShape;
+        var tNumR = texShape[0];
+        var tNumC = texShape[1];
+        if (tNumC === 1 && tNumR === 1) {
+            return "\n      float " + funcName + "(int index) {\n        return sampleTexture(" + texName + ", halfCR);\n      }\n    ";
+        }
+        if (tNumC === 1) {
+            return "\n      float " + funcName + "(int index) {\n        vec2 uv = vec2(0.5, (float(index) + 0.5) / " + tNumR + ".0);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
+        }
+        if (tNumR === 1) {
+            return "\n      float " + funcName + "(int index) {\n        vec2 uv = vec2((float(index) + 0.5) / " + tNumC + ".0, 0.5);\n        return sampleTexture(" + texName + ", uv);\n      }\n    ";
+        }
+        return "\n    float " + funcName + "(int index) {\n      vec2 uv = UVfrom1D(" + tNumR + ", " + tNumC + ", index);\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
+    }
+    function getBroadcastOutputCoordsSampler(inputInfo, outShapeInfo, texFuncSnippet, funcName) {
+        var inRank = inputInfo.shapeInfo.logicalShape.length;
+        var outRank = outShapeInfo.logicalShape.length;
+        var type = 'int';
+        if (outRank === 2) {
+            type = 'ivec2';
+        }
+        else if (outRank === 3) {
+            type = 'ivec3';
+        }
+        else if (outRank === 4) {
+            type = 'ivec4';
+        }
+        var broadcastDims = getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);
+        var rankDiff = outRank - inRank;
+        var coordsSnippet;
+        if (inRank === 0) {
+            coordsSnippet = '';
+        }
+        else if (outRank < 2 && broadcastDims.length >= 1) {
+            coordsSnippet = 'coords = 0;';
+        }
+        else {
+            coordsSnippet =
+                broadcastDims.map(function (d) { return "coords[" + (d + rankDiff) + "] = 0;"; }).join('\n');
+        }
+        var unpackedCoordsSnippet = '';
+        if (outRank < 2 && inRank > 0) {
+            unpackedCoordsSnippet = 'coords';
+        }
+        else {
+            unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape
+                .map(function (s, i) { return "coords[" + (i + rankDiff) + "]"; })
+                .join(', ');
+        }
+        return "\n    float " + funcName + "() {\n      " + type + " coords = getOutputCoords();\n      " + coordsSnippet + "\n      return get" + texFuncSnippet + "(" + unpackedCoordsSnippet + ");\n    }\n  ";
+    }
+    function getPackedSamplerAtOutputCoords(inputInfo, outShapeInfo, supportsBroadcasting) {
+        var texName = inputInfo.name;
+        var texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);
+        var texShape = inputInfo.shapeInfo.texShape;
+        var funcName = 'get' + texFuncSnippet + 'AtOutCoords';
+        var outTexShape = outShapeInfo.texShape;
+        var packedTexShape = [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];
+        var texNumR = packedTexShape[0];
+        var texNumC = packedTexShape[1];
+        var broadcastDims = getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);
+        var inRank = inputInfo.shapeInfo.logicalShape.length;
+        var outRank = outShapeInfo.logicalShape.length;
+        if (broadcastDims.length) {
+            throw Error('Packed broadcast sampling is not implemented yet.');
+        }
+        var inTexShape = inputInfo.shapeInfo.texShape;
+        if (arraysEqual(inTexShape, outTexShape)) {
+            return "\n      vec4 " + funcName + "() {\n        return texture2D(" + texName + ", resultUV);\n      }\n    ";
+        }
+        var output = "return texture2D(" + texName + ", uv)";
+        if (inRank === 1 && outRank > 1) {
+            output = "\n      vec4 sample = texture2D(" + texName + ", uv);\n      return vec4(sample.xy, sample.xy);\n    ";
+        }
+        else if (inRank === 0 && outRank > 0) {
+            if (outRank === 1) {
+                output = "\n        vec4 sample = texture2D(" + texName + ", uv);\n        return vec4(sample.x, sample.x, 0., 0.);\n      ";
+            }
+            else {
+                output = "\n        vec4 sample = texture2D(" + texName + ", uv);\n        return vec4(sample.x);\n      ";
+            }
+        }
+        return "\n    vec4 " + funcName + "() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + packedTexShape[0] + ", " + packedTexShape[1] + "));\n      int index = resTexRC.x * " + packedTexShape[1] + " + resTexRC.y;\n\n      int texR = index / " + texNumC + ";\n      int texC = index - texR * " + texNumC + ";\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(" + texNumC + ", " + texNumR + ");\n\n      " + output + ";\n    }\n  ";
+    }
+    function getSamplerAtOutputCoords(inputInfo, outShapeInfo, supportsBroadcasting) {
+        var texName = inputInfo.name;
+        var texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);
+        var funcName = 'get' + texFuncSnippet + 'AtOutCoords';
+        var broadcastDims = getBroadcastDims(inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);
+        var inRank = inputInfo.shapeInfo.logicalShape.length;
+        var outRank = outShapeInfo.logicalShape.length;
+        var doBroadcast = supportsBroadcasting && ((outRank > inRank) || broadcastDims.length > 0);
+        var broadcastOverOuter = broadcastDimsAreOuter(broadcastDims);
+        var isUniform = inputInfo.shapeInfo.isUniform;
+        if (doBroadcast && !broadcastOverOuter) {
+            return getBroadcastOutputCoordsSampler(inputInfo, outShapeInfo, texFuncSnippet, funcName);
+        }
+        var inSize = sizeFromShape(inputInfo.shapeInfo.logicalShape);
+        var broadcastSnippet = '';
+        if (doBroadcast && broadcastOverOuter) {
+            broadcastSnippet = "\n        int mainPart = index / " + inSize + ";\n        index -= mainPart * " + inSize + ";\n      ";
+        }
+        var outTexShape = outShapeInfo.texShape;
+        if (isUniform) {
+            if (inSize === 1) {
+                return "float " + funcName + "() {return " + texName + ";}";
+            }
+            return "\n      float " + funcName + "() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                              vec2(" + outTexShape[0] + ", " + outTexShape[1] + "));\n        int index = resTexRC.x * " + outTexShape[1] + " + resTexRC.y;\n        " + broadcastSnippet + "\n        return get" + texFuncSnippet + "Flat(index);\n      }\n    ";
+        }
+        var inTexShape = inputInfo.shapeInfo.texShape;
+        if (arraysEqual(inTexShape, outTexShape)) {
+            return "\n      float " + funcName + "() {\n        return sampleTexture(" + texName + ", resultUV);\n      }\n    ";
+        }
+        return "\n    float " + funcName + "() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(" + outTexShape[0] + ", " + outTexShape[1] + "));\n      int index = resTexRC.x * " + outTexShape[1] + " + resTexRC.y;\n      " + broadcastSnippet + "\n      int texR = index / " + inTexShape[1] + ";\n      int texC = index - texR * " + inTexShape[1] + ";\n      vec2 uv = (vec2(texC, texR) + halfCR) /\n                 vec2(" + inTexShape[1] + ".0, " + inTexShape[0] + ".0);\n\n      return sampleTexture(" + texName + ", uv);\n    }\n  ";
+    }
+    function getCoordsDataType(rank) {
+        if (rank <= 1) {
+            return 'int';
+        }
+        else if (rank === 2) {
+            return 'ivec2';
+        }
+        else if (rank === 3) {
+            return 'ivec3';
+        }
+        else if (rank === 4) {
+            return 'ivec4';
+        }
+        else if (rank === 5) {
+            return 'ivec5';
+        }
+        else if (rank === 6) {
+            return 'ivec6';
+        }
+        else {
+            throw Error("GPU for rank " + rank + " is not yet supported");
+        }
+    }
+    function squeezeInputInfo(inInfo, squeezedShape) {
+        var newInputInfo = JSON.parse(JSON.stringify(inInfo));
+        newInputInfo.shapeInfo.logicalShape = squeezedShape;
+        return newInputInfo;
+    }
+    function getSqueezedParams(params, keptDims) {
+        return keptDims.map(function (d) { return params[d]; }).join(', ');
+    }
+
+    var CumSumProgram = (function () {
+        function CumSumProgram(shape, exclusive, reverse) {
+            this.variableNames = ['x'];
+            this.outputShape = shape;
+            var rank = shape.length;
+            var finalDim = shape[shape.length - 1];
+            var comparator = reverse ? '<' : '>';
+            this.userCode = "\n      int getIndex(int i) {\n        " + (reverse ? "return " + finalDim + " -i - 1;" : 'return i;') + "\n      }\n\n      void main() {\n        " + getCoordsDataType(rank) + " coords = getOutputCoords();\n        int end = " + getFinalCoord(rank, 'coords') + ";\n        float val = 0.0;\n        for (int i = " + finalDim + " - 1; i >= 0; i -= 1) {\n          int idx = getIndex(i);\n          if (idx " + comparator + " end) {\n            continue;\n          }\n          if (idx == end && " + exclusive + ") {\n            continue;\n          }\n          " + getFinalCoord(rank, 'coords') + " = idx;\n          val += getX(" + getCoords(rank, 'coords') + ");\n        }\n        setOutput(val);\n      }\n    ";
+        }
+        return CumSumProgram;
+    }());
+    function getCoords(rank, name) {
+        if (rank === 1) {
+            return "" + name;
+        }
+        else if (rank === 2) {
+            return name + ".x, " + name + ".y";
+        }
+        else if (rank === 3) {
+            return name + ".x, " + name + ".y, " + name + ".z";
+        }
+        else if (rank === 4) {
+            return name + ".x, " + name + ".y, " + name + ".z, " + name + ".w";
+        }
+        else {
+            throw Error("Cumulative sum for rank " + rank + " is not yet supported");
+        }
+    }
+    function getFinalCoord(rank, name) {
+        if (rank === 1) {
+            return "" + name;
+        }
+        else if (rank === 2) {
+            return name + ".y";
+        }
+        else if (rank === 3) {
+            return name + ".z";
+        }
+        else if (rank === 4) {
+            return name + ".w";
+        }
+        else {
+            throw Error("Cumulative sum for rank " + rank + " is not yet supported");
+        }
+    }
+
+    var DepthToSpaceProgram = (function () {
+        function DepthToSpaceProgram(outputShape, blockSize, dataFormat) {
+            this.variableNames = ['x'];
+            this.outputShape = [];
+            this.outputShape = outputShape;
+            this.blockSize = blockSize;
+            this.dataFormat = dataFormat;
+            this.userCode = "\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = " + this.getHeightCoordString() + ";\n      int w = " + this.getWidthCoordString() + ";\n      int d = " + this.getDepthCoordString() + ";\n\n      int in_h = h / " + blockSize + ";\n      int offset_h = imod(h, " + blockSize + ");\n      int in_w = w / " + blockSize + ";\n      int offset_w = imod(w, " + blockSize + ");\n      int offset_d = (offset_h * " + blockSize + " + offset_w) *\n        " + this.getOutputDepthSize() + ";\n      int in_d = d + offset_d;\n\n      float result = " + this.getInputSamplingString() + ";\n      setOutput(result);\n    }\n  ";
+        }
+        DepthToSpaceProgram.prototype.getHeightCoordString = function () {
+            if (this.dataFormat === 'NHWC') {
+                return "coords[1]";
+            }
+            else {
+                return "coords[2]";
+            }
+        };
+        DepthToSpaceProgram.prototype.getWidthCoordString = function () {
+            if (this.dataFormat === 'NHWC') {
+                return "coords[2]";
+            }
+            else {
+                return "coords[3]";
+            }
+        };
+        DepthToSpaceProgram.prototype.getDepthCoordString = function () {
+            if (this.dataFormat === 'NHWC') {
+                return "coords[3]";
+            }
+            else {
+                return "coords[1]";
+            }
+        };
+        DepthToSpaceProgram.prototype.getOutputDepthSize = function () {
+            if (this.dataFormat === 'NHWC') {
+                return this.outputShape[3];
+            }
+            else {
+                return this.outputShape[1];
+            }
+        };
+        DepthToSpaceProgram.prototype.getInputSamplingString = function () {
+            if (this.dataFormat === 'NHWC') {
+                return "getX(b, in_h, in_w, in_d)";
+            }
+            else {
+                return "getX(b, in_d, in_h, in_w)";
+            }
+        };
+        return DepthToSpaceProgram;
+    }());
+
+    var EncodeFloatProgram = (function () {
+        function EncodeFloatProgram(outputShape) {
+            this.variableNames = ['A'];
+            this.outputShape = outputShape;
+            this.userCode = "\n      const float FLOAT_MAX = 1.70141184e38;\n      const float FLOAT_MIN = 1.17549435e-38;\n\n      lowp vec4 encode_float(highp float v) {\n        if (isNaN(v)) {\n          return vec4(255, 255, 255, 255);\n        }\n\n        highp float av = abs(v);\n\n        if(av < FLOAT_MIN) {\n          return vec4(0.0, 0.0, 0.0, 0.0);\n        } else if(v > FLOAT_MAX) {\n          return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n        } else if(v < -FLOAT_MAX) {\n          return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n        }\n\n        highp vec4 c = vec4(0,0,0,0);\n\n        highp float e = floor(log2(av));\n        highp float m = exp2(fract(log2(av))) - 1.0;\n\n        c[2] = floor(128.0 * m);\n        m -= c[2] / 128.0;\n        c[1] = floor(32768.0 * m);\n        m -= c[1] / 32768.0;\n        c[0] = floor(8388608.0 * m);\n\n        highp float ebias = e + 127.0;\n        c[3] = floor(ebias / 2.0);\n        ebias -= c[3] * 2.0;\n        c[2] += floor(ebias) * 128.0;\n\n        c[3] += 128.0 * step(0.0, -v);\n\n        return c / 255.0;\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        gl_FragColor = encode_float(x);\n      }\n    ";
+        }
+        return EncodeFloatProgram;
+    }());
+
+    var COMPLEX_FFT = {
+        REAL: 'return real * expR - imag * expI;',
+        IMAG: 'return real * expI + imag * expR;'
+    };
+    var FFTProgram = (function () {
+        function FFTProgram(op, inputShape, inverse) {
+            this.variableNames = ['real', 'imag'];
+            var innerDim = inputShape[1];
+            this.outputShape = inputShape;
+            var exponentMultiplierSnippet = inverse ? "2.0 * " + Math.PI : "-2.0 * " + Math.PI;
+            var resultDenominator = inverse ? innerDim + ".0" : '1.0';
+            this.userCode = "\n      const float exponentMultiplier = " + exponentMultiplierSnippet + ";\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        " + op + "\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float(" + innerDim + ");\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < " + innerDim + "; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / " + resultDenominator + ";\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    ";
+        }
+        return FFTProgram;
+    }());
+
+    var FromPixelsProgram = (function () {
+        function FromPixelsProgram(outputShape) {
+            this.variableNames = ['A'];
+            var height = outputShape[0], width = outputShape[1];
+            this.outputShape = outputShape;
+            this.userCode = "\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(" + width + ".0, " + height + ".0);\n\n        vec4 values = texture2D(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    ";
+        }
+        return FromPixelsProgram;
+    }());
+
+    var GatherProgram = (function () {
+        function GatherProgram(aShape, indicesLength, axis) {
+            this.variableNames = ['A', 'indices'];
+            var outputShape = aShape.slice();
+            outputShape[axis] = indicesLength;
+            this.outputShape = outputShape;
+            this.rank = outputShape.length;
+            var dtype = getCoordsDataType(this.rank);
+            var sourceCoords = getSourceCoords(aShape, axis);
+            this.userCode = "\n      void main() {\n        " + dtype + " resRC = getOutputCoords();\n        setOutput(getA(" + sourceCoords + "));\n      }\n    ";
+        }
+        return GatherProgram;
+    }());
+    function getSourceCoords(aShape, axis) {
+        var rank = aShape.length;
+        if (rank > 4) {
+            throw Error("Gather for rank " + rank + " is not yet supported");
+        }
+        if (rank === 1) {
+            return "int(getIndices(resRC))";
+        }
+        var currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];
+        var sourceCoords = [];
+        for (var i = 0; i < aShape.length; i++) {
+            if (i === axis) {
+                sourceCoords.push("int(getIndices(" + currentCoords[i] + "))");
+            }
+            else {
+                sourceCoords.push("" + currentCoords[i]);
+            }
+        }
+        return sourceCoords.join();
+    }
+
+    var GatherNDProgram = (function () {
+        function GatherNDProgram(sliceDim, strides, shape) {
+            this.sliceDim = sliceDim;
+            this.strides = strides;
+            this.variableNames = ['x', 'indices'];
+            this.outputShape = shape;
+            var stridesType = getCoordsDataType(strides.length);
+            var dtype = getCoordsDataType(shape.length);
+            var strideString = this.sliceDim > 1 ? 'strides[j]' : 'strides';
+            this.userCode = "\n        " + stridesType + " strides = " + stridesType + "(" + this.strides + ");\n         void main() {\n          " + dtype + " coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < " + this.sliceDim + "; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * " + strideString + ";\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      ";
+        }
+        return GatherNDProgram;
+    }());
+
+    var TextureUsage;
+    (function (TextureUsage) {
+        TextureUsage[TextureUsage["RENDER"] = 0] = "RENDER";
+        TextureUsage[TextureUsage["UPLOAD"] = 1] = "UPLOAD";
+        TextureUsage[TextureUsage["PIXELS"] = 2] = "PIXELS";
+        TextureUsage[TextureUsage["DOWNLOAD"] = 3] = "DOWNLOAD";
+    })(TextureUsage || (TextureUsage = {}));
+    var PhysicalTextureType;
+    (function (PhysicalTextureType) {
+        PhysicalTextureType[PhysicalTextureType["UNPACKED_FLOAT16"] = 0] = "UNPACKED_FLOAT16";
+        PhysicalTextureType[PhysicalTextureType["UNPACKED_FLOAT32"] = 1] = "UNPACKED_FLOAT32";
+        PhysicalTextureType[PhysicalTextureType["PACKED_4X1_UNSIGNED_BYTE"] = 2] = "PACKED_4X1_UNSIGNED_BYTE";
+        PhysicalTextureType[PhysicalTextureType["PACKED_2X2_FLOAT32"] = 3] = "PACKED_2X2_FLOAT32";
+        PhysicalTextureType[PhysicalTextureType["PACKED_2X2_FLOAT16"] = 4] = "PACKED_2X2_FLOAT16";
+    })(PhysicalTextureType || (PhysicalTextureType = {}));
+    function getUnpackedMatrixTextureShapeWidthHeight(rows, columns) {
+        return [columns, rows];
+    }
+    function getUnpackedArraySizeFromMatrixSize(matrixSize, channelsPerTexture) {
+        return matrixSize * channelsPerTexture;
+    }
+    function getMatrixSizeFromUnpackedArraySize(unpackedSize, channelsPerTexture) {
+        if (unpackedSize % channelsPerTexture !== 0) {
+            throw new Error("unpackedSize (" + unpackedSize + ") must be a multiple of " +
+                ("" + channelsPerTexture));
+        }
+        return unpackedSize / channelsPerTexture;
+    }
+    function encodeMatrixToUnpackedArray(matrix, unpackedArray, channelsPerTexture) {
+        var requiredSize = getUnpackedArraySizeFromMatrixSize(matrix.length, channelsPerTexture);
+        if (unpackedArray.length < requiredSize) {
+            throw new Error("unpackedArray length (" + unpackedArray.length + ") must be >= " +
+                ("" + requiredSize));
+        }
+        var dst = 0;
+        for (var src = 0; src < matrix.length; ++src) {
+            unpackedArray[dst] = matrix[src];
+            dst += channelsPerTexture;
+        }
+    }
+    function decodeMatrixFromUnpackedArray(unpackedArray, matrix, channelsPerTexture) {
+        var requiredSize = getMatrixSizeFromUnpackedArraySize(unpackedArray.length, channelsPerTexture);
+        if (matrix.length < requiredSize) {
+            throw new Error("matrix length (" + matrix.length + ") must be >= " + requiredSize);
+        }
+        var dst = 0;
+        for (var src = 0; src < unpackedArray.length; src += channelsPerTexture) {
+            matrix[dst++] = unpackedArray[src];
+        }
+    }
+    function getPackedMatrixTextureShapeWidthHeight(rows, columns) {
+        return [
+            Math.max(1, Math.ceil(columns / 2)), Math.max(1, Math.ceil(rows / 2))
+        ];
+    }
+    function getPackedRGBAArraySizeFromMatrixShape(rows, columns) {
+        var _a = getPackedMatrixTextureShapeWidthHeight(rows, columns), w = _a[0], h = _a[1];
+        return w * h * 4;
+    }
+    function encodeMatrixToPackedRGBA(matrix, batches, rows, columns, packedRGBA) {
+        var requiredSize = getPackedRGBAArraySizeFromMatrixShape(rows, columns);
+        if (packedRGBA.length < requiredSize) {
+            throw new Error("packedRGBA length (" + packedRGBA.length + ") must be >=\n        " + requiredSize);
+        }
+        var oddWidth = (columns % 2) === 1;
+        var oddHeight = (rows % 2) === 1;
+        var widthInFullBlocks = Math.floor(columns / 2);
+        var heightInFullBlocks = Math.floor(rows / 2);
+        var texelsPerRow = Math.ceil(columns / 2);
+        var texelsPerBatch = texelsPerRow * Math.ceil(rows / 2);
+        var flattenedMatrixSize = nearestLargerEven(rows) * nearestLargerEven(columns);
+        for (var batch = 0; batch < batches; batch++) {
+            var sourceOffset = batch * rows * columns;
+            var batchOffset = batch * flattenedMatrixSize;
+            {
+                var dstStride = (oddWidth ? 4 : 0);
+                var oneRow = columns;
+                var dst = batchOffset;
+                for (var blockY = 0; blockY < heightInFullBlocks; ++blockY) {
+                    var matrixSrcRow = (blockY * 2 * columns);
+                    for (var blockX = 0; blockX < widthInFullBlocks; ++blockX) {
+                        var matrixSrcCol = blockX * 2;
+                        var src = sourceOffset + matrixSrcRow + matrixSrcCol;
+                        packedRGBA[dst] = matrix[src];
+                        packedRGBA[dst + 1] = matrix[src + 1];
+                        packedRGBA[dst + 2] = matrix[src + oneRow];
+                        packedRGBA[dst + 3] = matrix[src + oneRow + 1];
+                        dst += 4;
+                    }
+                    dst += dstStride;
+                }
+            }
+            if (oddWidth) {
+                var src = sourceOffset + columns - 1;
+                var dst = batchOffset + (texelsPerRow - 1) * 4;
+                var srcStride = 2 * columns;
+                var dstStride = texelsPerRow * 4;
+                for (var blockY = 0; blockY < heightInFullBlocks; ++blockY) {
+                    packedRGBA[dst] = matrix[src];
+                    packedRGBA[dst + 2] = matrix[src + columns];
+                    src += srcStride;
+                    dst += dstStride;
+                }
+            }
+            if (oddHeight) {
+                var src = sourceOffset + (rows - 1) * columns;
+                var dst = batchOffset + (texelsPerBatch - texelsPerRow) * 4;
+                for (var blockX = 0; blockX < widthInFullBlocks; ++blockX) {
+                    packedRGBA[dst++] = matrix[src++];
+                    packedRGBA[dst++] = matrix[src++];
+                    dst += 2;
+                }
+                if (oddWidth && oddHeight) {
+                    packedRGBA[batchOffset + flattenedMatrixSize - 4] = matrix[src];
+                }
+            }
+        }
+        return packedRGBA;
+    }
+    function decodeMatrixFromPackedRGBA(packedRGBA, batches, rows, columns, matrix) {
+        var requiredSize = rows * columns;
+        if (matrix.length < requiredSize) {
+            throw new Error("matrix length (" + matrix.length + ") must be >= " + requiredSize);
+        }
+        var oddWidth = (columns % 2) === 1;
+        var oddHeight = (rows % 2) === 1;
+        var widthInFullBlocks = Math.floor(columns / 2);
+        var heightInFullBlocks = Math.floor(rows / 2);
+        var texelsPerRow = Math.ceil(columns / 2);
+        var texelsPerBatch = texelsPerRow * Math.ceil(rows / 2);
+        var flattenedMatrixSize = nearestLargerEven(rows) * nearestLargerEven(columns);
+        for (var batch = 0; batch < batches; batch++) {
+            var batchOffset = batch * rows * columns;
+            var sourceOffset = batch * flattenedMatrixSize;
+            {
+                var srcStride = oddWidth ? 4 : 0;
+                var dstStride = columns + (oddWidth ? 1 : 0);
+                var src = sourceOffset;
+                var dstRow1 = batchOffset;
+                var dstRow2 = batchOffset + columns;
+                for (var blockY = 0; blockY < heightInFullBlocks; ++blockY) {
+                    for (var blockX = 0; blockX < widthInFullBlocks; ++blockX) {
+                        matrix[dstRow1++] = packedRGBA[src++];
+                        matrix[dstRow1++] = packedRGBA[src++];
+                        matrix[dstRow2++] = packedRGBA[src++];
+                        matrix[dstRow2++] = packedRGBA[src++];
+                    }
+                    src += srcStride;
+                    dstRow1 += dstStride;
+                    dstRow2 += dstStride;
+                }
+            }
+            if (oddWidth) {
+                var src = sourceOffset + (texelsPerRow - 1) * 4;
+                var dst = batchOffset + columns - 1;
+                var srcStride = texelsPerRow * 4;
+                var dstStride = 2 * columns;
+                for (var blockY = 0; blockY < heightInFullBlocks; ++blockY) {
+                    matrix[dst] = packedRGBA[src];
+                    matrix[dst + columns] = packedRGBA[src + 2];
+                    src += srcStride;
+                    dst += dstStride;
+                }
+            }
+            if (oddHeight) {
+                var src = sourceOffset + (texelsPerBatch - texelsPerRow) * 4;
+                var dst = batchOffset + (rows - 1) * columns;
+                for (var blockX = 0; blockX < widthInFullBlocks; ++blockX) {
+                    matrix[dst++] = packedRGBA[src++];
+                    matrix[dst++] = packedRGBA[src++];
+                    src += 2;
+                }
+                if (oddWidth) {
+                    matrix[batchOffset + (rows * columns) - 1] = packedRGBA[src];
+                }
+            }
+        }
+        return matrix;
+    }
+
+    function callAndCheck(gl, func) {
+        var returnValue = func();
+        checkWebGLError(gl);
+        return returnValue;
+    }
+    var webGLDebugErrorCheckingEnabled = false;
+    function enableDebugWebGLErrorChecking(enabled) {
+        webGLDebugErrorCheckingEnabled = enabled;
+    }
+    function checkWebGLError(gl) {
+        if (webGLDebugErrorCheckingEnabled) {
+            var error = gl.getError();
+            if (error !== gl.NO_ERROR) {
+                throw new Error('WebGL Error: ' + getWebGLErrorMessage(gl, error));
+            }
+        }
+    }
+    function getWebGLErrorMessage(gl, status) {
+        switch (status) {
+            case gl.NO_ERROR:
+                return 'NO_ERROR';
+            case gl.INVALID_ENUM:
+                return 'INVALID_ENUM';
+            case gl.INVALID_VALUE:
+                return 'INVALID_VALUE';
+            case gl.INVALID_OPERATION:
+                return 'INVALID_OPERATION';
+            case gl.INVALID_FRAMEBUFFER_OPERATION:
+                return 'INVALID_FRAMEBUFFER_OPERATION';
+            case gl.OUT_OF_MEMORY:
+                return 'OUT_OF_MEMORY';
+            case gl.CONTEXT_LOST_WEBGL:
+                return 'CONTEXT_LOST_WEBGL';
+            default:
+                return "Unknown error code " + status;
+        }
+    }
+    function getExtensionOrThrow(gl, extensionName) {
+        return throwIfNull(gl, function () { return gl.getExtension(extensionName); }, 'Extension "' + extensionName + '" not supported on this browser.');
+    }
+    function createVertexShader(gl, vertexShaderSource) {
+        var vertexShader = throwIfNull(gl, function () { return gl.createShader(gl.VERTEX_SHADER); }, 'Unable to create vertex WebGLShader.');
+        callAndCheck(gl, function () { return gl.shaderSource(vertexShader, vertexShaderSource); });
+        callAndCheck(gl, function () { return gl.compileShader(vertexShader); });
+        if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {
+            console.log(gl.getShaderInfoLog(vertexShader));
+            throw new Error('Failed to compile vertex shader.');
+        }
+        return vertexShader;
+    }
+    function createFragmentShader(gl, fragmentShaderSource) {
+        var fragmentShader = throwIfNull(gl, function () { return gl.createShader(gl.FRAGMENT_SHADER); }, 'Unable to create fragment WebGLShader.');
+        callAndCheck(gl, function () { return gl.shaderSource(fragmentShader, fragmentShaderSource); });
+        callAndCheck(gl, function () { return gl.compileShader(fragmentShader); });
+        if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {
+            logShaderSourceAndInfoLog(fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));
+            throw new Error('Failed to compile fragment shader.');
+        }
+        return fragmentShader;
+    }
+    var lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;
+    function logShaderSourceAndInfoLog(shaderSource, shaderInfoLog) {
+        var lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);
+        if (lineNumberRegexResult == null) {
+            console.log("Couldn't parse line number in error: " + shaderInfoLog);
+            console.log(shaderSource);
+            return;
+        }
+        var lineNumber = +lineNumberRegexResult[1];
+        var shaderLines = shaderSource.split('\n');
+        var pad = shaderLines.length.toString().length + 2;
+        var linesWithLineNumbers = shaderLines.map(function (line, lineNumber) {
+            return rightPad((lineNumber + 1).toString(), pad) + line;
+        });
+        var maxLineLength = 0;
+        for (var i = 0; i < linesWithLineNumbers.length; i++) {
+            maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength);
+        }
+        var beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);
+        var errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);
+        var afterErrorLines = linesWithLineNumbers.slice(lineNumber);
+        console.log(beforeErrorLines.join('\n'));
+        console.log(shaderInfoLog.split('\n')[0]);
+        console.log("%c " + rightPad(errorLine[0], maxLineLength), 'border:1px solid red; background-color:#e3d2d2; color:#a61717');
+        console.log(afterErrorLines.join('\n'));
+    }
+    function createProgram(gl) {
+        return throwIfNull(gl, function () { return gl.createProgram(); }, 'Unable to create WebGLProgram.');
+    }
+    function linkProgram(gl, program) {
+        callAndCheck(gl, function () { return gl.linkProgram(program); });
+        if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {
+            console.log(gl.getProgramInfoLog(program));
+            throw new Error('Failed to link vertex and fragment shaders.');
+        }
+    }
+    function validateProgram(gl, program) {
+        callAndCheck(gl, function () { return gl.validateProgram(program); });
+        if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {
+            console.log(gl.getProgramInfoLog(program));
+            throw new Error('Shader program validation failed.');
+        }
+    }
+    function createStaticVertexBuffer(gl, data) {
+        var buffer = throwIfNull(gl, function () { return gl.createBuffer(); }, 'Unable to create WebGLBuffer');
+        callAndCheck(gl, function () { return gl.bindBuffer(gl.ARRAY_BUFFER, buffer); });
+        callAndCheck(gl, function () { return gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW); });
+        return buffer;
+    }
+    function createStaticIndexBuffer(gl, data) {
+        var buffer = throwIfNull(gl, function () { return gl.createBuffer(); }, 'Unable to create WebGLBuffer');
+        callAndCheck(gl, function () { return gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer); });
+        callAndCheck(gl, function () { return gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW); });
+        return buffer;
+    }
+    function getNumChannels() {
+        if (ENV.get('WEBGL_VERSION') === 2) {
+            return 1;
+        }
+        return 4;
+    }
+    function createTexture(gl) {
+        return throwIfNull(gl, function () { return gl.createTexture(); }, 'Unable to create WebGLTexture.');
+    }
+    function validateTextureSize(width, height) {
+        var maxTextureSize = ENV.get('WEBGL_MAX_TEXTURE_SIZE');
+        if ((width <= 0) || (height <= 0)) {
+            var requested = "[" + width + "x" + height + "]";
+            throw new Error('Requested texture size ' + requested + ' is invalid.');
+        }
+        if ((width > maxTextureSize) || (height > maxTextureSize)) {
+            var requested = "[" + width + "x" + height + "]";
+            var max = "[" + maxTextureSize + "x" + maxTextureSize + "]";
+            throw new Error('Requested texture size ' + requested +
+                ' greater than WebGL maximum on this browser / GPU ' + max + '.');
+        }
+    }
+    function createFramebuffer(gl) {
+        return throwIfNull(gl, function () { return gl.createFramebuffer(); }, 'Unable to create WebGLFramebuffer.');
+    }
+    function bindVertexBufferToProgramAttribute(gl, program, attribute, buffer, arrayEntriesPerItem, itemStrideInBytes, itemOffsetInBytes) {
+        var loc = gl.getAttribLocation(program, attribute);
+        if (loc === -1) {
+            return false;
+        }
+        callAndCheck(gl, function () { return gl.bindBuffer(gl.ARRAY_BUFFER, buffer); });
+        callAndCheck(gl, function () { return gl.vertexAttribPointer(loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes, itemOffsetInBytes); });
+        callAndCheck(gl, function () { return gl.enableVertexAttribArray(loc); });
+        return true;
+    }
+    function bindTextureUnit(gl, texture, textureUnit) {
+        validateTextureUnit(gl, textureUnit);
+        callAndCheck(gl, function () { return gl.activeTexture(gl.TEXTURE0 + textureUnit); });
+        callAndCheck(gl, function () { return gl.bindTexture(gl.TEXTURE_2D, texture); });
+    }
+    function unbindTextureUnit(gl, textureUnit) {
+        validateTextureUnit(gl, textureUnit);
+        callAndCheck(gl, function () { return gl.activeTexture(gl.TEXTURE0 + textureUnit); });
+        callAndCheck(gl, function () { return gl.bindTexture(gl.TEXTURE_2D, null); });
+    }
+    function getProgramUniformLocationOrThrow(gl, program, uniformName) {
+        return throwIfNull(gl, function () { return gl.getUniformLocation(program, uniformName); }, 'uniform "' + uniformName + '" not present in program.');
+    }
+    function getProgramUniformLocation(gl, program, uniformName) {
+        return gl.getUniformLocation(program, uniformName);
+    }
+    function bindTextureToProgramUniformSampler(gl, program, texture, uniformSamplerLocation, textureUnit) {
+        callAndCheck(gl, function () { return bindTextureUnit(gl, texture, textureUnit); });
+        callAndCheck(gl, function () { return gl.uniform1i(uniformSamplerLocation, textureUnit); });
+    }
+    function bindCanvasToFramebuffer(gl) {
+        callAndCheck(gl, function () { return gl.bindFramebuffer(gl.FRAMEBUFFER, null); });
+        callAndCheck(gl, function () { return gl.viewport(0, 0, gl.canvas.width, gl.canvas.height); });
+        callAndCheck(gl, function () { return gl.scissor(0, 0, gl.canvas.width, gl.canvas.height); });
+    }
+    function bindColorTextureToFramebuffer(gl, texture, framebuffer) {
+        callAndCheck(gl, function () { return gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer); });
+        callAndCheck(gl, function () { return gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0); });
+    }
+    function unbindColorTextureFromFramebuffer(gl, framebuffer) {
+        callAndCheck(gl, function () { return gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer); });
+        callAndCheck(gl, function () { return gl.framebufferTexture2D(gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0); });
+    }
+    function validateFramebuffer(gl) {
+        var status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);
+        if (status !== gl.FRAMEBUFFER_COMPLETE) {
+            throw new Error('Error binding framebuffer: ' + getFramebufferErrorMessage(gl, status));
+        }
+    }
+    function getFramebufferErrorMessage(gl, status) {
+        switch (status) {
+            case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:
+                return 'FRAMEBUFFER_INCOMPLETE_ATTACHMENT';
+            case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:
+                return 'FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT';
+            case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:
+                return 'FRAMEBUFFER_INCOMPLETE_DIMENSIONS';
+            case gl.FRAMEBUFFER_UNSUPPORTED:
+                return 'FRAMEBUFFER_UNSUPPORTED';
+            default:
+                return "unknown error " + status;
+        }
+    }
+    function throwIfNull(gl, returnTOrNull, failureMessage) {
+        var tOrNull = callAndCheck(gl, function () { return returnTOrNull(); });
+        if (tOrNull == null) {
+            throw new Error(failureMessage);
+        }
+        return tOrNull;
+    }
+    function validateTextureUnit(gl, textureUnit) {
+        var maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;
+        var glTextureUnit = textureUnit + gl.TEXTURE0;
+        if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {
+            var textureUnitRange = "[gl.TEXTURE0, gl.TEXTURE" + maxTextureUnit + "]";
+            throw new Error("textureUnit must be in " + textureUnitRange + ".");
+        }
+    }
+    function getTextureShapeFromLogicalShape(logShape, isPacked) {
+        if (isPacked === void 0) { isPacked = false; }
+        var maxTexSize = ENV.get('WEBGL_MAX_TEXTURE_SIZE');
+        if (isPacked) {
+            maxTexSize = maxTexSize * 2;
+            logShape = logShape.map(function (d, i) { return i >= logShape.length - 2 ?
+                nearestLargerEven(logShape[i]) :
+                logShape[i]; });
+        }
+        if (logShape.length !== 2) {
+            var squeezeResult = squeezeShape(logShape);
+            logShape = squeezeResult.newShape;
+        }
+        var size = sizeFromShape(logShape);
+        if (logShape.length <= 1 && size <= maxTexSize) {
+            return [1, size];
+        }
+        else if (logShape.length === 2 && logShape[0] <= maxTexSize &&
+            logShape[1] <= maxTexSize) {
+            return logShape;
+        }
+        else if (logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize &&
+            logShape[2] <= maxTexSize) {
+            return [logShape[0] * logShape[1], logShape[2]];
+        }
+        else if (logShape.length === 3 && logShape[0] <= maxTexSize &&
+            logShape[1] * logShape[2] <= maxTexSize) {
+            return [logShape[0], logShape[1] * logShape[2]];
+        }
+        else if (logShape.length === 4 &&
+            logShape[0] * logShape[1] * logShape[2] <= maxTexSize &&
+            logShape[3] <= maxTexSize) {
+            return [logShape[0] * logShape[1] * logShape[2], logShape[3]];
+        }
+        else if (logShape.length === 4 && logShape[0] <= maxTexSize &&
+            logShape[1] * logShape[2] * logShape[3] <= maxTexSize) {
+            return [logShape[0], logShape[1] * logShape[2] * logShape[3]];
+        }
+        else {
+            return sizeToSquarishShape(size);
+        }
+    }
+    function isEven(n) {
+        return n % 2 === 0;
+    }
+    function isReshapeFree(shape1, shape2) {
+        shape1 = shape1.slice(-2);
+        shape2 = shape2.slice(-2);
+        if (arraysEqual(shape1, shape2)) {
+            return true;
+        }
+        if (!shape1.length || !shape2.length) {
+            return true;
+        }
+        if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 ||
+            shape2[1] === 0) {
+            return true;
+        }
+        if (shape1.length !== shape2.length) {
+            var shape1Cols = shape1.slice(-1)[0];
+            var shape2Cols = shape2.slice(-1)[0];
+            if (shape1Cols === shape2Cols) {
+                return true;
+            }
+            if (isEven(shape1Cols) && isEven(shape2Cols) &&
+                (shape1[0] === 1 || shape2[0] === 1)) {
+                return true;
+            }
+        }
+        else {
+            if (isEven(shape1[0]) && isEven(shape2[0])) {
+                if (isEven(shape1[1]) && isEven(shape2[1])) {
+                    return true;
+                }
+                if (shape1[1] === shape2[1]) {
+                    return true;
+                }
+            }
+        }
+        return false;
+    }
+
+    var webgl_util = /*#__PURE__*/Object.freeze({
+        callAndCheck: callAndCheck,
+        enableDebugWebGLErrorChecking: enableDebugWebGLErrorChecking,
+        checkWebGLError: checkWebGLError,
+        getWebGLErrorMessage: getWebGLErrorMessage,
+        getExtensionOrThrow: getExtensionOrThrow,
+        createVertexShader: createVertexShader,
+        createFragmentShader: createFragmentShader,
+        createProgram: createProgram,
+        linkProgram: linkProgram,
+        validateProgram: validateProgram,
+        createStaticVertexBuffer: createStaticVertexBuffer,
+        createStaticIndexBuffer: createStaticIndexBuffer,
+        getNumChannels: getNumChannels,
+        createTexture: createTexture,
+        validateTextureSize: validateTextureSize,
+        createFramebuffer: createFramebuffer,
+        bindVertexBufferToProgramAttribute: bindVertexBufferToProgramAttribute,
+        bindTextureUnit: bindTextureUnit,
+        unbindTextureUnit: unbindTextureUnit,
+        getProgramUniformLocationOrThrow: getProgramUniformLocationOrThrow,
+        getProgramUniformLocation: getProgramUniformLocation,
+        bindTextureToProgramUniformSampler: bindTextureToProgramUniformSampler,
+        bindCanvasToFramebuffer: bindCanvasToFramebuffer,
+        bindColorTextureToFramebuffer: bindColorTextureToFramebuffer,
+        unbindColorTextureFromFramebuffer: unbindColorTextureFromFramebuffer,
+        validateFramebuffer: validateFramebuffer,
+        getFramebufferErrorMessage: getFramebufferErrorMessage,
+        getTextureShapeFromLogicalShape: getTextureShapeFromLogicalShape,
+        isReshapeFree: isReshapeFree
+    });
+
+    function createVertexShader$1(gl) {
+        var vertexShaderSource = "\n    precision highp float;\n    attribute vec3 clipSpacePos;\n    attribute vec2 uv;\n    varying vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }";
+        return createVertexShader(gl, vertexShaderSource);
+    }
+    function createVertexBuffer(gl) {
+        var vertexArray = new Float32Array([-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);
+        return createStaticVertexBuffer(gl, vertexArray);
+    }
+    function createIndexBuffer(gl) {
+        var triangleVertexIndices = new Uint16Array([0, 1, 2, 2, 1, 3]);
+        return createStaticIndexBuffer(gl, triangleVertexIndices);
+    }
+    function getTextureConfig(gl, textureHalfFloatExtension) {
+        var glany = gl;
+        var internalFormatFloat;
+        var internalFormatHalfFloat;
+        var internalFormatPackedFloat;
+        var textureFormatFloat;
+        var downloadTextureFormat;
+        var downloadUnpackNumChannels;
+        var defaultNumChannels;
+        var textureTypeHalfFloat;
+        if (ENV.get('WEBGL_VERSION') === 2) {
+            internalFormatFloat = glany.R32F;
+            internalFormatHalfFloat = glany.R16F;
+            internalFormatPackedFloat = glany.RGBA32F;
+            textureFormatFloat = glany.RED;
+            downloadUnpackNumChannels = 4;
+            defaultNumChannels = 1;
+            textureTypeHalfFloat = glany.HALF_FLOAT;
+        }
+        else {
+            internalFormatFloat = gl.RGBA;
+            internalFormatHalfFloat = gl.RGBA;
+            internalFormatPackedFloat = glany.RGBA;
+            textureFormatFloat = gl.RGBA;
+            downloadUnpackNumChannels = 4;
+            defaultNumChannels = 4;
+            textureTypeHalfFloat = textureHalfFloatExtension != null ?
+                textureHalfFloatExtension.HALF_FLOAT_OES :
+                null;
+        }
+        downloadTextureFormat = gl.RGBA;
+        return {
+            internalFormatFloat: internalFormatFloat,
+            internalFormatHalfFloat: internalFormatHalfFloat,
+            internalFormatPackedFloat: internalFormatPackedFloat,
+            textureFormatFloat: textureFormatFloat,
+            downloadTextureFormat: downloadTextureFormat,
+            downloadUnpackNumChannels: downloadUnpackNumChannels,
+            defaultNumChannels: defaultNumChannels,
+            textureTypeHalfFloat: textureTypeHalfFloat
+        };
+    }
+    function createAndConfigureTexture(gl, width, height, internalFormat, textureFormat, textureType) {
+        validateTextureSize(width, height);
+        var texture = createTexture(gl);
+        var tex2d = gl.TEXTURE_2D;
+        callAndCheck(gl, function () { return gl.bindTexture(tex2d, texture); });
+        callAndCheck(gl, function () { return gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE); });
+        callAndCheck(gl, function () { return gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE); });
+        callAndCheck(gl, function () { return gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST); });
+        callAndCheck(gl, function () { return gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST); });
+        callAndCheck(gl, function () { return gl.texImage2D(tex2d, 0, internalFormat, width, height, 0, textureFormat, textureType, null); });
+        callAndCheck(gl, function () { return gl.bindTexture(gl.TEXTURE_2D, null); });
+        return texture;
+    }
+    function createFloat32MatrixTexture(gl, rows, columns, textureConfig) {
+        var _a = getUnpackedMatrixTextureShapeWidthHeight(rows, columns), width = _a[0], height = _a[1];
+        return createAndConfigureTexture(gl, width, height, textureConfig.internalFormatFloat, textureConfig.textureFormatFloat, gl.FLOAT);
+    }
+    function createFloat16MatrixTexture(gl, rows, columns, textureConfig) {
+        var _a = getUnpackedMatrixTextureShapeWidthHeight(rows, columns), width = _a[0], height = _a[1];
+        return createAndConfigureTexture(gl, width, height, textureConfig.internalFormatFloat, textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);
+    }
+    function createUnsignedBytesMatrixTexture(gl, rows, columns, textureConfig) {
+        var _a = getUnpackedMatrixTextureShapeWidthHeight(rows, columns), width = _a[0], height = _a[1];
+        return createAndConfigureTexture(gl, width, height, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE);
+    }
+    function createPackedMatrixTexture(gl, rows, columns, textureConfig) {
+        var _a = getPackedMatrixTextureShapeWidthHeight(rows, columns), width = _a[0], height = _a[1];
+        return createAndConfigureTexture(gl, width, height, textureConfig.internalFormatPackedFloat, gl.RGBA, gl.FLOAT);
+    }
+    function createFloat16PackedMatrixTexture(gl, rows, columns, textureConfig) {
+        var _a = getPackedMatrixTextureShapeWidthHeight(rows, columns), width = _a[0], height = _a[1];
+        return createAndConfigureTexture(gl, width, height, textureConfig.internalFormatHalfFloat, gl.RGBA, textureConfig.textureTypeHalfFloat);
+    }
+    function bindVertexProgramAttributeStreams(gl, program, vertexBuffer) {
+        var posOffset = 0;
+        var uvOffset = 3 * 4;
+        var stride = (3 * 4) + (2 * 4);
+        callAndCheck(gl, function () { return gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer); });
+        var success = bindVertexBufferToProgramAttribute(gl, program, 'clipSpacePos', vertexBuffer, 3, stride, posOffset);
+        return success &&
+            bindVertexBufferToProgramAttribute(gl, program, 'uv', vertexBuffer, 2, stride, uvOffset);
+    }
+    function uploadPixelDataToTexture(gl, texture, pixels) {
+        callAndCheck(gl, function () { return gl.bindTexture(gl.TEXTURE_2D, texture); });
+        callAndCheck(gl, function () { return gl.texImage2D(gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels); });
+        callAndCheck(gl, function () { return gl.bindTexture(gl.TEXTURE_2D, null); });
+    }
+    function uploadDataToTexture(gl, texture, width, height, data, textureFormat) {
+        validateTextureSize(width, height);
+        callAndCheck(gl, function () { return gl.bindTexture(gl.TEXTURE_2D, texture); });
+        callAndCheck(gl, function () { return gl.texSubImage2D(gl.TEXTURE_2D, 0, 0, 0, width, height, textureFormat, gl.FLOAT, data); });
+        callAndCheck(gl, function () { return gl.bindTexture(gl.TEXTURE_2D, null); });
+    }
+    function uploadMatrixToTexture(gl, texture, rows, columns, matrix, numChannels, textureConfig) {
+        var _a = getUnpackedMatrixTextureShapeWidthHeight(rows, columns), w = _a[0], h = _a[1];
+        var unpackedArray;
+        if (textureConfig.defaultNumChannels === 1) {
+            unpackedArray = matrix;
+        }
+        else {
+            unpackedArray =
+                new Float32Array(getUnpackedArraySizeFromMatrixSize(matrix.length, numChannels));
+            encodeMatrixToUnpackedArray(matrix, unpackedArray, numChannels);
+        }
+        uploadDataToTexture(gl, texture, w, h, unpackedArray, textureConfig.textureFormatFloat);
+    }
+    function uploadMatrixToPackedTexture(gl, texture, batch, rows, columns, physicalRows, physicalCols, matrix, textureConfig) {
+        var _a = getPackedMatrixTextureShapeWidthHeight(physicalRows, physicalCols), w = _a[0], h = _a[1];
+        var packedRGBA = new Float32Array(batch * getPackedRGBAArraySizeFromMatrixShape(rows, columns));
+        encodeMatrixToPackedRGBA(matrix, batch, rows, columns, packedRGBA);
+        uploadDataToTexture(gl, texture, w, h, packedRGBA, gl.RGBA);
+    }
+    function maybeCreateBufferFromOutputTexture(gl, texture, rows, columns, textureConfig) {
+        var bufferOrTexture = texture;
+        if (ENV.get('WEBGL_VERSION') === 2) {
+            var gl2_1 = gl;
+            var buffer_1 = gl2_1.createBuffer();
+            callAndCheck(gl, function () { return gl.bindBuffer(gl2_1.PIXEL_PACK_BUFFER, buffer_1); });
+            var bytesPerFloat = 4;
+            var bufferSizeBytes_1 = bytesPerFloat *
+                getUnpackedArraySizeFromMatrixSize(rows * columns, textureConfig.downloadUnpackNumChannels);
+            callAndCheck(gl, function () { return gl.bufferData(gl2_1.PIXEL_PACK_BUFFER, bufferSizeBytes_1, gl.STATIC_DRAW); });
+            callAndCheck(gl, function () { return gl2_1.readPixels(0, 0, columns, rows, gl.RGBA, gl.FLOAT, 0); });
+            callAndCheck(gl, function () { return gl.bindBuffer(gl2_1.PIXEL_PACK_BUFFER, null); });
+            bufferOrTexture = buffer_1;
+        }
+        return bufferOrTexture;
+    }
+    function downloadFloat32MatrixFromBuffer(gl, buffer, rows, columns, textureConfig) {
+        var gl2 = gl;
+        var downloadTarget = new Float32Array(getUnpackedArraySizeFromMatrixSize(rows * columns, textureConfig.downloadUnpackNumChannels));
+        gl2.bindBuffer(gl.ARRAY_BUFFER, buffer);
+        gl2.getBufferSubData(gl.ARRAY_BUFFER, 0, downloadTarget);
+        gl2.bindBuffer(gl.ARRAY_BUFFER, null);
+        var matrix = new Float32Array(rows * columns);
+        decodeMatrixFromUnpackedArray(downloadTarget, matrix, textureConfig.downloadUnpackNumChannels);
+        return matrix;
+    }
+    function downloadFloat32MatrixFromOutputTexture(gl, rows, columns, textureConfig) {
+        var _a = getUnpackedMatrixTextureShapeWidthHeight(rows, columns), w = _a[0], h = _a[1];
+        var downloadTarget = new Float32Array(getUnpackedArraySizeFromMatrixSize(rows * columns, textureConfig.downloadUnpackNumChannels));
+        callAndCheck(gl, function () { return gl.readPixels(0, 0, w, h, textureConfig.downloadTextureFormat, gl.FLOAT, downloadTarget); });
+        var matrix = new Float32Array(rows * columns);
+        decodeMatrixFromUnpackedArray(downloadTarget, matrix, textureConfig.downloadUnpackNumChannels);
+        return matrix;
+    }
+    function downloadByteEncodedFloatMatrixFromOutputTexture(gl, rows, columns, textureConfig) {
+        var _a = getUnpackedMatrixTextureShapeWidthHeight(rows, columns), w = _a[0], h = _a[1];
+        var numChannels = 4;
+        var downloadTarget = new Uint8Array(getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));
+        callAndCheck(gl, function () { return gl.readPixels(0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE, downloadTarget); });
+        return new Float32Array(downloadTarget.buffer);
+    }
+    function downloadPackedMatrixFromBuffer(gl, buffer, batch, rows, cols, physicalRows, physicalCols, textureConfig) {
+        var gl2 = gl;
+        var downloadTarget = new Float32Array(getPackedRGBAArraySizeFromMatrixShape(physicalRows, physicalCols));
+        gl2.bindBuffer(gl.ARRAY_BUFFER, buffer);
+        gl2.getBufferSubData(gl.ARRAY_BUFFER, 0, downloadTarget);
+        gl2.bindBuffer(gl.ARRAY_BUFFER, null);
+        var matrix = new Float32Array(sizeFromShape([batch, rows, cols]));
+        decodeMatrixFromPackedRGBA(downloadTarget, batch, rows, cols, matrix);
+        return matrix;
+    }
+    function downloadMatrixFromPackedOutputTexture(gl, batch, rows, cols, physicalRows, physicalCols, textureConfig) {
+        var _a = getPackedMatrixTextureShapeWidthHeight(physicalRows, physicalCols), w = _a[0], h = _a[1];
+        var packedRGBA = new Float32Array(getPackedRGBAArraySizeFromMatrixShape(physicalRows, physicalCols));
+        callAndCheck(gl, function () { return gl.readPixels(0, 0, w, h, gl.RGBA, gl.FLOAT, packedRGBA); });
+        var matrix = new Float32Array(sizeFromShape([batch, rows, cols]));
+        return decodeMatrixFromPackedRGBA(packedRGBA, batch, rows, cols, matrix);
+    }
+
+    var gpgpu_util = /*#__PURE__*/Object.freeze({
+        createVertexShader: createVertexShader$1,
+        createVertexBuffer: createVertexBuffer,
+        createIndexBuffer: createIndexBuffer,
+        getTextureConfig: getTextureConfig,
+        createFloat32MatrixTexture: createFloat32MatrixTexture,
+        createFloat16MatrixTexture: createFloat16MatrixTexture,
+        createUnsignedBytesMatrixTexture: createUnsignedBytesMatrixTexture,
+        createPackedMatrixTexture: createPackedMatrixTexture,
+        createFloat16PackedMatrixTexture: createFloat16PackedMatrixTexture,
+        bindVertexProgramAttributeStreams: bindVertexProgramAttributeStreams,
+        uploadPixelDataToTexture: uploadPixelDataToTexture,
+        uploadMatrixToTexture: uploadMatrixToTexture,
+        uploadMatrixToPackedTexture: uploadMatrixToPackedTexture,
+        maybeCreateBufferFromOutputTexture: maybeCreateBufferFromOutputTexture,
+        downloadFloat32MatrixFromBuffer: downloadFloat32MatrixFromBuffer,
+        downloadFloat32MatrixFromOutputTexture: downloadFloat32MatrixFromOutputTexture,
+        downloadByteEncodedFloatMatrixFromOutputTexture: downloadByteEncodedFloatMatrixFromOutputTexture,
+        downloadPackedMatrixFromBuffer: downloadPackedMatrixFromBuffer,
+        downloadMatrixFromPackedOutputTexture: downloadMatrixFromPackedOutputTexture
+    });
+
+    var GPGPUContext = (function () {
+        function GPGPUContext(gl) {
+            this.outputTexture = null;
+            this.program = null;
+            this.disposed = false;
+            this.autoDebugValidate = false;
+            this.vertexAttrsAreBound = false;
+            this.itemsToPoll = [];
+            if (gl != null) {
+                this.gl = gl;
+            }
+            else {
+                this.gl = getWebGLContext(ENV.get('WEBGL_VERSION'));
+            }
+            if (ENV.get('WEBGL_VERSION') === 1) {
+                this.textureFloatExtension =
+                    getExtensionOrThrow(this.gl, 'OES_texture_float');
+                this.colorBufferFloatExtension =
+                    this.gl.getExtension('WEBGL_color_buffer_float');
+                if (!ENV.get('WEBGL_RENDER_FLOAT32_ENABLED')) {
+                    this.textureHalfFloatExtension =
+                        getExtensionOrThrow(this.gl, 'OES_texture_half_float');
+                    this.colorBufferHalfFloatExtension =
+                        this.gl.getExtension('EXT_color_buffer_half_float');
+                }
+            }
+            else {
+                this.colorBufferFloatExtension =
+                    getExtensionOrThrow(this.gl, 'EXT_color_buffer_float');
+            }
+            this.vertexBuffer = createVertexBuffer(this.gl);
+            this.indexBuffer = createIndexBuffer(this.gl);
+            this.framebuffer = createFramebuffer(this.gl);
+            this.textureConfig =
+                getTextureConfig(this.gl, this.textureHalfFloatExtension);
+        }
+        GPGPUContext.prototype.dispose = function () {
+            var _this = this;
+            if (this.disposed) {
+                return;
+            }
+            if (this.program != null) {
+                console.warn('Disposing a GPGPUContext that still has a bound WebGLProgram.' +
+                    ' This is probably a resource leak, delete the program with ' +
+                    'GPGPUContext.deleteProgram before disposing.');
+            }
+            if (this.outputTexture != null) {
+                console.warn('Disposing a GPGPUContext that still has a bound output matrix ' +
+                    'texture.  This is probably a resource leak, delete the output ' +
+                    'matrix texture with GPGPUContext.deleteMatrixTexture before ' +
+                    'disposing.');
+            }
+            var gl = this.gl;
+            callAndCheck(gl, function () { return gl.finish(); });
+            callAndCheck(gl, function () { return gl.bindFramebuffer(gl.FRAMEBUFFER, null); });
+            callAndCheck(gl, function () { return gl.deleteFramebuffer(_this.framebuffer); });
+            callAndCheck(gl, function () { return gl.bindBuffer(gl.ARRAY_BUFFER, null); });
+            callAndCheck(gl, function () { return gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null); });
+            callAndCheck(gl, function () { return gl.deleteBuffer(_this.indexBuffer); });
+            this.disposed = true;
+        };
+        GPGPUContext.prototype.enableAutomaticDebugValidation = function (enabled) {
+            this.autoDebugValidate = enabled;
+            enableDebugWebGLErrorChecking(enabled);
+        };
+        GPGPUContext.prototype.createFloat32MatrixTexture = function (rows, columns) {
+            this.throwIfDisposed();
+            return createFloat32MatrixTexture(this.gl, rows, columns, this.textureConfig);
+        };
+        GPGPUContext.prototype.createFloat16MatrixTexture = function (rows, columns) {
+            this.throwIfDisposed();
+            return createFloat16MatrixTexture(this.gl, rows, columns, this.textureConfig);
+        };
+        GPGPUContext.prototype.createUnsignedBytesMatrixTexture = function (rows, columns) {
+            this.throwIfDisposed();
+            return createUnsignedBytesMatrixTexture(this.gl, rows, columns, this.textureConfig);
+        };
+        GPGPUContext.prototype.uploadPixelDataToTexture = function (texture, pixels) {
+            this.throwIfDisposed();
+            uploadPixelDataToTexture(this.gl, texture, pixels);
+        };
+        GPGPUContext.prototype.createFloat16PackedMatrixTexture = function (rows, columns) {
+            this.throwIfDisposed();
+            return createFloat16PackedMatrixTexture(this.gl, rows, columns, this.textureConfig);
+        };
+        GPGPUContext.prototype.createPackedMatrixTexture = function (rows, columns) {
+            this.throwIfDisposed();
+            return createPackedMatrixTexture(this.gl, rows, columns, this.textureConfig);
+        };
+        GPGPUContext.prototype.deleteMatrixTexture = function (texture) {
+            var _this = this;
+            this.throwIfDisposed();
+            if (this.outputTexture === texture) {
+                unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);
+                this.outputTexture = null;
+            }
+            callAndCheck(this.gl, function () { return _this.gl.deleteTexture(texture); });
+        };
+        GPGPUContext.prototype.uploadMatrixToTexture = function (texture, rows, columns, matrix) {
+            this.throwIfDisposed();
+            var numChannels = getNumChannels();
+            return uploadMatrixToTexture(this.gl, texture, rows, columns, matrix, numChannels, this.textureConfig);
+        };
+        GPGPUContext.prototype.uploadMatrixToPackedTexture = function (texture, batch, rows, columns, physicalRows, physicalCols, matrix) {
+            this.throwIfDisposed();
+            return uploadMatrixToPackedTexture(this.gl, texture, batch, rows, columns, physicalRows, physicalCols, matrix, this.textureConfig);
+        };
+        GPGPUContext.prototype.downloadFloat32MatrixFromOutputTexture = function (texture, rows, columns) {
+            var _this = this;
+            return this.downloadMatrixDriver(texture, function () { return downloadFloat32MatrixFromOutputTexture(_this.gl, rows, columns, _this.textureConfig); });
+        };
+        GPGPUContext.prototype.downloadByteEncodedFloatMatrixFromOutputTexture = function (texture, rows, columns) {
+            var _this = this;
+            return this.downloadMatrixDriver(texture, function () { return downloadByteEncodedFloatMatrixFromOutputTexture(_this.gl, rows, columns, _this.textureConfig); });
+        };
+        GPGPUContext.prototype.downloadPackedMatrixFromBuffer = function (buffer, batch, rows, columns, physicalRows, physicalCols) {
+            return downloadPackedMatrixFromBuffer(this.gl, buffer, batch, rows, columns, physicalRows, physicalCols, this.textureConfig);
+        };
+        GPGPUContext.prototype.downloadFloat32MatrixFromBuffer = function (buffer, rows, columns) {
+            return downloadFloat32MatrixFromBuffer(this.gl, buffer, rows, columns, this.textureConfig);
+        };
+        GPGPUContext.prototype.maybeCreateBufferFromTexture = function (texture, rows, columns) {
+            this.bindTextureToFrameBuffer(texture);
+            var result = maybeCreateBufferFromOutputTexture(this.gl, texture, rows, columns, this.textureConfig);
+            this.unbindTextureToFrameBuffer();
+            return result;
+        };
+        GPGPUContext.prototype.createAndWaitForFence = function () {
+            var fenceContext = this.createFence(this.gl);
+            return this.pollFence(fenceContext);
+        };
+        GPGPUContext.prototype.createFence = function (gl) {
+            var _this = this;
+            var query;
+            var isFencePassed;
+            if (ENV.get('WEBGL_FENCE_API_ENABLED')) {
+                var gl2_1 = gl;
+                var sync_1 = gl2_1.fenceSync(gl2_1.SYNC_GPU_COMMANDS_COMPLETE, 0);
+                gl.flush();
+                isFencePassed = function () {
+                    var status = gl2_1.clientWaitSync(sync_1, 0, 0);
+                    return status === gl2_1.ALREADY_SIGNALED ||
+                        status === gl2_1.CONDITION_SATISFIED;
+                };
+                query = sync_1;
+            }
+            else if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {
+                query = this.beginQuery();
+                this.endQuery();
+                isFencePassed = function () { return _this.isQueryAvailable(query, ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION')); };
+            }
+            else {
+                isFencePassed = function () { return true; };
+            }
+            return { query: query, isFencePassed: isFencePassed };
+        };
+        GPGPUContext.prototype.downloadMatrixFromPackedTexture = function (texture, batch, rows, columns, physicalRows, physicalCols) {
+            var _this = this;
+            return this.downloadMatrixDriver(texture, function () { return downloadMatrixFromPackedOutputTexture(_this.gl, batch, rows, columns, physicalRows, physicalCols, _this.textureConfig); });
+        };
+        GPGPUContext.prototype.createProgram = function (fragmentShaderSource) {
+            this.throwIfDisposed();
+            var gl = this.gl;
+            var fragmentShader = createFragmentShader(gl, fragmentShaderSource);
+            var vertexShader = createVertexShader$1(gl);
+            var program = createProgram(gl);
+            callAndCheck(gl, function () { return gl.attachShader(program, vertexShader); });
+            callAndCheck(gl, function () { return gl.attachShader(program, fragmentShader); });
+            linkProgram(gl, program);
+            if (this.autoDebugValidate) {
+                validateProgram(gl, program);
+            }
+            if (!this.vertexAttrsAreBound) {
+                this.setProgram(program);
+                this.vertexAttrsAreBound = bindVertexProgramAttributeStreams(gl, this.program, this.vertexBuffer);
+            }
+            return program;
+        };
+        GPGPUContext.prototype.deleteProgram = function (program) {
+            var _this = this;
+            this.throwIfDisposed();
+            if (program === this.program) {
+                this.program = null;
+            }
+            if (program != null) {
+                callAndCheck(this.gl, function () { return _this.gl.deleteProgram(program); });
+            }
+        };
+        GPGPUContext.prototype.setProgram = function (program) {
+            var _this = this;
+            this.throwIfDisposed();
+            this.program = program;
+            if ((this.program != null) && this.autoDebugValidate) {
+                validateProgram(this.gl, this.program);
+            }
+            callAndCheck(this.gl, function () { return _this.gl.useProgram(program); });
+        };
+        GPGPUContext.prototype.getUniformLocation = function (program, uniformName, shouldThrow) {
+            if (shouldThrow === void 0) { shouldThrow = true; }
+            this.throwIfDisposed();
+            if (shouldThrow) {
+                return getProgramUniformLocationOrThrow(this.gl, program, uniformName);
+            }
+            else {
+                return getProgramUniformLocation(this.gl, program, uniformName);
+            }
+        };
+        GPGPUContext.prototype.getAttributeLocation = function (program, attribute) {
+            var _this = this;
+            this.throwIfDisposed();
+            return callAndCheck(this.gl, function () { return _this.gl.getAttribLocation(program, attribute); });
+        };
+        GPGPUContext.prototype.getUniformLocationNoThrow = function (program, uniformName) {
+            this.throwIfDisposed();
+            return this.gl.getUniformLocation(program, uniformName);
+        };
+        GPGPUContext.prototype.setInputMatrixTexture = function (inputMatrixTexture, uniformLocation, textureUnit) {
+            this.throwIfDisposed();
+            this.throwIfNoProgram();
+            bindTextureToProgramUniformSampler(this.gl, this.program, inputMatrixTexture, uniformLocation, textureUnit);
+        };
+        GPGPUContext.prototype.setOutputMatrixTexture = function (outputMatrixTexture, rows, columns) {
+            this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);
+        };
+        GPGPUContext.prototype.setOutputPackedMatrixTexture = function (outputPackedMatrixTexture, rows, columns) {
+            this.throwIfDisposed();
+            var _a = getPackedMatrixTextureShapeWidthHeight(rows, columns), width = _a[0], height = _a[1];
+            this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);
+        };
+        GPGPUContext.prototype.setOutputMatrixWriteRegion = function (startRow, numRows, startColumn, numColumns) {
+            this.setOutputMatrixWriteRegionDriver(startColumn, startRow, numColumns, numRows);
+        };
+        GPGPUContext.prototype.setOutputPackedMatrixWriteRegion = function (startRow, numRows, startColumn, numColumns) {
+            throw new Error('setOutputPackedMatrixWriteRegion not implemented.');
+        };
+        GPGPUContext.prototype.debugValidate = function () {
+            if (this.program != null) {
+                validateProgram(this.gl, this.program);
+            }
+            validateFramebuffer(this.gl);
+        };
+        GPGPUContext.prototype.executeProgram = function () {
+            this.throwIfDisposed();
+            this.throwIfNoProgram();
+            var gl = this.gl;
+            if (this.autoDebugValidate) {
+                this.debugValidate();
+            }
+            callAndCheck(gl, function () { return gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0); });
+        };
+        GPGPUContext.prototype.blockUntilAllProgramsCompleted = function () {
+            var _this = this;
+            this.throwIfDisposed();
+            callAndCheck(this.gl, function () { return _this.gl.finish(); });
+        };
+        GPGPUContext.prototype.getQueryTimerExtension = function () {
+            if (this.disjointQueryTimerExtension == null) {
+                this.disjointQueryTimerExtension =
+                    getExtensionOrThrow(this.gl, ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2 ?
+                        'EXT_disjoint_timer_query_webgl2' :
+                        'EXT_disjoint_timer_query');
+            }
+            return this.disjointQueryTimerExtension;
+        };
+        GPGPUContext.prototype.getQueryTimerExtensionWebGL2 = function () {
+            return this.getQueryTimerExtension();
+        };
+        GPGPUContext.prototype.getQueryTimerExtensionWebGL1 = function () {
+            return this.getQueryTimerExtension();
+        };
+        GPGPUContext.prototype.beginQuery = function () {
+            if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {
+                var gl2 = this.gl;
+                var ext_1 = this.getQueryTimerExtensionWebGL2();
+                var query_1 = gl2.createQuery();
+                gl2.beginQuery(ext_1.TIME_ELAPSED_EXT, query_1);
+                return query_1;
+            }
+            var ext = this.getQueryTimerExtensionWebGL1();
+            var query = ext.createQueryEXT();
+            ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);
+            return query;
+        };
+        GPGPUContext.prototype.endQuery = function () {
+            if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {
+                var gl2 = this.gl;
+                var ext_2 = this.getQueryTimerExtensionWebGL2();
+                gl2.endQuery(ext_2.TIME_ELAPSED_EXT);
+                return;
+            }
+            var ext = this.getQueryTimerExtensionWebGL1();
+            ext.endQueryEXT(ext.TIME_ELAPSED_EXT);
+        };
+        GPGPUContext.prototype.waitForQueryAndGetTime = function (query) {
+            return __awaiter(this, void 0, void 0, function () {
+                var _this = this;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0: return [4, repeatedTry(function () { return _this.disposed ||
+                                _this.isQueryAvailable(query, ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION')); })];
+                        case 1:
+                            _a.sent();
+                            return [2, this.getQueryTime(query, ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'))];
+                    }
+                });
+            });
+        };
+        GPGPUContext.prototype.getQueryTime = function (query, queryTimerVersion) {
+            if (queryTimerVersion === 0) {
+                return null;
+            }
+            if (queryTimerVersion === 2) {
+                var gl2 = this.gl;
+                var timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);
+                return timeElapsedNanos / 1000000;
+            }
+            else {
+                var ext = this.getQueryTimerExtensionWebGL1();
+                var timeElapsedNanos = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);
+                return timeElapsedNanos / 1000000;
+            }
+        };
+        GPGPUContext.prototype.isQueryAvailable = function (query, queryTimerVersion) {
+            if (queryTimerVersion === 0) {
+                return true;
+            }
+            if (queryTimerVersion === 2) {
+                var gl2 = this.gl;
+                var ext = this.getQueryTimerExtensionWebGL2();
+                var available = gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);
+                if (this.disjoint == null) {
+                    this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);
+                }
+                return available && !this.disjoint;
+            }
+            else {
+                var ext = this.getQueryTimerExtensionWebGL1();
+                var available = ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);
+                if (this.disjoint == null) {
+                    this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);
+                }
+                return available && !this.disjoint;
+            }
+        };
+        GPGPUContext.prototype.pollFence = function (fenceContext) {
+            var _this = this;
+            return new Promise(function (resolve) {
+                _this.addItemToPoll(function () { return fenceContext.isFencePassed(); }, function () { return resolve(); });
+            });
+        };
+        GPGPUContext.prototype.pollItems = function () {
+            var index = binSearchLastTrue(this.itemsToPoll.map(function (x) { return x.isDoneFn; }));
+            for (var i = 0; i <= index; ++i) {
+                var resolveFn = this.itemsToPoll[i].resolveFn;
+                resolveFn();
+            }
+            this.itemsToPoll = this.itemsToPoll.slice(index + 1);
+        };
+        GPGPUContext.prototype.addItemToPoll = function (isDoneFn, resolveFn) {
+            var _this = this;
+            this.itemsToPoll.push({ isDoneFn: isDoneFn, resolveFn: resolveFn });
+            if (this.itemsToPoll.length > 1) {
+                return;
+            }
+            repeatedTry(function () {
+                _this.pollItems();
+                return _this.itemsToPoll.length === 0;
+            });
+        };
+        GPGPUContext.prototype.bindTextureToFrameBuffer = function (texture) {
+            this.throwIfDisposed();
+            bindColorTextureToFramebuffer(this.gl, texture, this.framebuffer);
+            if (this.autoDebugValidate) {
+                validateFramebuffer(this.gl);
+            }
+        };
+        GPGPUContext.prototype.unbindTextureToFrameBuffer = function () {
+            if (this.outputTexture != null) {
+                bindColorTextureToFramebuffer(this.gl, this.outputTexture, this.framebuffer);
+                if (this.autoDebugValidate) {
+                    validateFramebuffer(this.gl);
+                }
+            }
+            else {
+                unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);
+            }
+        };
+        GPGPUContext.prototype.downloadMatrixDriver = function (texture, downloadAndDecode) {
+            this.bindTextureToFrameBuffer(texture);
+            var result = downloadAndDecode();
+            this.unbindTextureToFrameBuffer();
+            return result;
+        };
+        GPGPUContext.prototype.setOutputMatrixTextureDriver = function (outputMatrixTextureMaybePacked, width, height) {
+            this.throwIfDisposed();
+            var gl = this.gl;
+            bindColorTextureToFramebuffer(gl, outputMatrixTextureMaybePacked, this.framebuffer);
+            if (this.autoDebugValidate) {
+                validateFramebuffer(gl);
+            }
+            this.outputTexture = outputMatrixTextureMaybePacked;
+            callAndCheck(gl, function () { return gl.viewport(0, 0, width, height); });
+            callAndCheck(gl, function () { return gl.scissor(0, 0, width, height); });
+        };
+        GPGPUContext.prototype.setOutputMatrixWriteRegionDriver = function (x, y, width, height) {
+            var _this = this;
+            this.throwIfDisposed();
+            callAndCheck(this.gl, function () { return _this.gl.scissor(x, y, width, height); });
+        };
+        GPGPUContext.prototype.throwIfDisposed = function () {
+            if (this.disposed) {
+                throw new Error('Attempted to use disposed GPGPUContext.');
+            }
+        };
+        GPGPUContext.prototype.throwIfNoProgram = function () {
+            if (this.program == null) {
+                throw new Error('No GPU program is currently set.');
+            }
+        };
+        return GPGPUContext;
+    }());
+    function binSearchLastTrue(arr) {
+        var start = 0;
+        var end = arr.length - 1;
+        var best = -1;
+        while (start <= end) {
+            var mid = (start + end) >> 1;
+            var isDone = arr[mid]();
+            if (isDone) {
+                best = mid;
+                start = mid + 1;
+            }
+            else {
+                end = mid - 1;
+            }
+        }
+        return best;
+    }
+
+    function compileProgram(gpgpu, program, inputs, output) {
+        var userCode = program.userCode;
+        var inputInfos = inputs.map(function (input, i) {
+            var shapeInfo = {
+                logicalShape: input.shape,
+                texShape: input.isUniform ? null : input.texData.texShape,
+                isUniform: input.isUniform,
+                isPacked: input.isUniform ? false : input.texData.isPacked
+            };
+            return { name: program.variableNames[i], shapeInfo: shapeInfo };
+        });
+        var inShapeInfos = inputInfos.map(function (x) { return x.shapeInfo; });
+        var outShapeInfo = {
+            logicalShape: output.shape,
+            texShape: output.texData.texShape,
+            isUniform: false,
+            isPacked: output.texData.isPacked
+        };
+        var source = makeShader(inputInfos, outShapeInfo, userCode, program.supportsBroadcasting === true, program.usesPackedTextures);
+        var webGLProgram = gpgpu.createProgram(source);
+        var uniformLocations = {};
+        for (var i = 0; i < program.variableNames.length; i++) {
+            var uniformName = program.variableNames[i];
+            var shouldThrow = false;
+            uniformLocations[uniformName] =
+                gpgpu.getUniformLocation(webGLProgram, uniformName, shouldThrow);
+        }
+        return {
+            program: program,
+            source: source,
+            webGLProgram: webGLProgram,
+            uniformLocations: uniformLocations,
+            gpgpu: gpgpu,
+            inShapeInfos: inShapeInfos,
+            outShapeInfo: outShapeInfo
+        };
+    }
+    function validateBinaryAndProgram(shapeInfos, inputs) {
+        if (shapeInfos.length !== inputs.length) {
+            throw Error("Binary was compiled with " + shapeInfos.length + " inputs, but " +
+                ("was executed with " + inputs.length + " inputs"));
+        }
+        shapeInfos.forEach(function (s, i) {
+            var shapeA = s.logicalShape;
+            var input = inputs[i];
+            var shapeB = input.shape;
+            if (!arraysEqual(shapeA, shapeB)) {
+                throw Error("Binary was compiled with different shapes than " +
+                    ("the current args. Shapes " + shapeA + " and " + shapeB + " must match"));
+            }
+            if (s.isUniform && input.isUniform) {
+                return;
+            }
+            var texShapeA = s.texShape;
+            var texShapeB = input.isUniform ? null : input.texData.texShape;
+            if (!arraysEqual(texShapeA, texShapeB)) {
+                throw Error("Binary was compiled with different texture shapes than the" +
+                    (" current args. Shape " + texShapeA + " and " + texShapeB + " must match"));
+            }
+        });
+    }
+    function runProgram(binary, inputs, output, customSetup) {
+        validateBinaryAndProgram(binary.inShapeInfos, inputs);
+        validateBinaryAndProgram([binary.outShapeInfo], [output]);
+        var outTex = output.texData.texture;
+        var outTexShape = output.texData.texShape;
+        var gpgpu = binary.gpgpu;
+        if (output.texData.isPacked) {
+            gpgpu.setOutputPackedMatrixTexture(outTex, outTexShape[0], outTexShape[1]);
+        }
+        else {
+            gpgpu.setOutputMatrixTexture(outTex, outTexShape[0], outTexShape[1]);
+        }
+        gpgpu.setProgram(binary.webGLProgram);
+        inputs.forEach(function (input, i) {
+            var variableName = binary.program.variableNames[i];
+            var variableUniformLocation = binary.uniformLocations[variableName];
+            if (variableUniformLocation != null) {
+                if (input.isUniform) {
+                    if (sizeFromShape(input.shape) === 1) {
+                        gpgpu.gl.uniform1f(variableUniformLocation, input.uniformValues[0]);
+                    }
+                    else {
+                        var vals = input.uniformValues;
+                        if (!(vals instanceof Float32Array)) {
+                            vals = new Float32Array(vals);
+                        }
+                        gpgpu.gl.uniform1fv(variableUniformLocation, vals);
+                    }
+                    return;
+                }
+                var tex = input.texData.texture;
+                gpgpu.setInputMatrixTexture(tex, variableUniformLocation, i);
+            }
+        });
+        if (customSetup != null) {
+            customSetup(gpgpu, binary.webGLProgram);
+        }
+        gpgpu.executeProgram();
+    }
+    function makeShaderKey(program, inputs, output) {
+        var keyInputs = '';
+        inputs.concat(output).forEach(function (x) {
+            keyInputs += x.shape + "_" + (x.isUniform ? 'uniform' : x.texData.texShape);
+        });
+        var keyUserCode = program.userCode;
+        var keyBroadcast = (program.supportsBroadcasting === true).toString();
+        var key = program.constructor.name;
+        key += '_' + keyBroadcast + '_' + keyInputs + '_' + keyUserCode;
+        return key;
+    }
+
+    var Im2ColProgram = (function () {
+        function Im2ColProgram(outputShape, inputShape, convInfo) {
+            this.variableNames = ['A'];
+            this.outputShape = outputShape;
+            var filterWidth = convInfo.filterWidth, inChannels = convInfo.inChannels, strideWidth = convInfo.strideWidth, strideHeight = convInfo.strideHeight, padInfo = convInfo.padInfo, outWidth = convInfo.outWidth, dilationWidth = convInfo.dilationWidth, dilationHeight = convInfo.dilationHeight;
+            var left = padInfo.left, top = padInfo.top;
+            var itemsPerBlockRow = inChannels * filterWidth;
+            this.userCode = "\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            int blockIndex = rc.y + col;\n            int pos = rc.x + row;\n\n            if(blockIndex >= " + outputShape[1] + " || pos >= " + outputShape[0] + ") continue;\n\n            int offsetY = int(blockIndex / (" + outWidth + ")) * " + strideHeight + " - " + top + ";\n            int d0 = offsetY + " + dilationHeight + " * (pos / " + itemsPerBlockRow + ");\n\n            if(d0 >= " + inputShape[0] + " || d0 < 0) continue;\n\n            int offsetX = int(mod(float(blockIndex), " + outWidth + ".) * " + strideWidth + ". - " + left + ".);\n            int d1 = offsetX + " + dilationWidth + " * (int(mod(float(pos), " + itemsPerBlockRow + ".) / " + inChannels + ".));\n\n            if(d1 >= " + inputShape[1] + " || d1 < 0) continue;\n\n            result[row * 2 + col] = getA(d0, d1, int(mod(float(pos), " + inChannels + ".)));\n          }\n        }\n\n        gl_FragColor = result;\n      }\n    ";
+        }
+        return Im2ColProgram;
+    }());
+
+    var LRNProgram = (function () {
+        function LRNProgram(xShape, radius, bias, alpha, beta) {
+            this.variableNames = ['x'];
+            this.outputShape = [];
+            var rad = radius;
+            var maxD = xShape[3] - 1;
+            this.outputShape = xShape;
+            var powOperator;
+            var basis = "float(" + bias + ") + float(" + alpha + ") * sum";
+            if (beta === 0.5) {
+                powOperator = "inversesqrt(" + basis + ")";
+            }
+            else if (beta === 1.0) {
+                powOperator = "1.0/(" + basis + ")";
+            }
+            else {
+                powOperator = "exp(log(" + basis + ") * float(-" + beta + "));";
+            }
+            this.userCode = "\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -" + rad + "; j <= " + rad + "; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  " + maxD + ") {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * " + powOperator + ";\n        setOutput(val);\n      }\n    ";
+        }
+        return LRNProgram;
+    }());
+
+    var LRNGradProgram = (function () {
+        function LRNGradProgram(inputShape, depthRadius, bias, alpha, beta) {
+            this.variableNames = ['inputImage', 'outputImage', 'dy'];
+            this.outputShape = [];
+            this.outputShape = inputShape;
+            this.depth = inputShape[3];
+            this.depthRadius = depthRadius;
+            this.bias = bias;
+            this.alpha = alpha;
+            this.beta = beta;
+            this.userCode = "\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < " + this.depth + "; ++d) {\n          int depthBegin = int(max(0.0, float(d - " + depthRadius + ")));\n          int depthEnd = int(min(float(" + this.depth + "),\n              float(d + " + depthRadius + " + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = " + this.depth + ";\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(" + alpha + ") * norm + float(" + bias + ");\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(" + alpha + ")\n                * float(" + beta + ")\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * " + beta + ");\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    ";
+        }
+        return LRNGradProgram;
+    }());
+
+    var MaxPool2DBackpropProgram = (function () {
+        function MaxPool2DBackpropProgram(convInfo) {
+            this.variableNames = ['dy', 'maxPos'];
+            this.outputShape = convInfo.inShape;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var dilationHeight = convInfo.dilationHeight;
+            var effectiveFilterHeight = convInfo.effectiveFilterHeight;
+            var effectiveFilterWidth = convInfo.effectiveFilterWidth;
+            var padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+            var padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+            var lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;
+            this.userCode = "\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < " + effectiveFilterHeight + ";\n          wR += " + dilationHeight + ") {\n          float dyR = float(dyRCorner + wR) / " + strideHeight + ".0;\n\n          if (dyR < 0.0 || dyR >= " + convInfo.outHeight + ".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < " + effectiveFilterWidth + "; wC++) {\n            float dyC = float(dyCCorner + wC) / " + strideWidth + ".0;\n\n            if (dyC < 0.0 || dyC >= " + convInfo.outWidth + ".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = " + lastIndex + " - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * " + effectiveFilterWidth + " + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";
+        }
+        return MaxPool2DBackpropProgram;
+    }());
+
+    var MatMulProgram = (function () {
+        function MatMulProgram(aShape, bShape, transposeA, transposeB) {
+            if (transposeA === void 0) { transposeA = false; }
+            if (transposeB === void 0) { transposeB = false; }
+            this.variableNames = ['matrixA', 'matrixB'];
+            var batchSize = aShape[0];
+            var outerShapeA = transposeA ? aShape[2] : aShape[1];
+            var outerShapeB = transposeB ? bShape[1] : bShape[2];
+            var sharedDim = transposeA ? aShape[1] : aShape[2];
+            this.outputShape = [batchSize, outerShapeA, outerShapeB];
+            var aSnippetFromOffset = function (vec4Offset, indexVar) {
+                return transposeA ? "batch, " + indexVar + " + " + vec4Offset + ", aRow" :
+                    "batch, aRow, " + indexVar + " + " + vec4Offset;
+            };
+            var bSnippetFromOffset = function (vec4Offset, indexVar) {
+                return transposeB ? "batch, bCol, " + indexVar + " + " + vec4Offset :
+                    "batch, " + indexVar + " + " + vec4Offset + ", bCol";
+            };
+            var sharedDimNearestVec4 = Math.floor(sharedDim / 4) * 4;
+            var sharedDimVec4Remainder = sharedDim % 4;
+            this.userCode = " float dotARowBCol(int batch, int aRow, int bCol) {\n      float result = 0.0;\n      for (int i = 0; i < " + sharedDimNearestVec4 + "; i += 4) {\n        vec4 a = vec4(\n          getMatrixA(" + aSnippetFromOffset(0, 'i') + "),\n          getMatrixA(" + aSnippetFromOffset(1, 'i') + "),\n          getMatrixA(" + aSnippetFromOffset(2, 'i') + "),\n          getMatrixA(" + aSnippetFromOffset(3, 'i') + ")\n        );\n        vec4 b = vec4(\n          getMatrixB(" + bSnippetFromOffset(0, 'i') + "),\n          getMatrixB(" + bSnippetFromOffset(1, 'i') + "),\n          getMatrixB(" + bSnippetFromOffset(2, 'i') + "),\n          getMatrixB(" + bSnippetFromOffset(3, 'i') + ")\n        );\n\n        result += dot(a, b);\n      }\n\n      if (" + (sharedDimVec4Remainder === 1) + ") {\n        result += getMatrixA(" + aSnippetFromOffset(0, sharedDimNearestVec4) + ") *\n          getMatrixB(" + bSnippetFromOffset(0, sharedDimNearestVec4) + ");\n      } else if (" + (sharedDimVec4Remainder === 2) + ") {\n        vec2 a = vec2(\n          getMatrixA(" + aSnippetFromOffset(0, sharedDimNearestVec4) + "),\n          getMatrixA(" + aSnippetFromOffset(1, sharedDimNearestVec4) + ")\n        );\n        vec2 b = vec2(\n          getMatrixB(" + bSnippetFromOffset(0, sharedDimNearestVec4) + "),\n          getMatrixB(" + bSnippetFromOffset(1, sharedDimNearestVec4) + ")\n        );\n        result += dot(a, b);\n      } else if (" + (sharedDimVec4Remainder === 3) + ") {\n        vec3 a = vec3(\n          getMatrixA(" + aSnippetFromOffset(0, sharedDimNearestVec4) + "),\n          getMatrixA(" + aSnippetFromOffset(1, sharedDimNearestVec4) + "),\n          getMatrixA(" + aSnippetFromOffset(2, sharedDimNearestVec4) + ")\n        );\n        vec3 b = vec3(\n          getMatrixB(" + bSnippetFromOffset(0, sharedDimNearestVec4) + "),\n          getMatrixB(" + bSnippetFromOffset(1, sharedDimNearestVec4) + "),\n          getMatrixB(" + bSnippetFromOffset(2, sharedDimNearestVec4) + ")\n        );\n        result += dot(a, b);\n      }\n\n      return result;\n    }\n\n    void main() {\n      ivec3 resBRC = getOutputCoords();\n      setOutput(dotARowBCol(resBRC.x, resBRC.y, resBRC.z));\n    }\n    ";
+        }
+        return MatMulProgram;
+    }());
+
+    var MatMulPackedProgram = (function () {
+        function MatMulPackedProgram(aShape, bShape, outputShape, transposeA, transposeB) {
+            if (transposeA === void 0) { transposeA = false; }
+            if (transposeB === void 0) { transposeB = false; }
+            this.variableNames = ['matrixA', 'matrixB'];
+            this.usesPackedTextures = true;
+            this.outputShape = outputShape;
+            var sharedDim = transposeA ? aShape[0] : aShape[1];
+            var sharedDimensionPacked = Math.ceil(sharedDim / 2);
+            var aSample = transposeA ? 'i * 2, rc.x' : 'rc.x, i * 2';
+            var bSample = transposeB ? 'rc.y, i * 2' : 'i * 2, rc.y';
+            var aSwizzle = transposeA ? ['a.xxyy', 'a.zzww'] : ['a.xxzz', 'a.yyww'];
+            var bSwizzle = transposeB ? ['b.xzxz', 'b.ywyw'] : ['b.xyxy', 'b.zwzw'];
+            this.userCode = "\n      const float sharedDimension = " + sharedDimensionPacked + ".0;\n\n      vec4 dot2x2ARowBCol(ivec2 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < " + sharedDimensionPacked + "; i++) {\n          vec4 a = getMatrixA(" + aSample + ");\n          vec4 b = getMatrixB(" + bSample + ");\n\n          result += (" + aSwizzle[0] + " * " + bSwizzle[0] + ") + (" + aSwizzle[1] + " * " + bSwizzle[1] + ");\n        }\n        return result;\n      }\n\n      void main() {\n        ivec2 rc = getOutputCoords();\n        setOutput(dot2x2ARowBCol(rc));\n      }\n    ";
+        }
+        return MatMulPackedProgram;
+    }());
+
+    var MultinomialProgram = (function () {
+        function MultinomialProgram(batchSize, numOutcomes, numSamples) {
+            this.variableNames = ['probs'];
+            this.outputShape = [batchSize, numSamples];
+            this.userCode = "\n      uniform float seed;\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < " + (numOutcomes - 1) + "; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float(" + (numOutcomes - 1) + "));\n      }\n    ";
+        }
+        MultinomialProgram.prototype.getCustomSetupFunc = function (seed) {
+            var _this = this;
+            return function (gpgpu, webGLProgram) {
+                if (_this.seedLoc == null) {
+                    _this.seedLoc = gpgpu.getUniformLocation(webGLProgram, 'seed');
+                }
+                gpgpu.gl.uniform1f(_this.seedLoc, seed);
+            };
+        };
+        return MultinomialProgram;
+    }());
+
+    var OneHotProgram = (function () {
+        function OneHotProgram(numIndices, depth, onValue, offValue) {
+            this.variableNames = ['indices'];
+            this.outputShape = [numIndices, depth];
+            this.userCode = "\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float(" + offValue + "), float(" + onValue + "),\n                      float(index == coords.y)));\n      }\n    ";
+        }
+        return OneHotProgram;
+    }());
+
+    function getVecChannels(name, rank) {
+        return ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank).map(function (d) { return name + "." + d; });
+    }
+    function getChannels(name, rank) {
+        if (rank === 1) {
+            return [name];
+        }
+        return getVecChannels(name, rank);
+    }
+    function getSourceCoords$1(rank, dims) {
+        if (rank === 1) {
+            return 'rc';
+        }
+        var coords = '';
+        for (var i = 0; i < rank; i++) {
+            coords += dims[i];
+            if (i < rank - 1) {
+                coords += ',';
+            }
+        }
+        return coords;
+    }
+
+    var PackProgram = (function () {
+        function PackProgram(outputShape) {
+            this.variableNames = ['A'];
+            this.isPackShader = true;
+            this.outputShape = outputShape;
+            var rank = outputShape.length;
+            if (rank === 0) {
+                this.userCode = "\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      ";
+            }
+            else {
+                var channels = getChannels('rc', rank);
+                var dtype = getCoordsDataType(rank);
+                var outOfBoundsCondition = getOutOfBoundsCondition(rank, outputShape, channels);
+                var setup = getSetup(rank, outputShape[outputShape.length - 1], outputShape[outputShape.length - 2], channels);
+                var output = getOutput(outputShape, channels);
+                this.userCode = "\n        void main() {\n          " + dtype + " rc = getOutputCoords();\n\n          if(" + outOfBoundsCondition + ") {\n            setOutput(vec4(0));\n          } else {\n            " + setup + "\n\n            setOutput(vec4(" + output + "));\n          }\n        }\n      ";
+            }
+        }
+        return PackProgram;
+    }());
+    function getSourceCoordsArr(rank, dims) {
+        var coords = [];
+        for (var row = 0; row <= 1; row++) {
+            for (var col = 0; col <= 1; col++) {
+                var coord = (row === 0 ? 'r' : 'rp1') + ", " + (col === 0 ? 'c' : 'cp1');
+                for (var d = 2; d < rank; d++) {
+                    coord = dims[dims.length - 1 - d] + "," + coord;
+                }
+                coords.push(coord);
+            }
+        }
+        return coords;
+    }
+    function getOutOfBoundsCondition(rank, shape, dims) {
+        if (rank === 1) {
+            return "rc > " + shape[0];
+        }
+        var cond = '';
+        for (var i = rank - 2; i < rank; i++) {
+            cond += dims[i] + " >= " + shape[i];
+            if (i < rank - 1) {
+                cond += '||';
+            }
+        }
+        return cond;
+    }
+    function getSetup(rank, cols, rows, dims) {
+        if (rank === 1) {
+            return '';
+        }
+        var innerDims = dims.slice(-2);
+        return "\n    int r = " + innerDims[0] + ";\n    int c = " + innerDims[1] + ";\n    int rp1 = r + 1;\n    int cp1 = c + 1;\n\n    bool cEdge = cp1 >= " + cols + ";\n    bool rEdge = rp1 >= " + rows + ";\n  ";
+    }
+    function getOutput(shape, dims) {
+        var rank = shape.length;
+        var sourceCoords = getSourceCoordsArr(rank, dims);
+        if (rank === 1) {
+            return "getA(rc),\n            rc + 1 >= " + shape[0] + " ? 0. : getA(rc + 1),\n            0, 0";
+        }
+        return "getA(" + sourceCoords[0] + "),\n          cEdge ? 0. : getA(" + sourceCoords[1] + "),\n          rEdge ? 0. : getA(" + sourceCoords[2] + "),\n          rEdge || cEdge ? 0. : getA(" + sourceCoords[3] + ")";
+    }
+
+    var PadProgram = (function () {
+        function PadProgram(xShape, paddings, constantValue) {
+            this.variableNames = ['x'];
+            this.outputShape = paddings.map(function (p, i) { return p[0] + xShape[i] + p[1]; });
+            var rank = xShape.length;
+            var type = getCoordsDataType(rank);
+            var start = paddings.map(function (p) { return p[0]; }).join(',');
+            var end = paddings.map(function (p, i) { return p[0] + xShape[i]; }).join(',');
+            var unpackedCoords = ['coords[0]', 'coords[1]', 'coords[2]', 'coords[3]'].slice(0, rank);
+            if (rank === 1) {
+                this.userCode = "\n        int start = " + start + ";\n        int end = " + end + ";\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(float(" + constantValue + "));\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      ";
+                return;
+            }
+            this.userCode = "\n      " + type + " start = " + type + "(" + start + ");\n      " + type + " end = " + type + "(" + end + ");\n\n      void main() {\n        " + type + " outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(float(" + constantValue + "));\n        } else {\n          " + type + " coords = outC - start;\n          setOutput(getX(" + unpackedCoords + "));\n        }\n      }\n    ";
+        }
+        return PadProgram;
+    }());
+
+    var Pool2DProgram = (function () {
+        function Pool2DProgram(convInfo, poolType, computePositions) {
+            this.variableNames = ['x'];
+            if (poolType === 'avg' && computePositions) {
+                throw new Error('Cannot compute positions for average pool.');
+            }
+            var filterWidth = convInfo.filterWidth;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var dilationHeight = convInfo.dilationHeight;
+            var dilationWidth = convInfo.dilationWidth;
+            var effectiveFilterHeight = convInfo.effectiveFilterHeight;
+            var effectiveFilterWidth = convInfo.effectiveFilterWidth;
+            var padTop = convInfo.padInfo.top;
+            var padLeft = convInfo.padInfo.left;
+            this.outputShape = convInfo.outShape;
+            var isAvgPool = poolType === 'avg';
+            var initializationValue = '0.0';
+            if (!isAvgPool) {
+                initializationValue = '-1.0 / 0.0';
+            }
+            if (computePositions) {
+                var compareOp_1 = '>=';
+                this.userCode = "\n        const ivec2 strides = ivec2(" + strideHeight + ", " + strideWidth + ");\n        const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < " + effectiveFilterHeight + ";\n              wR += " + dilationHeight + ") {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n              continue;\n            }\n\n            for (int wC = 0; wC < " + effectiveFilterWidth + ";\n                wC += " + dilationWidth + ") {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value " + compareOp_1 + " currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = wR * " + effectiveFilterWidth + " + wC;\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      ";
+                return;
+            }
+            var compareOp = 'max';
+            var returnValue = poolType + "(" + poolType + "(" + poolType + "(" +
+                'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';
+            if (poolType === 'avg') {
+                returnValue = "avgValue / count";
+            }
+            var filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;
+            var filterWidthVec4Remainder = filterWidth % 4;
+            var updateSnippet = "\n      if (" + isAvgPool + ") {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = " + compareOp + "(values, minMaxValue);\n      }\n    ";
+            this.userCode = "\n      const ivec2 strides = ivec2(" + strideHeight + ", " + strideWidth + ");\n      const ivec2 pads = ivec2(" + padTop + ", " + padLeft + ");\n      const float initializationValue = " + initializationValue + ";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= " + convInfo.inWidth + ") {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(" + initializationValue + ");\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < " + effectiveFilterHeight + ";\n            wR += " + dilationHeight + ") {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= " + convInfo.inHeight + ") {\n            continue;\n          }\n\n          for (int wC = 0; wC < " + filterWidthNearestVec4 + "; wC += 4) {\n            int xC = xCCorner + wC * " + dilationWidth + ";\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + " + dilationWidth + ", d),\n              getValue(batch, xR, xC + 2 * " + dilationWidth + ", d),\n              getValue(batch, xR, xC + 3 * " + dilationWidth + ", d)\n            );\n\n            " + updateSnippet + "\n          }\n\n          int xC = xCCorner + " + filterWidthNearestVec4 + ";\n          if (" + (filterWidthVec4Remainder === 1) + ") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            " + updateSnippet + "\n          } else if (" + (filterWidthVec4Remainder === 2) + ") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + " + dilationWidth + ", d),\n              initializationValue,\n              initializationValue\n            );\n\n            " + updateSnippet + "\n          } else if (" + (filterWidthVec4Remainder === 3) + ") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + " + dilationWidth + ", d),\n              getValue(batch, xR, xC + 2 * " + dilationWidth + ", d),\n              initializationValue\n            );\n\n            " + updateSnippet + "\n          }\n        }\n        setOutput(" + returnValue + ");\n      }\n    ";
+        }
+        return Pool2DProgram;
+    }());
+
+    var ReduceProgram = (function () {
+        function ReduceProgram(reduceInfo, reduceType) {
+            this.variableNames = ['x'];
+            var windowSize = reduceInfo.windowSize;
+            var batchSize = reduceInfo.batchSize;
+            var inSize = reduceInfo.inSize;
+            var outSize = Math.ceil(inSize / windowSize);
+            this.outputShape = [batchSize, outSize];
+            var initializationValue = '0.0';
+            var compareOp = "";
+            if (reduceType === 'prod') {
+                initializationValue = '1.0';
+            }
+            else if (reduceType === 'min') {
+                initializationValue = '1.0 / 0.0';
+                compareOp = "min";
+            }
+            else if (reduceType === 'max') {
+                initializationValue = '-1.0 / 0.0';
+                compareOp = "max";
+            }
+            var returnValue = reduceType + "(" + reduceType + "(" + reduceType + "(" +
+                'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';
+            if (reduceType === 'sum') {
+                returnValue = "sumValue";
+            }
+            else if (reduceType === 'prod') {
+                returnValue = "prodValue";
+            }
+            else if (reduceType === 'all') {
+                returnValue = "allValue";
+            }
+            else if (reduceType === 'any') {
+                returnValue = "anyValue";
+            }
+            var windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;
+            var windowSizeVec4Remainder = windowSize % 4;
+            var updateSnippet = "\n      if (" + (reduceType === 'sum') + ") {\n        sumValue += dot(values, ones);\n      } else if (" + (reduceType === 'prod') + ") {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = " + compareOp + "(values, minMaxValue);\n      }\n    ";
+            var vecType = "vec4";
+            if (reduceType === 'all') {
+                initializationValue = '1.0';
+                updateSnippet = "\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      ";
+                vecType = "bvec4";
+            }
+            else if (reduceType === 'any') {
+                initializationValue = '0.0';
+                updateSnippet = "\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      ";
+                vecType = "bvec4";
+            }
+            var checkOutOfBounds = '';
+            if (inSize % windowSize > 0) {
+                checkOutOfBounds = "\n        if (inIdx < 0 || inIdx >= " + inSize + ") {\n          return initializationValue;\n        }\n      ";
+            }
+            this.userCode = "\n      const float initializationValue = " + initializationValue + ";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        " + checkOutOfBounds + "\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * " + windowSize + ";\n\n        vec4 minMaxValue = vec4(" + initializationValue + ");\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < " + windowSizeNearestVec4 + "; i += 4) {\n          int inIdx = inOffset + i;\n          " + vecType + " values = " + vecType + "(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          " + updateSnippet + "\n        }\n\n        int inIdx = inOffset + " + windowSizeNearestVec4 + ";\n        if (" + (windowSizeVec4Remainder === 1) + ") {\n          " + vecType + " values = " + vecType + "(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          " + updateSnippet + "\n        } else if (" + (windowSizeVec4Remainder === 2) + ") {\n          " + vecType + " values = " + vecType + "(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          " + updateSnippet + "\n        } else if (" + (windowSizeVec4Remainder === 3) + ") {\n          " + vecType + " values = " + vecType + "(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          " + updateSnippet + "\n        }\n        setOutput(" + returnValue + ");\n      }\n    ";
+        }
+        return ReduceProgram;
+    }());
+
+    var ReshapePackedProgram = (function () {
+        function ReshapePackedProgram(outputShape, inputShape) {
+            this.variableNames = ['A'];
+            this.usesPackedTextures = true;
+            this.outputShape = outputShape;
+            var mainLoop = "";
+            for (var i = 0; i < 4; i++) {
+                var thisRC = "thisRC = rc;";
+                if (i % 2 === 1) {
+                    thisRC += "thisRC.z += 1;";
+                }
+                if (i > 1) {
+                    thisRC += "thisRC.y += 1;";
+                }
+                mainLoop += "\n        " + thisRC + "\n        " + (i > 0 ? "if(thisRC.y < rows && thisRC.z < cols){" : '') + "\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[" + i + "] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        " + (i > 0 ? '}' : '') + "\n      ";
+            }
+            this.userCode = "\n      " + getReshapedInputCoords(inputShape) + "\n      " + getFlatIndex(outputShape) + "\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = " + outputShape[1] + ";\n        int cols = " + outputShape[2] + ";\n\n        " + mainLoop + "\n\n        setOutput(result);\n      }\n    ";
+        }
+        return ReshapePackedProgram;
+    }());
+    function getFlatIndex(shape) {
+        var dotCoordsWithStrides = dotify(['coords.x', 'coords.y', 'coords.z'], computeStrides(shape).map(function (d) { return d.toString(); }).concat(['1.']));
+        return "\n    int getFlatIndex(ivec3 coords) {\n      return round(" + dotCoordsWithStrides + ");\n    }\n  ";
+    }
+    function getReshapedInputCoords(shape) {
+        var coordsFromIndexSnippet = getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);
+        return "\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      " + coordsFromIndexSnippet + "\n      return ivec3(r, c, d);\n    }\n  ";
+    }
+
+    var ResizeBilinearBackpropProgram = (function () {
+        function ResizeBilinearBackpropProgram(dy, x, alignCorners) {
+            this.variableNames = ['dy'];
+            this.outputShape = [];
+            this.outputShape = x.shape;
+            var _a = x.shape, xHeight = _a[1], xWidth = _a[2];
+            var _b = dy.shape, yHeight = _b[1], yWidth = _b[2];
+            var effectiveXSize = [
+                (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,
+                (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth
+            ];
+            var effectiveYSize = [
+                (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,
+                (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth
+            ];
+            var heightScale = effectiveXSize[0] / effectiveYSize[0];
+            var widthScale = effectiveXSize[1] / effectiveYSize[1];
+            var invHeightScale = 1 / heightScale;
+            var invWidthScale = 1 / widthScale;
+            var winHeight = (Math.ceil(invHeightScale) * 2) + 2;
+            var winWidth = (Math.ceil(invWidthScale) * 2) + 2;
+            this.userCode = "\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(" + heightScale + ");\n        const float widthScale = float(" + widthScale + ");\n\n        const float invHeightScale = float(" + invHeightScale + ");\n        const float invWidthScale = float(" + invWidthScale + ");\n\n        const int winHeight = int(" + winHeight + ");\n        const int winWidth = int(" + winWidth + ");\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= " + yHeight + ") {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= " + yWidth + ") {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), " + (xHeight - 1) + ".0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), " + (xWidth - 1) + ".0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    ";
+        }
+        return ResizeBilinearBackpropProgram;
+    }());
+
+    var ResizeBilinearProgram = (function () {
+        function ResizeBilinearProgram(inputShape, newHeight, newWidth, alignCorners) {
+            this.variableNames = ['A'];
+            this.outputShape = [];
+            var batch = inputShape[0], oldHeight = inputShape[1], oldWidth = inputShape[2], depth = inputShape[3];
+            this.outputShape = [batch, newHeight, newWidth, depth];
+            var effectiveInSize = [
+                (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,
+                (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth
+            ];
+            var effectiveOutSize = [
+                (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,
+                (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth
+            ];
+            this.userCode = "\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          " + effectiveInSize[0] / effectiveOutSize[0] + ",\n          " + effectiveInSize[1] / effectiveOutSize[1] + ");\n      const vec2 inputShapeRC = vec2(" + oldHeight + ".0, " + oldWidth + ".0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    ";
+        }
+        return ResizeBilinearProgram;
+    }());
+
+    var ResizeNearestNeigborBackpropProgram = (function () {
+        function ResizeNearestNeigborBackpropProgram(dy, x, alignCorners) {
+            this.variableNames = ['dy'];
+            this.outputShape = [];
+            this.outputShape = x.shape;
+            var _a = x.shape, xHeight = _a[1], xWidth = _a[2];
+            var _b = dy.shape, yHeight = _b[1], yWidth = _b[2];
+            var effectiveXSize = [
+                (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,
+                (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth
+            ];
+            var effectiveYSize = [
+                (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,
+                (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth
+            ];
+            var heightScale = effectiveXSize[0] / effectiveYSize[0];
+            var widthScale = effectiveXSize[1] / effectiveYSize[1];
+            var invHeightScale = 1 / heightScale;
+            var invWidthScale = 1 / widthScale;
+            var winHeight = (Math.ceil(invHeightScale) * 2) + 2;
+            var winWidth = (Math.ceil(invWidthScale) * 2) + 2;
+            this.userCode = "\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(" + heightScale + ");\n        const float widthScale = float(" + widthScale + ");\n\n        const float invHeightScale = float(" + invHeightScale + ");\n        const float invWidthScale = float(" + invWidthScale + ");\n\n        const int winHeight = int(" + winHeight + ");\n        const int winWidth = int(" + winWidth + ");\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= " + yHeight + ") {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= " + yWidth + ") {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(" + effectiveXSize[0] + ") *\n                (float(dyR) / float(" + effectiveYSize[0] + "));\n\n            float sourceFracCol =\n                float(" + effectiveXSize[1] + ") *\n                  (float(dyC) / float(" + effectiveYSize[1] + "));\n\n            int sourceNearestRow = int(min(\n                float(int(" + xHeight + ") - 1),\n                " + alignCorners + " ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(" + xWidth + ") - 1),\n                " + alignCorners + " ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    ";
+        }
+        return ResizeNearestNeigborBackpropProgram;
+    }());
+
+    var ResizeNearestNeighborProgram = (function () {
+        function ResizeNearestNeighborProgram(inputShape, newHeight, newWidth, alignCorners) {
+            this.variableNames = ['A'];
+            this.outputShape = [];
+            var batch = inputShape[0], oldHeight = inputShape[1], oldWidth = inputShape[2], depth = inputShape[3];
+            this.outputShape = [batch, newHeight, newWidth, depth];
+            var effectiveInSize = [
+                (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,
+                (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth
+            ];
+            var effectiveOutSize = [
+                (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,
+                (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth
+            ];
+            var roundBase = alignCorners ? '0.5' : '0.0';
+            this.userCode = "\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          " + effectiveInSize[0] / effectiveOutSize[0] + ",\n          " + effectiveInSize[1] / effectiveOutSize[1] + ");\n      const vec2 inputShapeRC = vec2(" + oldHeight + ".0, " + oldWidth + ".0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + " + roundBase + ")));\n\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    ";
+        }
+        return ResizeNearestNeighborProgram;
+    }());
+
+    var ReverseProgram = (function () {
+        function ReverseProgram(xShape, axis) {
+            this.variableNames = ['x'];
+            var rank = xShape.length;
+            if (rank > 4) {
+                throw new Error("WebGL backend: Reverse of rank-" + rank + " tensor is not yet supported");
+            }
+            this.outputShape = xShape;
+            if (rank === 1) {
+                this.userCode = "\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(" + xShape[0] + " - coord - 1));\n        }\n      ";
+                return;
+            }
+            var getInCoord = function (i) {
+                if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {
+                    return xShape[i] + " - coords[" + i + "] - 1";
+                }
+                return "coords[" + i + "]";
+            };
+            var inCoords = xShape.map(function (_, i) { return getInCoord(i); }).join(',');
+            var type = getCoordsDataType(rank);
+            this.userCode = "\n      void main() {\n        " + type + " coords = getOutputCoords();\n        setOutput(getX(" + inCoords + "));\n      }\n    ";
+        }
+        return ReverseProgram;
+    }());
+
+    var ScatterProgram = (function () {
+        function ScatterProgram(updateSize, sliceDim, indicesRank, updatesRank, strides, shape, summingDupeIndex) {
+            if (summingDupeIndex === void 0) { summingDupeIndex = true; }
+            this.variableNames = ['updates', 'indices', 'defaultValue'];
+            this.outputShape = shape;
+            var stridesType = getCoordsDataType(strides.length);
+            var dtype = getCoordsDataType(shape.length);
+            var indicesString = '';
+            if (indicesRank === 1) {
+                indicesString = 'i';
+            }
+            else if (indicesRank === 2) {
+                indicesString = 'i, j';
+            }
+            var indicesSnippet = "getIndices(" + indicesString + ")";
+            var updatesString = '';
+            if (updatesRank === 1) {
+                updatesString = 'i';
+            }
+            else if (updatesRank === 2) {
+                updatesString = 'i, coords[1]';
+            }
+            var updatesSnippet = "getUpdates(" + updatesString + ")";
+            var strideString = sliceDim > 1 ? 'strides[j]' : 'strides';
+            this.userCode = "\n        " + stridesType + " strides = " + stridesType + "(" + strides + ");\n\n        void main() {\n          " + dtype + " coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < " + updateSize + "; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < " + sliceDim + "; j++) {\n              int index = round(" + indicesSnippet + ");\n              flattenedIndex += index * " + strideString + ";\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += " + updatesSnippet + ";\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      ";
+        }
+        return ScatterProgram;
+    }());
+
+    var SegmentOpProgram = (function () {
+        function SegmentOpProgram(segOpInfo, segOpType) {
+            this.variableNames = ['x', 'segmentIds'];
+            var windowSize = segOpInfo.windowSize;
+            var batchSize = segOpInfo.batchSize;
+            var inSize = segOpInfo.inSize;
+            var numSegments = segOpInfo.numSegments;
+            var outSize = numSegments * Math.ceil(inSize / windowSize);
+            this.outputShape = [batchSize, outSize];
+            var initializationValue = '0.0';
+            var returnValue = "sumValue";
+            var windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;
+            var windowSizeVec4Remainder = windowSize % 4;
+            var updateSnippet = "\n        sumValue += dot(values, filter);\n    ";
+            var checkValueOutOfBounds = '';
+            if (inSize % windowSize > 0) {
+                checkValueOutOfBounds = "\n        if (inIdx < 0 || inIdx >= " + inSize + ") {\n          return initializationValue;\n        }\n      ";
+            }
+            var checkSegmentIdOutOfBounds = '';
+            if (inSize % windowSize > 0) {
+                checkSegmentIdOutOfBounds = "\n        if (inIdx < 0 || inIdx >= " + inSize + ") {\n          return -1.0;\n        }\n      ";
+            }
+            this.userCode = "\n      const float initializationValue = " + initializationValue + ";\n\n      float getValue(int batch, int inIdx) {\n        " + checkValueOutOfBounds + "\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        " + checkSegmentIdOutOfBounds + "\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          " + numSegments + ")) * float(" + windowSize + "));\n        int currentSeg = int(mod(float(outIdx), float(" + numSegments + ")));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < " + windowSizeNearestVec4 + "; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          " + updateSnippet + "\n        }\n\n        int inIdx = inOffset + " + windowSizeNearestVec4 + ";\n        if (" + (windowSizeVec4Remainder === 1) + ") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          " + updateSnippet + "\n        } else if (" + (windowSizeVec4Remainder === 2) + ") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          " + updateSnippet + "\n        } else if (" + (windowSizeVec4Remainder === 3) + ") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          " + updateSnippet + "\n        }\n        setOutput(" + returnValue + ");\n      }\n    ";
+        }
+        return SegmentOpProgram;
+    }());
+
+    var SelectProgram = (function () {
+        function SelectProgram(cRank, shape, rank) {
+            this.variableNames = ['c', 'a', 'b'];
+            this.outputShape = shape;
+            var cCoords;
+            var abCoords;
+            if (rank > 4) {
+                throw Error("Where for rank " + rank + " is not yet supported");
+            }
+            if (rank === 1) {
+                abCoords = "resRC";
+                cCoords = "resRC";
+            }
+            else {
+                var currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];
+                var cCoordVars = [];
+                var abCoordVars = [];
+                for (var i = 0; i < shape.length; i++) {
+                    abCoordVars.push("" + currentCoords[i]);
+                    if (i < cRank) {
+                        cCoordVars.push("" + currentCoords[i]);
+                    }
+                }
+                cCoords = cCoordVars.join();
+                abCoords = abCoordVars.join();
+            }
+            var dtype = getCoordsDataType(rank);
+            this.userCode = "\n      void main() {\n        " + dtype + " resRC = getOutputCoords();\n        float cVal = getC(" + cCoords + ");\n        if (cVal >= 1.0) {\n          setOutput(getA(" + abCoords + "));\n        } else {\n          setOutput(getB(" + abCoords + "));\n        }\n      }\n    ";
+        }
+        return SelectProgram;
+    }());
+
+    var SliceProgram = (function () {
+        function SliceProgram(destSize) {
+            this.variableNames = ['source'];
+            this.outputShape = destSize;
+            this.rank = destSize.length;
+            var dtype = getCoordsDataType(this.rank);
+            var sourceCoords = getCoords$1(this.rank);
+            this.userCode = "\n      uniform " + dtype + " start;\n\n      void main() {\n        " + dtype + " sourceLoc = start + getOutputCoords();\n        setOutput(getSource(" + sourceCoords + "));\n      }\n    ";
+        }
+        SliceProgram.prototype.getCustomSetupFunc = function (start) {
+            var _this = this;
+            if (start.length !== this.rank) {
+                throw Error("The rank (" + this.rank + ") of the program must match the " +
+                    ("length of start (" + start.length + ")"));
+            }
+            return function (gpgpu, webGLProgram) {
+                if (_this.startLoc == null) {
+                    _this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');
+                    if (_this.startLoc == null) {
+                        return;
+                    }
+                }
+                if (_this.rank === 1) {
+                    gpgpu.gl.uniform1i(_this.startLoc, start[0]);
+                }
+                else if (_this.rank === 2) {
+                    gpgpu.gl.uniform2i(_this.startLoc, start[0], start[1]);
+                }
+                else if (_this.rank === 3) {
+                    gpgpu.gl.uniform3i(_this.startLoc, start[0], start[1], start[2]);
+                }
+                else if (_this.rank === 4) {
+                    gpgpu.gl.uniform4i(_this.startLoc, start[0], start[1], start[2], start[3]);
+                }
+                else {
+                    throw Error("Slicing for rank " + _this.rank + " is not yet supported");
+                }
+            };
+        };
+        return SliceProgram;
+    }());
+    function getCoords$1(rank) {
+        if (rank === 1) {
+            return 'sourceLoc';
+        }
+        else if (rank === 2) {
+            return 'sourceLoc.x, sourceLoc.y';
+        }
+        else if (rank === 3) {
+            return 'sourceLoc.x, sourceLoc.y, sourceLoc.z';
+        }
+        else if (rank === 4) {
+            return 'sourceLoc.x, sourceLoc.y, sourceLoc.z, sourceLoc.w';
+        }
+        else {
+            throw Error("Slicing for rank " + rank + " is not yet supported");
+        }
+    }
+
+    var StridedSliceProgram = (function () {
+        function StridedSliceProgram(begin, strides, size, shrinkAxis) {
+            this.variableNames = ['x'];
+            var shape = size.filter(function (v, index) { return shrinkAxis.indexOf(index) === -1; });
+            this.outputShape = shape;
+            var rank = size.length;
+            var inputDtype = getCoordsDataType(size.length);
+            var dtype = getCoordsDataType(shape.length);
+            var newCoords = '';
+            if (rank === 1) {
+                newCoords = 'coords * strides + begin';
+            }
+            else {
+                var outputAxis_1 = 0;
+                newCoords =
+                    size.map(function (_, i) {
+                        if (shrinkAxis.indexOf(i) === -1) {
+                            outputAxis_1++;
+                            return shape.length === 1 ?
+                                "coords * strides[" + i + "] + begin[" + i + "]" :
+                                "coords[" + (outputAxis_1 - 1) + "] * strides[" + i + "] + begin[" + i + "]";
+                        }
+                        else {
+                            return "begin[" + i + "]";
+                        }
+                    })
+                        .join(',');
+            }
+            this.userCode = "\n      " + inputDtype + " begin = " + inputDtype + "(" + begin + ");\n      " + inputDtype + " strides = " + inputDtype + "(" + strides + ");\n\n      void main() {\n        " + dtype + " coords = getOutputCoords();\n        setOutput(getX(" + newCoords + "));\n      }\n    ";
+        }
+        return StridedSliceProgram;
+    }());
+
+    var TextureManager = (function () {
+        function TextureManager(gpgpu) {
+            this.gpgpu = gpgpu;
+            this.numUsedTextures = 0;
+            this.numFreeTextures = 0;
+            this.freeTextures = {};
+            this.logEnabled = false;
+            this.usedTextures = {};
+        }
+        TextureManager.prototype.acquireTexture = function (shapeRC, usage, isPacked) {
+            var physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);
+            var shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);
+            if (!(shapeKey in this.freeTextures)) {
+                this.freeTextures[shapeKey] = [];
+            }
+            if (!(shapeKey in this.usedTextures)) {
+                this.usedTextures[shapeKey] = [];
+            }
+            if (this.freeTextures[shapeKey].length > 0) {
+                this.numFreeTextures--;
+                this.numUsedTextures++;
+                this.log();
+                var newTexture_1 = this.freeTextures[shapeKey].shift();
+                this.usedTextures[shapeKey].push(newTexture_1);
+                return newTexture_1;
+            }
+            this.numUsedTextures++;
+            this.log();
+            var newTexture;
+            if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) {
+                newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);
+            }
+            else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) {
+                newTexture =
+                    this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);
+            }
+            else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) {
+                newTexture =
+                    this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);
+            }
+            else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) {
+                newTexture =
+                    this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);
+            }
+            else if (physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) {
+                newTexture =
+                    this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);
+            }
+            this.usedTextures[shapeKey].push(newTexture);
+            return newTexture;
+        };
+        TextureManager.prototype.releaseTexture = function (texture, shape, logicalTexType, isPacked) {
+            if (this.freeTextures == null) {
+                return;
+            }
+            var physicalTexType = getPhysicalFromLogicalTextureType(logicalTexType, isPacked);
+            var shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);
+            if (!(shapeKey in this.freeTextures)) {
+                this.freeTextures[shapeKey] = [];
+            }
+            this.freeTextures[shapeKey].push(texture);
+            this.numFreeTextures++;
+            this.numUsedTextures--;
+            var texList = this.usedTextures[shapeKey];
+            var texIndex = texList.indexOf(texture);
+            if (texIndex < 0) {
+                throw new Error('Cannot release a texture that was never provided by this ' +
+                    'texture manager');
+            }
+            texList.splice(texIndex, 1);
+            this.log();
+        };
+        TextureManager.prototype.log = function () {
+            if (!this.logEnabled) {
+                return;
+            }
+            var total = this.numFreeTextures + this.numUsedTextures;
+            console.log('Free/Used', this.numFreeTextures + " / " + this.numUsedTextures, "(" + total + ")");
+        };
+        TextureManager.prototype.getNumUsedTextures = function () {
+            return this.numUsedTextures;
+        };
+        TextureManager.prototype.getNumFreeTextures = function () {
+            return this.numFreeTextures;
+        };
+        TextureManager.prototype.dispose = function () {
+            var _this = this;
+            if (this.freeTextures == null) {
+                return;
+            }
+            for (var texShape in this.freeTextures) {
+                this.freeTextures[texShape].forEach(function (tex) {
+                    _this.gpgpu.deleteMatrixTexture(tex);
+                });
+            }
+            for (var texShape in this.usedTextures) {
+                this.usedTextures[texShape].forEach(function (tex) {
+                    _this.gpgpu.deleteMatrixTexture(tex);
+                });
+            }
+            this.freeTextures = null;
+            this.usedTextures = null;
+            this.numUsedTextures = 0;
+            this.numFreeTextures = 0;
+        };
+        return TextureManager;
+    }());
+    function getPhysicalFromLogicalTextureType(logicalTexType, isPacked) {
+        if (logicalTexType === TextureUsage.UPLOAD) {
+            return isPacked ? PhysicalTextureType.PACKED_2X2_FLOAT32 :
+                PhysicalTextureType.UNPACKED_FLOAT32;
+        }
+        else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) {
+            if (isPacked) {
+                return ENV.get('WEBGL_RENDER_FLOAT32_ENABLED') ?
+                    PhysicalTextureType.PACKED_2X2_FLOAT32 :
+                    PhysicalTextureType.PACKED_2X2_FLOAT16;
+            }
+            return ENV.get('WEBGL_RENDER_FLOAT32_ENABLED') ?
+                PhysicalTextureType.UNPACKED_FLOAT32 :
+                PhysicalTextureType.UNPACKED_FLOAT16;
+        }
+        else if (logicalTexType === TextureUsage.DOWNLOAD ||
+            logicalTexType === TextureUsage.PIXELS) {
+            return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;
+        }
+        throw new Error("Unknown logical texture type " + logicalTexType);
+    }
+    function getKeyFromTextureShape(shapeRowsCol, physicalTexType, isPacked) {
+        return shapeRowsCol[0] + "_" + shapeRowsCol[1] + "_" + physicalTexType + "_" + isPacked;
+    }
+
+    var TileProgram = (function () {
+        function TileProgram(aShape, reps) {
+            this.variableNames = ['A'];
+            var outputShape = new Array(aShape.length);
+            for (var i = 0; i < outputShape.length; i++) {
+                outputShape[i] = aShape[i] * reps[i];
+            }
+            this.outputShape = outputShape;
+            this.rank = outputShape.length;
+            var dtype = getCoordsDataType(this.rank);
+            var sourceCoords = getSourceCoords$2(aShape);
+            this.userCode = "\n      void main() {\n        " + dtype + " resRC = getOutputCoords();\n        setOutput(getA(" + sourceCoords + "));\n      }\n    ";
+        }
+        return TileProgram;
+    }());
+    function getSourceCoords$2(aShape) {
+        var rank = aShape.length;
+        if (rank > 5) {
+            throw Error("Tile for rank " + rank + " is not yet supported");
+        }
+        if (rank === 1) {
+            return "imod(resRC, " + aShape[0] + ")";
+        }
+        var currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u'];
+        var sourceCoords = [];
+        for (var i = 0; i < aShape.length; i++) {
+            sourceCoords.push("imod(" + currentCoords[i] + ", " + aShape[i] + ")");
+        }
+        return sourceCoords.join();
+    }
+
+    var TransposeProgram = (function () {
+        function TransposeProgram(aShape, newDim) {
+            this.variableNames = ['A'];
+            var outputShape = new Array(aShape.length);
+            for (var i = 0; i < outputShape.length; i++) {
+                outputShape[i] = aShape[newDim[i]];
+            }
+            this.outputShape = outputShape;
+            this.rank = outputShape.length;
+            var dtype = getCoordsDataType(this.rank);
+            var switched = getSwitchedCoords(newDim);
+            this.userCode = "\n    void main() {\n      " + dtype + " resRC = getOutputCoords();\n      setOutput(getA(" + switched + "));\n    }\n    ";
+        }
+        return TransposeProgram;
+    }());
+    function getSwitchedCoords(newDim) {
+        var rank = newDim.length;
+        if (rank > 6) {
+            throw Error("Transpose for rank " + rank + " is not yet supported");
+        }
+        var originalOrder = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u', 'resRC.v'];
+        var switchedCoords = new Array(rank);
+        for (var i = 0; i < newDim.length; i++) {
+            switchedCoords[newDim[i]] = originalOrder[i];
+        }
+        return switchedCoords.join();
+    }
+
+    var ERF_P = 0.3275911;
+    var ERF_A1 = 0.254829592;
+    var ERF_A2 = -0.284496736;
+    var ERF_A3 = 1.421413741;
+    var ERF_A4 = -1.453152027;
+    var ERF_A5 = 1.061405429;
+
+    var SELU_SCALEALPHA = 1.7580993408473768599402175208123;
+    var SELU_SCALE = 1.0507009873554804934193349852946;
+
+    var UnaryOpProgram = (function () {
+        function UnaryOpProgram(aShape, opSnippet) {
+            this.variableNames = ['A'];
+            this.outputShape = aShape;
+            this.userCode = "\n      uniform float NAN;\n      float unaryOperation(float x) {\n        " + opSnippet + "\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    ";
+        }
+        UnaryOpProgram.prototype.getCustomSetupFunc = function () {
+            var _this = this;
+            return function (gpgpu, webGLProgram) {
+                if (_this.startLoc == null) {
+                    _this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'NAN');
+                    if (_this.startLoc == null) {
+                        return;
+                    }
+                }
+                gpgpu.gl.uniform1f(_this.startLoc, NaN);
+            };
+        };
+        return UnaryOpProgram;
+    }());
+    var CHECK_NAN_SNIPPET$1 = "if (isNaN(x)) return x;";
+    var ABS = "return abs(x);";
+    var RELU = CHECK_NAN_SNIPPET$1 + "\n  return (x < 0.0) ? 0.0 : x;\n";
+    var ELU = "return (x >= 0.0) ? x : (exp(x) - 1.0);";
+    var SELU = "\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = " + SELU_SCALEALPHA + ";\n  float scale = " + SELU_SCALE + ";\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n";
+    function STEP(alpha) {
+        if (alpha === void 0) { alpha = 0.0; }
+        return CHECK_NAN_SNIPPET$1 + ("\n    return x > 0.0 ? 1.0 : float(" + alpha + ");\n  ");
+    }
+    var NEG = "return -x;";
+    var CEIL = "return ceil(x);";
+    var FLOOR = "return floor(x);";
+    var SIGN = "\n  if (isNaN(x)) { return 0.0; }\n  return sign(x);\n";
+    var ROUND = "\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n";
+    var EXP = "return exp(x);";
+    var EXPM1 = "return exp(x) - 1.0;";
+    var LOG = "if (x < 0.0) return NAN;\n  return log(x);";
+    var LOG1P = "return log(1.0 + x);";
+    var SQRT = "return sqrt(x);";
+    var RSQRT = "return inversesqrt(x);";
+    var SIGMOID = "return 1.0 / (1.0 + exp(-1.0 * x));";
+    var SOFTPLUS = "\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n";
+    var SIN = CHECK_NAN_SNIPPET$1 + "\n  return sin(x);\n";
+    var COS = CHECK_NAN_SNIPPET$1 + "\n  return cos(x);\n";
+    var TAN = "return tan(x);";
+    var ASIN = "return asin(x);";
+    var ACOS = "return acos(x);";
+    var ATAN = CHECK_NAN_SNIPPET$1 + "\n  return atan(x);\n";
+    var SINH = "\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n";
+    var COSH = "\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n";
+    var TANH = "\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n";
+    var ASINH = "return log(x + sqrt(x * x + 1.0));";
+    var ACOSH = CHECK_NAN_SNIPPET$1 + "\n  if (x < 1.0) return NAN;\n  return log(x + sqrt(x * x - 1.0));";
+    var ATANH = CHECK_NAN_SNIPPET$1 + "\n  if ((x < -1.0) || (x > 1.0)) return NAN;\n  return (log(1.0 + x) - log(1.0 - x)) / 2.0;";
+    var ERF = "\n  // Error function is calculated approximately with elementary function.\n  // See \"Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n  float p = " + ERF_P + ";\n  float a1 = " + ERF_A1 + ";\n  float a2 = " + ERF_A2 + ";\n  float a3 = " + ERF_A3 + ";\n  float a4 = " + ERF_A4 + ";\n  float a5 = " + ERF_A5 + ";\n\n  float t = 1.0 / (1.0 + p * x);\n  return 1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x);\n";
+    var SQUARE = "return x * x;";
+    var RECIPROCAL = "return 1.0 / x;";
+    var LOGICAL_NOT = "return float(!(x >= 1.0));";
+    var TO_INT = "return float(int(x));";
+
+    var UnpackProgram = (function () {
+        function UnpackProgram(outputShape) {
+            this.variableNames = ['A'];
+            this.usesPackedTextures = true;
+            this.outputShape = outputShape;
+            var rank = outputShape.length;
+            var channels = getChannels('rc', rank);
+            var dtype = getCoordsDataType(rank);
+            var sourceCoords = getSourceCoords$1(rank, channels);
+            var innerDims = channels.slice(-2);
+            var coords = rank <= 1 ? 'rc' : "vec2(" + innerDims.join(',') + ")";
+            this.userCode = "\n      void main() {\n        " + dtype + " rc = getOutputCoords();\n        vec4 packedInput = getA(" + sourceCoords + ");\n\n        setOutput(getChannel(packedInput, " + coords + "));\n      }\n    ";
+        }
+        return UnpackProgram;
+    }());
+
+    function concat1d_(tensors) {
+        return concat(tensors, 0);
+    }
+    function concat2d_(tensors, axis) {
+        return concat(tensors, axis);
+    }
+    function concat3d_(tensors, axis) {
+        return concat(tensors, axis);
+    }
+    function concat4d_(tensors, axis) {
+        return concat(tensors, axis);
+    }
+    function concat_(tensors, axis) {
+        if (axis === void 0) { axis = 0; }
+        assert(tensors.length >= 1, 'Pass at least one tensor to concat');
+        var $tensors = convertToTensorArray(tensors, 'tensors', 'concat');
+        axis = parseAxisParam(axis, $tensors[0].shape)[0];
+        var outShape = computeOutShape($tensors.map(function (t) { return t.shape; }), axis);
+        if (sizeFromShape(outShape) === 0) {
+            return tensor([], outShape);
+        }
+        $tensors = $tensors.filter(function (t) { return t.size > 0; });
+        if ($tensors.length === 1) {
+            return $tensors[0];
+        }
+        var shapes = $tensors.map(function (t) { return t.shape; });
+        assertParamsConsistent(shapes, axis);
+        var der = function (dy) {
+            var sizeSplits = shapes.map(function (s) { return s[axis]; });
+            var derTensors = split$1(dy, sizeSplits, axis);
+            return derTensors.map(function (t) { return function () { return t; }; });
+        };
+        var inputs = $tensors;
+        return ENV.engine.runKernel(function (backend) { return backend.concat($tensors, axis); }, inputs, der);
+    }
+    function split_(x, numOrSizeSplits, axis) {
+        if (axis === void 0) { axis = 0; }
+        var $x = convertToTensor(x, 'x', 'split');
+        axis = parseAxisParam(axis, $x.shape)[0];
+        var splitSizes;
+        if (typeof (numOrSizeSplits) === 'number') {
+            assert($x.shape[axis] % numOrSizeSplits === 0, 'Number of splits must evenly divide the axis.');
+            splitSizes = Array(numOrSizeSplits).fill($x.shape[axis] / numOrSizeSplits);
+        }
+        else {
+            assert($x.shape[axis] === numOrSizeSplits.reduce(function (a, b) { return a + b; }), 'The sum of sizes must match the size of the axis dimension.');
+            splitSizes = numOrSizeSplits;
+        }
+        var der = function (dy) { return ({ $x: function () { return concat(dy, axis); } }); };
+        return ENV.engine.runKernel(function (backend) { return backend.split($x, splitSizes, axis); }, { $x: $x }, der);
+    }
+    var concat = op({ concat_: concat_ });
+    var concat1d = op({ concat1d_: concat1d_ });
+    var concat2d = op({ concat2d_: concat2d_ });
+    var concat3d = op({ concat3d_: concat3d_ });
+    var concat4d = op({ concat4d_: concat4d_ });
+    var split$1 = op({ split_: split_ });
+
+    var commonjsGlobal = typeof window !== 'undefined' ? window : typeof global !== 'undefined' ? global : typeof self !== 'undefined' ? self : {};
+
+    function createCommonjsModule(fn, module) {
+    	return module = { exports: {} }, fn(module, module.exports), module.exports;
+    }
+
+    var alea = createCommonjsModule(function (module) {
+    // A port of an algorithm by Johannes Baagøe <baagoe@baagoe.com>, 2010
+    // http://baagoe.com/en/RandomMusings/javascript/
+    // https://github.com/nquinlan/better-random-numbers-for-javascript-mirror
+    // Original work is under MIT license -
+
+    // Copyright (C) 2010 by Johannes Baagøe <baagoe@baagoe.org>
+    //
+    // Permission is hereby granted, free of charge, to any person obtaining a copy
+    // of this software and associated documentation files (the "Software"), to deal
+    // in the Software without restriction, including without limitation the rights
+    // to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    // copies of the Software, and to permit persons to whom the Software is
+    // furnished to do so, subject to the following conditions:
+    // 
+    // The above copyright notice and this permission notice shall be included in
+    // all copies or substantial portions of the Software.
+    // 
+    // THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    // IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    // FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    // AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    // LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    // OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+    // THE SOFTWARE.
+
+
+
+    (function(global, module, define) {
+
+    function Alea(seed) {
+      var me = this, mash = Mash();
+
+      me.next = function() {
+        var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32
+        me.s0 = me.s1;
+        me.s1 = me.s2;
+        return me.s2 = t - (me.c = t | 0);
+      };
+
+      // Apply the seeding algorithm from Baagoe.
+      me.c = 1;
+      me.s0 = mash(' ');
+      me.s1 = mash(' ');
+      me.s2 = mash(' ');
+      me.s0 -= mash(seed);
+      if (me.s0 < 0) { me.s0 += 1; }
+      me.s1 -= mash(seed);
+      if (me.s1 < 0) { me.s1 += 1; }
+      me.s2 -= mash(seed);
+      if (me.s2 < 0) { me.s2 += 1; }
+      mash = null;
+    }
+
+    function copy(f, t) {
+      t.c = f.c;
+      t.s0 = f.s0;
+      t.s1 = f.s1;
+      t.s2 = f.s2;
+      return t;
+    }
+
+    function impl(seed, opts) {
+      var xg = new Alea(seed),
+          state = opts && opts.state,
+          prng = xg.next;
+      prng.int32 = function() { return (xg.next() * 0x100000000) | 0; };
+      prng.double = function() {
+        return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53
+      };
+      prng.quick = prng;
+      if (state) {
+        if (typeof(state) == 'object') copy(state, xg);
+        prng.state = function() { return copy(xg, {}); };
+      }
+      return prng;
+    }
+
+    function Mash() {
+      var n = 0xefc8249d;
+
+      var mash = function(data) {
+        data = data.toString();
+        for (var i = 0; i < data.length; i++) {
+          n += data.charCodeAt(i);
+          var h = 0.02519603282416938 * n;
+          n = h >>> 0;
+          h -= n;
+          h *= n;
+          n = h >>> 0;
+          h -= n;
+          n += h * 0x100000000; // 2^32
+        }
+        return (n >>> 0) * 2.3283064365386963e-10; // 2^-32
+      };
+
+      return mash;
+    }
+
+
+    if (module && module.exports) {
+      module.exports = impl;
+    } else if (define && define.amd) {
+      define(function() { return impl; });
+    } else {
+      this.alea = impl;
+    }
+
+    })(
+      commonjsGlobal,
+      module,    // present in node.js
+      (typeof undefined) == 'function' && undefined   // present with an AMD loader
+    );
+    });
+
+    var xor128 = createCommonjsModule(function (module) {
+    // A Javascript implementaion of the "xor128" prng algorithm by
+    // George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper
+
+    (function(global, module, define) {
+
+    function XorGen(seed) {
+      var me = this, strseed = '';
+
+      me.x = 0;
+      me.y = 0;
+      me.z = 0;
+      me.w = 0;
+
+      // Set up generator function.
+      me.next = function() {
+        var t = me.x ^ (me.x << 11);
+        me.x = me.y;
+        me.y = me.z;
+        me.z = me.w;
+        return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);
+      };
+
+      if (seed === (seed | 0)) {
+        // Integer seed.
+        me.x = seed;
+      } else {
+        // String seed.
+        strseed += seed;
+      }
+
+      // Mix in string seed, then discard an initial batch of 64 values.
+      for (var k = 0; k < strseed.length + 64; k++) {
+        me.x ^= strseed.charCodeAt(k) | 0;
+        me.next();
+      }
+    }
+
+    function copy(f, t) {
+      t.x = f.x;
+      t.y = f.y;
+      t.z = f.z;
+      t.w = f.w;
+      return t;
+    }
+
+    function impl(seed, opts) {
+      var xg = new XorGen(seed),
+          state = opts && opts.state,
+          prng = function() { return (xg.next() >>> 0) / 0x100000000; };
+      prng.double = function() {
+        do {
+          var top = xg.next() >>> 11,
+              bot = (xg.next() >>> 0) / 0x100000000,
+              result = (top + bot) / (1 << 21);
+        } while (result === 0);
+        return result;
+      };
+      prng.int32 = xg.next;
+      prng.quick = prng;
+      if (state) {
+        if (typeof(state) == 'object') copy(state, xg);
+        prng.state = function() { return copy(xg, {}); };
+      }
+      return prng;
+    }
+
+    if (module && module.exports) {
+      module.exports = impl;
+    } else if (define && define.amd) {
+      define(function() { return impl; });
+    } else {
+      this.xor128 = impl;
+    }
+
+    })(
+      commonjsGlobal,
+      module,    // present in node.js
+      (typeof undefined) == 'function' && undefined   // present with an AMD loader
+    );
+    });
+
+    var xorwow = createCommonjsModule(function (module) {
+    // A Javascript implementaion of the "xorwow" prng algorithm by
+    // George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper
+
+    (function(global, module, define) {
+
+    function XorGen(seed) {
+      var me = this, strseed = '';
+
+      // Set up generator function.
+      me.next = function() {
+        var t = (me.x ^ (me.x >>> 2));
+        me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;
+        return (me.d = (me.d + 362437 | 0)) +
+           (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;
+      };
+
+      me.x = 0;
+      me.y = 0;
+      me.z = 0;
+      me.w = 0;
+      me.v = 0;
+
+      if (seed === (seed | 0)) {
+        // Integer seed.
+        me.x = seed;
+      } else {
+        // String seed.
+        strseed += seed;
+      }
+
+      // Mix in string seed, then discard an initial batch of 64 values.
+      for (var k = 0; k < strseed.length + 64; k++) {
+        me.x ^= strseed.charCodeAt(k) | 0;
+        if (k == strseed.length) {
+          me.d = me.x << 10 ^ me.x >>> 4;
+        }
+        me.next();
+      }
+    }
+
+    function copy(f, t) {
+      t.x = f.x;
+      t.y = f.y;
+      t.z = f.z;
+      t.w = f.w;
+      t.v = f.v;
+      t.d = f.d;
+      return t;
+    }
+
+    function impl(seed, opts) {
+      var xg = new XorGen(seed),
+          state = opts && opts.state,
+          prng = function() { return (xg.next() >>> 0) / 0x100000000; };
+      prng.double = function() {
+        do {
+          var top = xg.next() >>> 11,
+              bot = (xg.next() >>> 0) / 0x100000000,
+              result = (top + bot) / (1 << 21);
+        } while (result === 0);
+        return result;
+      };
+      prng.int32 = xg.next;
+      prng.quick = prng;
+      if (state) {
+        if (typeof(state) == 'object') copy(state, xg);
+        prng.state = function() { return copy(xg, {}); };
+      }
+      return prng;
+    }
+
+    if (module && module.exports) {
+      module.exports = impl;
+    } else if (define && define.amd) {
+      define(function() { return impl; });
+    } else {
+      this.xorwow = impl;
+    }
+
+    })(
+      commonjsGlobal,
+      module,    // present in node.js
+      (typeof undefined) == 'function' && undefined   // present with an AMD loader
+    );
+    });
+
+    var xorshift7 = createCommonjsModule(function (module) {
+    // A Javascript implementaion of the "xorshift7" algorithm by
+    // François Panneton and Pierre L'ecuyer:
+    // "On the Xorgshift Random Number Generators"
+    // http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf
+
+    (function(global, module, define) {
+
+    function XorGen(seed) {
+      var me = this;
+
+      // Set up generator function.
+      me.next = function() {
+        // Update xor generator.
+        var X = me.x, i = me.i, t, v;
+        t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);
+        t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);
+        t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);
+        t = X[(i + 4) & 7]; v ^= t ^ (t << 7);
+        t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);
+        X[i] = v;
+        me.i = (i + 1) & 7;
+        return v;
+      };
+
+      function init(me, seed) {
+        var j, w, X = [];
+
+        if (seed === (seed | 0)) {
+          // Seed state array using a 32-bit integer.
+          w = X[0] = seed;
+        } else {
+          // Seed state using a string.
+          seed = '' + seed;
+          for (j = 0; j < seed.length; ++j) {
+            X[j & 7] = (X[j & 7] << 15) ^
+                (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);
+          }
+        }
+        // Enforce an array length of 8, not all zeroes.
+        while (X.length < 8) X.push(0);
+        for (j = 0; j < 8 && X[j] === 0; ++j);
+        if (j == 8) w = X[7] = -1; else w = X[j];
+
+        me.x = X;
+        me.i = 0;
+
+        // Discard an initial 256 values.
+        for (j = 256; j > 0; --j) {
+          me.next();
+        }
+      }
+
+      init(me, seed);
+    }
+
+    function copy(f, t) {
+      t.x = f.x.slice();
+      t.i = f.i;
+      return t;
+    }
+
+    function impl(seed, opts) {
+      if (seed == null) seed = +(new Date);
+      var xg = new XorGen(seed),
+          state = opts && opts.state,
+          prng = function() { return (xg.next() >>> 0) / 0x100000000; };
+      prng.double = function() {
+        do {
+          var top = xg.next() >>> 11,
+              bot = (xg.next() >>> 0) / 0x100000000,
+              result = (top + bot) / (1 << 21);
+        } while (result === 0);
+        return result;
+      };
+      prng.int32 = xg.next;
+      prng.quick = prng;
+      if (state) {
+        if (state.x) copy(state, xg);
+        prng.state = function() { return copy(xg, {}); };
+      }
+      return prng;
+    }
+
+    if (module && module.exports) {
+      module.exports = impl;
+    } else if (define && define.amd) {
+      define(function() { return impl; });
+    } else {
+      this.xorshift7 = impl;
+    }
+
+    })(
+      commonjsGlobal,
+      module,    // present in node.js
+      (typeof undefined) == 'function' && undefined   // present with an AMD loader
+    );
+    });
+
+    var xor4096 = createCommonjsModule(function (module) {
+    // A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.
+    //
+    // This fast non-cryptographic random number generator is designed for
+    // use in Monte-Carlo algorithms. It combines a long-period xorshift
+    // generator with a Weyl generator, and it passes all common batteries
+    // of stasticial tests for randomness while consuming only a few nanoseconds
+    // for each prng generated.  For background on the generator, see Brent's
+    // paper: "Some long-period random number generators using shifts and xors."
+    // http://arxiv.org/pdf/1004.3115v1.pdf
+    //
+    // Usage:
+    //
+    // var xor4096 = require('xor4096');
+    // random = xor4096(1);                        // Seed with int32 or string.
+    // assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.
+    // assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.
+    //
+    // For nonzero numeric keys, this impelementation provides a sequence
+    // identical to that by Brent's xorgens 3 implementaion in C.  This
+    // implementation also provides for initalizing the generator with
+    // string seeds, or for saving and restoring the state of the generator.
+    //
+    // On Chrome, this prng benchmarks about 2.1 times slower than
+    // Javascript's built-in Math.random().
+
+    (function(global, module, define) {
+
+    function XorGen(seed) {
+      var me = this;
+
+      // Set up generator function.
+      me.next = function() {
+        var w = me.w,
+            X = me.X, i = me.i, t, v;
+        // Update Weyl generator.
+        me.w = w = (w + 0x61c88647) | 0;
+        // Update xor generator.
+        v = X[(i + 34) & 127];
+        t = X[i = ((i + 1) & 127)];
+        v ^= v << 13;
+        t ^= t << 17;
+        v ^= v >>> 15;
+        t ^= t >>> 12;
+        // Update Xor generator array state.
+        v = X[i] = v ^ t;
+        me.i = i;
+        // Result is the combination.
+        return (v + (w ^ (w >>> 16))) | 0;
+      };
+
+      function init(me, seed) {
+        var t, v, i, j, w, X = [], limit = 128;
+        if (seed === (seed | 0)) {
+          // Numeric seeds initialize v, which is used to generates X.
+          v = seed;
+          seed = null;
+        } else {
+          // String seeds are mixed into v and X one character at a time.
+          seed = seed + '\0';
+          v = 0;
+          limit = Math.max(limit, seed.length);
+        }
+        // Initialize circular array and weyl value.
+        for (i = 0, j = -32; j < limit; ++j) {
+          // Put the unicode characters into the array, and shuffle them.
+          if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);
+          // After 32 shuffles, take v as the starting w value.
+          if (j === 0) w = v;
+          v ^= v << 10;
+          v ^= v >>> 15;
+          v ^= v << 4;
+          v ^= v >>> 13;
+          if (j >= 0) {
+            w = (w + 0x61c88647) | 0;     // Weyl.
+            t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.
+            i = (0 == t) ? i + 1 : 0;     // Count zeroes.
+          }
+        }
+        // We have detected all zeroes; make the key nonzero.
+        if (i >= 128) {
+          X[(seed && seed.length || 0) & 127] = -1;
+        }
+        // Run the generator 512 times to further mix the state before using it.
+        // Factoring this as a function slows the main generator, so it is just
+        // unrolled here.  The weyl generator is not advanced while warming up.
+        i = 127;
+        for (j = 4 * 128; j > 0; --j) {
+          v = X[(i + 34) & 127];
+          t = X[i = ((i + 1) & 127)];
+          v ^= v << 13;
+          t ^= t << 17;
+          v ^= v >>> 15;
+          t ^= t >>> 12;
+          X[i] = v ^ t;
+        }
+        // Storing state as object members is faster than using closure variables.
+        me.w = w;
+        me.X = X;
+        me.i = i;
+      }
+
+      init(me, seed);
+    }
+
+    function copy(f, t) {
+      t.i = f.i;
+      t.w = f.w;
+      t.X = f.X.slice();
+      return t;
+    }
+    function impl(seed, opts) {
+      if (seed == null) seed = +(new Date);
+      var xg = new XorGen(seed),
+          state = opts && opts.state,
+          prng = function() { return (xg.next() >>> 0) / 0x100000000; };
+      prng.double = function() {
+        do {
+          var top = xg.next() >>> 11,
+              bot = (xg.next() >>> 0) / 0x100000000,
+              result = (top + bot) / (1 << 21);
+        } while (result === 0);
+        return result;
+      };
+      prng.int32 = xg.next;
+      prng.quick = prng;
+      if (state) {
+        if (state.X) copy(state, xg);
+        prng.state = function() { return copy(xg, {}); };
+      }
+      return prng;
+    }
+
+    if (module && module.exports) {
+      module.exports = impl;
+    } else if (define && define.amd) {
+      define(function() { return impl; });
+    } else {
+      this.xor4096 = impl;
+    }
+
+    })(
+      commonjsGlobal,                                     // window object or global
+      module,    // present in node.js
+      (typeof undefined) == 'function' && undefined   // present with an AMD loader
+    );
+    });
+
+    var tychei = createCommonjsModule(function (module) {
+    // A Javascript implementaion of the "Tyche-i" prng algorithm by
+    // Samuel Neves and Filipe Araujo.
+    // See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf
+
+    (function(global, module, define) {
+
+    function XorGen(seed) {
+      var me = this, strseed = '';
+
+      // Set up generator function.
+      me.next = function() {
+        var b = me.b, c = me.c, d = me.d, a = me.a;
+        b = (b << 25) ^ (b >>> 7) ^ c;
+        c = (c - d) | 0;
+        d = (d << 24) ^ (d >>> 8) ^ a;
+        a = (a - b) | 0;
+        me.b = b = (b << 20) ^ (b >>> 12) ^ c;
+        me.c = c = (c - d) | 0;
+        me.d = (d << 16) ^ (c >>> 16) ^ a;
+        return me.a = (a - b) | 0;
+      };
+
+      /* The following is non-inverted tyche, which has better internal
+       * bit diffusion, but which is about 25% slower than tyche-i in JS.
+      me.next = function() {
+        var a = me.a, b = me.b, c = me.c, d = me.d;
+        a = (me.a + me.b | 0) >>> 0;
+        d = me.d ^ a; d = d << 16 ^ d >>> 16;
+        c = me.c + d | 0;
+        b = me.b ^ c; b = b << 12 ^ d >>> 20;
+        me.a = a = a + b | 0;
+        d = d ^ a; me.d = d = d << 8 ^ d >>> 24;
+        me.c = c = c + d | 0;
+        b = b ^ c;
+        return me.b = (b << 7 ^ b >>> 25);
+      }
+      */
+
+      me.a = 0;
+      me.b = 0;
+      me.c = 2654435769 | 0;
+      me.d = 1367130551;
+
+      if (seed === Math.floor(seed)) {
+        // Integer seed.
+        me.a = (seed / 0x100000000) | 0;
+        me.b = seed | 0;
+      } else {
+        // String seed.
+        strseed += seed;
+      }
+
+      // Mix in string seed, then discard an initial batch of 64 values.
+      for (var k = 0; k < strseed.length + 20; k++) {
+        me.b ^= strseed.charCodeAt(k) | 0;
+        me.next();
+      }
+    }
+
+    function copy(f, t) {
+      t.a = f.a;
+      t.b = f.b;
+      t.c = f.c;
+      t.d = f.d;
+      return t;
+    }
+    function impl(seed, opts) {
+      var xg = new XorGen(seed),
+          state = opts && opts.state,
+          prng = function() { return (xg.next() >>> 0) / 0x100000000; };
+      prng.double = function() {
+        do {
+          var top = xg.next() >>> 11,
+              bot = (xg.next() >>> 0) / 0x100000000,
+              result = (top + bot) / (1 << 21);
+        } while (result === 0);
+        return result;
+      };
+      prng.int32 = xg.next;
+      prng.quick = prng;
+      if (state) {
+        if (typeof(state) == 'object') copy(state, xg);
+        prng.state = function() { return copy(xg, {}); };
+      }
+      return prng;
+    }
+
+    if (module && module.exports) {
+      module.exports = impl;
+    } else if (define && define.amd) {
+      define(function() { return impl; });
+    } else {
+      this.tychei = impl;
+    }
+
+    })(
+      commonjsGlobal,
+      module,    // present in node.js
+      (typeof undefined) == 'function' && undefined   // present with an AMD loader
+    );
+    });
+
+    var seedrandom = createCommonjsModule(function (module) {
+    /*
+    Copyright 2014 David Bau.
+
+    Permission is hereby granted, free of charge, to any person obtaining
+    a copy of this software and associated documentation files (the
+    "Software"), to deal in the Software without restriction, including
+    without limitation the rights to use, copy, modify, merge, publish,
+    distribute, sublicense, and/or sell copies of the Software, and to
+    permit persons to whom the Software is furnished to do so, subject to
+    the following conditions:
+
+    The above copyright notice and this permission notice shall be
+    included in all copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+    EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+    MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+    IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+    CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+    TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+    SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+    */
+
+    (function (pool, math) {
+    //
+    // The following constants are related to IEEE 754 limits.
+    //
+    var global = this,
+        width = 256,        // each RC4 output is 0 <= x < 256
+        chunks = 6,         // at least six RC4 outputs for each double
+        digits = 52,        // there are 52 significant digits in a double
+        rngname = 'random', // rngname: name for Math.random and Math.seedrandom
+        startdenom = math.pow(width, chunks),
+        significance = math.pow(2, digits),
+        overflow = significance * 2,
+        mask = width - 1,
+        nodecrypto;         // node.js crypto module, initialized at the bottom.
+
+    //
+    // seedrandom()
+    // This is the seedrandom function described above.
+    //
+    function seedrandom(seed, options, callback) {
+      var key = [];
+      options = (options == true) ? { entropy: true } : (options || {});
+
+      // Flatten the seed string or build one from local entropy if needed.
+      var shortseed = mixkey(flatten(
+        options.entropy ? [seed, tostring(pool)] :
+        (seed == null) ? autoseed() : seed, 3), key);
+
+      // Use the seed to initialize an ARC4 generator.
+      var arc4 = new ARC4(key);
+
+      // This function returns a random double in [0, 1) that contains
+      // randomness in every bit of the mantissa of the IEEE 754 value.
+      var prng = function() {
+        var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48
+            d = startdenom,                 //   and denominator d = 2 ^ 48.
+            x = 0;                          //   and no 'extra last byte'.
+        while (n < significance) {          // Fill up all significant digits by
+          n = (n + x) * width;              //   shifting numerator and
+          d *= width;                       //   denominator and generating a
+          x = arc4.g(1);                    //   new least-significant-byte.
+        }
+        while (n >= overflow) {             // To avoid rounding up, before adding
+          n /= 2;                           //   last byte, shift everything
+          d /= 2;                           //   right using integer math until
+          x >>>= 1;                         //   we have exactly the desired bits.
+        }
+        return (n + x) / d;                 // Form the number within [0, 1).
+      };
+
+      prng.int32 = function() { return arc4.g(4) | 0; };
+      prng.quick = function() { return arc4.g(4) / 0x100000000; };
+      prng.double = prng;
+
+      // Mix the randomness into accumulated entropy.
+      mixkey(tostring(arc4.S), pool);
+
+      // Calling convention: what to return as a function of prng, seed, is_math.
+      return (options.pass || callback ||
+          function(prng, seed, is_math_call, state) {
+            if (state) {
+              // Load the arc4 state from the given state if it has an S array.
+              if (state.S) { copy(state, arc4); }
+              // Only provide the .state method if requested via options.state.
+              prng.state = function() { return copy(arc4, {}); };
+            }
+
+            // If called as a method of Math (Math.seedrandom()), mutate
+            // Math.random because that is how seedrandom.js has worked since v1.0.
+            if (is_math_call) { math[rngname] = prng; return seed; }
+
+            // Otherwise, it is a newer calling convention, so return the
+            // prng directly.
+            else return prng;
+          })(
+      prng,
+      shortseed,
+      'global' in options ? options.global : (this == math),
+      options.state);
+    }
+    math['seed' + rngname] = seedrandom;
+
+    //
+    // ARC4
+    //
+    // An ARC4 implementation.  The constructor takes a key in the form of
+    // an array of at most (width) integers that should be 0 <= x < (width).
+    //
+    // The g(count) method returns a pseudorandom integer that concatenates
+    // the next (count) outputs from ARC4.  Its return value is a number x
+    // that is in the range 0 <= x < (width ^ count).
+    //
+    function ARC4(key) {
+      var t, keylen = key.length,
+          me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];
+
+      // The empty key [] is treated as [0].
+      if (!keylen) { key = [keylen++]; }
+
+      // Set up S using the standard key scheduling algorithm.
+      while (i < width) {
+        s[i] = i++;
+      }
+      for (i = 0; i < width; i++) {
+        s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];
+        s[j] = t;
+      }
+
+      // The "g" method returns the next (count) outputs as one number.
+      (me.g = function(count) {
+        // Using instance members instead of closure state nearly doubles speed.
+        var t, r = 0,
+            i = me.i, j = me.j, s = me.S;
+        while (count--) {
+          t = s[i = mask & (i + 1)];
+          r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];
+        }
+        me.i = i; me.j = j;
+        return r;
+        // For robust unpredictability, the function call below automatically
+        // discards an initial batch of values.  This is called RC4-drop[256].
+        // See http://google.com/search?q=rsa+fluhrer+response&btnI
+      })(width);
+    }
+
+    //
+    // copy()
+    // Copies internal state of ARC4 to or from a plain object.
+    //
+    function copy(f, t) {
+      t.i = f.i;
+      t.j = f.j;
+      t.S = f.S.slice();
+      return t;
+    }
+    //
+    // flatten()
+    // Converts an object tree to nested arrays of strings.
+    //
+    function flatten(obj, depth) {
+      var result = [], typ = (typeof obj), prop;
+      if (depth && typ == 'object') {
+        for (prop in obj) {
+          try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}
+        }
+      }
+      return (result.length ? result : typ == 'string' ? obj : obj + '\0');
+    }
+
+    //
+    // mixkey()
+    // Mixes a string seed into a key that is an array of integers, and
+    // returns a shortened string seed that is equivalent to the result key.
+    //
+    function mixkey(seed, key) {
+      var stringseed = seed + '', smear, j = 0;
+      while (j < stringseed.length) {
+        key[mask & j] =
+          mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));
+      }
+      return tostring(key);
+    }
+
+    //
+    // autoseed()
+    // Returns an object for autoseeding, using window.crypto and Node crypto
+    // module if available.
+    //
+    function autoseed() {
+      try {
+        var out;
+        if (nodecrypto && (out = nodecrypto.randomBytes)) {
+          // The use of 'out' to remember randomBytes makes tight minified code.
+          out = out(width);
+        } else {
+          out = new Uint8Array(width);
+          (global.crypto || global.msCrypto).getRandomValues(out);
+        }
+        return tostring(out);
+      } catch (e) {
+        var browser = global.navigator,
+            plugins = browser && browser.plugins;
+        return [+new Date, global, plugins, global.screen, tostring(pool)];
+      }
+    }
+
+    //
+    // tostring()
+    // Converts an array of charcodes to a string
+    //
+    function tostring(a) {
+      return String.fromCharCode.apply(0, a);
+    }
+
+    //
+    // When seedrandom.js is loaded, we immediately mix a few bits
+    // from the built-in RNG into the entropy pool.  Because we do
+    // not want to interfere with deterministic PRNG state later,
+    // seedrandom will not call math.random on its own again after
+    // initialization.
+    //
+    mixkey(math.random(), pool);
+
+    //
+    // Nodejs and AMD support: export the implementation as a module using
+    // either convention.
+    //
+    if (module.exports) {
+      module.exports = seedrandom;
+      // When in node.js, try using crypto package for autoseeding.
+      try {
+        nodecrypto = require('crypto');
+      } catch (ex) {}
+    } else if ((typeof undefined) == 'function' && undefined.amd) {
+      undefined(function() { return seedrandom; });
+    }
+
+    // End anonymous scope, and pass initial values.
+    })(
+      [],     // pool: entropy pool starts empty
+      Math    // math: package containing random, pow, and seedrandom
+    );
+    });
+
+    // A library of seedable RNGs implemented in Javascript.
+    //
+    // Usage:
+    //
+    // var seedrandom = require('seedrandom');
+    // var random = seedrandom(1); // or any seed.
+    // var x = random();       // 0 <= x < 1.  Every bit is random.
+    // var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.
+
+    // alea, a 53-bit multiply-with-carry generator by Johannes Baagøe.
+    // Period: ~2^116
+    // Reported to pass all BigCrush tests.
+
+
+    // xor128, a pure xor-shift generator by George Marsaglia.
+    // Period: 2^128-1.
+    // Reported to fail: MatrixRank and LinearComp.
+
+
+    // xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.
+    // Period: 2^192-2^32
+    // Reported to fail: CollisionOver, SimpPoker, and LinearComp.
+
+
+    // xorshift7, by François Panneton and Pierre L'ecuyer, takes
+    // a different approach: it adds robustness by allowing more shifts
+    // than Marsaglia's original three.  It is a 7-shift generator
+    // with 256 bits, that passes BigCrush with no systmatic failures.
+    // Period 2^256-1.
+    // No systematic BigCrush failures reported.
+
+
+    // xor4096, by Richard Brent, is a 4096-bit xor-shift with a
+    // very long period that also adds a Weyl generator. It also passes
+    // BigCrush with no systematic failures.  Its long period may
+    // be useful if you have many generators and need to avoid
+    // collisions.
+    // Period: 2^4128-2^32.
+    // No systematic BigCrush failures reported.
+
+
+    // Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random
+    // number generator derived from ChaCha, a modern stream cipher.
+    // https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf
+    // Period: ~2^127
+    // No systematic BigCrush failures reported.
+
+
+    // The original ARC4-based prng included in this library.
+    // Period: ~2^1600
+
+
+    seedrandom.alea = alea;
+    seedrandom.xor128 = xor128;
+    seedrandom.xorwow = xorwow;
+    seedrandom.xorshift7 = xorshift7;
+    seedrandom.xor4096 = xor4096;
+    seedrandom.tychei = tychei;
+
+    var seedrandom$1 = seedrandom;
+    var seedrandom_1 = seedrandom$1.alea;
+
+    var MPRandGauss = (function () {
+        function MPRandGauss(mean, stdDeviation, dtype, truncated, seed) {
+            this.mean = mean;
+            this.stdDev = stdDeviation;
+            this.dtype = dtype;
+            this.nextVal = NaN;
+            this.truncated = truncated;
+            if (this.truncated) {
+                this.upper = this.mean + this.stdDev * 2;
+                this.lower = this.mean - this.stdDev * 2;
+            }
+            var seedValue = seed ? seed : Math.random();
+            this.random = seedrandom_1(seedValue.toString());
+        }
+        MPRandGauss.prototype.nextValue = function () {
+            if (!isNaN(this.nextVal)) {
+                var value = this.nextVal;
+                this.nextVal = NaN;
+                return value;
+            }
+            var resultX, resultY;
+            var isValid = false;
+            while (!isValid) {
+                var v1 = void 0, v2 = void 0, s = void 0;
+                do {
+                    v1 = 2 * this.random() - 1;
+                    v2 = 2 * this.random() - 1;
+                    s = v1 * v1 + v2 * v2;
+                } while (s >= 1 || s === 0);
+                var mul = Math.sqrt(-2.0 * Math.log(s) / s);
+                resultX = this.mean + this.stdDev * v1 * mul;
+                resultY = this.mean + this.stdDev * v2 * mul;
+                if (!this.truncated || this.isValidTruncated(resultX)) {
+                    isValid = true;
+                }
+            }
+            if (!this.truncated || this.isValidTruncated(resultY)) {
+                this.nextVal = this.convertValue(resultY);
+            }
+            return this.convertValue(resultX);
+        };
+        MPRandGauss.prototype.convertValue = function (value) {
+            if (this.dtype == null || this.dtype === 'float32') {
+                return value;
+            }
+            return Math.round(value);
+        };
+        MPRandGauss.prototype.isValidTruncated = function (value) {
+            return value <= this.upper && value >= this.lower;
+        };
+        return MPRandGauss;
+    }());
+
+    function clone_(x) {
+        var $x = convertToTensor(x, 'x', 'clone', null);
+        var der = function (dy) {
+            return { $x: function () { return dy.toFloat(); } };
+        };
+        return ENV.engine.runKernel(function (backend) {
+            return Tensor.make($x.shape, { dataId: $x.dataId }, $x.dtype);
+        }, { $x: $x }, der);
+    }
+    function eye_(numRows, numColumns, batchShape, dtype) {
+        if (dtype === void 0) { dtype = 'float32'; }
+        if (numColumns == null) {
+            numColumns = numRows;
+        }
+        var buff = buffer([numRows, numColumns], dtype);
+        var n = numRows <= numColumns ? numRows : numColumns;
+        for (var i = 0; i < n; ++i) {
+            buff.set(1, i, i);
+        }
+        var out = buff.toTensor().as2D(numRows, numColumns);
+        if (batchShape == null) {
+            return out;
+        }
+        else {
+            if (batchShape.length === 1) {
+                return tile(expandDims(out, 0), [batchShape[0], 1, 1]);
+            }
+            else if (batchShape.length === 2) {
+                return tile(expandDims(expandDims(out, 0), 0), [batchShape[0], batchShape[1], 1, 1]);
+            }
+            else if (batchShape.length === 3) {
+                return tile(expandDims(expandDims(expandDims(out, 0), 0), 0), [batchShape[0], batchShape[1], batchShape[2], 1, 1]);
+            }
+            else {
+                throw new Error("eye() currently supports only 1D and 2D " +
+                    ("batchShapes, but received " + batchShape.length + "D."));
+            }
+        }
+    }
+    function randomNormal_(shape, mean, stdDev, dtype, seed) {
+        if (mean === void 0) { mean = 0; }
+        if (stdDev === void 0) { stdDev = 1; }
+        if (dtype != null && dtype === 'bool') {
+            throw new Error("Unsupported data type " + dtype);
+        }
+        var randGauss = new MPRandGauss(mean, stdDev, dtype, false, seed);
+        var res = buffer(shape, dtype);
+        for (var i = 0; i < res.values.length; i++) {
+            res.values[i] = randGauss.nextValue();
+        }
+        return res.toTensor();
+    }
+    function truncatedNormal_(shape, mean, stdDev, dtype, seed) {
+        if (mean === void 0) { mean = 0; }
+        if (stdDev === void 0) { stdDev = 1; }
+        if (dtype != null && dtype === 'bool') {
+            throw new Error("Unsupported data type " + dtype);
+        }
+        var randGauss = new MPRandGauss(mean, stdDev, dtype, true, seed);
+        var res = buffer(shape, dtype);
+        for (var i = 0; i < res.values.length; i++) {
+            res.values[i] = randGauss.nextValue();
+        }
+        return res.toTensor();
+    }
+    function randomUniform_(shape, minval, maxval, dtype) {
+        if (minval === void 0) { minval = 0; }
+        if (maxval === void 0) { maxval = 1; }
+        if (dtype === void 0) { dtype = 'float32'; }
+        var res = buffer(shape, dtype);
+        for (var i = 0; i < res.values.length; i++) {
+            res.values[i] = randUniform(minval, maxval);
+        }
+        return res.toTensor();
+    }
+    function rand_(shape, randFunction, dtype) {
+        var size = sizeFromShape(shape);
+        var values = null;
+        if (dtype == null || dtype === 'float32') {
+            values = new Float32Array(size);
+        }
+        else if (dtype === 'int32') {
+            values = new Int32Array(size);
+        }
+        else if (dtype === 'bool') {
+            values = new Uint8Array(size);
+        }
+        else {
+            throw new Error("Unknown data type " + dtype);
+        }
+        for (var i = 0; i < size; i++) {
+            values[i] = randFunction();
+        }
+        return Tensor.make(shape, { values: values }, dtype);
+    }
+    function multinomial_(logits, numSamples, seed, normalized) {
+        if (normalized === void 0) { normalized = false; }
+        var $logits = convertToTensor(logits, 'logits', 'multinomial');
+        var numOutcomes = $logits.size;
+        var origRank = $logits.rank;
+        if (numOutcomes < 2) {
+            throw new Error("Error in multinomial: you need at least 2 outcomes, but got " +
+                (numOutcomes + "."));
+        }
+        if (origRank > 2) {
+            throw new Error("Rank of probabilities must be 1 or 2, but is " + origRank);
+        }
+        seed = seed || Math.random();
+        var logits2D = origRank === 1 ? $logits.as2D(1, -1) : $logits;
+        var res = ENV.engine.runKernel(function (backend) { return backend.multinomial(logits2D, normalized, numSamples, seed); }, { logits2D: logits2D });
+        return origRank === 1 ? res.as1D() : res;
+    }
+    function oneHot_(indices, depth, onValue, offValue) {
+        if (onValue === void 0) { onValue = 1; }
+        if (offValue === void 0) { offValue = 0; }
+        var $indices = convertToTensor(indices, 'indices', 'oneHot', 'int32');
+        if (depth < 2) {
+            throw new Error("Error in oneHot: depth must be >=2, but it is " + depth);
+        }
+        var grad = function (dy) {
+            return { $indices: function () { return zeros($indices.shape, 'float32'); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.oneHot($indices, depth, onValue, offValue); }, { $indices: $indices }, grad);
+    }
+    function fromPixels_(pixels, numChannels) {
+        if (numChannels === void 0) { numChannels = 3; }
+        if (numChannels > 4) {
+            throw new Error('Cannot construct Tensor with more than 4 channels from pixels.');
+        }
+        return ENV.engine.fromPixels(pixels, numChannels);
+    }
+    function toPixels(img, canvas) {
+        return __awaiter(this, void 0, void 0, function () {
+            var $img, _a, height, width, depth, minTensor, maxTensor, min, max, data, multiplier, bytes, i, r, g, b, a, j, ctx, imageData;
+            return __generator(this, function (_b) {
+                switch (_b.label) {
+                    case 0:
+                        $img = convertToTensor(img, 'img', 'toPixels');
+                        if (!(img instanceof Tensor)) {
+                            $img = $img.toInt();
+                        }
+                        if ($img.rank !== 2 && $img.rank !== 3) {
+                            throw new Error("toPixels only supports rank 2 or 3 tensors, got rank " + $img.rank + ".");
+                        }
+                        _a = $img.shape.slice(0, 2), height = _a[0], width = _a[1];
+                        depth = $img.rank === 2 ? 1 : $img.shape[2];
+                        if (depth > 4 || depth === 2) {
+                            throw new Error("toPixels only supports depth of size " +
+                                ("1, 3 or 4 but got " + depth));
+                        }
+                        minTensor = $img.min();
+                        maxTensor = $img.max();
+                        return [4, minTensor.data()];
+                    case 1:
+                        min = (_b.sent())[0];
+                        return [4, maxTensor.data()];
+                    case 2:
+                        max = (_b.sent())[0];
+                        minTensor.dispose();
+                        maxTensor.dispose();
+                        if ($img.dtype === 'float32') {
+                            if (min < 0 || max > 1) {
+                                throw new Error("Tensor values for a float32 Tensor must be in the " +
+                                    ("range [0 - 1] but got range [" + min + " - " + max + "]."));
+                            }
+                        }
+                        else if ($img.dtype === 'int32') {
+                            if (min < 0 || max > 255) {
+                                throw new Error("Tensor values for a int32 Tensor must be in the " +
+                                    ("range [0 - 255] but got range [" + min + " - " + max + "]."));
+                            }
+                        }
+                        else {
+                            throw new Error("Unsupported type for toPixels: " + $img.dtype + "." +
+                                " Please use float32 or int32 tensors.");
+                        }
+                        return [4, $img.data()];
+                    case 3:
+                        data = _b.sent();
+                        multiplier = $img.dtype === 'float32' ? 255 : 1;
+                        bytes = new Uint8ClampedArray(width * height * 4);
+                        for (i = 0; i < height * width; ++i) {
+                            r = void 0, g = void 0, b = void 0, a = void 0;
+                            if (depth === 1) {
+                                r = data[i] * multiplier;
+                                g = data[i] * multiplier;
+                                b = data[i] * multiplier;
+                                a = 255;
+                            }
+                            else if (depth === 3) {
+                                r = data[i * 3] * multiplier;
+                                g = data[i * 3 + 1] * multiplier;
+                                b = data[i * 3 + 2] * multiplier;
+                                a = 255;
+                            }
+                            else if (depth === 4) {
+                                r = data[i * 4] * multiplier;
+                                g = data[i * 4 + 1] * multiplier;
+                                b = data[i * 4 + 2] * multiplier;
+                                a = data[i * 4 + 3] * multiplier;
+                            }
+                            j = i * 4;
+                            bytes[j + 0] = Math.round(r);
+                            bytes[j + 1] = Math.round(g);
+                            bytes[j + 2] = Math.round(b);
+                            bytes[j + 3] = Math.round(a);
+                        }
+                        if (canvas != null) {
+                            canvas.width = width;
+                            canvas.height = height;
+                            ctx = canvas.getContext('2d');
+                            imageData = new ImageData(bytes, width, height);
+                            ctx.putImageData(imageData, 0, 0);
+                        }
+                        if ($img !== img) {
+                            $img.dispose();
+                        }
+                        return [2, bytes];
+                }
+            });
+        });
+    }
+    function reshape_(x, shape) {
+        var $x = convertToTensor(x, 'x', 'reshape', null);
+        shape = inferFromImplicitShape(shape, $x.size);
+        assert($x.size === sizeFromShape(shape), 'new shape and old shape must have the same number of elements.');
+        var grad = function (dy) {
+            return { $x: function () { return dy.reshape($x.shape); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.reshape($x, shape); }, { $x: $x }, grad);
+    }
+    function squeeze_(x, axis) {
+        var $x = convertToTensor(x, 'x', 'squeeze');
+        return reshape($x, squeezeShape($x.shape, axis).newShape);
+    }
+    function cast_(x, dtype) {
+        var $x = convertToTensor(x, 'x', 'cast');
+        var grad = function (dy) {
+            return { $x: function () { return dy.clone(); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.cast($x, dtype); }, { $x: $x }, grad);
+    }
+    function tile_(x, reps) {
+        var $x = convertToTensor(x, 'x', 'tile');
+        assert($x.rank === reps.length, "Error in transpose: rank of input " + $x.rank + " " +
+            ("must match length of reps " + reps + "."));
+        var grad = function (dy) {
+            var derX = function () {
+                var xGrad = zerosLike($x);
+                if ($x.rank === 1) {
+                    for (var i = 0; i < reps[0]; ++i) {
+                        xGrad = xGrad.add(dy.slice([i * $x.shape[0]], [$x.shape[0]]));
+                    }
+                }
+                else if ($x.rank === 2) {
+                    for (var i = 0; i < reps[0]; ++i) {
+                        for (var j = 0; j < reps[1]; ++j) {
+                            xGrad = xGrad.add(dy.slice([i * $x.shape[0], j * $x.shape[1]], [$x.shape[0], $x.shape[1]]));
+                        }
+                    }
+                }
+                else if ($x.rank === 3) {
+                    for (var i = 0; i < reps[0]; ++i) {
+                        for (var j = 0; j < reps[1]; ++j) {
+                            for (var k = 0; k < reps[2]; ++k) {
+                                xGrad = xGrad.add(dy.slice([i * $x.shape[0], j * $x.shape[1], k * $x.shape[2]], [$x.shape[0], $x.shape[1], $x.shape[2]]));
+                            }
+                        }
+                    }
+                }
+                else if ($x.rank === 4) {
+                    for (var i = 0; i < reps[0]; ++i) {
+                        for (var j = 0; j < reps[1]; ++j) {
+                            for (var k = 0; k < reps[2]; ++k) {
+                                for (var l = 0; l < reps[3]; ++l) {
+                                    xGrad = xGrad.add(dy.slice([
+                                        i * $x.shape[0], j * $x.shape[1], k * $x.shape[2],
+                                        l * $x.shape[3]
+                                    ], [$x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]));
+                                }
+                            }
+                        }
+                    }
+                }
+                else {
+                    throw new Error("Gradient for tile operation is not implemented for rank-" +
+                        ($x.rank + " tensors yet."));
+                }
+                return xGrad;
+            };
+            return { $x: derX };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.tile($x, reps); }, { $x: $x }, grad);
+    }
+    function pad1d_(x, paddings, constantValue) {
+        if (constantValue === void 0) { constantValue = 0; }
+        assert(paddings.length === 2, 'Invalid number of paddings. Must be length of 2.');
+        return pad(x, [paddings], constantValue);
+    }
+    function pad2d_(x, paddings, constantValue) {
+        if (constantValue === void 0) { constantValue = 0; }
+        assert(paddings.length === 2 && paddings[0].length === 2 &&
+            paddings[1].length === 2, 'Invalid number of paddings. Must be length of 2 each.');
+        return pad(x, paddings, constantValue);
+    }
+    function pad3d_(x, paddings, constantValue) {
+        if (constantValue === void 0) { constantValue = 0; }
+        assert(paddings.length === 3 && paddings[0].length === 2 &&
+            paddings[1].length === 2 && paddings[2].length === 2, 'Invalid number of paddings. Must be length of 2 each.');
+        return pad(x, paddings, constantValue);
+    }
+    function pad4d_(x, paddings, constantValue) {
+        if (constantValue === void 0) { constantValue = 0; }
+        assert(paddings.length === 4 && paddings[0].length === 2 &&
+            paddings[1].length === 2 && paddings[2].length === 2 &&
+            paddings[3].length === 2, 'Invalid number of paddings. Must be length of 2 each.');
+        return pad(x, paddings, constantValue);
+    }
+    function pad_(x, paddings, constantValue) {
+        if (constantValue === void 0) { constantValue = 0; }
+        var $x = convertToTensor(x, 'x', 'pad');
+        if ($x.rank === 0) {
+            throw new Error('pad(scalar) is not defined. Pass non-scalar to pad');
+        }
+        var begin = paddings.map(function (p) { return p[0]; });
+        var grad = function (dy) {
+            return { $x: function () { return dy.slice(begin, $x.shape); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.pad($x, paddings, constantValue); }, { $x: $x }, grad);
+    }
+    function stack_(tensors, axis) {
+        if (axis === void 0) { axis = 0; }
+        var $tensors = convertToTensorArray(tensors, 'tensors', 'stack');
+        assert($tensors.length >= 1, 'Pass at least one tensor to tf.stack');
+        if ($tensors.length === 1) {
+            return $tensors[0].expandDims(axis);
+        }
+        var rank = $tensors[0].rank;
+        var shape = $tensors[0].shape;
+        var dtype = $tensors[0].dtype;
+        assert(axis <= rank, 'Axis must be <= rank of the tensor');
+        $tensors.forEach(function (t) {
+            assertShapesMatch(shape, t.shape, 'All tensors passed to stack must have matching shapes');
+        });
+        $tensors.forEach(function (t) {
+            assert(dtype === t.dtype, 'All tensors passed to stack must have matching dtypes');
+        });
+        var expandedTensors = $tensors.map(function (t) { return t.expandDims(axis); });
+        return concat(expandedTensors, axis);
+    }
+    function batchToSpaceND_(x, blockShape, crops) {
+        var $x = convertToTensor(x, 'x', 'batchToSpaceND');
+        var prod = blockShape.reduce(function (a, b) { return a * b; });
+        assert($x.rank >= 1 + blockShape.length, "input rank is " + $x.rank + " but should be > than blockShape.length " + blockShape.length);
+        assert(crops.length === blockShape.length, "crops.length is " + crops.length + " but should be equal to blockShape.length  " + blockShape.length);
+        assert($x.shape[0] % prod === 0, "input tensor batch is " + $x.shape[0] + " but is not divisible by the product of " +
+            ("the elements of blockShape " + blockShape.join(' * ') + " === " + prod));
+        var grad = function (dy) {
+            return { $x: function () { return dy.spaceToBatchND(blockShape, crops); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.batchToSpaceND($x, blockShape, crops); }, { $x: $x }, grad);
+    }
+    function spaceToBatchND_(x, blockShape, paddings) {
+        var $x = convertToTensor(x, 'x', 'spaceToBatchND');
+        assert($x.rank >= 1 + blockShape.length, "input rank " + $x.rank + " should be > than [blockShape] " + blockShape.length);
+        assert(paddings.length === blockShape.length, "paddings.shape[0] " + paddings.length + " must be equal to [blockShape] " + blockShape.length);
+        assert($x.shape.reduce(function (a, b, i) {
+            if (i > 0 && i <= blockShape.length) {
+                return a &&
+                    ((b + paddings[i - 1][0] + paddings[i - 1][1]) %
+                        blockShape[i - 1] ===
+                        0);
+            }
+            return a;
+        }, true), "input spatial dimensions " + $x.shape.slice(1) + " with paddings " + paddings.toString() + " must be divisible by blockShapes " + blockShape.toString());
+        var grad = function (dy) {
+            return { $x: function () { return dy.batchToSpaceND(blockShape, paddings); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.spaceToBatchND($x, blockShape, paddings); }, { $x: $x }, grad);
+    }
+    function unstack_(x, axis) {
+        if (axis === void 0) { axis = 0; }
+        var $x = convertToTensor(x, 'x', 'unstack');
+        var num = $x.shape[axis];
+        var outputShape = Array($x.rank - 1).fill(0);
+        var outIndex = 0;
+        for (var i = 0; i < $x.rank; i++) {
+            if (i !== axis) {
+                outputShape[outIndex] = $x.shape[i];
+                outIndex++;
+            }
+        }
+        var splitSizes;
+        splitSizes = Array(num).fill(1);
+        var begin = Array($x.rank).fill(0);
+        var size = $x.shape.slice();
+        return splitSizes.map(function (s) {
+            size[axis] = s;
+            var slice = $x.slice(begin, size);
+            begin[axis] += s;
+            return slice.reshape(outputShape);
+        });
+    }
+    function cumsum_(x, axis, exclusive, reverse) {
+        if (axis === void 0) { axis = 0; }
+        if (exclusive === void 0) { exclusive = false; }
+        if (reverse === void 0) { reverse = false; }
+        var $x = convertToTensor(x, 'x', 'cumsum');
+        axis = axis | 0;
+        var permutation = getAxesPermutation([axis], $x.rank);
+        var permutedX = $x;
+        if (permutation != null) {
+            permutedX = $x.transpose(permutation);
+        }
+        var permutedAxis = getInnerMostAxes(1, $x.rank)[0];
+        var grad = function (dy) {
+            return { permutedX: function () { return dy.cumsum(axis, exclusive, !reverse); } };
+        };
+        var value = ENV.engine.runKernel(function (backend) { return backend.cumsum(permutedX, permutedAxis, exclusive, reverse); }, { permutedX: permutedX }, grad);
+        if (permutation != null) {
+            value = value.transpose(permutation);
+        }
+        return value;
+    }
+    function expandDims_(x, axis) {
+        if (axis === void 0) { axis = 0; }
+        var $x = convertToTensor(x, 'x', 'expandDims');
+        assert(axis <= $x.rank, 'Axis must be <= rank of the tensor');
+        var newShape = $x.shape.slice();
+        if (axis < 0) {
+            assert(-($x.rank + 1) <= axis, "Axis must be in the interval [" + -($x.rank + 1) + ", " + $x.rank + "]");
+            axis = $x.rank + axis + 1;
+        }
+        newShape.splice(axis, 0, 1);
+        return reshape($x, newShape);
+    }
+    function depthToSpace_(x, blockSize, dataFormat) {
+        if (dataFormat === void 0) { dataFormat = 'NHWC'; }
+        var $x = convertToTensor(x, 'x', 'depthToSpace');
+        var inputHeight = (dataFormat === 'NHWC') ? $x.shape[1] : $x.shape[2];
+        var inputWidth = (dataFormat === 'NHWC') ? $x.shape[2] : $x.shape[3];
+        var inputDepth = (dataFormat === 'NHWC') ? $x.shape[3] : $x.shape[1];
+        assert(inputHeight * blockSize >= 0, "Negative dimension size caused by overflow when multiplying\n      " + inputHeight + " and " + blockSize + "  for depthToSpace with input shape\n      " + $x.shape);
+        assert(inputWidth * blockSize >= 0, "Negative dimension size caused by overflow when multiplying\n      " + inputWidth + " and " + blockSize + " for depthToSpace with input shape\n          " + $x.shape);
+        assert((inputDepth % (blockSize * blockSize) === 0), "Dimension size must be evenly divisible by " + blockSize * blockSize + " but is " + inputDepth + " for depthToSpace with input shape " + $x.shape);
+        return ENV.engine.runKernel(function (backend) { return backend.depthToSpace($x, blockSize, dataFormat); }, { $x: $x });
+    }
+    function setdiff1dAsync_(x, y) {
+        return __awaiter(this, void 0, void 0, function () {
+            var $x, $y, xVals, yVals, ySet, outputSize, i, buffer, indices, i, p;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        $x = convertToTensor(x, 'x', 'setdiff1d');
+                        $y = convertToTensor(y, 'y', 'setdiff1d');
+                        assert($x.dtype === $y.dtype, "x and y should have the same dtype, but got x (" + $x.dtype + ") and y (" + $y.dtype + ").");
+                        assert($x.rank === 1, "x should be 1D tensor, but got x (" + $x.shape + ").");
+                        assert($y.rank === 1, "y should be 1D tensor, but got y (" + $y.shape + ").");
+                        return [4, $x.data()];
+                    case 1:
+                        xVals = _a.sent();
+                        return [4, $y.data()];
+                    case 2:
+                        yVals = _a.sent();
+                        ySet = new Set(yVals);
+                        outputSize = 0;
+                        for (i = 0; i < xVals.length; i++) {
+                            if (!ySet.has(xVals[i])) {
+                                outputSize++;
+                            }
+                        }
+                        buffer = new TensorBuffer([outputSize], $x.dtype);
+                        indices = new TensorBuffer([outputSize], 'int32');
+                        for (i = 0, p = 0; i < xVals.length; i++) {
+                            if (!ySet.has(xVals[i])) {
+                                buffer.values[p] = xVals[i];
+                                indices.values[p] = i;
+                                p++;
+                            }
+                        }
+                        return [2, [buffer.toTensor(), indices.toTensor()]];
+                }
+            });
+        });
+    }
+    function buffer(shape, dtype, values) {
+        if (dtype === void 0) { dtype = 'float32'; }
+        dtype = dtype || 'float32';
+        return new TensorBuffer(shape, dtype, values);
+    }
+    function print(x, verbose) {
+        if (verbose === void 0) { verbose = false; }
+        console.log(x.toString(verbose));
+    }
+    var batchToSpaceND = op({ batchToSpaceND_: batchToSpaceND_ });
+    var cast = op({ cast_: cast_ });
+    var clone = op({ clone_: clone_ });
+    var cumsum = op({ cumsum_: cumsum_ });
+    var depthToSpace = op({ depthToSpace_: depthToSpace_ });
+    var expandDims = op({ expandDims_: expandDims_ });
+    var eye = op({ eye_: eye_ });
+    var fromPixels = op({ fromPixels_: fromPixels_ });
+    var multinomial = op({ multinomial_: multinomial_ });
+    var oneHot = op({ oneHot_: oneHot_ });
+    var pad = op({ pad_: pad_ });
+    var pad1d = op({ pad1d_: pad1d_ });
+    var pad2d = op({ pad2d_: pad2d_ });
+    var pad3d = op({ pad3d_: pad3d_ });
+    var pad4d = op({ pad4d_: pad4d_ });
+    var rand = op({ rand_: rand_ });
+    var randomNormal = op({ randomNormal_: randomNormal_ });
+    var randomUniform = op({ randomUniform_: randomUniform_ });
+    var reshape = op({ reshape_: reshape_ });
+    var spaceToBatchND = op({ spaceToBatchND_: spaceToBatchND_ });
+    var squeeze = op({ squeeze_: squeeze_ });
+    var stack = op({ stack_: stack_ });
+    var tile = op({ tile_: tile_ });
+    var truncatedNormal = op({ truncatedNormal_: truncatedNormal_ });
+    var unstack = op({ unstack_: unstack_ });
+    var setdiff1dAsync = setdiff1dAsync_;
+
+    function whereImpl(condShape, condVals) {
+        var indices = [];
+        for (var i = 0; i < condVals.length; i++) {
+            if (condVals[i]) {
+                indices.push(i);
+            }
+        }
+        var inBuffer = buffer(condShape, 'int32');
+        var out = buffer([indices.length, condShape.length], 'int32');
+        for (var i = 0; i < indices.length; i++) {
+            var loc = inBuffer.indexToLoc(indices[i]);
+            var offset = i * condShape.length;
+            out.values.set(loc, offset);
+        }
+        return out.toTensor();
+    }
+
+    var CPU_HANDOFF_SIZE_THRESHOLD = 10;
+    var BEFORE_PAGING_CONSTANT = 300;
+    var MATMUL_SHARED_DIM_THRESHOLD = 1000;
+    var MathBackendWebGL = (function () {
+        function MathBackendWebGL(gpgpu, delayedStorage) {
+            if (delayedStorage === void 0) { delayedStorage = true; }
+            this.gpgpu = gpgpu;
+            this.delayedStorage = delayedStorage;
+            this.pendingRead = new WeakMap();
+            this.pendingDisposal = new WeakSet();
+            this.lruDataGPU = [];
+            this.numBytesInGPU = 0;
+            this.uploadWaitMs = 0;
+            this.downloadWaitMs = 0;
+            this.binaryCache = {};
+            this.disposed = false;
+            if (ENV.get('WEBGL_VERSION') < 1) {
+                throw new Error('WebGL is not supported on this device');
+            }
+            if (gpgpu == null) {
+                var gl = getWebGLContext(ENV.get('WEBGL_VERSION'));
+                this.gpgpu = new GPGPUContext(gl);
+                this.canvas = gl.canvas;
+                this.gpgpuCreatedLocally = true;
+            }
+            else {
+                this.gpgpuCreatedLocally = false;
+                this.canvas = gpgpu.gl.canvas;
+            }
+            if (ENV.get('WEBGL_PAGING_ENABLED')) {
+                this.NUM_BYTES_BEFORE_PAGING =
+                    (window.screen.height * window.screen.width *
+                        window.devicePixelRatio) *
+                        BEFORE_PAGING_CONSTANT;
+            }
+            this.textureManager = new TextureManager(this.gpgpu);
+        }
+        MathBackendWebGL.prototype.register = function (dataId, shape, dtype) {
+            if (this.texData.has(dataId)) {
+                throw new Error('Data buffer is already registered');
+            }
+            this.texData.set(dataId, { shape: shape, dtype: dtype });
+        };
+        MathBackendWebGL.prototype.setDataMover = function (dataMover) {
+            this.texData = new DataStorage(dataMover);
+        };
+        MathBackendWebGL.prototype.fromPixels = function (pixels, numChannels) {
+            if (pixels == null) {
+                throw new Error('pixels passed to tf.fromPixels() can not be null');
+            }
+            var texShape = [pixels.height, pixels.width];
+            var outShape = [pixels.height, pixels.width, numChannels];
+            if (!(pixels instanceof HTMLVideoElement) &&
+                !(pixels instanceof HTMLImageElement) &&
+                !(pixels instanceof HTMLCanvasElement) &&
+                !(pixels instanceof ImageData)) {
+                throw new Error('pixels passed to tf.fromPixels() must be either an ' +
+                    "HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or " +
+                    ("ImageData, but was " + pixels.constructor.name));
+            }
+            if (pixels instanceof HTMLVideoElement) {
+                if (this.fromPixels2DContext == null) {
+                    if (!ENV.get('IS_BROWSER')) {
+                        throw new Error('Can\'t read pixels from HTMLImageElement outside the browser.');
+                    }
+                    if (document.readyState !== 'complete') {
+                        throw new Error('The DOM is not ready yet. Please call tf.fromPixels() ' +
+                            'once the DOM is ready. One way to do that is to add an event ' +
+                            'listener for `DOMContentLoaded` on the document object');
+                    }
+                    this.fromPixels2DContext =
+                        document.createElement('canvas').getContext('2d');
+                }
+                this.fromPixels2DContext.canvas.width = pixels.width;
+                this.fromPixels2DContext.canvas.height = pixels.height;
+                this.fromPixels2DContext.drawImage(pixels, 0, 0, pixels.width, pixels.height);
+                pixels = this.fromPixels2DContext.canvas;
+            }
+            var tempPixelHandle = this.makeTensorHandle(texShape, 'int32');
+            this.texData.get(tempPixelHandle.dataId).usage = TextureUsage.PIXELS;
+            this.gpgpu.uploadPixelDataToTexture(this.getTexture(tempPixelHandle.dataId), pixels);
+            var program = new FromPixelsProgram(outShape);
+            var res = this.compileAndRun(program, [tempPixelHandle]);
+            this.disposeData(tempPixelHandle.dataId);
+            return res;
+        };
+        MathBackendWebGL.prototype.makeTensorHandle = function (shape, dtype) {
+            var dataId = {};
+            this.register(dataId, shape, dtype);
+            return { dataId: dataId, shape: shape, dtype: dtype };
+        };
+        MathBackendWebGL.prototype.write = function (dataId, values) {
+            if (values == null) {
+                throw new Error('MathBackendWebGL.write(): values can not be null');
+            }
+            var texData = this.texData.get(dataId);
+            var texture = texData.texture, texShape = texData.texShape, usage = texData.usage, dtype = texData.dtype, isPacked = texData.isPacked;
+            if (dtype === 'complex64') {
+                throw new Error("Cannot write to a complex64 dtype. " +
+                    "Please use tf.complex(real, imag).");
+            }
+            if (texture != null) {
+                this.releaseTexture(dataId, texture, texShape, usage, isPacked);
+                texData.texture = null;
+                texData.texShape = null;
+            }
+            texData.usage = TextureUsage.UPLOAD;
+            texData.values = values;
+            if (!this.delayedStorage) {
+                this.uploadToGPU(dataId);
+            }
+        };
+        MathBackendWebGL.prototype.readSync = function (dataId) {
+            var texData = this.texData.get(dataId);
+            var values = texData.values, dtype = texData.dtype, complexTensors = texData.complexTensors;
+            if (values != null) {
+                return this.convertAndCacheOnCPU(dataId);
+            }
+            if (dtype === 'string') {
+                return values;
+            }
+            var shouldTimeProgram = this.activeTimers != null;
+            var start;
+            if (shouldTimeProgram) {
+                start = performance.now();
+            }
+            var result;
+            if (dtype === 'complex64') {
+                var realValues = complexTensors.real.dataSync();
+                var imagValues = complexTensors.imag.dataSync();
+                result = mergeRealAndImagArrays(realValues, imagValues);
+            }
+            else {
+                result = this.getValuesFromTexture(dataId);
+            }
+            if (shouldTimeProgram) {
+                this.downloadWaitMs += performance.now() - start;
+            }
+            return this.convertAndCacheOnCPU(dataId, result);
+        };
+        MathBackendWebGL.prototype.read = function (dataId) {
+            return __awaiter(this, void 0, void 0, function () {
+                var _a, _b, subscribers_1, texData, texture, values, texShape, isPacked, shape, width, height, bufferOrTexture, vals, batch, rows, cols, dTypeVals, subscribers;
+                return __generator(this, function (_c) {
+                    switch (_c.label) {
+                        case 0:
+                            if (this.pendingRead.has(dataId)) {
+                                subscribers_1 = this.pendingRead.get(dataId);
+                                return [2, new Promise(function (resolve) { return subscribers_1.push(resolve); })];
+                            }
+                            texData = this.texData.get(dataId);
+                            texture = texData.texture, values = texData.values, texShape = texData.texShape, isPacked = texData.isPacked, shape = texData.shape;
+                            if (values != null) {
+                                return [2, this.convertAndCacheOnCPU(dataId)];
+                            }
+                            this.pendingRead.set(dataId, []);
+                            if (!ENV.get('WEBGL_DOWNLOAD_FLOAT_ENABLED') &&
+                                ENV.get('WEBGL_VERSION') === 2) {
+                                throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and " +
+                                    "WEBGL_VERSION=2 not yet supported.");
+                            }
+                            width = texShape[1];
+                            height = texShape[0];
+                            if (isPacked) {
+                                _a = getPackedMatrixTextureShapeWidthHeight(texShape[0], texShape[1]), width = _a[0], height = _a[1];
+                            }
+                            bufferOrTexture = this.gpgpu.maybeCreateBufferFromTexture(texture, height, width);
+                            return [4, this.gpgpu.createAndWaitForFence()];
+                        case 1:
+                            _c.sent();
+                            if (bufferOrTexture instanceof WebGLTexture) {
+                                vals = this.getValuesFromTexture(dataId);
+                            }
+                            else {
+                                if (isPacked) {
+                                    batch = this.getBatchDim(shape);
+                                    rows = 1, cols = 1;
+                                    if (shape.length) {
+                                        _b = this.getRowsCols(shape), rows = _b[0], cols = _b[1];
+                                    }
+                                    vals = this.gpgpu.downloadPackedMatrixFromBuffer(bufferOrTexture, batch, rows, cols, texShape[0], texShape[1]);
+                                }
+                                else {
+                                    vals = this.gpgpu.downloadFloat32MatrixFromBuffer(bufferOrTexture, texShape[0], texShape[1]);
+                                }
+                            }
+                            dTypeVals = this.convertAndCacheOnCPU(dataId, vals);
+                            subscribers = this.pendingRead.get(dataId);
+                            this.pendingRead.delete(dataId);
+                            subscribers.forEach(function (resolve) { return resolve(dTypeVals); });
+                            if (this.pendingDisposal.has(dataId)) {
+                                this.pendingDisposal.delete(dataId);
+                                this.disposeData(dataId);
+                            }
+                            return [2, dTypeVals];
+                    }
+                });
+            });
+        };
+        MathBackendWebGL.prototype.getValuesFromTexture = function (dataId) {
+            var _a;
+            var _b = this.texData.get(dataId), shape = _b.shape, dtype = _b.dtype, texture = _b.texture, texShape = _b.texShape;
+            if (ENV.get('WEBGL_DOWNLOAD_FLOAT_ENABLED')) {
+                if (this.texData.get(dataId).isPacked) {
+                    var batch = this.getBatchDim(shape);
+                    var rows = 1, cols = 1;
+                    if (shape.length) {
+                        _a = this.getRowsCols(shape), rows = _a[0], cols = _a[1];
+                    }
+                    return this.gpgpu.downloadMatrixFromPackedTexture(texture, batch, rows, cols, texShape[0], texShape[1]);
+                }
+                else {
+                    return this.gpgpu.downloadFloat32MatrixFromOutputTexture(texture, texShape[0], texShape[1]);
+                }
+            }
+            var tmpTarget = this.makeTensorHandle(shape, 'float32');
+            tmpTarget.size = sizeFromShape(shape);
+            this.texData.get(tmpTarget.dataId).usage = TextureUsage.DOWNLOAD;
+            var program = new EncodeFloatProgram(shape);
+            var pageToCpu = false;
+            this.compileAndRun(program, [{ shape: shape, dtype: dtype, dataId: dataId }], tmpTarget, null, pageToCpu);
+            var tmpData = this.texData.get(tmpTarget.dataId);
+            var vals = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(tmpData.texture, tmpData.texShape[0], tmpData.texShape[1]);
+            this.disposeData(tmpTarget.dataId);
+            return vals;
+        };
+        MathBackendWebGL.prototype.time = function (f) {
+            return __awaiter(this, void 0, void 0, function () {
+                var oldActiveTimers, newActiveTimers, outerMostTime, flattenedActiveTimerQueries, flattenedActiveTimerNames, kernelMs, res;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0:
+                            oldActiveTimers = this.activeTimers;
+                            newActiveTimers = [];
+                            outerMostTime = false;
+                            if (this.programTimersStack == null) {
+                                this.programTimersStack = newActiveTimers;
+                                outerMostTime = true;
+                            }
+                            else {
+                                this.activeTimers.push(newActiveTimers);
+                            }
+                            this.activeTimers = newActiveTimers;
+                            f();
+                            flattenedActiveTimerQueries = flatten(this.activeTimers.map(function (d) { return d.query; }))
+                                .filter(function (d) { return d != null; });
+                            flattenedActiveTimerNames = flatten(this.activeTimers.map(function (d) { return d.name; }))
+                                .filter(function (d) { return d != null; });
+                            this.activeTimers = oldActiveTimers;
+                            if (outerMostTime) {
+                                this.programTimersStack = null;
+                            }
+                            return [4, Promise.all(flattenedActiveTimerQueries)];
+                        case 1:
+                            kernelMs = _a.sent();
+                            res = {
+                                uploadWaitMs: this.uploadWaitMs,
+                                downloadWaitMs: this.downloadWaitMs,
+                                kernelMs: sum(kernelMs),
+                                getExtraProfileInfo: function () {
+                                    return kernelMs.map(function (d, i) { return ({ name: flattenedActiveTimerNames[i], ms: d }); })
+                                        .map(function (d) { return d.name + ": " + d.ms; })
+                                        .join(', ');
+                                },
+                                wallMs: null
+                            };
+                            this.uploadWaitMs = 0;
+                            this.downloadWaitMs = 0;
+                            return [2, res];
+                    }
+                });
+            });
+        };
+        MathBackendWebGL.prototype.memory = function () {
+            return { unreliable: false, numBytesInGPU: this.numBytesInGPU };
+        };
+        MathBackendWebGL.prototype.startTimer = function () {
+            if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {
+                return this.gpgpu.beginQuery();
+            }
+            return { startMs: performance.now(), endMs: null };
+        };
+        MathBackendWebGL.prototype.endTimer = function (query) {
+            if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {
+                this.gpgpu.endQuery();
+                return query;
+            }
+            query.endMs = performance.now();
+            return query;
+        };
+        MathBackendWebGL.prototype.getQueryTime = function (query) {
+            return __awaiter(this, void 0, void 0, function () {
+                var timerQuery;
+                return __generator(this, function (_a) {
+                    if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {
+                        return [2, this.gpgpu.waitForQueryAndGetTime(query)];
+                    }
+                    timerQuery = query;
+                    return [2, timerQuery.endMs - timerQuery.startMs];
+                });
+            });
+        };
+        MathBackendWebGL.prototype.disposeData = function (dataId) {
+            if (this.pendingDisposal.has(dataId)) {
+                return;
+            }
+            if (this.pendingRead.has(dataId)) {
+                this.pendingDisposal.add(dataId);
+                return;
+            }
+            if (this.texData.has(dataId)) {
+                var _a = this.texData.get(dataId), texture = _a.texture, texShape = _a.texShape, usage = _a.usage, complexTensors = _a.complexTensors, isPacked = _a.isPacked;
+                if (texture != null) {
+                    this.releaseTexture(dataId, texture, texShape, usage, isPacked);
+                }
+                if (complexTensors != null) {
+                    complexTensors.real.dispose();
+                    complexTensors.imag.dispose();
+                }
+                this.texData.delete(dataId);
+            }
+        };
+        MathBackendWebGL.prototype.getTexture = function (dataId) {
+            this.uploadToGPU(dataId);
+            return this.texData.get(dataId).texture;
+        };
+        MathBackendWebGL.prototype.getCPUBackend = function () {
+            if (!ENV.get('WEBGL_CPU_FORWARD')) {
+                return null;
+            }
+            if (this.cpuBackend == null) {
+                this.cpuBackend = ENV.findBackend('cpu');
+            }
+            return this.cpuBackend;
+        };
+        MathBackendWebGL.prototype.shouldExecuteOnCPU = function (inputs, sizeThreshold) {
+            var _this = this;
+            if (sizeThreshold === void 0) { sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD; }
+            return this.getCPUBackend() != null &&
+                inputs.every(function (input) { return _this.texData.get(input.dataId).texture == null &&
+                    input.size < sizeThreshold; });
+        };
+        MathBackendWebGL.prototype.getGPGPUContext = function () {
+            return this.gpgpu;
+        };
+        MathBackendWebGL.prototype.getCanvas = function () {
+            return this.canvas;
+        };
+        MathBackendWebGL.prototype.complex = function (real, imag) {
+            var result = this.makeOutputArray(real.shape, 'complex64');
+            var resultData = this.texData.get(result.dataId);
+            resultData.complexTensors = {
+                real: ENV.engine.keep(real.clone()),
+                imag: ENV.engine.keep(imag.clone())
+            };
+            return result;
+        };
+        MathBackendWebGL.prototype.real = function (input) {
+            var resultData = this.texData.get(input.dataId);
+            return resultData.complexTensors.real.clone();
+        };
+        MathBackendWebGL.prototype.imag = function (input) {
+            var resultData = this.texData.get(input.dataId);
+            return resultData.complexTensors.imag.clone();
+        };
+        MathBackendWebGL.prototype.slice = function (x, begin, size) {
+            if (this.shouldExecuteOnCPU([x])) {
+                return this.cpuBackend.slice(x, begin, size);
+            }
+            var program = new SliceProgram(size);
+            var customSetup = program.getCustomSetupFunc(begin);
+            return this.compileAndRun(program, [x], null, customSetup);
+        };
+        MathBackendWebGL.prototype.stridedSlice = function (x, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {
+            if (this.shouldExecuteOnCPU([x])) {
+                return this.cpuBackend.stridedSlice(x, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask);
+            }
+            var _a = getStridedSlicedInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask), beginIndex = _a[0], size = _a[1], shrinkAxis = _a[2];
+            var shape = size.filter(function (v, index) { return shrinkAxis.indexOf(index) === -1; });
+            if (shape.some(function (axis) { return axis === 0; })) {
+                return tensor([], shape);
+            }
+            var program = new StridedSliceProgram(beginIndex, strides, size, shrinkAxis);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.reverse = function (x, axis) {
+            var program = new ReverseProgram(x.shape, axis);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.concat2Tensors = function (a, b, axis) {
+            var outShape = computeOutShape([a.shape, b.shape], axis);
+            var a2D = a.as2D(-1, sizeFromShape(a.shape.slice(axis)));
+            var b2D = b.as2D(-1, sizeFromShape(b.shape.slice(axis)));
+            var program = new ConcatProgram(a2D.shape, b2D.shape);
+            var res = this.compileAndRun(program, [a2D, b2D]);
+            return res.reshape(outShape);
+        };
+        MathBackendWebGL.prototype.concat = function (tensors, axis) {
+            if (this.shouldExecuteOnCPU(tensors)) {
+                return this.cpuBackend.concat(tensors, axis);
+            }
+            if (tensors.length === 1) {
+                return tensors[0];
+            }
+            var result = tensors[0];
+            for (var i = 1; i < tensors.length; ++i) {
+                result = this.concat2Tensors(result, tensors[i], axis);
+            }
+            return result;
+        };
+        MathBackendWebGL.prototype.neg = function (x) {
+            var program = new UnaryOpProgram(x.shape, NEG);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.batchMatMul = function (a, b, transposeA, transposeB) {
+            var outerShapeA = transposeA ? a.shape[2] : a.shape[1];
+            var outerShapeB = transposeB ? b.shape[1] : b.shape[2];
+            var sharedDim = transposeA ? a.shape[1] : a.shape[2];
+            var _a = a.shape, batch = _a[0];
+            if ((outerShapeA === 1 || outerShapeB === 1) &&
+                sharedDim > MATMUL_SHARED_DIM_THRESHOLD) {
+                if (transposeA) {
+                    a = a.transpose([0, 2, 1]);
+                }
+                if (transposeB) {
+                    b = b.transpose([0, 2, 1]);
+                }
+                var a3D = outerShapeB === 1 ? a : a.as3D(batch, sharedDim, 1);
+                var axis = outerShapeB === 1 ? 2 : 1;
+                var b3D = outerShapeB === 1 ? b.as3D(batch, 1, sharedDim) : b;
+                return this.multiply(a3D, b3D).sum(axis, true);
+            }
+            var dtype = upcastType(a.dtype, b.dtype);
+            if (batch === 1) {
+                var aSqueezed = a.as2D(a.shape[1], a.shape[2]);
+                var bSqueezed = b.as2D(b.shape[1], b.shape[2]);
+                var program = new MatMulPackedProgram(aSqueezed.shape, bSqueezed.shape, [outerShapeA, outerShapeB], transposeA, transposeB);
+                var output = this.makePackedTensor(program.outputShape, dtype);
+                var result = this.compileAndRun(program, [aSqueezed, bSqueezed], output);
+                return result.reshape([1, result.shape[0], result.shape[1]]);
+            }
+            else {
+                var program = new MatMulProgram(a.shape, b.shape, transposeA, transposeB);
+                var output = this.makeOutputArray(program.outputShape, dtype);
+                return this.compileAndRun(program, [a, b], output);
+            }
+        };
+        MathBackendWebGL.prototype.multiply = function (a, b) {
+            if (a.dtype === 'complex64') {
+                var aData = this.texData.get(a.dataId);
+                var bData = this.texData.get(b.dataId);
+                var realProgram = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.REAL, a.shape, b.shape);
+                var imagProgram = new BinaryOpComplexProgram(COMPLEX_MULTIPLY.IMAG, a.shape, b.shape);
+                var inputs = [
+                    this.makeComplexComponentTensorHandle(a, aData.complexTensors.real),
+                    this.makeComplexComponentTensorHandle(a, aData.complexTensors.imag),
+                    this.makeComplexComponentTensorHandle(b, bData.complexTensors.real),
+                    this.makeComplexComponentTensorHandle(b, bData.complexTensors.imag)
+                ];
+                var real = this.compileAndRun(realProgram, inputs);
+                var imag = this.compileAndRun(imagProgram, inputs);
+                var complex = this.complex(real, imag);
+                real.dispose();
+                imag.dispose();
+                return complex;
+            }
+            if (this.shouldExecuteOnCPU([a, b])) {
+                return this.cpuBackend.multiply(a, b);
+            }
+            var program = new BinaryOpProgram(MUL, a.shape, b.shape);
+            var output = this.makeOutputArray(program.outputShape, a.dtype);
+            return this.compileAndRun(program, [a, b], output);
+        };
+        MathBackendWebGL.prototype.batchNormalization = function (x, mean, variance, varianceEpsilon, scale, offset) {
+            var inputs = [x, mean, variance];
+            var offsetShape = null;
+            if (offset != null) {
+                offsetShape = offset.shape;
+                inputs.push(offset);
+            }
+            var scaleShape = null;
+            if (scale != null) {
+                scaleShape = scale.shape;
+                inputs.push(scale);
+            }
+            if (ENV.get('WEBGL_PACK_BATCHNORMALIZATION')) {
+                var batchNormPackedProgram = new BatchNormPackedProgram(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon);
+                return this.compileAndRun(batchNormPackedProgram, inputs);
+            }
+            var batchNormProgram = new BatchNormProgram(x.shape, mean.shape, variance.shape, offsetShape, scaleShape, varianceEpsilon);
+            return this.compileAndRun(batchNormProgram, inputs);
+        };
+        MathBackendWebGL.prototype.localResponseNormalization4D = function (x, radius, bias, alpha, beta) {
+            var program = new LRNProgram(x.shape, radius, bias, alpha, beta);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.LRNGrad = function (dy, inputImage, outputImage, depthRadius, bias, alpha, beta) {
+            var program = new LRNGradProgram(inputImage.shape, depthRadius, bias, alpha, beta);
+            return this.compileAndRun(program, [inputImage, outputImage, dy]);
+        };
+        MathBackendWebGL.prototype.tile = function (x, reps) {
+            var program = new TileProgram(x.shape, reps);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.pad = function (x, paddings, constantValue) {
+            var program = new PadProgram(x.shape, paddings, constantValue);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.transpose = function (x, perm) {
+            var program = new TransposeProgram(x.shape, perm);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.gather = function (x, indices, axis) {
+            var program = new GatherProgram(x.shape, indices.size, axis);
+            return this.compileAndRun(program, [x, indices]);
+        };
+        MathBackendWebGL.prototype.batchToSpaceND = function (x, blockShape, crops) {
+            assert(x.rank <= 4, 'batchToSpaceND for rank > 4 with a WebGL backend not implemented yet');
+            var prod = blockShape.reduce(function (a, b) { return a * b; });
+            var reshaped = getReshaped(x.shape, blockShape, prod);
+            var permuted = getPermuted(reshaped.length, blockShape.length);
+            var reshapedPermuted = getReshapedPermuted(x.shape, blockShape, prod);
+            var sliceBeginCoords = getSliceBeginCoords(crops, blockShape.length);
+            var sliceSize = getSliceSize(reshapedPermuted, crops, blockShape.length);
+            return x.reshape(reshaped)
+                .transpose(permuted)
+                .reshape(reshapedPermuted)
+                .slice(sliceBeginCoords, sliceSize);
+        };
+        MathBackendWebGL.prototype.spaceToBatchND = function (x, blockShape, paddings) {
+            assert(x.rank <= 4, 'spaceToBatchND for rank > 4 with a WebGL backend not implemented yet');
+            var prod = blockShape.reduce(function (a, b) { return a * b; });
+            var completePaddings = [[0, 0]];
+            completePaddings.push.apply(completePaddings, paddings);
+            for (var i = 1 + blockShape.length; i < x.shape.length; ++i) {
+                completePaddings.push([0, 0]);
+            }
+            var paddedX = x.pad(completePaddings);
+            var reshapedPaddedShape = getReshaped(paddedX.shape, blockShape, prod, false);
+            var permutedReshapedPaddedPermutation = getPermuted(reshapedPaddedShape.length, blockShape.length, false);
+            var flattenShape = getReshapedPermuted(paddedX.shape, blockShape, prod, false);
+            return paddedX.reshape(reshapedPaddedShape)
+                .transpose(permutedReshapedPaddedPermutation)
+                .reshape(flattenShape);
+        };
+        MathBackendWebGL.prototype.reduce = function (x, reduceType, dtype) {
+            var batchSize = x.shape[0];
+            var inSize = x.shape[1];
+            var windowSize = computeOptimalWindowSize(inSize);
+            var reduceInfo = { windowSize: windowSize, inSize: inSize, batchSize: batchSize };
+            var program = new ReduceProgram(reduceInfo, reduceType);
+            var _a = program.outputShape, rows = _a[0], cols = _a[1];
+            var output = this.makeOutputArray([rows, cols], dtype);
+            this.compileAndRun(program, [x], output);
+            if (output.shape[1] === 1) {
+                return output;
+            }
+            return this.reduce(output, reduceType, dtype);
+        };
+        MathBackendWebGL.prototype.argReduce = function (x, reduceType, bestIndicesA) {
+            if (bestIndicesA === void 0) { bestIndicesA = null; }
+            var batchSize = x.shape[0];
+            var inSize = x.shape[1];
+            if (bestIndicesA != null) {
+                batchSize = bestIndicesA.shape[0];
+                inSize = bestIndicesA.shape[1];
+            }
+            var windowSize = computeOptimalWindowSize(inSize);
+            var reduceInfo = { windowSize: windowSize, inSize: inSize, batchSize: batchSize };
+            var program = new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null);
+            var _a = program.outputShape, rows = _a[0], cols = _a[1];
+            var output = this.makeOutputArray([rows, cols], 'int32');
+            var inputs = [x];
+            if (bestIndicesA != null) {
+                inputs.push(bestIndicesA);
+            }
+            this.compileAndRun(program, inputs, output);
+            if (output.shape[1] === 1) {
+                return output;
+            }
+            return this.argReduce(x, reduceType, output);
+        };
+        MathBackendWebGL.prototype.sum = function (x, axes) {
+            assertAxesAreInnerMostDims('sum', axes, x.rank);
+            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+            var inSize = sizeFromShape(reduceShape);
+            var a2D = x.as2D(-1, inSize);
+            var outputDType = sumOutType(x.dtype);
+            return this.reduce(a2D, 'sum', outputDType).reshape(outShape);
+        };
+        MathBackendWebGL.prototype.prod = function (x, axes) {
+            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+            var inSize = sizeFromShape(reduceShape);
+            var a2D = x.as2D(-1, inSize);
+            var outputDType = sumOutType(x.dtype);
+            return this.reduce(a2D, 'prod', outputDType).reshape(outShape);
+        };
+        MathBackendWebGL.prototype.unsortedSegmentSum = function (x, segmentIds, numSegments) {
+            var axis = 0;
+            var permutation = getAxesPermutation([axis], x.rank);
+            var permutedX = x;
+            if (permutation != null) {
+                permutedX = x.transpose(permutation);
+                axis = getInnerMostAxes(1, x.rank)[0];
+            }
+            var outShape = computeOutShape$1(permutedX.shape, axis, numSegments);
+            var inSize = sizeFromShape([permutedX.shape[axis]]);
+            var a2D = permutedX.as2D(-1, inSize);
+            var outputDType = sumOutType(x.dtype);
+            var result = this.segOpCompute(a2D, 'unsortedSegmentSum', segmentIds, outputDType, numSegments)
+                .reshape(outShape);
+            if (permutation != null) {
+                result = result.transpose(getUndoAxesPermutation(permutation));
+            }
+            return result;
+        };
+        MathBackendWebGL.prototype.segOpCompute = function (x, segOpType, segmentIds, dtype, numSegments) {
+            var batchSize = x.shape[0];
+            var inSize = x.shape[1];
+            var windowSize = segOpComputeOptimalWindowSize(inSize, numSegments);
+            var segOpInfo = { windowSize: windowSize, inSize: inSize, batchSize: batchSize, numSegments: numSegments };
+            var program = new SegmentOpProgram(segOpInfo, segOpType);
+            var _a = program.outputShape, rows = _a[0], cols = _a[1];
+            var output = this.makeOutputArray([rows, cols], dtype);
+            this.compileAndRun(program, [x, segmentIds], output);
+            if (output.shape[1] === numSegments) {
+                return output;
+            }
+            segmentIds = range(0, numSegments).tile([inSize / windowSize]);
+            return this.segOpCompute(output, segOpType, segmentIds, dtype, numSegments);
+        };
+        MathBackendWebGL.prototype.argMin = function (x, axis) {
+            var axes = [axis];
+            assertAxesAreInnerMostDims('argMin', axes, x.rank);
+            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+            var inSize = sizeFromShape(reduceShape);
+            var a2D = x.as2D(-1, inSize);
+            return this.argReduce(a2D, 'min').reshape(outShape);
+        };
+        MathBackendWebGL.prototype.argMax = function (x, axis) {
+            var axes = [axis];
+            assertAxesAreInnerMostDims('argMax', axes, x.rank);
+            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+            var inSize = sizeFromShape(reduceShape);
+            var a2D = x.as2D(-1, inSize);
+            return this.argReduce(a2D, 'max').reshape(outShape);
+        };
+        MathBackendWebGL.prototype.cumsum = function (x, axis, exclusive, reverse) {
+            if (axis !== x.rank - 1) {
+                throw new Error("WebGL cumsum shader expects an inner-most axis=" + (x.rank - 1) + " " +
+                    ("but got axis=" + axis));
+            }
+            var program = new CumSumProgram(x.shape, exclusive, reverse);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.equal = function (a, b) {
+            var program = new BinaryOpProgram(EQUAL, a.shape, b.shape);
+            var output = this.makeOutputArray(program.outputShape, 'bool');
+            return this.compileAndRun(program, [a, b], output);
+        };
+        MathBackendWebGL.prototype.notEqual = function (a, b) {
+            var program = new BinaryOpProgram(NOT_EQUAL, a.shape, b.shape);
+            var output = this.makeOutputArray(program.outputShape, 'bool');
+            return this.compileAndRun(program, [a, b], output);
+        };
+        MathBackendWebGL.prototype.less = function (a, b) {
+            if (this.shouldExecuteOnCPU([a, b])) {
+                return this.cpuBackend.less(a, b);
+            }
+            var program = new BinaryOpProgram(LESS, a.shape, b.shape);
+            var output = this.makeOutputArray(program.outputShape, 'bool');
+            return this.compileAndRun(program, [a, b], output);
+        };
+        MathBackendWebGL.prototype.lessEqual = function (a, b) {
+            var program = new BinaryOpProgram(LESS_EQUAL, a.shape, b.shape);
+            var output = this.makeOutputArray(program.outputShape, 'bool');
+            return this.compileAndRun(program, [a, b], output);
+        };
+        MathBackendWebGL.prototype.greater = function (a, b) {
+            if (this.shouldExecuteOnCPU([a, b])) {
+                return this.cpuBackend.greater(a, b);
+            }
+            var program = new BinaryOpProgram(GREATER, a.shape, b.shape);
+            var output = this.makeOutputArray(program.outputShape, 'bool');
+            return this.compileAndRun(program, [a, b], output);
+        };
+        MathBackendWebGL.prototype.greaterEqual = function (a, b) {
+            var program = new BinaryOpProgram(GREATER_EQUAL, a.shape, b.shape);
+            var output = this.makeOutputArray(program.outputShape, 'bool');
+            return this.compileAndRun(program, [a, b], output);
+        };
+        MathBackendWebGL.prototype.logicalNot = function (x) {
+            var program = new UnaryOpProgram(x.shape, LOGICAL_NOT);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.logicalAnd = function (a, b) {
+            var program = new BinaryOpProgram(LOGICAL_AND, a.shape, b.shape);
+            var output = this.makeOutputArray(program.outputShape, 'bool');
+            return this.compileAndRun(program, [a, b], output);
+        };
+        MathBackendWebGL.prototype.logicalOr = function (a, b) {
+            var program = new BinaryOpProgram(LOGICAL_OR, a.shape, b.shape);
+            var output = this.makeOutputArray(program.outputShape, 'bool');
+            return this.compileAndRun(program, [a, b], output);
+        };
+        MathBackendWebGL.prototype.select = function (condition, a, b) {
+            var program = new SelectProgram(condition.rank, a.shape, a.rank);
+            var output = this.makeOutputArray(program.outputShape, upcastType(a.dtype, b.dtype));
+            return this.compileAndRun(program, [condition, a, b], output);
+        };
+        MathBackendWebGL.prototype.where = function (condition) {
+            warn('tf.where() in webgl locks the UI thread. ' +
+                'Call tf.whereAsync() instead');
+            var condVals = condition.dataSync();
+            return whereImpl(condition.shape, condVals);
+        };
+        MathBackendWebGL.prototype.topk = function (x, k, sorted) {
+            var xVals = x.dataSync();
+            return topkImpl(xVals, x.shape, x.dtype, k, sorted);
+        };
+        MathBackendWebGL.prototype.min = function (x, axes) {
+            assertAxesAreInnerMostDims('min', axes, x.rank);
+            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+            var inSize = sizeFromShape(reduceShape);
+            var a2D = x.as2D(-1, inSize);
+            return this.reduce(a2D, 'min', a2D.dtype).reshape(outShape);
+        };
+        MathBackendWebGL.prototype.minimum = function (a, b) {
+            if (this.shouldExecuteOnCPU([a, b])) {
+                return this.cpuBackend.minimum(a, b);
+            }
+            var program = new BinaryOpProgram(MIN, a.shape, b.shape);
+            return this.compileAndRun(program, [a, b]);
+        };
+        MathBackendWebGL.prototype.mod = function (a, b) {
+            var program = new BinaryOpProgram(MOD, a.shape, b.shape);
+            var customSetup = program.getCustomSetupFunc();
+            return this.compileAndRun(program, [a, b], null, customSetup);
+        };
+        MathBackendWebGL.prototype.max = function (x, axes) {
+            assertAxesAreInnerMostDims('max', axes, x.rank);
+            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+            var inSize = sizeFromShape(reduceShape);
+            var a2D = x.as2D(-1, inSize);
+            return this.reduce(a2D, 'max', a2D.dtype).reshape(outShape);
+        };
+        MathBackendWebGL.prototype.maximum = function (a, b) {
+            if (this.shouldExecuteOnCPU([a, b])) {
+                return this.cpuBackend.maximum(a, b);
+            }
+            var program = new BinaryOpProgram(MAX, a.shape, b.shape);
+            return this.compileAndRun(program, [a, b]);
+        };
+        MathBackendWebGL.prototype.all = function (x, axes) {
+            assertAxesAreInnerMostDims('all', axes, x.rank);
+            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+            var inSize = sizeFromShape(reduceShape);
+            var a2D = x.as2D(-1, inSize);
+            return this.reduce(a2D, 'all', a2D.dtype).reshape(outShape);
+        };
+        MathBackendWebGL.prototype.any = function (x, axes) {
+            assertAxesAreInnerMostDims('any', axes, x.rank);
+            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+            var inSize = sizeFromShape(reduceShape);
+            var a2D = x.as2D(-1, inSize);
+            return this.reduce(a2D, 'any', a2D.dtype).reshape(outShape);
+        };
+        MathBackendWebGL.prototype.squaredDifference = function (a, b) {
+            var program = new BinaryOpProgram(SQUARED_DIFFERENCE, a.shape, b.shape);
+            return this.compileAndRun(program, [a, b]);
+        };
+        MathBackendWebGL.prototype.realDivide = function (a, b) {
+            var op = DIV;
+            var outputDtype = 'float32';
+            var program = new BinaryOpProgram(op, a.shape, b.shape);
+            var output = this.makeOutputArray(program.outputShape, outputDtype);
+            return this.compileAndRun(program, [a, b], output);
+        };
+        MathBackendWebGL.prototype.floorDiv = function (a, b) {
+            var op = INT_DIV;
+            var outputDtype = 'int32';
+            var program = new BinaryOpProgram(op, a.shape, b.shape);
+            var output = this.makeOutputArray(program.outputShape, outputDtype);
+            return this.compileAndRun(program, [a, b], output);
+        };
+        MathBackendWebGL.prototype.add = function (a, b) {
+            if (a.dtype === 'complex64' && b.dtype === 'complex64') {
+                return this.complexSeparableBinaryOp(a, b, ADD);
+            }
+            var program = new BinaryOpProgram(ADD, a.shape, b.shape);
+            var output = this.makeOutputArray(program.outputShape, upcastType(a.dtype, b.dtype));
+            return this.compileAndRun(program, [a, b], output);
+        };
+        MathBackendWebGL.prototype.complexSeparableBinaryOp = function (a, b, op) {
+            var _this = this;
+            var aData = this.texData.get(a.dataId);
+            var bData = this.texData.get(b.dataId);
+            var _a = [
+                [aData.complexTensors.real, bData.complexTensors.real],
+                [aData.complexTensors.imag, bData.complexTensors.imag]
+            ].map(function (complexParts) {
+                var aPart = complexParts[0], bPart = complexParts[1];
+                var program = new BinaryOpProgram(op, a.shape, b.shape);
+                var output = _this.makeOutputArray(program.outputShape, upcastType(aPart.dtype, bPart.dtype));
+                var aHandle = _this.makeComplexComponentTensorHandle(a, aPart);
+                var bHandle = _this.makeComplexComponentTensorHandle(b, bPart);
+                return _this.compileAndRun(program, [aHandle, bHandle], output);
+            }), real = _a[0], imag = _a[1];
+            var complex = this.complex(real, imag);
+            real.dispose();
+            imag.dispose();
+            return complex;
+        };
+        MathBackendWebGL.prototype.makeComplexComponentTensorHandle = function (complexTensor, complexPart) {
+            return {
+                dataId: complexPart.dataId,
+                dtype: complexPart.dtype,
+                shape: complexTensor.shape
+            };
+        };
+        MathBackendWebGL.prototype.addN = function (tensors) {
+            var res = tensors[0];
+            for (var i = 1; i < tensors.length; i++) {
+                res = this.add(res, tensors[i]);
+            }
+            return res;
+        };
+        MathBackendWebGL.prototype.subtract = function (a, b) {
+            if (a.dtype === 'complex64' && b.dtype === 'complex64') {
+                return this.complexSeparableBinaryOp(a, b, SUB);
+            }
+            if (this.shouldExecuteOnCPU([a, b])) {
+                return this.cpuBackend.subtract(a, b);
+            }
+            var program = new BinaryOpProgram(SUB, a.shape, b.shape);
+            var output = this.makeOutputArray(program.outputShape, upcastType(a.dtype, b.dtype));
+            return this.compileAndRun(program, [a, b], output);
+        };
+        MathBackendWebGL.prototype.pow = function (a, b) {
+            var program = new BinaryOpProgram(POW, a.shape, b.shape);
+            var customSetup = program.getCustomSetupFunc();
+            var output = this.makeOutputArray(program.outputShape, upcastType(a.dtype, b.dtype));
+            return this.compileAndRun(program, [a, b], output, customSetup);
+        };
+        MathBackendWebGL.prototype.ceil = function (x) {
+            var program = new UnaryOpProgram(x.shape, CEIL);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.floor = function (x) {
+            var program = new UnaryOpProgram(x.shape, FLOOR);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.sign = function (x) {
+            var program = new UnaryOpProgram(x.shape, SIGN);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.round = function (x) {
+            var program = new UnaryOpProgram(x.shape, ROUND);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.exp = function (x) {
+            var program = new UnaryOpProgram(x.shape, EXP);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.expm1 = function (x) {
+            var program = new UnaryOpProgram(x.shape, EXPM1);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.log = function (x) {
+            var program = new UnaryOpProgram(x.shape, LOG);
+            var customSetup = program.getCustomSetupFunc();
+            return this.compileAndRun(program, [x], null, customSetup);
+        };
+        MathBackendWebGL.prototype.log1p = function (x) {
+            var program = new UnaryOpProgram(x.shape, LOG1P);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.sqrt = function (x) {
+            var program = new UnaryOpProgram(x.shape, SQRT);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.rsqrt = function (x) {
+            var program = new UnaryOpProgram(x.shape, RSQRT);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.square = function (x) {
+            var program = new UnaryOpProgram(x.shape, SQUARE);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.reciprocal = function (x) {
+            var program = new UnaryOpProgram(x.shape, RECIPROCAL);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.relu = function (x) {
+            var program = new UnaryOpProgram(x.shape, RELU);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.elu = function (x) {
+            var program = new UnaryOpProgram(x.shape, ELU);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.eluDer = function (dy, y) {
+            var program = new BinaryOpProgram(ELU_DER, dy.shape, y.shape);
+            return this.compileAndRun(program, [dy, y]);
+        };
+        MathBackendWebGL.prototype.selu = function (x) {
+            var program = new UnaryOpProgram(x.shape, SELU);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.int = function (x) {
+            var program = new UnaryOpProgram(x.shape, TO_INT);
+            var output = this.makeOutputArray(program.outputShape, 'int32');
+            return this.compileAndRun(program, [x], output);
+        };
+        MathBackendWebGL.prototype.clip = function (x, min, max) {
+            var program;
+            if (ENV.get('WEBGL_PACK_CLIP')) {
+                program = new ClipPackedProgram(x.shape, min, max);
+            }
+            else {
+                program = new ClipProgram(x.shape, min, max);
+            }
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.abs = function (x) {
+            var program = new UnaryOpProgram(x.shape, ABS);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.complexAbs = function (x) {
+            var xData = this.texData.get(x.dataId);
+            var program = new ComplexAbsProgram(x.shape);
+            var inputs = [
+                this.makeComplexComponentTensorHandle(x, xData.complexTensors.real),
+                this.makeComplexComponentTensorHandle(x, xData.complexTensors.imag),
+            ];
+            return this.compileAndRun(program, inputs);
+        };
+        MathBackendWebGL.prototype.sigmoid = function (x) {
+            var program = new UnaryOpProgram(x.shape, SIGMOID);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.softplus = function (x) {
+            var program = new UnaryOpProgram(x.shape, SOFTPLUS);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.sin = function (x) {
+            var program = new UnaryOpProgram(x.shape, SIN);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.cos = function (x) {
+            var program = new UnaryOpProgram(x.shape, COS);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.tan = function (x) {
+            var program = new UnaryOpProgram(x.shape, TAN);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.asin = function (x) {
+            var program = new UnaryOpProgram(x.shape, ASIN);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.acos = function (x) {
+            var program = new UnaryOpProgram(x.shape, ACOS);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.atan = function (x) {
+            var program = new UnaryOpProgram(x.shape, ATAN);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.atan2 = function (a, b) {
+            var program = new BinaryOpProgram(ATAN2, a.shape, b.shape);
+            return this.compileAndRun(program, [a, b]);
+        };
+        MathBackendWebGL.prototype.sinh = function (x) {
+            var program = new UnaryOpProgram(x.shape, SINH);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.cosh = function (x) {
+            var program = new UnaryOpProgram(x.shape, COSH);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.tanh = function (x) {
+            var program = new UnaryOpProgram(x.shape, TANH);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.asinh = function (x) {
+            var program = new UnaryOpProgram(x.shape, ASINH);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.acosh = function (x) {
+            var program = new UnaryOpProgram(x.shape, ACOSH);
+            var customSetup = program.getCustomSetupFunc();
+            return this.compileAndRun(program, [x], null, customSetup);
+        };
+        MathBackendWebGL.prototype.atanh = function (x) {
+            var program = new UnaryOpProgram(x.shape, ATANH);
+            var customSetup = program.getCustomSetupFunc();
+            return this.compileAndRun(program, [x], null, customSetup);
+        };
+        MathBackendWebGL.prototype.erf = function (x) {
+            var program = new UnaryOpProgram(x.shape, ERF);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.step = function (x, alpha) {
+            var program = new UnaryOpProgram(x.shape, STEP(alpha));
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.conv2dWithIm2Row = function (x, filter, convInfo) {
+            var filterWidth = convInfo.filterWidth, filterHeight = convInfo.filterHeight, inChannels = convInfo.inChannels, outWidth = convInfo.outWidth, outHeight = convInfo.outHeight;
+            var sharedDim = filterWidth * filterHeight * inChannels;
+            var numCols = outHeight * outWidth;
+            var x2ColShape = [sharedDim, numCols];
+            var xSqueezed = x.squeeze([0]);
+            var w2Row = filter.reshape([sharedDim, -1]);
+            var im2ColProgram = new Im2ColProgram(x2ColShape, xSqueezed.shape, convInfo);
+            var im2Col = this.compileAndRun(im2ColProgram, [xSqueezed]);
+            var matmulProgram = new MatMulPackedProgram(im2Col.shape, w2Row.shape, [numCols, convInfo.outChannels], true, false);
+            var product = this.compileAndRun(matmulProgram, [im2Col, w2Row]);
+            return product.reshape([1, outHeight, outWidth, convInfo.outChannels]);
+        };
+        MathBackendWebGL.prototype.conv2d = function (x, filter, convInfo) {
+            if (ENV.get('WEBGL_CONV_IM2COL') && x.shape[0] === 1) {
+                return this.conv2dWithIm2Row(x, filter, convInfo);
+            }
+            var program = new Conv2DProgram(convInfo);
+            return this.compileAndRun(program, [x, filter]);
+        };
+        MathBackendWebGL.prototype.conv2dDerInput = function (dy, filter, convInfo) {
+            var program = new Conv2DDerInputProgram(convInfo);
+            return this.compileAndRun(program, [dy, filter]);
+        };
+        MathBackendWebGL.prototype.conv2dDerFilter = function (x, dy, convInfo) {
+            var program = new Conv2DDerFilterProgram(convInfo);
+            return this.compileAndRun(program, [x, dy]);
+        };
+        MathBackendWebGL.prototype.depthwiseConv2D = function (x, filter, convInfo) {
+            var program;
+            if (ENV.get('WEBGL_PACK_DEPTHWISECONV') && convInfo.dilationWidth === 1 &&
+                convInfo.dilationHeight === 1 && convInfo.padInfo.left <= 1 &&
+                convInfo.strideWidth <= 2 &&
+                convInfo.outChannels / convInfo.inChannels === 1) {
+                program = new DepthwiseConvPacked2DProgram(convInfo);
+                return this.compileAndRun(program, [x, filter], this.makePackedTensor(convInfo.outShape, x.dtype));
+            }
+            program = new DepthwiseConv2DProgram(convInfo);
+            return this.compileAndRun(program, [x, filter]);
+        };
+        MathBackendWebGL.prototype.depthwiseConv2DDerInput = function (dy, filter, convInfo) {
+            var program = new DepthwiseConv2DDerInputProgram(convInfo);
+            return this.compileAndRun(program, [dy, filter]);
+        };
+        MathBackendWebGL.prototype.depthwiseConv2DDerFilter = function (x, dy, convInfo) {
+            var program = new DepthwiseConv2DDerFilterProgram(convInfo);
+            return this.compileAndRun(program, [x, dy]);
+        };
+        MathBackendWebGL.prototype.conv3d = function (x, filter, convInfo) {
+            var program = new Conv3DProgram(convInfo);
+            return this.compileAndRun(program, [x, filter]);
+        };
+        MathBackendWebGL.prototype.conv3dDerInput = function (dy, filter, convInfo) {
+            var program = new Conv3DDerInputProgram(convInfo);
+            return this.compileAndRun(program, [dy, filter]);
+        };
+        MathBackendWebGL.prototype.conv3dDerFilter = function (x, dy, convInfo) {
+            var program = new Conv3DDerFilterProgram(convInfo);
+            return this.compileAndRun(program, [x, dy]);
+        };
+        MathBackendWebGL.prototype.maxPool = function (x, convInfo) {
+            var program = new Pool2DProgram(convInfo, 'max', false);
+            var output = this.makeOutputArray(program.outputShape, x.dtype);
+            return this.compileAndRun(program, [x], output);
+        };
+        MathBackendWebGL.prototype.avgPool = function (x, convInfo) {
+            var program = new Pool2DProgram(convInfo, 'avg', false);
+            var output = this.makeOutputArray(program.outputShape, 'float32');
+            return this.compileAndRun(program, [x], output);
+        };
+        MathBackendWebGL.prototype.maxPoolBackprop = function (dy, x, y, convInfo) {
+            var getPositions = true;
+            var maxPoolPositionsProgram = new Pool2DProgram(convInfo, 'max', getPositions);
+            var maxPoolPositions = this.compileAndRun(maxPoolPositionsProgram, [x]);
+            var maxPoolBackPropProgram = new MaxPool2DBackpropProgram(convInfo);
+            var output = this.makeOutputArray(maxPoolBackPropProgram.outputShape, x.dtype);
+            var result = this.compileAndRun(maxPoolBackPropProgram, [dy, maxPoolPositions], output);
+            maxPoolPositions.dispose();
+            return result;
+        };
+        MathBackendWebGL.prototype.avgPoolBackprop = function (dy, x, convInfo) {
+            var avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo);
+            var output = this.makeOutputArray(avgPoolBackpropProgram.outputShape, x.dtype);
+            return this.compileAndRun(avgPoolBackpropProgram, [dy], output);
+        };
+        MathBackendWebGL.prototype.cast = function (x, dtype) {
+            return castTensor(x, dtype, this);
+        };
+        MathBackendWebGL.prototype.reshape = function (x, shape) {
+            if (this.texData.get(x.dataId).isPacked &&
+                !isReshapeFree(x.shape, shape)) {
+                return this.packedReshape(x, shape);
+            }
+            return reshapeTensor(x, shape);
+        };
+        MathBackendWebGL.prototype.resizeBilinear = function (x, newHeight, newWidth, alignCorners) {
+            var program = new ResizeBilinearProgram(x.shape, newHeight, newWidth, alignCorners);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.resizeBilinearBackprop = function (dy, x, alignCorners) {
+            var program = new ResizeBilinearBackpropProgram(dy, x, alignCorners);
+            return this.compileAndRun(program, [dy]);
+        };
+        MathBackendWebGL.prototype.resizeNearestNeighbor = function (x, newHeight, newWidth, alignCorners) {
+            var program = new ResizeNearestNeighborProgram(x.shape, newHeight, newWidth, alignCorners);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.resizeNearestNeighborBackprop = function (dy, x, alignCorners) {
+            var program = new ResizeNearestNeigborBackpropProgram(dy, x, alignCorners);
+            return this.compileAndRun(program, [dy]);
+        };
+        MathBackendWebGL.prototype.multinomial = function (logits, normalized, numSamples, seed) {
+            var probs = normalized ? logits : softmax(logits);
+            var batchSize = probs.shape[0];
+            var numOutcomes = probs.shape[1];
+            var program = new MultinomialProgram(batchSize, numOutcomes, numSamples);
+            var output = this.makeOutputArray(program.outputShape, 'int32');
+            var customSetup = program.getCustomSetupFunc(seed);
+            return this.compileAndRun(program, [probs], output, customSetup);
+        };
+        MathBackendWebGL.prototype.oneHot = function (indices, depth, onValue, offValue) {
+            var program = new OneHotProgram(indices.size, depth, onValue, offValue);
+            return this.compileAndRun(program, [indices]);
+        };
+        MathBackendWebGL.prototype.nonMaxSuppression = function (boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+            warn('tf.nonMaxSuppression() in webgl locks the UI thread. ' +
+                'Call tf.nonMaxSuppressionAsync() instead');
+            var boxesVals = boxes.dataSync();
+            var scoresVals = scores.dataSync();
+            return nonMaxSuppressionImpl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
+        };
+        MathBackendWebGL.prototype.cropAndResize = function (image, boxes, boxIndex, cropSize, method, extrapolationValue) {
+            var program = new CropAndResizeProgram(image.shape, boxes.shape, cropSize, method, extrapolationValue);
+            return this.compileAndRun(program, [image, boxes, boxIndex]);
+        };
+        MathBackendWebGL.prototype.depthToSpace = function (x, blockSize, dataFormat) {
+            assert(blockSize > 1, "blockSize should be > 1 for depthToSpace, but was: " + blockSize);
+            var batchSize = x.shape[0];
+            var inputHeight = (dataFormat === 'NHWC') ? x.shape[1] : x.shape[2];
+            var inputWidth = (dataFormat === 'NHWC') ? x.shape[2] : x.shape[3];
+            var inputDepth = (dataFormat === 'NHWC') ? x.shape[3] : x.shape[1];
+            var outputHeight = inputHeight * blockSize;
+            var outputWidth = inputWidth * blockSize;
+            var outputDepth = inputDepth / (blockSize * blockSize);
+            var outputShape = (dataFormat === 'NHWC') ?
+                [batchSize, outputHeight, outputWidth, outputDepth] :
+                [batchSize, outputDepth, outputHeight, outputWidth];
+            var program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat);
+            return this.compileAndRun(program, [x]);
+        };
+        MathBackendWebGL.prototype.split = function (x, sizeSplits, axis) {
+            return split(x, sizeSplits, axis);
+        };
+        MathBackendWebGL.prototype.scatterND = function (indices, updates, shape) {
+            var _a = calculateShapes(updates, indices, shape), sliceRank = _a.sliceRank, numUpdates = _a.numUpdates, sliceSize = _a.sliceSize, strides = _a.strides, outputSize = _a.outputSize;
+            var flattenShape = [outputSize / sliceSize, sliceSize];
+            var flattenIndices = indices.reshape([numUpdates, sliceRank]);
+            var flattenX = updates.reshape([numUpdates, sliceSize]);
+            if (outputSize === 0) {
+                return reshapeTensor(tensor([]), shape);
+            }
+            var defaultValue = scalar(0);
+            var program = new ScatterProgram(numUpdates, sliceRank, flattenIndices.rank, flattenX.rank, strides, flattenShape);
+            return this.compileAndRun(program, [flattenX, flattenIndices, defaultValue])
+                .reshape(shape);
+        };
+        MathBackendWebGL.prototype.sparseToDense = function (sparseIndices, sparseValues, outputShape, defaultValue) {
+            var _a = calculateShapes(sparseValues, sparseIndices, outputShape), sliceRank = _a.sliceRank, numUpdates = _a.numUpdates, strides = _a.strides, outputSize = _a.outputSize;
+            var sumDupeIndices = false;
+            var program = new ScatterProgram(numUpdates, sliceRank, sparseIndices.rank, sparseValues.rank, strides, [outputSize, 1], sumDupeIndices);
+            return this.compileAndRun(program, [sparseValues, sparseIndices, defaultValue])
+                .reshape(outputShape);
+        };
+        MathBackendWebGL.prototype.fft = function (x) {
+            var inverse = false;
+            return this.fftImpl(x, inverse);
+        };
+        MathBackendWebGL.prototype.ifft = function (x) {
+            var inverse = true;
+            return this.fftImpl(x, inverse);
+        };
+        MathBackendWebGL.prototype.fftImpl = function (x, inverse) {
+            var xData = this.texData.get(x.dataId);
+            var realProgram = new FFTProgram(COMPLEX_FFT.REAL, x.shape, inverse);
+            var imagProgram = new FFTProgram(COMPLEX_FFT.IMAG, x.shape, inverse);
+            var inputs = [
+                this.makeComplexComponentTensorHandle(x, xData.complexTensors.real),
+                this.makeComplexComponentTensorHandle(x, xData.complexTensors.imag),
+            ];
+            var real = this.compileAndRun(realProgram, inputs);
+            var imag = this.compileAndRun(imagProgram, inputs);
+            var complex = this.complex(real, imag).as2D(x.shape[0], x.shape[1]);
+            real.dispose();
+            imag.dispose();
+            return complex;
+        };
+        MathBackendWebGL.prototype.gatherND = function (x, indices) {
+            var indicesShape = indices.shape;
+            var sliceRank = indicesShape[indicesShape.length - 1];
+            var _a = prepareAndValidate(x, indices), resultShape = _a[0], numSlices = _a[1], sliceSize = _a[2], strides = _a[3];
+            var flattenIndices = indices.reshape([numSlices, sliceRank]);
+            var flattenX = x.reshape([x.size / sliceSize, sliceSize]);
+            var program = new GatherNDProgram(sliceRank, strides, [numSlices, sliceSize]);
+            return this.compileAndRun(program, [flattenX, flattenIndices])
+                .reshape(resultShape);
+        };
+        MathBackendWebGL.prototype.makeOutputArray = function (shape, dtype) {
+            return Tensor.make(shape, {}, dtype);
+        };
+        MathBackendWebGL.prototype.makePackedTensor = function (shape, dtype) {
+            var packedTensor = Tensor.make(shape, {}, dtype);
+            this.texData.get(packedTensor.dataId).isPacked = true;
+            return packedTensor;
+        };
+        MathBackendWebGL.prototype.unpackTensor = function (input) {
+            var program = new UnpackProgram(input.shape);
+            return this.compileAndRun(program, [input], Tensor.make(program.outputShape, {}, input.dtype));
+        };
+        MathBackendWebGL.prototype.getBatchDim = function (shape, dimsToSkip) {
+            if (dimsToSkip === void 0) { dimsToSkip = 2; }
+            return sizeFromShape(shape.slice(0, shape.length - dimsToSkip));
+        };
+        MathBackendWebGL.prototype.getRowsCols = function (shape) {
+            if (shape.length === 0) {
+                throw Error('Cannot get rows and columns of an empty shape array.');
+            }
+            return [
+                shape.length > 1 ? shape[shape.length - 2] : 1, shape[shape.length - 1]
+            ];
+        };
+        MathBackendWebGL.prototype.packedReshape = function (input, afterShape) {
+            var inputAs3D = input.reshape([this.getBatchDim(input.shape)].concat(this.getRowsCols(input.shape)));
+            var afterShapeAs3D = [this.getBatchDim(afterShape)].concat(this.getRowsCols(afterShape));
+            var program = new ReshapePackedProgram(afterShapeAs3D, inputAs3D.shape);
+            return this.compileAndRun(program, [inputAs3D])
+                .reshape(afterShape);
+        };
+        MathBackendWebGL.prototype.compileAndRun = function (program, inputs, output, customSetup, pageToCpu) {
+            var _this = this;
+            if (pageToCpu === void 0) { pageToCpu = true; }
+            if (output == null) {
+                if (program.usesPackedTextures) {
+                    output = this.makePackedTensor(program.outputShape, inputs[0].dtype);
+                }
+                else {
+                    output = this.makeOutputArray(program.outputShape, inputs[0].dtype);
+                }
+            }
+            if (output.size === 0) {
+                this.texData.get(output.dataId).values =
+                    getTypedArrayFromDType(output.dtype, 0);
+                return output;
+            }
+            var inputsData = inputs.map(function (input) {
+                if (input.dtype === 'complex64') {
+                    throw new Error("GPGPUProgram does not support complex64 input. For complex64 " +
+                        "dtypes, please separate the program into real and imaginary " +
+                        "parts.");
+                }
+                var texData = _this.texData.get(input.dataId);
+                if (texData.texture == null) {
+                    if (!program.usesPackedTextures &&
+                        sizeFromShape(input.shape) <=
+                            ENV.get('WEBGL_SIZE_UPLOAD_UNIFORM')) {
+                        return {
+                            shape: input.shape,
+                            texData: null,
+                            isUniform: true,
+                            uniformValues: _this.readSync(input.dataId)
+                        };
+                    }
+                    if (program.usesPackedTextures) {
+                        texData.isPacked = true;
+                        texData.shape = input.shape;
+                    }
+                }
+                else if (!!texData.isPacked !== !!program.usesPackedTextures) {
+                    var preProcessProgram = void 0;
+                    var processedInput = void 0;
+                    if (texData.isPacked) {
+                        preProcessProgram = new UnpackProgram(input.shape);
+                        processedInput = _this.compileAndRun(preProcessProgram, [input], Tensor.make(preProcessProgram.outputShape, {}, input.dtype));
+                    }
+                    else {
+                        preProcessProgram = new PackProgram(input.shape);
+                        processedInput = _this.compileAndRun(preProcessProgram, [input], _this.makePackedTensor(input.shape, input.dtype));
+                    }
+                    texData = _this.texData.get(processedInput.dataId);
+                    input = processedInput;
+                }
+                else if (texData.isPacked &&
+                    !isReshapeFree(texData.shape, input.shape)) {
+                    _this.delayedStorage = false;
+                    var inputValues = input.dataSync();
+                    _this.delayedStorage = true;
+                    input = Tensor.make(input.shape, { values: inputValues }, input.dtype);
+                    texData = _this.texData.get(input.dataId);
+                    texData.isPacked = true;
+                }
+                _this.uploadToGPU(input.dataId);
+                return { shape: input.shape, texData: texData, isUniform: false };
+            });
+            this.uploadToGPU(output.dataId);
+            var outputData = {
+                shape: output.shape,
+                texData: this.texData.get(output.dataId),
+                isUniform: false
+            };
+            var key = makeShaderKey(program, inputsData, outputData);
+            var binary = this.getAndSaveBinary(key, function () {
+                return compileProgram(_this.gpgpu, program, inputsData, outputData);
+            });
+            var shouldTimeProgram = this.activeTimers != null;
+            var query;
+            if (shouldTimeProgram) {
+                query = this.startTimer();
+            }
+            runProgram(binary, inputsData, outputData, customSetup);
+            if (ENV.get('WEBGL_PAGING_ENABLED') && pageToCpu &&
+                this.numBytesInGPU > this.NUM_BYTES_BEFORE_PAGING) {
+                var numBytesToPage = this.numBytesInGPU - this.NUM_BYTES_BEFORE_PAGING;
+                while (numBytesToPage > 0 && this.lruDataGPU.length > 0) {
+                    var dataId = this.lruDataGPU.shift();
+                    var _a = this.texData.get(dataId), shape = _a.shape, dtype = _a.dtype;
+                    numBytesToPage -= this.computeBytes(shape, dtype);
+                    this.read(dataId);
+                }
+            }
+            if (shouldTimeProgram) {
+                query = this.endTimer(query);
+                this.activeTimers.push({ name: program.constructor.name, query: this.getQueryTime(query) });
+            }
+            if (!ENV.get('WEBGL_LAZILY_UNPACK') &&
+                this.texData.get(output.dataId).isPacked && !program.isPackShader) {
+                return this.unpackTensor(output);
+            }
+            return output;
+        };
+        MathBackendWebGL.prototype.getAndSaveBinary = function (key, getBinary) {
+            if (!(key in this.binaryCache)) {
+                this.binaryCache[key] = getBinary();
+            }
+            return this.binaryCache[key];
+        };
+        MathBackendWebGL.prototype.getTextureManager = function () {
+            return this.textureManager;
+        };
+        MathBackendWebGL.prototype.dispose = function () {
+            if (this.disposed) {
+                return;
+            }
+            for (var key in this.binaryCache) {
+                this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram);
+            }
+            this.textureManager.dispose();
+            this.canvas.remove();
+            if (this.fromPixels2DContext != null) {
+                this.fromPixels2DContext.canvas.remove();
+            }
+            if (this.gpgpuCreatedLocally) {
+                this.gpgpu.dispose();
+            }
+            this.disposed = true;
+        };
+        MathBackendWebGL.prototype.floatPrecision = function () {
+            var _this = this;
+            return tidy(function () {
+                if (_this.abs(scalar(1e-8)).get() > 0) {
+                    return 32;
+                }
+                return 16;
+            });
+        };
+        MathBackendWebGL.prototype.uploadToGPU = function (dataId) {
+            var _a;
+            var texData = this.texData.get(dataId);
+            var shape = texData.shape, values = texData.values, texture = texData.texture, usage = texData.usage, isPacked = texData.isPacked;
+            if (texture != null) {
+                if (ENV.get('WEBGL_PAGING_ENABLED')) {
+                    var index = this.lruDataGPU.indexOf(dataId);
+                    if (index >= 0) {
+                        this.lruDataGPU.splice(this.lruDataGPU.indexOf(dataId), 1);
+                        this.lruDataGPU.push(dataId);
+                    }
+                }
+                return;
+            }
+            var shouldTimeProgram = this.activeTimers != null;
+            var start;
+            if (shouldTimeProgram) {
+                start = performance.now();
+            }
+            var texShape = getTextureShapeFromLogicalShape(shape, isPacked);
+            texData.texShape = texShape;
+            var newTexture = this.acquireTexture(dataId, texShape, usage, isPacked);
+            texData.texture = newTexture;
+            if (values != null) {
+                if (isPacked) {
+                    var batch = this.getBatchDim(shape);
+                    var rows = 1, cols = 1;
+                    if (shape.length) {
+                        _a = this.getRowsCols(shape), rows = _a[0], cols = _a[1];
+                    }
+                    this.gpgpu.uploadMatrixToPackedTexture(newTexture, batch, rows, cols, texShape[0], texShape[1], typedArrayToFloat32(values));
+                }
+                else {
+                    this.gpgpu.uploadMatrixToTexture(newTexture, texShape[0], texShape[1], typedArrayToFloat32(values));
+                }
+                texData.values = null;
+                if (shouldTimeProgram) {
+                    this.uploadWaitMs += performance.now() - start;
+                }
+            }
+        };
+        MathBackendWebGL.prototype.convertAndCacheOnCPU = function (dataId, float32Values) {
+            var dontKeepCopyOnGPU = this.delayedStorage;
+            var texData = this.texData.get(dataId);
+            var texture = texData.texture, texShape = texData.texShape, dtype = texData.dtype, usage = texData.usage, isPacked = texData.isPacked;
+            if (dontKeepCopyOnGPU && texture != null) {
+                this.releaseTexture(dataId, texture, texShape, usage, isPacked);
+                texData.texture = null;
+                texData.texShape = null;
+            }
+            texData.usage = TextureUsage.UPLOAD;
+            if (float32Values != null) {
+                texData.values = float32ToTypedArray(float32Values, dtype);
+            }
+            return texData.values;
+        };
+        MathBackendWebGL.prototype.releaseTexture = function (dataId, texture, texShape, texType, isPacked) {
+            var _a = this.texData.get(dataId), shape = _a.shape, dtype = _a.dtype;
+            if (ENV.get('WEBGL_PAGING_ENABLED')) {
+                var idx = this.lruDataGPU.indexOf(dataId);
+                if (idx >= 0) {
+                    this.lruDataGPU.splice(idx, 1);
+                }
+            }
+            this.numBytesInGPU -= this.computeBytes(shape, dtype);
+            this.textureManager.releaseTexture(texture, texShape, texType, isPacked);
+        };
+        MathBackendWebGL.prototype.acquireTexture = function (dataId, texShape, texType, isPacked) {
+            var _a = this.texData.get(dataId), shape = _a.shape, dtype = _a.dtype;
+            if (ENV.get('WEBGL_PAGING_ENABLED')) {
+                this.lruDataGPU.push(dataId);
+            }
+            this.numBytesInGPU += this.computeBytes(shape, dtype);
+            return this.textureManager.acquireTexture(texShape, texType, isPacked);
+        };
+        MathBackendWebGL.prototype.computeBytes = function (shape, dtype) {
+            return sizeFromShape(shape) * bytesPerElement(dtype);
+        };
+        return MathBackendWebGL;
+    }());
+    if (ENV.get('IS_BROWSER')) {
+        ENV.registerBackend('webgl', function () { return new MathBackendWebGL(); }, 2, setTensorTracker);
+    }
+    function float32ToTypedArray(a, dtype) {
+        if (dtype === 'float32' || dtype === 'complex64') {
+            return a;
+        }
+        else if (dtype === 'int32' || dtype === 'bool') {
+            var result = (dtype === 'int32') ? new Int32Array(a.length) :
+                new Uint8Array(a.length);
+            for (var i = 0; i < result.length; ++i) {
+                result[i] = Math.round(a[i]);
+            }
+            return result;
+        }
+        else {
+            throw new Error("Unknown dtype " + dtype);
+        }
+    }
+    function typedArrayToFloat32(a) {
+        return (a instanceof Float32Array) ? a : new Float32Array(a);
+    }
+
+    function neg_(x) {
+        var $x = convertToTensor(x, 'x', 'neg');
+        var grad = function (dy) {
+            return { $x: function () { return dy.neg(); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.neg($x); }, { $x: $x }, grad);
+    }
+    function ceil_(x) {
+        var $x = convertToTensor(x, 'x', 'ceil');
+        var grad = function (dy) {
+            return { $x: function () { return zerosLike(dy); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.ceil($x); }, { $x: $x }, grad);
+    }
+    function floor_(x) {
+        var $x = convertToTensor(x, 'x', 'floor');
+        var grad = function (dy) {
+            return { $x: function () { return zerosLike(dy); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.floor($x); }, { $x: $x }, grad);
+    }
+    function sign_(x) {
+        var $x = convertToTensor(x, 'x', 'sign');
+        var grad = function (dy) {
+            return { $x: function () { return zerosLike(dy); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.sign($x); }, { $x: $x }, grad);
+    }
+    function round_(x) {
+        var $x = convertToTensor(x, 'x', 'round');
+        var grad = function (dy) {
+            return { $x: function () { return zerosLike(dy); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.round($x); }, { $x: $x }, grad);
+    }
+    function exp_(x) {
+        var $x = convertToTensor(x, 'x', 'exp');
+        var bck = function (dy, saved) {
+            var y = saved[0];
+            return { $x: function () { return dy.mulStrict(y); } };
+        };
+        return ENV.engine.runKernel(function (backend, save) { return save(backend.exp($x)); }, { $x: $x }, bck);
+    }
+    function expm1_(x) {
+        var $x = convertToTensor(x, 'x', 'expm1');
+        var grad = function (dy) {
+            return { $x: function () { return dy.mulStrict($x.exp()); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.expm1($x); }, { $x: $x }, grad);
+    }
+    function log_(x) {
+        var $x = convertToTensor(x, 'x', 'log');
+        var grad = function (dy) {
+            return { $x: function () { return dy.divStrict($x.toFloat()); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.log($x); }, { $x: $x }, grad);
+    }
+    function log1p_(x) {
+        var $x = convertToTensor(x, 'x', 'log1p');
+        var grad = function (dy) {
+            return { $x: function () { return dy.div($x.add(1)); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.log1p($x); }, { $x: $x }, grad);
+    }
+    function sqrt_(x) {
+        var $x = convertToTensor(x, 'x', 'sqrt');
+        var grad = function (dy) {
+            return { $x: function () { return dy.div($x.toFloat().sqrt().mul(2)); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.sqrt($x); }, { $x: $x }, grad);
+    }
+    function rsqrt_(x) {
+        var $x = convertToTensor(x, 'x', 'rsqrt');
+        var grad = function (dy) {
+            return { $x: function () { return dy.div($x.pow(1.5).mul(2)).neg(); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.rsqrt($x); }, { $x: $x }, grad);
+    }
+    function square_(x) {
+        var $x = convertToTensor(x, 'x', 'square');
+        var grad = function (dy) {
+            return { $x: function () { return dy.mul($x.toFloat().mul(2)); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.square($x); }, { $x: $x }, grad);
+    }
+    function reciprocal_(x) {
+        var $x = convertToTensor(x, 'x', 'reciprocal');
+        var grad = function (dy) {
+            return { $x: function () { return dy.divStrict($x.square().neg()); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.reciprocal($x); }, { $x: $x }, grad);
+    }
+    function abs_(x) {
+        var $x = convertToTensor(x, 'x', 'abs');
+        if ($x.dtype === 'complex64') {
+            return ENV.engine.runKernel(function (backend) { return backend.complexAbs($x); }, { $x: $x });
+        }
+        var grad = function (dy) {
+            return { $x: function () { return dy.mulStrict($x.toFloat().step(-1)); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.abs($x); }, { $x: $x }, grad);
+    }
+    function clipByValue_(x, clipValueMin, clipValueMax) {
+        var $x = convertToTensor(x, 'x', 'clipByValue');
+        assert((clipValueMin <= clipValueMax), "Error in clip: min (" + clipValueMin + ") must be " +
+            ("less than or equal to max (" + clipValueMax + ")."));
+        var grad = function (dy) {
+            return {
+                $x: function () { return dy.where($x.greaterEqual(clipValueMin)
+                    .logicalAnd($x.lessEqual(clipValueMax)), zerosLike(dy)); },
+            };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.clip($x, clipValueMin, clipValueMax); }, { $x: $x }, grad);
+    }
+    function sigmoid_(x) {
+        var $x = convertToTensor(x, 'x', 'sigmoid');
+        var grad = function (dy, saved) {
+            var y = saved[0];
+            return { $x: function () { return dy.mul(y.mul(scalar(1).sub(y))); } };
+        };
+        return ENV.engine.runKernel(function (backend, save) { return save(backend.sigmoid($x)); }, { $x: $x }, grad);
+    }
+    function logSigmoid_(x) {
+        var $x = convertToTensor(x, 'x', 'logSigmoid');
+        var grad = function (dy) {
+            return { $x: function () { return dy.mulStrict($x.neg().sigmoid()); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.softplus($x.neg()).neg(); }, { $x: $x }, grad);
+    }
+    function softplus_(x) {
+        var $x = convertToTensor(x, 'x', 'softplus');
+        var grad = function (dy) {
+            return { $x: function () { return dy.mulStrict($x.sigmoid()); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.softplus($x); }, { $x: $x }, grad);
+    }
+    function sin_(x) {
+        var $x = convertToTensor(x, 'x', 'sin');
+        var grad = function (dy) {
+            return { $x: function () { return $x.toFloat().cos().mulStrict(dy); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.sin($x); }, { $x: $x }, grad);
+    }
+    function cos_(x) {
+        var $x = convertToTensor(x, 'x', 'cos');
+        var grad = function (dy) {
+            return { $x: function () { return $x.toFloat().sin().neg().mulStrict(dy); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.cos($x); }, { $x: $x }, grad);
+    }
+    function tan_(x) {
+        var $x = convertToTensor(x, 'x', 'tan');
+        var grad = function (dy) {
+            return { $x: function () { return dy.divStrict($x.cos().square()); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.tan($x); }, { $x: $x }, grad);
+    }
+    function asin_(x) {
+        var $x = convertToTensor(x, 'x', 'asin');
+        var grad = function (dy) {
+            return {
+                $x: function () { return dy.divStrict(scalar(1).sub($x.toFloat().square()).sqrt()); }
+            };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.asin($x); }, { $x: $x }, grad);
+    }
+    function acos_(x) {
+        var $x = convertToTensor(x, 'x', 'acos');
+        var grad = function (dy) {
+            return {
+                $x: function () {
+                    return dy.divStrict(scalar(1).sub($x.toFloat().square()).sqrt()).neg();
+                }
+            };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.acos($x); }, { $x: $x }, grad);
+    }
+    function atan_(x) {
+        var $x = convertToTensor(x, 'x', 'atan');
+        var grad = function (dy) {
+            return { $x: function () { return dy.div($x.toFloat().square().add(1)); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.atan($x); }, { $x: $x }, grad);
+    }
+    function sinh_(x) {
+        var $x = convertToTensor(x, 'x', 'sinh');
+        var grad = function (dy) {
+            return { $x: function () { return $x.toFloat().cosh().mulStrict(dy); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.sinh($x); }, { $x: $x }, grad);
+    }
+    function cosh_(x) {
+        var $x = convertToTensor(x, 'x', 'cosh');
+        var grad = function (dy) {
+            return { $x: function () { return $x.toFloat().sinh().mulStrict(dy); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.cosh($x); }, { $x: $x }, grad);
+    }
+    function tanh_(x) {
+        var $x = convertToTensor(x, 'x', 'tanh');
+        var grad = function (dy, saved) {
+            var y = saved[0];
+            return { $x: function () { return scalar(1).sub(y.square()).mulStrict(dy); } };
+        };
+        return ENV.engine.runKernel(function (backend, save) { return save(backend.tanh($x)); }, { $x: $x }, grad);
+    }
+    function asinh_(x) {
+        var $x = convertToTensor(x, 'x', 'asinh');
+        var grad = function (dy) {
+            return {
+                $x: function () { return dy.divStrict(scalar(1).add($x.toFloat().square()).sqrt()); }
+            };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.asinh($x); }, { $x: $x }, grad);
+    }
+    function acosh_(x) {
+        var $x = convertToTensor(x, 'x', 'acosh');
+        var grad = function (dy) {
+            return { $x: function () { return dy.divStrict($x.toFloat().square().sub(1).sqrt()); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.acosh($x); }, { $x: $x }, grad);
+    }
+    function atanh_(x) {
+        var $x = convertToTensor(x, 'x', 'atanh');
+        var grad = function (dy) {
+            return { $x: function () { return dy.div(scalar(1).sub($x.toFloat().square())); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.atanh($x); }, { $x: $x }, grad);
+    }
+    function erf_(x) {
+        var $x = convertToTensor(x, 'x', 'erf');
+        assert($x.dtype === 'int32' || $x.dtype === 'float32', 'Input dtype must be `int32` or `float32`.');
+        if ($x.dtype === 'int32') {
+            $x = $x.toFloat();
+        }
+        var grad = function (dy) {
+            return {
+                $x: function () { return dy.mul($x.square().neg().exp().mul(2 / Math.sqrt(Math.PI))); }
+            };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.erf($x); }, { $x: $x }, grad);
+    }
+    function step_(x, alpha) {
+        if (alpha === void 0) { alpha = 0.0; }
+        var $x = convertToTensor(x, 'x', 'step');
+        var grad = function (dy) {
+            return { $x: function () { return zerosLike(dy); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.step($x, alpha); }, { $x: $x }, grad);
+    }
+    var abs = op({ abs_: abs_ });
+    var acos = op({ acos_: acos_ });
+    var acosh = op({ acosh_: acosh_ });
+    var asin = op({ asin_: asin_ });
+    var asinh = op({ asinh_: asinh_ });
+    var atan = op({ atan_: atan_ });
+    var atanh = op({ atanh_: atanh_ });
+    var ceil = op({ ceil_: ceil_ });
+    var clipByValue = op({ clipByValue_: clipByValue_ });
+    var cos = op({ cos_: cos_ });
+    var cosh = op({ cosh_: cosh_ });
+    var erf = op({ erf_: erf_ });
+    var exp = op({ exp_: exp_ });
+    var expm1 = op({ expm1_: expm1_ });
+    var floor = op({ floor_: floor_ });
+    var log$1 = op({ log_: log_ });
+    var log1p = op({ log1p_: log1p_ });
+    var logSigmoid = op({ logSigmoid_: logSigmoid_ });
+    var neg = op({ neg_: neg_ });
+    var reciprocal = op({ reciprocal_: reciprocal_ });
+    var round = op({ round_: round_ });
+    var rsqrt = op({ rsqrt_: rsqrt_ });
+    var sigmoid = op({ sigmoid_: sigmoid_ });
+    var sign = op({ sign_: sign_ });
+    var sin = op({ sin_: sin_ });
+    var sinh = op({ sinh_: sinh_ });
+    var softplus = op({ softplus_: softplus_ });
+    var sqrt = op({ sqrt_: sqrt_ });
+    var square = op({ square_: square_ });
+    var step = op({ step_: step_ });
+    var tan = op({ tan_: tan_ });
+    var tanh$1 = op({ tanh_: tanh_ });
+
+    function batchNormalization2d_(x, mean, variance, varianceEpsilon, scale, offset) {
+        if (varianceEpsilon === void 0) { varianceEpsilon = .001; }
+        var $x = convertToTensor(x, 'x', 'batchNormalization');
+        var $mean = convertToTensor(mean, 'mean', 'batchNormalization');
+        var $variance = convertToTensor(variance, 'variance', 'batchNormalization');
+        var $scale;
+        if (scale != null) {
+            $scale = convertToTensor(scale, 'scale', 'batchNormalization');
+        }
+        var $offset;
+        if (offset != null) {
+            $offset = convertToTensor(offset, 'offset', 'batchNormalization');
+        }
+        assert($x.rank === 2, "Error in batchNormalization3D: x must be rank 3 but got rank " +
+            ($x.rank + "."));
+        assert($mean.rank === 2 || $mean.rank === 1, "Error in batchNormalization2D: mean must be rank 2 or rank 1 but " +
+            ("got rank " + $mean.rank + "."));
+        assert($variance.rank === 2 || $variance.rank === 1, "Error in batchNormalization2D: variance must be rank 2 or rank 1 " +
+            ("but got rank " + $variance.rank + "."));
+        if ($scale != null) {
+            assert($scale.rank === 2 || $scale.rank === 1, "Error in batchNormalization2D: scale must be rank 2 or rank 1 " +
+                ("but got rank " + $scale.rank + "."));
+        }
+        if ($offset != null) {
+            assert($offset.rank === 2 || $offset.rank === 1, "Error in batchNormalization2D: offset must be rank 2 or rank 1 " +
+                ("but got rank " + $offset.rank + "."));
+        }
+        return batchNormalization($x, $mean, $variance, varianceEpsilon, $scale, $offset);
+    }
+    function batchNormalization3d_(x, mean, variance, varianceEpsilon, scale, offset) {
+        if (varianceEpsilon === void 0) { varianceEpsilon = .001; }
+        var $x = convertToTensor(x, 'x', 'batchNormalization');
+        var $mean = convertToTensor(mean, 'mean', 'batchNormalization');
+        var $variance = convertToTensor(variance, 'variance', 'batchNormalization');
+        var $scale;
+        if (scale != null) {
+            $scale = convertToTensor(scale, 'scale', 'batchNormalization');
+        }
+        var $offset;
+        if (offset != null) {
+            $offset = convertToTensor(offset, 'offset', 'batchNormalization');
+        }
+        assert($x.rank === 3, "Error in batchNormalization3D: x must be rank 3 but got rank " +
+            ($x.rank + "."));
+        assert($mean.rank === 3 || $mean.rank === 1, "Error in batchNormalization3D: mean must be rank 3 or rank 1 but " +
+            ("got rank " + $mean.rank + "."));
+        assert($variance.rank === 3 || $variance.rank === 1, "Error in batchNormalization3D: variance must be rank 3 or rank 1 " +
+            ("but got rank " + $variance.rank + "."));
+        if ($scale != null) {
+            assert($scale.rank === 3 || $scale.rank === 1, "Error in batchNormalization3D: scale must be rank 3 or rank 1 " +
+                ("but got rank " + $scale.rank + "."));
+        }
+        if ($offset != null) {
+            assert($offset.rank === 3 || $offset.rank === 1, "Error in batchNormalization3D: offset must be rank 3 or rank 1 " +
+                ("but got rank " + $offset.rank + "."));
+        }
+        return batchNormalization($x, $mean, $variance, varianceEpsilon, $scale, $offset);
+    }
+    function batchNormalization4d_(x, mean, variance, varianceEpsilon, scale, offset) {
+        if (varianceEpsilon === void 0) { varianceEpsilon = .001; }
+        var $x = convertToTensor(x, 'x', 'batchNormalization');
+        var $mean = convertToTensor(mean, 'mean', 'batchNormalization');
+        var $variance = convertToTensor(variance, 'variance', 'batchNormalization');
+        var $scale;
+        if (scale != null) {
+            $scale = convertToTensor(scale, 'scale', 'batchNormalization');
+        }
+        var $offset;
+        if (offset != null) {
+            $offset = convertToTensor(offset, 'offset', 'batchNormalization');
+        }
+        assert($x.rank === 4, "Error in batchNormalization4D: x must be rank 4 but got rank " +
+            ($x.rank + "."));
+        assert($mean.rank === 4 || $mean.rank === 1, "Error in batchNormalization4D: mean must be rank 4 or rank 1 but " +
+            ("got rank " + $mean.rank + "."));
+        assert($variance.rank === 4 || $variance.rank === 1, "Error in batchNormalization4D: variance must be rank 4 or rank 1 " +
+            ("but got rank " + $variance.rank + "."));
+        if ($scale != null) {
+            assert($scale.rank === 4 || $scale.rank === 1, "Error in batchNormalization4D: scale must be rank 4 or rank 1 " +
+                ("but got rank " + $scale.rank + "."));
+        }
+        if ($offset != null) {
+            assert($offset.rank === 4 || $offset.rank === 1, "Error in batchNormalization4D: offset must be rank 4 or rank 1 " +
+                ("but got rank " + $offset.rank + "."));
+        }
+        return batchNormalization($x, $mean, $variance, varianceEpsilon, $scale, $offset);
+    }
+    function batchNormalization_(x, mean, variance, varianceEpsilon, scale, offset) {
+        if (varianceEpsilon === void 0) { varianceEpsilon = .001; }
+        var $x = convertToTensor(x, 'x', 'batchNormalization');
+        var $mean = convertToTensor(mean, 'mean', 'batchNormalization');
+        var $variance = convertToTensor(variance, 'variance', 'batchNormalization');
+        var $scale;
+        if (scale != null) {
+            $scale = convertToTensor(scale, 'scale', 'batchNormalization');
+        }
+        var $offset;
+        if (offset != null) {
+            $offset = convertToTensor(offset, 'offset', 'batchNormalization');
+        }
+        assert($mean.rank === $variance.rank, 'Batch normalization gradient requires mean and variance to have ' +
+            'equal ranks.');
+        assert($offset == null || $mean.rank === $offset.rank, 'Batch normalization gradient requires mean and offset to have ' +
+            'equal ranks.');
+        assert($scale == null || $mean.rank === $scale.rank, 'Batch normalization gradient requires mean and scale to have ' +
+            'equal ranks.');
+        var x4D;
+        if ($x.rank === 0 || $x.rank === 1) {
+            x4D = $x.as4D(1, 1, 1, $x.size);
+        }
+        else if ($x.rank === 2) {
+            x4D = $x.as4D(1, 1, $x.shape[0], $x.shape[1]);
+        }
+        else if ($x.rank === 3) {
+            x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
+        }
+        else {
+            x4D = $x;
+        }
+        var der = function (dy) {
+            var scaleValue = $scale == null ? scalar(1) : $scale;
+            var reductionAxes = getReductionAxes($mean.shape, x4D.shape);
+            var tileShape = [];
+            if ($mean.rank === 1) {
+                for (var i = 0; i < x4D.shape.length - 1; ++i) {
+                    tileShape.push(x4D.shape[i]);
+                }
+                tileShape.push(1);
+            }
+            var xMinusMean = $x.sub($mean);
+            var dyTimesScaleValue = dy.mul(scaleValue);
+            var oneOverSqrtVariance = rsqrt($variance.add(scalar(varianceEpsilon)));
+            var minusHalfRCube = oneOverSqrtVariance.mul(oneOverSqrtVariance)
+                .mul(oneOverSqrtVariance)
+                .mul(scalar(-0.5));
+            var derX = function () {
+                if ($mean.rank === 1) {
+                    return dy
+                        .mul(tile(oneOverSqrtVariance.as4D(1, 1, 1, $mean.shape[0]), tileShape))
+                        .mul(scaleValue)
+                        .reshape($x.shape);
+                }
+                else {
+                    return dy.mul(oneOverSqrtVariance).mul(scaleValue).reshape($x.shape);
+                }
+            };
+            var derMean = function () {
+                var meanDer = oneOverSqrtVariance.mul(scalar(-1)).mul(dyTimesScaleValue);
+                if ($mean.rank === 1) {
+                    meanDer = meanDer.sum(reductionAxes);
+                }
+                return meanDer.reshape($mean.shape);
+            };
+            var derVariance = function () {
+                var varianceDer = minusHalfRCube.mul(xMinusMean).mul(dyTimesScaleValue);
+                if ($mean.rank === 1) {
+                    varianceDer = varianceDer.sum(reductionAxes);
+                }
+                return varianceDer.reshape($mean.shape);
+            };
+            var derScale = function () {
+                var xMinusMean2TimesRsqrt = xMinusMean.mul(oneOverSqrtVariance);
+                var scaleDer = dy.mul(xMinusMean2TimesRsqrt);
+                if ($mean.rank === 1) {
+                    scaleDer = scaleDer.sum(reductionAxes);
+                }
+                return scaleDer.reshape($mean.shape);
+            };
+            var derOffset = function () {
+                var offsetDer = dy;
+                if ($mean.rank === 1) {
+                    offsetDer = offsetDer.sum(reductionAxes);
+                }
+                return offsetDer.reshape($mean.shape);
+            };
+            return {
+                $x: derX,
+                $mean: derMean,
+                $variance: derVariance,
+                $scale: derScale,
+                $offset: derOffset
+            };
+        };
+        var res = ENV.engine.runKernel(function (backend) { return backend.batchNormalization(x4D, batchnormReshape4D($mean), batchnormReshape4D($variance), varianceEpsilon, batchnormReshape4D($scale), batchnormReshape4D($offset)); }, { $x: $x, $mean: $mean, $variance: $variance, $scale: $scale, $offset: $offset }, der);
+        return res.reshape($x.shape);
+    }
+    function batchnormReshape4D(x) {
+        if (x == null) {
+            return null;
+        }
+        if (x.rank === 0) {
+            return x.as1D();
+        }
+        else if (x.rank === 1) {
+            return x;
+        }
+        else if (x.rank === 2) {
+            return x.as4D(1, 1, x.shape[0], x.shape[1]);
+        }
+        else if (x.rank === 3) {
+            return x.as4D(1, x.shape[0], x.shape[1], x.shape[2]);
+        }
+        return x;
+    }
+    var batchNormalization2d = op({ batchNormalization2d_: batchNormalization2d_ });
+    var batchNormalization3d = op({ batchNormalization3d_: batchNormalization3d_ });
+    var batchNormalization4d = op({ batchNormalization4d_: batchNormalization4d_ });
+    var batchNormalization = op({ batchNormalization_: batchNormalization_ });
+
+    function computePool2DInfo(inShape, filterSize, strides, dilations, pad, roundingMode, dataFormat) {
+        if (dataFormat === void 0) { dataFormat = 'channelsLast'; }
+        var _a = parseTupleParam(filterSize), filterHeight = _a[0], filterWidth = _a[1];
+        var filterShape;
+        if (dataFormat === 'channelsLast') {
+            filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];
+        }
+        else if (dataFormat === 'channelsFirst') {
+            filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];
+        }
+        else {
+            throw new Error("Unknown dataFormat " + dataFormat);
+        }
+        return computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, false, dataFormat);
+    }
+    function computeConv2DInfo(inShape, filterShape, strides, dilations, pad, roundingMode, depthwise, dataFormat) {
+        if (depthwise === void 0) { depthwise = false; }
+        if (dataFormat === void 0) { dataFormat = 'channelsLast'; }
+        var _a = [-1, -1, -1, -1], batchSize = _a[0], inHeight = _a[1], inWidth = _a[2], inChannels = _a[3];
+        if (dataFormat === 'channelsLast') {
+            batchSize = inShape[0], inHeight = inShape[1], inWidth = inShape[2], inChannels = inShape[3];
+        }
+        else if (dataFormat === 'channelsFirst') {
+            batchSize = inShape[0], inChannels = inShape[1], inHeight = inShape[2], inWidth = inShape[3];
+        }
+        else {
+            throw new Error("Unknown dataFormat " + dataFormat);
+        }
+        var filterHeight = filterShape[0], filterWidth = filterShape[1], filterChannels = filterShape[3];
+        var _b = parseTupleParam(strides), strideHeight = _b[0], strideWidth = _b[1];
+        var _c = parseTupleParam(dilations), dilationHeight = _c[0], dilationWidth = _c[1];
+        var effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
+        var effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
+        var _d = getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight, effectiveFilterWidth, roundingMode), padInfo = _d.padInfo, outHeight = _d.outHeight, outWidth = _d.outWidth;
+        var outChannels = depthwise ? filterChannels * inChannels : filterChannels;
+        var outShape;
+        if (dataFormat === 'channelsFirst') {
+            outShape = [batchSize, outChannels, outHeight, outWidth];
+        }
+        else if (dataFormat === 'channelsLast') {
+            outShape = [batchSize, outHeight, outWidth, outChannels];
+        }
+        return {
+            batchSize: batchSize,
+            dataFormat: dataFormat,
+            inHeight: inHeight,
+            inWidth: inWidth,
+            inChannels: inChannels,
+            outHeight: outHeight,
+            outWidth: outWidth,
+            outChannels: outChannels,
+            padInfo: padInfo,
+            strideHeight: strideHeight,
+            strideWidth: strideWidth,
+            filterHeight: filterHeight,
+            filterWidth: filterWidth,
+            effectiveFilterHeight: effectiveFilterHeight,
+            effectiveFilterWidth: effectiveFilterWidth,
+            dilationHeight: dilationHeight,
+            dilationWidth: dilationWidth,
+            inShape: inShape,
+            outShape: outShape,
+            filterShape: filterShape
+        };
+    }
+    function computeConv3DInfo(inShape, filterShape, strides, dilations, pad, depthwise, dataFormat) {
+        if (depthwise === void 0) { depthwise = false; }
+        if (dataFormat === void 0) { dataFormat = 'channelsLast'; }
+        var _a = [-1, -1, -1, -1, -1], batchSize = _a[0], inDepth = _a[1], inHeight = _a[2], inWidth = _a[3], inChannels = _a[4];
+        if (dataFormat === 'channelsLast') {
+            batchSize = inShape[0], inDepth = inShape[1], inHeight = inShape[2], inWidth = inShape[3], inChannels = inShape[4];
+        }
+        else if (dataFormat === 'channelsFirst') {
+            batchSize = inShape[0], inChannels = inShape[1], inDepth = inShape[2], inHeight = inShape[3], inWidth = inShape[4];
+        }
+        else {
+            throw new Error("Unknown dataFormat " + dataFormat);
+        }
+        var filterDepth = filterShape[0], filterHeight = filterShape[1], filterWidth = filterShape[2], filterChannels = filterShape[4];
+        var _b = parse3TupleParam(strides), strideDepth = _b[0], strideHeight = _b[1], strideWidth = _b[2];
+        var _c = parse3TupleParam(dilations), dilationDepth = _c[0], dilationHeight = _c[1], dilationWidth = _c[2];
+        var effectiveFilterDepth = getEffectiveFilterSize(filterDepth, dilationDepth);
+        var effectiveFilterHeight = getEffectiveFilterSize(filterHeight, dilationHeight);
+        var effectiveFilterWidth = getEffectiveFilterSize(filterWidth, dilationWidth);
+        var _d = get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth), padInfo = _d.padInfo, outDepth = _d.outDepth, outHeight = _d.outHeight, outWidth = _d.outWidth;
+        var outChannels = depthwise ? filterChannels * inChannels : filterChannels;
+        var outShape;
+        if (dataFormat === 'channelsFirst') {
+            outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];
+        }
+        else if (dataFormat === 'channelsLast') {
+            outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];
+        }
+        return {
+            batchSize: batchSize,
+            dataFormat: dataFormat,
+            inDepth: inDepth,
+            inHeight: inHeight,
+            inWidth: inWidth,
+            inChannels: inChannels,
+            outDepth: outDepth,
+            outHeight: outHeight,
+            outWidth: outWidth,
+            outChannels: outChannels,
+            padInfo: padInfo,
+            strideDepth: strideDepth,
+            strideHeight: strideHeight,
+            strideWidth: strideWidth,
+            filterDepth: filterDepth,
+            filterHeight: filterHeight,
+            filterWidth: filterWidth,
+            dilationDepth: dilationDepth,
+            dilationHeight: dilationHeight,
+            dilationWidth: dilationWidth,
+            inShape: inShape,
+            outShape: outShape,
+            filterShape: filterShape
+        };
+    }
+    function computeOutputShape3D(inShape, fieldSize, outDepth, stride, zeroPad, roundingMode) {
+        if (zeroPad == null) {
+            zeroPad = computeDefaultPad(inShape, fieldSize, stride);
+        }
+        var inputRows = inShape[0];
+        var inputCols = inShape[1];
+        var outputRows = conditionalRound((inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
+        assert(isInt(outputRows), "The output # of rows (" + outputRows + ") must be an integer. Change the " +
+            "stride and/or zero pad parameters");
+        var outputCols = conditionalRound((inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);
+        assert(isInt(outputCols), "The output # of columns (" + outputCols + ") must be an integer. Change " +
+            "the stride and/or zero pad parameters");
+        return [outputRows, outputCols, outDepth];
+    }
+    function computeDefaultPad(inputShape, fieldSize, stride, dilation) {
+        if (dilation === void 0) { dilation = 1; }
+        var effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);
+        return Math.floor((inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);
+    }
+    function parseTupleParam(param) {
+        return typeof param === 'number' ? [param, param] : param;
+    }
+    function parse3TupleParam(param) {
+        return typeof param === 'number' ? [param, param, param] : param;
+    }
+    function getEffectiveFilterSize(filterSize, dilation) {
+        if (dilation <= 1) {
+            return filterSize;
+        }
+        return filterSize + (filterSize - 1) * (dilation - 1);
+    }
+    function getPadAndOutInfo(pad, inHeight, inWidth, strideHeight, strideWidth, filterHeight, filterWidth, roundingMode) {
+        var padInfo;
+        var outHeight;
+        var outWidth;
+        if (typeof pad === 'number') {
+            var padType = (pad === 0) ? 'VALID' : 'NUMBER';
+            padInfo = { top: pad, bottom: pad, left: pad, right: pad, type: padType };
+            var outShape = computeOutputShape3D([inHeight, inWidth, 1], filterHeight, 1, strideHeight, pad, roundingMode);
+            outHeight = outShape[0];
+            outWidth = outShape[1];
+        }
+        else if (pad === 'same') {
+            outHeight = Math.ceil(inHeight / strideHeight);
+            outWidth = Math.ceil(inWidth / strideWidth);
+            var padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;
+            var padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;
+            var top_1 = Math.floor(padAlongHeight / 2);
+            var bottom = padAlongHeight - top_1;
+            var left = Math.floor(padAlongWidth / 2);
+            var right = padAlongWidth - left;
+            padInfo = { top: top_1, bottom: bottom, left: left, right: right, type: 'SAME' };
+        }
+        else if (pad === 'valid') {
+            padInfo = { top: 0, bottom: 0, left: 0, right: 0, type: 'VALID' };
+            outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);
+            outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);
+        }
+        else {
+            throw Error("Unknown padding parameter: " + pad);
+        }
+        return { padInfo: padInfo, outHeight: outHeight, outWidth: outWidth };
+    }
+    function get3DPadAndOutInfo(pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth, filterDepth, filterHeight, filterWidth) {
+        var padInfo;
+        var outDepth;
+        var outHeight;
+        var outWidth;
+        if (pad === 'same') {
+            outDepth = Math.ceil(inDepth / strideDepth);
+            outHeight = Math.ceil(inHeight / strideHeight);
+            outWidth = Math.ceil(inWidth / strideWidth);
+            var padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;
+            var padAlongHeight = (outHeight - 1) * strideHeight + filterHeight - inHeight;
+            var padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;
+            var front = Math.floor(padAlongDepth / 2);
+            var back = padAlongDepth - front;
+            var top_2 = Math.floor(padAlongHeight / 2);
+            var bottom = padAlongHeight - top_2;
+            var left = Math.floor(padAlongWidth / 2);
+            var right = padAlongWidth - left;
+            padInfo = { top: top_2, bottom: bottom, left: left, right: right, front: front, back: back, type: 'SAME' };
+        }
+        else if (pad === 'valid') {
+            padInfo = {
+                top: 0,
+                bottom: 0,
+                left: 0,
+                right: 0,
+                front: 0,
+                back: 0,
+                type: 'VALID'
+            };
+            outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);
+            outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);
+            outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);
+        }
+        else {
+            throw Error("Unknown padding parameter: " + pad);
+        }
+        return { padInfo: padInfo, outDepth: outDepth, outHeight: outHeight, outWidth: outWidth };
+    }
+    function conditionalRound(value, roundingMode) {
+        if (!roundingMode) {
+            return value;
+        }
+        switch (roundingMode) {
+            case 'round':
+                return Math.round(value);
+            case 'ceil':
+                return Math.ceil(value);
+            case 'floor':
+                return Math.floor(value);
+            default:
+                throw new Error("Unknown roundingMode " + roundingMode);
+        }
+    }
+    function tupleValuesAreOne(param) {
+        var _a = parseTupleParam(param), dimA = _a[0], dimB = _a[1];
+        return dimA === 1 && dimB === 1;
+    }
+    function eitherStridesOrDilationsAreOne(strides, dilations) {
+        return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);
+    }
+
+    function matMul_(a, b, transposeA, transposeB) {
+        if (transposeA === void 0) { transposeA = false; }
+        if (transposeB === void 0) { transposeB = false; }
+        var _a;
+        var $a = convertToTensor(a, 'a', 'matMul');
+        var $b = convertToTensor(b, 'b', 'matMul');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        var innerShapeA = transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];
+        var innerShapeB = transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];
+        var outerShapeA = transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];
+        var outerShapeB = transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];
+        var outerDimsA = $a.shape.slice(0, -2);
+        var outerDimsB = $b.shape.slice(0, -2);
+        var batchDimA = sizeFromShape(outerDimsA);
+        var batchDimB = sizeFromShape(outerDimsB);
+        assert($a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank, "Error in matMul: inputs must have the same rank of at least 2, " +
+            ("got ranks " + $a.rank + " and " + $b.rank + "."));
+        assert(arraysEqual(outerDimsA, outerDimsB), "Error in matMul: outer dimensions (" + outerDimsA + ") and (" +
+            (outerDimsB + ") of Tensors with shapes " + $a.shape + " and ") +
+            ($b.shape + " must match."));
+        assert(innerShapeA === innerShapeB, "Error in matMul: inner shapes (" + innerShapeA + ") and (" +
+            (innerShapeB + ") of Tensors with shapes " + $a.shape + " and ") +
+            ($b.shape + " and transposeA=" + transposeA) +
+            (" and transposeB=" + transposeB + " must match."));
+        var outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);
+        var a3D = transposeA ? $a.as3D(batchDimA, innerShapeA, outerShapeA) :
+            $a.as3D(batchDimA, outerShapeA, innerShapeA);
+        var b3D = transposeB ? $b.as3D(batchDimB, outerShapeB, innerShapeB) :
+            $b.as3D(batchDimB, innerShapeB, outerShapeB);
+        var grad = function (dy) {
+            if (!transposeA && !transposeB) {
+                return {
+                    $a: function () { return dy.matMul(b3D, false, true); },
+                    $b: function () { return a3D.matMul(dy, true, false); }
+                };
+            }
+            else if (!transposeA && transposeB) {
+                return {
+                    $a: function () { return dy.matMul(b3D, false, false); },
+                    $b: function () { return dy.matMul(a3D, true, false); }
+                };
+            }
+            else if (transposeA && !transposeB) {
+                return {
+                    $a: function () { return b3D.matMul(dy, false, true); },
+                    $b: function () { return a3D.matMul(dy, false, false); }
+                };
+            }
+            else {
+                return {
+                    $a: function () { return b3D.matMul(dy, true, true); },
+                    $b: function () { return dy.matMul(a3D, true, true); }
+                };
+            }
+        };
+        var res = ENV.engine.runKernel(function (backend) { return backend.batchMatMul(a3D, b3D, transposeA, transposeB); }, { $a: a3D, $b: b3D }, grad);
+        return res.reshape(outShape);
+    }
+    function outerProduct_(v1, v2) {
+        var $v1 = convertToTensor(v1, 'v1', 'outerProduct');
+        var $v2 = convertToTensor(v2, 'v2', 'outerProduct');
+        assert($v1.rank === 1 && $v2.rank === 1, "Error in outerProduct: inputs must be rank 1, but got ranks " +
+            ($v1.rank + " and " + $v2.rank + "."));
+        return $v1.as2D(-1, 1).matMul($v2.as2D(1, -1));
+    }
+    function dot_(t1, t2) {
+        var $t1 = convertToTensor(t1, 't1', 'dot');
+        var $t2 = convertToTensor(t2, 't2', 'dot');
+        assert(($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2), "Error in dot: inputs must all be rank 1 or 2, but got ranks " +
+            ($t1.rank + " and " + $t2.rank + "."));
+        var t1Inner = ($t1.rank === 1 ? $t1.size : $t1.shape[1]);
+        var t2Inner = ($t2.rank === 1 ? $t2.size : $t2.shape[0]);
+        assert(t1Inner === t2Inner, "Error in dot: inner dimensions of inputs must match, but got " +
+            (t1Inner + " and " + t2Inner + "."));
+        if ($t1.rank === 1 && $t2.rank === 1) {
+            return $t1.as2D(1, -1).matMul($t2.as2D(-1, 1)).asScalar();
+        }
+        else if ($t1.rank === 1 && $t2.rank === 2) {
+            return $t1.as2D(1, -1).matMul($t2.as2D($t2.shape[0], $t2.shape[1])).as1D();
+        }
+        else if ($t1.rank === 2 && $t2.rank === 1) {
+            return $t1.matMul($t2.as2D(-1, 1)).as1D();
+        }
+        else {
+            return $t1.matMul($t2.as2D($t2.shape[0], $t2.shape[1]));
+        }
+    }
+    var matMul = op({ matMul_: matMul_ });
+    var dot = op({ dot_: dot_ });
+    var outerProduct = op({ outerProduct_: outerProduct_ });
+
+    function conv1d_(x, filter, stride, pad, dataFormat, dilation, dimRoundingMode) {
+        if (dataFormat === void 0) { dataFormat = 'NWC'; }
+        if (dilation === void 0) { dilation = 1; }
+        var $x = convertToTensor(x, 'x', 'conv1d');
+        var $filter = convertToTensor(filter, 'filter', 'conv1d');
+        var x3D = $x;
+        var reshapedTo3D = false;
+        if ($x.rank === 2) {
+            reshapedTo3D = true;
+            x3D = $x.as3D(1, $x.shape[0], $x.shape[1]);
+        }
+        assert(x3D.rank === 3, "Error in conv1d: input must be rank 3, but got rank " + x3D.rank + ".");
+        assert($filter.rank === 3, "Error in conv1d: filter must be rank 3, but got rank " +
+            ($filter.rank + "."));
+        if (dimRoundingMode != null) {
+            assert(isInt(pad), "Error in conv1d: pad must be an integer when using, " +
+                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."));
+        }
+        assert(x3D.shape[2] === $filter.shape[1], "Error in conv1d: depth of input (" + x3D.shape[2] + ") must match " +
+            ("input depth for filter " + $filter.shape[1] + "."));
+        assert(eitherStridesOrDilationsAreOne(stride, dilation), 'Error in conv1D: Either stride or dilation must be 1. ' +
+            ("Got stride " + stride + " and dilation '" + dilation + "'"));
+        assert(dataFormat === 'NWC', "Error in conv1d: got dataFormat of " + dataFormat + " but only NWC is currently supported.");
+        var filter4D = $filter.as4D(1, $filter.shape[0], $filter.shape[1], $filter.shape[2]);
+        var input4D = x3D.as4D(x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]);
+        var strides = [1, stride];
+        var dilations = [1, dilation];
+        var conv2dDataFormat = 'NHWC';
+        var res = conv2d(input4D, filter4D, strides, pad, conv2dDataFormat, dilations, dimRoundingMode);
+        if (reshapedTo3D) {
+            return res.as2D(res.shape[2], res.shape[3]);
+        }
+        return res.as3D(res.shape[0], res.shape[2], res.shape[3]);
+    }
+    function conv2d_(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode) {
+        if (dataFormat === void 0) { dataFormat = 'NHWC'; }
+        if (dilations === void 0) { dilations = [1, 1]; }
+        var $x = convertToTensor(x, 'x', 'conv2d');
+        var $filter = convertToTensor(filter, 'filter', 'conv2d');
+        var x4D = $x;
+        var reshapedTo4D = false;
+        if ($x.rank === 3) {
+            reshapedTo4D = true;
+            x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
+        }
+        assert(x4D.rank === 4, "Error in conv2d: input must be rank 4, but got rank " + x4D.rank + ".");
+        assert($filter.rank === 4, "Error in conv2d: filter must be rank 4, but got rank " +
+            ($filter.rank + "."));
+        if (dimRoundingMode != null) {
+            assert(isInt(pad), "Error in conv2d: pad must be an integer when using, " +
+                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."));
+        }
+        assert(x4D.shape[3] === $filter.shape[2], "Error in conv2d: depth of input (" + x4D.shape[3] + ") must match " +
+            ("input depth for filter " + $filter.shape[2] + "."));
+        assert(eitherStridesOrDilationsAreOne(strides, dilations), 'Error in conv2D: Either strides or dilations must be 1. ' +
+            ("Got strides " + strides + " and dilations '" + dilations + "'"));
+        assert(dataFormat === 'NHWC', "Error in conv2d: got dataFormat of " + dataFormat + " but only NHWC is currently supported.");
+        var convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);
+        var res;
+        if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&
+            convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&
+            convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&
+            (convInfo.padInfo.type === 'SAME' || convInfo.padInfo.type === 'VALID')) {
+            var x2d = x4D.reshape([-1, convInfo.inChannels]);
+            var w2d = $filter.reshape([convInfo.inChannels, convInfo.outChannels]);
+            res = matMul(x2d, w2d).reshape(convInfo.outShape);
+        }
+        else {
+            var grad = function (dy) {
+                assert(tupleValuesAreOne(dilations), 'Error in gradient of conv2D: dilation rates greater than 1 are not' +
+                    ("yet supported in gradients. Got dilations '" + dilations + "'"));
+                return {
+                    x: function () { return conv2dDerInput_(x4D.shape, dy, $filter, strides, pad); },
+                    $filter: function () { return conv2dDerFilter_(x4D, dy, $filter.shape, strides, pad); }
+                };
+            };
+            res = ENV.engine.runKernel(function (backend) { return backend.conv2d(x4D, $filter, convInfo); }, { x: x4D, $filter: $filter }, grad);
+        }
+        if (reshapedTo4D) {
+            return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
+        }
+        return res;
+    }
+    function conv2dDerInput_(xShape, dy, filter, strides, pad, dimRoundingMode) {
+        assert(xShape.length === dy.rank, "Length of inShape " +
+            ("(" + xShape.length + ") and rank of dy (" + dy.rank + ") must match"));
+        var xShape4D = xShape;
+        var dy4D = dy;
+        var reshapedTo4D = false;
+        if (dy.rank === 3) {
+            reshapedTo4D = true;
+            dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);
+            xShape4D = [1, xShape[0], xShape[1], xShape[2]];
+        }
+        var inDepth = xShape4D[3];
+        var outDepth = dy4D.shape[3];
+        assert(xShape4D.length === 4, "Error in conv2dDerInput: inShape must be length 4, but got length " +
+            (xShape4D.length + "."));
+        assert(dy4D.rank === 4, "Error in conv2dDerInput: dy must be rank 4, but got " +
+            ("rank " + dy4D.rank));
+        assert(filter.rank === 4, "Error in conv2dDerInput: filter must be rank 4, but got " +
+            ("rank " + filter.rank));
+        assert(inDepth === filter.shape[2], "Error in conv2dDerInput: depth of input (" + inDepth + ") must " +
+            ("match input depth for filter " + filter.shape[2] + "."));
+        assert(outDepth === filter.shape[3], "Error in conv2dDerInput: depth of output (" + outDepth + ") must " +
+            ("match output depth for filter " + filter.shape[3] + "."));
+        if (dimRoundingMode != null) {
+            assert(isInt(pad), "Error in conv2dDerInput: pad must be an integer when using, " +
+                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."));
+        }
+        var dilations = 1;
+        var grad = function (ddx) {
+            var dataFormat = 'NHWC';
+            return {
+                dy4D: function () { return conv2d(ddx, filter, strides, pad, dataFormat, dilations, dimRoundingMode); },
+                filter: function () { return conv2dDerFilter(ddx, dy4D, filter.shape, strides, pad, dimRoundingMode); }
+            };
+        };
+        var convInfo = computeConv2DInfo(xShape4D, filter.shape, strides, dilations, pad, dimRoundingMode);
+        var res = ENV.engine.runKernel(function (backend) { return backend.conv2dDerInput(dy4D, filter, convInfo); }, { dy4D: dy4D, filter: filter }, grad);
+        if (reshapedTo4D) {
+            return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
+        }
+        return res;
+    }
+    function conv2dDerFilter_(x, dy, filterShape, strides, pad, dimRoundingMode) {
+        var x4D = x;
+        if (x.rank === 3) {
+            x4D = x.as4D(1, x.shape[0], x.shape[1], x.shape[2]);
+        }
+        var dy4D = dy;
+        if (dy4D.rank === 3) {
+            dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);
+        }
+        assert(x4D.rank === 4, "Error in conv2dDerFilter: input must be rank 4, but got shape " +
+            (x4D.shape + "."));
+        assert(dy4D.rank === 4, "Error in conv2dDerFilter: dy must be rank 4, but got shape " +
+            (dy4D.shape + "."));
+        assert(filterShape.length === 4, "Error in conv2dDerFilter: filterShape must be length 4, but got " +
+            (filterShape + "."));
+        assert(x4D.shape[3] === filterShape[2], "Error in conv2dDerFilter: depth of input " + x4D.shape[3] + ") must " +
+            ("match input depth in filter (" + filterShape[2] + "."));
+        assert(dy4D.shape[3] === filterShape[3], "Error in conv2dDerFilter: depth of dy (" + dy4D.shape[3] + ") must " +
+            ("match output depth for filter (" + filterShape[3] + ")."));
+        if (dimRoundingMode != null) {
+            assert(isInt(pad), "Error in conv2dDerFilter: pad must be an integer when using, " +
+                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."));
+        }
+        var dilations = 1;
+        var convInfo = computeConv2DInfo(x4D.shape, filterShape, strides, dilations, pad, dimRoundingMode);
+        return ENV.engine.runKernel(function (backend) { return backend.conv2dDerFilter(x4D, dy4D, convInfo); }, { x4D: x4D, dy4D: dy4D });
+    }
+    function conv2dTranspose_(x, filter, outputShape, strides, pad, dimRoundingMode) {
+        var $x = convertToTensor(x, 'x', 'conv2dTranspose');
+        var $filter = convertToTensor(filter, 'filter', 'conv2dTranspose');
+        return conv2dDerInput_(outputShape, $x, $filter, strides, pad, dimRoundingMode);
+    }
+    function depthwiseConv2d_(x, filter, strides, pad, dataFormat, dilations, dimRoundingMode) {
+        if (dataFormat === void 0) { dataFormat = 'NHWC'; }
+        if (dilations === void 0) { dilations = [1, 1]; }
+        var $x = convertToTensor(x, 'x', 'depthwiseConv2d');
+        var $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');
+        var x4D = $x;
+        var reshapedTo4D = false;
+        if ($x.rank === 3) {
+            reshapedTo4D = true;
+            x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
+        }
+        assert(x4D.rank === 4, "Error in depthwiseConv2d: input must be rank 4, but got " +
+            ("rank " + x4D.rank + "."));
+        assert($filter.rank === 4, "Error in depthwiseConv2d: filter must be rank 4, but got rank " +
+            ($filter.rank + "."));
+        assert(x4D.shape[3] === $filter.shape[2], "Error in depthwiseConv2d: number of input channels " +
+            ("(" + x4D.shape[3] + ") must match the inChannels dimension in ") +
+            ("filter " + $filter.shape[2] + "."));
+        if (dilations == null) {
+            dilations = [1, 1];
+        }
+        assert(eitherStridesOrDilationsAreOne(strides, dilations), 'Error in depthwiseConv2d: Either strides or dilations must be 1. ' +
+            ("Got strides " + strides + " and dilations '" + dilations + "'"));
+        if (dimRoundingMode != null) {
+            assert(isInt(pad), "Error in depthwiseConv2d: pad must be an integer when using, " +
+                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad + "."));
+        }
+        var convInfo = computeConv2DInfo(x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode, true);
+        var grad = function (dy) {
+            assert(tupleValuesAreOne(dilations), 'Error in gradient of depthwiseConv2d: dilation rates greater than ' +
+                ("1 are not yet supported. Got dilations '" + dilations + "'"));
+            return {
+                x: function () { return depthwiseConv2dDerInput(x4D.shape, dy, $filter, convInfo); },
+                $filter: function () { return depthwiseConv2dDerFilter(x4D, dy, $filter.shape, convInfo); },
+            };
+        };
+        var res = ENV.engine.runKernel(function (backend) { return backend.depthwiseConv2D(x4D, $filter, convInfo); }, { x: x4D, $filter: $filter }, grad);
+        if (reshapedTo4D) {
+            return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
+        }
+        return res;
+    }
+    function separableConv2d_(x, depthwiseFilter, pointwiseFilter, strides, pad, dilation, dataFormat) {
+        if (dilation === void 0) { dilation = [1, 1]; }
+        if (dataFormat === void 0) { dataFormat = 'NHWC'; }
+        var $x = convertToTensor(x, 'x', 'separableConv2d');
+        var $depthwiseFilter = convertToTensor(depthwiseFilter, 'depthwiseFilter', 'separableConv2d');
+        var $pointwiseFilter = convertToTensor(pointwiseFilter, 'pointwiseFilter', 'separableConv2d');
+        var x4D = $x;
+        var reshapedTo4D = false;
+        if ($x.rank === 3) {
+            reshapedTo4D = true;
+            x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
+        }
+        if (dataFormat === 'NCHW') {
+            throw new Error('separableConv2d currently does not support dataFormat NCHW; only ' +
+                'NHWC is supported');
+        }
+        assert(x4D.rank === 4, "Error in separableConv2d: input must be rank 4, but got " +
+            ("rank " + x4D.rank + "."));
+        assert($depthwiseFilter.rank === 4, "Error in separableConv2d: depthwise filter must be rank 4, but got " +
+            ("rank " + $depthwiseFilter.rank + "."));
+        assert($pointwiseFilter.rank === 4, "Error in separableConv2d: pointwise filter must be rank 4, but got " +
+            ("rank " + $depthwiseFilter.rank + "."));
+        assert($pointwiseFilter.shape[0] === 1, "Error in separableConv2d: the first dimension of pointwise filter " +
+            (" must be 1, but got " + $pointwiseFilter.shape[0] + "."));
+        assert($pointwiseFilter.shape[1] === 1, "Error in separableConv2d: the second dimension of pointwise filter " +
+            (" must be 1, but got " + $pointwiseFilter.shape[1] + "."));
+        var inChannels = $depthwiseFilter.shape[2];
+        var channelMultiplier = $depthwiseFilter.shape[3];
+        assert($pointwiseFilter.shape[2] === inChannels * channelMultiplier, "Error in separableConv2d: the third dimension of pointwise filter " +
+            ("must be " + inChannels * channelMultiplier + ", ") +
+            ("but got " + $pointwiseFilter.shape[2] + "."));
+        var depthwise = depthwiseConv2d(x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);
+        var pointwiseStride = 1;
+        var res = conv2d(depthwise, $pointwiseFilter, pointwiseStride, 'valid', dataFormat);
+        if (reshapedTo4D) {
+            return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
+        }
+        return res;
+    }
+    function parseTupleParam$1(param) {
+        if (typeof param === 'number') {
+            return [param, param, param];
+        }
+        if (param.length === 2) {
+            return [param[0], param[1], 1];
+        }
+        return param;
+    }
+    function tupleValuesAreOne$1(param) {
+        var _a = parseTupleParam$1(param), dimA = _a[0], dimB = _a[1], dimC = _a[2];
+        return dimA === 1 && dimB === 1 && dimC === 1;
+    }
+    function eitherStridesOrDilationsAreOne$1(strides, dilations) {
+        return tupleValuesAreOne$1(strides) || tupleValuesAreOne$1(dilations);
+    }
+    function depthwiseConv2dDerInput(xShape, dy, filter, convInfo) {
+        var dy4D = dy;
+        var reshapedTo4D = false;
+        if (dy.rank === 3) {
+            reshapedTo4D = true;
+            dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);
+        }
+        var res = ENV.engine.runKernel(function (backend) { return backend.depthwiseConv2DDerInput(dy4D, filter, convInfo); }, { dy4D: dy4D });
+        if (reshapedTo4D) {
+            return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
+        }
+        return res;
+    }
+    function depthwiseConv2dDerFilter(x, dy, filterShape, convInfo) {
+        var x4D = x;
+        if (x.rank === 3) {
+            x4D = x.as4D(1, x.shape[0], x.shape[1], x.shape[2]);
+        }
+        var dy4D = dy;
+        if (dy4D.rank === 3) {
+            dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);
+        }
+        return ENV.engine.runKernel(function (backend) { return backend.depthwiseConv2DDerFilter(x4D, dy4D, convInfo); }, { x4D: x4D, dy4D: dy4D });
+    }
+    function conv3d_(x, filter, strides, pad, dataFormat, dilations) {
+        if (dataFormat === void 0) { dataFormat = 'NHWC'; }
+        if (dilations === void 0) { dilations = [1, 1, 1]; }
+        var $x = convertToTensor(x, 'x', 'conv3d');
+        var $filter = convertToTensor(filter, 'filter', 'conv3d');
+        var x5D = $x;
+        var reshapedTo5D = false;
+        if ($x.rank === 4) {
+            reshapedTo5D = true;
+            x5D = $x.as5D(1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]);
+        }
+        assert(x5D.rank === 5, "Error in conv3d: input must be rank 5, but got rank " + x5D.rank + ".");
+        assert($filter.rank === 5, "Error in conv3d: filter must be rank 5, but got rank " +
+            ($filter.rank + "."));
+        assert(x5D.shape[4] === $filter.shape[3], "Error in conv3d: depth of input (" + x5D.shape[4] + ") must match " +
+            ("input depth for filter " + $filter.shape[3] + "."));
+        assert(eitherStridesOrDilationsAreOne$1(strides, dilations), 'Error in conv3D: Either strides or dilations must be 1. ' +
+            ("Got strides " + strides + " and dilations '" + dilations + "'"));
+        assert(dataFormat === 'NHWC', "Error in conv3d: got dataFormat of " + dataFormat + " but only NHWC is currently supported.");
+        var convInfo = computeConv3DInfo(x5D.shape, $filter.shape, strides, dilations, pad);
+        var grad = function (dy) {
+            assert(tupleValuesAreOne$1(dilations), 'Error in gradient of conv3D: dilation rates greater than 1 are not' +
+                ("yet supported in gradients. Got dilations '" + dilations + "'"));
+            return {
+                x: function () { return conv3dDerInput_(x5D.shape, dy, $filter, strides, pad); },
+                $filter: function () { return conv3dDerFilter_(x5D, dy, $filter.shape, strides, pad); }
+            };
+        };
+        var res = ENV.engine.runKernel(function (backend) { return backend.conv3d(x5D, $filter, convInfo); }, { x: x5D, $filter: $filter }, grad);
+        if (reshapedTo5D) {
+            return res.as4D(res.shape[1], res.shape[2], res.shape[3], res.shape[4]);
+        }
+        return res;
+    }
+    function conv3dDerInput_(xShape, dy, filter, strides, pad) {
+        assert(xShape.length === dy.rank, "Length of inShape " +
+            ("(" + xShape.length + ") and rank of dy (" + dy.rank + ") must match"));
+        var xShape5D = xShape;
+        var dy5D = dy;
+        var reshapedTo5D = false;
+        if (dy.rank === 4) {
+            reshapedTo5D = true;
+            dy5D = dy.as5D(1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]);
+            xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];
+        }
+        var inDepth = xShape5D[4];
+        var outDepth = dy5D.shape[4];
+        assert(xShape5D.length === 5, "Error in conv3dDerInput: inShape must be length 5, but got length " +
+            (xShape5D.length + "."));
+        assert(dy5D.rank === 5, "Error in conv3dDerInput: dy must be rank 5, but got " +
+            ("rank " + dy5D.rank));
+        assert(filter.rank === 5, "Error in conv3dDerInput: filter must be rank 5, but got " +
+            ("rank " + filter.rank));
+        assert(inDepth === filter.shape[3], "Error in conv3dDerInput: depth of input (" + inDepth + ") must " +
+            ("match input depth for filter " + filter.shape[3] + "."));
+        assert(outDepth === filter.shape[4], "Error in conv3dDerInput: depth of output (" + outDepth + ") must " +
+            ("match output depth for filter " + filter.shape[4] + "."));
+        var dilations = 1;
+        var convInfo = computeConv3DInfo(xShape5D, filter.shape, strides, dilations, pad);
+        var res = ENV.engine.runKernel(function (backend) { return backend.conv3dDerInput(dy5D, filter, convInfo); }, { dy5D: dy5D });
+        if (reshapedTo5D) {
+            return res.as4D(res.shape[1], res.shape[2], res.shape[3], res.shape[4]);
+        }
+        return res;
+    }
+    function conv3dDerFilter_(x, dy, filterShape, strides, pad) {
+        var x5D = x;
+        if (x.rank === 4) {
+            x5D = x.as5D(1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]);
+        }
+        var dy5D = dy;
+        if (dy5D.rank === 4) {
+            dy5D = dy.as5D(1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]);
+        }
+        assert(x5D.rank === 5, "Error in conv3dDerFilter: input must be rank 5, but got shape " +
+            (x5D.shape + "."));
+        assert(dy5D.rank === 5, "Error in conv3dDerFilter: dy must be rank 5, but got shape " +
+            (dy5D.shape + "."));
+        assert(filterShape.length === 5, "Error in conv3dDerFilter: filterShape must be length 5, but got " +
+            (filterShape + "."));
+        assert(x5D.shape[4] === filterShape[3], "Error in conv3dDerFilter: depth of input " + x5D.shape[4] + ") must " +
+            ("match input depth in filter (" + filterShape[3] + "."));
+        assert(dy5D.shape[4] === filterShape[4], "Error in conv3dDerFilter: depth of dy (" + dy5D.shape[4] + ") must " +
+            ("match output depth for filter (" + filterShape[4] + ")."));
+        var dilations = 1;
+        var convInfo = computeConv3DInfo(x5D.shape, filterShape, strides, dilations, pad);
+        return ENV.engine.runKernel(function (backend) { return backend.conv3dDerFilter(x5D, dy5D, convInfo); }, { x5D: x5D, dy5D: dy5D });
+    }
+    var conv1d = op({ conv1d_: conv1d_ });
+    var conv2d = op({ conv2d_: conv2d_ });
+    var conv3d = op({ conv3d_: conv3d_ });
+    var conv2dDerFilter = op({ conv2dDerFilter_: conv2dDerFilter_ });
+    var depthwiseConv2d = op({ depthwiseConv2d_: depthwiseConv2d_ });
+    var separableConv2d = op({ separableConv2d_: separableConv2d_ });
+    var conv2dTranspose = op({ conv2dTranspose_: conv2dTranspose_ });
+
+    function reverse1d_(x) {
+        var $x = convertToTensor(x, 'x', 'reverse');
+        assert($x.rank === 1, "Error in reverse1D: x must be rank 1 but got\n             rank " + $x.rank + ".");
+        return reverse($x, 0);
+    }
+    function reverse2d_(x, axis) {
+        var $x = convertToTensor(x, 'x', 'reverse');
+        assert($x.rank === 2, "Error in reverse2D: x must be rank 2 but got\n             rank " + $x.rank + ".");
+        return reverse($x, axis);
+    }
+    function reverse3d_(x, axis) {
+        var $x = convertToTensor(x, 'x', 'reverse');
+        assert($x.rank === 3, "Error in reverse3D: x must be rank 3 but got\n             rank " + $x.rank + ".");
+        return reverse($x, axis);
+    }
+    function reverse4d_(x, axis) {
+        var $x = convertToTensor(x, 'x', 'reverse');
+        assert($x.rank === 4, "Error in reverse4D: x must be rank 4 but got\n             rank " + $x.rank + ".");
+        return reverse($x, axis);
+    }
+    function reverse_(x, axis) {
+        var $x = convertToTensor(x, 'x', 'reverse');
+        if ($x.rank === 0) {
+            return $x.clone();
+        }
+        var axes = parseAxisParam(axis, $x.shape);
+        var grad = function (dy) {
+            return { $x: function () { return dy.reverse(axes); } };
+        };
+        var res = ENV.engine.runKernel(function (backend) { return backend.reverse($x, axes); }, { $x: $x }, grad);
+        return res.reshapeAs($x);
+    }
+    var reverse = op({ reverse_: reverse_ });
+    var reverse1d = op({ reverse1d_: reverse1d_ });
+    var reverse2d = op({ reverse2d_: reverse2d_ });
+    var reverse3d = op({ reverse3d_: reverse3d_ });
+    var reverse4d = op({ reverse4d_: reverse4d_ });
+
+    function maxPoolImpl_(x, filterSize, strides, dilations, pad$$1, dimRoundingMode) {
+        var $x = convertToTensor(x, 'x', 'maxPool');
+        var x4D = $x;
+        var reshapedTo4D = false;
+        if ($x.rank === 3) {
+            reshapedTo4D = true;
+            x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
+        }
+        if (dilations == null) {
+            dilations = [1, 1];
+        }
+        assert(x4D.rank === 4, "Error in maxPool: input must be rank 4 but got rank " + x4D.rank + ".");
+        assert(eitherStridesOrDilationsAreOne(strides, dilations), 'Error in maxPool: Either strides or dilations must be 1. ' +
+            ("Got strides " + strides + " and dilations '" + dilations + "'"));
+        if (dimRoundingMode != null) {
+            assert(isInt(pad$$1), "Error in maxPool: pad must be an integer when using, " +
+                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad$$1 + "."));
+        }
+        var convInfo = computePool2DInfo(x4D.shape, filterSize, strides, dilations, pad$$1, dimRoundingMode);
+        var grad = function (dy, saved) {
+            var y4D = saved[0];
+            return {
+                x: function () { return maxPoolBackprop(dy, x4D, y4D, filterSize, strides, dilations, pad$$1); }
+            };
+        };
+        var res = ENV.engine.runKernel(function (backend, save) { return save(backend.maxPool(x4D, convInfo)); }, { x: x4D }, grad);
+        if (reshapedTo4D) {
+            return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
+        }
+        return res;
+    }
+    function maxPool_(x, filterSize, strides, pad$$1, dimRoundingMode) {
+        return maxPoolImpl_(x, filterSize, strides, 1, pad$$1, dimRoundingMode);
+    }
+    function avgPoolImpl_(x, filterSize, strides, dilations, pad$$1, dimRoundingMode) {
+        var $x = convertToTensor(x, 'x', 'avgPool', 'float32');
+        if (dilations == null) {
+            dilations = [1, 1];
+        }
+        assert(eitherStridesOrDilationsAreOne(strides, dilations), 'Error in avgPool: Either strides or dilations must be 1. ' +
+            ("Got strides " + strides + " and dilations '" + dilations + "'"));
+        var x4D = $x;
+        var reshapedTo4D = false;
+        if ($x.rank === 3) {
+            reshapedTo4D = true;
+            x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
+        }
+        assert(x4D.rank === 4, "Error in avgPool: x must be rank 4 but got rank " + x4D.rank + ".");
+        if (dimRoundingMode != null) {
+            assert(isInt(pad$$1), "Error in avgPool: pad must be an integer when using, " +
+                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad$$1 + "."));
+        }
+        var convInfo = computePool2DInfo(x4D.shape, filterSize, strides, dilations, pad$$1);
+        var grad = function (dy) {
+            return {
+                x: function () { return avgPoolBackprop(dy, x4D, filterSize, strides, dilations, pad$$1); }
+            };
+        };
+        var res = ENV.engine.runKernel(function (backend) { return backend.avgPool(x4D, convInfo); }, { x: x4D }, grad);
+        res = res.cast($x.dtype);
+        if (reshapedTo4D) {
+            return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
+        }
+        return res;
+    }
+    function avgPool_(x, filterSize, strides, pad$$1, dimRoundingMode) {
+        return avgPoolImpl_(x, filterSize, strides, 1, pad$$1, dimRoundingMode);
+    }
+    function pool_(input, windowShape, poolingType, pad$$1, dilations, strides) {
+        if (dilations == null) {
+            dilations = [1, 1];
+        }
+        if (strides == null) {
+            strides = 1;
+        }
+        if (pad$$1 === 0) {
+            pad$$1 = 'valid';
+        }
+        var $x = convertToTensor(input, 'x', 'maxPool');
+        var x4D = $x;
+        var reshapedTo4D = false;
+        if ($x.rank === 3) {
+            reshapedTo4D = true;
+            x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
+        }
+        assert(eitherStridesOrDilationsAreOne(strides, dilations), 'Error in pool: Either strides or dilations must be 1. ' +
+            ("Got strides " + strides + " and dilations '" + dilations + "'"));
+        var convInfo = computePool2DInfo(x4D.shape, windowShape, strides, dilations, pad$$1);
+        var dilation = [convInfo.dilationHeight, convInfo.dilationWidth];
+        var basePadding;
+        if (pad$$1 === 'same') {
+            basePadding = withSpaceToBatchBasePaddings([convInfo.filterHeight, convInfo.filterWidth], dilation);
+        }
+        else {
+            basePadding = [[0, 0], [0, 0]];
+        }
+        var isDilationOne = dilation[0] === 1 && dilation[1] === 1;
+        var _a = requiredSpaceToBatchPaddings([convInfo.inHeight, convInfo.inWidth], dilation, basePadding), adjustedPadding = _a[0], adjustedCrops = _a[1];
+        var convertedPad = isDilationOne ? pad$$1 : 'valid';
+        var convertedX = isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);
+        var forwardOp = poolingType === 'avg' ?
+            function () { return avgPoolImpl_(convertedX, windowShape, strides, 1, convertedPad); } :
+            function () { return maxPoolImpl_(convertedX, windowShape, strides, 1, convertedPad); };
+        var y = forwardOp();
+        var res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);
+        if (reshapedTo4D) {
+            return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
+        }
+        return res;
+    }
+    function maxPoolBackprop(dy, input, output, filterSize, strides, dilations, pad$$1, dimRoundingMode) {
+        var $dy = convertToTensor(dy, 'dy', 'maxPoolBackprop');
+        var $input = convertToTensor(input, 'input', 'maxPoolBackprop');
+        var $output = convertToTensor(output, 'output', 'maxPoolBackprop');
+        assert($input.rank === $dy.rank, "Rank of input (" + $input.rank + ") does not match rank of dy (" + $dy.rank + ")");
+        if (dilations == null) {
+            dilations = [1, 1];
+        }
+        assert(eitherStridesOrDilationsAreOne(strides, dilations), 'Error in maxPoolBackProp: Either strides or dilations must be 1. ' +
+            ("Got strides " + strides + " and dilations '" + dilations + "'"));
+        assert($dy.rank === 4, "Error in maxPoolBackprop: dy must be rank 4 but got rank " +
+            ($dy.rank + "."));
+        assert($input.rank === 4, "Error in maxPoolBackprop: input must be rank 4 but got rank " +
+            ($input.rank + "."));
+        if (dimRoundingMode != null) {
+            assert(isInt(pad$$1), "Error in maxPoolBackprop: pad must be an integer when using, " +
+                ("dimRoundingMode " + dimRoundingMode + " but got pad " + pad$$1 + "."));
+        }
+        var convInfo = computePool2DInfo($input.shape, filterSize, strides, dilations, pad$$1, dimRoundingMode);
+        var res = ENV.engine.runKernel(function (backend) { return backend.maxPoolBackprop($dy, $input, $output, convInfo); }, { $dy: $dy, $input: $input });
+        return res;
+    }
+    function avgPoolBackprop(dy, input, filterSize, strides, dilations, pad$$1) {
+        var $dy = convertToTensor(dy, 'dy', 'avgPoolBackprop');
+        var $input = convertToTensor(input, 'input', 'avgPoolBackprop');
+        assert($input.rank === $dy.rank, "Rank of input (" + $input.rank + ") does not match rank of dy (" + $dy.rank + ")");
+        if (dilations == null) {
+            dilations = [1, 1];
+        }
+        assert(eitherStridesOrDilationsAreOne(strides, dilations), 'Error in avgPoolBackprop: Either strides or dilations must be 1. ' +
+            ("Got strides " + strides + " and dilations '" + dilations + "'"));
+        var input4D = $input;
+        var dy4D = $dy;
+        var reshapedTo4D = false;
+        if ($input.rank === 3) {
+            reshapedTo4D = true;
+            input4D = $input.as4D(1, $input.shape[0], $input.shape[1], $input.shape[2]);
+            dy4D = $dy.as4D(1, $dy.shape[0], $dy.shape[1], $dy.shape[2]);
+        }
+        assert(dy4D.rank === 4, "Error in avgPoolBackprop: dy must be rank 4 but got rank " +
+            (dy4D.rank + "."));
+        assert(input4D.rank === 4, "Error in avgPoolBackprop: input must be rank 4 but got rank " +
+            (input4D.rank + "."));
+        var convInfo = computePool2DInfo(input4D.shape, filterSize, strides, dilations, pad$$1);
+        var res = ENV.engine.runKernel(function (backend) { return backend.avgPoolBackprop(dy4D, input4D, convInfo); }, { dy4D: dy4D, input4D: input4D });
+        if (reshapedTo4D) {
+            return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
+        }
+        return res;
+    }
+    function requiredSpaceToBatchPaddings(inputShape, blockShape, basePadding) {
+        var padStart = basePadding.map(function (b) { return b[0]; });
+        var origPadEnd = basePadding.map(function (b) { return b[1]; });
+        var fullInputShape = inputShape.concat(padStart, origPadEnd);
+        var padEndExtra = blockShape.map(function (b, i) { return (b - fullInputShape[i] % b) % b; });
+        var padEnd = origPadEnd.map(function (s, i) { return s + padEndExtra[i]; });
+        var paddings = blockShape.map(function (_, i) { return [padStart[i], padEnd[i]]; });
+        var crops = blockShape.map(function (_, i) { return [0, padEndExtra[i]]; });
+        return [paddings, crops];
+    }
+    function withSpaceToBatchBasePaddings(filterShape, dilation) {
+        var dilatedFilterShape = filterShape.map(function (s, i) {
+            return s + (s - 1) * (dilation[i] - 1);
+        });
+        var padExtraShape = dilatedFilterShape.map(function (s) { return s - 1; });
+        var padExtraStart = padExtraShape.map(function (s) { return Math.floor(s / 2); });
+        var padExtraEnd = padExtraShape.map(function (s, i) { return s - padExtraStart[i]; });
+        return padExtraShape.map(function (_, i) {
+            return [padExtraStart[i], padExtraEnd[i]];
+        });
+    }
+    var maxPool = op({ maxPool_: maxPool_ });
+    var avgPool = op({ avgPool_: avgPool_ });
+    var pool = op({ pool_: pool_ });
+
+    function slice1d_(x, begin, size) {
+        var $x = convertToTensor(x, 'x', 'slice1d');
+        assert($x.rank === 1, "slice1d expects a rank-1 tensor, but got a rank-" + $x.rank + " tensor");
+        return slice($x, [begin], [size]);
+    }
+    function slice2d_(x, begin, size) {
+        var $x = convertToTensor(x, 'x', 'slice2d');
+        assert($x.rank === 2, "slice2d expects a rank-2 tensor, but got a rank-" + $x.rank + " tensor");
+        return slice($x, begin, size);
+    }
+    function slice3d_(x, begin, size) {
+        var $x = convertToTensor(x, 'x', 'slice3d');
+        assert($x.rank === 3, "slice3d expects a rank-3 tensor, but got a rank-" + $x.rank + " tensor");
+        return slice($x, begin, size);
+    }
+    function slice4d_(x, begin, size) {
+        var $x = convertToTensor(x, 'x', 'slice4d');
+        assert($x.rank === 4, "slice4d expects a rank-4 tensor, but got a rank-" + $x.rank + " tensor");
+        return slice($x, begin, size);
+    }
+    function slice_(x, begin, size) {
+        var $x = convertToTensor(x, 'x', 'slice');
+        if ($x.rank === 0) {
+            throw new Error('Slicing scalar is not possible');
+        }
+        var begin_;
+        if (typeof begin === 'number') {
+            begin_ = [begin].concat(new Array($x.rank - 1).fill(0));
+        }
+        else if (begin.length < $x.rank) {
+            begin_ = begin.concat(new Array($x.rank - begin.length).fill(0));
+        }
+        else {
+            begin_ = begin.slice();
+        }
+        var size_;
+        if (size == null) {
+            size_ = new Array($x.rank).fill(-1);
+        }
+        else if (typeof size === 'number') {
+            size_ = [size].concat(new Array($x.rank - 1).fill(-1));
+        }
+        else if (size.length < $x.rank) {
+            size_ = size.concat(new Array($x.rank - size.length).fill(-1));
+        }
+        else {
+            size_ = size;
+        }
+        size_ = size_.map(function (d, i) {
+            if (d >= 0) {
+                return d;
+            }
+            else {
+                assert(d === -1, 'Bad value in size');
+                return $x.shape[i] - begin_[i];
+            }
+        });
+        assertParamsValid($x, begin_, size_);
+        var inputShape = $x.shape;
+        var grad = function (dy) {
+            var paddings = [];
+            for (var i = 0; i < dy.rank; i++) {
+                paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);
+            }
+            return { $x: function () { return dy.pad(paddings); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.slice($x, begin_, size_); }, { $x: $x }, grad);
+    }
+    var slice = op({ slice_: slice_ });
+    var slice1d = op({ slice1d_: slice1d_ });
+    var slice2d = op({ slice2d_: slice2d_ });
+    var slice3d = op({ slice3d_: slice3d_ });
+    var slice4d = op({ slice4d_: slice4d_ });
+
+    function logSumExp_(x, axis, keepDims) {
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        var $x = convertToTensor(x, 'x', 'logSumExp');
+        var axes = parseAxisParam(axis, $x.shape);
+        var xMax = $x.max(axes, true);
+        var a = $x.sub(xMax);
+        var b = a.exp();
+        var c = b.sum(axes);
+        var d = c.log();
+        var res = xMax.reshape(d.shape).add(d);
+        if (keepDims) {
+            var newShape = expandShapeToKeepDim(res.shape, axes);
+            return res.reshape(newShape);
+        }
+        return res;
+    }
+    function sum_(x, axis, keepDims) {
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        var $x = convertToTensor(x, 'x', 'sum');
+        if ($x.dtype === 'bool') {
+            $x = $x.toInt();
+        }
+        var axes = parseAxisParam(axis, $x.shape);
+        var customOp = customGrad(function (x) {
+            var permutation = getAxesPermutation(axes, x.rank);
+            var reductionAxes = axes;
+            var permutedX = x;
+            if (permutation != null) {
+                permutedX = x.transpose(permutation);
+                reductionAxes = getInnerMostAxes(reductionAxes.length, x.rank);
+            }
+            var value = ENV.engine.runKernel(function (backend) { return backend.sum(permutedX, reductionAxes); }, { permutedX: permutedX });
+            if (keepDims) {
+                var newShape = expandShapeToKeepDim(value.shape, axes);
+                value = value.reshape(newShape);
+            }
+            var gradFunc = function (dy) {
+                var expandedDyShape = x.shape.slice();
+                axes.forEach(function (axis) {
+                    expandedDyShape[axis] = 1;
+                });
+                var expandedDy = dy.reshape(expandedDyShape);
+                var derX = expandedDy.mul(ones$1(x.shape, 'float32'));
+                return derX;
+            };
+            return { value: value, gradFunc: gradFunc };
+        });
+        return customOp($x);
+    }
+    function prod_(x, axis, keepDims) {
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        var $x = convertToTensor(x, 'x', 'prod');
+        if ($x.dtype === 'bool') {
+            $x = $x.toInt();
+        }
+        var axes = parseAxisParam(axis, $x.shape);
+        var permutation = getAxesPermutation(axes, $x.rank);
+        var reductionAxes = axes;
+        var permutedX = $x;
+        if (permutation != null) {
+            permutedX = $x.transpose(permutation);
+            reductionAxes = getInnerMostAxes(reductionAxes.length, $x.rank);
+        }
+        var value = ENV.engine.runKernel(function (backend) { return backend.prod(permutedX, reductionAxes); }, { permutedX: permutedX });
+        if (keepDims) {
+            var newShape = expandShapeToKeepDim(value.shape, axes);
+            value = value.reshape(newShape);
+        }
+        return value;
+    }
+    function mean_(x, axis, keepDims) {
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        var $x = convertToTensor(x, 'x', 'mean');
+        var axes = parseAxisParam(axis, $x.shape);
+        var shapes = computeOutAndReduceShapes($x.shape, axes);
+        var reduceShape = shapes[1];
+        var reduceSize = sizeFromShape(reduceShape);
+        var customOp = customGrad(function (x) {
+            var reduceSizeScalar = scalar(reduceSize);
+            var xReduce = reduceSizeScalar.dtype === x.dtype ? x : x.cast(reduceSizeScalar.dtype);
+            var res = xReduce.div(reduceSizeScalar);
+            var value = res.sum(axis, keepDims);
+            var gradFunc = function (dy) {
+                var expandedDyShape = x.shape.slice();
+                axes.forEach(function (axis) {
+                    expandedDyShape[axis] = 1;
+                });
+                var expandedDy = dy.reshape(expandedDyShape);
+                var derX = expandedDy.mul(ones$1(x.shape, 'float32')).div(reduceSizeScalar);
+                return derX;
+            };
+            return { value: value, gradFunc: gradFunc };
+        });
+        return customOp($x);
+    }
+    function gradForMinAndMax(dy, saved, xOrig, origAxes, permutedAxes) {
+        var y = saved[0];
+        if (y.rank < xOrig.rank) {
+            y = y.reshape(expandShapeToKeepDim(y.shape, origAxes));
+        }
+        if (dy.rank < xOrig.rank) {
+            dy = dy.reshape(expandShapeToKeepDim(dy.shape, origAxes));
+        }
+        return {
+            $x: function () {
+                var dx = dy.mul(xOrig.equal(y).cast(dy.dtype));
+                return permutedAxes == null ? dx : dx.transpose(permutedAxes);
+            }
+        };
+    }
+    function min_(x, axis, keepDims) {
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        var $x = convertToTensor(x, 'x', 'min');
+        var xOrig = $x;
+        var origAxes = parseAxisParam(axis, $x.shape);
+        var axes = origAxes;
+        var permutedAxes = getAxesPermutation(axes, $x.rank);
+        if (permutedAxes != null) {
+            $x = $x.transpose(permutedAxes);
+            axes = getInnerMostAxes(axes.length, $x.rank);
+        }
+        var grad$$1 = function (dy, saved) {
+            return gradForMinAndMax(dy, saved, xOrig, origAxes, permutedAxes);
+        };
+        var res = ENV.engine.runKernel(function (backend, save) { return save(backend.min($x, axes)); }, { $x: $x }, grad$$1);
+        if (keepDims) {
+            var newShape = expandShapeToKeepDim(res.shape, origAxes);
+            res = res.reshape(newShape);
+        }
+        return res;
+    }
+    function max_(x, axis, keepDims) {
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        var $x = convertToTensor(x, 'x', 'max');
+        var xOrig = $x;
+        var origAxes = parseAxisParam(axis, $x.shape);
+        var axes = origAxes;
+        var permutedAxes = getAxesPermutation(axes, $x.rank);
+        if (permutedAxes != null) {
+            $x = $x.transpose(permutedAxes);
+            axes = getInnerMostAxes(axes.length, $x.rank);
+        }
+        var grad$$1 = function (dy, saved) {
+            return gradForMinAndMax(dy, saved, xOrig, origAxes, permutedAxes);
+        };
+        var res = ENV.engine.runKernel(function (backend, save) { return save(backend.max($x, axes)); }, { $x: $x }, grad$$1);
+        if (keepDims) {
+            var newShape = expandShapeToKeepDim(res.shape, origAxes);
+            res = res.reshape(newShape);
+        }
+        return res;
+    }
+    function argMin_(x, axis) {
+        if (axis === void 0) { axis = 0; }
+        var $x = convertToTensor(x, 'x', 'argMin');
+        if (axis == null) {
+            axis = 0;
+        }
+        var axes = parseAxisParam(axis, $x.shape);
+        var permutedAxes = getAxesPermutation(axes, $x.rank);
+        if (permutedAxes != null) {
+            $x = $x.transpose(permutedAxes);
+            axes = getInnerMostAxes(axes.length, $x.rank);
+        }
+        var grad$$1 = function (dy) {
+            return { $x: function () { return zerosLike($x); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.argMin($x, axes[0]); }, { $x: $x }, grad$$1);
+    }
+    function argMax_(x, axis) {
+        if (axis === void 0) { axis = 0; }
+        var $x = convertToTensor(x, 'x', 'argMax');
+        if (axis == null) {
+            axis = 0;
+        }
+        var axes = parseAxisParam(axis, $x.shape);
+        var permutedAxes = getAxesPermutation(axes, $x.rank);
+        if (permutedAxes != null) {
+            $x = $x.transpose(permutedAxes);
+            axes = getInnerMostAxes(axes.length, $x.rank);
+        }
+        var grad$$1 = function (dy) {
+            return { $x: function () { return zerosLike($x); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.argMax($x, axes[0]); }, { $x: $x }, grad$$1);
+    }
+    function all_(x, axis, keepDims) {
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        var $x = convertToTensor(x, 'x', 'all', 'bool');
+        var origAxes = parseAxisParam(axis, $x.shape);
+        var axes = origAxes;
+        var permutedAxes = getAxesPermutation(axes, $x.rank);
+        if (permutedAxes != null) {
+            $x = $x.transpose(permutedAxes);
+            axes = getInnerMostAxes(axes.length, $x.rank);
+        }
+        var res = ENV.engine.runKernel(function (backend) { return backend.all($x, axes); }, { $x: $x });
+        if (keepDims) {
+            var newShape = expandShapeToKeepDim(res.shape, origAxes);
+            return res.reshape(newShape);
+        }
+        return res;
+    }
+    function any_(x, axis, keepDims) {
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        var $x = convertToTensor(x, 'x', 'any', 'bool');
+        var origAxes = parseAxisParam(axis, $x.shape);
+        var axes = origAxes;
+        var permutedAxes = getAxesPermutation(axes, $x.rank);
+        if (permutedAxes != null) {
+            $x = $x.transpose(permutedAxes);
+            axes = getInnerMostAxes(axes.length, $x.rank);
+        }
+        var res = ENV.engine.runKernel(function (backend) { return backend.any($x, axes); }, { $x: $x });
+        if (keepDims) {
+            var newShape = expandShapeToKeepDim(res.shape, origAxes);
+            return res.reshape(newShape);
+        }
+        return res;
+    }
+    function moments_(x, axis, keepDims) {
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        x = convertToTensor(x, 'x', 'moments');
+        var axes = parseAxisParam(axis, x.shape);
+        var mean = x.mean(axes, keepDims);
+        var keepDimsShape = mean.shape;
+        if (!keepDims) {
+            keepDimsShape = expandShapeToKeepDim(mean.shape, axes);
+        }
+        var devSquared = x.toFloat().sub(mean.reshape(keepDimsShape)).square();
+        var variance = devSquared.mean(axes, keepDims);
+        return { mean: mean, variance: variance };
+    }
+    var all = op({ all_: all_ });
+    var any = op({ any_: any_ });
+    var argMax = op({ argMax_: argMax_ });
+    var argMin = op({ argMin_: argMin_ });
+    var logSumExp = op({ logSumExp_: logSumExp_ });
+    var max = op({ max_: max_ });
+    var mean = op({ mean_: mean_ });
+    var min = op({ min_: min_ });
+    var moments = op({ moments_: moments_ });
+    var sum$1 = op({ sum_: sum_ });
+    var prod = op({ prod_: prod_ });
+
+    function notEqual_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'notEqual');
+        var $b = convertToTensor(b, 'b', 'notEqual');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        assertAndGetBroadcastShape($a.shape, $b.shape);
+        return ENV.engine.runKernel(function (backend) { return backend.notEqual($a, $b); }, { $a: $a, $b: $b });
+    }
+    function notEqualStrict_(a, b) {
+        var $a = convertToTensor(a, 'a', 'notEqualStrict');
+        var $b = convertToTensor(b, 'b', 'notEqualStrict');
+        assertShapesMatch($a.shape, $b.shape, 'Error in notEqualStrict: ');
+        return $a.notEqual($b);
+    }
+    function less_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'less');
+        var $b = convertToTensor(b, 'b', 'less');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        assertAndGetBroadcastShape($a.shape, $b.shape);
+        return ENV.engine.runKernel(function (backend) { return backend.less($a, $b); }, { $a: $a, $b: $b });
+    }
+    function lessStrict_(a, b) {
+        var $a = convertToTensor(a, 'a', 'lessStrict');
+        var $b = convertToTensor(b, 'b', 'lessStrict');
+        assertShapesMatch($a.shape, $b.shape, 'Error in lessStrict: ');
+        return $a.less($b);
+    }
+    function equal_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'equal');
+        var $b = convertToTensor(b, 'b', 'equal');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        assertAndGetBroadcastShape($a.shape, $b.shape);
+        return ENV.engine.runKernel(function (backend) { return backend.equal($a, $b); }, { $a: $a, $b: $b });
+    }
+    function equalStrict_(a, b) {
+        var $a = convertToTensor(a, 'a', 'equalStrict');
+        var $b = convertToTensor(b, 'b', 'equalStrict');
+        assertShapesMatch($a.shape, $b.shape, 'Error in equalStrict: ');
+        return $a.equal($b);
+    }
+    function lessEqual_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'lessEqual');
+        var $b = convertToTensor(b, 'b', 'lessEqual');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        assertAndGetBroadcastShape($a.shape, $b.shape);
+        return ENV.engine.runKernel(function (backend) { return backend.lessEqual($a, $b); }, { $a: $a, $b: $b });
+    }
+    function lessEqualStrict_(a, b) {
+        var $a = convertToTensor(a, 'a', 'lessEqualStrict');
+        var $b = convertToTensor(b, 'b', 'lessEqualStrict');
+        assertShapesMatch($a.shape, $b.shape, 'Error in lessEqualStrict: ');
+        return $a.lessEqual($b);
+    }
+    function greater_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'greater');
+        var $b = convertToTensor(b, 'b', 'greater');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        assertAndGetBroadcastShape($a.shape, $b.shape);
+        return ENV.engine.runKernel(function (backend) { return backend.greater($a, $b); }, { $a: $a, $b: $b });
+    }
+    function greaterStrict_(a, b) {
+        var $a = convertToTensor(a, 'a', 'greaterStrict');
+        var $b = convertToTensor(b, 'b', 'greaterStrict');
+        assertShapesMatch($a.shape, $b.shape, 'Error in greaterStrict: ');
+        return $a.greater($b);
+    }
+    function greaterEqual_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'greaterEqual');
+        var $b = convertToTensor(b, 'b', 'greaterEqual');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        assertAndGetBroadcastShape($a.shape, $b.shape);
+        var grad = function (dy) {
+            return { $a: function () { return zerosLike($a); }, $b: function () { return zerosLike($b); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.greaterEqual($a, $b); }, { $a: $a, $b: $b }, grad);
+    }
+    function greaterEqualStrict_(a, b) {
+        var $a = convertToTensor(a, 'a', 'greaterEqualStrict');
+        var $b = convertToTensor(b, 'b', 'greaterEqualStrict');
+        assertShapesMatch($a.shape, $b.shape, 'Error in greaterEqualStrict: ');
+        return $a.greaterEqual($b);
+    }
+    var equal = op({ equal_: equal_ });
+    var equalStrict = op({ equalStrict_: equalStrict_ });
+    var greater = op({ greater_: greater_ });
+    var greaterEqual = op({ greaterEqual_: greaterEqual_ });
+    var greaterEqualStrict = op({ greaterEqualStrict_: greaterEqualStrict_ });
+    var greaterStrict = op({ greaterStrict_: greaterStrict_ });
+    var less = op({ less_: less_ });
+    var lessEqual = op({ lessEqual_: lessEqual_ });
+    var lessEqualStrict = op({ lessEqualStrict_: lessEqualStrict_ });
+    var lessStrict = op({ lessStrict_: lessStrict_ });
+    var notEqual = op({ notEqual_: notEqual_ });
+    var notEqualStrict = op({ notEqualStrict_: notEqualStrict_ });
+
+    function add_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'add');
+        var $b = convertToTensor(b, 'b', 'add');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        var outShape = assertAndGetBroadcastShape($a.shape, $b.shape);
+        var der = function (dy) {
+            var derA = function () {
+                var res = dy;
+                var reduceAxes = getReductionAxes($a.shape, outShape);
+                if (reduceAxes.length > 0) {
+                    res = res.sum(reduceAxes);
+                }
+                return res.reshape($a.shape);
+            };
+            var derB = function () {
+                var res = dy;
+                var reduceAxes = getReductionAxes($b.shape, outShape);
+                if (reduceAxes.length > 0) {
+                    res = res.sum(reduceAxes);
+                }
+                return res.reshape($b.shape);
+            };
+            return { $a: derA, $b: derB };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.add($a, $b); }, { $a: $a, $b: $b }, der);
+    }
+    function addN_(tensors) {
+        assert(Array.isArray(tensors), function () { return 'The argument passed to tf.addN() must be a list of tensors'; });
+        assert(tensors.length >= 1, function () { return "Must pass at least one tensor to tf.addN(), but got " +
+            ("" + tensors.length); });
+        var $tensors = tensors.map(function (t, i) { return convertToTensor(t, "tensors" + i, 'addN'); });
+        var firstTensor = $tensors[0];
+        $tensors.forEach(function (t) {
+            if (t.dtype !== firstTensor.dtype) {
+                throw new Error('All tensors passed to tf.addN() must have the same dtype');
+            }
+        });
+        $tensors.forEach(function (t) {
+            if (!arraysEqual(t.shape, firstTensor.shape)) {
+                throw new Error('All tensors passed to tf.addN() must have the same shape');
+            }
+        });
+        var der = function (dy) {
+            var ders = {};
+            $tensors.forEach(function (t, i) {
+                ders[i] = function () { return dy.clone(); };
+            });
+            return ders;
+        };
+        var inputs = $tensors;
+        return ENV.engine.runKernel(function (backend) { return backend.addN($tensors); }, inputs, der);
+    }
+    function addStrict_(a, b) {
+        var $a = convertToTensor(a, 'a', 'addStrict');
+        var $b = convertToTensor(b, 'b', 'addStrict');
+        assertShapesMatch($a.shape, $b.shape, 'Error in addStrict: ');
+        return $a.add($b);
+    }
+    function sub_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'sub');
+        var $b = convertToTensor(b, 'b', 'sub');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        var outShape = assertAndGetBroadcastShape($a.shape, $b.shape);
+        var der = function (dy) {
+            var derA = function () {
+                var res = dy;
+                var reduceAxes = getReductionAxes($a.shape, outShape);
+                if (reduceAxes.length > 0) {
+                    res = res.sum(reduceAxes);
+                }
+                return res.reshape($a.shape);
+            };
+            var derB = function () {
+                var res = dy;
+                var reduceAxes = getReductionAxes($b.shape, outShape);
+                if (reduceAxes.length > 0) {
+                    res = res.sum(reduceAxes);
+                }
+                return res.neg().reshape($b.shape);
+            };
+            return { $a: derA, $b: derB };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.subtract($a, $b); }, { $a: $a, $b: $b }, der);
+    }
+    function subStrict_(a, b) {
+        var $a = convertToTensor(a, 'a', 'subStrict');
+        var $b = convertToTensor(b, 'b', 'subStrict');
+        assertShapesMatch($a.shape, $b.shape, 'Error in subStrict: ');
+        return $a.sub($b);
+    }
+    function pow_(base, exp$$1) {
+        var $base = convertToTensor(base, 'base', 'pow');
+        var $exp = convertToTensor(exp$$1, 'exp', 'pow');
+        var outShape = assertAndGetBroadcastShape($base.shape, $exp.shape);
+        base = $base.cast(upcastType($base.dtype, $exp.dtype));
+        exp$$1 = $exp.cast(upcastType($base.dtype, $exp.dtype));
+        var grad = function (dy, saved) {
+            var y = saved[0];
+            var derBase = function () {
+                var expFloat = $exp.toFloat();
+                var res = dy.mul(expFloat.mul($base.pow(expFloat.sub(scalar(1)))));
+                var reduceAxes = getReductionAxes($base.shape, outShape);
+                if (reduceAxes.length > 0) {
+                    res = res.sum(reduceAxes);
+                }
+                return res.reshape($base.shape);
+            };
+            var derExp = function () {
+                var res = dy.mul(y.mul($base.log()).toFloat());
+                var reduceAxes = getReductionAxes($exp.shape, outShape);
+                if (reduceAxes.length > 0) {
+                    res = res.sum(reduceAxes);
+                }
+                return res.reshape($exp.shape);
+            };
+            return { $base: derBase, $exp: derExp };
+        };
+        return ENV.engine.runKernel(function (backend, save) { return save(backend.pow($base, $exp)); }, { $base: $base, $exp: $exp }, grad);
+    }
+    function powStrict_(base, exp$$1) {
+        assertShapesMatch(base.shape, exp$$1.shape, 'Error in powStrict: ');
+        return base.pow(exp$$1);
+    }
+    function mul_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'mul');
+        var $b = convertToTensor(b, 'b', 'mul');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        var outShape = assertAndGetBroadcastShape($a.shape, $b.shape);
+        var der = function (dy) {
+            var derA = function () {
+                var res = dy.mul($b.toFloat());
+                var reduceAxes = getReductionAxes($a.shape, outShape);
+                if (reduceAxes.length > 0) {
+                    return res.sum(reduceAxes).reshape($a.shape);
+                }
+                return res;
+            };
+            var derB = function () {
+                var res = dy.mul($a.toFloat());
+                var reduceAxes = getReductionAxes($b.shape, outShape);
+                if (reduceAxes.length > 0) {
+                    return res.sum(reduceAxes).reshape($b.shape);
+                }
+                return res;
+            };
+            return { $a: derA, $b: derB };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.multiply($a, $b); }, { $a: $a, $b: $b }, der);
+    }
+    function mulStrict_(a, b) {
+        var $a = convertToTensor(a, 'a', 'mul');
+        var $b = convertToTensor(b, 'b', 'mul');
+        assertShapesMatch($a.shape, $b.shape, 'Error in multiplyStrict: ');
+        return $a.mul($b);
+    }
+    function div_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'div');
+        var $b = convertToTensor(b, 'b', 'div');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        var forwardFunc;
+        if ($a.dtype === 'int32' && $b.dtype === 'int32') {
+            return floorDiv($a, $b);
+        }
+        else {
+            forwardFunc = function (backend) { return backend.realDivide($a, $b); };
+        }
+        var outShape = assertAndGetBroadcastShape($a.shape, $b.shape);
+        var der = function (dy) {
+            var derA = function () {
+                var res = dy.div($b.toFloat());
+                var reduceAxes = getReductionAxes($a.shape, outShape);
+                if (reduceAxes.length > 0) {
+                    return res.sum(reduceAxes).reshape($a.shape);
+                }
+                return res;
+            };
+            var derB = function () {
+                var res = dy.mul($a.toFloat());
+                var reduceAxes = getReductionAxes($b.shape, outShape);
+                if (reduceAxes.length > 0) {
+                    res = res.sum(reduceAxes).reshape($b.shape);
+                }
+                var tmp = $b.square();
+                return res.div(tmp.toFloat()).neg();
+            };
+            return { $a: derA, $b: derB };
+        };
+        return ENV.engine.runKernel(forwardFunc, { $a: $a, $b: $b }, der);
+    }
+    function floorDiv_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'floorDiv');
+        var $b = convertToTensor(b, 'b', 'floorDiv');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        var forwardFunc = function (backend) { return backend.floorDiv($a, $b); };
+        var outShape = assertAndGetBroadcastShape($a.shape, $b.shape);
+        var der = function (dy) {
+            var derA = function () {
+                var res = dy.div($b.toFloat());
+                var reduceAxes = getReductionAxes($a.shape, outShape);
+                if (reduceAxes.length > 0) {
+                    return res.sum(reduceAxes).reshape($a.shape);
+                }
+                return res;
+            };
+            var derB = function () {
+                var res = dy.mul($a.toFloat());
+                var reduceAxes = getReductionAxes($b.shape, outShape);
+                if (reduceAxes.length > 0) {
+                    res = res.sum(reduceAxes).reshape($b.shape);
+                }
+                var tmp = $b.square();
+                return res.div(tmp.toFloat()).neg();
+            };
+            return { $a: derA, $b: derB };
+        };
+        return ENV.engine.runKernel(forwardFunc, { $a: $a, $b: $b }, der);
+    }
+    function divStrict_(a, b) {
+        var $a = convertToTensor(a, 'a', 'div');
+        var $b = convertToTensor(b, 'b', 'div');
+        assertShapesMatch($a.shape, $b.shape, 'Error in divideStrict: ');
+        return $a.div($b);
+    }
+    function mod_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'mod');
+        var $b = convertToTensor(b, 'b', 'mod');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        var outShape = assertAndGetBroadcastShape($a.shape, $b.shape);
+        var der = function (dy) {
+            var derA = function () {
+                var reduceAxes = getReductionAxes($a.shape, outShape);
+                if (reduceAxes.length > 0) {
+                    return dy.sum(reduceAxes).reshape($a.shape);
+                }
+                return dy;
+            };
+            var derB = function () {
+                var res = dy.mul($a.div($b).floor().neg());
+                var reduceAxes = getReductionAxes($b.shape, outShape);
+                if (reduceAxes.length > 0) {
+                    return res.sum(reduceAxes).reshape($b.shape);
+                }
+                return res;
+            };
+            return { $a: derA, $b: derB };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.mod($a, $b); }, { $a: $a, $b: $b }, der);
+    }
+    function modStrict_(a, b) {
+        var $a = convertToTensor(a, 'a', 'modStrict');
+        var $b = convertToTensor(b, 'b', 'modStrict');
+        assertShapesMatch($a.shape, $b.shape, 'Error in modStrict: ');
+        return $a.mod($b);
+    }
+    function minimum_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'minimum');
+        var $b = convertToTensor(b, 'b', 'minimum');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        if ($a.dtype === 'bool') {
+            $a = $a.toInt();
+            $b = $b.toInt();
+        }
+        assertAndGetBroadcastShape($a.shape, $b.shape);
+        var der = function (dy) {
+            var derA = function () { return dy.mul($a.lessEqual($b).toFloat()); };
+            var derB = function () { return dy.mul($a.greater($b).toFloat()); };
+            return { $a: derA, $b: derB };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.minimum($a, $b); }, { $a: $a, $b: $b }, der);
+    }
+    function minimumStrict_(a, b) {
+        var $a = convertToTensor(a, 'a', 'minimumStrict');
+        var $b = convertToTensor(b, 'b', 'minimumStrict');
+        assertShapesMatch($a.shape, $b.shape, 'Error in minimumStrict: ');
+        return $a.minimum($b);
+    }
+    function maximum_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'maximum');
+        var $b = convertToTensor(b, 'b', 'maximum');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        if ($a.dtype === 'bool') {
+            $a = $a.toInt();
+            $b = $b.toInt();
+        }
+        assertAndGetBroadcastShape($a.shape, $b.shape);
+        var der = function (dy) {
+            var derA = function () { return dy.mul($a.greaterEqual($b).toFloat()); };
+            var derB = function () { return dy.mul($a.less($b).toFloat()); };
+            return { $a: derA, $b: derB };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.maximum($a, $b); }, { $a: $a, $b: $b }, der);
+    }
+    function maximumStrict_(a, b) {
+        var $a = convertToTensor(a, 'a', 'maximumStrict');
+        var $b = convertToTensor(b, 'b', 'maximumStrict');
+        assertShapesMatch($a.shape, $b.shape, 'Error in maximumStrict: ');
+        return $a.maximum($b);
+    }
+    function squaredDifference_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'squaredDifference');
+        var $b = convertToTensor(b, 'b', 'squaredDifference');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        assertAndGetBroadcastShape($a.shape, $b.shape);
+        var der = function (dy) {
+            var two = scalar(2);
+            var derA = function () { return dy.mul($a.sub($b).mul(two)); };
+            var derB = function () { return dy.mul($b.sub($a).mul(two)); };
+            return { $a: derA, $b: derB };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.squaredDifference($a, $b); }, { $a: $a, $b: $b }, der);
+    }
+    function squaredDifferenceStrict_(a, b) {
+        var $a = convertToTensor(a, 'a', 'squaredDifferenceStrict');
+        var $b = convertToTensor(b, 'b', 'squaredDifferenceStrict');
+        assertShapesMatch($a.shape, $b.shape, 'Error in squaredDifferenceStrict: ');
+        return $a.squaredDifference($b);
+    }
+    function atan2_(a, b) {
+        var _a;
+        var $a = convertToTensor(a, 'a', 'atan2');
+        var $b = convertToTensor(b, 'b', 'atan2');
+        _a = makeTypesMatch($a, $b), $a = _a[0], $b = _a[1];
+        var outShape = assertAndGetBroadcastShape($a.shape, $b.shape);
+        var der = function (dy) {
+            var derA = function () {
+                var d = add($a.square(), $b.square());
+                var res = dy.mul($b.div(d));
+                var reduceAxes = getReductionAxes($a.shape, outShape);
+                if (reduceAxes.length > 0) {
+                    res = res.sum(reduceAxes);
+                }
+                return res.reshape($a.shape);
+            };
+            var derB = function () {
+                var d = add($a.square(), $b.square());
+                var res = neg(dy.mul($a.div(d)));
+                var reduceAxes = getReductionAxes($b.shape, outShape);
+                if (reduceAxes.length > 0) {
+                    res = res.sum(reduceAxes);
+                }
+                return res.reshape($b.shape);
+            };
+            return { $a: derA, $b: derB };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.atan2($a, $b); }, { $a: $a, $b: $b }, der);
+    }
+    var add = op({ add_: add_ });
+    var addN = op({ addN_: addN_ });
+    var addStrict = op({ addStrict_: addStrict_ });
+    var atan2 = op({ atan2_: atan2_ });
+    var div = op({ div_: div_ });
+    var divStrict = op({ divStrict_: divStrict_ });
+    var floorDiv = op({ floorDiv_: floorDiv_ });
+    var maximum = op({ maximum_: maximum_ });
+    var maximumStrict = op({ maximumStrict_: maximumStrict_ });
+    var minimum = op({ minimum_: minimum_ });
+    var minimumStrict = op({ minimumStrict_: minimumStrict_ });
+    var mod = op({ mod_: mod_ });
+    var modStrict = op({ modStrict_: modStrict_ });
+    var mul = op({ mul_: mul_ });
+    var mulStrict = op({ mulStrict_: mulStrict_ });
+    var pow = op({ pow_: pow_ });
+    var powStrict = op({ powStrict_: powStrict_ });
+    var squaredDifference = op({ squaredDifference_: squaredDifference_ });
+    var squaredDifferenceStrict = op({ squaredDifferenceStrict_: squaredDifferenceStrict_ });
+    var sub = op({ sub_: sub_ });
+    var subStrict = op({ subStrict_: subStrict_ });
+
+    function logicalNot_(x) {
+        var $x = convertToTensor(x, 'x', 'logicalNot', 'bool');
+        return ENV.engine.runKernel(function (backend) { return backend.logicalNot($x); }, { $x: $x });
+    }
+    function logicalAnd_(a, b) {
+        var $a = convertToTensor(a, 'a', 'logicalAnd', 'bool');
+        var $b = convertToTensor(b, 'b', 'logicalAnd', 'bool');
+        assertAndGetBroadcastShape($a.shape, $b.shape);
+        return ENV.engine.runKernel(function (backend) { return backend.logicalAnd($a, $b); }, { $a: $a, $b: $b });
+    }
+    function logicalOr_(a, b) {
+        var $a = convertToTensor(a, 'a', 'logicalOr', 'bool');
+        var $b = convertToTensor(b, 'b', 'logicalOr', 'bool');
+        assertAndGetBroadcastShape($a.shape, $b.shape);
+        return ENV.engine.runKernel(function (backend) { return backend.logicalOr($a, $b); }, { $a: $a, $b: $b });
+    }
+    function logicalXor_(a, b) {
+        var $a = convertToTensor(a, 'a', 'logicalXor', 'bool');
+        var $b = convertToTensor(b, 'b', 'logicalXor', 'bool');
+        assertAndGetBroadcastShape($a.shape, $b.shape);
+        return logicalOr(a, b).logicalAnd(logicalAnd(a, b).logicalNot());
+    }
+    function where_(condition, a, b) {
+        var $a = convertToTensor(a, 'a', 'where');
+        var $b = convertToTensor(b, 'b', 'where');
+        var $condition = convertToTensor(condition, 'condition', 'where', 'bool');
+        assertShapesMatch($a.shape, $b.shape, 'Error in where: ');
+        if ($condition.rank === 1) {
+            assert($condition.shape[0] === $a.shape[0], 'The first dimension of `a` must match the size of `condition`.');
+        }
+        else {
+            assertShapesMatch($condition.shape, $b.shape, 'Error in where: ');
+        }
+        var grad = function (dy) { return ({
+            $condition: function () { return zerosLike($condition).toFloat(); },
+            $a: function () { return dy.mul($condition.cast(dy.dtype)); },
+            $b: function () { return dy.mul($condition.logicalNot().cast(dy.dtype)); }
+        }); };
+        return ENV.engine.runKernel(function (backend) { return backend.select($condition, $a, $b); }, { $condition: $condition, $a: $a, $b: $b }, grad);
+    }
+    function whereAsync_(condition) {
+        return __awaiter(this, void 0, void 0, function () {
+            var $condition, vals, res;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        $condition = convertToTensor(condition, 'condition', 'whereAsync', 'bool');
+                        return [4, $condition.data()];
+                    case 1:
+                        vals = _a.sent();
+                        res = whereImpl($condition.shape, vals);
+                        if (condition !== $condition) {
+                            $condition.dispose();
+                        }
+                        return [2, res];
+                }
+            });
+        });
+    }
+    var logicalAnd = op({ logicalAnd_: logicalAnd_ });
+    var logicalNot = op({ logicalNot_: logicalNot_ });
+    var logicalOr = op({ logicalOr_: logicalOr_ });
+    var logicalXor = op({ logicalXor_: logicalXor_ });
+    var where = op({ where_: where_ });
+    var whereAsync = whereAsync_;
+
+    function relu_(x) {
+        var $x = convertToTensor(x, 'x', 'relu');
+        if ($x.dtype === 'bool') {
+            return $x.toInt();
+        }
+        var grad = function (dy) {
+            var stepRes = $x.step();
+            return { $x: function () { return dy.mulStrict(stepRes.toFloat()); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.relu($x); }, { $x: $x }, grad);
+    }
+    function elu_(x) {
+        var $x = convertToTensor(x, 'x', 'elu');
+        var grad = function (dy, saved) {
+            var y = saved[0];
+            return {
+                $x: function () {
+                    return ENV.engine.runKernel(function (backend) { return backend.eluDer(dy, y); }, { dy: dy, y: y });
+                }
+            };
+        };
+        return ENV.engine.runKernel(function (backend, save) { return save(backend.elu($x)); }, { $x: $x }, grad);
+    }
+    function selu_(x) {
+        var $x = convertToTensor(x, 'x', 'selu');
+        var grad = function (dy) {
+            return {
+                $x: function () {
+                    var mask = $x.greater(scalar(0));
+                    var scaleAlpha = scalar(SELU_SCALEALPHA);
+                    var scale = scalar(SELU_SCALE);
+                    var greaterThanZeroDer = dy.mul(scale);
+                    var lessEqualZeroDer = dy.mul(scaleAlpha).mul($x.toFloat().exp());
+                    return where(mask, greaterThanZeroDer, lessEqualZeroDer);
+                }
+            };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.selu($x); }, { $x: $x }, grad);
+    }
+    function leakyRelu_(x, alpha) {
+        if (alpha === void 0) { alpha = 0.2; }
+        var $x = convertToTensor(x, 'x', 'leakyRelu');
+        return maximum(scalar(alpha).mul($x), $x);
+    }
+    function prelu_(x, alpha) {
+        var $x = convertToTensor(x, 'x', 'prelu');
+        var $alpha = convertToTensor(alpha, 'alpha', 'prelu');
+        var zero = scalar(0);
+        return maximum(zero, $x).add($alpha.mul(minimum(zero, $x)));
+    }
+    var elu = op({ elu_: elu_ });
+    var leakyRelu = op({ leakyRelu_: leakyRelu_ });
+    var prelu = op({ prelu_: prelu_ });
+    var relu = op({ relu_: relu_ });
+    var selu = op({ selu_: selu_ });
+
+    function transpose_(x, perm) {
+        var $x = convertToTensor(x, 'x', 'transpose');
+        if (perm == null) {
+            perm = $x.shape.map(function (s, i) { return i; }).reverse();
+        }
+        assert($x.rank === perm.length, "Error in transpose: rank of input " + $x.rank + " " +
+            ("must match length of perm " + perm + "."));
+        perm.forEach(function (axis) {
+            assert(axis >= 0 && axis < $x.rank, "All entries in 'perm' must be between 0 and " + ($x.rank - 1) +
+                (" but got " + perm));
+        });
+        if ($x.rank <= 1) {
+            return $x.clone();
+        }
+        var der = function (dy) {
+            var undoPerm = getUndoAxesPermutation(perm);
+            return { $x: function () { return dy.transpose(undoPerm); } };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.transpose($x, perm); }, { $x: $x }, der);
+    }
+    var transpose = op({ transpose_: transpose_ });
+
+    function localResponseNormalization_(x, depthRadius, bias, alpha, beta) {
+        if (depthRadius === void 0) { depthRadius = 5; }
+        if (bias === void 0) { bias = 1; }
+        if (alpha === void 0) { alpha = 1; }
+        if (beta === void 0) { beta = 0.5; }
+        var $x = convertToTensor(x, 'x', 'localResponseNormalization');
+        assert($x.rank === 4 || $x.rank === 3, "Error in localResponseNormalization: x must be rank 3 or 4 but got\n               rank " + $x.rank + ".");
+        assert(isInt(depthRadius), "Error in localResponseNormalization: depthRadius must be an integer\n                     but got depthRadius " + depthRadius + ".");
+        var x4D = $x;
+        var reshapedTo4D = false;
+        if ($x.rank === 3) {
+            reshapedTo4D = true;
+            x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);
+        }
+        var backward = function (dy, saved) {
+            var outputImage = saved[0];
+            return {
+                x4D: function () { return ENV.engine.runKernel(function (backend) { return backend.LRNGrad(dy, x4D, outputImage, depthRadius, bias, alpha, beta); }, {}); }
+            };
+        };
+        var res = ENV.engine.runKernel(function (backend, save) { return save(backend.localResponseNormalization4D(x4D, depthRadius, bias, alpha, beta)); }, { x4D: x4D }, backward);
+        if (reshapedTo4D) {
+            return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
+        }
+        else {
+            return res;
+        }
+    }
+    var localResponseNormalization = op({ localResponseNormalization_: localResponseNormalization_ });
+
+    function norm_(x, ord, axis, keepDims) {
+        if (ord === void 0) { ord = 'euclidean'; }
+        if (axis === void 0) { axis = null; }
+        if (keepDims === void 0) { keepDims = false; }
+        x = convertToTensor(x, 'x', 'norm');
+        var norm = normImpl(x, ord, axis);
+        var keepDimsShape = norm.shape;
+        if (keepDims) {
+            var axes = parseAxisParam(axis, x.shape);
+            keepDimsShape = expandShapeToKeepDim(norm.shape, axes);
+        }
+        return norm.reshape(keepDimsShape);
+    }
+    function normImpl(x, p, axis) {
+        if (axis === void 0) { axis = null; }
+        if (x.rank === 0) {
+            return x.abs();
+        }
+        if (x.rank !== 1 && axis === null) {
+            return normImpl(x.reshape([-1]), p, axis);
+        }
+        if (x.rank === 1 || typeof axis === 'number' ||
+            axis instanceof Array && axis.length === 1) {
+            if (p === 1) {
+                return x.abs().sum(axis);
+            }
+            if (p === Infinity) {
+                return x.abs().max(axis);
+            }
+            if (p === -Infinity) {
+                return x.abs().min(axis);
+            }
+            if (p === 'euclidean' || p === 2) {
+                return x.abs().pow(scalar(2, 'int32')).sum(axis).sqrt();
+            }
+            throw new Error("Error in norm: invalid ord value: " + p);
+        }
+        if (axis instanceof Array && axis.length === 2) {
+            if (p === 1) {
+                return x.abs().sum(axis[0]).max(axis[1] - 1);
+            }
+            if (p === Infinity) {
+                return x.abs().sum(axis[1]).max(axis[0]);
+            }
+            if (p === -Infinity) {
+                return x.abs().sum(axis[1]).min(axis[0]);
+            }
+            if (p === 'fro' || p === 'euclidean') {
+                return x.square().sum(axis).sqrt();
+            }
+            throw new Error("Error in norm: invalid ord value: " + p);
+        }
+        throw new Error("Error in norm: invalid axis: " + axis);
+    }
+    var norm = op({ norm_: norm_ });
+
+    function unsortedSegmentSum_(x, segmentIds, numSegments) {
+        var $x = convertToTensor(x, 'x', 'unsortedSegmentSum');
+        var $segmentIds = convertToTensor(segmentIds, 'segmentIds', 'unsortedSegmentSum', 'int32');
+        assert(isInt(numSegments), 'numSegments must be of dtype int');
+        var gradFunc = function (dy) {
+            var derX = function () {
+                return gatherDropNegatives(dy, $segmentIds);
+            };
+            return { $x: derX };
+        };
+        return ENV.engine.runKernel(function (backend) {
+            return backend.unsortedSegmentSum($x, $segmentIds, numSegments);
+        }, { $x: $x }, gradFunc);
+    }
+    function gather_(x, indices, axis) {
+        if (axis === void 0) { axis = 0; }
+        var $x = convertToTensor(x, 'x', 'gather');
+        var $indices = convertToTensor(indices, 'indices', 'gather', 'int32');
+        axis = parseAxisParam(axis, $x.shape)[0];
+        var grad = function (dy) {
+            var derX = function () {
+                if (axis === 0) {
+                    return unsortedSegmentSum(dy, $indices, $x.shape[axis]);
+                }
+                var paramsShape = $x.shape;
+                var indicesSize = $indices.size;
+                var outerShape = paramsShape.slice(0, axis);
+                var outerDims = outerShape.length;
+                var innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);
+                var innerDims = innerShape.length;
+                var outerAxesIndices = arrayRange(0, outerDims);
+                var innerAxesIndices = arrayRange(outerDims + 1, outerDims + 1 + innerDims);
+                var valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);
+                var values = dy.reshape(valuesShape);
+                var reshapedIndices = $indices.reshape([indicesSize]);
+                var transposeDims = arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);
+                var valuesTranspose = values.transpose(transposeDims);
+                var paramsGrad = unsortedSegmentSum(valuesTranspose, reshapedIndices, $x.shape[axis]);
+                var invertTransposeDims = getUndoAxesPermutation(transposeDims);
+                paramsGrad = paramsGrad.transpose(invertTransposeDims);
+                return paramsGrad;
+            };
+            return { $x: derX };
+        };
+        return ENV.engine.runKernel(function (backend) { return backend.gather($x, $indices, axis); }, { $x: $x }, grad);
+    }
+    function arrayRange(start, stop) {
+        var result = [];
+        for (var i = start; i < stop; ++i) {
+            result.push(i);
+        }
+        return result;
+    }
+    function arrayConcat(arrays) {
+        var result = [];
+        for (var i = 0; i < arrays.length; ++i) {
+            for (var j = 0; j < arrays[i].length; ++j) {
+                result.push(arrays[i][j]);
+            }
+        }
+        return result;
+    }
+    function gatherDropNegatives(x, indices) {
+        var zeroClippedIndices = maximum(indices, zerosLike(indices));
+        var gathered = gather(x, zeroClippedIndices);
+        var isPositive = greaterEqual(indices, scalar(0, 'int32'));
+        var numIters = gathered.rank - isPositive.rank;
+        for (var i = 0; i < numIters; ++i) {
+            isPositive = expandDims(isPositive, i + 1);
+        }
+        isPositive = logicalAnd(isPositive, ones$1(gathered.shape, 'bool'));
+        var zeroSlice = zerosLike(gathered);
+        return where(isPositive, gathered, zeroSlice);
+    }
+    var gather = op({ gather_: gather_ });
+    var unsortedSegmentSum = op({ unsortedSegmentSum_: unsortedSegmentSum_ });
+
+    function multiRNNCell_(lstmCells, data, c, h) {
+        var $data = convertToTensor(data, 'data', 'multiRNNCell');
+        var $c = convertToTensorArray(c, 'c', 'multiRNNCell');
+        var $h = convertToTensorArray(h, 'h', 'multiRNNCell');
+        var input = $data;
+        var newStates = [];
+        for (var i = 0; i < lstmCells.length; i++) {
+            var output = lstmCells[i](input, $c[i], $h[i]);
+            newStates.push(output[0]);
+            newStates.push(output[1]);
+            input = output[1];
+        }
+        var newC = [];
+        var newH = [];
+        for (var i = 0; i < newStates.length; i += 2) {
+            newC.push(newStates[i]);
+            newH.push(newStates[i + 1]);
+        }
+        return [newC, newH];
+    }
+    function basicLSTMCell_(forgetBias, lstmKernel, lstmBias, data, c, h) {
+        var $forgetBias = convertToTensor(forgetBias, 'forgetBias', 'basicLSTMCell');
+        var $lstmKernel = convertToTensor(lstmKernel, 'lstmKernel', 'basicLSTMCell');
+        var $lstmBias = convertToTensor(lstmBias, 'lstmBias', 'basicLSTMCell');
+        var $data = convertToTensor(data, 'data', 'basicLSTMCell');
+        var $c = convertToTensor(c, 'c', 'basicLSTMCell');
+        var $h = convertToTensor(h, 'h', 'basicLSTMCell');
+        var combined = $data.concat($h, 1);
+        var weighted = combined.matMul($lstmKernel);
+        var res = weighted.add($lstmBias);
+        var batchSize = res.shape[0];
+        var sliceCols = res.shape[1] / 4;
+        var sliceSize = [batchSize, sliceCols];
+        var i = res.slice([0, 0], sliceSize);
+        var j = res.slice([0, sliceCols], sliceSize);
+        var f = res.slice([0, sliceCols * 2], sliceSize);
+        var o = res.slice([0, sliceCols * 3], sliceSize);
+        var newC = i.sigmoid().mulStrict(j.tanh()).addStrict($c.mulStrict($forgetBias.add(f).sigmoid()));
+        var newH = newC.tanh().mulStrict(o.sigmoid());
+        return [newC, newH];
+    }
+    var basicLSTMCell = op({ basicLSTMCell_: basicLSTMCell_ });
+    var multiRNNCell = op({ multiRNNCell_: multiRNNCell_ });
+
+    function movingAverage_(v, x, decay, step, zeroDebias) {
+        if (zeroDebias === void 0) { zeroDebias = true; }
+        var $v = convertToTensor(v, 'v', 'movingAverage');
+        var $x = convertToTensor(x, 'x', 'movingAverage');
+        var $decay = convertToTensor(decay, 'decay', 'movingAverage');
+        assertTypesMatch($v, $x);
+        assert(arraysEqual($v.shape, $x.shape), 'Shape mismatch in v and x');
+        var one = scalar(1);
+        var oneMinusDecay = one.sub($decay);
+        var update = $x.sub($v).mul(oneMinusDecay);
+        if (zeroDebias) {
+            assert(step != null, 'When using zeroDebias: true, step is required.');
+            var $step = convertToTensor(step, 'step', 'movingAverage');
+            update = update.div(one.sub(pow($decay, $step)));
+        }
+        return $v.add(update);
+    }
+    var movingAverage = op({ movingAverage_: movingAverage_ });
+
+    function stridedSlice_(x, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {
+        if (beginMask === void 0) { beginMask = 0; }
+        if (endMask === void 0) { endMask = 0; }
+        if (ellipsisMask === void 0) { ellipsisMask = 0; }
+        if (newAxisMask === void 0) { newAxisMask = 0; }
+        if (shrinkAxisMask === void 0) { shrinkAxisMask = 0; }
+        if (ellipsisMask !== 0) {
+            throw new Error('ellipsis mask is not yet supported');
+        }
+        if (newAxisMask !== 0) {
+            throw new Error('new axis mask is not yet supported');
+        }
+        var $x = convertToTensor(x, 'x', 'stridedSlice');
+        return ENV.engine.runKernel(function (backend) { return backend.stridedSlice($x, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask); }, { $x: $x });
+    }
+    var stridedSlice = op({ stridedSlice_: stridedSlice_ });
+
+    function topk_(x, k, sorted) {
+        if (k === void 0) { k = 1; }
+        if (sorted === void 0) { sorted = true; }
+        var $x = convertToTensor(x, 'x', 'topk');
+        if ($x.rank === 0) {
+            throw new Error('topk() expects the input to be of rank 1 or higher');
+        }
+        var lastDim = $x.shape[$x.shape.length - 1];
+        if (k > lastDim) {
+            throw new Error("'k' passed to topk() must be <= the last dimension (" + lastDim + ") " +
+                ("but got " + k));
+        }
+        var _a = ENV.engine.runKernel(function (b) { return b.topk($x, k, sorted); }, { $x: $x }), values = _a[0], indices = _a[1];
+        return { values: values, indices: indices };
+    }
+    var topk = op({ topk_: topk_ });
+
+    function scatterND_(indices, updates, shape) {
+        var $indices = convertToTensor(indices, 'indices', 'scatterND', 'int32');
+        var $updates = convertToTensor(updates, 'updates', 'scatterND');
+        validateInput($updates, $indices, shape);
+        return ENV.engine.runKernel(function (backend) { return backend.scatterND($indices, $updates, shape); }, { $indices: $indices, $updates: $updates });
+    }
+    var scatterND = op({ scatterND_: scatterND_ });
+
+    function fft_(input) {
+        assert(input.dtype === 'complex64', "The dtype for tf.spectral.fft() must be complex64 " +
+            ("but got " + input.dtype + "."));
+        var innerDimensionSize = input.shape[input.shape.length - 1];
+        var batch = input.size / innerDimensionSize;
+        var input2D = input.as2D(batch, innerDimensionSize);
+        var ret = ENV.engine.runKernel(function (backend) { return backend.fft(input2D); }, { input: input });
+        return ret.reshape(input.shape);
+    }
+    function ifft_(input) {
+        assert(input.dtype === 'complex64', "The dtype for tf.spectral.ifft() must be complex64 " +
+            ("but got " + input.dtype + "."));
+        var innerDimensionSize = input.shape[input.shape.length - 1];
+        var batch = input.size / innerDimensionSize;
+        var input2D = input.as2D(batch, innerDimensionSize);
+        var ret = ENV.engine.runKernel(function (backend) { return backend.ifft(input2D); }, { input: input });
+        return ret.reshape(input.shape);
+    }
+    function rfft_(input) {
+        assert(input.dtype === 'float32', "The dtype for rfft() must be real value but\n    got " + input.dtype);
+        var innerDimensionSize = input.shape[input.shape.length - 1];
+        var batch = input.size / innerDimensionSize;
+        var zeros$$1 = input.zerosLike();
+        var complexInput = complex(input, zeros$$1).as2D(batch, innerDimensionSize);
+        var ret = fft(complexInput);
+        var half = Math.floor(innerDimensionSize / 2) + 1;
+        var realValues = real(ret);
+        var imagValues = imag(ret);
+        var realComplexConjugate = realValues.split([half, innerDimensionSize - half], realValues.shape.length - 1);
+        var imagComplexConjugate = imagValues.split([half, innerDimensionSize - half], imagValues.shape.length - 1);
+        var outputShape = input.shape.slice();
+        outputShape[input.shape.length - 1] = half;
+        return complex(realComplexConjugate[0], imagComplexConjugate[0])
+            .reshape(outputShape);
+    }
+    function irfft_(input) {
+        var innerDimensionSize = input.shape[input.shape.length - 1];
+        var batch = input.size / innerDimensionSize;
+        if (innerDimensionSize <= 2) {
+            var complexInput = input.as2D(batch, innerDimensionSize);
+            var ret = ifft(complexInput);
+            return real(ret);
+        }
+        else {
+            var outputShape = [batch, 2 * (innerDimensionSize - 1)];
+            var realInput = real(input).as2D(batch, innerDimensionSize);
+            var imagInput = imag(input).as2D(batch, innerDimensionSize);
+            var realConjugate = realInput.slice([0, 1], [batch, innerDimensionSize - 2]).reverse(1);
+            var imagConjugate = imagInput.slice([0, 1], [batch, innerDimensionSize - 2])
+                .reverse(1)
+                .mul(scalar(-1));
+            var r = realInput.concat(realConjugate, 1);
+            var i = imagInput.concat(imagConjugate, 1);
+            var complexInput = complex(r, i).as2D(outputShape[0], outputShape[1]);
+            var ret = ifft(complexInput);
+            return real(ret);
+        }
+    }
+    var fft = op({ fft_: fft_ });
+    var ifft = op({ ifft_: ifft_ });
+    var rfft = op({ rfft_: rfft_ });
+    var irfft = op({ irfft_: irfft_ });
+
+    var spectral_ops = /*#__PURE__*/Object.freeze({
+        fft: fft,
+        ifft: ifft,
+        rfft: rfft,
+        irfft: irfft
+    });
+
+    function validateInput$1(sparseIndices, sparseValues, outputShape, defaultValues) {
+        if (sparseIndices.dtype !== 'int32') {
+            throw new Error('tf.sparseToDense() expects the indices to be int32 type,' +
+                (" but the dtype was " + sparseIndices.dtype + "."));
+        }
+        if (sparseIndices.rank > 2) {
+            throw new Error('sparseIndices should be a scalar, vector, or matrix,' +
+                (" but got shape " + sparseIndices.shape + "."));
+        }
+        var numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;
+        var numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;
+        if (outputShape.length !== numDims) {
+            throw new Error('outputShape has incorrect number of elements:,' +
+                (" " + outputShape.length + ", should be: " + numDims + "."));
+        }
+        var numValues = sparseValues.size;
+        if (!(sparseValues.rank === 0 ||
+            sparseValues.rank === 1 && numValues === numElems)) {
+            throw new Error('sparseValues has incorrect shape ' +
+                (sparseValues.shape + ", should be [] or [" + numElems + "]"));
+        }
+        if (sparseValues.dtype !== defaultValues.dtype) {
+            throw new Error('sparseValues.dtype must match defaultValues.dtype');
+        }
+    }
+
+    function sparseToDense_(sparseIndices, sparseValues, outputShape, defaultValue) {
+        var $sparseIndices = convertToTensor(sparseIndices, 'sparseIndices', 'sparseToDense', 'int32');
+        var $sparseValues = convertToTensor(sparseValues, 'sparseValues', 'sparseToDense');
+        var $defaultValue = convertToTensor(defaultValue, 'defaultValue', 'sparseToDense', $sparseValues.dtype);
+        validateInput$1($sparseIndices, $sparseValues, outputShape, $defaultValue);
+        return ENV.engine.runKernel(function (backend) { return backend.sparseToDense($sparseIndices, $sparseValues, outputShape, $defaultValue); }, { $sparseIndices: $sparseIndices, $sparseValues: $sparseValues, $defaultValue: $defaultValue });
+    }
+    var sparseToDense = op({ sparseToDense_: sparseToDense_ });
+
+    function gatherND_(x, indices) {
+        var $indices = convertToTensor(indices, 'indices', 'gatherND', 'int32');
+        var $x = convertToTensor(x, 'x', 'gatherND');
+        return ENV.engine.runKernel(function (backend) { return backend.gatherND($x, $indices); }, { $x: $x, $indices: $indices });
+    }
+    var gatherND = op({ gatherND_: gatherND_ });
+
+    (function (Reduction) {
+        Reduction[Reduction["NONE"] = 0] = "NONE";
+        Reduction[Reduction["MEAN"] = 1] = "MEAN";
+        Reduction[Reduction["SUM"] = 2] = "SUM";
+        Reduction[Reduction["SUM_BY_NONZERO_WEIGHTS"] = 3] = "SUM_BY_NONZERO_WEIGHTS";
+    })(exports.Reduction || (exports.Reduction = {}));
+    function computeWeightedLoss_(losses, weights, reduction) {
+        if (reduction === void 0) { reduction = exports.Reduction.SUM_BY_NONZERO_WEIGHTS; }
+        var $losses = convertToTensor(losses, 'losses', 'computeWeightedLoss');
+        var $weights = null;
+        if (weights != null) {
+            $weights = convertToTensor(weights, 'weights', 'computeWeightedLoss');
+        }
+        var weightedLoss = ($weights == null) ? $losses : $losses.mul($weights);
+        if (reduction === exports.Reduction.NONE) {
+            return weightedLoss;
+        }
+        if (reduction === exports.Reduction.SUM) {
+            return weightedLoss.sum();
+        }
+        if (reduction === exports.Reduction.MEAN) {
+            if ($weights == null) {
+                return weightedLoss.mean();
+            }
+            else {
+                var broadcastFactor = sizeFromShape($losses.shape) / sizeFromShape($weights.shape);
+                var result = weightedLoss.sum().div($weights.sum());
+                return broadcastFactor > 1 ? result.div(scalar(broadcastFactor)) :
+                    result;
+            }
+        }
+        if (reduction === exports.Reduction.SUM_BY_NONZERO_WEIGHTS) {
+            if ($weights == null) {
+                return weightedLoss.sum().div(scalar($losses.size));
+            }
+            else {
+                var broadcastedWeights = $weights.mul(ones$1($losses.shape));
+                var numNonZeros = broadcastedWeights.notEqual(scalar(0)).sum().toFloat();
+                return weightedLoss.sum().div(numNonZeros);
+            }
+        }
+        throw Error("Unknown reduction: " + reduction);
+    }
+    function absoluteDifference_(labels, predictions, weights, reduction) {
+        if (reduction === void 0) { reduction = exports.Reduction.SUM_BY_NONZERO_WEIGHTS; }
+        var $labels = convertToTensor(labels, 'labels', 'absoluteDifference');
+        var $predictions = convertToTensor(predictions, 'predictions', 'absoluteDifference');
+        var $weights = null;
+        if (weights != null) {
+            $weights = convertToTensor(weights, 'weights', 'absoluteDifference');
+        }
+        assertShapesMatch($labels.shape, $predictions.shape, 'Error in absoluteDifference: ');
+        var losses = $labels.sub($predictions).abs();
+        return computeWeightedLoss(losses, $weights, reduction);
+    }
+    function meanSquaredError_(labels, predictions, weights, reduction) {
+        if (reduction === void 0) { reduction = exports.Reduction.SUM_BY_NONZERO_WEIGHTS; }
+        var $labels = convertToTensor(labels, 'labels', 'meanSquaredError');
+        var $predictions = convertToTensor(predictions, 'predictions', 'meanSquaredError');
+        var $weights = null;
+        if (weights != null) {
+            $weights = convertToTensor(weights, 'weights', 'meanSquaredError');
+        }
+        assertShapesMatch($labels.shape, $predictions.shape, 'Error in meanSquaredError: ');
+        var losses = $labels.squaredDifference($predictions);
+        return computeWeightedLoss(losses, $weights, reduction);
+    }
+    function cosineDistance_(labels, predictions, axis, weights, reduction) {
+        if (reduction === void 0) { reduction = exports.Reduction.SUM_BY_NONZERO_WEIGHTS; }
+        var $labels = convertToTensor(labels, 'labels', 'cosineDistance');
+        var $predictions = convertToTensor(predictions, 'predictions', 'cosineDistance');
+        var $weights = null;
+        if (weights != null) {
+            $weights = convertToTensor(weights, 'weights', 'cosineDistance');
+        }
+        assertShapesMatch($labels.shape, $predictions.shape, 'Error in cosineDistance: ');
+        var one = scalar(1);
+        var losses = one.sub($labels.mul($predictions).sum(axis, true));
+        return computeWeightedLoss(losses, $weights, reduction);
+    }
+    function hingeLoss_(labels, predictions, weights, reduction) {
+        if (reduction === void 0) { reduction = exports.Reduction.SUM_BY_NONZERO_WEIGHTS; }
+        var $labels = convertToTensor(labels, 'labels', 'hingeLoss');
+        var $predictions = convertToTensor(predictions, 'predictions', 'hingeLoss');
+        var $weights = null;
+        if (weights != null) {
+            $weights = convertToTensor(weights, 'weights', 'hingeLoss');
+        }
+        assertShapesMatch($labels.shape, $predictions.shape, 'Error in hingeLoss: ');
+        var one = scalar(1);
+        $labels = scalar(2).mul($labels).sub(one);
+        var losses = one.sub($labels.mul($predictions)).relu();
+        return computeWeightedLoss(losses, $weights, reduction);
+    }
+    function logLoss_(labels, predictions, weights, epsilon, reduction) {
+        if (epsilon === void 0) { epsilon = 1e-7; }
+        if (reduction === void 0) { reduction = exports.Reduction.SUM_BY_NONZERO_WEIGHTS; }
+        var $labels = convertToTensor(labels, 'labels', 'logLoss');
+        var $predictions = convertToTensor(predictions, 'predictions', 'logLoss');
+        var $weights = null;
+        if (weights != null) {
+            $weights = convertToTensor(weights, 'weights', 'logLoss');
+        }
+        assertShapesMatch($labels.shape, $predictions.shape, 'Error in logLoss: ');
+        var one = scalar(1);
+        var epsilonScalar = scalar(epsilon);
+        var losses = $labels.mul($predictions.add(epsilonScalar).log())
+            .neg()
+            .sub(one.sub($labels).mul(one.sub($predictions).add(epsilonScalar).log()));
+        return computeWeightedLoss(losses, $weights, reduction);
+    }
+    function sigmoidCrossEntropyWithLogits_(labels, logits) {
+        var $labels = convertToTensor(labels, 'labels', 'sigmoidCrossEntropyWithLogits');
+        var $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropyWithLogits');
+        assertShapesMatch($labels.shape, $logits.shape, 'Error in sigmoidCrossEntropyWithLogits: ');
+        var maxOutput = $logits.relu();
+        var outputXTarget = $logits.mul($labels);
+        var sigmoidOutput = $logits.abs().neg().exp().log1p();
+        return maxOutput.sub(outputXTarget).add(sigmoidOutput);
+    }
+    function sigmoidCrossEntropy_(multiClassLabels, logits, weights, labelSmoothing, reduction) {
+        if (labelSmoothing === void 0) { labelSmoothing = 0; }
+        if (reduction === void 0) { reduction = exports.Reduction.SUM_BY_NONZERO_WEIGHTS; }
+        var $multiClassLabels = convertToTensor(multiClassLabels, 'multiClassLabels', 'sigmoidCrossEntropy');
+        var $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropy');
+        var $weights = null;
+        if (weights != null) {
+            $weights = convertToTensor(weights, 'weights', 'sigmoidCrossEntropy');
+        }
+        assertShapesMatch($multiClassLabels.shape, $logits.shape, 'Error in sigmoidCrossEntropy: ');
+        if (labelSmoothing > 0) {
+            var labelSmoothingScalar = scalar(labelSmoothing);
+            var one = scalar(1);
+            var half = scalar(0.5);
+            $multiClassLabels = $multiClassLabels.mul(one.sub(labelSmoothingScalar))
+                .add(half.mul(labelSmoothingScalar));
+        }
+        var losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);
+        return computeWeightedLoss(losses, $weights, reduction);
+    }
+    function huberLoss_(labels, predictions, weights, delta, reduction) {
+        if (delta === void 0) { delta = 1.0; }
+        if (reduction === void 0) { reduction = exports.Reduction.SUM_BY_NONZERO_WEIGHTS; }
+        var $labels = convertToTensor(labels, 'labels', 'huberLoss');
+        var $predictions = convertToTensor(predictions, 'predictions', 'huberLoss');
+        var $weights = null;
+        if (weights != null) {
+            $weights = convertToTensor(weights, 'weights', 'huberLoss');
+        }
+        assertShapesMatch($labels.shape, $predictions.shape, 'Error in huberLoss: ');
+        var deltaScalar = scalar(delta);
+        var error = $predictions.sub($labels).abs();
+        var quadratic = minimum(error, deltaScalar);
+        var linear = error.sub(quadratic);
+        var losses = scalar(0.5).mul(quadratic.square()).add(deltaScalar.mul(linear));
+        return computeWeightedLoss(losses, $weights, reduction);
+    }
+    function softmaxCrossEntropyWithLogits_(labels, logits, dim) {
+        if (dim === void 0) { dim = -1; }
+        if (dim === -1) {
+            dim = logits.rank - 1;
+        }
+        if (dim !== logits.rank - 1) {
+            throw Error("Softmax cross entropy along a non-last dimension is not yet " +
+                ("supported. Labels / logits was rank " + logits.rank + " ") +
+                ("and dim was " + dim));
+        }
+        var customOp = customGrad(function (labels, logits) {
+            var keepDims = true;
+            var lse = logits.logSumExp([dim], keepDims);
+            var logResult = logits.toFloat().sub(lse);
+            var costVector = logResult.mul(labels).neg();
+            var value = costVector.sum([dim]);
+            var gradFunc = function (dy) {
+                var dyShape = expandShapeToKeepDim(dy.shape, [dim]);
+                return [
+                    dy.reshape(dyShape).mul(labels.toFloat().sub(logResult.exp())),
+                    dy.reshape(dyShape).mul(logResult.exp().sub(labels.toFloat())),
+                ];
+            };
+            return { value: value, gradFunc: gradFunc };
+        });
+        return customOp(labels, logits);
+    }
+    function softmaxCrossEntropy_(onehotLabels, logits, weights, labelSmoothing, reduction) {
+        if (labelSmoothing === void 0) { labelSmoothing = 0; }
+        if (reduction === void 0) { reduction = exports.Reduction.SUM_BY_NONZERO_WEIGHTS; }
+        var $onehotLabels = convertToTensor(onehotLabels, 'onehotLabels', 'softmaxCrossEntropy');
+        var $logits = convertToTensor(logits, 'logits', 'softmaxCrossEntropy');
+        var $weights = null;
+        if (weights != null) {
+            $weights = convertToTensor(weights, 'weights', 'softmaxCrossEntropy');
+        }
+        assertShapesMatch($onehotLabels.shape, $logits.shape, 'Error in softmaxCrossEntropy: ');
+        if (labelSmoothing > 0) {
+            var labelSmoothingScalar = scalar(labelSmoothing);
+            var one = scalar(1);
+            var numClasses = scalar($onehotLabels.shape[1]);
+            $onehotLabels = $onehotLabels.mul(one.sub(labelSmoothingScalar))
+                .add(labelSmoothingScalar.div(numClasses));
+        }
+        var losses = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);
+        return computeWeightedLoss(losses, $weights, reduction);
+    }
+    var absoluteDifference = op({ absoluteDifference_: absoluteDifference_ });
+    var computeWeightedLoss = op({ computeWeightedLoss_: computeWeightedLoss_ });
+    var cosineDistance = op({ cosineDistance_: cosineDistance_ });
+    var hingeLoss = op({ hingeLoss_: hingeLoss_ });
+    var huberLoss = op({ huberLoss_: huberLoss_ });
+    var logLoss = op({ logLoss_: logLoss_ });
+    var meanSquaredError = op({ meanSquaredError_: meanSquaredError_ });
+    var sigmoidCrossEntropy = op({ sigmoidCrossEntropy_: sigmoidCrossEntropy_ });
+    var softmaxCrossEntropy = op({ softmaxCrossEntropy_: softmaxCrossEntropy_ });
+
+    var loss_ops = /*#__PURE__*/Object.freeze({
+        get Reduction () { return exports.Reduction; },
+        absoluteDifference: absoluteDifference,
+        computeWeightedLoss: computeWeightedLoss,
+        cosineDistance: cosineDistance,
+        hingeLoss: hingeLoss,
+        huberLoss: huberLoss,
+        logLoss: logLoss,
+        meanSquaredError: meanSquaredError,
+        sigmoidCrossEntropy: sigmoidCrossEntropy,
+        softmaxCrossEntropy: softmaxCrossEntropy
+    });
+
+    function gramSchmidt_(xs) {
+        var inputIsTensor2D;
+        if (Array.isArray(xs)) {
+            inputIsTensor2D = false;
+            assert(xs != null && xs.length > 0, 'Gram-Schmidt process: input must not be null, undefined, or empty');
+            var dim = xs[0].shape[0];
+            for (var i = 1; i < xs.length; ++i) {
+                assert(xs[i].shape[0] === dim, 'Gram-Schmidt: Non-unique lengths found in the input vectors: ' +
+                    ("(" + xs[i].shape[0] + " vs. " + dim + ")"));
+            }
+        }
+        else {
+            inputIsTensor2D = true;
+            xs = split$1(xs, xs.shape[0], 0).map(function (x) { return squeeze(x, [0]); });
+        }
+        assert(xs.length <= xs[0].shape[0], "Gram-Schmidt: Number of vectors (" + xs.length + ") exceeds " +
+            ("number of dimensions (" + xs[0].shape[0] + ")."));
+        var ys = [];
+        var xs1d = xs;
+        var _loop_1 = function (i) {
+            ys.push(ENV.engine.tidy(function () {
+                var x = xs1d[i];
+                if (i > 0) {
+                    for (var j = 0; j < i; ++j) {
+                        var proj = sum$1(ys[j].mulStrict(x)).mul(ys[j]);
+                        x = x.sub(proj);
+                    }
+                }
+                return x.div(norm(x, 'euclidean'));
+            }));
+        };
+        for (var i = 0; i < xs.length; ++i) {
+            _loop_1(i);
+        }
+        if (inputIsTensor2D) {
+            return stack(ys, 0);
+        }
+        else {
+            return ys;
+        }
+    }
+    function qr_(x, fullMatrices) {
+        if (fullMatrices === void 0) { fullMatrices = false; }
+        if (x.rank < 2) {
+            throw new Error("qr() requires input tensor to have a rank >= 2, but got rank " + x.rank);
+        }
+        else if (x.rank === 2) {
+            return qr2d(x, fullMatrices);
+        }
+        else {
+            var outerDimsProd = x.shape.slice(0, x.shape.length - 2)
+                .reduce(function (value, prev) { return value * prev; });
+            var x2ds = unstack(x.reshape([
+                outerDimsProd, x.shape[x.shape.length - 2],
+                x.shape[x.shape.length - 1]
+            ]), 0);
+            var q2ds_1 = [];
+            var r2ds_1 = [];
+            x2ds.forEach(function (x2d) {
+                var _a = qr2d(x2d, fullMatrices), q2d = _a[0], r2d = _a[1];
+                q2ds_1.push(q2d);
+                r2ds_1.push(r2d);
+            });
+            var q = stack(q2ds_1, 0).reshape(x.shape);
+            var r = stack(r2ds_1, 0).reshape(x.shape);
+            return [q, r];
+        }
+    }
+    function qr2d(x, fullMatrices) {
+        if (fullMatrices === void 0) { fullMatrices = false; }
+        return ENV.engine.tidy(function () {
+            if (x.shape.length !== 2) {
+                throw new Error("qr2d() requires a 2D Tensor, but got a " + x.shape.length + "D Tensor.");
+            }
+            var m = x.shape[0];
+            var n = x.shape[1];
+            var q = eye(m);
+            var r = x.clone();
+            var one2D = tensor2d([[1]], [1, 1]);
+            var w = one2D.clone();
+            var iters = m >= n ? n : m;
+            var _loop_2 = function (j) {
+                var _a;
+                var rTemp = r;
+                var wTemp = w;
+                var qTemp = q;
+                _a = ENV.engine.tidy(function () {
+                    var rjEnd1 = r.slice([j, j], [m - j, 1]);
+                    var normX = rjEnd1.norm();
+                    var rjj = r.slice([j, j], [1, 1]);
+                    var s = rjj.sign().neg();
+                    var u1 = rjj.sub(s.mul(normX));
+                    var wPre = rjEnd1.div(u1);
+                    if (wPre.shape[0] === 1) {
+                        w = one2D.clone();
+                    }
+                    else {
+                        w = one2D.concat(wPre.slice([1, 0], [wPre.shape[0] - 1, wPre.shape[1]]), 0);
+                    }
+                    var tau = s.matMul(u1).div(normX).neg();
+                    var rjEndAll = r.slice([j, 0], [m - j, n]);
+                    var tauTimesW = tau.mul(w);
+                    if (j === 0) {
+                        r = rjEndAll.sub(tauTimesW.matMul(w.transpose().matMul(rjEndAll)));
+                    }
+                    else {
+                        r = r.slice([0, 0], [j, n])
+                            .concat(rjEndAll.sub(tauTimesW.matMul(w.transpose().matMul(rjEndAll))), 0);
+                    }
+                    var qAllJEnd = q.slice([0, j], [m, q.shape[1] - j]);
+                    if (j === 0) {
+                        q = qAllJEnd.sub(qAllJEnd.matMul(w).matMul(tauTimesW.transpose()));
+                    }
+                    else {
+                        q = q.slice([0, 0], [m, j])
+                            .concat(qAllJEnd.sub(qAllJEnd.matMul(w).matMul(tauTimesW.transpose())), 1);
+                    }
+                    return [w, r, q];
+                }), w = _a[0], r = _a[1], q = _a[2];
+                dispose([rTemp, wTemp, qTemp]);
+            };
+            for (var j = 0; j < iters; ++j) {
+                _loop_2(j);
+            }
+            if (!fullMatrices && m > n) {
+                q = q.slice([0, 0], [m, n]);
+                r = r.slice([0, 0], [n, n]);
+            }
+            return [q, r];
+        });
+    }
+    var gramSchmidt = op({ gramSchmidt_: gramSchmidt_ });
+    var qr = op({ qr_: qr_ });
+
+    var linalg_ops = /*#__PURE__*/Object.freeze({
+        gramSchmidt: gramSchmidt,
+        qr: qr
+    });
+
+    function resizeBilinear_(images, size, alignCorners) {
+        if (alignCorners === void 0) { alignCorners = false; }
+        var $images = convertToTensor(images, 'images', 'resizeBilinear');
+        assert($images.rank === 3 || $images.rank === 4, "Error in resizeBilinear: x must be rank 3 or 4, but got " +
+            ("rank " + $images.rank + "."));
+        assert(size.length === 2, "Error in resizeBilinear: new shape must 2D, but got shape " +
+            (size + "."));
+        var batchImages = $images;
+        var reshapedTo4D = false;
+        if ($images.rank === 3) {
+            reshapedTo4D = true;
+            batchImages =
+                $images.as4D(1, $images.shape[0], $images.shape[1], $images.shape[2]);
+        }
+        var newHeight = size[0], newWidth = size[1];
+        var forward = function (backend, save) {
+            return backend.resizeBilinear(batchImages, newHeight, newWidth, alignCorners);
+        };
+        var backward = function (dy, saved) {
+            return {
+                batchImages: function () { return ENV.engine.runKernel(function (backend) {
+                    return backend.resizeBilinearBackprop(dy, batchImages, alignCorners);
+                }, {}); }
+            };
+        };
+        var res = ENV.engine.runKernel(forward, { batchImages: batchImages }, backward);
+        if (reshapedTo4D) {
+            return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
+        }
+        return res;
+    }
+    function resizeNearestNeighbor_(images, size, alignCorners) {
+        if (alignCorners === void 0) { alignCorners = false; }
+        var $images = convertToTensor(images, 'images', 'resizeNearestNeighbor');
+        assert($images.rank === 3 || $images.rank === 4, "Error in resizeNearestNeighbor: x must be rank 3 or 4, but got " +
+            ("rank " + $images.rank + "."));
+        assert(size.length === 2, "Error in resizeNearestNeighbor: new shape must 2D, but got shape " +
+            (size + "."));
+        assert($images.dtype === 'float32' || $images.dtype === 'int32', '`images` must have `int32` or `float32` as dtype');
+        var batchImages = $images;
+        var reshapedTo4D = false;
+        if ($images.rank === 3) {
+            reshapedTo4D = true;
+            batchImages =
+                $images.as4D(1, $images.shape[0], $images.shape[1], $images.shape[2]);
+        }
+        var newHeight = size[0], newWidth = size[1];
+        var forward = function (backend, save) {
+            return backend.resizeNearestNeighbor(batchImages, newHeight, newWidth, alignCorners);
+        };
+        var backward = function (dy, saved) {
+            return {
+                batchImages: function () { return ENV.engine.runKernel(function (backend) { return backend.resizeNearestNeighborBackprop(dy, batchImages, alignCorners); }, {}); }
+            };
+        };
+        var res = ENV.engine.runKernel(forward, { batchImages: batchImages }, backward);
+        if (reshapedTo4D) {
+            return res.as3D(res.shape[1], res.shape[2], res.shape[3]);
+        }
+        return res;
+    }
+    function nonMaxSuppression_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+        if (iouThreshold === void 0) { iouThreshold = 0.5; }
+        if (scoreThreshold === void 0) { scoreThreshold = Number.NEGATIVE_INFINITY; }
+        var $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');
+        var $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');
+        var inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
+        maxOutputSize = inputs.maxOutputSize;
+        iouThreshold = inputs.iouThreshold;
+        scoreThreshold = inputs.scoreThreshold;
+        return ENV.engine.runKernel(function (b) { return b.nonMaxSuppression($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold); }, { $boxes: $boxes });
+    }
+    function nonMaxSuppressionAsync_(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+        if (iouThreshold === void 0) { iouThreshold = 0.5; }
+        if (scoreThreshold === void 0) { scoreThreshold = Number.NEGATIVE_INFINITY; }
+        return __awaiter(this, void 0, void 0, function () {
+            var $boxes, $scores, inputs, boxesVals, scoresVals, res;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');
+                        $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');
+                        inputs = nonMaxSuppSanityCheck($boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);
+                        maxOutputSize = inputs.maxOutputSize;
+                        iouThreshold = inputs.iouThreshold;
+                        scoreThreshold = inputs.scoreThreshold;
+                        return [4, $boxes.data()];
+                    case 1:
+                        boxesVals = _a.sent();
+                        return [4, $scores.data()];
+                    case 2:
+                        scoresVals = _a.sent();
+                        res = nonMaxSuppressionImpl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
+                        if ($boxes !== boxes) {
+                            $boxes.dispose();
+                        }
+                        if ($scores !== scores) {
+                            $scores.dispose();
+                        }
+                        return [2, res];
+                }
+            });
+        });
+    }
+    function nonMaxSuppSanityCheck(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+        if (iouThreshold == null) {
+            iouThreshold = 0.5;
+        }
+        if (scoreThreshold == null) {
+            scoreThreshold = Number.NEGATIVE_INFINITY;
+        }
+        var numBoxes = boxes.shape[0];
+        maxOutputSize = Math.min(maxOutputSize, numBoxes);
+        assert(0 <= iouThreshold && iouThreshold <= 1, "iouThreshold must be in [0, 1], but was '" + iouThreshold + "'");
+        assert(boxes.rank === 2, "boxes must be a 2D tensor, but was of rank '" + boxes.rank + "'");
+        assert(boxes.shape[1] === 4, "boxes must have 4 columns, but 2nd dimension was " + boxes.shape[1]);
+        assert(scores.rank === 1, 'scores must be a 1D tensor');
+        assert(scores.shape[0] === numBoxes, "scores has incompatible shape with boxes. Expected " + numBoxes + ", " +
+            ("but was " + scores.shape[0]));
+        return { maxOutputSize: maxOutputSize, iouThreshold: iouThreshold, scoreThreshold: scoreThreshold };
+    }
+    function cropAndResize_(image, boxes, boxInd, cropSize, method, extrapolationValue) {
+        var $image = convertToTensor(image, 'image', 'cropAndResize', 'float32');
+        var $boxes = convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');
+        var $boxInd = convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');
+        method = method || 'bilinear';
+        extrapolationValue = extrapolationValue || 0;
+        var numBoxes = $boxes.shape[0];
+        assert($image.rank === 4, 'Error in cropAndResize: image must be rank 4,' +
+            ("but got rank " + $image.rank + "."));
+        assert($boxes.rank === 2 && $boxes.shape[1] === 4, "Error in cropAndResize: boxes must be have size [" + numBoxes + ",4] " +
+            ("but had shape " + $boxes.shape + "."));
+        assert($boxInd.rank === 1 && $boxInd.shape[0] === numBoxes, "Error in cropAndResize: boxInd must be have size [" + numBoxes + "] " +
+            ("but had shape " + $boxes.shape + "."));
+        assert(cropSize.length === 2, "Error in cropAndResize: cropSize must be of length 2, but got length " +
+            (cropSize.length + "."));
+        assert(cropSize[0] >= 1 && cropSize[1] >= 1, "cropSize must be atleast [1,1], but was " + cropSize);
+        assert(method === 'bilinear' || method === 'nearest', "method must be bilinear or nearest, but was " + method);
+        var forward = function (backend, save) {
+            return backend.cropAndResize($image, $boxes, $boxInd, cropSize, method, extrapolationValue);
+        };
+        var res = ENV.engine.runKernel(forward, { $image: $image, $boxes: $boxes });
+        return res;
+    }
+    var resizeBilinear = op({ resizeBilinear_: resizeBilinear_ });
+    var resizeNearestNeighbor = op({ resizeNearestNeighbor_: resizeNearestNeighbor_ });
+    var nonMaxSuppression = op({ nonMaxSuppression_: nonMaxSuppression_ });
+    var nonMaxSuppressionAsync = nonMaxSuppressionAsync_;
+    var cropAndResize = cropAndResize_;
+
+    var image_ops = /*#__PURE__*/Object.freeze({
+        resizeBilinear: resizeBilinear,
+        resizeNearestNeighbor: resizeNearestNeighbor,
+        nonMaxSuppression: nonMaxSuppression,
+        nonMaxSuppressionAsync: nonMaxSuppressionAsync,
+        cropAndResize: cropAndResize
+    });
+
+
+
+    var ops = /*#__PURE__*/Object.freeze({
+        image: image_ops,
+        linalg: linalg_ops,
+        losses: loss_ops,
+        spectral: spectral_ops,
+        op: op,
+        batchNormalization2d: batchNormalization2d,
+        batchNormalization3d: batchNormalization3d,
+        batchNormalization4d: batchNormalization4d,
+        batchNormalization: batchNormalization,
+        complex: complex,
+        real: real,
+        imag: imag,
+        concat: concat,
+        concat1d: concat1d,
+        concat2d: concat2d,
+        concat3d: concat3d,
+        concat4d: concat4d,
+        split: split$1,
+        conv1d: conv1d,
+        conv2d: conv2d,
+        conv3d: conv3d,
+        conv2dDerFilter: conv2dDerFilter,
+        depthwiseConv2d: depthwiseConv2d,
+        separableConv2d: separableConv2d,
+        conv2dTranspose: conv2dTranspose,
+        matMul: matMul,
+        dot: dot,
+        outerProduct: outerProduct,
+        reverse: reverse,
+        reverse1d: reverse1d,
+        reverse2d: reverse2d,
+        reverse3d: reverse3d,
+        reverse4d: reverse4d,
+        maxPool: maxPool,
+        avgPool: avgPool,
+        pool: pool,
+        slice: slice,
+        slice1d: slice1d,
+        slice2d: slice2d,
+        slice3d: slice3d,
+        slice4d: slice4d,
+        abs: abs,
+        acos: acos,
+        acosh: acosh,
+        asin: asin,
+        asinh: asinh,
+        atan: atan,
+        atanh: atanh,
+        ceil: ceil,
+        clipByValue: clipByValue,
+        cos: cos,
+        cosh: cosh,
+        erf: erf,
+        exp: exp,
+        expm1: expm1,
+        floor: floor,
+        log: log$1,
+        log1p: log1p,
+        logSigmoid: logSigmoid,
+        neg: neg,
+        reciprocal: reciprocal,
+        round: round,
+        rsqrt: rsqrt,
+        sigmoid: sigmoid,
+        sign: sign,
+        sin: sin,
+        sinh: sinh,
+        softplus: softplus,
+        sqrt: sqrt,
+        square: square,
+        step: step,
+        tan: tan,
+        tanh: tanh$1,
+        all: all,
+        any: any,
+        argMax: argMax,
+        argMin: argMin,
+        logSumExp: logSumExp,
+        max: max,
+        mean: mean,
+        min: min,
+        moments: moments,
+        sum: sum$1,
+        prod: prod,
+        equal: equal,
+        equalStrict: equalStrict,
+        greater: greater,
+        greaterEqual: greaterEqual,
+        greaterEqualStrict: greaterEqualStrict,
+        greaterStrict: greaterStrict,
+        less: less,
+        lessEqual: lessEqual,
+        lessEqualStrict: lessEqualStrict,
+        lessStrict: lessStrict,
+        notEqual: notEqual,
+        notEqualStrict: notEqualStrict,
+        add: add,
+        addN: addN,
+        addStrict: addStrict,
+        atan2: atan2,
+        div: div,
+        divStrict: divStrict,
+        floorDiv: floorDiv,
+        maximum: maximum,
+        maximumStrict: maximumStrict,
+        minimum: minimum,
+        minimumStrict: minimumStrict,
+        mod: mod,
+        modStrict: modStrict,
+        mul: mul,
+        mulStrict: mulStrict,
+        pow: pow,
+        powStrict: powStrict,
+        squaredDifference: squaredDifference,
+        squaredDifferenceStrict: squaredDifferenceStrict,
+        sub: sub,
+        subStrict: subStrict,
+        elu: elu,
+        leakyRelu: leakyRelu,
+        prelu: prelu,
+        relu: relu,
+        selu: selu,
+        logicalAnd: logicalAnd,
+        logicalNot: logicalNot,
+        logicalOr: logicalOr,
+        logicalXor: logicalXor,
+        where: where,
+        whereAsync: whereAsync,
+        buffer: buffer,
+        toPixels: toPixels,
+        print: print,
+        batchToSpaceND: batchToSpaceND,
+        cast: cast,
+        clone: clone,
+        cumsum: cumsum,
+        depthToSpace: depthToSpace,
+        expandDims: expandDims,
+        eye: eye,
+        fromPixels: fromPixels,
+        multinomial: multinomial,
+        oneHot: oneHot,
+        pad: pad,
+        pad1d: pad1d,
+        pad2d: pad2d,
+        pad3d: pad3d,
+        pad4d: pad4d,
+        rand: rand,
+        randomNormal: randomNormal,
+        randomUniform: randomUniform,
+        reshape: reshape,
+        spaceToBatchND: spaceToBatchND,
+        squeeze: squeeze,
+        stack: stack,
+        tile: tile,
+        truncatedNormal: truncatedNormal,
+        unstack: unstack,
+        setdiff1dAsync: setdiff1dAsync,
+        fill: fill,
+        linspace: linspace,
+        ones: ones$1,
+        range: range,
+        scalar: scalar,
+        tensor: tensor,
+        tensor1d: tensor1d,
+        tensor2d: tensor2d,
+        tensor3d: tensor3d,
+        tensor4d: tensor4d,
+        tensor5d: tensor5d,
+        tensor6d: tensor6d,
+        zeros: zeros,
+        onesLike: onesLike,
+        zerosLike: zerosLike,
+        transpose: transpose,
+        softmax: softmax,
+        logSoftmax: logSoftmax,
+        localResponseNormalization: localResponseNormalization,
+        norm: norm,
+        gather: gather,
+        unsortedSegmentSum: unsortedSegmentSum,
+        basicLSTMCell: basicLSTMCell,
+        multiRNNCell: multiRNNCell,
+        movingAverage: movingAverage,
+        stridedSlice: stridedSlice,
+        topk: topk,
+        scatterND: scatterND,
+        fft: fft,
+        ifft: ifft,
+        rfft: rfft,
+        irfft: irfft,
+        sparseToDense: sparseToDense,
+        gatherND: gatherND
+    });
+
+    var MathBackendCPU = (function () {
+        function MathBackendCPU() {
+            this.blockSize = 48;
+            this.firstUse = true;
+            if (ENV.get('IS_BROWSER')) {
+                this.fromPixels2DContext =
+                    document.createElement('canvas').getContext('2d');
+            }
+        }
+        MathBackendCPU.prototype.setDataMover = function (dataMover) {
+            this.data = new DataStorage(dataMover);
+        };
+        MathBackendCPU.prototype.register = function (dataId, shape, dtype) {
+            if (this.firstUse) {
+                this.firstUse = false;
+                if (ENV.get('IS_NODE')) {
+                    warn('\n============================\n' +
+                        'Hi there 👋. Looks like you are running TensorFlow.js in ' +
+                        'Node.js. To speed things up dramatically, install our node ' +
+                        'backend, which binds to TensorFlow C++, by running ' +
+                        'npm i @tensorflow/tfjs-node, ' +
+                        'or npm i @tensorflow/tfjs-node-gpu if you have CUDA. ' +
+                        'Then call require(\'@tensorflow/tfjs-node\'); (-gpu ' +
+                        'suffix for CUDA) at the start of your program. ' +
+                        'Visit https://github.com/tensorflow/tfjs-node for more details.' +
+                        '\n============================\n');
+                }
+            }
+            if (this.data.has(dataId)) {
+                throw new Error("Data buffer is already registered");
+            }
+            this.data.set(dataId, { dtype: dtype });
+        };
+        MathBackendCPU.prototype.write = function (dataId, values) {
+            if (values == null) {
+                throw new Error('MathBackendCPU.write(): values can not be null');
+            }
+            this.data.get(dataId).values = values;
+        };
+        MathBackendCPU.prototype.fromPixels = function (pixels, numChannels) {
+            if (pixels == null) {
+                throw new Error('pixels passed to tf.fromPixels() can not be null');
+            }
+            var vals;
+            if (ENV.get('IS_NODE') && pixels.getContext == null) {
+                throw new Error('When running in node, pixels must be an HTMLCanvasElement ' +
+                    'like the one returned by the `canvas` npm package');
+            }
+            if (pixels.getContext != null) {
+                vals = pixels
+                    .getContext('2d')
+                    .getImageData(0, 0, pixels.width, pixels.height)
+                    .data;
+            }
+            else if (pixels instanceof ImageData) {
+                vals = pixels.data;
+            }
+            else if (pixels instanceof HTMLImageElement ||
+                pixels instanceof HTMLVideoElement) {
+                if (this.fromPixels2DContext == null) {
+                    throw new Error('Can\'t read pixels from HTMLImageElement outside ' +
+                        'the browser.');
+                }
+                this.fromPixels2DContext.canvas.width = pixels.width;
+                this.fromPixels2DContext.canvas.height = pixels.height;
+                this.fromPixels2DContext.drawImage(pixels, 0, 0, pixels.width, pixels.height);
+                vals = this.fromPixels2DContext
+                    .getImageData(0, 0, pixels.width, pixels.height)
+                    .data;
+            }
+            else {
+                throw new Error('pixels passed to tf.fromPixels() must be either an ' +
+                    "HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or " +
+                    ("ImageData, but was " + pixels.constructor.name));
+            }
+            var values;
+            if (numChannels === 4) {
+                values = new Int32Array(vals);
+            }
+            else {
+                var numPixels = pixels.width * pixels.height;
+                values = new Int32Array(numPixels * numChannels);
+                for (var i = 0; i < numPixels; i++) {
+                    for (var channel = 0; channel < numChannels; ++channel) {
+                        values[i * numChannels + channel] = vals[i * 4 + channel];
+                    }
+                }
+            }
+            var outShape = [pixels.height, pixels.width, numChannels];
+            return tensor3d(values, outShape, 'int32');
+        };
+        MathBackendCPU.prototype.read = function (dataId) {
+            return __awaiter(this, void 0, void 0, function () {
+                return __generator(this, function (_a) {
+                    return [2, this.readSync(dataId)];
+                });
+            });
+        };
+        MathBackendCPU.prototype.readSync = function (dataId) {
+            var _a = this.data.get(dataId), dtype = _a.dtype, complexTensors = _a.complexTensors;
+            if (dtype === 'complex64') {
+                var realValues = complexTensors.real.dataSync();
+                var imagValues = complexTensors.imag.dataSync();
+                return mergeRealAndImagArrays(realValues, imagValues);
+            }
+            return this.data.get(dataId).values;
+        };
+        MathBackendCPU.prototype.disposeData = function (dataId) {
+            if (this.data.has(dataId)) {
+                var complexTensors = this.data.get(dataId).complexTensors;
+                if (complexTensors != null) {
+                    complexTensors.real.dispose();
+                    complexTensors.imag.dispose();
+                }
+                this.data.delete(dataId);
+            }
+        };
+        MathBackendCPU.prototype.time = function (f) {
+            return __awaiter(this, void 0, void 0, function () {
+                var start, kernelMs;
+                return __generator(this, function (_a) {
+                    start = now();
+                    f();
+                    kernelMs = now() - start;
+                    return [2, { kernelMs: kernelMs }];
+                });
+            });
+        };
+        MathBackendCPU.prototype.memory = function () {
+            return {
+                unreliable: true,
+                reasons: ['The reported memory is an upper bound. Due to automatic garbage ' +
+                        'collection, the true allocated memory may be less.']
+            };
+        };
+        MathBackendCPU.prototype.complex = function (real$$1, imag$$1) {
+            var result = Tensor.make(real$$1.shape, {}, 'complex64');
+            var resultData = this.data.get(result.dataId);
+            resultData.complexTensors = {
+                real: ENV.engine.keep(real$$1.clone()),
+                imag: ENV.engine.keep(imag$$1.clone())
+            };
+            return result;
+        };
+        MathBackendCPU.prototype.real = function (input) {
+            var resultData = this.data.get(input.dataId);
+            return resultData.complexTensors.real.clone();
+        };
+        MathBackendCPU.prototype.imag = function (input) {
+            var resultData = this.data.get(input.dataId);
+            return resultData.complexTensors.imag.clone();
+        };
+        MathBackendCPU.prototype.assertNotComplex = function (tensor$$1, opName) {
+            if (!Array.isArray(tensor$$1)) {
+                tensor$$1 = [tensor$$1];
+            }
+            tensor$$1.forEach(function (t) {
+                if (t != null) {
+                    assert(t.dtype !== 'complex64', opName + " does not support complex64 tensors.");
+                }
+            });
+        };
+        MathBackendCPU.prototype.slice = function (x, begin, size) {
+            this.assertNotComplex(x, 'slice');
+            var buffer$$1 = buffer(size, x.dtype);
+            for (var i = 0; i < buffer$$1.size; ++i) {
+                var loc = buffer$$1.indexToLoc(i);
+                var xLoc = loc.map(function (idx, j) { return idx + begin[j]; });
+                buffer$$1.set.apply(buffer$$1, [x.get.apply(x, xLoc)].concat(loc));
+            }
+            return buffer$$1.toTensor();
+        };
+        MathBackendCPU.prototype.stridedSlice = function (x, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask) {
+            this.assertNotComplex(x, 'stridedSlice');
+            var _a = getStridedSlicedInfo(x.shape, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask, shrinkAxisMask), beginIndex = _a[0], size = _a[1], shrinkAxis = _a[2];
+            var shape = size.filter(function (v, index) { return shrinkAxis.indexOf(index) === -1; });
+            if (shape.some(function (axis) { return axis === 0; })) {
+                return tensor([], shape);
+            }
+            var buffer$$1 = buffer(size, x.dtype);
+            for (var i = 0; i < buffer$$1.size; i++) {
+                var loc = buffer$$1.indexToLoc(i);
+                var newLoc = new Array(loc.length);
+                for (var j = 0; j < newLoc.length; j++) {
+                    newLoc[j] = loc[j] * strides[j] + beginIndex[j];
+                }
+                buffer$$1.set.apply(buffer$$1, [x.get.apply(x, newLoc)].concat(loc));
+            }
+            return buffer$$1.toTensor().reshape(shape);
+        };
+        MathBackendCPU.prototype.reverse = function (x, axis) {
+            this.assertNotComplex(x, 'reverse');
+            var buffer$$1 = buffer(x.shape, x.dtype);
+            var xBuffer = x.buffer();
+            var _loop_1 = function (i) {
+                var outLoc = buffer$$1.indexToLoc(i);
+                var inLoc = outLoc.slice();
+                axis.forEach(function (ax) { return inLoc[ax] = x.shape[ax] - 1 - inLoc[ax]; });
+                buffer$$1.set.apply(buffer$$1, [xBuffer.get.apply(xBuffer, inLoc)].concat(outLoc));
+            };
+            for (var i = 0; i < buffer$$1.size; i++) {
+                _loop_1(i);
+            }
+            return buffer$$1.toTensor();
+        };
+        MathBackendCPU.prototype.concat = function (tensors, axis) {
+            this.assertNotComplex(tensors, 'concat');
+            var tensors2D = tensors.map(function (t) {
+                var innerSize = sizeFromShape(t.shape.slice(axis));
+                return t.as2D(-1, innerSize);
+            });
+            var outShape = computeOutShape(tensors2D.map(function (t) { return t.shape; }), 1);
+            var values = buffer(outShape, tensors[0].dtype)
+                .values;
+            if (tensors2D[0].shape[0] === 1) {
+                var offset_1 = 0;
+                tensors2D.forEach(function (t) {
+                    values.set(t.dataSync(), offset_1);
+                    offset_1 += t.size;
+                });
+            }
+            else {
+                var colOffset_1 = 0;
+                tensors2D.forEach(function (t) {
+                    var tVals = t.dataSync();
+                    var tIdx = 0;
+                    for (var row = 0; row < t.shape[0]; ++row) {
+                        var resIdx = row * outShape[1] + colOffset_1;
+                        for (var col = 0; col < t.shape[1]; ++col) {
+                            values[resIdx + col] = tVals[tIdx++];
+                        }
+                    }
+                    colOffset_1 += t.shape[1];
+                });
+            }
+            var finalOutShape = computeOutShape(tensors.map(function (t) { return t.shape; }), axis);
+            return tensor(values, finalOutShape, tensors[0].dtype);
+        };
+        MathBackendCPU.prototype.neg = function (x) {
+            this.assertNotComplex(x, 'neg');
+            return this.multiply(scalar(-1), x);
+        };
+        MathBackendCPU.prototype.add = function (a, b) {
+            if (a.dtype === 'complex64' || b.dtype === 'complex64') {
+                return this.broadcastedBinaryComplexOp(a.cast('complex64'), b.cast('complex64'), function (aReal, aImag, bReal, bImag) {
+                    return { real: aReal + bReal, imag: aImag + bImag };
+                });
+            }
+            return this.broadcastedBinaryOp(a, b, upcastType(a.dtype, b.dtype), function (aValue, bValue) { return aValue + bValue; });
+        };
+        MathBackendCPU.prototype.addN = function (tensors) {
+            this.assertNotComplex(tensors, 'addN');
+            var vals = tensors.map(function (t) { return t.dataSync(); });
+            var result = buffer(tensors[0].shape, tensors[0].dtype);
+            var resultVals = result.values;
+            for (var i = 0; i < tensors.length; i++) {
+                var currVals = vals[i];
+                for (var j = 0; j < resultVals.length; j++) {
+                    resultVals[j] += currVals[j];
+                }
+            }
+            return result.toTensor();
+        };
+        MathBackendCPU.prototype.subtract = function (a, b) {
+            if (a.dtype === 'complex64' || b.dtype === 'complex64') {
+                return this.broadcastedBinaryComplexOp(a.cast('complex64'), b.cast('complex64'), function (aReal, aImag, bReal, bImag) {
+                    return { real: aReal - bReal, imag: aImag - bImag };
+                });
+            }
+            return this.broadcastedBinaryOp(a, b, upcastType(a.dtype, b.dtype), function (aValue, bValue) { return aValue - bValue; });
+        };
+        MathBackendCPU.prototype.pow = function (a, b) {
+            this.assertNotComplex([a, b], 'pow');
+            return this.broadcastedBinaryOp(a, b, a.dtype, function (aValue, bValue) { return Math.pow(aValue, bValue); });
+        };
+        MathBackendCPU.prototype.batchMatMul = function (a, b, transposeA, transposeB) {
+            this.assertNotComplex([a, b], 'matMul');
+            var sharedDim = transposeA ? a.shape[1] : a.shape[2];
+            var leftDim = transposeA ? a.shape[2] : a.shape[1];
+            var rightDim = transposeB ? b.shape[1] : b.shape[2];
+            var batchDim = a.shape[0];
+            var aValues = a.dataSync();
+            var bValues = b.dataSync();
+            var _a = transposeA ?
+                [a.strides[0], 1, a.strides[1]] :
+                [a.strides[0], a.strides[1], 1], aBatch = _a[0], aOuterStep = _a[1], aInnerStep = _a[2];
+            var _b = transposeB ?
+                [1, b.strides[1], b.strides[0]] :
+                [b.strides[1], 1, b.strides[0]], bInnerStep = _b[0], bOuterStep = _b[1], bBatch = _b[2];
+            var size = leftDim * rightDim;
+            var result = buffer([batchDim, leftDim, rightDim], a.dtype);
+            var resVals = result.values;
+            var blockSize = this.blockSize;
+            for (var b_1 = 0; b_1 < batchDim; b_1++) {
+                for (var i0 = 0; i0 < leftDim; i0 += blockSize) {
+                    for (var j0 = 0; j0 < rightDim; j0 += blockSize) {
+                        for (var k0 = 0; k0 < sharedDim; k0 += blockSize) {
+                            var iBlock = Math.min(i0 + blockSize, leftDim);
+                            var jBlock = Math.min(j0 + blockSize, rightDim);
+                            var kBlock = Math.min(k0 + blockSize, sharedDim);
+                            for (var i = i0; i < iBlock; i++) {
+                                for (var j = j0; j < jBlock; j++) {
+                                    var sum$$1 = 0.0;
+                                    for (var k = k0; k < kBlock; k++) {
+                                        sum$$1 += aValues[b_1 * aBatch + i * aOuterStep + k * aInnerStep] *
+                                            bValues[k * bInnerStep + j * bOuterStep + b_1 * bBatch];
+                                    }
+                                    resVals[b_1 * size + (i * rightDim + j)] += sum$$1;
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+            return result.toTensor();
+        };
+        MathBackendCPU.prototype.multiply = function (a, b) {
+            if (a.dtype === 'complex64' || b.dtype === 'complex64') {
+                return this.broadcastedBinaryComplexOp(a.cast('complex64'), b.cast('complex64'), function (aReal, aImag, bReal, bImag) {
+                    return {
+                        real: aReal * bReal - aImag * bImag,
+                        imag: aReal * bImag + aImag * bReal
+                    };
+                });
+            }
+            return this.broadcastedBinaryOp(a, b, upcastType(a.dtype, b.dtype), function (aValue, bValue) { return aValue * bValue; });
+        };
+        MathBackendCPU.prototype.realDivide = function (a, b) {
+            this.assertNotComplex([a, b], 'realDivide');
+            var op$$1 = function (a, b) { return a / b; };
+            var outputDtype = 'float32';
+            return this.broadcastedBinaryOp(a, b, outputDtype, op$$1);
+        };
+        MathBackendCPU.prototype.floorDiv = function (a, b) {
+            this.assertNotComplex([a, b], 'floorDiv');
+            var op$$1 = function (a, b) { return Math.floor(a / b); };
+            var outputDtype = 'int32';
+            return this.broadcastedBinaryOp(a, b, outputDtype, op$$1);
+        };
+        MathBackendCPU.prototype.sum = function (x, axes) {
+            this.assertNotComplex(x, 'sum');
+            assertAxesAreInnerMostDims('sum', axes, x.rank);
+            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+            var resultDtype = upcastType(x.dtype, 'int32');
+            var result = zeros(outShape, resultDtype);
+            var reduceSize = sizeFromShape(reduceShape);
+            var vals = result.dataSync();
+            var aVals = x.dataSync();
+            for (var i = 0; i < vals.length; ++i) {
+                var offset = i * reduceSize;
+                var sum$$1 = 0;
+                for (var j = 0; j < reduceSize; ++j) {
+                    sum$$1 += aVals[offset + j];
+                }
+                vals[i] = sum$$1;
+            }
+            return result;
+        };
+        MathBackendCPU.prototype.prod = function (x, axes) {
+            this.assertNotComplex(x, 'sum');
+            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+            var resultDtype = upcastType(x.dtype, 'int32');
+            var result = zeros(outShape, resultDtype);
+            var reduceSize = sizeFromShape(reduceShape);
+            var vals = result.dataSync();
+            var aVals = x.dataSync();
+            for (var i = 0; i < vals.length; ++i) {
+                var offset = i * reduceSize;
+                var prod$$1 = 1;
+                for (var j = 0; j < reduceSize; ++j) {
+                    prod$$1 *= aVals[offset + j];
+                }
+                vals[i] = prod$$1;
+            }
+            return result;
+        };
+        MathBackendCPU.prototype.unsortedSegmentSum = function (x, segmentIds, numSegments) {
+            this.assertNotComplex(x, 'unsortedSegmentSum');
+            var res = [];
+            var numIters = x.rank - segmentIds.rank;
+            for (var i = 0; i < numIters; ++i) {
+                segmentIds = segmentIds.expandDims(i + 1);
+            }
+            for (var i = 0; i < numSegments; ++i) {
+                var segmentId = scalar(i, 'int32');
+                var mask = equal(segmentId, segmentIds).asType('float32');
+                var sum$$1 = mask.mul(x).sum(0);
+                res.push(sum$$1);
+            }
+            return stack(res);
+        };
+        MathBackendCPU.prototype.argMin = function (x, axis) {
+            this.assertNotComplex(x, 'argMin');
+            var axes = [axis];
+            assertAxesAreInnerMostDims('argMin', axes, x.rank);
+            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+            var result = zeros(outShape, 'int32');
+            var reduceSize = sizeFromShape(reduceShape);
+            var vals = result.dataSync();
+            var aVals = x.dataSync();
+            for (var i = 0; i < vals.length; ++i) {
+                var offset = i * reduceSize;
+                var min$$1 = aVals[offset];
+                var minIndex = 0;
+                for (var j = 0; j < reduceSize; ++j) {
+                    var value = aVals[offset + j];
+                    if (value < min$$1) {
+                        min$$1 = value;
+                        minIndex = j;
+                    }
+                }
+                vals[i] = minIndex;
+            }
+            return result;
+        };
+        MathBackendCPU.prototype.argMax = function (x, axis) {
+            this.assertNotComplex(x, 'argMax');
+            var axes = [axis];
+            assertAxesAreInnerMostDims('argMax', axes, x.rank);
+            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+            var result = zeros(outShape, 'int32');
+            var reduceSize = sizeFromShape(reduceShape);
+            var vals = result.dataSync();
+            var aVals = x.dataSync();
+            for (var i = 0; i < vals.length; ++i) {
+                var offset = i * reduceSize;
+                var max$$1 = aVals[offset];
+                var maxIndex = 0;
+                for (var j = 0; j < reduceSize; ++j) {
+                    var value = aVals[offset + j];
+                    if (value > max$$1) {
+                        max$$1 = value;
+                        maxIndex = j;
+                    }
+                }
+                vals[i] = maxIndex;
+            }
+            return result;
+        };
+        MathBackendCPU.prototype.cumsum = function (x, axis, exclusive, reverse$$1) {
+            this.assertNotComplex(x, 'cumsum');
+            if (axis !== x.rank - 1) {
+                throw new Error("backend.cumsum in CPU expects an inner-most axis=" + (x.rank - 1) + " " +
+                    ("but got axis=" + axis));
+            }
+            var resultDtype = upcastType(x.dtype, 'int32');
+            var result = zeros(x.shape, resultDtype);
+            var vals = result.dataSync();
+            var aVals = x.dataSync();
+            var finalDim = x.shape[x.rank - 1];
+            var indexAdjuster = reverse$$1 ?
+                function (i, j) { return i + finalDim - j - 1; } :
+                function (i, j) { return i + j; };
+            for (var i = 0; i < aVals.length; i += finalDim) {
+                for (var j = 0; j < finalDim; j++) {
+                    var idx = indexAdjuster(i, j);
+                    if (j === 0) {
+                        vals[idx] = exclusive ? 0 : aVals[idx];
+                    }
+                    else {
+                        var prevIdx = indexAdjuster(i, j - 1);
+                        vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] :
+                            aVals[idx] + vals[prevIdx];
+                    }
+                }
+            }
+            return result;
+        };
+        MathBackendCPU.prototype.equal = function (a, b) {
+            this.assertNotComplex([a, b], 'equal');
+            return this.broadcastedBinaryOp(a, b, 'bool', function (aVal, bVal) {
+                return (aVal === bVal) ? 1 : 0;
+            });
+        };
+        MathBackendCPU.prototype.notEqual = function (a, b) {
+            this.assertNotComplex([a, b], 'notEqual');
+            return this.broadcastedBinaryOp(a, b, 'bool', function (aVal, bVal) {
+                return (aVal !== bVal) ? 1 : 0;
+            });
+        };
+        MathBackendCPU.prototype.less = function (a, b) {
+            this.assertNotComplex([a, b], 'less');
+            return this.broadcastedBinaryOp(a, b, 'bool', function (aVal, bVal) {
+                return (aVal < bVal) ? 1 : 0;
+            });
+        };
+        MathBackendCPU.prototype.lessEqual = function (a, b) {
+            this.assertNotComplex([a, b], 'lessEqual');
+            return this.broadcastedBinaryOp(a, b, 'bool', function (aVal, bVal) {
+                return (aVal <= bVal) ? 1 : 0;
+            });
+        };
+        MathBackendCPU.prototype.greater = function (a, b) {
+            this.assertNotComplex([a, b], 'greater');
+            return this.broadcastedBinaryOp(a, b, 'bool', function (aVal, bVal) {
+                return (aVal > bVal) ? 1 : 0;
+            });
+        };
+        MathBackendCPU.prototype.greaterEqual = function (a, b) {
+            this.assertNotComplex([a, b], 'greaterEqual');
+            return this.broadcastedBinaryOp(a, b, 'bool', function (aVal, bVal) {
+                return (aVal >= bVal) ? 1 : 0;
+            });
+        };
+        MathBackendCPU.prototype.logicalNot = function (x) {
+            this.assertNotComplex(x, 'logicalNot');
+            var values = x.dataSync();
+            var newValues = new Uint8Array(values.length);
+            for (var i = 0; i < values.length; ++i) {
+                newValues[i] = values[i] ? 0 : 1;
+            }
+            return Tensor.make(x.shape, { values: newValues }, 'bool');
+        };
+        MathBackendCPU.prototype.logicalAnd = function (a, b) {
+            this.assertNotComplex([a, b], 'logicalAnd');
+            return this.broadcastedBinaryOp(a, b, 'bool', function (aVal, bVal) {
+                return aVal && bVal;
+            });
+        };
+        MathBackendCPU.prototype.logicalOr = function (a, b) {
+            this.assertNotComplex([a, b], 'logicalOr');
+            return this.broadcastedBinaryOp(a, b, 'bool', function (aVal, bVal) {
+                return aVal || bVal;
+            });
+        };
+        MathBackendCPU.prototype.select = function (condition, a, b) {
+            this.assertNotComplex([condition, a, b], 'select');
+            var values = condition.dataSync();
+            var aValues = a.dataSync();
+            var bValues = b.dataSync();
+            var result = zeros(a.shape, upcastType(a.dtype, b.dtype));
+            var newValues = result.dataSync();
+            var index = 0;
+            var offset = condition.rank === 0 || condition.rank > 1 || a.rank === 1 ?
+                1 :
+                a.shape[1];
+            for (var i = 0; i < values.length; i++) {
+                for (var j = 0; j < offset; j++) {
+                    if (values[i] === 1) {
+                        newValues[index++] = aValues[i];
+                    }
+                    else {
+                        newValues[index++] = bValues[i];
+                    }
+                }
+            }
+            return result;
+        };
+        MathBackendCPU.prototype.where = function (condition) {
+            this.assertNotComplex([condition], 'where');
+            var condVals = condition.dataSync();
+            return whereImpl(condition.shape, condVals);
+        };
+        MathBackendCPU.prototype.topk = function (x, k, sorted) {
+            this.assertNotComplex(x, 'topk');
+            var xVals = x.dataSync();
+            return topkImpl(xVals, x.shape, x.dtype, k, sorted);
+        };
+        MathBackendCPU.prototype.min = function (x, axes) {
+            this.assertNotComplex(x, 'min');
+            assertAxesAreInnerMostDims('min', axes, x.rank);
+            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+            var result = zeros(outShape, x.dtype);
+            var reduceSize = sizeFromShape(reduceShape);
+            var vals = result.dataSync();
+            var aVals = x.dataSync();
+            for (var i = 0; i < vals.length; ++i) {
+                var offset = i * reduceSize;
+                var min$$1 = aVals[offset];
+                for (var j = 0; j < reduceSize; ++j) {
+                    var value = aVals[offset + j];
+                    if (value < min$$1) {
+                        min$$1 = value;
+                    }
+                }
+                vals[i] = min$$1;
+            }
+            return result;
+        };
+        MathBackendCPU.prototype.minimum = function (a, b) {
+            this.assertNotComplex([a, b], 'minimum');
+            return this.broadcastedBinaryOp(a, b, a.dtype, function (aVal, bVal) { return Math.min(aVal, bVal); });
+        };
+        MathBackendCPU.prototype.mod = function (a, b) {
+            this.assertNotComplex([a, b], 'mod');
+            return this.broadcastedBinaryOp(a, b, a.dtype, function (aVal, bVal) {
+                var rem = aVal % bVal;
+                if ((aVal < 0 && bVal < 0) || (aVal >= 0 && bVal >= 0)) {
+                    return rem;
+                }
+                else {
+                    return (rem + bVal) % bVal;
+                }
+            });
+        };
+        MathBackendCPU.prototype.max = function (x, axes) {
+            this.assertNotComplex(x, 'max');
+            assertAxesAreInnerMostDims('max', axes, x.rank);
+            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+            var result = zeros(outShape, x.dtype);
+            var reduceSize = sizeFromShape(reduceShape);
+            var vals = result.dataSync();
+            var aVals = x.dataSync();
+            for (var i = 0; i < vals.length; ++i) {
+                var offset = i * reduceSize;
+                var max$$1 = aVals[offset];
+                for (var j = 0; j < reduceSize; ++j) {
+                    var value = aVals[offset + j];
+                    if (value > max$$1) {
+                        max$$1 = value;
+                    }
+                }
+                vals[i] = max$$1;
+            }
+            return result;
+        };
+        MathBackendCPU.prototype.maximum = function (a, b) {
+            this.assertNotComplex([a, b], 'maximum');
+            return this.broadcastedBinaryOp(a, b, a.dtype, function (aVal, bVal) { return Math.max(aVal, bVal); });
+        };
+        MathBackendCPU.prototype.all = function (x, axes) {
+            this.assertNotComplex(x, 'all');
+            assertAxesAreInnerMostDims('all', axes, x.rank);
+            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+            var result = zeros(outShape, x.dtype);
+            var reduceSize = sizeFromShape(reduceShape);
+            var vals = result.dataSync();
+            var aVals = x.dataSync();
+            for (var i = 0; i < vals.length; ++i) {
+                var offset = i * reduceSize;
+                var all$$1 = aVals[offset];
+                for (var j = 0; j < reduceSize; ++j) {
+                    var value = aVals[offset + j];
+                    all$$1 = all$$1 && value;
+                }
+                vals[i] = all$$1;
+            }
+            return result;
+        };
+        MathBackendCPU.prototype.any = function (x, axes) {
+            this.assertNotComplex(x, 'any');
+            assertAxesAreInnerMostDims('any', axes, x.rank);
+            var _a = computeOutAndReduceShapes(x.shape, axes), outShape = _a[0], reduceShape = _a[1];
+            var result = zeros(outShape, x.dtype);
+            var reduceSize = sizeFromShape(reduceShape);
+            var vals = result.dataSync();
+            var aVals = x.dataSync();
+            for (var i = 0; i < vals.length; ++i) {
+                var offset = i * reduceSize;
+                var anyVal = aVals[offset];
+                for (var j = 0; j < reduceSize; ++j) {
+                    var value = aVals[offset + j];
+                    anyVal = anyVal || value;
+                }
+                vals[i] = anyVal;
+            }
+            return result;
+        };
+        MathBackendCPU.prototype.squaredDifference = function (a, b) {
+            this.assertNotComplex([a, b], 'squaredDifference');
+            return this.broadcastedBinaryOp(a, b, a.dtype, function (aVal, bVal) {
+                var diff = aVal - bVal;
+                return diff * diff;
+            });
+        };
+        MathBackendCPU.prototype.ceil = function (x) {
+            this.assertNotComplex(x, 'ceil');
+            var values = x.dataSync();
+            var newValues = new Float32Array(values.length);
+            for (var i = 0; i < values.length; ++i) {
+                newValues[i] = Math.ceil(values[i]);
+            }
+            return Tensor.make(x.shape, { values: newValues });
+        };
+        MathBackendCPU.prototype.floor = function (x) {
+            this.assertNotComplex(x, 'floor');
+            var values = x.dataSync();
+            var newValues = new Float32Array(values.length);
+            for (var i = 0; i < values.length; ++i) {
+                newValues[i] = Math.floor(values[i]);
+            }
+            return Tensor.make(x.shape, { values: newValues });
+        };
+        MathBackendCPU.prototype.sign = function (x) {
+            this.assertNotComplex(x, 'x');
+            var values = x.dataSync();
+            var newValues = new Float32Array(values.length);
+            for (var i = 0; i < values.length; ++i) {
+                if (values[i] < 0) {
+                    newValues[i] = -1;
+                }
+                else if (values[i] > 0) {
+                    newValues[i] = 1;
+                }
+                else {
+                    newValues[i] = 0;
+                }
+            }
+            return Tensor.make(x.shape, { values: newValues });
+        };
+        MathBackendCPU.prototype.round = function (x) {
+            this.assertNotComplex(x, 'round');
+            var values = x.dataSync();
+            var newValues = new Float32Array(values.length);
+            for (var i = 0; i < values.length; ++i) {
+                var base = Math.floor(values[i]);
+                if (values[i] - base < 0.5) {
+                    newValues[i] = Math.floor(values[i]);
+                }
+                else if (values[i] - base > 0.5) {
+                    newValues[i] = Math.ceil(values[i]);
+                }
+                else {
+                    if (base % 2.0 === 0.0) {
+                        newValues[i] = base;
+                    }
+                    else {
+                        newValues[i] = base + 1.0;
+                    }
+                }
+            }
+            return Tensor.make(x.shape, { values: newValues });
+        };
+        MathBackendCPU.prototype.exp = function (x) {
+            this.assertNotComplex(x, 'exp');
+            var values = x.dataSync();
+            var newValues = new Float32Array(values.length);
+            for (var i = 0; i < values.length; ++i) {
+                newValues[i] = Math.exp(values[i]);
+            }
+            return Tensor.make(x.shape, { values: newValues });
+        };
+        MathBackendCPU.prototype.expm1 = function (x) {
+            this.assertNotComplex(x, 'expm1');
+            var values = x.dataSync();
+            var newValues = new Float32Array(values.length);
+            for (var i = 0; i < values.length; ++i) {
+                newValues[i] = Math.expm1(values[i]);
+            }
+            return Tensor.make(x.shape, { values: newValues });
+        };
+        MathBackendCPU.prototype.log = function (x) {
+            this.assertNotComplex(x, 'log');
+            var values = x.dataSync();
+            var newValues = new Float32Array(values.length);
+            for (var i = 0; i < values.length; ++i) {
+                var value = values[i];
+                newValues[i] = Math.log(value);
+            }
+            return Tensor.make(x.shape, { values: newValues });
+        };
+        MathBackendCPU.prototype.log1p = function (x) {
+            this.assertNotComplex(x, 'log1p');
+            var values = x.dataSync();
+            var newValues = new Float32Array(values.length);
+            for (var i = 0; i < values.length; ++i) {
+                var value = values[i];
+                newValues[i] = Math.log1p(value);
+            }
+            return Tensor.make(x.shape, { values: newValues });
+        };
+        MathBackendCPU.prototype.sqrt = function (x) {
+            this.assertNotComplex(x, 'sqrt');
+            var values = x.dataSync();
+            var newValues = new Float32Array(values.length);
+            for (var i = 0; i < values.length; ++i) {
+                var value = values[i];
+                newValues[i] = Math.sqrt(value);
+            }
+            return Tensor.make(x.shape, { values: newValues });
+        };
+        MathBackendCPU.prototype.rsqrt = function (x) {
+            this.assertNotComplex(x, 'rsqrt');
+            var values = x.dataSync();
+            var newValues = new Float32Array(values.length);
+            for (var i = 0; i < values.length; ++i) {
+                var value = values[i];
+                newValues[i] = 1 / Math.sqrt(value);
+            }
+            return Tensor.make(x.shape, { values: newValues });
+        };
+        MathBackendCPU.prototype.square = function (x) {
+            this.assertNotComplex(x, 'square');
+            var values = x.dataSync();
+            var newValues = new Float32Array(values.length);
+            for (var i = 0; i < values.length; ++i) {
+                var value = values[i];
+                newValues[i] = value * value;
+            }
+            return Tensor.make(x.shape, { values: newValues });
+        };
+        MathBackendCPU.prototype.reciprocal = function (x) {
+            this.assertNotComplex(x, 'reciprocal');
+            var values = x.dataSync();
+            var newValues = new Float32Array(values.length);
+            for (var i = 0; i < values.length; ++i) {
+                newValues[i] = 1 / values[i];
+            }
+            return Tensor.make(x.shape, { values: newValues });
+        };
+        MathBackendCPU.prototype.relu = function (x) {
+            this.assertNotComplex(x, 'relu');
+            var res = zeros(x.shape, x.dtype);
+            var resVals = res.dataSync();
+            var inVals = x.dataSync();
+            for (var i = 0; i < inVals.length; ++i) {
+                resVals[i] = Math.max(0, inVals[i]);
+            }
+            return res;
+        };
+        MathBackendCPU.prototype.elu = function (x) {
+            this.assertNotComplex(x, 'elu');
+            var resultValues = new Float32Array(x.size);
+            var values = x.dataSync();
+            for (var i = 0; i < values.length; ++i) {
+                var v = values[i];
+                if (v >= 0) {
+                    resultValues[i] = v;
+                }
+                else {
+                    resultValues[i] = (Math.exp(v) - 1);
+                }
+            }
+            return Tensor.make(x.shape, { values: resultValues });
+        };
+        MathBackendCPU.prototype.eluDer = function (dy, y) {
+            this.assertNotComplex([dy, y], 'eluDer');
+            var resultValues = new Float32Array(y.size);
+            var values = y.dataSync();
+            var dyValues = dy.dataSync();
+            for (var i = 0; i < values.length; ++i) {
+                var v = values[i];
+                if (v >= 1) {
+                    resultValues[i] = dyValues[i];
+                }
+                else {
+                    resultValues[i] = dyValues[i] * (v + 1);
+                }
+            }
+            return Tensor.make(y.shape, { values: resultValues });
+        };
+        MathBackendCPU.prototype.selu = function (x) {
+            this.assertNotComplex(x, 'selu');
+            var scaleAlpha = SELU_SCALEALPHA;
+            var scale = SELU_SCALE;
+            var resultValues = new Float32Array(x.size);
+            var values = x.dataSync();
+            for (var i = 0; i < values.length; ++i) {
+                var v = values[i];
+                if (v >= 0) {
+                    resultValues[i] = scale * v;
+                }
+                else {
+                    resultValues[i] = scaleAlpha * (Math.exp(v) - 1);
+                }
+            }
+            return Tensor.make(x.shape, { values: resultValues });
+        };
+        MathBackendCPU.prototype.clip = function (x, min$$1, max$$1) {
+            this.assertNotComplex(x, 'clip');
+            var resultValues = new Float32Array(x.size);
+            var values = x.dataSync();
+            for (var i = 0; i < values.length; ++i) {
+                var v = values[i];
+                resultValues[i] = v > max$$1 ? max$$1 : (v < min$$1 ? min$$1 : v);
+            }
+            return Tensor.make(x.shape, { values: resultValues });
+        };
+        MathBackendCPU.prototype.abs = function (x) {
+            var resultValues = new Float32Array(x.size);
+            var values = x.dataSync();
+            for (var i = 0; i < values.length; ++i) {
+                resultValues[i] = Math.abs(values[i]);
+            }
+            return Tensor.make(x.shape, { values: resultValues });
+        };
+        MathBackendCPU.prototype.complexAbs = function (x) {
+            var resultValues = new Float32Array(x.size);
+            var values = x.dataSync();
+            for (var i = 0; i < x.size; ++i) {
+                var real$$1 = values[i * 2];
+                var imag$$1 = values[i * 2 + 1];
+                resultValues[i] = Math.hypot(real$$1, imag$$1);
+            }
+            return Tensor.make(x.shape, { values: resultValues });
+        };
+        MathBackendCPU.prototype.int = function (x) {
+            this.assertNotComplex(x, 'int');
+            var resultValues = new Int32Array(x.size);
+            var values = x.dataSync();
+            for (var i = 0; i < values.length; ++i) {
+                resultValues[i] = values[i];
+            }
+            return Tensor.make(x.shape, { values: resultValues }, 'int32');
+        };
+        MathBackendCPU.prototype.sigmoid = function (x) {
+            this.assertNotComplex(x, 'sigmoid');
+            var resultValues = new Float32Array(x.size);
+            var values = x.dataSync();
+            for (var i = 0; i < values.length; ++i) {
+                resultValues[i] = 1 / (1 + Math.exp(-values[i]));
+            }
+            return Tensor.make(x.shape, { values: resultValues });
+        };
+        MathBackendCPU.prototype.softplus = function (x) {
+            this.assertNotComplex(x, 'softplus');
+            var epsilon = 1.1920928955078125e-7;
+            var threshold = Math.log(epsilon) + 2.0;
+            var resultValues = new Float32Array(x.size);
+            var values = x.dataSync();
+            for (var i = 0; i < values.length; ++i) {
+                var tooLarge = values[i] > -threshold;
+                var tooSmall = values[i] < threshold;
+                var expX = Math.exp(values[i]);
+                var result = void 0;
+                if (tooSmall) {
+                    result = expX;
+                }
+                else if (tooLarge) {
+                    result = values[i];
+                }
+                else {
+                    result = Math.log(1.0 + expX);
+                }
+                resultValues[i] = result;
+            }
+            return Tensor.make(x.shape, { values: resultValues });
+        };
+        MathBackendCPU.prototype.sin = function (x) {
+            this.assertNotComplex(x, 'sin');
+            var resultValues = new Float32Array(x.size);
+            var values = x.dataSync();
+            for (var i = 0; i < values.length; ++i) {
+                resultValues[i] = Math.sin(values[i]);
+            }
+            return Tensor.make(x.shape, { values: resultValues });
+        };
+        MathBackendCPU.prototype.cos = function (x) {
+            this.assertNotComplex(x, 'cos');
+            var resultValues = new Float32Array(x.size);
+            var values = x.dataSync();
+            for (var i = 0; i < values.length; ++i) {
+                resultValues[i] = Math.cos(values[i]);
+            }
+            return Tensor.make(x.shape, { values: resultValues });
+        };
+        MathBackendCPU.prototype.tan = function (x) {
+            this.assertNotComplex(x, 'tan');
+            var resultValues = new Float32Array(x.size);
+            var values = x.dataSync();
+            for (var i = 0; i < values.length; ++i) {
+                resultValues[i] = Math.tan(values[i]);
+            }
+            return Tensor.make(x.shape, { values: resultValues });
+        };
+        MathBackendCPU.prototype.asin = function (x) {
+            this.assertNotComplex(x, 'asin');
+            var resultValues = new Float32Array(x.size);
+            var values = x.dataSync();
+            for (var i = 0; i < values.length; ++i) {
+                resultValues[i] = Math.asin(values[i]);
+            }
+            return Tensor.make(x.shape, { values: resultValues });
+        };
+        MathBackendCPU.prototype.acos = function (x) {
+            this.assertNotComplex(x, 'acos');
+            var resultValues = new Float32Array(x.size);
+            var values = x.dataSync();
+            for (var i = 0; i < values.length; ++i) {
+                resultValues[i] = Math.acos(values[i]);
+            }
+            return Tensor.make(x.shape, { values: resultValues });
+        };
+        MathBackendCPU.prototype.atan = function (x) {
+            this.assertNotComplex(x, 'atan');
+            var resultValues = new Float32Array(x.size);
+            var values = x.dataSync();
+            for (var i = 0; i < values.length; ++i) {
+                resultValues[i] = Math.atan(values[i]);
+            }
+            return Tensor.make(x.shape, { values: resultValues });
+        };
+        MathBackendCPU.prototype.atan2 = function (a, b) {
+            this.assertNotComplex([a, b], 'atan2');
+            return this.broadcastedBinaryOp(a, b, a.dtype, function (aValue, bValue) { return Math.atan2(aValue, bValue); });
+        };
+        MathBackendCPU.prototype.sinh = function (x) {
+            this.assertNotComplex(x, 'sinh');
+            var resultValues = new Float32Array(x.size);
+            var values = x.dataSync();
+            for (var i = 0; i < values.length; ++i) {
+                resultValues[i] = Math.sinh(values[i]);
+            }
+            return Tensor.make(x.shape, { values: resultValues });
+        };
+        MathBackendCPU.prototype.cosh = function (x) {
+            this.assertNotComplex(x, 'cosh');
+            var resultValues = new Float32Array(x.size);
+            var values = x.dataSync();
+            for (var i = 0; i < values.length; ++i) {
+                resultValues[i] = Math.cosh(values[i]);
+            }
+            return Tensor.make(x.shape, { values: resultValues });
+        };
+        MathBackendCPU.prototype.tanh = function (x) {
+            this.assertNotComplex(x, 'tanh');
+            var resultValues = new Float32Array(x.size);
+            var values = x.dataSync();
+            for (var i = 0; i < values.length; ++i) {
+                resultValues[i] = tanh(values[i]);
+            }
+            return Tensor.make(x.shape, { values: resultValues });
+        };
+        MathBackendCPU.prototype.asinh = function (x) {
+            this.assertNotComplex(x, 'asinh');
+            var resultValues = new Float32Array(x.size);
+            var values = x.dataSync();
+            for (var i = 0; i < values.length; ++i) {
+                resultValues[i] = Math.asinh(values[i]);
+            }
+            return Tensor.make(x.shape, { values: resultValues });
+        };
+        MathBackendCPU.prototype.acosh = function (x) {
+            this.assertNotComplex(x, 'acosh');
+            var resultValues = new Float32Array(x.size);
+            var values = x.dataSync();
+            for (var i = 0; i < values.length; ++i) {
+                resultValues[i] = Math.acosh(values[i]);
+            }
+            return Tensor.make(x.shape, { values: resultValues });
+        };
+        MathBackendCPU.prototype.atanh = function (x) {
+            this.assertNotComplex(x, 'atanh');
+            var resultValues = new Float32Array(x.size);
+            var values = x.dataSync();
+            for (var i = 0; i < values.length; ++i) {
+                resultValues[i] = Math.atanh(values[i]);
+            }
+            return Tensor.make(x.shape, { values: resultValues });
+        };
+        MathBackendCPU.prototype.erf = function (x) {
+            this.assertNotComplex(x, 'erf');
+            var resultValues = new Float32Array(x.size);
+            var values = x.dataSync();
+            var p = ERF_P;
+            var a1 = ERF_A1;
+            var a2 = ERF_A2;
+            var a3 = ERF_A3;
+            var a4 = ERF_A4;
+            var a5 = ERF_A5;
+            for (var i = 0; i < values.length; ++i) {
+                var v = values[i];
+                var t = 1.0 / (1.0 + p * v);
+                resultValues[i] = 1.0 -
+                    (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t *
+                        Math.exp(-v * v);
+            }
+            return Tensor.make(x.shape, { values: resultValues });
+        };
+        MathBackendCPU.prototype.step = function (x, alpha) {
+            if (alpha === void 0) { alpha = 0; }
+            this.assertNotComplex(x, 'step');
+            var resultValues = new Float32Array(x.size);
+            var values = x.dataSync();
+            for (var i = 0; i < values.length; ++i) {
+                var value = values[i];
+                if (isNaN(value)) {
+                    resultValues[i] = NaN;
+                }
+                else {
+                    resultValues[i] = value > 0 ? 1 : alpha;
+                }
+            }
+            return Tensor.make(x.shape, { values: resultValues });
+        };
+        MathBackendCPU.prototype.conv2d = function (x, filter, convInfo) {
+            this.assertNotComplex([x, filter], 'conv2d');
+            var filterHeight = convInfo.filterHeight;
+            var filterWidth = convInfo.filterWidth;
+            var dilationHeight = convInfo.dilationHeight;
+            var dilationWidth = convInfo.dilationWidth;
+            var padLeft = convInfo.padInfo.left;
+            var padTop = convInfo.padInfo.top;
+            var y = buffer(convInfo.outShape, x.dtype);
+            var xVals = x.dataSync();
+            var wVals = filter.dataSync();
+            var yVals = y.values;
+            for (var b = 0; b < convInfo.batchSize; ++b) {
+                var xOffset1 = b * x.strides[0];
+                var yOffset1 = b * y.strides[0];
+                for (var yR = 0; yR < convInfo.outHeight; ++yR) {
+                    var yOffset2 = yOffset1 + yR * y.strides[1];
+                    var xRCorner = yR * convInfo.strideHeight - padLeft;
+                    for (var wR = 0; wR < filterHeight; wR++) {
+                        var xR = xRCorner + wR * dilationHeight;
+                        if (xR < 0 || xR >= convInfo.inHeight) {
+                            continue;
+                        }
+                        var wOffset1 = wR * filter.strides[0];
+                        var xOffset2 = xOffset1 + xR * x.strides[1];
+                        for (var yC = 0; yC < convInfo.outWidth; ++yC) {
+                            var yOffset3 = yOffset2 + yC * convInfo.outChannels;
+                            var xCCorner = yC * convInfo.strideWidth - padTop;
+                            for (var wC = 0; wC < filterWidth; wC++) {
+                                var xC = xCCorner + wC * dilationWidth;
+                                if (xC < 0 || xC >= convInfo.inWidth) {
+                                    continue;
+                                }
+                                var wOffset2 = wOffset1 + wC * filter.strides[1];
+                                var xOffset3 = xOffset2 + xC * convInfo.inChannels;
+                                var wOffset3 = wOffset2;
+                                for (var d1 = 0; d1 < convInfo.inChannels; ++d1) {
+                                    var xVal = xVals[xOffset3 + d1];
+                                    for (var d2 = 0; d2 < convInfo.outChannels; ++d2) {
+                                        yVals[yOffset3 + d2] += xVal * wVals[wOffset3 + d2];
+                                    }
+                                    wOffset3 += convInfo.outChannels;
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+            return y.toTensor();
+        };
+        MathBackendCPU.prototype.conv3d = function (x, filter, convInfo) {
+            var filterDepth = convInfo.filterDepth;
+            var filterHeight = convInfo.filterHeight;
+            var filterWidth = convInfo.filterWidth;
+            var dilationDepth = convInfo.dilationDepth;
+            var dilationHeight = convInfo.dilationHeight;
+            var dilationWidth = convInfo.dilationWidth;
+            var padFront = convInfo.padInfo.front;
+            var padLeft = convInfo.padInfo.left;
+            var padTop = convInfo.padInfo.top;
+            var y = buffer(convInfo.outShape, x.dtype);
+            var xVals = x.dataSync();
+            var wVals = filter.dataSync();
+            var yVals = y.values;
+            for (var b = 0; b < convInfo.batchSize; ++b) {
+                var xOffset1 = b * x.strides[0];
+                var yOffset1 = b * y.strides[0];
+                for (var yF = 0; yF < convInfo.outDepth; ++yF) {
+                    var yOffset2 = yOffset1 + yF * y.strides[1];
+                    var xFCorner = yF * convInfo.strideDepth - padFront;
+                    for (var wF = 0; wF < filterDepth; wF++) {
+                        var xF = xFCorner + wF * dilationDepth;
+                        if (xF < 0 || xF >= convInfo.inDepth) {
+                            continue;
+                        }
+                        var wOffset1 = wF * filter.strides[0];
+                        var xOffset2 = xOffset1 + xF * x.strides[1];
+                        for (var yR = 0; yR < convInfo.outHeight; ++yR) {
+                            var yOffset3 = yOffset2 + yR * y.strides[2];
+                            var xRCorner = yR * convInfo.strideHeight - padTop;
+                            for (var wR = 0; wR < filterHeight; wR++) {
+                                var xR = xRCorner + wR * dilationHeight;
+                                if (xR < 0 || xR >= convInfo.inHeight) {
+                                    continue;
+                                }
+                                var wOffset2 = wOffset1 + wR * filter.strides[1];
+                                var xOffset3 = xOffset2 + xR * x.strides[2];
+                                for (var yC = 0; yC < convInfo.outWidth; ++yC) {
+                                    var yOffset4 = yOffset3 + yC * convInfo.outChannels;
+                                    var xCCorner = yC * convInfo.strideWidth - padLeft;
+                                    for (var wC = 0; wC < filterWidth; wC++) {
+                                        var xC = xCCorner + wC * dilationWidth;
+                                        if (xC < 0 || xC >= convInfo.inWidth) {
+                                            continue;
+                                        }
+                                        var wOffset3 = wOffset2 + wC * filter.strides[2];
+                                        var xOffset4 = xOffset3 + xC * convInfo.inChannels;
+                                        var wOffset4 = wOffset3;
+                                        for (var d1 = 0; d1 < convInfo.inChannels; ++d1) {
+                                            var xVal = xVals[xOffset4 + d1];
+                                            for (var d2 = 0; d2 < convInfo.outChannels; ++d2) {
+                                                yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];
+                                            }
+                                            wOffset4 += convInfo.outChannels;
+                                        }
+                                    }
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+            return y.toTensor();
+        };
+        MathBackendCPU.prototype.conv2dDerInput = function (dy, filter, convInfo) {
+            this.assertNotComplex([dy, filter], 'conv2dDerInput');
+            var dx = buffer(convInfo.inShape, 'float32');
+            var dxValues = dx.values;
+            var _a = dx.strides, dxS0 = _a[0], dxS1 = _a[1], dxS2 = _a[2];
+            var dyValues = dy.dataSync();
+            var _b = dy.strides, dyS0 = _b[0], dyS1 = _b[1], dyS2 = _b[2];
+            var fltValues = filter.dataSync();
+            var _c = filter.strides, fltS0 = _c[0], fltS1 = _c[1], fltS2 = _c[2];
+            var batchSize = convInfo.batchSize, filterHeight = convInfo.filterHeight, filterWidth = convInfo.filterWidth, inChannels = convInfo.inChannels, inHeight = convInfo.inHeight, inWidth = convInfo.inWidth, outChannels = convInfo.outChannels, outHeight = convInfo.outHeight, outWidth = convInfo.outWidth, strideHeight = convInfo.strideHeight, strideWidth = convInfo.strideWidth;
+            var topPad = filterHeight - 1 - convInfo.padInfo.top;
+            var leftPad = filterWidth - 1 - convInfo.padInfo.left;
+            for (var b = 0; b < batchSize; ++b) {
+                for (var d1 = 0; d1 < inChannels; ++d1) {
+                    for (var xR = 0; xR < inHeight; ++xR) {
+                        var xRCorner = xR - topPad;
+                        var xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
+                        var yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
+                        for (var xC = 0; xC < inWidth; ++xC) {
+                            var xCCorner = xC - leftPad;
+                            var xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
+                            var yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
+                            var dotProd = 0;
+                            for (var yR = xRMin; yR < yRMax; ++yR) {
+                                var wR = yR * strideHeight - xRCorner;
+                                for (var yC = xCMin; yC < yCMax; ++yC) {
+                                    var wC = yC * strideWidth - xCCorner;
+                                    var dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;
+                                    var fltOffset = fltS0 * (filterHeight - 1 - wR) +
+                                        fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;
+                                    for (var d2 = 0; d2 < outChannels; ++d2) {
+                                        var pixel = dyValues[dyOffset + d2];
+                                        var weight = fltValues[fltOffset + d2];
+                                        dotProd += pixel * weight;
+                                    }
+                                }
+                            }
+                            dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;
+                        }
+                    }
+                }
+            }
+            return dx.toTensor();
+        };
+        MathBackendCPU.prototype.conv3dDerInput = function (dy, filter, convInfo) {
+            var dx = buffer(convInfo.inShape, 'float32');
+            var dxValues = dx.values;
+            var _a = dx.strides, dxS0 = _a[0], dxS1 = _a[1], dxS2 = _a[2], dxS3 = _a[3];
+            var dyValues = dy.dataSync();
+            var _b = dy.strides, dyS0 = _b[0], dyS1 = _b[1], dyS2 = _b[2], dyS3 = _b[3];
+            var fltValues = filter.dataSync();
+            var _c = filter.strides, fltS0 = _c[0], fltS1 = _c[1], fltS2 = _c[2], fltS3 = _c[3];
+            var batchSize = convInfo.batchSize, filterDepth = convInfo.filterDepth, filterHeight = convInfo.filterHeight, filterWidth = convInfo.filterWidth, inChannels = convInfo.inChannels, inDepth = convInfo.inDepth, inHeight = convInfo.inHeight, inWidth = convInfo.inWidth, outChannels = convInfo.outChannels, outDepth = convInfo.outDepth, outHeight = convInfo.outHeight, outWidth = convInfo.outWidth, strideDepth = convInfo.strideDepth, strideHeight = convInfo.strideHeight, strideWidth = convInfo.strideWidth;
+            var frontPad = filterDepth - 1 - convInfo.padInfo.front;
+            var topPad = filterHeight - 1 - convInfo.padInfo.top;
+            var leftPad = filterWidth - 1 - convInfo.padInfo.left;
+            for (var b = 0; b < batchSize; ++b) {
+                for (var d1 = 0; d1 < inChannels; ++d1) {
+                    for (var xF = 0; xF < inDepth; ++xF) {
+                        var xFCorner = xF - frontPad;
+                        var xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));
+                        var yFMax = Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);
+                        for (var xR = 0; xR < inHeight; ++xR) {
+                            var xRCorner = xR - topPad;
+                            var xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
+                            var yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
+                            for (var xC = 0; xC < inWidth; ++xC) {
+                                var xCCorner = xC - leftPad;
+                                var xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
+                                var yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
+                                var dotProd = 0;
+                                for (var yF = xFMin; yF < yFMax; ++yF) {
+                                    var wF = yF * strideDepth - xFCorner;
+                                    for (var yR = xRMin; yR < yRMax; ++yR) {
+                                        var wR = yR * strideHeight - xRCorner;
+                                        for (var yC = xCMin; yC < yCMax; ++yC) {
+                                            var wC = yC * strideWidth - xCCorner;
+                                            var dyOffset = dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;
+                                            var fltOffset = fltS0 * (filterDepth - 1 - wF) +
+                                                fltS1 * (filterHeight - 1 - wR) +
+                                                fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;
+                                            for (var d2 = 0; d2 < outChannels; ++d2) {
+                                                var pixel = dyValues[dyOffset + d2];
+                                                var weight = fltValues[fltOffset + d2];
+                                                dotProd += pixel * weight;
+                                            }
+                                        }
+                                    }
+                                }
+                                dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] =
+                                    dotProd;
+                            }
+                        }
+                    }
+                }
+            }
+            return dx.toTensor();
+        };
+        MathBackendCPU.prototype.conv2dDerFilter = function (x, dy, convInfo) {
+            this.assertNotComplex([x, dy], 'conv2dDerFilter');
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var filterHeight = convInfo.filterHeight;
+            var filterWidth = convInfo.filterWidth;
+            var dW = buffer(convInfo.filterShape, 'float32');
+            var leftPad = convInfo.padInfo.left;
+            var topPad = convInfo.padInfo.top;
+            for (var wR = 0; wR < filterHeight; ++wR) {
+                var yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
+                var yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
+                for (var wC = 0; wC < filterWidth; ++wC) {
+                    var yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
+                    var yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
+                    for (var d1 = 0; d1 < convInfo.inChannels; ++d1) {
+                        for (var d2 = 0; d2 < convInfo.outChannels; ++d2) {
+                            var dotProd = 0;
+                            for (var b = 0; b < convInfo.batchSize; ++b) {
+                                for (var yR = yRMin; yR < yRMax; ++yR) {
+                                    var xR = wR + yR * strideHeight - topPad;
+                                    for (var yC = yCMin; yC < yCMax; ++yC) {
+                                        var xC = wC + yC * strideWidth - leftPad;
+                                        dotProd += x.get(b, xR, xC, d1) * dy.get(b, yR, yC, d2);
+                                    }
+                                }
+                            }
+                            dW.set(dotProd, wR, wC, d1, d2);
+                        }
+                    }
+                }
+            }
+            return dW.toTensor();
+        };
+        MathBackendCPU.prototype.conv3dDerFilter = function (x, dy, convInfo) {
+            var strideDepth = convInfo.strideDepth;
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var filterDepth = convInfo.filterDepth;
+            var filterHeight = convInfo.filterHeight;
+            var filterWidth = convInfo.filterWidth;
+            var dw = buffer(convInfo.filterShape, 'float32');
+            var dwValues = dw.values;
+            var _a = dw.strides, dwS0 = _a[0], dwS1 = _a[1], dwS2 = _a[2], dwS3 = _a[3];
+            var dyValues = dy.dataSync();
+            var _b = dy.strides, dyS0 = _b[0], dyS1 = _b[1], dyS2 = _b[2], dyS3 = _b[3];
+            var xValues = x.dataSync();
+            var _c = x.strides, xS0 = _c[0], xS1 = _c[1], xS2 = _c[2], xS3 = _c[3];
+            var frontPad = convInfo.padInfo.front;
+            var leftPad = convInfo.padInfo.left;
+            var topPad = convInfo.padInfo.top;
+            for (var wF = 0; wF < filterDepth; ++wF) {
+                var yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));
+                var yFMax = Math.min(convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);
+                var wOffset1 = wF * dwS0;
+                for (var wR = 0; wR < filterHeight; ++wR) {
+                    var yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
+                    var yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
+                    var wOffset2 = wR * dwS1 + wOffset1;
+                    for (var wC = 0; wC < filterWidth; ++wC) {
+                        var yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
+                        var yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
+                        var wOffset3 = wC * dwS2 + wOffset2;
+                        for (var d1 = 0; d1 < convInfo.inChannels; ++d1) {
+                            var wOffset4 = d1 * dwS3 + wOffset3;
+                            for (var d2 = 0; d2 < convInfo.outChannels; ++d2) {
+                                var dotProd = 0;
+                                for (var b = 0; b < convInfo.batchSize; ++b) {
+                                    var xOffset1 = b * xS0;
+                                    var yOffset1 = b * dyS0;
+                                    for (var yF = yFMin; yF < yFMax; ++yF) {
+                                        var xF = wF + yF * strideDepth - frontPad;
+                                        var xOffset2 = xF * xS1 + xOffset1;
+                                        var yOffset2 = yF * dyS1 + yOffset1;
+                                        for (var yR = yRMin; yR < yRMax; ++yR) {
+                                            var xR = wR + yR * strideHeight - topPad;
+                                            var xOffset3 = xR * xS2 + xOffset2;
+                                            var yOffset3 = yR * dyS2 + yOffset2;
+                                            for (var yC = yCMin; yC < yCMax; ++yC) {
+                                                var xC = wC + yC * strideWidth - leftPad;
+                                                var xOffset4 = xC * xS3 + xOffset3;
+                                                var yOffset4 = yC * dyS3 + yOffset3;
+                                                dotProd +=
+                                                    xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];
+                                            }
+                                        }
+                                    }
+                                }
+                                dwValues[wOffset4 + d2] = dotProd;
+                            }
+                        }
+                    }
+                }
+            }
+            return dw.toTensor();
+        };
+        MathBackendCPU.prototype.depthwiseConv2D = function (x, filter, convInfo) {
+            this.assertNotComplex([x, filter], 'depthwiseConv2D');
+            var filterHeight = convInfo.filterHeight;
+            var filterWidth = convInfo.filterWidth;
+            var dilationHeight = convInfo.dilationHeight;
+            var dilationWidth = convInfo.dilationWidth;
+            var padLeft = convInfo.padInfo.left;
+            var padTop = convInfo.padInfo.top;
+            var chMul = convInfo.outChannels / convInfo.inChannels;
+            var y = buffer(convInfo.outShape, x.dtype);
+            var xVals = x.dataSync();
+            var wVals = filter.dataSync();
+            var yVals = y.values;
+            for (var b = 0; b < convInfo.batchSize; ++b) {
+                var xOffset1 = b * x.strides[0];
+                var yOffset1 = b * y.strides[0];
+                for (var yR = 0; yR < convInfo.outHeight; ++yR) {
+                    var yOffset2 = yOffset1 + yR * y.strides[1];
+                    var xRCorner = yR * convInfo.strideHeight - padLeft;
+                    for (var wR = 0; wR < filterHeight; ++wR) {
+                        var xR = xRCorner + wR * dilationHeight;
+                        if (xR < 0 || xR >= convInfo.inHeight) {
+                            continue;
+                        }
+                        var wOffset1 = wR * filter.strides[0];
+                        var xOffset2 = xOffset1 + xR * x.strides[1];
+                        for (var yC = 0; yC < convInfo.outWidth; ++yC) {
+                            var yOffset3 = yOffset2 + yC * y.strides[2];
+                            var xCCorner = yC * convInfo.strideWidth - padTop;
+                            for (var wC = 0; wC < filterWidth; ++wC) {
+                                var xC = xCCorner + wC * dilationWidth;
+                                if (xC < 0 || xC >= convInfo.inWidth) {
+                                    continue;
+                                }
+                                var wOffset2 = wOffset1 + wC * filter.strides[1];
+                                var xOffset3 = xOffset2 + xC * convInfo.inChannels;
+                                var yOffset4 = yOffset3;
+                                var wOffset3 = wOffset2;
+                                for (var d1 = 0; d1 < convInfo.inChannels; ++d1) {
+                                    var xVal = xVals[xOffset3 + d1];
+                                    for (var q = 0; q < chMul; ++q) {
+                                        yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q];
+                                    }
+                                    yOffset4 += chMul;
+                                    wOffset3 += chMul;
+                                }
+                            }
+                        }
+                    }
+                }
+            }
+            return y.toTensor();
+        };
+        MathBackendCPU.prototype.depthwiseConv2DDerInput = function (dy, filter, convInfo) {
+            this.assertNotComplex([dy, filter], 'depthwiseConv2DDerInput');
+            var dx = buffer(convInfo.inShape, 'float32');
+            var dxValues = dx.values;
+            var _a = dx.strides, dxS0 = _a[0], dxS1 = _a[1], dxS2 = _a[2];
+            var dyValues = dy.dataSync();
+            var _b = dy.strides, dyS0 = _b[0], dyS1 = _b[1], dyS2 = _b[2];
+            var fltValues = filter.dataSync();
+            var _c = filter.strides, fltS0 = _c[0], fltS1 = _c[1], fltS2 = _c[2];
+            var batchSize = convInfo.batchSize, filterHeight = convInfo.filterHeight, filterWidth = convInfo.filterWidth, inChannels = convInfo.inChannels, inHeight = convInfo.inHeight, inWidth = convInfo.inWidth, outChannels = convInfo.outChannels, outHeight = convInfo.outHeight, outWidth = convInfo.outWidth, strideHeight = convInfo.strideHeight, strideWidth = convInfo.strideWidth;
+            var topPad = filterHeight - 1 - convInfo.padInfo.top;
+            var leftPad = filterWidth - 1 - convInfo.padInfo.left;
+            var chMul = outChannels / inChannels;
+            for (var b = 0; b < batchSize; ++b) {
+                for (var d1 = 0; d1 < inChannels; ++d1) {
+                    for (var xR = 0; xR < inHeight; ++xR) {
+                        var xRCorner = xR - topPad;
+                        var xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));
+                        var yRMax = Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);
+                        for (var xC = 0; xC < inWidth; ++xC) {
+                            var xCCorner = xC - leftPad;
+                            var xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));
+                            var yCMax = Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);
+                            var dotProd = 0;
+                            for (var yR = xRMin; yR < yRMax; ++yR) {
+                                var wR = yR * strideHeight - xRCorner;
+                                for (var yC = xCMin; yC < yCMax; ++yC) {
+                                    var wC = yC * strideWidth - xCCorner;
+                                    var dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;
+                                    var fltOffset = fltS0 * (filterHeight - 1 - wR) +
+                                        fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;
+                                    for (var dm = 0; dm < chMul; ++dm) {
+                                        var d2 = d1 * chMul + dm;
+                                        var pixel = dyValues[dyOffset + d2];
+                                        var weight = fltValues[fltOffset + dm];
+                                        dotProd += pixel * weight;
+                                    }
+                                }
+                            }
+                            dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;
+                        }
+                    }
+                }
+            }
+            return dx.toTensor();
+        };
+        MathBackendCPU.prototype.depthwiseConv2DDerFilter = function (x, dy, convInfo) {
+            this.assertNotComplex([x, dy], 'depthwiseConv2DDerFilter');
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var filterHeight = convInfo.filterHeight;
+            var filterWidth = convInfo.filterWidth;
+            var dW = buffer(convInfo.filterShape, 'float32');
+            var leftPad = convInfo.padInfo.left;
+            var topPad = convInfo.padInfo.top;
+            var chMul = convInfo.outChannels / convInfo.inChannels;
+            for (var wR = 0; wR < filterHeight; ++wR) {
+                var yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));
+                var yRMax = Math.min(convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);
+                for (var wC = 0; wC < filterWidth; ++wC) {
+                    var yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));
+                    var yCMax = Math.min(convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);
+                    for (var d2 = 0; d2 < convInfo.outChannels; ++d2) {
+                        var d1 = Math.trunc(d2 / chMul);
+                        var dm = d2 % chMul;
+                        var dotProd = 0;
+                        for (var b = 0; b < convInfo.batchSize; ++b) {
+                            for (var yR = yRMin; yR < yRMax; ++yR) {
+                                var xR = wR + yR * strideHeight - topPad;
+                                for (var yC = yCMin; yC < yCMax; ++yC) {
+                                    var xC = wC + yC * strideWidth - leftPad;
+                                    dotProd += x.get(b, xR, xC, d1) * dy.get(b, yR, yC, d2);
+                                }
+                            }
+                        }
+                        dW.set(dotProd, wR, wC, d1, dm);
+                    }
+                }
+            }
+            return dW.toTensor();
+        };
+        MathBackendCPU.prototype.tile = function (x, reps) {
+            this.assertNotComplex(x, 'tile');
+            var newShape = new Array(x.rank);
+            for (var i = 0; i < newShape.length; i++) {
+                newShape[i] = x.shape[i] * reps[i];
+            }
+            var result = buffer(newShape, x.dtype);
+            var xBuf = x.buffer();
+            for (var i = 0; i < result.values.length; ++i) {
+                var newLoc = result.indexToLoc(i);
+                var originalLoc = new Array(x.rank);
+                for (var i_1 = 0; i_1 < originalLoc.length; i_1++) {
+                    originalLoc[i_1] = newLoc[i_1] % x.shape[i_1];
+                }
+                var originalIndex = xBuf.locToIndex(originalLoc);
+                result.values[i] = xBuf.values[originalIndex];
+            }
+            return result.toTensor();
+        };
+        MathBackendCPU.prototype.pad = function (x, paddings, constantValue) {
+            this.assertNotComplex(x, 'pad');
+            var outShape = paddings.map(function (p, i) { return p[0] + x.shape[i] + p[1]; });
+            var start = paddings.map(function (p) { return p[0]; });
+            var xBuffer = x.buffer();
+            var buffer$$1 = buffer(outShape, x.dtype);
+            if (constantValue !== 0) {
+                buffer$$1.values.fill(constantValue);
+            }
+            for (var i = 0; i < x.size; i++) {
+                var coords = xBuffer.indexToLoc(i);
+                var outCoords = coords.map(function (c, i) { return c + start[i]; });
+                buffer$$1.set.apply(buffer$$1, [x.get.apply(x, coords)].concat(outCoords));
+            }
+            return buffer$$1.toTensor();
+        };
+        MathBackendCPU.prototype.transpose = function (x, perm) {
+            this.assertNotComplex(x, 'transpose');
+            var newShape = new Array(x.rank);
+            for (var i = 0; i < newShape.length; i++) {
+                newShape[i] = x.shape[perm[i]];
+            }
+            var values = x.dataSync();
+            var result = buffer(newShape, x.dtype);
+            var xBuf = x.buffer();
+            for (var i = 0; i < x.size; ++i) {
+                var loc = xBuf.indexToLoc(i);
+                var newLoc = new Array(loc.length);
+                for (var i_2 = 0; i_2 < newLoc.length; i_2++) {
+                    newLoc[i_2] = loc[perm[i_2]];
+                }
+                var newIndex = result.locToIndex(newLoc);
+                result.values[newIndex] = values[i];
+            }
+            return result.toTensor();
+        };
+        MathBackendCPU.prototype.gather = function (x, indices, axis) {
+            this.assertNotComplex([x, indices], 'gather');
+            var newShape = x.shape.slice();
+            var indicesValues = indices.dataSync();
+            newShape[axis] = indicesValues.length;
+            var result = buffer(newShape, x.dtype);
+            var xBuf = x.buffer();
+            for (var i = 0; i < result.size; ++i) {
+                var newLoc = result.indexToLoc(i);
+                var originalLoc = newLoc.slice();
+                originalLoc[axis] = indicesValues[newLoc[axis]];
+                var originalIndex = xBuf.locToIndex(originalLoc);
+                result.values[i] = xBuf.values[originalIndex];
+            }
+            return result.toTensor();
+        };
+        MathBackendCPU.prototype.batchToSpaceND = function (x, blockShape, crops) {
+            this.assertNotComplex([x], 'batchToSpaceND');
+            var prod$$1 = blockShape.reduce(function (a, b) { return a * b; });
+            var reshaped = getReshaped(x.shape, blockShape, prod$$1);
+            var permuted = getPermuted(reshaped.length, blockShape.length);
+            var reshapedPermuted = getReshapedPermuted(x.shape, blockShape, prod$$1);
+            var sliceBeginCoords = getSliceBeginCoords(crops, blockShape.length);
+            var sliceSize = getSliceSize(reshapedPermuted, crops, blockShape.length);
+            return x.reshape(reshaped)
+                .transpose(permuted)
+                .reshape(reshapedPermuted)
+                .slice(sliceBeginCoords, sliceSize);
+        };
+        MathBackendCPU.prototype.spaceToBatchND = function (x, blockShape, paddings) {
+            this.assertNotComplex([x], 'spaceToBatchND');
+            var prod$$1 = blockShape.reduce(function (a, b) { return a * b; });
+            var completePaddings = [[0, 0]];
+            completePaddings.push.apply(completePaddings, paddings);
+            for (var i = 1 + blockShape.length; i < x.shape.length; ++i) {
+                completePaddings.push([0, 0]);
+            }
+            var paddedX = x.pad(completePaddings);
+            var reshapedPaddedShape = getReshaped(paddedX.shape, blockShape, prod$$1, false);
+            var permutedReshapedPaddedPermutation = getPermuted(reshapedPaddedShape.length, blockShape.length, false);
+            var flattenShape = getReshapedPermuted(paddedX.shape, blockShape, prod$$1, false);
+            return paddedX.reshape(reshapedPaddedShape)
+                .transpose(permutedReshapedPaddedPermutation)
+                .reshape(flattenShape);
+        };
+        MathBackendCPU.prototype.pool = function (x, convInfo, poolType) {
+            this.assertNotComplex(x, 'pool');
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var dilationHeight = convInfo.dilationHeight;
+            var dilationWidth = convInfo.dilationWidth;
+            var effectiveFilterHeight = convInfo.effectiveFilterHeight;
+            var effectiveFilterWidth = convInfo.effectiveFilterWidth;
+            var padTop = convInfo.padInfo.top;
+            var padLeft = convInfo.padInfo.left;
+            var initialValue = (poolType === 'max' ? Number.NEGATIVE_INFINITY :
+                Number.POSITIVE_INFINITY);
+            var xValues = x.dataSync();
+            var output = buffer(convInfo.outShape, x.dtype);
+            var outputVals = output.values;
+            var outputBatchStrides = convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3];
+            var outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3];
+            var outputColStrides = convInfo.outShape[3];
+            for (var b = 0; b < convInfo.batchSize; ++b) {
+                var outputBatchOffset = b * outputBatchStrides;
+                var inputBatchOffset = b * x.strides[0];
+                for (var d = 0; d < convInfo.inChannels; ++d) {
+                    for (var yR = 0; yR < convInfo.outHeight; ++yR) {
+                        var xRCorner = yR * strideHeight - padTop;
+                        var xRMin = Math.max(0, xRCorner);
+                        var xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);
+                        var outputRowOffset = outputBatchOffset + yR * outputRowStrides;
+                        for (var yC = 0; yC < convInfo.outWidth; ++yC) {
+                            var xCCorner = yC * strideWidth - padLeft;
+                            var xCMin = Math.max(0, xCCorner);
+                            var xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);
+                            var minMaxValue = initialValue;
+                            var avgValue = 0;
+                            var count = 0;
+                            for (var xR = xRMin; xR < xRMax; xR += dilationHeight) {
+                                var xROffset = inputBatchOffset + xR * x.strides[1];
+                                for (var xC = xCMin; xC < xCMax; xC += dilationWidth) {
+                                    var xCOffset = xROffset + xC * x.strides[2];
+                                    var pixel = xValues[xCOffset + d];
+                                    if ((poolType === 'max' && pixel > minMaxValue)) {
+                                        minMaxValue = pixel;
+                                    }
+                                    else if (poolType === 'avg') {
+                                        avgValue += pixel;
+                                        count++;
+                                    }
+                                }
+                                if (isNaN(minMaxValue)) {
+                                    break;
+                                }
+                            }
+                            var outputOffset = outputRowOffset + yC * outputColStrides + d;
+                            outputVals[outputOffset] =
+                                poolType === 'avg' ? avgValue / count : minMaxValue;
+                        }
+                    }
+                }
+            }
+            return output.toTensor();
+        };
+        MathBackendCPU.prototype.maxPool = function (x, convInfo) {
+            return this.pool(x, convInfo, 'max');
+        };
+        MathBackendCPU.prototype.maxPoolPositions = function (x, convInfo) {
+            var maxPositions = buffer(convInfo.outShape, 'int32');
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var dilationHeight = convInfo.dilationHeight;
+            var dilationWidth = convInfo.dilationWidth;
+            var effectiveFilterHeight = convInfo.effectiveFilterHeight;
+            var effectiveFilterWidth = convInfo.effectiveFilterWidth;
+            var padTop = convInfo.padInfo.top;
+            var padLeft = convInfo.padInfo.left;
+            for (var b = 0; b < convInfo.batchSize; ++b) {
+                for (var d = 0; d < convInfo.inChannels; ++d) {
+                    for (var yR = 0; yR < convInfo.outHeight; ++yR) {
+                        var xRCorner = yR * strideHeight - padTop;
+                        var xRMin = xRCorner;
+                        while (xRMin < 0) {
+                            xRMin += dilationHeight;
+                        }
+                        var xRMax = Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);
+                        for (var yC = 0; yC < convInfo.outWidth; ++yC) {
+                            var xCCorner = yC * strideWidth - padLeft;
+                            var xCMin = xCCorner;
+                            while (xCMin < 0) {
+                                xCMin += dilationWidth;
+                            }
+                            var xCMax = Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);
+                            var maxValue = Number.NEGATIVE_INFINITY;
+                            var maxPosition = -1;
+                            for (var xR = xRMin; xR < xRMax; xR += dilationHeight) {
+                                var wR = xR - xRCorner;
+                                for (var xC = xCMin; xC < xCMax; xC += dilationWidth) {
+                                    var wC = xC - xCCorner;
+                                    var pixel = x.get(b, xR, xC, d);
+                                    if (pixel > maxValue) {
+                                        maxValue = pixel;
+                                        maxPosition = wR * effectiveFilterWidth + wC;
+                                    }
+                                }
+                            }
+                            maxPositions.set(maxPosition, b, yR, yC, d);
+                        }
+                    }
+                }
+            }
+            return maxPositions.toTensor();
+        };
+        MathBackendCPU.prototype.maxPoolBackprop = function (dy, x, y, convInfo) {
+            this.assertNotComplex([x, y], 'maxPoolBackprop');
+            var maxPositions = this.maxPoolPositions(x, convInfo);
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var dilationHeight = convInfo.dilationHeight;
+            var dilationWidth = convInfo.dilationWidth;
+            var effectiveFilterHeight = convInfo.effectiveFilterHeight;
+            var effectiveFilterWidth = convInfo.effectiveFilterWidth;
+            var padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+            var padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+            var dx = buffer(x.shape, 'float32');
+            for (var b = 0; b < convInfo.batchSize; ++b) {
+                for (var d = 0; d < convInfo.inChannels; ++d) {
+                    for (var dxR = 0; dxR < convInfo.inHeight; ++dxR) {
+                        for (var dxC = 0; dxC < convInfo.inWidth; ++dxC) {
+                            var dyRCorner = dxR - padTop;
+                            var dyCCorner = dxC - padLeft;
+                            var dotProd = 0;
+                            for (var wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {
+                                var dyR = (dyRCorner + wR) / strideHeight;
+                                if (dyR < 0 || dyR >= convInfo.outHeight ||
+                                    Math.floor(dyR) !== dyR) {
+                                    continue;
+                                }
+                                for (var wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {
+                                    var dyC = (dyCCorner + wC) / strideWidth;
+                                    if (dyC < 0 || dyC >= convInfo.outWidth ||
+                                        Math.floor(dyC) !== dyC) {
+                                        continue;
+                                    }
+                                    var maxPos = effectiveFilterHeight * effectiveFilterWidth -
+                                        1 - maxPositions.get(b, dyR, dyC, d);
+                                    var curPos = wR * effectiveFilterWidth + wC;
+                                    var mask = maxPos === curPos ? 1 : 0;
+                                    if (mask === 0) {
+                                        continue;
+                                    }
+                                    var pixel = dy.get(b, dyR, dyC, d);
+                                    dotProd += pixel * mask;
+                                }
+                            }
+                            dx.set(dotProd, b, dxR, dxC, d);
+                        }
+                    }
+                }
+            }
+            return dx.toTensor();
+        };
+        MathBackendCPU.prototype.avgPoolBackprop = function (dy, x, convInfo) {
+            this.assertNotComplex([dy, x], 'avgPoolBackprop');
+            var strideHeight = convInfo.strideHeight;
+            var strideWidth = convInfo.strideWidth;
+            var filterHeight = convInfo.filterHeight;
+            var filterWidth = convInfo.filterWidth;
+            var dilationHeight = convInfo.dilationHeight;
+            var dilationWidth = convInfo.dilationWidth;
+            var effectiveFilterHeight = convInfo.effectiveFilterHeight;
+            var effectiveFilterWidth = convInfo.effectiveFilterWidth;
+            var padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;
+            var padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;
+            var dx = buffer(x.shape, 'float32');
+            var avgMultiplier = 1 / (filterHeight * filterWidth);
+            for (var b = 0; b < convInfo.batchSize; ++b) {
+                for (var d = 0; d < convInfo.inChannels; ++d) {
+                    for (var dxR = 0; dxR < convInfo.inHeight; ++dxR) {
+                        for (var dxC = 0; dxC < convInfo.inWidth; ++dxC) {
+                            var dyRCorner = dxR - padTop;
+                            var dyCCorner = dxC - padLeft;
+                            var dotProd = 0;
+                            for (var wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {
+                                var dyR = (dyRCorner + wR) / strideHeight;
+                                if (dyR < 0 || dyR >= convInfo.outHeight ||
+                                    Math.floor(dyR) !== dyR) {
+                                    continue;
+                                }
+                                for (var wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {
+                                    var dyC = (dyCCorner + wC) / strideWidth;
+                                    if (dyC < 0 || dyC >= convInfo.outWidth ||
+                                        Math.floor(dyC) !== dyC) {
+                                        continue;
+                                    }
+                                    var pixel = dy.get(b, dyR, dyC, d);
+                                    dotProd += pixel;
+                                }
+                            }
+                            dx.set(dotProd * avgMultiplier, b, dxR, dxC, d);
+                        }
+                    }
+                }
+            }
+            return dx.toTensor();
+        };
+        MathBackendCPU.prototype.cast = function (x, dtype) {
+            return castTensor(x, dtype, this);
+        };
+        MathBackendCPU.prototype.reshape = function (x, shape) {
+            return reshapeTensor(x, shape);
+        };
+        MathBackendCPU.prototype.avgPool = function (x, convInfo) {
+            this.assertNotComplex(x, 'avgPool');
+            return this.pool(x, convInfo, 'avg').toFloat();
+        };
+        MathBackendCPU.prototype.resizeBilinear = function (x, newHeight, newWidth, alignCorners) {
+            this.assertNotComplex(x, 'resizeBilinear');
+            var _a = x.shape, batch = _a[0], oldHeight = _a[1], oldWidth = _a[2], numChannels = _a[3];
+            var xValues = x.dataSync();
+            var result = new Float32Array(sizeFromShape([batch, newHeight, newWidth, numChannels]));
+            var effectiveInputSize = [
+                (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,
+                (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth
+            ];
+            var effectiveOutputSize = [
+                (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,
+                (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth
+            ];
+            var outputIdx = 0;
+            var effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];
+            var effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];
+            for (var b = 0; b < batch; b++) {
+                for (var r = 0; r < newHeight; r++) {
+                    var sourceFracRow = effectiveRowSizeRatio * r;
+                    var sourceRowFloor = Math.floor(sourceFracRow);
+                    var rowFrac = sourceFracRow - sourceRowFloor;
+                    var sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));
+                    var topRowOffset = b * x.strides[0] + sourceRowFloor * x.strides[1];
+                    var botRowOffset = b * x.strides[0] + sourceRowCeil * x.strides[1];
+                    for (var c = 0; c < newWidth; c++) {
+                        var sourceFracCol = effectiveColSizeRatio * c;
+                        var sourceColFloor = Math.floor(sourceFracCol);
+                        var colFrac = sourceFracCol - sourceColFloor;
+                        var sourceColCeil = Math.min(oldWidth - 1, Math.ceil(sourceFracCol));
+                        var topLeftOffest = topRowOffset + sourceColFloor * x.strides[2];
+                        var botLeftOffset = botRowOffset + sourceColFloor * x.strides[2];
+                        var topRightOffset = topRowOffset + +sourceColCeil * x.strides[2];
+                        var botRightOffest = botRowOffset + sourceColCeil * x.strides[2];
+                        for (var d = 0; d < numChannels; d++) {
+                            var topLeft = xValues[topLeftOffest + d];
+                            var bottomLeft = xValues[botLeftOffset + d];
+                            var topRight = xValues[topRightOffset + d];
+                            var bottomRight = xValues[botRightOffest + d];
+                            var top_1 = topLeft + (topRight - topLeft) * colFrac;
+                            var bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;
+                            var newValue = top_1 + (bottom - top_1) * rowFrac;
+                            result[outputIdx++] = newValue;
+                        }
+                    }
+                }
+            }
+            return tensor(result, [batch, newHeight, newWidth, numChannels]);
+        };
+        MathBackendCPU.prototype.resizeBilinearBackprop = function (dy, x, alignCorners) {
+            this.assertNotComplex([dy, x], 'resizeBilinearBackprop');
+            var _a = x.shape, batch = _a[0], xHeight = _a[1], xWidth = _a[2], depth = _a[3];
+            var _b = dy.shape, yHeight = _b[1], yWidth = _b[2];
+            var output = new Float32Array(batch * xHeight * xWidth * depth);
+            var effectiveXSize = [
+                (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,
+                (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth
+            ];
+            var effectiveYSize = [
+                (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,
+                (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth
+            ];
+            var heightScale = effectiveXSize[0] / effectiveYSize[0];
+            var widthScale = effectiveXSize[1] / effectiveYSize[1];
+            var dyValues = dy.dataSync();
+            var offset = 0;
+            for (var b = 0; b < batch; b++) {
+                var bOffset = b * x.strides[0];
+                for (var r = 0; r < yHeight; r++) {
+                    var dxR = r * heightScale;
+                    var topDxRIndex = Math.floor(dxR);
+                    var bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);
+                    var topDxROffset = bOffset + topDxRIndex * x.strides[1];
+                    var bottomDxROffset = bOffset + bottomDxRIndex * x.strides[1];
+                    var dxRLerp = dxR - topDxRIndex;
+                    var inverseDxRLerp = 1.0 - dxRLerp;
+                    for (var c = 0; c < yWidth; c++) {
+                        var dxC = c * widthScale;
+                        var leftDxCIndex = Math.floor(dxC);
+                        var rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1);
+                        var dxCLerp = dxC - leftDxCIndex;
+                        var inverseDxCLerp = 1.0 - dxCLerp;
+                        var topLeftRCOffset = topDxROffset + leftDxCIndex * x.strides[2];
+                        var topRightRCOffset = topDxROffset + rightDxCIndex * x.strides[2];
+                        var bottomLeftRCOffset = bottomDxROffset + leftDxCIndex * x.strides[2];
+                        var bottomRightRCOffset = bottomDxROffset + rightDxCIndex * x.strides[2];
+                        var inverseDxRLerpTimesInverseDxCLerp = inverseDxRLerp * inverseDxCLerp;
+                        var inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp;
+                        var dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp;
+                        var dxRLerpTimesDxCLerp = dxRLerp * dxCLerp;
+                        for (var d = 0; d < depth; d++) {
+                            var dyVal = dyValues[offset++];
+                            output[topLeftRCOffset + d] +=
+                                dyVal * inverseDxRLerpTimesInverseDxCLerp;
+                            output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp;
+                            output[bottomLeftRCOffset + d] +=
+                                dyVal * dxRLerpTimesInverseDxCLerp;
+                            output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp;
+                        }
+                    }
+                }
+            }
+            return tensor4d(output, [batch, xWidth, xHeight, depth], x.dtype);
+        };
+        MathBackendCPU.prototype.resizeNearestNeighbor = function (x, newHeight, newWidth, alignCorners) {
+            this.assertNotComplex(x, 'resizeNearestNeighbor');
+            var _a = x.shape, batch = _a[0], oldHeight = _a[1], oldWidth = _a[2], numChannels = _a[3];
+            var xValues = x.dataSync();
+            var output = new Float32Array(batch * newHeight * newWidth * numChannels);
+            var effectiveInputSize = [
+                (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,
+                (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth
+            ];
+            var effectiveOutputSize = [
+                (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,
+                (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth
+            ];
+            var effectiveRowSizeRatio = effectiveInputSize[0] / effectiveOutputSize[0];
+            var effectiveColSizeRatio = effectiveInputSize[1] / effectiveOutputSize[1];
+            var outputOffset = 0;
+            for (var b = 0; b < batch; b++) {
+                var batchOffset = b * x.strides[0];
+                for (var r = 0; r < newHeight; r++) {
+                    var sourceFracRow = effectiveRowSizeRatio * r;
+                    var sourceNearestRow = Math.min(oldHeight - 1, alignCorners ? Math.round(sourceFracRow) :
+                        Math.floor(sourceFracRow));
+                    var rowOffset = batchOffset + sourceNearestRow * x.strides[1];
+                    for (var c = 0; c < newWidth; c++) {
+                        var sourceFracCol = effectiveColSizeRatio * c;
+                        var sourceNearestCol = Math.min(oldWidth - 1, alignCorners ? Math.round(sourceFracCol) :
+                            Math.floor(sourceFracCol));
+                        var colOffset = rowOffset + sourceNearestCol * x.strides[2];
+                        for (var d = 0; d < numChannels; d++) {
+                            var newVal = xValues[colOffset + d];
+                            output[outputOffset++] = newVal;
+                        }
+                    }
+                }
+            }
+            return tensor(output, [batch, newHeight, newWidth, numChannels], x.dtype);
+        };
+        MathBackendCPU.prototype.resizeNearestNeighborBackprop = function (dy, x, alignCorners) {
+            this.assertNotComplex([dy, x], 'resizeNearestNeighborBackprop');
+            var _a = x.shape, batch = _a[0], xHeight = _a[1], xWidth = _a[2], depth = _a[3];
+            var _b = dy.shape, yHeight = _b[1], yWidth = _b[2];
+            var output = new Float32Array(batch * xHeight * xWidth * depth);
+            var dyValues = dy.dataSync();
+            var effectiveXSize = [
+                (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,
+                (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth
+            ];
+            var effectiveYSize = [
+                (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,
+                (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth
+            ];
+            var heightScale = effectiveXSize[0] / effectiveYSize[0];
+            var widthScale = effectiveXSize[1] / effectiveYSize[1];
+            var invHeightScale = 1 / heightScale;
+            var invWidthScale = 1 / widthScale;
+            var winHeight = (Math.ceil(invHeightScale) * 2) + 2;
+            var winWidth = (Math.ceil(invWidthScale) * 2) + 2;
+            for (var b = 0; b < batch; b++) {
+                var batchOffset = b * x.strides[0];
+                for (var r = 0; r < xHeight; r++) {
+                    var rowOffset = batchOffset + r * x.strides[1];
+                    var startRLerp = Math.floor(r * invHeightScale);
+                    var startDyR = Math.floor(startRLerp - (winHeight / 2));
+                    for (var c = 0; c < xWidth; c++) {
+                        var colOffset = rowOffset + c * x.strides[2];
+                        var startCLerp = Math.floor(c * invWidthScale);
+                        var startDyC = Math.floor(startCLerp - (winWidth / 2));
+                        for (var d = 0; d < depth; d++) {
+                            var accum = 0;
+                            for (var dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {
+                                var dyR = dyRIndex + startDyR;
+                                if (dyR < 0 || dyR >= yHeight) {
+                                    continue;
+                                }
+                                var dyROffset = batchOffset + dyR * dy.strides[1];
+                                var sourceFracRow = dyR * heightScale;
+                                var sourceNearestRow = Math.min(xHeight - 1, alignCorners ? Math.round(sourceFracRow) :
+                                    Math.floor(sourceFracRow));
+                                if (r !== sourceNearestRow) {
+                                    continue;
+                                }
+                                for (var dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {
+                                    var dyC = dyCIndex + startDyC;
+                                    if (dyC < 0 || dyC >= yWidth) {
+                                        continue;
+                                    }
+                                    var dyCOffset = dyROffset + dyC * dy.strides[2];
+                                    var sourceFracCol = dyC * widthScale;
+                                    var sourceNearestCol = Math.min(xWidth - 1, alignCorners ? Math.round(sourceFracCol) :
+                                        Math.floor(sourceFracCol));
+                                    if (c === sourceNearestCol) {
+                                        accum += dyValues[dyCOffset + d];
+                                    }
+                                }
+                            }
+                            output[colOffset + d] = accum;
+                        }
+                    }
+                }
+            }
+            return tensor4d(output, x.shape, x.dtype);
+        };
+        MathBackendCPU.prototype.batchNormalization = function (x, mean$$1, variance, varianceEpsilon, scale, offset) {
+            this.assertNotComplex([x, mean$$1, variance, scale, offset], 'batchNormalization');
+            var xVals = x.dataSync();
+            var mVals = mean$$1.dataSync();
+            var varVals = variance.dataSync();
+            var sVals = scale ? scale.dataSync() : new Float32Array([1]);
+            var offVals = offset ? offset.dataSync() : new Float32Array([0]);
+            var outVals = new Float32Array(xVals.length);
+            var offValsLength = offVals.length;
+            var sValsLength = sVals.length;
+            var varValsLength = varVals.length;
+            var mValsLength = mVals.length;
+            var offi = 0;
+            var mi = 0;
+            var si = 0;
+            var vi = 0;
+            for (var i = 0; i < xVals.length; ++i) {
+                outVals[i] = offVals[offi++] +
+                    (xVals[i] - mVals[mi++]) * sVals[si++] /
+                        Math.sqrt(varVals[vi++] + varianceEpsilon);
+                if (offi >= offValsLength) {
+                    offi = 0;
+                }
+                if (mi >= mValsLength) {
+                    mi = 0;
+                }
+                if (si >= sValsLength) {
+                    si = 0;
+                }
+                if (vi >= varValsLength) {
+                    vi = 0;
+                }
+            }
+            return tensor4d(outVals, x.shape);
+        };
+        MathBackendCPU.prototype.localResponseNormalization4D = function (x, depthRadius, bias, alpha, beta) {
+            this.assertNotComplex(x, 'localResponseNormalization4D');
+            var channels = x.shape[3];
+            var maxD = channels - 1;
+            var xValues = x.dataSync();
+            var size = sizeFromShape(x.shape);
+            var result = new Float32Array(size);
+            function sumAcrossChannels(offset) {
+                var currentChannel = offset % channels;
+                var beginSumOffset = offset - currentChannel + Math.max(0, currentChannel - depthRadius);
+                var endSumOffset = offset - currentChannel +
+                    Math.min(currentChannel + depthRadius, maxD);
+                var sum$$1 = 0.0;
+                for (; beginSumOffset <= endSumOffset; beginSumOffset++) {
+                    var z = xValues[beginSumOffset];
+                    sum$$1 += z * z;
+                }
+                return sum$$1;
+            }
+            for (var offset = 0; offset < size; offset++) {
+                var sum$$1 = sumAcrossChannels(offset);
+                var val = xValues[offset] * Math.pow(bias + alpha * sum$$1, -beta);
+                result[offset] = val;
+            }
+            return tensor4d(result, x.shape);
+        };
+        MathBackendCPU.prototype.LRNGrad = function (dy, inputImage, outputImage, depthRadius, bias, alpha, beta) {
+            this.assertNotComplex(dy, 'LRNGrad');
+            var channels = dy.shape[3];
+            var dyValues = dy.dataSync();
+            var inputImageValues = inputImage.dataSync();
+            var outputImageValues = outputImage.dataSync();
+            var result = new Float32Array(sizeFromShape(dy.shape));
+            var size = sizeFromShape(dy.shape);
+            for (var offset = 0; offset < size; offset++) {
+                var currentChannel = offset % channels;
+                var depthBegin = (offset - currentChannel) + Math.max(0, currentChannel - depthRadius);
+                var depthEnd = (offset - currentChannel) +
+                    Math.min(channels, currentChannel + depthRadius + 1);
+                var norm$$1 = 0;
+                for (var k = depthBegin; k < depthEnd; k++) {
+                    norm$$1 += Math.pow(inputImageValues[k], 2);
+                }
+                norm$$1 = alpha * norm$$1 + bias;
+                for (var k = depthBegin; k < depthEnd; k++) {
+                    var dyi = -2 * alpha * beta * inputImageValues[k] *
+                        outputImageValues[offset] / norm$$1;
+                    if (offset === k) {
+                        dyi += Math.pow(norm$$1, -beta);
+                    }
+                    dyi *= dyValues[offset];
+                    result[k] += dyi;
+                }
+            }
+            return tensor4d(result, dy.shape);
+        };
+        MathBackendCPU.prototype.multinomial = function (logits, normalized, numSamples, seed) {
+            this.assertNotComplex(logits, 'multinomial');
+            var probabilities = normalized ? logits : softmax(logits);
+            var batchSize = probabilities.shape[0];
+            var numEvents = probabilities.shape[1];
+            var res = zeros([batchSize, numSamples], 'int32');
+            var resVals = res.dataSync();
+            var probVals = probabilities.dataSync();
+            for (var b = 0; b < batchSize; ++b) {
+                var offset = b * numEvents;
+                var cdf = new Float32Array(numEvents - 1);
+                cdf[0] = probVals[offset];
+                for (var event_1 = 1; event_1 < cdf.length; ++event_1) {
+                    cdf[event_1] = cdf[event_1 - 1] + probVals[offset + event_1];
+                }
+                var random = seedrandom_1(seed.toString());
+                var outOffset = b * numSamples;
+                for (var sampleId = 0; sampleId < numSamples; ++sampleId) {
+                    var r = random();
+                    resVals[outOffset + sampleId] = cdf.length;
+                    for (var event_2 = 0; event_2 < cdf.length; event_2++) {
+                        if (r < cdf[event_2]) {
+                            resVals[outOffset + sampleId] = event_2;
+                            break;
+                        }
+                    }
+                }
+            }
+            return res;
+        };
+        MathBackendCPU.prototype.oneHot = function (indices, depth, onValue, offValue) {
+            this.assertNotComplex(indices, 'oneHot');
+            var res = new Float32Array(indices.size * depth);
+            res.fill(offValue);
+            for (var event_3 = 0; event_3 < indices.size; ++event_3) {
+                if (indices.get(event_3) >= 0 && indices.get(event_3) < depth) {
+                    res[event_3 * depth + indices.get(event_3)] = onValue;
+                }
+            }
+            return tensor2d(res, [indices.size, depth], 'int32');
+        };
+        MathBackendCPU.prototype.nonMaxSuppression = function (boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+            this.assertNotComplex(boxes, 'nonMaxSuppression');
+            var boxesVals = boxes.dataSync();
+            var scoresVals = scores.dataSync();
+            return nonMaxSuppressionImpl(boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);
+        };
+        MathBackendCPU.prototype.fft = function (x) {
+            return this.fftBatch(x, false);
+        };
+        MathBackendCPU.prototype.ifft = function (x) {
+            return this.fftBatch(x, true);
+        };
+        MathBackendCPU.prototype.fftBatch = function (x, inverse) {
+            var batch = x.shape[0];
+            var innerDim = x.shape[1];
+            var realResult = buffer(x.shape, 'float32');
+            var imagResult = buffer(x.shape, 'float32');
+            var real$$1 = real(x).as2D(batch, innerDim);
+            var imag$$1 = imag(x).as2D(batch, innerDim);
+            for (var b = 0; b < batch; b++) {
+                var r = real$$1.slice([b, 0], [1, innerDim]);
+                var i = imag$$1.slice([b, 0], [1, innerDim]);
+                var input = complex(r, i);
+                var res = this.fftImpl(input, inverse).dataSync();
+                for (var d = 0; d < innerDim; d++) {
+                    var c = getComplexWithIndex(res, d);
+                    realResult.values[b * innerDim + d] = c.real;
+                    imagResult.values[b * innerDim + d] = c.imag;
+                }
+            }
+            var t = complex(realResult.toTensor(), imagResult.toTensor());
+            return t.as2D(batch, innerDim);
+        };
+        MathBackendCPU.prototype.fftImpl = function (x, inverse) {
+            var x1D = x.as1D();
+            var n = x1D.size;
+            if (this.isExponentOf2(n)) {
+                var result = this.fftRadix2(x1D, n, inverse).as2D(x.shape[0], x.shape[1]);
+                if (inverse) {
+                    result = complex(real(result).div(scalar(n)), imag(result).div(scalar(n)));
+                }
+                return result;
+            }
+            else {
+                var data = x.dataSync();
+                var rawOutput = this.fourierTransformByMatmul(data, n, inverse);
+                var output = splitRealAndImagArrays(rawOutput);
+                return complex(output.real, output.imag).as2D(x.shape[0], x.shape[1]);
+            }
+        };
+        MathBackendCPU.prototype.isExponentOf2 = function (size) {
+            return (size & size - 1) === 0;
+        };
+        MathBackendCPU.prototype.fftRadix2 = function (input, size, inverse) {
+            if (size === 1) {
+                return input;
+            }
+            var data = input.dataSync();
+            var half = size / 2;
+            var evenComplex = complexWithEvenIndex(data);
+            var evenTensor = complex(evenComplex.real, evenComplex.imag).as1D();
+            var oddComplex = complexWithOddIndex(data);
+            var oddTensor = complex(oddComplex.real, oddComplex.imag).as1D();
+            evenTensor = this.fftRadix2(evenTensor, half, inverse);
+            oddTensor = this.fftRadix2(oddTensor, half, inverse);
+            var e = exponents(size, inverse);
+            var exponent$$1 = complex(e.real, e.imag).mul(oddTensor);
+            var addPart = evenTensor.add(exponent$$1);
+            var subPart = evenTensor.sub(exponent$$1);
+            var realTensor = real(addPart).concat(real(subPart));
+            var imagTensor = imag(addPart).concat(imag(subPart));
+            return complex(realTensor, imagTensor).as1D();
+        };
+        MathBackendCPU.prototype.fourierTransformByMatmul = function (data, size, inverse) {
+            var ret = new Float32Array(size * 2);
+            for (var r = 0; r < size; r++) {
+                var real$$1 = 0.0;
+                var imag$$1 = 0.0;
+                for (var c = 0; c < size; c++) {
+                    var e = exponent(r * c, size, inverse);
+                    var term = getComplexWithIndex(data, c);
+                    real$$1 += term.real * e.real - term.imag * e.imag;
+                    imag$$1 += term.real * e.imag + term.imag * e.real;
+                }
+                if (inverse) {
+                    real$$1 /= size;
+                    imag$$1 /= size;
+                }
+                assignToTypedArray(ret, real$$1, imag$$1, r);
+            }
+            return ret;
+        };
+        MathBackendCPU.prototype.depthToSpace = function (x, blockSize, dataFormat) {
+            assert(dataFormat === 'NHWC', "Only NHWC dataFormat supported on CPU for depthToSpace. Got " + dataFormat);
+            assert(blockSize > 1, "blockSize should be > 1 for depthToSpace, but was: " + blockSize);
+            var batchSize = x.shape[0];
+            var inputHeight = x.shape[1];
+            var inputWidth = x.shape[2];
+            var inputDepth = x.shape[3];
+            var outputHeight = inputHeight * blockSize;
+            var outputWidth = inputWidth * blockSize;
+            var outputDepth = inputDepth / (blockSize * blockSize);
+            var xValues = x.dataSync();
+            var result = new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);
+            var outputIdx = 0;
+            for (var b = 0; b < batchSize; ++b) {
+                for (var h = 0; h < outputHeight; ++h) {
+                    var inH = Math.floor(h / blockSize);
+                    var offsetH = (h % blockSize);
+                    for (var w = 0; w < outputWidth; ++w) {
+                        var inW = Math.floor(w / blockSize);
+                        var offsetW = (w % blockSize);
+                        var offsetD = (offsetH * blockSize + offsetW) * outputDepth;
+                        for (var d = 0; d < outputDepth; ++d) {
+                            var inD = d + offsetD;
+                            var inputIdx = inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));
+                            result[outputIdx++] = xValues[inputIdx];
+                        }
+                    }
+                }
+            }
+            return tensor4d(result, [batchSize, outputHeight, outputWidth, outputDepth]);
+        };
+        MathBackendCPU.prototype.broadcastedBinaryOp = function (a, b, dtype, op$$1) {
+            var newShape = assertAndGetBroadcastShape(a.shape, b.shape);
+            var result = buffer(newShape, dtype);
+            var aVals = a.dataSync();
+            var bVals = b.dataSync();
+            var aBroadcastDims = getBroadcastDims(a.shape, newShape);
+            var bBroadcastDims = getBroadcastDims(b.shape, newShape);
+            var resVals = result.values;
+            if (aBroadcastDims.length + bBroadcastDims.length === 0) {
+                for (var i = 0; i < resVals.length; ++i) {
+                    resVals[i] = op$$1(aVals[i % aVals.length], bVals[i % bVals.length]);
+                }
+            }
+            else {
+                var aBuf = a.buffer();
+                var bBuf = b.buffer();
+                var _loop_2 = function (i) {
+                    var loc = result.indexToLoc(i);
+                    var aLoc = loc.slice(-a.rank);
+                    aBroadcastDims.forEach(function (d) { return aLoc[d] = 0; });
+                    var aIndex = aBuf.locToIndex(aLoc);
+                    var bLoc = loc.slice(-b.rank);
+                    bBroadcastDims.forEach(function (d) { return bLoc[d] = 0; });
+                    var bIndex = bBuf.locToIndex(bLoc);
+                    resVals[i] = op$$1(aVals[aIndex], bVals[bIndex]);
+                };
+                for (var i = 0; i < resVals.length; ++i) {
+                    _loop_2(i);
+                }
+            }
+            return result.toTensor();
+        };
+        MathBackendCPU.prototype.broadcastedBinaryComplexOp = function (a, b, op$$1) {
+            var newShape = assertAndGetBroadcastShape(a.shape, b.shape);
+            var realResult = buffer(newShape, 'float32');
+            var imagResult = buffer(newShape, 'float32');
+            var aVals = a.dataSync();
+            var bVals = b.dataSync();
+            var aBroadcastDims = getBroadcastDims(a.shape, newShape);
+            var bBroadcastDims = getBroadcastDims(b.shape, newShape);
+            var realVals = realResult.values;
+            var imagVals = imagResult.values;
+            if (aBroadcastDims.length + bBroadcastDims.length === 0) {
+                for (var i = 0; i < realVals.length; i++) {
+                    var aIdx = i % aVals.length;
+                    var bIdx = i % bVals.length;
+                    var result = op$$1(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2], bVals[bIdx * 2 + 1]);
+                    realVals[i] = result.real;
+                    imagVals[i] = result.imag;
+                }
+            }
+            else {
+                var aRealBuf = this.data.get(a.dataId).complexTensors.real.buffer();
+                var bRealBuf = this.data.get(b.dataId).complexTensors.real.buffer();
+                var _loop_3 = function (i) {
+                    var loc = realResult.indexToLoc(i);
+                    var aLoc = loc.slice(-a.rank);
+                    aBroadcastDims.forEach(function (d) { return aLoc[d] = 0; });
+                    var aIndex = aRealBuf.locToIndex(aLoc);
+                    var bLoc = loc.slice(-b.rank);
+                    bBroadcastDims.forEach(function (d) { return bLoc[d] = 0; });
+                    var bIndex = bRealBuf.locToIndex(bLoc);
+                    var opResult = op$$1(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2], bVals[bIndex * 2 + 1]);
+                    realVals[i] = opResult.real;
+                    imagVals[i] = opResult.imag;
+                };
+                for (var i = 0; i < realVals.length; i++) {
+                    _loop_3(i);
+                }
+            }
+            return this.complex(realResult.toTensor(), imagResult.toTensor());
+        };
+        MathBackendCPU.prototype.split = function (x, sizeSplits, axis) {
+            return split(x, sizeSplits, axis);
+        };
+        MathBackendCPU.prototype.dispose = function () { };
+        MathBackendCPU.prototype.floatPrecision = function () {
+            return 32;
+        };
+        MathBackendCPU.prototype.cropAndResize = function (images, boxes, boxIndex, cropSize, method, extrapolationValue) {
+            var _a = images.shape, batch = _a[0], imageHeight = _a[1], imageWidth = _a[2], numChannels = _a[3];
+            var numBoxes = boxes.shape[0];
+            var cropHeight = cropSize[0], cropWidth = cropSize[1];
+            var output = buffer([numBoxes, cropHeight, cropWidth, numChannels], images.dtype);
+            var boxVals = boxes.dataSync();
+            var boxIndVals = boxIndex.dataSync();
+            var imageVals = images.dataSync();
+            var inStride = images.strides;
+            var outStride = output.strides;
+            for (var b = 0; b < numBoxes; b++) {
+                var startInd = b * 4;
+                var y1 = boxVals[startInd];
+                var x1 = boxVals[startInd + 1];
+                var y2 = boxVals[startInd + 2];
+                var x2 = boxVals[startInd + 3];
+                var bInd = boxIndVals[b];
+                if (bInd >= batch) {
+                    continue;
+                }
+                var heightScale = (cropHeight > 1) ?
+                    (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) :
+                    0;
+                var widthScale = (cropWidth > 1) ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;
+                for (var y = 0; y < cropHeight; y++) {
+                    var yInd = (cropHeight > 1) ?
+                        y1 * (imageHeight - 1) + y * (heightScale) :
+                        0.5 * (y1 + y2) * (imageHeight - 1);
+                    if (yInd < 0 || yInd > imageHeight - 1) {
+                        for (var x = 0; x < cropWidth; x++) {
+                            for (var c = 0; c < numChannels; c++) {
+                                var ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
+                                output.values[ind] = extrapolationValue;
+                            }
+                        }
+                        continue;
+                    }
+                    if (method === 'bilinear') {
+                        var topInd = Math.floor(yInd);
+                        var bottomInd = Math.ceil(yInd);
+                        var yLerp = yInd - topInd;
+                        for (var x = 0; x < cropWidth; x++) {
+                            var xInd = (cropWidth > 1) ?
+                                x1 * (imageWidth - 1) + x * widthScale :
+                                0.5 * (x1 + x2) * (imageWidth - 1);
+                            if (xInd < 0 || xInd > imageWidth - 1) {
+                                for (var c = 0; c < numChannels; c++) {
+                                    var ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
+                                    output.values[ind] = extrapolationValue;
+                                }
+                                continue;
+                            }
+                            var leftInd = Math.floor(xInd);
+                            var rightInd = Math.ceil(xInd);
+                            var xLerp = xInd - leftInd;
+                            for (var c = 0; c < numChannels; c++) {
+                                var ind = c + leftInd * inStride[2] + topInd * inStride[1] +
+                                    bInd * inStride[0];
+                                var topLeft = imageVals[ind];
+                                ind = c + rightInd * inStride[2] + topInd * inStride[1] +
+                                    bInd * inStride[0];
+                                var topRight = imageVals[ind];
+                                ind = c + leftInd * inStride[2] + bottomInd * inStride[1] +
+                                    bInd * inStride[0];
+                                var bottomLeft = imageVals[ind];
+                                ind = c + rightInd * inStride[2] + bottomInd * inStride[1] +
+                                    bInd * inStride[0];
+                                var bottomRight = imageVals[ind];
+                                var top_2 = topLeft + (topRight - topLeft) * xLerp;
+                                var bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;
+                                ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
+                                output.values[ind] = top_2 + ((bottom - top_2) * yLerp);
+                            }
+                        }
+                    }
+                    else {
+                        for (var x = 0; x < cropWidth; ++x) {
+                            var xInd = (cropWidth > 1) ?
+                                x1 * (imageWidth - 1) + x * widthScale :
+                                0.5 * (x1 + x2) * (imageWidth - 1);
+                            if (xInd < 0 || xInd > imageWidth - 1) {
+                                for (var c = 0; c < numChannels; c++) {
+                                    var ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
+                                    output.values[ind] = extrapolationValue;
+                                }
+                                continue;
+                            }
+                            var closestX = Math.round(xInd);
+                            var closestY = Math.round(yInd);
+                            for (var c = 0; c < numChannels; c++) {
+                                var inInd = c + closestX * inStride[2] +
+                                    closestY * inStride[1] + bInd * inStride[0];
+                                var outInd = c + x * outStride[2] + y * outStride[1] + b * outStride[0];
+                                output.values[outInd] = imageVals[inInd];
+                            }
+                        }
+                    }
+                }
+            }
+            return output.toTensor();
+        };
+        MathBackendCPU.prototype.sparseToDense = function (sparseIndices, sparseValues, outputShape, defaultValue) {
+            var _a = calculateShapes(sparseValues, sparseIndices, outputShape), sliceRank = _a.sliceRank, numUpdates = _a.numUpdates, sliceSize = _a.sliceSize, strides = _a.strides, outputSize = _a.outputSize;
+            var sumDupeIndices = false;
+            return this.scatter(sparseIndices, sparseValues, outputShape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);
+        };
+        MathBackendCPU.prototype.gatherND = function (x, indices) {
+            var indicesShape = indices.shape;
+            var sliceRank = indicesShape[indicesShape.length - 1];
+            var _a = prepareAndValidate(x, indices), resultShape = _a[0], numSlices = _a[1], sliceSize = _a[2], strides = _a[3];
+            if (numSlices === 0) {
+                return tensor([], resultShape, x.dtype);
+            }
+            var buffer$$1 = new TensorBuffer([numSlices, sliceSize], x.dtype);
+            var indicesData = indices.dataSync();
+            var xData = x.dataSync();
+            for (var i = 0; i < numSlices; i++) {
+                var index = [];
+                var flattenIndex = 0;
+                for (var j = 0; j < sliceRank; j++) {
+                    var dim = indicesData[i * sliceRank + j];
+                    flattenIndex += dim * strides[j];
+                    index.push(dim);
+                }
+                if (flattenIndex < 0 || flattenIndex >= x.size / sliceSize) {
+                    throw new Error("Invalid indices: " + index + " does not index into " + x.shape);
+                }
+                for (var k = 0; k < sliceSize; k++) {
+                    buffer$$1.values[i * sliceSize + k] = xData[flattenIndex * sliceSize + k];
+                }
+            }
+            return buffer$$1.toTensor().reshape(resultShape);
+        };
+        MathBackendCPU.prototype.scatterND = function (indices, updates, shape) {
+            var _a = calculateShapes(updates, indices, shape), sliceRank = _a.sliceRank, numUpdates = _a.numUpdates, sliceSize = _a.sliceSize, strides = _a.strides, outputSize = _a.outputSize;
+            var defaultValue = scalar(0);
+            var sumDupeIndices = true;
+            return this.scatter(indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);
+        };
+        MathBackendCPU.prototype.scatter = function (indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank, strides, defaultValue, sumDupeIndices) {
+            var flattenShape = [outputSize / sliceSize, sliceSize];
+            var indicesData = indices.dataSync();
+            var updatesData = updates.dataSync();
+            if (outputSize === 0) {
+                return tensor([], shape, updates.dtype);
+            }
+            var buffer$$1 = new TensorBuffer(flattenShape, updates.dtype);
+            buffer$$1.values.fill(defaultValue.dataSync()[0]);
+            for (var i = 0; i < numUpdates; i++) {
+                var index = [];
+                var flattenIndex = 0;
+                for (var j = 0; j < sliceRank; j++) {
+                    var dim = indicesData[i * sliceRank + j];
+                    index.push(dim);
+                    flattenIndex += dim * strides[j];
+                }
+                if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) {
+                    throw new Error("Invalid indices: " + index + " does not index into " + shape);
+                }
+                for (var k = 0; k < sliceSize; k++) {
+                    if (sumDupeIndices) {
+                        buffer$$1.values[flattenIndex * sliceSize + k] +=
+                            updatesData[i * sliceSize + k];
+                    }
+                    else {
+                        buffer$$1.values[flattenIndex * sliceSize + k] = updates.rank === 0 ?
+                            updatesData[0] :
+                            updatesData[i * sliceSize + k];
+                    }
+                }
+            }
+            return buffer$$1.toTensor().reshape(shape);
+        };
+        return MathBackendCPU;
+    }());
+    ENV.registerBackend('cpu', function () { return new MathBackendCPU(); }, 1, setTensorTracker);
+
+    var delayCallback = typeof requestAnimationFrame !== 'undefined' ?
+        requestAnimationFrame :
+        setImmediate;
+    function nextFrame() {
+        return new Promise(function (resolve) { return delayCallback(function () { return resolve(); }); });
+    }
+
+    var DTYPE_VALUE_SIZE_MAP = {
+        'float32': 4,
+        'int32': 4,
+        'uint16': 2,
+        'uint8': 1,
+        'bool': 1,
+    };
+
+    function encodeWeights(tensors) {
+        return __awaiter(this, void 0, void 0, function () {
+            var specs, dataPromises, name_1, t, tensorValues;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        specs = [];
+                        dataPromises = [];
+                        for (name_1 in tensors) {
+                            t = tensors[name_1];
+                            if (t.dtype !== 'float32' && t.dtype !== 'int32' && t.dtype !== 'bool') {
+                                throw new Error("Unsupported dtype in weight '" + name_1 + "': " + t.dtype);
+                            }
+                            specs.push({ name: name_1, shape: t.shape, dtype: t.dtype });
+                            dataPromises.push(t.data());
+                        }
+                        return [4, Promise.all(dataPromises)];
+                    case 1:
+                        tensorValues = _a.sent();
+                        return [2, { data: concatenateTypedArrays(tensorValues), specs: specs }];
+                }
+            });
+        });
+    }
+    function decodeWeights(buffer, specs) {
+        var out = {};
+        var offset = 0;
+        var _loop_1 = function (spec) {
+            var name_2 = spec.name;
+            var dtype = spec.dtype;
+            var shape = spec.shape;
+            var size = sizeFromShape(shape);
+            var typedArray = void 0;
+            if ('quantization' in spec) {
+                var quantization_1 = spec.quantization;
+                if (quantization_1.dtype !== 'uint8' && quantization_1.dtype !== 'uint16') {
+                    throw new Error("Weight " + spec.name + " has unknown " +
+                        ("quantization dtype " + quantization_1.dtype + ". ") +
+                        "Supported quantization dtypes are: 'uint8' and 'uint16'.");
+                }
+                var quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization_1.dtype];
+                var byteBuffer = buffer.slice(offset, offset + size * quantizationSizeFactor);
+                var quantizedArray = (quantization_1.dtype === 'uint8') ?
+                    new Uint8Array(byteBuffer) :
+                    new Uint16Array(byteBuffer);
+                if (dtype === 'float32') {
+                    typedArray = Float32Array.from(quantizedArray, function (v) { return v * quantization_1.scale + quantization_1.min; });
+                }
+                else if (dtype === 'int32') {
+                    typedArray = Int32Array.from(quantizedArray, function (v) { return Math.round(v * quantization_1.scale + quantization_1.min); });
+                }
+                else {
+                    throw new Error("Unsupported dtype in weight '" + name_2 + "': " + dtype);
+                }
+                offset += size * quantizationSizeFactor;
+            }
+            else {
+                var dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];
+                var byteBuffer = buffer.slice(offset, offset + size * dtypeFactor);
+                if (dtype === 'float32') {
+                    typedArray = new Float32Array(byteBuffer);
+                }
+                else if (dtype === 'int32') {
+                    typedArray = new Int32Array(byteBuffer);
+                }
+                else if (dtype === 'bool') {
+                    typedArray = new Uint8Array(byteBuffer);
+                }
+                else {
+                    throw new Error("Unsupported dtype in weight '" + name_2 + "': " + dtype);
+                }
+                offset += size * dtypeFactor;
+            }
+            var value = void 0;
+            if (dtype === 'float32') {
+                value = tensor(typedArray, shape, 'float32');
+            }
+            else if (dtype === 'int32') {
+                value = tensor(typedArray, shape, 'int32');
+            }
+            else if (dtype === 'bool') {
+                value = tensor(typedArray, shape, 'bool');
+            }
+            else {
+                throw new Error("Unsupported dtype in weight '" + name_2 + "': " + dtype);
+            }
+            out[name_2] = value;
+        };
+        for (var _i = 0, specs_1 = specs; _i < specs_1.length; _i++) {
+            var spec = specs_1[_i];
+            _loop_1(spec);
+        }
+        return out;
+    }
+    function concatenateTypedArrays(xs) {
+        if (xs === null) {
+            throw new Error("Invalid input value: " + JSON.stringify(xs));
+        }
+        var totalByteLength = 0;
+        var normalizedXs = [];
+        xs.forEach(function (x) {
+            totalByteLength += x.byteLength;
+            normalizedXs.push(x.byteLength === x.buffer.byteLength ? x :
+                new x.constructor(x));
+            if (!(x instanceof Float32Array || x instanceof Int32Array ||
+                x instanceof Uint8Array)) {
+                throw new Error("Unsupported TypedArray subtype: " + x.constructor.name);
+            }
+        });
+        var y = new Uint8Array(totalByteLength);
+        var offset = 0;
+        normalizedXs.forEach(function (x) {
+            y.set(new Uint8Array(x.buffer), offset);
+            offset += x.byteLength;
+        });
+        return y.buffer;
+    }
+    var useNodeBuffer = typeof Buffer !== 'undefined' &&
+        (typeof Blob === 'undefined' || typeof atob === 'undefined' ||
+            typeof btoa === 'undefined');
+    function stringByteLength(str) {
+        if (useNodeBuffer) {
+            return Buffer.byteLength(str);
+        }
+        return new Blob([str]).size;
+    }
+    function arrayBufferToBase64String(buffer) {
+        if (useNodeBuffer) {
+            return Buffer.from(buffer).toString('base64');
+        }
+        return btoa(String.fromCharCode.apply(null, new Uint8Array(buffer)));
+    }
+    function base64StringToArrayBuffer(str) {
+        if (useNodeBuffer) {
+            var buf = Buffer.from(str, 'base64');
+            return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);
+        }
+        var s = atob(str);
+        var buffer = new Uint8Array(s.length);
+        for (var i = 0; i < s.length; ++i) {
+            buffer.set([s.charCodeAt(i)], i);
+        }
+        return buffer.buffer;
+    }
+    function concatenateArrayBuffers(buffers) {
+        var totalByteLength = 0;
+        buffers.forEach(function (buffer) {
+            totalByteLength += buffer.byteLength;
+        });
+        var temp = new Uint8Array(totalByteLength);
+        var offset = 0;
+        buffers.forEach(function (buffer) {
+            temp.set(new Uint8Array(buffer), offset);
+            offset += buffer.byteLength;
+        });
+        return temp.buffer;
+    }
+    function basename(path) {
+        var SEPARATOR = '/';
+        path = path.trim();
+        while (path.endsWith(SEPARATOR)) {
+            path = path.slice(0, path.length - 1);
+        }
+        var items = path.split(SEPARATOR);
+        return items[items.length - 1];
+    }
+    function getModelArtifactsInfoForJSON(modelArtifacts) {
+        if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
+            throw new Error('Expected JSON model topology, received ArrayBuffer.');
+        }
+        return {
+            dateSaved: new Date(),
+            modelTopologyType: 'JSON',
+            modelTopologyBytes: modelArtifacts.modelTopology == null ?
+                0 :
+                stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),
+            weightSpecsBytes: modelArtifacts.weightSpecs == null ?
+                0 :
+                stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),
+            weightDataBytes: modelArtifacts.weightData == null ?
+                0 :
+                modelArtifacts.weightData.byteLength,
+        };
+    }
+
+    var IORouterRegistry = (function () {
+        function IORouterRegistry() {
+            this.saveRouters = [];
+            this.loadRouters = [];
+        }
+        IORouterRegistry.getInstance = function () {
+            if (IORouterRegistry.instance == null) {
+                IORouterRegistry.instance = new IORouterRegistry();
+            }
+            return IORouterRegistry.instance;
+        };
+        IORouterRegistry.registerSaveRouter = function (saveRouter) {
+            IORouterRegistry.getInstance().saveRouters.push(saveRouter);
+        };
+        IORouterRegistry.registerLoadRouter = function (loadRouter) {
+            IORouterRegistry.getInstance().loadRouters.push(loadRouter);
+        };
+        IORouterRegistry.getSaveHandlers = function (url) {
+            return IORouterRegistry.getHandlers(url, 'save');
+        };
+        IORouterRegistry.getLoadHandlers = function (url) {
+            return IORouterRegistry.getHandlers(url, 'load');
+        };
+        IORouterRegistry.getHandlers = function (url, handlerType) {
+            var validHandlers = [];
+            var routers = handlerType === 'load' ? this.getInstance().loadRouters :
+                this.getInstance().saveRouters;
+            routers.forEach(function (router) {
+                var handler = router(url);
+                if (handler !== null) {
+                    validHandlers.push(handler);
+                }
+            });
+            return validHandlers;
+        };
+        return IORouterRegistry;
+    }());
+
+    var URL_SCHEME_SUFFIX = '://';
+    var ModelStoreManagerRegistry = (function () {
+        function ModelStoreManagerRegistry() {
+            this.managers = {};
+        }
+        ModelStoreManagerRegistry.getInstance = function () {
+            if (ModelStoreManagerRegistry.instance == null) {
+                ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();
+            }
+            return ModelStoreManagerRegistry.instance;
+        };
+        ModelStoreManagerRegistry.registerManager = function (scheme, manager) {
+            assert(scheme != null, 'scheme must not be undefined or null.');
+            if (scheme.endsWith(URL_SCHEME_SUFFIX)) {
+                scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));
+            }
+            assert(scheme.length > 0, 'scheme must not be an empty string.');
+            var registry = ModelStoreManagerRegistry.getInstance();
+            assert(registry.managers[scheme] == null, "A model store manager is already registered for scheme '" + scheme + "'.");
+            registry.managers[scheme] = manager;
+        };
+        ModelStoreManagerRegistry.getManager = function (scheme) {
+            var manager = this.getInstance().managers[scheme];
+            if (manager == null) {
+                throw new Error("Cannot find model manager for scheme '" + scheme + "'");
+            }
+            return manager;
+        };
+        ModelStoreManagerRegistry.getSchemes = function () {
+            return Object.keys(this.getInstance().managers);
+        };
+        return ModelStoreManagerRegistry;
+    }());
+    function parseURL(url) {
+        if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {
+            throw new Error("The url string provided does not contain a scheme. " +
+                "Supported schemes are: " +
+                ("" + ModelStoreManagerRegistry.getSchemes().join(',')));
+        }
+        return {
+            scheme: url.split(URL_SCHEME_SUFFIX)[0],
+            path: url.split(URL_SCHEME_SUFFIX)[1],
+        };
+    }
+    function cloneModelInternal(sourceURL, destURL, deleteSource) {
+        if (deleteSource === void 0) { deleteSource = false; }
+        return __awaiter(this, void 0, void 0, function () {
+            var loadHandlers, loadHandler, saveHandlers, saveHandler, sourceScheme, sourcePath, sameMedium, modelArtifacts, saveResult;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        assert(sourceURL !== destURL, "Old path and new path are the same: '" + sourceURL + "'");
+                        loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);
+                        assert(loadHandlers.length > 0, "Copying failed because no load handler is found for source URL " + sourceURL + ".");
+                        assert(loadHandlers.length < 2, "Copying failed because more than one (" + loadHandlers.length + ") " +
+                            ("load handlers for source URL " + sourceURL + "."));
+                        loadHandler = loadHandlers[0];
+                        saveHandlers = IORouterRegistry.getSaveHandlers(destURL);
+                        assert(saveHandlers.length > 0, "Copying failed because no save handler is found for destination URL " +
+                            (destURL + "."));
+                        assert(saveHandlers.length < 2, "Copying failed because more than one (" + loadHandlers.length + ") " +
+                            ("save handlers for destination URL " + destURL + "."));
+                        saveHandler = saveHandlers[0];
+                        sourceScheme = parseURL(sourceURL).scheme;
+                        sourcePath = parseURL(sourceURL).path;
+                        sameMedium = sourceScheme === parseURL(sourceURL).scheme;
+                        return [4, loadHandler.load()];
+                    case 1:
+                        modelArtifacts = _a.sent();
+                        if (!(deleteSource && sameMedium)) return [3, 3];
+                        return [4, ModelStoreManagerRegistry.getManager(sourceScheme)
+                                .removeModel(sourcePath)];
+                    case 2:
+                        _a.sent();
+                        _a.label = 3;
+                    case 3: return [4, saveHandler.save(modelArtifacts)];
+                    case 4:
+                        saveResult = _a.sent();
+                        if (!(deleteSource && !sameMedium)) return [3, 6];
+                        return [4, ModelStoreManagerRegistry.getManager(sourceScheme)
+                                .removeModel(sourcePath)];
+                    case 5:
+                        _a.sent();
+                        _a.label = 6;
+                    case 6: return [2, saveResult.modelArtifactsInfo];
+                }
+            });
+        });
+    }
+    function listModels() {
+        return __awaiter(this, void 0, void 0, function () {
+            var schemes, out, _i, schemes_1, scheme, schemeOut, path, url;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        schemes = ModelStoreManagerRegistry.getSchemes();
+                        out = {};
+                        _i = 0, schemes_1 = schemes;
+                        _a.label = 1;
+                    case 1:
+                        if (!(_i < schemes_1.length)) return [3, 4];
+                        scheme = schemes_1[_i];
+                        return [4, ModelStoreManagerRegistry.getManager(scheme).listModels()];
+                    case 2:
+                        schemeOut = _a.sent();
+                        for (path in schemeOut) {
+                            url = scheme + URL_SCHEME_SUFFIX + path;
+                            out[url] = schemeOut[path];
+                        }
+                        _a.label = 3;
+                    case 3:
+                        _i++;
+                        return [3, 1];
+                    case 4: return [2, out];
+                }
+            });
+        });
+    }
+    function removeModel(url) {
+        return __awaiter(this, void 0, void 0, function () {
+            var schemeAndPath, manager;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        schemeAndPath = parseURL(url);
+                        manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);
+                        return [4, manager.removeModel(schemeAndPath.path)];
+                    case 1: return [2, _a.sent()];
+                }
+            });
+        });
+    }
+    function copyModel(sourceURL, destURL) {
+        return __awaiter(this, void 0, void 0, function () {
+            var deleteSource;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        deleteSource = false;
+                        return [4, cloneModelInternal(sourceURL, destURL, deleteSource)];
+                    case 1: return [2, _a.sent()];
+                }
+            });
+        });
+    }
+    function moveModel(sourceURL, destURL) {
+        return __awaiter(this, void 0, void 0, function () {
+            var deleteSource;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        deleteSource = true;
+                        return [4, cloneModelInternal(sourceURL, destURL, deleteSource)];
+                    case 1: return [2, _a.sent()];
+                }
+            });
+        });
+    }
+
+    var DATABASE_NAME = 'tensorflowjs';
+    var DATABASE_VERSION = 1;
+    var MODEL_STORE_NAME = 'models_store';
+    var INFO_STORE_NAME = 'model_info_store';
+    function getIndexedDBFactory() {
+        if (!ENV.get('IS_BROWSER')) {
+            throw new Error('Failed to obtain IndexedDB factory because the current environment' +
+                'is not a web browser.');
+        }
+        var theWindow = window;
+        var factory = theWindow.indexedDB || theWindow.mozIndexedDB ||
+            theWindow.webkitIndexedDB || theWindow.msIndexedDB ||
+            theWindow.shimIndexedDB;
+        if (factory == null) {
+            throw new Error('The current browser does not appear to support IndexedDB.');
+        }
+        return factory;
+    }
+    function setUpDatabase(openRequest) {
+        var db = openRequest.result;
+        db.createObjectStore(MODEL_STORE_NAME, { keyPath: 'modelPath' });
+        db.createObjectStore(INFO_STORE_NAME, { keyPath: 'modelPath' });
+    }
+    var BrowserIndexedDB = (function () {
+        function BrowserIndexedDB(modelPath) {
+            this.indexedDB = getIndexedDBFactory();
+            if (modelPath == null || !modelPath) {
+                throw new Error('For IndexedDB, modelPath must not be null, undefined or empty.');
+            }
+            this.modelPath = modelPath;
+        }
+        BrowserIndexedDB.prototype.save = function (modelArtifacts) {
+            return __awaiter(this, void 0, void 0, function () {
+                return __generator(this, function (_a) {
+                    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
+                        throw new Error('BrowserLocalStorage.save() does not support saving model topology ' +
+                            'in binary formats yet.');
+                    }
+                    return [2, this.databaseAction(this.modelPath, modelArtifacts)];
+                });
+            });
+        };
+        BrowserIndexedDB.prototype.load = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                return __generator(this, function (_a) {
+                    return [2, this.databaseAction(this.modelPath)];
+                });
+            });
+        };
+        BrowserIndexedDB.prototype.databaseAction = function (modelPath, modelArtifacts) {
+            var _this = this;
+            return new Promise(function (resolve, reject) {
+                var openRequest = _this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
+                openRequest.onupgradeneeded = function () { return setUpDatabase(openRequest); };
+                openRequest.onsuccess = function () {
+                    var db = openRequest.result;
+                    if (modelArtifacts == null) {
+                        var modelTx = db.transaction(MODEL_STORE_NAME, 'readonly');
+                        var modelStore = modelTx.objectStore(MODEL_STORE_NAME);
+                        var getRequest_1 = modelStore.get(_this.modelPath);
+                        getRequest_1.onsuccess = function () {
+                            if (getRequest_1.result == null) {
+                                db.close();
+                                return reject(new Error("Cannot find model with path '" + _this.modelPath + "' " +
+                                    "in IndexedDB."));
+                            }
+                            else {
+                                resolve(getRequest_1.result.modelArtifacts);
+                            }
+                        };
+                        getRequest_1.onerror = function (error) {
+                            db.close();
+                            return reject(getRequest_1.error);
+                        };
+                        modelTx.oncomplete = function () { return db.close(); };
+                    }
+                    else {
+                        var modelArtifactsInfo_1 = getModelArtifactsInfoForJSON(modelArtifacts);
+                        var infoTx_1 = db.transaction(INFO_STORE_NAME, 'readwrite');
+                        var infoStore_1 = infoTx_1.objectStore(INFO_STORE_NAME);
+                        var putInfoRequest_1 = infoStore_1.put({ modelPath: _this.modelPath, modelArtifactsInfo: modelArtifactsInfo_1 });
+                        var modelTx_1;
+                        putInfoRequest_1.onsuccess = function () {
+                            modelTx_1 = db.transaction(MODEL_STORE_NAME, 'readwrite');
+                            var modelStore = modelTx_1.objectStore(MODEL_STORE_NAME);
+                            var putModelRequest = modelStore.put({
+                                modelPath: _this.modelPath,
+                                modelArtifacts: modelArtifacts,
+                                modelArtifactsInfo: modelArtifactsInfo_1
+                            });
+                            putModelRequest.onsuccess = function () { return resolve({ modelArtifactsInfo: modelArtifactsInfo_1 }); };
+                            putModelRequest.onerror = function (error) {
+                                infoStore_1 = infoTx_1.objectStore(INFO_STORE_NAME);
+                                var deleteInfoRequest = infoStore_1.delete(_this.modelPath);
+                                deleteInfoRequest.onsuccess = function () {
+                                    db.close();
+                                    return reject(putModelRequest.error);
+                                };
+                                deleteInfoRequest.onerror = function (error) {
+                                    db.close();
+                                    return reject(putModelRequest.error);
+                                };
+                            };
+                        };
+                        putInfoRequest_1.onerror = function (error) {
+                            db.close();
+                            return reject(putInfoRequest_1.error);
+                        };
+                        infoTx_1.oncomplete = function () {
+                            if (modelTx_1 == null) {
+                                db.close();
+                            }
+                            else {
+                                modelTx_1.oncomplete = function () { return db.close(); };
+                            }
+                        };
+                    }
+                };
+                openRequest.onerror = function (error) { return reject(openRequest.error); };
+            });
+        };
+        BrowserIndexedDB.URL_SCHEME = 'indexeddb://';
+        return BrowserIndexedDB;
+    }());
+    var indexedDBRouter = function (url) {
+        if (!ENV.get('IS_BROWSER')) {
+            return null;
+        }
+        else {
+            if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {
+                return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));
+            }
+            else {
+                return null;
+            }
+        }
+    };
+    IORouterRegistry.registerSaveRouter(indexedDBRouter);
+    IORouterRegistry.registerLoadRouter(indexedDBRouter);
+    function browserIndexedDB(modelPath) {
+        return new BrowserIndexedDB(modelPath);
+    }
+    function maybeStripScheme(key) {
+        return key.startsWith(BrowserIndexedDB.URL_SCHEME) ?
+            key.slice(BrowserIndexedDB.URL_SCHEME.length) :
+            key;
+    }
+    var BrowserIndexedDBManager = (function () {
+        function BrowserIndexedDBManager() {
+            this.indexedDB = getIndexedDBFactory();
+        }
+        BrowserIndexedDBManager.prototype.listModels = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                var _this = this;
+                return __generator(this, function (_a) {
+                    return [2, new Promise(function (resolve, reject) {
+                            var openRequest = _this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
+                            openRequest.onupgradeneeded = function () { return setUpDatabase(openRequest); };
+                            openRequest.onsuccess = function () {
+                                var db = openRequest.result;
+                                var tx = db.transaction(INFO_STORE_NAME, 'readonly');
+                                var store = tx.objectStore(INFO_STORE_NAME);
+                                var getAllInfoRequest = store.getAll();
+                                getAllInfoRequest.onsuccess = function () {
+                                    var out = {};
+                                    for (var _i = 0, _a = getAllInfoRequest.result; _i < _a.length; _i++) {
+                                        var item = _a[_i];
+                                        out[item.modelPath] = item.modelArtifactsInfo;
+                                    }
+                                    resolve(out);
+                                };
+                                getAllInfoRequest.onerror = function (error) {
+                                    db.close();
+                                    return reject(getAllInfoRequest.error);
+                                };
+                                tx.oncomplete = function () { return db.close(); };
+                            };
+                            openRequest.onerror = function (error) { return reject(openRequest.error); };
+                        })];
+                });
+            });
+        };
+        BrowserIndexedDBManager.prototype.removeModel = function (path) {
+            return __awaiter(this, void 0, void 0, function () {
+                var _this = this;
+                return __generator(this, function (_a) {
+                    path = maybeStripScheme(path);
+                    return [2, new Promise(function (resolve, reject) {
+                            var openRequest = _this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);
+                            openRequest.onupgradeneeded = function () { return setUpDatabase(openRequest); };
+                            openRequest.onsuccess = function () {
+                                var db = openRequest.result;
+                                var infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');
+                                var infoStore = infoTx.objectStore(INFO_STORE_NAME);
+                                var getInfoRequest = infoStore.get(path);
+                                var modelTx;
+                                getInfoRequest.onsuccess = function () {
+                                    if (getInfoRequest.result == null) {
+                                        db.close();
+                                        return reject(new Error("Cannot find model with path '" + path + "' " +
+                                            "in IndexedDB."));
+                                    }
+                                    else {
+                                        var deleteInfoRequest = infoStore.delete(path);
+                                        var deleteModelData_1 = function () {
+                                            modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');
+                                            var modelStore = modelTx.objectStore(MODEL_STORE_NAME);
+                                            var deleteModelRequest = modelStore.delete(path);
+                                            deleteModelRequest.onsuccess = function () {
+                                                return resolve(getInfoRequest.result.modelArtifactsInfo);
+                                            };
+                                            deleteModelRequest.onerror = function (error) {
+                                                return reject(getInfoRequest.error);
+                                            };
+                                        };
+                                        deleteInfoRequest.onsuccess = deleteModelData_1;
+                                        deleteInfoRequest.onerror = function (error) {
+                                            deleteModelData_1();
+                                            db.close();
+                                            return reject(getInfoRequest.error);
+                                        };
+                                    }
+                                };
+                                getInfoRequest.onerror = function (error) {
+                                    db.close();
+                                    return reject(getInfoRequest.error);
+                                };
+                                infoTx.oncomplete = function () {
+                                    if (modelTx == null) {
+                                        db.close();
+                                    }
+                                    else {
+                                        modelTx.oncomplete = function () { return db.close(); };
+                                    }
+                                };
+                            };
+                            openRequest.onerror = function (error) { return reject(openRequest.error); };
+                        })];
+                });
+            });
+        };
+        return BrowserIndexedDBManager;
+    }());
+    if (ENV.get('IS_BROWSER')) {
+        try {
+            ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());
+        }
+        catch (err) {
+        }
+    }
+
+    var PATH_SEPARATOR = '/';
+    var PATH_PREFIX = 'tensorflowjs_models';
+    var INFO_SUFFIX = 'info';
+    var MODEL_TOPOLOGY_SUFFIX = 'model_topology';
+    var WEIGHT_SPECS_SUFFIX = 'weight_specs';
+    var WEIGHT_DATA_SUFFIX = 'weight_data';
+    function getModelKeys(path) {
+        return {
+            info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),
+            topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),
+            weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),
+            weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR)
+        };
+    }
+    function getModelPathFromKey(key) {
+        var items = key.split(PATH_SEPARATOR);
+        if (items.length < 3) {
+            throw new Error("Invalid key format: " + key);
+        }
+        return items.slice(1, items.length - 1).join(PATH_SEPARATOR);
+    }
+    function maybeStripScheme$1(key) {
+        return key.startsWith(BrowserLocalStorage.URL_SCHEME) ?
+            key.slice(BrowserLocalStorage.URL_SCHEME.length) :
+            key;
+    }
+    var BrowserLocalStorage = (function () {
+        function BrowserLocalStorage(modelPath) {
+            if (!ENV.get('IS_BROWSER') || typeof window.localStorage === 'undefined') {
+                throw new Error('The current environment does not support local storage.');
+            }
+            this.LS = window.localStorage;
+            if (modelPath == null || !modelPath) {
+                throw new Error('For local storage, modelPath must not be null, undefined or empty.');
+            }
+            this.modelPath = modelPath;
+            this.keys = getModelKeys(this.modelPath);
+        }
+        BrowserLocalStorage.prototype.save = function (modelArtifacts) {
+            return __awaiter(this, void 0, void 0, function () {
+                var topology, weightSpecs, modelArtifactsInfo, key;
+                return __generator(this, function (_a) {
+                    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
+                        throw new Error('BrowserLocalStorage.save() does not support saving model topology ' +
+                            'in binary formats yet.');
+                    }
+                    else {
+                        topology = JSON.stringify(modelArtifacts.modelTopology);
+                        weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);
+                        modelArtifactsInfo = getModelArtifactsInfoForJSON(modelArtifacts);
+                        try {
+                            this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));
+                            this.LS.setItem(this.keys.topology, topology);
+                            this.LS.setItem(this.keys.weightSpecs, weightSpecs);
+                            this.LS.setItem(this.keys.weightData, arrayBufferToBase64String(modelArtifacts.weightData));
+                            return [2, { modelArtifactsInfo: modelArtifactsInfo }];
+                        }
+                        catch (err) {
+                            for (key in this.keys) {
+                                this.LS.removeItem(this.keys[key]);
+                            }
+                            throw new Error("Failed to save model '" + this.modelPath + "' to local storage: " +
+                                "size quota being exceeded is a possible cause of this failure: " +
+                                ("modelTopologyBytes=" + modelArtifactsInfo.modelTopologyBytes + ", ") +
+                                ("weightSpecsBytes=" + modelArtifactsInfo.weightSpecsBytes + ", ") +
+                                ("weightDataBytes=" + modelArtifactsInfo.weightDataBytes + "."));
+                        }
+                    }
+                    return [2];
+                });
+            });
+        };
+        BrowserLocalStorage.prototype.load = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                var info, out, topology, weightSpecs, weightDataBase64;
+                return __generator(this, function (_a) {
+                    info = JSON.parse(this.LS.getItem(this.keys.info));
+                    if (info == null) {
+                        throw new Error("In local storage, there is no model with name '" + this.modelPath + "'");
+                    }
+                    if (info.modelTopologyType !== 'JSON') {
+                        throw new Error('BrowserLocalStorage does not support loading non-JSON model ' +
+                            'topology yet.');
+                    }
+                    out = {};
+                    topology = JSON.parse(this.LS.getItem(this.keys.topology));
+                    if (topology == null) {
+                        throw new Error("In local storage, the topology of model '" + this.modelPath + "' " +
+                            "is missing.");
+                    }
+                    out.modelTopology = topology;
+                    weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));
+                    if (weightSpecs == null) {
+                        throw new Error("In local storage, the weight specs of model '" + this.modelPath + "' " +
+                            "are missing.");
+                    }
+                    out.weightSpecs = weightSpecs;
+                    weightDataBase64 = this.LS.getItem(this.keys.weightData);
+                    if (weightDataBase64 == null) {
+                        throw new Error("In local storage, the binary weight values of model " +
+                            ("'" + this.modelPath + "' are missing."));
+                    }
+                    out.weightData = base64StringToArrayBuffer(weightDataBase64);
+                    return [2, out];
+                });
+            });
+        };
+        BrowserLocalStorage.URL_SCHEME = 'localstorage://';
+        return BrowserLocalStorage;
+    }());
+    var localStorageRouter = function (url) {
+        if (!ENV.get('IS_BROWSER')) {
+            return null;
+        }
+        else {
+            if (!Array.isArray(url) &&
+                url.startsWith(BrowserLocalStorage.URL_SCHEME)) {
+                return browserLocalStorage(url.slice(BrowserLocalStorage.URL_SCHEME.length));
+            }
+            else {
+                return null;
+            }
+        }
+    };
+    IORouterRegistry.registerSaveRouter(localStorageRouter);
+    IORouterRegistry.registerLoadRouter(localStorageRouter);
+    function browserLocalStorage(modelPath) {
+        return new BrowserLocalStorage(modelPath);
+    }
+    var BrowserLocalStorageManager = (function () {
+        function BrowserLocalStorageManager() {
+            assert(ENV.get('IS_BROWSER'), 'Current environment is not a web browser');
+            assert(typeof window.localStorage !== 'undefined', 'Current browser does not appear to support localStorage');
+            this.LS = window.localStorage;
+        }
+        BrowserLocalStorageManager.prototype.listModels = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                var out, prefix, suffix, i, key, modelPath;
+                return __generator(this, function (_a) {
+                    out = {};
+                    prefix = PATH_PREFIX + PATH_SEPARATOR;
+                    suffix = PATH_SEPARATOR + INFO_SUFFIX;
+                    for (i = 0; i < this.LS.length; ++i) {
+                        key = this.LS.key(i);
+                        if (key.startsWith(prefix) && key.endsWith(suffix)) {
+                            modelPath = getModelPathFromKey(key);
+                            out[modelPath] = JSON.parse(this.LS.getItem(key));
+                        }
+                    }
+                    return [2, out];
+                });
+            });
+        };
+        BrowserLocalStorageManager.prototype.removeModel = function (path) {
+            return __awaiter(this, void 0, void 0, function () {
+                var keys, info;
+                return __generator(this, function (_a) {
+                    path = maybeStripScheme$1(path);
+                    keys = getModelKeys(path);
+                    if (this.LS.getItem(keys.info) == null) {
+                        throw new Error("Cannot find model at path '" + path + "'");
+                    }
+                    info = JSON.parse(this.LS.getItem(keys.info));
+                    this.LS.removeItem(keys.info);
+                    this.LS.removeItem(keys.topology);
+                    this.LS.removeItem(keys.weightSpecs);
+                    this.LS.removeItem(keys.weightData);
+                    return [2, info];
+                });
+            });
+        };
+        return BrowserLocalStorageManager;
+    }());
+    if (ENV.get('IS_BROWSER')) {
+        try {
+            ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());
+        }
+        catch (err) {
+        }
+    }
+
+    var DEFAULT_FILE_NAME_PREFIX = 'model';
+    var DEFAULT_JSON_EXTENSION_NAME = '.json';
+    var DEFAULT_WEIGHT_DATA_EXTENSION_NAME = '.weights.bin';
+    var BrowserDownloads = (function () {
+        function BrowserDownloads(fileNamePrefix) {
+            if (!ENV.get('IS_BROWSER')) {
+                throw new Error('browserDownloads() cannot proceed because the current environment ' +
+                    'is not a browser.');
+            }
+            if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {
+                fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);
+            }
+            if (fileNamePrefix == null || fileNamePrefix.length === 0) {
+                fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;
+            }
+            this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;
+            this.weightDataFileName =
+                fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;
+        }
+        BrowserDownloads.prototype.save = function (modelArtifacts) {
+            return __awaiter(this, void 0, void 0, function () {
+                var weightsURL, weightsManifest, modelTopologyAndWeightManifest, modelTopologyAndWeightManifestURL, jsonAnchor, weightDataAnchor;
+                return __generator(this, function (_a) {
+                    weightsURL = window.URL.createObjectURL(new Blob([modelArtifacts.weightData], { type: 'application/octet-stream' }));
+                    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
+                        throw new Error('BrowserDownloads.save() does not support saving model topology ' +
+                            'in binary formats yet.');
+                    }
+                    else {
+                        weightsManifest = [{
+                                paths: ['./' + this.weightDataFileName],
+                                weights: modelArtifacts.weightSpecs
+                            }];
+                        modelTopologyAndWeightManifest = {
+                            modelTopology: modelArtifacts.modelTopology,
+                            weightsManifest: weightsManifest
+                        };
+                        modelTopologyAndWeightManifestURL = window.URL.createObjectURL(new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: 'application/json' }));
+                        jsonAnchor = this.jsonAnchor == null ? document.createElement('a') :
+                            this.jsonAnchor;
+                        jsonAnchor.download = this.modelTopologyFileName;
+                        jsonAnchor.href = modelTopologyAndWeightManifestURL;
+                        jsonAnchor.click();
+                        if (modelArtifacts.weightData != null) {
+                            weightDataAnchor = this.weightDataAnchor == null ?
+                                document.createElement('a') :
+                                this.weightDataAnchor;
+                            weightDataAnchor.download = this.weightDataFileName;
+                            weightDataAnchor.href = weightsURL;
+                            weightDataAnchor.click();
+                        }
+                        return [2, { modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts) }];
+                    }
+                    return [2];
+                });
+            });
+        };
+        BrowserDownloads.URL_SCHEME = 'downloads://';
+        return BrowserDownloads;
+    }());
+    var BrowserFiles = (function () {
+        function BrowserFiles(files) {
+            if (files == null || files.length < 1) {
+                throw new Error("When calling browserFiles, at least 1 file is required, " +
+                    ("but received " + files));
+            }
+            this.files = files;
+        }
+        BrowserFiles.prototype.load = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                var jsonFile, weightFiles;
+                var _this = this;
+                return __generator(this, function (_a) {
+                    jsonFile = this.files[0];
+                    weightFiles = this.files.slice(1);
+                    return [2, new Promise(function (resolve, reject) {
+                            var jsonReader = new FileReader();
+                            jsonReader.onload = function (event) {
+                                var modelJSON = JSON.parse(event.target.result);
+                                var modelTopology = modelJSON.modelTopology;
+                                if (modelTopology == null) {
+                                    reject(new Error("modelTopology field is missing from file " + jsonFile.name));
+                                    return;
+                                }
+                                if (weightFiles.length === 0) {
+                                    resolve({ modelTopology: modelTopology });
+                                }
+                                var weightsManifest = modelJSON.weightsManifest;
+                                if (weightsManifest == null) {
+                                    reject(new Error("weightManifest field is missing from file " + jsonFile.name));
+                                    return;
+                                }
+                                var pathToFile;
+                                try {
+                                    pathToFile =
+                                        _this.checkManifestAndWeightFiles(weightsManifest, weightFiles);
+                                }
+                                catch (err) {
+                                    reject(err);
+                                    return;
+                                }
+                                var weightSpecs = [];
+                                var paths = [];
+                                var perFileBuffers = [];
+                                weightsManifest.forEach(function (weightsGroup) {
+                                    weightsGroup.paths.forEach(function (path) {
+                                        paths.push(path);
+                                        perFileBuffers.push(null);
+                                    });
+                                    weightSpecs.push.apply(weightSpecs, weightsGroup.weights);
+                                });
+                                weightsManifest.forEach(function (weightsGroup) {
+                                    weightsGroup.paths.forEach(function (path) {
+                                        var weightFileReader = new FileReader();
+                                        weightFileReader.onload = function (event) {
+                                            var weightData = event.target.result;
+                                            var index = paths.indexOf(path);
+                                            perFileBuffers[index] = weightData;
+                                            if (perFileBuffers.indexOf(null) === -1) {
+                                                resolve({
+                                                    modelTopology: modelTopology,
+                                                    weightSpecs: weightSpecs,
+                                                    weightData: concatenateArrayBuffers(perFileBuffers),
+                                                });
+                                            }
+                                        };
+                                        weightFileReader.onerror = function (error) {
+                                            return reject("Failed to weights data from file of path '" + path + "'.");
+                                        };
+                                        weightFileReader.readAsArrayBuffer(pathToFile[path]);
+                                    });
+                                });
+                            };
+                            jsonReader.onerror = function (error) { return reject("Failed to read model topology and weights manifest JSON " +
+                                ("from file '" + jsonFile.name + "'. BrowserFiles supports loading ") +
+                                "Keras-style tf.Model artifacts only."); };
+                            jsonReader.readAsText(jsonFile);
+                        })];
+                });
+            });
+        };
+        BrowserFiles.prototype.checkManifestAndWeightFiles = function (manifest, files) {
+            var basenames = [];
+            var fileNames = files.map(function (file) { return basename(file.name); });
+            var pathToFile = {};
+            for (var _i = 0, manifest_1 = manifest; _i < manifest_1.length; _i++) {
+                var group = manifest_1[_i];
+                group.paths.forEach(function (path) {
+                    var pathBasename = basename(path);
+                    if (basenames.indexOf(pathBasename) !== -1) {
+                        throw new Error("Duplicate file basename found in weights manifest: " +
+                            ("'" + pathBasename + "'"));
+                    }
+                    basenames.push(pathBasename);
+                    if (fileNames.indexOf(pathBasename) === -1) {
+                        throw new Error("Weight file with basename '" + pathBasename + "' is not provided.");
+                    }
+                    else {
+                        pathToFile[path] = files[fileNames.indexOf(pathBasename)];
+                    }
+                });
+            }
+            if (basenames.length !== files.length) {
+                throw new Error("Mismatch in the number of files in weights manifest " +
+                    ("(" + basenames.length + ") and the number of weight files provided ") +
+                    ("(" + files.length + ")."));
+            }
+            return pathToFile;
+        };
+        return BrowserFiles;
+    }());
+    var browserDownloadsRouter = function (url) {
+        if (!ENV.get('IS_BROWSER')) {
+            return null;
+        }
+        else {
+            if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {
+                return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));
+            }
+            else {
+                return null;
+            }
+        }
+    };
+    IORouterRegistry.registerSaveRouter(browserDownloadsRouter);
+    function browserDownloads(fileNamePrefix) {
+        if (fileNamePrefix === void 0) { fileNamePrefix = 'model'; }
+        return new BrowserDownloads(fileNamePrefix);
+    }
+    function browserFiles(files) {
+        return new BrowserFiles(files);
+    }
+
+    function loadWeightsAsArrayBuffer(fetchURLs, requestOptions, fetchFunc) {
+        return __awaiter(this, void 0, void 0, function () {
+            var requests, responses, buffers;
+            return __generator(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        if (fetchFunc == null) {
+                            fetchFunc = fetch;
+                        }
+                        requests = fetchURLs.map(function (fetchURL) { return fetchFunc(fetchURL, requestOptions); });
+                        return [4, Promise.all(requests)];
+                    case 1:
+                        responses = _a.sent();
+                        return [4, Promise.all(responses.map(function (response) { return response.arrayBuffer(); }))];
+                    case 2:
+                        buffers = _a.sent();
+                        return [2, buffers];
+                }
+            });
+        });
+    }
+    function loadWeights(manifest, filePathPrefix, weightNames, requestOptions) {
+        if (filePathPrefix === void 0) { filePathPrefix = ''; }
+        return __awaiter(this, void 0, void 0, function () {
+            var fetchWeights, loadWeights;
+            return __generator(this, function (_a) {
+                fetchWeights = function (fetchUrls) {
+                    return loadWeightsAsArrayBuffer(fetchUrls, requestOptions);
+                };
+                loadWeights = weightsLoaderFactory(fetchWeights);
+                return [2, loadWeights(manifest, filePathPrefix, weightNames)];
+            });
+        });
+    }
+    function weightsLoaderFactory(fetchWeightsFunction) {
+        var _this = this;
+        return function (manifest, filePathPrefix, weightNames) {
+            if (filePathPrefix === void 0) { filePathPrefix = ''; }
+            return __awaiter(_this, void 0, void 0, function () {
+                var groupIndicesToFetchMap, groupWeightsToFetch, weightsFound, allManifestWeightNames, weightsNotFound, groupIndicesToFetch, fetchUrls, buffers, weightsTensorMap, bufferIndexOffset;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0:
+                            groupIndicesToFetchMap = manifest.map(function () { return false; });
+                            groupWeightsToFetch = {};
+                            weightsFound = weightNames != null
+                                ? weightNames.map(function () { return false; })
+                                : [];
+                            allManifestWeightNames = [];
+                            manifest.forEach(function (manifestGroupConfig, groupIndex) {
+                                var groupOffset = 0;
+                                manifestGroupConfig.weights.forEach(function (weightsEntry) {
+                                    var rawDtype = ('quantization' in weightsEntry) ?
+                                        weightsEntry.quantization.dtype :
+                                        weightsEntry.dtype;
+                                    var weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] *
+                                        sizeFromShape(weightsEntry.shape);
+                                    var enqueueWeightsForFetchingFn = function () {
+                                        groupIndicesToFetchMap[groupIndex] = true;
+                                        if (groupWeightsToFetch[groupIndex] == null) {
+                                            groupWeightsToFetch[groupIndex] = [];
+                                        }
+                                        groupWeightsToFetch[groupIndex].push({
+                                            manifestEntry: weightsEntry,
+                                            groupOffset: groupOffset,
+                                            sizeBytes: weightsBytes
+                                        });
+                                    };
+                                    if (weightNames != null) {
+                                        weightNames.forEach(function (weightName, weightIndex) {
+                                            if (weightName === weightsEntry.name) {
+                                                enqueueWeightsForFetchingFn();
+                                                weightsFound[weightIndex] = true;
+                                            }
+                                        });
+                                    }
+                                    else {
+                                        enqueueWeightsForFetchingFn();
+                                    }
+                                    allManifestWeightNames.push(weightsEntry.name);
+                                    groupOffset += weightsBytes;
+                                });
+                            });
+                            if (!weightsFound.every(function (found) { return found; })) {
+                                weightsNotFound = weightNames.filter(function (_, i) { return !weightsFound[i]; });
+                                throw new Error("Could not find weights in manifest with names: " +
+                                    (weightsNotFound.join(', ') + ". \n") +
+                                    "Manifest JSON has weights with names: " +
+                                    (allManifestWeightNames.join(', ') + "."));
+                            }
+                            groupIndicesToFetch = groupIndicesToFetchMap.reduce(function (accumulator, shouldFetch, i) {
+                                if (shouldFetch) {
+                                    accumulator.push(i);
+                                }
+                                return accumulator;
+                            }, []);
+                            fetchUrls = [];
+                            groupIndicesToFetch.forEach(function (i) {
+                                manifest[i].paths.forEach(function (filepath) {
+                                    var fetchUrl = filePathPrefix +
+                                        (!filePathPrefix.endsWith('/') ? '/' : '') + filepath;
+                                    fetchUrls.push(fetchUrl);
+                                });
+                            });
+                            return [4, fetchWeightsFunction(fetchUrls)];
+                        case 1:
+                            buffers = _a.sent();
+                            weightsTensorMap = {};
+                            bufferIndexOffset = 0;
+                            groupIndicesToFetch.forEach(function (i) {
+                                var numBuffers = manifest[i].paths.length;
+                                var groupBytes = 0;
+                                for (var i_1 = 0; i_1 < numBuffers; i_1++) {
+                                    groupBytes += buffers[bufferIndexOffset + i_1].byteLength;
+                                }
+                                var groupBuffer = new ArrayBuffer(groupBytes);
+                                var groupByteBuffer = new Uint8Array(groupBuffer);
+                                var groupBufferOffset = 0;
+                                for (var i_2 = 0; i_2 < numBuffers; i_2++) {
+                                    var buffer = new Uint8Array(buffers[bufferIndexOffset + i_2]);
+                                    groupByteBuffer.set(buffer, groupBufferOffset);
+                                    groupBufferOffset += buffer.byteLength;
+                                }
+                                var weightsEntries = groupWeightsToFetch[i];
+                                weightsEntries.forEach(function (weightsEntry) {
+                                    var byteBuffer = groupBuffer.slice(weightsEntry.groupOffset, weightsEntry.groupOffset + weightsEntry.sizeBytes);
+                                    var nameToTensorMap = decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);
+                                    for (var name_1 in nameToTensorMap) {
+                                        weightsTensorMap[name_1] = nameToTensorMap[name_1];
+                                    }
+                                });
+                                bufferIndexOffset += numBuffers;
+                            });
+                            return [2, weightsTensorMap];
+                    }
+                });
+            });
+        };
+    }
+
+    var BrowserHTTPRequest = (function () {
+        function BrowserHTTPRequest(path, requestInit, weightPathPrefix, fetchFunc) {
+            this.weightPathPrefix = weightPathPrefix;
+            this.DEFAULT_METHOD = 'POST';
+            if (fetchFunc == null) {
+                if (typeof fetch === 'undefined') {
+                    throw new Error('browserHTTPRequest is not supported outside the web browser ' +
+                        'without a fetch polyfill.');
+                }
+                this.fetchFunc =
+                    fetch.bind(typeof window === 'undefined' ? null : window);
+            }
+            else {
+                assert(typeof fetchFunc === 'function', 'Must pass a function that matches the signature of ' +
+                    '`fetch` (see ' +
+                    'https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)');
+                this.fetchFunc = fetchFunc;
+            }
+            assert(path != null && path.length > 0, 'URL path for browserHTTPRequest must not be null, undefined or ' +
+                'empty.');
+            if (Array.isArray(path)) {
+                assert(path.length === 2, 'URL paths for browserHTTPRequest must have a length of 2, ' +
+                    ("(actual length is " + path.length + ")."));
+            }
+            this.path = path;
+            if (requestInit != null && requestInit.body != null) {
+                throw new Error('requestInit is expected to have no pre-existing body, but has one.');
+            }
+            this.requestInit = requestInit || {};
+        }
+        BrowserHTTPRequest.prototype.save = function (modelArtifacts) {
+            return __awaiter(this, void 0, void 0, function () {
+                var init, weightsManifest, modelTopologyAndWeightManifest, response;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0:
+                            if (modelArtifacts.modelTopology instanceof ArrayBuffer) {
+                                throw new Error('BrowserHTTPRequest.save() does not support saving model topology ' +
+                                    'in binary formats yet.');
+                            }
+                            init = Object.assign({ method: this.DEFAULT_METHOD }, this.requestInit);
+                            init.body = new FormData();
+                            weightsManifest = [{
+                                    paths: ['./model.weights.bin'],
+                                    weights: modelArtifacts.weightSpecs,
+                                }];
+                            modelTopologyAndWeightManifest = {
+                                modelTopology: modelArtifacts.modelTopology,
+                                weightsManifest: weightsManifest
+                            };
+                            init.body.append('model.json', new Blob([JSON.stringify(modelTopologyAndWeightManifest)], { type: 'application/json' }), 'model.json');
+                            if (modelArtifacts.weightData != null) {
+                                init.body.append('model.weights.bin', new Blob([modelArtifacts.weightData], { type: 'application/octet-stream' }), 'model.weights.bin');
+                            }
+                            return [4, this.getFetchFunc()(this.path, init)];
+                        case 1:
+                            response = _a.sent();
+                            if (response.ok) {
+                                return [2, {
+                                        modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),
+                                        responses: [response],
+                                    }];
+                            }
+                            else {
+                                throw new Error("BrowserHTTPRequest.save() failed due to HTTP response status " +
+                                    (response.status + "."));
+                            }
+                            return [2];
+                    }
+                });
+            });
+        };
+        BrowserHTTPRequest.prototype.load = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                return __generator(this, function (_a) {
+                    return [2, Array.isArray(this.path) ? this.loadBinaryModel() :
+                            this.loadJSONModel()];
+                });
+            });
+        };
+        BrowserHTTPRequest.prototype.loadBinaryTopology = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                var response, error_1;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0:
+                            _a.trys.push([0, 3, , 4]);
+                            return [4, this.getFetchFunc()(this.path[0], this.requestInit)];
+                        case 1:
+                            response = _a.sent();
+                            if (!response.ok) {
+                                throw new Error("BrowserHTTPRequest.load() failed due to HTTP response: " + response.statusText);
+                            }
+                            return [4, response.arrayBuffer()];
+                        case 2: return [2, _a.sent()];
+                        case 3:
+                            error_1 = _a.sent();
+                            throw new Error(this.path[0] + " not found. " + error_1);
+                        case 4: return [2];
+                    }
+                });
+            });
+        };
+        BrowserHTTPRequest.prototype.loadBinaryModel = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                var graphPromise, manifestPromise, results, modelTopology, weightsManifestResponse, weightsManifest, weightSpecs, weightData, results_1;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0:
+                            graphPromise = this.loadBinaryTopology();
+                            return [4, this.getFetchFunc()(this.path[1], this.requestInit)];
+                        case 1:
+                            manifestPromise = _a.sent();
+                            if (!manifestPromise.ok) {
+                                throw new Error("BrowserHTTPRequest.load() failed due to HTTP response: " + manifestPromise.statusText);
+                            }
+                            return [4, Promise.all([graphPromise, manifestPromise])];
+                        case 2:
+                            results = _a.sent();
+                            modelTopology = results[0], weightsManifestResponse = results[1];
+                            return [4, weightsManifestResponse.json()];
+                        case 3:
+                            weightsManifest = _a.sent();
+                            if (!(weightsManifest != null)) return [3, 5];
+                            return [4, this.loadWeights(weightsManifest)];
+                        case 4:
+                            results_1 = _a.sent();
+                            weightSpecs = results_1[0], weightData = results_1[1];
+                            _a.label = 5;
+                        case 5: return [2, { modelTopology: modelTopology, weightSpecs: weightSpecs, weightData: weightData }];
+                    }
+                });
+            });
+        };
+        BrowserHTTPRequest.prototype.loadJSONModel = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                var modelConfigRequest, modelConfig, modelTopology, weightsManifest, weightSpecs, weightData, weightsManifest_1, results;
+                return __generator(this, function (_a) {
+                    switch (_a.label) {
+                        case 0: return [4, this.getFetchFunc()(this.path, this.requestInit)];
+                        case 1:
+                            modelConfigRequest = _a.sent();
+                            if (!modelConfigRequest.ok) {
+                                throw new Error("BrowserHTTPRequest.load() failed due to HTTP response: " + modelConfigRequest.statusText);
+                            }
+                            return [4, modelConfigRequest.json()];
+                        case 2:
+                            modelConfig = _a.sent();
+                            modelTopology = modelConfig['modelTopology'];
+                            weightsManifest = modelConfig['weightsManifest'];
+                            if (modelTopology == null && weightsManifest == null) {
+                                throw new Error("The JSON from HTTP path " + this.path + " contains neither model " +
+                                    "topology or manifest for weights.");
+                            }
+                            if (!(weightsManifest != null)) return [3, 4];
+                            weightsManifest_1 = modelConfig['weightsManifest'];
+                            return [4, this.loadWeights(weightsManifest_1)];
+                        case 3:
+                            results = _a.sent();
+                            weightSpecs = results[0], weightData = results[1];
+                            _a.label = 4;
+                        case 4: return [2, { modelTopology: modelTopology, weightSpecs: weightSpecs, weightData: weightData }];
+                    }
+                });
+            });
+        };
+        BrowserHTTPRequest.prototype.loadWeights = function (weightsManifest) {
+            return __awaiter(this, void 0, void 0, function () {
+                var weightPath, _a, prefix, suffix, pathPrefix, weightSpecs, _i, weightsManifest_2, entry, fetchURLs, _b, _c;
+                return __generator(this, function (_d) {
+                    switch (_d.label) {
+                        case 0:
+                            weightPath = Array.isArray(this.path) ? this.path[1] : this.path;
+                            _a = parseUrl(weightPath), prefix = _a[0], suffix = _a[1];
+                            pathPrefix = this.weightPathPrefix || prefix;
+                            weightSpecs = [];
+                            for (_i = 0, weightsManifest_2 = weightsManifest; _i < weightsManifest_2.length; _i++) {
+                                entry = weightsManifest_2[_i];
+                                weightSpecs.push.apply(weightSpecs, entry.weights);
+                            }
+                            fetchURLs = [];
+                            weightsManifest.forEach(function (weightsGroup) {
+                                weightsGroup.paths.forEach(function (path) {
+                                    fetchURLs.push(pathPrefix + path + suffix);
+                                });
+                            });
+                            _b = [weightSpecs];
+                            _c = concatenateArrayBuffers;
+                            return [4, loadWeightsAsArrayBuffer(fetchURLs, this.requestInit, this.getFetchFunc())];
+                        case 1: return [2, _b.concat([
+                                _c.apply(void 0, [_d.sent()])
+                            ])];
+                    }
+                });
+            });
+        };
+        BrowserHTTPRequest.prototype.getFetchFunc = function () {
+            return this.fetchFunc;
+        };
+        BrowserHTTPRequest.URL_SCHEME_REGEX = /^https?:\/\//;
+        return BrowserHTTPRequest;
+    }());
+    function parseUrl(url) {
+        var lastSlash = url.lastIndexOf('/');
+        var lastSearchParam = url.lastIndexOf('?');
+        var prefix = url.substring(0, lastSlash);
+        var suffix = lastSearchParam > lastSlash ? url.substring(lastSearchParam) : '';
+        return [prefix + '/', suffix];
+    }
+    function isHTTPScheme(url) {
+        return url.match(BrowserHTTPRequest.URL_SCHEME_REGEX) != null;
+    }
+    var httpRequestRouter = function (url) {
+        if (typeof fetch === 'undefined') {
+            return null;
+        }
+        else {
+            var isHTTP = true;
+            if (Array.isArray(url)) {
+                isHTTP = url.every(function (urlItem) { return isHTTPScheme(urlItem); });
+            }
+            else {
+                isHTTP = isHTTPScheme(url);
+            }
+            if (isHTTP) {
+                return browserHTTPRequest(url);
+            }
+        }
+        return null;
+    };
+    IORouterRegistry.registerSaveRouter(httpRequestRouter);
+    IORouterRegistry.registerLoadRouter(httpRequestRouter);
+    function browserHTTPRequest(path, requestInit, weightPathPrefix, fetchFunc) {
+        return new BrowserHTTPRequest(path, requestInit, weightPathPrefix, fetchFunc);
+    }
+
+    var PassthroughLoader = (function () {
+        function PassthroughLoader(modelTopology, weightSpecs, weightData) {
+            this.modelTopology = modelTopology;
+            this.weightSpecs = weightSpecs;
+            this.weightData = weightData;
+        }
+        PassthroughLoader.prototype.load = function () {
+            return __awaiter(this, void 0, void 0, function () {
+                var result;
+                return __generator(this, function (_a) {
+                    result = {};
+                    if (this.modelTopology != null) {
+                        result = __assign({ modelTopology: this.modelTopology }, result);
+                    }
+                    if (this.weightSpecs != null && this.weightSpecs.length > 0) {
+                        result = __assign({ weightSpecs: this.weightSpecs }, result);
+                    }
+                    if (this.weightData != null && this.weightData.byteLength > 0) {
+                        result = __assign({ weightData: this.weightData }, result);
+                    }
+                    return [2, result];
+                });
+            });
+        };
+        return PassthroughLoader;
+    }());
+    var PassthroughSaver = (function () {
+        function PassthroughSaver(saveHandler) {
+            this.saveHandler = saveHandler;
+        }
+        PassthroughSaver.prototype.save = function (modelArtifacts) {
+            return __awaiter(this, void 0, void 0, function () {
+                return __generator(this, function (_a) {
+                    return [2, this.saveHandler(modelArtifacts)];
+                });
+            });
+        };
+        return PassthroughSaver;
+    }());
+    function fromMemory(modelTopology, weightSpecs, weightData) {
+        return new PassthroughLoader(modelTopology, weightSpecs, weightData);
+    }
+    function withSaveHandler(saveHandler) {
+        return new PassthroughSaver(saveHandler);
+    }
+
+    var registerSaveRouter = IORouterRegistry.registerSaveRouter;
+    var registerLoadRouter = IORouterRegistry.registerLoadRouter;
+    var getSaveHandlers = IORouterRegistry.getSaveHandlers;
+    var getLoadHandlers = IORouterRegistry.getLoadHandlers;
+
+    var io = /*#__PURE__*/Object.freeze({
+        browserFiles: browserFiles,
+        browserHTTPRequest: browserHTTPRequest,
+        concatenateArrayBuffers: concatenateArrayBuffers,
+        decodeWeights: decodeWeights,
+        encodeWeights: encodeWeights,
+        fromMemory: fromMemory,
+        getLoadHandlers: getLoadHandlers,
+        getModelArtifactsInfoForJSON: getModelArtifactsInfoForJSON,
+        getSaveHandlers: getSaveHandlers,
+        isHTTPScheme: isHTTPScheme,
+        loadWeights: loadWeights,
+        registerLoadRouter: registerLoadRouter,
+        registerSaveRouter: registerSaveRouter,
+        weightsLoaderFactory: weightsLoaderFactory,
+        withSaveHandler: withSaveHandler,
+        copyModel: copyModel,
+        listModels: listModels,
+        moveModel: moveModel,
+        removeModel: removeModel
+    });
+
+    function confusionMatrix_(labels, predictions, numClasses) {
+        var $labels = convertToTensor(labels, 'labels', 'confusionMatrix');
+        var $predictions = convertToTensor(predictions, 'predictions', 'confusionMatrix');
+        assert(numClasses == null || numClasses > 0 && Number.isInteger(numClasses), "If provided, numClasses must be a positive integer, " +
+            ("but got " + numClasses));
+        assert($labels.rank === 1, "Expected the rank of labels to be 1, but got " + $labels.rank);
+        assert($predictions.rank === 1, "Expected the rank of predictions to be 1, " +
+            ("but got " + $predictions.rank));
+        assert($labels.shape[0] === $predictions.shape[0], "Mismatch in the number of examples: " +
+            ($labels.shape[0] + " vs. " + $predictions.shape[0] + ". ") +
+            "Labels and predictions should have the same number of elements.");
+        assert(numClasses > 0 && Number.isInteger(numClasses), "numClasses is required to be a positive integer, but got " + numClasses);
+        var oneHotLabels = oneHot($labels.asType('int32'), numClasses);
+        var oneHotPredictions = oneHot($predictions.asType('int32'), numClasses);
+        return oneHotLabels.transpose().matMul(oneHotPredictions).asType('int32');
+    }
+    var confusionMatrix = op({ confusionMatrix_: confusionMatrix_ });
+
+
+
+    var math = /*#__PURE__*/Object.freeze({
+        confusionMatrix: confusionMatrix
+    });
+
+    var Serializable = (function () {
+        function Serializable() {
+        }
+        Serializable.prototype.getClassName = function () {
+            return this.constructor
+                .className;
+        };
+        Serializable.fromConfig = function (cls, config) {
+            return new cls(config);
+        };
+        return Serializable;
+    }());
+    var SerializationMap = (function () {
+        function SerializationMap() {
+            this.classNameMap = {};
+        }
+        SerializationMap.getMap = function () {
+            if (SerializationMap.instance == null) {
+                SerializationMap.instance = new SerializationMap();
+            }
+            return SerializationMap.instance;
+        };
+        SerializationMap.register = function (cls) {
+            SerializationMap.getMap().classNameMap[cls.className] =
+                [cls, cls.fromConfig];
+        };
+        return SerializationMap;
+    }());
+    function registerClass(cls) {
+        assert(cls.className != null, "Class being registered does not have the static className property " +
+            "defined.");
+        assert(typeof cls.className === 'string', "className is required to be a string, but got type " +
+            typeof cls.className);
+        assert(cls.className.length > 0, "Class being registered has an empty-string as its className, which " +
+            "is disallowed.");
+        SerializationMap.register(cls);
+    }
+
+    var serialization = /*#__PURE__*/Object.freeze({
+        Serializable: Serializable,
+        SerializationMap: SerializationMap,
+        registerClass: registerClass
+    });
+
+    var WEBGL_ENVS = {
+        'HAS_WEBGL': true
+    };
+    var NODE_ENVS = {
+        'IS_NODE': true
+    };
+    var CHROME_ENVS = {
+        'IS_CHROME': true
+    };
+    var BROWSER_ENVS = {
+        'IS_BROWSER': true
+    };
+    var CPU_ENVS = {
+        'HAS_WEBGL': false
+    };
+    var ALL_ENVS = {};
+    function expectArraysClose(actual, expected, epsilon) {
+        if (epsilon == null) {
+            epsilon = ENV.get('TEST_EPSILON');
+        }
+        return expectArraysPredicate(actual, expected, function (a, b) { return areClose(a, Number(b), epsilon); });
+    }
+    function expectArraysPredicate(actual, expected, predicate) {
+        if (!(actual instanceof Tensor) && !(expected instanceof Tensor)) {
+            var aType = actual.constructor.name;
+            var bType = expected.constructor.name;
+            if (aType !== bType) {
+                throw new Error("Arrays are of different type actual: " + aType + " " +
+                    ("vs expected: " + bType));
+            }
+        }
+        else if (actual instanceof Tensor && expected instanceof Tensor) {
+            if (actual.dtype !== expected.dtype) {
+                throw new Error("Arrays are of different type actual: " + actual.dtype + " " +
+                    ("vs expected: " + expected.dtype + "."));
+            }
+            if (!arraysEqual(actual.shape, expected.shape)) {
+                throw new Error("Arrays are of different shape actual: " + actual.shape + " " +
+                    ("vs expected: " + expected.shape + "."));
+            }
+        }
+        var actualValues;
+        var expectedValues;
+        if (actual instanceof Tensor) {
+            actualValues = actual.dataSync();
+        }
+        else {
+            actualValues = actual;
+        }
+        if (expected instanceof Tensor) {
+            expectedValues = expected.dataSync();
+        }
+        else {
+            expectedValues = expected;
+        }
+        if (actualValues.length !== expectedValues.length) {
+            throw new Error("Arrays have different lengths actual: " + actualValues.length + " vs " +
+                ("expected: " + expectedValues.length + ".\n") +
+                ("Actual:   " + actualValues + ".\n") +
+                ("Expected: " + expectedValues + "."));
+        }
+        for (var i = 0; i < expectedValues.length; ++i) {
+            var a = actualValues[i];
+            var e = expectedValues[i];
+            if (!predicate(a, e)) {
+                throw new Error("Arrays differ: actual[" + i + "] = " + a + ", expected[" + i + "] = " + e + ".\n" +
+                    ("Actual:   " + actualValues + ".\n") +
+                    ("Expected: " + expectedValues + "."));
+            }
+        }
+    }
+    function expectPromiseToFail(fn, done) {
+        fn().then(function () { return done.fail(); }, function () { return done(); });
+    }
+    function expectArraysEqual(actual, expected) {
+        if (actual instanceof Tensor && actual.dtype === 'string' ||
+            expected instanceof Tensor && expected.dtype === 'string' ||
+            actual instanceof Array && isString(actual[0]) ||
+            expected instanceof Array && isString(expected[0])) {
+            return expectArraysPredicate(actual, expected, function (a, b) { return a == b; });
+        }
+        return expectArraysClose(actual, expected, 0);
+    }
+    function expectNumbersClose(a, e, epsilon) {
+        if (epsilon == null) {
+            epsilon = ENV.get('TEST_EPSILON');
+        }
+        if (!areClose(a, e, epsilon)) {
+            throw new Error("Numbers differ: actual === " + a + ", expected === " + e);
+        }
+    }
+    function areClose(a, e, epsilon) {
+        if (isNaN(a) && isNaN(e)) {
+            return true;
+        }
+        if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon) {
+            return false;
+        }
+        return true;
+    }
+    function expectValuesInRange(actual, low, high) {
+        var actualVals;
+        if (actual instanceof Tensor) {
+            actualVals = actual.dataSync();
+        }
+        else {
+            actualVals = actual;
+        }
+        for (var i = 0; i < actualVals.length; i++) {
+            if (actualVals[i] < low || actualVals[i] > high) {
+                throw new Error("Value out of range:" + actualVals[i] + " low: " + low + ", high: " + high);
+            }
+        }
+    }
+    function expectArrayBuffersEqual(actual, expected) {
+        expect(new Float32Array(actual)).toEqual(new Float32Array(expected));
+    }
+
+    var test_util = /*#__PURE__*/Object.freeze({
+        WEBGL_ENVS: WEBGL_ENVS,
+        NODE_ENVS: NODE_ENVS,
+        CHROME_ENVS: CHROME_ENVS,
+        BROWSER_ENVS: BROWSER_ENVS,
+        CPU_ENVS: CPU_ENVS,
+        ALL_ENVS: ALL_ENVS,
+        expectArraysClose: expectArraysClose,
+        expectPromiseToFail: expectPromiseToFail,
+        expectArraysEqual: expectArraysEqual,
+        expectNumbersClose: expectNumbersClose,
+        expectValuesInRange: expectValuesInRange,
+        expectArrayBuffersEqual: expectArrayBuffersEqual
+    });
+
+    var version = '0.14.2';
+
+
+
+    var webgl = /*#__PURE__*/Object.freeze({
+        gpgpu_util: gpgpu_util,
+        webgl_util: webgl_util,
+        MathBackendWebGL: MathBackendWebGL,
+        GPGPUContext: GPGPUContext
+    });
+
+    var Optimizer = (function (_super) {
+        __extends(Optimizer, _super);
+        function Optimizer() {
+            return _super !== null && _super.apply(this, arguments) || this;
+        }
+        Optimizer.prototype.minimize = function (f, returnCost, varList) {
+            if (returnCost === void 0) { returnCost = false; }
+            var _a = this.computeGradients(f, varList), value = _a.value, grads$$1 = _a.grads;
+            this.applyGradients(grads$$1);
+            var varNames = Object.keys(grads$$1);
+            varNames.forEach(function (varName) { return grads$$1[varName].dispose(); });
+            if (returnCost) {
+                return value;
+            }
+            else {
+                value.dispose();
+                return null;
+            }
+        };
+        Optimizer.prototype.computeGradients = function (f, varList) {
+            return variableGrads(f, varList);
+        };
+        return Optimizer;
+    }(Serializable));
+
+    var AdadeltaOptimizer = (function (_super) {
+        __extends(AdadeltaOptimizer, _super);
+        function AdadeltaOptimizer(learningRate, rho, epsilon) {
+            if (epsilon === void 0) { epsilon = null; }
+            var _this = _super.call(this) || this;
+            _this.learningRate = learningRate;
+            _this.rho = rho;
+            _this.epsilon = epsilon;
+            _this.accumulatedGrads = {};
+            _this.accumulatedUpdates = {};
+            _this.c = keep(scalar(-learningRate));
+            _this.rhoScalar = keep(scalar(rho));
+            _this.oneMinusRho = keep(scalar(1 - rho));
+            if (epsilon === null) {
+                epsilon = ENV.get('EPSILON');
+            }
+            _this.epsilonScalar = keep(scalar(epsilon));
+            return _this;
+        }
+        AdadeltaOptimizer.prototype.applyGradients = function (variableGradients) {
+            var _this = this;
+            var _loop_1 = function (variableName) {
+                var value = ENV.engine.registeredVariables[variableName];
+                if (this_1.accumulatedGrads[variableName] == null) {
+                    var trainable_1 = false;
+                    tidy(function () {
+                        _this.accumulatedGrads[variableName] =
+                            zerosLike(value).variable(trainable_1);
+                    });
+                }
+                if (this_1.accumulatedUpdates[variableName] == null) {
+                    var trainable_2 = false;
+                    tidy(function () {
+                        _this.accumulatedUpdates[variableName] =
+                            zerosLike(value).variable(trainable_2);
+                    });
+                }
+                var gradient = variableGradients[variableName];
+                var accumulatedGrad = this_1.accumulatedGrads[variableName];
+                var accumulatedUpdate = this_1.accumulatedUpdates[variableName];
+                tidy(function () {
+                    var newAccumulatedGrad = _this.rhoScalar.mul(accumulatedGrad)
+                        .add(_this.oneMinusRho.mul(gradient.square()));
+                    var updates = accumulatedUpdate.add(_this.epsilonScalar)
+                        .sqrt()
+                        .div(accumulatedGrad.add(_this.epsilonScalar).sqrt())
+                        .mul(gradient);
+                    var newAccumulatedUpdate = _this.rhoScalar.mul(accumulatedUpdate)
+                        .add(_this.oneMinusRho.mul(updates.square()));
+                    _this.accumulatedGrads[variableName].assign(newAccumulatedGrad);
+                    _this.accumulatedUpdates[variableName].assign(newAccumulatedUpdate);
+                    var newValue = _this.c.mul(updates).add(value);
+                    value.assign(newValue);
+                });
+            };
+            var this_1 = this;
+            for (var variableName in variableGradients) {
+                _loop_1(variableName);
+            }
+        };
+        AdadeltaOptimizer.prototype.dispose = function () {
+            var _this = this;
+            this.c.dispose();
+            this.epsilonScalar.dispose();
+            this.rhoScalar.dispose();
+            this.oneMinusRho.dispose();
+            if (this.accumulatedUpdates != null) {
+                Object.keys(this.accumulatedUpdates)
+                    .forEach(function (name) { return _this.accumulatedUpdates[name].dispose(); });
+                Object.keys(this.accumulatedGrads)
+                    .forEach(function (name) { return _this.accumulatedGrads[name].dispose(); });
+            }
+        };
+        AdadeltaOptimizer.prototype.getConfig = function () {
+            return {
+                learningRate: this.learningRate,
+                rho: this.rho,
+                epsilon: this.epsilon
+            };
+        };
+        AdadeltaOptimizer.fromConfig = function (cls, config) {
+            return new cls(config.learningRate, config.rho, config.epsilon);
+        };
+        AdadeltaOptimizer.className = 'AdadeltaOptimizer';
+        return AdadeltaOptimizer;
+    }(Optimizer));
+    registerClass(AdadeltaOptimizer);
+
+    var AdagradOptimizer = (function (_super) {
+        __extends(AdagradOptimizer, _super);
+        function AdagradOptimizer(learningRate, initialAccumulatorValue) {
+            if (initialAccumulatorValue === void 0) { initialAccumulatorValue = 0.1; }
+            var _this = _super.call(this) || this;
+            _this.learningRate = learningRate;
+            _this.initialAccumulatorValue = initialAccumulatorValue;
+            _this.accumulatedGrads = {};
+            _this.c = keep(scalar(-learningRate));
+            _this.epsilon = keep(scalar(ENV.get('EPSILON')));
+            return _this;
+        }
+        AdagradOptimizer.prototype.applyGradients = function (variableGradients) {
+            var _this = this;
+            var _loop_1 = function (variableName) {
+                var value = ENV.engine.registeredVariables[variableName];
+                if (this_1.accumulatedGrads[variableName] == null) {
+                    var trainable_1 = false;
+                    tidy(function () {
+                        _this.accumulatedGrads[variableName] =
+                            fill(value.shape, _this.initialAccumulatorValue)
+                                .variable(trainable_1);
+                    });
+                }
+                var gradient = variableGradients[variableName];
+                var accumulatedGrad = this_1.accumulatedGrads[variableName];
+                tidy(function () {
+                    var newAccumulatedGrad = accumulatedGrad.add(gradient.square());
+                    _this.accumulatedGrads[variableName].assign(newAccumulatedGrad);
+                    var newValue = _this.c
+                        .mul(gradient.div(newAccumulatedGrad.add(_this.epsilon).sqrt()))
+                        .add(value);
+                    value.assign(newValue);
+                });
+            };
+            var this_1 = this;
+            for (var variableName in variableGradients) {
+                _loop_1(variableName);
+            }
+        };
+        AdagradOptimizer.prototype.dispose = function () {
+            var _this = this;
+            this.epsilon.dispose();
+            this.c.dispose();
+            if (this.accumulatedGrads != null) {
+                Object.keys(this.accumulatedGrads)
+                    .forEach(function (name) { return _this.accumulatedGrads[name].dispose(); });
+            }
+        };
+        AdagradOptimizer.prototype.getConfig = function () {
+            return {
+                learningRate: this.learningRate,
+                initialAccumulatorValue: this.initialAccumulatorValue,
+            };
+        };
+        AdagradOptimizer.fromConfig = function (cls, config) {
+            return new cls(config.learningRate, config.initialAccumulatorValue);
+        };
+        AdagradOptimizer.className = 'AdagradOptimizer';
+        return AdagradOptimizer;
+    }(Optimizer));
+    registerClass(AdagradOptimizer);
+
+    var AdamOptimizer = (function (_super) {
+        __extends(AdamOptimizer, _super);
+        function AdamOptimizer(learningRate, beta1, beta2, epsilon) {
+            if (epsilon === void 0) { epsilon = null; }
+            var _this = _super.call(this) || this;
+            _this.learningRate = learningRate;
+            _this.beta1 = beta1;
+            _this.beta2 = beta2;
+            _this.epsilon = epsilon;
+            _this.accumulatedFirstMoment = {};
+            _this.accumulatedSecondMoment = {};
+            _this.c = keep(scalar(-learningRate));
+            _this.beta1Scalar = keep(scalar(beta1));
+            _this.beta2Scalar = keep(scalar(beta2));
+            tidy(function () {
+                _this.accBeta1 = scalar(beta1).variable();
+                _this.accBeta2 = scalar(beta2).variable();
+            });
+            _this.oneMinusBeta1 = keep(scalar(1 - beta1));
+            _this.oneMinusBeta2 = keep(scalar(1 - beta2));
+            _this.one = keep(scalar(1));
+            if (epsilon === null) {
+                epsilon = ENV.get('EPSILON');
+            }
+            _this.epsScalar = keep(scalar(epsilon));
+            return _this;
+        }
+        AdamOptimizer.prototype.applyGradients = function (variableGradients) {
+            var _this = this;
+            tidy(function () {
+                var oneMinusAccBeta1 = _this.one.sub(_this.accBeta1);
+                var oneMinusAccBeta2 = _this.one.sub(_this.accBeta2);
+                for (var variableName in variableGradients) {
+                    var value = ENV.engine.registeredVariables[variableName];
+                    if (_this.accumulatedFirstMoment[variableName] == null) {
+                        var trainable = false;
+                        _this.accumulatedFirstMoment[variableName] =
+                            zerosLike(value).variable(trainable);
+                    }
+                    if (_this.accumulatedSecondMoment[variableName] == null) {
+                        var trainable = false;
+                        _this.accumulatedSecondMoment[variableName] =
+                            zerosLike(value).variable(trainable);
+                    }
+                    var gradient = variableGradients[variableName];
+                    var firstMoment = _this.accumulatedFirstMoment[variableName];
+                    var secondMoment = _this.accumulatedSecondMoment[variableName];
+                    var newFirstMoment = _this.beta1Scalar.mul(firstMoment)
+                        .add(_this.oneMinusBeta1.mul(gradient));
+                    var newSecondMoment = _this.beta2Scalar.mul(secondMoment)
+                        .add(_this.oneMinusBeta2.mul(gradient.square()));
+                    var biasCorrectedFirstMoment = newFirstMoment.div(oneMinusAccBeta1);
+                    var biasCorrectedSecondMoment = newSecondMoment.div(oneMinusAccBeta2);
+                    _this.accumulatedFirstMoment[variableName].assign(newFirstMoment);
+                    _this.accumulatedSecondMoment[variableName].assign(newSecondMoment);
+                    var newValue = _this.c
+                        .mul(biasCorrectedFirstMoment.div(_this.epsScalar.add(biasCorrectedSecondMoment.sqrt())))
+                        .add(value);
+                    value.assign(newValue);
+                }
+                _this.accBeta1.assign(_this.accBeta1.mul(_this.beta1Scalar));
+                _this.accBeta2.assign(_this.accBeta2.mul(_this.beta2Scalar));
+            });
+        };
+        AdamOptimizer.prototype.dispose = function () {
+            var _this = this;
+            this.c.dispose();
+            this.epsScalar.dispose();
+            this.beta1Scalar.dispose();
+            this.beta2Scalar.dispose();
+            this.accBeta1.dispose();
+            this.accBeta2.dispose();
+            this.oneMinusBeta1.dispose();
+            this.oneMinusBeta2.dispose();
+            this.one.dispose();
+            if (this.accumulatedFirstMoment != null) {
+                Object.keys(this.accumulatedFirstMoment)
+                    .forEach(function (name) { return _this.accumulatedFirstMoment[name].dispose(); });
+            }
+            if (this.accumulatedSecondMoment != null) {
+                Object.keys(this.accumulatedSecondMoment)
+                    .forEach(function (name) { return _this.accumulatedSecondMoment[name].dispose(); });
+            }
+        };
+        AdamOptimizer.prototype.getConfig = function () {
+            return {
+                learningRate: this.learningRate,
+                beta1: this.beta1,
+                beta2: this.beta2,
+                epsilon: this.epsilon,
+            };
+        };
+        AdamOptimizer.fromConfig = function (cls, config) {
+            return new cls(config.learningRate, config.beta1, config.beta2, config.epsilon);
+        };
+        AdamOptimizer.className = 'AdamOptimizer';
+        return AdamOptimizer;
+    }(Optimizer));
+    registerClass(AdamOptimizer);
+
+    var AdamaxOptimizer = (function (_super) {
+        __extends(AdamaxOptimizer, _super);
+        function AdamaxOptimizer(learningRate, beta1, beta2, epsilon, decay) {
+            if (epsilon === void 0) { epsilon = null; }
+            if (decay === void 0) { decay = 0.0; }
+            var _this = _super.call(this) || this;
+            _this.learningRate = learningRate;
+            _this.beta1 = beta1;
+            _this.beta2 = beta2;
+            _this.epsilon = epsilon;
+            _this.decay = decay;
+            _this.accumulatedFirstMoment = {};
+            _this.accumulatedWeightedInfNorm = {};
+            _this.c = keep(scalar(-learningRate));
+            _this.beta1Scalar = keep(scalar(beta1));
+            _this.beta2Scalar = keep(scalar(beta2));
+            _this.decayScalar = keep(scalar(decay));
+            tidy(function () {
+                _this.iteration = scalar(0).variable();
+                _this.accBeta1 = scalar(beta1).variable();
+            });
+            _this.oneMinusBeta1 = keep(scalar(1 - beta1));
+            _this.one = keep(scalar(1));
+            if (epsilon === null) {
+                epsilon = ENV.get('EPSILON');
+            }
+            _this.epsScalar = keep(scalar(epsilon));
+            return _this;
+        }
+        AdamaxOptimizer.prototype.applyGradients = function (variableGradients) {
+            var _this = this;
+            tidy(function () {
+                var oneMinusAccBeta1 = _this.one.sub(_this.accBeta1);
+                var lr = _this.c.div(_this.one.add(_this.decayScalar.mul(_this.iteration)));
+                for (var variableName in variableGradients) {
+                    var value = ENV.engine.registeredVariables[variableName];
+                    if (_this.accumulatedFirstMoment[variableName] == null) {
+                        var trainable = false;
+                        _this.accumulatedFirstMoment[variableName] =
+                            zerosLike(value).variable(trainable);
+                    }
+                    if (_this.accumulatedWeightedInfNorm[variableName] == null) {
+                        var trainable = false;
+                        _this.accumulatedWeightedInfNorm[variableName] =
+                            zerosLike(value).variable(trainable);
+                    }
+                    var gradient = variableGradients[variableName];
+                    var firstMoment = _this.accumulatedFirstMoment[variableName];
+                    var weightedInfNorm = _this.accumulatedWeightedInfNorm[variableName];
+                    var newFirstMoment = _this.beta1Scalar.mul(firstMoment)
+                        .add(_this.oneMinusBeta1.mul(gradient));
+                    var ut0 = _this.beta2Scalar.mul(weightedInfNorm);
+                    var ut1 = gradient.abs();
+                    var newWeightedInfNorm = ut0.maximum(ut1);
+                    _this.accumulatedFirstMoment[variableName].assign(newFirstMoment);
+                    _this.accumulatedWeightedInfNorm[variableName].assign(newWeightedInfNorm);
+                    var newValue = lr.div(oneMinusAccBeta1)
+                        .mul(newFirstMoment.div(_this.epsScalar.add(newWeightedInfNorm)))
+                        .add(value);
+                    value.assign(newValue);
+                }
+                _this.iteration.assign(_this.iteration.add(_this.one));
+                _this.accBeta1.assign(_this.accBeta1.mul(_this.beta1Scalar));
+            });
+        };
+        AdamaxOptimizer.prototype.dispose = function () {
+            var _this = this;
+            this.c.dispose();
+            this.epsScalar.dispose();
+            this.accBeta1.dispose();
+            this.beta1Scalar.dispose();
+            this.beta2Scalar.dispose();
+            this.oneMinusBeta1.dispose();
+            this.decayScalar.dispose();
+            this.iteration.dispose();
+            this.one.dispose();
+            if (this.accumulatedFirstMoment != null) {
+                Object.keys(this.accumulatedFirstMoment)
+                    .forEach(function (name) { return _this.accumulatedFirstMoment[name].dispose(); });
+            }
+            if (this.accumulatedWeightedInfNorm != null) {
+                Object.keys(this.accumulatedWeightedInfNorm)
+                    .forEach(function (name) { return _this.accumulatedWeightedInfNorm[name].dispose(); });
+            }
+        };
+        AdamaxOptimizer.prototype.getConfig = function () {
+            return {
+                learningRate: this.learningRate,
+                beta1: this.beta1,
+                beta2: this.beta2,
+                epsilon: this.epsilon,
+                decay: this.decay
+            };
+        };
+        AdamaxOptimizer.fromConfig = function (cls, config) {
+            return new cls(config.learningRate, config.beta1, config.beta2, config.epsilon, config.decay);
+        };
+        AdamaxOptimizer.className = 'AdamaxOptimizer';
+        return AdamaxOptimizer;
+    }(Optimizer));
+    registerClass(AdamaxOptimizer);
+
+    var SGDOptimizer = (function (_super) {
+        __extends(SGDOptimizer, _super);
+        function SGDOptimizer(learningRate) {
+            var _this = _super.call(this) || this;
+            _this.learningRate = learningRate;
+            _this.setLearningRate(learningRate);
+            return _this;
+        }
+        SGDOptimizer.prototype.applyGradients = function (variableGradients) {
+            var _this = this;
+            var varNames = Object.keys(variableGradients);
+            varNames.forEach(function (varName) {
+                var gradient = variableGradients[varName];
+                var value = ENV.engine.registeredVariables[varName];
+                tidy(function () {
+                    var newValue = _this.c.mul(gradient).add(value);
+                    value.assign(newValue);
+                });
+            });
+        };
+        SGDOptimizer.prototype.setLearningRate = function (learningRate) {
+            this.learningRate = learningRate;
+            if (this.c != null) {
+                this.c.dispose();
+            }
+            this.c = keep(scalar(-learningRate));
+        };
+        SGDOptimizer.prototype.dispose = function () {
+            this.c.dispose();
+        };
+        SGDOptimizer.prototype.getConfig = function () {
+            return { learningRate: this.learningRate };
+        };
+        SGDOptimizer.fromConfig = function (cls, config) {
+            return new cls(config.learningRate);
+        };
+        SGDOptimizer.className = 'SGDOptimizer';
+        return SGDOptimizer;
+    }(Optimizer));
+    registerClass(SGDOptimizer);
+
+    var MomentumOptimizer = (function (_super) {
+        __extends(MomentumOptimizer, _super);
+        function MomentumOptimizer(learningRate, momentum, useNesterov) {
+            if (useNesterov === void 0) { useNesterov = false; }
+            var _this = _super.call(this, learningRate) || this;
+            _this.learningRate = learningRate;
+            _this.momentum = momentum;
+            _this.useNesterov = useNesterov;
+            _this.m = scalar(_this.momentum);
+            _this.accumulations = {};
+            return _this;
+        }
+        MomentumOptimizer.prototype.applyGradients = function (variableGradients) {
+            var _this = this;
+            var _loop_1 = function (variableName) {
+                var value = ENV.engine.registeredVariables[variableName];
+                if (this_1.accumulations[variableName] == null) {
+                    var trainable_1 = false;
+                    tidy(function () {
+                        _this.accumulations[variableName] =
+                            zerosLike(value).variable(trainable_1);
+                    });
+                }
+                var accumulation = this_1.accumulations[variableName];
+                var gradient = variableGradients[variableName];
+                tidy(function () {
+                    var newValue;
+                    var newAccumulation = _this.m.mul(accumulation).add(gradient);
+                    if (_this.useNesterov) {
+                        newValue =
+                            _this.c.mul(gradient.add(newAccumulation.mul(_this.m))).add(value);
+                    }
+                    else {
+                        newValue = _this.c.mul(newAccumulation).add(value);
+                    }
+                    _this.accumulations[variableName].assign(newAccumulation);
+                    value.assign(newValue);
+                });
+            };
+            var this_1 = this;
+            for (var variableName in variableGradients) {
+                _loop_1(variableName);
+            }
+        };
+        MomentumOptimizer.prototype.dispose = function () {
+            _super.prototype.dispose.call(this);
+            this.m.dispose();
+            if (this.accumulations != null) {
+                for (var variableName in this.accumulations) {
+                    this.accumulations[variableName].dispose();
+                }
+            }
+        };
+        MomentumOptimizer.prototype.setMomentum = function (momentum) {
+            this.momentum = momentum;
+        };
+        MomentumOptimizer.prototype.getConfig = function () {
+            return {
+                learningRate: this.learningRate,
+                momentum: this.momentum,
+                useNesterov: this.useNesterov
+            };
+        };
+        MomentumOptimizer.fromConfig = function (cls, config) {
+            return new cls(config.learningRate, config.momentum, config.useNesterov);
+        };
+        MomentumOptimizer.className = 'MomentumOptimizer';
+        return MomentumOptimizer;
+    }(SGDOptimizer));
+    registerClass(MomentumOptimizer);
+
+    var RMSPropOptimizer = (function (_super) {
+        __extends(RMSPropOptimizer, _super);
+        function RMSPropOptimizer(learningRate, decay, momentum, epsilon, centered) {
+            if (decay === void 0) { decay = 0.9; }
+            if (momentum === void 0) { momentum = 0.0; }
+            if (epsilon === void 0) { epsilon = null; }
+            if (centered === void 0) { centered = false; }
+            var _this = _super.call(this) || this;
+            _this.learningRate = learningRate;
+            _this.decay = decay;
+            _this.momentum = momentum;
+            _this.epsilon = epsilon;
+            _this.accumulatedMeanSquares = {};
+            _this.accumulatedMeanGrads = {};
+            _this.accumulatedMoments = {};
+            _this.c = keep(scalar(learningRate));
+            _this.decayScalar = keep(scalar(decay));
+            _this.momentumScalar = keep(scalar(momentum));
+            _this.oneMinusDecay = keep(scalar(1 - decay));
+            _this.centered = centered;
+            if (epsilon === null) {
+                epsilon = ENV.get('EPSILON');
+            }
+            _this.epsilonScalar = keep(scalar(epsilon));
+            return _this;
+        }
+        RMSPropOptimizer.prototype.applyGradients = function (variableGradients) {
+            var _this = this;
+            var _loop_1 = function (variableName) {
+                var value = ENV.engine.registeredVariables[variableName];
+                if (this_1.accumulatedMeanSquares[variableName] == null) {
+                    var trainable_1 = false;
+                    tidy(function () {
+                        _this.accumulatedMeanSquares[variableName] =
+                            zerosLike(value).variable(trainable_1);
+                    });
+                }
+                if (this_1.accumulatedMeanGrads[variableName] == null && this_1.centered) {
+                    var trainable_2 = false;
+                    tidy(function () {
+                        _this.accumulatedMeanGrads[variableName] =
+                            zerosLike(value).variable(trainable_2);
+                    });
+                }
+                if (this_1.accumulatedMoments[variableName] == null) {
+                    var trainable_3 = false;
+                    tidy(function () {
+                        _this.accumulatedMoments[variableName] =
+                            zerosLike(value).variable(trainable_3);
+                    });
+                }
+                var accumulatedMeanSquare = this_1.accumulatedMeanSquares[variableName];
+                var accumulatedMeanGrad = this_1.accumulatedMeanGrads[variableName];
+                var accumulatedMoments = this_1.accumulatedMoments[variableName];
+                var gradient = variableGradients[variableName];
+                tidy(function () {
+                    var newAccumulatedMeanSquare = _this.decayScalar.mul(accumulatedMeanSquare)
+                        .add(_this.oneMinusDecay.mul(gradient.square()));
+                    if (_this.centered) {
+                        var newAccumulatedMeanGrad = _this.decayScalar.mul(accumulatedMeanGrad)
+                            .add(_this.oneMinusDecay.mul(gradient));
+                        var newAccumulatedMoments = _this.momentumScalar.mul(accumulatedMoments)
+                            .add(_this.c.mul(gradient).div(newAccumulatedMeanSquare
+                            .sub(newAccumulatedMeanGrad.square().add(_this.epsilonScalar))
+                            .sqrt()));
+                        _this.accumulatedMeanSquares[variableName].assign(newAccumulatedMeanSquare);
+                        _this.accumulatedMeanGrads[variableName].assign(newAccumulatedMeanGrad);
+                        _this.accumulatedMoments[variableName].assign(newAccumulatedMoments);
+                        var newValue = value.sub(newAccumulatedMoments);
+                        value.assign(newValue);
+                    }
+                    else {
+                        var newAccumulatedMeanSquare_1 = _this.decayScalar.mul(accumulatedMeanSquare)
+                            .add(_this.oneMinusDecay.mul(gradient.square()));
+                        var newAccumulatedMoments = _this.momentumScalar.mul(accumulatedMoments)
+                            .add(_this.c.mul(gradient).div(newAccumulatedMeanSquare_1.add(_this.epsilonScalar).sqrt()));
+                        _this.accumulatedMeanSquares[variableName].assign(newAccumulatedMeanSquare_1);
+                        _this.accumulatedMoments[variableName].assign(newAccumulatedMoments);
+                        var newValue = value.sub(newAccumulatedMoments);
+                        value.assign(newValue);
+                    }
+                });
+            };
+            var this_1 = this;
+            for (var variableName in variableGradients) {
+                _loop_1(variableName);
+            }
+        };
+        RMSPropOptimizer.prototype.dispose = function () {
+            var _this = this;
+            this.c.dispose();
+            this.epsilonScalar.dispose();
+            this.decayScalar.dispose();
+            this.momentumScalar.dispose();
+            this.oneMinusDecay.dispose();
+            if (this.accumulatedMeanSquares != null) {
+                Object.keys(this.accumulatedMeanSquares)
+                    .forEach(function (name) { return _this.accumulatedMeanSquares[name].dispose(); });
+            }
+            if (this.accumulatedMeanGrads != null && this.centered) {
+                Object.keys(this.accumulatedMeanGrads)
+                    .forEach(function (name) { return _this.accumulatedMeanGrads[name].dispose(); });
+            }
+            if (this.accumulatedMoments != null) {
+                Object.keys(this.accumulatedMoments)
+                    .forEach(function (name) { return _this.accumulatedMoments[name].dispose(); });
+            }
+        };
+        RMSPropOptimizer.prototype.getConfig = function () {
+            return {
+                learningRate: this.learningRate,
+                decay: this.decay,
+                momentum: this.momentum,
+                epsilon: this.epsilon,
+                centered: this.centered
+            };
+        };
+        RMSPropOptimizer.fromConfig = function (cls, config) {
+            return new cls(config.learningRate, config.decay, config.momentum, config.epsilon, config.centered);
+        };
+        RMSPropOptimizer.className = 'RMSPropOptimizer';
+        return RMSPropOptimizer;
+    }(Optimizer));
+    registerClass(RMSPropOptimizer);
+
+    var OptimizerConstructors = (function () {
+        function OptimizerConstructors() {
+        }
+        OptimizerConstructors.sgd = function (learningRate) {
+            return new SGDOptimizer(learningRate);
+        };
+        OptimizerConstructors.momentum = function (learningRate, momentum, useNesterov) {
+            if (useNesterov === void 0) { useNesterov = false; }
+            return new MomentumOptimizer(learningRate, momentum, useNesterov);
+        };
+        OptimizerConstructors.rmsprop = function (learningRate, decay, momentum, epsilon, centered) {
+            if (decay === void 0) { decay = .9; }
+            if (momentum === void 0) { momentum = 0.0; }
+            if (epsilon === void 0) { epsilon = null; }
+            if (centered === void 0) { centered = false; }
+            return new RMSPropOptimizer(learningRate, decay, momentum, epsilon, centered);
+        };
+        OptimizerConstructors.adam = function (learningRate, beta1, beta2, epsilon) {
+            if (learningRate === void 0) { learningRate = 0.001; }
+            if (beta1 === void 0) { beta1 = 0.9; }
+            if (beta2 === void 0) { beta2 = 0.999; }
+            if (epsilon === void 0) { epsilon = null; }
+            return new AdamOptimizer(learningRate, beta1, beta2, epsilon);
+        };
+        OptimizerConstructors.adadelta = function (learningRate, rho, epsilon) {
+            if (learningRate === void 0) { learningRate = .001; }
+            if (rho === void 0) { rho = .95; }
+            if (epsilon === void 0) { epsilon = null; }
+            return new AdadeltaOptimizer(learningRate, rho, epsilon);
+        };
+        OptimizerConstructors.adamax = function (learningRate, beta1, beta2, epsilon, decay) {
+            if (learningRate === void 0) { learningRate = 0.002; }
+            if (beta1 === void 0) { beta1 = 0.9; }
+            if (beta2 === void 0) { beta2 = 0.999; }
+            if (epsilon === void 0) { epsilon = null; }
+            if (decay === void 0) { decay = 0.0; }
+            return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon, decay);
+        };
+        OptimizerConstructors.adagrad = function (learningRate, initialAccumulatorValue) {
+            if (initialAccumulatorValue === void 0) { initialAccumulatorValue = 0.1; }
+            return new AdagradOptimizer(learningRate, initialAccumulatorValue);
+        };
+        return OptimizerConstructors;
+    }());
+
+    var train = {
+        sgd: OptimizerConstructors.sgd,
+        momentum: OptimizerConstructors.momentum,
+        adadelta: OptimizerConstructors.adadelta,
+        adagrad: OptimizerConstructors.adagrad,
+        rmsprop: OptimizerConstructors.rmsprop,
+        adamax: OptimizerConstructors.adamax,
+        adam: OptimizerConstructors.adam
+    };
+
+    var setBackend = Environment.setBackend;
+    var getBackend = Environment.getBackend;
+    var disposeVariables = Environment.disposeVariables;
+    var memory = Environment.memory;
+    setOpHandler(ops);
+
+    exports.setBackend = setBackend;
+    exports.getBackend = getBackend;
+    exports.disposeVariables = disposeVariables;
+    exports.memory = memory;
+    exports.version_core = version;
+    exports.nextFrame = nextFrame;
+    exports.environment = environment;
+    exports.io = io;
+    exports.math = math;
+    exports.serialization = serialization;
+    exports.test_util = test_util;
+    exports.util = util;
+    exports.webgl = webgl;
+    exports.AdadeltaOptimizer = AdadeltaOptimizer;
+    exports.AdagradOptimizer = AdagradOptimizer;
+    exports.AdamOptimizer = AdamOptimizer;
+    exports.AdamaxOptimizer = AdamaxOptimizer;
+    exports.MomentumOptimizer = MomentumOptimizer;
+    exports.Optimizer = Optimizer;
+    exports.RMSPropOptimizer = RMSPropOptimizer;
+    exports.SGDOptimizer = SGDOptimizer;
+    exports.Tensor = Tensor;
+    exports.TensorBuffer = TensorBuffer;
+    exports.variable = variable;
+    exports.Variable = Variable;
+    exports.ENV = ENV;
+    exports.Environment = Environment;
+    exports.KernelBackend = KernelBackend;
+    exports.DataStorage = DataStorage;
+    exports.image = image_ops;
+    exports.linalg = linalg_ops;
+    exports.losses = loss_ops;
+    exports.spectral = spectral_ops;
+    exports.op = op;
+    exports.batchNormalization2d = batchNormalization2d;
+    exports.batchNormalization3d = batchNormalization3d;
+    exports.batchNormalization4d = batchNormalization4d;
+    exports.batchNormalization = batchNormalization;
+    exports.complex = complex;
+    exports.real = real;
+    exports.imag = imag;
+    exports.concat = concat;
+    exports.concat1d = concat1d;
+    exports.concat2d = concat2d;
+    exports.concat3d = concat3d;
+    exports.concat4d = concat4d;
+    exports.split = split$1;
+    exports.conv1d = conv1d;
+    exports.conv2d = conv2d;
+    exports.conv3d = conv3d;
+    exports.conv2dDerFilter = conv2dDerFilter;
+    exports.depthwiseConv2d = depthwiseConv2d;
+    exports.separableConv2d = separableConv2d;
+    exports.conv2dTranspose = conv2dTranspose;
+    exports.matMul = matMul;
+    exports.dot = dot;
+    exports.outerProduct = outerProduct;
+    exports.reverse = reverse;
+    exports.reverse1d = reverse1d;
+    exports.reverse2d = reverse2d;
+    exports.reverse3d = reverse3d;
+    exports.reverse4d = reverse4d;
+    exports.maxPool = maxPool;
+    exports.avgPool = avgPool;
+    exports.pool = pool;
+    exports.slice = slice;
+    exports.slice1d = slice1d;
+    exports.slice2d = slice2d;
+    exports.slice3d = slice3d;
+    exports.slice4d = slice4d;
+    exports.abs = abs;
+    exports.acos = acos;
+    exports.acosh = acosh;
+    exports.asin = asin;
+    exports.asinh = asinh;
+    exports.atan = atan;
+    exports.atanh = atanh;
+    exports.ceil = ceil;
+    exports.clipByValue = clipByValue;
+    exports.cos = cos;
+    exports.cosh = cosh;
+    exports.erf = erf;
+    exports.exp = exp;
+    exports.expm1 = expm1;
+    exports.floor = floor;
+    exports.log = log$1;
+    exports.log1p = log1p;
+    exports.logSigmoid = logSigmoid;
+    exports.neg = neg;
+    exports.reciprocal = reciprocal;
+    exports.round = round;
+    exports.rsqrt = rsqrt;
+    exports.sigmoid = sigmoid;
+    exports.sign = sign;
+    exports.sin = sin;
+    exports.sinh = sinh;
+    exports.softplus = softplus;
+    exports.sqrt = sqrt;
+    exports.square = square;
+    exports.step = step;
+    exports.tan = tan;
+    exports.tanh = tanh$1;
+    exports.all = all;
+    exports.any = any;
+    exports.argMax = argMax;
+    exports.argMin = argMin;
+    exports.logSumExp = logSumExp;
+    exports.max = max;
+    exports.mean = mean;
+    exports.min = min;
+    exports.moments = moments;
+    exports.sum = sum$1;
+    exports.prod = prod;
+    exports.equal = equal;
+    exports.equalStrict = equalStrict;
+    exports.greater = greater;
+    exports.greaterEqual = greaterEqual;
+    exports.greaterEqualStrict = greaterEqualStrict;
+    exports.greaterStrict = greaterStrict;
+    exports.less = less;
+    exports.lessEqual = lessEqual;
+    exports.lessEqualStrict = lessEqualStrict;
+    exports.lessStrict = lessStrict;
+    exports.notEqual = notEqual;
+    exports.notEqualStrict = notEqualStrict;
+    exports.add = add;
+    exports.addN = addN;
+    exports.addStrict = addStrict;
+    exports.atan2 = atan2;
+    exports.div = div;
+    exports.divStrict = divStrict;
+    exports.floorDiv = floorDiv;
+    exports.maximum = maximum;
+    exports.maximumStrict = maximumStrict;
+    exports.minimum = minimum;
+    exports.minimumStrict = minimumStrict;
+    exports.mod = mod;
+    exports.modStrict = modStrict;
+    exports.mul = mul;
+    exports.mulStrict = mulStrict;
+    exports.pow = pow;
+    exports.powStrict = powStrict;
+    exports.squaredDifference = squaredDifference;
+    exports.squaredDifferenceStrict = squaredDifferenceStrict;
+    exports.sub = sub;
+    exports.subStrict = subStrict;
+    exports.elu = elu;
+    exports.leakyRelu = leakyRelu;
+    exports.prelu = prelu;
+    exports.relu = relu;
+    exports.selu = selu;
+    exports.logicalAnd = logicalAnd;
+    exports.logicalNot = logicalNot;
+    exports.logicalOr = logicalOr;
+    exports.logicalXor = logicalXor;
+    exports.where = where;
+    exports.whereAsync = whereAsync;
+    exports.buffer = buffer;
+    exports.toPixels = toPixels;
+    exports.print = print;
+    exports.batchToSpaceND = batchToSpaceND;
+    exports.cast = cast;
+    exports.clone = clone;
+    exports.cumsum = cumsum;
+    exports.depthToSpace = depthToSpace;
+    exports.expandDims = expandDims;
+    exports.eye = eye;
+    exports.fromPixels = fromPixels;
+    exports.multinomial = multinomial;
+    exports.oneHot = oneHot;
+    exports.pad = pad;
+    exports.pad1d = pad1d;
+    exports.pad2d = pad2d;
+    exports.pad3d = pad3d;
+    exports.pad4d = pad4d;
+    exports.rand = rand;
+    exports.randomNormal = randomNormal;
+    exports.randomUniform = randomUniform;
+    exports.reshape = reshape;
+    exports.spaceToBatchND = spaceToBatchND;
+    exports.squeeze = squeeze;
+    exports.stack = stack;
+    exports.tile = tile;
+    exports.truncatedNormal = truncatedNormal;
+    exports.unstack = unstack;
+    exports.setdiff1dAsync = setdiff1dAsync;
+    exports.fill = fill;
+    exports.linspace = linspace;
+    exports.ones = ones$1;
+    exports.range = range;
+    exports.scalar = scalar;
+    exports.tensor = tensor;
+    exports.tensor1d = tensor1d;
+    exports.tensor2d = tensor2d;
+    exports.tensor3d = tensor3d;
+    exports.tensor4d = tensor4d;
+    exports.tensor5d = tensor5d;
+    exports.tensor6d = tensor6d;
+    exports.zeros = zeros;
+    exports.onesLike = onesLike;
+    exports.zerosLike = zerosLike;
+    exports.transpose = transpose;
+    exports.softmax = softmax;
+    exports.logSoftmax = logSoftmax;
+    exports.localResponseNormalization = localResponseNormalization;
+    exports.norm = norm;
+    exports.gather = gather;
+    exports.unsortedSegmentSum = unsortedSegmentSum;
+    exports.basicLSTMCell = basicLSTMCell;
+    exports.multiRNNCell = multiRNNCell;
+    exports.movingAverage = movingAverage;
+    exports.stridedSlice = stridedSlice;
+    exports.topk = topk;
+    exports.scatterND = scatterND;
+    exports.fft = fft;
+    exports.ifft = ifft;
+    exports.rfft = rfft;
+    exports.irfft = irfft;
+    exports.sparseToDense = sparseToDense;
+    exports.gatherND = gatherND;
+    exports.train = train;
+    exports.tidy = tidy;
+    exports.keep = keep;
+    exports.dispose = dispose;
+    exports.time = time;
+    exports.profile = profile;
+    exports.customGrad = customGrad;
+    exports.grad = grad;
+    exports.grads = grads;
+    exports.valueAndGrad = valueAndGrad;
+    exports.valueAndGrads = valueAndGrads;
+    exports.variableGrads = variableGrads;
+
+    Object.defineProperty(exports, '__esModule', { value: true });
+
+})));
+//# sourceMappingURL=tf-core.js.map
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tf-core.js.map b/node_modules/@tensorflow/tfjs-core/dist/tf-core.js.map
new file mode 100644
index 0000000..87fc020
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tf-core.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"tf-core.js","sources":["../src/canvas_util.ts","../src/device_util.ts","../src/util.ts","../src/profiler.ts","../src/tensor_format.ts","../src/tensor.ts","../src/tape.ts","../src/types.ts","../src/tensor_util.ts","../src/engine.ts","../src/environment_util.ts","../src/environment.ts","../src/gradients.ts","../src/globals.ts","../src/log.ts","../src/ops/array_ops_util.ts","../src/ops/axis_util.ts","../src/ops/concat_util.ts","../src/ops/gather_nd_util.ts","../src/ops/reduce_util.ts","../src/ops/scatter_nd_util.ts","../src/ops/segment_util.ts","../src/ops/slice_util.ts","../src/tensor_util_env.ts","../src/ops/operation.ts","../src/ops/softmax.ts","../src/ops/complex_ops.ts","../src/ops/tensor_ops.ts","../src/kernels/backend.ts","../src/kernels/backend_util.ts","../src/kernels/complex_util.ts","../src/kernels/non_max_suppression_impl.ts","../src/kernels/split_shared.ts","../src/kernels/topk_impl.ts","../src/kernels/webgl/argminmax_gpu.ts","../src/kernels/webgl/avg_pool_backprop_gpu.ts","../src/ops/broadcast_util.ts","../src/kernels/webgl/batchnorm_gpu.ts","../src/kernels/webgl/batchnorm_packed_gpu.ts","../src/kernels/webgl/binaryop_complex_gpu.ts","../src/kernels/webgl/binaryop_gpu.ts","../src/kernels/webgl/clip_gpu.ts","../src/kernels/webgl/clip_packed_gpu.ts","../src/kernels/webgl/complex_abs_gpu.ts","../src/kernels/webgl/concat_gpu.ts","../src/kernels/webgl/conv_backprop_gpu.ts","../src/kernels/webgl/conv_backprop_gpu_depthwise.ts","../src/kernels/webgl/conv_gpu.ts","../src/kernels/webgl/conv_gpu_depthwise.ts","../src/kernels/webgl/conv_packed_gpu_depthwise.ts","../src/kernels/webgl/crop_and_resize_gpu.ts","../src/kernels/webgl/shader_compiler_util.ts","../src/kernels/webgl/shader_compiler.ts","../src/kernels/webgl/cumsum_gpu.ts","../src/kernels/webgl/depth_to_space_gpu.ts","../src/kernels/webgl/encode_float_gpu.ts","../src/kernels/webgl/fft_gpu.ts","../src/kernels/webgl/from_pixels_gpu.ts","../src/kernels/webgl/gather_gpu.ts","../src/kernels/webgl/gather_nd_gpu.ts","../src/kernels/webgl/tex_util.ts","../src/kernels/webgl/webgl_util.ts","../src/kernels/webgl/gpgpu_util.ts","../src/kernels/webgl/gpgpu_context.ts","../src/kernels/webgl/gpgpu_math.ts","../src/kernels/webgl/im2col_gpu.ts","../src/kernels/webgl/lrn_gpu.ts","../src/kernels/webgl/lrn_grad_gpu.ts","../src/kernels/webgl/max_pool_backprop_gpu.ts","../src/kernels/webgl/mulmat_gpu.ts","../src/kernels/webgl/mulmat_packed_gpu.ts","../src/kernels/webgl/multinomial_gpu.ts","../src/kernels/webgl/onehot_gpu.ts","../src/kernels/packing_util.ts","../src/kernels/webgl/pack_gpu.ts","../src/kernels/webgl/pad_gpu.ts","../src/kernels/webgl/pool_gpu.ts","../src/kernels/webgl/reduce_gpu.ts","../src/kernels/webgl/reshape_packed_gpu.ts","../src/kernels/webgl/resize_bilinear_backprop_gpu.ts","../src/kernels/webgl/resize_bilinear_gpu.ts","../src/kernels/webgl/resize_nearest_neighbor_backprop_gpu.ts","../src/kernels/webgl/resize_nearest_neighbor_gpu.ts","../src/kernels/webgl/reverse_gpu.ts","../src/kernels/webgl/scatter_gpu.ts","../src/kernels/webgl/segment_gpu.ts","../src/kernels/webgl/select_gpu.ts","../src/kernels/webgl/slice_gpu.ts","../src/kernels/webgl/strided_slice_gpu.ts","../src/kernels/webgl/texture_manager.ts","../src/kernels/webgl/tile_gpu.ts","../src/kernels/webgl/transpose_gpu.ts","../src/ops/erf_util.ts","../src/ops/selu_util.ts","../src/kernels/webgl/unaryop_gpu.ts","../src/kernels/webgl/unpack_gpu.ts","../src/ops/concat_split.ts","../node_modules/seedrandom/lib/alea.js","../node_modules/seedrandom/lib/xor128.js","../node_modules/seedrandom/lib/xorwow.js","../node_modules/seedrandom/lib/xorshift7.js","../node_modules/seedrandom/lib/xor4096.js","../node_modules/seedrandom/lib/tychei.js","../node_modules/seedrandom/seedrandom.js","../node_modules/seedrandom/index.js","../src/ops/rand.ts","../src/ops/array_ops.ts","../src/kernels/where_impl.ts","../src/kernels/backend_webgl.ts","../src/ops/unary_ops.ts","../src/ops/batchnorm.ts","../src/ops/conv_util.ts","../src/ops/matmul.ts","../src/ops/conv.ts","../src/ops/reverse.ts","../src/ops/pool.ts","../src/ops/slice.ts","../src/ops/reduction_ops.ts","../src/ops/compare.ts","../src/ops/binary_ops.ts","../src/ops/logical_ops.ts","../src/ops/relu_ops.ts","../src/ops/transpose.ts","../src/ops/lrn.ts","../src/ops/norm.ts","../src/ops/segment_ops.ts","../src/ops/lstm.ts","../src/ops/moving_average.ts","../src/ops/strided_slice.ts","../src/ops/topk.ts","../src/ops/scatter_nd.ts","../src/ops/spectral_ops.ts","../src/ops/sparse_to_dense_util.ts","../src/ops/sparse_to_dense.ts","../src/ops/gather_nd.ts","../src/ops/loss_ops.ts","../src/ops/linalg_ops.ts","../src/ops/image_ops.ts","../src/kernels/backend_cpu.ts","../src/browser_util.ts","../src/io/types.ts","../src/io/io_utils.ts","../src/io/router_registry.ts","../src/io/model_management.ts","../src/io/indexed_db.ts","../src/io/local_storage.ts","../src/io/browser_files.ts","../src/io/weights_loader.ts","../src/io/browser_http.ts","../src/io/passthrough.ts","../src/io/io.ts","../src/ops/confusion_matrix.ts","../src/serialization.ts","../src/test_util.ts","../src/version.ts","../src/optimizers/optimizer.ts","../src/optimizers/adadelta_optimizer.ts","../src/optimizers/adagrad_optimizer.ts","../src/optimizers/adam_optimizer.ts","../src/optimizers/adamax_optimizer.ts","../src/optimizers/sgd_optimizer.ts","../src/optimizers/momentum_optimizer.ts","../src/optimizers/rmsprop_optimizer.ts","../src/optimizers/optimizer_constructors.ts","../src/train.ts","../src/index.ts"],"sourcesContent":["/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nconst contexts: {[key: string]: WebGLRenderingContext} = {};\n\nconst WEBGL_ATTRIBUTES: WebGLContextAttributes = {\n  alpha: false,\n  antialias: false,\n  premultipliedAlpha: false,\n  preserveDrawingBuffer: false,\n  depth: false,\n  stencil: false,\n  failIfMajorPerformanceCaveat: true\n};\n\nexport function getWebGLContext(webGLVersion: number): WebGLRenderingContext {\n  if (!(webGLVersion in contexts)) {\n    const canvas = document.createElement('canvas');\n    canvas.addEventListener('webglcontextlost', ev => {\n      ev.preventDefault();\n      delete contexts[webGLVersion];\n    }, false);\n    contexts[webGLVersion] = getWebGLRenderingContext(webGLVersion);\n  }\n  const gl = contexts[webGLVersion];\n  if (gl.isContextLost()) {\n    delete contexts[webGLVersion];\n    return getWebGLContext(webGLVersion);\n  }\n\n  gl.disable(gl.DEPTH_TEST);\n  gl.disable(gl.STENCIL_TEST);\n  gl.disable(gl.BLEND);\n  gl.disable(gl.DITHER);\n  gl.disable(gl.POLYGON_OFFSET_FILL);\n  gl.disable(gl.SAMPLE_COVERAGE);\n  gl.enable(gl.SCISSOR_TEST);\n  gl.enable(gl.CULL_FACE);\n  gl.cullFace(gl.BACK);\n\n  return contexts[webGLVersion];\n}\n\nfunction getWebGLRenderingContext(webGLVersion: number): WebGLRenderingContext {\n  if (webGLVersion !== 1 && webGLVersion !== 2) {\n    throw new Error('Cannot get WebGL rendering context, WebGL is disabled.');\n  }\n\n  const canvas = document.createElement('canvas');\n  if (webGLVersion === 1) {\n    return (canvas.getContext('webgl', WEBGL_ATTRIBUTES) ||\n            canvas.getContext('experimental-webgl', WEBGL_ATTRIBUTES)) as\n        WebGLRenderingContext;\n  }\n  return canvas.getContext('webgl2', WEBGL_ATTRIBUTES) as WebGLRenderingContext;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport function isMobile(): boolean {\n  // tslint:disable-next-line:no-any\n  const a = navigator.userAgent || navigator.vendor || (window as any).opera;\n  // tslint:disable-next-line:max-line-length\n  return /(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i\n             .test(a) ||\n      // tslint:disable-next-line:max-line-length\n      /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\\-(n|u)|c55\\/|capi|ccwa|cdm\\-|cell|chtm|cldc|cmd\\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\\-s|devi|dica|dmob|do(c|p)o|ds(12|\\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\\-|_)|g1 u|g560|gene|gf\\-5|g\\-mo|go(\\.w|od)|gr(ad|un)|haie|hcit|hd\\-(m|p|t)|hei\\-|hi(pt|ta)|hp( i|ip)|hs\\-c|ht(c(\\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\\-(20|go|ma)|i230|iac( |\\-|\\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\\/)|klon|kpt |kwc\\-|kyo(c|k)|le(no|xi)|lg( g|\\/(k|l|u)|50|54|\\-[a-w])|libw|lynx|m1\\-w|m3ga|m50\\/|ma(te|ui|xo)|mc(01|21|ca)|m\\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\\-2|po(ck|rt|se)|prox|psio|pt\\-g|qa\\-a|qc(07|12|21|32|60|\\-[2-7]|i\\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\\-|oo|p\\-)|sdk\\/|se(c(\\-|0|1)|47|mc|nd|ri)|sgh\\-|shar|sie(\\-|m)|sk\\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\\-|v\\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\\-|tdg\\-|tel(i|m)|tim\\-|t\\-mo|to(pl|sh)|ts(70|m\\-|m3|m5)|tx\\-9|up(\\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\\-|your|zeto|zte\\-/i\n          .test(a.substr(0, 4));\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, DataTypeMap, FlatVector, NumericDataType, RecursiveArray, TensorLike, TypedArray} from './types';\n\n/** Shuffles the array using Fisher-Yates algorithm. */\n// tslint:disable-next-line:no-any\nexport function shuffle(array: any[]|Uint32Array|Int32Array|\n                        Float32Array): void {\n  let counter = array.length;\n  let temp = 0;\n  let index = 0;\n  // While there are elements in the array\n  while (counter > 0) {\n    // Pick a random index\n    index = (Math.random() * counter) | 0;\n    // Decrease counter by 1\n    counter--;\n    // And swap the last element with it\n    temp = array[counter];\n    array[counter] = array[index];\n    array[index] = temp;\n  }\n}\n\n/** Clamps a value to a specified range. */\nexport function clamp(min: number, x: number, max: number): number {\n  return Math.max(min, Math.min(x, max));\n}\n\nexport function nearestLargerEven(val: number): number {\n  return val % 2 === 0 ? val : val + 1;\n}\n\nexport function sum(arr: number[]): number {\n  let sum = 0;\n  for (let i = 0; i < arr.length; i++) {\n    sum += arr[i];\n  }\n  return sum;\n}\n\n/**\n * Returns a sample from a uniform [a, b) distribution.\n *\n * @param a The minimum support (inclusive).\n * @param b The maximum support (exclusive).\n * @return A pseudorandom number on the half-open interval [a,b).\n */\nexport function randUniform(a: number, b: number) {\n  const r = Math.random();\n  return (b * r) + (1 - r) * a;\n}\n\n/** Returns the squared Euclidean distance between two vectors. */\nexport function distSquared(a: FlatVector, b: FlatVector): number {\n  let result = 0;\n  for (let i = 0; i < a.length; i++) {\n    const diff = Number(a[i]) - Number(b[i]);\n    result += diff * diff;\n  }\n  return result;\n}\n\nexport function assert(expr: boolean, msg: string|(() => string)) {\n  if (!expr) {\n    throw new Error(typeof msg === 'string' ? msg : msg());\n  }\n}\n\nexport function assertShapesMatch(\n    shapeA: number[], shapeB: number[], errorMessagePrefix = ''): void {\n  assert(\n      arraysEqual(shapeA, shapeB),\n      errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\n\nexport function assertNonNull(a: TensorLike): void {\n  assert(\n      a != null,\n      `The input to the tensor constructor must be a non-null value.`);\n}\n\n// NOTE: We explicitly type out what T extends instead of any so that\n// util.flatten on a nested array of number doesn't try to infer T as a\n// number[][], causing us to explicitly type util.flatten<number>().\nexport function\nflatten<T extends number|boolean|string|Promise<number>|TypedArray>(\n    arr: T|RecursiveArray<T>, ret: T[] = []): T[] {\n  if (Array.isArray(arr) || isTypedArray(arr)) {\n    for (let i = 0; i < arr.length; ++i) {\n      flatten(arr[i], ret);\n    }\n  } else {\n    ret.push(arr as T);\n  }\n  return ret;\n}\n\nexport function sizeFromShape(shape: number[]): number {\n  if (shape.length === 0) {\n    // Scalar.\n    return 1;\n  }\n  let size = shape[0];\n  for (let i = 1; i < shape.length; i++) {\n    size *= shape[i];\n  }\n  return size;\n}\n\nexport function isScalarShape(shape: number[]): boolean {\n  return shape.length === 0;\n}\n\nexport function arraysEqual(n1: FlatVector, n2: FlatVector) {\n  if (n1 === n2) {\n    return true;\n  }\n  if (n1 == null || n2 == null) {\n    return false;\n  }\n\n  if (n1.length !== n2.length) {\n    return false;\n  }\n  for (let i = 0; i < n1.length; i++) {\n    if (n1[i] !== n2[i]) {\n      return false;\n    }\n  }\n  return true;\n}\n\nexport function isInt(a: number): boolean {\n  return a % 1 === 0;\n}\n\nexport function tanh(x: number): number {\n  // tslint:disable-next-line:no-any\n  if ((Math as any).tanh != null) {\n    // tslint:disable-next-line:no-any\n    return (Math as any).tanh(x);\n  }\n  if (x === Infinity) {\n    return 1;\n  } else if (x === -Infinity) {\n    return -1;\n  } else {\n    const e2x = Math.exp(2 * x);\n    return (e2x - 1) / (e2x + 1);\n  }\n}\n\nexport function sizeToSquarishShape(size: number): [number, number] {\n  for (let a = Math.floor(Math.sqrt(size)); a > 1; --a) {\n    if (size % a === 0) {\n      return [a, size / a];\n    }\n  }\n  return [1, size];\n}\n\nexport function createShuffledIndices(n: number): Uint32Array {\n  const shuffledIndices = new Uint32Array(n);\n  for (let i = 0; i < n; ++i) {\n    shuffledIndices[i] = i;\n  }\n  shuffle(shuffledIndices);\n  return shuffledIndices;\n}\n\nexport function rightPad(a: string, size: number): string {\n  if (size <= a.length) {\n    return a;\n  }\n  return a + ' '.repeat(size - a.length);\n}\n\nexport function repeatedTry(\n    checkFn: () => boolean, delayFn = (counter: number) => 0,\n    maxCounter?: number): Promise<void> {\n  return new Promise<void>((resolve, reject) => {\n    let tryCount = 0;\n\n    const tryFn = () => {\n      if (checkFn()) {\n        resolve();\n        return;\n      }\n\n      tryCount++;\n\n      const nextBackoff = delayFn(tryCount);\n\n      if (maxCounter != null && tryCount >= maxCounter) {\n        reject();\n        return;\n      }\n      setTimeout(tryFn, nextBackoff);\n    };\n\n    tryFn();\n  });\n}\n\n/**\n * Given the full size of the array and a shape that may contain -1 as the\n * implicit dimension, returns the inferred shape where -1 is replaced.\n * E.g. For shape=[2, -1, 3] and size=24, it will return [2, 4, 3].\n *\n * @param shape The shape, which may contain -1 in some dimension.\n * @param size The full size (number of elements) of the array.\n * @return The inferred shape where -1 is replaced with the inferred size.\n */\nexport function inferFromImplicitShape(\n    shape: number[], size: number): number[] {\n  let shapeProd = 1;\n  let implicitIdx = -1;\n\n  for (let i = 0; i < shape.length; ++i) {\n    if (shape[i] >= 0) {\n      shapeProd *= shape[i];\n    } else if (shape[i] === -1) {\n      if (implicitIdx !== -1) {\n        throw Error(\n            `Shapes can only have 1 implicit size. ` +\n            `Found -1 at dim ${implicitIdx} and dim ${i}`);\n      }\n      implicitIdx = i;\n    } else if (shape[i] < 0) {\n      throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);\n    }\n  }\n\n  if (implicitIdx === -1) {\n    if (size > 0 && size !== shapeProd) {\n      throw Error(`Size(${size}) must match the product of shape ${shape}`);\n    }\n    return shape;\n  }\n\n  if (shapeProd === 0) {\n    throw Error(\n        `Cannot infer the missing size in [${shape}] when ` +\n        `there are 0 elements`);\n  }\n  if (size % shapeProd !== 0) {\n    throw Error(\n        `The implicit shape can't be a fractional number. ` +\n        `Got ${size} / ${shapeProd}`);\n  }\n\n  const newShape = shape.slice();\n  newShape[implicitIdx] = size / shapeProd;\n  return newShape;\n}\n\n/** Reduces the shape by removing all dimensions of shape 1. */\nexport function squeezeShape(shape: number[], axis?: number[]):\n    {newShape: number[], keptDims: number[]} {\n  const newShape: number[] = [];\n  const keptDims: number[] = [];\n  let j = 0;\n  for (let i = 0; i < shape.length; ++i) {\n    if (axis != null) {\n      if (axis[j] === i && shape[i] !== 1) {\n        throw new Error(\n            `Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);\n      }\n      if ((axis[j] == null || axis[j] > i) && shape[i] === 1) {\n        newShape.push(shape[i]);\n        keptDims.push(i);\n      }\n      if (axis[j] <= i) {\n        j++;\n      }\n    }\n    if (shape[i] !== 1) {\n      newShape.push(shape[i]);\n      keptDims.push(i);\n    }\n  }\n  return {newShape, keptDims};\n}\n\nexport function getTypedArrayFromDType<D extends NumericDataType>(\n    dtype: D, size: number): DataTypeMap[D] {\n  let values = null;\n  if (dtype == null || dtype === 'float32') {\n    values = new Float32Array(size);\n  } else if (dtype === 'int32') {\n    values = new Int32Array(size);\n  } else if (dtype === 'bool') {\n    values = new Uint8Array(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n  return values;\n}\n\nexport function getArrayFromDType<D extends DataType>(\n    dtype: D, size: number): DataTypeMap[D] {\n  let values = null;\n  if (dtype == null || dtype === 'float32') {\n    values = new Float32Array(size);\n  } else if (dtype === 'int32') {\n    values = new Int32Array(size);\n  } else if (dtype === 'bool') {\n    values = new Uint8Array(size);\n  } else if (dtype === 'string') {\n    values = new Array<'string'>(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n  return values;\n}\n\nexport function checkComputationForNaN<D extends DataType>(\n    vals: DataTypeMap[D], dtype: D, name: string): void {\n  if (dtype !== 'float32') {\n    // Only floating point computations will generate NaN values\n    return;\n  }\n  for (let i = 0; i < vals.length; i++) {\n    if (isNaN(vals[i] as number)) {\n      throw Error(`The result of the '${name}' has NaNs.`);\n    }\n  }\n}\n\nexport function checkConversionForNaN<D extends DataType>(\n    vals: DataTypeMap[D]|number[], dtype: D): void {\n  if (dtype === 'float32') {\n    // NaN is valid for floating point conversions\n    return;\n  }\n\n  for (let i = 0; i < vals.length; i++) {\n    if (isNaN(vals[i] as number)) {\n      throw Error(`NaN is not a valid value for dtype: '${dtype}'.`);\n    }\n  }\n}\n\n/**\n * Returns true if the new type can't encode the old type without loss of\n * precision.\n */\nexport function hasEncodingLoss(oldType: DataType, newType: DataType): boolean {\n  if (newType === 'complex64') {\n    return false;\n  }\n  if (newType === 'float32' && oldType !== 'complex64') {\n    return false;\n  }\n  if (newType === 'int32' && oldType !== 'float32' && oldType !== 'complex64') {\n    return false;\n  }\n  if (newType === 'bool' && oldType === 'bool') {\n    return false;\n  }\n  return true;\n}\n\nexport function isTypedArray(a: {}): a is Float32Array|Int32Array|Uint8Array {\n  return a instanceof Float32Array || a instanceof Int32Array ||\n      a instanceof Uint8Array;\n}\n\nexport function bytesPerElement(dtype: DataType): number {\n  if (dtype === 'float32' || dtype === 'int32') {\n    return 4;\n  } else if (dtype === 'complex64') {\n    return 8;\n  } else if (dtype === 'bool') {\n    return 1;\n  } else {\n    throw new Error(`Unknown dtype ${dtype}`);\n  }\n}\n\n/**\n * Returns the approximate number of bytes allocated in the string array - 2\n * bytes per character. Computing the exact bytes for a native string in JS is\n * not possible since it depends on the encoding of the html page that serves\n * the website.\n */\nexport function bytesFromStringArray(arr: string[]): number {\n  if (arr == null) {\n    return 0;\n  }\n  let bytes = 0;\n  arr.forEach(x => bytes += x.length * 2);\n  return bytes;\n}\n\n/** Returns true if the value is a string. */\nexport function isString(value: {}): value is string {\n  return typeof value === 'string' || value instanceof String;\n}\n\nexport function isBoolean(value: {}): boolean {\n  return typeof value === 'boolean';\n}\n\nexport function isNumber(value: {}): boolean {\n  return typeof value === 'number';\n}\n\nexport function inferDtype(values: TensorLike): DataType {\n  if (values instanceof Array) {\n    return inferDtype(values[0]);\n  }\n  if (values instanceof Float32Array) {\n    return 'float32';\n  } else if (values instanceof Int32Array || values instanceof Uint8Array) {\n    return 'int32';\n  } else if (isNumber(values)) {\n    return 'float32';\n  } else if (isString(values)) {\n    return 'string';\n  } else if (isBoolean(values)) {\n    return 'bool';\n  }\n  return 'float32';\n}\n\nexport function isFunction(f: Function) {\n  return !!(f && f.constructor && f.call && f.apply);\n}\n\nexport function nearestDivisor(size: number, start: number): number {\n  for (let i = start; i < size; ++i) {\n    if (size % i === 0) {\n      return i;\n    }\n  }\n  return size;\n}\n\nexport function computeStrides(shape: number[]): number[] {\n  const rank = shape.length;\n  if (rank < 2) {\n    return [];\n  }\n\n  // Last dimension has implicit stride of 1, thus having D-1 (instead of D)\n  // strides.\n  const strides = new Array(rank - 1);\n  strides[rank - 2] = shape[rank - 1];\n  for (let i = rank - 3; i >= 0; --i) {\n    strides[i] = strides[i + 1] * shape[i + 1];\n  }\n  return strides;\n}\n\nexport function toTypedArray(\n    a: TensorLike, dtype: DataType, debugMode: boolean): TypedArray {\n  if (dtype === 'string') {\n    throw new Error('Cannot convert a string[] to a TypedArray');\n  }\n  if (noConversionNeeded(a, dtype)) {\n    return a as TypedArray;\n  }\n  if (Array.isArray(a)) {\n    a = flatten(a as number[]);\n  }\n  if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n    return new Float32Array(a as number[]);\n  } else if (dtype === 'int32') {\n    if (debugMode) {\n      checkConversionForNaN(a as number[], dtype);\n    }\n    return new Int32Array(a as number[]);\n  } else if (dtype === 'bool') {\n    const bool = new Uint8Array((a as number[]).length);\n    for (let i = 0; i < bool.length; ++i) {\n      if (Math.round((a as number[])[i] as number) !== 0) {\n        bool[i] = 1;\n      }\n    }\n    return bool;\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n}\n\nfunction noConversionNeeded(a: TensorLike, dtype: DataType): boolean {\n  return (a instanceof Float32Array && dtype === 'float32') ||\n      (a instanceof Int32Array && dtype === 'int32') ||\n      (a instanceof Uint8Array && dtype === 'bool');\n}\n\nexport function makeOnesTypedArray<D extends DataType>(\n    size: number, dtype: D): DataTypeMap[D] {\n  const array = makeZerosTypedArray(size, dtype);\n  for (let i = 0; i < array.length; i++) {\n    array[i] = 1;\n  }\n  return array;\n}\n\nexport function makeZerosTypedArray<D extends DataType>(\n    size: number, dtype: D): DataTypeMap[D] {\n  if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n    return new Float32Array(size);\n  } else if (dtype === 'int32') {\n    return new Int32Array(size);\n  } else if (dtype === 'bool') {\n    return new Uint8Array(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n}\n\n/**\n * Returns the current high-resolution real time in milliseconds. It is\n * relative to an arbitrary time in the past.\n */\nexport function now(): number {\n  if (typeof performance !== 'undefined') {\n    return performance.now();\n  } else if (typeof process !== 'undefined') {\n    const time = process.hrtime();\n    return time[0] * 1000 + time[1] / 1000000;\n  } else {\n    throw new Error(\n        'Cannot measure time in this environment. You should run tf.js ' +\n        'in the browser or in Node.js');\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {BackendTimer} from './kernels/backend';\nimport {Tensor} from './tensor';\nimport {TypedArray} from './types';\nimport * as util from './util';\n\nexport class Profiler {\n  constructor(private backendTimer: BackendTimer, private logger?: Logger) {\n    if (logger == null) {\n      this.logger = new Logger();\n    }\n  }\n\n  profileKernel<T extends Tensor|Tensor[]>(name: string, f: () => T | Tensor[]):\n      T {\n    let result: T|Tensor[];\n    const holdResultWrapperFn = () => {\n      result = f();\n    };\n    const timer = this.backendTimer.time(holdResultWrapperFn);\n\n    const results: Tensor[] =\n        Array.isArray(result) ? result : [result] as Tensor[];\n    results.forEach(r => {\n      const vals = r.dataSync();\n      util.checkComputationForNaN(vals, r.dtype, name);\n\n      timer.then(timing => {\n        let extraInfo = '';\n        if (timing.getExtraProfileInfo != null) {\n          extraInfo = timing.getExtraProfileInfo();\n        }\n\n        this.logger.logKernelProfile(name, r, vals, timing.kernelMs, extraInfo);\n      });\n    });\n\n    return result as T;\n  }\n}\n\nexport class Logger {\n  logKernelProfile(\n      name: string, result: Tensor, vals: TypedArray, timeMs: number,\n      extraInfo?: string) {\n    const time = util.rightPad(`${timeMs}ms`, 9);\n    const paddedName = util.rightPad(name, 25);\n    const rank = result.rank;\n    const size = result.size;\n    const shape = util.rightPad(result.shape.toString(), 14);\n\n    console.log(\n        `%c${paddedName}\\t%c${time}\\t%c${rank}D ${shape}\\t%c${size}\\t%c${\n            extraInfo}`,\n        'font-weight:bold', 'color:red', 'color:blue', 'color: orange',\n        'color: green');\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, TypedArray} from './types';\nimport {computeStrides, isString, rightPad, sizeFromShape} from './util';\n\n// Maximum number of values before we decide to show ellipsis.\nconst FORMAT_LIMIT_NUM_VALS = 20;\n// Number of first and last values to show when displaying a, b,...,y, z.\nconst FORMAT_NUM_FIRST_LAST_VALS = 3;\n// Number of significant digits to show.\nconst FORMAT_NUM_SIG_DIGITS = 7;\n\nexport function tensorToString(\n    vals: TypedArray|string[], shape: number[], dtype: DataType,\n    verbose: boolean) {\n  const strides = computeStrides(shape);\n  const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);\n  const rank = shape.length;\n  const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);\n  const lines = ['Tensor'];\n  if (verbose) {\n    lines.push(`  dtype: ${dtype}`);\n    lines.push(`  rank: ${rank}`);\n    lines.push(`  shape: [${shape}]`);\n    lines.push(`  values:`);\n  }\n  lines.push(valsLines.map(l => '    ' + l).join('\\n'));\n  return lines.join('\\n');\n}\n\nfunction computeMaxSizePerColumn(\n    vals: TypedArray|string[], shape: number[], dtype: DataType,\n    strides: number[]): number[] {\n  const n = sizeFromShape(shape);\n  const numCols = strides[strides.length - 1];\n  const padPerCol = new Array(numCols).fill(0);\n  const rank = shape.length;\n  const valuesOrTuples =\n      dtype === 'complex64' ? createComplexTuples(vals) : vals;\n\n  if (rank > 1) {\n    for (let row = 0; row < n / numCols; row++) {\n      const offset = row * numCols;\n      for (let j = 0; j < numCols; j++) {\n        padPerCol[j] = Math.max(\n            padPerCol[j], valToString(valuesOrTuples[offset + j], 0).length);\n      }\n    }\n  }\n  return padPerCol;\n}\n\nfunction valToString(val: number|string|[number, number], pad: number) {\n  let valStr: string;\n  if (Array.isArray(val)) {\n    valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ` +\n        `${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;\n  } else if (isString(val)) {\n    valStr = `'${val}'`;\n  } else {\n    valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();\n  }\n\n  return rightPad(valStr, pad);\n}\n\nfunction subTensorToString(\n    vals: TypedArray|string[], shape: number[], dtype: DataType,\n    strides: number[], padPerCol: number[], isLast = true): string[] {\n  const storagePerElement = dtype === 'complex64' ? 2 : 1;\n\n  const size = shape[0];\n  const rank = shape.length;\n  if (rank === 0) {\n    if (dtype === 'complex64') {\n      const complexTuple = createComplexTuples(vals);\n      return [valToString(complexTuple[0], 0)];\n    }\n    return [vals[0].toString()];\n  }\n\n  if (rank === 1) {\n    if (size > FORMAT_LIMIT_NUM_VALS) {\n      const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;\n\n      let firstVals = Array.from<number|string|[number, number]>(\n          vals.slice(0, firstValsSize));\n      let lastVals = Array.from<number|string|[number, number]>(vals.slice(\n          size - FORMAT_NUM_FIRST_LAST_VALS * storagePerElement, size));\n      if (dtype === 'complex64') {\n        firstVals = createComplexTuples(firstVals);\n        lastVals = createComplexTuples(lastVals);\n      }\n      return [\n        '[' + firstVals.map((x, i) => valToString(x, padPerCol[i])).join(', ') +\n        ', ..., ' +\n        lastVals\n            .map(\n                (x, i) => valToString(\n                    x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i]))\n            .join(', ') +\n        ']'\n      ];\n    }\n    const displayVals: Array<number|string|[number, number]> =\n        dtype === 'complex64' ? createComplexTuples(vals) :\n                                Array.from<number|string>(vals);\n\n    return [\n      '[' + displayVals.map((x, i) => valToString(x, padPerCol[i])).join(', ') +\n      ']'\n    ];\n  }\n\n  // The array is rank 2 or more.\n  const subshape = shape.slice(1);\n  const substrides = strides.slice(1);\n  const stride = strides[0] * storagePerElement;\n  const lines: string[] = [];\n  if (size > FORMAT_LIMIT_NUM_VALS) {\n    for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {\n      const start = i * stride;\n      const end = start + stride;\n      lines.push(...subTensorToString(\n          vals.slice(start, end), subshape, dtype, substrides, padPerCol,\n          false /* isLast */));\n    }\n    lines.push('...');\n    for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {\n      const start = i * stride;\n      const end = start + stride;\n      lines.push(...subTensorToString(\n          vals.slice(start, end), subshape, dtype, substrides, padPerCol,\n          i === size - 1 /* isLast */));\n    }\n  } else {\n    for (let i = 0; i < size; i++) {\n      const start = i * stride;\n      const end = start + stride;\n      lines.push(...subTensorToString(\n          vals.slice(start, end), subshape, dtype, substrides, padPerCol,\n          i === size - 1 /* isLast */));\n    }\n  }\n  const sep = rank === 2 ? ',' : '';\n  lines[0] = '[' + lines[0] + sep;\n  for (let i = 1; i < lines.length - 1; i++) {\n    lines[i] = ' ' + lines[i] + sep;\n  }\n  let newLineSep = ',\\n';\n  for (let i = 2; i < rank; i++) {\n    newLineSep += '\\n';\n  }\n  lines[lines.length - 1] =\n      ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep);\n  return lines;\n}\n\nfunction createComplexTuples(vals: Array<{}>|\n                             TypedArray): Array<[number, number]> {\n  const complexTuples: Array<[number, number]> = [];\n  for (let i = 0; i < vals.length; i += 2) {\n    complexTuples.push([vals[i], vals[i + 1]] as [number, number]);\n  }\n  return complexTuples;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {tensorToString} from './tensor_format';\nimport {DataType, DataTypeMap, DataValues, NumericDataType, Rank, ShapeMap, SingleValueMap, TensorLike, TensorLike1D, TensorLike3D, TensorLike4D, TypedArray} from './types';\nimport * as util from './util';\nimport {computeStrides} from './util';\n\nexport interface TensorData<D extends DataType> {\n  dataId?: DataId;\n  values?: DataTypeMap[D];\n}\n\n/**\n * A mutable object, similar to `tf.Tensor`, that allows users to set values\n * at locations before converting to an immutable `tf.Tensor`.\n *\n * See `tf.buffer` for creating a tensor buffer.\n */\n/** @doc {heading: 'Tensors', subheading: 'Classes'} */\nexport class TensorBuffer<R extends Rank, D extends DataType = 'float32'> {\n  size: number;\n  shape: ShapeMap[R];\n  strides: number[];\n  values: DataTypeMap[D];\n\n  constructor(shape: ShapeMap[R], public dtype: D, values?: DataTypeMap[D]) {\n    this.shape = shape.slice();\n    this.size = util.sizeFromShape(shape);\n\n    if (values != null) {\n      const n = values.length;\n      util.assert(\n          n === this.size,\n          `Length of values '${n}' does not match the size ` +\n              `inferred by the shape '${this.size}'.`);\n    }\n    if (dtype === 'complex64') {\n      throw new Error(\n          `complex64 dtype TensorBuffers are not supported. Please create ` +\n          `a TensorBuffer for the real and imaginary parts separately and ` +\n          `call tf.complex(real, imag).`);\n    }\n    this.values =\n        values || util.getArrayFromDType(dtype, util.sizeFromShape(this.shape));\n    this.strides = computeStrides(shape);\n  }\n\n  /**\n   * Sets a value in the buffer at a given location.\n   *\n   * @param value The value to set.\n   * @param locs  The location indices.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Creation'} */\n  set(value: SingleValueMap[D], ...locs: number[]): void {\n    if (locs.length === 0) {\n      locs = [0];\n    }\n    util.assert(\n        locs.length === this.rank,\n        `The number of provided coordinates (${locs.length}) must ` +\n            `match the rank (${this.rank})`);\n\n    const index = this.locToIndex(locs);\n    this.values[index] = value as number;\n  }\n\n  /**\n   * Returns the value in the buffer at the provided location.\n   *\n   * @param locs The location indices.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Creation'} */\n  get(...locs: number[]): SingleValueMap[D] {\n    if (locs.length === 0) {\n      locs = [0];\n    }\n    let index = locs[locs.length - 1];\n    for (let i = 0; i < locs.length - 1; ++i) {\n      index += this.strides[i] * locs[i];\n    }\n    return this.values[index];\n  }\n\n  locToIndex(locs: number[]): number {\n    if (this.rank === 0) {\n      return 0;\n    } else if (this.rank === 1) {\n      return locs[0];\n    }\n    let index = locs[locs.length - 1];\n    for (let i = 0; i < locs.length - 1; ++i) {\n      index += this.strides[i] * locs[i];\n    }\n    return index;\n  }\n\n  indexToLoc(index: number): number[] {\n    if (this.rank === 0) {\n      return [];\n    } else if (this.rank === 1) {\n      return [index];\n    }\n    const locs: number[] = new Array(this.shape.length);\n    for (let i = 0; i < locs.length - 1; ++i) {\n      locs[i] = Math.floor(index / this.strides[i]);\n      index -= locs[i] * this.strides[i];\n    }\n    locs[locs.length - 1] = index;\n    return locs;\n  }\n\n  get rank() {\n    return this.shape.length;\n  }\n\n  /**\n   * Creates an immutable `tf.Tensor` object from the buffer.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Creation'} */\n  toTensor(): Tensor<R> {\n    return Tensor.make(this.shape, {values: this.values}, this.dtype);\n  }\n}\n\nexport interface TensorTracker {\n  registerTensor(t: Tensor): void;\n  disposeTensor(t: Tensor): void;\n  write(dataId: DataId, values: DataValues): void;\n  read(dataId: DataId): Promise<DataValues>;\n  readSync(dataId: DataId): DataValues;\n  registerVariable(v: Variable): void;\n  nextTensorId(): number;\n  nextVariableId(): number;\n}\n\n/**\n * The Tensor class calls into this handler to delegate chaining operations.\n */\nexport interface OpHandler {\n  cast<T extends Tensor>(x: T, dtype: DataType): T;\n  buffer<R extends Rank, D extends DataType>(\n      shape: ShapeMap[R], dtype: D,\n      values?: DataTypeMap[D]): TensorBuffer<R, D>;\n  print<T extends Tensor>(x: T, verbose: boolean): void;\n  reshape<R2 extends Rank>(x: Tensor, shape: ShapeMap[R2]): Tensor<R2>;\n  expandDims<R2 extends Rank>(x: Tensor, axis: number): Tensor<R2>;\n  cumsum<T extends Tensor>(\n      x: Tensor, axis: number, exclusive: boolean, reverse: boolean): T;\n  squeeze<T extends Tensor>(x: Tensor, axis?: number[]): T;\n  clone<T extends Tensor>(x: T): T;\n  tile<T extends Tensor>(x: T, reps: number[]): T;\n  gather<T extends Tensor>(x: T, indices: Tensor1D|TensorLike1D, axis: number):\n      T;\n  matMul<T extends Tensor>(\n      a: T, b: T|TensorLike, transposeA: boolean, transposeB: boolean): T;\n  dot(t1: Tensor, t2: Tensor|TensorLike): Tensor;\n  norm(\n      x: Tensor, ord: number|'euclidean'|'fro', axis: number|number[],\n      keepDims: boolean): Tensor;\n  slice<R extends Rank, T extends Tensor<R>>(\n      x: T, begin: number|number[], size?: number|number[]): T;\n  split<T extends Tensor>(\n      x: T, numOrSizeSplits: number[]|number, axis?: number): T[];\n  reverse<T extends Tensor>(x: T, axis?: number|number[]): T;\n  concat<T extends Tensor>(tensors: Array<T|TensorLike>, axis: number): T;\n  stack<T extends Tensor>(tensors: Array<T|TensorLike>, axis: number): Tensor;\n  unstack<T extends Tensor>(value: T, axis: number): Tensor[];\n  pad<T extends Tensor>(\n      x: T, paddings: Array<[number, number]>, constantValue: number): T;\n  batchNormalization<R extends Rank>(\n      x: Tensor<R>, mean: Tensor<R>|Tensor1D|TensorLike,\n      variance: Tensor<R>|Tensor1D|TensorLike, varianceEpsilon: number,\n      scale?: Tensor<R>|Tensor1D|TensorLike,\n      offset?: Tensor<R>|Tensor1D|TensorLike): Tensor<R>;\n  all<T extends Tensor>(x: Tensor, axis: number|number[], keepDims: boolean): T;\n  any<T extends Tensor>(x: Tensor, axis: number|number[], keepDims: boolean): T;\n  logSumExp<T extends Tensor>(\n      x: Tensor, axis: number|number[], keepDims: boolean): T;\n  sum<T extends Tensor>(x: Tensor, axis: number|number[], keepDims: boolean): T;\n  prod<T extends Tensor>(x: Tensor, axis: number|number[], keepDims: boolean):\n      T;\n  mean<T extends Tensor>(x: Tensor, axis: number|number[], keepDims: boolean):\n      T;\n  min<T extends Tensor>(x: Tensor, axis: number|number[], keepDims: boolean): T;\n  max<T extends Tensor>(x: Tensor, axis: number|number[], keepDims: boolean): T;\n  argMin<T extends Tensor>(x: Tensor, axis: number): T;\n  argMax<T extends Tensor>(x: Tensor, axis: number): T;\n  add<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  addStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  atan2<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  sub<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  subStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  pow<T extends Tensor>(base: T, exp: Tensor|TensorLike): T;\n  powStrict<T extends Tensor>(base: T, exp: Tensor|TensorLike): T;\n  mul<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  mulStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  div<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  floorDiv<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  divStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  mod<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  modStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  minimum<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  minimumStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  maximum<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  maximumStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  squaredDifference<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  squaredDifferenceStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  transpose<T extends Tensor>(x: T, perm?: number[]): T;\n  logicalNot<T extends Tensor>(x: T): T;\n  logicalAnd<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  logicalOr<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  logicalXor<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  where<T extends Tensor>(condition: Tensor|TensorLike, a: T, b: T|TensorLike):\n      T;\n  notEqual<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  notEqualStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  less<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  lessStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  equal<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  equalStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  lessEqual<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  lessEqualStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  greater<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  greaterStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  greaterEqual<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  greaterEqualStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  neg<T extends Tensor>(x: T): T;\n  ceil<T extends Tensor>(x: T): T;\n  floor<T extends Tensor>(x: T): T;\n  sign<T extends Tensor>(x: T): T;\n  round<T extends Tensor>(x: T): T;\n  exp<T extends Tensor>(x: T): T;\n  expm1<T extends Tensor>(x: T): T;\n  log<T extends Tensor>(x: T): T;\n  log1p<T extends Tensor>(x: T): T;\n  sqrt<T extends Tensor>(x: T): T;\n  rsqrt<T extends Tensor>(x: T): T;\n  square<T extends Tensor>(x: T): T;\n  reciprocal<T extends Tensor>(x: T): T;\n  abs<T extends Tensor>(x: T): T;\n  clipByValue<T extends Tensor>(\n      x: T, clipValueMin: number, clipValueMax: number): T;\n  sigmoid<T extends Tensor>(x: T): T;\n  logSigmoid<T extends Tensor>(x: T): T;\n  softplus<T extends Tensor>(x: T): T;\n  zerosLike<T extends Tensor>(x: T): T;\n  onesLike<T extends Tensor>(x: T): T;\n  sin<T extends Tensor>(x: T): T;\n  cos<T extends Tensor>(x: T): T;\n  tan<T extends Tensor>(x: T): T;\n  asin<T extends Tensor>(x: T): T;\n  acos<T extends Tensor>(x: T): T;\n  atan<T extends Tensor>(x: T): T;\n  sinh<T extends Tensor>(x: T): T;\n  cosh<T extends Tensor>(x: T): T;\n  tanh<T extends Tensor>(x: T): T;\n  asinh<T extends Tensor>(x: T): T;\n  acosh<T extends Tensor>(x: T): T;\n  atanh<T extends Tensor>(x: T): T;\n  erf<T extends Tensor>(x: T): T;\n  step<T extends Tensor>(x: T, alpha: number): T;\n  relu<T extends Tensor>(x: T): T;\n  elu<T extends Tensor>(x: T): T;\n  selu<T extends Tensor>(x: T): T;\n  leakyRelu<T extends Tensor>(x: T, alpha: number): T;\n  prelu<T extends Tensor>(x: T, alpha: T|TensorLike): T;\n  softmax<T extends Tensor>(logits: T, dim: number): T;\n  logSoftmax<T extends Tensor>(logits: T, axis: number): T;\n  image: {\n    resizeBilinear<T extends Tensor3D|Tensor4D>(\n        images: T, size: [number, number], alignCorners: boolean): T;\n    resizeNearestNeighbor<T extends Tensor3D|Tensor4D>(\n        images: T, size: [number, number], alignCorners: boolean): T;\n  };\n  conv1d<T extends Tensor2D|Tensor3D>(\n      x: T, filter: Tensor3D|TensorLike3D, stride: number,\n      pad: 'valid'|'same'|number, dataFormat: 'NWC'|'NCW', dilation: number,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n  conv2d<T extends Tensor3D|Tensor4D>(\n      x: T, filter: Tensor4D|TensorLike4D, strides: [number, number]|number,\n      pad: 'valid'|'same'|number, dataFormat: 'NHWC'|'NCHW',\n      dilations: [number, number]|number,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n  conv2dTranspose<T extends Tensor3D|Tensor4D>(\n      x: T, filter: Tensor4D|TensorLike4D,\n      outputShape: [number, number, number, number]|[number, number, number],\n      strides: [number, number]|number, pad: 'valid'|'same'|number,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n  depthwiseConv2d<T extends Tensor3D|Tensor4D>(\n      x: T, filter: Tensor4D|TensorLike4D, strides: [number, number]|number,\n      pad: 'valid'|'same'|number, dataFormat: 'NHWC'|'NCHW',\n      dilations: [number, number]|number,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n  separableConv2d<T extends Tensor3D|Tensor4D>(\n      x: T|TensorLike, depthwiseFilter: Tensor4D|TensorLike4D,\n      pointwiseFilter: Tensor4D|TensorLike, strides: [number, number]|number,\n      pad: 'valid'|'same', dilation: [number, number]|number,\n      dataFormat: 'NHWC'|'NCHW'): T;\n  maxPool<T extends Tensor3D|Tensor4D>(\n      x: T, filterSize: [number, number]|number,\n      strides: [number, number]|number, pad: 'valid'|'same'|number,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n  avgPool<T extends Tensor3D|Tensor4D>(\n      x: T, filterSize: [number, number]|number,\n      strides: [number, number]|number, pad: 'valid'|'same'|number,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n  pool<T extends Tensor3D|Tensor4D>(\n      input: T, windowShape: [number, number]|number, poolingType: 'avg'|'max',\n      padding: 'valid'|'same'|number, diationRate?: [number, number]|number,\n      strides?: [number, number]|number): T;\n  localResponseNormalization<T extends Tensor3D|Tensor4D>(\n      x: T, depthRadius: number, bias: number, alpha: number, beta: number): T;\n  unsortedSegmentSum<T extends Tensor>(\n      x: T, segmentIds: Tensor1D|TensorLike1D, numSegments: number): T;\n  batchToSpaceND<T extends Tensor>(\n      x: T, blockShape: number[], crops: number[][]): T;\n  spaceToBatchND<T extends Tensor>(\n      x: T, blockShape: number[], paddings: number[][]): T;\n  topk<T extends Tensor>(x: T, k: number, sorted: boolean):\n      {values: T, indices: T};\n  stridedSlice<T extends Tensor>(\n      x: T, begin: number[], end: number[], strides: number[],\n      beginMask: number, endMask: number): T;\n  depthToSpace(x: Tensor4D, blockSize: number, dataFormat: string): Tensor4D;\n  spectral: {fft(x: Tensor): Tensor; ifft(x: Tensor): Tensor;};\n}\n\n// For tracking tensor creation and disposal.\nlet trackerFn: () => TensorTracker = null;\n// Used by chaining methods to call into ops.\nlet opHandler: OpHandler = null;\n\n/**\n * An external consumer can register itself as the tensor tracker. This way\n * the Tensor class can notify the tracker for every tensor created and\n * disposed.\n */\nexport function setTensorTracker(fn: () => TensorTracker) {\n  trackerFn = fn;\n}\n\n/**\n * An external consumer can register itself as the op handler. This way the\n * Tensor class can have chaining methods that call into ops via the op handler.\n */\nexport function setOpHandler(handler: OpHandler) {\n  opHandler = handler;\n}\n\n/**\n * We wrap data id since we use weak map to avoid memory leaks.\n * Since we have our own memory management, we have a reference counter\n * mapping a tensor to its data, so there is always a pointer (even if that\n * data is otherwise garbage collectable).\n * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/\n * Global_Objects/WeakMap\n */\nexport type DataId = object;  // object instead of {} to force non-primitive.\n\n/**\n * A `tf.Tensor` object represents an immutable, multidimensional array of\n * numbers that has a shape and a data type.\n *\n * See `tf.tensor` for details on how to create a `tf.Tensor`.\n */\n/** @doc {heading: 'Tensors', subheading: 'Classes'} */\nexport class Tensor<R extends Rank = Rank> {\n  /** Unique id of this tensor. */\n  readonly id: number;\n  /**\n   * Id of the bucket holding the data for this tensor. Multiple arrays can\n   * point to the same bucket (e.g. when calling array.reshape()).\n   */\n  dataId: DataId;\n  /** The shape of the tensor. */\n  readonly shape: ShapeMap[R];\n  /** Number of elements in the tensor. */\n  readonly size: number;\n  /** The data type for the array. */\n  readonly dtype: DataType;\n  /** The rank type for the array (see `Rank` enum). */\n  readonly rankType: R;\n\n  /**\n   * Number of elements to skip in each dimension when indexing. See\n   * https://docs.scipy.org/doc/numpy/reference/generated/\\\n   * numpy.ndarray.strides.html\n   */\n  readonly strides: number[];\n\n  protected constructor(\n      shape: ShapeMap[R], dtype: DataType, values?: DataValues,\n      dataId?: DataId) {\n    this.shape = shape.slice();\n    this.dtype = dtype || 'float32';\n    this.size = util.sizeFromShape(shape);\n    this.strides = computeStrides(shape);\n    this.dataId = dataId != null ? dataId : {};\n    this.id = trackerFn().nextTensorId();\n    this.rankType = (this.rank < 5 ? this.rank.toString() : 'higher') as R;\n    trackerFn().registerTensor(this);\n    if (values != null) {\n      trackerFn().write(this.dataId, values);\n    }\n  }\n\n  /**\n   * Makes a new tensor with the provided shape and values. Values should be in\n   * a flat array.\n   */\n  static make<T extends Tensor<R>, D extends DataType = 'float32',\n                                             R extends Rank = Rank>(\n      shape: ShapeMap[R], data: TensorData<D>, dtype?: D): T {\n    return new Tensor(shape, dtype, data.values, data.dataId) as T;\n  }\n\n  /** Flatten a Tensor to a 1D array. */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  flatten(): Tensor1D {\n    this.throwIfDisposed();\n    return this.as1D();\n  }\n\n  /** Converts a size-1 `tf.Tensor` to a `tf.Scalar`. */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  asScalar(): Scalar {\n    this.throwIfDisposed();\n    util.assert(this.size === 1, 'The array must have only 1 element.');\n    return this.reshape<Rank.R0>([]);\n  }\n\n  /** Converts a `tf.Tensor` to a `tf.Tensor1D`. */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  as1D(): Tensor1D {\n    this.throwIfDisposed();\n    return this.reshape<Rank.R1>([this.size]);\n  }\n\n  /**\n   * Converts a `tf.Tensor` to a `tf.Tensor2D`.\n   *\n   * @param rows Number of rows in `tf.Tensor2D`.\n   * @param columns Number of columns in `tf.Tensor2D`.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  as2D(rows: number, columns: number): Tensor2D {\n    this.throwIfDisposed();\n    return this.reshape<Rank.R2>([rows, columns]);\n  }\n\n  /**\n   * Converts a `tf.Tensor` to a `tf.Tensor3D`.\n   *\n   * @param rows Number of rows in `tf.Tensor3D`.\n   * @param columns Number of columns in `tf.Tensor3D`.\n   * @param depth Depth of `tf.Tensor3D`.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  as3D(rows: number, columns: number, depth: number): Tensor3D {\n    this.throwIfDisposed();\n    return this.reshape<Rank.R3>([rows, columns, depth]);\n  }\n\n  /**\n   * Converts a `tf.Tensor` to a `tf.Tensor4D`.\n   *\n   * @param rows Number of rows in `tf.Tensor4D`.\n   * @param columns Number of columns in `tf.Tensor4D`.\n   * @param depth Depth of `tf.Tensor4D`.\n   * @param depth2 4th dimension of `tf.Tensor4D`.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  as4D(rows: number, columns: number, depth: number, depth2: number): Tensor4D {\n    this.throwIfDisposed();\n    return this.reshape<Rank.R4>([rows, columns, depth, depth2]);\n  }\n\n  /**\n   * Converts a `tf.Tensor` to a `tf.Tensor5D`.\n   *\n   * @param rows Number of rows in `tf.Tensor5D`.\n   * @param columns Number of columns in `tf.Tensor5D`.\n   * @param depth Depth of `tf.Tensor5D`.\n   * @param depth2 4th dimension of `tf.Tensor5D`.\n   * @param depth3 5th dimension of 'tf.Tensor5D'\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  as5D(\n      rows: number, columns: number, depth: number, depth2: number,\n      depth3: number): Tensor5D {\n    this.throwIfDisposed();\n    return this.reshape<Rank.R5>([rows, columns, depth, depth2, depth3]);\n  }\n\n  /**\n   * Casts a `tf.Tensor` to a specified dtype.\n   *\n   * @param dtype Data-type to cast the tensor to.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  asType<T extends this>(this: T, dtype: DataType): T {\n    this.throwIfDisposed();\n    return opHandler.cast(this, dtype) as T;\n  }\n\n  get rank(): number {\n    return this.shape.length;\n  }\n\n  /**\n   * Returns the value in the tensor at the provided location.\n   * If using WebGL backend, this is a blocking call.\n   * Prefer calling the `async data()[flatIndex]` method instead.\n   *\n   * @param locs The location indices.\n   */\n  get(...locs: number[]) {\n    util.assert(\n        locs.length === this.rank,\n        'Number of coordinates in get() must match the rank of the tensor');\n    util.assert(\n        this.dtype !== 'complex64',\n        'Tensor.get() is not supported for complex64 tensors yet.');\n    this.throwIfDisposed();\n    if (locs.length === 0) {\n      locs = [0];\n    }\n    let index = locs[locs.length - 1];\n    for (let i = 0; i < locs.length - 1; ++i) {\n      index += this.strides[i] * locs[i];\n    }\n    return this.dataSync()[index];\n  }\n\n  /** Returns a `tf.TensorBuffer` that holds the underlying data. */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  buffer<D extends DataType>(): TensorBuffer<R, D> {\n    return opHandler.buffer(this.shape, this.dtype as D, this.dataSync());\n  }\n\n  /**\n   * Asynchronously downloads the values from the `tf.Tensor`. Returns a promise\n   * of `TypedArray` that resolves when the computation has finished.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  async data<D extends DataType = NumericDataType>(): Promise<DataTypeMap[D]> {\n    this.throwIfDisposed();\n    return trackerFn().read(this.dataId);\n  }\n\n  /**\n   * Synchronously downloads the values from the `tf.Tensor`. This blocks the UI\n   * thread until the values are ready, which can cause performance issues.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  dataSync<D extends DataType = NumericDataType>(): DataTypeMap[D] {\n    this.throwIfDisposed();\n    return trackerFn().readSync(this.dataId);\n  }\n\n  /**\n   * Disposes `tf.Tensor` from memory.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  dispose(): void {\n    if (this.isDisposed) {\n      return;\n    }\n    trackerFn().disposeTensor(this);\n    this.isDisposedInternal = true;\n  }\n\n  private isDisposedInternal = false;\n  get isDisposed(): boolean {\n    return this.isDisposedInternal;\n  }\n\n  private throwIfDisposed() {\n    if (this.isDisposed) {\n      throw new Error(`Tensor is disposed.`);\n    }\n  }\n\n  /** Casts the array to type `float32` */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  toFloat<T extends this>(this: T): T {\n    return this.asType('float32');\n  }\n\n  /** Casts the array to type `int32` */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  toInt() {\n    return this.asType('int32');\n  }\n\n  /** Casts the array to type `bool` */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  toBool() {\n    return this.asType('bool');\n  }\n\n  /**\n   * Prints the `tf.Tensor`. See `tf.print` for details.\n   *\n   * @param verbose Whether to print verbose information about the tensor,\n   *    including dtype and size.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  print(verbose = false): void {\n    return opHandler.print(this, verbose);\n  }\n\n  /**\n   * Reshapes the tensor into the provided shape.\n   * See `tf.reshape` for more details.\n   *\n   * @param newShape An array of integers defining the output tensor shape.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  reshape<R2 extends Rank>(newShape: ShapeMap[R2]): Tensor<R2> {\n    this.throwIfDisposed();\n    return opHandler.reshape(this, newShape);\n  }\n\n  /**\n   * Reshapes the tensor into the shape of the provided tensor.\n   *\n   * @param x The tensor of required shape.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  reshapeAs<T extends Tensor>(x: T): T {\n    this.throwIfDisposed();\n    return this.reshape(x.shape) as T;\n  }\n\n  /**\n   * Returns a `tf.Tensor` that has expanded rank, by inserting a dimension\n   * into the tensor's shape. See `tf.expandDims` for details.\n   *\n   * @param axis The dimension index at which to insert shape of 1. Defaults to\n   *    0 (the first dimension).\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  expandDims<R2 extends Rank>(axis = 0): Tensor<R2> {\n    return opHandler.expandDims(this, axis);\n  }\n\n  /**\n   * Returns the cumulative sum of the `tf.Tensor` along `axis`.\n   *\n   * @param axis The axis along which to sum. Optional. Defaults to 0.\n   * @param exclusive Whether to perform exclusive cumulative sum. Defaults to\n   *    false. If set to true then the sum of each tensor entry does not include\n   *    its own value, but only the values previous to it along the specified\n   *    axis.\n   * @param reverse Whether to sum in the opposite direction. Defaults to\n   *    false.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  cumsum<T extends Tensor>(axis = 0, exclusive = false, reverse = false): T {\n    return opHandler.cumsum(this, axis, exclusive, reverse);\n  }\n\n  /**\n   * Returns a `tf.Tensor` with dimensions of size 1 removed from the shape.\n   * See `tf.squeeze` for more details.\n   *\n   * @param axis A list of numbers. If specified, only squeezes the\n   *    dimensions listed. The dimension index starts at 0. It is an error to\n   *    squeeze a dimension that is not 1.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  squeeze<T extends Tensor>(axis?: number[]): T {\n    this.throwIfDisposed();\n    return opHandler.squeeze(this, axis);\n  }\n\n  /** Returns a copy of the tensor. See `tf.clone` for details. */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  clone<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.clone(this);\n  }\n\n  /** Returns a human-readable description of the tensor. Useful for logging. */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  toString(verbose = false): string {\n    const vals = this.dataSync();\n    return tensorToString(vals, this.shape, this.dtype, verbose);\n  }\n\n  // Below is chain API that is not exposed to docs to avoid repetition. To\n  // expose a method, move it above this comment and add @doc and jsdoc.\n\n  tile<T extends this>(this: T, reps: number[]): T {\n    this.throwIfDisposed();\n    return opHandler.tile(this, reps) as T;\n  }\n\n  gather<T extends this>(this: T, indices: Tensor1D|TensorLike1D, axis = 0): T {\n    this.throwIfDisposed();\n    return opHandler.gather(this, indices, axis) as T;\n  }\n\n  matMul<T extends Tensor>(\n      this: T, b: T|TensorLike, transposeA = false, transposeB = false): T {\n    this.throwIfDisposed();\n    return opHandler.matMul(this, b, transposeA, transposeB);\n  }\n  dot(b: Tensor|TensorLike): Tensor {\n    this.throwIfDisposed();\n    return opHandler.dot(this, b);\n  }\n  norm(\n      ord: number|'euclidean'|'fro' = 'euclidean', axis: number|number[] = null,\n      keepDims = false): Tensor {\n    this.throwIfDisposed();\n    return opHandler.norm(this, ord, axis, keepDims);\n  }\n  slice<T extends Tensor<R>>(\n      this: T, begin: number|number[], size?: number|number[]): T {\n    this.throwIfDisposed();\n    return opHandler.slice(this, begin, size);\n  }\n  reverse<T extends Tensor>(this: T, axis?: number|number[]): T {\n    this.throwIfDisposed();\n    return opHandler.reverse(this, axis);\n  }\n  concat<T extends Tensor>(this: T, x: T|Array<T|TensorLike>, axis = 0): T {\n    this.throwIfDisposed();\n    if (x instanceof Tensor) {\n      x = [x];\n    }\n    return opHandler.concat([this, ...x], axis);\n  }\n  split<T extends Tensor>(this: T, numOrSizeSplits: number[]|number, axis = 0):\n      T[] {\n    this.throwIfDisposed();\n    return opHandler.split(this, numOrSizeSplits, axis);\n  }\n  stack(x: Tensor, axis = 0): Tensor {\n    return opHandler.stack([this, x], axis);\n  }\n  unstack(x: Tensor, axis = 0): Tensor[] {\n    return opHandler.unstack(this, axis);\n  }\n  pad<T extends Tensor>(\n      this: T, paddings: Array<[number, number]>, constantValue = 0): T {\n    return opHandler.pad(this, paddings, constantValue);\n  }\n  batchNormalization(\n      mean: Tensor<R>|Tensor1D|TensorLike,\n      variance: Tensor<R>|Tensor1D|TensorLike, varianceEpsilon = .001,\n      scale?: Tensor<R>|Tensor1D|TensorLike,\n      offset?: Tensor<R>|Tensor1D|TensorLike): Tensor<R> {\n    this.throwIfDisposed();\n    return opHandler.batchNormalization(\n        this, mean, variance, varianceEpsilon, scale, offset);\n  }\n\n  // Reduction ops.\n  all<T extends Tensor>(axis: number|number[] = null, keepDims = false): T {\n    this.throwIfDisposed();\n    return opHandler.all(this, axis, keepDims);\n  }\n  any<T extends Tensor>(axis: number|number[] = null, keepDims = false): T {\n    this.throwIfDisposed();\n    return opHandler.any(this, axis, keepDims);\n  }\n  logSumExp<T extends Tensor>(axis: number|number[] = null, keepDims = false):\n      T {\n    this.throwIfDisposed();\n    return opHandler.logSumExp(this, axis, keepDims);\n  }\n  sum<T extends Tensor>(axis: number|number[] = null, keepDims = false): T {\n    this.throwIfDisposed();\n    return opHandler.sum(this, axis, keepDims);\n  }\n  prod<T extends Tensor>(axis: number|number[] = null, keepDims = false): T {\n    this.throwIfDisposed();\n    return opHandler.prod(this, axis, keepDims);\n  }\n  mean<T extends Tensor>(axis: number|number[] = null, keepDims = false): T {\n    this.throwIfDisposed();\n    return opHandler.mean(this, axis, keepDims);\n  }\n  min<T extends Tensor>(axis: number|number[] = null, keepDims = false): T {\n    this.throwIfDisposed();\n    return opHandler.min(this, axis, keepDims);\n  }\n  max<T extends Tensor>(axis: number|number[] = null, keepDims = false): T {\n    this.throwIfDisposed();\n    return opHandler.max(this, axis, keepDims);\n  }\n  argMin<T extends Tensor>(axis: number = null): T {\n    this.throwIfDisposed();\n    return opHandler.argMin(this, axis);\n  }\n  argMax<T extends Tensor>(axis: number = null): T {\n    this.throwIfDisposed();\n    return opHandler.argMax(this, axis);\n  }\n\n  // Transformations\n  cast<T extends this>(dtype: DataType): T {\n    this.throwIfDisposed();\n    return opHandler.cast(this as T, dtype) as T;\n  }\n\n  // Binary ops.\n\n  add<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.add(this, x);\n  }\n  addStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.addStrict(this, x) as T;\n  }\n  atan2<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.atan2(this, x) as T;\n  }\n  sub<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.sub(this, x);\n  }\n  subStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.subStrict(this, x) as T;\n  }\n  pow<T extends Tensor>(this: T, exp: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.pow(this, exp);\n  }\n  powStrict(exp: Tensor|TensorLike): Tensor<R> {\n    this.throwIfDisposed();\n    return opHandler.powStrict(this, exp);\n  }\n  mul<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.mul(this, x);\n  }\n  mulStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.mulStrict(this, x) as T;\n  }\n  div<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.div(this, x);\n  }\n  floorDiv<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.floorDiv(this, x);\n  }\n  divStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.divStrict(this, x) as T;\n  }\n  minimum<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.minimum(this, x);\n  }\n  minimumStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.minimumStrict(this, x) as T;\n  }\n  maximum<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.maximum(this, x);\n  }\n  maximumStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.maximumStrict(this, x) as T;\n  }\n  mod<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.mod(this, x);\n  }\n  modStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.modStrict(this, x) as T;\n  }\n  squaredDifference<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.squaredDifference(this, x);\n  }\n  squaredDifferenceStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.squaredDifferenceStrict(this, x) as T;\n  }\n  transpose<T extends Tensor>(this: T, perm?: number[]): T {\n    this.throwIfDisposed();\n    return opHandler.transpose(this, perm);\n  }\n\n  // Compare ops.\n\n  notEqual<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.notEqual(this, x);\n  }\n  notEqualStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.notEqualStrict(this, x) as T;\n  }\n  less<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.less(this, x);\n  }\n  lessStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.lessStrict(this, x) as T;\n  }\n  equal<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.equal(this, x);\n  }\n  equalStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.equalStrict(this, x) as T;\n  }\n  lessEqual<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.lessEqual(this, x);\n  }\n  lessEqualStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.lessEqualStrict(this, x) as T;\n  }\n  greater<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.greater(this, x);\n  }\n  greaterStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.greaterStrict(this, x) as T;\n  }\n  greaterEqual<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.greaterEqual(this, x);\n  }\n  greaterEqualStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.greaterEqualStrict(this, x) as T;\n  }\n\n  // Compare ops.\n  logicalAnd(x: Tensor|TensorLike): Tensor {\n    this.throwIfDisposed();\n    return opHandler.logicalAnd(this, x);\n  }\n  logicalOr(x: Tensor|TensorLike): Tensor {\n    this.throwIfDisposed();\n    return opHandler.logicalOr(this, x);\n  }\n  logicalNot<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.logicalNot(this);\n  }\n  logicalXor(x: Tensor|TensorLike): Tensor {\n    this.throwIfDisposed();\n    return opHandler.logicalXor(this, x);\n  }\n  where(condition: Tensor|TensorLike, x: Tensor|TensorLike): Tensor {\n    this.throwIfDisposed();\n    return opHandler.where(condition, this, x);\n  }\n\n  // Unary ops.\n  neg<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.neg(this);\n  }\n  ceil<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.ceil(this);\n  }\n  floor<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.floor(this);\n  }\n  sign<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.sign(this);\n  }\n  exp<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.exp(this);\n  }\n  expm1<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.expm1(this);\n  }\n  log<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.log(this);\n  }\n  log1p<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.log1p(this);\n  }\n  sqrt<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.sqrt(this);\n  }\n  rsqrt<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.rsqrt(this);\n  }\n  square<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.square(this);\n  }\n  reciprocal<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.reciprocal(this);\n  }\n  abs<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.abs(this);\n  }\n  clipByValue(min: number, max: number): Tensor<R> {\n    this.throwIfDisposed();\n    return opHandler.clipByValue(this, min, max);\n  }\n  relu<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.relu(this);\n  }\n  elu<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.elu(this);\n  }\n  selu<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.selu(this);\n  }\n  leakyRelu(alpha = 0.2): Tensor<R> {\n    this.throwIfDisposed();\n    return opHandler.leakyRelu(this, alpha);\n  }\n  prelu(alpha: Tensor<R>|TensorLike): Tensor<R> {\n    this.throwIfDisposed();\n    return opHandler.prelu(this, alpha);\n  }\n  sigmoid<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.sigmoid(this);\n  }\n  logSigmoid<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.logSigmoid(this);\n  }\n  softplus<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.softplus(this);\n  }\n  zerosLike<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.zerosLike(this);\n  }\n  onesLike<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.onesLike(this);\n  }\n  sin<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.sin(this);\n  }\n  cos<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.cos(this);\n  }\n  tan<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.tan(this);\n  }\n  asin<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.asin(this);\n  }\n  acos<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.acos(this);\n  }\n  atan<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.atan(this);\n  }\n  sinh<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.sinh(this);\n  }\n  cosh<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.cosh(this);\n  }\n  tanh<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.tanh(this);\n  }\n  asinh<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.asinh(this);\n  }\n  acosh<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.acosh(this);\n  }\n  atanh<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.atanh(this);\n  }\n  erf<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.erf(this);\n  }\n  round<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.round(this);\n  }\n  step<T extends Tensor>(this: T, alpha = 0.0): T {\n    this.throwIfDisposed();\n    return opHandler.step(this, alpha);\n  }\n  softmax<T extends this>(this: T, dim = -1): T {\n    this.throwIfDisposed();\n    return opHandler.softmax(this, dim) as T;\n  }\n  logSoftmax<T extends this>(this: T, axis = -1): T {\n    this.throwIfDisposed();\n    return opHandler.logSoftmax(this, axis) as T;\n  }\n\n  // Image ops.\n  resizeBilinear<T extends Tensor3D|Tensor4D>(\n      this: T, newShape2D: [number, number], alignCorners = false): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.image.resizeBilinear(this, newShape2D, alignCorners);\n  }\n\n  resizeNearestNeighbor<T extends Tensor3D|Tensor4D>(\n      this: T, newShape2D: [number, number], alignCorners = false): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.image.resizeNearestNeighbor(\n        this, newShape2D, alignCorners);\n  }\n\n  // Convolutions.\n  conv1d<T extends Tensor2D|Tensor3D>(\n      this: T, filter: Tensor3D|TensorLike3D, stride: number,\n      pad: 'valid'|'same'|number, dataFormat: 'NWC'|'NCW' = 'NWC', dilation = 1,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.conv1d(\n        this, filter, stride, pad, dataFormat, dilation, dimRoundingMode);\n  }\n  conv2d<T extends Tensor3D|Tensor4D>(\n      this: T, filter: Tensor4D|TensorLike4D, strides: [number, number]|number,\n      pad: 'valid'|'same'|number, dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n      dilations: [number, number]|number = [1, 1],\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.conv2d(\n        this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n  }\n  conv2dTranspose<T extends Tensor3D|Tensor4D>(\n      this: T, filter: Tensor4D|TensorLike4D,\n      outputShape: [number, number, number, number]|[number, number, number],\n      strides: [number, number]|number, pad: 'valid'|'same'|number,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.conv2dTranspose(\n        this, filter, outputShape, strides, pad, dimRoundingMode);\n  }\n  depthwiseConv2D<T extends Tensor3D|Tensor4D>(\n      this: T, filter: Tensor4D|TensorLike4D, strides: [number, number]|number,\n      pad: 'valid'|'same'|number, dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n      dilations: [number, number]|number = [1, 1],\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.depthwiseConv2d(\n        this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n  }\n\n  separableConv2d<T extends Tensor3D|Tensor4D>(\n      this: T|TensorLike, depthwiseFilter: Tensor4D|TensorLike4D,\n      pointwiseFilter: Tensor4D|TensorLike, strides: [number, number]|number,\n      pad: 'valid'|'same', dilation: [number, number]|number = [1, 1],\n      dataFormat: 'NHWC'|'NCHW' = 'NHWC'): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.separableConv2d(\n        this, depthwiseFilter, pointwiseFilter, strides, pad, dilation,\n        dataFormat);\n  }\n\n  // Pooling.\n  avgPool<T extends Tensor3D|Tensor4D>(\n      this: T, filterSize: [number, number]|number,\n      strides: [number, number]|number, pad: 'valid'|'same'|number,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.avgPool(this, filterSize, strides, pad, dimRoundingMode);\n  }\n  maxPool<T extends Tensor3D|Tensor4D>(\n      this: T, filterSize: [number, number]|number,\n      strides: [number, number]|number, pad: 'valid'|'same'|number,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.maxPool(this, filterSize, strides, pad, dimRoundingMode);\n  }\n  localResponseNormalization<T extends Tensor3D|Tensor4D>(\n      this: T, radius = 5, bias = 1, alpha = 1, beta = 0.5): T {\n    return opHandler.localResponseNormalization(\n        this, radius, bias, alpha, beta);\n  }\n  pool<T extends Tensor3D|Tensor4D>(\n      this: T, windowShape: [number, number]|number, poolingType: 'max'|'avg',\n      padding: 'valid'|'same'|number, dilationRate?: [number, number]|number,\n      strides?: [number, number]|number): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.pool(\n        this, windowShape, poolingType, padding, dilationRate, strides);\n  }\n\n  variable(trainable = true, name?: string, dtype?: DataType): Variable<R> {\n    this.throwIfDisposed();\n    return Variable.variable(this, trainable, name, dtype);\n  }\n\n  unsortedSegmentSum<T extends Tensor>(\n      this: T, segmentIds: Tensor1D|TensorLike1D, numSegments: number): T {\n    this.throwIfDisposed();\n    return opHandler.unsortedSegmentSum(this, segmentIds, numSegments);\n  }\n\n  batchToSpaceND<T extends Tensor>(\n      this: T, blockShape: number[], crops: number[][]): T {\n    this.throwIfDisposed();\n    return opHandler.batchToSpaceND(this, blockShape, crops);\n  }\n\n  spaceToBatchND<T extends Tensor>(\n      this: T, blockShape: number[], paddings: number[][]): T {\n    this.throwIfDisposed();\n    return opHandler.spaceToBatchND(this, blockShape, paddings);\n  }\n\n  topk<T extends Tensor>(this: T, k = 1, sorted = true):\n      {values: T, indices: T} {\n    this.throwIfDisposed();\n    return opHandler.topk(this, k, sorted);\n  }\n\n  stridedSlice<T extends Tensor>(\n      this: T, begin: number[], end: number[], strides: number[], beginMask = 0,\n      endMask = 0): T {\n    this.throwIfDisposed();\n    return opHandler.stridedSlice(\n        this, begin, end, strides, beginMask, endMask);\n  }\n\n  depthToSpace(this: Tensor4D, blockSize: number, dataFormat: 'NHWC'|'NCHW'):\n      Tensor4D {\n    this.throwIfDisposed();\n    return opHandler.depthToSpace(this, blockSize, dataFormat);\n  }\n\n  fft(this: Tensor): Tensor {\n    this.throwIfDisposed();\n    return opHandler.spectral.fft(this);\n  }\n\n  ifft(this: Tensor): Tensor {\n    this.throwIfDisposed();\n    return opHandler.spectral.ifft(this);\n  }\n}\nObject.defineProperty(Tensor, Symbol.hasInstance, {\n  value: (instance: Tensor) => {\n    return !!instance && instance.shape != null && instance.dtype != null;\n  }\n});\n\nexport interface NumericTensor<R extends Rank = Rank> extends Tensor<R> {\n  dtype: NumericDataType;\n  data(): Promise<TypedArray>;\n  dataSync(): TypedArray;\n}\n\nexport interface StringTensor<R extends Rank = Rank> extends Tensor<R> {\n  dtype: 'string';\n  dataSync(): string[];\n  data(): Promise<string[]>;\n}\n\n/** @doclink Tensor */\nexport type Scalar = Tensor<Rank.R0>;\n/** @doclink Tensor */\nexport type Tensor1D = Tensor<Rank.R1>;\n/** @doclink Tensor */\nexport type Tensor2D = Tensor<Rank.R2>;\n/** @doclink Tensor */\nexport type Tensor3D = Tensor<Rank.R3>;\n/** @doclink Tensor */\nexport type Tensor4D = Tensor<Rank.R4>;\n/** @doclink Tensor */\nexport type Tensor5D = Tensor<Rank.R5>;\n/** @doclink Tensor */\nexport type Tensor6D = Tensor<Rank.R6>;\n\n/**\n * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.\n */\n/** @doc {heading: 'Tensors', subheading: 'Classes'} */\nexport class Variable<R extends Rank = Rank> extends Tensor<R> {\n  name: string;\n\n  /**\n   * Private constructor since we cannot add logic before calling `super()`.\n   * Instead, we expose static `Variable.variable` method below, which will be\n   * added to global namespace.\n   */\n  private constructor(\n      initialValue: Tensor<R>, public trainable = true, name?: string) {\n    super(\n        initialValue.shape, initialValue.dtype, null /* values */,\n        initialValue.dataId);\n    this.name = name;\n    if (this.name == null) {\n      this.name = trackerFn().nextVariableId().toString();\n    }\n    try {\n      trackerFn().registerVariable(this);\n    } catch (ex) {\n      trackerFn().disposeTensor(this);\n      throw ex;\n    }\n  }\n\n  /**\n   * Creates a new variable with the provided initial value.\n   * ```js\n   * const x = tf.variable(tf.tensor([1, 2, 3]));\n   * x.assign(tf.tensor([4, 5, 6]));\n   *\n   * x.print();\n   * ```\n   *\n   * @param initialValue Initial value for the tensor.\n   * @param trainable If true, optimizers are allowed to update it.\n   * @param name Name of the variable. Defaults to a unique id.\n   * @param dtype If set, initialValue will be converted to the given type.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Creation'} */\n  static variable<R extends Rank>(\n      initialValue: Tensor<R>, trainable = true, name?: string,\n      dtype?: DataType): Variable<R> {\n    if (dtype != null && dtype !== initialValue.dtype) {\n      initialValue = initialValue.asType(dtype) as Tensor<R>;\n    }\n    return new Variable(initialValue, trainable, name);\n  }\n\n  /**\n   * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have\n   * the same shape and dtype as the old `tf.Tensor`.\n   *\n   * @param newValue New tensor to be assigned to this variable.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  assign(newValue: Tensor<R>): void {\n    if (newValue.dtype !== this.dtype) {\n      throw new Error(\n          `dtype of the new value (${newValue.dtype}) and ` +\n          `previous value (${this.dtype}) must match`);\n    }\n    if (!util.arraysEqual(newValue.shape, this.shape)) {\n      throw new Error(\n          `shape of the new value (${newValue.shape}) and ` +\n          `previous value (${this.shape}) must match`);\n    }\n    trackerFn().disposeTensor(this);\n    this.dataId = newValue.dataId;\n    trackerFn().registerTensor(this);\n  }\n}\nObject.defineProperty(Variable, Symbol.hasInstance, {\n  value: (instance: Variable) => {\n    return instance instanceof Tensor && instance.assign != null &&\n        instance.assign instanceof Function;\n  }\n});\n\nconst variable = Variable.variable;\nexport {variable};\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from './tensor';\nimport {NamedTensorMap} from './tensor_types';\nimport * as util from './util';\n\nexport interface TapeNode {\n  id: number;\n  name: string;\n  outputs: Tensor[];\n  inputs: NamedTensorMap;\n  // Optional params, defined only for ops with gradient impl.\n  gradient?: (dy: Tensor|Tensor[]) => NamedGradientMap;\n}\n\nexport type NamedGradientMap = {\n  [inputName: string]: () => Tensor;\n};\n\n/**\n * Computes a list of TapeNodes that connect x to y, filtering everything else\n * out and preserving the order of the original tape elements.\n *\n * @param tape The tape elements to filter.\n * @param xs The input Tensors.\n * @param y The output Tensor.\n */\nexport function getFilteredNodesXToY(\n    tape: TapeNode[], xs: Tensor[], y: Tensor): TapeNode[] {\n  // Forward pass to compute all the nodes and Tensors that are transitively a\n  // function of x.\n  const tensorsFromX: {[tensorId: number]: boolean} = {};\n  const nodesFromX: {[nodeId: number]: boolean} = {};\n  for (let i = 0; i < xs.length; i++) {\n    tensorsFromX[xs[i].id] = true;\n  }\n\n  for (let i = 0; i < tape.length; i++) {\n    const node = tape[i];\n    const nodeInputs = node.inputs;\n    for (const inputName in nodeInputs) {\n      const input = nodeInputs[inputName];\n\n      let anyInputFromX = false;\n      for (let j = 0; j < xs.length; j++) {\n        if (tensorsFromX[input.id]) {\n          node.outputs.forEach(output => tensorsFromX[output.id] = true);\n          anyInputFromX = true;\n          nodesFromX[node.id] = true;\n          break;\n        }\n      }\n\n      if (anyInputFromX) {\n        break;\n      }\n    }\n  }\n\n  // Backward pass to find all of the nodes and Tensors that lead to y.\n  const tensorsLeadToY: {[tensorId: number]: boolean} = {};\n  tensorsLeadToY[y.id] = true;\n  const nodesToY: {[nodeId: number]: boolean} = {};\n\n  for (let i = tape.length - 1; i >= 0; i--) {\n    const node = tape[i];\n    const nodeInputs = node.inputs;\n\n    // If any of the outputs lead to y, mark all of the inputs as leading to y.\n    for (let j = 0; j < node.outputs.length; j++) {\n      if (tensorsLeadToY[node.outputs[j].id]) {\n        for (const inputName in nodeInputs) {\n          tensorsLeadToY[nodeInputs[inputName].id] = true;\n          nodesToY[node.id] = true;\n        }\n        break;\n      }\n    }\n  }\n\n  // Return the paths that come from x and lead to y.\n  const filteredTape: TapeNode[] = [];\n  for (let i = 0; i < tape.length; i++) {\n    const node = tape[i];\n\n    if (nodesFromX[node.id] && nodesToY[node.id]) {\n      // Prune the inputs from the node that aren't a function of x.\n      const prunedInputs: {[inputName: string]: Tensor} = {};\n      for (const inputName in node.inputs) {\n        const nodeInput = node.inputs[inputName];\n        if (tensorsFromX[nodeInput.id]) {\n          prunedInputs[inputName] = nodeInput;\n        }\n      }\n\n      // Copy the node and overwrite inputsAndArgs to the pruned version.\n      const prunedNode = Object.assign({}, node) as TapeNode;\n      prunedNode.inputs = prunedInputs;\n      prunedNode.outputs = node.outputs;\n\n      filteredTape.push(prunedNode);\n    }\n  }\n\n  return filteredTape;\n}\n\n/**\n * Backpropagate gradients through the filtered TapeNodes.\n *\n * @param tensorAccumulatedGradientMap A map of Tensor to its gradient. This map\n * is mutated by this method.\n * @param filteredTape The filtered TapeNodes to backprop through.\n */\nexport function backpropagateGradients(\n    tensorAccumulatedGradientMap: {[tensorId: number]: Tensor},\n    filteredTape: TapeNode[]) {\n  // Walk the tape backward and keep a map of Tensor to its gradient.\n  for (let i = filteredTape.length - 1; i >= 0; i--) {\n    const node = filteredTape[i];\n\n    const dys: Tensor[] = [];\n    node.outputs.forEach(o => {\n      const gradTensor = tensorAccumulatedGradientMap[o.id];\n      if (gradTensor != null) {\n        dys.push(gradTensor);\n      } else {\n        // This particular output is not in the back-propagation subgraph, so it\n        // does not affect the final output, thus we put zeros for its dy.\n        const dy = Tensor.make(\n            o.shape, {values: util.makeZerosTypedArray(o.size, o.dtype)},\n            o.dtype);\n        dys.push(dy);\n      }\n    });\n\n    if (node.gradient == null) {\n      throw new Error(\n          `Cannot compute gradient: gradient function not found ` +\n          `for ${node.name}.`);\n    }\n\n    // Backprop dy through this node and accumulate gradients over the inputs.\n    const inputGradients =\n        // Grad functions of ops with single outputs expect a dy, while ops\n        // with multiple outputs expect dys (array of dy).\n        node.gradient(node.outputs.length === 1 ? dys[0] : dys);\n    for (const inputName in node.inputs) {\n      if (!(inputName in inputGradients)) {\n        throw new Error(\n            `Cannot backprop through input ${inputName}. ` +\n            `Available gradients found: ${Object.keys(inputGradients)}.`);\n      }\n\n      // Call the gradient function.\n      const dx = inputGradients[inputName]();\n      if (dx.dtype !== 'float32') {\n        throw new Error(\n            `Error in gradient for op ${node.name}. The gradient of input ` +\n            `${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);\n      }\n      const x = node.inputs[inputName];\n      if (!util.arraysEqual(dx.shape, x.shape)) {\n        throw new Error(\n            `Error in gradient for op ${node.name}. The gradient of input ` +\n            `'${inputName}' has shape '${dx.shape}', which does not match ` +\n            `the shape of the input '${x.shape}'`);\n      }\n\n      if (tensorAccumulatedGradientMap[x.id] == null) {\n        tensorAccumulatedGradientMap[x.id] = dx;\n      } else {\n        const curGradient = tensorAccumulatedGradientMap[x.id];\n        tensorAccumulatedGradientMap[x.id] = curGradient.add(dx);\n        curGradient.dispose();\n      }\n    }\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/** @docalias number[] */\nexport interface ShapeMap {\n  R0: number[];\n  R1: [number];\n  R2: [number, number];\n  R3: [number, number, number];\n  R4: [number, number, number, number];\n  R5: [number, number, number, number, number];\n  R6: [number, number, number, number, number, number];\n}\n\nexport interface DataTypeMap {\n  float32: Float32Array;\n  int32: Int32Array;\n  bool: Uint8Array;\n  complex64: Float32Array;\n  string: string[];\n}\n\nexport interface SingleValueMap {\n  bool: boolean;\n  int32: number;\n  float32: number;\n  complex64: number;\n  string: string;\n}\n\n/** @docalias 'float32'|'int32'|'bool'|'complex64'|'string' */\nexport type DataType = keyof DataTypeMap;\nexport type NumericDataType = 'float32'|'int32'|'bool'|'complex64';\nexport type TypedArray = Float32Array|Int32Array|Uint8Array;\nexport type DataValues = DataTypeMap[DataType];\n\nexport enum Rank {\n  R0 = 'R0',\n  R1 = 'R1',\n  R2 = 'R2',\n  R3 = 'R3',\n  R4 = 'R4',\n  R5 = 'R5',\n  R6 = 'R6'\n}\n\nexport type FlatVector = boolean[]|number[]|TypedArray;\nexport type RegularArray<T> =\n    T[]|T[][]|T[][][]|T[][][][]|T[][][][][]|T[][][][][][];\n\n// tslint:disable-next-line:no-any\nexport interface RecursiveArray<T extends any> {\n  [index: number]: T|RecursiveArray<T>;\n}\n\n// Looks for upcasting types. Used, for example, in operations with mixed dtype\n// inputs.\nenum UpcastInt32AndMap {\n  'float32' = 'float32',\n  'int32' = 'int32',\n  'bool' = 'int32',\n  'complex64' = 'complex64'\n}\n\nenum UpcastBoolAndMap {\n  'float32' = 'float32',\n  'int32' = 'int32',\n  'bool' = 'bool',\n  'complex64' = 'complex64'\n}\n\nenum UpcastFloat32AndMap {\n  'float32' = 'float32',\n  'int32' = 'float32',\n  'bool' = 'float32',\n  'complex64' = 'complex64'\n}\n\nenum UpcastComplex64AndMap {\n  'float32' = 'complex64',\n  'int32' = 'complex64',\n  'bool' = 'complex64',\n  'complex64' = 'complex64'\n}\n\nconst upcastTypeMap = {\n  'float32': UpcastFloat32AndMap,\n  'int32': UpcastInt32AndMap,\n  'bool': UpcastBoolAndMap,\n  'complex64': UpcastComplex64AndMap\n};\n\nexport function upcastType(typeA: DataType, typeB: DataType): DataType {\n  if (typeA === 'string' || typeB === 'string') {\n    if (typeA === 'string' && typeB === 'string') {\n      return 'string';\n    }\n    throw new Error(`Can not upcast ${typeA} with ${typeB}`);\n  }\n  return upcastTypeMap[typeA][typeB];\n}\n\n/** Returns the output type after summation. */\nexport function sumOutType(type: DataType): DataType {\n  return upcastType(type, 'int32');\n}\n\n/** @docalias TypedArray|Array */\nexport type TensorLike =\n    TypedArray|number|boolean|string|RegularArray<number|number[]|TypedArray>|\n    RegularArray<boolean>|RegularArray<string>;\n/** @docalias TypedArray|Array */\nexport type TensorLike1D = TypedArray|number[]|boolean[]|string[];\n/** @docalias TypedArray|Array */\nexport type TensorLike2D =\n    TypedArray|number[]|number[][]|boolean[]|boolean[][]|string[]|string[][];\n/** @docalias TypedArray|Array */\nexport type TensorLike3D = TypedArray|number[]|number[][][]|boolean[]|\n    boolean[][][]|string[]|string[][][];\n/** @docalias TypedArray|Array */\nexport type TensorLike4D = TypedArray|number[]|number[][][][]|boolean[]|\n    boolean[][][][]|string[]|string[][][][];\n/** @docalias TypedArray|Array */\nexport type TensorLike5D = TypedArray|number[]|number[][][][][]|boolean[]|\n    boolean[][][][][]|string[]|string[][][][][];\n/** @docalias TypedArray|Array */\nexport type TensorLike6D = TypedArray|number[]|number[][][][][][]|boolean[]|\n    boolean[][][][][][]|string[]|string[][][][][][];\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from './tensor';\nimport {NamedTensorMap, TensorContainer, TensorContainerArray} from './tensor_types';\nimport {upcastType} from './types';\nimport {assert} from './util';\n\nexport function makeTypesMatch<T extends Tensor>(a: T, b: T): [T, T] {\n  if (a.dtype === b.dtype) {\n    return [a, b];\n  }\n  const dtype = upcastType(a.dtype, b.dtype);\n  return [a.cast(dtype), b.cast(dtype)];\n}\n\nexport function assertTypesMatch(a: Tensor, b: Tensor): void {\n  assert(\n      a.dtype === b.dtype,\n      `The dtypes of the first(${a.dtype}) and` +\n          ` second(${b.dtype}) input must match`);\n}\n\nexport function isTensorInList(tensor: Tensor, tensorList: Tensor[]): boolean {\n  for (let i = 0; i < tensorList.length; i++) {\n    if (tensorList[i].id === tensor.id) {\n      return true;\n    }\n  }\n  return false;\n}\n\nexport function flattenNameArrayMap(\n    nameArrayMap: Tensor|NamedTensorMap, keys?: string[]): Tensor[] {\n  const xs: Tensor[] = [];\n  if (nameArrayMap instanceof Tensor) {\n    xs.push(nameArrayMap);\n  } else {\n    const xMap = nameArrayMap as {[xName: string]: Tensor};\n    for (let i = 0; i < keys.length; i++) {\n      xs.push(xMap[keys[i]]);\n    }\n  }\n  return xs;\n}\n\nexport function unflattenToNameArrayMap(\n    keys: string[], flatArrays: Tensor[]): NamedTensorMap {\n  if (keys.length !== flatArrays.length) {\n    throw new Error(\n        `Cannot unflatten Tensor[], keys and arrays are not of same length.`);\n  }\n  const result: NamedTensorMap = {};\n  for (let i = 0; i < keys.length; i++) {\n    result[keys[i]] = flatArrays[i];\n  }\n  return result;\n}\n\n/**\n * Extracts any `Tensor`s found within the provided object.\n *\n * @param container an object that may be a `Tensor` or may directly contain\n *   `Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. In general it\n *   is safe to pass any object here, except that `Promise`s are not\n *   supported.\n * @returns An array of `Tensors` found within the passed object. If the\n *   argument is simply a `Tensor', a list containing that `Tensor` is\n *   returned. If the object is not a `Tensor` or does not\n *   contain `Tensors`, an empty list is returned.\n */\nexport function getTensorsInContainer(result: TensorContainer): Tensor[] {\n  const list: Tensor[] = [];\n  const seen = new Set<{}|void>();\n  walkTensorContainer(result, list, seen);\n  return list;\n}\n\nfunction walkTensorContainer(\n    container: TensorContainer, list: Tensor[], seen: Set<{}|void>): void {\n  if (container == null) {\n    return;\n  }\n  if (container instanceof Tensor) {\n    list.push(container);\n    return;\n  }\n  if (!isIterable(container)) {\n    return;\n  }\n  // Iteration over keys works also for arrays.\n  const iterable = container as TensorContainerArray;\n  for (const k in iterable) {\n    const val = iterable[k];\n    if (!seen.has(val)) {\n      seen.add(val);\n      walkTensorContainer(val, list, seen);\n    }\n  }\n}\n\n// tslint:disable-next-line:no-any\nfunction isIterable(obj: any): boolean {\n  return Array.isArray(obj) || typeof obj === 'object';\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {BackendTimingInfo, DataMover, KernelBackend} from './kernels/backend';\nimport {Profiler} from './profiler';\nimport {backpropagateGradients, getFilteredNodesXToY, NamedGradientMap, TapeNode} from './tape';\nimport {DataId, Tensor, Tensor3D, Variable} from './tensor';\nimport {NamedTensorMap, NamedVariableMap, TensorContainer} from './tensor_types';\nimport {getTensorsInContainer, isTensorInList} from './tensor_util';\nimport {DataType, DataValues} from './types';\nimport * as util from './util';\nimport {bytesFromStringArray, makeOnesTypedArray, now, sizeFromShape} from './util';\n\n/**\n * A function that computes an output. The save function is for saving tensors\n * computed in the forward pass, that we need in the backward pass.\n */\nexport type ForwardFunc<T> =\n    (backend: KernelBackend, save?: <S extends Tensor>(tensor: S) => S) => T;\n\n/**\n * @docalias (a: Tensor, b: Tensor,...) => {\n * value: Tensor, * gradFunc: (dy: Tensor) => Tensor | Tensor[] * }\n */\nexport type CustomGradientFunc<T extends Tensor> = (...args: Tensor[]) => {\n  value: T, gradFunc: (dy: T) => Tensor | Tensor[];\n};\n\nexport type MemoryInfo = {\n  numTensors: number; numDataBuffers: number; numBytes: number;\n  unreliable?: boolean; reasons: string[];\n};\n\ntype KernelProfile = {\n  name: string; bytesAdded: number; totalBytesSnapshot: number;\n  tensorsAdded: number;\n  totalTensorsSnapshot: number;\n  inputShapes: number[][];\n  outputShape: number[] | number[][];\n};\n\nexport type ProfileInfo = {\n  newBytes: number; newTensors: number; peakBytes: number;\n  kernels: KernelProfile[];\n  result: TensorContainer;\n};\n\nexport interface TimingInfo extends BackendTimingInfo {\n  wallMs: number;\n}\n\n/** @docalias Function */\nexport type ScopeFn<T extends TensorContainer> = () => T;\n\nexport interface TensorManager {\n  registerTensor(a: Tensor): void;\n  registerVariable(v: Variable): void;\n  disposeTensor(a: Tensor): void;\n  memory(): {numDataBuffers: number; numBytes: number;};\n}\n\ninterface ScopeState {\n  track: Tensor[];\n  name: string;\n}\n\nexport class Engine implements TensorManager, DataMover {\n  // Public since optimizers will use it.\n  registeredVariables: NamedVariableMap = {};\n\n  private nextTapeNodeId = 0;\n  private numBytes = 0;\n  private numTensors = 0;\n  private numStringTensors = 0;\n  private numDataBuffers = 0;\n\n  private profiling = false;\n  private activeProfile: ProfileInfo;\n\n  private activeTape: TapeNode[];\n  private gradientScopeCount = 0;\n  private customGradientDepth = 0;\n\n  // Keep Tensors that parallel the tapes.\n  private activeScope: ScopeState;\n  private scopeStack: ScopeState[] = [];\n  private keepTensors: Set<number> = new Set();\n  private profiler: Profiler;\n\n  private tensorInfo = new WeakMap<DataId, {\n    backend: KernelBackend,\n    bytes: number,\n    dtype: DataType,\n    shape: number[],\n    refCount: number\n  }>();\n\n  constructor(\n      public backend: KernelBackend, public safeMode: boolean,\n      private debugMode: () => boolean) {\n    this.profiler = new Profiler(backend);\n    this.activeProfile =\n        {newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null};\n  }\n\n  moveData(dataId: DataId) {\n    this.write(dataId, this.readSync(dataId));\n  }\n\n  tidy<T extends TensorContainer>(\n      nameOrFn: string|ScopeFn<T>, fn?: ScopeFn<T>, gradMode = false): T {\n    // gradMode Primarily for internal use during backprop\n    //          If true, will start a tape if it is the outermost tidy.\n\n    let name: string = null;\n    if (fn == null) {\n      // Called with only 1 argument.\n      if (typeof nameOrFn !== 'function') {\n        throw new Error('Please provide a function to tidy()');\n      }\n      fn = nameOrFn;\n    } else {\n      // Called with 2 arguments.\n      if (typeof nameOrFn !== 'string' && !(nameOrFn instanceof String)) {\n        throw new Error(\n            'When calling with two arguments, the first argument ' +\n            'to tidy() must be a string');\n      }\n      if (typeof fn !== 'function') {\n        throw new Error(\n            'When calling with two arguments, the 2nd argument ' +\n            'to tidy() must be a function');\n      }\n      name = nameOrFn as string;\n      // TODO(nsthorat,smilkov): Do operation logging and performance\n      // profiling.\n    }\n    let result: T;\n    return this.scopedRun(\n        () => this.startScope(name, gradMode),\n        () => this.endScope(result, gradMode), () => {\n          result = fn();\n          if (result instanceof Promise) {\n            console.error('Cannot return a Promise inside of tidy.');\n          }\n          return result;\n        });\n  }\n\n  private scopedRun<T>(start: () => void, end: () => void, f: () => T): T {\n    start();\n    try {\n      const res = f();\n      end();\n      return res;\n    } catch (ex) {\n      end();\n      throw ex;\n    }\n  }\n\n  private static nextTensorId = 0;\n  nextTensorId(): number {\n    return Engine.nextTensorId++;\n  }\n\n  private static nextVariableId = 0;\n  nextVariableId(): number {\n    return Engine.nextVariableId++;\n  }\n\n  runKernel<T extends Tensor|Tensor[], I extends NamedTensorMap>(\n      forwardFunc: ForwardFunc<T>,\n      inputs: I,\n      backwardsFunc?: (dy: T, saved: Tensor[]) => {[P in keyof I]: () => I[P]},\n      ): T {\n    let result: T;\n    const saved: Tensor[] = [];\n    const saveFunc = <T extends Tensor>(x: T): T => {\n      saved.push(x);\n      return x;\n    };\n    const scopeName = this.activeScope.name;\n    const startingBytecount = this.numBytes;\n    const startingNumTensors = this.numTensors;\n\n    // Stop recording to a tape when running a kernel.\n    this.scopedRun(\n        () => this.customGradientDepth++, () => this.customGradientDepth--,\n        () => {\n          if (!this.debugMode()) {\n            result = forwardFunc(this.backend, saveFunc);\n          } else {\n            result = this.profiler.profileKernel(\n                scopeName, () => forwardFunc(this.backend, saveFunc));\n          }\n        });\n\n    if (this.shouldRecord()) {\n      const tapeNode: TapeNode = {\n        id: this.nextTapeNodeId++,\n        name: scopeName,\n        inputs,\n        outputs: Array.isArray(result) ? result : [result] as Tensor[]\n      };\n      if (backwardsFunc != null) {\n        tapeNode.gradient =\n            ((dy: T) => backwardsFunc(dy, saved)) as (dy: Tensor) =>\n                NamedGradientMap;\n      }\n      this.activeTape.push(tapeNode);\n    }\n\n    if (this.profiling) {\n      this.activeProfile.kernels.push({\n        name: scopeName,\n        bytesAdded: this.numBytes - startingBytecount,\n        totalBytesSnapshot: this.numBytes,\n        tensorsAdded: this.numTensors - startingNumTensors,\n        totalTensorsSnapshot: this.numTensors,\n        inputShapes: Object.keys(inputs).map(key => inputs[key].shape),\n        outputShape: Array.isArray(result) ?\n            (result as Tensor[]).map(item => (item as Tensor).shape) :\n            (result as Tensor).shape\n      });\n    }\n\n    return result;\n  }\n\n  // TensorManager implementation.\n\n  registerTensor(a: Tensor|Variable): void {\n    const refCount = this.tensorInfo.has(a.dataId) ?\n        this.tensorInfo.get(a.dataId).refCount :\n        0;\n    this.numTensors++;\n    if (a.dtype === 'string') {\n      this.numStringTensors++;\n    }\n    if (refCount === 0) {\n      this.numDataBuffers++;\n\n      // Bytes for complex numbers are counted by their components. Bytes for\n      // string tensors are counted when writing values.\n      let bytes = 0;\n      if (a.dtype !== 'complex64' && a.dtype !== 'string') {\n        bytes = util.sizeFromShape(a.shape) * util.bytesPerElement(a.dtype);\n      }\n      this.tensorInfo.set(a.dataId, {\n        backend: this.backend,\n        dtype: a.dtype,\n        shape: a.shape,\n        bytes,\n        refCount: 0\n      });\n      this.numBytes += bytes;\n      this.backend.register(a.dataId, a.shape, a.dtype);\n    }\n    this.tensorInfo.get(a.dataId).refCount++;\n    if (!(a instanceof Variable)) {\n      this.track(a);\n    }\n  }\n\n  registerVariable(v: Variable) {\n    if (this.registeredVariables[v.name] != null) {\n      throw new Error(`Variable with name ${v.name} was already registered`);\n    }\n    this.registeredVariables[v.name] = v;\n  }\n\n  disposeTensor(a: Tensor): void {\n    if (!this.tensorInfo.has(a.dataId)) {\n      return;\n    }\n    if (this.keepTensors.has(a.id)) {\n      this.keepTensors.delete(a.id);\n    }\n    this.numTensors--;\n    if (a.dtype === 'string') {\n      this.numStringTensors--;\n    }\n    const info = this.tensorInfo.get(a.dataId);\n    const refCount = info.refCount;\n    if (refCount <= 1) {\n      // Don't count bytes for complex numbers as they are counted by their\n      // components.\n      if (a.dtype !== 'complex64') {\n        this.numBytes -= info.bytes;\n      }\n      this.numDataBuffers--;\n      info.backend.disposeData(a.dataId);\n      this.tensorInfo.delete(a.dataId);\n    } else {\n      this.tensorInfo.get(a.dataId).refCount--;\n    }\n    // TODO(nsthorat): Construct an error and save the stack trace for\n    // debugging when in debug mode. Creating a stack trace is too expensive\n    // to do unconditionally.\n  }\n\n  disposeVariables(): void {\n    for (const varName in this.registeredVariables) {\n      const v = this.registeredVariables[varName];\n      this.disposeTensor(v);\n      delete this.registeredVariables[varName];\n    }\n  }\n\n  memory(): MemoryInfo {\n    const info = this.backend.memory() as MemoryInfo;\n    info.numTensors = this.numTensors;\n    info.numDataBuffers = this.numDataBuffers;\n    info.numBytes = this.numBytes;\n    if (this.numStringTensors > 0) {\n      info.unreliable = true;\n      if (info.reasons == null) {\n        info.reasons = [];\n      }\n      info.reasons.push(\n          'Memory usage by string tensors is approximate ' +\n          '(2 bytes per character)');\n    }\n    return info;\n  }\n\n  async profile(query: () => TensorContainer): Promise<ProfileInfo> {\n    this.profiling = true;\n\n    const startBytes = this.numBytes;\n    const startNumTensors = this.numTensors;\n\n    this.activeProfile.kernels = [];\n    this.activeProfile.result = query();\n\n    this.profiling = false;\n\n    this.activeProfile.peakBytes =\n        Math.max(...this.activeProfile.kernels.map(d => d.totalBytesSnapshot));\n    this.activeProfile.newBytes = this.numBytes - startBytes;\n    this.activeProfile.newTensors = this.numTensors - startNumTensors;\n    return this.activeProfile;\n  }\n\n  private shouldRecord(): boolean {\n    return this.activeTape != null && this.customGradientDepth === 0;\n  }\n\n  private addTapeNode(\n      inputs: Tensor[], result: Tensor,\n      gradientsFunc: (dy: Tensor) => Tensor[]): void {\n    const inputsMap: NamedTensorMap = {};\n    inputs.forEach((input, idx) => {\n      inputsMap[idx] = input;\n    });\n\n    const gradient = (dy: Tensor) => {\n      const res = gradientsFunc(dy);\n      const resMap: NamedGradientMap = {};\n      res.forEach((r, idx) => {\n        resMap[idx] = () => r;\n      });\n      return resMap;\n    };\n\n    const tapeNode: TapeNode = {\n      id: this.nextTapeNodeId++,\n      name: this.activeScope.name,\n      inputs: inputsMap,\n      outputs: [result],\n      gradient\n    };\n    this.activeTape.push(tapeNode);\n  }\n\n  keep<T extends Tensor>(result: T): T {\n    if (this.scopeStack.length === 1 && this.safeMode) {\n      throw new Error(\n          'Safe mode is ON. Enclose all tensor operations inside tf.tidy(): ' +\n          'tf.tidy(() => {...}) to avoid memory leaks.');\n    }\n    this.keepTensors.add(result.id);\n    return result;\n  }\n\n  /**\n   * Start a scope. Use this with endScope() to achieve the same functionality\n   * as scope() without the need for a function closure.\n   */\n  startScope(name?: string, gradientsMode = false) {\n    if (gradientsMode && this.gradientScopeCount === 0) {\n      this.activeTape = [];\n    }\n    if (gradientsMode) {\n      this.gradientScopeCount++;\n    }\n\n    const scopeInfo: ScopeState = {track: [], name: 'unnamed scope'};\n    if (name) {\n      scopeInfo.name = name;\n    }\n    this.scopeStack.push(scopeInfo);\n    this.activeScope = scopeInfo;\n  }\n\n  /**\n   * End a scope. Use this with startScope() to achieve the same functionality\n   * as scope() without the need for a function closure.\n   */\n  endScope(result?: TensorContainer, gradientsMode = false) {\n    if (gradientsMode) {\n      this.gradientScopeCount--;\n      if (this.gradientScopeCount === 0) {\n        this.activeTape = null;\n      }\n    }\n\n    const tensorsToKeep = new Set(this.keepTensors);\n\n    const tensorsToTrackInParent = getTensorsInContainer(result);\n    tensorsToTrackInParent.forEach(tensor => tensorsToKeep.add(tensor.id));\n\n    // Dispose the arrays tracked in this scope.\n    for (let i = 0; i < this.activeScope.track.length; i++) {\n      const tensor = this.activeScope.track[i];\n      if (tensorsToKeep.has(tensor.id)) {\n        continue;\n      }\n\n      if (this.activeTape != null) {\n        tensorsToTrackInParent.push(tensor);\n      } else {\n        tensor.dispose();\n      }\n    }\n\n    const oldScope = this.scopeStack.pop();\n    this.activeScope = this.scopeStack.length === 0 ?\n        null :\n        this.scopeStack[this.scopeStack.length - 1];\n\n    // Track the current result in the parent scope.\n    tensorsToTrackInParent.forEach(tensor => {\n      // Only track the tensor if was allocated in the inner scope and is not\n      // globally kept.\n      if (!this.keepTensors.has(tensor.id) &&\n          isTensorInList(tensor, oldScope.track)) {\n        this.track(tensor);\n      }\n    });\n  }\n\n  /**\n   * Returns gradients of `f` with respect to each of the `xs`. The gradients\n   * returned are of the same length as `xs`, but some might be null if `f` was\n   * not a function of that `x`. It also takes optional dy to multiply the\n   * gradient, which defaults to `1`.\n   */\n  gradients<T extends Tensor>(\n      f: () => T, xs: Tensor[], dy?: T,\n      allowNoGradients = false): {value: T, grads: Tensor[]} {\n    util.assert(xs.length > 0, 'gradients() received an empty list of xs.');\n    if (dy != null && dy.dtype !== 'float32') {\n      throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);\n    }\n\n    return this.tidy('gradients', () => {\n      const y = f();\n      util.assert(\n          y instanceof Tensor,\n          'The result y returned by f() must be a tensor.');\n      // Filter out the nodes that don't connect x => y.\n      const filteredTape = getFilteredNodesXToY(this.activeTape, xs, y);\n      if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {\n        throw new Error(\n            'Cannot compute gradient of y=f(x) with respect to x. Make sure ' +\n            'that the f you passed encloses all operations that lead from x ' +\n            'to y.');\n      }\n\n      const accumulatedGradientMap: {[tensorId: number]: Tensor} = {};\n      accumulatedGradientMap[y.id] = (dy == null) ? ones(y.shape) : dy;\n\n      // Backprop gradients through the filtered nodes.\n      backpropagateGradients(accumulatedGradientMap, filteredTape);\n\n      const grads = xs.map(x => accumulatedGradientMap[x.id]);\n      return {value: y, grads};\n    }, true /* gradientsMode */);\n  }\n\n  customGrad<T extends Tensor>(f: CustomGradientFunc<T>):\n      (...args: Tensor[]) => T {\n    util.assert(\n        util.isFunction(f),\n        'The f passed in customGrad(f) must be a function.');\n    return (...inputs: Tensor[]): T => {\n      util.assert(\n          inputs.every(t => t instanceof Tensor),\n          'The args passed in customGrad(f)(x1, x2,...) must all be tensors');\n\n      let gradientsFunc: (dy: T) => Tensor | Tensor[];\n      let result: T;\n      this.scopedRun(\n          () => this.customGradientDepth++, () => this.customGradientDepth--,\n          () => {\n            const gradientsMode = true;\n            result = this.tidy(f.name, () => {\n              const {value, gradFunc} = f(...inputs);\n              util.assert(\n                  value instanceof Tensor,\n                  'The function f passed in customGrad(f) must return an ' +\n                      'object where `obj.value` is a tensor');\n              util.assert(\n                  util.isFunction(gradFunc),\n                  'The function f passed in customGrad(f) must return an ' +\n                      'object where `obj.gradFunc` is a function.');\n              gradientsFunc = gradFunc;\n              return value;\n            }, gradientsMode);\n          });\n\n      if (this.shouldRecord()) {\n        const gradFunc = (dy: T): Tensor[] => {\n          const res = gradientsFunc(dy);\n          const grads: Tensor[] = Array.isArray(res) ? res : [res];\n          util.assert(\n              grads.length === inputs.length,\n              'The function f passed in customGrad(f) must return an object ' +\n                  'where `obj.gradFunc` is a function that returns the same ' +\n                  'number of tensors as inputs passed to f(...).');\n          util.assert(\n              grads.every(t => t instanceof Tensor),\n              'The function f passed in customGrad(f) must return an object ' +\n                  'where `obj.gradFunc` is a function that returns a list of ' +\n                  'only tensors.');\n          return grads;\n        };\n        this.addTapeNode(inputs, result, gradFunc);\n      }\n      return result;\n    };\n  }\n\n  // Forwarding to backend.\n  write(dataId: DataId, values: DataValues): void {\n    const info = this.tensorInfo.get(dataId);\n    // Bytes for string tensors are counted when writing.\n    if (info.dtype === 'string') {\n      const newBytes = bytesFromStringArray(values as string[]);\n      this.numBytes += newBytes - info.bytes;\n      info.bytes = newBytes;\n    }\n\n    if (this.backend !== info.backend) {\n      // Delete the tensor from the old backend and move it to the new backend.\n      info.backend.disposeData(dataId);\n      info.backend = this.backend;\n      this.backend.register(dataId, info.shape, info.dtype);\n    }\n    this.backend.write(dataId, values);\n  }\n  readSync(dataId: DataId): DataValues {\n    // Route the read to the correct backend.\n    const info = this.tensorInfo.get(dataId);\n    return info.backend.readSync(dataId);\n  }\n  read(dataId: DataId): Promise<DataValues> {\n    // Route the read to the correct backend.\n    const info = this.tensorInfo.get(dataId);\n    return info.backend.read(dataId);\n  }\n  fromPixels(\n      pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement,\n      numChannels: number): Tensor3D {\n    return this.backend.fromPixels(pixels, numChannels);\n  }\n  async time(query: () => void): Promise<TimingInfo> {\n    const start = now();\n    const timingInfo = await this.backend.time(query) as TimingInfo;\n    timingInfo.wallMs = now() - start;\n    return timingInfo;\n  }\n\n  /**\n   * Tracks a Tensor in the current scope to be automatically cleaned up\n   * when the current scope ends, and returns the value.\n   *\n   * @param result The Tensor to track in the current scope.\n   */\n  private track<T extends Tensor>(result: T): T {\n    if (this.scopeStack.length === 1 && this.safeMode) {\n      throw new Error(\n          'Safe mode is ON. Enclose all tensor operations inside tf.tidy(): ' +\n          'tf.tidy(() => {op();...}); to avoid memory leaks.');\n    }\n    if (this.activeScope != null) {\n      this.activeScope.track.push(result);\n    }\n    return result;\n  }\n}\n\nfunction ones(shape: number[]): Tensor {\n  const values = makeOnesTypedArray(sizeFromShape(shape), 'float32');\n  return Tensor.make(shape, {values});\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getWebGLContext} from './canvas_util';\n\nexport interface Features {\n  // Whether to enable debug mode.\n  'DEBUG'?: boolean;\n  // Whether we are in a browser (as versus, say, node.js) environment.\n  'IS_BROWSER'?: boolean;\n  // Whether we are in the Node.js environment.\n  'IS_NODE'?: boolean;\n  // Whether packed WebGL kernels lazily unpack their outputs.\n  'WEBGL_LAZILY_UNPACK'?: boolean;\n  // Whether the WebGL backend will sometimes forward ops to the CPU.\n  'WEBGL_CPU_FORWARD'?: boolean;\n  // Whether to turn all packing related flags on.\n  'WEBGL_PACK'?: boolean;\n  // Whether we will pack the batchnormalization op.\n  'WEBGL_PACK_BATCHNORMALIZATION'?: boolean;\n  // Whether we will pack the clipping op.\n  'WEBGL_PACK_CLIP'?: boolean;\n  // Whether we pack the depthwise convolution op.\n  'WEBGL_PACK_DEPTHWISECONV'?: boolean;\n  // Whether we will use the im2col algorithm to speed up convolutions.\n  'WEBGL_CONV_IM2COL'?: boolean;\n  // Whether we will perform memory paging.\n  'WEBGL_PAGING_ENABLED'?: boolean;\n  // The maximum texture dimension.\n  'WEBGL_MAX_TEXTURE_SIZE'?: number;\n  // The disjoint_query_timer extension version.\n  // 0: disabled, 1: EXT_disjoint_timer_query, 2:\n  // EXT_disjoint_timer_query_webgl2.\n  // In Firefox with WebGL 2.0,\n  // EXT_disjoint_timer_query_webgl2 is not available, so we must use the\n  // WebGL 1.0 extension.\n  'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'?: number;\n  // Whether the timer object from the disjoint_query_timer extension gives\n  // timing information that is reliable.\n  'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE'?: boolean;\n  // 0: No WebGL, 1: WebGL 1.0, 2: WebGL 2.0.\n  'WEBGL_VERSION'?: number;\n  // True if WebGL is supported.\n  'HAS_WEBGL'?: boolean;\n  // Whether rendering to float32 textures is enabled. If disabled, renders to\n  // float16 textures.\n  'WEBGL_RENDER_FLOAT32_ENABLED'?: boolean;\n  // Whether downloading float textures is enabled. If disabled, uses IEEE 754\n  // encoding of the float32 values to 4 uint8 when downloading.\n  'WEBGL_DOWNLOAD_FLOAT_ENABLED'?: boolean;\n  // Whether the fence API is available.\n  'WEBGL_FENCE_API_ENABLED'?: boolean;\n  // Tensors with size <= than this will be uploaded as uniforms, not textures.\n  'WEBGL_SIZE_UPLOAD_UNIFORM'?: number;\n  'BACKEND'?: string;\n  // Test precision for unit tests. This is decreased when we can't render\n  // float32 textures.\n  'TEST_EPSILON'?: number;\n  'IS_CHROME'?: boolean;\n  // True if running unit tests.\n  'IS_TEST'?: boolean;\n  // Smallest positive value used to make ops like division and log numerically\n  // stable.\n  'EPSILON'?: number;\n  // True when the environment is \"production\" where we disable safety checks\n  // to gain performance.\n  'PROD'?: boolean;\n  // Whether to do sanity checks when inferring a shape from user-provided\n  // values, used when creating a new tensor.\n  'TENSORLIKE_CHECK_SHAPE_CONSISTENCY'?: boolean;\n}\n\nexport enum Type {\n  NUMBER,\n  BOOLEAN,\n  STRING\n}\n\nexport const URL_PROPERTIES: URLProperty[] = [\n  {name: 'DEBUG', type: Type.BOOLEAN},\n  {name: 'IS_BROWSER', type: Type.BOOLEAN},\n  {name: 'WEBGL_LAZILY_UNPACK', type: Type.BOOLEAN},\n  {name: 'WEBGL_CPU_FORWARD', type: Type.BOOLEAN},\n  {name: 'WEBGL_PACK', type: Type.BOOLEAN},\n  {name: 'WEBGL_PACK_BATCHNORMALIZATION', type: Type.BOOLEAN},\n  {name: 'WEBGL_PACK_CLIP', type: Type.BOOLEAN},\n  {name: 'WEBGL_PACK_DEPTHWISECONV', type: Type.BOOLEAN},\n  {name: 'WEBGL_CONV_IM2COL', type: Type.BOOLEAN},\n  {name: 'WEBGL_MAX_TEXTURE_SIZE', type: Type.NUMBER},\n  {name: 'WEBGL_PAGING_ENABLED', type: Type.BOOLEAN},\n  {name: 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION', type: Type.NUMBER},\n  {name: 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE', type: Type.BOOLEAN},\n  {name: 'WEBGL_VERSION', type: Type.NUMBER},\n  {name: 'WEBGL_RENDER_FLOAT32_ENABLED', type: Type.BOOLEAN},\n  {name: 'WEBGL_DOWNLOAD_FLOAT_ENABLED', type: Type.BOOLEAN},\n  {name: 'WEBGL_FENCE_API_ENABLED', type: Type.BOOLEAN},\n  {name: 'WEBGL_SIZE_UPLOAD_UNIFORM', type: Type.NUMBER},\n  {name: 'BACKEND', type: Type.STRING},\n  {name: 'EPSILON', type: Type.NUMBER},\n  {name: 'PROD', type: Type.BOOLEAN},\n  {name: 'TENSORLIKE_CHECK_SHAPE_CONSISTENCY', type: Type.BOOLEAN},\n];\n\nexport interface URLProperty {\n  name: keyof Features;\n  type: Type;\n}\n\nexport function isWebGLVersionEnabled(webGLVersion: 1|2) {\n  try {\n    const gl = getWebGLContext(webGLVersion);\n    if (gl != null) {\n      return true;\n    }\n  } catch (e) {\n    return false;\n  }\n  return false;\n}\n\nlet MAX_TEXTURE_SIZE: number;\n// Caching MAX_TEXTURE_SIZE here because the environment gets reset between\n// unit tests and we don't want to constantly query the WebGLContext for\n// MAX_TEXTURE_SIZE.\nexport function getWebGLMaxTextureSize(webGLVersion: number): number {\n  if (MAX_TEXTURE_SIZE == null) {\n    const gl = getWebGLContext(webGLVersion);\n    MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);\n  }\n  return MAX_TEXTURE_SIZE;\n}\n\nexport function getWebGLDisjointQueryTimerVersion(webGLVersion: number):\n    number {\n  if (webGLVersion === 0) {\n    return 0;\n  }\n\n  let queryTimerVersion: number;\n  const gl = getWebGLContext(webGLVersion);\n\n  if (hasExtension(gl, 'EXT_disjoint_timer_query_webgl2') &&\n      webGLVersion === 2) {\n    queryTimerVersion = 2;\n  } else if (hasExtension(gl, 'EXT_disjoint_timer_query')) {\n    queryTimerVersion = 1;\n  } else {\n    queryTimerVersion = 0;\n  }\n  return queryTimerVersion;\n}\n\nexport function isRenderToFloatTextureEnabled(webGLVersion: number): boolean {\n  if (webGLVersion === 0) {\n    return false;\n  }\n\n  const gl = getWebGLContext(webGLVersion);\n\n  if (webGLVersion === 1) {\n    if (!hasExtension(gl, 'OES_texture_float')) {\n      return false;\n    }\n  } else {\n    if (!hasExtension(gl, 'EXT_color_buffer_float')) {\n      return false;\n    }\n  }\n\n  const isFrameBufferComplete =\n      createFloatTextureAndBindToFramebuffer(gl, webGLVersion);\n  return isFrameBufferComplete;\n}\n\nexport function isDownloadFloatTextureEnabled(webGLVersion: number): boolean {\n  if (webGLVersion === 0) {\n    return false;\n  }\n\n  const gl = getWebGLContext(webGLVersion);\n\n  if (webGLVersion === 1) {\n    if (!hasExtension(gl, 'OES_texture_float')) {\n      return false;\n    }\n    if (!hasExtension(gl, 'WEBGL_color_buffer_float')) {\n      return false;\n    }\n  } else {\n    if (!hasExtension(gl, 'EXT_color_buffer_float')) {\n      return false;\n    }\n  }\n\n  const isFrameBufferComplete =\n      createFloatTextureAndBindToFramebuffer(gl, webGLVersion);\n  return isFrameBufferComplete;\n}\n\nexport function isWebGLFenceEnabled(webGLVersion: number) {\n  if (webGLVersion !== 2) {\n    return false;\n  }\n  const gl = getWebGLContext(webGLVersion);\n\n  // tslint:disable-next-line:no-any\n  const isEnabled = (gl as any).fenceSync != null;\n  return isEnabled;\n}\n\nexport function isChrome() {\n  return typeof navigator !== 'undefined' && navigator != null &&\n      navigator.userAgent != null && /Chrome/.test(navigator.userAgent) &&\n      /Google Inc/.test(navigator.vendor);\n}\n\n// Expects flags from URL in the format ?tfjsflags=FLAG1:1,FLAG2:true.\nconst TENSORFLOWJS_FLAGS_PREFIX = 'tfjsflags';\nexport function getFeaturesFromURL(): Features {\n  const features: Features = {};\n\n  if (typeof window === 'undefined' || typeof window.location === 'undefined' ||\n      typeof window.location.search === 'undefined') {\n    return features;\n  }\n\n  const urlParams = getQueryParams(window.location.search);\n  if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {\n    const urlFlags: {[key: string]: string} = {};\n\n    const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(',');\n    keyValues.forEach(keyValue => {\n      const [key, value] = keyValue.split(':') as [string, string];\n      urlFlags[key] = value;\n    });\n\n    URL_PROPERTIES.forEach(urlProperty => {\n      if (urlProperty.name in urlFlags) {\n        console.log(\n            `Setting feature override from URL ${urlProperty.name}: ` +\n            `${urlFlags[urlProperty.name]}`);\n        if (urlProperty.type === Type.NUMBER) {\n          features[urlProperty.name] = +urlFlags[urlProperty.name];\n        } else if (urlProperty.type === Type.BOOLEAN) {\n          features[urlProperty.name] = urlFlags[urlProperty.name] === 'true';\n        } else if (urlProperty.type === Type.STRING) {\n          // tslint:disable-next-line:no-any\n          features[urlProperty.name] = urlFlags[urlProperty.name] as any;\n        } else {\n          console.warn(`Unknown URL param: ${urlProperty.name}.`);\n        }\n      }\n    });\n  }\n\n  return features;\n}\n\nfunction hasExtension(gl: WebGLRenderingContext, extensionName: string) {\n  const ext = gl.getExtension(extensionName);\n  return ext != null;\n}\n\nfunction createFloatTextureAndBindToFramebuffer(\n    gl: WebGLRenderingContext, webGLVersion: number): boolean {\n  const frameBuffer = gl.createFramebuffer();\n  const texture = gl.createTexture();\n\n  gl.bindTexture(gl.TEXTURE_2D, texture);\n\n  // tslint:disable-next-line:no-any\n  const internalFormat = webGLVersion === 2 ? (gl as any).RGBA32F : gl.RGBA;\n  gl.texImage2D(\n      gl.TEXTURE_2D, 0, internalFormat, 1, 1, 0, gl.RGBA, gl.FLOAT, null);\n\n  gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n  gl.framebufferTexture2D(\n      gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n\n  const isFrameBufferComplete =\n      gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n\n  gl.bindTexture(gl.TEXTURE_2D, null);\n  gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n  gl.deleteTexture(texture);\n  gl.deleteFramebuffer(frameBuffer);\n\n  return isFrameBufferComplete;\n}\n\nexport function getQueryParams(queryString: string): {[key: string]: string} {\n  const params = {};\n  queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {\n    decodeParam(params, t[0], t[1]);\n    return t.join('=');\n  });\n  return params;\n}\n\nfunction decodeParam(\n    params: {[key: string]: string}, name: string, value?: string) {\n  params[decodeURIComponent(name)] = decodeURIComponent(value || '');\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as device_util from './device_util';\nimport {Engine, MemoryInfo, ProfileInfo, ScopeFn, TimingInfo} from './engine';\nimport {Features, getFeaturesFromURL, getWebGLDisjointQueryTimerVersion, getWebGLMaxTextureSize, isChrome, isDownloadFloatTextureEnabled, isRenderToFloatTextureEnabled, isWebGLFenceEnabled, isWebGLVersionEnabled} from './environment_util';\nimport {KernelBackend} from './kernels/backend';\nimport {DataId, setTensorTracker, Tensor, TensorTracker} from './tensor';\nimport {TensorContainer} from './tensor_types';\nimport {getTensorsInContainer} from './tensor_util';\n\nexport const EPSILON_FLOAT16 = 1e-4;\nconst TEST_EPSILON_FLOAT16 = 1e-1;\n\nexport const EPSILON_FLOAT32 = 1e-7;\nconst TEST_EPSILON_FLOAT32 = 1e-3;\n\nexport class Environment {\n  private features: Features = {};\n  private globalEngine: Engine;\n  private registry:\n      {[id: string]: {backend: KernelBackend, priority: number}} = {};\n  backendName: string;\n\n  constructor(features?: Features) {\n    if (features != null) {\n      this.features = features;\n    }\n\n    if (this.get('DEBUG')) {\n      console.warn(\n          'Debugging mode is ON. The output of every math call will ' +\n          'be downloaded to CPU and checked for NaNs. ' +\n          'This significantly impacts performance.');\n    }\n  }\n\n  /**\n   * Sets the backend (cpu, webgl, etc) responsible for creating tensors and\n   * executing operations on those tensors.\n   *\n   * Note this disposes the current backend, if any, as well as any tensors\n   * associated with it. A new backend is initialized, even if it is of the\n   * same type as the previous one.\n   *\n   * @param backendName The name of the backend. Currently supports\n   *     `'webgl'|'cpu'` in the browser, and `'tensorflow'` under node.js\n   *     (requires tfjs-node).\n   * @param safeMode Defaults to false. In safe mode, you are forced to\n   *     construct tensors and call math operations inside a `tidy()` which\n   *     will automatically clean up intermediate tensors.\n   */\n  /** @doc {heading: 'Environment'} */\n  static setBackend(backendName: string, safeMode = false) {\n    if (!(backendName in ENV.registry)) {\n      throw new Error(`Backend name '${backendName}' not found in registry`);\n    }\n    ENV.engine.backend = ENV.findBackend(backendName);\n    ENV.backendName = backendName;\n  }\n\n  /**\n   * Returns the current backend name (cpu, webgl, etc). The backend is\n   * responsible for creating tensors and executing operations on those tensors.\n   */\n  /** @doc {heading: 'Environment'} */\n  static getBackend(): string {\n    ENV.initEngine();\n    return ENV.backendName;\n  }\n\n  /**\n   * Dispose all variables kept in backend engine.\n   */\n  /** @doc {heading: 'Environment'} */\n  static disposeVariables(): void {\n    ENV.engine.disposeVariables();\n  }\n\n  /**\n   * Returns memory info at the current time in the program. The result is an\n   * object with the following properties:\n   *\n   * - `numBytes`: Number of bytes allocated (undisposed) at this time.\n   * - `numTensors`: Number of unique tensors allocated.\n   * - `numDataBuffers`: Number of unique data buffers allocated\n   *   (undisposed) at this time, which is ≤ the number of tensors\n   *   (e.g. `a.reshape(newShape)` makes a new Tensor that shares the same\n   *   data buffer with `a`).\n   * - `unreliable`: True if the memory usage is unreliable. See `reasons` when\n   *    `unrealible` is true.\n   * - `reasons`: `string[]`, reasons why the memory is unreliable, present if\n   *    `unreliable` is true.\n   */\n  /** @doc {heading: 'Performance', subheading: 'Memory'} */\n  static memory(): MemoryInfo {\n    return ENV.engine.memory();\n  }\n\n  /**\n   * Executes the provided function `f()` and returns a promise that resolves\n   * with information about the function's memory use:\n   * - `newBytes`: tne number of new bytes allocated\n   * - `newTensors`: the number of new tensors created\n   * - `peakBytes`: the peak number of bytes allocated\n   * - `kernels`: an array of objects for each kernel involved that reports\n   * their input and output shapes, number of bytes used, and number of new\n   * tensors created.\n   *\n   * ```js\n   * const profile = await tf.profile(() => {\n   *   const x = tf.tensor1d([1, 2, 3]);\n   *   let x2 = x.square();\n   *   x2.dispose();\n   *   x2 = x.square();\n   *   x2.dispose();\n   *   return x;\n   * });\n   *\n   * console.log(`newBytes: ${profile.newBytes}`);\n   * console.log(`newTensors: ${profile.newTensors}`);\n   * console.log(`byte usage over all kernels: ${profile.kernels.map(k =>\n   * k.totalBytesSnapshot)}`);\n   * ```\n   *\n   */\n  /** @doc {heading: 'Performance', subheading: 'Profile'} */\n  static profile(f: () => TensorContainer): Promise<ProfileInfo> {\n    return ENV.engine.profile(f);\n  }\n\n  /**\n   * Executes the provided function `fn` and after it is executed, cleans up all\n   * intermediate tensors allocated by `fn` except those returned by `fn`.\n   * `fn` must not return a Promise (async functions not allowed). The returned\n   * result can be a complex object.\n   *\n   * Using this method helps avoid memory leaks. In general, wrap calls to\n   * operations in `tf.tidy` for automatic memory cleanup.\n   *\n   * When in safe mode, you must enclose all `tf.Tensor` creation and ops\n   * inside a `tf.tidy` to prevent memory leaks.\n   *\n   * ```js\n   * // y = 2 ^ 2 + 1\n   * const y = tf.tidy(() => {\n   *   // a, b, and one will be cleaned up when the tidy ends.\n   *   const one = tf.scalar(1);\n   *   const a = tf.scalar(2);\n   *   const b = a.square();\n   *\n   *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n   *\n   *   // The value returned inside the tidy function will return\n   *   // through the tidy, in this case to the variable y.\n   *   return b.add(one);\n   * });\n   *\n   * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n   * y.print();\n   * ```\n   *\n   * @param nameOrFn The name of the closure, or the function to execute.\n   *     If a name is provided, the 2nd argument should be the function.\n   *     If debug mode is on, the timing and the memory usage of the function\n   *     will be tracked and displayed on the console using the provided name.\n   * @param fn The function to execute.\n   */\n  /** @doc {heading: 'Performance', subheading: 'Memory'} */\n  static tidy<T extends TensorContainer>(\n      nameOrFn: string|ScopeFn<T>, fn?: ScopeFn<T>): T {\n    return ENV.engine.tidy(nameOrFn, fn);\n  }\n\n  /**\n   * Disposes any `tf.Tensor`s found within the provided object.\n   *\n   * @param container an object that may be a `tf.Tensor` or may directly\n   *     contain `tf.Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. If\n   *     the object is not a `tf.Tensor` or does not contain `Tensors`, nothing\n   *     happens. In general it is safe to pass any object here, except that\n   *     `Promise`s are not supported.\n   */\n  /** @doc {heading: 'Performance', subheading: 'Memory'} */\n  static dispose(container: TensorContainer) {\n    const tensors = getTensorsInContainer(container);\n    tensors.forEach(tensor => tensor.dispose());\n  }\n\n  /**\n   * Keeps a `tf.Tensor` generated inside a `tf.tidy` from being disposed\n   * automatically.\n   *\n   * ```js\n   * let b;\n   * const y = tf.tidy(() => {\n   *   const one = tf.scalar(1);\n   *   const a = tf.scalar(2);\n   *\n   *   // b will not be cleaned up by the tidy. a and one will be cleaned up\n   *   // when the tidy ends.\n   *   b = tf.keep(a.square());\n   *\n   *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n   *\n   *   // The value returned inside the tidy function will return\n   *   // through the tidy, in this case to the variable y.\n   *   return b.add(one);\n   * });\n   *\n   * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n   * console.log('y:');\n   * y.print();\n   * console.log('b:');\n   * b.print();\n   * ```\n   *\n   * @param result The tensor to keep from being disposed.\n   */\n  /** @doc {heading: 'Performance', subheading: 'Memory'} */\n  static keep<T extends Tensor>(result: T): T {\n    return ENV.engine.keep(result);\n  }\n\n  /**\n   * Executes `f()` and returns a promise that resolves with timing\n   * information.\n   *\n   * The result is an object with the following properties:\n   *\n   * - `wallMs`: Wall execution time.\n   * - `kernelMs`: Kernel execution time, ignoring data transfer.\n   * - On `WebGL` The following additional properties exist:\n   *   - `uploadWaitMs`: CPU blocking time on texture uploads.\n   *   - `downloadWaitMs`: CPU blocking time on texture downloads (readPixels).\n   *\n   * ```js\n   * const x = tf.randomNormal([20, 20]);\n   * const time = await tf.time(() => x.matMul(x));\n   *\n   * console.log(`kernelMs: ${time.kernelMs}, wallTimeMs: ${time.wallMs}`);\n   * ```\n   *\n   * @param f The function to execute and time.\n   */\n  /** @doc {heading: 'Performance', subheading: 'Timing'} */\n  static time(f: () => void): Promise<TimingInfo> {\n    return ENV.engine.time(f);\n  }\n\n  get<K extends keyof Features>(feature: K): Features[K] {\n    if (feature in this.features) {\n      return this.features[feature];\n    }\n\n    this.features[feature] = this.evaluateFeature(feature);\n\n    return this.features[feature];\n  }\n\n  getFeatures(): Features {\n    return this.features;\n  }\n\n  set<K extends keyof Features>(feature: K, value: Features[K]): void {\n    this.features[feature] = value;\n  }\n\n  private getBestBackendName(): string {\n    if (Object.keys(this.registry).length === 0) {\n      throw new Error('No backend found in registry.');\n    }\n    const sortedBackends = Object.keys(this.registry)\n                               .map(name => {\n                                 return {name, entry: this.registry[name]};\n                               })\n                               .sort((a, b) => {\n                                 // Highest priority comes first.\n                                 return b.entry.priority - a.entry.priority;\n                               });\n    return sortedBackends[0].name;\n  }\n\n  private evaluateFeature<K extends keyof Features>(feature: K): Features[K] {\n    if (feature === 'DEBUG') {\n      return false;\n    } else if (feature === 'IS_BROWSER') {\n      return typeof window !== 'undefined';\n    } else if (feature === 'IS_NODE') {\n      return (typeof process !== 'undefined') &&\n          (typeof process.versions !== 'undefined') &&\n          (typeof process.versions.node !== 'undefined');\n    } else if (feature === 'IS_CHROME') {\n      return isChrome();\n    } else if (feature === 'WEBGL_CPU_FORWARD') {\n      return true;\n    } else if (feature === 'WEBGL_PACK') {\n      return false;\n    } else if (feature === 'WEBGL_PACK_BATCHNORMALIZATION') {\n      return this.get('WEBGL_PACK');\n    } else if (feature === 'WEBGL_PACK_CLIP') {\n      return this.get('WEBGL_PACK');\n    } else if (feature === 'WEBGL_PACK_DEPTHWISECONV') {\n      return this.get('WEBGL_PACK');\n    } else if (feature === 'WEBGL_LAZILY_UNPACK') {\n      return this.get('WEBGL_PACK');\n    } else if (feature === 'WEBGL_CONV_IM2COL') {\n      return this.get('WEBGL_PACK');\n    } else if (feature === 'WEBGL_PAGING_ENABLED') {\n      return this.get('IS_BROWSER') && !this.get('PROD');\n    } else if (feature === 'WEBGL_MAX_TEXTURE_SIZE') {\n      return getWebGLMaxTextureSize(this.get('WEBGL_VERSION'));\n    } else if (feature === 'IS_TEST') {\n      return false;\n    } else if (feature === 'BACKEND') {\n      return this.getBestBackendName();\n    } else if (feature === 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') {\n      const webGLVersion = this.get('WEBGL_VERSION');\n\n      if (webGLVersion === 0) {\n        return 0;\n      }\n      return getWebGLDisjointQueryTimerVersion(webGLVersion);\n    } else if (feature === 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') {\n      return this.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0 &&\n          !device_util.isMobile();\n    } else if (feature === 'HAS_WEBGL') {\n      return this.get('WEBGL_VERSION') > 0;\n    } else if (feature === 'WEBGL_VERSION') {\n      if (isWebGLVersionEnabled(2)) {\n        return 2;\n      } else if (isWebGLVersionEnabled(1)) {\n        return 1;\n      }\n      return 0;\n    } else if (feature === 'WEBGL_RENDER_FLOAT32_ENABLED') {\n      return isRenderToFloatTextureEnabled(this.get('WEBGL_VERSION'));\n    } else if (feature === 'WEBGL_DOWNLOAD_FLOAT_ENABLED') {\n      return isDownloadFloatTextureEnabled(this.get('WEBGL_VERSION'));\n    } else if (feature === 'WEBGL_FENCE_API_ENABLED') {\n      return isWebGLFenceEnabled(this.get('WEBGL_VERSION'));\n    } else if (feature === 'WEBGL_SIZE_UPLOAD_UNIFORM') {\n      // Use uniform uploads only when 32bit floats are supported. In 16bit\n      // environments there are problems with comparing a 16bit texture value\n      // with a 32bit uniform value.\n      const useUniforms = this.get('WEBGL_RENDER_FLOAT32_ENABLED');\n      return useUniforms ? 4 : 0;\n    } else if (feature === 'TEST_EPSILON') {\n      return this.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 :\n                                                    TEST_EPSILON_FLOAT16;\n    } else if (feature === 'EPSILON') {\n      return this.backend.floatPrecision() === 32 ? EPSILON_FLOAT32 :\n                                                    EPSILON_FLOAT16;\n    } else if (feature === 'PROD') {\n      return false;\n    } else if (feature === 'TENSORLIKE_CHECK_SHAPE_CONSISTENCY') {\n      return !this.get('PROD');\n    }\n    throw new Error(`Unknown feature ${feature}.`);\n  }\n\n  setFeatures(features: Features) {\n    this.features = Object.assign({}, features);\n  }\n\n  reset() {\n    this.features = getFeaturesFromURL();\n    if (this.globalEngine != null) {\n      this.globalEngine = null;\n    }\n  }\n\n  get backend(): KernelBackend {\n    return this.engine.backend;\n  }\n\n  findBackend(name: string): KernelBackend {\n    if (!(name in this.registry)) {\n      return null;\n    }\n    return this.registry[name].backend;\n  }\n\n  /**\n   * Registers a global backend. The registration should happen when importing\n   * a module file (e.g. when importing `backend_webgl.ts`), and is used for\n   * modular builds (e.g. custom tfjs bundle with only webgl support).\n   *\n   * @param factory The backend factory function. When called, it should\n   * return an instance of the backend.\n   * @param priority The priority of the backend (higher = more important).\n   *     In case multiple backends are registered, the priority is used to find\n   *     the best backend. Defaults to 1.\n   * @return False if the creation/registration failed. True otherwise.\n   */\n  registerBackend(\n      name: string, factory: () => KernelBackend, priority = 1,\n      setTensorTrackerFn?: (f: () => TensorTracker) => void): boolean {\n    if (name in this.registry) {\n      console.warn(\n          `${name} backend was already registered. Reusing existing backend`);\n      if (setTensorTrackerFn != null) {\n        setTensorTrackerFn(() => this.engine);\n      }\n      return false;\n    }\n    try {\n      const backend = factory();\n      backend.setDataMover(\n          {moveData: (dataId: DataId) => this.engine.moveData(dataId)});\n      this.registry[name] = {backend, priority};\n      return true;\n    } catch (err) {\n      console.warn(`Registration of backend ${name} failed`);\n      console.warn(err.stack || err.message);\n      return false;\n    }\n  }\n\n  removeBackend(name: string): void {\n    if (!(name in this.registry)) {\n      throw new Error(`${name} backend not found in registry`);\n    }\n    this.registry[name].backend.dispose();\n    delete this.registry[name];\n  }\n\n  get engine(): Engine {\n    this.initEngine();\n    return this.globalEngine;\n  }\n\n  private initEngine() {\n    if (this.globalEngine == null) {\n      this.backendName = this.get('BACKEND');\n      const backend = this.findBackend(this.backendName);\n      this.globalEngine =\n          new Engine(backend, false /* safeMode */, () => this.get('DEBUG'));\n    }\n  }\n}\n\nfunction getGlobalNamespace(): {ENV: Environment} {\n  // tslint:disable-next-line:no-any\n  let ns: any;\n  if (typeof (window) !== 'undefined') {\n    ns = window;\n  } else if (typeof (process) !== 'undefined') {\n    ns = process;\n  } else {\n    throw new Error('Could not find a global object');\n  }\n  return ns;\n}\n\nfunction getOrMakeEnvironment(): Environment {\n  const ns = getGlobalNamespace();\n  if (ns.ENV == null) {\n    ns.ENV = new Environment(getFeaturesFromURL());\n    setTensorTracker(() => ns.ENV.engine);\n  }\n  return ns.ENV;\n}\n\nexport let ENV = getOrMakeEnvironment();\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {CustomGradientFunc, ScopeFn} from './engine';\nimport {ENV} from './environment';\nimport {Scalar, Tensor, Variable} from './tensor';\nimport {NamedTensorMap, TensorContainer} from './tensor_types';\nimport * as util from './util';\n\n/**\n * Create a new gradient scope. Similar to scope, but forces all inner scopes\n * to not clean up so that gradient operations can be used inside of this\n * scope.\n * @param nameOrScopeFn The name of the scope, or the function to execute.\n *     If a name is provided, the 2nd argument should be the function.\n *     If a name is provided, and debug mode is on, the timing and the memory\n *     usage of the function will be tracked and displayed on the console\n *     using the provided name.\n * @param scopeFn The function to execute.\n */\nfunction gradScope<T extends TensorContainer>(\n    nameOrScopeFn: string|ScopeFn<T>, scopeFn?: ScopeFn<T>): T {\n  return ENV.engine.tidy(nameOrScopeFn, scopeFn, true /* gradScope */);\n}\n\n/**\n * Provided `f(x)`, returns another function `g(x, dy?)`, which gives the\n * gradient of `f(x)` with respect to `x`.\n *\n * If `dy` is provided, the gradient of `f(x).mul(dy).sum()` with respect to\n * `x` is computed instead. `f(x)` must take a single tensor `x` and return a\n * single tensor `y`. If `f()` takes multiple inputs, use `tf.grads` instead.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.grad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * g(x).print();\n * ```\n *\n * ```js\n * // f(x) = x ^ 3\n * const f = x => x.pow(tf.scalar(3, 'int32'));\n * // f'(x) = 3x ^ 2\n * const g = tf.grad(f);\n * // f''(x) = 6x\n * const gg = tf.grad(g);\n *\n * const x = tf.tensor1d([2, 3]);\n * gg(x).print();\n * ```\n *\n * @param f The function f(x), to compute gradient for.\n */\n/** @doc {heading: 'Training', subheading: 'Gradients'} */\nfunction grad<I extends Tensor, O extends Tensor>(f: (x: I) => O): (\n    x: I, dy?: O) => I {\n  util.assert(util.isFunction(f), 'The f passed in grad(f) must be a function');\n  return (x: I, dy?: O): I => {\n    util.assert(\n        x instanceof Tensor, 'The x passed in grad(f)(x) must be a tensor');\n    util.assert(\n        dy == null || dy instanceof Tensor,\n        'The dy passed in grad(f)(x, dy) must be a tensor');\n    return ENV.engine.tidy(() => {\n      const {value, grads} = ENV.engine.gradients(() => f(x), [x], dy);\n      if (dy != null) {\n        util.assertShapesMatch(\n            value.shape, dy.shape,\n            'The shape of dy passed in grad(f)(x, dy) must match the shape ' +\n                'returned by f(x)');\n      }\n      checkGrads(grads);\n      return grads[0] as I;\n    });\n  };\n}\n\n/**\n * Provided `f(x1, x2,...)`, returns another function `g([x1, x2,...], dy?)`,\n * which gives an array of gradients of `f()` with respect to each input\n * [`x1`,`x2`,...].\n *\n * If `dy` is passed when calling `g()`, the gradient of\n * `f(x1,...).mul(dy).sum()` with respect to each input is computed instead.\n * The provided `f` must take one or more tensors and return a single tensor\n * `y`. If `f()` takes a single input, we recommend using `tf.grad` instead.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df / da = b, df / db = a\n * const g = tf.grads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const [da, db] = g([a, b]);\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @param f The function `f(x1, x2,...)` to compute gradients for.\n */\n/** @doc {heading: 'Training', subheading: 'Gradients'} */\nfunction grads<O extends Tensor>(f: (...args: Tensor[]) => O): (\n    args: Tensor[], dy?: O) => Tensor[] {\n  util.assert(\n      util.isFunction(f), 'The f passed in grads(f) must be a function');\n  return (args: Tensor[], dy?: O): Tensor[] => {\n    util.assert(\n        Array.isArray(args) && args.every(arg => arg instanceof Tensor),\n        'The args passed in grads(f)(args) must be an array of tensors');\n    util.assert(\n        dy == null || dy instanceof Tensor,\n        'The dy passed in grads(f)(args, dy) must be a tensor');\n    return ENV.engine.tidy(() => {\n      const {value, grads} = ENV.engine.gradients(() => f(...args), args, dy);\n      if (dy != null) {\n        util.assertShapesMatch(\n            value.shape, dy.shape,\n            'The shape of dy passed in grads(f)([x1,...], dy) must ' +\n                'match the shape returned by f([x1,...])');\n      }\n      checkGrads(grads);\n      return grads;\n    });\n  };\n}\n\n/**\n * Like `tf.grad`, but also returns the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grad: The gradient of `f(x)` w.r.t `x` (result of `tf.grad`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.valueAndGrad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * const {value, grad} = g(x);\n *\n * console.log('value');\n * value.print();\n * console.log('grad');\n * grad.print();\n * ```\n */\n/** @doc {heading: 'Training', subheading: 'Gradients'} */\nfunction valueAndGrad<I extends Tensor, O extends Tensor>(f: (x: I) => O): (\n    x: I, dy?: O) => {\n  value: O;\n  grad: I;\n} {\n  util.assert(\n      util.isFunction(f), 'The f passed in valueAndGrad(f) must be a function');\n  return (x: I, dy?: O) => {\n    util.assert(\n        x instanceof Tensor,\n        'The x passed in valueAndGrad(f)(x) must be a tensor');\n    util.assert(\n        dy == null || dy instanceof Tensor,\n        'The dy passed in valueAndGrad(f)(x, dy) must be a tensor');\n    const {grads, value} = ENV.engine.gradients(() => f(x), [x], dy);\n    checkGrads(grads);\n    return {grad: grads[0] as I, value: value as O};\n  };\n}\n\n/**\n * Like `tf.grads`, but returns also the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grads: The gradients of `f()` w.r.t each input (result of `tf.grads`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df/da = b, df/db = a\n * const g = tf.valueAndGrads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const {value, grads} = g([a, b]);\n *\n * const [da, db] = grads;\n *\n * console.log('value');\n * value.print();\n *\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n */\n/** @doc {heading: 'Training', subheading: 'Gradients'} */\nfunction valueAndGrads<O extends Tensor>(f: (...args: Tensor[]) => O): (\n    args: Tensor[], dy?: O) => {\n  grads: Tensor[];\n  value: O;\n} {\n  util.assert(\n      util.isFunction(f),\n      'The f passed in valueAndGrads(f) must be a function');\n  return (args: Tensor[], dy?: O) => {\n    util.assert(\n        Array.isArray(args) && args.every(arg => arg instanceof Tensor),\n        'The args passed in valueAndGrads(f)(args) must be array of tensors');\n    util.assert(\n        dy == null || dy instanceof Tensor,\n        'The dy passed in valueAndGrads(f)(args, dy) must be a tensor');\n    const res = ENV.engine.gradients(() => f(...args), args, dy);\n    if (dy != null) {\n      util.assertShapesMatch(\n          res.value.shape, dy.shape,\n          'The shape of dy passed in valueAndGrads(f)([x1,...], dy) must ' +\n              'match the shape returned by f([x1,...])');\n    }\n    checkGrads(res.grads);\n    return res;\n  };\n}\n\n/**\n * Computes and returns the gradient of f(x) with respect to the list of\n * trainable variables provided by `varList`. If no list is provided, it\n * defaults to all trainable variables.\n *\n * ```js\n * const a = tf.variable(tf.tensor1d([3, 4]));\n * const b = tf.variable(tf.tensor1d([5, 6]));\n * const x = tf.tensor1d([1, 2]);\n *\n * // f(a, b) = a * x ^ 2 + b * x\n * const f = () => a.mul(x.square()).add(b.mul(x)).sum();\n * // df/da = x ^ 2, df/db = x\n * const {value, grads} = tf.variableGrads(f);\n *\n * Object.keys(grads).forEach(varName => grads[varName].print());\n * ```\n *\n * @param f The function to execute. f() should return a scalar.\n * @param varList The list of trainable variables. Defaults to all variables.\n */\n/** @doc {heading: 'Training', subheading: 'Gradients'} */\nfunction variableGrads(f: () => Scalar, varList?: Variable[]):\n    {value: Scalar, grads: NamedTensorMap} {\n  util.assert(\n      util.isFunction(f),\n      'The f passed in variableGrads(f) must be a function');\n  util.assert(\n      varList == null ||\n          Array.isArray(varList) && varList.every(v => v instanceof Variable),\n      'The varList passed in variableGrads(f, varList) must be an array ' +\n          'of variables');\n  if (varList == null) {\n    // Get all of the trainable variables.\n    varList = [];\n    for (const varName in ENV.engine.registeredVariables) {\n      varList.push(ENV.engine.registeredVariables[varName]);\n    }\n  }\n  // Prune non-trainable variables.\n  const originalVarCount = varList.length;\n  varList = varList.filter(variable => variable.trainable);\n  util.assert(\n      varList.length > 0,\n      `variableGrads() expects at least one of the input variables to be ` +\n          `trainable, but none of the ${originalVarCount} variables is ` +\n          `trainable.`);\n\n  const allowNoGradients = true;\n  const {value, grads} =\n      ENV.engine.gradients(f, varList, null, allowNoGradients);\n\n  util.assert(\n      grads.some(g => g != null),\n      'Cannot find a connection between any variable and the result of the ' +\n          'loss function y=f(x). Please make sure the operations that use ' +\n          'variables are inside the function f passed to minimize().');\n  util.assert(\n      value.rank === 0,\n      `The f passed in variableGrads(f) must return a scalar, but it ` +\n          `returned a rank-${value.rank} tensor`);\n\n  const namedGrads: NamedTensorMap = {};\n  varList.forEach((v, i) => {\n    if (grads[i] != null) {\n      namedGrads[v.name] = grads[i];\n    }\n  });\n  return {value, grads: namedGrads};\n}\n\n/**\n * Overrides the gradient computation of a function `f`.\n *\n * Takes a function\n * `f(...inputs) => {value: Tensor, gradFunc: dy => Tensor[]}` and returns\n * another function `g(...inputs)` which takes the same inputs as `f`. When\n * called, `g` returns `f().value`. In backward mode, custom gradients with\n * respect to each input of `f` are computed using `f().gradFunc`.\n *\n * ```js\n * const customOp = tf.customGrad(x => {\n *   // Override gradient of our custom x ^ 2 op to be dy * abs(x);\n *   return {value: x.square(), gradFunc: dy => [dy.mul(x.abs())]};\n * });\n *\n * const x = tf.tensor1d([-1, -2, 3]);\n * const dx = tf.grad(x => customOp(x));\n *\n * console.log(`f(x):`);\n * customOp(x).print();\n * console.log(`f'(x):`);\n * dx(x).print();\n * ```\n *\n * @param f The function to evaluate in forward mode, which should return\n *     `{value: Tensor, gradFunc: (dy) => Tensor[]}`, where `gradFunc` returns\n *     the custom gradients of `f` with respect to its inputs.\n */\n/** @doc {heading: 'Training', subheading: 'Gradients'} */\nfunction customGrad<T extends Tensor>(f: CustomGradientFunc<T>):\n    (...args: Tensor[]) => T {\n  return ENV.engine.customGrad(f);\n}\n\nfunction checkGrads(grads: Tensor[]) {\n  const numNullGradients = grads.filter(g => g == null).length;\n  if (numNullGradients > 0) {\n    throw new Error(\n        `Cannot compute gradient of y=f(x) with respect to x. Make sure that\n    the f you passed encloses all operations that lead from x to y.`);\n  }\n}\n\nexport {\n  gradScope,\n  customGrad,\n  variableGrads,\n  valueAndGrad,\n  valueAndGrads,\n  grad,\n  grads,\n};\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Environment} from './environment';\nexport {customGrad, grad, grads, valueAndGrad, valueAndGrads, variableGrads} from './gradients';\n\nexport const tidy = Environment.tidy;\nexport const keep = Environment.keep;\nexport const dispose = Environment.dispose;\nexport const time = Environment.time;\nexport const profile = Environment.profile;","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from './environment';\n\nexport function warn(...msg: Array<{}>): void {\n  if (!ENV.get('IS_TEST')) {\n    console.warn(...msg);\n  }\n}\n\nexport function log(...msg: Array<{}>): void {\n  if (!ENV.get('IS_TEST')) {\n    console.log(...msg);\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Gets the new shape of the input Tensor after it's been reshaped\n * to:\n * [blockShape[0], ..., blockShape[M-1], batch / prod(blockShape),\n * inputShape[1], ..., inputShape[N-1]]\n *\n * See step 1: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshaped(\n    inputShape: number[], blockShape: number[], prod: number,\n    batchToSpace = true): number[] {\n  let reshaped: number[] = [];\n  if (batchToSpace) {\n    reshaped = reshaped.concat(blockShape.slice(0));\n    reshaped.push(inputShape[0] / prod);\n    reshaped = reshaped.concat(inputShape.slice(1));\n  } else {\n    reshaped = reshaped.concat(inputShape[0]);\n    const spatialLength = blockShape.length;\n    for (let i = 0; i < spatialLength; ++i) {\n      reshaped =\n          reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);\n    }\n    reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));\n  }\n  return reshaped;\n}\n\n/**\n * Gets the permutation that will transpose the dimensions of the\n * reshaped tensor to shape:\n *\n * [batch / prod(block_shape),inputShape[1], blockShape[0], ...,\n * inputShape[M], blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * see step 2: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getPermuted(\n    reshapedRank: number, blockShapeRank: number,\n    batchToSpace = true): number[] {\n  const permuted = [];\n  if (batchToSpace) {\n    permuted.push(blockShapeRank);\n    for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {\n      if (i <= 2 * blockShapeRank) {\n        permuted.push(i);\n        permuted.push(i - (blockShapeRank + 1));\n      } else {\n        permuted.push(i);\n      }\n    }\n  } else {\n    const permutedBeforeBatch = [];\n    const permutedAfterBatch = [];\n    for (let i = 1; i < reshapedRank; ++i) {\n      if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {\n        permutedAfterBatch.push(i);\n      } else {\n        permutedBeforeBatch.push(i);\n      }\n    }\n    permuted.push(...permutedBeforeBatch);\n    permuted.push(0);\n    permuted.push(...permutedAfterBatch);\n  }\n  return permuted;\n}\n\n/**\n * Gets the shape of the reshaped and permuted input Tensor before any cropping\n * is applied.  The new shape will be:\n *\n * [batch / prod(blockShape),inputShape[1] * blockShape[0], ...,\n * inputShape[M] * blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 3: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshapedPermuted(\n    inputShape: number[], blockShape: number[], prod: number,\n    batchToSpace = true): number[] {\n  const reshapedPermuted = [];\n\n  if (batchToSpace) {\n    reshapedPermuted.push(inputShape[0] / prod);\n  } else {\n    reshapedPermuted.push(inputShape[0] * prod);\n  }\n\n  for (let i = 1; i < inputShape.length; ++i) {\n    if (i <= blockShape.length) {\n      if (batchToSpace) {\n        reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);\n      } else {\n        reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);\n      }\n    } else {\n      reshapedPermuted.push(inputShape[i]);\n    }\n  }\n\n  return reshapedPermuted;\n}\n\n/**\n * Converts the crops argument into the beginning coordinates of a slice\n * operation.\n */\nexport function getSliceBeginCoords(\n    crops: number[][], blockShape: number): number[] {\n  const sliceBeginCoords = [0];\n  for (let i = 0; i < blockShape; ++i) {\n    sliceBeginCoords.push(crops[i][0]);\n  }\n  return sliceBeginCoords;\n}\n\n/**\n * Converts the crops argument into the size of a slice operation.  When\n * combined with getSliceBeginCoords this function allows the reshaped and\n * permuted Tensor to be cropped to its final output shape of:\n *\n * inputShape[1] * blockShape[0] - crops[0,0] - crops[0,1], ...,\n * inputShape[M] * blockShape[M-1] -crops[M-1,0] -\n * crops[M-1,1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 4: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getSliceSize(\n    uncroppedShape: number[], crops: number[][], blockShape: number): number[] {\n  const sliceSize = uncroppedShape.slice(0, 1);\n  for (let i = 0; i < blockShape; ++i) {\n    sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);\n  }\n\n  return sliceSize;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as util from '../util';\n\n/**\n * Returns true if the axis specifies the inner most dimensions of the\n * array.\n */\nexport function axesAreInnerMostDims(axes: number[], rank: number): boolean {\n  for (let i = 0; i < axes.length; ++i) {\n    if (axes[axes.length - i - 1] !== rank - 1 - i) {\n      return false;\n    }\n  }\n  return true;\n}\n\nexport function combineLocations(\n    outputLoc: number[], reduceLoc: number[], axes: number[]): number[] {\n  const rank = outputLoc.length + reduceLoc.length;\n  const loc = [];\n  let outIdx = 0;\n  let reduceIdx = 0;\n    for (let dim = 0; dim < rank; dim++) {\n    if (axes.indexOf(dim) === -1) {\n      loc.push(outputLoc[outIdx++]);\n    } else {\n      loc.push(reduceLoc[reduceIdx++]);\n    }\n  }\n  return loc;\n}\n\nexport function computeOutAndReduceShapes(\n    aShape: number[], axes: number[]): [number[], number[]] {\n  const outShape = [];\n  const rank = aShape.length;\n  for (let dim = 0; dim < rank; dim++) {\n    if (axes.indexOf(dim) === -1) {\n      outShape.push(aShape[dim]);\n    }\n  }\n  const reduceShape = axes.map(dim => aShape[dim]);\n  return [outShape, reduceShape];\n}\n\nexport function expandShapeToKeepDim(\n    shape: number[], axes: number[]): number[] {\n  const reduceSubShape = axes.map(x => 1);\n  return combineLocations(shape, reduceSubShape, axes);\n}\n\nexport function parseAxisParam(\n    axis: number|number[], shape: number[]): number[] {\n  const rank = shape.length;\n\n  // Normalize input\n  axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);\n\n  // Check for valid range\n  util.assert(\n      axis.every(ax => ax >= -rank && ax < rank),\n      `All values in axis param must be in range [-${rank}, ${rank}) but ` +\n          `got axis ${axis}`);\n\n  // Check for only integers\n  util.assert(\n      axis.every(ax => util.isInt(ax)),\n      `All values in axis param must be integers but ` +\n          `got axis ${axis}`);\n\n  // Handle negative axis.\n  return axis.map(a => a < 0 ? rank + a : a);\n}\n\nexport function assertAxesAreInnerMostDims(\n    msg: string, axes: number[], rank: number): void {\n  util.assert(\n      axesAreInnerMostDims(axes, rank),\n      `${msg} supports only inner-most axes for now. ` +\n          `Got axes ${axes} and rank-${rank} input.`);\n}\n\n/**\n * Returns the axes permutation to be used with `tf.transpose`, if such\n * permutation is necessary. Otherwise it returns null. This method is used by\n * operations that operate only on inner-most axes.\n */\nexport function getAxesPermutation(axes: number[], rank: number): number[]|\n    null {\n  if (axesAreInnerMostDims(axes, rank)) {\n    return null;\n  }\n  const result: number[] = [];\n  for (let i = 0; i < rank; ++i) {\n    if (axes.indexOf(i) === -1) {\n      result.push(i);\n    }\n  }\n  axes.forEach(axis => result.push(axis));\n  return result;\n}\n\n/** Returns the axes permutation that undoes the original permutation. */\nexport function getUndoAxesPermutation(axes: number[]): number[] {\n  return axes.map((axis, i) => [i, axis])\n      .sort((a, b) => a[1] - b[1])\n      .map(x => x[0]);\n}\n\nexport function getInnerMostAxes(numAxes: number, rank: number): number[] {\n  const res: number[] = [];\n  for (let i = rank - numAxes; i < rank; ++i) {\n    res.push(i);\n  }\n  return res;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as util from '../util';\n\nexport function assertParamsConsistent(shapes: number[][], axis: number) {\n  const rank = shapes[0].length;\n  shapes.forEach((shape, i) => {\n    util.assert(\n        shape.length === rank,\n        `Error in concat${rank}D: rank of tensors[${i}] must be the same ` +\n            `as the rank of the rest (${rank})`);\n  });\n\n  util.assert(\n      axis >= 0 && axis < rank,\n      `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);\n\n  const firstShape = shapes[0];\n  shapes.forEach((shape, i) => {\n    for (let r = 0; r < rank; r++) {\n      util.assert(\n          (r === axis) || (shape[r] === firstShape[r]),\n          `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) ` +\n              `does not match the shape of the rest (${firstShape}) ` +\n              `along the non-concatenated axis ${i}.`);\n    }\n  });\n}\n\nexport function computeOutShape(shapes: number[][], axis: number): number[] {\n  const outputShape = shapes[0].slice();\n  for (let i = 1; i < shapes.length; i++) {\n    outputShape[axis] += shapes[i][axis];\n  }\n  return outputShape;\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor} from '../tensor';\nimport {computeStrides} from '../util';\n\n/**\n * Validate gather nd inputs.\n *\n * @param tensor The tensor contains the source values.\n * @param indices The tensor contains the indices to slice the source.\n *\n * @returns [resultShape, numUpdates, sliceSize, strides]\n */\nexport function prepareAndValidate(\n    tensor: Tensor, indices: Tensor): [number[], number, number, number[]] {\n  if (tensor.rank < 1) {\n    throw new Error(\n        'tf.gatherND() expects the input to be rank 1 or higher,' +\n        ` but the rank was ${tensor.rank}.`);\n  }\n  if (indices.rank < 1) {\n    throw new Error(\n        'tf.gatherND() expects the indices to be rank 1 or higher,' +\n        ` but the rank was ${indices.rank}.`);\n  }\n  if (indices.dtype !== 'int32') {\n    throw new Error(\n        'tf.gatherND() expects the indices to be int32 type,' +\n        ` but the dtype was ${indices.dtype}.`);\n  }\n  if (indices.shape[indices.rank - 1] > tensor.rank) {\n    throw new Error(\n        'index innermost dimension length must be <= tensor rank; saw: ' +\n        `${indices.shape[indices.rank - 1]} vs. ${tensor.rank}`);\n  }\n\n  if (tensor.size === 0) {\n    throw new Error(\n        'Requested more than 0 entries, but input is empty.' +\n        ` Input shape: ${tensor.shape}.`);\n  }\n\n  const indicesShape = indices.shape;\n  const sliceRank = indicesShape[indicesShape.length - 1];\n\n  // The result shape is\n  //   indices.shape[:-1] + params.shape[indices.shape[-1]:]\n  let nResult = 1;\n  for (let i = 0; i < indicesShape.length - 1; ++i) {\n    nResult *= indicesShape[i];\n  }\n\n  const inputShape = tensor.shape;\n\n  const resultShape = indicesShape.slice();\n  resultShape.pop();\n\n  let sliceSize = 1;\n  for (let i = sliceRank; i < tensor.rank; ++i) {\n    sliceSize *= inputShape[i];\n    resultShape.push(inputShape[i]);\n  }\n\n  const strides =\n      [...computeStrides(tensor.shape).map(stride => stride / sliceSize),\n       1].slice(0, sliceRank);\n\n  return [resultShape, nResult, sliceSize, strides];\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Inputs of size above this threshold will be parallelized by calling multiple\n * shader programs.\n */\nimport {nearestDivisor} from '../util';\n\nexport const PARALLELIZE_THRESHOLD = 30;\n\nexport interface ReduceInfo {\n  windowSize: number;\n  batchSize: number;\n  inSize: number;\n}\n\nexport function computeOptimalWindowSize(inSize: number): number {\n  if (inSize <= PARALLELIZE_THRESHOLD) {\n    return inSize;\n  }\n  return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor} from '../tensor';\nimport {computeStrides, sizeFromShape} from '../util';\n\n/**\n * Check whether updates.shape = indices.shape[:batchDim] +\n * shape[sliceDim:]\n *\n * @param x The input tensor.\n */\nexport function validateUpdateShape(\n    shape: number[], indices: Tensor, updates: Tensor) {\n  const sliceDim = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;\n  const batchDim = (indices.rank > 1) ? indices.rank - 1 : 1;\n\n  const shapeError = 'Must have updates.shape = indices.shape[:batchDim] + ' +\n      `shape[sliceDim:], got updates.shape: ${updates.shape}` +\n      `, indices.shape: ${indices.shape}, shape: ${shape}` +\n      `, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;\n\n  if (updates.rank < batchDim) {\n    throw new Error(shapeError + ` update.rank < ${batchDim}. `);\n  }\n  if (shape.length < sliceDim + (updates.rank - batchDim)) {\n    throw new Error(\n        shapeError +\n        ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);\n  }\n  if (updates.rank !== batchDim + shape.length - sliceDim) {\n    throw new Error(\n        shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);\n  }\n  for (let d = 0; d < batchDim; ++d) {\n    if (updates.shape[d] !== indices.shape[d]) {\n      throw new Error(\n          shapeError +\n          ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${\n              indices.shape[d]}).`);\n    }\n  }\n  for (let d = 0; d < updates.rank - batchDim; ++d) {\n    if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {\n      throw new Error(\n          shapeError +\n          ` updates.shape[${d + batchDim}] (${\n              updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${\n              shape[d + batchDim]})`);\n    }\n  }\n}\n\nexport interface ScatterShapeInfo {\n  sliceRank: number;\n  numUpdates: number;\n  sliceSize: number;\n  strides: number[];\n  outputSize: number;\n}\n/**\n * Validate scatter nd inputs.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n */\nexport function validateInput(\n    updates: Tensor, indices: Tensor, shape: number[]) {\n  if (indices.rank < 1) {\n    throw new Error(\n        'tf.scatterND() expects the indices to be rank 1 or higher,' +\n        ` but the rank was ${indices.rank}.`);\n  }\n  if (updates.rank < 1) {\n    throw new Error(\n        'tf.scatterND() expects the updates to be rank 1 or higher,' +\n        ` but the rank was ${updates.rank}.`);\n  }\n  if (indices.dtype !== 'int32') {\n    throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${\n        indices.dtype}`);\n  }\n  if (shape.length < 1) {\n    throw new Error(\n        `Output rank must be greater or equal to 1, but got shape: ${shape}`);\n  }\n\n  if (shape.length === 0) {\n    if (indices.size === 0) {\n      throw new Error(`Indices specified for empty output. indices shape: ${\n          indices.shape}`);\n    }\n    if (updates.size === 0) {\n      throw new Error(`Updates specified for empty output. updates shape: ${\n          updates.shape}`);\n    }\n  }\n\n  validateUpdateShape(shape, indices, updates);\n}\n\n/**\n * Calculate the shape information for the output.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n *\n * @returns ScatterShapeInfo\n */\nexport function calculateShapes(\n    updates: Tensor, indices: Tensor, shape: number[]): ScatterShapeInfo {\n  // Calculate the number of dimensions in indices\n  const sliceRank = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;\n\n  // Calculate the number of elements that make up each slice of our updated\n  // tensor. This allows us to work with flattened tensors and copy over whole\n  // slices at a time.\n  const totalNd = shape.length;\n\n  let sliceSize = 1;\n  for (let i = sliceRank; i < totalNd; ++i) {\n    sliceSize *= shape[i];\n  }\n\n  const safeSliceDim = (sliceRank < 1) ? 1 : sliceRank;\n  const numUpdates = indices.size / safeSliceDim;\n\n  const outputStrides = [...computeStrides(shape), 1];\n  const strides = outputStrides.slice(\n      outputStrides.length - sliceRank, outputStrides.length);\n  const outputSize = sizeFromShape(shape);\n  return {sliceRank, numUpdates, sliceSize, strides, outputSize};\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {nearestDivisor} from '../util';\nimport {PARALLELIZE_THRESHOLD} from './reduce_util';\n\nexport interface SegOpInfo {\n  windowSize: number;\n  batchSize: number;\n  inSize: number;\n  numSegments: number;\n}\n\nexport function segOpComputeOptimalWindowSize(\n    inSize: number, numSegments: number): number {\n  let done = false;\n  let res;\n\n  if (inSize <= PARALLELIZE_THRESHOLD) {\n    res = inSize;\n    done = true;\n  } else {\n    res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n  }\n\n  while (!done) {\n    if (res > numSegments || res === inSize) {\n      done = true;\n      break;\n    } else {\n      res = nearestDivisor(inSize, res + 1);\n    }\n  }\n  return res;\n}\n\nexport function computeOutShape(\n    aShape: number[], axis: number, numSegments: number): number[] {\n  const outShape = [];\n  const rank = aShape.length;\n  for (let dim = 0; dim < rank; dim++) {\n    if (dim !== axis) {\n      outShape.push(aShape[dim]);\n    } else {\n      outShape.push(numSegments);\n    }\n  }\n  return outShape;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport * as util from '../util';\n\nexport function assertParamsValid(\n    input: Tensor, begin: number[], size: number[]): void {\n  util.assert(\n      input.rank === begin.length,\n      `Error in slice${input.rank}D: Length of begin ${begin} must ` +\n          `match the rank of the array (${input.rank}).`);\n  util.assert(\n      input.rank === size.length,\n      `Error in slice${input.rank}D: Length of size ${size} must ` +\n          `match the rank of the array (${input.rank}).`);\n\n  for (let i = 0; i < input.rank; ++i) {\n    util.assert(\n        begin[i] + size[i] <= input.shape[i],\n        `Error in slice${input.rank}D: begin[${i}] + size[${i}] ` +\n            `(${begin[i] + size[i]}) would overflow input.shape[${i}] (${\n                input.shape[i]})`);\n  }\n}\n\n/**\n * Calculate the start index and output tensor shape for strided slice op.\n * @returns array of [startIndex, size, shrinkAxis]\n */\nexport function getStridedSlicedInfo(\n    shape: number[], begin: number[], end: number[], strides: number[],\n    beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0,\n    shrinkAxisMask = 0): [number[], number[], number[]] {\n  if (ellipsisMask !== 0) {\n    throw new Error('ellipsis mask is not yet supported');\n  }\n  if (newAxisMask !== 0) {\n    throw new Error('new axis mask is not yet supported');\n  }\n  // Note that the axis orders are reversed for runtime ops, so the indices,\n  // strides and masks must be as well too.\n  const startIndex: number[] = [];\n  const endIndex: number[] = [];\n  const shrinkAxis: number[] = [];\n  for (let i = 0; i < shape.length; i++) {\n    startIndex[i] = startForAxis(beginMask, begin, strides, shape, i);\n    endIndex[i] = stopForAxis(endMask, end, strides, shape, i);\n    // When shrinking an axis, use startIndex + 1 for endIndex.\n    // Check the axis bit from right of shrinkAxisMask\n    if (shrinkAxisMask & 1 << i) {\n      endIndex[i] = startIndex[i] + 1;\n      shrinkAxis.push(i);\n    }\n  }\n\n  let size = new Array(shape.length).fill(0);\n  size = size.map((d, i) => {\n    let count = 0;\n    for (let start = startIndex[i];\n         !(strides[i] > 0 ? start >= endIndex[i] : start <= endIndex[i]);\n         start += strides[i]) {\n      count += 1;\n    }\n    return count;\n  });\n\n  return [startIndex, size, shrinkAxis];\n}\n\nexport function startForAxis(\n    beginMask: number, startIndices: number[], strides: number[],\n    inputShape: number[], axis: number): number {\n  // Begin with the specified index\n  let start = startIndices[axis];\n\n  // Check the axis bit from right of beginMask\n  if (beginMask & 1 << axis) {\n    if (strides[axis] > 0) {\n      // Forward iteration - use the first element. These values will get\n      // clamped below (Note: We could have set them to 0 and axis_size-1, but\n      // use lowest() and max() to maintain symmetry with StopForAxis())\n      start = Number.MIN_SAFE_INTEGER;\n    } else {\n      // Backward iteration - use the last element.\n      start = Number.MAX_SAFE_INTEGER;\n    }\n  }\n\n  // Handle negative indices\n  const axisSize = inputShape[axis];\n  if (start < 0) {\n    start += axisSize;\n  }\n\n  // Clamping\n  start = util.clamp(0, start, axisSize - 1);\n\n  return start;\n}\n\nexport function stopForAxis(\n    endMask: number, stopIndices: number[], strides: number[],\n    inputShape: number[], axis: number): number {\n  // Begin with the specified index\n  let stop = stopIndices[axis];\n\n  // Check the axis bit from right of endMask\n  if (endMask & (1 << axis)) {\n    if (strides[axis] > 0) {\n      // Forward iteration - use the last element. These values will get\n      // clamped below\n      stop = Number.MAX_SAFE_INTEGER;\n    } else {\n      // Backward iteration - use the first element.\n      stop = Number.MIN_SAFE_INTEGER;\n    }\n  }\n\n  // Handle negative indices\n  const axisSize = inputShape[axis];\n  if (stop < 0) {\n    stop += axisSize;\n  }\n\n  // Clamping\n  // Because the end index points one past the last element, we need slightly\n  // different clamping ranges depending on the direction.\n  if (strides[axis] > 0) {\n    // Forward iteration\n    stop = util.clamp(0, stop, axisSize);\n  } else {\n    // Backward iteration\n    stop = util.clamp(-1, stop, axisSize - 1);\n  }\n\n  return stop;\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from './environment';\nimport {Tensor} from './tensor';\nimport {DataType, TensorLike, TypedArray} from './types';\nimport {assert, flatten, inferDtype, isTypedArray, toTypedArray} from './util';\n\nexport function inferShape(val: TensorLike): number[] {\n  let firstElem: typeof val = val;\n\n  if (isTypedArray(val)) {\n    return [(val as TypedArray).length];\n  }\n  if (!Array.isArray(val)) {\n    return [];  // Scalar.\n  }\n  const shape: number[] = [];\n\n  while (firstElem instanceof Array || isTypedArray(firstElem)) {\n    shape.push(firstElem.length);\n    firstElem = firstElem[0];\n  }\n  if (val instanceof Array && ENV.get('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')) {\n    deepAssertShapeConsistency(val, shape, []);\n  }\n\n  return shape;\n}\n\nfunction deepAssertShapeConsistency(\n    val: TensorLike, shape: number[], indices: number[]) {\n  indices = indices || [];\n  if (!(val instanceof Array) && !isTypedArray(val)) {\n    assert(\n        shape.length === 0,\n        () => `Element arr[${indices.join('][')}] is a primitive, ` +\n            `but should be an array/TypedArray of ${shape[0]} elements`);\n    return;\n  }\n  assert(\n      shape.length > 0,\n      () => `Element arr[${indices.join('][')}] should be a primitive, ` +\n          `but is an array of ${val.length} elements`);\n  assert(\n      val.length === shape[0],\n      () => `Element arr[${indices.join('][')}] should have ${shape[0]} ` +\n          `elements, but has ${val.length} elements`);\n  const subShape = shape.slice(1);\n  for (let i = 0; i < val.length; ++i) {\n    deepAssertShapeConsistency(val[i], subShape, indices.concat(i));\n  }\n}\n\nfunction assertDtype(\n    expectedDtype: DataType|'numeric', actualDType: DataType, argName: string,\n    functionName: string) {\n  if (expectedDtype == null) {\n    return;\n  }\n  if (expectedDtype !== 'numeric' && expectedDtype !== actualDType ||\n      expectedDtype === 'numeric' && actualDType === 'string') {\n    throw new Error(\n        `Argument '${argName}' passed to '${functionName}' must ` +\n        `be ${expectedDtype} tensor, but got ${actualDType} tensor`);\n  }\n}\n\nexport function convertToTensor<T extends Tensor>(\n    x: T|TensorLike, argName: string, functionName: string,\n    parseAsDtype: DataType|'numeric' = 'numeric'): T {\n  if (x instanceof Tensor) {\n    assertDtype(parseAsDtype, x.dtype, argName, functionName);\n    return x;\n  }\n  let inferredDtype = inferDtype(x);\n  // If the user expects a bool/int/float, use that info to update the\n  // inferredDtype when it is not a string.\n  if (inferredDtype !== 'string' &&\n      ['bool', 'int32', 'float32'].indexOf(parseAsDtype) >= 0) {\n    inferredDtype = parseAsDtype as DataType;\n  }\n  assertDtype(parseAsDtype, inferredDtype, argName, functionName);\n\n  if (!isTypedArray(x) && !Array.isArray(x) && typeof x !== 'number' &&\n      typeof x !== 'boolean' && typeof x !== 'string') {\n    throw new Error(\n        `Argument '${argName}' passed to '${functionName}' must be a ` +\n        `Tensor or TensorLike, but got '${(x as {}).constructor.name}'`);\n  }\n  const inferredShape = inferShape(x);\n  if (!isTypedArray(x) && !Array.isArray(x)) {\n    x = [x] as number[];\n  }\n  const values = inferredDtype !== 'string' ?\n      toTypedArray(x, inferredDtype as DataType, ENV.get('DEBUG')) :\n      flatten(x as string[]) as string[];\n  return Tensor.make(inferredShape, {values}, inferredDtype);\n}\n\nexport function convertToTensorArray<T extends Tensor>(\n    arg: Array<T|TensorLike>, argName: string, functionName: string): T[] {\n  if (!Array.isArray(arg)) {\n    throw new Error(\n        `Argument ${argName} passed to ${functionName} must be a ` +\n        '`Tensor[]` or `TensorLike[]`');\n  }\n  const tensors = arg as T[];\n  return tensors.map(\n      (t, i) => convertToTensor(t, `${argName}[${i}]`, functionName));\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\n\n/**\n * Used for wrapping functions that perform math operations on\n * Tensors. The function will be wrapped in a named scope that cleans all\n * memory usage after the function is done.\n */\nexport function op<T extends Function>(f: {[name: string]: T}): T {\n  const keys = Object.keys(f);\n  if (keys.length !== 1) {\n    throw new Error(\n        `Please provide an object with a single key ` +\n        `(operation name) mapping to a function. Got an object with ` +\n        `${keys.length} keys.`);\n  }\n\n  let opName = keys[0];\n  const fn = f[opName];\n\n  // Strip the underscore from the end of the function name.\n  if (opName.endsWith('_')) {\n    opName = opName.substring(0, opName.length - 1);\n  }\n\n  // tslint:disable-next-line:no-any\n  const f2 = (...args: any[]) => {\n    ENV.engine.startScope(opName);\n    try {\n      const result = fn(...args);\n      if (result instanceof Promise) {\n        console.error('Cannot return a Promise inside of tidy.');\n      }\n      ENV.engine.endScope(result);\n      return result;\n    } catch (ex) {\n      ENV.engine.endScope(null);\n      throw ex;\n    }\n  };\n  Object.defineProperty(f2, 'name', {value: opName, configurable: true});\n\n  // tslint:disable-next-line:no-any\n  return f2 as any as T;\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {customGrad} from '../gradients';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * Computes the softmax normalized vector given the logits.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n */\n/** @doc {heading: 'Operations', subheading: 'Normalization'} */\nfunction softmax_<T extends Tensor>(logits: T|TensorLike, dim = -1): T {\n  const $logits = convertToTensor(logits, 'logits', 'softmax');\n\n  if (dim === -1) {\n    dim = $logits.rank - 1;\n  }\n  if (dim !== $logits.rank - 1) {\n    throw Error(\n        'Softmax along a non-last dimension is not yet supported. ' +\n        `Logits was rank ${$logits.rank} and dim was ${dim}`);\n  }\n\n  const customOp = customGrad(logits => {\n    // Do it in log space for numerical stability.\n    // exp(X - logSumExp(X))\n    const keepDims = true;\n    const lse = logits.logSumExp([dim], keepDims);\n    const logResult = logits.toFloat().sub(lse);\n    const y = logResult.exp() as T;\n\n    const gradFunc = (dy: T) => {\n      const dyTimesY = dy.mul(y);\n      const keepDims = true;\n      return dyTimesY.sub(dyTimesY.sum([dim], keepDims).mul(y));\n    };\n\n    return {value: y, gradFunc};\n  });\n\n  return customOp($logits);\n}\n\n/**\n * Computes the log softmax.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param axis The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n */\n/** @doc {heading: 'Operations', subheading: 'Normalization'} */\nfunction logSoftmax_<T extends Tensor>(logits: T|TensorLike, axis = -1): T {\n  const $logits = convertToTensor(logits, 'logits', 'logSoftmax');\n\n  if (axis === -1) {\n    axis = $logits.rank - 1;\n  }\n  if (axis !== $logits.rank - 1) {\n    throw Error(\n        'Log Softmax along a non-last dimension is not yet supported. ' +\n        `Logits was rank ${$logits.rank} and axis was ${axis}`);\n  }\n\n  const customOp = customGrad(logits => {\n    const keepDims = true;\n    const xMax = logits.max(axis, true);\n    const shifted = logits.sub(xMax);\n    const value =\n        shifted.toFloat().sub(shifted.exp().sum(axis, keepDims).log()) as T;\n\n    const gradFunc = (dy: T) => {\n      const softmax = value.exp();\n      return dy.sub(dy.sum(axis, keepDims).mul(softmax));\n    };\n\n    return {value, gradFunc};\n  });\n\n  return customOp($logits);\n}\n\nexport const softmax = op({softmax_});\nexport const logSoftmax = op({logSoftmax_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\n\n/**\n * Converts two real numbers to a complex number.\n *\n * Given a tensor `real` representing the real part of a complex number, and a\n * tensor `imag` representing the imaginary part of a complex number, this\n * operation returns complex numbers elementwise of the form [r0, i0, r1, i1],\n * where r represents the real part and i represents the imag part.\n *\n * The input tensors real and imag must have the same shape.\n *\n * ```js\n * const real = tf.tensor1d([2.25, 3.25]);\n * const imag = tf.tensor1d([4.75, 5.75]);\n * const complex = tf.complex(real, imag);\n *\n * complex.print();\n * ```\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction complex_<T extends Tensor>(real: T|TensorLike, imag: T|TensorLike): T {\n  const $real = convertToTensor(real, 'real', 'complex');\n  const $imag = convertToTensor(imag, 'imag', 'complex');\n  util.assertShapesMatch(\n      $real.shape, $imag.shape,\n      `real and imag shapes, ${$real.shape} and ${$imag.shape}, ` +\n          `must match in call to tf.complex().`);\n\n  return ENV.engine.runKernel(\n             backend => backend.complex($real, $imag), {$real, $imag}) as T;\n}\n\n/**\n * Returns the real part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the real part of each element in input considered as a complex number.\n *\n * If the input is real, it simply makes a clone.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.real(x).print();\n * ```\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction real_<T extends Tensor>(input: T|TensorLike): T {\n  const $input = convertToTensor(input, 'input', 'real');\n\n  return ENV.engine.runKernel(backend => backend.real($input), {$input}) as T;\n}\n\n/**\n * Returns the imaginary part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the imaginary part of each element in input considered as a complex number.\n * If input is real, a tensor of all zeros is returned.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.imag(x).print();\n * ```\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction imag_<T extends Tensor>(input: T|TensorLike): T {\n  const $input = convertToTensor(input, 'input', 'imag');\n\n  return ENV.engine.runKernel(backend => backend.imag($input), {$input}) as T;\n}\n\nexport const complex = op({complex_});\nexport const real = op({real_});\nexport const imag = op({imag_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Scalar, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, Tensor5D, Tensor6D} from '../tensor';\nimport {convertToTensor, inferShape} from '../tensor_util_env';\nimport {TensorLike, TensorLike1D, TensorLike2D, TensorLike3D, TensorLike4D, TensorLike5D, TensorLike6D, TypedArray} from '../types';\nimport {DataType, Rank, ShapeMap} from '../types';\nimport {assert, assertNonNull, flatten, getArrayFromDType, inferDtype, isTypedArray, makeOnesTypedArray, makeZerosTypedArray, sizeFromShape, toTypedArray} from '../util';\n\nimport {complex} from './complex_ops';\nimport {op} from './operation';\n\n/**\n * Creates a `tf.Tensor` with the provided values, shape and dtype.\n *\n * ```js\n * // Pass an array of values to create a vector.\n * tf.tensor([1, 2, 3, 4]).print();\n * ```\n *\n * ```js\n * // Pass a nested array of values to make a matrix or a higher\n * // dimensional tensor.\n * tf.tensor([[1, 2], [3, 4]]).print();\n * ```\n *\n * ```js\n * // Pass a flat array and specify a shape yourself.\n * tf.tensor([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor<R extends Rank>(\n    values: TensorLike, shape?: ShapeMap[R], dtype?: DataType): Tensor<R> {\n  if (dtype == null) {\n    dtype = inferDtype(values);\n  }\n  if (dtype === 'complex64') {\n    throw new Error(\n        `Cannot construct a complex64 tensor directly. ` +\n        `Please use tf.complex(real, imag).`);\n  }\n  if (!isTypedArray(values) && !Array.isArray(values) &&\n      typeof values !== 'number' && typeof values !== 'boolean' &&\n      typeof values !== 'string') {\n    throw new Error(\n        'values passed to tensor(values) must be a number/boolean/string or ' +\n        'an array of numbers/booleans/strings, or a TypedArray');\n  }\n  const inferredShape = inferShape(values);\n  if (shape != null) {\n    const providedSize = sizeFromShape(shape);\n    const inferredSize = sizeFromShape(inferredShape);\n    assert(\n        providedSize === inferredSize,\n        () =>\n            `Based on the provided shape, [${shape}], the tensor should have ` +\n            `${providedSize} values but has ${inferredSize}`);\n\n    for (let i = 0; i < inferredShape.length; ++i) {\n      const inferred = inferredShape[i];\n      const flatDimsDontMatch = i === inferredShape.length - 1 ?\n          inferred !== sizeFromShape(shape.slice(i)) :\n          true;\n      assert(\n          inferredShape[i] === shape[i] || !flatDimsDontMatch,\n          () => `Error creating a new Tensor. Inferred shape ` +\n              `(${inferredShape}) does not match the provided ` +\n              `shape (${shape}). `);\n    }\n  }\n  if (!isTypedArray(values) && !Array.isArray(values)) {\n    values = [values] as number[];\n  }\n  shape = shape || inferredShape;\n  values = dtype !== 'string' ? toTypedArray(values, dtype, ENV.get('DEBUG')) :\n                                flatten(values as string[]) as string[];\n  return Tensor.make(shape, {values}, dtype);\n}\n\n/**\n * Creates rank-0 `tf.Tensor` (scalar) with the provided value and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.scalar` as it makes the code more readable.\n *\n * ```js\n * tf.scalar(3.14).print();\n * ```\n *\n * @param value The value of the scalar.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction scalar(value: number|boolean|string, dtype?: DataType): Scalar {\n  if ((isTypedArray(value) || Array.isArray(value)) && dtype !== 'complex64') {\n    throw new Error(\n        'Error creating a new Scalar: value must be a primitive ' +\n        '(number|boolean|string)');\n  }\n  return tensor(value, [], dtype);\n}\n\n/**\n * Creates rank-1 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor1d` as it makes the code more readable.\n *\n * ```js\n * tf.tensor1d([1, 2, 3]).print();\n * ```\n *\n * @param values The values of the tensor. Can be array of numbers,\n *     or a `TypedArray`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor1d(values: TensorLike1D, dtype?: DataType): Tensor1D {\n  assertNonNull(values);\n  const inferredShape = inferShape(values);\n  if (inferredShape.length !== 1) {\n    throw new Error('tensor1d() requires values to be a flat/TypedArray');\n  }\n  return tensor(values, inferredShape as [number], dtype);\n}\n\n/**\n * Creates rank-2 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor2d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor2d([[1, 2], [3, 4]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided, it is inferred from\n *     `values`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor2d(\n    values: TensorLike2D, shape?: [number, number],\n    dtype?: DataType): Tensor2D {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 2) {\n    throw new Error('tensor2d() requires shape to have two numbers');\n  }\n  const inferredShape = inferShape(values);\n  if (inferredShape.length !== 2 && inferredShape.length !== 1) {\n    throw new Error(\n        'tensor2d() requires values to be number[][] or flat/TypedArray');\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\n        'tensor2d() requires shape to be provided when `values` ' +\n        'are a flat/TypedArray');\n  }\n  shape = shape || inferredShape as [number, number];\n  return tensor(values, shape, dtype);\n}\n\n/**\n * Creates rank-3 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor3d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor3d([[[1], [2]], [[3], [4]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided,  it is inferred from\n *     `values`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor3d(\n    values: TensorLike3D, shape?: [number, number, number],\n    dtype?: DataType): Tensor3D {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 3) {\n    throw new Error('tensor3d() requires shape to have three numbers');\n  }\n  const inferredShape = inferShape(values);\n  if (inferredShape.length !== 3 && inferredShape.length !== 1) {\n    throw new Error(\n        'tensor3d() requires values to be number[][][] or flat/TypedArray');\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\n        'tensor3d() requires shape to be provided when `values` ' +\n        'are a flat array');\n  }\n  shape = shape || inferredShape as [number, number, number];\n  return tensor(values, shape, dtype);\n}\n\n/**\n * Creates rank-4 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor4d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor4d(\n    values: TensorLike4D, shape?: [number, number, number, number],\n    dtype?: DataType): Tensor4D {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 4) {\n    throw new Error('tensor4d() requires shape to have four numbers');\n  }\n  const inferredShape = inferShape(values);\n  if (inferredShape.length !== 4 && inferredShape.length !== 1) {\n    throw new Error(\n        'tensor4d() requires values to be number[][][][] or flat/TypedArray');\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\n        'tensor4d() requires shape to be provided when `values` ' +\n        'are a flat array');\n  }\n  shape = shape || inferredShape as [number, number, number, number];\n  return tensor(values, shape, dtype);\n}\n\n/**\n * Creates rank-5 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor5d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor5d([[[[[1], [2]], [[3], [4]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor5d(\n    values: TensorLike5D, shape?: [number, number, number, number, number],\n    dtype?: DataType): Tensor5D {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 5) {\n    throw new Error('tensor5d() requires shape to have five numbers');\n  }\n  const inferredShape = inferShape(values);\n  if (inferredShape.length !== 5 && inferredShape.length !== 1) {\n    throw new Error(\n        'tensor5d() requires values to be ' +\n        'number[][][][][] or flat/TypedArray');\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\n        'tensor5d() requires shape to be provided when `values` ' +\n        'are a flat array');\n  }\n  shape = shape || inferredShape as [number, number, number, number, number];\n  return tensor(values, shape, dtype);\n}\n\n/**\n * Creates rank-6 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor6d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor6d([[[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor6d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor6d(\n    values: TensorLike6D,\n    shape?: [number, number, number, number, number, number],\n    dtype?: DataType): Tensor6D {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 6) {\n    throw new Error('tensor6d() requires shape to have six numbers');\n  }\n  const inferredShape = inferShape(values);\n  if (inferredShape.length !== 6 && inferredShape.length !== 1) {\n    throw new Error(\n        'tensor6d() requires values to be number[][][][] or flat/TypedArray');\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\n        'tensor6d() requires shape to be provided when `values` ' +\n        'are a flat array');\n  }\n  shape = shape ||\n      inferredShape as [number, number, number, number, number, number];\n  return tensor(values, shape, dtype);\n}\n\n/**\n * Creates a `tf.Tensor` with all elements set to 1.\n *\n * ```js\n * tf.ones([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n *     'float'.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction ones<R extends Rank>(\n    shape: ShapeMap[R], dtype: DataType = 'float32'): Tensor<R> {\n  if (dtype === 'complex64') {\n    const real = ones(shape, 'float32');\n    const imag = ones(shape, 'float32');\n    return complex(real, imag);\n  }\n  const values = makeOnesTypedArray(sizeFromShape(shape), dtype);\n  return Tensor.make(shape, {values}, dtype);\n}\n\n/**\n * Creates a `tf.Tensor` with all elements set to 0.\n *\n * ```js\n * tf.zeros([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Can\n *     be 'float32', 'int32' or 'bool'. Defaults to 'float'.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction zeros<R extends Rank>(\n    shape: ShapeMap[R], dtype: DataType = 'float32'): Tensor<R> {\n  if (dtype === 'complex64') {\n    const real = zeros(shape, 'float32');\n    const imag = zeros(shape, 'float32');\n    return complex(real, imag);\n  }\n  const values = makeZerosTypedArray(sizeFromShape(shape), dtype);\n  return Tensor.make(shape, {values}, dtype);\n}\n\n/**\n * Creates a `tf.Tensor` filled with a scalar value.\n *\n * ```js\n * tf.fill([2, 2], 4).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param value The scalar value to fill the tensor with.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n * 'float'.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction fill<R extends Rank>(\n    shape: ShapeMap[R], value: number|string, dtype?: DataType): Tensor<R> {\n  dtype = dtype || inferDtype(value);\n  const values = getArrayFromDType(dtype, sizeFromShape(shape)) as TypedArray;\n  values.fill(value as number);\n  return Tensor.make(shape, {values}, dtype);\n}\n\n/**\n * Creates a `tf.Tensor` with all elements set to 1 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.onesLike(x).print();\n * ```\n * @param x A tensor.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction onesLike_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'onesLike');\n  return ones($x.shape, $x.dtype) as T;\n}\n\n/**\n * Creates a `tf.Tensor` with all elements set to 0 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.zerosLike(x).print();\n * ```\n *\n * @param x The tensor of required shape.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction zerosLike_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'zerosLike');\n  return zeros($x.shape, $x.dtype) as T;\n}\n\n/**\n * Return an evenly spaced sequence of numbers over the given interval.\n *\n * ```js\n * tf.linspace(0, 9, 10).print();\n * ```\n * @param start The start value of the sequence.\n * @param stop The end value of the sequence.\n * @param num The number of values to generate.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction linspace(start: number, stop: number, num: number): Tensor1D {\n  if (num === 0) {\n    throw new Error('Cannot request zero samples');\n  }\n\n  const step = (stop - start) / (num - 1);\n\n  const values = makeZerosTypedArray(num, 'float32');\n  values[0] = start;\n  for (let i = 1; i < values.length; i++) {\n    values[i] = values[i - 1] + step;\n  }\n\n  return tensor1d(values, 'float32');\n}\n\n/**\n * Creates a new `tf.Tensor1D` filled with the numbers in the range provided.\n *\n * The tensor is a is half-open interval meaning it includes start, but\n * excludes stop. Decrementing ranges and negative step values are also\n * supported.\n *\n * ```js\n * tf.range(0, 9, 2).print();\n * ```\n *\n * @param start An integer start value\n * @param stop An integer stop value\n * @param step An integer increment (will default to 1 or -1)\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction range(\n    start: number, stop: number, step = 1,\n    dtype: 'float32'|'int32' = 'float32'): Tensor1D {\n  if (step === 0) {\n    throw new Error('Cannot have a step of zero');\n  }\n\n  const sameStartStop = start === stop;\n  const increasingRangeNegativeStep = start < stop && step < 0;\n  const decreasingRangePositiveStep = stop < start && step > 1;\n\n  if (sameStartStop || increasingRangeNegativeStep ||\n      decreasingRangePositiveStep) {\n    return zeros([0], dtype);\n  }\n\n  const numElements = Math.abs(Math.ceil((stop - start) / step));\n  const values = makeZerosTypedArray(numElements, dtype);\n\n  if (stop < start && step === 1) {\n    // Auto adjust the step's sign if it hasn't been set\n    // (or was set to 1)\n    step = -1;\n  }\n\n  values[0] = start;\n  for (let i = 1; i < values.length; i++) {\n    values[i] = values[i - 1] + step;\n  }\n\n  return tensor1d(values, dtype);\n}\n\nexport {\n  fill,\n  linspace,\n  ones,\n  range,\n  scalar,\n  tensor,\n  tensor1d,\n  tensor2d,\n  tensor3d,\n  tensor4d,\n  tensor5d,\n  tensor6d,\n  zeros\n};\n\nexport const onesLike = op({onesLike_});\nexport const zerosLike = op({zerosLike_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo, Conv3DInfo} from '../ops/conv_util';\nimport {DataId, Scalar, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, Tensor5D} from '../tensor';\nimport {DataType, DataValues, Rank, ShapeMap} from '../types';\n\n// Required information for all backends.\nexport interface BackendTimingInfo {\n  kernelMs: number;\n  getExtraProfileInfo?(): string;  // a field for additional timing information\n                                   // e.g. packing / unpacking for WebGL backend\n}\n\nexport interface TensorStorage {\n  read(dataId: DataId): Promise<DataValues>;\n  readSync(dataId: DataId): DataValues;\n  disposeData(dataId: DataId): void;\n  write(dataId: DataId, values: DataValues): void;\n  fromPixels(\n      pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement,\n      numChannels: number): Tensor3D;\n  register(dataId: DataId, shape: number[], dtype: DataType): void;\n  memory(): {unreliable: boolean;};  // Backend-specific information.\n}\n\n/** Convenient class for storing tensor-related data. */\nexport class DataStorage<T> {\n  private data = new WeakMap<DataId, T>();\n\n  constructor(private dataMover: DataMover) {}\n\n  get(dataId: DataId) {\n    if (!this.data.has(dataId)) {\n      this.dataMover.moveData(dataId);\n    }\n    return this.data.get(dataId);\n  }\n\n  set(dataId: DataId, value: T): void {\n    this.data.set(dataId, value);\n  }\n\n  has(dataId: DataId): boolean {\n    return this.data.has(dataId);\n  }\n\n  delete(dataId: DataId): boolean {\n    return this.data.delete(dataId);\n  }\n}\n\nexport interface DataMover {\n  /**\n   * To be called by backends whenever they see a dataId that they don't own.\n   * Upon calling this method, the mover will fetch the tensor from another\n   * backend and register it with the current active backend.\n   */\n  moveData(dataId: DataId): void;\n}\n\nexport interface BackendTimer {\n  time(f: () => void): Promise<BackendTimingInfo>;\n}\n\n/**\n * The interface that defines the kernels that should be implemented when\n * adding a new backend. New backends don't need to implement every one of the\n * methods, this can be done gradually (throw an error for unimplemented\n * methods).\n */\nexport class KernelBackend implements TensorStorage, BackendTimer {\n  time(f: () => void): Promise<BackendTimingInfo> {\n    throw new Error('Not yet implemented.');\n  }\n  read(dataId: object): Promise<DataValues> {\n    throw new Error('Not yet implemented.');\n  }\n  readSync(dataId: object): DataValues {\n    throw new Error('Not yet implemented.');\n  }\n  disposeData(dataId: object): void {\n    throw new Error('Not yet implemented.');\n  }\n  write(dataId: object, values: DataValues): void {\n    throw new Error('Not yet implemented.');\n  }\n  fromPixels(\n      pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement,\n      numChannels: number): Tensor<Rank.R3> {\n    throw new Error('Not yet implemented.');\n  }\n  register(dataId: object, shape: number[], dtype: DataType): void {\n    throw new Error('Not yet implemented.');\n  }\n  memory(): {unreliable: boolean; reasons?: string[]} {\n    throw new Error('Not yet implemented.');\n  }\n  /** Returns the highest precision for floats in bits (e.g. 16 or 32) */\n  floatPrecision(): number {\n    throw new Error('Not yet implemented');\n  }\n\n  batchMatMul(\n      a: Tensor3D, b: Tensor3D, transposeA: boolean,\n      transposeB: boolean): Tensor3D {\n    throw new Error('Not yet implemented');\n  }\n\n  slice<T extends Tensor>(x: T, begin: number[], size: number[]): T {\n    throw new Error('Not yet implemented');\n  }\n  stridedSlice<T extends Tensor>(\n      x: T, begin: number[], end: number[], strides: number[],\n      beginMask: number, endMask: number, ellipsisMask: number,\n      newAxisMask: number, shrinkAxisMask: number): T {\n    throw new Error('Not yet implemented');\n  }\n  reverse<T extends Tensor>(a: T, axis: number[]): T {\n    throw new Error('Not yet implemented');\n  }\n\n  concat(tensors: Tensor[], axis: number): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  neg<T extends Tensor>(a: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  add(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  addN<T extends Tensor>(tensors: T[]): T {\n    throw new Error('Not yet implemented');\n  }\n  subtract(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  multiply(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  realDivide(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  floorDiv(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  sum(x: Tensor, axes: number[]): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  prod(x: Tensor, axes: number[]): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  unsortedSegmentSum<T extends Tensor>(\n      x: T, segmentIds: Tensor1D, numSegments: number): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  argMin(x: Tensor, axis: number): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  argMax(x: Tensor, axis: number): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  equal(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  notEqual(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  less(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  lessEqual(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  greater(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  greaterEqual(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  logicalNot<T extends Tensor>(a: T): T {\n    throw new Error('Not yet implemented');\n  }\n  logicalAnd(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  logicalOr(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  where(condition: Tensor): Tensor2D {\n    throw new Error('Not yet implemented');\n  }\n  select(condition: Tensor, a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  topk<T extends Tensor>(x: T, k: number, sorted: boolean): [T, T] {\n    throw new Error('Not yet implemented');\n  }\n\n  min(x: Tensor, axes: number[]): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  minimum(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  mod(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  max(x: Tensor, axes: number[]): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  maximum(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  all(x: Tensor, axes: number[]): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  any(x: Tensor, axes: number[]): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  squaredDifference(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  ceil<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  floor<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  round<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  sign<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  pow<T extends Tensor>(a: T, b: Tensor): T {\n    throw new Error('Not yet implemented');\n  }\n  exp<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  expm1<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  log<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  log1p<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  sqrt<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  rsqrt<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  square<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  reciprocal<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  relu<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  elu<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  eluDer<T extends Tensor>(dy: T, y: T): T {\n    throw new Error('Not yet implemented');\n  }\n  selu<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  int<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  clip<T extends Tensor>(x: T, min: number, max: number): T {\n    throw new Error('Not yet implemented');\n  }\n\n  abs<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  complexAbs<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  sigmoid<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  softplus<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  sin<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  cos<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  tan<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  asin<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  acos<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  atan<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  atan2<T extends Tensor>(a: T, b: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  sinh<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  cosh<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  tanh<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  asinh<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  acosh<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  atanh<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  erf<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  step<T extends Tensor>(x: T, alpha: number): T {\n    throw new Error('Not yet implemented');\n  }\n\n  conv2d(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n  conv2dDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n  conv2dDerFilter(x: Tensor4D, dY: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  depthwiseConv2D(input: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n  depthwiseConv2DDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n  depthwiseConv2DDerFilter(x: Tensor4D, dY: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n  conv3d(x: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n    throw new Error('Not yet implemented');\n  }\n  conv3dDerInput(dy: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo):\n      Tensor5D {\n    throw new Error('Not yet implemented');\n  }\n  conv3dDerFilter(x: Tensor5D, dY: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n    throw new Error('Not yet implemented');\n  }\n  maxPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n  maxPoolBackprop(dy: Tensor4D, x: Tensor4D, y: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n  avgPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n  avgPoolBackprop(dy: Tensor4D, x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  reshape<T extends Tensor, R extends Rank>(x: T, shape: ShapeMap[R]):\n      Tensor<R> {\n    throw new Error('Not yet implemented');\n  }\n  cast<T extends Tensor>(x: T, dtype: DataType): T {\n    throw new Error('Not yet implemented');\n  }\n\n  tile<T extends Tensor>(x: T, reps: number[]): T {\n    throw new Error('Not yet implemented');\n  }\n\n  pad<T extends Tensor>(\n      x: T, paddings: Array<[number, number]>, constantValue: number): T {\n    throw new Error('Not yet implemented');\n  }\n\n  transpose<T extends Tensor>(x: T, perm: number[]): T {\n    throw new Error('Not yet implemented');\n  }\n\n  gather<T extends Tensor>(x: T, indices: Tensor1D, axis: number): T {\n    throw new Error('Not yet implemented');\n  }\n\n  gatherND(x: Tensor, indices: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  scatterND<R extends Rank>(\n      indices: Tensor, updates: Tensor, shape: ShapeMap[R]): Tensor<R> {\n    throw new Error('Not yet implemented');\n  }\n\n  batchToSpaceND<T extends Tensor>(\n      x: T, blockShape: number[], crops: number[][]): T {\n    throw new Error('Not yet implemented');\n  }\n\n  spaceToBatchND<T extends Tensor>(\n      x: T, blockShape: number[], paddings: number[][]): T {\n    throw new Error('Not yet implemented');\n  }\n\n  resizeBilinear(\n      x: Tensor4D, newHeight: number, newWidth: number,\n      alignCorners: boolean): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  resizeBilinearBackprop(dy: Tensor4D, x: Tensor4D, alignCorners: boolean):\n      Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  resizeNearestNeighbor(\n      x: Tensor4D, newHEight: number, newWidth: number,\n      alignCorners: boolean): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  resizeNearestNeighborBackprop(\n      dy: Tensor4D, x: Tensor4D, alignCorners: boolean): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  batchNormalization(\n      x: Tensor4D, mean: Tensor4D|Tensor1D, variance: Tensor4D|Tensor1D,\n      varianceEpsilon: number, scale?: Tensor4D|Tensor1D,\n      offset?: Tensor4D|Tensor1D): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  localResponseNormalization4D(\n      x: Tensor4D, radius: number, bias: number, alpha: number,\n      beta: number): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  LRNGrad(\n      dy: Tensor4D, inputImage: Tensor4D, outputImage: Tensor4D, radius: number,\n      bias: number, alpha: number, beta: number): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  multinomial(\n      logits: Tensor2D, normalized: boolean, numSamples: number,\n      seed: number): Tensor2D {\n    throw new Error('Not yet implemented');\n  }\n\n  oneHot(indices: Tensor1D, depth: number, onValue: number, offValue: number):\n      Tensor2D {\n    throw new Error('Not yet implemented');\n  }\n\n  cumsum(x: Tensor, axis: number, exclusive: boolean, reverse: boolean):\n      Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  nonMaxSuppression(\n      boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number,\n      iouThreshold: number, scoreThreshold?: number): Tensor1D {\n    throw new Error('Not yet implemented');\n  }\n\n  fft(x: Tensor2D): Tensor2D {\n    throw new Error('Not yet implemented');\n  }\n  ifft(x: Tensor2D): Tensor2D {\n    throw new Error('Not yet implemented');\n  }\n  complex<T extends Tensor>(real: T, imag: T): T {\n    throw new Error('Not yet implemented');\n  }\n  real<T extends Tensor>(input: T): T {\n    throw new Error('Not yet implemented');\n  }\n  imag<T extends Tensor>(input: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  cropAndResize(\n      image: Tensor4D, boxes: Tensor2D, boxIndex: Tensor1D,\n      cropSize: [number, number], method: 'bilinear'|'nearest',\n      extrapolationValue: number): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  depthToSpace(x: Tensor4D, blockSize: number, dataFormat: string): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  // Aligns with the \"SplitV\" kernel in TensorFlow.\n  split<T extends Tensor>(value: T, sizeSplits: number[], axis: number): T[] {\n    throw new Error('Not yet implemented');\n  }\n\n  sparseToDense<R extends Rank>(\n      sparseIndices: Tensor, sparseValues: Tensor, outputShape: ShapeMap[R],\n      defaultValue: Scalar): Tensor<R> {\n    throw new Error('Not yet implemented');\n  }\n  /**\n   * Sets the data mover for this backend. Backends should use the mover to\n   * move data from other backends to this backend.\n   */\n  setDataMover(dataMover: DataMover): void {\n    throw new Error('Not yet implemented');\n  }\n\n  dispose(): void {\n    throw new Error('Not yet implemented');\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {scalar, zeros} from '../ops/tensor_ops';\nimport {Tensor} from '../tensor';\nimport {Rank} from '../types';\nimport {DataType, ShapeMap} from '../types';\nimport {hasEncodingLoss} from '../util';\nimport {KernelBackend} from './backend';\n\nexport function castTensor<T extends Tensor>(\n    x: T, dtype: DataType, backend: KernelBackend): T {\n  if (dtype === 'complex64') {\n    if (x.dtype === 'complex64') {\n      return x.clone();\n    }\n    const zerosTensor = zeros(x.shape);\n    const floatX = x.toFloat();\n    const result = backend.complex(floatX, zerosTensor);\n    zerosTensor.dispose();\n    floatX.dispose();\n    return result as T;\n  }\n\n  if (!hasEncodingLoss(x.dtype, dtype)) {\n    // We don't change the underlying data, since we cast to higher\n    // precision.\n    return Tensor.make(x.shape, {dataId: x.dataId}, dtype) as T;\n  }\n  if (x.dtype === 'complex64') {\n    const real = backend.real(x);\n    const result = real.cast(dtype);\n    real.dispose();\n    return result;\n  }\n  if (dtype === 'int32') {\n    return backend.int(x);\n  } else if (dtype === 'bool') {\n    const zero = scalar(0, x.dtype);\n    const result = backend.notEqual(x, zero) as T;\n    zero.dispose();\n    return result;\n  } else {\n    throw new Error(`Error in Cast: unknown dtype argument (${dtype})`);\n  }\n}\n\nexport function reshapeTensor<T extends Tensor, R extends Rank>(\n    x: T, shape: ShapeMap[R]): Tensor<R> {\n  return Tensor.make(shape, {dataId: x.dataId}, x.dtype);\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {TypedArray} from '../types';\n/**\n * Merges real and imaginary Float32Arrays into a single complex Float32Array.\n *\n * The memory layout is interleaved as follows:\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n * complex: [r0, i0, r1, i1, r2, i2]\n *\n * This is the inverse of splitRealAndImagArrays.\n *\n * @param real The real values of the complex tensor values.\n * @param imag The imag values of the complex tensor values.\n * @returns A complex tensor as a Float32Array with merged values.\n */\nexport function mergeRealAndImagArrays(\n    real: Float32Array, imag: Float32Array): Float32Array {\n  if (real.length !== imag.length) {\n    throw new Error(\n        `Cannot merge real and imag arrays of different lengths. real:` +\n        `${real.length}, imag: ${imag.length}.`);\n  }\n  const result = new Float32Array(real.length * 2);\n  for (let i = 0; i < result.length; i += 2) {\n    result[i] = real[i / 2];\n    result[i + 1] = imag[i / 2];\n  }\n  return result;\n}\n\n/**\n * Splits a complex Float32Array into real and imag parts.\n *\n * The memory layout is interleaved as follows:\n * complex: [r0, i0, r1, i1, r2, i2]\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n *\n * This is the inverse of mergeRealAndImagArrays.\n *\n * @param complex The complex tensor values.\n * @returns An object with real and imag Float32Array components of the complex\n *     tensor.\n */\nexport function splitRealAndImagArrays(complex: Float32Array):\n    {real: Float32Array, imag: Float32Array} {\n  const real = new Float32Array(complex.length / 2);\n  const imag = new Float32Array(complex.length / 2);\n  for (let i = 0; i < complex.length; i += 2) {\n    real[i / 2] = complex[i];\n    imag[i / 2] = complex[i + 1];\n  }\n  return {real, imag};\n}\n\n/**\n * Extracts even indexed complex values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithEvenIndex(complex: Float32Array):\n    {real: Float32Array, imag: Float32Array} {\n  const len = Math.ceil(complex.length / 4);\n  const real = new Float32Array(len);\n  const imag = new Float32Array(len);\n  for (let i = 0; i < complex.length; i += 4) {\n    real[Math.floor(i / 4)] = complex[i];\n    imag[Math.floor(i / 4)] = complex[i + 1];\n  }\n  return {real, imag};\n}\n\n/**\n * Extracts odd indexed comple values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithOddIndex(complex: Float32Array):\n    {real: Float32Array, imag: Float32Array} {\n  const len = Math.floor(complex.length / 4);\n  const real = new Float32Array(len);\n  const imag = new Float32Array(len);\n  for (let i = 2; i < complex.length; i += 4) {\n    real[Math.floor(i / 4)] = complex[i];\n    imag[Math.floor(i / 4)] = complex[i + 1];\n  }\n  return {real, imag};\n}\n\n/**\n * Get the map representing a complex value in the given array.\n * @param complex The complex tensor values.\n * @param index An index of the target complex value.\n */\nexport function getComplexWithIndex(\n    complex: Float32Array, index: number): {real: number, imag: number} {\n  const real = complex[index * 2];\n  const imag = complex[index * 2 + 1];\n  return {real, imag};\n}\n\n/**\n * Insert a given complex value into the TypedArray.\n * @param data The array in which the complex value is inserted.\n * @param c The complex value to be inserted.\n * @param index An index of the target complex value.\n */\nexport function assignToTypedArray(\n    data: TypedArray, real: number, imag: number, index: number) {\n  data[index * 2] = real;\n  data[index * 2 + 1] = imag;\n}\n\n/**\n * Make the list of exponent terms used by FFT.\n */\nexport function exponents(\n    n: number, inverse: boolean): {real: Float32Array, imag: Float32Array} {\n  const real = new Float32Array(n / 2);\n  const imag = new Float32Array(n / 2);\n  for (let i = 0; i < Math.ceil(n / 2); i++) {\n    const x = (inverse ? 2 : -2) * Math.PI * (i / n);\n    real[i] = Math.cos(x);\n    imag[i] = Math.sin(x);\n  }\n  return {real, imag};\n}\n\n/**\n * Make the exponent term used by FFT.\n */\nexport function exponent(\n    k: number, n: number, inverse: boolean): {real: number, imag: number} {\n  const x = (inverse ? 2 : -2) * Math.PI * (k / n);\n  const real = Math.cos(x);\n  const imag = Math.sin(x);\n  return {real, imag};\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Implementation of the NonMaxSuppression kernel shared between webgl and cpu.\n */\n\nimport {tensor1d} from '../ops/tensor_ops';\nimport {Tensor1D} from '../tensor';\nimport {TypedArray} from '../types';\n\nexport function nonMaxSuppressionImpl(\n    boxes: TypedArray, scores: TypedArray, maxOutputSize: number,\n    iouThreshold: number, scoreThreshold: number): Tensor1D {\n  const candidates = Array.from(scores)\n                         .map((score, boxIndex) => ({score, boxIndex}))\n                         .filter(c => c.score > scoreThreshold)\n                         .sort((c1, c2) => c2.score - c1.score);\n\n  const selected: number[] = [];\n\n  for (let i = 0; i < candidates.length; i++) {\n    const {score, boxIndex} = candidates[i];\n    if (score < scoreThreshold) {\n      break;\n    }\n\n    let ignoreCandidate = false;\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = intersectionOverUnion(boxes, boxIndex, selected[j]);\n      if (iou >= iouThreshold) {\n        ignoreCandidate = true;\n        break;\n      }\n    }\n\n    if (!ignoreCandidate) {\n      selected.push(boxIndex);\n      if (selected.length >= maxOutputSize) {\n        break;\n      }\n    }\n  }\n\n  return tensor1d(selected, 'int32');\n}\n\nfunction intersectionOverUnion(boxes: TypedArray, i: number, j: number) {\n  const iCoord = boxes.subarray(i * 4, i * 4 + 4);\n  const jCoord = boxes.subarray(j * 4, j * 4 + 4);\n  const yminI = Math.min(iCoord[0], iCoord[2]);\n  const xminI = Math.min(iCoord[1], iCoord[3]);\n  const ymaxI = Math.max(iCoord[0], iCoord[2]);\n  const xmaxI = Math.max(iCoord[1], iCoord[3]);\n  const yminJ = Math.min(jCoord[0], jCoord[2]);\n  const xminJ = Math.min(jCoord[1], jCoord[3]);\n  const ymaxJ = Math.max(jCoord[0], jCoord[2]);\n  const xmaxJ = Math.max(jCoord[1], jCoord[3]);\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0;\n  }\n  const intersectionYmin = Math.max(yminI, yminJ);\n  const intersectionXmin = Math.max(xminI, xminJ);\n  const intersectionYmax = Math.min(ymaxI, ymaxJ);\n  const intersectionXmax = Math.min(xmaxI, xmaxJ);\n  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *\n      Math.max(intersectionXmax - intersectionXmin, 0.0);\n  return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\n\n/** Shared implementation of the split kernel across WebGL and CPU. */\nexport function split<T extends Tensor>(\n    x: T, sizeSplits: number[], axis: number): T[] {\n  const begin = Array(x.rank).fill(0);\n  const size = x.shape.slice();\n  return sizeSplits.map(s => {\n    size[axis] = s;\n    const slice = x.slice(begin, size);\n    begin[axis] += s;\n    return slice;\n  });\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/** An implementation of the TopK kernel shared between webgl and cpu. */\n\nimport {tensor} from '../ops/tensor_ops';\nimport {Tensor} from '../tensor';\nimport {NumericDataType, TypedArray} from '../types';\nimport {getTypedArrayFromDType} from '../util';\n\nexport function topkImpl<T extends Tensor>(\n    x: TypedArray, xShape: number[], xDtype: NumericDataType, k: number,\n    sorted: boolean): [T, T] {\n  // Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim.\n  const lastDim = xShape[xShape.length - 1];\n  const [batch, size] = [x.length / lastDim, lastDim];\n  const allTopKVals = getTypedArrayFromDType(xDtype, batch * k);\n  const allTopKIndices = getTypedArrayFromDType('int32', batch * k);\n\n  for (let b = 0; b < batch; b++) {\n    const offset = b * size;\n    const vals = x.subarray(offset, offset + size);\n    const valAndInd: Array<{value: number, index: number}> = [];\n    for (let i = 0; i < vals.length; i++) {\n      valAndInd.push({value: vals[i], index: i});\n    }\n    valAndInd.sort((a, b) => b.value - a.value);\n\n    const outOffset = b * k;\n    const topKVals = allTopKVals.subarray(outOffset, outOffset + k);\n    const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);\n    for (let i = 0; i < k; i++) {\n      topKVals[i] = valAndInd[i].value;\n      topKIndices[i] = valAndInd[i].index;\n    }\n  }\n  // Reshape back to the original input shape, except that the last\n  // dimension is k.\n  const outputShape = xShape.slice();\n  outputShape[outputShape.length - 1] = k;\n  return [\n    tensor(allTopKVals, outputShape, xDtype) as T,\n    tensor(allTopKIndices, outputShape, 'int32') as T\n  ];\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ReduceInfo} from '../../ops/reduce_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ArgMinMaxProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(reduceInfo: ReduceInfo, op: 'max'|'min', firstPass: boolean) {\n    const windowSize = reduceInfo.windowSize;\n    const batchSize = reduceInfo.batchSize;\n    const inSize = reduceInfo.inSize;\n    const outSize = Math.ceil(inSize / windowSize);\n    if (!firstPass) {\n      this.variableNames.push('bestIndicesA');\n    }\n    this.outputShape = [batchSize, outSize];\n    const compOp = (op === 'max') ? '>' : '<';\n    const indexSnippet = firstPass ?\n        'inOffset + i;' :\n        'round(getBestIndicesA(batch, inOffset + i));';\n\n    this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          int inIdx = ${indexSnippet};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${compOp} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class AvgPool2DBackpropProgram implements GPGPUProgram {\n  variableNames = ['dy'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv2DInfo) {\n    this.outputShape = convInfo.inShape;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n\n    const avgMultiplier = 1 / (filterHeight * filterWidth);\n\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth};\n            wC+= ${dilationWidth}) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Returns the dimensions in the input shape that are broadcasted to\n * produce the provided output shape.\n *\n * The returned dimensions are 0-indexed and sorted. An example:\n * inShape = [4, 1, 3]\n * outShape = [5, 4, 3, 3]\n * result = [1]. Dimension 1 (2nd dimension of input) gets broadcasted 1 => 3.\n */\nexport function getBroadcastDims(\n    inShape: number[], outShape: number[]): number[] {\n  const inRank = inShape.length;\n  const dims: number[] = [];\n  for (let i = 0; i < inRank; i++) {\n    const dim = inRank - 1 - i;\n    const a = inShape[dim] || 1;\n    const b = outShape[outShape.length - 1 - i] || 1;\n    if (b > 1 && a === 1) {\n      dims.unshift(dim);\n    }\n  }\n  return dims;\n}\n\n/**\n * Returns the axes in the output space that should be reduced to produce\n * the input space.\n */\nexport function getReductionAxes(\n    inShape: number[], outShape: number[]): number[] {\n  const result: number[] = [];\n  for (let i = 0; i < outShape.length; i++) {\n    const inDim = inShape[inShape.length - i - 1];\n    const outAxis = outShape.length - i - 1;\n    const outDim = outShape[outAxis];\n    if (inDim == null || (inDim === 1 && outDim > 1)) {\n      result.unshift(outAxis);\n    }\n  }\n  return result;\n}\n\n/**\n * Given the output of `getBroadcastDims()`, returns true if the broadcasting\n * is along the outer-most dimensions of the input.\n */\nexport function broadcastDimsAreOuter(dims: number[]): boolean {\n  for (let i = 0; i < dims.length; i++) {\n    if (dims[i] !== i) {\n      return false;\n    }\n  }\n  return true;\n}\n\nexport function assertAndGetBroadcastShape(\n    shapeA: number[], shapeB: number[]): number[] {\n  const result: number[] = [];\n  const l = Math.max(shapeA.length, shapeB.length);\n\n  for (let i = 0; i < l; i++) {\n    let a = shapeA[shapeA.length - i - 1];\n    if (a == null) {\n      a = 1;\n    }\n    let b = shapeB[shapeB.length - i - 1];\n    if (b == null) {\n      b = 1;\n    }\n    if (a === 1) {\n      result.unshift(b);\n    } else if (b === 1) {\n      result.unshift(a);\n    } else if (a !== b) {\n      const errMsg = `Operands could not be broadcast together with shapes ` +\n          `${shapeA} and ${shapeB}.`;\n      throw Error(errMsg);\n    } else {\n      result.unshift(a);\n    }\n  }\n  return result;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as broadcast_util from '../../ops/broadcast_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class BatchNormProgram implements GPGPUProgram {\n  variableNames: string[];\n  outputShape: number[] = [];\n  userCode: string;\n  supportsBroadcasting = true;\n\n  constructor(\n      xShape: number[], meanShape: number[], varianceShape: number[],\n      offsetShape: number[]|null, scaleShape: number[]|null,\n      varianceEpsilon: number) {\n    this.variableNames = ['x', 'mean', 'variance'];\n    broadcast_util.assertAndGetBroadcastShape(xShape, meanShape);\n    broadcast_util.assertAndGetBroadcastShape(xShape, varianceShape);\n\n    let offsetSnippet = '0.0';\n    if (offsetShape != null) {\n      broadcast_util.assertAndGetBroadcastShape(xShape, offsetShape);\n      this.variableNames.push('offset');\n      offsetSnippet = 'getOffsetAtOutCoords()';\n    }\n\n    let scaleSnippet = '1.0';\n    if (scaleShape != null) {\n      broadcast_util.assertAndGetBroadcastShape(xShape, scaleShape);\n      this.variableNames.push('scale');\n      scaleSnippet = 'getScaleAtOutCoords()';\n    }\n\n    this.outputShape = xShape;\n    this.userCode = `\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = ${offsetSnippet};\n        float scale = ${scaleSnippet};\n        float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as broadcast_util from '../../ops/broadcast_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class BatchNormPackedProgram implements GPGPUProgram {\n  variableNames: string[];\n  outputShape: number[];\n  userCode: string;\n  supportsBroadcasting = true;\n  usesPackedTextures = true;\n\n  constructor(\n      xShape: number[], meanShape: number[], varianceShape: number[],\n      offsetShape: number[]|null, scaleShape: number[]|null,\n      varianceEpsilon: number) {\n    this.variableNames = ['x', 'mean', 'variance'];\n    broadcast_util.assertAndGetBroadcastShape(xShape, meanShape);\n    broadcast_util.assertAndGetBroadcastShape(xShape, varianceShape);\n\n    let offsetSnippet = 'vec4(0.0)';\n    if (offsetShape != null) {\n      broadcast_util.assertAndGetBroadcastShape(xShape, offsetShape);\n      this.variableNames.push('offset');\n      offsetSnippet = 'getOffsetAtOutCoords()';\n    }\n\n    let scaleSnippet = 'vec4(1.0)';\n    if (scaleShape != null) {\n      broadcast_util.assertAndGetBroadcastShape(xShape, scaleShape);\n      this.variableNames.push('scale');\n      scaleSnippet = 'getScaleAtOutCoords()';\n    }\n\n    this.outputShape = xShape;\n    this.userCode = `\n      void main() {\n        vec4 offset = ${offsetSnippet};\n        vec4 scale = ${scaleSnippet};\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as broadcast_util from '../../ops/broadcast_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\n// (Ar + Ai)(Br + Bi) =\n// ArBr + ArBi + AiBr + AiBi = ArBr - AB + ArBi + AiBr\n// Yr = ArBr - AB\n// Yi = ArBi + AiBr\nexport const COMPLEX_MULTIPLY = {\n  REAL: 'return areal * breal - aimag * bimag;',\n  IMAG: 'return areal * bimag + aimag * breal;'\n};\n\nexport class BinaryOpComplexProgram implements GPGPUProgram {\n  variableNames = ['AReal', 'AImag', 'BReal', 'BImag'];\n  userCode: string;\n  outputShape: number[];\n  supportsBroadcasting = true;\n\n  constructor(op: string, aShape: number[], bShape: number[]) {\n    this.outputShape =\n        broadcast_util.assertAndGetBroadcastShape(aShape, bShape);\n\n    this.userCode = `\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ${op}\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as broadcast_util from '../../ops/broadcast_util';\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\n\nconst CHECK_NAN_SNIPPET = `\n  if (isNaN(a)) return a;\n  if (isNaN(b)) return b;\n`;\n\nexport const ADD = 'return a + b;';\nexport const SUB = 'return a - b;';\nexport const MUL = 'return a * b;';\nexport const DIV = `if (a == b) return 1.0;\n  return a / b;`;\n\n// We use native integer division to deal with floating point imprecision. Since\n// we implement floor division and glsl implements truncated division, we\n// correct for this by subtracting 1 from result when the result is negative and\n// there is a remainder.\nexport const INT_DIV = `\n  float resultSign = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  int result = ia / ib;\n  int amodb = ia - ib * result;\n\n  if (resultSign < 0.0 && amodb != 0) {\n    result -= 1;\n  }\n  return float(result);\n`;\n\nexport const POW = `\nif(a < 0.0 && floor(b) < b){\n  return NAN;\n}\nreturn (round(mod(b, 2.0)) == 0 || round(mod(b, 2.0)) == 2) ?\n    pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`;\nexport const SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';\n\nexport const EQUAL = `return float(a == b);`;\n\nexport const NOT_EQUAL = `return float(a != b);`;\n\nexport const LESS = `return float(a < b);`;\n\nexport const LESS_EQUAL = `return float(a <= b);`;\n\nexport const GREATER = `return float(a > b);`;\n\nexport const GREATER_EQUAL = `return float(a >= b);`;\n\nexport const LOGICAL_AND = `return float(a >= 1.0 && b >= 1.0);`;\n\nexport const LOGICAL_OR = `return float(a >= 1.0 || b >= 1.0);`;\n\nexport const MAX = CHECK_NAN_SNIPPET + `\n  return max(a, b);\n`;\nexport const MIN = CHECK_NAN_SNIPPET + `\n  return min(a, b);\n`;\nexport const MOD = `if (b == 0.0) return NAN;\n  return mod(a, b);`;\n\nexport const ATAN2 = CHECK_NAN_SNIPPET + `\n  return atan(a, b);\n`;\n\nexport const ELU_DER = `return (b >= 1.0) ? a : a * (b + 1.0);`;\n\nexport class BinaryOpProgram implements GPGPUProgram {\n  variableNames = ['A', 'B'];\n  outputShape: number[];\n  userCode: string;\n  supportsBroadcasting = true;\n\n  // Caching uniform location for speed.\n  startLoc: WebGLUniformLocation;\n\n  constructor(op: string, aShape: number[], bShape: number[]) {\n    this.outputShape =\n        broadcast_util.assertAndGetBroadcastShape(aShape, bShape);\n    this.userCode = `\n      uniform float NAN;\n      float binaryOperation(float a, float b) {\n        ${op}\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    `;\n  }\n\n  getCustomSetupFunc() {\n    return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n      if (this.startLoc == null) {\n        this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'NAN');\n        if (this.startLoc == null) {\n          // This means the compiler has optimized and realized it doesn't need\n          // the uniform.\n          return;\n        }\n      }\n      gpgpu.gl.uniform1f(this.startLoc, NaN);\n    };\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ClipProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  userCode: string;\n  outputShape: number[];\n\n  constructor(aShape: number[], min: number, max: number) {\n    this.outputShape = aShape;\n    this.userCode = `\n      void main() {\n        float value = getAAtOutCoords();\n        if (isNaN(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, float(${min}), float(${max})));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ClipPackedProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  usesPackedTextures = true;\n  userCode: string;\n  outputShape: number[];\n\n  constructor(aShape: number[], min: number, max: number) {\n    this.outputShape = aShape;\n    this.userCode = `\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (hasNaN(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(${min}), vec4(${max})));\n      }\n    `;\n  }\n}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ComplexAbsProgram implements GPGPUProgram {\n  variableNames = ['real', 'imag'];\n  userCode: string;\n  outputShape: number[];\n\n  constructor(shape: number[]) {\n    this.outputShape = shape;\n    this.userCode = `\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as concat_util from '../../ops/concat_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ConcatProgram implements GPGPUProgram {\n  variableNames = ['A', 'B'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  // Concats 2d tensors along axis=1. See comments in MathBackendWebGL.concat().\n  constructor(aShape: [number, number], bShape: [number, number]) {\n    this.outputShape =\n        concat_util.computeOutShape([aShape, bShape], 1 /* axis */);\n\n    this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        float value = 0.0;\n        if (yC < ${aShape[1]}) {\n          value = getA(yR, yC);\n        } else {\n          yC -= ${aShape[1]};\n          value = getB(yR, yC);\n        }\n\n        setOutput(value);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo, Conv3DInfo} from '../../ops/conv_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class Conv2DDerFilterProgram implements GPGPUProgram {\n  variableNames = ['x', 'dy'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv2DInfo) {\n    this.outputShape = convInfo.filterShape;\n\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n\nexport class Conv2DDerInputProgram implements GPGPUProgram {\n  variableNames = ['dy', 'W'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv2DInfo) {\n    this.outputShape = convInfo.inShape;\n\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n\n    const padTop = filterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, d2);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n\nexport class Conv3DDerFilterProgram implements GPGPUProgram {\n  variableNames = ['x', 'dy'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv3DInfo) {\n    this.outputShape = convInfo.filterShape;\n\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const padFront = convInfo.padInfo.front;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n\n    this.userCode = `\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {\n            int xF = wF + yF * ${strideDepth} - ${padFront};\n\n            if (xF < 0 || xF >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n              int xR = wR + yR * ${strideHeight} - ${padTop};\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n                int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n\nexport class Conv3DDerInputProgram implements GPGPUProgram {\n  variableNames = ['dy', 'W'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv3DInfo) {\n    this.outputShape = convInfo.inShape;\n\n    const filterDepth = convInfo.filterDepth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n\n    const padFront = filterDepth - 1 - convInfo.padInfo.front;\n    const padTop = filterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n\n    this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          float dyF = float(dyFCorner + wF) / ${strideDepth}.0;\n\n          if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${filterDepth} - 1 - wF;\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ${filterHeight} - 1 - wR;\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ${filterWidth} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class DepthwiseConv2DDerFilterProgram implements GPGPUProgram {\n  variableNames = ['x', 'dy'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv2DInfo) {\n    this.outputShape = convInfo.filterShape;\n\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const channelMul = convInfo.outChannels / convInfo.inChannels;\n\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ${channelMul} + dm;\n\n        float dotProd = 0.0;\n\n        // TODO: Vec4 over the batch size\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n\nexport class DepthwiseConv2DDerInputProgram implements GPGPUProgram {\n  variableNames = ['dy', 'W'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv2DInfo) {\n    this.outputShape = convInfo.inShape;\n\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n\n    const padTop = filterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n    const channelMul = convInfo.outChannels / convInfo.inChannels;\n\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            // TODO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${channelMul}; dm++) {\n              int d2 = d1 * ${channelMul} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo, Conv3DInfo} from '../../ops/conv_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class Conv2DProgram implements GPGPUProgram {\n  variableNames = ['x', 'W'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv2DInfo) {\n    this.outputShape = convInfo.outShape;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n\n    const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n    const inputDepthVec4Remainder = convInfo.inChannels % 4;\n\n    this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${convInfo.inWidth}) {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n              vec4 xValues = vec4(\n                getX(batch, xR, xC, d1),\n                getX(batch, xR, xC, d1 + 1),\n                getX(batch, xR, xC, d1 + 2),\n                getX(batch, xR, xC, d1 + 3)\n              );\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              dotProd += dot(xValues, wValues);\n            }\n\n            if (${inputDepthVec4Remainder === 1}) {\n              dotProd +=\n                getX(batch, xR, xC, ${inputDepthNearestVec4}) *\n                getW(wR, wC, ${inputDepthNearestVec4}, d2);\n            } else if (${inputDepthVec4Remainder === 2}) {\n              vec2 xValues = vec2(\n                getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)\n              );\n              vec2 wValues = vec2(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)\n              );\n              dotProd += dot(xValues, wValues);\n            } else if (${inputDepthVec4Remainder === 3}) {\n              vec3 xValues = vec3(\n                getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),\n                getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)\n              );\n              vec3 wValues = vec3(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)\n              );\n              dotProd += dot(xValues, wValues);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n\nexport class Conv3DProgram implements GPGPUProgram {\n  variableNames = ['x', 'W'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv3DInfo) {\n    this.outputShape = convInfo.outShape;\n    const padFront = convInfo.padInfo.front;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationDepth = convInfo.dilationDepth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const filterDepth = convInfo.filterDepth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n\n    const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n    const inputDepthVec4Remainder = convInfo.inChannels % 4;\n\n    this.userCode = `\n      const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${\n        strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          int xF = xFCorner + wF * ${dilationDepth};\n\n          if (xF < 0 || xF >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            int xR = xRCorner + wR * ${dilationHeight};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${inputDepthVec4Remainder === 1}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);\n              } else if (${inputDepthVec4Remainder === 2}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${inputDepthVec4Remainder === 3}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class DepthwiseConv2DProgram implements GPGPUProgram {\n  variableNames = ['x', 'W'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv2DInfo) {\n    this.outputShape = convInfo.outShape;\n\n    const xNumRows = convInfo.inHeight;\n    const xNumCols = convInfo.inWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const channelMul = convInfo.outChannels / convInfo.inChannels;\n\n    this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${channelMul};\n        int q = d2 - d1 * ${channelMul};\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TODO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${xNumRows}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${xNumCols}) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class DepthwiseConvPacked2DProgram implements GPGPUProgram {\n  variableNames = ['x', 'W'];\n  usesPackedTextures = true;\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv2DInfo) {\n    this.outputShape = convInfo.outShape;\n\n    const xNumRows = convInfo.inHeight;\n    const xNumCols = convInfo.inWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const texelsAcross = Math.ceil((filterWidth + 1) / 2);\n\n    let mainLoop = `int xR; int xC;`;\n\n    for (let r = 0; r < filterHeight; r++) {\n      for (let c = -padLeft; c < texelsAcross * 2; c++) {\n        mainLoop += `vec4 ${xTexelName(r, c)} = vec4(0.);`;\n      }\n\n      for (let c = 0; c < filterWidth; c++) {\n        mainLoop += `\n          vec4 wR${r}C${c} = vec4(0.);\n          vec4 xR${r}C${c} = vec4(0.);`;\n      }\n    }\n\n    /**\n     * This vectorized implementation of depthwiseConv works by gathering the\n     * values needed for each output channel's dot product into vec4's and then\n     * multiplying them all together (this happens in the final double for-loop\n     * below). Most of the main loop consists of constructing these vec4's with\n     * the minimum number of texture2D calls as possible, which entails logic\n     * for making use of all four returned values from a texture2D call at once.\n     */\n    for (let r = 0; r < filterHeight; r++) {\n      for (let c = 0; c < texelsAcross; c++) {\n        const col = c * 2;\n        const left = c * 2 + padLeft;\n\n        mainLoop += `\n          xR = xRCorner + ${r};\n          xC = xCCorner + ${left};\n\n          if(xR >= 0 && xR < ${xNumRows} && xC >= 0 && xC < ${xNumCols}) {\n            ${xTexelName(r, left)} = getX(batch, xR, xC, d1);\n          }`;\n\n        if (padLeft === 0) {\n          if (col < filterWidth && c === texelsAcross - 1) {\n            if (strideWidth > 1) {\n              mainLoop += `\n                vec4 ${xTexelName(r, left + 2)} = vec4(0.);\n\n                if(xR >= 0 && xR < ${xNumRows} && xC + 2 < ${xNumCols}) {\n                  ${xTexelName(r, left + 2)} = getX(batch, xR, xC + 2, d1);\n                }`;\n            }\n\n            mainLoop += `\n              xR${r}C${left} = ${constructTexel(r, left, strideWidth, padLeft)};\n            `;\n          }\n        } else if (c === 0) {\n          mainLoop += `\n            if(xR >= 0 && xR < ${xNumRows} && xC - 2 >= 0) {\n              ${xTexelName(r, left - 2)} = getX(batch, xR, xC - 2, d1);\n            }`;\n        }\n\n        if (col > 0) {\n          mainLoop += `xR${r}C${left - 2} =\n            ${constructTexel(r, left - 2, strideWidth, padLeft)};`;\n        }\n\n        if (left - 1 >= 0 && left - 1 < filterWidth) {\n          mainLoop += `xR${r}C${left - 1} =\n              ${constructTexel(r, left - 1, strideWidth, padLeft)};`;\n        }\n\n        if (col < filterWidth) {\n          mainLoop += `\n            vec4 wTexel${r}C${col} = getW(${r}, ${col}, d1, q);\n            wR${r}C${col} = vec4(wTexel${r}C${col}.xz, wTexel${r}C${col}.xz);\n          `;\n\n          if (col + 1 < filterWidth) {\n            mainLoop += `\n              vec4 wTexelR${r}C${col + 1} = getW(${r}, ${col + 1}, d1, q);\n              wR${r}C${col + 1} =\n                vec4(wTexelR${r}C${col + 1}.xz, wTexelR${r}C${col + 1}.xz);`;\n          }\n        }\n      }\n    }\n\n    for (let r = 0; r < filterHeight; r++) {\n      for (let c = 0; c < filterWidth; c++) {\n        mainLoop += `result += xR${r}C${c} * wR${r}C${c};`;\n      }\n    }\n\n    this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2;\n        int q = 0;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        vec4 result = vec4(0.);\n\n        ${mainLoop}\n\n        setOutput(result);\n      }\n    `;\n  }\n}\n\nfunction xTexelName(r: number, c: number): string {\n  return `xTexelR${r}C${c < 0 ? 'minus' + Math.abs(c).toString() : c}`;\n}\n\n/**\n * Given a 2x2 filter, we want to multiply xR0C0, wR0C0, xR0C1, wR0C1, xR1C0,\n * wR1C0, xR1C1, xR1C1. The xRC's are constructed out of xTexelRC's, which are\n * the vec4's returned from sampling calls. Sometimes this means mixing channels\n * from adjacent samples, which constructTexel handles.\n */\nfunction constructTexel(\n    r: number, c: number, stride: number, padLeft: number): string {\n  if (stride === 1) {\n    if (padLeft % 2 === c % 2) {\n      return xTexelName(r, c);\n    }\n    return `vec4(${xTexelName(r, c - 1)}.zw, ${xTexelName(r, c + 1)}.xy)`;\n  }\n\n  if (padLeft % 2 === c % 2) {\n    return `vec4(${xTexelName(r, c)}.xy, ${xTexelName(r, c + 2)}.xy)`;\n  }\n  return `vec4(${xTexelName(r, c - 1)}.zw, ${xTexelName(r, c + 1)}.zw)`;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport { GPGPUProgram } from './gpgpu_math';\n\nexport class CropAndResizeProgram implements GPGPUProgram {\n  variableNames = ['Image', 'Boxes', 'BoxInd'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(\n    imageShape: [number, number, number, number], boxShape: [number, number],\n    cropSize: [number, number], method: 'bilinear' | 'nearest',\n    extrapolationValue: number) {\n    const [batch, imageHeight, imageWidth, depth] = imageShape;\n    const [numBoxes,] = boxShape;\n    const [cropHeight, cropWidth] = cropSize;\n    this.outputShape = [numBoxes, cropHeight, cropWidth, depth];\n    const methodId = method === 'bilinear' ? 1 : 0;\n\n    const [inputHeightFloat, inputWidthFloat] =\n      [`${imageHeight - 1}.0`, `${imageWidth - 1}.0`];\n\n    const [heightRatio, heightScale, inY] = cropHeight > 1 ?\n      [\n        `${(imageHeight-1)/(cropHeight-1)}`,\n        '(y2-y1) * height_ratio',\n        `y1*${inputHeightFloat} + float(y)*(height_scale)`,\n      ] :\n      [\n        '0.0',\n        '0.0',\n        `0.5 * (y1+y2) * ${inputHeightFloat}`,\n      ];\n    const [widthRatio, widthScale, inX] = cropWidth > 1 ?\n      [\n        `${(imageWidth-1)/(cropWidth-1)}`,\n        '(x2-x1) * width_ratio',\n        `x1*${inputWidthFloat} + float(x)*(width_scale)`,\n      ] :\n      [\n        '0.0',\n        '0.0',\n        `0.5 * (x1+x2) * ${inputWidthFloat}`,\n      ];\n\n    // Reference implementation\n    // tslint:disable-next-line:max-line-length\n    // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op_gpu.cu.cc\n    this.userCode = `\n      const float height_ratio = float(${heightRatio});\n      const float width_ratio = float(${widthRatio});\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= ${batch}) {\n          return;\n        }\n\n        float height_scale = ${heightScale};\n        float width_scale = ${widthScale};\n\n        float in_y = ${inY};\n        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n        float in_x = ${inX};\n        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n\n        vec2 sourceFracIndexRC = vec2(in_y,in_x);\n        if(${methodId} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n          ivec2 sourceCeilRC = ivec2(ceil(sourceFracIndexRC));\n\n          float topLeft = getImage(b, sourceFloorRC.x, sourceFloorRC.y, d);\n          float bottomLeft = getImage(b, sourceCeilRC.x, sourceFloorRC.y, d);\n          float topRight = getImage(b, sourceFloorRC.x, sourceCeilRC.y, d);\n          float bottomRight = getImage(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n          vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n          float top = topLeft + (topRight - topLeft) * fracRC.y;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n          float newValue = top + (bottom - top) * fracRC.x;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestRC = ivec2(floor(\n            sourceFracIndexRC + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestRC.x, sourceNearestRC.y, d);\n          setOutput(newValue);\n        }\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as util from '../../util';\n\n/**\n * Produces GLSL code that derives logical coordinates from a flat\n * index. The code performs integer division with each stride and decrements\n * the index until the index equals the final dimension coordinate.\n */\nexport function getLogicalCoordinatesFromFlatIndex(\n    coords: string[], shape: number[], index = 'index'): string {\n  const strides = util.computeStrides(shape);\n  return strides\n      .map((stride, i) => {\n        const line1 = `int ${coords[i]} = ${index} / ${stride}`;\n        const line2 = i === strides.length - 1 ?\n            `int ${coords[i + 1]} = ${index} - ${coords[i]} * ${stride}` :\n            `index -= ${coords[i]} * ${stride}`;\n        return `${line1}; ${line2};`;\n      })\n      .join('');\n}\n\nfunction buildVec(x: string[]): string {\n  if (x.length === 1) {\n    return `${x[0]}`;\n  }\n  return `vec${x.length}(${x.join(',')})`;\n}\n\n/**\n * Produces GLSL code that computes the dot product of the input x and y\n * vectors. Handles splitting inputs into increments of vec4s when necessary.\n */\nexport function dotify(x: string[], y: string[]): string {\n  if (x.length !== y.length) {\n    throw new Error(\n        `Vectors to be dotted must be of the same length -` +\n        `got ${x.length} and ${y.length}`);\n  }\n\n  const slices: string[] = [];\n  const nearestVec4 = Math.floor(x.length / 4);\n  const nearestVec4Remainder = x.length % 4;\n\n  for (let i = 0; i < nearestVec4; i++) {\n    const xSlice = x.slice(i * 4, i * 4 + 4);\n    const ySlice = y.slice(i * 4, i * 4 + 4);\n    slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n  }\n\n  if (nearestVec4Remainder !== 0) {\n    let xSlice = x.slice(nearestVec4 * 4);\n    let ySlice = y.slice(nearestVec4 * 4);\n    if (xSlice.length === 1) {\n      xSlice = xSlice.map(d => `float(${d})`);\n      ySlice = ySlice.map(d => `float(${d})`);\n    }\n    slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n  }\n\n  return slices.map((d, i) => `dot(${d})`).join('+');\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENV} from '../../environment';\nimport * as broadcast_util from '../../ops/broadcast_util';\nimport * as util from '../../util';\n\nimport * as shader_util from './shader_compiler_util';\n\nexport type ShapeInfo = {\n  logicalShape: number[],\n  texShape: [number, number],\n  isUniform: boolean,\n  isPacked: boolean\n};\n\nexport type InputInfo = {\n  name: string,\n  shapeInfo: ShapeInfo\n};\n\nexport function makeShader(\n    inputsInfo: InputInfo[], outputShape: ShapeInfo, userCode: string,\n    broadcast: boolean, usesPackedTextures: boolean): string {\n  let inputPrefixSnippet: string[]|string = inputsInfo.map(x => {\n    const size = util.sizeFromShape(x.shapeInfo.logicalShape);\n    if (x.shapeInfo.isUniform) {\n      return `uniform float ${x.name}${size > 1 ? `[${size}]` : ''};`;\n    }\n    return `uniform sampler2D ${x.name};`;\n  });\n  inputPrefixSnippet = inputPrefixSnippet.join('\\n');\n\n  const inputSamplingSnippet =\n      inputsInfo\n          .map(\n              x => getInputSamplingSnippet(\n                  x, outputShape, broadcast, usesPackedTextures))\n          .join('\\n');\n  const outTexShape = outputShape.texShape;\n  let outputSamplingSnippet: string;\n  let floatTextureSetOutputSnippet: string;\n  let shaderPrefix = SHADER_PREFIX;\n\n  if (outputShape.isPacked) {\n    outputSamplingSnippet =\n        getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n    floatTextureSetOutputSnippet = FLOAT_TEXTURE_SET_RGBA_SNIPPET;\n  } else {\n    outputSamplingSnippet =\n        getOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n    floatTextureSetOutputSnippet = FLOAT_TEXTURE_SET_R_SNIPPET;\n  }\n\n  if (usesPackedTextures) {\n    shaderPrefix += SHADER_PACKED_PREFIX;\n  }\n\n  const source = [\n    shaderPrefix, FLOAT_TEXTURE_SAMPLE_SNIPPET, floatTextureSetOutputSnippet,\n    inputPrefixSnippet, outputSamplingSnippet, inputSamplingSnippet, userCode\n  ].join('\\n');\n  return source;\n}\n\nfunction getSamplerFromInInfo(inInfo: InputInfo): string {\n  const shape = inInfo.shapeInfo.logicalShape;\n  switch (shape.length) {\n    case 0:\n      return getSamplerScalar(inInfo);\n    case 1:\n      return getSampler1D(inInfo);\n    case 2:\n      return getSampler2D(inInfo);\n    case 3:\n      return getSampler3D(inInfo);\n    case 4:\n      return getSampler4D(inInfo);\n    case 5:\n      return getSampler5D(inInfo);\n    case 6:\n      return getSampler6D(inInfo);\n    default:\n      throw new Error(\n          `${shape.length}-D input sampling` +\n          ` is not yet supported`);\n  }\n}\n\nfunction getPackedSamplerFromInInfo(inInfo: InputInfo): string {\n  const shape = inInfo.shapeInfo.logicalShape;\n  switch (shape.length) {\n    case 0:\n      return getPackedSamplerScalar(inInfo);\n    case 1:\n      return getPackedSampler1D(inInfo);\n    case 2:\n      return getPackedSampler2D(inInfo);\n    case 3:\n      return getPackedSampler3D(inInfo);\n    case 4:\n      return getPackedSampler4D(inInfo);\n    default:\n      throw new Error(\n          `Packed ${shape.length}-D input sampling` +\n          ` is not yet supported`);\n  }\n}\n\nfunction getInputSamplingSnippet(\n    inInfo: InputInfo, outShapeInfo: ShapeInfo, broadcast: boolean,\n    usesPackedTextures = false): string {\n  let res = getSamplerFlat(inInfo);\n  if (usesPackedTextures) {\n    res += getPackedSamplerFromInInfo(inInfo);\n  } else {\n    res += getSamplerFromInInfo(inInfo);\n  }\n\n  // If input and output have matching logical shapes, add\n  // getTexNameAtOutCoord() method that samples the input textureSampler using\n  // the output coordinates.\n  if (broadcast ||\n      util.arraysEqual(\n          inInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape)) {\n    if (usesPackedTextures) {\n      res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo, broadcast);\n    } else {\n      res += getSamplerAtOutputCoords(inInfo, outShapeInfo, broadcast);\n    }\n  }\n  return res;\n}\n\nfunction getPackedOutputSamplingSnippet(\n    outShape: number[], outTexShape: [number, number]): string {\n  switch (outShape.length) {\n    case 0:\n      return getOutputScalarCoords();\n    case 1:\n      return getOutputPacked1DCoords(outShape as [number], outTexShape);\n    case 2:\n      return getOutputPacked2DCoords(outShape as [number, number], outTexShape);\n    case 3:\n      return getOutputPacked3DCoords(\n          outShape as [number, number, number], outTexShape);\n    case 4:\n      return getOutputPacked4DCoords(\n          outShape as [number, number, number, number], outTexShape);\n    default:\n      throw new Error(\n          `${outShape.length}-D packed output ` +\n          `coordinate fetching is not yet supported`);\n  }\n}\n\nfunction getOutputSamplingSnippet(\n    outShape: number[], outTexShape: [number, number]): string {\n  switch (outShape.length) {\n    case 0:\n      return getOutputScalarCoords();\n    case 1:\n      return getOutput1DCoords(outShape as [number], outTexShape);\n    case 2:\n      return getOutput2DCoords(outShape as [number, number], outTexShape);\n    case 3:\n      return getOutput3DCoords(\n          outShape as [number, number, number], outTexShape);\n    case 4:\n      return getOutput4DCoords(\n          outShape as [number, number, number, number], outTexShape);\n    case 5:\n      return getOutput5DCoords(\n          outShape as [number, number, number, number, number], outTexShape);\n    case 6:\n      return getOutput6DCoords(\n          outShape as [number, number, number, number, number, number],\n          outTexShape);\n    default:\n      throw new Error(\n          `${outShape.length}-D output sampling is not yet supported`);\n  }\n}\n\nconst SAMPLE_1D_SNIPPET = `\nvec2 UVfrom1D(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\n\nconst SAMPLE_2D_SNIPPET = `\nvec2 UVfrom2D(int texNumR, int texNumC, int numC, int row, int col) {\n  int index = row * numC + col;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\n\nconst SAMPLE_3D_SNIPPET = `\nvec2 UVfrom3D(int texNumR, int texNumC, int stride0,\n    int stride1, int row, int col, int depth) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\n\nconst SAMPLE_4D_SNIPPET = `\nvec2 UVfrom4D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int row, int col, int depth,\n    int depth2) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth * stride2 + depth2;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom4D(int texNumR, int texNumC, int texelsInBatch2,\n    int texelsInBatch, int texelsInLogicalRow, int b2, int b,\n    int row, int col) {\n  int index = b2 * texelsInBatch2 + b * texelsInBatch +\n    (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\n\nconst SAMPLE_5D_SNIPPET = `\nvec2 UVfrom5D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int stride3, int row, int col, int depth,\n    int depth2, int depth3) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 +\n              depth * stride2 + depth2 * stride3 + depth3;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\n\nconst SAMPLE_6D_SNIPPET = `\nvec2 UVfrom6D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int stride3, int stride4,\n    int row, int col, int depth, int depth2, int depth3, int depth4) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth * stride2 + depth2 *\n    stride3 + depth3 * stride4 + depth4;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\n\nconst FLOAT_TEXTURE_SAMPLE_SNIPPET = `\n  float sampleTexture(sampler2D textureSampler, vec2 uv) {\n    return texture2D(textureSampler, uv).r;\n  }\n`;\n\nconst FLOAT_TEXTURE_SET_R_SNIPPET = `\n  void setOutput(float val) {\n    gl_FragColor = vec4(val, 0, 0, 0);\n  }\n`;\n\nconst FLOAT_TEXTURE_SET_RGBA_SNIPPET = `\n  void setOutput(vec4 val) {\n    gl_FragColor = val;\n  }\n`;\n\nlet NAN_CHECKS = '';\nif (ENV.get('PROD')) {\n  NAN_CHECKS = `\n    bool isNaN(float val) {\n      return false;\n    }\n\n    bool hasNaN(vec4 values) {\n      return false;\n    }\n  `;\n} else {\n  /**\n   * Previous NaN check '(val < 0.0 || 0.0 < val || val == 0.0) ? false : true'\n   * does not work on iOS 12\n   */\n  NAN_CHECKS = `\n    bool isNaN(float val) {\n      return (val < 1.0 || 0.0 < val || val == 0.0) ? false : true;\n    }\n\n    bool hasNaN(vec4 values) {\n      return any(bvec4(\n        isNaN(values.x),\n        isNaN(values.y),\n        isNaN(values.z),\n        isNaN(values.w)\n      ));\n    }\n  `;\n}\n\nconst SHADER_PREFIX = `\n  precision highp float;\n  precision highp int;\n  varying vec2 resultUV;\n  const vec2 halfCR = vec2(0.5, 0.5);\n\n  struct ivec5\n  {\n    int x;\n    int y;\n    int z;\n    int w;\n    int u;\n  };\n\n  struct ivec6\n  {\n    int x;\n    int y;\n    int z;\n    int w;\n    int u;\n    int v;\n  };\n\n  ${NAN_CHECKS}\n\n  float getNaN(vec4 values) {\n    return dot(vec4(1), values);\n  }\n\n  int round(float value) {\n    return int(floor(value + 0.5));\n  }\n\n  int imod(int x, int y) {\n    return x - y * (x / y);\n  }\n\n  //Based on the work of Dave Hoskins\n  //https://www.shadertoy.com/view/4djSRW\n  #define HASHSCALE1 443.8975\n  float random(float seed){\n    vec2 p = resultUV * seed;\n    vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n    p3 += dot(p3, p3.yzx + 19.19);\n    return fract((p3.x + p3.y) * p3.z);\n  }\n\n  ${SAMPLE_1D_SNIPPET}\n  ${SAMPLE_2D_SNIPPET}\n  ${SAMPLE_3D_SNIPPET}\n  ${SAMPLE_4D_SNIPPET}\n  ${SAMPLE_5D_SNIPPET}\n  ${SAMPLE_6D_SNIPPET}\n`;\n\nconst SHADER_PACKED_PREFIX = `\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n`;\n\nfunction getOutputScalarCoords() {\n  return `\n    int getOutputCoords() {\n      return 0;\n    }\n  `;\n}\n\nfunction getOutputPacked1DCoords(\n    shape: [number], texShape: [number, number]): string {\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  if (texShape[0] === 1) {\n    return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ${packedTexShape[1]}.0);\n      }\n    `;\n  }\n\n  if (texShape[1] === 1) {\n    return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ${packedTexShape[0]}.0);\n      }\n    `;\n  }\n\n  return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      return resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n    }\n  `;\n}\n\nfunction getOutput1DCoords(\n    shape: [number], texShape: [number, number]): string {\n  if (texShape[0] === 1) {\n    return `\n      int getOutputCoords() {\n        return int(resultUV.x * ${texShape[1]}.0);\n      }\n    `;\n  }\n  if (texShape[1] === 1) {\n    return `\n      int getOutputCoords() {\n        return int(resultUV.y * ${texShape[0]}.0);\n      }\n    `;\n  }\n  return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      return resTexRC.x * ${texShape[1]} + resTexRC.y;\n    }\n  `;\n}\n\nfunction getOutputPacked3DCoords(\n    shape: [number, number, number], texShape: [number, number]): string {\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const texelsInLogicalRow = Math.ceil(shape[2] / 2);\n  const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);\n\n  return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;\n}\n\nfunction getOutput3DCoords(\n    shape: [number, number, number], texShape: [number, number]): string {\n  const coordsFromIndexSnippet =\n      shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n\n  return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\n\nfunction getOutputPacked4DCoords(\n    shape: [number, number, number, number],\n    texShape: [number, number]): string {\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n\n  const texelsInLogicalRow = Math.ceil(shape[3] / 2);\n  const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[2] / 2);\n  const texelsInBatch2 = texelsInBatch * shape[1];\n\n  return `\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      int b2 = index / ${texelsInBatch2};\n      index -= b2 * ${texelsInBatch2};\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec4(b2, b, r, c);\n    }\n  `;\n}\n\nfunction getOutput4DCoords(\n    shape: [number, number, number, number],\n    texShape: [number, number]): string {\n  const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(\n      ['r', 'c', 'd', 'd2'], shape);\n\n  return `\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec4(r, c, d, d2);\n    }\n  `;\n}\n\nfunction getOutput5DCoords(\n    shape: [number, number, number, number, number],\n    texShape: [number, number]): string {\n  const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(\n      ['r', 'c', 'd', 'd2', 'd3'], shape);\n\n  return `\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},\n                             ${texShape[1]}));\n\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `;\n}\n\nfunction getOutput6DCoords(\n    shape: [number, number, number, number, number, number],\n    texShape: [number, number]): string {\n  const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(\n      ['r', 'c', 'd', 'd2', 'd3', 'd4'], shape);\n\n  return `\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `;\n}\n\nfunction getOutputPacked2DCoords(\n    shape: [number, number], texShape: [number, number]): string {\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  if (util.arraysEqual(shape, texShape)) {\n    return `\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${\n        packedTexShape[1]}));\n      }\n    `;\n  }\n\n  // texels needed to accommodate a logical row\n  const texelsInLogicalRow = Math.ceil(shape[1] / 2);\n\n  /**\n   * getOutputCoords\n   *\n   * resTexRC: The rows and columns of the texels. If you move over one\n   * texel to the right in the packed texture, you are moving over one column\n   * (not two).\n   *\n   * index: The texel index\n   */\n  return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec2(r, c);\n    }\n  `;\n}\n\nfunction getOutput2DCoords(\n    shape: [number, number], texShape: [number, number]): string {\n  if (util.arraysEqual(shape, texShape)) {\n    return `\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));\n      }\n    `;\n  }\n  if (shape[1] === 1) {\n    return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `;\n  }\n  if (shape[0] === 1) {\n    return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `;\n  }\n  return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      int r = index / ${shape[1]};\n      int c = index - r * ${shape[1]};\n      return ivec2(r, c);\n    }\n  `;\n}\n\nfunction getPackedSamplerScalar(inputInfo: InputInfo): string {\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  return `\n    vec4 ${funcName}() {\n      return texture2D(${texName}, halfCR);\n    }\n  `;\n}\n\nfunction getSamplerScalar(inputInfo: InputInfo): string {\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  if (inputInfo.shapeInfo.isUniform) {\n    return `float ${funcName}() {return ${texName};}`;\n  }\n  return `\n    float ${funcName}() {\n      return sampleTexture(${texName}, halfCR);\n    }\n  `;\n}\n\nfunction getPackedSampler1D(inputInfo: InputInfo): string {\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n\n  return `\n    vec4 ${funcName}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${packedTexShape[0]}, ${packedTexShape[1]}, index);\n      return texture2D(${texName}, uv);\n    }\n  `;\n}\n\nfunction getSampler1D(inputInfo: InputInfo): string {\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n\n  return `\n    float ${funcName}(int index) {\n      return ${funcName}Flat(index);\n    }\n  `;\n}\n\nfunction getPackedSampler2D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  if (texShape != null && util.arraysEqual(shape, texShape)) {\n    return `\n      vec4 ${funcName}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n\n        return texture2D(${texName}, uv);\n      }\n    `;\n  }\n\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const valuesPerRow = Math.ceil(shape[1] / 2);\n\n  return `\n    vec4 ${funcName}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${\n      packedTexShape[1]}, row, col);\n      return texture2D(${texName}, uv);\n    }\n  `;\n}\n\nfunction getSampler2D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n\n  const texShape = inputInfo.shapeInfo.texShape;\n  if (texShape != null && util.arraysEqual(shape, texShape)) {\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    return `\n    float ${funcName}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n\n  const {newShape, keptDims} = util.squeezeShape(shape);\n  const squeezedShape = newShape;\n  if (squeezedShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n    const params = ['row', 'col'];\n    return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n\n  if (inputInfo.shapeInfo.isUniform) {\n    // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col) {\n        float index = dot(vec2(row, col), vec2(${shape[1]}, 1));\n        return ${funcName}Flat(round(index));\n      }\n    `;\n  }\n\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  if (texNumC === 1) {\n    // index is used directly as physical (no risk of float16 overflow).\n    return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec2(row, col), vec2(${shape[1]}, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  if (texNumR === 1) {\n    // index is used directly as physical (no risk of float16 overflow).\n    return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec2(row, col), vec2(${shape[1]}, 1));\n      vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  return `\n  float ${funcName}(int row, int col) {\n    vec2 uv = UVfrom2D(${texNumR}, ${texNumC}, ${shape[1]}, row, col);\n    return sampleTexture(${texName}, uv);\n  }\n`;\n}\n\nfunction getPackedSampler3D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n\n  if (shape[0] === 1) {\n    const squeezedShape = shape.slice(1);\n    const keptDims = [1, 2];\n    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n    const params = ['b', 'row', 'col'];\n    return `\n        ${getPackedSamplerFromInInfo(newInputInfo)}\n        vec4 ${funcName}(int b, int row, int col) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n  }\n\n  const texNumR = packedTexShape[0];\n  const texNumC = packedTexShape[1];\n\n  const valuesPerRow = Math.ceil(shape[2] / 2);\n  const texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);\n\n  return `\n    vec4 ${funcName}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);\n      return texture2D(${texName}, uv);\n    }\n  `;\n}\n\nfunction getSampler3D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const stride0 = shape[1] * shape[2];\n  const stride1 = shape[2];\n\n  const {newShape, keptDims} = util.squeezeShape(shape);\n  const squeezedShape = newShape;\n  if (squeezedShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n    const params = ['row', 'col', 'depth'];\n    return `\n        ${getSamplerFromInInfo(newInputInfo)}\n        float ${funcName}(int row, int col, int depth) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n  }\n\n  if (inputInfo.shapeInfo.isUniform) {\n    // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth) {\n        float index = dot(vec3(row, col, depth),\n                          vec3(${stride0}, ${stride1}, 1));\n        return ${funcName}Flat(round(index));\n      }\n    `;\n  }\n\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  if (texNumC === stride0) {\n    // texC is used directly as physical (no risk of float16 overflow).\n    return `\n        float ${funcName}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${stride1}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${texNumC}.0, ${texNumR}.0);\n          return sampleTexture(${texName}, uv);\n        }\n      `;\n  }\n\n  if (texNumC === stride1) {\n    // texR is used directly as physical (no risk of float16 overflow).\n    return `\n    float ${funcName}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n\n  return `\n      float ${funcName}(int row, int col, int depth) {\n        vec2 uv = UVfrom3D(\n            ${texNumR}, ${texNumC}, ${stride0}, ${stride1}, row, col, depth);\n        return sampleTexture(${texName}, uv);\n      }\n  `;\n}\n\nfunction getPackedSampler4D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const texNumR = packedTexShape[0];\n  const texNumC = packedTexShape[1];\n\n  const valuesPerRow = Math.ceil(shape[3] / 2);\n  const texelsInBatch = valuesPerRow * Math.ceil(shape[2] / 2);\n  const texelsInBatch2 = texelsInBatch * shape[1];\n\n  return `\n    vec4 ${funcName}(int b2, int b, int row, int col) {\n      vec2 uv = packedUVfrom4D(\n        ${texNumR}, ${texNumC}, ${texelsInBatch2},\n        ${texelsInBatch}, ${valuesPerRow}, b2, b, row, col);\n      return texture2D(${texName}, uv);\n    }\n  `;\n}\n\nfunction getSampler4D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const stride2 = shape[3];\n  const stride1 = shape[2] * stride2;\n  const stride0 = shape[1] * stride1;\n\n  const {newShape, keptDims} = util.squeezeShape(shape);\n  if (newShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n    const params = ['row', 'col', 'depth', 'depth2'];\n    return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n\n  if (inputInfo.shapeInfo.isUniform) {\n    // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float index = dot(vec4(row, col, depth, depth2),\n                          vec4(${stride0}, ${stride1}, ${stride2}, 1));\n        return ${funcName}Flat(round(index));\n      }\n    `;\n  }\n\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  if (texNumC === stride0) {\n    // texC is used directly as physical (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2), vec3(${stride1}, ${stride2}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  if (texNumC === stride2) {\n    // texR is used directly as physical (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${shape[1] * shape[2]}, ${shape[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  return `\n    float ${funcName}(int row, int col, int depth, int depth2) {\n      vec2 uv = UVfrom4D(${texNumR}, ${texNumC}, ${stride0}, ${stride1},\n          ${stride2}, row, col, depth, depth2);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\n\nfunction getSampler5D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const stride3 = shape[4];\n  const stride2 = shape[3] * stride3;\n  const stride1 = shape[2] * stride2;\n  const stride0 = shape[1] * stride1;\n\n  const {newShape, keptDims} = util.squeezeShape(shape);\n  if (newShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n    const params = ['row', 'col', 'depth', 'depth2', 'depth3'];\n    return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n\n  if (inputInfo.shapeInfo.isUniform) {\n    // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          depth3;\n        return ${funcName}Flat(index);\n      }\n    `;\n  }\n\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n\n  if (texNumC === stride0) {\n    // texC is used directly as physical (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(\n          vec4(col, depth, depth2, depth3),\n          vec4(${stride1}, ${stride2}, ${stride3}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n\n  if (texNumC === stride3) {\n    // texR is used directly as physical (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3]}, ${shape[2] * shape[3]},\n            ${shape[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n\n  return `\n    float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n      vec2 uv = UVfrom5D(${texNumR}, ${texNumC}, ${stride0}, ${stride1},\n          ${stride2}, ${stride3}, row, col, depth, depth2, depth3);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\n\nfunction getSampler6D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const stride4 = shape[5];\n  const stride3 = shape[4] * stride4;\n  const stride2 = shape[3] * stride3;\n  const stride1 = shape[2] * stride2;\n  const stride0 = shape[1] * stride1;\n  const {newShape, keptDims} = util.squeezeShape(shape);\n  if (newShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n    const params = ['row', 'col', 'depth', 'depth2', 'depth3', 'depth4'];\n    return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n\n  if (inputInfo.shapeInfo.isUniform) {\n    // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${stride4}, 1));\n        return ${funcName}Flat(index);\n      }\n    `;\n  }\n\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  if (texNumC === stride0) {\n    // texC is used directly as physical (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(\n          vec4(col, depth, depth2, depth3),\n          vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) + depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  if (texNumC === stride4) {\n    // texR is used directly as physical (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3] * shape[4]},\n               ${shape[2] * shape[3] * shape[4]},\n               ${shape[3] * shape[4]},\n               ${shape[4]})) + depth3;\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  return `\n    float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      vec2 uv = UVfrom6D(${texNumR}, ${texNumC}, ${stride0}, ${stride1},\n          ${stride2}, ${stride3}, ${stride4}\n          ,row, col, depth, depth2, depth3, depth4);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\n\nfunction getSamplerFlat(inputInfo: InputInfo): string {\n  const texName = inputInfo.name;\n  const funcName =\n      'get' + texName.charAt(0).toUpperCase() + texName.slice(1) + 'Flat';\n  const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n\n  if (inputInfo.shapeInfo.isUniform) {\n    if (inSize === 1) {\n      return `float ${funcName}(int index) {return ${texName};}`;\n    }\n    return `\n      float ${funcName}(int index) {\n        for (int i = 0; i < ${inSize}; i++) {\n          if (i == index) {\n            return ${texName}[i];\n          }\n        }\n      }\n    `;\n  }\n\n  const texShape = inputInfo.shapeInfo.texShape;\n  const tNumR = texShape[0];\n  const tNumC = texShape[1];\n  if (tNumC === 1 && tNumR === 1) {\n    return `\n      float ${funcName}(int index) {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n  }\n  if (tNumC === 1) {\n    return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2(0.5, (float(index) + 0.5) / ${tNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  if (tNumR === 1) {\n    return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2((float(index) + 0.5) / ${tNumC}.0, 0.5);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  return `\n    float ${funcName}(int index) {\n      vec2 uv = UVfrom1D(${tNumR}, ${tNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\n\nfunction getBroadcastOutputCoordsSampler(\n    inputInfo: InputInfo, outShapeInfo: ShapeInfo, texFuncSnippet: string,\n    funcName: string): string {\n  const inRank = inputInfo.shapeInfo.logicalShape.length;\n  const outRank = outShapeInfo.logicalShape.length;\n\n  let type = 'int';\n  if (outRank === 2) {\n    type = 'ivec2';\n  } else if (outRank === 3) {\n    type = 'ivec3';\n  } else if (outRank === 4) {\n    type = 'ivec4';\n  }\n  const broadcastDims = broadcast_util.getBroadcastDims(\n      inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n  const rankDiff = outRank - inRank;\n  let coordsSnippet: string;\n  if (inRank === 0) {\n    coordsSnippet = '';\n  } else if (outRank < 2 && broadcastDims.length >= 1) {\n    coordsSnippet = 'coords = 0;';\n  } else {\n    coordsSnippet =\n        broadcastDims.map(d => `coords[${d + rankDiff}] = 0;`).join('\\n');\n  }\n  let unpackedCoordsSnippet = '';\n  if (outRank < 2 && inRank > 0) {\n    unpackedCoordsSnippet = 'coords';\n  } else {\n    unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape\n                                .map((s, i) => `coords[${i + rankDiff}]`)\n                                .join(', ');\n  }\n  return `\n    float ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      return get${texFuncSnippet}(${unpackedCoordsSnippet});\n    }\n  `;\n}\n\nfunction getPackedSamplerAtOutputCoords(\n    inputInfo: InputInfo, outShapeInfo: ShapeInfo,\n    supportsBroadcasting: boolean) {\n  const texName = inputInfo.name;\n  const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n  const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n  const outTexShape = outShapeInfo.texShape;\n\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const texNumR = packedTexShape[0];\n  const texNumC = packedTexShape[1];\n\n  const broadcastDims = broadcast_util.getBroadcastDims(\n      inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n  const inRank = inputInfo.shapeInfo.logicalShape.length;\n  const outRank = outShapeInfo.logicalShape.length;\n  if (broadcastDims.length) {\n    throw Error('Packed broadcast sampling is not implemented yet.');\n  }\n\n  const inTexShape = inputInfo.shapeInfo.texShape;\n  if (util.arraysEqual(inTexShape, outTexShape)) {\n    return `\n      vec4 ${funcName}() {\n        return texture2D(${texName}, resultUV);\n      }\n    `;\n  }\n\n  let output = `return texture2D(${texName}, uv)`;\n\n  if (inRank === 1 && outRank > 1) {\n    output = `\n      vec4 sample = texture2D(${texName}, uv);\n      return vec4(sample.xy, sample.xy);\n    `;\n  } else if (inRank === 0 && outRank > 0) {\n    if (outRank === 1) {\n      output = `\n        vec4 sample = texture2D(${texName}, uv);\n        return vec4(sample.x, sample.x, 0., 0.);\n      `;\n    } else {\n      output = `\n        vec4 sample = texture2D(${texName}, uv);\n        return vec4(sample.x);\n      `;\n    }\n  }\n\n  return `\n    vec4 ${funcName}() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      int texR = index / ${texNumC};\n      int texC = index - texR * ${texNumC};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});\n\n      ${output};\n    }\n  `;\n}\n\nfunction getSamplerAtOutputCoords(\n    inputInfo: InputInfo, outShapeInfo: ShapeInfo,\n    supportsBroadcasting: boolean) {\n  const texName = inputInfo.name;\n  const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n  const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n\n  const broadcastDims = broadcast_util.getBroadcastDims(\n      inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n  const inRank = inputInfo.shapeInfo.logicalShape.length;\n  const outRank = outShapeInfo.logicalShape.length;\n  const doBroadcast =\n      supportsBroadcasting && ((outRank > inRank) || broadcastDims.length > 0);\n  const broadcastOverOuter =\n      broadcast_util.broadcastDimsAreOuter(broadcastDims);\n  const isUniform = inputInfo.shapeInfo.isUniform;\n\n  if (doBroadcast && !broadcastOverOuter) {\n    return getBroadcastOutputCoordsSampler(\n        inputInfo, outShapeInfo, texFuncSnippet, funcName);\n  }\n\n  const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n  let broadcastSnippet = '';\n  if (doBroadcast && broadcastOverOuter) {\n    broadcastSnippet = `\n        int mainPart = index / ${inSize};\n        index -= mainPart * ${inSize};\n      `;\n  }\n\n  const outTexShape = outShapeInfo.texShape;\n  if (isUniform) {\n    if (inSize === 1) {\n      return `float ${funcName}() {return ${texName};}`;\n    }\n    return `\n      float ${funcName}() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                              vec2(${outTexShape[0]}, ${outTexShape[1]}));\n        int index = resTexRC.x * ${outTexShape[1]} + resTexRC.y;\n        ${broadcastSnippet}\n        return get${texFuncSnippet}Flat(index);\n      }\n    `;\n  }\n\n  // At this point, the input is not a uniform.\n  const inTexShape = inputInfo.shapeInfo.texShape;\n  if (util.arraysEqual(inTexShape, outTexShape)) {\n    return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, resultUV);\n      }\n    `;\n  }\n\n  return `\n    float ${funcName}() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${outTexShape[0]}, ${outTexShape[1]}));\n      int index = resTexRC.x * ${outTexShape[1]} + resTexRC.y;\n      ${broadcastSnippet}\n      int texR = index / ${inTexShape[1]};\n      int texC = index - texR * ${inTexShape[1]};\n      vec2 uv = (vec2(texC, texR) + halfCR) /\n                 vec2(${inTexShape[1]}.0, ${inTexShape[0]}.0);\n\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\n\nexport function getCoordsDataType(rank: number): string {\n  if (rank <= 1) {\n    return 'int';\n  } else if (rank === 2) {\n    return 'ivec2';\n  } else if (rank === 3) {\n    return 'ivec3';\n  } else if (rank === 4) {\n    return 'ivec4';\n  } else if (rank === 5) {\n    return 'ivec5';\n  } else if (rank === 6) {\n    return 'ivec6';\n  } else {\n    throw Error(`GPU for rank ${rank} is not yet supported`);\n  }\n}\n\n/** Returns a new input info (a copy) that has a squeezed logical shape. */\nfunction squeezeInputInfo(\n    inInfo: InputInfo, squeezedShape: number[]): InputInfo {\n  // Deep copy.\n  const newInputInfo: InputInfo = JSON.parse(JSON.stringify(inInfo));\n  newInputInfo.shapeInfo.logicalShape = squeezedShape;\n  return newInputInfo;\n}\n\nfunction getSqueezedParams(params: string[], keptDims: number[]): string {\n  return keptDims.map(d => params[d]).join(', ');\n}","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class CumSumProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(shape: number[], exclusive: boolean, reverse: boolean) {\n    this.outputShape = shape;\n    const rank = shape.length;\n    const finalDim = shape[shape.length - 1];\n    const comparator = reverse ? '<' : '>';\n\n    this.userCode = `\n      int getIndex(int i) {\n        ${reverse ? `return ${finalDim} -i - 1;` : 'return i;'}\n      }\n\n      void main() {\n        ${getCoordsDataType(rank)} coords = getOutputCoords();\n        int end = ${getFinalCoord(rank, 'coords')};\n        float val = 0.0;\n        for (int i = ${finalDim} - 1; i >= 0; i -= 1) {\n          int idx = getIndex(i);\n          if (idx ${comparator} end) {\n            continue;\n          }\n          if (idx == end && ${exclusive}) {\n            continue;\n          }\n          ${getFinalCoord(rank, 'coords')} = idx;\n          val += getX(${getCoords(rank, 'coords')});\n        }\n        setOutput(val);\n      }\n    `;\n  }\n}\n\nfunction getCoords(rank: number, name: string): string {\n  if (rank === 1) {\n    return `${name}`;\n  } else if (rank === 2) {\n    return `${name}.x, ${name}.y`;\n  } else if (rank === 3) {\n    return `${name}.x, ${name}.y, ${name}.z`;\n  } else if (rank === 4) {\n    return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;\n  } else {\n    throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n  }\n}\n\nfunction getFinalCoord(rank: number, name: string): string {\n  if (rank === 1) {\n    return `${name}`;\n  } else if (rank === 2) {\n    return `${name}.y`;\n  } else if (rank === 3) {\n    return `${name}.z`;\n  } else if (rank === 4) {\n    return `${name}.w`;\n  } else {\n    throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class DepthToSpaceProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[] = [];\n  userCode: string;\n  blockSize: number;\n  dataFormat: string;\n\n  constructor(\n      outputShape: number[], blockSize: number, dataFormat: 'NHWC'|'NCHW') {\n    this.outputShape = outputShape;\n    this.blockSize = blockSize;\n    this.dataFormat = dataFormat;\n    this.userCode = `\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = ${this.getHeightCoordString()};\n      int w = ${this.getWidthCoordString()};\n      int d = ${this.getDepthCoordString()};\n\n      int in_h = h / ${blockSize};\n      int offset_h = imod(h, ${blockSize});\n      int in_w = w / ${blockSize};\n      int offset_w = imod(w, ${blockSize});\n      int offset_d = (offset_h * ${blockSize} + offset_w) *\n        ${this.getOutputDepthSize()};\n      int in_d = d + offset_d;\n\n      float result = ${this.getInputSamplingString()};\n      setOutput(result);\n    }\n  `;\n  }\n\n  private getHeightCoordString(): string {\n    if (this.dataFormat === 'NHWC') {\n      return `coords[1]`;\n    } else {\n      return `coords[2]`;\n    }\n  }\n\n  private getWidthCoordString(): string {\n    if (this.dataFormat === 'NHWC') {\n      return `coords[2]`;\n    } else {\n      return `coords[3]`;\n    }\n  }\n\n  private getDepthCoordString(): string {\n    if (this.dataFormat === 'NHWC') {\n      return `coords[3]`;\n    } else {\n      return `coords[1]`;\n    }\n  }\n\n  private getOutputDepthSize(): number {\n    if (this.dataFormat === 'NHWC') {\n      return this.outputShape[3];\n    } else {\n      return this.outputShape[1];\n    }\n  }\n\n  private getInputSamplingString(): string {\n    if (this.dataFormat === 'NHWC') {\n      return `getX(b, in_h, in_w, in_d)`;\n    } else {\n      return `getX(b, in_d, in_h, in_w)`;\n    }\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class EncodeFloatProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  userCode: string;\n  outputShape: number[];\n\n  constructor(outputShape: number[]) {\n    this.outputShape = outputShape;\n    this.userCode = `\n      const float FLOAT_MAX = 1.70141184e38;\n      const float FLOAT_MIN = 1.17549435e-38;\n\n      lowp vec4 encode_float(highp float v) {\n        if (isNaN(v)) {\n          return vec4(255, 255, 255, 255);\n        }\n\n        highp float av = abs(v);\n\n        if(av < FLOAT_MIN) {\n          return vec4(0.0, 0.0, 0.0, 0.0);\n        } else if(v > FLOAT_MAX) {\n          return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n        } else if(v < -FLOAT_MAX) {\n          return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n        }\n\n        highp vec4 c = vec4(0,0,0,0);\n\n        highp float e = floor(log2(av));\n        highp float m = exp2(fract(log2(av))) - 1.0;\n\n        c[2] = floor(128.0 * m);\n        m -= c[2] / 128.0;\n        c[1] = floor(32768.0 * m);\n        m -= c[1] / 32768.0;\n        c[0] = floor(8388608.0 * m);\n\n        highp float ebias = e + 127.0;\n        c[3] = floor(ebias / 2.0);\n        ebias -= c[3] * 2.0;\n        c[2] += floor(ebias) * 128.0;\n\n        c[3] += 128.0 * step(0.0, -v);\n\n        return c / 255.0;\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        gl_FragColor = encode_float(x);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport const COMPLEX_FFT = {\n  REAL: 'return real * expR - imag * expI;',\n  IMAG: 'return real * expI + imag * expR;'\n};\n\nexport class FFTProgram implements GPGPUProgram {\n  variableNames = ['real', 'imag'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(op: string, inputShape: [number, number], inverse: boolean) {\n    const innerDim = inputShape[1];\n    this.outputShape = inputShape;\n\n    const exponentMultiplierSnippet =\n        inverse ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`;\n    const resultDenominator = inverse ? `${innerDim}.0` : '1.0';\n\n    this.userCode = `\n      const float exponentMultiplier = ${exponentMultiplierSnippet};\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        ${op}\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float(${innerDim});\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < ${innerDim}; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / ${resultDenominator};\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class FromPixelsProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  userCode: string;\n  outputShape: number[];\n\n  constructor(outputShape: number[]) {\n    const [height, width, ] = outputShape;\n    this.outputShape = outputShape;\n    this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0);\n\n        vec4 values = texture2D(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class GatherProgram implements GPGPUProgram {\n  variableNames = ['A', 'indices'];\n  outputShape: number[];\n  userCode: string;\n  rank: number;\n\n  constructor(aShape: number[], indicesLength: number, axis: number) {\n    const outputShape: number[] = aShape.slice();\n    outputShape[axis] = indicesLength;\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    const dtype = getCoordsDataType(this.rank);\n    const sourceCoords = getSourceCoords(aShape, axis);\n\n    this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n  }\n}\n\nfunction getSourceCoords(aShape: number[], axis: number): string {\n  const rank = aShape.length;\n  if (rank > 4) {\n    throw Error(`Gather for rank ${rank} is not yet supported`);\n  }\n  if (rank === 1) {\n    return `int(getIndices(resRC))`;\n  }\n\n  const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n\n  const sourceCoords = [];\n  for (let i = 0; i < aShape.length; i++) {\n    if (i === axis) {\n      sourceCoords.push(`int(getIndices(${currentCoords[i]}))`);\n    } else {\n      sourceCoords.push(`${currentCoords[i]}`);\n    }\n  }\n  return sourceCoords.join();\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class GatherNDProgram implements GPGPUProgram {\n  variableNames = ['x', 'indices'];\n  outputShape: number[];\n  userCode: string;\n  constructor(\n      private sliceDim: number, private strides: number[], shape: number[]) {\n    this.outputShape = shape;\n    const stridesType = getCoordsDataType(strides.length);\n    const dtype = getCoordsDataType(shape.length);\n    const strideString = this.sliceDim > 1 ? 'strides[j]' : 'strides';\n    this.userCode = `\n        ${stridesType} strides = ${stridesType}(${this.strides});\n         void main() {\n          ${dtype} coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < ${this.sliceDim}; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * ${strideString};\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../../tensor';\nimport {DataType, DataValues} from '../../types';\nimport * as util from '../../util';\n\nexport enum TextureUsage {\n  RENDER,\n  UPLOAD,\n  PIXELS,\n  DOWNLOAD\n}\n\nexport enum PhysicalTextureType {\n  UNPACKED_FLOAT16,\n  UNPACKED_FLOAT32,\n  PACKED_4X1_UNSIGNED_BYTE,\n  PACKED_2X2_FLOAT32,\n  PACKED_2X2_FLOAT16\n}\n\nexport interface TextureData {\n  // Required.\n  shape: number[];\n  dtype: DataType;\n\n  // Optional.\n  values?: DataValues;\n  texture?: WebGLTexture;\n  // For complex numbers, the real and imaginary parts are stored as their own\n  // individual tensors, with a parent joining the two with the\n  // complexTensors field. When this is defined, texture will be null.\n  complexTensors?: {real: Tensor, imag: Tensor};\n  /** [rows, columns] shape of the texture. */\n  texShape?: [number, number];\n  usage?: TextureUsage;\n  isPacked?: boolean;\n}\n\nexport function getUnpackedMatrixTextureShapeWidthHeight(\n    rows: number, columns: number): [number, number] {\n  return [columns, rows];\n}\n\nexport function getUnpackedArraySizeFromMatrixSize(\n    matrixSize: number, channelsPerTexture: number): number {\n  return matrixSize * channelsPerTexture;\n}\n\nexport function getColorMatrixTextureShapeWidthHeight(\n    rows: number, columns: number): [number, number] {\n  return [columns * 4, rows];\n}\n\nexport function getMatrixSizeFromUnpackedArraySize(\n    unpackedSize: number, channelsPerTexture: number): number {\n  if (unpackedSize % channelsPerTexture !== 0) {\n    throw new Error(\n        `unpackedSize (${unpackedSize}) must be a multiple of ` +\n        `${channelsPerTexture}`);\n  }\n  return unpackedSize / channelsPerTexture;\n}\n\nexport function encodeMatrixToUnpackedArray(\n    matrix: Float32Array|Uint8Array, unpackedArray: Float32Array|Uint8Array,\n    channelsPerTexture: number) {\n  const requiredSize =\n      getUnpackedArraySizeFromMatrixSize(matrix.length, channelsPerTexture);\n  if (unpackedArray.length < requiredSize) {\n    throw new Error(\n        `unpackedArray length (${unpackedArray.length}) must be >= ` +\n        `${requiredSize}`);\n  }\n  let dst = 0;\n  for (let src = 0; src < matrix.length; ++src) {\n    unpackedArray[dst] = matrix[src];\n    dst += channelsPerTexture;\n  }\n}\n\nexport function decodeMatrixFromUnpackedArray(\n    unpackedArray: Float32Array, matrix: Float32Array,\n    channelsPerTexture: number) {\n  const requiredSize = getMatrixSizeFromUnpackedArraySize(\n      unpackedArray.length, channelsPerTexture);\n  if (matrix.length < requiredSize) {\n    throw new Error(\n        `matrix length (${matrix.length}) must be >= ${requiredSize}`);\n  }\n  let dst = 0;\n  for (let src = 0; src < unpackedArray.length; src += channelsPerTexture) {\n    matrix[dst++] = unpackedArray[src];\n  }\n}\n\nexport function decodeMatrixFromUnpackedColorRGBAArray(\n    unpackedArray: Float32Array, matrix: Float32Array, channels: number) {\n  const requiredSize = unpackedArray.length * channels / 4;\n  if (matrix.length < requiredSize) {\n    throw new Error(\n        `matrix length (${matrix.length}) must be >= ${requiredSize}`);\n  }\n  let dst = 0;\n  for (let src = 0; src < unpackedArray.length; src += 4) {\n    for (let c = 0; c < channels; c++) {\n      matrix[dst++] = unpackedArray[src + c];\n    }\n  }\n}\n\nexport function getPackedMatrixTextureShapeWidthHeight(\n    rows: number, columns: number): [number, number] {\n  return [\n    Math.max(1, Math.ceil(columns / 2)), Math.max(1, Math.ceil(rows / 2))\n  ];\n}\n\nexport function getPackedRGBAArraySizeFromMatrixShape(\n    rows: number, columns: number): number {\n  const [w, h] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n  return w * h * 4;\n}\n\n/*\nThis is how encodeMatrixToPackedRGBA encodes a tensor with shape = [2, 3, 5]\n(indices are [batch, row, col]).\n\n000|001   002|003   004|xxx   020|021   022|023   024|xxx\n-------   -------   -------   -------   -------   -------\n010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n\n100|101   102|103   104|xxx   120|121   122|123   124|xxx\n-------   -------   -------   -------   -------   -------\n110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n\nSingle texels contain only values from the same batch, and from adjacent rows\nand columns.\n\nNote the batch dimension is needed so xxx's are inserted below 020, 021, 022,\n023, and 024.\n */\n\nexport function encodeMatrixToPackedRGBA(\n    matrix: Float32Array, batches: number, rows: number, columns: number,\n    packedRGBA: Float32Array) {\n  const requiredSize = getPackedRGBAArraySizeFromMatrixShape(rows, columns);\n  if (packedRGBA.length < requiredSize) {\n    throw new Error(`packedRGBA length (${packedRGBA.length}) must be >=\n        ${requiredSize}`);\n  }\n\n  const oddWidth = (columns % 2) === 1;\n  const oddHeight = (rows % 2) === 1;\n  const widthInFullBlocks = Math.floor(columns / 2);\n  const heightInFullBlocks = Math.floor(rows / 2);\n\n  const texelsPerRow = Math.ceil(columns / 2);\n  const texelsPerBatch = texelsPerRow * Math.ceil(rows / 2);\n\n  const flattenedMatrixSize =\n      util.nearestLargerEven(rows) * util.nearestLargerEven(columns);\n\n  for (let batch = 0; batch < batches; batch++) {\n    const sourceOffset = batch * rows * columns;\n    const batchOffset = batch * flattenedMatrixSize;\n\n    // loop over full 2x2 blocks\n    {\n      const dstStride = (oddWidth ? 4 : 0);\n      const oneRow = columns;\n      let dst = batchOffset;\n      for (let blockY = 0; blockY < heightInFullBlocks; ++blockY) {\n        const matrixSrcRow = (blockY * 2 * columns);\n        for (let blockX = 0; blockX < widthInFullBlocks; ++blockX) {\n          const matrixSrcCol = blockX * 2;\n          const src = sourceOffset + matrixSrcRow + matrixSrcCol;\n          packedRGBA[dst] = matrix[src];\n          packedRGBA[dst + 1] = matrix[src + 1];\n          packedRGBA[dst + 2] = matrix[src + oneRow];\n          packedRGBA[dst + 3] = matrix[src + oneRow + 1];\n          dst += 4;\n        }\n        dst += dstStride;\n      }\n    }\n\n    // loop down final odd column\n    if (oddWidth) {\n      let src = sourceOffset + columns - 1;\n      let dst = batchOffset + (texelsPerRow - 1) * 4;\n      const srcStride = 2 * columns;\n      const dstStride = texelsPerRow * 4;\n      for (let blockY = 0; blockY < heightInFullBlocks; ++blockY) {\n        packedRGBA[dst] = matrix[src];\n        packedRGBA[dst + 2] = matrix[src + columns];\n        src += srcStride;\n        dst += dstStride;\n      }\n    }\n\n    // loop across final row\n    if (oddHeight) {\n      let src = sourceOffset + (rows - 1) * columns;\n      let dst = batchOffset + (texelsPerBatch - texelsPerRow) * 4;\n      for (let blockX = 0; blockX < widthInFullBlocks; ++blockX) {\n        packedRGBA[dst++] = matrix[src++];\n        packedRGBA[dst++] = matrix[src++];\n        dst += 2;\n      }\n\n      // fill in bottom-right texel\n      if (oddWidth && oddHeight) {\n        packedRGBA[batchOffset + flattenedMatrixSize - 4] = matrix[src];\n      }\n    }\n  }\n\n  return packedRGBA;\n}\n\nexport function decodeMatrixFromPackedRGBA(\n    packedRGBA: Float32Array, batches: number, rows: number, columns: number,\n    matrix: Float32Array): Float32Array {\n  const requiredSize = rows * columns;\n  if (matrix.length < requiredSize) {\n    throw new Error(\n        `matrix length (${matrix.length}) must be >= ${requiredSize}`);\n  }\n\n  const oddWidth = (columns % 2) === 1;\n  const oddHeight = (rows % 2) === 1;\n  const widthInFullBlocks = Math.floor(columns / 2);\n  const heightInFullBlocks = Math.floor(rows / 2);\n\n  const texelsPerRow = Math.ceil(columns / 2);\n  const texelsPerBatch = texelsPerRow * Math.ceil(rows / 2);\n\n  const flattenedMatrixSize =\n      util.nearestLargerEven(rows) * util.nearestLargerEven(columns);\n\n  for (let batch = 0; batch < batches; batch++) {\n    const batchOffset = batch * rows * columns;\n    const sourceOffset = batch * flattenedMatrixSize;\n\n    // loop over full 2x2 blocks\n    {\n      const srcStride = oddWidth ? 4 : 0;\n      const dstStride = columns + (oddWidth ? 1 : 0);\n      let src = sourceOffset;\n      let dstRow1 = batchOffset;\n      let dstRow2 = batchOffset + columns;\n      for (let blockY = 0; blockY < heightInFullBlocks; ++blockY) {\n        for (let blockX = 0; blockX < widthInFullBlocks; ++blockX) {\n          matrix[dstRow1++] = packedRGBA[src++];\n          matrix[dstRow1++] = packedRGBA[src++];\n          matrix[dstRow2++] = packedRGBA[src++];\n          matrix[dstRow2++] = packedRGBA[src++];\n        }\n        src += srcStride;\n        dstRow1 += dstStride;\n        dstRow2 += dstStride;\n      }\n    }\n\n    // loop down final column\n    if (oddWidth) {\n      let src = sourceOffset + (texelsPerRow - 1) * 4;\n      let dst = batchOffset + columns - 1;\n      const srcStride = texelsPerRow * 4;\n      const dstStride = 2 * columns;\n      for (let blockY = 0; blockY < heightInFullBlocks; ++blockY) {\n        matrix[dst] = packedRGBA[src];\n        matrix[dst + columns] = packedRGBA[src + 2];\n        src += srcStride;\n        dst += dstStride;\n      }\n    }\n\n    // loop across final row\n    if (oddHeight) {\n      let src = sourceOffset + (texelsPerBatch - texelsPerRow) * 4;\n      let dst = batchOffset + (rows - 1) * columns;\n      for (let blockX = 0; blockX < widthInFullBlocks; ++blockX) {\n        matrix[dst++] = packedRGBA[src++];\n        matrix[dst++] = packedRGBA[src++];\n        src += 2;\n      }\n\n      // fill in bottom-right cell\n      if (oddWidth) {\n        matrix[batchOffset + (rows * columns) - 1] = packedRGBA[src];\n      }\n    }\n  }\n\n  return matrix;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../../environment';\nimport * as util from '../../util';\n\nexport function callAndCheck<T>(gl: WebGLRenderingContext, func: () => T): T {\n  const returnValue = func();\n  checkWebGLError(gl);\n  return returnValue;\n}\n\nlet webGLDebugErrorCheckingEnabled = false;\n\nexport function enableDebugWebGLErrorChecking(enabled: boolean) {\n  webGLDebugErrorCheckingEnabled = enabled;\n}\n\nexport function checkWebGLError(gl: WebGLRenderingContext) {\n  if (webGLDebugErrorCheckingEnabled) {\n    const error = gl.getError();\n    if (error !== gl.NO_ERROR) {\n      throw new Error('WebGL Error: ' + getWebGLErrorMessage(gl, error));\n    }\n  }\n}\n\nexport function getWebGLErrorMessage(\n    gl: WebGLRenderingContext, status: number): string {\n  switch (status) {\n    case gl.NO_ERROR:\n      return 'NO_ERROR';\n    case gl.INVALID_ENUM:\n      return 'INVALID_ENUM';\n    case gl.INVALID_VALUE:\n      return 'INVALID_VALUE';\n    case gl.INVALID_OPERATION:\n      return 'INVALID_OPERATION';\n    case gl.INVALID_FRAMEBUFFER_OPERATION:\n      return 'INVALID_FRAMEBUFFER_OPERATION';\n    case gl.OUT_OF_MEMORY:\n      return 'OUT_OF_MEMORY';\n    case gl.CONTEXT_LOST_WEBGL:\n      return 'CONTEXT_LOST_WEBGL';\n    default:\n      return `Unknown error code ${status}`;\n  }\n}\n\nexport function getExtensionOrThrow(\n    gl: WebGLRenderingContext, extensionName: string): {} {\n  return throwIfNull<{}>(\n      gl, () => gl.getExtension(extensionName),\n      'Extension \"' + extensionName + '\" not supported on this browser.');\n}\n\nexport function createVertexShader(\n    gl: WebGLRenderingContext, vertexShaderSource: string): WebGLShader {\n  const vertexShader: WebGLShader = throwIfNull<WebGLShader>(\n      gl, () => gl.createShader(gl.VERTEX_SHADER),\n      'Unable to create vertex WebGLShader.');\n  callAndCheck(gl, () => gl.shaderSource(vertexShader, vertexShaderSource));\n  callAndCheck(gl, () => gl.compileShader(vertexShader));\n  if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {\n    console.log(gl.getShaderInfoLog(vertexShader));\n    throw new Error('Failed to compile vertex shader.');\n  }\n  return vertexShader;\n}\n\nexport function createFragmentShader(\n    gl: WebGLRenderingContext, fragmentShaderSource: string): WebGLShader {\n  const fragmentShader: WebGLShader = throwIfNull<WebGLShader>(\n      gl, () => gl.createShader(gl.FRAGMENT_SHADER),\n      'Unable to create fragment WebGLShader.');\n  callAndCheck(gl, () => gl.shaderSource(fragmentShader, fragmentShaderSource));\n  callAndCheck(gl, () => gl.compileShader(fragmentShader));\n  if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {\n    logShaderSourceAndInfoLog(\n        fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));\n    throw new Error('Failed to compile fragment shader.');\n  }\n  return fragmentShader;\n}\n\nconst lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;\nfunction logShaderSourceAndInfoLog(\n    shaderSource: string, shaderInfoLog: string) {\n  const lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);\n  if (lineNumberRegexResult == null) {\n    console.log(`Couldn't parse line number in error: ${shaderInfoLog}`);\n    console.log(shaderSource);\n    return;\n  }\n\n  const lineNumber = +lineNumberRegexResult[1];\n\n  const shaderLines = shaderSource.split('\\n');\n  const pad = shaderLines.length.toString().length + 2;\n  const linesWithLineNumbers = shaderLines.map(\n      (line, lineNumber) =>\n          util.rightPad((lineNumber + 1).toString(), pad) + line);\n  let maxLineLength = 0;\n  for (let i = 0; i < linesWithLineNumbers.length; i++) {\n    maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength);\n  }\n\n  const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);\n  const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);\n  const afterErrorLines = linesWithLineNumbers.slice(lineNumber);\n\n  console.log(beforeErrorLines.join('\\n'));\n  console.log(shaderInfoLog.split('\\n')[0]);\n  console.log(\n      `%c ${util.rightPad(errorLine[0], maxLineLength)}`,\n      'border:1px solid red; background-color:#e3d2d2; color:#a61717');\n  console.log(afterErrorLines.join('\\n'));\n}\n\nexport function createProgram(gl: WebGLRenderingContext): WebGLProgram {\n  return throwIfNull<WebGLProgram>(\n      gl, () => gl.createProgram(), 'Unable to create WebGLProgram.');\n}\n\nexport function linkProgram(gl: WebGLRenderingContext, program: WebGLProgram) {\n  callAndCheck(gl, () => gl.linkProgram(program));\n  if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {\n    console.log(gl.getProgramInfoLog(program));\n    throw new Error('Failed to link vertex and fragment shaders.');\n  }\n}\n\nexport function validateProgram(\n    gl: WebGLRenderingContext, program: WebGLProgram) {\n  callAndCheck(gl, () => gl.validateProgram(program));\n  if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {\n    console.log(gl.getProgramInfoLog(program));\n    throw new Error('Shader program validation failed.');\n  }\n}\n\nexport function createStaticVertexBuffer(\n    gl: WebGLRenderingContext, data: Float32Array): WebGLBuffer {\n  const buffer: WebGLBuffer = throwIfNull<WebGLBuffer>(\n      gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n  callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n  callAndCheck(gl, () => gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW));\n  return buffer;\n}\n\nexport function createStaticIndexBuffer(\n    gl: WebGLRenderingContext, data: Uint16Array): WebGLBuffer {\n  const buffer: WebGLBuffer = throwIfNull<WebGLBuffer>(\n      gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n  callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer));\n  callAndCheck(\n      gl, () => gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW));\n  return buffer;\n}\n\nexport function getNumChannels(): number {\n  if (ENV.get('WEBGL_VERSION') === 2) {\n    return 1;\n  }\n  return 4;\n}\n\nexport function createTexture(gl: WebGLRenderingContext): WebGLTexture {\n  return throwIfNull<WebGLTexture>(\n      gl, () => gl.createTexture(), 'Unable to create WebGLTexture.');\n}\n\nexport function validateTextureSize(width: number, height: number) {\n  const maxTextureSize = ENV.get('WEBGL_MAX_TEXTURE_SIZE');\n  if ((width <= 0) || (height <= 0)) {\n    const requested = `[${width}x${height}]`;\n    throw new Error('Requested texture size ' + requested + ' is invalid.');\n  }\n  if ((width > maxTextureSize) || (height > maxTextureSize)) {\n    const requested = `[${width}x${height}]`;\n    const max = `[${maxTextureSize}x${maxTextureSize}]`;\n    throw new Error(\n        'Requested texture size ' + requested +\n        ' greater than WebGL maximum on this browser / GPU ' + max + '.');\n  }\n}\n\nexport function createFramebuffer(gl: WebGLRenderingContext): WebGLFramebuffer {\n  return throwIfNull<WebGLFramebuffer>(\n      gl, () => gl.createFramebuffer(), 'Unable to create WebGLFramebuffer.');\n}\n\nexport function bindVertexBufferToProgramAttribute(\n    gl: WebGLRenderingContext, program: WebGLProgram, attribute: string,\n    buffer: WebGLBuffer, arrayEntriesPerItem: number, itemStrideInBytes: number,\n    itemOffsetInBytes: number): boolean {\n  const loc = gl.getAttribLocation(program, attribute);\n  if (loc === -1) {\n    // The GPU compiler decided to strip out this attribute because it's unused,\n    // thus no need to bind.\n    return false;\n  }\n  callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n  callAndCheck(\n      gl,\n      () => gl.vertexAttribPointer(\n          loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes,\n          itemOffsetInBytes));\n  callAndCheck(gl, () => gl.enableVertexAttribArray(loc));\n  return true;\n}\n\nexport function bindTextureUnit(\n    gl: WebGLRenderingContext, texture: WebGLTexture, textureUnit: number) {\n  validateTextureUnit(gl, textureUnit);\n  callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n}\n\nexport function unbindTextureUnit(\n    gl: WebGLRenderingContext, textureUnit: number) {\n  validateTextureUnit(gl, textureUnit);\n  callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\n\nexport function getProgramUniformLocationOrThrow(\n    gl: WebGLRenderingContext, program: WebGLProgram,\n    uniformName: string): WebGLUniformLocation {\n  return throwIfNull<WebGLUniformLocation>(\n      gl, () => gl.getUniformLocation(program, uniformName),\n      'uniform \"' + uniformName + '\" not present in program.');\n}\n\nexport function getProgramUniformLocation(\n    gl: WebGLRenderingContext, program: WebGLProgram,\n    uniformName: string): WebGLUniformLocation {\n  return gl.getUniformLocation(program, uniformName);\n}\n\nexport function bindTextureToProgramUniformSampler(\n    gl: WebGLRenderingContext, program: WebGLProgram, texture: WebGLTexture,\n    uniformSamplerLocation: WebGLUniformLocation, textureUnit: number) {\n  callAndCheck(gl, () => bindTextureUnit(gl, texture, textureUnit));\n  callAndCheck(gl, () => gl.uniform1i(uniformSamplerLocation, textureUnit));\n}\n\nexport function bindCanvasToFramebuffer(gl: WebGLRenderingContext) {\n  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n  callAndCheck(gl, () => gl.viewport(0, 0, gl.canvas.width, gl.canvas.height));\n  callAndCheck(gl, () => gl.scissor(0, 0, gl.canvas.width, gl.canvas.height));\n}\n\nexport function bindColorTextureToFramebuffer(\n    gl: WebGLRenderingContext, texture: WebGLTexture,\n    framebuffer: WebGLFramebuffer) {\n  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n  callAndCheck(\n      gl,\n      () => gl.framebufferTexture2D(\n          gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0));\n}\n\nexport function unbindColorTextureFromFramebuffer(\n    gl: WebGLRenderingContext, framebuffer: WebGLFramebuffer) {\n  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n  callAndCheck(\n      gl,\n      () => gl.framebufferTexture2D(\n          gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0));\n}\n\nexport function validateFramebuffer(gl: WebGLRenderingContext) {\n  const status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);\n  if (status !== gl.FRAMEBUFFER_COMPLETE) {\n    throw new Error(\n        'Error binding framebuffer: ' + getFramebufferErrorMessage(gl, status));\n  }\n}\n\nexport function getFramebufferErrorMessage(\n    gl: WebGLRenderingContext, status: number): string {\n  switch (status) {\n    case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:\n      return 'FRAMEBUFFER_INCOMPLETE_ATTACHMENT';\n    case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:\n      return 'FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT';\n    case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:\n      return 'FRAMEBUFFER_INCOMPLETE_DIMENSIONS';\n    case gl.FRAMEBUFFER_UNSUPPORTED:\n      return 'FRAMEBUFFER_UNSUPPORTED';\n    default:\n      return `unknown error ${status}`;\n  }\n}\n\nfunction throwIfNull<T>(\n    gl: WebGLRenderingContext, returnTOrNull: () => T | null,\n    failureMessage: string): T {\n  const tOrNull: T|null = callAndCheck(gl, () => returnTOrNull());\n  if (tOrNull == null) {\n    throw new Error(failureMessage);\n  }\n  return tOrNull as T;\n}\n\nfunction validateTextureUnit(gl: WebGLRenderingContext, textureUnit: number) {\n  const maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;\n  const glTextureUnit = textureUnit + gl.TEXTURE0;\n  if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {\n    const textureUnitRange = `[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;\n    throw new Error(`textureUnit must be in ${textureUnitRange}.`);\n  }\n}\n\nexport function getTextureShapeFromLogicalShape(\n    logShape: number[], isPacked = false): [number, number] {\n  let maxTexSize = ENV.get('WEBGL_MAX_TEXTURE_SIZE');\n  if (isPacked) {\n    maxTexSize = maxTexSize * 2;\n\n    // This logic ensures we accurately count the number of packed texels needed\n    // to accommodate the tensor. We can only pack values in the same texel if\n    // they are from adjacent pairs of rows/cols within the same batch. So if a\n    // tensor has 3 rows, we pretend it has 4 rows in order to account for the\n    // fact that the texels containing the third row are half empty.\n    logShape = logShape.map(\n        (d, i) => i >= logShape.length - 2 ?\n            util.nearestLargerEven(logShape[i]) :\n            logShape[i]);\n  }\n\n  // If logical shape is 2, we don't squeeze, since we want to match physical.\n  if (logShape.length !== 2) {\n    const squeezeResult = util.squeezeShape(logShape);\n    logShape = squeezeResult.newShape;\n  }\n\n  const size = util.sizeFromShape(logShape);\n  if (logShape.length <= 1 && size <= maxTexSize) {\n    return [1, size];\n  } else if (\n      logShape.length === 2 && logShape[0] <= maxTexSize &&\n      logShape[1] <= maxTexSize) {\n    return logShape as [number, number];\n  } else if (\n      logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize &&\n      logShape[2] <= maxTexSize) {\n    return [logShape[0] * logShape[1], logShape[2]];\n  } else if (\n      logShape.length === 3 && logShape[0] <= maxTexSize &&\n      logShape[1] * logShape[2] <= maxTexSize) {\n    return [logShape[0], logShape[1] * logShape[2]];\n  } else if (\n      logShape.length === 4 &&\n      logShape[0] * logShape[1] * logShape[2] <= maxTexSize &&\n      logShape[3] <= maxTexSize) {\n    return [logShape[0] * logShape[1] * logShape[2], logShape[3]];\n  } else if (\n      logShape.length === 4 && logShape[0] <= maxTexSize &&\n      logShape[1] * logShape[2] * logShape[3] <= maxTexSize) {\n    return [logShape[0], logShape[1] * logShape[2] * logShape[3]];\n  } else {\n    return util.sizeToSquarishShape(size);\n  }\n}\n\nfunction isEven(n: number): boolean {\n  return n % 2 === 0;\n}\n\n/**\n * This determines whether reshaping a packed texture requires rearranging\n * the data within the texture, assuming 2x2 packing.\n */\nexport function isReshapeFree(shape1: number[], shape2: number[]): boolean {\n  shape1 = shape1.slice(-2);\n  shape2 = shape2.slice(-2);\n\n  if (util.arraysEqual(shape1, shape2)) {\n    return true;\n  }\n\n  if (!shape1.length || !shape2.length) {  // One of the shapes is a scalar.\n    return true;\n  }\n\n  if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 ||\n      shape2[1] === 0) {\n    return true;\n  }\n\n  if (shape1.length !== shape2.length) {  // One of the shapes is a vector.\n    const shape1Cols = shape1.slice(-1)[0];\n    const shape2Cols = shape2.slice(-1)[0];\n    if (shape1Cols === shape2Cols) {\n      return true;\n    }\n\n    if (isEven(shape1Cols) && isEven(shape2Cols) &&\n        (shape1[0] === 1 || shape2[0] === 1)) {\n      return true;\n    }\n  } else {\n    if (isEven(shape1[0]) && isEven(shape2[0])) {\n      if (isEven(shape1[1]) && isEven(shape2[1])) {\n        return true;\n      }\n\n      if (shape1[1] === shape2[1]) {\n        return true;\n      }\n    }\n  }\n\n  return false;\n}","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../../environment';\nimport * as util from '../../util';\n\nimport * as tex_util from './tex_util';\nimport * as webgl_util from './webgl_util';\n\nexport interface TextureConfig {\n  internalFormatFloat: number;\n  textureFormatFloat: number;\n  internalFormatHalfFloat: number;\n  internalFormatPackedFloat: number;\n\n  // The format to use during a gl.readPixels call.\n  downloadTextureFormat: number;\n  // How many channels need to be unpacked after a gl.readPixels call.\n  downloadUnpackNumChannels: number;\n\n  defaultNumChannels: number;\n  textureTypeHalfFloat: number;\n}\n\nexport function createVertexShader(gl: WebGLRenderingContext): WebGLShader {\n  const vertexShaderSource = `\n    precision highp float;\n    attribute vec3 clipSpacePos;\n    attribute vec2 uv;\n    varying vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }`;\n  return webgl_util.createVertexShader(gl, vertexShaderSource);\n}\n\nexport function createVertexBuffer(gl: WebGLRenderingContext): WebGLBuffer {\n  // [x y z u v] * [upper-left, lower-left, upper-right, lower-right]\n  const vertexArray = new Float32Array(\n      [-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);\n  return webgl_util.createStaticVertexBuffer(gl, vertexArray);\n}\n\nexport function createIndexBuffer(gl: WebGLRenderingContext): WebGLBuffer {\n  // OpenGL (and WebGL) have \"CCW == front\" winding\n  const triangleVertexIndices = new Uint16Array([0, 1, 2, 2, 1, 3]);\n  return webgl_util.createStaticIndexBuffer(gl, triangleVertexIndices);\n}\n\nexport function getTextureConfig(\n    // tslint:disable-next-line:no-any\n    gl: WebGLRenderingContext, textureHalfFloatExtension?: any): TextureConfig {\n  // tslint:disable-next-line:no-any\n  const glany = gl as any;\n\n  let internalFormatFloat: number;\n  let internalFormatHalfFloat: number;\n  let internalFormatPackedFloat: number;\n  let textureFormatFloat: number;\n\n  let downloadTextureFormat: number;\n  let downloadUnpackNumChannels: number;\n\n  let defaultNumChannels: number;\n  let textureTypeHalfFloat: number;\n\n  if (ENV.get('WEBGL_VERSION') === 2) {\n    internalFormatFloat = glany.R32F;\n    internalFormatHalfFloat = glany.R16F;\n    internalFormatPackedFloat = glany.RGBA32F;\n    textureFormatFloat = glany.RED;\n    downloadUnpackNumChannels = 4;\n    defaultNumChannels = 1;\n    textureTypeHalfFloat = glany.HALF_FLOAT;\n  } else {\n    internalFormatFloat = gl.RGBA;\n    internalFormatHalfFloat = gl.RGBA;\n    internalFormatPackedFloat = glany.RGBA;\n    textureFormatFloat = gl.RGBA;\n    downloadUnpackNumChannels = 4;\n    defaultNumChannels = 4;\n    textureTypeHalfFloat = textureHalfFloatExtension != null ?\n        textureHalfFloatExtension.HALF_FLOAT_OES :\n        null;\n  }\n  downloadTextureFormat = gl.RGBA;\n\n  return {\n    internalFormatFloat,\n    internalFormatHalfFloat,\n    internalFormatPackedFloat,\n    textureFormatFloat,\n    downloadTextureFormat,\n    downloadUnpackNumChannels,\n    defaultNumChannels,\n    textureTypeHalfFloat\n  };\n}\n\nfunction createAndConfigureTexture(\n    gl: WebGLRenderingContext, width: number, height: number,\n    internalFormat: number, textureFormat: number,\n    textureType: number): WebGLTexture {\n  webgl_util.validateTextureSize(width, height);\n  const texture = webgl_util.createTexture(gl);\n\n  const tex2d = gl.TEXTURE_2D;\n  webgl_util.callAndCheck(gl, () => gl.bindTexture(tex2d, texture));\n  webgl_util.callAndCheck(\n      gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE));\n  webgl_util.callAndCheck(\n      gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE));\n  webgl_util.callAndCheck(\n      gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST));\n  webgl_util.callAndCheck(\n      gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST));\n  webgl_util.callAndCheck(\n      gl,\n      () => gl.texImage2D(\n          tex2d, 0, internalFormat, width, height, 0, textureFormat,\n          textureType, null));\n  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n  return texture;\n}\n\nexport function createFloat32MatrixTexture(\n    gl: WebGLRenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig): WebGLTexture {\n  const [width, height] =\n      tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(\n      gl, width, height, textureConfig.internalFormatFloat,\n      textureConfig.textureFormatFloat, gl.FLOAT);\n}\n\nexport function createFloat16MatrixTexture(\n    gl: WebGLRenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig): WebGLTexture {\n  const [width, height] =\n      tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(\n      gl, width, height, textureConfig.internalFormatFloat,\n      textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);\n}\n\nexport function createUnsignedBytesMatrixTexture(\n    gl: WebGLRenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig): WebGLTexture {\n  const [width, height] =\n      tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(\n      gl, width, height, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE);\n}\n\nexport function createPackedMatrixTexture(\n    gl: WebGLRenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig): WebGLTexture {\n  const [width, height] =\n      tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(\n      gl, width, height, textureConfig.internalFormatPackedFloat, gl.RGBA,\n      gl.FLOAT);\n}\n\nexport function createFloat16PackedMatrixTexture(\n    gl: WebGLRenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig): WebGLTexture {\n  const [width, height] =\n      tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(\n      gl, width, height, textureConfig.internalFormatHalfFloat, gl.RGBA,\n      textureConfig.textureTypeHalfFloat);\n}\n\nexport function bindVertexProgramAttributeStreams(\n    gl: WebGLRenderingContext, program: WebGLProgram,\n    vertexBuffer: WebGLBuffer): boolean {\n  const posOffset = 0;               // x is the first buffer element\n  const uvOffset = 3 * 4;            // uv comes after [x y z]\n  const stride = (3 * 4) + (2 * 4);  // xyz + uv, each entry is 4-byte float.\n  webgl_util.callAndCheck(\n      gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer));\n  const success = webgl_util.bindVertexBufferToProgramAttribute(\n      gl, program, 'clipSpacePos', vertexBuffer, 3, stride, posOffset);\n  return success &&\n      webgl_util.bindVertexBufferToProgramAttribute(\n          gl, program, 'uv', vertexBuffer, 2, stride, uvOffset);\n}\n\nexport function uploadPixelDataToTexture(\n    gl: WebGLRenderingContext, texture: WebGLTexture,\n    pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement) {\n  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n  webgl_util.callAndCheck(\n      gl,\n      () => gl.texImage2D(\n          gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels));\n  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\n\nfunction uploadDataToTexture(\n    gl: WebGLRenderingContext, texture: WebGLTexture, width: number,\n    height: number, data: Float32Array, textureFormat: number) {\n  webgl_util.validateTextureSize(width, height);\n  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n  webgl_util.callAndCheck(\n      gl,\n      () => gl.texSubImage2D(\n          gl.TEXTURE_2D, 0, 0, 0, width, height, textureFormat, gl.FLOAT,\n          data));\n\n  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\n\nexport function uploadMatrixToTexture(\n    gl: WebGLRenderingContext, texture: WebGLTexture, rows: number,\n    columns: number, matrix: Float32Array, numChannels: number,\n    textureConfig: TextureConfig) {\n  const [w, h] =\n      tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n\n  let unpackedArray: Float32Array;\n  if (textureConfig.defaultNumChannels === 1) {\n    // No need to allocate a temporary array.\n    unpackedArray = matrix;\n  } else {\n    unpackedArray =\n        new Float32Array(tex_util.getUnpackedArraySizeFromMatrixSize(\n            matrix.length, numChannels));\n    tex_util.encodeMatrixToUnpackedArray(matrix, unpackedArray, numChannels);\n  }\n\n  uploadDataToTexture(\n      gl, texture, w, h, unpackedArray, textureConfig.textureFormatFloat);\n}\n\n/**\n * This method writes a tensor to a packed texture in a way that respects how we\n * represent data using each texel's r,g,b,a channels. Specifically, we lay\n * out the four channels in two rows each containing two channels, so a single\n * texel can represent up to four values from the tensor. That means a texture\n * that has a channel width of 11 and channel height of 4 will have a texel\n * width of 6 and texel height of 2.\n *\n * rows, columns: Logical number of rows and columns in the tensor to be\n * uploaded.\n *\n * physicalRows, physicalCols: Channel dimensions of the texture that will hold\n * the tensor.\n *\n * width, height (internal parameters): Texel dimensions of the texture.\n */\nexport function uploadMatrixToPackedTexture(\n    gl: WebGLRenderingContext, texture: WebGLTexture, batch: number,\n    rows: number, columns: number, physicalRows: number, physicalCols: number,\n    matrix: Float32Array, textureConfig: TextureConfig) {\n  const [w, h] = tex_util.getPackedMatrixTextureShapeWidthHeight(\n      physicalRows, physicalCols);\n  const packedRGBA = new Float32Array(\n      batch * tex_util.getPackedRGBAArraySizeFromMatrixShape(rows, columns));\n  tex_util.encodeMatrixToPackedRGBA(matrix, batch, rows, columns, packedRGBA);\n  uploadDataToTexture(gl, texture, w, h, packedRGBA, gl.RGBA);\n}\n\nexport function maybeCreateBufferFromOutputTexture(\n    gl: WebGLRenderingContext, texture: WebGLTexture, rows: number,\n    columns: number, textureConfig: TextureConfig): WebGLBuffer|WebGLTexture {\n  let bufferOrTexture: WebGLBuffer|WebGLTexture = texture;\n\n  if (ENV.get('WEBGL_VERSION') === 2) {\n    const gl2 = gl as WebGL2RenderingContext;\n\n    // Create and bind the buffer.\n    const buffer = gl2.createBuffer();\n    webgl_util.callAndCheck(\n        gl, () => gl.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer));\n\n    // Initialize the buffer to the size of the texture in bytes.\n    const bytesPerFloat = 4;\n    const bufferSizeBytes = bytesPerFloat *\n        tex_util.getUnpackedArraySizeFromMatrixSize(\n            rows * columns, textureConfig.downloadUnpackNumChannels);\n\n    webgl_util.callAndCheck(\n        gl,\n        () => gl.bufferData(\n            gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl.STATIC_DRAW));\n\n    // Enqueue a command on the GPU command queue to copy of texture into the\n    // buffer.\n    webgl_util.callAndCheck(\n        gl, () => gl2.readPixels(0, 0, columns, rows, gl.RGBA, gl.FLOAT, 0));\n\n    webgl_util.callAndCheck(\n        gl, () => gl.bindBuffer(gl2.PIXEL_PACK_BUFFER, null));\n\n    bufferOrTexture = buffer;\n  }\n\n  return bufferOrTexture;\n}\n\nexport function downloadFloat32MatrixFromBuffer(\n    gl: WebGLRenderingContext, buffer: WebGLBuffer, rows: number,\n    columns: number, textureConfig: TextureConfig): Float32Array {\n  const gl2 = gl as WebGL2RenderingContext;\n\n  const downloadTarget =\n      new Float32Array(tex_util.getUnpackedArraySizeFromMatrixSize(\n          rows * columns, textureConfig.downloadUnpackNumChannels));\n\n  gl2.bindBuffer(gl.ARRAY_BUFFER, buffer);\n  gl2.getBufferSubData(gl.ARRAY_BUFFER, 0, downloadTarget);\n  gl2.bindBuffer(gl.ARRAY_BUFFER, null);\n\n  const matrix = new Float32Array(rows * columns);\n  tex_util.decodeMatrixFromUnpackedArray(\n      downloadTarget as Float32Array, matrix,\n      textureConfig.downloadUnpackNumChannels);\n\n  return matrix;\n}\n\nexport function downloadFloat32MatrixFromOutputTexture(\n    gl: WebGLRenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig): Float32Array {\n  const [w, h] =\n      tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n\n  const downloadTarget =\n      new Float32Array(tex_util.getUnpackedArraySizeFromMatrixSize(\n          rows * columns, textureConfig.downloadUnpackNumChannels));\n\n  webgl_util.callAndCheck(\n      gl,\n      () => gl.readPixels(\n          0, 0, w, h, textureConfig.downloadTextureFormat, gl.FLOAT,\n          downloadTarget));\n\n  const matrix = new Float32Array(rows * columns);\n  tex_util.decodeMatrixFromUnpackedArray(\n      downloadTarget as Float32Array, matrix,\n      textureConfig.downloadUnpackNumChannels);\n  return matrix;\n}\n\nexport function downloadByteEncodedFloatMatrixFromOutputTexture(\n    gl: WebGLRenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig) {\n  const [w, h] =\n      tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n\n  const numChannels = 4;\n  const downloadTarget = new Uint8Array(\n      tex_util.getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));\n\n  webgl_util.callAndCheck(\n      gl,\n      () => gl.readPixels(\n          0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE,\n          downloadTarget));\n\n  // By wrapping the buffer in a Float32Array, we use native browser IEEE 754\n  // decoding of the 4 bytes that back each 32 bit float.\n  return new Float32Array(downloadTarget.buffer);\n}\n\nexport function downloadPackedMatrixFromBuffer(\n    gl: WebGLRenderingContext, buffer: WebGLBuffer, batch: number, rows: number,\n    cols: number, physicalRows: number, physicalCols: number,\n    textureConfig: TextureConfig): Float32Array {\n  const gl2 = gl as WebGL2RenderingContext;\n\n  const downloadTarget =\n      new Float32Array(tex_util.getPackedRGBAArraySizeFromMatrixShape(\n          physicalRows, physicalCols));\n\n  gl2.bindBuffer(gl.ARRAY_BUFFER, buffer);\n  gl2.getBufferSubData(gl.ARRAY_BUFFER, 0, downloadTarget);\n  gl2.bindBuffer(gl.ARRAY_BUFFER, null);\n\n  const matrix = new Float32Array(util.sizeFromShape([batch, rows, cols]));\n  tex_util.decodeMatrixFromPackedRGBA(\n      downloadTarget, batch, rows, cols, matrix);\n  return matrix;\n}\n\nexport function downloadMatrixFromPackedOutputTexture(\n    gl: WebGLRenderingContext, batch: number, rows: number, cols: number,\n    physicalRows: number, physicalCols: number,\n    textureConfig: TextureConfig): Float32Array {\n  const [w, h] = tex_util.getPackedMatrixTextureShapeWidthHeight(\n      physicalRows, physicalCols);\n\n  const packedRGBA =\n      new Float32Array(tex_util.getPackedRGBAArraySizeFromMatrixShape(\n          physicalRows, physicalCols));\n  webgl_util.callAndCheck(\n      gl, () => gl.readPixels(0, 0, w, h, gl.RGBA, gl.FLOAT, packedRGBA));\n  const matrix = new Float32Array(util.sizeFromShape([batch, rows, cols]));\n  return tex_util.decodeMatrixFromPackedRGBA(\n      packedRGBA, batch, rows, cols, matrix);\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getWebGLContext} from '../../canvas_util';\nimport {ENV} from '../../environment';\nimport * as util from '../../util';\nimport * as gpgpu_util from './gpgpu_util';\nimport {TextureConfig} from './gpgpu_util';\nimport * as tex_util from './tex_util';\nimport {WebGL1DisjointQueryTimerExtension, WebGL2DisjointQueryTimerExtension} from './webgl_types';\nimport * as webgl_util from './webgl_util';\n\nexport interface FenceContext {\n  query: WebGLQuery|WebGLSync;\n  isFencePassed(): boolean;\n}\n\nexport class GPGPUContext {\n  gl: WebGLRenderingContext;\n  textureFloatExtension: {};\n  textureHalfFloatExtension: {};\n  colorBufferFloatExtension: {};\n  colorBufferHalfFloatExtension: {};\n  getBufferSubDataAsyncExtension: {};\n  disjointQueryTimerExtension: WebGL2DisjointQueryTimerExtension|\n      WebGL1DisjointQueryTimerExtension;\n  vertexBuffer: WebGLBuffer;\n  indexBuffer: WebGLBuffer;\n  framebuffer: WebGLFramebuffer;\n  outputTexture: WebGLTexture|null = null;\n  program: WebGLProgram|null = null;\n  private disposed = false;\n  private autoDebugValidate = false;\n  private disjoint: boolean;\n  private textureConfig: TextureConfig;\n\n  constructor(gl?: WebGLRenderingContext) {\n    if (gl != null) {\n      this.gl = gl;\n    } else {\n      this.gl = getWebGLContext(ENV.get('WEBGL_VERSION'));\n    }\n    // WebGL 2.0 enables texture floats without an extension.\n    if (ENV.get('WEBGL_VERSION') === 1) {\n      this.textureFloatExtension =\n          webgl_util.getExtensionOrThrow(this.gl, 'OES_texture_float');\n      this.colorBufferFloatExtension =\n          this.gl.getExtension('WEBGL_color_buffer_float');\n\n      if (!ENV.get('WEBGL_RENDER_FLOAT32_ENABLED')) {\n        this.textureHalfFloatExtension =\n            webgl_util.getExtensionOrThrow(this.gl, 'OES_texture_half_float');\n        this.colorBufferHalfFloatExtension =\n            this.gl.getExtension('EXT_color_buffer_half_float');\n      }\n    } else {\n      this.colorBufferFloatExtension =\n          webgl_util.getExtensionOrThrow(this.gl, 'EXT_color_buffer_float');\n    }\n\n    this.vertexBuffer = gpgpu_util.createVertexBuffer(this.gl);\n    this.indexBuffer = gpgpu_util.createIndexBuffer(this.gl);\n    this.framebuffer = webgl_util.createFramebuffer(this.gl);\n\n    this.textureConfig =\n        gpgpu_util.getTextureConfig(this.gl, this.textureHalfFloatExtension);\n  }\n\n  public dispose() {\n    if (this.disposed) {\n      return;\n    }\n    if (this.program != null) {\n      console.warn(\n          'Disposing a GPGPUContext that still has a bound WebGLProgram.' +\n          ' This is probably a resource leak, delete the program with ' +\n          'GPGPUContext.deleteProgram before disposing.');\n    }\n    if (this.outputTexture != null) {\n      console.warn(\n          'Disposing a GPGPUContext that still has a bound output matrix ' +\n          'texture.  This is probably a resource leak, delete the output ' +\n          'matrix texture with GPGPUContext.deleteMatrixTexture before ' +\n          'disposing.');\n    }\n    const gl = this.gl;\n    webgl_util.callAndCheck(gl, () => gl.finish());\n    webgl_util.callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n    webgl_util.callAndCheck(gl, () => gl.deleteFramebuffer(this.framebuffer));\n    webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, null));\n    webgl_util.callAndCheck(\n        gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null));\n    webgl_util.callAndCheck(gl, () => gl.deleteBuffer(this.indexBuffer));\n    this.disposed = true;\n  }\n\n  public enableAutomaticDebugValidation(enabled: boolean) {\n    this.autoDebugValidate = enabled;\n    webgl_util.enableDebugWebGLErrorChecking(enabled);\n  }\n\n  public createFloat32MatrixTexture(rows: number, columns: number):\n      WebGLTexture {\n    this.throwIfDisposed();\n    return gpgpu_util.createFloat32MatrixTexture(\n        this.gl, rows, columns, this.textureConfig);\n  }\n\n  public createFloat16MatrixTexture(rows: number, columns: number):\n      WebGLTexture {\n    this.throwIfDisposed();\n    return gpgpu_util.createFloat16MatrixTexture(\n        this.gl, rows, columns, this.textureConfig);\n  }\n\n  public createUnsignedBytesMatrixTexture(rows: number, columns: number):\n      WebGLTexture {\n    this.throwIfDisposed();\n    return gpgpu_util.createUnsignedBytesMatrixTexture(\n        this.gl, rows, columns, this.textureConfig);\n  }\n\n  public uploadPixelDataToTexture(\n      texture: WebGLTexture,\n      pixels: ImageData|HTMLImageElement|HTMLCanvasElement) {\n    this.throwIfDisposed();\n    gpgpu_util.uploadPixelDataToTexture(this.gl, texture, pixels);\n  }\n\n  public createFloat16PackedMatrixTexture(rows: number, columns: number):\n      WebGLTexture {\n    this.throwIfDisposed();\n    return gpgpu_util.createFloat16PackedMatrixTexture(\n        this.gl, rows, columns, this.textureConfig);\n  }\n\n  public createPackedMatrixTexture(rows: number, columns: number):\n      WebGLTexture {\n    this.throwIfDisposed();\n    return gpgpu_util.createPackedMatrixTexture(\n        this.gl, rows, columns, this.textureConfig);\n  }\n\n  public deleteMatrixTexture(texture: WebGLTexture) {\n    this.throwIfDisposed();\n    if (this.outputTexture === texture) {\n      webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n      this.outputTexture = null;\n    }\n    webgl_util.callAndCheck(this.gl, () => this.gl.deleteTexture(texture));\n  }\n\n  public uploadMatrixToTexture(\n      texture: WebGLTexture, rows: number, columns: number,\n      matrix: Float32Array) {\n    this.throwIfDisposed();\n    const numChannels = webgl_util.getNumChannels();\n    return gpgpu_util.uploadMatrixToTexture(\n        this.gl, texture, rows, columns, matrix, numChannels,\n        this.textureConfig);\n  }\n\n  public uploadMatrixToPackedTexture(\n      texture: WebGLTexture, batch: number, rows: number, columns: number,\n      physicalRows: number, physicalCols: number, matrix: Float32Array) {\n    this.throwIfDisposed();\n    return gpgpu_util.uploadMatrixToPackedTexture(\n        this.gl, texture, batch, rows, columns, physicalRows, physicalCols,\n        matrix, this.textureConfig);\n  }\n\n  public downloadFloat32MatrixFromOutputTexture(\n      texture: WebGLTexture, rows: number, columns: number): Float32Array {\n    return this.downloadMatrixDriver(\n        texture,\n        () => gpgpu_util.downloadFloat32MatrixFromOutputTexture(\n            this.gl, rows, columns, this.textureConfig));\n  }\n\n  public downloadByteEncodedFloatMatrixFromOutputTexture(\n      texture: WebGLTexture, rows: number, columns: number): Float32Array {\n    return this.downloadMatrixDriver(\n        texture,\n        () => gpgpu_util.downloadByteEncodedFloatMatrixFromOutputTexture(\n            this.gl, rows, columns, this.textureConfig));\n  }\n\n  public downloadPackedMatrixFromBuffer(\n      buffer: WebGLBuffer, batch: number, rows: number, columns: number,\n      physicalRows: number, physicalCols: number): Float32Array {\n    return gpgpu_util.downloadPackedMatrixFromBuffer(\n        this.gl, buffer, batch, rows, columns, physicalRows, physicalCols,\n        this.textureConfig);\n  }\n\n  public downloadFloat32MatrixFromBuffer(\n      buffer: WebGLBuffer, rows: number, columns: number): Float32Array {\n    return gpgpu_util.downloadFloat32MatrixFromBuffer(\n        this.gl, buffer, rows, columns, this.textureConfig);\n  }\n\n  public maybeCreateBufferFromTexture(\n      texture: WebGLTexture, rows: number, columns: number): WebGLBuffer\n      |WebGLTexture {\n    this.bindTextureToFrameBuffer(texture);\n    const result = gpgpu_util.maybeCreateBufferFromOutputTexture(\n        this.gl, texture, rows, columns, this.textureConfig);\n    this.unbindTextureToFrameBuffer();\n    return result;\n  }\n\n  public createAndWaitForFence(): Promise<void> {\n    const fenceContext = this.createFence(this.gl);\n    return this.pollFence(fenceContext);\n  }\n\n  private createFence(gl: WebGLRenderingContext): FenceContext {\n    let query: WebGLQuery|WebGLSync;\n    let isFencePassed: () => boolean;\n\n    if (ENV.get('WEBGL_FENCE_API_ENABLED')) {\n      const gl2 = gl as WebGL2RenderingContext;\n\n      const sync = gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE, 0);\n      gl.flush();\n\n      isFencePassed = () => {\n        const status = gl2.clientWaitSync(sync, 0, 0);\n        return status === gl2.ALREADY_SIGNALED ||\n            status === gl2.CONDITION_SATISFIED;\n      };\n\n      query = sync;\n    } else if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {\n      query = this.beginQuery();\n      this.endQuery();\n      isFencePassed = () => this.isQueryAvailable(\n          query, ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n    } else {\n      // If we have no way to fence, return true immediately. This will fire in\n      // WebGL 1.0 when there is no disjoint query timer. In this case, because\n      // the fence passes immediately, we'll immediately ask for a download of\n      // the texture, which will cause the UI thread to hang.\n      isFencePassed = () => true;\n    }\n\n    return {query, isFencePassed};\n  }\n\n  public downloadMatrixFromPackedTexture(\n      texture: WebGLTexture, batch: number, rows: number, columns: number,\n      physicalRows: number, physicalCols: number): Float32Array {\n    return this.downloadMatrixDriver(\n        texture,\n        () => gpgpu_util.downloadMatrixFromPackedOutputTexture(\n            this.gl, batch, rows, columns, physicalRows, physicalCols,\n            this.textureConfig));\n  }\n\n  private vertexAttrsAreBound = false;\n\n  public createProgram(fragmentShaderSource: string): WebGLProgram {\n    this.throwIfDisposed();\n    const gl = this.gl;\n    const fragmentShader: WebGLShader =\n        webgl_util.createFragmentShader(gl, fragmentShaderSource);\n    const vertexShader: WebGLShader = gpgpu_util.createVertexShader(gl);\n    const program: WebGLProgram = webgl_util.createProgram(gl);\n    webgl_util.callAndCheck(gl, () => gl.attachShader(program, vertexShader));\n    webgl_util.callAndCheck(gl, () => gl.attachShader(program, fragmentShader));\n    webgl_util.linkProgram(gl, program);\n    if (this.autoDebugValidate) {\n      webgl_util.validateProgram(gl, program);\n    }\n    if (!this.vertexAttrsAreBound) {\n      this.setProgram(program);\n      this.vertexAttrsAreBound = gpgpu_util.bindVertexProgramAttributeStreams(\n          gl, this.program, this.vertexBuffer);\n    }\n    return program;\n  }\n\n  public deleteProgram(program: WebGLProgram) {\n    this.throwIfDisposed();\n    if (program === this.program) {\n      this.program = null;\n    }\n    if (program != null) {\n      webgl_util.callAndCheck(this.gl, () => this.gl.deleteProgram(program));\n    }\n  }\n\n  public setProgram(program: WebGLProgram|null) {\n    this.throwIfDisposed();\n    this.program = program;\n    if ((this.program != null) && this.autoDebugValidate) {\n      webgl_util.validateProgram(this.gl, this.program);\n    }\n    webgl_util.callAndCheck(this.gl, () => this.gl.useProgram(program));\n  }\n\n  public getUniformLocation(\n      program: WebGLProgram, uniformName: string,\n      shouldThrow = true): WebGLUniformLocation {\n    this.throwIfDisposed();\n    if (shouldThrow) {\n      return webgl_util.getProgramUniformLocationOrThrow(\n          this.gl, program, uniformName);\n    } else {\n      return webgl_util.getProgramUniformLocation(\n          this.gl, program, uniformName);\n    }\n  }\n\n  public getAttributeLocation(program: WebGLProgram, attribute: string):\n      number {\n    this.throwIfDisposed();\n    return webgl_util.callAndCheck(\n        this.gl, () => this.gl.getAttribLocation(program, attribute));\n  }\n\n  public getUniformLocationNoThrow(program: WebGLProgram, uniformName: string):\n      WebGLUniformLocation {\n    this.throwIfDisposed();\n    return this.gl.getUniformLocation(program, uniformName);\n  }\n\n  public setInputMatrixTexture(\n      inputMatrixTexture: WebGLTexture, uniformLocation: WebGLUniformLocation,\n      textureUnit: number) {\n    this.throwIfDisposed();\n    this.throwIfNoProgram();\n    webgl_util.bindTextureToProgramUniformSampler(\n        this.gl, this.program, inputMatrixTexture, uniformLocation,\n        textureUnit);\n  }\n\n  public setOutputMatrixTexture(\n      outputMatrixTexture: WebGLTexture, rows: number, columns: number) {\n    this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);\n  }\n\n  public setOutputPackedMatrixTexture(\n      outputPackedMatrixTexture: WebGLTexture, rows: number, columns: number) {\n    this.throwIfDisposed();\n    const [width, height] =\n        tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);\n  }\n\n  public setOutputMatrixWriteRegion(\n      startRow: number, numRows: number, startColumn: number,\n      numColumns: number) {\n    this.setOutputMatrixWriteRegionDriver(\n        startColumn, startRow, numColumns, numRows);\n  }\n\n  public setOutputPackedMatrixWriteRegion(\n      startRow: number, numRows: number, startColumn: number,\n      numColumns: number) {\n    throw new Error('setOutputPackedMatrixWriteRegion not implemented.');\n  }\n\n  public debugValidate() {\n    if (this.program != null) {\n      webgl_util.validateProgram(this.gl, this.program);\n    }\n    webgl_util.validateFramebuffer(this.gl);\n  }\n\n  public executeProgram() {\n    this.throwIfDisposed();\n    this.throwIfNoProgram();\n    const gl = this.gl;\n    if (this.autoDebugValidate) {\n      this.debugValidate();\n    }\n    webgl_util.callAndCheck(\n        gl, () => gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0));\n  }\n\n  public blockUntilAllProgramsCompleted() {\n    this.throwIfDisposed();\n    webgl_util.callAndCheck(this.gl, () => this.gl.finish());\n  }\n\n  private getQueryTimerExtension(): WebGL1DisjointQueryTimerExtension\n      |WebGL2DisjointQueryTimerExtension {\n    if (this.disjointQueryTimerExtension == null) {\n      this.disjointQueryTimerExtension =\n          webgl_util.getExtensionOrThrow(\n              this.gl,\n              ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2 ?\n                  'EXT_disjoint_timer_query_webgl2' :\n                  'EXT_disjoint_timer_query') as\n              WebGL1DisjointQueryTimerExtension |\n          WebGL2DisjointQueryTimerExtension;\n    }\n    return this.disjointQueryTimerExtension;\n  }\n\n  private getQueryTimerExtensionWebGL2(): WebGL2DisjointQueryTimerExtension {\n    return this.getQueryTimerExtension();\n  }\n\n  private getQueryTimerExtensionWebGL1(): WebGL1DisjointQueryTimerExtension {\n    return this.getQueryTimerExtension() as WebGL1DisjointQueryTimerExtension;\n  }\n\n  beginQuery(): WebGLQuery {\n    if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n      const gl2 = this.gl as WebGL2RenderingContext;\n      const ext = this.getQueryTimerExtensionWebGL2();\n\n      const query = gl2.createQuery();\n      gl2.beginQuery(ext.TIME_ELAPSED_EXT, query);\n      return query;\n    }\n    const ext = this.getQueryTimerExtensionWebGL1();\n    const query = ext.createQueryEXT() as WebGLQuery;\n    ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);\n    return query;\n  }\n\n  endQuery() {\n    if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n      const gl2 = this.gl as WebGL2RenderingContext;\n      const ext = this.getQueryTimerExtensionWebGL2();\n      gl2.endQuery(ext.TIME_ELAPSED_EXT);\n      return;\n    }\n    const ext = this.getQueryTimerExtensionWebGL1();\n    ext.endQueryEXT(ext.TIME_ELAPSED_EXT);\n  }\n\n  public async waitForQueryAndGetTime(query: WebGLQuery): Promise<number> {\n    await util.repeatedTry(\n        () => this.disposed ||  // while testing contexts are created / disposed\n                                // in rapid succession, so without this check we\n                                // may poll for the query timer indefinitely\n            this.isQueryAvailable(\n                query,\n                ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION')));\n    return this.getQueryTime(\n        query, ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n  }\n\n  private getQueryTime(query: WebGLQuery, queryTimerVersion: number): number {\n    if (queryTimerVersion === 0) {\n      return null;\n    }\n\n    if (queryTimerVersion === 2) {\n      const gl2 = this.gl as WebGL2RenderingContext;\n\n      const timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);\n      // Return milliseconds.\n      return timeElapsedNanos / 1000000;\n    } else {\n      const ext = this.getQueryTimerExtensionWebGL1();\n\n      const timeElapsedNanos =\n          ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);\n      // Return milliseconds.\n      return timeElapsedNanos / 1000000;\n    }\n  }\n\n  private isQueryAvailable(query: WebGLQuery, queryTimerVersion: number):\n      boolean {\n    if (queryTimerVersion === 0) {\n      return true;\n    }\n\n    if (queryTimerVersion === 2) {\n      const gl2 = this.gl as WebGL2RenderingContext;\n      const ext = this.getQueryTimerExtensionWebGL2();\n\n      const available =\n          gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);\n      if (this.disjoint == null) {\n        this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n      }\n\n      return available && !this.disjoint;\n    } else {\n      const ext = this.getQueryTimerExtensionWebGL1();\n\n      const available =\n          ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);\n      if (this.disjoint == null) {\n        this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n      }\n\n      return available && !this.disjoint;\n    }\n  }\n\n  pollFence(fenceContext: FenceContext) {\n    return new Promise<void>(resolve => {\n      this.addItemToPoll(() => fenceContext.isFencePassed(), () => resolve());\n    });\n  }\n\n  private itemsToPoll: PollItem[] = [];\n\n  pollItems(): void {\n    // Find the last query that has finished using binary search.\n    // All other queries before it are also done.\n    const index = binSearchLastTrue(this.itemsToPoll.map(x => x.isDoneFn));\n    for (let i = 0; i <= index; ++i) {\n      const {resolveFn} = this.itemsToPoll[i];\n      resolveFn();\n    }\n    this.itemsToPoll = this.itemsToPoll.slice(index + 1);\n  }\n\n  private addItemToPoll(isDoneFn: () => boolean, resolveFn: () => void) {\n    this.itemsToPoll.push({isDoneFn, resolveFn});\n    if (this.itemsToPoll.length > 1) {\n      // We already have a running loop that polls.\n      return;\n    }\n    // Start a new loop that polls.\n    util.repeatedTry(() => {\n      this.pollItems();\n      // End the loop if no more items to poll.\n      return this.itemsToPoll.length === 0;\n    });\n  }\n\n  private bindTextureToFrameBuffer(texture: WebGLTexture) {\n    this.throwIfDisposed();\n    webgl_util.bindColorTextureToFramebuffer(\n        this.gl, texture, this.framebuffer);\n    if (this.autoDebugValidate) {\n      webgl_util.validateFramebuffer(this.gl);\n    }\n  }\n\n  private unbindTextureToFrameBuffer() {\n    if (this.outputTexture != null) {\n      webgl_util.bindColorTextureToFramebuffer(\n          this.gl, this.outputTexture, this.framebuffer);\n      if (this.autoDebugValidate) {\n        webgl_util.validateFramebuffer(this.gl);\n      }\n    } else {\n      webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n    }\n  }\n\n  private downloadMatrixDriver(\n      texture: WebGLTexture,\n      downloadAndDecode: () => Float32Array): Float32Array {\n    this.bindTextureToFrameBuffer(texture);\n    const result = downloadAndDecode();\n    this.unbindTextureToFrameBuffer();\n\n    return result;\n  }\n\n  private setOutputMatrixTextureDriver(\n      outputMatrixTextureMaybePacked: WebGLTexture, width: number,\n      height: number) {\n    this.throwIfDisposed();\n    const gl = this.gl;\n    webgl_util.bindColorTextureToFramebuffer(\n        gl, outputMatrixTextureMaybePacked, this.framebuffer);\n    if (this.autoDebugValidate) {\n      webgl_util.validateFramebuffer(gl);\n    }\n    this.outputTexture = outputMatrixTextureMaybePacked;\n    webgl_util.callAndCheck(gl, () => gl.viewport(0, 0, width, height));\n    webgl_util.callAndCheck(gl, () => gl.scissor(0, 0, width, height));\n  }\n\n  private setOutputMatrixWriteRegionDriver(\n      x: number, y: number, width: number, height: number) {\n    this.throwIfDisposed();\n    webgl_util.callAndCheck(\n        this.gl, () => this.gl.scissor(x, y, width, height));\n  }\n\n  private throwIfDisposed() {\n    if (this.disposed) {\n      throw new Error('Attempted to use disposed GPGPUContext.');\n    }\n  }\n\n  private throwIfNoProgram() {\n    if (this.program == null) {\n      throw new Error('No GPU program is currently set.');\n    }\n  }\n}\n\ntype PollItem = {\n  isDoneFn: () => boolean,\n  resolveFn: () => void\n};\n\n/**\n * Finds the index of the last true element using binary search where\n * evaluation of an entry is expensive.\n */\nexport function binSearchLastTrue(arr: Array<() => boolean>): number {\n  let start = 0;\n  let end = arr.length - 1;\n  let best = -1;\n  while (start <= end) {\n    const mid = (start + end) >> 1;\n    const isDone = arr[mid]();\n    if (isDone) {\n      best = mid;\n      start = mid + 1;\n    } else {\n      end = mid - 1;\n    }\n  }\n  return best;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../../tensor';\nimport {TypedArray} from '../../types';\nimport * as util from '../../util';\n\nimport {GPGPUContext} from './gpgpu_context';\nimport * as shader_compiler from './shader_compiler';\nimport {InputInfo, ShapeInfo} from './shader_compiler';\nimport {TextureData} from './tex_util';\n\nexport interface GPGPUProgram {\n  variableNames: string[];\n  outputShape: number[];\n  userCode: string;\n  usesPackedTextures?: boolean;\n  supportsBroadcasting?: boolean;\n  isPackShader?: boolean;  // This property is used to single out the packing\n                           // shader so its output does not get eagerly unpacked\n                           // by backend_webgl.compileAndRun.\n}\n\nexport interface GPGPUBinary {\n  webGLProgram: WebGLProgram;\n  program: GPGPUProgram;\n  uniformLocations: {[name: string]: WebGLUniformLocation};\n  gpgpu: GPGPUContext;\n  source: string;\n  inShapeInfos: ShapeInfo[];\n  outShapeInfo: ShapeInfo;\n}\n\nexport interface TensorData {\n  shape: number[];\n  texData: TextureData;\n  isUniform: boolean;\n  uniformValues?: TypedArray;\n}\n\nexport function compileProgram<T extends Tensor, K extends Tensor>(\n    gpgpu: GPGPUContext, program: GPGPUProgram, inputs: TensorData[],\n    output: TensorData): GPGPUBinary {\n  const userCode = program.userCode;\n  const inputInfos: InputInfo[] = inputs.map((input, i) => {\n    const shapeInfo = {\n      logicalShape: input.shape,\n      texShape: input.isUniform ? null : input.texData.texShape,\n      isUniform: input.isUniform,\n      isPacked: input.isUniform ? false : input.texData.isPacked\n    };\n    return {name: program.variableNames[i], shapeInfo};\n  });\n  const inShapeInfos = inputInfos.map(x => x.shapeInfo);\n  const outShapeInfo = {\n    logicalShape: output.shape,\n    texShape: output.texData.texShape,\n    isUniform: false,\n    isPacked: output.texData.isPacked\n  };\n  const source = shader_compiler.makeShader(\n      inputInfos, outShapeInfo, userCode, program.supportsBroadcasting === true,\n      program.usesPackedTextures);\n\n  const webGLProgram = gpgpu.createProgram(source);\n\n  const uniformLocations: {[name: string]: WebGLUniformLocation} = {};\n  for (let i = 0; i < program.variableNames.length; i++) {\n    const uniformName = program.variableNames[i];\n    const shouldThrow = false;\n    uniformLocations[uniformName] =\n        gpgpu.getUniformLocation(webGLProgram, uniformName, shouldThrow);\n  }\n\n  return {\n    program,\n    source,\n    webGLProgram,\n    uniformLocations,\n    gpgpu,\n    inShapeInfos,\n    outShapeInfo\n  };\n}\n\nfunction validateBinaryAndProgram(\n    shapeInfos: ShapeInfo[], inputs: TensorData[]) {\n  if (shapeInfos.length !== inputs.length) {\n    throw Error(\n        `Binary was compiled with ${shapeInfos.length} inputs, but ` +\n        `was executed with ${inputs.length} inputs`);\n  }\n\n  shapeInfos.forEach((s, i) => {\n    const shapeA = s.logicalShape;\n    const input = inputs[i];\n    const shapeB = input.shape;\n\n    if (!util.arraysEqual(shapeA, shapeB)) {\n      throw Error(\n          `Binary was compiled with different shapes than ` +\n          `the current args. Shapes ${shapeA} and ${shapeB} must match`);\n    }\n    // The input is uploaded as uniform.\n    if (s.isUniform && input.isUniform) {\n      return;\n    }\n\n    const texShapeA = s.texShape;\n    const texShapeB = input.isUniform ? null : input.texData.texShape;\n    if (!util.arraysEqual(texShapeA, texShapeB)) {\n      throw Error(\n          `Binary was compiled with different texture shapes than the` +\n          ` current args. Shape ${texShapeA} and ${texShapeB} must match`);\n    }\n  });\n}\n\nexport function runProgram<T extends Tensor, K extends Tensor>(\n    binary: GPGPUBinary, inputs: TensorData[], output: TensorData,\n    customSetup?: (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) =>\n        void): void {\n  validateBinaryAndProgram(binary.inShapeInfos, inputs);\n  validateBinaryAndProgram([binary.outShapeInfo], [output]);\n\n  const outTex = output.texData.texture;\n  const outTexShape = output.texData.texShape;\n  const gpgpu = binary.gpgpu;\n  if (output.texData.isPacked) {\n    gpgpu.setOutputPackedMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n  } else {\n    gpgpu.setOutputMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n  }\n  gpgpu.setProgram(binary.webGLProgram);\n  inputs.forEach((input, i) => {\n    const variableName = binary.program.variableNames[i];\n    const variableUniformLocation = binary.uniformLocations[variableName];\n    if (variableUniformLocation != null) {\n      if (input.isUniform) {\n        if (util.sizeFromShape(input.shape) === 1) {\n          gpgpu.gl.uniform1f(variableUniformLocation, input.uniformValues[0]);\n        } else {\n          let vals = input.uniformValues;\n          if (!(vals instanceof Float32Array)) {\n            vals = new Float32Array(vals);\n          }\n          gpgpu.gl.uniform1fv(variableUniformLocation, vals);\n        }\n        return;\n      }\n      const tex = input.texData.texture;\n      gpgpu.setInputMatrixTexture(tex, variableUniformLocation, i);\n    }\n  });\n\n  if (customSetup != null) {\n    customSetup(gpgpu, binary.webGLProgram);\n  }\n  gpgpu.executeProgram();\n}\n\nexport function makeShaderKey(\n    program: GPGPUProgram, inputs: TensorData[], output: TensorData): string {\n  let keyInputs = '';\n  inputs.concat(output).forEach(x => {\n    keyInputs += `${x.shape}_${x.isUniform ? 'uniform' : x.texData.texShape}`;\n  });\n  const keyUserCode = program.userCode;\n  const keyBroadcast = (program.supportsBroadcasting === true).toString();\n  let key = program.constructor.name;\n  // Fast string concat. See https://jsperf.com/string-concatenation/14.\n  key += '_' + keyBroadcast + '_' + keyInputs + '_' + keyUserCode;\n  return key;\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class Im2ColProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      outputShape: number[], inputShape: number[], convInfo: Conv2DInfo) {\n    this.outputShape = outputShape;\n\n    const {\n      filterWidth,\n      inChannels,\n      strideWidth,\n      strideHeight,\n      padInfo,\n      outWidth,\n      dilationWidth,\n      dilationHeight\n    } = convInfo;\n    const {left, top} = padInfo;\n    const itemsPerBlockRow = inChannels * filterWidth;\n\n    this.userCode = `\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            int blockIndex = rc.y + col;\n            int pos = rc.x + row;\n\n            if(blockIndex >= ${outputShape[1]} || pos >= ${\n        outputShape[0]}) continue;\n\n            int offsetY = int(blockIndex / (${outWidth})) * ${strideHeight} - ${\n        top};\n            int d0 = offsetY + ${dilationHeight} * (pos / ${itemsPerBlockRow});\n\n            if(d0 >= ${inputShape[0]} || d0 < 0) continue;\n\n            int offsetX = int(mod(float(blockIndex), ${outWidth}.) * ${\n        strideWidth}. - ${left}.);\n            int d1 = offsetX + ${dilationWidth} * (int(mod(float(pos), ${\n        itemsPerBlockRow}.) / ${inChannels}.));\n\n            if(d1 >= ${inputShape[1]} || d1 < 0) continue;\n\n            result[row * 2 + col] = getA(d0, d1, int(mod(float(pos), ${\n        inChannels}.)));\n          }\n        }\n\n        gl_FragColor = result;\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class LRNProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(\n      xShape: number[], radius: number, bias: number, alpha: number,\n      beta: number) {\n    const rad = radius;\n    const maxD = xShape[3] - 1;\n    this.outputShape = xShape;\n\n    // optimize pow(bias + alpha * sum, -beta)\n    // src: https://github.com/tensorflow/tensorflow/..\n    // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..\n    // tensorflow/core/kernels/mkl_lrn_op.cc#L320\n    let powOperator;\n    const basis = `float(${bias}) + float(${alpha}) * sum`;\n    if (beta === 0.5) {\n      powOperator = `inversesqrt(${basis})`;\n    } else if (beta === 1.0) {\n      powOperator = `1.0/(${basis})`;\n    } else {\n      powOperator = `exp(log(${basis}) * float(-${beta}));`;\n    }\n\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${rad}; j <= ${rad}; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  ${maxD}) {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ${powOperator};\n        setOutput(val);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class LRNGradProgram implements GPGPUProgram {\n  variableNames = ['inputImage', 'outputImage', 'dy'];\n  outputShape: number[] = [];\n  userCode: string;\n  depthRadius: number;\n  bias: number;\n  alpha: number;\n  beta: number;\n  depth: number;\n\n  constructor(\n      inputShape: number[], depthRadius: number, bias: number, alpha: number,\n      beta: number) {\n    this.outputShape = inputShape;\n    this.depth = inputShape[3];\n    this.depthRadius = depthRadius;\n    this.bias = bias;\n    this.alpha = alpha;\n    this.beta = beta;\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < ${this.depth}; ++d) {\n          int depthBegin = int(max(0.0, float(d - ${depthRadius})));\n          int depthEnd = int(min(float(${this.depth}),\n              float(d + ${depthRadius} + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${alpha}) * norm + float(${bias});\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(${alpha})\n                * float(${beta})\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${beta});\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class MaxPool2DBackpropProgram implements GPGPUProgram {\n  variableNames = ['dy', 'maxPos'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv2DInfo) {\n    this.outputShape = convInfo.inShape;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n\n    const lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n          wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * ${effectiveFilterWidth} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class MatMulProgram implements GPGPUProgram {\n  variableNames = ['matrixA', 'matrixB'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      aShape: [number, number, number], bShape: [number, number, number],\n      transposeA = false, transposeB = false) {\n    const batchSize = aShape[0];\n    const outerShapeA = transposeA ? aShape[2] : aShape[1];\n    const outerShapeB = transposeB ? bShape[1] : bShape[2];\n    const sharedDim = transposeA ? aShape[1] : aShape[2];\n    this.outputShape = [batchSize, outerShapeA, outerShapeB];\n\n    const aSnippetFromOffset = (vec4Offset: number, indexVar: string|number) =>\n        transposeA ? `batch, ${indexVar} + ${vec4Offset}, aRow` :\n                     `batch, aRow, ${indexVar} + ${vec4Offset}`;\n    const bSnippetFromOffset = (vec4Offset: number, indexVar: string|number) =>\n        transposeB ? `batch, bCol, ${indexVar} + ${vec4Offset}` :\n                     `batch, ${indexVar} + ${vec4Offset}, bCol`;\n\n    const sharedDimNearestVec4 = Math.floor(sharedDim / 4) * 4;\n    const sharedDimVec4Remainder = sharedDim % 4;\n\n    this.userCode = ` float dotARowBCol(int batch, int aRow, int bCol) {\n      float result = 0.0;\n      for (int i = 0; i < ${sharedDimNearestVec4}; i += 4) {\n        vec4 a = vec4(\n          getMatrixA(${aSnippetFromOffset(0, 'i')}),\n          getMatrixA(${aSnippetFromOffset(1, 'i')}),\n          getMatrixA(${aSnippetFromOffset(2, 'i')}),\n          getMatrixA(${aSnippetFromOffset(3, 'i')})\n        );\n        vec4 b = vec4(\n          getMatrixB(${bSnippetFromOffset(0, 'i')}),\n          getMatrixB(${bSnippetFromOffset(1, 'i')}),\n          getMatrixB(${bSnippetFromOffset(2, 'i')}),\n          getMatrixB(${bSnippetFromOffset(3, 'i')})\n        );\n\n        result += dot(a, b);\n      }\n\n      if (${sharedDimVec4Remainder === 1}) {\n        result += getMatrixA(${aSnippetFromOffset(0, sharedDimNearestVec4)}) *\n          getMatrixB(${bSnippetFromOffset(0, sharedDimNearestVec4)});\n      } else if (${sharedDimVec4Remainder === 2}) {\n        vec2 a = vec2(\n          getMatrixA(${aSnippetFromOffset(0, sharedDimNearestVec4)}),\n          getMatrixA(${aSnippetFromOffset(1, sharedDimNearestVec4)})\n        );\n        vec2 b = vec2(\n          getMatrixB(${bSnippetFromOffset(0, sharedDimNearestVec4)}),\n          getMatrixB(${bSnippetFromOffset(1, sharedDimNearestVec4)})\n        );\n        result += dot(a, b);\n      } else if (${sharedDimVec4Remainder === 3}) {\n        vec3 a = vec3(\n          getMatrixA(${aSnippetFromOffset(0, sharedDimNearestVec4)}),\n          getMatrixA(${aSnippetFromOffset(1, sharedDimNearestVec4)}),\n          getMatrixA(${aSnippetFromOffset(2, sharedDimNearestVec4)})\n        );\n        vec3 b = vec3(\n          getMatrixB(${bSnippetFromOffset(0, sharedDimNearestVec4)}),\n          getMatrixB(${bSnippetFromOffset(1, sharedDimNearestVec4)}),\n          getMatrixB(${bSnippetFromOffset(2, sharedDimNearestVec4)})\n        );\n        result += dot(a, b);\n      }\n\n      return result;\n    }\n\n    void main() {\n      ivec3 resBRC = getOutputCoords();\n      setOutput(dotARowBCol(resBRC.x, resBRC.y, resBRC.z));\n    }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class MatMulPackedProgram implements GPGPUProgram {\n  variableNames = ['matrixA', 'matrixB'];\n  usesPackedTextures = true;\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      aShape: [number, number], bShape: [number, number],\n      outputShape: [number, number], transposeA = false, transposeB = false) {\n    this.outputShape = outputShape;\n\n    const sharedDim = transposeA ? aShape[0] : aShape[1];\n    const sharedDimensionPacked = Math.ceil(sharedDim / 2);\n\n    const aSample = transposeA ? 'i * 2, rc.x' : 'rc.x, i * 2';\n    const bSample = transposeB ? 'rc.y, i * 2' : 'i * 2, rc.y';\n    const aSwizzle = transposeA ? ['a.xxyy', 'a.zzww'] : ['a.xxzz', 'a.yyww'];\n    const bSwizzle = transposeB ? ['b.xzxz', 'b.ywyw'] : ['b.xyxy', 'b.zwzw'];\n\n    this.userCode = `\n      const float sharedDimension = ${sharedDimensionPacked}.0;\n\n      vec4 dot2x2ARowBCol(ivec2 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < ${sharedDimensionPacked}; i++) {\n          vec4 a = getMatrixA(${aSample});\n          vec4 b = getMatrixB(${bSample});\n\n          result += (${aSwizzle[0]} * ${bSwizzle[0]}) + (${aSwizzle[1]} * ${\n        bSwizzle[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec2 rc = getOutputCoords();\n        setOutput(dot2x2ARowBCol(rc));\n      }\n    `;\n  }\n}","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class MultinomialProgram implements GPGPUProgram {\n  variableNames = ['probs'];\n  outputShape: number[];\n  userCode: string;\n\n  // Caching uniform location for speed.\n  seedLoc: WebGLUniformLocation;\n\n  constructor(batchSize: number, numOutcomes: number, numSamples: number) {\n    this.outputShape = [batchSize, numSamples];\n\n    this.userCode = `\n      uniform float seed;\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < ${numOutcomes - 1}; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float(${numOutcomes - 1}));\n      }\n    `;\n  }\n\n  getCustomSetupFunc(seed: number) {\n    return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n      if (this.seedLoc == null) {\n        this.seedLoc = gpgpu.getUniformLocation(webGLProgram, 'seed');\n      }\n      gpgpu.gl.uniform1f(this.seedLoc, seed);\n    };\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class OneHotProgram implements GPGPUProgram {\n  variableNames = ['indices'];\n  outputShape: number[];\n  userCode: string;\n\n  // Caching uniform location for speed.\n  seedLoc: WebGLUniformLocation;\n\n  constructor(\n      numIndices: number, depth: number, onValue: number, offValue: number) {\n    this.outputShape = [numIndices, depth];\n\n    this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float(${offValue}), float(${onValue}),\n                      float(index == coords.y)));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport function getVecChannels(name: string, rank: number): string[] {\n  return ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank).map(d => `${name}.${d}`);\n}\n\nexport function getChannels(name: string, rank: number): string[] {\n  if (rank === 1) {\n    return [name];\n  }\n  return getVecChannels(name, rank);\n}\n\nexport function getSourceCoords(rank: number, dims: string[]): string {\n  if (rank === 1) {\n    return 'rc';\n  }\n\n  let coords = '';\n  for (let i = 0; i < rank; i++) {\n    coords += dims[i];\n    if (i < rank - 1) {\n      coords += ',';\n    }\n  }\n  return coords;\n}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getChannels} from '../packing_util';\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class PackProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  isPackShader = true;\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      outputShape:\n          number[]) {  // TODO(https://github.com/tensorflow/tfjs/issues/893):\n                       // Only input / output 3D tensors.\n    this.outputShape = outputShape;\n    const rank = outputShape.length;\n\n    if (rank === 0) {\n      this.userCode = `\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      `;\n    } else {\n      const channels = getChannels('rc', rank);\n      const dtype = getCoordsDataType(rank);\n      const outOfBoundsCondition =\n          getOutOfBoundsCondition(rank, outputShape, channels);\n      const setup = getSetup(\n          rank, outputShape[outputShape.length - 1],\n          outputShape[outputShape.length - 2], channels);\n      const output = getOutput(outputShape, channels);\n\n      this.userCode = `\n        void main() {\n          ${dtype} rc = getOutputCoords();\n\n          if(${outOfBoundsCondition}) {\n            setOutput(vec4(0));\n          } else {\n            ${setup}\n\n            setOutput(vec4(${output}));\n          }\n        }\n      `;\n    }\n  }\n}\n\nfunction getSourceCoordsArr(rank: number, dims: string[]): string[] {\n  const coords = [];\n\n  for (let row = 0; row <= 1; row++) {\n    for (let col = 0; col <= 1; col++) {\n      let coord = `${row === 0 ? 'r' : 'rp1'}, ${col === 0 ? 'c' : 'cp1'}`;\n\n      for (let d = 2; d < rank; d++) {\n        coord = `${dims[dims.length - 1 - d]},` + coord;\n      }\n\n      coords.push(coord);\n    }\n  }\n  return coords;\n}\n\nfunction getOutOfBoundsCondition(\n    rank: number, shape: number[], dims: string[]): string {\n  if (rank === 1) {\n    return `rc > ${shape[0]}`;\n  }\n\n  let cond = '';\n  for (let i = rank - 2; i < rank; i++) {\n    cond += `${dims[i]} >= ${shape[i]}`;\n    if (i < rank - 1) {\n      cond += '||';\n    }\n  }\n\n  return cond;\n}\n\nfunction getSetup(\n    rank: number, cols: number, rows: number, dims: string[]): string {\n  if (rank === 1) {\n    return '';\n  }\n\n  const innerDims = dims.slice(-2);\n\n  return `\n    int r = ${innerDims[0]};\n    int c = ${innerDims[1]};\n    int rp1 = r + 1;\n    int cp1 = c + 1;\n\n    bool cEdge = cp1 >= ${cols};\n    bool rEdge = rp1 >= ${rows};\n  `;\n}\n\nfunction getOutput(shape: number[], dims: string[]): string {\n  const rank = shape.length;\n  const sourceCoords = getSourceCoordsArr(rank, dims);\n  if (rank === 1) {\n    return `getA(rc),\n            rc + 1 >= ${shape[0]} ? 0. : getA(rc + 1),\n            0, 0`;\n  }\n\n  return `getA(${sourceCoords[0]}),\n          cEdge ? 0. : getA(${sourceCoords[1]}),\n          rEdge ? 0. : getA(${sourceCoords[2]}),\n          rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`;\n}","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class PadProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      xShape: number[], paddings: Array<[number, number]>,\n      constantValue: number) {\n    this.outputShape = paddings.map(\n        (p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n    const rank = xShape.length;\n    const type = getCoordsDataType(rank);\n\n    const start = paddings.map(p => p[0]).join(',');\n    const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n    const unpackedCoords =\n         ['coords[0]', 'coords[1]', 'coords[2]','coords[3]'].slice(0, rank);\n\n    if (rank === 1) {\n      this.userCode = `\n        int start = ${start};\n        int end = ${end};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(float(${constantValue}));\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      `;\n      return;\n    }\n    this.userCode = `\n      ${type} start = ${type}(${start});\n      ${type} end = ${type}(${end});\n\n      void main() {\n        ${type} outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(float(${constantValue}));\n        } else {\n          ${type} coords = outC - start;\n          setOutput(getX(${unpackedCoords}));\n        }\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class Pool2DProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      convInfo: Conv2DInfo, poolType: 'max'|'avg', computePositions: boolean) {\n    if (poolType === 'avg' && computePositions) {\n      throw new Error('Cannot compute positions for average pool.');\n    }\n\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    this.outputShape = convInfo.outShape;\n\n    const isAvgPool = poolType === 'avg';\n\n    let initializationValue = '0.0';\n    if (!isAvgPool) {\n      initializationValue = '-1.0 / 0.0';\n    }\n\n    if (computePositions) {\n      const compareOp = '>=';\n\n      this.userCode = `\n        const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n        const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ${compareOp} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = wR * ${effectiveFilterWidth} + wC;\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n      return;\n    }\n\n    const compareOp = 'max';\n\n    let returnValue = `${poolType}(${poolType}(${poolType}(` +\n        'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n    if (poolType === 'avg') {\n      returnValue = `avgValue / count`;\n    }\n\n    const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n    const filterWidthVec4Remainder = filterWidth % 4;\n\n    const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n\n    this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              getValue(batch, xR, xC + 3 * ${dilationWidth}, d)\n            );\n\n            ${updateSnippet}\n          }\n\n          int xC = xCCorner + ${filterWidthNearestVec4};\n          if (${filterWidthVec4Remainder === 1}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 2}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 3}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              initializationValue\n            );\n\n            ${updateSnippet}\n          }\n        }\n        setOutput(${returnValue});\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ReduceInfo} from '../../ops/reduce_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ReduceProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      reduceInfo: ReduceInfo,\n      reduceType: 'all'|'any'|'max'|'min'|'sum'|'prod') {\n    const windowSize = reduceInfo.windowSize;\n    const batchSize = reduceInfo.batchSize;\n    const inSize = reduceInfo.inSize;\n    const outSize = Math.ceil(inSize / windowSize);\n    this.outputShape = [batchSize, outSize];\n\n    let initializationValue = '0.0';\n    let compareOp = ``;\n\n    if (reduceType === 'prod') {\n      initializationValue = '1.0';\n    } else if (reduceType === 'min') {\n      initializationValue = '1.0 / 0.0';\n      compareOp = `min`;\n    } else if (reduceType === 'max') {\n      initializationValue = '-1.0 / 0.0';\n      compareOp = `max`;\n    }\n\n    let returnValue = `${reduceType}(${reduceType}(${reduceType}(` +\n        'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n\n    if (reduceType === 'sum') {\n      returnValue = `sumValue`;\n    } else if (reduceType === 'prod') {\n      returnValue = `prodValue`;\n    } else if (reduceType === 'all') {\n      returnValue = `allValue`;\n    } else if (reduceType === 'any') {\n      returnValue = `anyValue`;\n    }\n\n    const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n    const windowSizeVec4Remainder = windowSize % 4;\n\n    let updateSnippet = `\n      if (${reduceType === 'sum'}) {\n        sumValue += dot(values, ones);\n      } else if (${reduceType === 'prod'}) {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n\n    let vecType = `vec4`;\n\n    if (reduceType === 'all') {\n      initializationValue = '1.0';\n      updateSnippet = `\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      `;\n      vecType = `bvec4`;\n    } else if (reduceType === 'any') {\n      initializationValue = '0.0';\n      updateSnippet = `\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      `;\n      vecType = `bvec4`;\n    }\n\n    let checkOutOfBounds = '';\n    if (inSize % windowSize > 0) {\n      checkOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n    }\n    this.userCode = `\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${checkOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        vec4 minMaxValue = vec4(${initializationValue});\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as util from '../../util';\nimport {GPGPUProgram} from './gpgpu_math';\nimport * as shader_util from './shader_compiler_util';\n\nexport class ReshapePackedProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  usesPackedTextures = true;\n  outputShape: number[];\n  userCode: string;\n\n  constructor(outputShape: [number, number, number], inputShape: [\n    number, number, number\n  ]) {\n    this.outputShape = outputShape;\n\n    let mainLoop = ``;\n    for (let i = 0; i < 4; i++) {\n      let thisRC = `thisRC = rc;`;\n      if (i % 2 === 1) {\n        thisRC += `thisRC.z += 1;`;\n      }\n      if (i > 1) {\n        thisRC += `thisRC.y += 1;`;\n      }\n\n      mainLoop += `\n        ${thisRC}\n        ${i > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : ''}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${i}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${i > 0 ? '}' : ''}\n      `;\n    }\n\n    this.userCode = `\n      ${getReshapedInputCoords(inputShape)}\n      ${getFlatIndex(outputShape)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${outputShape[1]};\n        int cols = ${outputShape[2]};\n\n        ${mainLoop}\n\n        setOutput(result);\n      }\n    `;\n  }\n}\n\nfunction getFlatIndex(shape: [number, number, number]): string {\n  const dotCoordsWithStrides = shader_util.dotify(\n      ['coords.x', 'coords.y', 'coords.z'],\n      util.computeStrides(shape).map(d => d.toString()).concat(['1.']));\n\n  return `\n    int getFlatIndex(ivec3 coords) {\n      return round(${dotCoordsWithStrides});\n    }\n  `;\n}\n\nfunction getReshapedInputCoords(shape: [number, number, number]): string {\n  const coordsFromIndexSnippet =\n      shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n\n  return `\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor4D} from '../../tensor';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ResizeBilinearBackpropProgram implements GPGPUProgram {\n  variableNames = ['dy'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(dy: Tensor4D, x: Tensor4D, alignCorners: boolean) {\n    this.outputShape = x.shape;\n    const [, xHeight, xWidth, ] = x.shape;\n    const [, yHeight, yWidth] = dy.shape;\n\n    // In the backwards pass, we want to find the pixels that were generated for\n    // each pixel in the input image the forward pass and add the corresponding\n    // coefficient from dy to the gradient (with some interpolation).\n\n    const effectiveXSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n      (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n    ];\n\n    const effectiveYSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n      (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n    ];\n\n    const heightScale = effectiveXSize[0] / effectiveYSize[0];\n    const widthScale = effectiveXSize[1] / effectiveYSize[1];\n\n    const invHeightScale = 1 / heightScale;\n    const invWidthScale = 1 / widthScale;\n\n    // This defines the size of the window of values around a particular\n    // index in dy that we want to search for contributions to dx.\n    const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n    const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ${xHeight - 1}.0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ${xWidth - 1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ResizeBilinearProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(\n      inputShape: [number, number, number, number], newHeight: number,\n      newWidth: number, alignCorners: boolean) {\n    const [batch, oldHeight, oldWidth, depth] = inputShape;\n    this.outputShape = [batch, newHeight, newWidth, depth];\n\n    const effectiveInSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n      (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n    ];\n\n    const effectiveOutSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n      (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n    ];\n\n    this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor4D} from '../../tensor';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ResizeNearestNeigborBackpropProgram implements GPGPUProgram {\n  variableNames = ['dy'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(dy: Tensor4D, x: Tensor4D, alignCorners: boolean) {\n    this.outputShape = x.shape;\n    const [, xHeight, xWidth, ] = x.shape;\n    const [, yHeight, yWidth] = dy.shape;\n\n    // In the backwards pass, we want to find the pixels that were generated for\n    // each pixel in the input image the forward pass and add the corresponding\n    // coefficient from dy to the gradient (with some interpolation).\n\n    const effectiveXSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n      (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n    ];\n\n    const effectiveYSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n      (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n    ];\n\n    const heightScale = effectiveXSize[0] / effectiveYSize[0];\n    const widthScale = effectiveXSize[1] / effectiveYSize[1];\n\n    const invHeightScale = 1 / heightScale;\n    const invWidthScale = 1 / widthScale;\n\n    // This defines the size of the window of values around a particular\n    // index in dy that we want to search for contributions to dx.\n    const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n    const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${effectiveXSize[0]}) *\n                (float(dyR) / float(${effectiveYSize[0]}));\n\n            float sourceFracCol =\n                float(${effectiveXSize[1]}) *\n                  (float(dyC) / float(${effectiveYSize[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${xHeight}) - 1),\n                ${alignCorners} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${xWidth}) - 1),\n                ${alignCorners} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ResizeNearestNeighborProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(\n      inputShape: [number, number, number, number], newHeight: number,\n      newWidth: number, alignCorners: boolean) {\n    const [batch, oldHeight, oldWidth, depth] = inputShape;\n    this.outputShape = [batch, newHeight, newWidth, depth];\n\n    const effectiveInSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n      (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n    ];\n\n    const effectiveOutSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n      (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n    ];\n\n    // When align corners is false, we rounds the value with floor.\n    const roundBase = alignCorners ? '0.5' : '0.0';\n\n    this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));\n\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class ReverseProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(xShape: number[], axis: number[]) {\n    const rank = xShape.length;\n    if (rank > 4) {\n      throw new Error(\n          `WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n    }\n    this.outputShape = xShape;\n\n    if (rank === 1) {\n      this.userCode = `\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(${xShape[0]} - coord - 1));\n        }\n      `;\n      return;\n    }\n    const getInCoord = (i: number) => {\n      if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n        return `${xShape[i]} - coords[${i}] - 1`;\n      }\n      return `coords[${i}]`;\n    };\n    const inCoords = xShape.map((_, i) => getInCoord(i)).join(',');\n    const type = getCoordsDataType(rank);\n\n    this.userCode = `\n      void main() {\n        ${type} coords = getOutputCoords();\n        setOutput(getX(${inCoords}));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class ScatterProgram implements GPGPUProgram {\n  variableNames = ['updates', 'indices', 'defaultValue'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      updateSize: number, sliceDim: number, indicesRank: number,\n      updatesRank: number, strides: number[], shape: number[],\n      summingDupeIndex = true) {\n    this.outputShape = shape;\n    const stridesType = getCoordsDataType(strides.length);\n    const dtype = getCoordsDataType(shape.length);\n    let indicesString = '';\n    if (indicesRank === 1) {\n      indicesString = 'i';\n    } else if (indicesRank === 2) {\n      indicesString = 'i, j';\n    }\n    const indicesSnippet = `getIndices(${indicesString})`;\n\n    let updatesString = '';\n    if (updatesRank === 1) {\n      updatesString = 'i';\n    } else if (updatesRank === 2) {\n      updatesString = 'i, coords[1]';\n    }\n    const updatesSnippet = `getUpdates(${updatesString})`;\n\n    const strideString = sliceDim > 1 ? 'strides[j]' : 'strides';\n    this.userCode = `\n        ${stridesType} strides = ${stridesType}(${strides});\n\n        void main() {\n          ${dtype} coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < ${updateSize}; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < ${sliceDim}; j++) {\n              int index = round(${indicesSnippet});\n              flattenedIndex += index * ${strideString};\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += ${updatesSnippet};\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {SegOpInfo} from '../../ops/segment_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class SegmentOpProgram implements GPGPUProgram {\n  variableNames = ['x', 'segmentIds'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(segOpInfo: SegOpInfo, segOpType: 'unsortedSegmentSum') {\n    const windowSize = segOpInfo.windowSize;\n    const batchSize = segOpInfo.batchSize;\n    const inSize = segOpInfo.inSize;\n    const numSegments = segOpInfo.numSegments;\n    const outSize = numSegments * Math.ceil(inSize / windowSize);\n    this.outputShape = [batchSize, outSize];\n\n    const initializationValue = '0.0';\n    const returnValue = `sumValue`;\n\n    const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n    const windowSizeVec4Remainder = windowSize % 4;\n\n    const updateSnippet = `\n        sumValue += dot(values, filter);\n    `;\n\n    let checkValueOutOfBounds = '';\n    if (inSize % windowSize > 0) {\n      checkValueOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n    }\n\n    let checkSegmentIdOutOfBounds = '';\n    if (inSize % windowSize > 0) {\n      checkSegmentIdOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return -1.0;\n        }\n      `;\n    }\n\n    this.userCode = `\n      const float initializationValue = ${initializationValue};\n\n      float getValue(int batch, int inIdx) {\n        ${checkValueOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${checkSegmentIdOutOfBounds}\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          ${numSegments})) * float(${windowSize}));\n        int currentSeg = int(mod(float(outIdx), float(${numSegments})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class SelectProgram implements GPGPUProgram {\n  variableNames = ['c', 'a', 'b'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(cRank: number, shape: number[], rank: number) {\n    this.outputShape = shape;\n\n    let cCoords;\n    let abCoords;\n    if (rank > 4) {\n      throw Error(`Where for rank ${rank} is not yet supported`);\n    }\n\n    if (rank === 1) {\n      abCoords = `resRC`;\n      cCoords = `resRC`;\n    } else {\n      const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n      const cCoordVars = [];\n      const abCoordVars = [];\n      for (let i = 0; i < shape.length; i++) {\n        abCoordVars.push(`${currentCoords[i]}`);\n        if (i < cRank) {\n          cCoordVars.push(`${currentCoords[i]}`);\n        }\n      }\n      cCoords = cCoordVars.join();\n      abCoords = abCoordVars.join();\n    }\n\n    const dtype = getCoordsDataType(rank);\n\n    this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        float cVal = getC(${cCoords});\n        if (cVal >= 1.0) {\n          setOutput(getA(${abCoords}));\n        } else {\n          setOutput(getB(${abCoords}));\n        }\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class SliceProgram implements GPGPUProgram {\n  variableNames = ['source'];\n  outputShape: number[];\n  userCode: string;\n  rank: number;\n\n  // Caching uniform location for speed.\n  startLoc: WebGLUniformLocation;\n\n  constructor(destSize: number[]) {\n    this.outputShape = destSize;\n    this.rank = destSize.length;\n\n    const dtype = getCoordsDataType(this.rank);\n    const sourceCoords = getCoords(this.rank);\n\n    this.userCode = `\n      uniform ${dtype} start;\n\n      void main() {\n        ${dtype} sourceLoc = start + getOutputCoords();\n        setOutput(getSource(${sourceCoords}));\n      }\n    `;\n  }\n\n  getCustomSetupFunc(start: number[]) {\n    if (start.length !== this.rank) {\n      throw Error(\n          `The rank (${this.rank}) of the program must match the ` +\n          `length of start (${start.length})`);\n    }\n    return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n      if (this.startLoc == null) {\n        this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');\n        if (this.startLoc == null) {\n          // This means the compiler has optimized and realized it doesn't need\n          // the uniform.\n          return;\n        }\n      }\n      if (this.rank === 1) {\n        gpgpu.gl.uniform1i(this.startLoc, start[0]);\n      } else if (this.rank === 2) {\n        gpgpu.gl.uniform2i(this.startLoc, start[0], start[1]);\n      } else if (this.rank === 3) {\n        gpgpu.gl.uniform3i(this.startLoc, start[0], start[1], start[2]);\n      } else if (this.rank === 4) {\n        gpgpu.gl.uniform4i(\n            this.startLoc, start[0], start[1], start[2], start[3]);\n      } else {\n        throw Error(`Slicing for rank ${this.rank} is not yet supported`);\n      }\n    };\n  }\n}\n\nfunction getCoords(rank: number): string {\n  if (rank === 1) {\n    return 'sourceLoc';\n  } else if (rank === 2) {\n    return 'sourceLoc.x, sourceLoc.y';\n  } else if (rank === 3) {\n    return 'sourceLoc.x, sourceLoc.y, sourceLoc.z';\n  } else if (rank === 4) {\n    return 'sourceLoc.x, sourceLoc.y, sourceLoc.z, sourceLoc.w';\n  } else {\n    throw Error(`Slicing for rank ${rank} is not yet supported`);\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class StridedSliceProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      begin: number[], strides: number[], size: number[],\n      shrinkAxis: number[]) {\n    const shape = size.filter((v, index) => shrinkAxis.indexOf(index) === -1);\n    this.outputShape = shape;\n    const rank = size.length;\n    const inputDtype = getCoordsDataType(size.length);\n    const dtype = getCoordsDataType(shape.length);\n\n    let newCoords = '';\n    if (rank === 1) {\n      newCoords = 'coords * strides + begin';\n    } else {\n      let outputAxis = 0;\n      newCoords =\n          size.map((_, i) => {\n                if (shrinkAxis.indexOf(i) === -1) {\n                  outputAxis++;\n                  return shape.length === 1 ?\n                      `coords * strides[${i}] + begin[${i}]` :\n                      `coords[${outputAxis - 1}] * strides[${i}] + begin[${i}]`;\n                } else {\n                  return `begin[${i}]`;\n                }\n              })\n              .join(',');\n    }\n\n    this.userCode = `\n      ${inputDtype} begin = ${inputDtype}(${begin});\n      ${inputDtype} strides = ${inputDtype}(${strides});\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        setOutput(getX(${newCoords}));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../../environment';\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {PhysicalTextureType, TextureUsage} from './tex_util';\n\nexport class TextureManager {\n  private numUsedTextures = 0;\n  private numFreeTextures = 0;\n  private freeTextures: {[shape: string]: WebGLTexture[]} = {};\n  private logEnabled = false;\n  private usedTextures: {[shape: string]: WebGLTexture[]} = {};\n\n  constructor(private gpgpu: GPGPUContext) {}\n\n  acquireTexture(\n      shapeRC: [number, number], usage: TextureUsage,\n      isPacked: boolean): WebGLTexture {\n    const physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);\n\n    const shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);\n    if (!(shapeKey in this.freeTextures)) {\n      this.freeTextures[shapeKey] = [];\n    }\n    if (!(shapeKey in this.usedTextures)) {\n      this.usedTextures[shapeKey] = [];\n    }\n\n    if (this.freeTextures[shapeKey].length > 0) {\n      this.numFreeTextures--;\n      this.numUsedTextures++;\n      this.log();\n      const newTexture = this.freeTextures[shapeKey].shift();\n      this.usedTextures[shapeKey].push(newTexture);\n      return newTexture;\n    }\n    this.numUsedTextures++;\n    this.log();\n\n    let newTexture: WebGLTexture;\n    if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) {\n      newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);\n    } else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) {\n      newTexture =\n          this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);\n    } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) {\n      newTexture =\n          this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);\n    } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) {\n      newTexture =\n          this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);\n\n    } else if (\n        physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) {\n      newTexture =\n          this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);\n    }\n    this.usedTextures[shapeKey].push(newTexture);\n\n    return newTexture;\n  }\n\n  releaseTexture(\n      texture: WebGLTexture, shape: [number, number],\n      logicalTexType: TextureUsage, isPacked: boolean): void {\n    if (this.freeTextures == null) {\n      // Already disposed.\n      return;\n    }\n    const physicalTexType =\n        getPhysicalFromLogicalTextureType(logicalTexType, isPacked);\n    const shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);\n    if (!(shapeKey in this.freeTextures)) {\n      this.freeTextures[shapeKey] = [];\n    }\n    this.freeTextures[shapeKey].push(texture);\n    this.numFreeTextures++;\n    this.numUsedTextures--;\n    const texList = this.usedTextures[shapeKey];\n    const texIndex = texList.indexOf(texture);\n    if (texIndex < 0) {\n      throw new Error(\n          'Cannot release a texture that was never provided by this ' +\n          'texture manager');\n    }\n    texList.splice(texIndex, 1);\n    this.log();\n  }\n\n  private log() {\n    if (!this.logEnabled) {\n      return;\n    }\n    const total = this.numFreeTextures + this.numUsedTextures;\n    console.log(\n        'Free/Used', `${this.numFreeTextures} / ${this.numUsedTextures}`,\n        `(${total})`);\n  }\n\n  getNumUsedTextures(): number {\n    return this.numUsedTextures;\n  }\n\n  getNumFreeTextures(): number {\n    return this.numFreeTextures;\n  }\n\n  dispose() {\n    if (this.freeTextures == null) {\n      // Already disposed.\n      return;\n    }\n    for (const texShape in this.freeTextures) {\n      this.freeTextures[texShape].forEach(tex => {\n        this.gpgpu.deleteMatrixTexture(tex);\n      });\n    }\n    for (const texShape in this.usedTextures) {\n      this.usedTextures[texShape].forEach(tex => {\n        this.gpgpu.deleteMatrixTexture(tex);\n      });\n    }\n    this.freeTextures = null;\n    this.usedTextures = null;\n    this.numUsedTextures = 0;\n    this.numFreeTextures = 0;\n  }\n}\n\nfunction getPhysicalFromLogicalTextureType(\n    logicalTexType: TextureUsage, isPacked: boolean): PhysicalTextureType {\n  if (logicalTexType === TextureUsage.UPLOAD) {\n    return isPacked ? PhysicalTextureType.PACKED_2X2_FLOAT32 :\n                      PhysicalTextureType.UNPACKED_FLOAT32;\n  } else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) {\n    if (isPacked) {\n      return ENV.get('WEBGL_RENDER_FLOAT32_ENABLED') ?\n          PhysicalTextureType.PACKED_2X2_FLOAT32 :\n          PhysicalTextureType.PACKED_2X2_FLOAT16;\n    }\n    return ENV.get('WEBGL_RENDER_FLOAT32_ENABLED') ?\n        PhysicalTextureType.UNPACKED_FLOAT32 :\n        PhysicalTextureType.UNPACKED_FLOAT16;\n  } else if (\n      logicalTexType === TextureUsage.DOWNLOAD ||\n      logicalTexType === TextureUsage.PIXELS) {\n    return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;\n  }\n  throw new Error(`Unknown logical texture type ${logicalTexType}`);\n}\n\nfunction getKeyFromTextureShape(\n    shapeRowsCol: [number, number], physicalTexType: PhysicalTextureType,\n    isPacked: boolean): string {\n  return `${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class TileProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  outputShape: number[];\n  userCode: string;\n  rank: number;\n\n  constructor(aShape: number[], reps: number[]) {\n    const outputShape: number[] = new Array(aShape.length);\n    for (let i = 0; i < outputShape.length; i++) {\n      outputShape[i] = aShape[i] * reps[i];\n    }\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    const dtype = getCoordsDataType(this.rank);\n    const sourceCoords = getSourceCoords(aShape);\n\n    this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n  }\n}\n\nfunction getSourceCoords(aShape: number[]): string {\n  const rank = aShape.length;\n  if (rank > 5) {\n    throw Error(`Tile for rank ${rank} is not yet supported`);\n  }\n  if (rank === 1) {\n    return `imod(resRC, ${aShape[0]})`;\n  }\n\n  const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u'];\n\n  const sourceCoords = [];\n  for (let i = 0; i < aShape.length; i++) {\n    sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);\n  }\n  return sourceCoords.join();\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class TransposeProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  outputShape: number[];\n  userCode: string;\n  rank: number;\n\n  constructor(aShape: number[], newDim: number[]) {\n    const outputShape: number[] = new Array(aShape.length);\n    for (let i = 0; i < outputShape.length; i++) {\n      outputShape[i] = aShape[newDim[i]];\n    }\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    const dtype = getCoordsDataType(this.rank);\n    const switched = getSwitchedCoords(newDim);\n\n    this.userCode = `\n    void main() {\n      ${dtype} resRC = getOutputCoords();\n      setOutput(getA(${switched}));\n    }\n    `;\n  }\n}\n\nfunction getSwitchedCoords(newDim: number[]): string {\n  const rank = newDim.length;\n  if (rank > 6) {\n    throw Error(`Transpose for rank ${rank} is not yet supported`);\n  }\n  const originalOrder =\n      ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u', 'resRC.v'];\n  const switchedCoords = new Array(rank);\n  for (let i = 0; i < newDim.length; i++) {\n    switchedCoords[newDim[i]] = originalOrder[i];\n  }\n  return switchedCoords.join();\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport const ERF_P = 0.3275911;\nexport const ERF_A1 = 0.254829592;\nexport const ERF_A2 = -0.284496736;\nexport const ERF_A3 = 1.421413741;\nexport const ERF_A4 = -1.453152027;\nexport const ERF_A5 = 1.061405429;\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport const SELU_SCALEALPHA = 1.7580993408473768599402175208123;\nexport const SELU_SCALE = 1.0507009873554804934193349852946;\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as erf_util from '../../ops/erf_util';\nimport * as selu_util from '../../ops/selu_util';\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {GPGPUContext} from './gpgpu_context';\n\nexport class UnaryOpProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  userCode: string;\n  outputShape: number[];\n\n  // Caching uniform location for speed.\n  startLoc: WebGLUniformLocation;\n\n  constructor(aShape: number[], opSnippet: string) {\n    this.outputShape = aShape;\n    this.userCode = `\n      uniform float NAN;\n      float unaryOperation(float x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n  }\n\n  getCustomSetupFunc() {\n    return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n      if (this.startLoc == null) {\n        this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'NAN');\n        if (this.startLoc == null) {\n          // This means the compiler has optimized and realized it doesn't need\n          // the uniform.\n          return;\n        }\n      }\n      gpgpu.gl.uniform1f(this.startLoc, NaN);\n    };\n  }\n}\n\nconst CHECK_NAN_SNIPPET = `if (isNaN(x)) return x;`;\n\nexport const ABS = `return abs(x);`;\n\nexport const RELU = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : x;\n`;\n\nexport const ELU = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;\n\nexport const SELU = `\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = ${selu_util.SELU_SCALEALPHA};\n  float scale = ${selu_util.SELU_SCALE};\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`;\n\nexport function STEP(alpha = 0.0) {\n  return CHECK_NAN_SNIPPET + `\n    return x > 0.0 ? 1.0 : float(${alpha});\n  `;\n}\n\nexport const NEG = `return -x;`;\n\nexport const CEIL = `return ceil(x);`;\n\nexport const FLOOR = `return floor(x);`;\n\nexport const SIGN = `\n  if (isNaN(x)) { return 0.0; }\n  return sign(x);\n`;\n\nexport const ROUND = `\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n`;\n\nexport const EXP = `return exp(x);`;\n\nexport const EXPM1 = `return exp(x) - 1.0;`;\n\nexport const LOG = `if (x < 0.0) return NAN;\n  return log(x);`;\n\nexport const LOG1P = `return log(1.0 + x);`;\n\nexport const SQRT = `return sqrt(x);`;\n\nexport const RSQRT = `return inversesqrt(x);`;\n\nexport const SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;\n\n/**\n * mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX\n *\n * epsilon is the difference between 1.0 and the next representable\n * float. For a single precision 32 bit float this should be 2^-23, see:\n * https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm\n *\n * too_large = (x > -threshold) is value above which exp(x) may overflow\n * but softplus(x) == x is within machine epsilon\n *\n * too_small = (x < threshold) is value below which exp(x) may underflow,\n * but softplus(x) == exp(x) is within machine epsilon.\n */\nexport const SOFTPLUS = `\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n`;\n\nexport const SIN = CHECK_NAN_SNIPPET + `\n  return sin(x);\n`;\n\nexport const COS = CHECK_NAN_SNIPPET + `\n  return cos(x);\n`;\n\nexport const TAN = `return tan(x);`;\n\nexport const ASIN = `return asin(x);`;\n\nexport const ACOS = `return acos(x);`;\n\nexport const ATAN = CHECK_NAN_SNIPPET + `\n  return atan(x);\n`;\n\nexport const SINH = `\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n`;\n\nexport const COSH = `\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n`;\n\nexport const TANH = `\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`;\n\nexport const ASINH = `return log(x + sqrt(x * x + 1.0));`;\n\nexport const ACOSH = CHECK_NAN_SNIPPET + `\n  if (x < 1.0) return NAN;\n  return log(x + sqrt(x * x - 1.0));`;\n\nexport const ATANH = CHECK_NAN_SNIPPET + `\n  if ((x < -1.0) || (x > 1.0)) return NAN;\n  return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;\n\nexport const ERF = `\n  // Error function is calculated approximately with elementary function.\n  // See \"Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n  float p = ${erf_util.ERF_P};\n  float a1 = ${erf_util.ERF_A1};\n  float a2 = ${erf_util.ERF_A2};\n  float a3 = ${erf_util.ERF_A3};\n  float a4 = ${erf_util.ERF_A4};\n  float a5 = ${erf_util.ERF_A5};\n\n  float t = 1.0 / (1.0 + p * x);\n  return 1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x);\n`;\n\nexport const SQUARE = `return x * x;`;\n\nexport const RECIPROCAL = `return 1.0 / x;`;\n\nexport const LOGICAL_NOT = `return float(!(x >= 1.0));`;\n\nexport const TO_INT = `return float(int(x));`;\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getChannels, getSourceCoords} from '../packing_util';\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class UnpackProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  usesPackedTextures = true;\n  outputShape: number[];\n  userCode: string;\n\n  constructor(outputShape: number[]) {\n    this.outputShape = outputShape;\n    const rank = outputShape.length;\n\n    const channels = getChannels('rc', rank);\n    const dtype = getCoordsDataType(rank);\n    const sourceCoords = getSourceCoords(rank, channels);\n    const innerDims = channels.slice(-2);\n    const coords = rank <= 1 ? 'rc' : `vec2(${innerDims.join(',')})`;\n\n    this.userCode = `\n      void main() {\n        ${dtype} rc = getOutputCoords();\n        vec4 packedInput = getA(${sourceCoords});\n\n        setOutput(getChannel(packedInput, ${coords}));\n      }\n    `;\n  }\n}","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor, convertToTensorArray} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assert, sizeFromShape} from '../util';\nimport {parseAxisParam} from './axis_util';\nimport {assertParamsConsistent, computeOutShape} from './concat_util';\nimport {op} from './operation';\nimport {tensor} from './tensor_ops';\n\n/**\n * Concatenates a list of`tf.Tensor1D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(3) = |r1, g1, b1|\n * B: shape(2) = |r2, g2|\n * C = tf.concat1d([A, B]) == |r1, g1, b1, r2, g2|\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @return The concatenated array.\n */\nfunction concat1d_(tensors: Array<Tensor1D|TensorLike>): Tensor1D {\n  return concat(tensors, 0 /* axis */);\n}\n\n/**\n * Concatenates a list of`tf.Tensor2D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *\n * B: shape(2, 3) = | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * C = tf.concat2d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *                  | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * if axis = 1:\n * C = shape(2, 6) = | r1, g1, b1, r3, g3, b3 |\n *                   | r2, g2, b2, r4, g4, b4 |\n *\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @param axis The axis to concatenate along.\n * @return The concatenated array.\n */\nfunction concat2d_(\n    tensors: Array<Tensor2D|TensorLike>, axis: number): Tensor2D {\n  return concat(tensors, axis);\n}\n\n/**\n * Concatenates a list of`tf.Tensor3D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *\n * B: shape(2, 1, 3) = | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * C = tf.concat3d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *                     | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * if axis = 1:\n * C: shape(2, 2, 3) = | r1, g1, b1, r3, g3, b3 |\n *                     | r2, g2, b2, r4, g4, b4 |\n *\n * if axis = 2:\n * C = shape(2, 1, 6) = | r1, g1, b1, r3, g3, b3 |\n *                      | r2, g2, b2, r4, g4, b4 |\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat3d_(\n    tensors: Array<Tensor3D|TensorLike>, axis: number): Tensor3D {\n  return concat(tensors, axis);\n}\n\n/**\n * Concatenates a list of`tf.Tensor4D`s along an axis. See `concat` for details.\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat4d_(\n    tensors: Array<Tensor4D|TensorLike>, axis: number): Tensor4D {\n  return concat(tensors, axis);\n}\n\n/**\n * Concatenates a list of`tf.Tensor`s along a given axis.\n *\n * The tensors ranks and types must match, and their sizes must match in all\n * dimensions except `axis`.\n *\n * Also available are stricter rank-specific methods that assert that\n * `tensors` are of the given rank:\n *   - `tf.concat1d`\n *   - `tf.concat2d`\n *   - `tf.concat3d`\n *   - `tf.concat4d`\n *\n * Except `tf.concat1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * a.concat(b).print();  // or a.concat(b)\n * ```\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.concat([a, b, c]).print();\n * ```\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [10, 20]]);\n * const b = tf.tensor2d([[3, 4], [30, 40]]);\n * const axis = 1;\n * tf.concat([a, b], axis).print();\n * ```\n * @param tensors A list of tensors to concatenate.\n * @param axis The axis to concate along. Defaults to 0 (the first dim).\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction concat_<T extends Tensor>(tensors: Array<T|TensorLike>, axis = 0): T {\n  assert(tensors.length >= 1, 'Pass at least one tensor to concat');\n  let $tensors = convertToTensorArray(tensors, 'tensors', 'concat');\n  axis = parseAxisParam(axis, $tensors[0].shape)[0];\n  const outShape = computeOutShape($tensors.map(t => t.shape), axis);\n  if (sizeFromShape(outShape) === 0) {\n    return tensor([], outShape) as T;\n  }\n  // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n  $tensors = $tensors.filter(t => t.size > 0);\n  if ($tensors.length === 1) {\n    return $tensors[0];\n  }\n\n  const shapes = $tensors.map(t => t.shape);\n  assertParamsConsistent(shapes, axis);\n  const der = (dy: T) => {\n    const sizeSplits = shapes.map(s => s[axis]);\n    const derTensors = split(dy, sizeSplits, axis);\n    return derTensors.map(t => () => t) as {};\n  };\n  const inputs = $tensors as {};\n  return ENV.engine.runKernel(\n      backend => backend.concat($tensors, axis) as T, inputs, der);\n}\n\n/**\n * Splits a`tf.Tensor` into sub tensors.\n *\n * If `numOrSizeSplits` is a number, splits `x` along dimension `axis`\n * into `numOrSizeSplits` smaller tensors.\n * Requires that `numOrSizeSplits` evenly divides `x.shape[axis]`.\n *\n * If `numOrSizeSplits` is a number array, splits `x` into\n * `(numOrSizeSplits.length` pieces. The shape of the `i`-th piece has the\n * same size as `x` except along dimension `axis` where the size is\n * `numOrSizeSplits[i]`.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);\n * const [a, b] = tf.split(x, 2, 1);\n * a.print();\n * b.print();\n *\n * const [c, d, e] = tf.split(x, [1, 2, 1], 1);\n * c.print();\n * d.print();\n * e.print();\n * ```\n *\n * @param x The input tensor to split.\n * @param numOrSizeSplits Either an integer indicating the number of\n * splits along the axis or an array of integers containing the sizes of\n * each output tensor along the axis. If a number then it must evenly divide\n * `x.shape[axis]`; otherwise the sum of sizes must match `x.shape[axis]`.\n * @param axis The dimension along which to split. Defaults to 0 (the first\n * dim).\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction split_<T extends Tensor>(\n    x: T|TensorLike, numOrSizeSplits: number[]|number, axis = 0): T[] {\n  const $x = convertToTensor(x, 'x', 'split');\n\n  axis = parseAxisParam(axis, $x.shape)[0];\n  let splitSizes: number[];\n  if (typeof (numOrSizeSplits) === 'number') {\n    assert(\n        $x.shape[axis] % numOrSizeSplits === 0,\n        'Number of splits must evenly divide the axis.');\n    splitSizes = Array(numOrSizeSplits).fill($x.shape[axis] / numOrSizeSplits);\n  } else {\n    assert(\n        $x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b),\n        'The sum of sizes must match the size of the axis dimension.');\n    splitSizes = numOrSizeSplits;\n  }\n  const der = (dy: T[]) => ({$x: () => concat(dy, axis)});\n  return ENV.engine.runKernel(\n      backend => backend.split($x, splitSizes, axis), {$x}, der);\n}\n\nexport const concat = op({concat_});\nexport const concat1d = op({concat1d_});\nexport const concat2d = op({concat2d_});\nexport const concat3d = op({concat3d_});\nexport const concat4d = op({concat4d_});\nexport const split = op({split_});\n","// A port of an algorithm by Johannes Baagøe <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baagøe <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n","// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n","// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n","// A Javascript implementaion of the \"xorshift7\" algorithm by\n// François Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n","// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n","// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n","/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n","// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baagøe.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by François Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as seedrandom from 'seedrandom';\n\nexport interface RandGauss {\n  nextValue(): number;\n}\n\nexport interface RandNormalDataTypes {\n  float32: Float32Array;\n  int32: Int32Array;\n}\n\n// https://en.wikipedia.org/wiki/Marsaglia_polar_method\nexport class MPRandGauss implements RandGauss {\n  private mean: number;\n  private stdDev: number;\n  private nextVal: number;\n  private dtype?: keyof RandNormalDataTypes;\n  private truncated?: boolean;\n  private upper?: number;\n  private lower?: number;\n  private random: seedrandom.prng;\n\n  constructor(\n      mean: number, stdDeviation: number, dtype?: keyof RandNormalDataTypes,\n      truncated?: boolean, seed?: number) {\n    this.mean = mean;\n    this.stdDev = stdDeviation;\n    this.dtype = dtype;\n    this.nextVal = NaN;\n    this.truncated = truncated;\n    if (this.truncated) {\n      this.upper = this.mean + this.stdDev * 2;\n      this.lower = this.mean - this.stdDev * 2;\n    }\n    const seedValue = seed ? seed : Math.random();\n    this.random = seedrandom.alea(seedValue.toString());\n  }\n\n  /** Returns next sample from a gaussian distribution. */\n  public nextValue(): number {\n    if (!isNaN(this.nextVal)) {\n      const value = this.nextVal;\n      this.nextVal = NaN;\n      return value;\n    }\n\n    let resultX: number, resultY: number;\n    let isValid = false;\n    while (!isValid) {\n      let v1: number, v2: number, s: number;\n      do {\n        v1 = 2 * this.random() - 1;\n        v2 = 2 * this.random() - 1;\n        s = v1 * v1 + v2 * v2;\n      } while (s >= 1 || s === 0);\n\n      const mul = Math.sqrt(-2.0 * Math.log(s) / s);\n      resultX = this.mean + this.stdDev * v1 * mul;\n      resultY = this.mean + this.stdDev * v2 * mul;\n\n      if (!this.truncated || this.isValidTruncated(resultX)) {\n        isValid = true;\n      }\n    }\n\n    if (!this.truncated || this.isValidTruncated(resultY)) {\n      this.nextVal = this.convertValue(resultY);\n    }\n    return this.convertValue(resultX);\n  }\n\n  /** Handles proper rounding for non floating point numbers. */\n  private convertValue(value: number): number {\n    if (this.dtype == null || this.dtype === 'float32') {\n      return value;\n    }\n    return Math.round(value);\n  }\n\n  /** Returns true if less than 2-standard-deviations from the mean. */\n  private isValidTruncated(value: number): boolean {\n    return value <= this.upper && value >= this.lower;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, TensorBuffer} from '../tensor';\nimport {convertToTensor, convertToTensorArray} from '../tensor_util_env';\nimport {DataType, DataTypeMap, Rank, ShapeMap, TensorLike, TensorLike1D, TensorLike4D} from '../types';\nimport * as util from '../util';\nimport {getAxesPermutation, getInnerMostAxes} from './axis_util';\nimport {concat} from './concat_split';\nimport {op} from './operation';\nimport {MPRandGauss} from './rand';\nimport {zeros, zerosLike} from './tensor_ops';\n\n/**\n * Creates a new tensor with the same values and shape as the specified\n * tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n *\n * x.clone().print();\n * ```\n *\n * @param x The tensor to clone.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction clone_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'clone', null);\n  const der = (dy: T) => {\n    return {$x: () => dy.toFloat()};\n  };\n\n  return ENV.engine.runKernel(\n             backend =>\n                 Tensor.make($x.shape, {dataId: $x.dataId}, $x.dtype) as T,\n             {$x}, der) as T;\n}\n\n/**\n * Create an identity matrix.\n *\n * @param numRows Number of rows.\n * @param numColumns Number of columns. Defaults to `numRows`.\n * @param batchShape If provided, will add the batch shape to the beginning\n *   of the shape of the returned `tf.Tensor` by repeating the identity\n *   matrix.\n * @param dtype Data type.\n * @returns Identity matrix of the specified size and data type, possibly\n *   with batch repetition if `batchShape` is specified.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction eye_(\n    numRows: number, numColumns?: number,\n    batchShape?:\n        [\n          number\n        ]|[number,\n           number]|[number, number, number]|[number, number, number, number],\n    dtype: DataType = 'float32'): Tensor2D {\n  if (numColumns == null) {\n    numColumns = numRows;\n  }\n  const buff = buffer([numRows, numColumns], dtype);\n  const n = numRows <= numColumns ? numRows : numColumns;\n  for (let i = 0; i < n; ++i) {\n    buff.set(1, i, i);\n  }\n  const out = buff.toTensor().as2D(numRows, numColumns);\n  if (batchShape == null) {\n    return out;\n  } else {\n    if (batchShape.length === 1) {\n      return tile(expandDims(out, 0), [batchShape[0], 1, 1]);\n    } else if (batchShape.length === 2) {\n      return tile(\n          expandDims(expandDims(out, 0), 0),\n          [batchShape[0], batchShape[1], 1, 1]);\n    } else if (batchShape.length === 3) {\n      return tile(\n          expandDims(expandDims(expandDims(out, 0), 0), 0),\n          [batchShape[0], batchShape[1], batchShape[2], 1, 1]);\n    } else {\n      throw new Error(\n          `eye() currently supports only 1D and 2D ` +\n          // tslint:disable-next-line:no-any\n          `batchShapes, but received ${(batchShape as any).length}D.`);\n    }\n  }\n}\n\n/**\n * Creates a `tf.Tensor` with values sampled from a normal distribution.\n *\n * ```js\n * tf.randomNormal([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output.\n * @param seed The seed for the random number generator.\n */\n/** @doc {heading: 'Tensors', subheading: 'Random'} */\nfunction randomNormal_<R extends Rank>(\n    shape: ShapeMap[R], mean = 0, stdDev = 1, dtype?: 'float32'|'int32',\n    seed?: number): Tensor<R> {\n  if (dtype != null && (dtype as DataType) === 'bool') {\n    throw new Error(`Unsupported data type ${dtype}`);\n  }\n  const randGauss =\n      new MPRandGauss(mean, stdDev, dtype, false /* truncated */, seed);\n  const res = buffer(shape, dtype);\n  for (let i = 0; i < res.values.length; i++) {\n    res.values[i] = randGauss.nextValue();\n  }\n  return res.toTensor();\n}\n\n/**\n * Creates a `tf.Tensor` with values sampled from a truncated normal\n * distribution.\n *\n * ```js\n * tf.truncatedNormal([2, 2]).print();\n * ```\n *\n * The generated values follow a normal distribution with specified mean and\n * standard deviation, except that values whose magnitude is more than 2\n * standard deviations from the mean are dropped and re-picked.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output tensor.\n * @param seed The seed for the random number generator.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction truncatedNormal_<R extends Rank>(\n    shape: ShapeMap[R], mean = 0, stdDev = 1, dtype?: 'float32'|'int32',\n    seed?: number): Tensor<R> {\n  if (dtype != null && (dtype as DataType) === 'bool') {\n    throw new Error(`Unsupported data type ${dtype}`);\n  }\n  const randGauss =\n      new MPRandGauss(mean, stdDev, dtype, true /* truncated */, seed);\n  const res = buffer(shape, dtype);\n  for (let i = 0; i < res.values.length; i++) {\n    res.values[i] = randGauss.nextValue();\n  }\n  return res.toTensor();\n}\n\n/**\n * Creates a `tf.Tensor` with values sampled from a uniform distribution.\n *\n * The generated values follow a uniform distribution in the range [minval,\n * maxval). The lower bound minval is included in the range, while the upper\n * bound maxval is excluded.\n *\n * ```js\n * tf.randomUniform([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param minval The lower bound on the range of random values to generate.\n *   Defaults to 0.\n * @param maxval The upper bound on the range of random values to generate.\n *   Defaults to 1.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n */\n/** @doc {heading: 'Tensors', subheading: 'Random'} */\nfunction randomUniform_<R extends Rank>(\n    shape: ShapeMap[R], minval = 0, maxval = 1,\n    dtype: DataType = 'float32'): Tensor<R> {\n  const res = buffer(shape, dtype);\n  for (let i = 0; i < res.values.length; i++) {\n    res.values[i] = util.randUniform(minval, maxval);\n  }\n  return res.toTensor();\n}\n\n/**\n * Creates a `tf.Tensor` with values sampled from a random number generator\n * function defined by the user.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param randFunction A random number generator function which is called\n * for each element in the output tensor.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n */\nfunction rand_<R extends Rank>(\n    shape: ShapeMap[R], randFunction: () => number,\n    dtype?: DataType): Tensor<R> {\n  const size = util.sizeFromShape(shape);\n\n  let values = null;\n  if (dtype == null || dtype === 'float32') {\n    values = new Float32Array(size);\n  } else if (dtype === 'int32') {\n    values = new Int32Array(size);\n  } else if (dtype === 'bool') {\n    values = new Uint8Array(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n\n  for (let i = 0; i < size; i++) {\n    values[i] = randFunction();\n  }\n  return Tensor.make(shape, {values}, dtype);\n}\n\n/**\n * Creates a `tf.Tensor` with values drawn from a multinomial distribution.\n *\n * ```js\n * const probs = tf.tensor([.75, .25]);\n * tf.multinomial(probs, 3).print();\n * ```\n *\n * @param logits 1D array with unnormalized log-probabilities, or\n *     2D array of shape `[batchSize, numOutcomes]`. See the `normalized`\n *     parameter.\n * @param numSamples Number of samples to draw for each row slice.\n * @param seed The seed number.\n * @param normalized Whether the provided `logits` are normalized true\n *     probabilities (sum to 1). Defaults to false.\n * @return 1D array of shape `[numSamples]`, or 2D array of shape\n *     `[batchSize, numSamples]`, depending on the rank of the input.\n */\n/** @doc {heading: 'Tensors', subheading: 'Random'} */\nfunction multinomial_(\n    logits: Tensor1D|Tensor2D|TensorLike, numSamples: number, seed?: number,\n    normalized = false): Tensor1D|Tensor2D {\n  const $logits = convertToTensor(logits, 'logits', 'multinomial');\n  const numOutcomes = $logits.size;\n  const origRank = $logits.rank;\n  if (numOutcomes < 2) {\n    throw new Error(\n        `Error in multinomial: you need at least 2 outcomes, but got ` +\n        `${numOutcomes}.`);\n  }\n  if (origRank > 2) {\n    throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);\n  }\n  seed = seed || Math.random();\n  const logits2D = origRank === 1 ? $logits.as2D(1, -1) : $logits as Tensor2D;\n  const res = ENV.engine.runKernel(\n      backend => backend.multinomial(logits2D, normalized, numSamples, seed),\n      {logits2D});\n\n  return origRank === 1 ? res.as1D() : res;\n}\n\n/**\n * Creates a one-hot `tf.Tensor`. The locations represented by `indices` take\n * value `onValue` (defaults to 1), while all other locations take value\n * `offValue` (defaults to 0).\n *\n * ```js\n * tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print();\n * ```\n *\n * @param indices `tf.Tensor1D` of indices with dtype `int32`.\n * @param depth The depth of the one hot dimension.\n * @param onValue A number used to fill in the output when the index matches\n * the location.\n * @param offValue A number used to fill in the output when the index does\n *     not match the location.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction oneHot_(\n    indices: Tensor1D|TensorLike1D, depth: number, onValue = 1,\n    offValue = 0): Tensor2D {\n  const $indices =\n      convertToTensor(indices, 'indices', 'oneHot', 'int32') as Tensor1D;\n\n  if (depth < 2) {\n    throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);\n  }\n  const grad = (dy: Tensor2D) => {\n    return {$indices: () => zeros($indices.shape, 'float32') as Tensor1D};\n  };\n  return ENV.engine.runKernel(\n      backend => backend.oneHot($indices, depth, onValue, offValue), {$indices},\n      grad);\n}\n\n/**\n * Creates a `tf.Tensor` from an image.\n *\n * ```js\n * const image = new ImageData(1, 1);\n * image.data[0] = 100;\n * image.data[1] = 150;\n * image.data[2] = 200;\n * image.data[3] = 255;\n *\n * tf.fromPixels(image).print();\n * ```\n *\n * @param pixels The input image to construct the tensor from. The\n * supported image types are all 4-channel.\n * @param numChannels The number of channels of the output tensor. A\n * numChannels value less than 4 allows you to ignore channels. Defaults to\n * 3 (ignores alpha channel of input image).\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction fromPixels_(\n    pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement,\n    numChannels = 3): Tensor3D {\n  if (numChannels > 4) {\n    throw new Error(\n        'Cannot construct Tensor with more than 4 channels from pixels.');\n  }\n  return ENV.engine.fromPixels(pixels, numChannels);\n}\n\n/**\n * Draws a `tf.Tensor` of pixel values to a byte array or optionally a\n * canvas.\n *\n * When the dtype of the input is 'float32', we assume values in the range\n * [0-1]. Otherwise, when input is 'int32', we assume values in the range\n * [0-255].\n *\n * Returns a promise that resolves when the canvas has been drawn to.\n *\n * @param img A rank-2 or rank-3 tensor. If rank-2, draws grayscale. If\n *     rank-3, must have depth of 1, 3 or 4. When depth of 1, draws\n * grayscale. When depth of 3, we draw with the first three components of\n * the depth dimension corresponding to r, g, b and alpha = 1. When depth of\n * 4, all four components of the depth dimension correspond to r, g, b, a.\n * @param canvas The canvas to draw to.\n */\n/** @doc {heading: 'Visualization'} */\nasync function toPixels(\n    img: Tensor2D|Tensor3D|TensorLike,\n    canvas?: HTMLCanvasElement): Promise<Uint8ClampedArray> {\n  let $img = convertToTensor(img, 'img', 'toPixels');\n  if (!(img instanceof Tensor)) {\n    // Assume int32 if user passed a native array.\n    $img = $img.toInt();\n  }\n  if ($img.rank !== 2 && $img.rank !== 3) {\n    throw new Error(\n        `toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);\n  }\n  const [height, width] = $img.shape.slice(0, 2);\n  const depth = $img.rank === 2 ? 1 : $img.shape[2];\n\n  if (depth > 4 || depth === 2) {\n    throw new Error(\n        `toPixels only supports depth of size ` +\n        `1, 3 or 4 but got ${depth}`);\n  }\n\n  const minTensor = $img.min();\n  const maxTensor = $img.max();\n  const min = (await minTensor.data())[0];\n  const max = (await maxTensor.data())[0];\n  minTensor.dispose();\n  maxTensor.dispose();\n  if ($img.dtype === 'float32') {\n    if (min < 0 || max > 1) {\n      throw new Error(\n          `Tensor values for a float32 Tensor must be in the ` +\n          `range [0 - 1] but got range [${min} - ${max}].`);\n    }\n  } else if ($img.dtype === 'int32') {\n    if (min < 0 || max > 255) {\n      throw new Error(\n          `Tensor values for a int32 Tensor must be in the ` +\n          `range [0 - 255] but got range [${min} - ${max}].`);\n    }\n  } else {\n    throw new Error(\n        `Unsupported type for toPixels: ${$img.dtype}.` +\n        ` Please use float32 or int32 tensors.`);\n  }\n\n  const data = await $img.data();\n  const multiplier = $img.dtype === 'float32' ? 255 : 1;\n  const bytes = new Uint8ClampedArray(width * height * 4);\n\n  for (let i = 0; i < height * width; ++i) {\n    let r, g, b, a;\n    if (depth === 1) {\n      r = data[i] * multiplier;\n      g = data[i] * multiplier;\n      b = data[i] * multiplier;\n      a = 255;\n    } else if (depth === 3) {\n      r = data[i * 3] * multiplier;\n      g = data[i * 3 + 1] * multiplier;\n      b = data[i * 3 + 2] * multiplier;\n      a = 255;\n    } else if (depth === 4) {\n      r = data[i * 4] * multiplier;\n      g = data[i * 4 + 1] * multiplier;\n      b = data[i * 4 + 2] * multiplier;\n      a = data[i * 4 + 3] * multiplier;\n    }\n\n    const j = i * 4;\n    bytes[j + 0] = Math.round(r);\n    bytes[j + 1] = Math.round(g);\n    bytes[j + 2] = Math.round(b);\n    bytes[j + 3] = Math.round(a);\n  }\n\n  if (canvas != null) {\n    canvas.width = width;\n    canvas.height = height;\n    const ctx = canvas.getContext('2d');\n    const imageData = new ImageData(bytes, width, height);\n    ctx.putImageData(imageData, 0, 0);\n  }\n  if ($img !== img) {\n    $img.dispose();\n  }\n  return bytes;\n}\n\n/**\n * Reshapes a `tf.Tensor` to a given shape.\n *\n * Given an input tensor, returns a new tensor with the same values as the\n * input tensor with shape `shape`.\n *\n * If one component of shape is the special value -1, the size of that\n * dimension is computed so that the total size remains constant. In\n * particular, a shape of [-1] flattens into 1-D. At most one component of\n * shape can be -1.\n *\n * If shape is 1-D or higher, then the operation returns a tensor with shape\n * shape filled with the values of tensor. In this case, the number of\n * elements implied by shape must be the same as the number of elements in\n * tensor.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.reshape([2, 2]).print();\n * ```\n *\n * @param x The input tensor to be reshaped.\n * @param shape An array of integers defining the output tensor shape.\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction reshape_<R2 extends Rank>(\n    x: Tensor|TensorLike, shape: ShapeMap[R2]): Tensor<R2> {\n  const $x = convertToTensor(x, 'x', 'reshape', null);\n  shape = util.inferFromImplicitShape(shape, $x.size);\n  util.assert(\n      $x.size === util.sizeFromShape(shape),\n      'new shape and old shape must have the same number of elements.');\n\n  const grad = (dy: Tensor<R2>) => {\n    return {$x: () => dy.reshape($x.shape)};\n  };\n  return ENV.engine.runKernel(\n      backend => backend.reshape($x, shape), {$x}, grad);\n}\n\n/**\n * Removes dimensions of size 1 from the shape of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4], [1, 1, 4]);\n * x.squeeze().print();\n * ```\n *\n * @param x The input tensor to be squeezed.\n * @param axis An optional list of numbers. If specified, only\n *     squeezes the dimensions listed. The dimension index starts at 0. It\n * is an error to squeeze a dimension that is not 1.\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction squeeze_<T extends Tensor>(x: Tensor|TensorLike, axis?: number[]): T {\n  const $x = convertToTensor(x, 'x', 'squeeze');\n  return reshape($x, util.squeezeShape($x.shape, axis).newShape) as T;\n}\n\n/**\n * Casts a `tf.Tensor` to a new dtype.\n *\n * ```js\n * const x = tf.tensor1d([1.5, 2.5, 3]);\n * tf.cast(x, 'int32').print();\n * ```\n * @param x The input tensor to be casted.\n * @param dtype The dtype to cast the input tensor to.\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction cast_<T extends Tensor>(x: T|TensorLike, dtype: DataType): T {\n  const $x = convertToTensor(x, 'x', 'cast');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.clone()};\n  };\n  return ENV.engine.runKernel(backend => backend.cast($x, dtype), {$x}, grad) as\n      T;\n}\n\n/**\n * Construct a tensor by repeating it the number of times given by reps.\n *\n * This operation creates a new tensor by replicating `input` `reps`\n * times. The output tensor's i'th dimension has `input.shape[i] *\n * reps[i]` elements, and the values of `input` are replicated\n * `reps[i]` times along the i'th dimension. For example, tiling\n * `[a, b, c, d]` by `[2]` produces `[a, b, c, d, a, b, c, d]`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n *\n * a.tile([2]).print();    // or a.tile([2])\n * ```\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.tile([1, 2]).print();  // or a.tile([1, 2])\n * ```\n * @param x The tensor to tile.\n * @param reps Determines the number of replications per dimension.\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction tile_<T extends Tensor>(x: T|TensorLike, reps: number[]): T {\n  const $x = convertToTensor(x, 'x', 'tile');\n\n  util.assert(\n      $x.rank === reps.length,\n      `Error in transpose: rank of input ${$x.rank} ` +\n          `must match length of reps ${reps}.`);\n  const grad = (dy: T) => {\n    const derX = () => {\n      let xGrad = zerosLike($x);\n      // TODO(cais): Maybe reduce memory footprint by avoiding repeated\n      // slicing.\n      if ($x.rank === 1) {\n        for (let i = 0; i < reps[0]; ++i) {\n          xGrad = xGrad.add(dy.slice([i * $x.shape[0]], [$x.shape[0]]));\n        }\n      } else if ($x.rank === 2) {\n        for (let i = 0; i < reps[0]; ++i) {\n          for (let j = 0; j < reps[1]; ++j) {\n            xGrad = xGrad.add(dy.slice(\n                [i * $x.shape[0], j * $x.shape[1]],\n                [$x.shape[0], $x.shape[1]]));\n          }\n        }\n      } else if ($x.rank === 3) {\n        for (let i = 0; i < reps[0]; ++i) {\n          for (let j = 0; j < reps[1]; ++j) {\n            for (let k = 0; k < reps[2]; ++k) {\n              xGrad = xGrad.add(dy.slice(\n                  [i * $x.shape[0], j * $x.shape[1], k * $x.shape[2]],\n                  [$x.shape[0], $x.shape[1], $x.shape[2]]));\n            }\n          }\n        }\n      } else if ($x.rank === 4) {\n        for (let i = 0; i < reps[0]; ++i) {\n          for (let j = 0; j < reps[1]; ++j) {\n            for (let k = 0; k < reps[2]; ++k) {\n              for (let l = 0; l < reps[3]; ++l) {\n                xGrad = xGrad.add(dy.slice(\n                    [\n                      i * $x.shape[0], j * $x.shape[1], k * $x.shape[2],\n                      l * $x.shape[3]\n                    ],\n                    [$x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]));\n              }\n            }\n          }\n        }\n      } else {\n        throw new Error(\n            `Gradient for tile operation is not implemented for rank-` +\n            `${$x.rank} tensors yet.`);\n      }\n      return xGrad;\n    };\n    return {$x: derX};\n  };\n  return ENV.engine.runKernel(backend => backend.tile($x, reps), {$x}, grad);\n}\n\n/**\n * Pads a `tf.Tensor1D` with a given value and paddings. See `pad` for details.\n */\nfunction pad1d_(\n    x: Tensor1D|TensorLike, paddings: [number, number],\n    constantValue = 0): Tensor1D {\n  util.assert(\n      paddings.length === 2,\n      'Invalid number of paddings. Must be length of 2.');\n  return pad(x, [paddings], constantValue);\n}\n\n/**\n * Pads a `tf.Tensor2D` with a given value and paddings. See `pad` for details.\n */\nfunction pad2d_(\n    x: Tensor2D|TensorLike, paddings: [[number, number], [number, number]],\n    constantValue = 0): Tensor2D {\n  util.assert(\n      paddings.length === 2 && paddings[0].length === 2 &&\n          paddings[1].length === 2,\n      'Invalid number of paddings. Must be length of 2 each.');\n  return pad(x, paddings, constantValue);\n}\n\n/**\n * Pads a `tf.Tensor3D` with a given value and paddings. See `pad` for details.\n */\nfunction pad3d_(\n    x: Tensor3D|TensorLike,\n    paddings: [[number, number], [number, number], [number, number]],\n    constantValue = 0): Tensor3D {\n  util.assert(\n      paddings.length === 3 && paddings[0].length === 2 &&\n          paddings[1].length === 2 && paddings[2].length === 2,\n      'Invalid number of paddings. Must be length of 2 each.');\n  return pad(x, paddings, constantValue);\n}\n\n/**\n * Pads a `tf.Tensor4D` with a given value and paddings. See `pad` for details.\n */\nfunction pad4d_(\n    x: Tensor4D|TensorLike,\n    paddings:\n        [\n          [number, number], [number, number], [number, number], [number, number]\n        ],\n    constantValue = 0): Tensor4D {\n  util.assert(\n      paddings.length === 4 && paddings[0].length === 2 &&\n          paddings[1].length === 2 && paddings[2].length === 2 &&\n          paddings[3].length === 2,\n      'Invalid number of paddings. Must be length of 2 each.');\n  return pad(x, paddings, constantValue);\n}\n\n/**\n * Pads a `tf.Tensor` with a given value and paddings.\n *\n * This operation currently only implements the `CONSTANT` mode.\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `paddings` is of given length.\n *   - `tf.pad1d`\n *   - `tf.pad2d`\n *   - `tf.pad3d`\n *   - `tf.pad4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.pad([[1, 2]]).print();\n * ```\n * @param x The tensor to pad.\n * @param paddings An array of length `R` (the rank of the tensor), where\n * each element is a length-2 tuple of ints `[padBefore, padAfter]`,\n * specifying how much to pad along each dimension of the tensor.\n * @param constantValue The pad value to use. Defaults to 0.\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction pad_<T extends Tensor>(\n    x: T|TensorLike, paddings: Array<[number, number]>, constantValue = 0): T {\n  const $x = convertToTensor(x, 'x', 'pad');\n\n  if ($x.rank === 0) {\n    throw new Error('pad(scalar) is not defined. Pass non-scalar to pad');\n  }\n  // Pad introduces values around the original tensor, so the gradient\n  // slices the original shape out of the gradient.\n  const begin = paddings.map(p => p[0]);\n  const grad = (dy: T) => {\n    return {$x: () => dy.slice(begin, $x.shape)};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.pad($x, paddings, constantValue), {$x}, grad) as\n      T;\n}\n\n/**\n * Stacks a list of rank-`R` `tf.Tensor`s into one rank-`(R+1)` `tf.Tensor`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.stack([a, b, c]).print();\n * ```\n *\n * @param tensors A list of tensor objects with the same shape and dtype.\n * @param axis The axis to stack along. Defaults to 0 (the first dim).\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction stack_<T extends Tensor>(\n    tensors: Array<T|TensorLike>, axis = 0): Tensor {\n  const $tensors = convertToTensorArray(tensors, 'tensors', 'stack');\n\n  util.assert($tensors.length >= 1, 'Pass at least one tensor to tf.stack');\n  if ($tensors.length === 1) {\n    return $tensors[0].expandDims(axis);\n  }\n  const rank = $tensors[0].rank;\n  const shape = $tensors[0].shape;\n  const dtype = $tensors[0].dtype;\n\n  util.assert(axis <= rank, 'Axis must be <= rank of the tensor');\n\n  $tensors.forEach(t => {\n    util.assertShapesMatch(\n        shape, t.shape,\n        'All tensors passed to stack must have matching shapes');\n  });\n\n  $tensors.forEach(t => {\n    util.assert(\n        dtype === t.dtype,\n        'All tensors passed to stack must have matching dtypes');\n  });\n  const expandedTensors = $tensors.map(t => t.expandDims(axis));\n  return concat(expandedTensors, axis);\n}\n\n/**\n * This operation reshapes the \"batch\" dimension 0 into `M + 1` dimensions of\n * shape `blockShape + [batch]`, interleaves these blocks back into the grid\n * defined by the spatial dimensions `[1, ..., M]`, to obtain a result with\n * the same rank as the input. The spatial dimensions of this intermediate\n * result are then optionally cropped according to `crops` to produce the\n * output. This is the reverse of `tf.spaceToBatchND`. See below for a precise\n * description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);\n * const blockShape = [2, 2];\n * const crops = [[0, 0], [0, 0]];\n *\n * x.batchToSpaceND(blockShape, crops).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param crops A 2-D array.  Must have shape `[M, 2]`, all values must be >= 0.\n * `crops[i] = [cropStart, cropEnd]` specifies the amount to crop from input\n * dimension `i + 1`, which corresponds to spatial dimension `i`. It is required\n * that `cropStart[i] + cropEnd[i] <= blockShape[i] * inputShape[i + 1]`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Reshape `x` to `reshaped` of shape: `[blockShape[0], ...,\n * blockShape[M-1], batch / prod(blockShape), x.shape[1], ...,\n * x.shape[N-1]]`\n *\n * 2. Permute dimensions of `reshaped`to produce `permuted` of shape `[batch /\n * prod(blockShape),x.shape[1], blockShape[0], ..., x.shape[M],\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 3. Reshape `permuted` to produce `reshapedPermuted` of shape `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0], ..., x.shape[M] *\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 4. Crop the start and end of dimensions `[1, ..., M]` of `reshapedPermuted`\n * according to `crops` to produce the output of shape: `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0] - crops[0,0] - crops[0,1],\n * ..., x.shape[M] * blockShape[M-1] - crops[M-1,0] -\n * crops[M-1,1],x.shape[M+1], ..., x.shape[N-1]]`\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction batchToSpaceND_<T extends Tensor>(\n    x: T|TensorLike, blockShape: number[], crops: number[][]): T {\n  const $x = convertToTensor(x, 'x', 'batchToSpaceND');\n  const prod = blockShape.reduce((a, b) => a * b);\n\n  util.assert(\n      $x.rank >= 1 + blockShape.length,\n      `input rank is ${$x.rank} but should be > than blockShape.length ${\n          blockShape.length}`);\n\n  util.assert(\n      crops.length === blockShape.length,\n      `crops.length is ${\n          crops.length} but should be equal to blockShape.length  ${\n          blockShape.length}`);\n\n  util.assert(\n      $x.shape[0] % prod === 0,\n      `input tensor batch is ${\n          $x.shape[0]} but is not divisible by the product of ` +\n          `the elements of blockShape ${blockShape.join(' * ')} === ${prod}`);\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.spaceToBatchND(blockShape, crops)};\n  };\n\n  return ENV.engine.runKernel(\n      backend => backend.batchToSpaceND($x, blockShape, crops), {$x}, grad);\n}\n\n/**\n * This operation divides \"spatial\" dimensions `[1, ..., M]` of the input into\n * a grid of blocks of shape `blockShape`, and interleaves these blocks with\n * the \"batch\" dimension (0) such that in the output, the spatial\n * dimensions `[1, ..., M]` correspond to the position within the grid,\n * and the batch dimension combines both the position within a spatial block\n * and the original batch position. Prior to division into blocks,\n * the spatial dimensions of the input are optionally zero padded\n * according to `paddings`. See below for a precise description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);\n * const blockShape = [2, 2];\n * const paddings = [[0, 0], [0, 0]];\n *\n * x.spaceToBatchND(blockShape, paddings).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param paddings A 2-D array. Must have shape `[M, 2]`, all values must be >=\n *     0. `paddings[i] = [padStart, padEnd]` specifies the amount to zero-pad\n * from input dimension `i + 1`, which corresponds to spatial dimension `i`. It\n * is required that\n * `(inputShape[i + 1] + padStart + padEnd) % blockShape[i] === 0`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the input\n * according to `paddings` to produce `padded` of shape paddedShape.\n *\n * 2. Reshape `padded` to `reshapedPadded` of shape:\n * `[batch] + [paddedShape[1] / blockShape[0], blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1], blockShape[M-1]] + remainingShape`\n *\n * 3. Permute dimensions of `reshapedPadded` to produce `permutedReshapedPadded`\n * of shape: `blockShape + [batch] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * 4. Reshape `permutedReshapedPadded` to flatten `blockShape` into the\n * batch dimension, producing an output tensor of shape:\n * `[batch * prod(blockShape)] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction spaceToBatchND_<T extends Tensor>(\n    x: T|TensorLike, blockShape: number[], paddings: number[][]): T {\n  const $x = convertToTensor(x, 'x', 'spaceToBatchND');\n\n  util.assert(\n      $x.rank >= 1 + blockShape.length,\n      `input rank ${$x.rank} should be > than [blockShape] ${\n          blockShape.length}`);\n\n  util.assert(\n      paddings.length === blockShape.length,\n      `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${\n          blockShape.length}`);\n\n  util.assert(\n      $x.shape.reduce(\n          (a, b, i) => {\n            if (i > 0 && i <= blockShape.length) {\n              return a &&\n                  ((b + paddings[i - 1][0] + paddings[i - 1][1]) %\n                       blockShape[i - 1] ===\n                   0);\n            }\n            return a;\n          },\n          true),\n      `input spatial dimensions ${$x.shape.slice(1)} with paddings ${\n          paddings.toString()} must be divisible by blockShapes ${\n          blockShape.toString()}`);\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.batchToSpaceND(blockShape, paddings)};\n  };\n\n  return ENV.engine.runKernel(\n      backend => backend.spaceToBatchND($x, blockShape, paddings), {$x}, grad);\n}\n\n/**\n * Unstacks a `tf.Tensor` of rank-`R` into a list of rank-`(R-1)` `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * tf.unstack(a).forEach(tensor => tensor.print());\n * ```\n *\n * @param x A tensor object.\n * @param axis The axis to unstack along. Defaults to 0 (the first dim).\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction unstack_<T extends Tensor>(x: T|TensorLike, axis = 0): Tensor[] {\n  const $x = convertToTensor(x, 'x', 'unstack');\n  const num = $x.shape[axis];\n  const outputShape: number[] = Array($x.rank - 1).fill(0);\n  let outIndex = 0;\n  for (let i = 0; i < $x.rank; i++) {\n    if (i !== axis) {\n      outputShape[outIndex] = $x.shape[i];\n      outIndex++;\n    }\n  }\n\n  let splitSizes: number[];\n  splitSizes = Array(num).fill(1);\n  const begin = Array($x.rank).fill(0);\n  const size = $x.shape.slice();\n  return splitSizes.map(s => {\n    size[axis] = s;\n    const slice = $x.slice(begin, size);\n    begin[axis] += s;\n    return slice.reshape(outputShape);\n  });\n}\n\n/**\n * Computes the cumulative sum of a `tf.Tensor` along `axis`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4]);\n * x.cumsum().print();\n * ```\n * ```js\n * const x = tf.tensor([[1, 2], [3, 4]]);\n * x.cumsum().print();\n * ```\n *\n * @param x The input tensor to be summed.\n * @param axis The axis along which to sum. Optional. Defaults to 0.\n * @param exclusive Whether to perform exclusive cumulative sum. Optional.\n *     Defaults to false. If set to true then the sum of each tensor entry\n *     does not include its own value, but only the values previous to it\n *     along the specified axis.\n * @param reverse Whether to sum in the opposite direction. Optional.\n *     Defaults to false.\n */\n/** @doc {heading: 'Operations', subheading: 'Scan'} */\nfunction cumsum_<T extends Tensor>(\n    x: Tensor|TensorLike, axis = 0, exclusive = false, reverse = false): T {\n  const $x = convertToTensor(x, 'x', 'cumsum');\n\n  axis = axis | 0;\n  const permutation = getAxesPermutation([axis], $x.rank);\n  let permutedX = $x;\n  if (permutation != null) {\n    permutedX = $x.transpose(permutation);\n  }\n  const permutedAxis = getInnerMostAxes(1, $x.rank)[0];\n\n  const grad = (dy: T) => {\n    return {permutedX: () => dy.cumsum(axis, exclusive, !reverse)};\n  };\n  let value = ENV.engine.runKernel(\n                  backend => backend.cumsum(\n                      permutedX, permutedAxis, exclusive, reverse),\n                  {permutedX}, grad) as T;\n\n  if (permutation != null) {\n    value = value.transpose(permutation);\n  }\n  return value;\n}\n\n/**\n * Returns a `tf.Tensor` that has expanded rank, by inserting a dimension\n * into the tensor's shape.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const axis = 1;\n * x.expandDims(axis).print();\n * ```\n *\n * @param x The input tensor whose dimensions to be expanded.\n * @param axis The dimension index at which to insert shape of `1`. Defaults\n *     to 0 (the first dimension).\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction expandDims_<R2 extends Rank>(\n    x: Tensor|TensorLike, axis = 0): Tensor<R2> {\n  const $x = convertToTensor(x, 'x', 'expandDims');\n\n  util.assert(axis <= $x.rank, 'Axis must be <= rank of the tensor');\n  const newShape = $x.shape.slice();\n  if (axis < 0) {\n    // Negative value is counted from the tail of rank.\n    util.assert(\n        -($x.rank + 1) <= axis,\n        `Axis must be in the interval [${- ($x.rank + 1)}, ${$x.rank}]`);\n    axis = $x.rank + axis + 1;\n  }\n  newShape.splice(axis, 0, 1);\n  return reshape($x, newShape);\n}\n\n/**\n * Rearranges data from depth into blocks of spatial data. More specifically,\n * this op outputs a copy of the input tensor where values from the `depth`\n * dimension are moved in spatial blocks to the `height` and `width` dimensions.\n * The attr `blockSize` indicates the input block size and how the data is\n * moved.\n *\n *  - Chunks of data of size `blockSize * blockSize` from depth are rearranged\n * into non-overlapping blocks of size `blockSize x blockSize`\n *\n *  - The width the output tensor is `inputWidth * blockSize`, whereas the\n * height is `inputHeight * blockSize`\n *\n *  - The Y, X coordinates within each block of the output image are determined\n * by the high order component of the input channel index\n *\n *  - The depth of the input tensor must be divisible by `blockSize *\n * blockSize`\n *\n * The `dataFormat` attr specifies the layout of the input and output tensors\n * with the following options: \"NHWC\": [ `batch, height, width, channels` ]\n * \"NCHW\": [ `batch, channels, height, width` ]\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]);\n * const blockSize = 2;\n * const dataFormat = \"NHWC\";\n *\n * tf.depthToSpace(x, blockSize, dataFormat).print();\n * ```\n *\n * @param x The input tensor of rank 4\n * @param blockSIze  An `int` that is `>= 2`. The size of the spatial block\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to \"NHWC\"\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction depthToSpace_(\n    x: Tensor4D|TensorLike4D, blockSize: number,\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC'): Tensor4D {\n  const $x = convertToTensor(x, 'x', 'depthToSpace') as Tensor4D;\n\n  const inputHeight = (dataFormat === 'NHWC') ? $x.shape[1] : $x.shape[2];\n  const inputWidth = (dataFormat === 'NHWC') ? $x.shape[2] : $x.shape[3];\n  const inputDepth = (dataFormat === 'NHWC') ? $x.shape[3] : $x.shape[1];\n\n  util.assert(\n      inputHeight * blockSize >= 0,\n      `Negative dimension size caused by overflow when multiplying\n      ${inputHeight} and ${blockSize}  for depthToSpace with input shape\n      ${$x.shape}`);\n\n  util.assert(\n      inputWidth * blockSize >= 0,\n      `Negative dimension size caused by overflow when multiplying\n      ${inputWidth} and ${blockSize} for depthToSpace with input shape\n          ${$x.shape}`);\n\n  util.assert(\n      (inputDepth % (blockSize * blockSize) === 0),\n      `Dimension size must be evenly divisible by ${\n          blockSize * blockSize} but is ${\n          inputDepth} for depthToSpace with input shape ${$x.shape}`);\n\n  return ENV.engine.runKernel(\n      backend => backend.depthToSpace($x, blockSize, dataFormat), {$x});\n}\n\n/**\n * Computes the difference between two lists of numbers.\n *\n * Given a Tensor `x` and a Tensor `y`, this operation returns a Tensor `out`\n * that represents all values that are in `x` but not in `y`. The returned\n * Tensor `out` is sorted in the same order that the numbers appear in `x`\n * (duplicates are preserved). This operation also returns a Tensor indices that\n * represents the position of each out element in `x`. In other words:\n *\n * `out[i] = x[idx[i]] for i in [0, 1, ..., out.length - 1]`\n *\n * ```js\n * const x = [1, 2, 3, 4, 5, 6];\n * const y = [1, 3, 5];\n *\n * const [out, indices] = await tf.setdiff1dAsync(x, y);\n * out.print(); // [2, 4, 6]\n * indices.print(); // [1, 3, 5]\n * ```\n *\n * @param x 1-D Tensor. Values to keep.\n * @param y 1-D Tensor. Must have the same type as x. Values to exclude in the\n *     output.\n * @returns Promise of Tensor tuple [out, indices].\n *  out: Tensor with the same type as x.\n *  indices: A Tensor of type int32.\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nasync function setdiff1dAsync_(\n    x: Tensor|TensorLike, y: Tensor|TensorLike): Promise<[Tensor, Tensor]> {\n  const $x = convertToTensor(x, 'x', 'setdiff1d');\n  const $y = convertToTensor(y, 'y', 'setdiff1d');\n\n  util.assert(\n      $x.dtype === $y.dtype,\n      `x and y should have the same dtype, but got x (${$x.dtype}) and y (${\n          $y.dtype}).`);\n\n  util.assert($x.rank === 1, `x should be 1D tensor, but got x (${$x.shape}).`);\n\n  util.assert($y.rank === 1, `y should be 1D tensor, but got y (${$y.shape}).`);\n\n  const xVals = await $x.data();\n  const yVals = await $y.data();\n  const ySet = new Set(yVals);\n\n  let outputSize = 0;\n  for (let i = 0; i < xVals.length; i++) {\n    if (!ySet.has(xVals[i])) {\n      outputSize++;\n    }\n  }\n\n  const buffer = new TensorBuffer([outputSize], $x.dtype);\n  const indices = new TensorBuffer([outputSize], 'int32');\n  for (let i = 0, p = 0; i < xVals.length; i++) {\n    if (!ySet.has(xVals[i])) {\n      buffer.values[p] = xVals[i];\n      indices.values[p] = i;\n      p++;\n    }\n  }\n  return [buffer.toTensor(), indices.toTensor()];\n}\n\n/**\n * Creates an empty `tf.TensorBuffer` with the specified `shape` and `dtype`.\n *\n * The values are stored in CPU as `TypedArray`. Fill the buffer using\n * `buffer.set()`, or by modifying directly `buffer.values`.\n *\n * When done, call `buffer.toTensor()` to get an immutable `tf.Tensor` with\n * those values.\n *\n * ```js\n * // Create a buffer and set values at particular indices.\n * const buffer = tf.buffer([2, 2]);\n * buffer.set(3, 0, 0);\n * buffer.set(5, 1, 0);\n *\n * // Convert the buffer back to a tensor.\n * buffer.toTensor().print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The dtype of the buffer. Defaults to 'float32'.\n * @param values The values of the buffer as `TypedArray`. Defaults to\n * zeros.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction buffer<R extends Rank, D extends DataType = 'float32'>(\n    shape: ShapeMap[R], dtype: D = 'float32' as D,\n    values?: DataTypeMap[D]): TensorBuffer<R, D> {\n  dtype = dtype || 'float32' as D;\n  return new TensorBuffer<R, D>(shape, dtype, values);\n}\n\n/**\n * Prints information about the `tf.Tensor` including its data.\n *\n * ```js\n * const verbose = true;\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose);\n * ```\n * @param x The tensor to be printed.\n * @param verbose Whether to print verbose information about the ` Tensor`,\n * including dtype and size.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction print<T extends Tensor>(x: T, verbose = false): void {\n  console.log(x.toString(verbose));\n}\n\nexport {\n  buffer,    // Not wrapped in op() since no tensors.\n  toPixels,  // Not wrapped in op() since async.\n  print      // Not wrapped in op() since no need to increase stack trace.\n};\n\nexport const batchToSpaceND = op({batchToSpaceND_});\nexport const cast = op({cast_});\nexport const clone = op({clone_});\nexport const cumsum = op({cumsum_});\nexport const depthToSpace = op({depthToSpace_});\nexport const expandDims = op({expandDims_});\nexport const eye = op({eye_});\nexport const fromPixels = op({fromPixels_});\nexport const multinomial = op({multinomial_});\nexport const oneHot = op({oneHot_});\nexport const pad = op({pad_});\nexport const pad1d = op({pad1d_});\nexport const pad2d = op({pad2d_});\nexport const pad3d = op({pad3d_});\nexport const pad4d = op({pad4d_});\nexport const rand = op({rand_});\nexport const randomNormal = op({randomNormal_});\nexport const randomUniform = op({randomUniform_});\nexport const reshape = op({reshape_});\nexport const spaceToBatchND = op({spaceToBatchND_});\nexport const squeeze = op({squeeze_});\nexport const stack = op({stack_});\nexport const tile = op({tile_});\nexport const truncatedNormal = op({truncatedNormal_});\nexport const unstack = op({unstack_});\nexport const setdiff1dAsync = setdiff1dAsync_;\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/** An implementation of the Where kernel shared between cpu and webgl */\n\nimport {buffer} from '../ops/array_ops';\nimport {Tensor2D} from '../tensor';\nimport {TypedArray} from '../types';\n\nexport function whereImpl(condShape: number[], condVals: TypedArray): Tensor2D {\n  const indices = [];\n  for (let i = 0; i < condVals.length; i++) {\n    if (condVals[i]) {\n      indices.push(i);\n    }\n  }\n\n  const inBuffer = buffer(condShape, 'int32');\n\n  const out = buffer([indices.length, condShape.length], 'int32');\n  for (let i = 0; i < indices.length; i++) {\n    const loc = inBuffer.indexToLoc(indices[i]);\n    const offset = i * condShape.length;\n    out.values.set(loc, offset);\n  }\n  return out.toTensor() as Tensor2D;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getWebGLContext} from '../canvas_util';\nimport {MemoryInfo, TimingInfo} from '../engine';\nimport {ENV} from '../environment';\nimport {tidy} from '../globals';\nimport {warn} from '../log';\nimport * as array_ops_util from '../ops/array_ops_util';\nimport * as axis_util from '../ops/axis_util';\nimport {computeOutShape} from '../ops/concat_util';\nimport {Conv2DInfo, Conv3DInfo} from '../ops/conv_util';\nimport * as gather_nd_util from '../ops/gather_nd_util';\nimport * as reduce_util from '../ops/reduce_util';\nimport * as scatter_nd_util from '../ops/scatter_nd_util';\nimport * as segment_util from '../ops/segment_util';\nimport {getStridedSlicedInfo} from '../ops/slice_util';\nimport {softmax} from '../ops/softmax';\nimport {range, scalar, tensor} from '../ops/tensor_ops';\nimport {DataId, Scalar, setTensorTracker, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, Tensor5D} from '../tensor';\nimport {DataType, DataTypeMap, DataValues, NumericDataType, Rank, RecursiveArray, ShapeMap, sumOutType, TypedArray, upcastType} from '../types';\nimport * as util from '../util';\nimport {getTypedArrayFromDType, sizeFromShape} from '../util';\nimport {DataMover, DataStorage, KernelBackend} from './backend';\nimport * as backend_util from './backend_util';\nimport {mergeRealAndImagArrays} from './complex_util';\nimport {nonMaxSuppressionImpl} from './non_max_suppression_impl';\nimport {split} from './split_shared';\nimport {topkImpl} from './topk_impl';\nimport {ArgMinMaxProgram} from './webgl/argminmax_gpu';\nimport {AvgPool2DBackpropProgram} from './webgl/avg_pool_backprop_gpu';\nimport {BatchNormProgram} from './webgl/batchnorm_gpu';\nimport {BatchNormPackedProgram} from './webgl/batchnorm_packed_gpu';\nimport * as binaryop_complex_gpu from './webgl/binaryop_complex_gpu';\nimport {BinaryOpComplexProgram} from './webgl/binaryop_complex_gpu';\nimport * as binaryop_gpu from './webgl/binaryop_gpu';\nimport {BinaryOpProgram} from './webgl/binaryop_gpu';\nimport {ClipProgram} from './webgl/clip_gpu';\nimport {ClipPackedProgram} from './webgl/clip_packed_gpu';\nimport {ComplexAbsProgram} from './webgl/complex_abs_gpu';\nimport {ConcatProgram} from './webgl/concat_gpu';\nimport {Conv2DDerFilterProgram, Conv2DDerInputProgram, Conv3DDerFilterProgram, Conv3DDerInputProgram} from './webgl/conv_backprop_gpu';\nimport {DepthwiseConv2DDerFilterProgram, DepthwiseConv2DDerInputProgram} from './webgl/conv_backprop_gpu_depthwise';\nimport {Conv2DProgram, Conv3DProgram} from './webgl/conv_gpu';\nimport {DepthwiseConv2DProgram} from './webgl/conv_gpu_depthwise';\nimport {DepthwiseConvPacked2DProgram} from './webgl/conv_packed_gpu_depthwise';\nimport {CropAndResizeProgram} from './webgl/crop_and_resize_gpu';\nimport {CumSumProgram} from './webgl/cumsum_gpu';\nimport {DepthToSpaceProgram} from './webgl/depth_to_space_gpu';\nimport {EncodeFloatProgram} from './webgl/encode_float_gpu';\nimport * as fft_gpu from './webgl/fft_gpu';\nimport {FFTProgram} from './webgl/fft_gpu';\nimport {FromPixelsProgram} from './webgl/from_pixels_gpu';\nimport {GatherProgram} from './webgl/gather_gpu';\nimport {GatherNDProgram} from './webgl/gather_nd_gpu';\nimport {GPGPUContext} from './webgl/gpgpu_context';\nimport * as gpgpu_math from './webgl/gpgpu_math';\nimport {GPGPUBinary, GPGPUProgram, TensorData} from './webgl/gpgpu_math';\nimport {Im2ColProgram} from './webgl/im2col_gpu';\nimport {LRNProgram} from './webgl/lrn_gpu';\nimport {LRNGradProgram} from './webgl/lrn_grad_gpu';\nimport {MaxPool2DBackpropProgram} from './webgl/max_pool_backprop_gpu';\nimport {MatMulProgram} from './webgl/mulmat_gpu';\nimport {MatMulPackedProgram} from './webgl/mulmat_packed_gpu';\nimport {MultinomialProgram} from './webgl/multinomial_gpu';\nimport {OneHotProgram} from './webgl/onehot_gpu';\nimport {PackProgram} from './webgl/pack_gpu';\nimport {PadProgram} from './webgl/pad_gpu';\nimport {Pool2DProgram} from './webgl/pool_gpu';\nimport {ReduceProgram} from './webgl/reduce_gpu';\nimport {ReshapePackedProgram} from './webgl/reshape_packed_gpu';\nimport {ResizeBilinearBackpropProgram} from './webgl/resize_bilinear_backprop_gpu';\nimport {ResizeBilinearProgram} from './webgl/resize_bilinear_gpu';\nimport {ResizeNearestNeigborBackpropProgram} from './webgl/resize_nearest_neighbor_backprop_gpu';\nimport {ResizeNearestNeighborProgram} from './webgl/resize_nearest_neighbor_gpu';\nimport {ReverseProgram} from './webgl/reverse_gpu';\nimport {ScatterProgram} from './webgl/scatter_gpu';\nimport {SegmentOpProgram} from './webgl/segment_gpu';\nimport {SelectProgram} from './webgl/select_gpu';\nimport {SliceProgram} from './webgl/slice_gpu';\nimport {StridedSliceProgram} from './webgl/strided_slice_gpu';\nimport * as tex_util from './webgl/tex_util';\nimport {TextureData, TextureUsage} from './webgl/tex_util';\nimport {TextureManager} from './webgl/texture_manager';\nimport {TileProgram} from './webgl/tile_gpu';\nimport {TransposeProgram} from './webgl/transpose_gpu';\nimport * as unary_op from './webgl/unaryop_gpu';\nimport {UnaryOpProgram} from './webgl/unaryop_gpu';\nimport {UnpackProgram} from './webgl/unpack_gpu';\nimport * as webgl_util from './webgl/webgl_util';\nimport {whereImpl} from './where_impl';\n\ntype KernelInfo = {\n  name: string; query: Promise<number>;\n};\n\nexport type TimerNode = RecursiveArray<KernelInfo>|KernelInfo;\nexport interface CPUTimerQuery {\n  startMs: number;\n  endMs?: number;\n}\n\nexport interface WebGLMemoryInfo extends MemoryInfo {\n  numBytesInGPU: number;\n  unreliable: boolean;\n}\n\nexport interface WebGLTimingInfo extends TimingInfo {\n  uploadWaitMs: number;\n  downloadWaitMs: number;\n}\n\n// Combines a dataId, a shape, and a dtype without a Tensor object so that\n// programs can be executed without a full Tensor object.\nexport interface TensorHandle {\n  dataId: DataId;\n  shape: number[];\n  dtype: DataType;\n}\n\n// Empirically determined constant used to determine size threshold for handing\n// off execution to the CPU.\nconst CPU_HANDOFF_SIZE_THRESHOLD = 10;\n// Empirically determined constant used to decide the number of bytes on GPU\n// before we start paging. The bytes are this constant * screen area * dpi.\nconst BEFORE_PAGING_CONSTANT = 300;\n// Tensors with size <= than this will be uploaded as uniforms, not textures.\nexport const SIZE_UPLOAD_UNIFORM = 4;\n// Empirically determined minimal shared dimension in matmul before we forward\n// to a.mul(b).sum() in order to take advantage of GPU parallelism. See\n// https://github.com/tensorflow/tfjs-core/pull/1379 for benchmarks.\nexport const MATMUL_SHARED_DIM_THRESHOLD = 1000;\n\nexport class MathBackendWebGL implements KernelBackend {\n  private texData: DataStorage<TextureData>;\n  // Maps data ids that have a pending read operation, to list of subscribers.\n  private pendingRead = new WeakMap<DataId, Array<(arr: TypedArray) => void>>();\n  // List of data ids that are scheduled for disposal, but are waiting on a\n  // pending read operation.\n  private pendingDisposal = new WeakSet<DataId>();\n  // List of data ids that are currently residing on gpu memory. Sorted with\n  // least recently used being first.\n  private lruDataGPU: DataId[] = [];\n  private numBytesInGPU = 0;\n  /**\n   * Number of bytes allocated on the GPU before we start moving data to cpu.\n   * Moving avoids gpu memory leaks and relies on JS's garbage collector.\n   */\n  private NUM_BYTES_BEFORE_PAGING: number;\n\n  private canvas: HTMLCanvasElement;\n  private fromPixels2DContext: CanvasRenderingContext2D;\n\n  private programTimersStack: TimerNode[];\n  private activeTimers: TimerNode[];\n  // Accumulated time spent (including blocking) in uploading data to webgl.\n  private uploadWaitMs = 0;\n  // Accumulated time spent (including blocking in downloading data from webgl.\n  private downloadWaitMs = 0;\n  private cpuBackend: KernelBackend;\n\n  register(dataId: DataId, shape: number[], dtype: DataType): void {\n    if (this.texData.has(dataId)) {\n      throw new Error('Data buffer is already registered');\n    }\n    this.texData.set(dataId, {shape, dtype});\n  }\n\n  setDataMover(dataMover: DataMover): void {\n    this.texData = new DataStorage(dataMover);\n  }\n\n  fromPixels(\n      pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement,\n      numChannels: number): Tensor3D {\n    if (pixels == null) {\n      throw new Error('pixels passed to tf.fromPixels() can not be null');\n    }\n    const texShape: [number, number] = [pixels.height, pixels.width];\n    const outShape = [pixels.height, pixels.width, numChannels];\n\n    if (!(pixels instanceof HTMLVideoElement) &&\n        !(pixels instanceof HTMLImageElement) &&\n        !(pixels instanceof HTMLCanvasElement) &&\n        !(pixels instanceof ImageData)) {\n      throw new Error(\n          'pixels passed to tf.fromPixels() must be either an ' +\n          `HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ` +\n          `ImageData, but was ${(pixels as {}).constructor.name}`);\n    }\n    if (pixels instanceof HTMLVideoElement) {\n      if (this.fromPixels2DContext == null) {\n        if (!ENV.get('IS_BROWSER')) {\n          throw new Error(\n              'Can\\'t read pixels from HTMLImageElement outside the browser.');\n        }\n        if (document.readyState !== 'complete') {\n          throw new Error(\n              'The DOM is not ready yet. Please call tf.fromPixels() ' +\n              'once the DOM is ready. One way to do that is to add an event ' +\n              'listener for `DOMContentLoaded` on the document object');\n        }\n        this.fromPixels2DContext =\n            document.createElement('canvas').getContext('2d');\n      }\n      this.fromPixels2DContext.canvas.width = pixels.width;\n      this.fromPixels2DContext.canvas.height = pixels.height;\n      this.fromPixels2DContext.drawImage(\n          pixels, 0, 0, pixels.width, pixels.height);\n      pixels = this.fromPixels2DContext.canvas;\n    }\n    const tempPixelHandle = this.makeTensorHandle(texShape, 'int32');\n    // This is a byte texture with pixels.\n    this.texData.get(tempPixelHandle.dataId).usage = TextureUsage.PIXELS;\n    this.gpgpu.uploadPixelDataToTexture(\n        this.getTexture(tempPixelHandle.dataId), pixels);\n    const program = new FromPixelsProgram(outShape);\n    const res = this.compileAndRun(program, [tempPixelHandle]);\n\n    this.disposeData(tempPixelHandle.dataId);\n\n    return res as Tensor3D;\n  }\n\n  private makeTensorHandle(shape: number[], dtype: DataType): TensorHandle {\n    const dataId = {};\n    this.register(dataId, shape, dtype);\n    return {dataId, shape, dtype};\n  }\n\n  write(dataId: DataId, values: DataValues): void {\n    if (values == null) {\n      throw new Error('MathBackendWebGL.write(): values can not be null');\n    }\n    const texData = this.texData.get(dataId);\n    const {texture, texShape, usage, dtype, isPacked} = texData;\n    if (dtype === 'complex64') {\n      throw new Error(\n          `Cannot write to a complex64 dtype. ` +\n          `Please use tf.complex(real, imag).`);\n    }\n\n    if (texture != null) {\n      // Release the old texture.\n      this.releaseTexture(dataId, texture, texShape, usage, isPacked);\n      texData.texture = null;\n      texData.texShape = null;\n    }\n    texData.usage = TextureUsage.UPLOAD;\n    texData.values = values;\n\n    if (!this.delayedStorage) {\n      this.uploadToGPU(dataId);\n    }\n  }\n  readSync(dataId: DataId): DataValues {\n    const texData = this.texData.get(dataId);\n    const {values, dtype, complexTensors} = texData;\n    if (values != null) {\n      return this.convertAndCacheOnCPU(dataId);\n    }\n    if (dtype === 'string') {\n      return values;\n    }\n    const shouldTimeProgram = this.activeTimers != null;\n    let start: number;\n    if (shouldTimeProgram) {\n      start = performance.now();\n    }\n\n    let result: Float32Array;\n    if (dtype === 'complex64') {\n      const realValues = complexTensors.real.dataSync() as Float32Array;\n      const imagValues = complexTensors.imag.dataSync() as Float32Array;\n      result = mergeRealAndImagArrays(realValues, imagValues);\n    } else {\n      result = this.getValuesFromTexture(dataId);\n    }\n\n    if (shouldTimeProgram) {\n      this.downloadWaitMs += performance.now() - start;\n    }\n    return this.convertAndCacheOnCPU(dataId, result);\n  }\n\n  async read(dataId: DataId): Promise<DataValues> {\n    if (this.pendingRead.has(dataId)) {\n      const subscribers = this.pendingRead.get(dataId);\n      return new Promise<TypedArray>(resolve => subscribers.push(resolve));\n    }\n    const texData = this.texData.get(dataId);\n    const {texture, values, texShape, isPacked, shape} = texData;\n    if (values != null) {\n      return this.convertAndCacheOnCPU(dataId);\n    }\n\n    this.pendingRead.set(dataId, []);\n\n    if (!ENV.get('WEBGL_DOWNLOAD_FLOAT_ENABLED') &&\n        ENV.get('WEBGL_VERSION') === 2) {\n      throw new Error(\n          `tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and ` +\n          `WEBGL_VERSION=2 not yet supported.`);\n    }\n\n    // Possibly copy the texture into a buffer before inserting a fence.\n    let width = texShape[1];\n    let height = texShape[0];\n    if (isPacked) {\n      [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(\n          texShape[0], texShape[1]);\n    }\n    const bufferOrTexture =\n        this.gpgpu.maybeCreateBufferFromTexture(texture, height, width);\n\n    // Create a fence and wait for it to resolve.\n    await this.gpgpu.createAndWaitForFence();\n\n    // Download the values from the GPU.\n    let vals: Float32Array;\n    if (bufferOrTexture instanceof WebGLTexture) {\n      vals = this.getValuesFromTexture(dataId);\n    } else {\n      if (isPacked) {\n        const batch = this.getBatchDim(shape);\n        let rows = 1, cols = 1;\n        if (shape.length) {\n          [rows, cols] = this.getRowsCols(shape);\n        }\n        vals = this.gpgpu.downloadPackedMatrixFromBuffer(\n            bufferOrTexture, batch, rows, cols, texShape[0], texShape[1]);\n      } else {\n        vals = this.gpgpu.downloadFloat32MatrixFromBuffer(\n            bufferOrTexture, texShape[0], texShape[1]);\n      }\n    }\n    const dTypeVals = this.convertAndCacheOnCPU(dataId, vals);\n\n    const subscribers = this.pendingRead.get(dataId);\n    this.pendingRead.delete(dataId);\n\n    // Notify all pending reads.\n    subscribers.forEach(resolve => resolve(dTypeVals));\n    if (this.pendingDisposal.has(dataId)) {\n      this.pendingDisposal.delete(dataId);\n      this.disposeData(dataId);\n    }\n    return dTypeVals;\n  }\n\n  private getValuesFromTexture(dataId: DataId): Float32Array {\n    const {shape, dtype, texture, texShape} = this.texData.get(dataId);\n    if (ENV.get('WEBGL_DOWNLOAD_FLOAT_ENABLED')) {\n      if (this.texData.get(dataId).isPacked) {\n        const batch = this.getBatchDim(shape);\n        let rows = 1, cols = 1;\n        if (shape.length) {\n          [rows, cols] = this.getRowsCols(shape);\n        }\n        return this.gpgpu.downloadMatrixFromPackedTexture(\n            texture, batch, rows, cols, texShape[0], texShape[1]);\n      } else {\n        return this.gpgpu.downloadFloat32MatrixFromOutputTexture(\n            texture, texShape[0], texShape[1]);\n      }\n    }\n\n    const tmpTarget = this.makeTensorHandle(shape, 'float32') as TensorHandle &\n        {size: number};\n    tmpTarget.size = sizeFromShape(shape);\n    this.texData.get(tmpTarget.dataId).usage = TextureUsage.DOWNLOAD;\n    const program = new EncodeFloatProgram(shape);\n    const pageToCpu = false;\n    this.compileAndRun(\n        program, [{shape, dtype, dataId}], tmpTarget, null, pageToCpu);\n    const tmpData = this.texData.get(tmpTarget.dataId);\n    const vals = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(\n        tmpData.texture, tmpData.texShape[0], tmpData.texShape[1]);\n    this.disposeData(tmpTarget.dataId);\n\n    return vals;\n  }\n\n  async time(f: () => void): Promise<WebGLTimingInfo> {\n    const oldActiveTimers = this.activeTimers;\n    const newActiveTimers: TimerNode[] = [];\n\n    let outerMostTime = false;\n    if (this.programTimersStack == null) {\n      this.programTimersStack = newActiveTimers;\n      outerMostTime = true;\n    } else {\n      this.activeTimers.push(newActiveTimers);\n    }\n    this.activeTimers = newActiveTimers;\n\n    f();\n\n    // needing to split these up because util.flatten only accepts certain types\n    const flattenedActiveTimerQueries =\n        util.flatten(this.activeTimers.map((d: KernelInfo) => d.query))\n            .filter(d => d != null);\n    const flattenedActiveTimerNames =\n        util.flatten(this.activeTimers.map((d: KernelInfo) => d.name))\n            .filter(d => d != null);\n\n    this.activeTimers = oldActiveTimers;\n\n    if (outerMostTime) {\n      this.programTimersStack = null;\n    }\n\n    const kernelMs = await Promise.all(flattenedActiveTimerQueries);\n\n    const res: WebGLTimingInfo = {\n      uploadWaitMs: this.uploadWaitMs,\n      downloadWaitMs: this.downloadWaitMs,\n      kernelMs: util.sum(kernelMs),\n      getExtraProfileInfo: () =>\n          kernelMs.map((d, i) => ({name: flattenedActiveTimerNames[i], ms: d}))\n              .map(d => `${d.name}: ${d.ms}`)\n              .join(', '),\n      wallMs: null  // will be filled by the engine\n    };\n    this.uploadWaitMs = 0;\n    this.downloadWaitMs = 0;\n    return res;\n  }\n  memory(): WebGLMemoryInfo {\n    return {unreliable: false, numBytesInGPU: this.numBytesInGPU} as\n        WebGLMemoryInfo;\n  }\n\n  private startTimer(): WebGLQuery|CPUTimerQuery {\n    if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {\n      return this.gpgpu.beginQuery();\n    }\n    return {startMs: performance.now(), endMs: null};\n  }\n\n  private endTimer(query: WebGLQuery|CPUTimerQuery): WebGLQuery|CPUTimerQuery {\n    if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {\n      this.gpgpu.endQuery();\n      return query;\n    }\n    (query as CPUTimerQuery).endMs = performance.now();\n    return query;\n  }\n\n  private async getQueryTime(query: WebGLQuery|CPUTimerQuery): Promise<number> {\n    if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {\n      return this.gpgpu.waitForQueryAndGetTime(query as WebGLQuery);\n    }\n    const timerQuery = query as CPUTimerQuery;\n    return timerQuery.endMs - timerQuery.startMs;\n  }\n\n  disposeData(dataId: DataId): void {\n    if (this.pendingDisposal.has(dataId)) {\n      return;\n    }\n    if (this.pendingRead.has(dataId)) {\n      this.pendingDisposal.add(dataId);\n      return;\n    }\n    if (this.texData.has(dataId)) {\n      const {texture, texShape, usage, complexTensors, isPacked} =\n          this.texData.get(dataId);\n      if (texture != null) {\n        this.releaseTexture(dataId, texture, texShape, usage, isPacked);\n      }\n      if (complexTensors != null) {\n        complexTensors.real.dispose();\n        complexTensors.imag.dispose();\n      }\n      this.texData.delete(dataId);\n    }\n  }\n\n  getTexture(dataId: DataId): WebGLTexture {\n    this.uploadToGPU(dataId);\n    return this.texData.get(dataId).texture;\n  }\n\n  private textureManager: TextureManager;\n  private binaryCache: {[key: string]: GPGPUBinary} = {};\n  private gpgpuCreatedLocally: boolean;\n\n  constructor(private gpgpu?: GPGPUContext, private delayedStorage = true) {\n    if (ENV.get('WEBGL_VERSION') < 1) {\n      throw new Error('WebGL is not supported on this device');\n    }\n\n    if (gpgpu == null) {\n      const gl = getWebGLContext(ENV.get('WEBGL_VERSION'));\n      this.gpgpu = new GPGPUContext(gl);\n      this.canvas = gl.canvas;\n      this.gpgpuCreatedLocally = true;\n    } else {\n      this.gpgpuCreatedLocally = false;\n      this.canvas = gpgpu.gl.canvas;\n    }\n    if (ENV.get('WEBGL_PAGING_ENABLED')) {\n      // Use the device screen's resolution as a heuristic to decide on the\n      // maximum memory allocated on the GPU before starting to page.\n      this.NUM_BYTES_BEFORE_PAGING =\n          (window.screen.height * window.screen.width *\n           window.devicePixelRatio) *\n          BEFORE_PAGING_CONSTANT;\n    }\n    this.textureManager = new TextureManager(this.gpgpu);\n  }\n\n  private getCPUBackend(): KernelBackend|null {\n    if (!ENV.get('WEBGL_CPU_FORWARD')) {\n      return null;\n    }\n\n    if (this.cpuBackend == null) {\n      this.cpuBackend = ENV.findBackend('cpu');\n    }\n\n    return this.cpuBackend;\n  }\n\n  /*\n  Tests whether all the inputs to an op are small and on the CPU. This heuristic\n  determines when it would be faster to execute a kernel on the CPU. WebGL\n  kernels opt into running this check and forwarding when appropriate.\n  TODO(https://github.com/tensorflow/tfjs/issues/872): Develop a more\n  sustainable strategy for optimizing backend execution of ops.\n   */\n  private shouldExecuteOnCPU(\n      inputs: Tensor[], sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD): boolean {\n    return this.getCPUBackend() != null &&\n        inputs.every(\n            input => this.texData.get(input.dataId).texture == null &&\n                input.size < sizeThreshold);\n  }\n\n  getGPGPUContext(): GPGPUContext {\n    return this.gpgpu;\n  }\n  getCanvas(): HTMLCanvasElement {\n    return this.canvas;\n  }\n\n  complex<T extends Tensor>(real: T, imag: T): T {\n    const result = this.makeOutputArray(real.shape, 'complex64') as T;\n    const resultData = this.texData.get(result.dataId);\n    // The backend owns the reference to the underlying real and imaginary\n    // clones. These will explicitly get disposed when the complex tensor is\n    // disposed.\n    resultData.complexTensors = {\n      real: ENV.engine.keep(real.clone()),\n      imag: ENV.engine.keep(imag.clone())\n    };\n\n    return result;\n  }\n  real<T extends Tensor>(input: T): T {\n    const resultData = this.texData.get(input.dataId);\n    return resultData.complexTensors.real.clone() as T;\n  }\n  imag<T extends Tensor>(input: T): T {\n    const resultData = this.texData.get(input.dataId);\n    return resultData.complexTensors.imag.clone() as T;\n  }\n\n  slice<T extends Tensor>(x: T, begin: number[], size: number[]): T {\n    if (this.shouldExecuteOnCPU([x])) {\n      return this.cpuBackend.slice(x, begin, size);\n    }\n\n    const program = new SliceProgram(size);\n    const customSetup = program.getCustomSetupFunc(begin);\n    return this.compileAndRun(program, [x], null, customSetup);\n  }\n\n  stridedSlice<T extends Tensor>(\n      x: T, begin: number[], end: number[], strides: number[],\n      beginMask: number, endMask: number, ellipsisMask: number,\n      newAxisMask: number, shrinkAxisMask: number): T {\n    if (this.shouldExecuteOnCPU([x])) {\n      return this.cpuBackend.stridedSlice(\n          x, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask,\n          shrinkAxisMask);\n    }\n\n    const [beginIndex, size, shrinkAxis] = getStridedSlicedInfo(\n        x.shape, begin, end, strides, beginMask, endMask, ellipsisMask,\n        newAxisMask, shrinkAxisMask);\n\n    const shape = size.filter((v, index) => shrinkAxis.indexOf(index) === -1);\n    if (shape.some(axis => axis === 0)) {\n      return tensor([], shape) as T;\n    }\n\n    const program =\n        new StridedSliceProgram(beginIndex, strides, size, shrinkAxis);\n    return this.compileAndRun(program, [x]);\n  }\n\n  reverse<T extends Tensor>(x: T, axis: number[]): T {\n    const program = new ReverseProgram(x.shape, axis);\n    return this.compileAndRun(program, [x]);\n  }\n\n  private concat2Tensors<T extends Tensor>(a: T, b: T, axis: number): T {\n    // Any concat of n-dimensional tensors across any axis can be reduced to\n    // a concatenation of two-dimensional tensors across the axis 1 by first\n    // partitioning the axes of the original tensors into those less than the\n    // axis to be concatenated and the rest. Then reshape the tensors\n    // into a two-dimensional tensor by collapsing these two sets of axes and\n    // concatenate the resulting matrices across the axis 1, finally reshaping\n    // the result to have the proper shape.\n    const outShape = computeOutShape([a.shape, b.shape], axis);\n    const a2D = a.as2D(-1, sizeFromShape(a.shape.slice(axis)));\n    const b2D = b.as2D(-1, sizeFromShape(b.shape.slice(axis)));\n    const program = new ConcatProgram(a2D.shape, b2D.shape);\n    const res = this.compileAndRun(program, [a2D, b2D]) as Tensor;\n    return res.reshape(outShape) as T;\n  }\n\n  concat(tensors: Tensor[], axis: number): Tensor {\n    if (this.shouldExecuteOnCPU(tensors)) {\n      return this.cpuBackend.concat(tensors, axis);\n    }\n\n    if (tensors.length === 1) {\n      return tensors[0];\n    }\n    let result = tensors[0];\n    for (let i = 1; i < tensors.length; ++i) {\n      result = this.concat2Tensors(result, tensors[i], axis);\n    }\n    return result;\n  }\n\n  neg<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.NEG);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  batchMatMul(\n      a: Tensor3D, b: Tensor3D, transposeA: boolean,\n      transposeB: boolean): Tensor3D {\n    const outerShapeA = transposeA ? a.shape[2] : a.shape[1];\n    const outerShapeB = transposeB ? b.shape[1] : b.shape[2];\n    const sharedDim = transposeA ? a.shape[1] : a.shape[2];\n    const [batch, , ] = a.shape;\n\n    // Since the matrices are vectors, it is faster to call mul().sum()\n    // because sum() is O(sqrt(N)) due to divide-and-conquer.\n    if ((outerShapeA === 1 || outerShapeB === 1) &&\n        sharedDim > MATMUL_SHARED_DIM_THRESHOLD) {\n      if (transposeA) {\n        a = a.transpose([0, 2, 1]);\n      }\n      if (transposeB) {\n        b = b.transpose([0, 2, 1]);\n      }\n\n      const a3D = outerShapeB === 1 ? a : a.as3D(batch, sharedDim, 1);\n      const axis = outerShapeB === 1 ? 2 : 1;\n      const b3D = outerShapeB === 1 ? b.as3D(batch, 1, sharedDim) : b;\n      return this.multiply(a3D, b3D).sum(axis, true /* keepDims */);\n    }\n\n    const dtype = upcastType(a.dtype, b.dtype);\n\n    // TODO(https://github.com/tensorflow/tfjs/issues/693): Support 3D tensors\n    if (batch === 1) {\n      const aSqueezed = a.as2D(a.shape[1], a.shape[2]);\n      const bSqueezed = b.as2D(b.shape[1], b.shape[2]);\n\n      const program = new MatMulPackedProgram(\n          aSqueezed.shape, bSqueezed.shape, [outerShapeA, outerShapeB],\n          transposeA, transposeB);\n      const output =\n          this.makePackedTensor(program.outputShape, dtype) as Tensor2D;\n      const result =\n          this.compileAndRun<Tensor2D>(program, [aSqueezed, bSqueezed], output);\n      return result.reshape([1, result.shape[0], result.shape[1]]);\n    } else {\n      const program =\n          new MatMulProgram(a.shape, b.shape, transposeA, transposeB);\n      const output =\n          this.makeOutputArray(program.outputShape, dtype) as Tensor3D;\n      return this.compileAndRun(program, [a, b], output);\n    }\n  }\n\n  multiply(a: Tensor, b: Tensor): Tensor {\n    if (a.dtype === 'complex64') {\n      const aData = this.texData.get(a.dataId);\n      const bData = this.texData.get(b.dataId);\n\n      const realProgram = new BinaryOpComplexProgram(\n          binaryop_complex_gpu.COMPLEX_MULTIPLY.REAL, a.shape, b.shape);\n      const imagProgram = new BinaryOpComplexProgram(\n          binaryop_complex_gpu.COMPLEX_MULTIPLY.IMAG, a.shape, b.shape);\n\n      const inputs = [\n        this.makeComplexComponentTensorHandle(a, aData.complexTensors.real),\n        this.makeComplexComponentTensorHandle(a, aData.complexTensors.imag),\n        this.makeComplexComponentTensorHandle(b, bData.complexTensors.real),\n        this.makeComplexComponentTensorHandle(b, bData.complexTensors.imag)\n      ];\n      const real = this.compileAndRun<Tensor>(realProgram, inputs);\n      const imag = this.compileAndRun<Tensor>(imagProgram, inputs);\n\n      const complex = this.complex(real, imag);\n      real.dispose();\n      imag.dispose();\n      return complex;\n    }\n\n    if (this.shouldExecuteOnCPU([a, b])) {\n      return this.cpuBackend.multiply(a, b);\n    }\n\n    const program = new BinaryOpProgram(binaryop_gpu.MUL, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, a.dtype) as Tensor;\n    return this.compileAndRun(program, [a, b], output) as Tensor;\n  }\n\n  batchNormalization(\n      x: Tensor4D, mean: Tensor4D|Tensor1D, variance: Tensor4D|Tensor1D,\n      varianceEpsilon: number, scale?: Tensor4D|Tensor1D,\n      offset?: Tensor4D|Tensor1D): Tensor4D {\n    const inputs = [x, mean, variance];\n\n    let offsetShape = null;\n    if (offset != null) {\n      offsetShape = offset.shape;\n      inputs.push(offset);\n    }\n\n    let scaleShape = null;\n    if (scale != null) {\n      scaleShape = scale.shape;\n      inputs.push(scale);\n    }\n\n    if (ENV.get('WEBGL_PACK_BATCHNORMALIZATION')) {\n      const batchNormPackedProgram = new BatchNormPackedProgram(\n          x.shape, mean.shape, variance.shape, offsetShape, scaleShape,\n          varianceEpsilon);\n      return this.compileAndRun<Tensor4D>(batchNormPackedProgram, inputs);\n    }\n\n    const batchNormProgram = new BatchNormProgram(\n        x.shape, mean.shape, variance.shape, offsetShape, scaleShape,\n        varianceEpsilon);\n    return this.compileAndRun(batchNormProgram, inputs);\n  }\n\n  localResponseNormalization4D(\n      x: Tensor4D, radius: number, bias: number, alpha: number,\n      beta: number): Tensor4D {\n    const program = new LRNProgram(x.shape, radius, bias, alpha, beta);\n    return this.compileAndRun(program, [x]);\n  }\n\n  LRNGrad(\n      dy: Tensor4D, inputImage: Tensor4D, outputImage: Tensor4D,\n      depthRadius: number, bias: number, alpha: number,\n      beta: number): Tensor4D {\n    const program =\n        new LRNGradProgram(inputImage.shape, depthRadius, bias, alpha, beta);\n    return this.compileAndRun(program, [inputImage, outputImage, dy]);\n  }\n\n  tile<T extends Tensor>(x: T, reps: number[]): T {\n    const program = new TileProgram(x.shape, reps);\n    return this.compileAndRun(program, [x]);\n  }\n\n  pad<T extends Tensor>(\n      x: T, paddings: Array<[number, number]>, constantValue: number): T {\n    const program = new PadProgram(x.shape, paddings, constantValue);\n    return this.compileAndRun(program, [x]);\n  }\n\n  transpose<T extends Tensor>(x: T, perm: number[]): T {\n    const program = new TransposeProgram(x.shape, perm);\n    return this.compileAndRun(program, [x]);\n  }\n\n  gather<T extends Tensor>(x: T, indices: Tensor1D, axis: number): T {\n    const program = new GatherProgram(x.shape, indices.size, axis);\n    return this.compileAndRun(program, [x, indices]);\n  }\n\n  batchToSpaceND<T extends Tensor>(\n      x: T, blockShape: number[], crops: number[][]): T {\n    util.assert(\n        x.rank <= 4,\n        'batchToSpaceND for rank > 4 with a WebGL backend not implemented yet');\n    const prod = blockShape.reduce((a, b) => a * b);\n\n    const reshaped = array_ops_util.getReshaped(x.shape, blockShape, prod);\n    const permuted =\n        array_ops_util.getPermuted(reshaped.length, blockShape.length);\n    const reshapedPermuted =\n        array_ops_util.getReshapedPermuted(x.shape, blockShape, prod);\n    const sliceBeginCoords =\n        array_ops_util.getSliceBeginCoords(crops, blockShape.length);\n    const sliceSize =\n        array_ops_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n\n    return x.reshape(reshaped)\n               .transpose(permuted)\n               .reshape(reshapedPermuted)\n               .slice(sliceBeginCoords, sliceSize) as T;\n  }\n\n  spaceToBatchND<T extends Tensor>(\n      x: T, blockShape: number[], paddings: Array<[number, number]>): T {\n    util.assert(\n        x.rank <= 4,\n        'spaceToBatchND for rank > 4 with a WebGL backend not implemented yet');\n\n    const prod = blockShape.reduce((a, b) => a * b);\n\n    const completePaddings: Array<[number, number]> = [[0, 0]];\n    completePaddings.push(...paddings);\n    for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n      completePaddings.push([0, 0]);\n    }\n\n    const paddedX = x.pad(completePaddings);\n\n    const reshapedPaddedShape =\n        array_ops_util.getReshaped(paddedX.shape, blockShape, prod, false);\n\n    const permutedReshapedPaddedPermutation = array_ops_util.getPermuted(\n        reshapedPaddedShape.length, blockShape.length, false);\n\n    const flattenShape = array_ops_util.getReshapedPermuted(\n        paddedX.shape, blockShape, prod, false);\n\n    return paddedX.reshape(reshapedPaddedShape)\n               .transpose(permutedReshapedPaddedPermutation)\n               .reshape(flattenShape) as T;\n  }\n\n  private reduce(\n      x: Tensor2D, reduceType: 'all'|'any'|'max'|'min'|'sum'|'prod',\n      dtype: DataType): Tensor2D {\n    const batchSize = x.shape[0];\n    const inSize = x.shape[1];\n    const windowSize = reduce_util.computeOptimalWindowSize(inSize);\n    const reduceInfo = {windowSize, inSize, batchSize};\n    const program = new ReduceProgram(reduceInfo, reduceType);\n    const [rows, cols] = program.outputShape;\n    const output = this.makeOutputArray<Tensor2D>([rows, cols], dtype);\n\n    this.compileAndRun(program, [x], output);\n    // No need to run another GPGPU program.\n    if (output.shape[1] === 1) {\n      return output;\n    }\n    return this.reduce(output, reduceType, dtype);\n  }\n\n  private argReduce(\n      x: Tensor2D, reduceType: 'max'|'min',\n      bestIndicesA: Tensor2D = null): Tensor2D {\n    let batchSize = x.shape[0];\n    let inSize = x.shape[1];\n    if (bestIndicesA != null) {\n      batchSize = bestIndicesA.shape[0];\n      inSize = bestIndicesA.shape[1];\n    }\n    const windowSize = reduce_util.computeOptimalWindowSize(inSize);\n    const reduceInfo = {windowSize, inSize, batchSize};\n    const program =\n        new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null);\n    const [rows, cols] = program.outputShape;\n    const output = this.makeOutputArray<Tensor2D>([rows, cols], 'int32');\n    const inputs = [x];\n    if (bestIndicesA != null) {\n      inputs.push(bestIndicesA);\n    }\n    this.compileAndRun(program, inputs, output);\n    // No need to run another GPGPU program.\n    if (output.shape[1] === 1) {\n      return output;\n    }\n    return this.argReduce(x, reduceType, output);\n  }\n\n  sum(x: Tensor, axes: number[]): Tensor {\n    axis_util.assertAxesAreInnerMostDims('sum', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const inSize = util.sizeFromShape(reduceShape);\n    const a2D = x.as2D(-1, inSize);\n    const outputDType = sumOutType(x.dtype);\n    return this.reduce(a2D, 'sum', outputDType).reshape(outShape);\n  }\n\n  prod(x: Tensor, axes: number[]): Tensor {\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const inSize = util.sizeFromShape(reduceShape);\n    const a2D = x.as2D(-1, inSize);\n    const outputDType = sumOutType(x.dtype);\n    return this.reduce(a2D, 'prod', outputDType).reshape(outShape);\n  }\n\n  unsortedSegmentSum<T extends Tensor>(\n      x: T, segmentIds: Tensor1D, numSegments: number): Tensor {\n    let axis = 0;\n    const permutation = axis_util.getAxesPermutation([axis], x.rank);\n    let permutedX = x;\n    if (permutation != null) {\n      permutedX = x.transpose(permutation);\n      axis = axis_util.getInnerMostAxes(1, x.rank)[0];\n    }\n\n    const outShape =\n        segment_util.computeOutShape(permutedX.shape, axis, numSegments);\n    const inSize = util.sizeFromShape([permutedX.shape[axis]]);\n    const a2D = permutedX.as2D(-1, inSize);\n    const outputDType = sumOutType(x.dtype);\n    let result =\n        this.segOpCompute(\n                a2D, 'unsortedSegmentSum', segmentIds, outputDType, numSegments)\n            .reshape(outShape);\n    if (permutation != null) {\n      result = result.transpose(axis_util.getUndoAxesPermutation(permutation));\n    }\n    return result;\n  }\n\n  private segOpCompute(\n      x: Tensor2D, segOpType: 'unsortedSegmentSum', segmentIds: Tensor1D,\n      dtype: DataType, numSegments: number): Tensor2D {\n    const batchSize = x.shape[0];\n    const inSize = x.shape[1];\n    const windowSize =\n        segment_util.segOpComputeOptimalWindowSize(inSize, numSegments);\n    const segOpInfo = {windowSize, inSize, batchSize, numSegments};\n    const program = new SegmentOpProgram(segOpInfo, segOpType);\n    const [rows, cols] = program.outputShape;\n    const output = this.makeOutputArray<Tensor2D>([rows, cols], dtype);\n    this.compileAndRun(program, [x, segmentIds], output);\n    // No need to run another GPGPU program.\n    if (output.shape[1] === numSegments) {\n      return output;\n    }\n    segmentIds = range(0, numSegments).tile([inSize / windowSize]);\n    return this.segOpCompute(output, segOpType, segmentIds, dtype, numSegments);\n  }\n\n  argMin(x: Tensor, axis: number): Tensor {\n    const axes = [axis];\n    axis_util.assertAxesAreInnerMostDims('argMin', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const inSize = util.sizeFromShape(reduceShape);\n    const a2D = x.as2D(-1, inSize);\n    return this.argReduce(a2D, 'min').reshape(outShape);\n  }\n\n  argMax(x: Tensor, axis: number): Tensor {\n    const axes = [axis];\n    axis_util.assertAxesAreInnerMostDims('argMax', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const inSize = util.sizeFromShape(reduceShape);\n    const a2D = x.as2D(-1, inSize);\n    return this.argReduce(a2D, 'max').reshape(outShape);\n  }\n\n  cumsum(x: Tensor, axis: number, exclusive: boolean, reverse: boolean):\n      Tensor {\n    if (axis !== x.rank - 1) {\n      throw new Error(\n          `WebGL cumsum shader expects an inner-most axis=${x.rank - 1} ` +\n          `but got axis=${axis}`);\n    }\n    const program = new CumSumProgram(x.shape, exclusive, reverse);\n    return this.compileAndRun(program, [x]);\n  }\n\n  equal(a: Tensor, b: Tensor): Tensor {\n    const program = new BinaryOpProgram(binaryop_gpu.EQUAL, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, 'bool');\n    return this.compileAndRun(program, [a, b], output);\n  }\n\n  notEqual(a: Tensor, b: Tensor): Tensor {\n    const program =\n        new BinaryOpProgram(binaryop_gpu.NOT_EQUAL, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, 'bool');\n    return this.compileAndRun(program, [a, b], output);\n  }\n\n  less(a: Tensor, b: Tensor): Tensor {\n    if (this.shouldExecuteOnCPU([a, b])) {\n      return this.cpuBackend.less(a, b);\n    }\n\n    const program = new BinaryOpProgram(binaryop_gpu.LESS, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, 'bool');\n    return this.compileAndRun(program, [a, b], output);\n  }\n\n  lessEqual(a: Tensor, b: Tensor): Tensor {\n    const program =\n        new BinaryOpProgram(binaryop_gpu.LESS_EQUAL, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, 'bool');\n    return this.compileAndRun(program, [a, b], output);\n  }\n\n  greater(a: Tensor, b: Tensor): Tensor {\n    if (this.shouldExecuteOnCPU([a, b])) {\n      return this.cpuBackend.greater(a, b);\n    }\n\n    const program = new BinaryOpProgram(binaryop_gpu.GREATER, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, 'bool');\n    return this.compileAndRun(program, [a, b], output);\n  }\n\n  greaterEqual(a: Tensor, b: Tensor): Tensor {\n    const program =\n        new BinaryOpProgram(binaryop_gpu.GREATER_EQUAL, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, 'bool');\n    return this.compileAndRun(program, [a, b], output);\n  }\n\n  logicalNot<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.LOGICAL_NOT);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  logicalAnd(a: Tensor, b: Tensor): Tensor {\n    const program =\n        new BinaryOpProgram(binaryop_gpu.LOGICAL_AND, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, 'bool');\n    return this.compileAndRun(program, [a, b], output);\n  }\n\n  logicalOr(a: Tensor, b: Tensor): Tensor {\n    const program =\n        new BinaryOpProgram(binaryop_gpu.LOGICAL_OR, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, 'bool');\n    return this.compileAndRun(program, [a, b], output);\n  }\n\n  select(condition: Tensor, a: Tensor, b: Tensor): Tensor {\n    const program = new SelectProgram(condition.rank, a.shape, a.rank);\n    const output =\n        this.makeOutputArray(program.outputShape, upcastType(a.dtype, b.dtype));\n    return this.compileAndRun(program, [condition, a, b], output);\n  }\n\n  where(condition: Tensor): Tensor2D {\n    warn(\n        'tf.where() in webgl locks the UI thread. ' +\n        'Call tf.whereAsync() instead');\n    const condVals = condition.dataSync();\n    return whereImpl(condition.shape, condVals);\n  }\n\n  topk<T extends Tensor>(x: T, k: number, sorted: boolean): [T, T] {\n    const xVals = x.dataSync();\n    return topkImpl(xVals, x.shape, x.dtype as NumericDataType, k, sorted);\n  }\n\n  min(x: Tensor, axes: number[]): Tensor {\n    axis_util.assertAxesAreInnerMostDims('min', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const inSize = util.sizeFromShape(reduceShape);\n    const a2D = x.as2D(-1, inSize);\n    return this.reduce(a2D, 'min', a2D.dtype).reshape(outShape);\n  }\n\n  minimum(a: Tensor, b: Tensor): Tensor {\n    if (this.shouldExecuteOnCPU([a, b])) {\n      return this.cpuBackend.minimum(a, b);\n    }\n\n    const program = new BinaryOpProgram(binaryop_gpu.MIN, a.shape, b.shape);\n    return this.compileAndRun(program, [a, b]);\n  }\n\n  mod(a: Tensor, b: Tensor): Tensor {\n    const program = new BinaryOpProgram(binaryop_gpu.MOD, a.shape, b.shape);\n    const customSetup = program.getCustomSetupFunc();\n    return this.compileAndRun(program, [a, b], null, customSetup);\n  }\n\n  max(x: Tensor, axes: number[]): Tensor {\n    axis_util.assertAxesAreInnerMostDims('max', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const inSize = util.sizeFromShape(reduceShape);\n    const a2D = x.as2D(-1, inSize);\n    return this.reduce(a2D, 'max', a2D.dtype).reshape(outShape);\n  }\n\n  maximum(a: Tensor, b: Tensor): Tensor {\n    if (this.shouldExecuteOnCPU([a, b])) {\n      return this.cpuBackend.maximum(a, b);\n    }\n\n    const program = new BinaryOpProgram(binaryop_gpu.MAX, a.shape, b.shape);\n    return this.compileAndRun(program, [a, b]);\n  }\n\n  all(x: Tensor, axes: number[]): Tensor {\n    axis_util.assertAxesAreInnerMostDims('all', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const inSize = util.sizeFromShape(reduceShape);\n    const a2D = x.as2D(-1, inSize);\n    return this.reduce(a2D, 'all', a2D.dtype).reshape(outShape);\n  }\n\n  any(x: Tensor, axes: number[]): Tensor {\n    axis_util.assertAxesAreInnerMostDims('any', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const inSize = util.sizeFromShape(reduceShape);\n    const a2D = x.as2D(-1, inSize);\n    return this.reduce(a2D, 'any', a2D.dtype).reshape(outShape);\n  }\n\n  squaredDifference(a: Tensor, b: Tensor): Tensor {\n    const program =\n        new BinaryOpProgram(binaryop_gpu.SQUARED_DIFFERENCE, a.shape, b.shape);\n    return this.compileAndRun(program, [a, b]);\n  }\n\n  realDivide(a: Tensor, b: Tensor): Tensor {\n    const op = binaryop_gpu.DIV;\n    const outputDtype = 'float32';\n    const program = new BinaryOpProgram(op, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, outputDtype);\n    return this.compileAndRun<Tensor>(program, [a, b], output);\n  }\n\n  floorDiv(a: Tensor, b: Tensor): Tensor {\n    const op = binaryop_gpu.INT_DIV;\n    const outputDtype = 'int32';\n    const program = new BinaryOpProgram(op, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, outputDtype);\n    return this.compileAndRun<Tensor>(program, [a, b], output);\n  }\n\n  add(a: Tensor, b: Tensor): Tensor {\n    if (a.dtype === 'complex64' && b.dtype === 'complex64') {\n      return this.complexSeparableBinaryOp(a, b, binaryop_gpu.ADD);\n    }\n\n    const program = new BinaryOpProgram(binaryop_gpu.ADD, a.shape, b.shape);\n    const output =\n        this.makeOutputArray(\n            program.outputShape, upcastType(a.dtype, b.dtype)) as Tensor;\n    return this.compileAndRun<Tensor>(program, [a, b], output);\n  }\n\n  /**\n   * Computes a complex binary operation that can be decomposed into a simple\n   * binary operation on both the real and imagary parts.\n   */\n  private complexSeparableBinaryOp(a: Tensor, b: Tensor, op: string): Tensor {\n    const aData = this.texData.get(a.dataId);\n    const bData = this.texData.get(b.dataId);\n\n    const [real, imag] = [\n      [aData.complexTensors.real, bData.complexTensors.real],\n      [aData.complexTensors.imag, bData.complexTensors.imag]\n    ].map(complexParts => {\n      const [aPart, bPart] = complexParts;\n\n      const program = new BinaryOpProgram(op, a.shape, b.shape);\n      const output = this.makeOutputArray(\n                         program.outputShape,\n                         upcastType(aPart.dtype, bPart.dtype)) as Tensor;\n\n      const aHandle = this.makeComplexComponentTensorHandle(a, aPart);\n      const bHandle = this.makeComplexComponentTensorHandle(b, bPart);\n\n      return this.compileAndRun<Tensor>(program, [aHandle, bHandle], output);\n    });\n\n    const complex = this.complex(real, imag);\n    real.dispose();\n    imag.dispose();\n    return complex;\n  }\n\n  // Returns a TensorHandle with the complex shape and the dataId of the\n  // underlying part. We need to do this because a reshaped complex tensor is\n  // not reflected in its parts.\n  private makeComplexComponentTensorHandle(\n      complexTensor: Tensor, complexPart: Tensor): TensorHandle {\n    return {\n      dataId: complexPart.dataId,\n      dtype: complexPart.dtype,\n      shape: complexTensor.shape\n    };\n  }\n\n  addN<T extends Tensor>(tensors: T[]): T {\n    let res = tensors[0];\n    for (let i = 1; i < tensors.length; i++) {\n      res = this.add(res, tensors[i]) as T;\n    }\n    return res;\n  }\n\n  subtract(a: Tensor, b: Tensor): Tensor {\n    if (a.dtype === 'complex64' && b.dtype === 'complex64') {\n      return this.complexSeparableBinaryOp(a, b, binaryop_gpu.SUB);\n    }\n\n    if (this.shouldExecuteOnCPU([a, b])) {\n      return this.cpuBackend.subtract(a, b);\n    }\n\n    const program = new BinaryOpProgram(binaryop_gpu.SUB, a.shape, b.shape);\n    const output =\n        this.makeOutputArray(\n            program.outputShape, upcastType(a.dtype, b.dtype)) as Tensor;\n    return this.compileAndRun<Tensor>(program, [a, b], output);\n  }\n\n  pow<T extends Tensor>(a: T, b: Tensor): T {\n    const program = new BinaryOpProgram(binaryop_gpu.POW, a.shape, b.shape);\n    const customSetup = program.getCustomSetupFunc();\n    const output = this.makeOutputArray(\n                       program.outputShape, upcastType(a.dtype, b.dtype)) as T;\n    return this.compileAndRun<T>(program, [a, b], output, customSetup);\n  }\n\n  ceil<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.CEIL);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  floor<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.FLOOR);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  sign<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.SIGN);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  round<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ROUND);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  exp<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.EXP);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  expm1<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.EXPM1);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  log<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.LOG);\n    const customSetup = program.getCustomSetupFunc();\n    return this.compileAndRun(program, [x], null, customSetup) as T;\n  }\n\n  log1p<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.LOG1P);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  sqrt<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.SQRT);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  rsqrt<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.RSQRT);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  square<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.SQUARE);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  reciprocal<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.RECIPROCAL);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  relu<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.RELU);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  elu<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ELU);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  eluDer<T extends Tensor>(dy: T, y: T): T {\n    const program =\n        new BinaryOpProgram(binaryop_gpu.ELU_DER, dy.shape, y.shape);\n    return this.compileAndRun(program, [dy, y]) as T;\n  }\n\n  selu<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.SELU);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  int<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.TO_INT);\n    const output = this.makeOutputArray(program.outputShape, 'int32');\n    return this.compileAndRun(program, [x], output) as T;\n  }\n\n  clip<T extends Tensor>(x: T, min: number, max: number): T {\n    let program;\n    if (ENV.get('WEBGL_PACK_CLIP')) {\n      program = new ClipPackedProgram(x.shape, min, max);\n    } else {\n      program = new ClipProgram(x.shape, min, max);\n    }\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  abs<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ABS);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  complexAbs<T extends Tensor>(x: T): T {\n    const xData = this.texData.get(x.dataId);\n\n    const program = new ComplexAbsProgram(x.shape);\n    const inputs = [\n      this.makeComplexComponentTensorHandle(x, xData.complexTensors.real),\n      this.makeComplexComponentTensorHandle(x, xData.complexTensors.imag),\n    ];\n\n    return this.compileAndRun<Tensor>(program, inputs) as T;\n  }\n\n  sigmoid<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.SIGMOID);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  softplus<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.SOFTPLUS);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  sin<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.SIN);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  cos<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.COS);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  tan<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.TAN);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  asin<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ASIN);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  acos<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ACOS);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  atan<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ATAN);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  atan2<T extends Tensor>(a: T, b: T): T {\n    const program = new BinaryOpProgram(binaryop_gpu.ATAN2, a.shape, b.shape);\n    return this.compileAndRun(program, [a, b]) as T;\n  }\n\n  sinh<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.SINH);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  cosh<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.COSH);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  tanh<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.TANH);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  asinh<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ASINH);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  acosh<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ACOSH);\n    const customSetup = program.getCustomSetupFunc();\n    return this.compileAndRun(program, [x], null, customSetup) as T;\n  }\n\n  atanh<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ATANH);\n    const customSetup = program.getCustomSetupFunc();\n    return this.compileAndRun(program, [x], null, customSetup) as T;\n  }\n\n  erf<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ERF);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  step<T extends Tensor>(x: T, alpha: number): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.STEP(alpha));\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  conv2dWithIm2Row(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    // Rearranges conv2d input so each block to be convolved over forms the\n    // column of a new matrix with shape [filterWidth * filterHeight *\n    // inChannels, outHeight * outWidth]. The filter is also rearranged so each\n    // output channel forms a row of a new matrix with shape [outChannels,\n    // filterWidth * filterHeight * inChannels]. The convolution is then\n    // computed by multiplying these matrices and reshaping the result.\n    const {\n      filterWidth,\n      filterHeight,\n      inChannels,\n      outWidth,\n      outHeight,\n    } = convInfo;\n\n    const sharedDim = filterWidth * filterHeight * inChannels;\n    const numCols = outHeight * outWidth;\n    const x2ColShape = [sharedDim, numCols];\n\n    const xSqueezed = x.squeeze([0]);\n    const w2Row = filter.reshape([sharedDim, -1]) as Tensor2D;\n\n    const im2ColProgram =\n        new Im2ColProgram(x2ColShape, xSqueezed.shape, convInfo);\n    const im2Col = this.compileAndRun<Tensor2D>(im2ColProgram, [xSqueezed]);\n\n    const matmulProgram = new MatMulPackedProgram(\n        im2Col.shape, w2Row.shape, [numCols, convInfo.outChannels], true,\n        false);\n    const product =\n        this.compileAndRun<Tensor4D>(matmulProgram, [im2Col, w2Row]);\n\n    return product.reshape([1, outHeight, outWidth, convInfo.outChannels]);\n  }\n\n  conv2d(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    if (ENV.get('WEBGL_CONV_IM2COL') && x.shape[0] === 1) {\n      return this.conv2dWithIm2Row(x, filter, convInfo);\n    }\n    const program = new Conv2DProgram(convInfo);\n    return this.compileAndRun(program, [x, filter]);\n  }\n\n  conv2dDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    const program = new Conv2DDerInputProgram(convInfo);\n    return this.compileAndRun(program, [dy, filter]);\n  }\n\n  conv2dDerFilter(x: Tensor4D, dy: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    const program = new Conv2DDerFilterProgram(convInfo);\n    return this.compileAndRun(program, [x, dy]);\n  }\n\n  depthwiseConv2D(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    let program: DepthwiseConv2DProgram|DepthwiseConvPacked2DProgram;\n    if (ENV.get('WEBGL_PACK_DEPTHWISECONV') && convInfo.dilationWidth === 1 &&\n        convInfo.dilationHeight === 1 && convInfo.padInfo.left <= 1 &&\n        convInfo.strideWidth <= 2 &&\n        convInfo.outChannels / convInfo.inChannels === 1) {\n      program = new DepthwiseConvPacked2DProgram(convInfo);\n      return this.compileAndRun(\n          program, [x, filter],\n          this.makePackedTensor(convInfo.outShape, x.dtype));\n    }\n\n    program = new DepthwiseConv2DProgram(convInfo);\n    return this.compileAndRun(program, [x, filter]);\n  }\n\n  depthwiseConv2DDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    const program = new DepthwiseConv2DDerInputProgram(convInfo);\n    return this.compileAndRun(program, [dy, filter]);\n  }\n\n  depthwiseConv2DDerFilter(x: Tensor4D, dy: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    const program = new DepthwiseConv2DDerFilterProgram(convInfo);\n    return this.compileAndRun(program, [x, dy]);\n  }\n\n  conv3d(x: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n    const program = new Conv3DProgram(convInfo);\n    return this.compileAndRun(program, [x, filter]);\n  }\n\n  conv3dDerInput(dy: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo):\n      Tensor5D {\n    const program = new Conv3DDerInputProgram(convInfo);\n    return this.compileAndRun(program, [dy, filter]);\n  }\n\n  conv3dDerFilter(x: Tensor5D, dy: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n    const program = new Conv3DDerFilterProgram(convInfo);\n    return this.compileAndRun(program, [x, dy]);\n  }\n\n  maxPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    const program = new Pool2DProgram(convInfo, 'max', false);\n    const output =\n        this.makeOutputArray(program.outputShape, x.dtype) as Tensor4D;\n    return this.compileAndRun(program, [x], output);\n  }\n\n  avgPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    const program = new Pool2DProgram(convInfo, 'avg', false);\n    const output = this.makeOutputArray(program.outputShape, 'float32');\n    return this.compileAndRun(program, [x], output) as Tensor4D;\n  }\n\n  maxPoolBackprop(dy: Tensor4D, x: Tensor4D, y: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    const getPositions = true;\n    const maxPoolPositionsProgram =\n        new Pool2DProgram(convInfo, 'max', getPositions);\n    const maxPoolPositions: Tensor4D =\n        this.compileAndRun(maxPoolPositionsProgram, [x]);\n\n    const maxPoolBackPropProgram = new MaxPool2DBackpropProgram(convInfo);\n    const output =\n        this.makeOutputArray(maxPoolBackPropProgram.outputShape, x.dtype);\n    const result = this.compileAndRun(\n        maxPoolBackPropProgram, [dy, maxPoolPositions], output);\n    maxPoolPositions.dispose();\n    return result as Tensor4D;\n  }\n\n  avgPoolBackprop(dy: Tensor4D, x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    const avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo);\n    const output =\n        this.makeOutputArray(avgPoolBackpropProgram.outputShape, x.dtype);\n    return this.compileAndRun(avgPoolBackpropProgram, [dy], output) as Tensor4D;\n  }\n\n  cast<T extends Tensor>(x: T, dtype: DataType): T {\n    return backend_util.castTensor(x, dtype, this);\n  }\n\n  reshape<R extends Rank>(x: Tensor, shape: ShapeMap[R]): Tensor<R> {\n    if (this.texData.get(x.dataId).isPacked &&\n        !webgl_util.isReshapeFree(x.shape, shape)) {\n      return this.packedReshape(x, shape);\n    }\n    return backend_util.reshapeTensor(x, shape);\n  }\n\n  resizeBilinear(\n      x: Tensor4D, newHeight: number, newWidth: number,\n      alignCorners: boolean): Tensor4D {\n    const program =\n        new ResizeBilinearProgram(x.shape, newHeight, newWidth, alignCorners);\n    return this.compileAndRun(program, [x]);\n  }\n\n  resizeBilinearBackprop(dy: Tensor4D, x: Tensor4D, alignCorners: boolean):\n      Tensor4D {\n    const program = new ResizeBilinearBackpropProgram(dy, x, alignCorners);\n\n    return this.compileAndRun(program, [dy]);\n  }\n\n  resizeNearestNeighbor(\n      x: Tensor4D, newHeight: number, newWidth: number,\n      alignCorners: boolean): Tensor4D {\n    const program = new ResizeNearestNeighborProgram(\n        x.shape, newHeight, newWidth, alignCorners);\n    return this.compileAndRun(program, [x]);\n  }\n\n  resizeNearestNeighborBackprop(\n      dy: Tensor4D, x: Tensor4D, alignCorners: boolean): Tensor4D {\n    const program =\n        new ResizeNearestNeigborBackpropProgram(dy, x, alignCorners);\n    return this.compileAndRun(program, [dy]);\n  }\n\n  multinomial(\n      logits: Tensor2D, normalized: boolean, numSamples: number,\n      seed: number): Tensor2D {\n    const probs = normalized ? logits : softmax(logits);\n    const batchSize = probs.shape[0];\n    const numOutcomes = probs.shape[1];\n    const program = new MultinomialProgram(batchSize, numOutcomes, numSamples);\n    const output =\n        this.makeOutputArray(program.outputShape, 'int32') as Tensor2D;\n    const customSetup = program.getCustomSetupFunc(seed);\n    return this.compileAndRun(program, [probs], output, customSetup);\n  }\n\n  oneHot(indices: Tensor1D, depth: number, onValue: number, offValue: number):\n      Tensor2D {\n    const program = new OneHotProgram(indices.size, depth, onValue, offValue);\n    return this.compileAndRun(program, [indices]);\n  }\n\n  nonMaxSuppression(\n      boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number,\n      iouThreshold: number, scoreThreshold: number): Tensor1D {\n    warn(\n        'tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n        'Call tf.nonMaxSuppressionAsync() instead');\n    const boxesVals = boxes.dataSync();\n    const scoresVals = scores.dataSync();\n    return nonMaxSuppressionImpl(\n        boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n  }\n\n  cropAndResize(\n      image: Tensor4D, boxes: Tensor2D, boxIndex: Tensor1D,\n      cropSize: [number, number], method: 'bilinear'|'nearest',\n      extrapolationValue: number): Tensor4D {\n    const program = new CropAndResizeProgram(\n        image.shape, boxes.shape, cropSize, method, extrapolationValue);\n    return this.compileAndRun(program, [image, boxes, boxIndex]);\n  }\n\n  depthToSpace(x: Tensor4D, blockSize: number, dataFormat: 'NHWC'|'NCHW'):\n      Tensor4D {\n    util.assert(\n        blockSize > 1,\n        `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n\n    const batchSize = x.shape[0];\n    const inputHeight = (dataFormat === 'NHWC') ? x.shape[1] : x.shape[2];\n    const inputWidth = (dataFormat === 'NHWC') ? x.shape[2] : x.shape[3];\n    const inputDepth = (dataFormat === 'NHWC') ? x.shape[3] : x.shape[1];\n\n    const outputHeight = inputHeight * blockSize;\n    const outputWidth = inputWidth * blockSize;\n    const outputDepth = inputDepth / (blockSize * blockSize);\n\n    const outputShape = (dataFormat === 'NHWC') ?\n        [batchSize, outputHeight, outputWidth, outputDepth] :\n        [batchSize, outputDepth, outputHeight, outputWidth];\n\n    const program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat);\n    return this.compileAndRun(program, [x]);\n  }\n\n  split<T extends Tensor>(x: T, sizeSplits: number[], axis: number): T[] {\n    return split(x, sizeSplits, axis);\n  }\n\n  scatterND<R extends Rank>(\n      indices: Tensor, updates: Tensor, shape: ShapeMap[R]): Tensor<R> {\n    const {sliceRank, numUpdates, sliceSize, strides, outputSize} =\n        scatter_nd_util.calculateShapes(updates, indices, shape);\n\n    const flattenShape = [outputSize / sliceSize, sliceSize];\n    const flattenIndices = indices.reshape([numUpdates, sliceRank]);\n    const flattenX = updates.reshape([numUpdates, sliceSize]);\n\n    if (outputSize === 0) {\n      return backend_util.reshapeTensor(tensor([]), shape);\n    }\n    const defaultValue = scalar(0);\n    const program = new ScatterProgram(\n        numUpdates, sliceRank, flattenIndices.rank, flattenX.rank, strides,\n        flattenShape);\n    return (this.compileAndRun(\n                program, [flattenX, flattenIndices, defaultValue]) as Tensor)\n        .reshape(shape);\n  }\n\n  sparseToDense<R extends Rank>(\n      sparseIndices: Tensor, sparseValues: Tensor, outputShape: ShapeMap[R],\n      defaultValue: Scalar): Tensor<R> {\n    const {sliceRank, numUpdates, strides, outputSize} =\n        scatter_nd_util.calculateShapes(\n            sparseValues, sparseIndices, outputShape);\n\n    const sumDupeIndices = false;\n    const program = new ScatterProgram(\n        numUpdates, sliceRank, sparseIndices.rank, sparseValues.rank, strides,\n        [outputSize, 1], sumDupeIndices);\n    return (this.compileAndRun(\n                program, [sparseValues, sparseIndices, defaultValue]) as Tensor)\n        .reshape(outputShape);\n  }\n\n  fft(x: Tensor2D): Tensor2D {\n    const inverse = false;\n    return this.fftImpl(x, inverse);\n  }\n\n  ifft(x: Tensor2D): Tensor2D {\n    const inverse = true;\n    return this.fftImpl(x, inverse);\n  }\n\n  private fftImpl(x: Tensor2D, inverse: boolean): Tensor2D {\n    const xData = this.texData.get(x.dataId);\n\n    const realProgram =\n        new FFTProgram(fft_gpu.COMPLEX_FFT.REAL, x.shape, inverse);\n    const imagProgram =\n        new FFTProgram(fft_gpu.COMPLEX_FFT.IMAG, x.shape, inverse);\n    const inputs = [\n      this.makeComplexComponentTensorHandle(x, xData.complexTensors.real),\n      this.makeComplexComponentTensorHandle(x, xData.complexTensors.imag),\n    ];\n\n    const real = this.compileAndRun<Tensor>(realProgram, inputs);\n    const imag = this.compileAndRun<Tensor>(imagProgram, inputs);\n    const complex = this.complex(real, imag).as2D(x.shape[0], x.shape[1]);\n    real.dispose();\n    imag.dispose();\n    return complex;\n  }\n\n  gatherND(x: Tensor, indices: Tensor): Tensor<Rank> {\n    const indicesShape = indices.shape;\n    const sliceRank = indicesShape[indicesShape.length - 1];\n\n    const [resultShape, numSlices, sliceSize, strides] =\n        gather_nd_util.prepareAndValidate(x, indices);\n\n    const flattenIndices = indices.reshape([numSlices, sliceRank]);\n    const flattenX = x.reshape([x.size / sliceSize, sliceSize]);\n    const program =\n        new GatherNDProgram(sliceRank, strides, [numSlices, sliceSize]);\n    return (this.compileAndRun(program, [flattenX, flattenIndices]) as Tensor)\n        .reshape(resultShape);\n  }\n\n  private makeOutputArray<T extends Tensor>(shape: number[], dtype: DataType):\n      T {\n    return Tensor.make(shape, {}, dtype) as T;\n  }\n\n  private makePackedTensor<T extends Tensor>(shape: number[], dtype: DataType):\n      T {\n    const packedTensor = Tensor.make(shape, {}, dtype);\n    this.texData.get(packedTensor.dataId).isPacked = true;\n    return packedTensor as T;\n  }\n\n  private unpackTensor<T extends Tensor>(input: T): T {\n    const program = new UnpackProgram(input.shape);\n    return this.compileAndRun(\n        program, [input], Tensor.make(program.outputShape, {}, input.dtype));\n  }\n\n  private getBatchDim(shape: number[], dimsToSkip = 2): number {\n    return util.sizeFromShape(shape.slice(0, shape.length - dimsToSkip));\n  }\n\n  private getRowsCols(shape: number[]): [number, number] {\n    if (shape.length === 0) {\n      throw Error('Cannot get rows and columns of an empty shape array.');\n    }\n\n    return [\n      shape.length > 1 ? shape[shape.length - 2] : 1, shape[shape.length - 1]\n    ];\n  }\n\n  private packedReshape<R extends Rank>(input: Tensor, afterShape: ShapeMap[R]):\n      Tensor<R> {\n    const inputAs3D = input.reshape(\n        [this.getBatchDim(input.shape), ...this.getRowsCols(input.shape)]);\n    const afterShapeAs3D =\n        [this.getBatchDim(afterShape), ...this.getRowsCols(afterShape)];\n    const program = new ReshapePackedProgram(\n        afterShapeAs3D as [number, number, number],\n        inputAs3D.shape as [number, number, number]);\n    return this.compileAndRun<Tensor<R>>(program, [inputAs3D])\n        .reshape(afterShape);\n  }\n\n  public compileAndRun<\n      K extends {dtype: DataType, size: number, dataId: {}, shape: number[]}>(\n      program: GPGPUProgram, inputs: TensorHandle[], output?: K,\n      customSetup?: (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => void,\n      pageToCpu = true): K {\n    if (output == null) {\n      if (program.usesPackedTextures) {\n        output = this.makePackedTensor(program.outputShape, inputs[0].dtype) as\n            {} as K;\n      } else {\n        output = this.makeOutputArray(program.outputShape, inputs[0].dtype) as\n            {} as K;\n      }\n    }\n    if (output.size === 0) {\n      // Short-circuit the computation since the result is empty (has 0 in its\n      // shape).\n      this.texData.get(output.dataId).values =\n          getTypedArrayFromDType(output.dtype as 'float32', 0);\n      return output;\n    }\n\n    const inputsData: TensorData[] = inputs.map(input => {\n      if (input.dtype === 'complex64') {\n        throw new Error(\n            `GPGPUProgram does not support complex64 input. For complex64 ` +\n            `dtypes, please separate the program into real and imaginary ` +\n            `parts.`);\n      }\n\n      let texData = this.texData.get(input.dataId);\n\n      if (texData.texture == null) {\n        if (!program.usesPackedTextures &&\n            util.sizeFromShape(input.shape) <=\n                ENV.get('WEBGL_SIZE_UPLOAD_UNIFORM')) {\n          // Upload small tensors that live on the CPU as uniforms, not as\n          // textures. Do this only when the environment supports 32bit floats\n          // due to problems when comparing 16bit floats with 32bit floats.\n          // TODO(https://github.com/tensorflow/tfjs/issues/821): Make it\n          // possible for packed shaders to sample from uniforms.\n          return {\n            shape: input.shape,\n            texData: null,\n            isUniform: true,\n            uniformValues: this.readSync(input.dataId) as TypedArray\n          };\n        }\n\n        // This ensures that if a packed program's inputs have not yet been\n        // uploaded to the GPU, they get uploaded as packed right off the bat.\n        if (program.usesPackedTextures) {\n          texData.isPacked = true;\n          texData.shape = input.shape;\n        }\n      } else if (!!texData.isPacked !== !!program.usesPackedTextures) {\n        let preProcessProgram: UnpackProgram|PackProgram;\n        let processedInput: Tensor;\n\n        // Explicitly specifying output tensors because compileAndRun assumes\n        // that programs that take packed inputs produce a packed output.\n        if (texData.isPacked) {\n          preProcessProgram = new UnpackProgram(input.shape);\n          processedInput = this.compileAndRun(\n              preProcessProgram, [input],\n              Tensor.make(preProcessProgram.outputShape, {}, input.dtype));\n        } else {\n          preProcessProgram = new PackProgram(input.shape);\n          processedInput = this.compileAndRun(\n              preProcessProgram, [input],\n              this.makePackedTensor(input.shape, input.dtype));\n        }\n\n        texData = this.texData.get(processedInput.dataId);\n        input = processedInput;\n      } else if (\n          texData.isPacked &&\n          !webgl_util.isReshapeFree(texData.shape, input.shape)) {\n        // This is a special, temporary case where a texture exists for a tensor\n        // but the shapes are incompatible (due to packing constraints) because\n        // the tensor did not have a chance to go through the packed reshape\n        // shader. This only happens when we reshape the *same* tensor to form\n        // *distinct* inputs to an op, e.g. dotting a vector with itself. This\n        // case will disappear once packed uploading is the default.\n\n        // Temporarily disable delayedStorage so the texture isn't removed from\n        // the original input\n        this.delayedStorage = false;\n        const inputValues = (input as Tensor).dataSync();\n        this.delayedStorage = true;\n\n        input = Tensor.make(input.shape, {values: inputValues}, input.dtype);\n        texData = this.texData.get(input.dataId);\n        texData.isPacked = true;\n      }\n\n      this.uploadToGPU(input.dataId);\n      return {shape: input.shape, texData, isUniform: false};\n    });\n\n    this.uploadToGPU(output.dataId);\n    const outputData = {\n      shape: output.shape,\n      texData: this.texData.get(output.dataId),\n      isUniform: false\n    };\n    const key = gpgpu_math.makeShaderKey(program, inputsData, outputData);\n    const binary = this.getAndSaveBinary(key, () => {\n      return gpgpu_math.compileProgram(\n          this.gpgpu, program, inputsData, outputData);\n    });\n    const shouldTimeProgram = this.activeTimers != null;\n    let query: WebGLQuery|CPUTimerQuery;\n    if (shouldTimeProgram) {\n      query = this.startTimer();\n    }\n\n    gpgpu_math.runProgram(binary, inputsData, outputData, customSetup);\n\n    if (ENV.get('WEBGL_PAGING_ENABLED') && pageToCpu &&\n        this.numBytesInGPU > this.NUM_BYTES_BEFORE_PAGING) {\n      let numBytesToPage = this.numBytesInGPU - this.NUM_BYTES_BEFORE_PAGING;\n      while (numBytesToPage > 0 && this.lruDataGPU.length > 0) {\n        const dataId = this.lruDataGPU.shift();\n        const {shape, dtype} = this.texData.get(dataId);\n        numBytesToPage -= this.computeBytes(shape, dtype);\n        this.read(dataId);\n      }\n    }\n\n    if (shouldTimeProgram) {\n      query = this.endTimer(query);\n      this.activeTimers.push(\n          {name: program.constructor.name, query: this.getQueryTime(query)});\n    }\n\n    if (!ENV.get('WEBGL_LAZILY_UNPACK') &&\n        this.texData.get(output.dataId).isPacked && !program.isPackShader) {\n      return this.unpackTensor(output as {} as Tensor) as {} as K;\n    }\n    return output;\n  }\n\n  private getAndSaveBinary(key: string, getBinary: () => GPGPUBinary):\n      GPGPUBinary {\n    if (!(key in this.binaryCache)) {\n      this.binaryCache[key] = getBinary();\n    }\n    return this.binaryCache[key];\n  }\n\n  getTextureManager(): TextureManager {\n    return this.textureManager;\n  }\n\n  private disposed = false;\n\n  dispose() {\n    if (this.disposed) {\n      return;\n    }\n    for (const key in this.binaryCache) {\n      this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram);\n    }\n    this.textureManager.dispose();\n    this.canvas.remove();\n    if (this.fromPixels2DContext != null) {\n      this.fromPixels2DContext.canvas.remove();\n    }\n    if (this.gpgpuCreatedLocally) {\n      this.gpgpu.dispose();\n    }\n    this.disposed = true;\n  }\n\n  floatPrecision(): number {\n    return tidy(() => {\n      if (this.abs(scalar(1e-8)).get() > 0) {\n        return 32;\n      }\n      return 16;\n    });\n  }\n\n  private uploadToGPU(dataId: DataId): void {\n    const texData = this.texData.get(dataId);\n    const {shape, values, texture, usage, isPacked} = texData;\n    if (texture != null) {\n      // Array is already on GPU. No-op.\n      // Touching the texture.\n      if (ENV.get('WEBGL_PAGING_ENABLED')) {\n        const index = this.lruDataGPU.indexOf(dataId);\n        if (index >= 0) {\n          this.lruDataGPU.splice(this.lruDataGPU.indexOf(dataId), 1);\n          this.lruDataGPU.push(dataId);\n        }\n      }\n      return;\n    }\n    const shouldTimeProgram = this.activeTimers != null;\n    let start: number;\n    if (shouldTimeProgram) {\n      start = performance.now();\n    }\n    const texShape =\n        webgl_util.getTextureShapeFromLogicalShape(shape, isPacked);\n    texData.texShape = texShape;\n    const newTexture = this.acquireTexture(dataId, texShape, usage, isPacked);\n    texData.texture = newTexture;\n    if (values != null) {\n      // TODO(smilkov): Propagate the original typed array to gpgpu.\n      if (isPacked) {\n        const batch = this.getBatchDim(shape);\n        let rows = 1, cols = 1;\n        if (shape.length) {\n          [rows, cols] = this.getRowsCols(shape);\n        }\n        this.gpgpu.uploadMatrixToPackedTexture(\n            newTexture, batch, rows, cols, texShape[0], texShape[1],\n            typedArrayToFloat32(values as Float32Array));\n      } else {\n        this.gpgpu.uploadMatrixToTexture(\n            newTexture, texShape[0], texShape[1],\n            typedArrayToFloat32(values as Float32Array));\n      }\n      // Once uploaded, don't store the values on cpu.\n      texData.values = null;\n      if (shouldTimeProgram) {\n        this.uploadWaitMs += performance.now() - start;\n      }\n    }\n  }\n\n  private convertAndCacheOnCPU(dataId: DataId, float32Values?: Float32Array):\n      TypedArray {\n    // In delayed storage mode, when the user reads data, we don't keep a\n    // copy on the gpu, to minimize likelihood of memory leak. We re-upload\n    // to gpu the next time a gpgpu program needs the texture.\n    const dontKeepCopyOnGPU = this.delayedStorage;\n    const texData = this.texData.get(dataId);\n    const {texture, texShape, dtype, usage, isPacked} = texData;\n    if (dontKeepCopyOnGPU && texture != null) {\n      this.releaseTexture(dataId, texture, texShape, usage, isPacked);\n      texData.texture = null;\n      texData.texShape = null;\n    }\n    texData.usage = TextureUsage.UPLOAD;\n    if (float32Values != null) {\n      texData.values = float32ToTypedArray(float32Values, dtype as 'float32');\n    }\n    return texData.values as TypedArray;\n  }\n\n  private releaseTexture(\n      dataId: DataId, texture: WebGLTexture, texShape: [number, number],\n      texType: TextureUsage, isPacked: boolean) {\n    const {shape, dtype} = this.texData.get(dataId);\n\n    if (ENV.get('WEBGL_PAGING_ENABLED')) {\n      const idx = this.lruDataGPU.indexOf(dataId);\n      if (idx >= 0) {\n        this.lruDataGPU.splice(idx, 1);\n      }\n    }\n    this.numBytesInGPU -= this.computeBytes(shape, dtype);\n    this.textureManager.releaseTexture(texture, texShape, texType, isPacked);\n  }\n\n  private acquireTexture(\n      dataId: DataId, texShape: [number, number], texType: TextureUsage,\n      isPacked: boolean): WebGLTexture {\n    const {shape, dtype} = this.texData.get(dataId);\n    if (ENV.get('WEBGL_PAGING_ENABLED')) {\n      this.lruDataGPU.push(dataId);\n    }\n    this.numBytesInGPU += this.computeBytes(shape, dtype);\n    return this.textureManager.acquireTexture(texShape, texType, isPacked);\n  }\n\n  private computeBytes(shape: number[], dtype: DataType) {\n    return util.sizeFromShape(shape) * util.bytesPerElement(dtype);\n  }\n}\n\nif (ENV.get('IS_BROWSER')) {\n  ENV.registerBackend(\n      'webgl', () => new MathBackendWebGL(), 2 /* priority */,\n      setTensorTracker);\n}\n\nfunction float32ToTypedArray<D extends NumericDataType>(\n    a: Float32Array, dtype: D): DataTypeMap[D] {\n  if (dtype === 'float32' || dtype === 'complex64') {\n    return a;\n  } else if (dtype === 'int32' || dtype === 'bool') {\n    const result = (dtype === 'int32') ? new Int32Array(a.length) :\n                                         new Uint8Array(a.length);\n    for (let i = 0; i < result.length; ++i) {\n      result[i] = Math.round(a[i]);\n    }\n    return result;\n  } else {\n    throw new Error(`Unknown dtype ${dtype}`);\n  }\n}\n\nfunction typedArrayToFloat32(a: TypedArray): Float32Array {\n  return (a instanceof Float32Array) ? a : new Float32Array(a);\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\nimport {scalar, zerosLike} from './tensor_ops';\n\n/**\n * Computes `-1 * x` element-wise.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, -2, 0], [2, 2]);\n *\n * x.neg().print();  // or tf.neg(x)\n * ```\n *\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction neg_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'neg');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.neg()};\n  };\n  return ENV.engine.runKernel(backend => backend.neg($x), {$x}, grad);\n}\n\n/**\n * Computes ceiling of input `tf.Tensor` element-wise: `ceil(x)`\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.ceil().print();  // or tf.ceil(x)\n * ```\n * @param x The input Tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction ceil_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'ceil');\n\n  // TODO(manrajgrover): Return null for gradients when backprop supports it.\n  const grad = (dy: T) => {\n    return {$x: () => zerosLike(dy)};\n  };\n  return ENV.engine.runKernel(backend => backend.ceil($x), {$x}, grad);\n}\n\n/**\n * Computes floor of input `tf.Tensor` element-wise: `floor(x)`.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.floor().print();  // or tf.floor(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction floor_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'floor');\n\n  // TODO(nsthorat): Let gradients be null for cases where we want to stop\n  // backpropgation.\n  const grad = (dy: T) => {\n    return {$x: () => zerosLike(dy)};\n  };\n  return ENV.engine.runKernel(backend => backend.floor($x), {$x}, grad);\n}\n\n/**\n * Returns an element-wise indication of the sign of a number.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3, NaN, 0]);\n *\n * x.sign().print();  // or tf.sign(x)\n * ```\n * @param x The input Tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction sign_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'sign');\n\n  const grad = (dy: T) => {\n    return {$x: () => zerosLike(dy)};\n  };\n  return ENV.engine.runKernel(backend => backend.sign($x), {$x}, grad);\n}\n\n/**\n * Computes round of input `tf.Tensor` element-wise: `round(x)`.\n * It implements banker's rounding.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.round().print();  // or tf.round(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction round_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'round');\n\n  // TODO(nsthorat): Let gradients be null for cases where we want to stop\n  // backpropgation.\n  const grad = (dy: T) => {\n    return {$x: () => zerosLike(dy)};\n  };\n  return ENV.engine.runKernel(backend => backend.round($x), {$x}, grad);\n}\n\n/**\n * Computes exponential of the input `tf.Tensor` element-wise. `e ^ x`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.exp().print();  // or tf.exp(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction exp_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'exp');\n\n  const bck = (dy: T, saved: Tensor[]) => {\n    const [y] = saved;\n    return {$x: () => dy.mulStrict(y as T)};\n  };\n  return ENV.engine.runKernel(\n      (backend, save) => save(backend.exp($x)), {$x}, bck);\n}\n\n/**\n * Computes exponential of the input `tf.Tensor` minus one element-wise.\n * `e ^ x - 1`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.expm1().print();  // or tf.expm1(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction expm1_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'expm1');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.mulStrict($x.exp())};\n  };\n  return ENV.engine.runKernel(backend => backend.expm1($x), {$x}, grad);\n}\n\n/**\n * Computes natural logarithm of the input `tf.Tensor` element-wise: `ln(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E]);\n *\n * x.log().print();  // or tf.log(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction log_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'log');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.divStrict($x.toFloat())};\n  };\n  return ENV.engine.runKernel(backend => backend.log($x), {$x}, grad);\n}\n\n/**\n * Computes natural logarithm of the input `tf.Tensor` plus one\n * element-wise: `ln(1 + x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E - 1]);\n *\n * x.log1p().print();  // or tf.log1p(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction log1p_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'log1p');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.div($x.add(1)) as T};\n  };\n  return ENV.engine.runKernel(backend => backend.log1p($x), {$x}, grad);\n}\n\n/**\n * Computes square root of the input `tf.Tensor` element-wise: `y = sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.sqrt().print();  // or tf.sqrt(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction sqrt_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'sqrt');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.div($x.toFloat().sqrt().mul(2)) as T};\n  };\n  return ENV.engine.runKernel(backend => backend.sqrt($x), {$x}, grad);\n}\n\n/**\n * Computes reciprocal of square root of the input `tf.Tensor` element-wise:\n * `y = 1 / sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.rsqrt().print();  // or tf.rsqrt(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction rsqrt_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'rsqrt');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.div($x.pow(1.5).mul(2)).neg() as T};\n  };\n  return ENV.engine.runKernel(backend => backend.rsqrt($x), {$x}, grad);\n}\n\n/**\n * Computes square of `x` element-wise: `x ^ 2`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.sqrt(2), -1]);\n *\n * x.square().print();  // or tf.square(x)\n * ```\n * @param x The input Tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction square_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'square');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.mul($x.toFloat().mul(2)) as T};\n  };\n  return ENV.engine.runKernel(backend => backend.square($x), {$x}, grad);\n}\n\n/**\n * Computes reciprocal of x element-wise: `1 / x`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, 2]);\n *\n * x.reciprocal().print();  // or tf.reciprocal(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction reciprocal_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'reciprocal');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.divStrict($x.square().neg())};\n  };\n  return ENV.engine.runKernel(backend => backend.reciprocal($x), {$x}, grad);\n}\n\n/**\n * Computes absolute value element-wise: `abs(x)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.abs().print();  // or tf.abs(x)\n * ```\n * @param x The input `tf.Tensor`.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction abs_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'abs');\n\n  if ($x.dtype === 'complex64') {\n    return ENV.engine.runKernel(backend => backend.complexAbs($x), {$x});\n  }\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.mulStrict($x.toFloat().step(-1))};\n  };\n  return ENV.engine.runKernel(backend => backend.abs($x), {$x}, grad);\n}\n\n/**\n * Clips values element-wise. `max(min(x, clipValueMax), clipValueMin)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.clipByValue(-2, 3).print();  // or tf.clipByValue(x, -2, 3)\n * ```\n * @param x The input tensor.\n * @param clipValueMin Lower-bound of range to be clipped to.\n * @param clipValueMax Upper-bound of range to be clipped to.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction clipByValue_<T extends Tensor>(\n    x: T|TensorLike, clipValueMin: number, clipValueMax: number): T {\n  const $x = convertToTensor(x, 'x', 'clipByValue');\n  util.assert(\n      (clipValueMin <= clipValueMax),\n      `Error in clip: min (${clipValueMin}) must be ` +\n          `less than or equal to max (${clipValueMax}).`);\n\n  const grad = (dy: T) => {\n    return {\n      $x: () => dy.where(\n                    $x.greaterEqual(clipValueMin)\n                        .logicalAnd($x.lessEqual(clipValueMax)),\n                    zerosLike(dy)) as T,\n    };\n  };\n  return ENV.engine.runKernel(\n      backend => backend.clip($x, clipValueMin, clipValueMax), {$x}, grad);\n}\n\n/**\n * Computes sigmoid element-wise, `1 / (1 + exp(-x))`\n *\n * ```js\n * const x = tf.tensor1d([0, -1, 2, -3]);\n *\n * x.sigmoid().print();  // or tf.sigmoid(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction sigmoid_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'sigmoid');\n\n  const grad = (dy: T, saved: Tensor[]) => {\n    const [y] = saved;\n    return {$x: () => dy.mul(y.mul(scalar(1).sub(y))) as T};\n  };\n  return ENV.engine.runKernel(\n      (backend, save) => save(backend.sigmoid($x)), {$x}, grad);\n}\n\n/**\n * Computes log sigmoid of the input `tf.Tensor` element-wise:\n * `logSigmoid(x)`. For numerical stability, we use `-tf.softplus(-x)`.\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.logSigmoid().print();  // or tf.logSigmoid(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction logSigmoid_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'logSigmoid');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.mulStrict($x.neg().sigmoid())};\n  };\n  return ENV.engine.runKernel(\n      backend => backend.softplus($x.neg()).neg(), {$x}, grad);\n}\n\n/**\n * Computes softplus of the input `tf.Tensor` element-wise: `log(exp(x) + 1)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.softplus().print();  // or tf.softplus(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction softplus_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'softplus');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.mulStrict($x.sigmoid())};\n  };\n  return ENV.engine.runKernel(backend => backend.softplus($x), {$x}, grad);\n}\n\n/**\n * Computes sin of the input Tensor element-wise: `sin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.sin().print();  // or tf.sin(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction sin_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'sin');\n\n  const grad = (dy: T) => {\n    return {$x: () => $x.toFloat().cos().mulStrict(dy)};\n  };\n  return ENV.engine.runKernel(backend => backend.sin($x), {$x}, grad);\n}\n\n/**\n * Computes cos of the input `tf.Tensor` element-wise: `cos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.cos().print();  // or tf.cos(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction cos_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'cos');\n\n  const grad = (dy: T) => {\n    return {$x: () => $x.toFloat().sin().neg().mulStrict(dy)};\n  };\n  return ENV.engine.runKernel(backend => backend.cos($x), {$x}, grad);\n}\n\n/**\n * Computes tan of the input `tf.Tensor` element-wise, `tan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.tan().print();  // or tf.tan(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction tan_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'tan');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.divStrict($x.cos().square())};\n  };\n  return ENV.engine.runKernel(backend => backend.tan($x), {$x}, grad);\n}\n\n/**\n * Computes asin of the input `tf.Tensor` element-wise: `asin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asin().print();  // or tf.asin(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction asin_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'asin');\n\n  const grad = (dy: T) => {\n    return {\n      $x: () => dy.divStrict(scalar(1).sub($x.toFloat().square()).sqrt() as T)\n    };\n  };\n  return ENV.engine.runKernel(backend => backend.asin($x), {$x}, grad);\n}\n\n/**\n * Computes acos of the input `tf.Tensor` element-wise: `acos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.acos().print();  // or tf.acos(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction acos_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'acos');\n\n  const grad = (dy: T) => {\n    return {\n      $x: () =>\n          dy.divStrict(scalar(1).sub($x.toFloat().square()).sqrt() as T).neg()\n    };\n  };\n  return ENV.engine.runKernel(backend => backend.acos($x), {$x}, grad);\n}\n\n/**\n * Computes atan of the input `tf.Tensor` element-wise: `atan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.atan().print();  // or tf.atan(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction atan_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'atan');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.div($x.toFloat().square().add(1)) as T};\n  };\n  return ENV.engine.runKernel(backend => backend.atan($x), {$x}, grad);\n}\n\n/**\n * Computes hyperbolic sin of the input `tf.Tensor` element-wise: `sinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.sinh().print();  // or tf.sinh(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction sinh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'sinh');\n\n  const grad = (dy: T) => {\n    return {$x: () => $x.toFloat().cosh().mulStrict(dy)};\n  };\n  return ENV.engine.runKernel(backend => backend.sinh($x), {$x}, grad);\n}\n\n/**\n * Computes hyperbolic cos of the input `tf.Tensor` element-wise: `cosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.cosh().print();  // or tf.cosh(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction cosh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'cosh');\n\n  const grad = (dy: T) => {\n    return {$x: () => $x.toFloat().sinh().mulStrict(dy)};\n  };\n  return ENV.engine.runKernel(backend => backend.cosh($x), {$x}, grad);\n}\n\n/**\n * Computes hyperbolic tangent of the input `tf.Tensor` element-wise: `tanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, 70]);\n *\n * x.tanh().print();  // or tf.tanh(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction tanh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'tanh');\n\n  const grad = (dy: T, saved: Tensor[]) => {\n    const [y] = saved;\n    return {$x: () => scalar(1).sub(y.square()).mulStrict(dy) as T};\n  };\n  return ENV.engine.runKernel(\n      (backend, save) => save(backend.tanh($x)), {$x}, grad);\n}\n\n/**\n * Computes inverse hyperbolic sin of the input `tf.Tensor` element-wise:\n * `asinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asinh().print();  // or tf.asinh(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction asinh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'asinh');\n\n  const grad = (dy: T) => {\n    return {\n      $x: () => dy.divStrict(scalar(1).add($x.toFloat().square()).sqrt() as T)\n    };\n  };\n  return ENV.engine.runKernel(backend => backend.asinh($x), {$x}, grad);\n}\n\n/**\n * Computes the inverse hyperbolic cos of the input `tf.Tensor` element-wise:\n * `acosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([10, 1, 3, 5.7]);\n *\n * x.acosh().print();  // or tf.acosh(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction acosh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'acosh');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.divStrict($x.toFloat().square().sub(1).sqrt() as T)};\n  };\n  return ENV.engine.runKernel(backend => backend.acosh($x), {$x}, grad);\n}\n\n/**\n * Computes inverse hyperbolic tan of the input `tf.Tensor` element-wise:\n * `atanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.atanh().print();  // or tf.atanh(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction atanh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'atanh');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.div(scalar(1).sub($x.toFloat().square())) as T};\n  };\n  return ENV.engine.runKernel(backend => backend.atanh($x), {$x}, grad);\n}\n\n/**\n * Computes gause error function of the input `tf.Tensor` element-wise:\n * `erf(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.erf().print(); // or tf.erf(x);\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction erf_<T extends Tensor>(x: T|TensorLike): T {\n  let $x = convertToTensor(x, 'x', 'erf');\n  util.assert(\n      $x.dtype === 'int32' || $x.dtype === 'float32',\n      'Input dtype must be `int32` or `float32`.');\n\n  if ($x.dtype === 'int32') {\n    $x = $x.toFloat();\n  }\n\n  const grad = (dy: T) => {\n    return {\n      $x: () => dy.mul($x.square().neg().exp().mul(2 / Math.sqrt(Math.PI))) as T\n    };\n  };\n  return ENV.engine.runKernel(backend => backend.erf($x), {$x}, grad);\n}\n\n/**\n * Computes step of the input `tf.Tensor` element-wise: `x > 0 ? 1 : alpha * x`\n *\n * ```js\n * const x = tf.tensor1d([0, 2, -1, -3]);\n *\n * x.step(.5).print();  // or tf.step(x, .5)\n * ```\n * @param x The input tensor.\n * @param alpha The gradient when input is negative.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction step_<T extends Tensor>(x: T|TensorLike, alpha = 0.0): T {\n  const $x = convertToTensor(x, 'x', 'step');\n\n  // TODO(manrajgrover): Return null for gradients when backprop supports\n  // it.\n  const grad = (dy: T) => {\n    return {$x: () => zerosLike(dy)};\n  };\n  return ENV.engine.runKernel(backend => backend.step($x, alpha), {$x}, grad);\n}\n\nexport const abs = op({abs_});\nexport const acos = op({acos_});\nexport const acosh = op({acosh_});\nexport const asin = op({asin_});\nexport const asinh = op({asinh_});\nexport const atan = op({atan_});\nexport const atanh = op({atanh_});\nexport const ceil = op({ceil_});\nexport const clipByValue = op({clipByValue_});\nexport const cos = op({cos_});\nexport const cosh = op({cosh_});\nexport const erf = op({erf_});\nexport const exp = op({exp_});\nexport const expm1 = op({expm1_});\nexport const floor = op({floor_});\nexport const log = op({log_});\nexport const log1p = op({log1p_});\nexport const logSigmoid = op({logSigmoid_});\nexport const neg = op({neg_});\nexport const reciprocal = op({reciprocal_});\nexport const round = op({round_});\nexport const rsqrt = op({rsqrt_});\nexport const sigmoid = op({sigmoid_});\nexport const sign = op({sign_});\nexport const sin = op({sin_});\nexport const sinh = op({sinh_});\nexport const softplus = op({softplus_});\nexport const sqrt = op({sqrt_});\nexport const square = op({square_});\nexport const step = op({step_});\nexport const tan = op({tan_});\nexport const tanh = op({tanh_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {tile} from './array_ops';\nimport {getReductionAxes} from './broadcast_util';\nimport {op} from './operation';\nimport {scalar} from './tensor_ops';\nimport {rsqrt} from './unary_ops';\n\n/**\n * Batch normalization, strictly for 2D. For the more relaxed version, see\n * `tf.batchNormalization`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n * @param scale A scale Tensor.\n * @param offset An offset Tensor.\n */\nfunction batchNormalization2d_(\n    x: Tensor2D|TensorLike, mean: Tensor2D|Tensor1D|TensorLike,\n    variance: Tensor2D|Tensor1D|TensorLike, varianceEpsilon = .001,\n    scale?: Tensor2D|Tensor1D|TensorLike,\n    offset?: Tensor2D|Tensor1D|TensorLike): Tensor2D {\n  const $x = convertToTensor(x, 'x', 'batchNormalization');\n  const $mean = convertToTensor(mean, 'mean', 'batchNormalization');\n  const $variance = convertToTensor(variance, 'variance', 'batchNormalization');\n  let $scale: Tensor2D|Tensor1D;\n  if (scale != null) {\n    $scale = convertToTensor(scale, 'scale', 'batchNormalization');\n  }\n  let $offset: Tensor2D|Tensor1D;\n  if (offset != null) {\n    $offset = convertToTensor(offset, 'offset', 'batchNormalization');\n  }\n  util.assert(\n      $x.rank === 2,\n      `Error in batchNormalization3D: x must be rank 3 but got rank ` +\n          `${$x.rank}.`);\n  util.assert(\n      $mean.rank === 2 || $mean.rank === 1,\n      `Error in batchNormalization2D: mean must be rank 2 or rank 1 but ` +\n          `got rank ${$mean.rank}.`);\n  util.assert(\n      $variance.rank === 2 || $variance.rank === 1,\n      `Error in batchNormalization2D: variance must be rank 2 or rank 1 ` +\n          `but got rank ${$variance.rank}.`);\n  if ($scale != null) {\n    util.assert(\n        $scale.rank === 2 || $scale.rank === 1,\n        `Error in batchNormalization2D: scale must be rank 2 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n  }\n  if ($offset != null) {\n    util.assert(\n        $offset.rank === 2 || $offset.rank === 1,\n        `Error in batchNormalization2D: offset must be rank 2 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n  }\n\n  return batchNormalization(\n      $x, $mean, $variance, varianceEpsilon, $scale, $offset);\n}\n\n/**\n * Batch normalization, strictly for 3D. For the more relaxed version, see\n * `tf.batchNormalization`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n * @param scale A scale Tensor.\n * @param offset An offset Tensor.\n */\nfunction batchNormalization3d_(\n    x: Tensor3D|TensorLike, mean: Tensor3D|Tensor1D|TensorLike,\n    variance: Tensor3D|Tensor1D|TensorLike, varianceEpsilon = .001,\n    scale?: Tensor3D|Tensor1D|TensorLike,\n    offset?: Tensor3D|Tensor1D|TensorLike): Tensor3D {\n  const $x = convertToTensor(x, 'x', 'batchNormalization');\n  const $mean = convertToTensor(mean, 'mean', 'batchNormalization');\n  const $variance = convertToTensor(variance, 'variance', 'batchNormalization');\n  let $scale: Tensor3D|Tensor1D;\n  if (scale != null) {\n    $scale = convertToTensor(scale, 'scale', 'batchNormalization');\n  }\n  let $offset: Tensor3D|Tensor1D;\n  if (offset != null) {\n    $offset = convertToTensor(offset, 'offset', 'batchNormalization');\n  }\n  util.assert(\n      $x.rank === 3,\n      `Error in batchNormalization3D: x must be rank 3 but got rank ` +\n          `${$x.rank}.`);\n  util.assert(\n      $mean.rank === 3 || $mean.rank === 1,\n      `Error in batchNormalization3D: mean must be rank 3 or rank 1 but ` +\n          `got rank ${$mean.rank}.`);\n  util.assert(\n      $variance.rank === 3 || $variance.rank === 1,\n      `Error in batchNormalization3D: variance must be rank 3 or rank 1 ` +\n          `but got rank ${$variance.rank}.`);\n  if ($scale != null) {\n    util.assert(\n        $scale.rank === 3 || $scale.rank === 1,\n        `Error in batchNormalization3D: scale must be rank 3 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n  }\n  if ($offset != null) {\n    util.assert(\n        $offset.rank === 3 || $offset.rank === 1,\n        `Error in batchNormalization3D: offset must be rank 3 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n  }\n\n  return batchNormalization(\n      $x, $mean, $variance, varianceEpsilon, $scale, $offset);\n}\n\n/**\n * Batch normalization, strictly for 4D. For the more relaxed version, see\n * `tf.batchNormalization`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n * @param scale A scale Tensor.\n * @param offset An offset Tensor.\n */\nfunction batchNormalization4d_(\n    x: Tensor4D|TensorLike, mean: Tensor4D|Tensor1D|TensorLike,\n    variance: Tensor4D|Tensor1D|TensorLike, varianceEpsilon = .001,\n    scale?: Tensor4D|Tensor1D|TensorLike,\n    offset?: Tensor4D|Tensor1D|TensorLike): Tensor4D {\n  const $x = convertToTensor(x, 'x', 'batchNormalization');\n  const $mean = convertToTensor(mean, 'mean', 'batchNormalization');\n  const $variance = convertToTensor(variance, 'variance', 'batchNormalization');\n  let $scale: Tensor4D|Tensor1D;\n  if (scale != null) {\n    $scale = convertToTensor(scale, 'scale', 'batchNormalization');\n  }\n  let $offset: Tensor4D|Tensor1D;\n  if (offset != null) {\n    $offset = convertToTensor(offset, 'offset', 'batchNormalization');\n  }\n  util.assert(\n      $x.rank === 4,\n      `Error in batchNormalization4D: x must be rank 4 but got rank ` +\n          `${$x.rank}.`);\n  util.assert(\n      $mean.rank === 4 || $mean.rank === 1,\n      `Error in batchNormalization4D: mean must be rank 4 or rank 1 but ` +\n          `got rank ${$mean.rank}.`);\n  util.assert(\n      $variance.rank === 4 || $variance.rank === 1,\n      `Error in batchNormalization4D: variance must be rank 4 or rank 1 ` +\n          `but got rank ${$variance.rank}.`);\n  if ($scale != null) {\n    util.assert(\n        $scale.rank === 4 || $scale.rank === 1,\n        `Error in batchNormalization4D: scale must be rank 4 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n  }\n  if ($offset != null) {\n    util.assert(\n        $offset.rank === 4 || $offset.rank === 1,\n        `Error in batchNormalization4D: offset must be rank 4 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n  }\n  return batchNormalization(\n      $x, $mean, $variance, varianceEpsilon, $scale, $offset);\n}\n\n/**\n * Batch normalization.\n *\n * As described in\n * [http://arxiv.org/abs/1502.03167](http://arxiv.org/abs/1502.03167).\n *\n * Mean, variance, scale, and offset can be of two shapes:\n *   - The same shape as the input.\n *   - In the common case, the depth dimension is the last dimension of x, so\n *     the values would be an `tf.Tensor1D` of shape [depth].\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that parameters passed are of given rank\n *   - `tf.batchNormalization2d`\n *   - `tf.batchNormalization3d`\n *   - `tf.batchNormalization4d`\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n * @param scale A scale Tensor.\n * @param offset An offset Tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Normalization'} */\nfunction batchNormalization_<R extends Rank>(\n    x: Tensor<R>|Tensor1D|TensorLike, mean: Tensor<R>|Tensor1D|TensorLike,\n    variance: Tensor<R>|Tensor1D|TensorLike, varianceEpsilon = .001,\n    scale?: Tensor<R>|Tensor1D|TensorLike,\n    offset?: Tensor<R>|Tensor1D|TensorLike): Tensor<R> {\n  const $x = convertToTensor(x, 'x', 'batchNormalization');\n  const $mean = convertToTensor(mean, 'mean', 'batchNormalization');\n  const $variance = convertToTensor(variance, 'variance', 'batchNormalization');\n  let $scale: Tensor<R>|Tensor1D;\n  if (scale != null) {\n    $scale = convertToTensor(scale, 'scale', 'batchNormalization');\n  }\n  let $offset: Tensor<R>|Tensor1D;\n  if (offset != null) {\n    $offset = convertToTensor(offset, 'offset', 'batchNormalization');\n  }\n\n  util.assert(\n      $mean.rank === $variance.rank,\n      'Batch normalization gradient requires mean and variance to have ' +\n          'equal ranks.');\n  util.assert(\n      $offset == null || $mean.rank === $offset.rank,\n      'Batch normalization gradient requires mean and offset to have ' +\n          'equal ranks.');\n  util.assert(\n      $scale == null || $mean.rank === $scale.rank,\n      'Batch normalization gradient requires mean and scale to have ' +\n          'equal ranks.');\n\n  let x4D: Tensor4D;\n  if ($x.rank === 0 || $x.rank === 1) {\n    x4D = $x.as4D(1, 1, 1, $x.size);\n  } else if ($x.rank === 2) {\n    x4D = $x.as4D(1, 1, $x.shape[0], $x.shape[1]);\n  } else if ($x.rank === 3) {\n    x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]) as Tensor4D;\n  } else {\n    x4D = $x as Tensor4D;\n  }\n\n  const der = (dy: Tensor) => {\n    const scaleValue = $scale == null ? scalar(1) : $scale;\n    const reductionAxes = getReductionAxes($mean.shape, x4D.shape);\n    const tileShape: number[] = [];\n    if ($mean.rank === 1) {\n      for (let i = 0; i < x4D.shape.length - 1; ++i) {\n        tileShape.push(x4D.shape[i]);\n      }\n      tileShape.push(1);\n    }\n\n    const xMinusMean = $x.sub($mean);\n    const dyTimesScaleValue = dy.mul(scaleValue);\n    const oneOverSqrtVariance = rsqrt($variance.add(scalar(varianceEpsilon)));\n    const minusHalfRCube = oneOverSqrtVariance.mul(oneOverSqrtVariance)\n                               .mul(oneOverSqrtVariance)\n                               .mul(scalar(-0.5));\n    const derX = () => {\n      if ($mean.rank === 1) {\n        return dy\n            .mul(tile(\n                oneOverSqrtVariance.as4D(1, 1, 1, $mean.shape[0]), tileShape))\n            .mul(scaleValue)\n            .reshape($x.shape);\n      } else {\n        return dy.mul(oneOverSqrtVariance).mul(scaleValue).reshape($x.shape);\n      }\n    };\n    const derMean = () => {\n      let meanDer = oneOverSqrtVariance.mul(scalar(-1)).mul(dyTimesScaleValue);\n      if ($mean.rank === 1) {\n        meanDer = meanDer.sum(reductionAxes);\n      }\n      return meanDer.reshape($mean.shape);\n    };\n    const derVariance = () => {\n      let varianceDer = minusHalfRCube.mul(xMinusMean).mul(dyTimesScaleValue);\n      if ($mean.rank === 1) {\n        varianceDer = varianceDer.sum(reductionAxes);\n      }\n      return varianceDer.reshape($mean.shape);\n    };\n    const derScale = () => {\n      const xMinusMean2TimesRsqrt = xMinusMean.mul(oneOverSqrtVariance);\n      let scaleDer = dy.mul(xMinusMean2TimesRsqrt);\n      if ($mean.rank === 1) {\n        scaleDer = scaleDer.sum(reductionAxes);\n      }\n      return scaleDer.reshape($mean.shape);\n    };\n    const derOffset = () => {\n      let offsetDer = dy;\n      if ($mean.rank === 1) {\n        offsetDer = offsetDer.sum(reductionAxes);\n      }\n      return offsetDer.reshape($mean.shape);\n    };\n    return {\n      $x: derX,\n      $mean: derMean,\n      $variance: derVariance,\n      $scale: derScale,\n      $offset: derOffset\n    };\n  };\n\n  const res = ENV.engine.runKernel(\n      backend => backend.batchNormalization(\n          x4D, batchnormReshape4D($mean), batchnormReshape4D($variance),\n          varianceEpsilon, batchnormReshape4D($scale),\n          batchnormReshape4D($offset)),\n      {$x, $mean, $variance, $scale, $offset}, der);\n  return res.reshape($x.shape);\n}\n\nfunction batchnormReshape4D(x: Tensor): Tensor4D|Tensor1D {\n  if (x == null) {\n    return null;\n  }\n  if (x.rank === 0) {\n    return x.as1D();\n  } else if (x.rank === 1) {\n    return x as Tensor1D;\n  } else if (x.rank === 2) {\n    return x.as4D(1, 1, x.shape[0], x.shape[1]);\n  } else if (x.rank === 3) {\n    return x.as4D(1, x.shape[0], x.shape[1], x.shape[2]);\n  }\n  return x as Tensor4D;\n}\n\nexport const batchNormalization2d = op({batchNormalization2d_});\nexport const batchNormalization3d = op({batchNormalization3d_});\nexport const batchNormalization4d = op({batchNormalization4d_});\nexport const batchNormalization = op({batchNormalization_});\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as util from '../util';\n\nexport type PadInfo = {\n  top: number,\n  left: number,\n  right: number,\n  bottom: number,\n  type: string\n};\n\nexport type PadInfo3D = {\n  top: number,\n  left: number,\n  right: number,\n  bottom: number,\n  front: number,\n  back: number,\n  type: string\n};\n\n/**\n * Information about the forward pass of a convolution/pooling operation.\n * It includes input and output shape, strides, filter size and padding\n * information.\n */\nexport type Conv2DInfo = {\n  batchSize: number,\n  inHeight: number,\n  inWidth: number,\n  inChannels: number,\n  outHeight: number,\n  outWidth: number,\n  outChannels: number,\n  dataFormat: 'channelsFirst'|'channelsLast',\n  strideHeight: number,\n  strideWidth: number,\n  dilationHeight: number,\n  dilationWidth: number,\n  filterHeight: number,\n  filterWidth: number,\n  effectiveFilterHeight: number,\n  effectiveFilterWidth: number,\n  padInfo: PadInfo,\n  inShape: [number, number, number, number],\n  outShape: [number, number, number, number],\n  filterShape: [number, number, number, number]\n};\n\nexport function computePool2DInfo(\n    inShape: [number, number, number, number],\n    filterSize: [number, number]|number, strides: number|[number, number],\n    dilations: number|[number, number], pad: 'same'|'valid'|number,\n    roundingMode?: 'floor'|'round'|'ceil',\n    dataFormat: 'channelsFirst'|'channelsLast' = 'channelsLast'): Conv2DInfo {\n  const [filterHeight, filterWidth] = parseTupleParam(filterSize);\n\n  let filterShape: [number, number, number, number];\n  if (dataFormat === 'channelsLast') {\n    filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];\n  } else if (dataFormat === 'channelsFirst') {\n    filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n\n  return computeConv2DInfo(\n      inShape, filterShape, strides, dilations, pad, roundingMode, false,\n      dataFormat);\n}\n\n/**\n * Computes the information for a forward pass of a convolution/pooling\n * operation.\n */\nexport function computeConv2DInfo(\n    inShape: [number, number, number, number],\n    filterShape: [number, number, number, number],\n    strides: number|[number, number], dilations: number|[number, number],\n    pad: 'same'|'valid'|number, roundingMode?: 'floor'|'round'|'ceil',\n    depthwise = false,\n    dataFormat: 'channelsFirst'|'channelsLast' = 'channelsLast'): Conv2DInfo {\n  let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];\n  if (dataFormat === 'channelsLast') {\n    [batchSize, inHeight, inWidth, inChannels] = inShape;\n  } else if (dataFormat === 'channelsFirst') {\n    [batchSize, inChannels, inHeight, inWidth] = inShape;\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n\n  const [filterHeight, filterWidth, , filterChannels] = filterShape;\n  const [strideHeight, strideWidth] = parseTupleParam(strides);\n  const [dilationHeight, dilationWidth] = parseTupleParam(dilations);\n\n  const effectiveFilterHeight =\n      getEffectiveFilterSize(filterHeight, dilationHeight);\n  const effectiveFilterWidth =\n      getEffectiveFilterSize(filterWidth, dilationWidth);\n  const {padInfo, outHeight, outWidth} = getPadAndOutInfo(\n      pad, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight,\n      effectiveFilterWidth, roundingMode);\n\n  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n\n  let outShape: [number, number, number, number];\n  if (dataFormat === 'channelsFirst') {\n    outShape = [batchSize, outChannels, outHeight, outWidth];\n  } else if (dataFormat === 'channelsLast') {\n    outShape = [batchSize, outHeight, outWidth, outChannels];\n  }\n\n  return {\n    batchSize,\n    dataFormat,\n    inHeight,\n    inWidth,\n    inChannels,\n    outHeight,\n    outWidth,\n    outChannels,\n    padInfo,\n    strideHeight,\n    strideWidth,\n    filterHeight,\n    filterWidth,\n    effectiveFilterHeight,\n    effectiveFilterWidth,\n    dilationHeight,\n    dilationWidth,\n    inShape,\n    outShape,\n    filterShape\n  };\n}\n\n/**\n * Information about the forward pass of a 3D convolution/pooling operation.\n * It includes input and output shape, strides, filter size and padding\n * information.\n */\nexport type Conv3DInfo = {\n  batchSize: number,\n  inDepth: number,\n  inHeight: number,\n  inWidth: number,\n  inChannels: number,\n  outDepth: number,\n  outHeight: number,\n  outWidth: number,\n  outChannels: number,\n  dataFormat: 'channelsFirst'|'channelsLast',\n  strideDepth: number,\n  strideHeight: number,\n  strideWidth: number,\n  dilationDepth: number,\n  dilationHeight: number,\n  dilationWidth: number,\n  filterDepth: number,\n  filterHeight: number,\n  filterWidth: number,\n  padInfo: PadInfo3D,\n  inShape: [number, number, number, number, number],\n  outShape: [number, number, number, number, number],\n  filterShape: [number, number, number, number, number]\n};\n\n/**\n * Computes the information for a forward pass of a 3D convolution/pooling\n * operation.\n */\nexport function computeConv3DInfo(\n    inShape: [number, number, number, number, number],\n    filterShape: [number, number, number, number, number],\n    strides: number|[number, number, number],\n    dilations: number|[number, number, number], pad: 'same'|'valid',\n    depthwise = false,\n    dataFormat: 'channelsFirst'|'channelsLast' = 'channelsLast'): Conv3DInfo {\n  let [batchSize, inDepth, inHeight, inWidth, inChannels] =\n      [-1, -1, -1, -1, -1];\n  if (dataFormat === 'channelsLast') {\n    [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;\n  } else if (dataFormat === 'channelsFirst') {\n    [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n\n  const [filterDepth, filterHeight, filterWidth, , filterChannels] =\n      filterShape;\n  const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);\n  const [dilationDepth, dilationHeight, dilationWidth] =\n      parse3TupleParam(dilations);\n\n  const effectiveFilterDepth =\n      getEffectiveFilterSize(filterDepth, dilationDepth);\n  const effectiveFilterHeight =\n      getEffectiveFilterSize(filterHeight, dilationHeight);\n  const effectiveFilterWidth =\n      getEffectiveFilterSize(filterWidth, dilationWidth);\n  const {padInfo, outDepth, outHeight, outWidth} = get3DPadAndOutInfo(\n      pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth,\n      effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth);\n\n  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n\n  let outShape: [number, number, number, number, number];\n  if (dataFormat === 'channelsFirst') {\n    outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];\n  } else if (dataFormat === 'channelsLast') {\n    outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];\n  }\n\n  return {\n    batchSize,\n    dataFormat,\n    inDepth,\n    inHeight,\n    inWidth,\n    inChannels,\n    outDepth,\n    outHeight,\n    outWidth,\n    outChannels,\n    padInfo,\n    strideDepth,\n    strideHeight,\n    strideWidth,\n    filterDepth,\n    filterHeight,\n    filterWidth,\n    dilationDepth,\n    dilationHeight,\n    dilationWidth,\n    inShape,\n    outShape,\n    filterShape\n  };\n}\n\nfunction computeOutputShape3D(\n    inShape: [number, number, number], fieldSize: number, outDepth: number,\n    stride: number, zeroPad?: number,\n    roundingMode?: 'floor'|'round'|'ceil'): [number, number, number] {\n  if (zeroPad == null) {\n    zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n  }\n  const inputRows = inShape[0];\n  const inputCols = inShape[1];\n\n  const outputRows = conditionalRound(\n      (inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  util.assert(\n      util.isInt(outputRows),\n      `The output # of rows (${outputRows}) must be an integer. Change the ` +\n          `stride and/or zero pad parameters`);\n\n  const outputCols = conditionalRound(\n      (inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  util.assert(\n      util.isInt(outputCols),\n      `The output # of columns (${outputCols}) must be an integer. Change ` +\n          `the stride and/or zero pad parameters`);\n\n  return [outputRows, outputCols, outDepth];\n}\n\nexport function computeDefaultPad(\n    inputShape: [number, number, number], fieldSize: number, stride: number,\n    dilation = 1): number {\n  const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);\n  return Math.floor(\n      (inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);\n}\n\nfunction parseTupleParam(param: number|[number, number]): [number, number] {\n  return typeof param === 'number' ? [param, param] : param;\n}\n\nfunction parse3TupleParam(param: number|[number, number, number]):\n    [number, number, number] {\n  return typeof param === 'number' ? [param, param, param] : param;\n}\n\n/* See https://www.tensorflow.org/api_docs/python/tf/nn/atrous_conv2d\n * Atrous convolution is equivalent to standard convolution with upsampled\n * filters with effective_filter_height =\n * filter_height + (filter_height - 1) * (dilation - 1)\n * and effective_filter_width =\n * filter_width + (filter_width - 1) * (dilation - 1),\n * produced by inserting dilation - 1 zeros along consecutive elements across\n * the filters' spatial dimensions.\n * When there is a dilation, this converts a filter dimension to the\n * effective filter dimension, so it can be used in a standard convolution.\n */\nfunction getEffectiveFilterSize(filterSize: number, dilation: number) {\n  if (dilation <= 1) {\n    return filterSize;\n  }\n\n  return filterSize + (filterSize - 1) * (dilation - 1);\n}\n\nfunction getPadAndOutInfo(\n    pad: 'same'|'valid'|number, inHeight: number, inWidth: number,\n    strideHeight: number, strideWidth: number, filterHeight: number,\n    filterWidth: number, roundingMode?: 'floor'|'round'|'ceil'):\n    {padInfo: PadInfo, outHeight: number, outWidth: number} {\n  let padInfo: PadInfo;\n  let outHeight: number;\n  let outWidth: number;\n\n  if (typeof pad === 'number') {\n    const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n    padInfo = {top: pad, bottom: pad, left: pad, right: pad, type: padType};\n    const outShape = computeOutputShape3D(\n        [inHeight, inWidth, 1], filterHeight, 1, strideHeight, pad,\n        roundingMode);\n    outHeight = outShape[0];\n    outWidth = outShape[1];\n  } else if (pad === 'same') {\n    outHeight = Math.ceil(inHeight / strideHeight);\n    outWidth = Math.ceil(inWidth / strideWidth);\n    const padAlongHeight =\n        (outHeight - 1) * strideHeight + filterHeight - inHeight;\n    const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;\n    const top = Math.floor(padAlongHeight / 2);\n    const bottom = padAlongHeight - top;\n    const left = Math.floor(padAlongWidth / 2);\n    const right = padAlongWidth - left;\n    padInfo = {top, bottom, left, right, type: 'SAME'};\n  } else if (pad === 'valid') {\n    padInfo = {top: 0, bottom: 0, left: 0, right: 0, type: 'VALID'};\n    outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n    outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n  } else {\n    throw Error(`Unknown padding parameter: ${pad}`);\n  }\n  return {padInfo, outHeight, outWidth};\n}\n\nfunction get3DPadAndOutInfo(\n    pad: 'same'|'valid', inDepth: number, inHeight: number, inWidth: number,\n    strideDepth: number, strideHeight: number, strideWidth: number,\n    filterDepth: number, filterHeight: number, filterWidth: number): {\n  padInfo: PadInfo3D,\n  outDepth: number,\n  outHeight: number,\n  outWidth: number\n} {\n  let padInfo: PadInfo3D;\n  let outDepth: number;\n  let outHeight: number;\n  let outWidth: number;\n\n  if (pad === 'same') {\n    outDepth = Math.ceil(inDepth / strideDepth);\n    outHeight = Math.ceil(inHeight / strideHeight);\n    outWidth = Math.ceil(inWidth / strideWidth);\n    const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;\n    const padAlongHeight =\n        (outHeight - 1) * strideHeight + filterHeight - inHeight;\n    const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;\n    const front = Math.floor(padAlongDepth / 2);\n    const back = padAlongDepth - front;\n    const top = Math.floor(padAlongHeight / 2);\n    const bottom = padAlongHeight - top;\n    const left = Math.floor(padAlongWidth / 2);\n    const right = padAlongWidth - left;\n\n    padInfo = {top, bottom, left, right, front, back, type: 'SAME'};\n  } else if (pad === 'valid') {\n    padInfo = {\n      top: 0,\n      bottom: 0,\n      left: 0,\n      right: 0,\n      front: 0,\n      back: 0,\n      type: 'VALID'\n    };\n    outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);\n    outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n    outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n  } else {\n    throw Error(`Unknown padding parameter: ${pad}`);\n  }\n  return {padInfo, outDepth, outHeight, outWidth};\n}\n\n/**\n * Rounds a value depending on the rounding mode\n * @param value\n * @param roundingMode\n */\nfunction conditionalRound(\n    value: number, roundingMode?: 'floor'|'round'|'ceil') {\n  if (!roundingMode) {\n    return value;\n  }\n  switch (roundingMode) {\n    case 'round':\n      // used for Caffe Conv\n      return Math.round(value);\n    case 'ceil':\n      // used for Caffe Pool\n      return Math.ceil(value);\n    case 'floor':\n      return Math.floor(value);\n    default:\n      throw new Error(`Unknown roundingMode ${roundingMode}`);\n  }\n}\n\nexport function tupleValuesAreOne(param: number|[number, number]): boolean {\n  const [dimA, dimB] = parseTupleParam(param);\n  return dimA === 1 && dimB === 1;\n}\n\nexport function eitherStridesOrDilationsAreOne(\n    strides: number|[number, number],\n    dilations: number|[number, number]): boolean {\n  return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D} from '../tensor';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\n\n/**\n * Computes the dot product of two matrices, A * B. These must be matrices.\n *\n * ```js\n * const a = tf.tensor2d([1, 2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.matMul(b).print();  // or tf.matMul(a, b)\n * ```\n * @param a First matrix in dot product operation.\n * @param b Second matrix in dot product operation.\n * @param transposeA If true, `a` is transposed before multiplication.\n * @param transposeB If true, `b` is transposed before multiplication.\n */\n/** @doc {heading: 'Operations', subheading: 'Matrices'} */\nfunction matMul_<T extends Tensor>(\n    a: T|TensorLike, b: T|TensorLike, transposeA = false,\n    transposeB = false): T {\n  let $a = convertToTensor(a, 'a', 'matMul');\n  let $b = convertToTensor(b, 'b', 'matMul');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const innerShapeA =\n      transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n  const innerShapeB =\n      transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n\n  const outerShapeA =\n      transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n  const outerShapeB =\n      transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n\n  const outerDimsA = $a.shape.slice(0, -2);\n  const outerDimsB = $b.shape.slice(0, -2);\n  const batchDimA = util.sizeFromShape(outerDimsA);\n  const batchDimB = util.sizeFromShape(outerDimsB);\n\n  util.assert(\n      $a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank,\n      `Error in matMul: inputs must have the same rank of at least 2, ` +\n          `got ranks ${$a.rank} and ${$b.rank}.`);\n\n  util.assert(\n      util.arraysEqual(outerDimsA, outerDimsB),\n      `Error in matMul: outer dimensions (${outerDimsA}) and (` +\n          `${outerDimsB}) of Tensors with shapes ${$a.shape} and ` +\n          `${$b.shape} must match.`);\n\n  util.assert(\n      innerShapeA === innerShapeB,\n      `Error in matMul: inner shapes (${innerShapeA}) and (` +\n          `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n          `${$b.shape} and transposeA=${transposeA}` +\n          ` and transposeB=${transposeB} must match.`);\n\n  const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);\n\n  const a3D = transposeA ? $a.as3D(batchDimA, innerShapeA, outerShapeA) :\n                           $a.as3D(batchDimA, outerShapeA, innerShapeA);\n  const b3D = transposeB ? $b.as3D(batchDimB, outerShapeB, innerShapeB) :\n                           $b.as3D(batchDimB, innerShapeB, outerShapeB);\n\n  const grad = (dy: Tensor3D) => {\n    if (!transposeA && !transposeB) {\n      return {\n        $a: () => dy.matMul(b3D, false, true),\n        $b: () => a3D.matMul(dy, true, false)\n      };\n    } else if (!transposeA && transposeB) {\n      return {\n        $a: () => dy.matMul(b3D, false, false),\n        $b: () => dy.matMul(a3D, true, false)\n      };\n    } else if (transposeA && !transposeB) {\n      return {\n        $a: () => b3D.matMul(dy, false, true),\n        $b: () => a3D.matMul(dy, false, false)\n      };\n    } else {\n      return {\n        $a: () => b3D.matMul(dy, true, true),\n        $b: () => dy.matMul(a3D, true, true)\n      };\n    }\n  };\n\n  const res = ENV.engine.runKernel(\n      backend => backend.batchMatMul(a3D, b3D, transposeA, transposeB),\n      {$a: a3D, $b: b3D}, grad);\n  return res.reshape(outShape) as T;\n}\n\n/**\n * Computes the outer product of two vectors, `v1` and `v2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([3, 4, 5]);\n *\n * tf.outerProduct(a, b).print();\n * ```\n * @param v1 The first vector in the outer product operation.\n * @param v2 The second vector in the outer product operation.\n */\n/** @doc {heading: 'Operations', subheading: 'Matrices'} */\nfunction outerProduct_(\n    v1: Tensor1D|TensorLike, v2: Tensor1D|TensorLike): Tensor2D {\n  const $v1 = convertToTensor(v1, 'v1', 'outerProduct');\n  const $v2 = convertToTensor(v2, 'v2', 'outerProduct');\n\n  util.assert(\n      $v1.rank === 1 && $v2.rank === 1,\n      `Error in outerProduct: inputs must be rank 1, but got ranks ` +\n          `${$v1.rank} and ${$v2.rank}.`);\n\n  return $v1.as2D(-1, 1).matMul($v2.as2D(1, -1));\n}\n\n/**\n * Computes the dot product of two matrices and/or vectors, `t1` and `t2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor2d([[1, 2], [3, 4]]);\n * const c = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);\n *\n * a.dot(b).print();  // or tf.dot(a, b)\n * b.dot(a).print();\n * b.dot(c).print();\n * ```\n * @param t1 The first tensor in the dot operation.\n * @param t2 The second tensor in the dot operation.\n */\n/** @doc {heading: 'Operations', subheading: 'Matrices'} */\nfunction dot_(t1: Tensor|TensorLike, t2: Tensor|TensorLike): Tensor {\n  const $t1 = convertToTensor(t1, 't1', 'dot');\n  const $t2 = convertToTensor(t2, 't2', 'dot');\n  util.assert(\n      ($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2),\n      `Error in dot: inputs must all be rank 1 or 2, but got ranks ` +\n          `${$t1.rank} and ${$t2.rank}.`);\n\n  const t1Inner = ($t1.rank === 1 ? $t1.size : $t1.shape[1]);\n  const t2Inner = ($t2.rank === 1 ? $t2.size : $t2.shape[0]);\n\n  util.assert(\n      t1Inner === t2Inner,\n      `Error in dot: inner dimensions of inputs must match, but got ` +\n          `${t1Inner} and ${t2Inner}.`);\n\n  if ($t1.rank === 1 && $t2.rank === 1) {\n    return $t1.as2D(1, -1).matMul($t2.as2D(-1, 1)).asScalar();\n  } else if ($t1.rank === 1 && $t2.rank === 2) {\n    return $t1.as2D(1, -1).matMul($t2.as2D($t2.shape[0], $t2.shape[1])).as1D();\n  } else if ($t1.rank === 2 && $t2.rank === 1) {\n    return $t1.matMul($t2.as2D(-1, 1)).as1D();\n  } else {\n    return $t1.matMul($t2.as2D($t2.shape[0], $t2.shape[1]));\n  }\n}\n\nexport const matMul = op({matMul_});\nexport const dot = op({dot_});\nexport const outerProduct = op({outerProduct_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor2D, Tensor3D, Tensor4D, Tensor5D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, TensorLike} from '../types';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport {matMul} from './matmul';\nimport {op} from './operation';\n\n/**\n * Computes a 1D convolution over the input x.\n *\n * @param x The input tensor, of rank 3 or rank 2, of shape\n *     `[batch, width, inChannels]`. If rank 2, batch of 1 is assumed.\n * @param filter The filter, rank 3, of shape\n *     `[filterWidth, inDepth, outDepth]`.\n * @param stride The number of entries by which the filter is moved right at\n *     each step.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat An optional string from \"NWC\", \"NCW\". Defaults to \"NWC\",\n *     the data is stored in the order of [batch, in_width, in_channels]. Only\n *     \"NWC\" is currently supported.\n * @param dilation The dilation rate in which we sample input values in\n *     atrous convolution. Defaults to `1`. If it is greater than 1, then\n *     stride must be `1`.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction conv1d_<T extends Tensor2D|Tensor3D>(\n    x: T|TensorLike, filter: Tensor3D|TensorLike, stride: number,\n    pad: 'valid'|'same'|number, dataFormat: 'NWC'|'NCW' = 'NWC', dilation = 1,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'conv1d');\n  const $filter = convertToTensor(filter, 'filter', 'conv1d');\n\n  let x3D = $x as Tensor3D;\n  let reshapedTo3D = false;\n  if ($x.rank === 2) {\n    reshapedTo3D = true;\n    x3D = $x.as3D(1, $x.shape[0], $x.shape[1]);\n  }\n\n  util.assert(\n      x3D.rank === 3,\n      `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);\n  util.assert(\n      $filter.rank === 3,\n      `Error in conv1d: filter must be rank 3, but got rank ` +\n          `${$filter.rank}.`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        `Error in conv1d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  util.assert(\n      x3D.shape[2] === $filter.shape[1],\n      `Error in conv1d: depth of input (${x3D.shape[2]}) must match ` +\n          `input depth for filter ${$filter.shape[1]}.`);\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(stride, dilation),\n      'Error in conv1D: Either stride or dilation must be 1. ' +\n          `Got stride ${stride} and dilation '${dilation}'`);\n  util.assert(\n      dataFormat === 'NWC',\n      `Error in conv1d: got dataFormat of ${\n          dataFormat} but only NWC is currently supported.`);\n\n  const filter4D =\n      $filter.as4D(1, $filter.shape[0], $filter.shape[1], $filter.shape[2]);\n  const input4D = x3D.as4D(x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]);\n  const strides: [number, number] = [1, stride];\n  const dilations: [number, number] = [1, dilation];\n\n  const conv2dDataFormat = 'NHWC';\n\n  const res = conv2d(\n      input4D, filter4D, strides, pad, conv2dDataFormat, dilations,\n      dimRoundingMode);\n\n  if (reshapedTo3D) {\n    return res.as2D(res.shape[2], res.shape[3]) as T;\n  }\n  return res.as3D(res.shape[0], res.shape[2], res.shape[3]) as T;\n}\n\n/**\n * Computes a 2D convolution over the input x.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction conv2d_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filter: Tensor4D|TensorLike,\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n    dilations: [number, number]|number = [1, 1],\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'conv2d');\n  const $filter = convertToTensor(filter, 'filter', 'conv2d');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n  }\n  util.assert(\n      x4D.rank === 4,\n      `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n  util.assert(\n      $filter.rank === 4,\n      `Error in conv2d: filter must be rank 4, but got rank ` +\n          `${$filter.rank}.`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        `Error in conv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  util.assert(\n      x4D.shape[3] === $filter.shape[2],\n      `Error in conv2d: depth of input (${x4D.shape[3]}) must match ` +\n          `input depth for filter ${$filter.shape[2]}.`);\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      'Error in conv2D: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n  util.assert(\n      dataFormat === 'NHWC',\n      `Error in conv2d: got dataFormat of ${\n          dataFormat} but only NHWC is currently supported.`);\n\n  const convInfo = conv_util.computeConv2DInfo(\n      x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);\n\n  let res: Tensor3D|Tensor4D;\n  if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&\n      convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&\n      convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&\n      (convInfo.padInfo.type === 'SAME' || convInfo.padInfo.type === 'VALID')) {\n    const x2d = x4D.reshape([-1, convInfo.inChannels]) as Tensor2D;\n    const w2d = $filter.reshape([convInfo.inChannels, convInfo.outChannels]) as\n        Tensor2D;\n\n    res = matMul(x2d, w2d).reshape<Rank.R4>(convInfo.outShape);\n  } else {\n    const grad = (dy: Tensor4D) => {\n      util.assert(\n          conv_util.tupleValuesAreOne(dilations),\n          'Error in gradient of conv2D: dilation rates greater than 1 are not' +\n              `yet supported in gradients. Got dilations '${dilations}'`);\n\n      return {\n        x: () => conv2dDerInput_(x4D.shape, dy, $filter, strides, pad),\n        $filter: () => conv2dDerFilter_(x4D, dy, $filter.shape, strides, pad)\n      };\n    };\n\n    res = ENV.engine.runKernel(\n        backend => backend.conv2d(x4D, $filter, convInfo), {x: x4D, $filter},\n        grad);\n  }\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the derivative of the input of a 2D convolution.\n *\n * @param xShape The shape of the input: [batch, height, width, inDepth].\n * If length of 3, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 4 or rank 3 of shape\n *   `[batch, outHeight, outWidth, outDepth]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction conv2dDerInput_<T extends Tensor3D|Tensor4D>(\n    xShape: [number, number, number, number]|[number, number, number], dy: T,\n    filter: Tensor4D, strides: [number, number]|number,\n    pad: 'valid'|'same'|number, dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  util.assert(\n      xShape.length === dy.rank,\n      `Length of inShape ` +\n          `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n\n  let xShape4D = xShape as [number, number, number, number];\n  let dy4D = dy as Tensor4D;\n  let reshapedTo4D = false;\n  if (dy.rank === 3) {\n    reshapedTo4D = true;\n    dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);\n    xShape4D = [1, xShape[0], xShape[1], xShape[2]];\n  }\n\n  const inDepth = xShape4D[3];\n  const outDepth = dy4D.shape[3];\n  util.assert(\n      xShape4D.length === 4,\n      `Error in conv2dDerInput: inShape must be length 4, but got length ` +\n          `${xShape4D.length}.`);\n  util.assert(\n      dy4D.rank === 4,\n      `Error in conv2dDerInput: dy must be rank 4, but got ` +\n          `rank ${dy4D.rank}`);\n  util.assert(\n      filter.rank === 4,\n      `Error in conv2dDerInput: filter must be rank 4, but got ` +\n          `rank ${filter.rank}`);\n  util.assert(\n      inDepth === filter.shape[2],\n      `Error in conv2dDerInput: depth of input (${inDepth}) must ` +\n          `match input depth for filter ${filter.shape[2]}.`);\n  util.assert(\n      outDepth === filter.shape[3],\n      `Error in conv2dDerInput: depth of output (${outDepth}) must ` +\n          `match output depth for filter ${filter.shape[3]}.`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        `Error in conv2dDerInput: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const dilations = 1;\n\n  const grad = (ddx: Tensor4D) => {\n    const dataFormat = 'NHWC';\n    return {\n      dy4D: () => conv2d(\n          ddx, filter, strides, pad, dataFormat, dilations, dimRoundingMode),\n      filter: () => conv2dDerFilter(\n          ddx, dy4D, filter.shape, strides, pad, dimRoundingMode)\n    };\n  };\n\n  const convInfo = conv_util.computeConv2DInfo(\n      xShape4D, filter.shape, strides, dilations, pad, dimRoundingMode);\n  const res = ENV.engine.runKernel(\n      backend => backend.conv2dDerInput(dy4D, filter, convInfo), {dy4D, filter},\n      grad);\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the derivative of the filter of a 2D convolution.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.\n * @param dy The dy image, of rank 4 or rank 3, of shape\n *     [batch, height, width, outDepth]. If rank 3, batch of 1 is assumed.\n * @param filterShape The shape of the filter, length 4,\n *     [filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction conv2dDerFilter_<T extends Tensor3D|Tensor4D>(\n    x: T, dy: T, filterShape: [number, number, number, number],\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): Tensor4D {\n  let x4D = x as Tensor4D;\n  if (x.rank === 3) {\n    x4D = x.as4D(1, x.shape[0], x.shape[1], x.shape[2]);\n  }\n  let dy4D = dy as Tensor4D;\n  if (dy4D.rank === 3) {\n    dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);\n  }\n  util.assert(\n      x4D.rank === 4,\n      `Error in conv2dDerFilter: input must be rank 4, but got shape ` +\n          `${x4D.shape}.`);\n  util.assert(\n      dy4D.rank === 4,\n      `Error in conv2dDerFilter: dy must be rank 4, but got shape ` +\n          `${dy4D.shape}.`);\n  util.assert(\n      filterShape.length === 4,\n      `Error in conv2dDerFilter: filterShape must be length 4, but got ` +\n          `${filterShape}.`);\n  util.assert(\n      x4D.shape[3] === filterShape[2],\n      `Error in conv2dDerFilter: depth of input ${x4D.shape[3]}) must ` +\n          `match input depth in filter (${filterShape[2]}.`);\n  util.assert(\n      dy4D.shape[3] === filterShape[3],\n      `Error in conv2dDerFilter: depth of dy (${dy4D.shape[3]}) must ` +\n          `match output depth for filter (${filterShape[3]}).`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        `Error in conv2dDerFilter: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const dilations = 1;\n\n  const convInfo = conv_util.computeConv2DInfo(\n      x4D.shape, filterShape, strides, dilations, pad, dimRoundingMode);\n  return ENV.engine.runKernel(\n      backend => backend.conv2dDerFilter(x4D, dy4D, convInfo), {x4D, dy4D});\n}\n\n/**\n * Computes the transposed 2D convolution of an image, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 4 or rank 3, of shape\n *   `[batch, height, width, inDepth]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, outDepth, inDepth]`.\n *     `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 4 or rank 3:\n *     `[batch, height, width, outDepth]`. If rank 3, batch of 1 is assumed.\n * @param strides The strides of the original convolution:\n *     `[strideHeight, strideWidth]`.\n * @param pad  The type of padding algorithm used in the non-transpose version\n *    of the op.\n * @param dimRoundingMode The rounding mode used when computing output\n *    dimensions if pad is a number. If none is provided, it will not round\n *    and error if the output is of fractional size.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction conv2dTranspose_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filter: Tensor4D|TensorLike,\n    outputShape: [number, number, number, number]|[number, number, number],\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'conv2dTranspose');\n  const $filter = convertToTensor(filter, 'filter', 'conv2dTranspose');\n\n  return conv2dDerInput_(\n      outputShape, $x, $filter, strides, pad, dimRoundingMode);\n}\n\n/**\n * Depthwise 2D convolution.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction depthwiseConv2d_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filter: Tensor4D|TensorLike,\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n    dilations: [number, number]|number = [1, 1],\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'depthwiseConv2d');\n  const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n  }\n  util.assert(\n      x4D.rank === 4,\n      `Error in depthwiseConv2d: input must be rank 4, but got ` +\n          `rank ${x4D.rank}.`);\n  util.assert(\n      $filter.rank === 4,\n      `Error in depthwiseConv2d: filter must be rank 4, but got rank ` +\n          `${$filter.rank}.`);\n  util.assert(\n      x4D.shape[3] === $filter.shape[2],\n      `Error in depthwiseConv2d: number of input channels ` +\n          `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n          `filter ${$filter.shape[2]}.`);\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      'Error in depthwiseConv2d: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        `Error in depthwiseConv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const convInfo = conv_util.computeConv2DInfo(\n      x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode,\n      true /* depthwise */);\n\n  const grad = (dy: Tensor4D) => {\n    util.assert(\n        conv_util.tupleValuesAreOne(dilations),\n        'Error in gradient of depthwiseConv2d: dilation rates greater than ' +\n            `1 are not yet supported. Got dilations '${dilations}'`);\n    return {\n      x: () => depthwiseConv2dDerInput(x4D.shape, dy, $filter, convInfo),\n      $filter: () => depthwiseConv2dDerFilter(x4D, dy, $filter.shape, convInfo),\n    };\n  };\n\n  const res = ENV.engine.runKernel(\n      backend => backend.depthwiseConv2D(x4D, $filter, convInfo),\n      {x: x4D, $filter}, grad);\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\n/**\n * 2-D convolution with separable filters.\n *\n * Performs a depthwise convolution that acts separately on channels followed\n * by a pointwise convolution that mixes channels. Note that this is\n * separability between dimensions [1, 2] and 3, not spatial separability\n * between dimensions 1 and 2.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param depthwiseFilter The depthwise filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`. This is\n *     the filter used in the first step.\n * @param pointwiseFilter The pointwise filter tensor, rank 4, of shape\n *     `[1, 1, inChannels * channelMultiplier, outChannels]`. This is\n *     the filter used in the second step.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction separableConv2d_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, depthwiseFilter: Tensor4D|TensorLike,\n    pointwiseFilter: Tensor4D|TensorLike, strides: [number, number]|number,\n    pad: 'valid'|'same', dilation: [number, number]|number = [1, 1],\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC'): T {\n  const $x = convertToTensor(x, 'x', 'separableConv2d');\n  const $depthwiseFilter =\n      convertToTensor(depthwiseFilter, 'depthwiseFilter', 'separableConv2d');\n  const $pointwiseFilter =\n      convertToTensor(pointwiseFilter, 'pointwiseFilter', 'separableConv2d');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n  }\n\n  if (dataFormat === 'NCHW') {\n    throw new Error(\n        'separableConv2d currently does not support dataFormat NCHW; only ' +\n        'NHWC is supported');\n  }\n\n  util.assert(\n      x4D.rank === 4,\n      `Error in separableConv2d: input must be rank 4, but got ` +\n          `rank ${x4D.rank}.`);\n  util.assert(\n      $depthwiseFilter.rank === 4,\n      `Error in separableConv2d: depthwise filter must be rank 4, but got ` +\n          `rank ${$depthwiseFilter.rank}.`);\n  util.assert(\n      $pointwiseFilter.rank === 4,\n      `Error in separableConv2d: pointwise filter must be rank 4, but got ` +\n          `rank ${$depthwiseFilter.rank}.`);\n  util.assert(\n      $pointwiseFilter.shape[0] === 1,\n      `Error in separableConv2d: the first dimension of pointwise filter ` +\n          ` must be 1, but got ${$pointwiseFilter.shape[0]}.`);\n  util.assert(\n      $pointwiseFilter.shape[1] === 1,\n      `Error in separableConv2d: the second dimension of pointwise filter ` +\n          ` must be 1, but got ${$pointwiseFilter.shape[1]}.`);\n\n  const inChannels = $depthwiseFilter.shape[2];\n  const channelMultiplier = $depthwiseFilter.shape[3];\n  util.assert(\n      $pointwiseFilter.shape[2] === inChannels * channelMultiplier,\n      `Error in separableConv2d: the third dimension of pointwise filter ` +\n          `must be ${inChannels * channelMultiplier}, ` +\n          `but got ${$pointwiseFilter.shape[2]}.`);\n\n  const depthwise = depthwiseConv2d(\n      x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);\n  const pointwiseStride = 1;\n  const res =\n      conv2d(depthwise, $pointwiseFilter, pointwiseStride, 'valid', dataFormat);\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\nfunction parseTupleParam(\n    param: number|[number, number]|[number, number, number]):\n    [number, number, number] {\n  if (typeof param === 'number') {\n    return [param, param, param];\n  }\n  if (param.length === 2) {\n    return [param[0], param[1], 1];\n  }\n  return param;\n}\n\nfunction tupleValuesAreOne(\n    param: number|[number, number]|[number, number, number]): boolean {\n  const [dimA, dimB, dimC] = parseTupleParam(param);\n  return dimA === 1 && dimB === 1 && dimC === 1;\n}\n\nfunction eitherStridesOrDilationsAreOne(\n    strides: number|[number, number]|[number, number, number],\n    dilations: number|[number, number]|[number, number, number]): boolean {\n  return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);\n}\n\nfunction depthwiseConv2dDerInput<T extends Tensor3D|Tensor4D>(\n    xShape: [number, number, number, number]|[number, number, number], dy: T,\n    filter: Tensor4D, convInfo: conv_util.Conv2DInfo): T {\n  let dy4D = dy as Tensor4D;\n  let reshapedTo4D = false;\n  if (dy.rank === 3) {\n    reshapedTo4D = true;\n    dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);\n  }\n  const res = ENV.engine.runKernel(\n      backend => backend.depthwiseConv2DDerInput(dy4D, filter, convInfo),\n      {dy4D});\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\nfunction depthwiseConv2dDerFilter<T extends Tensor3D|Tensor4D>(\n    x: T, dy: T, filterShape: [number, number, number, number],\n    convInfo: conv_util.Conv2DInfo): Tensor4D {\n  let x4D = x as Tensor4D;\n  if (x.rank === 3) {\n    x4D = x.as4D(1, x.shape[0], x.shape[1], x.shape[2]);\n  }\n  let dy4D = dy as Tensor4D;\n  if (dy4D.rank === 3) {\n    dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);\n  }\n  return ENV.engine.runKernel(\n      backend => backend.depthwiseConv2DDerFilter(x4D, dy4D, convInfo),\n      {x4D, dy4D});\n}\n\n/**\n * Computes a 3D convolution over the input x.\n *\n * @param x The input tensor, of rank 5 or rank 4, of shape\n *     `[batch, depth, height, width, channels]`. If rank 4,\n * batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`.\n *      inChannels must match between input and filter.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationDepth, dilationHeight,\n *     dilationWidth]` in which we sample input values across the height\n *     and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`.\n *     If `dilations` is a single number, then\n *     `dilationDepth == dilationHeight == dilationWidth`. If it is greater\n *     than 1, then all values of `strides` must be 1.\n */\n\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction conv3d_<T extends Tensor4D|Tensor5D>(\n    x: T|TensorLike, filter: Tensor5D|TensorLike,\n    strides: [number, number, number]|number, pad: 'valid'|'same',\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n    dilations: [number, number, number]|number = [1, 1, 1]): T {\n  const $x = convertToTensor(x, 'x', 'conv3d');\n  const $filter = convertToTensor(filter, 'filter', 'conv3d');\n\n  let x5D = $x as Tensor5D;\n  let reshapedTo5D = false;\n\n  if ($x.rank === 4) {\n    reshapedTo5D = true;\n    x5D = $x.as5D(1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]);\n  }\n  util.assert(\n      x5D.rank === 5,\n      `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);\n  util.assert(\n      $filter.rank === 5,\n      `Error in conv3d: filter must be rank 5, but got rank ` +\n          `${$filter.rank}.`);\n  util.assert(\n      x5D.shape[4] === $filter.shape[3],\n      `Error in conv3d: depth of input (${x5D.shape[4]}) must match ` +\n          `input depth for filter ${$filter.shape[3]}.`);\n  util.assert(\n      eitherStridesOrDilationsAreOne(strides, dilations),\n      'Error in conv3D: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n  util.assert(\n      dataFormat === 'NHWC',\n      `Error in conv3d: got dataFormat of ${\n          dataFormat} but only NHWC is currently supported.`);\n\n  const convInfo = conv_util.computeConv3DInfo(\n      x5D.shape, $filter.shape, strides, dilations, pad);\n\n  const grad = (dy: Tensor5D) => {\n    util.assert(\n        tupleValuesAreOne(dilations),\n        'Error in gradient of conv3D: dilation rates greater than 1 are not' +\n            `yet supported in gradients. Got dilations '${dilations}'`);\n\n    return {\n      x: () => conv3dDerInput_(x5D.shape, dy, $filter, strides, pad),\n      $filter: () => conv3dDerFilter_(x5D, dy, $filter.shape, strides, pad)\n    };\n  };\n\n  const res = ENV.engine.runKernel(\n      backend => backend.conv3d(x5D, $filter, convInfo), {x: x5D, $filter},\n      grad);\n  if (reshapedTo5D) {\n    return res.as4D(res.shape[1], res.shape[2], res.shape[3], res.shape[4]) as\n        T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the derivative of the input of a 3D convolution.\n *\n * @param xShape The shape of the input: [batch, depth, height, width,\n * in_channels]. If length of 4, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 5 or rank 4 of shape\n *   `[batch, outDepth, outHeight, outWidth, in_channels]`.\n * If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n */\nfunction conv3dDerInput_<T extends Tensor4D|Tensor5D>(\n    xShape:\n        [number, number, number, number,\n         number]|[number, number, number, number],\n    dy: T, filter: Tensor5D, strides: [number, number, number]|number,\n    pad: 'valid'|'same'): T {\n  util.assert(\n      xShape.length === dy.rank,\n      `Length of inShape ` +\n          `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n\n  let xShape5D = xShape as [number, number, number, number, number];\n  let dy5D = dy as Tensor5D;\n  let reshapedTo5D = false;\n  if (dy.rank === 4) {\n    reshapedTo5D = true;\n    dy5D = dy.as5D(1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]);\n    xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];\n  }\n\n  const inDepth = xShape5D[4];\n  const outDepth = dy5D.shape[4];\n  util.assert(\n      xShape5D.length === 5,\n      `Error in conv3dDerInput: inShape must be length 5, but got length ` +\n          `${xShape5D.length}.`);\n  util.assert(\n      dy5D.rank === 5,\n      `Error in conv3dDerInput: dy must be rank 5, but got ` +\n          `rank ${dy5D.rank}`);\n  util.assert(\n      filter.rank === 5,\n      `Error in conv3dDerInput: filter must be rank 5, but got ` +\n          `rank ${filter.rank}`);\n  util.assert(\n      inDepth === filter.shape[3],\n      `Error in conv3dDerInput: depth of input (${inDepth}) must ` +\n          `match input depth for filter ${filter.shape[3]}.`);\n  util.assert(\n      outDepth === filter.shape[4],\n      `Error in conv3dDerInput: depth of output (${outDepth}) must ` +\n          `match output depth for filter ${filter.shape[4]}.`);\n\n  const dilations = 1;\n\n  const convInfo = conv_util.computeConv3DInfo(\n      xShape5D, filter.shape, strides, dilations, pad);\n  const res = ENV.engine.runKernel(\n      backend => backend.conv3dDerInput(dy5D, filter, convInfo), {dy5D});\n  if (reshapedTo5D) {\n    return res.as4D(res.shape[1], res.shape[2], res.shape[3], res.shape[4]) as\n        T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the derivative of the filter of a 3D convolution.\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     [batch, depth, height, width, inChannels]. If rank 4, batch of 1 is\n *     assumed.\n * @param dy The dy image, of rank 5 or rank 4, of shape\n *     [batch, depth, height, width, outDepth]. If rank 4, batch of 1 is\n *     assumed.\n * @param filterShape The shape of the filter, length 5,\n *     [filterDepth, filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideDepth, strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n */\nfunction conv3dDerFilter_<T extends Tensor4D|Tensor5D>(\n    x: T, dy: T, filterShape: [number, number, number, number, number],\n    strides: [number, number, number]|number, pad: 'valid'|'same'): Tensor5D {\n  let x5D = x as Tensor5D;\n  if (x.rank === 4) {\n    x5D = x.as5D(1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]);\n  }\n  let dy5D = dy as Tensor5D;\n  if (dy5D.rank === 4) {\n    dy5D = dy.as5D(1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]);\n  }\n  util.assert(\n      x5D.rank === 5,\n      `Error in conv3dDerFilter: input must be rank 5, but got shape ` +\n          `${x5D.shape}.`);\n  util.assert(\n      dy5D.rank === 5,\n      `Error in conv3dDerFilter: dy must be rank 5, but got shape ` +\n          `${dy5D.shape}.`);\n  util.assert(\n      filterShape.length === 5,\n      `Error in conv3dDerFilter: filterShape must be length 5, but got ` +\n          `${filterShape}.`);\n  util.assert(\n      x5D.shape[4] === filterShape[3],\n      `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must ` +\n          `match input depth in filter (${filterShape[3]}.`);\n  util.assert(\n      dy5D.shape[4] === filterShape[4],\n      `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must ` +\n          `match output depth for filter (${filterShape[4]}).`);\n\n  const dilations = 1;\n\n  const convInfo = conv_util.computeConv3DInfo(\n      x5D.shape, filterShape, strides, dilations, pad);\n  return ENV.engine.runKernel(\n      backend => backend.conv3dDerFilter(x5D, dy5D, convInfo), {x5D, dy5D});\n}\n\nexport const conv1d = op({conv1d_});\nexport const conv2d = op({conv2d_});\nexport const conv3d = op({conv3d_});\nexport const conv2dDerFilter = op({conv2dDerFilter_});\nexport const depthwiseConv2d = op({depthwiseConv2d_});\nexport const separableConv2d = op({separableConv2d_});\nexport const conv2dTranspose = op({conv2dTranspose_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {parseAxisParam} from './axis_util';\nimport {op} from './operation';\n\n/**\n * Reverses a `tf.Tensor1D`.\n *\n * @param x The input tensor.\n */\nfunction reverse1d_(x: Tensor1D|TensorLike): Tensor1D {\n  const $x = convertToTensor(x, 'x', 'reverse');\n  util.assert($x.rank === 1, `Error in reverse1D: x must be rank 1 but got\n             rank ${$x.rank}.`);\n  return reverse($x, 0);\n}\n\n/**\n * Reverses a `tf.Tensor2D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse2d_(x: Tensor2D|TensorLike, axis?: number|number[]): Tensor2D {\n  const $x = convertToTensor(x, 'x', 'reverse');\n  util.assert($x.rank === 2, `Error in reverse2D: x must be rank 2 but got\n             rank ${$x.rank}.`);\n  return reverse($x, axis);\n}\n\n/**\n * Reverses a `tf.Tensor3D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse3d_(x: Tensor3D|TensorLike, axis?: number|number[]): Tensor3D {\n  const $x = convertToTensor(x, 'x', 'reverse');\n  util.assert($x.rank === 3, `Error in reverse3D: x must be rank 3 but got\n             rank ${$x.rank}.`);\n  return reverse($x, axis);\n}\n\n/**\n * Reverses a `tf.Tensor4D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse4d_(x: Tensor4D|TensorLike, axis?: number|number[]): Tensor4D {\n  const $x = convertToTensor(x, 'x', 'reverse');\n  util.assert($x.rank === 4, `Error in reverse4D: x must be rank 4 but got\n             rank ${$x.rank}.`);\n  return reverse($x, axis);\n}\n\n/**\n * Reverses a `tf.Tensor` along a specified axis.\n *\n * Also available are stricter rank-specific methods that assert that `x` is\n * of the given rank:\n *   - `tf.reverse1d`\n *   - `tf.reverse2d`\n *   - `tf.reverse3d`\n *   - `tf.reverse4d`\n *\n * Except `tf.reverse1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.reverse().print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.reverse(axis).print();\n * ```\n * @param x The input tensor to be reversed.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction reverse_<T extends Tensor>(\n    x: T|TensorLike, axis?: number|number[]): T {\n  const $x = convertToTensor(x, 'x', 'reverse');\n\n  if ($x.rank === 0) {\n    return $x.clone();\n  }\n  const axes = parseAxisParam(axis, $x.shape);\n  const grad = (dy: T) => {\n    return {$x: () => dy.reverse(axes)};\n  };\n  const res =\n      ENV.engine.runKernel(backend => backend.reverse($x, axes), {$x}, grad);\n  return res.reshapeAs($x);\n}\n\nexport const reverse = op({reverse_});\nexport const reverse1d = op({reverse1d_});\nexport const reverse2d = op({reverse2d_});\nexport const reverse3d = op({reverse3d_});\nexport const reverse4d = op({reverse4d_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {batchToSpaceND, spaceToBatchND} from './array_ops';\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\n\n/**\n * Computes the 2D max pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction maxPoolImpl_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filterSize: [number, number]|number,\n    strides: [number, number]|number, dilations: [number, number]|number,\n    pad: 'valid'|'same'|number, dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'maxPool');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n  }\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  util.assert(\n      x4D.rank === 4,\n      `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      'Error in maxPool: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        `Error in maxPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n  const convInfo = conv_util.computePool2DInfo(\n      x4D.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n\n  const grad = (dy: Tensor4D, saved: Tensor[]) => {\n    const [y4D] = saved;\n    return {\n      x: () => maxPoolBackprop(\n          dy, x4D, y4D as Tensor4D, filterSize, strides, dilations, pad)\n    };\n  };\n\n  const res = ENV.engine.runKernel(\n      (backend, save) => save(backend.maxPool(x4D, convInfo)), {x: x4D}, grad);\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the 2D max pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction maxPool_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filterSize: [number, number]|number,\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  return maxPoolImpl_(x, filterSize, strides, 1, pad, dimRoundingMode);\n}\n\n/**\n * Computes the 2D average pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction avgPoolImpl_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filterSize: [number, number]|number,\n    strides: [number, number]|number, dilations: [number, number]|number,\n    pad: 'valid'|'same'|number, dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'avgPool', 'float32');\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      'Error in avgPool: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n  }\n  util.assert(\n      x4D.rank === 4,\n      `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        `Error in avgPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const convInfo = conv_util.computePool2DInfo(\n      x4D.shape, filterSize, strides, dilations, pad);\n\n  const grad = (dy: Tensor4D) => {\n    return {\n      x: () => avgPoolBackprop(dy, x4D, filterSize, strides, dilations, pad)\n    };\n  };\n  let res = ENV.engine.runKernel(\n      backend => backend.avgPool(x4D, convInfo), {x: x4D}, grad);\n  res = res.cast($x.dtype);\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the 2D average pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction avgPool_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filterSize: [number, number]|number,\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  return avgPoolImpl_(x, filterSize, strides, 1, pad, dimRoundingMode);\n}\n\n/**\n * Performs an N-D pooling operation\n *\n * @param input The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param windowShape The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param poolingType The type of pooling, either 'max' or 'avg'.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilationRate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction pool_<T extends Tensor3D|Tensor4D>(\n    input: T|TensorLike, windowShape: [number, number]|number,\n    poolingType: 'avg'|'max', pad: 'valid'|'same'|number,\n    dilations?: [number, number]|number, strides?: [number, number]|number) {\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  if (strides == null) {\n    strides = 1;\n  }\n  if (pad === 0) {\n    pad = 'valid';\n  }\n  const $x = convertToTensor(input, 'x', 'maxPool');\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n  }\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      'Error in pool: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n  const convInfo = conv_util.computePool2DInfo(\n      x4D.shape, windowShape, strides, dilations, pad);\n  const dilation: [number, number] =\n      [convInfo.dilationHeight, convInfo.dilationWidth];\n\n  // The following implementation does batchToSpace(pool(spaceToBatch(x)))\n  // whenever dilation > 1 since the TF kernels do not support dilation > 1.\n  // tslint:disable-next-line:max-line-length\n  // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L1037\n\n  let basePadding: number[][];\n  if (pad === 'same') {\n    basePadding = withSpaceToBatchBasePaddings(\n        [convInfo.filterHeight, convInfo.filterWidth], dilation);\n  } else {\n    basePadding = [[0, 0], [0, 0]];\n  }\n  const isDilationOne = dilation[0] === 1 && dilation[1] === 1;\n  const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings(\n      [convInfo.inHeight, convInfo.inWidth], dilation, basePadding);\n  const convertedPad = isDilationOne ? pad : 'valid';\n  const convertedX =\n      isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);\n  const forwardOp = poolingType === 'avg' ?\n      () => avgPoolImpl_(\n          convertedX, windowShape, strides, 1 /* dilation */, convertedPad) :\n      () => maxPoolImpl_(\n          convertedX, windowShape, strides, 1 /* dilation */, convertedPad);\n  const y = forwardOp();\n  const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the backprop of a max pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The original input image, of rank 4, of shape\n *     [batchSize, height, width, channels].\n * @param output The original output image, of rank 4, of shape\n *     [batchSize, outHeight, outWidth, channels].\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction maxPoolBackprop(\n    dy: Tensor4D|TensorLike, input: Tensor4D|TensorLike,\n    output: Tensor4D|TensorLike, filterSize: [number, number]|number,\n    strides: [number, number]|number, dilations: [number, number]|number,\n    pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): Tensor4D {\n  const $dy = convertToTensor(dy, 'dy', 'maxPoolBackprop');\n  const $input = convertToTensor(input, 'input', 'maxPoolBackprop');\n  const $output = convertToTensor(output, 'output', 'maxPoolBackprop');\n  util.assert(\n      $input.rank === $dy.rank,\n      `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      'Error in maxPoolBackProp: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n\n  util.assert(\n      $dy.rank === 4,\n      `Error in maxPoolBackprop: dy must be rank 4 but got rank ` +\n          `${$dy.rank}.`);\n  util.assert(\n      $input.rank === 4,\n      `Error in maxPoolBackprop: input must be rank 4 but got rank ` +\n          `${$input.rank}.`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        `Error in maxPoolBackprop: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const convInfo = conv_util.computePool2DInfo(\n      $input.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n  const res = ENV.engine.runKernel(\n      backend => backend.maxPoolBackprop($dy, $input, $output, convInfo),\n      {$dy, $input});\n  return res;\n}\n\n/**\n * Computes the backprop of an avg pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The input image, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n */\nfunction avgPoolBackprop<T extends Tensor3D|Tensor4D>(\n    dy: T|TensorLike, input: T|TensorLike, filterSize: [number, number]|number,\n    strides: [number, number]|number, dilations: [number, number]|number,\n    pad: 'valid'|'same'|number): T {\n  const $dy = convertToTensor(dy, 'dy', 'avgPoolBackprop');\n  const $input = convertToTensor(input, 'input', 'avgPoolBackprop');\n  util.assert(\n      $input.rank === $dy.rank,\n      `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      'Error in avgPoolBackprop: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n\n  let input4D = $input as Tensor4D;\n  let dy4D = $dy as Tensor4D;\n  let reshapedTo4D = false;\n  if ($input.rank === 3) {\n    reshapedTo4D = true;\n    input4D = $input.as4D(1, $input.shape[0], $input.shape[1], $input.shape[2]);\n    dy4D = $dy.as4D(1, $dy.shape[0], $dy.shape[1], $dy.shape[2]);\n  }\n\n  util.assert(\n      dy4D.rank === 4,\n      `Error in avgPoolBackprop: dy must be rank 4 but got rank ` +\n          `${dy4D.rank}.`);\n  util.assert(\n      input4D.rank === 4,\n      `Error in avgPoolBackprop: input must be rank 4 but got rank ` +\n          `${input4D.rank}.`);\n\n  const convInfo = conv_util.computePool2DInfo(\n      input4D.shape, filterSize, strides, dilations, pad);\n  const res = ENV.engine.runKernel(\n      backend => backend.avgPoolBackprop(dy4D, input4D, convInfo),\n      {dy4D, input4D});\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\n// Helper function to compute crops and paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/array_ops.py#L2184\nfunction requiredSpaceToBatchPaddings(\n    inputShape: [number, number], blockShape: [number, number],\n    basePadding: number[][]) {\n  const padStart = basePadding.map(b => b[0]);\n  const origPadEnd = basePadding.map(b => b[1]);\n  const fullInputShape = inputShape.concat(padStart, origPadEnd);\n  const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);\n  const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);\n  const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);\n  const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);\n  return [paddings, crops];\n}\n\n// Helper function to compute base paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L524\nfunction withSpaceToBatchBasePaddings(\n    filterShape: [number, number], dilation: [number, number]) {\n  // Spatial dimensions of the filters and the upsampled filters in which we\n  // introduce (rate - 1) zeros between consecutive filter values.\n  const dilatedFilterShape = filterShape.map((s, i) => {\n    return s + (s - 1) * (dilation[i] - 1);\n  });\n  const padExtraShape = dilatedFilterShape.map(s => s - 1);\n\n  // When padding is odd, we pad more at end, following the same\n  // convention as conv2d.\n  const padExtraStart = padExtraShape.map(s => Math.floor(s / 2));\n  const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);\n  return padExtraShape.map((_, i) => {\n    return [padExtraStart[i], padExtraEnd[i]];\n  });\n}\n\nexport const maxPool = op({maxPool_});\nexport const avgPool = op({avgPool_});\nexport const pool = op({pool_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\nimport * as slice_util from './slice_util';\n\n/**\n * Extracts a 1D slice from 1D array starting at coordinates `begin` and is\n * of length `size`. See `slice` for details.\n */\nfunction slice1d_(\n    x: Tensor1D|TensorLike, begin: number, size: number): Tensor1D {\n  const $x = convertToTensor(x, 'x', 'slice1d');\n  util.assert(\n      $x.rank === 1,\n      `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, [begin], [size]);\n}\n\n/**\n * Extracts a 2D slice from a 2D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice2d_(\n    x: Tensor2D|TensorLike, begin: [number, number],\n    size: [number, number]): Tensor2D {\n  const $x = convertToTensor(x, 'x', 'slice2d');\n  util.assert(\n      $x.rank === 2,\n      `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, begin, size);\n}\n\n/**\n * Extracts a 3D slice from a 3D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice3d_(\n    x: Tensor3D|TensorLike, begin: [number, number, number],\n    size: [number, number, number]): Tensor3D {\n  const $x = convertToTensor(x, 'x', 'slice3d');\n  util.assert(\n      $x.rank === 3,\n      `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, begin, size);\n}\n\n/**\n * Extracts a 4D slice from a 4D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice4d_(\n    x: Tensor4D|TensorLike, begin: [number, number, number, number],\n    size: [number, number, number, number]): Tensor4D {\n  const $x = convertToTensor(x, 'x', 'slice4d');\n  util.assert(\n      $x.rank === 4,\n      `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, begin, size);\n}\n\n/**\n * Extracts a slice from a `tf.Tensor` starting at coordinates `begin`\n * and is of size `size`.\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `x` is of the given rank:\n *   - `tf.slice1d`\n *   - `tf.slice2d`\n *   - `tf.slice3d`\n *   - `tf.slice4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.slice([1], [2]).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * x.slice([1, 0], [1, 2]).print();\n * ```\n * @param x The input `tf.Tensor` to slice from.\n * @param begin The coordinates to start the slice from. The length can be\n *     less than the rank of x - the rest of the axes will have implicit 0 as\n *     start. Can also be a single number, in which case it specifies the\n *     first axis.\n * @param size The size of the slice. The length can be less than the rank of\n *     x - the rest of the axes will have implicit -1. A value of -1 requests\n *     the rest of the dimensions in the axis. Can also be a single number,\n *     in which case it specifies the size of the first axis.\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction slice_<R extends Rank, T extends Tensor<R>>(\n    x: T|TensorLike, begin: number|number[], size?: number|number[]): T {\n  const $x = convertToTensor(x, 'x', 'slice');\n\n  if ($x.rank === 0) {\n    throw new Error('Slicing scalar is not possible');\n  }\n  // The following logic allows for more ergonomic calls.\n  let begin_: number[];\n  if (typeof begin === 'number') {\n    begin_ = [begin, ...new Array($x.rank - 1).fill(0)];\n  } else if (begin.length < $x.rank) {\n    begin_ = begin.concat(new Array($x.rank - begin.length).fill(0));\n  } else {\n    begin_ = begin.slice();\n  }\n  let size_: number[];\n  if (size == null) {\n    size_ = new Array($x.rank).fill(-1);\n  } else if (typeof size === 'number') {\n    size_ = [size, ...new Array($x.rank - 1).fill(-1)];\n  } else if (size.length < $x.rank) {\n    size_ = size.concat(new Array($x.rank - size.length).fill(-1));\n  } else {\n    size_ = size;\n  }\n  size_ = size_.map((d, i) => {\n    if (d >= 0) {\n      return d;\n    } else {\n      util.assert(d === -1, 'Bad value in size');\n      return $x.shape[i] - begin_[i];\n    }\n  });\n  slice_util.assertParamsValid($x, begin_, size_);\n  const inputShape = $x.shape;\n  const grad = (dy: T) => {\n    // Create an Nx2 padding where the first column represents how many\n    // zeros are prepended (at start) for each dimension, and the second\n    // column indicates how many zeros are appended (at end).\n\n    // The number of zeros to append is the shape of the input\n    // elementwise-subtracted by both the begin vector and sizes vector.\n    const paddings: Array<[number, number]> = [];\n    for (let i = 0; i < dy.rank; i++) {\n      paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);\n    }\n    return {$x: () => dy.pad(paddings)};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.slice($x, begin_, size_), {$x}, grad) as T;\n}\n\nexport const slice = op({slice_});\nexport const slice1d = op({slice1d_});\nexport const slice2d = op({slice2d_});\nexport const slice3d = op({slice3d_});\nexport const slice4d = op({slice4d_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {customGrad} from '../globals';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport {op} from './operation';\nimport {ones, scalar, zerosLike} from './tensor_ops';\n\n/**\n * Computes the log(sum(exp(elements across the reduction dimensions)).\n *\n * Reduces the input along the dimensions given in `axis`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.logSumExp().print();  // or tf.logSumExp(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.logSumExp(axis).print();  // or tf.logSumExp(a, axis)\n * ```\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. If null (the default),\n *     reduces all dimensions.\n * @param keepDims If true, retains reduced dimensions with length\n *     of 1. Defaults to false.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction logSumExp_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  const $x = convertToTensor(x, 'x', 'logSumExp');\n\n  const axes = axis_util.parseAxisParam(axis, $x.shape);\n  const xMax = $x.max(axes, true /* keepDims */);\n  const a = $x.sub(xMax);\n  const b = a.exp();\n  const c = b.sum(axes);\n  const d = c.log();\n  const res = xMax.reshape(d.shape).add(d);\n\n  if (keepDims) {\n    const newShape = axis_util.expandShapeToKeepDim(res.shape, axes);\n    return res.reshape(newShape) as T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the sum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If axes has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.sum().print();  // or tf.sum(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.sum(axis).print();  // or tf.sum(x, axis)\n * ```\n *\n * @param x The input tensor to compute the sum over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction sum_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  let $x = convertToTensor(x, 'x', 'sum');\n\n  if ($x.dtype === 'bool') {\n    $x = $x.toInt();\n  }\n  const axes = axis_util.parseAxisParam(axis, $x.shape);\n\n  // Use a custom gradient to bypass 2 gradient backprops since sum is used\n  // extremely often.\n  const customOp = customGrad(x => {\n    const permutation = axis_util.getAxesPermutation(axes, x.rank);\n    let reductionAxes = axes;\n    let permutedX = x;\n    if (permutation != null) {\n      permutedX = x.transpose(permutation);\n      reductionAxes = axis_util.getInnerMostAxes(reductionAxes.length, x.rank);\n    }\n    let value = ENV.engine.runKernel(\n        backend => backend.sum(permutedX, reductionAxes), {permutedX});\n    if (keepDims) {\n      const newShape = axis_util.expandShapeToKeepDim(value.shape, axes);\n      value = value.reshape(newShape);\n    }\n\n    const gradFunc = (dy: Tensor) => {\n      const expandedDyShape = x.shape.slice();\n      axes.forEach(axis => {\n        expandedDyShape[axis] = 1;\n      });\n      const expandedDy = dy.reshape(expandedDyShape);\n      const derX = expandedDy.mul(ones(x.shape, 'float32'));\n      return derX;\n    };\n    return {value, gradFunc};\n  });\n\n  return customOp($x) as T;\n}\n\n/**\n * Computes the product of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.prod().print();  // or tf.prod(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.prod(axis).print();  // or tf.prod(x, axis)\n * ```\n *\n * @param x The input tensor to compute the product over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction prod_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  let $x = convertToTensor(x, 'x', 'prod');\n\n  if ($x.dtype === 'bool') {\n    $x = $x.toInt();\n  }\n  const axes = axis_util.parseAxisParam(axis, $x.shape);\n\n  const permutation = axis_util.getAxesPermutation(axes, $x.rank);\n  let reductionAxes = axes;\n  let permutedX = $x;\n  if (permutation != null) {\n    permutedX = $x.transpose(permutation);\n    reductionAxes = axis_util.getInnerMostAxes(reductionAxes.length, $x.rank);\n  }\n  let value = ENV.engine.runKernel(\n      backend => backend.prod(permutedX, reductionAxes), {permutedX});\n  if (keepDims) {\n    const newShape = axis_util.expandShapeToKeepDim(value.shape, axes);\n    value = value.reshape(newShape);\n  }\n\n  return value as T;\n}\n/**\n * Computes the mean of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces `x` along the dimensions given in `axis`. Unless `keepDims` is\n * true, the rank of the `tf.Tensor` is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and a `tf.Tensor` with\n * a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.mean().print();  // or tf.mean(a)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.mean(axis).print();  // or tf.mean(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction mean_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  const $x = convertToTensor(x, 'x', 'mean');\n\n  const axes = axis_util.parseAxisParam(axis, $x.shape);\n  const shapes = axis_util.computeOutAndReduceShapes($x.shape, axes);\n  const reduceShape = shapes[1];\n  const reduceSize = util.sizeFromShape(reduceShape);\n\n  // Use a custom gradient to bypass 2 gradient backprops since mean is used\n  // extremely often.\n  const customOp = customGrad(x => {\n    const reduceSizeScalar = scalar(reduceSize);\n    // Cast if needed.\n    const xReduce =\n        reduceSizeScalar.dtype === x.dtype ? x : x.cast(reduceSizeScalar.dtype);\n    const res = xReduce.div(reduceSizeScalar);\n    const value = res.sum(axis, keepDims);\n\n    const gradFunc = (dy: Tensor) => {\n      const expandedDyShape = x.shape.slice();\n      axes.forEach(axis => {\n        expandedDyShape[axis] = 1;\n      });\n      const expandedDy = dy.reshape(expandedDyShape);\n      const derX =\n          expandedDy.mul(ones(x.shape, 'float32')).div(reduceSizeScalar);\n      return derX;\n    };\n    return {value, gradFunc};\n  });\n\n  return customOp($x) as T;\n}\n\n/**\n * Gradient helper function for the min and max operations.\n */\nfunction gradForMinAndMax<T extends Tensor>(\n    dy: T, saved: Tensor[], xOrig: Tensor, origAxes: number[],\n    permutedAxes: number[]) {\n  let [y] = saved;\n  if (y.rank < xOrig.rank) {\n    y = y.reshape(axis_util.expandShapeToKeepDim(y.shape, origAxes)) as T;\n  }\n  if (dy.rank < xOrig.rank) {\n    dy = dy.reshape(axis_util.expandShapeToKeepDim(dy.shape, origAxes)) as T;\n  }\n  return {\n    $x: () => {\n      const dx = dy.mul(xOrig.equal(y).cast(dy.dtype));\n      return permutedAxes == null ? dx : dx.transpose(permutedAxes);\n    }\n  };\n}\n\n/**\n * Computes the minimum value from the input.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axes`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axes` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.min().print();  // or tf.min(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.min(axis).print();  // or tf.min(x, axis)\n * ```\n *\n * @param x The input Tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction min_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  let $x = convertToTensor(x, 'x', 'min');\n  const xOrig = $x;\n\n  const origAxes = axis_util.parseAxisParam(axis, $x.shape);\n  let axes = origAxes;\n  const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n  if (permutedAxes != null) {\n    $x = $x.transpose(permutedAxes);\n    axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n  }\n\n  const grad = (dy: T, saved: Tensor[]) =>\n      gradForMinAndMax(dy, saved, xOrig, origAxes, permutedAxes);\n  let res = ENV.engine.runKernel(\n      (backend, save) => save(backend.min($x, axes)), {$x}, grad);\n  if (keepDims) {\n    const newShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);\n    res = res.reshape(newShape) as T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the maximum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.max().print();  // or tf.max(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.max(axis).print();  // or tf.max(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction max_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  let $x = convertToTensor(x, 'x', 'max');\n  const xOrig = $x;\n\n  const origAxes = axis_util.parseAxisParam(axis, $x.shape);\n  let axes = origAxes;\n  const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n  if (permutedAxes != null) {\n    $x = $x.transpose(permutedAxes);\n    axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n  }\n\n  const grad = (dy: T, saved: Tensor[]) =>\n      gradForMinAndMax(dy, saved, xOrig, origAxes, permutedAxes);\n  let res = ENV.engine.runKernel(\n      (backend, save) => save(backend.max($x, axes)), {$x}, grad);\n  if (keepDims) {\n    const newShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);\n    res = res.reshape(newShape) as T;\n  }\n  return res as T;\n}\n\n/**\n * Returns the indices of the minimum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMin().print();  // or tf.argMin(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMin(axis).print();  // or tf.argMin(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction argMin_<T extends Tensor>(x: Tensor|TensorLike, axis = 0): T {\n  let $x = convertToTensor(x, 'x', 'argMin');\n\n  if (axis == null) {\n    axis = 0;\n  }\n  let axes = axis_util.parseAxisParam(axis, $x.shape);\n  const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n  if (permutedAxes != null) {\n    $x = $x.transpose(permutedAxes);\n    axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n  }\n  const grad = (dy: T) => {\n    return {$x: () => zerosLike($x)};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.argMin($x, axes[0]), {$x}, grad) as T;\n}\n\n/**\n * Returns the indices of the maximum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMax().print();  // or tf.argMax(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMax(axis).print();  // or tf.argMax(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction argMax_<T extends Tensor>(x: Tensor|TensorLike, axis = 0): T {\n  let $x = convertToTensor(x, 'x', 'argMax');\n\n  if (axis == null) {\n    axis = 0;\n  }\n  let axes = axis_util.parseAxisParam(axis, $x.shape);\n  const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n  if (permutedAxes != null) {\n    $x = $x.transpose(permutedAxes);\n    axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n  }\n  const grad = (dy: T) => {\n    return {$x: () => zerosLike($x)};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.argMax($x, axes[0]), {$x}, grad) as T;\n}\n\n/**\n * Computes the logical and of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1]);\n *\n * x.all().print();  // or tf.all(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.all(axis).print();  // or tf.all(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction all_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  let $x = convertToTensor(x, 'x', 'all', 'bool');\n\n  const origAxes = axis_util.parseAxisParam(axis, $x.shape);\n  let axes = origAxes;\n  const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n  if (permutedAxes != null) {\n    $x = $x.transpose(permutedAxes);\n    axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n  }\n  const res = ENV.engine.runKernel(backend => backend.all($x, axes), {$x});\n  if (keepDims) {\n    const newShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);\n    return res.reshape(newShape) as T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the logical or of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1]);\n *\n * x.any().print();  // or tf.any(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.any(axis).print();  // or tf.any(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction any_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  let $x = convertToTensor(x, 'x', 'any', 'bool');\n\n  const origAxes = axis_util.parseAxisParam(axis, $x.shape);\n  let axes = origAxes;\n  const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n  if (permutedAxes != null) {\n    $x = $x.transpose(permutedAxes);\n    axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n  }\n  const res = ENV.engine.runKernel(backend => backend.any($x, axes), {$x});\n  if (keepDims) {\n    const newShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);\n    return res.reshape(newShape) as T;\n  }\n  return res as T;\n}\n\n/**\n * Calculates the mean and variance of `x`. The mean and variance are\n * calculated by aggregating the contents of `x` across `axes`. If `x` is\n * 1-D and `axes = [0]` this is just the mean and variance of a vector.\n *\n * @param x The input tensor.\n * @param axis The dimension(s) along with to compute mean and\n *     variance. By default it reduces all dimensions.\n * @param keepDims If true, the moments have the same dimensionality as the\n *     input.\n * @return An object with two keys: `mean` and `variance`.\n */\n/** @doc {heading: 'Operations', subheading: 'Normalization'} */\nfunction moments_(\n    x: Tensor|TensorLike, axis: number|number[] = null,\n    keepDims = false): {mean: Tensor, variance: Tensor} {\n  x = convertToTensor(x, 'x', 'moments');\n  const axes = axis_util.parseAxisParam(axis, x.shape);\n  const mean = x.mean(axes, keepDims);\n  let keepDimsShape = mean.shape;\n  if (!keepDims) {\n    keepDimsShape = axis_util.expandShapeToKeepDim(mean.shape, axes);\n  }\n  const devSquared = x.toFloat().sub(mean.reshape(keepDimsShape)).square();\n  const variance = devSquared.mean(axes, keepDims);\n  return {mean, variance};\n}\n\nexport const all = op({all_});\n// tslint:disable-next-line:variable-name\nexport const any = op({any_});\nexport const argMax = op({argMax_});\nexport const argMin = op({argMin_});\nexport const logSumExp = op({logSumExp_});\nexport const max = op({max_});\nexport const mean = op({mean_});\nexport const min = op({min_});\nexport const moments = op({moments_});\nexport const sum = op({sum_});\nexport const prod = op({prod_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assertShapesMatch} from '../util';\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\nimport {zerosLike} from './tensor_ops';\n\n/**\n * Returns the truth value of (a != b) element-wise. Supports broadcasting.\n *\n * We also expose `tf.notEqualStrict` which has the same signature as this op\n * and asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([0, 2, 3]);\n *\n * a.notEqual(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction notEqual_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'notEqual');\n  let $b = convertToTensor(b, 'b', 'notEqual');\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  return ENV.engine.runKernel(backend => backend.notEqual($a, $b), {$a, $b}) as\n      T;\n}\n\n/**\n * Strict version of `tf.notEqual` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n *     `a`.\n */\nfunction notEqualStrict_<T extends Tensor>(\n    a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'notEqualStrict');\n  const $b = convertToTensor(b, 'b', 'notEqualStrict');\n  assertShapesMatch($a.shape, $b.shape, 'Error in notEqualStrict: ');\n  return $a.notEqual($b);\n}\n\n/**\n * Returns the truth value of (a < b) element-wise. Supports broadcasting.\n *\n * We also expose `tf.lessStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.less(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction less_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'less');\n  let $b = convertToTensor(b, 'b', 'less');\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  return ENV.engine.runKernel(backend => backend.less($a, $b), {$a, $b}) as T;\n}\n\n/**\n * Strict version of `tf.less` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n *     `a`.\n */\nfunction lessStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'lessStrict');\n  const $b = convertToTensor(b, 'b', 'lessStrict');\n  assertShapesMatch($a.shape, $b.shape, 'Error in lessStrict: ');\n  return $a.less($b);\n}\n\n/**\n * Returns the truth value of (a == b) element-wise. Supports broadcasting.\n *\n * We also expose `tf.equalStrict` which has the same signature as this op\n * and asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.equal(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction equal_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'equal');\n  let $b = convertToTensor(b, 'b', 'equal');\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  return ENV.engine.runKernel(backend => backend.equal($a, $b), {$a, $b}) as T;\n}\n\nfunction equalStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'equalStrict');\n  const $b = convertToTensor(b, 'b', 'equalStrict');\n  assertShapesMatch($a.shape, $b.shape, 'Error in equalStrict: ');\n  return $a.equal($b);\n}\n\n/**\n * Returns the truth value of (a <= b) element-wise. Supports broadcasting.\n *\n * We also expose `tf.lessEqualStrict` which has the same signature as this op\n * and asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.lessEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction lessEqual_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'lessEqual');\n  let $b = convertToTensor(b, 'b', 'lessEqual');\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  return ENV.engine.runKernel(backend => backend.lessEqual($a, $b), {$a, $b}) as\n      T;\n}\n\nfunction lessEqualStrict_<T extends Tensor>(\n    a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'lessEqualStrict');\n  const $b = convertToTensor(b, 'b', 'lessEqualStrict');\n  assertShapesMatch($a.shape, $b.shape, 'Error in lessEqualStrict: ');\n  return $a.lessEqual($b);\n}\n\n/**\n * Returns the truth value of (a > b) element-wise. Supports broadcasting.\n *\n * We also expose `tf.greaterStrict` which has the same signature as this\n * op and asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greater(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction greater_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'greater');\n  let $b = convertToTensor(b, 'b', 'greater');\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  return ENV.engine.runKernel(backend => backend.greater($a, $b), {$a, $b}) as\n      T;\n}\n\nfunction greaterStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'greaterStrict');\n  const $b = convertToTensor(b, 'b', 'greaterStrict');\n  assertShapesMatch($a.shape, $b.shape, 'Error in greaterStrict: ');\n  return $a.greater($b);\n}\n\n/**\n * Returns the truth value of (a >= b) element-wise. Supports broadcasting.\n *\n * We also expose `tf.greaterEqualStrict` which has the same signature as this\n * op and asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greaterEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction greaterEqual_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'greaterEqual');\n  let $b = convertToTensor(b, 'b', 'greaterEqual');\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const grad = (dy: T) => {\n    return {$a: () => zerosLike($a), $b: () => zerosLike($b)};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.greaterEqual($a, $b), {$a, $b}, grad) as T;\n}\n\nfunction greaterEqualStrict_<T extends Tensor>(\n    a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'greaterEqualStrict');\n  const $b = convertToTensor(b, 'b', 'greaterEqualStrict');\n  assertShapesMatch($a.shape, $b.shape, 'Error in greaterEqualStrict: ');\n  return $a.greaterEqual($b);\n}\n\nexport const equal = op({equal_});\nexport const equalStrict = op({equalStrict_});\nexport const greater = op({greater_});\nexport const greaterEqual = op({greaterEqual_});\nexport const greaterEqualStrict = op({greaterEqualStrict_});\nexport const greaterStrict = op({greaterStrict_});\nexport const less = op({less_});\nexport const lessEqual = op({lessEqual_});\nexport const lessEqualStrict = op({lessEqualStrict_});\nexport const lessStrict = op({lessStrict_});\nexport const notEqual = op({notEqual_});\nexport const notEqualStrict = op({notEqualStrict_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {KernelBackend} from '../kernels/backend';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike, upcastType} from '../types';\nimport * as util from '../util';\nimport * as broadcast_util from './broadcast_util';\nimport {op} from './operation';\nimport {scalar} from './tensor_ops';\nimport {neg} from './unary_ops';\n\n/**\n * Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting.\n *\n * We also expose `tf.addStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n *\n * ```js\n * // Broadcast add a with b.\n * const a = tf.scalar(5);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n * @param a The first `tf.Tensor` to add.\n * @param b The second `tf.Tensor` to add. Must have the same type as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction add_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'add');\n  let $b = convertToTensor(b, 'b', 'add');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const outShape =\n      broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const der = (dy: Tensor) => {\n    const derA = () => {\n      let res = dy;\n      const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes);\n      }\n      return res.reshape($a.shape);\n    };\n    const derB = () => {\n      let res = dy;\n      const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes);\n      }\n      return res.reshape($b.shape);\n    };\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(backend => backend.add($a, $b), {$a, $b}, der) as\n      T;\n}\n\n/**\n * Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n *\n * tf.addN([a, b, c]).print();\n * ```\n * @param tensors A list of tensors with the same shape and dtype.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction addN_<T extends Tensor>(tensors: Array<T|TensorLike>): T {\n  util.assert(\n      Array.isArray(tensors),\n      () => 'The argument passed to tf.addN() must be a list of tensors');\n  util.assert(\n      tensors.length >= 1,\n      () => `Must pass at least one tensor to tf.addN(), but got ` +\n          `${tensors.length}`);\n  const $tensors =\n      tensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'addN'));\n  const firstTensor = $tensors[0];\n  $tensors.forEach(t => {\n    if (t.dtype !== firstTensor.dtype) {\n      throw new Error(\n          'All tensors passed to tf.addN() must have the same dtype');\n    }\n  });\n  $tensors.forEach(t => {\n    if (!util.arraysEqual(t.shape, firstTensor.shape)) {\n      throw new Error(\n          'All tensors passed to tf.addN() must have the same shape');\n    }\n  });\n\n  const der = (dy: T) => {\n    const ders: {[key: string]: () => Tensor} = {};\n    $tensors.forEach((t, i) => {\n      ders[i] = () => dy.clone();\n    });\n    return ders;\n  };\n  const inputs: NamedTensorMap = $tensors as {} as NamedTensorMap;\n  return ENV.engine.runKernel(backend => backend.addN($tensors), inputs, der);\n}\n\n/**\n * Adds two `tf.Tensor`s element-wise, A + B.\n *\n * Inputs must be the same shape. For broadcasting support, use add() instead.\n *\n * @param a The first Tensor to add element-wise.\n * @param b The second Tensor to add element-wise.\n */\nfunction addStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'addStrict');\n  const $b = convertToTensor(b, 'b', 'addStrict');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in addStrict: ');\n  return $a.add($b);\n}\n\n/**\n * Subtracts two `tf.Tensor`s element-wise, A - B. Supports broadcasting.\n *\n * We also expose `tf.subStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n *\n * ```js\n * // Broadcast subtract a with b.\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.scalar(5);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n * @param a The first `tf.Tensor` to subtract from.\n * @param b The second `tf.Tensor` to be subtracted. Must have the same dtype as\n * `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction sub_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'sub');\n  let $b = convertToTensor(b, 'b', 'sub');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const outShape =\n      broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const der = (dy: Tensor) => {\n    const derA = () => {\n      let res = dy;\n      const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes);\n      }\n      return res.reshape($a.shape);\n    };\n    const derB = () => {\n      let res = dy;\n      const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes);\n      }\n      return res.neg().reshape($b.shape);\n    };\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.subtract($a, $b), {$a, $b}, der) as T;\n}\n\n/**\n * Subtracts two `tf.Tensor`s element-wise, A - B. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.sub` instead.\n *\n * @param a The first Tensor to subtract element-wise.\n * @param b The second Tensor to subtract element-wise.\n */\nfunction subStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'subStrict');\n  const $b = convertToTensor(b, 'b', 'subStrict');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in subStrict: ');\n  return $a.sub($b);\n}\n\n/**\n * Computes the power of one `tf.Tensor` to another. Supports broadcasting.\n *\n * Given a `tf.Tensor` x and a `tf.Tensor` y, this operation computes x^y for\n * corresponding elements in x and y. The result's dtype will be the upcasted\n * type of the `base` and `exp` dtypes.\n *\n * ```js\n * const a = tf.tensor([[2, 3], [4, 5]])\n * const b = tf.tensor([[1, 2], [3, 0]]).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n *\n * ```js\n * const a = tf.tensor([[1, 2], [3, 4]])\n * const b = tf.tensor(2).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n * We also expose `powStrict` which has the same signature as this op and\n * asserts that `base` and `exp` are the same shape (does not broadcast).\n *\n * @param base The base `tf.Tensor` to pow element-wise.\n * @param exp The exponent `tf.Tensor` to pow element-wise.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction pow_<T extends Tensor>(base: T|TensorLike, exp: Tensor|TensorLike): T {\n  const $base = convertToTensor(base, 'base', 'pow');\n  const $exp = convertToTensor(exp, 'exp', 'pow');\n\n  const outShape =\n      broadcast_util.assertAndGetBroadcastShape($base.shape, $exp.shape);\n  base = $base.cast(upcastType($base.dtype, $exp.dtype));\n  exp = $exp.cast(upcastType($base.dtype, $exp.dtype));\n  const grad = (dy: Tensor, saved: Tensor[]) => {\n    const [y] = saved;\n    const derBase = () => {\n      const expFloat = $exp.toFloat();\n      let res = dy.mul(expFloat.mul($base.pow(expFloat.sub(scalar(1)))));\n      const reduceAxes = broadcast_util.getReductionAxes($base.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes);\n      }\n      return res.reshape($base.shape) as T;\n    };\n    const derExp = () => {\n      let res = dy.mul(y.mul($base.log()).toFloat());\n      const reduceAxes = broadcast_util.getReductionAxes($exp.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes);\n      }\n      return res.reshape($exp.shape);\n    };\n    return {$base: derBase, $exp: derExp};\n  };\n  return ENV.engine.runKernel(\n             (backend, save) => save(backend.pow($base, $exp)), {$base, $exp},\n             grad) as T;\n}\n\n/**\n * Computes the power of one `tf.Tensor` to another. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.pow` instead.\n *\n * @param base The base tensor to pow element-wise.\n * @param exp The exponent tensor to pow element-wise.\n */\nfunction powStrict_<T extends Tensor>(base: T, exp: Tensor): T {\n  util.assertShapesMatch(base.shape, exp.shape, 'Error in powStrict: ');\n  return base.pow(exp);\n}\n\n/**\n * Multiplies two `tf.Tensor`s element-wise, A * B. Supports broadcasting.\n *\n * We also expose `tf.mulStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([2, 3, 4, 5]);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n *\n * ```js\n * // Broadcast mul a with b.\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.scalar(5);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n * @param a The first tensor to multiply.\n * @param b The second tensor to multiply. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction mul_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'mul');\n  let $b = convertToTensor(b, 'b', 'mul');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const outShape =\n      broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const der = (dy: Tensor) => {\n    const derA = () => {\n      const res = dy.mul($b.toFloat());\n      const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return res.sum(reduceAxes).reshape($a.shape);\n      }\n      return res;\n    };\n    const derB = () => {\n      const res = dy.mul($a.toFloat());\n      const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return res.sum(reduceAxes).reshape($b.shape);\n      }\n      return res;\n    };\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.multiply($a, $b), {$a, $b}, der) as T;\n}\n\n/**\n * Multiplies two `tf.Tensor`s element-wise, A * B.\n *\n * Inputs must be the same shape. For broadcasting support, use `tf.mul`.\n *\n * @param a The first tensor to multiply.\n * @param b The first tensor to multiply. Must have the same\n *    dtype as `a`.\n */\nfunction mulStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'mul');\n  const $b = convertToTensor(b, 'b', 'mul');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in multiplyStrict: ');\n  return $a.mul($b) as T;\n}\n\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n *\n * We also expose `tf.divStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction div_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'div');\n  let $b = convertToTensor(b, 'b', 'div');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  let forwardFunc: (backend: KernelBackend) => Tensor;\n  if ($a.dtype === 'int32' && $b.dtype === 'int32') {\n    return floorDiv($a, $b);\n  } else {\n    forwardFunc = (backend: KernelBackend) => backend.realDivide($a, $b);\n  }\n\n  const outShape =\n      broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n  const der = (dy: Tensor) => {\n    const derA = () => {\n      const res = dy.div($b.toFloat());\n      const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return res.sum(reduceAxes).reshape($a.shape);\n      }\n      return res;\n    };\n    const derB = () => {\n      let res = dy.mul($a.toFloat());\n      const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes).reshape($b.shape);\n      }\n      const tmp = $b.square() as Tensor;\n      return res.div(tmp.toFloat()).neg() as Tensor;\n    };\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(forwardFunc, {$a, $b}, der) as T;\n}\n\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n * The result is rounded with floor function.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.floorDiv(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.floorDiv(b).print();  // or tf.floorDiv(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction floorDiv_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'floorDiv');\n  let $b = convertToTensor(b, 'b', 'floorDiv');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const forwardFunc = (backend: KernelBackend) => backend.floorDiv($a, $b);\n  const outShape =\n      broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n  const der = (dy: Tensor) => {\n    const derA = () => {\n      const res = dy.div($b.toFloat());\n      const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return res.sum(reduceAxes).reshape($a.shape);\n      }\n      return res;\n    };\n    const derB = () => {\n      let res = dy.mul($a.toFloat());\n      const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes).reshape($b.shape);\n      }\n      const tmp = $b.square() as Tensor;\n      return res.div(tmp.toFloat()).neg() as Tensor;\n    };\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(forwardFunc, {$a, $b}, der) as T;\n}\n\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Inputs must\n * be the same shape.\n *\n * @param a The first tensor as the numerator for element-wise division.\n * @param b The second tensor as the denominator for element-wise division.\n */\nfunction divStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'div');\n  const $b = convertToTensor(b, 'b', 'div');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in divideStrict: ');\n  return $a.div($b) as T;\n}\n\n/**\n * Returns the mod of a and b element-wise.\n * `floor(x / y) * y + mod(x, y) = x`\n * Supports broadcasting.\n *\n * We also expose `tf.modStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * ```js\n * // Broadcast a mod b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction mod_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'mod');\n  let $b = convertToTensor(b, 'b', 'mod');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const outShape =\n      broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n  const der = (dy: Tensor) => {\n    const derA = () => {\n      const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return dy.sum(reduceAxes).reshape($a.shape);\n      }\n      return dy;\n    };\n    const derB = () => {\n      const res = dy.mul($a.div($b).floor().neg());\n      const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return res.sum(reduceAxes).reshape($b.shape);\n      }\n      return res;\n    };\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(backend => backend.mod($a, $b), {$a, $b}, der) as\n      T;\n}\n\n/**\n * Returns the mod of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use mod().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction modStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'modStrict');\n  const $b = convertToTensor(b, 'b', 'modStrict');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in modStrict: ');\n  return $a.mod($b);\n}\n\n/**\n * Returns the min of a and b (`a < b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `minimumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * ```js\n * // Broadcast minimum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction minimum_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'minimum');\n  let $b = convertToTensor(b, 'b', 'minimum');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  if ($a.dtype === 'bool') {\n    $a = $a.toInt();\n    $b = $b.toInt();\n  }\n\n  broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n  const der = (dy: Tensor) => {\n    const derA = () => dy.mul($a.lessEqual($b).toFloat());\n    const derB = () => dy.mul($a.greater($b).toFloat());\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.minimum($a, $b), {$a, $b}, der) as T;\n}\n\n/**\n * Returns the min of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use minimum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction minimumStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'minimumStrict');\n  const $b = convertToTensor(b, 'b', 'minimumStrict');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in minimumStrict: ');\n  return $a.minimum($b);\n}\n\n/**\n * Returns the max of a and b (`a > b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `tf.maximumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * ```js\n * // Broadcast maximum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction maximum_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'maximum');\n  let $b = convertToTensor(b, 'b', 'maximum');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  if ($a.dtype === 'bool') {\n    $a = $a.toInt();\n    $b = $b.toInt();\n  }\n\n  broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n  const der = (dy: Tensor) => {\n    const derA = () => dy.mul($a.greaterEqual($b).toFloat());\n    const derB = () => dy.mul($a.less($b).toFloat());\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.maximum($a, $b), {$a, $b}, der) as T;\n}\n\n/**\n * Returns the max of a and b (`a > b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use maximum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction maximumStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'maximumStrict');\n  const $b = convertToTensor(b, 'b', 'maximumStrict');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in maximumStrict: ');\n  return $a.maximum($b);\n}\n\n/**\n * Returns (a - b) * (a - b) element-wise.\n * Supports broadcasting.\n *\n * We also expose `tf.squaredDifferenceStrict` which has the same signature as\n * this op and asserts that `a` and `b` are the same shape (does not\n * broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * ```js\n * // Broadcast squared difference  a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction squaredDifference_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'squaredDifference');\n  let $b = convertToTensor(b, 'b', 'squaredDifference');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n  const der = (dy: Tensor) => {\n    const two = scalar(2);\n    const derA = () => dy.mul($a.sub($b).mul(two));\n    const derB = () => dy.mul($b.sub($a).mul(two));\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.squaredDifference($a, $b), {$a, $b}, der) as T;\n}\n\n/**\n * Returns (a - b) * (a - b) element-wise.\n *\n * Inputs must be the same shape. For broadcasting support, use\n * `tf.squaredDifference` instead.\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\nfunction squaredDifferenceStrict_<T extends Tensor>(\n    a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'squaredDifferenceStrict');\n  const $b = convertToTensor(b, 'b', 'squaredDifferenceStrict');\n  util.assertShapesMatch(\n      $a.shape, $b.shape, 'Error in squaredDifferenceStrict: ');\n  return $a.squaredDifference($b);\n}\n\n/**\n * Computes arctangent of `tf.Tensor`s a / b element-wise: `atan2(a, b)`.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1.0, 1.0, -1.0, .7]);\n * const b = tf.tensor1d([2.0, 13.0, 3.5, .21]);\n *\n * tf.atan2(a, b).print()\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n *\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction atan2_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'atan2');\n  let $b = convertToTensor(b, 'b', 'atan2');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const outShape =\n      broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const der = (dy: Tensor) => {\n    const derA = () => {\n      const d = add($a.square(), $b.square());\n      let res = dy.mul($b.div(d));\n      const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes);\n      }\n      return res.reshape($a.shape);\n    };\n    const derB = () => {\n      const d = add($a.square(), $b.square()) as T;\n      let res = neg(dy.mul($a.div(d)));\n      const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes);\n      }\n      return res.reshape($b.shape);\n    };\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.atan2($a, $b), {$a, $b}, der) as T;\n}\n\nexport const add = op({add_});\nexport const addN = op({addN_});\nexport const addStrict = op({addStrict_});\nexport const atan2 = op({atan2_});\nexport const div = op({div_});\nexport const divStrict = op({divStrict_});\nexport const floorDiv = op({floorDiv_});\nexport const maximum = op({maximum_});\nexport const maximumStrict = op({maximumStrict_});\nexport const minimum = op({minimum_});\nexport const minimumStrict = op({minimumStrict_});\nexport const mod = op({mod_});\nexport const modStrict = op({modStrict_});\nexport const mul = op({mul_});\nexport const mulStrict = op({mulStrict_});\nexport const pow = op({pow_});\nexport const powStrict = op({powStrict_});\nexport const squaredDifference = op({squaredDifference_});\nexport const squaredDifferenceStrict = op({squaredDifferenceStrict_});\nexport const sub = op({sub_});\nexport const subStrict = op({subStrict_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {whereImpl} from '../kernels/where_impl';\nimport {Tensor, Tensor2D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assert, assertShapesMatch} from '../util';\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\nimport {zerosLike} from './tensor_ops';\n\n/**\n * Returns the truth value of `NOT x` element-wise.\n *\n * ```js\n * const a = tf.tensor1d([false, true], 'bool');\n *\n * a.logicalNot().print();\n * ```\n *\n * @param x The input tensor. Must be of dtype 'bool'.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction logicalNot_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'logicalNot', 'bool');\n  return ENV.engine.runKernel(backend => backend.logicalNot($x), {$x});\n}\n\n/**\n * Returns the truth value of `a AND b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalAnd(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction logicalAnd_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'logicalAnd', 'bool');\n  const $b = convertToTensor(b, 'b', 'logicalAnd', 'bool');\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  return ENV.engine.runKernel(\n             backend => backend.logicalAnd($a, $b), {$a, $b}) as T;\n}\n\n/**\n * Returns the truth value of `a OR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalOr(b).print();\n * ```\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction logicalOr_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'logicalOr', 'bool');\n  const $b = convertToTensor(b, 'b', 'logicalOr', 'bool');\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  return ENV.engine.runKernel(backend => backend.logicalOr($a, $b), {$a, $b}) as\n      T;\n}\n\n/**\n * Returns the truth value of `a XOR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalXor(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction logicalXor_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'logicalXor', 'bool');\n  const $b = convertToTensor(b, 'b', 'logicalXor', 'bool');\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  // x ^ y = (x | y) & ~(x & y)\n  return logicalOr(a, b).logicalAnd(logicalAnd(a, b).logicalNot()) as T;\n}\n\n/**\n * Returns the elements, either `a` or `b` depending on the `condition`.\n *\n * If the condition is true, select from `a`, otherwise select from `b`.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const a = tf.tensor1d([1 , 2, 3]);\n * const b = tf.tensor1d([-1, -2, -3]);\n *\n * a.where(cond, b).print();\n * ```\n *\n * @param condition The input condition. Must be of dtype bool.\n * @param a If `condition` is rank 1, `a` may have a higher rank but\n *     its first dimension must match the size of `condition`.\n * @param b A tensor with the same shape and type as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction where_<T extends Tensor>(\n    condition: Tensor|TensorLike, a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'where');\n  const $b = convertToTensor(b, 'b', 'where');\n  const $condition = convertToTensor(condition, 'condition', 'where', 'bool');\n\n  assertShapesMatch($a.shape, $b.shape, 'Error in where: ');\n\n  if ($condition.rank === 1) {\n    // If condition rank is 1, then the first dimension must match the size of\n    // condition.\n    assert(\n        $condition.shape[0] === $a.shape[0],\n        'The first dimension of `a` must match the size of `condition`.');\n  } else {\n    // A must have the same shape as condition.\n    assertShapesMatch($condition.shape, $b.shape, 'Error in where: ');\n  }\n\n  // TODO(julianoks): Return null for condition gradient\n  // when backprop supports it.\n  const grad = (dy: T) => ({\n    $condition: () => zerosLike($condition).toFloat(),\n    $a: () => dy.mul($condition.cast(dy.dtype)) as T,\n    $b: () => dy.mul($condition.logicalNot().cast(dy.dtype)) as T\n  });\n\n  return ENV.engine.runKernel(\n             backend => backend.select($condition, $a, $b),\n             {$condition, $a, $b}, grad) as T;\n}\n\n/**\n * Returns the coordinates of true elements of condition.\n *\n * The coordinates are returned in a 2-D tensor where the first dimension (rows)\n * represents the number of true elements, and the second dimension (columns)\n * represents the coordinates of the true elements. Keep in mind, the shape of\n * the output tensor can vary depending on how many true values there are in\n * input. Indices are output in row-major order. The resulting tensor has the\n * shape `[numTrueElems, condition.rank]`.\n *\n * This is analogous to calling the python `tf.where(cond)` without an x or y.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * tf.whereAsync(cond).then(result => result.print());\n * ```\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nasync function whereAsync_(condition: Tensor|TensorLike): Promise<Tensor2D> {\n  const $condition =\n      convertToTensor(condition, 'condition', 'whereAsync', 'bool');\n  const vals = await $condition.data();\n  const res = whereImpl($condition.shape, vals);\n  if (condition !== $condition) {\n    $condition.dispose();\n  }\n  return res;\n}\n\nexport const logicalAnd = op({logicalAnd_});\nexport const logicalNot = op({logicalNot_});\nexport const logicalOr = op({logicalOr_});\nexport const logicalXor = op({logicalXor_});\nexport const where = op({where_});\nexport const whereAsync = whereAsync_;\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {maximum, minimum} from './binary_ops';\nimport {where} from './logical_ops';\nimport {op} from './operation';\nimport {SELU_SCALE, SELU_SCALEALPHA} from './selu_util';\nimport {scalar} from './tensor_ops';\n\n/**\n * Computes rectified linear element-wise: `max(x, 0)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.relu().print();  // or tf.relu(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n *     `int32'.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction relu_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'relu');\n\n  if ($x.dtype === 'bool') {\n    return $x.toInt();\n  }\n  const grad = (dy: T) => {\n    const stepRes = $x.step();\n    return {$x: () => dy.mulStrict(stepRes.toFloat())};\n  };\n  return ENV.engine.runKernel(backend => backend.relu($x), {$x}, grad);\n}\n\n/**\n * Computes exponential linear element-wise: `x > 0 ? e ^ x - 1 : 0`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 1, -3, 2]);\n *\n * x.elu().print();  // or tf.elu(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction elu_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'elu');\n\n  const grad = (dy: T, saved: Tensor[]) => {\n    const [y] = saved;\n    return {\n      $x: () =>\n          ENV.engine.runKernel(backend => backend.eluDer(dy, y), {dy, y}) as T\n    };\n  };\n  return ENV.engine.runKernel(\n      (backend, save) => save(backend.elu($x)), {$x}, grad);\n}\n\n/**\n * Computes scaled exponential linear element-wise.\n *\n * `x < 0 ? scale * alpha * (exp(x) - 1) : x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.selu().print();  // or tf.selu(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction selu_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'selu');\n\n  const grad = (dy: T) => {\n    return {\n      $x: () => {\n        const mask = $x.greater(scalar(0));\n\n        const scaleAlpha = scalar(SELU_SCALEALPHA);\n        const scale = scalar(SELU_SCALE);\n\n        const greaterThanZeroDer = dy.mul(scale);\n        const lessEqualZeroDer = dy.mul(scaleAlpha).mul($x.toFloat().exp());\n\n        return where(mask, greaterThanZeroDer, lessEqualZeroDer) as T;\n      }\n    };\n  };\n  return ENV.engine.runKernel(backend => backend.selu($x), {$x}, grad);\n}\n\n/**\n * Computes leaky rectified linear element-wise.\n *\n * See\n * [http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf](\n *     http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf)\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.leakyRelu(0.1).print();  // or tf.leakyRelu(x, 0.1)\n * ```\n * @param x The input tensor.\n * @param alpha The scaling factor for negative values, defaults to 0.2.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction leakyRelu_<T extends Tensor>(x: T|TensorLike, alpha = 0.2): T {\n  const $x = convertToTensor(x, 'x', 'leakyRelu');\n  return maximum(scalar(alpha).mul($x), $x);\n}\n\n/**\n * Computes leaky rectified linear element-wise with parametric alphas.\n *\n * `x < 0 ? alpha * x : f(x) = x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n * const alpha = tf.scalar(0.1);\n *\n * x.prelu(alpha).print();  // or tf.prelu(x, alpha)\n * ```\n * @param x The input tensor.\n * @param alpha Scaling factor for negative values.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction prelu_<T extends Tensor>(x: T|TensorLike, alpha: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'prelu');\n  const $alpha = convertToTensor(alpha, 'alpha', 'prelu');\n\n  const zero = scalar(0);\n  return maximum(zero, $x).add($alpha.mul(minimum(zero, $x)));\n}\n\nexport const elu = op({elu_});\nexport const leakyRelu = op({leakyRelu_});\nexport const prelu = op({prelu_});\nexport const relu = op({relu_});\nexport const selu = op({selu_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport {op} from './operation';\n\n/**\n * Transposes the `tf.Tensor`. Permutes the dimensions according to `perm`.\n *\n * The returned `tf.Tensor`'s dimension `i` will correspond to the input\n * dimension `perm[i]`. If `perm` is not given, it is set to `[n-1...0]`,\n * where `n` is the rank of the input `tf.Tensor`. Hence by default, this\n * operation performs a regular matrix transpose on 2-D input `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n *\n * a.transpose().print();  // or tf.transpose(a)\n * ```\n *\n * @param x The tensor to transpose.\n * @param perm The permutation of the dimensions of a.\n */\n/** @doc {heading: 'Operations', subheading: 'Matrices'} */\nfunction transpose_<T extends Tensor>(x: T|TensorLike, perm?: number[]): T {\n  const $x = convertToTensor(x, 'x', 'transpose');\n\n  if (perm == null) {\n    perm = $x.shape.map((s, i) => i).reverse();\n  }\n  util.assert(\n      $x.rank === perm.length,\n      `Error in transpose: rank of input ${$x.rank} ` +\n          `must match length of perm ${perm}.`);\n  perm.forEach(axis => {\n    util.assert(\n        axis >= 0 && axis < $x.rank,\n        `All entries in 'perm' must be between 0 and ${$x.rank - 1}` +\n            ` but got ${perm}`);\n  });\n\n  if ($x.rank <= 1) {\n    return $x.clone();\n  }\n\n  const der = (dy: T) => {\n    const undoPerm = axis_util.getUndoAxesPermutation(perm);\n    return {$x: () => dy.transpose(undoPerm)};\n  };\n  return ENV.engine.runKernel(\n      backend => backend.transpose($x, perm), {$x}, der);\n}\n\nexport const transpose = op({transpose_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\n\n/**\n * Normalizes the activation of a local neighborhood across or within\n * channels.\n *\n * @param x The input tensor. The 4-D input tensor is treated as a 3-D array\n *     of 1D vectors (along the last dimension), and each vector is\n *     normalized independently.\n * @param depthRadius The number of adjacent channels in the 1D normalization\n *     window.\n * @param bias A constant bias term for the basis.\n * @param alpha A scale factor, usually positive.\n * @param beta An exponent.\n */\n/** @doc {heading: 'Operations', subheading: 'Normalization'} */\nfunction localResponseNormalization_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5): T {\n  const $x = convertToTensor(x, 'x', 'localResponseNormalization');\n  util.assert(\n      $x.rank === 4 || $x.rank === 3,\n      `Error in localResponseNormalization: x must be rank 3 or 4 but got\n               rank ${$x.rank}.`);\n  util.assert(\n      util.isInt(depthRadius),\n      `Error in localResponseNormalization: depthRadius must be an integer\n                     but got depthRadius ${depthRadius}.`);\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n  }\n  const backward = (dy: Tensor4D, saved: Tensor[]) => {\n    const [outputImage] = saved;\n    return {\n      x4D: () => ENV.engine.runKernel(\n          backend => backend.LRNGrad(\n              dy, x4D, outputImage as Tensor4D, depthRadius, bias, alpha, beta),\n          {})\n    };\n  };\n  const res = ENV.engine.runKernel(\n      (backend, save) => save(backend.localResponseNormalization4D(\n          x4D, depthRadius, bias, alpha, beta)),\n      {x4D}, backward);\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  } else {\n    return res as T;\n  }\n}\n\nexport const localResponseNormalization = op({localResponseNormalization_});","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as axis_util from './axis_util';\nimport {op} from './operation';\nimport {scalar} from './tensor_ops';\n\n/**\n * Computes the norm of scalar, vectors, and matrices.\n * This function can compute several different vector norms (the 1-norm, the\n * Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0)\n * and matrix norms (Frobenius, 1-norm, and inf-norm).\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.norm().print();  // or tf.norm(x)\n * ```\n *\n * @param x The input array.\n * @param ord Optional. Order of the norm. Supported norm types are\n * following:\n *\n *  | ord        | norm for matrices         | norm for vectors\n *  |------------|---------------------------|---------------------\n *  |'euclidean' |Frobenius norm             |2-norm\n *  |'fro'       |Frobenius norm\t           |\n *  |Infinity    |max(sum(abs(x), axis=1))   |max(abs(x))\n *  |-Infinity   |min(sum(abs(x), axis=1))   |min(abs(x))\n *  |1           |max(sum(abs(x), axis=0))   |sum(abs(x))\n *  |2           |                           |sum(abs(x)^2)^1/2*\n *\n * @param axis Optional. If axis is null (the default), the input is\n * considered a vector and a single vector norm is computed over the entire\n * set of values in the Tensor, i.e. norm(x, ord) is equivalent\n * to norm(x.reshape([-1]), ord). If axis is a integer, the input\n * is considered a batch of vectors, and axis determines the axis in x\n * over which to compute vector norms. If axis is a 2-tuple of integer it is\n * considered a batch of matrices and axis determines the axes in NDArray\n * over which to compute a matrix norm.\n * @param keepDims Optional. If true, the norm have the same dimensionality\n * as the input.\n */\n/** @doc {heading: 'Operations', subheading: 'Matrices'} */\nfunction norm_(\n    x: Tensor|TensorLike, ord: number|'euclidean'|'fro' = 'euclidean',\n    axis: number|number[] = null, keepDims = false): Tensor {\n  x = convertToTensor(x, 'x', 'norm');\n\n  const norm = normImpl(x, ord, axis);\n  let keepDimsShape = norm.shape;\n  if (keepDims) {\n    const axes = axis_util.parseAxisParam(axis, x.shape);\n    keepDimsShape = axis_util.expandShapeToKeepDim(norm.shape, axes);\n  }\n  return norm.reshape(keepDimsShape);\n}\n\nfunction normImpl(\n    x: Tensor, p: number|string, axis: number|number[] = null): Tensor {\n  if (x.rank === 0) {\n    return x.abs();\n  }\n\n  // consider vector when no axis is specified\n  if (x.rank !== 1 && axis === null) {\n    return normImpl(x.reshape([-1]), p, axis);\n  }\n\n  // vector\n  if (x.rank === 1 || typeof axis === 'number' ||\n      axis instanceof Array && axis.length === 1) {\n    if (p === 1) {\n      return x.abs().sum(axis);\n    }\n    if (p === Infinity) {\n      return x.abs().max(axis);\n    }\n    if (p === -Infinity) {\n      return x.abs().min(axis);\n    }\n    if (p === 'euclidean' || p === 2) {\n      // norm(x, 2) = sum(abs(xi) ^ 2) ^ 1/2\n      return x.abs().pow(scalar(2, 'int32')).sum(axis).sqrt() as Tensor;\n    }\n\n    throw new Error(`Error in norm: invalid ord value: ${p}`);\n  }\n\n  // matrix (assumption axis[0] < axis[1])\n  if (axis instanceof Array && axis.length === 2) {\n    if (p === 1) {\n      return x.abs().sum(axis[0]).max(axis[1] - 1);\n    }\n    if (p === Infinity) {\n      return x.abs().sum(axis[1]).max(axis[0]);\n    }\n    if (p === -Infinity) {\n      return x.abs().sum(axis[1]).min(axis[0]);\n    }\n    if (p === 'fro' || p === 'euclidean') {\n      // norm(x) = sqrt(sum(pow(x, 2)))\n      return x.square().sum(axis).sqrt();\n    }\n\n    throw new Error(`Error in norm: invalid ord value: ${p}`);\n  }\n\n  throw new Error(`Error in norm: invalid axis: ${axis}`);\n}\n\nexport const norm = op({norm_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor1D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assert, isInt} from '../util';\nimport {expandDims} from './array_ops';\nimport {getUndoAxesPermutation, parseAxisParam} from './axis_util';\nimport {maximum} from './binary_ops';\nimport {greaterEqual} from './compare';\nimport {logicalAnd, where} from './logical_ops';\nimport {op} from './operation';\nimport {ones, scalar, zerosLike} from './tensor_ops';\n\n/**\n * Computes the sum along segments of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const segmentIds = tf.tensor1d([1, 2, 0, 1], 'int32');\n * const numSegments = 3;\n *\n * x.unsortedSegmentSum(segmentIds, numSegments).print()\n * //or tf.unsortedSegmentSum(x, segmentIds, numSegments)\n * ```\n * @param x The `tf.Tensor` that will be summed along its segments.\n * @param segmentIds A `tf.Tensor1D` whose rank is equal to the rank of `x`'s\n * dimension along the `axis`.  Maps each element of `x` to a segment.\n * @param numSegments The number of distinct `segmentIds`.\n */\n/** @doc {heading: 'Operations', subheading: 'Segment'} */\nfunction unsortedSegmentSum_<T extends Tensor>(\n    x: T|TensorLike, segmentIds: Tensor1D|TensorLike, numSegments: number): T {\n  const $x = convertToTensor(x, 'x', 'unsortedSegmentSum');\n  const $segmentIds =\n      convertToTensor(segmentIds, 'segmentIds', 'unsortedSegmentSum', 'int32');\n  assert(isInt(numSegments), 'numSegments must be of dtype int');\n\n  const gradFunc = (dy: T) => {\n    const derX = () => {\n      return gatherDropNegatives(dy, $segmentIds);\n    };\n    return {$x: derX};\n  };\n  return ENV.engine.runKernel(\n             backend =>\n                 backend.unsortedSegmentSum($x, $segmentIds, numSegments),\n             {$x}, gradFunc) as T;\n}\n\n/**\n * Gather slices from tensor `x`'s axis `axis` according to `indices`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const indices = tf.tensor1d([1, 3, 3], 'int32');\n *\n * x.gather(indices).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const indices = tf.tensor1d([1, 1, 0], 'int32');\n *\n * x.gather(indices).print();\n * ```\n * @param x The input tensor whose slices to be gathered.\n * @param indices The indices of the values to extract.\n * @param axis The axis over which to select values. Defaults to 0.\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction gather_<T extends Tensor>(\n    x: T|TensorLike, indices: Tensor1D|TensorLike, axis = 0): T {\n  const $x = convertToTensor(x, 'x', 'gather');\n  const $indices = convertToTensor(indices, 'indices', 'gather', 'int32');\n  axis = parseAxisParam(axis, $x.shape)[0];\n  const grad = (dy: T) => {\n    const derX = () => {\n      if (axis === 0) {\n        return unsortedSegmentSum(dy, $indices, $x.shape[axis]);\n      }\n      const paramsShape = $x.shape;\n      const indicesSize = $indices.size;\n\n      const outerShape = paramsShape.slice(0, axis);\n      const outerDims = outerShape.length;\n      const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);\n      const innerDims = innerShape.length;\n\n      const outerAxesIndices = arrayRange(0, outerDims);\n      const innerAxesIndices =\n          arrayRange(outerDims + 1, outerDims + 1 + innerDims);\n\n      const valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);\n\n      const values = dy.reshape(valuesShape);\n      const reshapedIndices = $indices.reshape([indicesSize]);\n\n      const transposeDims =\n          arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);\n      const valuesTranspose = values.transpose(transposeDims);\n\n      let paramsGrad = unsortedSegmentSum(\n          valuesTranspose, reshapedIndices as Tensor1D, $x.shape[axis]);\n\n      const invertTransposeDims = getUndoAxesPermutation(transposeDims);\n      paramsGrad = paramsGrad.transpose(invertTransposeDims);\n\n      return paramsGrad as T;\n    };\n    return {$x: derX};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.gather($x, $indices as Tensor1D, axis), {$x},\n             grad) as T;\n}\n\nfunction arrayRange(start: number, stop: number): number[] {\n  const result = [];\n  for (let i = start; i < stop; ++i) {\n    result.push(i);\n  }\n  return result;\n}\n\nfunction arrayConcat(arrays: number[][]): number[] {\n  const result = [];\n  for (let i = 0; i < arrays.length; ++i) {\n    for (let j = 0; j < arrays[i].length; ++j) {\n      result.push(arrays[i][j]);\n    }\n  }\n  return result;\n}\n\nfunction gatherDropNegatives<T extends Tensor>(x: T, indices: Tensor1D) {\n  // Helper function for unsorted segment ops. Gathers params for\n  // positive segment ids and gathers 0 for inputs with negative segment id.\n  // Mirrors _GatherDropNegatives from tensorflow/python/ops/math_grad.py\n  const zeroClippedIndices = maximum(indices, zerosLike(indices));\n  const gathered = gather(x, zeroClippedIndices as Tensor1D);\n  let isPositive = greaterEqual(indices, scalar(0, 'int32'));\n  const numIters = gathered.rank - isPositive.rank;\n  for (let i = 0; i < numIters; ++i) {\n    isPositive = expandDims(isPositive, i + 1);\n  }\n  isPositive = logicalAnd(isPositive, ones(gathered.shape, 'bool'));\n  const zeroSlice = zerosLike(gathered);\n  return where(isPositive, gathered, zeroSlice);\n}\n\nexport const gather = op({gather_});\nexport const unsortedSegmentSum = op({unsortedSegmentSum_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Scalar, Tensor1D, Tensor2D} from '../tensor';\nimport {convertToTensor, convertToTensorArray} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * @docalias (data: Tensor2D, c: Tensor2D, h: Tensor2D): [Tensor2D, Tensor2D]\n */\nexport type LSTMCellFunc = {\n  (data: Tensor2D, c: Tensor2D, h: Tensor2D): [Tensor2D, Tensor2D];\n};\n\n/**\n * Computes the next states and outputs of a stack of LSTMCells.\n *\n * Each cell output is used as input to the next cell.\n *\n * Returns `[cellState, cellOutput]`.\n *\n * Derived from tf.contrib.rn.MultiRNNCell.\n *\n * @param lstmCells Array of LSTMCell functions.\n * @param data The input to the cell.\n * @param c Array of previous cell states.\n * @param h Array of previous cell outputs.\n */\n/** @doc {heading: 'Operations', subheading: 'RNN'} */\nfunction multiRNNCell_(\n    lstmCells: LSTMCellFunc[], data: Tensor2D|TensorLike,\n    c: Array<Tensor2D|TensorLike>,\n    h: Array<Tensor2D|TensorLike>): [Tensor2D[], Tensor2D[]] {\n  const $data = convertToTensor(data, 'data', 'multiRNNCell');\n  const $c = convertToTensorArray(c, 'c', 'multiRNNCell');\n  const $h = convertToTensorArray(h, 'h', 'multiRNNCell');\n\n  let input = $data;\n  const newStates = [];\n  for (let i = 0; i < lstmCells.length; i++) {\n    const output = lstmCells[i](input, $c[i], $h[i]);\n    newStates.push(output[0]);\n    newStates.push(output[1]);\n    input = output[1];\n  }\n  const newC: Tensor2D[] = [];\n  const newH: Tensor2D[] = [];\n  for (let i = 0; i < newStates.length; i += 2) {\n    newC.push(newStates[i]);\n    newH.push(newStates[i + 1]);\n  }\n  return [newC, newH];\n}\n\n/**\n * Computes the next state and output of a BasicLSTMCell.\n *\n * Returns `[newC, newH]`.\n *\n * Derived from tf.contrib.rnn.BasicLSTMCell.\n *\n * @param forgetBias Forget bias for the cell.\n * @param lstmKernel The weights for the cell.\n * @param lstmBias The bias for the cell.\n * @param data The input to the cell.\n * @param c Previous cell state.\n * @param h Previous cell output.\n */\n/** @doc {heading: 'Operations', subheading: 'RNN'} */\nfunction basicLSTMCell_(\n    forgetBias: Scalar|TensorLike, lstmKernel: Tensor2D|TensorLike,\n    lstmBias: Tensor1D|TensorLike, data: Tensor2D|TensorLike,\n    c: Tensor2D|TensorLike, h: Tensor2D|TensorLike): [Tensor2D, Tensor2D] {\n  const $forgetBias =\n      convertToTensor(forgetBias, 'forgetBias', 'basicLSTMCell');\n  const $lstmKernel =\n      convertToTensor(lstmKernel, 'lstmKernel', 'basicLSTMCell');\n  const $lstmBias = convertToTensor(lstmBias, 'lstmBias', 'basicLSTMCell');\n  const $data = convertToTensor(data, 'data', 'basicLSTMCell');\n  const $c = convertToTensor(c, 'c', 'basicLSTMCell');\n  const $h = convertToTensor(h, 'h', 'basicLSTMCell');\n\n  const combined = $data.concat($h, 1);\n  const weighted = combined.matMul($lstmKernel);\n  const res = weighted.add($lstmBias) as Tensor2D;\n\n  // i = input_gate, j = new_input, f = forget_gate, o = output_gate\n  const batchSize = res.shape[0];\n  const sliceCols = res.shape[1] / 4;\n  const sliceSize: [number, number] = [batchSize, sliceCols];\n  const i = res.slice([0, 0], sliceSize);\n  const j = res.slice([0, sliceCols], sliceSize);\n  const f = res.slice([0, sliceCols * 2], sliceSize);\n  const o = res.slice([0, sliceCols * 3], sliceSize);\n\n  const newC = i.sigmoid().mulStrict(j.tanh()).addStrict(\n      $c.mulStrict($forgetBias.add(f).sigmoid() as Tensor2D));\n  const newH = newC.tanh().mulStrict(o.sigmoid());\n  return [newC, newH];\n}\n\nexport const basicLSTMCell = op({basicLSTMCell_});\nexport const multiRNNCell = op({multiRNNCell_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Scalar, Tensor} from '../tensor';\nimport {assertTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {pow} from './binary_ops';\nimport {op} from './operation';\nimport {scalar} from './tensor_ops';\n\n/**\n * Compute the moving average of a variable.\n *\n * Without zeroDebias, the moving average operation is defined by:\n *   `v += delta`\n * where\n *   `delta = (1 - decay) * (x - v)`\n *\n * With zeroDebias (default), the `delta` term is scaled to debias the\n * effect of the (assumed) zero-initialization of `v`.\n *   `delta /= (1 - decay ^ step)`\n *\n * For more details on the zero-debiasing algorithm, see:\n *   https://arxiv.org/abs/1412.6980\n *\n * Note that this function is completely stateless and does not keep track of\n * step count. The step count needs to be maintained by the caller and passed\n * in as `step`.\n *\n * @param v The current moving average value.\n * @param x New input value, must have the same shape and dtype as `v`.\n * @param decay The decay factor. Typical values are 0.95 and 0.99.\n * @param step Step count.\n * @param zeroDebias: Whether zeroDebias is to be performed (default: `true`).\n * @returns The new moving average value.\n */\n/** @doc {heading: 'Operations', subheading: 'Moving Average'} */\nfunction movingAverage_<T extends Tensor>(\n    v: T|TensorLike, x: T|TensorLike, decay: number|Scalar,\n    step?: number|Scalar, zeroDebias = true): T {\n  const $v = convertToTensor(v, 'v', 'movingAverage');\n  const $x = convertToTensor(x, 'x', 'movingAverage');\n  const $decay = convertToTensor(decay, 'decay', 'movingAverage');\n\n  assertTypesMatch($v, $x);\n  util.assert(\n      util.arraysEqual($v.shape, $x.shape), 'Shape mismatch in v and x');\n\n  const one = scalar(1);\n  const oneMinusDecay = one.sub($decay);\n\n  let update = $x.sub($v).mul(oneMinusDecay);\n  if (zeroDebias) {\n    util.assert(step != null, 'When using zeroDebias: true, step is required.');\n    const $step = convertToTensor(step, 'step', 'movingAverage');\n    update = update.div(one.sub(pow($decay, $step)));\n  }\n  return $v.add(update);\n}\n\nexport const movingAverage = op({movingAverage_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * Extracts a strided slice of a tensor.\n *\n * Roughly speaking, this op extracts a slice of size (end-begin)/stride from\n * the given input_ tensor. Starting at the location specified by begin the\n * slice continues by adding stride to the index until all dimensions are not\n * less than end. Note that a stride can be negative, which causes a reverse\n * slice.\n *\n * ```js\n * t = tf.tensor3d([1, 1, 1 ,2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],\n *    [3, 2, 3]);\n * t.stridedSlice([1, 0, 0], [2, 1, 3], [1, 1, 1]).print()  // [[[3, 3, 3]]]\n * t.stridedSlice([1, 0, 0], [2, 2, 3], [1, 1, 1]).print()  // [[[3, 3, 3],\n *                                                     // [4, 4, 4]]]\n * t.stridedSlice([1, -1, 0], [2, -3, 3], [1, -1, 1]).print() // [[[4, 4, 4],\n *                                                     // [3, 3, 3]]]\n * ```\n *\n * @param x The tensor to stride slice.\n * @param begin The coordinates to start the slice from.\n * @param end: The coordinates to end the slice at.\n * @param strides: The size of the slice.\n * @param beginMask: If the ith bit of begin_mask is set, begin[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param endMask: If the ith bit of end_mask is set, end[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param shrinkAxisMask: a bitmask where bit i implies that\n * the ith specification should shrink the dimensionality. begin and end must\n * imply a slice of size 1 in the dimension.\n */\n/** @doc {heading: 'Operations', subheading: 'Slicing and Joining'} */\nfunction stridedSlice_<T extends Tensor>(\n    x: T|TensorLike, begin: number[], end: number[], strides: number[],\n    beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0,\n    shrinkAxisMask = 0): T {\n  if (ellipsisMask !== 0) {\n    throw new Error('ellipsis mask is not yet supported');\n  }\n  if (newAxisMask !== 0) {\n    throw new Error('new axis mask is not yet supported');\n  }\n  const $x = convertToTensor(x, 'x', 'stridedSlice');\n  return ENV.engine.runKernel(\n             backend => backend.stridedSlice(\n                 $x, begin, end, strides, beginMask, endMask, ellipsisMask,\n                 newAxisMask, shrinkAxisMask),\n             {$x}) as T;\n}\n\nexport const stridedSlice = op({stridedSlice_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {NumericTensor, Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * Finds the values and indices of the `k` largest entries along the last\n * dimension.\n *\n * If the input is a vector (rank=1), finds the k largest entries in the vector\n * and outputs their values and indices as vectors. Thus values[j] is the j-th\n * largest entry in input, and its index is indices[j].\n * For higher rank inputs, computes the top k entries along the last dimension.\n *\n * If two elements are equal, the lower-index element appears first.\n *\n * ```js\n * const a = tf.tensor2d([[1, 5], [4, 3]]);\n * const {values, indices} = tf.topk(a);\n * values.print();\n * indices.print();\n * ```\n * @param x 1-D or higher `tf.Tensor` with last dimension being at least `k`.\n * @param k Number of top elements to look for along the last dimension.\n * @param sorted If true, the resulting `k` elements will be sorted by the\n *     values in descending order.\n */\n/** @doc {heading: 'Operations', subheading: 'Evaluation'} */\nfunction topk_<T extends Tensor>(\n    x: T|TensorLike, k = 1, sorted = true): {values: T, indices: T} {\n  const $x = convertToTensor(x, 'x', 'topk');\n  if ($x.rank === 0) {\n    throw new Error('topk() expects the input to be of rank 1 or higher');\n  }\n  const lastDim = $x.shape[$x.shape.length - 1];\n  if (k > lastDim) {\n    throw new Error(\n        `'k' passed to topk() must be <= the last dimension (${lastDim}) ` +\n        `but got ${k}`);\n  }\n\n  const [values, indices] =\n      ENV.engine.runKernel(b => b.topk($x as NumericTensor, k, sorted), {$x});\n  return {values, indices} as {values: T, indices: T};\n}\n\nexport const topk = op({topk_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, ShapeMap, TensorLike} from '../types';\n\nimport {op} from './operation';\nimport * as scatter_nd_util from './scatter_nd_util';\n\n/**\n * Creates a new tensor by applying sparse updates to individual\n * values or slices within a zero tensor of the given shape tensor according to\n * indices. This operator is the inverse of the `tf.gatherND` operator which\n * extracts values or slices from a given tensor.\n *\n * ```js\n * const indices = tf.tensor2d([[4], [3], [1], [7]]);\n * const updates = tf.tensor2d([9, 10, 11, 12]);\n * const shape = [8];\n * tf.scatterND(indices, updates, shape]).print() //[0, 11, 0, 10, 9, 0, 0, 12]\n * ```\n *\n * @param indices The tensor contains the indices into the output tensor.\n * @param updates The tensor contains the value for the indices.\n * @param shape: The shape of the output tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Slicing and Joining'} */\nfunction scatterND_<R extends Rank>(\n    indices: Tensor|TensorLike, updates: Tensor|TensorLike,\n    shape: ShapeMap[R]): Tensor<R> {\n  const $indices = convertToTensor(indices, 'indices', 'scatterND', 'int32');\n  const $updates = convertToTensor(updates, 'updates', 'scatterND');\n  scatter_nd_util.validateInput($updates, $indices, shape);\n\n  return ENV.engine.runKernel(\n             backend => backend.scatterND($indices, $updates, shape),\n             {$indices, $updates}) as Tensor<R>;\n}\n\nexport const scatterND = op({scatterND_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {complex, imag, real} from '../ops/complex_ops';\nimport {op} from '../ops/operation';\nimport {Tensor, Tensor2D} from '../tensor';\nimport {assert} from '../util';\nimport {scalar} from './tensor_ops';\n\n/**\n * Fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the inner-most\n * dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.fft().print();  // tf.spectral.fft(x).print();\n * ```\n * @param input The complex input to compute an fft over.\n */\n/**\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction fft_(input: Tensor): Tensor {\n  assert(\n      input.dtype === 'complex64',\n      `The dtype for tf.spectral.fft() must be complex64 ` +\n          `but got ${input.dtype}.`);\n\n  // Collapse all outer dimensions to a single batch dimension.\n  const innerDimensionSize = input.shape[input.shape.length - 1];\n  const batch = input.size / innerDimensionSize;\n  const input2D = input.as2D(batch, innerDimensionSize);\n\n  const ret = ENV.engine.runKernel(backend => backend.fft(input2D), {input});\n\n  return ret.reshape(input.shape);\n}\n\n/**\n * Inverse fast Fourier transform.\n *\n * Computes the inverse 1-dimensional discrete Fourier transform over the\n * inner-most dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.ifft().print();  // tf.spectral.ifft(x).print();\n * ```\n * @param input The complex input to compute an ifft over.\n */\n/**\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction ifft_(input: Tensor): Tensor {\n  assert(\n      input.dtype === 'complex64',\n      `The dtype for tf.spectral.ifft() must be complex64 ` +\n          `but got ${input.dtype}.`);\n\n  // Collapse all outer dimensions to a single batch dimension.\n  const innerDimensionSize = input.shape[input.shape.length - 1];\n  const batch = input.size / innerDimensionSize;\n  const input2D = input.as2D(batch, innerDimensionSize);\n\n  const ret = ENV.engine.runKernel(backend => backend.ifft(input2D), {input});\n\n  return ret.reshape(input.shape);\n}\n\n/**\n * Real value input fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n *\n * x.rfft().print();\n * ```\n * @param input The real value input to compute an rfft over.\n */\n/**\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction rfft_(input: Tensor): Tensor {\n  assert(input.dtype === 'float32', `The dtype for rfft() must be real value but\n    got ${input.dtype}`);\n\n  const innerDimensionSize = input.shape[input.shape.length - 1];\n  const batch = input.size / innerDimensionSize;\n\n  // Complement the input with zero imaginary numbers.\n  const zeros = input.zerosLike();\n  const complexInput = complex(input, zeros).as2D(batch, innerDimensionSize);\n\n  const ret = fft(complexInput);\n\n  // Exclude complex conjugations. These conjugations are put symmetrically.\n  const half = Math.floor(innerDimensionSize / 2) + 1;\n  const realValues = real(ret);\n  const imagValues = imag(ret);\n  const realComplexConjugate = realValues.split(\n      [half, innerDimensionSize - half], realValues.shape.length - 1);\n  const imagComplexConjugate = imagValues.split(\n      [half, innerDimensionSize - half], imagValues.shape.length - 1);\n\n  const outputShape = input.shape.slice();\n  outputShape[input.shape.length - 1] = half;\n\n  return complex(realComplexConjugate[0], imagComplexConjugate[0])\n      .reshape(outputShape);\n}\n\n/**\n * Inversed real value input fast Fourier transform.\n *\n * Computes the 1-dimensional inversed discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n *\n * x.irfft().print();\n * ```\n * @param input The real value input to compute an irfft over.\n */\n/**\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction irfft_(input: Tensor): Tensor {\n  const innerDimensionSize = input.shape[input.shape.length - 1];\n  const batch = input.size / innerDimensionSize;\n\n  if (innerDimensionSize <= 2) {\n    const complexInput = input.as2D(batch, innerDimensionSize);\n    const ret = ifft(complexInput);\n    return real(ret);\n  } else {\n    // The length of unique components of the DFT of a real-valued signal\n    // is 2 * (input_len - 1)\n    const outputShape = [batch, 2 * (innerDimensionSize - 1)];\n    const realInput = real(input).as2D(batch, innerDimensionSize);\n    const imagInput = imag(input).as2D(batch, innerDimensionSize);\n\n    const realConjugate =\n        realInput.slice([0, 1], [batch, innerDimensionSize - 2]).reverse(1);\n    const imagConjugate =\n        imagInput.slice([0, 1], [batch, innerDimensionSize - 2])\n            .reverse(1)\n            .mul(scalar(-1)) as Tensor2D;\n\n    const r = realInput.concat(realConjugate, 1);\n    const i = imagInput.concat(imagConjugate, 1);\n    const complexInput = complex(r, i).as2D(outputShape[0], outputShape[1]);\n    const ret = ifft(complexInput);\n    return real(ret);\n  }\n}\n\nexport const fft = op({fft_});\nexport const ifft = op({ifft_});\nexport const rfft = op({rfft_});\nexport const irfft = op({irfft_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor} from '../tensor';\n\n/**\n * Validate sparseToDense inputs.\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape number[]. Shape of the dense output tensor.\n * @param validateIndices boolean. indice validation is not supported, error\n * will be thrown if it is set.\n */\nexport function validateInput(\n    sparseIndices: Tensor, sparseValues: Tensor, outputShape: number[],\n    defaultValues: Tensor) {\n  if (sparseIndices.dtype !== 'int32') {\n    throw new Error(\n        'tf.sparseToDense() expects the indices to be int32 type,' +\n        ` but the dtype was ${sparseIndices.dtype}.`);\n  }\n  if (sparseIndices.rank > 2) {\n    throw new Error(\n        'sparseIndices should be a scalar, vector, or matrix,' +\n        ` but got shape ${sparseIndices.shape}.`);\n  }\n\n  const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;\n  const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;\n\n  if (outputShape.length !== numDims) {\n    throw new Error(\n        'outputShape has incorrect number of elements:,' +\n        ` ${outputShape.length}, should be: ${numDims}.`);\n  }\n\n  const numValues = sparseValues.size;\n  if (!(sparseValues.rank === 0 ||\n        sparseValues.rank === 1 && numValues === numElems)) {\n    throw new Error(\n        'sparseValues has incorrect shape ' +\n        `${sparseValues.shape}, should be [] or [${numElems}]`);\n  }\n\n  if (sparseValues.dtype !== defaultValues.dtype) {\n    throw new Error('sparseValues.dtype must match defaultValues.dtype');\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport * as sparse_to_dense from '../ops/sparse_to_dense_util';\nimport {Scalar, Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, ShapeMap, TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Converts a sparse representation into a dense tensor.\n *\n * Builds an array dense with shape outputShape such that:\n *\n * // If sparseIndices is scalar\n * dense[i] = (i == sparseIndices ? sparseValues : defaultValue)\n *\n * // If sparseIndices is a vector, then for each i\n * dense[sparseIndices[i]] = sparseValues[i]\n *\n * // If sparseIndices is an n by d matrix, then for each i in [0, n)\n * dense[sparseIndices[i][0], ..., sparseIndices[i][d-1]] = sparseValues[i]\n * All other values in dense are set to defaultValue. If sparseValues is a\n * scalar, all sparse indices are set to this single value.\n *\n * ```js\n * const indices = tf.tensor1d([4, 5, 6, 1, 2, 3], 'int32');\n * const values = tf.tensor1d([10, 11, 12, 13, 14, 15], 'float32');\n * const shape = [8];\n * tf.sparseToDense(indices, values, shape).print();\n * ```\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape Shape of the dense output tensor. the type is inferred.\n * @param defaultValue Scalar. Value to set for indices not specified in\n * sparseIndices. Defaults to zero.\n */\n/** @doc {heading: 'Operations', subheading: 'Normalization'} */\nfunction sparseToDense_<R extends Rank>(\n    sparseIndices: Tensor|TensorLike, sparseValues: Tensor|TensorLike,\n    outputShape: ShapeMap[R], defaultValue: Scalar|TensorLike): Tensor<R> {\n  const $sparseIndices =\n      convertToTensor(sparseIndices, 'sparseIndices', 'sparseToDense', 'int32');\n  const $sparseValues =\n      convertToTensor(sparseValues, 'sparseValues', 'sparseToDense');\n  const $defaultValue = convertToTensor(\n      defaultValue, 'defaultValue', 'sparseToDense', $sparseValues.dtype);\n\n  sparse_to_dense.validateInput(\n      $sparseIndices, $sparseValues, outputShape, $defaultValue);\n\n  return ENV.engine.runKernel(\n      backend => backend.sparseToDense(\n          $sparseIndices, $sparseValues, outputShape, $defaultValue),\n      {$sparseIndices, $sparseValues, $defaultValue});\n}\n\nexport const sparseToDense = op({sparseToDense_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * Gather slices from input tensor into a Tensor with shape specified by\n * `indices`.\n *\n * `indices` is an K-dimensional integer tensor, best thought of as a\n * (K-1)-dimensional tensor of indices into input, where each element defines a\n * slice of input:\n * output[\\\\(i_0, ..., i_{K-2}\\\\)] = input[indices[\\\\(i_0, ..., i_{K-2}\\\\)]]\n *\n * Whereas in `tf.gather`, `indices` defines slices into the first dimension of\n * input, in `tf.gatherND`, `indices` defines slices into the first N dimensions\n * of input, where N = indices.shape[-1].\n *\n * The last dimension of indices can be at most the rank of input:\n * indices.shape[-1] <= input.rank\n *\n * The last dimension of `indices` corresponds to elements\n * (if indices.shape[-1] == input.rank) or slices\n * (if indices.shape[-1] < input.rank) along dimension indices.shape[-1] of\n * input.\n * The output tensor has shape\n * indices.shape[:-1] + input.shape[indices.shape[-1]:]\n *\n * Note that on CPU, if an out of bound index is found, an error is returned. On\n * GPU, if an out of bound index is found, a 0 is stored in the corresponding\n * output value.\n *\n * ```js\n * const indices = tf.tensor2d([0, 1, 1, 0], [2,2], 'int32');\n * const input = tf.tensor2d([9, 10, 11, 12], [2, 2]);\n * tf.gatherND(input, indices).print() //[10, 11]\n * ```\n *\n * @param x The tensor from which to gather values.\n * @param indices Index tensor, must be of type int32.\n */\n/** @doc {heading: 'Operations', subheading: 'Slicing and Joining'} */\nfunction gatherND_(\n    x: Tensor|TensorLike, indices: Tensor|TensorLike): Tensor<Rank> {\n  const $indices = convertToTensor(indices, 'indices', 'gatherND', 'int32');\n  const $x = convertToTensor(x, 'x', 'gatherND');\n  return ENV.engine.runKernel(\n             backend => backend.gatherND($x, $indices), {$x, $indices}) as\n      Tensor<Rank>;\n}\nexport const gatherND = op({gatherND_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {customGrad} from '../globals';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assertShapesMatch, sizeFromShape} from '../util';\n\nimport {expandShapeToKeepDim} from './axis_util';\n\nimport {minimum} from './binary_ops';\nimport {op} from './operation';\nimport {ones, scalar} from './tensor_ops';\n\nexport enum Reduction {\n  NONE,\n  MEAN,\n  SUM,\n  SUM_BY_NONZERO_WEIGHTS\n}\n\n/**\n * Computes the weighted loss between two tensors.\n *\n * @param losses Tensor of shape `[batch_size, d1, ... dN]`.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `losses`, and must be broadcastable to `losses` (i.e., all\n *    dimensions must be either `1`, or the same as the corresponding\n *    `losses` dimension).\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction computeWeightedLoss_<T extends Tensor, O extends Tensor>(\n    losses: T|TensorLike, weights?: Tensor|TensorLike,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $losses = convertToTensor(losses, 'losses', 'computeWeightedLoss');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'computeWeightedLoss');\n  }\n\n  const weightedLoss = ($weights == null) ? $losses : $losses.mul($weights);\n\n  if (reduction === Reduction.NONE) {\n    return weightedLoss as O;\n  }\n  if (reduction === Reduction.SUM) {\n    return weightedLoss.sum();\n  }\n  if (reduction === Reduction.MEAN) {\n    if ($weights == null) {\n      return weightedLoss.mean();\n    } else {\n      const broadcastFactor =\n          sizeFromShape($losses.shape) / sizeFromShape($weights.shape);\n      const result = weightedLoss.sum().div($weights.sum());\n      return broadcastFactor > 1 ? result.div(scalar(broadcastFactor)) :\n                                   result as O;\n    }\n  }\n  if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    if ($weights == null) {\n      return weightedLoss.sum().div(scalar($losses.size));\n    } else {\n      const broadcastedWeights = $weights.mul(ones($losses.shape));\n\n      const numNonZeros =\n          broadcastedWeights.notEqual(scalar(0)).sum().toFloat();\n      return weightedLoss.sum().div(numNonZeros);\n    }\n  }\n\n  throw Error(`Unknown reduction: ${reduction}`);\n}\n\n/**\n * Computes the absolute difference loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction absoluteDifference_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike,\n    weights?: Tensor|TensorLike,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $labels = convertToTensor(labels, 'labels', 'absoluteDifference');\n  const $predictions =\n      convertToTensor(predictions, 'predictions', 'absoluteDifference');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'absoluteDifference');\n  }\n  assertShapesMatch(\n      $labels.shape, $predictions.shape, 'Error in absoluteDifference: ');\n\n  const losses = $labels.sub($predictions).abs();\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\n/**\n * Computes the mean squared error between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction meanSquaredError_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike,\n    weights?: Tensor|TensorLike,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $labels = convertToTensor(labels, 'labels', 'meanSquaredError');\n  const $predictions =\n      convertToTensor(predictions, 'predictions', 'meanSquaredError');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'meanSquaredError');\n  }\n  assertShapesMatch(\n      $labels.shape, $predictions.shape, 'Error in meanSquaredError: ');\n\n  const losses = $labels.squaredDifference($predictions);\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\n/**\n * Computes the cosine distance loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param axis The dimension along which the cosine distance is computed.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction cosineDistance_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike, axis: number,\n    weights?: Tensor|TensorLike,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $labels = convertToTensor(labels, 'labels', 'cosineDistance');\n  const $predictions =\n      convertToTensor(predictions, 'predictions', 'cosineDistance');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'cosineDistance');\n  }\n  assertShapesMatch(\n      $labels.shape, $predictions.shape, 'Error in cosineDistance: ');\n\n  const one = scalar(1);\n  const losses = one.sub($labels.mul($predictions).sum(axis, true));\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\n/**\n * Computes the Hinge loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction hingeLoss_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike,\n    weights?: Tensor|TensorLike,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  let $labels = convertToTensor(labels, 'labels', 'hingeLoss');\n  const $predictions = convertToTensor(predictions, 'predictions', 'hingeLoss');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'hingeLoss');\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, 'Error in hingeLoss: ');\n\n  const one = scalar(1);\n  // Convert binary labels to (-1, 1)\n  $labels = scalar(2).mul($labels).sub(one);\n  const losses = one.sub($labels.mul($predictions)).relu();\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\n/**\n * Computes the log loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param epsilon A small increment to avoid taking log of zero\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction logLoss_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike,\n    weights?: Tensor|TensorLike, epsilon = 1e-7,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $labels = convertToTensor(labels, 'labels', 'logLoss');\n  const $predictions = convertToTensor(predictions, 'predictions', 'logLoss');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'logLoss');\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, 'Error in logLoss: ');\n\n  const one = scalar(1);\n  const epsilonScalar = scalar(epsilon);\n  const losses = $labels.mul($predictions.add(epsilonScalar).log())\n                     .neg()\n                     .sub(one.sub($labels).mul(\n                         one.sub($predictions).add(epsilonScalar).log()));\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\nfunction sigmoidCrossEntropyWithLogits_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, logits: T|TensorLike): O {\n  const $labels =\n      convertToTensor(labels, 'labels', 'sigmoidCrossEntropyWithLogits');\n  const $logits =\n      convertToTensor(logits, 'logits', 'sigmoidCrossEntropyWithLogits');\n  assertShapesMatch(\n      $labels.shape, $logits.shape, 'Error in sigmoidCrossEntropyWithLogits: ');\n\n  /**\n   * Implementation Details:\n   *\n   * For brevity, let `x = logits`, `z = labels`.  The logistic loss is\n   *     z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n   *   = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n   *   = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n   *   = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n   *   = (1 - z) * x + log(1 + exp(-x))\n   *   = x - x * z + log(1 + exp(-x))\n   *\n   *   For x < 0, to avoid overflow in exp(-x), we reformulate the above\n   *     x - x * z + log(1 + exp(-x))\n   *   = log(exp(x)) - x * z + log(1 + exp(-x))\n   *   = - x * z + log(1 + exp(x))\n   *\n   * Hence, to ensure stability and avoid overflow, the implementation uses\n   * this equivalent formulation:\n   *     max(x, 0) - x * z + log(1 + exp(-abs(x)))\n   */\n  const maxOutput = $logits.relu();\n  const outputXTarget = $logits.mul($labels);\n  const sigmoidOutput = $logits.abs().neg().exp().log1p();\n\n  return maxOutput.sub(outputXTarget).add(sigmoidOutput);\n}\n\n/**\n * Computes the sigmoid cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newMulticlassLabels = multiclassLabels * (1 - labelSmoothing)\n *                         + 0.5 * labelSmoothing\n *\n * @param multiClassLabels The ground truth output tensor of shape\n * [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n */\n/** @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' } */\nfunction sigmoidCrossEntropy_<T extends Tensor, O extends Tensor>(\n    multiClassLabels: T|TensorLike, logits: T|TensorLike,\n    weights?: Tensor|TensorLike, labelSmoothing = 0,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  let $multiClassLabels = convertToTensor(\n      multiClassLabels, 'multiClassLabels', 'sigmoidCrossEntropy');\n  const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropy');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'sigmoidCrossEntropy');\n  }\n  assertShapesMatch(\n      $multiClassLabels.shape, $logits.shape, 'Error in sigmoidCrossEntropy: ');\n\n  if (labelSmoothing > 0) {\n    const labelSmoothingScalar = scalar(labelSmoothing);\n    const one = scalar(1);\n    const half = scalar(0.5);\n\n    $multiClassLabels = $multiClassLabels.mul(one.sub(labelSmoothingScalar))\n                            .add(half.mul(labelSmoothingScalar));\n  }\n  const losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);\n\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\n/**\n * Computes the huber loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param delta Point where huber loss changes from quadratic to linear.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`.\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction huberLoss_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike,\n    weights?: Tensor|TensorLike, delta = 1.0,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $labels = convertToTensor(labels, 'labels', 'huberLoss');\n  const $predictions = convertToTensor(predictions, 'predictions', 'huberLoss');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'huberLoss');\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, 'Error in huberLoss: ');\n\n  const deltaScalar = scalar(delta);\n  const error = $predictions.sub($labels).abs();\n  const quadratic = minimum(error, deltaScalar);\n  const linear = error.sub(quadratic);\n\n  const losses =\n      scalar(0.5).mul(quadratic.square()).add(deltaScalar.mul(linear));\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\n/**\n * Computes softmax cross entropy between logits and labels.\n *\n * Measures the probability error in discrete classification tasks in which\n * the classes are mutually exclusive (each entry is in exactly one class).\n * For example, each CIFAR-10 image is labeled with one and only one label: an\n * image can be a dog or a truck, but not both.\n *\n * `NOTE`: While the classes are mutually exclusive, their probabilities need\n * not be. All that is required is that each row of labels is a valid\n * probability distribution. If they are not, the computation of the gradient\n * will be incorrect.\n *\n * `WARNING`: This op expects unscaled logits, since it performs a softmax on\n * logits internally for efficiency. Do not call this op with the output of\n * softmax, as it will produce incorrect results.\n *\n * logits and labels must have the same shape, e.g. [batch_size, num_classes]\n * and the same dtype.\n * @param labels The labels array.\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n */\nfunction softmaxCrossEntropyWithLogits_<T extends Tensor, O extends Tensor>(\n    labels: T, logits: T, dim = -1): O {\n  if (dim === -1) {\n    dim = logits.rank - 1;\n  }\n\n  if (dim !== logits.rank - 1) {\n    throw Error(\n        `Softmax cross entropy along a non-last dimension is not yet ` +\n        `supported. Labels / logits was rank ${logits.rank} ` +\n        `and dim was ${dim}`);\n  }\n  // Use a custom gradient for numerical stability.\n  const customOp = customGrad((labels, logits) => {\n    // Reference:\n    //   1. http://cs231n.github.io/linear-classify/#softmax\n    //   2. https://blog.feedly.com/tricks-of-the-trade-logsumexp/\n    const keepDims = true;\n    const lse = logits.logSumExp([dim], keepDims);\n\n    const logResult = logits.toFloat().sub(lse);\n    const costVector = logResult.mul(labels).neg();\n\n    const value = costVector.sum([dim]) as O;\n\n    const gradFunc = (dy: O) => {\n      const dyShape = expandShapeToKeepDim(dy.shape, [dim]);\n      return [\n        dy.reshape(dyShape).mul(labels.toFloat().sub(logResult.exp())),\n        dy.reshape(dyShape).mul(logResult.exp().sub(labels.toFloat())),\n      ];\n    };\n    return {value, gradFunc};\n  });\n\n  return customOp(labels, logits);\n}\n\n/**\n * Computes the softmax cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newOnehotLabels = onehotLabels * (1 - labelSmoothing)\n *                         + labelSmoothing / numClasses\n *\n * @param onehotLabels One hot encoded labels\n *    [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or 1, and must be\n *    broadcastable to `loss`  of shape [batch_size]\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n */\n/** @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' } */\nfunction softmaxCrossEntropy_<T extends Tensor, O extends Tensor>(\n    onehotLabels: T|TensorLike, logits: T|TensorLike,\n    weights?: Tensor|TensorLike, labelSmoothing = 0,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  let $onehotLabels =\n      convertToTensor(onehotLabels, 'onehotLabels', 'softmaxCrossEntropy');\n  const $logits = convertToTensor(logits, 'logits', 'softmaxCrossEntropy');\n  let $weights: Tensor = null;\n\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'softmaxCrossEntropy');\n  }\n\n  assertShapesMatch(\n      $onehotLabels.shape, $logits.shape, 'Error in softmaxCrossEntropy: ');\n\n  if (labelSmoothing > 0) {\n    const labelSmoothingScalar = scalar(labelSmoothing);\n    const one = scalar(1);\n    const numClasses = scalar($onehotLabels.shape[1]);\n\n    $onehotLabels = $onehotLabels.mul(one.sub(labelSmoothingScalar))\n                        .add(labelSmoothingScalar.div(numClasses));\n  }\n\n  const losses = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);\n\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\nexport const absoluteDifference = op({absoluteDifference_});\nexport const computeWeightedLoss = op({computeWeightedLoss_});\nexport const cosineDistance = op({cosineDistance_});\nexport const hingeLoss = op({hingeLoss_});\nexport const huberLoss = op({huberLoss_});\nexport const logLoss = op({logLoss_});\nexport const meanSquaredError = op({meanSquaredError_});\nexport const sigmoidCrossEntropy = op({sigmoidCrossEntropy_});\nexport const softmaxCrossEntropy = op({softmaxCrossEntropy_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Linear algebra ops.\n */\n\nimport {ENV} from '../environment';\nimport {dispose} from '../globals';\nimport {Tensor, Tensor1D, Tensor2D} from '../tensor';\nimport {assert} from '../util';\nimport {eye, squeeze, stack, unstack} from './array_ops';\nimport {split} from './concat_split';\nimport {norm} from './norm';\nimport {op} from './operation';\nimport {sum} from './reduction_ops';\nimport {tensor2d} from './tensor_ops';\n\n/**\n * Gram-Schmidt orthogonalization.\n *\n * ```js\n * const x = tf.tensor2d([[1, 2], [3, 4]]);\n * let y = tf.linalg.gramSchmidt(x);\n * y.print();\n * console.log('Othogonalized:');\n * y.dot(y.transpose()).print();  // should be nearly the identity matrix.\n * console.log('First row direction maintained:');\n * console.log(y.get(0, 1) / y.get(0, 0));  // should be nearly 2.\n * ```\n *\n * @param xs The vectors to be orthogonalized, in one of the two following\n *   formats:\n *   - An Array of `tf.Tensor1D`.\n *   - A `tf.Tensor2D`, i.e., a matrix, in which case the vectors are the rows\n *     of `xs`.\n *   In each case, all the vectors must have the same length and the length\n *   must be greater than or equal to the number of vectors.\n * @returns The orthogonalized and normalized vectors or matrix.\n *   Orthogonalization means that the vectors or the rows of the matrix\n *   are orthogonal (zero inner products). Normalization means that each\n *   vector or each row of the matrix has an L2 norm that equals `1`.\n */\n/**\n * @doc {heading:'Operations',\n *       subheading:'Linear Algebra',\n *       namespace:'linalg'}\n */\nfunction gramSchmidt_(xs: Tensor1D[]|Tensor2D): Tensor1D[]|Tensor2D {\n  let inputIsTensor2D: boolean;\n  if (Array.isArray(xs)) {\n    inputIsTensor2D = false;\n    assert(\n        xs != null && xs.length > 0,\n        'Gram-Schmidt process: input must not be null, undefined, or empty');\n    const dim = xs[0].shape[0];\n    for (let i = 1; i < xs.length; ++i) {\n      assert(\n          xs[i].shape[0] === dim,\n          'Gram-Schmidt: Non-unique lengths found in the input vectors: ' +\n              `(${xs[i].shape[0]} vs. ${dim})`);\n    }\n  } else {\n    inputIsTensor2D = true;\n    xs = split(xs, xs.shape[0], 0).map(x => squeeze(x, [0]));\n  }\n\n  assert(\n      xs.length <= xs[0].shape[0],\n      `Gram-Schmidt: Number of vectors (${xs.length}) exceeds ` +\n          `number of dimensions (${xs[0].shape[0]}).`);\n\n  const ys: Tensor1D[] = [];\n  const xs1d = xs as Tensor1D[];\n  for (let i = 0; i < xs.length; ++i) {\n    ys.push(ENV.engine.tidy(() => {\n      let x = xs1d[i];\n      if (i > 0) {\n        for (let j = 0; j < i; ++j) {\n          const proj = sum(ys[j].mulStrict(x)).mul(ys[j]);\n          x = x.sub(proj);\n        }\n      }\n      return x.div(norm(x, 'euclidean'));\n    }));\n  }\n\n  if (inputIsTensor2D) {\n    return stack(ys, 0) as Tensor2D;\n  } else {\n    return ys;\n  }\n}\n\n/**\n * Compute QR decomposition of m-by-n matrix using Householder transformation.\n *\n * Implementation based on\n *   [http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf]\n * (http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf)\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [3, 4]]);\n * let [q, r] = tf.linalg.qr(a);\n * console.log('Q');\n * q.print();\n * console.log('R');\n * r.print();\n * console.log('Orthogonalized');\n * q.dot(q.transpose()).print()  // should be nearly the identity matrix.\n * console.log('Reconstructed');\n * q.dot(r).print(); // should be nearly [[1, 2], [3, 4]];\n * ```\n *\n * @param x The `tf.Tensor` to be QR-decomposed. Must have rank >= 2. Suppose\n *   it has the shape `[..., M, N]`.\n * @param fullMatrices An optional boolean parameter. Defaults to `false`.\n *   If `true`, compute full-sized `Q`. If `false` (the default),\n *   compute only the leading N columns of `Q` and `R`.\n * @returns An `Array` of two `tf.Tensor`s: `[Q, R]`. `Q` is a unitary matrix,\n *   i.e., its columns all have unit norm and are mutually orthogonal.\n *   If `M >= N`,\n *     If `fullMatrices` is `false` (default),\n *       - `Q` has a shape of `[..., M, N]`,\n *       - `R` has a shape of `[..., N, N]`.\n *     If `fullMatrices` is `true` (default),\n *       - `Q` has a shape of `[..., M, M]`,\n *       - `R` has a shape of `[..., M, N]`.\n *   If `M < N`,\n *     - `Q` has a shape of `[..., M, M]`,\n *     - `R` has a shape of `[..., M, N]`.\n * @throws If the rank of `x` is less than 2.\n */\n/**\n * @doc {heading:'Operations',\n *       subheading:'Linear Algebra',\n *       namespace:'linalg'}\n */\nfunction qr_(x: Tensor, fullMatrices = false): [Tensor, Tensor] {\n  if (x.rank < 2) {\n    throw new Error(\n        `qr() requires input tensor to have a rank >= 2, but got rank ${\n            x.rank}`);\n  } else if (x.rank === 2) {\n    return qr2d(x as Tensor2D, fullMatrices);\n  } else {\n    // Rank > 2.\n    // TODO(cais): Below we split the input into individual 2D tensors,\n    //   perform QR decomposition on them and then stack the results back\n    //   together. We should explore whether this can be parallelized.\n    const outerDimsProd = x.shape.slice(0, x.shape.length - 2)\n                              .reduce((value, prev) => value * prev);\n    const x2ds = unstack(\n        x.reshape([\n          outerDimsProd, x.shape[x.shape.length - 2],\n          x.shape[x.shape.length - 1]\n        ]),\n        0);\n    const q2ds: Tensor2D[] = [];\n    const r2ds: Tensor2D[] = [];\n    x2ds.forEach(x2d => {\n      const [q2d, r2d] = qr2d(x2d as Tensor2D, fullMatrices);\n      q2ds.push(q2d);\n      r2ds.push(r2d);\n    });\n    const q = stack(q2ds, 0).reshape(x.shape);\n    const r = stack(r2ds, 0).reshape(x.shape);\n    return [q, r];\n  }\n}\n\nfunction qr2d(x: Tensor2D, fullMatrices = false): [Tensor2D, Tensor2D] {\n  return ENV.engine.tidy(() => {\n    if (x.shape.length !== 2) {\n      throw new Error(\n          `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);\n    }\n\n    const m = x.shape[0];\n    const n = x.shape[1];\n\n    let q = eye(m) as Tensor2D;  // Orthogonal transform so far.\n    let r = x.clone();           // Transformed matrix so far.\n\n    const one2D = tensor2d([[1]], [1, 1]);\n    let w: Tensor2D = one2D.clone();\n\n    const iters = m >= n ? n : m;\n    for (let j = 0; j < iters; ++j) {\n      // This tidy within the for-loop ensures we clean up temporary\n      // tensors as soon as they are no longer needed.\n      const rTemp = r;\n      const wTemp = w;\n      const qTemp = q;\n      [w, r, q] = ENV.engine.tidy((): [Tensor2D, Tensor2D, Tensor2D] => {\n        // Find H = I - tau * w * w', to put zeros below R(j, j).\n        const rjEnd1 = r.slice([j, j], [m - j, 1]);\n        const normX = rjEnd1.norm();\n        const rjj = r.slice([j, j], [1, 1]);\n        const s = rjj.sign().neg() as Tensor2D;\n        const u1 = rjj.sub(s.mul(normX)) as Tensor2D;\n        const wPre = rjEnd1.div(u1);\n        if (wPre.shape[0] === 1) {\n          w = one2D.clone();\n        } else {\n          w = one2D.concat(\n              wPre.slice([1, 0], [wPre.shape[0] - 1, wPre.shape[1]]) as\n                  Tensor2D,\n              0);\n        }\n        const tau = s.matMul(u1).div(normX).neg() as Tensor2D;\n\n        // -- R := HR, Q := QH.\n        const rjEndAll = r.slice([j, 0], [m - j, n]);\n        const tauTimesW = tau.mul(w) as Tensor2D;\n        if (j === 0) {\n          r = rjEndAll.sub(tauTimesW.matMul(w.transpose().matMul(rjEndAll)));\n        } else {\n          r = r.slice([0, 0], [j, n])\n                  .concat(\n                      rjEndAll.sub(tauTimesW.matMul(\n                          w.transpose().matMul(rjEndAll))) as Tensor2D,\n                      0) as Tensor2D;\n        }\n        const qAllJEnd = q.slice([0, j], [m, q.shape[1] - j]);\n        if (j === 0) {\n          q = qAllJEnd.sub(qAllJEnd.matMul(w).matMul(tauTimesW.transpose()));\n        } else {\n          q = q.slice([0, 0], [m, j])\n                  .concat(\n                      qAllJEnd.sub(qAllJEnd.matMul(w).matMul(\n                          tauTimesW.transpose())) as Tensor2D,\n                      1) as Tensor2D;\n        }\n        return [w, r, q];\n      });\n      dispose([rTemp, wTemp, qTemp]);\n    }\n\n    if (!fullMatrices && m > n) {\n      q = q.slice([0, 0], [m, n]);\n      r = r.slice([0, 0], [n, n]);\n    }\n\n    return [q, r];\n  }) as [Tensor2D, Tensor2D];\n}\n\nexport const gramSchmidt = op({gramSchmidt_});\nexport const qr = op({qr_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ForwardFunc} from '../engine';\nimport {ENV} from '../environment';\nimport {nonMaxSuppressionImpl} from '../kernels/non_max_suppression_impl';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\n\n/**\n * Bilinear resize a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction resizeBilinear_<T extends Tensor3D|Tensor4D>(\n    images: T|TensorLike, size: [number, number], alignCorners = false): T {\n  const $images = convertToTensor(images, 'images', 'resizeBilinear');\n  util.assert(\n      $images.rank === 3 || $images.rank === 4,\n      `Error in resizeBilinear: x must be rank 3 or 4, but got ` +\n          `rank ${$images.rank}.`);\n  util.assert(\n      size.length === 2,\n      `Error in resizeBilinear: new shape must 2D, but got shape ` +\n          `${size}.`);\n\n  let batchImages = $images as Tensor4D;\n  let reshapedTo4D = false;\n  if ($images.rank === 3) {\n    reshapedTo4D = true;\n    batchImages =\n        $images.as4D(1, $images.shape[0], $images.shape[1], $images.shape[2]);\n  }\n\n  const [newHeight, newWidth] = size;\n  const forward: ForwardFunc<Tensor4D> = (backend, save) =>\n      backend.resizeBilinear(batchImages, newHeight, newWidth, alignCorners);\n\n  const backward = (dy: Tensor4D, saved: Tensor[]) => {\n    return {\n      batchImages: () => ENV.engine.runKernel(\n          backend =>\n              backend.resizeBilinearBackprop(dy, batchImages, alignCorners),\n          {})\n    };\n  };\n\n  const res = ENV.engine.runKernel(forward, {batchImages}, backward);\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\n/**\n * NearestNeighbor resize a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction resizeNearestNeighbor_<T extends Tensor3D|Tensor4D>(\n    images: T|TensorLike, size: [number, number], alignCorners = false): T {\n  const $images = convertToTensor(images, 'images', 'resizeNearestNeighbor');\n  util.assert(\n      $images.rank === 3 || $images.rank === 4,\n      `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got ` +\n          `rank ${$images.rank}.`);\n  util.assert(\n      size.length === 2,\n      `Error in resizeNearestNeighbor: new shape must 2D, but got shape ` +\n          `${size}.`);\n  util.assert(\n      $images.dtype === 'float32' || $images.dtype === 'int32',\n      '`images` must have `int32` or `float32` as dtype');\n\n  let batchImages = $images as Tensor4D;\n  let reshapedTo4D = false;\n  if ($images.rank === 3) {\n    reshapedTo4D = true;\n    batchImages =\n        $images.as4D(1, $images.shape[0], $images.shape[1], $images.shape[2]);\n  }\n  const [newHeight, newWidth] = size;\n\n  const forward: ForwardFunc<Tensor4D> = (backend, save) =>\n      backend.resizeNearestNeighbor(\n          batchImages, newHeight, newWidth, alignCorners);\n\n  const backward = (dy: Tensor4D, saved: Tensor[]) => {\n    return {\n      batchImages: () => ENV.engine.runKernel(\n          backend => backend.resizeNearestNeighborBackprop(\n              dy, batchImages, alignCorners),\n          {})\n    };\n  };\n\n  const res = ENV.engine.runKernel(forward, {batchImages}, backward);\n\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union)\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @return A 1D tensor with the selected box indices.\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction nonMaxSuppression_(\n    boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,\n    maxOutputSize: number, iouThreshold = 0.5,\n    scoreThreshold = Number.NEGATIVE_INFINITY): Tensor1D {\n  const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n  const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n\n  const inputs = nonMaxSuppSanityCheck(\n      $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n  maxOutputSize = inputs.maxOutputSize;\n  iouThreshold = inputs.iouThreshold;\n  scoreThreshold = inputs.scoreThreshold;\n\n  return ENV.engine.runKernel(\n      b => b.nonMaxSuppression(\n          $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold),\n      {$boxes});\n}\n\n/** This is the async version of `nonMaxSuppression` */\nasync function nonMaxSuppressionAsync_(\n    boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,\n    maxOutputSize: number, iouThreshold = 0.5,\n    scoreThreshold = Number.NEGATIVE_INFINITY): Promise<Tensor1D> {\n  const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n  const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n\n  const inputs = nonMaxSuppSanityCheck(\n      $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n  maxOutputSize = inputs.maxOutputSize;\n  iouThreshold = inputs.iouThreshold;\n  scoreThreshold = inputs.scoreThreshold;\n\n  const boxesVals = await $boxes.data();\n  const scoresVals = await $scores.data();\n  const res = nonMaxSuppressionImpl(\n      boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n  if ($boxes !== boxes) {\n    $boxes.dispose();\n  }\n  if ($scores !== scores) {\n    $scores.dispose();\n  }\n  return res;\n}\n\nfunction nonMaxSuppSanityCheck(\n    boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number,\n    iouThreshold: number, scoreThreshold: number):\n    {maxOutputSize: number, iouThreshold: number, scoreThreshold: number} {\n  if (iouThreshold == null) {\n    iouThreshold = 0.5;\n  }\n  if (scoreThreshold == null) {\n    scoreThreshold = Number.NEGATIVE_INFINITY;\n  }\n  const numBoxes = boxes.shape[0];\n  maxOutputSize = Math.min(maxOutputSize, numBoxes);\n\n  util.assert(\n      0 <= iouThreshold && iouThreshold <= 1,\n      `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);\n  util.assert(\n      boxes.rank === 2,\n      `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);\n  util.assert(\n      boxes.shape[1] === 4,\n      `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);\n  util.assert(scores.rank === 1, 'scores must be a 1D tensor');\n  util.assert(\n      scores.shape[0] === numBoxes,\n      `scores has incompatible shape with boxes. Expected ${numBoxes}, ` +\n          `but was ${scores.shape[0]}`);\n  return {maxOutputSize, iouThreshold, scoreThreshold};\n}\n\n/**\n * Extracts crops from the input image tensor and resizes them using bilinear\n * sampling or nearest neighbor sampling (possibly with aspect ratio change)\n * to a common output size specified by crop_size.\n *\n * @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`,\n *     where imageHeight and imageWidth must be positive, specifying the\n *     batch of images from which to take crops\n * @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized\n *     coordinates of the box in the boxInd[i]'th image in the batch\n * @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range\n *     `[0, batch)` that specifies the image that the `i`-th box refers to.\n * @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`\n *     specifying the size to which all crops are resized to.\n * @param method Optional string from `'bilinear' | 'nearest'`,\n *     defaults to bilinear, which specifies the sampling method for resizing\n * @param extrapolationValue A threshold for deciding when to remove boxes based\n *     on score. Defaults to 0.\n * @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]`\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction cropAndResize_(\n    image: Tensor4D|TensorLike,\n    boxes: Tensor2D|TensorLike,\n    boxInd: Tensor1D|TensorLike,\n    cropSize: [number, number],\n    method?: 'bilinear'|'nearest',\n    extrapolationValue?: number,\n    ): Tensor4D {\n  const $image = convertToTensor(image, 'image', 'cropAndResize', 'float32');\n  const $boxes = convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');\n  const $boxInd = convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');\n  method = method || 'bilinear';\n  extrapolationValue = extrapolationValue || 0;\n\n  const numBoxes = $boxes.shape[0];\n\n  util.assert(\n      $image.rank === 4,\n      'Error in cropAndResize: image must be rank 4,' +\n          `but got rank ${$image.rank}.`);\n  util.assert(\n      $boxes.rank === 2 && $boxes.shape[1] === 4,\n      `Error in cropAndResize: boxes must be have size [${numBoxes},4] ` +\n          `but had shape ${$boxes.shape}.`);\n  util.assert(\n      $boxInd.rank === 1 && $boxInd.shape[0] === numBoxes,\n      `Error in cropAndResize: boxInd must be have size [${numBoxes}] ` +\n          `but had shape ${$boxes.shape}.`);\n  util.assert(\n      cropSize.length === 2,\n      `Error in cropAndResize: cropSize must be of length 2, but got length ` +\n          `${cropSize.length}.`);\n  util.assert(\n      cropSize[0] >= 1 && cropSize[1] >= 1,\n      `cropSize must be atleast [1,1], but was ${cropSize}`);\n  util.assert(\n      method === 'bilinear' || method === 'nearest',\n      `method must be bilinear or nearest, but was ${method}`);\n\n  const forward: ForwardFunc<Tensor4D> = (backend, save) =>\n      backend.cropAndResize(\n          $image, $boxes, $boxInd, cropSize, method, extrapolationValue);\n\n  const res = ENV.engine.runKernel(forward, {$image, $boxes});\n  return res as Tensor4D;\n}\n\nexport const resizeBilinear = op({resizeBilinear_});\nexport const resizeNearestNeighbor = op({resizeNearestNeighbor_});\nexport const nonMaxSuppression = op({nonMaxSuppression_});\nexport const nonMaxSuppressionAsync = nonMaxSuppressionAsync_;\nexport const cropAndResize = cropAndResize_;\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as seedrandom from 'seedrandom';\nimport {ENV} from '../environment';\nimport {warn} from '../log';\nimport * as array_ops_util from '../ops/array_ops_util';\nimport * as axis_util from '../ops/axis_util';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport * as concat_util from '../ops/concat_util';\nimport {Conv2DInfo, Conv3DInfo} from '../ops/conv_util';\nimport * as erf_util from '../ops/erf_util';\nimport * as gather_nd_util from '../ops/gather_nd_util';\nimport * as ops from '../ops/ops';\nimport {buffer, scalar, tensor, tensor3d, tensor4d} from '../ops/ops';\nimport * as scatter_nd_util from '../ops/scatter_nd_util';\nimport * as selu_util from '../ops/selu_util';\nimport {getStridedSlicedInfo} from '../ops/slice_util';\nimport {DataId, Scalar, setTensorTracker, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, Tensor5D, TensorBuffer} from '../tensor';\nimport {DataType, DataTypeMap, DataValues, NumericDataType, Rank, ShapeMap, TypedArray, upcastType} from '../types';\nimport * as util from '../util';\nimport {now} from '../util';\nimport {BackendTimingInfo, DataMover, DataStorage, KernelBackend} from './backend';\nimport * as backend_util from './backend_util';\nimport * as complex_util from './complex_util';\nimport {nonMaxSuppressionImpl} from './non_max_suppression_impl';\nimport {split} from './split_shared';\nimport {topkImpl} from './topk_impl';\nimport {whereImpl} from './where_impl';\n\ninterface TensorData<D extends DataType> {\n  values?: DataTypeMap[D];\n  dtype: D;\n  // For complex numbers, the real and imaginary parts are stored as their own\n  // individual tensors, with a parent joining the two with the\n  // complexTensors field. When this is defined, texture will be null.\n  complexTensors?: {real: Tensor, imag: Tensor};\n}\n\nexport class MathBackendCPU implements KernelBackend {\n  public blockSize = 48;\n\n  private data: DataStorage<TensorData<DataType>>;\n  private fromPixels2DContext: CanvasRenderingContext2D;\n  private firstUse = true;\n\n  constructor() {\n    if (ENV.get('IS_BROWSER')) {\n      this.fromPixels2DContext =\n          document.createElement('canvas').getContext('2d');\n    }\n  }\n\n  setDataMover(dataMover: DataMover): void {\n    this.data = new DataStorage(dataMover);\n  }\n\n  register(dataId: DataId, shape: number[], dtype: DataType): void {\n    if (this.firstUse) {\n      this.firstUse = false;\n      if (ENV.get('IS_NODE')) {\n        warn(\n            '\\n============================\\n' +\n            'Hi there 👋. Looks like you are running TensorFlow.js in ' +\n            'Node.js. To speed things up dramatically, install our node ' +\n            'backend, which binds to TensorFlow C++, by running ' +\n            'npm i @tensorflow/tfjs-node, ' +\n            'or npm i @tensorflow/tfjs-node-gpu if you have CUDA. ' +\n            'Then call require(\\'@tensorflow/tfjs-node\\'); (-gpu ' +\n            'suffix for CUDA) at the start of your program. ' +\n            'Visit https://github.com/tensorflow/tfjs-node for more details.' +\n            '\\n============================\\n');\n      }\n    }\n    if (this.data.has(dataId)) {\n      throw new Error(`Data buffer is already registered`);\n    }\n    this.data.set(dataId, {dtype});\n  }\n  write(dataId: DataId, values: DataValues): void {\n    if (values == null) {\n      throw new Error('MathBackendCPU.write(): values can not be null');\n    }\n    this.data.get(dataId).values = values;\n  }\n  fromPixels(\n      pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement,\n      numChannels: number): Tensor3D {\n    if (pixels == null) {\n      throw new Error('pixels passed to tf.fromPixels() can not be null');\n    }\n    let vals: Uint8ClampedArray;\n    // tslint:disable-next-line:no-any\n    if (ENV.get('IS_NODE') && (pixels as any).getContext == null) {\n      throw new Error(\n          'When running in node, pixels must be an HTMLCanvasElement ' +\n          'like the one returned by the `canvas` npm package');\n    }\n    // tslint:disable-next-line:no-any\n    if ((pixels as any).getContext != null) {\n      // tslint:disable-next-line:no-any\n      vals = (pixels as any)\n                 .getContext('2d')\n                 .getImageData(0, 0, pixels.width, pixels.height)\n                 .data;\n    } else if (pixels instanceof ImageData) {\n      vals = pixels.data;\n    } else if (\n        pixels instanceof HTMLImageElement ||\n        pixels instanceof HTMLVideoElement) {\n      if (this.fromPixels2DContext == null) {\n        throw new Error(\n            'Can\\'t read pixels from HTMLImageElement outside ' +\n            'the browser.');\n      }\n      this.fromPixels2DContext.canvas.width = pixels.width;\n      this.fromPixels2DContext.canvas.height = pixels.height;\n      this.fromPixels2DContext.drawImage(\n          pixels, 0, 0, pixels.width, pixels.height);\n      vals = this.fromPixels2DContext\n                 .getImageData(0, 0, pixels.width, pixels.height)\n                 .data;\n    } else {\n      throw new Error(\n          'pixels passed to tf.fromPixels() must be either an ' +\n          `HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ` +\n          `ImageData, but was ${(pixels as {}).constructor.name}`);\n    }\n    let values: Int32Array;\n    if (numChannels === 4) {\n      values = new Int32Array(vals);\n    } else {\n      const numPixels = pixels.width * pixels.height;\n      values = new Int32Array(numPixels * numChannels);\n      for (let i = 0; i < numPixels; i++) {\n        for (let channel = 0; channel < numChannels; ++channel) {\n          values[i * numChannels + channel] = vals[i * 4 + channel];\n        }\n      }\n    }\n    const outShape: [number, number, number] =\n        [pixels.height, pixels.width, numChannels];\n    return tensor3d(values, outShape, 'int32');\n  }\n  async read(dataId: DataId): Promise<DataValues> {\n    return this.readSync(dataId);\n  }\n  readSync(dataId: DataId): DataValues {\n    const {dtype, complexTensors} = this.data.get(dataId);\n    if (dtype === 'complex64') {\n      const realValues = complexTensors.real.dataSync() as Float32Array;\n      const imagValues = complexTensors.imag.dataSync() as Float32Array;\n      return complex_util.mergeRealAndImagArrays(realValues, imagValues);\n    }\n    return this.data.get(dataId).values;\n  }\n\n  disposeData(dataId: DataId): void {\n    if (this.data.has(dataId)) {\n      const {complexTensors} = this.data.get(dataId);\n      if (complexTensors != null) {\n        complexTensors.real.dispose();\n        complexTensors.imag.dispose();\n      }\n      this.data.delete(dataId);\n    }\n  }\n\n  async time(f: () => void): Promise<BackendTimingInfo> {\n    const start = now();\n    f();\n    const kernelMs = now() - start;\n    return {kernelMs};\n  }\n\n  memory() {\n    return {\n      // Unreliable due to automatic gc. The numbers above are cumulative.\n      unreliable: true,\n      reasons:\n          ['The reported memory is an upper bound. Due to automatic garbage ' +\n           'collection, the true allocated memory may be less.']\n    };\n  }\n\n  complex<T extends Tensor>(real: T, imag: T): T {\n    const result = Tensor.make(real.shape, {}, 'complex64') as T;\n\n    const resultData = this.data.get(result.dataId);\n    // The backend owns the reference to the underlying real and imaginary\n    // clones. These will explicitly get disposed when the complex tensor is\n    // disposed.\n    resultData.complexTensors = {\n      real: ENV.engine.keep(real.clone()),\n      imag: ENV.engine.keep(imag.clone())\n    };\n\n    return result;\n  }\n  real<T extends Tensor>(input: T): T {\n    const resultData = this.data.get(input.dataId);\n    return resultData.complexTensors.real.clone() as T;\n  }\n  imag<T extends Tensor>(input: T): T {\n    const resultData = this.data.get(input.dataId);\n    return resultData.complexTensors.imag.clone() as T;\n  }\n\n  private assertNotComplex(tensor: Tensor|Tensor[], opName: string) {\n    if (!Array.isArray(tensor)) {\n      tensor = [tensor];\n    }\n    tensor.forEach(t => {\n      if (t != null) {\n        util.assert(\n            t.dtype !== 'complex64',\n            `${opName} does not support complex64 tensors.`);\n      }\n    });\n  }\n\n  slice<T extends Tensor>(x: T, begin: number[], size: number[]): T {\n    this.assertNotComplex(x, 'slice');\n\n    const buffer = ops.buffer(size, x.dtype);\n\n    for (let i = 0; i < buffer.size; ++i) {\n      const loc = buffer.indexToLoc(i);\n      const xLoc = loc.map((idx, j) => idx + begin[j]);\n      buffer.set(x.get(...xLoc), ...loc);\n    }\n    return buffer.toTensor() as T;\n  }\n\n  stridedSlice<T extends Tensor>(\n      x: T, begin: number[], end: number[], strides: number[],\n      beginMask: number, endMask: number, ellipsisMask: number,\n      newAxisMask: number, shrinkAxisMask: number): T {\n    this.assertNotComplex(x, 'stridedSlice');\n\n    const [beginIndex, size, shrinkAxis] = getStridedSlicedInfo(\n        x.shape, begin, end, strides, beginMask, endMask, ellipsisMask,\n        newAxisMask, shrinkAxisMask);\n\n    const shape = size.filter((v, index) => shrinkAxis.indexOf(index) === -1);\n\n    if (shape.some(axis => axis === 0)) {\n      return ops.tensor([], shape) as T;\n    }\n\n    const buffer = ops.buffer(size, x.dtype);\n\n    for (let i = 0; i < buffer.size; i++) {\n      const loc = buffer.indexToLoc(i);\n\n      const newLoc: number[] = new Array(loc.length);\n      for (let j = 0; j < newLoc.length; j++) {\n        newLoc[j] = loc[j] * strides[j] + beginIndex[j];\n      }\n      buffer.set(x.get(...newLoc), ...loc);\n    }\n\n    return buffer.toTensor().reshape(shape) as T;\n  }\n\n  reverse<T extends Tensor>(x: T, axis: number[]): T {\n    this.assertNotComplex(x, 'reverse');\n\n    const buffer = ops.buffer(x.shape, x.dtype);\n    const xBuffer = x.buffer();\n\n    for (let i = 0; i < buffer.size; i++) {\n      const outLoc = buffer.indexToLoc(i);\n      const inLoc = outLoc.slice();\n      axis.forEach(ax => inLoc[ax] = x.shape[ax] - 1 - inLoc[ax]);\n      buffer.set(xBuffer.get(...inLoc), ...outLoc);\n    }\n\n    return buffer.toTensor() as T;\n  }\n\n  concat(tensors: Tensor[], axis: number): Tensor {\n    this.assertNotComplex(tensors, 'concat');\n    const tensors2D = tensors.map(t => {\n      const innerSize = util.sizeFromShape(t.shape.slice(axis));\n      return t.as2D(-1, innerSize);\n    });\n    const outShape =\n        concat_util.computeOutShape(tensors2D.map(t => t.shape), 1 /* axis */);\n    const values =\n        ops.buffer(outShape as [number, number], tensors[0].dtype as 'float32')\n            .values;\n    if (tensors2D[0].shape[0] === 1) {\n      // Use built-in TypedArray.set() method for speed.\n      let offset = 0;\n      tensors2D.forEach(t => {\n        values.set(t.dataSync(), offset);\n        offset += t.size;\n      });\n    } else {\n      let colOffset = 0;\n      tensors2D.forEach(t => {\n        const tVals = t.dataSync();\n        let tIdx = 0;\n        for (let row = 0; row < t.shape[0]; ++row) {\n          const resIdx = row * outShape[1] + colOffset;\n          for (let col = 0; col < t.shape[1]; ++col) {\n            values[resIdx + col] = tVals[tIdx++];\n          }\n        }\n        colOffset += t.shape[1];\n      });\n    }\n    const finalOutShape =\n        concat_util.computeOutShape(tensors.map(t => t.shape), axis);\n    return tensor(values, finalOutShape, tensors[0].dtype);\n  }\n\n  neg<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'neg');\n\n    return this.multiply(ops.scalar(-1), x) as T;\n  }\n\n  add(a: Tensor, b: Tensor): Tensor {\n    if (a.dtype === 'complex64' || b.dtype === 'complex64') {\n      return this.broadcastedBinaryComplexOp(\n                 a.cast('complex64'), b.cast('complex64'),\n                 (aReal, aImag, bReal, bImag) => {\n                   return {real: aReal + bReal, imag: aImag + bImag};\n                 }) as Tensor;\n    }\n\n    return this.broadcastedBinaryOp(\n               a, b, upcastType(a.dtype, b.dtype),\n               (aValue, bValue) => aValue + bValue) as Tensor;\n  }\n\n  addN<T extends Tensor>(tensors: T[]): T {\n    this.assertNotComplex(tensors, 'addN');\n\n    const vals = tensors.map(t => t.dataSync());\n    const result = ops.buffer(tensors[0].shape, tensors[0].dtype as 'float32');\n    const resultVals = result.values;\n    for (let i = 0; i < tensors.length; i++) {\n      const currVals = vals[i];\n      for (let j = 0; j < resultVals.length; j++) {\n        resultVals[j] += currVals[j];\n      }\n    }\n    return result.toTensor() as T;\n  }\n\n  subtract(a: Tensor, b: Tensor): Tensor {\n    if (a.dtype === 'complex64' || b.dtype === 'complex64') {\n      return this.broadcastedBinaryComplexOp(\n                 a.cast('complex64'), b.cast('complex64'),\n                 (aReal, aImag, bReal, bImag) => {\n                   return {real: aReal - bReal, imag: aImag - bImag};\n                 }) as Tensor;\n    }\n\n    return this.broadcastedBinaryOp(\n               a, b, upcastType(a.dtype, b.dtype),\n               (aValue, bValue) => aValue - bValue) as Tensor;\n  }\n\n  pow<T extends Tensor>(a: T, b: Tensor): T {\n    this.assertNotComplex([a, b], 'pow');\n\n    return this.broadcastedBinaryOp(\n               a, b, a.dtype, (aValue, bValue) => Math.pow(aValue, bValue)) as\n        T;\n  }\n\n  batchMatMul(\n      a: Tensor3D, b: Tensor3D, transposeA: boolean,\n      transposeB: boolean): Tensor3D {\n    this.assertNotComplex([a, b], 'matMul');\n\n    const sharedDim = transposeA ? a.shape[1] : a.shape[2];\n    const leftDim = transposeA ? a.shape[2] : a.shape[1];\n    const rightDim = transposeB ? b.shape[1] : b.shape[2];\n    const batchDim = a.shape[0];\n\n    const aValues = a.dataSync();\n    const bValues = b.dataSync();\n    const [aBatch, aOuterStep, aInnerStep] = transposeA ?\n        [a.strides[0], 1, a.strides[1]] :\n        [a.strides[0], a.strides[1], 1];\n    const [bInnerStep, bOuterStep, bBatch] = transposeB ?\n        [1, b.strides[1], b.strides[0]] :\n        [b.strides[1], 1, b.strides[0]];\n\n    const size = leftDim * rightDim;\n    const result = buffer([batchDim, leftDim, rightDim], a.dtype);\n    const resVals = result.values as TypedArray;\n    const blockSize = this.blockSize;\n\n    for (let b = 0; b < batchDim; b++) {\n      for (let i0 = 0; i0 < leftDim; i0 += blockSize) {\n        for (let j0 = 0; j0 < rightDim; j0 += blockSize) {\n          for (let k0 = 0; k0 < sharedDim; k0 += blockSize) {\n            // for when blockSize doesn't evenly divide the input\n            const iBlock = Math.min(i0 + blockSize, leftDim);\n            const jBlock = Math.min(j0 + blockSize, rightDim);\n            const kBlock = Math.min(k0 + blockSize, sharedDim);\n\n            for (let i = i0; i < iBlock; i++) {\n              for (let j = j0; j < jBlock; j++) {\n                let sum = 0.0;\n\n                for (let k = k0; k < kBlock; k++) {\n                  sum += aValues[b * aBatch + i * aOuterStep + k * aInnerStep] *\n                      bValues[k * bInnerStep + j * bOuterStep + b * bBatch];\n                }\n                resVals[b * size + (i * rightDim + j)] += sum;\n              }\n            }\n          }\n        }\n      }\n    }\n    return result.toTensor() as Tensor3D;\n  }\n\n  multiply(a: Tensor, b: Tensor): Tensor {\n    if (a.dtype === 'complex64' || b.dtype === 'complex64') {\n      return this.broadcastedBinaryComplexOp(\n                 a.cast('complex64'), b.cast('complex64'),\n                 (aReal, aImag, bReal, bImag) => {\n                   return {\n                     real: aReal * bReal - aImag * bImag,\n                     imag: aReal * bImag + aImag * bReal\n                   };\n                 }) as Tensor;\n    }\n\n    return this.broadcastedBinaryOp(\n               a, b, upcastType(a.dtype, b.dtype),\n               (aValue, bValue) => aValue * bValue) as Tensor;\n  }\n\n  realDivide(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'realDivide');\n\n    const op = (a: number, b: number) => a / b;\n    const outputDtype = 'float32';\n    return this.broadcastedBinaryOp(a, b, outputDtype, op) as Tensor;\n  }\n\n  floorDiv(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'floorDiv');\n\n    const op = (a: number, b: number) => Math.floor(a / b);\n    const outputDtype = 'int32';\n    return this.broadcastedBinaryOp(a, b, outputDtype, op) as Tensor;\n  }\n\n  sum(x: Tensor, axes: number[]): Tensor {\n    this.assertNotComplex(x, 'sum');\n\n    axis_util.assertAxesAreInnerMostDims('sum', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const resultDtype = upcastType(x.dtype, 'int32');\n    const result = ops.zeros(outShape, resultDtype);\n    const reduceSize = util.sizeFromShape(reduceShape);\n    const vals = result.dataSync();\n\n    const aVals = x.dataSync();\n    for (let i = 0; i < vals.length; ++i) {\n      const offset = i * reduceSize;\n      let sum = 0;\n      for (let j = 0; j < reduceSize; ++j) {\n        sum += aVals[offset + j];\n      }\n      vals[i] = sum;\n    }\n    return result;\n  }\n\n  prod(x: Tensor, axes: number[]): Tensor {\n    this.assertNotComplex(x, 'sum');\n\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const resultDtype = upcastType(x.dtype, 'int32');\n    const result = ops.zeros(outShape, resultDtype);\n    const reduceSize = util.sizeFromShape(reduceShape);\n    const vals = result.dataSync();\n\n    const aVals = x.dataSync();\n    for (let i = 0; i < vals.length; ++i) {\n      const offset = i * reduceSize;\n      let prod = 1;\n      for (let j = 0; j < reduceSize; ++j) {\n        prod *= aVals[offset + j];\n      }\n      vals[i] = prod;\n    }\n    return result;\n  }\n\n  unsortedSegmentSum<T extends Tensor>(\n      x: T, segmentIds: Tensor1D, numSegments: number): Tensor {\n    this.assertNotComplex(x, 'unsortedSegmentSum');\n\n    const res = [];\n\n    // Reshape the segment id's so that they can be broadcast with\n    // x. The new shape should be [segmentIds.shape, 1, ..., 1]\n    const numIters = x.rank - segmentIds.rank;\n    for (let i = 0; i < numIters; ++i) {\n      segmentIds = segmentIds.expandDims(i + 1);\n    }\n\n    for (let i = 0; i < numSegments; ++i) {\n      const segmentId = ops.scalar(i, 'int32');\n      const mask = ops.equal(segmentId, segmentIds).asType('float32');\n      const sum = mask.mul(x).sum(0);\n      res.push(sum);\n    }\n\n    return ops.stack(res);\n  }\n\n  argMin(x: Tensor, axis: number): Tensor {\n    this.assertNotComplex(x, 'argMin');\n\n    const axes = [axis];\n    axis_util.assertAxesAreInnerMostDims('argMin', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const result = ops.zeros(outShape, 'int32');\n    const reduceSize = util.sizeFromShape(reduceShape);\n    const vals = result.dataSync();\n\n    const aVals = x.dataSync();\n    for (let i = 0; i < vals.length; ++i) {\n      const offset = i * reduceSize;\n      let min = aVals[offset];\n      let minIndex = 0;\n      for (let j = 0; j < reduceSize; ++j) {\n        const value = aVals[offset + j];\n        if (value < min) {\n          min = value;\n          minIndex = j;\n        }\n      }\n      vals[i] = minIndex;\n    }\n    return result;\n  }\n\n  argMax(x: Tensor, axis: number): Tensor {\n    this.assertNotComplex(x, 'argMax');\n\n    const axes = [axis];\n    axis_util.assertAxesAreInnerMostDims('argMax', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const result = ops.zeros(outShape, 'int32');\n    const reduceSize = util.sizeFromShape(reduceShape);\n    const vals = result.dataSync();\n\n    const aVals = x.dataSync();\n    for (let i = 0; i < vals.length; ++i) {\n      const offset = i * reduceSize;\n      let max = aVals[offset];\n      let maxIndex = 0;\n      for (let j = 0; j < reduceSize; ++j) {\n        const value = aVals[offset + j];\n        if (value > max) {\n          max = value;\n          maxIndex = j;\n        }\n      }\n      vals[i] = maxIndex;\n    }\n    return result;\n  }\n\n  cumsum(x: Tensor, axis: number, exclusive: boolean, reverse: boolean):\n      Tensor {\n    this.assertNotComplex(x, 'cumsum');\n\n    if (axis !== x.rank - 1) {\n      throw new Error(\n          `backend.cumsum in CPU expects an inner-most axis=${x.rank - 1} ` +\n          `but got axis=${axis}`);\n    }\n    const resultDtype = upcastType(x.dtype, 'int32');\n    const result = ops.zeros(x.shape, resultDtype);\n    const vals = result.dataSync();\n\n    const aVals = x.dataSync();\n    const finalDim = x.shape[x.rank - 1];\n    const indexAdjuster = reverse ?\n        (i: number, j: number) => i + finalDim - j - 1 :\n        (i: number, j: number) => i + j;\n    for (let i = 0; i < aVals.length; i += finalDim) {\n      for (let j = 0; j < finalDim; j++) {\n        const idx = indexAdjuster(i, j);\n        if (j === 0) {\n          vals[idx] = exclusive ? 0 : aVals[idx];\n        } else {\n          const prevIdx = indexAdjuster(i, j - 1);\n          vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] :\n                                  aVals[idx] + vals[prevIdx];\n        }\n      }\n    }\n    return result;\n  }\n\n  equal(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'equal');\n\n    return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n      return (aVal === bVal) ? 1 : 0;\n    });\n  }\n\n  notEqual(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'notEqual');\n\n    return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n      return (aVal !== bVal) ? 1 : 0;\n    });\n  }\n\n  less(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'less');\n\n    return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n      return (aVal < bVal) ? 1 : 0;\n    });\n  }\n\n  lessEqual(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'lessEqual');\n\n    return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n      return (aVal <= bVal) ? 1 : 0;\n    });\n  }\n\n  greater(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'greater');\n\n    return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n      return (aVal > bVal) ? 1 : 0;\n    });\n  }\n\n  greaterEqual(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'greaterEqual');\n\n    return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n      return (aVal >= bVal) ? 1 : 0;\n    });\n  }\n\n  logicalNot<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'logicalNot');\n\n    const values = x.dataSync();\n    const newValues = new Uint8Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      newValues[i] = values[i] ? 0 : 1;\n    }\n    return Tensor.make(x.shape, {values: newValues}, 'bool') as T;\n  }\n\n  logicalAnd(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'logicalAnd');\n\n    return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n      return aVal && bVal;\n    });\n  }\n\n  logicalOr(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'logicalOr');\n\n    return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n      return aVal || bVal;\n    });\n  }\n\n  select(condition: Tensor, a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([condition, a, b], 'select');\n\n    const values = condition.dataSync();\n    const aValues = a.dataSync();\n    const bValues = b.dataSync();\n    const result = ops.zeros(a.shape, upcastType(a.dtype, b.dtype));\n    const newValues = result.dataSync();\n    let index = 0;\n    const offset = condition.rank === 0 || condition.rank > 1 || a.rank === 1 ?\n        1 :\n        a.shape[1];\n\n    for (let i = 0; i < values.length; i++) {\n      for (let j = 0; j < offset; j++) {\n        if (values[i] === 1) {\n          newValues[index++] = aValues[i];\n        } else {\n          newValues[index++] = bValues[i];\n        }\n      }\n    }\n    return result;\n  }\n\n  where(condition: Tensor): Tensor2D {\n    this.assertNotComplex([condition], 'where');\n\n    const condVals = condition.dataSync();\n    return whereImpl(condition.shape, condVals);\n  }\n\n  topk<T extends Tensor>(x: T, k: number, sorted: boolean): [T, T] {\n    this.assertNotComplex(x, 'topk');\n\n    const xVals = x.dataSync();\n    return topkImpl(xVals, x.shape, x.dtype as NumericDataType, k, sorted);\n  }\n\n  min(x: Tensor, axes: number[]): Tensor {\n    this.assertNotComplex(x, 'min');\n\n    axis_util.assertAxesAreInnerMostDims('min', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const result = ops.zeros(outShape, x.dtype);\n    const reduceSize = util.sizeFromShape(reduceShape);\n    const vals = result.dataSync();\n\n    const aVals = x.dataSync();\n    for (let i = 0; i < vals.length; ++i) {\n      const offset = i * reduceSize;\n      let min = aVals[offset];\n      for (let j = 0; j < reduceSize; ++j) {\n        const value = aVals[offset + j];\n        if (value < min) {\n          min = value;\n        }\n      }\n      vals[i] = min;\n    }\n    return result;\n  }\n\n  minimum(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'minimum');\n\n    return this.broadcastedBinaryOp(\n        a, b, a.dtype, (aVal, bVal) => Math.min(aVal, bVal));\n  }\n\n  mod(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'mod');\n\n    return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => {\n      const rem = aVal % bVal;\n      if ((aVal < 0 && bVal < 0) || (aVal >= 0 && bVal >= 0)) {\n        return rem;\n      } else {\n        return (rem + bVal) % bVal;\n      }\n    });\n  }\n\n  max(x: Tensor, axes: number[]): Tensor {\n    this.assertNotComplex(x, 'max');\n\n    axis_util.assertAxesAreInnerMostDims('max', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const result = ops.zeros(outShape, x.dtype);\n    const reduceSize = util.sizeFromShape(reduceShape);\n    const vals = result.dataSync();\n\n    const aVals = x.dataSync();\n    for (let i = 0; i < vals.length; ++i) {\n      const offset = i * reduceSize;\n      let max = aVals[offset];\n      for (let j = 0; j < reduceSize; ++j) {\n        const value = aVals[offset + j];\n        if (value > max) {\n          max = value;\n        }\n      }\n      vals[i] = max;\n    }\n    return result;\n  }\n\n  maximum(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'maximum');\n\n    return this.broadcastedBinaryOp(\n        a, b, a.dtype, (aVal, bVal) => Math.max(aVal, bVal));\n  }\n\n  all(x: Tensor, axes: number[]): Tensor {\n    this.assertNotComplex(x, 'all');\n\n    axis_util.assertAxesAreInnerMostDims('all', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const result = ops.zeros(outShape, x.dtype);\n    const reduceSize = util.sizeFromShape(reduceShape);\n    const vals = result.dataSync();\n\n    const aVals = x.dataSync();\n    for (let i = 0; i < vals.length; ++i) {\n      const offset = i * reduceSize;\n      let all = aVals[offset];\n      for (let j = 0; j < reduceSize; ++j) {\n        const value = aVals[offset + j];\n        all = all && value;\n      }\n      vals[i] = all;\n    }\n    return result;\n  }\n\n  any(x: Tensor, axes: number[]): Tensor {\n    this.assertNotComplex(x, 'any');\n\n    axis_util.assertAxesAreInnerMostDims('any', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const result = ops.zeros(outShape, x.dtype);\n    const reduceSize = util.sizeFromShape(reduceShape);\n    const vals = result.dataSync();\n\n    const aVals = x.dataSync();\n    for (let i = 0; i < vals.length; ++i) {\n      const offset = i * reduceSize;\n      let anyVal = aVals[offset];\n      for (let j = 0; j < reduceSize; ++j) {\n        const value = aVals[offset + j];\n        anyVal = anyVal || value;\n      }\n      vals[i] = anyVal;\n    }\n    return result;\n  }\n\n  squaredDifference(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'squaredDifference');\n\n    return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => {\n      const diff = aVal - bVal;\n      return diff * diff;\n    });\n  }\n\n  ceil<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'ceil');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      newValues[i] = Math.ceil(values[i]);\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  floor<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'floor');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      newValues[i] = Math.floor(values[i]);\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  sign<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'x');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      if (values[i] < 0) {\n        newValues[i] = -1;\n      } else if (values[i] > 0) {\n        newValues[i] = 1;\n      } else {\n        newValues[i] = 0;\n      }\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  round<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'round');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      // The algorithm is based on banker's rounding.\n      const base = Math.floor(values[i]);\n      if (values[i] - base < 0.5) {\n        newValues[i] = Math.floor(values[i]);\n      } else if (values[i] - base > 0.5) {\n        newValues[i] = Math.ceil(values[i]);\n      } else {\n        if (base % 2.0 === 0.0) {\n          newValues[i] = base;\n        } else {\n          newValues[i] = base + 1.0;\n        }\n      }\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  exp<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'exp');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      newValues[i] = Math.exp(values[i]);\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  expm1<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'expm1');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      newValues[i] = Math.expm1(values[i]);\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  log<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'log');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      const value = values[i];\n      newValues[i] = Math.log(value);\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  log1p<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'log1p');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      const value = values[i];\n      newValues[i] = Math.log1p(value);\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  sqrt<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'sqrt');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      const value = values[i];\n      newValues[i] = Math.sqrt(value);\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  rsqrt<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'rsqrt');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      const value = values[i];\n      newValues[i] = 1 / Math.sqrt(value);\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  square<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'square');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      const value = values[i];\n      newValues[i] = value * value;\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  reciprocal<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'reciprocal');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      newValues[i] = 1 / values[i];\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  relu<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'relu');\n\n    const res = ops.zeros(x.shape, x.dtype);\n    const resVals = res.dataSync();\n    const inVals = x.dataSync();\n    for (let i = 0; i < inVals.length; ++i) {\n      resVals[i] = Math.max(0, inVals[i]);\n    }\n    return res as T;\n  }\n\n  elu<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'elu');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      const v = values[i];\n      if (v >= 0) {\n        resultValues[i] = v;\n      } else {\n        resultValues[i] = (Math.exp(v) - 1);\n      }\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  eluDer<T extends Tensor>(dy: T, y: T): T {\n    this.assertNotComplex([dy, y], 'eluDer');\n\n    const resultValues = new Float32Array(y.size);\n    const values = y.dataSync();\n    const dyValues = dy.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      const v = values[i];\n      if (v >= 1) {\n        resultValues[i] = dyValues[i];\n      } else {\n        resultValues[i] = dyValues[i] * (v + 1);\n      }\n    }\n    return Tensor.make(y.shape, {values: resultValues}) as T;\n  }\n\n  selu<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'selu');\n\n    // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n    // see: https://arxiv.org/abs/1706.02515\n    const scaleAlpha = selu_util.SELU_SCALEALPHA;\n    const scale = selu_util.SELU_SCALE;\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      const v = values[i];\n      if (v >= 0) {\n        resultValues[i] = scale * v;\n      } else {\n        resultValues[i] = scaleAlpha * (Math.exp(v) - 1);\n      }\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  clip<T extends Tensor>(x: T, min: number, max: number): T {\n    this.assertNotComplex(x, 'clip');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      const v = values[i];\n      resultValues[i] = v > max ? max : (v < min ? min : v);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  abs<T extends Tensor>(x: T): T {\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.abs(values[i]);\n    }\n\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  complexAbs<T extends Tensor>(x: T): T {\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n\n    for (let i = 0; i < x.size; ++i) {\n      const real = values[i * 2];\n      const imag = values[i * 2 + 1];\n      resultValues[i] = Math.hypot(real, imag);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  int<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'int');\n\n    const resultValues = new Int32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = values[i];\n    }\n    return Tensor.make(x.shape, {values: resultValues}, 'int32');\n  }\n\n  sigmoid<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'sigmoid');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = 1 / (1 + Math.exp(-values[i]));\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  softplus<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'softplus');\n\n    // mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX\n\n    // epsilon is the difference between 1.0 and the next representable float.\n    // For a single precision 32 bit float this should be 2^-23, see:\n    // https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm\n    const epsilon = 1.1920928955078125e-7;\n    const threshold = Math.log(epsilon) + 2.0;\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n\n    for (let i = 0; i < values.length; ++i) {\n      // Value above which exp(x) may overflow, but softplus(x) == x\n      // is within machine epsilon.\n      const tooLarge = values[i] > -threshold;\n\n      // Value below which exp(x) may underflow, but softplus(x) == exp(x)\n      // is within machine epsilon.\n      const tooSmall = values[i] < threshold;\n\n      const expX = Math.exp(values[i]);\n      let result;\n\n      if (tooSmall) {\n        result = expX;\n      } else if (tooLarge) {\n        result = values[i];\n      } else {\n        result = Math.log(1.0 + expX);\n      }\n      resultValues[i] = result;\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  sin<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'sin');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.sin(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  cos<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'cos');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.cos(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  tan<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'tan');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.tan(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  asin<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'asin');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.asin(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  acos<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'acos');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.acos(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  atan<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'atan');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.atan(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  atan2<T extends Tensor>(a: T, b: T): T {\n    this.assertNotComplex([a, b], 'atan2');\n\n    return this.broadcastedBinaryOp(\n               a, b, a.dtype, (aValue, bValue) => Math.atan2(aValue, bValue)) as\n        T;\n  }\n\n  sinh<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'sinh');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.sinh(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  cosh<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'cosh');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.cosh(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  tanh<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'tanh');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = util.tanh(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  asinh<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'asinh');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.asinh(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  acosh<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'acosh');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.acosh(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  atanh<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'atanh');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.atanh(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  erf<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'erf');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    const p = erf_util.ERF_P;\n    const a1 = erf_util.ERF_A1;\n    const a2 = erf_util.ERF_A2;\n    const a3 = erf_util.ERF_A3;\n    const a4 = erf_util.ERF_A4;\n    const a5 = erf_util.ERF_A5;\n    for (let i = 0; i < values.length; ++i) {\n      const v = values[i];\n      const t = 1.0 / (1.0 + p * v);\n      resultValues[i] = 1.0 -\n          (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t *\n              Math.exp(-v * v);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  step<T extends Tensor>(x: T, alpha = 0): T {\n    this.assertNotComplex(x, 'step');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      const value = values[i];\n      if (isNaN(value)) {\n        resultValues[i] = NaN;\n      } else {\n        resultValues[i] = value > 0 ? 1 : alpha;\n      }\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  conv2d(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    this.assertNotComplex([x, filter], 'conv2d');\n\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const padLeft = convInfo.padInfo.left;\n    const padTop = convInfo.padInfo.top;\n    const y = ops.buffer(convInfo.outShape, x.dtype as 'float32');\n\n    const xVals = x.dataSync();\n    const wVals = filter.dataSync();\n    const yVals = y.values;\n\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n      const xOffset1 = b * x.strides[0];\n      const yOffset1 = b * y.strides[0];\n      for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n        const yOffset2 = yOffset1 + yR * y.strides[1];\n        const xRCorner = yR * convInfo.strideHeight - padLeft;\n        for (let wR = 0; wR < filterHeight; wR++) {\n          const xR = xRCorner + wR * dilationHeight;\n          if (xR < 0 || xR >= convInfo.inHeight) {\n            continue;\n          }\n          const wOffset1 = wR * filter.strides[0];\n          const xOffset2 = xOffset1 + xR * x.strides[1];\n          for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n            const yOffset3 = yOffset2 + yC * convInfo.outChannels;\n            const xCCorner = yC * convInfo.strideWidth - padTop;\n            for (let wC = 0; wC < filterWidth; wC++) {\n              const xC = xCCorner + wC * dilationWidth;\n              if (xC < 0 || xC >= convInfo.inWidth) {\n                continue;\n              }\n              const wOffset2 = wOffset1 + wC * filter.strides[1];\n              const xOffset3 = xOffset2 + xC * convInfo.inChannels;\n              let wOffset3 = wOffset2;\n              for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                const xVal = xVals[xOffset3 + d1];\n                for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                  yVals[yOffset3 + d2] += xVal * wVals[wOffset3 + d2];\n                }\n                wOffset3 += convInfo.outChannels;\n              }\n            }\n          }\n        }\n      }\n    }\n    return y.toTensor() as Tensor4D;\n  }\n\n  conv3d(x: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n    const filterDepth = convInfo.filterDepth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dilationDepth = convInfo.dilationDepth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const padFront = convInfo.padInfo.front;\n    const padLeft = convInfo.padInfo.left;\n    const padTop = convInfo.padInfo.top;\n    const y = ops.buffer<Rank.R5>(convInfo.outShape, x.dtype as 'float32');\n\n    const xVals = x.dataSync();\n    const wVals = filter.dataSync();\n    const yVals = y.values;\n\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n      const xOffset1 = b * x.strides[0];\n      const yOffset1 = b * y.strides[0];\n      for (let yF = 0; yF < convInfo.outDepth; ++yF) {\n        const yOffset2 = yOffset1 + yF * y.strides[1];\n        const xFCorner = yF * convInfo.strideDepth - padFront;\n        for (let wF = 0; wF < filterDepth; wF++) {\n          const xF = xFCorner + wF * dilationDepth;\n          if (xF < 0 || xF >= convInfo.inDepth) {\n            continue;\n          }\n          const wOffset1 = wF * filter.strides[0];\n          const xOffset2 = xOffset1 + xF * x.strides[1];\n\n          for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n            const yOffset3 = yOffset2 + yR * y.strides[2];\n            const xRCorner = yR * convInfo.strideHeight - padTop;\n            for (let wR = 0; wR < filterHeight; wR++) {\n              const xR = xRCorner + wR * dilationHeight;\n              if (xR < 0 || xR >= convInfo.inHeight) {\n                continue;\n              }\n              const wOffset2 = wOffset1 + wR * filter.strides[1];\n              const xOffset3 = xOffset2 + xR * x.strides[2];\n              for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                const yOffset4 = yOffset3 + yC * convInfo.outChannels;\n                const xCCorner = yC * convInfo.strideWidth - padLeft;\n                for (let wC = 0; wC < filterWidth; wC++) {\n                  const xC = xCCorner + wC * dilationWidth;\n                  if (xC < 0 || xC >= convInfo.inWidth) {\n                    continue;\n                  }\n                  const wOffset3 = wOffset2 + wC * filter.strides[2];\n                  const xOffset4 = xOffset3 + xC * convInfo.inChannels;\n                  let wOffset4 = wOffset3;\n                  for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                    const xVal = xVals[xOffset4 + d1];\n                    for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                      yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];\n                    }\n                    wOffset4 += convInfo.outChannels;\n                  }\n                }\n              }\n            }\n          }\n        }\n      }\n    }\n    return y.toTensor();\n  }\n\n  conv2dDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    this.assertNotComplex([dy, filter], 'conv2dDerInput');\n\n    const dx = ops.buffer<Rank.R4>(convInfo.inShape, 'float32');\n    const dxValues = dx.values;\n    const [dxS0, dxS1, dxS2] = dx.strides;\n    const dyValues = dy.dataSync();\n    const [dyS0, dyS1, dyS2] = dy.strides;\n    const fltValues = filter.dataSync();\n    const [fltS0, fltS1, fltS2] = filter.strides;\n    const {\n      batchSize,\n      filterHeight,\n      filterWidth,\n      inChannels,\n      inHeight,\n      inWidth,\n      outChannels,\n      outHeight,\n      outWidth,\n      strideHeight,\n      strideWidth\n    } = convInfo;\n    const topPad = filterHeight - 1 - convInfo.padInfo.top;\n    const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n\n    for (let b = 0; b < batchSize; ++b) {\n      for (let d1 = 0; d1 < inChannels; ++d1) {\n        for (let xR = 0; xR < inHeight; ++xR) {\n          const xRCorner = xR - topPad;\n          const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n          const yRMax =\n              Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n\n          for (let xC = 0; xC < inWidth; ++xC) {\n            const xCCorner = xC - leftPad;\n            const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n            const yCMax =\n                Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n\n            let dotProd = 0;\n            for (let yR = xRMin; yR < yRMax; ++yR) {\n              const wR = yR * strideHeight - xRCorner;\n\n              for (let yC = xCMin; yC < yCMax; ++yC) {\n                const wC = yC * strideWidth - xCCorner;\n                const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;\n                const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n                    fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n\n                for (let d2 = 0; d2 < outChannels; ++d2) {\n                  const pixel = dyValues[dyOffset + d2];\n                  const weight = fltValues[fltOffset + d2];\n                  dotProd += pixel * weight;\n                }\n              }\n            }\n            dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;\n          }\n        }\n      }\n    }\n    return dx.toTensor();\n  }\n\n  conv3dDerInput(dy: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo):\n      Tensor5D {\n    const dx = ops.buffer<Rank.R5>(convInfo.inShape, 'float32');\n    const dxValues = dx.values;\n    const [dxS0, dxS1, dxS2, dxS3] = dx.strides;\n    const dyValues = dy.dataSync();\n    const [dyS0, dyS1, dyS2, dyS3] = dy.strides;\n    const fltValues = filter.dataSync();\n    const [fltS0, fltS1, fltS2, fltS3] = filter.strides;\n    const {\n      batchSize,\n      filterDepth,\n      filterHeight,\n      filterWidth,\n      inChannels,\n      inDepth,\n      inHeight,\n      inWidth,\n      outChannels,\n      outDepth,\n      outHeight,\n      outWidth,\n      strideDepth,\n      strideHeight,\n      strideWidth\n    } = convInfo;\n    const frontPad = filterDepth - 1 - convInfo.padInfo.front;\n    const topPad = filterHeight - 1 - convInfo.padInfo.top;\n    const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n\n    for (let b = 0; b < batchSize; ++b) {\n      for (let d1 = 0; d1 < inChannels; ++d1) {\n        // Frames of depth\n        for (let xF = 0; xF < inDepth; ++xF) {\n          const xFCorner = xF - frontPad;\n          const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));\n          const yFMax =\n              Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);\n\n          // Rows as per standard 2d matrix notation\n          for (let xR = 0; xR < inHeight; ++xR) {\n            const xRCorner = xR - topPad;\n            const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n            const yRMax =\n                Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n            // Columns as per standard 2d matrix notation\n            for (let xC = 0; xC < inWidth; ++xC) {\n              const xCCorner = xC - leftPad;\n              const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n              const yCMax =\n                  Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n\n              let dotProd = 0;\n              for (let yF = xFMin; yF < yFMax; ++yF) {\n                const wF = yF * strideDepth - xFCorner;\n\n                for (let yR = xRMin; yR < yRMax; ++yR) {\n                  const wR = yR * strideHeight - xRCorner;\n\n                  for (let yC = xCMin; yC < yCMax; ++yC) {\n                    const wC = yC * strideWidth - xCCorner;\n                    const dyOffset =\n                        dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;\n                    const fltOffset = fltS0 * (filterDepth - 1 - wF) +\n                        fltS1 * (filterHeight - 1 - wR) +\n                        fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;\n\n                    for (let d2 = 0; d2 < outChannels; ++d2) {\n                      const pixel = dyValues[dyOffset + d2];\n                      const weight = fltValues[fltOffset + d2];\n                      dotProd += pixel * weight;\n                    }\n                  }\n                }\n              }\n              dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] =\n                  dotProd;\n            }\n          }\n        }\n      }\n    }\n    return dx.toTensor();\n  }\n\n  conv2dDerFilter(x: Tensor4D, dy: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    this.assertNotComplex([x, dy], 'conv2dDerFilter');\n\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dW = ops.buffer<Rank.R4>(convInfo.filterShape, 'float32');\n\n    const leftPad = convInfo.padInfo.left;\n    const topPad = convInfo.padInfo.top;\n\n    for (let wR = 0; wR < filterHeight; ++wR) {\n      const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n      const yRMax = Math.min(\n          convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n\n      for (let wC = 0; wC < filterWidth; ++wC) {\n        const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n        const yCMax = Math.min(\n            convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n\n        for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n          for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n            // Need to convolve.\n            let dotProd = 0;\n            for (let b = 0; b < convInfo.batchSize; ++b) {\n              for (let yR = yRMin; yR < yRMax; ++yR) {\n                const xR = wR + yR * strideHeight - topPad;\n                for (let yC = yCMin; yC < yCMax; ++yC) {\n                  const xC = wC + yC * strideWidth - leftPad;\n                  dotProd += x.get(b, xR, xC, d1) * dy.get(b, yR, yC, d2);\n                }\n              }\n            }\n            dW.set(dotProd, wR, wC, d1, d2);\n          }\n        }\n      }\n    }\n    return dW.toTensor();\n  }\n\n  conv3dDerFilter(x: Tensor5D, dy: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const filterDepth = convInfo.filterDepth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n\n    const dw = ops.buffer<Rank.R5>(convInfo.filterShape, 'float32');\n    const dwValues = dw.values;\n    const [dwS0, dwS1, dwS2, dwS3] = dw.strides;\n    const dyValues = dy.dataSync();\n    const [dyS0, dyS1, dyS2, dyS3] = dy.strides;\n    const xValues = x.dataSync();\n    const [xS0, xS1, xS2, xS3] = x.strides;\n\n    const frontPad = convInfo.padInfo.front;\n    const leftPad = convInfo.padInfo.left;\n    const topPad = convInfo.padInfo.top;\n\n    for (let wF = 0; wF < filterDepth; ++wF) {\n      const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));\n      const yFMax = Math.min(\n          convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);\n      const wOffset1 = wF * dwS0;\n\n      for (let wR = 0; wR < filterHeight; ++wR) {\n        const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n        const yRMax = Math.min(\n            convInfo.outHeight,\n            (convInfo.inHeight + topPad - wR) / strideHeight);\n        const wOffset2 = wR * dwS1 + wOffset1;\n\n        for (let wC = 0; wC < filterWidth; ++wC) {\n          const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n          const yCMax = Math.min(\n              convInfo.outWidth,\n              (convInfo.inWidth + leftPad - wC) / strideWidth);\n          const wOffset3 = wC * dwS2 + wOffset2;\n\n          for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n            const wOffset4 = d1 * dwS3 + wOffset3;\n\n            for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n              let dotProd = 0;\n              for (let b = 0; b < convInfo.batchSize; ++b) {\n                const xOffset1 = b * xS0;\n                const yOffset1 = b * dyS0;\n\n                for (let yF = yFMin; yF < yFMax; ++yF) {\n                  const xF = wF + yF * strideDepth - frontPad;\n                  const xOffset2 = xF * xS1 + xOffset1;\n                  const yOffset2 = yF * dyS1 + yOffset1;\n\n                  for (let yR = yRMin; yR < yRMax; ++yR) {\n                    const xR = wR + yR * strideHeight - topPad;\n                    const xOffset3 = xR * xS2 + xOffset2;\n                    const yOffset3 = yR * dyS2 + yOffset2;\n\n                    for (let yC = yCMin; yC < yCMax; ++yC) {\n                      const xC = wC + yC * strideWidth - leftPad;\n                      const xOffset4 = xC * xS3 + xOffset3;\n                      const yOffset4 = yC * dyS3 + yOffset3;\n\n                      dotProd +=\n                          xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];\n                    }\n                  }\n                }\n              }\n              dwValues[wOffset4 + d2] = dotProd;\n            }\n          }\n        }\n      }\n    }\n    return dw.toTensor();\n  }\n\n  depthwiseConv2D(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    this.assertNotComplex([x, filter], 'depthwiseConv2D');\n\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const padLeft = convInfo.padInfo.left;\n    const padTop = convInfo.padInfo.top;\n    const chMul = convInfo.outChannels / convInfo.inChannels;\n    const y = ops.buffer(convInfo.outShape, x.dtype as 'float32');\n    const xVals = x.dataSync();\n    const wVals = filter.dataSync();\n    const yVals = y.values;\n\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n      const xOffset1 = b * x.strides[0];\n      const yOffset1 = b * y.strides[0];\n      for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n        const yOffset2 = yOffset1 + yR * y.strides[1];\n        const xRCorner = yR * convInfo.strideHeight - padLeft;\n        for (let wR = 0; wR < filterHeight; ++wR) {\n          const xR = xRCorner + wR * dilationHeight;\n          if (xR < 0 || xR >= convInfo.inHeight) {\n            continue;\n          }\n          const wOffset1 = wR * filter.strides[0];\n          const xOffset2 = xOffset1 + xR * x.strides[1];\n          for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n            const yOffset3 = yOffset2 + yC * y.strides[2];\n            const xCCorner = yC * convInfo.strideWidth - padTop;\n            for (let wC = 0; wC < filterWidth; ++wC) {\n              const xC = xCCorner + wC * dilationWidth;\n              if (xC < 0 || xC >= convInfo.inWidth) {\n                continue;\n              }\n              const wOffset2 = wOffset1 + wC * filter.strides[1];\n              const xOffset3 = xOffset2 + xC * convInfo.inChannels;\n              let yOffset4 = yOffset3;\n              let wOffset3 = wOffset2;\n              for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                const xVal = xVals[xOffset3 + d1];\n                for (let q = 0; q < chMul; ++q) {\n                  yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q];\n                }\n                yOffset4 += chMul;\n                wOffset3 += chMul;\n              }\n            }\n          }\n        }\n      }\n    }\n\n    return y.toTensor() as Tensor4D;\n  }\n\n  depthwiseConv2DDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    this.assertNotComplex([dy, filter], 'depthwiseConv2DDerInput');\n\n    const dx = ops.buffer<Rank.R4>(convInfo.inShape, 'float32');\n    const dxValues = dx.values;\n    const [dxS0, dxS1, dxS2] = dx.strides;\n    const dyValues = dy.dataSync();\n    const [dyS0, dyS1, dyS2] = dy.strides;\n    const fltValues = filter.dataSync();\n    const [fltS0, fltS1, fltS2] = filter.strides;\n    const {\n      batchSize,\n      filterHeight,\n      filterWidth,\n      inChannels,\n      inHeight,\n      inWidth,\n      outChannels,\n      outHeight,\n      outWidth,\n      strideHeight,\n      strideWidth\n    } = convInfo;\n    const topPad = filterHeight - 1 - convInfo.padInfo.top;\n    const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n    const chMul = outChannels / inChannels;\n\n    for (let b = 0; b < batchSize; ++b) {\n      for (let d1 = 0; d1 < inChannels; ++d1) {\n        for (let xR = 0; xR < inHeight; ++xR) {\n          const xRCorner = xR - topPad;\n          const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n          const yRMax =\n              Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n\n          for (let xC = 0; xC < inWidth; ++xC) {\n            const xCCorner = xC - leftPad;\n            const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n            const yCMax =\n                Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n\n            let dotProd = 0;\n            for (let yR = xRMin; yR < yRMax; ++yR) {\n              const wR = yR * strideHeight - xRCorner;\n\n              for (let yC = xCMin; yC < yCMax; ++yC) {\n                const wC = yC * strideWidth - xCCorner;\n                const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;\n                const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n                    fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n\n                for (let dm = 0; dm < chMul; ++dm) {\n                  const d2 = d1 * chMul + dm;\n                  const pixel = dyValues[dyOffset + d2];\n                  const weight = fltValues[fltOffset + dm];\n                  dotProd += pixel * weight;\n                }\n              }\n            }\n            dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;\n          }\n        }\n      }\n    }\n    return dx.toTensor();\n  }\n\n  depthwiseConv2DDerFilter(x: Tensor4D, dy: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    this.assertNotComplex([x, dy], 'depthwiseConv2DDerFilter');\n\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dW = ops.buffer<Rank.R4>(convInfo.filterShape, 'float32');\n\n    const leftPad = convInfo.padInfo.left;\n    const topPad = convInfo.padInfo.top;\n    const chMul = convInfo.outChannels / convInfo.inChannels;\n\n    for (let wR = 0; wR < filterHeight; ++wR) {\n      const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n      const yRMax = Math.min(\n          convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n\n      for (let wC = 0; wC < filterWidth; ++wC) {\n        const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n        const yCMax = Math.min(\n            convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n\n        for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n          const d1 = Math.trunc(d2 / chMul);\n          const dm = d2 % chMul;\n\n          let dotProd = 0;\n          for (let b = 0; b < convInfo.batchSize; ++b) {\n            for (let yR = yRMin; yR < yRMax; ++yR) {\n              const xR = wR + yR * strideHeight - topPad;\n              for (let yC = yCMin; yC < yCMax; ++yC) {\n                const xC = wC + yC * strideWidth - leftPad;\n                dotProd += x.get(b, xR, xC, d1) * dy.get(b, yR, yC, d2);\n              }\n            }\n          }\n          dW.set(dotProd, wR, wC, d1, dm);\n        }\n      }\n    }\n    return dW.toTensor();\n  }\n\n  tile<T extends Tensor>(x: T, reps: number[]): T {\n    this.assertNotComplex(x, 'tile');\n\n    const newShape: number[] = new Array(x.rank);\n    for (let i = 0; i < newShape.length; i++) {\n      newShape[i] = x.shape[i] * reps[i];\n    }\n    const result = ops.buffer(newShape, x.dtype);\n    const xBuf = x.buffer();\n    for (let i = 0; i < result.values.length; ++i) {\n      const newLoc = result.indexToLoc(i);\n\n      const originalLoc: number[] = new Array(x.rank);\n      for (let i = 0; i < originalLoc.length; i++) {\n        originalLoc[i] = newLoc[i] % x.shape[i];\n      }\n\n      const originalIndex = xBuf.locToIndex(originalLoc);\n\n      result.values[i] = xBuf.values[originalIndex];\n    }\n    return result.toTensor() as T;\n  }\n\n  pad<T extends Tensor>(\n      x: T, paddings: Array<[number, number]>, constantValue: number): T {\n    this.assertNotComplex(x, 'pad');\n\n    const outShape = paddings.map(\n        (p, i) => p[0] /* beforePad */ + x.shape[i] + p[1] /* afterPad */);\n    const start = paddings.map(p => p[0]);\n    const xBuffer = x.buffer();\n    const buffer = ops.buffer(outShape, x.dtype as 'float32');\n    if (constantValue !== 0) {\n      buffer.values.fill(constantValue);\n    }\n\n    for (let i = 0; i < x.size; i++) {\n      const coords = xBuffer.indexToLoc(i);\n      const outCoords = coords.map((c, i) => c + start[i]);\n      buffer.set(x.get(...coords), ...outCoords);\n    }\n    return buffer.toTensor() as T;\n  }\n\n  transpose<T extends Tensor>(x: T, perm: number[]): T {\n    this.assertNotComplex(x, 'transpose');\n\n    const newShape: number[] = new Array(x.rank);\n    for (let i = 0; i < newShape.length; i++) {\n      newShape[i] = x.shape[perm[i]];\n    }\n    const values = x.dataSync();\n    const result = buffer(newShape, x.dtype);\n\n    const xBuf = x.buffer();\n    for (let i = 0; i < x.size; ++i) {\n      const loc = xBuf.indexToLoc(i);\n\n      // Permute location.\n      const newLoc: number[] = new Array(loc.length);\n      for (let i = 0; i < newLoc.length; i++) {\n        newLoc[i] = loc[perm[i]];\n      }\n\n      const newIndex = result.locToIndex(newLoc);\n      result.values[newIndex] = values[i];\n    }\n    return result.toTensor() as T;\n  }\n\n  gather<T extends Tensor>(x: T, indices: Tensor1D, axis: number): T {\n    this.assertNotComplex([x, indices], 'gather');\n\n    const newShape: number[] = x.shape.slice();\n    const indicesValues = indices.dataSync();\n    newShape[axis] = indicesValues.length;\n    const result = buffer(newShape, x.dtype);\n    const xBuf = x.buffer();\n\n    for (let i = 0; i < result.size; ++i) {\n      const newLoc = result.indexToLoc(i);\n\n      const originalLoc: number[] = newLoc.slice();\n      originalLoc[axis] = indicesValues[newLoc[axis]];\n\n      const originalIndex = xBuf.locToIndex(originalLoc);\n      result.values[i] = xBuf.values[originalIndex];\n    }\n    return result.toTensor() as T;\n  }\n\n  batchToSpaceND<T extends Tensor>(\n      x: T, blockShape: number[], crops: number[][]): T {\n    this.assertNotComplex([x], 'batchToSpaceND');\n\n    const prod = blockShape.reduce((a, b) => a * b);\n\n    const reshaped = array_ops_util.getReshaped(x.shape, blockShape, prod);\n    const permuted =\n        array_ops_util.getPermuted(reshaped.length, blockShape.length);\n    const reshapedPermuted =\n        array_ops_util.getReshapedPermuted(x.shape, blockShape, prod);\n    const sliceBeginCoords =\n        array_ops_util.getSliceBeginCoords(crops, blockShape.length);\n    const sliceSize =\n        array_ops_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n\n    return x.reshape(reshaped)\n               .transpose(permuted)\n               .reshape(reshapedPermuted)\n               .slice(sliceBeginCoords, sliceSize) as T;\n  }\n\n  spaceToBatchND<T extends Tensor>(\n      x: T, blockShape: number[], paddings: Array<[number, number]>): T {\n    this.assertNotComplex([x], 'spaceToBatchND');\n\n    const prod = blockShape.reduce((a, b) => a * b);\n\n    const completePaddings: Array<[number, number]> = [[0, 0]];\n    completePaddings.push(...paddings);\n    for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n      completePaddings.push([0, 0]);\n    }\n\n    const paddedX = x.pad(completePaddings);\n\n    const reshapedPaddedShape =\n        array_ops_util.getReshaped(paddedX.shape, blockShape, prod, false);\n    const permutedReshapedPaddedPermutation = array_ops_util.getPermuted(\n        reshapedPaddedShape.length, blockShape.length, false);\n    const flattenShape = array_ops_util.getReshapedPermuted(\n        paddedX.shape, blockShape, prod, false);\n\n    return paddedX.reshape(reshapedPaddedShape)\n               .transpose(permutedReshapedPaddedPermutation)\n               .reshape(flattenShape) as T;\n  }\n\n  private pool(x: Tensor4D, convInfo: Conv2DInfo, poolType: 'max'|'avg'):\n      Tensor4D {\n    this.assertNotComplex(x, 'pool');\n\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n\n    const initialValue =\n        (poolType === 'max' ? Number.NEGATIVE_INFINITY :\n                              Number.POSITIVE_INFINITY);\n\n    const xValues = x.dataSync();\n    const output = ops.buffer(convInfo.outShape, x.dtype);\n    const outputVals = output.values;\n\n    const outputBatchStrides =\n        convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3];\n    const outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3];\n    const outputColStrides = convInfo.outShape[3];\n\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n      const outputBatchOffset = b * outputBatchStrides;\n      const inputBatchOffset = b * x.strides[0];\n      for (let d = 0; d < convInfo.inChannels; ++d) {\n        for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n          const xRCorner = yR * strideHeight - padTop;\n          const xRMin = Math.max(0, xRCorner);\n          const xRMax =\n              Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n          const outputRowOffset = outputBatchOffset + yR * outputRowStrides;\n          for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n            const xCCorner = yC * strideWidth - padLeft;\n            const xCMin = Math.max(0, xCCorner);\n            const xCMax =\n                Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n            let minMaxValue = initialValue;\n            let avgValue = 0;\n            let count = 0;\n            for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n              const xROffset = inputBatchOffset + xR * x.strides[1];\n              for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n                const xCOffset = xROffset + xC * x.strides[2];\n                const pixel = xValues[xCOffset + d];\n                if ((poolType === 'max' && pixel > minMaxValue)) {\n                  minMaxValue = pixel;\n                } else if (poolType === 'avg') {\n                  avgValue += pixel;\n                  count++;\n                }\n              }\n              if (isNaN(minMaxValue)) {\n                break;\n              }\n            }\n            const outputOffset = outputRowOffset + yC * outputColStrides + d;\n            outputVals[outputOffset] =\n                poolType === 'avg' ? avgValue / count : minMaxValue;\n          }\n        }\n      }\n    }\n    return output.toTensor() as Tensor4D;\n  }\n\n  maxPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    return this.pool(x, convInfo, 'max');\n  }\n\n  private maxPoolPositions(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    const maxPositions = ops.buffer(convInfo.outShape, 'int32');\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n      for (let d = 0; d < convInfo.inChannels; ++d) {\n        for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n          const xRCorner = yR * strideHeight - padTop;\n          let xRMin = xRCorner;\n          while (xRMin < 0) {\n            xRMin += dilationHeight;\n          }\n          // const xRMin = Math.max(0, xRCorner);\n          const xRMax =\n              Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n          for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n            const xCCorner = yC * strideWidth - padLeft;\n            let xCMin = xCCorner;\n            while (xCMin < 0) {\n              xCMin += dilationWidth;\n            }\n            const xCMax =\n                Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n            let maxValue = Number.NEGATIVE_INFINITY;\n            let maxPosition = -1;\n\n            for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n              const wR = xR - xRCorner;\n              for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n                const wC = xC - xCCorner;\n                const pixel = x.get(b, xR, xC, d);\n                if (pixel > maxValue) {\n                  maxValue = pixel;\n                  maxPosition = wR * effectiveFilterWidth + wC;\n                }\n              }\n            }\n            maxPositions.set(maxPosition, b, yR, yC, d);\n          }\n        }\n      }\n    }\n    return maxPositions.toTensor() as Tensor4D;\n  }\n\n  maxPoolBackprop(dy: Tensor4D, x: Tensor4D, y: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    this.assertNotComplex([x, y], 'maxPoolBackprop');\n\n    const maxPositions = this.maxPoolPositions(x, convInfo);\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const dx = ops.buffer<Rank.R4>(x.shape, 'float32');\n\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n      for (let d = 0; d < convInfo.inChannels; ++d) {\n        for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n          for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n            // Shader code begins.\n            const dyRCorner = dxR - padTop;\n            const dyCCorner = dxC - padLeft;\n            let dotProd = 0;\n            for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n              const dyR = (dyRCorner + wR) / strideHeight;\n              if (dyR < 0 || dyR >= convInfo.outHeight ||\n                  Math.floor(dyR) !== dyR) {\n                continue;\n              }\n              for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n                const dyC = (dyCCorner + wC) / strideWidth;\n                if (dyC < 0 || dyC >= convInfo.outWidth ||\n                    Math.floor(dyC) !== dyC) {\n                  continue;\n                }\n                const maxPos = effectiveFilterHeight * effectiveFilterWidth -\n                    1 - maxPositions.get(b, dyR, dyC, d);\n                const curPos = wR * effectiveFilterWidth + wC;\n\n                const mask = maxPos === curPos ? 1 : 0;\n                if (mask === 0) {\n                  continue;\n                }\n\n                const pixel = dy.get(b, dyR, dyC, d);\n                dotProd += pixel * mask;\n              }\n            }\n            dx.set(dotProd, b, dxR, dxC, d);\n          }\n        }\n      }\n    }\n    return dx.toTensor();\n  }\n\n  avgPoolBackprop(dy: Tensor4D, x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    this.assertNotComplex([dy, x], 'avgPoolBackprop');\n\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const dx = ops.buffer<Rank.R4>(x.shape, 'float32');\n\n    const avgMultiplier = 1 / (filterHeight * filterWidth);\n\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n      for (let d = 0; d < convInfo.inChannels; ++d) {\n        for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n          for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n            // Shader code begins.\n            const dyRCorner = dxR - padTop;\n            const dyCCorner = dxC - padLeft;\n            let dotProd = 0;\n            for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n              const dyR = (dyRCorner + wR) / strideHeight;\n              if (dyR < 0 || dyR >= convInfo.outHeight ||\n                  Math.floor(dyR) !== dyR) {\n                continue;\n              }\n              for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n                const dyC = (dyCCorner + wC) / strideWidth;\n                if (dyC < 0 || dyC >= convInfo.outWidth ||\n                    Math.floor(dyC) !== dyC) {\n                  continue;\n                }\n\n                const pixel = dy.get(b, dyR, dyC, d);\n                dotProd += pixel;\n              }\n            }\n            dx.set(dotProd * avgMultiplier, b, dxR, dxC, d);\n          }\n        }\n      }\n    }\n    return dx.toTensor();\n  }\n\n  cast<T extends Tensor>(x: T, dtype: DataType): T {\n    return backend_util.castTensor(x, dtype, this);\n  }\n\n  reshape<R extends Rank>(x: Tensor, shape: ShapeMap[R]): Tensor<R> {\n    return backend_util.reshapeTensor(x, shape);\n  }\n\n  avgPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    this.assertNotComplex(x, 'avgPool');\n\n    return this.pool(x, convInfo, 'avg').toFloat();\n  }\n\n  resizeBilinear(\n      x: Tensor4D, newHeight: number, newWidth: number,\n      alignCorners: boolean): Tensor4D {\n    this.assertNotComplex(x, 'resizeBilinear');\n\n    const [batch, oldHeight, oldWidth, numChannels] = x.shape;\n    const xValues = x.dataSync();\n    const result = new Float32Array(\n        util.sizeFromShape([batch, newHeight, newWidth, numChannels]));\n\n    const effectiveInputSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n      (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n    ];\n\n    const effectiveOutputSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n      (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n    ];\n    let outputIdx = 0;\n    const effectiveRowSizeRatio =\n        effectiveInputSize[0] / effectiveOutputSize[0];\n    const effectiveColSizeRatio =\n        effectiveInputSize[1] / effectiveOutputSize[1];\n    for (let b = 0; b < batch; b++) {\n      for (let r = 0; r < newHeight; r++) {\n        const sourceFracRow = effectiveRowSizeRatio * r;\n        const sourceRowFloor = Math.floor(sourceFracRow);\n        const rowFrac = sourceFracRow - sourceRowFloor;\n        const sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));\n        const topRowOffset = b * x.strides[0] + sourceRowFloor * x.strides[1];\n        const botRowOffset = b * x.strides[0] + sourceRowCeil * x.strides[1];\n        for (let c = 0; c < newWidth; c++) {\n          const sourceFracCol = effectiveColSizeRatio * c;\n          const sourceColFloor = Math.floor(sourceFracCol);\n          const colFrac = sourceFracCol - sourceColFloor;\n          const sourceColCeil =\n              Math.min(oldWidth - 1, Math.ceil(sourceFracCol));\n          const topLeftOffest = topRowOffset + sourceColFloor * x.strides[2];\n          const botLeftOffset = botRowOffset + sourceColFloor * x.strides[2];\n          const topRightOffset = topRowOffset + +sourceColCeil * x.strides[2];\n          const botRightOffest = botRowOffset + sourceColCeil * x.strides[2];\n          for (let d = 0; d < numChannels; d++) {\n            // Begin shader.\n\n            // Compute the fractional index of the source.\n            const topLeft = xValues[topLeftOffest + d];\n            const bottomLeft = xValues[botLeftOffset + d];\n            const topRight = xValues[topRightOffset + d];\n            const bottomRight = xValues[botRightOffest + d];\n\n            const top = topLeft + (topRight - topLeft) * colFrac;\n            const bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;\n            const newValue = top + (bottom - top) * rowFrac;\n\n            result[outputIdx++] = newValue;\n          }\n        }\n      }\n    }\n    return ops.tensor(result, [batch, newHeight, newWidth, numChannels]);\n  }\n\n  resizeBilinearBackprop(dy: Tensor4D, x: Tensor4D, alignCorners: boolean) {\n    this.assertNotComplex([dy, x], 'resizeBilinearBackprop');\n\n    const [batch, xHeight, xWidth, depth] = x.shape;\n    const [, yHeight, yWidth] = dy.shape;\n\n    const output = new Float32Array(batch * xHeight * xWidth * depth);\n\n    // In the backwards pass, we want to find the pixels that were generated\n    // for each pixel in the input image the forward pass and add the\n    // corresponding coefficient from dy to the gradient (with some\n    // interpolation).\n\n    const effectiveXSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n      (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n    ];\n\n    const effectiveYSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n      (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n    ];\n\n    const heightScale = effectiveXSize[0] / effectiveYSize[0];\n    const widthScale = effectiveXSize[1] / effectiveYSize[1];\n\n    // Reference implementation\n    // tslint:disable-next-line:max-line-length\n    // https://github.com/tensorflow/tensorflow/blob/3039375c86a5bbc9610c7725dcaa95d635f87ba2/tensorflow/core/kernels/resize_bilinear_op.cc#L275\n\n    const dyValues = dy.dataSync();\n    let offset = 0;\n    for (let b = 0; b < batch; b++) {\n      const bOffset = b * x.strides[0];\n      for (let r = 0; r < yHeight; r++) {\n        const dxR = r * heightScale;\n        const topDxRIndex = Math.floor(dxR);\n        const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);\n\n        const topDxROffset = bOffset + topDxRIndex * x.strides[1];\n        const bottomDxROffset = bOffset + bottomDxRIndex * x.strides[1];\n\n        const dxRLerp = dxR - topDxRIndex;\n        const inverseDxRLerp = 1.0 - dxRLerp;\n        for (let c = 0; c < yWidth; c++) {\n          const dxC = c * widthScale;\n          const leftDxCIndex = Math.floor(dxC);\n          const rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1);\n          const dxCLerp = dxC - leftDxCIndex;\n          const inverseDxCLerp = 1.0 - dxCLerp;\n\n          const topLeftRCOffset = topDxROffset + leftDxCIndex * x.strides[2];\n          const topRightRCOffset = topDxROffset + rightDxCIndex * x.strides[2];\n          const bottomLeftRCOffset =\n              bottomDxROffset + leftDxCIndex * x.strides[2];\n          const bottomRightRCOffset =\n              bottomDxROffset + rightDxCIndex * x.strides[2];\n\n          const inverseDxRLerpTimesInverseDxCLerp =\n              inverseDxRLerp * inverseDxCLerp;\n          const inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp;\n          const dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp;\n          const dxRLerpTimesDxCLerp = dxRLerp * dxCLerp;\n          for (let d = 0; d < depth; d++) {\n            const dyVal = dyValues[offset++];\n            output[topLeftRCOffset + d] +=\n                dyVal * inverseDxRLerpTimesInverseDxCLerp;\n            output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp;\n            output[bottomLeftRCOffset + d] +=\n                dyVal * dxRLerpTimesInverseDxCLerp;\n            output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp;\n          }\n        }\n      }\n    }\n    return ops.tensor4d(output, [batch, xWidth, xHeight, depth], x.dtype);\n  }\n\n  resizeNearestNeighbor(\n      x: Tensor4D, newHeight: number, newWidth: number,\n      alignCorners: boolean): Tensor4D {\n    this.assertNotComplex(x, 'resizeNearestNeighbor');\n\n    const [batch, oldHeight, oldWidth, numChannels] = x.shape;\n    const xValues = x.dataSync();\n    const output = new Float32Array(batch * newHeight * newWidth * numChannels);\n\n    const effectiveInputSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n      (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n    ];\n\n    const effectiveOutputSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n      (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n    ];\n\n    const effectiveRowSizeRatio =\n        effectiveInputSize[0] / effectiveOutputSize[0];\n    const effectiveColSizeRatio =\n        effectiveInputSize[1] / effectiveOutputSize[1];\n\n    let outputOffset = 0;\n    for (let b = 0; b < batch; b++) {\n      const batchOffset = b * x.strides[0];\n      for (let r = 0; r < newHeight; r++) {\n        const sourceFracRow = effectiveRowSizeRatio * r;\n        const sourceNearestRow = Math.min(\n            oldHeight - 1,\n            alignCorners ? Math.round(sourceFracRow) :\n                           Math.floor(sourceFracRow));\n        const rowOffset = batchOffset + sourceNearestRow * x.strides[1];\n        for (let c = 0; c < newWidth; c++) {\n          const sourceFracCol = effectiveColSizeRatio * c;\n          const sourceNearestCol = Math.min(\n              oldWidth - 1,\n              alignCorners ? Math.round(sourceFracCol) :\n                             Math.floor(sourceFracCol));\n          const colOffset = rowOffset + sourceNearestCol * x.strides[2];\n          for (let d = 0; d < numChannels; d++) {\n            // Begin shader.\n            // Compute the fractional index of the source.\n            const newVal = xValues[colOffset + d];\n            output[outputOffset++] = newVal;\n          }\n        }\n      }\n    }\n    return ops.tensor(\n        output, [batch, newHeight, newWidth, numChannels], x.dtype);\n  }\n\n  resizeNearestNeighborBackprop(\n      dy: Tensor4D, x: Tensor4D, alignCorners: boolean) {\n    this.assertNotComplex([dy, x], 'resizeNearestNeighborBackprop');\n\n    const [batch, xHeight, xWidth, depth] = x.shape;\n    const [, yHeight, yWidth] = dy.shape;\n\n    const output = new Float32Array(batch * xHeight * xWidth * depth);\n    const dyValues = dy.dataSync();\n\n    // In the backwards pass, we want to find the pixels that were generated\n    // for each pixel in the input image the forward pass\n\n    const effectiveXSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n      (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n    ];\n\n    const effectiveYSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n      (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n    ];\n\n    const heightScale = effectiveXSize[0] / effectiveYSize[0];\n    const widthScale = effectiveXSize[1] / effectiveYSize[1];\n\n    const invHeightScale = 1 / heightScale;\n    const invWidthScale = 1 / widthScale;\n\n    // This defines the size of the window of values around a particular\n    // index in dy that we want to search for contributions to dx.\n    const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n    const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n\n    // Loop over the output space.\n    for (let b = 0; b < batch; b++) {\n      const batchOffset = b * x.strides[0];\n      for (let r = 0; r < xHeight; r++) {\n        const rowOffset = batchOffset + r * x.strides[1];\n\n        // Compute bounds for where in dy we will look\n        const startRLerp = Math.floor(r * invHeightScale);\n        const startDyR = Math.floor(startRLerp - (winHeight / 2));\n        for (let c = 0; c < xWidth; c++) {\n          const colOffset = rowOffset + c * x.strides[2];\n\n          // Compute bounds for where in dy we will look\n          const startCLerp = Math.floor(c * invWidthScale);\n          const startDyC = Math.floor(startCLerp - (winWidth / 2));\n\n          for (let d = 0; d < depth; d++) {\n            let accum = 0;\n            // loop over dy\n\n            for (let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {\n              const dyR = dyRIndex + startDyR;\n              // Guard against the window exceeding the bounds of dy\n              if (dyR < 0 || dyR >= yHeight) {\n                continue;\n              }\n\n              const dyROffset = batchOffset + dyR * dy.strides[1];\n              const sourceFracRow = dyR * heightScale;\n              const sourceNearestRow = Math.min(\n                  xHeight - 1,\n                  alignCorners ? Math.round(sourceFracRow) :\n                                 Math.floor(sourceFracRow));\n              if (r !== sourceNearestRow) {\n                continue;\n              }\n              for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {\n                const dyC = dyCIndex + startDyC;\n                // Guard against the window exceeding the bounds of dy\n                if (dyC < 0 || dyC >= yWidth) {\n                  continue;\n                }\n\n                const dyCOffset = dyROffset + dyC * dy.strides[2];\n                const sourceFracCol = dyC * widthScale;\n                const sourceNearestCol = Math.min(\n                    xWidth - 1,\n                    alignCorners ? Math.round(sourceFracCol) :\n                                   Math.floor(sourceFracCol));\n\n                if (c === sourceNearestCol) {\n                  accum += dyValues[dyCOffset + d];\n                }\n              }\n            }\n            output[colOffset + d] = accum;\n          }\n        }\n      }\n    }\n    return ops.tensor4d(output, x.shape, x.dtype);\n  }\n\n  batchNormalization(\n      x: Tensor4D, mean: Tensor4D|Tensor1D, variance: Tensor4D|Tensor1D,\n      varianceEpsilon: number, scale?: Tensor4D|Tensor1D,\n      offset?: Tensor4D|Tensor1D): Tensor4D {\n    this.assertNotComplex(\n        [x, mean, variance, scale, offset], 'batchNormalization');\n\n    const xVals = x.dataSync();\n    const mVals = mean.dataSync();\n    const varVals = variance.dataSync();\n    const sVals = scale ? scale.dataSync() : new Float32Array([1]);\n    const offVals = offset ? offset.dataSync() : new Float32Array([0]);\n    const outVals = new Float32Array(xVals.length);\n\n    const offValsLength = offVals.length;\n    const sValsLength = sVals.length;\n    const varValsLength = varVals.length;\n    const mValsLength = mVals.length;\n\n    let offi = 0;\n    let mi = 0;\n    let si = 0;\n    let vi = 0;\n    for (let i = 0; i < xVals.length; ++i) {\n      outVals[i] = offVals[offi++] +\n          (xVals[i] - mVals[mi++]) * sVals[si++] /\n              Math.sqrt(varVals[vi++] + varianceEpsilon);\n      if (offi >= offValsLength) {\n        offi = 0;\n      }\n      if (mi >= mValsLength) {\n        mi = 0;\n      }\n      if (si >= sValsLength) {\n        si = 0;\n      }\n      if (vi >= varValsLength) {\n        vi = 0;\n      }\n    }\n    return tensor4d(outVals, x.shape);\n  }\n\n  localResponseNormalization4D(\n      x: Tensor4D, depthRadius: number, bias: number, alpha: number,\n      beta: number): Tensor4D {\n    this.assertNotComplex(x, 'localResponseNormalization4D');\n\n    const channels = x.shape[3];\n    const maxD = channels - 1;\n    const xValues = x.dataSync();\n    const size = util.sizeFromShape(x.shape);\n    const result = new Float32Array(size);\n\n    function sumAcrossChannels(offset: number) {\n      const currentChannel = offset % channels;\n      let beginSumOffset =\n          offset - currentChannel + Math.max(0, currentChannel - depthRadius);\n      const endSumOffset = offset - currentChannel +\n          Math.min(currentChannel + depthRadius, maxD);\n\n      let sum = 0.0;\n      for (; beginSumOffset <= endSumOffset; beginSumOffset++) {\n        const z = xValues[beginSumOffset];\n        sum += z * z;\n      }\n      return sum;\n    }\n\n    for (let offset = 0; offset < size; offset++) {\n      const sum = sumAcrossChannels(offset);\n      const val = xValues[offset] * Math.pow(bias + alpha * sum, -beta);\n      result[offset] = val;\n    }\n\n    return ops.tensor4d(result, x.shape);\n  }\n\n  LRNGrad(\n      dy: Tensor4D, inputImage: Tensor4D, outputImage: Tensor4D,\n      depthRadius: number, bias: number, alpha: number,\n      beta: number): Tensor4D {\n    this.assertNotComplex(dy, 'LRNGrad');\n    const channels = dy.shape[3];\n    const dyValues = dy.dataSync();\n    const inputImageValues = inputImage.dataSync();\n    const outputImageValues = outputImage.dataSync();\n    const result = new Float32Array(util.sizeFromShape(dy.shape));\n    const size = util.sizeFromShape(dy.shape);\n\n    for (let offset = 0; offset < size; offset++) {\n      const currentChannel = offset % channels;\n      const depthBegin =\n          (offset - currentChannel) + Math.max(0, currentChannel - depthRadius);\n      const depthEnd = (offset - currentChannel) +\n          Math.min(channels, currentChannel + depthRadius + 1);\n\n      let norm = 0;\n      for (let k = depthBegin; k < depthEnd; k++) {\n        norm += Math.pow(inputImageValues[k], 2);\n      }\n      norm = alpha * norm + bias;\n\n      for (let k = depthBegin; k < depthEnd; k++) {\n        let dyi = -2 * alpha * beta * inputImageValues[k] *\n            outputImageValues[offset] / norm;\n        if (offset === k) {\n          dyi += Math.pow(norm, -beta);\n        }\n        dyi *= dyValues[offset];\n        result[k] += dyi;\n      }\n    }\n    return ops.tensor4d(result, dy.shape);\n  }\n\n  multinomial(\n      logits: Tensor2D, normalized: boolean, numSamples: number,\n      seed: number): Tensor2D {\n    this.assertNotComplex(logits, 'multinomial');\n\n    const probabilities = normalized ? logits : ops.softmax(logits);\n    const batchSize = probabilities.shape[0];\n    const numEvents = probabilities.shape[1];\n    const res = ops.zeros<Rank.R2>([batchSize, numSamples], 'int32');\n    const resVals = res.dataSync();\n    const probVals = probabilities.dataSync();\n\n    for (let b = 0; b < batchSize; ++b) {\n      const offset = b * numEvents;\n      // The cdf won't include the last event. It will be implicit if no other\n      // event happened.\n      const cdf = new Float32Array(numEvents - 1);\n      cdf[0] = probVals[offset];\n      for (let event = 1; event < cdf.length; ++event) {\n        cdf[event] = cdf[event - 1] + probVals[offset + event];\n      }\n\n      const random = seedrandom.alea(seed.toString());\n      const outOffset = b * numSamples;\n      for (let sampleId = 0; sampleId < numSamples; ++sampleId) {\n        const r = random();\n\n        // Assume last event happened by default.\n        resVals[outOffset + sampleId] = cdf.length;\n\n        for (let event = 0; event < cdf.length; event++) {\n          if (r < cdf[event]) {\n            resVals[outOffset + sampleId] = event;\n            break;\n          }\n        }\n      }\n    }\n    return res;\n  }\n\n  oneHot(indices: Tensor1D, depth: number, onValue: number, offValue: number):\n      Tensor2D {\n    this.assertNotComplex(indices, 'oneHot');\n\n    const res = new Float32Array(indices.size * depth);\n    res.fill(offValue);\n\n    for (let event = 0; event < indices.size; ++event) {\n      if (indices.get(event) >= 0 && indices.get(event) < depth) {\n        res[event * depth + indices.get(event)] = onValue;\n      }\n    }\n    return ops.tensor2d(res, [indices.size, depth], 'int32');\n  }\n\n  nonMaxSuppression(\n      boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number,\n      iouThreshold: number, scoreThreshold: number): Tensor1D {\n    this.assertNotComplex(boxes, 'nonMaxSuppression');\n\n    const boxesVals = boxes.dataSync();\n    const scoresVals = scores.dataSync();\n    return nonMaxSuppressionImpl(\n        boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n  }\n\n  fft(x: Tensor2D): Tensor2D {\n    return this.fftBatch(x, false);\n  }\n\n  ifft(x: Tensor2D): Tensor2D {\n    return this.fftBatch(x, true);\n  }\n\n  /**\n   * Calculate FFT of inner most elements of batch tensor.\n   */\n  private fftBatch(x: Tensor2D, inverse: boolean): Tensor2D {\n    const batch = x.shape[0];\n    const innerDim = x.shape[1];\n    // Collects real and imaginary values separately.\n    const realResult = ops.buffer(x.shape, 'float32');\n    const imagResult = ops.buffer(x.shape, 'float32');\n\n    const real = ops.real(x).as2D(batch, innerDim);\n    const imag = ops.imag(x).as2D(batch, innerDim);\n\n    for (let b = 0; b < batch; b++) {\n      // TODO: Support slice ops for complex type.\n      const r = real.slice([b, 0], [1, innerDim]);\n      const i = imag.slice([b, 0], [1, innerDim]);\n      const input = ops.complex(r, i);\n      // Run FFT by batch element.\n      const res = this.fftImpl(input, inverse).dataSync() as Float32Array;\n      for (let d = 0; d < innerDim; d++) {\n        const c = complex_util.getComplexWithIndex(res, d);\n        realResult.values[b * innerDim + d] = c.real;\n        imagResult.values[b * innerDim + d] = c.imag;\n      }\n    }\n\n    const t = ops.complex(realResult.toTensor(), imagResult.toTensor());\n    return t.as2D(batch, innerDim);\n  }\n\n  private fftImpl(x: Tensor2D, inverse: boolean): Tensor2D {\n    const x1D = x.as1D();\n\n    const n = x1D.size;\n\n    if (this.isExponentOf2(n)) {\n      let result = this.fftRadix2(x1D, n, inverse).as2D(x.shape[0], x.shape[1]);\n      if (inverse) {\n        result = ops.complex(\n                     ops.real(result).div(scalar(n)),\n                     ops.imag(result).div(scalar(n))) as Tensor2D;\n      }\n      return result;\n    } else {\n      const data = x.dataSync();\n      const rawOutput =\n          this.fourierTransformByMatmul(data, n, inverse) as Float32Array;\n      const output = complex_util.splitRealAndImagArrays(rawOutput);\n      return ops.complex(output.real, output.imag).as2D(x.shape[0], x.shape[1]);\n    }\n  }\n\n  private isExponentOf2(size: number): boolean {\n    return (size & size - 1) === 0;\n  }\n\n  // FFT using Cooley-Tukey algorithm on radix 2 dimensional input.\n  private fftRadix2(input: Tensor1D, size: number, inverse: boolean): Tensor1D {\n    if (size === 1) {\n      return input;\n    }\n    const data = input.dataSync() as Float32Array;\n    const half = size / 2;\n    const evenComplex = complex_util.complexWithEvenIndex(data);\n    let evenTensor = ops.complex(evenComplex.real, evenComplex.imag).as1D();\n    const oddComplex = complex_util.complexWithOddIndex(data);\n    let oddTensor = ops.complex(oddComplex.real, oddComplex.imag).as1D();\n\n    // Recursive call for half part of original input.\n    evenTensor = this.fftRadix2(evenTensor, half, inverse);\n    oddTensor = this.fftRadix2(oddTensor, half, inverse);\n\n    const e = complex_util.exponents(size, inverse);\n    const exponent = ops.complex(e.real, e.imag).mul(oddTensor);\n\n    const addPart = evenTensor.add(exponent);\n    const subPart = evenTensor.sub(exponent);\n\n    const realTensor = ops.real(addPart).concat(ops.real(subPart));\n    const imagTensor = ops.imag(addPart).concat(ops.imag(subPart));\n\n    return ops.complex(realTensor, imagTensor).as1D();\n  }\n\n  // Calculate fourier transform by multplying sinusoid matrix.\n  private fourierTransformByMatmul(\n      data: TypedArray, size: number, inverse: boolean): TypedArray {\n    const ret = new Float32Array(size * 2);\n    // TODO: Use matmul instead once it supports complex64 type.\n    for (let r = 0; r < size; r++) {\n      let real = 0.0;\n      let imag = 0.0;\n      for (let c = 0; c < size; c++) {\n        const e = complex_util.exponent(r * c, size, inverse);\n        const term = complex_util.getComplexWithIndex(data as Float32Array, c);\n        real += term.real * e.real - term.imag * e.imag;\n        imag += term.real * e.imag + term.imag * e.real;\n      }\n      if (inverse) {\n        real /= size;\n        imag /= size;\n      }\n      complex_util.assignToTypedArray(ret, real, imag, r);\n    }\n    return ret;\n  }\n\n  depthToSpace(x: Tensor4D, blockSize: number, dataFormat: 'NHWC'|'NCHW'):\n      Tensor4D {\n    util.assert(\n        dataFormat === 'NHWC',\n        `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${\n            dataFormat}`);\n    util.assert(\n        blockSize > 1,\n        `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n\n    const batchSize = x.shape[0];\n    const inputHeight = x.shape[1];\n    const inputWidth = x.shape[2];\n    const inputDepth = x.shape[3];\n\n    const outputHeight = inputHeight * blockSize;\n    const outputWidth = inputWidth * blockSize;\n    const outputDepth = inputDepth / (blockSize * blockSize);\n\n    const xValues = x.dataSync();\n    const result =\n        new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);\n\n    let outputIdx = 0;\n    for (let b = 0; b < batchSize; ++b) {\n      for (let h = 0; h < outputHeight; ++h) {\n        const inH = Math.floor(h / blockSize);\n        const offsetH = (h % blockSize);\n        for (let w = 0; w < outputWidth; ++w) {\n          const inW = Math.floor(w / blockSize);\n          const offsetW = (w % blockSize);\n          const offsetD = (offsetH * blockSize + offsetW) * outputDepth;\n          for (let d = 0; d < outputDepth; ++d) {\n            const inD = d + offsetD;\n            const inputIdx =\n                inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));\n            result[outputIdx++] = xValues[inputIdx];\n          }\n        }\n      }\n    }\n    return ops.tensor4d(\n        result, [batchSize, outputHeight, outputWidth, outputDepth]);\n  }\n\n  private broadcastedBinaryOp(\n      a: Tensor, b: Tensor, dtype: DataType,\n      op: (a: number, b: number) => number): Tensor {\n    const newShape =\n        broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n    const result = ops.buffer(newShape, dtype);\n    const aVals = a.dataSync();\n    const bVals = b.dataSync();\n    const aBroadcastDims = broadcast_util.getBroadcastDims(a.shape, newShape);\n    const bBroadcastDims = broadcast_util.getBroadcastDims(b.shape, newShape);\n\n    const resVals = result.values;\n    if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n      for (let i = 0; i < resVals.length; ++i) {\n        resVals[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);\n      }\n    } else {\n      const aBuf = a.buffer();\n      const bBuf = b.buffer();\n      for (let i = 0; i < resVals.length; ++i) {\n        const loc = result.indexToLoc(i);\n\n        const aLoc = loc.slice(-a.rank);\n        aBroadcastDims.forEach(d => aLoc[d] = 0);\n        const aIndex = aBuf.locToIndex(aLoc);\n\n        const bLoc = loc.slice(-b.rank);\n        bBroadcastDims.forEach(d => bLoc[d] = 0);\n        const bIndex = bBuf.locToIndex(bLoc);\n\n        resVals[i] = op(aVals[aIndex], bVals[bIndex]);\n      }\n    }\n    return result.toTensor();\n  }\n\n  private broadcastedBinaryComplexOp(\n      a: Tensor, b: Tensor,\n      op:\n          (aReal: number, aImag: number, bReal: number,\n           bImag: number) => {real: number, imag: number}): Tensor {\n    const newShape =\n        broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n    const realResult = ops.buffer(newShape, 'float32');\n    const imagResult = ops.buffer(newShape, 'float32');\n\n    const aVals = a.dataSync();\n    const bVals = b.dataSync();\n    const aBroadcastDims = broadcast_util.getBroadcastDims(a.shape, newShape);\n    const bBroadcastDims = broadcast_util.getBroadcastDims(b.shape, newShape);\n\n    const realVals = realResult.values;\n    const imagVals = imagResult.values;\n\n    if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n      for (let i = 0; i < realVals.length; i++) {\n        const aIdx = i % aVals.length;\n        const bIdx = i % bVals.length;\n\n        const result =\n            op(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2],\n               bVals[bIdx * 2 + 1]);\n\n        realVals[i] = result.real;\n        imagVals[i] = result.imag;\n      }\n    } else {\n      const aRealBuf = this.data.get(a.dataId).complexTensors.real.buffer();\n      const bRealBuf = this.data.get(b.dataId).complexTensors.real.buffer();\n      for (let i = 0; i < realVals.length; i++) {\n        const loc = realResult.indexToLoc(i);\n\n        const aLoc = loc.slice(-a.rank);\n        aBroadcastDims.forEach(d => aLoc[d] = 0);\n        const aIndex = aRealBuf.locToIndex(aLoc);\n\n        const bLoc = loc.slice(-b.rank);\n        bBroadcastDims.forEach(d => bLoc[d] = 0);\n        const bIndex = bRealBuf.locToIndex(bLoc);\n\n        const opResult =\n            op(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2],\n               bVals[bIndex * 2 + 1]);\n\n        realVals[i] = opResult.real;\n        imagVals[i] = opResult.imag;\n      }\n    }\n    return this.complex(realResult.toTensor(), imagResult.toTensor());\n  }\n\n  split<T extends Tensor>(x: T, sizeSplits: number[], axis: number): T[] {\n    return split(x, sizeSplits, axis);\n  }\n\n  dispose() {}\n\n  floatPrecision() {\n    return 32;\n  }\n\n  cropAndResize(\n      images: Tensor4D,\n      boxes: Tensor2D,\n      boxIndex: Tensor1D,\n      cropSize: [number, number],\n      method: string,\n      extrapolationValue: number,\n  ) {\n    const [batch, imageHeight, imageWidth, numChannels] = images.shape;\n    const numBoxes = boxes.shape[0];\n\n    const [cropHeight, cropWidth] = cropSize;\n    const output = ops.buffer(\n        [numBoxes, cropHeight, cropWidth, numChannels], images.dtype);\n\n    const boxVals = boxes.dataSync();\n    const boxIndVals = boxIndex.dataSync();\n    const imageVals = images.dataSync();\n\n    const inStride = images.strides;   // to calculate flat indexes into image\n    const outStride = output.strides;  // to calculate flat indexes into output\n\n    // Reference implementation\n    // tslint:disable-next-line:max-line-length\n    // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op.cc\n    for (let b = 0; b < numBoxes; b++) {\n      const startInd = b * 4;\n      const y1 = boxVals[startInd];\n      const x1 = boxVals[startInd + 1];\n      const y2 = boxVals[startInd + 2];\n      const x2 = boxVals[startInd + 3];\n\n      const bInd: number = boxIndVals[b];\n      if (bInd >= batch) {\n        continue;\n      }\n\n      const heightScale = (cropHeight > 1) ?\n          (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) :\n          0;\n      const widthScale =\n          (cropWidth > 1) ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;\n\n      for (let y = 0; y < cropHeight; y++) {\n        const yInd: number = (cropHeight > 1) ?\n            y1 * (imageHeight - 1) + y * (heightScale) :\n            0.5 * (y1 + y2) * (imageHeight - 1);\n\n        if (yInd < 0 || yInd > imageHeight - 1) {\n          for (let x = 0; x < cropWidth; x++) {\n            for (let c = 0; c < numChannels; c++) {\n              const ind =\n                  c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n              output.values[ind] = extrapolationValue;\n            }\n          }\n          continue;\n        }\n\n        if (method === 'bilinear') {\n          const topInd = Math.floor(yInd);\n          const bottomInd = Math.ceil(yInd);\n          const yLerp = yInd - topInd;\n\n          for (let x = 0; x < cropWidth; x++) {\n            const xInd = (cropWidth > 1) ?\n                x1 * (imageWidth - 1) + x * widthScale :\n                0.5 * (x1 + x2) * (imageWidth - 1);\n\n            if (xInd < 0 || xInd > imageWidth - 1) {\n              for (let c = 0; c < numChannels; c++) {\n                const ind =\n                    c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                output.values[ind] = extrapolationValue;\n              }\n              continue;\n            }\n\n            const leftInd = Math.floor(xInd);\n            const rightInd = Math.ceil(xInd);\n            const xLerp = xInd - leftInd;\n\n            for (let c = 0; c < numChannels; c++) {\n              let ind = c + leftInd * inStride[2] + topInd * inStride[1] +\n                  bInd * inStride[0];\n              const topLeft = imageVals[ind];\n\n              ind = c + rightInd * inStride[2] + topInd * inStride[1] +\n                  bInd * inStride[0];\n              const topRight = imageVals[ind];\n\n              ind = c + leftInd * inStride[2] + bottomInd * inStride[1] +\n                  bInd * inStride[0];\n              const bottomLeft = imageVals[ind];\n\n              ind = c + rightInd * inStride[2] + bottomInd * inStride[1] +\n                  bInd * inStride[0];\n              const bottomRight = imageVals[ind];\n\n              const top = topLeft + (topRight - topLeft) * xLerp;\n              const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;\n\n              ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n              output.values[ind] = top + ((bottom - top) * yLerp);\n            }\n          }\n        } else {  // method == \"nearest\"\n          for (let x = 0; x < cropWidth; ++x) {\n            const xInd = (cropWidth > 1) ?\n                x1 * (imageWidth - 1) + x * widthScale :\n                0.5 * (x1 + x2) * (imageWidth - 1);\n\n            if (xInd < 0 || xInd > imageWidth - 1) {\n              for (let c = 0; c < numChannels; c++) {\n                const ind =\n                    c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                output.values[ind] = extrapolationValue;\n              }\n              continue;\n            }\n\n            const closestX = Math.round(xInd);\n            const closestY = Math.round(yInd);\n            for (let c = 0; c < numChannels; c++) {\n              const inInd = c + closestX * inStride[2] +\n                  closestY * inStride[1] + bInd * inStride[0];\n              const outInd =\n                  c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n              output.values[outInd] = imageVals[inInd];\n            }\n          }\n        }\n      }\n    }\n    return output.toTensor() as Tensor4D;\n  }\n\n  sparseToDense<R extends Rank>(\n      sparseIndices: Tensor, sparseValues: Tensor, outputShape: ShapeMap[R],\n      defaultValue: Scalar): Tensor<R> {\n    const {sliceRank, numUpdates, sliceSize, strides, outputSize} =\n        scatter_nd_util.calculateShapes(\n            sparseValues, sparseIndices, outputShape);\n    const sumDupeIndices = false;\n    return this.scatter(\n        sparseIndices, sparseValues, outputShape, outputSize, sliceSize,\n        numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);\n  }\n\n  gatherND(x: Tensor, indices: Tensor): Tensor<Rank> {\n    const indicesShape = indices.shape;\n    const sliceRank = indicesShape[indicesShape.length - 1];\n\n    const [resultShape, numSlices, sliceSize, strides] =\n        gather_nd_util.prepareAndValidate(x, indices);\n    if (numSlices === 0) {\n      return tensor([], resultShape, x.dtype);\n    }\n\n    const buffer = new TensorBuffer([numSlices, sliceSize], x.dtype);\n    const indicesData = indices.dataSync();\n    const xData = x.dataSync();\n\n    for (let i = 0; i < numSlices; i++) {\n      const index = [];\n      let flattenIndex = 0;\n      for (let j = 0; j < sliceRank; j++) {\n        const dim = indicesData[i * sliceRank + j];\n        flattenIndex += dim * strides[j];\n        index.push(dim);\n      }\n      if (flattenIndex < 0 || flattenIndex >= x.size / sliceSize) {\n        throw new Error(\n            `Invalid indices: ${index} does not index into ${x.shape}`);\n      }\n\n      for (let k = 0; k < sliceSize; k++) {\n        buffer.values[i * sliceSize + k] = xData[flattenIndex * sliceSize + k];\n      }\n    }\n    return buffer.toTensor().reshape(resultShape);\n  }\n\n  scatterND<R extends Rank>(\n      indices: Tensor, updates: Tensor, shape: ShapeMap[R]): Tensor<R> {\n    const {sliceRank, numUpdates, sliceSize, strides, outputSize} =\n        scatter_nd_util.calculateShapes(updates, indices, shape);\n    const defaultValue = scalar(0);\n    const sumDupeIndices = true;\n    return this.scatter(\n        indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank,\n        strides, defaultValue, sumDupeIndices);\n  }\n\n  private scatter<R extends Rank>(\n      indices: Tensor, updates: Tensor, shape: ShapeMap[R], outputSize: number,\n      sliceSize: number, numUpdates: number, sliceRank: number,\n      strides: number[], defaultValue: Scalar,\n      sumDupeIndices: boolean): Tensor<R> {\n    const flattenShape = [outputSize / sliceSize, sliceSize];\n    const indicesData = indices.dataSync();\n    const updatesData = updates.dataSync();\n\n    if (outputSize === 0) {\n      return tensor([], shape, updates.dtype);\n    }\n\n    const buffer = new TensorBuffer(flattenShape, updates.dtype as 'float32');\n    buffer.values.fill(defaultValue.dataSync()[0]);\n\n    for (let i = 0; i < numUpdates; i++) {\n      const index = [];\n      let flattenIndex = 0;\n      for (let j = 0; j < sliceRank; j++) {\n        const dim = indicesData[i * sliceRank + j];\n        index.push(dim);\n        flattenIndex += dim * strides[j];\n      }\n\n      if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) {\n        throw new Error(\n            `Invalid indices: ${index} does not index into ${shape}`);\n      }\n\n      for (let k = 0; k < sliceSize; k++) {\n        if (sumDupeIndices) {\n          buffer.values[flattenIndex * sliceSize + k] +=\n              updatesData[i * sliceSize + k];\n        } else {\n          buffer.values[flattenIndex * sliceSize + k] = updates.rank === 0 ?\n              updatesData[0] :\n              updatesData[i * sliceSize + k];\n        }\n      }\n    }\n    return buffer.toTensor().reshape(shape);\n  }\n}\n\nENV.registerBackend(\n    'cpu', () => new MathBackendCPU(), 1 /* priority */, setTensorTracker);\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nconst delayCallback: Function = typeof requestAnimationFrame !== 'undefined' ?\n    requestAnimationFrame :  // Browsers\n    setImmediate;            // Node.js\n\n/**\n * Returns a promise that resolve when a requestAnimationFrame has completed.\n *\n * On Node.js this uses setImmediate instead of requestAnimationFrame.\n *\n * This is simply a sugar method so that users can do the following:\n * `await tf.nextFrame();`\n */\n/** @doc {heading: 'Performance', subheading: 'Timing'} */\nfunction nextFrame(): Promise<void> {\n  return new Promise<void>(resolve => delayCallback(() => resolve()));\n}\n\nexport {nextFrame};\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/* Type definitions for exporting and importing of models. */\n\n/**\n * A map from Tensor dtype to number of bytes per element of the Tensor.\n */\nexport const DTYPE_VALUE_SIZE_MAP: {[dtype: string]: number} = {\n  'float32': 4,\n  'int32': 4,\n  'uint16': 2,\n  'uint8': 1,\n  'bool': 1,\n};\n\n/**\n * A weight manifest.\n *\n * The weight manifest consists of an ordered list of weight-manifest groups.\n * Each weight-manifest group (\"group\" for short hereafter) consists of a\n * number of weight values stored in a number of paths.\n * See the documentation of `WeightManifestGroupConfig` below for more details.\n */\nexport declare type WeightsManifestConfig = WeightsManifestGroupConfig[];\n\n/**\n * A weight-manifest group.\n *\n * Consists of an ordered list of weight values encoded in binary format,\n * stored in an ordered list of paths.\n */\nexport declare interface WeightsManifestGroupConfig {\n  /**\n   * An ordered list of paths.\n   *\n   * Paths are intentionally abstract in order to be general. For example, they\n   * can be relative URL paths or relative paths on the file system.\n   */\n  paths: string[];\n\n  /**\n   * Specifications of the weights stored in the paths.\n   */\n  weights: WeightsManifestEntry[];\n}\n\n/**\n * An entry in the weight manifest.\n *\n * The entry contains specification of a weight.\n */\nexport declare interface WeightsManifestEntry {\n  /**\n   * Name of the weight, e.g., 'Dense_1/bias'\n   */\n  name: string;\n\n  /**\n   * Shape of the weight.\n   */\n  shape: number[];\n\n  /**\n   * Data type of the weight.\n   */\n  dtype: 'float32'|'int32'|'bool';\n\n  /**\n   * Information for dequantization of the weight.\n   */\n  quantization?: {\n    scale: number,           // The scaling constant to multiply by.\n    min: number,             // The (possibly nudged) minimum weight to add.\n    dtype: 'uint16'|'uint8'  // The dtype of the quantized weights.\n  };\n}\n\n/**\n * Options for saving a model.\n */\nexport interface SaveConfig {\n  /**\n   * Whether to save only the trainable weights of the model, ignoring the\n   * untrainable ones.\n   */\n  trainableOnly?: boolean;\n}\n\n/**\n * Result of a saving operation.\n */\nexport interface SaveResult {\n  /**\n   * Information about the model artifacts saved.\n   */\n  modelArtifactsInfo: ModelArtifactsInfo;\n\n  /**\n   * HTTP responses from the server that handled the model-saving request (if\n   * any). This is applicable only to server-based saving routes.\n   */\n  responses?: Response[];\n\n  /**\n   * Error messages and related data (if any).\n   */\n  errors?: Array<{}|string>;\n}\n\nexport declare interface ModelArtifactsInfo {\n  /**\n   * Timestamp for when the model is saved.\n   */\n  dateSaved: Date;\n\n  /**\n   * Type of the model topology\n   *\n   * Possible values:\n   *   - JSON: JSON config (human-readable, e.g., Keras JSON).\n   *   - GraphDef: TensorFlow\n   *     [GraphDef](https://www.tensorflow.org/extend/tool_developers/#graphdef)\n   *     protocol buffer (binary).\n   */\n  modelTopologyType: 'JSON'|'GraphDef';\n\n  /**\n   * Size of model topology (Keras JSON or GraphDef), in bytes.\n   */\n  modelTopologyBytes?: number;\n\n  /**\n   * Size of weight specification or manifest, in bytes.\n   */\n  weightSpecsBytes?: number;\n\n  /**\n   * Size of weight value data, in bytes.\n   */\n  weightDataBytes?: number;\n}\n\n/**\n * The serialized artifacts of a model, including topology and weights.\n *\n * The `modelTopology`, `weightSpecs` and `weightData` fields of this interface\n * are optional, in order to support topology- or weights-only saving and\n * loading.\n */\nexport declare interface ModelArtifacts {\n  /**\n   * Model topology.\n   *\n   * For Keras-style `tf.Model`s, this is a JSON object.\n   * For TensorFlow-style models (e.g., `FrozenModel`), this is a binary buffer\n   * carrying the `GraphDef` protocol buffer.\n   */\n  modelTopology?: {}|ArrayBuffer;\n\n  /**\n   * Weight specifications.\n   *\n   * This corresponds to the weightsData below.\n   */\n  weightSpecs?: WeightsManifestEntry[];\n\n  /**\n   * Binary buffer for all weight values concatenated in the order specified\n   * by `weightSpecs`.\n   */\n  weightData?: ArrayBuffer;\n}\n\n/**\n * Type definition for handlers of loading operations.\n */\nexport type LoadHandler = () => Promise<ModelArtifacts>;\n\n/**\n * Type definition for handlers of saving operations.\n */\nexport type SaveHandler = (modelArtifact: ModelArtifacts) =>\n    Promise<SaveResult>;\n\n/**\n * Interface for a model import/export handler.\n *\n * The `save` and `load` handlers are both optional, in order to allow handlers\n * that support only saving or loading.\n */\n// tslint:disable-next-line:interface-name\nexport interface IOHandler {\n  save?: SaveHandler;\n  load?: LoadHandler;\n}\n\n/**\n * An interface for the manager of a model store.\n *\n * A model store is defined as a storage medium on which multiple models can\n * be stored. Each stored model has a unique `path` as its identifier.\n * A `ModelStoreManager` for the store allows actions including\n *\n * - Listing the models stored in the store.\n * - Deleting a model from the store.\n */\nexport interface ModelStoreManager {\n  /**\n   * List all models in the model store.\n   *\n   * @returns A dictionary mapping paths of existing models to their\n   *   model artifacts info. Model artifacts info include type of the model's\n   *   topology, byte sizes of the topology, weights, etc.\n   */\n  listModels(): Promise<{[path: string]: ModelArtifactsInfo}>;\n\n  /**\n   * Remove a model specified by `path`.\n   *\n   * @param path\n   * @returns ModelArtifactsInfo of the deleted model (if and only if deletion\n   *   is successful).\n   * @throws Error if deletion fails, e.g., if no model exists at `path`.\n   */\n  removeModel(path: string): Promise<ModelArtifactsInfo>;\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {tensor} from '../ops/tensor_ops';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {TypedArray} from '../types';\nimport {sizeFromShape} from '../util';\nimport {DTYPE_VALUE_SIZE_MAP, ModelArtifacts, ModelArtifactsInfo, WeightsManifestEntry} from './types';\n\n/**\n * Encode a map from names to weight values as an ArrayBuffer, along with an\n * `Array` of `WeightsManifestEntry` as specification of the encoded weights.\n *\n * This function does not perform sharding.\n *\n * This function is the reverse of `decodeWeights`.\n *\n * @param tensors A map (\"dict\") from names to tensors.\n * @returns A `Promise` of\n *   - A flat `ArrayBuffer` with all the binary values of the `Tensor`s\n *     concatenated.\n *   - An `Array` of `WeightManifestEntry`s, carrying information including\n *     tensor names, `dtype`s and shapes.\n * @throws Error: on unsupported tensor `dtype`.\n */\nexport async function encodeWeights(tensors: NamedTensorMap):\n    Promise<{data: ArrayBuffer, specs: WeightsManifestEntry[]}> {\n  // TODO(adarob, cais): Support quantization.\n  const specs: WeightsManifestEntry[] = [];\n  const dataPromises: Array<Promise<TypedArray>> = [];\n  for (const name in tensors) {\n    const t = tensors[name];\n\n    if (t.dtype !== 'float32' && t.dtype !== 'int32' && t.dtype !== 'bool') {\n      throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);\n    }\n    specs.push({name, shape: t.shape, dtype: t.dtype});\n    dataPromises.push(t.data());\n  }\n  const tensorValues = await Promise.all(dataPromises);\n  return {data: concatenateTypedArrays(tensorValues), specs};\n}\n\n/**\n * Decode flat ArrayBuffer as weights.\n *\n * This function does not handle sharding.\n *\n * This function is the reverse of `encodeWeights`.\n *\n * @param buffer A flat ArrayBuffer carrying the binary values of the tensors\n *   concatenated in the order specified in `specs`.\n * @param specs Specifications of the names, dtypes and shapes of the tensors\n *   whose value are encoded by `buffer`.\n * @return A map from tensor name to tensor value, with the names corresponding\n *   to names in `specs`.\n * @throws Error, if any of the tensors has unsupported dtype.\n */\nexport function decodeWeights(\n    buffer: ArrayBuffer, specs: WeightsManifestEntry[]): NamedTensorMap {\n  // TODO(adarob, cais): Support quantization.\n  const out: NamedTensorMap = {};\n  let offset = 0;\n  for (const spec of specs) {\n    const name = spec.name;\n    const dtype = spec.dtype;\n    const shape = spec.shape;\n    const size = sizeFromShape(shape);\n    let typedArray: TypedArray;\n\n    if ('quantization' in spec) {\n      const quantization = spec.quantization;\n      if (quantization.dtype !== 'uint8' && quantization.dtype !== 'uint16') {\n        throw new Error(\n            `Weight ${spec.name} has unknown ` +\n            `quantization dtype ${quantization.dtype}. ` +\n            `Supported quantization dtypes are: 'uint8' and 'uint16'.`);\n      }\n      const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];\n      const byteBuffer =\n          buffer.slice(offset, offset + size * quantizationSizeFactor);\n      const quantizedArray = (quantization.dtype === 'uint8') ?\n          new Uint8Array(byteBuffer) :\n          new Uint16Array(byteBuffer);\n      if (dtype === 'float32') {\n        typedArray = Float32Array.from(\n            quantizedArray, v => v * quantization.scale + quantization.min);\n      } else if (dtype === 'int32') {\n        typedArray = Int32Array.from(\n            quantizedArray,\n            v => Math.round(v * quantization.scale + quantization.min));\n      } else {\n        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n      }\n      offset += size * quantizationSizeFactor;\n    } else {\n      const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];\n      const byteBuffer = buffer.slice(offset, offset + size * dtypeFactor);\n\n      if (dtype === 'float32') {\n        typedArray = new Float32Array(byteBuffer);\n      } else if (dtype === 'int32') {\n        typedArray = new Int32Array(byteBuffer);\n      } else if (dtype === 'bool') {\n        typedArray = new Uint8Array(byteBuffer);\n      } else {\n        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n      }\n      offset += size * dtypeFactor;\n    }\n\n    let value: Tensor;\n    if (dtype === 'float32') {\n      value = tensor(typedArray, shape, 'float32');\n    } else if (dtype === 'int32') {\n      value = tensor(typedArray, shape, 'int32');\n    } else if (dtype === 'bool') {\n      value = tensor(typedArray, shape, 'bool');\n    } else {\n      throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n    }\n    out[name] = value;\n  }\n  return out;\n}\n\n/**\n * Concatenate TypedArrays into an ArrayBuffer.\n */\nexport function concatenateTypedArrays(xs: TypedArray[]): ArrayBuffer {\n  // TODO(adarob, cais): Support quantization.\n  if (xs === null) {\n    throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);\n  }\n\n  let totalByteLength = 0;\n\n  // `normalizedXs` is here for this reason: a `TypedArray`'s `buffer'\n  // can have a different byte length from that of the `TypedArray` itself,\n  // for example, when the `TypedArray` is created from an offset in an\n  // `ArrayBuffer`. `normliazedXs` holds `TypedArray`s whose `buffer`s match\n  // the `TypedArray` in byte length. If an element of `xs` does not show\n  // this property, a new `TypedArray` that satisfy this property will be\n  // constructed and pushed into `normalizedXs`.\n  const normalizedXs: TypedArray[] = [];\n  xs.forEach((x: TypedArray) => {\n    totalByteLength += x.byteLength;\n    // tslint:disable:no-any\n    normalizedXs.push(\n        x.byteLength === x.buffer.byteLength ? x :\n                                               new (x.constructor as any)(x));\n    if (!(x as any instanceof Float32Array || x as any instanceof Int32Array ||\n          x as any instanceof Uint8Array)) {\n      throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);\n    }\n    // tslint:enable:no-any\n  });\n\n  const y = new Uint8Array(totalByteLength);\n  let offset = 0;\n  normalizedXs.forEach((x: TypedArray) => {\n    y.set(new Uint8Array(x.buffer), offset);\n    offset += x.byteLength;\n  });\n\n  return y.buffer;\n}\n\n// Use Buffer on Node.js instead of Blob/atob/btoa\nconst useNodeBuffer = typeof Buffer !== 'undefined' &&\n    (typeof Blob === 'undefined' || typeof atob === 'undefined' ||\n     typeof btoa === 'undefined');\n\n/**\n * Calculate the byte length of a JavaScript string.\n *\n * Note that a JavaScript string can contain wide characters, therefore the\n * length of the string is not necessarily equal to the byte length.\n *\n * @param str Input string.\n * @returns Byte length.\n */\nexport function stringByteLength(str: string): number {\n  if (useNodeBuffer) {\n    return Buffer.byteLength(str);\n  }\n  return new Blob([str]).size;\n}\n\n/**\n * Encode an ArrayBuffer as a base64 encoded string.\n *\n * @param buffer `ArrayBuffer` to be converted.\n * @returns A string that base64-encodes `buffer`.\n */\nexport function arrayBufferToBase64String(buffer: ArrayBuffer): string {\n  if (useNodeBuffer) {\n    return Buffer.from(buffer).toString('base64');\n  }\n  return btoa(String.fromCharCode.apply(null, new Uint8Array(buffer)));\n}\n\n/**\n * Decode a base64 string as an ArrayBuffer.\n *\n * @param str Base64 string.\n * @returns Decoded `ArrayBuffer`.\n */\nexport function base64StringToArrayBuffer(str: string): ArrayBuffer {\n  if (useNodeBuffer) {\n    const buf = Buffer.from(str, 'base64');\n    return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);\n  }\n  const s = atob(str);\n  const buffer = new Uint8Array(s.length);\n  for (let i = 0; i < s.length; ++i) {\n    buffer.set([s.charCodeAt(i)], i);\n  }\n  return buffer.buffer;\n}\n\n/**\n * Concatenate a number of ArrayBuffers into one.\n *\n * @param buffers A number of array buffers to concatenate.\n * @returns Result of concatenating `buffers` in order.\n */\nexport function concatenateArrayBuffers(buffers: ArrayBuffer[]): ArrayBuffer {\n  let totalByteLength = 0;\n  buffers.forEach((buffer: ArrayBuffer) => {\n    totalByteLength += buffer.byteLength;\n  });\n\n  const temp = new Uint8Array(totalByteLength);\n  let offset = 0;\n  buffers.forEach((buffer: ArrayBuffer) => {\n    temp.set(new Uint8Array(buffer), offset);\n    offset += buffer.byteLength;\n  });\n  return temp.buffer;\n}\n\n/**\n * Get the basename of a path.\n *\n * Behaves in a way analogous to Linux's basename command.\n *\n * @param path\n */\nexport function basename(path: string): string {\n  const SEPARATOR = '/';\n  path = path.trim();\n  while (path.endsWith(SEPARATOR)) {\n    path = path.slice(0, path.length - 1);\n  }\n  const items = path.split(SEPARATOR);\n  return items[items.length - 1];\n}\n\n/**\n * Populate ModelArtifactsInfo fields for a model with JSON topology.\n * @param modelArtifacts\n * @returns A ModelArtifactsInfo object.\n */\nexport function getModelArtifactsInfoForJSON(modelArtifacts: ModelArtifacts):\n    ModelArtifactsInfo {\n  if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n    throw new Error('Expected JSON model topology, received ArrayBuffer.');\n  }\n\n  return {\n    dateSaved: new Date(),\n    modelTopologyType: 'JSON',\n    modelTopologyBytes: modelArtifacts.modelTopology == null ?\n        0 :\n        stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),\n    weightSpecsBytes: modelArtifacts.weightSpecs == null ?\n        0 :\n        stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),\n    weightDataBytes: modelArtifacts.weightData == null ?\n        0 :\n        modelArtifacts.weightData.byteLength,\n  };\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {IOHandler} from './types';\n\nexport type IORouter = (url: string|string[]) => IOHandler;\n\nexport class IORouterRegistry {\n  // Singleton instance.\n  private static instance: IORouterRegistry;\n\n  private saveRouters: IORouter[];\n  private loadRouters: IORouter[];\n\n  private constructor() {\n    this.saveRouters = [];\n    this.loadRouters = [];\n  }\n\n  private static getInstance(): IORouterRegistry {\n    if (IORouterRegistry.instance == null) {\n      IORouterRegistry.instance = new IORouterRegistry();\n    }\n    return IORouterRegistry.instance;\n  }\n\n  /**\n   * Register a save-handler router.\n   *\n   * @param saveRouter A function that maps a URL-like string onto an instance\n   * of `IOHandler` with the `save` method defined or `null`.\n   */\n  static registerSaveRouter(saveRouter: IORouter) {\n    IORouterRegistry.getInstance().saveRouters.push(saveRouter);\n  }\n\n  /**\n   * Register a load-handler router.\n   *\n   * @param loadRouter A function that maps a URL-like string onto an instance\n   * of `IOHandler` with the `load` method defined or `null`.\n   */\n  static registerLoadRouter(loadRouter: IORouter) {\n    IORouterRegistry.getInstance().loadRouters.push(loadRouter);\n  }\n\n  /**\n   * Look up IOHandler for saving, given a URL-like string.\n   *\n   * @param url\n   * @returns If only one match is found, an instance of IOHandler with the\n   * `save` method defined. If no match is found, `null`.\n   * @throws Error, if more than one match is found.\n   */\n  static getSaveHandlers(url: string|string[]): IOHandler[] {\n    return IORouterRegistry.getHandlers(url, 'save');\n  }\n\n  /**\n   * Look up IOHandler for loading, given a URL-like string.\n   *\n   * @param url\n   * @returns All valid handlers for `url`, given the currently registered\n   *   handler routers.\n   */\n  static getLoadHandlers(url: string|string[]): IOHandler[] {\n    return IORouterRegistry.getHandlers(url, 'load');\n  }\n\n  private static getHandlers(url: string|string[], handlerType: 'save'|'load'):\n      IOHandler[] {\n    const validHandlers: IOHandler[] = [];\n    const routers = handlerType === 'load' ? this.getInstance().loadRouters :\n                                             this.getInstance().saveRouters;\n    routers.forEach(router => {\n      const handler = router(url);\n      if (handler !== null) {\n        validHandlers.push(handler);\n      }\n    });\n    return validHandlers;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Classes and functions for model management across multiple storage mediums.\n *\n * Supported client actions:\n * - Listing models on all registered storage mediums.\n * - Remove model by URL from any registered storage mediums, by using URL\n *   string.\n * - Moving or copying model from one path to another in the same medium or from\n *   one medium to another, by using URL strings.\n */\n\nimport {assert} from '../util';\n\nimport {IORouterRegistry} from './router_registry';\nimport {ModelArtifactsInfo, ModelStoreManager} from './types';\n\nconst URL_SCHEME_SUFFIX = '://';\n\nexport class ModelStoreManagerRegistry {\n  // Singleton instance.\n  private static instance: ModelStoreManagerRegistry;\n\n  private managers: {[scheme: string]: ModelStoreManager};\n\n  private constructor() {\n    this.managers = {};\n  }\n\n  private static getInstance(): ModelStoreManagerRegistry {\n    if (ModelStoreManagerRegistry.instance == null) {\n      ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();\n    }\n    return ModelStoreManagerRegistry.instance;\n  }\n\n  /**\n   * Register a save-handler router.\n   *\n   * @param saveRouter A function that maps a URL-like string onto an instance\n   * of `IOHandler` with the `save` method defined or `null`.\n   */\n  static registerManager(scheme: string, manager: ModelStoreManager) {\n    assert(scheme != null, 'scheme must not be undefined or null.');\n    if (scheme.endsWith(URL_SCHEME_SUFFIX)) {\n      scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));\n    }\n    assert(scheme.length > 0, 'scheme must not be an empty string.');\n    const registry = ModelStoreManagerRegistry.getInstance();\n    assert(\n        registry.managers[scheme] == null,\n        `A model store manager is already registered for scheme '${scheme}'.`);\n    registry.managers[scheme] = manager;\n  }\n\n  static getManager(scheme: string): ModelStoreManager {\n    const manager = this.getInstance().managers[scheme];\n    if (manager == null) {\n      throw new Error(`Cannot find model manager for scheme '${scheme}'`);\n    }\n    return manager;\n  }\n\n  static getSchemes(): string[] {\n    return Object.keys(this.getInstance().managers);\n  }\n}\n\n/**\n * Helper method for parsing a URL string into a scheme and a path.\n *\n * @param url E.g., 'localstorage://my-model'\n * @returns A dictionary with two fields: scheme and path.\n *   Scheme: e.g., 'localstorage' in the example above.\n *   Path: e.g., 'my-model' in the example above.\n */\nfunction parseURL(url: string): {scheme: string, path: string} {\n  if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {\n    throw new Error(\n        `The url string provided does not contain a scheme. ` +\n        `Supported schemes are: ` +\n        `${ModelStoreManagerRegistry.getSchemes().join(',')}`);\n  }\n  return {\n    scheme: url.split(URL_SCHEME_SUFFIX)[0],\n    path: url.split(URL_SCHEME_SUFFIX)[1],\n  };\n}\n\nasync function cloneModelInternal(\n    sourceURL: string, destURL: string,\n    deleteSource = false): Promise<ModelArtifactsInfo> {\n  assert(\n      sourceURL !== destURL,\n      `Old path and new path are the same: '${sourceURL}'`);\n\n  const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);\n  assert(\n      loadHandlers.length > 0,\n      `Copying failed because no load handler is found for source URL ${\n          sourceURL}.`);\n  assert(\n      loadHandlers.length < 2,\n      `Copying failed because more than one (${loadHandlers.length}) ` +\n          `load handlers for source URL ${sourceURL}.`);\n  const loadHandler = loadHandlers[0];\n\n  const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);\n  assert(\n      saveHandlers.length > 0,\n      `Copying failed because no save handler is found for destination URL ` +\n          `${destURL}.`);\n  assert(\n      saveHandlers.length < 2,\n      `Copying failed because more than one (${loadHandlers.length}) ` +\n          `save handlers for destination URL ${destURL}.`);\n  const saveHandler = saveHandlers[0];\n\n  const sourceScheme = parseURL(sourceURL).scheme;\n  const sourcePath = parseURL(sourceURL).path;\n  const sameMedium = sourceScheme === parseURL(sourceURL).scheme;\n\n  const modelArtifacts = await loadHandler.load();\n\n  // If moving within the same storage medium, remove the old model as soon as\n  // the loading is done. Without doing this, it is possible that the combined\n  // size of the two models will cause the cloning to fail.\n  if (deleteSource && sameMedium) {\n    await ModelStoreManagerRegistry.getManager(sourceScheme)\n        .removeModel(sourcePath);\n  }\n\n  const saveResult = await saveHandler.save(modelArtifacts);\n\n  // If moving between mediums, the deletion is done after the save succeeds.\n  // This guards against the case in which saving to the destination medium\n  // fails.\n  if (deleteSource && !sameMedium) {\n    await ModelStoreManagerRegistry.getManager(sourceScheme)\n        .removeModel(sourcePath);\n  }\n\n  return saveResult.modelArtifactsInfo;\n}\n\n/**\n * List all models stored in registered storage mediums.\n *\n * For a web browser environment, the registered mediums are Local Storage and\n * IndexedDB.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @returns A `Promise` of a dictionary mapping URLs of existing models to\n * their model artifacts info. URLs include medium-specific schemes, e.g.,\n *   'indexeddb://my/model/1'. Model artifacts info include type of the\n * model's topology, byte sizes of the topology, weights, etc.\n */\n/** @doc {heading: 'Models', subheading: 'Management', namespace: 'io'} */\nasync function listModels(): Promise<{[url: string]: ModelArtifactsInfo}> {\n  const schemes = ModelStoreManagerRegistry.getSchemes();\n  const out: {[url: string]: ModelArtifactsInfo} = {};\n  for (const scheme of schemes) {\n    const schemeOut =\n        await ModelStoreManagerRegistry.getManager(scheme).listModels();\n    for (const path in schemeOut) {\n      const url = scheme + URL_SCHEME_SUFFIX + path;\n      out[url] = schemeOut[path];\n    }\n  }\n  return out;\n}\n\n/**\n * Remove a model specified by URL from a reigstered storage medium.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @param url A URL to a stored model, with a scheme prefix, e.g.,\n *   'localstorage://my-model-1', 'indexeddb://my/model/2'.\n * @returns ModelArtifactsInfo of the deleted model (if and only if deletion\n *   is successful).\n * @throws Error if deletion fails, e.g., if no model exists at `path`.\n */\n/** @doc {heading: 'Models', subheading: 'Management', namespace: 'io'} */\nasync function removeModel(url: string): Promise<ModelArtifactsInfo> {\n  const schemeAndPath = parseURL(url);\n  const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);\n  return await manager.removeModel(schemeAndPath.path);\n}\n\n/**\n * Copy a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Copying within a storage medium, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Copying between two storage mediums, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Copy the model, from Local Storage to IndexedDB.\n * await tf.io.copyModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove both models.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of copying.\n * @param destURL Destination URL of copying.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n */\n/** @doc {heading: 'Models', subheading: 'Management', namespace: 'io'} */\nasync function copyModel(\n    sourceURL: string, destURL: string): Promise<ModelArtifactsInfo> {\n  const deleteSource = false;\n  return await cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n\n/**\n * Move a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Moving within a storage medium, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Moving between two storage mediums, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Move the model, from Local Storage to IndexedDB.\n * await tf.io.moveModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove the moved model.\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of moving.\n * @param destURL Destination URL of moving.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n */\n/** @doc {heading: 'Models', subheading: 'Management', namespace: 'io'} */\nasync function moveModel(\n    sourceURL: string, destURL: string): Promise<ModelArtifactsInfo> {\n  const deleteSource = true;\n  return await cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n\nexport {moveModel, copyModel, removeModel, listModels};\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {getModelArtifactsInfoForJSON} from './io_utils';\nimport {ModelStoreManagerRegistry} from './model_management';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, ModelArtifactsInfo, ModelStoreManager, SaveResult} from './types';\n\nconst DATABASE_NAME = 'tensorflowjs';\nconst DATABASE_VERSION = 1;\n\n// Model data and ModelArtifactsInfo (metadata) are stored in two separate\n// stores for efficient access of the list of stored models and their metadata.\n// 1. The object store for model data: topology, weights and weight manifests.\nconst MODEL_STORE_NAME = 'models_store';\n// 2. The object store for ModelArtifactsInfo, including meta-information such\n//    as the type of topology (JSON vs binary), byte size of the topology, byte\n//    size of the weights, etc.\nconst INFO_STORE_NAME = 'model_info_store';\n\n/**\n * Delete the entire database for tensorflow.js, including the models store.\n */\nexport async function deleteDatabase(): Promise<void> {\n  const idbFactory = getIndexedDBFactory();\n\n  return new Promise<void>((resolve, reject) => {\n    const deleteRequest = idbFactory.deleteDatabase(DATABASE_NAME);\n    deleteRequest.onsuccess = () => resolve();\n    deleteRequest.onerror = error => reject(error);\n  });\n}\n\nfunction getIndexedDBFactory(): IDBFactory {\n  if (!ENV.get('IS_BROWSER')) {\n    // TODO(cais): Add more info about what IOHandler subtypes are available.\n    //   Maybe point to a doc page on the web and/or automatically determine\n    //   the available IOHandlers and print them in the error message.\n    throw new Error(\n        'Failed to obtain IndexedDB factory because the current environment' +\n        'is not a web browser.');\n  }\n  // tslint:disable-next-line:no-any\n  const theWindow: any = window;\n  const factory = theWindow.indexedDB || theWindow.mozIndexedDB ||\n      theWindow.webkitIndexedDB || theWindow.msIndexedDB ||\n      theWindow.shimIndexedDB;\n  if (factory == null) {\n    throw new Error(\n        'The current browser does not appear to support IndexedDB.');\n  }\n  return factory;\n}\n\nfunction setUpDatabase(openRequest: IDBRequest) {\n  const db = openRequest.result as IDBDatabase;\n  db.createObjectStore(MODEL_STORE_NAME, {keyPath: 'modelPath'});\n  db.createObjectStore(INFO_STORE_NAME, {keyPath: 'modelPath'});\n}\n\n/**\n * IOHandler subclass: Browser IndexedDB.\n *\n * See the doc string of `browserIndexedDB` for more details.\n */\nexport class BrowserIndexedDB implements IOHandler {\n  protected readonly indexedDB: IDBFactory;\n  protected readonly modelPath: string;\n\n  static readonly URL_SCHEME = 'indexeddb://';\n\n  constructor(modelPath: string) {\n    this.indexedDB = getIndexedDBFactory();\n\n    if (modelPath == null || !modelPath) {\n      throw new Error(\n          'For IndexedDB, modelPath must not be null, undefined or empty.');\n    }\n    this.modelPath = modelPath;\n  }\n\n  async save(modelArtifacts: ModelArtifacts): Promise<SaveResult> {\n    // TODO(cais): Support saving GraphDef models.\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\n          'BrowserLocalStorage.save() does not support saving model topology ' +\n          'in binary formats yet.');\n    }\n\n    return this.databaseAction(this.modelPath, modelArtifacts) as\n        Promise<SaveResult>;\n  }\n\n  async load(): Promise<ModelArtifacts> {\n    return this.databaseAction(this.modelPath) as Promise<ModelArtifacts>;\n  }\n\n  /**\n   * Perform database action to put model artifacts into or read model artifacts\n   * from IndexedDB object store.\n   *\n   * Whether the action is put or get depends on whether `modelArtifacts` is\n   * specified. If it is specified, the action will be put; otherwise the action\n   * will be get.\n   *\n   * @param modelPath A unique string path for the model.\n   * @param modelArtifacts If specified, it will be the model artifacts to be\n   *   stored in IndexedDB.\n   * @returns A `Promise` of `SaveResult`, if the action is put, or a `Promise`\n   *   of `ModelArtifacts`, if the action is get.\n   */\n  private databaseAction(modelPath: string, modelArtifacts?: ModelArtifacts):\n      Promise<ModelArtifacts|SaveResult> {\n    return new Promise<ModelArtifacts|SaveResult>((resolve, reject) => {\n      const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n      openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n\n      openRequest.onsuccess = () => {\n        const db = openRequest.result as IDBDatabase;\n\n        if (modelArtifacts == null) {\n          // Read model out from object store.\n          const modelTx = db.transaction(MODEL_STORE_NAME, 'readonly');\n          const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n          const getRequest = modelStore.get(this.modelPath);\n          getRequest.onsuccess = () => {\n            if (getRequest.result == null) {\n              db.close();\n              return reject(new Error(\n                  `Cannot find model with path '${this.modelPath}' ` +\n                  `in IndexedDB.`));\n            } else {\n              resolve(getRequest.result.modelArtifacts);\n            }\n          };\n          getRequest.onerror = error => {\n            db.close();\n            return reject(getRequest.error);\n          };\n          modelTx.oncomplete = () => db.close();\n        } else {\n          // Put model into object store.\n          const modelArtifactsInfo: ModelArtifactsInfo =\n              getModelArtifactsInfoForJSON(modelArtifacts);\n          // First, put ModelArtifactsInfo into info store.\n          const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n          let infoStore = infoTx.objectStore(INFO_STORE_NAME);\n          const putInfoRequest =\n              infoStore.put({modelPath: this.modelPath, modelArtifactsInfo});\n          let modelTx: IDBTransaction;\n          putInfoRequest.onsuccess = () => {\n            // Second, put model data into model store.\n            modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n            const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n            const putModelRequest = modelStore.put({\n              modelPath: this.modelPath,\n              modelArtifacts,\n              modelArtifactsInfo\n            });\n            putModelRequest.onsuccess = () => resolve({modelArtifactsInfo});\n            putModelRequest.onerror = error => {\n              // If the put-model request fails, roll back the info entry as\n              // well.\n              infoStore = infoTx.objectStore(INFO_STORE_NAME);\n              const deleteInfoRequest = infoStore.delete(this.modelPath);\n              deleteInfoRequest.onsuccess = () => {\n                db.close();\n                return reject(putModelRequest.error);\n              };\n              deleteInfoRequest.onerror = error => {\n                db.close();\n                return reject(putModelRequest.error);\n              };\n            };\n          };\n          putInfoRequest.onerror = error => {\n            db.close();\n            return reject(putInfoRequest.error);\n          };\n          infoTx.oncomplete = () => {\n            if (modelTx == null) {\n              db.close();\n            } else {\n              modelTx.oncomplete = () => db.close();\n            }\n          };\n        }\n      };\n      openRequest.onerror = error => reject(openRequest.error);\n    });\n  }\n}\n\nexport const indexedDBRouter: IORouter = (url: string|string[]) => {\n  if (!ENV.get('IS_BROWSER')) {\n    return null;\n  } else {\n    if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {\n      return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));\n    } else {\n      return null;\n    }\n  }\n};\nIORouterRegistry.registerSaveRouter(indexedDBRouter);\nIORouterRegistry.registerLoadRouter(indexedDBRouter);\n\n/**\n * Creates a browser IndexedDB IOHandler for saving and loading models.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save('indexeddb://MyModel'));\n * console.log(saveResult);\n * ```\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `BrowserIndexedDB` (sublcass of `IOHandler`),\n *   which can be used with, e.g., `tf.Model.save`.\n */\nexport function browserIndexedDB(modelPath: string): IOHandler {\n  return new BrowserIndexedDB(modelPath);\n}\n\nfunction maybeStripScheme(key: string) {\n  return key.startsWith(BrowserIndexedDB.URL_SCHEME) ?\n      key.slice(BrowserIndexedDB.URL_SCHEME.length) :\n      key;\n}\n\nexport class BrowserIndexedDBManager implements ModelStoreManager {\n  private indexedDB: IDBFactory;\n\n  constructor() {\n    this.indexedDB = getIndexedDBFactory();\n  }\n\n  async listModels(): Promise<{[path: string]: ModelArtifactsInfo}> {\n    return new Promise<{[path: string]: ModelArtifactsInfo}>(\n        (resolve, reject) => {\n          const openRequest =\n              this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n          openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n\n          openRequest.onsuccess = () => {\n            const db = openRequest.result as IDBDatabase;\n            const tx = db.transaction(INFO_STORE_NAME, 'readonly');\n            const store = tx.objectStore(INFO_STORE_NAME);\n            // tslint:disable:max-line-length\n            // Need to cast `store` as `any` here because TypeScript's DOM\n            // library does not have the `getAll()` method even though the\n            // method is supported in the latest version of most mainstream\n            // browsers:\n            // https://developer.mozilla.org/en-US/docs/Web/API/IDBObjectStore/getAll\n            // tslint:enable:max-line-length\n            // tslint:disable-next-line:no-any\n            const getAllInfoRequest = (store as any).getAll() as IDBRequest;\n            getAllInfoRequest.onsuccess = () => {\n              const out: {[path: string]: ModelArtifactsInfo} = {};\n              for (const item of getAllInfoRequest.result) {\n                out[item.modelPath] = item.modelArtifactsInfo;\n              }\n              resolve(out);\n            };\n            getAllInfoRequest.onerror = error => {\n              db.close();\n              return reject(getAllInfoRequest.error);\n            };\n            tx.oncomplete = () => db.close();\n          };\n          openRequest.onerror = error => reject(openRequest.error);\n        });\n  }\n\n  async removeModel(path: string): Promise<ModelArtifactsInfo> {\n    path = maybeStripScheme(path);\n    return new Promise<ModelArtifactsInfo>((resolve, reject) => {\n      const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n      openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n\n      openRequest.onsuccess = () => {\n        const db = openRequest.result as IDBDatabase;\n        const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n        const infoStore = infoTx.objectStore(INFO_STORE_NAME);\n\n        const getInfoRequest = infoStore.get(path);\n        let modelTx: IDBTransaction;\n        getInfoRequest.onsuccess = () => {\n          if (getInfoRequest.result == null) {\n            db.close();\n            return reject(new Error(\n                `Cannot find model with path '${path}' ` +\n                `in IndexedDB.`));\n          } else {\n            // First, delete the entry in the info store.\n            const deleteInfoRequest = infoStore.delete(path);\n            const deleteModelData = () => {\n              // Second, delete the entry in the model store.\n              modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n              const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n              const deleteModelRequest = modelStore.delete(path);\n              deleteModelRequest.onsuccess = () =>\n                  resolve(getInfoRequest.result.modelArtifactsInfo);\n              deleteModelRequest.onerror = error =>\n                  reject(getInfoRequest.error);\n            };\n            // Proceed with deleting model data regardless of whether deletion\n            // of info data succeeds or not.\n            deleteInfoRequest.onsuccess = deleteModelData;\n            deleteInfoRequest.onerror = error => {\n              deleteModelData();\n              db.close();\n              return reject(getInfoRequest.error);\n            };\n          }\n        };\n        getInfoRequest.onerror = error => {\n          db.close();\n          return reject(getInfoRequest.error);\n        };\n\n        infoTx.oncomplete = () => {\n          if (modelTx == null) {\n            db.close();\n          } else {\n            modelTx.oncomplete = () => db.close();\n          }\n        };\n      };\n      openRequest.onerror = error => reject(openRequest.error);\n    });\n  }\n}\n\nif (ENV.get('IS_BROWSER')) {\n  // Wrap the construction and registration, to guard against browsers that\n  // don't support Local Storage.\n  try {\n    ModelStoreManagerRegistry.registerManager(\n        BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());\n  } catch (err) {\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {assert} from '../util';\nimport {arrayBufferToBase64String, base64StringToArrayBuffer, getModelArtifactsInfoForJSON} from './io_utils';\nimport {ModelStoreManagerRegistry} from './model_management';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, ModelArtifactsInfo, ModelStoreManager, SaveResult} from './types';\n\nconst PATH_SEPARATOR = '/';\nconst PATH_PREFIX = 'tensorflowjs_models';\nconst INFO_SUFFIX = 'info';\nconst MODEL_TOPOLOGY_SUFFIX = 'model_topology';\nconst WEIGHT_SPECS_SUFFIX = 'weight_specs';\nconst WEIGHT_DATA_SUFFIX = 'weight_data';\n\n/**\n * Purge all tensorflow.js-saved model artifacts from local storage.\n *\n * @returns Paths of the models purged.\n */\nexport function purgeLocalStorageArtifacts(): string[] {\n  if (!ENV.get('IS_BROWSER') || typeof window.localStorage === 'undefined') {\n    throw new Error(\n        'purgeLocalStorageModels() cannot proceed because local storage is ' +\n        'unavailable in the current environment.');\n  }\n  const LS = window.localStorage;\n  const purgedModelPaths: string[] = [];\n  for (let i = 0; i < LS.length; ++i) {\n    const key = LS.key(i);\n    const prefix = PATH_PREFIX + PATH_SEPARATOR;\n    if (key.startsWith(prefix) && key.length > prefix.length) {\n      LS.removeItem(key);\n      const modelName = getModelPathFromKey(key);\n      if (purgedModelPaths.indexOf(modelName) === -1) {\n        purgedModelPaths.push(modelName);\n      }\n    }\n  }\n  return purgedModelPaths;\n}\n\nfunction getModelKeys(path: string):\n    {info: string, topology: string, weightSpecs: string, weightData: string} {\n  return {\n    info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),\n    topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),\n    weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),\n    weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR)\n  };\n}\n\n/**\n * Get model path from a local-storage key.\n *\n * E.g., 'tensorflowjs_models/my/model/1/info' --> 'my/model/1'\n *\n * @param key\n */\nfunction getModelPathFromKey(key: string) {\n  const items = key.split(PATH_SEPARATOR);\n  if (items.length < 3) {\n    throw new Error(`Invalid key format: ${key}`);\n  }\n  return items.slice(1, items.length - 1).join(PATH_SEPARATOR);\n}\n\nfunction maybeStripScheme(key: string) {\n  return key.startsWith(BrowserLocalStorage.URL_SCHEME) ?\n      key.slice(BrowserLocalStorage.URL_SCHEME.length) :\n      key;\n}\n\n/**\n * IOHandler subclass: Browser Local Storage.\n *\n * See the doc string to `browserLocalStorage` for more details.\n */\nexport class BrowserLocalStorage implements IOHandler {\n  protected readonly LS: Storage;\n  protected readonly modelPath: string;\n  protected readonly keys: {[key: string]: string};\n\n  static readonly URL_SCHEME = 'localstorage://';\n\n  constructor(modelPath: string) {\n    if (!ENV.get('IS_BROWSER') || typeof window.localStorage === 'undefined') {\n      // TODO(cais): Add more info about what IOHandler subtypes are\n      // available.\n      //   Maybe point to a doc page on the web and/or automatically determine\n      //   the available IOHandlers and print them in the error message.\n      throw new Error(\n          'The current environment does not support local storage.');\n    }\n    this.LS = window.localStorage;\n\n    if (modelPath == null || !modelPath) {\n      throw new Error(\n          'For local storage, modelPath must not be null, undefined or empty.');\n    }\n    this.modelPath = modelPath;\n    this.keys = getModelKeys(this.modelPath);\n  }\n\n  /**\n   * Save model artifacts to browser local storage.\n   *\n   * See the documentation to `browserLocalStorage` for details on the saved\n   * artifacts.\n   *\n   * @param modelArtifacts The model artifacts to be stored.\n   * @returns An instance of SaveResult.\n   */\n  async save(modelArtifacts: ModelArtifacts): Promise<SaveResult> {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\n          'BrowserLocalStorage.save() does not support saving model topology ' +\n          'in binary formats yet.');\n    } else {\n      const topology = JSON.stringify(modelArtifacts.modelTopology);\n      const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);\n\n      const modelArtifactsInfo: ModelArtifactsInfo =\n          getModelArtifactsInfoForJSON(modelArtifacts);\n\n      try {\n        this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));\n        this.LS.setItem(this.keys.topology, topology);\n        this.LS.setItem(this.keys.weightSpecs, weightSpecs);\n        this.LS.setItem(\n            this.keys.weightData,\n            arrayBufferToBase64String(modelArtifacts.weightData));\n\n        return {modelArtifactsInfo};\n      } catch (err) {\n        // If saving failed, clean up all items saved so far.\n        for (const key in this.keys) {\n          this.LS.removeItem(this.keys[key]);\n        }\n\n        throw new Error(\n            `Failed to save model '${this.modelPath}' to local storage: ` +\n            `size quota being exceeded is a possible cause of this failure: ` +\n            `modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, ` +\n            `weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, ` +\n            `weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);\n      }\n    }\n  }\n\n  /**\n   * Load a model from local storage.\n   *\n   * See the documentation to `browserLocalStorage` for details on the saved\n   * artifacts.\n   *\n   * @returns The loaded model (if loading succeeds).\n   */\n  async load(): Promise<ModelArtifacts> {\n    const info =\n        JSON.parse(this.LS.getItem(this.keys.info)) as ModelArtifactsInfo;\n    if (info == null) {\n      throw new Error(\n          `In local storage, there is no model with name '${this.modelPath}'`);\n    }\n\n    if (info.modelTopologyType !== 'JSON') {\n      throw new Error(\n          'BrowserLocalStorage does not support loading non-JSON model ' +\n          'topology yet.');\n    }\n\n    const out: ModelArtifacts = {};\n\n    // Load topology.\n    const topology = JSON.parse(this.LS.getItem(this.keys.topology));\n    if (topology == null) {\n      throw new Error(\n          `In local storage, the topology of model '${this.modelPath}' ` +\n          `is missing.`);\n    }\n    out.modelTopology = topology;\n\n    // Load weight specs.\n    const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));\n    if (weightSpecs == null) {\n      throw new Error(\n          `In local storage, the weight specs of model '${this.modelPath}' ` +\n          `are missing.`);\n    }\n    out.weightSpecs = weightSpecs;\n\n    // Load weight data.\n    const weightDataBase64 = this.LS.getItem(this.keys.weightData);\n    if (weightDataBase64 == null) {\n      throw new Error(\n          `In local storage, the binary weight values of model ` +\n          `'${this.modelPath}' are missing.`);\n    }\n    out.weightData = base64StringToArrayBuffer(weightDataBase64);\n\n    return out;\n  }\n}\n\nexport const localStorageRouter: IORouter = (url: string|string[]) => {\n  if (!ENV.get('IS_BROWSER')) {\n    return null;\n  } else {\n    if (!Array.isArray(url) &&\n        url.startsWith(BrowserLocalStorage.URL_SCHEME)) {\n      return browserLocalStorage(\n          url.slice(BrowserLocalStorage.URL_SCHEME.length));\n    } else {\n      return null;\n    }\n  }\n};\nIORouterRegistry.registerSaveRouter(localStorageRouter);\nIORouterRegistry.registerLoadRouter(localStorageRouter);\n\n/**\n * Factory function for local storage IOHandler.\n *\n * This `IOHandler` supports both `save` and `load`.\n *\n * For each model's saved artifacts, four items are saved to local storage.\n *   - `${PATH_SEPARATOR}/${modelPath}/info`: Contains meta-info about the\n *     model, such as date saved, type of the topology, size in bytes, etc.\n *   - `${PATH_SEPARATOR}/${modelPath}/topology`: Model topology. For Keras-\n *     style models, this is a stringized JSON.\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_specs`: Weight specs of the\n *     model, can be used to decode the saved binary weight values (see\n *     item below).\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_data`: Concatenated binary\n *     weight values, stored as a base64-encoded string.\n *\n * Saving may throw an `Error` if the total size of the artifacts exceed the\n * browser-specific quota.\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `IOHandler`, which can be used with, e.g.,\n *   `tf.Model.save`.\n */\nexport function browserLocalStorage(modelPath: string): IOHandler {\n  return new BrowserLocalStorage(modelPath);\n}\n\nexport class BrowserLocalStorageManager implements ModelStoreManager {\n  private readonly LS: Storage;\n\n  constructor() {\n    assert(ENV.get('IS_BROWSER'), 'Current environment is not a web browser');\n    assert(\n        typeof window.localStorage !== 'undefined',\n        'Current browser does not appear to support localStorage');\n    this.LS = window.localStorage;\n  }\n\n  async listModels(): Promise<{[path: string]: ModelArtifactsInfo}> {\n    const out: {[path: string]: ModelArtifactsInfo} = {};\n    const prefix = PATH_PREFIX + PATH_SEPARATOR;\n    const suffix = PATH_SEPARATOR + INFO_SUFFIX;\n    for (let i = 0; i < this.LS.length; ++i) {\n      const key = this.LS.key(i);\n      if (key.startsWith(prefix) && key.endsWith(suffix)) {\n        const modelPath = getModelPathFromKey(key);\n        out[modelPath] = JSON.parse(this.LS.getItem(key)) as ModelArtifactsInfo;\n      }\n    }\n    return out;\n  }\n\n  async removeModel(path: string): Promise<ModelArtifactsInfo> {\n    path = maybeStripScheme(path);\n    const keys = getModelKeys(path);\n    if (this.LS.getItem(keys.info) == null) {\n      throw new Error(`Cannot find model at path '${path}'`);\n    }\n    const info = JSON.parse(this.LS.getItem(keys.info)) as ModelArtifactsInfo;\n\n    this.LS.removeItem(keys.info);\n    this.LS.removeItem(keys.topology);\n    this.LS.removeItem(keys.weightSpecs);\n    this.LS.removeItem(keys.weightData);\n    return info;\n  }\n}\n\nif (ENV.get('IS_BROWSER')) {\n  // Wrap the construction and registration, to guard against browsers that\n  // don't support Local Storage.\n  try {\n    ModelStoreManagerRegistry.registerManager(\n        BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());\n  } catch (err) {\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * IOHandlers related to files, such as browser-triggered file downloads,\n * user-selected files in browser.\n */\n\nimport {ENV} from '../environment';\nimport {basename, concatenateArrayBuffers, getModelArtifactsInfoForJSON} from './io_utils';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, SaveResult, WeightsManifestConfig, WeightsManifestEntry} from './types';\n\nconst DEFAULT_FILE_NAME_PREFIX = 'model';\nconst DEFAULT_JSON_EXTENSION_NAME = '.json';\nconst DEFAULT_WEIGHT_DATA_EXTENSION_NAME = '.weights.bin';\n\nexport class BrowserDownloads implements IOHandler {\n  private readonly modelTopologyFileName: string;\n  private readonly weightDataFileName: string;\n  private readonly jsonAnchor: HTMLAnchorElement;\n  private readonly weightDataAnchor: HTMLAnchorElement;\n\n  static readonly URL_SCHEME = 'downloads://';\n\n  constructor(fileNamePrefix?: string) {\n    if (!ENV.get('IS_BROWSER')) {\n      // TODO(cais): Provide info on what IOHandlers are available under the\n      //   current environment.\n      throw new Error(\n          'browserDownloads() cannot proceed because the current environment ' +\n          'is not a browser.');\n    }\n\n    if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {\n      fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);\n    }\n    if (fileNamePrefix == null || fileNamePrefix.length === 0) {\n      fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;\n    }\n\n    this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;\n    this.weightDataFileName =\n        fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;\n  }\n\n  async save(modelArtifacts: ModelArtifacts): Promise<SaveResult> {\n    const weightsURL = window.URL.createObjectURL(new Blob(\n        [modelArtifacts.weightData], {type: 'application/octet-stream'}));\n\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\n          'BrowserDownloads.save() does not support saving model topology ' +\n          'in binary formats yet.');\n    } else {\n      const weightsManifest: WeightsManifestConfig = [{\n        paths: ['./' + this.weightDataFileName],\n        weights: modelArtifacts.weightSpecs\n      }];\n      const modelTopologyAndWeightManifest = {\n        modelTopology: modelArtifacts.modelTopology,\n        weightsManifest\n      };\n      const modelTopologyAndWeightManifestURL =\n          window.URL.createObjectURL(new Blob(\n              [JSON.stringify(modelTopologyAndWeightManifest)],\n              {type: 'application/json'}));\n\n      // If anchor elements are not provided, create them without attaching them\n      // to parents, so that the downloaded file names can be controlled.\n      const jsonAnchor = this.jsonAnchor == null ? document.createElement('a') :\n                                                   this.jsonAnchor;\n      jsonAnchor.download = this.modelTopologyFileName;\n      jsonAnchor.href = modelTopologyAndWeightManifestURL;\n      // Trigger downloads by calling the `click` methods on the download\n      // anchors.\n      jsonAnchor.click();\n\n      if (modelArtifacts.weightData != null) {\n        const weightDataAnchor = this.weightDataAnchor == null ?\n            document.createElement('a') :\n            this.weightDataAnchor;\n        weightDataAnchor.download = this.weightDataFileName;\n        weightDataAnchor.href = weightsURL;\n        weightDataAnchor.click();\n      }\n\n      return {modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts)};\n    }\n  }\n}\n\nclass BrowserFiles implements IOHandler {\n  private readonly files: File[];\n\n  constructor(files: File[]) {\n    if (files == null || files.length < 1) {\n      throw new Error(\n          `When calling browserFiles, at least 1 file is required, ` +\n          `but received ${files}`);\n    }\n    this.files = files;\n  }\n\n  async load(): Promise<ModelArtifacts> {\n    const jsonFile = this.files[0];\n    const weightFiles = this.files.slice(1);\n\n    return new Promise<ModelArtifacts>((resolve, reject) => {\n      const jsonReader = new FileReader();\n      jsonReader.onload = (event: Event) => {\n        // tslint:disable-next-line:no-any\n        const modelJSON = JSON.parse((event.target as any).result);\n        const modelTopology = modelJSON.modelTopology as {};\n        if (modelTopology == null) {\n          reject(new Error(\n              `modelTopology field is missing from file ${jsonFile.name}`));\n          return;\n        }\n\n        if (weightFiles.length === 0) {\n          resolve({modelTopology});\n        }\n\n        const weightsManifest =\n            modelJSON.weightsManifest as WeightsManifestConfig;\n        if (weightsManifest == null) {\n          reject(new Error(\n              `weightManifest field is missing from file ${jsonFile.name}`));\n          return;\n        }\n\n        let pathToFile: {[path: string]: File};\n        try {\n          pathToFile =\n              this.checkManifestAndWeightFiles(weightsManifest, weightFiles);\n        } catch (err) {\n          reject(err);\n          return;\n        }\n\n        const weightSpecs: WeightsManifestEntry[] = [];\n        const paths: string[] = [];\n        const perFileBuffers: ArrayBuffer[] = [];\n        weightsManifest.forEach(weightsGroup => {\n          weightsGroup.paths.forEach(path => {\n            paths.push(path);\n            perFileBuffers.push(null);\n          });\n          weightSpecs.push(...weightsGroup.weights);\n        });\n\n        weightsManifest.forEach(weightsGroup => {\n          weightsGroup.paths.forEach(path => {\n            const weightFileReader = new FileReader();\n            weightFileReader.onload = (event: Event) => {\n              // tslint:disable-next-line:no-any\n              const weightData = (event.target as any).result as ArrayBuffer;\n              const index = paths.indexOf(path);\n              perFileBuffers[index] = weightData;\n              if (perFileBuffers.indexOf(null) === -1) {\n                resolve({\n                  modelTopology,\n                  weightSpecs,\n                  weightData: concatenateArrayBuffers(perFileBuffers),\n                });\n              }\n            };\n            weightFileReader.onerror = error =>\n                reject(`Failed to weights data from file of path '${path}'.`);\n            weightFileReader.readAsArrayBuffer(pathToFile[path]);\n          });\n        });\n      };\n      jsonReader.onerror = error => reject(\n          `Failed to read model topology and weights manifest JSON ` +\n          `from file '${jsonFile.name}'. BrowserFiles supports loading ` +\n          `Keras-style tf.Model artifacts only.`);\n      jsonReader.readAsText(jsonFile);\n    });\n  }\n\n  /**\n   * Check the compatibility between weights manifest and weight files.\n   */\n  private checkManifestAndWeightFiles(\n      manifest: WeightsManifestConfig, files: File[]): {[path: string]: File} {\n    const basenames: string[] = [];\n    const fileNames = files.map(file => basename(file.name));\n    const pathToFile: {[path: string]: File} = {};\n    for (const group of manifest) {\n      group.paths.forEach(path => {\n        const pathBasename = basename(path);\n        if (basenames.indexOf(pathBasename) !== -1) {\n          throw new Error(\n              `Duplicate file basename found in weights manifest: ` +\n              `'${pathBasename}'`);\n        }\n        basenames.push(pathBasename);\n        if (fileNames.indexOf(pathBasename) === -1) {\n          throw new Error(\n              `Weight file with basename '${pathBasename}' is not provided.`);\n        } else {\n          pathToFile[path] = files[fileNames.indexOf(pathBasename)];\n        }\n      });\n    }\n\n    if (basenames.length !== files.length) {\n      throw new Error(\n          `Mismatch in the number of files in weights manifest ` +\n          `(${basenames.length}) and the number of weight files provided ` +\n          `(${files.length}).`);\n    }\n    return pathToFile;\n  }\n}\n\nexport const browserDownloadsRouter: IORouter = (url: string|string[]) => {\n  if (!ENV.get('IS_BROWSER')) {\n    return null;\n  } else {\n    if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {\n      return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));\n    } else {\n      return null;\n    }\n  }\n};\nIORouterRegistry.registerSaveRouter(browserDownloadsRouter);\n\n/**\n * Creates an IOHandler that triggers file downloads from the browser.\n *\n * The returned `IOHandler` instance can be used as model exporting methods such\n * as `tf.Model.save` and supports only saving.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * const saveResult = await model.save('downloads://mymodel'));\n * // This will trigger downloading of two files:\n * //   'mymodel.json' and 'mymodel.weights.bin'.\n * console.log(saveResult);\n * ```\n *\n * @param fileNamePrefix Prefix name of the files to be downloaded. For use with\n *   `tf.Model`, `fileNamePrefix` should follow either of the following two\n *   formats:\n *   1. `null` or `undefined`, in which case the default file\n *      names will be used:\n *      - 'model.json' for the JSON file containing the model topology and\n *        weights manifest.\n *      - 'model.weights.bin' for the binary file containing the binary weight\n *        values.\n *   2. A single string or an Array of a single string, as the file name prefix.\n *      For example, if `'foo'` is provided, the downloaded JSON\n *      file and binary weights file will be named 'foo.json' and\n *      'foo.weights.bin', respectively.\n * @param config Additional configuration for triggering downloads.\n * @returns An instance of `BrowserDownloads` `IOHandler`.\n */\n/** @doc {heading: 'Models', subheading: 'Loading', namespace: 'io'} */\nexport function browserDownloads(fileNamePrefix = 'model'): IOHandler {\n  return new BrowserDownloads(fileNamePrefix);\n}\n\n/**\n * Creates an IOHandler that loads model artifacts from user-selected files.\n *\n * This method can be used for loading from files such as user-selected files\n * in the browser.\n * When used in conjunction with `tf.loadModel`, an instance of `tf.Model`\n * (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * // Note: This code snippet won't run properly without the actual file input\n * //   elements in the HTML DOM.\n *\n * // Suppose there are two HTML file input (`<input type=\"file\" ...>`)\n * // elements.\n * const uploadJSONInput = document.getElementById('upload-json');\n * const uploadWeightsInput = document.getElementById('upload-weights');\n * const model = await tfl.loadModel(tf.io.browserFiles(\n *     [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));\n * ```\n *\n * @param files `File`s to load from. Currently, this function supports only\n *   loading from files that contain Keras-style models (i.e., `tf.Model`s), for\n *   which an `Array` of `File`s is expected (in that order):\n *   - A JSON file containing the model topology and weight manifest.\n *   - Optionally, One or more binary files containing the binary weights.\n *     These files must have names that match the paths in the `weightsManifest`\n *     contained by the aforementioned JSON file, or errors will be thrown\n *     during loading. These weights files have the same format as the ones\n *     generated by `tensorflowjs_converter` that comes with the `tensorflowjs`\n *     Python PIP package. If no weights files are provided, only the model\n *     topology will be loaded from the JSON file above.\n * @returns An instance of `Files` `IOHandler`.\n */\n/** @doc {heading: 'Models', subheading: 'Loading', namespace: 'io'} */\nexport function browserFiles(files: File[]): IOHandler {\n  return new BrowserFiles(files);\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {NamedTensorMap} from '../tensor_types';\nimport * as util from '../util';\n\nimport {decodeWeights} from './io_utils';\nimport {DTYPE_VALUE_SIZE_MAP, WeightsManifestConfig, WeightsManifestEntry} from './types';\n\n/**\n * Reads binary weights data from a number of URLs.\n *\n * @param fetchURLs URLs to send the HTTP requests at, using `fetch` calls.\n * @param requestOptions RequestInit (options) for the HTTP requests.\n * @param fetchFunc Optional overriding value for the `window.fetch` function.\n * @returns A `Promise` of an Array of `ArrayBuffer`. The Array has the same\n *   length as `fetchURLs`.\n */\nexport async function loadWeightsAsArrayBuffer(\n    fetchURLs: string[], requestOptions?: RequestInit, fetchFunc?: Function):\n  Promise<ArrayBuffer[]> {\n  if (fetchFunc == null) {\n    fetchFunc = fetch;\n  }\n\n  // Create the requests for all of the weights in parallel.\n  const requests = fetchURLs.map(\n      fetchURL => fetchFunc(fetchURL, requestOptions));\n  const responses = await Promise.all(requests);\n  const buffers =\n      await Promise.all(responses.map(response => response.arrayBuffer()));\n  return buffers;\n}\n\n/**\n * Reads a weights manifest JSON configuration, fetches the weights and\n * returns them as `Tensor`s.\n *\n * @param manifest The weights manifest JSON.\n * @param filePathPrefix The path prefix for filenames given in the manifest.\n *     Defaults to the empty string.\n * @param weightNames The names of the weights to be fetched.\n */\nexport async function loadWeights(\n    manifest: WeightsManifestConfig,\n    filePathPrefix = '',\n    weightNames?: string[],\n    requestOptions?: RequestInit): Promise<NamedTensorMap> {\n  // TODO(nsthorat): Groups are currently fetched atomically. If you need a\n  // single weight from a group, the whole group will be fetched. At a future\n  // date, we should support fetching only the individual shards within a\n  // group that are needed to reconstruct the requested weight.\n  // TODO(cais): Use `decodeWeights` for implementation.\n\n  const fetchWeights = (fetchUrls: string[]) =>\n    loadWeightsAsArrayBuffer(fetchUrls, requestOptions);\n  const loadWeights = weightsLoaderFactory(fetchWeights);\n\n  return loadWeights(manifest, filePathPrefix, weightNames);\n}\n\n/**\n * Creates a function, which reads a weights manifest JSON configuration,\n * fetches the weight files using the specified function and returns them as\n * `Tensor`s.\n *\n * ```js\n * // example for creating a nodejs weight loader, which reads the weight files\n * // from disk using fs.readFileSync\n *\n * import * as fs from 'fs'\n *\n * const fetchWeightsFromDisk = (filePaths: string[]) =>\n *   filePaths.map(filePath => fs.readFileSync(filePath).buffer)\n *\n * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n *\n * const manifest = JSON.parse(\n *   fs.readFileSync('./my_model-weights_manifest').toString()\n * )\n * const weightMap = await loadWeights(manifest, './')\n * ```\n * @param fetchWeightsFunction The function used for fetching the weight files.\n * @returns Weight loading function.\n */\nexport function weightsLoaderFactory(\n  fetchWeightsFunction: (fetchUrls: string[]) => Promise<ArrayBuffer[]>\n): (\n  manifest: WeightsManifestConfig,\n  filePathPrefix?: string,\n  weightNames?: string[]\n) => Promise<NamedTensorMap> {\n\n  return async (\n    manifest: WeightsManifestConfig,\n    filePathPrefix = '',\n    weightNames?: string[]\n  ): Promise<NamedTensorMap> => {\n\n    // Collect all the groups, weights, and their relative offsets to be\n    // fetched.\n    const groupIndicesToFetchMap = manifest.map(() => false);\n    const groupWeightsToFetch: {\n      [group: number]: Array<{\n        manifestEntry: WeightsManifestEntry; groupOffset: number;\n        sizeBytes: number;\n      }>\n    } = {};\n    const weightsFound = weightNames != null\n      ? weightNames.map(() => false)\n      : [];\n    const allManifestWeightNames: string[] = [];\n    manifest.forEach((manifestGroupConfig, groupIndex) => {\n      let groupOffset = 0;\n      manifestGroupConfig.weights.forEach(weightsEntry => {\n        const rawDtype = ('quantization' in weightsEntry) ?\n            weightsEntry.quantization.dtype :\n            weightsEntry.dtype;\n\n        const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] *\n            util.sizeFromShape(weightsEntry.shape);\n\n        const enqueueWeightsForFetchingFn = () => {\n          groupIndicesToFetchMap[groupIndex] = true;\n          if (groupWeightsToFetch[groupIndex] == null) {\n            groupWeightsToFetch[groupIndex] = [];\n          }\n\n          groupWeightsToFetch[groupIndex].push({\n            manifestEntry: weightsEntry,\n            groupOffset,\n            sizeBytes: weightsBytes\n          });\n        };\n\n        if (weightNames != null) {\n          weightNames.forEach((weightName, weightIndex) => {\n            if (weightName === weightsEntry.name) {\n              enqueueWeightsForFetchingFn();\n              weightsFound[weightIndex] = true;\n            }\n          });\n        } else {\n          enqueueWeightsForFetchingFn();\n        }\n\n        allManifestWeightNames.push(weightsEntry.name);\n        groupOffset += weightsBytes;\n      });\n    });\n\n    if (!weightsFound.every(found => found)) {\n      const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);\n      throw new Error(\n          `Could not find weights in manifest with names: ` +\n          `${weightsNotFound.join(', ')}. \\n` +\n          `Manifest JSON has weights with names: ` +\n          `${allManifestWeightNames.join(', ')}.`);\n    }\n\n    // Convert the one-hot boolean groupId => shouldFetch map to a list of group\n    // IDs.\n    const groupIndicesToFetch =\n        groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {\n          if (shouldFetch) {\n            accumulator.push(i);\n          }\n          return accumulator;\n        }, []);\n\n    const fetchUrls: string[] = [];\n    groupIndicesToFetch.forEach(i => {\n      manifest[i].paths.forEach(filepath => {\n        const fetchUrl = filePathPrefix +\n            (!filePathPrefix.endsWith('/') ? '/' : '') + filepath;\n        fetchUrls.push(fetchUrl);\n      });\n    });\n    const buffers = await fetchWeightsFunction(fetchUrls);\n\n    const weightsTensorMap: NamedTensorMap = {};\n    let bufferIndexOffset = 0;\n    groupIndicesToFetch.forEach(i => {\n      const numBuffers = manifest[i].paths.length;\n\n      let groupBytes = 0;\n      for (let i = 0; i < numBuffers; i++) {\n        groupBytes += buffers[bufferIndexOffset + i].byteLength;\n      }\n\n      // Create a buffer for the whole group.\n      const groupBuffer = new ArrayBuffer(groupBytes);\n      const groupByteBuffer = new Uint8Array(groupBuffer);\n      let groupBufferOffset = 0;\n      for (let i = 0; i < numBuffers; i++) {\n        const buffer = new Uint8Array(buffers[bufferIndexOffset + i]);\n        groupByteBuffer.set(buffer, groupBufferOffset);\n        groupBufferOffset += buffer.byteLength;\n      }\n\n      const weightsEntries = groupWeightsToFetch[i];\n      weightsEntries.forEach(weightsEntry => {\n        const byteBuffer = groupBuffer.slice(\n            weightsEntry.groupOffset,\n            weightsEntry.groupOffset + weightsEntry.sizeBytes);\n        const nameToTensorMap =\n            decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);\n        for (const name in nameToTensorMap) {\n          weightsTensorMap[name] = nameToTensorMap[name];\n        }\n      });\n\n      bufferIndexOffset += numBuffers;\n    });\n\n    return weightsTensorMap;\n  };\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * IOHandler implementations based on HTTP requests in the web browser.\n *\n * Uses [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n */\n\nimport {assert} from '../util';\nimport {concatenateArrayBuffers, getModelArtifactsInfoForJSON} from './io_utils';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, SaveResult, WeightsManifestConfig, WeightsManifestEntry} from './types';\nimport {loadWeightsAsArrayBuffer} from './weights_loader';\n\nexport class BrowserHTTPRequest implements IOHandler {\n  protected readonly path: string|string[];\n  protected readonly requestInit: RequestInit;\n\n  private readonly fetchFunc: Function;\n\n  readonly DEFAULT_METHOD = 'POST';\n\n  static readonly URL_SCHEME_REGEX = /^https?:\\/\\//;\n\n  constructor(\n      path: string|string[], requestInit?: RequestInit,\n      private readonly weightPathPrefix?: string, fetchFunc?: Function) {\n    if (fetchFunc == null) {\n      if (typeof fetch === 'undefined') {\n        throw new Error(\n            'browserHTTPRequest is not supported outside the web browser ' +\n            'without a fetch polyfill.');\n      }\n      // Make sure fetch is always bound to window (the\n      // original object) when available.\n      this.fetchFunc =\n          fetch.bind(typeof window === 'undefined' ? null : window);\n    } else {\n      assert(\n          typeof fetchFunc === 'function',\n          'Must pass a function that matches the signature of ' +\n              '`fetch` (see ' +\n              'https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)');\n      this.fetchFunc = fetchFunc;\n    }\n\n    assert(\n        path != null && path.length > 0,\n        'URL path for browserHTTPRequest must not be null, undefined or ' +\n            'empty.');\n\n    if (Array.isArray(path)) {\n      assert(\n          path.length === 2,\n          'URL paths for browserHTTPRequest must have a length of 2, ' +\n              `(actual length is ${path.length}).`);\n    }\n    this.path = path;\n\n    if (requestInit != null && requestInit.body != null) {\n      throw new Error(\n          'requestInit is expected to have no pre-existing body, but has one.');\n    }\n    this.requestInit = requestInit || {};\n  }\n\n  async save(modelArtifacts: ModelArtifacts): Promise<SaveResult> {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\n          'BrowserHTTPRequest.save() does not support saving model topology ' +\n          'in binary formats yet.');\n    }\n\n    const init = Object.assign({method: this.DEFAULT_METHOD}, this.requestInit);\n    init.body = new FormData();\n\n    const weightsManifest: WeightsManifestConfig = [{\n      paths: ['./model.weights.bin'],\n      weights: modelArtifacts.weightSpecs,\n    }];\n    const modelTopologyAndWeightManifest = {\n      modelTopology: modelArtifacts.modelTopology,\n      weightsManifest\n    };\n\n    init.body.append(\n        'model.json',\n        new Blob(\n            [JSON.stringify(modelTopologyAndWeightManifest)],\n            {type: 'application/json'}),\n        'model.json');\n\n    if (modelArtifacts.weightData != null) {\n      init.body.append(\n          'model.weights.bin',\n          new Blob(\n              [modelArtifacts.weightData], {type: 'application/octet-stream'}),\n          'model.weights.bin');\n    }\n\n    const response = await this.getFetchFunc()(this.path as string, init);\n\n    if (response.ok) {\n      return {\n        modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),\n        responses: [response],\n      };\n    } else {\n      throw new Error(\n          `BrowserHTTPRequest.save() failed due to HTTP response status ` +\n          `${response.status}.`);\n    }\n  }\n\n  /**\n   * Load model artifacts via HTTP request(s).\n   *\n   * See the documentation to `browserHTTPRequest` for details on the saved\n   * artifacts.\n   *\n   * @returns The loaded model artifacts (if loading succeeds).\n   */\n  async load(): Promise<ModelArtifacts> {\n    return Array.isArray(this.path) ? this.loadBinaryModel() :\n                                      this.loadJSONModel();\n  }\n\n  /**\n   * Loads the model topology file and build the in memory graph of the model.\n   */\n  private async loadBinaryTopology(): Promise<ArrayBuffer> {\n    try {\n      const response =\n          await this.getFetchFunc()(this.path[0], this.requestInit);\n      if (!response.ok) {\n        throw new Error(\n            `BrowserHTTPRequest.load() failed due to HTTP response: ${\n                response.statusText}`);\n      }\n      return await response.arrayBuffer();\n    } catch (error) {\n      throw new Error(`${this.path[0]} not found. ${error}`);\n    }\n  }\n\n  protected async loadBinaryModel(): Promise<ModelArtifacts> {\n    const graphPromise = this.loadBinaryTopology();\n    const manifestPromise =\n        await this.getFetchFunc()(this.path[1], this.requestInit);\n    if (!manifestPromise.ok) {\n      throw new Error(`BrowserHTTPRequest.load() failed due to HTTP response: ${\n          manifestPromise.statusText}`);\n    }\n\n    const results = await Promise.all([graphPromise, manifestPromise]);\n    const [modelTopology, weightsManifestResponse] = results;\n\n    const weightsManifest =\n        await weightsManifestResponse.json() as WeightsManifestConfig;\n\n    let weightSpecs: WeightsManifestEntry[];\n    let weightData: ArrayBuffer;\n    if (weightsManifest != null) {\n      const results = await this.loadWeights(weightsManifest);\n      [weightSpecs, weightData] = results;\n    }\n\n    return {modelTopology, weightSpecs, weightData};\n  }\n\n  protected async loadJSONModel(): Promise<ModelArtifacts> {\n    const modelConfigRequest =\n        await this.getFetchFunc()(this.path as string, this.requestInit);\n    if (!modelConfigRequest.ok) {\n      throw new Error(`BrowserHTTPRequest.load() failed due to HTTP response: ${\n          modelConfigRequest.statusText}`);\n    }\n    const modelConfig = await modelConfigRequest.json();\n    const modelTopology = modelConfig['modelTopology'];\n    const weightsManifest = modelConfig['weightsManifest'];\n\n    // We do not allow both modelTopology and weightsManifest to be missing.\n    if (modelTopology == null && weightsManifest == null) {\n      throw new Error(\n          `The JSON from HTTP path ${this.path} contains neither model ` +\n          `topology or manifest for weights.`);\n    }\n\n    let weightSpecs: WeightsManifestEntry[];\n    let weightData: ArrayBuffer;\n    if (weightsManifest != null) {\n      const weightsManifest =\n          modelConfig['weightsManifest'] as WeightsManifestConfig;\n      const results = await this.loadWeights(weightsManifest);\n      [weightSpecs, weightData] = results;\n    }\n\n    return {modelTopology, weightSpecs, weightData};\n  }\n\n  private async loadWeights(weightsManifest: WeightsManifestConfig):\n      Promise<[WeightsManifestEntry[], ArrayBuffer]> {\n    const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;\n    const [prefix, suffix] = parseUrl(weightPath);\n    const pathPrefix = this.weightPathPrefix || prefix;\n\n    const weightSpecs = [];\n    for (const entry of weightsManifest) {\n      weightSpecs.push(...entry.weights);\n    }\n\n    const fetchURLs: string[] = [];\n    weightsManifest.forEach(weightsGroup => {\n      weightsGroup.paths.forEach(path => {\n        fetchURLs.push(pathPrefix + path + suffix);\n      });\n    });\n\n    return [\n      weightSpecs,\n      concatenateArrayBuffers(await loadWeightsAsArrayBuffer(\n          fetchURLs, this.requestInit, this.getFetchFunc()))\n    ];\n  }\n\n  /**\n   * Helper method to get the `fetch`-like function set for this instance.\n   *\n   * This is mainly for avoiding confusion with regard to what context\n   * the `fetch`-like function is bound to. In the default (browser) case,\n   * the function will be bound to `window`, instead of `this`.\n   */\n  private getFetchFunc() {\n    return this.fetchFunc;\n  }\n}\n\n/**\n * Extract the prefix and suffix of the url, where the prefix is the path before\n * the last file, and suffix is the search params after the last file.\n * ```\n * const url = 'http://tfhub.dev/model/1/tensorflowjs_model.pb?tfjs-format=file'\n * [prefix, suffix] = parseUrl(url)\n * // prefix = 'http://tfhub.dev/model/1/'\n * // suffix = '?tfjs-format=file'\n * ```\n * @param url the model url to be parsed.\n */\nexport function parseUrl(url: string): [string, string] {\n  const lastSlash = url.lastIndexOf('/');\n  const lastSearchParam = url.lastIndexOf('?');\n  const prefix = url.substring(0, lastSlash);\n  const suffix =\n      lastSearchParam > lastSlash ? url.substring(lastSearchParam) : '';\n  return [prefix + '/', suffix];\n}\n\nexport function isHTTPScheme(url: string): boolean {\n  return url.match(BrowserHTTPRequest.URL_SCHEME_REGEX) != null;\n}\n\nexport const httpRequestRouter: IORouter = (url: string|string[]) => {\n  if (typeof fetch === 'undefined') {\n    // browserHTTPRequest uses `fetch`, if one wants to use it in node.js\n    // they have to setup a global fetch polyfill.\n    return null;\n  } else {\n    let isHTTP = true;\n    if (Array.isArray(url)) {\n      isHTTP = url.every(urlItem => isHTTPScheme(urlItem));\n    } else {\n      isHTTP = isHTTPScheme(url);\n    }\n    if (isHTTP) {\n      return browserHTTPRequest(url);\n    }\n  }\n  return null;\n};\nIORouterRegistry.registerSaveRouter(httpRequestRouter);\nIORouterRegistry.registerLoadRouter(httpRequestRouter);\n\n/**\n * Creates an IOHandler subtype that sends model artifacts to HTTP server.\n *\n * An HTTP request of the `multipart/form-data` mime type will be sent to the\n * `path` URL. The form data includes artifacts that represent the topology\n * and/or weights of the model. In the case of Keras-style `tf.Model`, two\n * blobs (files) exist in form-data:\n *   - A JSON file consisting of `modelTopology` and `weightsManifest`.\n *   - A binary weights file consisting of the concatenated weight values.\n * These files are in the same format as the one generated by\n * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).\n *\n * The following code snippet exemplifies the client-side code that uses this\n * function:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save(tf.io.browserHTTPRequest(\n *     'http://model-server:5000/upload', {method: 'PUT'}));\n * console.log(saveResult);\n * ```\n *\n * If the default `POST` method is to be used, without any custom parameters\n * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:\n *\n * ```js\n * const saveResult = await model.save('http://model-server:5000/upload');\n * ```\n *\n * The following Python code snippet based on the\n * [flask](https://github.com/pallets/flask) server framework implements a\n * server that can receive the request. Upon receiving the model artifacts\n * via the requst, this particular server reconsistutes instances of\n * [Keras Models](https://keras.io/models/model/) in memory.\n *\n * ```python\n * # pip install -U flask flask-cors tensorflow tensorflowjs\n *\n * from __future__ import absolute_import\n * from __future__ import division\n * from __future__ import print_function\n *\n * import io\n *\n * from flask import Flask, Response, request\n * from flask_cors import CORS, cross_origin\n * import tensorflow as tf\n * import tensorflowjs as tfjs\n * import werkzeug.formparser\n *\n *\n * class ModelReceiver(object):\n *\n *   def __init__(self):\n *     self._model = None\n *     self._model_json_bytes = None\n *     self._model_json_writer = None\n *     self._weight_bytes = None\n *     self._weight_writer = None\n *\n *   @property\n *   def model(self):\n *     self._model_json_writer.flush()\n *     self._weight_writer.flush()\n *     self._model_json_writer.seek(0)\n *     self._weight_writer.seek(0)\n *\n *     json_content = self._model_json_bytes.read()\n *     weights_content = self._weight_bytes.read()\n *     return tfjs.converters.deserialize_keras_model(\n *         json_content,\n *         weight_data=[weights_content],\n *         use_unique_name_scope=True)\n *\n *   def stream_factory(self,\n *                      total_content_length,\n *                      content_type,\n *                      filename,\n *                      content_length=None):\n *     # Note: this example code is *not* thread-safe.\n *     if filename == 'model.json':\n *       self._model_json_bytes = io.BytesIO()\n *       self._model_json_writer = io.BufferedWriter(self._model_json_bytes)\n *       return self._model_json_writer\n *     elif filename == 'model.weights.bin':\n *       self._weight_bytes = io.BytesIO()\n *       self._weight_writer = io.BufferedWriter(self._weight_bytes)\n *       return self._weight_writer\n *\n *\n * def main():\n *   app = Flask('model-server')\n *   CORS(app)\n *   app.config['CORS_HEADER'] = 'Content-Type'\n *\n *   model_receiver = ModelReceiver()\n *\n *   @app.route('/upload', methods=['POST'])\n *   @cross_origin()\n *   def upload():\n *     print('Handling request...')\n *     werkzeug.formparser.parse_form_data(\n *         request.environ, stream_factory=model_receiver.stream_factory)\n *     print('Received model:')\n *     with tf.Graph().as_default(), tf.Session():\n *       model = model_receiver.model\n *       model.summary()\n *       # You can perform `model.predict()`, `model.fit()`,\n *       # `model.evaluate()` etc. here.\n *     return Response(status=200)\n *\n *   app.run('localhost', 5000)\n *\n *\n * if __name__ == '__main__':\n *   main()\n * ```\n *\n * @param path A single URL path or an Array of URL  paths.\n *   Currently, only a single URL path is supported. Array input is reserved\n *   for future development.\n *   Can be an absolute HTTP path (e.g.,\n *   'http://localhost:8000/model-upload)') or a relative path (e.g.,\n *   './model-upload').\n * @param requestInit Request configurations to be used when sending\n *    HTTP request to server using `fetch`. It can contain fields such as\n *    `method`, `credentials`, `headers`, `mode`, etc. See\n *    https://developer.mozilla.org/en-US/docs/Web/API/Request/Request\n *    for more information. `requestInit` must not have a body, because the\n * body will be set by TensorFlow.js. File blobs representing the model\n * topology (filename: 'model.json') and the weights of the model (filename:\n * 'model.weights.bin') will be appended to the body. If `requestInit` has a\n * `body`, an Error will be thrown.\n * @param weightPathPrefix Optional, this specifies the path prefix for weight\n *   files, by default this is calculated from the path param.\n * @param fetchFunc Optional, custom `fetch` function. E.g., in Node.js,\n *   the `fetch` from node-fetch can be used here.\n * @returns An instance of `IOHandler`.\n */\nexport function browserHTTPRequest(\n    path: string|string[], requestInit?: RequestInit, weightPathPrefix?: string,\n    fetchFunc?: Function): IOHandler {\n  return new BrowserHTTPRequest(path, requestInit, weightPathPrefix, fetchFunc);\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * IOHandlers that pass through the in-memory ModelArtifacts format.\n */\n\nimport {IOHandler, ModelArtifacts, SaveResult, WeightsManifestEntry} from './types';\n\nclass PassthroughLoader implements IOHandler {\n  constructor(\n      private readonly modelTopology?: {}|ArrayBuffer,\n      private readonly weightSpecs?: WeightsManifestEntry[],\n      private readonly weightData?: ArrayBuffer) {}\n\n  async load(): Promise<ModelArtifacts> {\n    let result = {};\n    if (this.modelTopology != null) {\n      result = {modelTopology: this.modelTopology, ...result};\n    }\n    if (this.weightSpecs != null && this.weightSpecs.length > 0) {\n      result = {weightSpecs: this.weightSpecs, ...result};\n    }\n    if (this.weightData != null && this.weightData.byteLength > 0) {\n      result = {weightData: this.weightData, ...result};\n    }\n    return result;\n  }\n}\n\nclass PassthroughSaver implements IOHandler {\n  constructor(\n      private readonly saveHandler:\n          (artifacts: ModelArtifacts) => Promise<SaveResult>) {}\n\n  async save(modelArtifacts: ModelArtifacts) {\n    return this.saveHandler(modelArtifacts);\n  }\n}\n\n/**\n * Creates an IOHandler that loads model artifacts from memory.\n *\n * When used in conjunction with `tf.loadModel`, an instance of `tf.Model`\n * (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * const model = await tf.loadModel(tf.io.fromMemory(\n *     modelTopology, weightSpecs, weightData));\n * ```\n *\n * @param modelTopology a object containing model topology (i.e., parsed from\n *   the JSON format).\n * @param weightSpecs An array of `WeightsManifestEntry` objects describing the\n *   names, shapes, types, and quantization of the weight data.\n * @param weightData A single `ArrayBuffer` containing the weight data,\n *   concatenated in the order described by the weightSpecs.\n *\n * @returns A passthrough `IOHandler` that simply loads the provided data.\n */\nexport function fromMemory(\n    modelTopology: {}, weightSpecs?: WeightsManifestEntry[],\n    weightData?: ArrayBuffer): IOHandler {\n  return new PassthroughLoader(modelTopology, weightSpecs, weightData);\n}\n\n/**\n * Creates an IOHandler that passes saved model artifacts to a callback.\n *\n * ```js\n * function handleSave(artifacts) {\n *   // ... do something with the artifacts ...\n *   return {modelArtifactsInfo: {...}, ...};\n * }\n *\n * const saveResult = model.save(tf.io.withSaveHandler(handleSave));\n * ```\n *\n * @param saveHandler A function that accepts a `ModelArtifacts` and returns a\n *     `SaveResult`.\n */\nexport function withSaveHandler(\n    saveHandler: (artifacts: ModelArtifacts) =>\n        Promise<SaveResult>): IOHandler {\n  return new PassthroughSaver(saveHandler);\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// Importing local_storage and indexed_db is necessary for the routers to be\n// registered.\nimport './indexed_db';\nimport './local_storage';\n\nimport {browserFiles} from './browser_files';\nimport {browserHTTPRequest, isHTTPScheme} from './browser_http';\nimport {concatenateArrayBuffers, decodeWeights, encodeWeights, getModelArtifactsInfoForJSON} from './io_utils';\nimport {fromMemory, withSaveHandler} from './passthrough';\nimport {IORouterRegistry} from './router_registry';\nimport {IOHandler, LoadHandler, ModelArtifacts, ModelStoreManager, SaveConfig, SaveHandler, SaveResult, WeightsManifestConfig, WeightsManifestEntry} from './types';\nimport {loadWeights, weightsLoaderFactory} from './weights_loader';\n\nconst registerSaveRouter = IORouterRegistry.registerSaveRouter;\nconst registerLoadRouter = IORouterRegistry.registerLoadRouter;\nconst getSaveHandlers = IORouterRegistry.getSaveHandlers;\nconst getLoadHandlers = IORouterRegistry.getLoadHandlers;\n\nexport {copyModel, listModels, moveModel, removeModel} from './model_management';\n\nexport {\n  browserFiles,\n  browserHTTPRequest,\n  concatenateArrayBuffers,\n  decodeWeights,\n  encodeWeights,\n  fromMemory,\n  getLoadHandlers,\n  getModelArtifactsInfoForJSON,\n  getSaveHandlers,\n  IOHandler,\n  isHTTPScheme,\n  LoadHandler,\n  loadWeights,\n  ModelArtifacts,\n  ModelStoreManager,\n  registerLoadRouter,\n  registerSaveRouter,\n  SaveConfig,\n  SaveHandler,\n  SaveResult,\n  WeightsManifestConfig,\n  WeightsManifestEntry,\n  weightsLoaderFactory,\n  withSaveHandler\n};\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor1D, Tensor2D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {oneHot} from './array_ops';\nimport {op} from './operation';\n\n/**\n * Computes the confusion matrix from true labels and predicted labels.\n *\n * ```js\n * const labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');\n * const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');\n * const numClasses = 3;\n * const out = tf.math.confusionMatrix(labels, predictions, numClasses);\n * out.print();\n * // Expected output matrix:\n * // [[2, 0, 0],\n * //  [0, 1, 1],\n * //  [0, 0, 1]]\n * ```\n *\n * @param labels The target labels, assumed to be 0-based integers\n *   for the classes. The shape is `[numExamples]`, where\n *   `numExamples` is the number of examples included.\n * @param predictions The predicted classes, assumed to be\n *   0-based integers for the classes. Must have the same shape as `labels`.\n * @param numClasses Number of all classes, as an integer.\n *   Its value must be larger than the largest element in `labels` and\n *   `predictions`.\n * @returns The confusion matrix as a int32-type 2D tensor. The value at\n *   row `r` and column `c` is the number of times examples of actual class\n *   `r` were predicted as class `c`.\n */\n/** @doc {heading: 'Operations', subheading: 'Evaluation'} */\nexport function confusionMatrix_(\n    labels: Tensor1D|TensorLike, predictions: Tensor1D|TensorLike,\n    numClasses: number): Tensor2D {\n  const $labels = convertToTensor(labels, 'labels', 'confusionMatrix');\n  const $predictions =\n      convertToTensor(predictions, 'predictions', 'confusionMatrix');\n\n  util.assert(\n      numClasses == null || numClasses > 0 && Number.isInteger(numClasses),\n      `If provided, numClasses must be a positive integer, ` +\n          `but got ${numClasses}`);\n  util.assert(\n      $labels.rank === 1,\n      `Expected the rank of labels to be 1, but got ${$labels.rank}`);\n  util.assert(\n      $predictions.rank === 1,\n      `Expected the rank of predictions to be 1, ` +\n          `but got ${$predictions.rank}`);\n  util.assert(\n      $labels.shape[0] === $predictions.shape[0],\n      `Mismatch in the number of examples: ` +\n          `${$labels.shape[0]} vs. ${$predictions.shape[0]}. ` +\n          `Labels and predictions should have the same number of elements.`);\n  util.assert(\n      numClasses > 0 && Number.isInteger(numClasses),\n      `numClasses is required to be a positive integer, but got ${numClasses}`);\n  // TODO(cais): In the future, if oneHot supports tensors inputs for\n  //   `numClasses`, `confusionMatrix` can make `numClasses` optional.\n\n  const oneHotLabels = oneHot($labels.asType('int32'), numClasses);\n  const oneHotPredictions = oneHot($predictions.asType('int32'), numClasses);\n  return oneHotLabels.transpose().matMul(oneHotPredictions).asType('int32');\n}\n\nexport const confusionMatrix = op({confusionMatrix_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {assert} from './util';\n\n/**\n * Types to support JSON-esque data structures internally.\n *\n * Internally ConfigDict's use camelCase keys and values where the\n * values are class names to be instantiated.  On the python side, these\n * will be snake_case.  Internally we allow Enums into the values for better\n * type safety, but these need to be converted to raw primitives (usually\n * strings) for round-tripping with python.\n *\n * toConfig returns the TS-friendly representation. model.toJSON() returns\n * the pythonic version as that's the portable format.  If you need to\n * python-ify a non-model level toConfig output, you'll need to use a\n * convertTsToPythonic from serialization_utils in -Layers.\n *\n */\nexport type ConfigDictValue =\n    boolean|number|string|null|ConfigDictArray|ConfigDict;\nexport interface ConfigDict {\n  [key: string]: ConfigDictValue;\n}\nexport interface ConfigDictArray extends Array<ConfigDictValue> {}\n\n/**\n * Type to represent the class-type of Serializable objects.\n *\n * Ie the class prototype with access to the constructor and any\n * static members/methods. Instance methods are not listed here.\n *\n * Source for this idea: https://stackoverflow.com/a/43607255\n */\nexport type SerializableConstructor<T extends Serializable> = {\n  // tslint:disable-next-line:no-any\n  new (...args: any[]): T; className: string; fromConfig: FromConfigMethod<T>;\n};\nexport type FromConfigMethod<T extends Serializable> =\n    (cls: SerializableConstructor<T>, config: ConfigDict) => T;\n\n/**\n * Serializable defines the serialization contract.\n *\n * TFJS requires serializable classes to return their className when asked\n * to avoid issues with minification.\n */\nexport abstract class Serializable {\n  /**\n   * Return the class name for this class to use in serialization contexts.\n   *\n   * Generally speaking this will be the same thing that constructor.name\n   * would have returned.  However, the class name needs to be robust\n   * against minification for serialization/deserialization to work properly.\n   *\n   * There's also places such as initializers.VarianceScaling, where\n   * implementation details between different languages led to different\n   * class hierarchies and a non-leaf node is used for serialization purposes.\n   */\n  getClassName(): string {\n    return (this.constructor as SerializableConstructor<Serializable>)\n        .className;\n  }\n\n  /**\n   * Return all the non-weight state needed to serialize this object.\n   */\n  abstract getConfig(): ConfigDict;\n\n  /**\n   * Creates an instance of T from a ConfigDict.\n   *\n   * This works for most descendants of serializable.  A few need to\n   * provide special handling.\n   * @param cls A Constructor for the class to instantiate.\n   * @param config The Configuration for the object.\n   */\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(config);\n  }\n}\n\n/**\n * Maps string keys to class constructors.\n *\n * Used during (de)serialization from the cross-language JSON format, which\n * requires the class name in the serialization format matches the class\n * names as used in Python, should it exist.\n */\nexport class SerializationMap {\n  private static instance: SerializationMap;\n  classNameMap: {\n    [className: string]:\n        [SerializableConstructor<Serializable>, FromConfigMethod<Serializable>]\n  };\n\n  private constructor() {\n    this.classNameMap = {};\n  }\n\n  /**\n   * Returns the singleton instance of the map.\n   */\n  static getMap(): SerializationMap {\n    if (SerializationMap.instance == null) {\n      SerializationMap.instance = new SerializationMap();\n    }\n    return SerializationMap.instance;\n  }\n\n  /**\n   * Registers the class as serializable.\n   */\n  static register<T extends Serializable>(cls: SerializableConstructor<T>) {\n    SerializationMap.getMap().classNameMap[cls.className] =\n        [cls, cls.fromConfig];\n  }\n}\n\n/**\n * Register a class with the serialization map of TensorFlow.js.\n *\n * This is often used for registering custom Layers, so they can be\n * serialized and deserialized.\n *\n * Example:\n *\n * ```js\n * class MyCustomLayer extends tf.layers.Layer {\n *   static className = 'MyCustomLayer';\n *\n *   constructor(config) {\n *     super(config);\n *   }\n * }\n * tf.serialization.registerClass(MyCustomLayer);\n * ```\n *\n * @param cls The class to be registered. It must have a public static member\n *   called `className` defined and the value must be a non-empty string.\n */\n/** @doc {heading: 'Models', subheading: 'Serialization'} */\nexport function registerClass<T extends Serializable>(\n    cls: SerializableConstructor<T>) {\n  assert(\n      cls.className != null,\n      `Class being registered does not have the static className property ` +\n          `defined.`);\n  assert(\n      typeof cls.className === 'string',\n      `className is required to be a string, but got type ` +\n          typeof cls.className);\n  assert(\n      cls.className.length > 0,\n      `Class being registered has an empty-string as its className, which ` +\n          `is disallowed.`);\n\n  SerializationMap.register(cls);\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from './environment';\nimport {Features} from './environment_util';\nimport {Tensor} from './tensor';\nimport {TypedArray} from './types';\nimport * as util from './util';\nimport {isString} from './util';\n\n// TODO(smilkov): Move these constants to jasmine_util.\nexport const WEBGL_ENVS: Features = {\n  'HAS_WEBGL': true\n};\nexport const NODE_ENVS: Features = {\n  'IS_NODE': true\n};\nexport const CHROME_ENVS: Features = {\n  'IS_CHROME': true\n};\nexport const BROWSER_ENVS: Features = {\n  'IS_BROWSER': true\n};\nexport const CPU_ENVS: Features = {\n  'HAS_WEBGL': false\n};\n\nexport const ALL_ENVS: Features = {};\n\nexport function expectArraysClose(\n    actual: Tensor|TypedArray|number[],\n    expected: Tensor|TypedArray|number[]|boolean[], epsilon?: number) {\n  if (epsilon == null) {\n    epsilon = ENV.get('TEST_EPSILON');\n  }\n  return expectArraysPredicate(\n      actual, expected, (a, b) => areClose(a as number, Number(b), epsilon));\n}\n\nfunction expectArraysPredicate(\n    actual: Tensor|TypedArray|number[]|string[],\n    expected: Tensor|TypedArray|number[]|boolean[]|string[],\n    predicate: (a: {}, b: {}) => boolean) {\n  if (!(actual instanceof Tensor) && !(expected instanceof Tensor)) {\n    const aType = actual.constructor.name;\n    const bType = expected.constructor.name;\n\n    if (aType !== bType) {\n      throw new Error(\n          `Arrays are of different type actual: ${aType} ` +\n          `vs expected: ${bType}`);\n    }\n  } else if (actual instanceof Tensor && expected instanceof Tensor) {\n    if (actual.dtype !== expected.dtype) {\n      throw new Error(\n          `Arrays are of different type actual: ${actual.dtype} ` +\n          `vs expected: ${expected.dtype}.`);\n    }\n    if (!util.arraysEqual(actual.shape, expected.shape)) {\n      throw new Error(\n          `Arrays are of different shape actual: ${actual.shape} ` +\n          `vs expected: ${expected.shape}.`);\n    }\n  }\n\n  let actualValues: TypedArray|number[]|string[];\n  let expectedValues: TypedArray|number[]|boolean[]|string[];\n  if (actual instanceof Tensor) {\n    actualValues = actual.dataSync();\n  } else {\n    actualValues = actual;\n  }\n  if (expected instanceof Tensor) {\n    expectedValues = expected.dataSync();\n  } else {\n    expectedValues = expected;\n  }\n\n  if (actualValues.length !== expectedValues.length) {\n    throw new Error(\n        `Arrays have different lengths actual: ${actualValues.length} vs ` +\n        `expected: ${expectedValues.length}.\\n` +\n        `Actual:   ${actualValues}.\\n` +\n        `Expected: ${expectedValues}.`);\n  }\n  for (let i = 0; i < expectedValues.length; ++i) {\n    const a = actualValues[i];\n    const e = expectedValues[i];\n\n    if (!predicate(a, e)) {\n      throw new Error(\n          `Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.\\n` +\n          `Actual:   ${actualValues}.\\n` +\n          `Expected: ${expectedValues}.`);\n    }\n  }\n}\n\nexport interface DoneFn {\n  (): void;\n  fail: (message?: Error|string) => void;\n}\n\nexport function expectPromiseToFail(fn: () => Promise<{}>, done: DoneFn): void {\n  fn().then(() => done.fail(), () => done());\n}\n\nexport function expectArraysEqual(\n    actual: Tensor|TypedArray|number[]|string[],\n    expected: Tensor|TypedArray|number[]|boolean[]|string[]) {\n  if (actual instanceof Tensor && actual.dtype === 'string' ||\n      expected instanceof Tensor && expected.dtype === 'string' ||\n      actual instanceof Array && isString(actual[0]) ||\n      expected instanceof Array && isString(expected[0])) {\n    // tslint:disable-next-line:triple-equals\n    return expectArraysPredicate(actual, expected, (a, b) => a == b);\n  }\n  return expectArraysClose(actual as Tensor, expected as Tensor, 0);\n}\n\nexport function expectNumbersClose(a: number, e: number, epsilon?: number) {\n  if (epsilon == null) {\n    epsilon = ENV.get('TEST_EPSILON');\n  }\n  if (!areClose(a, e, epsilon)) {\n    throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);\n  }\n}\n\nfunction areClose(a: number, e: number, epsilon: number): boolean {\n  if (isNaN(a) && isNaN(e)) {\n    return true;\n  }\n  if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon) {\n    return false;\n  }\n  return true;\n}\n\nexport function expectValuesInRange(\n    actual: Tensor|TypedArray|number[], low: number, high: number) {\n  let actualVals: TypedArray|number[];\n  if (actual instanceof Tensor) {\n    actualVals = actual.dataSync();\n  } else {\n    actualVals = actual;\n  }\n  for (let i = 0; i < actualVals.length; i++) {\n    if (actualVals[i] < low || actualVals[i] > high) {\n      throw new Error(\n          `Value out of range:${actualVals[i]} low: ${low}, high: ${high}`);\n    }\n  }\n}\n\nexport function expectArrayBuffersEqual(\n    actual: ArrayBuffer, expected: ArrayBuffer) {\n  // Safari & Jasmine don't like comparing ArrayBuffers directly. Wrapping in\n  // a Float32Array solves this issue.\n  expect(new Float32Array(actual)).toEqual(new Float32Array(expected));\n}\n","/** @license See the LICENSE file. */\n\n// This code is auto-generated, do not modify this file!\nconst version = '0.14.2';\nexport {version};\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {variableGrads} from '../globals';\nimport {Serializable} from '../serialization';\nimport {Scalar, Variable} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\n\n/** @doc {heading: 'Training', subheading: 'Classes', namespace: 'train'} */\nexport abstract class Optimizer extends Serializable {\n  /**\n   * Executes `f()` and minimizes the scalar output of `f()` by computing\n   * gradients of y with respect to the list of trainable variables provided by\n   * `varList`. If no list is provided, it defaults to all trainable variables.\n   *\n   * @param f The function to execute and whose output to minimize.\n   * @param returnCost Whether to return the scalar cost value produced by\n   * executing `f()`.\n   * @param varList An optional list of variables to update. If specified, only\n   * the trainable variables in varList will be updated by minimize. Defaults to\n   * all trainable variables.\n   */\n  /** @doc {heading: 'Training', subheading: 'Optimizers'} */\n  minimize(f: () => Scalar, returnCost = false, varList?: Variable[]): Scalar\n      |null {\n    const {value, grads} = this.computeGradients(f, varList);\n\n    this.applyGradients(grads);\n\n    // Dispose gradients.\n    const varNames = Object.keys(grads);\n    varNames.forEach(varName => grads[varName].dispose());\n\n    if (returnCost) {\n      return value as Scalar;\n    } else {\n      value.dispose();\n      return null;\n    }\n  }\n\n  /**\n   * Executes f() and computes the gradient of the scalar output of f() with\n   * respect to the list of trainable variables provided by `varList`. If no\n   * list is provided, it defaults to all trainable variables.\n   *\n   * @param f The function to execute and whose output to use for computing\n   * gradients with respect to variables.\n   * @param varList An optional list of variables to compute gradients with\n   * respect to. If specified, only the trainable variables in varList will have\n   * gradients computed with respect to. Defaults to all trainable variables.\n   */\n  computeGradients(f: () => Scalar, varList?: Variable[]):\n      {value: Scalar, grads: NamedTensorMap} {\n    return variableGrads(f, varList);\n  }\n\n  /**\n   * Updates variables by using the computed gradients.\n   *\n   * @param variableGradients A mapping of variable name to its gradient value.\n   */\n  abstract applyGradients(variableGradients: NamedTensorMap): void;\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {keep, tidy} from '../globals';\nimport {scalar, zerosLike} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar} from '../tensor';\nimport {NamedVariableMap} from '../tensor_types';\nimport {Optimizer} from './optimizer';\n\n/** @doclink Optimizer */\nexport class AdadeltaOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'AdadeltaOptimizer';\n  private c: Scalar;\n  private epsilonScalar: Scalar;\n  private rhoScalar: Scalar;\n  private oneMinusRho: Scalar;\n\n  private accumulatedGrads: NamedVariableMap = {};\n  private accumulatedUpdates: NamedVariableMap = {};\n\n  constructor(\n      protected learningRate: number, protected rho: number,\n      protected epsilon: number = null) {\n    super();\n\n    this.c = keep(scalar(-learningRate));\n    this.rhoScalar = keep(scalar(rho));\n    this.oneMinusRho = keep(scalar(1 - rho));\n\n    if (epsilon === null) {\n      epsilon = ENV.get('EPSILON');\n    }\n\n    this.epsilonScalar = keep(scalar(epsilon));\n  }\n\n  applyGradients(variableGradients: NamedVariableMap) {\n    for (const variableName in variableGradients) {\n      const value = ENV.engine.registeredVariables[variableName];\n      if (this.accumulatedGrads[variableName] == null) {\n        const trainable = false;\n        tidy(() => {\n          this.accumulatedGrads[variableName] =\n              zerosLike(value).variable(trainable);\n        });\n      }\n      if (this.accumulatedUpdates[variableName] == null) {\n        const trainable = false;\n        tidy(() => {\n          this.accumulatedUpdates[variableName] =\n              zerosLike(value).variable(trainable);\n        });\n      }\n\n      const gradient = variableGradients[variableName];\n      const accumulatedGrad = this.accumulatedGrads[variableName];\n      const accumulatedUpdate = this.accumulatedUpdates[variableName];\n\n      tidy(() => {\n        const newAccumulatedGrad =\n            this.rhoScalar.mul(accumulatedGrad)\n                .add(this.oneMinusRho.mul(gradient.square()));\n\n        const updates = accumulatedUpdate.add(this.epsilonScalar)\n                            .sqrt()\n                            .div(accumulatedGrad.add(this.epsilonScalar).sqrt())\n                            .mul(gradient);\n\n        const newAccumulatedUpdate =\n            this.rhoScalar.mul(accumulatedUpdate)\n                .add(this.oneMinusRho.mul(updates.square()));\n\n        this.accumulatedGrads[variableName].assign(newAccumulatedGrad);\n        this.accumulatedUpdates[variableName].assign(newAccumulatedUpdate);\n\n        const newValue = this.c.mul(updates).add(value);\n        value.assign(newValue);\n      });\n    }\n  }\n\n  dispose() {\n    this.c.dispose();\n    this.epsilonScalar.dispose();\n    this.rhoScalar.dispose();\n    this.oneMinusRho.dispose();\n    if (this.accumulatedUpdates != null) {\n      Object.keys(this.accumulatedUpdates)\n          .forEach(name => this.accumulatedUpdates[name].dispose());\n      Object.keys(this.accumulatedGrads)\n          .forEach(name => this.accumulatedGrads[name].dispose());\n    }\n  }\n  getConfig(): ConfigDict {\n    return {\n      learningRate: this.learningRate,\n      rho: this.rho,\n      epsilon: this.epsilon\n    };\n  }\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(config.learningRate, config.rho, config.epsilon);\n  }\n}\nregisterClass(AdadeltaOptimizer);\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {keep, tidy} from '../globals';\nimport {fill, scalar} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar} from '../tensor';\nimport {NamedVariableMap} from '../tensor_types';\nimport {Optimizer} from './optimizer';\n\n/** @doclink Optimizer */\nexport class AdagradOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'AdagradOptimizer';\n  private c: Scalar;\n  private epsilon: Scalar;\n\n  private accumulatedGrads: NamedVariableMap = {};\n\n  constructor(\n      protected learningRate: number, private initialAccumulatorValue = 0.1) {\n    super();\n    this.c = keep(scalar(-learningRate));\n\n    this.epsilon = keep(scalar(ENV.get('EPSILON')));\n  }\n\n  applyGradients(variableGradients: NamedVariableMap) {\n    for (const variableName in variableGradients) {\n      const value = ENV.engine.registeredVariables[variableName];\n      if (this.accumulatedGrads[variableName] == null) {\n        const trainable = false;\n        tidy(() => {\n          this.accumulatedGrads[variableName] =\n              fill(value.shape, this.initialAccumulatorValue)\n                  .variable(trainable);\n        });\n      }\n\n      const gradient = variableGradients[variableName];\n      const accumulatedGrad = this.accumulatedGrads[variableName];\n\n      tidy(() => {\n        const newAccumulatedGrad = accumulatedGrad.add(gradient.square());\n        this.accumulatedGrads[variableName].assign(newAccumulatedGrad);\n\n        const newValue =\n            this.c\n                .mul(gradient.div(newAccumulatedGrad.add(this.epsilon).sqrt()))\n                .add(value);\n        value.assign(newValue);\n      });\n    }\n  }\n\n  dispose() {\n    this.epsilon.dispose();\n    this.c.dispose();\n    if (this.accumulatedGrads != null) {\n      Object.keys(this.accumulatedGrads)\n          .forEach(name => this.accumulatedGrads[name].dispose());\n    }\n  }\n  getConfig(): ConfigDict {\n    return {\n      learningRate: this.learningRate,\n      initialAccumulatorValue: this.initialAccumulatorValue,\n    };\n  }\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(config.learningRate, config.initialAccumulatorValue);\n  }\n}\nregisterClass(AdagradOptimizer);\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {keep, tidy} from '../globals';\nimport {scalar, zerosLike} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar, Variable} from '../tensor';\nimport {NamedVariableMap} from '../tensor_types';\nimport {Optimizer} from './optimizer';\n\nexport class AdamOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'AdamOptimizer';\n  private c: Scalar;\n  private epsScalar: Scalar;\n  private beta1Scalar: Scalar;\n  private beta2Scalar: Scalar;\n  private accBeta1: Variable;\n  private accBeta2: Variable;\n  private oneMinusBeta1: Scalar;\n  private oneMinusBeta2: Scalar;\n  private one: Scalar;\n\n  private accumulatedFirstMoment: NamedVariableMap = {};\n  private accumulatedSecondMoment: NamedVariableMap = {};\n\n  constructor(\n      protected learningRate: number, protected beta1: number,\n      protected beta2: number, protected epsilon: number = null) {\n    super();\n    this.c = keep(scalar(-learningRate));\n    // b1, b2 keep initial value of beta* hyperparameters.\n    this.beta1Scalar = keep(scalar(beta1));\n    this.beta2Scalar = keep(scalar(beta2));\n    tidy(() => {\n      // accB* will be updated by batch.\n      this.accBeta1 = scalar(beta1).variable();\n      this.accBeta2 = scalar(beta2).variable();\n    });\n    this.oneMinusBeta1 = keep(scalar(1 - beta1));\n    this.oneMinusBeta2 = keep(scalar(1 - beta2));\n    this.one = keep(scalar(1));\n\n    if (epsilon === null) {\n      epsilon = ENV.get('EPSILON');\n    }\n\n    this.epsScalar = keep(scalar(epsilon));\n  }\n\n  applyGradients(variableGradients: NamedVariableMap) {\n    tidy(() => {\n      const oneMinusAccBeta1 = this.one.sub(this.accBeta1);\n      const oneMinusAccBeta2 = this.one.sub(this.accBeta2);\n\n      for (const variableName in variableGradients) {\n        const value = ENV.engine.registeredVariables[variableName];\n        if (this.accumulatedFirstMoment[variableName] == null) {\n          const trainable = false;\n          this.accumulatedFirstMoment[variableName] =\n              zerosLike(value).variable(trainable);\n        }\n        if (this.accumulatedSecondMoment[variableName] == null) {\n          const trainable = false;\n          this.accumulatedSecondMoment[variableName] =\n              zerosLike(value).variable(trainable);\n        }\n\n        const gradient = variableGradients[variableName];\n        const firstMoment = this.accumulatedFirstMoment[variableName];\n        const secondMoment = this.accumulatedSecondMoment[variableName];\n\n        const newFirstMoment = this.beta1Scalar.mul(firstMoment)\n                                   .add(this.oneMinusBeta1.mul(gradient));\n        const newSecondMoment =\n            this.beta2Scalar.mul(secondMoment)\n                .add(this.oneMinusBeta2.mul(gradient.square()));\n\n        const biasCorrectedFirstMoment = newFirstMoment.div(oneMinusAccBeta1);\n        const biasCorrectedSecondMoment = newSecondMoment.div(oneMinusAccBeta2);\n\n        this.accumulatedFirstMoment[variableName].assign(newFirstMoment);\n        this.accumulatedSecondMoment[variableName].assign(newSecondMoment);\n\n        const newValue =\n            this.c\n                .mul(biasCorrectedFirstMoment.div(\n                    this.epsScalar.add(biasCorrectedSecondMoment.sqrt())))\n                .add(value);\n        value.assign(newValue);\n      }\n\n      this.accBeta1.assign(this.accBeta1.mul(this.beta1Scalar));\n      this.accBeta2.assign(this.accBeta2.mul(this.beta2Scalar));\n    });\n  }\n\n  dispose() {\n    this.c.dispose();\n    this.epsScalar.dispose();\n    this.beta1Scalar.dispose();\n    this.beta2Scalar.dispose();\n    this.accBeta1.dispose();\n    this.accBeta2.dispose();\n    this.oneMinusBeta1.dispose();\n    this.oneMinusBeta2.dispose();\n    this.one.dispose();\n\n    if (this.accumulatedFirstMoment != null) {\n      Object.keys(this.accumulatedFirstMoment)\n          .forEach(name => this.accumulatedFirstMoment[name].dispose());\n    }\n\n    if (this.accumulatedSecondMoment != null) {\n      Object.keys(this.accumulatedSecondMoment)\n          .forEach(name => this.accumulatedSecondMoment[name].dispose());\n    }\n  }\n  getConfig(): ConfigDict {\n    return {\n      learningRate: this.learningRate,\n      beta1: this.beta1,\n      beta2: this.beta2,\n      epsilon: this.epsilon,\n    };\n  }\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(\n        config.learningRate, config.beta1, config.beta2, config.epsilon);\n  }\n}\nregisterClass(AdamOptimizer);\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {keep, tidy} from '../globals';\nimport {scalar, zerosLike} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar, Variable} from '../tensor';\nimport {NamedVariableMap} from '../tensor_types';\nimport {Optimizer} from './optimizer';\n\nexport class AdamaxOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'AdamaxOptimizer';\n  private c: Scalar;\n  private epsScalar: Scalar;\n  private accBeta1: Variable;\n  private beta1Scalar: Scalar;\n  private beta2Scalar: Scalar;\n  private decayScalar: Scalar;\n  private oneMinusBeta1: Scalar;\n  private one: Scalar;\n  private iteration: Variable;\n\n  private accumulatedFirstMoment: NamedVariableMap = {};\n  private accumulatedWeightedInfNorm: NamedVariableMap = {};\n\n  constructor(\n      protected learningRate: number, protected beta1: number,\n      protected beta2: number, protected epsilon: number = null,\n      protected decay = 0.0) {\n    super();\n    this.c = keep(scalar(-learningRate));\n\n    // b1, b2 keep initial value of beta* hyperparameters.\n    this.beta1Scalar = keep(scalar(beta1));\n    this.beta2Scalar = keep(scalar(beta2));\n\n    this.decayScalar = keep(scalar(decay));\n\n    tidy(() => {\n      this.iteration = scalar(0).variable();\n      this.accBeta1 = scalar(beta1).variable();\n    });\n\n    this.oneMinusBeta1 = keep(scalar(1 - beta1));\n    this.one = keep(scalar(1));\n\n    if (epsilon === null) {\n      epsilon = ENV.get('EPSILON');\n    }\n\n    this.epsScalar = keep(scalar(epsilon));\n  }\n\n  applyGradients(variableGradients: NamedVariableMap) {\n    tidy(() => {\n      const oneMinusAccBeta1 = this.one.sub(this.accBeta1);\n      const lr = this.c.div(this.one.add(this.decayScalar.mul(this.iteration)));\n\n      for (const variableName in variableGradients) {\n        const value = ENV.engine.registeredVariables[variableName];\n        if (this.accumulatedFirstMoment[variableName] == null) {\n          const trainable = false;\n          this.accumulatedFirstMoment[variableName] =\n              zerosLike(value).variable(trainable);\n        }\n        if (this.accumulatedWeightedInfNorm[variableName] == null) {\n          const trainable = false;\n          this.accumulatedWeightedInfNorm[variableName] =\n              zerosLike(value).variable(trainable);\n        }\n\n        const gradient = variableGradients[variableName];\n        const firstMoment = this.accumulatedFirstMoment[variableName];\n        const weightedInfNorm = this.accumulatedWeightedInfNorm[variableName];\n\n        const newFirstMoment = this.beta1Scalar.mul(firstMoment)\n                                   .add(this.oneMinusBeta1.mul(gradient));\n\n        const ut0 = this.beta2Scalar.mul(weightedInfNorm);\n        const ut1 = gradient.abs();\n\n        const newWeightedInfNorm = ut0.maximum(ut1);\n\n        this.accumulatedFirstMoment[variableName].assign(newFirstMoment);\n        this.accumulatedWeightedInfNorm[variableName].assign(\n            newWeightedInfNorm);\n\n        const newValue =\n            lr.div(oneMinusAccBeta1)\n                .mul(newFirstMoment.div(this.epsScalar.add(newWeightedInfNorm)))\n                .add(value);\n\n        value.assign(newValue);\n      }\n\n      this.iteration.assign(this.iteration.add(this.one));\n      this.accBeta1.assign(this.accBeta1.mul(this.beta1Scalar));\n    });\n  }\n\n  dispose() {\n    this.c.dispose();\n    this.epsScalar.dispose();\n    this.accBeta1.dispose();\n    this.beta1Scalar.dispose();\n    this.beta2Scalar.dispose();\n    this.oneMinusBeta1.dispose();\n\n    this.decayScalar.dispose();\n    this.iteration.dispose();\n\n    this.one.dispose();\n\n    if (this.accumulatedFirstMoment != null) {\n      Object.keys(this.accumulatedFirstMoment)\n          .forEach(name => this.accumulatedFirstMoment[name].dispose());\n    }\n\n    if (this.accumulatedWeightedInfNorm != null) {\n      Object.keys(this.accumulatedWeightedInfNorm)\n          .forEach(name => this.accumulatedWeightedInfNorm[name].dispose());\n    }\n  }\n  getConfig(): ConfigDict {\n    return {\n      learningRate: this.learningRate,\n      beta1: this.beta1,\n      beta2: this.beta2,\n      epsilon: this.epsilon,\n      decay: this.decay\n    };\n  }\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(\n        config.learningRate, config.beta1, config.beta2, config.epsilon,\n        config.decay);\n  }\n}\nregisterClass(AdamaxOptimizer);\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {keep, tidy} from '../globals';\nimport {scalar} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {Optimizer} from './optimizer';\n\n/** @doclink Optimizer */\nexport class SGDOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'SGDOptimizer';\n  protected c: Scalar;\n\n  constructor(protected learningRate: number) {\n    super();\n    this.setLearningRate(learningRate);\n  }\n\n  applyGradients(variableGradients: NamedTensorMap) {\n    const varNames = Object.keys(variableGradients);\n    varNames.forEach(varName => {\n      const gradient = variableGradients[varName];\n      const value = ENV.engine.registeredVariables[varName];\n\n      tidy(() => {\n        const newValue = this.c.mul(gradient).add(value);\n        value.assign(newValue);\n      });\n    });\n  }\n\n  /**\n   * Sets the learning rate of the optimizer.\n   */\n  setLearningRate(learningRate: number) {\n    this.learningRate = learningRate;\n    if (this.c != null) {\n      this.c.dispose();\n    }\n    this.c = keep(scalar(-learningRate));\n  }\n\n  dispose() {\n    this.c.dispose();\n  }\n\n  getConfig(): ConfigDict {\n    return {learningRate: this.learningRate};\n  }\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(config.learningRate);\n  }\n}\nregisterClass(SGDOptimizer);\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {tidy} from '../globals';\nimport {scalar, zerosLike} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar, Tensor} from '../tensor';\nimport {NamedVariableMap} from '../tensor_types';\nimport {SGDOptimizer} from './sgd_optimizer';\n\n/** @doclink Optimizer */\nexport class MomentumOptimizer extends SGDOptimizer {\n  /** @nocollapse */\n  static className = 'MomentumOptimizer';\n  private m: Scalar;\n  private accumulations: NamedVariableMap;\n\n  constructor(\n      protected learningRate: number, private momentum: number,\n      private useNesterov = false) {\n    super(learningRate);\n    this.m = scalar(this.momentum);\n    this.accumulations = {};\n  }\n\n  applyGradients(variableGradients: NamedVariableMap) {\n    for (const variableName in variableGradients) {\n      const value = ENV.engine.registeredVariables[variableName];\n      if (this.accumulations[variableName] == null) {\n        const trainable = false;\n        tidy(() => {\n          this.accumulations[variableName] =\n              zerosLike(value).variable(trainable);\n        });\n      }\n\n      const accumulation = this.accumulations[variableName];\n      const gradient = variableGradients[variableName];\n\n      tidy(() => {\n        let newValue: Tensor;\n        const newAccumulation = this.m.mul(accumulation).add(gradient);\n        if (this.useNesterov) {\n          newValue =\n              this.c.mul(gradient.add(newAccumulation.mul(this.m))).add(value);\n        } else {\n          newValue = this.c.mul(newAccumulation).add(value);\n        }\n        this.accumulations[variableName].assign(newAccumulation);\n        value.assign(newValue);\n      });\n    }\n  }\n\n  dispose() {\n    super.dispose();\n    this.m.dispose();\n    if (this.accumulations != null) {\n      for (const variableName in this.accumulations) {\n        this.accumulations[variableName].dispose();\n      }\n    }\n  }\n\n  /**\n   * Sets the momentum of the optimizer.\n   *\n   * @param momentum\n   */\n  setMomentum(momentum: number) {\n    this.momentum = momentum;\n  }\n\n  getConfig(): ConfigDict {\n    return {\n      learningRate: this.learningRate,\n      momentum: this.momentum,\n      useNesterov: this.useNesterov\n    };\n  }\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(config.learningRate, config.momentum, config.useNesterov);\n  }\n}\nregisterClass(MomentumOptimizer);\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {keep, tidy} from '../globals';\nimport {scalar, zerosLike} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar} from '../tensor';\nimport {NamedVariableMap} from '../tensor_types';\nimport {Optimizer} from './optimizer';\n\n/** @doclink Optimizer */\nexport class RMSPropOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'RMSPropOptimizer';\n  private c: Scalar;\n  private epsilonScalar: Scalar;\n  private decayScalar: Scalar;\n  private momentumScalar: Scalar;\n  private oneMinusDecay: Scalar;\n  private centered: boolean;\n\n  private accumulatedMeanSquares: NamedVariableMap = {};\n  private accumulatedMeanGrads: NamedVariableMap = {};\n  private accumulatedMoments: NamedVariableMap = {};\n\n  constructor(\n      protected learningRate: number, protected decay = 0.9,\n      protected momentum = 0.0, protected epsilon: number = null,\n      centered = false) {\n    super();\n\n    this.c = keep(scalar(learningRate));\n    this.decayScalar = keep(scalar(decay));\n    this.momentumScalar = keep(scalar(momentum));\n    this.oneMinusDecay = keep(scalar(1 - decay));\n    this.centered = centered;\n\n    if (epsilon === null) {\n      epsilon = ENV.get('EPSILON');\n    }\n\n    this.epsilonScalar = keep(scalar(epsilon));\n  }\n\n  applyGradients(variableGradients: NamedVariableMap) {\n    for (const variableName in variableGradients) {\n      const value = ENV.engine.registeredVariables[variableName];\n      if (this.accumulatedMeanSquares[variableName] == null) {\n        const trainable = false;\n        tidy(() => {\n          this.accumulatedMeanSquares[variableName] =\n              zerosLike(value).variable(trainable);\n        });\n      }\n      if (this.accumulatedMeanGrads[variableName] == null && this.centered) {\n        const trainable = false;\n        tidy(() => {\n          this.accumulatedMeanGrads[variableName] =\n              zerosLike(value).variable(trainable);\n        });\n      }\n      if (this.accumulatedMoments[variableName] == null) {\n        const trainable = false;\n        tidy(() => {\n          this.accumulatedMoments[variableName] =\n              zerosLike(value).variable(trainable);\n        });\n      }\n\n      const accumulatedMeanSquare = this.accumulatedMeanSquares[variableName];\n      const accumulatedMeanGrad = this.accumulatedMeanGrads[variableName];\n      const accumulatedMoments = this.accumulatedMoments[variableName];\n      const gradient = variableGradients[variableName];\n\n      tidy(() => {\n        const newAccumulatedMeanSquare =\n            this.decayScalar.mul(accumulatedMeanSquare)\n                .add(this.oneMinusDecay.mul(gradient.square()));\n\n        if (this.centered) {\n          // Centered gradient\n          const newAccumulatedMeanGrad =\n              this.decayScalar.mul(accumulatedMeanGrad)\n                  .add(this.oneMinusDecay.mul(gradient));\n\n          const newAccumulatedMoments =\n              this.momentumScalar.mul(accumulatedMoments)\n                  .add(this.c.mul(gradient).div(\n                      newAccumulatedMeanSquare\n                          .sub(newAccumulatedMeanGrad.square().add(\n                              this.epsilonScalar))\n                          .sqrt()));\n\n          this.accumulatedMeanSquares[variableName].assign(\n              newAccumulatedMeanSquare);\n          this.accumulatedMeanGrads[variableName].assign(\n              newAccumulatedMeanGrad);\n          this.accumulatedMoments[variableName].assign(newAccumulatedMoments);\n\n          const newValue = value.sub(newAccumulatedMoments);\n          value.assign(newValue);\n        } else {\n          // Plain gradient\n          const newAccumulatedMeanSquare =\n              this.decayScalar.mul(accumulatedMeanSquare)\n                  .add(this.oneMinusDecay.mul(gradient.square()));\n\n          const newAccumulatedMoments =\n              this.momentumScalar.mul(accumulatedMoments)\n                  .add(this.c.mul(gradient).div(\n                      newAccumulatedMeanSquare.add(this.epsilonScalar).sqrt()));\n\n          this.accumulatedMeanSquares[variableName].assign(\n              newAccumulatedMeanSquare);\n          this.accumulatedMoments[variableName].assign(newAccumulatedMoments);\n\n          const newValue = value.sub(newAccumulatedMoments);\n          value.assign(newValue);\n        }\n      });\n    }\n  }\n\n  dispose() {\n    this.c.dispose();\n    this.epsilonScalar.dispose();\n    this.decayScalar.dispose();\n    this.momentumScalar.dispose();\n    this.oneMinusDecay.dispose();\n    if (this.accumulatedMeanSquares != null) {\n      Object.keys(this.accumulatedMeanSquares)\n          .forEach(name => this.accumulatedMeanSquares[name].dispose());\n    }\n    if (this.accumulatedMeanGrads != null && this.centered) {\n      Object.keys(this.accumulatedMeanGrads)\n          .forEach(name => this.accumulatedMeanGrads[name].dispose());\n    }\n    if (this.accumulatedMoments != null) {\n      Object.keys(this.accumulatedMoments)\n          .forEach(name => this.accumulatedMoments[name].dispose());\n    }\n  }\n\n  getConfig(): ConfigDict {\n    return {\n      learningRate: this.learningRate,\n      decay: this.decay,\n      momentum: this.momentum,\n      epsilon: this.epsilon,\n      centered: this.centered\n    };\n  }\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(\n        config.learningRate, config.decay, config.momentum, config.epsilon,\n        config.centered);\n  }\n}\nregisterClass(RMSPropOptimizer);\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {AdadeltaOptimizer} from './adadelta_optimizer';\nimport {AdagradOptimizer} from './adagrad_optimizer';\nimport {AdamOptimizer} from './adam_optimizer';\nimport {AdamaxOptimizer} from './adamax_optimizer';\nimport {MomentumOptimizer} from './momentum_optimizer';\nimport {RMSPropOptimizer} from './rmsprop_optimizer';\nimport {SGDOptimizer} from './sgd_optimizer';\n\nexport class OptimizerConstructors {\n  /**\n   * Constructs a `tf.SGDOptimizer` that uses stochastic gradient descent.\n   *\n   * ```js\n   * // Fit a quadratic function by learning the coefficients a, b, c.\n   * const xs = tf.tensor1d([0, 1, 2, 3]);\n   * const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]);\n   *\n   * const a = tf.scalar(Math.random()).variable();\n   * const b = tf.scalar(Math.random()).variable();\n   * const c = tf.scalar(Math.random()).variable();\n   *\n   * // y = a * x^2 + b * x + c.\n   * const f = x => a.mul(x.square()).add(b.mul(x)).add(c);\n   * const loss = (pred, label) => pred.sub(label).square().mean();\n   *\n   * const learningRate = 0.01;\n   * const optimizer = tf.train.sgd(learningRate);\n   *\n   * // Train the model.\n   * for (let i = 0; i < 10; i++) {\n   *   optimizer.minimize(() => loss(f(xs), ys));\n   * }\n   *\n   * // Make predictions.\n   * console.log(\n   *     `a: ${a.dataSync()}, b: ${b.dataSync()}, c: ${c.dataSync()}`);\n   * const preds = f(xs).dataSync();\n   * preds.forEach((pred, i) => {\n   *   console.log(`x: ${i}, pred: ${pred}`);\n   * });\n   * ```\n   *\n   * @param learningRate The learning rate to use for the SGD algorithm.\n   */\n  /**\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static sgd(learningRate: number): SGDOptimizer {\n    return new SGDOptimizer(learningRate);\n  }\n\n  /**\n   * Constructs a `tf.MomentumOptimizer` that uses momentum gradient\n   * descent.\n   *\n   * See\n   * [http://proceedings.mlr.press/v28/sutskever13.pdf](\n   * http://proceedings.mlr.press/v28/sutskever13.pdf)\n   *\n   * @param learningRate The learning rate to use for the Momentum gradient\n   * descent algorithm.\n   * @param momentum The momentum to use for the momentum gradient descent\n   * algorithm.\n   */\n  /**\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static momentum(learningRate: number, momentum: number, useNesterov = false):\n      MomentumOptimizer {\n    return new MomentumOptimizer(learningRate, momentum, useNesterov);\n  }\n\n  /**\n   * Constructs a `tf.RMSPropOptimizer` that uses RMSProp gradient\n   * descent. This implementation uses plain momentum and is not centered\n   * version of RMSProp.\n   *\n   * See\n   * [http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf](\n   * http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)\n   *\n   * @param learningRate The learning rate to use for the RMSProp gradient\n   * descent algorithm.\n   * @param decay The discounting factor for the history/coming gradient.\n   * @param momentum The momentum to use for the RMSProp gradient descent\n   * algorithm.\n   * @param epsilon Small value to avoid zero denominator.\n   * @param centered If true, gradients are normalized by the estimated\n   * variance of the gradient.\n   */\n  /**\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static rmsprop(\n      learningRate: number, decay = .9, momentum = 0.0, epsilon: number = null,\n      centered = false): RMSPropOptimizer {\n    return new RMSPropOptimizer(\n        learningRate, decay, momentum, epsilon, centered);\n  }\n\n  /**\n   * Constructs a `tf.AdamOptimizer` that uses the Adam algorithm.\n   * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n   *\n   * @param learningRate The learning rate to use for the Adam gradient\n   * descent algorithm.\n   * @param beta1 The exponential decay rate for the 1st moment estimates.\n   * @param beta2 The exponential decay rate for the 2nd moment estimates.\n   * @param epsilon A small constant for numerical stability.\n   */\n  /**\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static adam(\n      learningRate = 0.001, beta1 = 0.9, beta2 = 0.999,\n      epsilon: number = null): AdamOptimizer {\n    return new AdamOptimizer(learningRate, beta1, beta2, epsilon);\n  }\n\n  /**\n   * Constructs a `tf.AdadeltaOptimizer` that uses the Adadelta algorithm.\n   * See [https://arxiv.org/abs/1212.5701](https://arxiv.org/abs/1212.5701)\n   *\n   * @param learningRate The learning rate to use for the Adadelta gradient\n   * descent algorithm.\n   * @param rho The learning rate decay over each update.\n   * @param epsilon A constant epsilon used to better condition the grad\n   * update.\n   */\n  /**\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static adadelta(learningRate = .001, rho = .95, epsilon: number = null):\n      AdadeltaOptimizer {\n    return new AdadeltaOptimizer(learningRate, rho, epsilon);\n  }\n\n  /**\n   * Constructs a `tf.AdamaxOptimizer` that uses the Adamax algorithm.\n   * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n   *\n   * @param learningRate The learning rate to use for the Adamax gradient\n   * descent algorithm.\n   * @param beta1 The exponential decay rate for the 1st moment estimates.\n   * @param beta2 The exponential decay rate for the 2nd moment estimates.\n   * @param epsilon A small constant for numerical stability.\n   * @param decay The learning rate decay over each update.\n   */\n  /**\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static adamax(\n      learningRate = 0.002, beta1 = 0.9, beta2 = 0.999, epsilon: number = null,\n      decay = 0.0): AdamaxOptimizer {\n    return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon, decay);\n  }\n\n  /**\n   * Constructs a `tf.AdagradOptimizer` that uses the Adagrad algorithm.\n   * See\n   * [http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf](\n   * http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)\n   * or\n   * [http://ruder.io/optimizing-gradient-descent/index.html#adagrad](\n   * http://ruder.io/optimizing-gradient-descent/index.html#adagrad)\n   *\n   * @param learningRate The learning rate to use for the Adagrad gradient\n   * descent algorithm.\n   * @param initialAccumulatorValue Starting value for the accumulators, must be\n   * positive.\n   */\n  /**\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static adagrad(learningRate: number, initialAccumulatorValue = 0.1):\n      AdagradOptimizer {\n    return new AdagradOptimizer(learningRate, initialAccumulatorValue);\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// So typings can propagate.\nimport {AdadeltaOptimizer} from './optimizers/adadelta_optimizer';\nimport {AdagradOptimizer} from './optimizers/adagrad_optimizer';\nimport {AdamOptimizer} from './optimizers/adam_optimizer';\nimport {AdamaxOptimizer} from './optimizers/adamax_optimizer';\nimport {MomentumOptimizer} from './optimizers/momentum_optimizer';\nimport {OptimizerConstructors} from './optimizers/optimizer_constructors';\nimport {RMSPropOptimizer} from './optimizers/rmsprop_optimizer';\nimport {SGDOptimizer} from './optimizers/sgd_optimizer';\n\n// tslint:disable-next-line:no-unused-expression\n[MomentumOptimizer, SGDOptimizer, AdadeltaOptimizer, AdagradOptimizer,\n RMSPropOptimizer, AdamaxOptimizer, AdamOptimizer];\n\nexport const train = {\n  sgd: OptimizerConstructors.sgd,\n  momentum: OptimizerConstructors.momentum,\n  adadelta: OptimizerConstructors.adadelta,\n  adagrad: OptimizerConstructors.adagrad,\n  rmsprop: OptimizerConstructors.rmsprop,\n  adamax: OptimizerConstructors.adamax,\n  adam: OptimizerConstructors.adam\n};\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// backend_cpu.ts and backend_webgl.ts are standalone files and should be\n// explicitly included here. Below, there is an export from backend_webgl, but\n// that doesn't count since it's exporting a Typescript interface.\nimport './kernels/backend_webgl';\nimport './kernels/backend_cpu';\n\nimport {nextFrame} from './browser_util';\nimport * as environment from './environment';\nimport {Environment} from './environment';\n\n// Serialization.\nimport * as io from './io/io';\nimport * as math from './math';\nimport * as serialization from './serialization';\nimport {setOpHandler} from './tensor';\nimport * as test_util from './test_util';\nimport * as util from './util';\nimport {version} from './version';\nimport * as webgl from './webgl';\n\nexport {InferenceModel, ModelPredictConfig} from './model_types';\n\n// Optimizers.\nexport {AdadeltaOptimizer} from './optimizers/adadelta_optimizer';\nexport {AdagradOptimizer} from './optimizers/adagrad_optimizer';\nexport {AdamOptimizer} from './optimizers/adam_optimizer';\nexport {AdamaxOptimizer} from './optimizers/adamax_optimizer';\nexport {MomentumOptimizer} from './optimizers/momentum_optimizer';\nexport {Optimizer} from './optimizers/optimizer';\nexport {RMSPropOptimizer} from './optimizers/rmsprop_optimizer';\nexport {SGDOptimizer} from './optimizers/sgd_optimizer';\nexport {Scalar, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, TensorBuffer, variable, Variable} from './tensor';\nexport {NamedTensorMap} from './tensor_types';\nexport {DataType, DataTypeMap, DataValues, Rank, ShapeMap} from './types';\n\nexport * from './ops/ops';\nexport {LSTMCellFunc} from './ops/lstm';\nexport {Reduction} from './ops/loss_ops';\n\nexport * from './train';\nexport * from './globals';\n\nexport {Features} from './environment_util';\nexport {TimingInfo} from './engine';\nexport {ENV, Environment} from './environment';\n\nexport const setBackend = Environment.setBackend;\nexport const getBackend = Environment.getBackend;\nexport const disposeVariables = Environment.disposeVariables;\nexport const memory = Environment.memory;\nexport {version as version_core};\n\nexport {nextFrame};\n\n// Second level exports.\nexport {environment, io, math, serialization, test_util, util, webgl};\n\n// Backend specific.\nexport {KernelBackend, BackendTimingInfo, DataMover, DataStorage} from './kernels/backend';\n\nimport * as ops from 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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tf-core.min.js b/node_modules/@tensorflow/tfjs-core/dist/tf-core.min.js
new file mode 100644
index 0000000..a2fcdef
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tf-core.min.js
@@ -0,0 +1,18 @@
+/**
+ * @license
+ * Copyright 2018 Google LLC. All Rights Reserved.
+ * Licensed under the Apache License, Version 2.0 (the "License");
+ * you may not use this file except in compliance with the License.
+ * You may obtain a copy of the License at
+ *
+ * http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ * =============================================================================
+ */
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this.throwIfDisposed(),rt.elu(this)},t.prototype.selu=function(){return this.throwIfDisposed(),rt.selu(this)},t.prototype.leakyRelu=function(t){return void 0===t&&(t=.2),this.throwIfDisposed(),rt.leakyRelu(this,t)},t.prototype.prelu=function(t){return this.throwIfDisposed(),rt.prelu(this,t)},t.prototype.sigmoid=function(){return this.throwIfDisposed(),rt.sigmoid(this)},t.prototype.logSigmoid=function(){return this.throwIfDisposed(),rt.logSigmoid(this)},t.prototype.softplus=function(){return this.throwIfDisposed(),rt.softplus(this)},t.prototype.zerosLike=function(){return this.throwIfDisposed(),rt.zerosLike(this)},t.prototype.onesLike=function(){return this.throwIfDisposed(),rt.onesLike(this)},t.prototype.sin=function(){return this.throwIfDisposed(),rt.sin(this)},t.prototype.cos=function(){return this.throwIfDisposed(),rt.cos(this)},t.prototype.tan=function(){return this.throwIfDisposed(),rt.tan(this)},t.prototype.asin=function(){return this.throwIfDisposed(),rt.asin(this)},t.prototype.acos=function(){return this.throwIfDisposed(),rt.acos(this)},t.prototype.atan=function(){return this.throwIfDisposed(),rt.atan(this)},t.prototype.sinh=function(){return this.throwIfDisposed(),rt.sinh(this)},t.prototype.cosh=function(){return this.throwIfDisposed(),rt.cosh(this)},t.prototype.tanh=function(){return this.throwIfDisposed(),rt.tanh(this)},t.prototype.asinh=function(){return this.throwIfDisposed(),rt.asinh(this)},t.prototype.acosh=function(){return this.throwIfDisposed(),rt.acosh(this)},t.prototype.atanh=function(){return this.throwIfDisposed(),rt.atanh(this)},t.prototype.erf=function(){return this.throwIfDisposed(),rt.erf(this)},t.prototype.round=function(){return this.throwIfDisposed(),rt.round(this)},t.prototype.step=function(t){return void 0===t&&(t=0),this.throwIfDisposed(),rt.step(this,t)},t.prototype.softmax=function(t){return void 0===t&&(t=-1),this.throwIfDisposed(),rt.softmax(this,t)},t.prototype.logSoftmax=function(t){return void 0===t&&(t=-1),this.throwIfDisposed(),rt.logSoftmax(this,t)},t.prototype.resizeBilinear=function(t,e){return void 0===e&&(e=!1),this.throwIfDisposed(),rt.image.resizeBilinear(this,t,e)},t.prototype.resizeNearestNeighbor=function(t,e){return void 0===e&&(e=!1),this.throwIfDisposed(),rt.image.resizeNearestNeighbor(this,t,e)},t.prototype.conv1d=function(t,e,n,r,o,a){return void 0===r&&(r="NWC"),void 0===o&&(o=1),this.throwIfDisposed(),rt.conv1d(this,t,e,n,r,o,a)},t.prototype.conv2d=function(t,e,n,r,o,a){return void 0===r&&(r="NHWC"),void 0===o&&(o=[1,1]),this.throwIfDisposed(),rt.conv2d(this,t,e,n,r,o,a)},t.prototype.conv2dTranspose=function(t,e,n,r,o){return this.throwIfDisposed(),rt.conv2dTranspose(this,t,e,n,r,o)},t.prototype.depthwiseConv2D=function(t,e,n,r,o,a){return void 0===r&&(r="NHWC"),void 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this.throwIfDisposed(),rt.batchToSpaceND(this,t,e)},t.prototype.spaceToBatchND=function(t,e){return this.throwIfDisposed(),rt.spaceToBatchND(this,t,e)},t.prototype.topk=function(t,e){return void 0===t&&(t=1),void 0===e&&(e=!0),this.throwIfDisposed(),rt.topk(this,t,e)},t.prototype.stridedSlice=function(t,e,n,r,o){return void 0===r&&(r=0),void 0===o&&(o=0),this.throwIfDisposed(),rt.stridedSlice(this,t,e,n,r,o)},t.prototype.depthToSpace=function(t,e){return this.throwIfDisposed(),rt.depthToSpace(this,t,e)},t.prototype.fft=function(){return this.throwIfDisposed(),rt.spectral.fft(this)},t.prototype.ifft=function(){return this.throwIfDisposed(),rt.spectral.ifft(this)},t}();Object.defineProperty(at,Symbol.hasInstance,{value:function(t){return!!t&&null!=t.shape&&null!=t.dtype}});var it=function(t){function e(e,n,r){void 0===n&&(n=!0);var o=t.call(this,e.shape,e.dtype,null,e.dataId)||this;o.trainable=n,o.name=r,null==o.name&&(o.name=nt().nextVariableId().toString());try{nt().registerVariable(o)}catch(t){throw nt().disposeTensor(o),t}return o}return n(e,t),e.variable=function(t,n,r,o){return void 0===n&&(n=!0),null!=o&&o!==t.dtype&&(t=t.asType(o)),new e(t,n,r)},e.prototype.assign=function(t){if(t.dtype!==this.dtype)throw new Error("dtype of the new value ("+t.dtype+") and previous value ("+this.dtype+") must match");if(!x(t.shape,this.shape))throw new Error("shape of the new value ("+t.shape+") and previous value ("+this.shape+") must match");nt().disposeTensor(this),this.dataId=t.dataId,nt().registerTensor(this)},e}(at);Object.defineProperty(it,Symbol.hasInstance,{value:function(t){return t instanceof at&&null!=t.assign&&t.assign instanceof Function}});var st,ut,lt,ct,ht,pt=it.variable;(st=t.Rank||(t.Rank={})).R0="R0",st.R1="R1",st.R2="R2",st.R3="R3",st.R4="R4",st.R5="R5",st.R6="R6",function(t){t.float32="float32",t.int32="int32",t.bool="int32",t.complex64="complex64"}(ut||(ut={})),function(t){t.float32="float32",t.int32="int32",t.bool="bool",t.complex64="complex64"}(lt||(lt={})),function(t){t.float32="float32",t.int32="float32",t.bool="float32",t.complex64="complex64"}(ct||(ct={})),function(t){t.float32="complex64",t.int32="complex64",t.bool="complex64",t.complex64="complex64"}(ht||(ht={}));var ft={float32:ct,int32:ut,bool:lt,complex64:ht};function dt(t,e){if("string"===t||"string"===e){if("string"===t&&"string"===e)return"string";throw new Error("Can not upcast "+t+" with "+e)}return ft[t][e]}function mt(t){return dt(t,"int32")}function vt(t,e){if(t.dtype===e.dtype)return[t,e];var n=dt(t.dtype,e.dtype);return[t.cast(n),e.cast(n)]}function gt(t){var e=[];return function t(e,n,r){if(null==e)return;if(e instanceof at)return void 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c={id:this.nextTapeNodeId++,name:s,inputs:e,outputs:Array.isArray(r)?r:[r]};null!=n&&(c.gradient=function(t){return n(t,a)}),this.activeTape.push(c)}return this.profiling&&this.activeProfile.kernels.push({name:s,bytesAdded:this.numBytes-u,totalBytesSnapshot:this.numBytes,tensorsAdded:this.numTensors-l,totalTensorsSnapshot:this.numTensors,inputShapes:Object.keys(e).map(function(t){return e[t].shape}),outputShape:Array.isArray(r)?r.map(function(t){return t.shape}):r.shape}),r},t.prototype.registerTensor=function(t){var e=this.tensorInfo.has(t.dataId)?this.tensorInfo.get(t.dataId).refCount:0;if(this.numTensors++,"string"===t.dtype&&this.numStringTensors++,0===e){this.numDataBuffers++;var n=0;"complex64"!==t.dtype&&"string"!==t.dtype&&(n=y(t.shape)*_(t.dtype)),this.tensorInfo.set(t.dataId,{backend:this.backend,dtype:t.dtype,shape:t.shape,bytes:n,refCount:0}),this.numBytes+=n,this.backend.register(t.dataId,t.shape,t.dtype)}this.tensorInfo.get(t.dataId).refCount++,t instanceof it||this.track(t)},t.prototype.registerVariable=function(t){if(null!=this.registeredVariables[t.name])throw new Error("Variable with name "+t.name+" was already registered");this.registeredVariables[t.name]=t},t.prototype.disposeTensor=function(t){if(this.tensorInfo.has(t.dataId)){this.keepTensors.has(t.id)&&this.keepTensors.delete(t.id),this.numTensors--,"string"===t.dtype&&this.numStringTensors--;var e=this.tensorInfo.get(t.dataId);e.refCount<=1?("complex64"!==t.dtype&&(this.numBytes-=e.bytes),this.numDataBuffers--,e.backend.disposeData(t.dataId),this.tensorInfo.delete(t.dataId)):this.tensorInfo.get(t.dataId).refCount--}},t.prototype.disposeVariables=function(){for(var t in this.registeredVariables){var e=this.registeredVariables[t];this.disposeTensor(e),delete this.registeredVariables[t]}},t.prototype.memory=function(){var t=this.backend.memory();return t.numTensors=this.numTensors,t.numDataBuffers=this.numDataBuffers,t.numBytes=this.numBytes,this.numStringTensors>0&&(t.unreliable=!0,null==t.reasons&&(t.reasons=[]),t.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),t},t.prototype.profile=function(t){return o(this,void 0,void 0,function(){var e,n;return a(this,function(r){return this.profiling=!0,e=this.numBytes,n=this.numTensors,this.activeProfile.kernels=[],this.activeProfile.result=t(),this.profiling=!1,this.activeProfile.peakBytes=Math.max.apply(Math,this.activeProfile.kernels.map(function(t){return t.totalBytesSnapshot})),this.activeProfile.newBytes=this.numBytes-e,this.activeProfile.newTensors=this.numTensors-n,[2,this.activeProfile]})})},t.prototype.shouldRecord=function(){return null!=this.activeTape&&0===this.customGradientDepth},t.prototype.addTapeNode=function(t,e,n){var r={};t.forEach(function(t,e){r[e]=t});var 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implemented")},t.prototype.rsqrt=function(t){throw new Error("Not yet implemented")},t.prototype.square=function(t){throw new Error("Not yet implemented")},t.prototype.reciprocal=function(t){throw new Error("Not yet implemented")},t.prototype.relu=function(t){throw new Error("Not yet implemented")},t.prototype.elu=function(t){throw new Error("Not yet implemented")},t.prototype.eluDer=function(t,e){throw new Error("Not yet implemented")},t.prototype.selu=function(t){throw new Error("Not yet implemented")},t.prototype.int=function(t){throw new Error("Not yet implemented")},t.prototype.clip=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.abs=function(t){throw new Error("Not yet implemented")},t.prototype.complexAbs=function(t){throw new Error("Not yet implemented")},t.prototype.sigmoid=function(t){throw new Error("Not yet implemented")},t.prototype.softplus=function(t){throw new Error("Not yet implemented")},t.prototype.sin=function(t){throw new Error("Not yet implemented")},t.prototype.cos=function(t){throw new Error("Not yet implemented")},t.prototype.tan=function(t){throw new Error("Not yet implemented")},t.prototype.asin=function(t){throw new Error("Not yet implemented")},t.prototype.acos=function(t){throw new Error("Not yet implemented")},t.prototype.atan=function(t){throw new Error("Not yet implemented")},t.prototype.atan2=function(t,e){throw new Error("Not yet implemented")},t.prototype.sinh=function(t){throw new Error("Not yet implemented")},t.prototype.cosh=function(t){throw new Error("Not yet implemented")},t.prototype.tanh=function(t){throw new Error("Not yet implemented")},t.prototype.asinh=function(t){throw new Error("Not yet implemented")},t.prototype.acosh=function(t){throw new Error("Not yet implemented")},t.prototype.atanh=function(t){throw new Error("Not yet implemented")},t.prototype.erf=function(t){throw new Error("Not yet implemented")},t.prototype.step=function(t,e){throw new Error("Not yet implemented")},t.prototype.conv2d=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.conv2dDerInput=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.conv2dDerFilter=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.depthwiseConv2D=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.depthwiseConv2DDerInput=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.depthwiseConv2DDerFilter=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.conv3d=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.conv3dDerInput=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.conv3dDerFilter=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.maxPool=function(t,e){throw new Error("Not yet implemented")},t.prototype.maxPoolBackprop=function(t,e,n,r){throw new Error("Not yet implemented")},t.prototype.avgPool=function(t,e){throw new Error("Not yet implemented")},t.prototype.avgPoolBackprop=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.reshape=function(t,e){throw new Error("Not yet implemented")},t.prototype.cast=function(t,e){throw new Error("Not yet implemented")},t.prototype.tile=function(t,e){throw new Error("Not yet implemented")},t.prototype.pad=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.transpose=function(t,e){throw new Error("Not yet implemented")},t.prototype.gather=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.gatherND=function(t,e){throw new Error("Not yet implemented")},t.prototype.scatterND=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.batchToSpaceND=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.spaceToBatchND=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.resizeBilinear=function(t,e,n,r){throw new Error("Not yet implemented")},t.prototype.resizeBilinearBackprop=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.resizeNearestNeighbor=function(t,e,n,r){throw new Error("Not yet implemented")},t.prototype.resizeNearestNeighborBackprop=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.batchNormalization=function(t,e,n,r,o,a){throw new Error("Not yet implemented")},t.prototype.localResponseNormalization4D=function(t,e,n,r,o){throw new Error("Not yet implemented")},t.prototype.LRNGrad=function(t,e,n,r,o,a,i){throw new Error("Not yet implemented")},t.prototype.multinomial=function(t,e,n,r){throw new Error("Not yet implemented")},t.prototype.oneHot=function(t,e,n,r){throw new Error("Not yet implemented")},t.prototype.cumsum=function(t,e,n,r){throw new Error("Not yet implemented")},t.prototype.nonMaxSuppression=function(t,e,n,r,o){throw new Error("Not yet implemented")},t.prototype.fft=function(t){throw new Error("Not yet implemented")},t.prototype.ifft=function(t){throw new Error("Not yet implemented")},t.prototype.complex=function(t,e){throw new Error("Not yet implemented")},t.prototype.real=function(t){throw new Error("Not yet implemented")},t.prototype.imag=function(t){throw new Error("Not yet implemented")},t.prototype.cropAndResize=function(t,e,n,r,o,a){throw new Error("Not yet implemented")},t.prototype.depthToSpace=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.split=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.sparseToDense=function(t,e,n,r){throw new Error("Not yet implemented")},t.prototype.setDataMover=function(t){throw new Error("Not yet implemented")},t.prototype.dispose=function(){throw new Error("Not yet implemented")},t}();function Fe(t,e,n){if("complex64"===e){if("complex64"===t.dtype)return t.clone();var r=ke(t.shape),o=t.toFloat(),a=n.complex(o,r);return r.dispose(),o.dispose(),a}if(!A(t.dtype,e))return at.make(t.shape,{dataId:t.dataId},e);if("complex64"===t.dtype){var i=n.real(t);a=i.cast(e);return i.dispose(),a}if("int32"===e)return n.int(t);if("bool"===e){var s=xe(0,t.dtype);a=n.notEqual(t,s);return s.dispose(),a}throw new Error("Error in Cast: unknown dtype argument ("+e+")")}function Be(t,e){return at.make(e,{dataId:t.dataId},t.dtype)}function Le(t,e){if(t.length!==e.length)throw new Error("Cannot merge real and imag arrays of different lengths. real:"+t.length+", imag: "+e.length+".");for(var n=new Float32Array(2*t.length),r=0;r<n.length;r+=2)n[r]=t[r/2],n[r+1]=e[r/2];return n}function Pe(t,e){return{real:t[2*e],imag:t[2*e+1]}}function Ue(t,e,n,r){t[2*r]=e,t[2*r+1]=n}function We(t,e,n){var r=(n?2:-2)*Math.PI*(t/e);return{real:Math.cos(r),imag:Math.sin(r)}}function ze(t,e,n,r,o){for(var a=Array.from(e).map(function(t,e){return{score:t,boxIndex:e}}).filter(function(t){return t.score>o}).sort(function(t,e){return e.score-t.score}),i=[],s=0;s<a.length;s++){var u=a[s],l=u.score,c=u.boxIndex;if(l<o)break;for(var h=!1,p=i.length-1;p>=0;--p){if(Ve(t,c,i[p])>=r){h=!0;break}}if(!h&&(i.push(c),i.length>=n))break}return we(i,"int32")}function Ve(t,e,n){var r=t.subarray(4*e,4*e+4),o=t.subarray(4*n,4*n+4),a=Math.min(r[0],r[2]),i=Math.min(r[1],r[3]),s=Math.max(r[0],r[2]),u=Math.max(r[1],r[3]),l=Math.min(o[0],o[2]),c=Math.min(o[1],o[3]),h=Math.max(o[0],o[2]),p=Math.max(o[1],o[3]),f=(s-a)*(u-i),d=(h-l)*(p-c);if(f<=0||d<=0)return 0;var m=Math.max(a,l),v=Math.max(i,c),g=Math.min(s,h),y=Math.min(u,p),x=Math.max(g-m,0)*Math.max(y-v,0);return x/(f+d-x)}function Ge(t,e,n){var r=Array(t.rank).fill(0),o=t.shape.slice();return e.map(function(e){o[n]=e;var a=t.slice(r,o);return r[n]+=e,a})}function He(t,e,n,r,o){for(var a=e[e.length-1],i=[t.length/a,a],s=i[0],u=i[1],l=k(n,s*r),c=k("int32",s*r),h=0;h<s;h++){for(var p=h*u,f=t.subarray(p,p+u),d=[],m=0;m<f.length;m++)d.push({value:f[m],index:m});d.sort(function(t,e){return e.value-t.value});var v=h*r,g=l.subarray(v,v+r),y=c.subarray(v,v+r);for(m=0;m<r;m++)g[m]=d[m].value,y[m]=d[m].index}var x=e.slice();return x[x.length-1]=r,[ye(l,x,n),ye(c,x,"int32")]}var qe=function(){return function(t,e,n){this.variableNames=["A"];var r=t.windowSize,o=t.batchSize,a=t.inSize,i=Math.ceil(a/r);n||this.variableNames.push("bestIndicesA"),this.outputShape=[o,i];var s="max"===e?">":"<",u=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode="\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * "+r+";\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < "+r+"; i++) {\n          int inIdx = "+u+";\n          float candidate = getA(batch, inIdx);\n          if (candidate "+s+" bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    "}}(),$e=function(){return function(t){this.variableNames=["dy"],this.outputShape=t.inShape;var e=t.filterHeight,n=t.filterWidth,r=t.strideHeight,o=t.strideWidth,a=t.dilationHeight,i=t.dilationWidth,s=t.effectiveFilterHeight,u=t.effectiveFilterWidth,l=s-1-t.padInfo.top,c=u-1-t.padInfo.left,h=1/(e*n);this.userCode="\n      const ivec2 pads = ivec2("+l+", "+c+");\n      const float avgMultiplier = float("+h+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+s+";\n            wR += "+a+") {\n          float dyR = float(dyRCorner + wR) / "+r+".0;\n\n          if (dyR < 0.0 || dyR >= "+t.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < "+u+";\n            wC+= "+i+") {\n            float dyC = float(dyCCorner + wC) / "+o+".0;\n\n            if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    "}}();function je(t,e){for(var n=t.length,r=[],o=0;o<n;o++){var a=n-1-o,i=t[a]||1;(e[e.length-1-o]||1)>1&&1===i&&r.unshift(a)}return r}function Ke(t,e){for(var n=[],r=0;r<e.length;r++){var o=t[t.length-r-1],a=e.length-r-1,i=e[a];(null==o||1===o&&i>1)&&n.unshift(a)}return n}function Xe(t,e){for(var n=[],r=Math.max(t.length,e.length),o=0;o<r;o++){var a=t[t.length-o-1];null==a&&(a=1);var i=e[e.length-o-1];if(null==i&&(i=1),1===a)n.unshift(i);else if(1===i)n.unshift(a);else{if(a!==i)throw Error("Operands could not be broadcast together with shapes "+t+" and "+e+".");n.unshift(a)}}return n}var Ye=function(){return function(t,e,n,r,o,a){this.outputShape=[],this.supportsBroadcasting=!0,this.variableNames=["x","mean","variance"],Xe(t,e),Xe(t,n);var i="0.0";null!=r&&(Xe(t,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");var s="1.0";null!=o&&(Xe(t,o),this.variableNames.push("scale"),s="getScaleAtOutCoords()"),this.outputShape=t,this.userCode="\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = "+i+";\n        float scale = "+s+";\n        float inv = scale * inversesqrt(variance + float("+a+"));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    "}}(),Qe=function(){return function(t,e,n,r,o,a){this.supportsBroadcasting=!0,this.usesPackedTextures=!0,this.variableNames=["x","mean","variance"],Xe(t,e),Xe(t,n);var i="vec4(0.0)";null!=r&&(Xe(t,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");var s="vec4(1.0)";null!=o&&(Xe(t,o),this.variableNames.push("scale"),s="getScaleAtOutCoords()"),this.outputShape=t,this.userCode="\n      void main() {\n        vec4 offset = "+i+";\n        vec4 scale = "+s+";\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4("+a+"));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    "}}(),Je="return areal * breal - aimag * bimag;",Ze="return areal * bimag + aimag * breal;",tn=function(){return function(t,e,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.supportsBroadcasting=!0,this.outputShape=Xe(e,n),this.userCode="\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        "+t+"\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    "}}(),en="return a + b;",nn="return a - b;",rn=function(){function t(t,e,n){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.outputShape=Xe(e,n),this.userCode="\n      uniform float NAN;\n      float binaryOperation(float a, float b) {\n        "+t+"\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    "}return t.prototype.getCustomSetupFunc=function(){var t=this;return function(e,n){null==t.startLoc&&(t.startLoc=e.getUniformLocationNoThrow(n,"NAN"),null==t.startLoc)||e.gl.uniform1f(t.startLoc,NaN)}},t}(),on=function(){return function(t,e,n){this.variableNames=["A"],this.outputShape=t,this.userCode="\n      void main() {\n        float value = getAAtOutCoords();\n        if (isNaN(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, float("+e+"), float("+n+")));\n      }\n    "}}(),an=function(){return function(t,e,n){this.variableNames=["A"],this.usesPackedTextures=!0,this.outputShape=t,this.userCode="\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (hasNaN(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4("+e+"), vec4("+n+")));\n      }\n    "}}(),sn=function(){return function(t){this.variableNames=["real","imag"],this.outputShape=t,this.userCode="\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    "}}(),un=function(){return function(t,e){this.variableNames=["A","B"],this.outputShape=[],this.outputShape=Zt([t,e],1),this.userCode="\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        float value = 0.0;\n        if (yC < "+t[1]+") {\n          value = getA(yR, yC);\n        } else {\n          yC -= "+t[1]+";\n          value = getB(yR, yC);\n        }\n\n        setOutput(value);\n      }\n    "}}(),ln=function(){return function(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;var e=t.strideHeight,n=t.strideWidth,r=t.padInfo.top,o=t.padInfo.left;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < "+t.batchSize+"; b++) {\n          for (int yR = 0; yR < "+t.outHeight+"; yR++) {\n            int xR = wR + yR * "+e+" - "+r+";\n\n            if (xR < 0 || xR >= "+t.inHeight+") {\n              continue;\n            }\n\n            for (int yC = 0; yC < "+t.outWidth+"; yC++) {\n              int xC = wC + yC * "+n+" - "+o+";\n\n              if (xC < 0 || xC >= "+t.inWidth+") {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "}}(),cn=function(){return function(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;var e=t.filterHeight,n=t.filterWidth,r=t.strideHeight,o=t.strideWidth,a=e-1-t.padInfo.top,i=n-1-t.padInfo.left;this.userCode="\n      const ivec2 pads = ivec2("+a+", "+i+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+e+"; wR++) {\n          float dyR = float(dyRCorner + wR) / "+r+".0;\n\n          if (dyR < 0.0 || dyR >= "+t.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = "+e+" - 1 - wR;\n\n          for (int wC = 0; wC < "+n+"; wC++) {\n            float dyC = float(dyCCorner + wC) / "+o+".0;\n\n            if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = "+n+" - 1 - wC;\n\n            for (int d2 = 0; d2 < "+t.outChannels+"; d2++) {\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, d2);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "}}(),hn=function(){return function(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;var e=t.strideDepth,n=t.strideHeight,r=t.strideWidth,o=t.padInfo.front,a=t.padInfo.top,i=t.padInfo.left;this.userCode="\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < "+t.batchSize+"; b++) {\n          for (int yF = 0; yF < "+t.outDepth+"; yF++) {\n            int xF = wF + yF * "+e+" - "+o+";\n\n            if (xF < 0 || xF >= "+t.inDepth+") {\n              continue;\n            }\n\n            for (int yR = 0; yR < "+t.outHeight+"; yR++) {\n              int xR = wR + yR * "+n+" - "+a+";\n\n              if (xR < 0 || xR >= "+t.inHeight+") {\n                continue;\n              }\n\n              for (int yC = 0; yC < "+t.outWidth+"; yC++) {\n                int xC = wC + yC * "+r+" - "+i+";\n\n                if (xC < 0 || xC >= "+t.inWidth+") {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "}}(),pn=function(){return function(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;var e=t.filterDepth,n=t.filterHeight,r=t.filterWidth,o=t.strideDepth,a=t.strideHeight,i=t.strideWidth,s=e-1-t.padInfo.front,u=n-1-t.padInfo.top,l=r-1-t.padInfo.left;this.userCode="\n      const ivec3 pads = ivec3("+s+", "+u+", "+l+");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < "+e+"; wF++) {\n          float dyF = float(dyFCorner + wF) / "+o+".0;\n\n          if (dyF < 0.0 || dyF >= "+t.outDepth+".0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = "+e+" - 1 - wF;\n\n          for (int wR = 0; wR < "+n+"; wR++) {\n            float dyR = float(dyRCorner + wR) / "+a+".0;\n\n            if (dyR < 0.0 || dyR >= "+t.outHeight+".0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = "+n+" - 1 - wR;\n\n            for (int wC = 0; wC < "+r+"; wC++) {\n              float dyC = float(dyCCorner + wC) / "+i+".0;\n\n              if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = "+r+" - 1 - wC;\n\n              for (int d2 = 0; d2 < "+t.outChannels+"; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "}}(),fn=function(){return function(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;var e=t.strideHeight,n=t.strideWidth,r=t.padInfo.top,o=t.padInfo.left,a=t.outChannels/t.inChannels;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * "+a+" + dm;\n\n        float dotProd = 0.0;\n\n        // TODO: Vec4 over the batch size\n        for (int b = 0; b < "+t.batchSize+"; b++) {\n          for (int yR = 0; yR < "+t.outHeight+"; yR++) {\n            int xR = wR + yR * "+e+" - "+r+";\n\n            if (xR < 0 || xR >= "+t.inHeight+") {\n              continue;\n            }\n\n            for (int yC = 0; yC < "+t.outWidth+"; yC++) {\n              int xC = wC + yC * "+n+" - "+o+";\n\n              if (xC < 0 || xC >= "+t.inWidth+") {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "}}(),dn=function(){return function(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;var e=t.filterHeight,n=t.filterWidth,r=t.strideHeight,o=t.strideWidth,a=e-1-t.padInfo.top,i=n-1-t.padInfo.left,s=t.outChannels/t.inChannels;this.userCode="\n      const ivec2 pads = ivec2("+a+", "+i+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < "+e+"; wR++) {\n          float dyR = float(dyRCorner + wR) / "+r+".0;\n\n          if (dyR < 0.0 || dyR >= "+t.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = "+e+" - 1 - wR;\n\n          for (int wC = 0; wC < "+n+"; wC++) {\n            float dyC = float(dyCCorner + wC) / "+o+".0;\n\n            if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = "+n+" - 1 - wC;\n\n            // TODO: Vec4 over the channelMul\n            for (int dm = 0; dm < "+s+"; dm++) {\n              int d2 = d1 * "+s+" + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "}}(),mn=function(){return function(t){this.variableNames=["x","W"],this.outputShape=t.outShape;var e=t.padInfo.top,n=t.padInfo.left,r=t.strideHeight,o=t.strideWidth,a=t.dilationHeight,i=t.dilationWidth,s=t.filterHeight,u=t.filterWidth,l=4*Math.floor(t.inChannels/4),c=t.inChannels%4;this.userCode="\n      const ivec2 strides = ivec2("+r+", "+o+");\n      const ivec2 pads = ivec2("+e+", "+n+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+s+"; wR++) {\n          int xR = xRCorner + wR * "+a+";\n\n          if (xR < 0 || xR >= "+t.inHeight+") {\n            continue;\n          }\n\n          for (int wC = 0; wC < "+u+"; wC++) {\n            int xC = xCCorner + wC * "+i+";\n\n            if (xC < 0 || xC >= "+t.inWidth+") {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < "+l+"; d1 += 4) {\n              vec4 xValues = vec4(\n                getX(batch, xR, xC, d1),\n                getX(batch, xR, xC, d1 + 1),\n                getX(batch, xR, xC, d1 + 2),\n                getX(batch, xR, xC, d1 + 3)\n              );\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              dotProd += dot(xValues, wValues);\n            }\n\n            if ("+(1===c)+") {\n              dotProd +=\n                getX(batch, xR, xC, "+l+") *\n                getW(wR, wC, "+l+", d2);\n            } else if ("+(2===c)+") {\n              vec2 xValues = vec2(\n                getX(batch, xR, xC, "+l+"),\n                getX(batch, xR, xC, "+l+" + 1)\n              );\n              vec2 wValues = vec2(\n                getW(wR, wC, "+l+", d2),\n                getW(wR, wC, "+l+" + 1, d2)\n              );\n              dotProd += dot(xValues, wValues);\n            } else if ("+(3===c)+") {\n              vec3 xValues = vec3(\n                getX(batch, xR, xC, "+l+"),\n                getX(batch, xR, xC, "+l+" + 1),\n                getX(batch, xR, xC, "+l+" + 2)\n              );\n              vec3 wValues = vec3(\n                getW(wR, wC, "+l+", d2),\n                getW(wR, wC, "+l+" + 1, d2),\n                getW(wR, wC, "+l+" + 2, d2)\n              );\n              dotProd += dot(xValues, wValues);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "}}(),vn=function(){return function(t){this.variableNames=["x","W"],this.outputShape=t.outShape;var e=t.padInfo.front,n=t.padInfo.top,r=t.padInfo.left,o=t.strideDepth,a=t.strideHeight,i=t.strideWidth,s=t.dilationDepth,u=t.dilationHeight,l=t.dilationWidth,c=t.filterDepth,h=t.filterHeight,p=t.filterWidth,f=4*Math.floor(t.inChannels/4),d=t.inChannels%4;this.userCode="\n      const ivec3 strides = ivec3("+o+", "+a+", "+i+");\n      const ivec3 pads = ivec3("+e+", "+n+", "+r+");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < "+c+"; wF++) {\n          int xF = xFCorner + wF * "+s+";\n\n          if (xF < 0 || xF >= "+t.inDepth+") {\n            continue;\n          }\n\n          for (int wR = 0; wR < "+h+"; wR++) {\n            int xR = xRCorner + wR * "+u+";\n\n            if (xR < 0 || xR >= "+t.inHeight+") {\n              continue;\n            }\n\n            for (int wC = 0; wC < "+p+"; wC++) {\n              int xC = xCCorner + wC * "+l+";\n\n              if (xC < 0 || xC >= "+t.inWidth+") {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < "+f+"; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if ("+(1===d)+") {\n                dotProd +=\n                  getX(batch, xF, xR, xC, "+f+") *\n                  getW(wF, wR, wC, "+f+", d2);\n              } else if ("+(2===d)+") {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, "+f+"),\n                  getX(batch, xF, xR, xC, "+f+" + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, "+f+", d2),\n                  getW(wF, wR, wC, "+f+" + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if ("+(3===d)+") {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, "+f+"),\n                  getX(batch, xF, xR, xC, "+f+" + 1),\n                  getX(batch, xF, xR, xC, "+f+" + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, "+f+", d2),\n                  getW(wF, wR, wC, "+f+" + 1, d2),\n                  getW(wF, wR, wC, "+f+" + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "}}(),gn=function(){return function(t){this.variableNames=["x","W"],this.outputShape=t.outShape;var e=t.inHeight,n=t.inWidth,r=t.padInfo.top,o=t.padInfo.left,a=t.strideHeight,i=t.strideWidth,s=t.dilationHeight,u=t.dilationWidth,l=t.filterHeight,c=t.filterWidth,h=t.outChannels/t.inChannels;this.userCode="\n      const ivec2 strides = ivec2("+a+", "+i+");\n      const ivec2 pads = ivec2("+r+", "+o+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / "+h+";\n        int q = d2 - d1 * "+h+";\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TODO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < "+l+"; wR++) {\n          int xR = xRCorner + wR * "+s+";\n\n          if (xR < 0 || xR >= "+e+") {\n            continue;\n          }\n\n          for (int wC = 0; wC < "+c+"; wC++) {\n            int xC = xCCorner + wC * "+u+";\n\n            if (xC < 0 || xC >= "+n+") {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n        setOutput(dotProd);\n      }\n    "}}(),yn=function(){return function(t){this.variableNames=["x","W"],this.usesPackedTextures=!0,this.outputShape=t.outShape;for(var e=t.inHeight,n=t.inWidth,r=t.padInfo.top,o=t.padInfo.left,a=t.strideHeight,i=t.strideWidth,s=t.filterHeight,u=t.filterWidth,l=Math.ceil((u+1)/2),c="int xR; int xC;",h=0;h<s;h++){for(var p=-o;p<2*l;p++)c+="vec4 "+xn(h,p)+" = vec4(0.);";for(p=0;p<u;p++)c+="\n          vec4 wR"+h+"C"+p+" = vec4(0.);\n          vec4 xR"+h+"C"+p+" = vec4(0.);"}for(h=0;h<s;h++)for(p=0;p<l;p++){var f=2*p,d=2*p+o;c+="\n          xR = xRCorner + "+h+";\n          xC = xCCorner + "+d+";\n\n          if(xR >= 0 && xR < "+e+" && xC >= 0 && xC < "+n+") {\n            "+xn(h,d)+" = getX(batch, xR, xC, d1);\n          }",0===o?f<u&&p===l-1&&(i>1&&(c+="\n                vec4 "+xn(h,d+2)+" = vec4(0.);\n\n                if(xR >= 0 && xR < "+e+" && xC + 2 < "+n+") {\n                  "+xn(h,d+2)+" = getX(batch, xR, xC + 2, d1);\n                }"),c+="\n              xR"+h+"C"+d+" = "+wn(h,d,i,o)+";\n            "):0===p&&(c+="\n            if(xR >= 0 && xR < "+e+" && xC - 2 >= 0) {\n              "+xn(h,d-2)+" = getX(batch, xR, xC - 2, d1);\n            }"),f>0&&(c+="xR"+h+"C"+(d-2)+" =\n            "+wn(h,d-2,i,o)+";"),d-1>=0&&d-1<u&&(c+="xR"+h+"C"+(d-1)+" =\n              "+wn(h,d-1,i,o)+";"),f<u&&(c+="\n            vec4 wTexel"+h+"C"+f+" = getW("+h+", "+f+", d1, q);\n            wR"+h+"C"+f+" = vec4(wTexel"+h+"C"+f+".xz, wTexel"+h+"C"+f+".xz);\n          ",f+1<u&&(c+="\n              vec4 wTexelR"+h+"C"+(f+1)+" = getW("+h+", "+(f+1)+", d1, q);\n              wR"+h+"C"+(f+1)+" =\n                vec4(wTexelR"+h+"C"+(f+1)+".xz, wTexelR"+h+"C"+(f+1)+".xz);"))}for(h=0;h<s;h++)for(p=0;p<u;p++)c+="result += xR"+h+"C"+p+" * wR"+h+"C"+p+";";this.userCode="\n      const ivec2 strides = ivec2("+a+", "+i+");\n      const ivec2 pads = ivec2("+r+", "+o+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2;\n        int q = 0;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        vec4 result = vec4(0.);\n\n        "+c+"\n\n        setOutput(result);\n      }\n    "}}();function xn(t,e){return"xTexelR"+t+"C"+(e<0?"minus"+Math.abs(e).toString():e)}function wn(t,e,n,r){return 1===n?r%2==e%2?xn(t,e):"vec4("+xn(t,e-1)+".zw, "+xn(t,e+1)+".xy)":r%2==e%2?"vec4("+xn(t,e)+".xy, "+xn(t,e+2)+".xy)":"vec4("+xn(t,e-1)+".zw, "+xn(t,e+1)+".zw)"}var bn=function(){return function(t,e,n,r,o){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];var a=t[0],i=t[1],s=t[2],u=t[3],l=e[0],c=n[0],h=n[1];this.outputShape=[l,c,h,u];var p="bilinear"===r?1:0,f=[i-1+".0",s-1+".0"],d=f[0],m=f[1],v=c>1?[""+(i-1)/(c-1),"(y2-y1) * height_ratio","y1*"+d+" + float(y)*(height_scale)"]:["0.0","0.0","0.5 * (y1+y2) * "+d],g=v[0],y=v[1],x=v[2],w=h>1?[""+(s-1)/(h-1),"(x2-x1) * width_ratio","x1*"+m+" + float(x)*(width_scale)"]:["0.0","0.0","0.5 * (x1+x2) * "+m],b=w[0],E=w[1],C=w[2];this.userCode="\n      const float height_ratio = float("+g+");\n      const float width_ratio = float("+b+");\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= "+a+") {\n          return;\n        }\n\n        float height_scale = "+y+";\n        float width_scale = "+E+";\n\n        float in_y = "+x+";\n        if( in_y < 0.0 || in_y > "+d+" ) {\n          setOutput(float("+o+"));\n          return;\n        }\n        float in_x = "+C+";\n        if( in_x < 0.0 || in_x > "+m+" ) {\n          setOutput(float("+o+"));\n          return;\n        }\n\n        vec2 sourceFracIndexRC = vec2(in_y,in_x);\n        if("+p+" == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n          ivec2 sourceCeilRC = ivec2(ceil(sourceFracIndexRC));\n\n          float topLeft = getImage(b, sourceFloorRC.x, sourceFloorRC.y, d);\n          float bottomLeft = getImage(b, sourceCeilRC.x, sourceFloorRC.y, d);\n          float topRight = getImage(b, sourceFloorRC.x, sourceCeilRC.y, d);\n          float bottomRight = getImage(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n          vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n          float top = topLeft + (topRight - topLeft) * fracRC.y;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n          float newValue = top + (bottom - top) * fracRC.x;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestRC = ivec2(floor(\n            sourceFracIndexRC + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestRC.x, sourceNearestRC.y, d);\n          setOutput(newValue);\n        }\n      }\n    "}}();function En(t,e,n){void 0===n&&(n="index");var r=z(e);return r.map(function(e,o){return"int "+t[o]+" = "+n+" / "+e+"; "+(o===r.length-1?"int "+t[o+1]+" = "+n+" - "+t[o]+" * "+e:"index -= "+t[o]+" * "+e)+";"}).join("")}function Cn(t){return 1===t.length?""+t[0]:"vec"+t.length+"("+t.join(",")+")"}function Sn(t,e,n,r,o){var a=t.map(function(t){var e=y(t.shapeInfo.logicalShape);return t.shapeInfo.isUniform?"uniform float "+t.name+(e>1?"["+e+"]":"")+";":"uniform sampler2D "+t.name+";"});a=a.join("\n");var i,s,u=t.map(function(t){return function(t,e,n,r){void 0===r&&(r=!1);var o=function(t){var e=t.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1)+"Flat",r=y(t.shapeInfo.logicalShape);if(t.shapeInfo.isUniform)return 1===r?"float "+n+"(int index) {return "+e+";}":"\n      float "+n+"(int index) {\n        for (int i = 0; i < "+r+"; i++) {\n          if (i == index) {\n            return "+e+"[i];\n          }\n        }\n      }\n    ";var o=t.shapeInfo.texShape,a=o[0],i=o[1];if(1===i&&1===a)return"\n      float "+n+"(int index) {\n        return sampleTexture("+e+", halfCR);\n      }\n    ";if(1===i)return"\n      float "+n+"(int index) {\n        vec2 uv = vec2(0.5, (float(index) + 0.5) / "+a+".0);\n        return sampleTexture("+e+", uv);\n      }\n    ";if(1===a)return"\n      float "+n+"(int index) {\n        vec2 uv = vec2((float(index) + 0.5) / "+i+".0, 0.5);\n        return sampleTexture("+e+", uv);\n      }\n    ";return"\n    float "+n+"(int index) {\n      vec2 uv = UVfrom1D("+a+", "+i+", index);\n      return sampleTexture("+e+", uv);\n    }\n  "}(t);o+=r?Nn(t):Rn(t);(n||x(t.shapeInfo.logicalShape,e.logicalShape))&&(o+=r?function(t,e,n){var r=t.name,o=r.charAt(0).toUpperCase()+r.slice(1),a=t.shapeInfo.texShape,i="get"+o+"AtOutCoords",s=e.texShape,u=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],l=u[0],c=u[1],h=je(t.shapeInfo.logicalShape,e.logicalShape),p=t.shapeInfo.logicalShape.length,f=e.logicalShape.length;if(h.length)throw Error("Packed broadcast sampling is not implemented yet.");if(x(t.shapeInfo.texShape,s))return"\n      vec4 "+i+"() {\n        return texture2D("+r+", resultUV);\n      }\n    ";var d="return texture2D("+r+", uv)";1===p&&f>1?d="\n      vec4 sample = texture2D("+r+", uv);\n      return vec4(sample.xy, sample.xy);\n    ":0===p&&f>0&&(d=1===f?"\n        vec4 sample = texture2D("+r+", uv);\n        return vec4(sample.x, sample.x, 0., 0.);\n      ":"\n        vec4 sample = texture2D("+r+", uv);\n        return vec4(sample.x);\n      ");return"\n    vec4 "+i+"() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+u[0]+", "+u[1]+"));\n      int index = resTexRC.x * "+u[1]+" + resTexRC.y;\n\n      int texR = index / "+c+";\n      int texC = index - texR * "+c+";\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2("+c+", "+l+");\n\n      "+d+";\n    }\n  "}(t,e):function(t,e,n){var r=t.name,o=r.charAt(0).toUpperCase()+r.slice(1),a="get"+o+"AtOutCoords",i=je(t.shapeInfo.logicalShape,e.logicalShape),s=t.shapeInfo.logicalShape.length,u=e.logicalShape.length,l=n&&(u>s||i.length>0),c=function(t){for(var e=0;e<t.length;e++)if(t[e]!==e)return!1;return!0}(i),h=t.shapeInfo.isUniform;if(l&&!c)return function(t,e,n,r){var o=t.shapeInfo.logicalShape.length,a=e.logicalShape.length,i="int";2===a?i="ivec2":3===a?i="ivec3":4===a&&(i="ivec4");var s,u=je(t.shapeInfo.logicalShape,e.logicalShape),l=a-o;s=0===o?"":a<2&&u.length>=1?"coords = 0;":u.map(function(t){return"coords["+(t+l)+"] = 0;"}).join("\n");var c="";c=a<2&&o>0?"coords":t.shapeInfo.logicalShape.map(function(t,e){return"coords["+(e+l)+"]"}).join(", ");return"\n    float "+r+"() {\n      "+i+" coords = getOutputCoords();\n      "+s+"\n      return get"+n+"("+c+");\n    }\n  "}(t,e,o,a);var p=y(t.shapeInfo.logicalShape),f="";l&&c&&(f="\n        int mainPart = index / "+p+";\n        index -= mainPart * "+p+";\n      ");var d=e.texShape;if(h)return 1===p?"float "+a+"() {return "+r+";}":"\n      float "+a+"() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                              vec2("+d[0]+", "+d[1]+"));\n        int index = resTexRC.x * "+d[1]+" + resTexRC.y;\n        "+f+"\n        return get"+o+"Flat(index);\n      }\n    ";var m=t.shapeInfo.texShape;if(x(m,d))return"\n      float "+a+"() {\n        return sampleTexture("+r+", resultUV);\n      }\n    ";return"\n    float "+a+"() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+d[0]+", "+d[1]+"));\n      int index = resTexRC.x * "+d[1]+" + resTexRC.y;\n      "+f+"\n      int texR = index / "+m[1]+";\n      int texC = index - texR * "+m[1]+";\n      vec2 uv = (vec2(texC, texR) + halfCR) /\n                 vec2("+m[1]+".0, "+m[0]+".0);\n\n      return sampleTexture("+r+", uv);\n    }\n  "}(t,e,n));return o}(t,e,r,o)}).join("\n"),l=e.texShape,c=Dn;return e.isPacked?(i=function(t,e){switch(t.length){case 0:return"\n    int getOutputCoords() {\n      return 0;\n    }\n  ";case 1:return function(t,e){var n=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(1===e[0])return"\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * "+n[1]+".0);\n      }\n    ";if(1===e[1])return"\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * "+n[0]+".0);\n      }\n    ";return"\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+n[0]+", "+n[1]+"));\n      return resTexRC.x * "+n[1]+" + resTexRC.y;\n    }\n  "}(0,e);case 2:return function(t,e){var n=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(x(t,e))return"\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2("+n[0]+", "+n[1]+"));\n      }\n    ";var r=Math.ceil(t[1]/2);return"\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+n[0]+", "+n[1]+"));\n\n      int index = resTexRC.x * "+n[1]+" + resTexRC.y;\n      int r = 2 * (index / "+r+");\n      int c = imod(index, "+r+") * 2;\n\n      return ivec2(r, c);\n    }\n  "}(t,e);case 3:return n=t,r=e,o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],a=Math.ceil(n[2]/2),i=a*Math.ceil(n[1]/2),"\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+o[0]+", "+o[1]+"));\n      int index = resTexRC.x * "+o[1]+" + resTexRC.y;\n\n      int b = index / "+i+";\n      index -= b * "+i+";\n\n      int r = 2 * (index / "+a+");\n      int c = imod(index, "+a+") * 2;\n\n      return ivec3(b, r, c);\n    }\n  ";case 4:return function(t,e){var n=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)],r=Math.ceil(t[3]/2),o=r*Math.ceil(t[2]/2),a=o*t[1];return"\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+n[0]+", "+n[1]+"));\n      int index = resTexRC.x * "+n[1]+" + resTexRC.y;\n\n      int b2 = index / "+a+";\n      index -= b2 * "+a+";\n\n      int b = index / "+o+";\n      index -= b * "+o+";\n\n      int r = 2 * (index / "+r+");\n      int c = imod(index, "+r+") * 2;\n\n      return ivec4(b2, b, r, c);\n    }\n  "}(t,e);default:throw new Error(t.length+"-D packed output coordinate fetching is not yet supported")}var n,r,o,a,i}(e.logicalShape,l),s=In):(i=function(t,e){switch(t.length){case 0:return"\n    int getOutputCoords() {\n      return 0;\n    }\n  ";case 1:return function(t,e){if(1===e[0])return"\n      int getOutputCoords() {\n        return int(resultUV.x * "+e[1]+".0);\n      }\n    ";if(1===e[1])return"\n      int getOutputCoords() {\n        return int(resultUV.y * "+e[0]+".0);\n      }\n    ";return"\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+e[0]+", "+e[1]+"));\n      return resTexRC.x * "+e[1]+" + resTexRC.y;\n    }\n  "}(0,e);case 2:return function(t,e){if(x(t,e))return"\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2("+e[0]+", "+e[1]+"));\n      }\n    ";if(1===t[1])return"\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2("+e[0]+", "+e[1]+"));\n        int index = resTexRC.x * "+e[1]+" + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    ";if(1===t[0])return"\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2("+e[0]+", "+e[1]+"));\n        int index = resTexRC.x * "+e[1]+" + resTexRC.y;\n        return ivec2(0, index);\n      }\n    ";return"\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+e[0]+", "+e[1]+"));\n      int index = resTexRC.x * "+e[1]+" + resTexRC.y;\n      int r = index / "+t[1]+";\n      int c = index - r * "+t[1]+";\n      return ivec2(r, c);\n    }\n  "}(t,e);case 3:return n=e,r=En(["r","c","d"],t),"\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+n[0]+", "+n[1]+"));\n      int index = resTexRC.x * "+n[1]+" + resTexRC.y;\n      "+r+"\n      return ivec3(r, c, d);\n    }\n  ";case 4:return function(t,e){var n=En(["r","c","d","d2"],t);return"\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2("+e[0]+", "+e[1]+"));\n      int index = resTexRC.x * "+e[1]+" + resTexRC.y;\n      "+n+"\n      return ivec4(r, c, d, d2);\n    }\n  "}(t,e);case 5:return function(t,e){var n=En(["r","c","d","d2","d3"],t);return"\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2("+e[0]+",\n                             "+e[1]+"));\n\n      int index = resTexRC.x * "+e[1]+" + resTexRC.y;\n\n      "+n+"\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  "}(t,e);case 6:return function(t,e){var n=En(["r","c","d","d2","d3","d4"],t);return"\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2("+e[0]+", "+e[1]+"));\n      int index = resTexRC.x * "+e[1]+" + resTexRC.y;\n\n      "+n+"\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  "}(t,e);default:throw new Error(t.length+"-D output sampling is not yet supported")}var n,r}(e.logicalShape,l),s=Tn),o&&(c+=An),[c,kn,s,a,i,u,n].join("\n")}function Rn(t){var e,n,r=t.shapeInfo.logicalShape;switch(r.length){case 0:return function(t){var e=t.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1);if(t.shapeInfo.isUniform)return"float "+n+"() {return "+e+";}";return"\n    float "+n+"() {\n      return sampleTexture("+e+", halfCR);\n    }\n  "}(t);case 1:return e=t.name,"\n    float "+(n="get"+e.charAt(0).toUpperCase()+e.slice(1))+"(int index) {\n      return "+n+"Flat(index);\n    }\n  ";case 2:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=t.shapeInfo.texShape;if(null!=o&&x(e,o)){var a=o[0],i=o[1];return"\n    float "+r+"(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2("+i+".0, "+a+".0);\n      return sampleTexture("+n+", uv);\n    }\n  "}var s=N(e),u=s.newShape,l=s.keptDims,c=u;if(c.length<e.length){var h=_n(t,c);return"\n      "+Rn(h)+"\n      float "+r+"(int row, int col) {\n        return "+r+"("+On(["row","col"],l)+");\n      }\n    "}if(t.shapeInfo.isUniform)return"\n      float "+r+"(int row, int col) {\n        float index = dot(vec2(row, col), vec2("+e[1]+", 1));\n        return "+r+"Flat(round(index));\n      }\n    ";var p=o[0],f=o[1];if(1===f)return"\n    float "+r+"(int row, int col) {\n      float index = dot(vec2(row, col), vec2("+e[1]+", 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / "+p+".0);\n      return sampleTexture("+n+", uv);\n    }\n  ";if(1===p)return"\n    float "+r+"(int row, int col) {\n      float index = dot(vec2(row, col), vec2("+e[1]+", 1));\n      vec2 uv = vec2((index + 0.5) / "+f+".0, 0.5);\n      return sampleTexture("+n+", uv);\n    }\n  ";return"\n  float "+r+"(int row, int col) {\n    vec2 uv = UVfrom2D("+p+", "+f+", "+e[1]+", row, col);\n    return sampleTexture("+n+", uv);\n  }\n"}(t);case 3:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e[1]*e[2],a=e[2],i=N(e),s=i.newShape,u=i.keptDims,l=s;if(l.length<e.length){var c=_n(t,l);return"\n        "+Rn(c)+"\n        float "+r+"(int row, int col, int depth) {\n          return "+r+"("+On(["row","col","depth"],u)+");\n        }\n      "}if(t.shapeInfo.isUniform)return"\n      float "+r+"(int row, int col, int depth) {\n        float index = dot(vec3(row, col, depth),\n                          vec3("+o+", "+a+", 1));\n        return "+r+"Flat(round(index));\n      }\n    ";var h=t.shapeInfo.texShape,p=h[0],f=h[1];if(f===o)return"\n        float "+r+"(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2("+a+", 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2("+f+".0, "+p+".0);\n          return sampleTexture("+n+", uv);\n        }\n      ";if(f===a)return"\n    float "+r+"(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2("+e[1]+", 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2("+f+".0, "+p+".0);\n      return sampleTexture("+n+", uv);\n    }\n  ";return"\n      float "+r+"(int row, int col, int depth) {\n        vec2 uv = UVfrom3D(\n            "+p+", "+f+", "+o+", "+a+", row, col, depth);\n        return sampleTexture("+n+", uv);\n      }\n  "}(t);case 4:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e[3],a=e[2]*o,i=e[1]*a,s=N(e),u=s.newShape,l=s.keptDims;if(u.length<e.length){var c=_n(t,u);return"\n      "+Rn(c)+"\n      float "+r+"(int row, int col, int depth, int depth2) {\n        return "+r+"("+On(["row","col","depth","depth2"],l)+");\n      }\n    "}if(t.shapeInfo.isUniform)return"\n      float "+r+"(int row, int col, int depth, int depth2) {\n        float index = dot(vec4(row, col, depth, depth2),\n                          vec4("+i+", "+a+", "+o+", 1));\n        return "+r+"Flat(round(index));\n      }\n    ";var h=t.shapeInfo.texShape,p=h[0],f=h[1];if(f===i)return"\n      float "+r+"(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2), vec3("+a+", "+o+", 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2("+f+".0, "+p+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ";if(f===o)return"\n      float "+r+"(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3("+e[1]*e[2]+", "+e[2]+", 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2("+f+".0, "+p+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ";return"\n    float "+r+"(int row, int col, int depth, int depth2) {\n      vec2 uv = UVfrom4D("+p+", "+f+", "+i+", "+a+",\n          "+o+", row, col, depth, depth2);\n      return sampleTexture("+n+", uv);\n    }\n  "}(t);case 5:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e[4],a=e[3]*o,i=e[2]*a,s=e[1]*i,u=N(e),l=u.newShape,c=u.keptDims;if(l.length<e.length){var h=_n(t,l);return"\n      "+Rn(h)+"\n      float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n        return "+r+"("+On(["row","col","depth","depth2","depth3"],c)+");\n      }\n    "}if(t.shapeInfo.isUniform)return"\n      float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4("+s+", "+i+", "+a+", "+o+")) +\n          depth3;\n        return "+r+"Flat(index);\n      }\n    ";var p=t.shapeInfo.texShape,f=p[0],d=p[1];if(d===s)return"\n      float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(\n          vec4(col, depth, depth2, depth3),\n          vec4("+i+", "+a+", "+o+", 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2("+d+".0, "+f+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ";if(d===o)return"\n      float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4("+e[1]*e[2]*e[3]+", "+e[2]*e[3]+",\n            "+e[3]+", 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2("+d+".0, "+f+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ";return"\n    float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n      vec2 uv = UVfrom5D("+f+", "+d+", "+s+", "+i+",\n          "+a+", "+o+", row, col, depth, depth2, depth3);\n      return sampleTexture("+n+", uv);\n    }\n  "}(t);case 6:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e[5],a=e[4]*o,i=e[3]*a,s=e[2]*i,u=e[1]*s,l=N(e),c=l.newShape,h=l.keptDims;if(c.length<e.length){var p=_n(t,c);return"\n      "+Rn(p)+"\n      float "+r+"(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return "+r+"("+On(["row","col","depth","depth2","depth3","depth4"],h)+");\n      }\n    "}if(t.shapeInfo.isUniform)return"\n      float "+r+"(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4("+u+", "+s+", "+i+", "+a+")) +\n          dot(\n            vec2(depth3, depth4),\n            vec2("+o+", 1));\n        return "+r+"Flat(index);\n      }\n    ";var f=t.shapeInfo.texShape,d=f[0],m=f[1];if(m===u)return"\n      float "+r+"(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(\n          vec4(col, depth, depth2, depth3),\n          vec4("+s+", "+i+", "+a+", "+o+")) + depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2("+m+".0, "+d+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ";if(m===o)return"\n      float "+r+"(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4("+e[1]*e[2]*e[3]*e[4]+",\n               "+e[2]*e[3]*e[4]+",\n               "+e[3]*e[4]+",\n               "+e[4]+")) + depth3;\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2("+m+".0, "+d+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ";return"\n    float "+r+"(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      vec2 uv = UVfrom6D("+d+", "+m+", "+u+", "+s+",\n          "+i+", "+a+", "+o+"\n          ,row, col, depth, depth2, depth3, depth4);\n      return sampleTexture("+n+", uv);\n    }\n  "}(t);default:throw new Error(r.length+"-D input sampling is not yet supported")}}function Nn(t){var e,n=t.shapeInfo.logicalShape;switch(n.length){case 0:return"\n    vec4 get"+(e=t.name).charAt(0).toUpperCase()+e.slice(1)+"() {\n      return texture2D("+e+", halfCR);\n    }\n  ";case 1:return function(t){var e=t.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1),r=t.shapeInfo.texShape,o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return"\n    vec4 "+n+"(int index) {\n      vec2 uv = packedUVfrom1D(\n        "+o[0]+", "+o[1]+", index);\n      return texture2D("+e+", uv);\n    }\n  "}(t);case 2:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=t.shapeInfo.texShape,a=o[0],i=o[1];if(null!=o&&x(e,o))return"\n      vec4 "+r+"(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2("+i+".0, "+a+".0);\n\n        return texture2D("+n+", uv);\n      }\n    ";var s=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],u=Math.ceil(e[1]/2);return"\n    vec4 "+r+"(int row, int col) {\n      vec2 uv = packedUVfrom2D("+u+", "+s[0]+", "+s[1]+", row, col);\n      return texture2D("+n+", uv);\n    }\n  "}(t);case 3:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=t.shapeInfo.texShape,a=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];if(1===e[0]){var i=e.slice(1),s=_n(t,i);return"\n        "+Nn(s)+"\n        vec4 "+r+"(int b, int row, int col) {\n          return "+r+"("+On(["b","row","col"],[1,2])+");\n        }\n      "}var u=a[0],l=a[1],c=Math.ceil(e[2]/2),h=c*Math.ceil(e[1]/2);return"\n    vec4 "+r+"(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        "+u+", "+l+", "+h+", "+c+", b, row, col);\n      return texture2D("+n+", uv);\n    }\n  "}(t);case 4:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=t.shapeInfo.texShape,a=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],i=a[0],s=a[1],u=Math.ceil(e[3]/2),l=u*Math.ceil(e[2]/2),c=l*e[1];return"\n    vec4 "+r+"(int b2, int b, int row, int col) {\n      vec2 uv = packedUVfrom4D(\n        "+i+", "+s+", "+c+",\n        "+l+", "+u+", b2, b, row, col);\n      return texture2D("+n+", uv);\n    }\n  "}(t);default:throw new Error("Packed "+n.length+"-D input sampling is not yet supported")}}var kn="\n  float sampleTexture(sampler2D textureSampler, vec2 uv) {\n    return texture2D(textureSampler, uv).r;\n  }\n",Tn="\n  void setOutput(float val) {\n    gl_FragColor = vec4(val, 0, 0, 0);\n  }\n",In="\n  void setOutput(vec4 val) {\n    gl_FragColor = val;\n  }\n",Dn="\n  precision highp float;\n  precision highp int;\n  varying vec2 resultUV;\n  const vec2 halfCR = vec2(0.5, 0.5);\n\n  struct ivec5\n  {\n    int x;\n    int y;\n    int z;\n    int w;\n    int u;\n  };\n\n  struct ivec6\n  {\n    int x;\n    int y;\n    int z;\n    int w;\n    int u;\n    int v;\n  };\n\n  "+(It.get("PROD")?"\n    bool isNaN(float val) {\n      return false;\n    }\n\n    bool hasNaN(vec4 values) {\n      return false;\n    }\n  ":"\n    bool isNaN(float val) {\n      return (val < 1.0 || 0.0 < val || val == 0.0) ? false : true;\n    }\n\n    bool hasNaN(vec4 values) {\n      return any(bvec4(\n        isNaN(values.x),\n        isNaN(values.y),\n        isNaN(values.z),\n        isNaN(values.w)\n      ));\n    }\n  ")+"\n\n  float getNaN(vec4 values) {\n    return dot(vec4(1), values);\n  }\n\n  int round(float value) {\n    return int(floor(value + 0.5));\n  }\n\n  int imod(int x, int y) {\n    return x - y * (x / y);\n  }\n\n  //Based on the work of Dave Hoskins\n  //https://www.shadertoy.com/view/4djSRW\n  #define HASHSCALE1 443.8975\n  float random(float seed){\n    vec2 p = resultUV * seed;\n    vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n    p3 += dot(p3, p3.yzx + 19.19);\n    return fract((p3.x + p3.y) * p3.z);\n  }\n\n  \nvec2 UVfrom1D(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n\n  \nvec2 UVfrom2D(int texNumR, int texNumC, int numC, int row, int col) {\n  int index = row * numC + col;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n\n  \nvec2 UVfrom3D(int texNumR, int texNumC, int stride0,\n    int stride1, int row, int col, int depth) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n\n  \nvec2 UVfrom4D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int row, int col, int depth,\n    int depth2) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth * stride2 + depth2;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom4D(int texNumR, int texNumC, int texelsInBatch2,\n    int texelsInBatch, int texelsInLogicalRow, int b2, int b,\n    int row, int col) {\n  int index = b2 * texelsInBatch2 + b * texelsInBatch +\n    (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n\n  \nvec2 UVfrom5D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int stride3, int row, int col, int depth,\n    int depth2, int depth3) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 +\n              depth * stride2 + depth2 * stride3 + depth3;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n\n  \nvec2 UVfrom6D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int stride3, int stride4,\n    int row, int col, int depth, int depth2, int depth3, int depth4) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth * stride2 + depth2 *\n    stride3 + depth3 * stride4 + depth4;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n\n",An="\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n";function Mn(t){if(t<=1)return"int";if(2===t)return"ivec2";if(3===t)return"ivec3";if(4===t)return"ivec4";if(5===t)return"ivec5";if(6===t)return"ivec6";throw Error("GPU for rank "+t+" is not yet supported")}function _n(t,e){var n=JSON.parse(JSON.stringify(t));return n.shapeInfo.logicalShape=e,n}function On(t,e){return e.map(function(e){return t[e]}).join(", ")}var Fn=function(){return function(t,e,n){this.variableNames=["x"],this.outputShape=t;var r=t.length,o=t[t.length-1],a=n?"<":">";this.userCode="\n      int getIndex(int i) {\n        "+(n?"return "+o+" -i - 1;":"return i;")+"\n      }\n\n      void main() {\n        "+Mn(r)+" coords = getOutputCoords();\n        int end = "+Bn(r,"coords")+";\n        float val = 0.0;\n        for (int i = "+o+" - 1; i >= 0; i -= 1) {\n          int idx = getIndex(i);\n          if (idx "+a+" end) {\n            continue;\n          }\n          if (idx == end && "+e+") {\n            continue;\n          }\n          "+Bn(r,"coords")+" = idx;\n          val += getX("+function(t,e){if(1===t)return""+e;if(2===t)return e+".x, "+e+".y";if(3===t)return e+".x, "+e+".y, "+e+".z";if(4===t)return e+".x, "+e+".y, "+e+".z, "+e+".w";throw Error("Cumulative sum for rank "+t+" is not yet supported")}(r,"coords")+");\n        }\n        setOutput(val);\n      }\n    "}}();function Bn(t,e){if(1===t)return""+e;if(2===t)return e+".y";if(3===t)return e+".z";if(4===t)return e+".w";throw Error("Cumulative sum for rank "+t+" is not yet supported")}var Ln=function(){function t(t,e,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=n,this.userCode="\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = "+this.getHeightCoordString()+";\n      int w = "+this.getWidthCoordString()+";\n      int d = "+this.getDepthCoordString()+";\n\n      int in_h = h / "+e+";\n      int offset_h = imod(h, "+e+");\n      int in_w = w / "+e+";\n      int offset_w = imod(w, "+e+");\n      int offset_d = (offset_h * "+e+" + offset_w) *\n        "+this.getOutputDepthSize()+";\n      int in_d = d + offset_d;\n\n      float result = "+this.getInputSamplingString()+";\n      setOutput(result);\n    }\n  "}return t.prototype.getHeightCoordString=function(){return"NHWC"===this.dataFormat?"coords[1]":"coords[2]"},t.prototype.getWidthCoordString=function(){return"NHWC"===this.dataFormat?"coords[2]":"coords[3]"},t.prototype.getDepthCoordString=function(){return"NHWC"===this.dataFormat?"coords[3]":"coords[1]"},t.prototype.getOutputDepthSize=function(){return"NHWC"===this.dataFormat?this.outputShape[3]:this.outputShape[1]},t.prototype.getInputSamplingString=function(){return"NHWC"===this.dataFormat?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"},t}(),Pn=function(){return function(t){this.variableNames=["A"],this.outputShape=t,this.userCode="\n      const float FLOAT_MAX = 1.70141184e38;\n      const float FLOAT_MIN = 1.17549435e-38;\n\n      lowp vec4 encode_float(highp float v) {\n        if (isNaN(v)) {\n          return vec4(255, 255, 255, 255);\n        }\n\n        highp float av = abs(v);\n\n        if(av < FLOAT_MIN) {\n          return vec4(0.0, 0.0, 0.0, 0.0);\n        } else if(v > FLOAT_MAX) {\n          return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n        } else if(v < -FLOAT_MAX) {\n          return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n        }\n\n        highp vec4 c = vec4(0,0,0,0);\n\n        highp float e = floor(log2(av));\n        highp float m = exp2(fract(log2(av))) - 1.0;\n\n        c[2] = floor(128.0 * m);\n        m -= c[2] / 128.0;\n        c[1] = floor(32768.0 * m);\n        m -= c[1] / 32768.0;\n        c[0] = floor(8388608.0 * m);\n\n        highp float ebias = e + 127.0;\n        c[3] = floor(ebias / 2.0);\n        ebias -= c[3] * 2.0;\n        c[2] += floor(ebias) * 128.0;\n\n        c[3] += 128.0 * step(0.0, -v);\n\n        return c / 255.0;\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        gl_FragColor = encode_float(x);\n      }\n    "}}(),Un="return real * expR - imag * expI;",Wn="return real * expI + imag * expR;",zn=function(){return function(t,e,n){this.variableNames=["real","imag"];var r=e[1];this.outputShape=e;var o=n?"2.0 * "+Math.PI:"-2.0 * "+Math.PI,a=n?r+".0":"1.0";this.userCode="\n      const float exponentMultiplier = "+o+";\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        "+t+"\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float("+r+");\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < "+r+"; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / "+a+";\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    "}}(),Vn=function(){return function(t){this.variableNames=["A"];var e=t[0],n=t[1];this.outputShape=t,this.userCode="\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2("+n+".0, "+e+".0);\n\n        vec4 values = texture2D(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    "}}(),Gn=function(){return function(t,e,n){this.variableNames=["A","indices"];var r=t.slice();r[n]=e,this.outputShape=r,this.rank=r.length;var o=Mn(this.rank),a=function(t,e){var n=t.length;if(n>4)throw Error("Gather for rank "+n+" is not yet supported");if(1===n)return"int(getIndices(resRC))";for(var r=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],a=0;a<t.length;a++)a===e?o.push("int(getIndices("+r[a]+"))"):o.push(""+r[a]);return o.join()}(t,n);this.userCode="\n      void main() {\n        "+o+" resRC = getOutputCoords();\n        setOutput(getA("+a+"));\n      }\n    "}}();var Hn,qn,$n=function(){return function(t,e,n){this.sliceDim=t,this.strides=e,this.variableNames=["x","indices"],this.outputShape=n;var r=Mn(e.length),o=Mn(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode="\n        "+r+" strides = "+r+"("+this.strides+");\n         void main() {\n          "+o+" coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < "+this.sliceDim+"; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * "+a+";\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      "}}();function jn(t,e){return[e,t]}function Kn(t,e){return t*e}function Xn(t,e,n){var r=function(t,e){if(t%e!=0)throw new Error("unpackedSize ("+t+") must be a multiple of "+e);return t/e}(t.length,n);if(e.length<r)throw new Error("matrix length ("+e.length+") must be >= "+r);for(var o=0,a=0;a<t.length;a+=n)e[o++]=t[a]}function Yn(t,e){return[Math.max(1,Math.ceil(e/2)),Math.max(1,Math.ceil(t/2))]}function Qn(t,e){var n=Yn(t,e);return n[0]*n[1]*4}function Jn(t,e,n,r,o){var a=n*r;if(o.length<a)throw new 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null==this.disjointQueryTimerExtension&&(this.disjointQueryTimerExtension=or(this.gl,2===It.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension},t.prototype.getQueryTimerExtensionWebGL2=function(){return this.getQueryTimerExtension()},t.prototype.getQueryTimerExtensionWebGL1=function(){return this.getQueryTimerExtension()},t.prototype.beginQuery=function(){if(2===It.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){var t=this.gl,e=this.getQueryTimerExtensionWebGL2(),n=t.createQuery();return t.beginQuery(e.TIME_ELAPSED_EXT,n),n}var r=this.getQueryTimerExtensionWebGL1(),o=r.createQueryEXT();return r.beginQueryEXT(r.TIME_ELAPSED_EXT,o),o},t.prototype.endQuery=function(){if(2!==It.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){var t=this.getQueryTimerExtensionWebGL1();t.endQueryEXT(t.TIME_ELAPSED_EXT)}else{var e=this.gl,n=this.getQueryTimerExtensionWebGL2();e.endQuery(n.TIME_ELAPSED_EXT)}},t.prototype.waitForQueryAndGetTime=function(t){return o(this,void 0,void 0,function(){var e=this;return a(this,function(n){switch(n.label){case 0:return[4,S(function(){return e.disposed||e.isQueryAvailable(t,It.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))})];case 1:return n.sent(),[2,this.getQueryTime(t,It.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))]}})})},t.prototype.getQueryTime=function(t,e){if(0===e)return null;if(2===e){var n=this.gl;return n.getQueryParameter(t,n.QUERY_RESULT)/1e6}var r=this.getQueryTimerExtensionWebGL1();return r.getQueryObjectEXT(t,r.QUERY_RESULT_EXT)/1e6},t.prototype.isQueryAvailable=function(t,e){if(0===e)return!0;if(2===e){var n=this.gl,r=this.getQueryTimerExtensionWebGL2(),o=n.getQueryParameter(t,n.QUERY_RESULT_AVAILABLE);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint}o=(r=this.getQueryTimerExtensionWebGL1()).getQueryObjectEXT(t,r.QUERY_RESULT_AVAILABLE_EXT);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint},t.prototype.pollFence=function(t){var e=this;return new Promise(function(n){e.addItemToPoll(function(){return t.isFencePassed()},function(){return n()})})},t.prototype.pollItems=function(){for(var t=function(t){var e=0,n=t.length-1,r=-1;for(;e<=n;){var o=e+n>>1,a=t[o]();a?(r=o,e=o+1):n=o-1}return r}(this.itemsToPoll.map(function(t){return t.isDoneFn})),e=0;e<=t;++e){(0,this.itemsToPoll[e].resolveFn)()}this.itemsToPoll=this.itemsToPoll.slice(t+1)},t.prototype.addItemToPoll=function(t,e){var n=this;this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),this.itemsToPoll.length>1||S(function(){return n.pollItems(),0===n.itemsToPoll.length})},t.prototype.bindTextureToFrameBuffer=function(t){this.throwIfDisposed(),Er(this.gl,t,this.framebuffer),this.autoDebugValidate&&Sr(this.gl)},t.prototype.unbindTextureToFrameBuffer=function(){null!=this.outputTexture?(Er(this.gl,this.outputTexture,this.framebuffer),this.autoDebugValidate&&Sr(this.gl)):Cr(this.gl,this.framebuffer)},t.prototype.downloadMatrixDriver=function(t,e){this.bindTextureToFrameBuffer(t);var n=e();return this.unbindTextureToFrameBuffer(),n},t.prototype.setOutputMatrixTextureDriver=function(t,e,n){this.throwIfDisposed();var r=this.gl;Er(r,t,this.framebuffer),this.autoDebugValidate&&Sr(r),this.outputTexture=t,Zn(r,function(){return r.viewport(0,0,e,n)}),Zn(r,function(){return r.scissor(0,0,e,n)})},t.prototype.setOutputMatrixWriteRegionDriver=function(t,e,n,r){var o=this;this.throwIfDisposed(),Zn(this.gl,function(){return o.gl.scissor(t,e,n,r)})},t.prototype.throwIfDisposed=function(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")},t.prototype.throwIfNoProgram=function(){if(null==this.program)throw new Error("No GPU program is currently set.")},t}();function eo(t,e){if(t.length!==e.length)throw Error("Binary was compiled with "+t.length+" inputs, but was executed with "+e.length+" inputs");t.forEach(function(t,n){var r=t.logicalShape,o=e[n],a=o.shape;if(!x(r,a))throw Error("Binary was compiled with different shapes than the current args. Shapes "+r+" and "+a+" must match");if(!t.isUniform||!o.isUniform){var i=t.texShape,s=o.isUniform?null:o.texData.texShape;if(!x(i,s))throw Error("Binary was compiled with different texture shapes than the current args. Shape "+i+" and "+s+" must match")}})}var no=function(){return function(t,e,n){this.variableNames=["A"],this.outputShape=t;var r=n.filterWidth,o=n.inChannels,a=n.strideWidth,i=n.strideHeight,s=n.padInfo,u=n.outWidth,l=n.dilationWidth,c=n.dilationHeight,h=s.left,p=s.top,f=o*r;this.userCode="\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            int blockIndex = rc.y + col;\n            int pos = rc.x + row;\n\n            if(blockIndex >= "+t[1]+" || pos >= "+t[0]+") continue;\n\n            int offsetY = int(blockIndex / ("+u+")) * "+i+" - "+p+";\n            int d0 = offsetY + "+c+" * (pos / "+f+");\n\n            if(d0 >= "+e[0]+" || d0 < 0) continue;\n\n            int offsetX = int(mod(float(blockIndex), "+u+".) * "+a+". - "+h+".);\n            int d1 = offsetX + "+l+" * (int(mod(float(pos), "+f+".) / "+o+".));\n\n            if(d1 >= "+e[1]+" || d1 < 0) continue;\n\n            result[row * 2 + col] = getA(d0, d1, int(mod(float(pos), "+o+".)));\n          }\n        }\n\n        gl_FragColor = result;\n      }\n    "}}(),ro=function(){return function(t,e,n,r,o){this.variableNames=["x"],this.outputShape=[];var a,i=e,s=t[3]-1;this.outputShape=t;var u="float("+n+") + float("+r+") * sum";a=.5===o?"inversesqrt("+u+")":1===o?"1.0/("+u+")":"exp(log("+u+") * float(-"+o+"));",this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -"+i+"; j <= "+i+"; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  "+s+") {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * "+a+";\n        setOutput(val);\n      }\n    "}}(),oo=function(){return function(t,e,n,r,o){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=n,this.alpha=r,this.beta=o,this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < "+this.depth+"; ++d) {\n          int depthBegin = int(max(0.0, float(d - "+e+")));\n          int depthEnd = int(min(float("+this.depth+"),\n              float(d + "+e+" + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = "+this.depth+";\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float("+r+") * norm + float("+n+");\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float("+r+")\n                * float("+o+")\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * "+o+");\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    "}}(),ao=function(){return function(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;var e=t.strideHeight,n=t.strideWidth,r=t.dilationHeight,o=t.effectiveFilterHeight,a=t.effectiveFilterWidth,i=o-1-t.padInfo.top,s=a-1-t.padInfo.left,u=o*a-1;this.userCode="\n      const ivec2 pads = ivec2("+i+", "+s+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+o+";\n          wR += "+r+") {\n          float dyR = float(dyRCorner + wR) / "+e+".0;\n\n          if (dyR < 0.0 || dyR >= "+t.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < "+a+"; wC++) {\n            float dyC = float(dyCCorner + wC) / "+n+".0;\n\n            if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = "+u+" - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * "+a+" + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    "}}(),io=function(){return function(t,e,n,r){void 0===n&&(n=!1),void 0===r&&(r=!1),this.variableNames=["matrixA","matrixB"];var o=t[0],a=n?t[2]:t[1],i=r?e[1]:e[2],s=n?t[1]:t[2];this.outputShape=[o,a,i];var u=function(t,e){return n?"batch, "+e+" + "+t+", aRow":"batch, aRow, "+e+" + "+t},l=function(t,e){return r?"batch, bCol, "+e+" + "+t:"batch, "+e+" + "+t+", bCol"},c=4*Math.floor(s/4),h=s%4;this.userCode=" float dotARowBCol(int batch, int aRow, int bCol) {\n      float result = 0.0;\n      for (int i = 0; i < "+c+"; i += 4) {\n        vec4 a = vec4(\n          getMatrixA("+u(0,"i")+"),\n          getMatrixA("+u(1,"i")+"),\n          getMatrixA("+u(2,"i")+"),\n          getMatrixA("+u(3,"i")+")\n        );\n        vec4 b = vec4(\n          getMatrixB("+l(0,"i")+"),\n          getMatrixB("+l(1,"i")+"),\n          getMatrixB("+l(2,"i")+"),\n          getMatrixB("+l(3,"i")+")\n        );\n\n        result += dot(a, b);\n      }\n\n      if ("+(1===h)+") {\n        result += getMatrixA("+u(0,c)+") *\n          getMatrixB("+l(0,c)+");\n      } else if ("+(2===h)+") {\n        vec2 a = vec2(\n          getMatrixA("+u(0,c)+"),\n          getMatrixA("+u(1,c)+")\n        );\n        vec2 b = vec2(\n          getMatrixB("+l(0,c)+"),\n          getMatrixB("+l(1,c)+")\n        );\n        result += dot(a, b);\n      } else if ("+(3===h)+") {\n        vec3 a = vec3(\n          getMatrixA("+u(0,c)+"),\n          getMatrixA("+u(1,c)+"),\n          getMatrixA("+u(2,c)+")\n        );\n        vec3 b = vec3(\n          getMatrixB("+l(0,c)+"),\n          getMatrixB("+l(1,c)+"),\n          getMatrixB("+l(2,c)+")\n        );\n        result += dot(a, b);\n      }\n\n      return result;\n    }\n\n    void main() {\n      ivec3 resBRC = getOutputCoords();\n      setOutput(dotARowBCol(resBRC.x, resBRC.y, resBRC.z));\n    }\n    "}}(),so=function(){return function(t,e,n,r,o){void 0===r&&(r=!1),void 0===o&&(o=!1),this.variableNames=["matrixA","matrixB"],this.usesPackedTextures=!0,this.outputShape=n;var a=r?t[0]:t[1],i=Math.ceil(a/2),s=r?"i * 2, rc.x":"rc.x, i * 2",u=o?"rc.y, i * 2":"i * 2, rc.y",l=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],c=o?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"];this.userCode="\n      const float sharedDimension = "+i+".0;\n\n      vec4 dot2x2ARowBCol(ivec2 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < "+i+"; i++) {\n          vec4 a = getMatrixA("+s+");\n          vec4 b = getMatrixB("+u+");\n\n          result += ("+l[0]+" * "+c[0]+") + ("+l[1]+" * "+c[1]+");\n        }\n        return result;\n      }\n\n      void main() {\n        ivec2 rc = getOutputCoords();\n        setOutput(dot2x2ARowBCol(rc));\n      }\n    "}}(),uo=function(){function t(t,e,n){this.variableNames=["probs"],this.outputShape=[t,n],this.userCode="\n      uniform float seed;\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < "+(e-1)+"; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float("+(e-1)+"));\n      }\n    "}return t.prototype.getCustomSetupFunc=function(t){var e=this;return function(n,r){null==e.seedLoc&&(e.seedLoc=n.getUniformLocation(r,"seed")),n.gl.uniform1f(e.seedLoc,t)}},t}(),lo=function(){return function(t,e,n,r){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode="\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float("+r+"), float("+n+"),\n                      float(index == coords.y)));\n      }\n    "}}();function co(t,e){return 1===e?[t]:function(t,e){return["x","y","z","w","u","v"].slice(0,e).map(function(e){return t+"."+e})}(t,e)}var ho=function(){return function(t){this.variableNames=["A"],this.isPackShader=!0,this.outputShape=t;var e=t.length;if(0===e)this.userCode="\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      ";else{var n=co("rc",e),r=Mn(e),o=function(t,e,n){if(1===t)return"rc > "+e[0];for(var r="",o=t-2;o<t;o++)r+=n[o]+" >= "+e[o],o<t-1&&(r+="||");return r}(e,t,n),a=function(t,e,n,r){if(1===t)return"";var o=r.slice(-2);return"\n    int r = "+o[0]+";\n    int c = "+o[1]+";\n    int rp1 = r + 1;\n    int cp1 = c + 1;\n\n    bool cEdge = cp1 >= "+e+";\n    bool rEdge = rp1 >= "+n+";\n  "}(e,t[t.length-1],t[t.length-2],n),i=function(t,e){var n=t.length,r=function(t,e){for(var n=[],r=0;r<=1;r++)for(var o=0;o<=1;o++){for(var a=(0===r?"r":"rp1")+", "+(0===o?"c":"cp1"),i=2;i<t;i++)a=e[e.length-1-i]+","+a;n.push(a)}return n}(n,e);return 1===n?"getA(rc),\n            rc + 1 >= "+t[0]+" ? 0. : getA(rc + 1),\n            0, 0":"getA("+r[0]+"),\n          cEdge ? 0. : getA("+r[1]+"),\n          rEdge ? 0. : getA("+r[2]+"),\n          rEdge || cEdge ? 0. : getA("+r[3]+")"}(t,n);this.userCode="\n        void main() {\n          "+r+" rc = getOutputCoords();\n\n          if("+o+") {\n            setOutput(vec4(0));\n          } else {\n            "+a+"\n\n            setOutput(vec4("+i+"));\n          }\n        }\n      "}}}();var po=function(){return function(t,e,n){this.variableNames=["x"],this.outputShape=e.map(function(e,n){return e[0]+t[n]+e[1]});var r=t.length,o=Mn(r),a=e.map(function(t){return t[0]}).join(","),i=e.map(function(e,n){return e[0]+t[n]}).join(","),s=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);this.userCode=1!==r?"\n      "+o+" start = "+o+"("+a+");\n      "+o+" end = "+o+"("+i+");\n\n      void main() {\n        "+o+" outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(float("+n+"));\n        } else {\n          "+o+" coords = outC - start;\n          setOutput(getX("+s+"));\n        }\n      }\n    ":"\n        int start = "+a+";\n        int end = "+i+";\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(float("+n+"));\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      "}}(),fo=function(){return function(t,e,n){if(this.variableNames=["x"],"avg"===e&&n)throw new Error("Cannot compute positions for average pool.");var r=t.filterWidth,o=t.strideHeight,a=t.strideWidth,i=t.dilationHeight,s=t.dilationWidth,u=t.effectiveFilterHeight,l=t.effectiveFilterWidth,c=t.padInfo.top,h=t.padInfo.left;this.outputShape=t.outShape;var p="avg"===e,f="0.0";if(p||(f="-1.0 / 0.0"),n)this.userCode="\n        const ivec2 strides = ivec2("+o+", "+a+");\n        const ivec2 pads = ivec2("+c+", "+h+");\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < "+u+";\n              wR += "+i+") {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= "+t.inHeight+") {\n              continue;\n            }\n\n            for (int wC = 0; wC < "+l+";\n                wC += "+s+") {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= "+t.inWidth+") {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value >= currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = wR * "+l+" + wC;\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      ";else{var d=e+"("+e+"("+e+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"avg"===e&&(d="avgValue / count");var m=4*Math.floor(r/4),v=r%4,g="\n      if ("+p+") {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = max(values, minMaxValue);\n      }\n    ";this.userCode="\n      const ivec2 strides = ivec2("+o+", "+a+");\n      const ivec2 pads = ivec2("+c+", "+h+");\n      const float initializationValue = "+f+";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= "+t.inWidth+") {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4("+f+");\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < "+u+";\n            wR += "+i+") {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= "+t.inHeight+") {\n            continue;\n          }\n\n          for (int wC = 0; wC < "+m+"; wC += 4) {\n            int xC = xCCorner + wC * "+s+";\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + "+s+", d),\n              getValue(batch, xR, xC + 2 * "+s+", d),\n              getValue(batch, xR, xC + 3 * "+s+", d)\n            );\n\n            "+g+"\n          }\n\n          int xC = xCCorner + "+m+";\n          if ("+(1===v)+") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            "+g+"\n          } else if ("+(2===v)+") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + "+s+", d),\n              initializationValue,\n              initializationValue\n            );\n\n            "+g+"\n          } else if ("+(3===v)+") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + "+s+", d),\n              getValue(batch, xR, xC + 2 * "+s+", d),\n              initializationValue\n            );\n\n            "+g+"\n          }\n        }\n        setOutput("+d+");\n      }\n    "}}}(),mo=function(){return function(t,e){this.variableNames=["x"];var n=t.windowSize,r=t.batchSize,o=t.inSize,a=Math.ceil(o/n);this.outputShape=[r,a];var i="0.0",s="";"prod"===e?i="1.0":"min"===e?(i="1.0 / 0.0",s="min"):"max"===e&&(i="-1.0 / 0.0",s="max");var u=e+"("+e+"("+e+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"sum"===e?u="sumValue":"prod"===e?u="prodValue":"all"===e?u="allValue":"any"===e&&(u="anyValue");var l=4*Math.floor(n/4),c=n%4,h="\n      if ("+("sum"===e)+") {\n        sumValue += dot(values, ones);\n      } else if ("+("prod"===e)+") {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = "+s+"(values, minMaxValue);\n      }\n    ",p="vec4";"all"===e?(i="1.0",h="\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      ",p="bvec4"):"any"===e&&(i="0.0",h="\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      ",p="bvec4");var f="";o%n>0&&(f="\n        if (inIdx < 0 || inIdx >= "+o+") {\n          return initializationValue;\n        }\n      "),this.userCode="\n      const float initializationValue = "+i+";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        "+f+"\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * "+n+";\n\n        vec4 minMaxValue = vec4("+i+");\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < "+l+"; i += 4) {\n          int inIdx = inOffset + i;\n          "+p+" values = "+p+"(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          "+h+"\n        }\n\n        int inIdx = inOffset + "+l+";\n        if ("+(1===c)+") {\n          "+p+" values = "+p+"(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          "+h+"\n        } else if ("+(2===c)+") {\n          "+p+" values = "+p+"(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          "+h+"\n        } else if ("+(3===c)+") {\n          "+p+" values = "+p+"(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          "+h+"\n        }\n        setOutput("+u+");\n      }\n    "}}(),vo=function(){return function(t,e){this.variableNames=["A"],this.usesPackedTextures=!0,this.outputShape=t;for(var n="",r=0;r<4;r++){var o="thisRC = rc;";r%2==1&&(o+="thisRC.z += 1;"),r>1&&(o+="thisRC.y += 1;"),n+="\n        "+o+"\n        "+(r>0?"if(thisRC.y < rows && thisRC.z < cols){":"")+"\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result["+r+"] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        "+(r>0?"}":"")+"\n      "}this.userCode="\n      \n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      "+En(["r","c","d"],e)+"\n      return ivec3(r, c, d);\n    }\n  \n      "+function(t){return"\n    int getFlatIndex(ivec3 coords) {\n      return round("+function(t,e){if(t.length!==e.length)throw new Error("Vectors to be dotted must be of the same length -got "+t.length+" and "+e.length);for(var n=[],r=Math.floor(t.length/4),o=t.length%4,a=0;a<r;a++){var i=t.slice(4*a,4*a+4),s=e.slice(4*a,4*a+4);n.push(Cn(i)+", "+Cn(s))}return 0!==o&&(i=t.slice(4*r),s=e.slice(4*r),1===i.length&&(i=i.map(function(t){return"float("+t+")"}),s=s.map(function(t){return"float("+t+")"})),n.push(Cn(i)+", "+Cn(s))),n.map(function(t,e){return"dot("+t+")"}).join("+")}(["coords.x","coords.y","coords.z"],z(t).map(function(t){return t.toString()}).concat(["1."]))+");\n    }\n  "}(t)+"\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = "+t[1]+";\n        int cols = "+t[2]+";\n\n        "+n+"\n\n        setOutput(result);\n      }\n    "}}();var go=function(){return function(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e.shape;var r=e.shape,o=r[1],a=r[2],i=t.shape,s=i[1],u=i[2],l=[n&&s>1?o-1:o,n&&u>1?a-1:a],c=[n&&s>1?s-1:s,n&&u>1?u-1:u],h=l[0]/c[0],p=l[1]/c[1],f=1/h,d=1/p,m=2*Math.ceil(f)+2,v=2*Math.ceil(d)+2;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float("+h+");\n        const float widthScale = float("+p+");\n\n        const float invHeightScale = float("+f+");\n        const float invWidthScale = float("+d+");\n\n        const int winHeight = int("+m+");\n        const int winWidth = int("+v+");\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= "+s+") {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= "+u+") {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), "+(o-1)+".0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), "+(a-1)+".0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    "}}(),yo=function(){return function(t,e,n,r){this.variableNames=["A"],this.outputShape=[];var o=t[0],a=t[1],i=t[2],s=t[3];this.outputShape=[o,e,n,s];var u=[r&&e>1?a-1:a,r&&n>1?i-1:i],l=[r&&e>1?e-1:e,r&&n>1?n-1:n];this.userCode="\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          "+u[0]/l[0]+",\n          "+u[1]/l[1]+");\n      const vec2 inputShapeRC = vec2("+a+".0, "+i+".0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    "}}(),xo=function(){return function(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e.shape;var r=e.shape,o=r[1],a=r[2],i=t.shape,s=i[1],u=i[2],l=[n&&s>1?o-1:o,n&&u>1?a-1:a],c=[n&&s>1?s-1:s,n&&u>1?u-1:u],h=l[0]/c[0],p=l[1]/c[1],f=1/h,d=1/p,m=2*Math.ceil(f)+2,v=2*Math.ceil(d)+2;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float("+h+");\n        const float widthScale = float("+p+");\n\n        const float invHeightScale = float("+f+");\n        const float invWidthScale = float("+d+");\n\n        const int winHeight = int("+m+");\n        const int winWidth = int("+v+");\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= "+s+") {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= "+u+") {\n              continue;\n            }\n\n            float sourceFracRow =\n              float("+l[0]+") *\n                (float(dyR) / float("+c[0]+"));\n\n            float sourceFracCol =\n                float("+l[1]+") *\n                  (float(dyC) / float("+c[1]+"));\n\n            int sourceNearestRow = int(min(\n                float(int("+o+") - 1),\n                "+n+" ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int("+a+") - 1),\n                "+n+" ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    "}}(),wo=function(){return function(t,e,n,r){this.variableNames=["A"],this.outputShape=[];var o=t[0],a=t[1],i=t[2],s=t[3];this.outputShape=[o,e,n,s];var u=[r&&e>1?a-1:a,r&&n>1?i-1:i],l=[r&&e>1?e-1:e,r&&n>1?n-1:n],c=r?"0.5":"0.0";this.userCode="\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          "+u[0]/l[0]+",\n          "+u[1]/l[1]+");\n      const vec2 inputShapeRC = vec2("+a+".0, "+i+".0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + "+c+")));\n\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    "}}(),bo=function(){return function(t,e){this.variableNames=["x"];var n=t.length;if(n>4)throw new Error("WebGL backend: Reverse of rank-"+n+" tensor is not yet supported");if(this.outputShape=t,1!==n){var r=t.map(function(n,r){return function(n){return-1!==e.indexOf(n)&&1!==t[n]?t[n]+" - coords["+n+"] - 1":"coords["+n+"]"}(r)}).join(","),o=Mn(n);this.userCode="\n      void main() {\n        "+o+" coords = getOutputCoords();\n        setOutput(getX("+r+"));\n      }\n    "}else this.userCode="\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX("+t[0]+" - coord - 1));\n        }\n      "}}(),Eo=function(){return function(t,e,n,r,o,a,i){void 0===i&&(i=!0),this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;var s=Mn(o.length),u=Mn(a.length),l="";1===n?l="i":2===n&&(l="i, j");var c="getIndices("+l+")",h="";1===r?h="i":2===r&&(h="i, coords[1]");var p="getUpdates("+h+")",f=e>1?"strides[j]":"strides";this.userCode="\n        "+s+" strides = "+s+"("+o+");\n\n        void main() {\n          "+u+" coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < "+t+"; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < "+e+"; j++) {\n              int index = round("+c+");\n              flattenedIndex += index * "+f+";\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += "+p+";\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      "}}(),Co=function(){return function(t,e){this.variableNames=["x","segmentIds"];var n=t.windowSize,r=t.batchSize,o=t.inSize,a=t.numSegments,i=a*Math.ceil(o/n);this.outputShape=[r,i];var s=4*Math.floor(n/4),u=n%4,l="\n        sumValue += dot(values, filter);\n    ",c="";o%n>0&&(c="\n        if (inIdx < 0 || inIdx >= "+o+") {\n          return initializationValue;\n        }\n      ");var h="";o%n>0&&(h="\n        if (inIdx < 0 || inIdx >= "+o+") {\n          return -1.0;\n        }\n      "),this.userCode="\n      const float initializationValue = 0.0;\n\n      float getValue(int batch, int inIdx) {\n        "+c+"\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        "+h+"\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          "+a+")) * float("+n+"));\n        int currentSeg = int(mod(float(outIdx), float("+a+")));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < "+s+"; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          "+l+"\n        }\n\n        int inIdx = inOffset + "+s+";\n        if ("+(1===u)+") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          "+l+"\n        } else if ("+(2===u)+") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          "+l+"\n        } else if ("+(3===u)+") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          "+l+"\n        }\n        setOutput(sumValue);\n      }\n    "}}(),So=function(){return function(t,e,n){var r,o;if(this.variableNames=["c","a","b"],this.outputShape=e,n>4)throw Error("Where for rank "+n+" is not yet supported");if(1===n)o="resRC",r="resRC";else{for(var a=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],s=[],u=0;u<e.length;u++)s.push(""+a[u]),u<t&&i.push(""+a[u]);r=i.join(),o=s.join()}var l=Mn(n);this.userCode="\n      void main() {\n        "+l+" resRC = getOutputCoords();\n        float cVal = getC("+r+");\n        if (cVal >= 1.0) {\n          setOutput(getA("+o+"));\n        } else {\n          setOutput(getB("+o+"));\n        }\n      }\n    "}}(),Ro=function(){function t(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;var e=Mn(this.rank),n=function(t){if(1===t)return"sourceLoc";if(2===t)return"sourceLoc.x, sourceLoc.y";if(3===t)return"sourceLoc.x, sourceLoc.y, sourceLoc.z";if(4===t)return"sourceLoc.x, sourceLoc.y, sourceLoc.z, sourceLoc.w";throw Error("Slicing for rank "+t+" is not yet supported")}(this.rank);this.userCode="\n      uniform "+e+" start;\n\n      void main() {\n        "+e+" sourceLoc = start + getOutputCoords();\n        setOutput(getSource("+n+"));\n      }\n    "}return t.prototype.getCustomSetupFunc=function(t){var e=this;if(t.length!==this.rank)throw Error("The rank ("+this.rank+") of the program must match the length of start ("+t.length+")");return function(n,r){if(null!=e.startLoc||(e.startLoc=n.getUniformLocationNoThrow(r,"start"),null!=e.startLoc))if(1===e.rank)n.gl.uniform1i(e.startLoc,t[0]);else if(2===e.rank)n.gl.uniform2i(e.startLoc,t[0],t[1]);else if(3===e.rank)n.gl.uniform3i(e.startLoc,t[0],t[1],t[2]);else{if(4!==e.rank)throw Error("Slicing for rank "+e.rank+" is not yet 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Pass non-scalar to pad");var o=e.map(function(t){return t[0]});return It.engine.runKernel(function(t){return t.pad(r,e,n)},{$x:r},function(t){return{$x:function(){return t.slice(o,r.shape)}}})}}),wa=pe({pad1d_:function(t,e,n){return void 0===n&&(n=0),d(2===e.length,"Invalid number of paddings. Must be length of 2."),xa(t,[e],n)}}),ba=pe({pad2d_:function(t,e,n){return void 0===n&&(n=0),d(2===e.length&&2===e[0].length&&2===e[1].length,"Invalid number of paddings. Must be length of 2 each."),xa(t,e,n)}}),Ea=pe({pad3d_:function(t,e,n){return void 0===n&&(n=0),d(3===e.length&&2===e[0].length&&2===e[1].length&&2===e[2].length,"Invalid number of paddings. Must be length of 2 each."),xa(t,e,n)}}),Ca=pe({pad4d_:function(t,e,n){return void 0===n&&(n=0),d(4===e.length&&2===e[0].length&&2===e[1].length&&2===e[2].length&&2===e[3].length,"Invalid number of paddings. Must be length of 2 each."),xa(t,e,n)}}),Sa=pe({rand_:function(t,e,n){var r=y(t),o=null;if(null==n||"float32"===n)o=new Float32Array(r);else if("int32"===n)o=new Int32Array(r);else{if("bool"!==n)throw new Error("Unknown data type "+n);o=new Uint8Array(r)}for(var a=0;a<r;a++)o[a]=e();return at.make(t,{values:o},n)}}),Ra=pe({randomNormal_:function(t,e,n,r,o){if(void 0===e&&(e=0),void 0===n&&(n=1),null!=r&&"bool"===r)throw new Error("Unsupported data type "+r);for(var a=new aa(e,n,r,!1,o),i=sa(t,r),s=0;s<i.values.length;s++)i.values[s]=a.nextValue();return i.toTensor()}}),Na=pe({randomUniform_:function(t,e,n,r){void 0===e&&(e=0),void 0===n&&(n=1),void 0===r&&(r="float32");for(var o=sa(t,r),a=0;a<o.values.length;a++)o.values[a]=f(e,n);return o.toTensor()}}),ka=pe({reshape_:function(t,e){var n=ce(t,"x","reshape",null);return e=R(e,n.size),d(n.size===y(e),"new shape and old shape must have the same number of elements."),It.engine.runKernel(function(t){return t.reshape(n,e)},{$x:n},function(t){return{$x:function(){return t.reshape(n.shape)}}})}}),Ta=pe({spaceToBatchND_:function(t,e,n){var r=ce(t,"x","spaceToBatchND");return d(r.rank>=1+e.length,"input rank "+r.rank+" should be > than [blockShape] "+e.length),d(n.length===e.length,"paddings.shape[0] "+n.length+" must be equal to [blockShape] "+e.length),d(r.shape.reduce(function(t,r,o){return o>0&&o<=e.length?t&&(r+n[o-1][0]+n[o-1][1])%e[o-1]==0:t},!0),"input spatial dimensions "+r.shape.slice(1)+" with paddings "+n.toString()+" must be divisible by blockShapes "+e.toString()),It.engine.runKernel(function(t){return t.spaceToBatchND(r,e,n)},{$x:r},function(t){return{$x:function(){return t.batchToSpaceND(e,n)}}})}}),Ia=pe({squeeze_:function(t,e){var n=ce(t,"x","squeeze");return ka(n,N(n.shape,e).newShape)}}),Da=pe({stack_:function(t,e){void 0===e&&(e=0);var n=he(t,"tensors","stack");if(d(n.length>=1,"Pass at least one tensor to tf.stack"),1===n.length)return n[0].expandDims(e);var r=n[0].rank,o=n[0].shape,a=n[0].dtype;d(e<=r,"Axis must be <= rank of the tensor"),n.forEach(function(t){m(o,t.shape,"All tensors passed to stack must have matching shapes")}),n.forEach(function(t){d(a===t.dtype,"All tensors passed to stack must have matching dtypes")});var i=n.map(function(t){return t.expandDims(e)});return Ho(i,e)}}),Aa=pe({tile_:function(t,e){var n=ce(t,"x","tile");return d(n.rank===e.length,"Error in transpose: rank of input "+n.rank+" must match length of reps "+e+"."),It.engine.runKernel(function(t){return t.tile(n,e)},{$x:n},function(t){return{$x:function(){var r=Me(n);if(1===n.rank)for(var o=0;o<e[0];++o)r=r.add(t.slice([o*n.shape[0]],[n.shape[0]]));else if(2===n.rank)for(o=0;o<e[0];++o)for(var a=0;a<e[1];++a)r=r.add(t.slice([o*n.shape[0],a*n.shape[1]],[n.shape[0],n.shape[1]]));else if(3===n.rank)for(o=0;o<e[0];++o)for(a=0;a<e[1];++a)for(var i=0;i<e[2];++i)r=r.add(t.slice([o*n.shape[0],a*n.shape[1],i*n.shape[2]],[n.shape[0],n.shape[1],n.shape[2]]));else{if(4!==n.rank)throw new Error("Gradient for tile operation is not implemented for rank-"+n.rank+" tensors yet.");for(o=0;o<e[0];++o)for(a=0;a<e[1];++a)for(i=0;i<e[2];++i)for(var s=0;s<e[3];++s)r=r.add(t.slice([o*n.shape[0],a*n.shape[1],i*n.shape[2],s*n.shape[3]],[n.shape[0],n.shape[1],n.shape[2],n.shape[3]]))}return r}}})}}),Ma=pe({truncatedNormal_:function(t,e,n,r,o){if(void 0===e&&(e=0),void 0===n&&(n=1),null!=r&&"bool"===r)throw new Error("Unsupported data type "+r);for(var a=new aa(e,n,r,!0,o),i=sa(t,r),s=0;s<i.values.length;s++)i.values[s]=a.nextValue();return i.toTensor()}}),_a=pe({unstack_:function(t,e){void 0===e&&(e=0);for(var n,r=ce(t,"x","unstack"),o=r.shape[e],a=Array(r.rank-1).fill(0),i=0,s=0;s<r.rank;s++)s!==e&&(a[i]=r.shape[s],i++);n=Array(o).fill(1);var u=Array(r.rank).fill(0),l=r.shape.slice();return n.map(function(t){l[e]=t;var n=r.slice(u,l);return u[e]+=t,n.reshape(a)})}}),Oa=function(t,e){return o(this,void 0,void 0,function(){var n,r,o,i,s,u,l,c,h,p;return a(this,function(a){switch(a.label){case 0:return n=ce(t,"x","setdiff1d"),r=ce(e,"y","setdiff1d"),d(n.dtype===r.dtype,"x and y should have the same dtype, but got x ("+n.dtype+") and y ("+r.dtype+")."),d(1===n.rank,"x should be 1D tensor, but got x ("+n.shape+")."),d(1===r.rank,"y should be 1D tensor, but got y ("+r.shape+")."),[4,n.data()];case 1:return o=a.sent(),[4,r.data()];case 2:for(i=a.sent(),s=new Set(i),u=0,h=0;h<o.length;h++)s.has(o[h])||u++;for(l=new et([u],n.dtype),c=new et([u],"int32"),h=0,p=0;h<o.length;h++)s.has(o[h])||(l.values[p]=o[h],c.values[p]=h,p++);return[2,[l.toTensor(),c.toTensor()]]}})})};function Fa(t,e){for(var n=[],r=0;r<e.length;r++)e[r]&&n.push(r);var o=sa(t,"int32"),a=sa([n.length,t.length],"int32");for(r=0;r<n.length;r++){var i=o.indexToLoc(n[r]),s=r*t.length;a.values.set(i,s)}return a.toTensor()}var Ba=300,La=function(){function t(t,e){if(void 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n=[t.height,t.width],r=[t.height,t.width,e];if(!(t instanceof HTMLVideoElement||t instanceof HTMLImageElement||t instanceof HTMLCanvasElement||t instanceof ImageData))throw new Error("pixels passed to tf.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ImageData, but was "+t.constructor.name);if(t instanceof HTMLVideoElement){if(null==this.fromPixels2DContext){if(!It.get("IS_BROWSER"))throw new Error("Can't read pixels from HTMLImageElement outside the browser.");if("complete"!==document.readyState)throw new Error("The DOM is not ready yet. 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One way to do that is to add an event listener for `DOMContentLoaded` on the document object");this.fromPixels2DContext=document.createElement("canvas").getContext("2d")}this.fromPixels2DContext.canvas.width=t.width,this.fromPixels2DContext.canvas.height=t.height,this.fromPixels2DContext.drawImage(t,0,0,t.width,t.height),t=this.fromPixels2DContext.canvas}var o=this.makeTensorHandle(n,"int32");this.texData.get(o.dataId).usage=Hn.PIXELS,this.gpgpu.uploadPixelDataToTexture(this.getTexture(o.dataId),t);var a=new Vn(r),i=this.compileAndRun(a,[o]);return this.disposeData(o.dataId),i},t.prototype.makeTensorHandle=function(t,e){var n={};return this.register(n,t,e),{dataId:n,shape:t,dtype:e}},t.prototype.write=function(t,e){if(null==e)throw new Error("MathBackendWebGL.write(): values can not be null");var n=this.texData.get(t),r=n.texture,o=n.texShape,a=n.usage,i=n.dtype,s=n.isPacked;if("complex64"===i)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");null!=r&&(this.releaseTexture(t,r,o,a,s),n.texture=null,n.texShape=null),n.usage=Hn.UPLOAD,n.values=e,this.delayedStorage||this.uploadToGPU(t)},t.prototype.readSync=function(t){var e=this.texData.get(t),n=e.values,r=e.dtype,o=e.complexTensors;if(null!=n)return this.convertAndCacheOnCPU(t);if("string"===r)return n;var a,i,s=null!=this.activeTimers;(s&&(a=performance.now()),"complex64"===r)?i=Le(o.real.dataSync(),o.imag.dataSync()):i=this.getValuesFromTexture(t);return s&&(this.downloadWaitMs+=performance.now()-a),this.convertAndCacheOnCPU(t,i)},t.prototype.read=function(t){return o(this,void 0,void 0,function(){var e,n,r,o,i,s,u,l,c,h,p,f,d,m,v,g,y,x;return a(this,function(a){switch(a.label){case 0:if(this.pendingRead.has(t))return r=this.pendingRead.get(t),[2,new Promise(function(t){return r.push(t)})];if(o=this.texData.get(t),i=o.texture,s=o.values,u=o.texShape,l=o.isPacked,c=o.shape,null!=s)return[2,this.convertAndCacheOnCPU(t)];if(this.pendingRead.set(t,[]),!It.get("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&2===It.get("WEBGL_VERSION"))throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");return h=u[1],p=u[0],l&&(e=Yn(u[0],u[1]),h=e[0],p=e[1]),f=this.gpgpu.maybeCreateBufferFromTexture(i,p,h),[4,this.gpgpu.createAndWaitForFence()];case 1:return a.sent(),f instanceof WebGLTexture?d=this.getValuesFromTexture(t):l?(m=this.getBatchDim(c),v=1,g=1,c.length&&(n=this.getRowsCols(c),v=n[0],g=n[1]),d=this.gpgpu.downloadPackedMatrixFromBuffer(f,m,v,g,u[0],u[1])):d=this.gpgpu.downloadFloat32MatrixFromBuffer(f,u[0],u[1]),y=this.convertAndCacheOnCPU(t,d),x=this.pendingRead.get(t),this.pendingRead.delete(t),x.forEach(function(t){return t(y)}),this.pendingDisposal.has(t)&&(this.pendingDisposal.delete(t),this.disposeData(t)),[2,y]}})})},t.prototype.getValuesFromTexture=function(t){var e,n=this.texData.get(t),r=n.shape,o=n.dtype,a=n.texture,i=n.texShape;if(It.get("WEBGL_DOWNLOAD_FLOAT_ENABLED")){if(this.texData.get(t).isPacked){var s=this.getBatchDim(r),u=1,l=1;return r.length&&(u=(e=this.getRowsCols(r))[0],l=e[1]),this.gpgpu.downloadMatrixFromPackedTexture(a,s,u,l,i[0],i[1])}return this.gpgpu.downloadFloat32MatrixFromOutputTexture(a,i[0],i[1])}var c=this.makeTensorHandle(r,"float32");c.size=y(r),this.texData.get(c.dataId).usage=Hn.DOWNLOAD;var h=new Pn(r);this.compileAndRun(h,[{shape:r,dtype:o,dataId:t}],c,null,!1);var p=this.texData.get(c.dataId),f=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(p.texture,p.texShape[0],p.texShape[1]);return this.disposeData(c.dataId),f},t.prototype.time=function(t){return o(this,void 0,void 0,function(){var e,n,r,o,i,s,u;return a(this,function(a){switch(a.label){case 0:return e=this.activeTimers,n=[],r=!1,null==this.programTimersStack?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,t(),o=g(this.activeTimers.map(function(t){return t.query})).filter(function(t){return null!=t}),i=g(this.activeTimers.map(function(t){return t.name})).filter(function(t){return null!=t}),this.activeTimers=e,r&&(this.programTimersStack=null),[4,Promise.all(o)];case 1:return s=a.sent(),u={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:p(s),getExtraProfileInfo:function(){return s.map(function(t,e){return{name:i[e],ms:t}}).map(function(t){return t.name+": "+t.ms}).join(", ")},wallMs:null},this.uploadWaitMs=0,this.downloadWaitMs=0,[2,u]}})})},t.prototype.memory=function(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU}},t.prototype.startTimer=function(){return It.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?this.gpgpu.beginQuery():{startMs:performance.now(),endMs:null}},t.prototype.endTimer=function(t){return It.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(this.gpgpu.endQuery(),t):(t.endMs=performance.now(),t)},t.prototype.getQueryTime=function(t){return o(this,void 0,void 0,function(){var e;return a(this,function(n){return It.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?[2,this.gpgpu.waitForQueryAndGetTime(t)]:[2,(e=t).endMs-e.startMs]})})},t.prototype.disposeData=function(t){if(!this.pendingDisposal.has(t))if(this.pendingRead.has(t))this.pendingDisposal.add(t);else if(this.texData.has(t)){var e=this.texData.get(t),n=e.texture,r=e.texShape,o=e.usage,a=e.complexTensors,i=e.isPacked;null!=n&&this.releaseTexture(t,n,r,o,i),null!=a&&(a.real.dispose(),a.imag.dispose()),this.texData.delete(t)}},t.prototype.getTexture=function(t){return this.uploadToGPU(t),this.texData.get(t).texture},t.prototype.getCPUBackend=function(){return It.get("WEBGL_CPU_FORWARD")?(null==this.cpuBackend&&(this.cpuBackend=It.findBackend("cpu")),this.cpuBackend):null},t.prototype.shouldExecuteOnCPU=function(t,e){var n=this;return void 0===e&&(e=10),null!=this.getCPUBackend()&&t.every(function(t){return null==n.texData.get(t.dataId).texture&&t.size<e})},t.prototype.getGPGPUContext=function(){return this.gpgpu},t.prototype.getCanvas=function(){return this.canvas},t.prototype.complex=function(t,e){var n=this.makeOutputArray(t.shape,"complex64");return this.texData.get(n.dataId).complexTensors={real:It.engine.keep(t.clone()),imag:It.engine.keep(e.clone())},n},t.prototype.real=function(t){return this.texData.get(t.dataId).complexTensors.real.clone()},t.prototype.imag=function(t){return this.texData.get(t.dataId).complexTensors.imag.clone()},t.prototype.slice=function(t,e,n){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.slice(t,e,n);var r=new Ro(n),o=r.getCustomSetupFunc(e);return this.compileAndRun(r,[t],null,o)},t.prototype.stridedSlice=function(t,e,n,r,o,a,i,s,u){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.stridedSlice(t,e,n,r,o,a,i,s,u);var l=ae(t.shape,e,n,r,o,a,i,s,u),c=l[0],h=l[1],p=l[2],f=h.filter(function(t,e){return-1===p.indexOf(e)});if(f.some(function(t){return 0===t}))return ye([],f);var d=new No(c,r,h,p);return this.compileAndRun(d,[t])},t.prototype.reverse=function(t,e){var n=new bo(t.shape,e);return this.compileAndRun(n,[t])},t.prototype.concat2Tensors=function(t,e,n){var r=Zt([t.shape,e.shape],n),o=t.as2D(-1,y(t.shape.slice(n))),a=e.as2D(-1,y(e.shape.slice(n))),i=new un(o.shape,a.shape);return this.compileAndRun(i,[o,a]).reshape(r)},t.prototype.concat=function(t,e){if(this.shouldExecuteOnCPU(t))return this.cpuBackend.concat(t,e);if(1===t.length)return t[0];for(var n=t[0],r=1;r<t.length;++r)n=this.concat2Tensors(n,t[r],e);return n},t.prototype.neg=function(t){var e=new Oo(t.shape,"return -x;");return this.compileAndRun(e,[t])},t.prototype.batchMatMul=function(t,e,n,r){var o=n?t.shape[2]:t.shape[1],a=r?e.shape[1]:e.shape[2],i=n?t.shape[1]:t.shape[2],s=t.shape[0];if((1===o||1===a)&&i>1e3){n&&(t=t.transpose([0,2,1])),r&&(e=e.transpose([0,2,1]));var u=1===a?t:t.as3D(s,i,1),l=1===a?2:1,c=1===a?e.as3D(s,1,i):e;return this.multiply(u,c).sum(l,!0)}var h=dt(t.dtype,e.dtype);if(1===s){var p=t.as2D(t.shape[1],t.shape[2]),f=e.as2D(e.shape[1],e.shape[2]),d=new so(p.shape,f.shape,[o,a],n,r),m=this.makePackedTensor(d.outputShape,h),v=this.compileAndRun(d,[p,f],m);return v.reshape([1,v.shape[0],v.shape[1]])}d=new io(t.shape,e.shape,n,r),m=this.makeOutputArray(d.outputShape,h);return this.compileAndRun(d,[t,e],m)},t.prototype.multiply=function(t,e){if("complex64"===t.dtype){var n=this.texData.get(t.dataId),r=this.texData.get(e.dataId),o=new tn(Je,t.shape,e.shape),a=new tn(Ze,t.shape,e.shape),i=[this.makeComplexComponentTensorHandle(t,n.complexTensors.real),this.makeComplexComponentTensorHandle(t,n.complexTensors.imag),this.makeComplexComponentTensorHandle(e,r.complexTensors.real),this.makeComplexComponentTensorHandle(e,r.complexTensors.imag)],s=this.compileAndRun(o,i),u=this.compileAndRun(a,i),l=this.complex(s,u);return s.dispose(),u.dispose(),l}if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.multiply(t,e);var c=new rn("return a * b;",t.shape,e.shape),h=this.makeOutputArray(c.outputShape,t.dtype);return this.compileAndRun(c,[t,e],h)},t.prototype.batchNormalization=function(t,e,n,r,o,a){var i=[t,e,n],s=null;null!=a&&(s=a.shape,i.push(a));var u=null;if(null!=o&&(u=o.shape,i.push(o)),It.get("WEBGL_PACK_BATCHNORMALIZATION")){var l=new Qe(t.shape,e.shape,n.shape,s,u,r);return this.compileAndRun(l,i)}var c=new Ye(t.shape,e.shape,n.shape,s,u,r);return this.compileAndRun(c,i)},t.prototype.localResponseNormalization4D=function(t,e,n,r,o){var a=new ro(t.shape,e,n,r,o);return this.compileAndRun(a,[t])},t.prototype.LRNGrad=function(t,e,n,r,o,a,i){var s=new oo(e.shape,r,o,a,i);return this.compileAndRun(s,[e,n,t])},t.prototype.tile=function(t,e){var n=new Do(t.shape,e);return this.compileAndRun(n,[t])},t.prototype.pad=function(t,e,n){var r=new po(t.shape,e,n);return this.compileAndRun(r,[t])},t.prototype.transpose=function(t,e){var n=new Ao(t.shape,e);return this.compileAndRun(n,[t])},t.prototype.gather=function(t,e,n){var r=new Gn(t.shape,e.size,n);return this.compileAndRun(r,[t,e])},t.prototype.batchToSpaceND=function(t,e,n){d(t.rank<=4,"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");var r=e.reduce(function(t,e){return t*e}),o=Wt(t.shape,e,r),a=zt(o.length,e.length),i=Vt(t.shape,e,r),s=Gt(n,e.length),u=Ht(i,n,e.length);return t.reshape(o).transpose(a).reshape(i).slice(s,u)},t.prototype.spaceToBatchND=function(t,e,n){d(t.rank<=4,"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");var r=e.reduce(function(t,e){return t*e}),o=[[0,0]];o.push.apply(o,n);for(var a=1+e.length;a<t.shape.length;++a)o.push([0,0]);var i=t.pad(o),s=Wt(i.shape,e,r,!1),u=zt(s.length,e.length,!1),l=Vt(i.shape,e,r,!1);return i.reshape(s).transpose(u).reshape(l)},t.prototype.reduce=function(t,e,n){var r=t.shape[0],o=t.shape[1],a=ne(o),i=new mo({windowSize:a,inSize:o,batchSize:r},e),s=i.outputShape,u=s[0],l=s[1],c=this.makeOutputArray([u,l],n);return this.compileAndRun(i,[t],c),1===c.shape[1]?c:this.reduce(c,e,n)},t.prototype.argReduce=function(t,e,n){void 0===n&&(n=null);var r=t.shape[0],o=t.shape[1];null!=n&&(r=n.shape[0],o=n.shape[1]);var a=ne(o),i=new qe({windowSize:a,inSize:o,batchSize:r},e,null==n),s=i.outputShape,u=s[0],l=s[1],c=this.makeOutputArray([u,l],"int32"),h=[t];return null!=n&&h.push(n),this.compileAndRun(i,h,c),1===c.shape[1]?c:this.argReduce(t,e,c)},t.prototype.sum=function(t,e){Xt("sum",e,t.rank);var n=$t(t.shape,e),r=n[0],o=y(n[1]),a=t.as2D(-1,o),i=mt(t.dtype);return this.reduce(a,"sum",i).reshape(r)},t.prototype.prod=function(t,e){var n=$t(t.shape,e),r=n[0],o=y(n[1]),a=t.as2D(-1,o),i=mt(t.dtype);return this.reduce(a,"prod",i).reshape(r)},t.prototype.unsortedSegmentSum=function(t,e,n){var r=0,o=Yt([r],t.rank),a=t;null!=o&&(a=t.transpose(o),r=Jt(1,t.rank)[0]);var i=function(t,e,n){for(var r=[],o=t.length,a=0;a<o;a++)a!==e?r.push(t[a]):r.push(n);return r}(a.shape,r,n),s=y([a.shape[r]]),u=a.as2D(-1,s),l=mt(t.dtype),c=this.segOpCompute(u,"unsortedSegmentSum",e,l,n).reshape(i);return null!=o&&(c=c.transpose(Qt(o))),c},t.prototype.segOpCompute=function(t,e,n,r,o){var a=t.shape[0],i=t.shape[1],s=function(t,e){var n,r=!1;for(t<=ee?(n=t,r=!0):n=W(t,Math.floor(Math.sqrt(t)));!r;){if(n>e||n===t){r=!0;break}n=W(t,n+1)}return n}(i,o),u=new Co({windowSize:s,inSize:i,batchSize:a,numSegments:o},e),l=u.outputShape,c=l[0],h=l[1],p=this.makeOutputArray([c,h],r);return this.compileAndRun(u,[t,n],p),p.shape[1]===o?p:(n=De(0,o).tile([i/s]),this.segOpCompute(p,e,n,r,o))},t.prototype.argMin=function(t,e){var n=[e];Xt("argMin",n,t.rank);var r=$t(t.shape,n),o=r[0],a=y(r[1]),i=t.as2D(-1,a);return this.argReduce(i,"min").reshape(o)},t.prototype.argMax=function(t,e){var n=[e];Xt("argMax",n,t.rank);var r=$t(t.shape,n),o=r[0],a=y(r[1]),i=t.as2D(-1,a);return this.argReduce(i,"max").reshape(o)},t.prototype.cumsum=function(t,e,n,r){if(e!==t.rank-1)throw new Error("WebGL cumsum shader expects an inner-most axis="+(t.rank-1)+" but got axis="+e);var o=new Fn(t.shape,n,r);return this.compileAndRun(o,[t])},t.prototype.equal=function(t,e){var n=new rn("return float(a == b);",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[t,e],r)},t.prototype.notEqual=function(t,e){var n=new rn("return float(a != b);",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[t,e],r)},t.prototype.less=function(t,e){if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.less(t,e);var n=new rn("return float(a < b);",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[t,e],r)},t.prototype.lessEqual=function(t,e){var n=new rn("return float(a <= b);",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[t,e],r)},t.prototype.greater=function(t,e){if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.greater(t,e);var n=new rn("return float(a > b);",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[t,e],r)},t.prototype.greaterEqual=function(t,e){var n=new rn("return float(a >= b);",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[t,e],r)},t.prototype.logicalNot=function(t){var e=new Oo(t.shape,"return float(!(x >= 1.0));");return this.compileAndRun(e,[t])},t.prototype.logicalAnd=function(t,e){var n=new rn("return float(a >= 1.0 && b >= 1.0);",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[t,e],r)},t.prototype.logicalOr=function(t,e){var n=new rn("return float(a >= 1.0 || b >= 1.0);",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[t,e],r)},t.prototype.select=function(t,e,n){var r=new So(t.rank,e.shape,e.rank),o=this.makeOutputArray(r.outputShape,dt(e.dtype,n.dtype));return this.compileAndRun(r,[t,e,n],o)},t.prototype.where=function(t){Ut("tf.where() in webgl locks the UI thread. 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r="";e.concat(n).forEach(function(t){r+=t.shape+"_"+(t.isUniform?"uniform":t.texData.texShape)});var o=t.userCode,a=(!0===t.supportsBroadcasting).toString(),i=t.constructor.name;return i+="_"+a+"_"+r+"_"+o}(t,i,u),c=this.getAndSaveBinary(l,function(){return function(t,e,n,r){for(var o=e.userCode,a=n.map(function(t,n){var r={logicalShape:t.shape,texShape:t.isUniform?null:t.texData.texShape,isUniform:t.isUniform,isPacked:!t.isUniform&&t.texData.isPacked};return{name:e.variableNames[n],shapeInfo:r}}),i=a.map(function(t){return t.shapeInfo}),s={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked},u=Sn(a,s,o,!0===e.supportsBroadcasting,e.usesPackedTextures),l=t.createProgram(u),c={},h=0;h<e.variableNames.length;h++){var p=e.variableNames[h];c[p]=t.getUniformLocation(l,p,!1)}return{program:e,source:u,webGLProgram:l,uniformLocations:c,gpgpu:t,inShapeInfos:i,outShapeInfo:s}}(a.gpgpu,t,i,u)}),h=null!=this.activeTimers;if(h&&(s=this.startTimer()),function(t,e,n,r){eo(t.inShapeInfos,e),eo([t.outShapeInfo],[n]);var o=n.texData.texture,a=n.texData.texShape,i=t.gpgpu;n.texData.isPacked?i.setOutputPackedMatrixTexture(o,a[0],a[1]):i.setOutputMatrixTexture(o,a[0],a[1]),i.setProgram(t.webGLProgram),e.forEach(function(e,n){var r=t.program.variableNames[n],o=t.uniformLocations[r];if(null!=o){if(e.isUniform){if(1===y(e.shape))i.gl.uniform1f(o,e.uniformValues[0]);else{var a=e.uniformValues;a instanceof Float32Array||(a=new Float32Array(a)),i.gl.uniform1fv(o,a)}return}var s=e.texData.texture;i.setInputMatrixTexture(s,o,n)}}),null!=r&&r(i,t.webGLProgram),i.executeProgram()}(c,i,u,r),It.get("WEBGL_PAGING_ENABLED")&&o&&this.numBytesInGPU>this.NUM_BYTES_BEFORE_PAGING)for(var p=this.numBytesInGPU-this.NUM_BYTES_BEFORE_PAGING;p>0&&this.lruDataGPU.length>0;){var f=this.lruDataGPU.shift(),d=this.texData.get(f),m=d.shape,v=d.dtype;p-=this.computeBytes(m,v),this.read(f)}return h&&(s=this.endTimer(s),this.activeTimers.push({name:t.constructor.name,query:this.getQueryTime(s)})),It.get("WEBGL_LAZILY_UNPACK")||!this.texData.get(n.dataId).isPacked||t.isPackShader?n:this.unpackTensor(n)},t.prototype.getAndSaveBinary=function(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]},t.prototype.getTextureManager=function(){return this.textureManager},t.prototype.dispose=function(){if(!this.disposed){for(var t in this.binaryCache)this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram);this.textureManager.dispose(),this.canvas.remove(),null!=this.fromPixels2DContext&&this.fromPixels2DContext.canvas.remove(),this.gpgpuCreatedLocally&&this.gpgpu.dispose(),this.disposed=!0}},t.prototype.floatPrecision=function(){var t=this;return Ot(function(){return t.abs(xe(1e-8)).get()>0?32:16})},t.prototype.uploadToGPU=function(t){var e,n=this.texData.get(t),r=n.shape,o=n.values,a=n.texture,i=n.usage,s=n.isPacked;if(null==a){var u,l=null!=this.activeTimers;l&&(u=performance.now());var c=Tr(r,s);n.texShape=c;var h=this.acquireTexture(t,c,i,s);if(n.texture=h,null!=o){if(s){var p=this.getBatchDim(r),f=1,d=1;r.length&&(f=(e=this.getRowsCols(r))[0],d=e[1]),this.gpgpu.uploadMatrixToPackedTexture(h,p,f,d,c[0],c[1],Pa(o))}else this.gpgpu.uploadMatrixToTexture(h,c[0],c[1],Pa(o));n.values=null,l&&(this.uploadWaitMs+=performance.now()-u)}}else{It.get("WEBGL_PAGING_ENABLED")&&this.lruDataGPU.indexOf(t)>=0&&(this.lruDataGPU.splice(this.lruDataGPU.indexOf(t),1),this.lruDataGPU.push(t))}},t.prototype.convertAndCacheOnCPU=function(t,e){var n=this.delayedStorage,r=this.texData.get(t),o=r.texture,a=r.texShape,i=r.dtype,s=r.usage,u=r.isPacked;return n&&null!=o&&(this.releaseTexture(t,o,a,s,u),r.texture=null,r.texShape=null),r.usage=Hn.UPLOAD,null!=e&&(r.values=function(t,e){if("float32"===e||"complex64"===e)return t;if("int32"===e||"bool"===e){for(var n="int32"===e?new Int32Array(t.length):new Uint8Array(t.length),r=0;r<n.length;++r)n[r]=Math.round(t[r]);return n}throw new Error("Unknown dtype "+e)}(e,i)),r.values},t.prototype.releaseTexture=function(t,e,n,r,o){var a=this.texData.get(t),i=a.shape,s=a.dtype;if(It.get("WEBGL_PAGING_ENABLED")){var u=this.lruDataGPU.indexOf(t);u>=0&&this.lruDataGPU.splice(u,1)}this.numBytesInGPU-=this.computeBytes(i,s),this.textureManager.releaseTexture(e,n,r,o)},t.prototype.acquireTexture=function(t,e,n,r){var o=this.texData.get(t),a=o.shape,i=o.dtype;return It.get("WEBGL_PAGING_ENABLED")&&this.lruDataGPU.push(t),this.numBytesInGPU+=this.computeBytes(a,i),this.textureManager.acquireTexture(e,n,r)},t.prototype.computeBytes=function(t,e){return y(t)*_(e)},t}();function Pa(t){return t instanceof Float32Array?t:new Float32Array(t)}It.get("IS_BROWSER")&&It.registerBackend("webgl",function(){return new La},2,ot);var Ua=pe({abs_:function(t){var e=ce(t,"x","abs");return"complex64"===e.dtype?It.engine.runKernel(function(t){return t.complexAbs(e)},{$x:e}):It.engine.runKernel(function(t){return t.abs(e)},{$x:e},function(t){return{$x:function(){return t.mulStrict(e.toFloat().step(-1))}}})}}),Wa=pe({acos_:function(t){var e=ce(t,"x","acos");return It.engine.runKernel(function(t){return t.acos(e)},{$x:e},function(t){return{$x:function(){return t.divStrict(xe(1).sub(e.toFloat().square()).sqrt()).neg()}}})}}),za=pe({acosh_:function(t){var e=ce(t,"x","acosh");return It.engine.runKernel(function(t){return t.acosh(e)},{$x:e},function(t){return{$x:function(){return t.divStrict(e.toFloat().square().sub(1).sqrt())}}})}}),Va=pe({asin_:function(t){var e=ce(t,"x","asin");return It.engine.runKernel(function(t){return t.asin(e)},{$x:e},function(t){return{$x:function(){return 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n(t.exp(e))},{$x:e},function(t,e){var n=e[0];return{$x:function(){return t.mulStrict(n)}}})}}),Ja=pe({expm1_:function(t){var e=ce(t,"x","expm1");return It.engine.runKernel(function(t){return t.expm1(e)},{$x:e},function(t){return{$x:function(){return t.mulStrict(e.exp())}}})}}),Za=pe({floor_:function(t){var e=ce(t,"x","floor");return It.engine.runKernel(function(t){return t.floor(e)},{$x:e},function(t){return{$x:function(){return Me(t)}}})}}),ti=pe({log_:function(t){var e=ce(t,"x","log");return It.engine.runKernel(function(t){return t.log(e)},{$x:e},function(t){return{$x:function(){return t.divStrict(e.toFloat())}}})}}),ei=pe({log1p_:function(t){var e=ce(t,"x","log1p");return It.engine.runKernel(function(t){return t.log1p(e)},{$x:e},function(t){return{$x:function(){return t.div(e.add(1))}}})}}),ni=pe({logSigmoid_:function(t){var e=ce(t,"x","logSigmoid");return It.engine.runKernel(function(t){return t.softplus(e.neg()).neg()},{$x:e},function(t){return{$x:function(){return 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t.mul(n.mul(xe(1).sub(n)))}}})}}),ui=pe({sign_:function(t){var e=ce(t,"x","sign");return It.engine.runKernel(function(t){return t.sign(e)},{$x:e},function(t){return{$x:function(){return Me(t)}}})}}),li=pe({sin_:function(t){var e=ce(t,"x","sin");return It.engine.runKernel(function(t){return t.sin(e)},{$x:e},function(t){return{$x:function(){return e.toFloat().cos().mulStrict(t)}}})}}),ci=pe({sinh_:function(t){var e=ce(t,"x","sinh");return It.engine.runKernel(function(t){return t.sinh(e)},{$x:e},function(t){return{$x:function(){return e.toFloat().cosh().mulStrict(t)}}})}}),hi=pe({softplus_:function(t){var e=ce(t,"x","softplus");return It.engine.runKernel(function(t){return t.softplus(e)},{$x:e},function(t){return{$x:function(){return t.mulStrict(e.sigmoid())}}})}}),pi=pe({sqrt_:function(t){var e=ce(t,"x","sqrt");return It.engine.runKernel(function(t){return t.sqrt(e)},{$x:e},function(t){return{$x:function(){return t.div(e.toFloat().sqrt().mul(2))}}})}}),fi=pe({square_:function(t){var e=ce(t,"x","square");return It.engine.runKernel(function(t){return t.square(e)},{$x:e},function(t){return{$x:function(){return t.mul(e.toFloat().mul(2))}}})}}),di=pe({step_:function(t,e){void 0===e&&(e=0);var n=ce(t,"x","step");return It.engine.runKernel(function(t){return t.step(n,e)},{$x:n},function(t){return{$x:function(){return Me(t)}}})}}),mi=pe({tan_:function(t){var e=ce(t,"x","tan");return It.engine.runKernel(function(t){return t.tan(e)},{$x:e},function(t){return{$x:function(){return t.divStrict(e.cos().square())}}})}}),vi=pe({tanh_:function(t){var e=ce(t,"x","tanh");return It.engine.runKernel(function(t,n){return n(t.tanh(e))},{$x:e},function(t,e){var n=e[0];return{$x:function(){return xe(1).sub(n.square()).mulStrict(t)}}})}});function gi(t){return null==t?null:0===t.rank?t.as1D():1===t.rank?t:2===t.rank?t.as4D(1,1,t.shape[0],t.shape[1]):3===t.rank?t.as4D(1,t.shape[0],t.shape[1],t.shape[2]):t}var yi=pe({batchNormalization2d_:function(t,e,n,r,o,a){void 0===r&&(r=.001);var i,s,u=ce(t,"x","batchNormalization"),l=ce(e,"mean","batchNormalization"),c=ce(n,"variance","batchNormalization");return null!=o&&(i=ce(o,"scale","batchNormalization")),null!=a&&(s=ce(a,"offset","batchNormalization")),d(2===u.rank,"Error in batchNormalization3D: x must be rank 3 but got rank "+u.rank+"."),d(2===l.rank||1===l.rank,"Error in batchNormalization2D: mean must be rank 2 or rank 1 but got rank "+l.rank+"."),d(2===c.rank||1===c.rank,"Error in batchNormalization2D: variance must be rank 2 or rank 1 but got rank "+c.rank+"."),null!=i&&d(2===i.rank||1===i.rank,"Error in batchNormalization2D: scale must be rank 2 or rank 1 but got rank "+i.rank+"."),null!=s&&d(2===s.rank||1===s.rank,"Error in batchNormalization2D: offset must be rank 2 or rank 1 but got rank "+s.rank+"."),bi(u,l,c,r,i,s)}}),xi=pe({batchNormalization3d_:function(t,e,n,r,o,a){void 0===r&&(r=.001);var i,s,u=ce(t,"x","batchNormalization"),l=ce(e,"mean","batchNormalization"),c=ce(n,"variance","batchNormalization");return null!=o&&(i=ce(o,"scale","batchNormalization")),null!=a&&(s=ce(a,"offset","batchNormalization")),d(3===u.rank,"Error in batchNormalization3D: x must be rank 3 but got rank "+u.rank+"."),d(3===l.rank||1===l.rank,"Error in batchNormalization3D: mean must be rank 3 or rank 1 but got rank "+l.rank+"."),d(3===c.rank||1===c.rank,"Error in batchNormalization3D: variance must be rank 3 or rank 1 but got rank "+c.rank+"."),null!=i&&d(3===i.rank||1===i.rank,"Error in batchNormalization3D: scale must be rank 3 or rank 1 but got rank "+i.rank+"."),null!=s&&d(3===s.rank||1===s.rank,"Error in batchNormalization3D: offset must be rank 3 or rank 1 but got rank "+s.rank+"."),bi(u,l,c,r,i,s)}}),wi=pe({batchNormalization4d_:function(t,e,n,r,o,a){void 0===r&&(r=.001);var i,s,u=ce(t,"x","batchNormalization"),l=ce(e,"mean","batchNormalization"),c=ce(n,"variance","batchNormalization");return null!=o&&(i=ce(o,"scale","batchNormalization")),null!=a&&(s=ce(a,"offset","batchNormalization")),d(4===u.rank,"Error in batchNormalization4D: x must be rank 4 but got rank "+u.rank+"."),d(4===l.rank||1===l.rank,"Error in batchNormalization4D: mean must be rank 4 or rank 1 but got rank "+l.rank+"."),d(4===c.rank||1===c.rank,"Error in batchNormalization4D: variance must be rank 4 or rank 1 but got rank "+c.rank+"."),null!=i&&d(4===i.rank||1===i.rank,"Error in batchNormalization4D: scale must be rank 4 or rank 1 but got rank "+i.rank+"."),null!=s&&d(4===s.rank||1===s.rank,"Error in batchNormalization4D: offset must be rank 4 or rank 1 but got rank "+s.rank+"."),bi(u,l,c,r,i,s)}}),bi=pe({batchNormalization_:function(t,e,n,r,o,a){void 0===r&&(r=.001);var i,s,u,l=ce(t,"x","batchNormalization"),c=ce(e,"mean","batchNormalization"),h=ce(n,"variance","batchNormalization");return null!=o&&(i=ce(o,"scale","batchNormalization")),null!=a&&(s=ce(a,"offset","batchNormalization")),d(c.rank===h.rank,"Batch normalization gradient requires mean and variance to have equal ranks."),d(null==s||c.rank===s.rank,"Batch normalization gradient requires mean and offset to have equal ranks."),d(null==i||c.rank===i.rank,"Batch normalization gradient requires mean and scale to have equal ranks."),u=0===l.rank||1===l.rank?l.as4D(1,1,1,l.size):2===l.rank?l.as4D(1,1,l.shape[0],l.shape[1]):3===l.rank?l.as4D(1,l.shape[0],l.shape[1],l.shape[2]):l,It.engine.runKernel(function(t){return t.batchNormalization(u,gi(c),gi(h),r,gi(i),gi(s))},{$x:l,$mean:c,$variance:h,$scale:i,$offset:s},function(t){var e=null==i?xe(1):i,n=Ke(c.shape,u.shape),o=[];if(1===c.rank){for(var a=0;a<u.shape.length-1;++a)o.push(u.shape[a]);o.push(1)}var s=l.sub(c),p=t.mul(e),f=ii(h.add(xe(r))),d=f.mul(f).mul(f).mul(xe(-.5));return{$x:function(){return 1===c.rank?t.mul(Aa(f.as4D(1,1,1,c.shape[0]),o)).mul(e).reshape(l.shape):t.mul(f).mul(e).reshape(l.shape)},$mean:function(){var t=f.mul(xe(-1)).mul(p);return 1===c.rank&&(t=t.sum(n)),t.reshape(c.shape)},$variance:function(){var t=d.mul(s).mul(p);return 1===c.rank&&(t=t.sum(n)),t.reshape(c.shape)},$scale:function(){var e=s.mul(f),r=t.mul(e);return 1===c.rank&&(r=r.sum(n)),r.reshape(c.shape)},$offset:function(){var e=t;return 1===c.rank&&(e=e.sum(n)),e.reshape(c.shape)}}}).reshape(l.shape)}});function Ei(t,e,n,r,o,a,i){void 0===i&&(i="channelsLast");var s,u=Ri(e),l=u[0],c=u[1];if("channelsLast"===i)s=[l,c,t[3],t[3]];else{if("channelsFirst"!==i)throw new Error("Unknown dataFormat "+i);s=[l,c,t[1],t[1]]}return Ci(t,s,n,r,o,a,!1,i)}function Ci(t,e,n,r,o,a,i,s){void 0===i&&(i=!1),void 0===s&&(s="channelsLast");var u=[-1,-1,-1,-1],l=u[0],c=u[1],h=u[2],p=u[3];if("channelsLast"===s)l=t[0],c=t[1],h=t[2],p=t[3];else{if("channelsFirst"!==s)throw new Error("Unknown dataFormat "+s);l=t[0],p=t[1],c=t[2],h=t[3]}var f,m=e[0],v=e[1],g=e[3],y=Ri(n),x=y[0],b=y[1],E=Ri(r),C=E[0],S=E[1],R=ki(m,C),N=ki(v,S),k=function(t,e,n,r,o,a,i,s){var u,l,c;if("number"==typeof t){var h=0===t?"VALID":"NUMBER";u={top:t,bottom:t,left:t,right:t,type:h};var p=function(t,e,n,r,o,a){null==o&&(o=function(t,e,n,r){void 0===r&&(r=1);var o=ki(e,r);return Math.floor((t[0]*(n-1)-n+o)/2)}(t,e,r));var i=t[0],s=t[1],u=Ti((i-e+2*o)/r+1,a);d(w(u),"The output # of rows ("+u+") must be an integer. 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Change the stride and/or zero pad parameters"),[u,l,n]}([e,n,1],a,1,r,t,s);l=p[0],c=p[1]}else if("same"===t){l=Math.ceil(e/r),c=Math.ceil(n/o);var f=(l-1)*r+a-e,m=(c-1)*o+i-n,v=Math.floor(f/2),g=f-v,y=Math.floor(m/2),x=m-y;u={top:v,bottom:g,left:y,right:x,type:"SAME"}}else{if("valid"!==t)throw Error("Unknown padding parameter: "+t);u={top:0,bottom:0,left:0,right:0,type:"VALID"},l=Math.ceil((e-a+1)/r),c=Math.ceil((n-i+1)/o)}return{padInfo:u,outHeight:l,outWidth:c}}(o,c,h,x,b,R,N,a),T=k.padInfo,I=k.outHeight,D=k.outWidth,A=i?g*p:g;return"channelsFirst"===s?f=[l,A,I,D]:"channelsLast"===s&&(f=[l,I,D,A]),{batchSize:l,dataFormat:s,inHeight:c,inWidth:h,inChannels:p,outHeight:I,outWidth:D,outChannels:A,padInfo:T,strideHeight:x,strideWidth:b,filterHeight:m,filterWidth:v,effectiveFilterHeight:R,effectiveFilterWidth:N,dilationHeight:C,dilationWidth:S,inShape:t,outShape:f,filterShape:e}}function Si(t,e,n,r,o,a,i){void 0===a&&(a=!1),void 0===i&&(i="channelsLast");var s=[-1,-1,-1,-1,-1],u=s[0],l=s[1],c=s[2],h=s[3],p=s[4];if("channelsLast"===i)u=t[0],l=t[1],c=t[2],h=t[3],p=t[4];else{if("channelsFirst"!==i)throw new Error("Unknown dataFormat "+i);u=t[0],p=t[1],l=t[2],c=t[3],h=t[4]}var f,d=e[0],m=e[1],v=e[2],g=e[4],y=Ni(n),x=y[0],w=y[1],b=y[2],E=Ni(r),C=E[0],S=E[1],R=E[2],N=function(t,e,n,r,o,a,i,s,u,l){var c,h,p,f;if("same"===t){h=Math.ceil(e/o),p=Math.ceil(n/a),f=Math.ceil(r/i);var d=(h-1)*o+s-e,m=(p-1)*a+u-n,v=(f-1)*i+l-r,g=Math.floor(d/2),y=d-g,x=Math.floor(m/2),w=m-x,b=Math.floor(v/2),E=v-b;c={top:x,bottom:w,left:b,right:E,front:g,back:y,type:"SAME"}}else{if("valid"!==t)throw Error("Unknown padding parameter: "+t);c={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},h=Math.ceil((e-s+1)/o),p=Math.ceil((n-u+1)/a),f=Math.ceil((r-l+1)/i)}return{padInfo:c,outDepth:h,outHeight:p,outWidth:f}}(o,l,c,h,x,w,b,ki(d,C),ki(m,S),ki(v,R)),k=N.padInfo,T=N.outDepth,I=N.outHeight,D=N.outWidth,A=a?g*p:g;return"channelsFirst"===i?f=[u,A,T,I,D]:"channelsLast"===i&&(f=[u,T,I,D,A]),{batchSize:u,dataFormat:i,inDepth:l,inHeight:c,inWidth:h,inChannels:p,outDepth:T,outHeight:I,outWidth:D,outChannels:A,padInfo:k,strideDepth:x,strideHeight:w,strideWidth:b,filterDepth:d,filterHeight:m,filterWidth:v,dilationDepth:C,dilationHeight:S,dilationWidth:R,inShape:t,outShape:f,filterShape:e}}function Ri(t){return"number"==typeof t?[t,t]:t}function Ni(t){return"number"==typeof t?[t,t,t]:t}function ki(t,e){return e<=1?t:t+(t-1)*(e-1)}function Ti(t,e){if(!e)return t;switch(e){case"round":return Math.round(t);case"ceil":return Math.ceil(t);case"floor":return Math.floor(t);default:throw new Error("Unknown roundingMode "+e)}}function Ii(t){var e=Ri(t),n=e[0],r=e[1];return 1===n&&1===r}function Di(t,e){return Ii(t)||Ii(e)}var Ai=pe({matMul_:function(t,e,n,r){var o;void 0===n&&(n=!1),void 0===r&&(r=!1);var a=ce(t,"a","matMul"),i=ce(e,"b","matMul");a=(o=vt(a,i))[0],i=o[1];var s=n?a.shape[a.rank-2]:a.shape[a.rank-1],u=r?i.shape[i.rank-1]:i.shape[i.rank-2],l=n?a.shape[a.rank-1]:a.shape[a.rank-2],c=r?i.shape[i.rank-2]:i.shape[i.rank-1],h=a.shape.slice(0,-2),p=i.shape.slice(0,-2),f=y(h),m=y(p);d(a.rank>=2&&i.rank>=2&&a.rank===i.rank,"Error in matMul: inputs must have the same rank of at least 2, got ranks "+a.rank+" and "+i.rank+"."),d(x(h,p),"Error in matMul: outer dimensions ("+h+") and ("+p+") of Tensors with shapes "+a.shape+" and "+i.shape+" must match."),d(s===u,"Error in matMul: inner shapes ("+s+") and ("+u+") of Tensors with shapes "+a.shape+" and "+i.shape+" and transposeA="+n+" and transposeB="+r+" must match.");var v=a.shape.slice(0,-2).concat([l,c]),g=n?a.as3D(f,s,l):a.as3D(f,l,s),w=r?i.as3D(m,c,u):i.as3D(m,u,c);return It.engine.runKernel(function(t){return t.batchMatMul(g,w,n,r)},{$a:g,$b:w},function(t){return n||r?!n&&r?{$a:function(){return t.matMul(w,!1,!1)},$b:function(){return t.matMul(g,!0,!1)}}:n&&!r?{$a:function(){return w.matMul(t,!1,!0)},$b:function(){return g.matMul(t,!1,!1)}}:{$a:function(){return w.matMul(t,!0,!0)},$b:function(){return t.matMul(g,!0,!0)}}:{$a:function(){return t.matMul(w,!1,!0)},$b:function(){return g.matMul(t,!0,!1)}}}).reshape(v)}}),Mi=pe({dot_:function(t,e){var n=ce(t,"t1","dot"),r=ce(e,"t2","dot");d(!(1!==n.rank&&2!==n.rank||1!==r.rank&&2!==r.rank),"Error in dot: inputs must all be rank 1 or 2, but got ranks "+n.rank+" and "+r.rank+".");var o=1===n.rank?n.size:n.shape[1],a=1===r.rank?r.size:r.shape[0];return d(o===a,"Error in dot: inner dimensions of inputs must match, but got "+o+" and "+a+"."),1===n.rank&&1===r.rank?n.as2D(1,-1).matMul(r.as2D(-1,1)).asScalar():1===n.rank&&2===r.rank?n.as2D(1,-1).matMul(r.as2D(r.shape[0],r.shape[1])).as1D():2===n.rank&&1===r.rank?n.matMul(r.as2D(-1,1)).as1D():n.matMul(r.as2D(r.shape[0],r.shape[1]))}}),_i=pe({outerProduct_:function(t,e){var n=ce(t,"v1","outerProduct"),r=ce(e,"v2","outerProduct");return d(1===n.rank&&1===r.rank,"Error in outerProduct: inputs must be rank 1, but got ranks "+n.rank+" and "+r.rank+"."),n.as2D(-1,1).matMul(r.as2D(1,-1))}});function Oi(t,e,n,r,o,a){d(t.length===e.rank,"Length of inShape ("+t.length+") and rank of dy ("+e.rank+") must match");var i=t,s=e,u=!1;3===e.rank&&(u=!0,s=e.as4D(1,e.shape[0],e.shape[1],e.shape[2]),i=[1,t[0],t[1],t[2]]);var l=i[3],c=s.shape[3];d(4===i.length,"Error in conv2dDerInput: inShape must be length 4, but got length "+i.length+"."),d(4===s.rank,"Error in conv2dDerInput: dy must be rank 4, but got rank "+s.rank),d(4===n.rank,"Error in conv2dDerInput: filter must be rank 4, but got rank "+n.rank),d(l===n.shape[2],"Error in conv2dDerInput: depth of input ("+l+") must match input depth for filter "+n.shape[2]+"."),d(c===n.shape[3],"Error in conv2dDerInput: depth of output ("+c+") must match output depth for filter "+n.shape[3]+"."),null!=a&&d(w(o),"Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode "+a+" but got pad "+o+".");var h=Ci(i,n.shape,r,1,o,a),p=It.engine.runKernel(function(t){return t.conv2dDerInput(s,n,h)},{dy4D:s,filter:n},function(t){return{dy4D:function(){return Pi(t,n,r,o,"NHWC",1,a)},filter:function(){return Wi(t,s,n.shape,r,o,a)}}});return u?p.as3D(p.shape[1],p.shape[2],p.shape[3]):p}function Fi(t,e,n,r,o,a){var i=t;3===t.rank&&(i=t.as4D(1,t.shape[0],t.shape[1],t.shape[2]));var s=e;3===s.rank&&(s=e.as4D(1,e.shape[0],e.shape[1],e.shape[2])),d(4===i.rank,"Error in conv2dDerFilter: input must be rank 4, but got shape "+i.shape+"."),d(4===s.rank,"Error in conv2dDerFilter: dy must be rank 4, but got shape "+s.shape+"."),d(4===n.length,"Error in conv2dDerFilter: filterShape must be length 4, but got "+n+"."),d(i.shape[3]===n[2],"Error in conv2dDerFilter: depth of input "+i.shape[3]+") must match input depth in filter ("+n[2]+"."),d(s.shape[3]===n[3],"Error in conv2dDerFilter: depth of dy ("+s.shape[3]+") must match output depth for filter ("+n[3]+")."),null!=a&&d(w(o),"Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode "+a+" but got pad "+o+".");var u=Ci(i.shape,n,r,1,o,a);return It.engine.runKernel(function(t){return t.conv2dDerFilter(i,s,u)},{x4D:i,dy4D:s})}function Bi(t){var e=function(t){return"number"==typeof t?[t,t,t]:2===t.length?[t[0],t[1],1]:t}(t),n=e[0],r=e[1],o=e[2];return 1===n&&1===r&&1===o}var Li=pe({conv1d_:function(t,e,n,r,o,a,i){void 0===o&&(o="NWC"),void 0===a&&(a=1);var s=ce(t,"x","conv1d"),u=ce(e,"filter","conv1d"),l=s,c=!1;2===s.rank&&(c=!0,l=s.as3D(1,s.shape[0],s.shape[1])),d(3===l.rank,"Error in conv1d: input must be rank 3, but got rank "+l.rank+"."),d(3===u.rank,"Error in conv1d: filter must be rank 3, but got rank "+u.rank+"."),null!=i&&d(w(r),"Error in conv1d: pad must be an integer when using, dimRoundingMode "+i+" but got pad "+r+"."),d(l.shape[2]===u.shape[1],"Error in conv1d: depth of input ("+l.shape[2]+") must match input depth for filter "+u.shape[1]+"."),d(Di(n,a),"Error in conv1D: Either stride or dilation must be 1. Got stride "+n+" and dilation '"+a+"'"),d("NWC"===o,"Error in conv1d: got dataFormat of "+o+" but only NWC is currently supported.");var h=u.as4D(1,u.shape[0],u.shape[1],u.shape[2]),p=l.as4D(l.shape[0],1,l.shape[1],l.shape[2]),f=Pi(p,h,[1,n],r,"NHWC",[1,a],i);return c?f.as2D(f.shape[2],f.shape[3]):f.as3D(f.shape[0],f.shape[2],f.shape[3])}}),Pi=pe({conv2d_:function(t,e,n,r,o,a,i){void 0===o&&(o="NHWC"),void 0===a&&(a=[1,1]);var s=ce(t,"x","conv2d"),u=ce(e,"filter","conv2d"),l=s,c=!1;3===s.rank&&(c=!0,l=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),d(4===l.rank,"Error in conv2d: input must be rank 4, but got rank "+l.rank+"."),d(4===u.rank,"Error in conv2d: filter must be rank 4, but got rank "+u.rank+"."),null!=i&&d(w(r),"Error in conv2d: pad must be an integer when using, dimRoundingMode "+i+" but got pad "+r+"."),d(l.shape[3]===u.shape[2],"Error in conv2d: depth of input ("+l.shape[3]+") must match input depth for filter "+u.shape[2]+"."),d(Di(n,a),"Error in conv2D: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+a+"'"),d("NHWC"===o,"Error in conv2d: got dataFormat of "+o+" but only NHWC is currently supported.");var h,p=Ci(l.shape,u.shape,n,a,r,i);if(1!==p.filterHeight||1!==p.filterWidth||1!==p.dilationHeight||1!==p.dilationWidth||1!==p.strideHeight||1!==p.strideWidth||"SAME"!==p.padInfo.type&&"VALID"!==p.padInfo.type)h=It.engine.runKernel(function(t){return t.conv2d(l,u,p)},{x:l,$filter:u},function(t){return d(Ii(a),"Error in gradient of conv2D: dilation rates greater than 1 are notyet supported in gradients. Got dilations '"+a+"'"),{x:function(){return Oi(l.shape,t,u,n,r)},$filter:function(){return Fi(l,t,u.shape,n,r)}}});else{var f=l.reshape([-1,p.inChannels]),m=u.reshape([p.inChannels,p.outChannels]);h=Ai(f,m).reshape(p.outShape)}return c?h.as3D(h.shape[1],h.shape[2],h.shape[3]):h}}),Ui=pe({conv3d_:function(t,e,n,r,o,a){void 0===o&&(o="NHWC"),void 0===a&&(a=[1,1,1]);var i=ce(t,"x","conv3d"),s=ce(e,"filter","conv3d"),u=i,l=!1;4===i.rank&&(l=!0,u=i.as5D(1,i.shape[0],i.shape[1],i.shape[2],i.shape[3])),d(5===u.rank,"Error in conv3d: input must be rank 5, but got rank "+u.rank+"."),d(5===s.rank,"Error in conv3d: filter must be rank 5, but got rank "+s.rank+"."),d(u.shape[4]===s.shape[3],"Error in conv3d: depth of input ("+u.shape[4]+") must match input depth for filter "+s.shape[3]+"."),d(function(t,e){return Bi(t)||Bi(e)}(n,a),"Error in conv3D: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+a+"'"),d("NHWC"===o,"Error in conv3d: got dataFormat of "+o+" but only NHWC is currently supported.");var c=Si(u.shape,s.shape,n,a,r),h=It.engine.runKernel(function(t){return t.conv3d(u,s,c)},{x:u,$filter:s},function(t){return d(Bi(a),"Error in gradient of conv3D: dilation rates greater than 1 are notyet supported in gradients. Got dilations '"+a+"'"),{x:function(){return function(t,e,n,r,o){d(t.length===e.rank,"Length of inShape ("+t.length+") and rank of dy ("+e.rank+") must match");var a=t,i=e,s=!1;4===e.rank&&(s=!0,i=e.as5D(1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]),a=[1,t[0],t[1],t[2],t[3]]);var u=a[4],l=i.shape[4];d(5===a.length,"Error in conv3dDerInput: inShape must be length 5, but got length "+a.length+"."),d(5===i.rank,"Error in conv3dDerInput: dy must be rank 5, but got rank "+i.rank),d(5===n.rank,"Error in conv3dDerInput: filter must be rank 5, but got rank "+n.rank),d(u===n.shape[3],"Error in conv3dDerInput: depth of input ("+u+") must match input depth for filter "+n.shape[3]+"."),d(l===n.shape[4],"Error in conv3dDerInput: depth of output ("+l+") must match output depth for filter "+n.shape[4]+".");var c=Si(a,n.shape,r,1,o),h=It.engine.runKernel(function(t){return t.conv3dDerInput(i,n,c)},{dy5D:i});return s?h.as4D(h.shape[1],h.shape[2],h.shape[3],h.shape[4]):h}(u.shape,t,s,n,r)},$filter:function(){return function(t,e,n,r,o){var a=t;4===t.rank&&(a=t.as5D(1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]));var i=e;4===i.rank&&(i=e.as5D(1,e.shape[0],e.shape[1],e.shape[2],e.shape[3])),d(5===a.rank,"Error in conv3dDerFilter: input must be rank 5, but got shape "+a.shape+"."),d(5===i.rank,"Error in conv3dDerFilter: dy must be rank 5, but got shape "+i.shape+"."),d(5===n.length,"Error in conv3dDerFilter: filterShape must be length 5, but got "+n+"."),d(a.shape[4]===n[3],"Error in conv3dDerFilter: depth of input "+a.shape[4]+") must match input depth in filter ("+n[3]+"."),d(i.shape[4]===n[4],"Error in conv3dDerFilter: depth of dy ("+i.shape[4]+") must match output depth for filter ("+n[4]+").");var s=Si(a.shape,n,r,1,o);return It.engine.runKernel(function(t){return t.conv3dDerFilter(a,i,s)},{x5D:a,dy5D:i})}(u,t,s.shape,n,r)}}});return l?h.as4D(h.shape[1],h.shape[2],h.shape[3],h.shape[4]):h}}),Wi=pe({conv2dDerFilter_:Fi}),zi=pe({depthwiseConv2d_:function(t,e,n,r,o,a,i){void 0===o&&(o="NHWC"),void 0===a&&(a=[1,1]);var s=ce(t,"x","depthwiseConv2d"),u=ce(e,"filter","depthwiseConv2d"),l=s,c=!1;3===s.rank&&(c=!0,l=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),d(4===l.rank,"Error in depthwiseConv2d: input must be rank 4, but got rank "+l.rank+"."),d(4===u.rank,"Error in depthwiseConv2d: filter must be rank 4, but got rank "+u.rank+"."),d(l.shape[3]===u.shape[2],"Error in depthwiseConv2d: number of input channels ("+l.shape[3]+") must match the inChannels dimension in filter "+u.shape[2]+"."),null==a&&(a=[1,1]),d(Di(n,a),"Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+a+"'"),null!=i&&d(w(r),"Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode "+i+" but got pad "+r+".");var h=Ci(l.shape,u.shape,n,a,r,i,!0),p=It.engine.runKernel(function(t){return t.depthwiseConv2D(l,u,h)},{x:l,$filter:u},function(t){return d(Ii(a),"Error in gradient of depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '"+a+"'"),{x:function(){return function(t,e,n,r){var o=e,a=!1;3===e.rank&&(a=!0,o=e.as4D(1,e.shape[0],e.shape[1],e.shape[2]));var i=It.engine.runKernel(function(t){return t.depthwiseConv2DDerInput(o,n,r)},{dy4D:o});return a?i.as3D(i.shape[1],i.shape[2],i.shape[3]):i}(l.shape,t,u,h)},$filter:function(){return function(t,e,n,r){var o=t;3===t.rank&&(o=t.as4D(1,t.shape[0],t.shape[1],t.shape[2]));var a=e;return 3===a.rank&&(a=e.as4D(1,e.shape[0],e.shape[1],e.shape[2])),It.engine.runKernel(function(t){return t.depthwiseConv2DDerFilter(o,a,r)},{x4D:o,dy4D:a})}(l,t,u.shape,h)}}});return c?p.as3D(p.shape[1],p.shape[2],p.shape[3]):p}}),Vi=pe({separableConv2d_:function(t,e,n,r,o,a,i){void 0===a&&(a=[1,1]),void 0===i&&(i="NHWC");var s=ce(t,"x","separableConv2d"),u=ce(e,"depthwiseFilter","separableConv2d"),l=ce(n,"pointwiseFilter","separableConv2d"),c=s,h=!1;if(3===s.rank&&(h=!0,c=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),"NCHW"===i)throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");d(4===c.rank,"Error in separableConv2d: input must be rank 4, but got rank "+c.rank+"."),d(4===u.rank,"Error in separableConv2d: depthwise filter must be rank 4, but got rank "+u.rank+"."),d(4===l.rank,"Error in separableConv2d: pointwise filter must be rank 4, but got rank "+u.rank+"."),d(1===l.shape[0],"Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got "+l.shape[0]+"."),d(1===l.shape[1],"Error in separableConv2d: the second dimension of pointwise filter  must be 1, but got "+l.shape[1]+".");var p=u.shape[2],f=u.shape[3];d(l.shape[2]===p*f,"Error in separableConv2d: the third dimension of pointwise filter must be "+p*f+", but got "+l.shape[2]+".");var m=zi(c,u,r,o,i,a),v=Pi(m,l,1,"valid",i);return h?v.as3D(v.shape[1],v.shape[2],v.shape[3]):v}}),Gi=pe({conv2dTranspose_:function(t,e,n,r,o,a){return Oi(n,ce(t,"x","conv2dTranspose"),ce(e,"filter","conv2dTranspose"),r,o,a)}});var Hi=pe({reverse_:function(t,e){var n=ce(t,"x","reverse");if(0===n.rank)return n.clone();var r=Kt(e,n.shape);return It.engine.runKernel(function(t){return t.reverse(n,r)},{$x:n},function(t){return{$x:function(){return t.reverse(r)}}}).reshapeAs(n)}}),qi=pe({reverse1d_:function(t){var e=ce(t,"x","reverse");return d(1===e.rank,"Error in reverse1D: x must be rank 1 but got\n             rank "+e.rank+"."),Hi(e,0)}}),$i=pe({reverse2d_:function(t,e){var n=ce(t,"x","reverse");return d(2===n.rank,"Error in reverse2D: x must be rank 2 but got\n             rank "+n.rank+"."),Hi(n,e)}}),ji=pe({reverse3d_:function(t,e){var n=ce(t,"x","reverse");return d(3===n.rank,"Error in reverse3D: x must be rank 3 but got\n             rank "+n.rank+"."),Hi(n,e)}}),Ki=pe({reverse4d_:function(t,e){var n=ce(t,"x","reverse");return d(4===n.rank,"Error in reverse4D: x must be rank 4 but got\n             rank "+n.rank+"."),Hi(n,e)}});function Xi(t,e,n,r,o,a){var i=ce(t,"x","maxPool"),s=i,u=!1;3===i.rank&&(u=!0,s=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),null==r&&(r=[1,1]),d(4===s.rank,"Error in maxPool: input must be rank 4 but got rank "+s.rank+"."),d(Di(n,r),"Error in maxPool: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+r+"'"),null!=a&&d(w(o),"Error in maxPool: pad must be an integer when using, dimRoundingMode "+a+" but got pad "+o+".");var l=Ei(s.shape,e,n,r,o,a),c=It.engine.runKernel(function(t,e){return e(t.maxPool(s,l))},{x:s},function(t,a){var i=a[0];return{x:function(){return function(t,e,n,r,o,a,i,s){var u=ce(t,"dy","maxPoolBackprop"),l=ce(e,"input","maxPoolBackprop"),c=ce(n,"output","maxPoolBackprop");d(l.rank===u.rank,"Rank of input ("+l.rank+") does not match rank of dy ("+u.rank+")"),null==a&&(a=[1,1]),d(Di(o,a),"Error in maxPoolBackProp: Either strides or dilations must be 1. 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Iu(u,s,o)}}),Fu=pe({sigmoidCrossEntropy_:function(e,n,r,o,a){void 0===o&&(o=0),void 0===a&&(a=t.Reduction.SUM_BY_NONZERO_WEIGHTS);var i=ce(e,"multiClassLabels","sigmoidCrossEntropy"),s=ce(n,"logits","sigmoidCrossEntropy"),u=null;if(null!=r&&(u=ce(r,"weights","sigmoidCrossEntropy")),m(i.shape,s.shape,"Error in sigmoidCrossEntropy: "),o>0){var l=xe(o),c=xe(1),h=xe(.5);i=i.mul(c.sub(l)).add(h.mul(l))}var p=function(t,e){var n=ce(t,"labels","sigmoidCrossEntropyWithLogits"),r=ce(e,"logits","sigmoidCrossEntropyWithLogits");m(n.shape,r.shape,"Error in sigmoidCrossEntropyWithLogits: ");var o=r.relu(),a=r.mul(n),i=r.abs().neg().exp().log1p();return o.sub(a).add(i)}(i,s);return Iu(p,u,a)}}),Bu=pe({softmaxCrossEntropy_:function(e,n,r,o,a){void 0===o&&(o=0),void 0===a&&(a=t.Reduction.SUM_BY_NONZERO_WEIGHTS);var i=ce(e,"onehotLabels","softmaxCrossEntropy"),s=ce(n,"logits","softmaxCrossEntropy"),u=null;if(null!=r&&(u=ce(r,"weights","softmaxCrossEntropy")),m(i.shape,s.shape,"Error in 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t(){this.blockSize=48,this.firstUse=!0,It.get("IS_BROWSER")&&(this.fromPixels2DContext=document.createElement("canvas").getContext("2d"))}return t.prototype.setDataMover=function(t){this.data=new _e(t)},t.prototype.register=function(t,e,n){if(this.firstUse&&(this.firstUse=!1,It.get("IS_NODE")&&Ut("\n============================\nHi there 👋. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. 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r=$t(t.shape,n),o=r[0],a=r[1],i=ke(o,"int32"),s=y(a),u=i.dataSync(),l=t.dataSync(),c=0;c<u.length;++c){for(var h=c*s,p=l[h],f=0,d=0;d<s;++d){var m=l[h+d];m>p&&(p=m,f=d)}u[c]=f}return i},t.prototype.cumsum=function(t,e,n,r){if(this.assertNotComplex(t,"cumsum"),e!==t.rank-1)throw new Error("backend.cumsum in CPU expects an inner-most axis="+(t.rank-1)+" but got axis="+e);for(var o=dt(t.dtype,"int32"),a=ke(t.shape,o),i=a.dataSync(),s=t.dataSync(),u=t.shape[t.rank-1],l=r?function(t,e){return t+u-e-1}:function(t,e){return t+e},c=0;c<s.length;c+=u)for(var h=0;h<u;h++){var p=l(c,h);if(0===h)i[p]=n?0:s[p];else{var f=l(c,h-1);i[p]=n?s[f]+i[f]:s[p]+i[f]}}return a},t.prototype.equal=function(t,e){return this.assertNotComplex([t,e],"equal"),this.broadcastedBinaryOp(t,e,"bool",function(t,e){return t===e?1:0})},t.prototype.notEqual=function(t,e){return this.assertNotComplex([t,e],"notEqual"),this.broadcastedBinaryOp(t,e,"bool",function(t,e){return t!==e?1:0})},t.prototype.less=function(t,e){return this.assertNotComplex([t,e],"less"),this.broadcastedBinaryOp(t,e,"bool",function(t,e){return t<e?1:0})},t.prototype.lessEqual=function(t,e){return this.assertNotComplex([t,e],"lessEqual"),this.broadcastedBinaryOp(t,e,"bool",function(t,e){return t<=e?1:0})},t.prototype.greater=function(t,e){return this.assertNotComplex([t,e],"greater"),this.broadcastedBinaryOp(t,e,"bool",function(t,e){return t>e?1:0})},t.prototype.greaterEqual=function(t,e){return this.assertNotComplex([t,e],"greaterEqual"),this.broadcastedBinaryOp(t,e,"bool",function(t,e){return t>=e?1:0})},t.prototype.logicalNot=function(t){this.assertNotComplex(t,"logicalNot");for(var e=t.dataSync(),n=new Uint8Array(e.length),r=0;r<e.length;++r)n[r]=e[r]?0:1;return at.make(t.shape,{values:n},"bool")},t.prototype.logicalAnd=function(t,e){return this.assertNotComplex([t,e],"logicalAnd"),this.broadcastedBinaryOp(t,e,"bool",function(t,e){return t&&e})},t.prototype.logicalOr=function(t,e){return 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Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=Math.abs(n[r]);return at.make(t.shape,{values:e})},t.prototype.complexAbs=function(t){for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<t.size;++r){var o=n[2*r],a=n[2*r+1];e[r]=Math.hypot(o,a)}return at.make(t.shape,{values:e})},t.prototype.int=function(t){this.assertNotComplex(t,"int");for(var e=new Int32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=n[r];return at.make(t.shape,{values:e},"int32")},t.prototype.sigmoid=function(t){this.assertNotComplex(t,"sigmoid");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=1/(1+Math.exp(-n[r]));return at.make(t.shape,{values:e})},t.prototype.softplus=function(t){this.assertNotComplex(t,"softplus");for(var e=Math.log(1.1920928955078125e-7)+2,n=new Float32Array(t.size),r=t.dataSync(),o=0;o<r.length;++o){var a=r[o]>-e,i=r[o]<e,s=Math.exp(r[o]),u=void 0;u=i?s:a?r[o]:Math.log(1+s),n[o]=u}return at.make(t.shape,{values:n})},t.prototype.sin=function(t){this.assertNotComplex(t,"sin");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=Math.sin(n[r]);return at.make(t.shape,{values:e})},t.prototype.cos=function(t){this.assertNotComplex(t,"cos");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=Math.cos(n[r]);return at.make(t.shape,{values:e})},t.prototype.tan=function(t){this.assertNotComplex(t,"tan");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=Math.tan(n[r]);return at.make(t.shape,{values:e})},t.prototype.asin=function(t){this.assertNotComplex(t,"asin");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=Math.asin(n[r]);return at.make(t.shape,{values:e})},t.prototype.acos=function(t){this.assertNotComplex(t,"acos");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=Math.acos(n[r]);return at.make(t.shape,{values:e})},t.prototype.atan=function(t){this.assertNotComplex(t,"atan");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=Math.atan(n[r]);return at.make(t.shape,{values:e})},t.prototype.atan2=function(t,e){return this.assertNotComplex([t,e],"atan2"),this.broadcastedBinaryOp(t,e,t.dtype,function(t,e){return Math.atan2(t,e)})},t.prototype.sinh=function(t){this.assertNotComplex(t,"sinh");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=Math.sinh(n[r]);return at.make(t.shape,{values:e})},t.prototype.cosh=function(t){this.assertNotComplex(t,"cosh");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=Math.cosh(n[r]);return at.make(t.shape,{values:e})},t.prototype.tanh=function(t){this.assertNotComplex(t,"tanh");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=b(n[r]);return at.make(t.shape,{values:e})},t.prototype.asinh=function(t){this.assertNotComplex(t,"asinh");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=Math.asinh(n[r]);return at.make(t.shape,{values:e})},t.prototype.acosh=function(t){this.assertNotComplex(t,"acosh");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=Math.acosh(n[r]);return at.make(t.shape,{values:e})},t.prototype.atanh=function(t){this.assertNotComplex(t,"atanh");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=Math.atanh(n[r]);return at.make(t.shape,{values:e})},t.prototype.erf=function(t){this.assertNotComplex(t,"erf");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r){var o=n[r],a=1/(1+.3275911*o);e[r]=1-((((1.061405429*a-1.453152027)*a+1.421413741)*a-.284496736)*a+.254829592)*a*Math.exp(-o*o)}return at.make(t.shape,{values:e})},t.prototype.step=function(t,e){void 0===e&&(e=0),this.assertNotComplex(t,"step");for(var n=new Float32Array(t.size),r=t.dataSync(),o=0;o<r.length;++o){var a=r[o];isNaN(a)?n[o]=NaN:n[o]=a>0?1:e}return at.make(t.shape,{values:n})},t.prototype.conv2d=function(t,e,n){this.assertNotComplex([t,e],"conv2d");for(var r=n.filterHeight,o=n.filterWidth,a=n.dilationHeight,i=n.dilationWidth,s=n.padInfo.left,u=n.padInfo.top,l=sa(n.outShape,t.dtype),c=t.dataSync(),h=e.dataSync(),p=l.values,f=0;f<n.batchSize;++f)for(var d=f*t.strides[0],m=f*l.strides[0],v=0;v<n.outHeight;++v)for(var g=m+v*l.strides[1],y=v*n.strideHeight-s,x=0;x<r;x++){var w=y+x*a;if(!(w<0||w>=n.inHeight))for(var b=x*e.strides[0],E=d+w*t.strides[1],C=0;C<n.outWidth;++C)for(var S=g+C*n.outChannels,R=C*n.strideWidth-u,N=0;N<o;N++){var k=R+N*i;if(!(k<0||k>=n.inWidth))for(var T=b+N*e.strides[1],I=E+k*n.inChannels,D=T,A=0;A<n.inChannels;++A){for(var M=c[I+A],_=0;_<n.outChannels;++_)p[S+_]+=M*h[D+_];D+=n.outChannels}}}return l.toTensor()},t.prototype.conv3d=function(t,e,n){for(var r=n.filterDepth,o=n.filterHeight,a=n.filterWidth,i=n.dilationDepth,s=n.dilationHeight,u=n.dilationWidth,l=n.padInfo.front,c=n.padInfo.left,h=n.padInfo.top,p=sa(n.outShape,t.dtype),f=t.dataSync(),d=e.dataSync(),m=p.values,v=0;v<n.batchSize;++v)for(var g=v*t.strides[0],y=v*p.strides[0],x=0;x<n.outDepth;++x)for(var w=y+x*p.strides[1],b=x*n.strideDepth-l,E=0;E<r;E++){var C=b+E*i;if(!(C<0||C>=n.inDepth))for(var S=E*e.strides[0],R=g+C*t.strides[1],N=0;N<n.outHeight;++N)for(var k=w+N*p.strides[2],T=N*n.strideHeight-h,I=0;I<o;I++){var D=T+I*s;if(!(D<0||D>=n.inHeight))for(var A=S+I*e.strides[1],M=R+D*t.strides[2],_=0;_<n.outWidth;++_)for(var O=k+_*n.outChannels,F=_*n.strideWidth-c,B=0;B<a;B++){var L=F+B*u;if(!(L<0||L>=n.inWidth))for(var P=A+B*e.strides[2],U=M+L*n.inChannels,W=P,z=0;z<n.inChannels;++z){for(var V=f[U+z],G=0;G<n.outChannels;++G)m[O+G]+=V*d[W+G];W+=n.outChannels}}}}return p.toTensor()},t.prototype.conv2dDerInput=function(t,e,n){this.assertNotComplex([t,e],"conv2dDerInput");for(var r=sa(n.inShape,"float32"),o=r.values,a=r.strides,i=a[0],s=a[1],u=a[2],l=t.dataSync(),c=t.strides,h=c[0],p=c[1],f=c[2],d=e.dataSync(),m=e.strides,v=m[0],g=m[1],y=m[2],x=n.batchSize,w=n.filterHeight,b=n.filterWidth,E=n.inChannels,C=n.inHeight,S=n.inWidth,R=n.outChannels,N=n.outHeight,k=n.outWidth,T=n.strideHeight,I=n.strideWidth,D=w-1-n.padInfo.top,A=b-1-n.padInfo.left,M=0;M<x;++M)for(var _=0;_<E;++_)for(var O=0;O<C;++O)for(var F=O-D,B=Math.max(0,Math.ceil(F/T)),L=Math.min(N,(w+F)/T),P=0;P<S;++P){for(var U=P-A,W=Math.max(0,Math.ceil(U/I)),z=Math.min(k,(b+U)/I),V=0,G=B;G<L;++G)for(var H=G*T-F,q=W;q<z;++q)for(var $=h*M+p*G+f*q,j=v*(w-1-H)+g*(b-1-(q*I-U))+y*_,K=0;K<R;++K){V+=l[$+K]*d[j+K]}o[i*M+s*O+u*P+_]=V}return r.toTensor()},t.prototype.conv3dDerInput=function(t,e,n){for(var r=sa(n.inShape,"float32"),o=r.values,a=r.strides,i=a[0],s=a[1],u=a[2],l=a[3],c=t.dataSync(),h=t.strides,p=h[0],f=h[1],d=h[2],m=h[3],v=e.dataSync(),g=e.strides,y=g[0],x=g[1],w=g[2],b=g[3],E=n.batchSize,C=n.filterDepth,S=n.filterHeight,R=n.filterWidth,N=n.inChannels,k=n.inDepth,T=n.inHeight,I=n.inWidth,D=n.outChannels,A=n.outDepth,M=n.outHeight,_=n.outWidth,O=n.strideDepth,F=n.strideHeight,B=n.strideWidth,L=C-1-n.padInfo.front,P=S-1-n.padInfo.top,U=R-1-n.padInfo.left,W=0;W<E;++W)for(var z=0;z<N;++z)for(var V=0;V<k;++V)for(var G=V-L,H=Math.max(0,Math.ceil(G/O)),q=Math.min(A,(C+G)/O),$=0;$<T;++$)for(var j=$-P,K=Math.max(0,Math.ceil(j/F)),X=Math.min(M,(S+j)/F),Y=0;Y<I;++Y){for(var Q=Y-U,J=Math.max(0,Math.ceil(Q/B)),Z=Math.min(_,(R+Q)/B),tt=0,et=H;et<q;++et)for(var nt=et*O-G,rt=K;rt<X;++rt)for(var ot=rt*F-j,at=J;at<Z;++at)for(var it=p*W+f*et+d*rt+m*at,st=y*(C-1-nt)+x*(S-1-ot)+w*(R-1-(at*B-Q))+b*z,ut=0;ut<D;++ut){tt+=c[it+ut]*v[st+ut]}o[i*W+s*V+u*$+l*Y+z]=tt}return r.toTensor()},t.prototype.conv2dDerFilter=function(t,e,n){this.assertNotComplex([t,e],"conv2dDerFilter");for(var r=n.strideHeight,o=n.strideWidth,a=n.filterHeight,i=n.filterWidth,s=sa(n.filterShape,"float32"),u=n.padInfo.left,l=n.padInfo.top,c=0;c<a;++c)for(var h=Math.max(0,Math.ceil((l-c)/r)),p=Math.min(n.outHeight,(n.inHeight+l-c)/r),f=0;f<i;++f)for(var d=Math.max(0,Math.ceil((u-f)/o)),m=Math.min(n.outWidth,(n.inWidth+u-f)/o),v=0;v<n.inChannels;++v)for(var g=0;g<n.outChannels;++g){for(var y=0,x=0;x<n.batchSize;++x)for(var w=h;w<p;++w)for(var b=c+w*r-l,E=d;E<m;++E){var C=f+E*o-u;y+=t.get(x,b,C,v)*e.get(x,w,E,g)}s.set(y,c,f,v,g)}return s.toTensor()},t.prototype.conv3dDerFilter=function(t,e,n){for(var r=n.strideDepth,o=n.strideHeight,a=n.strideWidth,i=n.filterDepth,s=n.filterHeight,u=n.filterWidth,l=sa(n.filterShape,"float32"),c=l.values,h=l.strides,p=h[0],f=h[1],d=h[2],m=h[3],v=e.dataSync(),g=e.strides,y=g[0],x=g[1],w=g[2],b=g[3],E=t.dataSync(),C=t.strides,S=C[0],R=C[1],N=C[2],k=C[3],T=n.padInfo.front,I=n.padInfo.left,D=n.padInfo.top,A=0;A<i;++A)for(var M=Math.max(0,Math.ceil((T-A)/r)),_=Math.min(n.outDepth,(n.inDepth+T-A)/r),O=A*p,F=0;F<s;++F)for(var B=Math.max(0,Math.ceil((D-F)/o)),L=Math.min(n.outHeight,(n.inHeight+D-F)/o),P=F*f+O,U=0;U<u;++U)for(var W=Math.max(0,Math.ceil((I-U)/a)),z=Math.min(n.outWidth,(n.inWidth+I-U)/a),V=U*d+P,G=0;G<n.inChannels;++G)for(var H=G*m+V,q=0;q<n.outChannels;++q){for(var $=0,j=0;j<n.batchSize;++j)for(var K=j*S,X=j*y,Y=M;Y<_;++Y)for(var Q=(A+Y*r-T)*R+K,J=Y*x+X,Z=B;Z<L;++Z)for(var tt=(F+Z*o-D)*N+Q,et=Z*w+J,nt=W;nt<z;++nt){var rt=nt*b+et;$+=E[(U+nt*a-I)*k+tt+G]*v[rt+q]}c[H+q]=$}return l.toTensor()},t.prototype.depthwiseConv2D=function(t,e,n){this.assertNotComplex([t,e],"depthwiseConv2D");for(var r=n.filterHeight,o=n.filterWidth,a=n.dilationHeight,i=n.dilationWidth,s=n.padInfo.left,u=n.padInfo.top,l=n.outChannels/n.inChannels,c=sa(n.outShape,t.dtype),h=t.dataSync(),p=e.dataSync(),f=c.values,d=0;d<n.batchSize;++d)for(var m=d*t.strides[0],v=d*c.strides[0],g=0;g<n.outHeight;++g)for(var y=v+g*c.strides[1],x=g*n.strideHeight-s,w=0;w<r;++w){var b=x+w*a;if(!(b<0||b>=n.inHeight))for(var E=w*e.strides[0],C=m+b*t.strides[1],S=0;S<n.outWidth;++S)for(var R=y+S*c.strides[2],N=S*n.strideWidth-u,k=0;k<o;++k){var T=N+k*i;if(!(T<0||T>=n.inWidth))for(var I=E+k*e.strides[1],D=C+T*n.inChannels,A=R,M=I,_=0;_<n.inChannels;++_){for(var O=h[D+_],F=0;F<l;++F)f[A+F]+=O*p[M+F];A+=l,M+=l}}}return c.toTensor()},t.prototype.depthwiseConv2DDerInput=function(t,e,n){this.assertNotComplex([t,e],"depthwiseConv2DDerInput");for(var r=sa(n.inShape,"float32"),o=r.values,a=r.strides,i=a[0],s=a[1],u=a[2],l=t.dataSync(),c=t.strides,h=c[0],p=c[1],f=c[2],d=e.dataSync(),m=e.strides,v=m[0],g=m[1],y=m[2],x=n.batchSize,w=n.filterHeight,b=n.filterWidth,E=n.inChannels,C=n.inHeight,S=n.inWidth,R=n.outChannels,N=n.outHeight,k=n.outWidth,T=n.strideHeight,I=n.strideWidth,D=w-1-n.padInfo.top,A=b-1-n.padInfo.left,M=R/E,_=0;_<x;++_)for(var O=0;O<E;++O)for(var F=0;F<C;++F)for(var B=F-D,L=Math.max(0,Math.ceil(B/T)),P=Math.min(N,(w+B)/T),U=0;U<S;++U){for(var W=U-A,z=Math.max(0,Math.ceil(W/I)),V=Math.min(k,(b+W)/I),G=0,H=L;H<P;++H)for(var q=H*T-B,$=z;$<V;++$)for(var j=h*_+p*H+f*$,K=v*(w-1-q)+g*(b-1-($*I-W))+y*O,X=0;X<M;++X){G+=l[j+(O*M+X)]*d[K+X]}o[i*_+s*F+u*U+O]=G}return r.toTensor()},t.prototype.depthwiseConv2DDerFilter=function(t,e,n){this.assertNotComplex([t,e],"depthwiseConv2DDerFilter");for(var r=n.strideHeight,o=n.strideWidth,a=n.filterHeight,i=n.filterWidth,s=sa(n.filterShape,"float32"),u=n.padInfo.left,l=n.padInfo.top,c=n.outChannels/n.inChannels,h=0;h<a;++h)for(var p=Math.max(0,Math.ceil((l-h)/r)),f=Math.min(n.outHeight,(n.inHeight+l-h)/r),d=0;d<i;++d)for(var m=Math.max(0,Math.ceil((u-d)/o)),v=Math.min(n.outWidth,(n.inWidth+u-d)/o),g=0;g<n.outChannels;++g){for(var y=Math.trunc(g/c),x=g%c,w=0,b=0;b<n.batchSize;++b)for(var E=p;E<f;++E)for(var C=h+E*r-l,S=m;S<v;++S){var R=d+S*o-u;w+=t.get(b,C,R,y)*e.get(b,E,S,g)}s.set(w,h,d,y,x)}return s.toTensor()},t.prototype.tile=function(t,e){this.assertNotComplex(t,"tile");for(var n=new Array(t.rank),r=0;r<n.length;r++)n[r]=t.shape[r]*e[r];var o=sa(n,t.dtype),a=t.buffer();for(r=0;r<o.values.length;++r){for(var i=o.indexToLoc(r),s=new Array(t.rank),u=0;u<s.length;u++)s[u]=i[u]%t.shape[u];var l=a.locToIndex(s);o.values[r]=a.values[l]}return o.toTensor()},t.prototype.pad=function(t,e,n){this.assertNotComplex(t,"pad");var r=e.map(function(e,n){return e[0]+t.shape[n]+e[1]}),o=e.map(function(t){return t[0]}),a=t.buffer(),i=sa(r,t.dtype);0!==n&&i.values.fill(n);for(var s=0;s<t.size;s++){var u=a.indexToLoc(s),l=u.map(function(t,e){return t+o[e]});i.set.apply(i,[t.get.apply(t,u)].concat(l))}return i.toTensor()},t.prototype.transpose=function(t,e){this.assertNotComplex(t,"transpose");for(var n=new Array(t.rank),r=0;r<n.length;r++)n[r]=t.shape[e[r]];var o=t.dataSync(),a=sa(n,t.dtype),i=t.buffer();for(r=0;r<t.size;++r){for(var s=i.indexToLoc(r),u=new Array(s.length),l=0;l<u.length;l++)u[l]=s[e[l]];var c=a.locToIndex(u);a.values[c]=o[r]}return a.toTensor()},t.prototype.gather=function(t,e,n){this.assertNotComplex([t,e],"gather");var r=t.shape.slice(),o=e.dataSync();r[n]=o.length;for(var a=sa(r,t.dtype),i=t.buffer(),s=0;s<a.size;++s){var u=a.indexToLoc(s),l=u.slice();l[n]=o[u[n]];var c=i.locToIndex(l);a.values[s]=i.values[c]}return a.toTensor()},t.prototype.batchToSpaceND=function(t,e,n){this.assertNotComplex([t],"batchToSpaceND");var r=e.reduce(function(t,e){return t*e}),o=Wt(t.shape,e,r),a=zt(o.length,e.length),i=Vt(t.shape,e,r),s=Gt(n,e.length),u=Ht(i,n,e.length);return t.reshape(o).transpose(a).reshape(i).slice(s,u)},t.prototype.spaceToBatchND=function(t,e,n){this.assertNotComplex([t],"spaceToBatchND");var r=e.reduce(function(t,e){return t*e}),o=[[0,0]];o.push.apply(o,n);for(var a=1+e.length;a<t.shape.length;++a)o.push([0,0]);var i=t.pad(o),s=Wt(i.shape,e,r,!1),u=zt(s.length,e.length,!1),l=Vt(i.shape,e,r,!1);return i.reshape(s).transpose(u).reshape(l)},t.prototype.pool=function(t,e,n){this.assertNotComplex(t,"pool");for(var r=e.strideHeight,o=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,s=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=e.padInfo.top,c=e.padInfo.left,h="max"===n?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,p=t.dataSync(),f=sa(e.outShape,t.dtype),d=f.values,m=e.outShape[1]*e.outShape[2]*e.outShape[3],v=e.outShape[2]*e.outShape[3],g=e.outShape[3],y=0;y<e.batchSize;++y)for(var x=y*m,w=y*t.strides[0],b=0;b<e.inChannels;++b)for(var E=0;E<e.outHeight;++E)for(var C=E*r-l,S=Math.max(0,C),R=Math.min(e.inHeight,s+C),N=x+E*v,k=0;k<e.outWidth;++k){for(var T=k*o-c,I=Math.max(0,T),D=Math.min(e.inWidth,u+T),A=h,M=0,_=0,O=S;O<R;O+=a){for(var F=w+O*t.strides[1],B=I;B<D;B+=i){var L=p[F+B*t.strides[2]+b];"max"===n&&L>A?A=L:"avg"===n&&(M+=L,_++)}if(isNaN(A))break}d[N+k*g+b]="avg"===n?M/_:A}return f.toTensor()},t.prototype.maxPool=function(t,e){return this.pool(t,e,"max")},t.prototype.maxPoolPositions=function(t,e){for(var n=sa(e.outShape,"int32"),r=e.strideHeight,o=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,s=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=e.padInfo.top,c=e.padInfo.left,h=0;h<e.batchSize;++h)for(var p=0;p<e.inChannels;++p)for(var f=0;f<e.outHeight;++f){for(var d=f*r-l,m=d;m<0;)m+=a;for(var v=Math.min(e.inHeight,s+d),g=0;g<e.outWidth;++g){for(var y=g*o-c,x=y;x<0;)x+=i;for(var w=Math.min(e.inWidth,u+y),b=Number.NEGATIVE_INFINITY,E=-1,C=m;C<v;C+=a)for(var S=C-d,R=x;R<w;R+=i){var N=R-y,k=t.get(h,C,R,p);k>b&&(b=k,E=S*u+N)}n.set(E,h,f,g,p)}}return n.toTensor()},t.prototype.maxPoolBackprop=function(t,e,n,r){this.assertNotComplex([e,n],"maxPoolBackprop");for(var o=this.maxPoolPositions(e,r),a=r.strideHeight,i=r.strideWidth,s=r.dilationHeight,u=r.dilationWidth,l=r.effectiveFilterHeight,c=r.effectiveFilterWidth,h=c-1-r.padInfo.left,p=l-1-r.padInfo.top,f=sa(e.shape,"float32"),d=0;d<r.batchSize;++d)for(var m=0;m<r.inChannels;++m)for(var v=0;v<r.inHeight;++v)for(var g=0;g<r.inWidth;++g){for(var y=v-p,x=g-h,w=0,b=0;b<l;b+=s){var E=(y+b)/a;if(!(E<0||E>=r.outHeight||Math.floor(E)!==E))for(var C=0;C<c;C+=u){var S=(x+C)/i;if(!(S<0||S>=r.outWidth||Math.floor(S)!==S)){var R=l*c-1-o.get(d,E,S,m)===b*c+C?1:0;if(0!==R)w+=t.get(d,E,S,m)*R}}}f.set(w,d,v,g,m)}return f.toTensor()},t.prototype.avgPoolBackprop=function(t,e,n){this.assertNotComplex([t,e],"avgPoolBackprop");for(var r=n.strideHeight,o=n.strideWidth,a=n.filterHeight,i=n.filterWidth,s=n.dilationHeight,u=n.dilationWidth,l=n.effectiveFilterHeight,c=n.effectiveFilterWidth,h=c-1-n.padInfo.left,p=l-1-n.padInfo.top,f=sa(e.shape,"float32"),d=1/(a*i),m=0;m<n.batchSize;++m)for(var v=0;v<n.inChannels;++v)for(var g=0;g<n.inHeight;++g)for(var y=0;y<n.inWidth;++y){for(var x=g-p,w=y-h,b=0,E=0;E<l;E+=s){var C=(x+E)/r;if(!(C<0||C>=n.outHeight||Math.floor(C)!==C))for(var S=0;S<c;S+=u){var R=(w+S)/o;if(!(R<0||R>=n.outWidth||Math.floor(R)!==R))b+=t.get(m,C,R,v)}}f.set(b*d,m,g,y,v)}return f.toTensor()},t.prototype.cast=function(t,e){return Fe(t,e,this)},t.prototype.reshape=function(t,e){return Be(t,e)},t.prototype.avgPool=function(t,e){return this.assertNotComplex(t,"avgPool"),this.pool(t,e,"avg").toFloat()},t.prototype.resizeBilinear=function(t,e,n,r){this.assertNotComplex(t,"resizeBilinear");for(var o=t.shape,a=o[0],i=o[1],s=o[2],u=o[3],l=t.dataSync(),c=new Float32Array(y([a,e,n,u])),h=[r&&e>1?i-1:i,r&&n>1?s-1:s],p=[r&&e>1?e-1:e,r&&n>1?n-1:n],f=0,d=h[0]/p[0],m=h[1]/p[1],v=0;v<a;v++)for(var g=0;g<e;g++)for(var x=d*g,w=Math.floor(x),b=x-w,E=Math.min(i-1,Math.ceil(x)),C=v*t.strides[0]+w*t.strides[1],S=v*t.strides[0]+E*t.strides[1],R=0;R<n;R++)for(var N=m*R,k=Math.floor(N),T=N-k,I=Math.min(s-1,Math.ceil(N)),D=C+k*t.strides[2],A=S+k*t.strides[2],M=C+ +I*t.strides[2],_=S+I*t.strides[2],O=0;O<u;O++){var F=l[D+O],B=l[A+O],L=F+(l[M+O]-F)*T,P=L+(B+(l[_+O]-B)*T-L)*b;c[f++]=P}return ye(c,[a,e,n,u])},t.prototype.resizeBilinearBackprop=function(t,e,n){this.assertNotComplex([t,e],"resizeBilinearBackprop");for(var r=e.shape,o=r[0],a=r[1],i=r[2],s=r[3],u=t.shape,l=u[1],c=u[2],h=new Float32Array(o*a*i*s),p=[n&&l>1?a-1:a,n&&c>1?i-1:i],f=[n&&l>1?l-1:l,n&&c>1?c-1:c],d=p[0]/f[0],m=p[1]/f[1],v=t.dataSync(),g=0,y=0;y<o;y++)for(var x=y*e.strides[0],w=0;w<l;w++)for(var b=w*d,E=Math.floor(b),C=Math.min(Math.ceil(b),a-1),S=x+E*e.strides[1],R=x+C*e.strides[1],N=b-E,k=1-N,T=0;T<c;T++)for(var I=T*m,D=Math.floor(I),A=Math.min(Math.ceil(I),i-1),M=I-D,_=1-M,O=S+D*e.strides[2],F=S+A*e.strides[2],B=R+D*e.strides[2],L=R+A*e.strides[2],P=k*_,U=k*M,W=N*_,z=N*M,V=0;V<s;V++){var G=v[g++];h[O+V]+=G*P,h[F+V]+=G*U,h[B+V]+=G*W,h[L+V]+=G*z}return Ce(h,[o,i,a,s],e.dtype)},t.prototype.resizeNearestNeighbor=function(t,e,n,r){this.assertNotComplex(t,"resizeNearestNeighbor");for(var o=t.shape,a=o[0],i=o[1],s=o[2],u=o[3],l=t.dataSync(),c=new Float32Array(a*e*n*u),h=[r&&e>1?i-1:i,r&&n>1?s-1:s],p=[r&&e>1?e-1:e,r&&n>1?n-1:n],f=h[0]/p[0],d=h[1]/p[1],m=0,v=0;v<a;v++)for(var g=v*t.strides[0],y=0;y<e;y++)for(var x=f*y,w=g+Math.min(i-1,r?Math.round(x):Math.floor(x))*t.strides[1],b=0;b<n;b++)for(var E=d*b,C=w+Math.min(s-1,r?Math.round(E):Math.floor(E))*t.strides[2],S=0;S<u;S++){var R=l[C+S];c[m++]=R}return ye(c,[a,e,n,u],t.dtype)},t.prototype.resizeNearestNeighborBackprop=function(t,e,n){this.assertNotComplex([t,e],"resizeNearestNeighborBackprop");for(var r=e.shape,o=r[0],a=r[1],i=r[2],s=r[3],u=t.shape,l=u[1],c=u[2],h=new Float32Array(o*a*i*s),p=t.dataSync(),f=[n&&l>1?a-1:a,n&&c>1?i-1:i],d=[n&&l>1?l-1:l,n&&c>1?c-1:c],m=f[0]/d[0],v=f[1]/d[1],g=1/m,y=1/v,x=2*Math.ceil(g)+2,w=2*Math.ceil(y)+2,b=0;b<o;b++)for(var E=b*e.strides[0],C=0;C<a;C++)for(var S=E+C*e.strides[1],R=Math.floor(C*g),N=Math.floor(R-x/2),k=0;k<i;k++)for(var T=S+k*e.strides[2],I=Math.floor(k*y),D=Math.floor(I-w/2),A=0;A<s;A++){for(var M=0,_=0;_<x;_++){var O=_+N;if(!(O<0||O>=l)){var F=E+O*t.strides[1],B=O*m;if(C===Math.min(a-1,n?Math.round(B):Math.floor(B)))for(var L=0;L<w;L++){var P=L+D;if(!(P<0||P>=c)){var U=F+P*t.strides[2],W=P*v;k===Math.min(i-1,n?Math.round(W):Math.floor(W))&&(M+=p[U+A])}}}}h[T+A]=M}return Ce(h,e.shape,e.dtype)},t.prototype.batchNormalization=function(t,e,n,r,o,a){this.assertNotComplex([t,e,n,o,a],"batchNormalization");for(var i=t.dataSync(),s=e.dataSync(),u=n.dataSync(),l=o?o.dataSync():new Float32Array([1]),c=a?a.dataSync():new Float32Array([0]),h=new Float32Array(i.length),p=c.length,f=l.length,d=u.length,m=s.length,v=0,g=0,y=0,x=0,w=0;w<i.length;++w)h[w]=c[v++]+(i[w]-s[g++])*l[y++]/Math.sqrt(u[x++]+r),v>=p&&(v=0),g>=m&&(g=0),y>=f&&(y=0),x>=d&&(x=0);return Ce(h,t.shape)},t.prototype.localResponseNormalization4D=function(t,e,n,r,o){this.assertNotComplex(t,"localResponseNormalization4D");var a=t.shape[3],i=a-1,s=t.dataSync(),u=y(t.shape),l=new Float32Array(u);function c(t){for(var n=t%a,r=t-n+Math.max(0,n-e),o=t-n+Math.min(n+e,i),u=0;r<=o;r++){var l=s[r];u+=l*l}return u}for(var h=0;h<u;h++){var p=c(h),f=s[h]*Math.pow(n+r*p,-o);l[h]=f}return Ce(l,t.shape)},t.prototype.LRNGrad=function(t,e,n,r,o,a,i){this.assertNotComplex(t,"LRNGrad");for(var s=t.shape[3],u=t.dataSync(),l=e.dataSync(),c=n.dataSync(),h=new Float32Array(y(t.shape)),p=y(t.shape),f=0;f<p;f++){for(var d=f%s,m=f-d+Math.max(0,d-r),v=f-d+Math.min(s,d+r+1),g=0,x=m;x<v;x++)g+=Math.pow(l[x],2);g=a*g+o;for(x=m;x<v;x++){var w=-2*a*i*l[x]*c[f]/g;f===x&&(w+=Math.pow(g,-i)),w*=u[f],h[x]+=w}}return Ce(h,t.shape)},t.prototype.multinomial=function(t,e,n,r){this.assertNotComplex(t,"multinomial");for(var o=e?t:fe(t),a=o.shape[0],i=o.shape[1],s=ke([a,n],"int32"),u=s.dataSync(),l=o.dataSync(),c=0;c<a;++c){var h=c*i,p=new Float32Array(i-1);p[0]=l[h];for(var f=1;f<p.length;++f)p[f]=p[f-1]+l[h+f];for(var d=oa(r.toString()),m=c*n,v=0;v<n;++v){var g=d();u[m+v]=p.length;for(var y=0;y<p.length;y++)if(g<p[y]){u[m+v]=y;break}}}return s},t.prototype.oneHot=function(t,e,n,r){this.assertNotComplex(t,"oneHot");var o=new Float32Array(t.size*e);o.fill(r);for(var a=0;a<t.size;++a)t.get(a)>=0&&t.get(a)<e&&(o[a*e+t.get(a)]=n);return be(o,[t.size,e],"int32")},t.prototype.nonMaxSuppression=function(t,e,n,r,o){return this.assertNotComplex(t,"nonMaxSuppression"),ze(t.dataSync(),e.dataSync(),n,r,o)},t.prototype.fft=function(t){return this.fftBatch(t,!1)},t.prototype.ifft=function(t){return this.fftBatch(t,!0)},t.prototype.fftBatch=function(t,e){for(var n=t.shape[0],r=t.shape[1],o=sa(t.shape,"float32"),a=sa(t.shape,"float32"),i=ve(t).as2D(n,r),s=ge(t).as2D(n,r),u=0;u<n;u++)for(var l=i.slice([u,0],[1,r]),c=s.slice([u,0],[1,r]),h=me(l,c),p=this.fftImpl(h,e).dataSync(),f=0;f<r;f++){var d=Pe(p,f);o.values[u*r+f]=d.real,a.values[u*r+f]=d.imag}return me(o.toTensor(),a.toTensor()).as2D(n,r)},t.prototype.fftImpl=function(t,e){var n=t.as1D(),r=n.size;if(this.isExponentOf2(r)){var o=this.fftRadix2(n,r,e).as2D(t.shape[0],t.shape[1]);return e&&(o=me(ve(o).div(xe(r)),ge(o).div(xe(r)))),o}var a=t.dataSync(),i=function(t){for(var e=new Float32Array(t.length/2),n=new Float32Array(t.length/2),r=0;r<t.length;r+=2)e[r/2]=t[r],n[r/2]=t[r+1];return{real:e,imag:n}}(this.fourierTransformByMatmul(a,r,e));return me(i.real,i.imag).as2D(t.shape[0],t.shape[1])},t.prototype.isExponentOf2=function(t){return 0==(t&t-1)},t.prototype.fftRadix2=function(t,e,n){if(1===e)return t;var r=t.dataSync(),o=e/2,a=function(t){for(var e=Math.ceil(t.length/4),n=new Float32Array(e),r=new Float32Array(e),o=0;o<t.length;o+=4)n[Math.floor(o/4)]=t[o],r[Math.floor(o/4)]=t[o+1];return{real:n,imag:r}}(r),i=me(a.real,a.imag).as1D(),s=function(t){for(var e=Math.floor(t.length/4),n=new Float32Array(e),r=new Float32Array(e),o=2;o<t.length;o+=4)n[Math.floor(o/4)]=t[o],r[Math.floor(o/4)]=t[o+1];return{real:n,imag:r}}(r),u=me(s.real,s.imag).as1D();i=this.fftRadix2(i,o,n),u=this.fftRadix2(u,o,n);var l=function(t,e){for(var n=new Float32Array(t/2),r=new Float32Array(t/2),o=0;o<Math.ceil(t/2);o++){var a=(e?2:-2)*Math.PI*(o/t);n[o]=Math.cos(a),r[o]=Math.sin(a)}return{real:n,imag:r}}(e,n),c=me(l.real,l.imag).mul(u),h=i.add(c),p=i.sub(c),f=ve(h).concat(ve(p)),d=ge(h).concat(ge(p));return me(f,d).as1D()},t.prototype.fourierTransformByMatmul=function(t,e,n){for(var r=new Float32Array(2*e),o=0;o<e;o++){for(var a=0,i=0,s=0;s<e;s++){var u=We(o*s,e,n),l=Pe(t,s);a+=l.real*u.real-l.imag*u.imag,i+=l.real*u.imag+l.imag*u.real}n&&(a/=e,i/=e),Ue(r,a,i,o)}return r},t.prototype.depthToSpace=function(t,e,n){d("NHWC"===n,"Only NHWC dataFormat supported on CPU for depthToSpace. 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o=(u=a.transaction(cl,"readwrite")).objectStore(cl).put({modelPath:n.modelPath,modelArtifacts:e,modelArtifactsInfo:l});o.onsuccess=function(){return t({modelArtifactsInfo:l})},o.onerror=function(t){var e=(h=c.objectStore(hl)).delete(n.modelPath);e.onsuccess=function(){return a.close(),r(o.error)},e.onerror=function(t){return a.close(),r(o.error)}}},p.onerror=function(t){return a.close(),r(p.error)},c.oncomplete=function(){null==u?a.close():u.oncomplete=function(){return a.close()}}}},o.onerror=function(t){return r(o.error)}})},t.URL_SCHEME="indexeddb://",t}(),ml=function(t){return It.get("IS_BROWSER")&&!Array.isArray(t)&&t.startsWith(dl.URL_SCHEME)?(e=t.slice(dl.URL_SCHEME.length),new dl(e)):null;var e};al.registerSaveRouter(ml),al.registerLoadRouter(ml);var vl=function(){function t(){this.indexedDB=pl()}return t.prototype.listModels=function(){return o(this,void 0,void 0,function(){var t=this;return a(this,function(e){return[2,new Promise(function(e,n){var r=t.indexedDB.open("tensorflowjs",1);r.onupgradeneeded=function(){return fl(r)},r.onsuccess=function(){var t=r.result,o=t.transaction(hl,"readonly"),a=o.objectStore(hl).getAll();a.onsuccess=function(){for(var t={},n=0,r=a.result;n<r.length;n++){var o=r[n];t[o.modelPath]=o.modelArtifactsInfo}e(t)},a.onerror=function(e){return t.close(),n(a.error)},o.oncomplete=function(){return t.close()}},r.onerror=function(t){return n(r.error)}})]})})},t.prototype.removeModel=function(t){return o(this,void 0,void 0,function(){var e=this;return a(this,function(n){var r;return t=(r=t).startsWith(dl.URL_SCHEME)?r.slice(dl.URL_SCHEME.length):r,[2,new Promise(function(n,r){var o=e.indexedDB.open("tensorflowjs",1);o.onupgradeneeded=function(){return fl(o)},o.onsuccess=function(){var e,a=o.result,i=a.transaction(hl,"readwrite"),s=i.objectStore(hl),u=s.get(t);u.onsuccess=function(){if(null==u.result)return a.close(),r(new Error("Cannot find model with path '"+t+"' in IndexedDB."));var o=s.delete(t),i=function(){var o=(e=a.transaction(cl,"readwrite")).objectStore(cl).delete(t);o.onsuccess=function(){return n(u.result.modelArtifactsInfo)},o.onerror=function(t){return r(u.error)}};o.onsuccess=i,o.onerror=function(t){return i(),a.close(),r(u.error)}},u.onerror=function(t){return a.close(),r(u.error)},i.oncomplete=function(){null==e?a.close():e.oncomplete=function(){return a.close()}}},o.onerror=function(t){return r(o.error)}})]})})},t}();if(It.get("IS_BROWSER"))try{sl.registerManager(dl.URL_SCHEME,new vl)}catch(t){}var gl="/",yl="tensorflowjs_models",xl="info",wl="model_topology",bl="weight_specs",El="weight_data";function Cl(t){return{info:[yl,t,xl].join(gl),topology:[yl,t,wl].join(gl),weightSpecs:[yl,t,bl].join(gl),weightData:[yl,t,El].join(gl)}}function Sl(t){var e=t.split(gl);if(e.length<3)throw new Error("Invalid key format: "+t);return e.slice(1,e.length-1).join(gl)}var Rl=function(){function t(t){if(!It.get("IS_BROWSER")||void 0===window.localStorage)throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,null==t||!t)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=t,this.keys=Cl(this.modelPath)}return t.prototype.save=function(t){return o(this,void 0,void 0,function(){var e,n,r,o;return a(this,function(a){if(t.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");e=JSON.stringify(t.modelTopology),n=JSON.stringify(t.weightSpecs),r=ol(t);try{return this.LS.setItem(this.keys.info,JSON.stringify(r)),this.LS.setItem(this.keys.topology,e),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,(i=t.weightData,tl?Buffer.from(i).toString("base64"):btoa(String.fromCharCode.apply(null,new Uint8Array(i))))),[2,{modelArtifactsInfo:r}]}catch(t){for(o in this.keys)this.LS.removeItem(this.keys[o]);throw new Error("Failed to save model 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Blob([JSON.stringify(r)],{type:"application/json"})),(i=null==this.jsonAnchor?document.createElement("a"):this.jsonAnchor).download=this.modelTopologyFileName,i.href=o,i.click(),null!=t.weightData&&((s=null==this.weightDataAnchor?document.createElement("a"):this.weightDataAnchor).download=this.weightDataFileName,s.href=e,s.click()),[2,{modelArtifactsInfo:ol(t)}]})})},t.URL_SCHEME="downloads://",t}(),Ml=function(){function t(t){if(null==t||t.length<1)throw new Error("When calling browserFiles, at least 1 file is required, but received "+t);this.files=t}return t.prototype.load=function(){return o(this,void 0,void 0,function(){var t,e,n=this;return a(this,function(r){return t=this.files[0],e=this.files.slice(1),[2,new Promise(function(r,o){var a=new FileReader;a.onload=function(a){var i=JSON.parse(a.target.result),s=i.modelTopology;if(null!=s){0===e.length&&r({modelTopology:s});var u=i.weightsManifest;if(null!=u){var l;try{l=n.checkManifestAndWeightFiles(u,e)}catch(t){return void o(t)}var 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"+e);case 4:return[2]}})})},t.prototype.loadBinaryModel=function(){return o(this,void 0,void 0,function(){var t,e,n,r,o,i,s,u;return a(this,function(a){switch(a.label){case 0:return t=this.loadBinaryTopology(),[4,this.getFetchFunc()(this.path[1],this.requestInit)];case 1:if(!(e=a.sent()).ok)throw new Error("BrowserHTTPRequest.load() failed due to HTTP response: "+e.statusText);return[4,Promise.all([t,e])];case 2:return n=a.sent(),r=n[0],[4,n[1].json()];case 3:return null==(o=a.sent())?[3,5]:[4,this.loadWeights(o)];case 4:u=a.sent(),i=u[0],s=u[1],a.label=5;case 5:return[2,{modelTopology:r,weightSpecs:i,weightData:s}]}})})},t.prototype.loadJSONModel=function(){return o(this,void 0,void 0,function(){var t,e,n,r,o,i,s,u;return a(this,function(a){switch(a.label){case 0:return[4,this.getFetchFunc()(this.path,this.requestInit)];case 1:if(!(t=a.sent()).ok)throw new Error("BrowserHTTPRequest.load() failed due to HTTP response: "+t.statusText);return[4,t.json()];case 2:if(e=a.sent(),n=e.modelTopology,r=e.weightsManifest,null==n&&null==r)throw new Error("The JSON from HTTP path "+this.path+" contains neither model topology or manifest for weights.");return null==r?[3,4]:(s=e.weightsManifest,[4,this.loadWeights(s)]);case 3:u=a.sent(),o=u[0],i=u[1],a.label=4;case 4:return[2,{modelTopology:n,weightSpecs:o,weightData:i}]}})})},t.prototype.loadWeights=function(t){return o(this,void 0,void 0,function(){var e,n,r,o,i,s,u,l,c,h,p,f;return a(this,function(a){switch(a.label){case 0:for(e=Array.isArray(this.path)?this.path[1]:this.path,n=function(t){var e=t.lastIndexOf("/"),n=t.lastIndexOf("?"),r=t.substring(0,e),o=n>e?t.substring(n):"";return[r+"/",o]}(e),r=n[0],o=n[1],i=this.weightPathPrefix||r,s=[],u=0,l=t;u<l.length;u++)c=l[u],s.push.apply(s,c.weights);return h=[],t.forEach(function(t){t.paths.forEach(function(t){h.push(i+t+o)})}),p=[s],f=nl,[4,_l(h,this.requestInit,this.getFetchFunc())];case 1:return[2,p.concat([f.apply(void 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new file mode 100644
index 0000000..3e15d3d
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+++ b/node_modules/@tensorflow/tfjs-core/dist/tf-core.min.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"tf-core.min.js","sources":["../src/canvas_util.ts","../src/util.ts","../src/profiler.ts","../src/tensor_format.ts","../src/tensor.ts","../src/types.ts","../src/tensor_util.ts","../src/engine.ts","../src/environment_util.ts","../src/tape.ts","../src/environment.ts","../src/device_util.ts","../src/gradients.ts","../src/globals.ts","../src/log.ts","../src/ops/array_ops_util.ts","../src/ops/axis_util.ts","../src/ops/concat_util.ts","../src/ops/gather_nd_util.ts","../src/ops/reduce_util.ts","../src/ops/scatter_nd_util.ts","../src/ops/slice_util.ts","../src/tensor_util_env.ts","../src/ops/operation.ts","../src/ops/softmax.ts","../src/ops/complex_ops.ts","../src/ops/tensor_ops.ts","../src/kernels/backend.ts","../src/kernels/backend_util.ts","../src/kernels/complex_util.ts","../src/kernels/non_max_suppression_impl.ts","../src/kernels/split_shared.ts","../src/kernels/topk_impl.ts","../src/kernels/webgl/argminmax_gpu.ts","../src/kernels/webgl/avg_pool_backprop_gpu.ts","../src/ops/broadcast_util.ts","../src/kernels/webgl/batchnorm_gpu.ts","../src/kernels/webgl/batchnorm_packed_gpu.ts","../src/kernels/webgl/binaryop_complex_gpu.ts","../src/kernels/webgl/binaryop_gpu.ts","../src/kernels/webgl/clip_gpu.ts","../src/kernels/webgl/clip_packed_gpu.ts","../src/kernels/webgl/complex_abs_gpu.ts","../src/kernels/webgl/concat_gpu.ts","../src/kernels/webgl/conv_backprop_gpu.ts","../src/kernels/webgl/conv_backprop_gpu_depthwise.ts","../src/kernels/webgl/conv_gpu.ts","../src/kernels/webgl/conv_gpu_depthwise.ts","../src/kernels/webgl/conv_packed_gpu_depthwise.ts","../src/kernels/webgl/crop_and_resize_gpu.ts","../src/kernels/webgl/shader_compiler_util.ts","../src/kernels/webgl/shader_compiler.ts","../src/kernels/webgl/cumsum_gpu.ts","../src/kernels/webgl/depth_to_space_gpu.ts","../src/kernels/webgl/encode_float_gpu.ts","../src/kernels/webgl/fft_gpu.ts","../src/kernels/webgl/from_pixels_gpu.ts","../src/kernels/webgl/gather_gpu.ts","../src/kernels/webgl/gather_nd_gpu.ts","../src/kernels/webgl/tex_util.ts","../src/kernels/webgl/webgl_util.ts","../src/kernels/webgl/gpgpu_util.ts","../src/kernels/webgl/gpgpu_context.ts","../src/kernels/webgl/gpgpu_math.ts","../src/kernels/webgl/im2col_gpu.ts","../src/kernels/webgl/lrn_gpu.ts","../src/kernels/webgl/lrn_grad_gpu.ts","../src/kernels/webgl/max_pool_backprop_gpu.ts","../src/kernels/webgl/mulmat_gpu.ts","../src/kernels/webgl/mulmat_packed_gpu.ts","../src/kernels/webgl/multinomial_gpu.ts","../src/kernels/webgl/onehot_gpu.ts","../src/kernels/packing_util.ts","../src/kernels/webgl/pack_gpu.ts","../src/kernels/webgl/pad_gpu.ts","../src/kernels/webgl/pool_gpu.ts","../src/kernels/webgl/reduce_gpu.ts","../src/kernels/webgl/reshape_packed_gpu.ts","../src/kernels/webgl/resize_bilinear_backprop_gpu.ts","../src/kernels/webgl/resize_bilinear_gpu.ts","../src/kernels/webgl/resize_nearest_neighbor_backprop_gpu.ts","../src/kernels/webgl/resize_nearest_neighbor_gpu.ts","../src/kernels/webgl/reverse_gpu.ts","../src/kernels/webgl/scatter_gpu.ts","../src/kernels/webgl/segment_gpu.ts","../src/kernels/webgl/select_gpu.ts","../src/kernels/webgl/slice_gpu.ts","../src/kernels/webgl/strided_slice_gpu.ts","../src/kernels/webgl/texture_manager.ts","../src/kernels/webgl/tile_gpu.ts","../src/kernels/webgl/transpose_gpu.ts","../src/ops/erf_util.ts","../src/ops/selu_util.ts","../src/kernels/webgl/unaryop_gpu.ts","../src/kernels/webgl/unpack_gpu.ts","../src/ops/concat_split.ts","../node_modules/seedrandom/lib/alea.js","../node_modules/seedrandom/lib/xor128.js","../node_modules/seedrandom/lib/xorwow.js","../node_modules/seedrandom/lib/xorshift7.js","../node_modules/seedrandom/lib/xor4096.js","../node_modules/seedrandom/lib/tychei.js","../node_modules/seedrandom/seedrandom.js","../node_modules/seedrandom/index.js","../src/ops/rand.ts","../src/ops/array_ops.ts","../src/kernels/where_impl.ts","../src/kernels/backend_webgl.ts","../src/ops/segment_util.ts","../src/ops/unary_ops.ts","../src/ops/batchnorm.ts","../src/ops/conv_util.ts","../src/ops/matmul.ts","../src/ops/conv.ts","../src/ops/reverse.ts","../src/ops/pool.ts","../src/ops/slice.ts","../src/ops/reduction_ops.ts","../src/ops/compare.ts","../src/ops/binary_ops.ts","../src/ops/logical_ops.ts","../src/ops/relu_ops.ts","../src/ops/transpose.ts","../src/ops/lrn.ts","../src/ops/norm.ts","../src/ops/segment_ops.ts","../src/ops/lstm.ts","../src/ops/moving_average.ts","../src/ops/strided_slice.ts","../src/ops/topk.ts","../src/ops/scatter_nd.ts","../src/ops/spectral_ops.ts","../src/ops/sparse_to_dense.ts","../src/ops/sparse_to_dense_util.ts","../src/ops/loss_ops.ts","../src/ops/gather_nd.ts","../src/ops/linalg_ops.ts","../src/ops/image_ops.ts","../src/kernels/backend_cpu.ts","../src/browser_util.ts","../src/io/types.ts","../src/io/io_utils.ts","../src/io/router_registry.ts","../src/io/model_management.ts","../src/io/indexed_db.ts","../src/io/local_storage.ts","../src/io/browser_files.ts","../src/io/weights_loader.ts","../src/io/browser_http.ts","../src/io/passthro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* @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nconst contexts: {[key: string]: WebGLRenderingContext} = {};\n\nconst WEBGL_ATTRIBUTES: WebGLContextAttributes = {\n  alpha: false,\n  antialias: false,\n  premultipliedAlpha: false,\n  preserveDrawingBuffer: false,\n  depth: false,\n  stencil: false,\n  failIfMajorPerformanceCaveat: true\n};\n\nexport function getWebGLContext(webGLVersion: number): WebGLRenderingContext {\n  if (!(webGLVersion in contexts)) {\n    const canvas = document.createElement('canvas');\n    canvas.addEventListener('webglcontextlost', ev => {\n      ev.preventDefault();\n      delete contexts[webGLVersion];\n    }, false);\n    contexts[webGLVersion] = getWebGLRenderingContext(webGLVersion);\n  }\n  const gl = contexts[webGLVersion];\n  if (gl.isContextLost()) {\n    delete contexts[webGLVersion];\n    return getWebGLContext(webGLVersion);\n  }\n\n  gl.disable(gl.DEPTH_TEST);\n  gl.disable(gl.STENCIL_TEST);\n  gl.disable(gl.BLEND);\n  gl.disable(gl.DITHER);\n  gl.disable(gl.POLYGON_OFFSET_FILL);\n  gl.disable(gl.SAMPLE_COVERAGE);\n  gl.enable(gl.SCISSOR_TEST);\n  gl.enable(gl.CULL_FACE);\n  gl.cullFace(gl.BACK);\n\n  return contexts[webGLVersion];\n}\n\nfunction getWebGLRenderingContext(webGLVersion: number): WebGLRenderingContext {\n  if (webGLVersion !== 1 && webGLVersion !== 2) {\n    throw new Error('Cannot get WebGL rendering context, WebGL is disabled.');\n  }\n\n  const canvas = document.createElement('canvas');\n  if (webGLVersion === 1) {\n    return (canvas.getContext('webgl', WEBGL_ATTRIBUTES) ||\n            canvas.getContext('experimental-webgl', WEBGL_ATTRIBUTES)) as\n        WebGLRenderingContext;\n  }\n  return canvas.getContext('webgl2', WEBGL_ATTRIBUTES) as WebGLRenderingContext;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, DataTypeMap, FlatVector, NumericDataType, RecursiveArray, TensorLike, TypedArray} from './types';\n\n/** Shuffles the array using Fisher-Yates algorithm. */\n// tslint:disable-next-line:no-any\nexport function shuffle(array: any[]|Uint32Array|Int32Array|\n                        Float32Array): void {\n  let counter = array.length;\n  let temp = 0;\n  let index = 0;\n  // While there are elements in the array\n  while (counter > 0) {\n    // Pick a random index\n    index = (Math.random() * counter) | 0;\n    // Decrease counter by 1\n    counter--;\n    // And swap the last element with it\n    temp = array[counter];\n    array[counter] = array[index];\n    array[index] = temp;\n  }\n}\n\n/** Clamps a value to a specified range. */\nexport function clamp(min: number, x: number, max: number): number {\n  return Math.max(min, Math.min(x, max));\n}\n\nexport function nearestLargerEven(val: number): number {\n  return val % 2 === 0 ? val : val + 1;\n}\n\nexport function sum(arr: number[]): number {\n  let sum = 0;\n  for (let i = 0; i < arr.length; i++) {\n    sum += arr[i];\n  }\n  return sum;\n}\n\n/**\n * Returns a sample from a uniform [a, b) distribution.\n *\n * @param a The minimum support (inclusive).\n * @param b The maximum support (exclusive).\n * @return A pseudorandom number on the half-open interval [a,b).\n */\nexport function randUniform(a: number, b: number) {\n  const r = Math.random();\n  return (b * r) + (1 - r) * a;\n}\n\n/** Returns the squared Euclidean distance between two vectors. */\nexport function distSquared(a: FlatVector, b: FlatVector): number {\n  let result = 0;\n  for (let i = 0; i < a.length; i++) {\n    const diff = Number(a[i]) - Number(b[i]);\n    result += diff * diff;\n  }\n  return result;\n}\n\nexport function assert(expr: boolean, msg: string|(() => string)) {\n  if (!expr) {\n    throw new Error(typeof msg === 'string' ? msg : msg());\n  }\n}\n\nexport function assertShapesMatch(\n    shapeA: number[], shapeB: number[], errorMessagePrefix = ''): void {\n  assert(\n      arraysEqual(shapeA, shapeB),\n      errorMessagePrefix + ` Shapes ${shapeA} and ${shapeB} must match`);\n}\n\nexport function assertNonNull(a: TensorLike): void {\n  assert(\n      a != null,\n      `The input to the tensor constructor must be a non-null value.`);\n}\n\n// NOTE: We explicitly type out what T extends instead of any so that\n// util.flatten on a nested array of number doesn't try to infer T as a\n// number[][], causing us to explicitly type util.flatten<number>().\nexport function\nflatten<T extends number|boolean|string|Promise<number>|TypedArray>(\n    arr: T|RecursiveArray<T>, ret: T[] = []): T[] {\n  if (Array.isArray(arr) || isTypedArray(arr)) {\n    for (let i = 0; i < arr.length; ++i) {\n      flatten(arr[i], ret);\n    }\n  } else {\n    ret.push(arr as T);\n  }\n  return ret;\n}\n\nexport function sizeFromShape(shape: number[]): number {\n  if (shape.length === 0) {\n    // Scalar.\n    return 1;\n  }\n  let size = shape[0];\n  for (let i = 1; i < shape.length; i++) {\n    size *= shape[i];\n  }\n  return size;\n}\n\nexport function isScalarShape(shape: number[]): boolean {\n  return shape.length === 0;\n}\n\nexport function arraysEqual(n1: FlatVector, n2: FlatVector) {\n  if (n1 === n2) {\n    return true;\n  }\n  if (n1 == null || n2 == null) {\n    return false;\n  }\n\n  if (n1.length !== n2.length) {\n    return false;\n  }\n  for (let i = 0; i < n1.length; i++) {\n    if (n1[i] !== n2[i]) {\n      return false;\n    }\n  }\n  return true;\n}\n\nexport function isInt(a: number): boolean {\n  return a % 1 === 0;\n}\n\nexport function tanh(x: number): number {\n  // tslint:disable-next-line:no-any\n  if ((Math as any).tanh != null) {\n    // tslint:disable-next-line:no-any\n    return (Math as any).tanh(x);\n  }\n  if (x === Infinity) {\n    return 1;\n  } else if (x === -Infinity) {\n    return -1;\n  } else {\n    const e2x = Math.exp(2 * x);\n    return (e2x - 1) / (e2x + 1);\n  }\n}\n\nexport function sizeToSquarishShape(size: number): [number, number] {\n  for (let a = Math.floor(Math.sqrt(size)); a > 1; --a) {\n    if (size % a === 0) {\n      return [a, size / a];\n    }\n  }\n  return [1, size];\n}\n\nexport function createShuffledIndices(n: number): Uint32Array {\n  const shuffledIndices = new Uint32Array(n);\n  for (let i = 0; i < n; ++i) {\n    shuffledIndices[i] = i;\n  }\n  shuffle(shuffledIndices);\n  return shuffledIndices;\n}\n\nexport function rightPad(a: string, size: number): string {\n  if (size <= a.length) {\n    return a;\n  }\n  return a + ' '.repeat(size - a.length);\n}\n\nexport function repeatedTry(\n    checkFn: () => boolean, delayFn = (counter: number) => 0,\n    maxCounter?: number): Promise<void> {\n  return new Promise<void>((resolve, reject) => {\n    let tryCount = 0;\n\n    const tryFn = () => {\n      if (checkFn()) {\n        resolve();\n        return;\n      }\n\n      tryCount++;\n\n      const nextBackoff = delayFn(tryCount);\n\n      if (maxCounter != null && tryCount >= maxCounter) {\n        reject();\n        return;\n      }\n      setTimeout(tryFn, nextBackoff);\n    };\n\n    tryFn();\n  });\n}\n\n/**\n * Given the full size of the array and a shape that may contain -1 as the\n * implicit dimension, returns the inferred shape where -1 is replaced.\n * E.g. For shape=[2, -1, 3] and size=24, it will return [2, 4, 3].\n *\n * @param shape The shape, which may contain -1 in some dimension.\n * @param size The full size (number of elements) of the array.\n * @return The inferred shape where -1 is replaced with the inferred size.\n */\nexport function inferFromImplicitShape(\n    shape: number[], size: number): number[] {\n  let shapeProd = 1;\n  let implicitIdx = -1;\n\n  for (let i = 0; i < shape.length; ++i) {\n    if (shape[i] >= 0) {\n      shapeProd *= shape[i];\n    } else if (shape[i] === -1) {\n      if (implicitIdx !== -1) {\n        throw Error(\n            `Shapes can only have 1 implicit size. ` +\n            `Found -1 at dim ${implicitIdx} and dim ${i}`);\n      }\n      implicitIdx = i;\n    } else if (shape[i] < 0) {\n      throw Error(`Shapes can not be < 0. Found ${shape[i]} at dim ${i}`);\n    }\n  }\n\n  if (implicitIdx === -1) {\n    if (size > 0 && size !== shapeProd) {\n      throw Error(`Size(${size}) must match the product of shape ${shape}`);\n    }\n    return shape;\n  }\n\n  if (shapeProd === 0) {\n    throw Error(\n        `Cannot infer the missing size in [${shape}] when ` +\n        `there are 0 elements`);\n  }\n  if (size % shapeProd !== 0) {\n    throw Error(\n        `The implicit shape can't be a fractional number. ` +\n        `Got ${size} / ${shapeProd}`);\n  }\n\n  const newShape = shape.slice();\n  newShape[implicitIdx] = size / shapeProd;\n  return newShape;\n}\n\n/** Reduces the shape by removing all dimensions of shape 1. */\nexport function squeezeShape(shape: number[], axis?: number[]):\n    {newShape: number[], keptDims: number[]} {\n  const newShape: number[] = [];\n  const keptDims: number[] = [];\n  let j = 0;\n  for (let i = 0; i < shape.length; ++i) {\n    if (axis != null) {\n      if (axis[j] === i && shape[i] !== 1) {\n        throw new Error(\n            `Can't squeeze axis ${i} since its dim '${shape[i]}' is not 1`);\n      }\n      if ((axis[j] == null || axis[j] > i) && shape[i] === 1) {\n        newShape.push(shape[i]);\n        keptDims.push(i);\n      }\n      if (axis[j] <= i) {\n        j++;\n      }\n    }\n    if (shape[i] !== 1) {\n      newShape.push(shape[i]);\n      keptDims.push(i);\n    }\n  }\n  return {newShape, keptDims};\n}\n\nexport function getTypedArrayFromDType<D extends NumericDataType>(\n    dtype: D, size: number): DataTypeMap[D] {\n  let values = null;\n  if (dtype == null || dtype === 'float32') {\n    values = new Float32Array(size);\n  } else if (dtype === 'int32') {\n    values = new Int32Array(size);\n  } else if (dtype === 'bool') {\n    values = new Uint8Array(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n  return values;\n}\n\nexport function getArrayFromDType<D extends DataType>(\n    dtype: D, size: number): DataTypeMap[D] {\n  let values = null;\n  if (dtype == null || dtype === 'float32') {\n    values = new Float32Array(size);\n  } else if (dtype === 'int32') {\n    values = new Int32Array(size);\n  } else if (dtype === 'bool') {\n    values = new Uint8Array(size);\n  } else if (dtype === 'string') {\n    values = new Array<'string'>(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n  return values;\n}\n\nexport function checkComputationForNaN<D extends DataType>(\n    vals: DataTypeMap[D], dtype: D, name: string): void {\n  if (dtype !== 'float32') {\n    // Only floating point computations will generate NaN values\n    return;\n  }\n  for (let i = 0; i < vals.length; i++) {\n    if (isNaN(vals[i] as number)) {\n      throw Error(`The result of the '${name}' has NaNs.`);\n    }\n  }\n}\n\nexport function checkConversionForNaN<D extends DataType>(\n    vals: DataTypeMap[D]|number[], dtype: D): void {\n  if (dtype === 'float32') {\n    // NaN is valid for floating point conversions\n    return;\n  }\n\n  for (let i = 0; i < vals.length; i++) {\n    if (isNaN(vals[i] as number)) {\n      throw Error(`NaN is not a valid value for dtype: '${dtype}'.`);\n    }\n  }\n}\n\n/**\n * Returns true if the new type can't encode the old type without loss of\n * precision.\n */\nexport function hasEncodingLoss(oldType: DataType, newType: DataType): boolean {\n  if (newType === 'complex64') {\n    return false;\n  }\n  if (newType === 'float32' && oldType !== 'complex64') {\n    return false;\n  }\n  if (newType === 'int32' && oldType !== 'float32' && oldType !== 'complex64') {\n    return false;\n  }\n  if (newType === 'bool' && oldType === 'bool') {\n    return false;\n  }\n  return true;\n}\n\nexport function isTypedArray(a: {}): a is Float32Array|Int32Array|Uint8Array {\n  return a instanceof Float32Array || a instanceof Int32Array ||\n      a instanceof Uint8Array;\n}\n\nexport function bytesPerElement(dtype: DataType): number {\n  if (dtype === 'float32' || dtype === 'int32') {\n    return 4;\n  } else if (dtype === 'complex64') {\n    return 8;\n  } else if (dtype === 'bool') {\n    return 1;\n  } else {\n    throw new Error(`Unknown dtype ${dtype}`);\n  }\n}\n\n/**\n * Returns the approximate number of bytes allocated in the string array - 2\n * bytes per character. Computing the exact bytes for a native string in JS is\n * not possible since it depends on the encoding of the html page that serves\n * the website.\n */\nexport function bytesFromStringArray(arr: string[]): number {\n  if (arr == null) {\n    return 0;\n  }\n  let bytes = 0;\n  arr.forEach(x => bytes += x.length * 2);\n  return bytes;\n}\n\n/** Returns true if the value is a string. */\nexport function isString(value: {}): value is string {\n  return typeof value === 'string' || value instanceof String;\n}\n\nexport function isBoolean(value: {}): boolean {\n  return typeof value === 'boolean';\n}\n\nexport function isNumber(value: {}): boolean {\n  return typeof value === 'number';\n}\n\nexport function inferDtype(values: TensorLike): DataType {\n  if (values instanceof Array) {\n    return inferDtype(values[0]);\n  }\n  if (values instanceof Float32Array) {\n    return 'float32';\n  } else if (values instanceof Int32Array || values instanceof Uint8Array) {\n    return 'int32';\n  } else if (isNumber(values)) {\n    return 'float32';\n  } else if (isString(values)) {\n    return 'string';\n  } else if (isBoolean(values)) {\n    return 'bool';\n  }\n  return 'float32';\n}\n\nexport function isFunction(f: Function) {\n  return !!(f && f.constructor && f.call && f.apply);\n}\n\nexport function nearestDivisor(size: number, start: number): number {\n  for (let i = start; i < size; ++i) {\n    if (size % i === 0) {\n      return i;\n    }\n  }\n  return size;\n}\n\nexport function computeStrides(shape: number[]): number[] {\n  const rank = shape.length;\n  if (rank < 2) {\n    return [];\n  }\n\n  // Last dimension has implicit stride of 1, thus having D-1 (instead of D)\n  // strides.\n  const strides = new Array(rank - 1);\n  strides[rank - 2] = shape[rank - 1];\n  for (let i = rank - 3; i >= 0; --i) {\n    strides[i] = strides[i + 1] * shape[i + 1];\n  }\n  return strides;\n}\n\nexport function toTypedArray(\n    a: TensorLike, dtype: DataType, debugMode: boolean): TypedArray {\n  if (dtype === 'string') {\n    throw new Error('Cannot convert a string[] to a TypedArray');\n  }\n  if (noConversionNeeded(a, dtype)) {\n    return a as TypedArray;\n  }\n  if (Array.isArray(a)) {\n    a = flatten(a as number[]);\n  }\n  if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n    return new Float32Array(a as number[]);\n  } else if (dtype === 'int32') {\n    if (debugMode) {\n      checkConversionForNaN(a as number[], dtype);\n    }\n    return new Int32Array(a as number[]);\n  } else if (dtype === 'bool') {\n    const bool = new Uint8Array((a as number[]).length);\n    for (let i = 0; i < bool.length; ++i) {\n      if (Math.round((a as number[])[i] as number) !== 0) {\n        bool[i] = 1;\n      }\n    }\n    return bool;\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n}\n\nfunction noConversionNeeded(a: TensorLike, dtype: DataType): boolean {\n  return (a instanceof Float32Array && dtype === 'float32') ||\n      (a instanceof Int32Array && dtype === 'int32') ||\n      (a instanceof Uint8Array && dtype === 'bool');\n}\n\nexport function makeOnesTypedArray<D extends DataType>(\n    size: number, dtype: D): DataTypeMap[D] {\n  const array = makeZerosTypedArray(size, dtype);\n  for (let i = 0; i < array.length; i++) {\n    array[i] = 1;\n  }\n  return array;\n}\n\nexport function makeZerosTypedArray<D extends DataType>(\n    size: number, dtype: D): DataTypeMap[D] {\n  if (dtype == null || dtype === 'float32' || dtype === 'complex64') {\n    return new Float32Array(size);\n  } else if (dtype === 'int32') {\n    return new Int32Array(size);\n  } else if (dtype === 'bool') {\n    return new Uint8Array(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n}\n\n/**\n * Returns the current high-resolution real time in milliseconds. It is\n * relative to an arbitrary time in the past.\n */\nexport function now(): number {\n  if (typeof performance !== 'undefined') {\n    return performance.now();\n  } else if (typeof process !== 'undefined') {\n    const time = process.hrtime();\n    return time[0] * 1000 + time[1] / 1000000;\n  } else {\n    throw new Error(\n        'Cannot measure time in this environment. You should run tf.js ' +\n        'in the browser or in Node.js');\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {BackendTimer} from './kernels/backend';\nimport {Tensor} from './tensor';\nimport {TypedArray} from './types';\nimport * as util from './util';\n\nexport class Profiler {\n  constructor(private backendTimer: BackendTimer, private logger?: Logger) {\n    if (logger == null) {\n      this.logger = new Logger();\n    }\n  }\n\n  profileKernel<T extends Tensor|Tensor[]>(name: string, f: () => T | Tensor[]):\n      T {\n    let result: T|Tensor[];\n    const holdResultWrapperFn = () => {\n      result = f();\n    };\n    const timer = this.backendTimer.time(holdResultWrapperFn);\n\n    const results: Tensor[] =\n        Array.isArray(result) ? result : [result] as Tensor[];\n    results.forEach(r => {\n      const vals = r.dataSync();\n      util.checkComputationForNaN(vals, r.dtype, name);\n\n      timer.then(timing => {\n        let extraInfo = '';\n        if (timing.getExtraProfileInfo != null) {\n          extraInfo = timing.getExtraProfileInfo();\n        }\n\n        this.logger.logKernelProfile(name, r, vals, timing.kernelMs, extraInfo);\n      });\n    });\n\n    return result as T;\n  }\n}\n\nexport class Logger {\n  logKernelProfile(\n      name: string, result: Tensor, vals: TypedArray, timeMs: number,\n      extraInfo?: string) {\n    const time = util.rightPad(`${timeMs}ms`, 9);\n    const paddedName = util.rightPad(name, 25);\n    const rank = result.rank;\n    const size = result.size;\n    const shape = util.rightPad(result.shape.toString(), 14);\n\n    console.log(\n        `%c${paddedName}\\t%c${time}\\t%c${rank}D ${shape}\\t%c${size}\\t%c${\n            extraInfo}`,\n        'font-weight:bold', 'color:red', 'color:blue', 'color: orange',\n        'color: green');\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {DataType, TypedArray} from './types';\nimport {computeStrides, isString, rightPad, sizeFromShape} from './util';\n\n// Maximum number of values before we decide to show ellipsis.\nconst FORMAT_LIMIT_NUM_VALS = 20;\n// Number of first and last values to show when displaying a, b,...,y, z.\nconst FORMAT_NUM_FIRST_LAST_VALS = 3;\n// Number of significant digits to show.\nconst FORMAT_NUM_SIG_DIGITS = 7;\n\nexport function tensorToString(\n    vals: TypedArray|string[], shape: number[], dtype: DataType,\n    verbose: boolean) {\n  const strides = computeStrides(shape);\n  const padPerCol = computeMaxSizePerColumn(vals, shape, dtype, strides);\n  const rank = shape.length;\n  const valsLines = subTensorToString(vals, shape, dtype, strides, padPerCol);\n  const lines = ['Tensor'];\n  if (verbose) {\n    lines.push(`  dtype: ${dtype}`);\n    lines.push(`  rank: ${rank}`);\n    lines.push(`  shape: [${shape}]`);\n    lines.push(`  values:`);\n  }\n  lines.push(valsLines.map(l => '    ' + l).join('\\n'));\n  return lines.join('\\n');\n}\n\nfunction computeMaxSizePerColumn(\n    vals: TypedArray|string[], shape: number[], dtype: DataType,\n    strides: number[]): number[] {\n  const n = sizeFromShape(shape);\n  const numCols = strides[strides.length - 1];\n  const padPerCol = new Array(numCols).fill(0);\n  const rank = shape.length;\n  const valuesOrTuples =\n      dtype === 'complex64' ? createComplexTuples(vals) : vals;\n\n  if (rank > 1) {\n    for (let row = 0; row < n / numCols; row++) {\n      const offset = row * numCols;\n      for (let j = 0; j < numCols; j++) {\n        padPerCol[j] = Math.max(\n            padPerCol[j], valToString(valuesOrTuples[offset + j], 0).length);\n      }\n    }\n  }\n  return padPerCol;\n}\n\nfunction valToString(val: number|string|[number, number], pad: number) {\n  let valStr: string;\n  if (Array.isArray(val)) {\n    valStr = `${parseFloat(val[0].toFixed(FORMAT_NUM_SIG_DIGITS))} + ` +\n        `${parseFloat(val[1].toFixed(FORMAT_NUM_SIG_DIGITS))}j`;\n  } else if (isString(val)) {\n    valStr = `'${val}'`;\n  } else {\n    valStr = parseFloat(val.toFixed(FORMAT_NUM_SIG_DIGITS)).toString();\n  }\n\n  return rightPad(valStr, pad);\n}\n\nfunction subTensorToString(\n    vals: TypedArray|string[], shape: number[], dtype: DataType,\n    strides: number[], padPerCol: number[], isLast = true): string[] {\n  const storagePerElement = dtype === 'complex64' ? 2 : 1;\n\n  const size = shape[0];\n  const rank = shape.length;\n  if (rank === 0) {\n    if (dtype === 'complex64') {\n      const complexTuple = createComplexTuples(vals);\n      return [valToString(complexTuple[0], 0)];\n    }\n    return [vals[0].toString()];\n  }\n\n  if (rank === 1) {\n    if (size > FORMAT_LIMIT_NUM_VALS) {\n      const firstValsSize = FORMAT_NUM_FIRST_LAST_VALS * storagePerElement;\n\n      let firstVals = Array.from<number|string|[number, number]>(\n          vals.slice(0, firstValsSize));\n      let lastVals = Array.from<number|string|[number, number]>(vals.slice(\n          size - FORMAT_NUM_FIRST_LAST_VALS * storagePerElement, size));\n      if (dtype === 'complex64') {\n        firstVals = createComplexTuples(firstVals);\n        lastVals = createComplexTuples(lastVals);\n      }\n      return [\n        '[' + firstVals.map((x, i) => valToString(x, padPerCol[i])).join(', ') +\n        ', ..., ' +\n        lastVals\n            .map(\n                (x, i) => valToString(\n                    x, padPerCol[size - FORMAT_NUM_FIRST_LAST_VALS + i]))\n            .join(', ') +\n        ']'\n      ];\n    }\n    const displayVals: Array<number|string|[number, number]> =\n        dtype === 'complex64' ? createComplexTuples(vals) :\n                                Array.from<number|string>(vals);\n\n    return [\n      '[' + displayVals.map((x, i) => valToString(x, padPerCol[i])).join(', ') +\n      ']'\n    ];\n  }\n\n  // The array is rank 2 or more.\n  const subshape = shape.slice(1);\n  const substrides = strides.slice(1);\n  const stride = strides[0] * storagePerElement;\n  const lines: string[] = [];\n  if (size > FORMAT_LIMIT_NUM_VALS) {\n    for (let i = 0; i < FORMAT_NUM_FIRST_LAST_VALS; i++) {\n      const start = i * stride;\n      const end = start + stride;\n      lines.push(...subTensorToString(\n          vals.slice(start, end), subshape, dtype, substrides, padPerCol,\n          false /* isLast */));\n    }\n    lines.push('...');\n    for (let i = size - FORMAT_NUM_FIRST_LAST_VALS; i < size; i++) {\n      const start = i * stride;\n      const end = start + stride;\n      lines.push(...subTensorToString(\n          vals.slice(start, end), subshape, dtype, substrides, padPerCol,\n          i === size - 1 /* isLast */));\n    }\n  } else {\n    for (let i = 0; i < size; i++) {\n      const start = i * stride;\n      const end = start + stride;\n      lines.push(...subTensorToString(\n          vals.slice(start, end), subshape, dtype, substrides, padPerCol,\n          i === size - 1 /* isLast */));\n    }\n  }\n  const sep = rank === 2 ? ',' : '';\n  lines[0] = '[' + lines[0] + sep;\n  for (let i = 1; i < lines.length - 1; i++) {\n    lines[i] = ' ' + lines[i] + sep;\n  }\n  let newLineSep = ',\\n';\n  for (let i = 2; i < rank; i++) {\n    newLineSep += '\\n';\n  }\n  lines[lines.length - 1] =\n      ' ' + lines[lines.length - 1] + ']' + (isLast ? '' : newLineSep);\n  return lines;\n}\n\nfunction createComplexTuples(vals: Array<{}>|\n                             TypedArray): Array<[number, number]> {\n  const complexTuples: Array<[number, number]> = [];\n  for (let i = 0; i < vals.length; i += 2) {\n    complexTuples.push([vals[i], vals[i + 1]] as [number, number]);\n  }\n  return complexTuples;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {tensorToString} from './tensor_format';\nimport {DataType, DataTypeMap, DataValues, NumericDataType, Rank, ShapeMap, SingleValueMap, TensorLike, TensorLike1D, TensorLike3D, TensorLike4D, TypedArray} from './types';\nimport * as util from './util';\nimport {computeStrides} from './util';\n\nexport interface TensorData<D extends DataType> {\n  dataId?: DataId;\n  values?: DataTypeMap[D];\n}\n\n/**\n * A mutable object, similar to `tf.Tensor`, that allows users to set values\n * at locations before converting to an immutable `tf.Tensor`.\n *\n * See `tf.buffer` for creating a tensor buffer.\n */\n/** @doc {heading: 'Tensors', subheading: 'Classes'} */\nexport class TensorBuffer<R extends Rank, D extends DataType = 'float32'> {\n  size: number;\n  shape: ShapeMap[R];\n  strides: number[];\n  values: DataTypeMap[D];\n\n  constructor(shape: ShapeMap[R], public dtype: D, values?: DataTypeMap[D]) {\n    this.shape = shape.slice();\n    this.size = util.sizeFromShape(shape);\n\n    if (values != null) {\n      const n = values.length;\n      util.assert(\n          n === this.size,\n          `Length of values '${n}' does not match the size ` +\n              `inferred by the shape '${this.size}'.`);\n    }\n    if (dtype === 'complex64') {\n      throw new Error(\n          `complex64 dtype TensorBuffers are not supported. Please create ` +\n          `a TensorBuffer for the real and imaginary parts separately and ` +\n          `call tf.complex(real, imag).`);\n    }\n    this.values =\n        values || util.getArrayFromDType(dtype, util.sizeFromShape(this.shape));\n    this.strides = computeStrides(shape);\n  }\n\n  /**\n   * Sets a value in the buffer at a given location.\n   *\n   * @param value The value to set.\n   * @param locs  The location indices.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Creation'} */\n  set(value: SingleValueMap[D], ...locs: number[]): void {\n    if (locs.length === 0) {\n      locs = [0];\n    }\n    util.assert(\n        locs.length === this.rank,\n        `The number of provided coordinates (${locs.length}) must ` +\n            `match the rank (${this.rank})`);\n\n    const index = this.locToIndex(locs);\n    this.values[index] = value as number;\n  }\n\n  /**\n   * Returns the value in the buffer at the provided location.\n   *\n   * @param locs The location indices.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Creation'} */\n  get(...locs: number[]): SingleValueMap[D] {\n    if (locs.length === 0) {\n      locs = [0];\n    }\n    let index = locs[locs.length - 1];\n    for (let i = 0; i < locs.length - 1; ++i) {\n      index += this.strides[i] * locs[i];\n    }\n    return this.values[index];\n  }\n\n  locToIndex(locs: number[]): number {\n    if (this.rank === 0) {\n      return 0;\n    } else if (this.rank === 1) {\n      return locs[0];\n    }\n    let index = locs[locs.length - 1];\n    for (let i = 0; i < locs.length - 1; ++i) {\n      index += this.strides[i] * locs[i];\n    }\n    return index;\n  }\n\n  indexToLoc(index: number): number[] {\n    if (this.rank === 0) {\n      return [];\n    } else if (this.rank === 1) {\n      return [index];\n    }\n    const locs: number[] = new Array(this.shape.length);\n    for (let i = 0; i < locs.length - 1; ++i) {\n      locs[i] = Math.floor(index / this.strides[i]);\n      index -= locs[i] * this.strides[i];\n    }\n    locs[locs.length - 1] = index;\n    return locs;\n  }\n\n  get rank() {\n    return this.shape.length;\n  }\n\n  /**\n   * Creates an immutable `tf.Tensor` object from the buffer.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Creation'} */\n  toTensor(): Tensor<R> {\n    return Tensor.make(this.shape, {values: this.values}, this.dtype);\n  }\n}\n\nexport interface TensorTracker {\n  registerTensor(t: Tensor): void;\n  disposeTensor(t: Tensor): void;\n  write(dataId: DataId, values: DataValues): void;\n  read(dataId: DataId): Promise<DataValues>;\n  readSync(dataId: DataId): DataValues;\n  registerVariable(v: Variable): void;\n  nextTensorId(): number;\n  nextVariableId(): number;\n}\n\n/**\n * The Tensor class calls into this handler to delegate chaining operations.\n */\nexport interface OpHandler {\n  cast<T extends Tensor>(x: T, dtype: DataType): T;\n  buffer<R extends Rank, D extends DataType>(\n      shape: ShapeMap[R], dtype: D,\n      values?: DataTypeMap[D]): TensorBuffer<R, D>;\n  print<T extends Tensor>(x: T, verbose: boolean): void;\n  reshape<R2 extends Rank>(x: Tensor, shape: ShapeMap[R2]): Tensor<R2>;\n  expandDims<R2 extends Rank>(x: Tensor, axis: number): Tensor<R2>;\n  cumsum<T extends Tensor>(\n      x: Tensor, axis: number, exclusive: boolean, reverse: boolean): T;\n  squeeze<T extends Tensor>(x: Tensor, axis?: number[]): T;\n  clone<T extends Tensor>(x: T): T;\n  tile<T extends Tensor>(x: T, reps: number[]): T;\n  gather<T extends Tensor>(x: T, indices: Tensor1D|TensorLike1D, axis: number):\n      T;\n  matMul<T extends Tensor>(\n      a: T, b: T|TensorLike, transposeA: boolean, transposeB: boolean): T;\n  dot(t1: Tensor, t2: Tensor|TensorLike): Tensor;\n  norm(\n      x: Tensor, ord: number|'euclidean'|'fro', axis: number|number[],\n      keepDims: boolean): Tensor;\n  slice<R extends Rank, T extends Tensor<R>>(\n      x: T, begin: number|number[], size?: number|number[]): T;\n  split<T extends Tensor>(\n      x: T, numOrSizeSplits: number[]|number, axis?: number): T[];\n  reverse<T extends Tensor>(x: T, axis?: number|number[]): T;\n  concat<T extends Tensor>(tensors: Array<T|TensorLike>, axis: number): T;\n  stack<T extends Tensor>(tensors: Array<T|TensorLike>, axis: number): Tensor;\n  unstack<T extends Tensor>(value: T, axis: number): Tensor[];\n  pad<T extends Tensor>(\n      x: T, paddings: Array<[number, number]>, constantValue: number): T;\n  batchNormalization<R extends Rank>(\n      x: Tensor<R>, mean: Tensor<R>|Tensor1D|TensorLike,\n      variance: Tensor<R>|Tensor1D|TensorLike, varianceEpsilon: number,\n      scale?: Tensor<R>|Tensor1D|TensorLike,\n      offset?: Tensor<R>|Tensor1D|TensorLike): Tensor<R>;\n  all<T extends Tensor>(x: Tensor, axis: number|number[], keepDims: boolean): T;\n  any<T extends Tensor>(x: Tensor, axis: number|number[], keepDims: boolean): T;\n  logSumExp<T extends Tensor>(\n      x: Tensor, axis: number|number[], keepDims: boolean): T;\n  sum<T extends Tensor>(x: Tensor, axis: number|number[], keepDims: boolean): T;\n  prod<T extends Tensor>(x: Tensor, axis: number|number[], keepDims: boolean):\n      T;\n  mean<T extends Tensor>(x: Tensor, axis: number|number[], keepDims: boolean):\n      T;\n  min<T extends Tensor>(x: Tensor, axis: number|number[], keepDims: boolean): T;\n  max<T extends Tensor>(x: Tensor, axis: number|number[], keepDims: boolean): T;\n  argMin<T extends Tensor>(x: Tensor, axis: number): T;\n  argMax<T extends Tensor>(x: Tensor, axis: number): T;\n  add<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  addStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  atan2<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  sub<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  subStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  pow<T extends Tensor>(base: T, exp: Tensor|TensorLike): T;\n  powStrict<T extends Tensor>(base: T, exp: Tensor|TensorLike): T;\n  mul<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  mulStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  div<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  floorDiv<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  divStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  mod<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  modStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  minimum<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  minimumStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  maximum<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  maximumStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  squaredDifference<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  squaredDifferenceStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  transpose<T extends Tensor>(x: T, perm?: number[]): T;\n  logicalNot<T extends Tensor>(x: T): T;\n  logicalAnd<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  logicalOr<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  logicalXor<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  where<T extends Tensor>(condition: Tensor|TensorLike, a: T, b: T|TensorLike):\n      T;\n  notEqual<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  notEqualStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  less<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  lessStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  equal<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  equalStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  lessEqual<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  lessEqualStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  greater<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  greaterStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  greaterEqual<T extends Tensor>(a: Tensor, b: Tensor|TensorLike): T;\n  greaterEqualStrict<T extends Tensor>(a: T, b: T|TensorLike): T;\n  neg<T extends Tensor>(x: T): T;\n  ceil<T extends Tensor>(x: T): T;\n  floor<T extends Tensor>(x: T): T;\n  sign<T extends Tensor>(x: T): T;\n  round<T extends Tensor>(x: T): T;\n  exp<T extends Tensor>(x: T): T;\n  expm1<T extends Tensor>(x: T): T;\n  log<T extends Tensor>(x: T): T;\n  log1p<T extends Tensor>(x: T): T;\n  sqrt<T extends Tensor>(x: T): T;\n  rsqrt<T extends Tensor>(x: T): T;\n  square<T extends Tensor>(x: T): T;\n  reciprocal<T extends Tensor>(x: T): T;\n  abs<T extends Tensor>(x: T): T;\n  clipByValue<T extends Tensor>(\n      x: T, clipValueMin: number, clipValueMax: number): T;\n  sigmoid<T extends Tensor>(x: T): T;\n  logSigmoid<T extends Tensor>(x: T): T;\n  softplus<T extends Tensor>(x: T): T;\n  zerosLike<T extends Tensor>(x: T): T;\n  onesLike<T extends Tensor>(x: T): T;\n  sin<T extends Tensor>(x: T): T;\n  cos<T extends Tensor>(x: T): T;\n  tan<T extends Tensor>(x: T): T;\n  asin<T extends Tensor>(x: T): T;\n  acos<T extends Tensor>(x: T): T;\n  atan<T extends Tensor>(x: T): T;\n  sinh<T extends Tensor>(x: T): T;\n  cosh<T extends Tensor>(x: T): T;\n  tanh<T extends Tensor>(x: T): T;\n  asinh<T extends Tensor>(x: T): T;\n  acosh<T extends Tensor>(x: T): T;\n  atanh<T extends Tensor>(x: T): T;\n  erf<T extends Tensor>(x: T): T;\n  step<T extends Tensor>(x: T, alpha: number): T;\n  relu<T extends Tensor>(x: T): T;\n  elu<T extends Tensor>(x: T): T;\n  selu<T extends Tensor>(x: T): T;\n  leakyRelu<T extends Tensor>(x: T, alpha: number): T;\n  prelu<T extends Tensor>(x: T, alpha: T|TensorLike): T;\n  softmax<T extends Tensor>(logits: T, dim: number): T;\n  logSoftmax<T extends Tensor>(logits: T, axis: number): T;\n  image: {\n    resizeBilinear<T extends Tensor3D|Tensor4D>(\n        images: T, size: [number, number], alignCorners: boolean): T;\n    resizeNearestNeighbor<T extends Tensor3D|Tensor4D>(\n        images: T, size: [number, number], alignCorners: boolean): T;\n  };\n  conv1d<T extends Tensor2D|Tensor3D>(\n      x: T, filter: Tensor3D|TensorLike3D, stride: number,\n      pad: 'valid'|'same'|number, dataFormat: 'NWC'|'NCW', dilation: number,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n  conv2d<T extends Tensor3D|Tensor4D>(\n      x: T, filter: Tensor4D|TensorLike4D, strides: [number, number]|number,\n      pad: 'valid'|'same'|number, dataFormat: 'NHWC'|'NCHW',\n      dilations: [number, number]|number,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n  conv2dTranspose<T extends Tensor3D|Tensor4D>(\n      x: T, filter: Tensor4D|TensorLike4D,\n      outputShape: [number, number, number, number]|[number, number, number],\n      strides: [number, number]|number, pad: 'valid'|'same'|number,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n  depthwiseConv2d<T extends Tensor3D|Tensor4D>(\n      x: T, filter: Tensor4D|TensorLike4D, strides: [number, number]|number,\n      pad: 'valid'|'same'|number, dataFormat: 'NHWC'|'NCHW',\n      dilations: [number, number]|number,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n  separableConv2d<T extends Tensor3D|Tensor4D>(\n      x: T|TensorLike, depthwiseFilter: Tensor4D|TensorLike4D,\n      pointwiseFilter: Tensor4D|TensorLike, strides: [number, number]|number,\n      pad: 'valid'|'same', dilation: [number, number]|number,\n      dataFormat: 'NHWC'|'NCHW'): T;\n  maxPool<T extends Tensor3D|Tensor4D>(\n      x: T, filterSize: [number, number]|number,\n      strides: [number, number]|number, pad: 'valid'|'same'|number,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n  avgPool<T extends Tensor3D|Tensor4D>(\n      x: T, filterSize: [number, number]|number,\n      strides: [number, number]|number, pad: 'valid'|'same'|number,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T;\n  pool<T extends Tensor3D|Tensor4D>(\n      input: T, windowShape: [number, number]|number, poolingType: 'avg'|'max',\n      padding: 'valid'|'same'|number, diationRate?: [number, number]|number,\n      strides?: [number, number]|number): T;\n  localResponseNormalization<T extends Tensor3D|Tensor4D>(\n      x: T, depthRadius: number, bias: number, alpha: number, beta: number): T;\n  unsortedSegmentSum<T extends Tensor>(\n      x: T, segmentIds: Tensor1D|TensorLike1D, numSegments: number): T;\n  batchToSpaceND<T extends Tensor>(\n      x: T, blockShape: number[], crops: number[][]): T;\n  spaceToBatchND<T extends Tensor>(\n      x: T, blockShape: number[], paddings: number[][]): T;\n  topk<T extends Tensor>(x: T, k: number, sorted: boolean):\n      {values: T, indices: T};\n  stridedSlice<T extends Tensor>(\n      x: T, begin: number[], end: number[], strides: number[],\n      beginMask: number, endMask: number): T;\n  depthToSpace(x: Tensor4D, blockSize: number, dataFormat: string): Tensor4D;\n  spectral: {fft(x: Tensor): Tensor; ifft(x: Tensor): Tensor;};\n}\n\n// For tracking tensor creation and disposal.\nlet trackerFn: () => TensorTracker = null;\n// Used by chaining methods to call into ops.\nlet opHandler: OpHandler = null;\n\n/**\n * An external consumer can register itself as the tensor tracker. This way\n * the Tensor class can notify the tracker for every tensor created and\n * disposed.\n */\nexport function setTensorTracker(fn: () => TensorTracker) {\n  trackerFn = fn;\n}\n\n/**\n * An external consumer can register itself as the op handler. This way the\n * Tensor class can have chaining methods that call into ops via the op handler.\n */\nexport function setOpHandler(handler: OpHandler) {\n  opHandler = handler;\n}\n\n/**\n * We wrap data id since we use weak map to avoid memory leaks.\n * Since we have our own memory management, we have a reference counter\n * mapping a tensor to its data, so there is always a pointer (even if that\n * data is otherwise garbage collectable).\n * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/\n * Global_Objects/WeakMap\n */\nexport type DataId = object;  // object instead of {} to force non-primitive.\n\n/**\n * A `tf.Tensor` object represents an immutable, multidimensional array of\n * numbers that has a shape and a data type.\n *\n * See `tf.tensor` for details on how to create a `tf.Tensor`.\n */\n/** @doc {heading: 'Tensors', subheading: 'Classes'} */\nexport class Tensor<R extends Rank = Rank> {\n  /** Unique id of this tensor. */\n  readonly id: number;\n  /**\n   * Id of the bucket holding the data for this tensor. Multiple arrays can\n   * point to the same bucket (e.g. when calling array.reshape()).\n   */\n  dataId: DataId;\n  /** The shape of the tensor. */\n  readonly shape: ShapeMap[R];\n  /** Number of elements in the tensor. */\n  readonly size: number;\n  /** The data type for the array. */\n  readonly dtype: DataType;\n  /** The rank type for the array (see `Rank` enum). */\n  readonly rankType: R;\n\n  /**\n   * Number of elements to skip in each dimension when indexing. See\n   * https://docs.scipy.org/doc/numpy/reference/generated/\\\n   * numpy.ndarray.strides.html\n   */\n  readonly strides: number[];\n\n  protected constructor(\n      shape: ShapeMap[R], dtype: DataType, values?: DataValues,\n      dataId?: DataId) {\n    this.shape = shape.slice();\n    this.dtype = dtype || 'float32';\n    this.size = util.sizeFromShape(shape);\n    this.strides = computeStrides(shape);\n    this.dataId = dataId != null ? dataId : {};\n    this.id = trackerFn().nextTensorId();\n    this.rankType = (this.rank < 5 ? this.rank.toString() : 'higher') as R;\n    trackerFn().registerTensor(this);\n    if (values != null) {\n      trackerFn().write(this.dataId, values);\n    }\n  }\n\n  /**\n   * Makes a new tensor with the provided shape and values. Values should be in\n   * a flat array.\n   */\n  static make<T extends Tensor<R>, D extends DataType = 'float32',\n                                             R extends Rank = Rank>(\n      shape: ShapeMap[R], data: TensorData<D>, dtype?: D): T {\n    return new Tensor(shape, dtype, data.values, data.dataId) as T;\n  }\n\n  /** Flatten a Tensor to a 1D array. */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  flatten(): Tensor1D {\n    this.throwIfDisposed();\n    return this.as1D();\n  }\n\n  /** Converts a size-1 `tf.Tensor` to a `tf.Scalar`. */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  asScalar(): Scalar {\n    this.throwIfDisposed();\n    util.assert(this.size === 1, 'The array must have only 1 element.');\n    return this.reshape<Rank.R0>([]);\n  }\n\n  /** Converts a `tf.Tensor` to a `tf.Tensor1D`. */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  as1D(): Tensor1D {\n    this.throwIfDisposed();\n    return this.reshape<Rank.R1>([this.size]);\n  }\n\n  /**\n   * Converts a `tf.Tensor` to a `tf.Tensor2D`.\n   *\n   * @param rows Number of rows in `tf.Tensor2D`.\n   * @param columns Number of columns in `tf.Tensor2D`.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  as2D(rows: number, columns: number): Tensor2D {\n    this.throwIfDisposed();\n    return this.reshape<Rank.R2>([rows, columns]);\n  }\n\n  /**\n   * Converts a `tf.Tensor` to a `tf.Tensor3D`.\n   *\n   * @param rows Number of rows in `tf.Tensor3D`.\n   * @param columns Number of columns in `tf.Tensor3D`.\n   * @param depth Depth of `tf.Tensor3D`.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  as3D(rows: number, columns: number, depth: number): Tensor3D {\n    this.throwIfDisposed();\n    return this.reshape<Rank.R3>([rows, columns, depth]);\n  }\n\n  /**\n   * Converts a `tf.Tensor` to a `tf.Tensor4D`.\n   *\n   * @param rows Number of rows in `tf.Tensor4D`.\n   * @param columns Number of columns in `tf.Tensor4D`.\n   * @param depth Depth of `tf.Tensor4D`.\n   * @param depth2 4th dimension of `tf.Tensor4D`.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  as4D(rows: number, columns: number, depth: number, depth2: number): Tensor4D {\n    this.throwIfDisposed();\n    return this.reshape<Rank.R4>([rows, columns, depth, depth2]);\n  }\n\n  /**\n   * Converts a `tf.Tensor` to a `tf.Tensor5D`.\n   *\n   * @param rows Number of rows in `tf.Tensor5D`.\n   * @param columns Number of columns in `tf.Tensor5D`.\n   * @param depth Depth of `tf.Tensor5D`.\n   * @param depth2 4th dimension of `tf.Tensor5D`.\n   * @param depth3 5th dimension of 'tf.Tensor5D'\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  as5D(\n      rows: number, columns: number, depth: number, depth2: number,\n      depth3: number): Tensor5D {\n    this.throwIfDisposed();\n    return this.reshape<Rank.R5>([rows, columns, depth, depth2, depth3]);\n  }\n\n  /**\n   * Casts a `tf.Tensor` to a specified dtype.\n   *\n   * @param dtype Data-type to cast the tensor to.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  asType<T extends this>(this: T, dtype: DataType): T {\n    this.throwIfDisposed();\n    return opHandler.cast(this, dtype) as T;\n  }\n\n  get rank(): number {\n    return this.shape.length;\n  }\n\n  /**\n   * Returns the value in the tensor at the provided location.\n   * If using WebGL backend, this is a blocking call.\n   * Prefer calling the `async data()[flatIndex]` method instead.\n   *\n   * @param locs The location indices.\n   */\n  get(...locs: number[]) {\n    util.assert(\n        locs.length === this.rank,\n        'Number of coordinates in get() must match the rank of the tensor');\n    util.assert(\n        this.dtype !== 'complex64',\n        'Tensor.get() is not supported for complex64 tensors yet.');\n    this.throwIfDisposed();\n    if (locs.length === 0) {\n      locs = [0];\n    }\n    let index = locs[locs.length - 1];\n    for (let i = 0; i < locs.length - 1; ++i) {\n      index += this.strides[i] * locs[i];\n    }\n    return this.dataSync()[index];\n  }\n\n  /** Returns a `tf.TensorBuffer` that holds the underlying data. */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  buffer<D extends DataType>(): TensorBuffer<R, D> {\n    return opHandler.buffer(this.shape, this.dtype as D, this.dataSync());\n  }\n\n  /**\n   * Asynchronously downloads the values from the `tf.Tensor`. Returns a promise\n   * of `TypedArray` that resolves when the computation has finished.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  async data<D extends DataType = NumericDataType>(): Promise<DataTypeMap[D]> {\n    this.throwIfDisposed();\n    return trackerFn().read(this.dataId);\n  }\n\n  /**\n   * Synchronously downloads the values from the `tf.Tensor`. This blocks the UI\n   * thread until the values are ready, which can cause performance issues.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  dataSync<D extends DataType = NumericDataType>(): DataTypeMap[D] {\n    this.throwIfDisposed();\n    return trackerFn().readSync(this.dataId);\n  }\n\n  /**\n   * Disposes `tf.Tensor` from memory.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  dispose(): void {\n    if (this.isDisposed) {\n      return;\n    }\n    trackerFn().disposeTensor(this);\n    this.isDisposedInternal = true;\n  }\n\n  private isDisposedInternal = false;\n  get isDisposed(): boolean {\n    return this.isDisposedInternal;\n  }\n\n  private throwIfDisposed() {\n    if (this.isDisposed) {\n      throw new Error(`Tensor is disposed.`);\n    }\n  }\n\n  /** Casts the array to type `float32` */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  toFloat<T extends this>(this: T): T {\n    return this.asType('float32');\n  }\n\n  /** Casts the array to type `int32` */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  toInt() {\n    return this.asType('int32');\n  }\n\n  /** Casts the array to type `bool` */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  toBool() {\n    return this.asType('bool');\n  }\n\n  /**\n   * Prints the `tf.Tensor`. See `tf.print` for details.\n   *\n   * @param verbose Whether to print verbose information about the tensor,\n   *    including dtype and size.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  print(verbose = false): void {\n    return opHandler.print(this, verbose);\n  }\n\n  /**\n   * Reshapes the tensor into the provided shape.\n   * See `tf.reshape` for more details.\n   *\n   * @param newShape An array of integers defining the output tensor shape.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  reshape<R2 extends Rank>(newShape: ShapeMap[R2]): Tensor<R2> {\n    this.throwIfDisposed();\n    return opHandler.reshape(this, newShape);\n  }\n\n  /**\n   * Reshapes the tensor into the shape of the provided tensor.\n   *\n   * @param x The tensor of required shape.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  reshapeAs<T extends Tensor>(x: T): T {\n    this.throwIfDisposed();\n    return this.reshape(x.shape) as T;\n  }\n\n  /**\n   * Returns a `tf.Tensor` that has expanded rank, by inserting a dimension\n   * into the tensor's shape. See `tf.expandDims` for details.\n   *\n   * @param axis The dimension index at which to insert shape of 1. Defaults to\n   *    0 (the first dimension).\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  expandDims<R2 extends Rank>(axis = 0): Tensor<R2> {\n    return opHandler.expandDims(this, axis);\n  }\n\n  /**\n   * Returns the cumulative sum of the `tf.Tensor` along `axis`.\n   *\n   * @param axis The axis along which to sum. Optional. Defaults to 0.\n   * @param exclusive Whether to perform exclusive cumulative sum. Defaults to\n   *    false. If set to true then the sum of each tensor entry does not include\n   *    its own value, but only the values previous to it along the specified\n   *    axis.\n   * @param reverse Whether to sum in the opposite direction. Defaults to\n   *    false.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  cumsum<T extends Tensor>(axis = 0, exclusive = false, reverse = false): T {\n    return opHandler.cumsum(this, axis, exclusive, reverse);\n  }\n\n  /**\n   * Returns a `tf.Tensor` with dimensions of size 1 removed from the shape.\n   * See `tf.squeeze` for more details.\n   *\n   * @param axis A list of numbers. If specified, only squeezes the\n   *    dimensions listed. The dimension index starts at 0. It is an error to\n   *    squeeze a dimension that is not 1.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  squeeze<T extends Tensor>(axis?: number[]): T {\n    this.throwIfDisposed();\n    return opHandler.squeeze(this, axis);\n  }\n\n  /** Returns a copy of the tensor. See `tf.clone` for details. */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  clone<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.clone(this);\n  }\n\n  /** Returns a human-readable description of the tensor. Useful for logging. */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  toString(verbose = false): string {\n    const vals = this.dataSync();\n    return tensorToString(vals, this.shape, this.dtype, verbose);\n  }\n\n  // Below is chain API that is not exposed to docs to avoid repetition. To\n  // expose a method, move it above this comment and add @doc and jsdoc.\n\n  tile<T extends this>(this: T, reps: number[]): T {\n    this.throwIfDisposed();\n    return opHandler.tile(this, reps) as T;\n  }\n\n  gather<T extends this>(this: T, indices: Tensor1D|TensorLike1D, axis = 0): T {\n    this.throwIfDisposed();\n    return opHandler.gather(this, indices, axis) as T;\n  }\n\n  matMul<T extends Tensor>(\n      this: T, b: T|TensorLike, transposeA = false, transposeB = false): T {\n    this.throwIfDisposed();\n    return opHandler.matMul(this, b, transposeA, transposeB);\n  }\n  dot(b: Tensor|TensorLike): Tensor {\n    this.throwIfDisposed();\n    return opHandler.dot(this, b);\n  }\n  norm(\n      ord: number|'euclidean'|'fro' = 'euclidean', axis: number|number[] = null,\n      keepDims = false): Tensor {\n    this.throwIfDisposed();\n    return opHandler.norm(this, ord, axis, keepDims);\n  }\n  slice<T extends Tensor<R>>(\n      this: T, begin: number|number[], size?: number|number[]): T {\n    this.throwIfDisposed();\n    return opHandler.slice(this, begin, size);\n  }\n  reverse<T extends Tensor>(this: T, axis?: number|number[]): T {\n    this.throwIfDisposed();\n    return opHandler.reverse(this, axis);\n  }\n  concat<T extends Tensor>(this: T, x: T|Array<T|TensorLike>, axis = 0): T {\n    this.throwIfDisposed();\n    if (x instanceof Tensor) {\n      x = [x];\n    }\n    return opHandler.concat([this, ...x], axis);\n  }\n  split<T extends Tensor>(this: T, numOrSizeSplits: number[]|number, axis = 0):\n      T[] {\n    this.throwIfDisposed();\n    return opHandler.split(this, numOrSizeSplits, axis);\n  }\n  stack(x: Tensor, axis = 0): Tensor {\n    return opHandler.stack([this, x], axis);\n  }\n  unstack(x: Tensor, axis = 0): Tensor[] {\n    return opHandler.unstack(this, axis);\n  }\n  pad<T extends Tensor>(\n      this: T, paddings: Array<[number, number]>, constantValue = 0): T {\n    return opHandler.pad(this, paddings, constantValue);\n  }\n  batchNormalization(\n      mean: Tensor<R>|Tensor1D|TensorLike,\n      variance: Tensor<R>|Tensor1D|TensorLike, varianceEpsilon = .001,\n      scale?: Tensor<R>|Tensor1D|TensorLike,\n      offset?: Tensor<R>|Tensor1D|TensorLike): Tensor<R> {\n    this.throwIfDisposed();\n    return opHandler.batchNormalization(\n        this, mean, variance, varianceEpsilon, scale, offset);\n  }\n\n  // Reduction ops.\n  all<T extends Tensor>(axis: number|number[] = null, keepDims = false): T {\n    this.throwIfDisposed();\n    return opHandler.all(this, axis, keepDims);\n  }\n  any<T extends Tensor>(axis: number|number[] = null, keepDims = false): T {\n    this.throwIfDisposed();\n    return opHandler.any(this, axis, keepDims);\n  }\n  logSumExp<T extends Tensor>(axis: number|number[] = null, keepDims = false):\n      T {\n    this.throwIfDisposed();\n    return opHandler.logSumExp(this, axis, keepDims);\n  }\n  sum<T extends Tensor>(axis: number|number[] = null, keepDims = false): T {\n    this.throwIfDisposed();\n    return opHandler.sum(this, axis, keepDims);\n  }\n  prod<T extends Tensor>(axis: number|number[] = null, keepDims = false): T {\n    this.throwIfDisposed();\n    return opHandler.prod(this, axis, keepDims);\n  }\n  mean<T extends Tensor>(axis: number|number[] = null, keepDims = false): T {\n    this.throwIfDisposed();\n    return opHandler.mean(this, axis, keepDims);\n  }\n  min<T extends Tensor>(axis: number|number[] = null, keepDims = false): T {\n    this.throwIfDisposed();\n    return opHandler.min(this, axis, keepDims);\n  }\n  max<T extends Tensor>(axis: number|number[] = null, keepDims = false): T {\n    this.throwIfDisposed();\n    return opHandler.max(this, axis, keepDims);\n  }\n  argMin<T extends Tensor>(axis: number = null): T {\n    this.throwIfDisposed();\n    return opHandler.argMin(this, axis);\n  }\n  argMax<T extends Tensor>(axis: number = null): T {\n    this.throwIfDisposed();\n    return opHandler.argMax(this, axis);\n  }\n\n  // Transformations\n  cast<T extends this>(dtype: DataType): T {\n    this.throwIfDisposed();\n    return opHandler.cast(this as T, dtype) as T;\n  }\n\n  // Binary ops.\n\n  add<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.add(this, x);\n  }\n  addStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.addStrict(this, x) as T;\n  }\n  atan2<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.atan2(this, x) as T;\n  }\n  sub<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.sub(this, x);\n  }\n  subStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.subStrict(this, x) as T;\n  }\n  pow<T extends Tensor>(this: T, exp: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.pow(this, exp);\n  }\n  powStrict(exp: Tensor|TensorLike): Tensor<R> {\n    this.throwIfDisposed();\n    return opHandler.powStrict(this, exp);\n  }\n  mul<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.mul(this, x);\n  }\n  mulStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.mulStrict(this, x) as T;\n  }\n  div<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.div(this, x);\n  }\n  floorDiv<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.floorDiv(this, x);\n  }\n  divStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.divStrict(this, x) as T;\n  }\n  minimum<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.minimum(this, x);\n  }\n  minimumStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.minimumStrict(this, x) as T;\n  }\n  maximum<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.maximum(this, x);\n  }\n  maximumStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.maximumStrict(this, x) as T;\n  }\n  mod<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.mod(this, x);\n  }\n  modStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.modStrict(this, x) as T;\n  }\n  squaredDifference<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.squaredDifference(this, x);\n  }\n  squaredDifferenceStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.squaredDifferenceStrict(this, x) as T;\n  }\n  transpose<T extends Tensor>(this: T, perm?: number[]): T {\n    this.throwIfDisposed();\n    return opHandler.transpose(this, perm);\n  }\n\n  // Compare ops.\n\n  notEqual<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.notEqual(this, x);\n  }\n  notEqualStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.notEqualStrict(this, x) as T;\n  }\n  less<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.less(this, x);\n  }\n  lessStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.lessStrict(this, x) as T;\n  }\n  equal<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.equal(this, x);\n  }\n  equalStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.equalStrict(this, x) as T;\n  }\n  lessEqual<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.lessEqual(this, x);\n  }\n  lessEqualStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.lessEqualStrict(this, x) as T;\n  }\n  greater<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.greater(this, x);\n  }\n  greaterStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.greaterStrict(this, x) as T;\n  }\n  greaterEqual<T extends Tensor>(x: Tensor|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.greaterEqual(this, x);\n  }\n  greaterEqualStrict<T extends this>(this: T, x: T|TensorLike): T {\n    this.throwIfDisposed();\n    return opHandler.greaterEqualStrict(this, x) as T;\n  }\n\n  // Compare ops.\n  logicalAnd(x: Tensor|TensorLike): Tensor {\n    this.throwIfDisposed();\n    return opHandler.logicalAnd(this, x);\n  }\n  logicalOr(x: Tensor|TensorLike): Tensor {\n    this.throwIfDisposed();\n    return opHandler.logicalOr(this, x);\n  }\n  logicalNot<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.logicalNot(this);\n  }\n  logicalXor(x: Tensor|TensorLike): Tensor {\n    this.throwIfDisposed();\n    return opHandler.logicalXor(this, x);\n  }\n  where(condition: Tensor|TensorLike, x: Tensor|TensorLike): Tensor {\n    this.throwIfDisposed();\n    return opHandler.where(condition, this, x);\n  }\n\n  // Unary ops.\n  neg<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.neg(this);\n  }\n  ceil<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.ceil(this);\n  }\n  floor<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.floor(this);\n  }\n  sign<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.sign(this);\n  }\n  exp<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.exp(this);\n  }\n  expm1<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.expm1(this);\n  }\n  log<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.log(this);\n  }\n  log1p<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.log1p(this);\n  }\n  sqrt<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.sqrt(this);\n  }\n  rsqrt<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.rsqrt(this);\n  }\n  square<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.square(this);\n  }\n  reciprocal<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.reciprocal(this);\n  }\n  abs<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.abs(this);\n  }\n  clipByValue(min: number, max: number): Tensor<R> {\n    this.throwIfDisposed();\n    return opHandler.clipByValue(this, min, max);\n  }\n  relu<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.relu(this);\n  }\n  elu<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.elu(this);\n  }\n  selu<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.selu(this);\n  }\n  leakyRelu(alpha = 0.2): Tensor<R> {\n    this.throwIfDisposed();\n    return opHandler.leakyRelu(this, alpha);\n  }\n  prelu(alpha: Tensor<R>|TensorLike): Tensor<R> {\n    this.throwIfDisposed();\n    return opHandler.prelu(this, alpha);\n  }\n  sigmoid<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.sigmoid(this);\n  }\n  logSigmoid<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.logSigmoid(this);\n  }\n  softplus<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.softplus(this);\n  }\n  zerosLike<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.zerosLike(this);\n  }\n  onesLike<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.onesLike(this);\n  }\n  sin<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.sin(this);\n  }\n  cos<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.cos(this);\n  }\n  tan<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.tan(this);\n  }\n  asin<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.asin(this);\n  }\n  acos<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.acos(this);\n  }\n  atan<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.atan(this);\n  }\n  sinh<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.sinh(this);\n  }\n  cosh<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.cosh(this);\n  }\n  tanh<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.tanh(this);\n  }\n  asinh<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.asinh(this);\n  }\n  acosh<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.acosh(this);\n  }\n  atanh<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.atanh(this);\n  }\n  erf<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.erf(this);\n  }\n  round<T extends Tensor>(this: T): T {\n    this.throwIfDisposed();\n    return opHandler.round(this);\n  }\n  step<T extends Tensor>(this: T, alpha = 0.0): T {\n    this.throwIfDisposed();\n    return opHandler.step(this, alpha);\n  }\n  softmax<T extends this>(this: T, dim = -1): T {\n    this.throwIfDisposed();\n    return opHandler.softmax(this, dim) as T;\n  }\n  logSoftmax<T extends this>(this: T, axis = -1): T {\n    this.throwIfDisposed();\n    return opHandler.logSoftmax(this, axis) as T;\n  }\n\n  // Image ops.\n  resizeBilinear<T extends Tensor3D|Tensor4D>(\n      this: T, newShape2D: [number, number], alignCorners = false): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.image.resizeBilinear(this, newShape2D, alignCorners);\n  }\n\n  resizeNearestNeighbor<T extends Tensor3D|Tensor4D>(\n      this: T, newShape2D: [number, number], alignCorners = false): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.image.resizeNearestNeighbor(\n        this, newShape2D, alignCorners);\n  }\n\n  // Convolutions.\n  conv1d<T extends Tensor2D|Tensor3D>(\n      this: T, filter: Tensor3D|TensorLike3D, stride: number,\n      pad: 'valid'|'same'|number, dataFormat: 'NWC'|'NCW' = 'NWC', dilation = 1,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.conv1d(\n        this, filter, stride, pad, dataFormat, dilation, dimRoundingMode);\n  }\n  conv2d<T extends Tensor3D|Tensor4D>(\n      this: T, filter: Tensor4D|TensorLike4D, strides: [number, number]|number,\n      pad: 'valid'|'same'|number, dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n      dilations: [number, number]|number = [1, 1],\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.conv2d(\n        this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n  }\n  conv2dTranspose<T extends Tensor3D|Tensor4D>(\n      this: T, filter: Tensor4D|TensorLike4D,\n      outputShape: [number, number, number, number]|[number, number, number],\n      strides: [number, number]|number, pad: 'valid'|'same'|number,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.conv2dTranspose(\n        this, filter, outputShape, strides, pad, dimRoundingMode);\n  }\n  depthwiseConv2D<T extends Tensor3D|Tensor4D>(\n      this: T, filter: Tensor4D|TensorLike4D, strides: [number, number]|number,\n      pad: 'valid'|'same'|number, dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n      dilations: [number, number]|number = [1, 1],\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.depthwiseConv2d(\n        this, filter, strides, pad, dataFormat, dilations, dimRoundingMode);\n  }\n\n  separableConv2d<T extends Tensor3D|Tensor4D>(\n      this: T|TensorLike, depthwiseFilter: Tensor4D|TensorLike4D,\n      pointwiseFilter: Tensor4D|TensorLike, strides: [number, number]|number,\n      pad: 'valid'|'same', dilation: [number, number]|number = [1, 1],\n      dataFormat: 'NHWC'|'NCHW' = 'NHWC'): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.separableConv2d(\n        this, depthwiseFilter, pointwiseFilter, strides, pad, dilation,\n        dataFormat);\n  }\n\n  // Pooling.\n  avgPool<T extends Tensor3D|Tensor4D>(\n      this: T, filterSize: [number, number]|number,\n      strides: [number, number]|number, pad: 'valid'|'same'|number,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.avgPool(this, filterSize, strides, pad, dimRoundingMode);\n  }\n  maxPool<T extends Tensor3D|Tensor4D>(\n      this: T, filterSize: [number, number]|number,\n      strides: [number, number]|number, pad: 'valid'|'same'|number,\n      dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.maxPool(this, filterSize, strides, pad, dimRoundingMode);\n  }\n  localResponseNormalization<T extends Tensor3D|Tensor4D>(\n      this: T, radius = 5, bias = 1, alpha = 1, beta = 0.5): T {\n    return opHandler.localResponseNormalization(\n        this, radius, bias, alpha, beta);\n  }\n  pool<T extends Tensor3D|Tensor4D>(\n      this: T, windowShape: [number, number]|number, poolingType: 'max'|'avg',\n      padding: 'valid'|'same'|number, dilationRate?: [number, number]|number,\n      strides?: [number, number]|number): T {\n    (this as Tensor).throwIfDisposed();\n    return opHandler.pool(\n        this, windowShape, poolingType, padding, dilationRate, strides);\n  }\n\n  variable(trainable = true, name?: string, dtype?: DataType): Variable<R> {\n    this.throwIfDisposed();\n    return Variable.variable(this, trainable, name, dtype);\n  }\n\n  unsortedSegmentSum<T extends Tensor>(\n      this: T, segmentIds: Tensor1D|TensorLike1D, numSegments: number): T {\n    this.throwIfDisposed();\n    return opHandler.unsortedSegmentSum(this, segmentIds, numSegments);\n  }\n\n  batchToSpaceND<T extends Tensor>(\n      this: T, blockShape: number[], crops: number[][]): T {\n    this.throwIfDisposed();\n    return opHandler.batchToSpaceND(this, blockShape, crops);\n  }\n\n  spaceToBatchND<T extends Tensor>(\n      this: T, blockShape: number[], paddings: number[][]): T {\n    this.throwIfDisposed();\n    return opHandler.spaceToBatchND(this, blockShape, paddings);\n  }\n\n  topk<T extends Tensor>(this: T, k = 1, sorted = true):\n      {values: T, indices: T} {\n    this.throwIfDisposed();\n    return opHandler.topk(this, k, sorted);\n  }\n\n  stridedSlice<T extends Tensor>(\n      this: T, begin: number[], end: number[], strides: number[], beginMask = 0,\n      endMask = 0): T {\n    this.throwIfDisposed();\n    return opHandler.stridedSlice(\n        this, begin, end, strides, beginMask, endMask);\n  }\n\n  depthToSpace(this: Tensor4D, blockSize: number, dataFormat: 'NHWC'|'NCHW'):\n      Tensor4D {\n    this.throwIfDisposed();\n    return opHandler.depthToSpace(this, blockSize, dataFormat);\n  }\n\n  fft(this: Tensor): Tensor {\n    this.throwIfDisposed();\n    return opHandler.spectral.fft(this);\n  }\n\n  ifft(this: Tensor): Tensor {\n    this.throwIfDisposed();\n    return opHandler.spectral.ifft(this);\n  }\n}\nObject.defineProperty(Tensor, Symbol.hasInstance, {\n  value: (instance: Tensor) => {\n    return !!instance && instance.shape != null && instance.dtype != null;\n  }\n});\n\nexport interface NumericTensor<R extends Rank = Rank> extends Tensor<R> {\n  dtype: NumericDataType;\n  data(): Promise<TypedArray>;\n  dataSync(): TypedArray;\n}\n\nexport interface StringTensor<R extends Rank = Rank> extends Tensor<R> {\n  dtype: 'string';\n  dataSync(): string[];\n  data(): Promise<string[]>;\n}\n\n/** @doclink Tensor */\nexport type Scalar = Tensor<Rank.R0>;\n/** @doclink Tensor */\nexport type Tensor1D = Tensor<Rank.R1>;\n/** @doclink Tensor */\nexport type Tensor2D = Tensor<Rank.R2>;\n/** @doclink Tensor */\nexport type Tensor3D = Tensor<Rank.R3>;\n/** @doclink Tensor */\nexport type Tensor4D = Tensor<Rank.R4>;\n/** @doclink Tensor */\nexport type Tensor5D = Tensor<Rank.R5>;\n/** @doclink Tensor */\nexport type Tensor6D = Tensor<Rank.R6>;\n\n/**\n * A mutable `tf.Tensor`, useful for persisting state, e.g. for training.\n */\n/** @doc {heading: 'Tensors', subheading: 'Classes'} */\nexport class Variable<R extends Rank = Rank> extends Tensor<R> {\n  name: string;\n\n  /**\n   * Private constructor since we cannot add logic before calling `super()`.\n   * Instead, we expose static `Variable.variable` method below, which will be\n   * added to global namespace.\n   */\n  private constructor(\n      initialValue: Tensor<R>, public trainable = true, name?: string) {\n    super(\n        initialValue.shape, initialValue.dtype, null /* values */,\n        initialValue.dataId);\n    this.name = name;\n    if (this.name == null) {\n      this.name = trackerFn().nextVariableId().toString();\n    }\n    try {\n      trackerFn().registerVariable(this);\n    } catch (ex) {\n      trackerFn().disposeTensor(this);\n      throw ex;\n    }\n  }\n\n  /**\n   * Creates a new variable with the provided initial value.\n   * ```js\n   * const x = tf.variable(tf.tensor([1, 2, 3]));\n   * x.assign(tf.tensor([4, 5, 6]));\n   *\n   * x.print();\n   * ```\n   *\n   * @param initialValue Initial value for the tensor.\n   * @param trainable If true, optimizers are allowed to update it.\n   * @param name Name of the variable. Defaults to a unique id.\n   * @param dtype If set, initialValue will be converted to the given type.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Creation'} */\n  static variable<R extends Rank>(\n      initialValue: Tensor<R>, trainable = true, name?: string,\n      dtype?: DataType): Variable<R> {\n    if (dtype != null && dtype !== initialValue.dtype) {\n      initialValue = initialValue.asType(dtype) as Tensor<R>;\n    }\n    return new Variable(initialValue, trainable, name);\n  }\n\n  /**\n   * Assign a new `tf.Tensor` to this variable. The new `tf.Tensor` must have\n   * the same shape and dtype as the old `tf.Tensor`.\n   *\n   * @param newValue New tensor to be assigned to this variable.\n   */\n  /** @doc {heading: 'Tensors', subheading: 'Classes'} */\n  assign(newValue: Tensor<R>): void {\n    if (newValue.dtype !== this.dtype) {\n      throw new Error(\n          `dtype of the new value (${newValue.dtype}) and ` +\n          `previous value (${this.dtype}) must match`);\n    }\n    if (!util.arraysEqual(newValue.shape, this.shape)) {\n      throw new Error(\n          `shape of the new value (${newValue.shape}) and ` +\n          `previous value (${this.shape}) must match`);\n    }\n    trackerFn().disposeTensor(this);\n    this.dataId = newValue.dataId;\n    trackerFn().registerTensor(this);\n  }\n}\nObject.defineProperty(Variable, Symbol.hasInstance, {\n  value: (instance: Variable) => {\n    return instance instanceof Tensor && instance.assign != null &&\n        instance.assign instanceof Function;\n  }\n});\n\nconst variable = Variable.variable;\nexport {variable};\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/** @docalias number[] */\nexport interface ShapeMap {\n  R0: number[];\n  R1: [number];\n  R2: [number, number];\n  R3: [number, number, number];\n  R4: [number, number, number, number];\n  R5: [number, number, number, number, number];\n  R6: [number, number, number, number, number, number];\n}\n\nexport interface DataTypeMap {\n  float32: Float32Array;\n  int32: Int32Array;\n  bool: Uint8Array;\n  complex64: Float32Array;\n  string: string[];\n}\n\nexport interface SingleValueMap {\n  bool: boolean;\n  int32: number;\n  float32: number;\n  complex64: number;\n  string: string;\n}\n\n/** @docalias 'float32'|'int32'|'bool'|'complex64'|'string' */\nexport type DataType = keyof DataTypeMap;\nexport type NumericDataType = 'float32'|'int32'|'bool'|'complex64';\nexport type TypedArray = Float32Array|Int32Array|Uint8Array;\nexport type DataValues = DataTypeMap[DataType];\n\nexport enum Rank {\n  R0 = 'R0',\n  R1 = 'R1',\n  R2 = 'R2',\n  R3 = 'R3',\n  R4 = 'R4',\n  R5 = 'R5',\n  R6 = 'R6'\n}\n\nexport type FlatVector = boolean[]|number[]|TypedArray;\nexport type RegularArray<T> =\n    T[]|T[][]|T[][][]|T[][][][]|T[][][][][]|T[][][][][][];\n\n// tslint:disable-next-line:no-any\nexport interface RecursiveArray<T extends any> {\n  [index: number]: T|RecursiveArray<T>;\n}\n\n// Looks for upcasting types. Used, for example, in operations with mixed dtype\n// inputs.\nenum UpcastInt32AndMap {\n  'float32' = 'float32',\n  'int32' = 'int32',\n  'bool' = 'int32',\n  'complex64' = 'complex64'\n}\n\nenum UpcastBoolAndMap {\n  'float32' = 'float32',\n  'int32' = 'int32',\n  'bool' = 'bool',\n  'complex64' = 'complex64'\n}\n\nenum UpcastFloat32AndMap {\n  'float32' = 'float32',\n  'int32' = 'float32',\n  'bool' = 'float32',\n  'complex64' = 'complex64'\n}\n\nenum UpcastComplex64AndMap {\n  'float32' = 'complex64',\n  'int32' = 'complex64',\n  'bool' = 'complex64',\n  'complex64' = 'complex64'\n}\n\nconst upcastTypeMap = {\n  'float32': UpcastFloat32AndMap,\n  'int32': UpcastInt32AndMap,\n  'bool': UpcastBoolAndMap,\n  'complex64': UpcastComplex64AndMap\n};\n\nexport function upcastType(typeA: DataType, typeB: DataType): DataType {\n  if (typeA === 'string' || typeB === 'string') {\n    if (typeA === 'string' && typeB === 'string') {\n      return 'string';\n    }\n    throw new Error(`Can not upcast ${typeA} with ${typeB}`);\n  }\n  return upcastTypeMap[typeA][typeB];\n}\n\n/** Returns the output type after summation. */\nexport function sumOutType(type: DataType): DataType {\n  return upcastType(type, 'int32');\n}\n\n/** @docalias TypedArray|Array */\nexport type TensorLike =\n    TypedArray|number|boolean|string|RegularArray<number|number[]|TypedArray>|\n    RegularArray<boolean>|RegularArray<string>;\n/** @docalias TypedArray|Array */\nexport type TensorLike1D = TypedArray|number[]|boolean[]|string[];\n/** @docalias TypedArray|Array */\nexport type TensorLike2D =\n    TypedArray|number[]|number[][]|boolean[]|boolean[][]|string[]|string[][];\n/** @docalias TypedArray|Array */\nexport type TensorLike3D = TypedArray|number[]|number[][][]|boolean[]|\n    boolean[][][]|string[]|string[][][];\n/** @docalias TypedArray|Array */\nexport type TensorLike4D = TypedArray|number[]|number[][][][]|boolean[]|\n    boolean[][][][]|string[]|string[][][][];\n/** @docalias TypedArray|Array */\nexport type TensorLike5D = TypedArray|number[]|number[][][][][]|boolean[]|\n    boolean[][][][][]|string[]|string[][][][][];\n/** @docalias TypedArray|Array */\nexport type TensorLike6D = TypedArray|number[]|number[][][][][][]|boolean[]|\n    boolean[][][][][][]|string[]|string[][][][][][];\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from './tensor';\nimport {NamedTensorMap, TensorContainer, TensorContainerArray} from './tensor_types';\nimport {upcastType} from './types';\nimport {assert} from './util';\n\nexport function makeTypesMatch<T extends Tensor>(a: T, b: T): [T, T] {\n  if (a.dtype === b.dtype) {\n    return [a, b];\n  }\n  const dtype = upcastType(a.dtype, b.dtype);\n  return [a.cast(dtype), b.cast(dtype)];\n}\n\nexport function assertTypesMatch(a: Tensor, b: Tensor): void {\n  assert(\n      a.dtype === b.dtype,\n      `The dtypes of the first(${a.dtype}) and` +\n          ` second(${b.dtype}) input must match`);\n}\n\nexport function isTensorInList(tensor: Tensor, tensorList: Tensor[]): boolean {\n  for (let i = 0; i < tensorList.length; i++) {\n    if (tensorList[i].id === tensor.id) {\n      return true;\n    }\n  }\n  return false;\n}\n\nexport function flattenNameArrayMap(\n    nameArrayMap: Tensor|NamedTensorMap, keys?: string[]): Tensor[] {\n  const xs: Tensor[] = [];\n  if (nameArrayMap instanceof Tensor) {\n    xs.push(nameArrayMap);\n  } else {\n    const xMap = nameArrayMap as {[xName: string]: Tensor};\n    for (let i = 0; i < keys.length; i++) {\n      xs.push(xMap[keys[i]]);\n    }\n  }\n  return xs;\n}\n\nexport function unflattenToNameArrayMap(\n    keys: string[], flatArrays: Tensor[]): NamedTensorMap {\n  if (keys.length !== flatArrays.length) {\n    throw new Error(\n        `Cannot unflatten Tensor[], keys and arrays are not of same length.`);\n  }\n  const result: NamedTensorMap = {};\n  for (let i = 0; i < keys.length; i++) {\n    result[keys[i]] = flatArrays[i];\n  }\n  return result;\n}\n\n/**\n * Extracts any `Tensor`s found within the provided object.\n *\n * @param container an object that may be a `Tensor` or may directly contain\n *   `Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. In general it\n *   is safe to pass any object here, except that `Promise`s are not\n *   supported.\n * @returns An array of `Tensors` found within the passed object. If the\n *   argument is simply a `Tensor', a list containing that `Tensor` is\n *   returned. If the object is not a `Tensor` or does not\n *   contain `Tensors`, an empty list is returned.\n */\nexport function getTensorsInContainer(result: TensorContainer): Tensor[] {\n  const list: Tensor[] = [];\n  const seen = new Set<{}|void>();\n  walkTensorContainer(result, list, seen);\n  return list;\n}\n\nfunction walkTensorContainer(\n    container: TensorContainer, list: Tensor[], seen: Set<{}|void>): void {\n  if (container == null) {\n    return;\n  }\n  if (container instanceof Tensor) {\n    list.push(container);\n    return;\n  }\n  if (!isIterable(container)) {\n    return;\n  }\n  // Iteration over keys works also for arrays.\n  const iterable = container as TensorContainerArray;\n  for (const k in iterable) {\n    const val = iterable[k];\n    if (!seen.has(val)) {\n      seen.add(val);\n      walkTensorContainer(val, list, seen);\n    }\n  }\n}\n\n// tslint:disable-next-line:no-any\nfunction isIterable(obj: any): boolean {\n  return Array.isArray(obj) || typeof obj === 'object';\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {BackendTimingInfo, DataMover, KernelBackend} from './kernels/backend';\nimport {Profiler} from './profiler';\nimport {backpropagateGradients, getFilteredNodesXToY, NamedGradientMap, TapeNode} from './tape';\nimport {DataId, Tensor, Tensor3D, Variable} from './tensor';\nimport {NamedTensorMap, NamedVariableMap, TensorContainer} from './tensor_types';\nimport {getTensorsInContainer, isTensorInList} from './tensor_util';\nimport {DataType, DataValues} from './types';\nimport * as util from './util';\nimport {bytesFromStringArray, makeOnesTypedArray, now, sizeFromShape} from './util';\n\n/**\n * A function that computes an output. The save function is for saving tensors\n * computed in the forward pass, that we need in the backward pass.\n */\nexport type ForwardFunc<T> =\n    (backend: KernelBackend, save?: <S extends Tensor>(tensor: S) => S) => T;\n\n/**\n * @docalias (a: Tensor, b: Tensor,...) => {\n * value: Tensor, * gradFunc: (dy: Tensor) => Tensor | Tensor[] * }\n */\nexport type CustomGradientFunc<T extends Tensor> = (...args: Tensor[]) => {\n  value: T, gradFunc: (dy: T) => Tensor | Tensor[];\n};\n\nexport type MemoryInfo = {\n  numTensors: number; numDataBuffers: number; numBytes: number;\n  unreliable?: boolean; reasons: string[];\n};\n\ntype KernelProfile = {\n  name: string; bytesAdded: number; totalBytesSnapshot: number;\n  tensorsAdded: number;\n  totalTensorsSnapshot: number;\n  inputShapes: number[][];\n  outputShape: number[] | number[][];\n};\n\nexport type ProfileInfo = {\n  newBytes: number; newTensors: number; peakBytes: number;\n  kernels: KernelProfile[];\n  result: TensorContainer;\n};\n\nexport interface TimingInfo extends BackendTimingInfo {\n  wallMs: number;\n}\n\n/** @docalias Function */\nexport type ScopeFn<T extends TensorContainer> = () => T;\n\nexport interface TensorManager {\n  registerTensor(a: Tensor): void;\n  registerVariable(v: Variable): void;\n  disposeTensor(a: Tensor): void;\n  memory(): {numDataBuffers: number; numBytes: number;};\n}\n\ninterface ScopeState {\n  track: Tensor[];\n  name: string;\n}\n\nexport class Engine implements TensorManager, DataMover {\n  // Public since optimizers will use it.\n  registeredVariables: NamedVariableMap = {};\n\n  private nextTapeNodeId = 0;\n  private numBytes = 0;\n  private numTensors = 0;\n  private numStringTensors = 0;\n  private numDataBuffers = 0;\n\n  private profiling = false;\n  private activeProfile: ProfileInfo;\n\n  private activeTape: TapeNode[];\n  private gradientScopeCount = 0;\n  private customGradientDepth = 0;\n\n  // Keep Tensors that parallel the tapes.\n  private activeScope: ScopeState;\n  private scopeStack: ScopeState[] = [];\n  private keepTensors: Set<number> = new Set();\n  private profiler: Profiler;\n\n  private tensorInfo = new WeakMap<DataId, {\n    backend: KernelBackend,\n    bytes: number,\n    dtype: DataType,\n    shape: number[],\n    refCount: number\n  }>();\n\n  constructor(\n      public backend: KernelBackend, public safeMode: boolean,\n      private debugMode: () => boolean) {\n    this.profiler = new Profiler(backend);\n    this.activeProfile =\n        {newBytes: 0, newTensors: 0, peakBytes: 0, kernels: [], result: null};\n  }\n\n  moveData(dataId: DataId) {\n    this.write(dataId, this.readSync(dataId));\n  }\n\n  tidy<T extends TensorContainer>(\n      nameOrFn: string|ScopeFn<T>, fn?: ScopeFn<T>, gradMode = false): T {\n    // gradMode Primarily for internal use during backprop\n    //          If true, will start a tape if it is the outermost tidy.\n\n    let name: string = null;\n    if (fn == null) {\n      // Called with only 1 argument.\n      if (typeof nameOrFn !== 'function') {\n        throw new Error('Please provide a function to tidy()');\n      }\n      fn = nameOrFn;\n    } else {\n      // Called with 2 arguments.\n      if (typeof nameOrFn !== 'string' && !(nameOrFn instanceof String)) {\n        throw new Error(\n            'When calling with two arguments, the first argument ' +\n            'to tidy() must be a string');\n      }\n      if (typeof fn !== 'function') {\n        throw new Error(\n            'When calling with two arguments, the 2nd argument ' +\n            'to tidy() must be a function');\n      }\n      name = nameOrFn as string;\n      // TODO(nsthorat,smilkov): Do operation logging and performance\n      // profiling.\n    }\n    let result: T;\n    return this.scopedRun(\n        () => this.startScope(name, gradMode),\n        () => this.endScope(result, gradMode), () => {\n          result = fn();\n          if (result instanceof Promise) {\n            console.error('Cannot return a Promise inside of tidy.');\n          }\n          return result;\n        });\n  }\n\n  private scopedRun<T>(start: () => void, end: () => void, f: () => T): T {\n    start();\n    try {\n      const res = f();\n      end();\n      return res;\n    } catch (ex) {\n      end();\n      throw ex;\n    }\n  }\n\n  private static nextTensorId = 0;\n  nextTensorId(): number {\n    return Engine.nextTensorId++;\n  }\n\n  private static nextVariableId = 0;\n  nextVariableId(): number {\n    return Engine.nextVariableId++;\n  }\n\n  runKernel<T extends Tensor|Tensor[], I extends NamedTensorMap>(\n      forwardFunc: ForwardFunc<T>,\n      inputs: I,\n      backwardsFunc?: (dy: T, saved: Tensor[]) => {[P in keyof I]: () => I[P]},\n      ): T {\n    let result: T;\n    const saved: Tensor[] = [];\n    const saveFunc = <T extends Tensor>(x: T): T => {\n      saved.push(x);\n      return x;\n    };\n    const scopeName = this.activeScope.name;\n    const startingBytecount = this.numBytes;\n    const startingNumTensors = this.numTensors;\n\n    // Stop recording to a tape when running a kernel.\n    this.scopedRun(\n        () => this.customGradientDepth++, () => this.customGradientDepth--,\n        () => {\n          if (!this.debugMode()) {\n            result = forwardFunc(this.backend, saveFunc);\n          } else {\n            result = this.profiler.profileKernel(\n                scopeName, () => forwardFunc(this.backend, saveFunc));\n          }\n        });\n\n    if (this.shouldRecord()) {\n      const tapeNode: TapeNode = {\n        id: this.nextTapeNodeId++,\n        name: scopeName,\n        inputs,\n        outputs: Array.isArray(result) ? result : [result] as Tensor[]\n      };\n      if (backwardsFunc != null) {\n        tapeNode.gradient =\n            ((dy: T) => backwardsFunc(dy, saved)) as (dy: Tensor) =>\n                NamedGradientMap;\n      }\n      this.activeTape.push(tapeNode);\n    }\n\n    if (this.profiling) {\n      this.activeProfile.kernels.push({\n        name: scopeName,\n        bytesAdded: this.numBytes - startingBytecount,\n        totalBytesSnapshot: this.numBytes,\n        tensorsAdded: this.numTensors - startingNumTensors,\n        totalTensorsSnapshot: this.numTensors,\n        inputShapes: Object.keys(inputs).map(key => inputs[key].shape),\n        outputShape: Array.isArray(result) ?\n            (result as Tensor[]).map(item => (item as Tensor).shape) :\n            (result as Tensor).shape\n      });\n    }\n\n    return result;\n  }\n\n  // TensorManager implementation.\n\n  registerTensor(a: Tensor|Variable): void {\n    const refCount = this.tensorInfo.has(a.dataId) ?\n        this.tensorInfo.get(a.dataId).refCount :\n        0;\n    this.numTensors++;\n    if (a.dtype === 'string') {\n      this.numStringTensors++;\n    }\n    if (refCount === 0) {\n      this.numDataBuffers++;\n\n      // Bytes for complex numbers are counted by their components. Bytes for\n      // string tensors are counted when writing values.\n      let bytes = 0;\n      if (a.dtype !== 'complex64' && a.dtype !== 'string') {\n        bytes = util.sizeFromShape(a.shape) * util.bytesPerElement(a.dtype);\n      }\n      this.tensorInfo.set(a.dataId, {\n        backend: this.backend,\n        dtype: a.dtype,\n        shape: a.shape,\n        bytes,\n        refCount: 0\n      });\n      this.numBytes += bytes;\n      this.backend.register(a.dataId, a.shape, a.dtype);\n    }\n    this.tensorInfo.get(a.dataId).refCount++;\n    if (!(a instanceof Variable)) {\n      this.track(a);\n    }\n  }\n\n  registerVariable(v: Variable) {\n    if (this.registeredVariables[v.name] != null) {\n      throw new Error(`Variable with name ${v.name} was already registered`);\n    }\n    this.registeredVariables[v.name] = v;\n  }\n\n  disposeTensor(a: Tensor): void {\n    if (!this.tensorInfo.has(a.dataId)) {\n      return;\n    }\n    if (this.keepTensors.has(a.id)) {\n      this.keepTensors.delete(a.id);\n    }\n    this.numTensors--;\n    if (a.dtype === 'string') {\n      this.numStringTensors--;\n    }\n    const info = this.tensorInfo.get(a.dataId);\n    const refCount = info.refCount;\n    if (refCount <= 1) {\n      // Don't count bytes for complex numbers as they are counted by their\n      // components.\n      if (a.dtype !== 'complex64') {\n        this.numBytes -= info.bytes;\n      }\n      this.numDataBuffers--;\n      info.backend.disposeData(a.dataId);\n      this.tensorInfo.delete(a.dataId);\n    } else {\n      this.tensorInfo.get(a.dataId).refCount--;\n    }\n    // TODO(nsthorat): Construct an error and save the stack trace for\n    // debugging when in debug mode. Creating a stack trace is too expensive\n    // to do unconditionally.\n  }\n\n  disposeVariables(): void {\n    for (const varName in this.registeredVariables) {\n      const v = this.registeredVariables[varName];\n      this.disposeTensor(v);\n      delete this.registeredVariables[varName];\n    }\n  }\n\n  memory(): MemoryInfo {\n    const info = this.backend.memory() as MemoryInfo;\n    info.numTensors = this.numTensors;\n    info.numDataBuffers = this.numDataBuffers;\n    info.numBytes = this.numBytes;\n    if (this.numStringTensors > 0) {\n      info.unreliable = true;\n      if (info.reasons == null) {\n        info.reasons = [];\n      }\n      info.reasons.push(\n          'Memory usage by string tensors is approximate ' +\n          '(2 bytes per character)');\n    }\n    return info;\n  }\n\n  async profile(query: () => TensorContainer): Promise<ProfileInfo> {\n    this.profiling = true;\n\n    const startBytes = this.numBytes;\n    const startNumTensors = this.numTensors;\n\n    this.activeProfile.kernels = [];\n    this.activeProfile.result = query();\n\n    this.profiling = false;\n\n    this.activeProfile.peakBytes =\n        Math.max(...this.activeProfile.kernels.map(d => d.totalBytesSnapshot));\n    this.activeProfile.newBytes = this.numBytes - startBytes;\n    this.activeProfile.newTensors = this.numTensors - startNumTensors;\n    return this.activeProfile;\n  }\n\n  private shouldRecord(): boolean {\n    return this.activeTape != null && this.customGradientDepth === 0;\n  }\n\n  private addTapeNode(\n      inputs: Tensor[], result: Tensor,\n      gradientsFunc: (dy: Tensor) => Tensor[]): void {\n    const inputsMap: NamedTensorMap = {};\n    inputs.forEach((input, idx) => {\n      inputsMap[idx] = input;\n    });\n\n    const gradient = (dy: Tensor) => {\n      const res = gradientsFunc(dy);\n      const resMap: NamedGradientMap = {};\n      res.forEach((r, idx) => {\n        resMap[idx] = () => r;\n      });\n      return resMap;\n    };\n\n    const tapeNode: TapeNode = {\n      id: this.nextTapeNodeId++,\n      name: this.activeScope.name,\n      inputs: inputsMap,\n      outputs: [result],\n      gradient\n    };\n    this.activeTape.push(tapeNode);\n  }\n\n  keep<T extends Tensor>(result: T): T {\n    if (this.scopeStack.length === 1 && this.safeMode) {\n      throw new Error(\n          'Safe mode is ON. Enclose all tensor operations inside tf.tidy(): ' +\n          'tf.tidy(() => {...}) to avoid memory leaks.');\n    }\n    this.keepTensors.add(result.id);\n    return result;\n  }\n\n  /**\n   * Start a scope. Use this with endScope() to achieve the same functionality\n   * as scope() without the need for a function closure.\n   */\n  startScope(name?: string, gradientsMode = false) {\n    if (gradientsMode && this.gradientScopeCount === 0) {\n      this.activeTape = [];\n    }\n    if (gradientsMode) {\n      this.gradientScopeCount++;\n    }\n\n    const scopeInfo: ScopeState = {track: [], name: 'unnamed scope'};\n    if (name) {\n      scopeInfo.name = name;\n    }\n    this.scopeStack.push(scopeInfo);\n    this.activeScope = scopeInfo;\n  }\n\n  /**\n   * End a scope. Use this with startScope() to achieve the same functionality\n   * as scope() without the need for a function closure.\n   */\n  endScope(result?: TensorContainer, gradientsMode = false) {\n    if (gradientsMode) {\n      this.gradientScopeCount--;\n      if (this.gradientScopeCount === 0) {\n        this.activeTape = null;\n      }\n    }\n\n    const tensorsToKeep = new Set(this.keepTensors);\n\n    const tensorsToTrackInParent = getTensorsInContainer(result);\n    tensorsToTrackInParent.forEach(tensor => tensorsToKeep.add(tensor.id));\n\n    // Dispose the arrays tracked in this scope.\n    for (let i = 0; i < this.activeScope.track.length; i++) {\n      const tensor = this.activeScope.track[i];\n      if (tensorsToKeep.has(tensor.id)) {\n        continue;\n      }\n\n      if (this.activeTape != null) {\n        tensorsToTrackInParent.push(tensor);\n      } else {\n        tensor.dispose();\n      }\n    }\n\n    const oldScope = this.scopeStack.pop();\n    this.activeScope = this.scopeStack.length === 0 ?\n        null :\n        this.scopeStack[this.scopeStack.length - 1];\n\n    // Track the current result in the parent scope.\n    tensorsToTrackInParent.forEach(tensor => {\n      // Only track the tensor if was allocated in the inner scope and is not\n      // globally kept.\n      if (!this.keepTensors.has(tensor.id) &&\n          isTensorInList(tensor, oldScope.track)) {\n        this.track(tensor);\n      }\n    });\n  }\n\n  /**\n   * Returns gradients of `f` with respect to each of the `xs`. The gradients\n   * returned are of the same length as `xs`, but some might be null if `f` was\n   * not a function of that `x`. It also takes optional dy to multiply the\n   * gradient, which defaults to `1`.\n   */\n  gradients<T extends Tensor>(\n      f: () => T, xs: Tensor[], dy?: T,\n      allowNoGradients = false): {value: T, grads: Tensor[]} {\n    util.assert(xs.length > 0, 'gradients() received an empty list of xs.');\n    if (dy != null && dy.dtype !== 'float32') {\n      throw new Error(`dy must have 'float32' dtype, but has '${dy.dtype}'`);\n    }\n\n    return this.tidy('gradients', () => {\n      const y = f();\n      util.assert(\n          y instanceof Tensor,\n          'The result y returned by f() must be a tensor.');\n      // Filter out the nodes that don't connect x => y.\n      const filteredTape = getFilteredNodesXToY(this.activeTape, xs, y);\n      if (!allowNoGradients && filteredTape.length === 0 && xs.length > 0) {\n        throw new Error(\n            'Cannot compute gradient of y=f(x) with respect to x. Make sure ' +\n            'that the f you passed encloses all operations that lead from x ' +\n            'to y.');\n      }\n\n      const accumulatedGradientMap: {[tensorId: number]: Tensor} = {};\n      accumulatedGradientMap[y.id] = (dy == null) ? ones(y.shape) : dy;\n\n      // Backprop gradients through the filtered nodes.\n      backpropagateGradients(accumulatedGradientMap, filteredTape);\n\n      const grads = xs.map(x => accumulatedGradientMap[x.id]);\n      return {value: y, grads};\n    }, true /* gradientsMode */);\n  }\n\n  customGrad<T extends Tensor>(f: CustomGradientFunc<T>):\n      (...args: Tensor[]) => T {\n    util.assert(\n        util.isFunction(f),\n        'The f passed in customGrad(f) must be a function.');\n    return (...inputs: Tensor[]): T => {\n      util.assert(\n          inputs.every(t => t instanceof Tensor),\n          'The args passed in customGrad(f)(x1, x2,...) must all be tensors');\n\n      let gradientsFunc: (dy: T) => Tensor | Tensor[];\n      let result: T;\n      this.scopedRun(\n          () => this.customGradientDepth++, () => this.customGradientDepth--,\n          () => {\n            const gradientsMode = true;\n            result = this.tidy(f.name, () => {\n              const {value, gradFunc} = f(...inputs);\n              util.assert(\n                  value instanceof Tensor,\n                  'The function f passed in customGrad(f) must return an ' +\n                      'object where `obj.value` is a tensor');\n              util.assert(\n                  util.isFunction(gradFunc),\n                  'The function f passed in customGrad(f) must return an ' +\n                      'object where `obj.gradFunc` is a function.');\n              gradientsFunc = gradFunc;\n              return value;\n            }, gradientsMode);\n          });\n\n      if (this.shouldRecord()) {\n        const gradFunc = (dy: T): Tensor[] => {\n          const res = gradientsFunc(dy);\n          const grads: Tensor[] = Array.isArray(res) ? res : [res];\n          util.assert(\n              grads.length === inputs.length,\n              'The function f passed in customGrad(f) must return an object ' +\n                  'where `obj.gradFunc` is a function that returns the same ' +\n                  'number of tensors as inputs passed to f(...).');\n          util.assert(\n              grads.every(t => t instanceof Tensor),\n              'The function f passed in customGrad(f) must return an object ' +\n                  'where `obj.gradFunc` is a function that returns a list of ' +\n                  'only tensors.');\n          return grads;\n        };\n        this.addTapeNode(inputs, result, gradFunc);\n      }\n      return result;\n    };\n  }\n\n  // Forwarding to backend.\n  write(dataId: DataId, values: DataValues): void {\n    const info = this.tensorInfo.get(dataId);\n    // Bytes for string tensors are counted when writing.\n    if (info.dtype === 'string') {\n      const newBytes = bytesFromStringArray(values as string[]);\n      this.numBytes += newBytes - info.bytes;\n      info.bytes = newBytes;\n    }\n\n    if (this.backend !== info.backend) {\n      // Delete the tensor from the old backend and move it to the new backend.\n      info.backend.disposeData(dataId);\n      info.backend = this.backend;\n      this.backend.register(dataId, info.shape, info.dtype);\n    }\n    this.backend.write(dataId, values);\n  }\n  readSync(dataId: DataId): DataValues {\n    // Route the read to the correct backend.\n    const info = this.tensorInfo.get(dataId);\n    return info.backend.readSync(dataId);\n  }\n  read(dataId: DataId): Promise<DataValues> {\n    // Route the read to the correct backend.\n    const info = this.tensorInfo.get(dataId);\n    return info.backend.read(dataId);\n  }\n  fromPixels(\n      pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement,\n      numChannels: number): Tensor3D {\n    return this.backend.fromPixels(pixels, numChannels);\n  }\n  async time(query: () => void): Promise<TimingInfo> {\n    const start = now();\n    const timingInfo = await this.backend.time(query) as TimingInfo;\n    timingInfo.wallMs = now() - start;\n    return timingInfo;\n  }\n\n  /**\n   * Tracks a Tensor in the current scope to be automatically cleaned up\n   * when the current scope ends, and returns the value.\n   *\n   * @param result The Tensor to track in the current scope.\n   */\n  private track<T extends Tensor>(result: T): T {\n    if (this.scopeStack.length === 1 && this.safeMode) {\n      throw new Error(\n          'Safe mode is ON. Enclose all tensor operations inside tf.tidy(): ' +\n          'tf.tidy(() => {op();...}); to avoid memory leaks.');\n    }\n    if (this.activeScope != null) {\n      this.activeScope.track.push(result);\n    }\n    return result;\n  }\n}\n\nfunction ones(shape: number[]): Tensor {\n  const values = makeOnesTypedArray(sizeFromShape(shape), 'float32');\n  return Tensor.make(shape, {values});\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getWebGLContext} from './canvas_util';\n\nexport interface Features {\n  // Whether to enable debug mode.\n  'DEBUG'?: boolean;\n  // Whether we are in a browser (as versus, say, node.js) environment.\n  'IS_BROWSER'?: boolean;\n  // Whether we are in the Node.js environment.\n  'IS_NODE'?: boolean;\n  // Whether packed WebGL kernels lazily unpack their outputs.\n  'WEBGL_LAZILY_UNPACK'?: boolean;\n  // Whether the WebGL backend will sometimes forward ops to the CPU.\n  'WEBGL_CPU_FORWARD'?: boolean;\n  // Whether to turn all packing related flags on.\n  'WEBGL_PACK'?: boolean;\n  // Whether we will pack the batchnormalization op.\n  'WEBGL_PACK_BATCHNORMALIZATION'?: boolean;\n  // Whether we will pack the clipping op.\n  'WEBGL_PACK_CLIP'?: boolean;\n  // Whether we pack the depthwise convolution op.\n  'WEBGL_PACK_DEPTHWISECONV'?: boolean;\n  // Whether we will use the im2col algorithm to speed up convolutions.\n  'WEBGL_CONV_IM2COL'?: boolean;\n  // Whether we will perform memory paging.\n  'WEBGL_PAGING_ENABLED'?: boolean;\n  // The maximum texture dimension.\n  'WEBGL_MAX_TEXTURE_SIZE'?: number;\n  // The disjoint_query_timer extension version.\n  // 0: disabled, 1: EXT_disjoint_timer_query, 2:\n  // EXT_disjoint_timer_query_webgl2.\n  // In Firefox with WebGL 2.0,\n  // EXT_disjoint_timer_query_webgl2 is not available, so we must use the\n  // WebGL 1.0 extension.\n  'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'?: number;\n  // Whether the timer object from the disjoint_query_timer extension gives\n  // timing information that is reliable.\n  'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE'?: boolean;\n  // 0: No WebGL, 1: WebGL 1.0, 2: WebGL 2.0.\n  'WEBGL_VERSION'?: number;\n  // True if WebGL is supported.\n  'HAS_WEBGL'?: boolean;\n  // Whether rendering to float32 textures is enabled. If disabled, renders to\n  // float16 textures.\n  'WEBGL_RENDER_FLOAT32_ENABLED'?: boolean;\n  // Whether downloading float textures is enabled. If disabled, uses IEEE 754\n  // encoding of the float32 values to 4 uint8 when downloading.\n  'WEBGL_DOWNLOAD_FLOAT_ENABLED'?: boolean;\n  // Whether the fence API is available.\n  'WEBGL_FENCE_API_ENABLED'?: boolean;\n  // Tensors with size <= than this will be uploaded as uniforms, not textures.\n  'WEBGL_SIZE_UPLOAD_UNIFORM'?: number;\n  'BACKEND'?: string;\n  // Test precision for unit tests. This is decreased when we can't render\n  // float32 textures.\n  'TEST_EPSILON'?: number;\n  'IS_CHROME'?: boolean;\n  // True if running unit tests.\n  'IS_TEST'?: boolean;\n  // Smallest positive value used to make ops like division and log numerically\n  // stable.\n  'EPSILON'?: number;\n  // True when the environment is \"production\" where we disable safety checks\n  // to gain performance.\n  'PROD'?: boolean;\n  // Whether to do sanity checks when inferring a shape from user-provided\n  // values, used when creating a new tensor.\n  'TENSORLIKE_CHECK_SHAPE_CONSISTENCY'?: boolean;\n}\n\nexport enum Type {\n  NUMBER,\n  BOOLEAN,\n  STRING\n}\n\nexport const URL_PROPERTIES: URLProperty[] = [\n  {name: 'DEBUG', type: Type.BOOLEAN},\n  {name: 'IS_BROWSER', type: Type.BOOLEAN},\n  {name: 'WEBGL_LAZILY_UNPACK', type: Type.BOOLEAN},\n  {name: 'WEBGL_CPU_FORWARD', type: Type.BOOLEAN},\n  {name: 'WEBGL_PACK', type: Type.BOOLEAN},\n  {name: 'WEBGL_PACK_BATCHNORMALIZATION', type: Type.BOOLEAN},\n  {name: 'WEBGL_PACK_CLIP', type: Type.BOOLEAN},\n  {name: 'WEBGL_PACK_DEPTHWISECONV', type: Type.BOOLEAN},\n  {name: 'WEBGL_CONV_IM2COL', type: Type.BOOLEAN},\n  {name: 'WEBGL_MAX_TEXTURE_SIZE', type: Type.NUMBER},\n  {name: 'WEBGL_PAGING_ENABLED', type: Type.BOOLEAN},\n  {name: 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION', type: Type.NUMBER},\n  {name: 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE', type: Type.BOOLEAN},\n  {name: 'WEBGL_VERSION', type: Type.NUMBER},\n  {name: 'WEBGL_RENDER_FLOAT32_ENABLED', type: Type.BOOLEAN},\n  {name: 'WEBGL_DOWNLOAD_FLOAT_ENABLED', type: Type.BOOLEAN},\n  {name: 'WEBGL_FENCE_API_ENABLED', type: Type.BOOLEAN},\n  {name: 'WEBGL_SIZE_UPLOAD_UNIFORM', type: Type.NUMBER},\n  {name: 'BACKEND', type: Type.STRING},\n  {name: 'EPSILON', type: Type.NUMBER},\n  {name: 'PROD', type: Type.BOOLEAN},\n  {name: 'TENSORLIKE_CHECK_SHAPE_CONSISTENCY', type: Type.BOOLEAN},\n];\n\nexport interface URLProperty {\n  name: keyof Features;\n  type: Type;\n}\n\nexport function isWebGLVersionEnabled(webGLVersion: 1|2) {\n  try {\n    const gl = getWebGLContext(webGLVersion);\n    if (gl != null) {\n      return true;\n    }\n  } catch (e) {\n    return false;\n  }\n  return false;\n}\n\nlet MAX_TEXTURE_SIZE: number;\n// Caching MAX_TEXTURE_SIZE here because the environment gets reset between\n// unit tests and we don't want to constantly query the WebGLContext for\n// MAX_TEXTURE_SIZE.\nexport function getWebGLMaxTextureSize(webGLVersion: number): number {\n  if (MAX_TEXTURE_SIZE == null) {\n    const gl = getWebGLContext(webGLVersion);\n    MAX_TEXTURE_SIZE = gl.getParameter(gl.MAX_TEXTURE_SIZE);\n  }\n  return MAX_TEXTURE_SIZE;\n}\n\nexport function getWebGLDisjointQueryTimerVersion(webGLVersion: number):\n    number {\n  if (webGLVersion === 0) {\n    return 0;\n  }\n\n  let queryTimerVersion: number;\n  const gl = getWebGLContext(webGLVersion);\n\n  if (hasExtension(gl, 'EXT_disjoint_timer_query_webgl2') &&\n      webGLVersion === 2) {\n    queryTimerVersion = 2;\n  } else if (hasExtension(gl, 'EXT_disjoint_timer_query')) {\n    queryTimerVersion = 1;\n  } else {\n    queryTimerVersion = 0;\n  }\n  return queryTimerVersion;\n}\n\nexport function isRenderToFloatTextureEnabled(webGLVersion: number): boolean {\n  if (webGLVersion === 0) {\n    return false;\n  }\n\n  const gl = getWebGLContext(webGLVersion);\n\n  if (webGLVersion === 1) {\n    if (!hasExtension(gl, 'OES_texture_float')) {\n      return false;\n    }\n  } else {\n    if (!hasExtension(gl, 'EXT_color_buffer_float')) {\n      return false;\n    }\n  }\n\n  const isFrameBufferComplete =\n      createFloatTextureAndBindToFramebuffer(gl, webGLVersion);\n  return isFrameBufferComplete;\n}\n\nexport function isDownloadFloatTextureEnabled(webGLVersion: number): boolean {\n  if (webGLVersion === 0) {\n    return false;\n  }\n\n  const gl = getWebGLContext(webGLVersion);\n\n  if (webGLVersion === 1) {\n    if (!hasExtension(gl, 'OES_texture_float')) {\n      return false;\n    }\n    if (!hasExtension(gl, 'WEBGL_color_buffer_float')) {\n      return false;\n    }\n  } else {\n    if (!hasExtension(gl, 'EXT_color_buffer_float')) {\n      return false;\n    }\n  }\n\n  const isFrameBufferComplete =\n      createFloatTextureAndBindToFramebuffer(gl, webGLVersion);\n  return isFrameBufferComplete;\n}\n\nexport function isWebGLFenceEnabled(webGLVersion: number) {\n  if (webGLVersion !== 2) {\n    return false;\n  }\n  const gl = getWebGLContext(webGLVersion);\n\n  // tslint:disable-next-line:no-any\n  const isEnabled = (gl as any).fenceSync != null;\n  return isEnabled;\n}\n\nexport function isChrome() {\n  return typeof navigator !== 'undefined' && navigator != null &&\n      navigator.userAgent != null && /Chrome/.test(navigator.userAgent) &&\n      /Google Inc/.test(navigator.vendor);\n}\n\n// Expects flags from URL in the format ?tfjsflags=FLAG1:1,FLAG2:true.\nconst TENSORFLOWJS_FLAGS_PREFIX = 'tfjsflags';\nexport function getFeaturesFromURL(): Features {\n  const features: Features = {};\n\n  if (typeof window === 'undefined' || typeof window.location === 'undefined' ||\n      typeof window.location.search === 'undefined') {\n    return features;\n  }\n\n  const urlParams = getQueryParams(window.location.search);\n  if (TENSORFLOWJS_FLAGS_PREFIX in urlParams) {\n    const urlFlags: {[key: string]: string} = {};\n\n    const keyValues = urlParams[TENSORFLOWJS_FLAGS_PREFIX].split(',');\n    keyValues.forEach(keyValue => {\n      const [key, value] = keyValue.split(':') as [string, string];\n      urlFlags[key] = value;\n    });\n\n    URL_PROPERTIES.forEach(urlProperty => {\n      if (urlProperty.name in urlFlags) {\n        console.log(\n            `Setting feature override from URL ${urlProperty.name}: ` +\n            `${urlFlags[urlProperty.name]}`);\n        if (urlProperty.type === Type.NUMBER) {\n          features[urlProperty.name] = +urlFlags[urlProperty.name];\n        } else if (urlProperty.type === Type.BOOLEAN) {\n          features[urlProperty.name] = urlFlags[urlProperty.name] === 'true';\n        } else if (urlProperty.type === Type.STRING) {\n          // tslint:disable-next-line:no-any\n          features[urlProperty.name] = urlFlags[urlProperty.name] as any;\n        } else {\n          console.warn(`Unknown URL param: ${urlProperty.name}.`);\n        }\n      }\n    });\n  }\n\n  return features;\n}\n\nfunction hasExtension(gl: WebGLRenderingContext, extensionName: string) {\n  const ext = gl.getExtension(extensionName);\n  return ext != null;\n}\n\nfunction createFloatTextureAndBindToFramebuffer(\n    gl: WebGLRenderingContext, webGLVersion: number): boolean {\n  const frameBuffer = gl.createFramebuffer();\n  const texture = gl.createTexture();\n\n  gl.bindTexture(gl.TEXTURE_2D, texture);\n\n  // tslint:disable-next-line:no-any\n  const internalFormat = webGLVersion === 2 ? (gl as any).RGBA32F : gl.RGBA;\n  gl.texImage2D(\n      gl.TEXTURE_2D, 0, internalFormat, 1, 1, 0, gl.RGBA, gl.FLOAT, null);\n\n  gl.bindFramebuffer(gl.FRAMEBUFFER, frameBuffer);\n  gl.framebufferTexture2D(\n      gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0);\n\n  const isFrameBufferComplete =\n      gl.checkFramebufferStatus(gl.FRAMEBUFFER) === gl.FRAMEBUFFER_COMPLETE;\n\n  gl.bindTexture(gl.TEXTURE_2D, null);\n  gl.bindFramebuffer(gl.FRAMEBUFFER, null);\n  gl.deleteTexture(texture);\n  gl.deleteFramebuffer(frameBuffer);\n\n  return isFrameBufferComplete;\n}\n\nexport function getQueryParams(queryString: string): {[key: string]: string} {\n  const params = {};\n  queryString.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g, (s, ...t) => {\n    decodeParam(params, t[0], t[1]);\n    return t.join('=');\n  });\n  return params;\n}\n\nfunction decodeParam(\n    params: {[key: string]: string}, name: string, value?: string) {\n  params[decodeURIComponent(name)] = decodeURIComponent(value || '');\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from './tensor';\nimport {NamedTensorMap} from './tensor_types';\nimport * as util from './util';\n\nexport interface TapeNode {\n  id: number;\n  name: string;\n  outputs: Tensor[];\n  inputs: NamedTensorMap;\n  // Optional params, defined only for ops with gradient impl.\n  gradient?: (dy: Tensor|Tensor[]) => NamedGradientMap;\n}\n\nexport type NamedGradientMap = {\n  [inputName: string]: () => Tensor;\n};\n\n/**\n * Computes a list of TapeNodes that connect x to y, filtering everything else\n * out and preserving the order of the original tape elements.\n *\n * @param tape The tape elements to filter.\n * @param xs The input Tensors.\n * @param y The output Tensor.\n */\nexport function getFilteredNodesXToY(\n    tape: TapeNode[], xs: Tensor[], y: Tensor): TapeNode[] {\n  // Forward pass to compute all the nodes and Tensors that are transitively a\n  // function of x.\n  const tensorsFromX: {[tensorId: number]: boolean} = {};\n  const nodesFromX: {[nodeId: number]: boolean} = {};\n  for (let i = 0; i < xs.length; i++) {\n    tensorsFromX[xs[i].id] = true;\n  }\n\n  for (let i = 0; i < tape.length; i++) {\n    const node = tape[i];\n    const nodeInputs = node.inputs;\n    for (const inputName in nodeInputs) {\n      const input = nodeInputs[inputName];\n\n      let anyInputFromX = false;\n      for (let j = 0; j < xs.length; j++) {\n        if (tensorsFromX[input.id]) {\n          node.outputs.forEach(output => tensorsFromX[output.id] = true);\n          anyInputFromX = true;\n          nodesFromX[node.id] = true;\n          break;\n        }\n      }\n\n      if (anyInputFromX) {\n        break;\n      }\n    }\n  }\n\n  // Backward pass to find all of the nodes and Tensors that lead to y.\n  const tensorsLeadToY: {[tensorId: number]: boolean} = {};\n  tensorsLeadToY[y.id] = true;\n  const nodesToY: {[nodeId: number]: boolean} = {};\n\n  for (let i = tape.length - 1; i >= 0; i--) {\n    const node = tape[i];\n    const nodeInputs = node.inputs;\n\n    // If any of the outputs lead to y, mark all of the inputs as leading to y.\n    for (let j = 0; j < node.outputs.length; j++) {\n      if (tensorsLeadToY[node.outputs[j].id]) {\n        for (const inputName in nodeInputs) {\n          tensorsLeadToY[nodeInputs[inputName].id] = true;\n          nodesToY[node.id] = true;\n        }\n        break;\n      }\n    }\n  }\n\n  // Return the paths that come from x and lead to y.\n  const filteredTape: TapeNode[] = [];\n  for (let i = 0; i < tape.length; i++) {\n    const node = tape[i];\n\n    if (nodesFromX[node.id] && nodesToY[node.id]) {\n      // Prune the inputs from the node that aren't a function of x.\n      const prunedInputs: {[inputName: string]: Tensor} = {};\n      for (const inputName in node.inputs) {\n        const nodeInput = node.inputs[inputName];\n        if (tensorsFromX[nodeInput.id]) {\n          prunedInputs[inputName] = nodeInput;\n        }\n      }\n\n      // Copy the node and overwrite inputsAndArgs to the pruned version.\n      const prunedNode = Object.assign({}, node) as TapeNode;\n      prunedNode.inputs = prunedInputs;\n      prunedNode.outputs = node.outputs;\n\n      filteredTape.push(prunedNode);\n    }\n  }\n\n  return filteredTape;\n}\n\n/**\n * Backpropagate gradients through the filtered TapeNodes.\n *\n * @param tensorAccumulatedGradientMap A map of Tensor to its gradient. This map\n * is mutated by this method.\n * @param filteredTape The filtered TapeNodes to backprop through.\n */\nexport function backpropagateGradients(\n    tensorAccumulatedGradientMap: {[tensorId: number]: Tensor},\n    filteredTape: TapeNode[]) {\n  // Walk the tape backward and keep a map of Tensor to its gradient.\n  for (let i = filteredTape.length - 1; i >= 0; i--) {\n    const node = filteredTape[i];\n\n    const dys: Tensor[] = [];\n    node.outputs.forEach(o => {\n      const gradTensor = tensorAccumulatedGradientMap[o.id];\n      if (gradTensor != null) {\n        dys.push(gradTensor);\n      } else {\n        // This particular output is not in the back-propagation subgraph, so it\n        // does not affect the final output, thus we put zeros for its dy.\n        const dy = Tensor.make(\n            o.shape, {values: util.makeZerosTypedArray(o.size, o.dtype)},\n            o.dtype);\n        dys.push(dy);\n      }\n    });\n\n    if (node.gradient == null) {\n      throw new Error(\n          `Cannot compute gradient: gradient function not found ` +\n          `for ${node.name}.`);\n    }\n\n    // Backprop dy through this node and accumulate gradients over the inputs.\n    const inputGradients =\n        // Grad functions of ops with single outputs expect a dy, while ops\n        // with multiple outputs expect dys (array of dy).\n        node.gradient(node.outputs.length === 1 ? dys[0] : dys);\n    for (const inputName in node.inputs) {\n      if (!(inputName in inputGradients)) {\n        throw new Error(\n            `Cannot backprop through input ${inputName}. ` +\n            `Available gradients found: ${Object.keys(inputGradients)}.`);\n      }\n\n      // Call the gradient function.\n      const dx = inputGradients[inputName]();\n      if (dx.dtype !== 'float32') {\n        throw new Error(\n            `Error in gradient for op ${node.name}. The gradient of input ` +\n            `${inputName} must have 'float32' dtype, but has '${dx.dtype}'`);\n      }\n      const x = node.inputs[inputName];\n      if (!util.arraysEqual(dx.shape, x.shape)) {\n        throw new Error(\n            `Error in gradient for op ${node.name}. The gradient of input ` +\n            `'${inputName}' has shape '${dx.shape}', which does not match ` +\n            `the shape of the input '${x.shape}'`);\n      }\n\n      if (tensorAccumulatedGradientMap[x.id] == null) {\n        tensorAccumulatedGradientMap[x.id] = dx;\n      } else {\n        const curGradient = tensorAccumulatedGradientMap[x.id];\n        tensorAccumulatedGradientMap[x.id] = curGradient.add(dx);\n        curGradient.dispose();\n      }\n    }\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as device_util from './device_util';\nimport {Engine, MemoryInfo, ProfileInfo, ScopeFn, TimingInfo} from './engine';\nimport {Features, getFeaturesFromURL, getWebGLDisjointQueryTimerVersion, getWebGLMaxTextureSize, isChrome, isDownloadFloatTextureEnabled, isRenderToFloatTextureEnabled, isWebGLFenceEnabled, isWebGLVersionEnabled} from './environment_util';\nimport {KernelBackend} from './kernels/backend';\nimport {DataId, setTensorTracker, Tensor, TensorTracker} from './tensor';\nimport {TensorContainer} from './tensor_types';\nimport {getTensorsInContainer} from './tensor_util';\n\nexport const EPSILON_FLOAT16 = 1e-4;\nconst TEST_EPSILON_FLOAT16 = 1e-1;\n\nexport const EPSILON_FLOAT32 = 1e-7;\nconst TEST_EPSILON_FLOAT32 = 1e-3;\n\nexport class Environment {\n  private features: Features = {};\n  private globalEngine: Engine;\n  private registry:\n      {[id: string]: {backend: KernelBackend, priority: number}} = {};\n  backendName: string;\n\n  constructor(features?: Features) {\n    if (features != null) {\n      this.features = features;\n    }\n\n    if (this.get('DEBUG')) {\n      console.warn(\n          'Debugging mode is ON. The output of every math call will ' +\n          'be downloaded to CPU and checked for NaNs. ' +\n          'This significantly impacts performance.');\n    }\n  }\n\n  /**\n   * Sets the backend (cpu, webgl, etc) responsible for creating tensors and\n   * executing operations on those tensors.\n   *\n   * Note this disposes the current backend, if any, as well as any tensors\n   * associated with it. A new backend is initialized, even if it is of the\n   * same type as the previous one.\n   *\n   * @param backendName The name of the backend. Currently supports\n   *     `'webgl'|'cpu'` in the browser, and `'tensorflow'` under node.js\n   *     (requires tfjs-node).\n   * @param safeMode Defaults to false. In safe mode, you are forced to\n   *     construct tensors and call math operations inside a `tidy()` which\n   *     will automatically clean up intermediate tensors.\n   */\n  /** @doc {heading: 'Environment'} */\n  static setBackend(backendName: string, safeMode = false) {\n    if (!(backendName in ENV.registry)) {\n      throw new Error(`Backend name '${backendName}' not found in registry`);\n    }\n    ENV.engine.backend = ENV.findBackend(backendName);\n    ENV.backendName = backendName;\n  }\n\n  /**\n   * Returns the current backend name (cpu, webgl, etc). The backend is\n   * responsible for creating tensors and executing operations on those tensors.\n   */\n  /** @doc {heading: 'Environment'} */\n  static getBackend(): string {\n    ENV.initEngine();\n    return ENV.backendName;\n  }\n\n  /**\n   * Dispose all variables kept in backend engine.\n   */\n  /** @doc {heading: 'Environment'} */\n  static disposeVariables(): void {\n    ENV.engine.disposeVariables();\n  }\n\n  /**\n   * Returns memory info at the current time in the program. The result is an\n   * object with the following properties:\n   *\n   * - `numBytes`: Number of bytes allocated (undisposed) at this time.\n   * - `numTensors`: Number of unique tensors allocated.\n   * - `numDataBuffers`: Number of unique data buffers allocated\n   *   (undisposed) at this time, which is ≤ the number of tensors\n   *   (e.g. `a.reshape(newShape)` makes a new Tensor that shares the same\n   *   data buffer with `a`).\n   * - `unreliable`: True if the memory usage is unreliable. See `reasons` when\n   *    `unrealible` is true.\n   * - `reasons`: `string[]`, reasons why the memory is unreliable, present if\n   *    `unreliable` is true.\n   */\n  /** @doc {heading: 'Performance', subheading: 'Memory'} */\n  static memory(): MemoryInfo {\n    return ENV.engine.memory();\n  }\n\n  /**\n   * Executes the provided function `f()` and returns a promise that resolves\n   * with information about the function's memory use:\n   * - `newBytes`: tne number of new bytes allocated\n   * - `newTensors`: the number of new tensors created\n   * - `peakBytes`: the peak number of bytes allocated\n   * - `kernels`: an array of objects for each kernel involved that reports\n   * their input and output shapes, number of bytes used, and number of new\n   * tensors created.\n   *\n   * ```js\n   * const profile = await tf.profile(() => {\n   *   const x = tf.tensor1d([1, 2, 3]);\n   *   let x2 = x.square();\n   *   x2.dispose();\n   *   x2 = x.square();\n   *   x2.dispose();\n   *   return x;\n   * });\n   *\n   * console.log(`newBytes: ${profile.newBytes}`);\n   * console.log(`newTensors: ${profile.newTensors}`);\n   * console.log(`byte usage over all kernels: ${profile.kernels.map(k =>\n   * k.totalBytesSnapshot)}`);\n   * ```\n   *\n   */\n  /** @doc {heading: 'Performance', subheading: 'Profile'} */\n  static profile(f: () => TensorContainer): Promise<ProfileInfo> {\n    return ENV.engine.profile(f);\n  }\n\n  /**\n   * Executes the provided function `fn` and after it is executed, cleans up all\n   * intermediate tensors allocated by `fn` except those returned by `fn`.\n   * `fn` must not return a Promise (async functions not allowed). The returned\n   * result can be a complex object.\n   *\n   * Using this method helps avoid memory leaks. In general, wrap calls to\n   * operations in `tf.tidy` for automatic memory cleanup.\n   *\n   * When in safe mode, you must enclose all `tf.Tensor` creation and ops\n   * inside a `tf.tidy` to prevent memory leaks.\n   *\n   * ```js\n   * // y = 2 ^ 2 + 1\n   * const y = tf.tidy(() => {\n   *   // a, b, and one will be cleaned up when the tidy ends.\n   *   const one = tf.scalar(1);\n   *   const a = tf.scalar(2);\n   *   const b = a.square();\n   *\n   *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n   *\n   *   // The value returned inside the tidy function will return\n   *   // through the tidy, in this case to the variable y.\n   *   return b.add(one);\n   * });\n   *\n   * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n   * y.print();\n   * ```\n   *\n   * @param nameOrFn The name of the closure, or the function to execute.\n   *     If a name is provided, the 2nd argument should be the function.\n   *     If debug mode is on, the timing and the memory usage of the function\n   *     will be tracked and displayed on the console using the provided name.\n   * @param fn The function to execute.\n   */\n  /** @doc {heading: 'Performance', subheading: 'Memory'} */\n  static tidy<T extends TensorContainer>(\n      nameOrFn: string|ScopeFn<T>, fn?: ScopeFn<T>): T {\n    return ENV.engine.tidy(nameOrFn, fn);\n  }\n\n  /**\n   * Disposes any `tf.Tensor`s found within the provided object.\n   *\n   * @param container an object that may be a `tf.Tensor` or may directly\n   *     contain `tf.Tensor`s, such as a `Tensor[]` or `{key: Tensor, ...}`. If\n   *     the object is not a `tf.Tensor` or does not contain `Tensors`, nothing\n   *     happens. In general it is safe to pass any object here, except that\n   *     `Promise`s are not supported.\n   */\n  /** @doc {heading: 'Performance', subheading: 'Memory'} */\n  static dispose(container: TensorContainer) {\n    const tensors = getTensorsInContainer(container);\n    tensors.forEach(tensor => tensor.dispose());\n  }\n\n  /**\n   * Keeps a `tf.Tensor` generated inside a `tf.tidy` from being disposed\n   * automatically.\n   *\n   * ```js\n   * let b;\n   * const y = tf.tidy(() => {\n   *   const one = tf.scalar(1);\n   *   const a = tf.scalar(2);\n   *\n   *   // b will not be cleaned up by the tidy. a and one will be cleaned up\n   *   // when the tidy ends.\n   *   b = tf.keep(a.square());\n   *\n   *   console.log('numTensors (in tidy): ' + tf.memory().numTensors);\n   *\n   *   // The value returned inside the tidy function will return\n   *   // through the tidy, in this case to the variable y.\n   *   return b.add(one);\n   * });\n   *\n   * console.log('numTensors (outside tidy): ' + tf.memory().numTensors);\n   * console.log('y:');\n   * y.print();\n   * console.log('b:');\n   * b.print();\n   * ```\n   *\n   * @param result The tensor to keep from being disposed.\n   */\n  /** @doc {heading: 'Performance', subheading: 'Memory'} */\n  static keep<T extends Tensor>(result: T): T {\n    return ENV.engine.keep(result);\n  }\n\n  /**\n   * Executes `f()` and returns a promise that resolves with timing\n   * information.\n   *\n   * The result is an object with the following properties:\n   *\n   * - `wallMs`: Wall execution time.\n   * - `kernelMs`: Kernel execution time, ignoring data transfer.\n   * - On `WebGL` The following additional properties exist:\n   *   - `uploadWaitMs`: CPU blocking time on texture uploads.\n   *   - `downloadWaitMs`: CPU blocking time on texture downloads (readPixels).\n   *\n   * ```js\n   * const x = tf.randomNormal([20, 20]);\n   * const time = await tf.time(() => x.matMul(x));\n   *\n   * console.log(`kernelMs: ${time.kernelMs}, wallTimeMs: ${time.wallMs}`);\n   * ```\n   *\n   * @param f The function to execute and time.\n   */\n  /** @doc {heading: 'Performance', subheading: 'Timing'} */\n  static time(f: () => void): Promise<TimingInfo> {\n    return ENV.engine.time(f);\n  }\n\n  get<K extends keyof Features>(feature: K): Features[K] {\n    if (feature in this.features) {\n      return this.features[feature];\n    }\n\n    this.features[feature] = this.evaluateFeature(feature);\n\n    return this.features[feature];\n  }\n\n  getFeatures(): Features {\n    return this.features;\n  }\n\n  set<K extends keyof Features>(feature: K, value: Features[K]): void {\n    this.features[feature] = value;\n  }\n\n  private getBestBackendName(): string {\n    if (Object.keys(this.registry).length === 0) {\n      throw new Error('No backend found in registry.');\n    }\n    const sortedBackends = Object.keys(this.registry)\n                               .map(name => {\n                                 return {name, entry: this.registry[name]};\n                               })\n                               .sort((a, b) => {\n                                 // Highest priority comes first.\n                                 return b.entry.priority - a.entry.priority;\n                               });\n    return sortedBackends[0].name;\n  }\n\n  private evaluateFeature<K extends keyof Features>(feature: K): Features[K] {\n    if (feature === 'DEBUG') {\n      return false;\n    } else if (feature === 'IS_BROWSER') {\n      return typeof window !== 'undefined';\n    } else if (feature === 'IS_NODE') {\n      return (typeof process !== 'undefined') &&\n          (typeof process.versions !== 'undefined') &&\n          (typeof process.versions.node !== 'undefined');\n    } else if (feature === 'IS_CHROME') {\n      return isChrome();\n    } else if (feature === 'WEBGL_CPU_FORWARD') {\n      return true;\n    } else if (feature === 'WEBGL_PACK') {\n      return false;\n    } else if (feature === 'WEBGL_PACK_BATCHNORMALIZATION') {\n      return this.get('WEBGL_PACK');\n    } else if (feature === 'WEBGL_PACK_CLIP') {\n      return this.get('WEBGL_PACK');\n    } else if (feature === 'WEBGL_PACK_DEPTHWISECONV') {\n      return this.get('WEBGL_PACK');\n    } else if (feature === 'WEBGL_LAZILY_UNPACK') {\n      return this.get('WEBGL_PACK');\n    } else if (feature === 'WEBGL_CONV_IM2COL') {\n      return this.get('WEBGL_PACK');\n    } else if (feature === 'WEBGL_PAGING_ENABLED') {\n      return this.get('IS_BROWSER') && !this.get('PROD');\n    } else if (feature === 'WEBGL_MAX_TEXTURE_SIZE') {\n      return getWebGLMaxTextureSize(this.get('WEBGL_VERSION'));\n    } else if (feature === 'IS_TEST') {\n      return false;\n    } else if (feature === 'BACKEND') {\n      return this.getBestBackendName();\n    } else if (feature === 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') {\n      const webGLVersion = this.get('WEBGL_VERSION');\n\n      if (webGLVersion === 0) {\n        return 0;\n      }\n      return getWebGLDisjointQueryTimerVersion(webGLVersion);\n    } else if (feature === 'WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE') {\n      return this.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0 &&\n          !device_util.isMobile();\n    } else if (feature === 'HAS_WEBGL') {\n      return this.get('WEBGL_VERSION') > 0;\n    } else if (feature === 'WEBGL_VERSION') {\n      if (isWebGLVersionEnabled(2)) {\n        return 2;\n      } else if (isWebGLVersionEnabled(1)) {\n        return 1;\n      }\n      return 0;\n    } else if (feature === 'WEBGL_RENDER_FLOAT32_ENABLED') {\n      return isRenderToFloatTextureEnabled(this.get('WEBGL_VERSION'));\n    } else if (feature === 'WEBGL_DOWNLOAD_FLOAT_ENABLED') {\n      return isDownloadFloatTextureEnabled(this.get('WEBGL_VERSION'));\n    } else if (feature === 'WEBGL_FENCE_API_ENABLED') {\n      return isWebGLFenceEnabled(this.get('WEBGL_VERSION'));\n    } else if (feature === 'WEBGL_SIZE_UPLOAD_UNIFORM') {\n      // Use uniform uploads only when 32bit floats are supported. In 16bit\n      // environments there are problems with comparing a 16bit texture value\n      // with a 32bit uniform value.\n      const useUniforms = this.get('WEBGL_RENDER_FLOAT32_ENABLED');\n      return useUniforms ? 4 : 0;\n    } else if (feature === 'TEST_EPSILON') {\n      return this.backend.floatPrecision() === 32 ? TEST_EPSILON_FLOAT32 :\n                                                    TEST_EPSILON_FLOAT16;\n    } else if (feature === 'EPSILON') {\n      return this.backend.floatPrecision() === 32 ? EPSILON_FLOAT32 :\n                                                    EPSILON_FLOAT16;\n    } else if (feature === 'PROD') {\n      return false;\n    } else if (feature === 'TENSORLIKE_CHECK_SHAPE_CONSISTENCY') {\n      return !this.get('PROD');\n    }\n    throw new Error(`Unknown feature ${feature}.`);\n  }\n\n  setFeatures(features: Features) {\n    this.features = Object.assign({}, features);\n  }\n\n  reset() {\n    this.features = getFeaturesFromURL();\n    if (this.globalEngine != null) {\n      this.globalEngine = null;\n    }\n  }\n\n  get backend(): KernelBackend {\n    return this.engine.backend;\n  }\n\n  findBackend(name: string): KernelBackend {\n    if (!(name in this.registry)) {\n      return null;\n    }\n    return this.registry[name].backend;\n  }\n\n  /**\n   * Registers a global backend. The registration should happen when importing\n   * a module file (e.g. when importing `backend_webgl.ts`), and is used for\n   * modular builds (e.g. custom tfjs bundle with only webgl support).\n   *\n   * @param factory The backend factory function. When called, it should\n   * return an instance of the backend.\n   * @param priority The priority of the backend (higher = more important).\n   *     In case multiple backends are registered, the priority is used to find\n   *     the best backend. Defaults to 1.\n   * @return False if the creation/registration failed. True otherwise.\n   */\n  registerBackend(\n      name: string, factory: () => KernelBackend, priority = 1,\n      setTensorTrackerFn?: (f: () => TensorTracker) => void): boolean {\n    if (name in this.registry) {\n      console.warn(\n          `${name} backend was already registered. Reusing existing backend`);\n      if (setTensorTrackerFn != null) {\n        setTensorTrackerFn(() => this.engine);\n      }\n      return false;\n    }\n    try {\n      const backend = factory();\n      backend.setDataMover(\n          {moveData: (dataId: DataId) => this.engine.moveData(dataId)});\n      this.registry[name] = {backend, priority};\n      return true;\n    } catch (err) {\n      console.warn(`Registration of backend ${name} failed`);\n      console.warn(err.stack || err.message);\n      return false;\n    }\n  }\n\n  removeBackend(name: string): void {\n    if (!(name in this.registry)) {\n      throw new Error(`${name} backend not found in registry`);\n    }\n    this.registry[name].backend.dispose();\n    delete this.registry[name];\n  }\n\n  get engine(): Engine {\n    this.initEngine();\n    return this.globalEngine;\n  }\n\n  private initEngine() {\n    if (this.globalEngine == null) {\n      this.backendName = this.get('BACKEND');\n      const backend = this.findBackend(this.backendName);\n      this.globalEngine =\n          new Engine(backend, false /* safeMode */, () => this.get('DEBUG'));\n    }\n  }\n}\n\nfunction getGlobalNamespace(): {ENV: Environment} {\n  // tslint:disable-next-line:no-any\n  let ns: any;\n  if (typeof (window) !== 'undefined') {\n    ns = window;\n  } else if (typeof (process) !== 'undefined') {\n    ns = process;\n  } else {\n    throw new Error('Could not find a global object');\n  }\n  return ns;\n}\n\nfunction getOrMakeEnvironment(): Environment {\n  const ns = getGlobalNamespace();\n  if (ns.ENV == null) {\n    ns.ENV = new Environment(getFeaturesFromURL());\n    setTensorTracker(() => ns.ENV.engine);\n  }\n  return ns.ENV;\n}\n\nexport let ENV = getOrMakeEnvironment();\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport function isMobile(): boolean {\n  // tslint:disable-next-line:no-any\n  const a = navigator.userAgent || navigator.vendor || (window as any).opera;\n  // tslint:disable-next-line:max-line-length\n  return /(android|bb\\d+|meego).+mobile|avantgo|bada\\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i\n             .test(a) ||\n      // tslint:disable-next-line:max-line-length\n      /1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\\-(n|u)|c55\\/|capi|ccwa|cdm\\-|cell|chtm|cldc|cmd\\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\\-s|devi|dica|dmob|do(c|p)o|ds(12|\\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\\-|_)|g1 u|g560|gene|gf\\-5|g\\-mo|go(\\.w|od)|gr(ad|un)|haie|hcit|hd\\-(m|p|t)|hei\\-|hi(pt|ta)|hp( i|ip)|hs\\-c|ht(c(\\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\\-(20|go|ma)|i230|iac( |\\-|\\/)|ibro|idea|ig01|ikom|im1k|inno|ipaq|iris|ja(t|v)a|jbro|jemu|jigs|kddi|keji|kgt( |\\/)|klon|kpt |kwc\\-|kyo(c|k)|le(no|xi)|lg( g|\\/(k|l|u)|50|54|\\-[a-w])|libw|lynx|m1\\-w|m3ga|m50\\/|ma(te|ui|xo)|mc(01|21|ca)|m\\-cr|me(rc|ri)|mi(o8|oa|ts)|mmef|mo(01|02|bi|de|do|t(\\-| |o|v)|zz)|mt(50|p1|v )|mwbp|mywa|n10[0-2]|n20[2-3]|n30(0|2)|n50(0|2|5)|n7(0(0|1)|10)|ne((c|m)\\-|on|tf|wf|wg|wt)|nok(6|i)|nzph|o2im|op(ti|wv)|oran|owg1|p800|pan(a|d|t)|pdxg|pg(13|\\-([1-8]|c))|phil|pire|pl(ay|uc)|pn\\-2|po(ck|rt|se)|prox|psio|pt\\-g|qa\\-a|qc(07|12|21|32|60|\\-[2-7]|i\\-)|qtek|r380|r600|raks|rim9|ro(ve|zo)|s55\\/|sa(ge|ma|mm|ms|ny|va)|sc(01|h\\-|oo|p\\-)|sdk\\/|se(c(\\-|0|1)|47|mc|nd|ri)|sgh\\-|shar|sie(\\-|m)|sk\\-0|sl(45|id)|sm(al|ar|b3|it|t5)|so(ft|ny)|sp(01|h\\-|v\\-|v )|sy(01|mb)|t2(18|50)|t6(00|10|18)|ta(gt|lk)|tcl\\-|tdg\\-|tel(i|m)|tim\\-|t\\-mo|to(pl|sh)|ts(70|m\\-|m3|m5)|tx\\-9|up(\\.b|g1|si)|utst|v400|v750|veri|vi(rg|te)|vk(40|5[0-3]|\\-v)|vm40|voda|vulc|vx(52|53|60|61|70|80|81|83|85|98)|w3c(\\-| )|webc|whit|wi(g |nc|nw)|wmlb|wonu|x700|yas\\-|your|zeto|zte\\-/i\n          .test(a.substr(0, 4));\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {CustomGradientFunc, ScopeFn} from './engine';\nimport {ENV} from './environment';\nimport {Scalar, Tensor, Variable} from './tensor';\nimport {NamedTensorMap, TensorContainer} from './tensor_types';\nimport * as util from './util';\n\n/**\n * Create a new gradient scope. Similar to scope, but forces all inner scopes\n * to not clean up so that gradient operations can be used inside of this\n * scope.\n * @param nameOrScopeFn The name of the scope, or the function to execute.\n *     If a name is provided, the 2nd argument should be the function.\n *     If a name is provided, and debug mode is on, the timing and the memory\n *     usage of the function will be tracked and displayed on the console\n *     using the provided name.\n * @param scopeFn The function to execute.\n */\nfunction gradScope<T extends TensorContainer>(\n    nameOrScopeFn: string|ScopeFn<T>, scopeFn?: ScopeFn<T>): T {\n  return ENV.engine.tidy(nameOrScopeFn, scopeFn, true /* gradScope */);\n}\n\n/**\n * Provided `f(x)`, returns another function `g(x, dy?)`, which gives the\n * gradient of `f(x)` with respect to `x`.\n *\n * If `dy` is provided, the gradient of `f(x).mul(dy).sum()` with respect to\n * `x` is computed instead. `f(x)` must take a single tensor `x` and return a\n * single tensor `y`. If `f()` takes multiple inputs, use `tf.grads` instead.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.grad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * g(x).print();\n * ```\n *\n * ```js\n * // f(x) = x ^ 3\n * const f = x => x.pow(tf.scalar(3, 'int32'));\n * // f'(x) = 3x ^ 2\n * const g = tf.grad(f);\n * // f''(x) = 6x\n * const gg = tf.grad(g);\n *\n * const x = tf.tensor1d([2, 3]);\n * gg(x).print();\n * ```\n *\n * @param f The function f(x), to compute gradient for.\n */\n/** @doc {heading: 'Training', subheading: 'Gradients'} */\nfunction grad<I extends Tensor, O extends Tensor>(f: (x: I) => O): (\n    x: I, dy?: O) => I {\n  util.assert(util.isFunction(f), 'The f passed in grad(f) must be a function');\n  return (x: I, dy?: O): I => {\n    util.assert(\n        x instanceof Tensor, 'The x passed in grad(f)(x) must be a tensor');\n    util.assert(\n        dy == null || dy instanceof Tensor,\n        'The dy passed in grad(f)(x, dy) must be a tensor');\n    return ENV.engine.tidy(() => {\n      const {value, grads} = ENV.engine.gradients(() => f(x), [x], dy);\n      if (dy != null) {\n        util.assertShapesMatch(\n            value.shape, dy.shape,\n            'The shape of dy passed in grad(f)(x, dy) must match the shape ' +\n                'returned by f(x)');\n      }\n      checkGrads(grads);\n      return grads[0] as I;\n    });\n  };\n}\n\n/**\n * Provided `f(x1, x2,...)`, returns another function `g([x1, x2,...], dy?)`,\n * which gives an array of gradients of `f()` with respect to each input\n * [`x1`,`x2`,...].\n *\n * If `dy` is passed when calling `g()`, the gradient of\n * `f(x1,...).mul(dy).sum()` with respect to each input is computed instead.\n * The provided `f` must take one or more tensors and return a single tensor\n * `y`. If `f()` takes a single input, we recommend using `tf.grad` instead.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df / da = b, df / db = a\n * const g = tf.grads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const [da, db] = g([a, b]);\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n *\n * @param f The function `f(x1, x2,...)` to compute gradients for.\n */\n/** @doc {heading: 'Training', subheading: 'Gradients'} */\nfunction grads<O extends Tensor>(f: (...args: Tensor[]) => O): (\n    args: Tensor[], dy?: O) => Tensor[] {\n  util.assert(\n      util.isFunction(f), 'The f passed in grads(f) must be a function');\n  return (args: Tensor[], dy?: O): Tensor[] => {\n    util.assert(\n        Array.isArray(args) && args.every(arg => arg instanceof Tensor),\n        'The args passed in grads(f)(args) must be an array of tensors');\n    util.assert(\n        dy == null || dy instanceof Tensor,\n        'The dy passed in grads(f)(args, dy) must be a tensor');\n    return ENV.engine.tidy(() => {\n      const {value, grads} = ENV.engine.gradients(() => f(...args), args, dy);\n      if (dy != null) {\n        util.assertShapesMatch(\n            value.shape, dy.shape,\n            'The shape of dy passed in grads(f)([x1,...], dy) must ' +\n                'match the shape returned by f([x1,...])');\n      }\n      checkGrads(grads);\n      return grads;\n    });\n  };\n}\n\n/**\n * Like `tf.grad`, but also returns the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grad: The gradient of `f(x)` w.r.t `x` (result of `tf.grad`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(x) = x ^ 2\n * const f = x => x.square();\n * // f'(x) = 2x\n * const g = tf.valueAndGrad(f);\n *\n * const x = tf.tensor1d([2, 3]);\n * const {value, grad} = g(x);\n *\n * console.log('value');\n * value.print();\n * console.log('grad');\n * grad.print();\n * ```\n */\n/** @doc {heading: 'Training', subheading: 'Gradients'} */\nfunction valueAndGrad<I extends Tensor, O extends Tensor>(f: (x: I) => O): (\n    x: I, dy?: O) => {\n  value: O;\n  grad: I;\n} {\n  util.assert(\n      util.isFunction(f), 'The f passed in valueAndGrad(f) must be a function');\n  return (x: I, dy?: O) => {\n    util.assert(\n        x instanceof Tensor,\n        'The x passed in valueAndGrad(f)(x) must be a tensor');\n    util.assert(\n        dy == null || dy instanceof Tensor,\n        'The dy passed in valueAndGrad(f)(x, dy) must be a tensor');\n    const {grads, value} = ENV.engine.gradients(() => f(x), [x], dy);\n    checkGrads(grads);\n    return {grad: grads[0] as I, value: value as O};\n  };\n}\n\n/**\n * Like `tf.grads`, but returns also the value of `f()`. Useful when `f()`\n * returns a metric you want to show.\n *\n * The result is a rich object with the following properties:\n * - grads: The gradients of `f()` w.r.t each input (result of `tf.grads`).\n * - value: The value returned by `f(x)`.\n *\n * ```js\n * // f(a, b) = a * b\n * const f = (a, b) => a.mul(b);\n * // df/da = b, df/db = a\n * const g = tf.valueAndGrads(f);\n *\n * const a = tf.tensor1d([2, 3]);\n * const b = tf.tensor1d([-2, -3]);\n * const {value, grads} = g([a, b]);\n *\n * const [da, db] = grads;\n *\n * console.log('value');\n * value.print();\n *\n * console.log('da');\n * da.print();\n * console.log('db');\n * db.print();\n * ```\n */\n/** @doc {heading: 'Training', subheading: 'Gradients'} */\nfunction valueAndGrads<O extends Tensor>(f: (...args: Tensor[]) => O): (\n    args: Tensor[], dy?: O) => {\n  grads: Tensor[];\n  value: O;\n} {\n  util.assert(\n      util.isFunction(f),\n      'The f passed in valueAndGrads(f) must be a function');\n  return (args: Tensor[], dy?: O) => {\n    util.assert(\n        Array.isArray(args) && args.every(arg => arg instanceof Tensor),\n        'The args passed in valueAndGrads(f)(args) must be array of tensors');\n    util.assert(\n        dy == null || dy instanceof Tensor,\n        'The dy passed in valueAndGrads(f)(args, dy) must be a tensor');\n    const res = ENV.engine.gradients(() => f(...args), args, dy);\n    if (dy != null) {\n      util.assertShapesMatch(\n          res.value.shape, dy.shape,\n          'The shape of dy passed in valueAndGrads(f)([x1,...], dy) must ' +\n              'match the shape returned by f([x1,...])');\n    }\n    checkGrads(res.grads);\n    return res;\n  };\n}\n\n/**\n * Computes and returns the gradient of f(x) with respect to the list of\n * trainable variables provided by `varList`. If no list is provided, it\n * defaults to all trainable variables.\n *\n * ```js\n * const a = tf.variable(tf.tensor1d([3, 4]));\n * const b = tf.variable(tf.tensor1d([5, 6]));\n * const x = tf.tensor1d([1, 2]);\n *\n * // f(a, b) = a * x ^ 2 + b * x\n * const f = () => a.mul(x.square()).add(b.mul(x)).sum();\n * // df/da = x ^ 2, df/db = x\n * const {value, grads} = tf.variableGrads(f);\n *\n * Object.keys(grads).forEach(varName => grads[varName].print());\n * ```\n *\n * @param f The function to execute. f() should return a scalar.\n * @param varList The list of trainable variables. Defaults to all variables.\n */\n/** @doc {heading: 'Training', subheading: 'Gradients'} */\nfunction variableGrads(f: () => Scalar, varList?: Variable[]):\n    {value: Scalar, grads: NamedTensorMap} {\n  util.assert(\n      util.isFunction(f),\n      'The f passed in variableGrads(f) must be a function');\n  util.assert(\n      varList == null ||\n          Array.isArray(varList) && varList.every(v => v instanceof Variable),\n      'The varList passed in variableGrads(f, varList) must be an array ' +\n          'of variables');\n  if (varList == null) {\n    // Get all of the trainable variables.\n    varList = [];\n    for (const varName in ENV.engine.registeredVariables) {\n      varList.push(ENV.engine.registeredVariables[varName]);\n    }\n  }\n  // Prune non-trainable variables.\n  const originalVarCount = varList.length;\n  varList = varList.filter(variable => variable.trainable);\n  util.assert(\n      varList.length > 0,\n      `variableGrads() expects at least one of the input variables to be ` +\n          `trainable, but none of the ${originalVarCount} variables is ` +\n          `trainable.`);\n\n  const allowNoGradients = true;\n  const {value, grads} =\n      ENV.engine.gradients(f, varList, null, allowNoGradients);\n\n  util.assert(\n      grads.some(g => g != null),\n      'Cannot find a connection between any variable and the result of the ' +\n          'loss function y=f(x). Please make sure the operations that use ' +\n          'variables are inside the function f passed to minimize().');\n  util.assert(\n      value.rank === 0,\n      `The f passed in variableGrads(f) must return a scalar, but it ` +\n          `returned a rank-${value.rank} tensor`);\n\n  const namedGrads: NamedTensorMap = {};\n  varList.forEach((v, i) => {\n    if (grads[i] != null) {\n      namedGrads[v.name] = grads[i];\n    }\n  });\n  return {value, grads: namedGrads};\n}\n\n/**\n * Overrides the gradient computation of a function `f`.\n *\n * Takes a function\n * `f(...inputs) => {value: Tensor, gradFunc: dy => Tensor[]}` and returns\n * another function `g(...inputs)` which takes the same inputs as `f`. When\n * called, `g` returns `f().value`. In backward mode, custom gradients with\n * respect to each input of `f` are computed using `f().gradFunc`.\n *\n * ```js\n * const customOp = tf.customGrad(x => {\n *   // Override gradient of our custom x ^ 2 op to be dy * abs(x);\n *   return {value: x.square(), gradFunc: dy => [dy.mul(x.abs())]};\n * });\n *\n * const x = tf.tensor1d([-1, -2, 3]);\n * const dx = tf.grad(x => customOp(x));\n *\n * console.log(`f(x):`);\n * customOp(x).print();\n * console.log(`f'(x):`);\n * dx(x).print();\n * ```\n *\n * @param f The function to evaluate in forward mode, which should return\n *     `{value: Tensor, gradFunc: (dy) => Tensor[]}`, where `gradFunc` returns\n *     the custom gradients of `f` with respect to its inputs.\n */\n/** @doc {heading: 'Training', subheading: 'Gradients'} */\nfunction customGrad<T extends Tensor>(f: CustomGradientFunc<T>):\n    (...args: Tensor[]) => T {\n  return ENV.engine.customGrad(f);\n}\n\nfunction checkGrads(grads: Tensor[]) {\n  const numNullGradients = grads.filter(g => g == null).length;\n  if (numNullGradients > 0) {\n    throw new Error(\n        `Cannot compute gradient of y=f(x) with respect to x. Make sure that\n    the f you passed encloses all operations that lead from x to y.`);\n  }\n}\n\nexport {\n  gradScope,\n  customGrad,\n  variableGrads,\n  valueAndGrad,\n  valueAndGrads,\n  grad,\n  grads,\n};\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Environment} from './environment';\nexport {customGrad, grad, grads, valueAndGrad, valueAndGrads, variableGrads} from './gradients';\n\nexport const tidy = Environment.tidy;\nexport const keep = Environment.keep;\nexport const dispose = Environment.dispose;\nexport const time = Environment.time;\nexport const profile = Environment.profile;","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from './environment';\n\nexport function warn(...msg: Array<{}>): void {\n  if (!ENV.get('IS_TEST')) {\n    console.warn(...msg);\n  }\n}\n\nexport function log(...msg: Array<{}>): void {\n  if (!ENV.get('IS_TEST')) {\n    console.log(...msg);\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Gets the new shape of the input Tensor after it's been reshaped\n * to:\n * [blockShape[0], ..., blockShape[M-1], batch / prod(blockShape),\n * inputShape[1], ..., inputShape[N-1]]\n *\n * See step 1: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshaped(\n    inputShape: number[], blockShape: number[], prod: number,\n    batchToSpace = true): number[] {\n  let reshaped: number[] = [];\n  if (batchToSpace) {\n    reshaped = reshaped.concat(blockShape.slice(0));\n    reshaped.push(inputShape[0] / prod);\n    reshaped = reshaped.concat(inputShape.slice(1));\n  } else {\n    reshaped = reshaped.concat(inputShape[0]);\n    const spatialLength = blockShape.length;\n    for (let i = 0; i < spatialLength; ++i) {\n      reshaped =\n          reshaped.concat([inputShape[i + 1] / blockShape[i], blockShape[i]]);\n    }\n    reshaped = reshaped.concat(inputShape.slice(spatialLength + 1));\n  }\n  return reshaped;\n}\n\n/**\n * Gets the permutation that will transpose the dimensions of the\n * reshaped tensor to shape:\n *\n * [batch / prod(block_shape),inputShape[1], blockShape[0], ...,\n * inputShape[M], blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * see step 2: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getPermuted(\n    reshapedRank: number, blockShapeRank: number,\n    batchToSpace = true): number[] {\n  const permuted = [];\n  if (batchToSpace) {\n    permuted.push(blockShapeRank);\n    for (let i = blockShapeRank + 1; i < reshapedRank; ++i) {\n      if (i <= 2 * blockShapeRank) {\n        permuted.push(i);\n        permuted.push(i - (blockShapeRank + 1));\n      } else {\n        permuted.push(i);\n      }\n    }\n  } else {\n    const permutedBeforeBatch = [];\n    const permutedAfterBatch = [];\n    for (let i = 1; i < reshapedRank; ++i) {\n      if (i >= blockShapeRank * 2 + 1 || i % 2 === 1) {\n        permutedAfterBatch.push(i);\n      } else {\n        permutedBeforeBatch.push(i);\n      }\n    }\n    permuted.push(...permutedBeforeBatch);\n    permuted.push(0);\n    permuted.push(...permutedAfterBatch);\n  }\n  return permuted;\n}\n\n/**\n * Gets the shape of the reshaped and permuted input Tensor before any cropping\n * is applied.  The new shape will be:\n *\n * [batch / prod(blockShape),inputShape[1] * blockShape[0], ...,\n * inputShape[M] * blockShape[M-1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 3: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getReshapedPermuted(\n    inputShape: number[], blockShape: number[], prod: number,\n    batchToSpace = true): number[] {\n  const reshapedPermuted = [];\n\n  if (batchToSpace) {\n    reshapedPermuted.push(inputShape[0] / prod);\n  } else {\n    reshapedPermuted.push(inputShape[0] * prod);\n  }\n\n  for (let i = 1; i < inputShape.length; ++i) {\n    if (i <= blockShape.length) {\n      if (batchToSpace) {\n        reshapedPermuted.push(blockShape[i - 1] * inputShape[i]);\n      } else {\n        reshapedPermuted.push(inputShape[i] / blockShape[i - 1]);\n      }\n    } else {\n      reshapedPermuted.push(inputShape[i]);\n    }\n  }\n\n  return reshapedPermuted;\n}\n\n/**\n * Converts the crops argument into the beginning coordinates of a slice\n * operation.\n */\nexport function getSliceBeginCoords(\n    crops: number[][], blockShape: number): number[] {\n  const sliceBeginCoords = [0];\n  for (let i = 0; i < blockShape; ++i) {\n    sliceBeginCoords.push(crops[i][0]);\n  }\n  return sliceBeginCoords;\n}\n\n/**\n * Converts the crops argument into the size of a slice operation.  When\n * combined with getSliceBeginCoords this function allows the reshaped and\n * permuted Tensor to be cropped to its final output shape of:\n *\n * inputShape[1] * blockShape[0] - crops[0,0] - crops[0,1], ...,\n * inputShape[M] * blockShape[M-1] -crops[M-1,0] -\n * crops[M-1,1],inputShape[M+1], ..., inputShape[N-1]]\n *\n * See step 4: https://www.tensorflow.org/api_docs/python/tf/batch_to_space_nd\n */\nexport function getSliceSize(\n    uncroppedShape: number[], crops: number[][], blockShape: number): number[] {\n  const sliceSize = uncroppedShape.slice(0, 1);\n  for (let i = 0; i < blockShape; ++i) {\n    sliceSize.push(uncroppedShape[i + 1] - crops[i][0] - crops[i][1]);\n  }\n\n  return sliceSize;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as util from '../util';\n\n/**\n * Returns true if the axis specifies the inner most dimensions of the\n * array.\n */\nexport function axesAreInnerMostDims(axes: number[], rank: number): boolean {\n  for (let i = 0; i < axes.length; ++i) {\n    if (axes[axes.length - i - 1] !== rank - 1 - i) {\n      return false;\n    }\n  }\n  return true;\n}\n\nexport function combineLocations(\n    outputLoc: number[], reduceLoc: number[], axes: number[]): number[] {\n  const rank = outputLoc.length + reduceLoc.length;\n  const loc = [];\n  let outIdx = 0;\n  let reduceIdx = 0;\n    for (let dim = 0; dim < rank; dim++) {\n    if (axes.indexOf(dim) === -1) {\n      loc.push(outputLoc[outIdx++]);\n    } else {\n      loc.push(reduceLoc[reduceIdx++]);\n    }\n  }\n  return loc;\n}\n\nexport function computeOutAndReduceShapes(\n    aShape: number[], axes: number[]): [number[], number[]] {\n  const outShape = [];\n  const rank = aShape.length;\n  for (let dim = 0; dim < rank; dim++) {\n    if (axes.indexOf(dim) === -1) {\n      outShape.push(aShape[dim]);\n    }\n  }\n  const reduceShape = axes.map(dim => aShape[dim]);\n  return [outShape, reduceShape];\n}\n\nexport function expandShapeToKeepDim(\n    shape: number[], axes: number[]): number[] {\n  const reduceSubShape = axes.map(x => 1);\n  return combineLocations(shape, reduceSubShape, axes);\n}\n\nexport function parseAxisParam(\n    axis: number|number[], shape: number[]): number[] {\n  const rank = shape.length;\n\n  // Normalize input\n  axis = axis == null ? shape.map((s, i) => i) : [].concat(axis);\n\n  // Check for valid range\n  util.assert(\n      axis.every(ax => ax >= -rank && ax < rank),\n      `All values in axis param must be in range [-${rank}, ${rank}) but ` +\n          `got axis ${axis}`);\n\n  // Check for only integers\n  util.assert(\n      axis.every(ax => util.isInt(ax)),\n      `All values in axis param must be integers but ` +\n          `got axis ${axis}`);\n\n  // Handle negative axis.\n  return axis.map(a => a < 0 ? rank + a : a);\n}\n\nexport function assertAxesAreInnerMostDims(\n    msg: string, axes: number[], rank: number): void {\n  util.assert(\n      axesAreInnerMostDims(axes, rank),\n      `${msg} supports only inner-most axes for now. ` +\n          `Got axes ${axes} and rank-${rank} input.`);\n}\n\n/**\n * Returns the axes permutation to be used with `tf.transpose`, if such\n * permutation is necessary. Otherwise it returns null. This method is used by\n * operations that operate only on inner-most axes.\n */\nexport function getAxesPermutation(axes: number[], rank: number): number[]|\n    null {\n  if (axesAreInnerMostDims(axes, rank)) {\n    return null;\n  }\n  const result: number[] = [];\n  for (let i = 0; i < rank; ++i) {\n    if (axes.indexOf(i) === -1) {\n      result.push(i);\n    }\n  }\n  axes.forEach(axis => result.push(axis));\n  return result;\n}\n\n/** Returns the axes permutation that undoes the original permutation. */\nexport function getUndoAxesPermutation(axes: number[]): number[] {\n  return axes.map((axis, i) => [i, axis])\n      .sort((a, b) => a[1] - b[1])\n      .map(x => x[0]);\n}\n\nexport function getInnerMostAxes(numAxes: number, rank: number): number[] {\n  const res: number[] = [];\n  for (let i = rank - numAxes; i < rank; ++i) {\n    res.push(i);\n  }\n  return res;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as util from '../util';\n\nexport function assertParamsConsistent(shapes: number[][], axis: number) {\n  const rank = shapes[0].length;\n  shapes.forEach((shape, i) => {\n    util.assert(\n        shape.length === rank,\n        `Error in concat${rank}D: rank of tensors[${i}] must be the same ` +\n            `as the rank of the rest (${rank})`);\n  });\n\n  util.assert(\n      axis >= 0 && axis < rank,\n      `Error in concat${rank}D: axis must be between 0 and ${rank - 1}.`);\n\n  const firstShape = shapes[0];\n  shapes.forEach((shape, i) => {\n    for (let r = 0; r < rank; r++) {\n      util.assert(\n          (r === axis) || (shape[r] === firstShape[r]),\n          `Error in concat${rank}D: Shape of tensors[${i}] (${shape}) ` +\n              `does not match the shape of the rest (${firstShape}) ` +\n              `along the non-concatenated axis ${i}.`);\n    }\n  });\n}\n\nexport function computeOutShape(shapes: number[][], axis: number): number[] {\n  const outputShape = shapes[0].slice();\n  for (let i = 1; i < shapes.length; i++) {\n    outputShape[axis] += shapes[i][axis];\n  }\n  return outputShape;\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor} from '../tensor';\nimport {computeStrides} from '../util';\n\n/**\n * Validate gather nd inputs.\n *\n * @param tensor The tensor contains the source values.\n * @param indices The tensor contains the indices to slice the source.\n *\n * @returns [resultShape, numUpdates, sliceSize, strides]\n */\nexport function prepareAndValidate(\n    tensor: Tensor, indices: Tensor): [number[], number, number, number[]] {\n  if (tensor.rank < 1) {\n    throw new Error(\n        'tf.gatherND() expects the input to be rank 1 or higher,' +\n        ` but the rank was ${tensor.rank}.`);\n  }\n  if (indices.rank < 1) {\n    throw new Error(\n        'tf.gatherND() expects the indices to be rank 1 or higher,' +\n        ` but the rank was ${indices.rank}.`);\n  }\n  if (indices.dtype !== 'int32') {\n    throw new Error(\n        'tf.gatherND() expects the indices to be int32 type,' +\n        ` but the dtype was ${indices.dtype}.`);\n  }\n  if (indices.shape[indices.rank - 1] > tensor.rank) {\n    throw new Error(\n        'index innermost dimension length must be <= tensor rank; saw: ' +\n        `${indices.shape[indices.rank - 1]} vs. ${tensor.rank}`);\n  }\n\n  if (tensor.size === 0) {\n    throw new Error(\n        'Requested more than 0 entries, but input is empty.' +\n        ` Input shape: ${tensor.shape}.`);\n  }\n\n  const indicesShape = indices.shape;\n  const sliceRank = indicesShape[indicesShape.length - 1];\n\n  // The result shape is\n  //   indices.shape[:-1] + params.shape[indices.shape[-1]:]\n  let nResult = 1;\n  for (let i = 0; i < indicesShape.length - 1; ++i) {\n    nResult *= indicesShape[i];\n  }\n\n  const inputShape = tensor.shape;\n\n  const resultShape = indicesShape.slice();\n  resultShape.pop();\n\n  let sliceSize = 1;\n  for (let i = sliceRank; i < tensor.rank; ++i) {\n    sliceSize *= inputShape[i];\n    resultShape.push(inputShape[i]);\n  }\n\n  const strides =\n      [...computeStrides(tensor.shape).map(stride => stride / sliceSize),\n       1].slice(0, sliceRank);\n\n  return [resultShape, nResult, sliceSize, strides];\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Inputs of size above this threshold will be parallelized by calling multiple\n * shader programs.\n */\nimport {nearestDivisor} from '../util';\n\nexport const PARALLELIZE_THRESHOLD = 30;\n\nexport interface ReduceInfo {\n  windowSize: number;\n  batchSize: number;\n  inSize: number;\n}\n\nexport function computeOptimalWindowSize(inSize: number): number {\n  if (inSize <= PARALLELIZE_THRESHOLD) {\n    return inSize;\n  }\n  return nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor} from '../tensor';\nimport {computeStrides, sizeFromShape} from '../util';\n\n/**\n * Check whether updates.shape = indices.shape[:batchDim] +\n * shape[sliceDim:]\n *\n * @param x The input tensor.\n */\nexport function validateUpdateShape(\n    shape: number[], indices: Tensor, updates: Tensor) {\n  const sliceDim = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;\n  const batchDim = (indices.rank > 1) ? indices.rank - 1 : 1;\n\n  const shapeError = 'Must have updates.shape = indices.shape[:batchDim] + ' +\n      `shape[sliceDim:], got updates.shape: ${updates.shape}` +\n      `, indices.shape: ${indices.shape}, shape: ${shape}` +\n      `, sliceDim: ${sliceDim}, and batchDim: ${batchDim}.`;\n\n  if (updates.rank < batchDim) {\n    throw new Error(shapeError + ` update.rank < ${batchDim}. `);\n  }\n  if (shape.length < sliceDim + (updates.rank - batchDim)) {\n    throw new Error(\n        shapeError +\n        ` Output shape length < ${sliceDim + (updates.rank - batchDim)}`);\n  }\n  if (updates.rank !== batchDim + shape.length - sliceDim) {\n    throw new Error(\n        shapeError + ` update.rank != ${batchDim + shape.length - sliceDim}`);\n  }\n  for (let d = 0; d < batchDim; ++d) {\n    if (updates.shape[d] !== indices.shape[d]) {\n      throw new Error(\n          shapeError +\n          ` updates.shape[${d}] (${updates.shape[d]}) != indices.shape[${d}] (${\n              indices.shape[d]}).`);\n    }\n  }\n  for (let d = 0; d < updates.rank - batchDim; ++d) {\n    if (updates.shape[d + batchDim] !== shape[d + sliceDim]) {\n      throw new Error(\n          shapeError +\n          ` updates.shape[${d + batchDim}] (${\n              updates.shape[d + batchDim]}) != shape[${d + batchDim}] (${\n              shape[d + batchDim]})`);\n    }\n  }\n}\n\nexport interface ScatterShapeInfo {\n  sliceRank: number;\n  numUpdates: number;\n  sliceSize: number;\n  strides: number[];\n  outputSize: number;\n}\n/**\n * Validate scatter nd inputs.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n */\nexport function validateInput(\n    updates: Tensor, indices: Tensor, shape: number[]) {\n  if (indices.rank < 1) {\n    throw new Error(\n        'tf.scatterND() expects the indices to be rank 1 or higher,' +\n        ` but the rank was ${indices.rank}.`);\n  }\n  if (updates.rank < 1) {\n    throw new Error(\n        'tf.scatterND() expects the updates to be rank 1 or higher,' +\n        ` but the rank was ${updates.rank}.`);\n  }\n  if (indices.dtype !== 'int32') {\n    throw new Error(`The dtype of 'indices' should be int32, but got dtype: ${\n        indices.dtype}`);\n  }\n  if (shape.length < 1) {\n    throw new Error(\n        `Output rank must be greater or equal to 1, but got shape: ${shape}`);\n  }\n\n  if (shape.length === 0) {\n    if (indices.size === 0) {\n      throw new Error(`Indices specified for empty output. indices shape: ${\n          indices.shape}`);\n    }\n    if (updates.size === 0) {\n      throw new Error(`Updates specified for empty output. updates shape: ${\n          updates.shape}`);\n    }\n  }\n\n  validateUpdateShape(shape, indices, updates);\n}\n\n/**\n * Calculate the shape information for the output.\n *\n * @param update The tensor contains the update values.\n * @param indices The tensor contains the indices for the update values.\n * @param shape The shape of the output tensor.\n *\n * @returns ScatterShapeInfo\n */\nexport function calculateShapes(\n    updates: Tensor, indices: Tensor, shape: number[]): ScatterShapeInfo {\n  // Calculate the number of dimensions in indices\n  const sliceRank = (indices.rank > 1) ? indices.shape[indices.rank - 1] : 1;\n\n  // Calculate the number of elements that make up each slice of our updated\n  // tensor. This allows us to work with flattened tensors and copy over whole\n  // slices at a time.\n  const totalNd = shape.length;\n\n  let sliceSize = 1;\n  for (let i = sliceRank; i < totalNd; ++i) {\n    sliceSize *= shape[i];\n  }\n\n  const safeSliceDim = (sliceRank < 1) ? 1 : sliceRank;\n  const numUpdates = indices.size / safeSliceDim;\n\n  const outputStrides = [...computeStrides(shape), 1];\n  const strides = outputStrides.slice(\n      outputStrides.length - sliceRank, outputStrides.length);\n  const outputSize = sizeFromShape(shape);\n  return {sliceRank, numUpdates, sliceSize, strides, outputSize};\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport * as util from '../util';\n\nexport function assertParamsValid(\n    input: Tensor, begin: number[], size: number[]): void {\n  util.assert(\n      input.rank === begin.length,\n      `Error in slice${input.rank}D: Length of begin ${begin} must ` +\n          `match the rank of the array (${input.rank}).`);\n  util.assert(\n      input.rank === size.length,\n      `Error in slice${input.rank}D: Length of size ${size} must ` +\n          `match the rank of the array (${input.rank}).`);\n\n  for (let i = 0; i < input.rank; ++i) {\n    util.assert(\n        begin[i] + size[i] <= input.shape[i],\n        `Error in slice${input.rank}D: begin[${i}] + size[${i}] ` +\n            `(${begin[i] + size[i]}) would overflow input.shape[${i}] (${\n                input.shape[i]})`);\n  }\n}\n\n/**\n * Calculate the start index and output tensor shape for strided slice op.\n * @returns array of [startIndex, size, shrinkAxis]\n */\nexport function getStridedSlicedInfo(\n    shape: number[], begin: number[], end: number[], strides: number[],\n    beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0,\n    shrinkAxisMask = 0): [number[], number[], number[]] {\n  if (ellipsisMask !== 0) {\n    throw new Error('ellipsis mask is not yet supported');\n  }\n  if (newAxisMask !== 0) {\n    throw new Error('new axis mask is not yet supported');\n  }\n  // Note that the axis orders are reversed for runtime ops, so the indices,\n  // strides and masks must be as well too.\n  const startIndex: number[] = [];\n  const endIndex: number[] = [];\n  const shrinkAxis: number[] = [];\n  for (let i = 0; i < shape.length; i++) {\n    startIndex[i] = startForAxis(beginMask, begin, strides, shape, i);\n    endIndex[i] = stopForAxis(endMask, end, strides, shape, i);\n    // When shrinking an axis, use startIndex + 1 for endIndex.\n    // Check the axis bit from right of shrinkAxisMask\n    if (shrinkAxisMask & 1 << i) {\n      endIndex[i] = startIndex[i] + 1;\n      shrinkAxis.push(i);\n    }\n  }\n\n  let size = new Array(shape.length).fill(0);\n  size = size.map((d, i) => {\n    let count = 0;\n    for (let start = startIndex[i];\n         !(strides[i] > 0 ? start >= endIndex[i] : start <= endIndex[i]);\n         start += strides[i]) {\n      count += 1;\n    }\n    return count;\n  });\n\n  return [startIndex, size, shrinkAxis];\n}\n\nexport function startForAxis(\n    beginMask: number, startIndices: number[], strides: number[],\n    inputShape: number[], axis: number): number {\n  // Begin with the specified index\n  let start = startIndices[axis];\n\n  // Check the axis bit from right of beginMask\n  if (beginMask & 1 << axis) {\n    if (strides[axis] > 0) {\n      // Forward iteration - use the first element. These values will get\n      // clamped below (Note: We could have set them to 0 and axis_size-1, but\n      // use lowest() and max() to maintain symmetry with StopForAxis())\n      start = Number.MIN_SAFE_INTEGER;\n    } else {\n      // Backward iteration - use the last element.\n      start = Number.MAX_SAFE_INTEGER;\n    }\n  }\n\n  // Handle negative indices\n  const axisSize = inputShape[axis];\n  if (start < 0) {\n    start += axisSize;\n  }\n\n  // Clamping\n  start = util.clamp(0, start, axisSize - 1);\n\n  return start;\n}\n\nexport function stopForAxis(\n    endMask: number, stopIndices: number[], strides: number[],\n    inputShape: number[], axis: number): number {\n  // Begin with the specified index\n  let stop = stopIndices[axis];\n\n  // Check the axis bit from right of endMask\n  if (endMask & (1 << axis)) {\n    if (strides[axis] > 0) {\n      // Forward iteration - use the last element. These values will get\n      // clamped below\n      stop = Number.MAX_SAFE_INTEGER;\n    } else {\n      // Backward iteration - use the first element.\n      stop = Number.MIN_SAFE_INTEGER;\n    }\n  }\n\n  // Handle negative indices\n  const axisSize = inputShape[axis];\n  if (stop < 0) {\n    stop += axisSize;\n  }\n\n  // Clamping\n  // Because the end index points one past the last element, we need slightly\n  // different clamping ranges depending on the direction.\n  if (strides[axis] > 0) {\n    // Forward iteration\n    stop = util.clamp(0, stop, axisSize);\n  } else {\n    // Backward iteration\n    stop = util.clamp(-1, stop, axisSize - 1);\n  }\n\n  return stop;\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from './environment';\nimport {Tensor} from './tensor';\nimport {DataType, TensorLike, TypedArray} from './types';\nimport {assert, flatten, inferDtype, isTypedArray, toTypedArray} from './util';\n\nexport function inferShape(val: TensorLike): number[] {\n  let firstElem: typeof val = val;\n\n  if (isTypedArray(val)) {\n    return [(val as TypedArray).length];\n  }\n  if (!Array.isArray(val)) {\n    return [];  // Scalar.\n  }\n  const shape: number[] = [];\n\n  while (firstElem instanceof Array || isTypedArray(firstElem)) {\n    shape.push(firstElem.length);\n    firstElem = firstElem[0];\n  }\n  if (val instanceof Array && ENV.get('TENSORLIKE_CHECK_SHAPE_CONSISTENCY')) {\n    deepAssertShapeConsistency(val, shape, []);\n  }\n\n  return shape;\n}\n\nfunction deepAssertShapeConsistency(\n    val: TensorLike, shape: number[], indices: number[]) {\n  indices = indices || [];\n  if (!(val instanceof Array) && !isTypedArray(val)) {\n    assert(\n        shape.length === 0,\n        () => `Element arr[${indices.join('][')}] is a primitive, ` +\n            `but should be an array/TypedArray of ${shape[0]} elements`);\n    return;\n  }\n  assert(\n      shape.length > 0,\n      () => `Element arr[${indices.join('][')}] should be a primitive, ` +\n          `but is an array of ${val.length} elements`);\n  assert(\n      val.length === shape[0],\n      () => `Element arr[${indices.join('][')}] should have ${shape[0]} ` +\n          `elements, but has ${val.length} elements`);\n  const subShape = shape.slice(1);\n  for (let i = 0; i < val.length; ++i) {\n    deepAssertShapeConsistency(val[i], subShape, indices.concat(i));\n  }\n}\n\nfunction assertDtype(\n    expectedDtype: DataType|'numeric', actualDType: DataType, argName: string,\n    functionName: string) {\n  if (expectedDtype == null) {\n    return;\n  }\n  if (expectedDtype !== 'numeric' && expectedDtype !== actualDType ||\n      expectedDtype === 'numeric' && actualDType === 'string') {\n    throw new Error(\n        `Argument '${argName}' passed to '${functionName}' must ` +\n        `be ${expectedDtype} tensor, but got ${actualDType} tensor`);\n  }\n}\n\nexport function convertToTensor<T extends Tensor>(\n    x: T|TensorLike, argName: string, functionName: string,\n    parseAsDtype: DataType|'numeric' = 'numeric'): T {\n  if (x instanceof Tensor) {\n    assertDtype(parseAsDtype, x.dtype, argName, functionName);\n    return x;\n  }\n  let inferredDtype = inferDtype(x);\n  // If the user expects a bool/int/float, use that info to update the\n  // inferredDtype when it is not a string.\n  if (inferredDtype !== 'string' &&\n      ['bool', 'int32', 'float32'].indexOf(parseAsDtype) >= 0) {\n    inferredDtype = parseAsDtype as DataType;\n  }\n  assertDtype(parseAsDtype, inferredDtype, argName, functionName);\n\n  if (!isTypedArray(x) && !Array.isArray(x) && typeof x !== 'number' &&\n      typeof x !== 'boolean' && typeof x !== 'string') {\n    throw new Error(\n        `Argument '${argName}' passed to '${functionName}' must be a ` +\n        `Tensor or TensorLike, but got '${(x as {}).constructor.name}'`);\n  }\n  const inferredShape = inferShape(x);\n  if (!isTypedArray(x) && !Array.isArray(x)) {\n    x = [x] as number[];\n  }\n  const values = inferredDtype !== 'string' ?\n      toTypedArray(x, inferredDtype as DataType, ENV.get('DEBUG')) :\n      flatten(x as string[]) as string[];\n  return Tensor.make(inferredShape, {values}, inferredDtype);\n}\n\nexport function convertToTensorArray<T extends Tensor>(\n    arg: Array<T|TensorLike>, argName: string, functionName: string): T[] {\n  if (!Array.isArray(arg)) {\n    throw new Error(\n        `Argument ${argName} passed to ${functionName} must be a ` +\n        '`Tensor[]` or `TensorLike[]`');\n  }\n  const tensors = arg as T[];\n  return tensors.map(\n      (t, i) => convertToTensor(t, `${argName}[${i}]`, functionName));\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\n\n/**\n * Used for wrapping functions that perform math operations on\n * Tensors. The function will be wrapped in a named scope that cleans all\n * memory usage after the function is done.\n */\nexport function op<T extends Function>(f: {[name: string]: T}): T {\n  const keys = Object.keys(f);\n  if (keys.length !== 1) {\n    throw new Error(\n        `Please provide an object with a single key ` +\n        `(operation name) mapping to a function. Got an object with ` +\n        `${keys.length} keys.`);\n  }\n\n  let opName = keys[0];\n  const fn = f[opName];\n\n  // Strip the underscore from the end of the function name.\n  if (opName.endsWith('_')) {\n    opName = opName.substring(0, opName.length - 1);\n  }\n\n  // tslint:disable-next-line:no-any\n  const f2 = (...args: any[]) => {\n    ENV.engine.startScope(opName);\n    try {\n      const result = fn(...args);\n      if (result instanceof Promise) {\n        console.error('Cannot return a Promise inside of tidy.');\n      }\n      ENV.engine.endScope(result);\n      return result;\n    } catch (ex) {\n      ENV.engine.endScope(null);\n      throw ex;\n    }\n  };\n  Object.defineProperty(f2, 'name', {value: opName, configurable: true});\n\n  // tslint:disable-next-line:no-any\n  return f2 as any as T;\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {customGrad} from '../gradients';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * Computes the softmax normalized vector given the logits.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.softmax().print();  // or tf.softmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n */\n/** @doc {heading: 'Operations', subheading: 'Normalization'} */\nfunction softmax_<T extends Tensor>(logits: T|TensorLike, dim = -1): T {\n  const $logits = convertToTensor(logits, 'logits', 'softmax');\n\n  if (dim === -1) {\n    dim = $logits.rank - 1;\n  }\n  if (dim !== $logits.rank - 1) {\n    throw Error(\n        'Softmax along a non-last dimension is not yet supported. ' +\n        `Logits was rank ${$logits.rank} and dim was ${dim}`);\n  }\n\n  const customOp = customGrad(logits => {\n    // Do it in log space for numerical stability.\n    // exp(X - logSumExp(X))\n    const keepDims = true;\n    const lse = logits.logSumExp([dim], keepDims);\n    const logResult = logits.toFloat().sub(lse);\n    const y = logResult.exp() as T;\n\n    const gradFunc = (dy: T) => {\n      const dyTimesY = dy.mul(y);\n      const keepDims = true;\n      return dyTimesY.sub(dyTimesY.sum([dim], keepDims).mul(y));\n    };\n\n    return {value: y, gradFunc};\n  });\n\n  return customOp($logits);\n}\n\n/**\n * Computes the log softmax.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * ```js\n * const a = tf.tensor2d([2, 4, 6, 1, 2, 3], [2, 3]);\n *\n * a.logSoftmax().print();  // or tf.logSoftmax(a)\n * ```\n *\n * @param logits The logits array.\n * @param axis The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n */\n/** @doc {heading: 'Operations', subheading: 'Normalization'} */\nfunction logSoftmax_<T extends Tensor>(logits: T|TensorLike, axis = -1): T {\n  const $logits = convertToTensor(logits, 'logits', 'logSoftmax');\n\n  if (axis === -1) {\n    axis = $logits.rank - 1;\n  }\n  if (axis !== $logits.rank - 1) {\n    throw Error(\n        'Log Softmax along a non-last dimension is not yet supported. ' +\n        `Logits was rank ${$logits.rank} and axis was ${axis}`);\n  }\n\n  const customOp = customGrad(logits => {\n    const keepDims = true;\n    const xMax = logits.max(axis, true);\n    const shifted = logits.sub(xMax);\n    const value =\n        shifted.toFloat().sub(shifted.exp().sum(axis, keepDims).log()) as T;\n\n    const gradFunc = (dy: T) => {\n      const softmax = value.exp();\n      return dy.sub(dy.sum(axis, keepDims).mul(softmax));\n    };\n\n    return {value, gradFunc};\n  });\n\n  return customOp($logits);\n}\n\nexport const softmax = op({softmax_});\nexport const logSoftmax = op({logSoftmax_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\n\n/**\n * Converts two real numbers to a complex number.\n *\n * Given a tensor `real` representing the real part of a complex number, and a\n * tensor `imag` representing the imaginary part of a complex number, this\n * operation returns complex numbers elementwise of the form [r0, i0, r1, i1],\n * where r represents the real part and i represents the imag part.\n *\n * The input tensors real and imag must have the same shape.\n *\n * ```js\n * const real = tf.tensor1d([2.25, 3.25]);\n * const imag = tf.tensor1d([4.75, 5.75]);\n * const complex = tf.complex(real, imag);\n *\n * complex.print();\n * ```\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction complex_<T extends Tensor>(real: T|TensorLike, imag: T|TensorLike): T {\n  const $real = convertToTensor(real, 'real', 'complex');\n  const $imag = convertToTensor(imag, 'imag', 'complex');\n  util.assertShapesMatch(\n      $real.shape, $imag.shape,\n      `real and imag shapes, ${$real.shape} and ${$imag.shape}, ` +\n          `must match in call to tf.complex().`);\n\n  return ENV.engine.runKernel(\n             backend => backend.complex($real, $imag), {$real, $imag}) as T;\n}\n\n/**\n * Returns the real part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the real part of each element in input considered as a complex number.\n *\n * If the input is real, it simply makes a clone.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.real(x).print();\n * ```\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction real_<T extends Tensor>(input: T|TensorLike): T {\n  const $input = convertToTensor(input, 'input', 'real');\n\n  return ENV.engine.runKernel(backend => backend.real($input), {$input}) as T;\n}\n\n/**\n * Returns the imaginary part of a complex (or real) tensor.\n *\n * Given a tensor input, this operation returns a tensor of type float that is\n * the imaginary part of each element in input considered as a complex number.\n * If input is real, a tensor of all zeros is returned.\n *\n * ```js\n * const x = tf.complex([-2.25, 3.25], [4.75, 5.75]);\n * tf.imag(x).print();\n * ```\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction imag_<T extends Tensor>(input: T|TensorLike): T {\n  const $input = convertToTensor(input, 'input', 'imag');\n\n  return ENV.engine.runKernel(backend => backend.imag($input), {$input}) as T;\n}\n\nexport const complex = op({complex_});\nexport const real = op({real_});\nexport const imag = op({imag_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Scalar, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, Tensor5D, Tensor6D} from '../tensor';\nimport {convertToTensor, inferShape} from '../tensor_util_env';\nimport {TensorLike, TensorLike1D, TensorLike2D, TensorLike3D, TensorLike4D, TensorLike5D, TensorLike6D, TypedArray} from '../types';\nimport {DataType, Rank, ShapeMap} from '../types';\nimport {assert, assertNonNull, flatten, getArrayFromDType, inferDtype, isTypedArray, makeOnesTypedArray, makeZerosTypedArray, sizeFromShape, toTypedArray} from '../util';\n\nimport {complex} from './complex_ops';\nimport {op} from './operation';\n\n/**\n * Creates a `tf.Tensor` with the provided values, shape and dtype.\n *\n * ```js\n * // Pass an array of values to create a vector.\n * tf.tensor([1, 2, 3, 4]).print();\n * ```\n *\n * ```js\n * // Pass a nested array of values to make a matrix or a higher\n * // dimensional tensor.\n * tf.tensor([[1, 2], [3, 4]]).print();\n * ```\n *\n * ```js\n * // Pass a flat array and specify a shape yourself.\n * tf.tensor([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor<R extends Rank>(\n    values: TensorLike, shape?: ShapeMap[R], dtype?: DataType): Tensor<R> {\n  if (dtype == null) {\n    dtype = inferDtype(values);\n  }\n  if (dtype === 'complex64') {\n    throw new Error(\n        `Cannot construct a complex64 tensor directly. ` +\n        `Please use tf.complex(real, imag).`);\n  }\n  if (!isTypedArray(values) && !Array.isArray(values) &&\n      typeof values !== 'number' && typeof values !== 'boolean' &&\n      typeof values !== 'string') {\n    throw new Error(\n        'values passed to tensor(values) must be a number/boolean/string or ' +\n        'an array of numbers/booleans/strings, or a TypedArray');\n  }\n  const inferredShape = inferShape(values);\n  if (shape != null) {\n    const providedSize = sizeFromShape(shape);\n    const inferredSize = sizeFromShape(inferredShape);\n    assert(\n        providedSize === inferredSize,\n        () =>\n            `Based on the provided shape, [${shape}], the tensor should have ` +\n            `${providedSize} values but has ${inferredSize}`);\n\n    for (let i = 0; i < inferredShape.length; ++i) {\n      const inferred = inferredShape[i];\n      const flatDimsDontMatch = i === inferredShape.length - 1 ?\n          inferred !== sizeFromShape(shape.slice(i)) :\n          true;\n      assert(\n          inferredShape[i] === shape[i] || !flatDimsDontMatch,\n          () => `Error creating a new Tensor. Inferred shape ` +\n              `(${inferredShape}) does not match the provided ` +\n              `shape (${shape}). `);\n    }\n  }\n  if (!isTypedArray(values) && !Array.isArray(values)) {\n    values = [values] as number[];\n  }\n  shape = shape || inferredShape;\n  values = dtype !== 'string' ? toTypedArray(values, dtype, ENV.get('DEBUG')) :\n                                flatten(values as string[]) as string[];\n  return Tensor.make(shape, {values}, dtype);\n}\n\n/**\n * Creates rank-0 `tf.Tensor` (scalar) with the provided value and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.scalar` as it makes the code more readable.\n *\n * ```js\n * tf.scalar(3.14).print();\n * ```\n *\n * @param value The value of the scalar.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction scalar(value: number|boolean|string, dtype?: DataType): Scalar {\n  if ((isTypedArray(value) || Array.isArray(value)) && dtype !== 'complex64') {\n    throw new Error(\n        'Error creating a new Scalar: value must be a primitive ' +\n        '(number|boolean|string)');\n  }\n  return tensor(value, [], dtype);\n}\n\n/**\n * Creates rank-1 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor1d` as it makes the code more readable.\n *\n * ```js\n * tf.tensor1d([1, 2, 3]).print();\n * ```\n *\n * @param values The values of the tensor. Can be array of numbers,\n *     or a `TypedArray`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor1d(values: TensorLike1D, dtype?: DataType): Tensor1D {\n  assertNonNull(values);\n  const inferredShape = inferShape(values);\n  if (inferredShape.length !== 1) {\n    throw new Error('tensor1d() requires values to be a flat/TypedArray');\n  }\n  return tensor(values, inferredShape as [number], dtype);\n}\n\n/**\n * Creates rank-2 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor2d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor2d([[1, 2], [3, 4]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided, it is inferred from\n *     `values`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor2d(\n    values: TensorLike2D, shape?: [number, number],\n    dtype?: DataType): Tensor2D {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 2) {\n    throw new Error('tensor2d() requires shape to have two numbers');\n  }\n  const inferredShape = inferShape(values);\n  if (inferredShape.length !== 2 && inferredShape.length !== 1) {\n    throw new Error(\n        'tensor2d() requires values to be number[][] or flat/TypedArray');\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\n        'tensor2d() requires shape to be provided when `values` ' +\n        'are a flat/TypedArray');\n  }\n  shape = shape || inferredShape as [number, number];\n  return tensor(values, shape, dtype);\n}\n\n/**\n * Creates rank-3 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor3d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor3d([[[1], [2]], [[3], [4]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor3d([1, 2, 3, 4], [2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. If not provided,  it is inferred from\n *     `values`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor3d(\n    values: TensorLike3D, shape?: [number, number, number],\n    dtype?: DataType): Tensor3D {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 3) {\n    throw new Error('tensor3d() requires shape to have three numbers');\n  }\n  const inferredShape = inferShape(values);\n  if (inferredShape.length !== 3 && inferredShape.length !== 1) {\n    throw new Error(\n        'tensor3d() requires values to be number[][][] or flat/TypedArray');\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\n        'tensor3d() requires shape to be provided when `values` ' +\n        'are a flat array');\n  }\n  shape = shape || inferredShape as [number, number, number];\n  return tensor(values, shape, dtype);\n}\n\n/**\n * Creates rank-4 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor4d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor4d([[[[1], [2]], [[3], [4]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor4d(\n    values: TensorLike4D, shape?: [number, number, number, number],\n    dtype?: DataType): Tensor4D {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 4) {\n    throw new Error('tensor4d() requires shape to have four numbers');\n  }\n  const inferredShape = inferShape(values);\n  if (inferredShape.length !== 4 && inferredShape.length !== 1) {\n    throw new Error(\n        'tensor4d() requires values to be number[][][][] or flat/TypedArray');\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\n        'tensor4d() requires shape to be provided when `values` ' +\n        'are a flat array');\n  }\n  shape = shape || inferredShape as [number, number, number, number];\n  return tensor(values, shape, dtype);\n}\n\n/**\n * Creates rank-5 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor5d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor5d([[[[[1], [2]], [[3], [4]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor5d([1, 2, 3, 4, 5, 6, 7, 8], [1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor5d(\n    values: TensorLike5D, shape?: [number, number, number, number, number],\n    dtype?: DataType): Tensor5D {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 5) {\n    throw new Error('tensor5d() requires shape to have five numbers');\n  }\n  const inferredShape = inferShape(values);\n  if (inferredShape.length !== 5 && inferredShape.length !== 1) {\n    throw new Error(\n        'tensor5d() requires values to be ' +\n        'number[][][][][] or flat/TypedArray');\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\n        'tensor5d() requires shape to be provided when `values` ' +\n        'are a flat array');\n  }\n  shape = shape || inferredShape as [number, number, number, number, number];\n  return tensor(values, shape, dtype);\n}\n\n/**\n * Creates rank-6 `tf.Tensor` with the provided values, shape and dtype.\n *\n * The same functionality can be achieved with `tf.tensor`, but in general\n * we recommend using `tf.tensor6d` as it makes the code more readable.\n *\n *  ```js\n * // Pass a nested array.\n * tf.tensor6d([[[[[[1],[2]],[[3],[4]]],[[[5],[6]],[[7],[8]]]]]]).print();\n * ```\n * ```js\n * // Pass a flat array and specify a shape.\n * tf.tensor6d([1, 2, 3, 4, 5, 6, 7, 8], [1, 1, 2, 2, 2, 1]).print();\n * ```\n *\n * @param values The values of the tensor. Can be nested array of numbers,\n *     or a flat array, or a `TypedArray`.\n * @param shape The shape of the tensor. Optional. If not provided,\n *   it is inferred from `values`.\n * @param dtype The data type.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction tensor6d(\n    values: TensorLike6D,\n    shape?: [number, number, number, number, number, number],\n    dtype?: DataType): Tensor6D {\n  assertNonNull(values);\n  if (shape != null && shape.length !== 6) {\n    throw new Error('tensor6d() requires shape to have six numbers');\n  }\n  const inferredShape = inferShape(values);\n  if (inferredShape.length !== 6 && inferredShape.length !== 1) {\n    throw new Error(\n        'tensor6d() requires values to be number[][][][] or flat/TypedArray');\n  }\n  if (inferredShape.length === 1 && shape == null) {\n    throw new Error(\n        'tensor6d() requires shape to be provided when `values` ' +\n        'are a flat array');\n  }\n  shape = shape ||\n      inferredShape as [number, number, number, number, number, number];\n  return tensor(values, shape, dtype);\n}\n\n/**\n * Creates a `tf.Tensor` with all elements set to 1.\n *\n * ```js\n * tf.ones([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n *     'float'.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction ones<R extends Rank>(\n    shape: ShapeMap[R], dtype: DataType = 'float32'): Tensor<R> {\n  if (dtype === 'complex64') {\n    const real = ones(shape, 'float32');\n    const imag = ones(shape, 'float32');\n    return complex(real, imag);\n  }\n  const values = makeOnesTypedArray(sizeFromShape(shape), dtype);\n  return Tensor.make(shape, {values}, dtype);\n}\n\n/**\n * Creates a `tf.Tensor` with all elements set to 0.\n *\n * ```js\n * tf.zeros([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The type of an element in the resulting tensor. Can\n *     be 'float32', 'int32' or 'bool'. Defaults to 'float'.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction zeros<R extends Rank>(\n    shape: ShapeMap[R], dtype: DataType = 'float32'): Tensor<R> {\n  if (dtype === 'complex64') {\n    const real = zeros(shape, 'float32');\n    const imag = zeros(shape, 'float32');\n    return complex(real, imag);\n  }\n  const values = makeZerosTypedArray(sizeFromShape(shape), dtype);\n  return Tensor.make(shape, {values}, dtype);\n}\n\n/**\n * Creates a `tf.Tensor` filled with a scalar value.\n *\n * ```js\n * tf.fill([2, 2], 4).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param value The scalar value to fill the tensor with.\n * @param dtype The type of an element in the resulting tensor. Defaults to\n * 'float'.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction fill<R extends Rank>(\n    shape: ShapeMap[R], value: number|string, dtype?: DataType): Tensor<R> {\n  dtype = dtype || inferDtype(value);\n  const values = getArrayFromDType(dtype, sizeFromShape(shape)) as TypedArray;\n  values.fill(value as number);\n  return Tensor.make(shape, {values}, dtype);\n}\n\n/**\n * Creates a `tf.Tensor` with all elements set to 1 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.onesLike(x).print();\n * ```\n * @param x A tensor.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction onesLike_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'onesLike');\n  return ones($x.shape, $x.dtype) as T;\n}\n\n/**\n * Creates a `tf.Tensor` with all elements set to 0 with the same shape as the\n * given tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n * tf.zerosLike(x).print();\n * ```\n *\n * @param x The tensor of required shape.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction zerosLike_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'zerosLike');\n  return zeros($x.shape, $x.dtype) as T;\n}\n\n/**\n * Return an evenly spaced sequence of numbers over the given interval.\n *\n * ```js\n * tf.linspace(0, 9, 10).print();\n * ```\n * @param start The start value of the sequence.\n * @param stop The end value of the sequence.\n * @param num The number of values to generate.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction linspace(start: number, stop: number, num: number): Tensor1D {\n  if (num === 0) {\n    throw new Error('Cannot request zero samples');\n  }\n\n  const step = (stop - start) / (num - 1);\n\n  const values = makeZerosTypedArray(num, 'float32');\n  values[0] = start;\n  for (let i = 1; i < values.length; i++) {\n    values[i] = values[i - 1] + step;\n  }\n\n  return tensor1d(values, 'float32');\n}\n\n/**\n * Creates a new `tf.Tensor1D` filled with the numbers in the range provided.\n *\n * The tensor is a is half-open interval meaning it includes start, but\n * excludes stop. Decrementing ranges and negative step values are also\n * supported.\n *\n * ```js\n * tf.range(0, 9, 2).print();\n * ```\n *\n * @param start An integer start value\n * @param stop An integer stop value\n * @param step An integer increment (will default to 1 or -1)\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction range(\n    start: number, stop: number, step = 1,\n    dtype: 'float32'|'int32' = 'float32'): Tensor1D {\n  if (step === 0) {\n    throw new Error('Cannot have a step of zero');\n  }\n\n  const sameStartStop = start === stop;\n  const increasingRangeNegativeStep = start < stop && step < 0;\n  const decreasingRangePositiveStep = stop < start && step > 1;\n\n  if (sameStartStop || increasingRangeNegativeStep ||\n      decreasingRangePositiveStep) {\n    return zeros([0], dtype);\n  }\n\n  const numElements = Math.abs(Math.ceil((stop - start) / step));\n  const values = makeZerosTypedArray(numElements, dtype);\n\n  if (stop < start && step === 1) {\n    // Auto adjust the step's sign if it hasn't been set\n    // (or was set to 1)\n    step = -1;\n  }\n\n  values[0] = start;\n  for (let i = 1; i < values.length; i++) {\n    values[i] = values[i - 1] + step;\n  }\n\n  return tensor1d(values, dtype);\n}\n\nexport {\n  fill,\n  linspace,\n  ones,\n  range,\n  scalar,\n  tensor,\n  tensor1d,\n  tensor2d,\n  tensor3d,\n  tensor4d,\n  tensor5d,\n  tensor6d,\n  zeros\n};\n\nexport const onesLike = op({onesLike_});\nexport const zerosLike = op({zerosLike_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo, Conv3DInfo} from '../ops/conv_util';\nimport {DataId, Scalar, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, Tensor5D} from '../tensor';\nimport {DataType, DataValues, Rank, ShapeMap} from '../types';\n\n// Required information for all backends.\nexport interface BackendTimingInfo {\n  kernelMs: number;\n  getExtraProfileInfo?(): string;  // a field for additional timing information\n                                   // e.g. packing / unpacking for WebGL backend\n}\n\nexport interface TensorStorage {\n  read(dataId: DataId): Promise<DataValues>;\n  readSync(dataId: DataId): DataValues;\n  disposeData(dataId: DataId): void;\n  write(dataId: DataId, values: DataValues): void;\n  fromPixels(\n      pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement,\n      numChannels: number): Tensor3D;\n  register(dataId: DataId, shape: number[], dtype: DataType): void;\n  memory(): {unreliable: boolean;};  // Backend-specific information.\n}\n\n/** Convenient class for storing tensor-related data. */\nexport class DataStorage<T> {\n  private data = new WeakMap<DataId, T>();\n\n  constructor(private dataMover: DataMover) {}\n\n  get(dataId: DataId) {\n    if (!this.data.has(dataId)) {\n      this.dataMover.moveData(dataId);\n    }\n    return this.data.get(dataId);\n  }\n\n  set(dataId: DataId, value: T): void {\n    this.data.set(dataId, value);\n  }\n\n  has(dataId: DataId): boolean {\n    return this.data.has(dataId);\n  }\n\n  delete(dataId: DataId): boolean {\n    return this.data.delete(dataId);\n  }\n}\n\nexport interface DataMover {\n  /**\n   * To be called by backends whenever they see a dataId that they don't own.\n   * Upon calling this method, the mover will fetch the tensor from another\n   * backend and register it with the current active backend.\n   */\n  moveData(dataId: DataId): void;\n}\n\nexport interface BackendTimer {\n  time(f: () => void): Promise<BackendTimingInfo>;\n}\n\n/**\n * The interface that defines the kernels that should be implemented when\n * adding a new backend. New backends don't need to implement every one of the\n * methods, this can be done gradually (throw an error for unimplemented\n * methods).\n */\nexport class KernelBackend implements TensorStorage, BackendTimer {\n  time(f: () => void): Promise<BackendTimingInfo> {\n    throw new Error('Not yet implemented.');\n  }\n  read(dataId: object): Promise<DataValues> {\n    throw new Error('Not yet implemented.');\n  }\n  readSync(dataId: object): DataValues {\n    throw new Error('Not yet implemented.');\n  }\n  disposeData(dataId: object): void {\n    throw new Error('Not yet implemented.');\n  }\n  write(dataId: object, values: DataValues): void {\n    throw new Error('Not yet implemented.');\n  }\n  fromPixels(\n      pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement,\n      numChannels: number): Tensor<Rank.R3> {\n    throw new Error('Not yet implemented.');\n  }\n  register(dataId: object, shape: number[], dtype: DataType): void {\n    throw new Error('Not yet implemented.');\n  }\n  memory(): {unreliable: boolean; reasons?: string[]} {\n    throw new Error('Not yet implemented.');\n  }\n  /** Returns the highest precision for floats in bits (e.g. 16 or 32) */\n  floatPrecision(): number {\n    throw new Error('Not yet implemented');\n  }\n\n  batchMatMul(\n      a: Tensor3D, b: Tensor3D, transposeA: boolean,\n      transposeB: boolean): Tensor3D {\n    throw new Error('Not yet implemented');\n  }\n\n  slice<T extends Tensor>(x: T, begin: number[], size: number[]): T {\n    throw new Error('Not yet implemented');\n  }\n  stridedSlice<T extends Tensor>(\n      x: T, begin: number[], end: number[], strides: number[],\n      beginMask: number, endMask: number, ellipsisMask: number,\n      newAxisMask: number, shrinkAxisMask: number): T {\n    throw new Error('Not yet implemented');\n  }\n  reverse<T extends Tensor>(a: T, axis: number[]): T {\n    throw new Error('Not yet implemented');\n  }\n\n  concat(tensors: Tensor[], axis: number): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  neg<T extends Tensor>(a: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  add(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  addN<T extends Tensor>(tensors: T[]): T {\n    throw new Error('Not yet implemented');\n  }\n  subtract(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  multiply(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  realDivide(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  floorDiv(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  sum(x: Tensor, axes: number[]): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  prod(x: Tensor, axes: number[]): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  unsortedSegmentSum<T extends Tensor>(\n      x: T, segmentIds: Tensor1D, numSegments: number): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  argMin(x: Tensor, axis: number): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  argMax(x: Tensor, axis: number): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  equal(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  notEqual(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  less(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  lessEqual(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  greater(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  greaterEqual(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  logicalNot<T extends Tensor>(a: T): T {\n    throw new Error('Not yet implemented');\n  }\n  logicalAnd(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  logicalOr(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  where(condition: Tensor): Tensor2D {\n    throw new Error('Not yet implemented');\n  }\n  select(condition: Tensor, a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  topk<T extends Tensor>(x: T, k: number, sorted: boolean): [T, T] {\n    throw new Error('Not yet implemented');\n  }\n\n  min(x: Tensor, axes: number[]): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  minimum(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  mod(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  max(x: Tensor, axes: number[]): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  maximum(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  all(x: Tensor, axes: number[]): Tensor {\n    throw new Error('Not yet implemented');\n  }\n  any(x: Tensor, axes: number[]): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  squaredDifference(a: Tensor, b: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  ceil<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  floor<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  round<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  sign<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  pow<T extends Tensor>(a: T, b: Tensor): T {\n    throw new Error('Not yet implemented');\n  }\n  exp<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  expm1<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  log<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  log1p<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  sqrt<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  rsqrt<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  square<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  reciprocal<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  relu<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  elu<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  eluDer<T extends Tensor>(dy: T, y: T): T {\n    throw new Error('Not yet implemented');\n  }\n  selu<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  int<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  clip<T extends Tensor>(x: T, min: number, max: number): T {\n    throw new Error('Not yet implemented');\n  }\n\n  abs<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  complexAbs<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  sigmoid<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  softplus<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  sin<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  cos<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  tan<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  asin<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  acos<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  atan<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  atan2<T extends Tensor>(a: T, b: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  sinh<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  cosh<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  tanh<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  asinh<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  acosh<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n  atanh<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  erf<T extends Tensor>(x: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  step<T extends Tensor>(x: T, alpha: number): T {\n    throw new Error('Not yet implemented');\n  }\n\n  conv2d(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n  conv2dDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n  conv2dDerFilter(x: Tensor4D, dY: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  depthwiseConv2D(input: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n  depthwiseConv2DDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n  depthwiseConv2DDerFilter(x: Tensor4D, dY: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n  conv3d(x: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n    throw new Error('Not yet implemented');\n  }\n  conv3dDerInput(dy: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo):\n      Tensor5D {\n    throw new Error('Not yet implemented');\n  }\n  conv3dDerFilter(x: Tensor5D, dY: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n    throw new Error('Not yet implemented');\n  }\n  maxPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n  maxPoolBackprop(dy: Tensor4D, x: Tensor4D, y: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n  avgPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n  avgPoolBackprop(dy: Tensor4D, x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  reshape<T extends Tensor, R extends Rank>(x: T, shape: ShapeMap[R]):\n      Tensor<R> {\n    throw new Error('Not yet implemented');\n  }\n  cast<T extends Tensor>(x: T, dtype: DataType): T {\n    throw new Error('Not yet implemented');\n  }\n\n  tile<T extends Tensor>(x: T, reps: number[]): T {\n    throw new Error('Not yet implemented');\n  }\n\n  pad<T extends Tensor>(\n      x: T, paddings: Array<[number, number]>, constantValue: number): T {\n    throw new Error('Not yet implemented');\n  }\n\n  transpose<T extends Tensor>(x: T, perm: number[]): T {\n    throw new Error('Not yet implemented');\n  }\n\n  gather<T extends Tensor>(x: T, indices: Tensor1D, axis: number): T {\n    throw new Error('Not yet implemented');\n  }\n\n  gatherND(x: Tensor, indices: Tensor): Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  scatterND<R extends Rank>(\n      indices: Tensor, updates: Tensor, shape: ShapeMap[R]): Tensor<R> {\n    throw new Error('Not yet implemented');\n  }\n\n  batchToSpaceND<T extends Tensor>(\n      x: T, blockShape: number[], crops: number[][]): T {\n    throw new Error('Not yet implemented');\n  }\n\n  spaceToBatchND<T extends Tensor>(\n      x: T, blockShape: number[], paddings: number[][]): T {\n    throw new Error('Not yet implemented');\n  }\n\n  resizeBilinear(\n      x: Tensor4D, newHeight: number, newWidth: number,\n      alignCorners: boolean): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  resizeBilinearBackprop(dy: Tensor4D, x: Tensor4D, alignCorners: boolean):\n      Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  resizeNearestNeighbor(\n      x: Tensor4D, newHEight: number, newWidth: number,\n      alignCorners: boolean): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  resizeNearestNeighborBackprop(\n      dy: Tensor4D, x: Tensor4D, alignCorners: boolean): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  batchNormalization(\n      x: Tensor4D, mean: Tensor4D|Tensor1D, variance: Tensor4D|Tensor1D,\n      varianceEpsilon: number, scale?: Tensor4D|Tensor1D,\n      offset?: Tensor4D|Tensor1D): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  localResponseNormalization4D(\n      x: Tensor4D, radius: number, bias: number, alpha: number,\n      beta: number): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  LRNGrad(\n      dy: Tensor4D, inputImage: Tensor4D, outputImage: Tensor4D, radius: number,\n      bias: number, alpha: number, beta: number): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  multinomial(\n      logits: Tensor2D, normalized: boolean, numSamples: number,\n      seed: number): Tensor2D {\n    throw new Error('Not yet implemented');\n  }\n\n  oneHot(indices: Tensor1D, depth: number, onValue: number, offValue: number):\n      Tensor2D {\n    throw new Error('Not yet implemented');\n  }\n\n  cumsum(x: Tensor, axis: number, exclusive: boolean, reverse: boolean):\n      Tensor {\n    throw new Error('Not yet implemented');\n  }\n\n  nonMaxSuppression(\n      boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number,\n      iouThreshold: number, scoreThreshold?: number): Tensor1D {\n    throw new Error('Not yet implemented');\n  }\n\n  fft(x: Tensor2D): Tensor2D {\n    throw new Error('Not yet implemented');\n  }\n  ifft(x: Tensor2D): Tensor2D {\n    throw new Error('Not yet implemented');\n  }\n  complex<T extends Tensor>(real: T, imag: T): T {\n    throw new Error('Not yet implemented');\n  }\n  real<T extends Tensor>(input: T): T {\n    throw new Error('Not yet implemented');\n  }\n  imag<T extends Tensor>(input: T): T {\n    throw new Error('Not yet implemented');\n  }\n\n  cropAndResize(\n      image: Tensor4D, boxes: Tensor2D, boxIndex: Tensor1D,\n      cropSize: [number, number], method: 'bilinear'|'nearest',\n      extrapolationValue: number): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  depthToSpace(x: Tensor4D, blockSize: number, dataFormat: string): Tensor4D {\n    throw new Error('Not yet implemented');\n  }\n\n  // Aligns with the \"SplitV\" kernel in TensorFlow.\n  split<T extends Tensor>(value: T, sizeSplits: number[], axis: number): T[] {\n    throw new Error('Not yet implemented');\n  }\n\n  sparseToDense<R extends Rank>(\n      sparseIndices: Tensor, sparseValues: Tensor, outputShape: ShapeMap[R],\n      defaultValue: Scalar): Tensor<R> {\n    throw new Error('Not yet implemented');\n  }\n  /**\n   * Sets the data mover for this backend. Backends should use the mover to\n   * move data from other backends to this backend.\n   */\n  setDataMover(dataMover: DataMover): void {\n    throw new Error('Not yet implemented');\n  }\n\n  dispose(): void {\n    throw new Error('Not yet implemented');\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {scalar, zeros} from '../ops/tensor_ops';\nimport {Tensor} from '../tensor';\nimport {Rank} from '../types';\nimport {DataType, ShapeMap} from '../types';\nimport {hasEncodingLoss} from '../util';\nimport {KernelBackend} from './backend';\n\nexport function castTensor<T extends Tensor>(\n    x: T, dtype: DataType, backend: KernelBackend): T {\n  if (dtype === 'complex64') {\n    if (x.dtype === 'complex64') {\n      return x.clone();\n    }\n    const zerosTensor = zeros(x.shape);\n    const floatX = x.toFloat();\n    const result = backend.complex(floatX, zerosTensor);\n    zerosTensor.dispose();\n    floatX.dispose();\n    return result as T;\n  }\n\n  if (!hasEncodingLoss(x.dtype, dtype)) {\n    // We don't change the underlying data, since we cast to higher\n    // precision.\n    return Tensor.make(x.shape, {dataId: x.dataId}, dtype) as T;\n  }\n  if (x.dtype === 'complex64') {\n    const real = backend.real(x);\n    const result = real.cast(dtype);\n    real.dispose();\n    return result;\n  }\n  if (dtype === 'int32') {\n    return backend.int(x);\n  } else if (dtype === 'bool') {\n    const zero = scalar(0, x.dtype);\n    const result = backend.notEqual(x, zero) as T;\n    zero.dispose();\n    return result;\n  } else {\n    throw new Error(`Error in Cast: unknown dtype argument (${dtype})`);\n  }\n}\n\nexport function reshapeTensor<T extends Tensor, R extends Rank>(\n    x: T, shape: ShapeMap[R]): Tensor<R> {\n  return Tensor.make(shape, {dataId: x.dataId}, x.dtype);\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {TypedArray} from '../types';\n/**\n * Merges real and imaginary Float32Arrays into a single complex Float32Array.\n *\n * The memory layout is interleaved as follows:\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n * complex: [r0, i0, r1, i1, r2, i2]\n *\n * This is the inverse of splitRealAndImagArrays.\n *\n * @param real The real values of the complex tensor values.\n * @param imag The imag values of the complex tensor values.\n * @returns A complex tensor as a Float32Array with merged values.\n */\nexport function mergeRealAndImagArrays(\n    real: Float32Array, imag: Float32Array): Float32Array {\n  if (real.length !== imag.length) {\n    throw new Error(\n        `Cannot merge real and imag arrays of different lengths. real:` +\n        `${real.length}, imag: ${imag.length}.`);\n  }\n  const result = new Float32Array(real.length * 2);\n  for (let i = 0; i < result.length; i += 2) {\n    result[i] = real[i / 2];\n    result[i + 1] = imag[i / 2];\n  }\n  return result;\n}\n\n/**\n * Splits a complex Float32Array into real and imag parts.\n *\n * The memory layout is interleaved as follows:\n * complex: [r0, i0, r1, i1, r2, i2]\n * real: [r0, r1, r2]\n * imag: [i0, i1, i2]\n *\n * This is the inverse of mergeRealAndImagArrays.\n *\n * @param complex The complex tensor values.\n * @returns An object with real and imag Float32Array components of the complex\n *     tensor.\n */\nexport function splitRealAndImagArrays(complex: Float32Array):\n    {real: Float32Array, imag: Float32Array} {\n  const real = new Float32Array(complex.length / 2);\n  const imag = new Float32Array(complex.length / 2);\n  for (let i = 0; i < complex.length; i += 2) {\n    real[i / 2] = complex[i];\n    imag[i / 2] = complex[i + 1];\n  }\n  return {real, imag};\n}\n\n/**\n * Extracts even indexed complex values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithEvenIndex(complex: Float32Array):\n    {real: Float32Array, imag: Float32Array} {\n  const len = Math.ceil(complex.length / 4);\n  const real = new Float32Array(len);\n  const imag = new Float32Array(len);\n  for (let i = 0; i < complex.length; i += 4) {\n    real[Math.floor(i / 4)] = complex[i];\n    imag[Math.floor(i / 4)] = complex[i + 1];\n  }\n  return {real, imag};\n}\n\n/**\n * Extracts odd indexed comple values in the given array.\n * @param complex The complex tensor values\n */\nexport function complexWithOddIndex(complex: Float32Array):\n    {real: Float32Array, imag: Float32Array} {\n  const len = Math.floor(complex.length / 4);\n  const real = new Float32Array(len);\n  const imag = new Float32Array(len);\n  for (let i = 2; i < complex.length; i += 4) {\n    real[Math.floor(i / 4)] = complex[i];\n    imag[Math.floor(i / 4)] = complex[i + 1];\n  }\n  return {real, imag};\n}\n\n/**\n * Get the map representing a complex value in the given array.\n * @param complex The complex tensor values.\n * @param index An index of the target complex value.\n */\nexport function getComplexWithIndex(\n    complex: Float32Array, index: number): {real: number, imag: number} {\n  const real = complex[index * 2];\n  const imag = complex[index * 2 + 1];\n  return {real, imag};\n}\n\n/**\n * Insert a given complex value into the TypedArray.\n * @param data The array in which the complex value is inserted.\n * @param c The complex value to be inserted.\n * @param index An index of the target complex value.\n */\nexport function assignToTypedArray(\n    data: TypedArray, real: number, imag: number, index: number) {\n  data[index * 2] = real;\n  data[index * 2 + 1] = imag;\n}\n\n/**\n * Make the list of exponent terms used by FFT.\n */\nexport function exponents(\n    n: number, inverse: boolean): {real: Float32Array, imag: Float32Array} {\n  const real = new Float32Array(n / 2);\n  const imag = new Float32Array(n / 2);\n  for (let i = 0; i < Math.ceil(n / 2); i++) {\n    const x = (inverse ? 2 : -2) * Math.PI * (i / n);\n    real[i] = Math.cos(x);\n    imag[i] = Math.sin(x);\n  }\n  return {real, imag};\n}\n\n/**\n * Make the exponent term used by FFT.\n */\nexport function exponent(\n    k: number, n: number, inverse: boolean): {real: number, imag: number} {\n  const x = (inverse ? 2 : -2) * Math.PI * (k / n);\n  const real = Math.cos(x);\n  const imag = Math.sin(x);\n  return {real, imag};\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Implementation of the NonMaxSuppression kernel shared between webgl and cpu.\n */\n\nimport {tensor1d} from '../ops/tensor_ops';\nimport {Tensor1D} from '../tensor';\nimport {TypedArray} from '../types';\n\nexport function nonMaxSuppressionImpl(\n    boxes: TypedArray, scores: TypedArray, maxOutputSize: number,\n    iouThreshold: number, scoreThreshold: number): Tensor1D {\n  const candidates = Array.from(scores)\n                         .map((score, boxIndex) => ({score, boxIndex}))\n                         .filter(c => c.score > scoreThreshold)\n                         .sort((c1, c2) => c2.score - c1.score);\n\n  const selected: number[] = [];\n\n  for (let i = 0; i < candidates.length; i++) {\n    const {score, boxIndex} = candidates[i];\n    if (score < scoreThreshold) {\n      break;\n    }\n\n    let ignoreCandidate = false;\n    for (let j = selected.length - 1; j >= 0; --j) {\n      const iou = intersectionOverUnion(boxes, boxIndex, selected[j]);\n      if (iou >= iouThreshold) {\n        ignoreCandidate = true;\n        break;\n      }\n    }\n\n    if (!ignoreCandidate) {\n      selected.push(boxIndex);\n      if (selected.length >= maxOutputSize) {\n        break;\n      }\n    }\n  }\n\n  return tensor1d(selected, 'int32');\n}\n\nfunction intersectionOverUnion(boxes: TypedArray, i: number, j: number) {\n  const iCoord = boxes.subarray(i * 4, i * 4 + 4);\n  const jCoord = boxes.subarray(j * 4, j * 4 + 4);\n  const yminI = Math.min(iCoord[0], iCoord[2]);\n  const xminI = Math.min(iCoord[1], iCoord[3]);\n  const ymaxI = Math.max(iCoord[0], iCoord[2]);\n  const xmaxI = Math.max(iCoord[1], iCoord[3]);\n  const yminJ = Math.min(jCoord[0], jCoord[2]);\n  const xminJ = Math.min(jCoord[1], jCoord[3]);\n  const ymaxJ = Math.max(jCoord[0], jCoord[2]);\n  const xmaxJ = Math.max(jCoord[1], jCoord[3]);\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI);\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);\n  if (areaI <= 0 || areaJ <= 0) {\n    return 0.0;\n  }\n  const intersectionYmin = Math.max(yminI, yminJ);\n  const intersectionXmin = Math.max(xminI, xminJ);\n  const intersectionYmax = Math.min(ymaxI, ymaxJ);\n  const intersectionXmax = Math.min(xmaxI, xmaxJ);\n  const intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *\n      Math.max(intersectionXmax - intersectionXmin, 0.0);\n  return intersectionArea / (areaI + areaJ - intersectionArea);\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\n\n/** Shared implementation of the split kernel across WebGL and CPU. */\nexport function split<T extends Tensor>(\n    x: T, sizeSplits: number[], axis: number): T[] {\n  const begin = Array(x.rank).fill(0);\n  const size = x.shape.slice();\n  return sizeSplits.map(s => {\n    size[axis] = s;\n    const slice = x.slice(begin, size);\n    begin[axis] += s;\n    return slice;\n  });\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/** An implementation of the TopK kernel shared between webgl and cpu. */\n\nimport {tensor} from '../ops/tensor_ops';\nimport {Tensor} from '../tensor';\nimport {NumericDataType, TypedArray} from '../types';\nimport {getTypedArrayFromDType} from '../util';\n\nexport function topkImpl<T extends Tensor>(\n    x: TypedArray, xShape: number[], xDtype: NumericDataType, k: number,\n    sorted: boolean): [T, T] {\n  // Reshape into a 2d tensor [batch, lastDim] and compute topk along lastDim.\n  const lastDim = xShape[xShape.length - 1];\n  const [batch, size] = [x.length / lastDim, lastDim];\n  const allTopKVals = getTypedArrayFromDType(xDtype, batch * k);\n  const allTopKIndices = getTypedArrayFromDType('int32', batch * k);\n\n  for (let b = 0; b < batch; b++) {\n    const offset = b * size;\n    const vals = x.subarray(offset, offset + size);\n    const valAndInd: Array<{value: number, index: number}> = [];\n    for (let i = 0; i < vals.length; i++) {\n      valAndInd.push({value: vals[i], index: i});\n    }\n    valAndInd.sort((a, b) => b.value - a.value);\n\n    const outOffset = b * k;\n    const topKVals = allTopKVals.subarray(outOffset, outOffset + k);\n    const topKIndices = allTopKIndices.subarray(outOffset, outOffset + k);\n    for (let i = 0; i < k; i++) {\n      topKVals[i] = valAndInd[i].value;\n      topKIndices[i] = valAndInd[i].index;\n    }\n  }\n  // Reshape back to the original input shape, except that the last\n  // dimension is k.\n  const outputShape = xShape.slice();\n  outputShape[outputShape.length - 1] = k;\n  return [\n    tensor(allTopKVals, outputShape, xDtype) as T,\n    tensor(allTopKIndices, outputShape, 'int32') as T\n  ];\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ReduceInfo} from '../../ops/reduce_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ArgMinMaxProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(reduceInfo: ReduceInfo, op: 'max'|'min', firstPass: boolean) {\n    const windowSize = reduceInfo.windowSize;\n    const batchSize = reduceInfo.batchSize;\n    const inSize = reduceInfo.inSize;\n    const outSize = Math.ceil(inSize / windowSize);\n    if (!firstPass) {\n      this.variableNames.push('bestIndicesA');\n    }\n    this.outputShape = [batchSize, outSize];\n    const compOp = (op === 'max') ? '>' : '<';\n    const indexSnippet = firstPass ?\n        'inOffset + i;' :\n        'round(getBestIndicesA(batch, inOffset + i));';\n\n    this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < ${windowSize}; i++) {\n          int inIdx = ${indexSnippet};\n          float candidate = getA(batch, inIdx);\n          if (candidate ${compOp} bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class AvgPool2DBackpropProgram implements GPGPUProgram {\n  variableNames = ['dy'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv2DInfo) {\n    this.outputShape = convInfo.inShape;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n\n    const avgMultiplier = 1 / (filterHeight * filterWidth);\n\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float avgMultiplier = float(${avgMultiplier});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth};\n            wC+= ${dilationWidth}) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Returns the dimensions in the input shape that are broadcasted to\n * produce the provided output shape.\n *\n * The returned dimensions are 0-indexed and sorted. An example:\n * inShape = [4, 1, 3]\n * outShape = [5, 4, 3, 3]\n * result = [1]. Dimension 1 (2nd dimension of input) gets broadcasted 1 => 3.\n */\nexport function getBroadcastDims(\n    inShape: number[], outShape: number[]): number[] {\n  const inRank = inShape.length;\n  const dims: number[] = [];\n  for (let i = 0; i < inRank; i++) {\n    const dim = inRank - 1 - i;\n    const a = inShape[dim] || 1;\n    const b = outShape[outShape.length - 1 - i] || 1;\n    if (b > 1 && a === 1) {\n      dims.unshift(dim);\n    }\n  }\n  return dims;\n}\n\n/**\n * Returns the axes in the output space that should be reduced to produce\n * the input space.\n */\nexport function getReductionAxes(\n    inShape: number[], outShape: number[]): number[] {\n  const result: number[] = [];\n  for (let i = 0; i < outShape.length; i++) {\n    const inDim = inShape[inShape.length - i - 1];\n    const outAxis = outShape.length - i - 1;\n    const outDim = outShape[outAxis];\n    if (inDim == null || (inDim === 1 && outDim > 1)) {\n      result.unshift(outAxis);\n    }\n  }\n  return result;\n}\n\n/**\n * Given the output of `getBroadcastDims()`, returns true if the broadcasting\n * is along the outer-most dimensions of the input.\n */\nexport function broadcastDimsAreOuter(dims: number[]): boolean {\n  for (let i = 0; i < dims.length; i++) {\n    if (dims[i] !== i) {\n      return false;\n    }\n  }\n  return true;\n}\n\nexport function assertAndGetBroadcastShape(\n    shapeA: number[], shapeB: number[]): number[] {\n  const result: number[] = [];\n  const l = Math.max(shapeA.length, shapeB.length);\n\n  for (let i = 0; i < l; i++) {\n    let a = shapeA[shapeA.length - i - 1];\n    if (a == null) {\n      a = 1;\n    }\n    let b = shapeB[shapeB.length - i - 1];\n    if (b == null) {\n      b = 1;\n    }\n    if (a === 1) {\n      result.unshift(b);\n    } else if (b === 1) {\n      result.unshift(a);\n    } else if (a !== b) {\n      const errMsg = `Operands could not be broadcast together with shapes ` +\n          `${shapeA} and ${shapeB}.`;\n      throw Error(errMsg);\n    } else {\n      result.unshift(a);\n    }\n  }\n  return result;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as broadcast_util from '../../ops/broadcast_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class BatchNormProgram implements GPGPUProgram {\n  variableNames: string[];\n  outputShape: number[] = [];\n  userCode: string;\n  supportsBroadcasting = true;\n\n  constructor(\n      xShape: number[], meanShape: number[], varianceShape: number[],\n      offsetShape: number[]|null, scaleShape: number[]|null,\n      varianceEpsilon: number) {\n    this.variableNames = ['x', 'mean', 'variance'];\n    broadcast_util.assertAndGetBroadcastShape(xShape, meanShape);\n    broadcast_util.assertAndGetBroadcastShape(xShape, varianceShape);\n\n    let offsetSnippet = '0.0';\n    if (offsetShape != null) {\n      broadcast_util.assertAndGetBroadcastShape(xShape, offsetShape);\n      this.variableNames.push('offset');\n      offsetSnippet = 'getOffsetAtOutCoords()';\n    }\n\n    let scaleSnippet = '1.0';\n    if (scaleShape != null) {\n      broadcast_util.assertAndGetBroadcastShape(xShape, scaleShape);\n      this.variableNames.push('scale');\n      scaleSnippet = 'getScaleAtOutCoords()';\n    }\n\n    this.outputShape = xShape;\n    this.userCode = `\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = ${offsetSnippet};\n        float scale = ${scaleSnippet};\n        float inv = scale * inversesqrt(variance + float(${varianceEpsilon}));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as broadcast_util from '../../ops/broadcast_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class BatchNormPackedProgram implements GPGPUProgram {\n  variableNames: string[];\n  outputShape: number[];\n  userCode: string;\n  supportsBroadcasting = true;\n  usesPackedTextures = true;\n\n  constructor(\n      xShape: number[], meanShape: number[], varianceShape: number[],\n      offsetShape: number[]|null, scaleShape: number[]|null,\n      varianceEpsilon: number) {\n    this.variableNames = ['x', 'mean', 'variance'];\n    broadcast_util.assertAndGetBroadcastShape(xShape, meanShape);\n    broadcast_util.assertAndGetBroadcastShape(xShape, varianceShape);\n\n    let offsetSnippet = 'vec4(0.0)';\n    if (offsetShape != null) {\n      broadcast_util.assertAndGetBroadcastShape(xShape, offsetShape);\n      this.variableNames.push('offset');\n      offsetSnippet = 'getOffsetAtOutCoords()';\n    }\n\n    let scaleSnippet = 'vec4(1.0)';\n    if (scaleShape != null) {\n      broadcast_util.assertAndGetBroadcastShape(xShape, scaleShape);\n      this.variableNames.push('scale');\n      scaleSnippet = 'getScaleAtOutCoords()';\n    }\n\n    this.outputShape = xShape;\n    this.userCode = `\n      void main() {\n        vec4 offset = ${offsetSnippet};\n        vec4 scale = ${scaleSnippet};\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4(${varianceEpsilon}));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as broadcast_util from '../../ops/broadcast_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\n// (Ar + Ai)(Br + Bi) =\n// ArBr + ArBi + AiBr + AiBi = ArBr - AB + ArBi + AiBr\n// Yr = ArBr - AB\n// Yi = ArBi + AiBr\nexport const COMPLEX_MULTIPLY = {\n  REAL: 'return areal * breal - aimag * bimag;',\n  IMAG: 'return areal * bimag + aimag * breal;'\n};\n\nexport class BinaryOpComplexProgram implements GPGPUProgram {\n  variableNames = ['AReal', 'AImag', 'BReal', 'BImag'];\n  userCode: string;\n  outputShape: number[];\n  supportsBroadcasting = true;\n\n  constructor(op: string, aShape: number[], bShape: number[]) {\n    this.outputShape =\n        broadcast_util.assertAndGetBroadcastShape(aShape, bShape);\n\n    this.userCode = `\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        ${op}\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as broadcast_util from '../../ops/broadcast_util';\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\n\nconst CHECK_NAN_SNIPPET = `\n  if (isNaN(a)) return a;\n  if (isNaN(b)) return b;\n`;\n\nexport const ADD = 'return a + b;';\nexport const SUB = 'return a - b;';\nexport const MUL = 'return a * b;';\nexport const DIV = `if (a == b) return 1.0;\n  return a / b;`;\n\n// We use native integer division to deal with floating point imprecision. Since\n// we implement floor division and glsl implements truncated division, we\n// correct for this by subtracting 1 from result when the result is negative and\n// there is a remainder.\nexport const INT_DIV = `\n  float resultSign = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  int result = ia / ib;\n  int amodb = ia - ib * result;\n\n  if (resultSign < 0.0 && amodb != 0) {\n    result -= 1;\n  }\n  return float(result);\n`;\n\nexport const POW = `\nif(a < 0.0 && floor(b) < b){\n  return NAN;\n}\nreturn (round(mod(b, 2.0)) == 0 || round(mod(b, 2.0)) == 2) ?\n    pow(abs(a), b) : sign(a) * pow(abs(a), b);\n`;\nexport const SQUARED_DIFFERENCE = 'return (a - b) * (a - b);';\n\nexport const EQUAL = `return float(a == b);`;\n\nexport const NOT_EQUAL = `return float(a != b);`;\n\nexport const LESS = `return float(a < b);`;\n\nexport const LESS_EQUAL = `return float(a <= b);`;\n\nexport const GREATER = `return float(a > b);`;\n\nexport const GREATER_EQUAL = `return float(a >= b);`;\n\nexport const LOGICAL_AND = `return float(a >= 1.0 && b >= 1.0);`;\n\nexport const LOGICAL_OR = `return float(a >= 1.0 || b >= 1.0);`;\n\nexport const MAX = CHECK_NAN_SNIPPET + `\n  return max(a, b);\n`;\nexport const MIN = CHECK_NAN_SNIPPET + `\n  return min(a, b);\n`;\nexport const MOD = `if (b == 0.0) return NAN;\n  return mod(a, b);`;\n\nexport const ATAN2 = CHECK_NAN_SNIPPET + `\n  return atan(a, b);\n`;\n\nexport const ELU_DER = `return (b >= 1.0) ? a : a * (b + 1.0);`;\n\nexport class BinaryOpProgram implements GPGPUProgram {\n  variableNames = ['A', 'B'];\n  outputShape: number[];\n  userCode: string;\n  supportsBroadcasting = true;\n\n  // Caching uniform location for speed.\n  startLoc: WebGLUniformLocation;\n\n  constructor(op: string, aShape: number[], bShape: number[]) {\n    this.outputShape =\n        broadcast_util.assertAndGetBroadcastShape(aShape, bShape);\n    this.userCode = `\n      uniform float NAN;\n      float binaryOperation(float a, float b) {\n        ${op}\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    `;\n  }\n\n  getCustomSetupFunc() {\n    return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n      if (this.startLoc == null) {\n        this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'NAN');\n        if (this.startLoc == null) {\n          // This means the compiler has optimized and realized it doesn't need\n          // the uniform.\n          return;\n        }\n      }\n      gpgpu.gl.uniform1f(this.startLoc, NaN);\n    };\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ClipProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  userCode: string;\n  outputShape: number[];\n\n  constructor(aShape: number[], min: number, max: number) {\n    this.outputShape = aShape;\n    this.userCode = `\n      void main() {\n        float value = getAAtOutCoords();\n        if (isNaN(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, float(${min}), float(${max})));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ClipPackedProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  usesPackedTextures = true;\n  userCode: string;\n  outputShape: number[];\n\n  constructor(aShape: number[], min: number, max: number) {\n    this.outputShape = aShape;\n    this.userCode = `\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (hasNaN(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4(${min}), vec4(${max})));\n      }\n    `;\n  }\n}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ComplexAbsProgram implements GPGPUProgram {\n  variableNames = ['real', 'imag'];\n  userCode: string;\n  outputShape: number[];\n\n  constructor(shape: number[]) {\n    this.outputShape = shape;\n    this.userCode = `\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as concat_util from '../../ops/concat_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ConcatProgram implements GPGPUProgram {\n  variableNames = ['A', 'B'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  // Concats 2d tensors along axis=1. See comments in MathBackendWebGL.concat().\n  constructor(aShape: [number, number], bShape: [number, number]) {\n    this.outputShape =\n        concat_util.computeOutShape([aShape, bShape], 1 /* axis */);\n\n    this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        float value = 0.0;\n        if (yC < ${aShape[1]}) {\n          value = getA(yR, yC);\n        } else {\n          yC -= ${aShape[1]};\n          value = getB(yR, yC);\n        }\n\n        setOutput(value);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo, Conv3DInfo} from '../../ops/conv_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class Conv2DDerFilterProgram implements GPGPUProgram {\n  variableNames = ['x', 'dy'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv2DInfo) {\n    this.outputShape = convInfo.filterShape;\n\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n\nexport class Conv2DDerInputProgram implements GPGPUProgram {\n  variableNames = ['dy', 'W'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv2DInfo) {\n    this.outputShape = convInfo.inShape;\n\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n\n    const padTop = filterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, d2);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n\nexport class Conv3DDerFilterProgram implements GPGPUProgram {\n  variableNames = ['x', 'dy'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv3DInfo) {\n    this.outputShape = convInfo.filterShape;\n\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const padFront = convInfo.padInfo.front;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n\n    this.userCode = `\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yF = 0; yF < ${convInfo.outDepth}; yF++) {\n            int xF = wF + yF * ${strideDepth} - ${padFront};\n\n            if (xF < 0 || xF >= ${convInfo.inDepth}) {\n              continue;\n            }\n\n            for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n              int xR = wR + yR * ${strideHeight} - ${padTop};\n\n              if (xR < 0 || xR >= ${convInfo.inHeight}) {\n                continue;\n              }\n\n              for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n                int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n                if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n\nexport class Conv3DDerInputProgram implements GPGPUProgram {\n  variableNames = ['dy', 'W'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv3DInfo) {\n    this.outputShape = convInfo.inShape;\n\n    const filterDepth = convInfo.filterDepth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n\n    const padFront = filterDepth - 1 - convInfo.padInfo.front;\n    const padTop = filterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n\n    this.userCode = `\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          float dyF = float(dyFCorner + wF) / ${strideDepth}.0;\n\n          if (dyF < 0.0 || dyF >= ${convInfo.outDepth}.0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = ${filterDepth} - 1 - wF;\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n            if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = ${filterHeight} - 1 - wR;\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n              if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = ${filterWidth} - 1 - wC;\n\n              for (int d2 = 0; d2 < ${convInfo.outChannels}; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class DepthwiseConv2DDerFilterProgram implements GPGPUProgram {\n  variableNames = ['x', 'dy'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv2DInfo) {\n    this.outputShape = convInfo.filterShape;\n\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const channelMul = convInfo.outChannels / convInfo.inChannels;\n\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * ${channelMul} + dm;\n\n        float dotProd = 0.0;\n\n        // TODO: Vec4 over the batch size\n        for (int b = 0; b < ${convInfo.batchSize}; b++) {\n          for (int yR = 0; yR < ${convInfo.outHeight}; yR++) {\n            int xR = wR + yR * ${strideHeight} - ${padTop};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int yC = 0; yC < ${convInfo.outWidth}; yC++) {\n              int xC = wC + yC * ${strideWidth} - ${padLeft};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n\nexport class DepthwiseConv2DDerInputProgram implements GPGPUProgram {\n  variableNames = ['dy', 'W'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv2DInfo) {\n    this.outputShape = convInfo.inShape;\n\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n\n    const padTop = filterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = filterWidth - 1 - convInfo.padInfo.left;\n    const channelMul = convInfo.outChannels / convInfo.inChannels;\n\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = ${filterHeight} - 1 - wR;\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = ${filterWidth} - 1 - wC;\n\n            // TODO: Vec4 over the channelMul\n            for (int dm = 0; dm < ${channelMul}; dm++) {\n              int d2 = d1 * ${channelMul} + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo, Conv3DInfo} from '../../ops/conv_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class Conv2DProgram implements GPGPUProgram {\n  variableNames = ['x', 'W'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv2DInfo) {\n    this.outputShape = convInfo.outShape;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n\n    const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n    const inputDepthVec4Remainder = convInfo.inChannels % 4;\n\n    this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${convInfo.inWidth}) {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n              vec4 xValues = vec4(\n                getX(batch, xR, xC, d1),\n                getX(batch, xR, xC, d1 + 1),\n                getX(batch, xR, xC, d1 + 2),\n                getX(batch, xR, xC, d1 + 3)\n              );\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              dotProd += dot(xValues, wValues);\n            }\n\n            if (${inputDepthVec4Remainder === 1}) {\n              dotProd +=\n                getX(batch, xR, xC, ${inputDepthNearestVec4}) *\n                getW(wR, wC, ${inputDepthNearestVec4}, d2);\n            } else if (${inputDepthVec4Remainder === 2}) {\n              vec2 xValues = vec2(\n                getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                getX(batch, xR, xC, ${inputDepthNearestVec4} + 1)\n              );\n              vec2 wValues = vec2(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2)\n              );\n              dotProd += dot(xValues, wValues);\n            } else if (${inputDepthVec4Remainder === 3}) {\n              vec3 xValues = vec3(\n                getX(batch, xR, xC, ${inputDepthNearestVec4}),\n                getX(batch, xR, xC, ${inputDepthNearestVec4} + 1),\n                getX(batch, xR, xC, ${inputDepthNearestVec4} + 2)\n              );\n              vec3 wValues = vec3(\n                getW(wR, wC, ${inputDepthNearestVec4}, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                getW(wR, wC, ${inputDepthNearestVec4} + 2, d2)\n              );\n              dotProd += dot(xValues, wValues);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n\nexport class Conv3DProgram implements GPGPUProgram {\n  variableNames = ['x', 'W'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv3DInfo) {\n    this.outputShape = convInfo.outShape;\n    const padFront = convInfo.padInfo.front;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationDepth = convInfo.dilationDepth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const filterDepth = convInfo.filterDepth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n\n    const inputDepthNearestVec4 = Math.floor(convInfo.inChannels / 4) * 4;\n    const inputDepthVec4Remainder = convInfo.inChannels % 4;\n\n    this.userCode = `\n      const ivec3 strides = ivec3(${strideDepth}, ${strideHeight}, ${\n        strideWidth});\n      const ivec3 pads = ivec3(${padFront}, ${padTop}, ${padLeft});\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < ${filterDepth}; wF++) {\n          int xF = xFCorner + wF * ${dilationDepth};\n\n          if (xF < 0 || xF >= ${convInfo.inDepth}) {\n            continue;\n          }\n\n          for (int wR = 0; wR < ${filterHeight}; wR++) {\n            int xR = xRCorner + wR * ${dilationHeight};\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${filterWidth}; wC++) {\n              int xC = xCCorner + wC * ${dilationWidth};\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < ${inputDepthNearestVec4}; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if (${inputDepthVec4Remainder === 1}) {\n                dotProd +=\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}) *\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2);\n              } else if (${inputDepthVec4Remainder === 2}) {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if (${inputDepthVec4Remainder === 3}) {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4}),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 1),\n                  getX(batch, xF, xR, xC, ${inputDepthNearestVec4} + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, ${inputDepthNearestVec4}, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 1, d2),\n                  getW(wF, wR, wC, ${inputDepthNearestVec4} + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class DepthwiseConv2DProgram implements GPGPUProgram {\n  variableNames = ['x', 'W'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv2DInfo) {\n    this.outputShape = convInfo.outShape;\n\n    const xNumRows = convInfo.inHeight;\n    const xNumCols = convInfo.inWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const channelMul = convInfo.outChannels / convInfo.inChannels;\n\n    this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / ${channelMul};\n        int q = d2 - d1 * ${channelMul};\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TODO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < ${filterHeight}; wR++) {\n          int xR = xRCorner + wR * ${dilationHeight};\n\n          if (xR < 0 || xR >= ${xNumRows}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidth}; wC++) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            if (xC < 0 || xC >= ${xNumCols}) {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class DepthwiseConvPacked2DProgram implements GPGPUProgram {\n  variableNames = ['x', 'W'];\n  usesPackedTextures = true;\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv2DInfo) {\n    this.outputShape = convInfo.outShape;\n\n    const xNumRows = convInfo.inHeight;\n    const xNumCols = convInfo.inWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const texelsAcross = Math.ceil((filterWidth + 1) / 2);\n\n    let mainLoop = `int xR; int xC;`;\n\n    for (let r = 0; r < filterHeight; r++) {\n      for (let c = -padLeft; c < texelsAcross * 2; c++) {\n        mainLoop += `vec4 ${xTexelName(r, c)} = vec4(0.);`;\n      }\n\n      for (let c = 0; c < filterWidth; c++) {\n        mainLoop += `\n          vec4 wR${r}C${c} = vec4(0.);\n          vec4 xR${r}C${c} = vec4(0.);`;\n      }\n    }\n\n    /**\n     * This vectorized implementation of depthwiseConv works by gathering the\n     * values needed for each output channel's dot product into vec4's and then\n     * multiplying them all together (this happens in the final double for-loop\n     * below). Most of the main loop consists of constructing these vec4's with\n     * the minimum number of texture2D calls as possible, which entails logic\n     * for making use of all four returned values from a texture2D call at once.\n     */\n    for (let r = 0; r < filterHeight; r++) {\n      for (let c = 0; c < texelsAcross; c++) {\n        const col = c * 2;\n        const left = c * 2 + padLeft;\n\n        mainLoop += `\n          xR = xRCorner + ${r};\n          xC = xCCorner + ${left};\n\n          if(xR >= 0 && xR < ${xNumRows} && xC >= 0 && xC < ${xNumCols}) {\n            ${xTexelName(r, left)} = getX(batch, xR, xC, d1);\n          }`;\n\n        if (padLeft === 0) {\n          if (col < filterWidth && c === texelsAcross - 1) {\n            if (strideWidth > 1) {\n              mainLoop += `\n                vec4 ${xTexelName(r, left + 2)} = vec4(0.);\n\n                if(xR >= 0 && xR < ${xNumRows} && xC + 2 < ${xNumCols}) {\n                  ${xTexelName(r, left + 2)} = getX(batch, xR, xC + 2, d1);\n                }`;\n            }\n\n            mainLoop += `\n              xR${r}C${left} = ${constructTexel(r, left, strideWidth, padLeft)};\n            `;\n          }\n        } else if (c === 0) {\n          mainLoop += `\n            if(xR >= 0 && xR < ${xNumRows} && xC - 2 >= 0) {\n              ${xTexelName(r, left - 2)} = getX(batch, xR, xC - 2, d1);\n            }`;\n        }\n\n        if (col > 0) {\n          mainLoop += `xR${r}C${left - 2} =\n            ${constructTexel(r, left - 2, strideWidth, padLeft)};`;\n        }\n\n        if (left - 1 >= 0 && left - 1 < filterWidth) {\n          mainLoop += `xR${r}C${left - 1} =\n              ${constructTexel(r, left - 1, strideWidth, padLeft)};`;\n        }\n\n        if (col < filterWidth) {\n          mainLoop += `\n            vec4 wTexel${r}C${col} = getW(${r}, ${col}, d1, q);\n            wR${r}C${col} = vec4(wTexel${r}C${col}.xz, wTexel${r}C${col}.xz);\n          `;\n\n          if (col + 1 < filterWidth) {\n            mainLoop += `\n              vec4 wTexelR${r}C${col + 1} = getW(${r}, ${col + 1}, d1, q);\n              wR${r}C${col + 1} =\n                vec4(wTexelR${r}C${col + 1}.xz, wTexelR${r}C${col + 1}.xz);`;\n          }\n        }\n      }\n    }\n\n    for (let r = 0; r < filterHeight; r++) {\n      for (let c = 0; c < filterWidth; c++) {\n        mainLoop += `result += xR${r}C${c} * wR${r}C${c};`;\n      }\n    }\n\n    this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2;\n        int q = 0;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        vec4 result = vec4(0.);\n\n        ${mainLoop}\n\n        setOutput(result);\n      }\n    `;\n  }\n}\n\nfunction xTexelName(r: number, c: number): string {\n  return `xTexelR${r}C${c < 0 ? 'minus' + Math.abs(c).toString() : c}`;\n}\n\n/**\n * Given a 2x2 filter, we want to multiply xR0C0, wR0C0, xR0C1, wR0C1, xR1C0,\n * wR1C0, xR1C1, xR1C1. The xRC's are constructed out of xTexelRC's, which are\n * the vec4's returned from sampling calls. Sometimes this means mixing channels\n * from adjacent samples, which constructTexel handles.\n */\nfunction constructTexel(\n    r: number, c: number, stride: number, padLeft: number): string {\n  if (stride === 1) {\n    if (padLeft % 2 === c % 2) {\n      return xTexelName(r, c);\n    }\n    return `vec4(${xTexelName(r, c - 1)}.zw, ${xTexelName(r, c + 1)}.xy)`;\n  }\n\n  if (padLeft % 2 === c % 2) {\n    return `vec4(${xTexelName(r, c)}.xy, ${xTexelName(r, c + 2)}.xy)`;\n  }\n  return `vec4(${xTexelName(r, c - 1)}.zw, ${xTexelName(r, c + 1)}.zw)`;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport { GPGPUProgram } from './gpgpu_math';\n\nexport class CropAndResizeProgram implements GPGPUProgram {\n  variableNames = ['Image', 'Boxes', 'BoxInd'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(\n    imageShape: [number, number, number, number], boxShape: [number, number],\n    cropSize: [number, number], method: 'bilinear' | 'nearest',\n    extrapolationValue: number) {\n    const [batch, imageHeight, imageWidth, depth] = imageShape;\n    const [numBoxes,] = boxShape;\n    const [cropHeight, cropWidth] = cropSize;\n    this.outputShape = [numBoxes, cropHeight, cropWidth, depth];\n    const methodId = method === 'bilinear' ? 1 : 0;\n\n    const [inputHeightFloat, inputWidthFloat] =\n      [`${imageHeight - 1}.0`, `${imageWidth - 1}.0`];\n\n    const [heightRatio, heightScale, inY] = cropHeight > 1 ?\n      [\n        `${(imageHeight-1)/(cropHeight-1)}`,\n        '(y2-y1) * height_ratio',\n        `y1*${inputHeightFloat} + float(y)*(height_scale)`,\n      ] :\n      [\n        '0.0',\n        '0.0',\n        `0.5 * (y1+y2) * ${inputHeightFloat}`,\n      ];\n    const [widthRatio, widthScale, inX] = cropWidth > 1 ?\n      [\n        `${(imageWidth-1)/(cropWidth-1)}`,\n        '(x2-x1) * width_ratio',\n        `x1*${inputWidthFloat} + float(x)*(width_scale)`,\n      ] :\n      [\n        '0.0',\n        '0.0',\n        `0.5 * (x1+x2) * ${inputWidthFloat}`,\n      ];\n\n    // Reference implementation\n    // tslint:disable-next-line:max-line-length\n    // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op_gpu.cu.cc\n    this.userCode = `\n      const float height_ratio = float(${heightRatio});\n      const float width_ratio = float(${widthRatio});\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= ${batch}) {\n          return;\n        }\n\n        float height_scale = ${heightScale};\n        float width_scale = ${widthScale};\n\n        float in_y = ${inY};\n        if( in_y < 0.0 || in_y > ${inputHeightFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n        float in_x = ${inX};\n        if( in_x < 0.0 || in_x > ${inputWidthFloat} ) {\n          setOutput(float(${extrapolationValue}));\n          return;\n        }\n\n        vec2 sourceFracIndexRC = vec2(in_y,in_x);\n        if(${methodId} == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n          ivec2 sourceCeilRC = ivec2(ceil(sourceFracIndexRC));\n\n          float topLeft = getImage(b, sourceFloorRC.x, sourceFloorRC.y, d);\n          float bottomLeft = getImage(b, sourceCeilRC.x, sourceFloorRC.y, d);\n          float topRight = getImage(b, sourceFloorRC.x, sourceCeilRC.y, d);\n          float bottomRight = getImage(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n          vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n          float top = topLeft + (topRight - topLeft) * fracRC.y;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n          float newValue = top + (bottom - top) * fracRC.x;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestRC = ivec2(floor(\n            sourceFracIndexRC + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestRC.x, sourceNearestRC.y, d);\n          setOutput(newValue);\n        }\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as util from '../../util';\n\n/**\n * Produces GLSL code that derives logical coordinates from a flat\n * index. The code performs integer division with each stride and decrements\n * the index until the index equals the final dimension coordinate.\n */\nexport function getLogicalCoordinatesFromFlatIndex(\n    coords: string[], shape: number[], index = 'index'): string {\n  const strides = util.computeStrides(shape);\n  return strides\n      .map((stride, i) => {\n        const line1 = `int ${coords[i]} = ${index} / ${stride}`;\n        const line2 = i === strides.length - 1 ?\n            `int ${coords[i + 1]} = ${index} - ${coords[i]} * ${stride}` :\n            `index -= ${coords[i]} * ${stride}`;\n        return `${line1}; ${line2};`;\n      })\n      .join('');\n}\n\nfunction buildVec(x: string[]): string {\n  if (x.length === 1) {\n    return `${x[0]}`;\n  }\n  return `vec${x.length}(${x.join(',')})`;\n}\n\n/**\n * Produces GLSL code that computes the dot product of the input x and y\n * vectors. Handles splitting inputs into increments of vec4s when necessary.\n */\nexport function dotify(x: string[], y: string[]): string {\n  if (x.length !== y.length) {\n    throw new Error(\n        `Vectors to be dotted must be of the same length -` +\n        `got ${x.length} and ${y.length}`);\n  }\n\n  const slices: string[] = [];\n  const nearestVec4 = Math.floor(x.length / 4);\n  const nearestVec4Remainder = x.length % 4;\n\n  for (let i = 0; i < nearestVec4; i++) {\n    const xSlice = x.slice(i * 4, i * 4 + 4);\n    const ySlice = y.slice(i * 4, i * 4 + 4);\n    slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n  }\n\n  if (nearestVec4Remainder !== 0) {\n    let xSlice = x.slice(nearestVec4 * 4);\n    let ySlice = y.slice(nearestVec4 * 4);\n    if (xSlice.length === 1) {\n      xSlice = xSlice.map(d => `float(${d})`);\n      ySlice = ySlice.map(d => `float(${d})`);\n    }\n    slices.push(`${buildVec(xSlice)}, ${buildVec(ySlice)}`);\n  }\n\n  return slices.map((d, i) => `dot(${d})`).join('+');\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENV} from '../../environment';\nimport * as broadcast_util from '../../ops/broadcast_util';\nimport * as util from '../../util';\n\nimport * as shader_util from './shader_compiler_util';\n\nexport type ShapeInfo = {\n  logicalShape: number[],\n  texShape: [number, number],\n  isUniform: boolean,\n  isPacked: boolean\n};\n\nexport type InputInfo = {\n  name: string,\n  shapeInfo: ShapeInfo\n};\n\nexport function makeShader(\n    inputsInfo: InputInfo[], outputShape: ShapeInfo, userCode: string,\n    broadcast: boolean, usesPackedTextures: boolean): string {\n  let inputPrefixSnippet: string[]|string = inputsInfo.map(x => {\n    const size = util.sizeFromShape(x.shapeInfo.logicalShape);\n    if (x.shapeInfo.isUniform) {\n      return `uniform float ${x.name}${size > 1 ? `[${size}]` : ''};`;\n    }\n    return `uniform sampler2D ${x.name};`;\n  });\n  inputPrefixSnippet = inputPrefixSnippet.join('\\n');\n\n  const inputSamplingSnippet =\n      inputsInfo\n          .map(\n              x => getInputSamplingSnippet(\n                  x, outputShape, broadcast, usesPackedTextures))\n          .join('\\n');\n  const outTexShape = outputShape.texShape;\n  let outputSamplingSnippet: string;\n  let floatTextureSetOutputSnippet: string;\n  let shaderPrefix = SHADER_PREFIX;\n\n  if (outputShape.isPacked) {\n    outputSamplingSnippet =\n        getPackedOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n    floatTextureSetOutputSnippet = FLOAT_TEXTURE_SET_RGBA_SNIPPET;\n  } else {\n    outputSamplingSnippet =\n        getOutputSamplingSnippet(outputShape.logicalShape, outTexShape);\n    floatTextureSetOutputSnippet = FLOAT_TEXTURE_SET_R_SNIPPET;\n  }\n\n  if (usesPackedTextures) {\n    shaderPrefix += SHADER_PACKED_PREFIX;\n  }\n\n  const source = [\n    shaderPrefix, FLOAT_TEXTURE_SAMPLE_SNIPPET, floatTextureSetOutputSnippet,\n    inputPrefixSnippet, outputSamplingSnippet, inputSamplingSnippet, userCode\n  ].join('\\n');\n  return source;\n}\n\nfunction getSamplerFromInInfo(inInfo: InputInfo): string {\n  const shape = inInfo.shapeInfo.logicalShape;\n  switch (shape.length) {\n    case 0:\n      return getSamplerScalar(inInfo);\n    case 1:\n      return getSampler1D(inInfo);\n    case 2:\n      return getSampler2D(inInfo);\n    case 3:\n      return getSampler3D(inInfo);\n    case 4:\n      return getSampler4D(inInfo);\n    case 5:\n      return getSampler5D(inInfo);\n    case 6:\n      return getSampler6D(inInfo);\n    default:\n      throw new Error(\n          `${shape.length}-D input sampling` +\n          ` is not yet supported`);\n  }\n}\n\nfunction getPackedSamplerFromInInfo(inInfo: InputInfo): string {\n  const shape = inInfo.shapeInfo.logicalShape;\n  switch (shape.length) {\n    case 0:\n      return getPackedSamplerScalar(inInfo);\n    case 1:\n      return getPackedSampler1D(inInfo);\n    case 2:\n      return getPackedSampler2D(inInfo);\n    case 3:\n      return getPackedSampler3D(inInfo);\n    case 4:\n      return getPackedSampler4D(inInfo);\n    default:\n      throw new Error(\n          `Packed ${shape.length}-D input sampling` +\n          ` is not yet supported`);\n  }\n}\n\nfunction getInputSamplingSnippet(\n    inInfo: InputInfo, outShapeInfo: ShapeInfo, broadcast: boolean,\n    usesPackedTextures = false): string {\n  let res = getSamplerFlat(inInfo);\n  if (usesPackedTextures) {\n    res += getPackedSamplerFromInInfo(inInfo);\n  } else {\n    res += getSamplerFromInInfo(inInfo);\n  }\n\n  // If input and output have matching logical shapes, add\n  // getTexNameAtOutCoord() method that samples the input textureSampler using\n  // the output coordinates.\n  if (broadcast ||\n      util.arraysEqual(\n          inInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape)) {\n    if (usesPackedTextures) {\n      res += getPackedSamplerAtOutputCoords(inInfo, outShapeInfo, broadcast);\n    } else {\n      res += getSamplerAtOutputCoords(inInfo, outShapeInfo, broadcast);\n    }\n  }\n  return res;\n}\n\nfunction getPackedOutputSamplingSnippet(\n    outShape: number[], outTexShape: [number, number]): string {\n  switch (outShape.length) {\n    case 0:\n      return getOutputScalarCoords();\n    case 1:\n      return getOutputPacked1DCoords(outShape as [number], outTexShape);\n    case 2:\n      return getOutputPacked2DCoords(outShape as [number, number], outTexShape);\n    case 3:\n      return getOutputPacked3DCoords(\n          outShape as [number, number, number], outTexShape);\n    case 4:\n      return getOutputPacked4DCoords(\n          outShape as [number, number, number, number], outTexShape);\n    default:\n      throw new Error(\n          `${outShape.length}-D packed output ` +\n          `coordinate fetching is not yet supported`);\n  }\n}\n\nfunction getOutputSamplingSnippet(\n    outShape: number[], outTexShape: [number, number]): string {\n  switch (outShape.length) {\n    case 0:\n      return getOutputScalarCoords();\n    case 1:\n      return getOutput1DCoords(outShape as [number], outTexShape);\n    case 2:\n      return getOutput2DCoords(outShape as [number, number], outTexShape);\n    case 3:\n      return getOutput3DCoords(\n          outShape as [number, number, number], outTexShape);\n    case 4:\n      return getOutput4DCoords(\n          outShape as [number, number, number, number], outTexShape);\n    case 5:\n      return getOutput5DCoords(\n          outShape as [number, number, number, number, number], outTexShape);\n    case 6:\n      return getOutput6DCoords(\n          outShape as [number, number, number, number, number, number],\n          outTexShape);\n    default:\n      throw new Error(\n          `${outShape.length}-D output sampling is not yet supported`);\n  }\n}\n\nconst SAMPLE_1D_SNIPPET = `\nvec2 UVfrom1D(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\n\nconst SAMPLE_2D_SNIPPET = `\nvec2 UVfrom2D(int texNumR, int texNumC, int numC, int row, int col) {\n  int index = row * numC + col;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\n\nconst SAMPLE_3D_SNIPPET = `\nvec2 UVfrom3D(int texNumR, int texNumC, int stride0,\n    int stride1, int row, int col, int depth) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\n\nconst SAMPLE_4D_SNIPPET = `\nvec2 UVfrom4D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int row, int col, int depth,\n    int depth2) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth * stride2 + depth2;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom4D(int texNumR, int texNumC, int texelsInBatch2,\n    int texelsInBatch, int texelsInLogicalRow, int b2, int b,\n    int row, int col) {\n  int index = b2 * texelsInBatch2 + b * texelsInBatch +\n    (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\n\nconst SAMPLE_5D_SNIPPET = `\nvec2 UVfrom5D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int stride3, int row, int col, int depth,\n    int depth2, int depth3) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 +\n              depth * stride2 + depth2 * stride3 + depth3;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\n\nconst SAMPLE_6D_SNIPPET = `\nvec2 UVfrom6D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int stride3, int stride4,\n    int row, int col, int depth, int depth2, int depth3, int depth4) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth * stride2 + depth2 *\n    stride3 + depth3 * stride4 + depth4;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n`;\n\nconst FLOAT_TEXTURE_SAMPLE_SNIPPET = `\n  float sampleTexture(sampler2D textureSampler, vec2 uv) {\n    return texture2D(textureSampler, uv).r;\n  }\n`;\n\nconst FLOAT_TEXTURE_SET_R_SNIPPET = `\n  void setOutput(float val) {\n    gl_FragColor = vec4(val, 0, 0, 0);\n  }\n`;\n\nconst FLOAT_TEXTURE_SET_RGBA_SNIPPET = `\n  void setOutput(vec4 val) {\n    gl_FragColor = val;\n  }\n`;\n\nlet NAN_CHECKS = '';\nif (ENV.get('PROD')) {\n  NAN_CHECKS = `\n    bool isNaN(float val) {\n      return false;\n    }\n\n    bool hasNaN(vec4 values) {\n      return false;\n    }\n  `;\n} else {\n  /**\n   * Previous NaN check '(val < 0.0 || 0.0 < val || val == 0.0) ? false : true'\n   * does not work on iOS 12\n   */\n  NAN_CHECKS = `\n    bool isNaN(float val) {\n      return (val < 1.0 || 0.0 < val || val == 0.0) ? false : true;\n    }\n\n    bool hasNaN(vec4 values) {\n      return any(bvec4(\n        isNaN(values.x),\n        isNaN(values.y),\n        isNaN(values.z),\n        isNaN(values.w)\n      ));\n    }\n  `;\n}\n\nconst SHADER_PREFIX = `\n  precision highp float;\n  precision highp int;\n  varying vec2 resultUV;\n  const vec2 halfCR = vec2(0.5, 0.5);\n\n  struct ivec5\n  {\n    int x;\n    int y;\n    int z;\n    int w;\n    int u;\n  };\n\n  struct ivec6\n  {\n    int x;\n    int y;\n    int z;\n    int w;\n    int u;\n    int v;\n  };\n\n  ${NAN_CHECKS}\n\n  float getNaN(vec4 values) {\n    return dot(vec4(1), values);\n  }\n\n  int round(float value) {\n    return int(floor(value + 0.5));\n  }\n\n  int imod(int x, int y) {\n    return x - y * (x / y);\n  }\n\n  //Based on the work of Dave Hoskins\n  //https://www.shadertoy.com/view/4djSRW\n  #define HASHSCALE1 443.8975\n  float random(float seed){\n    vec2 p = resultUV * seed;\n    vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n    p3 += dot(p3, p3.yzx + 19.19);\n    return fract((p3.x + p3.y) * p3.z);\n  }\n\n  ${SAMPLE_1D_SNIPPET}\n  ${SAMPLE_2D_SNIPPET}\n  ${SAMPLE_3D_SNIPPET}\n  ${SAMPLE_4D_SNIPPET}\n  ${SAMPLE_5D_SNIPPET}\n  ${SAMPLE_6D_SNIPPET}\n`;\n\nconst SHADER_PACKED_PREFIX = `\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n`;\n\nfunction getOutputScalarCoords() {\n  return `\n    int getOutputCoords() {\n      return 0;\n    }\n  `;\n}\n\nfunction getOutputPacked1DCoords(\n    shape: [number], texShape: [number, number]): string {\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  if (texShape[0] === 1) {\n    return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * ${packedTexShape[1]}.0);\n      }\n    `;\n  }\n\n  if (texShape[1] === 1) {\n    return `\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * ${packedTexShape[0]}.0);\n      }\n    `;\n  }\n\n  return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      return resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n    }\n  `;\n}\n\nfunction getOutput1DCoords(\n    shape: [number], texShape: [number, number]): string {\n  if (texShape[0] === 1) {\n    return `\n      int getOutputCoords() {\n        return int(resultUV.x * ${texShape[1]}.0);\n      }\n    `;\n  }\n  if (texShape[1] === 1) {\n    return `\n      int getOutputCoords() {\n        return int(resultUV.y * ${texShape[0]}.0);\n      }\n    `;\n  }\n  return `\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      return resTexRC.x * ${texShape[1]} + resTexRC.y;\n    }\n  `;\n}\n\nfunction getOutputPacked3DCoords(\n    shape: [number, number, number], texShape: [number, number]): string {\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const texelsInLogicalRow = Math.ceil(shape[2] / 2);\n  const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[1] / 2);\n\n  return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec3(b, r, c);\n    }\n  `;\n}\n\nfunction getOutput3DCoords(\n    shape: [number, number, number], texShape: [number, number]): string {\n  const coordsFromIndexSnippet =\n      shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n\n  return `\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}\n\nfunction getOutputPacked4DCoords(\n    shape: [number, number, number, number],\n    texShape: [number, number]): string {\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n\n  const texelsInLogicalRow = Math.ceil(shape[3] / 2);\n  const texelsInBatch = texelsInLogicalRow * Math.ceil(shape[2] / 2);\n  const texelsInBatch2 = texelsInBatch * shape[1];\n\n  return `\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      int b2 = index / ${texelsInBatch2};\n      index -= b2 * ${texelsInBatch2};\n\n      int b = index / ${texelsInBatch};\n      index -= b * ${texelsInBatch};\n\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec4(b2, b, r, c);\n    }\n  `;\n}\n\nfunction getOutput4DCoords(\n    shape: [number, number, number, number],\n    texShape: [number, number]): string {\n  const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(\n      ['r', 'c', 'd', 'd2'], shape);\n\n  return `\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      ${coordsFromIndexSnippet}\n      return ivec4(r, c, d, d2);\n    }\n  `;\n}\n\nfunction getOutput5DCoords(\n    shape: [number, number, number, number, number],\n    texShape: [number, number]): string {\n  const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(\n      ['r', 'c', 'd', 'd2', 'd3'], shape);\n\n  return `\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(${texShape[0]},\n                             ${texShape[1]}));\n\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  `;\n}\n\nfunction getOutput6DCoords(\n    shape: [number, number, number, number, number, number],\n    texShape: [number, number]): string {\n  const coordsFromIndexSnippet = shader_util.getLogicalCoordinatesFromFlatIndex(\n      ['r', 'c', 'd', 'd2', 'd3', 'd4'], shape);\n\n  return `\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n\n      ${coordsFromIndexSnippet}\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  `;\n}\n\nfunction getOutputPacked2DCoords(\n    shape: [number, number], texShape: [number, number]): string {\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  if (util.arraysEqual(shape, texShape)) {\n    return `\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2(${packedTexShape[0]}, ${\n        packedTexShape[1]}));\n      }\n    `;\n  }\n\n  // texels needed to accommodate a logical row\n  const texelsInLogicalRow = Math.ceil(shape[1] / 2);\n\n  /**\n   * getOutputCoords\n   *\n   * resTexRC: The rows and columns of the texels. If you move over one\n   * texel to the right in the packed texture, you are moving over one column\n   * (not two).\n   *\n   * index: The texel index\n   */\n  return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n      int r = 2 * (index / ${texelsInLogicalRow});\n      int c = imod(index, ${texelsInLogicalRow}) * 2;\n\n      return ivec2(r, c);\n    }\n  `;\n}\n\nfunction getOutput2DCoords(\n    shape: [number, number], texShape: [number, number]): string {\n  if (util.arraysEqual(shape, texShape)) {\n    return `\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2(${texShape[0]}, ${texShape[1]}));\n      }\n    `;\n  }\n  if (shape[1] === 1) {\n    return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    `;\n  }\n  if (shape[0] === 1) {\n    return `\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2(${texShape[0]}, ${texShape[1]}));\n        int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n        return ivec2(0, index);\n      }\n    `;\n  }\n  return `\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${texShape[0]}, ${texShape[1]}));\n      int index = resTexRC.x * ${texShape[1]} + resTexRC.y;\n      int r = index / ${shape[1]};\n      int c = index - r * ${shape[1]};\n      return ivec2(r, c);\n    }\n  `;\n}\n\nfunction getPackedSamplerScalar(inputInfo: InputInfo): string {\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  return `\n    vec4 ${funcName}() {\n      return texture2D(${texName}, halfCR);\n    }\n  `;\n}\n\nfunction getSamplerScalar(inputInfo: InputInfo): string {\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  if (inputInfo.shapeInfo.isUniform) {\n    return `float ${funcName}() {return ${texName};}`;\n  }\n  return `\n    float ${funcName}() {\n      return sampleTexture(${texName}, halfCR);\n    }\n  `;\n}\n\nfunction getPackedSampler1D(inputInfo: InputInfo): string {\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n\n  return `\n    vec4 ${funcName}(int index) {\n      vec2 uv = packedUVfrom1D(\n        ${packedTexShape[0]}, ${packedTexShape[1]}, index);\n      return texture2D(${texName}, uv);\n    }\n  `;\n}\n\nfunction getSampler1D(inputInfo: InputInfo): string {\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n\n  return `\n    float ${funcName}(int index) {\n      return ${funcName}Flat(index);\n    }\n  `;\n}\n\nfunction getPackedSampler2D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  if (texShape != null && util.arraysEqual(shape, texShape)) {\n    return `\n      vec4 ${funcName}(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n\n        return texture2D(${texName}, uv);\n      }\n    `;\n  }\n\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const valuesPerRow = Math.ceil(shape[1] / 2);\n\n  return `\n    vec4 ${funcName}(int row, int col) {\n      vec2 uv = packedUVfrom2D(${valuesPerRow}, ${packedTexShape[0]}, ${\n      packedTexShape[1]}, row, col);\n      return texture2D(${texName}, uv);\n    }\n  `;\n}\n\nfunction getSampler2D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n\n  const texShape = inputInfo.shapeInfo.texShape;\n  if (texShape != null && util.arraysEqual(shape, texShape)) {\n    const texNumR = texShape[0];\n    const texNumC = texShape[1];\n    return `\n    float ${funcName}(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n\n  const {newShape, keptDims} = util.squeezeShape(shape);\n  const squeezedShape = newShape;\n  if (squeezedShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n    const params = ['row', 'col'];\n    return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n\n  if (inputInfo.shapeInfo.isUniform) {\n    // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col) {\n        float index = dot(vec2(row, col), vec2(${shape[1]}, 1));\n        return ${funcName}Flat(round(index));\n      }\n    `;\n  }\n\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  if (texNumC === 1) {\n    // index is used directly as physical (no risk of float16 overflow).\n    return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec2(row, col), vec2(${shape[1]}, 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  if (texNumR === 1) {\n    // index is used directly as physical (no risk of float16 overflow).\n    return `\n    float ${funcName}(int row, int col) {\n      float index = dot(vec2(row, col), vec2(${shape[1]}, 1));\n      vec2 uv = vec2((index + 0.5) / ${texNumC}.0, 0.5);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n  return `\n  float ${funcName}(int row, int col) {\n    vec2 uv = UVfrom2D(${texNumR}, ${texNumC}, ${shape[1]}, row, col);\n    return sampleTexture(${texName}, uv);\n  }\n`;\n}\n\nfunction getPackedSampler3D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n\n  if (shape[0] === 1) {\n    const squeezedShape = shape.slice(1);\n    const keptDims = [1, 2];\n    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n    const params = ['b', 'row', 'col'];\n    return `\n        ${getPackedSamplerFromInInfo(newInputInfo)}\n        vec4 ${funcName}(int b, int row, int col) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n  }\n\n  const texNumR = packedTexShape[0];\n  const texNumC = packedTexShape[1];\n\n  const valuesPerRow = Math.ceil(shape[2] / 2);\n  const texelsInBatch = valuesPerRow * Math.ceil(shape[1] / 2);\n\n  return `\n    vec4 ${funcName}(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        ${texNumR}, ${texNumC}, ${texelsInBatch}, ${valuesPerRow}, b, row, col);\n      return texture2D(${texName}, uv);\n    }\n  `;\n}\n\nfunction getSampler3D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const stride0 = shape[1] * shape[2];\n  const stride1 = shape[2];\n\n  const {newShape, keptDims} = util.squeezeShape(shape);\n  const squeezedShape = newShape;\n  if (squeezedShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, squeezedShape);\n    const params = ['row', 'col', 'depth'];\n    return `\n        ${getSamplerFromInInfo(newInputInfo)}\n        float ${funcName}(int row, int col, int depth) {\n          return ${funcName}(${getSqueezedParams(params, keptDims)});\n        }\n      `;\n  }\n\n  if (inputInfo.shapeInfo.isUniform) {\n    // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth) {\n        float index = dot(vec3(row, col, depth),\n                          vec3(${stride0}, ${stride1}, 1));\n        return ${funcName}Flat(round(index));\n      }\n    `;\n  }\n\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  if (texNumC === stride0) {\n    // texC is used directly as physical (no risk of float16 overflow).\n    return `\n        float ${funcName}(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2(${stride1}, 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2(${texNumC}.0, ${texNumR}.0);\n          return sampleTexture(${texName}, uv);\n        }\n      `;\n  }\n\n  if (texNumC === stride1) {\n    // texR is used directly as physical (no risk of float16 overflow).\n    return `\n    float ${funcName}(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2(${shape[1]}, 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}.0, ${texNumR}.0);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n  }\n\n  return `\n      float ${funcName}(int row, int col, int depth) {\n        vec2 uv = UVfrom3D(\n            ${texNumR}, ${texNumC}, ${stride0}, ${stride1}, row, col, depth);\n        return sampleTexture(${texName}, uv);\n      }\n  `;\n}\n\nfunction getPackedSampler4D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const texNumR = packedTexShape[0];\n  const texNumC = packedTexShape[1];\n\n  const valuesPerRow = Math.ceil(shape[3] / 2);\n  const texelsInBatch = valuesPerRow * Math.ceil(shape[2] / 2);\n  const texelsInBatch2 = texelsInBatch * shape[1];\n\n  return `\n    vec4 ${funcName}(int b2, int b, int row, int col) {\n      vec2 uv = packedUVfrom4D(\n        ${texNumR}, ${texNumC}, ${texelsInBatch2},\n        ${texelsInBatch}, ${valuesPerRow}, b2, b, row, col);\n      return texture2D(${texName}, uv);\n    }\n  `;\n}\n\nfunction getSampler4D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const stride2 = shape[3];\n  const stride1 = shape[2] * stride2;\n  const stride0 = shape[1] * stride1;\n\n  const {newShape, keptDims} = util.squeezeShape(shape);\n  if (newShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n    const params = ['row', 'col', 'depth', 'depth2'];\n    return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n\n  if (inputInfo.shapeInfo.isUniform) {\n    // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float index = dot(vec4(row, col, depth, depth2),\n                          vec4(${stride0}, ${stride1}, ${stride2}, 1));\n        return ${funcName}Flat(round(index));\n      }\n    `;\n  }\n\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  if (texNumC === stride0) {\n    // texC is used directly as physical (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2), vec3(${stride1}, ${stride2}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  if (texNumC === stride2) {\n    // texR is used directly as physical (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3(${shape[1] * shape[2]}, ${shape[2]}, 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  return `\n    float ${funcName}(int row, int col, int depth, int depth2) {\n      vec2 uv = UVfrom4D(${texNumR}, ${texNumC}, ${stride0}, ${stride1},\n          ${stride2}, row, col, depth, depth2);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\n\nfunction getSampler5D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const stride3 = shape[4];\n  const stride2 = shape[3] * stride3;\n  const stride1 = shape[2] * stride2;\n  const stride0 = shape[1] * stride1;\n\n  const {newShape, keptDims} = util.squeezeShape(shape);\n  if (newShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n    const params = ['row', 'col', 'depth', 'depth2', 'depth3'];\n    return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n\n  if (inputInfo.shapeInfo.isUniform) {\n    // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          depth3;\n        return ${funcName}Flat(index);\n      }\n    `;\n  }\n\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n\n  if (texNumC === stride0) {\n    // texC is used directly as physical (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(\n          vec4(col, depth, depth2, depth3),\n          vec4(${stride1}, ${stride2}, ${stride3}, 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n\n  if (texNumC === stride3) {\n    // texR is used directly as physical (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3]}, ${shape[2] * shape[3]},\n            ${shape[3]}, 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n\n  return `\n    float ${funcName}(int row, int col, int depth, int depth2, int depth3) {\n      vec2 uv = UVfrom5D(${texNumR}, ${texNumC}, ${stride0}, ${stride1},\n          ${stride2}, ${stride3}, row, col, depth, depth2, depth3);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\n\nfunction getSampler6D(inputInfo: InputInfo): string {\n  const shape = inputInfo.shapeInfo.logicalShape;\n  const texName = inputInfo.name;\n  const funcName = 'get' + texName.charAt(0).toUpperCase() + texName.slice(1);\n  const stride4 = shape[5];\n  const stride3 = shape[4] * stride4;\n  const stride2 = shape[3] * stride3;\n  const stride1 = shape[2] * stride2;\n  const stride0 = shape[1] * stride1;\n  const {newShape, keptDims} = util.squeezeShape(shape);\n  if (newShape.length < shape.length) {\n    const newInputInfo = squeezeInputInfo(inputInfo, newShape);\n    const params = ['row', 'col', 'depth', 'depth2', 'depth3', 'depth4'];\n    return `\n      ${getSamplerFromInInfo(newInputInfo)}\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return ${funcName}(${getSqueezedParams(params, keptDims)});\n      }\n    `;\n  }\n\n  if (inputInfo.shapeInfo.isUniform) {\n    // Uniform arrays will be less than 65505 (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${stride0}, ${stride1}, ${stride2}, ${stride3})) +\n          dot(\n            vec2(depth3, depth4),\n            vec2(${stride4}, 1));\n        return ${funcName}Flat(index);\n      }\n    `;\n  }\n\n  const texShape = inputInfo.shapeInfo.texShape;\n  const texNumR = texShape[0];\n  const texNumC = texShape[1];\n  if (texNumC === stride0) {\n    // texC is used directly as physical (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(\n          vec4(col, depth, depth2, depth3),\n          vec4(${stride1}, ${stride2}, ${stride3}, ${stride4})) + depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  if (texNumC === stride4) {\n    // texR is used directly as physical (no risk of float16 overflow).\n    return `\n      float ${funcName}(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4(${shape[1] * shape[2] * shape[3] * shape[4]},\n               ${shape[2] * shape[3] * shape[4]},\n               ${shape[3] * shape[4]},\n               ${shape[4]})) + depth3;\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2(${texNumC}.0, ${texNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  return `\n    float ${funcName}(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      vec2 uv = UVfrom6D(${texNumR}, ${texNumC}, ${stride0}, ${stride1},\n          ${stride2}, ${stride3}, ${stride4}\n          ,row, col, depth, depth2, depth3, depth4);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\n\nfunction getSamplerFlat(inputInfo: InputInfo): string {\n  const texName = inputInfo.name;\n  const funcName =\n      'get' + texName.charAt(0).toUpperCase() + texName.slice(1) + 'Flat';\n  const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n\n  if (inputInfo.shapeInfo.isUniform) {\n    if (inSize === 1) {\n      return `float ${funcName}(int index) {return ${texName};}`;\n    }\n    return `\n      float ${funcName}(int index) {\n        for (int i = 0; i < ${inSize}; i++) {\n          if (i == index) {\n            return ${texName}[i];\n          }\n        }\n      }\n    `;\n  }\n\n  const texShape = inputInfo.shapeInfo.texShape;\n  const tNumR = texShape[0];\n  const tNumC = texShape[1];\n  if (tNumC === 1 && tNumR === 1) {\n    return `\n      float ${funcName}(int index) {\n        return sampleTexture(${texName}, halfCR);\n      }\n    `;\n  }\n  if (tNumC === 1) {\n    return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2(0.5, (float(index) + 0.5) / ${tNumR}.0);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  if (tNumR === 1) {\n    return `\n      float ${funcName}(int index) {\n        vec2 uv = vec2((float(index) + 0.5) / ${tNumC}.0, 0.5);\n        return sampleTexture(${texName}, uv);\n      }\n    `;\n  }\n  return `\n    float ${funcName}(int index) {\n      vec2 uv = UVfrom1D(${tNumR}, ${tNumC}, index);\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\n\nfunction getBroadcastOutputCoordsSampler(\n    inputInfo: InputInfo, outShapeInfo: ShapeInfo, texFuncSnippet: string,\n    funcName: string): string {\n  const inRank = inputInfo.shapeInfo.logicalShape.length;\n  const outRank = outShapeInfo.logicalShape.length;\n\n  let type = 'int';\n  if (outRank === 2) {\n    type = 'ivec2';\n  } else if (outRank === 3) {\n    type = 'ivec3';\n  } else if (outRank === 4) {\n    type = 'ivec4';\n  }\n  const broadcastDims = broadcast_util.getBroadcastDims(\n      inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n  const rankDiff = outRank - inRank;\n  let coordsSnippet: string;\n  if (inRank === 0) {\n    coordsSnippet = '';\n  } else if (outRank < 2 && broadcastDims.length >= 1) {\n    coordsSnippet = 'coords = 0;';\n  } else {\n    coordsSnippet =\n        broadcastDims.map(d => `coords[${d + rankDiff}] = 0;`).join('\\n');\n  }\n  let unpackedCoordsSnippet = '';\n  if (outRank < 2 && inRank > 0) {\n    unpackedCoordsSnippet = 'coords';\n  } else {\n    unpackedCoordsSnippet = inputInfo.shapeInfo.logicalShape\n                                .map((s, i) => `coords[${i + rankDiff}]`)\n                                .join(', ');\n  }\n  return `\n    float ${funcName}() {\n      ${type} coords = getOutputCoords();\n      ${coordsSnippet}\n      return get${texFuncSnippet}(${unpackedCoordsSnippet});\n    }\n  `;\n}\n\nfunction getPackedSamplerAtOutputCoords(\n    inputInfo: InputInfo, outShapeInfo: ShapeInfo,\n    supportsBroadcasting: boolean) {\n  const texName = inputInfo.name;\n  const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n  const texShape = inputInfo.shapeInfo.texShape;\n  const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n  const outTexShape = outShapeInfo.texShape;\n\n  const packedTexShape =\n      [Math.ceil(texShape[0] / 2), Math.ceil(texShape[1] / 2)];\n  const texNumR = packedTexShape[0];\n  const texNumC = packedTexShape[1];\n\n  const broadcastDims = broadcast_util.getBroadcastDims(\n      inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n  const inRank = inputInfo.shapeInfo.logicalShape.length;\n  const outRank = outShapeInfo.logicalShape.length;\n  if (broadcastDims.length) {\n    throw Error('Packed broadcast sampling is not implemented yet.');\n  }\n\n  const inTexShape = inputInfo.shapeInfo.texShape;\n  if (util.arraysEqual(inTexShape, outTexShape)) {\n    return `\n      vec4 ${funcName}() {\n        return texture2D(${texName}, resultUV);\n      }\n    `;\n  }\n\n  let output = `return texture2D(${texName}, uv)`;\n\n  if (inRank === 1 && outRank > 1) {\n    output = `\n      vec4 sample = texture2D(${texName}, uv);\n      return vec4(sample.xy, sample.xy);\n    `;\n  } else if (inRank === 0 && outRank > 0) {\n    if (outRank === 1) {\n      output = `\n        vec4 sample = texture2D(${texName}, uv);\n        return vec4(sample.x, sample.x, 0., 0.);\n      `;\n    } else {\n      output = `\n        vec4 sample = texture2D(${texName}, uv);\n        return vec4(sample.x);\n      `;\n    }\n  }\n\n  return `\n    vec4 ${funcName}() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${packedTexShape[0]}, ${packedTexShape[1]}));\n      int index = resTexRC.x * ${packedTexShape[1]} + resTexRC.y;\n\n      int texR = index / ${texNumC};\n      int texC = index - texR * ${texNumC};\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${texNumC}, ${texNumR});\n\n      ${output};\n    }\n  `;\n}\n\nfunction getSamplerAtOutputCoords(\n    inputInfo: InputInfo, outShapeInfo: ShapeInfo,\n    supportsBroadcasting: boolean) {\n  const texName = inputInfo.name;\n  const texFuncSnippet = texName.charAt(0).toUpperCase() + texName.slice(1);\n  const funcName = 'get' + texFuncSnippet + 'AtOutCoords';\n\n  const broadcastDims = broadcast_util.getBroadcastDims(\n      inputInfo.shapeInfo.logicalShape, outShapeInfo.logicalShape);\n  const inRank = inputInfo.shapeInfo.logicalShape.length;\n  const outRank = outShapeInfo.logicalShape.length;\n  const doBroadcast =\n      supportsBroadcasting && ((outRank > inRank) || broadcastDims.length > 0);\n  const broadcastOverOuter =\n      broadcast_util.broadcastDimsAreOuter(broadcastDims);\n  const isUniform = inputInfo.shapeInfo.isUniform;\n\n  if (doBroadcast && !broadcastOverOuter) {\n    return getBroadcastOutputCoordsSampler(\n        inputInfo, outShapeInfo, texFuncSnippet, funcName);\n  }\n\n  const inSize = util.sizeFromShape(inputInfo.shapeInfo.logicalShape);\n  let broadcastSnippet = '';\n  if (doBroadcast && broadcastOverOuter) {\n    broadcastSnippet = `\n        int mainPart = index / ${inSize};\n        index -= mainPart * ${inSize};\n      `;\n  }\n\n  const outTexShape = outShapeInfo.texShape;\n  if (isUniform) {\n    if (inSize === 1) {\n      return `float ${funcName}() {return ${texName};}`;\n    }\n    return `\n      float ${funcName}() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                              vec2(${outTexShape[0]}, ${outTexShape[1]}));\n        int index = resTexRC.x * ${outTexShape[1]} + resTexRC.y;\n        ${broadcastSnippet}\n        return get${texFuncSnippet}Flat(index);\n      }\n    `;\n  }\n\n  // At this point, the input is not a uniform.\n  const inTexShape = inputInfo.shapeInfo.texShape;\n  if (util.arraysEqual(inTexShape, outTexShape)) {\n    return `\n      float ${funcName}() {\n        return sampleTexture(${texName}, resultUV);\n      }\n    `;\n  }\n\n  return `\n    float ${funcName}() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2(${outTexShape[0]}, ${outTexShape[1]}));\n      int index = resTexRC.x * ${outTexShape[1]} + resTexRC.y;\n      ${broadcastSnippet}\n      int texR = index / ${inTexShape[1]};\n      int texC = index - texR * ${inTexShape[1]};\n      vec2 uv = (vec2(texC, texR) + halfCR) /\n                 vec2(${inTexShape[1]}.0, ${inTexShape[0]}.0);\n\n      return sampleTexture(${texName}, uv);\n    }\n  `;\n}\n\nexport function getCoordsDataType(rank: number): string {\n  if (rank <= 1) {\n    return 'int';\n  } else if (rank === 2) {\n    return 'ivec2';\n  } else if (rank === 3) {\n    return 'ivec3';\n  } else if (rank === 4) {\n    return 'ivec4';\n  } else if (rank === 5) {\n    return 'ivec5';\n  } else if (rank === 6) {\n    return 'ivec6';\n  } else {\n    throw Error(`GPU for rank ${rank} is not yet supported`);\n  }\n}\n\n/** Returns a new input info (a copy) that has a squeezed logical shape. */\nfunction squeezeInputInfo(\n    inInfo: InputInfo, squeezedShape: number[]): InputInfo {\n  // Deep copy.\n  const newInputInfo: InputInfo = JSON.parse(JSON.stringify(inInfo));\n  newInputInfo.shapeInfo.logicalShape = squeezedShape;\n  return newInputInfo;\n}\n\nfunction getSqueezedParams(params: string[], keptDims: number[]): string {\n  return keptDims.map(d => params[d]).join(', ');\n}","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class CumSumProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(shape: number[], exclusive: boolean, reverse: boolean) {\n    this.outputShape = shape;\n    const rank = shape.length;\n    const finalDim = shape[shape.length - 1];\n    const comparator = reverse ? '<' : '>';\n\n    this.userCode = `\n      int getIndex(int i) {\n        ${reverse ? `return ${finalDim} -i - 1;` : 'return i;'}\n      }\n\n      void main() {\n        ${getCoordsDataType(rank)} coords = getOutputCoords();\n        int end = ${getFinalCoord(rank, 'coords')};\n        float val = 0.0;\n        for (int i = ${finalDim} - 1; i >= 0; i -= 1) {\n          int idx = getIndex(i);\n          if (idx ${comparator} end) {\n            continue;\n          }\n          if (idx == end && ${exclusive}) {\n            continue;\n          }\n          ${getFinalCoord(rank, 'coords')} = idx;\n          val += getX(${getCoords(rank, 'coords')});\n        }\n        setOutput(val);\n      }\n    `;\n  }\n}\n\nfunction getCoords(rank: number, name: string): string {\n  if (rank === 1) {\n    return `${name}`;\n  } else if (rank === 2) {\n    return `${name}.x, ${name}.y`;\n  } else if (rank === 3) {\n    return `${name}.x, ${name}.y, ${name}.z`;\n  } else if (rank === 4) {\n    return `${name}.x, ${name}.y, ${name}.z, ${name}.w`;\n  } else {\n    throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n  }\n}\n\nfunction getFinalCoord(rank: number, name: string): string {\n  if (rank === 1) {\n    return `${name}`;\n  } else if (rank === 2) {\n    return `${name}.y`;\n  } else if (rank === 3) {\n    return `${name}.z`;\n  } else if (rank === 4) {\n    return `${name}.w`;\n  } else {\n    throw Error(`Cumulative sum for rank ${rank} is not yet supported`);\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class DepthToSpaceProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[] = [];\n  userCode: string;\n  blockSize: number;\n  dataFormat: string;\n\n  constructor(\n      outputShape: number[], blockSize: number, dataFormat: 'NHWC'|'NCHW') {\n    this.outputShape = outputShape;\n    this.blockSize = blockSize;\n    this.dataFormat = dataFormat;\n    this.userCode = `\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = ${this.getHeightCoordString()};\n      int w = ${this.getWidthCoordString()};\n      int d = ${this.getDepthCoordString()};\n\n      int in_h = h / ${blockSize};\n      int offset_h = imod(h, ${blockSize});\n      int in_w = w / ${blockSize};\n      int offset_w = imod(w, ${blockSize});\n      int offset_d = (offset_h * ${blockSize} + offset_w) *\n        ${this.getOutputDepthSize()};\n      int in_d = d + offset_d;\n\n      float result = ${this.getInputSamplingString()};\n      setOutput(result);\n    }\n  `;\n  }\n\n  private getHeightCoordString(): string {\n    if (this.dataFormat === 'NHWC') {\n      return `coords[1]`;\n    } else {\n      return `coords[2]`;\n    }\n  }\n\n  private getWidthCoordString(): string {\n    if (this.dataFormat === 'NHWC') {\n      return `coords[2]`;\n    } else {\n      return `coords[3]`;\n    }\n  }\n\n  private getDepthCoordString(): string {\n    if (this.dataFormat === 'NHWC') {\n      return `coords[3]`;\n    } else {\n      return `coords[1]`;\n    }\n  }\n\n  private getOutputDepthSize(): number {\n    if (this.dataFormat === 'NHWC') {\n      return this.outputShape[3];\n    } else {\n      return this.outputShape[1];\n    }\n  }\n\n  private getInputSamplingString(): string {\n    if (this.dataFormat === 'NHWC') {\n      return `getX(b, in_h, in_w, in_d)`;\n    } else {\n      return `getX(b, in_d, in_h, in_w)`;\n    }\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class EncodeFloatProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  userCode: string;\n  outputShape: number[];\n\n  constructor(outputShape: number[]) {\n    this.outputShape = outputShape;\n    this.userCode = `\n      const float FLOAT_MAX = 1.70141184e38;\n      const float FLOAT_MIN = 1.17549435e-38;\n\n      lowp vec4 encode_float(highp float v) {\n        if (isNaN(v)) {\n          return vec4(255, 255, 255, 255);\n        }\n\n        highp float av = abs(v);\n\n        if(av < FLOAT_MIN) {\n          return vec4(0.0, 0.0, 0.0, 0.0);\n        } else if(v > FLOAT_MAX) {\n          return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n        } else if(v < -FLOAT_MAX) {\n          return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n        }\n\n        highp vec4 c = vec4(0,0,0,0);\n\n        highp float e = floor(log2(av));\n        highp float m = exp2(fract(log2(av))) - 1.0;\n\n        c[2] = floor(128.0 * m);\n        m -= c[2] / 128.0;\n        c[1] = floor(32768.0 * m);\n        m -= c[1] / 32768.0;\n        c[0] = floor(8388608.0 * m);\n\n        highp float ebias = e + 127.0;\n        c[3] = floor(ebias / 2.0);\n        ebias -= c[3] * 2.0;\n        c[2] += floor(ebias) * 128.0;\n\n        c[3] += 128.0 * step(0.0, -v);\n\n        return c / 255.0;\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        gl_FragColor = encode_float(x);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport const COMPLEX_FFT = {\n  REAL: 'return real * expR - imag * expI;',\n  IMAG: 'return real * expI + imag * expR;'\n};\n\nexport class FFTProgram implements GPGPUProgram {\n  variableNames = ['real', 'imag'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(op: string, inputShape: [number, number], inverse: boolean) {\n    const innerDim = inputShape[1];\n    this.outputShape = inputShape;\n\n    const exponentMultiplierSnippet =\n        inverse ? `2.0 * ${Math.PI}` : `-2.0 * ${Math.PI}`;\n    const resultDenominator = inverse ? `${innerDim}.0` : '1.0';\n\n    this.userCode = `\n      const float exponentMultiplier = ${exponentMultiplierSnippet};\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        ${op}\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float(${innerDim});\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < ${innerDim}; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / ${resultDenominator};\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class FromPixelsProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  userCode: string;\n  outputShape: number[];\n\n  constructor(outputShape: number[]) {\n    const [height, width, ] = outputShape;\n    this.outputShape = outputShape;\n    this.userCode = `\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(${width}.0, ${height}.0);\n\n        vec4 values = texture2D(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class GatherProgram implements GPGPUProgram {\n  variableNames = ['A', 'indices'];\n  outputShape: number[];\n  userCode: string;\n  rank: number;\n\n  constructor(aShape: number[], indicesLength: number, axis: number) {\n    const outputShape: number[] = aShape.slice();\n    outputShape[axis] = indicesLength;\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    const dtype = getCoordsDataType(this.rank);\n    const sourceCoords = getSourceCoords(aShape, axis);\n\n    this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n  }\n}\n\nfunction getSourceCoords(aShape: number[], axis: number): string {\n  const rank = aShape.length;\n  if (rank > 4) {\n    throw Error(`Gather for rank ${rank} is not yet supported`);\n  }\n  if (rank === 1) {\n    return `int(getIndices(resRC))`;\n  }\n\n  const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n\n  const sourceCoords = [];\n  for (let i = 0; i < aShape.length; i++) {\n    if (i === axis) {\n      sourceCoords.push(`int(getIndices(${currentCoords[i]}))`);\n    } else {\n      sourceCoords.push(`${currentCoords[i]}`);\n    }\n  }\n  return sourceCoords.join();\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class GatherNDProgram implements GPGPUProgram {\n  variableNames = ['x', 'indices'];\n  outputShape: number[];\n  userCode: string;\n  constructor(\n      private sliceDim: number, private strides: number[], shape: number[]) {\n    this.outputShape = shape;\n    const stridesType = getCoordsDataType(strides.length);\n    const dtype = getCoordsDataType(shape.length);\n    const strideString = this.sliceDim > 1 ? 'strides[j]' : 'strides';\n    this.userCode = `\n        ${stridesType} strides = ${stridesType}(${this.strides});\n         void main() {\n          ${dtype} coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < ${this.sliceDim}; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * ${strideString};\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../../tensor';\nimport {DataType, DataValues} from '../../types';\nimport * as util from '../../util';\n\nexport enum TextureUsage {\n  RENDER,\n  UPLOAD,\n  PIXELS,\n  DOWNLOAD\n}\n\nexport enum PhysicalTextureType {\n  UNPACKED_FLOAT16,\n  UNPACKED_FLOAT32,\n  PACKED_4X1_UNSIGNED_BYTE,\n  PACKED_2X2_FLOAT32,\n  PACKED_2X2_FLOAT16\n}\n\nexport interface TextureData {\n  // Required.\n  shape: number[];\n  dtype: DataType;\n\n  // Optional.\n  values?: DataValues;\n  texture?: WebGLTexture;\n  // For complex numbers, the real and imaginary parts are stored as their own\n  // individual tensors, with a parent joining the two with the\n  // complexTensors field. When this is defined, texture will be null.\n  complexTensors?: {real: Tensor, imag: Tensor};\n  /** [rows, columns] shape of the texture. */\n  texShape?: [number, number];\n  usage?: TextureUsage;\n  isPacked?: boolean;\n}\n\nexport function getUnpackedMatrixTextureShapeWidthHeight(\n    rows: number, columns: number): [number, number] {\n  return [columns, rows];\n}\n\nexport function getUnpackedArraySizeFromMatrixSize(\n    matrixSize: number, channelsPerTexture: number): number {\n  return matrixSize * channelsPerTexture;\n}\n\nexport function getColorMatrixTextureShapeWidthHeight(\n    rows: number, columns: number): [number, number] {\n  return [columns * 4, rows];\n}\n\nexport function getMatrixSizeFromUnpackedArraySize(\n    unpackedSize: number, channelsPerTexture: number): number {\n  if (unpackedSize % channelsPerTexture !== 0) {\n    throw new Error(\n        `unpackedSize (${unpackedSize}) must be a multiple of ` +\n        `${channelsPerTexture}`);\n  }\n  return unpackedSize / channelsPerTexture;\n}\n\nexport function encodeMatrixToUnpackedArray(\n    matrix: Float32Array|Uint8Array, unpackedArray: Float32Array|Uint8Array,\n    channelsPerTexture: number) {\n  const requiredSize =\n      getUnpackedArraySizeFromMatrixSize(matrix.length, channelsPerTexture);\n  if (unpackedArray.length < requiredSize) {\n    throw new Error(\n        `unpackedArray length (${unpackedArray.length}) must be >= ` +\n        `${requiredSize}`);\n  }\n  let dst = 0;\n  for (let src = 0; src < matrix.length; ++src) {\n    unpackedArray[dst] = matrix[src];\n    dst += channelsPerTexture;\n  }\n}\n\nexport function decodeMatrixFromUnpackedArray(\n    unpackedArray: Float32Array, matrix: Float32Array,\n    channelsPerTexture: number) {\n  const requiredSize = getMatrixSizeFromUnpackedArraySize(\n      unpackedArray.length, channelsPerTexture);\n  if (matrix.length < requiredSize) {\n    throw new Error(\n        `matrix length (${matrix.length}) must be >= ${requiredSize}`);\n  }\n  let dst = 0;\n  for (let src = 0; src < unpackedArray.length; src += channelsPerTexture) {\n    matrix[dst++] = unpackedArray[src];\n  }\n}\n\nexport function decodeMatrixFromUnpackedColorRGBAArray(\n    unpackedArray: Float32Array, matrix: Float32Array, channels: number) {\n  const requiredSize = unpackedArray.length * channels / 4;\n  if (matrix.length < requiredSize) {\n    throw new Error(\n        `matrix length (${matrix.length}) must be >= ${requiredSize}`);\n  }\n  let dst = 0;\n  for (let src = 0; src < unpackedArray.length; src += 4) {\n    for (let c = 0; c < channels; c++) {\n      matrix[dst++] = unpackedArray[src + c];\n    }\n  }\n}\n\nexport function getPackedMatrixTextureShapeWidthHeight(\n    rows: number, columns: number): [number, number] {\n  return [\n    Math.max(1, Math.ceil(columns / 2)), Math.max(1, Math.ceil(rows / 2))\n  ];\n}\n\nexport function getPackedRGBAArraySizeFromMatrixShape(\n    rows: number, columns: number): number {\n  const [w, h] = getPackedMatrixTextureShapeWidthHeight(rows, columns);\n  return w * h * 4;\n}\n\n/*\nThis is how encodeMatrixToPackedRGBA encodes a tensor with shape = [2, 3, 5]\n(indices are [batch, row, col]).\n\n000|001   002|003   004|xxx   020|021   022|023   024|xxx\n-------   -------   -------   -------   -------   -------\n010|011   012|013   014|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n\n100|101   102|103   104|xxx   120|121   122|123   124|xxx\n-------   -------   -------   -------   -------   -------\n110|111   112|113   114|xxx   xxx|xxx   xxx|xxx   xxx|xxx\n\nSingle texels contain only values from the same batch, and from adjacent rows\nand columns.\n\nNote the batch dimension is needed so xxx's are inserted below 020, 021, 022,\n023, and 024.\n */\n\nexport function encodeMatrixToPackedRGBA(\n    matrix: Float32Array, batches: number, rows: number, columns: number,\n    packedRGBA: Float32Array) {\n  const requiredSize = getPackedRGBAArraySizeFromMatrixShape(rows, columns);\n  if (packedRGBA.length < requiredSize) {\n    throw new Error(`packedRGBA length (${packedRGBA.length}) must be >=\n        ${requiredSize}`);\n  }\n\n  const oddWidth = (columns % 2) === 1;\n  const oddHeight = (rows % 2) === 1;\n  const widthInFullBlocks = Math.floor(columns / 2);\n  const heightInFullBlocks = Math.floor(rows / 2);\n\n  const texelsPerRow = Math.ceil(columns / 2);\n  const texelsPerBatch = texelsPerRow * Math.ceil(rows / 2);\n\n  const flattenedMatrixSize =\n      util.nearestLargerEven(rows) * util.nearestLargerEven(columns);\n\n  for (let batch = 0; batch < batches; batch++) {\n    const sourceOffset = batch * rows * columns;\n    const batchOffset = batch * flattenedMatrixSize;\n\n    // loop over full 2x2 blocks\n    {\n      const dstStride = (oddWidth ? 4 : 0);\n      const oneRow = columns;\n      let dst = batchOffset;\n      for (let blockY = 0; blockY < heightInFullBlocks; ++blockY) {\n        const matrixSrcRow = (blockY * 2 * columns);\n        for (let blockX = 0; blockX < widthInFullBlocks; ++blockX) {\n          const matrixSrcCol = blockX * 2;\n          const src = sourceOffset + matrixSrcRow + matrixSrcCol;\n          packedRGBA[dst] = matrix[src];\n          packedRGBA[dst + 1] = matrix[src + 1];\n          packedRGBA[dst + 2] = matrix[src + oneRow];\n          packedRGBA[dst + 3] = matrix[src + oneRow + 1];\n          dst += 4;\n        }\n        dst += dstStride;\n      }\n    }\n\n    // loop down final odd column\n    if (oddWidth) {\n      let src = sourceOffset + columns - 1;\n      let dst = batchOffset + (texelsPerRow - 1) * 4;\n      const srcStride = 2 * columns;\n      const dstStride = texelsPerRow * 4;\n      for (let blockY = 0; blockY < heightInFullBlocks; ++blockY) {\n        packedRGBA[dst] = matrix[src];\n        packedRGBA[dst + 2] = matrix[src + columns];\n        src += srcStride;\n        dst += dstStride;\n      }\n    }\n\n    // loop across final row\n    if (oddHeight) {\n      let src = sourceOffset + (rows - 1) * columns;\n      let dst = batchOffset + (texelsPerBatch - texelsPerRow) * 4;\n      for (let blockX = 0; blockX < widthInFullBlocks; ++blockX) {\n        packedRGBA[dst++] = matrix[src++];\n        packedRGBA[dst++] = matrix[src++];\n        dst += 2;\n      }\n\n      // fill in bottom-right texel\n      if (oddWidth && oddHeight) {\n        packedRGBA[batchOffset + flattenedMatrixSize - 4] = matrix[src];\n      }\n    }\n  }\n\n  return packedRGBA;\n}\n\nexport function decodeMatrixFromPackedRGBA(\n    packedRGBA: Float32Array, batches: number, rows: number, columns: number,\n    matrix: Float32Array): Float32Array {\n  const requiredSize = rows * columns;\n  if (matrix.length < requiredSize) {\n    throw new Error(\n        `matrix length (${matrix.length}) must be >= ${requiredSize}`);\n  }\n\n  const oddWidth = (columns % 2) === 1;\n  const oddHeight = (rows % 2) === 1;\n  const widthInFullBlocks = Math.floor(columns / 2);\n  const heightInFullBlocks = Math.floor(rows / 2);\n\n  const texelsPerRow = Math.ceil(columns / 2);\n  const texelsPerBatch = texelsPerRow * Math.ceil(rows / 2);\n\n  const flattenedMatrixSize =\n      util.nearestLargerEven(rows) * util.nearestLargerEven(columns);\n\n  for (let batch = 0; batch < batches; batch++) {\n    const batchOffset = batch * rows * columns;\n    const sourceOffset = batch * flattenedMatrixSize;\n\n    // loop over full 2x2 blocks\n    {\n      const srcStride = oddWidth ? 4 : 0;\n      const dstStride = columns + (oddWidth ? 1 : 0);\n      let src = sourceOffset;\n      let dstRow1 = batchOffset;\n      let dstRow2 = batchOffset + columns;\n      for (let blockY = 0; blockY < heightInFullBlocks; ++blockY) {\n        for (let blockX = 0; blockX < widthInFullBlocks; ++blockX) {\n          matrix[dstRow1++] = packedRGBA[src++];\n          matrix[dstRow1++] = packedRGBA[src++];\n          matrix[dstRow2++] = packedRGBA[src++];\n          matrix[dstRow2++] = packedRGBA[src++];\n        }\n        src += srcStride;\n        dstRow1 += dstStride;\n        dstRow2 += dstStride;\n      }\n    }\n\n    // loop down final column\n    if (oddWidth) {\n      let src = sourceOffset + (texelsPerRow - 1) * 4;\n      let dst = batchOffset + columns - 1;\n      const srcStride = texelsPerRow * 4;\n      const dstStride = 2 * columns;\n      for (let blockY = 0; blockY < heightInFullBlocks; ++blockY) {\n        matrix[dst] = packedRGBA[src];\n        matrix[dst + columns] = packedRGBA[src + 2];\n        src += srcStride;\n        dst += dstStride;\n      }\n    }\n\n    // loop across final row\n    if (oddHeight) {\n      let src = sourceOffset + (texelsPerBatch - texelsPerRow) * 4;\n      let dst = batchOffset + (rows - 1) * columns;\n      for (let blockX = 0; blockX < widthInFullBlocks; ++blockX) {\n        matrix[dst++] = packedRGBA[src++];\n        matrix[dst++] = packedRGBA[src++];\n        src += 2;\n      }\n\n      // fill in bottom-right cell\n      if (oddWidth) {\n        matrix[batchOffset + (rows * columns) - 1] = packedRGBA[src];\n      }\n    }\n  }\n\n  return matrix;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../../environment';\nimport * as util from '../../util';\n\nexport function callAndCheck<T>(gl: WebGLRenderingContext, func: () => T): T {\n  const returnValue = func();\n  checkWebGLError(gl);\n  return returnValue;\n}\n\nlet webGLDebugErrorCheckingEnabled = false;\n\nexport function enableDebugWebGLErrorChecking(enabled: boolean) {\n  webGLDebugErrorCheckingEnabled = enabled;\n}\n\nexport function checkWebGLError(gl: WebGLRenderingContext) {\n  if (webGLDebugErrorCheckingEnabled) {\n    const error = gl.getError();\n    if (error !== gl.NO_ERROR) {\n      throw new Error('WebGL Error: ' + getWebGLErrorMessage(gl, error));\n    }\n  }\n}\n\nexport function getWebGLErrorMessage(\n    gl: WebGLRenderingContext, status: number): string {\n  switch (status) {\n    case gl.NO_ERROR:\n      return 'NO_ERROR';\n    case gl.INVALID_ENUM:\n      return 'INVALID_ENUM';\n    case gl.INVALID_VALUE:\n      return 'INVALID_VALUE';\n    case gl.INVALID_OPERATION:\n      return 'INVALID_OPERATION';\n    case gl.INVALID_FRAMEBUFFER_OPERATION:\n      return 'INVALID_FRAMEBUFFER_OPERATION';\n    case gl.OUT_OF_MEMORY:\n      return 'OUT_OF_MEMORY';\n    case gl.CONTEXT_LOST_WEBGL:\n      return 'CONTEXT_LOST_WEBGL';\n    default:\n      return `Unknown error code ${status}`;\n  }\n}\n\nexport function getExtensionOrThrow(\n    gl: WebGLRenderingContext, extensionName: string): {} {\n  return throwIfNull<{}>(\n      gl, () => gl.getExtension(extensionName),\n      'Extension \"' + extensionName + '\" not supported on this browser.');\n}\n\nexport function createVertexShader(\n    gl: WebGLRenderingContext, vertexShaderSource: string): WebGLShader {\n  const vertexShader: WebGLShader = throwIfNull<WebGLShader>(\n      gl, () => gl.createShader(gl.VERTEX_SHADER),\n      'Unable to create vertex WebGLShader.');\n  callAndCheck(gl, () => gl.shaderSource(vertexShader, vertexShaderSource));\n  callAndCheck(gl, () => gl.compileShader(vertexShader));\n  if (gl.getShaderParameter(vertexShader, gl.COMPILE_STATUS) === false) {\n    console.log(gl.getShaderInfoLog(vertexShader));\n    throw new Error('Failed to compile vertex shader.');\n  }\n  return vertexShader;\n}\n\nexport function createFragmentShader(\n    gl: WebGLRenderingContext, fragmentShaderSource: string): WebGLShader {\n  const fragmentShader: WebGLShader = throwIfNull<WebGLShader>(\n      gl, () => gl.createShader(gl.FRAGMENT_SHADER),\n      'Unable to create fragment WebGLShader.');\n  callAndCheck(gl, () => gl.shaderSource(fragmentShader, fragmentShaderSource));\n  callAndCheck(gl, () => gl.compileShader(fragmentShader));\n  if (gl.getShaderParameter(fragmentShader, gl.COMPILE_STATUS) === false) {\n    logShaderSourceAndInfoLog(\n        fragmentShaderSource, gl.getShaderInfoLog(fragmentShader));\n    throw new Error('Failed to compile fragment shader.');\n  }\n  return fragmentShader;\n}\n\nconst lineNumberRegex = /ERROR: [0-9]+:([0-9]+):/g;\nfunction logShaderSourceAndInfoLog(\n    shaderSource: string, shaderInfoLog: string) {\n  const lineNumberRegexResult = lineNumberRegex.exec(shaderInfoLog);\n  if (lineNumberRegexResult == null) {\n    console.log(`Couldn't parse line number in error: ${shaderInfoLog}`);\n    console.log(shaderSource);\n    return;\n  }\n\n  const lineNumber = +lineNumberRegexResult[1];\n\n  const shaderLines = shaderSource.split('\\n');\n  const pad = shaderLines.length.toString().length + 2;\n  const linesWithLineNumbers = shaderLines.map(\n      (line, lineNumber) =>\n          util.rightPad((lineNumber + 1).toString(), pad) + line);\n  let maxLineLength = 0;\n  for (let i = 0; i < linesWithLineNumbers.length; i++) {\n    maxLineLength = Math.max(linesWithLineNumbers[i].length, maxLineLength);\n  }\n\n  const beforeErrorLines = linesWithLineNumbers.slice(0, lineNumber - 1);\n  const errorLine = linesWithLineNumbers.slice(lineNumber - 1, lineNumber);\n  const afterErrorLines = linesWithLineNumbers.slice(lineNumber);\n\n  console.log(beforeErrorLines.join('\\n'));\n  console.log(shaderInfoLog.split('\\n')[0]);\n  console.log(\n      `%c ${util.rightPad(errorLine[0], maxLineLength)}`,\n      'border:1px solid red; background-color:#e3d2d2; color:#a61717');\n  console.log(afterErrorLines.join('\\n'));\n}\n\nexport function createProgram(gl: WebGLRenderingContext): WebGLProgram {\n  return throwIfNull<WebGLProgram>(\n      gl, () => gl.createProgram(), 'Unable to create WebGLProgram.');\n}\n\nexport function linkProgram(gl: WebGLRenderingContext, program: WebGLProgram) {\n  callAndCheck(gl, () => gl.linkProgram(program));\n  if (gl.getProgramParameter(program, gl.LINK_STATUS) === false) {\n    console.log(gl.getProgramInfoLog(program));\n    throw new Error('Failed to link vertex and fragment shaders.');\n  }\n}\n\nexport function validateProgram(\n    gl: WebGLRenderingContext, program: WebGLProgram) {\n  callAndCheck(gl, () => gl.validateProgram(program));\n  if (gl.getProgramParameter(program, gl.VALIDATE_STATUS) === false) {\n    console.log(gl.getProgramInfoLog(program));\n    throw new Error('Shader program validation failed.');\n  }\n}\n\nexport function createStaticVertexBuffer(\n    gl: WebGLRenderingContext, data: Float32Array): WebGLBuffer {\n  const buffer: WebGLBuffer = throwIfNull<WebGLBuffer>(\n      gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n  callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n  callAndCheck(gl, () => gl.bufferData(gl.ARRAY_BUFFER, data, gl.STATIC_DRAW));\n  return buffer;\n}\n\nexport function createStaticIndexBuffer(\n    gl: WebGLRenderingContext, data: Uint16Array): WebGLBuffer {\n  const buffer: WebGLBuffer = throwIfNull<WebGLBuffer>(\n      gl, () => gl.createBuffer(), 'Unable to create WebGLBuffer');\n  callAndCheck(gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, buffer));\n  callAndCheck(\n      gl, () => gl.bufferData(gl.ELEMENT_ARRAY_BUFFER, data, gl.STATIC_DRAW));\n  return buffer;\n}\n\nexport function getNumChannels(): number {\n  if (ENV.get('WEBGL_VERSION') === 2) {\n    return 1;\n  }\n  return 4;\n}\n\nexport function createTexture(gl: WebGLRenderingContext): WebGLTexture {\n  return throwIfNull<WebGLTexture>(\n      gl, () => gl.createTexture(), 'Unable to create WebGLTexture.');\n}\n\nexport function validateTextureSize(width: number, height: number) {\n  const maxTextureSize = ENV.get('WEBGL_MAX_TEXTURE_SIZE');\n  if ((width <= 0) || (height <= 0)) {\n    const requested = `[${width}x${height}]`;\n    throw new Error('Requested texture size ' + requested + ' is invalid.');\n  }\n  if ((width > maxTextureSize) || (height > maxTextureSize)) {\n    const requested = `[${width}x${height}]`;\n    const max = `[${maxTextureSize}x${maxTextureSize}]`;\n    throw new Error(\n        'Requested texture size ' + requested +\n        ' greater than WebGL maximum on this browser / GPU ' + max + '.');\n  }\n}\n\nexport function createFramebuffer(gl: WebGLRenderingContext): WebGLFramebuffer {\n  return throwIfNull<WebGLFramebuffer>(\n      gl, () => gl.createFramebuffer(), 'Unable to create WebGLFramebuffer.');\n}\n\nexport function bindVertexBufferToProgramAttribute(\n    gl: WebGLRenderingContext, program: WebGLProgram, attribute: string,\n    buffer: WebGLBuffer, arrayEntriesPerItem: number, itemStrideInBytes: number,\n    itemOffsetInBytes: number): boolean {\n  const loc = gl.getAttribLocation(program, attribute);\n  if (loc === -1) {\n    // The GPU compiler decided to strip out this attribute because it's unused,\n    // thus no need to bind.\n    return false;\n  }\n  callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, buffer));\n  callAndCheck(\n      gl,\n      () => gl.vertexAttribPointer(\n          loc, arrayEntriesPerItem, gl.FLOAT, false, itemStrideInBytes,\n          itemOffsetInBytes));\n  callAndCheck(gl, () => gl.enableVertexAttribArray(loc));\n  return true;\n}\n\nexport function bindTextureUnit(\n    gl: WebGLRenderingContext, texture: WebGLTexture, textureUnit: number) {\n  validateTextureUnit(gl, textureUnit);\n  callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n}\n\nexport function unbindTextureUnit(\n    gl: WebGLRenderingContext, textureUnit: number) {\n  validateTextureUnit(gl, textureUnit);\n  callAndCheck(gl, () => gl.activeTexture(gl.TEXTURE0 + textureUnit));\n  callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\n\nexport function getProgramUniformLocationOrThrow(\n    gl: WebGLRenderingContext, program: WebGLProgram,\n    uniformName: string): WebGLUniformLocation {\n  return throwIfNull<WebGLUniformLocation>(\n      gl, () => gl.getUniformLocation(program, uniformName),\n      'uniform \"' + uniformName + '\" not present in program.');\n}\n\nexport function getProgramUniformLocation(\n    gl: WebGLRenderingContext, program: WebGLProgram,\n    uniformName: string): WebGLUniformLocation {\n  return gl.getUniformLocation(program, uniformName);\n}\n\nexport function bindTextureToProgramUniformSampler(\n    gl: WebGLRenderingContext, program: WebGLProgram, texture: WebGLTexture,\n    uniformSamplerLocation: WebGLUniformLocation, textureUnit: number) {\n  callAndCheck(gl, () => bindTextureUnit(gl, texture, textureUnit));\n  callAndCheck(gl, () => gl.uniform1i(uniformSamplerLocation, textureUnit));\n}\n\nexport function bindCanvasToFramebuffer(gl: WebGLRenderingContext) {\n  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n  callAndCheck(gl, () => gl.viewport(0, 0, gl.canvas.width, gl.canvas.height));\n  callAndCheck(gl, () => gl.scissor(0, 0, gl.canvas.width, gl.canvas.height));\n}\n\nexport function bindColorTextureToFramebuffer(\n    gl: WebGLRenderingContext, texture: WebGLTexture,\n    framebuffer: WebGLFramebuffer) {\n  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n  callAndCheck(\n      gl,\n      () => gl.framebufferTexture2D(\n          gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, texture, 0));\n}\n\nexport function unbindColorTextureFromFramebuffer(\n    gl: WebGLRenderingContext, framebuffer: WebGLFramebuffer) {\n  callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, framebuffer));\n  callAndCheck(\n      gl,\n      () => gl.framebufferTexture2D(\n          gl.FRAMEBUFFER, gl.COLOR_ATTACHMENT0, gl.TEXTURE_2D, null, 0));\n}\n\nexport function validateFramebuffer(gl: WebGLRenderingContext) {\n  const status = gl.checkFramebufferStatus(gl.FRAMEBUFFER);\n  if (status !== gl.FRAMEBUFFER_COMPLETE) {\n    throw new Error(\n        'Error binding framebuffer: ' + getFramebufferErrorMessage(gl, status));\n  }\n}\n\nexport function getFramebufferErrorMessage(\n    gl: WebGLRenderingContext, status: number): string {\n  switch (status) {\n    case gl.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:\n      return 'FRAMEBUFFER_INCOMPLETE_ATTACHMENT';\n    case gl.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:\n      return 'FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT';\n    case gl.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:\n      return 'FRAMEBUFFER_INCOMPLETE_DIMENSIONS';\n    case gl.FRAMEBUFFER_UNSUPPORTED:\n      return 'FRAMEBUFFER_UNSUPPORTED';\n    default:\n      return `unknown error ${status}`;\n  }\n}\n\nfunction throwIfNull<T>(\n    gl: WebGLRenderingContext, returnTOrNull: () => T | null,\n    failureMessage: string): T {\n  const tOrNull: T|null = callAndCheck(gl, () => returnTOrNull());\n  if (tOrNull == null) {\n    throw new Error(failureMessage);\n  }\n  return tOrNull as T;\n}\n\nfunction validateTextureUnit(gl: WebGLRenderingContext, textureUnit: number) {\n  const maxTextureUnit = gl.MAX_COMBINED_TEXTURE_IMAGE_UNITS - 1;\n  const glTextureUnit = textureUnit + gl.TEXTURE0;\n  if (glTextureUnit < gl.TEXTURE0 || glTextureUnit > maxTextureUnit) {\n    const textureUnitRange = `[gl.TEXTURE0, gl.TEXTURE${maxTextureUnit}]`;\n    throw new Error(`textureUnit must be in ${textureUnitRange}.`);\n  }\n}\n\nexport function getTextureShapeFromLogicalShape(\n    logShape: number[], isPacked = false): [number, number] {\n  let maxTexSize = ENV.get('WEBGL_MAX_TEXTURE_SIZE');\n  if (isPacked) {\n    maxTexSize = maxTexSize * 2;\n\n    // This logic ensures we accurately count the number of packed texels needed\n    // to accommodate the tensor. We can only pack values in the same texel if\n    // they are from adjacent pairs of rows/cols within the same batch. So if a\n    // tensor has 3 rows, we pretend it has 4 rows in order to account for the\n    // fact that the texels containing the third row are half empty.\n    logShape = logShape.map(\n        (d, i) => i >= logShape.length - 2 ?\n            util.nearestLargerEven(logShape[i]) :\n            logShape[i]);\n  }\n\n  // If logical shape is 2, we don't squeeze, since we want to match physical.\n  if (logShape.length !== 2) {\n    const squeezeResult = util.squeezeShape(logShape);\n    logShape = squeezeResult.newShape;\n  }\n\n  const size = util.sizeFromShape(logShape);\n  if (logShape.length <= 1 && size <= maxTexSize) {\n    return [1, size];\n  } else if (\n      logShape.length === 2 && logShape[0] <= maxTexSize &&\n      logShape[1] <= maxTexSize) {\n    return logShape as [number, number];\n  } else if (\n      logShape.length === 3 && logShape[0] * logShape[1] <= maxTexSize &&\n      logShape[2] <= maxTexSize) {\n    return [logShape[0] * logShape[1], logShape[2]];\n  } else if (\n      logShape.length === 3 && logShape[0] <= maxTexSize &&\n      logShape[1] * logShape[2] <= maxTexSize) {\n    return [logShape[0], logShape[1] * logShape[2]];\n  } else if (\n      logShape.length === 4 &&\n      logShape[0] * logShape[1] * logShape[2] <= maxTexSize &&\n      logShape[3] <= maxTexSize) {\n    return [logShape[0] * logShape[1] * logShape[2], logShape[3]];\n  } else if (\n      logShape.length === 4 && logShape[0] <= maxTexSize &&\n      logShape[1] * logShape[2] * logShape[3] <= maxTexSize) {\n    return [logShape[0], logShape[1] * logShape[2] * logShape[3]];\n  } else {\n    return util.sizeToSquarishShape(size);\n  }\n}\n\nfunction isEven(n: number): boolean {\n  return n % 2 === 0;\n}\n\n/**\n * This determines whether reshaping a packed texture requires rearranging\n * the data within the texture, assuming 2x2 packing.\n */\nexport function isReshapeFree(shape1: number[], shape2: number[]): boolean {\n  shape1 = shape1.slice(-2);\n  shape2 = shape2.slice(-2);\n\n  if (util.arraysEqual(shape1, shape2)) {\n    return true;\n  }\n\n  if (!shape1.length || !shape2.length) {  // One of the shapes is a scalar.\n    return true;\n  }\n\n  if (shape1[0] === 0 || shape1[1] === 0 || shape2[0] === 0 ||\n      shape2[1] === 0) {\n    return true;\n  }\n\n  if (shape1.length !== shape2.length) {  // One of the shapes is a vector.\n    const shape1Cols = shape1.slice(-1)[0];\n    const shape2Cols = shape2.slice(-1)[0];\n    if (shape1Cols === shape2Cols) {\n      return true;\n    }\n\n    if (isEven(shape1Cols) && isEven(shape2Cols) &&\n        (shape1[0] === 1 || shape2[0] === 1)) {\n      return true;\n    }\n  } else {\n    if (isEven(shape1[0]) && isEven(shape2[0])) {\n      if (isEven(shape1[1]) && isEven(shape2[1])) {\n        return true;\n      }\n\n      if (shape1[1] === shape2[1]) {\n        return true;\n      }\n    }\n  }\n\n  return false;\n}","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../../environment';\nimport * as util from '../../util';\n\nimport * as tex_util from './tex_util';\nimport * as webgl_util from './webgl_util';\n\nexport interface TextureConfig {\n  internalFormatFloat: number;\n  textureFormatFloat: number;\n  internalFormatHalfFloat: number;\n  internalFormatPackedFloat: number;\n\n  // The format to use during a gl.readPixels call.\n  downloadTextureFormat: number;\n  // How many channels need to be unpacked after a gl.readPixels call.\n  downloadUnpackNumChannels: number;\n\n  defaultNumChannels: number;\n  textureTypeHalfFloat: number;\n}\n\nexport function createVertexShader(gl: WebGLRenderingContext): WebGLShader {\n  const vertexShaderSource = `\n    precision highp float;\n    attribute vec3 clipSpacePos;\n    attribute vec2 uv;\n    varying vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }`;\n  return webgl_util.createVertexShader(gl, vertexShaderSource);\n}\n\nexport function createVertexBuffer(gl: WebGLRenderingContext): WebGLBuffer {\n  // [x y z u v] * [upper-left, lower-left, upper-right, lower-right]\n  const vertexArray = new Float32Array(\n      [-1, 1, 0, 0, 1, -1, -1, 0, 0, 0, 1, 1, 0, 1, 1, 1, -1, 0, 1, 0]);\n  return webgl_util.createStaticVertexBuffer(gl, vertexArray);\n}\n\nexport function createIndexBuffer(gl: WebGLRenderingContext): WebGLBuffer {\n  // OpenGL (and WebGL) have \"CCW == front\" winding\n  const triangleVertexIndices = new Uint16Array([0, 1, 2, 2, 1, 3]);\n  return webgl_util.createStaticIndexBuffer(gl, triangleVertexIndices);\n}\n\nexport function getTextureConfig(\n    // tslint:disable-next-line:no-any\n    gl: WebGLRenderingContext, textureHalfFloatExtension?: any): TextureConfig {\n  // tslint:disable-next-line:no-any\n  const glany = gl as any;\n\n  let internalFormatFloat: number;\n  let internalFormatHalfFloat: number;\n  let internalFormatPackedFloat: number;\n  let textureFormatFloat: number;\n\n  let downloadTextureFormat: number;\n  let downloadUnpackNumChannels: number;\n\n  let defaultNumChannels: number;\n  let textureTypeHalfFloat: number;\n\n  if (ENV.get('WEBGL_VERSION') === 2) {\n    internalFormatFloat = glany.R32F;\n    internalFormatHalfFloat = glany.R16F;\n    internalFormatPackedFloat = glany.RGBA32F;\n    textureFormatFloat = glany.RED;\n    downloadUnpackNumChannels = 4;\n    defaultNumChannels = 1;\n    textureTypeHalfFloat = glany.HALF_FLOAT;\n  } else {\n    internalFormatFloat = gl.RGBA;\n    internalFormatHalfFloat = gl.RGBA;\n    internalFormatPackedFloat = glany.RGBA;\n    textureFormatFloat = gl.RGBA;\n    downloadUnpackNumChannels = 4;\n    defaultNumChannels = 4;\n    textureTypeHalfFloat = textureHalfFloatExtension != null ?\n        textureHalfFloatExtension.HALF_FLOAT_OES :\n        null;\n  }\n  downloadTextureFormat = gl.RGBA;\n\n  return {\n    internalFormatFloat,\n    internalFormatHalfFloat,\n    internalFormatPackedFloat,\n    textureFormatFloat,\n    downloadTextureFormat,\n    downloadUnpackNumChannels,\n    defaultNumChannels,\n    textureTypeHalfFloat\n  };\n}\n\nfunction createAndConfigureTexture(\n    gl: WebGLRenderingContext, width: number, height: number,\n    internalFormat: number, textureFormat: number,\n    textureType: number): WebGLTexture {\n  webgl_util.validateTextureSize(width, height);\n  const texture = webgl_util.createTexture(gl);\n\n  const tex2d = gl.TEXTURE_2D;\n  webgl_util.callAndCheck(gl, () => gl.bindTexture(tex2d, texture));\n  webgl_util.callAndCheck(\n      gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_S, gl.CLAMP_TO_EDGE));\n  webgl_util.callAndCheck(\n      gl, () => gl.texParameteri(tex2d, gl.TEXTURE_WRAP_T, gl.CLAMP_TO_EDGE));\n  webgl_util.callAndCheck(\n      gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MIN_FILTER, gl.NEAREST));\n  webgl_util.callAndCheck(\n      gl, () => gl.texParameteri(tex2d, gl.TEXTURE_MAG_FILTER, gl.NEAREST));\n  webgl_util.callAndCheck(\n      gl,\n      () => gl.texImage2D(\n          tex2d, 0, internalFormat, width, height, 0, textureFormat,\n          textureType, null));\n  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n  return texture;\n}\n\nexport function createFloat32MatrixTexture(\n    gl: WebGLRenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig): WebGLTexture {\n  const [width, height] =\n      tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(\n      gl, width, height, textureConfig.internalFormatFloat,\n      textureConfig.textureFormatFloat, gl.FLOAT);\n}\n\nexport function createFloat16MatrixTexture(\n    gl: WebGLRenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig): WebGLTexture {\n  const [width, height] =\n      tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(\n      gl, width, height, textureConfig.internalFormatFloat,\n      textureConfig.textureFormatFloat, textureConfig.textureTypeHalfFloat);\n}\n\nexport function createUnsignedBytesMatrixTexture(\n    gl: WebGLRenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig): WebGLTexture {\n  const [width, height] =\n      tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(\n      gl, width, height, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE);\n}\n\nexport function createPackedMatrixTexture(\n    gl: WebGLRenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig): WebGLTexture {\n  const [width, height] =\n      tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(\n      gl, width, height, textureConfig.internalFormatPackedFloat, gl.RGBA,\n      gl.FLOAT);\n}\n\nexport function createFloat16PackedMatrixTexture(\n    gl: WebGLRenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig): WebGLTexture {\n  const [width, height] =\n      tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n  return createAndConfigureTexture(\n      gl, width, height, textureConfig.internalFormatHalfFloat, gl.RGBA,\n      textureConfig.textureTypeHalfFloat);\n}\n\nexport function bindVertexProgramAttributeStreams(\n    gl: WebGLRenderingContext, program: WebGLProgram,\n    vertexBuffer: WebGLBuffer): boolean {\n  const posOffset = 0;               // x is the first buffer element\n  const uvOffset = 3 * 4;            // uv comes after [x y z]\n  const stride = (3 * 4) + (2 * 4);  // xyz + uv, each entry is 4-byte float.\n  webgl_util.callAndCheck(\n      gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, vertexBuffer));\n  const success = webgl_util.bindVertexBufferToProgramAttribute(\n      gl, program, 'clipSpacePos', vertexBuffer, 3, stride, posOffset);\n  return success &&\n      webgl_util.bindVertexBufferToProgramAttribute(\n          gl, program, 'uv', vertexBuffer, 2, stride, uvOffset);\n}\n\nexport function uploadPixelDataToTexture(\n    gl: WebGLRenderingContext, texture: WebGLTexture,\n    pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement) {\n  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n  webgl_util.callAndCheck(\n      gl,\n      () => gl.texImage2D(\n          gl.TEXTURE_2D, 0, gl.RGBA, gl.RGBA, gl.UNSIGNED_BYTE, pixels));\n  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\n\nfunction uploadDataToTexture(\n    gl: WebGLRenderingContext, texture: WebGLTexture, width: number,\n    height: number, data: Float32Array, textureFormat: number) {\n  webgl_util.validateTextureSize(width, height);\n  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, texture));\n  webgl_util.callAndCheck(\n      gl,\n      () => gl.texSubImage2D(\n          gl.TEXTURE_2D, 0, 0, 0, width, height, textureFormat, gl.FLOAT,\n          data));\n\n  webgl_util.callAndCheck(gl, () => gl.bindTexture(gl.TEXTURE_2D, null));\n}\n\nexport function uploadMatrixToTexture(\n    gl: WebGLRenderingContext, texture: WebGLTexture, rows: number,\n    columns: number, matrix: Float32Array, numChannels: number,\n    textureConfig: TextureConfig) {\n  const [w, h] =\n      tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n\n  let unpackedArray: Float32Array;\n  if (textureConfig.defaultNumChannels === 1) {\n    // No need to allocate a temporary array.\n    unpackedArray = matrix;\n  } else {\n    unpackedArray =\n        new Float32Array(tex_util.getUnpackedArraySizeFromMatrixSize(\n            matrix.length, numChannels));\n    tex_util.encodeMatrixToUnpackedArray(matrix, unpackedArray, numChannels);\n  }\n\n  uploadDataToTexture(\n      gl, texture, w, h, unpackedArray, textureConfig.textureFormatFloat);\n}\n\n/**\n * This method writes a tensor to a packed texture in a way that respects how we\n * represent data using each texel's r,g,b,a channels. Specifically, we lay\n * out the four channels in two rows each containing two channels, so a single\n * texel can represent up to four values from the tensor. That means a texture\n * that has a channel width of 11 and channel height of 4 will have a texel\n * width of 6 and texel height of 2.\n *\n * rows, columns: Logical number of rows and columns in the tensor to be\n * uploaded.\n *\n * physicalRows, physicalCols: Channel dimensions of the texture that will hold\n * the tensor.\n *\n * width, height (internal parameters): Texel dimensions of the texture.\n */\nexport function uploadMatrixToPackedTexture(\n    gl: WebGLRenderingContext, texture: WebGLTexture, batch: number,\n    rows: number, columns: number, physicalRows: number, physicalCols: number,\n    matrix: Float32Array, textureConfig: TextureConfig) {\n  const [w, h] = tex_util.getPackedMatrixTextureShapeWidthHeight(\n      physicalRows, physicalCols);\n  const packedRGBA = new Float32Array(\n      batch * tex_util.getPackedRGBAArraySizeFromMatrixShape(rows, columns));\n  tex_util.encodeMatrixToPackedRGBA(matrix, batch, rows, columns, packedRGBA);\n  uploadDataToTexture(gl, texture, w, h, packedRGBA, gl.RGBA);\n}\n\nexport function maybeCreateBufferFromOutputTexture(\n    gl: WebGLRenderingContext, texture: WebGLTexture, rows: number,\n    columns: number, textureConfig: TextureConfig): WebGLBuffer|WebGLTexture {\n  let bufferOrTexture: WebGLBuffer|WebGLTexture = texture;\n\n  if (ENV.get('WEBGL_VERSION') === 2) {\n    const gl2 = gl as WebGL2RenderingContext;\n\n    // Create and bind the buffer.\n    const buffer = gl2.createBuffer();\n    webgl_util.callAndCheck(\n        gl, () => gl.bindBuffer(gl2.PIXEL_PACK_BUFFER, buffer));\n\n    // Initialize the buffer to the size of the texture in bytes.\n    const bytesPerFloat = 4;\n    const bufferSizeBytes = bytesPerFloat *\n        tex_util.getUnpackedArraySizeFromMatrixSize(\n            rows * columns, textureConfig.downloadUnpackNumChannels);\n\n    webgl_util.callAndCheck(\n        gl,\n        () => gl.bufferData(\n            gl2.PIXEL_PACK_BUFFER, bufferSizeBytes, gl.STATIC_DRAW));\n\n    // Enqueue a command on the GPU command queue to copy of texture into the\n    // buffer.\n    webgl_util.callAndCheck(\n        gl, () => gl2.readPixels(0, 0, columns, rows, gl.RGBA, gl.FLOAT, 0));\n\n    webgl_util.callAndCheck(\n        gl, () => gl.bindBuffer(gl2.PIXEL_PACK_BUFFER, null));\n\n    bufferOrTexture = buffer;\n  }\n\n  return bufferOrTexture;\n}\n\nexport function downloadFloat32MatrixFromBuffer(\n    gl: WebGLRenderingContext, buffer: WebGLBuffer, rows: number,\n    columns: number, textureConfig: TextureConfig): Float32Array {\n  const gl2 = gl as WebGL2RenderingContext;\n\n  const downloadTarget =\n      new Float32Array(tex_util.getUnpackedArraySizeFromMatrixSize(\n          rows * columns, textureConfig.downloadUnpackNumChannels));\n\n  gl2.bindBuffer(gl.ARRAY_BUFFER, buffer);\n  gl2.getBufferSubData(gl.ARRAY_BUFFER, 0, downloadTarget);\n  gl2.bindBuffer(gl.ARRAY_BUFFER, null);\n\n  const matrix = new Float32Array(rows * columns);\n  tex_util.decodeMatrixFromUnpackedArray(\n      downloadTarget as Float32Array, matrix,\n      textureConfig.downloadUnpackNumChannels);\n\n  return matrix;\n}\n\nexport function downloadFloat32MatrixFromOutputTexture(\n    gl: WebGLRenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig): Float32Array {\n  const [w, h] =\n      tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n\n  const downloadTarget =\n      new Float32Array(tex_util.getUnpackedArraySizeFromMatrixSize(\n          rows * columns, textureConfig.downloadUnpackNumChannels));\n\n  webgl_util.callAndCheck(\n      gl,\n      () => gl.readPixels(\n          0, 0, w, h, textureConfig.downloadTextureFormat, gl.FLOAT,\n          downloadTarget));\n\n  const matrix = new Float32Array(rows * columns);\n  tex_util.decodeMatrixFromUnpackedArray(\n      downloadTarget as Float32Array, matrix,\n      textureConfig.downloadUnpackNumChannels);\n  return matrix;\n}\n\nexport function downloadByteEncodedFloatMatrixFromOutputTexture(\n    gl: WebGLRenderingContext, rows: number, columns: number,\n    textureConfig: TextureConfig) {\n  const [w, h] =\n      tex_util.getUnpackedMatrixTextureShapeWidthHeight(rows, columns);\n\n  const numChannels = 4;\n  const downloadTarget = new Uint8Array(\n      tex_util.getUnpackedArraySizeFromMatrixSize(rows * columns, numChannels));\n\n  webgl_util.callAndCheck(\n      gl,\n      () => gl.readPixels(\n          0, 0, w, h, textureConfig.downloadTextureFormat, gl.UNSIGNED_BYTE,\n          downloadTarget));\n\n  // By wrapping the buffer in a Float32Array, we use native browser IEEE 754\n  // decoding of the 4 bytes that back each 32 bit float.\n  return new Float32Array(downloadTarget.buffer);\n}\n\nexport function downloadPackedMatrixFromBuffer(\n    gl: WebGLRenderingContext, buffer: WebGLBuffer, batch: number, rows: number,\n    cols: number, physicalRows: number, physicalCols: number,\n    textureConfig: TextureConfig): Float32Array {\n  const gl2 = gl as WebGL2RenderingContext;\n\n  const downloadTarget =\n      new Float32Array(tex_util.getPackedRGBAArraySizeFromMatrixShape(\n          physicalRows, physicalCols));\n\n  gl2.bindBuffer(gl.ARRAY_BUFFER, buffer);\n  gl2.getBufferSubData(gl.ARRAY_BUFFER, 0, downloadTarget);\n  gl2.bindBuffer(gl.ARRAY_BUFFER, null);\n\n  const matrix = new Float32Array(util.sizeFromShape([batch, rows, cols]));\n  tex_util.decodeMatrixFromPackedRGBA(\n      downloadTarget, batch, rows, cols, matrix);\n  return matrix;\n}\n\nexport function downloadMatrixFromPackedOutputTexture(\n    gl: WebGLRenderingContext, batch: number, rows: number, cols: number,\n    physicalRows: number, physicalCols: number,\n    textureConfig: TextureConfig): Float32Array {\n  const [w, h] = tex_util.getPackedMatrixTextureShapeWidthHeight(\n      physicalRows, physicalCols);\n\n  const packedRGBA =\n      new Float32Array(tex_util.getPackedRGBAArraySizeFromMatrixShape(\n          physicalRows, physicalCols));\n  webgl_util.callAndCheck(\n      gl, () => gl.readPixels(0, 0, w, h, gl.RGBA, gl.FLOAT, packedRGBA));\n  const matrix = new Float32Array(util.sizeFromShape([batch, rows, cols]));\n  return tex_util.decodeMatrixFromPackedRGBA(\n      packedRGBA, batch, rows, cols, matrix);\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getWebGLContext} from '../../canvas_util';\nimport {ENV} from '../../environment';\nimport * as util from '../../util';\nimport * as gpgpu_util from './gpgpu_util';\nimport {TextureConfig} from './gpgpu_util';\nimport * as tex_util from './tex_util';\nimport {WebGL1DisjointQueryTimerExtension, WebGL2DisjointQueryTimerExtension} from './webgl_types';\nimport * as webgl_util from './webgl_util';\n\nexport interface FenceContext {\n  query: WebGLQuery|WebGLSync;\n  isFencePassed(): boolean;\n}\n\nexport class GPGPUContext {\n  gl: WebGLRenderingContext;\n  textureFloatExtension: {};\n  textureHalfFloatExtension: {};\n  colorBufferFloatExtension: {};\n  colorBufferHalfFloatExtension: {};\n  getBufferSubDataAsyncExtension: {};\n  disjointQueryTimerExtension: WebGL2DisjointQueryTimerExtension|\n      WebGL1DisjointQueryTimerExtension;\n  vertexBuffer: WebGLBuffer;\n  indexBuffer: WebGLBuffer;\n  framebuffer: WebGLFramebuffer;\n  outputTexture: WebGLTexture|null = null;\n  program: WebGLProgram|null = null;\n  private disposed = false;\n  private autoDebugValidate = false;\n  private disjoint: boolean;\n  private textureConfig: TextureConfig;\n\n  constructor(gl?: WebGLRenderingContext) {\n    if (gl != null) {\n      this.gl = gl;\n    } else {\n      this.gl = getWebGLContext(ENV.get('WEBGL_VERSION'));\n    }\n    // WebGL 2.0 enables texture floats without an extension.\n    if (ENV.get('WEBGL_VERSION') === 1) {\n      this.textureFloatExtension =\n          webgl_util.getExtensionOrThrow(this.gl, 'OES_texture_float');\n      this.colorBufferFloatExtension =\n          this.gl.getExtension('WEBGL_color_buffer_float');\n\n      if (!ENV.get('WEBGL_RENDER_FLOAT32_ENABLED')) {\n        this.textureHalfFloatExtension =\n            webgl_util.getExtensionOrThrow(this.gl, 'OES_texture_half_float');\n        this.colorBufferHalfFloatExtension =\n            this.gl.getExtension('EXT_color_buffer_half_float');\n      }\n    } else {\n      this.colorBufferFloatExtension =\n          webgl_util.getExtensionOrThrow(this.gl, 'EXT_color_buffer_float');\n    }\n\n    this.vertexBuffer = gpgpu_util.createVertexBuffer(this.gl);\n    this.indexBuffer = gpgpu_util.createIndexBuffer(this.gl);\n    this.framebuffer = webgl_util.createFramebuffer(this.gl);\n\n    this.textureConfig =\n        gpgpu_util.getTextureConfig(this.gl, this.textureHalfFloatExtension);\n  }\n\n  public dispose() {\n    if (this.disposed) {\n      return;\n    }\n    if (this.program != null) {\n      console.warn(\n          'Disposing a GPGPUContext that still has a bound WebGLProgram.' +\n          ' This is probably a resource leak, delete the program with ' +\n          'GPGPUContext.deleteProgram before disposing.');\n    }\n    if (this.outputTexture != null) {\n      console.warn(\n          'Disposing a GPGPUContext that still has a bound output matrix ' +\n          'texture.  This is probably a resource leak, delete the output ' +\n          'matrix texture with GPGPUContext.deleteMatrixTexture before ' +\n          'disposing.');\n    }\n    const gl = this.gl;\n    webgl_util.callAndCheck(gl, () => gl.finish());\n    webgl_util.callAndCheck(gl, () => gl.bindFramebuffer(gl.FRAMEBUFFER, null));\n    webgl_util.callAndCheck(gl, () => gl.deleteFramebuffer(this.framebuffer));\n    webgl_util.callAndCheck(gl, () => gl.bindBuffer(gl.ARRAY_BUFFER, null));\n    webgl_util.callAndCheck(\n        gl, () => gl.bindBuffer(gl.ELEMENT_ARRAY_BUFFER, null));\n    webgl_util.callAndCheck(gl, () => gl.deleteBuffer(this.indexBuffer));\n    this.disposed = true;\n  }\n\n  public enableAutomaticDebugValidation(enabled: boolean) {\n    this.autoDebugValidate = enabled;\n    webgl_util.enableDebugWebGLErrorChecking(enabled);\n  }\n\n  public createFloat32MatrixTexture(rows: number, columns: number):\n      WebGLTexture {\n    this.throwIfDisposed();\n    return gpgpu_util.createFloat32MatrixTexture(\n        this.gl, rows, columns, this.textureConfig);\n  }\n\n  public createFloat16MatrixTexture(rows: number, columns: number):\n      WebGLTexture {\n    this.throwIfDisposed();\n    return gpgpu_util.createFloat16MatrixTexture(\n        this.gl, rows, columns, this.textureConfig);\n  }\n\n  public createUnsignedBytesMatrixTexture(rows: number, columns: number):\n      WebGLTexture {\n    this.throwIfDisposed();\n    return gpgpu_util.createUnsignedBytesMatrixTexture(\n        this.gl, rows, columns, this.textureConfig);\n  }\n\n  public uploadPixelDataToTexture(\n      texture: WebGLTexture,\n      pixels: ImageData|HTMLImageElement|HTMLCanvasElement) {\n    this.throwIfDisposed();\n    gpgpu_util.uploadPixelDataToTexture(this.gl, texture, pixels);\n  }\n\n  public createFloat16PackedMatrixTexture(rows: number, columns: number):\n      WebGLTexture {\n    this.throwIfDisposed();\n    return gpgpu_util.createFloat16PackedMatrixTexture(\n        this.gl, rows, columns, this.textureConfig);\n  }\n\n  public createPackedMatrixTexture(rows: number, columns: number):\n      WebGLTexture {\n    this.throwIfDisposed();\n    return gpgpu_util.createPackedMatrixTexture(\n        this.gl, rows, columns, this.textureConfig);\n  }\n\n  public deleteMatrixTexture(texture: WebGLTexture) {\n    this.throwIfDisposed();\n    if (this.outputTexture === texture) {\n      webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n      this.outputTexture = null;\n    }\n    webgl_util.callAndCheck(this.gl, () => this.gl.deleteTexture(texture));\n  }\n\n  public uploadMatrixToTexture(\n      texture: WebGLTexture, rows: number, columns: number,\n      matrix: Float32Array) {\n    this.throwIfDisposed();\n    const numChannels = webgl_util.getNumChannels();\n    return gpgpu_util.uploadMatrixToTexture(\n        this.gl, texture, rows, columns, matrix, numChannels,\n        this.textureConfig);\n  }\n\n  public uploadMatrixToPackedTexture(\n      texture: WebGLTexture, batch: number, rows: number, columns: number,\n      physicalRows: number, physicalCols: number, matrix: Float32Array) {\n    this.throwIfDisposed();\n    return gpgpu_util.uploadMatrixToPackedTexture(\n        this.gl, texture, batch, rows, columns, physicalRows, physicalCols,\n        matrix, this.textureConfig);\n  }\n\n  public downloadFloat32MatrixFromOutputTexture(\n      texture: WebGLTexture, rows: number, columns: number): Float32Array {\n    return this.downloadMatrixDriver(\n        texture,\n        () => gpgpu_util.downloadFloat32MatrixFromOutputTexture(\n            this.gl, rows, columns, this.textureConfig));\n  }\n\n  public downloadByteEncodedFloatMatrixFromOutputTexture(\n      texture: WebGLTexture, rows: number, columns: number): Float32Array {\n    return this.downloadMatrixDriver(\n        texture,\n        () => gpgpu_util.downloadByteEncodedFloatMatrixFromOutputTexture(\n            this.gl, rows, columns, this.textureConfig));\n  }\n\n  public downloadPackedMatrixFromBuffer(\n      buffer: WebGLBuffer, batch: number, rows: number, columns: number,\n      physicalRows: number, physicalCols: number): Float32Array {\n    return gpgpu_util.downloadPackedMatrixFromBuffer(\n        this.gl, buffer, batch, rows, columns, physicalRows, physicalCols,\n        this.textureConfig);\n  }\n\n  public downloadFloat32MatrixFromBuffer(\n      buffer: WebGLBuffer, rows: number, columns: number): Float32Array {\n    return gpgpu_util.downloadFloat32MatrixFromBuffer(\n        this.gl, buffer, rows, columns, this.textureConfig);\n  }\n\n  public maybeCreateBufferFromTexture(\n      texture: WebGLTexture, rows: number, columns: number): WebGLBuffer\n      |WebGLTexture {\n    this.bindTextureToFrameBuffer(texture);\n    const result = gpgpu_util.maybeCreateBufferFromOutputTexture(\n        this.gl, texture, rows, columns, this.textureConfig);\n    this.unbindTextureToFrameBuffer();\n    return result;\n  }\n\n  public createAndWaitForFence(): Promise<void> {\n    const fenceContext = this.createFence(this.gl);\n    return this.pollFence(fenceContext);\n  }\n\n  private createFence(gl: WebGLRenderingContext): FenceContext {\n    let query: WebGLQuery|WebGLSync;\n    let isFencePassed: () => boolean;\n\n    if (ENV.get('WEBGL_FENCE_API_ENABLED')) {\n      const gl2 = gl as WebGL2RenderingContext;\n\n      const sync = gl2.fenceSync(gl2.SYNC_GPU_COMMANDS_COMPLETE, 0);\n      gl.flush();\n\n      isFencePassed = () => {\n        const status = gl2.clientWaitSync(sync, 0, 0);\n        return status === gl2.ALREADY_SIGNALED ||\n            status === gl2.CONDITION_SATISFIED;\n      };\n\n      query = sync;\n    } else if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {\n      query = this.beginQuery();\n      this.endQuery();\n      isFencePassed = () => this.isQueryAvailable(\n          query, ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n    } else {\n      // If we have no way to fence, return true immediately. This will fire in\n      // WebGL 1.0 when there is no disjoint query timer. In this case, because\n      // the fence passes immediately, we'll immediately ask for a download of\n      // the texture, which will cause the UI thread to hang.\n      isFencePassed = () => true;\n    }\n\n    return {query, isFencePassed};\n  }\n\n  public downloadMatrixFromPackedTexture(\n      texture: WebGLTexture, batch: number, rows: number, columns: number,\n      physicalRows: number, physicalCols: number): Float32Array {\n    return this.downloadMatrixDriver(\n        texture,\n        () => gpgpu_util.downloadMatrixFromPackedOutputTexture(\n            this.gl, batch, rows, columns, physicalRows, physicalCols,\n            this.textureConfig));\n  }\n\n  private vertexAttrsAreBound = false;\n\n  public createProgram(fragmentShaderSource: string): WebGLProgram {\n    this.throwIfDisposed();\n    const gl = this.gl;\n    const fragmentShader: WebGLShader =\n        webgl_util.createFragmentShader(gl, fragmentShaderSource);\n    const vertexShader: WebGLShader = gpgpu_util.createVertexShader(gl);\n    const program: WebGLProgram = webgl_util.createProgram(gl);\n    webgl_util.callAndCheck(gl, () => gl.attachShader(program, vertexShader));\n    webgl_util.callAndCheck(gl, () => gl.attachShader(program, fragmentShader));\n    webgl_util.linkProgram(gl, program);\n    if (this.autoDebugValidate) {\n      webgl_util.validateProgram(gl, program);\n    }\n    if (!this.vertexAttrsAreBound) {\n      this.setProgram(program);\n      this.vertexAttrsAreBound = gpgpu_util.bindVertexProgramAttributeStreams(\n          gl, this.program, this.vertexBuffer);\n    }\n    return program;\n  }\n\n  public deleteProgram(program: WebGLProgram) {\n    this.throwIfDisposed();\n    if (program === this.program) {\n      this.program = null;\n    }\n    if (program != null) {\n      webgl_util.callAndCheck(this.gl, () => this.gl.deleteProgram(program));\n    }\n  }\n\n  public setProgram(program: WebGLProgram|null) {\n    this.throwIfDisposed();\n    this.program = program;\n    if ((this.program != null) && this.autoDebugValidate) {\n      webgl_util.validateProgram(this.gl, this.program);\n    }\n    webgl_util.callAndCheck(this.gl, () => this.gl.useProgram(program));\n  }\n\n  public getUniformLocation(\n      program: WebGLProgram, uniformName: string,\n      shouldThrow = true): WebGLUniformLocation {\n    this.throwIfDisposed();\n    if (shouldThrow) {\n      return webgl_util.getProgramUniformLocationOrThrow(\n          this.gl, program, uniformName);\n    } else {\n      return webgl_util.getProgramUniformLocation(\n          this.gl, program, uniformName);\n    }\n  }\n\n  public getAttributeLocation(program: WebGLProgram, attribute: string):\n      number {\n    this.throwIfDisposed();\n    return webgl_util.callAndCheck(\n        this.gl, () => this.gl.getAttribLocation(program, attribute));\n  }\n\n  public getUniformLocationNoThrow(program: WebGLProgram, uniformName: string):\n      WebGLUniformLocation {\n    this.throwIfDisposed();\n    return this.gl.getUniformLocation(program, uniformName);\n  }\n\n  public setInputMatrixTexture(\n      inputMatrixTexture: WebGLTexture, uniformLocation: WebGLUniformLocation,\n      textureUnit: number) {\n    this.throwIfDisposed();\n    this.throwIfNoProgram();\n    webgl_util.bindTextureToProgramUniformSampler(\n        this.gl, this.program, inputMatrixTexture, uniformLocation,\n        textureUnit);\n  }\n\n  public setOutputMatrixTexture(\n      outputMatrixTexture: WebGLTexture, rows: number, columns: number) {\n    this.setOutputMatrixTextureDriver(outputMatrixTexture, columns, rows);\n  }\n\n  public setOutputPackedMatrixTexture(\n      outputPackedMatrixTexture: WebGLTexture, rows: number, columns: number) {\n    this.throwIfDisposed();\n    const [width, height] =\n        tex_util.getPackedMatrixTextureShapeWidthHeight(rows, columns);\n    this.setOutputMatrixTextureDriver(outputPackedMatrixTexture, width, height);\n  }\n\n  public setOutputMatrixWriteRegion(\n      startRow: number, numRows: number, startColumn: number,\n      numColumns: number) {\n    this.setOutputMatrixWriteRegionDriver(\n        startColumn, startRow, numColumns, numRows);\n  }\n\n  public setOutputPackedMatrixWriteRegion(\n      startRow: number, numRows: number, startColumn: number,\n      numColumns: number) {\n    throw new Error('setOutputPackedMatrixWriteRegion not implemented.');\n  }\n\n  public debugValidate() {\n    if (this.program != null) {\n      webgl_util.validateProgram(this.gl, this.program);\n    }\n    webgl_util.validateFramebuffer(this.gl);\n  }\n\n  public executeProgram() {\n    this.throwIfDisposed();\n    this.throwIfNoProgram();\n    const gl = this.gl;\n    if (this.autoDebugValidate) {\n      this.debugValidate();\n    }\n    webgl_util.callAndCheck(\n        gl, () => gl.drawElements(gl.TRIANGLES, 6, gl.UNSIGNED_SHORT, 0));\n  }\n\n  public blockUntilAllProgramsCompleted() {\n    this.throwIfDisposed();\n    webgl_util.callAndCheck(this.gl, () => this.gl.finish());\n  }\n\n  private getQueryTimerExtension(): WebGL1DisjointQueryTimerExtension\n      |WebGL2DisjointQueryTimerExtension {\n    if (this.disjointQueryTimerExtension == null) {\n      this.disjointQueryTimerExtension =\n          webgl_util.getExtensionOrThrow(\n              this.gl,\n              ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2 ?\n                  'EXT_disjoint_timer_query_webgl2' :\n                  'EXT_disjoint_timer_query') as\n              WebGL1DisjointQueryTimerExtension |\n          WebGL2DisjointQueryTimerExtension;\n    }\n    return this.disjointQueryTimerExtension;\n  }\n\n  private getQueryTimerExtensionWebGL2(): WebGL2DisjointQueryTimerExtension {\n    return this.getQueryTimerExtension();\n  }\n\n  private getQueryTimerExtensionWebGL1(): WebGL1DisjointQueryTimerExtension {\n    return this.getQueryTimerExtension() as WebGL1DisjointQueryTimerExtension;\n  }\n\n  beginQuery(): WebGLQuery {\n    if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n      const gl2 = this.gl as WebGL2RenderingContext;\n      const ext = this.getQueryTimerExtensionWebGL2();\n\n      const query = gl2.createQuery();\n      gl2.beginQuery(ext.TIME_ELAPSED_EXT, query);\n      return query;\n    }\n    const ext = this.getQueryTimerExtensionWebGL1();\n    const query = ext.createQueryEXT() as WebGLQuery;\n    ext.beginQueryEXT(ext.TIME_ELAPSED_EXT, query);\n    return query;\n  }\n\n  endQuery() {\n    if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') === 2) {\n      const gl2 = this.gl as WebGL2RenderingContext;\n      const ext = this.getQueryTimerExtensionWebGL2();\n      gl2.endQuery(ext.TIME_ELAPSED_EXT);\n      return;\n    }\n    const ext = this.getQueryTimerExtensionWebGL1();\n    ext.endQueryEXT(ext.TIME_ELAPSED_EXT);\n  }\n\n  public async waitForQueryAndGetTime(query: WebGLQuery): Promise<number> {\n    await util.repeatedTry(\n        () => this.disposed ||  // while testing contexts are created / disposed\n                                // in rapid succession, so without this check we\n                                // may poll for the query timer indefinitely\n            this.isQueryAvailable(\n                query,\n                ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION')));\n    return this.getQueryTime(\n        query, ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION'));\n  }\n\n  private getQueryTime(query: WebGLQuery, queryTimerVersion: number): number {\n    if (queryTimerVersion === 0) {\n      return null;\n    }\n\n    if (queryTimerVersion === 2) {\n      const gl2 = this.gl as WebGL2RenderingContext;\n\n      const timeElapsedNanos = gl2.getQueryParameter(query, gl2.QUERY_RESULT);\n      // Return milliseconds.\n      return timeElapsedNanos / 1000000;\n    } else {\n      const ext = this.getQueryTimerExtensionWebGL1();\n\n      const timeElapsedNanos =\n          ext.getQueryObjectEXT(query, ext.QUERY_RESULT_EXT);\n      // Return milliseconds.\n      return timeElapsedNanos / 1000000;\n    }\n  }\n\n  private isQueryAvailable(query: WebGLQuery, queryTimerVersion: number):\n      boolean {\n    if (queryTimerVersion === 0) {\n      return true;\n    }\n\n    if (queryTimerVersion === 2) {\n      const gl2 = this.gl as WebGL2RenderingContext;\n      const ext = this.getQueryTimerExtensionWebGL2();\n\n      const available =\n          gl2.getQueryParameter(query, gl2.QUERY_RESULT_AVAILABLE);\n      if (this.disjoint == null) {\n        this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n      }\n\n      return available && !this.disjoint;\n    } else {\n      const ext = this.getQueryTimerExtensionWebGL1();\n\n      const available =\n          ext.getQueryObjectEXT(query, ext.QUERY_RESULT_AVAILABLE_EXT);\n      if (this.disjoint == null) {\n        this.disjoint = this.gl.getParameter(ext.GPU_DISJOINT_EXT);\n      }\n\n      return available && !this.disjoint;\n    }\n  }\n\n  pollFence(fenceContext: FenceContext) {\n    return new Promise<void>(resolve => {\n      this.addItemToPoll(() => fenceContext.isFencePassed(), () => resolve());\n    });\n  }\n\n  private itemsToPoll: PollItem[] = [];\n\n  pollItems(): void {\n    // Find the last query that has finished using binary search.\n    // All other queries before it are also done.\n    const index = binSearchLastTrue(this.itemsToPoll.map(x => x.isDoneFn));\n    for (let i = 0; i <= index; ++i) {\n      const {resolveFn} = this.itemsToPoll[i];\n      resolveFn();\n    }\n    this.itemsToPoll = this.itemsToPoll.slice(index + 1);\n  }\n\n  private addItemToPoll(isDoneFn: () => boolean, resolveFn: () => void) {\n    this.itemsToPoll.push({isDoneFn, resolveFn});\n    if (this.itemsToPoll.length > 1) {\n      // We already have a running loop that polls.\n      return;\n    }\n    // Start a new loop that polls.\n    util.repeatedTry(() => {\n      this.pollItems();\n      // End the loop if no more items to poll.\n      return this.itemsToPoll.length === 0;\n    });\n  }\n\n  private bindTextureToFrameBuffer(texture: WebGLTexture) {\n    this.throwIfDisposed();\n    webgl_util.bindColorTextureToFramebuffer(\n        this.gl, texture, this.framebuffer);\n    if (this.autoDebugValidate) {\n      webgl_util.validateFramebuffer(this.gl);\n    }\n  }\n\n  private unbindTextureToFrameBuffer() {\n    if (this.outputTexture != null) {\n      webgl_util.bindColorTextureToFramebuffer(\n          this.gl, this.outputTexture, this.framebuffer);\n      if (this.autoDebugValidate) {\n        webgl_util.validateFramebuffer(this.gl);\n      }\n    } else {\n      webgl_util.unbindColorTextureFromFramebuffer(this.gl, this.framebuffer);\n    }\n  }\n\n  private downloadMatrixDriver(\n      texture: WebGLTexture,\n      downloadAndDecode: () => Float32Array): Float32Array {\n    this.bindTextureToFrameBuffer(texture);\n    const result = downloadAndDecode();\n    this.unbindTextureToFrameBuffer();\n\n    return result;\n  }\n\n  private setOutputMatrixTextureDriver(\n      outputMatrixTextureMaybePacked: WebGLTexture, width: number,\n      height: number) {\n    this.throwIfDisposed();\n    const gl = this.gl;\n    webgl_util.bindColorTextureToFramebuffer(\n        gl, outputMatrixTextureMaybePacked, this.framebuffer);\n    if (this.autoDebugValidate) {\n      webgl_util.validateFramebuffer(gl);\n    }\n    this.outputTexture = outputMatrixTextureMaybePacked;\n    webgl_util.callAndCheck(gl, () => gl.viewport(0, 0, width, height));\n    webgl_util.callAndCheck(gl, () => gl.scissor(0, 0, width, height));\n  }\n\n  private setOutputMatrixWriteRegionDriver(\n      x: number, y: number, width: number, height: number) {\n    this.throwIfDisposed();\n    webgl_util.callAndCheck(\n        this.gl, () => this.gl.scissor(x, y, width, height));\n  }\n\n  private throwIfDisposed() {\n    if (this.disposed) {\n      throw new Error('Attempted to use disposed GPGPUContext.');\n    }\n  }\n\n  private throwIfNoProgram() {\n    if (this.program == null) {\n      throw new Error('No GPU program is currently set.');\n    }\n  }\n}\n\ntype PollItem = {\n  isDoneFn: () => boolean,\n  resolveFn: () => void\n};\n\n/**\n * Finds the index of the last true element using binary search where\n * evaluation of an entry is expensive.\n */\nexport function binSearchLastTrue(arr: Array<() => boolean>): number {\n  let start = 0;\n  let end = arr.length - 1;\n  let best = -1;\n  while (start <= end) {\n    const mid = (start + end) >> 1;\n    const isDone = arr[mid]();\n    if (isDone) {\n      best = mid;\n      start = mid + 1;\n    } else {\n      end = mid - 1;\n    }\n  }\n  return best;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../../tensor';\nimport {TypedArray} from '../../types';\nimport * as util from '../../util';\n\nimport {GPGPUContext} from './gpgpu_context';\nimport * as shader_compiler from './shader_compiler';\nimport {InputInfo, ShapeInfo} from './shader_compiler';\nimport {TextureData} from './tex_util';\n\nexport interface GPGPUProgram {\n  variableNames: string[];\n  outputShape: number[];\n  userCode: string;\n  usesPackedTextures?: boolean;\n  supportsBroadcasting?: boolean;\n  isPackShader?: boolean;  // This property is used to single out the packing\n                           // shader so its output does not get eagerly unpacked\n                           // by backend_webgl.compileAndRun.\n}\n\nexport interface GPGPUBinary {\n  webGLProgram: WebGLProgram;\n  program: GPGPUProgram;\n  uniformLocations: {[name: string]: WebGLUniformLocation};\n  gpgpu: GPGPUContext;\n  source: string;\n  inShapeInfos: ShapeInfo[];\n  outShapeInfo: ShapeInfo;\n}\n\nexport interface TensorData {\n  shape: number[];\n  texData: TextureData;\n  isUniform: boolean;\n  uniformValues?: TypedArray;\n}\n\nexport function compileProgram<T extends Tensor, K extends Tensor>(\n    gpgpu: GPGPUContext, program: GPGPUProgram, inputs: TensorData[],\n    output: TensorData): GPGPUBinary {\n  const userCode = program.userCode;\n  const inputInfos: InputInfo[] = inputs.map((input, i) => {\n    const shapeInfo = {\n      logicalShape: input.shape,\n      texShape: input.isUniform ? null : input.texData.texShape,\n      isUniform: input.isUniform,\n      isPacked: input.isUniform ? false : input.texData.isPacked\n    };\n    return {name: program.variableNames[i], shapeInfo};\n  });\n  const inShapeInfos = inputInfos.map(x => x.shapeInfo);\n  const outShapeInfo = {\n    logicalShape: output.shape,\n    texShape: output.texData.texShape,\n    isUniform: false,\n    isPacked: output.texData.isPacked\n  };\n  const source = shader_compiler.makeShader(\n      inputInfos, outShapeInfo, userCode, program.supportsBroadcasting === true,\n      program.usesPackedTextures);\n\n  const webGLProgram = gpgpu.createProgram(source);\n\n  const uniformLocations: {[name: string]: WebGLUniformLocation} = {};\n  for (let i = 0; i < program.variableNames.length; i++) {\n    const uniformName = program.variableNames[i];\n    const shouldThrow = false;\n    uniformLocations[uniformName] =\n        gpgpu.getUniformLocation(webGLProgram, uniformName, shouldThrow);\n  }\n\n  return {\n    program,\n    source,\n    webGLProgram,\n    uniformLocations,\n    gpgpu,\n    inShapeInfos,\n    outShapeInfo\n  };\n}\n\nfunction validateBinaryAndProgram(\n    shapeInfos: ShapeInfo[], inputs: TensorData[]) {\n  if (shapeInfos.length !== inputs.length) {\n    throw Error(\n        `Binary was compiled with ${shapeInfos.length} inputs, but ` +\n        `was executed with ${inputs.length} inputs`);\n  }\n\n  shapeInfos.forEach((s, i) => {\n    const shapeA = s.logicalShape;\n    const input = inputs[i];\n    const shapeB = input.shape;\n\n    if (!util.arraysEqual(shapeA, shapeB)) {\n      throw Error(\n          `Binary was compiled with different shapes than ` +\n          `the current args. Shapes ${shapeA} and ${shapeB} must match`);\n    }\n    // The input is uploaded as uniform.\n    if (s.isUniform && input.isUniform) {\n      return;\n    }\n\n    const texShapeA = s.texShape;\n    const texShapeB = input.isUniform ? null : input.texData.texShape;\n    if (!util.arraysEqual(texShapeA, texShapeB)) {\n      throw Error(\n          `Binary was compiled with different texture shapes than the` +\n          ` current args. Shape ${texShapeA} and ${texShapeB} must match`);\n    }\n  });\n}\n\nexport function runProgram<T extends Tensor, K extends Tensor>(\n    binary: GPGPUBinary, inputs: TensorData[], output: TensorData,\n    customSetup?: (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) =>\n        void): void {\n  validateBinaryAndProgram(binary.inShapeInfos, inputs);\n  validateBinaryAndProgram([binary.outShapeInfo], [output]);\n\n  const outTex = output.texData.texture;\n  const outTexShape = output.texData.texShape;\n  const gpgpu = binary.gpgpu;\n  if (output.texData.isPacked) {\n    gpgpu.setOutputPackedMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n  } else {\n    gpgpu.setOutputMatrixTexture(outTex, outTexShape[0], outTexShape[1]);\n  }\n  gpgpu.setProgram(binary.webGLProgram);\n  inputs.forEach((input, i) => {\n    const variableName = binary.program.variableNames[i];\n    const variableUniformLocation = binary.uniformLocations[variableName];\n    if (variableUniformLocation != null) {\n      if (input.isUniform) {\n        if (util.sizeFromShape(input.shape) === 1) {\n          gpgpu.gl.uniform1f(variableUniformLocation, input.uniformValues[0]);\n        } else {\n          let vals = input.uniformValues;\n          if (!(vals instanceof Float32Array)) {\n            vals = new Float32Array(vals);\n          }\n          gpgpu.gl.uniform1fv(variableUniformLocation, vals);\n        }\n        return;\n      }\n      const tex = input.texData.texture;\n      gpgpu.setInputMatrixTexture(tex, variableUniformLocation, i);\n    }\n  });\n\n  if (customSetup != null) {\n    customSetup(gpgpu, binary.webGLProgram);\n  }\n  gpgpu.executeProgram();\n}\n\nexport function makeShaderKey(\n    program: GPGPUProgram, inputs: TensorData[], output: TensorData): string {\n  let keyInputs = '';\n  inputs.concat(output).forEach(x => {\n    keyInputs += `${x.shape}_${x.isUniform ? 'uniform' : x.texData.texShape}`;\n  });\n  const keyUserCode = program.userCode;\n  const keyBroadcast = (program.supportsBroadcasting === true).toString();\n  let key = program.constructor.name;\n  // Fast string concat. See https://jsperf.com/string-concatenation/14.\n  key += '_' + keyBroadcast + '_' + keyInputs + '_' + keyUserCode;\n  return key;\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class Im2ColProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      outputShape: number[], inputShape: number[], convInfo: Conv2DInfo) {\n    this.outputShape = outputShape;\n\n    const {\n      filterWidth,\n      inChannels,\n      strideWidth,\n      strideHeight,\n      padInfo,\n      outWidth,\n      dilationWidth,\n      dilationHeight\n    } = convInfo;\n    const {left, top} = padInfo;\n    const itemsPerBlockRow = inChannels * filterWidth;\n\n    this.userCode = `\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            int blockIndex = rc.y + col;\n            int pos = rc.x + row;\n\n            if(blockIndex >= ${outputShape[1]} || pos >= ${\n        outputShape[0]}) continue;\n\n            int offsetY = int(blockIndex / (${outWidth})) * ${strideHeight} - ${\n        top};\n            int d0 = offsetY + ${dilationHeight} * (pos / ${itemsPerBlockRow});\n\n            if(d0 >= ${inputShape[0]} || d0 < 0) continue;\n\n            int offsetX = int(mod(float(blockIndex), ${outWidth}.) * ${\n        strideWidth}. - ${left}.);\n            int d1 = offsetX + ${dilationWidth} * (int(mod(float(pos), ${\n        itemsPerBlockRow}.) / ${inChannels}.));\n\n            if(d1 >= ${inputShape[1]} || d1 < 0) continue;\n\n            result[row * 2 + col] = getA(d0, d1, int(mod(float(pos), ${\n        inChannels}.)));\n          }\n        }\n\n        gl_FragColor = result;\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class LRNProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(\n      xShape: number[], radius: number, bias: number, alpha: number,\n      beta: number) {\n    const rad = radius;\n    const maxD = xShape[3] - 1;\n    this.outputShape = xShape;\n\n    // optimize pow(bias + alpha * sum, -beta)\n    // src: https://github.com/tensorflow/tensorflow/..\n    // blob/26033a1644a9c4a5fbe3170ab2e864b6a4ccd4ca/..\n    // tensorflow/core/kernels/mkl_lrn_op.cc#L320\n    let powOperator;\n    const basis = `float(${bias}) + float(${alpha}) * sum`;\n    if (beta === 0.5) {\n      powOperator = `inversesqrt(${basis})`;\n    } else if (beta === 1.0) {\n      powOperator = `1.0/(${basis})`;\n    } else {\n      powOperator = `exp(log(${basis}) * float(-${beta}));`;\n    }\n\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -${rad}; j <= ${rad}; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  ${maxD}) {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * ${powOperator};\n        setOutput(val);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class LRNGradProgram implements GPGPUProgram {\n  variableNames = ['inputImage', 'outputImage', 'dy'];\n  outputShape: number[] = [];\n  userCode: string;\n  depthRadius: number;\n  bias: number;\n  alpha: number;\n  beta: number;\n  depth: number;\n\n  constructor(\n      inputShape: number[], depthRadius: number, bias: number, alpha: number,\n      beta: number) {\n    this.outputShape = inputShape;\n    this.depth = inputShape[3];\n    this.depthRadius = depthRadius;\n    this.bias = bias;\n    this.alpha = alpha;\n    this.beta = beta;\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < ${this.depth}; ++d) {\n          int depthBegin = int(max(0.0, float(d - ${depthRadius})));\n          int depthEnd = int(min(float(${this.depth}),\n              float(d + ${depthRadius} + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = ${this.depth};\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float(${alpha}) * norm + float(${bias});\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float(${alpha})\n                * float(${beta})\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * ${beta});\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class MaxPool2DBackpropProgram implements GPGPUProgram {\n  variableNames = ['dy', 'maxPos'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(convInfo: Conv2DInfo) {\n    this.outputShape = convInfo.inShape;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n\n    const lastIndex = effectiveFilterHeight * effectiveFilterWidth - 1;\n    this.userCode = `\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n          wR += ${dilationHeight}) {\n          float dyR = float(dyRCorner + wR) / ${strideHeight}.0;\n\n          if (dyR < 0.0 || dyR >= ${convInfo.outHeight}.0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < ${effectiveFilterWidth}; wC++) {\n            float dyC = float(dyCCorner + wC) / ${strideWidth}.0;\n\n            if (dyC < 0.0 || dyC >= ${convInfo.outWidth}.0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = ${lastIndex} - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * ${effectiveFilterWidth} + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class MatMulProgram implements GPGPUProgram {\n  variableNames = ['matrixA', 'matrixB'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      aShape: [number, number, number], bShape: [number, number, number],\n      transposeA = false, transposeB = false) {\n    const batchSize = aShape[0];\n    const outerShapeA = transposeA ? aShape[2] : aShape[1];\n    const outerShapeB = transposeB ? bShape[1] : bShape[2];\n    const sharedDim = transposeA ? aShape[1] : aShape[2];\n    this.outputShape = [batchSize, outerShapeA, outerShapeB];\n\n    const aSnippetFromOffset = (vec4Offset: number, indexVar: string|number) =>\n        transposeA ? `batch, ${indexVar} + ${vec4Offset}, aRow` :\n                     `batch, aRow, ${indexVar} + ${vec4Offset}`;\n    const bSnippetFromOffset = (vec4Offset: number, indexVar: string|number) =>\n        transposeB ? `batch, bCol, ${indexVar} + ${vec4Offset}` :\n                     `batch, ${indexVar} + ${vec4Offset}, bCol`;\n\n    const sharedDimNearestVec4 = Math.floor(sharedDim / 4) * 4;\n    const sharedDimVec4Remainder = sharedDim % 4;\n\n    this.userCode = ` float dotARowBCol(int batch, int aRow, int bCol) {\n      float result = 0.0;\n      for (int i = 0; i < ${sharedDimNearestVec4}; i += 4) {\n        vec4 a = vec4(\n          getMatrixA(${aSnippetFromOffset(0, 'i')}),\n          getMatrixA(${aSnippetFromOffset(1, 'i')}),\n          getMatrixA(${aSnippetFromOffset(2, 'i')}),\n          getMatrixA(${aSnippetFromOffset(3, 'i')})\n        );\n        vec4 b = vec4(\n          getMatrixB(${bSnippetFromOffset(0, 'i')}),\n          getMatrixB(${bSnippetFromOffset(1, 'i')}),\n          getMatrixB(${bSnippetFromOffset(2, 'i')}),\n          getMatrixB(${bSnippetFromOffset(3, 'i')})\n        );\n\n        result += dot(a, b);\n      }\n\n      if (${sharedDimVec4Remainder === 1}) {\n        result += getMatrixA(${aSnippetFromOffset(0, sharedDimNearestVec4)}) *\n          getMatrixB(${bSnippetFromOffset(0, sharedDimNearestVec4)});\n      } else if (${sharedDimVec4Remainder === 2}) {\n        vec2 a = vec2(\n          getMatrixA(${aSnippetFromOffset(0, sharedDimNearestVec4)}),\n          getMatrixA(${aSnippetFromOffset(1, sharedDimNearestVec4)})\n        );\n        vec2 b = vec2(\n          getMatrixB(${bSnippetFromOffset(0, sharedDimNearestVec4)}),\n          getMatrixB(${bSnippetFromOffset(1, sharedDimNearestVec4)})\n        );\n        result += dot(a, b);\n      } else if (${sharedDimVec4Remainder === 3}) {\n        vec3 a = vec3(\n          getMatrixA(${aSnippetFromOffset(0, sharedDimNearestVec4)}),\n          getMatrixA(${aSnippetFromOffset(1, sharedDimNearestVec4)}),\n          getMatrixA(${aSnippetFromOffset(2, sharedDimNearestVec4)})\n        );\n        vec3 b = vec3(\n          getMatrixB(${bSnippetFromOffset(0, sharedDimNearestVec4)}),\n          getMatrixB(${bSnippetFromOffset(1, sharedDimNearestVec4)}),\n          getMatrixB(${bSnippetFromOffset(2, sharedDimNearestVec4)})\n        );\n        result += dot(a, b);\n      }\n\n      return result;\n    }\n\n    void main() {\n      ivec3 resBRC = getOutputCoords();\n      setOutput(dotARowBCol(resBRC.x, resBRC.y, resBRC.z));\n    }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class MatMulPackedProgram implements GPGPUProgram {\n  variableNames = ['matrixA', 'matrixB'];\n  usesPackedTextures = true;\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      aShape: [number, number], bShape: [number, number],\n      outputShape: [number, number], transposeA = false, transposeB = false) {\n    this.outputShape = outputShape;\n\n    const sharedDim = transposeA ? aShape[0] : aShape[1];\n    const sharedDimensionPacked = Math.ceil(sharedDim / 2);\n\n    const aSample = transposeA ? 'i * 2, rc.x' : 'rc.x, i * 2';\n    const bSample = transposeB ? 'rc.y, i * 2' : 'i * 2, rc.y';\n    const aSwizzle = transposeA ? ['a.xxyy', 'a.zzww'] : ['a.xxzz', 'a.yyww'];\n    const bSwizzle = transposeB ? ['b.xzxz', 'b.ywyw'] : ['b.xyxy', 'b.zwzw'];\n\n    this.userCode = `\n      const float sharedDimension = ${sharedDimensionPacked}.0;\n\n      vec4 dot2x2ARowBCol(ivec2 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < ${sharedDimensionPacked}; i++) {\n          vec4 a = getMatrixA(${aSample});\n          vec4 b = getMatrixB(${bSample});\n\n          result += (${aSwizzle[0]} * ${bSwizzle[0]}) + (${aSwizzle[1]} * ${\n        bSwizzle[1]});\n        }\n        return result;\n      }\n\n      void main() {\n        ivec2 rc = getOutputCoords();\n        setOutput(dot2x2ARowBCol(rc));\n      }\n    `;\n  }\n}","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class MultinomialProgram implements GPGPUProgram {\n  variableNames = ['probs'];\n  outputShape: number[];\n  userCode: string;\n\n  // Caching uniform location for speed.\n  seedLoc: WebGLUniformLocation;\n\n  constructor(batchSize: number, numOutcomes: number, numSamples: number) {\n    this.outputShape = [batchSize, numSamples];\n\n    this.userCode = `\n      uniform float seed;\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < ${numOutcomes - 1}; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float(${numOutcomes - 1}));\n      }\n    `;\n  }\n\n  getCustomSetupFunc(seed: number) {\n    return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n      if (this.seedLoc == null) {\n        this.seedLoc = gpgpu.getUniformLocation(webGLProgram, 'seed');\n      }\n      gpgpu.gl.uniform1f(this.seedLoc, seed);\n    };\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class OneHotProgram implements GPGPUProgram {\n  variableNames = ['indices'];\n  outputShape: number[];\n  userCode: string;\n\n  // Caching uniform location for speed.\n  seedLoc: WebGLUniformLocation;\n\n  constructor(\n      numIndices: number, depth: number, onValue: number, offValue: number) {\n    this.outputShape = [numIndices, depth];\n\n    this.userCode = `\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float(${offValue}), float(${onValue}),\n                      float(index == coords.y)));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport function getVecChannels(name: string, rank: number): string[] {\n  return ['x', 'y', 'z', 'w', 'u', 'v'].slice(0, rank).map(d => `${name}.${d}`);\n}\n\nexport function getChannels(name: string, rank: number): string[] {\n  if (rank === 1) {\n    return [name];\n  }\n  return getVecChannels(name, rank);\n}\n\nexport function getSourceCoords(rank: number, dims: string[]): string {\n  if (rank === 1) {\n    return 'rc';\n  }\n\n  let coords = '';\n  for (let i = 0; i < rank; i++) {\n    coords += dims[i];\n    if (i < rank - 1) {\n      coords += ',';\n    }\n  }\n  return coords;\n}","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getChannels} from '../packing_util';\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class PackProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  isPackShader = true;\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      outputShape:\n          number[]) {  // TODO(https://github.com/tensorflow/tfjs/issues/893):\n                       // Only input / output 3D tensors.\n    this.outputShape = outputShape;\n    const rank = outputShape.length;\n\n    if (rank === 0) {\n      this.userCode = `\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      `;\n    } else {\n      const channels = getChannels('rc', rank);\n      const dtype = getCoordsDataType(rank);\n      const outOfBoundsCondition =\n          getOutOfBoundsCondition(rank, outputShape, channels);\n      const setup = getSetup(\n          rank, outputShape[outputShape.length - 1],\n          outputShape[outputShape.length - 2], channels);\n      const output = getOutput(outputShape, channels);\n\n      this.userCode = `\n        void main() {\n          ${dtype} rc = getOutputCoords();\n\n          if(${outOfBoundsCondition}) {\n            setOutput(vec4(0));\n          } else {\n            ${setup}\n\n            setOutput(vec4(${output}));\n          }\n        }\n      `;\n    }\n  }\n}\n\nfunction getSourceCoordsArr(rank: number, dims: string[]): string[] {\n  const coords = [];\n\n  for (let row = 0; row <= 1; row++) {\n    for (let col = 0; col <= 1; col++) {\n      let coord = `${row === 0 ? 'r' : 'rp1'}, ${col === 0 ? 'c' : 'cp1'}`;\n\n      for (let d = 2; d < rank; d++) {\n        coord = `${dims[dims.length - 1 - d]},` + coord;\n      }\n\n      coords.push(coord);\n    }\n  }\n  return coords;\n}\n\nfunction getOutOfBoundsCondition(\n    rank: number, shape: number[], dims: string[]): string {\n  if (rank === 1) {\n    return `rc > ${shape[0]}`;\n  }\n\n  let cond = '';\n  for (let i = rank - 2; i < rank; i++) {\n    cond += `${dims[i]} >= ${shape[i]}`;\n    if (i < rank - 1) {\n      cond += '||';\n    }\n  }\n\n  return cond;\n}\n\nfunction getSetup(\n    rank: number, cols: number, rows: number, dims: string[]): string {\n  if (rank === 1) {\n    return '';\n  }\n\n  const innerDims = dims.slice(-2);\n\n  return `\n    int r = ${innerDims[0]};\n    int c = ${innerDims[1]};\n    int rp1 = r + 1;\n    int cp1 = c + 1;\n\n    bool cEdge = cp1 >= ${cols};\n    bool rEdge = rp1 >= ${rows};\n  `;\n}\n\nfunction getOutput(shape: number[], dims: string[]): string {\n  const rank = shape.length;\n  const sourceCoords = getSourceCoordsArr(rank, dims);\n  if (rank === 1) {\n    return `getA(rc),\n            rc + 1 >= ${shape[0]} ? 0. : getA(rc + 1),\n            0, 0`;\n  }\n\n  return `getA(${sourceCoords[0]}),\n          cEdge ? 0. : getA(${sourceCoords[1]}),\n          rEdge ? 0. : getA(${sourceCoords[2]}),\n          rEdge || cEdge ? 0. : getA(${sourceCoords[3]})`;\n}","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class PadProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      xShape: number[], paddings: Array<[number, number]>,\n      constantValue: number) {\n    this.outputShape = paddings.map(\n        (p, i) => p[0] /* beforePad */ + xShape[i] + p[1] /* afterPad */);\n    const rank = xShape.length;\n    const type = getCoordsDataType(rank);\n\n    const start = paddings.map(p => p[0]).join(',');\n    const end = paddings.map((p, i) => p[0] + xShape[i]).join(',');\n    const unpackedCoords =\n         ['coords[0]', 'coords[1]', 'coords[2]','coords[3]'].slice(0, rank);\n\n    if (rank === 1) {\n      this.userCode = `\n        int start = ${start};\n        int end = ${end};\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(float(${constantValue}));\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      `;\n      return;\n    }\n    this.userCode = `\n      ${type} start = ${type}(${start});\n      ${type} end = ${type}(${end});\n\n      void main() {\n        ${type} outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(float(${constantValue}));\n        } else {\n          ${type} coords = outC - start;\n          setOutput(getX(${unpackedCoords}));\n        }\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Conv2DInfo} from '../../ops/conv_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class Pool2DProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      convInfo: Conv2DInfo, poolType: 'max'|'avg', computePositions: boolean) {\n    if (poolType === 'avg' && computePositions) {\n      throw new Error('Cannot compute positions for average pool.');\n    }\n\n    const filterWidth = convInfo.filterWidth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n    this.outputShape = convInfo.outShape;\n\n    const isAvgPool = poolType === 'avg';\n\n    let initializationValue = '0.0';\n    if (!isAvgPool) {\n      initializationValue = '-1.0 / 0.0';\n    }\n\n    if (computePositions) {\n      const compareOp = '>=';\n\n      this.userCode = `\n        const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n        const ivec2 pads = ivec2(${padTop}, ${padLeft});\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < ${effectiveFilterHeight};\n              wR += ${dilationHeight}) {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= ${convInfo.inHeight}) {\n              continue;\n            }\n\n            for (int wC = 0; wC < ${effectiveFilterWidth};\n                wC += ${dilationWidth}) {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= ${convInfo.inWidth}) {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value ${compareOp} currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = wR * ${effectiveFilterWidth} + wC;\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      `;\n      return;\n    }\n\n    const compareOp = 'max';\n\n    let returnValue = `${poolType}(${poolType}(${poolType}(` +\n        'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n    if (poolType === 'avg') {\n      returnValue = `avgValue / count`;\n    }\n\n    const filterWidthNearestVec4 = Math.floor(filterWidth / 4) * 4;\n    const filterWidthVec4Remainder = filterWidth % 4;\n\n    const updateSnippet = `\n      if (${isAvgPool}) {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n\n    this.userCode = `\n      const ivec2 strides = ivec2(${strideHeight}, ${strideWidth});\n      const ivec2 pads = ivec2(${padTop}, ${padLeft});\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= ${convInfo.inWidth}) {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4(${initializationValue});\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < ${effectiveFilterHeight};\n            wR += ${dilationHeight}) {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= ${convInfo.inHeight}) {\n            continue;\n          }\n\n          for (int wC = 0; wC < ${filterWidthNearestVec4}; wC += 4) {\n            int xC = xCCorner + wC * ${dilationWidth};\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              getValue(batch, xR, xC + 3 * ${dilationWidth}, d)\n            );\n\n            ${updateSnippet}\n          }\n\n          int xC = xCCorner + ${filterWidthNearestVec4};\n          if (${filterWidthVec4Remainder === 1}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 2}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              initializationValue,\n              initializationValue\n            );\n\n            ${updateSnippet}\n          } else if (${filterWidthVec4Remainder === 3}) {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + ${dilationWidth}, d),\n              getValue(batch, xR, xC + 2 * ${dilationWidth}, d),\n              initializationValue\n            );\n\n            ${updateSnippet}\n          }\n        }\n        setOutput(${returnValue});\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ReduceInfo} from '../../ops/reduce_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ReduceProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      reduceInfo: ReduceInfo,\n      reduceType: 'all'|'any'|'max'|'min'|'sum'|'prod') {\n    const windowSize = reduceInfo.windowSize;\n    const batchSize = reduceInfo.batchSize;\n    const inSize = reduceInfo.inSize;\n    const outSize = Math.ceil(inSize / windowSize);\n    this.outputShape = [batchSize, outSize];\n\n    let initializationValue = '0.0';\n    let compareOp = ``;\n\n    if (reduceType === 'prod') {\n      initializationValue = '1.0';\n    } else if (reduceType === 'min') {\n      initializationValue = '1.0 / 0.0';\n      compareOp = `min`;\n    } else if (reduceType === 'max') {\n      initializationValue = '-1.0 / 0.0';\n      compareOp = `max`;\n    }\n\n    let returnValue = `${reduceType}(${reduceType}(${reduceType}(` +\n        'minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])';\n\n    if (reduceType === 'sum') {\n      returnValue = `sumValue`;\n    } else if (reduceType === 'prod') {\n      returnValue = `prodValue`;\n    } else if (reduceType === 'all') {\n      returnValue = `allValue`;\n    } else if (reduceType === 'any') {\n      returnValue = `anyValue`;\n    }\n\n    const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n    const windowSizeVec4Remainder = windowSize % 4;\n\n    let updateSnippet = `\n      if (${reduceType === 'sum'}) {\n        sumValue += dot(values, ones);\n      } else if (${reduceType === 'prod'}) {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = ${compareOp}(values, minMaxValue);\n      }\n    `;\n\n    let vecType = `vec4`;\n\n    if (reduceType === 'all') {\n      initializationValue = '1.0';\n      updateSnippet = `\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      `;\n      vecType = `bvec4`;\n    } else if (reduceType === 'any') {\n      initializationValue = '0.0';\n      updateSnippet = `\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      `;\n      vecType = `bvec4`;\n    }\n\n    let checkOutOfBounds = '';\n    if (inSize % windowSize > 0) {\n      checkOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n    }\n    this.userCode = `\n      const float initializationValue = ${initializationValue};\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        ${checkOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * ${windowSize};\n\n        vec4 minMaxValue = vec4(${initializationValue});\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          ${vecType} values = ${vecType}(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as util from '../../util';\nimport {GPGPUProgram} from './gpgpu_math';\nimport * as shader_util from './shader_compiler_util';\n\nexport class ReshapePackedProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  usesPackedTextures = true;\n  outputShape: number[];\n  userCode: string;\n\n  constructor(outputShape: [number, number, number], inputShape: [\n    number, number, number\n  ]) {\n    this.outputShape = outputShape;\n\n    let mainLoop = ``;\n    for (let i = 0; i < 4; i++) {\n      let thisRC = `thisRC = rc;`;\n      if (i % 2 === 1) {\n        thisRC += `thisRC.z += 1;`;\n      }\n      if (i > 1) {\n        thisRC += `thisRC.y += 1;`;\n      }\n\n      mainLoop += `\n        ${thisRC}\n        ${i > 0 ? `if(thisRC.y < rows && thisRC.z < cols){` : ''}\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result[${i}] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        ${i > 0 ? '}' : ''}\n      `;\n    }\n\n    this.userCode = `\n      ${getReshapedInputCoords(inputShape)}\n      ${getFlatIndex(outputShape)}\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = ${outputShape[1]};\n        int cols = ${outputShape[2]};\n\n        ${mainLoop}\n\n        setOutput(result);\n      }\n    `;\n  }\n}\n\nfunction getFlatIndex(shape: [number, number, number]): string {\n  const dotCoordsWithStrides = shader_util.dotify(\n      ['coords.x', 'coords.y', 'coords.z'],\n      util.computeStrides(shape).map(d => d.toString()).concat(['1.']));\n\n  return `\n    int getFlatIndex(ivec3 coords) {\n      return round(${dotCoordsWithStrides});\n    }\n  `;\n}\n\nfunction getReshapedInputCoords(shape: [number, number, number]): string {\n  const coordsFromIndexSnippet =\n      shader_util.getLogicalCoordinatesFromFlatIndex(['r', 'c', 'd'], shape);\n\n  return `\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      ${coordsFromIndexSnippet}\n      return ivec3(r, c, d);\n    }\n  `;\n}","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor4D} from '../../tensor';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ResizeBilinearBackpropProgram implements GPGPUProgram {\n  variableNames = ['dy'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(dy: Tensor4D, x: Tensor4D, alignCorners: boolean) {\n    this.outputShape = x.shape;\n    const [, xHeight, xWidth, ] = x.shape;\n    const [, yHeight, yWidth] = dy.shape;\n\n    // In the backwards pass, we want to find the pixels that were generated for\n    // each pixel in the input image the forward pass and add the corresponding\n    // coefficient from dy to the gradient (with some interpolation).\n\n    const effectiveXSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n      (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n    ];\n\n    const effectiveYSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n      (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n    ];\n\n    const heightScale = effectiveXSize[0] / effectiveYSize[0];\n    const widthScale = effectiveXSize[1] / effectiveYSize[1];\n\n    const invHeightScale = 1 / heightScale;\n    const invWidthScale = 1 / widthScale;\n\n    // This defines the size of the window of values around a particular\n    // index in dy that we want to search for contributions to dx.\n    const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n    const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), ${xHeight - 1}.0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), ${xWidth - 1}.0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ResizeBilinearProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(\n      inputShape: [number, number, number, number], newHeight: number,\n      newWidth: number, alignCorners: boolean) {\n    const [batch, oldHeight, oldWidth, depth] = inputShape;\n    this.outputShape = [batch, newHeight, newWidth, depth];\n\n    const effectiveInSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n      (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n    ];\n\n    const effectiveOutSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n      (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n    ];\n\n    this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor4D} from '../../tensor';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ResizeNearestNeigborBackpropProgram implements GPGPUProgram {\n  variableNames = ['dy'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(dy: Tensor4D, x: Tensor4D, alignCorners: boolean) {\n    this.outputShape = x.shape;\n    const [, xHeight, xWidth, ] = x.shape;\n    const [, yHeight, yWidth] = dy.shape;\n\n    // In the backwards pass, we want to find the pixels that were generated for\n    // each pixel in the input image the forward pass and add the corresponding\n    // coefficient from dy to the gradient (with some interpolation).\n\n    const effectiveXSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n      (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n    ];\n\n    const effectiveYSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n      (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n    ];\n\n    const heightScale = effectiveXSize[0] / effectiveYSize[0];\n    const widthScale = effectiveXSize[1] / effectiveYSize[1];\n\n    const invHeightScale = 1 / heightScale;\n    const invWidthScale = 1 / widthScale;\n\n    // This defines the size of the window of values around a particular\n    // index in dy that we want to search for contributions to dx.\n    const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n    const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n\n    this.userCode = `\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float(${heightScale});\n        const float widthScale = float(${widthScale});\n\n        const float invHeightScale = float(${invHeightScale});\n        const float invWidthScale = float(${invWidthScale});\n\n        const int winHeight = int(${winHeight});\n        const int winWidth = int(${winWidth});\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= ${yHeight}) {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= ${yWidth}) {\n              continue;\n            }\n\n            float sourceFracRow =\n              float(${effectiveXSize[0]}) *\n                (float(dyR) / float(${effectiveYSize[0]}));\n\n            float sourceFracCol =\n                float(${effectiveXSize[1]}) *\n                  (float(dyC) / float(${effectiveYSize[1]}));\n\n            int sourceNearestRow = int(min(\n                float(int(${xHeight}) - 1),\n                ${alignCorners} ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int(${xWidth}) - 1),\n                ${alignCorners} ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class ResizeNearestNeighborProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  outputShape: number[] = [];\n  userCode: string;\n\n  constructor(\n      inputShape: [number, number, number, number], newHeight: number,\n      newWidth: number, alignCorners: boolean) {\n    const [batch, oldHeight, oldWidth, depth] = inputShape;\n    this.outputShape = [batch, newHeight, newWidth, depth];\n\n    const effectiveInSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n      (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n    ];\n\n    const effectiveOutSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n      (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n    ];\n\n    // When align corners is false, we rounds the value with floor.\n    const roundBase = alignCorners ? '0.5' : '0.0';\n\n    this.userCode = `\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          ${effectiveInSize[0] / effectiveOutSize[0]},\n          ${effectiveInSize[1] / effectiveOutSize[1]});\n      const vec2 inputShapeRC = vec2(${oldHeight}.0, ${oldWidth}.0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + ${roundBase})));\n\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class ReverseProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(xShape: number[], axis: number[]) {\n    const rank = xShape.length;\n    if (rank > 4) {\n      throw new Error(\n          `WebGL backend: Reverse of rank-${rank} tensor is not yet supported`);\n    }\n    this.outputShape = xShape;\n\n    if (rank === 1) {\n      this.userCode = `\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX(${xShape[0]} - coord - 1));\n        }\n      `;\n      return;\n    }\n    const getInCoord = (i: number) => {\n      if (axis.indexOf(i) !== -1 && xShape[i] !== 1) {\n        return `${xShape[i]} - coords[${i}] - 1`;\n      }\n      return `coords[${i}]`;\n    };\n    const inCoords = xShape.map((_, i) => getInCoord(i)).join(',');\n    const type = getCoordsDataType(rank);\n\n    this.userCode = `\n      void main() {\n        ${type} coords = getOutputCoords();\n        setOutput(getX(${inCoords}));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class ScatterProgram implements GPGPUProgram {\n  variableNames = ['updates', 'indices', 'defaultValue'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      updateSize: number, sliceDim: number, indicesRank: number,\n      updatesRank: number, strides: number[], shape: number[],\n      summingDupeIndex = true) {\n    this.outputShape = shape;\n    const stridesType = getCoordsDataType(strides.length);\n    const dtype = getCoordsDataType(shape.length);\n    let indicesString = '';\n    if (indicesRank === 1) {\n      indicesString = 'i';\n    } else if (indicesRank === 2) {\n      indicesString = 'i, j';\n    }\n    const indicesSnippet = `getIndices(${indicesString})`;\n\n    let updatesString = '';\n    if (updatesRank === 1) {\n      updatesString = 'i';\n    } else if (updatesRank === 2) {\n      updatesString = 'i, coords[1]';\n    }\n    const updatesSnippet = `getUpdates(${updatesString})`;\n\n    const strideString = sliceDim > 1 ? 'strides[j]' : 'strides';\n    this.userCode = `\n        ${stridesType} strides = ${stridesType}(${strides});\n\n        void main() {\n          ${dtype} coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < ${updateSize}; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < ${sliceDim}; j++) {\n              int index = round(${indicesSnippet});\n              flattenedIndex += index * ${strideString};\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += ${updatesSnippet};\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      `;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {SegOpInfo} from '../../ops/segment_util';\nimport {GPGPUProgram} from './gpgpu_math';\n\nexport class SegmentOpProgram implements GPGPUProgram {\n  variableNames = ['x', 'segmentIds'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(segOpInfo: SegOpInfo, segOpType: 'unsortedSegmentSum') {\n    const windowSize = segOpInfo.windowSize;\n    const batchSize = segOpInfo.batchSize;\n    const inSize = segOpInfo.inSize;\n    const numSegments = segOpInfo.numSegments;\n    const outSize = numSegments * Math.ceil(inSize / windowSize);\n    this.outputShape = [batchSize, outSize];\n\n    const initializationValue = '0.0';\n    const returnValue = `sumValue`;\n\n    const windowSizeNearestVec4 = Math.floor(windowSize / 4) * 4;\n    const windowSizeVec4Remainder = windowSize % 4;\n\n    const updateSnippet = `\n        sumValue += dot(values, filter);\n    `;\n\n    let checkValueOutOfBounds = '';\n    if (inSize % windowSize > 0) {\n      checkValueOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return initializationValue;\n        }\n      `;\n    }\n\n    let checkSegmentIdOutOfBounds = '';\n    if (inSize % windowSize > 0) {\n      checkSegmentIdOutOfBounds = `\n        if (inIdx < 0 || inIdx >= ${inSize}) {\n          return -1.0;\n        }\n      `;\n    }\n\n    this.userCode = `\n      const float initializationValue = ${initializationValue};\n\n      float getValue(int batch, int inIdx) {\n        ${checkValueOutOfBounds}\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        ${checkSegmentIdOutOfBounds}\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          ${numSegments})) * float(${windowSize}));\n        int currentSeg = int(mod(float(outIdx), float(${numSegments})));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < ${windowSizeNearestVec4}; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          ${updateSnippet}\n        }\n\n        int inIdx = inOffset + ${windowSizeNearestVec4};\n        if (${windowSizeVec4Remainder === 1}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 2}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          ${updateSnippet}\n        } else if (${windowSizeVec4Remainder === 3}) {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          ${updateSnippet}\n        }\n        setOutput(${returnValue});\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class SelectProgram implements GPGPUProgram {\n  variableNames = ['c', 'a', 'b'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(cRank: number, shape: number[], rank: number) {\n    this.outputShape = shape;\n\n    let cCoords;\n    let abCoords;\n    if (rank > 4) {\n      throw Error(`Where for rank ${rank} is not yet supported`);\n    }\n\n    if (rank === 1) {\n      abCoords = `resRC`;\n      cCoords = `resRC`;\n    } else {\n      const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w'];\n      const cCoordVars = [];\n      const abCoordVars = [];\n      for (let i = 0; i < shape.length; i++) {\n        abCoordVars.push(`${currentCoords[i]}`);\n        if (i < cRank) {\n          cCoordVars.push(`${currentCoords[i]}`);\n        }\n      }\n      cCoords = cCoordVars.join();\n      abCoords = abCoordVars.join();\n    }\n\n    const dtype = getCoordsDataType(rank);\n\n    this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        float cVal = getC(${cCoords});\n        if (cVal >= 1.0) {\n          setOutput(getA(${abCoords}));\n        } else {\n          setOutput(getB(${abCoords}));\n        }\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class SliceProgram implements GPGPUProgram {\n  variableNames = ['source'];\n  outputShape: number[];\n  userCode: string;\n  rank: number;\n\n  // Caching uniform location for speed.\n  startLoc: WebGLUniformLocation;\n\n  constructor(destSize: number[]) {\n    this.outputShape = destSize;\n    this.rank = destSize.length;\n\n    const dtype = getCoordsDataType(this.rank);\n    const sourceCoords = getCoords(this.rank);\n\n    this.userCode = `\n      uniform ${dtype} start;\n\n      void main() {\n        ${dtype} sourceLoc = start + getOutputCoords();\n        setOutput(getSource(${sourceCoords}));\n      }\n    `;\n  }\n\n  getCustomSetupFunc(start: number[]) {\n    if (start.length !== this.rank) {\n      throw Error(\n          `The rank (${this.rank}) of the program must match the ` +\n          `length of start (${start.length})`);\n    }\n    return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n      if (this.startLoc == null) {\n        this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'start');\n        if (this.startLoc == null) {\n          // This means the compiler has optimized and realized it doesn't need\n          // the uniform.\n          return;\n        }\n      }\n      if (this.rank === 1) {\n        gpgpu.gl.uniform1i(this.startLoc, start[0]);\n      } else if (this.rank === 2) {\n        gpgpu.gl.uniform2i(this.startLoc, start[0], start[1]);\n      } else if (this.rank === 3) {\n        gpgpu.gl.uniform3i(this.startLoc, start[0], start[1], start[2]);\n      } else if (this.rank === 4) {\n        gpgpu.gl.uniform4i(\n            this.startLoc, start[0], start[1], start[2], start[3]);\n      } else {\n        throw Error(`Slicing for rank ${this.rank} is not yet supported`);\n      }\n    };\n  }\n}\n\nfunction getCoords(rank: number): string {\n  if (rank === 1) {\n    return 'sourceLoc';\n  } else if (rank === 2) {\n    return 'sourceLoc.x, sourceLoc.y';\n  } else if (rank === 3) {\n    return 'sourceLoc.x, sourceLoc.y, sourceLoc.z';\n  } else if (rank === 4) {\n    return 'sourceLoc.x, sourceLoc.y, sourceLoc.z, sourceLoc.w';\n  } else {\n    throw Error(`Slicing for rank ${rank} is not yet supported`);\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class StridedSliceProgram implements GPGPUProgram {\n  variableNames = ['x'];\n  outputShape: number[];\n  userCode: string;\n\n  constructor(\n      begin: number[], strides: number[], size: number[],\n      shrinkAxis: number[]) {\n    const shape = size.filter((v, index) => shrinkAxis.indexOf(index) === -1);\n    this.outputShape = shape;\n    const rank = size.length;\n    const inputDtype = getCoordsDataType(size.length);\n    const dtype = getCoordsDataType(shape.length);\n\n    let newCoords = '';\n    if (rank === 1) {\n      newCoords = 'coords * strides + begin';\n    } else {\n      let outputAxis = 0;\n      newCoords =\n          size.map((_, i) => {\n                if (shrinkAxis.indexOf(i) === -1) {\n                  outputAxis++;\n                  return shape.length === 1 ?\n                      `coords * strides[${i}] + begin[${i}]` :\n                      `coords[${outputAxis - 1}] * strides[${i}] + begin[${i}]`;\n                } else {\n                  return `begin[${i}]`;\n                }\n              })\n              .join(',');\n    }\n\n    this.userCode = `\n      ${inputDtype} begin = ${inputDtype}(${begin});\n      ${inputDtype} strides = ${inputDtype}(${strides});\n\n      void main() {\n        ${dtype} coords = getOutputCoords();\n        setOutput(getX(${newCoords}));\n      }\n    `;\n  }\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../../environment';\n\nimport {GPGPUContext} from './gpgpu_context';\nimport {PhysicalTextureType, TextureUsage} from './tex_util';\n\nexport class TextureManager {\n  private numUsedTextures = 0;\n  private numFreeTextures = 0;\n  private freeTextures: {[shape: string]: WebGLTexture[]} = {};\n  private logEnabled = false;\n  private usedTextures: {[shape: string]: WebGLTexture[]} = {};\n\n  constructor(private gpgpu: GPGPUContext) {}\n\n  acquireTexture(\n      shapeRC: [number, number], usage: TextureUsage,\n      isPacked: boolean): WebGLTexture {\n    const physicalTexType = getPhysicalFromLogicalTextureType(usage, isPacked);\n\n    const shapeKey = getKeyFromTextureShape(shapeRC, physicalTexType, isPacked);\n    if (!(shapeKey in this.freeTextures)) {\n      this.freeTextures[shapeKey] = [];\n    }\n    if (!(shapeKey in this.usedTextures)) {\n      this.usedTextures[shapeKey] = [];\n    }\n\n    if (this.freeTextures[shapeKey].length > 0) {\n      this.numFreeTextures--;\n      this.numUsedTextures++;\n      this.log();\n      const newTexture = this.freeTextures[shapeKey].shift();\n      this.usedTextures[shapeKey].push(newTexture);\n      return newTexture;\n    }\n    this.numUsedTextures++;\n    this.log();\n\n    let newTexture: WebGLTexture;\n    if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT32) {\n      newTexture = this.gpgpu.createPackedMatrixTexture(shapeRC[0], shapeRC[1]);\n    } else if (physicalTexType === PhysicalTextureType.PACKED_2X2_FLOAT16) {\n      newTexture =\n          this.gpgpu.createFloat16PackedMatrixTexture(shapeRC[0], shapeRC[1]);\n    } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT32) {\n      newTexture =\n          this.gpgpu.createFloat32MatrixTexture(shapeRC[0], shapeRC[1]);\n    } else if (physicalTexType === PhysicalTextureType.UNPACKED_FLOAT16) {\n      newTexture =\n          this.gpgpu.createFloat16MatrixTexture(shapeRC[0], shapeRC[1]);\n\n    } else if (\n        physicalTexType === PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE) {\n      newTexture =\n          this.gpgpu.createUnsignedBytesMatrixTexture(shapeRC[0], shapeRC[1]);\n    }\n    this.usedTextures[shapeKey].push(newTexture);\n\n    return newTexture;\n  }\n\n  releaseTexture(\n      texture: WebGLTexture, shape: [number, number],\n      logicalTexType: TextureUsage, isPacked: boolean): void {\n    if (this.freeTextures == null) {\n      // Already disposed.\n      return;\n    }\n    const physicalTexType =\n        getPhysicalFromLogicalTextureType(logicalTexType, isPacked);\n    const shapeKey = getKeyFromTextureShape(shape, physicalTexType, isPacked);\n    if (!(shapeKey in this.freeTextures)) {\n      this.freeTextures[shapeKey] = [];\n    }\n    this.freeTextures[shapeKey].push(texture);\n    this.numFreeTextures++;\n    this.numUsedTextures--;\n    const texList = this.usedTextures[shapeKey];\n    const texIndex = texList.indexOf(texture);\n    if (texIndex < 0) {\n      throw new Error(\n          'Cannot release a texture that was never provided by this ' +\n          'texture manager');\n    }\n    texList.splice(texIndex, 1);\n    this.log();\n  }\n\n  private log() {\n    if (!this.logEnabled) {\n      return;\n    }\n    const total = this.numFreeTextures + this.numUsedTextures;\n    console.log(\n        'Free/Used', `${this.numFreeTextures} / ${this.numUsedTextures}`,\n        `(${total})`);\n  }\n\n  getNumUsedTextures(): number {\n    return this.numUsedTextures;\n  }\n\n  getNumFreeTextures(): number {\n    return this.numFreeTextures;\n  }\n\n  dispose() {\n    if (this.freeTextures == null) {\n      // Already disposed.\n      return;\n    }\n    for (const texShape in this.freeTextures) {\n      this.freeTextures[texShape].forEach(tex => {\n        this.gpgpu.deleteMatrixTexture(tex);\n      });\n    }\n    for (const texShape in this.usedTextures) {\n      this.usedTextures[texShape].forEach(tex => {\n        this.gpgpu.deleteMatrixTexture(tex);\n      });\n    }\n    this.freeTextures = null;\n    this.usedTextures = null;\n    this.numUsedTextures = 0;\n    this.numFreeTextures = 0;\n  }\n}\n\nfunction getPhysicalFromLogicalTextureType(\n    logicalTexType: TextureUsage, isPacked: boolean): PhysicalTextureType {\n  if (logicalTexType === TextureUsage.UPLOAD) {\n    return isPacked ? PhysicalTextureType.PACKED_2X2_FLOAT32 :\n                      PhysicalTextureType.UNPACKED_FLOAT32;\n  } else if (logicalTexType === TextureUsage.RENDER || logicalTexType == null) {\n    if (isPacked) {\n      return ENV.get('WEBGL_RENDER_FLOAT32_ENABLED') ?\n          PhysicalTextureType.PACKED_2X2_FLOAT32 :\n          PhysicalTextureType.PACKED_2X2_FLOAT16;\n    }\n    return ENV.get('WEBGL_RENDER_FLOAT32_ENABLED') ?\n        PhysicalTextureType.UNPACKED_FLOAT32 :\n        PhysicalTextureType.UNPACKED_FLOAT16;\n  } else if (\n      logicalTexType === TextureUsage.DOWNLOAD ||\n      logicalTexType === TextureUsage.PIXELS) {\n    return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;\n  }\n  throw new Error(`Unknown logical texture type ${logicalTexType}`);\n}\n\nfunction getKeyFromTextureShape(\n    shapeRowsCol: [number, number], physicalTexType: PhysicalTextureType,\n    isPacked: boolean): string {\n  return `${shapeRowsCol[0]}_${shapeRowsCol[1]}_${physicalTexType}_${isPacked}`;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class TileProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  outputShape: number[];\n  userCode: string;\n  rank: number;\n\n  constructor(aShape: number[], reps: number[]) {\n    const outputShape: number[] = new Array(aShape.length);\n    for (let i = 0; i < outputShape.length; i++) {\n      outputShape[i] = aShape[i] * reps[i];\n    }\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    const dtype = getCoordsDataType(this.rank);\n    const sourceCoords = getSourceCoords(aShape);\n\n    this.userCode = `\n      void main() {\n        ${dtype} resRC = getOutputCoords();\n        setOutput(getA(${sourceCoords}));\n      }\n    `;\n  }\n}\n\nfunction getSourceCoords(aShape: number[]): string {\n  const rank = aShape.length;\n  if (rank > 5) {\n    throw Error(`Tile for rank ${rank} is not yet supported`);\n  }\n  if (rank === 1) {\n    return `imod(resRC, ${aShape[0]})`;\n  }\n\n  const currentCoords = ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u'];\n\n  const sourceCoords = [];\n  for (let i = 0; i < aShape.length; i++) {\n    sourceCoords.push(`imod(${currentCoords[i]}, ${aShape[i]})`);\n  }\n  return sourceCoords.join();\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class TransposeProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  outputShape: number[];\n  userCode: string;\n  rank: number;\n\n  constructor(aShape: number[], newDim: number[]) {\n    const outputShape: number[] = new Array(aShape.length);\n    for (let i = 0; i < outputShape.length; i++) {\n      outputShape[i] = aShape[newDim[i]];\n    }\n    this.outputShape = outputShape;\n    this.rank = outputShape.length;\n    const dtype = getCoordsDataType(this.rank);\n    const switched = getSwitchedCoords(newDim);\n\n    this.userCode = `\n    void main() {\n      ${dtype} resRC = getOutputCoords();\n      setOutput(getA(${switched}));\n    }\n    `;\n  }\n}\n\nfunction getSwitchedCoords(newDim: number[]): string {\n  const rank = newDim.length;\n  if (rank > 6) {\n    throw Error(`Transpose for rank ${rank} is not yet supported`);\n  }\n  const originalOrder =\n      ['resRC.x', 'resRC.y', 'resRC.z', 'resRC.w', 'resRC.u', 'resRC.v'];\n  const switchedCoords = new Array(rank);\n  for (let i = 0; i < newDim.length; i++) {\n    switchedCoords[newDim[i]] = originalOrder[i];\n  }\n  return switchedCoords.join();\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport const ERF_P = 0.3275911;\nexport const ERF_A1 = 0.254829592;\nexport const ERF_A2 = -0.284496736;\nexport const ERF_A3 = 1.421413741;\nexport const ERF_A4 = -1.453152027;\nexport const ERF_A5 = 1.061405429;\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nexport const SELU_SCALEALPHA = 1.7580993408473768599402175208123;\nexport const SELU_SCALE = 1.0507009873554804934193349852946;\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as erf_util from '../../ops/erf_util';\nimport * as selu_util from '../../ops/selu_util';\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {GPGPUContext} from './gpgpu_context';\n\nexport class UnaryOpProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  userCode: string;\n  outputShape: number[];\n\n  // Caching uniform location for speed.\n  startLoc: WebGLUniformLocation;\n\n  constructor(aShape: number[], opSnippet: string) {\n    this.outputShape = aShape;\n    this.userCode = `\n      uniform float NAN;\n      float unaryOperation(float x) {\n        ${opSnippet}\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    `;\n  }\n\n  getCustomSetupFunc() {\n    return (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => {\n      if (this.startLoc == null) {\n        this.startLoc = gpgpu.getUniformLocationNoThrow(webGLProgram, 'NAN');\n        if (this.startLoc == null) {\n          // This means the compiler has optimized and realized it doesn't need\n          // the uniform.\n          return;\n        }\n      }\n      gpgpu.gl.uniform1f(this.startLoc, NaN);\n    };\n  }\n}\n\nconst CHECK_NAN_SNIPPET = `if (isNaN(x)) return x;`;\n\nexport const ABS = `return abs(x);`;\n\nexport const RELU = CHECK_NAN_SNIPPET + `\n  return (x < 0.0) ? 0.0 : x;\n`;\n\nexport const ELU = `return (x >= 0.0) ? x : (exp(x) - 1.0);`;\n\nexport const SELU = `\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = ${selu_util.SELU_SCALEALPHA};\n  float scale = ${selu_util.SELU_SCALE};\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n`;\n\nexport function STEP(alpha = 0.0) {\n  return CHECK_NAN_SNIPPET + `\n    return x > 0.0 ? 1.0 : float(${alpha});\n  `;\n}\n\nexport const NEG = `return -x;`;\n\nexport const CEIL = `return ceil(x);`;\n\nexport const FLOOR = `return floor(x);`;\n\nexport const SIGN = `\n  if (isNaN(x)) { return 0.0; }\n  return sign(x);\n`;\n\nexport const ROUND = `\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n`;\n\nexport const EXP = `return exp(x);`;\n\nexport const EXPM1 = `return exp(x) - 1.0;`;\n\nexport const LOG = `if (x < 0.0) return NAN;\n  return log(x);`;\n\nexport const LOG1P = `return log(1.0 + x);`;\n\nexport const SQRT = `return sqrt(x);`;\n\nexport const RSQRT = `return inversesqrt(x);`;\n\nexport const SIGMOID = `return 1.0 / (1.0 + exp(-1.0 * x));`;\n\n/**\n * mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX\n *\n * epsilon is the difference between 1.0 and the next representable\n * float. For a single precision 32 bit float this should be 2^-23, see:\n * https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm\n *\n * too_large = (x > -threshold) is value above which exp(x) may overflow\n * but softplus(x) == x is within machine epsilon\n *\n * too_small = (x < threshold) is value below which exp(x) may underflow,\n * but softplus(x) == exp(x) is within machine epsilon.\n */\nexport const SOFTPLUS = `\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n`;\n\nexport const SIN = CHECK_NAN_SNIPPET + `\n  return sin(x);\n`;\n\nexport const COS = CHECK_NAN_SNIPPET + `\n  return cos(x);\n`;\n\nexport const TAN = `return tan(x);`;\n\nexport const ASIN = `return asin(x);`;\n\nexport const ACOS = `return acos(x);`;\n\nexport const ATAN = CHECK_NAN_SNIPPET + `\n  return atan(x);\n`;\n\nexport const SINH = `\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n`;\n\nexport const COSH = `\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n`;\n\nexport const TANH = `\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n`;\n\nexport const ASINH = `return log(x + sqrt(x * x + 1.0));`;\n\nexport const ACOSH = CHECK_NAN_SNIPPET + `\n  if (x < 1.0) return NAN;\n  return log(x + sqrt(x * x - 1.0));`;\n\nexport const ATANH = CHECK_NAN_SNIPPET + `\n  if ((x < -1.0) || (x > 1.0)) return NAN;\n  return (log(1.0 + x) - log(1.0 - x)) / 2.0;`;\n\nexport const ERF = `\n  // Error function is calculated approximately with elementary function.\n  // See \"Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\n  float p = ${erf_util.ERF_P};\n  float a1 = ${erf_util.ERF_A1};\n  float a2 = ${erf_util.ERF_A2};\n  float a3 = ${erf_util.ERF_A3};\n  float a4 = ${erf_util.ERF_A4};\n  float a5 = ${erf_util.ERF_A5};\n\n  float t = 1.0 / (1.0 + p * x);\n  return 1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x);\n`;\n\nexport const SQUARE = `return x * x;`;\n\nexport const RECIPROCAL = `return 1.0 / x;`;\n\nexport const LOGICAL_NOT = `return float(!(x >= 1.0));`;\n\nexport const TO_INT = `return float(int(x));`;\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getChannels, getSourceCoords} from '../packing_util';\n\nimport {GPGPUProgram} from './gpgpu_math';\nimport {getCoordsDataType} from './shader_compiler';\n\nexport class UnpackProgram implements GPGPUProgram {\n  variableNames = ['A'];\n  usesPackedTextures = true;\n  outputShape: number[];\n  userCode: string;\n\n  constructor(outputShape: number[]) {\n    this.outputShape = outputShape;\n    const rank = outputShape.length;\n\n    const channels = getChannels('rc', rank);\n    const dtype = getCoordsDataType(rank);\n    const sourceCoords = getSourceCoords(rank, channels);\n    const innerDims = channels.slice(-2);\n    const coords = rank <= 1 ? 'rc' : `vec2(${innerDims.join(',')})`;\n\n    this.userCode = `\n      void main() {\n        ${dtype} rc = getOutputCoords();\n        vec4 packedInput = getA(${sourceCoords});\n\n        setOutput(getChannel(packedInput, ${coords}));\n      }\n    `;\n  }\n}","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor, convertToTensorArray} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assert, sizeFromShape} from '../util';\nimport {parseAxisParam} from './axis_util';\nimport {assertParamsConsistent, computeOutShape} from './concat_util';\nimport {op} from './operation';\nimport {tensor} from './tensor_ops';\n\n/**\n * Concatenates a list of`tf.Tensor1D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(3) = |r1, g1, b1|\n * B: shape(2) = |r2, g2|\n * C = tf.concat1d([A, B]) == |r1, g1, b1, r2, g2|\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @return The concatenated array.\n */\nfunction concat1d_(tensors: Array<Tensor1D|TensorLike>): Tensor1D {\n  return concat(tensors, 0 /* axis */);\n}\n\n/**\n * Concatenates a list of`tf.Tensor2D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *\n * B: shape(2, 3) = | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * C = tf.concat2d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 3) = | r1, g1, b1 |\n *                  | r2, g2, b2 |\n *                  | r3, g3, b3 |\n *                  | r4, g4, b4 |\n *\n * if axis = 1:\n * C = shape(2, 6) = | r1, g1, b1, r3, g3, b3 |\n *                   | r2, g2, b2, r4, g4, b4 |\n *\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @param axis The axis to concatenate along.\n * @return The concatenated array.\n */\nfunction concat2d_(\n    tensors: Array<Tensor2D|TensorLike>, axis: number): Tensor2D {\n  return concat(tensors, axis);\n}\n\n/**\n * Concatenates a list of`tf.Tensor3D`s along an axis. See `concat` for details.\n *\n * For example, if:\n * A: shape(2, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *\n * B: shape(2, 1, 3) = | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * C = tf.concat3d([A, B], axis)\n *\n * if axis = 0:\n * C: shape(4, 1, 3) = | r1, g1, b1 |\n *                     | r2, g2, b2 |\n *                     | r3, g3, b3 |\n *                     | r4, g4, b4 |\n *\n * if axis = 1:\n * C: shape(2, 2, 3) = | r1, g1, b1, r3, g3, b3 |\n *                     | r2, g2, b2, r4, g4, b4 |\n *\n * if axis = 2:\n * C = shape(2, 1, 6) = | r1, g1, b1, r3, g3, b3 |\n *                      | r2, g2, b2, r4, g4, b4 |\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat3d_(\n    tensors: Array<Tensor3D|TensorLike>, axis: number): Tensor3D {\n  return concat(tensors, axis);\n}\n\n/**\n * Concatenates a list of`tf.Tensor4D`s along an axis. See `concat` for details.\n *\n * @param tensors A list of`tf.Tensor`s to concatenate.\n * @param axis The axis to concate along.\n * @return The concatenated array.\n */\nfunction concat4d_(\n    tensors: Array<Tensor4D|TensorLike>, axis: number): Tensor4D {\n  return concat(tensors, axis);\n}\n\n/**\n * Concatenates a list of`tf.Tensor`s along a given axis.\n *\n * The tensors ranks and types must match, and their sizes must match in all\n * dimensions except `axis`.\n *\n * Also available are stricter rank-specific methods that assert that\n * `tensors` are of the given rank:\n *   - `tf.concat1d`\n *   - `tf.concat2d`\n *   - `tf.concat3d`\n *   - `tf.concat4d`\n *\n * Except `tf.concat1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * a.concat(b).print();  // or a.concat(b)\n * ```\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.concat([a, b, c]).print();\n * ```\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [10, 20]]);\n * const b = tf.tensor2d([[3, 4], [30, 40]]);\n * const axis = 1;\n * tf.concat([a, b], axis).print();\n * ```\n * @param tensors A list of tensors to concatenate.\n * @param axis The axis to concate along. Defaults to 0 (the first dim).\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction concat_<T extends Tensor>(tensors: Array<T|TensorLike>, axis = 0): T {\n  assert(tensors.length >= 1, 'Pass at least one tensor to concat');\n  let $tensors = convertToTensorArray(tensors, 'tensors', 'concat');\n  axis = parseAxisParam(axis, $tensors[0].shape)[0];\n  const outShape = computeOutShape($tensors.map(t => t.shape), axis);\n  if (sizeFromShape(outShape) === 0) {\n    return tensor([], outShape) as T;\n  }\n  // Keep only non-empty tensors (ignore tensors with 0 in their shape).\n  $tensors = $tensors.filter(t => t.size > 0);\n  if ($tensors.length === 1) {\n    return $tensors[0];\n  }\n\n  const shapes = $tensors.map(t => t.shape);\n  assertParamsConsistent(shapes, axis);\n  const der = (dy: T) => {\n    const sizeSplits = shapes.map(s => s[axis]);\n    const derTensors = split(dy, sizeSplits, axis);\n    return derTensors.map(t => () => t) as {};\n  };\n  const inputs = $tensors as {};\n  return ENV.engine.runKernel(\n      backend => backend.concat($tensors, axis) as T, inputs, der);\n}\n\n/**\n * Splits a`tf.Tensor` into sub tensors.\n *\n * If `numOrSizeSplits` is a number, splits `x` along dimension `axis`\n * into `numOrSizeSplits` smaller tensors.\n * Requires that `numOrSizeSplits` evenly divides `x.shape[axis]`.\n *\n * If `numOrSizeSplits` is a number array, splits `x` into\n * `(numOrSizeSplits.length` pieces. The shape of the `i`-th piece has the\n * same size as `x` except along dimension `axis` where the size is\n * `numOrSizeSplits[i]`.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4, 5, 6, 7, 8], [2, 4]);\n * const [a, b] = tf.split(x, 2, 1);\n * a.print();\n * b.print();\n *\n * const [c, d, e] = tf.split(x, [1, 2, 1], 1);\n * c.print();\n * d.print();\n * e.print();\n * ```\n *\n * @param x The input tensor to split.\n * @param numOrSizeSplits Either an integer indicating the number of\n * splits along the axis or an array of integers containing the sizes of\n * each output tensor along the axis. If a number then it must evenly divide\n * `x.shape[axis]`; otherwise the sum of sizes must match `x.shape[axis]`.\n * @param axis The dimension along which to split. Defaults to 0 (the first\n * dim).\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction split_<T extends Tensor>(\n    x: T|TensorLike, numOrSizeSplits: number[]|number, axis = 0): T[] {\n  const $x = convertToTensor(x, 'x', 'split');\n\n  axis = parseAxisParam(axis, $x.shape)[0];\n  let splitSizes: number[];\n  if (typeof (numOrSizeSplits) === 'number') {\n    assert(\n        $x.shape[axis] % numOrSizeSplits === 0,\n        'Number of splits must evenly divide the axis.');\n    splitSizes = Array(numOrSizeSplits).fill($x.shape[axis] / numOrSizeSplits);\n  } else {\n    assert(\n        $x.shape[axis] === numOrSizeSplits.reduce((a, b) => a + b),\n        'The sum of sizes must match the size of the axis dimension.');\n    splitSizes = numOrSizeSplits;\n  }\n  const der = (dy: T[]) => ({$x: () => concat(dy, axis)});\n  return ENV.engine.runKernel(\n      backend => backend.split($x, splitSizes, axis), {$x}, der);\n}\n\nexport const concat = op({concat_});\nexport const concat1d = op({concat1d_});\nexport const concat2d = op({concat2d_});\nexport const concat3d = op({concat3d_});\nexport const concat4d = op({concat4d_});\nexport const split = op({split_});\n","// A port of an algorithm by Johannes Baagøe <baagoe@baagoe.com>, 2010\n// http://baagoe.com/en/RandomMusings/javascript/\n// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror\n// Original work is under MIT license -\n\n// Copyright (C) 2010 by Johannes Baagøe <baagoe@baagoe.org>\n//\n// Permission is hereby granted, free of charge, to any person obtaining a copy\n// of this software and associated documentation files (the \"Software\"), to deal\n// in the Software without restriction, including without limitation the rights\n// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n// copies of the Software, and to permit persons to whom the Software is\n// furnished to do so, subject to the following conditions:\n// \n// The above copyright notice and this permission notice shall be included in\n// all copies or substantial portions of the Software.\n// \n// THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n// THE SOFTWARE.\n\n\n\n(function(global, module, define) {\n\nfunction Alea(seed) {\n  var me = this, mash = Mash();\n\n  me.next = function() {\n    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32\n    me.s0 = me.s1;\n    me.s1 = me.s2;\n    return me.s2 = t - (me.c = t | 0);\n  };\n\n  // Apply the seeding algorithm from Baagoe.\n  me.c = 1;\n  me.s0 = mash(' ');\n  me.s1 = mash(' ');\n  me.s2 = mash(' ');\n  me.s0 -= mash(seed);\n  if (me.s0 < 0) { me.s0 += 1; }\n  me.s1 -= mash(seed);\n  if (me.s1 < 0) { me.s1 += 1; }\n  me.s2 -= mash(seed);\n  if (me.s2 < 0) { me.s2 += 1; }\n  mash = null;\n}\n\nfunction copy(f, t) {\n  t.c = f.c;\n  t.s0 = f.s0;\n  t.s1 = f.s1;\n  t.s2 = f.s2;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new Alea(seed),\n      state = opts && opts.state,\n      prng = xg.next;\n  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }\n  prng.double = function() {\n    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53\n  };\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nfunction Mash() {\n  var n = 0xefc8249d;\n\n  var mash = function(data) {\n    data = data.toString();\n    for (var i = 0; i < data.length; i++) {\n      n += data.charCodeAt(i);\n      var h = 0.02519603282416938 * n;\n      n = h >>> 0;\n      h -= n;\n      h *= n;\n      n = h >>> 0;\n      h -= n;\n      n += h * 0x100000000; // 2^32\n    }\n    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32\n  };\n\n  return mash;\n}\n\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.alea = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n","// A Javascript implementaion of the \"xor128\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n\n  // Set up generator function.\n  me.next = function() {\n    var t = me.x ^ (me.x << 11);\n    me.x = me.y;\n    me.y = me.z;\n    me.z = me.w;\n    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);\n  };\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor128 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n","// A Javascript implementaion of the \"xorwow\" prng algorithm by\n// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var t = (me.x ^ (me.x >>> 2));\n    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;\n    return (me.d = (me.d + 362437 | 0)) +\n       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;\n  };\n\n  me.x = 0;\n  me.y = 0;\n  me.z = 0;\n  me.w = 0;\n  me.v = 0;\n\n  if (seed === (seed | 0)) {\n    // Integer seed.\n    me.x = seed;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 64; k++) {\n    me.x ^= strseed.charCodeAt(k) | 0;\n    if (k == strseed.length) {\n      me.d = me.x << 10 ^ me.x >>> 4;\n    }\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.x = f.x;\n  t.y = f.y;\n  t.z = f.z;\n  t.w = f.w;\n  t.v = f.v;\n  t.d = f.d;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorwow = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n","// A Javascript implementaion of the \"xorshift7\" algorithm by\n// François Panneton and Pierre L'ecuyer:\n// \"On the Xorgshift Random Number Generators\"\n// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    // Update xor generator.\n    var X = me.x, i = me.i, t, v, w;\n    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);\n    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);\n    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);\n    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);\n    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);\n    X[i] = v;\n    me.i = (i + 1) & 7;\n    return v;\n  };\n\n  function init(me, seed) {\n    var j, w, X = [];\n\n    if (seed === (seed | 0)) {\n      // Seed state array using a 32-bit integer.\n      w = X[0] = seed;\n    } else {\n      // Seed state using a string.\n      seed = '' + seed;\n      for (j = 0; j < seed.length; ++j) {\n        X[j & 7] = (X[j & 7] << 15) ^\n            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);\n      }\n    }\n    // Enforce an array length of 8, not all zeroes.\n    while (X.length < 8) X.push(0);\n    for (j = 0; j < 8 && X[j] === 0; ++j);\n    if (j == 8) w = X[7] = -1; else w = X[j];\n\n    me.x = X;\n    me.i = 0;\n\n    // Discard an initial 256 values.\n    for (j = 256; j > 0; --j) {\n      me.next();\n    }\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.x = f.x.slice();\n  t.i = f.i;\n  return t;\n}\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.x) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xorshift7 = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n","// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.\n//\n// This fast non-cryptographic random number generator is designed for\n// use in Monte-Carlo algorithms. It combines a long-period xorshift\n// generator with a Weyl generator, and it passes all common batteries\n// of stasticial tests for randomness while consuming only a few nanoseconds\n// for each prng generated.  For background on the generator, see Brent's\n// paper: \"Some long-period random number generators using shifts and xors.\"\n// http://arxiv.org/pdf/1004.3115v1.pdf\n//\n// Usage:\n//\n// var xor4096 = require('xor4096');\n// random = xor4096(1);                        // Seed with int32 or string.\n// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.\n// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.\n//\n// For nonzero numeric keys, this impelementation provides a sequence\n// identical to that by Brent's xorgens 3 implementaion in C.  This\n// implementation also provides for initalizing the generator with\n// string seeds, or for saving and restoring the state of the generator.\n//\n// On Chrome, this prng benchmarks about 2.1 times slower than\n// Javascript's built-in Math.random().\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this;\n\n  // Set up generator function.\n  me.next = function() {\n    var w = me.w,\n        X = me.X, i = me.i, t, v;\n    // Update Weyl generator.\n    me.w = w = (w + 0x61c88647) | 0;\n    // Update xor generator.\n    v = X[(i + 34) & 127];\n    t = X[i = ((i + 1) & 127)];\n    v ^= v << 13;\n    t ^= t << 17;\n    v ^= v >>> 15;\n    t ^= t >>> 12;\n    // Update Xor generator array state.\n    v = X[i] = v ^ t;\n    me.i = i;\n    // Result is the combination.\n    return (v + (w ^ (w >>> 16))) | 0;\n  };\n\n  function init(me, seed) {\n    var t, v, i, j, w, X = [], limit = 128;\n    if (seed === (seed | 0)) {\n      // Numeric seeds initialize v, which is used to generates X.\n      v = seed;\n      seed = null;\n    } else {\n      // String seeds are mixed into v and X one character at a time.\n      seed = seed + '\\0';\n      v = 0;\n      limit = Math.max(limit, seed.length);\n    }\n    // Initialize circular array and weyl value.\n    for (i = 0, j = -32; j < limit; ++j) {\n      // Put the unicode characters into the array, and shuffle them.\n      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);\n      // After 32 shuffles, take v as the starting w value.\n      if (j === 0) w = v;\n      v ^= v << 10;\n      v ^= v >>> 15;\n      v ^= v << 4;\n      v ^= v >>> 13;\n      if (j >= 0) {\n        w = (w + 0x61c88647) | 0;     // Weyl.\n        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.\n        i = (0 == t) ? i + 1 : 0;     // Count zeroes.\n      }\n    }\n    // We have detected all zeroes; make the key nonzero.\n    if (i >= 128) {\n      X[(seed && seed.length || 0) & 127] = -1;\n    }\n    // Run the generator 512 times to further mix the state before using it.\n    // Factoring this as a function slows the main generator, so it is just\n    // unrolled here.  The weyl generator is not advanced while warming up.\n    i = 127;\n    for (j = 4 * 128; j > 0; --j) {\n      v = X[(i + 34) & 127];\n      t = X[i = ((i + 1) & 127)];\n      v ^= v << 13;\n      t ^= t << 17;\n      v ^= v >>> 15;\n      t ^= t >>> 12;\n      X[i] = v ^ t;\n    }\n    // Storing state as object members is faster than using closure variables.\n    me.w = w;\n    me.X = X;\n    me.i = i;\n  }\n\n  init(me, seed);\n}\n\nfunction copy(f, t) {\n  t.i = f.i;\n  t.w = f.w;\n  t.X = f.X.slice();\n  return t;\n};\n\nfunction impl(seed, opts) {\n  if (seed == null) seed = +(new Date);\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (state.X) copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.xor4096 = impl;\n}\n\n})(\n  this,                                     // window object or global\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n","// A Javascript implementaion of the \"Tyche-i\" prng algorithm by\n// Samuel Neves and Filipe Araujo.\n// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n\n(function(global, module, define) {\n\nfunction XorGen(seed) {\n  var me = this, strseed = '';\n\n  // Set up generator function.\n  me.next = function() {\n    var b = me.b, c = me.c, d = me.d, a = me.a;\n    b = (b << 25) ^ (b >>> 7) ^ c;\n    c = (c - d) | 0;\n    d = (d << 24) ^ (d >>> 8) ^ a;\n    a = (a - b) | 0;\n    me.b = b = (b << 20) ^ (b >>> 12) ^ c;\n    me.c = c = (c - d) | 0;\n    me.d = (d << 16) ^ (c >>> 16) ^ a;\n    return me.a = (a - b) | 0;\n  };\n\n  /* The following is non-inverted tyche, which has better internal\n   * bit diffusion, but which is about 25% slower than tyche-i in JS.\n  me.next = function() {\n    var a = me.a, b = me.b, c = me.c, d = me.d;\n    a = (me.a + me.b | 0) >>> 0;\n    d = me.d ^ a; d = d << 16 ^ d >>> 16;\n    c = me.c + d | 0;\n    b = me.b ^ c; b = b << 12 ^ d >>> 20;\n    me.a = a = a + b | 0;\n    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;\n    me.c = c = c + d | 0;\n    b = b ^ c;\n    return me.b = (b << 7 ^ b >>> 25);\n  }\n  */\n\n  me.a = 0;\n  me.b = 0;\n  me.c = 2654435769 | 0;\n  me.d = 1367130551;\n\n  if (seed === Math.floor(seed)) {\n    // Integer seed.\n    me.a = (seed / 0x100000000) | 0;\n    me.b = seed | 0;\n  } else {\n    // String seed.\n    strseed += seed;\n  }\n\n  // Mix in string seed, then discard an initial batch of 64 values.\n  for (var k = 0; k < strseed.length + 20; k++) {\n    me.b ^= strseed.charCodeAt(k) | 0;\n    me.next();\n  }\n}\n\nfunction copy(f, t) {\n  t.a = f.a;\n  t.b = f.b;\n  t.c = f.c;\n  t.d = f.d;\n  return t;\n};\n\nfunction impl(seed, opts) {\n  var xg = new XorGen(seed),\n      state = opts && opts.state,\n      prng = function() { return (xg.next() >>> 0) / 0x100000000; };\n  prng.double = function() {\n    do {\n      var top = xg.next() >>> 11,\n          bot = (xg.next() >>> 0) / 0x100000000,\n          result = (top + bot) / (1 << 21);\n    } while (result === 0);\n    return result;\n  };\n  prng.int32 = xg.next;\n  prng.quick = prng;\n  if (state) {\n    if (typeof(state) == 'object') copy(state, xg);\n    prng.state = function() { return copy(xg, {}); }\n  }\n  return prng;\n}\n\nif (module && module.exports) {\n  module.exports = impl;\n} else if (define && define.amd) {\n  define(function() { return impl; });\n} else {\n  this.tychei = impl;\n}\n\n})(\n  this,\n  (typeof module) == 'object' && module,    // present in node.js\n  (typeof define) == 'function' && define   // present with an AMD loader\n);\n\n\n","/*\nCopyright 2014 David Bau.\n\nPermission is hereby granted, free of charge, to any person obtaining\na copy of this software and associated documentation files (the\n\"Software\"), to deal in the Software without restriction, including\nwithout limitation the rights to use, copy, modify, merge, publish,\ndistribute, sublicense, and/or sell copies of the Software, and to\npermit persons to whom the Software is furnished to do so, subject to\nthe following conditions:\n\nThe above copyright notice and this permission notice shall be\nincluded in all copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\nEXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\nMERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.\nIN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY\nCLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,\nTORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE\nSOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.\n\n*/\n\n(function (pool, math) {\n//\n// The following constants are related to IEEE 754 limits.\n//\nvar global = this,\n    width = 256,        // each RC4 output is 0 <= x < 256\n    chunks = 6,         // at least six RC4 outputs for each double\n    digits = 52,        // there are 52 significant digits in a double\n    rngname = 'random', // rngname: name for Math.random and Math.seedrandom\n    startdenom = math.pow(width, chunks),\n    significance = math.pow(2, digits),\n    overflow = significance * 2,\n    mask = width - 1,\n    nodecrypto;         // node.js crypto module, initialized at the bottom.\n\n//\n// seedrandom()\n// This is the seedrandom function described above.\n//\nfunction seedrandom(seed, options, callback) {\n  var key = [];\n  options = (options == true) ? { entropy: true } : (options || {});\n\n  // Flatten the seed string or build one from local entropy if needed.\n  var shortseed = mixkey(flatten(\n    options.entropy ? [seed, tostring(pool)] :\n    (seed == null) ? autoseed() : seed, 3), key);\n\n  // Use the seed to initialize an ARC4 generator.\n  var arc4 = new ARC4(key);\n\n  // This function returns a random double in [0, 1) that contains\n  // randomness in every bit of the mantissa of the IEEE 754 value.\n  var prng = function() {\n    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48\n        d = startdenom,                 //   and denominator d = 2 ^ 48.\n        x = 0;                          //   and no 'extra last byte'.\n    while (n < significance) {          // Fill up all significant digits by\n      n = (n + x) * width;              //   shifting numerator and\n      d *= width;                       //   denominator and generating a\n      x = arc4.g(1);                    //   new least-significant-byte.\n    }\n    while (n >= overflow) {             // To avoid rounding up, before adding\n      n /= 2;                           //   last byte, shift everything\n      d /= 2;                           //   right using integer math until\n      x >>>= 1;                         //   we have exactly the desired bits.\n    }\n    return (n + x) / d;                 // Form the number within [0, 1).\n  };\n\n  prng.int32 = function() { return arc4.g(4) | 0; }\n  prng.quick = function() { return arc4.g(4) / 0x100000000; }\n  prng.double = prng;\n\n  // Mix the randomness into accumulated entropy.\n  mixkey(tostring(arc4.S), pool);\n\n  // Calling convention: what to return as a function of prng, seed, is_math.\n  return (options.pass || callback ||\n      function(prng, seed, is_math_call, state) {\n        if (state) {\n          // Load the arc4 state from the given state if it has an S array.\n          if (state.S) { copy(state, arc4); }\n          // Only provide the .state method if requested via options.state.\n          prng.state = function() { return copy(arc4, {}); }\n        }\n\n        // If called as a method of Math (Math.seedrandom()), mutate\n        // Math.random because that is how seedrandom.js has worked since v1.0.\n        if (is_math_call) { math[rngname] = prng; return seed; }\n\n        // Otherwise, it is a newer calling convention, so return the\n        // prng directly.\n        else return prng;\n      })(\n  prng,\n  shortseed,\n  'global' in options ? options.global : (this == math),\n  options.state);\n}\nmath['seed' + rngname] = seedrandom;\n\n//\n// ARC4\n//\n// An ARC4 implementation.  The constructor takes a key in the form of\n// an array of at most (width) integers that should be 0 <= x < (width).\n//\n// The g(count) method returns a pseudorandom integer that concatenates\n// the next (count) outputs from ARC4.  Its return value is a number x\n// that is in the range 0 <= x < (width ^ count).\n//\nfunction ARC4(key) {\n  var t, keylen = key.length,\n      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];\n\n  // The empty key [] is treated as [0].\n  if (!keylen) { key = [keylen++]; }\n\n  // Set up S using the standard key scheduling algorithm.\n  while (i < width) {\n    s[i] = i++;\n  }\n  for (i = 0; i < width; i++) {\n    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];\n    s[j] = t;\n  }\n\n  // The \"g\" method returns the next (count) outputs as one number.\n  (me.g = function(count) {\n    // Using instance members instead of closure state nearly doubles speed.\n    var t, r = 0,\n        i = me.i, j = me.j, s = me.S;\n    while (count--) {\n      t = s[i = mask & (i + 1)];\n      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];\n    }\n    me.i = i; me.j = j;\n    return r;\n    // For robust unpredictability, the function call below automatically\n    // discards an initial batch of values.  This is called RC4-drop[256].\n    // See http://google.com/search?q=rsa+fluhrer+response&btnI\n  })(width);\n}\n\n//\n// copy()\n// Copies internal state of ARC4 to or from a plain object.\n//\nfunction copy(f, t) {\n  t.i = f.i;\n  t.j = f.j;\n  t.S = f.S.slice();\n  return t;\n};\n\n//\n// flatten()\n// Converts an object tree to nested arrays of strings.\n//\nfunction flatten(obj, depth) {\n  var result = [], typ = (typeof obj), prop;\n  if (depth && typ == 'object') {\n    for (prop in obj) {\n      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}\n    }\n  }\n  return (result.length ? result : typ == 'string' ? obj : obj + '\\0');\n}\n\n//\n// mixkey()\n// Mixes a string seed into a key that is an array of integers, and\n// returns a shortened string seed that is equivalent to the result key.\n//\nfunction mixkey(seed, key) {\n  var stringseed = seed + '', smear, j = 0;\n  while (j < stringseed.length) {\n    key[mask & j] =\n      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));\n  }\n  return tostring(key);\n}\n\n//\n// autoseed()\n// Returns an object for autoseeding, using window.crypto and Node crypto\n// module if available.\n//\nfunction autoseed() {\n  try {\n    var out;\n    if (nodecrypto && (out = nodecrypto.randomBytes)) {\n      // The use of 'out' to remember randomBytes makes tight minified code.\n      out = out(width);\n    } else {\n      out = new Uint8Array(width);\n      (global.crypto || global.msCrypto).getRandomValues(out);\n    }\n    return tostring(out);\n  } catch (e) {\n    var browser = global.navigator,\n        plugins = browser && browser.plugins;\n    return [+new Date, global, plugins, global.screen, tostring(pool)];\n  }\n}\n\n//\n// tostring()\n// Converts an array of charcodes to a string\n//\nfunction tostring(a) {\n  return String.fromCharCode.apply(0, a);\n}\n\n//\n// When seedrandom.js is loaded, we immediately mix a few bits\n// from the built-in RNG into the entropy pool.  Because we do\n// not want to interfere with deterministic PRNG state later,\n// seedrandom will not call math.random on its own again after\n// initialization.\n//\nmixkey(math.random(), pool);\n\n//\n// Nodejs and AMD support: export the implementation as a module using\n// either convention.\n//\nif ((typeof module) == 'object' && module.exports) {\n  module.exports = seedrandom;\n  // When in node.js, try using crypto package for autoseeding.\n  try {\n    nodecrypto = require('crypto');\n  } catch (ex) {}\n} else if ((typeof define) == 'function' && define.amd) {\n  define(function() { return seedrandom; });\n}\n\n// End anonymous scope, and pass initial values.\n})(\n  [],     // pool: entropy pool starts empty\n  Math    // math: package containing random, pow, and seedrandom\n);\n","// A library of seedable RNGs implemented in Javascript.\n//\n// Usage:\n//\n// var seedrandom = require('seedrandom');\n// var random = seedrandom(1); // or any seed.\n// var x = random();       // 0 <= x < 1.  Every bit is random.\n// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.\n\n// alea, a 53-bit multiply-with-carry generator by Johannes Baagøe.\n// Period: ~2^116\n// Reported to pass all BigCrush tests.\nvar alea = require('./lib/alea');\n\n// xor128, a pure xor-shift generator by George Marsaglia.\n// Period: 2^128-1.\n// Reported to fail: MatrixRank and LinearComp.\nvar xor128 = require('./lib/xor128');\n\n// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.\n// Period: 2^192-2^32\n// Reported to fail: CollisionOver, SimpPoker, and LinearComp.\nvar xorwow = require('./lib/xorwow');\n\n// xorshift7, by François Panneton and Pierre L'ecuyer, takes\n// a different approach: it adds robustness by allowing more shifts\n// than Marsaglia's original three.  It is a 7-shift generator\n// with 256 bits, that passes BigCrush with no systmatic failures.\n// Period 2^256-1.\n// No systematic BigCrush failures reported.\nvar xorshift7 = require('./lib/xorshift7');\n\n// xor4096, by Richard Brent, is a 4096-bit xor-shift with a\n// very long period that also adds a Weyl generator. It also passes\n// BigCrush with no systematic failures.  Its long period may\n// be useful if you have many generators and need to avoid\n// collisions.\n// Period: 2^4128-2^32.\n// No systematic BigCrush failures reported.\nvar xor4096 = require('./lib/xor4096');\n\n// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random\n// number generator derived from ChaCha, a modern stream cipher.\n// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf\n// Period: ~2^127\n// No systematic BigCrush failures reported.\nvar tychei = require('./lib/tychei');\n\n// The original ARC4-based prng included in this library.\n// Period: ~2^1600\nvar sr = require('./seedrandom');\n\nsr.alea = alea;\nsr.xor128 = xor128;\nsr.xorwow = xorwow;\nsr.xorshift7 = xorshift7;\nsr.xor4096 = xor4096;\nsr.tychei = tychei;\n\nmodule.exports = sr;\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as seedrandom from 'seedrandom';\n\nexport interface RandGauss {\n  nextValue(): number;\n}\n\nexport interface RandNormalDataTypes {\n  float32: Float32Array;\n  int32: Int32Array;\n}\n\n// https://en.wikipedia.org/wiki/Marsaglia_polar_method\nexport class MPRandGauss implements RandGauss {\n  private mean: number;\n  private stdDev: number;\n  private nextVal: number;\n  private dtype?: keyof RandNormalDataTypes;\n  private truncated?: boolean;\n  private upper?: number;\n  private lower?: number;\n  private random: seedrandom.prng;\n\n  constructor(\n      mean: number, stdDeviation: number, dtype?: keyof RandNormalDataTypes,\n      truncated?: boolean, seed?: number) {\n    this.mean = mean;\n    this.stdDev = stdDeviation;\n    this.dtype = dtype;\n    this.nextVal = NaN;\n    this.truncated = truncated;\n    if (this.truncated) {\n      this.upper = this.mean + this.stdDev * 2;\n      this.lower = this.mean - this.stdDev * 2;\n    }\n    const seedValue = seed ? seed : Math.random();\n    this.random = seedrandom.alea(seedValue.toString());\n  }\n\n  /** Returns next sample from a gaussian distribution. */\n  public nextValue(): number {\n    if (!isNaN(this.nextVal)) {\n      const value = this.nextVal;\n      this.nextVal = NaN;\n      return value;\n    }\n\n    let resultX: number, resultY: number;\n    let isValid = false;\n    while (!isValid) {\n      let v1: number, v2: number, s: number;\n      do {\n        v1 = 2 * this.random() - 1;\n        v2 = 2 * this.random() - 1;\n        s = v1 * v1 + v2 * v2;\n      } while (s >= 1 || s === 0);\n\n      const mul = Math.sqrt(-2.0 * Math.log(s) / s);\n      resultX = this.mean + this.stdDev * v1 * mul;\n      resultY = this.mean + this.stdDev * v2 * mul;\n\n      if (!this.truncated || this.isValidTruncated(resultX)) {\n        isValid = true;\n      }\n    }\n\n    if (!this.truncated || this.isValidTruncated(resultY)) {\n      this.nextVal = this.convertValue(resultY);\n    }\n    return this.convertValue(resultX);\n  }\n\n  /** Handles proper rounding for non floating point numbers. */\n  private convertValue(value: number): number {\n    if (this.dtype == null || this.dtype === 'float32') {\n      return value;\n    }\n    return Math.round(value);\n  }\n\n  /** Returns true if less than 2-standard-deviations from the mean. */\n  private isValidTruncated(value: number): boolean {\n    return value <= this.upper && value >= this.lower;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, TensorBuffer} from '../tensor';\nimport {convertToTensor, convertToTensorArray} from '../tensor_util_env';\nimport {DataType, DataTypeMap, Rank, ShapeMap, TensorLike, TensorLike1D, TensorLike4D} from '../types';\nimport * as util from '../util';\nimport {getAxesPermutation, getInnerMostAxes} from './axis_util';\nimport {concat} from './concat_split';\nimport {op} from './operation';\nimport {MPRandGauss} from './rand';\nimport {zeros, zerosLike} from './tensor_ops';\n\n/**\n * Creates a new tensor with the same values and shape as the specified\n * tensor.\n *\n * ```js\n * const x = tf.tensor([1, 2]);\n *\n * x.clone().print();\n * ```\n *\n * @param x The tensor to clone.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction clone_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'clone', null);\n  const der = (dy: T) => {\n    return {$x: () => dy.toFloat()};\n  };\n\n  return ENV.engine.runKernel(\n             backend =>\n                 Tensor.make($x.shape, {dataId: $x.dataId}, $x.dtype) as T,\n             {$x}, der) as T;\n}\n\n/**\n * Create an identity matrix.\n *\n * @param numRows Number of rows.\n * @param numColumns Number of columns. Defaults to `numRows`.\n * @param batchShape If provided, will add the batch shape to the beginning\n *   of the shape of the returned `tf.Tensor` by repeating the identity\n *   matrix.\n * @param dtype Data type.\n * @returns Identity matrix of the specified size and data type, possibly\n *   with batch repetition if `batchShape` is specified.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction eye_(\n    numRows: number, numColumns?: number,\n    batchShape?:\n        [\n          number\n        ]|[number,\n           number]|[number, number, number]|[number, number, number, number],\n    dtype: DataType = 'float32'): Tensor2D {\n  if (numColumns == null) {\n    numColumns = numRows;\n  }\n  const buff = buffer([numRows, numColumns], dtype);\n  const n = numRows <= numColumns ? numRows : numColumns;\n  for (let i = 0; i < n; ++i) {\n    buff.set(1, i, i);\n  }\n  const out = buff.toTensor().as2D(numRows, numColumns);\n  if (batchShape == null) {\n    return out;\n  } else {\n    if (batchShape.length === 1) {\n      return tile(expandDims(out, 0), [batchShape[0], 1, 1]);\n    } else if (batchShape.length === 2) {\n      return tile(\n          expandDims(expandDims(out, 0), 0),\n          [batchShape[0], batchShape[1], 1, 1]);\n    } else if (batchShape.length === 3) {\n      return tile(\n          expandDims(expandDims(expandDims(out, 0), 0), 0),\n          [batchShape[0], batchShape[1], batchShape[2], 1, 1]);\n    } else {\n      throw new Error(\n          `eye() currently supports only 1D and 2D ` +\n          // tslint:disable-next-line:no-any\n          `batchShapes, but received ${(batchShape as any).length}D.`);\n    }\n  }\n}\n\n/**\n * Creates a `tf.Tensor` with values sampled from a normal distribution.\n *\n * ```js\n * tf.randomNormal([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output.\n * @param seed The seed for the random number generator.\n */\n/** @doc {heading: 'Tensors', subheading: 'Random'} */\nfunction randomNormal_<R extends Rank>(\n    shape: ShapeMap[R], mean = 0, stdDev = 1, dtype?: 'float32'|'int32',\n    seed?: number): Tensor<R> {\n  if (dtype != null && (dtype as DataType) === 'bool') {\n    throw new Error(`Unsupported data type ${dtype}`);\n  }\n  const randGauss =\n      new MPRandGauss(mean, stdDev, dtype, false /* truncated */, seed);\n  const res = buffer(shape, dtype);\n  for (let i = 0; i < res.values.length; i++) {\n    res.values[i] = randGauss.nextValue();\n  }\n  return res.toTensor();\n}\n\n/**\n * Creates a `tf.Tensor` with values sampled from a truncated normal\n * distribution.\n *\n * ```js\n * tf.truncatedNormal([2, 2]).print();\n * ```\n *\n * The generated values follow a normal distribution with specified mean and\n * standard deviation, except that values whose magnitude is more than 2\n * standard deviations from the mean are dropped and re-picked.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param mean The mean of the normal distribution.\n * @param stdDev The standard deviation of the normal distribution.\n * @param dtype The data type of the output tensor.\n * @param seed The seed for the random number generator.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction truncatedNormal_<R extends Rank>(\n    shape: ShapeMap[R], mean = 0, stdDev = 1, dtype?: 'float32'|'int32',\n    seed?: number): Tensor<R> {\n  if (dtype != null && (dtype as DataType) === 'bool') {\n    throw new Error(`Unsupported data type ${dtype}`);\n  }\n  const randGauss =\n      new MPRandGauss(mean, stdDev, dtype, true /* truncated */, seed);\n  const res = buffer(shape, dtype);\n  for (let i = 0; i < res.values.length; i++) {\n    res.values[i] = randGauss.nextValue();\n  }\n  return res.toTensor();\n}\n\n/**\n * Creates a `tf.Tensor` with values sampled from a uniform distribution.\n *\n * The generated values follow a uniform distribution in the range [minval,\n * maxval). The lower bound minval is included in the range, while the upper\n * bound maxval is excluded.\n *\n * ```js\n * tf.randomUniform([2, 2]).print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param minval The lower bound on the range of random values to generate.\n *   Defaults to 0.\n * @param maxval The upper bound on the range of random values to generate.\n *   Defaults to 1.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n */\n/** @doc {heading: 'Tensors', subheading: 'Random'} */\nfunction randomUniform_<R extends Rank>(\n    shape: ShapeMap[R], minval = 0, maxval = 1,\n    dtype: DataType = 'float32'): Tensor<R> {\n  const res = buffer(shape, dtype);\n  for (let i = 0; i < res.values.length; i++) {\n    res.values[i] = util.randUniform(minval, maxval);\n  }\n  return res.toTensor();\n}\n\n/**\n * Creates a `tf.Tensor` with values sampled from a random number generator\n * function defined by the user.\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param randFunction A random number generator function which is called\n * for each element in the output tensor.\n * @param dtype The data type of the output tensor. Defaults to 'float32'.\n */\nfunction rand_<R extends Rank>(\n    shape: ShapeMap[R], randFunction: () => number,\n    dtype?: DataType): Tensor<R> {\n  const size = util.sizeFromShape(shape);\n\n  let values = null;\n  if (dtype == null || dtype === 'float32') {\n    values = new Float32Array(size);\n  } else if (dtype === 'int32') {\n    values = new Int32Array(size);\n  } else if (dtype === 'bool') {\n    values = new Uint8Array(size);\n  } else {\n    throw new Error(`Unknown data type ${dtype}`);\n  }\n\n  for (let i = 0; i < size; i++) {\n    values[i] = randFunction();\n  }\n  return Tensor.make(shape, {values}, dtype);\n}\n\n/**\n * Creates a `tf.Tensor` with values drawn from a multinomial distribution.\n *\n * ```js\n * const probs = tf.tensor([.75, .25]);\n * tf.multinomial(probs, 3).print();\n * ```\n *\n * @param logits 1D array with unnormalized log-probabilities, or\n *     2D array of shape `[batchSize, numOutcomes]`. See the `normalized`\n *     parameter.\n * @param numSamples Number of samples to draw for each row slice.\n * @param seed The seed number.\n * @param normalized Whether the provided `logits` are normalized true\n *     probabilities (sum to 1). Defaults to false.\n * @return 1D array of shape `[numSamples]`, or 2D array of shape\n *     `[batchSize, numSamples]`, depending on the rank of the input.\n */\n/** @doc {heading: 'Tensors', subheading: 'Random'} */\nfunction multinomial_(\n    logits: Tensor1D|Tensor2D|TensorLike, numSamples: number, seed?: number,\n    normalized = false): Tensor1D|Tensor2D {\n  const $logits = convertToTensor(logits, 'logits', 'multinomial');\n  const numOutcomes = $logits.size;\n  const origRank = $logits.rank;\n  if (numOutcomes < 2) {\n    throw new Error(\n        `Error in multinomial: you need at least 2 outcomes, but got ` +\n        `${numOutcomes}.`);\n  }\n  if (origRank > 2) {\n    throw new Error(`Rank of probabilities must be 1 or 2, but is ${origRank}`);\n  }\n  seed = seed || Math.random();\n  const logits2D = origRank === 1 ? $logits.as2D(1, -1) : $logits as Tensor2D;\n  const res = ENV.engine.runKernel(\n      backend => backend.multinomial(logits2D, normalized, numSamples, seed),\n      {logits2D});\n\n  return origRank === 1 ? res.as1D() : res;\n}\n\n/**\n * Creates a one-hot `tf.Tensor`. The locations represented by `indices` take\n * value `onValue` (defaults to 1), while all other locations take value\n * `offValue` (defaults to 0).\n *\n * ```js\n * tf.oneHot(tf.tensor1d([0, 1], 'int32'), 3).print();\n * ```\n *\n * @param indices `tf.Tensor1D` of indices with dtype `int32`.\n * @param depth The depth of the one hot dimension.\n * @param onValue A number used to fill in the output when the index matches\n * the location.\n * @param offValue A number used to fill in the output when the index does\n *     not match the location.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction oneHot_(\n    indices: Tensor1D|TensorLike1D, depth: number, onValue = 1,\n    offValue = 0): Tensor2D {\n  const $indices =\n      convertToTensor(indices, 'indices', 'oneHot', 'int32') as Tensor1D;\n\n  if (depth < 2) {\n    throw new Error(`Error in oneHot: depth must be >=2, but it is ${depth}`);\n  }\n  const grad = (dy: Tensor2D) => {\n    return {$indices: () => zeros($indices.shape, 'float32') as Tensor1D};\n  };\n  return ENV.engine.runKernel(\n      backend => backend.oneHot($indices, depth, onValue, offValue), {$indices},\n      grad);\n}\n\n/**\n * Creates a `tf.Tensor` from an image.\n *\n * ```js\n * const image = new ImageData(1, 1);\n * image.data[0] = 100;\n * image.data[1] = 150;\n * image.data[2] = 200;\n * image.data[3] = 255;\n *\n * tf.fromPixels(image).print();\n * ```\n *\n * @param pixels The input image to construct the tensor from. The\n * supported image types are all 4-channel.\n * @param numChannels The number of channels of the output tensor. A\n * numChannels value less than 4 allows you to ignore channels. Defaults to\n * 3 (ignores alpha channel of input image).\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction fromPixels_(\n    pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement,\n    numChannels = 3): Tensor3D {\n  if (numChannels > 4) {\n    throw new Error(\n        'Cannot construct Tensor with more than 4 channels from pixels.');\n  }\n  return ENV.engine.fromPixels(pixels, numChannels);\n}\n\n/**\n * Draws a `tf.Tensor` of pixel values to a byte array or optionally a\n * canvas.\n *\n * When the dtype of the input is 'float32', we assume values in the range\n * [0-1]. Otherwise, when input is 'int32', we assume values in the range\n * [0-255].\n *\n * Returns a promise that resolves when the canvas has been drawn to.\n *\n * @param img A rank-2 or rank-3 tensor. If rank-2, draws grayscale. If\n *     rank-3, must have depth of 1, 3 or 4. When depth of 1, draws\n * grayscale. When depth of 3, we draw with the first three components of\n * the depth dimension corresponding to r, g, b and alpha = 1. When depth of\n * 4, all four components of the depth dimension correspond to r, g, b, a.\n * @param canvas The canvas to draw to.\n */\n/** @doc {heading: 'Visualization'} */\nasync function toPixels(\n    img: Tensor2D|Tensor3D|TensorLike,\n    canvas?: HTMLCanvasElement): Promise<Uint8ClampedArray> {\n  let $img = convertToTensor(img, 'img', 'toPixels');\n  if (!(img instanceof Tensor)) {\n    // Assume int32 if user passed a native array.\n    $img = $img.toInt();\n  }\n  if ($img.rank !== 2 && $img.rank !== 3) {\n    throw new Error(\n        `toPixels only supports rank 2 or 3 tensors, got rank ${$img.rank}.`);\n  }\n  const [height, width] = $img.shape.slice(0, 2);\n  const depth = $img.rank === 2 ? 1 : $img.shape[2];\n\n  if (depth > 4 || depth === 2) {\n    throw new Error(\n        `toPixels only supports depth of size ` +\n        `1, 3 or 4 but got ${depth}`);\n  }\n\n  const minTensor = $img.min();\n  const maxTensor = $img.max();\n  const min = (await minTensor.data())[0];\n  const max = (await maxTensor.data())[0];\n  minTensor.dispose();\n  maxTensor.dispose();\n  if ($img.dtype === 'float32') {\n    if (min < 0 || max > 1) {\n      throw new Error(\n          `Tensor values for a float32 Tensor must be in the ` +\n          `range [0 - 1] but got range [${min} - ${max}].`);\n    }\n  } else if ($img.dtype === 'int32') {\n    if (min < 0 || max > 255) {\n      throw new Error(\n          `Tensor values for a int32 Tensor must be in the ` +\n          `range [0 - 255] but got range [${min} - ${max}].`);\n    }\n  } else {\n    throw new Error(\n        `Unsupported type for toPixels: ${$img.dtype}.` +\n        ` Please use float32 or int32 tensors.`);\n  }\n\n  const data = await $img.data();\n  const multiplier = $img.dtype === 'float32' ? 255 : 1;\n  const bytes = new Uint8ClampedArray(width * height * 4);\n\n  for (let i = 0; i < height * width; ++i) {\n    let r, g, b, a;\n    if (depth === 1) {\n      r = data[i] * multiplier;\n      g = data[i] * multiplier;\n      b = data[i] * multiplier;\n      a = 255;\n    } else if (depth === 3) {\n      r = data[i * 3] * multiplier;\n      g = data[i * 3 + 1] * multiplier;\n      b = data[i * 3 + 2] * multiplier;\n      a = 255;\n    } else if (depth === 4) {\n      r = data[i * 4] * multiplier;\n      g = data[i * 4 + 1] * multiplier;\n      b = data[i * 4 + 2] * multiplier;\n      a = data[i * 4 + 3] * multiplier;\n    }\n\n    const j = i * 4;\n    bytes[j + 0] = Math.round(r);\n    bytes[j + 1] = Math.round(g);\n    bytes[j + 2] = Math.round(b);\n    bytes[j + 3] = Math.round(a);\n  }\n\n  if (canvas != null) {\n    canvas.width = width;\n    canvas.height = height;\n    const ctx = canvas.getContext('2d');\n    const imageData = new ImageData(bytes, width, height);\n    ctx.putImageData(imageData, 0, 0);\n  }\n  if ($img !== img) {\n    $img.dispose();\n  }\n  return bytes;\n}\n\n/**\n * Reshapes a `tf.Tensor` to a given shape.\n *\n * Given an input tensor, returns a new tensor with the same values as the\n * input tensor with shape `shape`.\n *\n * If one component of shape is the special value -1, the size of that\n * dimension is computed so that the total size remains constant. In\n * particular, a shape of [-1] flattens into 1-D. At most one component of\n * shape can be -1.\n *\n * If shape is 1-D or higher, then the operation returns a tensor with shape\n * shape filled with the values of tensor. In this case, the number of\n * elements implied by shape must be the same as the number of elements in\n * tensor.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.reshape([2, 2]).print();\n * ```\n *\n * @param x The input tensor to be reshaped.\n * @param shape An array of integers defining the output tensor shape.\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction reshape_<R2 extends Rank>(\n    x: Tensor|TensorLike, shape: ShapeMap[R2]): Tensor<R2> {\n  const $x = convertToTensor(x, 'x', 'reshape', null);\n  shape = util.inferFromImplicitShape(shape, $x.size);\n  util.assert(\n      $x.size === util.sizeFromShape(shape),\n      'new shape and old shape must have the same number of elements.');\n\n  const grad = (dy: Tensor<R2>) => {\n    return {$x: () => dy.reshape($x.shape)};\n  };\n  return ENV.engine.runKernel(\n      backend => backend.reshape($x, shape), {$x}, grad);\n}\n\n/**\n * Removes dimensions of size 1 from the shape of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4], [1, 1, 4]);\n * x.squeeze().print();\n * ```\n *\n * @param x The input tensor to be squeezed.\n * @param axis An optional list of numbers. If specified, only\n *     squeezes the dimensions listed. The dimension index starts at 0. It\n * is an error to squeeze a dimension that is not 1.\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction squeeze_<T extends Tensor>(x: Tensor|TensorLike, axis?: number[]): T {\n  const $x = convertToTensor(x, 'x', 'squeeze');\n  return reshape($x, util.squeezeShape($x.shape, axis).newShape) as T;\n}\n\n/**\n * Casts a `tf.Tensor` to a new dtype.\n *\n * ```js\n * const x = tf.tensor1d([1.5, 2.5, 3]);\n * tf.cast(x, 'int32').print();\n * ```\n * @param x The input tensor to be casted.\n * @param dtype The dtype to cast the input tensor to.\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction cast_<T extends Tensor>(x: T|TensorLike, dtype: DataType): T {\n  const $x = convertToTensor(x, 'x', 'cast');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.clone()};\n  };\n  return ENV.engine.runKernel(backend => backend.cast($x, dtype), {$x}, grad) as\n      T;\n}\n\n/**\n * Construct a tensor by repeating it the number of times given by reps.\n *\n * This operation creates a new tensor by replicating `input` `reps`\n * times. The output tensor's i'th dimension has `input.shape[i] *\n * reps[i]` elements, and the values of `input` are replicated\n * `reps[i]` times along the i'th dimension. For example, tiling\n * `[a, b, c, d]` by `[2]` produces `[a, b, c, d, a, b, c, d]`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n *\n * a.tile([2]).print();    // or a.tile([2])\n * ```\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.tile([1, 2]).print();  // or a.tile([1, 2])\n * ```\n * @param x The tensor to tile.\n * @param reps Determines the number of replications per dimension.\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction tile_<T extends Tensor>(x: T|TensorLike, reps: number[]): T {\n  const $x = convertToTensor(x, 'x', 'tile');\n\n  util.assert(\n      $x.rank === reps.length,\n      `Error in transpose: rank of input ${$x.rank} ` +\n          `must match length of reps ${reps}.`);\n  const grad = (dy: T) => {\n    const derX = () => {\n      let xGrad = zerosLike($x);\n      // TODO(cais): Maybe reduce memory footprint by avoiding repeated\n      // slicing.\n      if ($x.rank === 1) {\n        for (let i = 0; i < reps[0]; ++i) {\n          xGrad = xGrad.add(dy.slice([i * $x.shape[0]], [$x.shape[0]]));\n        }\n      } else if ($x.rank === 2) {\n        for (let i = 0; i < reps[0]; ++i) {\n          for (let j = 0; j < reps[1]; ++j) {\n            xGrad = xGrad.add(dy.slice(\n                [i * $x.shape[0], j * $x.shape[1]],\n                [$x.shape[0], $x.shape[1]]));\n          }\n        }\n      } else if ($x.rank === 3) {\n        for (let i = 0; i < reps[0]; ++i) {\n          for (let j = 0; j < reps[1]; ++j) {\n            for (let k = 0; k < reps[2]; ++k) {\n              xGrad = xGrad.add(dy.slice(\n                  [i * $x.shape[0], j * $x.shape[1], k * $x.shape[2]],\n                  [$x.shape[0], $x.shape[1], $x.shape[2]]));\n            }\n          }\n        }\n      } else if ($x.rank === 4) {\n        for (let i = 0; i < reps[0]; ++i) {\n          for (let j = 0; j < reps[1]; ++j) {\n            for (let k = 0; k < reps[2]; ++k) {\n              for (let l = 0; l < reps[3]; ++l) {\n                xGrad = xGrad.add(dy.slice(\n                    [\n                      i * $x.shape[0], j * $x.shape[1], k * $x.shape[2],\n                      l * $x.shape[3]\n                    ],\n                    [$x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]]));\n              }\n            }\n          }\n        }\n      } else {\n        throw new Error(\n            `Gradient for tile operation is not implemented for rank-` +\n            `${$x.rank} tensors yet.`);\n      }\n      return xGrad;\n    };\n    return {$x: derX};\n  };\n  return ENV.engine.runKernel(backend => backend.tile($x, reps), {$x}, grad);\n}\n\n/**\n * Pads a `tf.Tensor1D` with a given value and paddings. See `pad` for details.\n */\nfunction pad1d_(\n    x: Tensor1D|TensorLike, paddings: [number, number],\n    constantValue = 0): Tensor1D {\n  util.assert(\n      paddings.length === 2,\n      'Invalid number of paddings. Must be length of 2.');\n  return pad(x, [paddings], constantValue);\n}\n\n/**\n * Pads a `tf.Tensor2D` with a given value and paddings. See `pad` for details.\n */\nfunction pad2d_(\n    x: Tensor2D|TensorLike, paddings: [[number, number], [number, number]],\n    constantValue = 0): Tensor2D {\n  util.assert(\n      paddings.length === 2 && paddings[0].length === 2 &&\n          paddings[1].length === 2,\n      'Invalid number of paddings. Must be length of 2 each.');\n  return pad(x, paddings, constantValue);\n}\n\n/**\n * Pads a `tf.Tensor3D` with a given value and paddings. See `pad` for details.\n */\nfunction pad3d_(\n    x: Tensor3D|TensorLike,\n    paddings: [[number, number], [number, number], [number, number]],\n    constantValue = 0): Tensor3D {\n  util.assert(\n      paddings.length === 3 && paddings[0].length === 2 &&\n          paddings[1].length === 2 && paddings[2].length === 2,\n      'Invalid number of paddings. Must be length of 2 each.');\n  return pad(x, paddings, constantValue);\n}\n\n/**\n * Pads a `tf.Tensor4D` with a given value and paddings. See `pad` for details.\n */\nfunction pad4d_(\n    x: Tensor4D|TensorLike,\n    paddings:\n        [\n          [number, number], [number, number], [number, number], [number, number]\n        ],\n    constantValue = 0): Tensor4D {\n  util.assert(\n      paddings.length === 4 && paddings[0].length === 2 &&\n          paddings[1].length === 2 && paddings[2].length === 2 &&\n          paddings[3].length === 2,\n      'Invalid number of paddings. Must be length of 2 each.');\n  return pad(x, paddings, constantValue);\n}\n\n/**\n * Pads a `tf.Tensor` with a given value and paddings.\n *\n * This operation currently only implements the `CONSTANT` mode.\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `paddings` is of given length.\n *   - `tf.pad1d`\n *   - `tf.pad2d`\n *   - `tf.pad3d`\n *   - `tf.pad4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * x.pad([[1, 2]]).print();\n * ```\n * @param x The tensor to pad.\n * @param paddings An array of length `R` (the rank of the tensor), where\n * each element is a length-2 tuple of ints `[padBefore, padAfter]`,\n * specifying how much to pad along each dimension of the tensor.\n * @param constantValue The pad value to use. Defaults to 0.\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction pad_<T extends Tensor>(\n    x: T|TensorLike, paddings: Array<[number, number]>, constantValue = 0): T {\n  const $x = convertToTensor(x, 'x', 'pad');\n\n  if ($x.rank === 0) {\n    throw new Error('pad(scalar) is not defined. Pass non-scalar to pad');\n  }\n  // Pad introduces values around the original tensor, so the gradient\n  // slices the original shape out of the gradient.\n  const begin = paddings.map(p => p[0]);\n  const grad = (dy: T) => {\n    return {$x: () => dy.slice(begin, $x.shape)};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.pad($x, paddings, constantValue), {$x}, grad) as\n      T;\n}\n\n/**\n * Stacks a list of rank-`R` `tf.Tensor`s into one rank-`(R+1)` `tf.Tensor`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n * tf.stack([a, b, c]).print();\n * ```\n *\n * @param tensors A list of tensor objects with the same shape and dtype.\n * @param axis The axis to stack along. Defaults to 0 (the first dim).\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction stack_<T extends Tensor>(\n    tensors: Array<T|TensorLike>, axis = 0): Tensor {\n  const $tensors = convertToTensorArray(tensors, 'tensors', 'stack');\n\n  util.assert($tensors.length >= 1, 'Pass at least one tensor to tf.stack');\n  if ($tensors.length === 1) {\n    return $tensors[0].expandDims(axis);\n  }\n  const rank = $tensors[0].rank;\n  const shape = $tensors[0].shape;\n  const dtype = $tensors[0].dtype;\n\n  util.assert(axis <= rank, 'Axis must be <= rank of the tensor');\n\n  $tensors.forEach(t => {\n    util.assertShapesMatch(\n        shape, t.shape,\n        'All tensors passed to stack must have matching shapes');\n  });\n\n  $tensors.forEach(t => {\n    util.assert(\n        dtype === t.dtype,\n        'All tensors passed to stack must have matching dtypes');\n  });\n  const expandedTensors = $tensors.map(t => t.expandDims(axis));\n  return concat(expandedTensors, axis);\n}\n\n/**\n * This operation reshapes the \"batch\" dimension 0 into `M + 1` dimensions of\n * shape `blockShape + [batch]`, interleaves these blocks back into the grid\n * defined by the spatial dimensions `[1, ..., M]`, to obtain a result with\n * the same rank as the input. The spatial dimensions of this intermediate\n * result are then optionally cropped according to `crops` to produce the\n * output. This is the reverse of `tf.spaceToBatchND`. See below for a precise\n * description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [4, 1, 1, 1]);\n * const blockShape = [2, 2];\n * const crops = [[0, 0], [0, 0]];\n *\n * x.batchToSpaceND(blockShape, crops).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param crops A 2-D array.  Must have shape `[M, 2]`, all values must be >= 0.\n * `crops[i] = [cropStart, cropEnd]` specifies the amount to crop from input\n * dimension `i + 1`, which corresponds to spatial dimension `i`. It is required\n * that `cropStart[i] + cropEnd[i] <= blockShape[i] * inputShape[i + 1]`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Reshape `x` to `reshaped` of shape: `[blockShape[0], ...,\n * blockShape[M-1], batch / prod(blockShape), x.shape[1], ...,\n * x.shape[N-1]]`\n *\n * 2. Permute dimensions of `reshaped`to produce `permuted` of shape `[batch /\n * prod(blockShape),x.shape[1], blockShape[0], ..., x.shape[M],\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 3. Reshape `permuted` to produce `reshapedPermuted` of shape `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0], ..., x.shape[M] *\n * blockShape[M-1],x.shape[M+1], ..., x.shape[N-1]]`\n *\n * 4. Crop the start and end of dimensions `[1, ..., M]` of `reshapedPermuted`\n * according to `crops` to produce the output of shape: `[batch /\n * prod(blockShape),x.shape[1] * blockShape[0] - crops[0,0] - crops[0,1],\n * ..., x.shape[M] * blockShape[M-1] - crops[M-1,0] -\n * crops[M-1,1],x.shape[M+1], ..., x.shape[N-1]]`\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction batchToSpaceND_<T extends Tensor>(\n    x: T|TensorLike, blockShape: number[], crops: number[][]): T {\n  const $x = convertToTensor(x, 'x', 'batchToSpaceND');\n  const prod = blockShape.reduce((a, b) => a * b);\n\n  util.assert(\n      $x.rank >= 1 + blockShape.length,\n      `input rank is ${$x.rank} but should be > than blockShape.length ${\n          blockShape.length}`);\n\n  util.assert(\n      crops.length === blockShape.length,\n      `crops.length is ${\n          crops.length} but should be equal to blockShape.length  ${\n          blockShape.length}`);\n\n  util.assert(\n      $x.shape[0] % prod === 0,\n      `input tensor batch is ${\n          $x.shape[0]} but is not divisible by the product of ` +\n          `the elements of blockShape ${blockShape.join(' * ')} === ${prod}`);\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.spaceToBatchND(blockShape, crops)};\n  };\n\n  return ENV.engine.runKernel(\n      backend => backend.batchToSpaceND($x, blockShape, crops), {$x}, grad);\n}\n\n/**\n * This operation divides \"spatial\" dimensions `[1, ..., M]` of the input into\n * a grid of blocks of shape `blockShape`, and interleaves these blocks with\n * the \"batch\" dimension (0) such that in the output, the spatial\n * dimensions `[1, ..., M]` correspond to the position within the grid,\n * and the batch dimension combines both the position within a spatial block\n * and the original batch position. Prior to division into blocks,\n * the spatial dimensions of the input are optionally zero padded\n * according to `paddings`. See below for a precise description.\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 2, 2, 1]);\n * const blockShape = [2, 2];\n * const paddings = [[0, 0], [0, 0]];\n *\n * x.spaceToBatchND(blockShape, paddings).print();\n * ```\n *\n * @param x A `tf.Tensor`. N-D with `x.shape` = `[batch] + spatialShape +\n * remainingShape`, where spatialShape has `M` dimensions.\n * @param blockShape A 1-D array. Must have shape `[M]`, all values must\n * be >= 1.\n * @param paddings A 2-D array. Must have shape `[M, 2]`, all values must be >=\n *     0. `paddings[i] = [padStart, padEnd]` specifies the amount to zero-pad\n * from input dimension `i + 1`, which corresponds to spatial dimension `i`. It\n * is required that\n * `(inputShape[i + 1] + padStart + padEnd) % blockShape[i] === 0`\n *\n * This operation is equivalent to the following steps:\n *\n * 1. Zero-pad the start and end of dimensions `[1, ..., M]` of the input\n * according to `paddings` to produce `padded` of shape paddedShape.\n *\n * 2. Reshape `padded` to `reshapedPadded` of shape:\n * `[batch] + [paddedShape[1] / blockShape[0], blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1], blockShape[M-1]] + remainingShape`\n *\n * 3. Permute dimensions of `reshapedPadded` to produce `permutedReshapedPadded`\n * of shape: `blockShape + [batch] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n *\n * 4. Reshape `permutedReshapedPadded` to flatten `blockShape` into the\n * batch dimension, producing an output tensor of shape:\n * `[batch * prod(blockShape)] + [paddedShape[1] / blockShape[0], ...,\n * paddedShape[M] / blockShape[M-1]] + remainingShape`\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction spaceToBatchND_<T extends Tensor>(\n    x: T|TensorLike, blockShape: number[], paddings: number[][]): T {\n  const $x = convertToTensor(x, 'x', 'spaceToBatchND');\n\n  util.assert(\n      $x.rank >= 1 + blockShape.length,\n      `input rank ${$x.rank} should be > than [blockShape] ${\n          blockShape.length}`);\n\n  util.assert(\n      paddings.length === blockShape.length,\n      `paddings.shape[0] ${paddings.length} must be equal to [blockShape] ${\n          blockShape.length}`);\n\n  util.assert(\n      $x.shape.reduce(\n          (a, b, i) => {\n            if (i > 0 && i <= blockShape.length) {\n              return a &&\n                  ((b + paddings[i - 1][0] + paddings[i - 1][1]) %\n                       blockShape[i - 1] ===\n                   0);\n            }\n            return a;\n          },\n          true),\n      `input spatial dimensions ${$x.shape.slice(1)} with paddings ${\n          paddings.toString()} must be divisible by blockShapes ${\n          blockShape.toString()}`);\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.batchToSpaceND(blockShape, paddings)};\n  };\n\n  return ENV.engine.runKernel(\n      backend => backend.spaceToBatchND($x, blockShape, paddings), {$x}, grad);\n}\n\n/**\n * Unstacks a `tf.Tensor` of rank-`R` into a list of rank-`(R-1)` `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * tf.unstack(a).forEach(tensor => tensor.print());\n * ```\n *\n * @param x A tensor object.\n * @param axis The axis to unstack along. Defaults to 0 (the first dim).\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction unstack_<T extends Tensor>(x: T|TensorLike, axis = 0): Tensor[] {\n  const $x = convertToTensor(x, 'x', 'unstack');\n  const num = $x.shape[axis];\n  const outputShape: number[] = Array($x.rank - 1).fill(0);\n  let outIndex = 0;\n  for (let i = 0; i < $x.rank; i++) {\n    if (i !== axis) {\n      outputShape[outIndex] = $x.shape[i];\n      outIndex++;\n    }\n  }\n\n  let splitSizes: number[];\n  splitSizes = Array(num).fill(1);\n  const begin = Array($x.rank).fill(0);\n  const size = $x.shape.slice();\n  return splitSizes.map(s => {\n    size[axis] = s;\n    const slice = $x.slice(begin, size);\n    begin[axis] += s;\n    return slice.reshape(outputShape);\n  });\n}\n\n/**\n * Computes the cumulative sum of a `tf.Tensor` along `axis`.\n *\n * ```js\n * const x = tf.tensor([1, 2, 3, 4]);\n * x.cumsum().print();\n * ```\n * ```js\n * const x = tf.tensor([[1, 2], [3, 4]]);\n * x.cumsum().print();\n * ```\n *\n * @param x The input tensor to be summed.\n * @param axis The axis along which to sum. Optional. Defaults to 0.\n * @param exclusive Whether to perform exclusive cumulative sum. Optional.\n *     Defaults to false. If set to true then the sum of each tensor entry\n *     does not include its own value, but only the values previous to it\n *     along the specified axis.\n * @param reverse Whether to sum in the opposite direction. Optional.\n *     Defaults to false.\n */\n/** @doc {heading: 'Operations', subheading: 'Scan'} */\nfunction cumsum_<T extends Tensor>(\n    x: Tensor|TensorLike, axis = 0, exclusive = false, reverse = false): T {\n  const $x = convertToTensor(x, 'x', 'cumsum');\n\n  axis = axis | 0;\n  const permutation = getAxesPermutation([axis], $x.rank);\n  let permutedX = $x;\n  if (permutation != null) {\n    permutedX = $x.transpose(permutation);\n  }\n  const permutedAxis = getInnerMostAxes(1, $x.rank)[0];\n\n  const grad = (dy: T) => {\n    return {permutedX: () => dy.cumsum(axis, exclusive, !reverse)};\n  };\n  let value = ENV.engine.runKernel(\n                  backend => backend.cumsum(\n                      permutedX, permutedAxis, exclusive, reverse),\n                  {permutedX}, grad) as T;\n\n  if (permutation != null) {\n    value = value.transpose(permutation);\n  }\n  return value;\n}\n\n/**\n * Returns a `tf.Tensor` that has expanded rank, by inserting a dimension\n * into the tensor's shape.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const axis = 1;\n * x.expandDims(axis).print();\n * ```\n *\n * @param x The input tensor whose dimensions to be expanded.\n * @param axis The dimension index at which to insert shape of `1`. Defaults\n *     to 0 (the first dimension).\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction expandDims_<R2 extends Rank>(\n    x: Tensor|TensorLike, axis = 0): Tensor<R2> {\n  const $x = convertToTensor(x, 'x', 'expandDims');\n\n  util.assert(axis <= $x.rank, 'Axis must be <= rank of the tensor');\n  const newShape = $x.shape.slice();\n  if (axis < 0) {\n    // Negative value is counted from the tail of rank.\n    util.assert(\n        -($x.rank + 1) <= axis,\n        `Axis must be in the interval [${- ($x.rank + 1)}, ${$x.rank}]`);\n    axis = $x.rank + axis + 1;\n  }\n  newShape.splice(axis, 0, 1);\n  return reshape($x, newShape);\n}\n\n/**\n * Rearranges data from depth into blocks of spatial data. More specifically,\n * this op outputs a copy of the input tensor where values from the `depth`\n * dimension are moved in spatial blocks to the `height` and `width` dimensions.\n * The attr `blockSize` indicates the input block size and how the data is\n * moved.\n *\n *  - Chunks of data of size `blockSize * blockSize` from depth are rearranged\n * into non-overlapping blocks of size `blockSize x blockSize`\n *\n *  - The width the output tensor is `inputWidth * blockSize`, whereas the\n * height is `inputHeight * blockSize`\n *\n *  - The Y, X coordinates within each block of the output image are determined\n * by the high order component of the input channel index\n *\n *  - The depth of the input tensor must be divisible by `blockSize *\n * blockSize`\n *\n * The `dataFormat` attr specifies the layout of the input and output tensors\n * with the following options: \"NHWC\": [ `batch, height, width, channels` ]\n * \"NCHW\": [ `batch, channels, height, width` ]\n *\n * ```js\n * const x = tf.tensor4d([1, 2, 3, 4], [1, 1, 1, 4]);\n * const blockSize = 2;\n * const dataFormat = \"NHWC\";\n *\n * tf.depthToSpace(x, blockSize, dataFormat).print();\n * ```\n *\n * @param x The input tensor of rank 4\n * @param blockSIze  An `int` that is `>= 2`. The size of the spatial block\n * @param dataFormat An optional string from: \"NHWC\", \"NCHW\". Defaults to \"NHWC\"\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nfunction depthToSpace_(\n    x: Tensor4D|TensorLike4D, blockSize: number,\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC'): Tensor4D {\n  const $x = convertToTensor(x, 'x', 'depthToSpace') as Tensor4D;\n\n  const inputHeight = (dataFormat === 'NHWC') ? $x.shape[1] : $x.shape[2];\n  const inputWidth = (dataFormat === 'NHWC') ? $x.shape[2] : $x.shape[3];\n  const inputDepth = (dataFormat === 'NHWC') ? $x.shape[3] : $x.shape[1];\n\n  util.assert(\n      inputHeight * blockSize >= 0,\n      `Negative dimension size caused by overflow when multiplying\n      ${inputHeight} and ${blockSize}  for depthToSpace with input shape\n      ${$x.shape}`);\n\n  util.assert(\n      inputWidth * blockSize >= 0,\n      `Negative dimension size caused by overflow when multiplying\n      ${inputWidth} and ${blockSize} for depthToSpace with input shape\n          ${$x.shape}`);\n\n  util.assert(\n      (inputDepth % (blockSize * blockSize) === 0),\n      `Dimension size must be evenly divisible by ${\n          blockSize * blockSize} but is ${\n          inputDepth} for depthToSpace with input shape ${$x.shape}`);\n\n  return ENV.engine.runKernel(\n      backend => backend.depthToSpace($x, blockSize, dataFormat), {$x});\n}\n\n/**\n * Computes the difference between two lists of numbers.\n *\n * Given a Tensor `x` and a Tensor `y`, this operation returns a Tensor `out`\n * that represents all values that are in `x` but not in `y`. The returned\n * Tensor `out` is sorted in the same order that the numbers appear in `x`\n * (duplicates are preserved). This operation also returns a Tensor indices that\n * represents the position of each out element in `x`. In other words:\n *\n * `out[i] = x[idx[i]] for i in [0, 1, ..., out.length - 1]`\n *\n * ```js\n * const x = [1, 2, 3, 4, 5, 6];\n * const y = [1, 3, 5];\n *\n * const [out, indices] = await tf.setdiff1dAsync(x, y);\n * out.print(); // [2, 4, 6]\n * indices.print(); // [1, 3, 5]\n * ```\n *\n * @param x 1-D Tensor. Values to keep.\n * @param y 1-D Tensor. Must have the same type as x. Values to exclude in the\n *     output.\n * @returns Promise of Tensor tuple [out, indices].\n *  out: Tensor with the same type as x.\n *  indices: A Tensor of type int32.\n */\n/** @doc {heading: 'Tensors', subheading: 'Transformations'} */\nasync function setdiff1dAsync_(\n    x: Tensor|TensorLike, y: Tensor|TensorLike): Promise<[Tensor, Tensor]> {\n  const $x = convertToTensor(x, 'x', 'setdiff1d');\n  const $y = convertToTensor(y, 'y', 'setdiff1d');\n\n  util.assert(\n      $x.dtype === $y.dtype,\n      `x and y should have the same dtype, but got x (${$x.dtype}) and y (${\n          $y.dtype}).`);\n\n  util.assert($x.rank === 1, `x should be 1D tensor, but got x (${$x.shape}).`);\n\n  util.assert($y.rank === 1, `y should be 1D tensor, but got y (${$y.shape}).`);\n\n  const xVals = await $x.data();\n  const yVals = await $y.data();\n  const ySet = new Set(yVals);\n\n  let outputSize = 0;\n  for (let i = 0; i < xVals.length; i++) {\n    if (!ySet.has(xVals[i])) {\n      outputSize++;\n    }\n  }\n\n  const buffer = new TensorBuffer([outputSize], $x.dtype);\n  const indices = new TensorBuffer([outputSize], 'int32');\n  for (let i = 0, p = 0; i < xVals.length; i++) {\n    if (!ySet.has(xVals[i])) {\n      buffer.values[p] = xVals[i];\n      indices.values[p] = i;\n      p++;\n    }\n  }\n  return [buffer.toTensor(), indices.toTensor()];\n}\n\n/**\n * Creates an empty `tf.TensorBuffer` with the specified `shape` and `dtype`.\n *\n * The values are stored in CPU as `TypedArray`. Fill the buffer using\n * `buffer.set()`, or by modifying directly `buffer.values`.\n *\n * When done, call `buffer.toTensor()` to get an immutable `tf.Tensor` with\n * those values.\n *\n * ```js\n * // Create a buffer and set values at particular indices.\n * const buffer = tf.buffer([2, 2]);\n * buffer.set(3, 0, 0);\n * buffer.set(5, 1, 0);\n *\n * // Convert the buffer back to a tensor.\n * buffer.toTensor().print();\n * ```\n *\n * @param shape An array of integers defining the output tensor shape.\n * @param dtype The dtype of the buffer. Defaults to 'float32'.\n * @param values The values of the buffer as `TypedArray`. Defaults to\n * zeros.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction buffer<R extends Rank, D extends DataType = 'float32'>(\n    shape: ShapeMap[R], dtype: D = 'float32' as D,\n    values?: DataTypeMap[D]): TensorBuffer<R, D> {\n  dtype = dtype || 'float32' as D;\n  return new TensorBuffer<R, D>(shape, dtype, values);\n}\n\n/**\n * Prints information about the `tf.Tensor` including its data.\n *\n * ```js\n * const verbose = true;\n * tf.tensor2d([1, 2, 3, 4], [2, 2]).print(verbose);\n * ```\n * @param x The tensor to be printed.\n * @param verbose Whether to print verbose information about the ` Tensor`,\n * including dtype and size.\n */\n/** @doc {heading: 'Tensors', subheading: 'Creation'} */\nfunction print<T extends Tensor>(x: T, verbose = false): void {\n  console.log(x.toString(verbose));\n}\n\nexport {\n  buffer,    // Not wrapped in op() since no tensors.\n  toPixels,  // Not wrapped in op() since async.\n  print      // Not wrapped in op() since no need to increase stack trace.\n};\n\nexport const batchToSpaceND = op({batchToSpaceND_});\nexport const cast = op({cast_});\nexport const clone = op({clone_});\nexport const cumsum = op({cumsum_});\nexport const depthToSpace = op({depthToSpace_});\nexport const expandDims = op({expandDims_});\nexport const eye = op({eye_});\nexport const fromPixels = op({fromPixels_});\nexport const multinomial = op({multinomial_});\nexport const oneHot = op({oneHot_});\nexport const pad = op({pad_});\nexport const pad1d = op({pad1d_});\nexport const pad2d = op({pad2d_});\nexport const pad3d = op({pad3d_});\nexport const pad4d = op({pad4d_});\nexport const rand = op({rand_});\nexport const randomNormal = op({randomNormal_});\nexport const randomUniform = op({randomUniform_});\nexport const reshape = op({reshape_});\nexport const spaceToBatchND = op({spaceToBatchND_});\nexport const squeeze = op({squeeze_});\nexport const stack = op({stack_});\nexport const tile = op({tile_});\nexport const truncatedNormal = op({truncatedNormal_});\nexport const unstack = op({unstack_});\nexport const setdiff1dAsync = setdiff1dAsync_;\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/** An implementation of the Where kernel shared between cpu and webgl */\n\nimport {buffer} from '../ops/array_ops';\nimport {Tensor2D} from '../tensor';\nimport {TypedArray} from '../types';\n\nexport function whereImpl(condShape: number[], condVals: TypedArray): Tensor2D {\n  const indices = [];\n  for (let i = 0; i < condVals.length; i++) {\n    if (condVals[i]) {\n      indices.push(i);\n    }\n  }\n\n  const inBuffer = buffer(condShape, 'int32');\n\n  const out = buffer([indices.length, condShape.length], 'int32');\n  for (let i = 0; i < indices.length; i++) {\n    const loc = inBuffer.indexToLoc(indices[i]);\n    const offset = i * condShape.length;\n    out.values.set(loc, offset);\n  }\n  return out.toTensor() as Tensor2D;\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {getWebGLContext} from '../canvas_util';\nimport {MemoryInfo, TimingInfo} from '../engine';\nimport {ENV} from '../environment';\nimport {tidy} from '../globals';\nimport {warn} from '../log';\nimport * as array_ops_util from '../ops/array_ops_util';\nimport * as axis_util from '../ops/axis_util';\nimport {computeOutShape} from '../ops/concat_util';\nimport {Conv2DInfo, Conv3DInfo} from '../ops/conv_util';\nimport * as gather_nd_util from '../ops/gather_nd_util';\nimport * as reduce_util from '../ops/reduce_util';\nimport * as scatter_nd_util from '../ops/scatter_nd_util';\nimport * as segment_util from '../ops/segment_util';\nimport {getStridedSlicedInfo} from '../ops/slice_util';\nimport {softmax} from '../ops/softmax';\nimport {range, scalar, tensor} from '../ops/tensor_ops';\nimport {DataId, Scalar, setTensorTracker, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, Tensor5D} from '../tensor';\nimport {DataType, DataTypeMap, DataValues, NumericDataType, Rank, RecursiveArray, ShapeMap, sumOutType, TypedArray, upcastType} from '../types';\nimport * as util from '../util';\nimport {getTypedArrayFromDType, sizeFromShape} from '../util';\nimport {DataMover, DataStorage, KernelBackend} from './backend';\nimport * as backend_util from './backend_util';\nimport {mergeRealAndImagArrays} from './complex_util';\nimport {nonMaxSuppressionImpl} from './non_max_suppression_impl';\nimport {split} from './split_shared';\nimport {topkImpl} from './topk_impl';\nimport {ArgMinMaxProgram} from './webgl/argminmax_gpu';\nimport {AvgPool2DBackpropProgram} from './webgl/avg_pool_backprop_gpu';\nimport {BatchNormProgram} from './webgl/batchnorm_gpu';\nimport {BatchNormPackedProgram} from './webgl/batchnorm_packed_gpu';\nimport * as binaryop_complex_gpu from './webgl/binaryop_complex_gpu';\nimport {BinaryOpComplexProgram} from './webgl/binaryop_complex_gpu';\nimport * as binaryop_gpu from './webgl/binaryop_gpu';\nimport {BinaryOpProgram} from './webgl/binaryop_gpu';\nimport {ClipProgram} from './webgl/clip_gpu';\nimport {ClipPackedProgram} from './webgl/clip_packed_gpu';\nimport {ComplexAbsProgram} from './webgl/complex_abs_gpu';\nimport {ConcatProgram} from './webgl/concat_gpu';\nimport {Conv2DDerFilterProgram, Conv2DDerInputProgram, Conv3DDerFilterProgram, Conv3DDerInputProgram} from './webgl/conv_backprop_gpu';\nimport {DepthwiseConv2DDerFilterProgram, DepthwiseConv2DDerInputProgram} from './webgl/conv_backprop_gpu_depthwise';\nimport {Conv2DProgram, Conv3DProgram} from './webgl/conv_gpu';\nimport {DepthwiseConv2DProgram} from './webgl/conv_gpu_depthwise';\nimport {DepthwiseConvPacked2DProgram} from './webgl/conv_packed_gpu_depthwise';\nimport {CropAndResizeProgram} from './webgl/crop_and_resize_gpu';\nimport {CumSumProgram} from './webgl/cumsum_gpu';\nimport {DepthToSpaceProgram} from './webgl/depth_to_space_gpu';\nimport {EncodeFloatProgram} from './webgl/encode_float_gpu';\nimport * as fft_gpu from './webgl/fft_gpu';\nimport {FFTProgram} from './webgl/fft_gpu';\nimport {FromPixelsProgram} from './webgl/from_pixels_gpu';\nimport {GatherProgram} from './webgl/gather_gpu';\nimport {GatherNDProgram} from './webgl/gather_nd_gpu';\nimport {GPGPUContext} from './webgl/gpgpu_context';\nimport * as gpgpu_math from './webgl/gpgpu_math';\nimport {GPGPUBinary, GPGPUProgram, TensorData} from './webgl/gpgpu_math';\nimport {Im2ColProgram} from './webgl/im2col_gpu';\nimport {LRNProgram} from './webgl/lrn_gpu';\nimport {LRNGradProgram} from './webgl/lrn_grad_gpu';\nimport {MaxPool2DBackpropProgram} from './webgl/max_pool_backprop_gpu';\nimport {MatMulProgram} from './webgl/mulmat_gpu';\nimport {MatMulPackedProgram} from './webgl/mulmat_packed_gpu';\nimport {MultinomialProgram} from './webgl/multinomial_gpu';\nimport {OneHotProgram} from './webgl/onehot_gpu';\nimport {PackProgram} from './webgl/pack_gpu';\nimport {PadProgram} from './webgl/pad_gpu';\nimport {Pool2DProgram} from './webgl/pool_gpu';\nimport {ReduceProgram} from './webgl/reduce_gpu';\nimport {ReshapePackedProgram} from './webgl/reshape_packed_gpu';\nimport {ResizeBilinearBackpropProgram} from './webgl/resize_bilinear_backprop_gpu';\nimport {ResizeBilinearProgram} from './webgl/resize_bilinear_gpu';\nimport {ResizeNearestNeigborBackpropProgram} from './webgl/resize_nearest_neighbor_backprop_gpu';\nimport {ResizeNearestNeighborProgram} from './webgl/resize_nearest_neighbor_gpu';\nimport {ReverseProgram} from './webgl/reverse_gpu';\nimport {ScatterProgram} from './webgl/scatter_gpu';\nimport {SegmentOpProgram} from './webgl/segment_gpu';\nimport {SelectProgram} from './webgl/select_gpu';\nimport {SliceProgram} from './webgl/slice_gpu';\nimport {StridedSliceProgram} from './webgl/strided_slice_gpu';\nimport * as tex_util from './webgl/tex_util';\nimport {TextureData, TextureUsage} from './webgl/tex_util';\nimport {TextureManager} from './webgl/texture_manager';\nimport {TileProgram} from './webgl/tile_gpu';\nimport {TransposeProgram} from './webgl/transpose_gpu';\nimport * as unary_op from './webgl/unaryop_gpu';\nimport {UnaryOpProgram} from './webgl/unaryop_gpu';\nimport {UnpackProgram} from './webgl/unpack_gpu';\nimport * as webgl_util from './webgl/webgl_util';\nimport {whereImpl} from './where_impl';\n\ntype KernelInfo = {\n  name: string; query: Promise<number>;\n};\n\nexport type TimerNode = RecursiveArray<KernelInfo>|KernelInfo;\nexport interface CPUTimerQuery {\n  startMs: number;\n  endMs?: number;\n}\n\nexport interface WebGLMemoryInfo extends MemoryInfo {\n  numBytesInGPU: number;\n  unreliable: boolean;\n}\n\nexport interface WebGLTimingInfo extends TimingInfo {\n  uploadWaitMs: number;\n  downloadWaitMs: number;\n}\n\n// Combines a dataId, a shape, and a dtype without a Tensor object so that\n// programs can be executed without a full Tensor object.\nexport interface TensorHandle {\n  dataId: DataId;\n  shape: number[];\n  dtype: DataType;\n}\n\n// Empirically determined constant used to determine size threshold for handing\n// off execution to the CPU.\nconst CPU_HANDOFF_SIZE_THRESHOLD = 10;\n// Empirically determined constant used to decide the number of bytes on GPU\n// before we start paging. The bytes are this constant * screen area * dpi.\nconst BEFORE_PAGING_CONSTANT = 300;\n// Tensors with size <= than this will be uploaded as uniforms, not textures.\nexport const SIZE_UPLOAD_UNIFORM = 4;\n// Empirically determined minimal shared dimension in matmul before we forward\n// to a.mul(b).sum() in order to take advantage of GPU parallelism. See\n// https://github.com/tensorflow/tfjs-core/pull/1379 for benchmarks.\nexport const MATMUL_SHARED_DIM_THRESHOLD = 1000;\n\nexport class MathBackendWebGL implements KernelBackend {\n  private texData: DataStorage<TextureData>;\n  // Maps data ids that have a pending read operation, to list of subscribers.\n  private pendingRead = new WeakMap<DataId, Array<(arr: TypedArray) => void>>();\n  // List of data ids that are scheduled for disposal, but are waiting on a\n  // pending read operation.\n  private pendingDisposal = new WeakSet<DataId>();\n  // List of data ids that are currently residing on gpu memory. Sorted with\n  // least recently used being first.\n  private lruDataGPU: DataId[] = [];\n  private numBytesInGPU = 0;\n  /**\n   * Number of bytes allocated on the GPU before we start moving data to cpu.\n   * Moving avoids gpu memory leaks and relies on JS's garbage collector.\n   */\n  private NUM_BYTES_BEFORE_PAGING: number;\n\n  private canvas: HTMLCanvasElement;\n  private fromPixels2DContext: CanvasRenderingContext2D;\n\n  private programTimersStack: TimerNode[];\n  private activeTimers: TimerNode[];\n  // Accumulated time spent (including blocking) in uploading data to webgl.\n  private uploadWaitMs = 0;\n  // Accumulated time spent (including blocking in downloading data from webgl.\n  private downloadWaitMs = 0;\n  private cpuBackend: KernelBackend;\n\n  register(dataId: DataId, shape: number[], dtype: DataType): void {\n    if (this.texData.has(dataId)) {\n      throw new Error('Data buffer is already registered');\n    }\n    this.texData.set(dataId, {shape, dtype});\n  }\n\n  setDataMover(dataMover: DataMover): void {\n    this.texData = new DataStorage(dataMover);\n  }\n\n  fromPixels(\n      pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement,\n      numChannels: number): Tensor3D {\n    if (pixels == null) {\n      throw new Error('pixels passed to tf.fromPixels() can not be null');\n    }\n    const texShape: [number, number] = [pixels.height, pixels.width];\n    const outShape = [pixels.height, pixels.width, numChannels];\n\n    if (!(pixels instanceof HTMLVideoElement) &&\n        !(pixels instanceof HTMLImageElement) &&\n        !(pixels instanceof HTMLCanvasElement) &&\n        !(pixels instanceof ImageData)) {\n      throw new Error(\n          'pixels passed to tf.fromPixels() must be either an ' +\n          `HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ` +\n          `ImageData, but was ${(pixels as {}).constructor.name}`);\n    }\n    if (pixels instanceof HTMLVideoElement) {\n      if (this.fromPixels2DContext == null) {\n        if (!ENV.get('IS_BROWSER')) {\n          throw new Error(\n              'Can\\'t read pixels from HTMLImageElement outside the browser.');\n        }\n        if (document.readyState !== 'complete') {\n          throw new Error(\n              'The DOM is not ready yet. Please call tf.fromPixels() ' +\n              'once the DOM is ready. One way to do that is to add an event ' +\n              'listener for `DOMContentLoaded` on the document object');\n        }\n        this.fromPixels2DContext =\n            document.createElement('canvas').getContext('2d');\n      }\n      this.fromPixels2DContext.canvas.width = pixels.width;\n      this.fromPixels2DContext.canvas.height = pixels.height;\n      this.fromPixels2DContext.drawImage(\n          pixels, 0, 0, pixels.width, pixels.height);\n      pixels = this.fromPixels2DContext.canvas;\n    }\n    const tempPixelHandle = this.makeTensorHandle(texShape, 'int32');\n    // This is a byte texture with pixels.\n    this.texData.get(tempPixelHandle.dataId).usage = TextureUsage.PIXELS;\n    this.gpgpu.uploadPixelDataToTexture(\n        this.getTexture(tempPixelHandle.dataId), pixels);\n    const program = new FromPixelsProgram(outShape);\n    const res = this.compileAndRun(program, [tempPixelHandle]);\n\n    this.disposeData(tempPixelHandle.dataId);\n\n    return res as Tensor3D;\n  }\n\n  private makeTensorHandle(shape: number[], dtype: DataType): TensorHandle {\n    const dataId = {};\n    this.register(dataId, shape, dtype);\n    return {dataId, shape, dtype};\n  }\n\n  write(dataId: DataId, values: DataValues): void {\n    if (values == null) {\n      throw new Error('MathBackendWebGL.write(): values can not be null');\n    }\n    const texData = this.texData.get(dataId);\n    const {texture, texShape, usage, dtype, isPacked} = texData;\n    if (dtype === 'complex64') {\n      throw new Error(\n          `Cannot write to a complex64 dtype. ` +\n          `Please use tf.complex(real, imag).`);\n    }\n\n    if (texture != null) {\n      // Release the old texture.\n      this.releaseTexture(dataId, texture, texShape, usage, isPacked);\n      texData.texture = null;\n      texData.texShape = null;\n    }\n    texData.usage = TextureUsage.UPLOAD;\n    texData.values = values;\n\n    if (!this.delayedStorage) {\n      this.uploadToGPU(dataId);\n    }\n  }\n  readSync(dataId: DataId): DataValues {\n    const texData = this.texData.get(dataId);\n    const {values, dtype, complexTensors} = texData;\n    if (values != null) {\n      return this.convertAndCacheOnCPU(dataId);\n    }\n    if (dtype === 'string') {\n      return values;\n    }\n    const shouldTimeProgram = this.activeTimers != null;\n    let start: number;\n    if (shouldTimeProgram) {\n      start = performance.now();\n    }\n\n    let result: Float32Array;\n    if (dtype === 'complex64') {\n      const realValues = complexTensors.real.dataSync() as Float32Array;\n      const imagValues = complexTensors.imag.dataSync() as Float32Array;\n      result = mergeRealAndImagArrays(realValues, imagValues);\n    } else {\n      result = this.getValuesFromTexture(dataId);\n    }\n\n    if (shouldTimeProgram) {\n      this.downloadWaitMs += performance.now() - start;\n    }\n    return this.convertAndCacheOnCPU(dataId, result);\n  }\n\n  async read(dataId: DataId): Promise<DataValues> {\n    if (this.pendingRead.has(dataId)) {\n      const subscribers = this.pendingRead.get(dataId);\n      return new Promise<TypedArray>(resolve => subscribers.push(resolve));\n    }\n    const texData = this.texData.get(dataId);\n    const {texture, values, texShape, isPacked, shape} = texData;\n    if (values != null) {\n      return this.convertAndCacheOnCPU(dataId);\n    }\n\n    this.pendingRead.set(dataId, []);\n\n    if (!ENV.get('WEBGL_DOWNLOAD_FLOAT_ENABLED') &&\n        ENV.get('WEBGL_VERSION') === 2) {\n      throw new Error(\n          `tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and ` +\n          `WEBGL_VERSION=2 not yet supported.`);\n    }\n\n    // Possibly copy the texture into a buffer before inserting a fence.\n    let width = texShape[1];\n    let height = texShape[0];\n    if (isPacked) {\n      [width, height] = tex_util.getPackedMatrixTextureShapeWidthHeight(\n          texShape[0], texShape[1]);\n    }\n    const bufferOrTexture =\n        this.gpgpu.maybeCreateBufferFromTexture(texture, height, width);\n\n    // Create a fence and wait for it to resolve.\n    await this.gpgpu.createAndWaitForFence();\n\n    // Download the values from the GPU.\n    let vals: Float32Array;\n    if (bufferOrTexture instanceof WebGLTexture) {\n      vals = this.getValuesFromTexture(dataId);\n    } else {\n      if (isPacked) {\n        const batch = this.getBatchDim(shape);\n        let rows = 1, cols = 1;\n        if (shape.length) {\n          [rows, cols] = this.getRowsCols(shape);\n        }\n        vals = this.gpgpu.downloadPackedMatrixFromBuffer(\n            bufferOrTexture, batch, rows, cols, texShape[0], texShape[1]);\n      } else {\n        vals = this.gpgpu.downloadFloat32MatrixFromBuffer(\n            bufferOrTexture, texShape[0], texShape[1]);\n      }\n    }\n    const dTypeVals = this.convertAndCacheOnCPU(dataId, vals);\n\n    const subscribers = this.pendingRead.get(dataId);\n    this.pendingRead.delete(dataId);\n\n    // Notify all pending reads.\n    subscribers.forEach(resolve => resolve(dTypeVals));\n    if (this.pendingDisposal.has(dataId)) {\n      this.pendingDisposal.delete(dataId);\n      this.disposeData(dataId);\n    }\n    return dTypeVals;\n  }\n\n  private getValuesFromTexture(dataId: DataId): Float32Array {\n    const {shape, dtype, texture, texShape} = this.texData.get(dataId);\n    if (ENV.get('WEBGL_DOWNLOAD_FLOAT_ENABLED')) {\n      if (this.texData.get(dataId).isPacked) {\n        const batch = this.getBatchDim(shape);\n        let rows = 1, cols = 1;\n        if (shape.length) {\n          [rows, cols] = this.getRowsCols(shape);\n        }\n        return this.gpgpu.downloadMatrixFromPackedTexture(\n            texture, batch, rows, cols, texShape[0], texShape[1]);\n      } else {\n        return this.gpgpu.downloadFloat32MatrixFromOutputTexture(\n            texture, texShape[0], texShape[1]);\n      }\n    }\n\n    const tmpTarget = this.makeTensorHandle(shape, 'float32') as TensorHandle &\n        {size: number};\n    tmpTarget.size = sizeFromShape(shape);\n    this.texData.get(tmpTarget.dataId).usage = TextureUsage.DOWNLOAD;\n    const program = new EncodeFloatProgram(shape);\n    const pageToCpu = false;\n    this.compileAndRun(\n        program, [{shape, dtype, dataId}], tmpTarget, null, pageToCpu);\n    const tmpData = this.texData.get(tmpTarget.dataId);\n    const vals = this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(\n        tmpData.texture, tmpData.texShape[0], tmpData.texShape[1]);\n    this.disposeData(tmpTarget.dataId);\n\n    return vals;\n  }\n\n  async time(f: () => void): Promise<WebGLTimingInfo> {\n    const oldActiveTimers = this.activeTimers;\n    const newActiveTimers: TimerNode[] = [];\n\n    let outerMostTime = false;\n    if (this.programTimersStack == null) {\n      this.programTimersStack = newActiveTimers;\n      outerMostTime = true;\n    } else {\n      this.activeTimers.push(newActiveTimers);\n    }\n    this.activeTimers = newActiveTimers;\n\n    f();\n\n    // needing to split these up because util.flatten only accepts certain types\n    const flattenedActiveTimerQueries =\n        util.flatten(this.activeTimers.map((d: KernelInfo) => d.query))\n            .filter(d => d != null);\n    const flattenedActiveTimerNames =\n        util.flatten(this.activeTimers.map((d: KernelInfo) => d.name))\n            .filter(d => d != null);\n\n    this.activeTimers = oldActiveTimers;\n\n    if (outerMostTime) {\n      this.programTimersStack = null;\n    }\n\n    const kernelMs = await Promise.all(flattenedActiveTimerQueries);\n\n    const res: WebGLTimingInfo = {\n      uploadWaitMs: this.uploadWaitMs,\n      downloadWaitMs: this.downloadWaitMs,\n      kernelMs: util.sum(kernelMs),\n      getExtraProfileInfo: () =>\n          kernelMs.map((d, i) => ({name: flattenedActiveTimerNames[i], ms: d}))\n              .map(d => `${d.name}: ${d.ms}`)\n              .join(', '),\n      wallMs: null  // will be filled by the engine\n    };\n    this.uploadWaitMs = 0;\n    this.downloadWaitMs = 0;\n    return res;\n  }\n  memory(): WebGLMemoryInfo {\n    return {unreliable: false, numBytesInGPU: this.numBytesInGPU} as\n        WebGLMemoryInfo;\n  }\n\n  private startTimer(): WebGLQuery|CPUTimerQuery {\n    if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {\n      return this.gpgpu.beginQuery();\n    }\n    return {startMs: performance.now(), endMs: null};\n  }\n\n  private endTimer(query: WebGLQuery|CPUTimerQuery): WebGLQuery|CPUTimerQuery {\n    if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {\n      this.gpgpu.endQuery();\n      return query;\n    }\n    (query as CPUTimerQuery).endMs = performance.now();\n    return query;\n  }\n\n  private async getQueryTime(query: WebGLQuery|CPUTimerQuery): Promise<number> {\n    if (ENV.get('WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION') > 0) {\n      return this.gpgpu.waitForQueryAndGetTime(query as WebGLQuery);\n    }\n    const timerQuery = query as CPUTimerQuery;\n    return timerQuery.endMs - timerQuery.startMs;\n  }\n\n  disposeData(dataId: DataId): void {\n    if (this.pendingDisposal.has(dataId)) {\n      return;\n    }\n    if (this.pendingRead.has(dataId)) {\n      this.pendingDisposal.add(dataId);\n      return;\n    }\n    if (this.texData.has(dataId)) {\n      const {texture, texShape, usage, complexTensors, isPacked} =\n          this.texData.get(dataId);\n      if (texture != null) {\n        this.releaseTexture(dataId, texture, texShape, usage, isPacked);\n      }\n      if (complexTensors != null) {\n        complexTensors.real.dispose();\n        complexTensors.imag.dispose();\n      }\n      this.texData.delete(dataId);\n    }\n  }\n\n  getTexture(dataId: DataId): WebGLTexture {\n    this.uploadToGPU(dataId);\n    return this.texData.get(dataId).texture;\n  }\n\n  private textureManager: TextureManager;\n  private binaryCache: {[key: string]: GPGPUBinary} = {};\n  private gpgpuCreatedLocally: boolean;\n\n  constructor(private gpgpu?: GPGPUContext, private delayedStorage = true) {\n    if (ENV.get('WEBGL_VERSION') < 1) {\n      throw new Error('WebGL is not supported on this device');\n    }\n\n    if (gpgpu == null) {\n      const gl = getWebGLContext(ENV.get('WEBGL_VERSION'));\n      this.gpgpu = new GPGPUContext(gl);\n      this.canvas = gl.canvas;\n      this.gpgpuCreatedLocally = true;\n    } else {\n      this.gpgpuCreatedLocally = false;\n      this.canvas = gpgpu.gl.canvas;\n    }\n    if (ENV.get('WEBGL_PAGING_ENABLED')) {\n      // Use the device screen's resolution as a heuristic to decide on the\n      // maximum memory allocated on the GPU before starting to page.\n      this.NUM_BYTES_BEFORE_PAGING =\n          (window.screen.height * window.screen.width *\n           window.devicePixelRatio) *\n          BEFORE_PAGING_CONSTANT;\n    }\n    this.textureManager = new TextureManager(this.gpgpu);\n  }\n\n  private getCPUBackend(): KernelBackend|null {\n    if (!ENV.get('WEBGL_CPU_FORWARD')) {\n      return null;\n    }\n\n    if (this.cpuBackend == null) {\n      this.cpuBackend = ENV.findBackend('cpu');\n    }\n\n    return this.cpuBackend;\n  }\n\n  /*\n  Tests whether all the inputs to an op are small and on the CPU. This heuristic\n  determines when it would be faster to execute a kernel on the CPU. WebGL\n  kernels opt into running this check and forwarding when appropriate.\n  TODO(https://github.com/tensorflow/tfjs/issues/872): Develop a more\n  sustainable strategy for optimizing backend execution of ops.\n   */\n  private shouldExecuteOnCPU(\n      inputs: Tensor[], sizeThreshold = CPU_HANDOFF_SIZE_THRESHOLD): boolean {\n    return this.getCPUBackend() != null &&\n        inputs.every(\n            input => this.texData.get(input.dataId).texture == null &&\n                input.size < sizeThreshold);\n  }\n\n  getGPGPUContext(): GPGPUContext {\n    return this.gpgpu;\n  }\n  getCanvas(): HTMLCanvasElement {\n    return this.canvas;\n  }\n\n  complex<T extends Tensor>(real: T, imag: T): T {\n    const result = this.makeOutputArray(real.shape, 'complex64') as T;\n    const resultData = this.texData.get(result.dataId);\n    // The backend owns the reference to the underlying real and imaginary\n    // clones. These will explicitly get disposed when the complex tensor is\n    // disposed.\n    resultData.complexTensors = {\n      real: ENV.engine.keep(real.clone()),\n      imag: ENV.engine.keep(imag.clone())\n    };\n\n    return result;\n  }\n  real<T extends Tensor>(input: T): T {\n    const resultData = this.texData.get(input.dataId);\n    return resultData.complexTensors.real.clone() as T;\n  }\n  imag<T extends Tensor>(input: T): T {\n    const resultData = this.texData.get(input.dataId);\n    return resultData.complexTensors.imag.clone() as T;\n  }\n\n  slice<T extends Tensor>(x: T, begin: number[], size: number[]): T {\n    if (this.shouldExecuteOnCPU([x])) {\n      return this.cpuBackend.slice(x, begin, size);\n    }\n\n    const program = new SliceProgram(size);\n    const customSetup = program.getCustomSetupFunc(begin);\n    return this.compileAndRun(program, [x], null, customSetup);\n  }\n\n  stridedSlice<T extends Tensor>(\n      x: T, begin: number[], end: number[], strides: number[],\n      beginMask: number, endMask: number, ellipsisMask: number,\n      newAxisMask: number, shrinkAxisMask: number): T {\n    if (this.shouldExecuteOnCPU([x])) {\n      return this.cpuBackend.stridedSlice(\n          x, begin, end, strides, beginMask, endMask, ellipsisMask, newAxisMask,\n          shrinkAxisMask);\n    }\n\n    const [beginIndex, size, shrinkAxis] = getStridedSlicedInfo(\n        x.shape, begin, end, strides, beginMask, endMask, ellipsisMask,\n        newAxisMask, shrinkAxisMask);\n\n    const shape = size.filter((v, index) => shrinkAxis.indexOf(index) === -1);\n    if (shape.some(axis => axis === 0)) {\n      return tensor([], shape) as T;\n    }\n\n    const program =\n        new StridedSliceProgram(beginIndex, strides, size, shrinkAxis);\n    return this.compileAndRun(program, [x]);\n  }\n\n  reverse<T extends Tensor>(x: T, axis: number[]): T {\n    const program = new ReverseProgram(x.shape, axis);\n    return this.compileAndRun(program, [x]);\n  }\n\n  private concat2Tensors<T extends Tensor>(a: T, b: T, axis: number): T {\n    // Any concat of n-dimensional tensors across any axis can be reduced to\n    // a concatenation of two-dimensional tensors across the axis 1 by first\n    // partitioning the axes of the original tensors into those less than the\n    // axis to be concatenated and the rest. Then reshape the tensors\n    // into a two-dimensional tensor by collapsing these two sets of axes and\n    // concatenate the resulting matrices across the axis 1, finally reshaping\n    // the result to have the proper shape.\n    const outShape = computeOutShape([a.shape, b.shape], axis);\n    const a2D = a.as2D(-1, sizeFromShape(a.shape.slice(axis)));\n    const b2D = b.as2D(-1, sizeFromShape(b.shape.slice(axis)));\n    const program = new ConcatProgram(a2D.shape, b2D.shape);\n    const res = this.compileAndRun(program, [a2D, b2D]) as Tensor;\n    return res.reshape(outShape) as T;\n  }\n\n  concat(tensors: Tensor[], axis: number): Tensor {\n    if (this.shouldExecuteOnCPU(tensors)) {\n      return this.cpuBackend.concat(tensors, axis);\n    }\n\n    if (tensors.length === 1) {\n      return tensors[0];\n    }\n    let result = tensors[0];\n    for (let i = 1; i < tensors.length; ++i) {\n      result = this.concat2Tensors(result, tensors[i], axis);\n    }\n    return result;\n  }\n\n  neg<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.NEG);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  batchMatMul(\n      a: Tensor3D, b: Tensor3D, transposeA: boolean,\n      transposeB: boolean): Tensor3D {\n    const outerShapeA = transposeA ? a.shape[2] : a.shape[1];\n    const outerShapeB = transposeB ? b.shape[1] : b.shape[2];\n    const sharedDim = transposeA ? a.shape[1] : a.shape[2];\n    const [batch, , ] = a.shape;\n\n    // Since the matrices are vectors, it is faster to call mul().sum()\n    // because sum() is O(sqrt(N)) due to divide-and-conquer.\n    if ((outerShapeA === 1 || outerShapeB === 1) &&\n        sharedDim > MATMUL_SHARED_DIM_THRESHOLD) {\n      if (transposeA) {\n        a = a.transpose([0, 2, 1]);\n      }\n      if (transposeB) {\n        b = b.transpose([0, 2, 1]);\n      }\n\n      const a3D = outerShapeB === 1 ? a : a.as3D(batch, sharedDim, 1);\n      const axis = outerShapeB === 1 ? 2 : 1;\n      const b3D = outerShapeB === 1 ? b.as3D(batch, 1, sharedDim) : b;\n      return this.multiply(a3D, b3D).sum(axis, true /* keepDims */);\n    }\n\n    const dtype = upcastType(a.dtype, b.dtype);\n\n    // TODO(https://github.com/tensorflow/tfjs/issues/693): Support 3D tensors\n    if (batch === 1) {\n      const aSqueezed = a.as2D(a.shape[1], a.shape[2]);\n      const bSqueezed = b.as2D(b.shape[1], b.shape[2]);\n\n      const program = new MatMulPackedProgram(\n          aSqueezed.shape, bSqueezed.shape, [outerShapeA, outerShapeB],\n          transposeA, transposeB);\n      const output =\n          this.makePackedTensor(program.outputShape, dtype) as Tensor2D;\n      const result =\n          this.compileAndRun<Tensor2D>(program, [aSqueezed, bSqueezed], output);\n      return result.reshape([1, result.shape[0], result.shape[1]]);\n    } else {\n      const program =\n          new MatMulProgram(a.shape, b.shape, transposeA, transposeB);\n      const output =\n          this.makeOutputArray(program.outputShape, dtype) as Tensor3D;\n      return this.compileAndRun(program, [a, b], output);\n    }\n  }\n\n  multiply(a: Tensor, b: Tensor): Tensor {\n    if (a.dtype === 'complex64') {\n      const aData = this.texData.get(a.dataId);\n      const bData = this.texData.get(b.dataId);\n\n      const realProgram = new BinaryOpComplexProgram(\n          binaryop_complex_gpu.COMPLEX_MULTIPLY.REAL, a.shape, b.shape);\n      const imagProgram = new BinaryOpComplexProgram(\n          binaryop_complex_gpu.COMPLEX_MULTIPLY.IMAG, a.shape, b.shape);\n\n      const inputs = [\n        this.makeComplexComponentTensorHandle(a, aData.complexTensors.real),\n        this.makeComplexComponentTensorHandle(a, aData.complexTensors.imag),\n        this.makeComplexComponentTensorHandle(b, bData.complexTensors.real),\n        this.makeComplexComponentTensorHandle(b, bData.complexTensors.imag)\n      ];\n      const real = this.compileAndRun<Tensor>(realProgram, inputs);\n      const imag = this.compileAndRun<Tensor>(imagProgram, inputs);\n\n      const complex = this.complex(real, imag);\n      real.dispose();\n      imag.dispose();\n      return complex;\n    }\n\n    if (this.shouldExecuteOnCPU([a, b])) {\n      return this.cpuBackend.multiply(a, b);\n    }\n\n    const program = new BinaryOpProgram(binaryop_gpu.MUL, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, a.dtype) as Tensor;\n    return this.compileAndRun(program, [a, b], output) as Tensor;\n  }\n\n  batchNormalization(\n      x: Tensor4D, mean: Tensor4D|Tensor1D, variance: Tensor4D|Tensor1D,\n      varianceEpsilon: number, scale?: Tensor4D|Tensor1D,\n      offset?: Tensor4D|Tensor1D): Tensor4D {\n    const inputs = [x, mean, variance];\n\n    let offsetShape = null;\n    if (offset != null) {\n      offsetShape = offset.shape;\n      inputs.push(offset);\n    }\n\n    let scaleShape = null;\n    if (scale != null) {\n      scaleShape = scale.shape;\n      inputs.push(scale);\n    }\n\n    if (ENV.get('WEBGL_PACK_BATCHNORMALIZATION')) {\n      const batchNormPackedProgram = new BatchNormPackedProgram(\n          x.shape, mean.shape, variance.shape, offsetShape, scaleShape,\n          varianceEpsilon);\n      return this.compileAndRun<Tensor4D>(batchNormPackedProgram, inputs);\n    }\n\n    const batchNormProgram = new BatchNormProgram(\n        x.shape, mean.shape, variance.shape, offsetShape, scaleShape,\n        varianceEpsilon);\n    return this.compileAndRun(batchNormProgram, inputs);\n  }\n\n  localResponseNormalization4D(\n      x: Tensor4D, radius: number, bias: number, alpha: number,\n      beta: number): Tensor4D {\n    const program = new LRNProgram(x.shape, radius, bias, alpha, beta);\n    return this.compileAndRun(program, [x]);\n  }\n\n  LRNGrad(\n      dy: Tensor4D, inputImage: Tensor4D, outputImage: Tensor4D,\n      depthRadius: number, bias: number, alpha: number,\n      beta: number): Tensor4D {\n    const program =\n        new LRNGradProgram(inputImage.shape, depthRadius, bias, alpha, beta);\n    return this.compileAndRun(program, [inputImage, outputImage, dy]);\n  }\n\n  tile<T extends Tensor>(x: T, reps: number[]): T {\n    const program = new TileProgram(x.shape, reps);\n    return this.compileAndRun(program, [x]);\n  }\n\n  pad<T extends Tensor>(\n      x: T, paddings: Array<[number, number]>, constantValue: number): T {\n    const program = new PadProgram(x.shape, paddings, constantValue);\n    return this.compileAndRun(program, [x]);\n  }\n\n  transpose<T extends Tensor>(x: T, perm: number[]): T {\n    const program = new TransposeProgram(x.shape, perm);\n    return this.compileAndRun(program, [x]);\n  }\n\n  gather<T extends Tensor>(x: T, indices: Tensor1D, axis: number): T {\n    const program = new GatherProgram(x.shape, indices.size, axis);\n    return this.compileAndRun(program, [x, indices]);\n  }\n\n  batchToSpaceND<T extends Tensor>(\n      x: T, blockShape: number[], crops: number[][]): T {\n    util.assert(\n        x.rank <= 4,\n        'batchToSpaceND for rank > 4 with a WebGL backend not implemented yet');\n    const prod = blockShape.reduce((a, b) => a * b);\n\n    const reshaped = array_ops_util.getReshaped(x.shape, blockShape, prod);\n    const permuted =\n        array_ops_util.getPermuted(reshaped.length, blockShape.length);\n    const reshapedPermuted =\n        array_ops_util.getReshapedPermuted(x.shape, blockShape, prod);\n    const sliceBeginCoords =\n        array_ops_util.getSliceBeginCoords(crops, blockShape.length);\n    const sliceSize =\n        array_ops_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n\n    return x.reshape(reshaped)\n               .transpose(permuted)\n               .reshape(reshapedPermuted)\n               .slice(sliceBeginCoords, sliceSize) as T;\n  }\n\n  spaceToBatchND<T extends Tensor>(\n      x: T, blockShape: number[], paddings: Array<[number, number]>): T {\n    util.assert(\n        x.rank <= 4,\n        'spaceToBatchND for rank > 4 with a WebGL backend not implemented yet');\n\n    const prod = blockShape.reduce((a, b) => a * b);\n\n    const completePaddings: Array<[number, number]> = [[0, 0]];\n    completePaddings.push(...paddings);\n    for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n      completePaddings.push([0, 0]);\n    }\n\n    const paddedX = x.pad(completePaddings);\n\n    const reshapedPaddedShape =\n        array_ops_util.getReshaped(paddedX.shape, blockShape, prod, false);\n\n    const permutedReshapedPaddedPermutation = array_ops_util.getPermuted(\n        reshapedPaddedShape.length, blockShape.length, false);\n\n    const flattenShape = array_ops_util.getReshapedPermuted(\n        paddedX.shape, blockShape, prod, false);\n\n    return paddedX.reshape(reshapedPaddedShape)\n               .transpose(permutedReshapedPaddedPermutation)\n               .reshape(flattenShape) as T;\n  }\n\n  private reduce(\n      x: Tensor2D, reduceType: 'all'|'any'|'max'|'min'|'sum'|'prod',\n      dtype: DataType): Tensor2D {\n    const batchSize = x.shape[0];\n    const inSize = x.shape[1];\n    const windowSize = reduce_util.computeOptimalWindowSize(inSize);\n    const reduceInfo = {windowSize, inSize, batchSize};\n    const program = new ReduceProgram(reduceInfo, reduceType);\n    const [rows, cols] = program.outputShape;\n    const output = this.makeOutputArray<Tensor2D>([rows, cols], dtype);\n\n    this.compileAndRun(program, [x], output);\n    // No need to run another GPGPU program.\n    if (output.shape[1] === 1) {\n      return output;\n    }\n    return this.reduce(output, reduceType, dtype);\n  }\n\n  private argReduce(\n      x: Tensor2D, reduceType: 'max'|'min',\n      bestIndicesA: Tensor2D = null): Tensor2D {\n    let batchSize = x.shape[0];\n    let inSize = x.shape[1];\n    if (bestIndicesA != null) {\n      batchSize = bestIndicesA.shape[0];\n      inSize = bestIndicesA.shape[1];\n    }\n    const windowSize = reduce_util.computeOptimalWindowSize(inSize);\n    const reduceInfo = {windowSize, inSize, batchSize};\n    const program =\n        new ArgMinMaxProgram(reduceInfo, reduceType, bestIndicesA == null);\n    const [rows, cols] = program.outputShape;\n    const output = this.makeOutputArray<Tensor2D>([rows, cols], 'int32');\n    const inputs = [x];\n    if (bestIndicesA != null) {\n      inputs.push(bestIndicesA);\n    }\n    this.compileAndRun(program, inputs, output);\n    // No need to run another GPGPU program.\n    if (output.shape[1] === 1) {\n      return output;\n    }\n    return this.argReduce(x, reduceType, output);\n  }\n\n  sum(x: Tensor, axes: number[]): Tensor {\n    axis_util.assertAxesAreInnerMostDims('sum', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const inSize = util.sizeFromShape(reduceShape);\n    const a2D = x.as2D(-1, inSize);\n    const outputDType = sumOutType(x.dtype);\n    return this.reduce(a2D, 'sum', outputDType).reshape(outShape);\n  }\n\n  prod(x: Tensor, axes: number[]): Tensor {\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const inSize = util.sizeFromShape(reduceShape);\n    const a2D = x.as2D(-1, inSize);\n    const outputDType = sumOutType(x.dtype);\n    return this.reduce(a2D, 'prod', outputDType).reshape(outShape);\n  }\n\n  unsortedSegmentSum<T extends Tensor>(\n      x: T, segmentIds: Tensor1D, numSegments: number): Tensor {\n    let axis = 0;\n    const permutation = axis_util.getAxesPermutation([axis], x.rank);\n    let permutedX = x;\n    if (permutation != null) {\n      permutedX = x.transpose(permutation);\n      axis = axis_util.getInnerMostAxes(1, x.rank)[0];\n    }\n\n    const outShape =\n        segment_util.computeOutShape(permutedX.shape, axis, numSegments);\n    const inSize = util.sizeFromShape([permutedX.shape[axis]]);\n    const a2D = permutedX.as2D(-1, inSize);\n    const outputDType = sumOutType(x.dtype);\n    let result =\n        this.segOpCompute(\n                a2D, 'unsortedSegmentSum', segmentIds, outputDType, numSegments)\n            .reshape(outShape);\n    if (permutation != null) {\n      result = result.transpose(axis_util.getUndoAxesPermutation(permutation));\n    }\n    return result;\n  }\n\n  private segOpCompute(\n      x: Tensor2D, segOpType: 'unsortedSegmentSum', segmentIds: Tensor1D,\n      dtype: DataType, numSegments: number): Tensor2D {\n    const batchSize = x.shape[0];\n    const inSize = x.shape[1];\n    const windowSize =\n        segment_util.segOpComputeOptimalWindowSize(inSize, numSegments);\n    const segOpInfo = {windowSize, inSize, batchSize, numSegments};\n    const program = new SegmentOpProgram(segOpInfo, segOpType);\n    const [rows, cols] = program.outputShape;\n    const output = this.makeOutputArray<Tensor2D>([rows, cols], dtype);\n    this.compileAndRun(program, [x, segmentIds], output);\n    // No need to run another GPGPU program.\n    if (output.shape[1] === numSegments) {\n      return output;\n    }\n    segmentIds = range(0, numSegments).tile([inSize / windowSize]);\n    return this.segOpCompute(output, segOpType, segmentIds, dtype, numSegments);\n  }\n\n  argMin(x: Tensor, axis: number): Tensor {\n    const axes = [axis];\n    axis_util.assertAxesAreInnerMostDims('argMin', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const inSize = util.sizeFromShape(reduceShape);\n    const a2D = x.as2D(-1, inSize);\n    return this.argReduce(a2D, 'min').reshape(outShape);\n  }\n\n  argMax(x: Tensor, axis: number): Tensor {\n    const axes = [axis];\n    axis_util.assertAxesAreInnerMostDims('argMax', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const inSize = util.sizeFromShape(reduceShape);\n    const a2D = x.as2D(-1, inSize);\n    return this.argReduce(a2D, 'max').reshape(outShape);\n  }\n\n  cumsum(x: Tensor, axis: number, exclusive: boolean, reverse: boolean):\n      Tensor {\n    if (axis !== x.rank - 1) {\n      throw new Error(\n          `WebGL cumsum shader expects an inner-most axis=${x.rank - 1} ` +\n          `but got axis=${axis}`);\n    }\n    const program = new CumSumProgram(x.shape, exclusive, reverse);\n    return this.compileAndRun(program, [x]);\n  }\n\n  equal(a: Tensor, b: Tensor): Tensor {\n    const program = new BinaryOpProgram(binaryop_gpu.EQUAL, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, 'bool');\n    return this.compileAndRun(program, [a, b], output);\n  }\n\n  notEqual(a: Tensor, b: Tensor): Tensor {\n    const program =\n        new BinaryOpProgram(binaryop_gpu.NOT_EQUAL, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, 'bool');\n    return this.compileAndRun(program, [a, b], output);\n  }\n\n  less(a: Tensor, b: Tensor): Tensor {\n    if (this.shouldExecuteOnCPU([a, b])) {\n      return this.cpuBackend.less(a, b);\n    }\n\n    const program = new BinaryOpProgram(binaryop_gpu.LESS, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, 'bool');\n    return this.compileAndRun(program, [a, b], output);\n  }\n\n  lessEqual(a: Tensor, b: Tensor): Tensor {\n    const program =\n        new BinaryOpProgram(binaryop_gpu.LESS_EQUAL, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, 'bool');\n    return this.compileAndRun(program, [a, b], output);\n  }\n\n  greater(a: Tensor, b: Tensor): Tensor {\n    if (this.shouldExecuteOnCPU([a, b])) {\n      return this.cpuBackend.greater(a, b);\n    }\n\n    const program = new BinaryOpProgram(binaryop_gpu.GREATER, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, 'bool');\n    return this.compileAndRun(program, [a, b], output);\n  }\n\n  greaterEqual(a: Tensor, b: Tensor): Tensor {\n    const program =\n        new BinaryOpProgram(binaryop_gpu.GREATER_EQUAL, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, 'bool');\n    return this.compileAndRun(program, [a, b], output);\n  }\n\n  logicalNot<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.LOGICAL_NOT);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  logicalAnd(a: Tensor, b: Tensor): Tensor {\n    const program =\n        new BinaryOpProgram(binaryop_gpu.LOGICAL_AND, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, 'bool');\n    return this.compileAndRun(program, [a, b], output);\n  }\n\n  logicalOr(a: Tensor, b: Tensor): Tensor {\n    const program =\n        new BinaryOpProgram(binaryop_gpu.LOGICAL_OR, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, 'bool');\n    return this.compileAndRun(program, [a, b], output);\n  }\n\n  select(condition: Tensor, a: Tensor, b: Tensor): Tensor {\n    const program = new SelectProgram(condition.rank, a.shape, a.rank);\n    const output =\n        this.makeOutputArray(program.outputShape, upcastType(a.dtype, b.dtype));\n    return this.compileAndRun(program, [condition, a, b], output);\n  }\n\n  where(condition: Tensor): Tensor2D {\n    warn(\n        'tf.where() in webgl locks the UI thread. ' +\n        'Call tf.whereAsync() instead');\n    const condVals = condition.dataSync();\n    return whereImpl(condition.shape, condVals);\n  }\n\n  topk<T extends Tensor>(x: T, k: number, sorted: boolean): [T, T] {\n    const xVals = x.dataSync();\n    return topkImpl(xVals, x.shape, x.dtype as NumericDataType, k, sorted);\n  }\n\n  min(x: Tensor, axes: number[]): Tensor {\n    axis_util.assertAxesAreInnerMostDims('min', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const inSize = util.sizeFromShape(reduceShape);\n    const a2D = x.as2D(-1, inSize);\n    return this.reduce(a2D, 'min', a2D.dtype).reshape(outShape);\n  }\n\n  minimum(a: Tensor, b: Tensor): Tensor {\n    if (this.shouldExecuteOnCPU([a, b])) {\n      return this.cpuBackend.minimum(a, b);\n    }\n\n    const program = new BinaryOpProgram(binaryop_gpu.MIN, a.shape, b.shape);\n    return this.compileAndRun(program, [a, b]);\n  }\n\n  mod(a: Tensor, b: Tensor): Tensor {\n    const program = new BinaryOpProgram(binaryop_gpu.MOD, a.shape, b.shape);\n    const customSetup = program.getCustomSetupFunc();\n    return this.compileAndRun(program, [a, b], null, customSetup);\n  }\n\n  max(x: Tensor, axes: number[]): Tensor {\n    axis_util.assertAxesAreInnerMostDims('max', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const inSize = util.sizeFromShape(reduceShape);\n    const a2D = x.as2D(-1, inSize);\n    return this.reduce(a2D, 'max', a2D.dtype).reshape(outShape);\n  }\n\n  maximum(a: Tensor, b: Tensor): Tensor {\n    if (this.shouldExecuteOnCPU([a, b])) {\n      return this.cpuBackend.maximum(a, b);\n    }\n\n    const program = new BinaryOpProgram(binaryop_gpu.MAX, a.shape, b.shape);\n    return this.compileAndRun(program, [a, b]);\n  }\n\n  all(x: Tensor, axes: number[]): Tensor {\n    axis_util.assertAxesAreInnerMostDims('all', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const inSize = util.sizeFromShape(reduceShape);\n    const a2D = x.as2D(-1, inSize);\n    return this.reduce(a2D, 'all', a2D.dtype).reshape(outShape);\n  }\n\n  any(x: Tensor, axes: number[]): Tensor {\n    axis_util.assertAxesAreInnerMostDims('any', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const inSize = util.sizeFromShape(reduceShape);\n    const a2D = x.as2D(-1, inSize);\n    return this.reduce(a2D, 'any', a2D.dtype).reshape(outShape);\n  }\n\n  squaredDifference(a: Tensor, b: Tensor): Tensor {\n    const program =\n        new BinaryOpProgram(binaryop_gpu.SQUARED_DIFFERENCE, a.shape, b.shape);\n    return this.compileAndRun(program, [a, b]);\n  }\n\n  realDivide(a: Tensor, b: Tensor): Tensor {\n    const op = binaryop_gpu.DIV;\n    const outputDtype = 'float32';\n    const program = new BinaryOpProgram(op, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, outputDtype);\n    return this.compileAndRun<Tensor>(program, [a, b], output);\n  }\n\n  floorDiv(a: Tensor, b: Tensor): Tensor {\n    const op = binaryop_gpu.INT_DIV;\n    const outputDtype = 'int32';\n    const program = new BinaryOpProgram(op, a.shape, b.shape);\n    const output = this.makeOutputArray(program.outputShape, outputDtype);\n    return this.compileAndRun<Tensor>(program, [a, b], output);\n  }\n\n  add(a: Tensor, b: Tensor): Tensor {\n    if (a.dtype === 'complex64' && b.dtype === 'complex64') {\n      return this.complexSeparableBinaryOp(a, b, binaryop_gpu.ADD);\n    }\n\n    const program = new BinaryOpProgram(binaryop_gpu.ADD, a.shape, b.shape);\n    const output =\n        this.makeOutputArray(\n            program.outputShape, upcastType(a.dtype, b.dtype)) as Tensor;\n    return this.compileAndRun<Tensor>(program, [a, b], output);\n  }\n\n  /**\n   * Computes a complex binary operation that can be decomposed into a simple\n   * binary operation on both the real and imagary parts.\n   */\n  private complexSeparableBinaryOp(a: Tensor, b: Tensor, op: string): Tensor {\n    const aData = this.texData.get(a.dataId);\n    const bData = this.texData.get(b.dataId);\n\n    const [real, imag] = [\n      [aData.complexTensors.real, bData.complexTensors.real],\n      [aData.complexTensors.imag, bData.complexTensors.imag]\n    ].map(complexParts => {\n      const [aPart, bPart] = complexParts;\n\n      const program = new BinaryOpProgram(op, a.shape, b.shape);\n      const output = this.makeOutputArray(\n                         program.outputShape,\n                         upcastType(aPart.dtype, bPart.dtype)) as Tensor;\n\n      const aHandle = this.makeComplexComponentTensorHandle(a, aPart);\n      const bHandle = this.makeComplexComponentTensorHandle(b, bPart);\n\n      return this.compileAndRun<Tensor>(program, [aHandle, bHandle], output);\n    });\n\n    const complex = this.complex(real, imag);\n    real.dispose();\n    imag.dispose();\n    return complex;\n  }\n\n  // Returns a TensorHandle with the complex shape and the dataId of the\n  // underlying part. We need to do this because a reshaped complex tensor is\n  // not reflected in its parts.\n  private makeComplexComponentTensorHandle(\n      complexTensor: Tensor, complexPart: Tensor): TensorHandle {\n    return {\n      dataId: complexPart.dataId,\n      dtype: complexPart.dtype,\n      shape: complexTensor.shape\n    };\n  }\n\n  addN<T extends Tensor>(tensors: T[]): T {\n    let res = tensors[0];\n    for (let i = 1; i < tensors.length; i++) {\n      res = this.add(res, tensors[i]) as T;\n    }\n    return res;\n  }\n\n  subtract(a: Tensor, b: Tensor): Tensor {\n    if (a.dtype === 'complex64' && b.dtype === 'complex64') {\n      return this.complexSeparableBinaryOp(a, b, binaryop_gpu.SUB);\n    }\n\n    if (this.shouldExecuteOnCPU([a, b])) {\n      return this.cpuBackend.subtract(a, b);\n    }\n\n    const program = new BinaryOpProgram(binaryop_gpu.SUB, a.shape, b.shape);\n    const output =\n        this.makeOutputArray(\n            program.outputShape, upcastType(a.dtype, b.dtype)) as Tensor;\n    return this.compileAndRun<Tensor>(program, [a, b], output);\n  }\n\n  pow<T extends Tensor>(a: T, b: Tensor): T {\n    const program = new BinaryOpProgram(binaryop_gpu.POW, a.shape, b.shape);\n    const customSetup = program.getCustomSetupFunc();\n    const output = this.makeOutputArray(\n                       program.outputShape, upcastType(a.dtype, b.dtype)) as T;\n    return this.compileAndRun<T>(program, [a, b], output, customSetup);\n  }\n\n  ceil<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.CEIL);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  floor<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.FLOOR);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  sign<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.SIGN);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  round<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ROUND);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  exp<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.EXP);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  expm1<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.EXPM1);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  log<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.LOG);\n    const customSetup = program.getCustomSetupFunc();\n    return this.compileAndRun(program, [x], null, customSetup) as T;\n  }\n\n  log1p<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.LOG1P);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  sqrt<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.SQRT);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  rsqrt<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.RSQRT);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  square<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.SQUARE);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  reciprocal<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.RECIPROCAL);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  relu<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.RELU);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  elu<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ELU);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  eluDer<T extends Tensor>(dy: T, y: T): T {\n    const program =\n        new BinaryOpProgram(binaryop_gpu.ELU_DER, dy.shape, y.shape);\n    return this.compileAndRun(program, [dy, y]) as T;\n  }\n\n  selu<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.SELU);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  int<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.TO_INT);\n    const output = this.makeOutputArray(program.outputShape, 'int32');\n    return this.compileAndRun(program, [x], output) as T;\n  }\n\n  clip<T extends Tensor>(x: T, min: number, max: number): T {\n    let program;\n    if (ENV.get('WEBGL_PACK_CLIP')) {\n      program = new ClipPackedProgram(x.shape, min, max);\n    } else {\n      program = new ClipProgram(x.shape, min, max);\n    }\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  abs<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ABS);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  complexAbs<T extends Tensor>(x: T): T {\n    const xData = this.texData.get(x.dataId);\n\n    const program = new ComplexAbsProgram(x.shape);\n    const inputs = [\n      this.makeComplexComponentTensorHandle(x, xData.complexTensors.real),\n      this.makeComplexComponentTensorHandle(x, xData.complexTensors.imag),\n    ];\n\n    return this.compileAndRun<Tensor>(program, inputs) as T;\n  }\n\n  sigmoid<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.SIGMOID);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  softplus<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.SOFTPLUS);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  sin<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.SIN);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  cos<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.COS);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  tan<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.TAN);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  asin<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ASIN);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  acos<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ACOS);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  atan<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ATAN);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  atan2<T extends Tensor>(a: T, b: T): T {\n    const program = new BinaryOpProgram(binaryop_gpu.ATAN2, a.shape, b.shape);\n    return this.compileAndRun(program, [a, b]) as T;\n  }\n\n  sinh<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.SINH);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  cosh<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.COSH);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  tanh<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.TANH);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  asinh<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ASINH);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  acosh<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ACOSH);\n    const customSetup = program.getCustomSetupFunc();\n    return this.compileAndRun(program, [x], null, customSetup) as T;\n  }\n\n  atanh<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ATANH);\n    const customSetup = program.getCustomSetupFunc();\n    return this.compileAndRun(program, [x], null, customSetup) as T;\n  }\n\n  erf<T extends Tensor>(x: T): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.ERF);\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  step<T extends Tensor>(x: T, alpha: number): T {\n    const program = new UnaryOpProgram(x.shape, unary_op.STEP(alpha));\n    return this.compileAndRun(program, [x]) as T;\n  }\n\n  conv2dWithIm2Row(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    // Rearranges conv2d input so each block to be convolved over forms the\n    // column of a new matrix with shape [filterWidth * filterHeight *\n    // inChannels, outHeight * outWidth]. The filter is also rearranged so each\n    // output channel forms a row of a new matrix with shape [outChannels,\n    // filterWidth * filterHeight * inChannels]. The convolution is then\n    // computed by multiplying these matrices and reshaping the result.\n    const {\n      filterWidth,\n      filterHeight,\n      inChannels,\n      outWidth,\n      outHeight,\n    } = convInfo;\n\n    const sharedDim = filterWidth * filterHeight * inChannels;\n    const numCols = outHeight * outWidth;\n    const x2ColShape = [sharedDim, numCols];\n\n    const xSqueezed = x.squeeze([0]);\n    const w2Row = filter.reshape([sharedDim, -1]) as Tensor2D;\n\n    const im2ColProgram =\n        new Im2ColProgram(x2ColShape, xSqueezed.shape, convInfo);\n    const im2Col = this.compileAndRun<Tensor2D>(im2ColProgram, [xSqueezed]);\n\n    const matmulProgram = new MatMulPackedProgram(\n        im2Col.shape, w2Row.shape, [numCols, convInfo.outChannels], true,\n        false);\n    const product =\n        this.compileAndRun<Tensor4D>(matmulProgram, [im2Col, w2Row]);\n\n    return product.reshape([1, outHeight, outWidth, convInfo.outChannels]);\n  }\n\n  conv2d(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    if (ENV.get('WEBGL_CONV_IM2COL') && x.shape[0] === 1) {\n      return this.conv2dWithIm2Row(x, filter, convInfo);\n    }\n    const program = new Conv2DProgram(convInfo);\n    return this.compileAndRun(program, [x, filter]);\n  }\n\n  conv2dDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    const program = new Conv2DDerInputProgram(convInfo);\n    return this.compileAndRun(program, [dy, filter]);\n  }\n\n  conv2dDerFilter(x: Tensor4D, dy: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    const program = new Conv2DDerFilterProgram(convInfo);\n    return this.compileAndRun(program, [x, dy]);\n  }\n\n  depthwiseConv2D(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    let program: DepthwiseConv2DProgram|DepthwiseConvPacked2DProgram;\n    if (ENV.get('WEBGL_PACK_DEPTHWISECONV') && convInfo.dilationWidth === 1 &&\n        convInfo.dilationHeight === 1 && convInfo.padInfo.left <= 1 &&\n        convInfo.strideWidth <= 2 &&\n        convInfo.outChannels / convInfo.inChannels === 1) {\n      program = new DepthwiseConvPacked2DProgram(convInfo);\n      return this.compileAndRun(\n          program, [x, filter],\n          this.makePackedTensor(convInfo.outShape, x.dtype));\n    }\n\n    program = new DepthwiseConv2DProgram(convInfo);\n    return this.compileAndRun(program, [x, filter]);\n  }\n\n  depthwiseConv2DDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    const program = new DepthwiseConv2DDerInputProgram(convInfo);\n    return this.compileAndRun(program, [dy, filter]);\n  }\n\n  depthwiseConv2DDerFilter(x: Tensor4D, dy: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    const program = new DepthwiseConv2DDerFilterProgram(convInfo);\n    return this.compileAndRun(program, [x, dy]);\n  }\n\n  conv3d(x: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n    const program = new Conv3DProgram(convInfo);\n    return this.compileAndRun(program, [x, filter]);\n  }\n\n  conv3dDerInput(dy: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo):\n      Tensor5D {\n    const program = new Conv3DDerInputProgram(convInfo);\n    return this.compileAndRun(program, [dy, filter]);\n  }\n\n  conv3dDerFilter(x: Tensor5D, dy: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n    const program = new Conv3DDerFilterProgram(convInfo);\n    return this.compileAndRun(program, [x, dy]);\n  }\n\n  maxPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    const program = new Pool2DProgram(convInfo, 'max', false);\n    const output =\n        this.makeOutputArray(program.outputShape, x.dtype) as Tensor4D;\n    return this.compileAndRun(program, [x], output);\n  }\n\n  avgPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    const program = new Pool2DProgram(convInfo, 'avg', false);\n    const output = this.makeOutputArray(program.outputShape, 'float32');\n    return this.compileAndRun(program, [x], output) as Tensor4D;\n  }\n\n  maxPoolBackprop(dy: Tensor4D, x: Tensor4D, y: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    const getPositions = true;\n    const maxPoolPositionsProgram =\n        new Pool2DProgram(convInfo, 'max', getPositions);\n    const maxPoolPositions: Tensor4D =\n        this.compileAndRun(maxPoolPositionsProgram, [x]);\n\n    const maxPoolBackPropProgram = new MaxPool2DBackpropProgram(convInfo);\n    const output =\n        this.makeOutputArray(maxPoolBackPropProgram.outputShape, x.dtype);\n    const result = this.compileAndRun(\n        maxPoolBackPropProgram, [dy, maxPoolPositions], output);\n    maxPoolPositions.dispose();\n    return result as Tensor4D;\n  }\n\n  avgPoolBackprop(dy: Tensor4D, x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    const avgPoolBackpropProgram = new AvgPool2DBackpropProgram(convInfo);\n    const output =\n        this.makeOutputArray(avgPoolBackpropProgram.outputShape, x.dtype);\n    return this.compileAndRun(avgPoolBackpropProgram, [dy], output) as Tensor4D;\n  }\n\n  cast<T extends Tensor>(x: T, dtype: DataType): T {\n    return backend_util.castTensor(x, dtype, this);\n  }\n\n  reshape<R extends Rank>(x: Tensor, shape: ShapeMap[R]): Tensor<R> {\n    if (this.texData.get(x.dataId).isPacked &&\n        !webgl_util.isReshapeFree(x.shape, shape)) {\n      return this.packedReshape(x, shape);\n    }\n    return backend_util.reshapeTensor(x, shape);\n  }\n\n  resizeBilinear(\n      x: Tensor4D, newHeight: number, newWidth: number,\n      alignCorners: boolean): Tensor4D {\n    const program =\n        new ResizeBilinearProgram(x.shape, newHeight, newWidth, alignCorners);\n    return this.compileAndRun(program, [x]);\n  }\n\n  resizeBilinearBackprop(dy: Tensor4D, x: Tensor4D, alignCorners: boolean):\n      Tensor4D {\n    const program = new ResizeBilinearBackpropProgram(dy, x, alignCorners);\n\n    return this.compileAndRun(program, [dy]);\n  }\n\n  resizeNearestNeighbor(\n      x: Tensor4D, newHeight: number, newWidth: number,\n      alignCorners: boolean): Tensor4D {\n    const program = new ResizeNearestNeighborProgram(\n        x.shape, newHeight, newWidth, alignCorners);\n    return this.compileAndRun(program, [x]);\n  }\n\n  resizeNearestNeighborBackprop(\n      dy: Tensor4D, x: Tensor4D, alignCorners: boolean): Tensor4D {\n    const program =\n        new ResizeNearestNeigborBackpropProgram(dy, x, alignCorners);\n    return this.compileAndRun(program, [dy]);\n  }\n\n  multinomial(\n      logits: Tensor2D, normalized: boolean, numSamples: number,\n      seed: number): Tensor2D {\n    const probs = normalized ? logits : softmax(logits);\n    const batchSize = probs.shape[0];\n    const numOutcomes = probs.shape[1];\n    const program = new MultinomialProgram(batchSize, numOutcomes, numSamples);\n    const output =\n        this.makeOutputArray(program.outputShape, 'int32') as Tensor2D;\n    const customSetup = program.getCustomSetupFunc(seed);\n    return this.compileAndRun(program, [probs], output, customSetup);\n  }\n\n  oneHot(indices: Tensor1D, depth: number, onValue: number, offValue: number):\n      Tensor2D {\n    const program = new OneHotProgram(indices.size, depth, onValue, offValue);\n    return this.compileAndRun(program, [indices]);\n  }\n\n  nonMaxSuppression(\n      boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number,\n      iouThreshold: number, scoreThreshold: number): Tensor1D {\n    warn(\n        'tf.nonMaxSuppression() in webgl locks the UI thread. ' +\n        'Call tf.nonMaxSuppressionAsync() instead');\n    const boxesVals = boxes.dataSync();\n    const scoresVals = scores.dataSync();\n    return nonMaxSuppressionImpl(\n        boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n  }\n\n  cropAndResize(\n      image: Tensor4D, boxes: Tensor2D, boxIndex: Tensor1D,\n      cropSize: [number, number], method: 'bilinear'|'nearest',\n      extrapolationValue: number): Tensor4D {\n    const program = new CropAndResizeProgram(\n        image.shape, boxes.shape, cropSize, method, extrapolationValue);\n    return this.compileAndRun(program, [image, boxes, boxIndex]);\n  }\n\n  depthToSpace(x: Tensor4D, blockSize: number, dataFormat: 'NHWC'|'NCHW'):\n      Tensor4D {\n    util.assert(\n        blockSize > 1,\n        `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n\n    const batchSize = x.shape[0];\n    const inputHeight = (dataFormat === 'NHWC') ? x.shape[1] : x.shape[2];\n    const inputWidth = (dataFormat === 'NHWC') ? x.shape[2] : x.shape[3];\n    const inputDepth = (dataFormat === 'NHWC') ? x.shape[3] : x.shape[1];\n\n    const outputHeight = inputHeight * blockSize;\n    const outputWidth = inputWidth * blockSize;\n    const outputDepth = inputDepth / (blockSize * blockSize);\n\n    const outputShape = (dataFormat === 'NHWC') ?\n        [batchSize, outputHeight, outputWidth, outputDepth] :\n        [batchSize, outputDepth, outputHeight, outputWidth];\n\n    const program = new DepthToSpaceProgram(outputShape, blockSize, dataFormat);\n    return this.compileAndRun(program, [x]);\n  }\n\n  split<T extends Tensor>(x: T, sizeSplits: number[], axis: number): T[] {\n    return split(x, sizeSplits, axis);\n  }\n\n  scatterND<R extends Rank>(\n      indices: Tensor, updates: Tensor, shape: ShapeMap[R]): Tensor<R> {\n    const {sliceRank, numUpdates, sliceSize, strides, outputSize} =\n        scatter_nd_util.calculateShapes(updates, indices, shape);\n\n    const flattenShape = [outputSize / sliceSize, sliceSize];\n    const flattenIndices = indices.reshape([numUpdates, sliceRank]);\n    const flattenX = updates.reshape([numUpdates, sliceSize]);\n\n    if (outputSize === 0) {\n      return backend_util.reshapeTensor(tensor([]), shape);\n    }\n    const defaultValue = scalar(0);\n    const program = new ScatterProgram(\n        numUpdates, sliceRank, flattenIndices.rank, flattenX.rank, strides,\n        flattenShape);\n    return (this.compileAndRun(\n                program, [flattenX, flattenIndices, defaultValue]) as Tensor)\n        .reshape(shape);\n  }\n\n  sparseToDense<R extends Rank>(\n      sparseIndices: Tensor, sparseValues: Tensor, outputShape: ShapeMap[R],\n      defaultValue: Scalar): Tensor<R> {\n    const {sliceRank, numUpdates, strides, outputSize} =\n        scatter_nd_util.calculateShapes(\n            sparseValues, sparseIndices, outputShape);\n\n    const sumDupeIndices = false;\n    const program = new ScatterProgram(\n        numUpdates, sliceRank, sparseIndices.rank, sparseValues.rank, strides,\n        [outputSize, 1], sumDupeIndices);\n    return (this.compileAndRun(\n                program, [sparseValues, sparseIndices, defaultValue]) as Tensor)\n        .reshape(outputShape);\n  }\n\n  fft(x: Tensor2D): Tensor2D {\n    const inverse = false;\n    return this.fftImpl(x, inverse);\n  }\n\n  ifft(x: Tensor2D): Tensor2D {\n    const inverse = true;\n    return this.fftImpl(x, inverse);\n  }\n\n  private fftImpl(x: Tensor2D, inverse: boolean): Tensor2D {\n    const xData = this.texData.get(x.dataId);\n\n    const realProgram =\n        new FFTProgram(fft_gpu.COMPLEX_FFT.REAL, x.shape, inverse);\n    const imagProgram =\n        new FFTProgram(fft_gpu.COMPLEX_FFT.IMAG, x.shape, inverse);\n    const inputs = [\n      this.makeComplexComponentTensorHandle(x, xData.complexTensors.real),\n      this.makeComplexComponentTensorHandle(x, xData.complexTensors.imag),\n    ];\n\n    const real = this.compileAndRun<Tensor>(realProgram, inputs);\n    const imag = this.compileAndRun<Tensor>(imagProgram, inputs);\n    const complex = this.complex(real, imag).as2D(x.shape[0], x.shape[1]);\n    real.dispose();\n    imag.dispose();\n    return complex;\n  }\n\n  gatherND(x: Tensor, indices: Tensor): Tensor<Rank> {\n    const indicesShape = indices.shape;\n    const sliceRank = indicesShape[indicesShape.length - 1];\n\n    const [resultShape, numSlices, sliceSize, strides] =\n        gather_nd_util.prepareAndValidate(x, indices);\n\n    const flattenIndices = indices.reshape([numSlices, sliceRank]);\n    const flattenX = x.reshape([x.size / sliceSize, sliceSize]);\n    const program =\n        new GatherNDProgram(sliceRank, strides, [numSlices, sliceSize]);\n    return (this.compileAndRun(program, [flattenX, flattenIndices]) as Tensor)\n        .reshape(resultShape);\n  }\n\n  private makeOutputArray<T extends Tensor>(shape: number[], dtype: DataType):\n      T {\n    return Tensor.make(shape, {}, dtype) as T;\n  }\n\n  private makePackedTensor<T extends Tensor>(shape: number[], dtype: DataType):\n      T {\n    const packedTensor = Tensor.make(shape, {}, dtype);\n    this.texData.get(packedTensor.dataId).isPacked = true;\n    return packedTensor as T;\n  }\n\n  private unpackTensor<T extends Tensor>(input: T): T {\n    const program = new UnpackProgram(input.shape);\n    return this.compileAndRun(\n        program, [input], Tensor.make(program.outputShape, {}, input.dtype));\n  }\n\n  private getBatchDim(shape: number[], dimsToSkip = 2): number {\n    return util.sizeFromShape(shape.slice(0, shape.length - dimsToSkip));\n  }\n\n  private getRowsCols(shape: number[]): [number, number] {\n    if (shape.length === 0) {\n      throw Error('Cannot get rows and columns of an empty shape array.');\n    }\n\n    return [\n      shape.length > 1 ? shape[shape.length - 2] : 1, shape[shape.length - 1]\n    ];\n  }\n\n  private packedReshape<R extends Rank>(input: Tensor, afterShape: ShapeMap[R]):\n      Tensor<R> {\n    const inputAs3D = input.reshape(\n        [this.getBatchDim(input.shape), ...this.getRowsCols(input.shape)]);\n    const afterShapeAs3D =\n        [this.getBatchDim(afterShape), ...this.getRowsCols(afterShape)];\n    const program = new ReshapePackedProgram(\n        afterShapeAs3D as [number, number, number],\n        inputAs3D.shape as [number, number, number]);\n    return this.compileAndRun<Tensor<R>>(program, [inputAs3D])\n        .reshape(afterShape);\n  }\n\n  public compileAndRun<\n      K extends {dtype: DataType, size: number, dataId: {}, shape: number[]}>(\n      program: GPGPUProgram, inputs: TensorHandle[], output?: K,\n      customSetup?: (gpgpu: GPGPUContext, webGLProgram: WebGLProgram) => void,\n      pageToCpu = true): K {\n    if (output == null) {\n      if (program.usesPackedTextures) {\n        output = this.makePackedTensor(program.outputShape, inputs[0].dtype) as\n            {} as K;\n      } else {\n        output = this.makeOutputArray(program.outputShape, inputs[0].dtype) as\n            {} as K;\n      }\n    }\n    if (output.size === 0) {\n      // Short-circuit the computation since the result is empty (has 0 in its\n      // shape).\n      this.texData.get(output.dataId).values =\n          getTypedArrayFromDType(output.dtype as 'float32', 0);\n      return output;\n    }\n\n    const inputsData: TensorData[] = inputs.map(input => {\n      if (input.dtype === 'complex64') {\n        throw new Error(\n            `GPGPUProgram does not support complex64 input. For complex64 ` +\n            `dtypes, please separate the program into real and imaginary ` +\n            `parts.`);\n      }\n\n      let texData = this.texData.get(input.dataId);\n\n      if (texData.texture == null) {\n        if (!program.usesPackedTextures &&\n            util.sizeFromShape(input.shape) <=\n                ENV.get('WEBGL_SIZE_UPLOAD_UNIFORM')) {\n          // Upload small tensors that live on the CPU as uniforms, not as\n          // textures. Do this only when the environment supports 32bit floats\n          // due to problems when comparing 16bit floats with 32bit floats.\n          // TODO(https://github.com/tensorflow/tfjs/issues/821): Make it\n          // possible for packed shaders to sample from uniforms.\n          return {\n            shape: input.shape,\n            texData: null,\n            isUniform: true,\n            uniformValues: this.readSync(input.dataId) as TypedArray\n          };\n        }\n\n        // This ensures that if a packed program's inputs have not yet been\n        // uploaded to the GPU, they get uploaded as packed right off the bat.\n        if (program.usesPackedTextures) {\n          texData.isPacked = true;\n          texData.shape = input.shape;\n        }\n      } else if (!!texData.isPacked !== !!program.usesPackedTextures) {\n        let preProcessProgram: UnpackProgram|PackProgram;\n        let processedInput: Tensor;\n\n        // Explicitly specifying output tensors because compileAndRun assumes\n        // that programs that take packed inputs produce a packed output.\n        if (texData.isPacked) {\n          preProcessProgram = new UnpackProgram(input.shape);\n          processedInput = this.compileAndRun(\n              preProcessProgram, [input],\n              Tensor.make(preProcessProgram.outputShape, {}, input.dtype));\n        } else {\n          preProcessProgram = new PackProgram(input.shape);\n          processedInput = this.compileAndRun(\n              preProcessProgram, [input],\n              this.makePackedTensor(input.shape, input.dtype));\n        }\n\n        texData = this.texData.get(processedInput.dataId);\n        input = processedInput;\n      } else if (\n          texData.isPacked &&\n          !webgl_util.isReshapeFree(texData.shape, input.shape)) {\n        // This is a special, temporary case where a texture exists for a tensor\n        // but the shapes are incompatible (due to packing constraints) because\n        // the tensor did not have a chance to go through the packed reshape\n        // shader. This only happens when we reshape the *same* tensor to form\n        // *distinct* inputs to an op, e.g. dotting a vector with itself. This\n        // case will disappear once packed uploading is the default.\n\n        // Temporarily disable delayedStorage so the texture isn't removed from\n        // the original input\n        this.delayedStorage = false;\n        const inputValues = (input as Tensor).dataSync();\n        this.delayedStorage = true;\n\n        input = Tensor.make(input.shape, {values: inputValues}, input.dtype);\n        texData = this.texData.get(input.dataId);\n        texData.isPacked = true;\n      }\n\n      this.uploadToGPU(input.dataId);\n      return {shape: input.shape, texData, isUniform: false};\n    });\n\n    this.uploadToGPU(output.dataId);\n    const outputData = {\n      shape: output.shape,\n      texData: this.texData.get(output.dataId),\n      isUniform: false\n    };\n    const key = gpgpu_math.makeShaderKey(program, inputsData, outputData);\n    const binary = this.getAndSaveBinary(key, () => {\n      return gpgpu_math.compileProgram(\n          this.gpgpu, program, inputsData, outputData);\n    });\n    const shouldTimeProgram = this.activeTimers != null;\n    let query: WebGLQuery|CPUTimerQuery;\n    if (shouldTimeProgram) {\n      query = this.startTimer();\n    }\n\n    gpgpu_math.runProgram(binary, inputsData, outputData, customSetup);\n\n    if (ENV.get('WEBGL_PAGING_ENABLED') && pageToCpu &&\n        this.numBytesInGPU > this.NUM_BYTES_BEFORE_PAGING) {\n      let numBytesToPage = this.numBytesInGPU - this.NUM_BYTES_BEFORE_PAGING;\n      while (numBytesToPage > 0 && this.lruDataGPU.length > 0) {\n        const dataId = this.lruDataGPU.shift();\n        const {shape, dtype} = this.texData.get(dataId);\n        numBytesToPage -= this.computeBytes(shape, dtype);\n        this.read(dataId);\n      }\n    }\n\n    if (shouldTimeProgram) {\n      query = this.endTimer(query);\n      this.activeTimers.push(\n          {name: program.constructor.name, query: this.getQueryTime(query)});\n    }\n\n    if (!ENV.get('WEBGL_LAZILY_UNPACK') &&\n        this.texData.get(output.dataId).isPacked && !program.isPackShader) {\n      return this.unpackTensor(output as {} as Tensor) as {} as K;\n    }\n    return output;\n  }\n\n  private getAndSaveBinary(key: string, getBinary: () => GPGPUBinary):\n      GPGPUBinary {\n    if (!(key in this.binaryCache)) {\n      this.binaryCache[key] = getBinary();\n    }\n    return this.binaryCache[key];\n  }\n\n  getTextureManager(): TextureManager {\n    return this.textureManager;\n  }\n\n  private disposed = false;\n\n  dispose() {\n    if (this.disposed) {\n      return;\n    }\n    for (const key in this.binaryCache) {\n      this.gpgpu.deleteProgram(this.binaryCache[key].webGLProgram);\n    }\n    this.textureManager.dispose();\n    this.canvas.remove();\n    if (this.fromPixels2DContext != null) {\n      this.fromPixels2DContext.canvas.remove();\n    }\n    if (this.gpgpuCreatedLocally) {\n      this.gpgpu.dispose();\n    }\n    this.disposed = true;\n  }\n\n  floatPrecision(): number {\n    return tidy(() => {\n      if (this.abs(scalar(1e-8)).get() > 0) {\n        return 32;\n      }\n      return 16;\n    });\n  }\n\n  private uploadToGPU(dataId: DataId): void {\n    const texData = this.texData.get(dataId);\n    const {shape, values, texture, usage, isPacked} = texData;\n    if (texture != null) {\n      // Array is already on GPU. No-op.\n      // Touching the texture.\n      if (ENV.get('WEBGL_PAGING_ENABLED')) {\n        const index = this.lruDataGPU.indexOf(dataId);\n        if (index >= 0) {\n          this.lruDataGPU.splice(this.lruDataGPU.indexOf(dataId), 1);\n          this.lruDataGPU.push(dataId);\n        }\n      }\n      return;\n    }\n    const shouldTimeProgram = this.activeTimers != null;\n    let start: number;\n    if (shouldTimeProgram) {\n      start = performance.now();\n    }\n    const texShape =\n        webgl_util.getTextureShapeFromLogicalShape(shape, isPacked);\n    texData.texShape = texShape;\n    const newTexture = this.acquireTexture(dataId, texShape, usage, isPacked);\n    texData.texture = newTexture;\n    if (values != null) {\n      // TODO(smilkov): Propagate the original typed array to gpgpu.\n      if (isPacked) {\n        const batch = this.getBatchDim(shape);\n        let rows = 1, cols = 1;\n        if (shape.length) {\n          [rows, cols] = this.getRowsCols(shape);\n        }\n        this.gpgpu.uploadMatrixToPackedTexture(\n            newTexture, batch, rows, cols, texShape[0], texShape[1],\n            typedArrayToFloat32(values as Float32Array));\n      } else {\n        this.gpgpu.uploadMatrixToTexture(\n            newTexture, texShape[0], texShape[1],\n            typedArrayToFloat32(values as Float32Array));\n      }\n      // Once uploaded, don't store the values on cpu.\n      texData.values = null;\n      if (shouldTimeProgram) {\n        this.uploadWaitMs += performance.now() - start;\n      }\n    }\n  }\n\n  private convertAndCacheOnCPU(dataId: DataId, float32Values?: Float32Array):\n      TypedArray {\n    // In delayed storage mode, when the user reads data, we don't keep a\n    // copy on the gpu, to minimize likelihood of memory leak. We re-upload\n    // to gpu the next time a gpgpu program needs the texture.\n    const dontKeepCopyOnGPU = this.delayedStorage;\n    const texData = this.texData.get(dataId);\n    const {texture, texShape, dtype, usage, isPacked} = texData;\n    if (dontKeepCopyOnGPU && texture != null) {\n      this.releaseTexture(dataId, texture, texShape, usage, isPacked);\n      texData.texture = null;\n      texData.texShape = null;\n    }\n    texData.usage = TextureUsage.UPLOAD;\n    if (float32Values != null) {\n      texData.values = float32ToTypedArray(float32Values, dtype as 'float32');\n    }\n    return texData.values as TypedArray;\n  }\n\n  private releaseTexture(\n      dataId: DataId, texture: WebGLTexture, texShape: [number, number],\n      texType: TextureUsage, isPacked: boolean) {\n    const {shape, dtype} = this.texData.get(dataId);\n\n    if (ENV.get('WEBGL_PAGING_ENABLED')) {\n      const idx = this.lruDataGPU.indexOf(dataId);\n      if (idx >= 0) {\n        this.lruDataGPU.splice(idx, 1);\n      }\n    }\n    this.numBytesInGPU -= this.computeBytes(shape, dtype);\n    this.textureManager.releaseTexture(texture, texShape, texType, isPacked);\n  }\n\n  private acquireTexture(\n      dataId: DataId, texShape: [number, number], texType: TextureUsage,\n      isPacked: boolean): WebGLTexture {\n    const {shape, dtype} = this.texData.get(dataId);\n    if (ENV.get('WEBGL_PAGING_ENABLED')) {\n      this.lruDataGPU.push(dataId);\n    }\n    this.numBytesInGPU += this.computeBytes(shape, dtype);\n    return this.textureManager.acquireTexture(texShape, texType, isPacked);\n  }\n\n  private computeBytes(shape: number[], dtype: DataType) {\n    return util.sizeFromShape(shape) * util.bytesPerElement(dtype);\n  }\n}\n\nif (ENV.get('IS_BROWSER')) {\n  ENV.registerBackend(\n      'webgl', () => new MathBackendWebGL(), 2 /* priority */,\n      setTensorTracker);\n}\n\nfunction float32ToTypedArray<D extends NumericDataType>(\n    a: Float32Array, dtype: D): DataTypeMap[D] {\n  if (dtype === 'float32' || dtype === 'complex64') {\n    return a;\n  } else if (dtype === 'int32' || dtype === 'bool') {\n    const result = (dtype === 'int32') ? new Int32Array(a.length) :\n                                         new Uint8Array(a.length);\n    for (let i = 0; i < result.length; ++i) {\n      result[i] = Math.round(a[i]);\n    }\n    return result;\n  } else {\n    throw new Error(`Unknown dtype ${dtype}`);\n  }\n}\n\nfunction typedArrayToFloat32(a: TypedArray): Float32Array {\n  return (a instanceof Float32Array) ? a : new Float32Array(a);\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {nearestDivisor} from '../util';\nimport {PARALLELIZE_THRESHOLD} from './reduce_util';\n\nexport interface SegOpInfo {\n  windowSize: number;\n  batchSize: number;\n  inSize: number;\n  numSegments: number;\n}\n\nexport function segOpComputeOptimalWindowSize(\n    inSize: number, numSegments: number): number {\n  let done = false;\n  let res;\n\n  if (inSize <= PARALLELIZE_THRESHOLD) {\n    res = inSize;\n    done = true;\n  } else {\n    res = nearestDivisor(inSize, Math.floor(Math.sqrt(inSize)));\n  }\n\n  while (!done) {\n    if (res > numSegments || res === inSize) {\n      done = true;\n      break;\n    } else {\n      res = nearestDivisor(inSize, res + 1);\n    }\n  }\n  return res;\n}\n\nexport function computeOutShape(\n    aShape: number[], axis: number, numSegments: number): number[] {\n  const outShape = [];\n  const rank = aShape.length;\n  for (let dim = 0; dim < rank; dim++) {\n    if (dim !== axis) {\n      outShape.push(aShape[dim]);\n    } else {\n      outShape.push(numSegments);\n    }\n  }\n  return outShape;\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {op} from './operation';\nimport {scalar, zerosLike} from './tensor_ops';\n\n/**\n * Computes `-1 * x` element-wise.\n *\n * ```js\n * const x = tf.tensor2d([1, 2, -2, 0], [2, 2]);\n *\n * x.neg().print();  // or tf.neg(x)\n * ```\n *\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction neg_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'neg');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.neg()};\n  };\n  return ENV.engine.runKernel(backend => backend.neg($x), {$x}, grad);\n}\n\n/**\n * Computes ceiling of input `tf.Tensor` element-wise: `ceil(x)`\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.ceil().print();  // or tf.ceil(x)\n * ```\n * @param x The input Tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction ceil_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'ceil');\n\n  // TODO(manrajgrover): Return null for gradients when backprop supports it.\n  const grad = (dy: T) => {\n    return {$x: () => zerosLike(dy)};\n  };\n  return ENV.engine.runKernel(backend => backend.ceil($x), {$x}, grad);\n}\n\n/**\n * Computes floor of input `tf.Tensor` element-wise: `floor(x)`.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.floor().print();  // or tf.floor(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction floor_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'floor');\n\n  // TODO(nsthorat): Let gradients be null for cases where we want to stop\n  // backpropgation.\n  const grad = (dy: T) => {\n    return {$x: () => zerosLike(dy)};\n  };\n  return ENV.engine.runKernel(backend => backend.floor($x), {$x}, grad);\n}\n\n/**\n * Returns an element-wise indication of the sign of a number.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3, NaN, 0]);\n *\n * x.sign().print();  // or tf.sign(x)\n * ```\n * @param x The input Tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction sign_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'sign');\n\n  const grad = (dy: T) => {\n    return {$x: () => zerosLike(dy)};\n  };\n  return ENV.engine.runKernel(backend => backend.sign($x), {$x}, grad);\n}\n\n/**\n * Computes round of input `tf.Tensor` element-wise: `round(x)`.\n * It implements banker's rounding.\n *\n * ```js\n * const x = tf.tensor1d([.6, 1.1, -3.3]);\n *\n * x.round().print();  // or tf.round(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction round_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'round');\n\n  // TODO(nsthorat): Let gradients be null for cases where we want to stop\n  // backpropgation.\n  const grad = (dy: T) => {\n    return {$x: () => zerosLike(dy)};\n  };\n  return ENV.engine.runKernel(backend => backend.round($x), {$x}, grad);\n}\n\n/**\n * Computes exponential of the input `tf.Tensor` element-wise. `e ^ x`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.exp().print();  // or tf.exp(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction exp_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'exp');\n\n  const bck = (dy: T, saved: Tensor[]) => {\n    const [y] = saved;\n    return {$x: () => dy.mulStrict(y as T)};\n  };\n  return ENV.engine.runKernel(\n      (backend, save) => save(backend.exp($x)), {$x}, bck);\n}\n\n/**\n * Computes exponential of the input `tf.Tensor` minus one element-wise.\n * `e ^ x - 1`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, -3]);\n *\n * x.expm1().print();  // or tf.expm1(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction expm1_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'expm1');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.mulStrict($x.exp())};\n  };\n  return ENV.engine.runKernel(backend => backend.expm1($x), {$x}, grad);\n}\n\n/**\n * Computes natural logarithm of the input `tf.Tensor` element-wise: `ln(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E]);\n *\n * x.log().print();  // or tf.log(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction log_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'log');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.divStrict($x.toFloat())};\n  };\n  return ENV.engine.runKernel(backend => backend.log($x), {$x}, grad);\n}\n\n/**\n * Computes natural logarithm of the input `tf.Tensor` plus one\n * element-wise: `ln(1 + x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.E - 1]);\n *\n * x.log1p().print();  // or tf.log1p(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction log1p_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'log1p');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.div($x.add(1)) as T};\n  };\n  return ENV.engine.runKernel(backend => backend.log1p($x), {$x}, grad);\n}\n\n/**\n * Computes square root of the input `tf.Tensor` element-wise: `y = sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.sqrt().print();  // or tf.sqrt(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction sqrt_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'sqrt');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.div($x.toFloat().sqrt().mul(2)) as T};\n  };\n  return ENV.engine.runKernel(backend => backend.sqrt($x), {$x}, grad);\n}\n\n/**\n * Computes reciprocal of square root of the input `tf.Tensor` element-wise:\n * `y = 1 / sqrt(x)`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 4, -1]);\n *\n * x.rsqrt().print();  // or tf.rsqrt(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction rsqrt_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'rsqrt');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.div($x.pow(1.5).mul(2)).neg() as T};\n  };\n  return ENV.engine.runKernel(backend => backend.rsqrt($x), {$x}, grad);\n}\n\n/**\n * Computes square of `x` element-wise: `x ^ 2`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, Math.sqrt(2), -1]);\n *\n * x.square().print();  // or tf.square(x)\n * ```\n * @param x The input Tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction square_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'square');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.mul($x.toFloat().mul(2)) as T};\n  };\n  return ENV.engine.runKernel(backend => backend.square($x), {$x}, grad);\n}\n\n/**\n * Computes reciprocal of x element-wise: `1 / x`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, 2]);\n *\n * x.reciprocal().print();  // or tf.reciprocal(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction reciprocal_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'reciprocal');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.divStrict($x.square().neg())};\n  };\n  return ENV.engine.runKernel(backend => backend.reciprocal($x), {$x}, grad);\n}\n\n/**\n * Computes absolute value element-wise: `abs(x)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.abs().print();  // or tf.abs(x)\n * ```\n * @param x The input `tf.Tensor`.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction abs_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'abs');\n\n  if ($x.dtype === 'complex64') {\n    return ENV.engine.runKernel(backend => backend.complexAbs($x), {$x});\n  }\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.mulStrict($x.toFloat().step(-1))};\n  };\n  return ENV.engine.runKernel(backend => backend.abs($x), {$x}, grad);\n}\n\n/**\n * Clips values element-wise. `max(min(x, clipValueMax), clipValueMin)`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.clipByValue(-2, 3).print();  // or tf.clipByValue(x, -2, 3)\n * ```\n * @param x The input tensor.\n * @param clipValueMin Lower-bound of range to be clipped to.\n * @param clipValueMax Upper-bound of range to be clipped to.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction clipByValue_<T extends Tensor>(\n    x: T|TensorLike, clipValueMin: number, clipValueMax: number): T {\n  const $x = convertToTensor(x, 'x', 'clipByValue');\n  util.assert(\n      (clipValueMin <= clipValueMax),\n      `Error in clip: min (${clipValueMin}) must be ` +\n          `less than or equal to max (${clipValueMax}).`);\n\n  const grad = (dy: T) => {\n    return {\n      $x: () => dy.where(\n                    $x.greaterEqual(clipValueMin)\n                        .logicalAnd($x.lessEqual(clipValueMax)),\n                    zerosLike(dy)) as T,\n    };\n  };\n  return ENV.engine.runKernel(\n      backend => backend.clip($x, clipValueMin, clipValueMax), {$x}, grad);\n}\n\n/**\n * Computes sigmoid element-wise, `1 / (1 + exp(-x))`\n *\n * ```js\n * const x = tf.tensor1d([0, -1, 2, -3]);\n *\n * x.sigmoid().print();  // or tf.sigmoid(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction sigmoid_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'sigmoid');\n\n  const grad = (dy: T, saved: Tensor[]) => {\n    const [y] = saved;\n    return {$x: () => dy.mul(y.mul(scalar(1).sub(y))) as T};\n  };\n  return ENV.engine.runKernel(\n      (backend, save) => save(backend.sigmoid($x)), {$x}, grad);\n}\n\n/**\n * Computes log sigmoid of the input `tf.Tensor` element-wise:\n * `logSigmoid(x)`. For numerical stability, we use `-tf.softplus(-x)`.\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.logSigmoid().print();  // or tf.logSigmoid(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction logSigmoid_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'logSigmoid');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.mulStrict($x.neg().sigmoid())};\n  };\n  return ENV.engine.runKernel(\n      backend => backend.softplus($x.neg()).neg(), {$x}, grad);\n}\n\n/**\n * Computes softplus of the input `tf.Tensor` element-wise: `log(exp(x) + 1)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.softplus().print();  // or tf.softplus(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction softplus_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'softplus');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.mulStrict($x.sigmoid())};\n  };\n  return ENV.engine.runKernel(backend => backend.softplus($x), {$x}, grad);\n}\n\n/**\n * Computes sin of the input Tensor element-wise: `sin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.sin().print();  // or tf.sin(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction sin_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'sin');\n\n  const grad = (dy: T) => {\n    return {$x: () => $x.toFloat().cos().mulStrict(dy)};\n  };\n  return ENV.engine.runKernel(backend => backend.sin($x), {$x}, grad);\n}\n\n/**\n * Computes cos of the input `tf.Tensor` element-wise: `cos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.cos().print();  // or tf.cos(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction cos_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'cos');\n\n  const grad = (dy: T) => {\n    return {$x: () => $x.toFloat().sin().neg().mulStrict(dy)};\n  };\n  return ENV.engine.runKernel(backend => backend.cos($x), {$x}, grad);\n}\n\n/**\n * Computes tan of the input `tf.Tensor` element-wise, `tan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, Math.PI / 2, Math.PI * 3 / 4]);\n *\n * x.tan().print();  // or tf.tan(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction tan_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'tan');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.divStrict($x.cos().square())};\n  };\n  return ENV.engine.runKernel(backend => backend.tan($x), {$x}, grad);\n}\n\n/**\n * Computes asin of the input `tf.Tensor` element-wise: `asin(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asin().print();  // or tf.asin(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction asin_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'asin');\n\n  const grad = (dy: T) => {\n    return {\n      $x: () => dy.divStrict(scalar(1).sub($x.toFloat().square()).sqrt() as T)\n    };\n  };\n  return ENV.engine.runKernel(backend => backend.asin($x), {$x}, grad);\n}\n\n/**\n * Computes acos of the input `tf.Tensor` element-wise: `acos(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.acos().print();  // or tf.acos(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction acos_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'acos');\n\n  const grad = (dy: T) => {\n    return {\n      $x: () =>\n          dy.divStrict(scalar(1).sub($x.toFloat().square()).sqrt() as T).neg()\n    };\n  };\n  return ENV.engine.runKernel(backend => backend.acos($x), {$x}, grad);\n}\n\n/**\n * Computes atan of the input `tf.Tensor` element-wise: `atan(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.atan().print();  // or tf.atan(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction atan_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'atan');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.div($x.toFloat().square().add(1)) as T};\n  };\n  return ENV.engine.runKernel(backend => backend.atan($x), {$x}, grad);\n}\n\n/**\n * Computes hyperbolic sin of the input `tf.Tensor` element-wise: `sinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.sinh().print();  // or tf.sinh(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction sinh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'sinh');\n\n  const grad = (dy: T) => {\n    return {$x: () => $x.toFloat().cosh().mulStrict(dy)};\n  };\n  return ENV.engine.runKernel(backend => backend.sinh($x), {$x}, grad);\n}\n\n/**\n * Computes hyperbolic cos of the input `tf.Tensor` element-wise: `cosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.cosh().print();  // or tf.cosh(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction cosh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'cosh');\n\n  const grad = (dy: T) => {\n    return {$x: () => $x.toFloat().sinh().mulStrict(dy)};\n  };\n  return ENV.engine.runKernel(backend => backend.cosh($x), {$x}, grad);\n}\n\n/**\n * Computes hyperbolic tangent of the input `tf.Tensor` element-wise: `tanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, 70]);\n *\n * x.tanh().print();  // or tf.tanh(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction tanh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'tanh');\n\n  const grad = (dy: T, saved: Tensor[]) => {\n    const [y] = saved;\n    return {$x: () => scalar(1).sub(y.square()).mulStrict(dy) as T};\n  };\n  return ENV.engine.runKernel(\n      (backend, save) => save(backend.tanh($x)), {$x}, grad);\n}\n\n/**\n * Computes inverse hyperbolic sin of the input `tf.Tensor` element-wise:\n * `asinh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, 1, -1, .7]);\n *\n * x.asinh().print();  // or tf.asinh(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction asinh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'asinh');\n\n  const grad = (dy: T) => {\n    return {\n      $x: () => dy.divStrict(scalar(1).add($x.toFloat().square()).sqrt() as T)\n    };\n  };\n  return ENV.engine.runKernel(backend => backend.asinh($x), {$x}, grad);\n}\n\n/**\n * Computes the inverse hyperbolic cos of the input `tf.Tensor` element-wise:\n * `acosh(x)`\n *\n * ```js\n * const x = tf.tensor1d([10, 1, 3, 5.7]);\n *\n * x.acosh().print();  // or tf.acosh(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction acosh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'acosh');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.divStrict($x.toFloat().square().sub(1).sqrt() as T)};\n  };\n  return ENV.engine.runKernel(backend => backend.acosh($x), {$x}, grad);\n}\n\n/**\n * Computes inverse hyperbolic tan of the input `tf.Tensor` element-wise:\n * `atanh(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.atanh().print();  // or tf.atanh(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction atanh_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'atanh');\n\n  const grad = (dy: T) => {\n    return {$x: () => dy.div(scalar(1).sub($x.toFloat().square())) as T};\n  };\n  return ENV.engine.runKernel(backend => backend.atanh($x), {$x}, grad);\n}\n\n/**\n * Computes gause error function of the input `tf.Tensor` element-wise:\n * `erf(x)`\n *\n * ```js\n * const x = tf.tensor1d([0, .1, -.1, .7]);\n *\n * x.erf().print(); // or tf.erf(x);\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction erf_<T extends Tensor>(x: T|TensorLike): T {\n  let $x = convertToTensor(x, 'x', 'erf');\n  util.assert(\n      $x.dtype === 'int32' || $x.dtype === 'float32',\n      'Input dtype must be `int32` or `float32`.');\n\n  if ($x.dtype === 'int32') {\n    $x = $x.toFloat();\n  }\n\n  const grad = (dy: T) => {\n    return {\n      $x: () => dy.mul($x.square().neg().exp().mul(2 / Math.sqrt(Math.PI))) as T\n    };\n  };\n  return ENV.engine.runKernel(backend => backend.erf($x), {$x}, grad);\n}\n\n/**\n * Computes step of the input `tf.Tensor` element-wise: `x > 0 ? 1 : alpha * x`\n *\n * ```js\n * const x = tf.tensor1d([0, 2, -1, -3]);\n *\n * x.step(.5).print();  // or tf.step(x, .5)\n * ```\n * @param x The input tensor.\n * @param alpha The gradient when input is negative.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction step_<T extends Tensor>(x: T|TensorLike, alpha = 0.0): T {\n  const $x = convertToTensor(x, 'x', 'step');\n\n  // TODO(manrajgrover): Return null for gradients when backprop supports\n  // it.\n  const grad = (dy: T) => {\n    return {$x: () => zerosLike(dy)};\n  };\n  return ENV.engine.runKernel(backend => backend.step($x, alpha), {$x}, grad);\n}\n\nexport const abs = op({abs_});\nexport const acos = op({acos_});\nexport const acosh = op({acosh_});\nexport const asin = op({asin_});\nexport const asinh = op({asinh_});\nexport const atan = op({atan_});\nexport const atanh = op({atanh_});\nexport const ceil = op({ceil_});\nexport const clipByValue = op({clipByValue_});\nexport const cos = op({cos_});\nexport const cosh = op({cosh_});\nexport const erf = op({erf_});\nexport const exp = op({exp_});\nexport const expm1 = op({expm1_});\nexport const floor = op({floor_});\nexport const log = op({log_});\nexport const log1p = op({log1p_});\nexport const logSigmoid = op({logSigmoid_});\nexport const neg = op({neg_});\nexport const reciprocal = op({reciprocal_});\nexport const round = op({round_});\nexport const rsqrt = op({rsqrt_});\nexport const sigmoid = op({sigmoid_});\nexport const sign = op({sign_});\nexport const sin = op({sin_});\nexport const sinh = op({sinh_});\nexport const softplus = op({softplus_});\nexport const sqrt = op({sqrt_});\nexport const square = op({square_});\nexport const step = op({step_});\nexport const tan = op({tan_});\nexport const tanh = op({tanh_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {tile} from './array_ops';\nimport {getReductionAxes} from './broadcast_util';\nimport {op} from './operation';\nimport {scalar} from './tensor_ops';\nimport {rsqrt} from './unary_ops';\n\n/**\n * Batch normalization, strictly for 2D. For the more relaxed version, see\n * `tf.batchNormalization`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n * @param scale A scale Tensor.\n * @param offset An offset Tensor.\n */\nfunction batchNormalization2d_(\n    x: Tensor2D|TensorLike, mean: Tensor2D|Tensor1D|TensorLike,\n    variance: Tensor2D|Tensor1D|TensorLike, varianceEpsilon = .001,\n    scale?: Tensor2D|Tensor1D|TensorLike,\n    offset?: Tensor2D|Tensor1D|TensorLike): Tensor2D {\n  const $x = convertToTensor(x, 'x', 'batchNormalization');\n  const $mean = convertToTensor(mean, 'mean', 'batchNormalization');\n  const $variance = convertToTensor(variance, 'variance', 'batchNormalization');\n  let $scale: Tensor2D|Tensor1D;\n  if (scale != null) {\n    $scale = convertToTensor(scale, 'scale', 'batchNormalization');\n  }\n  let $offset: Tensor2D|Tensor1D;\n  if (offset != null) {\n    $offset = convertToTensor(offset, 'offset', 'batchNormalization');\n  }\n  util.assert(\n      $x.rank === 2,\n      `Error in batchNormalization3D: x must be rank 3 but got rank ` +\n          `${$x.rank}.`);\n  util.assert(\n      $mean.rank === 2 || $mean.rank === 1,\n      `Error in batchNormalization2D: mean must be rank 2 or rank 1 but ` +\n          `got rank ${$mean.rank}.`);\n  util.assert(\n      $variance.rank === 2 || $variance.rank === 1,\n      `Error in batchNormalization2D: variance must be rank 2 or rank 1 ` +\n          `but got rank ${$variance.rank}.`);\n  if ($scale != null) {\n    util.assert(\n        $scale.rank === 2 || $scale.rank === 1,\n        `Error in batchNormalization2D: scale must be rank 2 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n  }\n  if ($offset != null) {\n    util.assert(\n        $offset.rank === 2 || $offset.rank === 1,\n        `Error in batchNormalization2D: offset must be rank 2 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n  }\n\n  return batchNormalization(\n      $x, $mean, $variance, varianceEpsilon, $scale, $offset);\n}\n\n/**\n * Batch normalization, strictly for 3D. For the more relaxed version, see\n * `tf.batchNormalization`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n * @param scale A scale Tensor.\n * @param offset An offset Tensor.\n */\nfunction batchNormalization3d_(\n    x: Tensor3D|TensorLike, mean: Tensor3D|Tensor1D|TensorLike,\n    variance: Tensor3D|Tensor1D|TensorLike, varianceEpsilon = .001,\n    scale?: Tensor3D|Tensor1D|TensorLike,\n    offset?: Tensor3D|Tensor1D|TensorLike): Tensor3D {\n  const $x = convertToTensor(x, 'x', 'batchNormalization');\n  const $mean = convertToTensor(mean, 'mean', 'batchNormalization');\n  const $variance = convertToTensor(variance, 'variance', 'batchNormalization');\n  let $scale: Tensor3D|Tensor1D;\n  if (scale != null) {\n    $scale = convertToTensor(scale, 'scale', 'batchNormalization');\n  }\n  let $offset: Tensor3D|Tensor1D;\n  if (offset != null) {\n    $offset = convertToTensor(offset, 'offset', 'batchNormalization');\n  }\n  util.assert(\n      $x.rank === 3,\n      `Error in batchNormalization3D: x must be rank 3 but got rank ` +\n          `${$x.rank}.`);\n  util.assert(\n      $mean.rank === 3 || $mean.rank === 1,\n      `Error in batchNormalization3D: mean must be rank 3 or rank 1 but ` +\n          `got rank ${$mean.rank}.`);\n  util.assert(\n      $variance.rank === 3 || $variance.rank === 1,\n      `Error in batchNormalization3D: variance must be rank 3 or rank 1 ` +\n          `but got rank ${$variance.rank}.`);\n  if ($scale != null) {\n    util.assert(\n        $scale.rank === 3 || $scale.rank === 1,\n        `Error in batchNormalization3D: scale must be rank 3 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n  }\n  if ($offset != null) {\n    util.assert(\n        $offset.rank === 3 || $offset.rank === 1,\n        `Error in batchNormalization3D: offset must be rank 3 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n  }\n\n  return batchNormalization(\n      $x, $mean, $variance, varianceEpsilon, $scale, $offset);\n}\n\n/**\n * Batch normalization, strictly for 4D. For the more relaxed version, see\n * `tf.batchNormalization`.\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n * @param scale A scale Tensor.\n * @param offset An offset Tensor.\n */\nfunction batchNormalization4d_(\n    x: Tensor4D|TensorLike, mean: Tensor4D|Tensor1D|TensorLike,\n    variance: Tensor4D|Tensor1D|TensorLike, varianceEpsilon = .001,\n    scale?: Tensor4D|Tensor1D|TensorLike,\n    offset?: Tensor4D|Tensor1D|TensorLike): Tensor4D {\n  const $x = convertToTensor(x, 'x', 'batchNormalization');\n  const $mean = convertToTensor(mean, 'mean', 'batchNormalization');\n  const $variance = convertToTensor(variance, 'variance', 'batchNormalization');\n  let $scale: Tensor4D|Tensor1D;\n  if (scale != null) {\n    $scale = convertToTensor(scale, 'scale', 'batchNormalization');\n  }\n  let $offset: Tensor4D|Tensor1D;\n  if (offset != null) {\n    $offset = convertToTensor(offset, 'offset', 'batchNormalization');\n  }\n  util.assert(\n      $x.rank === 4,\n      `Error in batchNormalization4D: x must be rank 4 but got rank ` +\n          `${$x.rank}.`);\n  util.assert(\n      $mean.rank === 4 || $mean.rank === 1,\n      `Error in batchNormalization4D: mean must be rank 4 or rank 1 but ` +\n          `got rank ${$mean.rank}.`);\n  util.assert(\n      $variance.rank === 4 || $variance.rank === 1,\n      `Error in batchNormalization4D: variance must be rank 4 or rank 1 ` +\n          `but got rank ${$variance.rank}.`);\n  if ($scale != null) {\n    util.assert(\n        $scale.rank === 4 || $scale.rank === 1,\n        `Error in batchNormalization4D: scale must be rank 4 or rank 1 ` +\n            `but got rank ${$scale.rank}.`);\n  }\n  if ($offset != null) {\n    util.assert(\n        $offset.rank === 4 || $offset.rank === 1,\n        `Error in batchNormalization4D: offset must be rank 4 or rank 1 ` +\n            `but got rank ${$offset.rank}.`);\n  }\n  return batchNormalization(\n      $x, $mean, $variance, varianceEpsilon, $scale, $offset);\n}\n\n/**\n * Batch normalization.\n *\n * As described in\n * [http://arxiv.org/abs/1502.03167](http://arxiv.org/abs/1502.03167).\n *\n * Mean, variance, scale, and offset can be of two shapes:\n *   - The same shape as the input.\n *   - In the common case, the depth dimension is the last dimension of x, so\n *     the values would be an `tf.Tensor1D` of shape [depth].\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that parameters passed are of given rank\n *   - `tf.batchNormalization2d`\n *   - `tf.batchNormalization3d`\n *   - `tf.batchNormalization4d`\n *\n * @param x The input Tensor.\n * @param mean A mean Tensor.\n * @param variance A variance Tensor.\n * @param varianceEpsilon A small float number to avoid dividing by 0.\n * @param scale A scale Tensor.\n * @param offset An offset Tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Normalization'} */\nfunction batchNormalization_<R extends Rank>(\n    x: Tensor<R>|Tensor1D|TensorLike, mean: Tensor<R>|Tensor1D|TensorLike,\n    variance: Tensor<R>|Tensor1D|TensorLike, varianceEpsilon = .001,\n    scale?: Tensor<R>|Tensor1D|TensorLike,\n    offset?: Tensor<R>|Tensor1D|TensorLike): Tensor<R> {\n  const $x = convertToTensor(x, 'x', 'batchNormalization');\n  const $mean = convertToTensor(mean, 'mean', 'batchNormalization');\n  const $variance = convertToTensor(variance, 'variance', 'batchNormalization');\n  let $scale: Tensor<R>|Tensor1D;\n  if (scale != null) {\n    $scale = convertToTensor(scale, 'scale', 'batchNormalization');\n  }\n  let $offset: Tensor<R>|Tensor1D;\n  if (offset != null) {\n    $offset = convertToTensor(offset, 'offset', 'batchNormalization');\n  }\n\n  util.assert(\n      $mean.rank === $variance.rank,\n      'Batch normalization gradient requires mean and variance to have ' +\n          'equal ranks.');\n  util.assert(\n      $offset == null || $mean.rank === $offset.rank,\n      'Batch normalization gradient requires mean and offset to have ' +\n          'equal ranks.');\n  util.assert(\n      $scale == null || $mean.rank === $scale.rank,\n      'Batch normalization gradient requires mean and scale to have ' +\n          'equal ranks.');\n\n  let x4D: Tensor4D;\n  if ($x.rank === 0 || $x.rank === 1) {\n    x4D = $x.as4D(1, 1, 1, $x.size);\n  } else if ($x.rank === 2) {\n    x4D = $x.as4D(1, 1, $x.shape[0], $x.shape[1]);\n  } else if ($x.rank === 3) {\n    x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]) as Tensor4D;\n  } else {\n    x4D = $x as Tensor4D;\n  }\n\n  const der = (dy: Tensor) => {\n    const scaleValue = $scale == null ? scalar(1) : $scale;\n    const reductionAxes = getReductionAxes($mean.shape, x4D.shape);\n    const tileShape: number[] = [];\n    if ($mean.rank === 1) {\n      for (let i = 0; i < x4D.shape.length - 1; ++i) {\n        tileShape.push(x4D.shape[i]);\n      }\n      tileShape.push(1);\n    }\n\n    const xMinusMean = $x.sub($mean);\n    const dyTimesScaleValue = dy.mul(scaleValue);\n    const oneOverSqrtVariance = rsqrt($variance.add(scalar(varianceEpsilon)));\n    const minusHalfRCube = oneOverSqrtVariance.mul(oneOverSqrtVariance)\n                               .mul(oneOverSqrtVariance)\n                               .mul(scalar(-0.5));\n    const derX = () => {\n      if ($mean.rank === 1) {\n        return dy\n            .mul(tile(\n                oneOverSqrtVariance.as4D(1, 1, 1, $mean.shape[0]), tileShape))\n            .mul(scaleValue)\n            .reshape($x.shape);\n      } else {\n        return dy.mul(oneOverSqrtVariance).mul(scaleValue).reshape($x.shape);\n      }\n    };\n    const derMean = () => {\n      let meanDer = oneOverSqrtVariance.mul(scalar(-1)).mul(dyTimesScaleValue);\n      if ($mean.rank === 1) {\n        meanDer = meanDer.sum(reductionAxes);\n      }\n      return meanDer.reshape($mean.shape);\n    };\n    const derVariance = () => {\n      let varianceDer = minusHalfRCube.mul(xMinusMean).mul(dyTimesScaleValue);\n      if ($mean.rank === 1) {\n        varianceDer = varianceDer.sum(reductionAxes);\n      }\n      return varianceDer.reshape($mean.shape);\n    };\n    const derScale = () => {\n      const xMinusMean2TimesRsqrt = xMinusMean.mul(oneOverSqrtVariance);\n      let scaleDer = dy.mul(xMinusMean2TimesRsqrt);\n      if ($mean.rank === 1) {\n        scaleDer = scaleDer.sum(reductionAxes);\n      }\n      return scaleDer.reshape($mean.shape);\n    };\n    const derOffset = () => {\n      let offsetDer = dy;\n      if ($mean.rank === 1) {\n        offsetDer = offsetDer.sum(reductionAxes);\n      }\n      return offsetDer.reshape($mean.shape);\n    };\n    return {\n      $x: derX,\n      $mean: derMean,\n      $variance: derVariance,\n      $scale: derScale,\n      $offset: derOffset\n    };\n  };\n\n  const res = ENV.engine.runKernel(\n      backend => backend.batchNormalization(\n          x4D, batchnormReshape4D($mean), batchnormReshape4D($variance),\n          varianceEpsilon, batchnormReshape4D($scale),\n          batchnormReshape4D($offset)),\n      {$x, $mean, $variance, $scale, $offset}, der);\n  return res.reshape($x.shape);\n}\n\nfunction batchnormReshape4D(x: Tensor): Tensor4D|Tensor1D {\n  if (x == null) {\n    return null;\n  }\n  if (x.rank === 0) {\n    return x.as1D();\n  } else if (x.rank === 1) {\n    return x as Tensor1D;\n  } else if (x.rank === 2) {\n    return x.as4D(1, 1, x.shape[0], x.shape[1]);\n  } else if (x.rank === 3) {\n    return x.as4D(1, x.shape[0], x.shape[1], x.shape[2]);\n  }\n  return x as Tensor4D;\n}\n\nexport const batchNormalization2d = op({batchNormalization2d_});\nexport const batchNormalization3d = op({batchNormalization3d_});\nexport const batchNormalization4d = op({batchNormalization4d_});\nexport const batchNormalization = op({batchNormalization_});\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as util from '../util';\n\nexport type PadInfo = {\n  top: number,\n  left: number,\n  right: number,\n  bottom: number,\n  type: string\n};\n\nexport type PadInfo3D = {\n  top: number,\n  left: number,\n  right: number,\n  bottom: number,\n  front: number,\n  back: number,\n  type: string\n};\n\n/**\n * Information about the forward pass of a convolution/pooling operation.\n * It includes input and output shape, strides, filter size and padding\n * information.\n */\nexport type Conv2DInfo = {\n  batchSize: number,\n  inHeight: number,\n  inWidth: number,\n  inChannels: number,\n  outHeight: number,\n  outWidth: number,\n  outChannels: number,\n  dataFormat: 'channelsFirst'|'channelsLast',\n  strideHeight: number,\n  strideWidth: number,\n  dilationHeight: number,\n  dilationWidth: number,\n  filterHeight: number,\n  filterWidth: number,\n  effectiveFilterHeight: number,\n  effectiveFilterWidth: number,\n  padInfo: PadInfo,\n  inShape: [number, number, number, number],\n  outShape: [number, number, number, number],\n  filterShape: [number, number, number, number]\n};\n\nexport function computePool2DInfo(\n    inShape: [number, number, number, number],\n    filterSize: [number, number]|number, strides: number|[number, number],\n    dilations: number|[number, number], pad: 'same'|'valid'|number,\n    roundingMode?: 'floor'|'round'|'ceil',\n    dataFormat: 'channelsFirst'|'channelsLast' = 'channelsLast'): Conv2DInfo {\n  const [filterHeight, filterWidth] = parseTupleParam(filterSize);\n\n  let filterShape: [number, number, number, number];\n  if (dataFormat === 'channelsLast') {\n    filterShape = [filterHeight, filterWidth, inShape[3], inShape[3]];\n  } else if (dataFormat === 'channelsFirst') {\n    filterShape = [filterHeight, filterWidth, inShape[1], inShape[1]];\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n\n  return computeConv2DInfo(\n      inShape, filterShape, strides, dilations, pad, roundingMode, false,\n      dataFormat);\n}\n\n/**\n * Computes the information for a forward pass of a convolution/pooling\n * operation.\n */\nexport function computeConv2DInfo(\n    inShape: [number, number, number, number],\n    filterShape: [number, number, number, number],\n    strides: number|[number, number], dilations: number|[number, number],\n    pad: 'same'|'valid'|number, roundingMode?: 'floor'|'round'|'ceil',\n    depthwise = false,\n    dataFormat: 'channelsFirst'|'channelsLast' = 'channelsLast'): Conv2DInfo {\n  let [batchSize, inHeight, inWidth, inChannels] = [-1, -1, -1, -1];\n  if (dataFormat === 'channelsLast') {\n    [batchSize, inHeight, inWidth, inChannels] = inShape;\n  } else if (dataFormat === 'channelsFirst') {\n    [batchSize, inChannels, inHeight, inWidth] = inShape;\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n\n  const [filterHeight, filterWidth, , filterChannels] = filterShape;\n  const [strideHeight, strideWidth] = parseTupleParam(strides);\n  const [dilationHeight, dilationWidth] = parseTupleParam(dilations);\n\n  const effectiveFilterHeight =\n      getEffectiveFilterSize(filterHeight, dilationHeight);\n  const effectiveFilterWidth =\n      getEffectiveFilterSize(filterWidth, dilationWidth);\n  const {padInfo, outHeight, outWidth} = getPadAndOutInfo(\n      pad, inHeight, inWidth, strideHeight, strideWidth, effectiveFilterHeight,\n      effectiveFilterWidth, roundingMode);\n\n  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n\n  let outShape: [number, number, number, number];\n  if (dataFormat === 'channelsFirst') {\n    outShape = [batchSize, outChannels, outHeight, outWidth];\n  } else if (dataFormat === 'channelsLast') {\n    outShape = [batchSize, outHeight, outWidth, outChannels];\n  }\n\n  return {\n    batchSize,\n    dataFormat,\n    inHeight,\n    inWidth,\n    inChannels,\n    outHeight,\n    outWidth,\n    outChannels,\n    padInfo,\n    strideHeight,\n    strideWidth,\n    filterHeight,\n    filterWidth,\n    effectiveFilterHeight,\n    effectiveFilterWidth,\n    dilationHeight,\n    dilationWidth,\n    inShape,\n    outShape,\n    filterShape\n  };\n}\n\n/**\n * Information about the forward pass of a 3D convolution/pooling operation.\n * It includes input and output shape, strides, filter size and padding\n * information.\n */\nexport type Conv3DInfo = {\n  batchSize: number,\n  inDepth: number,\n  inHeight: number,\n  inWidth: number,\n  inChannels: number,\n  outDepth: number,\n  outHeight: number,\n  outWidth: number,\n  outChannels: number,\n  dataFormat: 'channelsFirst'|'channelsLast',\n  strideDepth: number,\n  strideHeight: number,\n  strideWidth: number,\n  dilationDepth: number,\n  dilationHeight: number,\n  dilationWidth: number,\n  filterDepth: number,\n  filterHeight: number,\n  filterWidth: number,\n  padInfo: PadInfo3D,\n  inShape: [number, number, number, number, number],\n  outShape: [number, number, number, number, number],\n  filterShape: [number, number, number, number, number]\n};\n\n/**\n * Computes the information for a forward pass of a 3D convolution/pooling\n * operation.\n */\nexport function computeConv3DInfo(\n    inShape: [number, number, number, number, number],\n    filterShape: [number, number, number, number, number],\n    strides: number|[number, number, number],\n    dilations: number|[number, number, number], pad: 'same'|'valid',\n    depthwise = false,\n    dataFormat: 'channelsFirst'|'channelsLast' = 'channelsLast'): Conv3DInfo {\n  let [batchSize, inDepth, inHeight, inWidth, inChannels] =\n      [-1, -1, -1, -1, -1];\n  if (dataFormat === 'channelsLast') {\n    [batchSize, inDepth, inHeight, inWidth, inChannels] = inShape;\n  } else if (dataFormat === 'channelsFirst') {\n    [batchSize, inChannels, inDepth, inHeight, inWidth] = inShape;\n  } else {\n    throw new Error(`Unknown dataFormat ${dataFormat}`);\n  }\n\n  const [filterDepth, filterHeight, filterWidth, , filterChannels] =\n      filterShape;\n  const [strideDepth, strideHeight, strideWidth] = parse3TupleParam(strides);\n  const [dilationDepth, dilationHeight, dilationWidth] =\n      parse3TupleParam(dilations);\n\n  const effectiveFilterDepth =\n      getEffectiveFilterSize(filterDepth, dilationDepth);\n  const effectiveFilterHeight =\n      getEffectiveFilterSize(filterHeight, dilationHeight);\n  const effectiveFilterWidth =\n      getEffectiveFilterSize(filterWidth, dilationWidth);\n  const {padInfo, outDepth, outHeight, outWidth} = get3DPadAndOutInfo(\n      pad, inDepth, inHeight, inWidth, strideDepth, strideHeight, strideWidth,\n      effectiveFilterDepth, effectiveFilterHeight, effectiveFilterWidth);\n\n  const outChannels = depthwise ? filterChannels * inChannels : filterChannels;\n\n  let outShape: [number, number, number, number, number];\n  if (dataFormat === 'channelsFirst') {\n    outShape = [batchSize, outChannels, outDepth, outHeight, outWidth];\n  } else if (dataFormat === 'channelsLast') {\n    outShape = [batchSize, outDepth, outHeight, outWidth, outChannels];\n  }\n\n  return {\n    batchSize,\n    dataFormat,\n    inDepth,\n    inHeight,\n    inWidth,\n    inChannels,\n    outDepth,\n    outHeight,\n    outWidth,\n    outChannels,\n    padInfo,\n    strideDepth,\n    strideHeight,\n    strideWidth,\n    filterDepth,\n    filterHeight,\n    filterWidth,\n    dilationDepth,\n    dilationHeight,\n    dilationWidth,\n    inShape,\n    outShape,\n    filterShape\n  };\n}\n\nfunction computeOutputShape3D(\n    inShape: [number, number, number], fieldSize: number, outDepth: number,\n    stride: number, zeroPad?: number,\n    roundingMode?: 'floor'|'round'|'ceil'): [number, number, number] {\n  if (zeroPad == null) {\n    zeroPad = computeDefaultPad(inShape, fieldSize, stride);\n  }\n  const inputRows = inShape[0];\n  const inputCols = inShape[1];\n\n  const outputRows = conditionalRound(\n      (inputRows - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  util.assert(\n      util.isInt(outputRows),\n      `The output # of rows (${outputRows}) must be an integer. Change the ` +\n          `stride and/or zero pad parameters`);\n\n  const outputCols = conditionalRound(\n      (inputCols - fieldSize + 2 * zeroPad) / stride + 1, roundingMode);\n  util.assert(\n      util.isInt(outputCols),\n      `The output # of columns (${outputCols}) must be an integer. Change ` +\n          `the stride and/or zero pad parameters`);\n\n  return [outputRows, outputCols, outDepth];\n}\n\nexport function computeDefaultPad(\n    inputShape: [number, number, number], fieldSize: number, stride: number,\n    dilation = 1): number {\n  const effectiveFieldSize = getEffectiveFilterSize(fieldSize, dilation);\n  return Math.floor(\n      (inputShape[0] * (stride - 1) - stride + effectiveFieldSize) / 2);\n}\n\nfunction parseTupleParam(param: number|[number, number]): [number, number] {\n  return typeof param === 'number' ? [param, param] : param;\n}\n\nfunction parse3TupleParam(param: number|[number, number, number]):\n    [number, number, number] {\n  return typeof param === 'number' ? [param, param, param] : param;\n}\n\n/* See https://www.tensorflow.org/api_docs/python/tf/nn/atrous_conv2d\n * Atrous convolution is equivalent to standard convolution with upsampled\n * filters with effective_filter_height =\n * filter_height + (filter_height - 1) * (dilation - 1)\n * and effective_filter_width =\n * filter_width + (filter_width - 1) * (dilation - 1),\n * produced by inserting dilation - 1 zeros along consecutive elements across\n * the filters' spatial dimensions.\n * When there is a dilation, this converts a filter dimension to the\n * effective filter dimension, so it can be used in a standard convolution.\n */\nfunction getEffectiveFilterSize(filterSize: number, dilation: number) {\n  if (dilation <= 1) {\n    return filterSize;\n  }\n\n  return filterSize + (filterSize - 1) * (dilation - 1);\n}\n\nfunction getPadAndOutInfo(\n    pad: 'same'|'valid'|number, inHeight: number, inWidth: number,\n    strideHeight: number, strideWidth: number, filterHeight: number,\n    filterWidth: number, roundingMode?: 'floor'|'round'|'ceil'):\n    {padInfo: PadInfo, outHeight: number, outWidth: number} {\n  let padInfo: PadInfo;\n  let outHeight: number;\n  let outWidth: number;\n\n  if (typeof pad === 'number') {\n    const padType = (pad === 0) ? 'VALID' : 'NUMBER';\n    padInfo = {top: pad, bottom: pad, left: pad, right: pad, type: padType};\n    const outShape = computeOutputShape3D(\n        [inHeight, inWidth, 1], filterHeight, 1, strideHeight, pad,\n        roundingMode);\n    outHeight = outShape[0];\n    outWidth = outShape[1];\n  } else if (pad === 'same') {\n    outHeight = Math.ceil(inHeight / strideHeight);\n    outWidth = Math.ceil(inWidth / strideWidth);\n    const padAlongHeight =\n        (outHeight - 1) * strideHeight + filterHeight - inHeight;\n    const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;\n    const top = Math.floor(padAlongHeight / 2);\n    const bottom = padAlongHeight - top;\n    const left = Math.floor(padAlongWidth / 2);\n    const right = padAlongWidth - left;\n    padInfo = {top, bottom, left, right, type: 'SAME'};\n  } else if (pad === 'valid') {\n    padInfo = {top: 0, bottom: 0, left: 0, right: 0, type: 'VALID'};\n    outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n    outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n  } else {\n    throw Error(`Unknown padding parameter: ${pad}`);\n  }\n  return {padInfo, outHeight, outWidth};\n}\n\nfunction get3DPadAndOutInfo(\n    pad: 'same'|'valid', inDepth: number, inHeight: number, inWidth: number,\n    strideDepth: number, strideHeight: number, strideWidth: number,\n    filterDepth: number, filterHeight: number, filterWidth: number): {\n  padInfo: PadInfo3D,\n  outDepth: number,\n  outHeight: number,\n  outWidth: number\n} {\n  let padInfo: PadInfo3D;\n  let outDepth: number;\n  let outHeight: number;\n  let outWidth: number;\n\n  if (pad === 'same') {\n    outDepth = Math.ceil(inDepth / strideDepth);\n    outHeight = Math.ceil(inHeight / strideHeight);\n    outWidth = Math.ceil(inWidth / strideWidth);\n    const padAlongDepth = (outDepth - 1) * strideDepth + filterDepth - inDepth;\n    const padAlongHeight =\n        (outHeight - 1) * strideHeight + filterHeight - inHeight;\n    const padAlongWidth = (outWidth - 1) * strideWidth + filterWidth - inWidth;\n    const front = Math.floor(padAlongDepth / 2);\n    const back = padAlongDepth - front;\n    const top = Math.floor(padAlongHeight / 2);\n    const bottom = padAlongHeight - top;\n    const left = Math.floor(padAlongWidth / 2);\n    const right = padAlongWidth - left;\n\n    padInfo = {top, bottom, left, right, front, back, type: 'SAME'};\n  } else if (pad === 'valid') {\n    padInfo = {\n      top: 0,\n      bottom: 0,\n      left: 0,\n      right: 0,\n      front: 0,\n      back: 0,\n      type: 'VALID'\n    };\n    outDepth = Math.ceil((inDepth - filterDepth + 1) / strideDepth);\n    outHeight = Math.ceil((inHeight - filterHeight + 1) / strideHeight);\n    outWidth = Math.ceil((inWidth - filterWidth + 1) / strideWidth);\n  } else {\n    throw Error(`Unknown padding parameter: ${pad}`);\n  }\n  return {padInfo, outDepth, outHeight, outWidth};\n}\n\n/**\n * Rounds a value depending on the rounding mode\n * @param value\n * @param roundingMode\n */\nfunction conditionalRound(\n    value: number, roundingMode?: 'floor'|'round'|'ceil') {\n  if (!roundingMode) {\n    return value;\n  }\n  switch (roundingMode) {\n    case 'round':\n      // used for Caffe Conv\n      return Math.round(value);\n    case 'ceil':\n      // used for Caffe Pool\n      return Math.ceil(value);\n    case 'floor':\n      return Math.floor(value);\n    default:\n      throw new Error(`Unknown roundingMode ${roundingMode}`);\n  }\n}\n\nexport function tupleValuesAreOne(param: number|[number, number]): boolean {\n  const [dimA, dimB] = parseTupleParam(param);\n  return dimA === 1 && dimB === 1;\n}\n\nexport function eitherStridesOrDilationsAreOne(\n    strides: number|[number, number],\n    dilations: number|[number, number]): boolean {\n  return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D} from '../tensor';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\n\n/**\n * Computes the dot product of two matrices, A * B. These must be matrices.\n *\n * ```js\n * const a = tf.tensor2d([1, 2], [1, 2]);\n * const b = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * a.matMul(b).print();  // or tf.matMul(a, b)\n * ```\n * @param a First matrix in dot product operation.\n * @param b Second matrix in dot product operation.\n * @param transposeA If true, `a` is transposed before multiplication.\n * @param transposeB If true, `b` is transposed before multiplication.\n */\n/** @doc {heading: 'Operations', subheading: 'Matrices'} */\nfunction matMul_<T extends Tensor>(\n    a: T|TensorLike, b: T|TensorLike, transposeA = false,\n    transposeB = false): T {\n  let $a = convertToTensor(a, 'a', 'matMul');\n  let $b = convertToTensor(b, 'b', 'matMul');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const innerShapeA =\n      transposeA ? $a.shape[$a.rank - 2] : $a.shape[$a.rank - 1];\n  const innerShapeB =\n      transposeB ? $b.shape[$b.rank - 1] : $b.shape[$b.rank - 2];\n\n  const outerShapeA =\n      transposeA ? $a.shape[$a.rank - 1] : $a.shape[$a.rank - 2];\n  const outerShapeB =\n      transposeB ? $b.shape[$b.rank - 2] : $b.shape[$b.rank - 1];\n\n  const outerDimsA = $a.shape.slice(0, -2);\n  const outerDimsB = $b.shape.slice(0, -2);\n  const batchDimA = util.sizeFromShape(outerDimsA);\n  const batchDimB = util.sizeFromShape(outerDimsB);\n\n  util.assert(\n      $a.rank >= 2 && $b.rank >= 2 && $a.rank === $b.rank,\n      `Error in matMul: inputs must have the same rank of at least 2, ` +\n          `got ranks ${$a.rank} and ${$b.rank}.`);\n\n  util.assert(\n      util.arraysEqual(outerDimsA, outerDimsB),\n      `Error in matMul: outer dimensions (${outerDimsA}) and (` +\n          `${outerDimsB}) of Tensors with shapes ${$a.shape} and ` +\n          `${$b.shape} must match.`);\n\n  util.assert(\n      innerShapeA === innerShapeB,\n      `Error in matMul: inner shapes (${innerShapeA}) and (` +\n          `${innerShapeB}) of Tensors with shapes ${$a.shape} and ` +\n          `${$b.shape} and transposeA=${transposeA}` +\n          ` and transposeB=${transposeB} must match.`);\n\n  const outShape = $a.shape.slice(0, -2).concat([outerShapeA, outerShapeB]);\n\n  const a3D = transposeA ? $a.as3D(batchDimA, innerShapeA, outerShapeA) :\n                           $a.as3D(batchDimA, outerShapeA, innerShapeA);\n  const b3D = transposeB ? $b.as3D(batchDimB, outerShapeB, innerShapeB) :\n                           $b.as3D(batchDimB, innerShapeB, outerShapeB);\n\n  const grad = (dy: Tensor3D) => {\n    if (!transposeA && !transposeB) {\n      return {\n        $a: () => dy.matMul(b3D, false, true),\n        $b: () => a3D.matMul(dy, true, false)\n      };\n    } else if (!transposeA && transposeB) {\n      return {\n        $a: () => dy.matMul(b3D, false, false),\n        $b: () => dy.matMul(a3D, true, false)\n      };\n    } else if (transposeA && !transposeB) {\n      return {\n        $a: () => b3D.matMul(dy, false, true),\n        $b: () => a3D.matMul(dy, false, false)\n      };\n    } else {\n      return {\n        $a: () => b3D.matMul(dy, true, true),\n        $b: () => dy.matMul(a3D, true, true)\n      };\n    }\n  };\n\n  const res = ENV.engine.runKernel(\n      backend => backend.batchMatMul(a3D, b3D, transposeA, transposeB),\n      {$a: a3D, $b: b3D}, grad);\n  return res.reshape(outShape) as T;\n}\n\n/**\n * Computes the outer product of two vectors, `v1` and `v2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([3, 4, 5]);\n *\n * tf.outerProduct(a, b).print();\n * ```\n * @param v1 The first vector in the outer product operation.\n * @param v2 The second vector in the outer product operation.\n */\n/** @doc {heading: 'Operations', subheading: 'Matrices'} */\nfunction outerProduct_(\n    v1: Tensor1D|TensorLike, v2: Tensor1D|TensorLike): Tensor2D {\n  const $v1 = convertToTensor(v1, 'v1', 'outerProduct');\n  const $v2 = convertToTensor(v2, 'v2', 'outerProduct');\n\n  util.assert(\n      $v1.rank === 1 && $v2.rank === 1,\n      `Error in outerProduct: inputs must be rank 1, but got ranks ` +\n          `${$v1.rank} and ${$v2.rank}.`);\n\n  return $v1.as2D(-1, 1).matMul($v2.as2D(1, -1));\n}\n\n/**\n * Computes the dot product of two matrices and/or vectors, `t1` and `t2`.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor2d([[1, 2], [3, 4]]);\n * const c = tf.tensor2d([[1, 2, 3], [4, 5, 6]]);\n *\n * a.dot(b).print();  // or tf.dot(a, b)\n * b.dot(a).print();\n * b.dot(c).print();\n * ```\n * @param t1 The first tensor in the dot operation.\n * @param t2 The second tensor in the dot operation.\n */\n/** @doc {heading: 'Operations', subheading: 'Matrices'} */\nfunction dot_(t1: Tensor|TensorLike, t2: Tensor|TensorLike): Tensor {\n  const $t1 = convertToTensor(t1, 't1', 'dot');\n  const $t2 = convertToTensor(t2, 't2', 'dot');\n  util.assert(\n      ($t1.rank === 1 || $t1.rank === 2) && ($t2.rank === 1 || $t2.rank === 2),\n      `Error in dot: inputs must all be rank 1 or 2, but got ranks ` +\n          `${$t1.rank} and ${$t2.rank}.`);\n\n  const t1Inner = ($t1.rank === 1 ? $t1.size : $t1.shape[1]);\n  const t2Inner = ($t2.rank === 1 ? $t2.size : $t2.shape[0]);\n\n  util.assert(\n      t1Inner === t2Inner,\n      `Error in dot: inner dimensions of inputs must match, but got ` +\n          `${t1Inner} and ${t2Inner}.`);\n\n  if ($t1.rank === 1 && $t2.rank === 1) {\n    return $t1.as2D(1, -1).matMul($t2.as2D(-1, 1)).asScalar();\n  } else if ($t1.rank === 1 && $t2.rank === 2) {\n    return $t1.as2D(1, -1).matMul($t2.as2D($t2.shape[0], $t2.shape[1])).as1D();\n  } else if ($t1.rank === 2 && $t2.rank === 1) {\n    return $t1.matMul($t2.as2D(-1, 1)).as1D();\n  } else {\n    return $t1.matMul($t2.as2D($t2.shape[0], $t2.shape[1]));\n  }\n}\n\nexport const matMul = op({matMul_});\nexport const dot = op({dot_});\nexport const outerProduct = op({outerProduct_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor2D, Tensor3D, Tensor4D, Tensor5D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, TensorLike} from '../types';\nimport * as util from '../util';\nimport * as conv_util from './conv_util';\nimport {matMul} from './matmul';\nimport {op} from './operation';\n\n/**\n * Computes a 1D convolution over the input x.\n *\n * @param x The input tensor, of rank 3 or rank 2, of shape\n *     `[batch, width, inChannels]`. If rank 2, batch of 1 is assumed.\n * @param filter The filter, rank 3, of shape\n *     `[filterWidth, inDepth, outDepth]`.\n * @param stride The number of entries by which the filter is moved right at\n *     each step.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat An optional string from \"NWC\", \"NCW\". Defaults to \"NWC\",\n *     the data is stored in the order of [batch, in_width, in_channels]. Only\n *     \"NWC\" is currently supported.\n * @param dilation The dilation rate in which we sample input values in\n *     atrous convolution. Defaults to `1`. If it is greater than 1, then\n *     stride must be `1`.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction conv1d_<T extends Tensor2D|Tensor3D>(\n    x: T|TensorLike, filter: Tensor3D|TensorLike, stride: number,\n    pad: 'valid'|'same'|number, dataFormat: 'NWC'|'NCW' = 'NWC', dilation = 1,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'conv1d');\n  const $filter = convertToTensor(filter, 'filter', 'conv1d');\n\n  let x3D = $x as Tensor3D;\n  let reshapedTo3D = false;\n  if ($x.rank === 2) {\n    reshapedTo3D = true;\n    x3D = $x.as3D(1, $x.shape[0], $x.shape[1]);\n  }\n\n  util.assert(\n      x3D.rank === 3,\n      `Error in conv1d: input must be rank 3, but got rank ${x3D.rank}.`);\n  util.assert(\n      $filter.rank === 3,\n      `Error in conv1d: filter must be rank 3, but got rank ` +\n          `${$filter.rank}.`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        `Error in conv1d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  util.assert(\n      x3D.shape[2] === $filter.shape[1],\n      `Error in conv1d: depth of input (${x3D.shape[2]}) must match ` +\n          `input depth for filter ${$filter.shape[1]}.`);\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(stride, dilation),\n      'Error in conv1D: Either stride or dilation must be 1. ' +\n          `Got stride ${stride} and dilation '${dilation}'`);\n  util.assert(\n      dataFormat === 'NWC',\n      `Error in conv1d: got dataFormat of ${\n          dataFormat} but only NWC is currently supported.`);\n\n  const filter4D =\n      $filter.as4D(1, $filter.shape[0], $filter.shape[1], $filter.shape[2]);\n  const input4D = x3D.as4D(x3D.shape[0], 1, x3D.shape[1], x3D.shape[2]);\n  const strides: [number, number] = [1, stride];\n  const dilations: [number, number] = [1, dilation];\n\n  const conv2dDataFormat = 'NHWC';\n\n  const res = conv2d(\n      input4D, filter4D, strides, pad, conv2dDataFormat, dilations,\n      dimRoundingMode);\n\n  if (reshapedTo3D) {\n    return res.as2D(res.shape[2], res.shape[3]) as T;\n  }\n  return res.as3D(res.shape[0], res.shape[2], res.shape[3]) as T;\n}\n\n/**\n * Computes a 2D convolution over the input x.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction conv2d_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filter: Tensor4D|TensorLike,\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n    dilations: [number, number]|number = [1, 1],\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'conv2d');\n  const $filter = convertToTensor(filter, 'filter', 'conv2d');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n  }\n  util.assert(\n      x4D.rank === 4,\n      `Error in conv2d: input must be rank 4, but got rank ${x4D.rank}.`);\n  util.assert(\n      $filter.rank === 4,\n      `Error in conv2d: filter must be rank 4, but got rank ` +\n          `${$filter.rank}.`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        `Error in conv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  util.assert(\n      x4D.shape[3] === $filter.shape[2],\n      `Error in conv2d: depth of input (${x4D.shape[3]}) must match ` +\n          `input depth for filter ${$filter.shape[2]}.`);\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      'Error in conv2D: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n  util.assert(\n      dataFormat === 'NHWC',\n      `Error in conv2d: got dataFormat of ${\n          dataFormat} but only NHWC is currently supported.`);\n\n  const convInfo = conv_util.computeConv2DInfo(\n      x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode);\n\n  let res: Tensor3D|Tensor4D;\n  if (convInfo.filterHeight === 1 && convInfo.filterWidth === 1 &&\n      convInfo.dilationHeight === 1 && convInfo.dilationWidth === 1 &&\n      convInfo.strideHeight === 1 && convInfo.strideWidth === 1 &&\n      (convInfo.padInfo.type === 'SAME' || convInfo.padInfo.type === 'VALID')) {\n    const x2d = x4D.reshape([-1, convInfo.inChannels]) as Tensor2D;\n    const w2d = $filter.reshape([convInfo.inChannels, convInfo.outChannels]) as\n        Tensor2D;\n\n    res = matMul(x2d, w2d).reshape<Rank.R4>(convInfo.outShape);\n  } else {\n    const grad = (dy: Tensor4D) => {\n      util.assert(\n          conv_util.tupleValuesAreOne(dilations),\n          'Error in gradient of conv2D: dilation rates greater than 1 are not' +\n              `yet supported in gradients. Got dilations '${dilations}'`);\n\n      return {\n        x: () => conv2dDerInput_(x4D.shape, dy, $filter, strides, pad),\n        $filter: () => conv2dDerFilter_(x4D, dy, $filter.shape, strides, pad)\n      };\n    };\n\n    res = ENV.engine.runKernel(\n        backend => backend.conv2d(x4D, $filter, convInfo), {x: x4D, $filter},\n        grad);\n  }\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the derivative of the input of a 2D convolution.\n *\n * @param xShape The shape of the input: [batch, height, width, inDepth].\n * If length of 3, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 4 or rank 3 of shape\n *   `[batch, outHeight, outWidth, outDepth]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction conv2dDerInput_<T extends Tensor3D|Tensor4D>(\n    xShape: [number, number, number, number]|[number, number, number], dy: T,\n    filter: Tensor4D, strides: [number, number]|number,\n    pad: 'valid'|'same'|number, dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  util.assert(\n      xShape.length === dy.rank,\n      `Length of inShape ` +\n          `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n\n  let xShape4D = xShape as [number, number, number, number];\n  let dy4D = dy as Tensor4D;\n  let reshapedTo4D = false;\n  if (dy.rank === 3) {\n    reshapedTo4D = true;\n    dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);\n    xShape4D = [1, xShape[0], xShape[1], xShape[2]];\n  }\n\n  const inDepth = xShape4D[3];\n  const outDepth = dy4D.shape[3];\n  util.assert(\n      xShape4D.length === 4,\n      `Error in conv2dDerInput: inShape must be length 4, but got length ` +\n          `${xShape4D.length}.`);\n  util.assert(\n      dy4D.rank === 4,\n      `Error in conv2dDerInput: dy must be rank 4, but got ` +\n          `rank ${dy4D.rank}`);\n  util.assert(\n      filter.rank === 4,\n      `Error in conv2dDerInput: filter must be rank 4, but got ` +\n          `rank ${filter.rank}`);\n  util.assert(\n      inDepth === filter.shape[2],\n      `Error in conv2dDerInput: depth of input (${inDepth}) must ` +\n          `match input depth for filter ${filter.shape[2]}.`);\n  util.assert(\n      outDepth === filter.shape[3],\n      `Error in conv2dDerInput: depth of output (${outDepth}) must ` +\n          `match output depth for filter ${filter.shape[3]}.`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        `Error in conv2dDerInput: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const dilations = 1;\n\n  const grad = (ddx: Tensor4D) => {\n    const dataFormat = 'NHWC';\n    return {\n      dy4D: () => conv2d(\n          ddx, filter, strides, pad, dataFormat, dilations, dimRoundingMode),\n      filter: () => conv2dDerFilter(\n          ddx, dy4D, filter.shape, strides, pad, dimRoundingMode)\n    };\n  };\n\n  const convInfo = conv_util.computeConv2DInfo(\n      xShape4D, filter.shape, strides, dilations, pad, dimRoundingMode);\n  const res = ENV.engine.runKernel(\n      backend => backend.conv2dDerInput(dy4D, filter, convInfo), {dy4D, filter},\n      grad);\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the derivative of the filter of a 2D convolution.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     [batch, height, width, inChannels]. If rank 3, batch of 1 is assumed.\n * @param dy The dy image, of rank 4 or rank 3, of shape\n *     [batch, height, width, outDepth]. If rank 3, batch of 1 is assumed.\n * @param filterShape The shape of the filter, length 4,\n *     [filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction conv2dDerFilter_<T extends Tensor3D|Tensor4D>(\n    x: T, dy: T, filterShape: [number, number, number, number],\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): Tensor4D {\n  let x4D = x as Tensor4D;\n  if (x.rank === 3) {\n    x4D = x.as4D(1, x.shape[0], x.shape[1], x.shape[2]);\n  }\n  let dy4D = dy as Tensor4D;\n  if (dy4D.rank === 3) {\n    dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);\n  }\n  util.assert(\n      x4D.rank === 4,\n      `Error in conv2dDerFilter: input must be rank 4, but got shape ` +\n          `${x4D.shape}.`);\n  util.assert(\n      dy4D.rank === 4,\n      `Error in conv2dDerFilter: dy must be rank 4, but got shape ` +\n          `${dy4D.shape}.`);\n  util.assert(\n      filterShape.length === 4,\n      `Error in conv2dDerFilter: filterShape must be length 4, but got ` +\n          `${filterShape}.`);\n  util.assert(\n      x4D.shape[3] === filterShape[2],\n      `Error in conv2dDerFilter: depth of input ${x4D.shape[3]}) must ` +\n          `match input depth in filter (${filterShape[2]}.`);\n  util.assert(\n      dy4D.shape[3] === filterShape[3],\n      `Error in conv2dDerFilter: depth of dy (${dy4D.shape[3]}) must ` +\n          `match output depth for filter (${filterShape[3]}).`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        `Error in conv2dDerFilter: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const dilations = 1;\n\n  const convInfo = conv_util.computeConv2DInfo(\n      x4D.shape, filterShape, strides, dilations, pad, dimRoundingMode);\n  return ENV.engine.runKernel(\n      backend => backend.conv2dDerFilter(x4D, dy4D, convInfo), {x4D, dy4D});\n}\n\n/**\n * Computes the transposed 2D convolution of an image, also known as a\n * deconvolution.\n *\n * @param x The input image, of rank 4 or rank 3, of shape\n *   `[batch, height, width, inDepth]`. If rank 3, batch of 1 is assumed.\n * @param filter The filter, rank 4, of shape\n *     `[filterHeight, filterWidth, outDepth, inDepth]`.\n *     `inDepth` must match `inDepth` in `x`.\n * @param outputShape Output shape, of rank 4 or rank 3:\n *     `[batch, height, width, outDepth]`. If rank 3, batch of 1 is assumed.\n * @param strides The strides of the original convolution:\n *     `[strideHeight, strideWidth]`.\n * @param pad  The type of padding algorithm used in the non-transpose version\n *    of the op.\n * @param dimRoundingMode The rounding mode used when computing output\n *    dimensions if pad is a number. If none is provided, it will not round\n *    and error if the output is of fractional size.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction conv2dTranspose_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filter: Tensor4D|TensorLike,\n    outputShape: [number, number, number, number]|[number, number, number],\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'conv2dTranspose');\n  const $filter = convertToTensor(filter, 'filter', 'conv2dTranspose');\n\n  return conv2dDerInput_(\n      outputShape, $x, $filter, strides, pad, dimRoundingMode);\n}\n\n/**\n * Depthwise 2D convolution.\n *\n * Given a 4D `input` array and a `filter` array of shape\n * `[filterHeight, filterWidth, inChannels, channelMultiplier]` containing\n * `inChannels` convolutional filters of depth 1, this op applies a\n * different filter to each input channel (expanding from 1 channel to\n * `channelMultiplier` channels for each), then concatenates the results\n * together. The output has `inChannels * channelMultiplier` channels.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/depthwise_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param filter The filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction depthwiseConv2d_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filter: Tensor4D|TensorLike,\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n    dilations: [number, number]|number = [1, 1],\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'depthwiseConv2d');\n  const $filter = convertToTensor(filter, 'filter', 'depthwiseConv2d');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n  }\n  util.assert(\n      x4D.rank === 4,\n      `Error in depthwiseConv2d: input must be rank 4, but got ` +\n          `rank ${x4D.rank}.`);\n  util.assert(\n      $filter.rank === 4,\n      `Error in depthwiseConv2d: filter must be rank 4, but got rank ` +\n          `${$filter.rank}.`);\n  util.assert(\n      x4D.shape[3] === $filter.shape[2],\n      `Error in depthwiseConv2d: number of input channels ` +\n          `(${x4D.shape[3]}) must match the inChannels dimension in ` +\n          `filter ${$filter.shape[2]}.`);\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      'Error in depthwiseConv2d: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        `Error in depthwiseConv2d: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const convInfo = conv_util.computeConv2DInfo(\n      x4D.shape, $filter.shape, strides, dilations, pad, dimRoundingMode,\n      true /* depthwise */);\n\n  const grad = (dy: Tensor4D) => {\n    util.assert(\n        conv_util.tupleValuesAreOne(dilations),\n        'Error in gradient of depthwiseConv2d: dilation rates greater than ' +\n            `1 are not yet supported. Got dilations '${dilations}'`);\n    return {\n      x: () => depthwiseConv2dDerInput(x4D.shape, dy, $filter, convInfo),\n      $filter: () => depthwiseConv2dDerFilter(x4D, dy, $filter.shape, convInfo),\n    };\n  };\n\n  const res = ENV.engine.runKernel(\n      backend => backend.depthwiseConv2D(x4D, $filter, convInfo),\n      {x: x4D, $filter}, grad);\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\n/**\n * 2-D convolution with separable filters.\n *\n * Performs a depthwise convolution that acts separately on channels followed\n * by a pointwise convolution that mixes channels. Note that this is\n * separability between dimensions [1, 2] and 3, not spatial separability\n * between dimensions 1 and 2.\n *\n * See\n * [https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d](\n *     https://www.tensorflow.org/api_docs/python/tf/nn/separable_conv2d)\n * for more details.\n *\n * @param x The input tensor, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is\n * assumed.\n * @param depthwiseFilter The depthwise filter tensor, rank 4, of shape\n *     `[filterHeight, filterWidth, inChannels, channelMultiplier]`. This is\n *     the filter used in the first step.\n * @param pointwiseFilter The pointwise filter tensor, rank 4, of shape\n *     `[1, 1, inChannels * channelMultiplier, outChannels]`. This is\n *     the filter used in the second step.\n * @param strides The strides of the convolution: `[strideHeight,\n * strideWidth]`. If strides is a single number, then `strideHeight ==\n * strideWidth`.\n * @param pad The type of padding algorithm.\n *   - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *   - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in atrous convolution. Defaults to `[1, 1]`. If `rate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     height, width, channels]. Only \"NHWC\" is currently supported.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction separableConv2d_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, depthwiseFilter: Tensor4D|TensorLike,\n    pointwiseFilter: Tensor4D|TensorLike, strides: [number, number]|number,\n    pad: 'valid'|'same', dilation: [number, number]|number = [1, 1],\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC'): T {\n  const $x = convertToTensor(x, 'x', 'separableConv2d');\n  const $depthwiseFilter =\n      convertToTensor(depthwiseFilter, 'depthwiseFilter', 'separableConv2d');\n  const $pointwiseFilter =\n      convertToTensor(pointwiseFilter, 'pointwiseFilter', 'separableConv2d');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n  }\n\n  if (dataFormat === 'NCHW') {\n    throw new Error(\n        'separableConv2d currently does not support dataFormat NCHW; only ' +\n        'NHWC is supported');\n  }\n\n  util.assert(\n      x4D.rank === 4,\n      `Error in separableConv2d: input must be rank 4, but got ` +\n          `rank ${x4D.rank}.`);\n  util.assert(\n      $depthwiseFilter.rank === 4,\n      `Error in separableConv2d: depthwise filter must be rank 4, but got ` +\n          `rank ${$depthwiseFilter.rank}.`);\n  util.assert(\n      $pointwiseFilter.rank === 4,\n      `Error in separableConv2d: pointwise filter must be rank 4, but got ` +\n          `rank ${$depthwiseFilter.rank}.`);\n  util.assert(\n      $pointwiseFilter.shape[0] === 1,\n      `Error in separableConv2d: the first dimension of pointwise filter ` +\n          ` must be 1, but got ${$pointwiseFilter.shape[0]}.`);\n  util.assert(\n      $pointwiseFilter.shape[1] === 1,\n      `Error in separableConv2d: the second dimension of pointwise filter ` +\n          ` must be 1, but got ${$pointwiseFilter.shape[1]}.`);\n\n  const inChannels = $depthwiseFilter.shape[2];\n  const channelMultiplier = $depthwiseFilter.shape[3];\n  util.assert(\n      $pointwiseFilter.shape[2] === inChannels * channelMultiplier,\n      `Error in separableConv2d: the third dimension of pointwise filter ` +\n          `must be ${inChannels * channelMultiplier}, ` +\n          `but got ${$pointwiseFilter.shape[2]}.`);\n\n  const depthwise = depthwiseConv2d(\n      x4D, $depthwiseFilter, strides, pad, dataFormat, dilation);\n  const pointwiseStride = 1;\n  const res =\n      conv2d(depthwise, $pointwiseFilter, pointwiseStride, 'valid', dataFormat);\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\nfunction parseTupleParam(\n    param: number|[number, number]|[number, number, number]):\n    [number, number, number] {\n  if (typeof param === 'number') {\n    return [param, param, param];\n  }\n  if (param.length === 2) {\n    return [param[0], param[1], 1];\n  }\n  return param;\n}\n\nfunction tupleValuesAreOne(\n    param: number|[number, number]|[number, number, number]): boolean {\n  const [dimA, dimB, dimC] = parseTupleParam(param);\n  return dimA === 1 && dimB === 1 && dimC === 1;\n}\n\nfunction eitherStridesOrDilationsAreOne(\n    strides: number|[number, number]|[number, number, number],\n    dilations: number|[number, number]|[number, number, number]): boolean {\n  return tupleValuesAreOne(strides) || tupleValuesAreOne(dilations);\n}\n\nfunction depthwiseConv2dDerInput<T extends Tensor3D|Tensor4D>(\n    xShape: [number, number, number, number]|[number, number, number], dy: T,\n    filter: Tensor4D, convInfo: conv_util.Conv2DInfo): T {\n  let dy4D = dy as Tensor4D;\n  let reshapedTo4D = false;\n  if (dy.rank === 3) {\n    reshapedTo4D = true;\n    dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);\n  }\n  const res = ENV.engine.runKernel(\n      backend => backend.depthwiseConv2DDerInput(dy4D, filter, convInfo),\n      {dy4D});\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\nfunction depthwiseConv2dDerFilter<T extends Tensor3D|Tensor4D>(\n    x: T, dy: T, filterShape: [number, number, number, number],\n    convInfo: conv_util.Conv2DInfo): Tensor4D {\n  let x4D = x as Tensor4D;\n  if (x.rank === 3) {\n    x4D = x.as4D(1, x.shape[0], x.shape[1], x.shape[2]);\n  }\n  let dy4D = dy as Tensor4D;\n  if (dy4D.rank === 3) {\n    dy4D = dy.as4D(1, dy.shape[0], dy.shape[1], dy.shape[2]);\n  }\n  return ENV.engine.runKernel(\n      backend => backend.depthwiseConv2DDerFilter(x4D, dy4D, convInfo),\n      {x4D, dy4D});\n}\n\n/**\n * Computes a 3D convolution over the input x.\n *\n * @param x The input tensor, of rank 5 or rank 4, of shape\n *     `[batch, depth, height, width, channels]`. If rank 4,\n * batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inChannels, outChannels]`.\n *      inChannels must match between input and filter.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *   - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dataFormat: An optional string from: \"NHWC\", \"NCHW\". Defaults to\n *     \"NHWC\". Specify the data format of the input and output data. With the\n *     default format \"NHWC\", the data is stored in the order of: [batch,\n *     depth, height, width, channels]. Only \"NHWC\" is currently supported.\n * @param dilations The dilation rates: `[dilationDepth, dilationHeight,\n *     dilationWidth]` in which we sample input values across the height\n *     and width dimensions in atrous convolution. Defaults to `[1, 1, 1]`.\n *     If `dilations` is a single number, then\n *     `dilationDepth == dilationHeight == dilationWidth`. If it is greater\n *     than 1, then all values of `strides` must be 1.\n */\n\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction conv3d_<T extends Tensor4D|Tensor5D>(\n    x: T|TensorLike, filter: Tensor5D|TensorLike,\n    strides: [number, number, number]|number, pad: 'valid'|'same',\n    dataFormat: 'NHWC'|'NCHW' = 'NHWC',\n    dilations: [number, number, number]|number = [1, 1, 1]): T {\n  const $x = convertToTensor(x, 'x', 'conv3d');\n  const $filter = convertToTensor(filter, 'filter', 'conv3d');\n\n  let x5D = $x as Tensor5D;\n  let reshapedTo5D = false;\n\n  if ($x.rank === 4) {\n    reshapedTo5D = true;\n    x5D = $x.as5D(1, $x.shape[0], $x.shape[1], $x.shape[2], $x.shape[3]);\n  }\n  util.assert(\n      x5D.rank === 5,\n      `Error in conv3d: input must be rank 5, but got rank ${x5D.rank}.`);\n  util.assert(\n      $filter.rank === 5,\n      `Error in conv3d: filter must be rank 5, but got rank ` +\n          `${$filter.rank}.`);\n  util.assert(\n      x5D.shape[4] === $filter.shape[3],\n      `Error in conv3d: depth of input (${x5D.shape[4]}) must match ` +\n          `input depth for filter ${$filter.shape[3]}.`);\n  util.assert(\n      eitherStridesOrDilationsAreOne(strides, dilations),\n      'Error in conv3D: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n  util.assert(\n      dataFormat === 'NHWC',\n      `Error in conv3d: got dataFormat of ${\n          dataFormat} but only NHWC is currently supported.`);\n\n  const convInfo = conv_util.computeConv3DInfo(\n      x5D.shape, $filter.shape, strides, dilations, pad);\n\n  const grad = (dy: Tensor5D) => {\n    util.assert(\n        tupleValuesAreOne(dilations),\n        'Error in gradient of conv3D: dilation rates greater than 1 are not' +\n            `yet supported in gradients. Got dilations '${dilations}'`);\n\n    return {\n      x: () => conv3dDerInput_(x5D.shape, dy, $filter, strides, pad),\n      $filter: () => conv3dDerFilter_(x5D, dy, $filter.shape, strides, pad)\n    };\n  };\n\n  const res = ENV.engine.runKernel(\n      backend => backend.conv3d(x5D, $filter, convInfo), {x: x5D, $filter},\n      grad);\n  if (reshapedTo5D) {\n    return res.as4D(res.shape[1], res.shape[2], res.shape[3], res.shape[4]) as\n        T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the derivative of the input of a 3D convolution.\n *\n * @param xShape The shape of the input: [batch, depth, height, width,\n * in_channels]. If length of 4, batch of 1 is assumed.\n * @param dy The derivative of the output, of rank 5 or rank 4 of shape\n *   `[batch, outDepth, outHeight, outWidth, in_channels]`.\n * If rank 4, batch of 1 is assumed.\n * @param filter The filter, rank 5, of shape\n *     `[filterDepth, filterHeight, filterWidth, inDepth, outDepth]`.\n * @param strides The strides of the convolution: `[strideDepth, strideHeight,\n * strideWidth]`.\n * @param pad The type of padding algorithm used:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n */\nfunction conv3dDerInput_<T extends Tensor4D|Tensor5D>(\n    xShape:\n        [number, number, number, number,\n         number]|[number, number, number, number],\n    dy: T, filter: Tensor5D, strides: [number, number, number]|number,\n    pad: 'valid'|'same'): T {\n  util.assert(\n      xShape.length === dy.rank,\n      `Length of inShape ` +\n          `(${xShape.length}) and rank of dy (${dy.rank}) must match`);\n\n  let xShape5D = xShape as [number, number, number, number, number];\n  let dy5D = dy as Tensor5D;\n  let reshapedTo5D = false;\n  if (dy.rank === 4) {\n    reshapedTo5D = true;\n    dy5D = dy.as5D(1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]);\n    xShape5D = [1, xShape[0], xShape[1], xShape[2], xShape[3]];\n  }\n\n  const inDepth = xShape5D[4];\n  const outDepth = dy5D.shape[4];\n  util.assert(\n      xShape5D.length === 5,\n      `Error in conv3dDerInput: inShape must be length 5, but got length ` +\n          `${xShape5D.length}.`);\n  util.assert(\n      dy5D.rank === 5,\n      `Error in conv3dDerInput: dy must be rank 5, but got ` +\n          `rank ${dy5D.rank}`);\n  util.assert(\n      filter.rank === 5,\n      `Error in conv3dDerInput: filter must be rank 5, but got ` +\n          `rank ${filter.rank}`);\n  util.assert(\n      inDepth === filter.shape[3],\n      `Error in conv3dDerInput: depth of input (${inDepth}) must ` +\n          `match input depth for filter ${filter.shape[3]}.`);\n  util.assert(\n      outDepth === filter.shape[4],\n      `Error in conv3dDerInput: depth of output (${outDepth}) must ` +\n          `match output depth for filter ${filter.shape[4]}.`);\n\n  const dilations = 1;\n\n  const convInfo = conv_util.computeConv3DInfo(\n      xShape5D, filter.shape, strides, dilations, pad);\n  const res = ENV.engine.runKernel(\n      backend => backend.conv3dDerInput(dy5D, filter, convInfo), {dy5D});\n  if (reshapedTo5D) {\n    return res.as4D(res.shape[1], res.shape[2], res.shape[3], res.shape[4]) as\n        T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the derivative of the filter of a 3D convolution.\n *\n * @param x The input tensor, of rank 5 or rank 4 of shape\n *     [batch, depth, height, width, inChannels]. If rank 4, batch of 1 is\n *     assumed.\n * @param dy The dy image, of rank 5 or rank 4, of shape\n *     [batch, depth, height, width, outDepth]. If rank 4, batch of 1 is\n *     assumed.\n * @param filterShape The shape of the filter, length 5,\n *     [filterDepth, filterHeight, filterWidth, inDepth, outDepth].\n * @param strides The strides of the convolution: [strideDepth, strideHeight,\n * strideWidth].\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n */\nfunction conv3dDerFilter_<T extends Tensor4D|Tensor5D>(\n    x: T, dy: T, filterShape: [number, number, number, number, number],\n    strides: [number, number, number]|number, pad: 'valid'|'same'): Tensor5D {\n  let x5D = x as Tensor5D;\n  if (x.rank === 4) {\n    x5D = x.as5D(1, x.shape[0], x.shape[1], x.shape[2], x.shape[3]);\n  }\n  let dy5D = dy as Tensor5D;\n  if (dy5D.rank === 4) {\n    dy5D = dy.as5D(1, dy.shape[0], dy.shape[1], dy.shape[2], dy.shape[3]);\n  }\n  util.assert(\n      x5D.rank === 5,\n      `Error in conv3dDerFilter: input must be rank 5, but got shape ` +\n          `${x5D.shape}.`);\n  util.assert(\n      dy5D.rank === 5,\n      `Error in conv3dDerFilter: dy must be rank 5, but got shape ` +\n          `${dy5D.shape}.`);\n  util.assert(\n      filterShape.length === 5,\n      `Error in conv3dDerFilter: filterShape must be length 5, but got ` +\n          `${filterShape}.`);\n  util.assert(\n      x5D.shape[4] === filterShape[3],\n      `Error in conv3dDerFilter: depth of input ${x5D.shape[4]}) must ` +\n          `match input depth in filter (${filterShape[3]}.`);\n  util.assert(\n      dy5D.shape[4] === filterShape[4],\n      `Error in conv3dDerFilter: depth of dy (${dy5D.shape[4]}) must ` +\n          `match output depth for filter (${filterShape[4]}).`);\n\n  const dilations = 1;\n\n  const convInfo = conv_util.computeConv3DInfo(\n      x5D.shape, filterShape, strides, dilations, pad);\n  return ENV.engine.runKernel(\n      backend => backend.conv3dDerFilter(x5D, dy5D, convInfo), {x5D, dy5D});\n}\n\nexport const conv1d = op({conv1d_});\nexport const conv2d = op({conv2d_});\nexport const conv3d = op({conv3d_});\nexport const conv2dDerFilter = op({conv2dDerFilter_});\nexport const depthwiseConv2d = op({depthwiseConv2d_});\nexport const separableConv2d = op({separableConv2d_});\nexport const conv2dTranspose = op({conv2dTranspose_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {parseAxisParam} from './axis_util';\nimport {op} from './operation';\n\n/**\n * Reverses a `tf.Tensor1D`.\n *\n * @param x The input tensor.\n */\nfunction reverse1d_(x: Tensor1D|TensorLike): Tensor1D {\n  const $x = convertToTensor(x, 'x', 'reverse');\n  util.assert($x.rank === 1, `Error in reverse1D: x must be rank 1 but got\n             rank ${$x.rank}.`);\n  return reverse($x, 0);\n}\n\n/**\n * Reverses a `tf.Tensor2D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse2d_(x: Tensor2D|TensorLike, axis?: number|number[]): Tensor2D {\n  const $x = convertToTensor(x, 'x', 'reverse');\n  util.assert($x.rank === 2, `Error in reverse2D: x must be rank 2 but got\n             rank ${$x.rank}.`);\n  return reverse($x, axis);\n}\n\n/**\n * Reverses a `tf.Tensor3D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse3d_(x: Tensor3D|TensorLike, axis?: number|number[]): Tensor3D {\n  const $x = convertToTensor(x, 'x', 'reverse');\n  util.assert($x.rank === 3, `Error in reverse3D: x must be rank 3 but got\n             rank ${$x.rank}.`);\n  return reverse($x, axis);\n}\n\n/**\n * Reverses a `tf.Tensor4D` along a specified axis.\n *\n * @param x The input tensor.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\nfunction reverse4d_(x: Tensor4D|TensorLike, axis?: number|number[]): Tensor4D {\n  const $x = convertToTensor(x, 'x', 'reverse');\n  util.assert($x.rank === 4, `Error in reverse4D: x must be rank 4 but got\n             rank ${$x.rank}.`);\n  return reverse($x, axis);\n}\n\n/**\n * Reverses a `tf.Tensor` along a specified axis.\n *\n * Also available are stricter rank-specific methods that assert that `x` is\n * of the given rank:\n *   - `tf.reverse1d`\n *   - `tf.reverse2d`\n *   - `tf.reverse3d`\n *   - `tf.reverse4d`\n *\n * Except `tf.reverse1d` (which does not have axis param), all methods have\n * same signature as this method.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.reverse().print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.reverse(axis).print();\n * ```\n * @param x The input tensor to be reversed.\n * @param axis The set of dimensions to reverse. Must be in the\n *     range [-rank(x), rank(x)). Defaults to all axes.\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction reverse_<T extends Tensor>(\n    x: T|TensorLike, axis?: number|number[]): T {\n  const $x = convertToTensor(x, 'x', 'reverse');\n\n  if ($x.rank === 0) {\n    return $x.clone();\n  }\n  const axes = parseAxisParam(axis, $x.shape);\n  const grad = (dy: T) => {\n    return {$x: () => dy.reverse(axes)};\n  };\n  const res =\n      ENV.engine.runKernel(backend => backend.reverse($x, axes), {$x}, grad);\n  return res.reshapeAs($x);\n}\n\nexport const reverse = op({reverse_});\nexport const reverse1d = op({reverse1d_});\nexport const reverse2d = op({reverse2d_});\nexport const reverse3d = op({reverse3d_});\nexport const reverse4d = op({reverse4d_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {batchToSpaceND, spaceToBatchND} from './array_ops';\nimport * as conv_util from './conv_util';\nimport {op} from './operation';\n\n/**\n * Computes the 2D max pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction maxPoolImpl_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filterSize: [number, number]|number,\n    strides: [number, number]|number, dilations: [number, number]|number,\n    pad: 'valid'|'same'|number, dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'maxPool');\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n  }\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  util.assert(\n      x4D.rank === 4,\n      `Error in maxPool: input must be rank 4 but got rank ${x4D.rank}.`);\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      'Error in maxPool: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        `Error in maxPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n  const convInfo = conv_util.computePool2DInfo(\n      x4D.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n\n  const grad = (dy: Tensor4D, saved: Tensor[]) => {\n    const [y4D] = saved;\n    return {\n      x: () => maxPoolBackprop(\n          dy, x4D, y4D as Tensor4D, filterSize, strides, dilations, pad)\n    };\n  };\n\n  const res = ENV.engine.runKernel(\n      (backend, save) => save(backend.maxPool(x4D, convInfo)), {x: x4D}, grad);\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the 2D max pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm.\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *          https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction maxPool_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filterSize: [number, number]|number,\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  return maxPoolImpl_(x, filterSize, strides, 1, pad, dimRoundingMode);\n}\n\n/**\n * Computes the 2D average pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilations` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\nfunction avgPoolImpl_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filterSize: [number, number]|number,\n    strides: [number, number]|number, dilations: [number, number]|number,\n    pad: 'valid'|'same'|number, dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  const $x = convertToTensor(x, 'x', 'avgPool', 'float32');\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      'Error in avgPool: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n  }\n  util.assert(\n      x4D.rank === 4,\n      `Error in avgPool: x must be rank 4 but got rank ${x4D.rank}.`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        `Error in avgPool: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const convInfo = conv_util.computePool2DInfo(\n      x4D.shape, filterSize, strides, dilations, pad);\n\n  const grad = (dy: Tensor4D) => {\n    return {\n      x: () => avgPoolBackprop(dy, x4D, filterSize, strides, dilations, pad)\n    };\n  };\n  let res = ENV.engine.runKernel(\n      backend => backend.avgPool(x4D, convInfo), {x: x4D}, grad);\n  res = res.cast($x.dtype);\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the 2D average pooling of an image.\n *\n * @param x The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dimRoundingMode The rounding mode used when computing output\n *     dimensions if pad is a number. If none is provided, it will not round\n *     and error if the output is of fractional size.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction avgPool_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, filterSize: [number, number]|number,\n    strides: [number, number]|number, pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): T {\n  return avgPoolImpl_(x, filterSize, strides, 1, pad, dimRoundingMode);\n}\n\n/**\n * Performs an N-D pooling operation\n *\n * @param input The input tensor, of rank 4 or rank 3 of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param windowShape The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param poolingType The type of pooling, either 'max' or 'avg'.\n * @param pad The type of padding algorithm:\n *    - `same` and stride 1: output will be of same size as input,\n *       regardless of filter size.\n *    - `valid`: output will be smaller than input if filter is larger\n *       than 1x1.\n *    - For more info, see this guide:\n *     [https://www.tensorflow.org/api_guides/python/nn#Convolution](\n *         https://www.tensorflow.org/api_guides/python/nn#Convolution)\n * @param dilations The dilation rates: `[dilationHeight, dilationWidth]`\n *     in which we sample input values across the height and width dimensions\n *     in dilated pooling. Defaults to `[1, 1]`. If `dilationRate` is a single\n *     number, then `dilationHeight == dilationWidth`. If it is greater than\n *     1, then all values of `strides` must be 1.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n */\n/** @doc {heading: 'Operations', subheading: 'Convolution'} */\nfunction pool_<T extends Tensor3D|Tensor4D>(\n    input: T|TensorLike, windowShape: [number, number]|number,\n    poolingType: 'avg'|'max', pad: 'valid'|'same'|number,\n    dilations?: [number, number]|number, strides?: [number, number]|number) {\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  if (strides == null) {\n    strides = 1;\n  }\n  if (pad === 0) {\n    pad = 'valid';\n  }\n  const $x = convertToTensor(input, 'x', 'maxPool');\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n  }\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      'Error in pool: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n  const convInfo = conv_util.computePool2DInfo(\n      x4D.shape, windowShape, strides, dilations, pad);\n  const dilation: [number, number] =\n      [convInfo.dilationHeight, convInfo.dilationWidth];\n\n  // The following implementation does batchToSpace(pool(spaceToBatch(x)))\n  // whenever dilation > 1 since the TF kernels do not support dilation > 1.\n  // tslint:disable-next-line:max-line-length\n  // https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L1037\n\n  let basePadding: number[][];\n  if (pad === 'same') {\n    basePadding = withSpaceToBatchBasePaddings(\n        [convInfo.filterHeight, convInfo.filterWidth], dilation);\n  } else {\n    basePadding = [[0, 0], [0, 0]];\n  }\n  const isDilationOne = dilation[0] === 1 && dilation[1] === 1;\n  const [adjustedPadding, adjustedCrops] = requiredSpaceToBatchPaddings(\n      [convInfo.inHeight, convInfo.inWidth], dilation, basePadding);\n  const convertedPad = isDilationOne ? pad : 'valid';\n  const convertedX =\n      isDilationOne ? x4D : spaceToBatchND(x4D, dilation, adjustedPadding);\n  const forwardOp = poolingType === 'avg' ?\n      () => avgPoolImpl_(\n          convertedX, windowShape, strides, 1 /* dilation */, convertedPad) :\n      () => maxPoolImpl_(\n          convertedX, windowShape, strides, 1 /* dilation */, convertedPad);\n  const y = forwardOp();\n  const res = isDilationOne ? y : batchToSpaceND(y, dilation, adjustedCrops);\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the backprop of a max pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The original input image, of rank 4, of shape\n *     [batchSize, height, width, channels].\n * @param output The original output image, of rank 4, of shape\n *     [batchSize, outHeight, outWidth, channels].\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n * @param dimRoundingMode A string from: 'ceil', 'round', 'floor'. The\n *     rounding mode used when computing output dimensions if pad is a\n *     number. If none is provided, it will not round and error if the output\n *     is of fractional size.\n */\nfunction maxPoolBackprop(\n    dy: Tensor4D|TensorLike, input: Tensor4D|TensorLike,\n    output: Tensor4D|TensorLike, filterSize: [number, number]|number,\n    strides: [number, number]|number, dilations: [number, number]|number,\n    pad: 'valid'|'same'|number,\n    dimRoundingMode?: 'floor'|'round'|'ceil'): Tensor4D {\n  const $dy = convertToTensor(dy, 'dy', 'maxPoolBackprop');\n  const $input = convertToTensor(input, 'input', 'maxPoolBackprop');\n  const $output = convertToTensor(output, 'output', 'maxPoolBackprop');\n  util.assert(\n      $input.rank === $dy.rank,\n      `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      'Error in maxPoolBackProp: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n\n  util.assert(\n      $dy.rank === 4,\n      `Error in maxPoolBackprop: dy must be rank 4 but got rank ` +\n          `${$dy.rank}.`);\n  util.assert(\n      $input.rank === 4,\n      `Error in maxPoolBackprop: input must be rank 4 but got rank ` +\n          `${$input.rank}.`);\n  if (dimRoundingMode != null) {\n    util.assert(\n        util.isInt(pad as number),\n        `Error in maxPoolBackprop: pad must be an integer when using, ` +\n            `dimRoundingMode ${dimRoundingMode} but got pad ${pad}.`);\n  }\n\n  const convInfo = conv_util.computePool2DInfo(\n      $input.shape, filterSize, strides, dilations, pad, dimRoundingMode);\n  const res = ENV.engine.runKernel(\n      backend => backend.maxPoolBackprop($dy, $input, $output, convInfo),\n      {$dy, $input});\n  return res;\n}\n\n/**\n * Computes the backprop of an avg pool.\n *\n * @param dy The dy error, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param input The input image, of rank 4 or rank 3 of shape\n *     [batchSize, height, width, channels]. If rank 3, batch of 1 is\n * assumed.\n * @param filterSize The filter size: `[filterHeight, filterWidth]`. If\n *     `filterSize` is a single number, then `filterHeight == filterWidth`.\n * @param strides The strides of the pooling: `[strideHeight, strideWidth]`. If\n *     `strides` is a single number, then `strideHeight == strideWidth`.\n * @param pad A string from: 'same', 'valid'. The type of padding algorithm\n *     used in the forward prop of the op.\n */\nfunction avgPoolBackprop<T extends Tensor3D|Tensor4D>(\n    dy: T|TensorLike, input: T|TensorLike, filterSize: [number, number]|number,\n    strides: [number, number]|number, dilations: [number, number]|number,\n    pad: 'valid'|'same'|number): T {\n  const $dy = convertToTensor(dy, 'dy', 'avgPoolBackprop');\n  const $input = convertToTensor(input, 'input', 'avgPoolBackprop');\n  util.assert(\n      $input.rank === $dy.rank,\n      `Rank of input (${$input.rank}) does not match rank of dy (${$dy.rank})`);\n  if (dilations == null) {\n    dilations = [1, 1];\n  }\n  util.assert(\n      conv_util.eitherStridesOrDilationsAreOne(strides, dilations),\n      'Error in avgPoolBackprop: Either strides or dilations must be 1. ' +\n          `Got strides ${strides} and dilations '${dilations}'`);\n\n  let input4D = $input as Tensor4D;\n  let dy4D = $dy as Tensor4D;\n  let reshapedTo4D = false;\n  if ($input.rank === 3) {\n    reshapedTo4D = true;\n    input4D = $input.as4D(1, $input.shape[0], $input.shape[1], $input.shape[2]);\n    dy4D = $dy.as4D(1, $dy.shape[0], $dy.shape[1], $dy.shape[2]);\n  }\n\n  util.assert(\n      dy4D.rank === 4,\n      `Error in avgPoolBackprop: dy must be rank 4 but got rank ` +\n          `${dy4D.rank}.`);\n  util.assert(\n      input4D.rank === 4,\n      `Error in avgPoolBackprop: input must be rank 4 but got rank ` +\n          `${input4D.rank}.`);\n\n  const convInfo = conv_util.computePool2DInfo(\n      input4D.shape, filterSize, strides, dilations, pad);\n  const res = ENV.engine.runKernel(\n      backend => backend.avgPoolBackprop(dy4D, input4D, convInfo),\n      {dy4D, input4D});\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\n// Helper function to compute crops and paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/array_ops.py#L2184\nfunction requiredSpaceToBatchPaddings(\n    inputShape: [number, number], blockShape: [number, number],\n    basePadding: number[][]) {\n  const padStart = basePadding.map(b => b[0]);\n  const origPadEnd = basePadding.map(b => b[1]);\n  const fullInputShape = inputShape.concat(padStart, origPadEnd);\n  const padEndExtra = blockShape.map((b, i) => (b - fullInputShape[i] % b) % b);\n  const padEnd = origPadEnd.map((s, i) => s + padEndExtra[i]);\n  const paddings = blockShape.map((_, i) => [padStart[i], padEnd[i]]);\n  const crops = blockShape.map((_, i) => [0, padEndExtra[i]]);\n  return [paddings, crops];\n}\n\n// Helper function to compute base paddings for pool with dilation > 1.\n// tslint:disable-next-line:max-line-length\n// https://github.com/tensorflow/tensorflow/blob/50f6bb67dc98c9b74630b6047aae7a4f8a40fd02/tensorflow/python/ops/nn_ops.py#L524\nfunction withSpaceToBatchBasePaddings(\n    filterShape: [number, number], dilation: [number, number]) {\n  // Spatial dimensions of the filters and the upsampled filters in which we\n  // introduce (rate - 1) zeros between consecutive filter values.\n  const dilatedFilterShape = filterShape.map((s, i) => {\n    return s + (s - 1) * (dilation[i] - 1);\n  });\n  const padExtraShape = dilatedFilterShape.map(s => s - 1);\n\n  // When padding is odd, we pad more at end, following the same\n  // convention as conv2d.\n  const padExtraStart = padExtraShape.map(s => Math.floor(s / 2));\n  const padExtraEnd = padExtraShape.map((s, i) => s - padExtraStart[i]);\n  return padExtraShape.map((_, i) => {\n    return [padExtraStart[i], padExtraEnd[i]];\n  });\n}\n\nexport const maxPool = op({maxPool_});\nexport const avgPool = op({avgPool_});\nexport const pool = op({pool_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\nimport * as slice_util from './slice_util';\n\n/**\n * Extracts a 1D slice from 1D array starting at coordinates `begin` and is\n * of length `size`. See `slice` for details.\n */\nfunction slice1d_(\n    x: Tensor1D|TensorLike, begin: number, size: number): Tensor1D {\n  const $x = convertToTensor(x, 'x', 'slice1d');\n  util.assert(\n      $x.rank === 1,\n      `slice1d expects a rank-1 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, [begin], [size]);\n}\n\n/**\n * Extracts a 2D slice from a 2D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice2d_(\n    x: Tensor2D|TensorLike, begin: [number, number],\n    size: [number, number]): Tensor2D {\n  const $x = convertToTensor(x, 'x', 'slice2d');\n  util.assert(\n      $x.rank === 2,\n      `slice2d expects a rank-2 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, begin, size);\n}\n\n/**\n * Extracts a 3D slice from a 3D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice3d_(\n    x: Tensor3D|TensorLike, begin: [number, number, number],\n    size: [number, number, number]): Tensor3D {\n  const $x = convertToTensor(x, 'x', 'slice3d');\n  util.assert(\n      $x.rank === 3,\n      `slice3d expects a rank-3 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, begin, size);\n}\n\n/**\n * Extracts a 4D slice from a 4D array starting at coordinates `begin` and\n * is of size `size`. See `slice` for details.\n */\nfunction slice4d_(\n    x: Tensor4D|TensorLike, begin: [number, number, number, number],\n    size: [number, number, number, number]): Tensor4D {\n  const $x = convertToTensor(x, 'x', 'slice4d');\n  util.assert(\n      $x.rank === 4,\n      `slice4d expects a rank-4 tensor, but got a rank-${$x.rank} tensor`);\n  return slice($x, begin, size);\n}\n\n/**\n * Extracts a slice from a `tf.Tensor` starting at coordinates `begin`\n * and is of size `size`.\n *\n * Also available are stricter rank-specific methods with the same signature\n * as this method that assert that `x` is of the given rank:\n *   - `tf.slice1d`\n *   - `tf.slice2d`\n *   - `tf.slice3d`\n *   - `tf.slice4d`\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.slice([1], [2]).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * x.slice([1, 0], [1, 2]).print();\n * ```\n * @param x The input `tf.Tensor` to slice from.\n * @param begin The coordinates to start the slice from. The length can be\n *     less than the rank of x - the rest of the axes will have implicit 0 as\n *     start. Can also be a single number, in which case it specifies the\n *     first axis.\n * @param size The size of the slice. The length can be less than the rank of\n *     x - the rest of the axes will have implicit -1. A value of -1 requests\n *     the rest of the dimensions in the axis. Can also be a single number,\n *     in which case it specifies the size of the first axis.\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction slice_<R extends Rank, T extends Tensor<R>>(\n    x: T|TensorLike, begin: number|number[], size?: number|number[]): T {\n  const $x = convertToTensor(x, 'x', 'slice');\n\n  if ($x.rank === 0) {\n    throw new Error('Slicing scalar is not possible');\n  }\n  // The following logic allows for more ergonomic calls.\n  let begin_: number[];\n  if (typeof begin === 'number') {\n    begin_ = [begin, ...new Array($x.rank - 1).fill(0)];\n  } else if (begin.length < $x.rank) {\n    begin_ = begin.concat(new Array($x.rank - begin.length).fill(0));\n  } else {\n    begin_ = begin.slice();\n  }\n  let size_: number[];\n  if (size == null) {\n    size_ = new Array($x.rank).fill(-1);\n  } else if (typeof size === 'number') {\n    size_ = [size, ...new Array($x.rank - 1).fill(-1)];\n  } else if (size.length < $x.rank) {\n    size_ = size.concat(new Array($x.rank - size.length).fill(-1));\n  } else {\n    size_ = size;\n  }\n  size_ = size_.map((d, i) => {\n    if (d >= 0) {\n      return d;\n    } else {\n      util.assert(d === -1, 'Bad value in size');\n      return $x.shape[i] - begin_[i];\n    }\n  });\n  slice_util.assertParamsValid($x, begin_, size_);\n  const inputShape = $x.shape;\n  const grad = (dy: T) => {\n    // Create an Nx2 padding where the first column represents how many\n    // zeros are prepended (at start) for each dimension, and the second\n    // column indicates how many zeros are appended (at end).\n\n    // The number of zeros to append is the shape of the input\n    // elementwise-subtracted by both the begin vector and sizes vector.\n    const paddings: Array<[number, number]> = [];\n    for (let i = 0; i < dy.rank; i++) {\n      paddings.push([begin_[i], inputShape[i] - begin_[i] - size_[i]]);\n    }\n    return {$x: () => dy.pad(paddings)};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.slice($x, begin_, size_), {$x}, grad) as T;\n}\n\nexport const slice = op({slice_});\nexport const slice1d = op({slice1d_});\nexport const slice2d = op({slice2d_});\nexport const slice3d = op({slice3d_});\nexport const slice4d = op({slice4d_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {customGrad} from '../globals';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport {op} from './operation';\nimport {ones, scalar, zerosLike} from './tensor_ops';\n\n/**\n * Computes the log(sum(exp(elements across the reduction dimensions)).\n *\n * Reduces the input along the dimensions given in `axis`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.logSumExp().print();  // or tf.logSumExp(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.logSumExp(axis).print();  // or tf.logSumExp(a, axis)\n * ```\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. If null (the default),\n *     reduces all dimensions.\n * @param keepDims If true, retains reduced dimensions with length\n *     of 1. Defaults to false.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction logSumExp_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  const $x = convertToTensor(x, 'x', 'logSumExp');\n\n  const axes = axis_util.parseAxisParam(axis, $x.shape);\n  const xMax = $x.max(axes, true /* keepDims */);\n  const a = $x.sub(xMax);\n  const b = a.exp();\n  const c = b.sum(axes);\n  const d = c.log();\n  const res = xMax.reshape(d.shape).add(d);\n\n  if (keepDims) {\n    const newShape = axis_util.expandShapeToKeepDim(res.shape, axes);\n    return res.reshape(newShape) as T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the sum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If axes has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.sum().print();  // or tf.sum(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.sum(axis).print();  // or tf.sum(x, axis)\n * ```\n *\n * @param x The input tensor to compute the sum over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction sum_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  let $x = convertToTensor(x, 'x', 'sum');\n\n  if ($x.dtype === 'bool') {\n    $x = $x.toInt();\n  }\n  const axes = axis_util.parseAxisParam(axis, $x.shape);\n\n  // Use a custom gradient to bypass 2 gradient backprops since sum is used\n  // extremely often.\n  const customOp = customGrad(x => {\n    const permutation = axis_util.getAxesPermutation(axes, x.rank);\n    let reductionAxes = axes;\n    let permutedX = x;\n    if (permutation != null) {\n      permutedX = x.transpose(permutation);\n      reductionAxes = axis_util.getInnerMostAxes(reductionAxes.length, x.rank);\n    }\n    let value = ENV.engine.runKernel(\n        backend => backend.sum(permutedX, reductionAxes), {permutedX});\n    if (keepDims) {\n      const newShape = axis_util.expandShapeToKeepDim(value.shape, axes);\n      value = value.reshape(newShape);\n    }\n\n    const gradFunc = (dy: Tensor) => {\n      const expandedDyShape = x.shape.slice();\n      axes.forEach(axis => {\n        expandedDyShape[axis] = 1;\n      });\n      const expandedDy = dy.reshape(expandedDyShape);\n      const derX = expandedDy.mul(ones(x.shape, 'float32'));\n      return derX;\n    };\n    return {value, gradFunc};\n  });\n\n  return customOp($x) as T;\n}\n\n/**\n * Computes the product of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and a\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.prod().print();  // or tf.prod(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.prod(axis).print();  // or tf.prod(x, axis)\n * ```\n *\n * @param x The input tensor to compute the product over. If the dtype is `bool`\n *   it will be converted to `int32` and the output dtype will be `int32`.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction prod_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  let $x = convertToTensor(x, 'x', 'prod');\n\n  if ($x.dtype === 'bool') {\n    $x = $x.toInt();\n  }\n  const axes = axis_util.parseAxisParam(axis, $x.shape);\n\n  const permutation = axis_util.getAxesPermutation(axes, $x.rank);\n  let reductionAxes = axes;\n  let permutedX = $x;\n  if (permutation != null) {\n    permutedX = $x.transpose(permutation);\n    reductionAxes = axis_util.getInnerMostAxes(reductionAxes.length, $x.rank);\n  }\n  let value = ENV.engine.runKernel(\n      backend => backend.prod(permutedX, reductionAxes), {permutedX});\n  if (keepDims) {\n    const newShape = axis_util.expandShapeToKeepDim(value.shape, axes);\n    value = value.reshape(newShape);\n  }\n\n  return value as T;\n}\n/**\n * Computes the mean of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces `x` along the dimensions given in `axis`. Unless `keepDims` is\n * true, the rank of the `tf.Tensor` is reduced by 1 for each entry in `axis`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axis` has no entries, all dimensions are reduced, and a `tf.Tensor` with\n * a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.mean().print();  // or tf.mean(a)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.mean(axis).print();  // or tf.mean(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction mean_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  const $x = convertToTensor(x, 'x', 'mean');\n\n  const axes = axis_util.parseAxisParam(axis, $x.shape);\n  const shapes = axis_util.computeOutAndReduceShapes($x.shape, axes);\n  const reduceShape = shapes[1];\n  const reduceSize = util.sizeFromShape(reduceShape);\n\n  // Use a custom gradient to bypass 2 gradient backprops since mean is used\n  // extremely often.\n  const customOp = customGrad(x => {\n    const reduceSizeScalar = scalar(reduceSize);\n    // Cast if needed.\n    const xReduce =\n        reduceSizeScalar.dtype === x.dtype ? x : x.cast(reduceSizeScalar.dtype);\n    const res = xReduce.div(reduceSizeScalar);\n    const value = res.sum(axis, keepDims);\n\n    const gradFunc = (dy: Tensor) => {\n      const expandedDyShape = x.shape.slice();\n      axes.forEach(axis => {\n        expandedDyShape[axis] = 1;\n      });\n      const expandedDy = dy.reshape(expandedDyShape);\n      const derX =\n          expandedDy.mul(ones(x.shape, 'float32')).div(reduceSizeScalar);\n      return derX;\n    };\n    return {value, gradFunc};\n  });\n\n  return customOp($x) as T;\n}\n\n/**\n * Gradient helper function for the min and max operations.\n */\nfunction gradForMinAndMax<T extends Tensor>(\n    dy: T, saved: Tensor[], xOrig: Tensor, origAxes: number[],\n    permutedAxes: number[]) {\n  let [y] = saved;\n  if (y.rank < xOrig.rank) {\n    y = y.reshape(axis_util.expandShapeToKeepDim(y.shape, origAxes)) as T;\n  }\n  if (dy.rank < xOrig.rank) {\n    dy = dy.reshape(axis_util.expandShapeToKeepDim(dy.shape, origAxes)) as T;\n  }\n  return {\n    $x: () => {\n      const dx = dy.mul(xOrig.equal(y).cast(dy.dtype));\n      return permutedAxes == null ? dx : dx.transpose(permutedAxes);\n    }\n  };\n}\n\n/**\n * Computes the minimum value from the input.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the array is reduced by 1 for each entry in `axes`.\n * If `keepDims` is true, the reduced dimensions are retained with length 1.\n * If `axes` has no entries, all dimensions are reduced, and an array with a\n * single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.min().print();  // or tf.min(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.min(axis).print();  // or tf.min(x, axis)\n * ```\n *\n * @param x The input Tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction min_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  let $x = convertToTensor(x, 'x', 'min');\n  const xOrig = $x;\n\n  const origAxes = axis_util.parseAxisParam(axis, $x.shape);\n  let axes = origAxes;\n  const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n  if (permutedAxes != null) {\n    $x = $x.transpose(permutedAxes);\n    axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n  }\n\n  const grad = (dy: T, saved: Tensor[]) =>\n      gradForMinAndMax(dy, saved, xOrig, origAxes, permutedAxes);\n  let res = ENV.engine.runKernel(\n      (backend, save) => save(backend.min($x, axes)), {$x}, grad);\n  if (keepDims) {\n    const newShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);\n    res = res.reshape(newShape) as T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the maximum of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.max().print();  // or tf.max(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n *\n * const axis = 1;\n * x.max(axis).print();  // or tf.max(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction max_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  let $x = convertToTensor(x, 'x', 'max');\n  const xOrig = $x;\n\n  const origAxes = axis_util.parseAxisParam(axis, $x.shape);\n  let axes = origAxes;\n  const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n  if (permutedAxes != null) {\n    $x = $x.transpose(permutedAxes);\n    axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n  }\n\n  const grad = (dy: T, saved: Tensor[]) =>\n      gradForMinAndMax(dy, saved, xOrig, origAxes, permutedAxes);\n  let res = ENV.engine.runKernel(\n      (backend, save) => save(backend.max($x, axes)), {$x}, grad);\n  if (keepDims) {\n    const newShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);\n    res = res.reshape(newShape) as T;\n  }\n  return res as T;\n}\n\n/**\n * Returns the indices of the minimum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMin().print();  // or tf.argMin(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMin(axis).print();  // or tf.argMin(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n *\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction argMin_<T extends Tensor>(x: Tensor|TensorLike, axis = 0): T {\n  let $x = convertToTensor(x, 'x', 'argMin');\n\n  if (axis == null) {\n    axis = 0;\n  }\n  let axes = axis_util.parseAxisParam(axis, $x.shape);\n  const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n  if (permutedAxes != null) {\n    $x = $x.transpose(permutedAxes);\n    axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n  }\n  const grad = (dy: T) => {\n    return {$x: () => zerosLike($x)};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.argMin($x, axes[0]), {$x}, grad) as T;\n}\n\n/**\n * Returns the indices of the maximum values along an `axis`.\n *\n * The result has the same shape as `input` with the dimension along `axis`\n * removed.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3]);\n *\n * x.argMax().print();  // or tf.argMax(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 4, 3], [2, 2]);\n *\n * const axis = 1;\n * x.argMax(axis).print();  // or tf.argMax(x, axis)\n * ```\n *\n * @param x The input tensor.\n * @param axis The dimension to reduce. Defaults to 0 (outer-most dimension).\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction argMax_<T extends Tensor>(x: Tensor|TensorLike, axis = 0): T {\n  let $x = convertToTensor(x, 'x', 'argMax');\n\n  if (axis == null) {\n    axis = 0;\n  }\n  let axes = axis_util.parseAxisParam(axis, $x.shape);\n  const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n  if (permutedAxes != null) {\n    $x = $x.transpose(permutedAxes);\n    axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n  }\n  const grad = (dy: T) => {\n    return {$x: () => zerosLike($x)};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.argMax($x, axes[0]), {$x}, grad) as T;\n}\n\n/**\n * Computes the logical and of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1]);\n *\n * x.all().print();  // or tf.all(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.all(axis).print();  // or tf.all(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction all_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  let $x = convertToTensor(x, 'x', 'all', 'bool');\n\n  const origAxes = axis_util.parseAxisParam(axis, $x.shape);\n  let axes = origAxes;\n  const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n  if (permutedAxes != null) {\n    $x = $x.transpose(permutedAxes);\n    axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n  }\n  const res = ENV.engine.runKernel(backend => backend.all($x, axes), {$x});\n  if (keepDims) {\n    const newShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);\n    return res.reshape(newShape) as T;\n  }\n  return res as T;\n}\n\n/**\n * Computes the logical or of elements across dimensions of a `tf.Tensor`.\n *\n * Reduces the input along the dimensions given in `axes`. Unless `keepDims`\n * is true, the rank of the `tf.Tensor` is reduced by 1 for each entry in\n * `axes`. If `keepDims` is true, the reduced dimensions are retained with\n * length 1. If `axes` has no entries, all dimensions are reduced, and an\n * `tf.Tensor` with a single element is returned.\n *\n * ```js\n * const x = tf.tensor1d([1, 1, 1]);\n *\n * x.any().print();  // or tf.any(x)\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 1, 0, 0], [2, 2], 'bool');\n *\n * const axis = 1;\n * x.any(axis).print();  // or tf.any(x, axis)\n * ```\n *\n * @param x The input tensor. Must be of dtype bool.\n * @param axis The dimension(s) to reduce. By default it reduces\n *     all dimensions.\n * @param keepDims If true, retains reduced dimensions with size 1.\n */\n/** @doc {heading: 'Operations', subheading: 'Reduction'} */\nfunction any_<T extends Tensor>(\n    x: Tensor|TensorLike, axis: number|number[] = null, keepDims = false): T {\n  let $x = convertToTensor(x, 'x', 'any', 'bool');\n\n  const origAxes = axis_util.parseAxisParam(axis, $x.shape);\n  let axes = origAxes;\n  const permutedAxes = axis_util.getAxesPermutation(axes, $x.rank);\n  if (permutedAxes != null) {\n    $x = $x.transpose(permutedAxes);\n    axes = axis_util.getInnerMostAxes(axes.length, $x.rank);\n  }\n  const res = ENV.engine.runKernel(backend => backend.any($x, axes), {$x});\n  if (keepDims) {\n    const newShape = axis_util.expandShapeToKeepDim(res.shape, origAxes);\n    return res.reshape(newShape) as T;\n  }\n  return res as T;\n}\n\n/**\n * Calculates the mean and variance of `x`. The mean and variance are\n * calculated by aggregating the contents of `x` across `axes`. If `x` is\n * 1-D and `axes = [0]` this is just the mean and variance of a vector.\n *\n * @param x The input tensor.\n * @param axis The dimension(s) along with to compute mean and\n *     variance. By default it reduces all dimensions.\n * @param keepDims If true, the moments have the same dimensionality as the\n *     input.\n * @return An object with two keys: `mean` and `variance`.\n */\n/** @doc {heading: 'Operations', subheading: 'Normalization'} */\nfunction moments_(\n    x: Tensor|TensorLike, axis: number|number[] = null,\n    keepDims = false): {mean: Tensor, variance: Tensor} {\n  x = convertToTensor(x, 'x', 'moments');\n  const axes = axis_util.parseAxisParam(axis, x.shape);\n  const mean = x.mean(axes, keepDims);\n  let keepDimsShape = mean.shape;\n  if (!keepDims) {\n    keepDimsShape = axis_util.expandShapeToKeepDim(mean.shape, axes);\n  }\n  const devSquared = x.toFloat().sub(mean.reshape(keepDimsShape)).square();\n  const variance = devSquared.mean(axes, keepDims);\n  return {mean, variance};\n}\n\nexport const all = op({all_});\n// tslint:disable-next-line:variable-name\nexport const any = op({any_});\nexport const argMax = op({argMax_});\nexport const argMin = op({argMin_});\nexport const logSumExp = op({logSumExp_});\nexport const max = op({max_});\nexport const mean = op({mean_});\nexport const min = op({min_});\nexport const moments = op({moments_});\nexport const sum = op({sum_});\nexport const prod = op({prod_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assertShapesMatch} from '../util';\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\nimport {zerosLike} from './tensor_ops';\n\n/**\n * Returns the truth value of (a != b) element-wise. Supports broadcasting.\n *\n * We also expose `tf.notEqualStrict` which has the same signature as this op\n * and asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([0, 2, 3]);\n *\n * a.notEqual(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction notEqual_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'notEqual');\n  let $b = convertToTensor(b, 'b', 'notEqual');\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n  return ENV.engine.runKernel(backend => backend.notEqual($a, $b), {$a, $b}) as\n      T;\n}\n\n/**\n * Strict version of `tf.notEqual` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n *     `a`.\n */\nfunction notEqualStrict_<T extends Tensor>(\n    a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'notEqualStrict');\n  const $b = convertToTensor(b, 'b', 'notEqualStrict');\n  assertShapesMatch($a.shape, $b.shape, 'Error in notEqualStrict: ');\n  return $a.notEqual($b);\n}\n\n/**\n * Returns the truth value of (a < b) element-wise. Supports broadcasting.\n *\n * We also expose `tf.lessStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.less(b).print();\n * ```\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction less_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'less');\n  let $b = convertToTensor(b, 'b', 'less');\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  return ENV.engine.runKernel(backend => backend.less($a, $b), {$a, $b}) as T;\n}\n\n/**\n * Strict version of `tf.less` that forces `a` and `b` to be of the same\n * shape.\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same shape and dtype as\n *     `a`.\n */\nfunction lessStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'lessStrict');\n  const $b = convertToTensor(b, 'b', 'lessStrict');\n  assertShapesMatch($a.shape, $b.shape, 'Error in lessStrict: ');\n  return $a.less($b);\n}\n\n/**\n * Returns the truth value of (a == b) element-wise. Supports broadcasting.\n *\n * We also expose `tf.equalStrict` which has the same signature as this op\n * and asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.equal(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction equal_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'equal');\n  let $b = convertToTensor(b, 'b', 'equal');\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  return ENV.engine.runKernel(backend => backend.equal($a, $b), {$a, $b}) as T;\n}\n\nfunction equalStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'equalStrict');\n  const $b = convertToTensor(b, 'b', 'equalStrict');\n  assertShapesMatch($a.shape, $b.shape, 'Error in equalStrict: ');\n  return $a.equal($b);\n}\n\n/**\n * Returns the truth value of (a <= b) element-wise. Supports broadcasting.\n *\n * We also expose `tf.lessEqualStrict` which has the same signature as this op\n * and asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.lessEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction lessEqual_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'lessEqual');\n  let $b = convertToTensor(b, 'b', 'lessEqual');\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  return ENV.engine.runKernel(backend => backend.lessEqual($a, $b), {$a, $b}) as\n      T;\n}\n\nfunction lessEqualStrict_<T extends Tensor>(\n    a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'lessEqualStrict');\n  const $b = convertToTensor(b, 'b', 'lessEqualStrict');\n  assertShapesMatch($a.shape, $b.shape, 'Error in lessEqualStrict: ');\n  return $a.lessEqual($b);\n}\n\n/**\n * Returns the truth value of (a > b) element-wise. Supports broadcasting.\n *\n * We also expose `tf.greaterStrict` which has the same signature as this\n * op and asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greater(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction greater_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'greater');\n  let $b = convertToTensor(b, 'b', 'greater');\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  return ENV.engine.runKernel(backend => backend.greater($a, $b), {$a, $b}) as\n      T;\n}\n\nfunction greaterStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'greaterStrict');\n  const $b = convertToTensor(b, 'b', 'greaterStrict');\n  assertShapesMatch($a.shape, $b.shape, 'Error in greaterStrict: ');\n  return $a.greater($b);\n}\n\n/**\n * Returns the truth value of (a >= b) element-wise. Supports broadcasting.\n *\n * We also expose `tf.greaterEqualStrict` which has the same signature as this\n * op and asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3]);\n * const b = tf.tensor1d([2, 2, 2]);\n *\n * a.greaterEqual(b).print();\n * ```\n *\n * @param a The first input tensor.\n * @param b The second input tensor. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction greaterEqual_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'greaterEqual');\n  let $b = convertToTensor(b, 'b', 'greaterEqual');\n  [$a, $b] = makeTypesMatch($a, $b);\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const grad = (dy: T) => {\n    return {$a: () => zerosLike($a), $b: () => zerosLike($b)};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.greaterEqual($a, $b), {$a, $b}, grad) as T;\n}\n\nfunction greaterEqualStrict_<T extends Tensor>(\n    a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'greaterEqualStrict');\n  const $b = convertToTensor(b, 'b', 'greaterEqualStrict');\n  assertShapesMatch($a.shape, $b.shape, 'Error in greaterEqualStrict: ');\n  return $a.greaterEqual($b);\n}\n\nexport const equal = op({equal_});\nexport const equalStrict = op({equalStrict_});\nexport const greater = op({greater_});\nexport const greaterEqual = op({greaterEqual_});\nexport const greaterEqualStrict = op({greaterEqualStrict_});\nexport const greaterStrict = op({greaterStrict_});\nexport const less = op({less_});\nexport const lessEqual = op({lessEqual_});\nexport const lessEqualStrict = op({lessEqualStrict_});\nexport const lessStrict = op({lessStrict_});\nexport const notEqual = op({notEqual_});\nexport const notEqualStrict = op({notEqualStrict_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {KernelBackend} from '../kernels/backend';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {makeTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike, upcastType} from '../types';\nimport * as util from '../util';\nimport * as broadcast_util from './broadcast_util';\nimport {op} from './operation';\nimport {scalar} from './tensor_ops';\nimport {neg} from './unary_ops';\n\n/**\n * Adds two `tf.Tensor`s element-wise, A + B. Supports broadcasting.\n *\n * We also expose `tf.addStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n *\n * ```js\n * // Broadcast add a with b.\n * const a = tf.scalar(5);\n * const b = tf.tensor1d([10, 20, 30, 40]);\n *\n * a.add(b).print();  // or tf.add(a, b)\n * ```\n * @param a The first `tf.Tensor` to add.\n * @param b The second `tf.Tensor` to add. Must have the same type as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction add_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'add');\n  let $b = convertToTensor(b, 'b', 'add');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const outShape =\n      broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const der = (dy: Tensor) => {\n    const derA = () => {\n      let res = dy;\n      const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes);\n      }\n      return res.reshape($a.shape);\n    };\n    const derB = () => {\n      let res = dy;\n      const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes);\n      }\n      return res.reshape($b.shape);\n    };\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(backend => backend.add($a, $b), {$a, $b}, der) as\n      T;\n}\n\n/**\n * Adds a list of `tf.Tensor`s element-wise, each with the same shape and dtype.\n *\n * ```js\n * const a = tf.tensor1d([1, 2]);\n * const b = tf.tensor1d([3, 4]);\n * const c = tf.tensor1d([5, 6]);\n *\n * tf.addN([a, b, c]).print();\n * ```\n * @param tensors A list of tensors with the same shape and dtype.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction addN_<T extends Tensor>(tensors: Array<T|TensorLike>): T {\n  util.assert(\n      Array.isArray(tensors),\n      () => 'The argument passed to tf.addN() must be a list of tensors');\n  util.assert(\n      tensors.length >= 1,\n      () => `Must pass at least one tensor to tf.addN(), but got ` +\n          `${tensors.length}`);\n  const $tensors =\n      tensors.map((t, i) => convertToTensor(t, `tensors${i}`, 'addN'));\n  const firstTensor = $tensors[0];\n  $tensors.forEach(t => {\n    if (t.dtype !== firstTensor.dtype) {\n      throw new Error(\n          'All tensors passed to tf.addN() must have the same dtype');\n    }\n  });\n  $tensors.forEach(t => {\n    if (!util.arraysEqual(t.shape, firstTensor.shape)) {\n      throw new Error(\n          'All tensors passed to tf.addN() must have the same shape');\n    }\n  });\n\n  const der = (dy: T) => {\n    const ders: {[key: string]: () => Tensor} = {};\n    $tensors.forEach((t, i) => {\n      ders[i] = () => dy.clone();\n    });\n    return ders;\n  };\n  const inputs: NamedTensorMap = $tensors as {} as NamedTensorMap;\n  return ENV.engine.runKernel(backend => backend.addN($tensors), inputs, der);\n}\n\n/**\n * Adds two `tf.Tensor`s element-wise, A + B.\n *\n * Inputs must be the same shape. For broadcasting support, use add() instead.\n *\n * @param a The first Tensor to add element-wise.\n * @param b The second Tensor to add element-wise.\n */\nfunction addStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'addStrict');\n  const $b = convertToTensor(b, 'b', 'addStrict');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in addStrict: ');\n  return $a.add($b);\n}\n\n/**\n * Subtracts two `tf.Tensor`s element-wise, A - B. Supports broadcasting.\n *\n * We also expose `tf.subStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n *\n * ```js\n * // Broadcast subtract a with b.\n * const a = tf.tensor1d([10, 20, 30, 40]);\n * const b = tf.scalar(5);\n *\n * a.sub(b).print();  // or tf.sub(a, b)\n * ```\n * @param a The first `tf.Tensor` to subtract from.\n * @param b The second `tf.Tensor` to be subtracted. Must have the same dtype as\n * `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction sub_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'sub');\n  let $b = convertToTensor(b, 'b', 'sub');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const outShape =\n      broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const der = (dy: Tensor) => {\n    const derA = () => {\n      let res = dy;\n      const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes);\n      }\n      return res.reshape($a.shape);\n    };\n    const derB = () => {\n      let res = dy;\n      const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes);\n      }\n      return res.neg().reshape($b.shape);\n    };\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.subtract($a, $b), {$a, $b}, der) as T;\n}\n\n/**\n * Subtracts two `tf.Tensor`s element-wise, A - B. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.sub` instead.\n *\n * @param a The first Tensor to subtract element-wise.\n * @param b The second Tensor to subtract element-wise.\n */\nfunction subStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'subStrict');\n  const $b = convertToTensor(b, 'b', 'subStrict');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in subStrict: ');\n  return $a.sub($b);\n}\n\n/**\n * Computes the power of one `tf.Tensor` to another. Supports broadcasting.\n *\n * Given a `tf.Tensor` x and a `tf.Tensor` y, this operation computes x^y for\n * corresponding elements in x and y. The result's dtype will be the upcasted\n * type of the `base` and `exp` dtypes.\n *\n * ```js\n * const a = tf.tensor([[2, 3], [4, 5]])\n * const b = tf.tensor([[1, 2], [3, 0]]).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n *\n * ```js\n * const a = tf.tensor([[1, 2], [3, 4]])\n * const b = tf.tensor(2).toInt();\n *\n * a.pow(b).print();  // or tf.pow(a, b)\n * ```\n * We also expose `powStrict` which has the same signature as this op and\n * asserts that `base` and `exp` are the same shape (does not broadcast).\n *\n * @param base The base `tf.Tensor` to pow element-wise.\n * @param exp The exponent `tf.Tensor` to pow element-wise.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction pow_<T extends Tensor>(base: T|TensorLike, exp: Tensor|TensorLike): T {\n  const $base = convertToTensor(base, 'base', 'pow');\n  const $exp = convertToTensor(exp, 'exp', 'pow');\n\n  const outShape =\n      broadcast_util.assertAndGetBroadcastShape($base.shape, $exp.shape);\n  base = $base.cast(upcastType($base.dtype, $exp.dtype));\n  exp = $exp.cast(upcastType($base.dtype, $exp.dtype));\n  const grad = (dy: Tensor, saved: Tensor[]) => {\n    const [y] = saved;\n    const derBase = () => {\n      const expFloat = $exp.toFloat();\n      let res = dy.mul(expFloat.mul($base.pow(expFloat.sub(scalar(1)))));\n      const reduceAxes = broadcast_util.getReductionAxes($base.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes);\n      }\n      return res.reshape($base.shape) as T;\n    };\n    const derExp = () => {\n      let res = dy.mul(y.mul($base.log()).toFloat());\n      const reduceAxes = broadcast_util.getReductionAxes($exp.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes);\n      }\n      return res.reshape($exp.shape);\n    };\n    return {$base: derBase, $exp: derExp};\n  };\n  return ENV.engine.runKernel(\n             (backend, save) => save(backend.pow($base, $exp)), {$base, $exp},\n             grad) as T;\n}\n\n/**\n * Computes the power of one `tf.Tensor` to another. Inputs must\n * be the same shape.\n *\n * For broadcasting support, use `tf.pow` instead.\n *\n * @param base The base tensor to pow element-wise.\n * @param exp The exponent tensor to pow element-wise.\n */\nfunction powStrict_<T extends Tensor>(base: T, exp: Tensor): T {\n  util.assertShapesMatch(base.shape, exp.shape, 'Error in powStrict: ');\n  return base.pow(exp);\n}\n\n/**\n * Multiplies two `tf.Tensor`s element-wise, A * B. Supports broadcasting.\n *\n * We also expose `tf.mulStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.tensor1d([2, 3, 4, 5]);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n *\n * ```js\n * // Broadcast mul a with b.\n * const a = tf.tensor1d([1, 2, 3, 4]);\n * const b = tf.scalar(5);\n *\n * a.mul(b).print();  // or tf.mul(a, b)\n * ```\n * @param a The first tensor to multiply.\n * @param b The second tensor to multiply. Must have the same dtype as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction mul_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'mul');\n  let $b = convertToTensor(b, 'b', 'mul');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const outShape =\n      broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const der = (dy: Tensor) => {\n    const derA = () => {\n      const res = dy.mul($b.toFloat());\n      const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return res.sum(reduceAxes).reshape($a.shape);\n      }\n      return res;\n    };\n    const derB = () => {\n      const res = dy.mul($a.toFloat());\n      const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return res.sum(reduceAxes).reshape($b.shape);\n      }\n      return res;\n    };\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.multiply($a, $b), {$a, $b}, der) as T;\n}\n\n/**\n * Multiplies two `tf.Tensor`s element-wise, A * B.\n *\n * Inputs must be the same shape. For broadcasting support, use `tf.mul`.\n *\n * @param a The first tensor to multiply.\n * @param b The first tensor to multiply. Must have the same\n *    dtype as `a`.\n */\nfunction mulStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'mul');\n  const $b = convertToTensor(b, 'b', 'mul');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in multiplyStrict: ');\n  return $a.mul($b) as T;\n}\n\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n *\n * We also expose `tf.divStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.div(b).print();  // or tf.div(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction div_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'div');\n  let $b = convertToTensor(b, 'b', 'div');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  let forwardFunc: (backend: KernelBackend) => Tensor;\n  if ($a.dtype === 'int32' && $b.dtype === 'int32') {\n    return floorDiv($a, $b);\n  } else {\n    forwardFunc = (backend: KernelBackend) => backend.realDivide($a, $b);\n  }\n\n  const outShape =\n      broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n  const der = (dy: Tensor) => {\n    const derA = () => {\n      const res = dy.div($b.toFloat());\n      const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return res.sum(reduceAxes).reshape($a.shape);\n      }\n      return res;\n    };\n    const derB = () => {\n      let res = dy.mul($a.toFloat());\n      const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes).reshape($b.shape);\n      }\n      const tmp = $b.square() as Tensor;\n      return res.div(tmp.toFloat()).neg() as Tensor;\n    };\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(forwardFunc, {$a, $b}, der) as T;\n}\n\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Supports broadcasting.\n * The result is rounded with floor function.\n *\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 9, 16]);\n * const b = tf.tensor1d([1, 2, 3, 4]);\n *\n * a.floorDiv(b).print();  // or tf.div(a, b)\n * ```\n *\n * ```js\n * // Broadcast div a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(2);\n *\n * a.floorDiv(b).print();  // or tf.floorDiv(a, b)\n * ```\n *\n * @param a The first tensor as the numerator.\n * @param b The second tensor as the denominator. Must have the same dtype as\n * `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction floorDiv_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'floorDiv');\n  let $b = convertToTensor(b, 'b', 'floorDiv');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const forwardFunc = (backend: KernelBackend) => backend.floorDiv($a, $b);\n  const outShape =\n      broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n  const der = (dy: Tensor) => {\n    const derA = () => {\n      const res = dy.div($b.toFloat());\n      const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return res.sum(reduceAxes).reshape($a.shape);\n      }\n      return res;\n    };\n    const derB = () => {\n      let res = dy.mul($a.toFloat());\n      const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes).reshape($b.shape);\n      }\n      const tmp = $b.square() as Tensor;\n      return res.div(tmp.toFloat()).neg() as Tensor;\n    };\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(forwardFunc, {$a, $b}, der) as T;\n}\n\n/**\n * Divides two `tf.Tensor`s element-wise, A / B. Inputs must\n * be the same shape.\n *\n * @param a The first tensor as the numerator for element-wise division.\n * @param b The second tensor as the denominator for element-wise division.\n */\nfunction divStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'div');\n  const $b = convertToTensor(b, 'b', 'div');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in divideStrict: ');\n  return $a.div($b) as T;\n}\n\n/**\n * Returns the mod of a and b element-wise.\n * `floor(x / y) * y + mod(x, y) = x`\n * Supports broadcasting.\n *\n * We also expose `tf.modStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * ```js\n * // Broadcast a mod b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.mod(b).print();  // or tf.mod(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction mod_<T extends Tensor>(a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'mod');\n  let $b = convertToTensor(b, 'b', 'mod');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const outShape =\n      broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n  const der = (dy: Tensor) => {\n    const derA = () => {\n      const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return dy.sum(reduceAxes).reshape($a.shape);\n      }\n      return dy;\n    };\n    const derB = () => {\n      const res = dy.mul($a.div($b).floor().neg());\n      const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        return res.sum(reduceAxes).reshape($b.shape);\n      }\n      return res;\n    };\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(backend => backend.mod($a, $b), {$a, $b}, der) as\n      T;\n}\n\n/**\n * Returns the mod of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use mod().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction modStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'modStrict');\n  const $b = convertToTensor(b, 'b', 'modStrict');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in modStrict: ');\n  return $a.mod($b);\n}\n\n/**\n * Returns the min of a and b (`a < b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `minimumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * ```js\n * // Broadcast minimum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.minimum(b).print();  // or tf.minimum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction minimum_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'minimum');\n  let $b = convertToTensor(b, 'b', 'minimum');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  if ($a.dtype === 'bool') {\n    $a = $a.toInt();\n    $b = $b.toInt();\n  }\n\n  broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n  const der = (dy: Tensor) => {\n    const derA = () => dy.mul($a.lessEqual($b).toFloat());\n    const derB = () => dy.mul($a.greater($b).toFloat());\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.minimum($a, $b), {$a, $b}, der) as T;\n}\n\n/**\n * Returns the min of a and b (`a < b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use minimum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction minimumStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'minimumStrict');\n  const $b = convertToTensor(b, 'b', 'minimumStrict');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in minimumStrict: ');\n  return $a.minimum($b);\n}\n\n/**\n * Returns the max of a and b (`a > b ? a : b`) element-wise.\n * Supports broadcasting.\n *\n * We also expose `tf.maximumStrict` which has the same signature as this op and\n * asserts that `a` and `b` are the same shape (does not broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * ```js\n * // Broadcast maximum a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.maximum(b).print();  // or tf.maximum(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction maximum_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'maximum');\n  let $b = convertToTensor(b, 'b', 'maximum');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  if ($a.dtype === 'bool') {\n    $a = $a.toInt();\n    $b = $b.toInt();\n  }\n\n  broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n  const der = (dy: Tensor) => {\n    const derA = () => dy.mul($a.greaterEqual($b).toFloat());\n    const derB = () => dy.mul($a.less($b).toFloat());\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.maximum($a, $b), {$a, $b}, der) as T;\n}\n\n/**\n * Returns the max of a and b (`a > b ? a : b`) element-wise. Inputs must\n * be the same shape. For broadcasting support, use maximum().\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n */\nfunction maximumStrict_<T extends Tensor>(a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'maximumStrict');\n  const $b = convertToTensor(b, 'b', 'maximumStrict');\n  util.assertShapesMatch($a.shape, $b.shape, 'Error in maximumStrict: ');\n  return $a.maximum($b);\n}\n\n/**\n * Returns (a - b) * (a - b) element-wise.\n * Supports broadcasting.\n *\n * We also expose `tf.squaredDifferenceStrict` which has the same signature as\n * this op and asserts that `a` and `b` are the same shape (does not\n * broadcast).\n *\n * ```js\n * const a = tf.tensor1d([1, 4, 3, 16]);\n * const b = tf.tensor1d([1, 2, 9, 4]);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * ```js\n * // Broadcast squared difference  a with b.\n * const a = tf.tensor1d([2, 4, 6, 8]);\n * const b = tf.scalar(5);\n *\n * a.squaredDifference(b).print();  // or tf.squaredDifference(a, b)\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Arithmetic'} */\nfunction squaredDifference_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'squaredDifference');\n  let $b = convertToTensor(b, 'b', 'squaredDifference');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n  const der = (dy: Tensor) => {\n    const two = scalar(2);\n    const derA = () => dy.mul($a.sub($b).mul(two));\n    const derB = () => dy.mul($b.sub($a).mul(two));\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.squaredDifference($a, $b), {$a, $b}, der) as T;\n}\n\n/**\n * Returns (a - b) * (a - b) element-wise.\n *\n * Inputs must be the same shape. For broadcasting support, use\n * `tf.squaredDifference` instead.\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same type as `a`.\n */\nfunction squaredDifferenceStrict_<T extends Tensor>(\n    a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'squaredDifferenceStrict');\n  const $b = convertToTensor(b, 'b', 'squaredDifferenceStrict');\n  util.assertShapesMatch(\n      $a.shape, $b.shape, 'Error in squaredDifferenceStrict: ');\n  return $a.squaredDifference($b);\n}\n\n/**\n * Computes arctangent of `tf.Tensor`s a / b element-wise: `atan2(a, b)`.\n * Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([1.0, 1.0, -1.0, .7]);\n * const b = tf.tensor1d([2.0, 13.0, 3.5, .21]);\n *\n * tf.atan2(a, b).print()\n * ```\n *\n * @param a The first tensor.\n * @param b The second tensor. Must have the same dtype as `a`.\n *\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction atan2_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  let $a = convertToTensor(a, 'a', 'atan2');\n  let $b = convertToTensor(b, 'b', 'atan2');\n  [$a, $b] = makeTypesMatch($a, $b);\n\n  const outShape =\n      broadcast_util.assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  const der = (dy: Tensor) => {\n    const derA = () => {\n      const d = add($a.square(), $b.square());\n      let res = dy.mul($b.div(d));\n      const reduceAxes = broadcast_util.getReductionAxes($a.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes);\n      }\n      return res.reshape($a.shape);\n    };\n    const derB = () => {\n      const d = add($a.square(), $b.square()) as T;\n      let res = neg(dy.mul($a.div(d)));\n      const reduceAxes = broadcast_util.getReductionAxes($b.shape, outShape);\n      if (reduceAxes.length > 0) {\n        res = res.sum(reduceAxes);\n      }\n      return res.reshape($b.shape);\n    };\n    return {$a: derA, $b: derB};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.atan2($a, $b), {$a, $b}, der) as T;\n}\n\nexport const add = op({add_});\nexport const addN = op({addN_});\nexport const addStrict = op({addStrict_});\nexport const atan2 = op({atan2_});\nexport const div = op({div_});\nexport const divStrict = op({divStrict_});\nexport const floorDiv = op({floorDiv_});\nexport const maximum = op({maximum_});\nexport const maximumStrict = op({maximumStrict_});\nexport const minimum = op({minimum_});\nexport const minimumStrict = op({minimumStrict_});\nexport const mod = op({mod_});\nexport const modStrict = op({modStrict_});\nexport const mul = op({mul_});\nexport const mulStrict = op({mulStrict_});\nexport const pow = op({pow_});\nexport const powStrict = op({powStrict_});\nexport const squaredDifference = op({squaredDifference_});\nexport const squaredDifferenceStrict = op({squaredDifferenceStrict_});\nexport const sub = op({sub_});\nexport const subStrict = op({subStrict_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {whereImpl} from '../kernels/where_impl';\nimport {Tensor, Tensor2D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assert, assertShapesMatch} from '../util';\nimport {assertAndGetBroadcastShape} from './broadcast_util';\nimport {op} from './operation';\nimport {zerosLike} from './tensor_ops';\n\n/**\n * Returns the truth value of `NOT x` element-wise.\n *\n * ```js\n * const a = tf.tensor1d([false, true], 'bool');\n *\n * a.logicalNot().print();\n * ```\n *\n * @param x The input tensor. Must be of dtype 'bool'.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction logicalNot_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'logicalNot', 'bool');\n  return ENV.engine.runKernel(backend => backend.logicalNot($x), {$x});\n}\n\n/**\n * Returns the truth value of `a AND b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalAnd(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction logicalAnd_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'logicalAnd', 'bool');\n  const $b = convertToTensor(b, 'b', 'logicalAnd', 'bool');\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  return ENV.engine.runKernel(\n             backend => backend.logicalAnd($a, $b), {$a, $b}) as T;\n}\n\n/**\n * Returns the truth value of `a OR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalOr(b).print();\n * ```\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction logicalOr_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'logicalOr', 'bool');\n  const $b = convertToTensor(b, 'b', 'logicalOr', 'bool');\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  return ENV.engine.runKernel(backend => backend.logicalOr($a, $b), {$a, $b}) as\n      T;\n}\n\n/**\n * Returns the truth value of `a XOR b` element-wise. Supports broadcasting.\n *\n * ```js\n * const a = tf.tensor1d([false, false, true, true], 'bool');\n * const b = tf.tensor1d([false, true, false, true], 'bool');\n *\n * a.logicalXor(b).print();\n * ```\n *\n * @param a The first input tensor. Must be of dtype bool.\n * @param b The second input tensor. Must be of dtype bool.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction logicalXor_<T extends Tensor>(\n    a: Tensor|TensorLike, b: Tensor|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'logicalXor', 'bool');\n  const $b = convertToTensor(b, 'b', 'logicalXor', 'bool');\n  assertAndGetBroadcastShape($a.shape, $b.shape);\n\n  // x ^ y = (x | y) & ~(x & y)\n  return logicalOr(a, b).logicalAnd(logicalAnd(a, b).logicalNot()) as T;\n}\n\n/**\n * Returns the elements, either `a` or `b` depending on the `condition`.\n *\n * If the condition is true, select from `a`, otherwise select from `b`.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * const a = tf.tensor1d([1 , 2, 3]);\n * const b = tf.tensor1d([-1, -2, -3]);\n *\n * a.where(cond, b).print();\n * ```\n *\n * @param condition The input condition. Must be of dtype bool.\n * @param a If `condition` is rank 1, `a` may have a higher rank but\n *     its first dimension must match the size of `condition`.\n * @param b A tensor with the same shape and type as `a`.\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nfunction where_<T extends Tensor>(\n    condition: Tensor|TensorLike, a: T|TensorLike, b: T|TensorLike): T {\n  const $a = convertToTensor(a, 'a', 'where');\n  const $b = convertToTensor(b, 'b', 'where');\n  const $condition = convertToTensor(condition, 'condition', 'where', 'bool');\n\n  assertShapesMatch($a.shape, $b.shape, 'Error in where: ');\n\n  if ($condition.rank === 1) {\n    // If condition rank is 1, then the first dimension must match the size of\n    // condition.\n    assert(\n        $condition.shape[0] === $a.shape[0],\n        'The first dimension of `a` must match the size of `condition`.');\n  } else {\n    // A must have the same shape as condition.\n    assertShapesMatch($condition.shape, $b.shape, 'Error in where: ');\n  }\n\n  // TODO(julianoks): Return null for condition gradient\n  // when backprop supports it.\n  const grad = (dy: T) => ({\n    $condition: () => zerosLike($condition).toFloat(),\n    $a: () => dy.mul($condition.cast(dy.dtype)) as T,\n    $b: () => dy.mul($condition.logicalNot().cast(dy.dtype)) as T\n  });\n\n  return ENV.engine.runKernel(\n             backend => backend.select($condition, $a, $b),\n             {$condition, $a, $b}, grad) as T;\n}\n\n/**\n * Returns the coordinates of true elements of condition.\n *\n * The coordinates are returned in a 2-D tensor where the first dimension (rows)\n * represents the number of true elements, and the second dimension (columns)\n * represents the coordinates of the true elements. Keep in mind, the shape of\n * the output tensor can vary depending on how many true values there are in\n * input. Indices are output in row-major order. The resulting tensor has the\n * shape `[numTrueElems, condition.rank]`.\n *\n * This is analogous to calling the python `tf.where(cond)` without an x or y.\n *\n * ```js\n * const cond = tf.tensor1d([false, false, true], 'bool');\n * tf.whereAsync(cond).then(result => result.print());\n * ```\n */\n/** @doc {heading: 'Operations', subheading: 'Logical'} */\nasync function whereAsync_(condition: Tensor|TensorLike): Promise<Tensor2D> {\n  const $condition =\n      convertToTensor(condition, 'condition', 'whereAsync', 'bool');\n  const vals = await $condition.data();\n  const res = whereImpl($condition.shape, vals);\n  if (condition !== $condition) {\n    $condition.dispose();\n  }\n  return res;\n}\n\nexport const logicalAnd = op({logicalAnd_});\nexport const logicalNot = op({logicalNot_});\nexport const logicalOr = op({logicalOr_});\nexport const logicalXor = op({logicalXor_});\nexport const where = op({where_});\nexport const whereAsync = whereAsync_;\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {maximum, minimum} from './binary_ops';\nimport {where} from './logical_ops';\nimport {op} from './operation';\nimport {SELU_SCALE, SELU_SCALEALPHA} from './selu_util';\nimport {scalar} from './tensor_ops';\n\n/**\n * Computes rectified linear element-wise: `max(x, 0)`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.relu().print();  // or tf.relu(x)\n * ```\n * @param x The input tensor. If the dtype is `bool`, the output dtype will be\n *     `int32'.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction relu_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'relu');\n\n  if ($x.dtype === 'bool') {\n    return $x.toInt();\n  }\n  const grad = (dy: T) => {\n    const stepRes = $x.step();\n    return {$x: () => dy.mulStrict(stepRes.toFloat())};\n  };\n  return ENV.engine.runKernel(backend => backend.relu($x), {$x}, grad);\n}\n\n/**\n * Computes exponential linear element-wise: `x > 0 ? e ^ x - 1 : 0`.\n *\n * ```js\n * const x = tf.tensor1d([-1, 1, -3, 2]);\n *\n * x.elu().print();  // or tf.elu(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction elu_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'elu');\n\n  const grad = (dy: T, saved: Tensor[]) => {\n    const [y] = saved;\n    return {\n      $x: () =>\n          ENV.engine.runKernel(backend => backend.eluDer(dy, y), {dy, y}) as T\n    };\n  };\n  return ENV.engine.runKernel(\n      (backend, save) => save(backend.elu($x)), {$x}, grad);\n}\n\n/**\n * Computes scaled exponential linear element-wise.\n *\n * `x < 0 ? scale * alpha * (exp(x) - 1) : x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.selu().print();  // or tf.selu(x)\n * ```\n * @param x The input tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction selu_<T extends Tensor>(x: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'selu');\n\n  const grad = (dy: T) => {\n    return {\n      $x: () => {\n        const mask = $x.greater(scalar(0));\n\n        const scaleAlpha = scalar(SELU_SCALEALPHA);\n        const scale = scalar(SELU_SCALE);\n\n        const greaterThanZeroDer = dy.mul(scale);\n        const lessEqualZeroDer = dy.mul(scaleAlpha).mul($x.toFloat().exp());\n\n        return where(mask, greaterThanZeroDer, lessEqualZeroDer) as T;\n      }\n    };\n  };\n  return ENV.engine.runKernel(backend => backend.selu($x), {$x}, grad);\n}\n\n/**\n * Computes leaky rectified linear element-wise.\n *\n * See\n * [http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf](\n *     http://web.stanford.edu/~awni/papers/relu_hybrid_icml2013_final.pdf)\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n *\n * x.leakyRelu(0.1).print();  // or tf.leakyRelu(x, 0.1)\n * ```\n * @param x The input tensor.\n * @param alpha The scaling factor for negative values, defaults to 0.2.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction leakyRelu_<T extends Tensor>(x: T|TensorLike, alpha = 0.2): T {\n  const $x = convertToTensor(x, 'x', 'leakyRelu');\n  return maximum(scalar(alpha).mul($x), $x);\n}\n\n/**\n * Computes leaky rectified linear element-wise with parametric alphas.\n *\n * `x < 0 ? alpha * x : f(x) = x`\n *\n * ```js\n * const x = tf.tensor1d([-1, 2, -3, 4]);\n * const alpha = tf.scalar(0.1);\n *\n * x.prelu(alpha).print();  // or tf.prelu(x, alpha)\n * ```\n * @param x The input tensor.\n * @param alpha Scaling factor for negative values.\n */\n/** @doc {heading: 'Operations', subheading: 'Basic math'} */\nfunction prelu_<T extends Tensor>(x: T|TensorLike, alpha: T|TensorLike): T {\n  const $x = convertToTensor(x, 'x', 'prelu');\n  const $alpha = convertToTensor(alpha, 'alpha', 'prelu');\n\n  const zero = scalar(0);\n  return maximum(zero, $x).add($alpha.mul(minimum(zero, $x)));\n}\n\nexport const elu = op({elu_});\nexport const leakyRelu = op({leakyRelu_});\nexport const prelu = op({prelu_});\nexport const relu = op({relu_});\nexport const selu = op({selu_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport * as axis_util from './axis_util';\nimport {op} from './operation';\n\n/**\n * Transposes the `tf.Tensor`. Permutes the dimensions according to `perm`.\n *\n * The returned `tf.Tensor`'s dimension `i` will correspond to the input\n * dimension `perm[i]`. If `perm` is not given, it is set to `[n-1...0]`,\n * where `n` is the rank of the input `tf.Tensor`. Hence by default, this\n * operation performs a regular matrix transpose on 2-D input `tf.Tensor`s.\n *\n * ```js\n * const a = tf.tensor2d([1, 2, 3, 4, 5, 6], [2, 3]);\n *\n * a.transpose().print();  // or tf.transpose(a)\n * ```\n *\n * @param x The tensor to transpose.\n * @param perm The permutation of the dimensions of a.\n */\n/** @doc {heading: 'Operations', subheading: 'Matrices'} */\nfunction transpose_<T extends Tensor>(x: T|TensorLike, perm?: number[]): T {\n  const $x = convertToTensor(x, 'x', 'transpose');\n\n  if (perm == null) {\n    perm = $x.shape.map((s, i) => i).reverse();\n  }\n  util.assert(\n      $x.rank === perm.length,\n      `Error in transpose: rank of input ${$x.rank} ` +\n          `must match length of perm ${perm}.`);\n  perm.forEach(axis => {\n    util.assert(\n        axis >= 0 && axis < $x.rank,\n        `All entries in 'perm' must be between 0 and ${$x.rank - 1}` +\n            ` but got ${perm}`);\n  });\n\n  if ($x.rank <= 1) {\n    return $x.clone();\n  }\n\n  const der = (dy: T) => {\n    const undoPerm = axis_util.getUndoAxesPermutation(perm);\n    return {$x: () => dy.transpose(undoPerm)};\n  };\n  return ENV.engine.runKernel(\n      backend => backend.transpose($x, perm), {$x}, der);\n}\n\nexport const transpose = op({transpose_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\n\n/**\n * Normalizes the activation of a local neighborhood across or within\n * channels.\n *\n * @param x The input tensor. The 4-D input tensor is treated as a 3-D array\n *     of 1D vectors (along the last dimension), and each vector is\n *     normalized independently.\n * @param depthRadius The number of adjacent channels in the 1D normalization\n *     window.\n * @param bias A constant bias term for the basis.\n * @param alpha A scale factor, usually positive.\n * @param beta An exponent.\n */\n/** @doc {heading: 'Operations', subheading: 'Normalization'} */\nfunction localResponseNormalization_<T extends Tensor3D|Tensor4D>(\n    x: T|TensorLike, depthRadius = 5, bias = 1, alpha = 1, beta = 0.5): T {\n  const $x = convertToTensor(x, 'x', 'localResponseNormalization');\n  util.assert(\n      $x.rank === 4 || $x.rank === 3,\n      `Error in localResponseNormalization: x must be rank 3 or 4 but got\n               rank ${$x.rank}.`);\n  util.assert(\n      util.isInt(depthRadius),\n      `Error in localResponseNormalization: depthRadius must be an integer\n                     but got depthRadius ${depthRadius}.`);\n\n  let x4D = $x as Tensor4D;\n  let reshapedTo4D = false;\n  if ($x.rank === 3) {\n    reshapedTo4D = true;\n    x4D = $x.as4D(1, $x.shape[0], $x.shape[1], $x.shape[2]);\n  }\n  const backward = (dy: Tensor4D, saved: Tensor[]) => {\n    const [outputImage] = saved;\n    return {\n      x4D: () => ENV.engine.runKernel(\n          backend => backend.LRNGrad(\n              dy, x4D, outputImage as Tensor4D, depthRadius, bias, alpha, beta),\n          {})\n    };\n  };\n  const res = ENV.engine.runKernel(\n      (backend, save) => save(backend.localResponseNormalization4D(\n          x4D, depthRadius, bias, alpha, beta)),\n      {x4D}, backward);\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  } else {\n    return res as T;\n  }\n}\n\nexport const localResponseNormalization = op({localResponseNormalization_});","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as axis_util from './axis_util';\nimport {op} from './operation';\nimport {scalar} from './tensor_ops';\n\n/**\n * Computes the norm of scalar, vectors, and matrices.\n * This function can compute several different vector norms (the 1-norm, the\n * Euclidean or 2-norm, the inf-norm, and in general the p-norm for p > 0)\n * and matrix norms (Frobenius, 1-norm, and inf-norm).\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n *\n * x.norm().print();  // or tf.norm(x)\n * ```\n *\n * @param x The input array.\n * @param ord Optional. Order of the norm. Supported norm types are\n * following:\n *\n *  | ord        | norm for matrices         | norm for vectors\n *  |------------|---------------------------|---------------------\n *  |'euclidean' |Frobenius norm             |2-norm\n *  |'fro'       |Frobenius norm\t           |\n *  |Infinity    |max(sum(abs(x), axis=1))   |max(abs(x))\n *  |-Infinity   |min(sum(abs(x), axis=1))   |min(abs(x))\n *  |1           |max(sum(abs(x), axis=0))   |sum(abs(x))\n *  |2           |                           |sum(abs(x)^2)^1/2*\n *\n * @param axis Optional. If axis is null (the default), the input is\n * considered a vector and a single vector norm is computed over the entire\n * set of values in the Tensor, i.e. norm(x, ord) is equivalent\n * to norm(x.reshape([-1]), ord). If axis is a integer, the input\n * is considered a batch of vectors, and axis determines the axis in x\n * over which to compute vector norms. If axis is a 2-tuple of integer it is\n * considered a batch of matrices and axis determines the axes in NDArray\n * over which to compute a matrix norm.\n * @param keepDims Optional. If true, the norm have the same dimensionality\n * as the input.\n */\n/** @doc {heading: 'Operations', subheading: 'Matrices'} */\nfunction norm_(\n    x: Tensor|TensorLike, ord: number|'euclidean'|'fro' = 'euclidean',\n    axis: number|number[] = null, keepDims = false): Tensor {\n  x = convertToTensor(x, 'x', 'norm');\n\n  const norm = normImpl(x, ord, axis);\n  let keepDimsShape = norm.shape;\n  if (keepDims) {\n    const axes = axis_util.parseAxisParam(axis, x.shape);\n    keepDimsShape = axis_util.expandShapeToKeepDim(norm.shape, axes);\n  }\n  return norm.reshape(keepDimsShape);\n}\n\nfunction normImpl(\n    x: Tensor, p: number|string, axis: number|number[] = null): Tensor {\n  if (x.rank === 0) {\n    return x.abs();\n  }\n\n  // consider vector when no axis is specified\n  if (x.rank !== 1 && axis === null) {\n    return normImpl(x.reshape([-1]), p, axis);\n  }\n\n  // vector\n  if (x.rank === 1 || typeof axis === 'number' ||\n      axis instanceof Array && axis.length === 1) {\n    if (p === 1) {\n      return x.abs().sum(axis);\n    }\n    if (p === Infinity) {\n      return x.abs().max(axis);\n    }\n    if (p === -Infinity) {\n      return x.abs().min(axis);\n    }\n    if (p === 'euclidean' || p === 2) {\n      // norm(x, 2) = sum(abs(xi) ^ 2) ^ 1/2\n      return x.abs().pow(scalar(2, 'int32')).sum(axis).sqrt() as Tensor;\n    }\n\n    throw new Error(`Error in norm: invalid ord value: ${p}`);\n  }\n\n  // matrix (assumption axis[0] < axis[1])\n  if (axis instanceof Array && axis.length === 2) {\n    if (p === 1) {\n      return x.abs().sum(axis[0]).max(axis[1] - 1);\n    }\n    if (p === Infinity) {\n      return x.abs().sum(axis[1]).max(axis[0]);\n    }\n    if (p === -Infinity) {\n      return x.abs().sum(axis[1]).min(axis[0]);\n    }\n    if (p === 'fro' || p === 'euclidean') {\n      // norm(x) = sqrt(sum(pow(x, 2)))\n      return x.square().sum(axis).sqrt();\n    }\n\n    throw new Error(`Error in norm: invalid ord value: ${p}`);\n  }\n\n  throw new Error(`Error in norm: invalid axis: ${axis}`);\n}\n\nexport const norm = op({norm_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor, Tensor1D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assert, isInt} from '../util';\nimport {expandDims} from './array_ops';\nimport {getUndoAxesPermutation, parseAxisParam} from './axis_util';\nimport {maximum} from './binary_ops';\nimport {greaterEqual} from './compare';\nimport {logicalAnd, where} from './logical_ops';\nimport {op} from './operation';\nimport {ones, scalar, zerosLike} from './tensor_ops';\n\n/**\n * Computes the sum along segments of a `tf.Tensor`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const segmentIds = tf.tensor1d([1, 2, 0, 1], 'int32');\n * const numSegments = 3;\n *\n * x.unsortedSegmentSum(segmentIds, numSegments).print()\n * //or tf.unsortedSegmentSum(x, segmentIds, numSegments)\n * ```\n * @param x The `tf.Tensor` that will be summed along its segments.\n * @param segmentIds A `tf.Tensor1D` whose rank is equal to the rank of `x`'s\n * dimension along the `axis`.  Maps each element of `x` to a segment.\n * @param numSegments The number of distinct `segmentIds`.\n */\n/** @doc {heading: 'Operations', subheading: 'Segment'} */\nfunction unsortedSegmentSum_<T extends Tensor>(\n    x: T|TensorLike, segmentIds: Tensor1D|TensorLike, numSegments: number): T {\n  const $x = convertToTensor(x, 'x', 'unsortedSegmentSum');\n  const $segmentIds =\n      convertToTensor(segmentIds, 'segmentIds', 'unsortedSegmentSum', 'int32');\n  assert(isInt(numSegments), 'numSegments must be of dtype int');\n\n  const gradFunc = (dy: T) => {\n    const derX = () => {\n      return gatherDropNegatives(dy, $segmentIds);\n    };\n    return {$x: derX};\n  };\n  return ENV.engine.runKernel(\n             backend =>\n                 backend.unsortedSegmentSum($x, $segmentIds, numSegments),\n             {$x}, gradFunc) as T;\n}\n\n/**\n * Gather slices from tensor `x`'s axis `axis` according to `indices`.\n *\n * ```js\n * const x = tf.tensor1d([1, 2, 3, 4]);\n * const indices = tf.tensor1d([1, 3, 3], 'int32');\n *\n * x.gather(indices).print();\n * ```\n *\n * ```js\n * const x = tf.tensor2d([1, 2, 3, 4], [2, 2]);\n * const indices = tf.tensor1d([1, 1, 0], 'int32');\n *\n * x.gather(indices).print();\n * ```\n * @param x The input tensor whose slices to be gathered.\n * @param indices The indices of the values to extract.\n * @param axis The axis over which to select values. Defaults to 0.\n */\n/** @doc {heading: 'Tensors', subheading: 'Slicing and Joining'} */\nfunction gather_<T extends Tensor>(\n    x: T|TensorLike, indices: Tensor1D|TensorLike, axis = 0): T {\n  const $x = convertToTensor(x, 'x', 'gather');\n  const $indices = convertToTensor(indices, 'indices', 'gather', 'int32');\n  axis = parseAxisParam(axis, $x.shape)[0];\n  const grad = (dy: T) => {\n    const derX = () => {\n      if (axis === 0) {\n        return unsortedSegmentSum(dy, $indices, $x.shape[axis]);\n      }\n      const paramsShape = $x.shape;\n      const indicesSize = $indices.size;\n\n      const outerShape = paramsShape.slice(0, axis);\n      const outerDims = outerShape.length;\n      const innerShape = paramsShape.slice(axis, paramsShape.length).slice(1);\n      const innerDims = innerShape.length;\n\n      const outerAxesIndices = arrayRange(0, outerDims);\n      const innerAxesIndices =\n          arrayRange(outerDims + 1, outerDims + 1 + innerDims);\n\n      const valuesShape = arrayConcat([outerShape, [indicesSize], innerShape]);\n\n      const values = dy.reshape(valuesShape);\n      const reshapedIndices = $indices.reshape([indicesSize]);\n\n      const transposeDims =\n          arrayConcat([[outerDims], outerAxesIndices, innerAxesIndices]);\n      const valuesTranspose = values.transpose(transposeDims);\n\n      let paramsGrad = unsortedSegmentSum(\n          valuesTranspose, reshapedIndices as Tensor1D, $x.shape[axis]);\n\n      const invertTransposeDims = getUndoAxesPermutation(transposeDims);\n      paramsGrad = paramsGrad.transpose(invertTransposeDims);\n\n      return paramsGrad as T;\n    };\n    return {$x: derX};\n  };\n  return ENV.engine.runKernel(\n             backend => backend.gather($x, $indices as Tensor1D, axis), {$x},\n             grad) as T;\n}\n\nfunction arrayRange(start: number, stop: number): number[] {\n  const result = [];\n  for (let i = start; i < stop; ++i) {\n    result.push(i);\n  }\n  return result;\n}\n\nfunction arrayConcat(arrays: number[][]): number[] {\n  const result = [];\n  for (let i = 0; i < arrays.length; ++i) {\n    for (let j = 0; j < arrays[i].length; ++j) {\n      result.push(arrays[i][j]);\n    }\n  }\n  return result;\n}\n\nfunction gatherDropNegatives<T extends Tensor>(x: T, indices: Tensor1D) {\n  // Helper function for unsorted segment ops. Gathers params for\n  // positive segment ids and gathers 0 for inputs with negative segment id.\n  // Mirrors _GatherDropNegatives from tensorflow/python/ops/math_grad.py\n  const zeroClippedIndices = maximum(indices, zerosLike(indices));\n  const gathered = gather(x, zeroClippedIndices as Tensor1D);\n  let isPositive = greaterEqual(indices, scalar(0, 'int32'));\n  const numIters = gathered.rank - isPositive.rank;\n  for (let i = 0; i < numIters; ++i) {\n    isPositive = expandDims(isPositive, i + 1);\n  }\n  isPositive = logicalAnd(isPositive, ones(gathered.shape, 'bool'));\n  const zeroSlice = zerosLike(gathered);\n  return where(isPositive, gathered, zeroSlice);\n}\n\nexport const gather = op({gather_});\nexport const unsortedSegmentSum = op({unsortedSegmentSum_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Scalar, Tensor1D, Tensor2D} from '../tensor';\nimport {convertToTensor, convertToTensorArray} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * @docalias (data: Tensor2D, c: Tensor2D, h: Tensor2D): [Tensor2D, Tensor2D]\n */\nexport type LSTMCellFunc = {\n  (data: Tensor2D, c: Tensor2D, h: Tensor2D): [Tensor2D, Tensor2D];\n};\n\n/**\n * Computes the next states and outputs of a stack of LSTMCells.\n *\n * Each cell output is used as input to the next cell.\n *\n * Returns `[cellState, cellOutput]`.\n *\n * Derived from tf.contrib.rn.MultiRNNCell.\n *\n * @param lstmCells Array of LSTMCell functions.\n * @param data The input to the cell.\n * @param c Array of previous cell states.\n * @param h Array of previous cell outputs.\n */\n/** @doc {heading: 'Operations', subheading: 'RNN'} */\nfunction multiRNNCell_(\n    lstmCells: LSTMCellFunc[], data: Tensor2D|TensorLike,\n    c: Array<Tensor2D|TensorLike>,\n    h: Array<Tensor2D|TensorLike>): [Tensor2D[], Tensor2D[]] {\n  const $data = convertToTensor(data, 'data', 'multiRNNCell');\n  const $c = convertToTensorArray(c, 'c', 'multiRNNCell');\n  const $h = convertToTensorArray(h, 'h', 'multiRNNCell');\n\n  let input = $data;\n  const newStates = [];\n  for (let i = 0; i < lstmCells.length; i++) {\n    const output = lstmCells[i](input, $c[i], $h[i]);\n    newStates.push(output[0]);\n    newStates.push(output[1]);\n    input = output[1];\n  }\n  const newC: Tensor2D[] = [];\n  const newH: Tensor2D[] = [];\n  for (let i = 0; i < newStates.length; i += 2) {\n    newC.push(newStates[i]);\n    newH.push(newStates[i + 1]);\n  }\n  return [newC, newH];\n}\n\n/**\n * Computes the next state and output of a BasicLSTMCell.\n *\n * Returns `[newC, newH]`.\n *\n * Derived from tf.contrib.rnn.BasicLSTMCell.\n *\n * @param forgetBias Forget bias for the cell.\n * @param lstmKernel The weights for the cell.\n * @param lstmBias The bias for the cell.\n * @param data The input to the cell.\n * @param c Previous cell state.\n * @param h Previous cell output.\n */\n/** @doc {heading: 'Operations', subheading: 'RNN'} */\nfunction basicLSTMCell_(\n    forgetBias: Scalar|TensorLike, lstmKernel: Tensor2D|TensorLike,\n    lstmBias: Tensor1D|TensorLike, data: Tensor2D|TensorLike,\n    c: Tensor2D|TensorLike, h: Tensor2D|TensorLike): [Tensor2D, Tensor2D] {\n  const $forgetBias =\n      convertToTensor(forgetBias, 'forgetBias', 'basicLSTMCell');\n  const $lstmKernel =\n      convertToTensor(lstmKernel, 'lstmKernel', 'basicLSTMCell');\n  const $lstmBias = convertToTensor(lstmBias, 'lstmBias', 'basicLSTMCell');\n  const $data = convertToTensor(data, 'data', 'basicLSTMCell');\n  const $c = convertToTensor(c, 'c', 'basicLSTMCell');\n  const $h = convertToTensor(h, 'h', 'basicLSTMCell');\n\n  const combined = $data.concat($h, 1);\n  const weighted = combined.matMul($lstmKernel);\n  const res = weighted.add($lstmBias) as Tensor2D;\n\n  // i = input_gate, j = new_input, f = forget_gate, o = output_gate\n  const batchSize = res.shape[0];\n  const sliceCols = res.shape[1] / 4;\n  const sliceSize: [number, number] = [batchSize, sliceCols];\n  const i = res.slice([0, 0], sliceSize);\n  const j = res.slice([0, sliceCols], sliceSize);\n  const f = res.slice([0, sliceCols * 2], sliceSize);\n  const o = res.slice([0, sliceCols * 3], sliceSize);\n\n  const newC = i.sigmoid().mulStrict(j.tanh()).addStrict(\n      $c.mulStrict($forgetBias.add(f).sigmoid() as Tensor2D));\n  const newH = newC.tanh().mulStrict(o.sigmoid());\n  return [newC, newH];\n}\n\nexport const basicLSTMCell = op({basicLSTMCell_});\nexport const multiRNNCell = op({multiRNNCell_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Scalar, Tensor} from '../tensor';\nimport {assertTypesMatch} from '../tensor_util';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {pow} from './binary_ops';\nimport {op} from './operation';\nimport {scalar} from './tensor_ops';\n\n/**\n * Compute the moving average of a variable.\n *\n * Without zeroDebias, the moving average operation is defined by:\n *   `v += delta`\n * where\n *   `delta = (1 - decay) * (x - v)`\n *\n * With zeroDebias (default), the `delta` term is scaled to debias the\n * effect of the (assumed) zero-initialization of `v`.\n *   `delta /= (1 - decay ^ step)`\n *\n * For more details on the zero-debiasing algorithm, see:\n *   https://arxiv.org/abs/1412.6980\n *\n * Note that this function is completely stateless and does not keep track of\n * step count. The step count needs to be maintained by the caller and passed\n * in as `step`.\n *\n * @param v The current moving average value.\n * @param x New input value, must have the same shape and dtype as `v`.\n * @param decay The decay factor. Typical values are 0.95 and 0.99.\n * @param step Step count.\n * @param zeroDebias: Whether zeroDebias is to be performed (default: `true`).\n * @returns The new moving average value.\n */\n/** @doc {heading: 'Operations', subheading: 'Moving Average'} */\nfunction movingAverage_<T extends Tensor>(\n    v: T|TensorLike, x: T|TensorLike, decay: number|Scalar,\n    step?: number|Scalar, zeroDebias = true): T {\n  const $v = convertToTensor(v, 'v', 'movingAverage');\n  const $x = convertToTensor(x, 'x', 'movingAverage');\n  const $decay = convertToTensor(decay, 'decay', 'movingAverage');\n\n  assertTypesMatch($v, $x);\n  util.assert(\n      util.arraysEqual($v.shape, $x.shape), 'Shape mismatch in v and x');\n\n  const one = scalar(1);\n  const oneMinusDecay = one.sub($decay);\n\n  let update = $x.sub($v).mul(oneMinusDecay);\n  if (zeroDebias) {\n    util.assert(step != null, 'When using zeroDebias: true, step is required.');\n    const $step = convertToTensor(step, 'step', 'movingAverage');\n    update = update.div(one.sub(pow($decay, $step)));\n  }\n  return $v.add(update);\n}\n\nexport const movingAverage = op({movingAverage_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * Extracts a strided slice of a tensor.\n *\n * Roughly speaking, this op extracts a slice of size (end-begin)/stride from\n * the given input_ tensor. Starting at the location specified by begin the\n * slice continues by adding stride to the index until all dimensions are not\n * less than end. Note that a stride can be negative, which causes a reverse\n * slice.\n *\n * ```js\n * t = tf.tensor3d([1, 1, 1 ,2, 2, 2, 3, 3, 3, 4, 4, 4, 5, 5, 5, 6, 6, 6],\n *    [3, 2, 3]);\n * t.stridedSlice([1, 0, 0], [2, 1, 3], [1, 1, 1]).print()  // [[[3, 3, 3]]]\n * t.stridedSlice([1, 0, 0], [2, 2, 3], [1, 1, 1]).print()  // [[[3, 3, 3],\n *                                                     // [4, 4, 4]]]\n * t.stridedSlice([1, -1, 0], [2, -3, 3], [1, -1, 1]).print() // [[[4, 4, 4],\n *                                                     // [3, 3, 3]]]\n * ```\n *\n * @param x The tensor to stride slice.\n * @param begin The coordinates to start the slice from.\n * @param end: The coordinates to end the slice at.\n * @param strides: The size of the slice.\n * @param beginMask: If the ith bit of begin_mask is set, begin[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param endMask: If the ith bit of end_mask is set, end[i] is ignored\n *      and the fullest possible range in that dimension is used instead.\n * @param shrinkAxisMask: a bitmask where bit i implies that\n * the ith specification should shrink the dimensionality. begin and end must\n * imply a slice of size 1 in the dimension.\n */\n/** @doc {heading: 'Operations', subheading: 'Slicing and Joining'} */\nfunction stridedSlice_<T extends Tensor>(\n    x: T|TensorLike, begin: number[], end: number[], strides: number[],\n    beginMask = 0, endMask = 0, ellipsisMask = 0, newAxisMask = 0,\n    shrinkAxisMask = 0): T {\n  if (ellipsisMask !== 0) {\n    throw new Error('ellipsis mask is not yet supported');\n  }\n  if (newAxisMask !== 0) {\n    throw new Error('new axis mask is not yet supported');\n  }\n  const $x = convertToTensor(x, 'x', 'stridedSlice');\n  return ENV.engine.runKernel(\n             backend => backend.stridedSlice(\n                 $x, begin, end, strides, beginMask, endMask, ellipsisMask,\n                 newAxisMask, shrinkAxisMask),\n             {$x}) as T;\n}\n\nexport const stridedSlice = op({stridedSlice_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {NumericTensor, Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * Finds the values and indices of the `k` largest entries along the last\n * dimension.\n *\n * If the input is a vector (rank=1), finds the k largest entries in the vector\n * and outputs their values and indices as vectors. Thus values[j] is the j-th\n * largest entry in input, and its index is indices[j].\n * For higher rank inputs, computes the top k entries along the last dimension.\n *\n * If two elements are equal, the lower-index element appears first.\n *\n * ```js\n * const a = tf.tensor2d([[1, 5], [4, 3]]);\n * const {values, indices} = tf.topk(a);\n * values.print();\n * indices.print();\n * ```\n * @param x 1-D or higher `tf.Tensor` with last dimension being at least `k`.\n * @param k Number of top elements to look for along the last dimension.\n * @param sorted If true, the resulting `k` elements will be sorted by the\n *     values in descending order.\n */\n/** @doc {heading: 'Operations', subheading: 'Evaluation'} */\nfunction topk_<T extends Tensor>(\n    x: T|TensorLike, k = 1, sorted = true): {values: T, indices: T} {\n  const $x = convertToTensor(x, 'x', 'topk');\n  if ($x.rank === 0) {\n    throw new Error('topk() expects the input to be of rank 1 or higher');\n  }\n  const lastDim = $x.shape[$x.shape.length - 1];\n  if (k > lastDim) {\n    throw new Error(\n        `'k' passed to topk() must be <= the last dimension (${lastDim}) ` +\n        `but got ${k}`);\n  }\n\n  const [values, indices] =\n      ENV.engine.runKernel(b => b.topk($x as NumericTensor, k, sorted), {$x});\n  return {values, indices} as {values: T, indices: T};\n}\n\nexport const topk = op({topk_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, ShapeMap, TensorLike} from '../types';\n\nimport {op} from './operation';\nimport * as scatter_nd_util from './scatter_nd_util';\n\n/**\n * Creates a new tensor by applying sparse updates to individual\n * values or slices within a zero tensor of the given shape tensor according to\n * indices. This operator is the inverse of the `tf.gatherND` operator which\n * extracts values or slices from a given tensor.\n *\n * ```js\n * const indices = tf.tensor2d([[4], [3], [1], [7]]);\n * const updates = tf.tensor2d([9, 10, 11, 12]);\n * const shape = [8];\n * tf.scatterND(indices, updates, shape]).print() //[0, 11, 0, 10, 9, 0, 0, 12]\n * ```\n *\n * @param indices The tensor contains the indices into the output tensor.\n * @param updates The tensor contains the value for the indices.\n * @param shape: The shape of the output tensor.\n */\n/** @doc {heading: 'Operations', subheading: 'Slicing and Joining'} */\nfunction scatterND_<R extends Rank>(\n    indices: Tensor|TensorLike, updates: Tensor|TensorLike,\n    shape: ShapeMap[R]): Tensor<R> {\n  const $indices = convertToTensor(indices, 'indices', 'scatterND', 'int32');\n  const $updates = convertToTensor(updates, 'updates', 'scatterND');\n  scatter_nd_util.validateInput($updates, $indices, shape);\n\n  return ENV.engine.runKernel(\n             backend => backend.scatterND($indices, $updates, shape),\n             {$indices, $updates}) as Tensor<R>;\n}\n\nexport const scatterND = op({scatterND_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {complex, imag, real} from '../ops/complex_ops';\nimport {op} from '../ops/operation';\nimport {Tensor, Tensor2D} from '../tensor';\nimport {assert} from '../util';\nimport {scalar} from './tensor_ops';\n\n/**\n * Fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the inner-most\n * dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.fft().print();  // tf.spectral.fft(x).print();\n * ```\n * @param input The complex input to compute an fft over.\n */\n/**\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction fft_(input: Tensor): Tensor {\n  assert(\n      input.dtype === 'complex64',\n      `The dtype for tf.spectral.fft() must be complex64 ` +\n          `but got ${input.dtype}.`);\n\n  // Collapse all outer dimensions to a single batch dimension.\n  const innerDimensionSize = input.shape[input.shape.length - 1];\n  const batch = input.size / innerDimensionSize;\n  const input2D = input.as2D(batch, innerDimensionSize);\n\n  const ret = ENV.engine.runKernel(backend => backend.fft(input2D), {input});\n\n  return ret.reshape(input.shape);\n}\n\n/**\n * Inverse fast Fourier transform.\n *\n * Computes the inverse 1-dimensional discrete Fourier transform over the\n * inner-most dimension of input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n * const imag = tf.tensor1d([1, 2, 3]);\n * const x = tf.complex(real, imag);\n *\n * x.ifft().print();  // tf.spectral.ifft(x).print();\n * ```\n * @param input The complex input to compute an ifft over.\n */\n/**\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction ifft_(input: Tensor): Tensor {\n  assert(\n      input.dtype === 'complex64',\n      `The dtype for tf.spectral.ifft() must be complex64 ` +\n          `but got ${input.dtype}.`);\n\n  // Collapse all outer dimensions to a single batch dimension.\n  const innerDimensionSize = input.shape[input.shape.length - 1];\n  const batch = input.size / innerDimensionSize;\n  const input2D = input.as2D(batch, innerDimensionSize);\n\n  const ret = ENV.engine.runKernel(backend => backend.ifft(input2D), {input});\n\n  return ret.reshape(input.shape);\n}\n\n/**\n * Real value input fast Fourier transform.\n *\n * Computes the 1-dimensional discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n *\n * x.rfft().print();\n * ```\n * @param input The real value input to compute an rfft over.\n */\n/**\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction rfft_(input: Tensor): Tensor {\n  assert(input.dtype === 'float32', `The dtype for rfft() must be real value but\n    got ${input.dtype}`);\n\n  const innerDimensionSize = input.shape[input.shape.length - 1];\n  const batch = input.size / innerDimensionSize;\n\n  // Complement the input with zero imaginary numbers.\n  const zeros = input.zerosLike();\n  const complexInput = complex(input, zeros).as2D(batch, innerDimensionSize);\n\n  const ret = fft(complexInput);\n\n  // Exclude complex conjugations. These conjugations are put symmetrically.\n  const half = Math.floor(innerDimensionSize / 2) + 1;\n  const realValues = real(ret);\n  const imagValues = imag(ret);\n  const realComplexConjugate = realValues.split(\n      [half, innerDimensionSize - half], realValues.shape.length - 1);\n  const imagComplexConjugate = imagValues.split(\n      [half, innerDimensionSize - half], imagValues.shape.length - 1);\n\n  const outputShape = input.shape.slice();\n  outputShape[input.shape.length - 1] = half;\n\n  return complex(realComplexConjugate[0], imagComplexConjugate[0])\n      .reshape(outputShape);\n}\n\n/**\n * Inversed real value input fast Fourier transform.\n *\n * Computes the 1-dimensional inversed discrete Fourier transform over the\n * inner-most dimension of the real input.\n *\n * ```js\n * const real = tf.tensor1d([1, 2, 3]);\n *\n * x.irfft().print();\n * ```\n * @param input The real value input to compute an irfft over.\n */\n/**\n * @doc {heading: 'Operations', subheading: 'Spectral', namespace: 'spectral'}\n */\nfunction irfft_(input: Tensor): Tensor {\n  const innerDimensionSize = input.shape[input.shape.length - 1];\n  const batch = input.size / innerDimensionSize;\n\n  if (innerDimensionSize <= 2) {\n    const complexInput = input.as2D(batch, innerDimensionSize);\n    const ret = ifft(complexInput);\n    return real(ret);\n  } else {\n    // The length of unique components of the DFT of a real-valued signal\n    // is 2 * (input_len - 1)\n    const outputShape = [batch, 2 * (innerDimensionSize - 1)];\n    const realInput = real(input).as2D(batch, innerDimensionSize);\n    const imagInput = imag(input).as2D(batch, innerDimensionSize);\n\n    const realConjugate =\n        realInput.slice([0, 1], [batch, innerDimensionSize - 2]).reverse(1);\n    const imagConjugate =\n        imagInput.slice([0, 1], [batch, innerDimensionSize - 2])\n            .reverse(1)\n            .mul(scalar(-1)) as Tensor2D;\n\n    const r = realInput.concat(realConjugate, 1);\n    const i = imagInput.concat(imagConjugate, 1);\n    const complexInput = complex(r, i).as2D(outputShape[0], outputShape[1]);\n    const ret = ifft(complexInput);\n    return real(ret);\n  }\n}\n\nexport const fft = op({fft_});\nexport const ifft = op({ifft_});\nexport const rfft = op({rfft_});\nexport const irfft = op({irfft_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport * as sparse_to_dense from '../ops/sparse_to_dense_util';\nimport {Scalar, Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, ShapeMap, TensorLike} from '../types';\n\nimport {op} from './operation';\n\n/**\n * Converts a sparse representation into a dense tensor.\n *\n * Builds an array dense with shape outputShape such that:\n *\n * // If sparseIndices is scalar\n * dense[i] = (i == sparseIndices ? sparseValues : defaultValue)\n *\n * // If sparseIndices is a vector, then for each i\n * dense[sparseIndices[i]] = sparseValues[i]\n *\n * // If sparseIndices is an n by d matrix, then for each i in [0, n)\n * dense[sparseIndices[i][0], ..., sparseIndices[i][d-1]] = sparseValues[i]\n * All other values in dense are set to defaultValue. If sparseValues is a\n * scalar, all sparse indices are set to this single value.\n *\n * ```js\n * const indices = tf.tensor1d([4, 5, 6, 1, 2, 3], 'int32');\n * const values = tf.tensor1d([10, 11, 12, 13, 14, 15], 'float32');\n * const shape = [8];\n * tf.sparseToDense(indices, values, shape).print();\n * ```\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape Shape of the dense output tensor. the type is inferred.\n * @param defaultValue Scalar. Value to set for indices not specified in\n * sparseIndices. Defaults to zero.\n */\n/** @doc {heading: 'Operations', subheading: 'Normalization'} */\nfunction sparseToDense_<R extends Rank>(\n    sparseIndices: Tensor|TensorLike, sparseValues: Tensor|TensorLike,\n    outputShape: ShapeMap[R], defaultValue: Scalar|TensorLike): Tensor<R> {\n  const $sparseIndices =\n      convertToTensor(sparseIndices, 'sparseIndices', 'sparseToDense', 'int32');\n  const $sparseValues =\n      convertToTensor(sparseValues, 'sparseValues', 'sparseToDense');\n  const $defaultValue = convertToTensor(\n      defaultValue, 'defaultValue', 'sparseToDense', $sparseValues.dtype);\n\n  sparse_to_dense.validateInput(\n      $sparseIndices, $sparseValues, outputShape, $defaultValue);\n\n  return ENV.engine.runKernel(\n      backend => backend.sparseToDense(\n          $sparseIndices, $sparseValues, outputShape, $defaultValue),\n      {$sparseIndices, $sparseValues, $defaultValue});\n}\n\nexport const sparseToDense = op({sparseToDense_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {Tensor} from '../tensor';\n\n/**\n * Validate sparseToDense inputs.\n *\n * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.\n * sparseIndices[i] contains the complete index where sparseValues[i] will be\n * placed.\n * @param sparseValues A 0-D or 1-D Tensor. Values\n * corresponding to each row of sparseIndices, or a scalar value to be used for\n * all sparse indices.\n * @param outputShape number[]. Shape of the dense output tensor.\n * @param validateIndices boolean. indice validation is not supported, error\n * will be thrown if it is set.\n */\nexport function validateInput(\n    sparseIndices: Tensor, sparseValues: Tensor, outputShape: number[],\n    defaultValues: Tensor) {\n  if (sparseIndices.dtype !== 'int32') {\n    throw new Error(\n        'tf.sparseToDense() expects the indices to be int32 type,' +\n        ` but the dtype was ${sparseIndices.dtype}.`);\n  }\n  if (sparseIndices.rank > 2) {\n    throw new Error(\n        'sparseIndices should be a scalar, vector, or matrix,' +\n        ` but got shape ${sparseIndices.shape}.`);\n  }\n\n  const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;\n  const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;\n\n  if (outputShape.length !== numDims) {\n    throw new Error(\n        'outputShape has incorrect number of elements:,' +\n        ` ${outputShape.length}, should be: ${numDims}.`);\n  }\n\n  const numValues = sparseValues.size;\n  if (!(sparseValues.rank === 0 ||\n        sparseValues.rank === 1 && numValues === numElems)) {\n    throw new Error(\n        'sparseValues has incorrect shape ' +\n        `${sparseValues.shape}, should be [] or [${numElems}]`);\n  }\n\n  if (sparseValues.dtype !== defaultValues.dtype) {\n    throw new Error('sparseValues.dtype must match defaultValues.dtype');\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {customGrad} from '../globals';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport {assertShapesMatch, sizeFromShape} from '../util';\n\nimport {expandShapeToKeepDim} from './axis_util';\n\nimport {minimum} from './binary_ops';\nimport {op} from './operation';\nimport {ones, scalar} from './tensor_ops';\n\nexport enum Reduction {\n  NONE,\n  MEAN,\n  SUM,\n  SUM_BY_NONZERO_WEIGHTS\n}\n\n/**\n * Computes the weighted loss between two tensors.\n *\n * @param losses Tensor of shape `[batch_size, d1, ... dN]`.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `losses`, and must be broadcastable to `losses` (i.e., all\n *    dimensions must be either `1`, or the same as the corresponding\n *    `losses` dimension).\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction computeWeightedLoss_<T extends Tensor, O extends Tensor>(\n    losses: T|TensorLike, weights?: Tensor|TensorLike,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $losses = convertToTensor(losses, 'losses', 'computeWeightedLoss');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'computeWeightedLoss');\n  }\n\n  const weightedLoss = ($weights == null) ? $losses : $losses.mul($weights);\n\n  if (reduction === Reduction.NONE) {\n    return weightedLoss as O;\n  }\n  if (reduction === Reduction.SUM) {\n    return weightedLoss.sum();\n  }\n  if (reduction === Reduction.MEAN) {\n    if ($weights == null) {\n      return weightedLoss.mean();\n    } else {\n      const broadcastFactor =\n          sizeFromShape($losses.shape) / sizeFromShape($weights.shape);\n      const result = weightedLoss.sum().div($weights.sum());\n      return broadcastFactor > 1 ? result.div(scalar(broadcastFactor)) :\n                                   result as O;\n    }\n  }\n  if (reduction === Reduction.SUM_BY_NONZERO_WEIGHTS) {\n    if ($weights == null) {\n      return weightedLoss.sum().div(scalar($losses.size));\n    } else {\n      const broadcastedWeights = $weights.mul(ones($losses.shape));\n\n      const numNonZeros =\n          broadcastedWeights.notEqual(scalar(0)).sum().toFloat();\n      return weightedLoss.sum().div(numNonZeros);\n    }\n  }\n\n  throw Error(`Unknown reduction: ${reduction}`);\n}\n\n/**\n * Computes the absolute difference loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction absoluteDifference_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike,\n    weights?: Tensor|TensorLike,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $labels = convertToTensor(labels, 'labels', 'absoluteDifference');\n  const $predictions =\n      convertToTensor(predictions, 'predictions', 'absoluteDifference');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'absoluteDifference');\n  }\n  assertShapesMatch(\n      $labels.shape, $predictions.shape, 'Error in absoluteDifference: ');\n\n  const losses = $labels.sub($predictions).abs();\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\n/**\n * Computes the mean squared error between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction meanSquaredError_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike,\n    weights?: Tensor|TensorLike,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $labels = convertToTensor(labels, 'labels', 'meanSquaredError');\n  const $predictions =\n      convertToTensor(predictions, 'predictions', 'meanSquaredError');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'meanSquaredError');\n  }\n  assertShapesMatch(\n      $labels.shape, $predictions.shape, 'Error in meanSquaredError: ');\n\n  const losses = $labels.squaredDifference($predictions);\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\n/**\n * Computes the cosine distance loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param axis The dimension along which the cosine distance is computed.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction cosineDistance_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike, axis: number,\n    weights?: Tensor|TensorLike,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $labels = convertToTensor(labels, 'labels', 'cosineDistance');\n  const $predictions =\n      convertToTensor(predictions, 'predictions', 'cosineDistance');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'cosineDistance');\n  }\n  assertShapesMatch(\n      $labels.shape, $predictions.shape, 'Error in cosineDistance: ');\n\n  const one = scalar(1);\n  const losses = one.sub($labels.mul($predictions).sum(axis, true));\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\n/**\n * Computes the Hinge loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction hingeLoss_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike,\n    weights?: Tensor|TensorLike,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  let $labels = convertToTensor(labels, 'labels', 'hingeLoss');\n  const $predictions = convertToTensor(predictions, 'predictions', 'hingeLoss');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'hingeLoss');\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, 'Error in hingeLoss: ');\n\n  const one = scalar(1);\n  // Convert binary labels to (-1, 1)\n  $labels = scalar(2).mul($labels).sub(one);\n  const losses = one.sub($labels.mul($predictions)).relu();\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\n/**\n * Computes the log loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param epsilon A small increment to avoid taking log of zero\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction logLoss_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike,\n    weights?: Tensor|TensorLike, epsilon = 1e-7,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $labels = convertToTensor(labels, 'labels', 'logLoss');\n  const $predictions = convertToTensor(predictions, 'predictions', 'logLoss');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'logLoss');\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, 'Error in logLoss: ');\n\n  const one = scalar(1);\n  const epsilonScalar = scalar(epsilon);\n  const losses = $labels.mul($predictions.add(epsilonScalar).log())\n                     .neg()\n                     .sub(one.sub($labels).mul(\n                         one.sub($predictions).add(epsilonScalar).log()));\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\nfunction sigmoidCrossEntropyWithLogits_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, logits: T|TensorLike): O {\n  const $labels =\n      convertToTensor(labels, 'labels', 'sigmoidCrossEntropyWithLogits');\n  const $logits =\n      convertToTensor(logits, 'logits', 'sigmoidCrossEntropyWithLogits');\n  assertShapesMatch(\n      $labels.shape, $logits.shape, 'Error in sigmoidCrossEntropyWithLogits: ');\n\n  /**\n   * Implementation Details:\n   *\n   * For brevity, let `x = logits`, `z = labels`.  The logistic loss is\n   *     z * -log(sigmoid(x)) + (1 - z) * -log(1 - sigmoid(x))\n   *   = z * -log(1 / (1 + exp(-x))) + (1 - z) * -log(exp(-x) / (1 + exp(-x)))\n   *   = z * log(1 + exp(-x)) + (1 - z) * (-log(exp(-x)) + log(1 + exp(-x)))\n   *   = z * log(1 + exp(-x)) + (1 - z) * (x + log(1 + exp(-x))\n   *   = (1 - z) * x + log(1 + exp(-x))\n   *   = x - x * z + log(1 + exp(-x))\n   *\n   *   For x < 0, to avoid overflow in exp(-x), we reformulate the above\n   *     x - x * z + log(1 + exp(-x))\n   *   = log(exp(x)) - x * z + log(1 + exp(-x))\n   *   = - x * z + log(1 + exp(x))\n   *\n   * Hence, to ensure stability and avoid overflow, the implementation uses\n   * this equivalent formulation:\n   *     max(x, 0) - x * z + log(1 + exp(-abs(x)))\n   */\n  const maxOutput = $logits.relu();\n  const outputXTarget = $logits.mul($labels);\n  const sigmoidOutput = $logits.abs().neg().exp().log1p();\n\n  return maxOutput.sub(outputXTarget).add(sigmoidOutput);\n}\n\n/**\n * Computes the sigmoid cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newMulticlassLabels = multiclassLabels * (1 - labelSmoothing)\n *                         + 0.5 * labelSmoothing\n *\n * @param multiClassLabels The ground truth output tensor of shape\n * [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n */\n/** @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' } */\nfunction sigmoidCrossEntropy_<T extends Tensor, O extends Tensor>(\n    multiClassLabels: T|TensorLike, logits: T|TensorLike,\n    weights?: Tensor|TensorLike, labelSmoothing = 0,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  let $multiClassLabels = convertToTensor(\n      multiClassLabels, 'multiClassLabels', 'sigmoidCrossEntropy');\n  const $logits = convertToTensor(logits, 'logits', 'sigmoidCrossEntropy');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'sigmoidCrossEntropy');\n  }\n  assertShapesMatch(\n      $multiClassLabels.shape, $logits.shape, 'Error in sigmoidCrossEntropy: ');\n\n  if (labelSmoothing > 0) {\n    const labelSmoothingScalar = scalar(labelSmoothing);\n    const one = scalar(1);\n    const half = scalar(0.5);\n\n    $multiClassLabels = $multiClassLabels.mul(one.sub(labelSmoothingScalar))\n                            .add(half.mul(labelSmoothingScalar));\n  }\n  const losses = sigmoidCrossEntropyWithLogits_($multiClassLabels, $logits);\n\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\n/**\n * Computes the huber loss between two tensors.\n *\n * @param labels The ground truth output tensor, same dimensions as\n *    'predictions'.\n * @param predictions The predicted outputs.\n * @param weights Tensor whose rank is either 0, or the same rank as\n *    `labels`, and must be broadcastable to `labels` (i.e., all dimensions\n *    must be either `1`, or the same as the corresponding `losses`\n *    dimension).\n * @param delta Point where huber loss changes from quadratic to linear.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`.\n */\n/** @doc {heading: 'Training', subheading: 'Losses', namespace: 'losses'} */\nfunction huberLoss_<T extends Tensor, O extends Tensor>(\n    labels: T|TensorLike, predictions: T|TensorLike,\n    weights?: Tensor|TensorLike, delta = 1.0,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  const $labels = convertToTensor(labels, 'labels', 'huberLoss');\n  const $predictions = convertToTensor(predictions, 'predictions', 'huberLoss');\n  let $weights: Tensor = null;\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'huberLoss');\n  }\n  assertShapesMatch($labels.shape, $predictions.shape, 'Error in huberLoss: ');\n\n  const deltaScalar = scalar(delta);\n  const error = $predictions.sub($labels).abs();\n  const quadratic = minimum(error, deltaScalar);\n  const linear = error.sub(quadratic);\n\n  const losses =\n      scalar(0.5).mul(quadratic.square()).add(deltaScalar.mul(linear));\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\n/**\n * Computes softmax cross entropy between logits and labels.\n *\n * Measures the probability error in discrete classification tasks in which\n * the classes are mutually exclusive (each entry is in exactly one class).\n * For example, each CIFAR-10 image is labeled with one and only one label: an\n * image can be a dog or a truck, but not both.\n *\n * `NOTE`: While the classes are mutually exclusive, their probabilities need\n * not be. All that is required is that each row of labels is a valid\n * probability distribution. If they are not, the computation of the gradient\n * will be incorrect.\n *\n * `WARNING`: This op expects unscaled logits, since it performs a softmax on\n * logits internally for efficiency. Do not call this op with the output of\n * softmax, as it will produce incorrect results.\n *\n * logits and labels must have the same shape, e.g. [batch_size, num_classes]\n * and the same dtype.\n * @param labels The labels array.\n * @param logits The logits array.\n * @param dim The dimension softmax would be performed on. Defaults to `-1`\n *     which indicates the last dimension.\n */\nfunction softmaxCrossEntropyWithLogits_<T extends Tensor, O extends Tensor>(\n    labels: T, logits: T, dim = -1): O {\n  if (dim === -1) {\n    dim = logits.rank - 1;\n  }\n\n  if (dim !== logits.rank - 1) {\n    throw Error(\n        `Softmax cross entropy along a non-last dimension is not yet ` +\n        `supported. Labels / logits was rank ${logits.rank} ` +\n        `and dim was ${dim}`);\n  }\n  // Use a custom gradient for numerical stability.\n  const customOp = customGrad((labels, logits) => {\n    // Reference:\n    //   1. http://cs231n.github.io/linear-classify/#softmax\n    //   2. https://blog.feedly.com/tricks-of-the-trade-logsumexp/\n    const keepDims = true;\n    const lse = logits.logSumExp([dim], keepDims);\n\n    const logResult = logits.toFloat().sub(lse);\n    const costVector = logResult.mul(labels).neg();\n\n    const value = costVector.sum([dim]) as O;\n\n    const gradFunc = (dy: O) => {\n      const dyShape = expandShapeToKeepDim(dy.shape, [dim]);\n      return [\n        dy.reshape(dyShape).mul(labels.toFloat().sub(logResult.exp())),\n        dy.reshape(dyShape).mul(logResult.exp().sub(labels.toFloat())),\n      ];\n    };\n    return {value, gradFunc};\n  });\n\n  return customOp(labels, logits);\n}\n\n/**\n * Computes the softmax cross entropy loss between two tensors.\n *\n * If labelSmoothing is nonzero, smooth the labels towards 1/2:\n *\n *   newOnehotLabels = onehotLabels * (1 - labelSmoothing)\n *                         + labelSmoothing / numClasses\n *\n * @param onehotLabels One hot encoded labels\n *    [batch_size, num_classes], same dimensions as 'predictions'.\n * @param logits The predicted outputs.\n * @param weights Tensor whose rank is either 0, or 1, and must be\n *    broadcastable to `loss`  of shape [batch_size]\n * @param labelSmoothing If greater than 0, then smooth the labels.\n * @param reduction Type of reduction to apply to loss. Should be of type\n *    `Reduction`\n */\n/** @doc { heading: 'Training', subheading: 'Losses', namespace: 'losses' } */\nfunction softmaxCrossEntropy_<T extends Tensor, O extends Tensor>(\n    onehotLabels: T|TensorLike, logits: T|TensorLike,\n    weights?: Tensor|TensorLike, labelSmoothing = 0,\n    reduction = Reduction.SUM_BY_NONZERO_WEIGHTS): O {\n  let $onehotLabels =\n      convertToTensor(onehotLabels, 'onehotLabels', 'softmaxCrossEntropy');\n  const $logits = convertToTensor(logits, 'logits', 'softmaxCrossEntropy');\n  let $weights: Tensor = null;\n\n  if (weights != null) {\n    $weights = convertToTensor(weights, 'weights', 'softmaxCrossEntropy');\n  }\n\n  assertShapesMatch(\n      $onehotLabels.shape, $logits.shape, 'Error in softmaxCrossEntropy: ');\n\n  if (labelSmoothing > 0) {\n    const labelSmoothingScalar = scalar(labelSmoothing);\n    const one = scalar(1);\n    const numClasses = scalar($onehotLabels.shape[1]);\n\n    $onehotLabels = $onehotLabels.mul(one.sub(labelSmoothingScalar))\n                        .add(labelSmoothingScalar.div(numClasses));\n  }\n\n  const losses = softmaxCrossEntropyWithLogits_($onehotLabels, $logits);\n\n  return computeWeightedLoss(losses, $weights, reduction);\n}\n\nexport const absoluteDifference = op({absoluteDifference_});\nexport const computeWeightedLoss = op({computeWeightedLoss_});\nexport const cosineDistance = op({cosineDistance_});\nexport const hingeLoss = op({hingeLoss_});\nexport const huberLoss = op({huberLoss_});\nexport const logLoss = op({logLoss_});\nexport const meanSquaredError = op({meanSquaredError_});\nexport const sigmoidCrossEntropy = op({sigmoidCrossEntropy_});\nexport const softmaxCrossEntropy = op({softmaxCrossEntropy_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nimport {ENV} from '../environment';\nimport {Tensor} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {Rank, TensorLike} from '../types';\nimport {op} from './operation';\n\n/**\n * Gather slices from input tensor into a Tensor with shape specified by\n * `indices`.\n *\n * `indices` is an K-dimensional integer tensor, best thought of as a\n * (K-1)-dimensional tensor of indices into input, where each element defines a\n * slice of input:\n * output[\\\\(i_0, ..., i_{K-2}\\\\)] = input[indices[\\\\(i_0, ..., i_{K-2}\\\\)]]\n *\n * Whereas in `tf.gather`, `indices` defines slices into the first dimension of\n * input, in `tf.gatherND`, `indices` defines slices into the first N dimensions\n * of input, where N = indices.shape[-1].\n *\n * The last dimension of indices can be at most the rank of input:\n * indices.shape[-1] <= input.rank\n *\n * The last dimension of `indices` corresponds to elements\n * (if indices.shape[-1] == input.rank) or slices\n * (if indices.shape[-1] < input.rank) along dimension indices.shape[-1] of\n * input.\n * The output tensor has shape\n * indices.shape[:-1] + input.shape[indices.shape[-1]:]\n *\n * Note that on CPU, if an out of bound index is found, an error is returned. On\n * GPU, if an out of bound index is found, a 0 is stored in the corresponding\n * output value.\n *\n * ```js\n * const indices = tf.tensor2d([0, 1, 1, 0], [2,2], 'int32');\n * const input = tf.tensor2d([9, 10, 11, 12], [2, 2]);\n * tf.gatherND(input, indices).print() //[10, 11]\n * ```\n *\n * @param x The tensor from which to gather values.\n * @param indices Index tensor, must be of type int32.\n */\n/** @doc {heading: 'Operations', subheading: 'Slicing and Joining'} */\nfunction gatherND_(\n    x: Tensor|TensorLike, indices: Tensor|TensorLike): Tensor<Rank> {\n  const $indices = convertToTensor(indices, 'indices', 'gatherND', 'int32');\n  const $x = convertToTensor(x, 'x', 'gatherND');\n  return ENV.engine.runKernel(\n             backend => backend.gatherND($x, $indices), {$x, $indices}) as\n      Tensor<Rank>;\n}\nexport const gatherND = op({gatherND_});\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Linear algebra ops.\n */\n\nimport {ENV} from '../environment';\nimport {dispose} from '../globals';\nimport {Tensor, Tensor1D, Tensor2D} from '../tensor';\nimport {assert} from '../util';\nimport {eye, squeeze, stack, unstack} from './array_ops';\nimport {split} from './concat_split';\nimport {norm} from './norm';\nimport {op} from './operation';\nimport {sum} from './reduction_ops';\nimport {tensor2d} from './tensor_ops';\n\n/**\n * Gram-Schmidt orthogonalization.\n *\n * ```js\n * const x = tf.tensor2d([[1, 2], [3, 4]]);\n * let y = tf.linalg.gramSchmidt(x);\n * y.print();\n * console.log('Othogonalized:');\n * y.dot(y.transpose()).print();  // should be nearly the identity matrix.\n * console.log('First row direction maintained:');\n * console.log(y.get(0, 1) / y.get(0, 0));  // should be nearly 2.\n * ```\n *\n * @param xs The vectors to be orthogonalized, in one of the two following\n *   formats:\n *   - An Array of `tf.Tensor1D`.\n *   - A `tf.Tensor2D`, i.e., a matrix, in which case the vectors are the rows\n *     of `xs`.\n *   In each case, all the vectors must have the same length and the length\n *   must be greater than or equal to the number of vectors.\n * @returns The orthogonalized and normalized vectors or matrix.\n *   Orthogonalization means that the vectors or the rows of the matrix\n *   are orthogonal (zero inner products). Normalization means that each\n *   vector or each row of the matrix has an L2 norm that equals `1`.\n */\n/**\n * @doc {heading:'Operations',\n *       subheading:'Linear Algebra',\n *       namespace:'linalg'}\n */\nfunction gramSchmidt_(xs: Tensor1D[]|Tensor2D): Tensor1D[]|Tensor2D {\n  let inputIsTensor2D: boolean;\n  if (Array.isArray(xs)) {\n    inputIsTensor2D = false;\n    assert(\n        xs != null && xs.length > 0,\n        'Gram-Schmidt process: input must not be null, undefined, or empty');\n    const dim = xs[0].shape[0];\n    for (let i = 1; i < xs.length; ++i) {\n      assert(\n          xs[i].shape[0] === dim,\n          'Gram-Schmidt: Non-unique lengths found in the input vectors: ' +\n              `(${xs[i].shape[0]} vs. ${dim})`);\n    }\n  } else {\n    inputIsTensor2D = true;\n    xs = split(xs, xs.shape[0], 0).map(x => squeeze(x, [0]));\n  }\n\n  assert(\n      xs.length <= xs[0].shape[0],\n      `Gram-Schmidt: Number of vectors (${xs.length}) exceeds ` +\n          `number of dimensions (${xs[0].shape[0]}).`);\n\n  const ys: Tensor1D[] = [];\n  const xs1d = xs as Tensor1D[];\n  for (let i = 0; i < xs.length; ++i) {\n    ys.push(ENV.engine.tidy(() => {\n      let x = xs1d[i];\n      if (i > 0) {\n        for (let j = 0; j < i; ++j) {\n          const proj = sum(ys[j].mulStrict(x)).mul(ys[j]);\n          x = x.sub(proj);\n        }\n      }\n      return x.div(norm(x, 'euclidean'));\n    }));\n  }\n\n  if (inputIsTensor2D) {\n    return stack(ys, 0) as Tensor2D;\n  } else {\n    return ys;\n  }\n}\n\n/**\n * Compute QR decomposition of m-by-n matrix using Householder transformation.\n *\n * Implementation based on\n *   [http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf]\n * (http://www.cs.cornell.edu/~bindel/class/cs6210-f09/lec18.pdf)\n *\n * ```js\n * const a = tf.tensor2d([[1, 2], [3, 4]]);\n * let [q, r] = tf.linalg.qr(a);\n * console.log('Q');\n * q.print();\n * console.log('R');\n * r.print();\n * console.log('Orthogonalized');\n * q.dot(q.transpose()).print()  // should be nearly the identity matrix.\n * console.log('Reconstructed');\n * q.dot(r).print(); // should be nearly [[1, 2], [3, 4]];\n * ```\n *\n * @param x The `tf.Tensor` to be QR-decomposed. Must have rank >= 2. Suppose\n *   it has the shape `[..., M, N]`.\n * @param fullMatrices An optional boolean parameter. Defaults to `false`.\n *   If `true`, compute full-sized `Q`. If `false` (the default),\n *   compute only the leading N columns of `Q` and `R`.\n * @returns An `Array` of two `tf.Tensor`s: `[Q, R]`. `Q` is a unitary matrix,\n *   i.e., its columns all have unit norm and are mutually orthogonal.\n *   If `M >= N`,\n *     If `fullMatrices` is `false` (default),\n *       - `Q` has a shape of `[..., M, N]`,\n *       - `R` has a shape of `[..., N, N]`.\n *     If `fullMatrices` is `true` (default),\n *       - `Q` has a shape of `[..., M, M]`,\n *       - `R` has a shape of `[..., M, N]`.\n *   If `M < N`,\n *     - `Q` has a shape of `[..., M, M]`,\n *     - `R` has a shape of `[..., M, N]`.\n * @throws If the rank of `x` is less than 2.\n */\n/**\n * @doc {heading:'Operations',\n *       subheading:'Linear Algebra',\n *       namespace:'linalg'}\n */\nfunction qr_(x: Tensor, fullMatrices = false): [Tensor, Tensor] {\n  if (x.rank < 2) {\n    throw new Error(\n        `qr() requires input tensor to have a rank >= 2, but got rank ${\n            x.rank}`);\n  } else if (x.rank === 2) {\n    return qr2d(x as Tensor2D, fullMatrices);\n  } else {\n    // Rank > 2.\n    // TODO(cais): Below we split the input into individual 2D tensors,\n    //   perform QR decomposition on them and then stack the results back\n    //   together. We should explore whether this can be parallelized.\n    const outerDimsProd = x.shape.slice(0, x.shape.length - 2)\n                              .reduce((value, prev) => value * prev);\n    const x2ds = unstack(\n        x.reshape([\n          outerDimsProd, x.shape[x.shape.length - 2],\n          x.shape[x.shape.length - 1]\n        ]),\n        0);\n    const q2ds: Tensor2D[] = [];\n    const r2ds: Tensor2D[] = [];\n    x2ds.forEach(x2d => {\n      const [q2d, r2d] = qr2d(x2d as Tensor2D, fullMatrices);\n      q2ds.push(q2d);\n      r2ds.push(r2d);\n    });\n    const q = stack(q2ds, 0).reshape(x.shape);\n    const r = stack(r2ds, 0).reshape(x.shape);\n    return [q, r];\n  }\n}\n\nfunction qr2d(x: Tensor2D, fullMatrices = false): [Tensor2D, Tensor2D] {\n  return ENV.engine.tidy(() => {\n    if (x.shape.length !== 2) {\n      throw new Error(\n          `qr2d() requires a 2D Tensor, but got a ${x.shape.length}D Tensor.`);\n    }\n\n    const m = x.shape[0];\n    const n = x.shape[1];\n\n    let q = eye(m) as Tensor2D;  // Orthogonal transform so far.\n    let r = x.clone();           // Transformed matrix so far.\n\n    const one2D = tensor2d([[1]], [1, 1]);\n    let w: Tensor2D = one2D.clone();\n\n    const iters = m >= n ? n : m;\n    for (let j = 0; j < iters; ++j) {\n      // This tidy within the for-loop ensures we clean up temporary\n      // tensors as soon as they are no longer needed.\n      const rTemp = r;\n      const wTemp = w;\n      const qTemp = q;\n      [w, r, q] = ENV.engine.tidy((): [Tensor2D, Tensor2D, Tensor2D] => {\n        // Find H = I - tau * w * w', to put zeros below R(j, j).\n        const rjEnd1 = r.slice([j, j], [m - j, 1]);\n        const normX = rjEnd1.norm();\n        const rjj = r.slice([j, j], [1, 1]);\n        const s = rjj.sign().neg() as Tensor2D;\n        const u1 = rjj.sub(s.mul(normX)) as Tensor2D;\n        const wPre = rjEnd1.div(u1);\n        if (wPre.shape[0] === 1) {\n          w = one2D.clone();\n        } else {\n          w = one2D.concat(\n              wPre.slice([1, 0], [wPre.shape[0] - 1, wPre.shape[1]]) as\n                  Tensor2D,\n              0);\n        }\n        const tau = s.matMul(u1).div(normX).neg() as Tensor2D;\n\n        // -- R := HR, Q := QH.\n        const rjEndAll = r.slice([j, 0], [m - j, n]);\n        const tauTimesW = tau.mul(w) as Tensor2D;\n        if (j === 0) {\n          r = rjEndAll.sub(tauTimesW.matMul(w.transpose().matMul(rjEndAll)));\n        } else {\n          r = r.slice([0, 0], [j, n])\n                  .concat(\n                      rjEndAll.sub(tauTimesW.matMul(\n                          w.transpose().matMul(rjEndAll))) as Tensor2D,\n                      0) as Tensor2D;\n        }\n        const qAllJEnd = q.slice([0, j], [m, q.shape[1] - j]);\n        if (j === 0) {\n          q = qAllJEnd.sub(qAllJEnd.matMul(w).matMul(tauTimesW.transpose()));\n        } else {\n          q = q.slice([0, 0], [m, j])\n                  .concat(\n                      qAllJEnd.sub(qAllJEnd.matMul(w).matMul(\n                          tauTimesW.transpose())) as Tensor2D,\n                      1) as Tensor2D;\n        }\n        return [w, r, q];\n      });\n      dispose([rTemp, wTemp, qTemp]);\n    }\n\n    if (!fullMatrices && m > n) {\n      q = q.slice([0, 0], [m, n]);\n      r = r.slice([0, 0], [n, n]);\n    }\n\n    return [q, r];\n  }) as [Tensor2D, Tensor2D];\n}\n\nexport const gramSchmidt = op({gramSchmidt_});\nexport const qr = op({qr_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ForwardFunc} from '../engine';\nimport {ENV} from '../environment';\nimport {nonMaxSuppressionImpl} from '../kernels/non_max_suppression_impl';\nimport {Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\nimport {op} from './operation';\n\n/**\n * Bilinear resize a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction resizeBilinear_<T extends Tensor3D|Tensor4D>(\n    images: T|TensorLike, size: [number, number], alignCorners = false): T {\n  const $images = convertToTensor(images, 'images', 'resizeBilinear');\n  util.assert(\n      $images.rank === 3 || $images.rank === 4,\n      `Error in resizeBilinear: x must be rank 3 or 4, but got ` +\n          `rank ${$images.rank}.`);\n  util.assert(\n      size.length === 2,\n      `Error in resizeBilinear: new shape must 2D, but got shape ` +\n          `${size}.`);\n\n  let batchImages = $images as Tensor4D;\n  let reshapedTo4D = false;\n  if ($images.rank === 3) {\n    reshapedTo4D = true;\n    batchImages =\n        $images.as4D(1, $images.shape[0], $images.shape[1], $images.shape[2]);\n  }\n\n  const [newHeight, newWidth] = size;\n  const forward: ForwardFunc<Tensor4D> = (backend, save) =>\n      backend.resizeBilinear(batchImages, newHeight, newWidth, alignCorners);\n\n  const backward = (dy: Tensor4D, saved: Tensor[]) => {\n    return {\n      batchImages: () => ENV.engine.runKernel(\n          backend =>\n              backend.resizeBilinearBackprop(dy, batchImages, alignCorners),\n          {})\n    };\n  };\n\n  const res = ENV.engine.runKernel(forward, {batchImages}, backward);\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\n/**\n * NearestNeighbor resize a batch of 3D images to a new shape.\n *\n * @param images The images, of rank 4 or rank 3, of shape\n *     `[batch, height, width, inChannels]`. If rank 3, batch of 1 is assumed.\n * @param size The new shape `[newHeight, newWidth]` to resize the\n *     images to. Each channel is resized individually.\n * @param alignCorners Defaults to False. If true, rescale\n *     input by `(new_height - 1) / (height - 1)`, which exactly aligns the 4\n *     corners of images and resized images. If false, rescale by\n *     `new_height / height`. Treat similarly the width dimension.\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction resizeNearestNeighbor_<T extends Tensor3D|Tensor4D>(\n    images: T|TensorLike, size: [number, number], alignCorners = false): T {\n  const $images = convertToTensor(images, 'images', 'resizeNearestNeighbor');\n  util.assert(\n      $images.rank === 3 || $images.rank === 4,\n      `Error in resizeNearestNeighbor: x must be rank 3 or 4, but got ` +\n          `rank ${$images.rank}.`);\n  util.assert(\n      size.length === 2,\n      `Error in resizeNearestNeighbor: new shape must 2D, but got shape ` +\n          `${size}.`);\n  util.assert(\n      $images.dtype === 'float32' || $images.dtype === 'int32',\n      '`images` must have `int32` or `float32` as dtype');\n\n  let batchImages = $images as Tensor4D;\n  let reshapedTo4D = false;\n  if ($images.rank === 3) {\n    reshapedTo4D = true;\n    batchImages =\n        $images.as4D(1, $images.shape[0], $images.shape[1], $images.shape[2]);\n  }\n  const [newHeight, newWidth] = size;\n\n  const forward: ForwardFunc<Tensor4D> = (backend, save) =>\n      backend.resizeNearestNeighbor(\n          batchImages, newHeight, newWidth, alignCorners);\n\n  const backward = (dy: Tensor4D, saved: Tensor[]) => {\n    return {\n      batchImages: () => ENV.engine.runKernel(\n          backend => backend.resizeNearestNeighborBackprop(\n              dy, batchImages, alignCorners),\n          {})\n    };\n  };\n\n  const res = ENV.engine.runKernel(forward, {batchImages}, backward);\n\n  if (reshapedTo4D) {\n    return res.as3D(res.shape[1], res.shape[2], res.shape[3]) as T;\n  }\n  return res as T;\n}\n\n/**\n * Performs non maximum suppression of bounding boxes based on\n * iou (intersection over union)\n *\n * @param boxes a 2d tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the corners of\n *     the bounding box.\n * @param scores a 1d tensor providing the box scores of shape `[numBoxes]`.\n * @param maxOutputSize The maximum number of boxes to be selected.\n * @param iouThreshold A float representing the threshold for deciding whether\n *     boxes overlap too much with respect to IOU. Must be between [0, 1].\n *     Defaults to 0.5 (50% box overlap).\n * @param scoreThreshold A threshold for deciding when to remove boxes based\n *     on score. Defaults to -inf, which means any score is accepted.\n * @return A 1D tensor with the selected box indices.\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction nonMaxSuppression_(\n    boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,\n    maxOutputSize: number, iouThreshold = 0.5,\n    scoreThreshold = Number.NEGATIVE_INFINITY): Tensor1D {\n  const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppression');\n  const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppression');\n\n  const inputs = nonMaxSuppSanityCheck(\n      $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n  maxOutputSize = inputs.maxOutputSize;\n  iouThreshold = inputs.iouThreshold;\n  scoreThreshold = inputs.scoreThreshold;\n\n  return ENV.engine.runKernel(\n      b => b.nonMaxSuppression(\n          $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold),\n      {$boxes});\n}\n\n/** This is the async version of `nonMaxSuppression` */\nasync function nonMaxSuppressionAsync_(\n    boxes: Tensor2D|TensorLike, scores: Tensor1D|TensorLike,\n    maxOutputSize: number, iouThreshold = 0.5,\n    scoreThreshold = Number.NEGATIVE_INFINITY): Promise<Tensor1D> {\n  const $boxes = convertToTensor(boxes, 'boxes', 'nonMaxSuppressionAsync');\n  const $scores = convertToTensor(scores, 'scores', 'nonMaxSuppressionAsync');\n\n  const inputs = nonMaxSuppSanityCheck(\n      $boxes, $scores, maxOutputSize, iouThreshold, scoreThreshold);\n  maxOutputSize = inputs.maxOutputSize;\n  iouThreshold = inputs.iouThreshold;\n  scoreThreshold = inputs.scoreThreshold;\n\n  const boxesVals = await $boxes.data();\n  const scoresVals = await $scores.data();\n  const res = nonMaxSuppressionImpl(\n      boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n  if ($boxes !== boxes) {\n    $boxes.dispose();\n  }\n  if ($scores !== scores) {\n    $scores.dispose();\n  }\n  return res;\n}\n\nfunction nonMaxSuppSanityCheck(\n    boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number,\n    iouThreshold: number, scoreThreshold: number):\n    {maxOutputSize: number, iouThreshold: number, scoreThreshold: number} {\n  if (iouThreshold == null) {\n    iouThreshold = 0.5;\n  }\n  if (scoreThreshold == null) {\n    scoreThreshold = Number.NEGATIVE_INFINITY;\n  }\n  const numBoxes = boxes.shape[0];\n  maxOutputSize = Math.min(maxOutputSize, numBoxes);\n\n  util.assert(\n      0 <= iouThreshold && iouThreshold <= 1,\n      `iouThreshold must be in [0, 1], but was '${iouThreshold}'`);\n  util.assert(\n      boxes.rank === 2,\n      `boxes must be a 2D tensor, but was of rank '${boxes.rank}'`);\n  util.assert(\n      boxes.shape[1] === 4,\n      `boxes must have 4 columns, but 2nd dimension was ${boxes.shape[1]}`);\n  util.assert(scores.rank === 1, 'scores must be a 1D tensor');\n  util.assert(\n      scores.shape[0] === numBoxes,\n      `scores has incompatible shape with boxes. Expected ${numBoxes}, ` +\n          `but was ${scores.shape[0]}`);\n  return {maxOutputSize, iouThreshold, scoreThreshold};\n}\n\n/**\n * Extracts crops from the input image tensor and resizes them using bilinear\n * sampling or nearest neighbor sampling (possibly with aspect ratio change)\n * to a common output size specified by crop_size.\n *\n * @param image 4d tensor of shape `[batch,imageHeight,imageWidth, depth]`,\n *     where imageHeight and imageWidth must be positive, specifying the\n *     batch of images from which to take crops\n * @param boxes 2d float32 tensor of shape `[numBoxes, 4]`. Each entry is\n *     `[y1, x1, y2, x2]`, where `(y1, x1)` and `(y2, x2)` are the normalized\n *     coordinates of the box in the boxInd[i]'th image in the batch\n * @param boxInd 1d int32 tensor of shape `[numBoxes]` with values in range\n *     `[0, batch)` that specifies the image that the `i`-th box refers to.\n * @param cropSize 1d int32 tensor of 2 elements `[cropHeigh, cropWidth]`\n *     specifying the size to which all crops are resized to.\n * @param method Optional string from `'bilinear' | 'nearest'`,\n *     defaults to bilinear, which specifies the sampling method for resizing\n * @param extrapolationValue A threshold for deciding when to remove boxes based\n *     on score. Defaults to 0.\n * @return A 4D tensor of the shape `[numBoxes,cropHeight,cropWidth,depth]`\n */\n/** @doc {heading: 'Operations', subheading: 'Images', namespace: 'image'} */\nfunction cropAndResize_(\n    image: Tensor4D|TensorLike,\n    boxes: Tensor2D|TensorLike,\n    boxInd: Tensor1D|TensorLike,\n    cropSize: [number, number],\n    method?: 'bilinear'|'nearest',\n    extrapolationValue?: number,\n    ): Tensor4D {\n  const $image = convertToTensor(image, 'image', 'cropAndResize', 'float32');\n  const $boxes = convertToTensor(boxes, 'boxes', 'cropAndResize', 'float32');\n  const $boxInd = convertToTensor(boxInd, 'boxInd', 'cropAndResize', 'int32');\n  method = method || 'bilinear';\n  extrapolationValue = extrapolationValue || 0;\n\n  const numBoxes = $boxes.shape[0];\n\n  util.assert(\n      $image.rank === 4,\n      'Error in cropAndResize: image must be rank 4,' +\n          `but got rank ${$image.rank}.`);\n  util.assert(\n      $boxes.rank === 2 && $boxes.shape[1] === 4,\n      `Error in cropAndResize: boxes must be have size [${numBoxes},4] ` +\n          `but had shape ${$boxes.shape}.`);\n  util.assert(\n      $boxInd.rank === 1 && $boxInd.shape[0] === numBoxes,\n      `Error in cropAndResize: boxInd must be have size [${numBoxes}] ` +\n          `but had shape ${$boxes.shape}.`);\n  util.assert(\n      cropSize.length === 2,\n      `Error in cropAndResize: cropSize must be of length 2, but got length ` +\n          `${cropSize.length}.`);\n  util.assert(\n      cropSize[0] >= 1 && cropSize[1] >= 1,\n      `cropSize must be atleast [1,1], but was ${cropSize}`);\n  util.assert(\n      method === 'bilinear' || method === 'nearest',\n      `method must be bilinear or nearest, but was ${method}`);\n\n  const forward: ForwardFunc<Tensor4D> = (backend, save) =>\n      backend.cropAndResize(\n          $image, $boxes, $boxInd, cropSize, method, extrapolationValue);\n\n  const res = ENV.engine.runKernel(forward, {$image, $boxes});\n  return res as Tensor4D;\n}\n\nexport const resizeBilinear = op({resizeBilinear_});\nexport const resizeNearestNeighbor = op({resizeNearestNeighbor_});\nexport const nonMaxSuppression = op({nonMaxSuppression_});\nexport const nonMaxSuppressionAsync = nonMaxSuppressionAsync_;\nexport const cropAndResize = cropAndResize_;\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport * as seedrandom from 'seedrandom';\nimport {ENV} from '../environment';\nimport {warn} from '../log';\nimport * as array_ops_util from '../ops/array_ops_util';\nimport * as axis_util from '../ops/axis_util';\nimport * as broadcast_util from '../ops/broadcast_util';\nimport * as concat_util from '../ops/concat_util';\nimport {Conv2DInfo, Conv3DInfo} from '../ops/conv_util';\nimport * as erf_util from '../ops/erf_util';\nimport * as gather_nd_util from '../ops/gather_nd_util';\nimport * as ops from '../ops/ops';\nimport {buffer, scalar, tensor, tensor3d, tensor4d} from '../ops/ops';\nimport * as scatter_nd_util from '../ops/scatter_nd_util';\nimport * as selu_util from '../ops/selu_util';\nimport {getStridedSlicedInfo} from '../ops/slice_util';\nimport {DataId, Scalar, setTensorTracker, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, Tensor5D, TensorBuffer} from '../tensor';\nimport {DataType, DataTypeMap, DataValues, NumericDataType, Rank, ShapeMap, TypedArray, upcastType} from '../types';\nimport * as util from '../util';\nimport {now} from '../util';\nimport {BackendTimingInfo, DataMover, DataStorage, KernelBackend} from './backend';\nimport * as backend_util from './backend_util';\nimport * as complex_util from './complex_util';\nimport {nonMaxSuppressionImpl} from './non_max_suppression_impl';\nimport {split} from './split_shared';\nimport {topkImpl} from './topk_impl';\nimport {whereImpl} from './where_impl';\n\ninterface TensorData<D extends DataType> {\n  values?: DataTypeMap[D];\n  dtype: D;\n  // For complex numbers, the real and imaginary parts are stored as their own\n  // individual tensors, with a parent joining the two with the\n  // complexTensors field. When this is defined, texture will be null.\n  complexTensors?: {real: Tensor, imag: Tensor};\n}\n\nexport class MathBackendCPU implements KernelBackend {\n  public blockSize = 48;\n\n  private data: DataStorage<TensorData<DataType>>;\n  private fromPixels2DContext: CanvasRenderingContext2D;\n  private firstUse = true;\n\n  constructor() {\n    if (ENV.get('IS_BROWSER')) {\n      this.fromPixels2DContext =\n          document.createElement('canvas').getContext('2d');\n    }\n  }\n\n  setDataMover(dataMover: DataMover): void {\n    this.data = new DataStorage(dataMover);\n  }\n\n  register(dataId: DataId, shape: number[], dtype: DataType): void {\n    if (this.firstUse) {\n      this.firstUse = false;\n      if (ENV.get('IS_NODE')) {\n        warn(\n            '\\n============================\\n' +\n            'Hi there 👋. Looks like you are running TensorFlow.js in ' +\n            'Node.js. To speed things up dramatically, install our node ' +\n            'backend, which binds to TensorFlow C++, by running ' +\n            'npm i @tensorflow/tfjs-node, ' +\n            'or npm i @tensorflow/tfjs-node-gpu if you have CUDA. ' +\n            'Then call require(\\'@tensorflow/tfjs-node\\'); (-gpu ' +\n            'suffix for CUDA) at the start of your program. ' +\n            'Visit https://github.com/tensorflow/tfjs-node for more details.' +\n            '\\n============================\\n');\n      }\n    }\n    if (this.data.has(dataId)) {\n      throw new Error(`Data buffer is already registered`);\n    }\n    this.data.set(dataId, {dtype});\n  }\n  write(dataId: DataId, values: DataValues): void {\n    if (values == null) {\n      throw new Error('MathBackendCPU.write(): values can not be null');\n    }\n    this.data.get(dataId).values = values;\n  }\n  fromPixels(\n      pixels: ImageData|HTMLImageElement|HTMLCanvasElement|HTMLVideoElement,\n      numChannels: number): Tensor3D {\n    if (pixels == null) {\n      throw new Error('pixels passed to tf.fromPixels() can not be null');\n    }\n    let vals: Uint8ClampedArray;\n    // tslint:disable-next-line:no-any\n    if (ENV.get('IS_NODE') && (pixels as any).getContext == null) {\n      throw new Error(\n          'When running in node, pixels must be an HTMLCanvasElement ' +\n          'like the one returned by the `canvas` npm package');\n    }\n    // tslint:disable-next-line:no-any\n    if ((pixels as any).getContext != null) {\n      // tslint:disable-next-line:no-any\n      vals = (pixels as any)\n                 .getContext('2d')\n                 .getImageData(0, 0, pixels.width, pixels.height)\n                 .data;\n    } else if (pixels instanceof ImageData) {\n      vals = pixels.data;\n    } else if (\n        pixels instanceof HTMLImageElement ||\n        pixels instanceof HTMLVideoElement) {\n      if (this.fromPixels2DContext == null) {\n        throw new Error(\n            'Can\\'t read pixels from HTMLImageElement outside ' +\n            'the browser.');\n      }\n      this.fromPixels2DContext.canvas.width = pixels.width;\n      this.fromPixels2DContext.canvas.height = pixels.height;\n      this.fromPixels2DContext.drawImage(\n          pixels, 0, 0, pixels.width, pixels.height);\n      vals = this.fromPixels2DContext\n                 .getImageData(0, 0, pixels.width, pixels.height)\n                 .data;\n    } else {\n      throw new Error(\n          'pixels passed to tf.fromPixels() must be either an ' +\n          `HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ` +\n          `ImageData, but was ${(pixels as {}).constructor.name}`);\n    }\n    let values: Int32Array;\n    if (numChannels === 4) {\n      values = new Int32Array(vals);\n    } else {\n      const numPixels = pixels.width * pixels.height;\n      values = new Int32Array(numPixels * numChannels);\n      for (let i = 0; i < numPixels; i++) {\n        for (let channel = 0; channel < numChannels; ++channel) {\n          values[i * numChannels + channel] = vals[i * 4 + channel];\n        }\n      }\n    }\n    const outShape: [number, number, number] =\n        [pixels.height, pixels.width, numChannels];\n    return tensor3d(values, outShape, 'int32');\n  }\n  async read(dataId: DataId): Promise<DataValues> {\n    return this.readSync(dataId);\n  }\n  readSync(dataId: DataId): DataValues {\n    const {dtype, complexTensors} = this.data.get(dataId);\n    if (dtype === 'complex64') {\n      const realValues = complexTensors.real.dataSync() as Float32Array;\n      const imagValues = complexTensors.imag.dataSync() as Float32Array;\n      return complex_util.mergeRealAndImagArrays(realValues, imagValues);\n    }\n    return this.data.get(dataId).values;\n  }\n\n  disposeData(dataId: DataId): void {\n    if (this.data.has(dataId)) {\n      const {complexTensors} = this.data.get(dataId);\n      if (complexTensors != null) {\n        complexTensors.real.dispose();\n        complexTensors.imag.dispose();\n      }\n      this.data.delete(dataId);\n    }\n  }\n\n  async time(f: () => void): Promise<BackendTimingInfo> {\n    const start = now();\n    f();\n    const kernelMs = now() - start;\n    return {kernelMs};\n  }\n\n  memory() {\n    return {\n      // Unreliable due to automatic gc. The numbers above are cumulative.\n      unreliable: true,\n      reasons:\n          ['The reported memory is an upper bound. Due to automatic garbage ' +\n           'collection, the true allocated memory may be less.']\n    };\n  }\n\n  complex<T extends Tensor>(real: T, imag: T): T {\n    const result = Tensor.make(real.shape, {}, 'complex64') as T;\n\n    const resultData = this.data.get(result.dataId);\n    // The backend owns the reference to the underlying real and imaginary\n    // clones. These will explicitly get disposed when the complex tensor is\n    // disposed.\n    resultData.complexTensors = {\n      real: ENV.engine.keep(real.clone()),\n      imag: ENV.engine.keep(imag.clone())\n    };\n\n    return result;\n  }\n  real<T extends Tensor>(input: T): T {\n    const resultData = this.data.get(input.dataId);\n    return resultData.complexTensors.real.clone() as T;\n  }\n  imag<T extends Tensor>(input: T): T {\n    const resultData = this.data.get(input.dataId);\n    return resultData.complexTensors.imag.clone() as T;\n  }\n\n  private assertNotComplex(tensor: Tensor|Tensor[], opName: string) {\n    if (!Array.isArray(tensor)) {\n      tensor = [tensor];\n    }\n    tensor.forEach(t => {\n      if (t != null) {\n        util.assert(\n            t.dtype !== 'complex64',\n            `${opName} does not support complex64 tensors.`);\n      }\n    });\n  }\n\n  slice<T extends Tensor>(x: T, begin: number[], size: number[]): T {\n    this.assertNotComplex(x, 'slice');\n\n    const buffer = ops.buffer(size, x.dtype);\n\n    for (let i = 0; i < buffer.size; ++i) {\n      const loc = buffer.indexToLoc(i);\n      const xLoc = loc.map((idx, j) => idx + begin[j]);\n      buffer.set(x.get(...xLoc), ...loc);\n    }\n    return buffer.toTensor() as T;\n  }\n\n  stridedSlice<T extends Tensor>(\n      x: T, begin: number[], end: number[], strides: number[],\n      beginMask: number, endMask: number, ellipsisMask: number,\n      newAxisMask: number, shrinkAxisMask: number): T {\n    this.assertNotComplex(x, 'stridedSlice');\n\n    const [beginIndex, size, shrinkAxis] = getStridedSlicedInfo(\n        x.shape, begin, end, strides, beginMask, endMask, ellipsisMask,\n        newAxisMask, shrinkAxisMask);\n\n    const shape = size.filter((v, index) => shrinkAxis.indexOf(index) === -1);\n\n    if (shape.some(axis => axis === 0)) {\n      return ops.tensor([], shape) as T;\n    }\n\n    const buffer = ops.buffer(size, x.dtype);\n\n    for (let i = 0; i < buffer.size; i++) {\n      const loc = buffer.indexToLoc(i);\n\n      const newLoc: number[] = new Array(loc.length);\n      for (let j = 0; j < newLoc.length; j++) {\n        newLoc[j] = loc[j] * strides[j] + beginIndex[j];\n      }\n      buffer.set(x.get(...newLoc), ...loc);\n    }\n\n    return buffer.toTensor().reshape(shape) as T;\n  }\n\n  reverse<T extends Tensor>(x: T, axis: number[]): T {\n    this.assertNotComplex(x, 'reverse');\n\n    const buffer = ops.buffer(x.shape, x.dtype);\n    const xBuffer = x.buffer();\n\n    for (let i = 0; i < buffer.size; i++) {\n      const outLoc = buffer.indexToLoc(i);\n      const inLoc = outLoc.slice();\n      axis.forEach(ax => inLoc[ax] = x.shape[ax] - 1 - inLoc[ax]);\n      buffer.set(xBuffer.get(...inLoc), ...outLoc);\n    }\n\n    return buffer.toTensor() as T;\n  }\n\n  concat(tensors: Tensor[], axis: number): Tensor {\n    this.assertNotComplex(tensors, 'concat');\n    const tensors2D = tensors.map(t => {\n      const innerSize = util.sizeFromShape(t.shape.slice(axis));\n      return t.as2D(-1, innerSize);\n    });\n    const outShape =\n        concat_util.computeOutShape(tensors2D.map(t => t.shape), 1 /* axis */);\n    const values =\n        ops.buffer(outShape as [number, number], tensors[0].dtype as 'float32')\n            .values;\n    if (tensors2D[0].shape[0] === 1) {\n      // Use built-in TypedArray.set() method for speed.\n      let offset = 0;\n      tensors2D.forEach(t => {\n        values.set(t.dataSync(), offset);\n        offset += t.size;\n      });\n    } else {\n      let colOffset = 0;\n      tensors2D.forEach(t => {\n        const tVals = t.dataSync();\n        let tIdx = 0;\n        for (let row = 0; row < t.shape[0]; ++row) {\n          const resIdx = row * outShape[1] + colOffset;\n          for (let col = 0; col < t.shape[1]; ++col) {\n            values[resIdx + col] = tVals[tIdx++];\n          }\n        }\n        colOffset += t.shape[1];\n      });\n    }\n    const finalOutShape =\n        concat_util.computeOutShape(tensors.map(t => t.shape), axis);\n    return tensor(values, finalOutShape, tensors[0].dtype);\n  }\n\n  neg<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'neg');\n\n    return this.multiply(ops.scalar(-1), x) as T;\n  }\n\n  add(a: Tensor, b: Tensor): Tensor {\n    if (a.dtype === 'complex64' || b.dtype === 'complex64') {\n      return this.broadcastedBinaryComplexOp(\n                 a.cast('complex64'), b.cast('complex64'),\n                 (aReal, aImag, bReal, bImag) => {\n                   return {real: aReal + bReal, imag: aImag + bImag};\n                 }) as Tensor;\n    }\n\n    return this.broadcastedBinaryOp(\n               a, b, upcastType(a.dtype, b.dtype),\n               (aValue, bValue) => aValue + bValue) as Tensor;\n  }\n\n  addN<T extends Tensor>(tensors: T[]): T {\n    this.assertNotComplex(tensors, 'addN');\n\n    const vals = tensors.map(t => t.dataSync());\n    const result = ops.buffer(tensors[0].shape, tensors[0].dtype as 'float32');\n    const resultVals = result.values;\n    for (let i = 0; i < tensors.length; i++) {\n      const currVals = vals[i];\n      for (let j = 0; j < resultVals.length; j++) {\n        resultVals[j] += currVals[j];\n      }\n    }\n    return result.toTensor() as T;\n  }\n\n  subtract(a: Tensor, b: Tensor): Tensor {\n    if (a.dtype === 'complex64' || b.dtype === 'complex64') {\n      return this.broadcastedBinaryComplexOp(\n                 a.cast('complex64'), b.cast('complex64'),\n                 (aReal, aImag, bReal, bImag) => {\n                   return {real: aReal - bReal, imag: aImag - bImag};\n                 }) as Tensor;\n    }\n\n    return this.broadcastedBinaryOp(\n               a, b, upcastType(a.dtype, b.dtype),\n               (aValue, bValue) => aValue - bValue) as Tensor;\n  }\n\n  pow<T extends Tensor>(a: T, b: Tensor): T {\n    this.assertNotComplex([a, b], 'pow');\n\n    return this.broadcastedBinaryOp(\n               a, b, a.dtype, (aValue, bValue) => Math.pow(aValue, bValue)) as\n        T;\n  }\n\n  batchMatMul(\n      a: Tensor3D, b: Tensor3D, transposeA: boolean,\n      transposeB: boolean): Tensor3D {\n    this.assertNotComplex([a, b], 'matMul');\n\n    const sharedDim = transposeA ? a.shape[1] : a.shape[2];\n    const leftDim = transposeA ? a.shape[2] : a.shape[1];\n    const rightDim = transposeB ? b.shape[1] : b.shape[2];\n    const batchDim = a.shape[0];\n\n    const aValues = a.dataSync();\n    const bValues = b.dataSync();\n    const [aBatch, aOuterStep, aInnerStep] = transposeA ?\n        [a.strides[0], 1, a.strides[1]] :\n        [a.strides[0], a.strides[1], 1];\n    const [bInnerStep, bOuterStep, bBatch] = transposeB ?\n        [1, b.strides[1], b.strides[0]] :\n        [b.strides[1], 1, b.strides[0]];\n\n    const size = leftDim * rightDim;\n    const result = buffer([batchDim, leftDim, rightDim], a.dtype);\n    const resVals = result.values as TypedArray;\n    const blockSize = this.blockSize;\n\n    for (let b = 0; b < batchDim; b++) {\n      for (let i0 = 0; i0 < leftDim; i0 += blockSize) {\n        for (let j0 = 0; j0 < rightDim; j0 += blockSize) {\n          for (let k0 = 0; k0 < sharedDim; k0 += blockSize) {\n            // for when blockSize doesn't evenly divide the input\n            const iBlock = Math.min(i0 + blockSize, leftDim);\n            const jBlock = Math.min(j0 + blockSize, rightDim);\n            const kBlock = Math.min(k0 + blockSize, sharedDim);\n\n            for (let i = i0; i < iBlock; i++) {\n              for (let j = j0; j < jBlock; j++) {\n                let sum = 0.0;\n\n                for (let k = k0; k < kBlock; k++) {\n                  sum += aValues[b * aBatch + i * aOuterStep + k * aInnerStep] *\n                      bValues[k * bInnerStep + j * bOuterStep + b * bBatch];\n                }\n                resVals[b * size + (i * rightDim + j)] += sum;\n              }\n            }\n          }\n        }\n      }\n    }\n    return result.toTensor() as Tensor3D;\n  }\n\n  multiply(a: Tensor, b: Tensor): Tensor {\n    if (a.dtype === 'complex64' || b.dtype === 'complex64') {\n      return this.broadcastedBinaryComplexOp(\n                 a.cast('complex64'), b.cast('complex64'),\n                 (aReal, aImag, bReal, bImag) => {\n                   return {\n                     real: aReal * bReal - aImag * bImag,\n                     imag: aReal * bImag + aImag * bReal\n                   };\n                 }) as Tensor;\n    }\n\n    return this.broadcastedBinaryOp(\n               a, b, upcastType(a.dtype, b.dtype),\n               (aValue, bValue) => aValue * bValue) as Tensor;\n  }\n\n  realDivide(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'realDivide');\n\n    const op = (a: number, b: number) => a / b;\n    const outputDtype = 'float32';\n    return this.broadcastedBinaryOp(a, b, outputDtype, op) as Tensor;\n  }\n\n  floorDiv(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'floorDiv');\n\n    const op = (a: number, b: number) => Math.floor(a / b);\n    const outputDtype = 'int32';\n    return this.broadcastedBinaryOp(a, b, outputDtype, op) as Tensor;\n  }\n\n  sum(x: Tensor, axes: number[]): Tensor {\n    this.assertNotComplex(x, 'sum');\n\n    axis_util.assertAxesAreInnerMostDims('sum', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const resultDtype = upcastType(x.dtype, 'int32');\n    const result = ops.zeros(outShape, resultDtype);\n    const reduceSize = util.sizeFromShape(reduceShape);\n    const vals = result.dataSync();\n\n    const aVals = x.dataSync();\n    for (let i = 0; i < vals.length; ++i) {\n      const offset = i * reduceSize;\n      let sum = 0;\n      for (let j = 0; j < reduceSize; ++j) {\n        sum += aVals[offset + j];\n      }\n      vals[i] = sum;\n    }\n    return result;\n  }\n\n  prod(x: Tensor, axes: number[]): Tensor {\n    this.assertNotComplex(x, 'sum');\n\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const resultDtype = upcastType(x.dtype, 'int32');\n    const result = ops.zeros(outShape, resultDtype);\n    const reduceSize = util.sizeFromShape(reduceShape);\n    const vals = result.dataSync();\n\n    const aVals = x.dataSync();\n    for (let i = 0; i < vals.length; ++i) {\n      const offset = i * reduceSize;\n      let prod = 1;\n      for (let j = 0; j < reduceSize; ++j) {\n        prod *= aVals[offset + j];\n      }\n      vals[i] = prod;\n    }\n    return result;\n  }\n\n  unsortedSegmentSum<T extends Tensor>(\n      x: T, segmentIds: Tensor1D, numSegments: number): Tensor {\n    this.assertNotComplex(x, 'unsortedSegmentSum');\n\n    const res = [];\n\n    // Reshape the segment id's so that they can be broadcast with\n    // x. The new shape should be [segmentIds.shape, 1, ..., 1]\n    const numIters = x.rank - segmentIds.rank;\n    for (let i = 0; i < numIters; ++i) {\n      segmentIds = segmentIds.expandDims(i + 1);\n    }\n\n    for (let i = 0; i < numSegments; ++i) {\n      const segmentId = ops.scalar(i, 'int32');\n      const mask = ops.equal(segmentId, segmentIds).asType('float32');\n      const sum = mask.mul(x).sum(0);\n      res.push(sum);\n    }\n\n    return ops.stack(res);\n  }\n\n  argMin(x: Tensor, axis: number): Tensor {\n    this.assertNotComplex(x, 'argMin');\n\n    const axes = [axis];\n    axis_util.assertAxesAreInnerMostDims('argMin', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const result = ops.zeros(outShape, 'int32');\n    const reduceSize = util.sizeFromShape(reduceShape);\n    const vals = result.dataSync();\n\n    const aVals = x.dataSync();\n    for (let i = 0; i < vals.length; ++i) {\n      const offset = i * reduceSize;\n      let min = aVals[offset];\n      let minIndex = 0;\n      for (let j = 0; j < reduceSize; ++j) {\n        const value = aVals[offset + j];\n        if (value < min) {\n          min = value;\n          minIndex = j;\n        }\n      }\n      vals[i] = minIndex;\n    }\n    return result;\n  }\n\n  argMax(x: Tensor, axis: number): Tensor {\n    this.assertNotComplex(x, 'argMax');\n\n    const axes = [axis];\n    axis_util.assertAxesAreInnerMostDims('argMax', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const result = ops.zeros(outShape, 'int32');\n    const reduceSize = util.sizeFromShape(reduceShape);\n    const vals = result.dataSync();\n\n    const aVals = x.dataSync();\n    for (let i = 0; i < vals.length; ++i) {\n      const offset = i * reduceSize;\n      let max = aVals[offset];\n      let maxIndex = 0;\n      for (let j = 0; j < reduceSize; ++j) {\n        const value = aVals[offset + j];\n        if (value > max) {\n          max = value;\n          maxIndex = j;\n        }\n      }\n      vals[i] = maxIndex;\n    }\n    return result;\n  }\n\n  cumsum(x: Tensor, axis: number, exclusive: boolean, reverse: boolean):\n      Tensor {\n    this.assertNotComplex(x, 'cumsum');\n\n    if (axis !== x.rank - 1) {\n      throw new Error(\n          `backend.cumsum in CPU expects an inner-most axis=${x.rank - 1} ` +\n          `but got axis=${axis}`);\n    }\n    const resultDtype = upcastType(x.dtype, 'int32');\n    const result = ops.zeros(x.shape, resultDtype);\n    const vals = result.dataSync();\n\n    const aVals = x.dataSync();\n    const finalDim = x.shape[x.rank - 1];\n    const indexAdjuster = reverse ?\n        (i: number, j: number) => i + finalDim - j - 1 :\n        (i: number, j: number) => i + j;\n    for (let i = 0; i < aVals.length; i += finalDim) {\n      for (let j = 0; j < finalDim; j++) {\n        const idx = indexAdjuster(i, j);\n        if (j === 0) {\n          vals[idx] = exclusive ? 0 : aVals[idx];\n        } else {\n          const prevIdx = indexAdjuster(i, j - 1);\n          vals[idx] = exclusive ? aVals[prevIdx] + vals[prevIdx] :\n                                  aVals[idx] + vals[prevIdx];\n        }\n      }\n    }\n    return result;\n  }\n\n  equal(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'equal');\n\n    return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n      return (aVal === bVal) ? 1 : 0;\n    });\n  }\n\n  notEqual(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'notEqual');\n\n    return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n      return (aVal !== bVal) ? 1 : 0;\n    });\n  }\n\n  less(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'less');\n\n    return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n      return (aVal < bVal) ? 1 : 0;\n    });\n  }\n\n  lessEqual(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'lessEqual');\n\n    return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n      return (aVal <= bVal) ? 1 : 0;\n    });\n  }\n\n  greater(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'greater');\n\n    return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n      return (aVal > bVal) ? 1 : 0;\n    });\n  }\n\n  greaterEqual(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'greaterEqual');\n\n    return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n      return (aVal >= bVal) ? 1 : 0;\n    });\n  }\n\n  logicalNot<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'logicalNot');\n\n    const values = x.dataSync();\n    const newValues = new Uint8Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      newValues[i] = values[i] ? 0 : 1;\n    }\n    return Tensor.make(x.shape, {values: newValues}, 'bool') as T;\n  }\n\n  logicalAnd(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'logicalAnd');\n\n    return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n      return aVal && bVal;\n    });\n  }\n\n  logicalOr(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'logicalOr');\n\n    return this.broadcastedBinaryOp(a, b, 'bool', (aVal, bVal) => {\n      return aVal || bVal;\n    });\n  }\n\n  select(condition: Tensor, a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([condition, a, b], 'select');\n\n    const values = condition.dataSync();\n    const aValues = a.dataSync();\n    const bValues = b.dataSync();\n    const result = ops.zeros(a.shape, upcastType(a.dtype, b.dtype));\n    const newValues = result.dataSync();\n    let index = 0;\n    const offset = condition.rank === 0 || condition.rank > 1 || a.rank === 1 ?\n        1 :\n        a.shape[1];\n\n    for (let i = 0; i < values.length; i++) {\n      for (let j = 0; j < offset; j++) {\n        if (values[i] === 1) {\n          newValues[index++] = aValues[i];\n        } else {\n          newValues[index++] = bValues[i];\n        }\n      }\n    }\n    return result;\n  }\n\n  where(condition: Tensor): Tensor2D {\n    this.assertNotComplex([condition], 'where');\n\n    const condVals = condition.dataSync();\n    return whereImpl(condition.shape, condVals);\n  }\n\n  topk<T extends Tensor>(x: T, k: number, sorted: boolean): [T, T] {\n    this.assertNotComplex(x, 'topk');\n\n    const xVals = x.dataSync();\n    return topkImpl(xVals, x.shape, x.dtype as NumericDataType, k, sorted);\n  }\n\n  min(x: Tensor, axes: number[]): Tensor {\n    this.assertNotComplex(x, 'min');\n\n    axis_util.assertAxesAreInnerMostDims('min', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const result = ops.zeros(outShape, x.dtype);\n    const reduceSize = util.sizeFromShape(reduceShape);\n    const vals = result.dataSync();\n\n    const aVals = x.dataSync();\n    for (let i = 0; i < vals.length; ++i) {\n      const offset = i * reduceSize;\n      let min = aVals[offset];\n      for (let j = 0; j < reduceSize; ++j) {\n        const value = aVals[offset + j];\n        if (value < min) {\n          min = value;\n        }\n      }\n      vals[i] = min;\n    }\n    return result;\n  }\n\n  minimum(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'minimum');\n\n    return this.broadcastedBinaryOp(\n        a, b, a.dtype, (aVal, bVal) => Math.min(aVal, bVal));\n  }\n\n  mod(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'mod');\n\n    return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => {\n      const rem = aVal % bVal;\n      if ((aVal < 0 && bVal < 0) || (aVal >= 0 && bVal >= 0)) {\n        return rem;\n      } else {\n        return (rem + bVal) % bVal;\n      }\n    });\n  }\n\n  max(x: Tensor, axes: number[]): Tensor {\n    this.assertNotComplex(x, 'max');\n\n    axis_util.assertAxesAreInnerMostDims('max', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const result = ops.zeros(outShape, x.dtype);\n    const reduceSize = util.sizeFromShape(reduceShape);\n    const vals = result.dataSync();\n\n    const aVals = x.dataSync();\n    for (let i = 0; i < vals.length; ++i) {\n      const offset = i * reduceSize;\n      let max = aVals[offset];\n      for (let j = 0; j < reduceSize; ++j) {\n        const value = aVals[offset + j];\n        if (value > max) {\n          max = value;\n        }\n      }\n      vals[i] = max;\n    }\n    return result;\n  }\n\n  maximum(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'maximum');\n\n    return this.broadcastedBinaryOp(\n        a, b, a.dtype, (aVal, bVal) => Math.max(aVal, bVal));\n  }\n\n  all(x: Tensor, axes: number[]): Tensor {\n    this.assertNotComplex(x, 'all');\n\n    axis_util.assertAxesAreInnerMostDims('all', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const result = ops.zeros(outShape, x.dtype);\n    const reduceSize = util.sizeFromShape(reduceShape);\n    const vals = result.dataSync();\n\n    const aVals = x.dataSync();\n    for (let i = 0; i < vals.length; ++i) {\n      const offset = i * reduceSize;\n      let all = aVals[offset];\n      for (let j = 0; j < reduceSize; ++j) {\n        const value = aVals[offset + j];\n        all = all && value;\n      }\n      vals[i] = all;\n    }\n    return result;\n  }\n\n  any(x: Tensor, axes: number[]): Tensor {\n    this.assertNotComplex(x, 'any');\n\n    axis_util.assertAxesAreInnerMostDims('any', axes, x.rank);\n    const [outShape, reduceShape] =\n        axis_util.computeOutAndReduceShapes(x.shape, axes);\n    const result = ops.zeros(outShape, x.dtype);\n    const reduceSize = util.sizeFromShape(reduceShape);\n    const vals = result.dataSync();\n\n    const aVals = x.dataSync();\n    for (let i = 0; i < vals.length; ++i) {\n      const offset = i * reduceSize;\n      let anyVal = aVals[offset];\n      for (let j = 0; j < reduceSize; ++j) {\n        const value = aVals[offset + j];\n        anyVal = anyVal || value;\n      }\n      vals[i] = anyVal;\n    }\n    return result;\n  }\n\n  squaredDifference(a: Tensor, b: Tensor): Tensor {\n    this.assertNotComplex([a, b], 'squaredDifference');\n\n    return this.broadcastedBinaryOp(a, b, a.dtype, (aVal, bVal) => {\n      const diff = aVal - bVal;\n      return diff * diff;\n    });\n  }\n\n  ceil<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'ceil');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      newValues[i] = Math.ceil(values[i]);\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  floor<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'floor');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      newValues[i] = Math.floor(values[i]);\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  sign<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'x');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      if (values[i] < 0) {\n        newValues[i] = -1;\n      } else if (values[i] > 0) {\n        newValues[i] = 1;\n      } else {\n        newValues[i] = 0;\n      }\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  round<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'round');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      // The algorithm is based on banker's rounding.\n      const base = Math.floor(values[i]);\n      if (values[i] - base < 0.5) {\n        newValues[i] = Math.floor(values[i]);\n      } else if (values[i] - base > 0.5) {\n        newValues[i] = Math.ceil(values[i]);\n      } else {\n        if (base % 2.0 === 0.0) {\n          newValues[i] = base;\n        } else {\n          newValues[i] = base + 1.0;\n        }\n      }\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  exp<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'exp');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      newValues[i] = Math.exp(values[i]);\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  expm1<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'expm1');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      newValues[i] = Math.expm1(values[i]);\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  log<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'log');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      const value = values[i];\n      newValues[i] = Math.log(value);\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  log1p<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'log1p');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      const value = values[i];\n      newValues[i] = Math.log1p(value);\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  sqrt<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'sqrt');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      const value = values[i];\n      newValues[i] = Math.sqrt(value);\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  rsqrt<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'rsqrt');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      const value = values[i];\n      newValues[i] = 1 / Math.sqrt(value);\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  square<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'square');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      const value = values[i];\n      newValues[i] = value * value;\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  reciprocal<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'reciprocal');\n\n    const values = x.dataSync();\n    const newValues = new Float32Array(values.length);\n    for (let i = 0; i < values.length; ++i) {\n      newValues[i] = 1 / values[i];\n    }\n    return Tensor.make(x.shape, {values: newValues}) as T;\n  }\n\n  relu<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'relu');\n\n    const res = ops.zeros(x.shape, x.dtype);\n    const resVals = res.dataSync();\n    const inVals = x.dataSync();\n    for (let i = 0; i < inVals.length; ++i) {\n      resVals[i] = Math.max(0, inVals[i]);\n    }\n    return res as T;\n  }\n\n  elu<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'elu');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      const v = values[i];\n      if (v >= 0) {\n        resultValues[i] = v;\n      } else {\n        resultValues[i] = (Math.exp(v) - 1);\n      }\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  eluDer<T extends Tensor>(dy: T, y: T): T {\n    this.assertNotComplex([dy, y], 'eluDer');\n\n    const resultValues = new Float32Array(y.size);\n    const values = y.dataSync();\n    const dyValues = dy.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      const v = values[i];\n      if (v >= 1) {\n        resultValues[i] = dyValues[i];\n      } else {\n        resultValues[i] = dyValues[i] * (v + 1);\n      }\n    }\n    return Tensor.make(y.shape, {values: resultValues}) as T;\n  }\n\n  selu<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'selu');\n\n    // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n    // see: https://arxiv.org/abs/1706.02515\n    const scaleAlpha = selu_util.SELU_SCALEALPHA;\n    const scale = selu_util.SELU_SCALE;\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      const v = values[i];\n      if (v >= 0) {\n        resultValues[i] = scale * v;\n      } else {\n        resultValues[i] = scaleAlpha * (Math.exp(v) - 1);\n      }\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  clip<T extends Tensor>(x: T, min: number, max: number): T {\n    this.assertNotComplex(x, 'clip');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      const v = values[i];\n      resultValues[i] = v > max ? max : (v < min ? min : v);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  abs<T extends Tensor>(x: T): T {\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.abs(values[i]);\n    }\n\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  complexAbs<T extends Tensor>(x: T): T {\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n\n    for (let i = 0; i < x.size; ++i) {\n      const real = values[i * 2];\n      const imag = values[i * 2 + 1];\n      resultValues[i] = Math.hypot(real, imag);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  int<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'int');\n\n    const resultValues = new Int32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = values[i];\n    }\n    return Tensor.make(x.shape, {values: resultValues}, 'int32');\n  }\n\n  sigmoid<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'sigmoid');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = 1 / (1 + Math.exp(-values[i]));\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  softplus<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'softplus');\n\n    // mirrors the implementation of tf.nn.softplus: https://goo.gl/vkcvwX\n\n    // epsilon is the difference between 1.0 and the next representable float.\n    // For a single precision 32 bit float this should be 2^-23, see:\n    // https://math.byu.edu/~schow/work/IEEEFloatingPoint.htm\n    const epsilon = 1.1920928955078125e-7;\n    const threshold = Math.log(epsilon) + 2.0;\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n\n    for (let i = 0; i < values.length; ++i) {\n      // Value above which exp(x) may overflow, but softplus(x) == x\n      // is within machine epsilon.\n      const tooLarge = values[i] > -threshold;\n\n      // Value below which exp(x) may underflow, but softplus(x) == exp(x)\n      // is within machine epsilon.\n      const tooSmall = values[i] < threshold;\n\n      const expX = Math.exp(values[i]);\n      let result;\n\n      if (tooSmall) {\n        result = expX;\n      } else if (tooLarge) {\n        result = values[i];\n      } else {\n        result = Math.log(1.0 + expX);\n      }\n      resultValues[i] = result;\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  sin<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'sin');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.sin(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  cos<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'cos');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.cos(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  tan<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'tan');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.tan(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  asin<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'asin');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.asin(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  acos<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'acos');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.acos(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  atan<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'atan');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.atan(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  atan2<T extends Tensor>(a: T, b: T): T {\n    this.assertNotComplex([a, b], 'atan2');\n\n    return this.broadcastedBinaryOp(\n               a, b, a.dtype, (aValue, bValue) => Math.atan2(aValue, bValue)) as\n        T;\n  }\n\n  sinh<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'sinh');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.sinh(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  cosh<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'cosh');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.cosh(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  tanh<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'tanh');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = util.tanh(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  asinh<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'asinh');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.asinh(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  acosh<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'acosh');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.acosh(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  atanh<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'atanh');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      resultValues[i] = Math.atanh(values[i]);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  erf<T extends Tensor>(x: T): T {\n    this.assertNotComplex(x, 'erf');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    const p = erf_util.ERF_P;\n    const a1 = erf_util.ERF_A1;\n    const a2 = erf_util.ERF_A2;\n    const a3 = erf_util.ERF_A3;\n    const a4 = erf_util.ERF_A4;\n    const a5 = erf_util.ERF_A5;\n    for (let i = 0; i < values.length; ++i) {\n      const v = values[i];\n      const t = 1.0 / (1.0 + p * v);\n      resultValues[i] = 1.0 -\n          (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t *\n              Math.exp(-v * v);\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  step<T extends Tensor>(x: T, alpha = 0): T {\n    this.assertNotComplex(x, 'step');\n\n    const resultValues = new Float32Array(x.size);\n    const values = x.dataSync();\n    for (let i = 0; i < values.length; ++i) {\n      const value = values[i];\n      if (isNaN(value)) {\n        resultValues[i] = NaN;\n      } else {\n        resultValues[i] = value > 0 ? 1 : alpha;\n      }\n    }\n    return Tensor.make(x.shape, {values: resultValues}) as T;\n  }\n\n  conv2d(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    this.assertNotComplex([x, filter], 'conv2d');\n\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const padLeft = convInfo.padInfo.left;\n    const padTop = convInfo.padInfo.top;\n    const y = ops.buffer(convInfo.outShape, x.dtype as 'float32');\n\n    const xVals = x.dataSync();\n    const wVals = filter.dataSync();\n    const yVals = y.values;\n\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n      const xOffset1 = b * x.strides[0];\n      const yOffset1 = b * y.strides[0];\n      for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n        const yOffset2 = yOffset1 + yR * y.strides[1];\n        const xRCorner = yR * convInfo.strideHeight - padLeft;\n        for (let wR = 0; wR < filterHeight; wR++) {\n          const xR = xRCorner + wR * dilationHeight;\n          if (xR < 0 || xR >= convInfo.inHeight) {\n            continue;\n          }\n          const wOffset1 = wR * filter.strides[0];\n          const xOffset2 = xOffset1 + xR * x.strides[1];\n          for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n            const yOffset3 = yOffset2 + yC * convInfo.outChannels;\n            const xCCorner = yC * convInfo.strideWidth - padTop;\n            for (let wC = 0; wC < filterWidth; wC++) {\n              const xC = xCCorner + wC * dilationWidth;\n              if (xC < 0 || xC >= convInfo.inWidth) {\n                continue;\n              }\n              const wOffset2 = wOffset1 + wC * filter.strides[1];\n              const xOffset3 = xOffset2 + xC * convInfo.inChannels;\n              let wOffset3 = wOffset2;\n              for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                const xVal = xVals[xOffset3 + d1];\n                for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                  yVals[yOffset3 + d2] += xVal * wVals[wOffset3 + d2];\n                }\n                wOffset3 += convInfo.outChannels;\n              }\n            }\n          }\n        }\n      }\n    }\n    return y.toTensor() as Tensor4D;\n  }\n\n  conv3d(x: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n    const filterDepth = convInfo.filterDepth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dilationDepth = convInfo.dilationDepth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const padFront = convInfo.padInfo.front;\n    const padLeft = convInfo.padInfo.left;\n    const padTop = convInfo.padInfo.top;\n    const y = ops.buffer<Rank.R5>(convInfo.outShape, x.dtype as 'float32');\n\n    const xVals = x.dataSync();\n    const wVals = filter.dataSync();\n    const yVals = y.values;\n\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n      const xOffset1 = b * x.strides[0];\n      const yOffset1 = b * y.strides[0];\n      for (let yF = 0; yF < convInfo.outDepth; ++yF) {\n        const yOffset2 = yOffset1 + yF * y.strides[1];\n        const xFCorner = yF * convInfo.strideDepth - padFront;\n        for (let wF = 0; wF < filterDepth; wF++) {\n          const xF = xFCorner + wF * dilationDepth;\n          if (xF < 0 || xF >= convInfo.inDepth) {\n            continue;\n          }\n          const wOffset1 = wF * filter.strides[0];\n          const xOffset2 = xOffset1 + xF * x.strides[1];\n\n          for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n            const yOffset3 = yOffset2 + yR * y.strides[2];\n            const xRCorner = yR * convInfo.strideHeight - padTop;\n            for (let wR = 0; wR < filterHeight; wR++) {\n              const xR = xRCorner + wR * dilationHeight;\n              if (xR < 0 || xR >= convInfo.inHeight) {\n                continue;\n              }\n              const wOffset2 = wOffset1 + wR * filter.strides[1];\n              const xOffset3 = xOffset2 + xR * x.strides[2];\n              for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n                const yOffset4 = yOffset3 + yC * convInfo.outChannels;\n                const xCCorner = yC * convInfo.strideWidth - padLeft;\n                for (let wC = 0; wC < filterWidth; wC++) {\n                  const xC = xCCorner + wC * dilationWidth;\n                  if (xC < 0 || xC >= convInfo.inWidth) {\n                    continue;\n                  }\n                  const wOffset3 = wOffset2 + wC * filter.strides[2];\n                  const xOffset4 = xOffset3 + xC * convInfo.inChannels;\n                  let wOffset4 = wOffset3;\n                  for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                    const xVal = xVals[xOffset4 + d1];\n                    for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n                      yVals[yOffset4 + d2] += xVal * wVals[wOffset4 + d2];\n                    }\n                    wOffset4 += convInfo.outChannels;\n                  }\n                }\n              }\n            }\n          }\n        }\n      }\n    }\n    return y.toTensor();\n  }\n\n  conv2dDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    this.assertNotComplex([dy, filter], 'conv2dDerInput');\n\n    const dx = ops.buffer<Rank.R4>(convInfo.inShape, 'float32');\n    const dxValues = dx.values;\n    const [dxS0, dxS1, dxS2] = dx.strides;\n    const dyValues = dy.dataSync();\n    const [dyS0, dyS1, dyS2] = dy.strides;\n    const fltValues = filter.dataSync();\n    const [fltS0, fltS1, fltS2] = filter.strides;\n    const {\n      batchSize,\n      filterHeight,\n      filterWidth,\n      inChannels,\n      inHeight,\n      inWidth,\n      outChannels,\n      outHeight,\n      outWidth,\n      strideHeight,\n      strideWidth\n    } = convInfo;\n    const topPad = filterHeight - 1 - convInfo.padInfo.top;\n    const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n\n    for (let b = 0; b < batchSize; ++b) {\n      for (let d1 = 0; d1 < inChannels; ++d1) {\n        for (let xR = 0; xR < inHeight; ++xR) {\n          const xRCorner = xR - topPad;\n          const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n          const yRMax =\n              Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n\n          for (let xC = 0; xC < inWidth; ++xC) {\n            const xCCorner = xC - leftPad;\n            const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n            const yCMax =\n                Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n\n            let dotProd = 0;\n            for (let yR = xRMin; yR < yRMax; ++yR) {\n              const wR = yR * strideHeight - xRCorner;\n\n              for (let yC = xCMin; yC < yCMax; ++yC) {\n                const wC = yC * strideWidth - xCCorner;\n                const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;\n                const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n                    fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n\n                for (let d2 = 0; d2 < outChannels; ++d2) {\n                  const pixel = dyValues[dyOffset + d2];\n                  const weight = fltValues[fltOffset + d2];\n                  dotProd += pixel * weight;\n                }\n              }\n            }\n            dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;\n          }\n        }\n      }\n    }\n    return dx.toTensor();\n  }\n\n  conv3dDerInput(dy: Tensor5D, filter: Tensor5D, convInfo: Conv3DInfo):\n      Tensor5D {\n    const dx = ops.buffer<Rank.R5>(convInfo.inShape, 'float32');\n    const dxValues = dx.values;\n    const [dxS0, dxS1, dxS2, dxS3] = dx.strides;\n    const dyValues = dy.dataSync();\n    const [dyS0, dyS1, dyS2, dyS3] = dy.strides;\n    const fltValues = filter.dataSync();\n    const [fltS0, fltS1, fltS2, fltS3] = filter.strides;\n    const {\n      batchSize,\n      filterDepth,\n      filterHeight,\n      filterWidth,\n      inChannels,\n      inDepth,\n      inHeight,\n      inWidth,\n      outChannels,\n      outDepth,\n      outHeight,\n      outWidth,\n      strideDepth,\n      strideHeight,\n      strideWidth\n    } = convInfo;\n    const frontPad = filterDepth - 1 - convInfo.padInfo.front;\n    const topPad = filterHeight - 1 - convInfo.padInfo.top;\n    const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n\n    for (let b = 0; b < batchSize; ++b) {\n      for (let d1 = 0; d1 < inChannels; ++d1) {\n        // Frames of depth\n        for (let xF = 0; xF < inDepth; ++xF) {\n          const xFCorner = xF - frontPad;\n          const xFMin = Math.max(0, Math.ceil(xFCorner / strideDepth));\n          const yFMax =\n              Math.min(outDepth, (filterDepth + xFCorner) / strideDepth);\n\n          // Rows as per standard 2d matrix notation\n          for (let xR = 0; xR < inHeight; ++xR) {\n            const xRCorner = xR - topPad;\n            const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n            const yRMax =\n                Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n            // Columns as per standard 2d matrix notation\n            for (let xC = 0; xC < inWidth; ++xC) {\n              const xCCorner = xC - leftPad;\n              const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n              const yCMax =\n                  Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n\n              let dotProd = 0;\n              for (let yF = xFMin; yF < yFMax; ++yF) {\n                const wF = yF * strideDepth - xFCorner;\n\n                for (let yR = xRMin; yR < yRMax; ++yR) {\n                  const wR = yR * strideHeight - xRCorner;\n\n                  for (let yC = xCMin; yC < yCMax; ++yC) {\n                    const wC = yC * strideWidth - xCCorner;\n                    const dyOffset =\n                        dyS0 * b + dyS1 * yF + dyS2 * yR + dyS3 * yC;\n                    const fltOffset = fltS0 * (filterDepth - 1 - wF) +\n                        fltS1 * (filterHeight - 1 - wR) +\n                        fltS2 * (filterWidth - 1 - wC) + fltS3 * d1;\n\n                    for (let d2 = 0; d2 < outChannels; ++d2) {\n                      const pixel = dyValues[dyOffset + d2];\n                      const weight = fltValues[fltOffset + d2];\n                      dotProd += pixel * weight;\n                    }\n                  }\n                }\n              }\n              dxValues[dxS0 * b + dxS1 * xF + dxS2 * xR + dxS3 * xC + d1] =\n                  dotProd;\n            }\n          }\n        }\n      }\n    }\n    return dx.toTensor();\n  }\n\n  conv2dDerFilter(x: Tensor4D, dy: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    this.assertNotComplex([x, dy], 'conv2dDerFilter');\n\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dW = ops.buffer<Rank.R4>(convInfo.filterShape, 'float32');\n\n    const leftPad = convInfo.padInfo.left;\n    const topPad = convInfo.padInfo.top;\n\n    for (let wR = 0; wR < filterHeight; ++wR) {\n      const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n      const yRMax = Math.min(\n          convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n\n      for (let wC = 0; wC < filterWidth; ++wC) {\n        const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n        const yCMax = Math.min(\n            convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n\n        for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n          for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n            // Need to convolve.\n            let dotProd = 0;\n            for (let b = 0; b < convInfo.batchSize; ++b) {\n              for (let yR = yRMin; yR < yRMax; ++yR) {\n                const xR = wR + yR * strideHeight - topPad;\n                for (let yC = yCMin; yC < yCMax; ++yC) {\n                  const xC = wC + yC * strideWidth - leftPad;\n                  dotProd += x.get(b, xR, xC, d1) * dy.get(b, yR, yC, d2);\n                }\n              }\n            }\n            dW.set(dotProd, wR, wC, d1, d2);\n          }\n        }\n      }\n    }\n    return dW.toTensor();\n  }\n\n  conv3dDerFilter(x: Tensor5D, dy: Tensor5D, convInfo: Conv3DInfo): Tensor5D {\n    const strideDepth = convInfo.strideDepth;\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const filterDepth = convInfo.filterDepth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n\n    const dw = ops.buffer<Rank.R5>(convInfo.filterShape, 'float32');\n    const dwValues = dw.values;\n    const [dwS0, dwS1, dwS2, dwS3] = dw.strides;\n    const dyValues = dy.dataSync();\n    const [dyS0, dyS1, dyS2, dyS3] = dy.strides;\n    const xValues = x.dataSync();\n    const [xS0, xS1, xS2, xS3] = x.strides;\n\n    const frontPad = convInfo.padInfo.front;\n    const leftPad = convInfo.padInfo.left;\n    const topPad = convInfo.padInfo.top;\n\n    for (let wF = 0; wF < filterDepth; ++wF) {\n      const yFMin = Math.max(0, Math.ceil((frontPad - wF) / strideDepth));\n      const yFMax = Math.min(\n          convInfo.outDepth, (convInfo.inDepth + frontPad - wF) / strideDepth);\n      const wOffset1 = wF * dwS0;\n\n      for (let wR = 0; wR < filterHeight; ++wR) {\n        const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n        const yRMax = Math.min(\n            convInfo.outHeight,\n            (convInfo.inHeight + topPad - wR) / strideHeight);\n        const wOffset2 = wR * dwS1 + wOffset1;\n\n        for (let wC = 0; wC < filterWidth; ++wC) {\n          const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n          const yCMax = Math.min(\n              convInfo.outWidth,\n              (convInfo.inWidth + leftPad - wC) / strideWidth);\n          const wOffset3 = wC * dwS2 + wOffset2;\n\n          for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n            const wOffset4 = d1 * dwS3 + wOffset3;\n\n            for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n              let dotProd = 0;\n              for (let b = 0; b < convInfo.batchSize; ++b) {\n                const xOffset1 = b * xS0;\n                const yOffset1 = b * dyS0;\n\n                for (let yF = yFMin; yF < yFMax; ++yF) {\n                  const xF = wF + yF * strideDepth - frontPad;\n                  const xOffset2 = xF * xS1 + xOffset1;\n                  const yOffset2 = yF * dyS1 + yOffset1;\n\n                  for (let yR = yRMin; yR < yRMax; ++yR) {\n                    const xR = wR + yR * strideHeight - topPad;\n                    const xOffset3 = xR * xS2 + xOffset2;\n                    const yOffset3 = yR * dyS2 + yOffset2;\n\n                    for (let yC = yCMin; yC < yCMax; ++yC) {\n                      const xC = wC + yC * strideWidth - leftPad;\n                      const xOffset4 = xC * xS3 + xOffset3;\n                      const yOffset4 = yC * dyS3 + yOffset3;\n\n                      dotProd +=\n                          xValues[xOffset4 + d1] * dyValues[yOffset4 + d2];\n                    }\n                  }\n                }\n              }\n              dwValues[wOffset4 + d2] = dotProd;\n            }\n          }\n        }\n      }\n    }\n    return dw.toTensor();\n  }\n\n  depthwiseConv2D(x: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    this.assertNotComplex([x, filter], 'depthwiseConv2D');\n\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const padLeft = convInfo.padInfo.left;\n    const padTop = convInfo.padInfo.top;\n    const chMul = convInfo.outChannels / convInfo.inChannels;\n    const y = ops.buffer(convInfo.outShape, x.dtype as 'float32');\n    const xVals = x.dataSync();\n    const wVals = filter.dataSync();\n    const yVals = y.values;\n\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n      const xOffset1 = b * x.strides[0];\n      const yOffset1 = b * y.strides[0];\n      for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n        const yOffset2 = yOffset1 + yR * y.strides[1];\n        const xRCorner = yR * convInfo.strideHeight - padLeft;\n        for (let wR = 0; wR < filterHeight; ++wR) {\n          const xR = xRCorner + wR * dilationHeight;\n          if (xR < 0 || xR >= convInfo.inHeight) {\n            continue;\n          }\n          const wOffset1 = wR * filter.strides[0];\n          const xOffset2 = xOffset1 + xR * x.strides[1];\n          for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n            const yOffset3 = yOffset2 + yC * y.strides[2];\n            const xCCorner = yC * convInfo.strideWidth - padTop;\n            for (let wC = 0; wC < filterWidth; ++wC) {\n              const xC = xCCorner + wC * dilationWidth;\n              if (xC < 0 || xC >= convInfo.inWidth) {\n                continue;\n              }\n              const wOffset2 = wOffset1 + wC * filter.strides[1];\n              const xOffset3 = xOffset2 + xC * convInfo.inChannels;\n              let yOffset4 = yOffset3;\n              let wOffset3 = wOffset2;\n              for (let d1 = 0; d1 < convInfo.inChannels; ++d1) {\n                const xVal = xVals[xOffset3 + d1];\n                for (let q = 0; q < chMul; ++q) {\n                  yVals[yOffset4 + q] += xVal * wVals[wOffset3 + q];\n                }\n                yOffset4 += chMul;\n                wOffset3 += chMul;\n              }\n            }\n          }\n        }\n      }\n    }\n\n    return y.toTensor() as Tensor4D;\n  }\n\n  depthwiseConv2DDerInput(dy: Tensor4D, filter: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    this.assertNotComplex([dy, filter], 'depthwiseConv2DDerInput');\n\n    const dx = ops.buffer<Rank.R4>(convInfo.inShape, 'float32');\n    const dxValues = dx.values;\n    const [dxS0, dxS1, dxS2] = dx.strides;\n    const dyValues = dy.dataSync();\n    const [dyS0, dyS1, dyS2] = dy.strides;\n    const fltValues = filter.dataSync();\n    const [fltS0, fltS1, fltS2] = filter.strides;\n    const {\n      batchSize,\n      filterHeight,\n      filterWidth,\n      inChannels,\n      inHeight,\n      inWidth,\n      outChannels,\n      outHeight,\n      outWidth,\n      strideHeight,\n      strideWidth\n    } = convInfo;\n    const topPad = filterHeight - 1 - convInfo.padInfo.top;\n    const leftPad = filterWidth - 1 - convInfo.padInfo.left;\n    const chMul = outChannels / inChannels;\n\n    for (let b = 0; b < batchSize; ++b) {\n      for (let d1 = 0; d1 < inChannels; ++d1) {\n        for (let xR = 0; xR < inHeight; ++xR) {\n          const xRCorner = xR - topPad;\n          const xRMin = Math.max(0, Math.ceil(xRCorner / strideHeight));\n          const yRMax =\n              Math.min(outHeight, (filterHeight + xRCorner) / strideHeight);\n\n          for (let xC = 0; xC < inWidth; ++xC) {\n            const xCCorner = xC - leftPad;\n            const xCMin = Math.max(0, Math.ceil(xCCorner / strideWidth));\n            const yCMax =\n                Math.min(outWidth, (filterWidth + xCCorner) / strideWidth);\n\n            let dotProd = 0;\n            for (let yR = xRMin; yR < yRMax; ++yR) {\n              const wR = yR * strideHeight - xRCorner;\n\n              for (let yC = xCMin; yC < yCMax; ++yC) {\n                const wC = yC * strideWidth - xCCorner;\n                const dyOffset = dyS0 * b + dyS1 * yR + dyS2 * yC;\n                const fltOffset = fltS0 * (filterHeight - 1 - wR) +\n                    fltS1 * (filterWidth - 1 - wC) + fltS2 * d1;\n\n                for (let dm = 0; dm < chMul; ++dm) {\n                  const d2 = d1 * chMul + dm;\n                  const pixel = dyValues[dyOffset + d2];\n                  const weight = fltValues[fltOffset + dm];\n                  dotProd += pixel * weight;\n                }\n              }\n            }\n            dxValues[dxS0 * b + dxS1 * xR + dxS2 * xC + d1] = dotProd;\n          }\n        }\n      }\n    }\n    return dx.toTensor();\n  }\n\n  depthwiseConv2DDerFilter(x: Tensor4D, dy: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    this.assertNotComplex([x, dy], 'depthwiseConv2DDerFilter');\n\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dW = ops.buffer<Rank.R4>(convInfo.filterShape, 'float32');\n\n    const leftPad = convInfo.padInfo.left;\n    const topPad = convInfo.padInfo.top;\n    const chMul = convInfo.outChannels / convInfo.inChannels;\n\n    for (let wR = 0; wR < filterHeight; ++wR) {\n      const yRMin = Math.max(0, Math.ceil((topPad - wR) / strideHeight));\n      const yRMax = Math.min(\n          convInfo.outHeight, (convInfo.inHeight + topPad - wR) / strideHeight);\n\n      for (let wC = 0; wC < filterWidth; ++wC) {\n        const yCMin = Math.max(0, Math.ceil((leftPad - wC) / strideWidth));\n        const yCMax = Math.min(\n            convInfo.outWidth, (convInfo.inWidth + leftPad - wC) / strideWidth);\n\n        for (let d2 = 0; d2 < convInfo.outChannels; ++d2) {\n          const d1 = Math.trunc(d2 / chMul);\n          const dm = d2 % chMul;\n\n          let dotProd = 0;\n          for (let b = 0; b < convInfo.batchSize; ++b) {\n            for (let yR = yRMin; yR < yRMax; ++yR) {\n              const xR = wR + yR * strideHeight - topPad;\n              for (let yC = yCMin; yC < yCMax; ++yC) {\n                const xC = wC + yC * strideWidth - leftPad;\n                dotProd += x.get(b, xR, xC, d1) * dy.get(b, yR, yC, d2);\n              }\n            }\n          }\n          dW.set(dotProd, wR, wC, d1, dm);\n        }\n      }\n    }\n    return dW.toTensor();\n  }\n\n  tile<T extends Tensor>(x: T, reps: number[]): T {\n    this.assertNotComplex(x, 'tile');\n\n    const newShape: number[] = new Array(x.rank);\n    for (let i = 0; i < newShape.length; i++) {\n      newShape[i] = x.shape[i] * reps[i];\n    }\n    const result = ops.buffer(newShape, x.dtype);\n    const xBuf = x.buffer();\n    for (let i = 0; i < result.values.length; ++i) {\n      const newLoc = result.indexToLoc(i);\n\n      const originalLoc: number[] = new Array(x.rank);\n      for (let i = 0; i < originalLoc.length; i++) {\n        originalLoc[i] = newLoc[i] % x.shape[i];\n      }\n\n      const originalIndex = xBuf.locToIndex(originalLoc);\n\n      result.values[i] = xBuf.values[originalIndex];\n    }\n    return result.toTensor() as T;\n  }\n\n  pad<T extends Tensor>(\n      x: T, paddings: Array<[number, number]>, constantValue: number): T {\n    this.assertNotComplex(x, 'pad');\n\n    const outShape = paddings.map(\n        (p, i) => p[0] /* beforePad */ + x.shape[i] + p[1] /* afterPad */);\n    const start = paddings.map(p => p[0]);\n    const xBuffer = x.buffer();\n    const buffer = ops.buffer(outShape, x.dtype as 'float32');\n    if (constantValue !== 0) {\n      buffer.values.fill(constantValue);\n    }\n\n    for (let i = 0; i < x.size; i++) {\n      const coords = xBuffer.indexToLoc(i);\n      const outCoords = coords.map((c, i) => c + start[i]);\n      buffer.set(x.get(...coords), ...outCoords);\n    }\n    return buffer.toTensor() as T;\n  }\n\n  transpose<T extends Tensor>(x: T, perm: number[]): T {\n    this.assertNotComplex(x, 'transpose');\n\n    const newShape: number[] = new Array(x.rank);\n    for (let i = 0; i < newShape.length; i++) {\n      newShape[i] = x.shape[perm[i]];\n    }\n    const values = x.dataSync();\n    const result = buffer(newShape, x.dtype);\n\n    const xBuf = x.buffer();\n    for (let i = 0; i < x.size; ++i) {\n      const loc = xBuf.indexToLoc(i);\n\n      // Permute location.\n      const newLoc: number[] = new Array(loc.length);\n      for (let i = 0; i < newLoc.length; i++) {\n        newLoc[i] = loc[perm[i]];\n      }\n\n      const newIndex = result.locToIndex(newLoc);\n      result.values[newIndex] = values[i];\n    }\n    return result.toTensor() as T;\n  }\n\n  gather<T extends Tensor>(x: T, indices: Tensor1D, axis: number): T {\n    this.assertNotComplex([x, indices], 'gather');\n\n    const newShape: number[] = x.shape.slice();\n    const indicesValues = indices.dataSync();\n    newShape[axis] = indicesValues.length;\n    const result = buffer(newShape, x.dtype);\n    const xBuf = x.buffer();\n\n    for (let i = 0; i < result.size; ++i) {\n      const newLoc = result.indexToLoc(i);\n\n      const originalLoc: number[] = newLoc.slice();\n      originalLoc[axis] = indicesValues[newLoc[axis]];\n\n      const originalIndex = xBuf.locToIndex(originalLoc);\n      result.values[i] = xBuf.values[originalIndex];\n    }\n    return result.toTensor() as T;\n  }\n\n  batchToSpaceND<T extends Tensor>(\n      x: T, blockShape: number[], crops: number[][]): T {\n    this.assertNotComplex([x], 'batchToSpaceND');\n\n    const prod = blockShape.reduce((a, b) => a * b);\n\n    const reshaped = array_ops_util.getReshaped(x.shape, blockShape, prod);\n    const permuted =\n        array_ops_util.getPermuted(reshaped.length, blockShape.length);\n    const reshapedPermuted =\n        array_ops_util.getReshapedPermuted(x.shape, blockShape, prod);\n    const sliceBeginCoords =\n        array_ops_util.getSliceBeginCoords(crops, blockShape.length);\n    const sliceSize =\n        array_ops_util.getSliceSize(reshapedPermuted, crops, blockShape.length);\n\n    return x.reshape(reshaped)\n               .transpose(permuted)\n               .reshape(reshapedPermuted)\n               .slice(sliceBeginCoords, sliceSize) as T;\n  }\n\n  spaceToBatchND<T extends Tensor>(\n      x: T, blockShape: number[], paddings: Array<[number, number]>): T {\n    this.assertNotComplex([x], 'spaceToBatchND');\n\n    const prod = blockShape.reduce((a, b) => a * b);\n\n    const completePaddings: Array<[number, number]> = [[0, 0]];\n    completePaddings.push(...paddings);\n    for (let i = 1 + blockShape.length; i < x.shape.length; ++i) {\n      completePaddings.push([0, 0]);\n    }\n\n    const paddedX = x.pad(completePaddings);\n\n    const reshapedPaddedShape =\n        array_ops_util.getReshaped(paddedX.shape, blockShape, prod, false);\n    const permutedReshapedPaddedPermutation = array_ops_util.getPermuted(\n        reshapedPaddedShape.length, blockShape.length, false);\n    const flattenShape = array_ops_util.getReshapedPermuted(\n        paddedX.shape, blockShape, prod, false);\n\n    return paddedX.reshape(reshapedPaddedShape)\n               .transpose(permutedReshapedPaddedPermutation)\n               .reshape(flattenShape) as T;\n  }\n\n  private pool(x: Tensor4D, convInfo: Conv2DInfo, poolType: 'max'|'avg'):\n      Tensor4D {\n    this.assertNotComplex(x, 'pool');\n\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n\n    const initialValue =\n        (poolType === 'max' ? Number.NEGATIVE_INFINITY :\n                              Number.POSITIVE_INFINITY);\n\n    const xValues = x.dataSync();\n    const output = ops.buffer(convInfo.outShape, x.dtype);\n    const outputVals = output.values;\n\n    const outputBatchStrides =\n        convInfo.outShape[1] * convInfo.outShape[2] * convInfo.outShape[3];\n    const outputRowStrides = convInfo.outShape[2] * convInfo.outShape[3];\n    const outputColStrides = convInfo.outShape[3];\n\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n      const outputBatchOffset = b * outputBatchStrides;\n      const inputBatchOffset = b * x.strides[0];\n      for (let d = 0; d < convInfo.inChannels; ++d) {\n        for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n          const xRCorner = yR * strideHeight - padTop;\n          const xRMin = Math.max(0, xRCorner);\n          const xRMax =\n              Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n          const outputRowOffset = outputBatchOffset + yR * outputRowStrides;\n          for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n            const xCCorner = yC * strideWidth - padLeft;\n            const xCMin = Math.max(0, xCCorner);\n            const xCMax =\n                Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n            let minMaxValue = initialValue;\n            let avgValue = 0;\n            let count = 0;\n            for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n              const xROffset = inputBatchOffset + xR * x.strides[1];\n              for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n                const xCOffset = xROffset + xC * x.strides[2];\n                const pixel = xValues[xCOffset + d];\n                if ((poolType === 'max' && pixel > minMaxValue)) {\n                  minMaxValue = pixel;\n                } else if (poolType === 'avg') {\n                  avgValue += pixel;\n                  count++;\n                }\n              }\n              if (isNaN(minMaxValue)) {\n                break;\n              }\n            }\n            const outputOffset = outputRowOffset + yC * outputColStrides + d;\n            outputVals[outputOffset] =\n                poolType === 'avg' ? avgValue / count : minMaxValue;\n          }\n        }\n      }\n    }\n    return output.toTensor() as Tensor4D;\n  }\n\n  maxPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    return this.pool(x, convInfo, 'max');\n  }\n\n  private maxPoolPositions(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    const maxPositions = ops.buffer(convInfo.outShape, 'int32');\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padTop = convInfo.padInfo.top;\n    const padLeft = convInfo.padInfo.left;\n\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n      for (let d = 0; d < convInfo.inChannels; ++d) {\n        for (let yR = 0; yR < convInfo.outHeight; ++yR) {\n          const xRCorner = yR * strideHeight - padTop;\n          let xRMin = xRCorner;\n          while (xRMin < 0) {\n            xRMin += dilationHeight;\n          }\n          // const xRMin = Math.max(0, xRCorner);\n          const xRMax =\n              Math.min(convInfo.inHeight, effectiveFilterHeight + xRCorner);\n          for (let yC = 0; yC < convInfo.outWidth; ++yC) {\n            const xCCorner = yC * strideWidth - padLeft;\n            let xCMin = xCCorner;\n            while (xCMin < 0) {\n              xCMin += dilationWidth;\n            }\n            const xCMax =\n                Math.min(convInfo.inWidth, effectiveFilterWidth + xCCorner);\n            let maxValue = Number.NEGATIVE_INFINITY;\n            let maxPosition = -1;\n\n            for (let xR = xRMin; xR < xRMax; xR += dilationHeight) {\n              const wR = xR - xRCorner;\n              for (let xC = xCMin; xC < xCMax; xC += dilationWidth) {\n                const wC = xC - xCCorner;\n                const pixel = x.get(b, xR, xC, d);\n                if (pixel > maxValue) {\n                  maxValue = pixel;\n                  maxPosition = wR * effectiveFilterWidth + wC;\n                }\n              }\n            }\n            maxPositions.set(maxPosition, b, yR, yC, d);\n          }\n        }\n      }\n    }\n    return maxPositions.toTensor() as Tensor4D;\n  }\n\n  maxPoolBackprop(dy: Tensor4D, x: Tensor4D, y: Tensor4D, convInfo: Conv2DInfo):\n      Tensor4D {\n    this.assertNotComplex([x, y], 'maxPoolBackprop');\n\n    const maxPositions = this.maxPoolPositions(x, convInfo);\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const dx = ops.buffer<Rank.R4>(x.shape, 'float32');\n\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n      for (let d = 0; d < convInfo.inChannels; ++d) {\n        for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n          for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n            // Shader code begins.\n            const dyRCorner = dxR - padTop;\n            const dyCCorner = dxC - padLeft;\n            let dotProd = 0;\n            for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n              const dyR = (dyRCorner + wR) / strideHeight;\n              if (dyR < 0 || dyR >= convInfo.outHeight ||\n                  Math.floor(dyR) !== dyR) {\n                continue;\n              }\n              for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n                const dyC = (dyCCorner + wC) / strideWidth;\n                if (dyC < 0 || dyC >= convInfo.outWidth ||\n                    Math.floor(dyC) !== dyC) {\n                  continue;\n                }\n                const maxPos = effectiveFilterHeight * effectiveFilterWidth -\n                    1 - maxPositions.get(b, dyR, dyC, d);\n                const curPos = wR * effectiveFilterWidth + wC;\n\n                const mask = maxPos === curPos ? 1 : 0;\n                if (mask === 0) {\n                  continue;\n                }\n\n                const pixel = dy.get(b, dyR, dyC, d);\n                dotProd += pixel * mask;\n              }\n            }\n            dx.set(dotProd, b, dxR, dxC, d);\n          }\n        }\n      }\n    }\n    return dx.toTensor();\n  }\n\n  avgPoolBackprop(dy: Tensor4D, x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    this.assertNotComplex([dy, x], 'avgPoolBackprop');\n\n    const strideHeight = convInfo.strideHeight;\n    const strideWidth = convInfo.strideWidth;\n    const filterHeight = convInfo.filterHeight;\n    const filterWidth = convInfo.filterWidth;\n    const dilationHeight = convInfo.dilationHeight;\n    const dilationWidth = convInfo.dilationWidth;\n    const effectiveFilterHeight = convInfo.effectiveFilterHeight;\n    const effectiveFilterWidth = convInfo.effectiveFilterWidth;\n    const padLeft = effectiveFilterWidth - 1 - convInfo.padInfo.left;\n    const padTop = effectiveFilterHeight - 1 - convInfo.padInfo.top;\n    const dx = ops.buffer<Rank.R4>(x.shape, 'float32');\n\n    const avgMultiplier = 1 / (filterHeight * filterWidth);\n\n    for (let b = 0; b < convInfo.batchSize; ++b) {\n      for (let d = 0; d < convInfo.inChannels; ++d) {\n        for (let dxR = 0; dxR < convInfo.inHeight; ++dxR) {\n          for (let dxC = 0; dxC < convInfo.inWidth; ++dxC) {\n            // Shader code begins.\n            const dyRCorner = dxR - padTop;\n            const dyCCorner = dxC - padLeft;\n            let dotProd = 0;\n            for (let wR = 0; wR < effectiveFilterHeight; wR += dilationHeight) {\n              const dyR = (dyRCorner + wR) / strideHeight;\n              if (dyR < 0 || dyR >= convInfo.outHeight ||\n                  Math.floor(dyR) !== dyR) {\n                continue;\n              }\n              for (let wC = 0; wC < effectiveFilterWidth; wC += dilationWidth) {\n                const dyC = (dyCCorner + wC) / strideWidth;\n                if (dyC < 0 || dyC >= convInfo.outWidth ||\n                    Math.floor(dyC) !== dyC) {\n                  continue;\n                }\n\n                const pixel = dy.get(b, dyR, dyC, d);\n                dotProd += pixel;\n              }\n            }\n            dx.set(dotProd * avgMultiplier, b, dxR, dxC, d);\n          }\n        }\n      }\n    }\n    return dx.toTensor();\n  }\n\n  cast<T extends Tensor>(x: T, dtype: DataType): T {\n    return backend_util.castTensor(x, dtype, this);\n  }\n\n  reshape<R extends Rank>(x: Tensor, shape: ShapeMap[R]): Tensor<R> {\n    return backend_util.reshapeTensor(x, shape);\n  }\n\n  avgPool(x: Tensor4D, convInfo: Conv2DInfo): Tensor4D {\n    this.assertNotComplex(x, 'avgPool');\n\n    return this.pool(x, convInfo, 'avg').toFloat();\n  }\n\n  resizeBilinear(\n      x: Tensor4D, newHeight: number, newWidth: number,\n      alignCorners: boolean): Tensor4D {\n    this.assertNotComplex(x, 'resizeBilinear');\n\n    const [batch, oldHeight, oldWidth, numChannels] = x.shape;\n    const xValues = x.dataSync();\n    const result = new Float32Array(\n        util.sizeFromShape([batch, newHeight, newWidth, numChannels]));\n\n    const effectiveInputSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n      (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n    ];\n\n    const effectiveOutputSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n      (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n    ];\n    let outputIdx = 0;\n    const effectiveRowSizeRatio =\n        effectiveInputSize[0] / effectiveOutputSize[0];\n    const effectiveColSizeRatio =\n        effectiveInputSize[1] / effectiveOutputSize[1];\n    for (let b = 0; b < batch; b++) {\n      for (let r = 0; r < newHeight; r++) {\n        const sourceFracRow = effectiveRowSizeRatio * r;\n        const sourceRowFloor = Math.floor(sourceFracRow);\n        const rowFrac = sourceFracRow - sourceRowFloor;\n        const sourceRowCeil = Math.min(oldHeight - 1, Math.ceil(sourceFracRow));\n        const topRowOffset = b * x.strides[0] + sourceRowFloor * x.strides[1];\n        const botRowOffset = b * x.strides[0] + sourceRowCeil * x.strides[1];\n        for (let c = 0; c < newWidth; c++) {\n          const sourceFracCol = effectiveColSizeRatio * c;\n          const sourceColFloor = Math.floor(sourceFracCol);\n          const colFrac = sourceFracCol - sourceColFloor;\n          const sourceColCeil =\n              Math.min(oldWidth - 1, Math.ceil(sourceFracCol));\n          const topLeftOffest = topRowOffset + sourceColFloor * x.strides[2];\n          const botLeftOffset = botRowOffset + sourceColFloor * x.strides[2];\n          const topRightOffset = topRowOffset + +sourceColCeil * x.strides[2];\n          const botRightOffest = botRowOffset + sourceColCeil * x.strides[2];\n          for (let d = 0; d < numChannels; d++) {\n            // Begin shader.\n\n            // Compute the fractional index of the source.\n            const topLeft = xValues[topLeftOffest + d];\n            const bottomLeft = xValues[botLeftOffset + d];\n            const topRight = xValues[topRightOffset + d];\n            const bottomRight = xValues[botRightOffest + d];\n\n            const top = topLeft + (topRight - topLeft) * colFrac;\n            const bottom = bottomLeft + (bottomRight - bottomLeft) * colFrac;\n            const newValue = top + (bottom - top) * rowFrac;\n\n            result[outputIdx++] = newValue;\n          }\n        }\n      }\n    }\n    return ops.tensor(result, [batch, newHeight, newWidth, numChannels]);\n  }\n\n  resizeBilinearBackprop(dy: Tensor4D, x: Tensor4D, alignCorners: boolean) {\n    this.assertNotComplex([dy, x], 'resizeBilinearBackprop');\n\n    const [batch, xHeight, xWidth, depth] = x.shape;\n    const [, yHeight, yWidth] = dy.shape;\n\n    const output = new Float32Array(batch * xHeight * xWidth * depth);\n\n    // In the backwards pass, we want to find the pixels that were generated\n    // for each pixel in the input image the forward pass and add the\n    // corresponding coefficient from dy to the gradient (with some\n    // interpolation).\n\n    const effectiveXSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n      (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n    ];\n\n    const effectiveYSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n      (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n    ];\n\n    const heightScale = effectiveXSize[0] / effectiveYSize[0];\n    const widthScale = effectiveXSize[1] / effectiveYSize[1];\n\n    // Reference implementation\n    // tslint:disable-next-line:max-line-length\n    // https://github.com/tensorflow/tensorflow/blob/3039375c86a5bbc9610c7725dcaa95d635f87ba2/tensorflow/core/kernels/resize_bilinear_op.cc#L275\n\n    const dyValues = dy.dataSync();\n    let offset = 0;\n    for (let b = 0; b < batch; b++) {\n      const bOffset = b * x.strides[0];\n      for (let r = 0; r < yHeight; r++) {\n        const dxR = r * heightScale;\n        const topDxRIndex = Math.floor(dxR);\n        const bottomDxRIndex = Math.min(Math.ceil(dxR), xHeight - 1);\n\n        const topDxROffset = bOffset + topDxRIndex * x.strides[1];\n        const bottomDxROffset = bOffset + bottomDxRIndex * x.strides[1];\n\n        const dxRLerp = dxR - topDxRIndex;\n        const inverseDxRLerp = 1.0 - dxRLerp;\n        for (let c = 0; c < yWidth; c++) {\n          const dxC = c * widthScale;\n          const leftDxCIndex = Math.floor(dxC);\n          const rightDxCIndex = Math.min(Math.ceil(dxC), xWidth - 1);\n          const dxCLerp = dxC - leftDxCIndex;\n          const inverseDxCLerp = 1.0 - dxCLerp;\n\n          const topLeftRCOffset = topDxROffset + leftDxCIndex * x.strides[2];\n          const topRightRCOffset = topDxROffset + rightDxCIndex * x.strides[2];\n          const bottomLeftRCOffset =\n              bottomDxROffset + leftDxCIndex * x.strides[2];\n          const bottomRightRCOffset =\n              bottomDxROffset + rightDxCIndex * x.strides[2];\n\n          const inverseDxRLerpTimesInverseDxCLerp =\n              inverseDxRLerp * inverseDxCLerp;\n          const inverseDxRLerpTimesDxCLerp = inverseDxRLerp * dxCLerp;\n          const dxRLerpTimesInverseDxCLerp = dxRLerp * inverseDxCLerp;\n          const dxRLerpTimesDxCLerp = dxRLerp * dxCLerp;\n          for (let d = 0; d < depth; d++) {\n            const dyVal = dyValues[offset++];\n            output[topLeftRCOffset + d] +=\n                dyVal * inverseDxRLerpTimesInverseDxCLerp;\n            output[topRightRCOffset + d] += dyVal * inverseDxRLerpTimesDxCLerp;\n            output[bottomLeftRCOffset + d] +=\n                dyVal * dxRLerpTimesInverseDxCLerp;\n            output[bottomRightRCOffset + d] += dyVal * dxRLerpTimesDxCLerp;\n          }\n        }\n      }\n    }\n    return ops.tensor4d(output, [batch, xWidth, xHeight, depth], x.dtype);\n  }\n\n  resizeNearestNeighbor(\n      x: Tensor4D, newHeight: number, newWidth: number,\n      alignCorners: boolean): Tensor4D {\n    this.assertNotComplex(x, 'resizeNearestNeighbor');\n\n    const [batch, oldHeight, oldWidth, numChannels] = x.shape;\n    const xValues = x.dataSync();\n    const output = new Float32Array(batch * newHeight * newWidth * numChannels);\n\n    const effectiveInputSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? oldHeight - 1 : oldHeight,\n      (alignCorners && newWidth > 1) ? oldWidth - 1 : oldWidth\n    ];\n\n    const effectiveOutputSize: [number, number] = [\n      (alignCorners && newHeight > 1) ? newHeight - 1 : newHeight,\n      (alignCorners && newWidth > 1) ? newWidth - 1 : newWidth\n    ];\n\n    const effectiveRowSizeRatio =\n        effectiveInputSize[0] / effectiveOutputSize[0];\n    const effectiveColSizeRatio =\n        effectiveInputSize[1] / effectiveOutputSize[1];\n\n    let outputOffset = 0;\n    for (let b = 0; b < batch; b++) {\n      const batchOffset = b * x.strides[0];\n      for (let r = 0; r < newHeight; r++) {\n        const sourceFracRow = effectiveRowSizeRatio * r;\n        const sourceNearestRow = Math.min(\n            oldHeight - 1,\n            alignCorners ? Math.round(sourceFracRow) :\n                           Math.floor(sourceFracRow));\n        const rowOffset = batchOffset + sourceNearestRow * x.strides[1];\n        for (let c = 0; c < newWidth; c++) {\n          const sourceFracCol = effectiveColSizeRatio * c;\n          const sourceNearestCol = Math.min(\n              oldWidth - 1,\n              alignCorners ? Math.round(sourceFracCol) :\n                             Math.floor(sourceFracCol));\n          const colOffset = rowOffset + sourceNearestCol * x.strides[2];\n          for (let d = 0; d < numChannels; d++) {\n            // Begin shader.\n            // Compute the fractional index of the source.\n            const newVal = xValues[colOffset + d];\n            output[outputOffset++] = newVal;\n          }\n        }\n      }\n    }\n    return ops.tensor(\n        output, [batch, newHeight, newWidth, numChannels], x.dtype);\n  }\n\n  resizeNearestNeighborBackprop(\n      dy: Tensor4D, x: Tensor4D, alignCorners: boolean) {\n    this.assertNotComplex([dy, x], 'resizeNearestNeighborBackprop');\n\n    const [batch, xHeight, xWidth, depth] = x.shape;\n    const [, yHeight, yWidth] = dy.shape;\n\n    const output = new Float32Array(batch * xHeight * xWidth * depth);\n    const dyValues = dy.dataSync();\n\n    // In the backwards pass, we want to find the pixels that were generated\n    // for each pixel in the input image the forward pass\n\n    const effectiveXSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? xHeight - 1 : xHeight,\n      (alignCorners && yWidth > 1) ? xWidth - 1 : xWidth\n    ];\n\n    const effectiveYSize: [number, number] = [\n      (alignCorners && yHeight > 1) ? yHeight - 1 : yHeight,\n      (alignCorners && yWidth > 1) ? yWidth - 1 : yWidth\n    ];\n\n    const heightScale = effectiveXSize[0] / effectiveYSize[0];\n    const widthScale = effectiveXSize[1] / effectiveYSize[1];\n\n    const invHeightScale = 1 / heightScale;\n    const invWidthScale = 1 / widthScale;\n\n    // This defines the size of the window of values around a particular\n    // index in dy that we want to search for contributions to dx.\n    const winHeight = (Math.ceil(invHeightScale) * 2) + 2;\n    const winWidth = (Math.ceil(invWidthScale) * 2) + 2;\n\n    // Loop over the output space.\n    for (let b = 0; b < batch; b++) {\n      const batchOffset = b * x.strides[0];\n      for (let r = 0; r < xHeight; r++) {\n        const rowOffset = batchOffset + r * x.strides[1];\n\n        // Compute bounds for where in dy we will look\n        const startRLerp = Math.floor(r * invHeightScale);\n        const startDyR = Math.floor(startRLerp - (winHeight / 2));\n        for (let c = 0; c < xWidth; c++) {\n          const colOffset = rowOffset + c * x.strides[2];\n\n          // Compute bounds for where in dy we will look\n          const startCLerp = Math.floor(c * invWidthScale);\n          const startDyC = Math.floor(startCLerp - (winWidth / 2));\n\n          for (let d = 0; d < depth; d++) {\n            let accum = 0;\n            // loop over dy\n\n            for (let dyRIndex = 0; dyRIndex < winHeight; dyRIndex++) {\n              const dyR = dyRIndex + startDyR;\n              // Guard against the window exceeding the bounds of dy\n              if (dyR < 0 || dyR >= yHeight) {\n                continue;\n              }\n\n              const dyROffset = batchOffset + dyR * dy.strides[1];\n              const sourceFracRow = dyR * heightScale;\n              const sourceNearestRow = Math.min(\n                  xHeight - 1,\n                  alignCorners ? Math.round(sourceFracRow) :\n                                 Math.floor(sourceFracRow));\n              if (r !== sourceNearestRow) {\n                continue;\n              }\n              for (let dyCIndex = 0; dyCIndex < winWidth; dyCIndex++) {\n                const dyC = dyCIndex + startDyC;\n                // Guard against the window exceeding the bounds of dy\n                if (dyC < 0 || dyC >= yWidth) {\n                  continue;\n                }\n\n                const dyCOffset = dyROffset + dyC * dy.strides[2];\n                const sourceFracCol = dyC * widthScale;\n                const sourceNearestCol = Math.min(\n                    xWidth - 1,\n                    alignCorners ? Math.round(sourceFracCol) :\n                                   Math.floor(sourceFracCol));\n\n                if (c === sourceNearestCol) {\n                  accum += dyValues[dyCOffset + d];\n                }\n              }\n            }\n            output[colOffset + d] = accum;\n          }\n        }\n      }\n    }\n    return ops.tensor4d(output, x.shape, x.dtype);\n  }\n\n  batchNormalization(\n      x: Tensor4D, mean: Tensor4D|Tensor1D, variance: Tensor4D|Tensor1D,\n      varianceEpsilon: number, scale?: Tensor4D|Tensor1D,\n      offset?: Tensor4D|Tensor1D): Tensor4D {\n    this.assertNotComplex(\n        [x, mean, variance, scale, offset], 'batchNormalization');\n\n    const xVals = x.dataSync();\n    const mVals = mean.dataSync();\n    const varVals = variance.dataSync();\n    const sVals = scale ? scale.dataSync() : new Float32Array([1]);\n    const offVals = offset ? offset.dataSync() : new Float32Array([0]);\n    const outVals = new Float32Array(xVals.length);\n\n    const offValsLength = offVals.length;\n    const sValsLength = sVals.length;\n    const varValsLength = varVals.length;\n    const mValsLength = mVals.length;\n\n    let offi = 0;\n    let mi = 0;\n    let si = 0;\n    let vi = 0;\n    for (let i = 0; i < xVals.length; ++i) {\n      outVals[i] = offVals[offi++] +\n          (xVals[i] - mVals[mi++]) * sVals[si++] /\n              Math.sqrt(varVals[vi++] + varianceEpsilon);\n      if (offi >= offValsLength) {\n        offi = 0;\n      }\n      if (mi >= mValsLength) {\n        mi = 0;\n      }\n      if (si >= sValsLength) {\n        si = 0;\n      }\n      if (vi >= varValsLength) {\n        vi = 0;\n      }\n    }\n    return tensor4d(outVals, x.shape);\n  }\n\n  localResponseNormalization4D(\n      x: Tensor4D, depthRadius: number, bias: number, alpha: number,\n      beta: number): Tensor4D {\n    this.assertNotComplex(x, 'localResponseNormalization4D');\n\n    const channels = x.shape[3];\n    const maxD = channels - 1;\n    const xValues = x.dataSync();\n    const size = util.sizeFromShape(x.shape);\n    const result = new Float32Array(size);\n\n    function sumAcrossChannels(offset: number) {\n      const currentChannel = offset % channels;\n      let beginSumOffset =\n          offset - currentChannel + Math.max(0, currentChannel - depthRadius);\n      const endSumOffset = offset - currentChannel +\n          Math.min(currentChannel + depthRadius, maxD);\n\n      let sum = 0.0;\n      for (; beginSumOffset <= endSumOffset; beginSumOffset++) {\n        const z = xValues[beginSumOffset];\n        sum += z * z;\n      }\n      return sum;\n    }\n\n    for (let offset = 0; offset < size; offset++) {\n      const sum = sumAcrossChannels(offset);\n      const val = xValues[offset] * Math.pow(bias + alpha * sum, -beta);\n      result[offset] = val;\n    }\n\n    return ops.tensor4d(result, x.shape);\n  }\n\n  LRNGrad(\n      dy: Tensor4D, inputImage: Tensor4D, outputImage: Tensor4D,\n      depthRadius: number, bias: number, alpha: number,\n      beta: number): Tensor4D {\n    this.assertNotComplex(dy, 'LRNGrad');\n    const channels = dy.shape[3];\n    const dyValues = dy.dataSync();\n    const inputImageValues = inputImage.dataSync();\n    const outputImageValues = outputImage.dataSync();\n    const result = new Float32Array(util.sizeFromShape(dy.shape));\n    const size = util.sizeFromShape(dy.shape);\n\n    for (let offset = 0; offset < size; offset++) {\n      const currentChannel = offset % channels;\n      const depthBegin =\n          (offset - currentChannel) + Math.max(0, currentChannel - depthRadius);\n      const depthEnd = (offset - currentChannel) +\n          Math.min(channels, currentChannel + depthRadius + 1);\n\n      let norm = 0;\n      for (let k = depthBegin; k < depthEnd; k++) {\n        norm += Math.pow(inputImageValues[k], 2);\n      }\n      norm = alpha * norm + bias;\n\n      for (let k = depthBegin; k < depthEnd; k++) {\n        let dyi = -2 * alpha * beta * inputImageValues[k] *\n            outputImageValues[offset] / norm;\n        if (offset === k) {\n          dyi += Math.pow(norm, -beta);\n        }\n        dyi *= dyValues[offset];\n        result[k] += dyi;\n      }\n    }\n    return ops.tensor4d(result, dy.shape);\n  }\n\n  multinomial(\n      logits: Tensor2D, normalized: boolean, numSamples: number,\n      seed: number): Tensor2D {\n    this.assertNotComplex(logits, 'multinomial');\n\n    const probabilities = normalized ? logits : ops.softmax(logits);\n    const batchSize = probabilities.shape[0];\n    const numEvents = probabilities.shape[1];\n    const res = ops.zeros<Rank.R2>([batchSize, numSamples], 'int32');\n    const resVals = res.dataSync();\n    const probVals = probabilities.dataSync();\n\n    for (let b = 0; b < batchSize; ++b) {\n      const offset = b * numEvents;\n      // The cdf won't include the last event. It will be implicit if no other\n      // event happened.\n      const cdf = new Float32Array(numEvents - 1);\n      cdf[0] = probVals[offset];\n      for (let event = 1; event < cdf.length; ++event) {\n        cdf[event] = cdf[event - 1] + probVals[offset + event];\n      }\n\n      const random = seedrandom.alea(seed.toString());\n      const outOffset = b * numSamples;\n      for (let sampleId = 0; sampleId < numSamples; ++sampleId) {\n        const r = random();\n\n        // Assume last event happened by default.\n        resVals[outOffset + sampleId] = cdf.length;\n\n        for (let event = 0; event < cdf.length; event++) {\n          if (r < cdf[event]) {\n            resVals[outOffset + sampleId] = event;\n            break;\n          }\n        }\n      }\n    }\n    return res;\n  }\n\n  oneHot(indices: Tensor1D, depth: number, onValue: number, offValue: number):\n      Tensor2D {\n    this.assertNotComplex(indices, 'oneHot');\n\n    const res = new Float32Array(indices.size * depth);\n    res.fill(offValue);\n\n    for (let event = 0; event < indices.size; ++event) {\n      if (indices.get(event) >= 0 && indices.get(event) < depth) {\n        res[event * depth + indices.get(event)] = onValue;\n      }\n    }\n    return ops.tensor2d(res, [indices.size, depth], 'int32');\n  }\n\n  nonMaxSuppression(\n      boxes: Tensor2D, scores: Tensor1D, maxOutputSize: number,\n      iouThreshold: number, scoreThreshold: number): Tensor1D {\n    this.assertNotComplex(boxes, 'nonMaxSuppression');\n\n    const boxesVals = boxes.dataSync();\n    const scoresVals = scores.dataSync();\n    return nonMaxSuppressionImpl(\n        boxesVals, scoresVals, maxOutputSize, iouThreshold, scoreThreshold);\n  }\n\n  fft(x: Tensor2D): Tensor2D {\n    return this.fftBatch(x, false);\n  }\n\n  ifft(x: Tensor2D): Tensor2D {\n    return this.fftBatch(x, true);\n  }\n\n  /**\n   * Calculate FFT of inner most elements of batch tensor.\n   */\n  private fftBatch(x: Tensor2D, inverse: boolean): Tensor2D {\n    const batch = x.shape[0];\n    const innerDim = x.shape[1];\n    // Collects real and imaginary values separately.\n    const realResult = ops.buffer(x.shape, 'float32');\n    const imagResult = ops.buffer(x.shape, 'float32');\n\n    const real = ops.real(x).as2D(batch, innerDim);\n    const imag = ops.imag(x).as2D(batch, innerDim);\n\n    for (let b = 0; b < batch; b++) {\n      // TODO: Support slice ops for complex type.\n      const r = real.slice([b, 0], [1, innerDim]);\n      const i = imag.slice([b, 0], [1, innerDim]);\n      const input = ops.complex(r, i);\n      // Run FFT by batch element.\n      const res = this.fftImpl(input, inverse).dataSync() as Float32Array;\n      for (let d = 0; d < innerDim; d++) {\n        const c = complex_util.getComplexWithIndex(res, d);\n        realResult.values[b * innerDim + d] = c.real;\n        imagResult.values[b * innerDim + d] = c.imag;\n      }\n    }\n\n    const t = ops.complex(realResult.toTensor(), imagResult.toTensor());\n    return t.as2D(batch, innerDim);\n  }\n\n  private fftImpl(x: Tensor2D, inverse: boolean): Tensor2D {\n    const x1D = x.as1D();\n\n    const n = x1D.size;\n\n    if (this.isExponentOf2(n)) {\n      let result = this.fftRadix2(x1D, n, inverse).as2D(x.shape[0], x.shape[1]);\n      if (inverse) {\n        result = ops.complex(\n                     ops.real(result).div(scalar(n)),\n                     ops.imag(result).div(scalar(n))) as Tensor2D;\n      }\n      return result;\n    } else {\n      const data = x.dataSync();\n      const rawOutput =\n          this.fourierTransformByMatmul(data, n, inverse) as Float32Array;\n      const output = complex_util.splitRealAndImagArrays(rawOutput);\n      return ops.complex(output.real, output.imag).as2D(x.shape[0], x.shape[1]);\n    }\n  }\n\n  private isExponentOf2(size: number): boolean {\n    return (size & size - 1) === 0;\n  }\n\n  // FFT using Cooley-Tukey algorithm on radix 2 dimensional input.\n  private fftRadix2(input: Tensor1D, size: number, inverse: boolean): Tensor1D {\n    if (size === 1) {\n      return input;\n    }\n    const data = input.dataSync() as Float32Array;\n    const half = size / 2;\n    const evenComplex = complex_util.complexWithEvenIndex(data);\n    let evenTensor = ops.complex(evenComplex.real, evenComplex.imag).as1D();\n    const oddComplex = complex_util.complexWithOddIndex(data);\n    let oddTensor = ops.complex(oddComplex.real, oddComplex.imag).as1D();\n\n    // Recursive call for half part of original input.\n    evenTensor = this.fftRadix2(evenTensor, half, inverse);\n    oddTensor = this.fftRadix2(oddTensor, half, inverse);\n\n    const e = complex_util.exponents(size, inverse);\n    const exponent = ops.complex(e.real, e.imag).mul(oddTensor);\n\n    const addPart = evenTensor.add(exponent);\n    const subPart = evenTensor.sub(exponent);\n\n    const realTensor = ops.real(addPart).concat(ops.real(subPart));\n    const imagTensor = ops.imag(addPart).concat(ops.imag(subPart));\n\n    return ops.complex(realTensor, imagTensor).as1D();\n  }\n\n  // Calculate fourier transform by multplying sinusoid matrix.\n  private fourierTransformByMatmul(\n      data: TypedArray, size: number, inverse: boolean): TypedArray {\n    const ret = new Float32Array(size * 2);\n    // TODO: Use matmul instead once it supports complex64 type.\n    for (let r = 0; r < size; r++) {\n      let real = 0.0;\n      let imag = 0.0;\n      for (let c = 0; c < size; c++) {\n        const e = complex_util.exponent(r * c, size, inverse);\n        const term = complex_util.getComplexWithIndex(data as Float32Array, c);\n        real += term.real * e.real - term.imag * e.imag;\n        imag += term.real * e.imag + term.imag * e.real;\n      }\n      if (inverse) {\n        real /= size;\n        imag /= size;\n      }\n      complex_util.assignToTypedArray(ret, real, imag, r);\n    }\n    return ret;\n  }\n\n  depthToSpace(x: Tensor4D, blockSize: number, dataFormat: 'NHWC'|'NCHW'):\n      Tensor4D {\n    util.assert(\n        dataFormat === 'NHWC',\n        `Only NHWC dataFormat supported on CPU for depthToSpace. Got ${\n            dataFormat}`);\n    util.assert(\n        blockSize > 1,\n        `blockSize should be > 1 for depthToSpace, but was: ${blockSize}`);\n\n    const batchSize = x.shape[0];\n    const inputHeight = x.shape[1];\n    const inputWidth = x.shape[2];\n    const inputDepth = x.shape[3];\n\n    const outputHeight = inputHeight * blockSize;\n    const outputWidth = inputWidth * blockSize;\n    const outputDepth = inputDepth / (blockSize * blockSize);\n\n    const xValues = x.dataSync();\n    const result =\n        new Float32Array(batchSize * outputHeight * outputWidth * outputDepth);\n\n    let outputIdx = 0;\n    for (let b = 0; b < batchSize; ++b) {\n      for (let h = 0; h < outputHeight; ++h) {\n        const inH = Math.floor(h / blockSize);\n        const offsetH = (h % blockSize);\n        for (let w = 0; w < outputWidth; ++w) {\n          const inW = Math.floor(w / blockSize);\n          const offsetW = (w % blockSize);\n          const offsetD = (offsetH * blockSize + offsetW) * outputDepth;\n          for (let d = 0; d < outputDepth; ++d) {\n            const inD = d + offsetD;\n            const inputIdx =\n                inD + inputDepth * (inW + inputWidth * (inH + inputHeight * b));\n            result[outputIdx++] = xValues[inputIdx];\n          }\n        }\n      }\n    }\n    return ops.tensor4d(\n        result, [batchSize, outputHeight, outputWidth, outputDepth]);\n  }\n\n  private broadcastedBinaryOp(\n      a: Tensor, b: Tensor, dtype: DataType,\n      op: (a: number, b: number) => number): Tensor {\n    const newShape =\n        broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n    const result = ops.buffer(newShape, dtype);\n    const aVals = a.dataSync();\n    const bVals = b.dataSync();\n    const aBroadcastDims = broadcast_util.getBroadcastDims(a.shape, newShape);\n    const bBroadcastDims = broadcast_util.getBroadcastDims(b.shape, newShape);\n\n    const resVals = result.values;\n    if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n      for (let i = 0; i < resVals.length; ++i) {\n        resVals[i] = op(aVals[i % aVals.length], bVals[i % bVals.length]);\n      }\n    } else {\n      const aBuf = a.buffer();\n      const bBuf = b.buffer();\n      for (let i = 0; i < resVals.length; ++i) {\n        const loc = result.indexToLoc(i);\n\n        const aLoc = loc.slice(-a.rank);\n        aBroadcastDims.forEach(d => aLoc[d] = 0);\n        const aIndex = aBuf.locToIndex(aLoc);\n\n        const bLoc = loc.slice(-b.rank);\n        bBroadcastDims.forEach(d => bLoc[d] = 0);\n        const bIndex = bBuf.locToIndex(bLoc);\n\n        resVals[i] = op(aVals[aIndex], bVals[bIndex]);\n      }\n    }\n    return result.toTensor();\n  }\n\n  private broadcastedBinaryComplexOp(\n      a: Tensor, b: Tensor,\n      op:\n          (aReal: number, aImag: number, bReal: number,\n           bImag: number) => {real: number, imag: number}): Tensor {\n    const newShape =\n        broadcast_util.assertAndGetBroadcastShape(a.shape, b.shape);\n    const realResult = ops.buffer(newShape, 'float32');\n    const imagResult = ops.buffer(newShape, 'float32');\n\n    const aVals = a.dataSync();\n    const bVals = b.dataSync();\n    const aBroadcastDims = broadcast_util.getBroadcastDims(a.shape, newShape);\n    const bBroadcastDims = broadcast_util.getBroadcastDims(b.shape, newShape);\n\n    const realVals = realResult.values;\n    const imagVals = imagResult.values;\n\n    if (aBroadcastDims.length + bBroadcastDims.length === 0) {\n      for (let i = 0; i < realVals.length; i++) {\n        const aIdx = i % aVals.length;\n        const bIdx = i % bVals.length;\n\n        const result =\n            op(aVals[aIdx * 2], aVals[aIdx * 2 + 1], bVals[bIdx * 2],\n               bVals[bIdx * 2 + 1]);\n\n        realVals[i] = result.real;\n        imagVals[i] = result.imag;\n      }\n    } else {\n      const aRealBuf = this.data.get(a.dataId).complexTensors.real.buffer();\n      const bRealBuf = this.data.get(b.dataId).complexTensors.real.buffer();\n      for (let i = 0; i < realVals.length; i++) {\n        const loc = realResult.indexToLoc(i);\n\n        const aLoc = loc.slice(-a.rank);\n        aBroadcastDims.forEach(d => aLoc[d] = 0);\n        const aIndex = aRealBuf.locToIndex(aLoc);\n\n        const bLoc = loc.slice(-b.rank);\n        bBroadcastDims.forEach(d => bLoc[d] = 0);\n        const bIndex = bRealBuf.locToIndex(bLoc);\n\n        const opResult =\n            op(aVals[aIndex * 2], aVals[aIndex * 2 + 1], bVals[bIndex * 2],\n               bVals[bIndex * 2 + 1]);\n\n        realVals[i] = opResult.real;\n        imagVals[i] = opResult.imag;\n      }\n    }\n    return this.complex(realResult.toTensor(), imagResult.toTensor());\n  }\n\n  split<T extends Tensor>(x: T, sizeSplits: number[], axis: number): T[] {\n    return split(x, sizeSplits, axis);\n  }\n\n  dispose() {}\n\n  floatPrecision() {\n    return 32;\n  }\n\n  cropAndResize(\n      images: Tensor4D,\n      boxes: Tensor2D,\n      boxIndex: Tensor1D,\n      cropSize: [number, number],\n      method: string,\n      extrapolationValue: number,\n  ) {\n    const [batch, imageHeight, imageWidth, numChannels] = images.shape;\n    const numBoxes = boxes.shape[0];\n\n    const [cropHeight, cropWidth] = cropSize;\n    const output = ops.buffer(\n        [numBoxes, cropHeight, cropWidth, numChannels], images.dtype);\n\n    const boxVals = boxes.dataSync();\n    const boxIndVals = boxIndex.dataSync();\n    const imageVals = images.dataSync();\n\n    const inStride = images.strides;   // to calculate flat indexes into image\n    const outStride = output.strides;  // to calculate flat indexes into output\n\n    // Reference implementation\n    // tslint:disable-next-line:max-line-length\n    // https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/kernels/crop_and_resize_op.cc\n    for (let b = 0; b < numBoxes; b++) {\n      const startInd = b * 4;\n      const y1 = boxVals[startInd];\n      const x1 = boxVals[startInd + 1];\n      const y2 = boxVals[startInd + 2];\n      const x2 = boxVals[startInd + 3];\n\n      const bInd: number = boxIndVals[b];\n      if (bInd >= batch) {\n        continue;\n      }\n\n      const heightScale = (cropHeight > 1) ?\n          (y2 - y1) * (imageHeight - 1) / (cropHeight - 1) :\n          0;\n      const widthScale =\n          (cropWidth > 1) ? (x2 - x1) * (imageWidth - 1) / (cropWidth - 1) : 0;\n\n      for (let y = 0; y < cropHeight; y++) {\n        const yInd: number = (cropHeight > 1) ?\n            y1 * (imageHeight - 1) + y * (heightScale) :\n            0.5 * (y1 + y2) * (imageHeight - 1);\n\n        if (yInd < 0 || yInd > imageHeight - 1) {\n          for (let x = 0; x < cropWidth; x++) {\n            for (let c = 0; c < numChannels; c++) {\n              const ind =\n                  c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n              output.values[ind] = extrapolationValue;\n            }\n          }\n          continue;\n        }\n\n        if (method === 'bilinear') {\n          const topInd = Math.floor(yInd);\n          const bottomInd = Math.ceil(yInd);\n          const yLerp = yInd - topInd;\n\n          for (let x = 0; x < cropWidth; x++) {\n            const xInd = (cropWidth > 1) ?\n                x1 * (imageWidth - 1) + x * widthScale :\n                0.5 * (x1 + x2) * (imageWidth - 1);\n\n            if (xInd < 0 || xInd > imageWidth - 1) {\n              for (let c = 0; c < numChannels; c++) {\n                const ind =\n                    c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                output.values[ind] = extrapolationValue;\n              }\n              continue;\n            }\n\n            const leftInd = Math.floor(xInd);\n            const rightInd = Math.ceil(xInd);\n            const xLerp = xInd - leftInd;\n\n            for (let c = 0; c < numChannels; c++) {\n              let ind = c + leftInd * inStride[2] + topInd * inStride[1] +\n                  bInd * inStride[0];\n              const topLeft = imageVals[ind];\n\n              ind = c + rightInd * inStride[2] + topInd * inStride[1] +\n                  bInd * inStride[0];\n              const topRight = imageVals[ind];\n\n              ind = c + leftInd * inStride[2] + bottomInd * inStride[1] +\n                  bInd * inStride[0];\n              const bottomLeft = imageVals[ind];\n\n              ind = c + rightInd * inStride[2] + bottomInd * inStride[1] +\n                  bInd * inStride[0];\n              const bottomRight = imageVals[ind];\n\n              const top = topLeft + (topRight - topLeft) * xLerp;\n              const bottom = bottomLeft + (bottomRight - bottomLeft) * xLerp;\n\n              ind = c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n              output.values[ind] = top + ((bottom - top) * yLerp);\n            }\n          }\n        } else {  // method == \"nearest\"\n          for (let x = 0; x < cropWidth; ++x) {\n            const xInd = (cropWidth > 1) ?\n                x1 * (imageWidth - 1) + x * widthScale :\n                0.5 * (x1 + x2) * (imageWidth - 1);\n\n            if (xInd < 0 || xInd > imageWidth - 1) {\n              for (let c = 0; c < numChannels; c++) {\n                const ind =\n                    c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n                output.values[ind] = extrapolationValue;\n              }\n              continue;\n            }\n\n            const closestX = Math.round(xInd);\n            const closestY = Math.round(yInd);\n            for (let c = 0; c < numChannels; c++) {\n              const inInd = c + closestX * inStride[2] +\n                  closestY * inStride[1] + bInd * inStride[0];\n              const outInd =\n                  c + x * outStride[2] + y * outStride[1] + b * outStride[0];\n              output.values[outInd] = imageVals[inInd];\n            }\n          }\n        }\n      }\n    }\n    return output.toTensor() as Tensor4D;\n  }\n\n  sparseToDense<R extends Rank>(\n      sparseIndices: Tensor, sparseValues: Tensor, outputShape: ShapeMap[R],\n      defaultValue: Scalar): Tensor<R> {\n    const {sliceRank, numUpdates, sliceSize, strides, outputSize} =\n        scatter_nd_util.calculateShapes(\n            sparseValues, sparseIndices, outputShape);\n    const sumDupeIndices = false;\n    return this.scatter(\n        sparseIndices, sparseValues, outputShape, outputSize, sliceSize,\n        numUpdates, sliceRank, strides, defaultValue, sumDupeIndices);\n  }\n\n  gatherND(x: Tensor, indices: Tensor): Tensor<Rank> {\n    const indicesShape = indices.shape;\n    const sliceRank = indicesShape[indicesShape.length - 1];\n\n    const [resultShape, numSlices, sliceSize, strides] =\n        gather_nd_util.prepareAndValidate(x, indices);\n    if (numSlices === 0) {\n      return tensor([], resultShape, x.dtype);\n    }\n\n    const buffer = new TensorBuffer([numSlices, sliceSize], x.dtype);\n    const indicesData = indices.dataSync();\n    const xData = x.dataSync();\n\n    for (let i = 0; i < numSlices; i++) {\n      const index = [];\n      let flattenIndex = 0;\n      for (let j = 0; j < sliceRank; j++) {\n        const dim = indicesData[i * sliceRank + j];\n        flattenIndex += dim * strides[j];\n        index.push(dim);\n      }\n      if (flattenIndex < 0 || flattenIndex >= x.size / sliceSize) {\n        throw new Error(\n            `Invalid indices: ${index} does not index into ${x.shape}`);\n      }\n\n      for (let k = 0; k < sliceSize; k++) {\n        buffer.values[i * sliceSize + k] = xData[flattenIndex * sliceSize + k];\n      }\n    }\n    return buffer.toTensor().reshape(resultShape);\n  }\n\n  scatterND<R extends Rank>(\n      indices: Tensor, updates: Tensor, shape: ShapeMap[R]): Tensor<R> {\n    const {sliceRank, numUpdates, sliceSize, strides, outputSize} =\n        scatter_nd_util.calculateShapes(updates, indices, shape);\n    const defaultValue = scalar(0);\n    const sumDupeIndices = true;\n    return this.scatter(\n        indices, updates, shape, outputSize, sliceSize, numUpdates, sliceRank,\n        strides, defaultValue, sumDupeIndices);\n  }\n\n  private scatter<R extends Rank>(\n      indices: Tensor, updates: Tensor, shape: ShapeMap[R], outputSize: number,\n      sliceSize: number, numUpdates: number, sliceRank: number,\n      strides: number[], defaultValue: Scalar,\n      sumDupeIndices: boolean): Tensor<R> {\n    const flattenShape = [outputSize / sliceSize, sliceSize];\n    const indicesData = indices.dataSync();\n    const updatesData = updates.dataSync();\n\n    if (outputSize === 0) {\n      return tensor([], shape, updates.dtype);\n    }\n\n    const buffer = new TensorBuffer(flattenShape, updates.dtype as 'float32');\n    buffer.values.fill(defaultValue.dataSync()[0]);\n\n    for (let i = 0; i < numUpdates; i++) {\n      const index = [];\n      let flattenIndex = 0;\n      for (let j = 0; j < sliceRank; j++) {\n        const dim = indicesData[i * sliceRank + j];\n        index.push(dim);\n        flattenIndex += dim * strides[j];\n      }\n\n      if (flattenIndex < 0 || flattenIndex >= outputSize / sliceSize) {\n        throw new Error(\n            `Invalid indices: ${index} does not index into ${shape}`);\n      }\n\n      for (let k = 0; k < sliceSize; k++) {\n        if (sumDupeIndices) {\n          buffer.values[flattenIndex * sliceSize + k] +=\n              updatesData[i * sliceSize + k];\n        } else {\n          buffer.values[flattenIndex * sliceSize + k] = updates.rank === 0 ?\n              updatesData[0] :\n              updatesData[i * sliceSize + k];\n        }\n      }\n    }\n    return buffer.toTensor().reshape(shape);\n  }\n}\n\nENV.registerBackend(\n    'cpu', () => new MathBackendCPU(), 1 /* priority */, setTensorTracker);\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nconst delayCallback: Function = typeof requestAnimationFrame !== 'undefined' ?\n    requestAnimationFrame :  // Browsers\n    setImmediate;            // Node.js\n\n/**\n * Returns a promise that resolve when a requestAnimationFrame has completed.\n *\n * On Node.js this uses setImmediate instead of requestAnimationFrame.\n *\n * This is simply a sugar method so that users can do the following:\n * `await tf.nextFrame();`\n */\n/** @doc {heading: 'Performance', subheading: 'Timing'} */\nfunction nextFrame(): Promise<void> {\n  return new Promise<void>(resolve => delayCallback(() => resolve()));\n}\n\nexport {nextFrame};\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/* Type definitions for exporting and importing of models. */\n\n/**\n * A map from Tensor dtype to number of bytes per element of the Tensor.\n */\nexport const DTYPE_VALUE_SIZE_MAP: {[dtype: string]: number} = {\n  'float32': 4,\n  'int32': 4,\n  'uint16': 2,\n  'uint8': 1,\n  'bool': 1,\n};\n\n/**\n * A weight manifest.\n *\n * The weight manifest consists of an ordered list of weight-manifest groups.\n * Each weight-manifest group (\"group\" for short hereafter) consists of a\n * number of weight values stored in a number of paths.\n * See the documentation of `WeightManifestGroupConfig` below for more details.\n */\nexport declare type WeightsManifestConfig = WeightsManifestGroupConfig[];\n\n/**\n * A weight-manifest group.\n *\n * Consists of an ordered list of weight values encoded in binary format,\n * stored in an ordered list of paths.\n */\nexport declare interface WeightsManifestGroupConfig {\n  /**\n   * An ordered list of paths.\n   *\n   * Paths are intentionally abstract in order to be general. For example, they\n   * can be relative URL paths or relative paths on the file system.\n   */\n  paths: string[];\n\n  /**\n   * Specifications of the weights stored in the paths.\n   */\n  weights: WeightsManifestEntry[];\n}\n\n/**\n * An entry in the weight manifest.\n *\n * The entry contains specification of a weight.\n */\nexport declare interface WeightsManifestEntry {\n  /**\n   * Name of the weight, e.g., 'Dense_1/bias'\n   */\n  name: string;\n\n  /**\n   * Shape of the weight.\n   */\n  shape: number[];\n\n  /**\n   * Data type of the weight.\n   */\n  dtype: 'float32'|'int32'|'bool';\n\n  /**\n   * Information for dequantization of the weight.\n   */\n  quantization?: {\n    scale: number,           // The scaling constant to multiply by.\n    min: number,             // The (possibly nudged) minimum weight to add.\n    dtype: 'uint16'|'uint8'  // The dtype of the quantized weights.\n  };\n}\n\n/**\n * Options for saving a model.\n */\nexport interface SaveConfig {\n  /**\n   * Whether to save only the trainable weights of the model, ignoring the\n   * untrainable ones.\n   */\n  trainableOnly?: boolean;\n}\n\n/**\n * Result of a saving operation.\n */\nexport interface SaveResult {\n  /**\n   * Information about the model artifacts saved.\n   */\n  modelArtifactsInfo: ModelArtifactsInfo;\n\n  /**\n   * HTTP responses from the server that handled the model-saving request (if\n   * any). This is applicable only to server-based saving routes.\n   */\n  responses?: Response[];\n\n  /**\n   * Error messages and related data (if any).\n   */\n  errors?: Array<{}|string>;\n}\n\nexport declare interface ModelArtifactsInfo {\n  /**\n   * Timestamp for when the model is saved.\n   */\n  dateSaved: Date;\n\n  /**\n   * Type of the model topology\n   *\n   * Possible values:\n   *   - JSON: JSON config (human-readable, e.g., Keras JSON).\n   *   - GraphDef: TensorFlow\n   *     [GraphDef](https://www.tensorflow.org/extend/tool_developers/#graphdef)\n   *     protocol buffer (binary).\n   */\n  modelTopologyType: 'JSON'|'GraphDef';\n\n  /**\n   * Size of model topology (Keras JSON or GraphDef), in bytes.\n   */\n  modelTopologyBytes?: number;\n\n  /**\n   * Size of weight specification or manifest, in bytes.\n   */\n  weightSpecsBytes?: number;\n\n  /**\n   * Size of weight value data, in bytes.\n   */\n  weightDataBytes?: number;\n}\n\n/**\n * The serialized artifacts of a model, including topology and weights.\n *\n * The `modelTopology`, `weightSpecs` and `weightData` fields of this interface\n * are optional, in order to support topology- or weights-only saving and\n * loading.\n */\nexport declare interface ModelArtifacts {\n  /**\n   * Model topology.\n   *\n   * For Keras-style `tf.Model`s, this is a JSON object.\n   * For TensorFlow-style models (e.g., `FrozenModel`), this is a binary buffer\n   * carrying the `GraphDef` protocol buffer.\n   */\n  modelTopology?: {}|ArrayBuffer;\n\n  /**\n   * Weight specifications.\n   *\n   * This corresponds to the weightsData below.\n   */\n  weightSpecs?: WeightsManifestEntry[];\n\n  /**\n   * Binary buffer for all weight values concatenated in the order specified\n   * by `weightSpecs`.\n   */\n  weightData?: ArrayBuffer;\n}\n\n/**\n * Type definition for handlers of loading operations.\n */\nexport type LoadHandler = () => Promise<ModelArtifacts>;\n\n/**\n * Type definition for handlers of saving operations.\n */\nexport type SaveHandler = (modelArtifact: ModelArtifacts) =>\n    Promise<SaveResult>;\n\n/**\n * Interface for a model import/export handler.\n *\n * The `save` and `load` handlers are both optional, in order to allow handlers\n * that support only saving or loading.\n */\n// tslint:disable-next-line:interface-name\nexport interface IOHandler {\n  save?: SaveHandler;\n  load?: LoadHandler;\n}\n\n/**\n * An interface for the manager of a model store.\n *\n * A model store is defined as a storage medium on which multiple models can\n * be stored. Each stored model has a unique `path` as its identifier.\n * A `ModelStoreManager` for the store allows actions including\n *\n * - Listing the models stored in the store.\n * - Deleting a model from the store.\n */\nexport interface ModelStoreManager {\n  /**\n   * List all models in the model store.\n   *\n   * @returns A dictionary mapping paths of existing models to their\n   *   model artifacts info. Model artifacts info include type of the model's\n   *   topology, byte sizes of the topology, weights, etc.\n   */\n  listModels(): Promise<{[path: string]: ModelArtifactsInfo}>;\n\n  /**\n   * Remove a model specified by `path`.\n   *\n   * @param path\n   * @returns ModelArtifactsInfo of the deleted model (if and only if deletion\n   *   is successful).\n   * @throws Error if deletion fails, e.g., if no model exists at `path`.\n   */\n  removeModel(path: string): Promise<ModelArtifactsInfo>;\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {tensor} from '../ops/tensor_ops';\nimport {Tensor} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {TypedArray} from '../types';\nimport {sizeFromShape} from '../util';\nimport {DTYPE_VALUE_SIZE_MAP, ModelArtifacts, ModelArtifactsInfo, WeightsManifestEntry} from './types';\n\n/**\n * Encode a map from names to weight values as an ArrayBuffer, along with an\n * `Array` of `WeightsManifestEntry` as specification of the encoded weights.\n *\n * This function does not perform sharding.\n *\n * This function is the reverse of `decodeWeights`.\n *\n * @param tensors A map (\"dict\") from names to tensors.\n * @returns A `Promise` of\n *   - A flat `ArrayBuffer` with all the binary values of the `Tensor`s\n *     concatenated.\n *   - An `Array` of `WeightManifestEntry`s, carrying information including\n *     tensor names, `dtype`s and shapes.\n * @throws Error: on unsupported tensor `dtype`.\n */\nexport async function encodeWeights(tensors: NamedTensorMap):\n    Promise<{data: ArrayBuffer, specs: WeightsManifestEntry[]}> {\n  // TODO(adarob, cais): Support quantization.\n  const specs: WeightsManifestEntry[] = [];\n  const dataPromises: Array<Promise<TypedArray>> = [];\n  for (const name in tensors) {\n    const t = tensors[name];\n\n    if (t.dtype !== 'float32' && t.dtype !== 'int32' && t.dtype !== 'bool') {\n      throw new Error(`Unsupported dtype in weight '${name}': ${t.dtype}`);\n    }\n    specs.push({name, shape: t.shape, dtype: t.dtype});\n    dataPromises.push(t.data());\n  }\n  const tensorValues = await Promise.all(dataPromises);\n  return {data: concatenateTypedArrays(tensorValues), specs};\n}\n\n/**\n * Decode flat ArrayBuffer as weights.\n *\n * This function does not handle sharding.\n *\n * This function is the reverse of `encodeWeights`.\n *\n * @param buffer A flat ArrayBuffer carrying the binary values of the tensors\n *   concatenated in the order specified in `specs`.\n * @param specs Specifications of the names, dtypes and shapes of the tensors\n *   whose value are encoded by `buffer`.\n * @return A map from tensor name to tensor value, with the names corresponding\n *   to names in `specs`.\n * @throws Error, if any of the tensors has unsupported dtype.\n */\nexport function decodeWeights(\n    buffer: ArrayBuffer, specs: WeightsManifestEntry[]): NamedTensorMap {\n  // TODO(adarob, cais): Support quantization.\n  const out: NamedTensorMap = {};\n  let offset = 0;\n  for (const spec of specs) {\n    const name = spec.name;\n    const dtype = spec.dtype;\n    const shape = spec.shape;\n    const size = sizeFromShape(shape);\n    let typedArray: TypedArray;\n\n    if ('quantization' in spec) {\n      const quantization = spec.quantization;\n      if (quantization.dtype !== 'uint8' && quantization.dtype !== 'uint16') {\n        throw new Error(\n            `Weight ${spec.name} has unknown ` +\n            `quantization dtype ${quantization.dtype}. ` +\n            `Supported quantization dtypes are: 'uint8' and 'uint16'.`);\n      }\n      const quantizationSizeFactor = DTYPE_VALUE_SIZE_MAP[quantization.dtype];\n      const byteBuffer =\n          buffer.slice(offset, offset + size * quantizationSizeFactor);\n      const quantizedArray = (quantization.dtype === 'uint8') ?\n          new Uint8Array(byteBuffer) :\n          new Uint16Array(byteBuffer);\n      if (dtype === 'float32') {\n        typedArray = Float32Array.from(\n            quantizedArray, v => v * quantization.scale + quantization.min);\n      } else if (dtype === 'int32') {\n        typedArray = Int32Array.from(\n            quantizedArray,\n            v => Math.round(v * quantization.scale + quantization.min));\n      } else {\n        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n      }\n      offset += size * quantizationSizeFactor;\n    } else {\n      const dtypeFactor = DTYPE_VALUE_SIZE_MAP[dtype];\n      const byteBuffer = buffer.slice(offset, offset + size * dtypeFactor);\n\n      if (dtype === 'float32') {\n        typedArray = new Float32Array(byteBuffer);\n      } else if (dtype === 'int32') {\n        typedArray = new Int32Array(byteBuffer);\n      } else if (dtype === 'bool') {\n        typedArray = new Uint8Array(byteBuffer);\n      } else {\n        throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n      }\n      offset += size * dtypeFactor;\n    }\n\n    let value: Tensor;\n    if (dtype === 'float32') {\n      value = tensor(typedArray, shape, 'float32');\n    } else if (dtype === 'int32') {\n      value = tensor(typedArray, shape, 'int32');\n    } else if (dtype === 'bool') {\n      value = tensor(typedArray, shape, 'bool');\n    } else {\n      throw new Error(`Unsupported dtype in weight '${name}': ${dtype}`);\n    }\n    out[name] = value;\n  }\n  return out;\n}\n\n/**\n * Concatenate TypedArrays into an ArrayBuffer.\n */\nexport function concatenateTypedArrays(xs: TypedArray[]): ArrayBuffer {\n  // TODO(adarob, cais): Support quantization.\n  if (xs === null) {\n    throw new Error(`Invalid input value: ${JSON.stringify(xs)}`);\n  }\n\n  let totalByteLength = 0;\n\n  // `normalizedXs` is here for this reason: a `TypedArray`'s `buffer'\n  // can have a different byte length from that of the `TypedArray` itself,\n  // for example, when the `TypedArray` is created from an offset in an\n  // `ArrayBuffer`. `normliazedXs` holds `TypedArray`s whose `buffer`s match\n  // the `TypedArray` in byte length. If an element of `xs` does not show\n  // this property, a new `TypedArray` that satisfy this property will be\n  // constructed and pushed into `normalizedXs`.\n  const normalizedXs: TypedArray[] = [];\n  xs.forEach((x: TypedArray) => {\n    totalByteLength += x.byteLength;\n    // tslint:disable:no-any\n    normalizedXs.push(\n        x.byteLength === x.buffer.byteLength ? x :\n                                               new (x.constructor as any)(x));\n    if (!(x as any instanceof Float32Array || x as any instanceof Int32Array ||\n          x as any instanceof Uint8Array)) {\n      throw new Error(`Unsupported TypedArray subtype: ${x.constructor.name}`);\n    }\n    // tslint:enable:no-any\n  });\n\n  const y = new Uint8Array(totalByteLength);\n  let offset = 0;\n  normalizedXs.forEach((x: TypedArray) => {\n    y.set(new Uint8Array(x.buffer), offset);\n    offset += x.byteLength;\n  });\n\n  return y.buffer;\n}\n\n// Use Buffer on Node.js instead of Blob/atob/btoa\nconst useNodeBuffer = typeof Buffer !== 'undefined' &&\n    (typeof Blob === 'undefined' || typeof atob === 'undefined' ||\n     typeof btoa === 'undefined');\n\n/**\n * Calculate the byte length of a JavaScript string.\n *\n * Note that a JavaScript string can contain wide characters, therefore the\n * length of the string is not necessarily equal to the byte length.\n *\n * @param str Input string.\n * @returns Byte length.\n */\nexport function stringByteLength(str: string): number {\n  if (useNodeBuffer) {\n    return Buffer.byteLength(str);\n  }\n  return new Blob([str]).size;\n}\n\n/**\n * Encode an ArrayBuffer as a base64 encoded string.\n *\n * @param buffer `ArrayBuffer` to be converted.\n * @returns A string that base64-encodes `buffer`.\n */\nexport function arrayBufferToBase64String(buffer: ArrayBuffer): string {\n  if (useNodeBuffer) {\n    return Buffer.from(buffer).toString('base64');\n  }\n  return btoa(String.fromCharCode.apply(null, new Uint8Array(buffer)));\n}\n\n/**\n * Decode a base64 string as an ArrayBuffer.\n *\n * @param str Base64 string.\n * @returns Decoded `ArrayBuffer`.\n */\nexport function base64StringToArrayBuffer(str: string): ArrayBuffer {\n  if (useNodeBuffer) {\n    const buf = Buffer.from(str, 'base64');\n    return buf.buffer.slice(buf.byteOffset, buf.byteOffset + buf.byteLength);\n  }\n  const s = atob(str);\n  const buffer = new Uint8Array(s.length);\n  for (let i = 0; i < s.length; ++i) {\n    buffer.set([s.charCodeAt(i)], i);\n  }\n  return buffer.buffer;\n}\n\n/**\n * Concatenate a number of ArrayBuffers into one.\n *\n * @param buffers A number of array buffers to concatenate.\n * @returns Result of concatenating `buffers` in order.\n */\nexport function concatenateArrayBuffers(buffers: ArrayBuffer[]): ArrayBuffer {\n  let totalByteLength = 0;\n  buffers.forEach((buffer: ArrayBuffer) => {\n    totalByteLength += buffer.byteLength;\n  });\n\n  const temp = new Uint8Array(totalByteLength);\n  let offset = 0;\n  buffers.forEach((buffer: ArrayBuffer) => {\n    temp.set(new Uint8Array(buffer), offset);\n    offset += buffer.byteLength;\n  });\n  return temp.buffer;\n}\n\n/**\n * Get the basename of a path.\n *\n * Behaves in a way analogous to Linux's basename command.\n *\n * @param path\n */\nexport function basename(path: string): string {\n  const SEPARATOR = '/';\n  path = path.trim();\n  while (path.endsWith(SEPARATOR)) {\n    path = path.slice(0, path.length - 1);\n  }\n  const items = path.split(SEPARATOR);\n  return items[items.length - 1];\n}\n\n/**\n * Populate ModelArtifactsInfo fields for a model with JSON topology.\n * @param modelArtifacts\n * @returns A ModelArtifactsInfo object.\n */\nexport function getModelArtifactsInfoForJSON(modelArtifacts: ModelArtifacts):\n    ModelArtifactsInfo {\n  if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n    throw new Error('Expected JSON model topology, received ArrayBuffer.');\n  }\n\n  return {\n    dateSaved: new Date(),\n    modelTopologyType: 'JSON',\n    modelTopologyBytes: modelArtifacts.modelTopology == null ?\n        0 :\n        stringByteLength(JSON.stringify(modelArtifacts.modelTopology)),\n    weightSpecsBytes: modelArtifacts.weightSpecs == null ?\n        0 :\n        stringByteLength(JSON.stringify(modelArtifacts.weightSpecs)),\n    weightDataBytes: modelArtifacts.weightData == null ?\n        0 :\n        modelArtifacts.weightData.byteLength,\n  };\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {IOHandler} from './types';\n\nexport type IORouter = (url: string|string[]) => IOHandler;\n\nexport class IORouterRegistry {\n  // Singleton instance.\n  private static instance: IORouterRegistry;\n\n  private saveRouters: IORouter[];\n  private loadRouters: IORouter[];\n\n  private constructor() {\n    this.saveRouters = [];\n    this.loadRouters = [];\n  }\n\n  private static getInstance(): IORouterRegistry {\n    if (IORouterRegistry.instance == null) {\n      IORouterRegistry.instance = new IORouterRegistry();\n    }\n    return IORouterRegistry.instance;\n  }\n\n  /**\n   * Register a save-handler router.\n   *\n   * @param saveRouter A function that maps a URL-like string onto an instance\n   * of `IOHandler` with the `save` method defined or `null`.\n   */\n  static registerSaveRouter(saveRouter: IORouter) {\n    IORouterRegistry.getInstance().saveRouters.push(saveRouter);\n  }\n\n  /**\n   * Register a load-handler router.\n   *\n   * @param loadRouter A function that maps a URL-like string onto an instance\n   * of `IOHandler` with the `load` method defined or `null`.\n   */\n  static registerLoadRouter(loadRouter: IORouter) {\n    IORouterRegistry.getInstance().loadRouters.push(loadRouter);\n  }\n\n  /**\n   * Look up IOHandler for saving, given a URL-like string.\n   *\n   * @param url\n   * @returns If only one match is found, an instance of IOHandler with the\n   * `save` method defined. If no match is found, `null`.\n   * @throws Error, if more than one match is found.\n   */\n  static getSaveHandlers(url: string|string[]): IOHandler[] {\n    return IORouterRegistry.getHandlers(url, 'save');\n  }\n\n  /**\n   * Look up IOHandler for loading, given a URL-like string.\n   *\n   * @param url\n   * @returns All valid handlers for `url`, given the currently registered\n   *   handler routers.\n   */\n  static getLoadHandlers(url: string|string[]): IOHandler[] {\n    return IORouterRegistry.getHandlers(url, 'load');\n  }\n\n  private static getHandlers(url: string|string[], handlerType: 'save'|'load'):\n      IOHandler[] {\n    const validHandlers: IOHandler[] = [];\n    const routers = handlerType === 'load' ? this.getInstance().loadRouters :\n                                             this.getInstance().saveRouters;\n    routers.forEach(router => {\n      const handler = router(url);\n      if (handler !== null) {\n        validHandlers.push(handler);\n      }\n    });\n    return validHandlers;\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * Classes and functions for model management across multiple storage mediums.\n *\n * Supported client actions:\n * - Listing models on all registered storage mediums.\n * - Remove model by URL from any registered storage mediums, by using URL\n *   string.\n * - Moving or copying model from one path to another in the same medium or from\n *   one medium to another, by using URL strings.\n */\n\nimport {assert} from '../util';\n\nimport {IORouterRegistry} from './router_registry';\nimport {ModelArtifactsInfo, ModelStoreManager} from './types';\n\nconst URL_SCHEME_SUFFIX = '://';\n\nexport class ModelStoreManagerRegistry {\n  // Singleton instance.\n  private static instance: ModelStoreManagerRegistry;\n\n  private managers: {[scheme: string]: ModelStoreManager};\n\n  private constructor() {\n    this.managers = {};\n  }\n\n  private static getInstance(): ModelStoreManagerRegistry {\n    if (ModelStoreManagerRegistry.instance == null) {\n      ModelStoreManagerRegistry.instance = new ModelStoreManagerRegistry();\n    }\n    return ModelStoreManagerRegistry.instance;\n  }\n\n  /**\n   * Register a save-handler router.\n   *\n   * @param saveRouter A function that maps a URL-like string onto an instance\n   * of `IOHandler` with the `save` method defined or `null`.\n   */\n  static registerManager(scheme: string, manager: ModelStoreManager) {\n    assert(scheme != null, 'scheme must not be undefined or null.');\n    if (scheme.endsWith(URL_SCHEME_SUFFIX)) {\n      scheme = scheme.slice(0, scheme.indexOf(URL_SCHEME_SUFFIX));\n    }\n    assert(scheme.length > 0, 'scheme must not be an empty string.');\n    const registry = ModelStoreManagerRegistry.getInstance();\n    assert(\n        registry.managers[scheme] == null,\n        `A model store manager is already registered for scheme '${scheme}'.`);\n    registry.managers[scheme] = manager;\n  }\n\n  static getManager(scheme: string): ModelStoreManager {\n    const manager = this.getInstance().managers[scheme];\n    if (manager == null) {\n      throw new Error(`Cannot find model manager for scheme '${scheme}'`);\n    }\n    return manager;\n  }\n\n  static getSchemes(): string[] {\n    return Object.keys(this.getInstance().managers);\n  }\n}\n\n/**\n * Helper method for parsing a URL string into a scheme and a path.\n *\n * @param url E.g., 'localstorage://my-model'\n * @returns A dictionary with two fields: scheme and path.\n *   Scheme: e.g., 'localstorage' in the example above.\n *   Path: e.g., 'my-model' in the example above.\n */\nfunction parseURL(url: string): {scheme: string, path: string} {\n  if (url.indexOf(URL_SCHEME_SUFFIX) === -1) {\n    throw new Error(\n        `The url string provided does not contain a scheme. ` +\n        `Supported schemes are: ` +\n        `${ModelStoreManagerRegistry.getSchemes().join(',')}`);\n  }\n  return {\n    scheme: url.split(URL_SCHEME_SUFFIX)[0],\n    path: url.split(URL_SCHEME_SUFFIX)[1],\n  };\n}\n\nasync function cloneModelInternal(\n    sourceURL: string, destURL: string,\n    deleteSource = false): Promise<ModelArtifactsInfo> {\n  assert(\n      sourceURL !== destURL,\n      `Old path and new path are the same: '${sourceURL}'`);\n\n  const loadHandlers = IORouterRegistry.getLoadHandlers(sourceURL);\n  assert(\n      loadHandlers.length > 0,\n      `Copying failed because no load handler is found for source URL ${\n          sourceURL}.`);\n  assert(\n      loadHandlers.length < 2,\n      `Copying failed because more than one (${loadHandlers.length}) ` +\n          `load handlers for source URL ${sourceURL}.`);\n  const loadHandler = loadHandlers[0];\n\n  const saveHandlers = IORouterRegistry.getSaveHandlers(destURL);\n  assert(\n      saveHandlers.length > 0,\n      `Copying failed because no save handler is found for destination URL ` +\n          `${destURL}.`);\n  assert(\n      saveHandlers.length < 2,\n      `Copying failed because more than one (${loadHandlers.length}) ` +\n          `save handlers for destination URL ${destURL}.`);\n  const saveHandler = saveHandlers[0];\n\n  const sourceScheme = parseURL(sourceURL).scheme;\n  const sourcePath = parseURL(sourceURL).path;\n  const sameMedium = sourceScheme === parseURL(sourceURL).scheme;\n\n  const modelArtifacts = await loadHandler.load();\n\n  // If moving within the same storage medium, remove the old model as soon as\n  // the loading is done. Without doing this, it is possible that the combined\n  // size of the two models will cause the cloning to fail.\n  if (deleteSource && sameMedium) {\n    await ModelStoreManagerRegistry.getManager(sourceScheme)\n        .removeModel(sourcePath);\n  }\n\n  const saveResult = await saveHandler.save(modelArtifacts);\n\n  // If moving between mediums, the deletion is done after the save succeeds.\n  // This guards against the case in which saving to the destination medium\n  // fails.\n  if (deleteSource && !sameMedium) {\n    await ModelStoreManagerRegistry.getManager(sourceScheme)\n        .removeModel(sourcePath);\n  }\n\n  return saveResult.modelArtifactsInfo;\n}\n\n/**\n * List all models stored in registered storage mediums.\n *\n * For a web browser environment, the registered mediums are Local Storage and\n * IndexedDB.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @returns A `Promise` of a dictionary mapping URLs of existing models to\n * their model artifacts info. URLs include medium-specific schemes, e.g.,\n *   'indexeddb://my/model/1'. Model artifacts info include type of the\n * model's topology, byte sizes of the topology, weights, etc.\n */\n/** @doc {heading: 'Models', subheading: 'Management', namespace: 'io'} */\nasync function listModels(): Promise<{[url: string]: ModelArtifactsInfo}> {\n  const schemes = ModelStoreManagerRegistry.getSchemes();\n  const out: {[url: string]: ModelArtifactsInfo} = {};\n  for (const scheme of schemes) {\n    const schemeOut =\n        await ModelStoreManagerRegistry.getManager(scheme).listModels();\n    for (const path in schemeOut) {\n      const url = scheme + URL_SCHEME_SUFFIX + path;\n      out[url] = schemeOut[path];\n    }\n  }\n  return out;\n}\n\n/**\n * Remove a model specified by URL from a reigstered storage medium.\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Delete the model.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n * ```\n *\n * @param url A URL to a stored model, with a scheme prefix, e.g.,\n *   'localstorage://my-model-1', 'indexeddb://my/model/2'.\n * @returns ModelArtifactsInfo of the deleted model (if and only if deletion\n *   is successful).\n * @throws Error if deletion fails, e.g., if no model exists at `path`.\n */\n/** @doc {heading: 'Models', subheading: 'Management', namespace: 'io'} */\nasync function removeModel(url: string): Promise<ModelArtifactsInfo> {\n  const schemeAndPath = parseURL(url);\n  const manager = ModelStoreManagerRegistry.getManager(schemeAndPath.scheme);\n  return await manager.removeModel(schemeAndPath.path);\n}\n\n/**\n * Copy a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Copying within a storage medium, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Copying between two storage mediums, e.g.,\n *    `tf.io.copyModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Copy the model, from Local Storage to IndexedDB.\n * await tf.io.copyModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove both models.\n * await tf.io.removeModel('localstorage://demo/management/model1');\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of copying.\n * @param destURL Destination URL of copying.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if copying fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n */\n/** @doc {heading: 'Models', subheading: 'Management', namespace: 'io'} */\nasync function copyModel(\n    sourceURL: string, destURL: string): Promise<ModelArtifactsInfo> {\n  const deleteSource = false;\n  return await cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n\n/**\n * Move a model from one URL to another.\n *\n * This function supports:\n *\n * 1. Moving within a storage medium, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'localstorage://model-2')`\n * 2. Moving between two storage mediums, e.g.,\n *    `tf.io.moveModel('localstorage://model-1', 'indexeddb://model-1')`\n *\n * ```js\n * // First create and save a model.\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * await model.save('localstorage://demo/management/model1');\n *\n * // Then list existing models.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Move the model, from Local Storage to IndexedDB.\n * await tf.io.moveModel(\n *     'localstorage://demo/management/model1',\n *     'indexeddb://demo/management/model1');\n *\n * // List models again.\n * console.log(JSON.stringify(await tf.io.listModels()));\n *\n * // Remove the moved model.\n * await tf.io.removeModel('indexeddb://demo/management/model1');\n * ```\n *\n * @param sourceURL Source URL of moving.\n * @param destURL Destination URL of moving.\n * @returns ModelArtifactsInfo of the copied model (if and only if copying\n *   is successful).\n * @throws Error if moving fails, e.g., if no model exists at `sourceURL`, or\n *   if `oldPath` and `newPath` are identical.\n */\n/** @doc {heading: 'Models', subheading: 'Management', namespace: 'io'} */\nasync function moveModel(\n    sourceURL: string, destURL: string): Promise<ModelArtifactsInfo> {\n  const deleteSource = true;\n  return await cloneModelInternal(sourceURL, destURL, deleteSource);\n}\n\nexport {moveModel, copyModel, removeModel, listModels};\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {getModelArtifactsInfoForJSON} from './io_utils';\nimport {ModelStoreManagerRegistry} from './model_management';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, ModelArtifactsInfo, ModelStoreManager, SaveResult} from './types';\n\nconst DATABASE_NAME = 'tensorflowjs';\nconst DATABASE_VERSION = 1;\n\n// Model data and ModelArtifactsInfo (metadata) are stored in two separate\n// stores for efficient access of the list of stored models and their metadata.\n// 1. The object store for model data: topology, weights and weight manifests.\nconst MODEL_STORE_NAME = 'models_store';\n// 2. The object store for ModelArtifactsInfo, including meta-information such\n//    as the type of topology (JSON vs binary), byte size of the topology, byte\n//    size of the weights, etc.\nconst INFO_STORE_NAME = 'model_info_store';\n\n/**\n * Delete the entire database for tensorflow.js, including the models store.\n */\nexport async function deleteDatabase(): Promise<void> {\n  const idbFactory = getIndexedDBFactory();\n\n  return new Promise<void>((resolve, reject) => {\n    const deleteRequest = idbFactory.deleteDatabase(DATABASE_NAME);\n    deleteRequest.onsuccess = () => resolve();\n    deleteRequest.onerror = error => reject(error);\n  });\n}\n\nfunction getIndexedDBFactory(): IDBFactory {\n  if (!ENV.get('IS_BROWSER')) {\n    // TODO(cais): Add more info about what IOHandler subtypes are available.\n    //   Maybe point to a doc page on the web and/or automatically determine\n    //   the available IOHandlers and print them in the error message.\n    throw new Error(\n        'Failed to obtain IndexedDB factory because the current environment' +\n        'is not a web browser.');\n  }\n  // tslint:disable-next-line:no-any\n  const theWindow: any = window;\n  const factory = theWindow.indexedDB || theWindow.mozIndexedDB ||\n      theWindow.webkitIndexedDB || theWindow.msIndexedDB ||\n      theWindow.shimIndexedDB;\n  if (factory == null) {\n    throw new Error(\n        'The current browser does not appear to support IndexedDB.');\n  }\n  return factory;\n}\n\nfunction setUpDatabase(openRequest: IDBRequest) {\n  const db = openRequest.result as IDBDatabase;\n  db.createObjectStore(MODEL_STORE_NAME, {keyPath: 'modelPath'});\n  db.createObjectStore(INFO_STORE_NAME, {keyPath: 'modelPath'});\n}\n\n/**\n * IOHandler subclass: Browser IndexedDB.\n *\n * See the doc string of `browserIndexedDB` for more details.\n */\nexport class BrowserIndexedDB implements IOHandler {\n  protected readonly indexedDB: IDBFactory;\n  protected readonly modelPath: string;\n\n  static readonly URL_SCHEME = 'indexeddb://';\n\n  constructor(modelPath: string) {\n    this.indexedDB = getIndexedDBFactory();\n\n    if (modelPath == null || !modelPath) {\n      throw new Error(\n          'For IndexedDB, modelPath must not be null, undefined or empty.');\n    }\n    this.modelPath = modelPath;\n  }\n\n  async save(modelArtifacts: ModelArtifacts): Promise<SaveResult> {\n    // TODO(cais): Support saving GraphDef models.\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\n          'BrowserLocalStorage.save() does not support saving model topology ' +\n          'in binary formats yet.');\n    }\n\n    return this.databaseAction(this.modelPath, modelArtifacts) as\n        Promise<SaveResult>;\n  }\n\n  async load(): Promise<ModelArtifacts> {\n    return this.databaseAction(this.modelPath) as Promise<ModelArtifacts>;\n  }\n\n  /**\n   * Perform database action to put model artifacts into or read model artifacts\n   * from IndexedDB object store.\n   *\n   * Whether the action is put or get depends on whether `modelArtifacts` is\n   * specified. If it is specified, the action will be put; otherwise the action\n   * will be get.\n   *\n   * @param modelPath A unique string path for the model.\n   * @param modelArtifacts If specified, it will be the model artifacts to be\n   *   stored in IndexedDB.\n   * @returns A `Promise` of `SaveResult`, if the action is put, or a `Promise`\n   *   of `ModelArtifacts`, if the action is get.\n   */\n  private databaseAction(modelPath: string, modelArtifacts?: ModelArtifacts):\n      Promise<ModelArtifacts|SaveResult> {\n    return new Promise<ModelArtifacts|SaveResult>((resolve, reject) => {\n      const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n      openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n\n      openRequest.onsuccess = () => {\n        const db = openRequest.result as IDBDatabase;\n\n        if (modelArtifacts == null) {\n          // Read model out from object store.\n          const modelTx = db.transaction(MODEL_STORE_NAME, 'readonly');\n          const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n          const getRequest = modelStore.get(this.modelPath);\n          getRequest.onsuccess = () => {\n            if (getRequest.result == null) {\n              db.close();\n              return reject(new Error(\n                  `Cannot find model with path '${this.modelPath}' ` +\n                  `in IndexedDB.`));\n            } else {\n              resolve(getRequest.result.modelArtifacts);\n            }\n          };\n          getRequest.onerror = error => {\n            db.close();\n            return reject(getRequest.error);\n          };\n          modelTx.oncomplete = () => db.close();\n        } else {\n          // Put model into object store.\n          const modelArtifactsInfo: ModelArtifactsInfo =\n              getModelArtifactsInfoForJSON(modelArtifacts);\n          // First, put ModelArtifactsInfo into info store.\n          const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n          let infoStore = infoTx.objectStore(INFO_STORE_NAME);\n          const putInfoRequest =\n              infoStore.put({modelPath: this.modelPath, modelArtifactsInfo});\n          let modelTx: IDBTransaction;\n          putInfoRequest.onsuccess = () => {\n            // Second, put model data into model store.\n            modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n            const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n            const putModelRequest = modelStore.put({\n              modelPath: this.modelPath,\n              modelArtifacts,\n              modelArtifactsInfo\n            });\n            putModelRequest.onsuccess = () => resolve({modelArtifactsInfo});\n            putModelRequest.onerror = error => {\n              // If the put-model request fails, roll back the info entry as\n              // well.\n              infoStore = infoTx.objectStore(INFO_STORE_NAME);\n              const deleteInfoRequest = infoStore.delete(this.modelPath);\n              deleteInfoRequest.onsuccess = () => {\n                db.close();\n                return reject(putModelRequest.error);\n              };\n              deleteInfoRequest.onerror = error => {\n                db.close();\n                return reject(putModelRequest.error);\n              };\n            };\n          };\n          putInfoRequest.onerror = error => {\n            db.close();\n            return reject(putInfoRequest.error);\n          };\n          infoTx.oncomplete = () => {\n            if (modelTx == null) {\n              db.close();\n            } else {\n              modelTx.oncomplete = () => db.close();\n            }\n          };\n        }\n      };\n      openRequest.onerror = error => reject(openRequest.error);\n    });\n  }\n}\n\nexport const indexedDBRouter: IORouter = (url: string|string[]) => {\n  if (!ENV.get('IS_BROWSER')) {\n    return null;\n  } else {\n    if (!Array.isArray(url) && url.startsWith(BrowserIndexedDB.URL_SCHEME)) {\n      return browserIndexedDB(url.slice(BrowserIndexedDB.URL_SCHEME.length));\n    } else {\n      return null;\n    }\n  }\n};\nIORouterRegistry.registerSaveRouter(indexedDBRouter);\nIORouterRegistry.registerLoadRouter(indexedDBRouter);\n\n/**\n * Creates a browser IndexedDB IOHandler for saving and loading models.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save('indexeddb://MyModel'));\n * console.log(saveResult);\n * ```\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `BrowserIndexedDB` (sublcass of `IOHandler`),\n *   which can be used with, e.g., `tf.Model.save`.\n */\nexport function browserIndexedDB(modelPath: string): IOHandler {\n  return new BrowserIndexedDB(modelPath);\n}\n\nfunction maybeStripScheme(key: string) {\n  return key.startsWith(BrowserIndexedDB.URL_SCHEME) ?\n      key.slice(BrowserIndexedDB.URL_SCHEME.length) :\n      key;\n}\n\nexport class BrowserIndexedDBManager implements ModelStoreManager {\n  private indexedDB: IDBFactory;\n\n  constructor() {\n    this.indexedDB = getIndexedDBFactory();\n  }\n\n  async listModels(): Promise<{[path: string]: ModelArtifactsInfo}> {\n    return new Promise<{[path: string]: ModelArtifactsInfo}>(\n        (resolve, reject) => {\n          const openRequest =\n              this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n          openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n\n          openRequest.onsuccess = () => {\n            const db = openRequest.result as IDBDatabase;\n            const tx = db.transaction(INFO_STORE_NAME, 'readonly');\n            const store = tx.objectStore(INFO_STORE_NAME);\n            // tslint:disable:max-line-length\n            // Need to cast `store` as `any` here because TypeScript's DOM\n            // library does not have the `getAll()` method even though the\n            // method is supported in the latest version of most mainstream\n            // browsers:\n            // https://developer.mozilla.org/en-US/docs/Web/API/IDBObjectStore/getAll\n            // tslint:enable:max-line-length\n            // tslint:disable-next-line:no-any\n            const getAllInfoRequest = (store as any).getAll() as IDBRequest;\n            getAllInfoRequest.onsuccess = () => {\n              const out: {[path: string]: ModelArtifactsInfo} = {};\n              for (const item of getAllInfoRequest.result) {\n                out[item.modelPath] = item.modelArtifactsInfo;\n              }\n              resolve(out);\n            };\n            getAllInfoRequest.onerror = error => {\n              db.close();\n              return reject(getAllInfoRequest.error);\n            };\n            tx.oncomplete = () => db.close();\n          };\n          openRequest.onerror = error => reject(openRequest.error);\n        });\n  }\n\n  async removeModel(path: string): Promise<ModelArtifactsInfo> {\n    path = maybeStripScheme(path);\n    return new Promise<ModelArtifactsInfo>((resolve, reject) => {\n      const openRequest = this.indexedDB.open(DATABASE_NAME, DATABASE_VERSION);\n      openRequest.onupgradeneeded = () => setUpDatabase(openRequest);\n\n      openRequest.onsuccess = () => {\n        const db = openRequest.result as IDBDatabase;\n        const infoTx = db.transaction(INFO_STORE_NAME, 'readwrite');\n        const infoStore = infoTx.objectStore(INFO_STORE_NAME);\n\n        const getInfoRequest = infoStore.get(path);\n        let modelTx: IDBTransaction;\n        getInfoRequest.onsuccess = () => {\n          if (getInfoRequest.result == null) {\n            db.close();\n            return reject(new Error(\n                `Cannot find model with path '${path}' ` +\n                `in IndexedDB.`));\n          } else {\n            // First, delete the entry in the info store.\n            const deleteInfoRequest = infoStore.delete(path);\n            const deleteModelData = () => {\n              // Second, delete the entry in the model store.\n              modelTx = db.transaction(MODEL_STORE_NAME, 'readwrite');\n              const modelStore = modelTx.objectStore(MODEL_STORE_NAME);\n              const deleteModelRequest = modelStore.delete(path);\n              deleteModelRequest.onsuccess = () =>\n                  resolve(getInfoRequest.result.modelArtifactsInfo);\n              deleteModelRequest.onerror = error =>\n                  reject(getInfoRequest.error);\n            };\n            // Proceed with deleting model data regardless of whether deletion\n            // of info data succeeds or not.\n            deleteInfoRequest.onsuccess = deleteModelData;\n            deleteInfoRequest.onerror = error => {\n              deleteModelData();\n              db.close();\n              return reject(getInfoRequest.error);\n            };\n          }\n        };\n        getInfoRequest.onerror = error => {\n          db.close();\n          return reject(getInfoRequest.error);\n        };\n\n        infoTx.oncomplete = () => {\n          if (modelTx == null) {\n            db.close();\n          } else {\n            modelTx.oncomplete = () => db.close();\n          }\n        };\n      };\n      openRequest.onerror = error => reject(openRequest.error);\n    });\n  }\n}\n\nif (ENV.get('IS_BROWSER')) {\n  // Wrap the construction and registration, to guard against browsers that\n  // don't support Local Storage.\n  try {\n    ModelStoreManagerRegistry.registerManager(\n        BrowserIndexedDB.URL_SCHEME, new BrowserIndexedDBManager());\n  } catch (err) {\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {assert} from '../util';\nimport {arrayBufferToBase64String, base64StringToArrayBuffer, getModelArtifactsInfoForJSON} from './io_utils';\nimport {ModelStoreManagerRegistry} from './model_management';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, ModelArtifactsInfo, ModelStoreManager, SaveResult} from './types';\n\nconst PATH_SEPARATOR = '/';\nconst PATH_PREFIX = 'tensorflowjs_models';\nconst INFO_SUFFIX = 'info';\nconst MODEL_TOPOLOGY_SUFFIX = 'model_topology';\nconst WEIGHT_SPECS_SUFFIX = 'weight_specs';\nconst WEIGHT_DATA_SUFFIX = 'weight_data';\n\n/**\n * Purge all tensorflow.js-saved model artifacts from local storage.\n *\n * @returns Paths of the models purged.\n */\nexport function purgeLocalStorageArtifacts(): string[] {\n  if (!ENV.get('IS_BROWSER') || typeof window.localStorage === 'undefined') {\n    throw new Error(\n        'purgeLocalStorageModels() cannot proceed because local storage is ' +\n        'unavailable in the current environment.');\n  }\n  const LS = window.localStorage;\n  const purgedModelPaths: string[] = [];\n  for (let i = 0; i < LS.length; ++i) {\n    const key = LS.key(i);\n    const prefix = PATH_PREFIX + PATH_SEPARATOR;\n    if (key.startsWith(prefix) && key.length > prefix.length) {\n      LS.removeItem(key);\n      const modelName = getModelPathFromKey(key);\n      if (purgedModelPaths.indexOf(modelName) === -1) {\n        purgedModelPaths.push(modelName);\n      }\n    }\n  }\n  return purgedModelPaths;\n}\n\nfunction getModelKeys(path: string):\n    {info: string, topology: string, weightSpecs: string, weightData: string} {\n  return {\n    info: [PATH_PREFIX, path, INFO_SUFFIX].join(PATH_SEPARATOR),\n    topology: [PATH_PREFIX, path, MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),\n    weightSpecs: [PATH_PREFIX, path, WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),\n    weightData: [PATH_PREFIX, path, WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR)\n  };\n}\n\n/**\n * Get model path from a local-storage key.\n *\n * E.g., 'tensorflowjs_models/my/model/1/info' --> 'my/model/1'\n *\n * @param key\n */\nfunction getModelPathFromKey(key: string) {\n  const items = key.split(PATH_SEPARATOR);\n  if (items.length < 3) {\n    throw new Error(`Invalid key format: ${key}`);\n  }\n  return items.slice(1, items.length - 1).join(PATH_SEPARATOR);\n}\n\nfunction maybeStripScheme(key: string) {\n  return key.startsWith(BrowserLocalStorage.URL_SCHEME) ?\n      key.slice(BrowserLocalStorage.URL_SCHEME.length) :\n      key;\n}\n\n/**\n * IOHandler subclass: Browser Local Storage.\n *\n * See the doc string to `browserLocalStorage` for more details.\n */\nexport class BrowserLocalStorage implements IOHandler {\n  protected readonly LS: Storage;\n  protected readonly modelPath: string;\n  protected readonly keys: {[key: string]: string};\n\n  static readonly URL_SCHEME = 'localstorage://';\n\n  constructor(modelPath: string) {\n    if (!ENV.get('IS_BROWSER') || typeof window.localStorage === 'undefined') {\n      // TODO(cais): Add more info about what IOHandler subtypes are\n      // available.\n      //   Maybe point to a doc page on the web and/or automatically determine\n      //   the available IOHandlers and print them in the error message.\n      throw new Error(\n          'The current environment does not support local storage.');\n    }\n    this.LS = window.localStorage;\n\n    if (modelPath == null || !modelPath) {\n      throw new Error(\n          'For local storage, modelPath must not be null, undefined or empty.');\n    }\n    this.modelPath = modelPath;\n    this.keys = getModelKeys(this.modelPath);\n  }\n\n  /**\n   * Save model artifacts to browser local storage.\n   *\n   * See the documentation to `browserLocalStorage` for details on the saved\n   * artifacts.\n   *\n   * @param modelArtifacts The model artifacts to be stored.\n   * @returns An instance of SaveResult.\n   */\n  async save(modelArtifacts: ModelArtifacts): Promise<SaveResult> {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\n          'BrowserLocalStorage.save() does not support saving model topology ' +\n          'in binary formats yet.');\n    } else {\n      const topology = JSON.stringify(modelArtifacts.modelTopology);\n      const weightSpecs = JSON.stringify(modelArtifacts.weightSpecs);\n\n      const modelArtifactsInfo: ModelArtifactsInfo =\n          getModelArtifactsInfoForJSON(modelArtifacts);\n\n      try {\n        this.LS.setItem(this.keys.info, JSON.stringify(modelArtifactsInfo));\n        this.LS.setItem(this.keys.topology, topology);\n        this.LS.setItem(this.keys.weightSpecs, weightSpecs);\n        this.LS.setItem(\n            this.keys.weightData,\n            arrayBufferToBase64String(modelArtifacts.weightData));\n\n        return {modelArtifactsInfo};\n      } catch (err) {\n        // If saving failed, clean up all items saved so far.\n        for (const key in this.keys) {\n          this.LS.removeItem(this.keys[key]);\n        }\n\n        throw new Error(\n            `Failed to save model '${this.modelPath}' to local storage: ` +\n            `size quota being exceeded is a possible cause of this failure: ` +\n            `modelTopologyBytes=${modelArtifactsInfo.modelTopologyBytes}, ` +\n            `weightSpecsBytes=${modelArtifactsInfo.weightSpecsBytes}, ` +\n            `weightDataBytes=${modelArtifactsInfo.weightDataBytes}.`);\n      }\n    }\n  }\n\n  /**\n   * Load a model from local storage.\n   *\n   * See the documentation to `browserLocalStorage` for details on the saved\n   * artifacts.\n   *\n   * @returns The loaded model (if loading succeeds).\n   */\n  async load(): Promise<ModelArtifacts> {\n    const info =\n        JSON.parse(this.LS.getItem(this.keys.info)) as ModelArtifactsInfo;\n    if (info == null) {\n      throw new Error(\n          `In local storage, there is no model with name '${this.modelPath}'`);\n    }\n\n    if (info.modelTopologyType !== 'JSON') {\n      throw new Error(\n          'BrowserLocalStorage does not support loading non-JSON model ' +\n          'topology yet.');\n    }\n\n    const out: ModelArtifacts = {};\n\n    // Load topology.\n    const topology = JSON.parse(this.LS.getItem(this.keys.topology));\n    if (topology == null) {\n      throw new Error(\n          `In local storage, the topology of model '${this.modelPath}' ` +\n          `is missing.`);\n    }\n    out.modelTopology = topology;\n\n    // Load weight specs.\n    const weightSpecs = JSON.parse(this.LS.getItem(this.keys.weightSpecs));\n    if (weightSpecs == null) {\n      throw new Error(\n          `In local storage, the weight specs of model '${this.modelPath}' ` +\n          `are missing.`);\n    }\n    out.weightSpecs = weightSpecs;\n\n    // Load weight data.\n    const weightDataBase64 = this.LS.getItem(this.keys.weightData);\n    if (weightDataBase64 == null) {\n      throw new Error(\n          `In local storage, the binary weight values of model ` +\n          `'${this.modelPath}' are missing.`);\n    }\n    out.weightData = base64StringToArrayBuffer(weightDataBase64);\n\n    return out;\n  }\n}\n\nexport const localStorageRouter: IORouter = (url: string|string[]) => {\n  if (!ENV.get('IS_BROWSER')) {\n    return null;\n  } else {\n    if (!Array.isArray(url) &&\n        url.startsWith(BrowserLocalStorage.URL_SCHEME)) {\n      return browserLocalStorage(\n          url.slice(BrowserLocalStorage.URL_SCHEME.length));\n    } else {\n      return null;\n    }\n  }\n};\nIORouterRegistry.registerSaveRouter(localStorageRouter);\nIORouterRegistry.registerLoadRouter(localStorageRouter);\n\n/**\n * Factory function for local storage IOHandler.\n *\n * This `IOHandler` supports both `save` and `load`.\n *\n * For each model's saved artifacts, four items are saved to local storage.\n *   - `${PATH_SEPARATOR}/${modelPath}/info`: Contains meta-info about the\n *     model, such as date saved, type of the topology, size in bytes, etc.\n *   - `${PATH_SEPARATOR}/${modelPath}/topology`: Model topology. For Keras-\n *     style models, this is a stringized JSON.\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_specs`: Weight specs of the\n *     model, can be used to decode the saved binary weight values (see\n *     item below).\n *   - `${PATH_SEPARATOR}/${modelPath}/weight_data`: Concatenated binary\n *     weight values, stored as a base64-encoded string.\n *\n * Saving may throw an `Error` if the total size of the artifacts exceed the\n * browser-specific quota.\n *\n * @param modelPath A unique identifier for the model to be saved. Must be a\n *   non-empty string.\n * @returns An instance of `IOHandler`, which can be used with, e.g.,\n *   `tf.Model.save`.\n */\nexport function browserLocalStorage(modelPath: string): IOHandler {\n  return new BrowserLocalStorage(modelPath);\n}\n\nexport class BrowserLocalStorageManager implements ModelStoreManager {\n  private readonly LS: Storage;\n\n  constructor() {\n    assert(ENV.get('IS_BROWSER'), 'Current environment is not a web browser');\n    assert(\n        typeof window.localStorage !== 'undefined',\n        'Current browser does not appear to support localStorage');\n    this.LS = window.localStorage;\n  }\n\n  async listModels(): Promise<{[path: string]: ModelArtifactsInfo}> {\n    const out: {[path: string]: ModelArtifactsInfo} = {};\n    const prefix = PATH_PREFIX + PATH_SEPARATOR;\n    const suffix = PATH_SEPARATOR + INFO_SUFFIX;\n    for (let i = 0; i < this.LS.length; ++i) {\n      const key = this.LS.key(i);\n      if (key.startsWith(prefix) && key.endsWith(suffix)) {\n        const modelPath = getModelPathFromKey(key);\n        out[modelPath] = JSON.parse(this.LS.getItem(key)) as ModelArtifactsInfo;\n      }\n    }\n    return out;\n  }\n\n  async removeModel(path: string): Promise<ModelArtifactsInfo> {\n    path = maybeStripScheme(path);\n    const keys = getModelKeys(path);\n    if (this.LS.getItem(keys.info) == null) {\n      throw new Error(`Cannot find model at path '${path}'`);\n    }\n    const info = JSON.parse(this.LS.getItem(keys.info)) as ModelArtifactsInfo;\n\n    this.LS.removeItem(keys.info);\n    this.LS.removeItem(keys.topology);\n    this.LS.removeItem(keys.weightSpecs);\n    this.LS.removeItem(keys.weightData);\n    return info;\n  }\n}\n\nif (ENV.get('IS_BROWSER')) {\n  // Wrap the construction and registration, to guard against browsers that\n  // don't support Local Storage.\n  try {\n    ModelStoreManagerRegistry.registerManager(\n        BrowserLocalStorage.URL_SCHEME, new BrowserLocalStorageManager());\n  } catch (err) {\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * IOHandlers related to files, such as browser-triggered file downloads,\n * user-selected files in browser.\n */\n\nimport {ENV} from '../environment';\nimport {basename, concatenateArrayBuffers, getModelArtifactsInfoForJSON} from './io_utils';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, SaveResult, WeightsManifestConfig, WeightsManifestEntry} from './types';\n\nconst DEFAULT_FILE_NAME_PREFIX = 'model';\nconst DEFAULT_JSON_EXTENSION_NAME = '.json';\nconst DEFAULT_WEIGHT_DATA_EXTENSION_NAME = '.weights.bin';\n\nexport class BrowserDownloads implements IOHandler {\n  private readonly modelTopologyFileName: string;\n  private readonly weightDataFileName: string;\n  private readonly jsonAnchor: HTMLAnchorElement;\n  private readonly weightDataAnchor: HTMLAnchorElement;\n\n  static readonly URL_SCHEME = 'downloads://';\n\n  constructor(fileNamePrefix?: string) {\n    if (!ENV.get('IS_BROWSER')) {\n      // TODO(cais): Provide info on what IOHandlers are available under the\n      //   current environment.\n      throw new Error(\n          'browserDownloads() cannot proceed because the current environment ' +\n          'is not a browser.');\n    }\n\n    if (fileNamePrefix.startsWith(BrowserDownloads.URL_SCHEME)) {\n      fileNamePrefix = fileNamePrefix.slice(BrowserDownloads.URL_SCHEME.length);\n    }\n    if (fileNamePrefix == null || fileNamePrefix.length === 0) {\n      fileNamePrefix = DEFAULT_FILE_NAME_PREFIX;\n    }\n\n    this.modelTopologyFileName = fileNamePrefix + DEFAULT_JSON_EXTENSION_NAME;\n    this.weightDataFileName =\n        fileNamePrefix + DEFAULT_WEIGHT_DATA_EXTENSION_NAME;\n  }\n\n  async save(modelArtifacts: ModelArtifacts): Promise<SaveResult> {\n    const weightsURL = window.URL.createObjectURL(new Blob(\n        [modelArtifacts.weightData], {type: 'application/octet-stream'}));\n\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\n          'BrowserDownloads.save() does not support saving model topology ' +\n          'in binary formats yet.');\n    } else {\n      const weightsManifest: WeightsManifestConfig = [{\n        paths: ['./' + this.weightDataFileName],\n        weights: modelArtifacts.weightSpecs\n      }];\n      const modelTopologyAndWeightManifest = {\n        modelTopology: modelArtifacts.modelTopology,\n        weightsManifest\n      };\n      const modelTopologyAndWeightManifestURL =\n          window.URL.createObjectURL(new Blob(\n              [JSON.stringify(modelTopologyAndWeightManifest)],\n              {type: 'application/json'}));\n\n      // If anchor elements are not provided, create them without attaching them\n      // to parents, so that the downloaded file names can be controlled.\n      const jsonAnchor = this.jsonAnchor == null ? document.createElement('a') :\n                                                   this.jsonAnchor;\n      jsonAnchor.download = this.modelTopologyFileName;\n      jsonAnchor.href = modelTopologyAndWeightManifestURL;\n      // Trigger downloads by calling the `click` methods on the download\n      // anchors.\n      jsonAnchor.click();\n\n      if (modelArtifacts.weightData != null) {\n        const weightDataAnchor = this.weightDataAnchor == null ?\n            document.createElement('a') :\n            this.weightDataAnchor;\n        weightDataAnchor.download = this.weightDataFileName;\n        weightDataAnchor.href = weightsURL;\n        weightDataAnchor.click();\n      }\n\n      return {modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts)};\n    }\n  }\n}\n\nclass BrowserFiles implements IOHandler {\n  private readonly files: File[];\n\n  constructor(files: File[]) {\n    if (files == null || files.length < 1) {\n      throw new Error(\n          `When calling browserFiles, at least 1 file is required, ` +\n          `but received ${files}`);\n    }\n    this.files = files;\n  }\n\n  async load(): Promise<ModelArtifacts> {\n    const jsonFile = this.files[0];\n    const weightFiles = this.files.slice(1);\n\n    return new Promise<ModelArtifacts>((resolve, reject) => {\n      const jsonReader = new FileReader();\n      jsonReader.onload = (event: Event) => {\n        // tslint:disable-next-line:no-any\n        const modelJSON = JSON.parse((event.target as any).result);\n        const modelTopology = modelJSON.modelTopology as {};\n        if (modelTopology == null) {\n          reject(new Error(\n              `modelTopology field is missing from file ${jsonFile.name}`));\n          return;\n        }\n\n        if (weightFiles.length === 0) {\n          resolve({modelTopology});\n        }\n\n        const weightsManifest =\n            modelJSON.weightsManifest as WeightsManifestConfig;\n        if (weightsManifest == null) {\n          reject(new Error(\n              `weightManifest field is missing from file ${jsonFile.name}`));\n          return;\n        }\n\n        let pathToFile: {[path: string]: File};\n        try {\n          pathToFile =\n              this.checkManifestAndWeightFiles(weightsManifest, weightFiles);\n        } catch (err) {\n          reject(err);\n          return;\n        }\n\n        const weightSpecs: WeightsManifestEntry[] = [];\n        const paths: string[] = [];\n        const perFileBuffers: ArrayBuffer[] = [];\n        weightsManifest.forEach(weightsGroup => {\n          weightsGroup.paths.forEach(path => {\n            paths.push(path);\n            perFileBuffers.push(null);\n          });\n          weightSpecs.push(...weightsGroup.weights);\n        });\n\n        weightsManifest.forEach(weightsGroup => {\n          weightsGroup.paths.forEach(path => {\n            const weightFileReader = new FileReader();\n            weightFileReader.onload = (event: Event) => {\n              // tslint:disable-next-line:no-any\n              const weightData = (event.target as any).result as ArrayBuffer;\n              const index = paths.indexOf(path);\n              perFileBuffers[index] = weightData;\n              if (perFileBuffers.indexOf(null) === -1) {\n                resolve({\n                  modelTopology,\n                  weightSpecs,\n                  weightData: concatenateArrayBuffers(perFileBuffers),\n                });\n              }\n            };\n            weightFileReader.onerror = error =>\n                reject(`Failed to weights data from file of path '${path}'.`);\n            weightFileReader.readAsArrayBuffer(pathToFile[path]);\n          });\n        });\n      };\n      jsonReader.onerror = error => reject(\n          `Failed to read model topology and weights manifest JSON ` +\n          `from file '${jsonFile.name}'. BrowserFiles supports loading ` +\n          `Keras-style tf.Model artifacts only.`);\n      jsonReader.readAsText(jsonFile);\n    });\n  }\n\n  /**\n   * Check the compatibility between weights manifest and weight files.\n   */\n  private checkManifestAndWeightFiles(\n      manifest: WeightsManifestConfig, files: File[]): {[path: string]: File} {\n    const basenames: string[] = [];\n    const fileNames = files.map(file => basename(file.name));\n    const pathToFile: {[path: string]: File} = {};\n    for (const group of manifest) {\n      group.paths.forEach(path => {\n        const pathBasename = basename(path);\n        if (basenames.indexOf(pathBasename) !== -1) {\n          throw new Error(\n              `Duplicate file basename found in weights manifest: ` +\n              `'${pathBasename}'`);\n        }\n        basenames.push(pathBasename);\n        if (fileNames.indexOf(pathBasename) === -1) {\n          throw new Error(\n              `Weight file with basename '${pathBasename}' is not provided.`);\n        } else {\n          pathToFile[path] = files[fileNames.indexOf(pathBasename)];\n        }\n      });\n    }\n\n    if (basenames.length !== files.length) {\n      throw new Error(\n          `Mismatch in the number of files in weights manifest ` +\n          `(${basenames.length}) and the number of weight files provided ` +\n          `(${files.length}).`);\n    }\n    return pathToFile;\n  }\n}\n\nexport const browserDownloadsRouter: IORouter = (url: string|string[]) => {\n  if (!ENV.get('IS_BROWSER')) {\n    return null;\n  } else {\n    if (!Array.isArray(url) && url.startsWith(BrowserDownloads.URL_SCHEME)) {\n      return browserDownloads(url.slice(BrowserDownloads.URL_SCHEME.length));\n    } else {\n      return null;\n    }\n  }\n};\nIORouterRegistry.registerSaveRouter(browserDownloadsRouter);\n\n/**\n * Creates an IOHandler that triggers file downloads from the browser.\n *\n * The returned `IOHandler` instance can be used as model exporting methods such\n * as `tf.Model.save` and supports only saving.\n *\n * ```js\n * const model = tf.sequential();\n * model.add(tf.layers.dense(\n *     {units: 1, inputShape: [10], activation: 'sigmoid'}));\n * const saveResult = await model.save('downloads://mymodel'));\n * // This will trigger downloading of two files:\n * //   'mymodel.json' and 'mymodel.weights.bin'.\n * console.log(saveResult);\n * ```\n *\n * @param fileNamePrefix Prefix name of the files to be downloaded. For use with\n *   `tf.Model`, `fileNamePrefix` should follow either of the following two\n *   formats:\n *   1. `null` or `undefined`, in which case the default file\n *      names will be used:\n *      - 'model.json' for the JSON file containing the model topology and\n *        weights manifest.\n *      - 'model.weights.bin' for the binary file containing the binary weight\n *        values.\n *   2. A single string or an Array of a single string, as the file name prefix.\n *      For example, if `'foo'` is provided, the downloaded JSON\n *      file and binary weights file will be named 'foo.json' and\n *      'foo.weights.bin', respectively.\n * @param config Additional configuration for triggering downloads.\n * @returns An instance of `BrowserDownloads` `IOHandler`.\n */\n/** @doc {heading: 'Models', subheading: 'Loading', namespace: 'io'} */\nexport function browserDownloads(fileNamePrefix = 'model'): IOHandler {\n  return new BrowserDownloads(fileNamePrefix);\n}\n\n/**\n * Creates an IOHandler that loads model artifacts from user-selected files.\n *\n * This method can be used for loading from files such as user-selected files\n * in the browser.\n * When used in conjunction with `tf.loadModel`, an instance of `tf.Model`\n * (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * // Note: This code snippet won't run properly without the actual file input\n * //   elements in the HTML DOM.\n *\n * // Suppose there are two HTML file input (`<input type=\"file\" ...>`)\n * // elements.\n * const uploadJSONInput = document.getElementById('upload-json');\n * const uploadWeightsInput = document.getElementById('upload-weights');\n * const model = await tfl.loadModel(tf.io.browserFiles(\n *     [uploadJSONInput.files[0], uploadWeightsInput.files[0]]));\n * ```\n *\n * @param files `File`s to load from. Currently, this function supports only\n *   loading from files that contain Keras-style models (i.e., `tf.Model`s), for\n *   which an `Array` of `File`s is expected (in that order):\n *   - A JSON file containing the model topology and weight manifest.\n *   - Optionally, One or more binary files containing the binary weights.\n *     These files must have names that match the paths in the `weightsManifest`\n *     contained by the aforementioned JSON file, or errors will be thrown\n *     during loading. These weights files have the same format as the ones\n *     generated by `tensorflowjs_converter` that comes with the `tensorflowjs`\n *     Python PIP package. If no weights files are provided, only the model\n *     topology will be loaded from the JSON file above.\n * @returns An instance of `Files` `IOHandler`.\n */\n/** @doc {heading: 'Models', subheading: 'Loading', namespace: 'io'} */\nexport function browserFiles(files: File[]): IOHandler {\n  return new BrowserFiles(files);\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {NamedTensorMap} from '../tensor_types';\nimport * as util from '../util';\n\nimport {decodeWeights} from './io_utils';\nimport {DTYPE_VALUE_SIZE_MAP, WeightsManifestConfig, WeightsManifestEntry} from './types';\n\n/**\n * Reads binary weights data from a number of URLs.\n *\n * @param fetchURLs URLs to send the HTTP requests at, using `fetch` calls.\n * @param requestOptions RequestInit (options) for the HTTP requests.\n * @param fetchFunc Optional overriding value for the `window.fetch` function.\n * @returns A `Promise` of an Array of `ArrayBuffer`. The Array has the same\n *   length as `fetchURLs`.\n */\nexport async function loadWeightsAsArrayBuffer(\n    fetchURLs: string[], requestOptions?: RequestInit, fetchFunc?: Function):\n  Promise<ArrayBuffer[]> {\n  if (fetchFunc == null) {\n    fetchFunc = fetch;\n  }\n\n  // Create the requests for all of the weights in parallel.\n  const requests = fetchURLs.map(\n      fetchURL => fetchFunc(fetchURL, requestOptions));\n  const responses = await Promise.all(requests);\n  const buffers =\n      await Promise.all(responses.map(response => response.arrayBuffer()));\n  return buffers;\n}\n\n/**\n * Reads a weights manifest JSON configuration, fetches the weights and\n * returns them as `Tensor`s.\n *\n * @param manifest The weights manifest JSON.\n * @param filePathPrefix The path prefix for filenames given in the manifest.\n *     Defaults to the empty string.\n * @param weightNames The names of the weights to be fetched.\n */\nexport async function loadWeights(\n    manifest: WeightsManifestConfig,\n    filePathPrefix = '',\n    weightNames?: string[],\n    requestOptions?: RequestInit): Promise<NamedTensorMap> {\n  // TODO(nsthorat): Groups are currently fetched atomically. If you need a\n  // single weight from a group, the whole group will be fetched. At a future\n  // date, we should support fetching only the individual shards within a\n  // group that are needed to reconstruct the requested weight.\n  // TODO(cais): Use `decodeWeights` for implementation.\n\n  const fetchWeights = (fetchUrls: string[]) =>\n    loadWeightsAsArrayBuffer(fetchUrls, requestOptions);\n  const loadWeights = weightsLoaderFactory(fetchWeights);\n\n  return loadWeights(manifest, filePathPrefix, weightNames);\n}\n\n/**\n * Creates a function, which reads a weights manifest JSON configuration,\n * fetches the weight files using the specified function and returns them as\n * `Tensor`s.\n *\n * ```js\n * // example for creating a nodejs weight loader, which reads the weight files\n * // from disk using fs.readFileSync\n *\n * import * as fs from 'fs'\n *\n * const fetchWeightsFromDisk = (filePaths: string[]) =>\n *   filePaths.map(filePath => fs.readFileSync(filePath).buffer)\n *\n * const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)\n *\n * const manifest = JSON.parse(\n *   fs.readFileSync('./my_model-weights_manifest').toString()\n * )\n * const weightMap = await loadWeights(manifest, './')\n * ```\n * @param fetchWeightsFunction The function used for fetching the weight files.\n * @returns Weight loading function.\n */\nexport function weightsLoaderFactory(\n  fetchWeightsFunction: (fetchUrls: string[]) => Promise<ArrayBuffer[]>\n): (\n  manifest: WeightsManifestConfig,\n  filePathPrefix?: string,\n  weightNames?: string[]\n) => Promise<NamedTensorMap> {\n\n  return async (\n    manifest: WeightsManifestConfig,\n    filePathPrefix = '',\n    weightNames?: string[]\n  ): Promise<NamedTensorMap> => {\n\n    // Collect all the groups, weights, and their relative offsets to be\n    // fetched.\n    const groupIndicesToFetchMap = manifest.map(() => false);\n    const groupWeightsToFetch: {\n      [group: number]: Array<{\n        manifestEntry: WeightsManifestEntry; groupOffset: number;\n        sizeBytes: number;\n      }>\n    } = {};\n    const weightsFound = weightNames != null\n      ? weightNames.map(() => false)\n      : [];\n    const allManifestWeightNames: string[] = [];\n    manifest.forEach((manifestGroupConfig, groupIndex) => {\n      let groupOffset = 0;\n      manifestGroupConfig.weights.forEach(weightsEntry => {\n        const rawDtype = ('quantization' in weightsEntry) ?\n            weightsEntry.quantization.dtype :\n            weightsEntry.dtype;\n\n        const weightsBytes = DTYPE_VALUE_SIZE_MAP[rawDtype] *\n            util.sizeFromShape(weightsEntry.shape);\n\n        const enqueueWeightsForFetchingFn = () => {\n          groupIndicesToFetchMap[groupIndex] = true;\n          if (groupWeightsToFetch[groupIndex] == null) {\n            groupWeightsToFetch[groupIndex] = [];\n          }\n\n          groupWeightsToFetch[groupIndex].push({\n            manifestEntry: weightsEntry,\n            groupOffset,\n            sizeBytes: weightsBytes\n          });\n        };\n\n        if (weightNames != null) {\n          weightNames.forEach((weightName, weightIndex) => {\n            if (weightName === weightsEntry.name) {\n              enqueueWeightsForFetchingFn();\n              weightsFound[weightIndex] = true;\n            }\n          });\n        } else {\n          enqueueWeightsForFetchingFn();\n        }\n\n        allManifestWeightNames.push(weightsEntry.name);\n        groupOffset += weightsBytes;\n      });\n    });\n\n    if (!weightsFound.every(found => found)) {\n      const weightsNotFound = weightNames.filter((_, i) => !weightsFound[i]);\n      throw new Error(\n          `Could not find weights in manifest with names: ` +\n          `${weightsNotFound.join(', ')}. \\n` +\n          `Manifest JSON has weights with names: ` +\n          `${allManifestWeightNames.join(', ')}.`);\n    }\n\n    // Convert the one-hot boolean groupId => shouldFetch map to a list of group\n    // IDs.\n    const groupIndicesToFetch =\n        groupIndicesToFetchMap.reduce((accumulator, shouldFetch, i) => {\n          if (shouldFetch) {\n            accumulator.push(i);\n          }\n          return accumulator;\n        }, []);\n\n    const fetchUrls: string[] = [];\n    groupIndicesToFetch.forEach(i => {\n      manifest[i].paths.forEach(filepath => {\n        const fetchUrl = filePathPrefix +\n            (!filePathPrefix.endsWith('/') ? '/' : '') + filepath;\n        fetchUrls.push(fetchUrl);\n      });\n    });\n    const buffers = await fetchWeightsFunction(fetchUrls);\n\n    const weightsTensorMap: NamedTensorMap = {};\n    let bufferIndexOffset = 0;\n    groupIndicesToFetch.forEach(i => {\n      const numBuffers = manifest[i].paths.length;\n\n      let groupBytes = 0;\n      for (let i = 0; i < numBuffers; i++) {\n        groupBytes += buffers[bufferIndexOffset + i].byteLength;\n      }\n\n      // Create a buffer for the whole group.\n      const groupBuffer = new ArrayBuffer(groupBytes);\n      const groupByteBuffer = new Uint8Array(groupBuffer);\n      let groupBufferOffset = 0;\n      for (let i = 0; i < numBuffers; i++) {\n        const buffer = new Uint8Array(buffers[bufferIndexOffset + i]);\n        groupByteBuffer.set(buffer, groupBufferOffset);\n        groupBufferOffset += buffer.byteLength;\n      }\n\n      const weightsEntries = groupWeightsToFetch[i];\n      weightsEntries.forEach(weightsEntry => {\n        const byteBuffer = groupBuffer.slice(\n            weightsEntry.groupOffset,\n            weightsEntry.groupOffset + weightsEntry.sizeBytes);\n        const nameToTensorMap =\n            decodeWeights(byteBuffer, [weightsEntry.manifestEntry]);\n        for (const name in nameToTensorMap) {\n          weightsTensorMap[name] = nameToTensorMap[name];\n        }\n      });\n\n      bufferIndexOffset += numBuffers;\n    });\n\n    return weightsTensorMap;\n  };\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * IOHandler implementations based on HTTP requests in the web browser.\n *\n * Uses [`fetch`](https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API).\n */\n\nimport {assert} from '../util';\nimport {concatenateArrayBuffers, getModelArtifactsInfoForJSON} from './io_utils';\nimport {IORouter, IORouterRegistry} from './router_registry';\nimport {IOHandler, ModelArtifacts, SaveResult, WeightsManifestConfig, WeightsManifestEntry} from './types';\nimport {loadWeightsAsArrayBuffer} from './weights_loader';\n\nexport class BrowserHTTPRequest implements IOHandler {\n  protected readonly path: string|string[];\n  protected readonly requestInit: RequestInit;\n\n  private readonly fetchFunc: Function;\n\n  readonly DEFAULT_METHOD = 'POST';\n\n  static readonly URL_SCHEME_REGEX = /^https?:\\/\\//;\n\n  constructor(\n      path: string|string[], requestInit?: RequestInit,\n      private readonly weightPathPrefix?: string, fetchFunc?: Function) {\n    if (fetchFunc == null) {\n      if (typeof fetch === 'undefined') {\n        throw new Error(\n            'browserHTTPRequest is not supported outside the web browser ' +\n            'without a fetch polyfill.');\n      }\n      // Make sure fetch is always bound to window (the\n      // original object) when available.\n      this.fetchFunc =\n          fetch.bind(typeof window === 'undefined' ? null : window);\n    } else {\n      assert(\n          typeof fetchFunc === 'function',\n          'Must pass a function that matches the signature of ' +\n              '`fetch` (see ' +\n              'https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)');\n      this.fetchFunc = fetchFunc;\n    }\n\n    assert(\n        path != null && path.length > 0,\n        'URL path for browserHTTPRequest must not be null, undefined or ' +\n            'empty.');\n\n    if (Array.isArray(path)) {\n      assert(\n          path.length === 2,\n          'URL paths for browserHTTPRequest must have a length of 2, ' +\n              `(actual length is ${path.length}).`);\n    }\n    this.path = path;\n\n    if (requestInit != null && requestInit.body != null) {\n      throw new Error(\n          'requestInit is expected to have no pre-existing body, but has one.');\n    }\n    this.requestInit = requestInit || {};\n  }\n\n  async save(modelArtifacts: ModelArtifacts): Promise<SaveResult> {\n    if (modelArtifacts.modelTopology instanceof ArrayBuffer) {\n      throw new Error(\n          'BrowserHTTPRequest.save() does not support saving model topology ' +\n          'in binary formats yet.');\n    }\n\n    const init = Object.assign({method: this.DEFAULT_METHOD}, this.requestInit);\n    init.body = new FormData();\n\n    const weightsManifest: WeightsManifestConfig = [{\n      paths: ['./model.weights.bin'],\n      weights: modelArtifacts.weightSpecs,\n    }];\n    const modelTopologyAndWeightManifest = {\n      modelTopology: modelArtifacts.modelTopology,\n      weightsManifest\n    };\n\n    init.body.append(\n        'model.json',\n        new Blob(\n            [JSON.stringify(modelTopologyAndWeightManifest)],\n            {type: 'application/json'}),\n        'model.json');\n\n    if (modelArtifacts.weightData != null) {\n      init.body.append(\n          'model.weights.bin',\n          new Blob(\n              [modelArtifacts.weightData], {type: 'application/octet-stream'}),\n          'model.weights.bin');\n    }\n\n    const response = await this.getFetchFunc()(this.path as string, init);\n\n    if (response.ok) {\n      return {\n        modelArtifactsInfo: getModelArtifactsInfoForJSON(modelArtifacts),\n        responses: [response],\n      };\n    } else {\n      throw new Error(\n          `BrowserHTTPRequest.save() failed due to HTTP response status ` +\n          `${response.status}.`);\n    }\n  }\n\n  /**\n   * Load model artifacts via HTTP request(s).\n   *\n   * See the documentation to `browserHTTPRequest` for details on the saved\n   * artifacts.\n   *\n   * @returns The loaded model artifacts (if loading succeeds).\n   */\n  async load(): Promise<ModelArtifacts> {\n    return Array.isArray(this.path) ? this.loadBinaryModel() :\n                                      this.loadJSONModel();\n  }\n\n  /**\n   * Loads the model topology file and build the in memory graph of the model.\n   */\n  private async loadBinaryTopology(): Promise<ArrayBuffer> {\n    try {\n      const response =\n          await this.getFetchFunc()(this.path[0], this.requestInit);\n      if (!response.ok) {\n        throw new Error(\n            `BrowserHTTPRequest.load() failed due to HTTP response: ${\n                response.statusText}`);\n      }\n      return await response.arrayBuffer();\n    } catch (error) {\n      throw new Error(`${this.path[0]} not found. ${error}`);\n    }\n  }\n\n  protected async loadBinaryModel(): Promise<ModelArtifacts> {\n    const graphPromise = this.loadBinaryTopology();\n    const manifestPromise =\n        await this.getFetchFunc()(this.path[1], this.requestInit);\n    if (!manifestPromise.ok) {\n      throw new Error(`BrowserHTTPRequest.load() failed due to HTTP response: ${\n          manifestPromise.statusText}`);\n    }\n\n    const results = await Promise.all([graphPromise, manifestPromise]);\n    const [modelTopology, weightsManifestResponse] = results;\n\n    const weightsManifest =\n        await weightsManifestResponse.json() as WeightsManifestConfig;\n\n    let weightSpecs: WeightsManifestEntry[];\n    let weightData: ArrayBuffer;\n    if (weightsManifest != null) {\n      const results = await this.loadWeights(weightsManifest);\n      [weightSpecs, weightData] = results;\n    }\n\n    return {modelTopology, weightSpecs, weightData};\n  }\n\n  protected async loadJSONModel(): Promise<ModelArtifacts> {\n    const modelConfigRequest =\n        await this.getFetchFunc()(this.path as string, this.requestInit);\n    if (!modelConfigRequest.ok) {\n      throw new Error(`BrowserHTTPRequest.load() failed due to HTTP response: ${\n          modelConfigRequest.statusText}`);\n    }\n    const modelConfig = await modelConfigRequest.json();\n    const modelTopology = modelConfig['modelTopology'];\n    const weightsManifest = modelConfig['weightsManifest'];\n\n    // We do not allow both modelTopology and weightsManifest to be missing.\n    if (modelTopology == null && weightsManifest == null) {\n      throw new Error(\n          `The JSON from HTTP path ${this.path} contains neither model ` +\n          `topology or manifest for weights.`);\n    }\n\n    let weightSpecs: WeightsManifestEntry[];\n    let weightData: ArrayBuffer;\n    if (weightsManifest != null) {\n      const weightsManifest =\n          modelConfig['weightsManifest'] as WeightsManifestConfig;\n      const results = await this.loadWeights(weightsManifest);\n      [weightSpecs, weightData] = results;\n    }\n\n    return {modelTopology, weightSpecs, weightData};\n  }\n\n  private async loadWeights(weightsManifest: WeightsManifestConfig):\n      Promise<[WeightsManifestEntry[], ArrayBuffer]> {\n    const weightPath = Array.isArray(this.path) ? this.path[1] : this.path;\n    const [prefix, suffix] = parseUrl(weightPath);\n    const pathPrefix = this.weightPathPrefix || prefix;\n\n    const weightSpecs = [];\n    for (const entry of weightsManifest) {\n      weightSpecs.push(...entry.weights);\n    }\n\n    const fetchURLs: string[] = [];\n    weightsManifest.forEach(weightsGroup => {\n      weightsGroup.paths.forEach(path => {\n        fetchURLs.push(pathPrefix + path + suffix);\n      });\n    });\n\n    return [\n      weightSpecs,\n      concatenateArrayBuffers(await loadWeightsAsArrayBuffer(\n          fetchURLs, this.requestInit, this.getFetchFunc()))\n    ];\n  }\n\n  /**\n   * Helper method to get the `fetch`-like function set for this instance.\n   *\n   * This is mainly for avoiding confusion with regard to what context\n   * the `fetch`-like function is bound to. In the default (browser) case,\n   * the function will be bound to `window`, instead of `this`.\n   */\n  private getFetchFunc() {\n    return this.fetchFunc;\n  }\n}\n\n/**\n * Extract the prefix and suffix of the url, where the prefix is the path before\n * the last file, and suffix is the search params after the last file.\n * ```\n * const url = 'http://tfhub.dev/model/1/tensorflowjs_model.pb?tfjs-format=file'\n * [prefix, suffix] = parseUrl(url)\n * // prefix = 'http://tfhub.dev/model/1/'\n * // suffix = '?tfjs-format=file'\n * ```\n * @param url the model url to be parsed.\n */\nexport function parseUrl(url: string): [string, string] {\n  const lastSlash = url.lastIndexOf('/');\n  const lastSearchParam = url.lastIndexOf('?');\n  const prefix = url.substring(0, lastSlash);\n  const suffix =\n      lastSearchParam > lastSlash ? url.substring(lastSearchParam) : '';\n  return [prefix + '/', suffix];\n}\n\nexport function isHTTPScheme(url: string): boolean {\n  return url.match(BrowserHTTPRequest.URL_SCHEME_REGEX) != null;\n}\n\nexport const httpRequestRouter: IORouter = (url: string|string[]) => {\n  if (typeof fetch === 'undefined') {\n    // browserHTTPRequest uses `fetch`, if one wants to use it in node.js\n    // they have to setup a global fetch polyfill.\n    return null;\n  } else {\n    let isHTTP = true;\n    if (Array.isArray(url)) {\n      isHTTP = url.every(urlItem => isHTTPScheme(urlItem));\n    } else {\n      isHTTP = isHTTPScheme(url);\n    }\n    if (isHTTP) {\n      return browserHTTPRequest(url);\n    }\n  }\n  return null;\n};\nIORouterRegistry.registerSaveRouter(httpRequestRouter);\nIORouterRegistry.registerLoadRouter(httpRequestRouter);\n\n/**\n * Creates an IOHandler subtype that sends model artifacts to HTTP server.\n *\n * An HTTP request of the `multipart/form-data` mime type will be sent to the\n * `path` URL. The form data includes artifacts that represent the topology\n * and/or weights of the model. In the case of Keras-style `tf.Model`, two\n * blobs (files) exist in form-data:\n *   - A JSON file consisting of `modelTopology` and `weightsManifest`.\n *   - A binary weights file consisting of the concatenated weight values.\n * These files are in the same format as the one generated by\n * [tfjs_converter](https://js.tensorflow.org/tutorials/import-keras.html).\n *\n * The following code snippet exemplifies the client-side code that uses this\n * function:\n *\n * ```js\n * const model = tf.sequential();\n * model.add(\n *     tf.layers.dense({units: 1, inputShape: [100], activation: 'sigmoid'}));\n *\n * const saveResult = await model.save(tf.io.browserHTTPRequest(\n *     'http://model-server:5000/upload', {method: 'PUT'}));\n * console.log(saveResult);\n * ```\n *\n * If the default `POST` method is to be used, without any custom parameters\n * such as headers, you can simply pass an HTTP or HTTPS URL to `model.save`:\n *\n * ```js\n * const saveResult = await model.save('http://model-server:5000/upload');\n * ```\n *\n * The following Python code snippet based on the\n * [flask](https://github.com/pallets/flask) server framework implements a\n * server that can receive the request. Upon receiving the model artifacts\n * via the requst, this particular server reconsistutes instances of\n * [Keras Models](https://keras.io/models/model/) in memory.\n *\n * ```python\n * # pip install -U flask flask-cors tensorflow tensorflowjs\n *\n * from __future__ import absolute_import\n * from __future__ import division\n * from __future__ import print_function\n *\n * import io\n *\n * from flask import Flask, Response, request\n * from flask_cors import CORS, cross_origin\n * import tensorflow as tf\n * import tensorflowjs as tfjs\n * import werkzeug.formparser\n *\n *\n * class ModelReceiver(object):\n *\n *   def __init__(self):\n *     self._model = None\n *     self._model_json_bytes = None\n *     self._model_json_writer = None\n *     self._weight_bytes = None\n *     self._weight_writer = None\n *\n *   @property\n *   def model(self):\n *     self._model_json_writer.flush()\n *     self._weight_writer.flush()\n *     self._model_json_writer.seek(0)\n *     self._weight_writer.seek(0)\n *\n *     json_content = self._model_json_bytes.read()\n *     weights_content = self._weight_bytes.read()\n *     return tfjs.converters.deserialize_keras_model(\n *         json_content,\n *         weight_data=[weights_content],\n *         use_unique_name_scope=True)\n *\n *   def stream_factory(self,\n *                      total_content_length,\n *                      content_type,\n *                      filename,\n *                      content_length=None):\n *     # Note: this example code is *not* thread-safe.\n *     if filename == 'model.json':\n *       self._model_json_bytes = io.BytesIO()\n *       self._model_json_writer = io.BufferedWriter(self._model_json_bytes)\n *       return self._model_json_writer\n *     elif filename == 'model.weights.bin':\n *       self._weight_bytes = io.BytesIO()\n *       self._weight_writer = io.BufferedWriter(self._weight_bytes)\n *       return self._weight_writer\n *\n *\n * def main():\n *   app = Flask('model-server')\n *   CORS(app)\n *   app.config['CORS_HEADER'] = 'Content-Type'\n *\n *   model_receiver = ModelReceiver()\n *\n *   @app.route('/upload', methods=['POST'])\n *   @cross_origin()\n *   def upload():\n *     print('Handling request...')\n *     werkzeug.formparser.parse_form_data(\n *         request.environ, stream_factory=model_receiver.stream_factory)\n *     print('Received model:')\n *     with tf.Graph().as_default(), tf.Session():\n *       model = model_receiver.model\n *       model.summary()\n *       # You can perform `model.predict()`, `model.fit()`,\n *       # `model.evaluate()` etc. here.\n *     return Response(status=200)\n *\n *   app.run('localhost', 5000)\n *\n *\n * if __name__ == '__main__':\n *   main()\n * ```\n *\n * @param path A single URL path or an Array of URL  paths.\n *   Currently, only a single URL path is supported. Array input is reserved\n *   for future development.\n *   Can be an absolute HTTP path (e.g.,\n *   'http://localhost:8000/model-upload)') or a relative path (e.g.,\n *   './model-upload').\n * @param requestInit Request configurations to be used when sending\n *    HTTP request to server using `fetch`. It can contain fields such as\n *    `method`, `credentials`, `headers`, `mode`, etc. See\n *    https://developer.mozilla.org/en-US/docs/Web/API/Request/Request\n *    for more information. `requestInit` must not have a body, because the\n * body will be set by TensorFlow.js. File blobs representing the model\n * topology (filename: 'model.json') and the weights of the model (filename:\n * 'model.weights.bin') will be appended to the body. If `requestInit` has a\n * `body`, an Error will be thrown.\n * @param weightPathPrefix Optional, this specifies the path prefix for weight\n *   files, by default this is calculated from the path param.\n * @param fetchFunc Optional, custom `fetch` function. E.g., in Node.js,\n *   the `fetch` from node-fetch can be used here.\n * @returns An instance of `IOHandler`.\n */\nexport function browserHTTPRequest(\n    path: string|string[], requestInit?: RequestInit, weightPathPrefix?: string,\n    fetchFunc?: Function): IOHandler {\n  return new BrowserHTTPRequest(path, requestInit, weightPathPrefix, fetchFunc);\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n/**\n * IOHandlers that pass through the in-memory ModelArtifacts format.\n */\n\nimport {IOHandler, ModelArtifacts, SaveResult, WeightsManifestEntry} from './types';\n\nclass PassthroughLoader implements IOHandler {\n  constructor(\n      private readonly modelTopology?: {}|ArrayBuffer,\n      private readonly weightSpecs?: WeightsManifestEntry[],\n      private readonly weightData?: ArrayBuffer) {}\n\n  async load(): Promise<ModelArtifacts> {\n    let result = {};\n    if (this.modelTopology != null) {\n      result = {modelTopology: this.modelTopology, ...result};\n    }\n    if (this.weightSpecs != null && this.weightSpecs.length > 0) {\n      result = {weightSpecs: this.weightSpecs, ...result};\n    }\n    if (this.weightData != null && this.weightData.byteLength > 0) {\n      result = {weightData: this.weightData, ...result};\n    }\n    return result;\n  }\n}\n\nclass PassthroughSaver implements IOHandler {\n  constructor(\n      private readonly saveHandler:\n          (artifacts: ModelArtifacts) => Promise<SaveResult>) {}\n\n  async save(modelArtifacts: ModelArtifacts) {\n    return this.saveHandler(modelArtifacts);\n  }\n}\n\n/**\n * Creates an IOHandler that loads model artifacts from memory.\n *\n * When used in conjunction with `tf.loadModel`, an instance of `tf.Model`\n * (Keras-style) can be constructed from the loaded artifacts.\n *\n * ```js\n * const model = await tf.loadModel(tf.io.fromMemory(\n *     modelTopology, weightSpecs, weightData));\n * ```\n *\n * @param modelTopology a object containing model topology (i.e., parsed from\n *   the JSON format).\n * @param weightSpecs An array of `WeightsManifestEntry` objects describing the\n *   names, shapes, types, and quantization of the weight data.\n * @param weightData A single `ArrayBuffer` containing the weight data,\n *   concatenated in the order described by the weightSpecs.\n *\n * @returns A passthrough `IOHandler` that simply loads the provided data.\n */\nexport function fromMemory(\n    modelTopology: {}, weightSpecs?: WeightsManifestEntry[],\n    weightData?: ArrayBuffer): IOHandler {\n  return new PassthroughLoader(modelTopology, weightSpecs, weightData);\n}\n\n/**\n * Creates an IOHandler that passes saved model artifacts to a callback.\n *\n * ```js\n * function handleSave(artifacts) {\n *   // ... do something with the artifacts ...\n *   return {modelArtifactsInfo: {...}, ...};\n * }\n *\n * const saveResult = model.save(tf.io.withSaveHandler(handleSave));\n * ```\n *\n * @param saveHandler A function that accepts a `ModelArtifacts` and returns a\n *     `SaveResult`.\n */\nexport function withSaveHandler(\n    saveHandler: (artifacts: ModelArtifacts) =>\n        Promise<SaveResult>): IOHandler {\n  return new PassthroughSaver(saveHandler);\n}\n","/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// Importing local_storage and indexed_db is necessary for the routers to be\n// registered.\nimport './indexed_db';\nimport './local_storage';\n\nimport {browserFiles} from './browser_files';\nimport {browserHTTPRequest, isHTTPScheme} from './browser_http';\nimport {concatenateArrayBuffers, decodeWeights, encodeWeights, getModelArtifactsInfoForJSON} from './io_utils';\nimport {fromMemory, withSaveHandler} from './passthrough';\nimport {IORouterRegistry} from './router_registry';\nimport {IOHandler, LoadHandler, ModelArtifacts, ModelStoreManager, SaveConfig, SaveHandler, SaveResult, WeightsManifestConfig, WeightsManifestEntry} from './types';\nimport {loadWeights, weightsLoaderFactory} from './weights_loader';\n\nconst registerSaveRouter = IORouterRegistry.registerSaveRouter;\nconst registerLoadRouter = IORouterRegistry.registerLoadRouter;\nconst getSaveHandlers = IORouterRegistry.getSaveHandlers;\nconst getLoadHandlers = IORouterRegistry.getLoadHandlers;\n\nexport {copyModel, listModels, moveModel, removeModel} from './model_management';\n\nexport {\n  browserFiles,\n  browserHTTPRequest,\n  concatenateArrayBuffers,\n  decodeWeights,\n  encodeWeights,\n  fromMemory,\n  getLoadHandlers,\n  getModelArtifactsInfoForJSON,\n  getSaveHandlers,\n  IOHandler,\n  isHTTPScheme,\n  LoadHandler,\n  loadWeights,\n  ModelArtifacts,\n  ModelStoreManager,\n  registerLoadRouter,\n  registerSaveRouter,\n  SaveConfig,\n  SaveHandler,\n  SaveResult,\n  WeightsManifestConfig,\n  WeightsManifestEntry,\n  weightsLoaderFactory,\n  withSaveHandler\n};\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {Tensor1D, Tensor2D} from '../tensor';\nimport {convertToTensor} from '../tensor_util_env';\nimport {TensorLike} from '../types';\nimport * as util from '../util';\n\nimport {oneHot} from './array_ops';\nimport {op} from './operation';\n\n/**\n * Computes the confusion matrix from true labels and predicted labels.\n *\n * ```js\n * const labels = tf.tensor1d([0, 1, 2, 1, 0], 'int32');\n * const predictions = tf.tensor1d([0, 2, 2, 1, 0], 'int32');\n * const numClasses = 3;\n * const out = tf.math.confusionMatrix(labels, predictions, numClasses);\n * out.print();\n * // Expected output matrix:\n * // [[2, 0, 0],\n * //  [0, 1, 1],\n * //  [0, 0, 1]]\n * ```\n *\n * @param labels The target labels, assumed to be 0-based integers\n *   for the classes. The shape is `[numExamples]`, where\n *   `numExamples` is the number of examples included.\n * @param predictions The predicted classes, assumed to be\n *   0-based integers for the classes. Must have the same shape as `labels`.\n * @param numClasses Number of all classes, as an integer.\n *   Its value must be larger than the largest element in `labels` and\n *   `predictions`.\n * @returns The confusion matrix as a int32-type 2D tensor. The value at\n *   row `r` and column `c` is the number of times examples of actual class\n *   `r` were predicted as class `c`.\n */\n/** @doc {heading: 'Operations', subheading: 'Evaluation'} */\nexport function confusionMatrix_(\n    labels: Tensor1D|TensorLike, predictions: Tensor1D|TensorLike,\n    numClasses: number): Tensor2D {\n  const $labels = convertToTensor(labels, 'labels', 'confusionMatrix');\n  const $predictions =\n      convertToTensor(predictions, 'predictions', 'confusionMatrix');\n\n  util.assert(\n      numClasses == null || numClasses > 0 && Number.isInteger(numClasses),\n      `If provided, numClasses must be a positive integer, ` +\n          `but got ${numClasses}`);\n  util.assert(\n      $labels.rank === 1,\n      `Expected the rank of labels to be 1, but got ${$labels.rank}`);\n  util.assert(\n      $predictions.rank === 1,\n      `Expected the rank of predictions to be 1, ` +\n          `but got ${$predictions.rank}`);\n  util.assert(\n      $labels.shape[0] === $predictions.shape[0],\n      `Mismatch in the number of examples: ` +\n          `${$labels.shape[0]} vs. ${$predictions.shape[0]}. ` +\n          `Labels and predictions should have the same number of elements.`);\n  util.assert(\n      numClasses > 0 && Number.isInteger(numClasses),\n      `numClasses is required to be a positive integer, but got ${numClasses}`);\n  // TODO(cais): In the future, if oneHot supports tensors inputs for\n  //   `numClasses`, `confusionMatrix` can make `numClasses` optional.\n\n  const oneHotLabels = oneHot($labels.asType('int32'), numClasses);\n  const oneHotPredictions = oneHot($predictions.asType('int32'), numClasses);\n  return oneHotLabels.transpose().matMul(oneHotPredictions).asType('int32');\n}\n\nexport const confusionMatrix = op({confusionMatrix_});\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {assert} from './util';\n\n/**\n * Types to support JSON-esque data structures internally.\n *\n * Internally ConfigDict's use camelCase keys and values where the\n * values are class names to be instantiated.  On the python side, these\n * will be snake_case.  Internally we allow Enums into the values for better\n * type safety, but these need to be converted to raw primitives (usually\n * strings) for round-tripping with python.\n *\n * toConfig returns the TS-friendly representation. model.toJSON() returns\n * the pythonic version as that's the portable format.  If you need to\n * python-ify a non-model level toConfig output, you'll need to use a\n * convertTsToPythonic from serialization_utils in -Layers.\n *\n */\nexport type ConfigDictValue =\n    boolean|number|string|null|ConfigDictArray|ConfigDict;\nexport interface ConfigDict {\n  [key: string]: ConfigDictValue;\n}\nexport interface ConfigDictArray extends Array<ConfigDictValue> {}\n\n/**\n * Type to represent the class-type of Serializable objects.\n *\n * Ie the class prototype with access to the constructor and any\n * static members/methods. Instance methods are not listed here.\n *\n * Source for this idea: https://stackoverflow.com/a/43607255\n */\nexport type SerializableConstructor<T extends Serializable> = {\n  // tslint:disable-next-line:no-any\n  new (...args: any[]): T; className: string; fromConfig: FromConfigMethod<T>;\n};\nexport type FromConfigMethod<T extends Serializable> =\n    (cls: SerializableConstructor<T>, config: ConfigDict) => T;\n\n/**\n * Serializable defines the serialization contract.\n *\n * TFJS requires serializable classes to return their className when asked\n * to avoid issues with minification.\n */\nexport abstract class Serializable {\n  /**\n   * Return the class name for this class to use in serialization contexts.\n   *\n   * Generally speaking this will be the same thing that constructor.name\n   * would have returned.  However, the class name needs to be robust\n   * against minification for serialization/deserialization to work properly.\n   *\n   * There's also places such as initializers.VarianceScaling, where\n   * implementation details between different languages led to different\n   * class hierarchies and a non-leaf node is used for serialization purposes.\n   */\n  getClassName(): string {\n    return (this.constructor as SerializableConstructor<Serializable>)\n        .className;\n  }\n\n  /**\n   * Return all the non-weight state needed to serialize this object.\n   */\n  abstract getConfig(): ConfigDict;\n\n  /**\n   * Creates an instance of T from a ConfigDict.\n   *\n   * This works for most descendants of serializable.  A few need to\n   * provide special handling.\n   * @param cls A Constructor for the class to instantiate.\n   * @param config The Configuration for the object.\n   */\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(config);\n  }\n}\n\n/**\n * Maps string keys to class constructors.\n *\n * Used during (de)serialization from the cross-language JSON format, which\n * requires the class name in the serialization format matches the class\n * names as used in Python, should it exist.\n */\nexport class SerializationMap {\n  private static instance: SerializationMap;\n  classNameMap: {\n    [className: string]:\n        [SerializableConstructor<Serializable>, FromConfigMethod<Serializable>]\n  };\n\n  private constructor() {\n    this.classNameMap = {};\n  }\n\n  /**\n   * Returns the singleton instance of the map.\n   */\n  static getMap(): SerializationMap {\n    if (SerializationMap.instance == null) {\n      SerializationMap.instance = new SerializationMap();\n    }\n    return SerializationMap.instance;\n  }\n\n  /**\n   * Registers the class as serializable.\n   */\n  static register<T extends Serializable>(cls: SerializableConstructor<T>) {\n    SerializationMap.getMap().classNameMap[cls.className] =\n        [cls, cls.fromConfig];\n  }\n}\n\n/**\n * Register a class with the serialization map of TensorFlow.js.\n *\n * This is often used for registering custom Layers, so they can be\n * serialized and deserialized.\n *\n * Example:\n *\n * ```js\n * class MyCustomLayer extends tf.layers.Layer {\n *   static className = 'MyCustomLayer';\n *\n *   constructor(config) {\n *     super(config);\n *   }\n * }\n * tf.serialization.registerClass(MyCustomLayer);\n * ```\n *\n * @param cls The class to be registered. It must have a public static member\n *   called `className` defined and the value must be a non-empty string.\n */\n/** @doc {heading: 'Models', subheading: 'Serialization'} */\nexport function registerClass<T extends Serializable>(\n    cls: SerializableConstructor<T>) {\n  assert(\n      cls.className != null,\n      `Class being registered does not have the static className property ` +\n          `defined.`);\n  assert(\n      typeof cls.className === 'string',\n      `className is required to be a string, but got type ` +\n          typeof cls.className);\n  assert(\n      cls.className.length > 0,\n      `Class being registered has an empty-string as its className, which ` +\n          `is disallowed.`);\n\n  SerializationMap.register(cls);\n}\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from './environment';\nimport {Features} from './environment_util';\nimport {Tensor} from './tensor';\nimport {TypedArray} from './types';\nimport * as util from './util';\nimport {isString} from './util';\n\n// TODO(smilkov): Move these constants to jasmine_util.\nexport const WEBGL_ENVS: Features = {\n  'HAS_WEBGL': true\n};\nexport const NODE_ENVS: Features = {\n  'IS_NODE': true\n};\nexport const CHROME_ENVS: Features = {\n  'IS_CHROME': true\n};\nexport const BROWSER_ENVS: Features = {\n  'IS_BROWSER': true\n};\nexport const CPU_ENVS: Features = {\n  'HAS_WEBGL': false\n};\n\nexport const ALL_ENVS: Features = {};\n\nexport function expectArraysClose(\n    actual: Tensor|TypedArray|number[],\n    expected: Tensor|TypedArray|number[]|boolean[], epsilon?: number) {\n  if (epsilon == null) {\n    epsilon = ENV.get('TEST_EPSILON');\n  }\n  return expectArraysPredicate(\n      actual, expected, (a, b) => areClose(a as number, Number(b), epsilon));\n}\n\nfunction expectArraysPredicate(\n    actual: Tensor|TypedArray|number[]|string[],\n    expected: Tensor|TypedArray|number[]|boolean[]|string[],\n    predicate: (a: {}, b: {}) => boolean) {\n  if (!(actual instanceof Tensor) && !(expected instanceof Tensor)) {\n    const aType = actual.constructor.name;\n    const bType = expected.constructor.name;\n\n    if (aType !== bType) {\n      throw new Error(\n          `Arrays are of different type actual: ${aType} ` +\n          `vs expected: ${bType}`);\n    }\n  } else if (actual instanceof Tensor && expected instanceof Tensor) {\n    if (actual.dtype !== expected.dtype) {\n      throw new Error(\n          `Arrays are of different type actual: ${actual.dtype} ` +\n          `vs expected: ${expected.dtype}.`);\n    }\n    if (!util.arraysEqual(actual.shape, expected.shape)) {\n      throw new Error(\n          `Arrays are of different shape actual: ${actual.shape} ` +\n          `vs expected: ${expected.shape}.`);\n    }\n  }\n\n  let actualValues: TypedArray|number[]|string[];\n  let expectedValues: TypedArray|number[]|boolean[]|string[];\n  if (actual instanceof Tensor) {\n    actualValues = actual.dataSync();\n  } else {\n    actualValues = actual;\n  }\n  if (expected instanceof Tensor) {\n    expectedValues = expected.dataSync();\n  } else {\n    expectedValues = expected;\n  }\n\n  if (actualValues.length !== expectedValues.length) {\n    throw new Error(\n        `Arrays have different lengths actual: ${actualValues.length} vs ` +\n        `expected: ${expectedValues.length}.\\n` +\n        `Actual:   ${actualValues}.\\n` +\n        `Expected: ${expectedValues}.`);\n  }\n  for (let i = 0; i < expectedValues.length; ++i) {\n    const a = actualValues[i];\n    const e = expectedValues[i];\n\n    if (!predicate(a, e)) {\n      throw new Error(\n          `Arrays differ: actual[${i}] = ${a}, expected[${i}] = ${e}.\\n` +\n          `Actual:   ${actualValues}.\\n` +\n          `Expected: ${expectedValues}.`);\n    }\n  }\n}\n\nexport interface DoneFn {\n  (): void;\n  fail: (message?: Error|string) => void;\n}\n\nexport function expectPromiseToFail(fn: () => Promise<{}>, done: DoneFn): void {\n  fn().then(() => done.fail(), () => done());\n}\n\nexport function expectArraysEqual(\n    actual: Tensor|TypedArray|number[]|string[],\n    expected: Tensor|TypedArray|number[]|boolean[]|string[]) {\n  if (actual instanceof Tensor && actual.dtype === 'string' ||\n      expected instanceof Tensor && expected.dtype === 'string' ||\n      actual instanceof Array && isString(actual[0]) ||\n      expected instanceof Array && isString(expected[0])) {\n    // tslint:disable-next-line:triple-equals\n    return expectArraysPredicate(actual, expected, (a, b) => a == b);\n  }\n  return expectArraysClose(actual as Tensor, expected as Tensor, 0);\n}\n\nexport function expectNumbersClose(a: number, e: number, epsilon?: number) {\n  if (epsilon == null) {\n    epsilon = ENV.get('TEST_EPSILON');\n  }\n  if (!areClose(a, e, epsilon)) {\n    throw new Error(`Numbers differ: actual === ${a}, expected === ${e}`);\n  }\n}\n\nfunction areClose(a: number, e: number, epsilon: number): boolean {\n  if (isNaN(a) && isNaN(e)) {\n    return true;\n  }\n  if (isNaN(a) || isNaN(e) || Math.abs(a - e) > epsilon) {\n    return false;\n  }\n  return true;\n}\n\nexport function expectValuesInRange(\n    actual: Tensor|TypedArray|number[], low: number, high: number) {\n  let actualVals: TypedArray|number[];\n  if (actual instanceof Tensor) {\n    actualVals = actual.dataSync();\n  } else {\n    actualVals = actual;\n  }\n  for (let i = 0; i < actualVals.length; i++) {\n    if (actualVals[i] < low || actualVals[i] > high) {\n      throw new Error(\n          `Value out of range:${actualVals[i]} low: ${low}, high: ${high}`);\n    }\n  }\n}\n\nexport function expectArrayBuffersEqual(\n    actual: ArrayBuffer, expected: ArrayBuffer) {\n  // Safari & Jasmine don't like comparing ArrayBuffers directly. Wrapping in\n  // a Float32Array solves this issue.\n  expect(new Float32Array(actual)).toEqual(new Float32Array(expected));\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {variableGrads} from '../globals';\nimport {Serializable} from '../serialization';\nimport {Scalar, Variable} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\n\n/** @doc {heading: 'Training', subheading: 'Classes', namespace: 'train'} */\nexport abstract class Optimizer extends Serializable {\n  /**\n   * Executes `f()` and minimizes the scalar output of `f()` by computing\n   * gradients of y with respect to the list of trainable variables provided by\n   * `varList`. If no list is provided, it defaults to all trainable variables.\n   *\n   * @param f The function to execute and whose output to minimize.\n   * @param returnCost Whether to return the scalar cost value produced by\n   * executing `f()`.\n   * @param varList An optional list of variables to update. If specified, only\n   * the trainable variables in varList will be updated by minimize. Defaults to\n   * all trainable variables.\n   */\n  /** @doc {heading: 'Training', subheading: 'Optimizers'} */\n  minimize(f: () => Scalar, returnCost = false, varList?: Variable[]): Scalar\n      |null {\n    const {value, grads} = this.computeGradients(f, varList);\n\n    this.applyGradients(grads);\n\n    // Dispose gradients.\n    const varNames = Object.keys(grads);\n    varNames.forEach(varName => grads[varName].dispose());\n\n    if (returnCost) {\n      return value as Scalar;\n    } else {\n      value.dispose();\n      return null;\n    }\n  }\n\n  /**\n   * Executes f() and computes the gradient of the scalar output of f() with\n   * respect to the list of trainable variables provided by `varList`. If no\n   * list is provided, it defaults to all trainable variables.\n   *\n   * @param f The function to execute and whose output to use for computing\n   * gradients with respect to variables.\n   * @param varList An optional list of variables to compute gradients with\n   * respect to. If specified, only the trainable variables in varList will have\n   * gradients computed with respect to. Defaults to all trainable variables.\n   */\n  computeGradients(f: () => Scalar, varList?: Variable[]):\n      {value: Scalar, grads: NamedTensorMap} {\n    return variableGrads(f, varList);\n  }\n\n  /**\n   * Updates variables by using the computed gradients.\n   *\n   * @param variableGradients A mapping of variable name to its gradient value.\n   */\n  abstract applyGradients(variableGradients: NamedTensorMap): void;\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {keep, tidy} from '../globals';\nimport {scalar, zerosLike} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar} from '../tensor';\nimport {NamedVariableMap} from '../tensor_types';\nimport {Optimizer} from './optimizer';\n\n/** @doclink Optimizer */\nexport class AdadeltaOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'AdadeltaOptimizer';\n  private c: Scalar;\n  private epsilonScalar: Scalar;\n  private rhoScalar: Scalar;\n  private oneMinusRho: Scalar;\n\n  private accumulatedGrads: NamedVariableMap = {};\n  private accumulatedUpdates: NamedVariableMap = {};\n\n  constructor(\n      protected learningRate: number, protected rho: number,\n      protected epsilon: number = null) {\n    super();\n\n    this.c = keep(scalar(-learningRate));\n    this.rhoScalar = keep(scalar(rho));\n    this.oneMinusRho = keep(scalar(1 - rho));\n\n    if (epsilon === null) {\n      epsilon = ENV.get('EPSILON');\n    }\n\n    this.epsilonScalar = keep(scalar(epsilon));\n  }\n\n  applyGradients(variableGradients: NamedVariableMap) {\n    for (const variableName in variableGradients) {\n      const value = ENV.engine.registeredVariables[variableName];\n      if (this.accumulatedGrads[variableName] == null) {\n        const trainable = false;\n        tidy(() => {\n          this.accumulatedGrads[variableName] =\n              zerosLike(value).variable(trainable);\n        });\n      }\n      if (this.accumulatedUpdates[variableName] == null) {\n        const trainable = false;\n        tidy(() => {\n          this.accumulatedUpdates[variableName] =\n              zerosLike(value).variable(trainable);\n        });\n      }\n\n      const gradient = variableGradients[variableName];\n      const accumulatedGrad = this.accumulatedGrads[variableName];\n      const accumulatedUpdate = this.accumulatedUpdates[variableName];\n\n      tidy(() => {\n        const newAccumulatedGrad =\n            this.rhoScalar.mul(accumulatedGrad)\n                .add(this.oneMinusRho.mul(gradient.square()));\n\n        const updates = accumulatedUpdate.add(this.epsilonScalar)\n                            .sqrt()\n                            .div(accumulatedGrad.add(this.epsilonScalar).sqrt())\n                            .mul(gradient);\n\n        const newAccumulatedUpdate =\n            this.rhoScalar.mul(accumulatedUpdate)\n                .add(this.oneMinusRho.mul(updates.square()));\n\n        this.accumulatedGrads[variableName].assign(newAccumulatedGrad);\n        this.accumulatedUpdates[variableName].assign(newAccumulatedUpdate);\n\n        const newValue = this.c.mul(updates).add(value);\n        value.assign(newValue);\n      });\n    }\n  }\n\n  dispose() {\n    this.c.dispose();\n    this.epsilonScalar.dispose();\n    this.rhoScalar.dispose();\n    this.oneMinusRho.dispose();\n    if (this.accumulatedUpdates != null) {\n      Object.keys(this.accumulatedUpdates)\n          .forEach(name => this.accumulatedUpdates[name].dispose());\n      Object.keys(this.accumulatedGrads)\n          .forEach(name => this.accumulatedGrads[name].dispose());\n    }\n  }\n  getConfig(): ConfigDict {\n    return {\n      learningRate: this.learningRate,\n      rho: this.rho,\n      epsilon: this.epsilon\n    };\n  }\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(config.learningRate, config.rho, config.epsilon);\n  }\n}\nregisterClass(AdadeltaOptimizer);\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {keep, tidy} from '../globals';\nimport {fill, scalar} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar} from '../tensor';\nimport {NamedVariableMap} from '../tensor_types';\nimport {Optimizer} from './optimizer';\n\n/** @doclink Optimizer */\nexport class AdagradOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'AdagradOptimizer';\n  private c: Scalar;\n  private epsilon: Scalar;\n\n  private accumulatedGrads: NamedVariableMap = {};\n\n  constructor(\n      protected learningRate: number, private initialAccumulatorValue = 0.1) {\n    super();\n    this.c = keep(scalar(-learningRate));\n\n    this.epsilon = keep(scalar(ENV.get('EPSILON')));\n  }\n\n  applyGradients(variableGradients: NamedVariableMap) {\n    for (const variableName in variableGradients) {\n      const value = ENV.engine.registeredVariables[variableName];\n      if (this.accumulatedGrads[variableName] == null) {\n        const trainable = false;\n        tidy(() => {\n          this.accumulatedGrads[variableName] =\n              fill(value.shape, this.initialAccumulatorValue)\n                  .variable(trainable);\n        });\n      }\n\n      const gradient = variableGradients[variableName];\n      const accumulatedGrad = this.accumulatedGrads[variableName];\n\n      tidy(() => {\n        const newAccumulatedGrad = accumulatedGrad.add(gradient.square());\n        this.accumulatedGrads[variableName].assign(newAccumulatedGrad);\n\n        const newValue =\n            this.c\n                .mul(gradient.div(newAccumulatedGrad.add(this.epsilon).sqrt()))\n                .add(value);\n        value.assign(newValue);\n      });\n    }\n  }\n\n  dispose() {\n    this.epsilon.dispose();\n    this.c.dispose();\n    if (this.accumulatedGrads != null) {\n      Object.keys(this.accumulatedGrads)\n          .forEach(name => this.accumulatedGrads[name].dispose());\n    }\n  }\n  getConfig(): ConfigDict {\n    return {\n      learningRate: this.learningRate,\n      initialAccumulatorValue: this.initialAccumulatorValue,\n    };\n  }\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(config.learningRate, config.initialAccumulatorValue);\n  }\n}\nregisterClass(AdagradOptimizer);\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {keep, tidy} from '../globals';\nimport {scalar, zerosLike} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar, Variable} from '../tensor';\nimport {NamedVariableMap} from '../tensor_types';\nimport {Optimizer} from './optimizer';\n\nexport class AdamOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'AdamOptimizer';\n  private c: Scalar;\n  private epsScalar: Scalar;\n  private beta1Scalar: Scalar;\n  private beta2Scalar: Scalar;\n  private accBeta1: Variable;\n  private accBeta2: Variable;\n  private oneMinusBeta1: Scalar;\n  private oneMinusBeta2: Scalar;\n  private one: Scalar;\n\n  private accumulatedFirstMoment: NamedVariableMap = {};\n  private accumulatedSecondMoment: NamedVariableMap = {};\n\n  constructor(\n      protected learningRate: number, protected beta1: number,\n      protected beta2: number, protected epsilon: number = null) {\n    super();\n    this.c = keep(scalar(-learningRate));\n    // b1, b2 keep initial value of beta* hyperparameters.\n    this.beta1Scalar = keep(scalar(beta1));\n    this.beta2Scalar = keep(scalar(beta2));\n    tidy(() => {\n      // accB* will be updated by batch.\n      this.accBeta1 = scalar(beta1).variable();\n      this.accBeta2 = scalar(beta2).variable();\n    });\n    this.oneMinusBeta1 = keep(scalar(1 - beta1));\n    this.oneMinusBeta2 = keep(scalar(1 - beta2));\n    this.one = keep(scalar(1));\n\n    if (epsilon === null) {\n      epsilon = ENV.get('EPSILON');\n    }\n\n    this.epsScalar = keep(scalar(epsilon));\n  }\n\n  applyGradients(variableGradients: NamedVariableMap) {\n    tidy(() => {\n      const oneMinusAccBeta1 = this.one.sub(this.accBeta1);\n      const oneMinusAccBeta2 = this.one.sub(this.accBeta2);\n\n      for (const variableName in variableGradients) {\n        const value = ENV.engine.registeredVariables[variableName];\n        if (this.accumulatedFirstMoment[variableName] == null) {\n          const trainable = false;\n          this.accumulatedFirstMoment[variableName] =\n              zerosLike(value).variable(trainable);\n        }\n        if (this.accumulatedSecondMoment[variableName] == null) {\n          const trainable = false;\n          this.accumulatedSecondMoment[variableName] =\n              zerosLike(value).variable(trainable);\n        }\n\n        const gradient = variableGradients[variableName];\n        const firstMoment = this.accumulatedFirstMoment[variableName];\n        const secondMoment = this.accumulatedSecondMoment[variableName];\n\n        const newFirstMoment = this.beta1Scalar.mul(firstMoment)\n                                   .add(this.oneMinusBeta1.mul(gradient));\n        const newSecondMoment =\n            this.beta2Scalar.mul(secondMoment)\n                .add(this.oneMinusBeta2.mul(gradient.square()));\n\n        const biasCorrectedFirstMoment = newFirstMoment.div(oneMinusAccBeta1);\n        const biasCorrectedSecondMoment = newSecondMoment.div(oneMinusAccBeta2);\n\n        this.accumulatedFirstMoment[variableName].assign(newFirstMoment);\n        this.accumulatedSecondMoment[variableName].assign(newSecondMoment);\n\n        const newValue =\n            this.c\n                .mul(biasCorrectedFirstMoment.div(\n                    this.epsScalar.add(biasCorrectedSecondMoment.sqrt())))\n                .add(value);\n        value.assign(newValue);\n      }\n\n      this.accBeta1.assign(this.accBeta1.mul(this.beta1Scalar));\n      this.accBeta2.assign(this.accBeta2.mul(this.beta2Scalar));\n    });\n  }\n\n  dispose() {\n    this.c.dispose();\n    this.epsScalar.dispose();\n    this.beta1Scalar.dispose();\n    this.beta2Scalar.dispose();\n    this.accBeta1.dispose();\n    this.accBeta2.dispose();\n    this.oneMinusBeta1.dispose();\n    this.oneMinusBeta2.dispose();\n    this.one.dispose();\n\n    if (this.accumulatedFirstMoment != null) {\n      Object.keys(this.accumulatedFirstMoment)\n          .forEach(name => this.accumulatedFirstMoment[name].dispose());\n    }\n\n    if (this.accumulatedSecondMoment != null) {\n      Object.keys(this.accumulatedSecondMoment)\n          .forEach(name => this.accumulatedSecondMoment[name].dispose());\n    }\n  }\n  getConfig(): ConfigDict {\n    return {\n      learningRate: this.learningRate,\n      beta1: this.beta1,\n      beta2: this.beta2,\n      epsilon: this.epsilon,\n    };\n  }\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(\n        config.learningRate, config.beta1, config.beta2, config.epsilon);\n  }\n}\nregisterClass(AdamOptimizer);\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {keep, tidy} from '../globals';\nimport {scalar, zerosLike} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar, Variable} from '../tensor';\nimport {NamedVariableMap} from '../tensor_types';\nimport {Optimizer} from './optimizer';\n\nexport class AdamaxOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'AdamaxOptimizer';\n  private c: Scalar;\n  private epsScalar: Scalar;\n  private accBeta1: Variable;\n  private beta1Scalar: Scalar;\n  private beta2Scalar: Scalar;\n  private decayScalar: Scalar;\n  private oneMinusBeta1: Scalar;\n  private one: Scalar;\n  private iteration: Variable;\n\n  private accumulatedFirstMoment: NamedVariableMap = {};\n  private accumulatedWeightedInfNorm: NamedVariableMap = {};\n\n  constructor(\n      protected learningRate: number, protected beta1: number,\n      protected beta2: number, protected epsilon: number = null,\n      protected decay = 0.0) {\n    super();\n    this.c = keep(scalar(-learningRate));\n\n    // b1, b2 keep initial value of beta* hyperparameters.\n    this.beta1Scalar = keep(scalar(beta1));\n    this.beta2Scalar = keep(scalar(beta2));\n\n    this.decayScalar = keep(scalar(decay));\n\n    tidy(() => {\n      this.iteration = scalar(0).variable();\n      this.accBeta1 = scalar(beta1).variable();\n    });\n\n    this.oneMinusBeta1 = keep(scalar(1 - beta1));\n    this.one = keep(scalar(1));\n\n    if (epsilon === null) {\n      epsilon = ENV.get('EPSILON');\n    }\n\n    this.epsScalar = keep(scalar(epsilon));\n  }\n\n  applyGradients(variableGradients: NamedVariableMap) {\n    tidy(() => {\n      const oneMinusAccBeta1 = this.one.sub(this.accBeta1);\n      const lr = this.c.div(this.one.add(this.decayScalar.mul(this.iteration)));\n\n      for (const variableName in variableGradients) {\n        const value = ENV.engine.registeredVariables[variableName];\n        if (this.accumulatedFirstMoment[variableName] == null) {\n          const trainable = false;\n          this.accumulatedFirstMoment[variableName] =\n              zerosLike(value).variable(trainable);\n        }\n        if (this.accumulatedWeightedInfNorm[variableName] == null) {\n          const trainable = false;\n          this.accumulatedWeightedInfNorm[variableName] =\n              zerosLike(value).variable(trainable);\n        }\n\n        const gradient = variableGradients[variableName];\n        const firstMoment = this.accumulatedFirstMoment[variableName];\n        const weightedInfNorm = this.accumulatedWeightedInfNorm[variableName];\n\n        const newFirstMoment = this.beta1Scalar.mul(firstMoment)\n                                   .add(this.oneMinusBeta1.mul(gradient));\n\n        const ut0 = this.beta2Scalar.mul(weightedInfNorm);\n        const ut1 = gradient.abs();\n\n        const newWeightedInfNorm = ut0.maximum(ut1);\n\n        this.accumulatedFirstMoment[variableName].assign(newFirstMoment);\n        this.accumulatedWeightedInfNorm[variableName].assign(\n            newWeightedInfNorm);\n\n        const newValue =\n            lr.div(oneMinusAccBeta1)\n                .mul(newFirstMoment.div(this.epsScalar.add(newWeightedInfNorm)))\n                .add(value);\n\n        value.assign(newValue);\n      }\n\n      this.iteration.assign(this.iteration.add(this.one));\n      this.accBeta1.assign(this.accBeta1.mul(this.beta1Scalar));\n    });\n  }\n\n  dispose() {\n    this.c.dispose();\n    this.epsScalar.dispose();\n    this.accBeta1.dispose();\n    this.beta1Scalar.dispose();\n    this.beta2Scalar.dispose();\n    this.oneMinusBeta1.dispose();\n\n    this.decayScalar.dispose();\n    this.iteration.dispose();\n\n    this.one.dispose();\n\n    if (this.accumulatedFirstMoment != null) {\n      Object.keys(this.accumulatedFirstMoment)\n          .forEach(name => this.accumulatedFirstMoment[name].dispose());\n    }\n\n    if (this.accumulatedWeightedInfNorm != null) {\n      Object.keys(this.accumulatedWeightedInfNorm)\n          .forEach(name => this.accumulatedWeightedInfNorm[name].dispose());\n    }\n  }\n  getConfig(): ConfigDict {\n    return {\n      learningRate: this.learningRate,\n      beta1: this.beta1,\n      beta2: this.beta2,\n      epsilon: this.epsilon,\n      decay: this.decay\n    };\n  }\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(\n        config.learningRate, config.beta1, config.beta2, config.epsilon,\n        config.decay);\n  }\n}\nregisterClass(AdamaxOptimizer);\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {keep, tidy} from '../globals';\nimport {scalar} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar} from '../tensor';\nimport {NamedTensorMap} from '../tensor_types';\nimport {Optimizer} from './optimizer';\n\n/** @doclink Optimizer */\nexport class SGDOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'SGDOptimizer';\n  protected c: Scalar;\n\n  constructor(protected learningRate: number) {\n    super();\n    this.setLearningRate(learningRate);\n  }\n\n  applyGradients(variableGradients: NamedTensorMap) {\n    const varNames = Object.keys(variableGradients);\n    varNames.forEach(varName => {\n      const gradient = variableGradients[varName];\n      const value = ENV.engine.registeredVariables[varName];\n\n      tidy(() => {\n        const newValue = this.c.mul(gradient).add(value);\n        value.assign(newValue);\n      });\n    });\n  }\n\n  /**\n   * Sets the learning rate of the optimizer.\n   */\n  setLearningRate(learningRate: number) {\n    this.learningRate = learningRate;\n    if (this.c != null) {\n      this.c.dispose();\n    }\n    this.c = keep(scalar(-learningRate));\n  }\n\n  dispose() {\n    this.c.dispose();\n  }\n\n  getConfig(): ConfigDict {\n    return {learningRate: this.learningRate};\n  }\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(config.learningRate);\n  }\n}\nregisterClass(SGDOptimizer);\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {tidy} from '../globals';\nimport {scalar, zerosLike} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar, Tensor} from '../tensor';\nimport {NamedVariableMap} from '../tensor_types';\nimport {SGDOptimizer} from './sgd_optimizer';\n\n/** @doclink Optimizer */\nexport class MomentumOptimizer extends SGDOptimizer {\n  /** @nocollapse */\n  static className = 'MomentumOptimizer';\n  private m: Scalar;\n  private accumulations: NamedVariableMap;\n\n  constructor(\n      protected learningRate: number, private momentum: number,\n      private useNesterov = false) {\n    super(learningRate);\n    this.m = scalar(this.momentum);\n    this.accumulations = {};\n  }\n\n  applyGradients(variableGradients: NamedVariableMap) {\n    for (const variableName in variableGradients) {\n      const value = ENV.engine.registeredVariables[variableName];\n      if (this.accumulations[variableName] == null) {\n        const trainable = false;\n        tidy(() => {\n          this.accumulations[variableName] =\n              zerosLike(value).variable(trainable);\n        });\n      }\n\n      const accumulation = this.accumulations[variableName];\n      const gradient = variableGradients[variableName];\n\n      tidy(() => {\n        let newValue: Tensor;\n        const newAccumulation = this.m.mul(accumulation).add(gradient);\n        if (this.useNesterov) {\n          newValue =\n              this.c.mul(gradient.add(newAccumulation.mul(this.m))).add(value);\n        } else {\n          newValue = this.c.mul(newAccumulation).add(value);\n        }\n        this.accumulations[variableName].assign(newAccumulation);\n        value.assign(newValue);\n      });\n    }\n  }\n\n  dispose() {\n    super.dispose();\n    this.m.dispose();\n    if (this.accumulations != null) {\n      for (const variableName in this.accumulations) {\n        this.accumulations[variableName].dispose();\n      }\n    }\n  }\n\n  /**\n   * Sets the momentum of the optimizer.\n   *\n   * @param momentum\n   */\n  setMomentum(momentum: number) {\n    this.momentum = momentum;\n  }\n\n  getConfig(): ConfigDict {\n    return {\n      learningRate: this.learningRate,\n      momentum: this.momentum,\n      useNesterov: this.useNesterov\n    };\n  }\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(config.learningRate, config.momentum, config.useNesterov);\n  }\n}\nregisterClass(MomentumOptimizer);\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {ENV} from '../environment';\nimport {keep, tidy} from '../globals';\nimport {scalar, zerosLike} from '../ops/ops';\nimport {ConfigDict, registerClass, Serializable, SerializableConstructor} from '../serialization';\nimport {Scalar} from '../tensor';\nimport {NamedVariableMap} from '../tensor_types';\nimport {Optimizer} from './optimizer';\n\n/** @doclink Optimizer */\nexport class RMSPropOptimizer extends Optimizer {\n  /** @nocollapse */\n  static className = 'RMSPropOptimizer';\n  private c: Scalar;\n  private epsilonScalar: Scalar;\n  private decayScalar: Scalar;\n  private momentumScalar: Scalar;\n  private oneMinusDecay: Scalar;\n  private centered: boolean;\n\n  private accumulatedMeanSquares: NamedVariableMap = {};\n  private accumulatedMeanGrads: NamedVariableMap = {};\n  private accumulatedMoments: NamedVariableMap = {};\n\n  constructor(\n      protected learningRate: number, protected decay = 0.9,\n      protected momentum = 0.0, protected epsilon: number = null,\n      centered = false) {\n    super();\n\n    this.c = keep(scalar(learningRate));\n    this.decayScalar = keep(scalar(decay));\n    this.momentumScalar = keep(scalar(momentum));\n    this.oneMinusDecay = keep(scalar(1 - decay));\n    this.centered = centered;\n\n    if (epsilon === null) {\n      epsilon = ENV.get('EPSILON');\n    }\n\n    this.epsilonScalar = keep(scalar(epsilon));\n  }\n\n  applyGradients(variableGradients: NamedVariableMap) {\n    for (const variableName in variableGradients) {\n      const value = ENV.engine.registeredVariables[variableName];\n      if (this.accumulatedMeanSquares[variableName] == null) {\n        const trainable = false;\n        tidy(() => {\n          this.accumulatedMeanSquares[variableName] =\n              zerosLike(value).variable(trainable);\n        });\n      }\n      if (this.accumulatedMeanGrads[variableName] == null && this.centered) {\n        const trainable = false;\n        tidy(() => {\n          this.accumulatedMeanGrads[variableName] =\n              zerosLike(value).variable(trainable);\n        });\n      }\n      if (this.accumulatedMoments[variableName] == null) {\n        const trainable = false;\n        tidy(() => {\n          this.accumulatedMoments[variableName] =\n              zerosLike(value).variable(trainable);\n        });\n      }\n\n      const accumulatedMeanSquare = this.accumulatedMeanSquares[variableName];\n      const accumulatedMeanGrad = this.accumulatedMeanGrads[variableName];\n      const accumulatedMoments = this.accumulatedMoments[variableName];\n      const gradient = variableGradients[variableName];\n\n      tidy(() => {\n        const newAccumulatedMeanSquare =\n            this.decayScalar.mul(accumulatedMeanSquare)\n                .add(this.oneMinusDecay.mul(gradient.square()));\n\n        if (this.centered) {\n          // Centered gradient\n          const newAccumulatedMeanGrad =\n              this.decayScalar.mul(accumulatedMeanGrad)\n                  .add(this.oneMinusDecay.mul(gradient));\n\n          const newAccumulatedMoments =\n              this.momentumScalar.mul(accumulatedMoments)\n                  .add(this.c.mul(gradient).div(\n                      newAccumulatedMeanSquare\n                          .sub(newAccumulatedMeanGrad.square().add(\n                              this.epsilonScalar))\n                          .sqrt()));\n\n          this.accumulatedMeanSquares[variableName].assign(\n              newAccumulatedMeanSquare);\n          this.accumulatedMeanGrads[variableName].assign(\n              newAccumulatedMeanGrad);\n          this.accumulatedMoments[variableName].assign(newAccumulatedMoments);\n\n          const newValue = value.sub(newAccumulatedMoments);\n          value.assign(newValue);\n        } else {\n          // Plain gradient\n          const newAccumulatedMeanSquare =\n              this.decayScalar.mul(accumulatedMeanSquare)\n                  .add(this.oneMinusDecay.mul(gradient.square()));\n\n          const newAccumulatedMoments =\n              this.momentumScalar.mul(accumulatedMoments)\n                  .add(this.c.mul(gradient).div(\n                      newAccumulatedMeanSquare.add(this.epsilonScalar).sqrt()));\n\n          this.accumulatedMeanSquares[variableName].assign(\n              newAccumulatedMeanSquare);\n          this.accumulatedMoments[variableName].assign(newAccumulatedMoments);\n\n          const newValue = value.sub(newAccumulatedMoments);\n          value.assign(newValue);\n        }\n      });\n    }\n  }\n\n  dispose() {\n    this.c.dispose();\n    this.epsilonScalar.dispose();\n    this.decayScalar.dispose();\n    this.momentumScalar.dispose();\n    this.oneMinusDecay.dispose();\n    if (this.accumulatedMeanSquares != null) {\n      Object.keys(this.accumulatedMeanSquares)\n          .forEach(name => this.accumulatedMeanSquares[name].dispose());\n    }\n    if (this.accumulatedMeanGrads != null && this.centered) {\n      Object.keys(this.accumulatedMeanGrads)\n          .forEach(name => this.accumulatedMeanGrads[name].dispose());\n    }\n    if (this.accumulatedMoments != null) {\n      Object.keys(this.accumulatedMoments)\n          .forEach(name => this.accumulatedMoments[name].dispose());\n    }\n  }\n\n  getConfig(): ConfigDict {\n    return {\n      learningRate: this.learningRate,\n      decay: this.decay,\n      momentum: this.momentum,\n      epsilon: this.epsilon,\n      centered: this.centered\n    };\n  }\n  static fromConfig<T extends Serializable>(\n      cls: SerializableConstructor<T>, config: ConfigDict): T {\n    return new cls(\n        config.learningRate, config.decay, config.momentum, config.epsilon,\n        config.centered);\n  }\n}\nregisterClass(RMSPropOptimizer);\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\nimport {AdadeltaOptimizer} from './adadelta_optimizer';\nimport {AdagradOptimizer} from './adagrad_optimizer';\nimport {AdamOptimizer} from './adam_optimizer';\nimport {AdamaxOptimizer} from './adamax_optimizer';\nimport {MomentumOptimizer} from './momentum_optimizer';\nimport {RMSPropOptimizer} from './rmsprop_optimizer';\nimport {SGDOptimizer} from './sgd_optimizer';\n\nexport class OptimizerConstructors {\n  /**\n   * Constructs a `tf.SGDOptimizer` that uses stochastic gradient descent.\n   *\n   * ```js\n   * // Fit a quadratic function by learning the coefficients a, b, c.\n   * const xs = tf.tensor1d([0, 1, 2, 3]);\n   * const ys = tf.tensor1d([1.1, 5.9, 16.8, 33.9]);\n   *\n   * const a = tf.scalar(Math.random()).variable();\n   * const b = tf.scalar(Math.random()).variable();\n   * const c = tf.scalar(Math.random()).variable();\n   *\n   * // y = a * x^2 + b * x + c.\n   * const f = x => a.mul(x.square()).add(b.mul(x)).add(c);\n   * const loss = (pred, label) => pred.sub(label).square().mean();\n   *\n   * const learningRate = 0.01;\n   * const optimizer = tf.train.sgd(learningRate);\n   *\n   * // Train the model.\n   * for (let i = 0; i < 10; i++) {\n   *   optimizer.minimize(() => loss(f(xs), ys));\n   * }\n   *\n   * // Make predictions.\n   * console.log(\n   *     `a: ${a.dataSync()}, b: ${b.dataSync()}, c: ${c.dataSync()}`);\n   * const preds = f(xs).dataSync();\n   * preds.forEach((pred, i) => {\n   *   console.log(`x: ${i}, pred: ${pred}`);\n   * });\n   * ```\n   *\n   * @param learningRate The learning rate to use for the SGD algorithm.\n   */\n  /**\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static sgd(learningRate: number): SGDOptimizer {\n    return new SGDOptimizer(learningRate);\n  }\n\n  /**\n   * Constructs a `tf.MomentumOptimizer` that uses momentum gradient\n   * descent.\n   *\n   * See\n   * [http://proceedings.mlr.press/v28/sutskever13.pdf](\n   * http://proceedings.mlr.press/v28/sutskever13.pdf)\n   *\n   * @param learningRate The learning rate to use for the Momentum gradient\n   * descent algorithm.\n   * @param momentum The momentum to use for the momentum gradient descent\n   * algorithm.\n   */\n  /**\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static momentum(learningRate: number, momentum: number, useNesterov = false):\n      MomentumOptimizer {\n    return new MomentumOptimizer(learningRate, momentum, useNesterov);\n  }\n\n  /**\n   * Constructs a `tf.RMSPropOptimizer` that uses RMSProp gradient\n   * descent. This implementation uses plain momentum and is not centered\n   * version of RMSProp.\n   *\n   * See\n   * [http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf](\n   * http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)\n   *\n   * @param learningRate The learning rate to use for the RMSProp gradient\n   * descent algorithm.\n   * @param decay The discounting factor for the history/coming gradient.\n   * @param momentum The momentum to use for the RMSProp gradient descent\n   * algorithm.\n   * @param epsilon Small value to avoid zero denominator.\n   * @param centered If true, gradients are normalized by the estimated\n   * variance of the gradient.\n   */\n  /**\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static rmsprop(\n      learningRate: number, decay = .9, momentum = 0.0, epsilon: number = null,\n      centered = false): RMSPropOptimizer {\n    return new RMSPropOptimizer(\n        learningRate, decay, momentum, epsilon, centered);\n  }\n\n  /**\n   * Constructs a `tf.AdamOptimizer` that uses the Adam algorithm.\n   * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n   *\n   * @param learningRate The learning rate to use for the Adam gradient\n   * descent algorithm.\n   * @param beta1 The exponential decay rate for the 1st moment estimates.\n   * @param beta2 The exponential decay rate for the 2nd moment estimates.\n   * @param epsilon A small constant for numerical stability.\n   */\n  /**\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static adam(\n      learningRate = 0.001, beta1 = 0.9, beta2 = 0.999,\n      epsilon: number = null): AdamOptimizer {\n    return new AdamOptimizer(learningRate, beta1, beta2, epsilon);\n  }\n\n  /**\n   * Constructs a `tf.AdadeltaOptimizer` that uses the Adadelta algorithm.\n   * See [https://arxiv.org/abs/1212.5701](https://arxiv.org/abs/1212.5701)\n   *\n   * @param learningRate The learning rate to use for the Adadelta gradient\n   * descent algorithm.\n   * @param rho The learning rate decay over each update.\n   * @param epsilon A constant epsilon used to better condition the grad\n   * update.\n   */\n  /**\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static adadelta(learningRate = .001, rho = .95, epsilon: number = null):\n      AdadeltaOptimizer {\n    return new AdadeltaOptimizer(learningRate, rho, epsilon);\n  }\n\n  /**\n   * Constructs a `tf.AdamaxOptimizer` that uses the Adamax algorithm.\n   * See [https://arxiv.org/abs/1412.6980](https://arxiv.org/abs/1412.6980)\n   *\n   * @param learningRate The learning rate to use for the Adamax gradient\n   * descent algorithm.\n   * @param beta1 The exponential decay rate for the 1st moment estimates.\n   * @param beta2 The exponential decay rate for the 2nd moment estimates.\n   * @param epsilon A small constant for numerical stability.\n   * @param decay The learning rate decay over each update.\n   */\n  /**\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static adamax(\n      learningRate = 0.002, beta1 = 0.9, beta2 = 0.999, epsilon: number = null,\n      decay = 0.0): AdamaxOptimizer {\n    return new AdamaxOptimizer(learningRate, beta1, beta2, epsilon, decay);\n  }\n\n  /**\n   * Constructs a `tf.AdagradOptimizer` that uses the Adagrad algorithm.\n   * See\n   * [http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf](\n   * http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf)\n   * or\n   * [http://ruder.io/optimizing-gradient-descent/index.html#adagrad](\n   * http://ruder.io/optimizing-gradient-descent/index.html#adagrad)\n   *\n   * @param learningRate The learning rate to use for the Adagrad gradient\n   * descent algorithm.\n   * @param initialAccumulatorValue Starting value for the accumulators, must be\n   * positive.\n   */\n  /**\n   * @doc {heading: 'Training', subheading: 'Optimizers', namespace: 'train'}\n   */\n  static adagrad(learningRate: number, initialAccumulatorValue = 0.1):\n      AdagradOptimizer {\n    return new AdagradOptimizer(learningRate, initialAccumulatorValue);\n  }\n}\n","/**\n * @license\n * Copyright 2018 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// So typings can propagate.\nimport {AdadeltaOptimizer} from './optimizers/adadelta_optimizer';\nimport {AdagradOptimizer} from './optimizers/adagrad_optimizer';\nimport {AdamOptimizer} from './optimizers/adam_optimizer';\nimport {AdamaxOptimizer} from './optimizers/adamax_optimizer';\nimport {MomentumOptimizer} from './optimizers/momentum_optimizer';\nimport {OptimizerConstructors} from './optimizers/optimizer_constructors';\nimport {RMSPropOptimizer} from './optimizers/rmsprop_optimizer';\nimport {SGDOptimizer} from './optimizers/sgd_optimizer';\n\n// tslint:disable-next-line:no-unused-expression\n[MomentumOptimizer, SGDOptimizer, AdadeltaOptimizer, AdagradOptimizer,\n RMSPropOptimizer, AdamaxOptimizer, AdamOptimizer];\n\nexport const train = {\n  sgd: OptimizerConstructors.sgd,\n  momentum: OptimizerConstructors.momentum,\n  adadelta: OptimizerConstructors.adadelta,\n  adagrad: OptimizerConstructors.adagrad,\n  rmsprop: OptimizerConstructors.rmsprop,\n  adamax: OptimizerConstructors.adamax,\n  adam: OptimizerConstructors.adam\n};\n","/**\n * @license\n * Copyright 2017 Google Inc. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\n\n// backend_cpu.ts and backend_webgl.ts are standalone files and should be\n// explicitly included here. Below, there is an export from backend_webgl, but\n// that doesn't count since it's exporting a Typescript interface.\nimport './kernels/backend_webgl';\nimport './kernels/backend_cpu';\n\nimport {nextFrame} from './browser_util';\nimport * as environment from './environment';\nimport {Environment} from './environment';\n\n// Serialization.\nimport * as io from './io/io';\nimport * as math from './math';\nimport * as serialization from './serialization';\nimport {setOpHandler} from './tensor';\nimport * as test_util from './test_util';\nimport * as util from './util';\nimport {version} from './version';\nimport * as webgl from './webgl';\n\nexport {InferenceModel, ModelPredictConfig} from './model_types';\n\n// Optimizers.\nexport {AdadeltaOptimizer} from './optimizers/adadelta_optimizer';\nexport {AdagradOptimizer} from './optimizers/adagrad_optimizer';\nexport {AdamOptimizer} from './optimizers/adam_optimizer';\nexport {AdamaxOptimizer} from './optimizers/adamax_optimizer';\nexport {MomentumOptimizer} from './optimizers/momentum_optimizer';\nexport {Optimizer} from './optimizers/optimizer';\nexport {RMSPropOptimizer} from './optimizers/rmsprop_optimizer';\nexport {SGDOptimizer} from './optimizers/sgd_optimizer';\nexport {Scalar, Tensor, Tensor1D, Tensor2D, Tensor3D, Tensor4D, TensorBuffer, variable, Variable} from './tensor';\nexport {NamedTensorMap} from './tensor_types';\nexport {DataType, DataTypeMap, DataValues, Rank, ShapeMap} from './types';\n\nexport * from './ops/ops';\nexport {LSTMCellFunc} from './ops/lstm';\nexport {Reduction} from './ops/loss_ops';\n\nexport * from './train';\nexport * from './globals';\n\nexport {Features} from './environment_util';\nexport {TimingInfo} from './engine';\nexport {ENV, Environment} from './environment';\n\nexport const setBackend = Environment.setBackend;\nexport const getBackend = Environment.getBackend;\nexport const disposeVariables = Environment.disposeVariables;\nexport const memory = Environment.memory;\nexport {version as version_core};\n\nexport {nextFrame};\n\n// Second level exports.\nexport {environment, io, math, serialization, test_util, util, webgl};\n\n// Backend specific.\nexport {KernelBackend, BackendTimingInfo, DataMover, DataStorage} from './kernels/backend';\n\nimport * as ops from './ops/ops';\nsetOpHandler(ops);\n","/** @license See the LICENSE file. */\n\n// This code is auto-generated, do not modify this file!\nconst version = '0.14.2';\nexport 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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tracking_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/tracking_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tracking_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tracking_test.js b/node_modules/@tensorflow/tfjs-core/dist/tracking_test.js
new file mode 100644
index 0000000..dc4a31c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tracking_test.js
@@ -0,0 +1,300 @@
+"use strict";
+var __awaiter = (this && this.__awaiter) || function (thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+};
+var __generator = (this && this.__generator) || function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+var _this = this;
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("./index");
+var jasmine_util_1 = require("./jasmine_util");
+var test_util_1 = require("./test_util");
+jasmine_util_1.describeWithFlags('time webgl', test_util_1.WEBGL_ENVS, function () {
+    it('upload + compute', function () { return __awaiter(_this, void 0, void 0, function () {
+        var a, time;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    a = tf.zeros([10, 10]);
+                    return [4, tf.time(function () { return a.square(); })];
+                case 1:
+                    time = _a.sent();
+                    expect(time.uploadWaitMs > 0);
+                    expect(time.downloadWaitMs === 0);
+                    expect(time.kernelMs > 0);
+                    expect(time.wallMs >= time.kernelMs);
+                    return [2];
+            }
+        });
+    }); });
+    it('upload + compute + dataSync', function () { return __awaiter(_this, void 0, void 0, function () {
+        var a, time;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    a = tf.zeros([10, 10]);
+                    return [4, tf.time(function () { return a.square().dataSync(); })];
+                case 1:
+                    time = _a.sent();
+                    expect(time.uploadWaitMs > 0);
+                    expect(time.downloadWaitMs > 0);
+                    expect(time.kernelMs > 0);
+                    expect(time.wallMs >= time.kernelMs);
+                    return [2];
+            }
+        });
+    }); });
+    it('upload + compute + data', function () { return __awaiter(_this, void 0, void 0, function () {
+        var a, time;
+        var _this = this;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    a = tf.zeros([10, 10]);
+                    return [4, tf.time(function () { return __awaiter(_this, void 0, void 0, function () { return __generator(this, function (_a) {
+                            switch (_a.label) {
+                                case 0: return [4, a.square().data()];
+                                case 1: return [2, _a.sent()];
+                            }
+                        }); }); })];
+                case 1:
+                    time = _a.sent();
+                    expect(time.uploadWaitMs > 0);
+                    expect(time.downloadWaitMs > 0);
+                    expect(time.kernelMs > 0);
+                    expect(time.wallMs >= time.kernelMs);
+                    return [2];
+            }
+        });
+    }); });
+    it('preupload (not included) + compute + data', function () { return __awaiter(_this, void 0, void 0, function () {
+        var a, time;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    a = tf.zeros([10, 10]);
+                    a.square();
+                    return [4, tf.time(function () { return a.sqrt(); })];
+                case 1:
+                    time = _a.sent();
+                    expect(time.uploadWaitMs === 0);
+                    expect(time.downloadWaitMs === 0);
+                    expect(time.kernelMs > 0);
+                    expect(time.wallMs >= time.kernelMs);
+                    return [2];
+            }
+        });
+    }); });
+});
+jasmine_util_1.describeWithFlags('time cpu', test_util_1.NODE_ENVS, function () {
+    it('simple upload', function () { return __awaiter(_this, void 0, void 0, function () {
+        var a, time;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    a = tf.zeros([10, 10]);
+                    return [4, tf.time(function () { return a.square(); })];
+                case 1:
+                    time = _a.sent();
+                    expect(time.kernelMs > 0);
+                    expect(time.wallMs >= time.kernelMs);
+                    return [2];
+            }
+        });
+    }); });
+});
+jasmine_util_1.describeWithFlags('tidy', test_util_1.ALL_ENVS, function () {
+    it('returns Tensor', function () {
+        tf.tidy(function () {
+            var a = tf.tensor1d([1, 2, 3]);
+            var b = tf.tensor1d([0, 0, 0]);
+            expect(tf.memory().numTensors).toBe(2);
+            tf.tidy(function () {
+                var result = tf.tidy(function () {
+                    b = tf.addStrict(a, b);
+                    b = tf.addStrict(a, b);
+                    b = tf.addStrict(a, b);
+                    return tf.add(a, b);
+                });
+                expect(tf.memory().numTensors).toBe(2 + 1);
+                test_util_1.expectArraysClose(result, [4, 8, 12]);
+            });
+            expect(tf.memory().numTensors).toBe(2);
+        });
+        expect(tf.memory().numTensors).toBe(0);
+    });
+    it('multiple disposes does not affect num arrays', function () {
+        expect(tf.memory().numTensors).toBe(0);
+        var a = tf.tensor1d([1, 2, 3]);
+        var b = tf.tensor1d([1, 2, 3]);
+        expect(tf.memory().numTensors).toBe(2);
+        a.dispose();
+        a.dispose();
+        expect(tf.memory().numTensors).toBe(1);
+        b.dispose();
+        expect(tf.memory().numTensors).toBe(0);
+    });
+    it('allows primitive types', function () {
+        var a = tf.tidy(function () { return 5; });
+        expect(a).toBe(5);
+        var b = tf.tidy(function () { return 'hello'; });
+        expect(b).toBe('hello');
+    });
+    it('allows complex types', function () {
+        var res = tf.tidy(function () {
+            return { a: tf.scalar(1), b: 'hello', c: [tf.scalar(2), 'world'] };
+        });
+        test_util_1.expectArraysClose(res.a, [1]);
+        test_util_1.expectArraysClose(res.c[0], [2]);
+    });
+    it('returns Tensor[]', function () {
+        var a = tf.tensor1d([1, 2, 3]);
+        var b = tf.tensor1d([0, -1, 1]);
+        expect(tf.memory().numTensors).toBe(2);
+        tf.tidy(function () {
+            var result = tf.tidy(function () {
+                tf.add(a, b);
+                return [tf.add(a, b), tf.sub(a, b)];
+            });
+            expect(tf.memory().numTensors).toBe(4);
+            test_util_1.expectArraysClose(result[0], [1, 1, 4]);
+            test_util_1.expectArraysClose(result[1], [1, 3, 2]);
+            expect(tf.memory().numTensors).toBe(4);
+        });
+        expect(tf.memory().numTensors).toBe(2);
+        a.dispose();
+        b.dispose();
+        expect(tf.memory().numTensors).toBe(0);
+    });
+    it('basic usage without return', function () {
+        var a = tf.tensor1d([1, 2, 3]);
+        var b = tf.tensor1d([0, 0, 0]);
+        expect(tf.memory().numTensors).toBe(2);
+        tf.tidy(function () {
+            b = tf.addStrict(a, b);
+            b = tf.addStrict(a, b);
+            b = tf.addStrict(a, b);
+            tf.add(a, b);
+        });
+        expect(tf.memory().numTensors).toBe(2);
+    });
+    it('nested usage', function () {
+        var a = tf.tensor1d([1, 2, 3]);
+        var b = tf.tensor1d([0, 0, 0]);
+        expect(tf.memory().numTensors).toBe(2);
+        tf.tidy(function () {
+            var result = tf.tidy(function () {
+                b = tf.addStrict(a, b);
+                b = tf.tidy(function () {
+                    b = tf.tidy(function () {
+                        return tf.addStrict(a, b);
+                    });
+                    expect(tf.memory().numTensors).toBe(4);
+                    tf.tidy(function () {
+                        tf.addStrict(a, b);
+                    });
+                    expect(tf.memory().numTensors).toBe(4);
+                    return tf.addStrict(a, b);
+                });
+                expect(tf.memory().numTensors).toBe(4);
+                return tf.addStrict(a, b);
+            });
+            expect(tf.memory().numTensors).toBe(3);
+            test_util_1.expectArraysClose(result, [4, 8, 12]);
+        });
+        expect(tf.memory().numTensors).toBe(2);
+    });
+    it('nested usage returns tensor created from outside scope', function () {
+        var x = tf.scalar(1);
+        tf.tidy(function () {
+            tf.tidy(function () {
+                return x;
+            });
+        });
+        expect(x.isDisposed).toBe(false);
+    });
+    it('nested usage with keep works', function () {
+        var b;
+        tf.tidy(function () {
+            var a = tf.scalar(1);
+            tf.tidy(function () {
+                b = tf.keep(a);
+            });
+        });
+        expect(b.isDisposed).toBe(false);
+        b.dispose();
+    });
+    it('single argument', function () {
+        var hasRan = false;
+        tf.tidy(function () {
+            hasRan = true;
+        });
+        expect(hasRan).toBe(true);
+    });
+    it('single argument, but not a function throws error', function () {
+        expect(function () {
+            tf.tidy('asdf');
+        }).toThrowError();
+    });
+    it('2 arguments, first is string', function () {
+        var hasRan = false;
+        tf.tidy('name', function () {
+            hasRan = true;
+        });
+        expect(hasRan).toBe(true);
+    });
+    it('2 arguments, but first is not string throws error', function () {
+        expect(function () {
+            tf.tidy(4, function () { });
+        }).toThrowError();
+    });
+    it('2 arguments, but second is not a function throws error', function () {
+        expect(function () {
+            tf.tidy('name', 'another name');
+        }).toThrowError();
+    });
+    it('works with arbitrary depth of result', function () {
+        tf.tidy(function () {
+            var res = tf.tidy(function () {
+                return [tf.scalar(1), [[tf.scalar(2)]], { list: [tf.scalar(3)] }];
+            });
+            test_util_1.expectArraysEqual(res[0], [1]);
+            test_util_1.expectArraysEqual(res[1][0][0], [2]);
+            test_util_1.expectArraysEqual(res[2].list[0], [3]);
+            expect(tf.memory().numTensors).toBe(3);
+            return res[0];
+        });
+        expect(tf.memory().numTensors).toBe(1);
+    });
+});
+//# sourceMappingURL=tracking_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/tracking_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/tracking_test.js.map
new file mode 100644
index 0000000..47faf5a
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/tracking_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/train.d.ts b/node_modules/@tensorflow/tfjs-core/dist/train.d.ts
new file mode 100644
index 0000000..512e1b0
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/train.d.ts
@@ -0,0 +1,10 @@
+import { OptimizerConstructors } from './optimizers/optimizer_constructors';
+export declare const train: {
+    sgd: typeof OptimizerConstructors.sgd;
+    momentum: typeof OptimizerConstructors.momentum;
+    adadelta: typeof OptimizerConstructors.adadelta;
+    adagrad: typeof OptimizerConstructors.adagrad;
+    rmsprop: typeof OptimizerConstructors.rmsprop;
+    adamax: typeof OptimizerConstructors.adamax;
+    adam: typeof OptimizerConstructors.adam;
+};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/train.js b/node_modules/@tensorflow/tfjs-core/dist/train.js
new file mode 100644
index 0000000..4f2dd40
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/train.js
@@ -0,0 +1,22 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var adadelta_optimizer_1 = require("./optimizers/adadelta_optimizer");
+var adagrad_optimizer_1 = require("./optimizers/adagrad_optimizer");
+var adam_optimizer_1 = require("./optimizers/adam_optimizer");
+var adamax_optimizer_1 = require("./optimizers/adamax_optimizer");
+var momentum_optimizer_1 = require("./optimizers/momentum_optimizer");
+var optimizer_constructors_1 = require("./optimizers/optimizer_constructors");
+var rmsprop_optimizer_1 = require("./optimizers/rmsprop_optimizer");
+var sgd_optimizer_1 = require("./optimizers/sgd_optimizer");
+[momentum_optimizer_1.MomentumOptimizer, sgd_optimizer_1.SGDOptimizer, adadelta_optimizer_1.AdadeltaOptimizer, adagrad_optimizer_1.AdagradOptimizer,
+    rmsprop_optimizer_1.RMSPropOptimizer, adamax_optimizer_1.AdamaxOptimizer, adam_optimizer_1.AdamOptimizer];
+exports.train = {
+    sgd: optimizer_constructors_1.OptimizerConstructors.sgd,
+    momentum: optimizer_constructors_1.OptimizerConstructors.momentum,
+    adadelta: optimizer_constructors_1.OptimizerConstructors.adadelta,
+    adagrad: optimizer_constructors_1.OptimizerConstructors.adagrad,
+    rmsprop: optimizer_constructors_1.OptimizerConstructors.rmsprop,
+    adamax: optimizer_constructors_1.OptimizerConstructors.adamax,
+    adam: optimizer_constructors_1.OptimizerConstructors.adam
+};
+//# sourceMappingURL=train.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/train.js.map b/node_modules/@tensorflow/tfjs-core/dist/train.js.map
new file mode 100644
index 0000000..94b2e3b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/train.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"train.js","sourceRoot":"","sources":["../src/train.ts"],"names":[],"mappings":";;AAkBA,sEAAkE;AAClE,oEAAgE;AAChE,8DAA0D;AAC1D,kEAA8D;AAC9D,sEAAkE;AAClE,8EAA0E;AAC1E,oEAAgE;AAChE,4DAAwD;AAGxD,CAAC,sCAAiB,EAAE,4BAAY,EAAE,sCAAiB,EAAE,oCAAgB;IACpE,oCAAgB,EAAE,kCAAe,EAAE,8BAAa,CAAC,CAAC;AAEtC,QAAA,KAAK,GAAG;IACnB,GAAG,EAAE,8CAAqB,CAAC,GAAG;IAC9B,QAAQ,EAAE,8CAAqB,CAAC,QAAQ;IACxC,QAAQ,EAAE,8CAAqB,CAAC,QAAQ;IACxC,OAAO,EAAE,8CAAqB,CAAC,OAAO;IACtC,OAAO,EAAE,8CAAqB,CAAC,OAAO;IACtC,MAAM,EAAE,8CAAqB,CAAC,MAAM;IACpC,IAAI,EAAE,8CAAqB,CAAC,IAAI;CACjC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/types.d.ts b/node_modules/@tensorflow/tfjs-core/dist/types.d.ts
new file mode 100644
index 0000000..07c7fb1
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/types.d.ts
@@ -0,0 +1,50 @@
+export interface ShapeMap {
+    R0: number[];
+    R1: [number];
+    R2: [number, number];
+    R3: [number, number, number];
+    R4: [number, number, number, number];
+    R5: [number, number, number, number, number];
+    R6: [number, number, number, number, number, number];
+}
+export interface DataTypeMap {
+    float32: Float32Array;
+    int32: Int32Array;
+    bool: Uint8Array;
+    complex64: Float32Array;
+    string: string[];
+}
+export interface SingleValueMap {
+    bool: boolean;
+    int32: number;
+    float32: number;
+    complex64: number;
+    string: string;
+}
+export declare type DataType = keyof DataTypeMap;
+export declare type NumericDataType = 'float32' | 'int32' | 'bool' | 'complex64';
+export declare type TypedArray = Float32Array | Int32Array | Uint8Array;
+export declare type DataValues = DataTypeMap[DataType];
+export declare enum Rank {
+    R0 = "R0",
+    R1 = "R1",
+    R2 = "R2",
+    R3 = "R3",
+    R4 = "R4",
+    R5 = "R5",
+    R6 = "R6"
+}
+export declare type FlatVector = boolean[] | number[] | TypedArray;
+export declare type RegularArray<T> = T[] | T[][] | T[][][] | T[][][][] | T[][][][][] | T[][][][][][];
+export interface RecursiveArray<T extends any> {
+    [index: number]: T | RecursiveArray<T>;
+}
+export declare function upcastType(typeA: DataType, typeB: DataType): DataType;
+export declare function sumOutType(type: DataType): DataType;
+export declare type TensorLike = TypedArray | number | boolean | string | RegularArray<number | number[] | TypedArray> | RegularArray<boolean> | RegularArray<string>;
+export declare type TensorLike1D = TypedArray | number[] | boolean[] | string[];
+export declare type TensorLike2D = TypedArray | number[] | number[][] | boolean[] | boolean[][] | string[] | string[][];
+export declare type TensorLike3D = TypedArray | number[] | number[][][] | boolean[] | boolean[][][] | string[] | string[][][];
+export declare type TensorLike4D = TypedArray | number[] | number[][][][] | boolean[] | boolean[][][][] | string[] | string[][][][];
+export declare type TensorLike5D = TypedArray | number[] | number[][][][][] | boolean[] | boolean[][][][][] | string[] | string[][][][][];
+export declare type TensorLike6D = TypedArray | number[] | number[][][][][][] | boolean[] | boolean[][][][][][] | string[] | string[][][][][][];
diff --git a/node_modules/@tensorflow/tfjs-core/dist/types.js b/node_modules/@tensorflow/tfjs-core/dist/types.js
new file mode 100644
index 0000000..47f336e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/types.js
@@ -0,0 +1,61 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var Rank;
+(function (Rank) {
+    Rank["R0"] = "R0";
+    Rank["R1"] = "R1";
+    Rank["R2"] = "R2";
+    Rank["R3"] = "R3";
+    Rank["R4"] = "R4";
+    Rank["R5"] = "R5";
+    Rank["R6"] = "R6";
+})(Rank = exports.Rank || (exports.Rank = {}));
+var UpcastInt32AndMap;
+(function (UpcastInt32AndMap) {
+    UpcastInt32AndMap["float32"] = "float32";
+    UpcastInt32AndMap["int32"] = "int32";
+    UpcastInt32AndMap["bool"] = "int32";
+    UpcastInt32AndMap["complex64"] = "complex64";
+})(UpcastInt32AndMap || (UpcastInt32AndMap = {}));
+var UpcastBoolAndMap;
+(function (UpcastBoolAndMap) {
+    UpcastBoolAndMap["float32"] = "float32";
+    UpcastBoolAndMap["int32"] = "int32";
+    UpcastBoolAndMap["bool"] = "bool";
+    UpcastBoolAndMap["complex64"] = "complex64";
+})(UpcastBoolAndMap || (UpcastBoolAndMap = {}));
+var UpcastFloat32AndMap;
+(function (UpcastFloat32AndMap) {
+    UpcastFloat32AndMap["float32"] = "float32";
+    UpcastFloat32AndMap["int32"] = "float32";
+    UpcastFloat32AndMap["bool"] = "float32";
+    UpcastFloat32AndMap["complex64"] = "complex64";
+})(UpcastFloat32AndMap || (UpcastFloat32AndMap = {}));
+var UpcastComplex64AndMap;
+(function (UpcastComplex64AndMap) {
+    UpcastComplex64AndMap["float32"] = "complex64";
+    UpcastComplex64AndMap["int32"] = "complex64";
+    UpcastComplex64AndMap["bool"] = "complex64";
+    UpcastComplex64AndMap["complex64"] = "complex64";
+})(UpcastComplex64AndMap || (UpcastComplex64AndMap = {}));
+var upcastTypeMap = {
+    'float32': UpcastFloat32AndMap,
+    'int32': UpcastInt32AndMap,
+    'bool': UpcastBoolAndMap,
+    'complex64': UpcastComplex64AndMap
+};
+function upcastType(typeA, typeB) {
+    if (typeA === 'string' || typeB === 'string') {
+        if (typeA === 'string' && typeB === 'string') {
+            return 'string';
+        }
+        throw new Error("Can not upcast " + typeA + " with " + typeB);
+    }
+    return upcastTypeMap[typeA][typeB];
+}
+exports.upcastType = upcastType;
+function sumOutType(type) {
+    return upcastType(type, 'int32');
+}
+exports.sumOutType = sumOutType;
+//# sourceMappingURL=types.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/types.js.map b/node_modules/@tensorflow/tfjs-core/dist/types.js.map
new file mode 100644
index 0000000..cabf751
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/types.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"types.js","sourceRoot":"","sources":["../src/types.ts"],"names":[],"mappings":";;AAkDA,IAAY,IAQX;AARD,WAAY,IAAI;IACd,iBAAS,CAAA;IACT,iBAAS,CAAA;IACT,iBAAS,CAAA;IACT,iBAAS,CAAA;IACT,iBAAS,CAAA;IACT,iBAAS,CAAA;IACT,iBAAS,CAAA;AACX,CAAC,EARW,IAAI,GAAJ,YAAI,KAAJ,YAAI,QAQf;AAaD,IAAK,iBAKJ;AALD,WAAK,iBAAiB;IACpB,wCAAqB,CAAA;IACrB,oCAAiB,CAAA;IACjB,mCAAgB,CAAA;IAChB,4CAAyB,CAAA;AAC3B,CAAC,EALI,iBAAiB,KAAjB,iBAAiB,QAKrB;AAED,IAAK,gBAKJ;AALD,WAAK,gBAAgB;IACnB,uCAAqB,CAAA;IACrB,mCAAiB,CAAA;IACjB,iCAAe,CAAA;IACf,2CAAyB,CAAA;AAC3B,CAAC,EALI,gBAAgB,KAAhB,gBAAgB,QAKpB;AAED,IAAK,mBAKJ;AALD,WAAK,mBAAmB;IACtB,0CAAqB,CAAA;IACrB,wCAAmB,CAAA;IACnB,uCAAkB,CAAA;IAClB,8CAAyB,CAAA;AAC3B,CAAC,EALI,mBAAmB,KAAnB,mBAAmB,QAKvB;AAED,IAAK,qBAKJ;AALD,WAAK,qBAAqB;IACxB,8CAAuB,CAAA;IACvB,4CAAqB,CAAA;IACrB,2CAAoB,CAAA;IACpB,gDAAyB,CAAA;AAC3B,CAAC,EALI,qBAAqB,KAArB,qBAAqB,QAKzB;AAED,IAAM,aAAa,GAAG;IACpB,SAAS,EAAE,mBAAmB;IAC9B,OAAO,EAAE,iBAAiB;IAC1B,MAAM,EAAE,gBAAgB;IACxB,WAAW,EAAE,qBAAqB;CACnC,CAAC;AAEF,oBAA2B,KAAe,EAAE,KAAe;IACzD,IAAI,KAAK,KAAK,QAAQ,IAAI,KAAK,KAAK,QAAQ,EAAE;QAC5C,IAAI,KAAK,KAAK,QAAQ,IAAI,KAAK,KAAK,QAAQ,EAAE;YAC5C,OAAO,QAAQ,CAAC;SACjB;QACD,MAAM,IAAI,KAAK,CAAC,oBAAkB,KAAK,cAAS,KAAO,CAAC,CAAC;KAC1D;IACD,OAAO,aAAa,CAAC,KAAK,CAAC,CAAC,KAAK,CAAC,CAAC;AACrC,CAAC;AARD,gCAQC;AAGD,oBAA2B,IAAc;IACvC,OAAO,UAAU,CAAC,IAAI,EAAE,OAAO,CAAC,CAAC;AACnC,CAAC;AAFD,gCAEC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/types_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/types_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/types_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/types_test.js b/node_modules/@tensorflow/tfjs-core/dist/types_test.js
new file mode 100644
index 0000000..871108f
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/types_test.js
@@ -0,0 +1,31 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var types_1 = require("./types");
+describe('upcastType', function () {
+    it('upcasts bool to bool', function () {
+        expect(types_1.upcastType('bool', 'bool')).toBe('bool');
+    });
+    it('upcasts bool/int32 to int32', function () {
+        expect(types_1.upcastType('bool', 'int32')).toBe('int32');
+        expect(types_1.upcastType('int32', 'int32')).toBe('int32');
+    });
+    it('upcasts bool/int32/float32 to float32', function () {
+        expect(types_1.upcastType('bool', 'float32')).toBe('float32');
+        expect(types_1.upcastType('int32', 'float32')).toBe('float32');
+        expect(types_1.upcastType('float32', 'float32')).toBe('float32');
+    });
+    it('upcasts bool/int32/float32/complex64 to complex64', function () {
+        expect(types_1.upcastType('bool', 'complex64')).toBe('complex64');
+        expect(types_1.upcastType('int32', 'complex64')).toBe('complex64');
+        expect(types_1.upcastType('float32', 'complex64')).toBe('complex64');
+        expect(types_1.upcastType('complex64', 'complex64')).toBe('complex64');
+    });
+    it('fails to upcast anything other than string with string', function () {
+        expect(function () { return types_1.upcastType('bool', 'string'); }).toThrowError();
+        expect(function () { return types_1.upcastType('int32', 'string'); }).toThrowError();
+        expect(function () { return types_1.upcastType('float32', 'string'); }).toThrowError();
+        expect(function () { return types_1.upcastType('complex64', 'string'); }).toThrowError();
+        expect(types_1.upcastType('string', 'string')).toBe('string');
+    });
+});
+//# sourceMappingURL=types_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/types_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/types_test.js.map
new file mode 100644
index 0000000..2abfb13
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/types_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"types_test.js","sourceRoot":"","sources":["../src/types_test.ts"],"names":[],"mappings":";;AAiBA,iCAAmC;AAEnC,QAAQ,CAAC,YAAY,EAAE;IACrB,EAAE,CAAC,sBAAsB,EAAE;QACzB,MAAM,CAAC,kBAAU,CAAC,MAAM,EAAE,MAAM,CAAC,CAAC,CAAC,IAAI,CAAC,MAAM,CAAC,CAAC;IAClD,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,6BAA6B,EAAE;QAChC,MAAM,CAAC,kBAAU,CAAC,MAAM,EAAE,OAAO,CAAC,CAAC,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC;QAClD,MAAM,CAAC,kBAAU,CAAC,OAAO,EAAE,OAAO,CAAC,CAAC,CAAC,IAAI,CAAC,OAAO,CAAC,CAAC;IACrD,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,uCAAuC,EAAE;QAC1C,MAAM,CAAC,kBAAU,CAAC,MAAM,EAAE,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,SAAS,CAAC,CAAC;QACtD,MAAM,CAAC,kBAAU,CAAC,OAAO,EAAE,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,SAAS,CAAC,CAAC;QACvD,MAAM,CAAC,kBAAU,CAAC,SAAS,EAAE,SAAS,CAAC,CAAC,CAAC,IAAI,CAAC,SAAS,CAAC,CAAC;IAC3D,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,mDAAmD,EAAE;QACtD,MAAM,CAAC,kBAAU,CAAC,MAAM,EAAE,WAAW,CAAC,CAAC,CAAC,IAAI,CAAC,WAAW,CAAC,CAAC;QAC1D,MAAM,CAAC,kBAAU,CAAC,OAAO,EAAE,WAAW,CAAC,CAAC,CAAC,IAAI,CAAC,WAAW,CAAC,CAAC;QAC3D,MAAM,CAAC,kBAAU,CAAC,SAAS,EAAE,WAAW,CAAC,CAAC,CAAC,IAAI,CAAC,WAAW,CAAC,CAAC;QAC7D,MAAM,CAAC,kBAAU,CAAC,WAAW,EAAE,WAAW,CAAC,CAAC,CAAC,IAAI,CAAC,WAAW,CAAC,CAAC;IACjE,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,wDAAwD,EAAE;QAC3D,MAAM,CAAC,cAAM,OAAA,kBAAU,CAAC,MAAM,EAAE,QAAQ,CAAC,EAA5B,CAA4B,CAAC,CAAC,YAAY,EAAE,CAAC;QAC1D,MAAM,CAAC,cAAM,OAAA,kBAAU,CAAC,OAAO,EAAE,QAAQ,CAAC,EAA7B,CAA6B,CAAC,CAAC,YAAY,EAAE,CAAC;QAC3D,MAAM,CAAC,cAAM,OAAA,kBAAU,CAAC,SAAS,EAAE,QAAQ,CAAC,EAA/B,CAA+B,CAAC,CAAC,YAAY,EAAE,CAAC;QAC7D,MAAM,CAAC,cAAM,OAAA,kBAAU,CAAC,WAAW,EAAE,QAAQ,CAAC,EAAjC,CAAiC,CAAC,CAAC,YAAY,EAAE,CAAC;QAE/D,MAAM,CAAC,kBAAU,CAAC,QAAQ,EAAE,QAAQ,CAAC,CAAC,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC;IACxD,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/util.d.ts b/node_modules/@tensorflow/tfjs-core/dist/util.d.ts
new file mode 100644
index 0000000..8ac6b51
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/util.d.ts
@@ -0,0 +1,44 @@
+import { DataType, DataTypeMap, FlatVector, NumericDataType, RecursiveArray, TensorLike, TypedArray } from './types';
+export declare function shuffle(array: any[] | Uint32Array | Int32Array | Float32Array): void;
+export declare function clamp(min: number, x: number, max: number): number;
+export declare function nearestLargerEven(val: number): number;
+export declare function sum(arr: number[]): number;
+export declare function randUniform(a: number, b: number): number;
+export declare function distSquared(a: FlatVector, b: FlatVector): number;
+export declare function assert(expr: boolean, msg: string | (() => string)): void;
+export declare function assertShapesMatch(shapeA: number[], shapeB: number[], errorMessagePrefix?: string): void;
+export declare function assertNonNull(a: TensorLike): void;
+export declare function flatten<T extends number | boolean | string | Promise<number> | TypedArray>(arr: T | RecursiveArray<T>, ret?: T[]): T[];
+export declare function sizeFromShape(shape: number[]): number;
+export declare function isScalarShape(shape: number[]): boolean;
+export declare function arraysEqual(n1: FlatVector, n2: FlatVector): boolean;
+export declare function isInt(a: number): boolean;
+export declare function tanh(x: number): number;
+export declare function sizeToSquarishShape(size: number): [number, number];
+export declare function createShuffledIndices(n: number): Uint32Array;
+export declare function rightPad(a: string, size: number): string;
+export declare function repeatedTry(checkFn: () => boolean, delayFn?: (counter: number) => number, maxCounter?: number): Promise<void>;
+export declare function inferFromImplicitShape(shape: number[], size: number): number[];
+export declare function squeezeShape(shape: number[], axis?: number[]): {
+    newShape: number[];
+    keptDims: number[];
+};
+export declare function getTypedArrayFromDType<D extends NumericDataType>(dtype: D, size: number): DataTypeMap[D];
+export declare function getArrayFromDType<D extends DataType>(dtype: D, size: number): DataTypeMap[D];
+export declare function checkComputationForNaN<D extends DataType>(vals: DataTypeMap[D], dtype: D, name: string): void;
+export declare function checkConversionForNaN<D extends DataType>(vals: DataTypeMap[D] | number[], dtype: D): void;
+export declare function hasEncodingLoss(oldType: DataType, newType: DataType): boolean;
+export declare function isTypedArray(a: {}): a is Float32Array | Int32Array | Uint8Array;
+export declare function bytesPerElement(dtype: DataType): number;
+export declare function bytesFromStringArray(arr: string[]): number;
+export declare function isString(value: {}): value is string;
+export declare function isBoolean(value: {}): boolean;
+export declare function isNumber(value: {}): boolean;
+export declare function inferDtype(values: TensorLike): DataType;
+export declare function isFunction(f: Function): boolean;
+export declare function nearestDivisor(size: number, start: number): number;
+export declare function computeStrides(shape: number[]): number[];
+export declare function toTypedArray(a: TensorLike, dtype: DataType, debugMode: boolean): TypedArray;
+export declare function makeOnesTypedArray<D extends DataType>(size: number, dtype: D): DataTypeMap[D];
+export declare function makeZerosTypedArray<D extends DataType>(size: number, dtype: D): DataTypeMap[D];
+export declare function now(): number;
diff --git a/node_modules/@tensorflow/tfjs-core/dist/util.js b/node_modules/@tensorflow/tfjs-core/dist/util.js
new file mode 100644
index 0000000..dac715c
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/util.js
@@ -0,0 +1,474 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+function shuffle(array) {
+    var counter = array.length;
+    var temp = 0;
+    var index = 0;
+    while (counter > 0) {
+        index = (Math.random() * counter) | 0;
+        counter--;
+        temp = array[counter];
+        array[counter] = array[index];
+        array[index] = temp;
+    }
+}
+exports.shuffle = shuffle;
+function clamp(min, x, max) {
+    return Math.max(min, Math.min(x, max));
+}
+exports.clamp = clamp;
+function nearestLargerEven(val) {
+    return val % 2 === 0 ? val : val + 1;
+}
+exports.nearestLargerEven = nearestLargerEven;
+function sum(arr) {
+    var sum = 0;
+    for (var i = 0; i < arr.length; i++) {
+        sum += arr[i];
+    }
+    return sum;
+}
+exports.sum = sum;
+function randUniform(a, b) {
+    var r = Math.random();
+    return (b * r) + (1 - r) * a;
+}
+exports.randUniform = randUniform;
+function distSquared(a, b) {
+    var result = 0;
+    for (var i = 0; i < a.length; i++) {
+        var diff = Number(a[i]) - Number(b[i]);
+        result += diff * diff;
+    }
+    return result;
+}
+exports.distSquared = distSquared;
+function assert(expr, msg) {
+    if (!expr) {
+        throw new Error(typeof msg === 'string' ? msg : msg());
+    }
+}
+exports.assert = assert;
+function assertShapesMatch(shapeA, shapeB, errorMessagePrefix) {
+    if (errorMessagePrefix === void 0) { errorMessagePrefix = ''; }
+    assert(arraysEqual(shapeA, shapeB), errorMessagePrefix + (" Shapes " + shapeA + " and " + shapeB + " must match"));
+}
+exports.assertShapesMatch = assertShapesMatch;
+function assertNonNull(a) {
+    assert(a != null, "The input to the tensor constructor must be a non-null value.");
+}
+exports.assertNonNull = assertNonNull;
+function flatten(arr, ret) {
+    if (ret === void 0) { ret = []; }
+    if (Array.isArray(arr) || isTypedArray(arr)) {
+        for (var i = 0; i < arr.length; ++i) {
+            flatten(arr[i], ret);
+        }
+    }
+    else {
+        ret.push(arr);
+    }
+    return ret;
+}
+exports.flatten = flatten;
+function sizeFromShape(shape) {
+    if (shape.length === 0) {
+        return 1;
+    }
+    var size = shape[0];
+    for (var i = 1; i < shape.length; i++) {
+        size *= shape[i];
+    }
+    return size;
+}
+exports.sizeFromShape = sizeFromShape;
+function isScalarShape(shape) {
+    return shape.length === 0;
+}
+exports.isScalarShape = isScalarShape;
+function arraysEqual(n1, n2) {
+    if (n1 === n2) {
+        return true;
+    }
+    if (n1 == null || n2 == null) {
+        return false;
+    }
+    if (n1.length !== n2.length) {
+        return false;
+    }
+    for (var i = 0; i < n1.length; i++) {
+        if (n1[i] !== n2[i]) {
+            return false;
+        }
+    }
+    return true;
+}
+exports.arraysEqual = arraysEqual;
+function isInt(a) {
+    return a % 1 === 0;
+}
+exports.isInt = isInt;
+function tanh(x) {
+    if (Math.tanh != null) {
+        return Math.tanh(x);
+    }
+    if (x === Infinity) {
+        return 1;
+    }
+    else if (x === -Infinity) {
+        return -1;
+    }
+    else {
+        var e2x = Math.exp(2 * x);
+        return (e2x - 1) / (e2x + 1);
+    }
+}
+exports.tanh = tanh;
+function sizeToSquarishShape(size) {
+    for (var a = Math.floor(Math.sqrt(size)); a > 1; --a) {
+        if (size % a === 0) {
+            return [a, size / a];
+        }
+    }
+    return [1, size];
+}
+exports.sizeToSquarishShape = sizeToSquarishShape;
+function createShuffledIndices(n) {
+    var shuffledIndices = new Uint32Array(n);
+    for (var i = 0; i < n; ++i) {
+        shuffledIndices[i] = i;
+    }
+    shuffle(shuffledIndices);
+    return shuffledIndices;
+}
+exports.createShuffledIndices = createShuffledIndices;
+function rightPad(a, size) {
+    if (size <= a.length) {
+        return a;
+    }
+    return a + ' '.repeat(size - a.length);
+}
+exports.rightPad = rightPad;
+function repeatedTry(checkFn, delayFn, maxCounter) {
+    if (delayFn === void 0) { delayFn = function (counter) { return 0; }; }
+    return new Promise(function (resolve, reject) {
+        var tryCount = 0;
+        var tryFn = function () {
+            if (checkFn()) {
+                resolve();
+                return;
+            }
+            tryCount++;
+            var nextBackoff = delayFn(tryCount);
+            if (maxCounter != null && tryCount >= maxCounter) {
+                reject();
+                return;
+            }
+            setTimeout(tryFn, nextBackoff);
+        };
+        tryFn();
+    });
+}
+exports.repeatedTry = repeatedTry;
+function inferFromImplicitShape(shape, size) {
+    var shapeProd = 1;
+    var implicitIdx = -1;
+    for (var i = 0; i < shape.length; ++i) {
+        if (shape[i] >= 0) {
+            shapeProd *= shape[i];
+        }
+        else if (shape[i] === -1) {
+            if (implicitIdx !== -1) {
+                throw Error("Shapes can only have 1 implicit size. " +
+                    ("Found -1 at dim " + implicitIdx + " and dim " + i));
+            }
+            implicitIdx = i;
+        }
+        else if (shape[i] < 0) {
+            throw Error("Shapes can not be < 0. Found " + shape[i] + " at dim " + i);
+        }
+    }
+    if (implicitIdx === -1) {
+        if (size > 0 && size !== shapeProd) {
+            throw Error("Size(" + size + ") must match the product of shape " + shape);
+        }
+        return shape;
+    }
+    if (shapeProd === 0) {
+        throw Error("Cannot infer the missing size in [" + shape + "] when " +
+            "there are 0 elements");
+    }
+    if (size % shapeProd !== 0) {
+        throw Error("The implicit shape can't be a fractional number. " +
+            ("Got " + size + " / " + shapeProd));
+    }
+    var newShape = shape.slice();
+    newShape[implicitIdx] = size / shapeProd;
+    return newShape;
+}
+exports.inferFromImplicitShape = inferFromImplicitShape;
+function squeezeShape(shape, axis) {
+    var newShape = [];
+    var keptDims = [];
+    var j = 0;
+    for (var i = 0; i < shape.length; ++i) {
+        if (axis != null) {
+            if (axis[j] === i && shape[i] !== 1) {
+                throw new Error("Can't squeeze axis " + i + " since its dim '" + shape[i] + "' is not 1");
+            }
+            if ((axis[j] == null || axis[j] > i) && shape[i] === 1) {
+                newShape.push(shape[i]);
+                keptDims.push(i);
+            }
+            if (axis[j] <= i) {
+                j++;
+            }
+        }
+        if (shape[i] !== 1) {
+            newShape.push(shape[i]);
+            keptDims.push(i);
+        }
+    }
+    return { newShape: newShape, keptDims: keptDims };
+}
+exports.squeezeShape = squeezeShape;
+function getTypedArrayFromDType(dtype, size) {
+    var values = null;
+    if (dtype == null || dtype === 'float32') {
+        values = new Float32Array(size);
+    }
+    else if (dtype === 'int32') {
+        values = new Int32Array(size);
+    }
+    else if (dtype === 'bool') {
+        values = new Uint8Array(size);
+    }
+    else {
+        throw new Error("Unknown data type " + dtype);
+    }
+    return values;
+}
+exports.getTypedArrayFromDType = getTypedArrayFromDType;
+function getArrayFromDType(dtype, size) {
+    var values = null;
+    if (dtype == null || dtype === 'float32') {
+        values = new Float32Array(size);
+    }
+    else if (dtype === 'int32') {
+        values = new Int32Array(size);
+    }
+    else if (dtype === 'bool') {
+        values = new Uint8Array(size);
+    }
+    else if (dtype === 'string') {
+        values = new Array(size);
+    }
+    else {
+        throw new Error("Unknown data type " + dtype);
+    }
+    return values;
+}
+exports.getArrayFromDType = getArrayFromDType;
+function checkComputationForNaN(vals, dtype, name) {
+    if (dtype !== 'float32') {
+        return;
+    }
+    for (var i = 0; i < vals.length; i++) {
+        if (isNaN(vals[i])) {
+            throw Error("The result of the '" + name + "' has NaNs.");
+        }
+    }
+}
+exports.checkComputationForNaN = checkComputationForNaN;
+function checkConversionForNaN(vals, dtype) {
+    if (dtype === 'float32') {
+        return;
+    }
+    for (var i = 0; i < vals.length; i++) {
+        if (isNaN(vals[i])) {
+            throw Error("NaN is not a valid value for dtype: '" + dtype + "'.");
+        }
+    }
+}
+exports.checkConversionForNaN = checkConversionForNaN;
+function hasEncodingLoss(oldType, newType) {
+    if (newType === 'complex64') {
+        return false;
+    }
+    if (newType === 'float32' && oldType !== 'complex64') {
+        return false;
+    }
+    if (newType === 'int32' && oldType !== 'float32' && oldType !== 'complex64') {
+        return false;
+    }
+    if (newType === 'bool' && oldType === 'bool') {
+        return false;
+    }
+    return true;
+}
+exports.hasEncodingLoss = hasEncodingLoss;
+function isTypedArray(a) {
+    return a instanceof Float32Array || a instanceof Int32Array ||
+        a instanceof Uint8Array;
+}
+exports.isTypedArray = isTypedArray;
+function bytesPerElement(dtype) {
+    if (dtype === 'float32' || dtype === 'int32') {
+        return 4;
+    }
+    else if (dtype === 'complex64') {
+        return 8;
+    }
+    else if (dtype === 'bool') {
+        return 1;
+    }
+    else {
+        throw new Error("Unknown dtype " + dtype);
+    }
+}
+exports.bytesPerElement = bytesPerElement;
+function bytesFromStringArray(arr) {
+    if (arr == null) {
+        return 0;
+    }
+    var bytes = 0;
+    arr.forEach(function (x) { return bytes += x.length * 2; });
+    return bytes;
+}
+exports.bytesFromStringArray = bytesFromStringArray;
+function isString(value) {
+    return typeof value === 'string' || value instanceof String;
+}
+exports.isString = isString;
+function isBoolean(value) {
+    return typeof value === 'boolean';
+}
+exports.isBoolean = isBoolean;
+function isNumber(value) {
+    return typeof value === 'number';
+}
+exports.isNumber = isNumber;
+function inferDtype(values) {
+    if (values instanceof Array) {
+        return inferDtype(values[0]);
+    }
+    if (values instanceof Float32Array) {
+        return 'float32';
+    }
+    else if (values instanceof Int32Array || values instanceof Uint8Array) {
+        return 'int32';
+    }
+    else if (isNumber(values)) {
+        return 'float32';
+    }
+    else if (isString(values)) {
+        return 'string';
+    }
+    else if (isBoolean(values)) {
+        return 'bool';
+    }
+    return 'float32';
+}
+exports.inferDtype = inferDtype;
+function isFunction(f) {
+    return !!(f && f.constructor && f.call && f.apply);
+}
+exports.isFunction = isFunction;
+function nearestDivisor(size, start) {
+    for (var i = start; i < size; ++i) {
+        if (size % i === 0) {
+            return i;
+        }
+    }
+    return size;
+}
+exports.nearestDivisor = nearestDivisor;
+function computeStrides(shape) {
+    var rank = shape.length;
+    if (rank < 2) {
+        return [];
+    }
+    var strides = new Array(rank - 1);
+    strides[rank - 2] = shape[rank - 1];
+    for (var i = rank - 3; i >= 0; --i) {
+        strides[i] = strides[i + 1] * shape[i + 1];
+    }
+    return strides;
+}
+exports.computeStrides = computeStrides;
+function toTypedArray(a, dtype, debugMode) {
+    if (dtype === 'string') {
+        throw new Error('Cannot convert a string[] to a TypedArray');
+    }
+    if (noConversionNeeded(a, dtype)) {
+        return a;
+    }
+    if (Array.isArray(a)) {
+        a = flatten(a);
+    }
+    if (dtype == null || dtype === 'float32' || dtype === 'complex64') {
+        return new Float32Array(a);
+    }
+    else if (dtype === 'int32') {
+        if (debugMode) {
+            checkConversionForNaN(a, dtype);
+        }
+        return new Int32Array(a);
+    }
+    else if (dtype === 'bool') {
+        var bool = new Uint8Array(a.length);
+        for (var i = 0; i < bool.length; ++i) {
+            if (Math.round(a[i]) !== 0) {
+                bool[i] = 1;
+            }
+        }
+        return bool;
+    }
+    else {
+        throw new Error("Unknown data type " + dtype);
+    }
+}
+exports.toTypedArray = toTypedArray;
+function noConversionNeeded(a, dtype) {
+    return (a instanceof Float32Array && dtype === 'float32') ||
+        (a instanceof Int32Array && dtype === 'int32') ||
+        (a instanceof Uint8Array && dtype === 'bool');
+}
+function makeOnesTypedArray(size, dtype) {
+    var array = makeZerosTypedArray(size, dtype);
+    for (var i = 0; i < array.length; i++) {
+        array[i] = 1;
+    }
+    return array;
+}
+exports.makeOnesTypedArray = makeOnesTypedArray;
+function makeZerosTypedArray(size, dtype) {
+    if (dtype == null || dtype === 'float32' || dtype === 'complex64') {
+        return new Float32Array(size);
+    }
+    else if (dtype === 'int32') {
+        return new Int32Array(size);
+    }
+    else if (dtype === 'bool') {
+        return new Uint8Array(size);
+    }
+    else {
+        throw new Error("Unknown data type " + dtype);
+    }
+}
+exports.makeZerosTypedArray = makeZerosTypedArray;
+function now() {
+    if (typeof performance !== 'undefined') {
+        return performance.now();
+    }
+    else if (typeof process !== 'undefined') {
+        var time = process.hrtime();
+        return time[0] * 1000 + time[1] / 1000000;
+    }
+    else {
+        throw new Error('Cannot measure time in this environment. You should run tf.js ' +
+            'in the browser or in Node.js');
+    }
+}
+exports.now = now;
+//# sourceMappingURL=util.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/util.js.map b/node_modules/@tensorflow/tfjs-core/dist/util.js.map
new file mode 100644
index 0000000..642eda9
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/util.js.map
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/util_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/util_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/util_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/util_test.js b/node_modules/@tensorflow/tfjs-core/dist/util_test.js
new file mode 100644
index 0000000..dc2fe6d
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/util_test.js
@@ -0,0 +1,263 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tensor_util_env_1 = require("./tensor_util_env");
+var util = require("./util");
+describe('Util', function () {
+    it('Correctly gets size from shape', function () {
+        expect(util.sizeFromShape([1, 2, 3, 4])).toEqual(24);
+    });
+    it('Correctly identifies scalars', function () {
+        expect(util.isScalarShape([])).toBe(true);
+        expect(util.isScalarShape([1, 2])).toBe(false);
+        expect(util.isScalarShape([1])).toBe(false);
+    });
+    it('Number arrays equal', function () {
+        expect(util.arraysEqual([1, 2, 3, 6], [1, 2, 3, 6])).toBe(true);
+        expect(util.arraysEqual([1, 2], [1, 2, 3])).toBe(false);
+        expect(util.arraysEqual([1, 2, 5], [1, 2])).toBe(false);
+    });
+    it('Is integer', function () {
+        expect(util.isInt(0.5)).toBe(false);
+        expect(util.isInt(1)).toBe(true);
+    });
+    it('Size to squarish shape (perfect square)', function () {
+        expect(util.sizeToSquarishShape(9)).toEqual([3, 3]);
+    });
+    it('Size to squarish shape (prime number)', function () {
+        expect(util.sizeToSquarishShape(11)).toEqual([1, 11]);
+    });
+    it('Size to squarish shape (almost square)', function () {
+        expect(util.sizeToSquarishShape(35)).toEqual([5, 7]);
+    });
+    it('Size of 1 to squarish shape', function () {
+        expect(util.sizeToSquarishShape(1)).toEqual([1, 1]);
+    });
+    it('infer shape single number', function () {
+        expect(tensor_util_env_1.inferShape(4)).toEqual([]);
+    });
+    it('infer shape 1d array', function () {
+        expect(tensor_util_env_1.inferShape([1, 2, 5])).toEqual([3]);
+    });
+    it('infer shape 2d array', function () {
+        expect(tensor_util_env_1.inferShape([[1, 2, 5], [5, 4, 1]])).toEqual([2, 3]);
+    });
+    it('infer shape 3d array', function () {
+        var a = [[[1, 2], [2, 3], [5, 6]], [[5, 6], [4, 5], [1, 2]]];
+        expect(tensor_util_env_1.inferShape(a)).toEqual([2, 3, 2]);
+    });
+    it('infer shape 4d array', function () {
+        var a = [
+            [[[1], [2]], [[2], [3]], [[5], [6]]], [[[5], [6]], [[4], [5]], [[1], [2]]]
+        ];
+        expect(tensor_util_env_1.inferShape(a)).toEqual([2, 3, 2, 1]);
+    });
+    it('infer shape of typed array', function () {
+        var a = new Float32Array([1, 2, 3, 4, 5]);
+        expect(tensor_util_env_1.inferShape(a)).toEqual([5]);
+    });
+});
+describe('util.flatten', function () {
+    it('nested number arrays', function () {
+        expect(util.flatten([[1, 2, 3], [4, 5, 6]])).toEqual([1, 2, 3, 4, 5, 6]);
+        expect(util.flatten([[[1, 2], [3, 4], [5, 6], [7, 8]]])).toEqual([
+            1, 2, 3, 4, 5, 6, 7, 8
+        ]);
+        expect(util.flatten([1, 2, 3, 4, 5, 6])).toEqual([1, 2, 3, 4, 5, 6]);
+    });
+    it('nested string arrays', function () {
+        expect(util.flatten([['a', 'b'], ['c', [['d']]]])).toEqual([
+            'a', 'b', 'c', 'd'
+        ]);
+        expect(util.flatten([['a', ['b']], ['c', [['d']], 'e']])).toEqual([
+            'a', 'b', 'c', 'd', 'e'
+        ]);
+    });
+    it('mixed TypedArray and number[]', function () {
+        var data = [new Float32Array([1, 2]), 3, [4, 5, new Float32Array([6, 7])]];
+        expect(util.flatten(data)).toEqual([1, 2, 3, 4, 5, 6, 7]);
+    });
+});
+describe('util.bytesFromStringArray', function () {
+    it('count each character as 2 bytes', function () {
+        expect(util.bytesFromStringArray(['a', 'bb', 'ccc'])).toBe(6 * 2);
+        expect(util.bytesFromStringArray(['a', 'bb', 'cccddd'])).toBe(9 * 2);
+        expect(util.bytesFromStringArray(['даниел'])).toBe(6 * 2);
+    });
+});
+describe('util.inferDtype', function () {
+    it('a single string => string', function () {
+        expect(util.inferDtype('hello')).toBe('string');
+    });
+    it('a single boolean => bool', function () {
+        expect(util.inferDtype(true)).toBe('bool');
+        expect(util.inferDtype(false)).toBe('bool');
+    });
+    it('a single number => float32', function () {
+        expect(util.inferDtype(0)).toBe('float32');
+        expect(util.inferDtype(34)).toBe('float32');
+    });
+    it('a list of strings => string', function () {
+        expect(util.inferDtype(['a', 'b', 'c'])).toBe('string');
+        expect(util.inferDtype([
+            [['a']], [['b']], [['c']], [['d']]
+        ])).toBe('string');
+    });
+    it('a list of bools => float32', function () {
+        expect(util.inferDtype([false, true, false])).toBe('bool');
+        expect(util.inferDtype([
+            [[true]], [[false]], [[true]], [[true]]
+        ])).toBe('bool');
+    });
+    it('a list of numbers => float32', function () {
+        expect(util.inferDtype([0, 1, 2])).toBe('float32');
+        expect(util.inferDtype([[[0]], [[1]], [[2]], [[3]]])).toBe('float32');
+    });
+});
+describe('util.repeatedTry', function () {
+    it('resolves', function (doneFn) {
+        var counter = 0;
+        var checkFn = function () {
+            counter++;
+            if (counter === 2) {
+                return true;
+            }
+            return false;
+        };
+        util.repeatedTry(checkFn).then(doneFn).catch(function () {
+            throw new Error('Rejected backoff.');
+        });
+    });
+    it('rejects', function (doneFn) {
+        var checkFn = function () { return false; };
+        util.repeatedTry(checkFn, function () { return 0; }, 5)
+            .then(function () {
+            throw new Error('Backoff resolved');
+        })
+            .catch(doneFn);
+    });
+});
+describe('util.inferFromImplicitShape', function () {
+    it('empty shape', function () {
+        var result = util.inferFromImplicitShape([], 0);
+        expect(result).toEqual([]);
+    });
+    it('[2, 3, 4] -> [2, 3, 4]', function () {
+        var result = util.inferFromImplicitShape([2, 3, 4], 24);
+        expect(result).toEqual([2, 3, 4]);
+    });
+    it('[2, -1, 4] -> [2, 3, 4], size=24', function () {
+        var result = util.inferFromImplicitShape([2, -1, 4], 24);
+        expect(result).toEqual([2, 3, 4]);
+    });
+    it('[-1, 3, 4] -> [2, 3, 4], size=24', function () {
+        var result = util.inferFromImplicitShape([-1, 3, 4], 24);
+        expect(result).toEqual([2, 3, 4]);
+    });
+    it('[2, 3, -1] -> [2, 3, 4], size=24', function () {
+        var result = util.inferFromImplicitShape([2, 3, -1], 24);
+        expect(result).toEqual([2, 3, 4]);
+    });
+    it('[2, -1, -1] throws error', function () {
+        expect(function () { return util.inferFromImplicitShape([2, -1, -1], 24); }).toThrowError();
+    });
+    it('[2, 3, -1] size=13 throws error', function () {
+        expect(function () { return util.inferFromImplicitShape([2, 3, -1], 13); }).toThrowError();
+    });
+    it('[2, 3, 4] size=25 (should be 24) throws error', function () {
+        expect(function () { return util.inferFromImplicitShape([2, 3, 4], 25); }).toThrowError();
+    });
+});
+describe('util.squeezeShape', function () {
+    it('scalar', function () {
+        var _a = util.squeezeShape([]), newShape = _a.newShape, keptDims = _a.keptDims;
+        expect(newShape).toEqual([]);
+        expect(keptDims).toEqual([]);
+    });
+    it('1x1 reduced to scalar', function () {
+        var _a = util.squeezeShape([1, 1]), newShape = _a.newShape, keptDims = _a.keptDims;
+        expect(newShape).toEqual([]);
+        expect(keptDims).toEqual([]);
+    });
+    it('1x3x1 reduced to [3]', function () {
+        var _a = util.squeezeShape([1, 3, 1]), newShape = _a.newShape, keptDims = _a.keptDims;
+        expect(newShape).toEqual([3]);
+        expect(keptDims).toEqual([1]);
+    });
+    it('1x1x4 reduced to [4]', function () {
+        var _a = util.squeezeShape([1, 1, 4]), newShape = _a.newShape, keptDims = _a.keptDims;
+        expect(newShape).toEqual([4]);
+        expect(keptDims).toEqual([2]);
+    });
+    it('2x3x4 not reduction', function () {
+        var _a = util.squeezeShape([2, 3, 4]), newShape = _a.newShape, keptDims = _a.keptDims;
+        expect(newShape).toEqual([2, 3, 4]);
+        expect(keptDims).toEqual([0, 1, 2]);
+    });
+    describe('with axis', function () {
+        it('should only reduce dimensions specified by axis', function () {
+            var _a = util.squeezeShape([1, 1, 1, 1, 4], [1, 2]), newShape = _a.newShape, keptDims = _a.keptDims;
+            expect(newShape).toEqual([1, 1, 4]);
+            expect(keptDims).toEqual([0, 3, 4]);
+        });
+        it('throws error when specified axis is not squeezable', function () {
+            expect(function () { return util.squeezeShape([1, 1, 2, 1, 4], [1, 2]); }).toThrowError();
+        });
+    });
+});
+describe('util.checkComputationForNaN', function () {
+    it('Float32Array has NaN', function () {
+        expect(function () { return util.checkComputationForNaN(new Float32Array([1, 2, 3, NaN, 4, 255]), 'float32', ''); })
+            .toThrowError();
+    });
+    it('Float32Array no NaN', function () {
+        expect(function () { return util.checkComputationForNaN(new Float32Array([1, 2, 3, 4, -1, 255]), 'float32', ''); })
+            .not.toThrowError();
+    });
+});
+describe('util.checkConversionForNaN', function () {
+    it('Float32Array has NaN', function () {
+        expect(function () { return util.checkConversionForNaN(new Float32Array([1, 2, 3, NaN, 4, 255]), 'float32'); })
+            .not.toThrowError();
+    });
+    it('Int32Array has NaN', function () {
+        expect(function () { return util.checkConversionForNaN([1, 2, 3, 4, NaN], 'int32'); })
+            .toThrowError();
+    });
+});
+describe('util.hasEncodingLoss', function () {
+    it('complex64 to any', function () {
+        expect(util.hasEncodingLoss('complex64', 'complex64')).toBe(false);
+        expect(util.hasEncodingLoss('complex64', 'float32')).toBe(true);
+        expect(util.hasEncodingLoss('complex64', 'int32')).toBe(true);
+        expect(util.hasEncodingLoss('complex64', 'bool')).toBe(true);
+    });
+    it('any to complex64', function () {
+        expect(util.hasEncodingLoss('bool', 'complex64')).toBe(false);
+        expect(util.hasEncodingLoss('int32', 'complex64')).toBe(false);
+        expect(util.hasEncodingLoss('float32', 'complex64')).toBe(false);
+        expect(util.hasEncodingLoss('complex64', 'complex64')).toBe(false);
+    });
+    it('any to float32', function () {
+        expect(util.hasEncodingLoss('bool', 'float32')).toBe(false);
+        expect(util.hasEncodingLoss('int32', 'float32')).toBe(false);
+        expect(util.hasEncodingLoss('float32', 'float32')).toBe(false);
+        expect(util.hasEncodingLoss('complex64', 'float32')).toBe(true);
+    });
+    it('float32 to any', function () {
+        expect(util.hasEncodingLoss('float32', 'float32')).toBe(false);
+        expect(util.hasEncodingLoss('float32', 'int32')).toBe(true);
+        expect(util.hasEncodingLoss('float32', 'bool')).toBe(true);
+        expect(util.hasEncodingLoss('float32', 'complex64')).toBe(false);
+    });
+    it('int32 to lower', function () {
+        expect(util.hasEncodingLoss('int32', 'int32')).toBe(false);
+        expect(util.hasEncodingLoss('int32', 'bool')).toBe(true);
+    });
+    it('lower to int32', function () {
+        expect(util.hasEncodingLoss('bool', 'int32')).toBe(false);
+    });
+    it('bool to bool', function () {
+        expect(util.hasEncodingLoss('bool', 'bool')).toBe(false);
+    });
+});
+//# sourceMappingURL=util_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/util_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/util_test.js.map
new file mode 100644
index 0000000..e4a8329
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/util_test.js.map
@@ -0,0 +1 @@
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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/variable_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/variable_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/variable_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/variable_test.js b/node_modules/@tensorflow/tfjs-core/dist/variable_test.js
new file mode 100644
index 0000000..c98e1c3
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/variable_test.js
@@ -0,0 +1,137 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var tf = require("./index");
+var jasmine_util_1 = require("./jasmine_util");
+var tensor_1 = require("./tensor");
+var test_util_1 = require("./test_util");
+jasmine_util_1.describeWithFlags('variable', test_util_1.ALL_ENVS, function () {
+    it('simple assign', function () {
+        var v = tensor_1.variable(tf.tensor1d([1, 2, 3]));
+        test_util_1.expectArraysClose(v, [1, 2, 3]);
+        v.assign(tf.tensor1d([4, 5, 6]));
+        test_util_1.expectArraysClose(v, [4, 5, 6]);
+    });
+    it('simple chain assign', function () {
+        var v = tf.tensor1d([1, 2, 3]).variable();
+        test_util_1.expectArraysClose(v, [1, 2, 3]);
+        v.assign(tf.tensor1d([4, 5, 6]));
+        test_util_1.expectArraysClose(v, [4, 5, 6]);
+    });
+    it('default names are unique', function () {
+        var v = tensor_1.variable(tf.tensor1d([1, 2, 3]));
+        expect(v.name).not.toBeNull();
+        var v2 = tensor_1.variable(tf.tensor1d([1, 2, 3]));
+        expect(v2.name).not.toBeNull();
+        expect(v.name).not.toBe(v2.name);
+    });
+    it('user provided name', function () {
+        var v = tensor_1.variable(tf.tensor1d([1, 2, 3]), true, 'myName');
+        expect(v.name).toBe('myName');
+    });
+    it('if name already used, throw error', function () {
+        tensor_1.variable(tf.tensor1d([1, 2, 3]), true, 'myName');
+        expect(function () { return tensor_1.variable(tf.tensor1d([1, 2, 3]), true, 'myName'); })
+            .toThrowError();
+    });
+    it('ops can take variables', function () {
+        var value = tf.tensor1d([1, 2, 3]);
+        var v = tensor_1.variable(value);
+        var res = tf.sum(v);
+        test_util_1.expectArraysClose(res, [6]);
+    });
+    it('chained variables works', function () {
+        var v = tf.tensor1d([1, 2, 3]).variable();
+        var res = tf.sum(v);
+        test_util_1.expectArraysClose(res, [6]);
+    });
+    it('variables are not affected by tidy', function () {
+        var v;
+        expect(tf.memory().numTensors).toBe(0);
+        tf.tidy(function () {
+            var value = tf.tensor1d([1, 2, 3], 'float32');
+            expect(tf.memory().numTensors).toBe(1);
+            v = tensor_1.variable(value);
+            expect(tf.memory().numTensors).toBe(2);
+        });
+        expect(tf.memory().numTensors).toBe(1);
+        test_util_1.expectArraysClose(v, [1, 2, 3]);
+        v.dispose();
+        expect(tf.memory().numTensors).toBe(0);
+    });
+    it('constructor does not dispose', function () {
+        var a = tf.scalar(2);
+        var v = tf.variable(a);
+        expect(tf.memory().numTensors).toBe(2);
+        expect(tf.memory().numDataBuffers).toBe(1);
+        test_util_1.expectArraysClose(v, [2]);
+        test_util_1.expectArraysClose(a, [2]);
+    });
+    it('variables are assignable to tensors', function () {
+        var x0 = null;
+        var y0 = x0;
+        expect(y0).toBeNull();
+        var x1 = null;
+        var y1 = x1;
+        expect(y1).toBeNull();
+        var x2 = null;
+        var y2 = x2;
+        expect(y2).toBeNull();
+        var x3 = null;
+        var y3 = x3;
+        expect(y3).toBeNull();
+        var x4 = null;
+        var y4 = x4;
+        expect(y4).toBeNull();
+        var xh = null;
+        var yh = xh;
+        expect(yh).toBeNull();
+    });
+    it('assign does not dispose old data', function () {
+        var v;
+        v = tensor_1.variable(tf.tensor1d([1, 2, 3]));
+        expect(tf.memory().numTensors).toBe(2);
+        expect(tf.memory().numDataBuffers).toBe(1);
+        test_util_1.expectArraysClose(v, [1, 2, 3]);
+        var secondArray = tf.tensor1d([4, 5, 6]);
+        expect(tf.memory().numTensors).toBe(3);
+        expect(tf.memory().numDataBuffers).toBe(2);
+        v.assign(secondArray);
+        test_util_1.expectArraysClose(v, [4, 5, 6]);
+        expect(tf.memory().numTensors).toBe(3);
+        expect(tf.memory().numDataBuffers).toBe(2);
+        v.dispose();
+        expect(tf.memory().numTensors).toBe(2);
+        expect(tf.memory().numDataBuffers).toBe(2);
+    });
+    it('shape must match', function () {
+        var v = tensor_1.variable(tf.tensor1d([1, 2, 3]));
+        expect(function () { return v.assign(tf.tensor1d([1, 2])); }).toThrowError();
+        expect(function () { return v.assign(tf.tensor2d([3, 4], [1, 2])); }).toThrowError();
+    });
+    it('dtype must match', function () {
+        var v = tensor_1.variable(tf.tensor1d([1, 2, 3]));
+        expect(function () { return v.assign(tf.tensor1d([1, 1, 1], 'int32')); })
+            .toThrowError();
+        expect(function () { return v.assign(tf.tensor1d([true, false, true], 'bool')); })
+            .toThrowError();
+    });
+});
+jasmine_util_1.describeWithFlags('x instanceof Variable', test_util_1.ALL_ENVS, function () {
+    it('x: Variable', function () {
+        var t = tf.variable(tf.scalar(1));
+        expect(t instanceof tensor_1.Variable).toBe(true);
+    });
+    it('x: Variable-like', function () {
+        var t = { assign: function () { }, shape: [2], dtype: 'float32' };
+        expect(t instanceof tensor_1.Variable).toBe(true);
+    });
+    it('x: other object, fails', function () {
+        var t = { something: 'else' };
+        expect(t instanceof tensor_1.Variable).toBe(false);
+    });
+    it('x: Tensor, fails', function () {
+        var t = tf.scalar(1);
+        expect(t instanceof tensor_1.Variable).toBe(false);
+    });
+});
+//# sourceMappingURL=variable_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/variable_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/variable_test.js.map
new file mode 100644
index 0000000..25118c9
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/variable_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"variable_test.js","sourceRoot":"","sources":["../src/variable_test.ts"],"names":[],"mappings":";;AAiBA,4BAA8B;AAC9B,+CAAiD;AACjD,mCAAoG;AACpG,yCAAwD;AAGxD,gCAAiB,CAAC,UAAU,EAAE,oBAAQ,EAAE;IACtC,EAAE,CAAC,eAAe,EAAE;QAClB,IAAM,CAAC,GAAG,iBAAQ,CAAC,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QAC3C,6BAAiB,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAEhC,CAAC,CAAC,MAAM,CAAC,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QACjC,6BAAiB,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IAClC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,qBAAqB,EAAE;QACxB,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC;QAC5C,6BAAiB,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAEhC,CAAC,CAAC,MAAM,CAAC,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QACjC,6BAAiB,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;IAClC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,0BAA0B,EAAE;QAC7B,IAAM,CAAC,GAAG,iBAAQ,CAAC,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QAC3C,MAAM,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,QAAQ,EAAE,CAAC;QAE9B,IAAM,EAAE,GAAG,iBAAQ,CAAC,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QAC5C,MAAM,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,QAAQ,EAAE,CAAC;QAC/B,MAAM,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,GAAG,CAAC,IAAI,CAAC,EAAE,CAAC,IAAI,CAAC,CAAC;IACnC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,oBAAoB,EAAE;QACvB,IAAM,CAAC,GAAG,iBAAQ,CAAC,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,IAAI,EAAE,QAAQ,CAAC,CAAC;QAC3D,MAAM,CAAC,CAAC,CAAC,IAAI,CAAC,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC;IAChC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,mCAAmC,EAAE;QACtC,iBAAQ,CAAC,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,IAAI,EAAE,QAAQ,CAAC,CAAC;QACjD,MAAM,CAAC,cAAM,OAAA,iBAAQ,CAAC,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,EAAE,IAAI,EAAE,QAAQ,CAAC,EAAhD,CAAgD,CAAC;aACzD,YAAY,EAAE,CAAC;IACtB,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,wBAAwB,EAAE;QAC3B,IAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QACrC,IAAM,CAAC,GAAG,iBAAQ,CAAC,KAAK,CAAC,CAAC;QAC1B,IAAM,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC;QACtB,6BAAiB,CAAC,GAAG,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;IAC9B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,yBAAyB,EAAE;QAC5B,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,QAAQ,EAAE,CAAC;QAC5C,IAAM,GAAG,GAAG,EAAE,CAAC,GAAG,CAAC,CAAC,CAAC,CAAC;QACtB,6BAAiB,CAAC,GAAG,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;IAC9B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,oCAAoC,EAAE;QACvC,IAAI,CAAoB,CAAC;QACzB,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;QAEvC,EAAE,CAAC,IAAI,CAAC;YACN,IAAM,KAAK,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,EAAE,SAAS,CAAC,CAAC;YAChD,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;YAEvC,CAAC,GAAG,iBAAQ,CAAC,KAAK,CAAC,CAAC;YACpB,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;QACzC,CAAC,CAAC,CAAC;QAEH,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;QACvC,6BAAiB,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAEhC,CAAC,CAAC,OAAO,EAAE,CAAC;QACZ,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;IACzC,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,8BAA8B,EAAE;QACjC,IAAM,CAAC,GAAG,EAAE,CAAC,MAAM,CAAC,CAAC,CAAC,CAAC;QACvB,IAAM,CAAC,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,CAAC;QAEzB,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;QACvC,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,cAAc,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;QAC3C,6BAAiB,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QAC1B,6BAAiB,CAAC,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;IAC5B,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,qCAAqC,EAAE;QAExC,IAAM,EAAE,GAAsB,IAAI,CAAC;QACnC,IAAM,EAAE,GAAW,EAAE,CAAC;QACtB,MAAM,CAAC,EAAE,CAAC,CAAC,QAAQ,EAAE,CAAC;QAEtB,IAAM,EAAE,GAAsB,IAAI,CAAC;QACnC,IAAM,EAAE,GAAa,EAAE,CAAC;QACxB,MAAM,CAAC,EAAE,CAAC,CAAC,QAAQ,EAAE,CAAC;QAEtB,IAAM,EAAE,GAAsB,IAAI,CAAC;QACnC,IAAM,EAAE,GAAa,EAAE,CAAC;QACxB,MAAM,CAAC,EAAE,CAAC,CAAC,QAAQ,EAAE,CAAC;QAEtB,IAAM,EAAE,GAAsB,IAAI,CAAC;QACnC,IAAM,EAAE,GAAa,EAAE,CAAC;QACxB,MAAM,CAAC,EAAE,CAAC,CAAC,QAAQ,EAAE,CAAC;QAEtB,IAAM,EAAE,GAAsB,IAAI,CAAC;QACnC,IAAM,EAAE,GAAa,EAAE,CAAC;QACxB,MAAM,CAAC,EAAE,CAAC,CAAC,QAAQ,EAAE,CAAC;QAEtB,IAAM,EAAE,GAAa,IAAI,CAAC;QAC1B,IAAM,EAAE,GAAW,EAAE,CAAC;QACtB,MAAM,CAAC,EAAE,CAAC,CAAC,QAAQ,EAAE,CAAC;IACxB,CAAC,CAAC,CAAC;IAEH,EAAE,CAAC,kCAAkC,EAAE;QACrC,IAAI,CAAoB,CAAC;QACzB,CAAC,GAAG,iBAAQ,CAAC,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC,CAAC;QAErC,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;QACvC,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,cAAc,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;QAE3C,6BAAiB,CAAC,CAAC,EAAE,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAEhC,IAAM,WAAW,GAAG,EAAE,CAAC,QAAQ,CAAC,CAAC,CAAC,EAAE,CAAC,EAAE,CAAC,CAAC,CAAC,CAAC;QAC3C,MAAM,CAAC,EAAE,CAAC,MAAM,EAAE,CAAC,UAAU,CAAC,CAAC,IAAI,CAAC,CAAC,CAAC,CAAC;Q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\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/version.d.ts b/node_modules/@tensorflow/tfjs-core/dist/version.d.ts
new file mode 100644
index 0000000..5291a7b
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/version.d.ts
@@ -0,0 +1,2 @@
+declare const version = "0.14.2";
+export { version };
diff --git a/node_modules/@tensorflow/tfjs-core/dist/version.js b/node_modules/@tensorflow/tfjs-core/dist/version.js
new file mode 100644
index 0000000..9068c9e
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/version.js
@@ -0,0 +1,5 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var version = '0.14.2';
+exports.version = version;
+//# sourceMappingURL=version.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/version.js.map b/node_modules/@tensorflow/tfjs-core/dist/version.js.map
new file mode 100644
index 0000000..71e4f86
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/version.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"version.js","sourceRoot":"","sources":["../src/version.ts"],"names":[],"mappings":";;AAGA,IAAM,OAAO,GAAG,QAAQ,CAAC;AACjB,0BAAO"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/version_test.d.ts b/node_modules/@tensorflow/tfjs-core/dist/version_test.d.ts
new file mode 100644
index 0000000..509db18
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/version_test.d.ts
@@ -0,0 +1 @@
+export {};
diff --git a/node_modules/@tensorflow/tfjs-core/dist/version_test.js b/node_modules/@tensorflow/tfjs-core/dist/version_test.js
new file mode 100644
index 0000000..deb40b6
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/version_test.js
@@ -0,0 +1,10 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var index_1 = require("./index");
+describe('version', function () {
+    it('version is contained', function () {
+        var expected = require('../package.json').version;
+        expect(index_1.version_core).toBe(expected);
+    });
+});
+//# sourceMappingURL=version_test.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/version_test.js.map b/node_modules/@tensorflow/tfjs-core/dist/version_test.js.map
new file mode 100644
index 0000000..5a33330
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/version_test.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"version_test.js","sourceRoot":"","sources":["../src/version_test.ts"],"names":[],"mappings":";;AAiBA,iCAAqC;AAErC,QAAQ,CAAC,SAAS,EAAE;IAClB,EAAE,CAAC,sBAAsB,EAAE;QAEzB,IAAM,QAAQ,GAAG,OAAO,CAAC,iBAAiB,CAAC,CAAC,OAAO,CAAC;QACpD,MAAM,CAAC,oBAAY,CAAC,CAAC,IAAI,CAAC,QAAQ,CAAC,CAAC;IACtC,CAAC,CAAC,CAAC;AACL,CAAC,CAAC,CAAC"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/webgl.d.ts b/node_modules/@tensorflow/tfjs-core/dist/webgl.d.ts
new file mode 100644
index 0000000..310d773
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/webgl.d.ts
@@ -0,0 +1,6 @@
+import * as gpgpu_util from './kernels/webgl/gpgpu_util';
+import * as webgl_util from './kernels/webgl/webgl_util';
+export { MathBackendWebGL, WebGLMemoryInfo, WebGLTimingInfo } from './kernels/backend_webgl';
+export { GPGPUContext } from './kernels/webgl/gpgpu_context';
+export { GPGPUProgram } from './kernels/webgl/gpgpu_math';
+export { gpgpu_util, webgl_util };
diff --git a/node_modules/@tensorflow/tfjs-core/dist/webgl.js b/node_modules/@tensorflow/tfjs-core/dist/webgl.js
new file mode 100644
index 0000000..318be41
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/webgl.js
@@ -0,0 +1,11 @@
+"use strict";
+Object.defineProperty(exports, "__esModule", { value: true });
+var gpgpu_util = require("./kernels/webgl/gpgpu_util");
+exports.gpgpu_util = gpgpu_util;
+var webgl_util = require("./kernels/webgl/webgl_util");
+exports.webgl_util = webgl_util;
+var backend_webgl_1 = require("./kernels/backend_webgl");
+exports.MathBackendWebGL = backend_webgl_1.MathBackendWebGL;
+var gpgpu_context_1 = require("./kernels/webgl/gpgpu_context");
+exports.GPGPUContext = gpgpu_context_1.GPGPUContext;
+//# sourceMappingURL=webgl.js.map
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/dist/webgl.js.map b/node_modules/@tensorflow/tfjs-core/dist/webgl.js.map
new file mode 100644
index 0000000..004ecb6
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/dist/webgl.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"webgl.js","sourceRoot":"","sources":["../src/webgl.ts"],"names":[],"mappings":";;AAiBA,uDAAyD;AAMjD,gCAAU;AALlB,uDAAyD;AAKrC,gCAAU;AAJ9B,yDAA2F;AAAnF,2CAAA,gBAAgB,CAAA;AACxB,+DAA2D;AAAnD,uCAAA,YAAY,CAAA"}
\ No newline at end of file
diff --git a/node_modules/@tensorflow/tfjs-core/package.json b/node_modules/@tensorflow/tfjs-core/package.json
new file mode 100644
index 0000000..fa94d27
--- /dev/null
+++ b/node_modules/@tensorflow/tfjs-core/package.json
@@ -0,0 +1,99 @@
+{
+  "_args": [
+    [
+      "@tensorflow/tfjs-core@0.14.2",
+      "/Users/zamua/Dropbox/workspace/madify"
+    ]
+  ],
+  "_from": "@tensorflow/tfjs-core@0.14.2",
+  "_id": "@tensorflow/tfjs-core@0.14.2",
+  "_inBundle": false,
+  "_integrity": "sha512-VVbcu6H3ioKCkfkep/gQASfzPnQt3C5v+4ppH9pQ6Lf0lD+l3NMuMJYxa8Wjac1TfiWhFEX58bJvhpMfTGsUlg==",
+  "_location": "/@tensorflow/tfjs-core",
+  "_phantomChildren": {},
+  "_requested": {
+    "type": "version",
+    "registry": true,
+    "raw": "@tensorflow/tfjs-core@0.14.2",
+    "name": "@tensorflow/tfjs-core",
+    "escapedName": "@tensorflow%2ftfjs-core",
+    "scope": "@tensorflow",
+    "rawSpec": "0.14.2",
+    "saveSpec": null,
+    "fetchSpec": "0.14.2"
+  },
+  "_requiredBy": [
+    "/face-api.js",
+    "/tfjs-image-recognition-base",
+    "/tfjs-tiny-yolov2"
+  ],
+  "_resolved": "https://registry.npmjs.org/@tensorflow/tfjs-core/-/tfjs-core-0.14.2.tgz",
+  "_spec": "0.14.2",
+  "_where": "/Users/zamua/Dropbox/workspace/madify",
+  "bugs": {
+    "url": "https://github.com/tensorflow/tfjs-core/issues"
+  },
+  "dependencies": {
+    "@types/seedrandom": "2.4.27",
+    "@types/webgl-ext": "0.0.30",
+    "@types/webgl2": "0.0.4",
+    "seedrandom": "2.4.3"
+  },
+  "description": "Hardware-accelerated JavaScript library for machine intelligence",
+  "devDependencies": {
+    "@types/jasmine": "~2.5.53",
+    "@types/node": "~9.6.0",
+    "clang-format": "~1.2.4",
+    "jasmine": "~3.1.0",
+    "jasmine-core": "~3.1.0",
+    "karma": "~2.0.2",
+    "karma-browserstack-launcher": "~1.3.0",
+    "karma-chrome-launcher": "~2.2.0",
+    "karma-jasmine": "~1.1.0",
+    "karma-typescript": "~3.0.12",
+    "npm-run-all": "~4.1.3",
+    "rimraf": "~2.6.2",
+    "rollup": "~0.58.2",
+    "rollup-plugin-commonjs": "~9.1.3",
+    "rollup-plugin-node-resolve": "~3.3.0",
+    "rollup-plugin-typescript2": "~0.13.0",
+    "rollup-plugin-uglify": "~3.0.0",
+    "ts-node": "~7.0.0",
+    "tslint": "~5.11.0",
+    "tslint-no-circular-imports": "~0.5.0",
+    "typescript": "2.9.2",
+    "yalc": "~1.0.0-pre.21"
+  },
+  "engines": {
+    "yarn": ">= 1.3.2"
+  },
+  "homepage": "https://github.com/tensorflow/tfjs-core#readme",
+  "jsdelivr": "dist/tf-core.min.js",
+  "jsnext:main": "dist/tf-core.esm.js",
+  "license": "Apache-2.0",
+  "main": "dist/index.js",
+  "module": "dist/tf-core.esm.js",
+  "name": "@tensorflow/tfjs-core",
+  "private": false,
+  "repository": {
+    "type": "git",
+    "url": "git+https://github.com/tensorflow/tfjs-core.git"
+  },
+  "scripts": {
+    "build": "tsc",
+    "build-npm": "./scripts/build-npm.sh",
+    "coverage": "KARMA_COVERAGE=1 karma start --browsers='Chrome' --singleRun",
+    "link-local": "yalc link",
+    "lint": "tslint -p . -t verbose",
+    "publish-local": "rimraf dist/ && yarn build && yalc push",
+    "run-browserstack": "karma start --singleRun --reporters='dots,karma-typescript,BrowserStack' --hostname='bs-local.com'",
+    "test": "karma start",
+    "test-benchmark": "cd integration_tests/benchmarks && yarn benchmark-travis && cd ../../",
+    "test-integration": "./scripts/test-integration.sh",
+    "test-node": "ts-node src/test_node.ts",
+    "test-travis": "./scripts/test-travis.sh"
+  },
+  "types": "dist/index.d.ts",
+  "unpkg": "dist/tf-core.min.js",
+  "version": "0.14.2"
+}
diff --git a/node_modules/@types/seedrandom/README.md b/node_modules/@types/seedrandom/README.md
new file mode 100644
index 0000000..17037fc
--- /dev/null
+++ b/node_modules/@types/seedrandom/README.md
@@ -0,0 +1,18 @@
+# Installation
+> `npm install --save @types/seedrandom`
+
+# Summary
+This package contains type definitions for seedrandom 2.4.2 (https://github.com/davidbau/seedrandom).
+
+# Details
+Files were exported from https://www.github.com/DefinitelyTyped/DefinitelyTyped/tree/types-2.0/seedrandom
+
+Additional Details
+ * Last updated: Mon, 19 Sep 2016 17:28:59 GMT
+ * File structure: UMD
+ * Library Dependencies: none
+ * Module Dependencies: none
+ * Global values: seedrandom
+
+# Credits
+These definitions were written by Kern Handa <https://github.com/kernhanda>.
diff --git a/node_modules/@types/seedrandom/index.d.ts b/node_modules/@types/seedrandom/index.d.ts
new file mode 100644
index 0000000..4db5005
--- /dev/null
+++ b/node_modules/@types/seedrandom/index.d.ts
@@ -0,0 +1,45 @@
+// Type definitions for seedrandom 2.4.2
+// Project: https://github.com/davidbau/seedrandom
+// Definitions by: Kern Handa <https://github.com/kernhanda>
+// Definitions: https://github.com/DefinitelyTyped/DefinitelyTyped
+
+declare namespace seedrandom {
+
+  export type seedrandomStateType = boolean | (() => prng);
+
+  interface prng {
+    new (seed?: string, options?: seedRandomOptions, callback?: any): prng;
+    (): number;
+    quick(): number;
+    int32(): number;
+    double(): number;
+    state(): () => prng;
+  }
+
+  interface seedrandom_prng {
+    (seed?: string, options?: seedRandomOptions, callback?: any): prng;
+    alea: (seed?: string, options?: seedRandomOptions, callback?: seedrandomCallback) => prng;
+    xor128: (seed?: string, options?: seedRandomOptions, callback?: seedrandomCallback) => prng;
+    tychei: (seed?: string, options?: seedRandomOptions, callback?: seedrandomCallback) => prng;
+    xorwow: (seed?: string, options?: seedRandomOptions, callback?: seedrandomCallback) => prng;
+    xor4096: (seed?: string, options?: seedRandomOptions, callback?: seedrandomCallback) => prng;
+    xorshift7: (seed?: string, options?: seedRandomOptions, callback?: seedrandomCallback) => prng;
+    quick: (seed?: string, options?: seedRandomOptions, callback?: seedrandomCallback) => prng;
+  }
+
+  interface seedrandomCallback {
+    (prng?: prng, shortseed?: string, global?: boolean, state?: seedrandomStateType): prng;
+  }
+
+  interface seedRandomOptions {
+    entropy?: boolean;
+    'global'?: boolean;
+    state?: seedrandomStateType;
+    pass?: seedrandomCallback;
+  }
+}
+
+declare var seedrandom: seedrandom.seedrandom_prng;
+
+export = seedrandom;
+export as namespace seedrandom;
diff --git a/node_modules/@types/seedrandom/package.json b/node_modules/@types/seedrandom/package.json
new file mode 100644
index 0000000..47abfb4
--- /dev/null
+++ b/node_modules/@types/seedrandom/package.json
@@ -0,0 +1,52 @@
+{
+  "_args": [
+    [
+      "@types/seedrandom@2.4.27",
+      "/Users/zamua/Dropbox/workspace/madify"
+    ]
+  ],
+  "_from": "@types/seedrandom@2.4.27",
+  "_id": "@types/seedrandom@2.4.27",
+  "_inBundle": false,
+  "_integrity": "sha1-nbVjk33YaRX2kJK8QyWdL0hXjkE=",
+  "_location": "/@types/seedrandom",
+  "_phantomChildren": {},
+  "_requested": {
+    "type": "version",
+    "registry": true,
+    "raw": "@types/seedrandom@2.4.27",
+    "name": "@types/seedrandom",
+    "escapedName": "@types%2fseedrandom",
+    "scope": "@types",
+    "rawSpec": "2.4.27",
+    "saveSpec": null,
+    "fetchSpec": "2.4.27"
+  },
+  "_requiredBy": [
+    "/@tensorflow/tfjs-core"
+  ],
+  "_resolved": "https://registry.npmjs.org/@types/seedrandom/-/seedrandom-2.4.27.tgz",
+  "_spec": "2.4.27",
+  "_where": "/Users/zamua/Dropbox/workspace/madify",
+  "author": {
+    "name": "Kern Handa",
+    "email": "https://github.com/kernhanda"
+  },
+  "bugs": {
+    "url": "https://github.com/DefinitelyTyped/DefinitelyTyped/issues"
+  },
+  "dependencies": {},
+  "description": "TypeScript definitions for seedrandom 2.4.2",
+  "homepage": "https://github.com/DefinitelyTyped/DefinitelyTyped#readme",
+  "license": "MIT",
+  "main": "",
+  "name": "@types/seedrandom",
+  "repository": {
+    "type": "git",
+    "url": "git+https://github.com/DefinitelyTyped/DefinitelyTyped.git"
+  },
+  "scripts": {},
+  "typesPublisherContentHash": "6ad69483683964e97254f65e374a17f845154a74df41e5bf3ea0d53ec1b70e19",
+  "typings": "index.d.ts",
+  "version": "2.4.27"
+}
diff --git a/node_modules/@types/seedrandom/types-metadata.json b/node_modules/@types/seedrandom/types-metadata.json
new file mode 100644
index 0000000..22b3620
--- /dev/null
+++ b/node_modules/@types/seedrandom/types-metadata.json
@@ -0,0 +1,23 @@
+{
+    "authors": "Kern Handa <https://github.com/kernhanda>",
+    "definitionFilename": "index.d.ts",
+    "libraryDependencies": [],
+    "moduleDependencies": [],
+    "libraryMajorVersion": "2",
+    "libraryMinorVersion": "4",
+    "libraryName": "seedrandom 2.4.2",
+    "typingsPackageName": "seedrandom",
+    "projectName": "https://github.com/davidbau/seedrandom",
+    "sourceRepoURL": "https://www.github.com/DefinitelyTyped/DefinitelyTyped",
+    "sourceBranch": "types-2.0",
+    "kind": "UMD",
+    "globals": [
+        "seedrandom"
+    ],
+    "declaredModules": [],
+    "files": [
+        "index.d.ts"
+    ],
+    "hasPackageJson": false,
+    "contentHash": "6ad69483683964e97254f65e374a17f845154a74df41e5bf3ea0d53ec1b70e19"
+}
\ No newline at end of file
diff --git a/node_modules/@types/webgl-ext/LICENSE b/node_modules/@types/webgl-ext/LICENSE
new file mode 100644
index 0000000..4b1ad51
--- /dev/null
+++ b/node_modules/@types/webgl-ext/LICENSE
@@ -0,0 +1,21 @@
+    MIT License
+
+    Copyright (c) Microsoft Corporation. All rights reserved.
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in all
+    copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+    SOFTWARE
diff --git a/node_modules/@types/webgl-ext/README.md b/node_modules/@types/webgl-ext/README.md
new file mode 100644
index 0000000..5f8ad26
--- /dev/null
+++ b/node_modules/@types/webgl-ext/README.md
@@ -0,0 +1,16 @@
+# Installation
+> `npm install --save @types/webgl-ext`
+
+# Summary
+This package contains type definitions for WebGL Extensions (http://webgl.org/).
+
+# Details
+Files were exported from https://github.com/DefinitelyTyped/DefinitelyTyped/tree/master/types/webgl-ext
+
+Additional Details
+ * Last updated: Tue, 23 Oct 2018 17:03:26 GMT
+ * Dependencies: none
+ * Global values: none
+
+# Credits
+These definitions were written by Arthur Langereis <https://github.com/zenmumbler>.
diff --git a/node_modules/@types/webgl-ext/index.d.ts b/node_modules/@types/webgl-ext/index.d.ts
new file mode 100644
index 0000000..e8e2e94
--- /dev/null
+++ b/node_modules/@types/webgl-ext/index.d.ts
@@ -0,0 +1,82 @@
+// Type definitions for WebGL Extensions
+// Project: http://webgl.org/
+// Definitions by: Arthur Langereis <https://github.com/zenmumbler>
+// Definitions: https://github.com/DefinitelyTyped/DefinitelyTyped/webgl-ext
+// TypeScript Version: 2.7
+
+// render-gl1/declarations - WebGL 1 extension definitions (beyond TS lib)
+// Extracted from Stardazed - https://github.com/stardazed/stardazed
+
+interface HTMLCanvasElement {
+	getContext(contextId: "webgl" | "experimental-webgl", contextAttributes?: WebGLContextAttributes): (WebGLRenderingContext & WebGL1Extensions) | null;
+}
+
+interface WebGL1Extensions {
+	getExtension(name: "EXT_color_buffer_half_float"): EXT_color_buffer_half_float;
+
+	getExtension(name: "WEBGL_compressed_texture_atc"): WEBGL_compressed_texture_atc;
+	getExtension(name: "WEBGL_compressed_texture_etc1"): WEBGL_compressed_texture_etc1;
+	getExtension(name: "WEBGL_compressed_texture_pvrtc"): WEBKIT_WEBGL_compressed_texture_pvrtc;
+
+	// Prefixed versions appearing in the wild as per February 2018
+	getExtension(name: "WEBKIT_EXT_texture_filter_anisotropic"): EXT_texture_filter_anisotropic; // Chrome
+	getExtension(name: "WEBKIT_WEBGL_compressed_texture_atc"): WEBGL_compressed_texture_atc; // Android
+	getExtension(name: "WEBKIT_WEBGL_compressed_texture_pvrtc"): WEBKIT_WEBGL_compressed_texture_pvrtc; // Safari iOS
+	getExtension(name: "WEBKIT_WEBGL_compressed_texture_s3tc"): WEBGL_compressed_texture_s3tc; // Chrome
+	getExtension(name: "WEBKIT_WEBGL_depth_texture"): WEBGL_depth_texture; // Chrome
+	getExtension(name: "WEBKIT_WEBGL_lose_context"): WEBGL_lose_context; // Chrome
+}
+
+
+// WebGL 1 Type Branding
+interface WebGLObject { readonly __WebGLObject: void; }
+interface WebGLBuffer { readonly __WebGLBuffer: void; }
+interface WebGLFramebuffer { readonly __WebGLFramebuffer: void; }
+interface WebGLProgram { readonly __WebGLProgram: void; }
+interface WebGLRenderbuffer { readonly __WebGLRenderbuffer: void; }
+interface WebGLShader { readonly __WebGLShader: void; }
+interface WebGLTexture { readonly __WebGLTexture: void; }
+interface WebGLUniformLocation { readonly __WebGLUniformLocation: void; }
+interface WebGLVertexArrayObjectOES extends WebGLObject { readonly __WebGLVertexArrayObjectOES: void; }
+
+interface EXT_frag_depth { readonly __EXT_frag_depth: void; }
+interface EXT_shader_texture_lod { readonly __EXT_shader_texture_lod: void; }
+
+interface OES_element_index_uint { readonly __OESElementIndexUint: void; }
+interface OES_texture_float { readonly __OES_texture_float: void; }
+interface OES_texture_float_linear { readonly __OES_texture_float_linear: void; }
+interface OES_texture_half_float_linear { readonly __OES_texture_half_float_linear: void; }
+
+
+// WebGL 1 Extensions
+interface EXT_color_buffer_half_float {
+	readonly RGBA16F_EXT: number;
+	readonly RGB16F_EXT: number;
+	readonly FRAMEBUFFER_ATTACHMENT_COMPONENT_TYPE_EXT: number;
+	readonly UNSIGNED_NORMALIZED_EXT: number;
+}
+
+interface OES_vertex_array_object {
+	// TS's lib.dom (as of v3.1.3) does not specify the nulls
+	createVertexArrayOES(): WebGLVertexArrayObjectOES | null;
+	deleteVertexArrayOES(arrayObject: WebGLVertexArrayObjectOES | null): void;
+	isVertexArrayOES(arrayObject: WebGLVertexArrayObjectOES | null): boolean;
+	bindVertexArrayOES(arrayObject: WebGLVertexArrayObjectOES | null): void;
+}
+
+interface WEBGL_compressed_texture_atc {
+	readonly COMPRESSED_RGB_ATC_WEBGL: number;
+	readonly COMPRESSED_RGBA_ATC_EXPLICIT_ALPHA_WEBGL: number;
+	readonly COMPRESSED_RGBA_ATC_INTERPOLATED_ALPHA_WEBGL: number;
+}
+
+interface WEBGL_compressed_texture_etc1 {
+	readonly COMPRESSED_RGB_ETC1_WEBGL: number;
+}
+
+interface WEBKIT_WEBGL_compressed_texture_pvrtc {
+	readonly COMPRESSED_RGB_PVRTC_4BPPV1_IMG: number;
+	readonly COMPRESSED_RGB_PVRTC_2BPPV1_IMG: number;
+	readonly COMPRESSED_RGBA_PVRTC_4BPPV1_IMG: number;
+	readonly COMPRESSED_RGBA_PVRTC_2BPPV1_IMG: number;
+}
diff --git a/node_modules/@types/webgl-ext/package.json b/node_modules/@types/webgl-ext/package.json
new file mode 100644
index 0000000..8039bd0
--- /dev/null
+++ b/node_modules/@types/webgl-ext/package.json
@@ -0,0 +1,55 @@
+{
+  "_args": [
+    [
+      "@types/webgl-ext@0.0.30",
+      "/Users/zamua/Dropbox/workspace/madify"
+    ]
+  ],
+  "_from": "@types/webgl-ext@0.0.30",
+  "_id": "@types/webgl-ext@0.0.30",
+  "_inBundle": false,
+  "_integrity": "sha512-LKVgNmBxN0BbljJrVUwkxwRYqzsAEPcZOe6S2T6ZaBDIrFp0qu4FNlpc5sM1tGbXUYFgdVQIoeLk1Y1UoblyEg==",
+  "_location": "/@types/webgl-ext",
+  "_phantomChildren": {},
+  "_requested": {
+    "type": "version",
+    "registry": true,
+    "raw": "@types/webgl-ext@0.0.30",
+    "name": "@types/webgl-ext",
+    "escapedName": "@types%2fwebgl-ext",
+    "scope": "@types",
+    "rawSpec": "0.0.30",
+    "saveSpec": null,
+    "fetchSpec": "0.0.30"
+  },
+  "_requiredBy": [
+    "/@tensorflow/tfjs-core"
+  ],
+  "_resolved": "https://registry.npmjs.org/@types/webgl-ext/-/webgl-ext-0.0.30.tgz",
+  "_spec": "0.0.30",
+  "_where": "/Users/zamua/Dropbox/workspace/madify",
+  "bugs": {
+    "url": "https://github.com/DefinitelyTyped/DefinitelyTyped/issues"
+  },
+  "contributors": [
+    {
+      "name": "Arthur Langereis",
+      "url": "https://github.com/zenmumbler"
+    }
+  ],
+  "dependencies": {},
+  "description": "TypeScript definitions for WebGL Extensions",
+  "homepage": "https://github.com/DefinitelyTyped/DefinitelyTyped#readme",
+  "license": "MIT",
+  "main": "",
+  "name": "@types/webgl-ext",
+  "repository": {
+    "type": "git",
+    "url": "git+https://github.com/DefinitelyTyped/DefinitelyTyped.git"
+  },
+  "scripts": {},
+  "typeScriptVersion": "2.7",
+  "types": "",
+  "typesPublisherContentHash": "c4fbe5a8c8557daa65dacda7d74d61020264991e65ea85898689a7f2e6692e16",
+  "version": "0.0.30"
+}
diff --git a/node_modules/@types/webgl2/LICENSE b/node_modules/@types/webgl2/LICENSE
new file mode 100644
index 0000000..4b1ad51
--- /dev/null
+++ b/node_modules/@types/webgl2/LICENSE
@@ -0,0 +1,21 @@
+    MIT License
+
+    Copyright (c) Microsoft Corporation. All rights reserved.
+
+    Permission is hereby granted, free of charge, to any person obtaining a copy
+    of this software and associated documentation files (the "Software"), to deal
+    in the Software without restriction, including without limitation the rights
+    to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+    copies of the Software, and to permit persons to whom the Software is
+    furnished to do so, subject to the following conditions:
+
+    The above copyright notice and this permission notice shall be included in all
+    copies or substantial portions of the Software.
+
+    THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+    IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+    FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+    AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+    LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+    OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+    SOFTWARE
diff --git a/node_modules/@types/webgl2/README.md b/node_modules/@types/webgl2/README.md
new file mode 100644
index 0000000..476353a
--- /dev/null
+++ b/node_modules/@types/webgl2/README.md
@@ -0,0 +1,16 @@
+# Installation
+> `npm install --save @types/webgl2`
+
+# Summary
+This package contains type definitions for WebGL 2, Editor's Draft Fri Feb 24 16:10:18 2017 -0800 (https://www.khronos.org/registry/webgl/specs/latest/2.0/).
+
+# Details
+Files were exported from https://www.github.com/DefinitelyTyped/DefinitelyTyped/tree/master/types/webgl2
+
+Additional Details
+ * Last updated: Thu, 26 Apr 2018 22:15:55 GMT
+ * Dependencies: none
+ * Global values: WebGL2RenderingContext, WebGLQuery, WebGLSampler, WebGLSync, WebGLTransformFeedback, WebGLVertexArrayObject
+
+# Credits
+These definitions were written by Nico Kemnitz <https://github.com/nkemnitz>, Adrian Blumer <https://github.com/karhu>.
diff --git a/node_modules/@types/webgl2/index.d.ts b/node_modules/@types/webgl2/index.d.ts
new file mode 100644
index 0000000..e6ebfad
--- /dev/null
+++ b/node_modules/@types/webgl2/index.d.ts
@@ -0,0 +1,1194 @@
+// Type definitions for WebGL 2, Editor's Draft Fri Feb 24 16:10:18 2017 -0800
+// Project: https://www.khronos.org/registry/webgl/specs/latest/2.0/
+// Definitions by: Nico Kemnitz <https://github.com/nkemnitz>
+//                 Adrian Blumer <https://github.com/karhu>
+// Definitions: https://github.com/DefinitelyTyped/DefinitelyTyped
+
+interface HTMLCanvasElement extends HTMLElement {
+    getContext(contextId: "webgl2" | "experimental-webgl2", contextAttributes?: WebGLContextAttributes): WebGL2RenderingContext | null;
+}
+
+interface ImageBitmap {
+    readonly width: number;
+    readonly height: number;
+    close(): void;
+}
+
+interface WebGL2RenderingContext extends WebGLRenderingContext {
+    readonly READ_BUFFER: number;                                   // 0x0C02
+    readonly UNPACK_ROW_LENGTH: number;                             // 0x0CF2
+    readonly UNPACK_SKIP_ROWS: number;                              // 0x0CF3
+    readonly UNPACK_SKIP_PIXELS: number;                            // 0x0CF4
+    readonly PACK_ROW_LENGTH: number;                               // 0x0D02
+    readonly PACK_SKIP_ROWS: number;                                // 0x0D03
+    readonly PACK_SKIP_PIXELS: number;                              // 0x0D04
+    readonly COLOR: number;                                         // 0x1800
+    readonly DEPTH: number;                                         // 0x1801
+    readonly STENCIL: number;                                       // 0x1802
+    readonly RED: number;                                           // 0x1903
+    readonly RGB8: number;                                          // 0x8051
+    readonly RGBA8: number;                                         // 0x8058
+    readonly RGB10_A2: number;                                      // 0x8059
+    readonly TEXTURE_BINDING_3D: number;                            // 0x806A
+    readonly UNPACK_SKIP_IMAGES: number;                            // 0x806D
+    readonly UNPACK_IMAGE_HEIGHT: number;                           // 0x806E
+    readonly TEXTURE_3D: number;                                    // 0x806F
+    readonly TEXTURE_WRAP_R: number;                                // 0x8072
+    readonly MAX_3D_TEXTURE_SIZE: number;                           // 0x8073
+    readonly UNSIGNED_INT_2_10_10_10_REV: number;                   // 0x8368
+    readonly MAX_ELEMENTS_VERTICES: number;                         // 0x80E8
+    readonly MAX_ELEMENTS_INDICES: number;                          // 0x80E9
+    readonly TEXTURE_MIN_LOD: number;                               // 0x813A
+    readonly TEXTURE_MAX_LOD: number;                               // 0x813B
+    readonly TEXTURE_BASE_LEVEL: number;                            // 0x813C
+    readonly TEXTURE_MAX_LEVEL: number;                             // 0x813D
+    readonly MIN: number;                                           // 0x8007
+    readonly MAX: number;                                           // 0x8008
+    readonly DEPTH_COMPONENT24: number;                             // 0x81A6
+    readonly MAX_TEXTURE_LOD_BIAS: number;                          // 0x84FD
+    readonly TEXTURE_COMPARE_MODE: number;                          // 0x884C
+    readonly TEXTURE_COMPARE_FUNC: number;                          // 0x884D
+    readonly CURRENT_QUERY: number;                                 // 0x8865
+    readonly QUERY_RESULT: number;                                  // 0x8866
+    readonly QUERY_RESULT_AVAILABLE: number;                        // 0x8867
+    readonly STREAM_READ: number;                                   // 0x88E1
+    readonly STREAM_COPY: number;                                   // 0x88E2
+    readonly STATIC_READ: number;                                   // 0x88E5
+    readonly STATIC_COPY: number;                                   // 0x88E6
+    readonly DYNAMIC_READ: number;                                  // 0x88E9
+    readonly DYNAMIC_COPY: number;                                  // 0x88EA
+    readonly MAX_DRAW_BUFFERS: number;                              // 0x8824
+    readonly DRAW_BUFFER0: number;                                  // 0x8825
+    readonly DRAW_BUFFER1: number;                                  // 0x8826
+    readonly DRAW_BUFFER2: number;                                  // 0x8827
+    readonly DRAW_BUFFER3: number;                                  // 0x8828
+    readonly DRAW_BUFFER4: number;                                  // 0x8829
+    readonly DRAW_BUFFER5: number;                                  // 0x882A
+    readonly DRAW_BUFFER6: number;                                  // 0x882B
+    readonly DRAW_BUFFER7: number;                                  // 0x882C
+    readonly DRAW_BUFFER8: number;                                  // 0x882D
+    readonly DRAW_BUFFER9: number;                                  // 0x882E
+    readonly DRAW_BUFFER10: number;                                 // 0x882F
+    readonly DRAW_BUFFER11: number;                                 // 0x8830
+    readonly DRAW_BUFFER12: number;                                 // 0x8831
+    readonly DRAW_BUFFER13: number;                                 // 0x8832
+    readonly DRAW_BUFFER14: number;                                 // 0x8833
+    readonly DRAW_BUFFER15: number;                                 // 0x8834
+    readonly MAX_FRAGMENT_UNIFORM_COMPONENTS: number;               // 0x8B49
+    readonly MAX_VERTEX_UNIFORM_COMPONENTS: number;                 // 0x8B4A
+    readonly SAMPLER_3D: number;                                    // 0x8B5F
+    readonly SAMPLER_2D_SHADOW: number;                             // 0x8B62
+    readonly FRAGMENT_SHADER_DERIVATIVE_HINT: number;               // 0x8B8B
+    readonly PIXEL_PACK_BUFFER: number;                             // 0x88EB
+    readonly PIXEL_UNPACK_BUFFER: number;                           // 0x88EC
+    readonly PIXEL_PACK_BUFFER_BINDING: number;                     // 0x88ED
+    readonly PIXEL_UNPACK_BUFFER_BINDING: number;                   // 0x88EF
+    readonly FLOAT_MAT2x3: number;                                  // 0x8B65
+    readonly FLOAT_MAT2x4: number;                                  // 0x8B66
+    readonly FLOAT_MAT3x2: number;                                  // 0x8B67
+    readonly FLOAT_MAT3x4: number;                                  // 0x8B68
+    readonly FLOAT_MAT4x2: number;                                  // 0x8B69
+    readonly FLOAT_MAT4x3: number;                                  // 0x8B6A
+    readonly SRGB: number;                                          // 0x8C40
+    readonly SRGB8: number;                                         // 0x8C41
+    readonly SRGB8_ALPHA8: number;                                  // 0x8C43
+    readonly COMPARE_REF_TO_TEXTURE: number;                        // 0x884E
+    readonly RGBA32F: number;                                       // 0x8814
+    readonly RGB32F: number;                                        // 0x8815
+    readonly RGBA16F: number;                                       // 0x881A
+    readonly RGB16F: number;                                        // 0x881B
+    readonly VERTEX_ATTRIB_ARRAY_INTEGER: number;                   // 0x88FD
+    readonly MAX_ARRAY_TEXTURE_LAYERS: number;                      // 0x88FF
+    readonly MIN_PROGRAM_TEXEL_OFFSET: number;                      // 0x8904
+    readonly MAX_PROGRAM_TEXEL_OFFSET: number;                      // 0x8905
+    readonly MAX_VARYING_COMPONENTS: number;                        // 0x8B4B
+    readonly TEXTURE_2D_ARRAY: number;                              // 0x8C1A
+    readonly TEXTURE_BINDING_2D_ARRAY: number;                      // 0x8C1D
+    readonly R11F_G11F_B10F: number;                                // 0x8C3A
+    readonly UNSIGNED_INT_10F_11F_11F_REV: number;                  // 0x8C3B
+    readonly RGB9_E5: number;                                       // 0x8C3D
+    readonly UNSIGNED_INT_5_9_9_9_REV: number;                      // 0x8C3E
+    readonly TRANSFORM_FEEDBACK_BUFFER_MODE: number;                // 0x8C7F
+    readonly MAX_TRANSFORM_FEEDBACK_SEPARATE_COMPONENTS: number;    // 0x8C80
+    readonly TRANSFORM_FEEDBACK_VARYINGS: number;                   // 0x8C83
+    readonly TRANSFORM_FEEDBACK_BUFFER_START: number;               // 0x8C84
+    readonly TRANSFORM_FEEDBACK_BUFFER_SIZE: number;                // 0x8C85
+    readonly TRANSFORM_FEEDBACK_PRIMITIVES_WRITTEN: number;         // 0x8C88
+    readonly RASTERIZER_DISCARD: number;                            // 0x8C89
+    readonly MAX_TRANSFORM_FEEDBACK_INTERLEAVED_COMPONENTS: number; // 0x8C8A
+    readonly MAX_TRANSFORM_FEEDBACK_SEPARATE_ATTRIBS: number;       // 0x8C8B
+    readonly INTERLEAVED_ATTRIBS: number;                           // 0x8C8C
+    readonly SEPARATE_ATTRIBS: number;                              // 0x8C8D
+    readonly TRANSFORM_FEEDBACK_BUFFER: number;                     // 0x8C8E
+    readonly TRANSFORM_FEEDBACK_BUFFER_BINDING: number;             // 0x8C8F
+    readonly RGBA32UI: number;                                      // 0x8D70
+    readonly RGB32UI: number;                                       // 0x8D71
+    readonly RGBA16UI: number;                                      // 0x8D76
+    readonly RGB16UI: number;                                       // 0x8D77
+    readonly RGBA8UI: number;                                       // 0x8D7C
+    readonly RGB8UI: number;                                        // 0x8D7D
+    readonly RGBA32I: number;                                       // 0x8D82
+    readonly RGB32I: number;                                        // 0x8D83
+    readonly RGBA16I: number;                                       // 0x8D88
+    readonly RGB16I: number;                                        // 0x8D89
+    readonly RGBA8I: number;                                        // 0x8D8E
+    readonly RGB8I: number;                                         // 0x8D8F
+    readonly RED_INTEGER: number;                                   // 0x8D94
+    readonly RGB_INTEGER: number;                                   // 0x8D98
+    readonly RGBA_INTEGER: number;                                  // 0x8D99
+    readonly SAMPLER_2D_ARRAY: number;                              // 0x8DC1
+    readonly SAMPLER_2D_ARRAY_SHADOW: number;                       // 0x8DC4
+    readonly SAMPLER_CUBE_SHADOW: number;                           // 0x8DC5
+    readonly UNSIGNED_INT_VEC2: number;                             // 0x8DC6
+    readonly UNSIGNED_INT_VEC3: number;                             // 0x8DC7
+    readonly UNSIGNED_INT_VEC4: number;                             // 0x8DC8
+    readonly INT_SAMPLER_2D: number;                                // 0x8DCA
+    readonly INT_SAMPLER_3D: number;                                // 0x8DCB
+    readonly INT_SAMPLER_CUBE: number;                              // 0x8DCC
+    readonly INT_SAMPLER_2D_ARRAY: number;                          // 0x8DCF
+    readonly UNSIGNED_INT_SAMPLER_2D: number;                       // 0x8DD2
+    readonly UNSIGNED_INT_SAMPLER_3D: number;                       // 0x8DD3
+    readonly UNSIGNED_INT_SAMPLER_CUBE: number;                     // 0x8DD4
+    readonly UNSIGNED_INT_SAMPLER_2D_ARRAY: number;                 // 0x8DD7
+    readonly DEPTH_COMPONENT32F: number;                            // 0x8CAC
+    readonly DEPTH32F_STENCIL8: number;                             // 0x8CAD
+    readonly FLOAT_32_UNSIGNED_INT_24_8_REV: number;                // 0x8DAD
+    readonly FRAMEBUFFER_ATTACHMENT_COLOR_ENCODING: number;         // 0x8210
+    readonly FRAMEBUFFER_ATTACHMENT_COMPONENT_TYPE: number;         // 0x8211
+    readonly FRAMEBUFFER_ATTACHMENT_RED_SIZE: number;               // 0x8212
+    readonly FRAMEBUFFER_ATTACHMENT_GREEN_SIZE: number;             // 0x8213
+    readonly FRAMEBUFFER_ATTACHMENT_BLUE_SIZE: number;              // 0x8214
+    readonly FRAMEBUFFER_ATTACHMENT_ALPHA_SIZE: number;             // 0x8215
+    readonly FRAMEBUFFER_ATTACHMENT_DEPTH_SIZE: number;             // 0x8216
+    readonly FRAMEBUFFER_ATTACHMENT_STENCIL_SIZE: number;           // 0x8217
+    readonly FRAMEBUFFER_DEFAULT: number;                           // 0x8218
+    //readonly DEPTH_STENCIL_ATTACHMENT: number;                      // 0x821A /* Already defined in WebGL1 constants */
+    //readonly DEPTH_STENCIL: number;                                 // 0x84F9 /* Already defined in WebGL1 constants */
+    readonly UNSIGNED_INT_24_8: number;                             // 0x84FA
+    readonly DEPTH24_STENCIL8: number;                              // 0x88F0
+    readonly UNSIGNED_NORMALIZED: number;                           // 0x8C17
+    readonly DRAW_FRAMEBUFFER_BINDING: number;                      // 0x8CA6 /* Same as FRAMEBUFFER_BINDING */
+    readonly READ_FRAMEBUFFER: number;                              // 0x8CA8
+    readonly DRAW_FRAMEBUFFER: number;                              // 0x8CA9
+    readonly READ_FRAMEBUFFER_BINDING: number;                      // 0x8CAA
+    readonly RENDERBUFFER_SAMPLES: number;                          // 0x8CAB
+    readonly FRAMEBUFFER_ATTACHMENT_TEXTURE_LAYER: number;          // 0x8CD4
+    readonly MAX_COLOR_ATTACHMENTS: number;                         // 0x8CDF
+    readonly COLOR_ATTACHMENT1: number;                             // 0x8CE1
+    readonly COLOR_ATTACHMENT2: number;                             // 0x8CE2
+    readonly COLOR_ATTACHMENT3: number;                             // 0x8CE3
+    readonly COLOR_ATTACHMENT4: number;                             // 0x8CE4
+    readonly COLOR_ATTACHMENT5: number;                             // 0x8CE5
+    readonly COLOR_ATTACHMENT6: number;                             // 0x8CE6
+    readonly COLOR_ATTACHMENT7: number;                             // 0x8CE7
+    readonly COLOR_ATTACHMENT8: number;                             // 0x8CE8
+    readonly COLOR_ATTACHMENT9: number;                             // 0x8CE9
+    readonly COLOR_ATTACHMENT10: number;                            // 0x8CEA
+    readonly COLOR_ATTACHMENT11: number;                            // 0x8CEB
+    readonly COLOR_ATTACHMENT12: number;                            // 0x8CEC
+    readonly COLOR_ATTACHMENT13: number;                            // 0x8CED
+    readonly COLOR_ATTACHMENT14: number;                            // 0x8CEE
+    readonly COLOR_ATTACHMENT15: number;                            // 0x8CEF
+    readonly FRAMEBUFFER_INCOMPLETE_MULTISAMPLE: number;            // 0x8D56
+    readonly MAX_SAMPLES: number;                                   // 0x8D57
+    readonly HALF_FLOAT: number;                                    // 0x140B
+    readonly RG: number;                                            // 0x8227
+    readonly RG_INTEGER: number;                                    // 0x8228
+    readonly R8: number;                                            // 0x8229
+    readonly RG8: number;                                           // 0x822B
+    readonly R16F: number;                                          // 0x822D
+    readonly R32F: number;                                          // 0x822E
+    readonly RG16F: number;                                         // 0x822F
+    readonly RG32F: number;                                         // 0x8230
+    readonly R8I: number;                                           // 0x8231
+    readonly R8UI: number;                                          // 0x8232
+    readonly R16I: number;                                          // 0x8233
+    readonly R16UI: number;                                         // 0x8234
+    readonly R32I: number;                                          // 0x8235
+    readonly R32UI: number;                                         // 0x8236
+    readonly RG8I: number;                                          // 0x8237
+    readonly RG8UI: number;                                         // 0x8238
+    readonly RG16I: number;                                         // 0x8239
+    readonly RG16UI: number;                                        // 0x823A
+    readonly RG32I: number;                                         // 0x823B
+    readonly RG32UI: number;                                        // 0x823C
+    readonly VERTEX_ARRAY_BINDING: number;                          // 0x85B5
+    readonly R8_SNORM: number;                                      // 0x8F94
+    readonly RG8_SNORM: number;                                     // 0x8F95
+    readonly RGB8_SNORM: number;                                    // 0x8F96
+    readonly RGBA8_SNORM: number;                                   // 0x8F97
+    readonly SIGNED_NORMALIZED: number;                             // 0x8F9C
+    readonly COPY_READ_BUFFER: number;                              // 0x8F36
+    readonly COPY_WRITE_BUFFER: number;                             // 0x8F37
+    readonly COPY_READ_BUFFER_BINDING: number;                      // 0x8F36 /* Same as COPY_READ_BUFFER */
+    readonly COPY_WRITE_BUFFER_BINDING: number;                     // 0x8F37 /* Same as COPY_WRITE_BUFFER */
+    readonly UNIFORM_BUFFER: number;                                // 0x8A11
+    readonly UNIFORM_BUFFER_BINDING: number;                        // 0x8A28
+    readonly UNIFORM_BUFFER_START: number;                          // 0x8A29
+    readonly UNIFORM_BUFFER_SIZE: number;                           // 0x8A2A
+    readonly MAX_VERTEX_UNIFORM_BLOCKS: number;                     // 0x8A2B
+    readonly MAX_FRAGMENT_UNIFORM_BLOCKS: number;                   // 0x8A2D
+    readonly MAX_COMBINED_UNIFORM_BLOCKS: number;                   // 0x8A2E
+    readonly MAX_UNIFORM_BUFFER_BINDINGS: number;                   // 0x8A2F
+    readonly MAX_UNIFORM_BLOCK_SIZE: number;                        // 0x8A30
+    readonly MAX_COMBINED_VERTEX_UNIFORM_COMPONENTS: number;        // 0x8A31
+    readonly MAX_COMBINED_FRAGMENT_UNIFORM_COMPONENTS: number;      // 0x8A33
+    readonly UNIFORM_BUFFER_OFFSET_ALIGNMENT: number;               // 0x8A34
+    readonly ACTIVE_UNIFORM_BLOCKS: number;                         // 0x8A36
+    readonly UNIFORM_TYPE: number;                                  // 0x8A37
+    readonly UNIFORM_SIZE: number;                                  // 0x8A38
+    readonly UNIFORM_BLOCK_INDEX: number;                           // 0x8A3A
+    readonly UNIFORM_OFFSET: number;                                // 0x8A3B
+    readonly UNIFORM_ARRAY_STRIDE: number;                          // 0x8A3C
+    readonly UNIFORM_MATRIX_STRIDE: number;                         // 0x8A3D
+    readonly UNIFORM_IS_ROW_MAJOR: number;                          // 0x8A3E
+    readonly UNIFORM_BLOCK_BINDING: number;                         // 0x8A3F
+    readonly UNIFORM_BLOCK_DATA_SIZE: number;                       // 0x8A40
+    readonly UNIFORM_BLOCK_ACTIVE_UNIFORMS: number;                 // 0x8A42
+    readonly UNIFORM_BLOCK_ACTIVE_UNIFORM_INDICES: number;          // 0x8A43
+    readonly UNIFORM_BLOCK_REFERENCED_BY_VERTEX_SHADER: number;     // 0x8A44
+    readonly UNIFORM_BLOCK_REFERENCED_BY_FRAGMENT_SHADER: number;   // 0x8A46
+    readonly INVALID_INDEX: number;                                 // 0xFFFFFFFF
+    readonly MAX_VERTEX_OUTPUT_COMPONENTS: number;                  // 0x9122
+    readonly MAX_FRAGMENT_INPUT_COMPONENTS: number;                 // 0x9125
+    readonly MAX_SERVER_WAIT_TIMEOUT: number;                       // 0x9111
+    readonly OBJECT_TYPE: number;                                   // 0x9112
+    readonly SYNC_CONDITION: number;                                // 0x9113
+    readonly SYNC_STATUS: number;                                   // 0x9114
+    readonly SYNC_FLAGS: number;                                    // 0x9115
+    readonly SYNC_FENCE: number;                                    // 0x9116
+    readonly SYNC_GPU_COMMANDS_COMPLETE: number;                    // 0x9117
+    readonly UNSIGNALED: number;                                    // 0x9118
+    readonly SIGNALED: number;                                      // 0x9119
+    readonly ALREADY_SIGNALED: number;                              // 0x911A
+    readonly TIMEOUT_EXPIRED: number;                               // 0x911B
+    readonly CONDITION_SATISFIED: number;                           // 0x911C
+    readonly WAIT_FAILED: number;                                   // 0x911D
+    readonly SYNC_FLUSH_COMMANDS_BIT: number;                       // 0x00000001
+    readonly VERTEX_ATTRIB_ARRAY_DIVISOR: number;                   // 0x88FE
+    readonly ANY_SAMPLES_PASSED: number;                            // 0x8C2F
+    readonly ANY_SAMPLES_PASSED_CONSERVATIVE: number;               // 0x8D6A
+    readonly SAMPLER_BINDING: number;                               // 0x8919
+    readonly RGB10_A2UI: number;                                    // 0x906F
+    readonly INT_2_10_10_10_REV: number;                            // 0x8D9F
+    readonly TRANSFORM_FEEDBACK: number;                            // 0x8E22
+    readonly TRANSFORM_FEEDBACK_PAUSED: number;                     // 0x8E23
+    readonly TRANSFORM_FEEDBACK_ACTIVE: number;                     // 0x8E24
+    readonly TRANSFORM_FEEDBACK_BINDING: number;                    // 0x8E25
+    readonly TEXTURE_IMMUTABLE_FORMAT: number;                      // 0x912F
+    readonly MAX_ELEMENT_INDEX: number;                             // 0x8D6B
+    readonly TEXTURE_IMMUTABLE_LEVELS: number;                      // 0x82DF
+
+    readonly TIMEOUT_IGNORED: number;                               // -1
+
+    /* WebGL-specific enums */
+    readonly MAX_CLIENT_WAIT_TIMEOUT_WEBGL: number;                 // 0x9247
+
+
+    /* Buffer objects */
+    // WebGL1:
+    bufferData(target: number, sizeOrData: number | Int8Array | Int16Array | Int32Array | Uint8Array | Uint16Array |
+        Uint32Array | Uint8ClampedArray | Float32Array | Float64Array | DataView | ArrayBuffer | null, usage: number): void;
+    bufferSubData(target: number, dstByteOffset: number, srcData: Int8Array | Int16Array | Int32Array | Uint8Array |
+        Uint16Array | Uint32Array | Uint8ClampedArray | Float32Array | Float64Array | DataView | ArrayBuffer | null): void;
+    // For compatibility with WebGL 1 context in older Typescript versions.
+    bufferData(target: number, data: ArrayBufferView, usage: number): void;
+    bufferSubData(target: number, dstByteOffset: number, srcData: ArrayBufferView): void;
+    // WebGL2:
+    bufferData(target: number, srcData: Int8Array | Int16Array | Int32Array | Uint8Array | Uint16Array | Uint32Array |
+        Uint8ClampedArray | Float32Array | Float64Array | DataView | ArrayBuffer | null, usage: number, srcOffset: number, length?: number): void;
+    bufferSubData(target: number, dstByteOffset: number, srcData: ArrayBufferView,
+        srcOffset: number, length?: number): void;
+
+    copyBufferSubData(readTarget: number, writeTarget: number, readOffset: number,
+        writeOffset: number, size: number): void;
+    // MapBufferRange, in particular its read-only and write-only modes,
+    // can not be exposed safely to JavaScript. GetBufferSubData
+    // replaces it for the purpose of fetching data back from the GPU.
+    getBufferSubData(target: number, srcByteOffset: number, dstBuffer: ArrayBufferView,
+        dstOffset?: number, length?: number): void;
+
+    /* Framebuffer objects */
+    blitFramebuffer(srcX0: number, srcY0: number, srcX1: number, srcY1: number, dstX0: number, dstY0: number,
+        dstX1: number, dstY1: number, mask: number, filter: number): void;
+    framebufferTextureLayer(target: number, attachment: number, texture: WebGLTexture | null, level: number,
+        layer: number): void;
+    invalidateFramebuffer(target: number, attachments: number[]): void;
+    invalidateSubFramebuffer(target: number, attachments: number[],
+        x: number, y: number, width: number, height: number): void;
+    readBuffer(src: number): void;
+
+    /* Renderbuffer objects */
+    getInternalformatParameter(target: number, internalformat: number, pname: number): any;
+    renderbufferStorageMultisample(target: number, samples: number, internalformat: number,
+        width: number, height: number): void;
+
+    /* Texture objects */
+    texStorage2D(target: number, levels: number, internalformat: number, width: number,
+        height: number): void;
+    texStorage3D(target: number, levels: number, internalformat: number, width: number,
+        height: number, depth: number): void;
+
+    // WebGL1 legacy entrypoints:
+    texImage2D(target: number, level: number, internalformat: number,
+        width: number, height: number, border: number, format: number,
+        type: number, pixels?: ArrayBufferView | null): void;
+    texImage2D(target: number, level: number, internalformat: number,
+        format: number, type: number, source: ImageData | HTMLImageElement | HTMLCanvasElement | HTMLVideoElement): void; // May throw DOMException
+    texImage2D(target: number, level: number, internalformat: number,
+        format: number, type: number, source: ImageBitmap | ImageData | HTMLImageElement | HTMLCanvasElement | HTMLVideoElement): void; // May throw DOMException
+
+    texSubImage2D(target: number, level: number, xoffset: number, yoffset: number,
+        width: number, height: number,
+        format: number, type: number, pixels?: ArrayBufferView | null): void;
+    texSubImage2D(target: number, level: number, xoffset: number, yoffset: number,
+        format: number, type: number, source: ImageData | HTMLImageElement | HTMLCanvasElement | HTMLVideoElement): void; // May throw DOMException
+    texSubImage2D(target: number, level: number, xoffset: number, yoffset: number,
+        format: number, type: number, source: ImageBitmap | ImageData | HTMLImageElement | HTMLCanvasElement | HTMLVideoElement): void; // May throw DOMException
+
+    // WebGL2 entrypoints:
+    texImage2D(target: number, level: number, internalformat: number, width: number, height: number,
+        border: number, format: number, type: number, pboOffset: number): void;
+    texImage2D(target: number, level: number, internalformat: number, width: number, height: number,
+        border: number, format: number, type: number,
+        source: ImageBitmap | ImageData | HTMLImageElement | HTMLCanvasElement | HTMLVideoElement): void; // May throw DOMException
+    texImage2D(target: number, level: number, internalformat: number, width: number, height: number,
+        border: number, format: number, type: number, srcData: ArrayBufferView,
+        srcOffset: number): void;
+
+    texImage3D(target: number, level: number, internalformat: number, width: number, height: number,
+        depth: number, border: number, format: number, type: number, pboOffset: number): void;
+    texImage3D(target: number, level: number, internalformat: number, width: number, height: number,
+        depth: number, border: number, format: number, type: number,
+        source: ImageBitmap | ImageData | HTMLImageElement | HTMLCanvasElement | HTMLVideoElement): void; // May throw DOMException
+    texImage3D(target: number, level: number, internalformat: number, width: number, height: number,
+        depth: number, border: number, format: number, type: number, srcData: ArrayBufferView | null): void;
+    texImage3D(target: number, level: number, internalformat: number, width: number, height: number,
+        depth: number, border: number, format: number, type: number, srcData: ArrayBufferView,
+        srcOffset: number): void;
+
+    texSubImage2D(target: number, level: number, xoffset: number, yoffset: number, width: number,
+        height: number, format: number, type: number, pboOffset: number): void;
+    texSubImage2D(target: number, level: number, xoffset: number, yoffset: number, width: number,
+        height: number, format: number, type: number,
+        source: ImageBitmap | ImageData | HTMLImageElement | HTMLCanvasElement | HTMLVideoElement): void; // May throw DOMException
+    texSubImage2D(target: number, level: number, xoffset: number, yoffset: number, width: number,
+        height: number, format: number, type: number, srcData: ArrayBufferView,
+        srcOffset: number): void;
+
+    texSubImage3D(target: number, level: number, xoffset: number, yoffset: number, zoffset: number,
+        width: number, height: number, depth: number, format: number, type: number,
+        pboOffset: number): void;
+    texSubImage3D(target: number, level: number, xoffset: number, yoffset: number, zoffset: number,
+        width: number, height: number, depth: number, format: number, type: number,
+        source: ImageBitmap | ImageData | HTMLImageElement | HTMLCanvasElement | HTMLVideoElement): void; // May throw DOMException
+    texSubImage3D(target: number, level: number, xoffset: number, yoffset: number, zoffset: number,
+        width: number, height: number, depth: number, format: number, type: number,
+        srcData: ArrayBufferView | null, srcOffset?: number): void;
+
+    copyTexSubImage3D(target: number, level: number, xoffset: number, yoffset: number, zoffset: number,
+        x: number, y: number, width: number, height: number): void;
+
+    compressedTexImage2D(target: number, level: number, internalformat: number, width: number,
+        height: number, border: number, imageSize: number, offset: number): void;
+    compressedTexImage2D(target: number, level: number, internalformat: number, width: number,
+        height: number, border: number, srcData: Int8Array | Int16Array | Int32Array | Uint8Array | Uint16Array | Uint32Array |
+        Uint8ClampedArray | Float32Array | Float64Array | DataView | null, srcOffset?: number, srcLengthOverride?: number): void;
+    // For compatibility with WebGL 1 context in older Typescript versions.
+    compressedTexImage2D(target: number, level: number, internalformat: number, width: number,
+        height: number, border: number, srcData: ArrayBufferView,
+        srcOffset?: number, srcLengthOverride?: number): void;
+
+    compressedTexImage3D(target: number, level: number, internalformat: number, width: number,
+        height: number, depth: number, border: number, imageSize: number, offset: number): void;
+    compressedTexImage3D(target: number, level: number, internalformat: number, width: number,
+        height: number, depth: number, border: number, srcData: ArrayBufferView,
+        srcOffset?: number, srcLengthOverride?: number): void;
+
+    compressedTexSubImage2D(target: number, level: number, xoffset: number, yoffset: number,
+        width: number, height: number, format: number, imageSize: number, offset: number): void;
+    compressedTexSubImage2D(target: number, level: number, xoffset: number, yoffset: number,
+        width: number, height: number, format: number,
+        srcData: Int8Array | Int16Array | Int32Array | Uint8Array | Uint16Array | Uint32Array |
+        Uint8ClampedArray | Float32Array | Float64Array | DataView | null, srcOffset?: number, srcLengthOverride?: number): void;
+    // For compatibility with WebGL 1 context in older Typescript versions.
+    compressedTexSubImage2D(target: number, level: number, xoffset: number, yoffset: number,
+        width: number, height: number, format: number,
+        srcData: ArrayBufferView | null,
+        srcOffset?: number,
+        srcLengthOverride?: number): void;
+
+    compressedTexSubImage3D(target: number, level: number, xoffset: number, yoffset: number,
+        zoffset: number, width: number, height: number, depth: number,
+        format: number, imageSize: number, offset: number): void;
+    compressedTexSubImage3D(target: number, level: number, xoffset: number, yoffset: number,
+        zoffset: number, width: number, height: number, depth: number,
+        format: number, srcData: ArrayBufferView,
+        srcOffset?: number,
+        srcLengthOverride?: number): void;
+
+    /* Programs and shaders */
+    getFragDataLocation(program: WebGLProgram, name: string): number;
+
+    /* Uniforms */
+    uniform1ui(location: WebGLUniformLocation | null, v0: number): void;
+    uniform2ui(location: WebGLUniformLocation | null, v0: number, v1: number): void;
+    uniform3ui(location: WebGLUniformLocation | null, v0: number, v1: number, v2: number): void;
+    uniform4ui(location: WebGLUniformLocation | null, v0: number, v1: number, v2: number, v3: number): void;
+
+    uniform1fv(location: WebGLUniformLocation | null, data: Float32Array | ArrayLike<number>, srcOffset?: number,
+        srcLength?: number): void;
+    uniform2fv(location: WebGLUniformLocation | null, data: Float32Array | ArrayLike<number>, srcOffset?: number,
+        srcLength?: number): void;
+    uniform3fv(location: WebGLUniformLocation | null, data: Float32Array | ArrayLike<number>, srcOffset?: number,
+        srcLength?: number): void;
+    uniform4fv(location: WebGLUniformLocation | null, data: Float32Array | ArrayLike<number>, srcOffset?: number,
+        srcLength?: number): void;
+
+    uniform1iv(location: WebGLUniformLocation | null, data: Int32Array | ArrayLike<number>, srcOffset?: number,
+        srcLength?: number): void;
+    uniform2iv(location: WebGLUniformLocation | null, data: Int32Array | ArrayLike<number>, srcOffset?: number,
+        srcLength?: number): void;
+    uniform3iv(location: WebGLUniformLocation | null, data: Int32Array | ArrayLike<number>, srcOffset?: number,
+        srcLength?: number): void;
+    uniform4iv(location: WebGLUniformLocation | null, data: Int32Array | ArrayLike<number>, srcOffset?: number,
+        srcLength?: number): void;
+
+    uniform1uiv(location: WebGLUniformLocation | null, data: Uint32Array | ArrayLike<number>, srcOffset?: number,
+        srcLength?: number): void;
+    uniform2uiv(location: WebGLUniformLocation | null, data: Uint32Array | ArrayLike<number>, srcOffset?: number,
+        srcLength?: number): void;
+    uniform3uiv(location: WebGLUniformLocation | null, data: Uint32Array | ArrayLike<number>, srcOffset?: number,
+        srcLength?: number): void;
+    uniform4uiv(location: WebGLUniformLocation | null, data: Uint32Array | ArrayLike<number>, srcOffset?: number,
+        srcLength?: number): void;
+
+    uniformMatrix2fv(location: WebGLUniformLocation | null, transpose: boolean, data: Float32Array | ArrayLike<number>,
+        srcOffset?: number, srcLength?: number): void;
+    uniformMatrix3x2fv(location: WebGLUniformLocation | null, transpose: boolean, data: Float32Array | ArrayLike<number>,
+        srcOffset?: number, srcLength?: number): void;
+    uniformMatrix4x2fv(location: WebGLUniformLocation | null, transpose: boolean, data: Float32Array | ArrayLike<number>,
+        srcOffset?: number, srcLength?: number): void;
+
+    uniformMatrix2x3fv(location: WebGLUniformLocation | null, transpose: boolean, data: Float32Array | ArrayLike<number>,
+        srcOffset?: number, srcLength?: number): void;
+    uniformMatrix3fv(location: WebGLUniformLocation | null, transpose: boolean, data: Float32Array | ArrayLike<number>,
+        srcOffset?: number, srcLength?: number): void;
+    uniformMatrix4x3fv(location: WebGLUniformLocation | null, transpose: boolean, data: Float32Array | ArrayLike<number>,
+        srcOffset?: number, srcLength?: number): void;
+
+    uniformMatrix2x4fv(location: WebGLUniformLocation | null, transpose: boolean, data: Float32Array | ArrayLike<number>,
+        srcOffset?: number, srcLength?: number): void;
+    uniformMatrix3x4fv(location: WebGLUniformLocation | null, transpose: boolean, data: Float32Array | ArrayLike<number>,
+        srcOffset?: number, srcLength?: number): void;
+    uniformMatrix4fv(location: WebGLUniformLocation | null, transpose: boolean, data: Float32Array | ArrayLike<number>,
+        srcOffset?: number, srcLength?: number): void;
+
+    /* Vertex attribs */
+    vertexAttribI4i(index: number, x: number, y: number, z: number, w: number): void;
+    vertexAttribI4iv(index: number, values: Int32Array | ArrayLike<number>): void;
+    vertexAttribI4ui(index: number, x: number, y: number, z: number, w: number): void;
+    vertexAttribI4uiv(index: number, values: Uint32Array | ArrayLike<number>): void;
+    vertexAttribIPointer(index: number, size: number, type: number, stride: number, offset: number): void;
+
+    /* Writing to the drawing buffer */
+    vertexAttribDivisor(index: number, divisor: number): void;
+    drawArraysInstanced(mode: number, first: number, count: number, instanceCount: number): void;
+    drawElementsInstanced(mode: number, count: number, type: number, offset: number, instanceCount: number): void;
+    drawRangeElements(mode: number, start: number, end: number, count: number, type: number, offset: number): void;
+
+    /* Reading back pixels */
+    // WebGL1:
+    readPixels(x: number, y: number, width: number, height: number, format: number, type: number,
+        dstData: Int8Array | Int16Array | Int32Array | Uint8Array | Uint16Array | Uint32Array | Uint8ClampedArray |
+        Float32Array | Float64Array | DataView | null): void;
+    // For compatibility with WebGL 1 context in older Typescript versions.
+    readPixels(x: number, y: number, width: number, height: number, format: number, type: number,
+        dstData: ArrayBufferView | null): void;
+    // WebGL2:
+    readPixels(x: number, y: number, width: number, height: number, format: number, type: number,
+        offset: number): void;
+    readPixels(x: number, y: number, width: number, height: number, format: number, type: number,
+        dstData: ArrayBufferView, dstOffset: number): void;
+
+    /* Multiple Render Targets */
+    drawBuffers(buffers: number[]): void;
+
+    clearBufferfv(buffer: number, drawbuffer: number, values: Float32Array | ArrayLike<number>,
+        srcOffset?: number): void;
+    clearBufferiv(buffer: number, drawbuffer: number, values: Int32Array | ArrayLike<number>,
+        srcOffset?: number): void;
+    clearBufferuiv(buffer: number, drawbuffer: number, values: Uint32Array | ArrayLike<number>,
+        srcOffset?: number): void;
+
+    clearBufferfi(buffer: number, drawbuffer: number, depth: number, stencil: number): void;
+
+    /* Query Objects */
+    createQuery(): WebGLQuery | null;
+    deleteQuery(query: WebGLQuery | null): void;
+    isQuery(query: WebGLQuery | null): boolean; //[WebGLHandlesContextLoss]
+    beginQuery(target: number, query: WebGLQuery): void;
+    endQuery(target: number): void;
+    getQuery(target: number, pname: number): WebGLQuery | null;
+    getQueryParameter(query: WebGLQuery, pname: number): any;
+
+    /* Sampler Objects */
+    createSampler(): WebGLSampler | null;
+    deleteSampler(sampler: WebGLSampler | null): void;
+    isSampler(sampler: WebGLSampler | null): boolean; //[WebGLHandlesContextLoss]
+    bindSampler(unit: number, sampler: WebGLSampler | null): void;
+    samplerParameteri(sampler: WebGLSampler, pname: number, param: number): void;
+    samplerParameterf(sampler: WebGLSampler, pname: number, param: number): void;
+    getSamplerParameter(sampler: WebGLSampler, pname: number): any;
+
+    /* Sync objects */
+    fenceSync(condition: number, flags: number): WebGLSync | null;
+    isSync(sync: WebGLSync | null): boolean; //[WebGLHandlesContextLoss]
+    deleteSync(sync: WebGLSync | null): void;
+    clientWaitSync(sync: WebGLSync, flags: number, timeout: number): number;
+    waitSync(sync: WebGLSync, flags: number, timeout: number): void;
+    getSyncParameter(sync: WebGLSync, pname: number): any;
+
+    /* Transform Feedback */
+    createTransformFeedback(): WebGLTransformFeedback | null;
+    deleteTransformFeedback(tf: WebGLTransformFeedback | null): void;
+    isTransformFeedback(tf: WebGLTransformFeedback | null): boolean; //[WebGLHandlesContextLoss]
+    bindTransformFeedback(target: number, tf: WebGLTransformFeedback | null): void;
+    beginTransformFeedback(primitiveMode: number): void;
+    endTransformFeedback(): void;
+    transformFeedbackVaryings(program: WebGLProgram, varyings: string[], bufferMode: number): void;
+    getTransformFeedbackVarying(program: WebGLProgram, index: number): WebGLActiveInfo | null;
+    pauseTransformFeedback(): void;
+    resumeTransformFeedback(): void;
+
+    /* Uniform Buffer Objects and Transform Feedback Buffers */
+    bindBufferBase(target: number, index: number, buffer: WebGLBuffer | null): void;
+    bindBufferRange(target: number, index: number, buffer: WebGLBuffer | null, offset: number, size: number): void;
+    getIndexedParameter(target: number, index: number): any;
+    getUniformIndices(program: WebGLProgram, uniformNames: string[]): number[] | null;
+    getActiveUniforms(program: WebGLProgram, uniformIndices: number[], pname: number): any;
+    getUniformBlockIndex(program: WebGLProgram, uniformBlockName: string): number;
+    getActiveUniformBlockParameter(program: WebGLProgram, uniformBlockIndex: number, pname: number): any;
+    getActiveUniformBlockName(program: WebGLProgram, uniformBlockIndex: number): string | null;
+    uniformBlockBinding(program: WebGLProgram, uniformBlockIndex: number, uniformBlockBinding: number): void;
+
+    /* Vertex Array Objects */
+    createVertexArray(): WebGLVertexArrayObject | null;
+    deleteVertexArray(vertexArray: WebGLVertexArrayObject | null): void;
+    isVertexArray(vertexArray: WebGLVertexArrayObject | null): boolean; //[WebGLHandlesContextLoss]
+    bindVertexArray(array: WebGLVertexArrayObject | null): void;
+}
+
+declare var WebGL2RenderingContext: {
+    prototype: WebGL2RenderingContext;
+    new (): WebGL2RenderingContext;
+
+    /* WebGL1 constants */
+    readonly ACTIVE_ATTRIBUTES: number;
+    readonly ACTIVE_TEXTURE: number;
+    readonly ACTIVE_UNIFORMS: number;
+    readonly ALIASED_LINE_WIDTH_RANGE: number;
+    readonly ALIASED_POINT_SIZE_RANGE: number;
+    readonly ALPHA: number;
+    readonly ALPHA_BITS: number;
+    readonly ALWAYS: number;
+    readonly ARRAY_BUFFER: number;
+    readonly ARRAY_BUFFER_BINDING: number;
+    readonly ATTACHED_SHADERS: number;
+    readonly BACK: number;
+    readonly BLEND: number;
+    readonly BLEND_COLOR: number;
+    readonly BLEND_DST_ALPHA: number;
+    readonly BLEND_DST_RGB: number;
+    readonly BLEND_EQUATION: number;
+    readonly BLEND_EQUATION_ALPHA: number;
+    readonly BLEND_EQUATION_RGB: number;
+    readonly BLEND_SRC_ALPHA: number;
+    readonly BLEND_SRC_RGB: number;
+    readonly BLUE_BITS: number;
+    readonly BOOL: number;
+    readonly BOOL_VEC2: number;
+    readonly BOOL_VEC3: number;
+    readonly BOOL_VEC4: number;
+    readonly BROWSER_DEFAULT_WEBGL: number;
+    readonly BUFFER_SIZE: number;
+    readonly BUFFER_USAGE: number;
+    readonly BYTE: number;
+    readonly CCW: number;
+    readonly CLAMP_TO_EDGE: number;
+    readonly COLOR_ATTACHMENT0: number;
+    readonly COLOR_BUFFER_BIT: number;
+    readonly COLOR_CLEAR_VALUE: number;
+    readonly COLOR_WRITEMASK: number;
+    readonly COMPILE_STATUS: number;
+    readonly COMPRESSED_TEXTURE_FORMATS: number;
+    readonly CONSTANT_ALPHA: number;
+    readonly CONSTANT_COLOR: number;
+    readonly CONTEXT_LOST_WEBGL: number;
+    readonly CULL_FACE: number;
+    readonly CULL_FACE_MODE: number;
+    readonly CURRENT_PROGRAM: number;
+    readonly CURRENT_VERTEX_ATTRIB: number;
+    readonly CW: number;
+    readonly DECR: number;
+    readonly DECR_WRAP: number;
+    readonly DELETE_STATUS: number;
+    readonly DEPTH_ATTACHMENT: number;
+    readonly DEPTH_BITS: number;
+    readonly DEPTH_BUFFER_BIT: number;
+    readonly DEPTH_CLEAR_VALUE: number;
+    readonly DEPTH_COMPONENT: number;
+    readonly DEPTH_COMPONENT16: number;
+    readonly DEPTH_FUNC: number;
+    readonly DEPTH_RANGE: number;
+    readonly DEPTH_STENCIL: number;
+    readonly DEPTH_STENCIL_ATTACHMENT: number;
+    readonly DEPTH_TEST: number;
+    readonly DEPTH_WRITEMASK: number;
+    readonly DITHER: number;
+    readonly DONT_CARE: number;
+    readonly DST_ALPHA: number;
+    readonly DST_COLOR: number;
+    readonly DYNAMIC_DRAW: number;
+    readonly ELEMENT_ARRAY_BUFFER: number;
+    readonly ELEMENT_ARRAY_BUFFER_BINDING: number;
+    readonly EQUAL: number;
+    readonly FASTEST: number;
+    readonly FLOAT: number;
+    readonly FLOAT_MAT2: number;
+    readonly FLOAT_MAT3: number;
+    readonly FLOAT_MAT4: number;
+    readonly FLOAT_VEC2: number;
+    readonly FLOAT_VEC3: number;
+    readonly FLOAT_VEC4: number;
+    readonly FRAGMENT_SHADER: number;
+    readonly FRAMEBUFFER: number;
+    readonly FRAMEBUFFER_ATTACHMENT_OBJECT_NAME: number;
+    readonly FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE: number;
+    readonly FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE: number;
+    readonly FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL: number;
+    readonly FRAMEBUFFER_BINDING: number;
+    readonly FRAMEBUFFER_COMPLETE: number;
+    readonly FRAMEBUFFER_INCOMPLETE_ATTACHMENT: number;
+    readonly FRAMEBUFFER_INCOMPLETE_DIMENSIONS: number;
+    readonly FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT: number;
+    readonly FRAMEBUFFER_UNSUPPORTED: number;
+    readonly FRONT: number;
+    readonly FRONT_AND_BACK: number;
+    readonly FRONT_FACE: number;
+    readonly FUNC_ADD: number;
+    readonly FUNC_REVERSE_SUBTRACT: number;
+    readonly FUNC_SUBTRACT: number;
+    readonly GENERATE_MIPMAP_HINT: number;
+    readonly GEQUAL: number;
+    readonly GREATER: number;
+    readonly GREEN_BITS: number;
+    readonly HIGH_FLOAT: number;
+    readonly HIGH_INT: number;
+    readonly IMPLEMENTATION_COLOR_READ_FORMAT: number;
+    readonly IMPLEMENTATION_COLOR_READ_TYPE: number;
+    readonly INCR: number;
+    readonly INCR_WRAP: number;
+    readonly INT: number;
+    readonly INT_VEC2: number;
+    readonly INT_VEC3: number;
+    readonly INT_VEC4: number;
+    readonly INVALID_ENUM: number;
+    readonly INVALID_FRAMEBUFFER_OPERATION: number;
+    readonly INVALID_OPERATION: number;
+    readonly INVALID_VALUE: number;
+    readonly INVERT: number;
+    readonly KEEP: number;
+    readonly LEQUAL: number;
+    readonly LESS: number;
+    readonly LINEAR: number;
+    readonly LINEAR_MIPMAP_LINEAR: number;
+    readonly LINEAR_MIPMAP_NEAREST: number;
+    readonly LINES: number;
+    readonly LINE_LOOP: number;
+    readonly LINE_STRIP: number;
+    readonly LINE_WIDTH: number;
+    readonly LINK_STATUS: number;
+    readonly LOW_FLOAT: number;
+    readonly LOW_INT: number;
+    readonly LUMINANCE: number;
+    readonly LUMINANCE_ALPHA: number;
+    readonly MAX_COMBINED_TEXTURE_IMAGE_UNITS: number;
+    readonly MAX_CUBE_MAP_TEXTURE_SIZE: number;
+    readonly MAX_FRAGMENT_UNIFORM_VECTORS: number;
+    readonly MAX_RENDERBUFFER_SIZE: number;
+    readonly MAX_TEXTURE_IMAGE_UNITS: number;
+    readonly MAX_TEXTURE_SIZE: number;
+    readonly MAX_VARYING_VECTORS: number;
+    readonly MAX_VERTEX_ATTRIBS: number;
+    readonly MAX_VERTEX_TEXTURE_IMAGE_UNITS: number;
+    readonly MAX_VERTEX_UNIFORM_VECTORS: number;
+    readonly MAX_VIEWPORT_DIMS: number;
+    readonly MEDIUM_FLOAT: number;
+    readonly MEDIUM_INT: number;
+    readonly MIRRORED_REPEAT: number;
+    readonly NEAREST: number;
+    readonly NEAREST_MIPMAP_LINEAR: number;
+    readonly NEAREST_MIPMAP_NEAREST: number;
+    readonly NEVER: number;
+    readonly NICEST: number;
+    readonly NONE: number;
+    readonly NOTEQUAL: number;
+    readonly NO_ERROR: number;
+    readonly ONE: number;
+    readonly ONE_MINUS_CONSTANT_ALPHA: number;
+    readonly ONE_MINUS_CONSTANT_COLOR: number;
+    readonly ONE_MINUS_DST_ALPHA: number;
+    readonly ONE_MINUS_DST_COLOR: number;
+    readonly ONE_MINUS_SRC_ALPHA: number;
+    readonly ONE_MINUS_SRC_COLOR: number;
+    readonly OUT_OF_MEMORY: number;
+    readonly PACK_ALIGNMENT: number;
+    readonly POINTS: number;
+    readonly POLYGON_OFFSET_FACTOR: number;
+    readonly POLYGON_OFFSET_FILL: number;
+    readonly POLYGON_OFFSET_UNITS: number;
+    readonly RED_BITS: number;
+    readonly RENDERBUFFER: number;
+    readonly RENDERBUFFER_ALPHA_SIZE: number;
+    readonly RENDERBUFFER_BINDING: number;
+    readonly RENDERBUFFER_BLUE_SIZE: number;
+    readonly RENDERBUFFER_DEPTH_SIZE: number;
+    readonly RENDERBUFFER_GREEN_SIZE: number;
+    readonly RENDERBUFFER_HEIGHT: number;
+    readonly RENDERBUFFER_INTERNAL_FORMAT: number;
+    readonly RENDERBUFFER_RED_SIZE: number;
+    readonly RENDERBUFFER_STENCIL_SIZE: number;
+    readonly RENDERBUFFER_WIDTH: number;
+    readonly RENDERER: number;
+    readonly REPEAT: number;
+    readonly REPLACE: number;
+    readonly RGB: number;
+    readonly RGB565: number;
+    readonly RGB5_A1: number;
+    readonly RGBA: number;
+    readonly RGBA4: number;
+    readonly SAMPLER_2D: number;
+    readonly SAMPLER_CUBE: number;
+    readonly SAMPLES: number;
+    readonly SAMPLE_ALPHA_TO_COVERAGE: number;
+    readonly SAMPLE_BUFFERS: number;
+    readonly SAMPLE_COVERAGE: number;
+    readonly SAMPLE_COVERAGE_INVERT: number;
+    readonly SAMPLE_COVERAGE_VALUE: number;
+    readonly SCISSOR_BOX: number;
+    readonly SCISSOR_TEST: number;
+    readonly SHADER_TYPE: number;
+    readonly SHADING_LANGUAGE_VERSION: number;
+    readonly SHORT: number;
+    readonly SRC_ALPHA: number;
+    readonly SRC_ALPHA_SATURATE: number;
+    readonly SRC_COLOR: number;
+    readonly STATIC_DRAW: number;
+    readonly STENCIL_ATTACHMENT: number;
+    readonly STENCIL_BACK_FAIL: number;
+    readonly STENCIL_BACK_FUNC: number;
+    readonly STENCIL_BACK_PASS_DEPTH_FAIL: number;
+    readonly STENCIL_BACK_PASS_DEPTH_PASS: number;
+    readonly STENCIL_BACK_REF: number;
+    readonly STENCIL_BACK_VALUE_MASK: number;
+    readonly STENCIL_BACK_WRITEMASK: number;
+    readonly STENCIL_BITS: number;
+    readonly STENCIL_BUFFER_BIT: number;
+    readonly STENCIL_CLEAR_VALUE: number;
+    readonly STENCIL_FAIL: number;
+    readonly STENCIL_FUNC: number;
+    readonly STENCIL_INDEX: number;
+    readonly STENCIL_INDEX8: number;
+    readonly STENCIL_PASS_DEPTH_FAIL: number;
+    readonly STENCIL_PASS_DEPTH_PASS: number;
+    readonly STENCIL_REF: number;
+    readonly STENCIL_TEST: number;
+    readonly STENCIL_VALUE_MASK: number;
+    readonly STENCIL_WRITEMASK: number;
+    readonly STREAM_DRAW: number;
+    readonly SUBPIXEL_BITS: number;
+    readonly TEXTURE: number;
+    readonly TEXTURE0: number;
+    readonly TEXTURE1: number;
+    readonly TEXTURE10: number;
+    readonly TEXTURE11: number;
+    readonly TEXTURE12: number;
+    readonly TEXTURE13: number;
+    readonly TEXTURE14: number;
+    readonly TEXTURE15: number;
+    readonly TEXTURE16: number;
+    readonly TEXTURE17: number;
+    readonly TEXTURE18: number;
+    readonly TEXTURE19: number;
+    readonly TEXTURE2: number;
+    readonly TEXTURE20: number;
+    readonly TEXTURE21: number;
+    readonly TEXTURE22: number;
+    readonly TEXTURE23: number;
+    readonly TEXTURE24: number;
+    readonly TEXTURE25: number;
+    readonly TEXTURE26: number;
+    readonly TEXTURE27: number;
+    readonly TEXTURE28: number;
+    readonly TEXTURE29: number;
+    readonly TEXTURE3: number;
+    readonly TEXTURE30: number;
+    readonly TEXTURE31: number;
+    readonly TEXTURE4: number;
+    readonly TEXTURE5: number;
+    readonly TEXTURE6: number;
+    readonly TEXTURE7: number;
+    readonly TEXTURE8: number;
+    readonly TEXTURE9: number;
+    readonly TEXTURE_2D: number;
+    readonly TEXTURE_BINDING_2D: number;
+    readonly TEXTURE_BINDING_CUBE_MAP: number;
+    readonly TEXTURE_CUBE_MAP: number;
+    readonly TEXTURE_CUBE_MAP_NEGATIVE_X: number;
+    readonly TEXTURE_CUBE_MAP_NEGATIVE_Y: number;
+    readonly TEXTURE_CUBE_MAP_NEGATIVE_Z: number;
+    readonly TEXTURE_CUBE_MAP_POSITIVE_X: number;
+    readonly TEXTURE_CUBE_MAP_POSITIVE_Y: number;
+    readonly TEXTURE_CUBE_MAP_POSITIVE_Z: number;
+    readonly TEXTURE_MAG_FILTER: number;
+    readonly TEXTURE_MIN_FILTER: number;
+    readonly TEXTURE_WRAP_S: number;
+    readonly TEXTURE_WRAP_T: number;
+    readonly TRIANGLES: number;
+    readonly TRIANGLE_FAN: number;
+    readonly TRIANGLE_STRIP: number;
+    readonly UNPACK_ALIGNMENT: number;
+    readonly UNPACK_COLORSPACE_CONVERSION_WEBGL: number;
+    readonly UNPACK_FLIP_Y_WEBGL: number;
+    readonly UNPACK_PREMULTIPLY_ALPHA_WEBGL: number;
+    readonly UNSIGNED_BYTE: number;
+    readonly UNSIGNED_INT: number;
+    readonly UNSIGNED_SHORT: number;
+    readonly UNSIGNED_SHORT_4_4_4_4: number;
+    readonly UNSIGNED_SHORT_5_5_5_1: number;
+    readonly UNSIGNED_SHORT_5_6_5: number;
+    readonly VALIDATE_STATUS: number;
+    readonly VENDOR: number;
+    readonly VERSION: number;
+    readonly VERTEX_ATTRIB_ARRAY_BUFFER_BINDING: number;
+    readonly VERTEX_ATTRIB_ARRAY_ENABLED: number;
+    readonly VERTEX_ATTRIB_ARRAY_NORMALIZED: number;
+    readonly VERTEX_ATTRIB_ARRAY_POINTER: number;
+    readonly VERTEX_ATTRIB_ARRAY_SIZE: number;
+    readonly VERTEX_ATTRIB_ARRAY_STRIDE: number;
+    readonly VERTEX_ATTRIB_ARRAY_TYPE: number;
+    readonly VERTEX_SHADER: number;
+    readonly VIEWPORT: number;
+    readonly ZERO: number;
+
+    /* WebGL2 constants */
+    readonly READ_BUFFER: number;                                   // 0x0C02
+    readonly UNPACK_ROW_LENGTH: number;                             // 0x0CF2
+    readonly UNPACK_SKIP_ROWS: number;                              // 0x0CF3
+    readonly UNPACK_SKIP_PIXELS: number;                            // 0x0CF4
+    readonly PACK_ROW_LENGTH: number;                               // 0x0D02
+    readonly PACK_SKIP_ROWS: number;                                // 0x0D03
+    readonly PACK_SKIP_PIXELS: number;                              // 0x0D04
+    readonly COLOR: number;                                         // 0x1800
+    readonly DEPTH: number;                                         // 0x1801
+    readonly STENCIL: number;                                       // 0x1802
+    readonly RED: number;                                           // 0x1903
+    readonly RGB8: number;                                          // 0x8051
+    readonly RGBA8: number;                                         // 0x8058
+    readonly RGB10_A2: number;                                      // 0x8059
+    readonly TEXTURE_BINDING_3D: number;                            // 0x806A
+    readonly UNPACK_SKIP_IMAGES: number;                            // 0x806D
+    readonly UNPACK_IMAGE_HEIGHT: number;                           // 0x806E
+    readonly TEXTURE_3D: number;                                    // 0x806F
+    readonly TEXTURE_WRAP_R: number;                                // 0x8072
+    readonly MAX_3D_TEXTURE_SIZE: number;                           // 0x8073
+    readonly UNSIGNED_INT_2_10_10_10_REV: number;                   // 0x8368
+    readonly MAX_ELEMENTS_VERTICES: number;                         // 0x80E8
+    readonly MAX_ELEMENTS_INDICES: number;                          // 0x80E9
+    readonly TEXTURE_MIN_LOD: number;                               // 0x813A
+    readonly TEXTURE_MAX_LOD: number;                               // 0x813B
+    readonly TEXTURE_BASE_LEVEL: number;                            // 0x813C
+    readonly TEXTURE_MAX_LEVEL: number;                             // 0x813D
+    readonly MIN: number;                                           // 0x8007
+    readonly MAX: number;                                           // 0x8008
+    readonly DEPTH_COMPONENT24: number;                             // 0x81A6
+    readonly MAX_TEXTURE_LOD_BIAS: number;                          // 0x84FD
+    readonly TEXTURE_COMPARE_MODE: number;                          // 0x884C
+    readonly TEXTURE_COMPARE_FUNC: number;                          // 0x884D
+    readonly CURRENT_QUERY: number;                                 // 0x8865
+    readonly QUERY_RESULT: number;                                  // 0x8866
+    readonly QUERY_RESULT_AVAILABLE: number;                        // 0x8867
+    readonly STREAM_READ: number;                                   // 0x88E1
+    readonly STREAM_COPY: number;                                   // 0x88E2
+    readonly STATIC_READ: number;                                   // 0x88E5
+    readonly STATIC_COPY: number;                                   // 0x88E6
+    readonly DYNAMIC_READ: number;                                  // 0x88E9
+    readonly DYNAMIC_COPY: number;                                  // 0x88EA
+    readonly MAX_DRAW_BUFFERS: number;                              // 0x8824
+    readonly DRAW_BUFFER0: number;                                  // 0x8825
+    readonly DRAW_BUFFER1: number;                                  // 0x8826
+    readonly DRAW_BUFFER2: number;                                  // 0x8827
+    readonly DRAW_BUFFER3: number;                                  // 0x8828
+    readonly DRAW_BUFFER4: number;                                  // 0x8829
+    readonly DRAW_BUFFER5: number;                                  // 0x882A
+    readonly DRAW_BUFFER6: number;                                  // 0x882B
+    readonly DRAW_BUFFER7: number;                                  // 0x882C
+    readonly DRAW_BUFFER8: number;                                  // 0x882D
+    readonly DRAW_BUFFER9: number;                                  // 0x882E
+    readonly DRAW_BUFFER10: number;                                 // 0x882F
+    readonly DRAW_BUFFER11: number;                                 // 0x8830
+    readonly DRAW_BUFFER12: number;                                 // 0x8831
+    readonly DRAW_BUFFER13: number;                                 // 0x8832
+    readonly DRAW_BUFFER14: number;                                 // 0x8833
+    readonly DRAW_BUFFER15: number;                                 // 0x8834
+    readonly MAX_FRAGMENT_UNIFORM_COMPONENTS: number;               // 0x8B49
+    readonly MAX_VERTEX_UNIFORM_COMPONENTS: number;                 // 0x8B4A
+    readonly SAMPLER_3D: number;                                    // 0x8B5F
+    readonly SAMPLER_2D_SHADOW: number;                             // 0x8B62
+    readonly FRAGMENT_SHADER_DERIVATIVE_HINT: number;               // 0x8B8B
+    readonly PIXEL_PACK_BUFFER: number;                             // 0x88EB
+    readonly PIXEL_UNPACK_BUFFER: number;                           // 0x88EC
+    readonly PIXEL_PACK_BUFFER_BINDING: number;                     // 0x88ED
+    readonly PIXEL_UNPACK_BUFFER_BINDING: number;                   // 0x88EF
+    readonly FLOAT_MAT2x3: number;                                  // 0x8B65
+    readonly FLOAT_MAT2x4: number;                                  // 0x8B66
+    readonly FLOAT_MAT3x2: number;                                  // 0x8B67
+    readonly FLOAT_MAT3x4: number;                                  // 0x8B68
+    readonly FLOAT_MAT4x2: number;                                  // 0x8B69
+    readonly FLOAT_MAT4x3: number;                                  // 0x8B6A
+    readonly SRGB: number;                                          // 0x8C40
+    readonly SRGB8: number;                                         // 0x8C41
+    readonly SRGB8_ALPHA8: number;                                  // 0x8C43
+    readonly COMPARE_REF_TO_TEXTURE: number;                        // 0x884E
+    readonly RGBA32F: number;                                       // 0x8814
+    readonly RGB32F: number;                                        // 0x8815
+    readonly RGBA16F: number;                                       // 0x881A
+    readonly RGB16F: number;                                        // 0x881B
+    readonly VERTEX_ATTRIB_ARRAY_INTEGER: number;                   // 0x88FD
+    readonly MAX_ARRAY_TEXTURE_LAYERS: number;                      // 0x88FF
+    readonly MIN_PROGRAM_TEXEL_OFFSET: number;                      // 0x8904
+    readonly MAX_PROGRAM_TEXEL_OFFSET: number;                      // 0x8905
+    readonly MAX_VARYING_COMPONENTS: number;                        // 0x8B4B
+    readonly TEXTURE_2D_ARRAY: number;                              // 0x8C1A
+    readonly TEXTURE_BINDING_2D_ARRAY: number;                      // 0x8C1D
+    readonly R11F_G11F_B10F: number;                                // 0x8C3A
+    readonly UNSIGNED_INT_10F_11F_11F_REV: number;                  // 0x8C3B
+    readonly RGB9_E5: number;                                       // 0x8C3D
+    readonly UNSIGNED_INT_5_9_9_9_REV: number;                      // 0x8C3E
+    readonly TRANSFORM_FEEDBACK_BUFFER_MODE: number;                // 0x8C7F
+    readonly MAX_TRANSFORM_FEEDBACK_SEPARATE_COMPONENTS: number;    // 0x8C80
+    readonly TRANSFORM_FEEDBACK_VARYINGS: number;                   // 0x8C83
+    readonly TRANSFORM_FEEDBACK_BUFFER_START: number;               // 0x8C84
+    readonly TRANSFORM_FEEDBACK_BUFFER_SIZE: number;                // 0x8C85
+    readonly TRANSFORM_FEEDBACK_PRIMITIVES_WRITTEN: number;         // 0x8C88
+    readonly RASTERIZER_DISCARD: number;                            // 0x8C89
+    readonly MAX_TRANSFORM_FEEDBACK_INTERLEAVED_COMPONENTS: number; // 0x8C8A
+    readonly MAX_TRANSFORM_FEEDBACK_SEPARATE_ATTRIBS: number;       // 0x8C8B
+    readonly INTERLEAVED_ATTRIBS: number;                           // 0x8C8C
+    readonly SEPARATE_ATTRIBS: number;                              // 0x8C8D
+    readonly TRANSFORM_FEEDBACK_BUFFER: number;                     // 0x8C8E
+    readonly TRANSFORM_FEEDBACK_BUFFER_BINDING: number;             // 0x8C8F
+    readonly RGBA32UI: number;                                      // 0x8D70
+    readonly RGB32UI: number;                                       // 0x8D71
+    readonly RGBA16UI: number;                                      // 0x8D76
+    readonly RGB16UI: number;                                       // 0x8D77
+    readonly RGBA8UI: number;                                       // 0x8D7C
+    readonly RGB8UI: number;                                        // 0x8D7D
+    readonly RGBA32I: number;                                       // 0x8D82
+    readonly RGB32I: number;                                        // 0x8D83
+    readonly RGBA16I: number;                                       // 0x8D88
+    readonly RGB16I: number;                                        // 0x8D89
+    readonly RGBA8I: number;                                        // 0x8D8E
+    readonly RGB8I: number;                                         // 0x8D8F
+    readonly RED_INTEGER: number;                                   // 0x8D94
+    readonly RGB_INTEGER: number;                                   // 0x8D98
+    readonly RGBA_INTEGER: number;                                  // 0x8D99
+    readonly SAMPLER_2D_ARRAY: number;                              // 0x8DC1
+    readonly SAMPLER_2D_ARRAY_SHADOW: number;                       // 0x8DC4
+    readonly SAMPLER_CUBE_SHADOW: number;                           // 0x8DC5
+    readonly UNSIGNED_INT_VEC2: number;                             // 0x8DC6
+    readonly UNSIGNED_INT_VEC3: number;                             // 0x8DC7
+    readonly UNSIGNED_INT_VEC4: number;                             // 0x8DC8
+    readonly INT_SAMPLER_2D: number;                                // 0x8DCA
+    readonly INT_SAMPLER_3D: number;                                // 0x8DCB
+    readonly INT_SAMPLER_CUBE: number;                              // 0x8DCC
+    readonly INT_SAMPLER_2D_ARRAY: number;                          // 0x8DCF
+    readonly UNSIGNED_INT_SAMPLER_2D: number;                       // 0x8DD2
+    readonly UNSIGNED_INT_SAMPLER_3D: number;                       // 0x8DD3
+    readonly UNSIGNED_INT_SAMPLER_CUBE: number;                     // 0x8DD4
+    readonly UNSIGNED_INT_SAMPLER_2D_ARRAY: number;                 // 0x8DD7
+    readonly DEPTH_COMPONENT32F: number;                            // 0x8CAC
+    readonly DEPTH32F_STENCIL8: number;                             // 0x8CAD
+    readonly FLOAT_32_UNSIGNED_INT_24_8_REV: number;                // 0x8DAD
+    readonly FRAMEBUFFER_ATTACHMENT_COLOR_ENCODING: number;         // 0x8210
+    readonly FRAMEBUFFER_ATTACHMENT_COMPONENT_TYPE: number;         // 0x8211
+    readonly FRAMEBUFFER_ATTACHMENT_RED_SIZE: number;               // 0x8212
+    readonly FRAMEBUFFER_ATTACHMENT_GREEN_SIZE: number;             // 0x8213
+    readonly FRAMEBUFFER_ATTACHMENT_BLUE_SIZE: number;              // 0x8214
+    readonly FRAMEBUFFER_ATTACHMENT_ALPHA_SIZE: number;             // 0x8215
+    readonly FRAMEBUFFER_ATTACHMENT_DEPTH_SIZE: number;             // 0x8216
+    readonly FRAMEBUFFER_ATTACHMENT_STENCIL_SIZE: number;           // 0x8217
+    readonly FRAMEBUFFER_DEFAULT: number;                           // 0x8218
+    //readonly DEPTH_STENCIL_ATTACHMENT: number;                    // 0x821A /* Already defined in WebGL1 constants */
+    //readonly DEPTH_STENCIL: number;                               // 0x84F9 /* Already defined in WebGL1 constants */
+    readonly UNSIGNED_INT_24_8: number;                             // 0x84FA
+    readonly DEPTH24_STENCIL8: number;                              // 0x88F0
+    readonly UNSIGNED_NORMALIZED: number;                           // 0x8C17
+    readonly DRAW_FRAMEBUFFER_BINDING: number;                      // 0x8CA6 /* Same as FRAMEBUFFER_BINDING */
+    readonly READ_FRAMEBUFFER: number;                              // 0x8CA8
+    readonly DRAW_FRAMEBUFFER: number;                              // 0x8CA9
+    readonly READ_FRAMEBUFFER_BINDING: number;                      // 0x8CAA
+    readonly RENDERBUFFER_SAMPLES: number;                          // 0x8CAB
+    readonly FRAMEBUFFER_ATTACHMENT_TEXTURE_LAYER: number;          // 0x8CD4
+    readonly MAX_COLOR_ATTACHMENTS: number;                         // 0x8CDF
+    readonly COLOR_ATTACHMENT1: number;                             // 0x8CE1
+    readonly COLOR_ATTACHMENT2: number;                             // 0x8CE2
+    readonly COLOR_ATTACHMENT3: number;                             // 0x8CE3
+    readonly COLOR_ATTACHMENT4: number;                             // 0x8CE4
+    readonly COLOR_ATTACHMENT5: number;                             // 0x8CE5
+    readonly COLOR_ATTACHMENT6: number;                             // 0x8CE6
+    readonly COLOR_ATTACHMENT7: number;                             // 0x8CE7
+    readonly COLOR_ATTACHMENT8: number;                             // 0x8CE8
+    readonly COLOR_ATTACHMENT9: number;                             // 0x8CE9
+    readonly COLOR_ATTACHMENT10: number;                            // 0x8CEA
+    readonly COLOR_ATTACHMENT11: number;                            // 0x8CEB
+    readonly COLOR_ATTACHMENT12: number;                            // 0x8CEC
+    readonly COLOR_ATTACHMENT13: number;                            // 0x8CED
+    readonly COLOR_ATTACHMENT14: number;                            // 0x8CEE
+    readonly COLOR_ATTACHMENT15: number;                            // 0x8CEF
+    readonly FRAMEBUFFER_INCOMPLETE_MULTISAMPLE: number;            // 0x8D56
+    readonly MAX_SAMPLES: number;                                   // 0x8D57
+    readonly HALF_FLOAT: number;                                    // 0x140B
+    readonly RG: number;                                            // 0x8227
+    readonly RG_INTEGER: number;                                    // 0x8228
+    readonly R8: number;                                            // 0x8229
+    readonly RG8: number;                                           // 0x822B
+    readonly R16F: number;                                          // 0x822D
+    readonly R32F: number;                                          // 0x822E
+    readonly RG16F: number;                                         // 0x822F
+    readonly RG32F: number;                                         // 0x8230
+    readonly R8I: number;                                           // 0x8231
+    readonly R8UI: number;                                          // 0x8232
+    readonly R16I: number;                                          // 0x8233
+    readonly R16UI: number;                                         // 0x8234
+    readonly R32I: number;                                          // 0x8235
+    readonly R32UI: number;                                         // 0x8236
+    readonly RG8I: number;                                          // 0x8237
+    readonly RG8UI: number;                                         // 0x8238
+    readonly RG16I: number;                                         // 0x8239
+    readonly RG16UI: number;                                        // 0x823A
+    readonly RG32I: number;                                         // 0x823B
+    readonly RG32UI: number;                                        // 0x823C
+    readonly VERTEX_ARRAY_BINDING: number;                          // 0x85B5
+    readonly R8_SNORM: number;                                      // 0x8F94
+    readonly RG8_SNORM: number;                                     // 0x8F95
+    readonly RGB8_SNORM: number;                                    // 0x8F96
+    readonly RGBA8_SNORM: number;                                   // 0x8F97
+    readonly SIGNED_NORMALIZED: number;                             // 0x8F9C
+    readonly COPY_READ_BUFFER: number;                              // 0x8F36
+    readonly COPY_WRITE_BUFFER: number;                             // 0x8F37
+    readonly COPY_READ_BUFFER_BINDING: number;                      // 0x8F36 /* Same as COPY_READ_BUFFER */
+    readonly COPY_WRITE_BUFFER_BINDING: number;                     // 0x8F37 /* Same as COPY_WRITE_BUFFER */
+    readonly UNIFORM_BUFFER: number;                                // 0x8A11
+    readonly UNIFORM_BUFFER_BINDING: number;                        // 0x8A28
+    readonly UNIFORM_BUFFER_START: number;                          // 0x8A29
+    readonly UNIFORM_BUFFER_SIZE: number;                           // 0x8A2A
+    readonly MAX_VERTEX_UNIFORM_BLOCKS: number;                     // 0x8A2B
+    readonly MAX_FRAGMENT_UNIFORM_BLOCKS: number;                   // 0x8A2D
+    readonly MAX_COMBINED_UNIFORM_BLOCKS: number;                   // 0x8A2E
+    readonly MAX_UNIFORM_BUFFER_BINDINGS: number;                   // 0x8A2F
+    readonly MAX_UNIFORM_BLOCK_SIZE: number;                        // 0x8A30
+    readonly MAX_COMBINED_VERTEX_UNIFORM_COMPONENTS: number;        // 0x8A31
+    readonly MAX_COMBINED_FRAGMENT_UNIFORM_COMPONENTS: number;      // 0x8A33
+    readonly UNIFORM_BUFFER_OFFSET_ALIGNMENT: number;               // 0x8A34
+    readonly ACTIVE_UNIFORM_BLOCKS: number;                         // 0x8A36
+    readonly UNIFORM_TYPE: number;                                  // 0x8A37
+    readonly UNIFORM_SIZE: number;                                  // 0x8A38
+    readonly UNIFORM_BLOCK_INDEX: number;                           // 0x8A3A
+    readonly UNIFORM_OFFSET: number;                                // 0x8A3B
+    readonly UNIFORM_ARRAY_STRIDE: number;                          // 0x8A3C
+    readonly UNIFORM_MATRIX_STRIDE: number;                         // 0x8A3D
+    readonly UNIFORM_IS_ROW_MAJOR: number;                          // 0x8A3E
+    readonly UNIFORM_BLOCK_BINDING: number;                         // 0x8A3F
+    readonly UNIFORM_BLOCK_DATA_SIZE: number;                       // 0x8A40
+    readonly UNIFORM_BLOCK_ACTIVE_UNIFORMS: number;                 // 0x8A42
+    readonly UNIFORM_BLOCK_ACTIVE_UNIFORM_INDICES: number;          // 0x8A43
+    readonly UNIFORM_BLOCK_REFERENCED_BY_VERTEX_SHADER: number;     // 0x8A44
+    readonly UNIFORM_BLOCK_REFERENCED_BY_FRAGMENT_SHADER: number;   // 0x8A46
+    readonly INVALID_INDEX: number;                                 // 0xFFFFFFFF
+    readonly MAX_VERTEX_OUTPUT_COMPONENTS: number;                  // 0x9122
+    readonly MAX_FRAGMENT_INPUT_COMPONENTS: number;                 // 0x9125
+    readonly MAX_SERVER_WAIT_TIMEOUT: number;                       // 0x9111
+    readonly OBJECT_TYPE: number;                                   // 0x9112
+    readonly SYNC_CONDITION: number;                                // 0x9113
+    readonly SYNC_STATUS: number;                                   // 0x9114
+    readonly SYNC_FLAGS: number;                                    // 0x9115
+    readonly SYNC_FENCE: number;                                    // 0x9116
+    readonly SYNC_GPU_COMMANDS_COMPLETE: number;                    // 0x9117
+    readonly UNSIGNALED: number;                                    // 0x9118
+    readonly SIGNALED: number;                                      // 0x9119
+    readonly ALREADY_SIGNALED: number;                              // 0x911A
+    readonly TIMEOUT_EXPIRED: number;                               // 0x911B
+    readonly CONDITION_SATISFIED: number;                           // 0x911C
+    readonly WAIT_FAILED: number;                                   // 0x911D
+    readonly SYNC_FLUSH_COMMANDS_BIT: number;                       // 0x00000001
+    readonly VERTEX_ATTRIB_ARRAY_DIVISOR: number;                   // 0x88FE
+    readonly ANY_SAMPLES_PASSED: number;                            // 0x8C2F
+    readonly ANY_SAMPLES_PASSED_CONSERVATIVE: number;               // 0x8D6A
+    readonly SAMPLER_BINDING: number;                               // 0x8919
+    readonly RGB10_A2UI: number;                                    // 0x906F
+    readonly INT_2_10_10_10_REV: number;                            // 0x8D9F
+    readonly TRANSFORM_FEEDBACK: number;                            // 0x8E22
+    readonly TRANSFORM_FEEDBACK_PAUSED: number;                     // 0x8E23
+    readonly TRANSFORM_FEEDBACK_ACTIVE: number;                     // 0x8E24
+    readonly TRANSFORM_FEEDBACK_BINDING: number;                    // 0x8E25
+    readonly TEXTURE_IMMUTABLE_FORMAT: number;                      // 0x912F
+    readonly MAX_ELEMENT_INDEX: number;                             // 0x8D6B
+    readonly TEXTURE_IMMUTABLE_LEVELS: number;                      // 0x82DF
+
+    readonly TIMEOUT_IGNORED: number;                               // -1
+
+    /* WebGL-specific enums */
+    readonly MAX_CLIENT_WAIT_TIMEOUT_WEBGL: number;                 // 0x9247
+};
+
+interface WebGLQuery extends WebGLObject {
+}
+
+declare var WebGLQuery: {
+    prototype: WebGLQuery;
+    new (): WebGLQuery;
+};
+
+interface WebGLSampler extends WebGLObject {
+}
+
+declare var WebGLSampler: {
+    prototype: WebGLSampler;
+    new (): WebGLSampler;
+};
+
+interface WebGLSync extends WebGLObject {
+}
+
+declare var WebGLSync: {
+    prototype: WebGLSync;
+    new (): WebGLSync;
+};
+
+interface WebGLTransformFeedback extends WebGLObject {
+}
+
+declare var WebGLTransformFeedback: {
+    prototype: WebGLTransformFeedback;
+    new (): WebGLTransformFeedback;
+};
+
+interface WebGLVertexArrayObject extends WebGLObject {
+}
+
+declare var WebGLVertexArrayObject: {
+    prototype: WebGLVertexArrayObject;
+    new (): WebGLVertexArrayObject;
+};
diff --git a/node_modules/@types/webgl2/package.json b/node_modules/@types/webgl2/package.json
new file mode 100644
index 0000000..5e001d7
--- /dev/null
+++ b/node_modules/@types/webgl2/package.json
@@ -0,0 +1,58 @@
+{
+  "_args": [
+    [
+      "@types/webgl2@0.0.4",
+      "/Users/zamua/Dropbox/workspace/madify"
+    ]
+  ],
+  "_from": "@types/webgl2@0.0.4",
+  "_id": "@types/webgl2@0.0.4",
+  "_inBundle": false,
+  "_integrity": "sha512-PACt1xdErJbMUOUweSrbVM7gSIYm1vTncW2hF6Os/EeWi6TXYAYMPp+8v6rzHmypE5gHrxaxZNXgMkJVIdZpHw==",
+  "_location": "/@types/webgl2",
+  "_phantomChildren": {},
+  "_requested": {
+    "type": "version",
+    "registry": true,
+    "raw": "@types/webgl2@0.0.4",
+    "name": "@types/webgl2",
+    "escapedName": "@types%2fwebgl2",
+    "scope": "@types",
+    "rawSpec": "0.0.4",
+    "saveSpec": null,
+    "fetchSpec": "0.0.4"
+  },
+  "_requiredBy": [
+    "/@tensorflow/tfjs-core"
+  ],
+  "_resolved": "https://registry.npmjs.org/@types/webgl2/-/webgl2-0.0.4.tgz",
+  "_spec": "0.0.4",
+  "_where": "/Users/zamua/Dropbox/workspace/madify",
+  "bugs": {
+    "url": "https://github.com/DefinitelyTyped/DefinitelyTyped/issues"
+  },
+  "contributors": [
+    {
+      "name": "Nico Kemnitz",
+      "url": "https://github.com/nkemnitz"
+    },
+    {
+      "name": "Adrian Blumer",
+      "url": "https://github.com/karhu"
+    }
+  ],
+  "dependencies": {},
+  "description": "TypeScript definitions for WebGL 2, Editor's Draft Fri Feb 24 16:10:18 2017 -0800",
+  "homepage": "https://github.com/DefinitelyTyped/DefinitelyTyped#readme",
+  "license": "MIT",
+  "main": "",
+  "name": "@types/webgl2",
+  "repository": {
+    "type": "git",
+    "url": "git+https://github.com/DefinitelyTyped/DefinitelyTyped.git"
+  },
+  "scripts": {},
+  "typeScriptVersion": "2.0",
+  "typesPublisherContentHash": "4b06a12ad7c8229a033ac12d69a54d58274e48369d24481a61e344c8605d3d2f",
+  "version": "0.0.4"
+}
diff --git a/node_modules/face-api.js/.travis.yml b/node_modules/face-api.js/.travis.yml
new file mode 100644
index 0000000..3347c72
--- /dev/null
+++ b/node_modules/face-api.js/.travis.yml
@@ -0,0 +1,24 @@
+sudo: required
+language: node_js
+node_js:
+  - "node"
+  - "10"
+  - "8"
+  # node 6 is not compatible with tfjs-node
+  # - "6"
+env:
+  global:
+    - BACKEND_CPU=true EXCLUDE_UNCOMPRESSED=true
+  matrix:
+    - ENV=browser
+    - ENV=node
+addons:
+  chrome: stable
+install: npm install
+before_install:
+ - export DISPLAY=:99.0
+ - sh -e /etc/init.d/xvfb start
+script:
+  - if [ $ENV == 'browser' ]; then npm run test-browser; fi
+  - if [ $ENV == 'node' ]; then npm run test-node; fi
+  - npm run build
\ No newline at end of file
diff --git a/node_modules/face-api.js/LICENSE b/node_modules/face-api.js/LICENSE
new file mode 100644
index 0000000..2fafbb1
--- /dev/null
+++ b/node_modules/face-api.js/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2018 Vincent Mühler
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/node_modules/face-api.js/README.md b/node_modules/face-api.js/README.md
new file mode 100644
index 0000000..1fe8681
--- /dev/null
+++ b/node_modules/face-api.js/README.md
@@ -0,0 +1,779 @@
+# face-api.js
+
+[![Build Status](https://travis-ci.org/justadudewhohacks/face-api.js.svg?branch=master)](https://travis-ci.org/justadudewhohacks/face-api.js)
+[![Slack](https://slack.bri.im/badge.svg)](https://slack.bri.im)
+
+**JavaScript API for face detection and face recognition in the browser implemented on top of the tensorflow.js core API ([tensorflow/tfjs-core](https://github.com/tensorflow/tfjs-core))**
+
+Table of Contents:
+
+* **[Resources](#resources)**
+  * **[Live Demos](#live-demos)**
+  * **[Tutorials](#tutorials)**
+* **[Examples](#examples)**
+  * **[Running the Examples](#running-the-examples)**
+* **[Available Models](#models)**
+  * **[Face Detection Models](#models-face-detection)**
+  * **[68 Point Face Landmark Detection Models](#models-face-landmark-detection)**
+  * **[Face Recognition Model](#models-face-recognition)**
+  * **[Face Expression Recognition Model](#models-face-expression-recognition)**
+* **[Getting Started](#getting-started)**
+  * **[face-api.js for the Browser](#getting-started-browser)**
+  * **[face-api.js for Nodejs](#getting-started-nodejs)**
+* **[Usage](#usage)**
+  * **[Loading the Models](#usage-loading-models)**
+  * **[High Level API](#usage-high-level-api)**
+  * **[Displaying Detection Results](#usage-displaying-detection-results)**
+  * **[Face Detection Options](#usage-face-detection-options)**
+  * **[Utility Classes](#usage-utility-classes)**
+  * **[Other Useful Utility](#other-useful-utility)**
+* **[API Documentation](https://justadudewhohacks.github.io/face-api.js/docs/globals.html)**
+
+<a name="resources"></a>
+
+# Resources
+
+<a name="live-demos"></a>
+
+## Live Demos
+
+**[Check out the live demos!](https://justadudewhohacks.github.io/face-api.js/)**
+
+<a name="tutorials"></a>
+
+## Tutorials
+
+Check out my face-api.js tutorials:
+
+* **[face-api.js — JavaScript API for Face Recognition in the Browser with tensorflow.js](https://itnext.io/face-api-js-javascript-api-for-face-recognition-in-the-browser-with-tensorflow-js-bcc2a6c4cf07)**
+* **[Realtime JavaScript Face Tracking and Face Recognition using face-api.js’ MTCNN Face Detector](https://itnext.io/realtime-javascript-face-tracking-and-face-recognition-using-face-api-js-mtcnn-face-detector-d924dd8b5740)**
+
+<a name="examples"></a>
+
+# Examples
+
+## Face Recognition
+
+![preview_face-detection-and-recognition](https://user-images.githubusercontent.com/31125521/41526995-1a90e4e6-72e6-11e8-96d4-8b2ccdee5f79.gif)
+
+![face-recognition-preview](https://user-images.githubusercontent.com/31125521/47384002-41e36f80-d706-11e8-8cd9-b3102c1bee67.png)
+
+## Face Similarity
+
+![preview_face-similarity](https://user-images.githubusercontent.com/31125521/40316573-0a1190c0-5d1f-11e8-8797-f6deaa344523.gif)
+
+## Face Expression Recognition
+
+![preview_face-expression-recognition](https://user-images.githubusercontent.com/31125521/50575270-f501d080-0dfb-11e9-9676-8f419efdade4.png)
+
+## Face Landmark Detection
+
+![face_landmarks_boxes_2](https://user-images.githubusercontent.com/31125521/46063404-00928b00-c16d-11e8-8f29-e9c50afd2bc8.jpg)
+
+![preview_face_landmarks](https://user-images.githubusercontent.com/31125521/41507950-e121b05e-723c-11e8-89f2-d8f9348a8e86.png)
+
+## Realtime Face Tracking
+
+![output](https://user-images.githubusercontent.com/31125521/47383860-ea450400-d705-11e8-9880-d5d15d952661.gif)
+
+## MTCNN
+
+![mtcnn-preview](https://user-images.githubusercontent.com/31125521/42756818-0a41edaa-88fe-11e8-9033-8cd141b0fa09.gif)
+
+<a name="running-the-examples"></a>
+
+## Running the Examples
+
+Clone the repository:
+
+``` bash
+git clone https://github.com/justadudewhohacks/face-api.js.git
+```
+
+### Running the Browser Examples
+
+``` bash
+cd face-api.js/examples/examples-browser
+npm i
+npm start
+```
+
+Browse to http://localhost:3000/.
+
+### Running the Nodejs Examples
+
+``` bash
+cd face-api.js/examples/examples-nodejs
+npm i
+```
+
+Now run one of the examples using ts-node:
+
+``` bash
+ts-node faceDetection.ts
+```
+
+Or simply compile and run them with node:
+
+``` bash
+tsc faceDetection.ts
+node faceDetection.js
+```
+
+<a name="models"></a>
+
+# Available Models
+
+<a name="models-face-detection"></a>
+
+## Face Detection Models
+
+### SSD Mobilenet V1
+
+For face detection, this project implements a SSD (Single Shot Multibox Detector) based on MobileNetV1. The neural net will compute the locations of each face in an image and will return the bounding boxes together with it's probability for each face. This face detector is aiming towards obtaining high accuracy in detecting face bounding boxes instead of low inference time. The size of the quantized model is about 5.4 MB (**ssd_mobilenetv1_model**).
+
+The face detection model has been trained on the [WIDERFACE dataset](http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/) and the weights are provided by [yeephycho](https://github.com/yeephycho) in [this](https://github.com/yeephycho/tensorflow-face-detection) repo.
+
+### Tiny Face Detector
+
+The Tiny Face Detector is a very performant, realtime face detector, which is much faster, smaller and less resource consuming compared to the SSD Mobilenet V1 face detector, in return it performs slightly less well on detecting small faces. This model is extremely mobile and web friendly, thus it should be your GO-TO face detector on mobile devices and resource limited clients. The size of the quantized model is only 190 KB (**tiny_face_detector_model**).
+
+The face detector has been trained on a custom dataset of ~14K images labeled with bounding boxes. Furthermore the model has been trained to predict bounding boxes, which entirely cover facial feature points, thus it in general produces better results in combination with subsequent face landmark detection than SSD Mobilenet V1.
+
+This model is basically an even tinier version of Tiny Yolo V2, replacing the regular convolutions of Yolo with depthwise separable convolutions. Yolo is fully convolutional, thus can easily adapt to different input image sizes to trade off accuracy for performance (inference time).
+
+### MTCNN
+
+**Note, this model is mostly kept in this repo for experimental reasons. In general the other face detectors should perform better, but of course you are free to play around with MTCNN.**
+
+MTCNN (Multi-task Cascaded Convolutional Neural Networks) represents an alternative face detector to SSD Mobilenet v1 and Tiny Yolo v2, which offers much more room for configuration. By tuning the input parameters, MTCNN should be able to detect a wide range of face bounding box sizes. MTCNN is a 3 stage cascaded CNN, which simultaneously returns 5 face landmark points along with the bounding boxes and scores for each face. Additionally the model size is only 2MB.
+
+MTCNN has been presented in the paper [Joint Face Detection and Alignment using Multi-task Cascaded Convolutional Networks](https://kpzhang93.github.io/MTCNN_face_detection_alignment/paper/spl.pdf) by Zhang et al. and the model weights are provided in the official [repo](https://github.com/kpzhang93/MTCNN_face_detection_alignment) of the MTCNN implementation.
+
+<a name="models-face-landmark-detection"></a>
+
+## 68 Point Face Landmark Detection Models
+
+This package implements a very lightweight and fast, yet accurate 68 point face landmark detector. The default model has a size of only 350kb (**face_landmark_68_model**) and the tiny model is only 80kb (**face_landmark_68_tiny_model**). Both models employ the ideas of depthwise separable convolutions as well as densely connected blocks. The models have been trained on a dataset of ~35k face images labeled with 68 face landmark points.
+
+<a name="models-face-recognition"></a>
+
+## Face Recognition Model
+
+For face recognition, a ResNet-34 like architecture is implemented to compute a face descriptor (a feature vector with 128 values) from any given face image, which is used to describe the characteristics of a persons face. The model is **not** limited to the set of faces used for training, meaning you can use it for face recognition of any person, for example yourself. You can determine the similarity of two arbitrary faces by comparing their face descriptors, for example by computing the euclidean distance or using any other classifier of your choice.
+
+The neural net is equivalent to the **FaceRecognizerNet** used in [face-recognition.js](https://github.com/justadudewhohacks/face-recognition.js) and the net used in the [dlib](https://github.com/davisking/dlib/blob/master/examples/dnn_face_recognition_ex.cpp) face recognition example. The weights have been trained by [davisking](https://github.com/davisking) and the model achieves a prediction accuracy of 99.38% on the LFW (Labeled Faces in the Wild) benchmark for face recognition.
+
+The size of the quantized model is roughly 6.2 MB (**face_recognition_model**).
+
+<a name="models-face-expression-recognition"></a>
+
+## Face Expression Recognition Model
+
+The face expression recognition model is lightweight, fast and provides reasonable accuracy. The model has a size of roughly 310kb and it employs depthwise separable convolutions and densely connected blocks. It has been trained on a variety of images from publicly available datasets as well as images scraped from the web. Note, that wearing glasses might decrease the accuracy of the prediction results.
+
+<a name="getting-started"></a>
+
+# Getting Started
+
+<a name="getting-started-browser"></a>
+
+## face-api.js for the Browser
+
+Simply include the latest script from [dist/face-api.js](https://github.com/justadudewhohacks/face-api.js/tree/master/dist).
+
+Or install it via npm:
+
+``` bash
+npm i face-api.js
+```
+
+<a name="getting-started-nodejs"></a>
+
+## face-api.js for Nodejs
+
+We can use the equivalent API in a nodejs environment by polyfilling some browser specifics, such as HTMLImageElement, HTMLCanvasElement and ImageData. The easiest way to do so is by installing the node-canvas package.
+
+Alternatively you can simply construct your own tensors from image data and pass tensors as inputs to the API.
+
+Furthermore you want to install @tensorflow/tfjs-node (not required, but highly recommended), which speeds things up drastically by compiling and binding to the native Tensorflow C++ library:
+
+``` bash
+npm i face-api.js canvas @tensorflow/tfjs-node
+```
+
+Now we simply monkey patch the environment to use the polyfills:
+
+``` javascript
+// import nodejs bindings to native tensorflow,
+// not required, but will speed up things drastically (python required)
+import '@tensorflow/tfjs-node';
+
+// implements nodejs wrappers for HTMLCanvasElement, HTMLImageElement, ImageData
+import * as canvas from 'canvas';
+
+import * as faceapi from 'face-api.js';
+
+// patch nodejs environment, we need to provide an implementation of
+// HTMLCanvasElement and HTMLImageElement, additionally an implementation
+// of ImageData is required, in case you want to use the MTCNN
+const { Canvas, Image, ImageData } = canvas
+faceapi.env.monkeyPatch({ Canvas, Image, ImageData })
+```
+
+# Usage
+
+<a name="usage-loading-models"></a>
+
+## Loading the Models
+
+To load a model, you have provide the corresponding manifest.json file as well as the model weight files (shards) as assets. Simply copy them to your public or assets folder. The manifest.json and shard files of a model have to be located in the same directory / accessible under the same route.
+
+Assuming the models reside in **public/models**:
+
+``` javascript
+await faceapi.loadSsdMobilenetv1Model('/models')
+// accordingly for the other models:
+// await faceapi.loadTinyFaceDetectorModel('/models')
+// await faceapi.loadMtcnnModel('/models')
+// await faceapi.loadFaceLandmarkModel('/models')
+// await faceapi.loadFaceLandmarkTinyModel('/models')
+// await faceapi.loadFaceRecognitionModel('/models')
+// await faceapi.loadFaceExpressionModel('/models')
+```
+
+All global neural network instances are exported via faceapi.nets:
+
+``` javascript
+console.log(faceapi.nets)
+```
+
+The following is equivalent to `await faceapi.loadSsdMobilenetv1Model('/models')`:
+
+``` javascript
+await faceapi.nets.ssdMobilenetv1.loadFromUri('/models')
+```
+
+In a nodejs environment you can furthermore load the models directly from disk:
+
+``` javascript
+await faceapi.nets.ssdMobilenetv1.loadFromDisk('./models')
+```
+
+You can also load the model from a tf.NamedTensorMap:
+
+``` javascript
+await faceapi.nets.ssdMobilenetv1.loadFromWeightMap(weightMap)
+```
+
+Alternatively, you can also create own instances of the neural nets:
+
+``` javascript
+const net = new faceapi.SsdMobilenetv1()
+await net.load('/models')
+```
+
+You can also load the weights as a Float32Array (in case you want to use the uncompressed models):
+
+``` javascript
+// using fetch
+net.load(await faceapi.fetchNetWeights('/models/face_detection_model.weights'))
+
+// using axios
+const res = await axios.get('/models/face_detection_model.weights', { responseType: 'arraybuffer' })
+const weights = new Float32Array(res.data)
+net.load(weights)
+```
+
+## High Level API
+
+In the following **input** can be an HTML img, video or canvas element or the id of that element.
+
+``` html
+<img id="myImg" src="images/example.png" />
+<video id="myVideo" src="media/example.mp4" />
+<canvas id="myCanvas" />
+```
+
+``` javascript
+const input = document.getElementById('myImg')
+// const input = document.getElementById('myVideo')
+// const input = document.getElementById('myCanvas')
+// or simply:
+// const input = 'myImg'
+```
+
+### Detecting Faces
+
+Detect all faces in an image. Returns **Array<[FaceDetection](#interface-face-detection)>**:
+
+``` javascript
+const detections = await faceapi.detectAllFaces(input)
+```
+
+Detect the face with the highest confidence score in an image. Returns **[FaceDetection](#interface-face-detection) | undefined**:
+
+``` javascript
+const detection = await faceapi.detectSingleFace(input)
+```
+
+By default **detectAllFaces** and **detectSingleFace** utilize the SSD Mobilenet V1 Face Detector. You can specify the face detector by passing the corresponding options object:
+
+``` javascript
+const detections1 = await faceapi.detectAllFaces(input, new faceapi.SsdMobilenetv1Options())
+const detections2 = await faceapi.detectAllFaces(input, new faceapi.TinyFaceDetectorOptions())
+const detections3 = await faceapi.detectAllFaces(input, new faceapi.MtcnnOptions())
+```
+
+You can tune the options of each face detector as shown [here](#usage-face-detection-options).
+
+### Detecting 68 Face Landmark Points
+
+**After face detection, we can furthermore predict the facial landmarks for each detected face as follows:**
+
+Detect all faces in an image + computes 68 Point Face Landmarks for each detected face. Returns **Array<[WithFaceLandmarks<WithFaceDetection<{}>>](#usage-utility-classes)>**:
+
+``` javascript
+const detectionsWithLandmarks = await faceapi.detectAllFaces(input).withFaceLandmarks()
+```
+
+Detect the face with the highest confidence score in an image + computes 68 Point Face Landmarks for that face. Returns **[WithFaceLandmarks<WithFaceDetection<{}>>](#usage-utility-classes) | undefined**:
+
+``` javascript
+const detectionWithLandmarks = await faceapi.detectSingleFace(input).withFaceLandmarks()
+```
+
+You can also specify to use the tiny model instead of the default model:
+
+``` javascript
+const useTinyModel = true
+const detectionsWithLandmarks = await faceapi.detectAllFaces(input).withFaceLandmarks(useTinyModel)
+```
+
+### Computing Face Descriptors
+
+**After face detection and facial landmark prediction the face descriptors for each face can be computed as follows:**
+
+Detect all faces in an image + computes 68 Point Face Landmarks for each detected face. Returns **Array<[WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>](#usage-utility-classes)>**:
+
+``` javascript
+const results = await faceapi.detectAllFaces(input).withFaceLandmarks().withFaceDescriptors()
+```
+
+Detect the face with the highest confidence score in an image + computes 68 Point Face Landmarks and face descriptor for that face. Returns **[WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>](#usage-utility-classes) | undefined**:
+
+``` javascript
+const result = await faceapi.detectSingleFace(input).withFaceLandmarks().withFaceDescriptor()
+```
+
+### Recognizing Face Expressions
+
+**Face expression recognition can be performed for detected faces as follows:**
+
+Detect all faces in an image + recognize face expressions. Returns **Array<[WithFaceExpressions<WithFaceDetection<{}>>](#usage-utility-classes)>**:
+
+``` javascript
+const detectionsWithExpressions = await faceapi.detectAllFaces(input).withFaceExpressions()
+```
+
+Detect the face with the highest confidence score in an image + recognize the face expression for that face. Returns **[WithFaceExpressions<WithFaceDetection<{}>>](#usage-utility-classes) | undefined**:
+
+``` javascript
+const detectionWithExpressions = await faceapi.detectSingleFace(input).withFaceExpressions()
+```
+
+### Composition of Tasks
+
+**Tasks can be composed as follows:**
+
+``` javascript
+// all faces
+await faceapi.detectAllFaces(input)
+await faceapi.detectAllFaces(input).withFaceExpressions()
+await faceapi.detectAllFaces(input).withFaceLandmarks()
+await faceapi.detectAllFaces(input).withFaceExpressions().withFaceLandmarks()
+await faceapi.detectAllFaces(input).withFaceExpressions().withFaceLandmarks().withFaceDescriptors()
+
+// single face
+await faceapi.detectSingleFace(input)
+await faceapi.detectSingleFace(input).withFaceExpressions()
+await faceapi.detectSingleFace(input).withFaceLandmarks()
+await faceapi.detectSingleFace(input).withFaceExpressions().withFaceLandmarks()
+await faceapi.detectSingleFace(input).withFaceExpressions().withFaceLandmarks().withFaceDescriptor()
+```
+
+### Face Recognition by Matching Descriptors
+
+To perform face recognition, one can use faceapi.FaceMatcher to compare reference face descriptors to query face descriptors.
+
+First, we initialize the FaceMatcher with the reference data, for example we can simply detect faces in a **referenceImage** and match the descriptors of the detected faces to faces of subsquent images:
+
+``` javascript
+const results = await faceapi
+  .detectAllFaces(referenceImage)
+  .withFaceLandmarks()
+  .withFaceDescriptors()
+
+if (!results.length) {
+  return
+}
+
+// create FaceMatcher with automatically assigned labels
+// from the detection results for the reference image
+const faceMatcher = new faceapi.FaceMatcher(results)
+```
+
+Now we can recognize a persons face shown in **queryImage1**:
+
+``` javascript
+const singleResult = await faceapi
+  .detectSingleFace(queryImage1)
+  .withFaceLandmarks()
+  .withFaceDescriptor()
+
+if (singleResult) {
+  const bestMatch = faceMatcher.findBestMatch(singleResult.descriptor)
+  console.log(bestMatch.toString())
+}
+```
+
+Or we can recognize all faces shown in **queryImage2**:
+
+``` javascript
+const results = await faceapi
+  .detectAllFaces(queryImage2)
+  .withFaceLandmarks()
+  .withFaceDescriptors()
+
+results.forEach(fd => {
+  const bestMatch = faceMatcher.findBestMatch(fd.descriptor)
+  console.log(bestMatch.toString())
+})
+```
+
+You can also create labeled reference descriptors as follows:
+
+``` javascript
+const labeledDescriptors = [
+  new faceapi.LabeledFaceDescriptors(
+    'obama',
+    [descriptorObama1, descriptorObama2]
+  ),
+  new faceapi.LabeledFaceDescriptors(
+    'trump',
+    [descriptorTrump]
+  )
+]
+
+const faceMatcher = new faceapi.FaceMatcher(labeledDescriptors)
+```
+
+<a name="usage-displaying-detection-results"></a>
+
+## Displaying Detection Results
+
+Drawing the detected faces into a canvas:
+
+``` javascript
+const detections = await faceapi.detectAllFaces(input)
+
+// resize the detected boxes in case your displayed image has a different size then the original
+const detectionsForSize = faceapi.resizeResults(detections, { width: input.width, height: input.height })
+// draw them into a canvas
+const canvas = document.getElementById('overlay')
+canvas.width = input.width
+canvas.height = input.height
+faceapi.drawDetection(canvas, detectionsForSize, { withScore: true })
+```
+
+Drawing face landmarks into a canvas:
+
+``` javascript
+const detectionsWithLandmarks = await faceapi
+  .detectAllFaces(input)
+  .withFaceLandmarks()
+
+// resize the detected boxes and landmarks in case your displayed image has a different size then the original
+const detectionsWithLandmarksForSize = faceapi.resizeResults(detectionsWithLandmarks, { width: input.width, height: input.height })
+// draw them into a canvas
+const canvas = document.getElementById('overlay')
+canvas.width = input.width
+canvas.height = input.height
+faceapi.drawLandmarks(canvas, detectionsWithLandmarks, { drawLines: true })
+```
+
+Finally you can also draw boxes with custom text:
+
+``` javascript
+const boxesWithText = [
+  new faceapi.BoxWithText(new faceapi.Rect(x, y, width, height), text))
+  new faceapi.BoxWithText(new faceapi.Rect(0, 0, 50, 50), 'some text'))
+]
+
+const canvas = document.getElementById('overlay')
+faceapi.drawDetection(canvas, boxesWithText)
+```
+
+<a name="usage-face-detection-options"></a>
+
+## Face Detection Options
+
+### SsdMobilenetv1Options
+
+``` javascript
+export interface ISsdMobilenetv1Options {
+  // minimum confidence threshold
+  // default: 0.5
+  minConfidence?: number
+
+  // maximum number of faces to return
+  // default: 100
+  maxResults?: number
+}
+
+// example
+const options = new faceapi.SsdMobilenetv1Options({ minConfidence: 0.8 })
+```
+
+### TinyFaceDetectorOptions
+
+``` javascript
+export interface ITinyFaceDetectorOptions {
+  // size at which image is processed, the smaller the faster,
+  // but less precise in detecting smaller faces, must be divisible
+  // by 32, common sizes are 128, 160, 224, 320, 416, 512, 608,
+  // for face tracking via webcam I would recommend using smaller sizes,
+  // e.g. 128, 160, for detecting smaller faces use larger sizes, e.g. 512, 608
+  // default: 416
+  inputSize?: number
+
+  // minimum confidence threshold
+  // default: 0.5
+  scoreThreshold?: number
+}
+
+// example
+const options = new faceapi.TinyFaceDetectorOptions({ inputSize: 320 })
+```
+
+### MtcnnOptions
+
+``` javascript
+export interface IMtcnnOptions {
+  // minimum face size to expect, the higher the faster processing will be,
+  // but smaller faces won't be detected
+  // default: 20
+  minFaceSize?: number
+
+  // the score threshold values used to filter the bounding
+  // boxes of stage 1, 2 and 3
+  // default: [0.6, 0.7, 0.7]
+  scoreThresholds?: number[]
+
+  // scale factor used to calculate the scale steps of the image
+  // pyramid used in stage 1
+  // default: 0.709
+  scaleFactor?: number
+
+  // number of scaled versions of the input image passed through the CNN
+  // of the first stage, lower numbers will result in lower inference time,
+  // but will also be less accurate
+  // default: 10
+  maxNumScales?: number
+
+  // instead of specifying scaleFactor and maxNumScales you can also
+  // set the scaleSteps manually
+  scaleSteps?: number[]
+}
+
+// example
+const options = new faceapi.MtcnnOptions({ minFaceSize: 100, scaleFactor: 0.8 })
+```
+
+<a name="usage-utility-classes"></a>
+
+## Utility Classes
+
+### IBox
+
+``` javascript
+export interface IBox {
+  x: number
+  y: number
+  width: number
+  height: number
+}
+```
+
+<a name="interface-face-detection"></a>
+
+### IFaceDetection
+
+``` javascript
+export interface IFaceDetection {
+  score: number
+  box: Box
+}
+```
+
+<a name="interface-face-landmarks"></a>
+
+### IFaceLandmarks
+
+``` javascript
+export interface IFaceLandmarks {
+  positions: Point[]
+  shift: Point
+}
+```
+
+<a name="with-face-detection"></a>
+
+### WithFaceDetection
+
+``` javascript
+export type WithFaceDetection<TSource> TSource & {
+  detection: FaceDetection
+}
+```
+
+<a name="with-face-landmarks"></a>
+
+### WithFaceLandmarks
+
+``` javascript
+export type WithFaceLandmarks<TSource> TSource & {
+  unshiftedLandmarks: FaceLandmarks
+  landmarks: FaceLandmarks
+  alignedRect: FaceDetection
+}
+```
+
+<a name="with-face-descriptor"></a>
+
+### WithFaceDescriptor
+
+``` javascript
+export type WithFaceDescriptor<TSource> TSource & {
+  descriptor: Float32Array
+}
+```
+
+<a name="with-face-expressions"></a>
+
+### WithFaceExpressions
+
+``` javascript
+export type FaceExpression = 'neutral' | 'happy' | 'sad' | 'angry' | 'fearful' | 'disgusted' | 'surprised'
+
+export type FaceExpressionPrediction = {
+  expression: FaceExpression,
+  probability: number
+}
+
+export type WithFaceExpressions<TSource> TSource & {
+  expressions: FaceExpressionPrediction[]
+}
+```
+
+<a name="other-useful-utility"></a>
+
+## Other Useful Utility
+
+### Using the Low Level API
+
+Instead of using the high level API, you can directly use the forward methods of each neural network:
+
+``` javascript
+const detections1 = await faceapi.ssdMobilenetv1(input, options)
+const detections2 = await faceapi.tinyFaceDetector(input, options)
+const detections3 = await faceapi.mtcnn(input, options)
+const landmarks1 = await faceapi.detectFaceLandmarks(faceImage)
+const landmarks2 = await faceapi.detectFaceLandmarksTiny(faceImage)
+const descriptor = await faceapi.computeFaceDescriptor(alignedFaceImage)
+```
+
+### Extracting a Canvas for an Image Region
+
+``` javascript
+const regionsToExtract = [
+  new faceapi.Rect(0, 0, 100, 100)
+]
+// actually extractFaces is meant to extract face regions from bounding boxes
+// but you can also use it to extract any other region
+const canvases = await faceapi.extractFaces(input, regionsToExtract)
+```
+
+### Euclidean Distance
+
+``` javascript
+// ment to be used for computing the euclidean distance between two face descriptors
+const dist = faceapi.euclideanDistance([0, 0], [0, 10])
+console.log(dist) // 10
+```
+
+### Retrieve the Face Landmark Points and Contours
+
+``` javascript
+const landmarkPositions = landmarks.positions
+
+// or get the positions of individual contours,
+// only available for 68 point face ladnamrks (FaceLandmarks68)
+const jawOutline = landmarks.getJawOutline()
+const nose = landmarks.getNose()
+const mouth = landmarks.getMouth()
+const leftEye = landmarks.getLeftEye()
+const rightEye = landmarks.getRightEye()
+const leftEyeBbrow = landmarks.getLeftEyeBrow()
+const rightEyeBrow = landmarks.getRightEyeBrow()
+```
+
+### Fetch and Display Images from an URL
+
+``` html
+<img id="myImg" src="">
+```
+
+``` javascript
+const image = await faceapi.fetchImage('/images/example.png')
+
+console.log(image instanceof HTMLImageElement) // true
+
+// displaying the fetched image content
+const myImg = document.getElementById('myImg')
+myImg.src = image.src
+```
+
+### Fetching JSON
+
+``` javascript
+const json = await faceapi.fetchJson('/files/example.json')
+```
+
+### Creating an Image Picker
+
+``` html
+<img id="myImg" src="">
+<input id="myFileUpload" type="file" onchange="uploadImage()" accept=".jpg, .jpeg, .png">
+```
+
+``` javascript
+async function uploadImage() {
+  const imgFile = document.getElementById('myFileUpload').files[0]
+  // create an HTMLImageElement from a Blob
+  const img = await faceapi.bufferToImage(imgFile)
+  document.getElementById('myImg').src = img.src
+}
+```
+
+### Creating a Canvas Element from an Image or Video Element
+
+``` html
+<img id="myImg" src="images/example.png" />
+<video id="myVideo" src="media/example.mp4" />
+```
+
+``` javascript
+const canvas1 = faceapi.createCanvasFromMedia(document.getElementById('myImg'))
+const canvas2 = faceapi.createCanvasFromMedia(document.getElementById('myVideo'))
+```
diff --git a/node_modules/face-api.js/dist/face-api.js b/node_modules/face-api.js/dist/face-api.js
new file mode 100644
index 0000000..2363777
--- /dev/null
+++ b/node_modules/face-api.js/dist/face-api.js
@@ -0,0 +1,6150 @@
+(function (global, factory) {
+    typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports) :
+    typeof define === 'function' && define.amd ? define(['exports'], factory) :
+    (factory((global.faceapi = global.faceapi || {})));
+}(this, (function (exports) { 'use strict';
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var extendStatics=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t;}||function(e,t){for(var n in t)t.hasOwnProperty(n)&&(e[n]=t[n]);};function __extends(e,t){function n(){this.constructor=e;}extendStatics(e,t),e.prototype=null===t?Object.create(t):(n.prototype=t.prototype,new n);}var __assign=Object.assign||function(e){for(var t,n=1,r=arguments.length;n<r;n++)for(var o in t=arguments[n])Object.prototype.hasOwnProperty.call(t,o)&&(e[o]=t[o]);return e};function __awaiter(e,t,n,r){return new(n||(n=Promise))(function(o,a){function i(e){try{u(r.next(e));}catch(e){a(e);}}function s(e){try{u(r.throw(e));}catch(e){a(e);}}function u(e){e.done?o(e.value):new n(function(t){t(e.value);}).then(i,s);}u((r=r.apply(e,t||[])).next());})}function __generator(e,t){var n,r,o,a,i={label:0,sent:function(){if(1&o[0])throw o[1];return o[1]},trys:[],ops:[]};return a={next:s(0),throw:s(1),return:s(2)},"function"==typeof Symbol&&(a[Symbol.iterator]=function(){return this}),a;function s(a){return function(s){return function(a){if(n)throw new TypeError("Generator is already executing.");for(;i;)try{if(n=1,r&&(o=2&a[0]?r.return:a[0]?r.throw||((o=r.return)&&o.call(r),0):r.next)&&!(o=o.call(r,a[1])).done)return o;switch(r=0,o&&(a=[2&a[0],o.value]),a[0]){case 0:case 1:o=a;break;case 4:return i.label++,{value:a[1],done:!1};case 5:i.label++,r=a[1],a=[0];continue;case 7:a=i.ops.pop(),i.trys.pop();continue;default:if(!(o=(o=i.trys).length>0&&o[o.length-1])&&(6===a[0]||2===a[0])){i=0;continue}if(3===a[0]&&(!o||a[1]>o[0]&&a[1]<o[3])){i.label=a[1];break}if(6===a[0]&&i.label<o[1]){i.label=o[1],o=a;break}if(o&&i.label<o[2]){i.label=o[2],i.ops.push(a);break}o[2]&&i.ops.pop(),i.trys.pop();continue}a=t.call(e,i);}catch(e){a=[6,e],r=0;}finally{n=o=0;}if(5&a[0])throw a[1];return {value:a[0]?a[1]:void 0,done:!0}}([a,s])}}}var contexts={},WEBGL_ATTRIBUTES={alpha:!1,antialias:!1,premultipliedAlpha:!1,preserveDrawingBuffer:!1,depth:!1,stencil:!1,failIfMajorPerformanceCaveat:!0};function getWebGLContext(e){e in contexts||(document.createElement("canvas").addEventListener("webglcontextlost",function(t){t.preventDefault(),delete contexts[e];},!1),contexts[e]=getWebGLRenderingContext(e));var t=contexts[e];return t.isContextLost()?(delete contexts[e],getWebGLContext(e)):(t.disable(t.DEPTH_TEST),t.disable(t.STENCIL_TEST),t.disable(t.BLEND),t.disable(t.DITHER),t.disable(t.POLYGON_OFFSET_FILL),t.disable(t.SAMPLE_COVERAGE),t.enable(t.SCISSOR_TEST),t.enable(t.CULL_FACE),t.cullFace(t.BACK),contexts[e])}function getWebGLRenderingContext(e){if(1!==e&&2!==e)throw new Error("Cannot get WebGL rendering context, WebGL is disabled.");var t=document.createElement("canvas");return 1===e?t.getContext("webgl",WEBGL_ATTRIBUTES)||t.getContext("experimental-webgl",WEBGL_ATTRIBUTES):t.getContext("webgl2",WEBGL_ATTRIBUTES)}function 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Found -1 at dim "+r+" and dim "+o);r=o;}else if(e[o]<0)throw Error("Shapes can not be < 0. Found "+e[o]+" at dim "+o);if(-1===r){if(t>0&&t!==n)throw Error("Size("+t+") must match the product of shape "+e);return e}if(0===n)throw Error("Cannot infer the missing size in ["+e+"] when there are 0 elements");if(t%n!=0)throw Error("The implicit shape can't be a fractional number. Got "+t+" / "+n);var a=e.slice();return a[r]=t/n,a}function squeezeShape(e,t){for(var n=[],r=[],o=0,a=0;a<e.length;++a){if(null!=t){if(t[o]===a&&1!==e[a])throw new Error("Can't squeeze axis "+a+" since its dim '"+e[a]+"' is not 1");(null==t[o]||t[o]>a)&&1===e[a]&&(n.push(e[a]),r.push(a)),t[o]<=a&&o++;}1!==e[a]&&(n.push(e[a]),r.push(a));}return {newShape:n,keptDims:r}}function getTypedArrayFromDType(e,t){var n=null;if(null==e||"float32"===e)n=new Float32Array(t);else if("int32"===e)n=new Int32Array(t);else{if("bool"!==e)throw new Error("Unknown data type "+e);n=new Uint8Array(t);}return n}function getArrayFromDType(e,t){var n=null;if(null==e||"float32"===e)n=new Float32Array(t);else if("int32"===e)n=new Int32Array(t);else if("bool"===e)n=new Uint8Array(t);else{if("string"!==e)throw new Error("Unknown data type "+e);n=new Array(t);}return n}function checkComputationForNaN(e,t,n){if("float32"===t)for(var r=0;r<e.length;r++)if(isNaN(e[r]))throw Error("The result of the '"+n+"' has NaNs.")}function checkConversionForNaN(e,t){if("float32"!==t)for(var n=0;n<e.length;n++)if(isNaN(e[n]))throw Error("NaN is not a valid value for dtype: '"+t+"'.")}function hasEncodingLoss(e,t){return "complex64"!==t&&(("float32"!==t||"complex64"===e)&&(("int32"!==t||"float32"===e||"complex64"===e)&&("bool"!==t||"bool"!==e)))}function isTypedArray(e){return e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array}function bytesPerElement(e){if("float32"===e||"int32"===e)return 4;if("complex64"===e)return 8;if("bool"===e)return 1;throw new Error("Unknown dtype "+e)}function bytesFromStringArray(e){if(null==e)return 0;var t=0;return e.forEach(function(e){return t+=2*e.length}),t}function isString(e){return "string"==typeof e||e instanceof String}function isBoolean(e){return "boolean"==typeof e}function isNumber(e){return "number"==typeof e}function inferDtype(e){return e instanceof Array?inferDtype(e[0]):e instanceof Float32Array?"float32":e instanceof Int32Array||e instanceof Uint8Array?"int32":isNumber(e)?"float32":isString(e)?"string":isBoolean(e)?"bool":"float32"}function isFunction(e){return !!(e&&e.constructor&&e.call&&e.apply)}function nearestDivisor(e,t){for(var n=t;n<e;++n)if(e%n==0)return n;return e}function computeStrides(e){var t=e.length;if(t<2)return [];var n=new Array(t-1);n[t-2]=e[t-1];for(var r=t-3;r>=0;--r)n[r]=n[r+1]*e[r+1];return n}function toTypedArray(e,t,n){if("string"===t)throw new Error("Cannot convert a string[] to a TypedArray");if(noConversionNeeded(e,t))return e;if(Array.isArray(e)&&(e=flatten(e)),null==t||"float32"===t||"complex64"===t)return new Float32Array(e);if("int32"===t)return n&&checkConversionForNaN(e,t),new Int32Array(e);if("bool"===t){for(var r=new Uint8Array(e.length),o=0;o<r.length;++o)0!==Math.round(e[o])&&(r[o]=1);return r}throw new Error("Unknown data type "+t)}function noConversionNeeded(e,t){return e instanceof Float32Array&&"float32"===t||e instanceof Int32Array&&"int32"===t||e instanceof Uint8Array&&"bool"===t}function makeOnesTypedArray(e,t){for(var n=makeZerosTypedArray(e,t),r=0;r<n.length;r++)n[r]=1;return n}function makeZerosTypedArray(e,t){if(null==t||"float32"===t||"complex64"===t)return new Float32Array(e);if("int32"===t)return new Int32Array(e);if("bool"===t)return new Uint8Array(e);throw new Error("Unknown data type "+t)}function now(){if("undefined"!=typeof performance)return performance.now();if("undefined"!=typeof process){var e=process.hrtime();return 1e3*e[0]+e[1]/1e6}throw new Error("Cannot measure time in this environment. You should run tf.js in the browser or in Node.js")}var util=Object.freeze({shuffle:shuffle,clamp:clamp,nearestLargerEven:nearestLargerEven,sum:sum,randUniform:randUniform,distSquared:distSquared,assert:assert,assertShapesMatch:assertShapesMatch,assertNonNull:assertNonNull,flatten:flatten,sizeFromShape:sizeFromShape,isScalarShape:isScalarShape,arraysEqual:arraysEqual,isInt:isInt,tanh:tanh,sizeToSquarishShape:sizeToSquarishShape,createShuffledIndices:createShuffledIndices,rightPad:rightPad,repeatedTry:repeatedTry,inferFromImplicitShape:inferFromImplicitShape,squeezeShape:squeezeShape,getTypedArrayFromDType:getTypedArrayFromDType,getArrayFromDType:getArrayFromDType,checkComputationForNaN:checkComputationForNaN,checkConversionForNaN:checkConversionForNaN,hasEncodingLoss:hasEncodingLoss,isTypedArray:isTypedArray,bytesPerElement:bytesPerElement,bytesFromStringArray:bytesFromStringArray,isString:isString,isBoolean:isBoolean,isNumber:isNumber,inferDtype:inferDtype,isFunction:isFunction,nearestDivisor:nearestDivisor,computeStrides:computeStrides,toTypedArray:toTypedArray,makeOnesTypedArray:makeOnesTypedArray,makeZerosTypedArray:makeZerosTypedArray,now:now}),Profiler=function(){function e(e,t){this.backendTimer=e,this.logger=t,null==t&&(this.logger=new Logger);}return e.prototype.profileKernel=function(e,t){var n,r=this,o=this.backendTimer.time(function(){n=t();});return (Array.isArray(n)?n:[n]).forEach(function(t){var n=t.dataSync();checkComputationForNaN(n,t.dtype,e),o.then(function(o){var a="";null!=o.getExtraProfileInfo&&(a=o.getExtraProfileInfo()),r.logger.logKernelProfile(e,t,n,o.kernelMs,a);});}),n},e}(),Logger=function(){function e(){}return e.prototype.logKernelProfile=function(e,t,n,r,o){var a=rightPad(r+"ms",9),i=rightPad(e,25),s=t.rank,u=t.size,l=rightPad(t.shape.toString(),14);console.log("%c"+i+"\t%c"+a+"\t%c"+s+"D "+l+"\t%c"+u+"\t%c"+o,"font-weight:bold","color:red","color:blue","color: orange","color: green");},e}(),FORMAT_LIMIT_NUM_VALS=20,FORMAT_NUM_FIRST_LAST_VALS=3,FORMAT_NUM_SIG_DIGITS=7;function tensorToString(e,t,n,r){var o=computeStrides(t),a=computeMaxSizePerColumn(e,t,n,o),i=t.length,s=subTensorToString(e,t,n,o,a),u=["Tensor"];return r&&(u.push("  dtype: "+n),u.push("  rank: "+i),u.push("  shape: ["+t+"]"),u.push("  values:")),u.push(s.map(function(e){return "    "+e}).join("\n")),u.join("\n")}function computeMaxSizePerColumn(e,t,n,r){var o=sizeFromShape(t),a=r[r.length-1],i=new Array(a).fill(0),s=t.length,u="complex64"===n?createComplexTuples(e):e;if(s>1)for(var l=0;l<o/a;l++)for(var c=l*a,p=0;p<a;p++)i[p]=Math.max(i[p],valToString(u[c+p],0).length);return i}function valToString(e,t){return rightPad(Array.isArray(e)?parseFloat(e[0].toFixed(FORMAT_NUM_SIG_DIGITS))+" + "+parseFloat(e[1].toFixed(FORMAT_NUM_SIG_DIGITS))+"j":isString(e)?"'"+e+"'":parseFloat(e.toFixed(FORMAT_NUM_SIG_DIGITS)).toString(),t)}function subTensorToString(e,t,n,r,o,a){void 0===a&&(a=!0);var i="complex64"===n?2:1,s=t[0],u=t.length;if(0===u)return "complex64"===n?[valToString(createComplexTuples(e)[0],0)]:[e[0].toString()];if(1===u){if(s>FORMAT_LIMIT_NUM_VALS){var l=FORMAT_NUM_FIRST_LAST_VALS*i,c=Array.from(e.slice(0,l)),p=Array.from(e.slice(s-FORMAT_NUM_FIRST_LAST_VALS*i,s));return "complex64"===n&&(c=createComplexTuples(c),p=createComplexTuples(p)),["["+c.map(function(e,t){return valToString(e,o[t])}).join(", ")+", ..., "+p.map(function(e,t){return valToString(e,o[s-FORMAT_NUM_FIRST_LAST_VALS+t])}).join(", ")+"]"]}return ["["+("complex64"===n?createComplexTuples(e):Array.from(e)).map(function(e,t){return valToString(e,o[t])}).join(", ")+"]"]}var d=t.slice(1),h=r.slice(1),f=r[0]*i,m=[];if(s>FORMAT_LIMIT_NUM_VALS){for(var g=0;g<FORMAT_NUM_FIRST_LAST_VALS;g++){var v=(y=g*f)+f;m.push.apply(m,subTensorToString(e.slice(y,v),d,n,h,o,!1));}m.push("...");for(g=s-FORMAT_NUM_FIRST_LAST_VALS;g<s;g++){v=(y=g*f)+f;m.push.apply(m,subTensorToString(e.slice(y,v),d,n,h,o,g===s-1));}}else for(g=0;g<s;g++){var y;v=(y=g*f)+f;m.push.apply(m,subTensorToString(e.slice(y,v),d,n,h,o,g===s-1));}var x=2===u?",":"";m[0]="["+m[0]+x;for(g=1;g<m.length-1;g++)m[g]=" "+m[g]+x;var T=",\n";for(g=2;g<u;g++)T+="\n";return m[m.length-1]=" "+m[m.length-1]+"]"+(a?"":T),m}function createComplexTuples(e){for(var t=[],n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var TensorBuffer=function(){function e(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=sizeFromShape(e),null!=n){var r=n.length;assert(r===this.size,"Length of values '"+r+"' does not match the size inferred by the shape '"+this.size+"'.");}if("complex64"===t)throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||getArrayFromDType(t,sizeFromShape(this.shape)),this.strides=computeStrides(e);}return e.prototype.set=function(e){for(var t=[],n=1;n<arguments.length;n++)t[n-1]=arguments[n];0===t.length&&(t=[0]),assert(t.length===this.rank,"The number of provided coordinates ("+t.length+") must match the rank ("+this.rank+")");var r=this.locToIndex(t);this.values[r]=e;},e.prototype.get=function(){for(var e=[],t=0;t<arguments.length;t++)e[t]=arguments[t];0===e.length&&(e=[0]);for(var n=e[e.length-1],r=0;r<e.length-1;++r)n+=this.strides[r]*e[r];return this.values[n]},e.prototype.locToIndex=function(e){if(0===this.rank)return 0;if(1===this.rank)return e[0];for(var t=e[e.length-1],n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t},e.prototype.indexToLoc=function(e){if(0===this.rank)return [];if(1===this.rank)return [e];for(var t=new Array(this.shape.length),n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t},Object.defineProperty(e.prototype,"rank",{get:function(){return this.shape.length},enumerable:!0,configurable:!0}),e.prototype.toTensor=function(){return Tensor.make(this.shape,{values:this.values},this.dtype)},e}(),trackerFn=null,opHandler=null;function setTensorTracker(e){trackerFn=e;}function setOpHandler(e){opHandler=e;}var Tensor=function(){function e(e,t,n,r){this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=sizeFromShape(e),this.strides=computeStrides(e),this.dataId=null!=r?r:{},this.id=trackerFn().nextTensorId(),this.rankType=this.rank<5?this.rank.toString():"higher",trackerFn().registerTensor(this),null!=n&&trackerFn().write(this.dataId,n);}return e.make=function(t,n,r){return new e(t,r,n.values,n.dataId)},e.prototype.flatten=function(){return this.throwIfDisposed(),this.as1D()},e.prototype.asScalar=function(){return this.throwIfDisposed(),assert(1===this.size,"The array must have only 1 element."),this.reshape([])},e.prototype.as1D=function(){return this.throwIfDisposed(),this.reshape([this.size])},e.prototype.as2D=function(e,t){return this.throwIfDisposed(),this.reshape([e,t])},e.prototype.as3D=function(e,t,n){return this.throwIfDisposed(),this.reshape([e,t,n])},e.prototype.as4D=function(e,t,n,r){return this.throwIfDisposed(),this.reshape([e,t,n,r])},e.prototype.as5D=function(e,t,n,r,o){return this.throwIfDisposed(),this.reshape([e,t,n,r,o])},e.prototype.asType=function(e){return this.throwIfDisposed(),opHandler.cast(this,e)},Object.defineProperty(e.prototype,"rank",{get:function(){return this.shape.length},enumerable:!0,configurable:!0}),e.prototype.get=function(){for(var e=[],t=0;t<arguments.length;t++)e[t]=arguments[t];assert(e.length===this.rank,"Number of coordinates in get() must match the rank of the tensor"),assert("complex64"!==this.dtype,"Tensor.get() is not supported for complex64 tensors yet."),this.throwIfDisposed(),0===e.length&&(e=[0]);for(var n=e[e.length-1],r=0;r<e.length-1;++r)n+=this.strides[r]*e[r];return this.dataSync()[n]},e.prototype.buffer=function(){return opHandler.buffer(this.shape,this.dtype,this.dataSync())},e.prototype.data=function(){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(e){return this.throwIfDisposed(),[2,trackerFn().read(this.dataId)]})})},e.prototype.dataSync=function(){return this.throwIfDisposed(),trackerFn().readSync(this.dataId)},e.prototype.dispose=function(){this.isDisposed||(trackerFn().disposeTensor(this),this.isDisposedInternal=!0);},Object.defineProperty(e.prototype,"isDisposed",{get:function(){return this.isDisposedInternal},enumerable:!0,configurable:!0}),e.prototype.throwIfDisposed=function(){if(this.isDisposed)throw new Error("Tensor is disposed.")},e.prototype.toFloat=function(){return this.asType("float32")},e.prototype.toInt=function(){return this.asType("int32")},e.prototype.toBool=function(){return this.asType("bool")},e.prototype.print=function(e){return void 0===e&&(e=!1),opHandler.print(this,e)},e.prototype.reshape=function(e){return this.throwIfDisposed(),opHandler.reshape(this,e)},e.prototype.reshapeAs=function(e){return this.throwIfDisposed(),this.reshape(e.shape)},e.prototype.expandDims=function(e){return void 0===e&&(e=0),opHandler.expandDims(this,e)},e.prototype.cumsum=function(e,t,n){return void 0===e&&(e=0),void 0===t&&(t=!1),void 0===n&&(n=!1),opHandler.cumsum(this,e,t,n)},e.prototype.squeeze=function(e){return this.throwIfDisposed(),opHandler.squeeze(this,e)},e.prototype.clone=function(){return this.throwIfDisposed(),opHandler.clone(this)},e.prototype.toString=function(e){return void 0===e&&(e=!1),tensorToString(this.dataSync(),this.shape,this.dtype,e)},e.prototype.tile=function(e){return this.throwIfDisposed(),opHandler.tile(this,e)},e.prototype.gather=function(e,t){return void 0===t&&(t=0),this.throwIfDisposed(),opHandler.gather(this,e,t)},e.prototype.matMul=function(e,t,n){return void 0===t&&(t=!1),void 0===n&&(n=!1),this.throwIfDisposed(),opHandler.matMul(this,e,t,n)},e.prototype.dot=function(e){return this.throwIfDisposed(),opHandler.dot(this,e)},e.prototype.norm=function(e,t,n){return void 0===e&&(e="euclidean"),void 0===t&&(t=null),void 0===n&&(n=!1),this.throwIfDisposed(),opHandler.norm(this,e,t,n)},e.prototype.slice=function(e,t){return this.throwIfDisposed(),opHandler.slice(this,e,t)},e.prototype.reverse=function(e){return this.throwIfDisposed(),opHandler.reverse(this,e)},e.prototype.concat=function(t,n){return void 0===n&&(n=0),this.throwIfDisposed(),t instanceof e&&(t=[t]),opHandler.concat([this].concat(t),n)},e.prototype.split=function(e,t){return void 0===t&&(t=0),this.throwIfDisposed(),opHandler.split(this,e,t)},e.prototype.stack=function(e,t){return void 0===t&&(t=0),opHandler.stack([this,e],t)},e.prototype.unstack=function(e,t){return void 0===t&&(t=0),opHandler.unstack(this,t)},e.prototype.pad=function(e,t){return void 0===t&&(t=0),opHandler.pad(this,e,t)},e.prototype.batchNormalization=function(e,t,n,r,o){return void 0===n&&(n=.001),this.throwIfDisposed(),opHandler.batchNormalization(this,e,t,n,r,o)},e.prototype.all=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.all(this,e,t)},e.prototype.any=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.any(this,e,t)},e.prototype.logSumExp=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.logSumExp(this,e,t)},e.prototype.sum=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.sum(this,e,t)},e.prototype.prod=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.prod(this,e,t)},e.prototype.mean=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.mean(this,e,t)},e.prototype.min=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.min(this,e,t)},e.prototype.max=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.max(this,e,t)},e.prototype.argMin=function(e){return void 0===e&&(e=null),this.throwIfDisposed(),opHandler.argMin(this,e)},e.prototype.argMax=function(e){return void 0===e&&(e=null),this.throwIfDisposed(),opHandler.argMax(this,e)},e.prototype.cast=function(e){return this.throwIfDisposed(),opHandler.cast(this,e)},e.prototype.add=function(e){return this.throwIfDisposed(),opHandler.add(this,e)},e.prototype.addStrict=function(e){return this.throwIfDisposed(),opHandler.addStrict(this,e)},e.prototype.atan2=function(e){return this.throwIfDisposed(),opHandler.atan2(this,e)},e.prototype.sub=function(e){return this.throwIfDisposed(),opHandler.sub(this,e)},e.prototype.subStrict=function(e){return this.throwIfDisposed(),opHandler.subStrict(this,e)},e.prototype.pow=function(e){return this.throwIfDisposed(),opHandler.pow(this,e)},e.prototype.powStrict=function(e){return this.throwIfDisposed(),opHandler.powStrict(this,e)},e.prototype.mul=function(e){return this.throwIfDisposed(),opHandler.mul(this,e)},e.prototype.mulStrict=function(e){return this.throwIfDisposed(),opHandler.mulStrict(this,e)},e.prototype.div=function(e){return this.throwIfDisposed(),opHandler.div(this,e)},e.prototype.floorDiv=function(e){return this.throwIfDisposed(),opHandler.floorDiv(this,e)},e.prototype.divStrict=function(e){return this.throwIfDisposed(),opHandler.divStrict(this,e)},e.prototype.minimum=function(e){return this.throwIfDisposed(),opHandler.minimum(this,e)},e.prototype.minimumStrict=function(e){return this.throwIfDisposed(),opHandler.minimumStrict(this,e)},e.prototype.maximum=function(e){return this.throwIfDisposed(),opHandler.maximum(this,e)},e.prototype.maximumStrict=function(e){return this.throwIfDisposed(),opHandler.maximumStrict(this,e)},e.prototype.mod=function(e){return this.throwIfDisposed(),opHandler.mod(this,e)},e.prototype.modStrict=function(e){return this.throwIfDisposed(),opHandler.modStrict(this,e)},e.prototype.squaredDifference=function(e){return this.throwIfDisposed(),opHandler.squaredDifference(this,e)},e.prototype.squaredDifferenceStrict=function(e){return this.throwIfDisposed(),opHandler.squaredDifferenceStrict(this,e)},e.prototype.transpose=function(e){return this.throwIfDisposed(),opHandler.transpose(this,e)},e.prototype.notEqual=function(e){return this.throwIfDisposed(),opHandler.notEqual(this,e)},e.prototype.notEqualStrict=function(e){return this.throwIfDisposed(),opHandler.notEqualStrict(this,e)},e.prototype.less=function(e){return this.throwIfDisposed(),opHandler.less(this,e)},e.prototype.lessStrict=function(e){return this.throwIfDisposed(),opHandler.lessStrict(this,e)},e.prototype.equal=function(e){return this.throwIfDisposed(),opHandler.equal(this,e)},e.prototype.equalStrict=function(e){return this.throwIfDisposed(),opHandler.equalStrict(this,e)},e.prototype.lessEqual=function(e){return this.throwIfDisposed(),opHandler.lessEqual(this,e)},e.prototype.lessEqualStrict=function(e){return this.throwIfDisposed(),opHandler.lessEqualStrict(this,e)},e.prototype.greater=function(e){return this.throwIfDisposed(),opHandler.greater(this,e)},e.prototype.greaterStrict=function(e){return this.throwIfDisposed(),opHandler.greaterStrict(this,e)},e.prototype.greaterEqual=function(e){return this.throwIfDisposed(),opHandler.greaterEqual(this,e)},e.prototype.greaterEqualStrict=function(e){return this.throwIfDisposed(),opHandler.greaterEqualStrict(this,e)},e.prototype.logicalAnd=function(e){return this.throwIfDisposed(),opHandler.logicalAnd(this,e)},e.prototype.logicalOr=function(e){return this.throwIfDisposed(),opHandler.logicalOr(this,e)},e.prototype.logicalNot=function(){return this.throwIfDisposed(),opHandler.logicalNot(this)},e.prototype.logicalXor=function(e){return this.throwIfDisposed(),opHandler.logicalXor(this,e)},e.prototype.where=function(e,t){return this.throwIfDisposed(),opHandler.where(e,this,t)},e.prototype.neg=function(){return this.throwIfDisposed(),opHandler.neg(this)},e.prototype.ceil=function(){return this.throwIfDisposed(),opHandler.ceil(this)},e.prototype.floor=function(){return this.throwIfDisposed(),opHandler.floor(this)},e.prototype.sign=function(){return this.throwIfDisposed(),opHandler.sign(this)},e.prototype.exp=function(){return this.throwIfDisposed(),opHandler.exp(this)},e.prototype.expm1=function(){return this.throwIfDisposed(),opHandler.expm1(this)},e.prototype.log=function(){return this.throwIfDisposed(),opHandler.log(this)},e.prototype.log1p=function(){return this.throwIfDisposed(),opHandler.log1p(this)},e.prototype.sqrt=function(){return this.throwIfDisposed(),opHandler.sqrt(this)},e.prototype.rsqrt=function(){return this.throwIfDisposed(),opHandler.rsqrt(this)},e.prototype.square=function(){return this.throwIfDisposed(),opHandler.square(this)},e.prototype.reciprocal=function(){return this.throwIfDisposed(),opHandler.reciprocal(this)},e.prototype.abs=function(){return this.throwIfDisposed(),opHandler.abs(this)},e.prototype.clipByValue=function(e,t){return this.throwIfDisposed(),opHandler.clipByValue(this,e,t)},e.prototype.relu=function(){return this.throwIfDisposed(),opHandler.relu(this)},e.prototype.elu=function(){return this.throwIfDisposed(),opHandler.elu(this)},e.prototype.selu=function(){return this.throwIfDisposed(),opHandler.selu(this)},e.prototype.leakyRelu=function(e){return void 0===e&&(e=.2),this.throwIfDisposed(),opHandler.leakyRelu(this,e)},e.prototype.prelu=function(e){return this.throwIfDisposed(),opHandler.prelu(this,e)},e.prototype.sigmoid=function(){return this.throwIfDisposed(),opHandler.sigmoid(this)},e.prototype.logSigmoid=function(){return this.throwIfDisposed(),opHandler.logSigmoid(this)},e.prototype.softplus=function(){return this.throwIfDisposed(),opHandler.softplus(this)},e.prototype.zerosLike=function(){return this.throwIfDisposed(),opHandler.zerosLike(this)},e.prototype.onesLike=function(){return this.throwIfDisposed(),opHandler.onesLike(this)},e.prototype.sin=function(){return this.throwIfDisposed(),opHandler.sin(this)},e.prototype.cos=function(){return this.throwIfDisposed(),opHandler.cos(this)},e.prototype.tan=function(){return this.throwIfDisposed(),opHandler.tan(this)},e.prototype.asin=function(){return this.throwIfDisposed(),opHandler.asin(this)},e.prototype.acos=function(){return this.throwIfDisposed(),opHandler.acos(this)},e.prototype.atan=function(){return this.throwIfDisposed(),opHandler.atan(this)},e.prototype.sinh=function(){return this.throwIfDisposed(),opHandler.sinh(this)},e.prototype.cosh=function(){return this.throwIfDisposed(),opHandler.cosh(this)},e.prototype.tanh=function(){return this.throwIfDisposed(),opHandler.tanh(this)},e.prototype.asinh=function(){return this.throwIfDisposed(),opHandler.asinh(this)},e.prototype.acosh=function(){return this.throwIfDisposed(),opHandler.acosh(this)},e.prototype.atanh=function(){return this.throwIfDisposed(),opHandler.atanh(this)},e.prototype.erf=function(){return this.throwIfDisposed(),opHandler.erf(this)},e.prototype.round=function(){return this.throwIfDisposed(),opHandler.round(this)},e.prototype.step=function(e){return void 0===e&&(e=0),this.throwIfDisposed(),opHandler.step(this,e)},e.prototype.softmax=function(e){return void 0===e&&(e=-1),this.throwIfDisposed(),opHandler.softmax(this,e)},e.prototype.logSoftmax=function(e){return void 0===e&&(e=-1),this.throwIfDisposed(),opHandler.logSoftmax(this,e)},e.prototype.resizeBilinear=function(e,t){return void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.image.resizeBilinear(this,e,t)},e.prototype.resizeNearestNeighbor=function(e,t){return void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.image.resizeNearestNeighbor(this,e,t)},e.prototype.conv1d=function(e,t,n,r,o,a){return void 0===r&&(r="NWC"),void 0===o&&(o=1),this.throwIfDisposed(),opHandler.conv1d(this,e,t,n,r,o,a)},e.prototype.conv2d=function(e,t,n,r,o,a){return void 0===r&&(r="NHWC"),void 0===o&&(o=[1,1]),this.throwIfDisposed(),opHandler.conv2d(this,e,t,n,r,o,a)},e.prototype.conv2dTranspose=function(e,t,n,r,o){return this.throwIfDisposed(),opHandler.conv2dTranspose(this,e,t,n,r,o)},e.prototype.depthwiseConv2D=function(e,t,n,r,o,a){return void 0===r&&(r="NHWC"),void 0===o&&(o=[1,1]),this.throwIfDisposed(),opHandler.depthwiseConv2d(this,e,t,n,r,o,a)},e.prototype.separableConv2d=function(e,t,n,r,o,a){return void 0===o&&(o=[1,1]),void 0===a&&(a="NHWC"),this.throwIfDisposed(),opHandler.separableConv2d(this,e,t,n,r,o,a)},e.prototype.avgPool=function(e,t,n,r){return this.throwIfDisposed(),opHandler.avgPool(this,e,t,n,r)},e.prototype.maxPool=function(e,t,n,r){return this.throwIfDisposed(),opHandler.maxPool(this,e,t,n,r)},e.prototype.localResponseNormalization=function(e,t,n,r){return void 0===e&&(e=5),void 0===t&&(t=1),void 0===n&&(n=1),void 0===r&&(r=.5),opHandler.localResponseNormalization(this,e,t,n,r)},e.prototype.pool=function(e,t,n,r,o){return this.throwIfDisposed(),opHandler.pool(this,e,t,n,r,o)},e.prototype.variable=function(e,t,n){return void 0===e&&(e=!0),this.throwIfDisposed(),Variable.variable(this,e,t,n)},e.prototype.unsortedSegmentSum=function(e,t){return this.throwIfDisposed(),opHandler.unsortedSegmentSum(this,e,t)},e.prototype.batchToSpaceND=function(e,t){return this.throwIfDisposed(),opHandler.batchToSpaceND(this,e,t)},e.prototype.spaceToBatchND=function(e,t){return this.throwIfDisposed(),opHandler.spaceToBatchND(this,e,t)},e.prototype.topk=function(e,t){return void 0===e&&(e=1),void 0===t&&(t=!0),this.throwIfDisposed(),opHandler.topk(this,e,t)},e.prototype.stridedSlice=function(e,t,n,r,o){return void 0===r&&(r=0),void 0===o&&(o=0),this.throwIfDisposed(),opHandler.stridedSlice(this,e,t,n,r,o)},e.prototype.depthToSpace=function(e,t){return this.throwIfDisposed(),opHandler.depthToSpace(this,e,t)},e.prototype.fft=function(){return this.throwIfDisposed(),opHandler.spectral.fft(this)},e.prototype.ifft=function(){return this.throwIfDisposed(),opHandler.spectral.ifft(this)},e}();Object.defineProperty(Tensor,Symbol.hasInstance,{value:function(e){return !!e&&null!=e.shape&&null!=e.dtype}});var Variable=function(e){function t(t,n,r){void 0===n&&(n=!0);var o=e.call(this,t.shape,t.dtype,null,t.dataId)||this;o.trainable=n,o.name=r,null==o.name&&(o.name=trackerFn().nextVariableId().toString());try{trackerFn().registerVariable(o);}catch(e){throw trackerFn().disposeTensor(o),e}return o}return __extends(t,e),t.variable=function(e,n,r,o){return void 0===n&&(n=!0),null!=o&&o!==e.dtype&&(e=e.asType(o)),new t(e,n,r)},t.prototype.assign=function(e){if(e.dtype!==this.dtype)throw new Error("dtype of the new value ("+e.dtype+") and previous value ("+this.dtype+") must match");if(!arraysEqual(e.shape,this.shape))throw new Error("shape of the new value ("+e.shape+") and previous value ("+this.shape+") must match");trackerFn().disposeTensor(this),this.dataId=e.dataId,trackerFn().registerTensor(this);},t}(Tensor);Object.defineProperty(Variable,Symbol.hasInstance,{value:function(e){return e instanceof Tensor&&null!=e.assign&&e.assign instanceof Function}});var Rank,UpcastInt32AndMap,UpcastBoolAndMap,UpcastFloat32AndMap,UpcastComplex64AndMap,variable=Variable.variable;function getFilteredNodesXToY(e,t,n){for(var r={},o={},a=0;a<t.length;a++)r[t[a].id]=!0;for(a=0;a<e.length;a++){var i=(f=e[a]).inputs;for(var s in i){for(var u=i[s],l=!1,c=0;c<t.length;c++)if(r[u.id]){f.outputs.forEach(function(e){return r[e.id]=!0}),l=!0,o[f.id]=!0;break}if(l)break}}var p={};p[n.id]=!0;var d={};for(a=e.length-1;a>=0;a--)for(i=(f=e[a]).inputs,c=0;c<f.outputs.length;c++)if(p[f.outputs[c].id]){for(var s in i)p[i[s].id]=!0,d[f.id]=!0;break}var h=[];for(a=0;a<e.length;a++){var f;if(o[(f=e[a]).id]&&d[f.id]){var m={};for(var s in f.inputs){var g=f.inputs[s];r[g.id]&&(m[s]=g);}var v=Object.assign({},f);v.inputs=m,v.outputs=f.outputs,h.push(v);}}return h}function backpropagateGradients(e,t){for(var n=function(n){var r=t[n],o=[];if(r.outputs.forEach(function(t){var n=e[t.id];if(null!=n)o.push(n);else{var r=Tensor.make(t.shape,{values:makeZerosTypedArray(t.size,t.dtype)},t.dtype);o.push(r);}}),null==r.gradient)throw new Error("Cannot compute gradient: gradient function not found for "+r.name+".");var a=r.gradient(1===r.outputs.length?o[0]:o);for(var i in r.inputs){if(!(i in a))throw new Error("Cannot backprop through input "+i+". Available gradients found: "+Object.keys(a)+".");var s=a[i]();if("float32"!==s.dtype)throw new Error("Error in gradient for op "+r.name+". The gradient of input "+i+" must have 'float32' dtype, but has '"+s.dtype+"'");var u=r.inputs[i];if(!arraysEqual(s.shape,u.shape))throw new Error("Error in gradient for op "+r.name+". The gradient of input '"+i+"' has shape '"+s.shape+"', which does not match the shape of the input '"+u.shape+"'");if(null==e[u.id])e[u.id]=s;else{var l=e[u.id];e[u.id]=l.add(s),l.dispose();}}},r=t.length-1;r>=0;r--)n(r);}!function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6";}(Rank||(Rank={})),function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64";}(UpcastInt32AndMap||(UpcastInt32AndMap={})),function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64";}(UpcastBoolAndMap||(UpcastBoolAndMap={})),function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64";}(UpcastFloat32AndMap||(UpcastFloat32AndMap={})),function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64";}(UpcastComplex64AndMap||(UpcastComplex64AndMap={}));var upcastTypeMap={float32:UpcastFloat32AndMap,int32:UpcastInt32AndMap,bool:UpcastBoolAndMap,complex64:UpcastComplex64AndMap};function upcastType(e,t){if("string"===e||"string"===t){if("string"===e&&"string"===t)return "string";throw new Error("Can not upcast "+e+" with "+t)}return upcastTypeMap[e][t]}function sumOutType(e){return upcastType(e,"int32")}function makeTypesMatch(e,t){if(e.dtype===t.dtype)return [e,t];var n=upcastType(e.dtype,t.dtype);return [e.cast(n),t.cast(n)]}function assertTypesMatch(e,t){assert(e.dtype===t.dtype,"The dtypes of the first("+e.dtype+") and second("+t.dtype+") input must match");}function isTensorInList(e,t){for(var n=0;n<t.length;n++)if(t[n].id===e.id)return !0;return !1}function getTensorsInContainer(e){var t=[];return walkTensorContainer(e,t,new Set),t}function walkTensorContainer(e,t,n){if(null!=e)if(e instanceof Tensor)t.push(e);else if(isIterable(e)){var r=e;for(var o in r){var a=r[o];n.has(a)||(n.add(a),walkTensorContainer(a,t,n));}}}function isIterable(e){return Array.isArray(e)||"object"==typeof e}var Type,Engine=function(){function e(e,t,n){this.backend=e,this.safeMode=t,this.debugMode=n,this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.profiling=!1,this.gradientScopeCount=0,this.customGradientDepth=0,this.scopeStack=[],this.keepTensors=new Set,this.tensorInfo=new WeakMap,this.profiler=new Profiler(e),this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null};}return e.prototype.moveData=function(e){this.write(e,this.readSync(e));},e.prototype.tidy=function(e,t,n){var r=this;void 0===n&&(n=!1);var o,a=null;if(null==t){if("function"!=typeof e)throw new Error("Please provide a function to tidy()");t=e;}else{if("string"!=typeof e&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if("function"!=typeof t)throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");a=e;}return this.scopedRun(function(){return r.startScope(a,n)},function(){return r.endScope(o,n)},function(){return (o=t())instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),o})},e.prototype.scopedRun=function(e,t,n){e();try{var r=n();return t(),r}catch(e){throw t(),e}},e.prototype.nextTensorId=function(){return e.nextTensorId++},e.prototype.nextVariableId=function(){return e.nextVariableId++},e.prototype.runKernel=function(e,t,n){var r,o=this,a=[],i=function(e){return a.push(e),e},s=this.activeScope.name,u=this.numBytes,l=this.numTensors;if(this.scopedRun(function(){return o.customGradientDepth++},function(){return o.customGradientDepth--},function(){r=o.debugMode()?o.profiler.profileKernel(s,function(){return e(o.backend,i)}):e(o.backend,i);}),this.shouldRecord()){var c={id:this.nextTapeNodeId++,name:s,inputs:t,outputs:Array.isArray(r)?r:[r]};null!=n&&(c.gradient=function(e){return n(e,a)}),this.activeTape.push(c);}return this.profiling&&this.activeProfile.kernels.push({name:s,bytesAdded:this.numBytes-u,totalBytesSnapshot:this.numBytes,tensorsAdded:this.numTensors-l,totalTensorsSnapshot:this.numTensors,inputShapes:Object.keys(t).map(function(e){return t[e].shape}),outputShape:Array.isArray(r)?r.map(function(e){return e.shape}):r.shape}),r},e.prototype.registerTensor=function(e){var t=this.tensorInfo.has(e.dataId)?this.tensorInfo.get(e.dataId).refCount:0;if(this.numTensors++,"string"===e.dtype&&this.numStringTensors++,0===t){this.numDataBuffers++;var n=0;"complex64"!==e.dtype&&"string"!==e.dtype&&(n=sizeFromShape(e.shape)*bytesPerElement(e.dtype)),this.tensorInfo.set(e.dataId,{backend:this.backend,dtype:e.dtype,shape:e.shape,bytes:n,refCount:0}),this.numBytes+=n,this.backend.register(e.dataId,e.shape,e.dtype);}this.tensorInfo.get(e.dataId).refCount++,e instanceof Variable||this.track(e);},e.prototype.registerVariable=function(e){if(null!=this.registeredVariables[e.name])throw new Error("Variable with name "+e.name+" was already registered");this.registeredVariables[e.name]=e;},e.prototype.disposeTensor=function(e){if(this.tensorInfo.has(e.dataId)){this.keepTensors.has(e.id)&&this.keepTensors.delete(e.id),this.numTensors--,"string"===e.dtype&&this.numStringTensors--;var t=this.tensorInfo.get(e.dataId);t.refCount<=1?("complex64"!==e.dtype&&(this.numBytes-=t.bytes),this.numDataBuffers--,t.backend.disposeData(e.dataId),this.tensorInfo.delete(e.dataId)):this.tensorInfo.get(e.dataId).refCount--;}},e.prototype.disposeVariables=function(){for(var e in this.registeredVariables){var t=this.registeredVariables[e];this.disposeTensor(t),delete this.registeredVariables[e];}},e.prototype.memory=function(){var e=this.backend.memory();return e.numTensors=this.numTensors,e.numDataBuffers=this.numDataBuffers,e.numBytes=this.numBytes,this.numStringTensors>0&&(e.unreliable=!0,null==e.reasons&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e},e.prototype.profile=function(e){return __awaiter(this,void 0,void 0,function(){var t,n;return __generator(this,function(r){return this.profiling=!0,t=this.numBytes,n=this.numTensors,this.activeProfile.kernels=[],this.activeProfile.result=e(),this.profiling=!1,this.activeProfile.peakBytes=Math.max.apply(Math,this.activeProfile.kernels.map(function(e){return e.totalBytesSnapshot})),this.activeProfile.newBytes=this.numBytes-t,this.activeProfile.newTensors=this.numTensors-n,[2,this.activeProfile]})})},e.prototype.shouldRecord=function(){return null!=this.activeTape&&0===this.customGradientDepth},e.prototype.addTapeNode=function(e,t,n){var r={};e.forEach(function(e,t){r[t]=e;});var o={id:this.nextTapeNodeId++,name:this.activeScope.name,inputs:r,outputs:[t],gradient:function(e){var t={};return n(e).forEach(function(e,n){t[n]=function(){return e};}),t}};this.activeTape.push(o);},e.prototype.keep=function(e){if(1===this.scopeStack.length&&this.safeMode)throw new Error("Safe mode is ON. Enclose all tensor operations inside tf.tidy(): tf.tidy(() => {...}) to avoid memory leaks.");return this.keepTensors.add(e.id),e},e.prototype.startScope=function(e,t){void 0===t&&(t=!1),t&&0===this.gradientScopeCount&&(this.activeTape=[]),t&&this.gradientScopeCount++;var n={track:[],name:"unnamed scope"};e&&(n.name=e),this.scopeStack.push(n),this.activeScope=n;},e.prototype.endScope=function(e,t){var n=this;void 0===t&&(t=!1),t&&(this.gradientScopeCount--,0===this.gradientScopeCount&&(this.activeTape=null));var r=new Set(this.keepTensors),o=getTensorsInContainer(e);o.forEach(function(e){return r.add(e.id)});for(var a=0;a<this.activeScope.track.length;a++){var i=this.activeScope.track[a];r.has(i.id)||(null!=this.activeTape?o.push(i):i.dispose());}var s=this.scopeStack.pop();this.activeScope=0===this.scopeStack.length?null:this.scopeStack[this.scopeStack.length-1],o.forEach(function(e){!n.keepTensors.has(e.id)&&isTensorInList(e,s.track)&&n.track(e);});},e.prototype.gradients=function(e,t,n,r){var o=this;if(void 0===r&&(r=!1),assert(t.length>0,"gradients() received an empty list of xs."),null!=n&&"float32"!==n.dtype)throw new Error("dy must have 'float32' dtype, but has '"+n.dtype+"'");return this.tidy("gradients",function(){var a=e();assert(a instanceof Tensor,"The result y returned by f() must be a tensor.");var i=getFilteredNodesXToY(o.activeTape,t,a);if(!r&&0===i.length&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");var s={};return s[a.id]=null==n?ones(a.shape):n,backpropagateGradients(s,i),{value:a,grads:t.map(function(e){return s[e.id]})}},!0)},e.prototype.customGrad=function(e){var t=this;return assert(isFunction(e),"The f passed in customGrad(f) must be a function."),function(){for(var n,r,o=[],a=0;a<arguments.length;a++)o[a]=arguments[a];if(assert(o.every(function(e){return e instanceof Tensor}),"The args passed in customGrad(f)(x1, x2,...) must all be tensors"),t.scopedRun(function(){return t.customGradientDepth++},function(){return t.customGradientDepth--},function(){r=t.tidy(e.name,function(){var t=e.apply(void 0,o),r=t.value,a=t.gradFunc;return assert(r instanceof Tensor,"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),assert(isFunction(a),"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n=a,r},!0);}),t.shouldRecord()){t.addTapeNode(o,r,function(e){var t=n(e),r=Array.isArray(t)?t:[t];return assert(r.length===o.length,"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),assert(r.every(function(e){return e instanceof Tensor}),"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors."),r});}return r}},e.prototype.write=function(e,t){var n=this.tensorInfo.get(e);if("string"===n.dtype){var r=bytesFromStringArray(t);this.numBytes+=r-n.bytes,n.bytes=r;}this.backend!==n.backend&&(n.backend.disposeData(e),n.backend=this.backend,this.backend.register(e,n.shape,n.dtype)),this.backend.write(e,t);},e.prototype.readSync=function(e){return this.tensorInfo.get(e).backend.readSync(e)},e.prototype.read=function(e){return this.tensorInfo.get(e).backend.read(e)},e.prototype.fromPixels=function(e,t){return this.backend.fromPixels(e,t)},e.prototype.time=function(e){return __awaiter(this,void 0,void 0,function(){var t,n;return __generator(this,function(r){switch(r.label){case 0:return t=now(),[4,this.backend.time(e)];case 1:return (n=r.sent()).wallMs=now()-t,[2,n]}})})},e.prototype.track=function(e){if(1===this.scopeStack.length&&this.safeMode)throw new Error("Safe mode is ON. Enclose all tensor operations inside tf.tidy(): tf.tidy(() => {op();...}); to avoid memory leaks.");return null!=this.activeScope&&this.activeScope.track.push(e),e},e.nextTensorId=0,e.nextVariableId=0,e}();function ones(e){var t=makeOnesTypedArray(sizeFromShape(e),"float32");return Tensor.make(e,{values:t})}!function(e){e[e.NUMBER=0]="NUMBER",e[e.BOOLEAN=1]="BOOLEAN",e[e.STRING=2]="STRING";}(Type||(Type={}));var MAX_TEXTURE_SIZE,URL_PROPERTIES=[{name:"DEBUG",type:Type.BOOLEAN},{name:"IS_BROWSER",type:Type.BOOLEAN},{name:"WEBGL_LAZILY_UNPACK",type:Type.BOOLEAN},{name:"WEBGL_CPU_FORWARD",type:Type.BOOLEAN},{name:"WEBGL_PACK",type:Type.BOOLEAN},{name:"WEBGL_PACK_BATCHNORMALIZATION",type:Type.BOOLEAN},{name:"WEBGL_PACK_CLIP",type:Type.BOOLEAN},{name:"WEBGL_PACK_DEPTHWISECONV",type:Type.BOOLEAN},{name:"WEBGL_CONV_IM2COL",type:Type.BOOLEAN},{name:"WEBGL_MAX_TEXTURE_SIZE",type:Type.NUMBER},{name:"WEBGL_PAGING_ENABLED",type:Type.BOOLEAN},{name:"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",type:Type.NUMBER},{name:"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",type:Type.BOOLEAN},{name:"WEBGL_VERSION",type:Type.NUMBER},{name:"WEBGL_RENDER_FLOAT32_ENABLED",type:Type.BOOLEAN},{name:"WEBGL_DOWNLOAD_FLOAT_ENABLED",type:Type.BOOLEAN},{name:"WEBGL_FENCE_API_ENABLED",type:Type.BOOLEAN},{name:"WEBGL_SIZE_UPLOAD_UNIFORM",type:Type.NUMBER},{name:"BACKEND",type:Type.STRING},{name:"EPSILON",type:Type.NUMBER},{name:"PROD",type:Type.BOOLEAN},{name:"TENSORLIKE_CHECK_SHAPE_CONSISTENCY",type:Type.BOOLEAN}];function isWebGLVersionEnabled(e){try{if(null!=getWebGLContext(e))return !0}catch(e){return !1}return !1}function getWebGLMaxTextureSize(e){if(null==MAX_TEXTURE_SIZE){var t=getWebGLContext(e);MAX_TEXTURE_SIZE=t.getParameter(t.MAX_TEXTURE_SIZE);}return MAX_TEXTURE_SIZE}function getWebGLDisjointQueryTimerVersion(e){if(0===e)return 0;var t=getWebGLContext(e);return hasExtension(t,"EXT_disjoint_timer_query_webgl2")&&2===e?2:hasExtension(t,"EXT_disjoint_timer_query")?1:0}function isRenderToFloatTextureEnabled(e){if(0===e)return !1;var t=getWebGLContext(e);if(1===e){if(!hasExtension(t,"OES_texture_float"))return !1}else if(!hasExtension(t,"EXT_color_buffer_float"))return !1;return createFloatTextureAndBindToFramebuffer(t,e)}function isDownloadFloatTextureEnabled(e){if(0===e)return !1;var t=getWebGLContext(e);if(1===e){if(!hasExtension(t,"OES_texture_float"))return !1;if(!hasExtension(t,"WEBGL_color_buffer_float"))return !1}else if(!hasExtension(t,"EXT_color_buffer_float"))return !1;return createFloatTextureAndBindToFramebuffer(t,e)}function isWebGLFenceEnabled(e){return 2===e&&null!=getWebGLContext(e).fenceSync}function isChrome(){return "undefined"!=typeof navigator&&null!=navigator&&null!=navigator.userAgent&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor)}var TENSORFLOWJS_FLAGS_PREFIX="tfjsflags";function getFeaturesFromURL(){var e={};if("undefined"==typeof window||void 0===window.location||void 0===window.location.search)return e;var t=getQueryParams(window.location.search);if(TENSORFLOWJS_FLAGS_PREFIX in t){var n={};t[TENSORFLOWJS_FLAGS_PREFIX].split(",").forEach(function(e){var t=e.split(":"),r=t[0],o=t[1];n[r]=o;}),URL_PROPERTIES.forEach(function(t){t.name in n&&(console.log("Setting feature override from URL "+t.name+": "+n[t.name]),t.type===Type.NUMBER?e[t.name]=+n[t.name]:t.type===Type.BOOLEAN?e[t.name]="true"===n[t.name]:t.type===Type.STRING?e[t.name]=n[t.name]:console.warn("Unknown URL param: "+t.name+"."));});}return e}function hasExtension(e,t){return null!=e.getExtension(t)}function createFloatTextureAndBindToFramebuffer(e,t){var n=e.createFramebuffer(),r=e.createTexture();e.bindTexture(e.TEXTURE_2D,r);var o=2===t?e.RGBA32F:e.RGBA;e.texImage2D(e.TEXTURE_2D,0,o,1,1,0,e.RGBA,e.FLOAT,null),e.bindFramebuffer(e.FRAMEBUFFER,n),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,r,0);var a=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(r),e.deleteFramebuffer(n),a}function getQueryParams(e){var t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,function(e){for(var n=[],r=1;r<arguments.length;r++)n[r-1]=arguments[r];return decodeParam(t,n[0],n[1]),n.join("=")}),t}function decodeParam(e,t,n){e[decodeURIComponent(t)]=decodeURIComponent(n||"");}var EPSILON_FLOAT16=1e-4,TEST_EPSILON_FLOAT16=.1,EPSILON_FLOAT32=1e-7,TEST_EPSILON_FLOAT32=.001,Environment=function(){function e(e){this.features={},this.registry={},null!=e&&(this.features=e),this.get("DEBUG")&&console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.");}return e.setBackend=function(e,t){if(void 0===t&&(t=!1),!(e in ENV.registry))throw new Error("Backend name '"+e+"' not found in registry");ENV.engine.backend=ENV.findBackend(e),ENV.backendName=e;},e.getBackend=function(){return ENV.initEngine(),ENV.backendName},e.disposeVariables=function(){ENV.engine.disposeVariables();},e.memory=function(){return ENV.engine.memory()},e.profile=function(e){return ENV.engine.profile(e)},e.tidy=function(e,t){return ENV.engine.tidy(e,t)},e.dispose=function(e){getTensorsInContainer(e).forEach(function(e){return e.dispose()});},e.keep=function(e){return ENV.engine.keep(e)},e.time=function(e){return ENV.engine.time(e)},e.prototype.get=function(e){return e in this.features?this.features[e]:(this.features[e]=this.evaluateFeature(e),this.features[e])},e.prototype.getFeatures=function(){return this.features},e.prototype.set=function(e,t){this.features[e]=t;},e.prototype.getBestBackendName=function(){var e=this;if(0===Object.keys(this.registry).length)throw new Error("No backend found in registry.");return Object.keys(this.registry).map(function(t){return {name:t,entry:e.registry[t]}}).sort(function(e,t){return t.entry.priority-e.entry.priority})[0].name},e.prototype.evaluateFeature=function(e){if("DEBUG"===e)return !1;if("IS_BROWSER"===e)return "undefined"!=typeof window;if("IS_NODE"===e)return "undefined"!=typeof process&&void 0!==process.versions&&void 0!==process.versions.node;if("IS_CHROME"===e)return isChrome();if("WEBGL_CPU_FORWARD"===e)return !0;if("WEBGL_PACK"===e)return !1;if("WEBGL_PACK_BATCHNORMALIZATION"===e)return this.get("WEBGL_PACK");if("WEBGL_PACK_CLIP"===e)return this.get("WEBGL_PACK");if("WEBGL_PACK_DEPTHWISECONV"===e)return this.get("WEBGL_PACK");if("WEBGL_LAZILY_UNPACK"===e)return this.get("WEBGL_PACK");if("WEBGL_CONV_IM2COL"===e)return this.get("WEBGL_PACK");if("WEBGL_PAGING_ENABLED"===e)return this.get("IS_BROWSER")&&!this.get("PROD");if("WEBGL_MAX_TEXTURE_SIZE"===e)return getWebGLMaxTextureSize(this.get("WEBGL_VERSION"));if("IS_TEST"===e)return !1;if("BACKEND"===e)return this.getBestBackendName();if("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"===e){var t=this.get("WEBGL_VERSION");return 0===t?0:getWebGLDisjointQueryTimerVersion(t)}if("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE"===e)return this.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!isMobile();if("HAS_WEBGL"===e)return this.get("WEBGL_VERSION")>0;if("WEBGL_VERSION"===e)return isWebGLVersionEnabled(2)?2:isWebGLVersionEnabled(1)?1:0;if("WEBGL_RENDER_FLOAT32_ENABLED"===e)return isRenderToFloatTextureEnabled(this.get("WEBGL_VERSION"));if("WEBGL_DOWNLOAD_FLOAT_ENABLED"===e)return isDownloadFloatTextureEnabled(this.get("WEBGL_VERSION"));if("WEBGL_FENCE_API_ENABLED"===e)return isWebGLFenceEnabled(this.get("WEBGL_VERSION"));if("WEBGL_SIZE_UPLOAD_UNIFORM"===e)return this.get("WEBGL_RENDER_FLOAT32_ENABLED")?4:0;if("TEST_EPSILON"===e)return 32===this.backend.floatPrecision()?TEST_EPSILON_FLOAT32:TEST_EPSILON_FLOAT16;if("EPSILON"===e)return 32===this.backend.floatPrecision()?EPSILON_FLOAT32:EPSILON_FLOAT16;if("PROD"===e)return !1;if("TENSORLIKE_CHECK_SHAPE_CONSISTENCY"===e)return !this.get("PROD");throw new Error("Unknown feature "+e+".")},e.prototype.setFeatures=function(e){this.features=Object.assign({},e);},e.prototype.reset=function(){this.features=getFeaturesFromURL(),null!=this.globalEngine&&(this.globalEngine=null);},Object.defineProperty(e.prototype,"backend",{get:function(){return this.engine.backend},enumerable:!0,configurable:!0}),e.prototype.findBackend=function(e){return e in this.registry?this.registry[e].backend:null},e.prototype.registerBackend=function(e,t,n,r){var o=this;if(void 0===n&&(n=1),e in this.registry)return console.warn(e+" backend was already registered. Reusing existing backend"),null!=r&&r(function(){return o.engine}),!1;try{var a=t();return a.setDataMover({moveData:function(e){return o.engine.moveData(e)}}),this.registry[e]={backend:a,priority:n},!0}catch(t){return console.warn("Registration of backend "+e+" failed"),console.warn(t.stack||t.message),!1}},e.prototype.removeBackend=function(e){if(!(e in this.registry))throw new Error(e+" backend not found in registry");this.registry[e].backend.dispose(),delete this.registry[e];},Object.defineProperty(e.prototype,"engine",{get:function(){return this.initEngine(),this.globalEngine},enumerable:!0,configurable:!0}),e.prototype.initEngine=function(){var e=this;if(null==this.globalEngine){this.backendName=this.get("BACKEND");var t=this.findBackend(this.backendName);this.globalEngine=new Engine(t,!1,function(){return e.get("DEBUG")});}},e}();function getGlobalNamespace(){var e;if("undefined"!=typeof window)e=window;else{if("undefined"==typeof process)throw new Error("Could not find a global object");e=process;}return e}function getOrMakeEnvironment(){var e=getGlobalNamespace();return null==e.ENV&&(e.ENV=new Environment(getFeaturesFromURL()),setTensorTracker(function(){return e.ENV.engine})),e.ENV}var ENV=getOrMakeEnvironment(),environment=Object.freeze({EPSILON_FLOAT16:EPSILON_FLOAT16,EPSILON_FLOAT32:EPSILON_FLOAT32,Environment:Environment,ENV:ENV});function grad(e){return assert(isFunction(e),"The f passed in grad(f) must be a function"),function(t,n){return assert(t instanceof Tensor,"The x passed in grad(f)(x) must be a tensor"),assert(null==n||n instanceof Tensor,"The dy passed in grad(f)(x, dy) must be a tensor"),ENV.engine.tidy(function(){var r=ENV.engine.gradients(function(){return e(t)},[t],n),o=r.value,a=r.grads;return null!=n&&assertShapesMatch(o.shape,n.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),checkGrads(a),a[0]})}}function grads(e){return assert(isFunction(e),"The f passed in grads(f) must be a function"),function(t,n){return assert(Array.isArray(t)&&t.every(function(e){return e instanceof Tensor}),"The args passed in grads(f)(args) must be an array of tensors"),assert(null==n||n instanceof Tensor,"The dy passed in grads(f)(args, dy) must be a tensor"),ENV.engine.tidy(function(){var r=ENV.engine.gradients(function(){return e.apply(void 0,t)},t,n),o=r.value,a=r.grads;return null!=n&&assertShapesMatch(o.shape,n.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(a),a})}}function valueAndGrad(e){return assert(isFunction(e),"The f passed in valueAndGrad(f) must be a function"),function(t,n){assert(t instanceof Tensor,"The x passed in valueAndGrad(f)(x) must be a tensor"),assert(null==n||n instanceof Tensor,"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");var r=ENV.engine.gradients(function(){return e(t)},[t],n),o=r.grads,a=r.value;return checkGrads(o),{grad:o[0],value:a}}}function valueAndGrads(e){return assert(isFunction(e),"The f passed in valueAndGrads(f) must be a function"),function(t,n){assert(Array.isArray(t)&&t.every(function(e){return e instanceof Tensor}),"The args passed in valueAndGrads(f)(args) must be array of tensors"),assert(null==n||n instanceof Tensor,"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");var r=ENV.engine.gradients(function(){return e.apply(void 0,t)},t,n);return null!=n&&assertShapesMatch(r.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(r.grads),r}}function variableGrads(e,t){if(assert(isFunction(e),"The f passed in variableGrads(f) must be a function"),assert(null==t||Array.isArray(t)&&t.every(function(e){return e instanceof Variable}),"The varList passed in variableGrads(f, varList) must be an array of variables"),null==t)for(var n in t=[],ENV.engine.registeredVariables)t.push(ENV.engine.registeredVariables[n]);var r=t.length;assert((t=t.filter(function(e){return e.trainable})).length>0,"variableGrads() expects at least one of the input variables to be trainable, but none of the "+r+" variables is trainable.");var o=ENV.engine.gradients(e,t,null,!0),a=o.value,i=o.grads;assert(i.some(function(e){return null!=e}),"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),assert(0===a.rank,"The f passed in variableGrads(f) must return a scalar, but it returned a rank-"+a.rank+" tensor");var s={};return t.forEach(function(e,t){null!=i[t]&&(s[e.name]=i[t]);}),{value:a,grads:s}}function customGrad(e){return ENV.engine.customGrad(e)}function checkGrads(e){if(e.filter(function(e){return null==e}).length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that\n    the f you passed encloses all operations that lead from x to y.")}var tidy=Environment.tidy,keep=Environment.keep,dispose=Environment.dispose,time=Environment.time,profile=Environment.profile;function warn(){for(var e=[],t=0;t<arguments.length;t++)e[t]=arguments[t];ENV.get("IS_TEST")||console.warn.apply(console,e);}function getReshaped(e,t,n,r){void 0===r&&(r=!0);var o=[];if(r)(o=o.concat(t.slice(0))).push(e[0]/n),o=o.concat(e.slice(1));else{o=o.concat(e[0]);for(var a=t.length,i=0;i<a;++i)o=o.concat([e[i+1]/t[i],t[i]]);o=o.concat(e.slice(a+1));}return o}function getPermuted(e,t,n){void 0===n&&(n=!0);var r=[];if(n){r.push(t);for(var o=t+1;o<e;++o)o<=2*t?(r.push(o),r.push(o-(t+1))):r.push(o);}else{var a=[],i=[];for(o=1;o<e;++o)o>=2*t+1||o%2==1?i.push(o):a.push(o);r.push.apply(r,a),r.push(0),r.push.apply(r,i);}return r}function getReshapedPermuted(e,t,n,r){void 0===r&&(r=!0);var o=[];r?o.push(e[0]/n):o.push(e[0]*n);for(var a=1;a<e.length;++a)a<=t.length?r?o.push(t[a-1]*e[a]):o.push(e[a]/t[a-1]):o.push(e[a]);return o}function getSliceBeginCoords(e,t){for(var n=[0],r=0;r<t;++r)n.push(e[r][0]);return n}function getSliceSize(e,t,n){for(var r=e.slice(0,1),o=0;o<n;++o)r.push(e[o+1]-t[o][0]-t[o][1]);return r}function axesAreInnerMostDims(e,t){for(var n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return !1;return !0}function combineLocations(e,t,n){for(var r=e.length+t.length,o=[],a=0,i=0,s=0;s<r;s++)-1===n.indexOf(s)?o.push(e[a++]):o.push(t[i++]);return o}function computeOutAndReduceShapes(e,t){for(var n=[],r=e.length,o=0;o<r;o++)-1===t.indexOf(o)&&n.push(e[o]);return [n,t.map(function(t){return e[t]})]}function expandShapeToKeepDim(e,t){return combineLocations(e,t.map(function(e){return 1}),t)}function parseAxisParam(e,t){var n=t.length;return assert((e=null==e?t.map(function(e,t){return t}):[].concat(e)).every(function(e){return e>=-n&&e<n}),"All values in axis param must be in range [-"+n+", "+n+") but got axis "+e),assert(e.every(function(e){return isInt(e)}),"All values in axis param must be integers but got axis "+e),e.map(function(e){return e<0?n+e:e})}function assertAxesAreInnerMostDims(e,t,n){assert(axesAreInnerMostDims(t,n),e+" supports only inner-most axes for now. Got axes "+t+" and rank-"+n+" input.");}function getAxesPermutation(e,t){if(axesAreInnerMostDims(e,t))return null;for(var n=[],r=0;r<t;++r)-1===e.indexOf(r)&&n.push(r);return e.forEach(function(e){return n.push(e)}),n}function getUndoAxesPermutation(e){return e.map(function(e,t){return [t,e]}).sort(function(e,t){return e[1]-t[1]}).map(function(e){return e[0]})}function getInnerMostAxes(e,t){for(var n=[],r=t-e;r<t;++r)n.push(r);return n}function assertParamsConsistent(e,t){var n=e[0].length;e.forEach(function(e,t){assert(e.length===n,"Error in concat"+n+"D: rank of tensors["+t+"] must be the same as the rank of the rest ("+n+")");}),assert(t>=0&&t<n,"Error in concat"+n+"D: axis must be between 0 and "+(n-1)+".");var r=e[0];e.forEach(function(e,o){for(var a=0;a<n;a++)assert(a===t||e[a]===r[a],"Error in concat"+n+"D: Shape of tensors["+o+"] ("+e+") does not match the shape of the rest ("+r+") along the non-concatenated axis "+o+".");});}function computeOutShape(e,t){for(var n=e[0].slice(),r=1;r<e.length;r++)n[t]+=e[r][t];return n}function prepareAndValidate(e,t){if(e.rank<1)throw new Error("tf.gatherND() expects the input to be rank 1 or higher, but the rank was "+e.rank+".");if(t.rank<1)throw new Error("tf.gatherND() expects the indices to be rank 1 or higher, but the rank was "+t.rank+".");if("int32"!==t.dtype)throw new Error("tf.gatherND() expects the indices to be int32 type, but the dtype was "+t.dtype+".");if(t.shape[t.rank-1]>e.rank)throw new Error("index innermost dimension length must be <= tensor rank; saw: "+t.shape[t.rank-1]+" vs. "+e.rank);if(0===e.size)throw new Error("Requested more than 0 entries, but input is empty. Input shape: "+e.shape+".");for(var n=t.shape,r=n[n.length-1],o=1,a=0;a<n.length-1;++a)o*=n[a];var i=e.shape,s=n.slice();s.pop();var u=1;for(a=r;a<e.rank;++a)u*=i[a],s.push(i[a]);var l=computeStrides(e.shape).map(function(e){return e/u}).concat([1]).slice(0,r);return [s,o,u,l]}var PARALLELIZE_THRESHOLD=30;function computeOptimalWindowSize(e){return e<=PARALLELIZE_THRESHOLD?e:nearestDivisor(e,Math.floor(Math.sqrt(e)))}function validateUpdateShape(e,t,n){var r=t.rank>1?t.shape[t.rank-1]:1,o=t.rank>1?t.rank-1:1,a="Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: "+n.shape+", indices.shape: "+t.shape+", shape: "+e+", sliceDim: "+r+", and batchDim: "+o+".";if(n.rank<o)throw new Error(a+" update.rank < "+o+". ");if(e.length<r+(n.rank-o))throw new Error(a+" Output shape length < "+(r+(n.rank-o)));if(n.rank!==o+e.length-r)throw new Error(a+" update.rank != "+(o+e.length-r));for(var i=0;i<o;++i)if(n.shape[i]!==t.shape[i])throw new Error(a+" updates.shape["+i+"] ("+n.shape[i]+") != indices.shape["+i+"] ("+t.shape[i]+").");for(i=0;i<n.rank-o;++i)if(n.shape[i+o]!==e[i+r])throw new Error(a+" updates.shape["+(i+o)+"] ("+n.shape[i+o]+") != shape["+(i+o)+"] ("+e[i+o]+")")}function validateInput(e,t,n){if(t.rank<1)throw new Error("tf.scatterND() expects the indices to be rank 1 or higher, but the rank was "+t.rank+".");if(e.rank<1)throw new Error("tf.scatterND() expects the updates to be rank 1 or higher, but the rank was "+e.rank+".");if("int32"!==t.dtype)throw new Error("The dtype of 'indices' should be int32, but got dtype: "+t.dtype);if(n.length<1)throw new Error("Output rank must be greater or equal to 1, but got shape: "+n);if(0===n.length){if(0===t.size)throw new Error("Indices specified for empty output. indices shape: "+t.shape);if(0===e.size)throw new Error("Updates specified for empty output. updates shape: "+e.shape)}validateUpdateShape(n,t,e);}function calculateShapes(e,t,n){for(var r=t.rank>1?t.shape[t.rank-1]:1,o=n.length,a=1,i=r;i<o;++i)a*=n[i];var s=r<1?1:r,u=t.size/s,l=computeStrides(n).concat([1]);return {sliceRank:r,numUpdates:u,sliceSize:a,strides:l.slice(l.length-r,l.length),outputSize:sizeFromShape(n)}}function segOpComputeOptimalWindowSize(e,t){var n,r=!1;for(e<=PARALLELIZE_THRESHOLD?(n=e,r=!0):n=nearestDivisor(e,Math.floor(Math.sqrt(e)));!r;){if(n>t||n===e){r=!0;break}n=nearestDivisor(e,n+1);}return n}function computeOutShape$1(e,t,n){for(var r=[],o=e.length,a=0;a<o;a++)a!==t?r.push(e[a]):r.push(n);return r}function assertParamsValid(e,t,n){assert(e.rank===t.length,"Error in slice"+e.rank+"D: Length of begin "+t+" must match the rank of the array ("+e.rank+")."),assert(e.rank===n.length,"Error in slice"+e.rank+"D: Length of size "+n+" must match the rank of the array ("+e.rank+").");for(var r=0;r<e.rank;++r)assert(t[r]+n[r]<=e.shape[r],"Error in slice"+e.rank+"D: begin["+r+"] + size["+r+"] ("+(t[r]+n[r])+") would overflow input.shape["+r+"] ("+e.shape[r]+")");}function getStridedSlicedInfo(e,t,n,r,o,a,i,s,u){if(void 0===o&&(o=0),void 0===a&&(a=0),void 0===i&&(i=0),void 0===s&&(s=0),void 0===u&&(u=0),0!==i)throw new Error("ellipsis mask is not yet supported");if(0!==s)throw new Error("new axis mask is not yet supported");for(var l=[],c=[],p=[],d=0;d<e.length;d++)l[d]=startForAxis(o,t,r,e,d),c[d]=stopForAxis(a,n,r,e,d),u&1<<d&&(c[d]=l[d]+1,p.push(d));var h=new Array(e.length).fill(0);return h=h.map(function(e,t){for(var n=0,o=l[t];!(r[t]>0?o>=c[t]:o<=c[t]);o+=r[t])n+=1;return n}),[l,h,p]}function startForAxis(e,t,n,r,o){var a=t[o];e&1<<o&&(a=n[o]>0?Number.MIN_SAFE_INTEGER:Number.MAX_SAFE_INTEGER);var i=r[o];return a<0&&(a+=i),a=clamp(0,a,i-1)}function stopForAxis(e,t,n,r,o){var a=t[o];e&1<<o&&(a=n[o]>0?Number.MAX_SAFE_INTEGER:Number.MIN_SAFE_INTEGER);var i=r[o];return a<0&&(a+=i),a=n[o]>0?clamp(0,a,i):clamp(-1,a,i-1)}function inferShape(e){var t=e;if(isTypedArray(e))return [e.length];if(!Array.isArray(e))return [];for(var n=[];t instanceof Array||isTypedArray(t);)n.push(t.length),t=t[0];return e instanceof Array&&ENV.get("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&deepAssertShapeConsistency(e,n,[]),n}function deepAssertShapeConsistency(e,t,n){if(n=n||[],e instanceof Array||isTypedArray(e)){assert(t.length>0,function(){return "Element arr["+n.join("][")+"] should be a primitive, but is an array of "+e.length+" elements"}),assert(e.length===t[0],function(){return "Element arr["+n.join("][")+"] should have "+t[0]+" elements, but has "+e.length+" elements"});for(var r=t.slice(1),o=0;o<e.length;++o)deepAssertShapeConsistency(e[o],r,n.concat(o));}else assert(0===t.length,function(){return "Element arr["+n.join("][")+"] is a primitive, but should be an array/TypedArray of "+t[0]+" elements"});}function assertDtype(e,t,n,r){if(null!=e&&("numeric"!==e&&e!==t||"numeric"===e&&"string"===t))throw new Error("Argument '"+n+"' passed to '"+r+"' must be "+e+" tensor, but got "+t+" tensor")}function convertToTensor(e,t,n,r){if(void 0===r&&(r="numeric"),e instanceof Tensor)return assertDtype(r,e.dtype,t,n),e;var o=inferDtype(e);if("string"!==o&&["bool","int32","float32"].indexOf(r)>=0&&(o=r),assertDtype(r,o,t,n),!isTypedArray(e)&&!Array.isArray(e)&&"number"!=typeof e&&"boolean"!=typeof e&&"string"!=typeof e)throw new Error("Argument '"+t+"' passed to '"+n+"' must be a Tensor or TensorLike, but got '"+e.constructor.name+"'");var a=inferShape(e);isTypedArray(e)||Array.isArray(e)||(e=[e]);var i="string"!==o?toTypedArray(e,o,ENV.get("DEBUG")):flatten(e);return Tensor.make(a,{values:i},o)}function convertToTensorArray(e,t,n){if(!Array.isArray(e))throw new Error("Argument "+t+" passed to "+n+" must be a `Tensor[]` or `TensorLike[]`");return e.map(function(e,r){return convertToTensor(e,t+"["+r+"]",n)})}function op(e){var t=Object.keys(e);if(1!==t.length)throw new Error("Please provide an object with a single key (operation name) mapping to a function. Got an object with "+t.length+" keys.");var n=t[0],r=e[n];n.endsWith("_")&&(n=n.substring(0,n.length-1));var o=function(){for(var e=[],t=0;t<arguments.length;t++)e[t]=arguments[t];ENV.engine.startScope(n);try{var o=r.apply(void 0,e);return o instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),ENV.engine.endScope(o),o}catch(e){throw ENV.engine.endScope(null),e}};return Object.defineProperty(o,"name",{value:n,configurable:!0}),o}function softmax_(e,t){void 0===t&&(t=-1);var n=convertToTensor(e,"logits","softmax");if(-1===t&&(t=n.rank-1),t!==n.rank-1)throw Error("Softmax along a non-last dimension is not yet supported. Logits was rank "+n.rank+" and dim was "+t);return customGrad(function(e){var n=e.logSumExp([t],!0),r=e.toFloat().sub(n).exp();return {value:r,gradFunc:function(e){var n=e.mul(r);return n.sub(n.sum([t],!0).mul(r))}}})(n)}function logSoftmax_(e,t){void 0===t&&(t=-1);var n=convertToTensor(e,"logits","logSoftmax");if(-1===t&&(t=n.rank-1),t!==n.rank-1)throw Error("Log Softmax along a non-last dimension is not yet supported. Logits was rank "+n.rank+" and axis was "+t);return customGrad(function(e){var n=e.max(t,!0),r=e.sub(n),o=r.toFloat().sub(r.exp().sum(t,!0).log());return {value:o,gradFunc:function(e){var n=o.exp();return e.sub(e.sum(t,!0).mul(n))}}})(n)}var softmax=op({softmax_:softmax_}),logSoftmax=op({logSoftmax_:logSoftmax_});function complex_(e,t){var n=convertToTensor(e,"real","complex"),r=convertToTensor(t,"imag","complex");return assertShapesMatch(n.shape,r.shape,"real and imag shapes, "+n.shape+" and "+r.shape+", must match in call to tf.complex()."),ENV.engine.runKernel(function(e){return e.complex(n,r)},{$real:n,$imag:r})}function real_(e){var t=convertToTensor(e,"input","real");return ENV.engine.runKernel(function(e){return e.real(t)},{$input:t})}function imag_(e){var t=convertToTensor(e,"input","imag");return ENV.engine.runKernel(function(e){return e.imag(t)},{$input:t})}var complex=op({complex_:complex_}),real=op({real_:real_}),imag=op({imag_:imag_});function tensor(e,t,n){if(null==n&&(n=inferDtype(e)),"complex64"===n)throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!isTypedArray(e)&&!Array.isArray(e)&&"number"!=typeof e&&"boolean"!=typeof e&&"string"!=typeof e)throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");var r=inferShape(e);if(null!=t){var o=sizeFromShape(t),a=sizeFromShape(r);assert(o===a,function(){return "Based on the provided shape, ["+t+"], the tensor should have "+o+" values but has "+a});for(var i=0;i<r.length;++i){var s=r[i],u=i!==r.length-1||s!==sizeFromShape(t.slice(i));assert(r[i]===t[i]||!u,function(){return "Error creating a new Tensor. Inferred shape ("+r+") does not match the provided shape ("+t+"). "});}}return isTypedArray(e)||Array.isArray(e)||(e=[e]),t=t||r,e="string"!==n?toTypedArray(e,n,ENV.get("DEBUG")):flatten(e),Tensor.make(t,{values:e},n)}function scalar(e,t){if((isTypedArray(e)||Array.isArray(e))&&"complex64"!==t)throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");return tensor(e,[],t)}function tensor1d(e,t){assertNonNull(e);var n=inferShape(e);if(1!==n.length)throw new Error("tensor1d() requires values to be a flat/TypedArray");return tensor(e,n,t)}function tensor2d(e,t,n){if(assertNonNull(e),null!=t&&2!==t.length)throw new Error("tensor2d() requires shape to have two numbers");var r=inferShape(e);if(2!==r.length&&1!==r.length)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return tensor(e,t=t||r,n)}function tensor3d(e,t,n){if(assertNonNull(e),null!=t&&3!==t.length)throw new Error("tensor3d() requires shape to have three numbers");var r=inferShape(e);if(3!==r.length&&1!==r.length)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return tensor(e,t=t||r,n)}function tensor4d(e,t,n){if(assertNonNull(e),null!=t&&4!==t.length)throw new Error("tensor4d() requires shape to have four numbers");var r=inferShape(e);if(4!==r.length&&1!==r.length)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return tensor(e,t=t||r,n)}function tensor5d(e,t,n){if(assertNonNull(e),null!=t&&5!==t.length)throw new Error("tensor5d() requires shape to have five numbers");var r=inferShape(e);if(5!==r.length&&1!==r.length)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return tensor(e,t=t||r,n)}function tensor6d(e,t,n){if(assertNonNull(e),null!=t&&6!==t.length)throw new Error("tensor6d() requires shape to have six numbers");var r=inferShape(e);if(6!==r.length&&1!==r.length)throw new Error("tensor6d() requires values to be number[][][][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return tensor(e,t=t||r,n)}function ones$1(e,t){if(void 0===t&&(t="float32"),"complex64"===t){var n=ones$1(e,"float32"),r=ones$1(e,"float32");return complex(n,r)}var o=makeOnesTypedArray(sizeFromShape(e),t);return Tensor.make(e,{values:o},t)}function zeros(e,t){if(void 0===t&&(t="float32"),"complex64"===t){var n=zeros(e,"float32"),r=zeros(e,"float32");return complex(n,r)}var o=makeZerosTypedArray(sizeFromShape(e),t);return Tensor.make(e,{values:o},t)}function fill(e,t,n){var r=getArrayFromDType(n=n||inferDtype(t),sizeFromShape(e));return r.fill(t),Tensor.make(e,{values:r},n)}function onesLike_(e){var t=convertToTensor(e,"x","onesLike");return ones$1(t.shape,t.dtype)}function zerosLike_(e){var t=convertToTensor(e,"x","zerosLike");return zeros(t.shape,t.dtype)}function linspace(e,t,n){if(0===n)throw new Error("Cannot request zero samples");var r=(t-e)/(n-1),o=makeZerosTypedArray(n,"float32");o[0]=e;for(var a=1;a<o.length;a++)o[a]=o[a-1]+r;return tensor1d(o,"float32")}function range(e,t,n,r){if(void 0===n&&(n=1),void 0===r&&(r="float32"),0===n)throw new Error("Cannot have a step of zero");if(e===t||e<t&&n<0||t<e&&n>1)return zeros([0],r);var o=makeZerosTypedArray(Math.abs(Math.ceil((t-e)/n)),r);t<e&&1===n&&(n=-1),o[0]=e;for(var a=1;a<o.length;a++)o[a]=o[a-1]+n;return tensor1d(o,r)}var onesLike=op({onesLike_:onesLike_}),zerosLike=op({zerosLike_:zerosLike_}),DataStorage=function(){function e(e){this.dataMover=e,this.data=new WeakMap;}return e.prototype.get=function(e){return this.data.has(e)||this.dataMover.moveData(e),this.data.get(e)},e.prototype.set=function(e,t){this.data.set(e,t);},e.prototype.has=function(e){return this.data.has(e)},e.prototype.delete=function(e){return this.data.delete(e)},e}(),KernelBackend=function(){function e(){}return e.prototype.time=function(e){throw new Error("Not yet implemented.")},e.prototype.read=function(e){throw new Error("Not yet implemented.")},e.prototype.readSync=function(e){throw new Error("Not yet implemented.")},e.prototype.disposeData=function(e){throw new Error("Not yet implemented.")},e.prototype.write=function(e,t){throw new Error("Not yet implemented.")},e.prototype.fromPixels=function(e,t){throw new Error("Not yet implemented.")},e.prototype.register=function(e,t,n){throw new Error("Not yet implemented.")},e.prototype.memory=function(){throw new Error("Not yet implemented.")},e.prototype.floatPrecision=function(){throw new Error("Not yet implemented")},e.prototype.batchMatMul=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.slice=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.stridedSlice=function(e,t,n,r,o,a,i,s,u){throw new Error("Not yet implemented")},e.prototype.reverse=function(e,t){throw new Error("Not yet implemented")},e.prototype.concat=function(e,t){throw new Error("Not yet implemented")},e.prototype.neg=function(e){throw new Error("Not yet implemented")},e.prototype.add=function(e,t){throw new Error("Not yet implemented")},e.prototype.addN=function(e){throw new Error("Not yet implemented")},e.prototype.subtract=function(e,t){throw new Error("Not yet implemented")},e.prototype.multiply=function(e,t){throw new Error("Not yet implemented")},e.prototype.realDivide=function(e,t){throw new Error("Not yet implemented")},e.prototype.floorDiv=function(e,t){throw new Error("Not yet implemented")},e.prototype.sum=function(e,t){throw new Error("Not yet implemented")},e.prototype.prod=function(e,t){throw new Error("Not yet implemented")},e.prototype.unsortedSegmentSum=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.argMin=function(e,t){throw new Error("Not yet implemented")},e.prototype.argMax=function(e,t){throw new Error("Not yet implemented")},e.prototype.equal=function(e,t){throw new Error("Not yet implemented")},e.prototype.notEqual=function(e,t){throw new Error("Not yet implemented")},e.prototype.less=function(e,t){throw new Error("Not yet implemented")},e.prototype.lessEqual=function(e,t){throw new Error("Not yet implemented")},e.prototype.greater=function(e,t){throw new Error("Not yet implemented")},e.prototype.greaterEqual=function(e,t){throw new Error("Not yet implemented")},e.prototype.logicalNot=function(e){throw new Error("Not yet implemented")},e.prototype.logicalAnd=function(e,t){throw new Error("Not yet implemented")},e.prototype.logicalOr=function(e,t){throw new Error("Not yet implemented")},e.prototype.where=function(e){throw new Error("Not yet implemented")},e.prototype.select=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.topk=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.min=function(e,t){throw new Error("Not yet implemented")},e.prototype.minimum=function(e,t){throw new Error("Not yet implemented")},e.prototype.mod=function(e,t){throw new Error("Not yet implemented")},e.prototype.max=function(e,t){throw new Error("Not yet implemented")},e.prototype.maximum=function(e,t){throw new Error("Not yet implemented")},e.prototype.all=function(e,t){throw new Error("Not yet implemented")},e.prototype.any=function(e,t){throw new Error("Not yet implemented")},e.prototype.squaredDifference=function(e,t){throw new Error("Not yet implemented")},e.prototype.ceil=function(e){throw new Error("Not yet implemented")},e.prototype.floor=function(e){throw new Error("Not yet implemented")},e.prototype.round=function(e){throw new Error("Not yet implemented")},e.prototype.sign=function(e){throw new Error("Not yet implemented")},e.prototype.pow=function(e,t){throw new Error("Not yet implemented")},e.prototype.exp=function(e){throw new Error("Not yet implemented")},e.prototype.expm1=function(e){throw new Error("Not yet implemented")},e.prototype.log=function(e){throw new Error("Not yet implemented")},e.prototype.log1p=function(e){throw new Error("Not yet implemented")},e.prototype.sqrt=function(e){throw new Error("Not yet implemented")},e.prototype.rsqrt=function(e){throw new Error("Not yet implemented")},e.prototype.square=function(e){throw new Error("Not yet implemented")},e.prototype.reciprocal=function(e){throw new Error("Not yet implemented")},e.prototype.relu=function(e){throw new Error("Not yet implemented")},e.prototype.elu=function(e){throw new Error("Not yet implemented")},e.prototype.eluDer=function(e,t){throw new Error("Not yet implemented")},e.prototype.selu=function(e){throw new Error("Not yet implemented")},e.prototype.int=function(e){throw new Error("Not yet implemented")},e.prototype.clip=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.abs=function(e){throw new Error("Not yet implemented")},e.prototype.complexAbs=function(e){throw new Error("Not yet implemented")},e.prototype.sigmoid=function(e){throw new Error("Not yet implemented")},e.prototype.softplus=function(e){throw new Error("Not yet implemented")},e.prototype.sin=function(e){throw new Error("Not yet implemented")},e.prototype.cos=function(e){throw new Error("Not yet implemented")},e.prototype.tan=function(e){throw new Error("Not yet implemented")},e.prototype.asin=function(e){throw new Error("Not yet implemented")},e.prototype.acos=function(e){throw new Error("Not yet implemented")},e.prototype.atan=function(e){throw new Error("Not yet implemented")},e.prototype.atan2=function(e,t){throw new Error("Not yet implemented")},e.prototype.sinh=function(e){throw new Error("Not yet implemented")},e.prototype.cosh=function(e){throw new Error("Not yet implemented")},e.prototype.tanh=function(e){throw new Error("Not yet implemented")},e.prototype.asinh=function(e){throw new Error("Not yet implemented")},e.prototype.acosh=function(e){throw new Error("Not yet implemented")},e.prototype.atanh=function(e){throw new Error("Not yet implemented")},e.prototype.erf=function(e){throw new Error("Not yet implemented")},e.prototype.step=function(e,t){throw new Error("Not yet implemented")},e.prototype.conv2d=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.conv2dDerInput=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.conv2dDerFilter=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.depthwiseConv2D=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.depthwiseConv2DDerInput=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.depthwiseConv2DDerFilter=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.conv3d=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.conv3dDerInput=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.conv3dDerFilter=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.maxPool=function(e,t){throw new Error("Not yet implemented")},e.prototype.maxPoolBackprop=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.avgPool=function(e,t){throw new Error("Not yet implemented")},e.prototype.avgPoolBackprop=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.reshape=function(e,t){throw new Error("Not yet implemented")},e.prototype.cast=function(e,t){throw new Error("Not yet implemented")},e.prototype.tile=function(e,t){throw new Error("Not yet implemented")},e.prototype.pad=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.transpose=function(e,t){throw new Error("Not yet implemented")},e.prototype.gather=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.gatherND=function(e,t){throw new Error("Not yet implemented")},e.prototype.scatterND=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.batchToSpaceND=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.spaceToBatchND=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.resizeBilinear=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.resizeBilinearBackprop=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.resizeNearestNeighbor=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.resizeNearestNeighborBackprop=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.batchNormalization=function(e,t,n,r,o,a){throw new Error("Not yet implemented")},e.prototype.localResponseNormalization4D=function(e,t,n,r,o){throw new Error("Not yet implemented")},e.prototype.LRNGrad=function(e,t,n,r,o,a,i){throw new Error("Not yet implemented")},e.prototype.multinomial=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.oneHot=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.cumsum=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.nonMaxSuppression=function(e,t,n,r,o){throw new Error("Not yet implemented")},e.prototype.fft=function(e){throw new Error("Not yet implemented")},e.prototype.ifft=function(e){throw new Error("Not yet implemented")},e.prototype.complex=function(e,t){throw new Error("Not yet implemented")},e.prototype.real=function(e){throw new Error("Not yet implemented")},e.prototype.imag=function(e){throw new Error("Not yet implemented")},e.prototype.cropAndResize=function(e,t,n,r,o,a){throw new Error("Not yet implemented")},e.prototype.depthToSpace=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.split=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.sparseToDense=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.setDataMover=function(e){throw new Error("Not yet implemented")},e.prototype.dispose=function(){throw new Error("Not yet implemented")},e}();function castTensor(e,t,n){if("complex64"===t){if("complex64"===e.dtype)return e.clone();var r=zeros(e.shape),o=e.toFloat(),a=n.complex(o,r);return r.dispose(),o.dispose(),a}if(!hasEncodingLoss(e.dtype,t))return Tensor.make(e.shape,{dataId:e.dataId},t);if("complex64"===e.dtype){var i=n.real(e);a=i.cast(t);return i.dispose(),a}if("int32"===t)return n.int(e);if("bool"===t){var s=scalar(0,e.dtype);a=n.notEqual(e,s);return s.dispose(),a}throw new Error("Error in Cast: unknown dtype argument ("+t+")")}function reshapeTensor(e,t){return Tensor.make(t,{dataId:e.dataId},e.dtype)}function mergeRealAndImagArrays(e,t){if(e.length!==t.length)throw new Error("Cannot merge real and imag arrays of different lengths. real:"+e.length+", imag: "+t.length+".");for(var n=new Float32Array(2*e.length),r=0;r<n.length;r+=2)n[r]=e[r/2],n[r+1]=t[r/2];return n}function splitRealAndImagArrays(e){for(var t=new Float32Array(e.length/2),n=new Float32Array(e.length/2),r=0;r<e.length;r+=2)t[r/2]=e[r],n[r/2]=e[r+1];return {real:t,imag:n}}function complexWithEvenIndex(e){for(var t=Math.ceil(e.length/4),n=new Float32Array(t),r=new Float32Array(t),o=0;o<e.length;o+=4)n[Math.floor(o/4)]=e[o],r[Math.floor(o/4)]=e[o+1];return {real:n,imag:r}}function complexWithOddIndex(e){for(var t=Math.floor(e.length/4),n=new Float32Array(t),r=new Float32Array(t),o=2;o<e.length;o+=4)n[Math.floor(o/4)]=e[o],r[Math.floor(o/4)]=e[o+1];return {real:n,imag:r}}function getComplexWithIndex(e,t){return {real:e[2*t],imag:e[2*t+1]}}function assignToTypedArray(e,t,n,r){e[2*r]=t,e[2*r+1]=n;}function exponents(e,t){for(var n=new Float32Array(e/2),r=new Float32Array(e/2),o=0;o<Math.ceil(e/2);o++){var a=(t?2:-2)*Math.PI*(o/e);n[o]=Math.cos(a),r[o]=Math.sin(a);}return {real:n,imag:r}}function exponent(e,t,n){var r=(n?2:-2)*Math.PI*(e/t);return {real:Math.cos(r),imag:Math.sin(r)}}function nonMaxSuppressionImpl(e,t,n,r,o){for(var a=Array.from(t).map(function(e,t){return {score:e,boxIndex:t}}).filter(function(e){return e.score>o}).sort(function(e,t){return t.score-e.score}),i=[],s=0;s<a.length;s++){var u=a[s],l=u.score,c=u.boxIndex;if(l<o)break;for(var p=!1,d=i.length-1;d>=0;--d){if(intersectionOverUnion(e,c,i[d])>=r){p=!0;break}}if(!p&&(i.push(c),i.length>=n))break}return tensor1d(i,"int32")}function intersectionOverUnion(e,t,n){var r=e.subarray(4*t,4*t+4),o=e.subarray(4*n,4*n+4),a=Math.min(r[0],r[2]),i=Math.min(r[1],r[3]),s=Math.max(r[0],r[2]),u=Math.max(r[1],r[3]),l=Math.min(o[0],o[2]),c=Math.min(o[1],o[3]),p=Math.max(o[0],o[2]),d=Math.max(o[1],o[3]),h=(s-a)*(u-i),f=(p-l)*(d-c);if(h<=0||f<=0)return 0;var m=Math.max(a,l),g=Math.max(i,c),v=Math.min(s,p),y=Math.min(u,d),x=Math.max(v-m,0)*Math.max(y-g,0);return x/(h+f-x)}function split(e,t,n){var r=Array(e.rank).fill(0),o=e.shape.slice();return t.map(function(t){o[n]=t;var a=e.slice(r,o);return r[n]+=t,a})}function topkImpl(e,t,n,r,o){for(var a=t[t.length-1],i=[e.length/a,a],s=i[0],u=i[1],l=getTypedArrayFromDType(n,s*r),c=getTypedArrayFromDType("int32",s*r),p=0;p<s;p++){for(var d=p*u,h=e.subarray(d,d+u),f=[],m=0;m<h.length;m++)f.push({value:h[m],index:m});f.sort(function(e,t){return t.value-e.value});var g=p*r,v=l.subarray(g,g+r),y=c.subarray(g,g+r);for(m=0;m<r;m++)v[m]=f[m].value,y[m]=f[m].index;}var x=t.slice();return x[x.length-1]=r,[tensor(l,x,n),tensor(c,x,"int32")]}var ArgMinMaxProgram=function(){return function(e,t,n){this.variableNames=["A"];var r=e.windowSize,o=e.batchSize,a=e.inSize,i=Math.ceil(a/r);n||this.variableNames.push("bestIndicesA"),this.outputShape=[o,i];var s="max"===t?">":"<",u=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode="\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * "+r+";\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < "+r+"; i++) {\n          int inIdx = "+u+";\n          float candidate = getA(batch, inIdx);\n          if (candidate "+s+" bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    ";}}(),AvgPool2DBackpropProgram=function(){return function(e){this.variableNames=["dy"],this.outputShape=e.inShape;var t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,o=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,s=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=s-1-e.padInfo.top,c=u-1-e.padInfo.left,p=1/(t*n);this.userCode="\n      const ivec2 pads = ivec2("+l+", "+c+");\n      const float avgMultiplier = float("+p+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+s+";\n            wR += "+a+") {\n          float dyR = float(dyRCorner + wR) / "+r+".0;\n\n          if (dyR < 0.0 || dyR >= "+e.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < "+u+";\n            wC+= "+i+") {\n            float dyC = float(dyCCorner + wC) / "+o+".0;\n\n            if (dyC < 0.0 || dyC >= "+e.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";}}();function getBroadcastDims(e,t){for(var n=e.length,r=[],o=0;o<n;o++){var a=n-1-o,i=e[a]||1;(t[t.length-1-o]||1)>1&&1===i&&r.unshift(a);}return r}function getReductionAxes(e,t){for(var n=[],r=0;r<t.length;r++){var o=e[e.length-r-1],a=t.length-r-1,i=t[a];(null==o||1===o&&i>1)&&n.unshift(a);}return n}function broadcastDimsAreOuter(e){for(var t=0;t<e.length;t++)if(e[t]!==t)return !1;return !0}function assertAndGetBroadcastShape(e,t){for(var n=[],r=Math.max(e.length,t.length),o=0;o<r;o++){var a=e[e.length-o-1];null==a&&(a=1);var i=t[t.length-o-1];if(null==i&&(i=1),1===a)n.unshift(i);else if(1===i)n.unshift(a);else{if(a!==i)throw Error("Operands could not be broadcast together with shapes "+e+" and "+t+".");n.unshift(a);}}return n}var BatchNormProgram=function(){return function(e,t,n,r,o,a){this.outputShape=[],this.supportsBroadcasting=!0,this.variableNames=["x","mean","variance"],assertAndGetBroadcastShape(e,t),assertAndGetBroadcastShape(e,n);var i="0.0";null!=r&&(assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");var s="1.0";null!=o&&(assertAndGetBroadcastShape(e,o),this.variableNames.push("scale"),s="getScaleAtOutCoords()"),this.outputShape=e,this.userCode="\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = "+i+";\n        float scale = "+s+";\n        float inv = scale * inversesqrt(variance + float("+a+"));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    ";}}(),BatchNormPackedProgram=function(){return function(e,t,n,r,o,a){this.supportsBroadcasting=!0,this.usesPackedTextures=!0,this.variableNames=["x","mean","variance"],assertAndGetBroadcastShape(e,t),assertAndGetBroadcastShape(e,n);var i="vec4(0.0)";null!=r&&(assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");var s="vec4(1.0)";null!=o&&(assertAndGetBroadcastShape(e,o),this.variableNames.push("scale"),s="getScaleAtOutCoords()"),this.outputShape=e,this.userCode="\n      void main() {\n        vec4 offset = "+i+";\n        vec4 scale = "+s+";\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4("+a+"));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    ";}}(),COMPLEX_MULTIPLY={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},BinaryOpComplexProgram=function(){return function(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.supportsBroadcasting=!0,this.outputShape=assertAndGetBroadcastShape(t,n),this.userCode="\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        "+e+"\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    ";}}(),CHECK_NAN_SNIPPET="\n  if (isNaN(a)) return a;\n  if (isNaN(b)) return b;\n",ADD="return a + b;",SUB="return a - b;",MUL="return a * b;",DIV="if (a == b) return 1.0;\n  return a / b;",INT_DIV="\n  float resultSign = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  int result = ia / ib;\n  int amodb = ia - ib * result;\n\n  if (resultSign < 0.0 && amodb != 0) {\n    result -= 1;\n  }\n  return float(result);\n",POW="\nif(a < 0.0 && floor(b) < b){\n  return NAN;\n}\nreturn (round(mod(b, 2.0)) == 0 || round(mod(b, 2.0)) == 2) ?\n    pow(abs(a), b) : sign(a) * pow(abs(a), b);\n",SQUARED_DIFFERENCE="return (a - b) * (a - b);",EQUAL="return float(a == b);",NOT_EQUAL="return float(a != b);",LESS="return float(a < b);",LESS_EQUAL="return float(a <= b);",GREATER="return float(a > b);",GREATER_EQUAL="return float(a >= b);",LOGICAL_AND="return float(a >= 1.0 && b >= 1.0);",LOGICAL_OR="return float(a >= 1.0 || b >= 1.0);",MAX=CHECK_NAN_SNIPPET+"\n  return max(a, b);\n",MIN=CHECK_NAN_SNIPPET+"\n  return min(a, b);\n",MOD="if (b == 0.0) return NAN;\n  return mod(a, b);",ATAN2=CHECK_NAN_SNIPPET+"\n  return atan(a, b);\n",ELU_DER="return (b >= 1.0) ? a : a * (b + 1.0);",BinaryOpProgram=function(){function e(e,t,n){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.outputShape=assertAndGetBroadcastShape(t,n),this.userCode="\n      uniform float NAN;\n      float binaryOperation(float a, float b) {\n        "+e+"\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    ";}return e.prototype.getCustomSetupFunc=function(){var e=this;return function(t,n){null==e.startLoc&&(e.startLoc=t.getUniformLocationNoThrow(n,"NAN"),null==e.startLoc)||t.gl.uniform1f(e.startLoc,NaN);}},e}(),ClipProgram=function(){return function(e,t,n){this.variableNames=["A"],this.outputShape=e,this.userCode="\n      void main() {\n        float value = getAAtOutCoords();\n        if (isNaN(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, float("+t+"), float("+n+")));\n      }\n    ";}}(),ClipPackedProgram=function(){return function(e,t,n){this.variableNames=["A"],this.usesPackedTextures=!0,this.outputShape=e,this.userCode="\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (hasNaN(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4("+t+"), vec4("+n+")));\n      }\n    ";}}(),ComplexAbsProgram=function(){return function(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode="\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    ";}}(),ConcatProgram=function(){return function(e,t){this.variableNames=["A","B"],this.outputShape=[],this.outputShape=computeOutShape([e,t],1),this.userCode="\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        float value = 0.0;\n        if (yC < "+e[1]+") {\n          value = getA(yR, yC);\n        } else {\n          yC -= "+e[1]+";\n          value = getB(yR, yC);\n        }\n\n        setOutput(value);\n      }\n    ";}}(),Conv2DDerFilterProgram=function(){return function(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;var t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,o=e.padInfo.left;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < "+e.batchSize+"; b++) {\n          for (int yR = 0; yR < "+e.outHeight+"; yR++) {\n            int xR = wR + yR * "+t+" - "+r+";\n\n            if (xR < 0 || xR >= "+e.inHeight+") {\n              continue;\n            }\n\n            for (int yC = 0; yC < "+e.outWidth+"; yC++) {\n              int xC = wC + yC * "+n+" - "+o+";\n\n              if (xC < 0 || xC >= "+e.inWidth+") {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";}}(),Conv2DDerInputProgram=function(){return function(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;var t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,o=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left;this.userCode="\n      const ivec2 pads = ivec2("+a+", "+i+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+t+"; wR++) {\n          float dyR = float(dyRCorner + wR) / "+r+".0;\n\n          if (dyR < 0.0 || dyR >= "+e.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = "+t+" - 1 - wR;\n\n          for (int wC = 0; wC < "+n+"; wC++) {\n            float dyC = float(dyCCorner + wC) / "+o+".0;\n\n            if (dyC < 0.0 || dyC >= "+e.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = "+n+" - 1 - wC;\n\n            for (int d2 = 0; d2 < "+e.outChannels+"; d2++) {\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, d2);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";}}(),Conv3DDerFilterProgram=function(){return function(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;var t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,o=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode="\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < "+e.batchSize+"; b++) {\n          for (int yF = 0; yF < "+e.outDepth+"; yF++) {\n            int xF = wF + yF * "+t+" - "+o+";\n\n            if (xF < 0 || xF >= "+e.inDepth+") {\n              continue;\n            }\n\n            for (int yR = 0; yR < "+e.outHeight+"; yR++) {\n              int xR = wR + yR * "+n+" - "+a+";\n\n              if (xR < 0 || xR >= "+e.inHeight+") {\n                continue;\n              }\n\n              for (int yC = 0; yC < "+e.outWidth+"; yC++) {\n                int xC = wC + yC * "+r+" - "+i+";\n\n                if (xC < 0 || xC >= "+e.inWidth+") {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";}}(),Conv3DDerInputProgram=function(){return function(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;var t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,o=e.strideDepth,a=e.strideHeight,i=e.strideWidth,s=t-1-e.padInfo.front,u=n-1-e.padInfo.top,l=r-1-e.padInfo.left;this.userCode="\n      const ivec3 pads = ivec3("+s+", "+u+", "+l+");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < "+t+"; wF++) {\n          float dyF = float(dyFCorner + wF) / "+o+".0;\n\n          if (dyF < 0.0 || dyF >= "+e.outDepth+".0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = "+t+" - 1 - wF;\n\n          for (int wR = 0; wR < "+n+"; wR++) {\n            float dyR = float(dyRCorner + wR) / "+a+".0;\n\n            if (dyR < 0.0 || dyR >= "+e.outHeight+".0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = "+n+" - 1 - wR;\n\n            for (int wC = 0; wC < "+r+"; wC++) {\n              float dyC = float(dyCCorner + wC) / "+i+".0;\n\n              if (dyC < 0.0 || dyC >= "+e.outWidth+".0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = "+r+" - 1 - wC;\n\n              for (int d2 = 0; d2 < "+e.outChannels+"; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";}}(),DepthwiseConv2DDerFilterProgram=function(){return function(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;var t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,o=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * "+a+" + dm;\n\n        float dotProd = 0.0;\n\n        // TODO: Vec4 over the batch size\n        for (int b = 0; b < "+e.batchSize+"; b++) {\n          for (int yR = 0; yR < "+e.outHeight+"; yR++) {\n            int xR = wR + yR * "+t+" - "+r+";\n\n            if (xR < 0 || xR >= "+e.inHeight+") {\n              continue;\n            }\n\n            for (int yC = 0; yC < "+e.outWidth+"; yC++) {\n              int xC = wC + yC * "+n+" - "+o+";\n\n              if (xC < 0 || xC >= "+e.inWidth+") {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";}}(),DepthwiseConv2DDerInputProgram=function(){return function(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;var t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,o=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode="\n      const ivec2 pads = ivec2("+a+", "+i+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < "+t+"; wR++) {\n          float dyR = float(dyRCorner + wR) / "+r+".0;\n\n          if (dyR < 0.0 || dyR >= "+e.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = "+t+" - 1 - wR;\n\n          for (int wC = 0; wC < "+n+"; wC++) {\n            float dyC = float(dyCCorner + wC) / "+o+".0;\n\n            if (dyC < 0.0 || dyC >= "+e.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = "+n+" - 1 - wC;\n\n            // TODO: Vec4 over the channelMul\n            for (int dm = 0; dm < "+s+"; dm++) {\n              int d2 = d1 * "+s+" + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";}}(),Conv2DProgram=function(){return function(e){this.variableNames=["x","W"],this.outputShape=e.outShape;var t=e.padInfo.top,n=e.padInfo.left,r=e.strideHeight,o=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,s=e.filterHeight,u=e.filterWidth,l=4*Math.floor(e.inChannels/4),c=e.inChannels%4;this.userCode="\n      const ivec2 strides = ivec2("+r+", "+o+");\n      const ivec2 pads = ivec2("+t+", "+n+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+s+"; wR++) {\n          int xR = xRCorner + wR * "+a+";\n\n          if (xR < 0 || xR >= "+e.inHeight+") {\n            continue;\n          }\n\n          for (int wC = 0; wC < "+u+"; wC++) {\n            int xC = xCCorner + wC * "+i+";\n\n            if (xC < 0 || xC >= "+e.inWidth+") {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < "+l+"; d1 += 4) {\n              vec4 xValues = vec4(\n                getX(batch, xR, xC, d1),\n                getX(batch, xR, xC, d1 + 1),\n                getX(batch, xR, xC, d1 + 2),\n                getX(batch, xR, xC, d1 + 3)\n              );\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              dotProd += dot(xValues, wValues);\n            }\n\n            if ("+(1===c)+") {\n              dotProd +=\n                getX(batch, xR, xC, "+l+") *\n                getW(wR, wC, "+l+", d2);\n            } else if ("+(2===c)+") {\n              vec2 xValues = vec2(\n                getX(batch, xR, xC, "+l+"),\n                getX(batch, xR, xC, "+l+" + 1)\n              );\n              vec2 wValues = vec2(\n                getW(wR, wC, "+l+", d2),\n                getW(wR, wC, "+l+" + 1, d2)\n              );\n              dotProd += dot(xValues, wValues);\n            } else if ("+(3===c)+") {\n              vec3 xValues = vec3(\n                getX(batch, xR, xC, "+l+"),\n                getX(batch, xR, xC, "+l+" + 1),\n                getX(batch, xR, xC, "+l+" + 2)\n              );\n              vec3 wValues = vec3(\n                getW(wR, wC, "+l+", d2),\n                getW(wR, wC, "+l+" + 1, d2),\n                getW(wR, wC, "+l+" + 2, d2)\n              );\n              dotProd += dot(xValues, wValues);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";}}(),Conv3DProgram=function(){return function(e){this.variableNames=["x","W"],this.outputShape=e.outShape;var t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,o=e.strideDepth,a=e.strideHeight,i=e.strideWidth,s=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=4*Math.floor(e.inChannels/4),f=e.inChannels%4;this.userCode="\n      const ivec3 strides = ivec3("+o+", "+a+", "+i+");\n      const ivec3 pads = ivec3("+t+", "+n+", "+r+");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < "+c+"; wF++) {\n          int xF = xFCorner + wF * "+s+";\n\n          if (xF < 0 || xF >= "+e.inDepth+") {\n            continue;\n          }\n\n          for (int wR = 0; wR < "+p+"; wR++) {\n            int xR = xRCorner + wR * "+u+";\n\n            if (xR < 0 || xR >= "+e.inHeight+") {\n              continue;\n            }\n\n            for (int wC = 0; wC < "+d+"; wC++) {\n              int xC = xCCorner + wC * "+l+";\n\n              if (xC < 0 || xC >= "+e.inWidth+") {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < "+h+"; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if ("+(1===f)+") {\n                dotProd +=\n                  getX(batch, xF, xR, xC, "+h+") *\n                  getW(wF, wR, wC, "+h+", d2);\n              } else if ("+(2===f)+") {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, "+h+"),\n                  getX(batch, xF, xR, xC, "+h+" + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, "+h+", d2),\n                  getW(wF, wR, wC, "+h+" + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if ("+(3===f)+") {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, "+h+"),\n                  getX(batch, xF, xR, xC, "+h+" + 1),\n                  getX(batch, xF, xR, xC, "+h+" + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, "+h+", d2),\n                  getW(wF, wR, wC, "+h+" + 1, d2),\n                  getW(wF, wR, wC, "+h+" + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";}}(),DepthwiseConv2DProgram=function(){return function(e){this.variableNames=["x","W"],this.outputShape=e.outShape;var t=e.inHeight,n=e.inWidth,r=e.padInfo.top,o=e.padInfo.left,a=e.strideHeight,i=e.strideWidth,s=e.dilationHeight,u=e.dilationWidth,l=e.filterHeight,c=e.filterWidth,p=e.outChannels/e.inChannels;this.userCode="\n      const ivec2 strides = ivec2("+a+", "+i+");\n      const ivec2 pads = ivec2("+r+", "+o+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / "+p+";\n        int q = d2 - d1 * "+p+";\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TODO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < "+l+"; wR++) {\n          int xR = xRCorner + wR * "+s+";\n\n          if (xR < 0 || xR >= "+t+") {\n            continue;\n          }\n\n          for (int wC = 0; wC < "+c+"; wC++) {\n            int xC = xCCorner + wC * "+u+";\n\n            if (xC < 0 || xC >= "+n+") {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";}}(),DepthwiseConvPacked2DProgram=function(){return function(e){this.variableNames=["x","W"],this.usesPackedTextures=!0,this.outputShape=e.outShape;for(var t=e.inHeight,n=e.inWidth,r=e.padInfo.top,o=e.padInfo.left,a=e.strideHeight,i=e.strideWidth,s=e.filterHeight,u=e.filterWidth,l=Math.ceil((u+1)/2),c="int xR; int xC;",p=0;p<s;p++){for(var d=-o;d<2*l;d++)c+="vec4 "+xTexelName(p,d)+" = vec4(0.);";for(d=0;d<u;d++)c+="\n          vec4 wR"+p+"C"+d+" = vec4(0.);\n          vec4 xR"+p+"C"+d+" = vec4(0.);";}for(p=0;p<s;p++)for(d=0;d<l;d++){var h=2*d,f=2*d+o;c+="\n          xR = xRCorner + "+p+";\n          xC = xCCorner + "+f+";\n\n          if(xR >= 0 && xR < "+t+" && xC >= 0 && xC < "+n+") {\n            "+xTexelName(p,f)+" = getX(batch, xR, xC, d1);\n          }",0===o?h<u&&d===l-1&&(i>1&&(c+="\n                vec4 "+xTexelName(p,f+2)+" = vec4(0.);\n\n                if(xR >= 0 && xR < "+t+" && xC + 2 < "+n+") {\n                  "+xTexelName(p,f+2)+" = getX(batch, xR, xC + 2, d1);\n                }"),c+="\n              xR"+p+"C"+f+" = "+constructTexel(p,f,i,o)+";\n            "):0===d&&(c+="\n            if(xR >= 0 && xR < "+t+" && xC - 2 >= 0) {\n              "+xTexelName(p,f-2)+" = getX(batch, xR, xC - 2, d1);\n            }"),h>0&&(c+="xR"+p+"C"+(f-2)+" =\n            "+constructTexel(p,f-2,i,o)+";"),f-1>=0&&f-1<u&&(c+="xR"+p+"C"+(f-1)+" =\n              "+constructTexel(p,f-1,i,o)+";"),h<u&&(c+="\n            vec4 wTexel"+p+"C"+h+" = getW("+p+", "+h+", d1, q);\n            wR"+p+"C"+h+" = vec4(wTexel"+p+"C"+h+".xz, wTexel"+p+"C"+h+".xz);\n          ",h+1<u&&(c+="\n              vec4 wTexelR"+p+"C"+(h+1)+" = getW("+p+", "+(h+1)+", d1, q);\n              wR"+p+"C"+(h+1)+" =\n                vec4(wTexelR"+p+"C"+(h+1)+".xz, wTexelR"+p+"C"+(h+1)+".xz);"));}for(p=0;p<s;p++)for(d=0;d<u;d++)c+="result += xR"+p+"C"+d+" * wR"+p+"C"+d+";";this.userCode="\n      const ivec2 strides = ivec2("+a+", "+i+");\n      const ivec2 pads = ivec2("+r+", "+o+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2;\n        int q = 0;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        vec4 result = vec4(0.);\n\n        "+c+"\n\n        setOutput(result);\n      }\n    ";}}();function xTexelName(e,t){return "xTexelR"+e+"C"+(t<0?"minus"+Math.abs(t).toString():t)}function constructTexel(e,t,n,r){return 1===n?r%2==t%2?xTexelName(e,t):"vec4("+xTexelName(e,t-1)+".zw, "+xTexelName(e,t+1)+".xy)":r%2==t%2?"vec4("+xTexelName(e,t)+".xy, "+xTexelName(e,t+2)+".xy)":"vec4("+xTexelName(e,t-1)+".zw, "+xTexelName(e,t+1)+".zw)"}var CropAndResizeProgram=function(){return function(e,t,n,r,o){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];var a=e[0],i=e[1],s=e[2],u=e[3],l=t[0],c=n[0],p=n[1];this.outputShape=[l,c,p,u];var d="bilinear"===r?1:0,h=[i-1+".0",s-1+".0"],f=h[0],m=h[1],g=c>1?[""+(i-1)/(c-1),"(y2-y1) * height_ratio","y1*"+f+" + float(y)*(height_scale)"]:["0.0","0.0","0.5 * (y1+y2) * "+f],v=g[0],y=g[1],x=g[2],T=p>1?[""+(s-1)/(p-1),"(x2-x1) * width_ratio","x1*"+m+" + float(x)*(width_scale)"]:["0.0","0.0","0.5 * (x1+x2) * "+m],w=T[0],E=T[1],S=T[2];this.userCode="\n      const float height_ratio = float("+v+");\n      const float width_ratio = float("+w+");\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= "+a+") {\n          return;\n        }\n\n        float height_scale = "+y+";\n        float width_scale = "+E+";\n\n        float in_y = "+x+";\n        if( in_y < 0.0 || in_y > "+f+" ) {\n          setOutput(float("+o+"));\n          return;\n        }\n        float in_x = "+S+";\n        if( in_x < 0.0 || in_x > "+m+" ) {\n          setOutput(float("+o+"));\n          return;\n        }\n\n        vec2 sourceFracIndexRC = vec2(in_y,in_x);\n        if("+d+" == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n          ivec2 sourceCeilRC = ivec2(ceil(sourceFracIndexRC));\n\n          float topLeft = getImage(b, sourceFloorRC.x, sourceFloorRC.y, d);\n          float bottomLeft = getImage(b, sourceCeilRC.x, sourceFloorRC.y, d);\n          float topRight = getImage(b, sourceFloorRC.x, sourceCeilRC.y, d);\n          float bottomRight = getImage(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n          vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n          float top = topLeft + (topRight - topLeft) * fracRC.y;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n          float newValue = top + (bottom - top) * fracRC.x;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestRC = ivec2(floor(\n            sourceFracIndexRC + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestRC.x, sourceNearestRC.y, d);\n          setOutput(newValue);\n        }\n      }\n    ";}}();function getLogicalCoordinatesFromFlatIndex(e,t,n){void 0===n&&(n="index");var r=computeStrides(t);return r.map(function(t,o){return "int "+e[o]+" = "+n+" / "+t+"; "+(o===r.length-1?"int "+e[o+1]+" = "+n+" - "+e[o]+" * "+t:"index -= "+e[o]+" * "+t)+";"}).join("")}function buildVec(e){return 1===e.length?""+e[0]:"vec"+e.length+"("+e.join(",")+")"}function dotify(e,t){if(e.length!==t.length)throw new Error("Vectors to be dotted must be of the same length -got "+e.length+" and "+t.length);for(var n=[],r=Math.floor(e.length/4),o=e.length%4,a=0;a<r;a++){var i=e.slice(4*a,4*a+4),s=t.slice(4*a,4*a+4);n.push(buildVec(i)+", "+buildVec(s));}if(0!==o){i=e.slice(4*r),s=t.slice(4*r);1===i.length&&(i=i.map(function(e){return "float("+e+")"}),s=s.map(function(e){return "float("+e+")"})),n.push(buildVec(i)+", "+buildVec(s));}return n.map(function(e,t){return "dot("+e+")"}).join("+")}function makeShader(e,t,n,r,o){var a=e.map(function(e){var t=sizeFromShape(e.shapeInfo.logicalShape);return e.shapeInfo.isUniform?"uniform float "+e.name+(t>1?"["+t+"]":"")+";":"uniform sampler2D "+e.name+";"});a=a.join("\n");var i,s,u=e.map(function(e){return getInputSamplingSnippet(e,t,r,o)}).join("\n"),l=t.texShape,c=SHADER_PREFIX;return t.isPacked?(i=getPackedOutputSamplingSnippet(t.logicalShape,l),s=FLOAT_TEXTURE_SET_RGBA_SNIPPET):(i=getOutputSamplingSnippet(t.logicalShape,l),s=FLOAT_TEXTURE_SET_R_SNIPPET),o&&(c+=SHADER_PACKED_PREFIX),[c,FLOAT_TEXTURE_SAMPLE_SNIPPET,s,a,i,u,n].join("\n")}function getSamplerFromInInfo(e){var t=e.shapeInfo.logicalShape;switch(t.length){case 0:return getSamplerScalar(e);case 1:return getSampler1D(e);case 2:return getSampler2D(e);case 3:return getSampler3D(e);case 4:return getSampler4D(e);case 5:return getSampler5D(e);case 6:return getSampler6D(e);default:throw new Error(t.length+"-D input sampling is not yet supported")}}function getPackedSamplerFromInInfo(e){var t=e.shapeInfo.logicalShape;switch(t.length){case 0:return getPackedSamplerScalar(e);case 1:return getPackedSampler1D(e);case 2:return getPackedSampler2D(e);case 3:return getPackedSampler3D(e);case 4:return getPackedSampler4D(e);default:throw new Error("Packed "+t.length+"-D input sampling is not yet supported")}}function getInputSamplingSnippet(e,t,n,r){void 0===r&&(r=!1);var o=getSamplerFlat(e);return o+=r?getPackedSamplerFromInInfo(e):getSamplerFromInInfo(e),(n||arraysEqual(e.shapeInfo.logicalShape,t.logicalShape))&&(o+=r?getPackedSamplerAtOutputCoords(e,t,n):getSamplerAtOutputCoords(e,t,n)),o}function getPackedOutputSamplingSnippet(e,t){switch(e.length){case 0:return getOutputScalarCoords();case 1:return getOutputPacked1DCoords(e,t);case 2:return getOutputPacked2DCoords(e,t);case 3:return getOutputPacked3DCoords(e,t);case 4:return getOutputPacked4DCoords(e,t);default:throw new Error(e.length+"-D packed output coordinate fetching is not yet supported")}}function getOutputSamplingSnippet(e,t){switch(e.length){case 0:return getOutputScalarCoords();case 1:return getOutput1DCoords(e,t);case 2:return getOutput2DCoords(e,t);case 3:return getOutput3DCoords(e,t);case 4:return getOutput4DCoords(e,t);case 5:return getOutput5DCoords(e,t);case 6:return getOutput6DCoords(e,t);default:throw new Error(e.length+"-D output sampling is not yet supported")}}var SAMPLE_1D_SNIPPET="\nvec2 UVfrom1D(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",SAMPLE_2D_SNIPPET="\nvec2 UVfrom2D(int texNumR, int texNumC, int numC, int row, int col) {\n  int index = row * numC + col;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",SAMPLE_3D_SNIPPET="\nvec2 UVfrom3D(int texNumR, int texNumC, int stride0,\n    int stride1, int row, int col, int depth) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",SAMPLE_4D_SNIPPET="\nvec2 UVfrom4D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int row, int col, int depth,\n    int depth2) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth * stride2 + depth2;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom4D(int texNumR, int texNumC, int texelsInBatch2,\n    int texelsInBatch, int texelsInLogicalRow, int b2, int b,\n    int row, int col) {\n  int index = b2 * texelsInBatch2 + b * texelsInBatch +\n    (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",SAMPLE_5D_SNIPPET="\nvec2 UVfrom5D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int stride3, int row, int col, int depth,\n    int depth2, int depth3) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 +\n              depth * stride2 + depth2 * stride3 + depth3;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",SAMPLE_6D_SNIPPET="\nvec2 UVfrom6D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int stride3, int stride4,\n    int row, int col, int depth, int depth2, int depth3, int depth4) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth * stride2 + depth2 *\n    stride3 + depth3 * stride4 + depth4;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",FLOAT_TEXTURE_SAMPLE_SNIPPET="\n  float sampleTexture(sampler2D textureSampler, vec2 uv) {\n    return texture2D(textureSampler, uv).r;\n  }\n",FLOAT_TEXTURE_SET_R_SNIPPET="\n  void setOutput(float val) {\n    gl_FragColor = vec4(val, 0, 0, 0);\n  }\n",FLOAT_TEXTURE_SET_RGBA_SNIPPET="\n  void setOutput(vec4 val) {\n    gl_FragColor = val;\n  }\n",NAN_CHECKS="",SHADER_PREFIX="\n  precision highp float;\n  precision highp int;\n  varying vec2 resultUV;\n  const vec2 halfCR = vec2(0.5, 0.5);\n\n  struct ivec5\n  {\n    int x;\n    int y;\n    int z;\n    int w;\n    int u;\n  };\n\n  struct ivec6\n  {\n    int x;\n    int y;\n    int z;\n    int w;\n    int u;\n    int v;\n  };\n\n  "+(NAN_CHECKS=ENV.get("PROD")?"\n    bool isNaN(float val) {\n      return false;\n    }\n\n    bool hasNaN(vec4 values) {\n      return false;\n    }\n  ":"\n    bool isNaN(float val) {\n      return (val < 1.0 || 0.0 < val || val == 0.0) ? false : true;\n    }\n\n    bool hasNaN(vec4 values) {\n      return any(bvec4(\n        isNaN(values.x),\n        isNaN(values.y),\n        isNaN(values.z),\n        isNaN(values.w)\n      ));\n    }\n  ")+"\n\n  float getNaN(vec4 values) {\n    return dot(vec4(1), values);\n  }\n\n  int round(float value) {\n    return int(floor(value + 0.5));\n  }\n\n  int imod(int x, int y) {\n    return x - y * (x / y);\n  }\n\n  //Based on the work of Dave Hoskins\n  //https://www.shadertoy.com/view/4djSRW\n  #define HASHSCALE1 443.8975\n  float random(float seed){\n    vec2 p = resultUV * seed;\n    vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n    p3 += dot(p3, p3.yzx + 19.19);\n    return fract((p3.x + p3.y) * p3.z);\n  }\n\n  "+SAMPLE_1D_SNIPPET+"\n  "+SAMPLE_2D_SNIPPET+"\n  "+SAMPLE_3D_SNIPPET+"\n  "+SAMPLE_4D_SNIPPET+"\n  "+SAMPLE_5D_SNIPPET+"\n  "+SAMPLE_6D_SNIPPET+"\n",SHADER_PACKED_PREFIX="\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n";function getOutputScalarCoords(){return "\n    int getOutputCoords() {\n      return 0;\n    }\n  "}function getOutputPacked1DCoords(e,t){var n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return 1===t[0]?"\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * "+n[1]+".0);\n      }\n    ":1===t[1]?"\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * "+n[0]+".0);\n      }\n    ":"\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+n[0]+", "+n[1]+"));\n      return resTexRC.x * "+n[1]+" + resTexRC.y;\n    }\n  "}function getOutput1DCoords(e,t){return 1===t[0]?"\n      int getOutputCoords() {\n        return int(resultUV.x * "+t[1]+".0);\n      }\n    ":1===t[1]?"\n      int getOutputCoords() {\n        return int(resultUV.y * "+t[0]+".0);\n      }\n    ":"\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+t[0]+", "+t[1]+"));\n      return resTexRC.x * "+t[1]+" + resTexRC.y;\n    }\n  "}function getOutputPacked3DCoords(e,t){var n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),o=r*Math.ceil(e[1]/2);return "\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+n[0]+", "+n[1]+"));\n      int index = resTexRC.x * "+n[1]+" + resTexRC.y;\n\n      int b = index / "+o+";\n      index -= b * "+o+";\n\n      int r = 2 * (index / "+r+");\n      int c = imod(index, "+r+") * 2;\n\n      return ivec3(b, r, c);\n    }\n  "}function getOutput3DCoords(e,t){var n=getLogicalCoordinatesFromFlatIndex(["r","c","d"],e);return "\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+t[0]+", "+t[1]+"));\n      int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n      "+n+"\n      return ivec3(r, c, d);\n    }\n  "}function getOutputPacked4DCoords(e,t){var n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[3]/2),o=r*Math.ceil(e[2]/2),a=o*e[1];return "\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+n[0]+", "+n[1]+"));\n      int index = resTexRC.x * "+n[1]+" + resTexRC.y;\n\n      int b2 = index / "+a+";\n      index -= b2 * "+a+";\n\n      int b = index / "+o+";\n      index -= b * "+o+";\n\n      int r = 2 * (index / "+r+");\n      int c = imod(index, "+r+") * 2;\n\n      return ivec4(b2, b, r, c);\n    }\n  "}function getOutput4DCoords(e,t){var n=getLogicalCoordinatesFromFlatIndex(["r","c","d","d2"],e);return "\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2("+t[0]+", "+t[1]+"));\n      int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n      "+n+"\n      return ivec4(r, c, d, d2);\n    }\n  "}function getOutput5DCoords(e,t){var n=getLogicalCoordinatesFromFlatIndex(["r","c","d","d2","d3"],e);return "\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2("+t[0]+",\n                             "+t[1]+"));\n\n      int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n\n      "+n+"\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  "}function getOutput6DCoords(e,t){var n=getLogicalCoordinatesFromFlatIndex(["r","c","d","d2","d3","d4"],e);return "\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2("+t[0]+", "+t[1]+"));\n      int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n\n      "+n+"\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  "}function getOutputPacked2DCoords(e,t){var n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(arraysEqual(e,t))return "\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2("+n[0]+", "+n[1]+"));\n      }\n    ";var r=Math.ceil(e[1]/2);return "\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+n[0]+", "+n[1]+"));\n\n      int index = resTexRC.x * "+n[1]+" + resTexRC.y;\n      int r = 2 * (index / "+r+");\n      int c = imod(index, "+r+") * 2;\n\n      return ivec2(r, c);\n    }\n  "}function getOutput2DCoords(e,t){return arraysEqual(e,t)?"\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2("+t[0]+", "+t[1]+"));\n      }\n    ":1===e[1]?"\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2("+t[0]+", "+t[1]+"));\n        int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    ":1===e[0]?"\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2("+t[0]+", "+t[1]+"));\n        int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n        return ivec2(0, index);\n      }\n    ":"\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+t[0]+", "+t[1]+"));\n      int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n      int r = index / "+e[1]+";\n      int c = index - r * "+e[1]+";\n      return ivec2(r, c);\n    }\n  "}function getPackedSamplerScalar(e){var t=e.name;return "\n    vec4 "+("get"+t.charAt(0).toUpperCase()+t.slice(1))+"() {\n      return texture2D("+t+", halfCR);\n    }\n  "}function getSamplerScalar(e){var t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);return e.shapeInfo.isUniform?"float "+n+"() {return "+t+";}":"\n    float "+n+"() {\n      return sampleTexture("+t+", halfCR);\n    }\n  "}function getPackedSampler1D(e){var t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=e.shapeInfo.texShape,o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return "\n    vec4 "+n+"(int index) {\n      vec2 uv = packedUVfrom1D(\n        "+o[0]+", "+o[1]+", index);\n      return texture2D("+t+", uv);\n    }\n  "}function getSampler1D(e){var t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);return "\n    float "+n+"(int index) {\n      return "+n+"Flat(index);\n    }\n  "}function getPackedSampler2D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e.shapeInfo.texShape,a=o[0],i=o[1];if(null!=o&&arraysEqual(t,o))return "\n      vec4 "+r+"(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2("+i+".0, "+a+".0);\n\n        return texture2D("+n+", uv);\n      }\n    ";var s=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];return "\n    vec4 "+r+"(int row, int col) {\n      vec2 uv = packedUVfrom2D("+Math.ceil(t[1]/2)+", "+s[0]+", "+s[1]+", row, col);\n      return texture2D("+n+", uv);\n    }\n  "}function getSampler2D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e.shapeInfo.texShape;if(null!=o&&arraysEqual(t,o)){var a=o[0];return "\n    float "+r+"(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2("+o[1]+".0, "+a+".0);\n      return sampleTexture("+n+", uv);\n    }\n  "}var i=squeezeShape(t),s=i.newShape,u=i.keptDims,l=s;if(l.length<t.length){return "\n      "+getSamplerFromInInfo(squeezeInputInfo(e,l))+"\n      float "+r+"(int row, int col) {\n        return "+r+"("+getSqueezedParams(["row","col"],u)+");\n      }\n    "}if(e.shapeInfo.isUniform)return "\n      float "+r+"(int row, int col) {\n        float index = dot(vec2(row, col), vec2("+t[1]+", 1));\n        return "+r+"Flat(round(index));\n      }\n    ";var c=o[0],p=o[1];return 1===p?"\n    float "+r+"(int row, int col) {\n      float index = dot(vec2(row, col), vec2("+t[1]+", 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / "+c+".0);\n      return sampleTexture("+n+", uv);\n    }\n  ":1===c?"\n    float "+r+"(int row, int col) {\n      float index = dot(vec2(row, col), vec2("+t[1]+", 1));\n      vec2 uv = vec2((index + 0.5) / "+p+".0, 0.5);\n      return sampleTexture("+n+", uv);\n    }\n  ":"\n  float "+r+"(int row, int col) {\n    vec2 uv = UVfrom2D("+c+", "+p+", "+t[1]+", row, col);\n    return sampleTexture("+n+", uv);\n  }\n"}function getPackedSampler3D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e.shapeInfo.texShape,a=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];if(1===t[0]){return "\n        "+getPackedSamplerFromInInfo(squeezeInputInfo(e,t.slice(1)))+"\n        vec4 "+r+"(int b, int row, int col) {\n          return "+r+"("+getSqueezedParams(["b","row","col"],[1,2])+");\n        }\n      "}var i=a[0],s=a[1],u=Math.ceil(t[2]/2);return "\n    vec4 "+r+"(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        "+i+", "+s+", "+u*Math.ceil(t[1]/2)+", "+u+", b, row, col);\n      return texture2D("+n+", uv);\n    }\n  "}function getSampler3D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=t[1]*t[2],a=t[2],i=squeezeShape(t),s=i.newShape,u=i.keptDims,l=s;if(l.length<t.length){return "\n        "+getSamplerFromInInfo(squeezeInputInfo(e,l))+"\n        float "+r+"(int row, int col, int depth) {\n          return "+r+"("+getSqueezedParams(["row","col","depth"],u)+");\n        }\n      "}if(e.shapeInfo.isUniform)return "\n      float "+r+"(int row, int col, int depth) {\n        float index = dot(vec3(row, col, depth),\n                          vec3("+o+", "+a+", 1));\n        return "+r+"Flat(round(index));\n      }\n    ";var c=e.shapeInfo.texShape,p=c[0],d=c[1];return d===o?"\n        float "+r+"(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2("+a+", 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2("+d+".0, "+p+".0);\n          return sampleTexture("+n+", uv);\n        }\n      ":d===a?"\n    float "+r+"(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2("+t[1]+", 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2("+d+".0, "+p+".0);\n      return sampleTexture("+n+", uv);\n    }\n  ":"\n      float "+r+"(int row, int col, int depth) {\n        vec2 uv = UVfrom3D(\n            "+p+", "+d+", "+o+", "+a+", row, col, depth);\n        return sampleTexture("+n+", uv);\n      }\n  "}function getPackedSampler4D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e.shapeInfo.texShape,a=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],i=a[0],s=a[1],u=Math.ceil(t[3]/2),l=u*Math.ceil(t[2]/2);return "\n    vec4 "+r+"(int b2, int b, int row, int col) {\n      vec2 uv = packedUVfrom4D(\n        "+i+", "+s+", "+l*t[1]+",\n        "+l+", "+u+", b2, b, row, col);\n      return texture2D("+n+", uv);\n    }\n  "}function getSampler4D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=t[3],a=t[2]*o,i=t[1]*a,s=squeezeShape(t),u=s.newShape,l=s.keptDims;if(u.length<t.length){return "\n      "+getSamplerFromInInfo(squeezeInputInfo(e,u))+"\n      float "+r+"(int row, int col, int depth, int depth2) {\n        return "+r+"("+getSqueezedParams(["row","col","depth","depth2"],l)+");\n      }\n    "}if(e.shapeInfo.isUniform)return "\n      float "+r+"(int row, int col, int depth, int depth2) {\n        float index = dot(vec4(row, col, depth, depth2),\n                          vec4("+i+", "+a+", "+o+", 1));\n        return "+r+"Flat(round(index));\n      }\n    ";var c=e.shapeInfo.texShape,p=c[0],d=c[1];return d===i?"\n      float "+r+"(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2), vec3("+a+", "+o+", 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2("+d+".0, "+p+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":d===o?"\n      float "+r+"(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3("+t[1]*t[2]+", "+t[2]+", 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2("+d+".0, "+p+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":"\n    float "+r+"(int row, int col, int depth, int depth2) {\n      vec2 uv = UVfrom4D("+p+", "+d+", "+i+", "+a+",\n          "+o+", row, col, depth, depth2);\n      return sampleTexture("+n+", uv);\n    }\n  "}function getSampler5D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=t[4],a=t[3]*o,i=t[2]*a,s=t[1]*i,u=squeezeShape(t),l=u.newShape,c=u.keptDims;if(l.length<t.length){return "\n      "+getSamplerFromInInfo(squeezeInputInfo(e,l))+"\n      float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n        return "+r+"("+getSqueezedParams(["row","col","depth","depth2","depth3"],c)+");\n      }\n    "}if(e.shapeInfo.isUniform)return "\n      float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4("+s+", "+i+", "+a+", "+o+")) +\n          depth3;\n        return "+r+"Flat(index);\n      }\n    ";var p=e.shapeInfo.texShape,d=p[0],h=p[1];return h===s?"\n      float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(\n          vec4(col, depth, depth2, depth3),\n          vec4("+i+", "+a+", "+o+", 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2("+h+".0, "+d+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":h===o?"\n      float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4("+t[1]*t[2]*t[3]+", "+t[2]*t[3]+",\n            "+t[3]+", 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2("+h+".0, "+d+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":"\n    float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n      vec2 uv = UVfrom5D("+d+", "+h+", "+s+", "+i+",\n          "+a+", "+o+", row, col, depth, depth2, depth3);\n      return sampleTexture("+n+", uv);\n    }\n  "}function getSampler6D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=t[5],a=t[4]*o,i=t[3]*a,s=t[2]*i,u=t[1]*s,l=squeezeShape(t),c=l.newShape,p=l.keptDims;if(c.length<t.length){return "\n      "+getSamplerFromInInfo(squeezeInputInfo(e,c))+"\n      float "+r+"(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return "+r+"("+getSqueezedParams(["row","col","depth","depth2","depth3","depth4"],p)+");\n      }\n    "}if(e.shapeInfo.isUniform)return "\n      float "+r+"(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4("+u+", "+s+", "+i+", "+a+")) +\n          dot(\n            vec2(depth3, depth4),\n            vec2("+o+", 1));\n        return "+r+"Flat(index);\n      }\n    ";var d=e.shapeInfo.texShape,h=d[0],f=d[1];return f===u?"\n      float "+r+"(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(\n          vec4(col, depth, depth2, depth3),\n          vec4("+s+", "+i+", "+a+", "+o+")) + depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2("+f+".0, "+h+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":f===o?"\n      float "+r+"(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4("+t[1]*t[2]*t[3]*t[4]+",\n               "+t[2]*t[3]*t[4]+",\n               "+t[3]*t[4]+",\n               "+t[4]+")) + depth3;\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2("+f+".0, "+h+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":"\n    float "+r+"(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      vec2 uv = UVfrom6D("+h+", "+f+", "+u+", "+s+",\n          "+i+", "+a+", "+o+"\n          ,row, col, depth, depth2, depth3, depth4);\n      return sampleTexture("+n+", uv);\n    }\n  "}function getSamplerFlat(e){var t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1)+"Flat",r=sizeFromShape(e.shapeInfo.logicalShape);if(e.shapeInfo.isUniform)return 1===r?"float "+n+"(int index) {return "+t+";}":"\n      float "+n+"(int index) {\n        for (int i = 0; i < "+r+"; i++) {\n          if (i == index) {\n            return "+t+"[i];\n          }\n        }\n      }\n    ";var o=e.shapeInfo.texShape,a=o[0],i=o[1];return 1===i&&1===a?"\n      float "+n+"(int index) {\n        return sampleTexture("+t+", halfCR);\n      }\n    ":1===i?"\n      float "+n+"(int index) {\n        vec2 uv = vec2(0.5, (float(index) + 0.5) / "+a+".0);\n        return sampleTexture("+t+", uv);\n      }\n    ":1===a?"\n      float "+n+"(int index) {\n        vec2 uv = vec2((float(index) + 0.5) / "+i+".0, 0.5);\n        return sampleTexture("+t+", uv);\n      }\n    ":"\n    float "+n+"(int index) {\n      vec2 uv = UVfrom1D("+a+", "+i+", index);\n      return sampleTexture("+t+", uv);\n    }\n  "}function getBroadcastOutputCoordsSampler(e,t,n,r){var o=e.shapeInfo.logicalShape.length,a=t.logicalShape.length,i="int";2===a?i="ivec2":3===a?i="ivec3":4===a&&(i="ivec4");var s=getBroadcastDims(e.shapeInfo.logicalShape,t.logicalShape),u=a-o;return "\n    float "+r+"() {\n      "+i+" coords = getOutputCoords();\n      "+(0===o?"":a<2&&s.length>=1?"coords = 0;":s.map(function(e){return "coords["+(e+u)+"] = 0;"}).join("\n"))+"\n      return get"+n+"("+(a<2&&o>0?"coords":e.shapeInfo.logicalShape.map(function(e,t){return "coords["+(t+u)+"]"}).join(", "))+");\n    }\n  "}function getPackedSamplerAtOutputCoords(e,t,n){var r=e.name,o=r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape,i="get"+o+"AtOutCoords",s=t.texShape,u=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],l=u[0],c=u[1],p=getBroadcastDims(e.shapeInfo.logicalShape,t.logicalShape),d=e.shapeInfo.logicalShape.length,h=t.logicalShape.length;if(p.length)throw Error("Packed broadcast sampling is not implemented yet.");if(arraysEqual(e.shapeInfo.texShape,s))return "\n      vec4 "+i+"() {\n        return texture2D("+r+", resultUV);\n      }\n    ";var f="return texture2D("+r+", uv)";return 1===d&&h>1?f="\n      vec4 sample = texture2D("+r+", uv);\n      return vec4(sample.xy, sample.xy);\n    ":0===d&&h>0&&(f=1===h?"\n        vec4 sample = texture2D("+r+", uv);\n        return vec4(sample.x, sample.x, 0., 0.);\n      ":"\n        vec4 sample = texture2D("+r+", uv);\n        return vec4(sample.x);\n      "),"\n    vec4 "+i+"() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+u[0]+", "+u[1]+"));\n      int index = resTexRC.x * "+u[1]+" + resTexRC.y;\n\n      int texR = index / "+c+";\n      int texC = index - texR * "+c+";\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2("+c+", "+l+");\n\n      "+f+";\n    }\n  "}function getSamplerAtOutputCoords(e,t,n){var r=e.name,o=r.charAt(0).toUpperCase()+r.slice(1),a="get"+o+"AtOutCoords",i=getBroadcastDims(e.shapeInfo.logicalShape,t.logicalShape),s=e.shapeInfo.logicalShape.length,u=t.logicalShape.length,l=n&&(u>s||i.length>0),c=broadcastDimsAreOuter(i),p=e.shapeInfo.isUniform;if(l&&!c)return getBroadcastOutputCoordsSampler(e,t,o,a);var d=sizeFromShape(e.shapeInfo.logicalShape),h="";l&&c&&(h="\n        int mainPart = index / "+d+";\n        index -= mainPart * "+d+";\n      ");var f=t.texShape;if(p)return 1===d?"float "+a+"() {return "+r+";}":"\n      float "+a+"() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                              vec2("+f[0]+", "+f[1]+"));\n        int index = resTexRC.x * "+f[1]+" + resTexRC.y;\n        "+h+"\n        return get"+o+"Flat(index);\n      }\n    ";var m=e.shapeInfo.texShape;return arraysEqual(m,f)?"\n      float "+a+"() {\n        return sampleTexture("+r+", resultUV);\n      }\n    ":"\n    float "+a+"() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+f[0]+", "+f[1]+"));\n      int index = resTexRC.x * "+f[1]+" + resTexRC.y;\n      "+h+"\n      int texR = index / "+m[1]+";\n      int texC = index - texR * "+m[1]+";\n      vec2 uv = (vec2(texC, texR) + halfCR) /\n                 vec2("+m[1]+".0, "+m[0]+".0);\n\n      return sampleTexture("+r+", uv);\n    }\n  "}function getCoordsDataType(e){if(e<=1)return "int";if(2===e)return "ivec2";if(3===e)return "ivec3";if(4===e)return "ivec4";if(5===e)return "ivec5";if(6===e)return "ivec6";throw Error("GPU for rank "+e+" is not yet supported")}function squeezeInputInfo(e,t){var n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function getSqueezedParams(e,t){return t.map(function(t){return e[t]}).join(", ")}var CumSumProgram=function(){return function(e,t,n){this.variableNames=["x"],this.outputShape=e;var r=e.length,o=e[e.length-1],a=n?"<":">";this.userCode="\n      int getIndex(int i) {\n        "+(n?"return "+o+" -i - 1;":"return i;")+"\n      }\n\n      void main() {\n        "+getCoordsDataType(r)+" coords = getOutputCoords();\n        int end = "+getFinalCoord(r,"coords")+";\n        float val = 0.0;\n        for (int i = "+o+" - 1; i >= 0; i -= 1) {\n          int idx = getIndex(i);\n          if (idx "+a+" end) {\n            continue;\n          }\n          if (idx == end && "+t+") {\n            continue;\n          }\n          "+getFinalCoord(r,"coords")+" = idx;\n          val += getX("+getCoords(r,"coords")+");\n        }\n        setOutput(val);\n      }\n    ";}}();function getCoords(e,t){if(1===e)return ""+t;if(2===e)return t+".x, "+t+".y";if(3===e)return t+".x, "+t+".y, "+t+".z";if(4===e)return t+".x, "+t+".y, "+t+".z, "+t+".w";throw Error("Cumulative sum for rank "+e+" is not yet supported")}function getFinalCoord(e,t){if(1===e)return ""+t;if(2===e)return t+".y";if(3===e)return t+".z";if(4===e)return t+".w";throw Error("Cumulative sum for rank "+e+" is not yet supported")}var DepthToSpaceProgram=function(){function e(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode="\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = "+this.getHeightCoordString()+";\n      int w = "+this.getWidthCoordString()+";\n      int d = "+this.getDepthCoordString()+";\n\n      int in_h = h / "+t+";\n      int offset_h = imod(h, "+t+");\n      int in_w = w / "+t+";\n      int offset_w = imod(w, "+t+");\n      int offset_d = (offset_h * "+t+" + offset_w) *\n        "+this.getOutputDepthSize()+";\n      int in_d = d + offset_d;\n\n      float result = "+this.getInputSamplingString()+";\n      setOutput(result);\n    }\n  ";}return e.prototype.getHeightCoordString=function(){return "NHWC"===this.dataFormat?"coords[1]":"coords[2]"},e.prototype.getWidthCoordString=function(){return "NHWC"===this.dataFormat?"coords[2]":"coords[3]"},e.prototype.getDepthCoordString=function(){return "NHWC"===this.dataFormat?"coords[3]":"coords[1]"},e.prototype.getOutputDepthSize=function(){return "NHWC"===this.dataFormat?this.outputShape[3]:this.outputShape[1]},e.prototype.getInputSamplingString=function(){return "NHWC"===this.dataFormat?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"},e}(),EncodeFloatProgram=function(){return function(e){this.variableNames=["A"],this.outputShape=e,this.userCode="\n      const float FLOAT_MAX = 1.70141184e38;\n      const float FLOAT_MIN = 1.17549435e-38;\n\n      lowp vec4 encode_float(highp float v) {\n        if (isNaN(v)) {\n          return vec4(255, 255, 255, 255);\n        }\n\n        highp float av = abs(v);\n\n        if(av < FLOAT_MIN) {\n          return vec4(0.0, 0.0, 0.0, 0.0);\n        } else if(v > FLOAT_MAX) {\n          return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n        } else if(v < -FLOAT_MAX) {\n          return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n        }\n\n        highp vec4 c = vec4(0,0,0,0);\n\n        highp float e = floor(log2(av));\n        highp float m = exp2(fract(log2(av))) - 1.0;\n\n        c[2] = floor(128.0 * m);\n        m -= c[2] / 128.0;\n        c[1] = floor(32768.0 * m);\n        m -= c[1] / 32768.0;\n        c[0] = floor(8388608.0 * m);\n\n        highp float ebias = e + 127.0;\n        c[3] = floor(ebias / 2.0);\n        ebias -= c[3] * 2.0;\n        c[2] += floor(ebias) * 128.0;\n\n        c[3] += 128.0 * step(0.0, -v);\n\n        return c / 255.0;\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        gl_FragColor = encode_float(x);\n      }\n    ";}}(),COMPLEX_FFT={REAL:"return real * expR - imag * expI;",IMAG:"return real * expI + imag * expR;"},FFTProgram=function(){return function(e,t,n){this.variableNames=["real","imag"];var r=t[1];this.outputShape=t;var o=n?"2.0 * "+Math.PI:"-2.0 * "+Math.PI,a=n?r+".0":"1.0";this.userCode="\n      const float exponentMultiplier = "+o+";\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        "+e+"\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float("+r+");\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < "+r+"; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / "+a+";\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    ";}}(),FromPixelsProgram=function(){return function(e){this.variableNames=["A"];var t=e[0],n=e[1];this.outputShape=e,this.userCode="\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2("+n+".0, "+t+".0);\n\n        vec4 values = texture2D(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    ";}}(),GatherProgram=function(){return function(e,t,n){this.variableNames=["A","indices"];var r=e.slice();r[n]=t,this.outputShape=r,this.rank=r.length;var o=getCoordsDataType(this.rank),a=getSourceCoords(e,n);this.userCode="\n      void main() {\n        "+o+" resRC = getOutputCoords();\n        setOutput(getA("+a+"));\n      }\n    ";}}();function getSourceCoords(e,t){var n=e.length;if(n>4)throw Error("Gather for rank "+n+" is not yet supported");if(1===n)return "int(getIndices(resRC))";for(var r=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],a=0;a<e.length;a++)a===t?o.push("int(getIndices("+r[a]+"))"):o.push(""+r[a]);return o.join()}var TextureUsage,PhysicalTextureType,GatherNDProgram=function(){return function(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;var r=getCoordsDataType(t.length),o=getCoordsDataType(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode="\n        "+r+" strides = "+r+"("+this.strides+");\n         void main() {\n          "+o+" coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < "+this.sliceDim+"; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * "+a+";\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      ";}}();function getUnpackedMatrixTextureShapeWidthHeight(e,t){return [t,e]}function getUnpackedArraySizeFromMatrixSize(e,t){return e*t}function getMatrixSizeFromUnpackedArraySize(e,t){if(e%t!=0)throw new Error("unpackedSize ("+e+") must be a multiple of "+t);return e/t}function encodeMatrixToUnpackedArray(e,t,n){var r=getUnpackedArraySizeFromMatrixSize(e.length,n);if(t.length<r)throw new Error("unpackedArray length ("+t.length+") must be >= "+r);for(var o=0,a=0;a<e.length;++a)t[o]=e[a],o+=n;}function decodeMatrixFromUnpackedArray(e,t,n){var r=getMatrixSizeFromUnpackedArraySize(e.length,n);if(t.length<r)throw new Error("matrix length ("+t.length+") must be >= "+r);for(var o=0,a=0;a<e.length;a+=n)t[o++]=e[a];}function getPackedMatrixTextureShapeWidthHeight(e,t){return [Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function getPackedRGBAArraySizeFromMatrixShape(e,t){var n=getPackedMatrixTextureShapeWidthHeight(e,t);return n[0]*n[1]*4}function encodeMatrixToPackedRGBA(e,t,n,r,o){var a=getPackedRGBAArraySizeFromMatrixShape(n,r);if(o.length<a)throw new Error("packedRGBA length ("+o.length+") must be >=\n        "+a);for(var i=r%2==1,s=n%2==1,u=Math.floor(r/2),l=Math.floor(n/2),c=Math.ceil(r/2),p=c*Math.ceil(n/2),d=nearestLargerEven(n)*nearestLargerEven(r),h=0;h<t;h++){for(var f=h*n*r,m=h*d,g=i?4:0,v=r,y=m,x=0;x<l;++x){for(var T=2*x*r,w=0;w<u;++w){var E=f+T+2*w;o[y]=e[E],o[y+1]=e[E+1],o[y+2]=e[E+v],o[y+3]=e[E+v+1],y+=4;}y+=g;}if(i){E=f+r-1,y=m+4*(c-1);var S=2*r;for(g=4*c,x=0;x<l;++x)o[y]=e[E],o[y+2]=e[E+r],E+=S,y+=g;}if(s){for(E=f+(n-1)*r,y=m+4*(p-c),w=0;w<u;++w)o[y++]=e[E++],o[y++]=e[E++],y+=2;i&&s&&(o[m+d-4]=e[E]);}}return o}function decodeMatrixFromPackedRGBA(e,t,n,r,o){var a=n*r;if(o.length<a)throw new Error("matrix length ("+o.length+") must be >= "+a);for(var i=r%2==1,s=n%2==1,u=Math.floor(r/2),l=Math.floor(n/2),c=Math.ceil(r/2),p=c*Math.ceil(n/2),d=nearestLargerEven(n)*nearestLargerEven(r),h=0;h<t;h++){for(var f=h*n*r,m=h*d,g=i?4:0,v=r+(i?1:0),y=m,x=f,T=f+r,w=0;w<l;++w){for(var E=0;E<u;++E)o[x++]=e[y++],o[x++]=e[y++],o[T++]=e[y++],o[T++]=e[y++];y+=g,x+=v,T+=v;}if(i){y=m+4*(c-1);var S=f+r-1;for(g=4*c,v=2*r,w=0;w<l;++w)o[S]=e[y],o[S+r]=e[y+2],y+=g,S+=v;}if(s){for(y=m+4*(p-c),S=f+(n-1)*r,E=0;E<u;++E)o[S++]=e[y++],o[S++]=e[y++],y+=2;i&&(o[f+n*r-1]=e[y]);}}return o}function callAndCheck(e,t){var n=t();return checkWebGLError(e),n}!function(e){e[e.RENDER=0]="RENDER",e[e.UPLOAD=1]="UPLOAD",e[e.PIXELS=2]="PIXELS",e[e.DOWNLOAD=3]="DOWNLOAD";}(TextureUsage||(TextureUsage={})),function(e){e[e.UNPACKED_FLOAT16=0]="UNPACKED_FLOAT16",e[e.UNPACKED_FLOAT32=1]="UNPACKED_FLOAT32",e[e.PACKED_4X1_UNSIGNED_BYTE=2]="PACKED_4X1_UNSIGNED_BYTE",e[e.PACKED_2X2_FLOAT32=3]="PACKED_2X2_FLOAT32",e[e.PACKED_2X2_FLOAT16=4]="PACKED_2X2_FLOAT16";}(PhysicalTextureType||(PhysicalTextureType={}));var webGLDebugErrorCheckingEnabled=!1;function enableDebugWebGLErrorChecking(e){webGLDebugErrorCheckingEnabled=e;}function checkWebGLError(e){if(webGLDebugErrorCheckingEnabled){var t=e.getError();if(t!==e.NO_ERROR)throw new Error("WebGL Error: "+getWebGLErrorMessage(e,t))}}function getWebGLErrorMessage(e,t){switch(t){case e.NO_ERROR:return "NO_ERROR";case e.INVALID_ENUM:return "INVALID_ENUM";case e.INVALID_VALUE:return "INVALID_VALUE";case e.INVALID_OPERATION:return "INVALID_OPERATION";case e.INVALID_FRAMEBUFFER_OPERATION:return "INVALID_FRAMEBUFFER_OPERATION";case e.OUT_OF_MEMORY:return "OUT_OF_MEMORY";case e.CONTEXT_LOST_WEBGL:return "CONTEXT_LOST_WEBGL";default:return "Unknown error code "+t}}function getExtensionOrThrow(e,t){return throwIfNull(e,function(){return e.getExtension(t)},'Extension "'+t+'" not supported on this browser.')}function createVertexShader(e,t){var n=throwIfNull(e,function(){return e.createShader(e.VERTEX_SHADER)},"Unable to create vertex WebGLShader.");if(callAndCheck(e,function(){return e.shaderSource(n,t)}),callAndCheck(e,function(){return e.compileShader(n)}),!1===e.getShaderParameter(n,e.COMPILE_STATUS))throw console.log(e.getShaderInfoLog(n)),new Error("Failed to compile vertex shader.");return n}function createFragmentShader(e,t){var n=throwIfNull(e,function(){return e.createShader(e.FRAGMENT_SHADER)},"Unable to create fragment WebGLShader.");if(callAndCheck(e,function(){return e.shaderSource(n,t)}),callAndCheck(e,function(){return e.compileShader(n)}),!1===e.getShaderParameter(n,e.COMPILE_STATUS))throw logShaderSourceAndInfoLog(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}var lineNumberRegex=/ERROR: [0-9]+:([0-9]+):/g;function logShaderSourceAndInfoLog(e,t){var n=lineNumberRegex.exec(t);if(null==n)return console.log("Couldn't parse line number in error: "+t),void console.log(e);for(var r=+n[1],o=e.split("\n"),a=o.length.toString().length+2,i=o.map(function(e,t){return rightPad((t+1).toString(),a)+e}),s=0,u=0;u<i.length;u++)s=Math.max(i[u].length,s);var l=i.slice(0,r-1),c=i.slice(r-1,r),p=i.slice(r);console.log(l.join("\n")),console.log(t.split("\n")[0]),console.log("%c "+rightPad(c[0],s),"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(p.join("\n"));}function createProgram(e){return throwIfNull(e,function(){return e.createProgram()},"Unable to create WebGLProgram.")}function linkProgram(e,t){if(callAndCheck(e,function(){return e.linkProgram(t)}),!1===e.getProgramParameter(t,e.LINK_STATUS))throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function validateProgram(e,t){if(callAndCheck(e,function(){return e.validateProgram(t)}),!1===e.getProgramParameter(t,e.VALIDATE_STATUS))throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function createStaticVertexBuffer(e,t){var n=throwIfNull(e,function(){return e.createBuffer()},"Unable to create WebGLBuffer");return callAndCheck(e,function(){return e.bindBuffer(e.ARRAY_BUFFER,n)}),callAndCheck(e,function(){return e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)}),n}function createStaticIndexBuffer(e,t){var n=throwIfNull(e,function(){return e.createBuffer()},"Unable to create WebGLBuffer");return callAndCheck(e,function(){return e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)}),callAndCheck(e,function(){return e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)}),n}function getNumChannels(){return 2===ENV.get("WEBGL_VERSION")?1:4}function createTexture(e){return throwIfNull(e,function(){return e.createTexture()},"Unable to create WebGLTexture.")}function validateTextureSize(e,t){var n=ENV.get("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){var r="["+e+"x"+t+"]";throw new Error("Requested texture size "+r+" is invalid.")}if(e>n||t>n){r="["+e+"x"+t+"]";throw new Error("Requested texture size "+r+" greater than WebGL maximum on this browser / GPU "+("["+n+"x"+n+"]")+".")}}function createFramebuffer(e){return throwIfNull(e,function(){return e.createFramebuffer()},"Unable to create WebGLFramebuffer.")}function bindVertexBufferToProgramAttribute(e,t,n,r,o,a,i){var s=e.getAttribLocation(t,n);return -1!==s&&(callAndCheck(e,function(){return e.bindBuffer(e.ARRAY_BUFFER,r)}),callAndCheck(e,function(){return e.vertexAttribPointer(s,o,e.FLOAT,!1,a,i)}),callAndCheck(e,function(){return e.enableVertexAttribArray(s)}),!0)}function bindTextureUnit(e,t,n){validateTextureUnit(e,n),callAndCheck(e,function(){return e.activeTexture(e.TEXTURE0+n)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,t)});}function unbindTextureUnit(e,t){validateTextureUnit(e,t),callAndCheck(e,function(){return e.activeTexture(e.TEXTURE0+t)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)});}function getProgramUniformLocationOrThrow(e,t,n){return throwIfNull(e,function(){return e.getUniformLocation(t,n)},'uniform "'+n+'" not present in program.')}function getProgramUniformLocation(e,t,n){return e.getUniformLocation(t,n)}function bindTextureToProgramUniformSampler(e,t,n,r,o){callAndCheck(e,function(){return bindTextureUnit(e,n,o)}),callAndCheck(e,function(){return e.uniform1i(r,o)});}function bindCanvasToFramebuffer(e){callAndCheck(e,function(){return e.bindFramebuffer(e.FRAMEBUFFER,null)}),callAndCheck(e,function(){return e.viewport(0,0,e.canvas.width,e.canvas.height)}),callAndCheck(e,function(){return e.scissor(0,0,e.canvas.width,e.canvas.height)});}function bindColorTextureToFramebuffer(e,t,n){callAndCheck(e,function(){return e.bindFramebuffer(e.FRAMEBUFFER,n)}),callAndCheck(e,function(){return e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0)});}function unbindColorTextureFromFramebuffer(e,t){callAndCheck(e,function(){return e.bindFramebuffer(e.FRAMEBUFFER,t)}),callAndCheck(e,function(){return e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0)});}function validateFramebuffer(e){var t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+getFramebufferErrorMessage(e,t))}function getFramebufferErrorMessage(e,t){switch(t){case e.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return "FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return "FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return "FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case e.FRAMEBUFFER_UNSUPPORTED:return "FRAMEBUFFER_UNSUPPORTED";default:return "unknown error "+t}}function throwIfNull(e,t,n){var r=callAndCheck(e,function(){return t()});if(null==r)throw new Error(n);return r}function validateTextureUnit(e,t){var n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,r=t+e.TEXTURE0;if(r<e.TEXTURE0||r>n)throw new Error("textureUnit must be in "+("[gl.TEXTURE0, gl.TEXTURE"+n+"]")+".")}function getTextureShapeFromLogicalShape(e,t){void 0===t&&(t=!1);var n=ENV.get("WEBGL_MAX_TEXTURE_SIZE");if(t&&(n*=2,e=e.map(function(t,n){return n>=e.length-2?nearestLargerEven(e[n]):e[n]})),2!==e.length){var r=squeezeShape(e);e=r.newShape;}var o=sizeFromShape(e);return e.length<=1&&o<=n?[1,o]:2===e.length&&e[0]<=n&&e[1]<=n?e:3===e.length&&e[0]*e[1]<=n&&e[2]<=n?[e[0]*e[1],e[2]]:3===e.length&&e[0]<=n&&e[1]*e[2]<=n?[e[0],e[1]*e[2]]:4===e.length&&e[0]*e[1]*e[2]<=n&&e[3]<=n?[e[0]*e[1]*e[2],e[3]]:4===e.length&&e[0]<=n&&e[1]*e[2]*e[3]<=n?[e[0],e[1]*e[2]*e[3]]:sizeToSquarishShape(o)}function isEven(e){return e%2==0}function isReshapeFree(e,t){if(arraysEqual(e=e.slice(-2),t=t.slice(-2)))return !0;if(!e.length||!t.length)return !0;if(0===e[0]||0===e[1]||0===t[0]||0===t[1])return !0;if(e.length!==t.length){var n=e.slice(-1)[0],r=t.slice(-1)[0];if(n===r)return !0;if(isEven(n)&&isEven(r)&&(1===e[0]||1===t[0]))return !0}else if(isEven(e[0])&&isEven(t[0])){if(isEven(e[1])&&isEven(t[1]))return !0;if(e[1]===t[1])return !0}return !1}var webgl_util=Object.freeze({callAndCheck:callAndCheck,enableDebugWebGLErrorChecking:enableDebugWebGLErrorChecking,checkWebGLError:checkWebGLError,getWebGLErrorMessage:getWebGLErrorMessage,getExtensionOrThrow:getExtensionOrThrow,createVertexShader:createVertexShader,createFragmentShader:createFragmentShader,createProgram:createProgram,linkProgram:linkProgram,validateProgram:validateProgram,createStaticVertexBuffer:createStaticVertexBuffer,createStaticIndexBuffer:createStaticIndexBuffer,getNumChannels:getNumChannels,createTexture:createTexture,validateTextureSize:validateTextureSize,createFramebuffer:createFramebuffer,bindVertexBufferToProgramAttribute:bindVertexBufferToProgramAttribute,bindTextureUnit:bindTextureUnit,unbindTextureUnit:unbindTextureUnit,getProgramUniformLocationOrThrow:getProgramUniformLocationOrThrow,getProgramUniformLocation:getProgramUniformLocation,bindTextureToProgramUniformSampler:bindTextureToProgramUniformSampler,bindCanvasToFramebuffer:bindCanvasToFramebuffer,bindColorTextureToFramebuffer:bindColorTextureToFramebuffer,unbindColorTextureFromFramebuffer:unbindColorTextureFromFramebuffer,validateFramebuffer:validateFramebuffer,getFramebufferErrorMessage:getFramebufferErrorMessage,getTextureShapeFromLogicalShape:getTextureShapeFromLogicalShape,isReshapeFree:isReshapeFree});function createVertexShader$1(e){return createVertexShader(e,"\n    precision highp float;\n    attribute vec3 clipSpacePos;\n    attribute vec2 uv;\n    varying vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }")}function createVertexBuffer(e){return createStaticVertexBuffer(e,new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]))}function createIndexBuffer(e){return createStaticIndexBuffer(e,new Uint16Array([0,1,2,2,1,3]))}function getTextureConfig(e,t){var n,r,o,a,i,s,u,l=e;return 2===ENV.get("WEBGL_VERSION")?(n=l.R32F,r=l.R16F,o=l.RGBA32F,a=l.RED,i=4,s=1,u=l.HALF_FLOAT):(n=e.RGBA,r=e.RGBA,o=l.RGBA,a=e.RGBA,i=4,s=4,u=null!=t?t.HALF_FLOAT_OES:null),{internalFormatFloat:n,internalFormatHalfFloat:r,internalFormatPackedFloat:o,textureFormatFloat:a,downloadTextureFormat:e.RGBA,downloadUnpackNumChannels:i,defaultNumChannels:s,textureTypeHalfFloat:u}}function createAndConfigureTexture(e,t,n,r,o,a){validateTextureSize(t,n);var i=createTexture(e),s=e.TEXTURE_2D;return callAndCheck(e,function(){return e.bindTexture(s,i)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_MIN_FILTER,e.NEAREST)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_MAG_FILTER,e.NEAREST)}),callAndCheck(e,function(){return e.texImage2D(s,0,r,t,n,0,o,a,null)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)}),i}function createFloat32MatrixTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],r.internalFormatFloat,r.textureFormatFloat,e.FLOAT)}function createFloat16MatrixTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],r.internalFormatFloat,r.textureFormatFloat,r.textureTypeHalfFloat)}function createUnsignedBytesMatrixTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],e.RGBA,e.RGBA,e.UNSIGNED_BYTE)}function createPackedMatrixTexture(e,t,n,r){var o=getPackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],r.internalFormatPackedFloat,e.RGBA,e.FLOAT)}function createFloat16PackedMatrixTexture(e,t,n,r){var o=getPackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],r.internalFormatHalfFloat,e.RGBA,r.textureTypeHalfFloat)}function bindVertexProgramAttributeStreams(e,t,n){return callAndCheck(e,function(){return e.bindBuffer(e.ARRAY_BUFFER,n)}),bindVertexBufferToProgramAttribute(e,t,"clipSpacePos",n,3,20,0)&&bindVertexBufferToProgramAttribute(e,t,"uv",n,2,20,12)}function uploadPixelDataToTexture(e,t,n){callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,t)}),callAndCheck(e,function(){return e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)});}function uploadDataToTexture(e,t,n,r,o,a){validateTextureSize(n,r),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,t)}),callAndCheck(e,function(){return e.texSubImage2D(e.TEXTURE_2D,0,0,0,n,r,a,e.FLOAT,o)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)});}function uploadMatrixToTexture(e,t,n,r,o,a,i){var s,u=getUnpackedMatrixTextureShapeWidthHeight(n,r),l=u[0],c=u[1];1===i.defaultNumChannels?s=o:encodeMatrixToUnpackedArray(o,s=new Float32Array(getUnpackedArraySizeFromMatrixSize(o.length,a)),a),uploadDataToTexture(e,t,l,c,s,i.textureFormatFloat);}function uploadMatrixToPackedTexture(e,t,n,r,o,a,i,s,u){var l=getPackedMatrixTextureShapeWidthHeight(a,i),c=l[0],p=l[1],d=new Float32Array(n*getPackedRGBAArraySizeFromMatrixShape(r,o));encodeMatrixToPackedRGBA(s,n,r,o,d),uploadDataToTexture(e,t,c,p,d,e.RGBA);}function maybeCreateBufferFromOutputTexture(e,t,n,r,o){var a=t;if(2===ENV.get("WEBGL_VERSION")){var i=e,s=i.createBuffer();callAndCheck(e,function(){return e.bindBuffer(i.PIXEL_PACK_BUFFER,s)});var u=4*getUnpackedArraySizeFromMatrixSize(n*r,o.downloadUnpackNumChannels);callAndCheck(e,function(){return e.bufferData(i.PIXEL_PACK_BUFFER,u,e.STATIC_DRAW)}),callAndCheck(e,function(){return i.readPixels(0,0,r,n,e.RGBA,e.FLOAT,0)}),callAndCheck(e,function(){return e.bindBuffer(i.PIXEL_PACK_BUFFER,null)}),a=s;}return a}function downloadFloat32MatrixFromBuffer(e,t,n,r,o){var a=e,i=new Float32Array(getUnpackedArraySizeFromMatrixSize(n*r,o.downloadUnpackNumChannels));a.bindBuffer(e.ARRAY_BUFFER,t),a.getBufferSubData(e.ARRAY_BUFFER,0,i),a.bindBuffer(e.ARRAY_BUFFER,null);var s=new Float32Array(n*r);return decodeMatrixFromUnpackedArray(i,s,o.downloadUnpackNumChannels),s}function downloadFloat32MatrixFromOutputTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n),a=o[0],i=o[1],s=new Float32Array(getUnpackedArraySizeFromMatrixSize(t*n,r.downloadUnpackNumChannels));callAndCheck(e,function(){return e.readPixels(0,0,a,i,r.downloadTextureFormat,e.FLOAT,s)});var u=new Float32Array(t*n);return decodeMatrixFromUnpackedArray(s,u,r.downloadUnpackNumChannels),u}function downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n),a=o[0],i=o[1],s=new Uint8Array(getUnpackedArraySizeFromMatrixSize(t*n,4));return callAndCheck(e,function(){return e.readPixels(0,0,a,i,r.downloadTextureFormat,e.UNSIGNED_BYTE,s)}),new Float32Array(s.buffer)}function downloadPackedMatrixFromBuffer(e,t,n,r,o,a,i,s){var u=e,l=new Float32Array(getPackedRGBAArraySizeFromMatrixShape(a,i));u.bindBuffer(e.ARRAY_BUFFER,t),u.getBufferSubData(e.ARRAY_BUFFER,0,l),u.bindBuffer(e.ARRAY_BUFFER,null);var c=new Float32Array(sizeFromShape([n,r,o]));return decodeMatrixFromPackedRGBA(l,n,r,o,c),c}function downloadMatrixFromPackedOutputTexture(e,t,n,r,o,a,i){var s=getPackedMatrixTextureShapeWidthHeight(o,a),u=s[0],l=s[1],c=new Float32Array(getPackedRGBAArraySizeFromMatrixShape(o,a));callAndCheck(e,function(){return e.readPixels(0,0,u,l,e.RGBA,e.FLOAT,c)});var p=new Float32Array(sizeFromShape([t,n,r]));return decodeMatrixFromPackedRGBA(c,t,n,r,p)}var gpgpu_util=Object.freeze({createVertexShader:createVertexShader$1,createVertexBuffer:createVertexBuffer,createIndexBuffer:createIndexBuffer,getTextureConfig:getTextureConfig,createFloat32MatrixTexture:createFloat32MatrixTexture,createFloat16MatrixTexture:createFloat16MatrixTexture,createUnsignedBytesMatrixTexture:createUnsignedBytesMatrixTexture,createPackedMatrixTexture:createPackedMatrixTexture,createFloat16PackedMatrixTexture:createFloat16PackedMatrixTexture,bindVertexProgramAttributeStreams:bindVertexProgramAttributeStreams,uploadPixelDataToTexture:uploadPixelDataToTexture,uploadMatrixToTexture:uploadMatrixToTexture,uploadMatrixToPackedTexture:uploadMatrixToPackedTexture,maybeCreateBufferFromOutputTexture:maybeCreateBufferFromOutputTexture,downloadFloat32MatrixFromBuffer:downloadFloat32MatrixFromBuffer,downloadFloat32MatrixFromOutputTexture:downloadFloat32MatrixFromOutputTexture,downloadByteEncodedFloatMatrixFromOutputTexture:downloadByteEncodedFloatMatrixFromOutputTexture,downloadPackedMatrixFromBuffer:downloadPackedMatrixFromBuffer,downloadMatrixFromPackedOutputTexture:downloadMatrixFromPackedOutputTexture}),GPGPUContext=function(){function e(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.autoDebugValidate=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[],this.gl=null!=e?e:getWebGLContext(ENV.get("WEBGL_VERSION")),1===ENV.get("WEBGL_VERSION")?(this.textureFloatExtension=getExtensionOrThrow(this.gl,"OES_texture_float"),this.colorBufferFloatExtension=this.gl.getExtension("WEBGL_color_buffer_float"),ENV.get("WEBGL_RENDER_FLOAT32_ENABLED")||(this.textureHalfFloatExtension=getExtensionOrThrow(this.gl,"OES_texture_half_float"),this.colorBufferHalfFloatExtension=this.gl.getExtension("EXT_color_buffer_half_float"))):this.colorBufferFloatExtension=getExtensionOrThrow(this.gl,"EXT_color_buffer_float"),this.vertexBuffer=createVertexBuffer(this.gl),this.indexBuffer=createIndexBuffer(this.gl),this.framebuffer=createFramebuffer(this.gl),this.textureConfig=getTextureConfig(this.gl,this.textureHalfFloatExtension);}return e.prototype.dispose=function(){var e=this;if(!this.disposed){null!=this.program&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),null!=this.outputTexture&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");var t=this.gl;callAndCheck(t,function(){return t.finish()}),callAndCheck(t,function(){return t.bindFramebuffer(t.FRAMEBUFFER,null)}),callAndCheck(t,function(){return t.deleteFramebuffer(e.framebuffer)}),callAndCheck(t,function(){return t.bindBuffer(t.ARRAY_BUFFER,null)}),callAndCheck(t,function(){return t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)}),callAndCheck(t,function(){return t.deleteBuffer(e.indexBuffer)}),this.disposed=!0;}},e.prototype.enableAutomaticDebugValidation=function(e){this.autoDebugValidate=e,enableDebugWebGLErrorChecking(e);},e.prototype.createFloat32MatrixTexture=function(e,t){return this.throwIfDisposed(),createFloat32MatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.createFloat16MatrixTexture=function(e,t){return this.throwIfDisposed(),createFloat16MatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.createUnsignedBytesMatrixTexture=function(e,t){return this.throwIfDisposed(),createUnsignedBytesMatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.uploadPixelDataToTexture=function(e,t){this.throwIfDisposed(),uploadPixelDataToTexture(this.gl,e,t);},e.prototype.createFloat16PackedMatrixTexture=function(e,t){return this.throwIfDisposed(),createFloat16PackedMatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.createPackedMatrixTexture=function(e,t){return this.throwIfDisposed(),createPackedMatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.deleteMatrixTexture=function(e){var t=this;this.throwIfDisposed(),this.outputTexture===e&&(unbindColorTextureFromFramebuffer(this.gl,this.framebuffer),this.outputTexture=null),callAndCheck(this.gl,function(){return t.gl.deleteTexture(e)});},e.prototype.uploadMatrixToTexture=function(e,t,n,r){this.throwIfDisposed();var o=getNumChannels();return uploadMatrixToTexture(this.gl,e,t,n,r,o,this.textureConfig)},e.prototype.uploadMatrixToPackedTexture=function(e,t,n,r,o,a,i){return this.throwIfDisposed(),uploadMatrixToPackedTexture(this.gl,e,t,n,r,o,a,i,this.textureConfig)},e.prototype.downloadFloat32MatrixFromOutputTexture=function(e,t,n){var r=this;return this.downloadMatrixDriver(e,function(){return downloadFloat32MatrixFromOutputTexture(r.gl,t,n,r.textureConfig)})},e.prototype.downloadByteEncodedFloatMatrixFromOutputTexture=function(e,t,n){var r=this;return this.downloadMatrixDriver(e,function(){return downloadByteEncodedFloatMatrixFromOutputTexture(r.gl,t,n,r.textureConfig)})},e.prototype.downloadPackedMatrixFromBuffer=function(e,t,n,r,o,a){return downloadPackedMatrixFromBuffer(this.gl,e,t,n,r,o,a,this.textureConfig)},e.prototype.downloadFloat32MatrixFromBuffer=function(e,t,n){return downloadFloat32MatrixFromBuffer(this.gl,e,t,n,this.textureConfig)},e.prototype.maybeCreateBufferFromTexture=function(e,t,n){this.bindTextureToFrameBuffer(e);var r=maybeCreateBufferFromOutputTexture(this.gl,e,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r},e.prototype.createAndWaitForFence=function(){var e=this.createFence(this.gl);return this.pollFence(e)},e.prototype.createFence=function(e){var t,n,r=this;if(ENV.get("WEBGL_FENCE_API_ENABLED")){var o=e,a=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=function(){var e=o.clientWaitSync(a,0,0);return e===o.ALREADY_SIGNALED||e===o.CONDITION_SATISFIED},t=a;}else ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=function(){return r.isQueryAvailable(t,ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}):n=function(){return !0};return {query:t,isFencePassed:n}},e.prototype.downloadMatrixFromPackedTexture=function(e,t,n,r,o,a){var i=this;return this.downloadMatrixDriver(e,function(){return downloadMatrixFromPackedOutputTexture(i.gl,t,n,r,o,a,i.textureConfig)})},e.prototype.createProgram=function(e){this.throwIfDisposed();var t=this.gl,n=createFragmentShader(t,e),r=createVertexShader$1(t),o=createProgram(t);return callAndCheck(t,function(){return t.attachShader(o,r)}),callAndCheck(t,function(){return t.attachShader(o,n)}),linkProgram(t,o),this.autoDebugValidate&&validateProgram(t,o),this.vertexAttrsAreBound||(this.setProgram(o),this.vertexAttrsAreBound=bindVertexProgramAttributeStreams(t,this.program,this.vertexBuffer)),o},e.prototype.deleteProgram=function(e){var t=this;this.throwIfDisposed(),e===this.program&&(this.program=null),null!=e&&callAndCheck(this.gl,function(){return t.gl.deleteProgram(e)});},e.prototype.setProgram=function(e){var t=this;this.throwIfDisposed(),this.program=e,null!=this.program&&this.autoDebugValidate&&validateProgram(this.gl,this.program),callAndCheck(this.gl,function(){return t.gl.useProgram(e)});},e.prototype.getUniformLocation=function(e,t,n){return void 0===n&&(n=!0),this.throwIfDisposed(),n?getProgramUniformLocationOrThrow(this.gl,e,t):getProgramUniformLocation(this.gl,e,t)},e.prototype.getAttributeLocation=function(e,t){var n=this;return this.throwIfDisposed(),callAndCheck(this.gl,function(){return n.gl.getAttribLocation(e,t)})},e.prototype.getUniformLocationNoThrow=function(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)},e.prototype.setInputMatrixTexture=function(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),bindTextureToProgramUniformSampler(this.gl,this.program,e,t,n);},e.prototype.setOutputMatrixTexture=function(e,t,n){this.setOutputMatrixTextureDriver(e,n,t);},e.prototype.setOutputPackedMatrixTexture=function(e,t,n){this.throwIfDisposed();var r=getPackedMatrixTextureShapeWidthHeight(t,n),o=r[0],a=r[1];this.setOutputMatrixTextureDriver(e,o,a);},e.prototype.setOutputMatrixWriteRegion=function(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t);},e.prototype.setOutputPackedMatrixWriteRegion=function(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")},e.prototype.debugValidate=function(){null!=this.program&&validateProgram(this.gl,this.program),validateFramebuffer(this.gl);},e.prototype.executeProgram=function(){this.throwIfDisposed(),this.throwIfNoProgram();var e=this.gl;this.autoDebugValidate&&this.debugValidate(),callAndCheck(e,function(){return e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0)});},e.prototype.blockUntilAllProgramsCompleted=function(){var e=this;this.throwIfDisposed(),callAndCheck(this.gl,function(){return e.gl.finish()});},e.prototype.getQueryTimerExtension=function(){return null==this.disjointQueryTimerExtension&&(this.disjointQueryTimerExtension=getExtensionOrThrow(this.gl,2===ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension},e.prototype.getQueryTimerExtensionWebGL2=function(){return this.getQueryTimerExtension()},e.prototype.getQueryTimerExtensionWebGL1=function(){return this.getQueryTimerExtension()},e.prototype.beginQuery=function(){if(2===ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){var e=this.gl,t=this.getQueryTimerExtensionWebGL2(),n=e.createQuery();return e.beginQuery(t.TIME_ELAPSED_EXT,n),n}var r=this.getQueryTimerExtensionWebGL1(),o=r.createQueryEXT();return r.beginQueryEXT(r.TIME_ELAPSED_EXT,o),o},e.prototype.endQuery=function(){if(2!==ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){var e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT);}else{var t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);}},e.prototype.waitForQueryAndGetTime=function(e){return __awaiter(this,void 0,void 0,function(){var t=this;return __generator(this,function(n){switch(n.label){case 0:return [4,repeatedTry(function(){return t.disposed||t.isQueryAvailable(e,ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))})];case 1:return n.sent(),[2,this.getQueryTime(e,ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))]}})})},e.prototype.getQueryTime=function(e,t){if(0===t)return null;if(2===t){var n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}var r=this.getQueryTimerExtensionWebGL1();return r.getQueryObjectEXT(e,r.QUERY_RESULT_EXT)/1e6},e.prototype.isQueryAvailable=function(e,t){if(0===t)return !0;if(2===t){var n=this.gl,r=this.getQueryTimerExtensionWebGL2(),o=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint}o=(r=this.getQueryTimerExtensionWebGL1()).getQueryObjectEXT(e,r.QUERY_RESULT_AVAILABLE_EXT);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint},e.prototype.pollFence=function(e){var t=this;return new Promise(function(n){t.addItemToPoll(function(){return e.isFencePassed()},function(){return n()});})},e.prototype.pollItems=function(){for(var e=binSearchLastTrue(this.itemsToPoll.map(function(e){return e.isDoneFn})),t=0;t<=e;++t){(0, this.itemsToPoll[t].resolveFn)();}this.itemsToPoll=this.itemsToPoll.slice(e+1);},e.prototype.addItemToPoll=function(e,t){var n=this;this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1||repeatedTry(function(){return n.pollItems(),0===n.itemsToPoll.length});},e.prototype.bindTextureToFrameBuffer=function(e){this.throwIfDisposed(),bindColorTextureToFramebuffer(this.gl,e,this.framebuffer),this.autoDebugValidate&&validateFramebuffer(this.gl);},e.prototype.unbindTextureToFrameBuffer=function(){null!=this.outputTexture?(bindColorTextureToFramebuffer(this.gl,this.outputTexture,this.framebuffer),this.autoDebugValidate&&validateFramebuffer(this.gl)):unbindColorTextureFromFramebuffer(this.gl,this.framebuffer);},e.prototype.downloadMatrixDriver=function(e,t){this.bindTextureToFrameBuffer(e);var n=t();return this.unbindTextureToFrameBuffer(),n},e.prototype.setOutputMatrixTextureDriver=function(e,t,n){this.throwIfDisposed();var r=this.gl;bindColorTextureToFramebuffer(r,e,this.framebuffer),this.autoDebugValidate&&validateFramebuffer(r),this.outputTexture=e,callAndCheck(r,function(){return r.viewport(0,0,t,n)}),callAndCheck(r,function(){return r.scissor(0,0,t,n)});},e.prototype.setOutputMatrixWriteRegionDriver=function(e,t,n,r){var o=this;this.throwIfDisposed(),callAndCheck(this.gl,function(){return o.gl.scissor(e,t,n,r)});},e.prototype.throwIfDisposed=function(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")},e.prototype.throwIfNoProgram=function(){if(null==this.program)throw new Error("No GPU program is currently set.")},e}();function binSearchLastTrue(e){for(var t=0,n=e.length-1,r=-1;t<=n;){var o=t+n>>1;e[o]()?(r=o,t=o+1):n=o-1;}return r}function compileProgram(e,t,n,r){for(var o=t.userCode,a=n.map(function(e,n){var r={logicalShape:e.shape,texShape:e.isUniform?null:e.texData.texShape,isUniform:e.isUniform,isPacked:!e.isUniform&&e.texData.isPacked};return {name:t.variableNames[n],shapeInfo:r}}),i=a.map(function(e){return e.shapeInfo}),s={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked},u=makeShader(a,s,o,!0===t.supportsBroadcasting,t.usesPackedTextures),l=e.createProgram(u),c={},p=0;p<t.variableNames.length;p++){var d=t.variableNames[p];c[d]=e.getUniformLocation(l,d,!1);}return {program:t,source:u,webGLProgram:l,uniformLocations:c,gpgpu:e,inShapeInfos:i,outShapeInfo:s}}function validateBinaryAndProgram(e,t){if(e.length!==t.length)throw Error("Binary was compiled with "+e.length+" inputs, but was executed with "+t.length+" inputs");e.forEach(function(e,n){var r=e.logicalShape,o=t[n],a=o.shape;if(!arraysEqual(r,a))throw Error("Binary was compiled with different shapes than the current args. Shapes "+r+" and "+a+" must match");if(!e.isUniform||!o.isUniform){var i=e.texShape,s=o.isUniform?null:o.texData.texShape;if(!arraysEqual(i,s))throw Error("Binary was compiled with different texture shapes than the current args. Shape "+i+" and "+s+" must match")}});}function runProgram(e,t,n,r){validateBinaryAndProgram(e.inShapeInfos,t),validateBinaryAndProgram([e.outShapeInfo],[n]);var o=n.texData.texture,a=n.texData.texShape,i=e.gpgpu;n.texData.isPacked?i.setOutputPackedMatrixTexture(o,a[0],a[1]):i.setOutputMatrixTexture(o,a[0],a[1]),i.setProgram(e.webGLProgram),t.forEach(function(t,n){var r=e.program.variableNames[n],o=e.uniformLocations[r];if(null!=o){if(t.isUniform){if(1===sizeFromShape(t.shape))i.gl.uniform1f(o,t.uniformValues[0]);else{var a=t.uniformValues;a instanceof Float32Array||(a=new Float32Array(a)),i.gl.uniform1fv(o,a);}return}var s=t.texData.texture;i.setInputMatrixTexture(s,o,n);}}),null!=r&&r(i,e.webGLProgram),i.executeProgram();}function makeShaderKey(e,t,n){var r="";t.concat(n).forEach(function(e){r+=e.shape+"_"+(e.isUniform?"uniform":e.texData.texShape);});var o=e.userCode,a=(!0===e.supportsBroadcasting).toString(),i=e.constructor.name;return i+="_"+a+"_"+r+"_"+o}var Im2ColProgram=function(){return function(e,t,n){this.variableNames=["A"],this.outputShape=e;var r=n.filterWidth,o=n.inChannels,a=n.strideWidth,i=n.strideHeight,s=n.padInfo,u=n.outWidth,l=n.dilationWidth,c=n.dilationHeight,p=s.left,d=s.top,h=o*r;this.userCode="\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            int blockIndex = rc.y + col;\n            int pos = rc.x + row;\n\n            if(blockIndex >= "+e[1]+" || pos >= "+e[0]+") continue;\n\n            int offsetY = int(blockIndex / ("+u+")) * "+i+" - "+d+";\n            int d0 = offsetY + "+c+" * (pos / "+h+");\n\n            if(d0 >= "+t[0]+" || d0 < 0) continue;\n\n            int offsetX = int(mod(float(blockIndex), "+u+".) * "+a+". - "+p+".);\n            int d1 = offsetX + "+l+" * (int(mod(float(pos), "+h+".) / "+o+".));\n\n            if(d1 >= "+t[1]+" || d1 < 0) continue;\n\n            result[row * 2 + col] = getA(d0, d1, int(mod(float(pos), "+o+".)));\n          }\n        }\n\n        gl_FragColor = result;\n      }\n    ";}}(),LRNProgram=function(){return function(e,t,n,r,o){this.variableNames=["x"],this.outputShape=[];var a,i=t,s=e[3]-1;this.outputShape=e;var u="float("+n+") + float("+r+") * sum";a=.5===o?"inversesqrt("+u+")":1===o?"1.0/("+u+")":"exp(log("+u+") * float(-"+o+"));",this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -"+i+"; j <= "+i+"; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  "+s+") {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * "+a+";\n        setOutput(val);\n      }\n    ";}}(),LRNGradProgram=function(){return function(e,t,n,r,o){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=o,this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < "+this.depth+"; ++d) {\n          int depthBegin = int(max(0.0, float(d - "+t+")));\n          int depthEnd = int(min(float("+this.depth+"),\n              float(d + "+t+" + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = "+this.depth+";\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float("+r+") * norm + float("+n+");\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float("+r+")\n                * float("+o+")\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * "+o+");\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    ";}}(),MaxPool2DBackpropProgram=function(){return function(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;var t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,o=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=o-1-e.padInfo.top,s=a-1-e.padInfo.left,u=o*a-1;this.userCode="\n      const ivec2 pads = ivec2("+i+", "+s+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+o+";\n          wR += "+r+") {\n          float dyR = float(dyRCorner + wR) / "+t+".0;\n\n          if (dyR < 0.0 || dyR >= "+e.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < "+a+"; wC++) {\n            float dyC = float(dyCCorner + wC) / "+n+".0;\n\n            if (dyC < 0.0 || dyC >= "+e.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = "+u+" - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * "+a+" + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";}}(),MatMulProgram=function(){return function(e,t,n,r){void 0===n&&(n=!1),void 0===r&&(r=!1),this.variableNames=["matrixA","matrixB"];var o=e[0],a=n?e[2]:e[1],i=r?t[1]:t[2],s=n?e[1]:e[2];this.outputShape=[o,a,i];var u=function(e,t){return n?"batch, "+t+" + "+e+", aRow":"batch, aRow, "+t+" + "+e},l=function(e,t){return r?"batch, bCol, "+t+" + "+e:"batch, "+t+" + "+e+", bCol"},c=4*Math.floor(s/4),p=s%4;this.userCode=" float dotARowBCol(int batch, int aRow, int bCol) {\n      float result = 0.0;\n      for (int i = 0; i < "+c+"; i += 4) {\n        vec4 a = vec4(\n          getMatrixA("+u(0,"i")+"),\n          getMatrixA("+u(1,"i")+"),\n          getMatrixA("+u(2,"i")+"),\n          getMatrixA("+u(3,"i")+")\n        );\n        vec4 b = vec4(\n          getMatrixB("+l(0,"i")+"),\n          getMatrixB("+l(1,"i")+"),\n          getMatrixB("+l(2,"i")+"),\n          getMatrixB("+l(3,"i")+")\n        );\n\n        result += dot(a, b);\n      }\n\n      if ("+(1===p)+") {\n        result += getMatrixA("+u(0,c)+") *\n          getMatrixB("+l(0,c)+");\n      } else if ("+(2===p)+") {\n        vec2 a = vec2(\n          getMatrixA("+u(0,c)+"),\n          getMatrixA("+u(1,c)+")\n        );\n        vec2 b = vec2(\n          getMatrixB("+l(0,c)+"),\n          getMatrixB("+l(1,c)+")\n        );\n        result += dot(a, b);\n      } else if ("+(3===p)+") {\n        vec3 a = vec3(\n          getMatrixA("+u(0,c)+"),\n          getMatrixA("+u(1,c)+"),\n          getMatrixA("+u(2,c)+")\n        );\n        vec3 b = vec3(\n          getMatrixB("+l(0,c)+"),\n          getMatrixB("+l(1,c)+"),\n          getMatrixB("+l(2,c)+")\n        );\n        result += dot(a, b);\n      }\n\n      return result;\n    }\n\n    void main() {\n      ivec3 resBRC = getOutputCoords();\n      setOutput(dotARowBCol(resBRC.x, resBRC.y, resBRC.z));\n    }\n    ";}}(),MatMulPackedProgram=function(){return function(e,t,n,r,o){void 0===r&&(r=!1),void 0===o&&(o=!1),this.variableNames=["matrixA","matrixB"],this.usesPackedTextures=!0,this.outputShape=n;var a=r?e[0]:e[1],i=Math.ceil(a/2),s=r?"i * 2, rc.x":"rc.x, i * 2",u=o?"rc.y, i * 2":"i * 2, rc.y",l=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],c=o?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"];this.userCode="\n      const float sharedDimension = "+i+".0;\n\n      vec4 dot2x2ARowBCol(ivec2 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < "+i+"; i++) {\n          vec4 a = getMatrixA("+s+");\n          vec4 b = getMatrixB("+u+");\n\n          result += ("+l[0]+" * "+c[0]+") + ("+l[1]+" * "+c[1]+");\n        }\n        return result;\n      }\n\n      void main() {\n        ivec2 rc = getOutputCoords();\n        setOutput(dot2x2ARowBCol(rc));\n      }\n    ";}}(),MultinomialProgram=function(){function e(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode="\n      uniform float seed;\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < "+(t-1)+"; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float("+(t-1)+"));\n      }\n    ";}return e.prototype.getCustomSetupFunc=function(e){var t=this;return function(n,r){null==t.seedLoc&&(t.seedLoc=n.getUniformLocation(r,"seed")),n.gl.uniform1f(t.seedLoc,e);}},e}(),OneHotProgram=function(){return function(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode="\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float("+r+"), float("+n+"),\n                      float(index == coords.y)));\n      }\n    ";}}();function getVecChannels(e,t){return ["x","y","z","w","u","v"].slice(0,t).map(function(t){return e+"."+t})}function getChannels(e,t){return 1===t?[e]:getVecChannels(e,t)}function getSourceCoords$1(e,t){if(1===e)return "rc";for(var n="",r=0;r<e;r++)n+=t[r],r<e-1&&(n+=",");return n}var PackProgram=function(){return function(e){this.variableNames=["A"],this.isPackShader=!0,this.outputShape=e;var t=e.length;if(0===t)this.userCode="\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      ";else{var n=getChannels("rc",t),r=getCoordsDataType(t),o=getOutOfBoundsCondition(t,e,n),a=getSetup(t,e[e.length-1],e[e.length-2],n),i=getOutput(e,n);this.userCode="\n        void main() {\n          "+r+" rc = getOutputCoords();\n\n          if("+o+") {\n            setOutput(vec4(0));\n          } else {\n            "+a+"\n\n            setOutput(vec4("+i+"));\n          }\n        }\n      ";}}}();function getSourceCoordsArr(e,t){for(var n=[],r=0;r<=1;r++)for(var o=0;o<=1;o++){for(var a=(0===r?"r":"rp1")+", "+(0===o?"c":"cp1"),i=2;i<e;i++)a=t[t.length-1-i]+","+a;n.push(a);}return n}function getOutOfBoundsCondition(e,t,n){if(1===e)return "rc > "+t[0];for(var r="",o=e-2;o<e;o++)r+=n[o]+" >= "+t[o],o<e-1&&(r+="||");return r}function getSetup(e,t,n,r){if(1===e)return "";var o=r.slice(-2);return "\n    int r = "+o[0]+";\n    int c = "+o[1]+";\n    int rp1 = r + 1;\n    int cp1 = c + 1;\n\n    bool cEdge = cp1 >= "+t+";\n    bool rEdge = rp1 >= "+n+";\n  "}function getOutput(e,t){var n=e.length,r=getSourceCoordsArr(n,t);return 1===n?"getA(rc),\n            rc + 1 >= "+e[0]+" ? 0. : getA(rc + 1),\n            0, 0":"getA("+r[0]+"),\n          cEdge ? 0. : getA("+r[1]+"),\n          rEdge ? 0. : getA("+r[2]+"),\n          rEdge || cEdge ? 0. : getA("+r[3]+")"}var PadProgram=function(){return function(e,t,n){this.variableNames=["x"],this.outputShape=t.map(function(t,n){return t[0]+e[n]+t[1]});var r=e.length,o=getCoordsDataType(r),a=t.map(function(e){return e[0]}).join(","),i=t.map(function(t,n){return t[0]+e[n]}).join(","),s=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);this.userCode=1!==r?"\n      "+o+" start = "+o+"("+a+");\n      "+o+" end = "+o+"("+i+");\n\n      void main() {\n        "+o+" outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(float("+n+"));\n        } else {\n          "+o+" coords = outC - start;\n          setOutput(getX("+s+"));\n        }\n      }\n    ":"\n        int start = "+a+";\n        int end = "+i+";\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(float("+n+"));\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      ";}}(),Pool2DProgram=function(){return function(e,t,n){if(this.variableNames=["x"],"avg"===t&&n)throw new Error("Cannot compute positions for average pool.");var r=e.filterWidth,o=e.strideHeight,a=e.strideWidth,i=e.dilationHeight,s=e.dilationWidth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;var d="avg"===t,h="0.0";if(d||(h="-1.0 / 0.0"),n)this.userCode="\n        const ivec2 strides = ivec2("+o+", "+a+");\n        const ivec2 pads = ivec2("+c+", "+p+");\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < "+u+";\n              wR += "+i+") {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= "+e.inHeight+") {\n              continue;\n            }\n\n            for (int wC = 0; wC < "+l+";\n                wC += "+s+") {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= "+e.inWidth+") {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value >= currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = wR * "+l+" + wC;\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      ";else{var f=t+"("+t+"("+t+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"avg"===t&&(f="avgValue / count");var m=4*Math.floor(r/4),g=r%4,v="\n      if ("+d+") {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = max(values, minMaxValue);\n      }\n    ";this.userCode="\n      const ivec2 strides = ivec2("+o+", "+a+");\n      const ivec2 pads = ivec2("+c+", "+p+");\n      const float initializationValue = "+h+";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= "+e.inWidth+") {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4("+h+");\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < "+u+";\n            wR += "+i+") {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= "+e.inHeight+") {\n            continue;\n          }\n\n          for (int wC = 0; wC < "+m+"; wC += 4) {\n            int xC = xCCorner + wC * "+s+";\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + "+s+", d),\n              getValue(batch, xR, xC + 2 * "+s+", d),\n              getValue(batch, xR, xC + 3 * "+s+", d)\n            );\n\n            "+v+"\n          }\n\n          int xC = xCCorner + "+m+";\n          if ("+(1===g)+") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            "+v+"\n          } else if ("+(2===g)+") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + "+s+", d),\n              initializationValue,\n              initializationValue\n            );\n\n            "+v+"\n          } else if ("+(3===g)+") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + "+s+", d),\n              getValue(batch, xR, xC + 2 * "+s+", d),\n              initializationValue\n            );\n\n            "+v+"\n          }\n        }\n        setOutput("+f+");\n      }\n    ";}}}(),ReduceProgram=function(){return function(e,t){this.variableNames=["x"];var n=e.windowSize,r=e.batchSize,o=e.inSize,a=Math.ceil(o/n);this.outputShape=[r,a];var i="0.0",s="";"prod"===t?i="1.0":"min"===t?(i="1.0 / 0.0",s="min"):"max"===t&&(i="-1.0 / 0.0",s="max");var u=t+"("+t+"("+t+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"sum"===t?u="sumValue":"prod"===t?u="prodValue":"all"===t?u="allValue":"any"===t&&(u="anyValue");var l=4*Math.floor(n/4),c=n%4,p="\n      if ("+("sum"===t)+") {\n        sumValue += dot(values, ones);\n      } else if ("+("prod"===t)+") {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = "+s+"(values, minMaxValue);\n      }\n    ",d="vec4";"all"===t?(i="1.0",p="\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      ",d="bvec4"):"any"===t&&(i="0.0",p="\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      ",d="bvec4");var h="";o%n>0&&(h="\n        if (inIdx < 0 || inIdx >= "+o+") {\n          return initializationValue;\n        }\n      "),this.userCode="\n      const float initializationValue = "+i+";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        "+h+"\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * "+n+";\n\n        vec4 minMaxValue = vec4("+i+");\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < "+l+"; i += 4) {\n          int inIdx = inOffset + i;\n          "+d+" values = "+d+"(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          "+p+"\n        }\n\n        int inIdx = inOffset + "+l+";\n        if ("+(1===c)+") {\n          "+d+" values = "+d+"(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          "+p+"\n        } else if ("+(2===c)+") {\n          "+d+" values = "+d+"(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          "+p+"\n        } else if ("+(3===c)+") {\n          "+d+" values = "+d+"(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          "+p+"\n        }\n        setOutput("+u+");\n      }\n    ";}}(),ReshapePackedProgram=function(){return function(e,t){this.variableNames=["A"],this.usesPackedTextures=!0,this.outputShape=e;for(var n="",r=0;r<4;r++){var o="thisRC = rc;";r%2==1&&(o+="thisRC.z += 1;"),r>1&&(o+="thisRC.y += 1;"),n+="\n        "+o+"\n        "+(r>0?"if(thisRC.y < rows && thisRC.z < cols){":"")+"\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result["+r+"] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        "+(r>0?"}":"")+"\n      ";}this.userCode="\n      "+getReshapedInputCoords(t)+"\n      "+getFlatIndex(e)+"\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = "+e[1]+";\n        int cols = "+e[2]+";\n\n        "+n+"\n\n        setOutput(result);\n      }\n    ";}}();function getFlatIndex(e){return "\n    int getFlatIndex(ivec3 coords) {\n      return round("+dotify(["coords.x","coords.y","coords.z"],computeStrides(e).map(function(e){return e.toString()}).concat(["1."]))+");\n    }\n  "}function getReshapedInputCoords(e){return "\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      "+getLogicalCoordinatesFromFlatIndex(["r","c","d"],e)+"\n      return ivec3(r, c, d);\n    }\n  "}var ResizeBilinearBackpropProgram=function(){return function(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t.shape;var r=t.shape,o=r[1],a=r[2],i=e.shape,s=i[1],u=i[2],l=[n&&s>1?o-1:o,n&&u>1?a-1:a],c=[n&&s>1?s-1:s,n&&u>1?u-1:u],p=l[0]/c[0],d=l[1]/c[1],h=1/p,f=1/d,m=2*Math.ceil(h)+2,g=2*Math.ceil(f)+2;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float("+p+");\n        const float widthScale = float("+d+");\n\n        const float invHeightScale = float("+h+");\n        const float invWidthScale = float("+f+");\n\n        const int winHeight = int("+m+");\n        const int winWidth = int("+g+");\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= "+s+") {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= "+u+") {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), "+(o-1)+".0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), "+(a-1)+".0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    ";}}(),ResizeBilinearProgram=function(){return function(e,t,n,r){this.variableNames=["A"],this.outputShape=[];var o=e[0],a=e[1],i=e[2],s=e[3];this.outputShape=[o,t,n,s];var u=[r&&t>1?a-1:a,r&&n>1?i-1:i],l=[r&&t>1?t-1:t,r&&n>1?n-1:n];this.userCode="\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          "+u[0]/l[0]+",\n          "+u[1]/l[1]+");\n      const vec2 inputShapeRC = vec2("+a+".0, "+i+".0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    ";}}(),ResizeNearestNeigborBackpropProgram=function(){return function(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t.shape;var r=t.shape,o=r[1],a=r[2],i=e.shape,s=i[1],u=i[2],l=[n&&s>1?o-1:o,n&&u>1?a-1:a],c=[n&&s>1?s-1:s,n&&u>1?u-1:u],p=l[0]/c[0],d=l[1]/c[1],h=1/p,f=1/d,m=2*Math.ceil(h)+2,g=2*Math.ceil(f)+2;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float("+p+");\n        const float widthScale = float("+d+");\n\n        const float invHeightScale = float("+h+");\n        const float invWidthScale = float("+f+");\n\n        const int winHeight = int("+m+");\n        const int winWidth = int("+g+");\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= "+s+") {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= "+u+") {\n              continue;\n            }\n\n            float sourceFracRow =\n              float("+l[0]+") *\n                (float(dyR) / float("+c[0]+"));\n\n            float sourceFracCol =\n                float("+l[1]+") *\n                  (float(dyC) / float("+c[1]+"));\n\n            int sourceNearestRow = int(min(\n                float(int("+o+") - 1),\n                "+n+" ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int("+a+") - 1),\n                "+n+" ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    ";}}(),ResizeNearestNeighborProgram=function(){return function(e,t,n,r){this.variableNames=["A"],this.outputShape=[];var o=e[0],a=e[1],i=e[2],s=e[3];this.outputShape=[o,t,n,s];var u=[r&&t>1?a-1:a,r&&n>1?i-1:i],l=[r&&t>1?t-1:t,r&&n>1?n-1:n],c=r?"0.5":"0.0";this.userCode="\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          "+u[0]/l[0]+",\n          "+u[1]/l[1]+");\n      const vec2 inputShapeRC = vec2("+a+".0, "+i+".0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + "+c+")));\n\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    ";}}(),ReverseProgram=function(){return function(e,t){this.variableNames=["x"];var n=e.length;if(n>4)throw new Error("WebGL backend: Reverse of rank-"+n+" tensor is not yet supported");if(this.outputShape=e,1!==n){var r=e.map(function(n,r){return function(n){return -1!==t.indexOf(n)&&1!==e[n]?e[n]+" - coords["+n+"] - 1":"coords["+n+"]"}(r)}).join(","),o=getCoordsDataType(n);this.userCode="\n      void main() {\n        "+o+" coords = getOutputCoords();\n        setOutput(getX("+r+"));\n      }\n    ";}else this.userCode="\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX("+e[0]+" - coord - 1));\n        }\n      ";}}(),ScatterProgram=function(){return function(e,t,n,r,o,a,i){void 0===i&&(i=!0),this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;var s=getCoordsDataType(o.length),u=getCoordsDataType(a.length),l="";1===n?l="i":2===n&&(l="i, j");var c="getIndices("+l+")",p="";1===r?p="i":2===r&&(p="i, coords[1]");var d="getUpdates("+p+")",h=t>1?"strides[j]":"strides";this.userCode="\n        "+s+" strides = "+s+"("+o+");\n\n        void main() {\n          "+u+" coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < "+e+"; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < "+t+"; j++) {\n              int index = round("+c+");\n              flattenedIndex += index * "+h+";\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += "+d+";\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      ";}}(),SegmentOpProgram=function(){return function(e,t){this.variableNames=["x","segmentIds"];var n=e.windowSize,r=e.batchSize,o=e.inSize,a=e.numSegments,i=a*Math.ceil(o/n);this.outputShape=[r,i];var s=4*Math.floor(n/4),u=n%4,l="\n        sumValue += dot(values, filter);\n    ",c="";o%n>0&&(c="\n        if (inIdx < 0 || inIdx >= "+o+") {\n          return initializationValue;\n        }\n      ");var p="";o%n>0&&(p="\n        if (inIdx < 0 || inIdx >= "+o+") {\n          return -1.0;\n        }\n      "),this.userCode="\n      const float initializationValue = 0.0;\n\n      float getValue(int batch, int inIdx) {\n        "+c+"\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        "+p+"\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          "+a+")) * float("+n+"));\n        int currentSeg = int(mod(float(outIdx), float("+a+")));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < "+s+"; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          "+l+"\n        }\n\n        int inIdx = inOffset + "+s+";\n        if ("+(1===u)+") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          "+l+"\n        } else if ("+(2===u)+") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          "+l+"\n        } else if ("+(3===u)+") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          "+l+"\n        }\n        setOutput(sumValue);\n      }\n    ";}}(),SelectProgram=function(){return function(e,t,n){var r,o;if(this.variableNames=["c","a","b"],this.outputShape=t,n>4)throw Error("Where for rank "+n+" is not yet supported");if(1===n)o="resRC",r="resRC";else{for(var a=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],s=[],u=0;u<t.length;u++)s.push(""+a[u]),u<e&&i.push(""+a[u]);r=i.join(),o=s.join();}var l=getCoordsDataType(n);this.userCode="\n      void main() {\n        "+l+" resRC = getOutputCoords();\n        float cVal = getC("+r+");\n        if (cVal >= 1.0) {\n          setOutput(getA("+o+"));\n        } else {\n          setOutput(getB("+o+"));\n        }\n      }\n    ";}}(),SliceProgram=function(){function e(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;var t=getCoordsDataType(this.rank),n=getCoords$1(this.rank);this.userCode="\n      uniform "+t+" start;\n\n      void main() {\n        "+t+" sourceLoc = start + getOutputCoords();\n        setOutput(getSource("+n+"));\n      }\n    ";}return e.prototype.getCustomSetupFunc=function(e){var t=this;if(e.length!==this.rank)throw Error("The rank ("+this.rank+") of the program must match the length of start ("+e.length+")");return function(n,r){if(null!=t.startLoc||(t.startLoc=n.getUniformLocationNoThrow(r,"start"),null!=t.startLoc))if(1===t.rank)n.gl.uniform1i(t.startLoc,e[0]);else if(2===t.rank)n.gl.uniform2i(t.startLoc,e[0],e[1]);else if(3===t.rank)n.gl.uniform3i(t.startLoc,e[0],e[1],e[2]);else{if(4!==t.rank)throw Error("Slicing for rank "+t.rank+" is not yet supported");n.gl.uniform4i(t.startLoc,e[0],e[1],e[2],e[3]);}}},e}();function getCoords$1(e){if(1===e)return "sourceLoc";if(2===e)return "sourceLoc.x, sourceLoc.y";if(3===e)return "sourceLoc.x, sourceLoc.y, sourceLoc.z";if(4===e)return "sourceLoc.x, sourceLoc.y, sourceLoc.z, sourceLoc.w";throw Error("Slicing for rank "+e+" is not yet supported")}var StridedSliceProgram=function(){return function(e,t,n,r){this.variableNames=["x"];var o=n.filter(function(e,t){return -1===r.indexOf(t)});this.outputShape=o;var a=n.length,i=getCoordsDataType(n.length),s=getCoordsDataType(o.length),u="";if(1===a)u="coords * strides + begin";else{var l=0;u=n.map(function(e,t){return -1===r.indexOf(t)?(l++,1===o.length?"coords * strides["+t+"] + begin["+t+"]":"coords["+(l-1)+"] * strides["+t+"] + begin["+t+"]"):"begin["+t+"]"}).join(",");}this.userCode="\n      "+i+" begin = "+i+"("+e+");\n      "+i+" strides = "+i+"("+t+");\n\n      void main() {\n        "+s+" coords = getOutputCoords();\n        setOutput(getX("+u+"));\n      }\n    ";}}(),TextureManager=function(){function e(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={};}return e.prototype.acquireTexture=function(e,t,n){var r,o=getPhysicalFromLogicalTextureType(t,n),a=getKeyFromTextureShape(e,o,n);if(a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]),this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this.log();var i=this.freeTextures[a].shift();return this.usedTextures[a].push(i),i}return this.numUsedTextures++,this.log(),o===PhysicalTextureType.PACKED_2X2_FLOAT32?r=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):o===PhysicalTextureType.PACKED_2X2_FLOAT16?r=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):o===PhysicalTextureType.UNPACKED_FLOAT32?r=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):o===PhysicalTextureType.UNPACKED_FLOAT16?r=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):o===PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE&&(r=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(r),r},e.prototype.releaseTexture=function(e,t,n,r){if(null!=this.freeTextures){var o=getKeyFromTextureShape(t,getPhysicalFromLogicalTextureType(n,r),r);o in this.freeTextures||(this.freeTextures[o]=[]),this.freeTextures[o].push(e),this.numFreeTextures++,this.numUsedTextures--;var a=this.usedTextures[o],i=a.indexOf(e);if(i<0)throw new Error("Cannot release a texture that was never provided by this texture manager");a.splice(i,1),this.log();}},e.prototype.log=function(){if(this.logEnabled){var e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",this.numFreeTextures+" / "+this.numUsedTextures,"("+e+")");}},e.prototype.getNumUsedTextures=function(){return this.numUsedTextures},e.prototype.getNumFreeTextures=function(){return this.numFreeTextures},e.prototype.dispose=function(){var e=this;if(null!=this.freeTextures){for(var t in this.freeTextures)this.freeTextures[t].forEach(function(t){e.gpgpu.deleteMatrixTexture(t);});for(var t in this.usedTextures)this.usedTextures[t].forEach(function(t){e.gpgpu.deleteMatrixTexture(t);});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0;}},e}();function getPhysicalFromLogicalTextureType(e,t){if(e===TextureUsage.UPLOAD)return t?PhysicalTextureType.PACKED_2X2_FLOAT32:PhysicalTextureType.UNPACKED_FLOAT32;if(e===TextureUsage.RENDER||null==e)return t?ENV.get("WEBGL_RENDER_FLOAT32_ENABLED")?PhysicalTextureType.PACKED_2X2_FLOAT32:PhysicalTextureType.PACKED_2X2_FLOAT16:ENV.get("WEBGL_RENDER_FLOAT32_ENABLED")?PhysicalTextureType.UNPACKED_FLOAT32:PhysicalTextureType.UNPACKED_FLOAT16;if(e===TextureUsage.DOWNLOAD||e===TextureUsage.PIXELS)return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;throw new Error("Unknown logical texture type "+e)}function getKeyFromTextureShape(e,t,n){return e[0]+"_"+e[1]+"_"+t+"_"+n}var TileProgram=function(){return function(e,t){this.variableNames=["A"];for(var n=new Array(e.length),r=0;r<n.length;r++)n[r]=e[r]*t[r];this.outputShape=n,this.rank=n.length;var o=getCoordsDataType(this.rank),a=getSourceCoords$2(e);this.userCode="\n      void main() {\n        "+o+" resRC = getOutputCoords();\n        setOutput(getA("+a+"));\n      }\n    ";}}();function getSourceCoords$2(e){var t=e.length;if(t>5)throw Error("Tile for rank "+t+" is not yet supported");if(1===t)return "imod(resRC, "+e[0]+")";for(var n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[],o=0;o<e.length;o++)r.push("imod("+n[o]+", "+e[o]+")");return r.join()}var TransposeProgram=function(){return function(e,t){this.variableNames=["A"];for(var n=new Array(e.length),r=0;r<n.length;r++)n[r]=e[t[r]];this.outputShape=n,this.rank=n.length;var o=getCoordsDataType(this.rank),a=getSwitchedCoords(t);this.userCode="\n    void main() {\n      "+o+" resRC = getOutputCoords();\n      setOutput(getA("+a+"));\n    }\n    ";}}();function getSwitchedCoords(e){var t=e.length;if(t>6)throw Error("Transpose for rank "+t+" is not yet supported");for(var n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t),o=0;o<e.length;o++)r[e[o]]=n[o];return r.join()}var ERF_P=.3275911,ERF_A1=.254829592,ERF_A2=-.284496736,ERF_A3=1.421413741,ERF_A4=-1.453152027,ERF_A5=1.061405429,SELU_SCALEALPHA=1.7580993408473768,SELU_SCALE=1.0507009873554805,UnaryOpProgram=function(){function e(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode="\n      uniform float NAN;\n      float unaryOperation(float x) {\n        "+t+"\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    ";}return e.prototype.getCustomSetupFunc=function(){var e=this;return function(t,n){null==e.startLoc&&(e.startLoc=t.getUniformLocationNoThrow(n,"NAN"),null==e.startLoc)||t.gl.uniform1f(e.startLoc,NaN);}},e}(),CHECK_NAN_SNIPPET$1="if (isNaN(x)) return x;",ABS="return abs(x);",RELU=CHECK_NAN_SNIPPET$1+"\n  return (x < 0.0) ? 0.0 : x;\n",ELU="return (x >= 0.0) ? x : (exp(x) - 1.0);",SELU="\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = "+SELU_SCALEALPHA+";\n  float scale = "+SELU_SCALE+";\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n";function STEP(e){return void 0===e&&(e=0),CHECK_NAN_SNIPPET$1+"\n    return x > 0.0 ? 1.0 : float("+e+");\n  "}var NEG="return -x;",CEIL="return ceil(x);",FLOOR="return floor(x);",SIGN="\n  if (isNaN(x)) { return 0.0; }\n  return sign(x);\n",ROUND="\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n",EXP="return exp(x);",EXPM1="return exp(x) - 1.0;",LOG="if (x < 0.0) return NAN;\n  return log(x);",LOG1P="return log(1.0 + x);",SQRT="return sqrt(x);",RSQRT="return inversesqrt(x);",SIGMOID="return 1.0 / (1.0 + exp(-1.0 * x));",SOFTPLUS="\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n",SIN=CHECK_NAN_SNIPPET$1+"\n  return sin(x);\n",COS=CHECK_NAN_SNIPPET$1+"\n  return cos(x);\n",TAN="return tan(x);",ASIN="return asin(x);",ACOS="return acos(x);",ATAN=CHECK_NAN_SNIPPET$1+"\n  return atan(x);\n",SINH="\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n",COSH="\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n",TANH="\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n",ASINH="return log(x + sqrt(x * x + 1.0));",ACOSH=CHECK_NAN_SNIPPET$1+"\n  if (x < 1.0) return NAN;\n  return log(x + sqrt(x * x - 1.0));",ATANH=CHECK_NAN_SNIPPET$1+"\n  if ((x < -1.0) || (x > 1.0)) return NAN;\n  return (log(1.0 + x) - log(1.0 - x)) / 2.0;",ERF='\n  // Error function is calculated approximately with elementary function.\n  // See "Handbook of Mathematical Functions with 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s=1===i?o.as2D(1,-1):o,u=ENV.engine.runKernel(function(e){return e.multinomial(s,r,t,n)},{logits2D:s});return 1===i?u.as1D():u}function oneHot_(e,t,n,r){void 0===n&&(n=1),void 0===r&&(r=0);var o=convertToTensor(e,"indices","oneHot","int32");if(t<2)throw new Error("Error in oneHot: depth must be >=2, but it is "+t);return ENV.engine.runKernel(function(e){return e.oneHot(o,t,n,r)},{$indices:o},function(e){return {$indices:function(){return zeros(o.shape,"float32")}}})}function fromPixels_(e,t){if(void 0===t&&(t=3),t>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");return ENV.engine.fromPixels(e,t)}function toPixels(e,t){return __awaiter(this,void 0,void 0,function(){var n,r,o,a,i,s,u,l,c,p,d,h,f,m,g,v,y,x,T,w;return __generator(this,function(E){switch(E.label){case 0:if(n=convertToTensor(e,"img","toPixels"),e instanceof Tensor||(n=n.toInt()),2!==n.rank&&3!==n.rank)throw new Error("toPixels only supports rank 2 or 3 tensors, got rank "+n.rank+".");if(r=n.shape.slice(0,2),o=r[0],a=r[1],(i=2===n.rank?1:n.shape[2])>4||2===i)throw new Error("toPixels only supports depth of size 1, 3 or 4 but got "+i);return s=n.min(),u=n.max(),[4,s.data()];case 1:return l=E.sent()[0],[4,u.data()];case 2:if(c=E.sent()[0],s.dispose(),u.dispose(),"float32"===n.dtype){if(l<0||c>1)throw new Error("Tensor values for a float32 Tensor must be in the range [0 - 1] but got range ["+l+" - "+c+"].")}else{if("int32"!==n.dtype)throw new Error("Unsupported type for toPixels: "+n.dtype+". Please use float32 or int32 tensors.");if(l<0||c>255)throw new Error("Tensor values for a int32 Tensor must be in the range [0 - 255] but got range ["+l+" - "+c+"].")}return [4,n.data()];case 3:for(p=E.sent(),d="float32"===n.dtype?255:1,h=new Uint8ClampedArray(a*o*4),f=0;f<o*a;++f)m=void 0,g=void 0,v=void 0,y=void 0,1===i?(m=p[f]*d,g=p[f]*d,v=p[f]*d,y=255):3===i?(m=p[3*f]*d,g=p[3*f+1]*d,v=p[3*f+2]*d,y=255):4===i&&(m=p[4*f]*d,g=p[4*f+1]*d,v=p[4*f+2]*d,y=p[4*f+3]*d),h[(x=4*f)+0]=Math.round(m),h[x+1]=Math.round(g),h[x+2]=Math.round(v),h[x+3]=Math.round(y);return null!=t&&(t.width=a,t.height=o,T=t.getContext("2d"),w=new ImageData(h,a,o),T.putImageData(w,0,0)),n!==e&&n.dispose(),[2,h]}})})}function reshape_(e,t){var n=convertToTensor(e,"x","reshape",null);t=inferFromImplicitShape(t,n.size),assert(n.size===sizeFromShape(t),"new shape and old shape must have the same number of elements.");return ENV.engine.runKernel(function(e){return e.reshape(n,t)},{$x:n},function(e){return {$x:function(){return e.reshape(n.shape)}}})}function squeeze_(e,t){var n=convertToTensor(e,"x","squeeze");return reshape(n,squeezeShape(n.shape,t).newShape)}function cast_(e,t){var n=convertToTensor(e,"x","cast");return ENV.engine.runKernel(function(e){return e.cast(n,t)},{$x:n},function(e){return {$x:function(){return e.clone()}}})}function tile_(e,t){var n=convertToTensor(e,"x","tile");assert(n.rank===t.length,"Error in transpose: rank of input "+n.rank+" must match length of reps "+t+".");return ENV.engine.runKernel(function(e){return e.tile(n,t)},{$x:n},function(e){return {$x:function(){var r=zerosLike(n);if(1===n.rank)for(var o=0;o<t[0];++o)r=r.add(e.slice([o*n.shape[0]],[n.shape[0]]));else if(2===n.rank)for(o=0;o<t[0];++o)for(var a=0;a<t[1];++a)r=r.add(e.slice([o*n.shape[0],a*n.shape[1]],[n.shape[0],n.shape[1]]));else if(3===n.rank)for(o=0;o<t[0];++o)for(a=0;a<t[1];++a)for(var i=0;i<t[2];++i)r=r.add(e.slice([o*n.shape[0],a*n.shape[1],i*n.shape[2]],[n.shape[0],n.shape[1],n.shape[2]]));else{if(4!==n.rank)throw new Error("Gradient for tile operation is not implemented for rank-"+n.rank+" tensors yet.");for(o=0;o<t[0];++o)for(a=0;a<t[1];++a)for(i=0;i<t[2];++i)for(var s=0;s<t[3];++s)r=r.add(e.slice([o*n.shape[0],a*n.shape[1],i*n.shape[2],s*n.shape[3]],[n.shape[0],n.shape[1],n.shape[2],n.shape[3]]));}return r}}})}function pad1d_(e,t,n){return void 0===n&&(n=0),assert(2===t.length,"Invalid number of paddings. Must be length of 2."),pad(e,[t],n)}function pad2d_(e,t,n){return void 0===n&&(n=0),assert(2===t.length&&2===t[0].length&&2===t[1].length,"Invalid number of paddings. Must be length of 2 each."),pad(e,t,n)}function pad3d_(e,t,n){return void 0===n&&(n=0),assert(3===t.length&&2===t[0].length&&2===t[1].length&&2===t[2].length,"Invalid number of paddings. Must be length of 2 each."),pad(e,t,n)}function pad4d_(e,t,n){return void 0===n&&(n=0),assert(4===t.length&&2===t[0].length&&2===t[1].length&&2===t[2].length&&2===t[3].length,"Invalid number of paddings. Must be length of 2 each."),pad(e,t,n)}function pad_(e,t,n){void 0===n&&(n=0);var r=convertToTensor(e,"x","pad");if(0===r.rank)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");var o=t.map(function(e){return e[0]});return ENV.engine.runKernel(function(e){return e.pad(r,t,n)},{$x:r},function(e){return {$x:function(){return e.slice(o,r.shape)}}})}function stack_(e,t){void 0===t&&(t=0);var n=convertToTensorArray(e,"tensors","stack");if(assert(n.length>=1,"Pass at least one tensor to tf.stack"),1===n.length)return n[0].expandDims(t);var r=n[0].rank,o=n[0].shape,a=n[0].dtype;assert(t<=r,"Axis must be <= rank of the tensor"),n.forEach(function(e){assertShapesMatch(o,e.shape,"All tensors passed to stack must have matching shapes");}),n.forEach(function(e){assert(a===e.dtype,"All tensors passed to stack must have matching dtypes");});var i=n.map(function(e){return e.expandDims(t)});return concat(i,t)}function batchToSpaceND_(e,t,n){var r=convertToTensor(e,"x","batchToSpaceND"),o=t.reduce(function(e,t){return e*t});assert(r.rank>=1+t.length,"input rank is "+r.rank+" but should be > than blockShape.length "+t.length),assert(n.length===t.length,"crops.length is "+n.length+" but should be equal to blockShape.length  "+t.length),assert(r.shape[0]%o==0,"input tensor batch is "+r.shape[0]+" but is not divisible by the product of the elements of blockShape "+t.join(" * ")+" === "+o);return ENV.engine.runKernel(function(e){return e.batchToSpaceND(r,t,n)},{$x:r},function(e){return {$x:function(){return e.spaceToBatchND(t,n)}}})}function spaceToBatchND_(e,t,n){var r=convertToTensor(e,"x","spaceToBatchND");assert(r.rank>=1+t.length,"input rank "+r.rank+" should be > than [blockShape] "+t.length),assert(n.length===t.length,"paddings.shape[0] "+n.length+" must be equal to [blockShape] "+t.length),assert(r.shape.reduce(function(e,r,o){return o>0&&o<=t.length?e&&(r+n[o-1][0]+n[o-1][1])%t[o-1]==0:e},!0),"input spatial dimensions "+r.shape.slice(1)+" with paddings "+n.toString()+" must be divisible by blockShapes "+t.toString());return ENV.engine.runKernel(function(e){return e.spaceToBatchND(r,t,n)},{$x:r},function(e){return {$x:function(){return e.batchToSpaceND(t,n)}}})}function unstack_(e,t){void 0===t&&(t=0);for(var n,r=convertToTensor(e,"x","unstack"),o=r.shape[t],a=Array(r.rank-1).fill(0),i=0,s=0;s<r.rank;s++)s!==t&&(a[i]=r.shape[s],i++);n=Array(o).fill(1);var u=Array(r.rank).fill(0),l=r.shape.slice();return n.map(function(e){l[t]=e;var n=r.slice(u,l);return u[t]+=e,n.reshape(a)})}function cumsum_(e,t,n,r){void 0===t&&(t=0),void 0===n&&(n=!1),void 0===r&&(r=!1);var o=convertToTensor(e,"x","cumsum"),a=getAxesPermutation([t|=0],o.rank),i=o;null!=a&&(i=o.transpose(a));var s=getInnerMostAxes(1,o.rank)[0],u=ENV.engine.runKernel(function(e){return e.cumsum(i,s,n,r)},{permutedX:i},function(e){return {permutedX:function(){return e.cumsum(t,n,!r)}}});return null!=a&&(u=u.transpose(a)),u}function expandDims_(e,t){void 0===t&&(t=0);var n=convertToTensor(e,"x","expandDims");assert(t<=n.rank,"Axis must be <= rank of the tensor");var r=n.shape.slice();return t<0&&(assert(-(n.rank+1)<=t,"Axis must be in the interval ["+-(n.rank+1)+", "+n.rank+"]"),t=n.rank+t+1),r.splice(t,0,1),reshape(n,r)}function depthToSpace_(e,t,n){void 0===n&&(n="NHWC");var r=convertToTensor(e,"x","depthToSpace"),o="NHWC"===n?r.shape[1]:r.shape[2],a="NHWC"===n?r.shape[2]:r.shape[3],i="NHWC"===n?r.shape[3]:r.shape[1];return assert(o*t>=0,"Negative dimension size caused by overflow when multiplying\n      "+o+" and "+t+"  for depthToSpace with input shape\n      "+r.shape),assert(a*t>=0,"Negative dimension size caused by overflow when multiplying\n      "+a+" and "+t+" for depthToSpace with input shape\n          "+r.shape),assert(i%(t*t)==0,"Dimension size must be evenly divisible by "+t*t+" but is "+i+" for depthToSpace with input shape "+r.shape),ENV.engine.runKernel(function(e){return e.depthToSpace(r,t,n)},{$x:r})}function setdiff1dAsync_(e,t){return __awaiter(this,void 0,void 0,function(){var n,r,o,a,i,s,u,l,c,p;return __generator(this,function(d){switch(d.label){case 0:return n=convertToTensor(e,"x","setdiff1d"),r=convertToTensor(t,"y","setdiff1d"),assert(n.dtype===r.dtype,"x and y should have the same dtype, but got x ("+n.dtype+") and y ("+r.dtype+")."),assert(1===n.rank,"x should be 1D tensor, but got x ("+n.shape+")."),assert(1===r.rank,"y should be 1D tensor, but got y ("+r.shape+")."),[4,n.data()];case 1:return o=d.sent(),[4,r.data()];case 2:for(a=d.sent(),i=new Set(a),s=0,c=0;c<o.length;c++)i.has(o[c])||s++;for(u=new TensorBuffer([s],n.dtype),l=new TensorBuffer([s],"int32"),c=0,p=0;c<o.length;c++)i.has(o[c])||(u.values[p]=o[c],l.values[p]=c,p++);return [2,[u.toTensor(),l.toTensor()]]}})})}function buffer(e,t,n){return void 0===t&&(t="float32"),new TensorBuffer(e,t=t||"float32",n)}function print(e,t){void 0===t&&(t=!1),console.log(e.toString(t));}var batchToSpaceND=op({batchToSpaceND_:batchToSpaceND_}),cast=op({cast_:cast_}),clone=op({clone_:clone_}),cumsum=op({cumsum_:cumsum_}),depthToSpace=op({depthToSpace_:depthToSpace_}),expandDims=op({expandDims_:expandDims_}),eye=op({eye_:eye_}),fromPixels=op({fromPixels_:fromPixels_}),multinomial=op({multinomial_:multinomial_}),oneHot=op({oneHot_:oneHot_}),pad=op({pad_:pad_}),pad1d=op({pad1d_:pad1d_}),pad2d=op({pad2d_:pad2d_}),pad3d=op({pad3d_:pad3d_}),pad4d=op({pad4d_:pad4d_}),rand=op({rand_:rand_}),randomNormal=op({randomNormal_:randomNormal_}),randomUniform=op({randomUniform_:randomUniform_}),reshape=op({reshape_:reshape_}),spaceToBatchND=op({spaceToBatchND_:spaceToBatchND_}),squeeze=op({squeeze_:squeeze_}),stack=op({stack_:stack_}),tile=op({tile_:tile_}),truncatedNormal=op({truncatedNormal_:truncatedNormal_}),unstack=op({unstack_:unstack_}),setdiff1dAsync=setdiff1dAsync_;function whereImpl(e,t){for(var n=[],r=0;r<t.length;r++)t[r]&&n.push(r);var o=buffer(e,"int32"),a=buffer([n.length,e.length],"int32");for(r=0;r<n.length;r++){var i=o.indexToLoc(n[r]),s=r*e.length;a.values.set(i,s);}return a.toTensor()}var CPU_HANDOFF_SIZE_THRESHOLD=10,BEFORE_PAGING_CONSTANT=300,MATMUL_SHARED_DIM_THRESHOLD=1e3,MathBackendWebGL=function(){function e(e,t){if(void 0===t&&(t=!0),this.gpgpu=e,this.delayedStorage=t,this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.lruDataGPU=[],this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.binaryCache={},this.disposed=!1,ENV.get("WEBGL_VERSION")<1)throw new Error("WebGL is not supported on this device");if(null==e){var n=getWebGLContext(ENV.get("WEBGL_VERSION"));this.gpgpu=new GPGPUContext(n),this.canvas=n.canvas,this.gpgpuCreatedLocally=!0;}else this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;ENV.get("WEBGL_PAGING_ENABLED")&&(this.NUM_BYTES_BEFORE_PAGING=window.screen.height*window.screen.width*window.devicePixelRatio*BEFORE_PAGING_CONSTANT),this.textureManager=new TextureManager(this.gpgpu);}return e.prototype.register=function(e,t,n){if(this.texData.has(e))throw new Error("Data buffer is already registered");this.texData.set(e,{shape:t,dtype:n});},e.prototype.setDataMover=function(e){this.texData=new DataStorage(e);},e.prototype.fromPixels=function(e,t){if(null==e)throw new Error("pixels passed to tf.fromPixels() can not be null");var n=[e.height,e.width],r=[e.height,e.width,t];if(!(e instanceof HTMLVideoElement||e instanceof HTMLImageElement||e instanceof HTMLCanvasElement||e instanceof ImageData))throw new Error("pixels passed to tf.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ImageData, but was "+e.constructor.name);if(e instanceof HTMLVideoElement){if(null==this.fromPixels2DContext){if(!ENV.get("IS_BROWSER"))throw new Error("Can't read pixels from HTMLImageElement outside the browser.");if("complete"!==document.readyState)throw new Error("The DOM is not ready yet. Please call tf.fromPixels() once the DOM is ready. One way to do that is to add an event listener for `DOMContentLoaded` on the document object");this.fromPixels2DContext=document.createElement("canvas").getContext("2d");}this.fromPixels2DContext.canvas.width=e.width,this.fromPixels2DContext.canvas.height=e.height,this.fromPixels2DContext.drawImage(e,0,0,e.width,e.height),e=this.fromPixels2DContext.canvas;}var o=this.makeTensorHandle(n,"int32");this.texData.get(o.dataId).usage=TextureUsage.PIXELS,this.gpgpu.uploadPixelDataToTexture(this.getTexture(o.dataId),e);var a=new FromPixelsProgram(r),i=this.compileAndRun(a,[o]);return this.disposeData(o.dataId),i},e.prototype.makeTensorHandle=function(e,t){var n={};return this.register(n,e,t),{dataId:n,shape:e,dtype:t}},e.prototype.write=function(e,t){if(null==t)throw new Error("MathBackendWebGL.write(): values can not be null");var n=this.texData.get(e),r=n.texture,o=n.texShape,a=n.usage,i=n.dtype,s=n.isPacked;if("complex64"===i)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");null!=r&&(this.releaseTexture(e,r,o,a,s),n.texture=null,n.texShape=null),n.usage=TextureUsage.UPLOAD,n.values=t,this.delayedStorage||this.uploadToGPU(e);},e.prototype.readSync=function(e){var t=this.texData.get(e),n=t.values,r=t.dtype,o=t.complexTensors;if(null!=n)return this.convertAndCacheOnCPU(e);if("string"===r)return n;var a,i,s=null!=this.activeTimers;(s&&(a=performance.now()),"complex64"===r)?i=mergeRealAndImagArrays(o.real.dataSync(),o.imag.dataSync()):i=this.getValuesFromTexture(e);return s&&(this.downloadWaitMs+=performance.now()-a),this.convertAndCacheOnCPU(e,i)},e.prototype.read=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o,a,i,s,u,l,c,p,d,h,f,m,g,v,y;return __generator(this,function(x){switch(x.label){case 0:if(this.pendingRead.has(e))return r=this.pendingRead.get(e),[2,new Promise(function(e){return r.push(e)})];if(o=this.texData.get(e),a=o.texture,i=o.values,s=o.texShape,u=o.isPacked,l=o.shape,null!=i)return [2,this.convertAndCacheOnCPU(e)];if(this.pendingRead.set(e,[]),!ENV.get("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&2===ENV.get("WEBGL_VERSION"))throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");return c=s[1],p=s[0],u&&(t=getPackedMatrixTextureShapeWidthHeight(s[0],s[1]),c=t[0],p=t[1]),d=this.gpgpu.maybeCreateBufferFromTexture(a,p,c),[4,this.gpgpu.createAndWaitForFence()];case 1:return x.sent(),d instanceof WebGLTexture?h=this.getValuesFromTexture(e):u?(f=this.getBatchDim(l),m=1,g=1,l.length&&(n=this.getRowsCols(l),m=n[0],g=n[1]),h=this.gpgpu.downloadPackedMatrixFromBuffer(d,f,m,g,s[0],s[1])):h=this.gpgpu.downloadFloat32MatrixFromBuffer(d,s[0],s[1]),v=this.convertAndCacheOnCPU(e,h),y=this.pendingRead.get(e),this.pendingRead.delete(e),y.forEach(function(e){return e(v)}),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)),[2,v]}})})},e.prototype.getValuesFromTexture=function(e){var t,n=this.texData.get(e),r=n.shape,o=n.dtype,a=n.texture,i=n.texShape;if(ENV.get("WEBGL_DOWNLOAD_FLOAT_ENABLED")){if(this.texData.get(e).isPacked){var s=this.getBatchDim(r),u=1,l=1;return r.length&&(u=(t=this.getRowsCols(r))[0],l=t[1]),this.gpgpu.downloadMatrixFromPackedTexture(a,s,u,l,i[0],i[1])}return this.gpgpu.downloadFloat32MatrixFromOutputTexture(a,i[0],i[1])}var c=this.makeTensorHandle(r,"float32");c.size=sizeFromShape(r),this.texData.get(c.dataId).usage=TextureUsage.DOWNLOAD;var p=new EncodeFloatProgram(r);this.compileAndRun(p,[{shape:r,dtype:o,dataId:e}],c,null,!1);var d=this.texData.get(c.dataId),h=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(d.texture,d.texShape[0],d.texShape[1]);return this.disposeData(c.dataId),h},e.prototype.time=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o,a,i,s;return __generator(this,function(u){switch(u.label){case 0:return t=this.activeTimers,n=[],r=!1,null==this.programTimersStack?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e(),o=flatten(this.activeTimers.map(function(e){return e.query})).filter(function(e){return null!=e}),a=flatten(this.activeTimers.map(function(e){return e.name})).filter(function(e){return null!=e}),this.activeTimers=t,r&&(this.programTimersStack=null),[4,Promise.all(o)];case 1:return i=u.sent(),s={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:sum(i),getExtraProfileInfo:function(){return i.map(function(e,t){return {name:a[t],ms:e}}).map(function(e){return e.name+": "+e.ms}).join(", ")},wallMs:null},this.uploadWaitMs=0,this.downloadWaitMs=0,[2,s]}})})},e.prototype.memory=function(){return {unreliable:!1,numBytesInGPU:this.numBytesInGPU}},e.prototype.startTimer=function(){return ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?this.gpgpu.beginQuery():{startMs:performance.now(),endMs:null}},e.prototype.endTimer=function(e){return ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(this.gpgpu.endQuery(),e):(e.endMs=performance.now(),e)},e.prototype.getQueryTime=function(e){return __awaiter(this,void 0,void 0,function(){var t;return __generator(this,function(n){return ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?[2,this.gpgpu.waitForQueryAndGetTime(e)]:[2,(t=e).endMs-t.startMs]})})},e.prototype.disposeData=function(e){if(!this.pendingDisposal.has(e))if(this.pendingRead.has(e))this.pendingDisposal.add(e);else if(this.texData.has(e)){var t=this.texData.get(e),n=t.texture,r=t.texShape,o=t.usage,a=t.complexTensors,i=t.isPacked;null!=n&&this.releaseTexture(e,n,r,o,i),null!=a&&(a.real.dispose(),a.imag.dispose()),this.texData.delete(e);}},e.prototype.getTexture=function(e){return this.uploadToGPU(e),this.texData.get(e).texture},e.prototype.getCPUBackend=function(){return ENV.get("WEBGL_CPU_FORWARD")?(null==this.cpuBackend&&(this.cpuBackend=ENV.findBackend("cpu")),this.cpuBackend):null},e.prototype.shouldExecuteOnCPU=function(e,t){var n=this;return void 0===t&&(t=CPU_HANDOFF_SIZE_THRESHOLD),null!=this.getCPUBackend()&&e.every(function(e){return null==n.texData.get(e.dataId).texture&&e.size<t})},e.prototype.getGPGPUContext=function(){return this.gpgpu},e.prototype.getCanvas=function(){return this.canvas},e.prototype.complex=function(e,t){var n=this.makeOutputArray(e.shape,"complex64");return this.texData.get(n.dataId).complexTensors={real:ENV.engine.keep(e.clone()),imag:ENV.engine.keep(t.clone())},n},e.prototype.real=function(e){return this.texData.get(e.dataId).complexTensors.real.clone()},e.prototype.imag=function(e){return this.texData.get(e.dataId).complexTensors.imag.clone()},e.prototype.slice=function(e,t,n){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.slice(e,t,n);var r=new SliceProgram(n),o=r.getCustomSetupFunc(t);return this.compileAndRun(r,[e],null,o)},e.prototype.stridedSlice=function(e,t,n,r,o,a,i,s,u){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.stridedSlice(e,t,n,r,o,a,i,s,u);var l=getStridedSlicedInfo(e.shape,t,n,r,o,a,i,s,u),c=l[0],p=l[1],d=l[2],h=p.filter(function(e,t){return -1===d.indexOf(t)});if(h.some(function(e){return 0===e}))return tensor([],h);var f=new StridedSliceProgram(c,r,p,d);return this.compileAndRun(f,[e])},e.prototype.reverse=function(e,t){var n=new ReverseProgram(e.shape,t);return this.compileAndRun(n,[e])},e.prototype.concat2Tensors=function(e,t,n){var r=computeOutShape([e.shape,t.shape],n),o=e.as2D(-1,sizeFromShape(e.shape.slice(n))),a=t.as2D(-1,sizeFromShape(t.shape.slice(n))),i=new ConcatProgram(o.shape,a.shape);return this.compileAndRun(i,[o,a]).reshape(r)},e.prototype.concat=function(e,t){if(this.shouldExecuteOnCPU(e))return this.cpuBackend.concat(e,t);if(1===e.length)return e[0];for(var n=e[0],r=1;r<e.length;++r)n=this.concat2Tensors(n,e[r],t);return n},e.prototype.neg=function(e){var t=new UnaryOpProgram(e.shape,NEG);return this.compileAndRun(t,[e])},e.prototype.batchMatMul=function(e,t,n,r){var o=n?e.shape[2]:e.shape[1],a=r?t.shape[1]:t.shape[2],i=n?e.shape[1]:e.shape[2],s=e.shape[0];if((1===o||1===a)&&i>MATMUL_SHARED_DIM_THRESHOLD){n&&(e=e.transpose([0,2,1])),r&&(t=t.transpose([0,2,1]));var u=1===a?e:e.as3D(s,i,1),l=1===a?2:1,c=1===a?t.as3D(s,1,i):t;return this.multiply(u,c).sum(l,!0)}var p=upcastType(e.dtype,t.dtype);if(1===s){var d=e.as2D(e.shape[1],e.shape[2]),h=t.as2D(t.shape[1],t.shape[2]),f=new MatMulPackedProgram(d.shape,h.shape,[o,a],n,r),m=this.makePackedTensor(f.outputShape,p),g=this.compileAndRun(f,[d,h],m);return g.reshape([1,g.shape[0],g.shape[1]])}f=new MatMulProgram(e.shape,t.shape,n,r),m=this.makeOutputArray(f.outputShape,p);return this.compileAndRun(f,[e,t],m)},e.prototype.multiply=function(e,t){if("complex64"===e.dtype){var n=this.texData.get(e.dataId),r=this.texData.get(t.dataId),o=new BinaryOpComplexProgram(COMPLEX_MULTIPLY.REAL,e.shape,t.shape),a=new BinaryOpComplexProgram(COMPLEX_MULTIPLY.IMAG,e.shape,t.shape),i=[this.makeComplexComponentTensorHandle(e,n.complexTensors.real),this.makeComplexComponentTensorHandle(e,n.complexTensors.imag),this.makeComplexComponentTensorHandle(t,r.complexTensors.real),this.makeComplexComponentTensorHandle(t,r.complexTensors.imag)],s=this.compileAndRun(o,i),u=this.compileAndRun(a,i),l=this.complex(s,u);return s.dispose(),u.dispose(),l}if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.multiply(e,t);var c=new BinaryOpProgram(MUL,e.shape,t.shape),p=this.makeOutputArray(c.outputShape,e.dtype);return this.compileAndRun(c,[e,t],p)},e.prototype.batchNormalization=function(e,t,n,r,o,a){var i=[e,t,n],s=null;null!=a&&(s=a.shape,i.push(a));var u=null;if(null!=o&&(u=o.shape,i.push(o)),ENV.get("WEBGL_PACK_BATCHNORMALIZATION")){var l=new BatchNormPackedProgram(e.shape,t.shape,n.shape,s,u,r);return this.compileAndRun(l,i)}var c=new BatchNormProgram(e.shape,t.shape,n.shape,s,u,r);return this.compileAndRun(c,i)},e.prototype.localResponseNormalization4D=function(e,t,n,r,o){var a=new LRNProgram(e.shape,t,n,r,o);return this.compileAndRun(a,[e])},e.prototype.LRNGrad=function(e,t,n,r,o,a,i){var s=new LRNGradProgram(t.shape,r,o,a,i);return this.compileAndRun(s,[t,n,e])},e.prototype.tile=function(e,t){var n=new TileProgram(e.shape,t);return this.compileAndRun(n,[e])},e.prototype.pad=function(e,t,n){var r=new PadProgram(e.shape,t,n);return this.compileAndRun(r,[e])},e.prototype.transpose=function(e,t){var n=new TransposeProgram(e.shape,t);return this.compileAndRun(n,[e])},e.prototype.gather=function(e,t,n){var r=new GatherProgram(e.shape,t.size,n);return this.compileAndRun(r,[e,t])},e.prototype.batchToSpaceND=function(e,t,n){assert(e.rank<=4,"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");var r=t.reduce(function(e,t){return e*t}),o=getReshaped(e.shape,t,r),a=getPermuted(o.length,t.length),i=getReshapedPermuted(e.shape,t,r),s=getSliceBeginCoords(n,t.length),u=getSliceSize(i,n,t.length);return e.reshape(o).transpose(a).reshape(i).slice(s,u)},e.prototype.spaceToBatchND=function(e,t,n){assert(e.rank<=4,"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");var r=t.reduce(function(e,t){return e*t}),o=[[0,0]];o.push.apply(o,n);for(var a=1+t.length;a<e.shape.length;++a)o.push([0,0]);var i=e.pad(o),s=getReshaped(i.shape,t,r,!1),u=getPermuted(s.length,t.length,!1),l=getReshapedPermuted(i.shape,t,r,!1);return i.reshape(s).transpose(u).reshape(l)},e.prototype.reduce=function(e,t,n){var r=e.shape[0],o=e.shape[1],a=computeOptimalWindowSize(o),i=new ReduceProgram({windowSize:a,inSize:o,batchSize:r},t),s=i.outputShape,u=s[0],l=s[1],c=this.makeOutputArray([u,l],n);return this.compileAndRun(i,[e],c),1===c.shape[1]?c:this.reduce(c,t,n)},e.prototype.argReduce=function(e,t,n){void 0===n&&(n=null);var r=e.shape[0],o=e.shape[1];null!=n&&(r=n.shape[0],o=n.shape[1]);var a=computeOptimalWindowSize(o),i=new ArgMinMaxProgram({windowSize:a,inSize:o,batchSize:r},t,null==n),s=i.outputShape,u=s[0],l=s[1],c=this.makeOutputArray([u,l],"int32"),p=[e];return null!=n&&p.push(n),this.compileAndRun(i,p,c),1===c.shape[1]?c:this.argReduce(e,t,c)},e.prototype.sum=function(e,t){assertAxesAreInnerMostDims("sum",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o),i=sumOutType(e.dtype);return this.reduce(a,"sum",i).reshape(r)},e.prototype.prod=function(e,t){var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o),i=sumOutType(e.dtype);return this.reduce(a,"prod",i).reshape(r)},e.prototype.unsortedSegmentSum=function(e,t,n){var r=0,o=getAxesPermutation([r],e.rank),a=e;null!=o&&(a=e.transpose(o),r=getInnerMostAxes(1,e.rank)[0]);var i=computeOutShape$1(a.shape,r,n),s=sizeFromShape([a.shape[r]]),u=a.as2D(-1,s),l=sumOutType(e.dtype),c=this.segOpCompute(u,"unsortedSegmentSum",t,l,n).reshape(i);return null!=o&&(c=c.transpose(getUndoAxesPermutation(o))),c},e.prototype.segOpCompute=function(e,t,n,r,o){var a=e.shape[0],i=e.shape[1],s=segOpComputeOptimalWindowSize(i,o),u=new SegmentOpProgram({windowSize:s,inSize:i,batchSize:a,numSegments:o},t),l=u.outputShape,c=l[0],p=l[1],d=this.makeOutputArray([c,p],r);return this.compileAndRun(u,[e,n],d),d.shape[1]===o?d:(n=range(0,o).tile([i/s]),this.segOpCompute(d,t,n,r,o))},e.prototype.argMin=function(e,t){var n=[t];assertAxesAreInnerMostDims("argMin",n,e.rank);var r=computeOutAndReduceShapes(e.shape,n),o=r[0],a=sizeFromShape(r[1]),i=e.as2D(-1,a);return this.argReduce(i,"min").reshape(o)},e.prototype.argMax=function(e,t){var n=[t];assertAxesAreInnerMostDims("argMax",n,e.rank);var r=computeOutAndReduceShapes(e.shape,n),o=r[0],a=sizeFromShape(r[1]),i=e.as2D(-1,a);return this.argReduce(i,"max").reshape(o)},e.prototype.cumsum=function(e,t,n,r){if(t!==e.rank-1)throw new Error("WebGL cumsum shader expects an inner-most axis="+(e.rank-1)+" but got axis="+t);var o=new CumSumProgram(e.shape,n,r);return this.compileAndRun(o,[e])},e.prototype.equal=function(e,t){var n=new BinaryOpProgram(EQUAL,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.notEqual=function(e,t){var n=new BinaryOpProgram(NOT_EQUAL,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.less=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.less(e,t);var n=new BinaryOpProgram(LESS,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.lessEqual=function(e,t){var n=new BinaryOpProgram(LESS_EQUAL,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.greater=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.greater(e,t);var n=new BinaryOpProgram(GREATER,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.greaterEqual=function(e,t){var n=new BinaryOpProgram(GREATER_EQUAL,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.logicalNot=function(e){var t=new UnaryOpProgram(e.shape,LOGICAL_NOT);return this.compileAndRun(t,[e])},e.prototype.logicalAnd=function(e,t){var n=new BinaryOpProgram(LOGICAL_AND,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.logicalOr=function(e,t){var n=new BinaryOpProgram(LOGICAL_OR,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.select=function(e,t,n){var r=new SelectProgram(e.rank,t.shape,t.rank),o=this.makeOutputArray(r.outputShape,upcastType(t.dtype,n.dtype));return this.compileAndRun(r,[e,t,n],o)},e.prototype.where=function(e){warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");var t=e.dataSync();return whereImpl(e.shape,t)},e.prototype.topk=function(e,t,n){return topkImpl(e.dataSync(),e.shape,e.dtype,t,n)},e.prototype.min=function(e,t){assertAxesAreInnerMostDims("min",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o);return this.reduce(a,"min",a.dtype).reshape(r)},e.prototype.minimum=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.minimum(e,t);var n=new BinaryOpProgram(MIN,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.mod=function(e,t){var n=new BinaryOpProgram(MOD,e.shape,t.shape),r=n.getCustomSetupFunc();return this.compileAndRun(n,[e,t],null,r)},e.prototype.max=function(e,t){assertAxesAreInnerMostDims("max",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o);return this.reduce(a,"max",a.dtype).reshape(r)},e.prototype.maximum=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.maximum(e,t);var n=new BinaryOpProgram(MAX,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.all=function(e,t){assertAxesAreInnerMostDims("all",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o);return this.reduce(a,"all",a.dtype).reshape(r)},e.prototype.any=function(e,t){assertAxesAreInnerMostDims("any",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o);return this.reduce(a,"any",a.dtype).reshape(r)},e.prototype.squaredDifference=function(e,t){var n=new BinaryOpProgram(SQUARED_DIFFERENCE,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.realDivide=function(e,t){var n=new BinaryOpProgram(DIV,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"float32");return this.compileAndRun(n,[e,t],r)},e.prototype.floorDiv=function(e,t){var n=new BinaryOpProgram(INT_DIV,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"int32");return this.compileAndRun(n,[e,t],r)},e.prototype.add=function(e,t){if("complex64"===e.dtype&&"complex64"===t.dtype)return this.complexSeparableBinaryOp(e,t,ADD);var n=new BinaryOpProgram(ADD,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,upcastType(e.dtype,t.dtype));return this.compileAndRun(n,[e,t],r)},e.prototype.complexSeparableBinaryOp=function(e,t,n){var r=this,o=this.texData.get(e.dataId),a=this.texData.get(t.dataId),i=[[o.complexTensors.real,a.complexTensors.real],[o.complexTensors.imag,a.complexTensors.imag]].map(function(o){var a=o[0],i=o[1],s=new BinaryOpProgram(n,e.shape,t.shape),u=r.makeOutputArray(s.outputShape,upcastType(a.dtype,i.dtype)),l=r.makeComplexComponentTensorHandle(e,a),c=r.makeComplexComponentTensorHandle(t,i);return r.compileAndRun(s,[l,c],u)}),s=i[0],u=i[1],l=this.complex(s,u);return s.dispose(),u.dispose(),l},e.prototype.makeComplexComponentTensorHandle=function(e,t){return {dataId:t.dataId,dtype:t.dtype,shape:e.shape}},e.prototype.addN=function(e){for(var t=e[0],n=1;n<e.length;n++)t=this.add(t,e[n]);return t},e.prototype.subtract=function(e,t){if("complex64"===e.dtype&&"complex64"===t.dtype)return this.complexSeparableBinaryOp(e,t,SUB);if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.subtract(e,t);var n=new BinaryOpProgram(SUB,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,upcastType(e.dtype,t.dtype));return this.compileAndRun(n,[e,t],r)},e.prototype.pow=function(e,t){var n=new BinaryOpProgram(POW,e.shape,t.shape),r=n.getCustomSetupFunc(),o=this.makeOutputArray(n.outputShape,upcastType(e.dtype,t.dtype));return this.compileAndRun(n,[e,t],o,r)},e.prototype.ceil=function(e){var t=new UnaryOpProgram(e.shape,CEIL);return this.compileAndRun(t,[e])},e.prototype.floor=function(e){var t=new UnaryOpProgram(e.shape,FLOOR);return this.compileAndRun(t,[e])},e.prototype.sign=function(e){var t=new UnaryOpProgram(e.shape,SIGN);return this.compileAndRun(t,[e])},e.prototype.round=function(e){var t=new UnaryOpProgram(e.shape,ROUND);return this.compileAndRun(t,[e])},e.prototype.exp=function(e){var t=new UnaryOpProgram(e.shape,EXP);return this.compileAndRun(t,[e])},e.prototype.expm1=function(e){var t=new UnaryOpProgram(e.shape,EXPM1);return this.compileAndRun(t,[e])},e.prototype.log=function(e){var t=new UnaryOpProgram(e.shape,LOG),n=t.getCustomSetupFunc();return this.compileAndRun(t,[e],null,n)},e.prototype.log1p=function(e){var t=new UnaryOpProgram(e.shape,LOG1P);return this.compileAndRun(t,[e])},e.prototype.sqrt=function(e){var t=new UnaryOpProgram(e.shape,SQRT);return this.compileAndRun(t,[e])},e.prototype.rsqrt=function(e){var t=new UnaryOpProgram(e.shape,RSQRT);return this.compileAndRun(t,[e])},e.prototype.square=function(e){var t=new UnaryOpProgram(e.shape,SQUARE);return this.compileAndRun(t,[e])},e.prototype.reciprocal=function(e){var t=new UnaryOpProgram(e.shape,RECIPROCAL);return this.compileAndRun(t,[e])},e.prototype.relu=function(e){var t=new UnaryOpProgram(e.shape,RELU);return this.compileAndRun(t,[e])},e.prototype.elu=function(e){var t=new UnaryOpProgram(e.shape,ELU);return this.compileAndRun(t,[e])},e.prototype.eluDer=function(e,t){var n=new BinaryOpProgram(ELU_DER,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.selu=function(e){var t=new UnaryOpProgram(e.shape,SELU);return this.compileAndRun(t,[e])},e.prototype.int=function(e){var t=new UnaryOpProgram(e.shape,TO_INT),n=this.makeOutputArray(t.outputShape,"int32");return this.compileAndRun(t,[e],n)},e.prototype.clip=function(e,t,n){var r;return r=ENV.get("WEBGL_PACK_CLIP")?new ClipPackedProgram(e.shape,t,n):new ClipProgram(e.shape,t,n),this.compileAndRun(r,[e])},e.prototype.abs=function(e){var t=new UnaryOpProgram(e.shape,ABS);return this.compileAndRun(t,[e])},e.prototype.complexAbs=function(e){var t=this.texData.get(e.dataId),n=new ComplexAbsProgram(e.shape),r=[this.makeComplexComponentTensorHandle(e,t.complexTensors.real),this.makeComplexComponentTensorHandle(e,t.complexTensors.imag)];return this.compileAndRun(n,r)},e.prototype.sigmoid=function(e){var t=new UnaryOpProgram(e.shape,SIGMOID);return this.compileAndRun(t,[e])},e.prototype.softplus=function(e){var t=new UnaryOpProgram(e.shape,SOFTPLUS);return this.compileAndRun(t,[e])},e.prototype.sin=function(e){var t=new UnaryOpProgram(e.shape,SIN);return this.compileAndRun(t,[e])},e.prototype.cos=function(e){var t=new UnaryOpProgram(e.shape,COS);return this.compileAndRun(t,[e])},e.prototype.tan=function(e){var t=new UnaryOpProgram(e.shape,TAN);return this.compileAndRun(t,[e])},e.prototype.asin=function(e){var t=new UnaryOpProgram(e.shape,ASIN);return this.compileAndRun(t,[e])},e.prototype.acos=function(e){var t=new UnaryOpProgram(e.shape,ACOS);return this.compileAndRun(t,[e])},e.prototype.atan=function(e){var t=new UnaryOpProgram(e.shape,ATAN);return this.compileAndRun(t,[e])},e.prototype.atan2=function(e,t){var n=new BinaryOpProgram(ATAN2,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.sinh=function(e){var t=new UnaryOpProgram(e.shape,SINH);return this.compileAndRun(t,[e])},e.prototype.cosh=function(e){var t=new UnaryOpProgram(e.shape,COSH);return this.compileAndRun(t,[e])},e.prototype.tanh=function(e){var t=new UnaryOpProgram(e.shape,TANH);return this.compileAndRun(t,[e])},e.prototype.asinh=function(e){var t=new UnaryOpProgram(e.shape,ASINH);return this.compileAndRun(t,[e])},e.prototype.acosh=function(e){var t=new UnaryOpProgram(e.shape,ACOSH),n=t.getCustomSetupFunc();return this.compileAndRun(t,[e],null,n)},e.prototype.atanh=function(e){var t=new UnaryOpProgram(e.shape,ATANH),n=t.getCustomSetupFunc();return this.compileAndRun(t,[e],null,n)},e.prototype.erf=function(e){var t=new UnaryOpProgram(e.shape,ERF);return this.compileAndRun(t,[e])},e.prototype.step=function(e,t){var n=new UnaryOpProgram(e.shape,STEP(t));return this.compileAndRun(n,[e])},e.prototype.conv2dWithIm2Row=function(e,t,n){var r=n.filterWidth,o=n.filterHeight,a=n.inChannels,i=n.outWidth,s=n.outHeight,u=r*o*a,l=s*i,c=[u,l],p=e.squeeze([0]),d=t.reshape([u,-1]),h=new Im2ColProgram(c,p.shape,n),f=this.compileAndRun(h,[p]),m=new MatMulPackedProgram(f.shape,d.shape,[l,n.outChannels],!0,!1);return this.compileAndRun(m,[f,d]).reshape([1,s,i,n.outChannels])},e.prototype.conv2d=function(e,t,n){if(ENV.get("WEBGL_CONV_IM2COL")&&1===e.shape[0])return this.conv2dWithIm2Row(e,t,n);var r=new Conv2DProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv2dDerInput=function(e,t,n){var r=new Conv2DDerInputProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv2dDerFilter=function(e,t,n){var r=new Conv2DDerFilterProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.depthwiseConv2D=function(e,t,n){var r;return ENV.get("WEBGL_PACK_DEPTHWISECONV")&&1===n.dilationWidth&&1===n.dilationHeight&&n.padInfo.left<=1&&n.strideWidth<=2&&n.outChannels/n.inChannels==1?(r=new DepthwiseConvPacked2DProgram(n),this.compileAndRun(r,[e,t],this.makePackedTensor(n.outShape,e.dtype))):(r=new DepthwiseConv2DProgram(n),this.compileAndRun(r,[e,t]))},e.prototype.depthwiseConv2DDerInput=function(e,t,n){var r=new DepthwiseConv2DDerInputProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.depthwiseConv2DDerFilter=function(e,t,n){var r=new DepthwiseConv2DDerFilterProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv3d=function(e,t,n){var r=new Conv3DProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv3dDerInput=function(e,t,n){var r=new Conv3DDerInputProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv3dDerFilter=function(e,t,n){var r=new Conv3DDerFilterProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.maxPool=function(e,t){var n=new Pool2DProgram(t,"max",!1),r=this.makeOutputArray(n.outputShape,e.dtype);return this.compileAndRun(n,[e],r)},e.prototype.avgPool=function(e,t){var n=new Pool2DProgram(t,"avg",!1),r=this.makeOutputArray(n.outputShape,"float32");return this.compileAndRun(n,[e],r)},e.prototype.maxPoolBackprop=function(e,t,n,r){var o=new Pool2DProgram(r,"max",!0),a=this.compileAndRun(o,[t]),i=new MaxPool2DBackpropProgram(r),s=this.makeOutputArray(i.outputShape,t.dtype),u=this.compileAndRun(i,[e,a],s);return a.dispose(),u},e.prototype.avgPoolBackprop=function(e,t,n){var r=new AvgPool2DBackpropProgram(n),o=this.makeOutputArray(r.outputShape,t.dtype);return this.compileAndRun(r,[e],o)},e.prototype.cast=function(e,t){return castTensor(e,t,this)},e.prototype.reshape=function(e,t){return this.texData.get(e.dataId).isPacked&&!isReshapeFree(e.shape,t)?this.packedReshape(e,t):reshapeTensor(e,t)},e.prototype.resizeBilinear=function(e,t,n,r){var o=new ResizeBilinearProgram(e.shape,t,n,r);return this.compileAndRun(o,[e])},e.prototype.resizeBilinearBackprop=function(e,t,n){var r=new ResizeBilinearBackpropProgram(e,t,n);return this.compileAndRun(r,[e])},e.prototype.resizeNearestNeighbor=function(e,t,n,r){var o=new ResizeNearestNeighborProgram(e.shape,t,n,r);return this.compileAndRun(o,[e])},e.prototype.resizeNearestNeighborBackprop=function(e,t,n){var r=new ResizeNearestNeigborBackpropProgram(e,t,n);return this.compileAndRun(r,[e])},e.prototype.multinomial=function(e,t,n,r){var o=t?e:softmax(e),a=o.shape[0],i=o.shape[1],s=new MultinomialProgram(a,i,n),u=this.makeOutputArray(s.outputShape,"int32"),l=s.getCustomSetupFunc(r);return this.compileAndRun(s,[o],u,l)},e.prototype.oneHot=function(e,t,n,r){var o=new OneHotProgram(e.size,t,n,r);return this.compileAndRun(o,[e])},e.prototype.nonMaxSuppression=function(e,t,n,r,o){return warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"),nonMaxSuppressionImpl(e.dataSync(),t.dataSync(),n,r,o)},e.prototype.cropAndResize=function(e,t,n,r,o,a){var i=new CropAndResizeProgram(e.shape,t.shape,r,o,a);return this.compileAndRun(i,[e,t,n])},e.prototype.depthToSpace=function(e,t,n){assert(t>1,"blockSize should be > 1 for depthToSpace, but was: "+t);var r=e.shape[0],o=("NHWC"===n?e.shape[1]:e.shape[2])*t,a=("NHWC"===n?e.shape[2]:e.shape[3])*t,i=("NHWC"===n?e.shape[3]:e.shape[1])/(t*t),s=new DepthToSpaceProgram("NHWC"===n?[r,o,a,i]:[r,i,o,a],t,n);return this.compileAndRun(s,[e])},e.prototype.split=function(e,t,n){return split(e,t,n)},e.prototype.scatterND=function(e,t,n){var r=calculateShapes(t,e,n),o=r.sliceRank,a=r.numUpdates,i=r.sliceSize,s=r.strides,u=r.outputSize,l=[u/i,i],c=e.reshape([a,o]),p=t.reshape([a,i]);if(0===u)return reshapeTensor(tensor([]),n);var d=scalar(0),h=new ScatterProgram(a,o,c.rank,p.rank,s,l);return this.compileAndRun(h,[p,c,d]).reshape(n)},e.prototype.sparseToDense=function(e,t,n,r){var o=calculateShapes(t,e,n),a=o.sliceRank,i=o.numUpdates,s=o.strides,u=o.outputSize,l=new ScatterProgram(i,a,e.rank,t.rank,s,[u,1],!1);return this.compileAndRun(l,[t,e,r]).reshape(n)},e.prototype.fft=function(e){return this.fftImpl(e,!1)},e.prototype.ifft=function(e){return this.fftImpl(e,!0)},e.prototype.fftImpl=function(e,t){var n=this.texData.get(e.dataId),r=new FFTProgram(COMPLEX_FFT.REAL,e.shape,t),o=new FFTProgram(COMPLEX_FFT.IMAG,e.shape,t),a=[this.makeComplexComponentTensorHandle(e,n.complexTensors.real),this.makeComplexComponentTensorHandle(e,n.complexTensors.imag)],i=this.compileAndRun(r,a),s=this.compileAndRun(o,a),u=this.complex(i,s).as2D(e.shape[0],e.shape[1]);return i.dispose(),s.dispose(),u},e.prototype.gatherND=function(e,t){var n=t.shape,r=n[n.length-1],o=prepareAndValidate(e,t),a=o[0],i=o[1],s=o[2],u=o[3],l=t.reshape([i,r]),c=e.reshape([e.size/s,s]),p=new GatherNDProgram(r,u,[i,s]);return this.compileAndRun(p,[c,l]).reshape(a)},e.prototype.makeOutputArray=function(e,t){return Tensor.make(e,{},t)},e.prototype.makePackedTensor=function(e,t){var n=Tensor.make(e,{},t);return this.texData.get(n.dataId).isPacked=!0,n},e.prototype.unpackTensor=function(e){var t=new UnpackProgram(e.shape);return this.compileAndRun(t,[e],Tensor.make(t.outputShape,{},e.dtype))},e.prototype.getBatchDim=function(e,t){return void 0===t&&(t=2),sizeFromShape(e.slice(0,e.length-t))},e.prototype.getRowsCols=function(e){if(0===e.length)throw Error("Cannot get rows and columns of an empty shape array.");return [e.length>1?e[e.length-2]:1,e[e.length-1]]},e.prototype.packedReshape=function(e,t){var n=e.reshape([this.getBatchDim(e.shape)].concat(this.getRowsCols(e.shape))),r=[this.getBatchDim(t)].concat(this.getRowsCols(t)),o=new ReshapePackedProgram(r,n.shape);return this.compileAndRun(o,[n]).reshape(t)},e.prototype.compileAndRun=function(e,t,n,r,o){var a=this;if(void 0===o&&(o=!0),null==n&&(n=e.usesPackedTextures?this.makePackedTensor(e.outputShape,t[0].dtype):this.makeOutputArray(e.outputShape,t[0].dtype)),0===n.size)return this.texData.get(n.dataId).values=getTypedArrayFromDType(n.dtype,0),n;var i=t.map(function(t){if("complex64"===t.dtype)throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");var n=a.texData.get(t.dataId);if(null==n.texture){if(!e.usesPackedTextures&&sizeFromShape(t.shape)<=ENV.get("WEBGL_SIZE_UPLOAD_UNIFORM"))return {shape:t.shape,texData:null,isUniform:!0,uniformValues:a.readSync(t.dataId)};e.usesPackedTextures&&(n.isPacked=!0,n.shape=t.shape);}else if(!!n.isPacked!=!!e.usesPackedTextures){var r=void 0,o=void 0;n.isPacked?(r=new UnpackProgram(t.shape),o=a.compileAndRun(r,[t],Tensor.make(r.outputShape,{},t.dtype))):(r=new PackProgram(t.shape),o=a.compileAndRun(r,[t],a.makePackedTensor(t.shape,t.dtype))),n=a.texData.get(o.dataId),t=o;}else if(n.isPacked&&!isReshapeFree(n.shape,t.shape)){a.delayedStorage=!1;var i=t.dataSync();a.delayedStorage=!0,t=Tensor.make(t.shape,{values:i},t.dtype),(n=a.texData.get(t.dataId)).isPacked=!0;}return a.uploadToGPU(t.dataId),{shape:t.shape,texData:n,isUniform:!1}});this.uploadToGPU(n.dataId);var s,u={shape:n.shape,texData:this.texData.get(n.dataId),isUniform:!1},l=makeShaderKey(e,i,u),c=this.getAndSaveBinary(l,function(){return compileProgram(a.gpgpu,e,i,u)}),p=null!=this.activeTimers;if(p&&(s=this.startTimer()),runProgram(c,i,u,r),ENV.get("WEBGL_PAGING_ENABLED")&&o&&this.numBytesInGPU>this.NUM_BYTES_BEFORE_PAGING)for(var d=this.numBytesInGPU-this.NUM_BYTES_BEFORE_PAGING;d>0&&this.lruDataGPU.length>0;){var h=this.lruDataGPU.shift(),f=this.texData.get(h),m=f.shape,g=f.dtype;d-=this.computeBytes(m,g),this.read(h);}return p&&(s=this.endTimer(s),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(s)})),ENV.get("WEBGL_LAZILY_UNPACK")||!this.texData.get(n.dataId).isPacked||e.isPackShader?n:this.unpackTensor(n)},e.prototype.getAndSaveBinary=function(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]},e.prototype.getTextureManager=function(){return this.textureManager},e.prototype.dispose=function(){if(!this.disposed){for(var e in this.binaryCache)this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram);this.textureManager.dispose(),this.canvas.remove(),null!=this.fromPixels2DContext&&this.fromPixels2DContext.canvas.remove(),this.gpgpuCreatedLocally&&this.gpgpu.dispose(),this.disposed=!0;}},e.prototype.floatPrecision=function(){var e=this;return tidy(function(){return e.abs(scalar(1e-8)).get()>0?32:16})},e.prototype.uploadToGPU=function(e){var t,n=this.texData.get(e),r=n.shape,o=n.values,a=n.texture,i=n.usage,s=n.isPacked;if(null==a){var u,l=null!=this.activeTimers;l&&(u=performance.now());var c=getTextureShapeFromLogicalShape(r,s);n.texShape=c;var p=this.acquireTexture(e,c,i,s);if(n.texture=p,null!=o){if(s){var d=this.getBatchDim(r),h=1,f=1;r.length&&(h=(t=this.getRowsCols(r))[0],f=t[1]),this.gpgpu.uploadMatrixToPackedTexture(p,d,h,f,c[0],c[1],typedArrayToFloat32(o));}else this.gpgpu.uploadMatrixToTexture(p,c[0],c[1],typedArrayToFloat32(o));n.values=null,l&&(this.uploadWaitMs+=performance.now()-u);}}else{ENV.get("WEBGL_PAGING_ENABLED")&&this.lruDataGPU.indexOf(e)>=0&&(this.lruDataGPU.splice(this.lruDataGPU.indexOf(e),1),this.lruDataGPU.push(e));}},e.prototype.convertAndCacheOnCPU=function(e,t){var n=this.delayedStorage,r=this.texData.get(e),o=r.texture,a=r.texShape,i=r.dtype,s=r.usage,u=r.isPacked;return n&&null!=o&&(this.releaseTexture(e,o,a,s,u),r.texture=null,r.texShape=null),r.usage=TextureUsage.UPLOAD,null!=t&&(r.values=float32ToTypedArray(t,i)),r.values},e.prototype.releaseTexture=function(e,t,n,r,o){var a=this.texData.get(e),i=a.shape,s=a.dtype;if(ENV.get("WEBGL_PAGING_ENABLED")){var u=this.lruDataGPU.indexOf(e);u>=0&&this.lruDataGPU.splice(u,1);}this.numBytesInGPU-=this.computeBytes(i,s),this.textureManager.releaseTexture(t,n,r,o);},e.prototype.acquireTexture=function(e,t,n,r){var o=this.texData.get(e),a=o.shape,i=o.dtype;return ENV.get("WEBGL_PAGING_ENABLED")&&this.lruDataGPU.push(e),this.numBytesInGPU+=this.computeBytes(a,i),this.textureManager.acquireTexture(t,n,r)},e.prototype.computeBytes=function(e,t){return sizeFromShape(e)*bytesPerElement(t)},e}();function float32ToTypedArray(e,t){if("float32"===t||"complex64"===t)return e;if("int32"===t||"bool"===t){for(var n="int32"===t?new Int32Array(e.length):new Uint8Array(e.length),r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}throw new Error("Unknown dtype "+t)}function typedArrayToFloat32(e){return e instanceof Float32Array?e:new Float32Array(e)}function neg_(e){var t=convertToTensor(e,"x","neg");return ENV.engine.runKernel(function(e){return e.neg(t)},{$x:t},function(e){return {$x:function(){return e.neg()}}})}function ceil_(e){var t=convertToTensor(e,"x","ceil");return ENV.engine.runKernel(function(e){return e.ceil(t)},{$x:t},function(e){return {$x:function(){return zerosLike(e)}}})}function floor_(e){var t=convertToTensor(e,"x","floor");return ENV.engine.runKernel(function(e){return e.floor(t)},{$x:t},function(e){return {$x:function(){return zerosLike(e)}}})}function sign_(e){var t=convertToTensor(e,"x","sign");return ENV.engine.runKernel(function(e){return e.sign(t)},{$x:t},function(e){return {$x:function(){return zerosLike(e)}}})}function round_(e){var t=convertToTensor(e,"x","round");return ENV.engine.runKernel(function(e){return e.round(t)},{$x:t},function(e){return {$x:function(){return zerosLike(e)}}})}function exp_(e){var t=convertToTensor(e,"x","exp");return ENV.engine.runKernel(function(e,n){return n(e.exp(t))},{$x:t},function(e,t){var n=t[0];return {$x:function(){return e.mulStrict(n)}}})}function expm1_(e){var t=convertToTensor(e,"x","expm1");return ENV.engine.runKernel(function(e){return e.expm1(t)},{$x:t},function(e){return {$x:function(){return e.mulStrict(t.exp())}}})}function log_(e){var t=convertToTensor(e,"x","log");return ENV.engine.runKernel(function(e){return e.log(t)},{$x:t},function(e){return {$x:function(){return e.divStrict(t.toFloat())}}})}function log1p_(e){var t=convertToTensor(e,"x","log1p");return ENV.engine.runKernel(function(e){return e.log1p(t)},{$x:t},function(e){return {$x:function(){return e.div(t.add(1))}}})}function sqrt_(e){var t=convertToTensor(e,"x","sqrt");return ENV.engine.runKernel(function(e){return e.sqrt(t)},{$x:t},function(e){return {$x:function(){return e.div(t.toFloat().sqrt().mul(2))}}})}function rsqrt_(e){var t=convertToTensor(e,"x","rsqrt");return ENV.engine.runKernel(function(e){return e.rsqrt(t)},{$x:t},function(e){return {$x:function(){return e.div(t.pow(1.5).mul(2)).neg()}}})}function square_(e){var t=convertToTensor(e,"x","square");return ENV.engine.runKernel(function(e){return e.square(t)},{$x:t},function(e){return {$x:function(){return e.mul(t.toFloat().mul(2))}}})}function reciprocal_(e){var t=convertToTensor(e,"x","reciprocal");return ENV.engine.runKernel(function(e){return e.reciprocal(t)},{$x:t},function(e){return {$x:function(){return e.divStrict(t.square().neg())}}})}function abs_(e){var t=convertToTensor(e,"x","abs");if("complex64"===t.dtype)return ENV.engine.runKernel(function(e){return e.complexAbs(t)},{$x:t});return ENV.engine.runKernel(function(e){return e.abs(t)},{$x:t},function(e){return {$x:function(){return e.mulStrict(t.toFloat().step(-1))}}})}function clipByValue_(e,t,n){var r=convertToTensor(e,"x","clipByValue");assert(t<=n,"Error in clip: min ("+t+") must be less than or equal to max ("+n+").");return ENV.engine.runKernel(function(e){return e.clip(r,t,n)},{$x:r},function(e){return {$x:function(){return e.where(r.greaterEqual(t).logicalAnd(r.lessEqual(n)),zerosLike(e))}}})}function sigmoid_(e){var t=convertToTensor(e,"x","sigmoid");return ENV.engine.runKernel(function(e,n){return n(e.sigmoid(t))},{$x:t},function(e,t){var n=t[0];return {$x:function(){return e.mul(n.mul(scalar(1).sub(n)))}}})}function logSigmoid_(e){var t=convertToTensor(e,"x","logSigmoid");return ENV.engine.runKernel(function(e){return e.softplus(t.neg()).neg()},{$x:t},function(e){return {$x:function(){return e.mulStrict(t.neg().sigmoid())}}})}function softplus_(e){var t=convertToTensor(e,"x","softplus");return ENV.engine.runKernel(function(e){return e.softplus(t)},{$x:t},function(e){return {$x:function(){return e.mulStrict(t.sigmoid())}}})}function sin_(e){var t=convertToTensor(e,"x","sin");return ENV.engine.runKernel(function(e){return e.sin(t)},{$x:t},function(e){return {$x:function(){return t.toFloat().cos().mulStrict(e)}}})}function cos_(e){var t=convertToTensor(e,"x","cos");return ENV.engine.runKernel(function(e){return e.cos(t)},{$x:t},function(e){return {$x:function(){return t.toFloat().sin().neg().mulStrict(e)}}})}function tan_(e){var t=convertToTensor(e,"x","tan");return ENV.engine.runKernel(function(e){return e.tan(t)},{$x:t},function(e){return {$x:function(){return e.divStrict(t.cos().square())}}})}function asin_(e){var t=convertToTensor(e,"x","asin");return ENV.engine.runKernel(function(e){return e.asin(t)},{$x:t},function(e){return {$x:function(){return e.divStrict(scalar(1).sub(t.toFloat().square()).sqrt())}}})}function acos_(e){var t=convertToTensor(e,"x","acos");return ENV.engine.runKernel(function(e){return e.acos(t)},{$x:t},function(e){return {$x:function(){return e.divStrict(scalar(1).sub(t.toFloat().square()).sqrt()).neg()}}})}function atan_(e){var t=convertToTensor(e,"x","atan");return ENV.engine.runKernel(function(e){return e.atan(t)},{$x:t},function(e){return {$x:function(){return e.div(t.toFloat().square().add(1))}}})}function sinh_(e){var t=convertToTensor(e,"x","sinh");return ENV.engine.runKernel(function(e){return e.sinh(t)},{$x:t},function(e){return {$x:function(){return t.toFloat().cosh().mulStrict(e)}}})}function cosh_(e){var t=convertToTensor(e,"x","cosh");return ENV.engine.runKernel(function(e){return e.cosh(t)},{$x:t},function(e){return {$x:function(){return t.toFloat().sinh().mulStrict(e)}}})}function tanh_(e){var t=convertToTensor(e,"x","tanh");return ENV.engine.runKernel(function(e,n){return n(e.tanh(t))},{$x:t},function(e,t){var n=t[0];return {$x:function(){return scalar(1).sub(n.square()).mulStrict(e)}}})}function asinh_(e){var t=convertToTensor(e,"x","asinh");return ENV.engine.runKernel(function(e){return e.asinh(t)},{$x:t},function(e){return {$x:function(){return e.divStrict(scalar(1).add(t.toFloat().square()).sqrt())}}})}function acosh_(e){var t=convertToTensor(e,"x","acosh");return ENV.engine.runKernel(function(e){return e.acosh(t)},{$x:t},function(e){return {$x:function(){return e.divStrict(t.toFloat().square().sub(1).sqrt())}}})}function atanh_(e){var t=convertToTensor(e,"x","atanh");return ENV.engine.runKernel(function(e){return e.atanh(t)},{$x:t},function(e){return {$x:function(){return e.div(scalar(1).sub(t.toFloat().square()))}}})}function erf_(e){var t=convertToTensor(e,"x","erf");assert("int32"===t.dtype||"float32"===t.dtype,"Input dtype must be `int32` or `float32`."),"int32"===t.dtype&&(t=t.toFloat());return ENV.engine.runKernel(function(e){return e.erf(t)},{$x:t},function(e){return {$x:function(){return e.mul(t.square().neg().exp().mul(2/Math.sqrt(Math.PI)))}}})}function step_(e,t){void 0===t&&(t=0);var n=convertToTensor(e,"x","step");return ENV.engine.runKernel(function(e){return e.step(n,t)},{$x:n},function(e){return {$x:function(){return zerosLike(e)}}})}ENV.get("IS_BROWSER")&&ENV.registerBackend("webgl",function(){return new MathBackendWebGL},2,setTensorTracker);var abs=op({abs_:abs_}),acos=op({acos_:acos_}),acosh=op({acosh_:acosh_}),asin=op({asin_:asin_}),asinh=op({asinh_:asinh_}),atan=op({atan_:atan_}),atanh=op({atanh_:atanh_}),ceil=op({ceil_:ceil_}),clipByValue=op({clipByValue_:clipByValue_}),cos=op({cos_:cos_}),cosh=op({cosh_:cosh_}),erf=op({erf_:erf_}),exp=op({exp_:exp_}),expm1=op({expm1_:expm1_}),floor=op({floor_:floor_}),log$1=op({log_:log_}),log1p=op({log1p_:log1p_}),logSigmoid=op({logSigmoid_:logSigmoid_}),neg=op({neg_:neg_}),reciprocal=op({reciprocal_:reciprocal_}),round=op({round_:round_}),rsqrt=op({rsqrt_:rsqrt_}),sigmoid=op({sigmoid_:sigmoid_}),sign=op({sign_:sign_}),sin=op({sin_:sin_}),sinh=op({sinh_:sinh_}),softplus=op({softplus_:softplus_}),sqrt=op({sqrt_:sqrt_}),square=op({square_:square_}),step=op({step_:step_}),tan=op({tan_:tan_}),tanh$1=op({tanh_:tanh_});function batchNormalization2d_(e,t,n,r,o,a){void 0===r&&(r=.001);var i,s,u=convertToTensor(e,"x","batchNormalization"),l=convertToTensor(t,"mean","batchNormalization"),c=convertToTensor(n,"variance","batchNormalization");return null!=o&&(i=convertToTensor(o,"scale","batchNormalization")),null!=a&&(s=convertToTensor(a,"offset","batchNormalization")),assert(2===u.rank,"Error in batchNormalization3D: x must be rank 3 but got rank "+u.rank+"."),assert(2===l.rank||1===l.rank,"Error in batchNormalization2D: mean must be rank 2 or rank 1 but got rank "+l.rank+"."),assert(2===c.rank||1===c.rank,"Error in batchNormalization2D: variance must be rank 2 or rank 1 but got rank "+c.rank+"."),null!=i&&assert(2===i.rank||1===i.rank,"Error in batchNormalization2D: scale must be rank 2 or rank 1 but got rank "+i.rank+"."),null!=s&&assert(2===s.rank||1===s.rank,"Error in batchNormalization2D: offset must be rank 2 or rank 1 but got rank "+s.rank+"."),batchNormalization(u,l,c,r,i,s)}function batchNormalization3d_(e,t,n,r,o,a){void 0===r&&(r=.001);var i,s,u=convertToTensor(e,"x","batchNormalization"),l=convertToTensor(t,"mean","batchNormalization"),c=convertToTensor(n,"variance","batchNormalization");return null!=o&&(i=convertToTensor(o,"scale","batchNormalization")),null!=a&&(s=convertToTensor(a,"offset","batchNormalization")),assert(3===u.rank,"Error in batchNormalization3D: x must be rank 3 but got rank "+u.rank+"."),assert(3===l.rank||1===l.rank,"Error in batchNormalization3D: mean must be rank 3 or rank 1 but got rank "+l.rank+"."),assert(3===c.rank||1===c.rank,"Error in batchNormalization3D: variance must be rank 3 or rank 1 but got rank "+c.rank+"."),null!=i&&assert(3===i.rank||1===i.rank,"Error in batchNormalization3D: scale must be rank 3 or rank 1 but got rank "+i.rank+"."),null!=s&&assert(3===s.rank||1===s.rank,"Error in batchNormalization3D: offset must be rank 3 or rank 1 but got rank "+s.rank+"."),batchNormalization(u,l,c,r,i,s)}function batchNormalization4d_(e,t,n,r,o,a){void 0===r&&(r=.001);var i,s,u=convertToTensor(e,"x","batchNormalization"),l=convertToTensor(t,"mean","batchNormalization"),c=convertToTensor(n,"variance","batchNormalization");return null!=o&&(i=convertToTensor(o,"scale","batchNormalization")),null!=a&&(s=convertToTensor(a,"offset","batchNormalization")),assert(4===u.rank,"Error in batchNormalization4D: x must be rank 4 but got rank "+u.rank+"."),assert(4===l.rank||1===l.rank,"Error in batchNormalization4D: mean must be rank 4 or rank 1 but got rank "+l.rank+"."),assert(4===c.rank||1===c.rank,"Error in batchNormalization4D: variance must be rank 4 or rank 1 but got rank "+c.rank+"."),null!=i&&assert(4===i.rank||1===i.rank,"Error in batchNormalization4D: scale must be rank 4 or rank 1 but got rank "+i.rank+"."),null!=s&&assert(4===s.rank||1===s.rank,"Error in batchNormalization4D: offset must be rank 4 or rank 1 but got rank "+s.rank+"."),batchNormalization(u,l,c,r,i,s)}function batchNormalization_(e,t,n,r,o,a){void 0===r&&(r=.001);var i,s,u,l=convertToTensor(e,"x","batchNormalization"),c=convertToTensor(t,"mean","batchNormalization"),p=convertToTensor(n,"variance","batchNormalization");null!=o&&(i=convertToTensor(o,"scale","batchNormalization")),null!=a&&(s=convertToTensor(a,"offset","batchNormalization")),assert(c.rank===p.rank,"Batch normalization gradient requires mean and variance to have equal ranks."),assert(null==s||c.rank===s.rank,"Batch normalization gradient requires mean and offset to have equal ranks."),assert(null==i||c.rank===i.rank,"Batch normalization gradient requires mean and scale to have equal ranks."),u=0===l.rank||1===l.rank?l.as4D(1,1,1,l.size):2===l.rank?l.as4D(1,1,l.shape[0],l.shape[1]):3===l.rank?l.as4D(1,l.shape[0],l.shape[1],l.shape[2]):l;return ENV.engine.runKernel(function(e){return e.batchNormalization(u,batchnormReshape4D(c),batchnormReshape4D(p),r,batchnormReshape4D(i),batchnormReshape4D(s))},{$x:l,$mean:c,$variance:p,$scale:i,$offset:s},function(e){var t=null==i?scalar(1):i,n=getReductionAxes(c.shape,u.shape),o=[];if(1===c.rank){for(var a=0;a<u.shape.length-1;++a)o.push(u.shape[a]);o.push(1);}var s=l.sub(c),d=e.mul(t),h=rsqrt(p.add(scalar(r))),f=h.mul(h).mul(h).mul(scalar(-.5));return {$x:function(){return 1===c.rank?e.mul(tile(h.as4D(1,1,1,c.shape[0]),o)).mul(t).reshape(l.shape):e.mul(h).mul(t).reshape(l.shape)},$mean:function(){var e=h.mul(scalar(-1)).mul(d);return 1===c.rank&&(e=e.sum(n)),e.reshape(c.shape)},$variance:function(){var e=f.mul(s).mul(d);return 1===c.rank&&(e=e.sum(n)),e.reshape(c.shape)},$scale:function(){var t=s.mul(h),r=e.mul(t);return 1===c.rank&&(r=r.sum(n)),r.reshape(c.shape)},$offset:function(){var t=e;return 1===c.rank&&(t=t.sum(n)),t.reshape(c.shape)}}}).reshape(l.shape)}function batchnormReshape4D(e){return null==e?null:0===e.rank?e.as1D():1===e.rank?e:2===e.rank?e.as4D(1,1,e.shape[0],e.shape[1]):3===e.rank?e.as4D(1,e.shape[0],e.shape[1],e.shape[2]):e}var batchNormalization2d=op({batchNormalization2d_:batchNormalization2d_}),batchNormalization3d=op({batchNormalization3d_:batchNormalization3d_}),batchNormalization4d=op({batchNormalization4d_:batchNormalization4d_}),batchNormalization=op({batchNormalization_:batchNormalization_});function computePool2DInfo(e,t,n,r,o,a,i){void 0===i&&(i="channelsLast");var s,u=parseTupleParam(t),l=u[0],c=u[1];if("channelsLast"===i)s=[l,c,e[3],e[3]];else{if("channelsFirst"!==i)throw new Error("Unknown dataFormat "+i);s=[l,c,e[1],e[1]];}return computeConv2DInfo(e,s,n,r,o,a,!1,i)}function computeConv2DInfo(e,t,n,r,o,a,i,s){void 0===i&&(i=!1),void 0===s&&(s="channelsLast");var u=[-1,-1,-1,-1],l=u[0],c=u[1],p=u[2],d=u[3];if("channelsLast"===s)l=e[0],c=e[1],p=e[2],d=e[3];else{if("channelsFirst"!==s)throw new Error("Unknown dataFormat "+s);l=e[0],d=e[1],c=e[2],p=e[3];}var h,f=t[0],m=t[1],g=t[3],v=parseTupleParam(n),y=v[0],x=v[1],T=parseTupleParam(r),w=T[0],E=T[1],S=getEffectiveFilterSize(f,w),b=getEffectiveFilterSize(m,E),C=getPadAndOutInfo(o,c,p,y,x,S,b,a),A=C.padInfo,_=C.outHeight,N=C.outWidth,R=i?g*d:g;return "channelsFirst"===s?h=[l,R,_,N]:"channelsLast"===s&&(h=[l,_,N,R]),{batchSize:l,dataFormat:s,inHeight:c,inWidth:p,inChannels:d,outHeight:_,outWidth:N,outChannels:R,padInfo:A,strideHeight:y,strideWidth:x,filterHeight:f,filterWidth:m,effectiveFilterHeight:S,effectiveFilterWidth:b,dilationHeight:w,dilationWidth:E,inShape:e,outShape:h,filterShape:t}}function computeConv3DInfo(e,t,n,r,o,a,i){void 0===a&&(a=!1),void 0===i&&(i="channelsLast");var s=[-1,-1,-1,-1,-1],u=s[0],l=s[1],c=s[2],p=s[3],d=s[4];if("channelsLast"===i)u=e[0],l=e[1],c=e[2],p=e[3],d=e[4];else{if("channelsFirst"!==i)throw new Error("Unknown dataFormat "+i);u=e[0],d=e[1],l=e[2],c=e[3],p=e[4];}var h,f=t[0],m=t[1],g=t[2],v=t[4],y=parse3TupleParam(n),x=y[0],T=y[1],w=y[2],E=parse3TupleParam(r),S=E[0],b=E[1],C=E[2],A=get3DPadAndOutInfo(o,l,c,p,x,T,w,getEffectiveFilterSize(f,S),getEffectiveFilterSize(m,b),getEffectiveFilterSize(g,C)),_=A.padInfo,N=A.outDepth,R=A.outHeight,k=A.outWidth,I=a?v*d:v;return "channelsFirst"===i?h=[u,I,N,R,k]:"channelsLast"===i&&(h=[u,N,R,k,I]),{batchSize:u,dataFormat:i,inDepth:l,inHeight:c,inWidth:p,inChannels:d,outDepth:N,outHeight:R,outWidth:k,outChannels:I,padInfo:_,strideDepth:x,strideHeight:T,strideWidth:w,filterDepth:f,filterHeight:m,filterWidth:g,dilationDepth:S,dilationHeight:b,dilationWidth:C,inShape:e,outShape:h,filterShape:t}}function computeOutputShape3D(e,t,n,r,o,a){null==o&&(o=computeDefaultPad(e,t,r));var i=e[0],s=e[1],u=conditionalRound((i-t+2*o)/r+1,a);assert(isInt(u),"The output # of rows ("+u+") must be an integer. Change the stride and/or zero pad parameters");var l=conditionalRound((s-t+2*o)/r+1,a);return assert(isInt(l),"The output # of columns ("+l+") must be an integer. Change the stride and/or zero pad parameters"),[u,l,n]}function computeDefaultPad(e,t,n,r){void 0===r&&(r=1);var o=getEffectiveFilterSize(t,r);return Math.floor((e[0]*(n-1)-n+o)/2)}function parseTupleParam(e){return "number"==typeof e?[e,e]:e}function parse3TupleParam(e){return "number"==typeof e?[e,e,e]:e}function getEffectiveFilterSize(e,t){return t<=1?e:e+(e-1)*(t-1)}function getPadAndOutInfo(e,t,n,r,o,a,i,s){var u,l,c;if("number"==typeof e){u={top:e,bottom:e,left:e,right:e,type:0===e?"VALID":"NUMBER"};var p=computeOutputShape3D([t,n,1],a,1,r,e,s);l=p[0],c=p[1];}else if("same"===e){var d=((l=Math.ceil(t/r))-1)*r+a-t,h=((c=Math.ceil(n/o))-1)*o+i-n,f=Math.floor(d/2),m=d-f,g=Math.floor(h/2);u={top:f,bottom:m,left:g,right:h-g,type:"SAME"};}else{if("valid"!==e)throw Error("Unknown padding parameter: "+e);u={top:0,bottom:0,left:0,right:0,type:"VALID"},l=Math.ceil((t-a+1)/r),c=Math.ceil((n-i+1)/o);}return {padInfo:u,outHeight:l,outWidth:c}}function get3DPadAndOutInfo(e,t,n,r,o,a,i,s,u,l){var c,p,d,h;if("same"===e){var f=((p=Math.ceil(t/o))-1)*o+s-t,m=((d=Math.ceil(n/a))-1)*a+u-n,g=((h=Math.ceil(r/i))-1)*i+l-r,v=Math.floor(f/2),y=f-v,x=Math.floor(m/2),T=m-x,w=Math.floor(g/2);c={top:x,bottom:T,left:w,right:g-w,front:v,back:y,type:"SAME"};}else{if("valid"!==e)throw Error("Unknown padding parameter: "+e);c={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},p=Math.ceil((t-s+1)/o),d=Math.ceil((n-u+1)/a),h=Math.ceil((r-l+1)/i);}return {padInfo:c,outDepth:p,outHeight:d,outWidth:h}}function conditionalRound(e,t){if(!t)return e;switch(t){case"round":return Math.round(e);case"ceil":return Math.ceil(e);case"floor":return Math.floor(e);default:throw new Error("Unknown roundingMode "+t)}}function tupleValuesAreOne(e){var t=parseTupleParam(e),n=t[0],r=t[1];return 1===n&&1===r}function eitherStridesOrDilationsAreOne(e,t){return tupleValuesAreOne(e)||tupleValuesAreOne(t)}function matMul_(e,t,n,r){var o;void 0===n&&(n=!1),void 0===r&&(r=!1);var a=convertToTensor(e,"a","matMul"),i=convertToTensor(t,"b","matMul");a=(o=makeTypesMatch(a,i))[0],i=o[1];var s=n?a.shape[a.rank-2]:a.shape[a.rank-1],u=r?i.shape[i.rank-1]:i.shape[i.rank-2],l=n?a.shape[a.rank-1]:a.shape[a.rank-2],c=r?i.shape[i.rank-2]:i.shape[i.rank-1],p=a.shape.slice(0,-2),d=i.shape.slice(0,-2),h=sizeFromShape(p),f=sizeFromShape(d);assert(a.rank>=2&&i.rank>=2&&a.rank===i.rank,"Error in matMul: inputs must have the same rank of at least 2, got ranks "+a.rank+" and "+i.rank+"."),assert(arraysEqual(p,d),"Error in matMul: outer dimensions ("+p+") and ("+d+") of Tensors with shapes "+a.shape+" and "+i.shape+" must match."),assert(s===u,"Error in matMul: inner shapes ("+s+") and ("+u+") of Tensors with shapes "+a.shape+" and "+i.shape+" and transposeA="+n+" and transposeB="+r+" must match.");var m=a.shape.slice(0,-2).concat([l,c]),g=n?a.as3D(h,s,l):a.as3D(h,l,s),v=r?i.as3D(f,c,u):i.as3D(f,u,c);return ENV.engine.runKernel(function(e){return e.batchMatMul(g,v,n,r)},{$a:g,$b:v},function(e){return n||r?!n&&r?{$a:function(){return e.matMul(v,!1,!1)},$b:function(){return e.matMul(g,!0,!1)}}:n&&!r?{$a:function(){return v.matMul(e,!1,!0)},$b:function(){return g.matMul(e,!1,!1)}}:{$a:function(){return v.matMul(e,!0,!0)},$b:function(){return e.matMul(g,!0,!0)}}:{$a:function(){return e.matMul(v,!1,!0)},$b:function(){return g.matMul(e,!0,!1)}}}).reshape(m)}function outerProduct_(e,t){var n=convertToTensor(e,"v1","outerProduct"),r=convertToTensor(t,"v2","outerProduct");return assert(1===n.rank&&1===r.rank,"Error in outerProduct: inputs must be rank 1, but got ranks "+n.rank+" and "+r.rank+"."),n.as2D(-1,1).matMul(r.as2D(1,-1))}function dot_(e,t){var n=convertToTensor(e,"t1","dot"),r=convertToTensor(t,"t2","dot");assert(!(1!==n.rank&&2!==n.rank||1!==r.rank&&2!==r.rank),"Error in dot: inputs must all be rank 1 or 2, but got ranks "+n.rank+" and "+r.rank+".");var o=1===n.rank?n.size:n.shape[1],a=1===r.rank?r.size:r.shape[0];return assert(o===a,"Error in dot: inner dimensions of inputs must match, but got "+o+" and "+a+"."),1===n.rank&&1===r.rank?n.as2D(1,-1).matMul(r.as2D(-1,1)).asScalar():1===n.rank&&2===r.rank?n.as2D(1,-1).matMul(r.as2D(r.shape[0],r.shape[1])).as1D():2===n.rank&&1===r.rank?n.matMul(r.as2D(-1,1)).as1D():n.matMul(r.as2D(r.shape[0],r.shape[1]))}var matMul=op({matMul_:matMul_}),dot=op({dot_:dot_}),outerProduct=op({outerProduct_:outerProduct_});function conv1d_(e,t,n,r,o,a,i){void 0===o&&(o="NWC"),void 0===a&&(a=1);var s=convertToTensor(e,"x","conv1d"),u=convertToTensor(t,"filter","conv1d"),l=s,c=!1;2===s.rank&&(c=!0,l=s.as3D(1,s.shape[0],s.shape[1])),assert(3===l.rank,"Error in conv1d: input must be rank 3, but got rank "+l.rank+"."),assert(3===u.rank,"Error in conv1d: filter must be rank 3, but got rank "+u.rank+"."),null!=i&&assert(isInt(r),"Error in conv1d: pad must be an integer when using, dimRoundingMode "+i+" but got pad "+r+"."),assert(l.shape[2]===u.shape[1],"Error in conv1d: depth of input ("+l.shape[2]+") must match input depth for filter "+u.shape[1]+"."),assert(eitherStridesOrDilationsAreOne(n,a),"Error in conv1D: Either stride or dilation must be 1. Got stride "+n+" and dilation '"+a+"'"),assert("NWC"===o,"Error in conv1d: got dataFormat of "+o+" but only NWC is currently supported.");var p=u.as4D(1,u.shape[0],u.shape[1],u.shape[2]),d=l.as4D(l.shape[0],1,l.shape[1],l.shape[2]),h=conv2d(d,p,[1,n],r,"NHWC",[1,a],i);return c?h.as2D(h.shape[2],h.shape[3]):h.as3D(h.shape[0],h.shape[2],h.shape[3])}function conv2d_(e,t,n,r,o,a,i){void 0===o&&(o="NHWC"),void 0===a&&(a=[1,1]);var s=convertToTensor(e,"x","conv2d"),u=convertToTensor(t,"filter","conv2d"),l=s,c=!1;3===s.rank&&(c=!0,l=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),assert(4===l.rank,"Error in conv2d: input must be rank 4, but got rank "+l.rank+"."),assert(4===u.rank,"Error in conv2d: filter must be rank 4, but got rank "+u.rank+"."),null!=i&&assert(isInt(r),"Error in conv2d: pad must be an integer when using, dimRoundingMode "+i+" but got pad "+r+"."),assert(l.shape[3]===u.shape[2],"Error in conv2d: depth of input ("+l.shape[3]+") must match input depth for filter "+u.shape[2]+"."),assert(eitherStridesOrDilationsAreOne(n,a),"Error in conv2D: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+a+"'"),assert("NHWC"===o,"Error in conv2d: got dataFormat of "+o+" but only NHWC is currently supported.");var p,d=computeConv2DInfo(l.shape,u.shape,n,a,r,i);if(1!==d.filterHeight||1!==d.filterWidth||1!==d.dilationHeight||1!==d.dilationWidth||1!==d.strideHeight||1!==d.strideWidth||"SAME"!==d.padInfo.type&&"VALID"!==d.padInfo.type){p=ENV.engine.runKernel(function(e){return e.conv2d(l,u,d)},{x:l,$filter:u},function(e){return assert(tupleValuesAreOne(a),"Error in gradient of conv2D: dilation rates greater than 1 are notyet supported in gradients. Got dilations '"+a+"'"),{x:function(){return conv2dDerInput_(l.shape,e,u,n,r)},$filter:function(){return conv2dDerFilter_(l,e,u.shape,n,r)}}});}else{var h=l.reshape([-1,d.inChannels]),f=u.reshape([d.inChannels,d.outChannels]);p=matMul(h,f).reshape(d.outShape);}return c?p.as3D(p.shape[1],p.shape[2],p.shape[3]):p}function conv2dDerInput_(e,t,n,r,o,a){assert(e.length===t.rank,"Length of inShape ("+e.length+") and rank of dy ("+t.rank+") must match");var i=e,s=t,u=!1;3===t.rank&&(u=!0,s=t.as4D(1,t.shape[0],t.shape[1],t.shape[2]),i=[1,e[0],e[1],e[2]]);var l=i[3],c=s.shape[3];assert(4===i.length,"Error in conv2dDerInput: inShape must be length 4, but got length "+i.length+"."),assert(4===s.rank,"Error in conv2dDerInput: dy must be rank 4, but got rank "+s.rank),assert(4===n.rank,"Error in conv2dDerInput: filter must be rank 4, but got rank "+n.rank),assert(l===n.shape[2],"Error in conv2dDerInput: depth of input ("+l+") must match input depth for filter "+n.shape[2]+"."),assert(c===n.shape[3],"Error in conv2dDerInput: depth of output ("+c+") must match output depth for filter "+n.shape[3]+"."),null!=a&&assert(isInt(o),"Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode "+a+" but got pad "+o+".");var p=computeConv2DInfo(i,n.shape,r,1,o,a),d=ENV.engine.runKernel(function(e){return e.conv2dDerInput(s,n,p)},{dy4D:s,filter:n},function(e){return {dy4D:function(){return conv2d(e,n,r,o,"NHWC",1,a)},filter:function(){return conv2dDerFilter(e,s,n.shape,r,o,a)}}});return u?d.as3D(d.shape[1],d.shape[2],d.shape[3]):d}function conv2dDerFilter_(e,t,n,r,o,a){var i=e;3===e.rank&&(i=e.as4D(1,e.shape[0],e.shape[1],e.shape[2]));var s=t;3===s.rank&&(s=t.as4D(1,t.shape[0],t.shape[1],t.shape[2])),assert(4===i.rank,"Error in conv2dDerFilter: input must be rank 4, but got shape "+i.shape+"."),assert(4===s.rank,"Error in conv2dDerFilter: dy must be rank 4, but got shape "+s.shape+"."),assert(4===n.length,"Error in conv2dDerFilter: filterShape must be length 4, but got "+n+"."),assert(i.shape[3]===n[2],"Error in conv2dDerFilter: depth of input "+i.shape[3]+") must match input depth in filter ("+n[2]+"."),assert(s.shape[3]===n[3],"Error in conv2dDerFilter: depth of dy ("+s.shape[3]+") must match output depth for filter ("+n[3]+")."),null!=a&&assert(isInt(o),"Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode "+a+" but got pad "+o+".");var u=computeConv2DInfo(i.shape,n,r,1,o,a);return ENV.engine.runKernel(function(e){return e.conv2dDerFilter(i,s,u)},{x4D:i,dy4D:s})}function conv2dTranspose_(e,t,n,r,o,a){return conv2dDerInput_(n,convertToTensor(e,"x","conv2dTranspose"),convertToTensor(t,"filter","conv2dTranspose"),r,o,a)}function depthwiseConv2d_(e,t,n,r,o,a,i){void 0===o&&(o="NHWC"),void 0===a&&(a=[1,1]);var s=convertToTensor(e,"x","depthwiseConv2d"),u=convertToTensor(t,"filter","depthwiseConv2d"),l=s,c=!1;3===s.rank&&(c=!0,l=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),assert(4===l.rank,"Error in depthwiseConv2d: input must be rank 4, but got rank "+l.rank+"."),assert(4===u.rank,"Error in depthwiseConv2d: filter must be rank 4, but got rank "+u.rank+"."),assert(l.shape[3]===u.shape[2],"Error in depthwiseConv2d: number of input channels ("+l.shape[3]+") must match the inChannels dimension in filter "+u.shape[2]+"."),null==a&&(a=[1,1]),assert(eitherStridesOrDilationsAreOne(n,a),"Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+a+"'"),null!=i&&assert(isInt(r),"Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode "+i+" but got pad "+r+".");var p=computeConv2DInfo(l.shape,u.shape,n,a,r,i,!0),d=ENV.engine.runKernel(function(e){return e.depthwiseConv2D(l,u,p)},{x:l,$filter:u},function(e){return assert(tupleValuesAreOne(a),"Error in gradient of depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '"+a+"'"),{x:function(){return depthwiseConv2dDerInput(l.shape,e,u,p)},$filter:function(){return depthwiseConv2dDerFilter(l,e,u.shape,p)}}});return c?d.as3D(d.shape[1],d.shape[2],d.shape[3]):d}function separableConv2d_(e,t,n,r,o,a,i){void 0===a&&(a=[1,1]),void 0===i&&(i="NHWC");var s=convertToTensor(e,"x","separableConv2d"),u=convertToTensor(t,"depthwiseFilter","separableConv2d"),l=convertToTensor(n,"pointwiseFilter","separableConv2d"),c=s,p=!1;if(3===s.rank&&(p=!0,c=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),"NCHW"===i)throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");assert(4===c.rank,"Error in separableConv2d: input must be rank 4, but got rank "+c.rank+"."),assert(4===u.rank,"Error in separableConv2d: depthwise filter must be rank 4, but got rank "+u.rank+"."),assert(4===l.rank,"Error in separableConv2d: pointwise filter must be rank 4, but got rank "+u.rank+"."),assert(1===l.shape[0],"Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got "+l.shape[0]+"."),assert(1===l.shape[1],"Error in separableConv2d: the second dimension of pointwise filter  must be 1, but got "+l.shape[1]+".");var d=u.shape[2],h=u.shape[3];assert(l.shape[2]===d*h,"Error in separableConv2d: the third dimension of pointwise filter must be "+d*h+", but got "+l.shape[2]+".");var f=depthwiseConv2d(c,u,r,o,i,a),m=conv2d(f,l,1,"valid",i);return p?m.as3D(m.shape[1],m.shape[2],m.shape[3]):m}function parseTupleParam$1(e){return "number"==typeof e?[e,e,e]:2===e.length?[e[0],e[1],1]:e}function tupleValuesAreOne$1(e){var t=parseTupleParam$1(e),n=t[0],r=t[1],o=t[2];return 1===n&&1===r&&1===o}function eitherStridesOrDilationsAreOne$1(e,t){return tupleValuesAreOne$1(e)||tupleValuesAreOne$1(t)}function depthwiseConv2dDerInput(e,t,n,r){var o=t,a=!1;3===t.rank&&(a=!0,o=t.as4D(1,t.shape[0],t.shape[1],t.shape[2]));var i=ENV.engine.runKernel(function(e){return e.depthwiseConv2DDerInput(o,n,r)},{dy4D:o});return a?i.as3D(i.shape[1],i.shape[2],i.shape[3]):i}function depthwiseConv2dDerFilter(e,t,n,r){var o=e;3===e.rank&&(o=e.as4D(1,e.shape[0],e.shape[1],e.shape[2]));var a=t;return 3===a.rank&&(a=t.as4D(1,t.shape[0],t.shape[1],t.shape[2])),ENV.engine.runKernel(function(e){return e.depthwiseConv2DDerFilter(o,a,r)},{x4D:o,dy4D:a})}function conv3d_(e,t,n,r,o,a){void 0===o&&(o="NHWC"),void 0===a&&(a=[1,1,1]);var i=convertToTensor(e,"x","conv3d"),s=convertToTensor(t,"filter","conv3d"),u=i,l=!1;4===i.rank&&(l=!0,u=i.as5D(1,i.shape[0],i.shape[1],i.shape[2],i.shape[3])),assert(5===u.rank,"Error in conv3d: input must be rank 5, but got rank "+u.rank+"."),assert(5===s.rank,"Error in conv3d: filter must be rank 5, but got rank "+s.rank+"."),assert(u.shape[4]===s.shape[3],"Error in conv3d: depth of input ("+u.shape[4]+") must match input depth for filter "+s.shape[3]+"."),assert(eitherStridesOrDilationsAreOne$1(n,a),"Error in conv3D: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+a+"'"),assert("NHWC"===o,"Error in conv3d: got dataFormat of "+o+" but only NHWC is currently supported.");var c=computeConv3DInfo(u.shape,s.shape,n,a,r),p=ENV.engine.runKernel(function(e){return e.conv3d(u,s,c)},{x:u,$filter:s},function(e){return assert(tupleValuesAreOne$1(a),"Error in gradient of conv3D: dilation rates greater than 1 are notyet supported in gradients. Got dilations '"+a+"'"),{x:function(){return conv3dDerInput_(u.shape,e,s,n,r)},$filter:function(){return conv3dDerFilter_(u,e,s.shape,n,r)}}});return l?p.as4D(p.shape[1],p.shape[2],p.shape[3],p.shape[4]):p}function conv3dDerInput_(e,t,n,r,o){assert(e.length===t.rank,"Length of inShape ("+e.length+") and rank of dy ("+t.rank+") must match");var a=e,i=t,s=!1;4===t.rank&&(s=!0,i=t.as5D(1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]),a=[1,e[0],e[1],e[2],e[3]]);var u=a[4],l=i.shape[4];assert(5===a.length,"Error in conv3dDerInput: inShape must be length 5, but got length "+a.length+"."),assert(5===i.rank,"Error in conv3dDerInput: dy must be rank 5, but got rank "+i.rank),assert(5===n.rank,"Error in conv3dDerInput: filter must be rank 5, but got rank "+n.rank),assert(u===n.shape[3],"Error in conv3dDerInput: depth of input ("+u+") must match input depth for filter "+n.shape[3]+"."),assert(l===n.shape[4],"Error in conv3dDerInput: depth of output ("+l+") must match output depth for filter "+n.shape[4]+".");var c=computeConv3DInfo(a,n.shape,r,1,o),p=ENV.engine.runKernel(function(e){return e.conv3dDerInput(i,n,c)},{dy5D:i});return s?p.as4D(p.shape[1],p.shape[2],p.shape[3],p.shape[4]):p}function conv3dDerFilter_(e,t,n,r,o){var a=e;4===e.rank&&(a=e.as5D(1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]));var i=t;4===i.rank&&(i=t.as5D(1,t.shape[0],t.shape[1],t.shape[2],t.shape[3])),assert(5===a.rank,"Error in conv3dDerFilter: input must be rank 5, but got shape "+a.shape+"."),assert(5===i.rank,"Error in conv3dDerFilter: dy must be rank 5, but got shape "+i.shape+"."),assert(5===n.length,"Error in conv3dDerFilter: filterShape must be length 5, but got "+n+"."),assert(a.shape[4]===n[3],"Error in conv3dDerFilter: depth of input "+a.shape[4]+") must match input depth in filter ("+n[3]+"."),assert(i.shape[4]===n[4],"Error in conv3dDerFilter: depth of dy ("+i.shape[4]+") must match output depth for filter ("+n[4]+").");var s=computeConv3DInfo(a.shape,n,r,1,o);return ENV.engine.runKernel(function(e){return e.conv3dDerFilter(a,i,s)},{x5D:a,dy5D:i})}var conv1d=op({conv1d_:conv1d_}),conv2d=op({conv2d_:conv2d_}),conv3d=op({conv3d_:conv3d_}),conv2dDerFilter=op({conv2dDerFilter_:conv2dDerFilter_}),depthwiseConv2d=op({depthwiseConv2d_:depthwiseConv2d_}),separableConv2d=op({separableConv2d_:separableConv2d_}),conv2dTranspose=op({conv2dTranspose_:conv2dTranspose_});function reverse1d_(e){var t=convertToTensor(e,"x","reverse");return assert(1===t.rank,"Error in reverse1D: x must be rank 1 but got\n             rank "+t.rank+"."),reverse(t,0)}function reverse2d_(e,t){var n=convertToTensor(e,"x","reverse");return assert(2===n.rank,"Error in reverse2D: x must be rank 2 but got\n             rank "+n.rank+"."),reverse(n,t)}function reverse3d_(e,t){var n=convertToTensor(e,"x","reverse");return assert(3===n.rank,"Error in reverse3D: x must be rank 3 but got\n             rank "+n.rank+"."),reverse(n,t)}function reverse4d_(e,t){var n=convertToTensor(e,"x","reverse");return assert(4===n.rank,"Error in reverse4D: x must be rank 4 but got\n             rank "+n.rank+"."),reverse(n,t)}function reverse_(e,t){var n=convertToTensor(e,"x","reverse");if(0===n.rank)return n.clone();var r=parseAxisParam(t,n.shape);return ENV.engine.runKernel(function(e){return e.reverse(n,r)},{$x:n},function(e){return {$x:function(){return e.reverse(r)}}}).reshapeAs(n)}var reverse=op({reverse_:reverse_}),reverse1d=op({reverse1d_:reverse1d_}),reverse2d=op({reverse2d_:reverse2d_}),reverse3d=op({reverse3d_:reverse3d_}),reverse4d=op({reverse4d_:reverse4d_});function maxPoolImpl_(e,t,n,r,o,a){var i=convertToTensor(e,"x","maxPool"),s=i,u=!1;3===i.rank&&(u=!0,s=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),null==r&&(r=[1,1]),assert(4===s.rank,"Error in maxPool: input must be rank 4 but got rank "+s.rank+"."),assert(eitherStridesOrDilationsAreOne(n,r),"Error in maxPool: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+r+"'"),null!=a&&assert(isInt(o),"Error in maxPool: pad must be an integer when using, dimRoundingMode "+a+" but got pad "+o+".");var l=computePool2DInfo(s.shape,t,n,r,o,a),c=ENV.engine.runKernel(function(e,t){return t(e.maxPool(s,l))},{x:s},function(e,a){var i=a[0];return {x:function(){return maxPoolBackprop(e,s,i,t,n,r,o)}}});return u?c.as3D(c.shape[1],c.shape[2],c.shape[3]):c}function maxPool_(e,t,n,r,o){return maxPoolImpl_(e,t,n,1,r,o)}function avgPoolImpl_(e,t,n,r,o,a){var i=convertToTensor(e,"x","avgPool","float32");null==r&&(r=[1,1]),assert(eitherStridesOrDilationsAreOne(n,r),"Error in avgPool: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+r+"'");var s=i,u=!1;3===i.rank&&(u=!0,s=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),assert(4===s.rank,"Error in avgPool: x must be rank 4 but got rank "+s.rank+"."),null!=a&&assert(isInt(o),"Error in avgPool: pad must be an integer when using, dimRoundingMode "+a+" but got pad "+o+".");var l=computePool2DInfo(s.shape,t,n,r,o),c=ENV.engine.runKernel(function(e){return e.avgPool(s,l)},{x:s},function(e){return {x:function(){return avgPoolBackprop(e,s,t,n,r,o)}}});return c=c.cast(i.dtype),u?c.as3D(c.shape[1],c.shape[2],c.shape[3]):c}function avgPool_(e,t,n,r,o){return avgPoolImpl_(e,t,n,1,r,o)}function pool_(e,t,n,r,o,a){null==o&&(o=[1,1]),null==a&&(a=1),0===r&&(r="valid");var i=convertToTensor(e,"x","maxPool"),s=i,u=!1;3===i.rank&&(u=!0,s=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),assert(eitherStridesOrDilationsAreOne(a,o),"Error in pool: Either strides or dilations must be 1. Got strides "+a+" and dilations '"+o+"'");var l,c=computePool2DInfo(s.shape,t,a,o,r),p=[c.dilationHeight,c.dilationWidth];l="same"===r?withSpaceToBatchBasePaddings([c.filterHeight,c.filterWidth],p):[[0,0],[0,0]];var d=1===p[0]&&1===p[1],h=requiredSpaceToBatchPaddings([c.inHeight,c.inWidth],p,l),f=h[0],m=h[1],g=d?r:"valid",v=d?s:spaceToBatchND(s,p,f),y=("avg"===n?function(){return avgPoolImpl_(v,t,a,1,g)}:function(){return maxPoolImpl_(v,t,a,1,g)})(),x=d?y:batchToSpaceND(y,p,m);return u?x.as3D(x.shape[1],x.shape[2],x.shape[3]):x}function maxPoolBackprop(e,t,n,r,o,a,i,s){var u=convertToTensor(e,"dy","maxPoolBackprop"),l=convertToTensor(t,"input","maxPoolBackprop"),c=convertToTensor(n,"output","maxPoolBackprop");assert(l.rank===u.rank,"Rank of input ("+l.rank+") does not match rank of dy ("+u.rank+")"),null==a&&(a=[1,1]),assert(eitherStridesOrDilationsAreOne(o,a),"Error in maxPoolBackProp: Either strides or dilations must be 1. Got strides "+o+" and dilations '"+a+"'"),assert(4===u.rank,"Error in maxPoolBackprop: dy must be rank 4 but got rank "+u.rank+"."),assert(4===l.rank,"Error in maxPoolBackprop: input must be rank 4 but got rank "+l.rank+"."),null!=s&&assert(isInt(i),"Error in maxPoolBackprop: pad must be an integer when using, dimRoundingMode "+s+" but got pad "+i+".");var p=computePool2DInfo(l.shape,r,o,a,i,s);return ENV.engine.runKernel(function(e){return e.maxPoolBackprop(u,l,c,p)},{$dy:u,$input:l})}function avgPoolBackprop(e,t,n,r,o,a){var i=convertToTensor(e,"dy","avgPoolBackprop"),s=convertToTensor(t,"input","avgPoolBackprop");assert(s.rank===i.rank,"Rank of input ("+s.rank+") does not match rank of dy ("+i.rank+")"),null==o&&(o=[1,1]),assert(eitherStridesOrDilationsAreOne(r,o),"Error in avgPoolBackprop: Either strides or dilations must be 1. Got strides "+r+" and dilations '"+o+"'");var u=s,l=i,c=!1;3===s.rank&&(c=!0,u=s.as4D(1,s.shape[0],s.shape[1],s.shape[2]),l=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),assert(4===l.rank,"Error in avgPoolBackprop: dy must be rank 4 but got rank "+l.rank+"."),assert(4===u.rank,"Error in avgPoolBackprop: input must be rank 4 but got rank "+u.rank+".");var p=computePool2DInfo(u.shape,n,r,o,a),d=ENV.engine.runKernel(function(e){return e.avgPoolBackprop(l,u,p)},{dy4D:l,input4D:u});return c?d.as3D(d.shape[1],d.shape[2],d.shape[3]):d}function requiredSpaceToBatchPaddings(e,t,n){var r=n.map(function(e){return e[0]}),o=n.map(function(e){return e[1]}),a=e.concat(r,o),i=t.map(function(e,t){return (e-a[t]%e)%e}),s=o.map(function(e,t){return e+i[t]});return [t.map(function(e,t){return [r[t],s[t]]}),t.map(function(e,t){return [0,i[t]]})]}function withSpaceToBatchBasePaddings(e,t){var n=e.map(function(e,n){return e+(e-1)*(t[n]-1)}).map(function(e){return e-1}),r=n.map(function(e){return Math.floor(e/2)}),o=n.map(function(e,t){return e-r[t]});return n.map(function(e,t){return [r[t],o[t]]})}var maxPool=op({maxPool_:maxPool_}),avgPool=op({avgPool_:avgPool_}),pool=op({pool_:pool_});function slice1d_(e,t,n){var r=convertToTensor(e,"x","slice1d");return assert(1===r.rank,"slice1d expects a rank-1 tensor, but got a rank-"+r.rank+" tensor"),slice(r,[t],[n])}function slice2d_(e,t,n){var r=convertToTensor(e,"x","slice2d");return assert(2===r.rank,"slice2d expects a rank-2 tensor, but got a rank-"+r.rank+" tensor"),slice(r,t,n)}function slice3d_(e,t,n){var r=convertToTensor(e,"x","slice3d");return assert(3===r.rank,"slice3d expects a rank-3 tensor, but got a rank-"+r.rank+" tensor"),slice(r,t,n)}function slice4d_(e,t,n){var r=convertToTensor(e,"x","slice4d");return assert(4===r.rank,"slice4d expects a rank-4 tensor, but got a rank-"+r.rank+" tensor"),slice(r,t,n)}function slice_(e,t,n){var r,o,a=convertToTensor(e,"x","slice");if(0===a.rank)throw new Error("Slicing scalar is not possible");r="number"==typeof t?[t].concat(new Array(a.rank-1).fill(0)):t.length<a.rank?t.concat(new Array(a.rank-t.length).fill(0)):t.slice(),o=(o=null==n?new Array(a.rank).fill(-1):"number"==typeof n?[n].concat(new Array(a.rank-1).fill(-1)):n.length<a.rank?n.concat(new Array(a.rank-n.length).fill(-1)):n).map(function(e,t){return e>=0?e:(assert(-1===e,"Bad value in size"),a.shape[t]-r[t])}),assertParamsValid(a,r,o);var i=a.shape;return ENV.engine.runKernel(function(e){return e.slice(a,r,o)},{$x:a},function(e){for(var t=[],n=0;n<e.rank;n++)t.push([r[n],i[n]-r[n]-o[n]]);return {$x:function(){return e.pad(t)}}})}var slice=op({slice_:slice_}),slice1d=op({slice1d_:slice1d_}),slice2d=op({slice2d_:slice2d_}),slice3d=op({slice3d_:slice3d_}),slice4d=op({slice4d_:slice4d_});function logSumExp_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","logSumExp"),o=parseAxisParam(t,r.shape),a=r.max(o,!0),i=r.sub(a).exp().sum(o).log(),s=a.reshape(i.shape).add(i);if(n){var u=expandShapeToKeepDim(s.shape,o);return s.reshape(u)}return s}function sum_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","sum");"bool"===r.dtype&&(r=r.toInt());var o=parseAxisParam(t,r.shape);return customGrad(function(e){var t=getAxesPermutation(o,e.rank),r=o,a=e;null!=t&&(a=e.transpose(t),r=getInnerMostAxes(r.length,e.rank));var i=ENV.engine.runKernel(function(e){return e.sum(a,r)},{permutedX:a});if(n){var s=expandShapeToKeepDim(i.shape,o);i=i.reshape(s);}return {value:i,gradFunc:function(t){var n=e.shape.slice();return o.forEach(function(e){n[e]=1;}),t.reshape(n).mul(ones$1(e.shape,"float32"))}}})(r)}function prod_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","prod");"bool"===r.dtype&&(r=r.toInt());var o=parseAxisParam(t,r.shape),a=getAxesPermutation(o,r.rank),i=o,s=r;null!=a&&(s=r.transpose(a),i=getInnerMostAxes(i.length,r.rank));var u=ENV.engine.runKernel(function(e){return e.prod(s,i)},{permutedX:s});if(n){var l=expandShapeToKeepDim(u.shape,o);u=u.reshape(l);}return u}function mean_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","mean"),o=parseAxisParam(t,r.shape),a=sizeFromShape(computeOutAndReduceShapes(r.shape,o)[1]);return customGrad(function(e){var r=scalar(a);return {value:(r.dtype===e.dtype?e:e.cast(r.dtype)).div(r).sum(t,n),gradFunc:function(t){var n=e.shape.slice();return o.forEach(function(e){n[e]=1;}),t.reshape(n).mul(ones$1(e.shape,"float32")).div(r)}}})(r)}function gradForMinAndMax(e,t,n,r,o){var a=t[0];return a.rank<n.rank&&(a=a.reshape(expandShapeToKeepDim(a.shape,r))),e.rank<n.rank&&(e=e.reshape(expandShapeToKeepDim(e.shape,r))),{$x:function(){var t=e.mul(n.equal(a).cast(e.dtype));return null==o?t:t.transpose(o)}}}function min_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","min"),o=r,a=parseAxisParam(t,r.shape),i=a,s=getAxesPermutation(i,r.rank);null!=s&&(r=r.transpose(s),i=getInnerMostAxes(i.length,r.rank));var u=ENV.engine.runKernel(function(e,t){return t(e.min(r,i))},{$x:r},function(e,t){return gradForMinAndMax(e,t,o,a,s)});if(n){var l=expandShapeToKeepDim(u.shape,a);u=u.reshape(l);}return u}function max_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","max"),o=r,a=parseAxisParam(t,r.shape),i=a,s=getAxesPermutation(i,r.rank);null!=s&&(r=r.transpose(s),i=getInnerMostAxes(i.length,r.rank));var u=ENV.engine.runKernel(function(e,t){return t(e.max(r,i))},{$x:r},function(e,t){return gradForMinAndMax(e,t,o,a,s)});if(n){var l=expandShapeToKeepDim(u.shape,a);u=u.reshape(l);}return u}function argMin_(e,t){void 0===t&&(t=0);var n=convertToTensor(e,"x","argMin");null==t&&(t=0);var r=parseAxisParam(t,n.shape),o=getAxesPermutation(r,n.rank);null!=o&&(n=n.transpose(o),r=getInnerMostAxes(r.length,n.rank));return ENV.engine.runKernel(function(e){return e.argMin(n,r[0])},{$x:n},function(e){return {$x:function(){return zerosLike(n)}}})}function argMax_(e,t){void 0===t&&(t=0);var n=convertToTensor(e,"x","argMax");null==t&&(t=0);var r=parseAxisParam(t,n.shape),o=getAxesPermutation(r,n.rank);null!=o&&(n=n.transpose(o),r=getInnerMostAxes(r.length,n.rank));return ENV.engine.runKernel(function(e){return e.argMax(n,r[0])},{$x:n},function(e){return {$x:function(){return zerosLike(n)}}})}function all_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","all","bool"),o=parseAxisParam(t,r.shape),a=o,i=getAxesPermutation(a,r.rank);null!=i&&(r=r.transpose(i),a=getInnerMostAxes(a.length,r.rank));var s=ENV.engine.runKernel(function(e){return e.all(r,a)},{$x:r});if(n){var u=expandShapeToKeepDim(s.shape,o);return s.reshape(u)}return s}function any_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","any","bool"),o=parseAxisParam(t,r.shape),a=o,i=getAxesPermutation(a,r.rank);null!=i&&(r=r.transpose(i),a=getInnerMostAxes(a.length,r.rank));var s=ENV.engine.runKernel(function(e){return e.any(r,a)},{$x:r});if(n){var u=expandShapeToKeepDim(s.shape,o);return s.reshape(u)}return s}function moments_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=parseAxisParam(t,(e=convertToTensor(e,"x","moments")).shape),o=e.mean(r,n),a=o.shape;return n||(a=expandShapeToKeepDim(o.shape,r)),{mean:o,variance:e.toFloat().sub(o.reshape(a)).square().mean(r,n)}}var all=op({all_:all_}),any=op({any_:any_}),argMax=op({argMax_:argMax_}),argMin=op({argMin_:argMin_}),logSumExp=op({logSumExp_:logSumExp_}),max=op({max_:max_}),mean=op({mean_:mean_}),min=op({min_:min_}),moments=op({moments_:moments_}),sum$1=op({sum_:sum_}),prod=op({prod_:prod_});function notEqual_(e,t){var n,r=convertToTensor(e,"a","notEqual"),o=convertToTensor(t,"b","notEqual");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.notEqual(r,o)},{$a:r,$b:o})}function notEqualStrict_(e,t){var n=convertToTensor(e,"a","notEqualStrict"),r=convertToTensor(t,"b","notEqualStrict");return assertShapesMatch(n.shape,r.shape,"Error in notEqualStrict: "),n.notEqual(r)}function less_(e,t){var n,r=convertToTensor(e,"a","less"),o=convertToTensor(t,"b","less");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.less(r,o)},{$a:r,$b:o})}function lessStrict_(e,t){var n=convertToTensor(e,"a","lessStrict"),r=convertToTensor(t,"b","lessStrict");return assertShapesMatch(n.shape,r.shape,"Error in lessStrict: "),n.less(r)}function equal_(e,t){var n,r=convertToTensor(e,"a","equal"),o=convertToTensor(t,"b","equal");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.equal(r,o)},{$a:r,$b:o})}function equalStrict_(e,t){var n=convertToTensor(e,"a","equalStrict"),r=convertToTensor(t,"b","equalStrict");return assertShapesMatch(n.shape,r.shape,"Error in equalStrict: "),n.equal(r)}function lessEqual_(e,t){var n,r=convertToTensor(e,"a","lessEqual"),o=convertToTensor(t,"b","lessEqual");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.lessEqual(r,o)},{$a:r,$b:o})}function lessEqualStrict_(e,t){var n=convertToTensor(e,"a","lessEqualStrict"),r=convertToTensor(t,"b","lessEqualStrict");return assertShapesMatch(n.shape,r.shape,"Error in lessEqualStrict: "),n.lessEqual(r)}function greater_(e,t){var n,r=convertToTensor(e,"a","greater"),o=convertToTensor(t,"b","greater");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.greater(r,o)},{$a:r,$b:o})}function greaterStrict_(e,t){var n=convertToTensor(e,"a","greaterStrict"),r=convertToTensor(t,"b","greaterStrict");return assertShapesMatch(n.shape,r.shape,"Error in greaterStrict: "),n.greater(r)}function greaterEqual_(e,t){var n,r=convertToTensor(e,"a","greaterEqual"),o=convertToTensor(t,"b","greaterEqual");n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.greaterEqual(r,o)},{$a:r,$b:o},function(e){return {$a:function(){return zerosLike(r)},$b:function(){return zerosLike(o)}}})}function greaterEqualStrict_(e,t){var n=convertToTensor(e,"a","greaterEqualStrict"),r=convertToTensor(t,"b","greaterEqualStrict");return assertShapesMatch(n.shape,r.shape,"Error in greaterEqualStrict: "),n.greaterEqual(r)}var equal=op({equal_:equal_}),equalStrict=op({equalStrict_:equalStrict_}),greater=op({greater_:greater_}),greaterEqual=op({greaterEqual_:greaterEqual_}),greaterEqualStrict=op({greaterEqualStrict_:greaterEqualStrict_}),greaterStrict=op({greaterStrict_:greaterStrict_}),less=op({less_:less_}),lessEqual=op({lessEqual_:lessEqual_}),lessEqualStrict=op({lessEqualStrict_:lessEqualStrict_}),lessStrict=op({lessStrict_:lessStrict_}),notEqual=op({notEqual_:notEqual_}),notEqualStrict=op({notEqualStrict_:notEqualStrict_});function add_(e,t){var n,r=convertToTensor(e,"a","add"),o=convertToTensor(t,"b","add");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.add(r,o)},{$a:r,$b:o},function(e){return {$a:function(){var t=e,n=getReductionAxes(r.shape,a);return n.length>0&&(t=t.sum(n)),t.reshape(r.shape)},$b:function(){var t=e,n=getReductionAxes(o.shape,a);return n.length>0&&(t=t.sum(n)),t.reshape(o.shape)}}})}function addN_(e){assert(Array.isArray(e),function(){return "The argument passed to tf.addN() must be a list of tensors"}),assert(e.length>=1,function(){return "Must pass at least one tensor to tf.addN(), but got "+e.length});var t=e.map(function(e,t){return convertToTensor(e,"tensors"+t,"addN")}),n=t[0];t.forEach(function(e){if(e.dtype!==n.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),t.forEach(function(e){if(!arraysEqual(e.shape,n.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});var r=t;return ENV.engine.runKernel(function(e){return e.addN(t)},r,function(e){var n={};return t.forEach(function(t,r){n[r]=function(){return e.clone()};}),n})}function addStrict_(e,t){var n=convertToTensor(e,"a","addStrict"),r=convertToTensor(t,"b","addStrict");return assertShapesMatch(n.shape,r.shape,"Error in addStrict: "),n.add(r)}function sub_(e,t){var n,r=convertToTensor(e,"a","sub"),o=convertToTensor(t,"b","sub");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.subtract(r,o)},{$a:r,$b:o},function(e){return {$a:function(){var t=e,n=getReductionAxes(r.shape,a);return n.length>0&&(t=t.sum(n)),t.reshape(r.shape)},$b:function(){var t=e,n=getReductionAxes(o.shape,a);return n.length>0&&(t=t.sum(n)),t.neg().reshape(o.shape)}}})}function subStrict_(e,t){var n=convertToTensor(e,"a","subStrict"),r=convertToTensor(t,"b","subStrict");return assertShapesMatch(n.shape,r.shape,"Error in subStrict: "),n.sub(r)}function pow_(e,t){var n=convertToTensor(e,"base","pow"),r=convertToTensor(t,"exp","pow"),o=assertAndGetBroadcastShape(n.shape,r.shape);e=n.cast(upcastType(n.dtype,r.dtype)),t=r.cast(upcastType(n.dtype,r.dtype));return ENV.engine.runKernel(function(e,t){return t(e.pow(n,r))},{$base:n,$exp:r},function(e,t){var a=t[0];return {$base:function(){var t=r.toFloat(),a=e.mul(t.mul(n.pow(t.sub(scalar(1))))),i=getReductionAxes(n.shape,o);return i.length>0&&(a=a.sum(i)),a.reshape(n.shape)},$exp:function(){var t=e.mul(a.mul(n.log()).toFloat()),i=getReductionAxes(r.shape,o);return i.length>0&&(t=t.sum(i)),t.reshape(r.shape)}}})}function powStrict_(e,t){return assertShapesMatch(e.shape,t.shape,"Error in powStrict: "),e.pow(t)}function mul_(e,t){var n,r=convertToTensor(e,"a","mul"),o=convertToTensor(t,"b","mul");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.multiply(r,o)},{$a:r,$b:o},function(e){return {$a:function(){var t=e.mul(o.toFloat()),n=getReductionAxes(r.shape,a);return n.length>0?t.sum(n).reshape(r.shape):t},$b:function(){var t=e.mul(r.toFloat()),n=getReductionAxes(o.shape,a);return n.length>0?t.sum(n).reshape(o.shape):t}}})}function mulStrict_(e,t){var n=convertToTensor(e,"a","mul"),r=convertToTensor(t,"b","mul");return assertShapesMatch(n.shape,r.shape,"Error in multiplyStrict: "),n.mul(r)}function div_(e,t){var n,r,o=convertToTensor(e,"a","div"),a=convertToTensor(t,"b","div");if(n=makeTypesMatch(o,a),o=n[0],a=n[1],"int32"===o.dtype&&"int32"===a.dtype)return floorDiv(o,a);r=function(e){return e.realDivide(o,a)};var i=assertAndGetBroadcastShape(o.shape,a.shape);return ENV.engine.runKernel(r,{$a:o,$b:a},function(e){return {$a:function(){var t=e.div(a.toFloat()),n=getReductionAxes(o.shape,i);return n.length>0?t.sum(n).reshape(o.shape):t},$b:function(){var t=e.mul(o.toFloat()),n=getReductionAxes(a.shape,i);n.length>0&&(t=t.sum(n).reshape(a.shape));var r=a.square();return t.div(r.toFloat()).neg()}}})}function floorDiv_(e,t){var n,r=convertToTensor(e,"a","floorDiv"),o=convertToTensor(t,"b","floorDiv");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.floorDiv(r,o)},{$a:r,$b:o},function(e){return {$a:function(){var t=e.div(o.toFloat()),n=getReductionAxes(r.shape,a);return n.length>0?t.sum(n).reshape(r.shape):t},$b:function(){var t=e.mul(r.toFloat()),n=getReductionAxes(o.shape,a);n.length>0&&(t=t.sum(n).reshape(o.shape));var i=o.square();return t.div(i.toFloat()).neg()}}})}function divStrict_(e,t){var n=convertToTensor(e,"a","div"),r=convertToTensor(t,"b","div");return assertShapesMatch(n.shape,r.shape,"Error in divideStrict: "),n.div(r)}function mod_(e,t){var n,r=convertToTensor(e,"a","mod"),o=convertToTensor(t,"b","mod");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.mod(r,o)},{$a:r,$b:o},function(e){return {$a:function(){var t=getReductionAxes(r.shape,a);return t.length>0?e.sum(t).reshape(r.shape):e},$b:function(){var t=e.mul(r.div(o).floor().neg()),n=getReductionAxes(o.shape,a);return n.length>0?t.sum(n).reshape(o.shape):t}}})}function modStrict_(e,t){var n=convertToTensor(e,"a","modStrict"),r=convertToTensor(t,"b","modStrict");return assertShapesMatch(n.shape,r.shape,"Error in modStrict: "),n.mod(r)}function minimum_(e,t){var n,r=convertToTensor(e,"a","minimum"),o=convertToTensor(t,"b","minimum");n=makeTypesMatch(r,o),r=n[0],o=n[1],"bool"===r.dtype&&(r=r.toInt(),o=o.toInt()),assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.minimum(r,o)},{$a:r,$b:o},function(e){return {$a:function(){return e.mul(r.lessEqual(o).toFloat())},$b:function(){return e.mul(r.greater(o).toFloat())}}})}function minimumStrict_(e,t){var n=convertToTensor(e,"a","minimumStrict"),r=convertToTensor(t,"b","minimumStrict");return assertShapesMatch(n.shape,r.shape,"Error in minimumStrict: "),n.minimum(r)}function maximum_(e,t){var n,r=convertToTensor(e,"a","maximum"),o=convertToTensor(t,"b","maximum");n=makeTypesMatch(r,o),r=n[0],o=n[1],"bool"===r.dtype&&(r=r.toInt(),o=o.toInt()),assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.maximum(r,o)},{$a:r,$b:o},function(e){return {$a:function(){return e.mul(r.greaterEqual(o).toFloat())},$b:function(){return e.mul(r.less(o).toFloat())}}})}function maximumStrict_(e,t){var n=convertToTensor(e,"a","maximumStrict"),r=convertToTensor(t,"b","maximumStrict");return assertShapesMatch(n.shape,r.shape,"Error in maximumStrict: "),n.maximum(r)}function squaredDifference_(e,t){var n,r=convertToTensor(e,"a","squaredDifference"),o=convertToTensor(t,"b","squaredDifference");n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.squaredDifference(r,o)},{$a:r,$b:o},function(e){var t=scalar(2);return {$a:function(){return e.mul(r.sub(o).mul(t))},$b:function(){return e.mul(o.sub(r).mul(t))}}})}function squaredDifferenceStrict_(e,t){var n=convertToTensor(e,"a","squaredDifferenceStrict"),r=convertToTensor(t,"b","squaredDifferenceStrict");return assertShapesMatch(n.shape,r.shape,"Error in squaredDifferenceStrict: "),n.squaredDifference(r)}function atan2_(e,t){var n,r=convertToTensor(e,"a","atan2"),o=convertToTensor(t,"b","atan2");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.atan2(r,o)},{$a:r,$b:o},function(e){return {$a:function(){var t=add(r.square(),o.square()),n=e.mul(o.div(t)),i=getReductionAxes(r.shape,a);return i.length>0&&(n=n.sum(i)),n.reshape(r.shape)},$b:function(){var t=add(r.square(),o.square()),n=neg(e.mul(r.div(t))),i=getReductionAxes(o.shape,a);return i.length>0&&(n=n.sum(i)),n.reshape(o.shape)}}})}var add=op({add_:add_}),addN=op({addN_:addN_}),addStrict=op({addStrict_:addStrict_}),atan2=op({atan2_:atan2_}),div=op({div_:div_}),divStrict=op({divStrict_:divStrict_}),floorDiv=op({floorDiv_:floorDiv_}),maximum=op({maximum_:maximum_}),maximumStrict=op({maximumStrict_:maximumStrict_}),minimum=op({minimum_:minimum_}),minimumStrict=op({minimumStrict_:minimumStrict_}),mod=op({mod_:mod_}),modStrict=op({modStrict_:modStrict_}),mul=op({mul_:mul_}),mulStrict=op({mulStrict_:mulStrict_}),pow=op({pow_:pow_}),powStrict=op({powStrict_:powStrict_}),squaredDifference=op({squaredDifference_:squaredDifference_}),squaredDifferenceStrict=op({squaredDifferenceStrict_:squaredDifferenceStrict_}),sub=op({sub_:sub_}),subStrict=op({subStrict_:subStrict_});function logicalNot_(e){var t=convertToTensor(e,"x","logicalNot","bool");return ENV.engine.runKernel(function(e){return e.logicalNot(t)},{$x:t})}function logicalAnd_(e,t){var n=convertToTensor(e,"a","logicalAnd","bool"),r=convertToTensor(t,"b","logicalAnd","bool");return assertAndGetBroadcastShape(n.shape,r.shape),ENV.engine.runKernel(function(e){return e.logicalAnd(n,r)},{$a:n,$b:r})}function logicalOr_(e,t){var n=convertToTensor(e,"a","logicalOr","bool"),r=convertToTensor(t,"b","logicalOr","bool");return assertAndGetBroadcastShape(n.shape,r.shape),ENV.engine.runKernel(function(e){return e.logicalOr(n,r)},{$a:n,$b:r})}function logicalXor_(e,t){var n=convertToTensor(e,"a","logicalXor","bool"),r=convertToTensor(t,"b","logicalXor","bool");return assertAndGetBroadcastShape(n.shape,r.shape),logicalOr(e,t).logicalAnd(logicalAnd(e,t).logicalNot())}function where_(e,t,n){var r=convertToTensor(t,"a","where"),o=convertToTensor(n,"b","where"),a=convertToTensor(e,"condition","where","bool");assertShapesMatch(r.shape,o.shape,"Error in where: "),1===a.rank?assert(a.shape[0]===r.shape[0],"The first dimension of `a` must match the size of `condition`."):assertShapesMatch(a.shape,o.shape,"Error in where: ");return ENV.engine.runKernel(function(e){return e.select(a,r,o)},{$condition:a,$a:r,$b:o},function(e){return {$condition:function(){return zerosLike(a).toFloat()},$a:function(){return e.mul(a.cast(e.dtype))},$b:function(){return e.mul(a.logicalNot().cast(e.dtype))}}})}function whereAsync_(e){return __awaiter(this,void 0,void 0,function(){var t,n,r;return __generator(this,function(o){switch(o.label){case 0:return [4,(t=convertToTensor(e,"condition","whereAsync","bool")).data()];case 1:return n=o.sent(),r=whereImpl(t.shape,n),e!==t&&t.dispose(),[2,r]}})})}var logicalAnd=op({logicalAnd_:logicalAnd_}),logicalNot=op({logicalNot_:logicalNot_}),logicalOr=op({logicalOr_:logicalOr_}),logicalXor=op({logicalXor_:logicalXor_}),where=op({where_:where_}),whereAsync=whereAsync_;function relu_(e){var t=convertToTensor(e,"x","relu");if("bool"===t.dtype)return t.toInt();return ENV.engine.runKernel(function(e){return e.relu(t)},{$x:t},function(e){var n=t.step();return {$x:function(){return e.mulStrict(n.toFloat())}}})}function elu_(e){var t=convertToTensor(e,"x","elu");return ENV.engine.runKernel(function(e,n){return n(e.elu(t))},{$x:t},function(e,t){var n=t[0];return {$x:function(){return ENV.engine.runKernel(function(t){return t.eluDer(e,n)},{dy:e,y:n})}}})}function selu_(e){var t=convertToTensor(e,"x","selu");return ENV.engine.runKernel(function(e){return e.selu(t)},{$x:t},function(e){return {$x:function(){var n=t.greater(scalar(0)),r=scalar(SELU_SCALEALPHA),o=scalar(SELU_SCALE),a=e.mul(o),i=e.mul(r).mul(t.toFloat().exp());return where(n,a,i)}}})}function leakyRelu_(e,t){void 0===t&&(t=.2);var n=convertToTensor(e,"x","leakyRelu");return maximum(scalar(t).mul(n),n)}function prelu_(e,t){var n=convertToTensor(e,"x","prelu"),r=convertToTensor(t,"alpha","prelu"),o=scalar(0);return maximum(o,n).add(r.mul(minimum(o,n)))}var elu=op({elu_:elu_}),leakyRelu=op({leakyRelu_:leakyRelu_}),prelu=op({prelu_:prelu_}),relu=op({relu_:relu_}),selu=op({selu_:selu_});function transpose_(e,t){var n=convertToTensor(e,"x","transpose");if(null==t&&(t=n.shape.map(function(e,t){return t}).reverse()),assert(n.rank===t.length,"Error in transpose: rank of input "+n.rank+" must match length of perm "+t+"."),t.forEach(function(e){assert(e>=0&&e<n.rank,"All entries in 'perm' must be between 0 and "+(n.rank-1)+" but got "+t);}),n.rank<=1)return n.clone();return ENV.engine.runKernel(function(e){return e.transpose(n,t)},{$x:n},function(e){var n=getUndoAxesPermutation(t);return {$x:function(){return e.transpose(n)}}})}var transpose=op({transpose_:transpose_});function localResponseNormalization_(e,t,n,r,o){void 0===t&&(t=5),void 0===n&&(n=1),void 0===r&&(r=1),void 0===o&&(o=.5);var a=convertToTensor(e,"x","localResponseNormalization");assert(4===a.rank||3===a.rank,"Error in localResponseNormalization: x must be rank 3 or 4 but got\n               rank "+a.rank+"."),assert(isInt(t),"Error in localResponseNormalization: depthRadius must be an integer\n                     but got depthRadius "+t+".");var i=a,s=!1;3===a.rank&&(s=!0,i=a.as4D(1,a.shape[0],a.shape[1],a.shape[2]));var u=ENV.engine.runKernel(function(e,a){return a(e.localResponseNormalization4D(i,t,n,r,o))},{x4D:i},function(e,a){var s=a[0];return {x4D:function(){return ENV.engine.runKernel(function(a){return a.LRNGrad(e,i,s,t,n,r,o)},{})}}});return s?u.as3D(u.shape[1],u.shape[2],u.shape[3]):u}var localResponseNormalization=op({localResponseNormalization_:localResponseNormalization_});function norm_(e,t,n,r){void 0===t&&(t="euclidean"),void 0===n&&(n=null),void 0===r&&(r=!1);var o=normImpl(e=convertToTensor(e,"x","norm"),t,n),a=o.shape;if(r){var i=parseAxisParam(n,e.shape);a=expandShapeToKeepDim(o.shape,i);}return o.reshape(a)}function normImpl(e,t,n){if(void 0===n&&(n=null),0===e.rank)return e.abs();if(1!==e.rank&&null===n)return normImpl(e.reshape([-1]),t,n);if(1===e.rank||"number"==typeof n||n instanceof Array&&1===n.length){if(1===t)return e.abs().sum(n);if(t===1/0)return e.abs().max(n);if(t===-1/0)return e.abs().min(n);if("euclidean"===t||2===t)return e.abs().pow(scalar(2,"int32")).sum(n).sqrt();throw new Error("Error in norm: invalid ord value: "+t)}if(n instanceof Array&&2===n.length){if(1===t)return e.abs().sum(n[0]).max(n[1]-1);if(t===1/0)return e.abs().sum(n[1]).max(n[0]);if(t===-1/0)return e.abs().sum(n[1]).min(n[0]);if("fro"===t||"euclidean"===t)return e.square().sum(n).sqrt();throw new Error("Error in norm: invalid ord value: "+t)}throw new Error("Error in norm: invalid axis: "+n)}var norm=op({norm_:norm_});function unsortedSegmentSum_(e,t,n){var r=convertToTensor(e,"x","unsortedSegmentSum"),o=convertToTensor(t,"segmentIds","unsortedSegmentSum","int32");assert(isInt(n),"numSegments must be of dtype int");return ENV.engine.runKernel(function(e){return e.unsortedSegmentSum(r,o,n)},{$x:r},function(e){return {$x:function(){return gatherDropNegatives(e,o)}}})}function gather_(e,t,n){void 0===n&&(n=0);var r=convertToTensor(e,"x","gather"),o=convertToTensor(t,"indices","gather","int32");n=parseAxisParam(n,r.shape)[0];return ENV.engine.runKernel(function(e){return e.gather(r,o,n)},{$x:r},function(e){return {$x:function(){if(0===n)return unsortedSegmentSum(e,o,r.shape[n]);var t=r.shape,a=o.size,i=t.slice(0,n),s=i.length,u=t.slice(n,t.length).slice(1),l=u.length,c=arrayRange(0,s),p=arrayRange(s+1,s+1+l),d=arrayConcat([i,[a],u]),h=e.reshape(d),f=o.reshape([a]),m=arrayConcat([[s],c,p]),g=h.transpose(m),v=unsortedSegmentSum(g,f,r.shape[n]),y=getUndoAxesPermutation(m);return v=v.transpose(y)}}})}function arrayRange(e,t){for(var n=[],r=e;r<t;++r)n.push(r);return n}function arrayConcat(e){for(var t=[],n=0;n<e.length;++n)for(var r=0;r<e[n].length;++r)t.push(e[n][r]);return t}function gatherDropNegatives(e,t){for(var n=maximum(t,zerosLike(t)),r=gather(e,n),o=greaterEqual(t,scalar(0,"int32")),a=r.rank-o.rank,i=0;i<a;++i)o=expandDims(o,i+1);o=logicalAnd(o,ones$1(r.shape,"bool"));var s=zerosLike(r);return where(o,r,s)}var gather=op({gather_:gather_}),unsortedSegmentSum=op({unsortedSegmentSum_:unsortedSegmentSum_});function multiRNNCell_(e,t,n,r){for(var o=convertToTensor(t,"data","multiRNNCell"),a=convertToTensorArray(n,"c","multiRNNCell"),i=convertToTensorArray(r,"h","multiRNNCell"),s=o,u=[],l=0;l<e.length;l++){var c=e[l](s,a[l],i[l]);u.push(c[0]),u.push(c[1]),s=c[1];}var p=[],d=[];for(l=0;l<u.length;l+=2)p.push(u[l]),d.push(u[l+1]);return [p,d]}function basicLSTMCell_(e,t,n,r,o,a){var i=convertToTensor(e,"forgetBias","basicLSTMCell"),s=convertToTensor(t,"lstmKernel","basicLSTMCell"),u=convertToTensor(n,"lstmBias","basicLSTMCell"),l=convertToTensor(r,"data","basicLSTMCell"),c=convertToTensor(o,"c","basicLSTMCell"),p=convertToTensor(a,"h","basicLSTMCell"),d=l.concat(p,1).matMul(s).add(u),h=d.shape[0],f=d.shape[1]/4,m=[h,f],g=d.slice([0,0],m),v=d.slice([0,f],m),y=d.slice([0,2*f],m),x=d.slice([0,3*f],m),T=g.sigmoid().mulStrict(v.tanh()).addStrict(c.mulStrict(i.add(y).sigmoid()));return [T,T.tanh().mulStrict(x.sigmoid())]}var basicLSTMCell=op({basicLSTMCell_:basicLSTMCell_}),multiRNNCell=op({multiRNNCell_:multiRNNCell_});function movingAverage_(e,t,n,r,o){void 0===o&&(o=!0);var a=convertToTensor(e,"v","movingAverage"),i=convertToTensor(t,"x","movingAverage"),s=convertToTensor(n,"decay","movingAverage");assertTypesMatch(a,i),assert(arraysEqual(a.shape,i.shape),"Shape mismatch in v and x");var u=scalar(1),l=u.sub(s),c=i.sub(a).mul(l);if(o){assert(null!=r,"When using zeroDebias: true, step is required.");var p=convertToTensor(r,"step","movingAverage");c=c.div(u.sub(pow(s,p)));}return a.add(c)}var movingAverage=op({movingAverage_:movingAverage_});function stridedSlice_(e,t,n,r,o,a,i,s,u){if(void 0===o&&(o=0),void 0===a&&(a=0),void 0===i&&(i=0),void 0===s&&(s=0),void 0===u&&(u=0),0!==i)throw new Error("ellipsis mask is not yet supported");if(0!==s)throw new Error("new axis mask is not yet supported");var l=convertToTensor(e,"x","stridedSlice");return ENV.engine.runKernel(function(e){return e.stridedSlice(l,t,n,r,o,a,i,s,u)},{$x:l})}var stridedSlice=op({stridedSlice_:stridedSlice_});function topk_(e,t,n){void 0===t&&(t=1),void 0===n&&(n=!0);var r=convertToTensor(e,"x","topk");if(0===r.rank)throw new Error("topk() expects the input to be of rank 1 or higher");var o=r.shape[r.shape.length-1];if(t>o)throw new Error("'k' passed to topk() must be <= the last dimension ("+o+") but got "+t);var a=ENV.engine.runKernel(function(e){return e.topk(r,t,n)},{$x:r});return {values:a[0],indices:a[1]}}var topk=op({topk_:topk_});function scatterND_(e,t,n){var r=convertToTensor(e,"indices","scatterND","int32"),o=convertToTensor(t,"updates","scatterND");return validateInput(o,r,n),ENV.engine.runKernel(function(e){return e.scatterND(r,o,n)},{$indices:r,$updates:o})}var scatterND=op({scatterND_:scatterND_});function fft_(e){assert("complex64"===e.dtype,"The dtype for tf.spectral.fft() must be complex64 but got "+e.dtype+".");var t=e.shape[e.shape.length-1],n=e.size/t,r=e.as2D(n,t);return ENV.engine.runKernel(function(e){return e.fft(r)},{input:e}).reshape(e.shape)}function ifft_(e){assert("complex64"===e.dtype,"The dtype for tf.spectral.ifft() must be complex64 but got "+e.dtype+".");var t=e.shape[e.shape.length-1],n=e.size/t,r=e.as2D(n,t);return ENV.engine.runKernel(function(e){return e.ifft(r)},{input:e}).reshape(e.shape)}function rfft_(e){assert("float32"===e.dtype,"The dtype for rfft() must be real value but\n    got "+e.dtype);var t=e.shape[e.shape.length-1],n=e.size/t,r=e.zerosLike(),o=complex(e,r).as2D(n,t),a=fft(o),i=Math.floor(t/2)+1,s=real(a),u=imag(a),l=s.split([i,t-i],s.shape.length-1),c=u.split([i,t-i],u.shape.length-1),p=e.shape.slice();return p[e.shape.length-1]=i,complex(l[0],c[0]).reshape(p)}function irfft_(e){var t=e.shape[e.shape.length-1],n=e.size/t;if(t<=2){var r=e.as2D(n,t),o=ifft(r);return real(o)}var a=[n,2*(t-1)],i=real(e).as2D(n,t),s=imag(e).as2D(n,t),u=i.slice([0,1],[n,t-2]).reverse(1),l=s.slice([0,1],[n,t-2]).reverse(1).mul(scalar(-1)),c=i.concat(u,1),p=s.concat(l,1);r=complex(c,p).as2D(a[0],a[1]),o=ifft(r);return real(o)}var fft=op({fft_:fft_}),ifft=op({ifft_:ifft_}),rfft=op({rfft_:rfft_}),irfft=op({irfft_:irfft_}),spectral_ops=Object.freeze({fft:fft,ifft:ifft,rfft:rfft,irfft:irfft});function validateInput$1(e,t,n,r){if("int32"!==e.dtype)throw new Error("tf.sparseToDense() expects the indices to be int32 type, but the dtype was "+e.dtype+".");if(e.rank>2)throw new Error("sparseIndices should be a scalar, vector, or matrix, but got shape "+e.shape+".");var o=e.rank>0?e.shape[0]:1,a=e.rank>1?e.shape[1]:1;if(n.length!==a)throw new Error("outputShape has incorrect number of elements:, "+n.length+", should be: "+a+".");var i=t.size;if(0!==t.rank&&(1!==t.rank||i!==o))throw new Error("sparseValues has incorrect shape "+t.shape+", should be [] or ["+o+"]");if(t.dtype!==r.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function sparseToDense_(e,t,n,r){var o=convertToTensor(e,"sparseIndices","sparseToDense","int32"),a=convertToTensor(t,"sparseValues","sparseToDense"),i=convertToTensor(r,"defaultValue","sparseToDense",a.dtype);return validateInput$1(o,a,n,i),ENV.engine.runKernel(function(e){return e.sparseToDense(o,a,n,i)},{$sparseIndices:o,$sparseValues:a,$defaultValue:i})}var sparseToDense=op({sparseToDense_:sparseToDense_});function gatherND_(e,t){var n=convertToTensor(t,"indices","gatherND","int32"),r=convertToTensor(e,"x","gatherND");return ENV.engine.runKernel(function(e){return e.gatherND(r,n)},{$x:r,$indices:n})}var Reduction,gatherND=op({gatherND_:gatherND_});function computeWeightedLoss_(e,t,n){void 0===n&&(n=Reduction.SUM_BY_NONZERO_WEIGHTS);var r=convertToTensor(e,"losses","computeWeightedLoss"),o=null;null!=t&&(o=convertToTensor(t,"weights","computeWeightedLoss"));var a=null==o?r:r.mul(o);if(n===Reduction.NONE)return a;if(n===Reduction.SUM)return a.sum();if(n===Reduction.MEAN){if(null==o)return a.mean();var i=sizeFromShape(r.shape)/sizeFromShape(o.shape),s=a.sum().div(o.sum());return i>1?s.div(scalar(i)):s}if(n===Reduction.SUM_BY_NONZERO_WEIGHTS){if(null==o)return a.sum().div(scalar(r.size));var u=o.mul(ones$1(r.shape)).notEqual(scalar(0)).sum().toFloat();return a.sum().div(u)}throw Error("Unknown reduction: "+n)}function absoluteDifference_(e,t,n,r){void 0===r&&(r=Reduction.SUM_BY_NONZERO_WEIGHTS);var o=convertToTensor(e,"labels","absoluteDifference"),a=convertToTensor(t,"predictions","absoluteDifference"),i=null;null!=n&&(i=convertToTensor(n,"weights","absoluteDifference")),assertShapesMatch(o.shape,a.shape,"Error in absoluteDifference: ");var s=o.sub(a).abs();return computeWeightedLoss(s,i,r)}function meanSquaredError_(e,t,n,r){void 0===r&&(r=Reduction.SUM_BY_NONZERO_WEIGHTS);var o=convertToTensor(e,"labels","meanSquaredError"),a=convertToTensor(t,"predictions","meanSquaredError"),i=null;null!=n&&(i=convertToTensor(n,"weights","meanSquaredError")),assertShapesMatch(o.shape,a.shape,"Error in meanSquaredError: ");var s=o.squaredDifference(a);return computeWeightedLoss(s,i,r)}function cosineDistance_(e,t,n,r,o){void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,"labels","cosineDistance"),i=convertToTensor(t,"predictions","cosineDistance"),s=null;null!=r&&(s=convertToTensor(r,"weights","cosineDistance")),assertShapesMatch(a.shape,i.shape,"Error in cosineDistance: ");var u=scalar(1).sub(a.mul(i).sum(n,!0));return computeWeightedLoss(u,s,o)}function hingeLoss_(e,t,n,r){void 0===r&&(r=Reduction.SUM_BY_NONZERO_WEIGHTS);var o=convertToTensor(e,"labels","hingeLoss"),a=convertToTensor(t,"predictions","hingeLoss"),i=null;null!=n&&(i=convertToTensor(n,"weights","hingeLoss")),assertShapesMatch(o.shape,a.shape,"Error in hingeLoss: ");var s=scalar(1);o=scalar(2).mul(o).sub(s);var u=s.sub(o.mul(a)).relu();return computeWeightedLoss(u,i,r)}function logLoss_(e,t,n,r,o){void 0===r&&(r=1e-7),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,"labels","logLoss"),i=convertToTensor(t,"predictions","logLoss"),s=null;null!=n&&(s=convertToTensor(n,"weights","logLoss")),assertShapesMatch(a.shape,i.shape,"Error in logLoss: ");var u=scalar(1),l=scalar(r),c=a.mul(i.add(l).log()).neg().sub(u.sub(a).mul(u.sub(i).add(l).log()));return computeWeightedLoss(c,s,o)}function sigmoidCrossEntropyWithLogits_(e,t){var n=convertToTensor(e,"labels","sigmoidCrossEntropyWithLogits"),r=convertToTensor(t,"logits","sigmoidCrossEntropyWithLogits");assertShapesMatch(n.shape,r.shape,"Error in sigmoidCrossEntropyWithLogits: ");var o=r.relu(),a=r.mul(n),i=r.abs().neg().exp().log1p();return o.sub(a).add(i)}function sigmoidCrossEntropy_(e,t,n,r,o){void 0===r&&(r=0),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,"multiClassLabels","sigmoidCrossEntropy"),i=convertToTensor(t,"logits","sigmoidCrossEntropy"),s=null;if(null!=n&&(s=convertToTensor(n,"weights","sigmoidCrossEntropy")),assertShapesMatch(a.shape,i.shape,"Error in sigmoidCrossEntropy: "),r>0){var u=scalar(r),l=scalar(1),c=scalar(.5);a=a.mul(l.sub(u)).add(c.mul(u));}var p=sigmoidCrossEntropyWithLogits_(a,i);return computeWeightedLoss(p,s,o)}function huberLoss_(e,t,n,r,o){void 0===r&&(r=1),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,"labels","huberLoss"),i=convertToTensor(t,"predictions","huberLoss"),s=null;null!=n&&(s=convertToTensor(n,"weights","huberLoss")),assertShapesMatch(a.shape,i.shape,"Error in huberLoss: ");var u=scalar(r),l=i.sub(a).abs(),c=minimum(l,u),p=l.sub(c),d=scalar(.5).mul(c.square()).add(u.mul(p));return computeWeightedLoss(d,s,o)}function softmaxCrossEntropyWithLogits_(e,t,n){if(void 0===n&&(n=-1),-1===n&&(n=t.rank-1),n!==t.rank-1)throw Error("Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank "+t.rank+" and dim was "+n);return customGrad(function(e,t){var r=t.logSumExp([n],!0),o=t.toFloat().sub(r);return {value:o.mul(e).neg().sum([n]),gradFunc:function(t){var r=expandShapeToKeepDim(t.shape,[n]);return [t.reshape(r).mul(e.toFloat().sub(o.exp())),t.reshape(r).mul(o.exp().sub(e.toFloat()))]}}})(e,t)}function softmaxCrossEntropy_(e,t,n,r,o){void 0===r&&(r=0),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,"onehotLabels","softmaxCrossEntropy"),i=convertToTensor(t,"logits","softmaxCrossEntropy"),s=null;if(null!=n&&(s=convertToTensor(n,"weights","softmaxCrossEntropy")),assertShapesMatch(a.shape,i.shape,"Error in softmaxCrossEntropy: "),r>0){var u=scalar(r),l=scalar(1),c=scalar(a.shape[1]);a=a.mul(l.sub(u)).add(u.div(c));}var p=softmaxCrossEntropyWithLogits_(a,i);return computeWeightedLoss(p,s,o)}!function(e){e[e.NONE=0]="NONE",e[e.MEAN=1]="MEAN",e[e.SUM=2]="SUM",e[e.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS";}(Reduction||(Reduction={}));var absoluteDifference=op({absoluteDifference_:absoluteDifference_}),computeWeightedLoss=op({computeWeightedLoss_:computeWeightedLoss_}),cosineDistance=op({cosineDistance_:cosineDistance_}),hingeLoss=op({hingeLoss_:hingeLoss_}),huberLoss=op({huberLoss_:huberLoss_}),logLoss=op({logLoss_:logLoss_}),meanSquaredError=op({meanSquaredError_:meanSquaredError_}),sigmoidCrossEntropy=op({sigmoidCrossEntropy_:sigmoidCrossEntropy_}),softmaxCrossEntropy=op({softmaxCrossEntropy_:softmaxCrossEntropy_}),loss_ops=Object.freeze({get Reduction(){return Reduction},absoluteDifference:absoluteDifference,computeWeightedLoss:computeWeightedLoss,cosineDistance:cosineDistance,hingeLoss:hingeLoss,huberLoss:huberLoss,logLoss:logLoss,meanSquaredError:meanSquaredError,sigmoidCrossEntropy:sigmoidCrossEntropy,softmaxCrossEntropy:softmaxCrossEntropy});function gramSchmidt_(e){var t;if(Array.isArray(e)){t=!1,assert(null!=e&&e.length>0,"Gram-Schmidt process: input must not be null, undefined, or empty");for(var n=e[0].shape[0],r=1;r<e.length;++r)assert(e[r].shape[0]===n,"Gram-Schmidt: Non-unique lengths found in the input vectors: ("+e[r].shape[0]+" vs. "+n+")");}else t=!0,e=split$1(e,e.shape[0],0).map(function(e){return squeeze(e,[0])});assert(e.length<=e[0].shape[0],"Gram-Schmidt: Number of vectors ("+e.length+") exceeds number of dimensions ("+e[0].shape[0]+").");var o=[],a=e,i=function(e){o.push(ENV.engine.tidy(function(){var t=a[e];if(e>0)for(var n=0;n<e;++n){var r=sum$1(o[n].mulStrict(t)).mul(o[n]);t=t.sub(r);}return t.div(norm(t,"euclidean"))}));};for(r=0;r<e.length;++r)i(r);return t?stack(o,0):o}function qr_(e,t){if(void 0===t&&(t=!1),e.rank<2)throw new Error("qr() requires input tensor to have a rank >= 2, but got rank "+e.rank);if(2===e.rank)return qr2d(e,t);var n=e.shape.slice(0,e.shape.length-2).reduce(function(e,t){return e*t}),r=[],o=[];return unstack(e.reshape([n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0).forEach(function(e){var n=qr2d(e,t),a=n[0],i=n[1];r.push(a),o.push(i);}),[stack(r,0).reshape(e.shape),stack(o,0).reshape(e.shape)]}function qr2d(e,t){return void 0===t&&(t=!1),ENV.engine.tidy(function(){if(2!==e.shape.length)throw new Error("qr2d() requires a 2D Tensor, but got a "+e.shape.length+"D Tensor.");for(var n=e.shape[0],r=e.shape[1],o=eye(n),a=e.clone(),i=tensor2d([[1]],[1,1]),s=i.clone(),u=n>=r?r:n,l=function(e){var t,u=a,l=s,c=o;t=ENV.engine.tidy(function(){var t=a.slice([e,e],[n-e,1]),u=t.norm(),l=a.slice([e,e],[1,1]),c=l.sign().neg(),p=l.sub(c.mul(u)),d=t.div(p);s=1===d.shape[0]?i.clone():i.concat(d.slice([1,0],[d.shape[0]-1,d.shape[1]]),0);var h=c.matMul(p).div(u).neg(),f=a.slice([e,0],[n-e,r]),m=h.mul(s);a=0===e?f.sub(m.matMul(s.transpose().matMul(f))):a.slice([0,0],[e,r]).concat(f.sub(m.matMul(s.transpose().matMul(f))),0);var g=o.slice([0,e],[n,o.shape[1]-e]);return o=0===e?g.sub(g.matMul(s).matMul(m.transpose())):o.slice([0,0],[n,e]).concat(g.sub(g.matMul(s).matMul(m.transpose())),1),[s,a,o]}),s=t[0],a=t[1],o=t[2],dispose([u,l,c]);},c=0;c<u;++c)l(c);return !t&&n>r&&(o=o.slice([0,0],[n,r]),a=a.slice([0,0],[r,r])),[o,a]})}var gramSchmidt=op({gramSchmidt_:gramSchmidt_}),qr=op({qr_:qr_}),linalg_ops=Object.freeze({gramSchmidt:gramSchmidt,qr:qr});function resizeBilinear_(e,t,n){void 0===n&&(n=!1);var r=convertToTensor(e,"images","resizeBilinear");assert(3===r.rank||4===r.rank,"Error in resizeBilinear: x must be rank 3 or 4, but got rank "+r.rank+"."),assert(2===t.length,"Error in resizeBilinear: new shape must 2D, but got shape "+t+".");var o=r,a=!1;3===r.rank&&(a=!0,o=r.as4D(1,r.shape[0],r.shape[1],r.shape[2]));var i=t[0],s=t[1],u=ENV.engine.runKernel(function(e,t){return e.resizeBilinear(o,i,s,n)},{batchImages:o},function(e,t){return {batchImages:function(){return ENV.engine.runKernel(function(t){return t.resizeBilinearBackprop(e,o,n)},{})}}});return a?u.as3D(u.shape[1],u.shape[2],u.shape[3]):u}function resizeNearestNeighbor_(e,t,n){void 0===n&&(n=!1);var r=convertToTensor(e,"images","resizeNearestNeighbor");assert(3===r.rank||4===r.rank,"Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank "+r.rank+"."),assert(2===t.length,"Error in resizeNearestNeighbor: new shape must 2D, but got shape "+t+"."),assert("float32"===r.dtype||"int32"===r.dtype,"`images` must have `int32` or `float32` as dtype");var o=r,a=!1;3===r.rank&&(a=!0,o=r.as4D(1,r.shape[0],r.shape[1],r.shape[2]));var i=t[0],s=t[1],u=ENV.engine.runKernel(function(e,t){return e.resizeNearestNeighbor(o,i,s,n)},{batchImages:o},function(e,t){return {batchImages:function(){return ENV.engine.runKernel(function(t){return t.resizeNearestNeighborBackprop(e,o,n)},{})}}});return a?u.as3D(u.shape[1],u.shape[2],u.shape[3]):u}function nonMaxSuppression_(e,t,n,r,o){void 0===r&&(r=.5),void 0===o&&(o=Number.NEGATIVE_INFINITY);var a=convertToTensor(e,"boxes","nonMaxSuppression"),i=convertToTensor(t,"scores","nonMaxSuppression"),s=nonMaxSuppSanityCheck(a,i,n,r,o);return n=s.maxOutputSize,r=s.iouThreshold,o=s.scoreThreshold,ENV.engine.runKernel(function(e){return e.nonMaxSuppression(a,i,n,r,o)},{$boxes:a})}function nonMaxSuppressionAsync_(e,t,n,r,o){return void 0===r&&(r=.5),void 0===o&&(o=Number.NEGATIVE_INFINITY),__awaiter(this,void 0,void 0,function(){var a,i,s,u,l,c;return __generator(this,function(p){switch(p.label){case 0:return a=convertToTensor(e,"boxes","nonMaxSuppressionAsync"),i=convertToTensor(t,"scores","nonMaxSuppressionAsync"),s=nonMaxSuppSanityCheck(a,i,n,r,o),n=s.maxOutputSize,r=s.iouThreshold,o=s.scoreThreshold,[4,a.data()];case 1:return u=p.sent(),[4,i.data()];case 2:return l=p.sent(),c=nonMaxSuppressionImpl(u,l,n,r,o),a!==e&&a.dispose(),i!==t&&i.dispose(),[2,c]}})})}function nonMaxSuppSanityCheck(e,t,n,r,o){null==r&&(r=.5),null==o&&(o=Number.NEGATIVE_INFINITY);var a=e.shape[0];return n=Math.min(n,a),assert(0<=r&&r<=1,"iouThreshold must be in [0, 1], but was '"+r+"'"),assert(2===e.rank,"boxes must be a 2D tensor, but was of rank '"+e.rank+"'"),assert(4===e.shape[1],"boxes must have 4 columns, but 2nd dimension was "+e.shape[1]),assert(1===t.rank,"scores must be a 1D tensor"),assert(t.shape[0]===a,"scores has incompatible shape with boxes. Expected "+a+", but was "+t.shape[0]),{maxOutputSize:n,iouThreshold:r,scoreThreshold:o}}function cropAndResize_(e,t,n,r,o,a){var i=convertToTensor(e,"image","cropAndResize","float32"),s=convertToTensor(t,"boxes","cropAndResize","float32"),u=convertToTensor(n,"boxInd","cropAndResize","int32");o=o||"bilinear",a=a||0;var l=s.shape[0];assert(4===i.rank,"Error in cropAndResize: image must be rank 4,but got rank "+i.rank+"."),assert(2===s.rank&&4===s.shape[1],"Error in cropAndResize: boxes must be have size ["+l+",4] but had shape "+s.shape+"."),assert(1===u.rank&&u.shape[0]===l,"Error in cropAndResize: boxInd must be have size ["+l+"] but had shape "+s.shape+"."),assert(2===r.length,"Error in cropAndResize: cropSize must be of length 2, but got length "+r.length+"."),assert(r[0]>=1&&r[1]>=1,"cropSize must be atleast [1,1], but was "+r),assert("bilinear"===o||"nearest"===o,"method must be bilinear or nearest, but was "+o);return ENV.engine.runKernel(function(e,t){return e.cropAndResize(i,s,u,r,o,a)},{$image:i,$boxes:s})}var resizeBilinear=op({resizeBilinear_:resizeBilinear_}),resizeNearestNeighbor=op({resizeNearestNeighbor_:resizeNearestNeighbor_}),nonMaxSuppression=op({nonMaxSuppression_:nonMaxSuppression_}),nonMaxSuppressionAsync=nonMaxSuppressionAsync_,cropAndResize=cropAndResize_,image_ops=Object.freeze({resizeBilinear:resizeBilinear,resizeNearestNeighbor:resizeNearestNeighbor,nonMaxSuppression:nonMaxSuppression,nonMaxSuppressionAsync:nonMaxSuppressionAsync,cropAndResize:cropAndResize}),ops=Object.freeze({image:image_ops,linalg:linalg_ops,losses:loss_ops,spectral:spectral_ops,op:op,batchNormalization2d:batchNormalization2d,batchNormalization3d:batchNormalization3d,batchNormalization4d:batchNormalization4d,batchNormalization:batchNormalization,complex:complex,real:real,imag:imag,concat:concat,concat1d:concat1d,concat2d:concat2d,concat3d:concat3d,concat4d:concat4d,split:split$1,conv1d:conv1d,conv2d:conv2d,conv3d:conv3d,conv2dDerFilter:conv2dDerFilter,depthwiseConv2d:depthwiseConv2d,separableConv2d:separableConv2d,conv2dTranspose:conv2dTranspose,matMul:matMul,dot:dot,outerProduct:outerProduct,reverse:reverse,reverse1d:reverse1d,reverse2d:reverse2d,reverse3d:reverse3d,reverse4d:reverse4d,maxPool:maxPool,avgPool:avgPool,pool:pool,slice:slice,slice1d:slice1d,slice2d:slice2d,slice3d:slice3d,slice4d:slice4d,abs:abs,acos:acos,acosh:acosh,asin:asin,asinh:asinh,atan:atan,atanh:atanh,ceil:ceil,clipByValue:clipByValue,cos:cos,cosh:cosh,erf:erf,exp:exp,expm1:expm1,floor:floor,log:log$1,log1p:log1p,logSigmoid:logSigmoid,neg:neg,reciprocal:reciprocal,round:round,rsqrt:rsqrt,sigmoid:sigmoid,sign:sign,sin:sin,sinh:sinh,softplus:softplus,sqrt:sqrt,square:square,step:step,tan:tan,tanh:tanh$1,all:all,any:any,argMax:argMax,argMin:argMin,logSumExp:logSumExp,max:max,mean:mean,min:min,moments:moments,sum:sum$1,prod:prod,equal:equal,equalStrict:equalStrict,greater:greater,greaterEqual:greaterEqual,greaterEqualStrict:greaterEqualStrict,greaterStrict:greaterStrict,less:less,lessEqual:lessEqual,lessEqualStrict:lessEqualStrict,lessStrict:lessStrict,notEqual:notEqual,notEqualStrict:notEqualStrict,add:add,addN:addN,addStrict:addStrict,atan2:atan2,div:div,divStrict:divStrict,floorDiv:floorDiv,maximum:maximum,maximumStrict:maximumStrict,minimum:minimum,minimumStrict:minimumStrict,mod:mod,modStrict:modStrict,mul:mul,mulStrict:mulStrict,pow:pow,powStrict:powStrict,squaredDifference:squaredDifference,squaredDifferenceStrict:squaredDifferenceStrict,sub:sub,subStrict:subStrict,elu:elu,leakyRelu:leakyRelu,prelu:prelu,relu:relu,selu:selu,logicalAnd:logicalAnd,logicalNot:logicalNot,logicalOr:logicalOr,logicalXor:logicalXor,where:where,whereAsync:whereAsync,buffer:buffer,toPixels:toPixels,print:print,batchToSpaceND:batchToSpaceND,cast:cast,clone:clone,cumsum:cumsum,depthToSpace:depthToSpace,expandDims:expandDims,eye:eye,fromPixels:fromPixels,multinomial:multinomial,oneHot:oneHot,pad:pad,pad1d:pad1d,pad2d:pad2d,pad3d:pad3d,pad4d:pad4d,rand:rand,randomNormal:randomNormal,randomUniform:randomUniform,reshape:reshape,spaceToBatchND:spaceToBatchND,squeeze:squeeze,stack:stack,tile:tile,truncatedNormal:truncatedNormal,unstack:unstack,setdiff1dAsync:setdiff1dAsync,fill:fill,linspace:linspace,ones:ones$1,range:range,scalar:scalar,tensor:tensor,tensor1d:tensor1d,tensor2d:tensor2d,tensor3d:tensor3d,tensor4d:tensor4d,tensor5d:tensor5d,tensor6d:tensor6d,zeros:zeros,onesLike:onesLike,zerosLike:zerosLike,transpose:transpose,softmax:softmax,logSoftmax:logSoftmax,localResponseNormalization:localResponseNormalization,norm:norm,gather:gather,unsortedSegmentSum:unsortedSegmentSum,basicLSTMCell:basicLSTMCell,multiRNNCell:multiRNNCell,movingAverage:movingAverage,stridedSlice:stridedSlice,topk:topk,scatterND:scatterND,fft:fft,ifft:ifft,rfft:rfft,irfft:irfft,sparseToDense:sparseToDense,gatherND:gatherND}),MathBackendCPU=function(){function e(){this.blockSize=48,this.firstUse=!0,ENV.get("IS_BROWSER")&&(this.fromPixels2DContext=document.createElement("canvas").getContext("2d"));}return e.prototype.setDataMover=function(e){this.data=new DataStorage(e);},e.prototype.register=function(e,t,n){if(this.firstUse&&(this.firstUse=!1,ENV.get("IS_NODE")&&warn("\n============================\nHi there 👋. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.\n============================\n")),this.data.has(e))throw new Error("Data buffer is already registered");this.data.set(e,{dtype:n});},e.prototype.write=function(e,t){if(null==t)throw new Error("MathBackendCPU.write(): values can not be null");this.data.get(e).values=t;},e.prototype.fromPixels=function(e,t){if(null==e)throw new Error("pixels passed to tf.fromPixels() can not be null");var n,r;if(ENV.get("IS_NODE")&&null==e.getContext)throw new Error("When running in node, pixels must be an HTMLCanvasElement like the one returned by the `canvas` npm package");if(null!=e.getContext)n=e.getContext("2d").getImageData(0,0,e.width,e.height).data;else if(e instanceof ImageData)n=e.data;else{if(!(e instanceof HTMLImageElement||e instanceof HTMLVideoElement))throw new Error("pixels passed to tf.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ImageData, but was "+e.constructor.name);if(null==this.fromPixels2DContext)throw new Error("Can't read pixels from HTMLImageElement outside the browser.");this.fromPixels2DContext.canvas.width=e.width,this.fromPixels2DContext.canvas.height=e.height,this.fromPixels2DContext.drawImage(e,0,0,e.width,e.height),n=this.fromPixels2DContext.getImageData(0,0,e.width,e.height).data;}if(4===t)r=new Int32Array(n);else{var o=e.width*e.height;r=new Int32Array(o*t);for(var a=0;a<o;a++)for(var i=0;i<t;++i)r[a*t+i]=n[4*a+i];}return tensor3d(r,[e.height,e.width,t],"int32")},e.prototype.read=function(e){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(t){return [2,this.readSync(e)]})})},e.prototype.readSync=function(e){var t=this.data.get(e),n=t.dtype,r=t.complexTensors;return "complex64"===n?mergeRealAndImagArrays(r.real.dataSync(),r.imag.dataSync()):this.data.get(e).values},e.prototype.disposeData=function(e){if(this.data.has(e)){var t=this.data.get(e).complexTensors;null!=t&&(t.real.dispose(),t.imag.dispose()),this.data.delete(e);}},e.prototype.time=function(e){return __awaiter(this,void 0,void 0,function(){var t;return __generator(this,function(n){return t=now(),e(),[2,{kernelMs:now()-t}]})})},e.prototype.memory=function(){return {unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}},e.prototype.complex=function(e,t){var n=Tensor.make(e.shape,{},"complex64");return this.data.get(n.dataId).complexTensors={real:ENV.engine.keep(e.clone()),imag:ENV.engine.keep(t.clone())},n},e.prototype.real=function(e){return this.data.get(e.dataId).complexTensors.real.clone()},e.prototype.imag=function(e){return this.data.get(e.dataId).complexTensors.imag.clone()},e.prototype.assertNotComplex=function(e,t){Array.isArray(e)||(e=[e]),e.forEach(function(e){null!=e&&assert("complex64"!==e.dtype,t+" does not support complex64 tensors.");});},e.prototype.slice=function(e,t,n){this.assertNotComplex(e,"slice");for(var r=buffer(n,e.dtype),o=0;o<r.size;++o){var a=r.indexToLoc(o),i=a.map(function(e,n){return e+t[n]});r.set.apply(r,[e.get.apply(e,i)].concat(a));}return r.toTensor()},e.prototype.stridedSlice=function(e,t,n,r,o,a,i,s,u){this.assertNotComplex(e,"stridedSlice");var l=getStridedSlicedInfo(e.shape,t,n,r,o,a,i,s,u),c=l[0],p=l[1],d=l[2],h=p.filter(function(e,t){return -1===d.indexOf(t)});if(h.some(function(e){return 0===e}))return tensor([],h);for(var f=buffer(p,e.dtype),m=0;m<f.size;m++){for(var g=f.indexToLoc(m),v=new Array(g.length),y=0;y<v.length;y++)v[y]=g[y]*r[y]+c[y];f.set.apply(f,[e.get.apply(e,v)].concat(g));}return f.toTensor().reshape(h)},e.prototype.reverse=function(e,t){this.assertNotComplex(e,"reverse");for(var n=buffer(e.shape,e.dtype),r=e.buffer(),o=function(o){var a=n.indexToLoc(o),i=a.slice();t.forEach(function(t){return i[t]=e.shape[t]-1-i[t]}),n.set.apply(n,[r.get.apply(r,i)].concat(a));},a=0;a<n.size;a++)o(a);return n.toTensor()},e.prototype.concat=function(e,t){this.assertNotComplex(e,"concat");var n=e.map(function(e){var n=sizeFromShape(e.shape.slice(t));return e.as2D(-1,n)}),r=computeOutShape(n.map(function(e){return e.shape}),1),o=buffer(r,e[0].dtype).values;if(1===n[0].shape[0]){var a=0;n.forEach(function(e){o.set(e.dataSync(),a),a+=e.size;});}else{var i=0;n.forEach(function(e){for(var t=e.dataSync(),n=0,a=0;a<e.shape[0];++a)for(var s=a*r[1]+i,u=0;u<e.shape[1];++u)o[s+u]=t[n++];i+=e.shape[1];});}var s=computeOutShape(e.map(function(e){return e.shape}),t);return tensor(o,s,e[0].dtype)},e.prototype.neg=function(e){return this.assertNotComplex(e,"neg"),this.multiply(scalar(-1),e)},e.prototype.add=function(e,t){return "complex64"===e.dtype||"complex64"===t.dtype?this.broadcastedBinaryComplexOp(e.cast("complex64"),t.cast("complex64"),function(e,t,n,r){return {real:e+n,imag:t+r}}):this.broadcastedBinaryOp(e,t,upcastType(e.dtype,t.dtype),function(e,t){return e+t})},e.prototype.addN=function(e){this.assertNotComplex(e,"addN");for(var t=e.map(function(e){return e.dataSync()}),n=buffer(e[0].shape,e[0].dtype),r=n.values,o=0;o<e.length;o++)for(var a=t[o],i=0;i<r.length;i++)r[i]+=a[i];return n.toTensor()},e.prototype.subtract=function(e,t){return "complex64"===e.dtype||"complex64"===t.dtype?this.broadcastedBinaryComplexOp(e.cast("complex64"),t.cast("complex64"),function(e,t,n,r){return {real:e-n,imag:t-r}}):this.broadcastedBinaryOp(e,t,upcastType(e.dtype,t.dtype),function(e,t){return e-t})},e.prototype.pow=function(e,t){return this.assertNotComplex([e,t],"pow"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){return Math.pow(e,t)})},e.prototype.batchMatMul=function(e,t,n,r){this.assertNotComplex([e,t],"matMul");for(var o=n?e.shape[1]:e.shape[2],a=n?e.shape[2]:e.shape[1],i=r?t.shape[1]:t.shape[2],s=e.shape[0],u=e.dataSync(),l=t.dataSync(),c=n?[e.strides[0],1,e.strides[1]]:[e.strides[0],e.strides[1],1],p=c[0],d=c[1],h=c[2],f=r?[1,t.strides[1],t.strides[0]]:[t.strides[1],1,t.strides[0]],m=f[0],g=f[1],v=f[2],y=a*i,x=buffer([s,a,i],e.dtype),T=x.values,w=this.blockSize,E=0;E<s;E++)for(var S=0;S<a;S+=w)for(var b=0;b<i;b+=w)for(var C=0;C<o;C+=w)for(var A=Math.min(S+w,a),_=Math.min(b+w,i),N=Math.min(C+w,o),R=S;R<A;R++)for(var k=b;k<_;k++){for(var I=0,D=C;D<N;D++)I+=u[E*p+R*d+D*h]*l[D*m+k*g+E*v];T[E*y+(R*i+k)]+=I;}return x.toTensor()},e.prototype.multiply=function(e,t){return "complex64"===e.dtype||"complex64"===t.dtype?this.broadcastedBinaryComplexOp(e.cast("complex64"),t.cast("complex64"),function(e,t,n,r){return {real:e*n-t*r,imag:e*r+t*n}}):this.broadcastedBinaryOp(e,t,upcastType(e.dtype,t.dtype),function(e,t){return e*t})},e.prototype.realDivide=function(e,t){this.assertNotComplex([e,t],"realDivide");return this.broadcastedBinaryOp(e,t,"float32",function(e,t){return e/t})},e.prototype.floorDiv=function(e,t){this.assertNotComplex([e,t],"floorDiv");return this.broadcastedBinaryOp(e,t,"int32",function(e,t){return Math.floor(e/t)})},e.prototype.sum=function(e,t){this.assertNotComplex(e,"sum"),assertAxesAreInnerMostDims("sum",t,e.rank);for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,upcastType(e.dtype,"int32")),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),l=0;l<s.length;++l){for(var c=l*i,p=0,d=0;d<i;++d)p+=u[c+d];s[l]=p;}return a},e.prototype.prod=function(e,t){this.assertNotComplex(e,"sum");for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,upcastType(e.dtype,"int32")),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),l=0;l<s.length;++l){for(var c=l*i,p=1,d=0;d<i;++d)p*=u[c+d];s[l]=p;}return a},e.prototype.unsortedSegmentSum=function(e,t,n){this.assertNotComplex(e,"unsortedSegmentSum");for(var r=[],o=e.rank-t.rank,a=0;a<o;++a)t=t.expandDims(a+1);for(a=0;a<n;++a){var i=scalar(a,"int32"),s=equal(i,t).asType("float32").mul(e).sum(0);r.push(s);}return stack(r)},e.prototype.argMin=function(e,t){this.assertNotComplex(e,"argMin");var n=[t];assertAxesAreInnerMostDims("argMin",n,e.rank);for(var r=computeOutAndReduceShapes(e.shape,n),o=r[0],a=r[1],i=zeros(o,"int32"),s=sizeFromShape(a),u=i.dataSync(),l=e.dataSync(),c=0;c<u.length;++c){for(var p=c*s,d=l[p],h=0,f=0;f<s;++f){var m=l[p+f];m<d&&(d=m,h=f);}u[c]=h;}return i},e.prototype.argMax=function(e,t){this.assertNotComplex(e,"argMax");var n=[t];assertAxesAreInnerMostDims("argMax",n,e.rank);for(var r=computeOutAndReduceShapes(e.shape,n),o=r[0],a=r[1],i=zeros(o,"int32"),s=sizeFromShape(a),u=i.dataSync(),l=e.dataSync(),c=0;c<u.length;++c){for(var p=c*s,d=l[p],h=0,f=0;f<s;++f){var m=l[p+f];m>d&&(d=m,h=f);}u[c]=h;}return i},e.prototype.cumsum=function(e,t,n,r){if(this.assertNotComplex(e,"cumsum"),t!==e.rank-1)throw new Error("backend.cumsum in CPU expects an inner-most axis="+(e.rank-1)+" but got axis="+t);for(var o=upcastType(e.dtype,"int32"),a=zeros(e.shape,o),i=a.dataSync(),s=e.dataSync(),u=e.shape[e.rank-1],l=r?function(e,t){return e+u-t-1}:function(e,t){return e+t},c=0;c<s.length;c+=u)for(var p=0;p<u;p++){var d=l(c,p);if(0===p)i[d]=n?0:s[d];else{var h=l(c,p-1);i[d]=n?s[h]+i[h]:s[d]+i[h];}}return a},e.prototype.equal=function(e,t){return this.assertNotComplex([e,t],"equal"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e===t?1:0})},e.prototype.notEqual=function(e,t){return this.assertNotComplex([e,t],"notEqual"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e!==t?1:0})},e.prototype.less=function(e,t){return this.assertNotComplex([e,t],"less"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e<t?1:0})},e.prototype.lessEqual=function(e,t){return this.assertNotComplex([e,t],"lessEqual"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e<=t?1:0})},e.prototype.greater=function(e,t){return this.assertNotComplex([e,t],"greater"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e>t?1:0})},e.prototype.greaterEqual=function(e,t){return this.assertNotComplex([e,t],"greaterEqual"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e>=t?1:0})},e.prototype.logicalNot=function(e){this.assertNotComplex(e,"logicalNot");for(var t=e.dataSync(),n=new Uint8Array(t.length),r=0;r<t.length;++r)n[r]=t[r]?0:1;return Tensor.make(e.shape,{values:n},"bool")},e.prototype.logicalAnd=function(e,t){return this.assertNotComplex([e,t],"logicalAnd"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e&&t})},e.prototype.logicalOr=function(e,t){return this.assertNotComplex([e,t],"logicalOr"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e||t})},e.prototype.select=function(e,t,n){this.assertNotComplex([e,t,n],"select");for(var r=e.dataSync(),o=t.dataSync(),a=n.dataSync(),i=zeros(t.shape,upcastType(t.dtype,n.dtype)),s=i.dataSync(),u=0,l=0===e.rank||e.rank>1||1===t.rank?1:t.shape[1],c=0;c<r.length;c++)for(var p=0;p<l;p++)1===r[c]?s[u++]=o[c]:s[u++]=a[c];return i},e.prototype.where=function(e){this.assertNotComplex([e],"where");var t=e.dataSync();return whereImpl(e.shape,t)},e.prototype.topk=function(e,t,n){return this.assertNotComplex(e,"topk"),topkImpl(e.dataSync(),e.shape,e.dtype,t,n)},e.prototype.min=function(e,t){this.assertNotComplex(e,"min"),assertAxesAreInnerMostDims("min",t,e.rank);for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,e.dtype),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),l=0;l<s.length;++l){for(var c=l*i,p=u[c],d=0;d<i;++d){var h=u[c+d];h<p&&(p=h);}s[l]=p;}return a},e.prototype.minimum=function(e,t){return this.assertNotComplex([e,t],"minimum"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){return Math.min(e,t)})},e.prototype.mod=function(e,t){return this.assertNotComplex([e,t],"mod"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){var n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t})},e.prototype.max=function(e,t){this.assertNotComplex(e,"max"),assertAxesAreInnerMostDims("max",t,e.rank);for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,e.dtype),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),l=0;l<s.length;++l){for(var c=l*i,p=u[c],d=0;d<i;++d){var h=u[c+d];h>p&&(p=h);}s[l]=p;}return a},e.prototype.maximum=function(e,t){return this.assertNotComplex([e,t],"maximum"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){return Math.max(e,t)})},e.prototype.all=function(e,t){this.assertNotComplex(e,"all"),assertAxesAreInnerMostDims("all",t,e.rank);for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,e.dtype),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),l=0;l<s.length;++l){for(var c=l*i,p=u[c],d=0;d<i;++d){var h=u[c+d];p=p&&h;}s[l]=p;}return a},e.prototype.any=function(e,t){this.assertNotComplex(e,"any"),assertAxesAreInnerMostDims("any",t,e.rank);for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,e.dtype),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),l=0;l<s.length;++l){for(var c=l*i,p=u[c],d=0;d<i;++d){var h=u[c+d];p=p||h;}s[l]=p;}return a},e.prototype.squaredDifference=function(e,t){return this.assertNotComplex([e,t],"squaredDifference"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){var n=e-t;return n*n})},e.prototype.ceil=function(e){this.assertNotComplex(e,"ceil");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)n[r]=Math.ceil(t[r]);return Tensor.make(e.shape,{values:n})},e.prototype.floor=function(e){this.assertNotComplex(e,"floor");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)n[r]=Math.floor(t[r]);return Tensor.make(e.shape,{values:n})},e.prototype.sign=function(e){this.assertNotComplex(e,"x");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)t[r]<0?n[r]=-1:t[r]>0?n[r]=1:n[r]=0;return Tensor.make(e.shape,{values:n})},e.prototype.round=function(e){this.assertNotComplex(e,"round");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=Math.floor(t[r]);t[r]-o<.5?n[r]=Math.floor(t[r]):t[r]-o>.5?n[r]=Math.ceil(t[r]):n[r]=o%2==0?o:o+1;}return Tensor.make(e.shape,{values:n})},e.prototype.exp=function(e){this.assertNotComplex(e,"exp");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)n[r]=Math.exp(t[r]);return Tensor.make(e.shape,{values:n})},e.prototype.expm1=function(e){this.assertNotComplex(e,"expm1");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)n[r]=Math.expm1(t[r]);return Tensor.make(e.shape,{values:n})},e.prototype.log=function(e){this.assertNotComplex(e,"log");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=Math.log(o);}return Tensor.make(e.shape,{values:n})},e.prototype.log1p=function(e){this.assertNotComplex(e,"log1p");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=Math.log1p(o);}return Tensor.make(e.shape,{values:n})},e.prototype.sqrt=function(e){this.assertNotComplex(e,"sqrt");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=Math.sqrt(o);}return Tensor.make(e.shape,{values:n})},e.prototype.rsqrt=function(e){this.assertNotComplex(e,"rsqrt");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=1/Math.sqrt(o);}return Tensor.make(e.shape,{values:n})},e.prototype.square=function(e){this.assertNotComplex(e,"square");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=o*o;}return Tensor.make(e.shape,{values:n})},e.prototype.reciprocal=function(e){this.assertNotComplex(e,"reciprocal");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)n[r]=1/t[r];return Tensor.make(e.shape,{values:n})},e.prototype.relu=function(e){this.assertNotComplex(e,"relu");for(var t=zeros(e.shape,e.dtype),n=t.dataSync(),r=e.dataSync(),o=0;o<r.length;++o)n[o]=Math.max(0,r[o]);return t},e.prototype.elu=function(e){this.assertNotComplex(e,"elu");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r){var o=n[r];t[r]=o>=0?o:Math.exp(o)-1;}return Tensor.make(e.shape,{values:t})},e.prototype.eluDer=function(e,t){this.assertNotComplex([e,t],"eluDer");for(var n=new Float32Array(t.size),r=t.dataSync(),o=e.dataSync(),a=0;a<r.length;++a){var i=r[a];n[a]=i>=1?o[a]:o[a]*(i+1);}return Tensor.make(t.shape,{values:n})},e.prototype.selu=function(e){this.assertNotComplex(e,"selu");for(var t=SELU_SCALEALPHA,n=SELU_SCALE,r=new Float32Array(e.size),o=e.dataSync(),a=0;a<o.length;++a){var i=o[a];r[a]=i>=0?n*i:t*(Math.exp(i)-1);}return Tensor.make(e.shape,{values:r})},e.prototype.clip=function(e,t,n){this.assertNotComplex(e,"clip");for(var r=new Float32Array(e.size),o=e.dataSync(),a=0;a<o.length;++a){var i=o[a];r[a]=i>n?n:i<t?t:i;}return Tensor.make(e.shape,{values:r})},e.prototype.abs=function(e){for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.abs(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.complexAbs=function(e){for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<e.size;++r){var o=n[2*r],a=n[2*r+1];t[r]=Math.hypot(o,a);}return Tensor.make(e.shape,{values:t})},e.prototype.int=function(e){this.assertNotComplex(e,"int");for(var t=new Int32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=n[r];return Tensor.make(e.shape,{values:t},"int32")},e.prototype.sigmoid=function(e){this.assertNotComplex(e,"sigmoid");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=1/(1+Math.exp(-n[r]));return Tensor.make(e.shape,{values:t})},e.prototype.softplus=function(e){this.assertNotComplex(e,"softplus");for(var t=Math.log(1.1920928955078125e-7)+2,n=new Float32Array(e.size),r=e.dataSync(),o=0;o<r.length;++o){var a=r[o]>-t,i=r[o]<t,s=Math.exp(r[o]),u=void 0;u=i?s:a?r[o]:Math.log(1+s),n[o]=u;}return Tensor.make(e.shape,{values:n})},e.prototype.sin=function(e){this.assertNotComplex(e,"sin");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.sin(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.cos=function(e){this.assertNotComplex(e,"cos");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.cos(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.tan=function(e){this.assertNotComplex(e,"tan");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.tan(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.asin=function(e){this.assertNotComplex(e,"asin");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.asin(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.acos=function(e){this.assertNotComplex(e,"acos");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.acos(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.atan=function(e){this.assertNotComplex(e,"atan");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.atan(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.atan2=function(e,t){return this.assertNotComplex([e,t],"atan2"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){return Math.atan2(e,t)})},e.prototype.sinh=function(e){this.assertNotComplex(e,"sinh");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.sinh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.cosh=function(e){this.assertNotComplex(e,"cosh");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.cosh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.tanh=function(e){this.assertNotComplex(e,"tanh");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=tanh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.asinh=function(e){this.assertNotComplex(e,"asinh");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.asinh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.acosh=function(e){this.assertNotComplex(e,"acosh");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.acosh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.atanh=function(e){this.assertNotComplex(e,"atanh");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.atanh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.erf=function(e){this.assertNotComplex(e,"erf");for(var t=new Float32Array(e.size),n=e.dataSync(),r=ERF_P,o=ERF_A1,a=ERF_A2,i=ERF_A3,s=ERF_A4,u=ERF_A5,l=0;l<n.length;++l){var c=n[l],p=1/(1+r*c);t[l]=1-((((u*p+s)*p+i)*p+a)*p+o)*p*Math.exp(-c*c);}return Tensor.make(e.shape,{values:t})},e.prototype.step=function(e,t){void 0===t&&(t=0),this.assertNotComplex(e,"step");for(var n=new Float32Array(e.size),r=e.dataSync(),o=0;o<r.length;++o){var a=r[o];isNaN(a)?n[o]=NaN:n[o]=a>0?1:t;}return Tensor.make(e.shape,{values:n})},e.prototype.conv2d=function(e,t,n){this.assertNotComplex([e,t],"conv2d");for(var r=n.filterHeight,o=n.filterWidth,a=n.dilationHeight,i=n.dilationWidth,s=n.padInfo.left,u=n.padInfo.top,l=buffer(n.outShape,e.dtype),c=e.dataSync(),p=t.dataSync(),d=l.values,h=0;h<n.batchSize;++h)for(var f=h*e.strides[0],m=h*l.strides[0],g=0;g<n.outHeight;++g)for(var v=m+g*l.strides[1],y=g*n.strideHeight-s,x=0;x<r;x++){var T=y+x*a;if(!(T<0||T>=n.inHeight))for(var w=x*t.strides[0],E=f+T*e.strides[1],S=0;S<n.outWidth;++S)for(var b=v+S*n.outChannels,C=S*n.strideWidth-u,A=0;A<o;A++){var _=C+A*i;if(!(_<0||_>=n.inWidth))for(var N=w+A*t.strides[1],R=E+_*n.inChannels,k=N,I=0;I<n.inChannels;++I){for(var D=c[R+I],M=0;M<n.outChannels;++M)d[b+M]+=D*p[k+M];k+=n.outChannels;}}}return l.toTensor()},e.prototype.conv3d=function(e,t,n){for(var r=n.filterDepth,o=n.filterHeight,a=n.filterWidth,i=n.dilationDepth,s=n.dilationHeight,u=n.dilationWidth,l=n.padInfo.front,c=n.padInfo.left,p=n.padInfo.top,d=buffer(n.outShape,e.dtype),h=e.dataSync(),f=t.dataSync(),m=d.values,g=0;g<n.batchSize;++g)for(var v=g*e.strides[0],y=g*d.strides[0],x=0;x<n.outDepth;++x)for(var T=y+x*d.strides[1],w=x*n.strideDepth-l,E=0;E<r;E++){var S=w+E*i;if(!(S<0||S>=n.inDepth))for(var b=E*t.strides[0],C=v+S*e.strides[1],A=0;A<n.outHeight;++A)for(var _=T+A*d.strides[2],N=A*n.strideHeight-p,R=0;R<o;R++){var k=N+R*s;if(!(k<0||k>=n.inHeight))for(var I=b+R*t.strides[1],D=C+k*e.strides[2],M=0;M<n.outWidth;++M)for(var P=_+M*n.outChannels,O=M*n.strideWidth-c,F=0;F<a;F++){var L=O+F*u;if(!(L<0||L>=n.inWidth))for(var B=I+F*t.strides[2],U=D+L*n.inChannels,V=B,z=0;z<n.inChannels;++z){for(var H=h[U+z],W=0;W<n.outChannels;++W)m[P+W]+=H*f[V+W];V+=n.outChannels;}}}}return d.toTensor()},e.prototype.conv2dDerInput=function(e,t,n){this.assertNotComplex([e,t],"conv2dDerInput");for(var r=buffer(n.inShape,"float32"),o=r.values,a=r.strides,i=a[0],s=a[1],u=a[2],l=e.dataSync(),c=e.strides,p=c[0],d=c[1],h=c[2],f=t.dataSync(),m=t.strides,g=m[0],v=m[1],y=m[2],x=n.batchSize,T=n.filterHeight,w=n.filterWidth,E=n.inChannels,S=n.inHeight,b=n.inWidth,C=n.outChannels,A=n.outHeight,_=n.outWidth,N=n.strideHeight,R=n.strideWidth,k=T-1-n.padInfo.top,I=w-1-n.padInfo.left,D=0;D<x;++D)for(var M=0;M<E;++M)for(var P=0;P<S;++P)for(var O=P-k,F=Math.max(0,Math.ceil(O/N)),L=Math.min(A,(T+O)/N),B=0;B<b;++B){for(var U=B-I,V=Math.max(0,Math.ceil(U/R)),z=Math.min(_,(w+U)/R),H=0,W=F;W<L;++W)for(var G=W*N-O,q=V;q<z;++q)for(var $=p*D+d*W+h*q,X=g*(T-1-G)+v*(w-1-(q*R-U))+y*M,K=0;K<C;++K){H+=l[$+K]*f[X+K];}o[i*D+s*P+u*B+M]=H;}return r.toTensor()},e.prototype.conv3dDerInput=function(e,t,n){for(var r=buffer(n.inShape,"float32"),o=r.values,a=r.strides,i=a[0],s=a[1],u=a[2],l=a[3],c=e.dataSync(),p=e.strides,d=p[0],h=p[1],f=p[2],m=p[3],g=t.dataSync(),v=t.strides,y=v[0],x=v[1],T=v[2],w=v[3],E=n.batchSize,S=n.filterDepth,b=n.filterHeight,C=n.filterWidth,A=n.inChannels,_=n.inDepth,N=n.inHeight,R=n.inWidth,k=n.outChannels,I=n.outDepth,D=n.outHeight,M=n.outWidth,P=n.strideDepth,O=n.strideHeight,F=n.strideWidth,L=S-1-n.padInfo.front,B=b-1-n.padInfo.top,U=C-1-n.padInfo.left,V=0;V<E;++V)for(var z=0;z<A;++z)for(var H=0;H<_;++H)for(var W=H-L,G=Math.max(0,Math.ceil(W/P)),q=Math.min(I,(S+W)/P),$=0;$<N;++$)for(var X=$-B,K=Math.max(0,Math.ceil(X/O)),j=Math.min(D,(b+X)/O),Y=0;Y<R;++Y){for(var Q=Y-U,J=Math.max(0,Math.ceil(Q/F)),Z=Math.min(M,(C+Q)/F),ee=0,te=G;te<q;++te)for(var ne=te*P-W,re=K;re<j;++re)for(var oe=re*O-X,ae=J;ae<Z;++ae)for(var ie=d*V+h*te+f*re+m*ae,se=y*(S-1-ne)+x*(b-1-oe)+T*(C-1-(ae*F-Q))+w*z,ue=0;ue<k;++ue){ee+=c[ie+ue]*g[se+ue];}o[i*V+s*H+u*$+l*Y+z]=ee;}return r.toTensor()},e.prototype.conv2dDerFilter=function(e,t,n){this.assertNotComplex([e,t],"conv2dDerFilter");for(var r=n.strideHeight,o=n.strideWidth,a=n.filterHeight,i=n.filterWidth,s=buffer(n.filterShape,"float32"),u=n.padInfo.left,l=n.padInfo.top,c=0;c<a;++c)for(var p=Math.max(0,Math.ceil((l-c)/r)),d=Math.min(n.outHeight,(n.inHeight+l-c)/r),h=0;h<i;++h)for(var f=Math.max(0,Math.ceil((u-h)/o)),m=Math.min(n.outWidth,(n.inWidth+u-h)/o),g=0;g<n.inChannels;++g)for(var v=0;v<n.outChannels;++v){for(var y=0,x=0;x<n.batchSize;++x)for(var T=p;T<d;++T)for(var w=c+T*r-l,E=f;E<m;++E){var S=h+E*o-u;y+=e.get(x,w,S,g)*t.get(x,T,E,v);}s.set(y,c,h,g,v);}return s.toTensor()},e.prototype.conv3dDerFilter=function(e,t,n){for(var r=n.strideDepth,o=n.strideHeight,a=n.strideWidth,i=n.filterDepth,s=n.filterHeight,u=n.filterWidth,l=buffer(n.filterShape,"float32"),c=l.values,p=l.strides,d=p[0],h=p[1],f=p[2],m=p[3],g=t.dataSync(),v=t.strides,y=v[0],x=v[1],T=v[2],w=v[3],E=e.dataSync(),S=e.strides,b=S[0],C=S[1],A=S[2],_=S[3],N=n.padInfo.front,R=n.padInfo.left,k=n.padInfo.top,I=0;I<i;++I)for(var D=Math.max(0,Math.ceil((N-I)/r)),M=Math.min(n.outDepth,(n.inDepth+N-I)/r),P=I*d,O=0;O<s;++O)for(var F=Math.max(0,Math.ceil((k-O)/o)),L=Math.min(n.outHeight,(n.inHeight+k-O)/o),B=O*h+P,U=0;U<u;++U)for(var V=Math.max(0,Math.ceil((R-U)/a)),z=Math.min(n.outWidth,(n.inWidth+R-U)/a),H=U*f+B,W=0;W<n.inChannels;++W)for(var G=W*m+H,q=0;q<n.outChannels;++q){for(var $=0,X=0;X<n.batchSize;++X)for(var K=X*b,j=X*y,Y=D;Y<M;++Y)for(var Q=(I+Y*r-N)*C+K,J=Y*x+j,Z=F;Z<L;++Z)for(var ee=(O+Z*o-k)*A+Q,te=Z*T+J,ne=V;ne<z;++ne){var re=ne*w+te;$+=E[(U+ne*a-R)*_+ee+W]*g[re+q];}c[G+q]=$;}return l.toTensor()},e.prototype.depthwiseConv2D=function(e,t,n){this.assertNotComplex([e,t],"depthwiseConv2D");for(var r=n.filterHeight,o=n.filterWidth,a=n.dilationHeight,i=n.dilationWidth,s=n.padInfo.left,u=n.padInfo.top,l=n.outChannels/n.inChannels,c=buffer(n.outShape,e.dtype),p=e.dataSync(),d=t.dataSync(),h=c.values,f=0;f<n.batchSize;++f)for(var m=f*e.strides[0],g=f*c.strides[0],v=0;v<n.outHeight;++v)for(var y=g+v*c.strides[1],x=v*n.strideHeight-s,T=0;T<r;++T){var w=x+T*a;if(!(w<0||w>=n.inHeight))for(var E=T*t.strides[0],S=m+w*e.strides[1],b=0;b<n.outWidth;++b)for(var C=y+b*c.strides[2],A=b*n.strideWidth-u,_=0;_<o;++_){var N=A+_*i;if(!(N<0||N>=n.inWidth))for(var R=E+_*t.strides[1],k=S+N*n.inChannels,I=C,D=R,M=0;M<n.inChannels;++M){for(var P=p[k+M],O=0;O<l;++O)h[I+O]+=P*d[D+O];I+=l,D+=l;}}}return c.toTensor()},e.prototype.depthwiseConv2DDerInput=function(e,t,n){this.assertNotComplex([e,t],"depthwiseConv2DDerInput");for(var r=buffer(n.inShape,"float32"),o=r.values,a=r.strides,i=a[0],s=a[1],u=a[2],l=e.dataSync(),c=e.strides,p=c[0],d=c[1],h=c[2],f=t.dataSync(),m=t.strides,g=m[0],v=m[1],y=m[2],x=n.batchSize,T=n.filterHeight,w=n.filterWidth,E=n.inChannels,S=n.inHeight,b=n.inWidth,C=n.outChannels,A=n.outHeight,_=n.outWidth,N=n.strideHeight,R=n.strideWidth,k=T-1-n.padInfo.top,I=w-1-n.padInfo.left,D=C/E,M=0;M<x;++M)for(var P=0;P<E;++P)for(var O=0;O<S;++O)for(var F=O-k,L=Math.max(0,Math.ceil(F/N)),B=Math.min(A,(T+F)/N),U=0;U<b;++U){for(var V=U-I,z=Math.max(0,Math.ceil(V/R)),H=Math.min(_,(w+V)/R),W=0,G=L;G<B;++G)for(var q=G*N-F,$=z;$<H;++$)for(var X=p*M+d*G+h*$,K=g*(T-1-q)+v*(w-1-($*R-V))+y*P,j=0;j<D;++j){W+=l[X+(P*D+j)]*f[K+j];}o[i*M+s*O+u*U+P]=W;}return r.toTensor()},e.prototype.depthwiseConv2DDerFilter=function(e,t,n){this.assertNotComplex([e,t],"depthwiseConv2DDerFilter");for(var r=n.strideHeight,o=n.strideWidth,a=n.filterHeight,i=n.filterWidth,s=buffer(n.filterShape,"float32"),u=n.padInfo.left,l=n.padInfo.top,c=n.outChannels/n.inChannels,p=0;p<a;++p)for(var d=Math.max(0,Math.ceil((l-p)/r)),h=Math.min(n.outHeight,(n.inHeight+l-p)/r),f=0;f<i;++f)for(var m=Math.max(0,Math.ceil((u-f)/o)),g=Math.min(n.outWidth,(n.inWidth+u-f)/o),v=0;v<n.outChannels;++v){for(var y=Math.trunc(v/c),x=v%c,T=0,w=0;w<n.batchSize;++w)for(var E=d;E<h;++E)for(var S=p+E*r-l,b=m;b<g;++b){var C=f+b*o-u;T+=e.get(w,S,C,y)*t.get(w,E,b,v);}s.set(T,p,f,y,x);}return s.toTensor()},e.prototype.tile=function(e,t){this.assertNotComplex(e,"tile");for(var n=new Array(e.rank),r=0;r<n.length;r++)n[r]=e.shape[r]*t[r];var o=buffer(n,e.dtype),a=e.buffer();for(r=0;r<o.values.length;++r){for(var i=o.indexToLoc(r),s=new Array(e.rank),u=0;u<s.length;u++)s[u]=i[u]%e.shape[u];var l=a.locToIndex(s);o.values[r]=a.values[l];}return o.toTensor()},e.prototype.pad=function(e,t,n){this.assertNotComplex(e,"pad");var r=t.map(function(t,n){return t[0]+e.shape[n]+t[1]}),o=t.map(function(e){return e[0]}),a=e.buffer(),i=buffer(r,e.dtype);0!==n&&i.values.fill(n);for(var s=0;s<e.size;s++){var u=a.indexToLoc(s),l=u.map(function(e,t){return e+o[t]});i.set.apply(i,[e.get.apply(e,u)].concat(l));}return i.toTensor()},e.prototype.transpose=function(e,t){this.assertNotComplex(e,"transpose");for(var n=new Array(e.rank),r=0;r<n.length;r++)n[r]=e.shape[t[r]];var o=e.dataSync(),a=buffer(n,e.dtype),i=e.buffer();for(r=0;r<e.size;++r){for(var s=i.indexToLoc(r),u=new Array(s.length),l=0;l<u.length;l++)u[l]=s[t[l]];var c=a.locToIndex(u);a.values[c]=o[r];}return a.toTensor()},e.prototype.gather=function(e,t,n){this.assertNotComplex([e,t],"gather");var r=e.shape.slice(),o=t.dataSync();r[n]=o.length;for(var a=buffer(r,e.dtype),i=e.buffer(),s=0;s<a.size;++s){var u=a.indexToLoc(s),l=u.slice();l[n]=o[u[n]];var c=i.locToIndex(l);a.values[s]=i.values[c];}return a.toTensor()},e.prototype.batchToSpaceND=function(e,t,n){this.assertNotComplex([e],"batchToSpaceND");var r=t.reduce(function(e,t){return e*t}),o=getReshaped(e.shape,t,r),a=getPermuted(o.length,t.length),i=getReshapedPermuted(e.shape,t,r),s=getSliceBeginCoords(n,t.length),u=getSliceSize(i,n,t.length);return e.reshape(o).transpose(a).reshape(i).slice(s,u)},e.prototype.spaceToBatchND=function(e,t,n){this.assertNotComplex([e],"spaceToBatchND");var r=t.reduce(function(e,t){return e*t}),o=[[0,0]];o.push.apply(o,n);for(var a=1+t.length;a<e.shape.length;++a)o.push([0,0]);var i=e.pad(o),s=getReshaped(i.shape,t,r,!1),u=getPermuted(s.length,t.length,!1),l=getReshapedPermuted(i.shape,t,r,!1);return i.reshape(s).transpose(u).reshape(l)},e.prototype.pool=function(e,t,n){this.assertNotComplex(e,"pool");for(var r=t.strideHeight,o=t.strideWidth,a=t.dilationHeight,i=t.dilationWidth,s=t.effectiveFilterHeight,u=t.effectiveFilterWidth,l=t.padInfo.top,c=t.padInfo.left,p="max"===n?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,d=e.dataSync(),h=buffer(t.outShape,e.dtype),f=h.values,m=t.outShape[1]*t.outShape[2]*t.outShape[3],g=t.outShape[2]*t.outShape[3],v=t.outShape[3],y=0;y<t.batchSize;++y)for(var x=y*m,T=y*e.strides[0],w=0;w<t.inChannels;++w)for(var E=0;E<t.outHeight;++E)for(var S=E*r-l,b=Math.max(0,S),C=Math.min(t.inHeight,s+S),A=x+E*g,_=0;_<t.outWidth;++_){for(var N=_*o-c,R=Math.max(0,N),k=Math.min(t.inWidth,u+N),I=p,D=0,M=0,P=b;P<C;P+=a){for(var O=T+P*e.strides[1],F=R;F<k;F+=i){var L=d[O+F*e.strides[2]+w];"max"===n&&L>I?I=L:"avg"===n&&(D+=L,M++);}if(isNaN(I))break}f[A+_*v+w]="avg"===n?D/M:I;}return h.toTensor()},e.prototype.maxPool=function(e,t){return this.pool(e,t,"max")},e.prototype.maxPoolPositions=function(e,t){for(var n=buffer(t.outShape,"int32"),r=t.strideHeight,o=t.strideWidth,a=t.dilationHeight,i=t.dilationWidth,s=t.effectiveFilterHeight,u=t.effectiveFilterWidth,l=t.padInfo.top,c=t.padInfo.left,p=0;p<t.batchSize;++p)for(var d=0;d<t.inChannels;++d)for(var h=0;h<t.outHeight;++h){for(var f=h*r-l,m=f;m<0;)m+=a;for(var g=Math.min(t.inHeight,s+f),v=0;v<t.outWidth;++v){for(var y=v*o-c,x=y;x<0;)x+=i;for(var T=Math.min(t.inWidth,u+y),w=Number.NEGATIVE_INFINITY,E=-1,S=m;S<g;S+=a)for(var b=S-f,C=x;C<T;C+=i){var A=C-y,_=e.get(p,S,C,d);_>w&&(w=_,E=b*u+A);}n.set(E,p,h,v,d);}}return n.toTensor()},e.prototype.maxPoolBackprop=function(e,t,n,r){this.assertNotComplex([t,n],"maxPoolBackprop");for(var o=this.maxPoolPositions(t,r),a=r.strideHeight,i=r.strideWidth,s=r.dilationHeight,u=r.dilationWidth,l=r.effectiveFilterHeight,c=r.effectiveFilterWidth,p=c-1-r.padInfo.left,d=l-1-r.padInfo.top,h=buffer(t.shape,"float32"),f=0;f<r.batchSize;++f)for(var m=0;m<r.inChannels;++m)for(var g=0;g<r.inHeight;++g)for(var v=0;v<r.inWidth;++v){for(var y=g-d,x=v-p,T=0,w=0;w<l;w+=s){var E=(y+w)/a;if(!(E<0||E>=r.outHeight||Math.floor(E)!==E))for(var S=0;S<c;S+=u){var b=(x+S)/i;if(!(b<0||b>=r.outWidth||Math.floor(b)!==b)){var C=l*c-1-o.get(f,E,b,m)===w*c+S?1:0;if(0!==C)T+=e.get(f,E,b,m)*C;}}}h.set(T,f,g,v,m);}return h.toTensor()},e.prototype.avgPoolBackprop=function(e,t,n){this.assertNotComplex([e,t],"avgPoolBackprop");for(var r=n.strideHeight,o=n.strideWidth,a=n.filterHeight,i=n.filterWidth,s=n.dilationHeight,u=n.dilationWidth,l=n.effectiveFilterHeight,c=n.effectiveFilterWidth,p=c-1-n.padInfo.left,d=l-1-n.padInfo.top,h=buffer(t.shape,"float32"),f=1/(a*i),m=0;m<n.batchSize;++m)for(var g=0;g<n.inChannels;++g)for(var v=0;v<n.inHeight;++v)for(var y=0;y<n.inWidth;++y){for(var x=v-d,T=y-p,w=0,E=0;E<l;E+=s){var S=(x+E)/r;if(!(S<0||S>=n.outHeight||Math.floor(S)!==S))for(var b=0;b<c;b+=u){var C=(T+b)/o;if(!(C<0||C>=n.outWidth||Math.floor(C)!==C))w+=e.get(m,S,C,g);}}h.set(w*f,m,v,y,g);}return h.toTensor()},e.prototype.cast=function(e,t){return castTensor(e,t,this)},e.prototype.reshape=function(e,t){return reshapeTensor(e,t)},e.prototype.avgPool=function(e,t){return this.assertNotComplex(e,"avgPool"),this.pool(e,t,"avg").toFloat()},e.prototype.resizeBilinear=function(e,t,n,r){this.assertNotComplex(e,"resizeBilinear");for(var o=e.shape,a=o[0],i=o[1],s=o[2],u=o[3],l=e.dataSync(),c=new Float32Array(sizeFromShape([a,t,n,u])),p=[r&&t>1?i-1:i,r&&n>1?s-1:s],d=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=0,f=p[0]/d[0],m=p[1]/d[1],g=0;g<a;g++)for(var v=0;v<t;v++)for(var y=f*v,x=Math.floor(y),T=y-x,w=Math.min(i-1,Math.ceil(y)),E=g*e.strides[0]+x*e.strides[1],S=g*e.strides[0]+w*e.strides[1],b=0;b<n;b++)for(var C=m*b,A=Math.floor(C),_=C-A,N=Math.min(s-1,Math.ceil(C)),R=E+A*e.strides[2],k=S+A*e.strides[2],I=E+ +N*e.strides[2],D=S+N*e.strides[2],M=0;M<u;M++){var P=l[R+M],O=l[k+M],F=P+(l[I+M]-P)*_,L=F+(O+(l[D+M]-O)*_-F)*T;c[h++]=L;}return tensor(c,[a,t,n,u])},e.prototype.resizeBilinearBackprop=function(e,t,n){this.assertNotComplex([e,t],"resizeBilinearBackprop");for(var r=t.shape,o=r[0],a=r[1],i=r[2],s=r[3],u=e.shape,l=u[1],c=u[2],p=new Float32Array(o*a*i*s),d=[n&&l>1?a-1:a,n&&c>1?i-1:i],h=[n&&l>1?l-1:l,n&&c>1?c-1:c],f=d[0]/h[0],m=d[1]/h[1],g=e.dataSync(),v=0,y=0;y<o;y++)for(var x=y*t.strides[0],T=0;T<l;T++)for(var w=T*f,E=Math.floor(w),S=Math.min(Math.ceil(w),a-1),b=x+E*t.strides[1],C=x+S*t.strides[1],A=w-E,_=1-A,N=0;N<c;N++)for(var R=N*m,k=Math.floor(R),I=Math.min(Math.ceil(R),i-1),D=R-k,M=1-D,P=b+k*t.strides[2],O=b+I*t.strides[2],F=C+k*t.strides[2],L=C+I*t.strides[2],B=_*M,U=_*D,V=A*M,z=A*D,H=0;H<s;H++){var W=g[v++];p[P+H]+=W*B,p[O+H]+=W*U,p[F+H]+=W*V,p[L+H]+=W*z;}return tensor4d(p,[o,i,a,s],t.dtype)},e.prototype.resizeNearestNeighbor=function(e,t,n,r){this.assertNotComplex(e,"resizeNearestNeighbor");for(var o=e.shape,a=o[0],i=o[1],s=o[2],u=o[3],l=e.dataSync(),c=new Float32Array(a*t*n*u),p=[r&&t>1?i-1:i,r&&n>1?s-1:s],d=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=p[0]/d[0],f=p[1]/d[1],m=0,g=0;g<a;g++)for(var v=g*e.strides[0],y=0;y<t;y++)for(var x=h*y,T=v+Math.min(i-1,r?Math.round(x):Math.floor(x))*e.strides[1],w=0;w<n;w++)for(var E=f*w,S=T+Math.min(s-1,r?Math.round(E):Math.floor(E))*e.strides[2],b=0;b<u;b++){var C=l[S+b];c[m++]=C;}return tensor(c,[a,t,n,u],e.dtype)},e.prototype.resizeNearestNeighborBackprop=function(e,t,n){this.assertNotComplex([e,t],"resizeNearestNeighborBackprop");for(var r=t.shape,o=r[0],a=r[1],i=r[2],s=r[3],u=e.shape,l=u[1],c=u[2],p=new Float32Array(o*a*i*s),d=e.dataSync(),h=[n&&l>1?a-1:a,n&&c>1?i-1:i],f=[n&&l>1?l-1:l,n&&c>1?c-1:c],m=h[0]/f[0],g=h[1]/f[1],v=1/m,y=1/g,x=2*Math.ceil(v)+2,T=2*Math.ceil(y)+2,w=0;w<o;w++)for(var E=w*t.strides[0],S=0;S<a;S++)for(var b=E+S*t.strides[1],C=Math.floor(S*v),A=Math.floor(C-x/2),_=0;_<i;_++)for(var N=b+_*t.strides[2],R=Math.floor(_*y),k=Math.floor(R-T/2),I=0;I<s;I++){for(var D=0,M=0;M<x;M++){var P=M+A;if(!(P<0||P>=l)){var O=E+P*e.strides[1],F=P*m;if(S===Math.min(a-1,n?Math.round(F):Math.floor(F)))for(var L=0;L<T;L++){var B=L+k;if(!(B<0||B>=c)){var U=O+B*e.strides[2],V=B*g;_===Math.min(i-1,n?Math.round(V):Math.floor(V))&&(D+=d[U+I]);}}}}p[N+I]=D;}return tensor4d(p,t.shape,t.dtype)},e.prototype.batchNormalization=function(e,t,n,r,o,a){this.assertNotComplex([e,t,n,o,a],"batchNormalization");for(var i=e.dataSync(),s=t.dataSync(),u=n.dataSync(),l=o?o.dataSync():new Float32Array([1]),c=a?a.dataSync():new Float32Array([0]),p=new Float32Array(i.length),d=c.length,h=l.length,f=u.length,m=s.length,g=0,v=0,y=0,x=0,T=0;T<i.length;++T)p[T]=c[g++]+(i[T]-s[v++])*l[y++]/Math.sqrt(u[x++]+r),g>=d&&(g=0),v>=m&&(v=0),y>=h&&(y=0),x>=f&&(x=0);return tensor4d(p,e.shape)},e.prototype.localResponseNormalization4D=function(e,t,n,r,o){this.assertNotComplex(e,"localResponseNormalization4D");var a=e.shape[3],i=a-1,s=e.dataSync(),u=sizeFromShape(e.shape),l=new Float32Array(u);function c(e){for(var n=e%a,r=e-n+Math.max(0,n-t),o=e-n+Math.min(n+t,i),u=0;r<=o;r++){var l=s[r];u+=l*l;}return u}for(var p=0;p<u;p++){var d=c(p),h=s[p]*Math.pow(n+r*d,-o);l[p]=h;}return tensor4d(l,e.shape)},e.prototype.LRNGrad=function(e,t,n,r,o,a,i){this.assertNotComplex(e,"LRNGrad");for(var s=e.shape[3],u=e.dataSync(),l=t.dataSync(),c=n.dataSync(),p=new Float32Array(sizeFromShape(e.shape)),d=sizeFromShape(e.shape),h=0;h<d;h++){for(var f=h%s,m=h-f+Math.max(0,f-r),g=h-f+Math.min(s,f+r+1),v=0,y=m;y<g;y++)v+=Math.pow(l[y],2);v=a*v+o;for(y=m;y<g;y++){var x=-2*a*i*l[y]*c[h]/v;h===y&&(x+=Math.pow(v,-i)),x*=u[h],p[y]+=x;}}return tensor4d(p,e.shape)},e.prototype.multinomial=function(e,t,n,r){this.assertNotComplex(e,"multinomial");for(var o=t?e:softmax(e),a=o.shape[0],i=o.shape[1],s=zeros([a,n],"int32"),u=s.dataSync(),l=o.dataSync(),c=0;c<a;++c){var p=c*i,d=new Float32Array(i-1);d[0]=l[p];for(var h=1;h<d.length;++h)d[h]=d[h-1]+l[p+h];for(var f=seedrandom_1(r.toString()),m=c*n,g=0;g<n;++g){var v=f();u[m+g]=d.length;for(var y=0;y<d.length;y++)if(v<d[y]){u[m+g]=y;break}}}return s},e.prototype.oneHot=function(e,t,n,r){this.assertNotComplex(e,"oneHot");var o=new Float32Array(e.size*t);o.fill(r);for(var a=0;a<e.size;++a)e.get(a)>=0&&e.get(a)<t&&(o[a*t+e.get(a)]=n);return tensor2d(o,[e.size,t],"int32")},e.prototype.nonMaxSuppression=function(e,t,n,r,o){return this.assertNotComplex(e,"nonMaxSuppression"),nonMaxSuppressionImpl(e.dataSync(),t.dataSync(),n,r,o)},e.prototype.fft=function(e){return this.fftBatch(e,!1)},e.prototype.ifft=function(e){return this.fftBatch(e,!0)},e.prototype.fftBatch=function(e,t){for(var n=e.shape[0],r=e.shape[1],o=buffer(e.shape,"float32"),a=buffer(e.shape,"float32"),i=real(e).as2D(n,r),s=imag(e).as2D(n,r),u=0;u<n;u++)for(var l=i.slice([u,0],[1,r]),c=s.slice([u,0],[1,r]),p=complex(l,c),d=this.fftImpl(p,t).dataSync(),h=0;h<r;h++){var f=getComplexWithIndex(d,h);o.values[u*r+h]=f.real,a.values[u*r+h]=f.imag;}return complex(o.toTensor(),a.toTensor()).as2D(n,r)},e.prototype.fftImpl=function(e,t){var n=e.as1D(),r=n.size;if(this.isExponentOf2(r)){var o=this.fftRadix2(n,r,t).as2D(e.shape[0],e.shape[1]);return t&&(o=complex(real(o).div(scalar(r)),imag(o).div(scalar(r)))),o}var a=e.dataSync(),i=splitRealAndImagArrays(this.fourierTransformByMatmul(a,r,t));return complex(i.real,i.imag).as2D(e.shape[0],e.shape[1])},e.prototype.isExponentOf2=function(e){return 0==(e&e-1)},e.prototype.fftRadix2=function(e,t,n){if(1===t)return e;var r=e.dataSync(),o=t/2,a=complexWithEvenIndex(r),i=complex(a.real,a.imag).as1D(),s=complexWithOddIndex(r),u=complex(s.real,s.imag).as1D();i=this.fftRadix2(i,o,n),u=this.fftRadix2(u,o,n);var l=exponents(t,n),c=complex(l.real,l.imag).mul(u),p=i.add(c),d=i.sub(c),h=real(p).concat(real(d)),f=imag(p).concat(imag(d));return complex(h,f).as1D()},e.prototype.fourierTransformByMatmul=function(e,t,n){for(var r=new Float32Array(2*t),o=0;o<t;o++){for(var a=0,i=0,s=0;s<t;s++){var u=exponent(o*s,t,n),l=getComplexWithIndex(e,s);a+=l.real*u.real-l.imag*u.imag,i+=l.real*u.imag+l.imag*u.real;}n&&(a/=t,i/=t),assignToTypedArray(r,a,i,o);}return r},e.prototype.depthToSpace=function(e,t,n){assert("NHWC"===n,"Only NHWC dataFormat supported on CPU for depthToSpace. Got "+n),assert(t>1,"blockSize should be > 1 for depthToSpace, but was: "+t);for(var r=e.shape[0],o=e.shape[1],a=e.shape[2],i=e.shape[3],s=o*t,u=a*t,l=i/(t*t),c=e.dataSync(),p=new Float32Array(r*s*u*l),d=0,h=0;h<r;++h)for(var f=0;f<s;++f)for(var m=Math.floor(f/t),g=f%t,v=0;v<u;++v)for(var y=Math.floor(v/t),x=(g*t+v%t)*l,T=0;T<l;++T){var w=T+x+i*(y+a*(m+o*h));p[d++]=c[w];}return tensor4d(p,[r,s,u,l])},e.prototype.broadcastedBinaryOp=function(e,t,n,r){var o=assertAndGetBroadcastShape(e.shape,t.shape),a=buffer(o,n),i=e.dataSync(),s=t.dataSync(),u=getBroadcastDims(e.shape,o),l=getBroadcastDims(t.shape,o),c=a.values;if(u.length+l.length===0)for(var p=0;p<c.length;++p)c[p]=r(i[p%i.length],s[p%s.length]);else{var d=e.buffer(),h=t.buffer(),f=function(n){var o=a.indexToLoc(n),p=o.slice(-e.rank);u.forEach(function(e){return p[e]=0});var f=d.locToIndex(p),m=o.slice(-t.rank);l.forEach(function(e){return m[e]=0});var g=h.locToIndex(m);c[n]=r(i[f],s[g]);};for(p=0;p<c.length;++p)f(p);}return a.toTensor()},e.prototype.broadcastedBinaryComplexOp=function(e,t,n){var r=assertAndGetBroadcastShape(e.shape,t.shape),o=buffer(r,"float32"),a=buffer(r,"float32"),i=e.dataSync(),s=t.dataSync(),u=getBroadcastDims(e.shape,r),l=getBroadcastDims(t.shape,r),c=o.values,p=a.values;if(u.length+l.length===0)for(var d=0;d<c.length;d++){var h=d%i.length,f=d%s.length,m=n(i[2*h],i[2*h+1],s[2*f],s[2*f+1]);c[d]=m.real,p[d]=m.imag;}else{var g=this.data.get(e.dataId).complexTensors.real.buffer(),v=this.data.get(t.dataId).complexTensors.real.buffer(),y=function(r){var a=o.indexToLoc(r),d=a.slice(-e.rank);u.forEach(function(e){return d[e]=0});var h=g.locToIndex(d),f=a.slice(-t.rank);l.forEach(function(e){return f[e]=0});var m=v.locToIndex(f),y=n(i[2*h],i[2*h+1],s[2*m],s[2*m+1]);c[r]=y.real,p[r]=y.imag;};for(d=0;d<c.length;d++)y(d);}return this.complex(o.toTensor(),a.toTensor())},e.prototype.split=function(e,t,n){return split(e,t,n)},e.prototype.dispose=function(){},e.prototype.floatPrecision=function(){return 32},e.prototype.cropAndResize=function(e,t,n,r,o,a){for(var i=e.shape,s=i[0],u=i[1],l=i[2],c=i[3],p=t.shape[0],d=r[0],h=r[1],f=buffer([p,d,h,c],e.dtype),m=t.dataSync(),g=n.dataSync(),v=e.dataSync(),y=e.strides,x=f.strides,T=0;T<p;T++){var w=4*T,E=m[w],S=m[w+1],b=m[w+2],C=m[w+3],A=g[T];if(!(A>=s))for(var _=d>1?(b-E)*(u-1)/(d-1):0,N=h>1?(C-S)*(l-1)/(h-1):0,R=0;R<d;R++){var k=d>1?E*(u-1)+R*_:.5*(E+b)*(u-1);if(k<0||k>u-1)for(var I=0;I<h;I++)for(var D=0;D<c;D++){var M=D+I*x[2]+R*x[1]+T*x[0];f.values[M]=a;}else if("bilinear"===o){var P=Math.floor(k),O=Math.ceil(k),F=k-P;for(I=0;I<h;I++){if((q=h>1?S*(l-1)+I*N:.5*(S+C)*(l-1))<0||q>l-1)for(D=0;D<c;D++){M=D+I*x[2]+R*x[1]+T*x[0];f.values[M]=a;}else{var L=Math.floor(q),B=Math.ceil(q),U=q-L;for(D=0;D<c;D++){var V=v[M=D+L*y[2]+P*y[1]+A*y[0]],z=v[M=D+B*y[2]+P*y[1]+A*y[0]],H=v[M=D+L*y[2]+O*y[1]+A*y[0]],W=V+(z-V)*U,G=H+(v[M=D+B*y[2]+O*y[1]+A*y[0]]-H)*U;M=D+I*x[2]+R*x[1]+T*x[0],f.values[M]=W+(G-W)*F;}}}}else for(I=0;I<h;++I){var q;if((q=h>1?S*(l-1)+I*N:.5*(S+C)*(l-1))<0||q>l-1)for(D=0;D<c;D++){M=D+I*x[2]+R*x[1]+T*x[0];f.values[M]=a;}else{var $=Math.round(q),X=Math.round(k);for(D=0;D<c;D++){var K=D+$*y[2]+X*y[1]+A*y[0],j=D+I*x[2]+R*x[1]+T*x[0];f.values[j]=v[K];}}}}}return f.toTensor()},e.prototype.sparseToDense=function(e,t,n,r){var o=calculateShapes(t,e,n),a=o.sliceRank,i=o.numUpdates,s=o.sliceSize,u=o.strides,l=o.outputSize;return this.scatter(e,t,n,l,s,i,a,u,r,!1)},e.prototype.gatherND=function(e,t){var n=t.shape,r=n[n.length-1],o=prepareAndValidate(e,t),a=o[0],i=o[1],s=o[2],u=o[3];if(0===i)return tensor([],a,e.dtype);for(var l=new TensorBuffer([i,s],e.dtype),c=t.dataSync(),p=e.dataSync(),d=0;d<i;d++){for(var h=[],f=0,m=0;m<r;m++){var g=c[d*r+m];f+=g*u[m],h.push(g);}if(f<0||f>=e.size/s)throw new Error("Invalid indices: "+h+" does not index into "+e.shape);for(var v=0;v<s;v++)l.values[d*s+v]=p[f*s+v];}return l.toTensor().reshape(a)},e.prototype.scatterND=function(e,t,n){var r=calculateShapes(t,e,n),o=r.sliceRank,a=r.numUpdates,i=r.sliceSize,s=r.strides,u=r.outputSize,l=scalar(0);return this.scatter(e,t,n,u,i,a,o,s,l,!0)},e.prototype.scatter=function(e,t,n,r,o,a,i,s,u,l){var c=[r/o,o],p=e.dataSync(),d=t.dataSync();if(0===r)return tensor([],n,t.dtype);var h=new TensorBuffer(c,t.dtype);h.values.fill(u.dataSync()[0]);for(var f=0;f<a;f++){for(var m=[],g=0,v=0;v<i;v++){var y=p[f*i+v];m.push(y),g+=y*s[v];}if(g<0||g>=r/o)throw new Error("Invalid indices: "+m+" does not index into "+n);for(var x=0;x<o;x++)l?h.values[g*o+x]+=d[f*o+x]:h.values[g*o+x]=0===t.rank?d[0]:d[f*o+x];}return h.toTensor().reshape(n)},e}();ENV.registerBackend("cpu",function(){return new MathBackendCPU},1,setTensorTracker);var delayCallback="undefined"!=typeof requestAnimationFrame?requestAnimationFrame:setImmediate;function nextFrame(){return new Promise(function(e){return delayCallback(function(){return e()})})}var DTYPE_VALUE_SIZE_MAP={float32:4,int32:4,uint16:2,uint8:1,bool:1};function encodeWeights(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o;return __generator(this,function(a){switch(a.label){case 0:for(r in t=[],n=[],e){if("float32"!==(o=e[r]).dtype&&"int32"!==o.dtype&&"bool"!==o.dtype)throw new Error("Unsupported dtype in weight '"+r+"': "+o.dtype);t.push({name:r,shape:o.shape,dtype:o.dtype}),n.push(o.data());}return [4,Promise.all(n)];case 1:return [2,{data:concatenateTypedArrays(a.sent()),specs:t}]}})})}function decodeWeights(e,t){for(var n={},r=0,o=function(t){var o=t.name,a=t.dtype,i=t.shape,s=sizeFromShape(i),u=void 0;if("quantization"in t){var l=t.quantization;if("uint8"!==l.dtype&&"uint16"!==l.dtype)throw new Error("Weight "+t.name+" has unknown quantization dtype "+l.dtype+". Supported quantization dtypes are: 'uint8' and 'uint16'.");var c=DTYPE_VALUE_SIZE_MAP[l.dtype],p=e.slice(r,r+s*c),d="uint8"===l.dtype?new Uint8Array(p):new Uint16Array(p);if("float32"===a)u=Float32Array.from(d,function(e){return e*l.scale+l.min});else{if("int32"!==a)throw new Error("Unsupported dtype in weight '"+o+"': "+a);u=Int32Array.from(d,function(e){return Math.round(e*l.scale+l.min)});}r+=s*c;}else{var h=DTYPE_VALUE_SIZE_MAP[a];p=e.slice(r,r+s*h);if("float32"===a)u=new Float32Array(p);else if("int32"===a)u=new Int32Array(p);else{if("bool"!==a)throw new Error("Unsupported dtype in weight '"+o+"': "+a);u=new Uint8Array(p);}r+=s*h;}var f=void 0;if("float32"===a)f=tensor(u,i,"float32");else if("int32"===a)f=tensor(u,i,"int32");else{if("bool"!==a)throw new Error("Unsupported dtype in weight '"+o+"': "+a);f=tensor(u,i,"bool");}n[o]=f;},a=0,i=t;a<i.length;a++){o(i[a]);}return n}function concatenateTypedArrays(e){if(null===e)throw new Error("Invalid input value: "+JSON.stringify(e));var t=0,n=[];e.forEach(function(e){if(t+=e.byteLength,n.push(e.byteLength===e.buffer.byteLength?e:new e.constructor(e)),!(e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array))throw new Error("Unsupported TypedArray subtype: "+e.constructor.name)});var r=new Uint8Array(t),o=0;return n.forEach(function(e){r.set(new Uint8Array(e.buffer),o),o+=e.byteLength;}),r.buffer}var useNodeBuffer="undefined"!=typeof Buffer&&("undefined"==typeof Blob||"undefined"==typeof atob||"undefined"==typeof btoa);function stringByteLength(e){return useNodeBuffer?Buffer.byteLength(e):new Blob([e]).size}function arrayBufferToBase64String(e){return useNodeBuffer?Buffer.from(e).toString("base64"):btoa(String.fromCharCode.apply(null,new Uint8Array(e)))}function base64StringToArrayBuffer(e){if(useNodeBuffer){var t=Buffer.from(e,"base64");return t.buffer.slice(t.byteOffset,t.byteOffset+t.byteLength)}for(var n=atob(e),r=new Uint8Array(n.length),o=0;o<n.length;++o)r.set([n.charCodeAt(o)],o);return r.buffer}function concatenateArrayBuffers(e){var t=0;e.forEach(function(e){t+=e.byteLength;});var n=new Uint8Array(t),r=0;return e.forEach(function(e){n.set(new Uint8Array(e),r),r+=e.byteLength;}),n.buffer}function basename(e){for(e=e.trim();e.endsWith("/");)e=e.slice(0,e.length-1);var t=e.split("/");return t[t.length-1]}function getModelArtifactsInfoForJSON(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return {dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:null==e.modelTopology?0:stringByteLength(JSON.stringify(e.modelTopology)),weightSpecsBytes:null==e.weightSpecs?0:stringByteLength(JSON.stringify(e.weightSpecs)),weightDataBytes:null==e.weightData?0:e.weightData.byteLength}}var IORouterRegistry=function(){function e(){this.saveRouters=[],this.loadRouters=[];}return e.getInstance=function(){return null==e.instance&&(e.instance=new e),e.instance},e.registerSaveRouter=function(t){e.getInstance().saveRouters.push(t);},e.registerLoadRouter=function(t){e.getInstance().loadRouters.push(t);},e.getSaveHandlers=function(t){return e.getHandlers(t,"save")},e.getLoadHandlers=function(t){return e.getHandlers(t,"load")},e.getHandlers=function(e,t){var n=[];return ("load"===t?this.getInstance().loadRouters:this.getInstance().saveRouters).forEach(function(t){var r=t(e);null!==r&&n.push(r);}),n},e}(),URL_SCHEME_SUFFIX="://",ModelStoreManagerRegistry=function(){function e(){this.managers={};}return e.getInstance=function(){return null==e.instance&&(e.instance=new e),e.instance},e.registerManager=function(t,n){assert(null!=t,"scheme must not be undefined or null."),t.endsWith(URL_SCHEME_SUFFIX)&&(t=t.slice(0,t.indexOf(URL_SCHEME_SUFFIX))),assert(t.length>0,"scheme must not be an empty string.");var r=e.getInstance();assert(null==r.managers[t],"A model store manager is already registered for scheme '"+t+"'."),r.managers[t]=n;},e.getManager=function(e){var t=this.getInstance().managers[e];if(null==t)throw new Error("Cannot find model manager for scheme '"+e+"'");return t},e.getSchemes=function(){return Object.keys(this.getInstance().managers)},e}();function parseURL(e){if(-1===e.indexOf(URL_SCHEME_SUFFIX))throw new Error("The url string provided does not contain a scheme. Supported schemes are: "+ModelStoreManagerRegistry.getSchemes().join(","));return {scheme:e.split(URL_SCHEME_SUFFIX)[0],path:e.split(URL_SCHEME_SUFFIX)[1]}}function cloneModelInternal(e,t,n){return void 0===n&&(n=!1),__awaiter(this,void 0,void 0,function(){var r,o,a,i,s,u,l,c,p;return __generator(this,function(d){switch(d.label){case 0:return assert(e!==t,"Old path and new path are the same: '"+e+"'"),assert((r=IORouterRegistry.getLoadHandlers(e)).length>0,"Copying failed because no load handler is found for source URL "+e+"."),assert(r.length<2,"Copying failed because more than one ("+r.length+") load handlers for source URL "+e+"."),o=r[0],assert((a=IORouterRegistry.getSaveHandlers(t)).length>0,"Copying failed because no save handler is found for destination URL "+t+"."),assert(a.length<2,"Copying failed because more than one ("+r.length+") save handlers for destination URL "+t+"."),i=a[0],s=parseURL(e).scheme,u=parseURL(e).path,l=s===parseURL(e).scheme,[4,o.load()];case 1:return c=d.sent(),n&&l?[4,ModelStoreManagerRegistry.getManager(s).removeModel(u)]:[3,3];case 2:d.sent(),d.label=3;case 3:return [4,i.save(c)];case 4:return p=d.sent(),!n||l?[3,6]:[4,ModelStoreManagerRegistry.getManager(s).removeModel(u)];case 5:d.sent(),d.label=6;case 6:return [2,p.modelArtifactsInfo]}})})}function listModels(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o,a,i;return __generator(this,function(s){switch(s.label){case 0:e=ModelStoreManagerRegistry.getSchemes(),t={},n=0,r=e,s.label=1;case 1:return n<r.length?(o=r[n],[4,ModelStoreManagerRegistry.getManager(o).listModels()]):[3,4];case 2:for(i in a=s.sent())t[o+URL_SCHEME_SUFFIX+i]=a[i];s.label=3;case 3:return n++,[3,1];case 4:return [2,t]}})})}function removeModel(e){return __awaiter(this,void 0,void 0,function(){var t;return __generator(this,function(n){switch(n.label){case 0:return t=parseURL(e),[4,ModelStoreManagerRegistry.getManager(t.scheme).removeModel(t.path)];case 1:return [2,n.sent()]}})})}function copyModel(e,t){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(n){switch(n.label){case 0:return [4,cloneModelInternal(e,t,!1)];case 1:return [2,n.sent()]}})})}function moveModel(e,t){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(n){switch(n.label){case 0:return [4,cloneModelInternal(e,t,!0)];case 1:return [2,n.sent()]}})})}var DATABASE_NAME="tensorflowjs",DATABASE_VERSION=1,MODEL_STORE_NAME="models_store",INFO_STORE_NAME="model_info_store";function getIndexedDBFactory(){if(!ENV.get("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");var e=window,t=e.indexedDB||e.mozIndexedDB||e.webkitIndexedDB||e.msIndexedDB||e.shimIndexedDB;if(null==t)throw new Error("The current browser does not appear to support IndexedDB.");return t}function setUpDatabase(e){var t=e.result;t.createObjectStore(MODEL_STORE_NAME,{keyPath:"modelPath"}),t.createObjectStore(INFO_STORE_NAME,{keyPath:"modelPath"});}var BrowserIndexedDB=function(){function e(e){if(this.indexedDB=getIndexedDBFactory(),null==e||!e)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=e;}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(t){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return [2,this.databaseAction(this.modelPath,e)]})})},e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(e){return [2,this.databaseAction(this.modelPath)]})})},e.prototype.databaseAction=function(e,t){var n=this;return new Promise(function(e,r){var o=n.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);o.onupgradeneeded=function(){return setUpDatabase(o)},o.onsuccess=function(){var a=o.result;if(null==t){var i=a.transaction(MODEL_STORE_NAME,"readonly"),s=i.objectStore(MODEL_STORE_NAME).get(n.modelPath);s.onsuccess=function(){if(null==s.result)return a.close(),r(new Error("Cannot find model with path '"+n.modelPath+"' in IndexedDB."));e(s.result.modelArtifacts);},s.onerror=function(e){return a.close(),r(s.error)},i.oncomplete=function(){return a.close()};}else{var u,l=getModelArtifactsInfoForJSON(t),c=a.transaction(INFO_STORE_NAME,"readwrite"),p=c.objectStore(INFO_STORE_NAME),d=p.put({modelPath:n.modelPath,modelArtifactsInfo:l});d.onsuccess=function(){var o=(u=a.transaction(MODEL_STORE_NAME,"readwrite")).objectStore(MODEL_STORE_NAME).put({modelPath:n.modelPath,modelArtifacts:t,modelArtifactsInfo:l});o.onsuccess=function(){return e({modelArtifactsInfo:l})},o.onerror=function(e){var t=(p=c.objectStore(INFO_STORE_NAME)).delete(n.modelPath);t.onsuccess=function(){return a.close(),r(o.error)},t.onerror=function(e){return a.close(),r(o.error)};};},d.onerror=function(e){return a.close(),r(d.error)},c.oncomplete=function(){null==u?a.close():u.oncomplete=function(){return a.close()};};}},o.onerror=function(e){return r(o.error)};})},e.URL_SCHEME="indexeddb://",e}(),indexedDBRouter=function(e){return ENV.get("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(BrowserIndexedDB.URL_SCHEME)?browserIndexedDB(e.slice(BrowserIndexedDB.URL_SCHEME.length)):null};function browserIndexedDB(e){return new BrowserIndexedDB(e)}function maybeStripScheme(e){return e.startsWith(BrowserIndexedDB.URL_SCHEME)?e.slice(BrowserIndexedDB.URL_SCHEME.length):e}IORouterRegistry.registerSaveRouter(indexedDBRouter),IORouterRegistry.registerLoadRouter(indexedDBRouter);var BrowserIndexedDBManager=function(){function e(){this.indexedDB=getIndexedDBFactory();}return e.prototype.listModels=function(){return __awaiter(this,void 0,void 0,function(){var e=this;return __generator(this,function(t){return [2,new Promise(function(t,n){var r=e.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);r.onupgradeneeded=function(){return setUpDatabase(r)},r.onsuccess=function(){var e=r.result,o=e.transaction(INFO_STORE_NAME,"readonly"),a=o.objectStore(INFO_STORE_NAME).getAll();a.onsuccess=function(){for(var e={},n=0,r=a.result;n<r.length;n++){var o=r[n];e[o.modelPath]=o.modelArtifactsInfo;}t(e);},a.onerror=function(t){return e.close(),n(a.error)},o.oncomplete=function(){return e.close()};},r.onerror=function(e){return n(r.error)};})]})})},e.prototype.removeModel=function(e){return __awaiter(this,void 0,void 0,function(){var t=this;return __generator(this,function(n){return e=maybeStripScheme(e),[2,new Promise(function(n,r){var o=t.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);o.onupgradeneeded=function(){return setUpDatabase(o)},o.onsuccess=function(){var t,a=o.result,i=a.transaction(INFO_STORE_NAME,"readwrite"),s=i.objectStore(INFO_STORE_NAME),u=s.get(e);u.onsuccess=function(){if(null==u.result)return a.close(),r(new Error("Cannot find model with path '"+e+"' in IndexedDB."));var o=s.delete(e),i=function(){var o=(t=a.transaction(MODEL_STORE_NAME,"readwrite")).objectStore(MODEL_STORE_NAME).delete(e);o.onsuccess=function(){return n(u.result.modelArtifactsInfo)},o.onerror=function(e){return r(u.error)};};o.onsuccess=i,o.onerror=function(e){return i(),a.close(),r(u.error)};},u.onerror=function(e){return a.close(),r(u.error)},i.oncomplete=function(){null==t?a.close():t.oncomplete=function(){return a.close()};};},o.onerror=function(e){return r(o.error)};})]})})},e}();if(ENV.get("IS_BROWSER"))try{ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME,new BrowserIndexedDBManager);}catch(e){}var PATH_SEPARATOR="/",PATH_PREFIX="tensorflowjs_models",INFO_SUFFIX="info",MODEL_TOPOLOGY_SUFFIX="model_topology",WEIGHT_SPECS_SUFFIX="weight_specs",WEIGHT_DATA_SUFFIX="weight_data";function getModelKeys(e){return {info:[PATH_PREFIX,e,INFO_SUFFIX].join(PATH_SEPARATOR),topology:[PATH_PREFIX,e,MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),weightSpecs:[PATH_PREFIX,e,WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),weightData:[PATH_PREFIX,e,WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR)}}function getModelPathFromKey(e){var t=e.split(PATH_SEPARATOR);if(t.length<3)throw new Error("Invalid key format: "+e);return t.slice(1,t.length-1).join(PATH_SEPARATOR)}function maybeStripScheme$1(e){return e.startsWith(BrowserLocalStorage.URL_SCHEME)?e.slice(BrowserLocalStorage.URL_SCHEME.length):e}var BrowserLocalStorage=function(){function e(e){if(!ENV.get("IS_BROWSER")||void 0===window.localStorage)throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,null==e||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=getModelKeys(this.modelPath);}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o;return __generator(this,function(a){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),r=getModelArtifactsInfoForJSON(e);try{return this.LS.setItem(this.keys.info,JSON.stringify(r)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,arrayBufferToBase64String(e.weightData)),[2,{modelArtifactsInfo:r}]}catch(e){for(o in this.keys)this.LS.removeItem(this.keys[o]);throw new Error("Failed to save model '"+this.modelPath+"' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes="+r.modelTopologyBytes+", weightSpecsBytes="+r.weightSpecsBytes+", weightDataBytes="+r.weightDataBytes+".")}return [2]})})},e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o;return __generator(this,function(a){if(null==(e=JSON.parse(this.LS.getItem(this.keys.info))))throw new Error("In local storage, there is no model with name '"+this.modelPath+"'");if("JSON"!==e.modelTopologyType)throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");if(t={},null==(n=JSON.parse(this.LS.getItem(this.keys.topology))))throw new Error("In local storage, the topology of model '"+this.modelPath+"' is missing.");if(t.modelTopology=n,null==(r=JSON.parse(this.LS.getItem(this.keys.weightSpecs))))throw new Error("In local storage, the weight specs of model '"+this.modelPath+"' are missing.");if(t.weightSpecs=r,null==(o=this.LS.getItem(this.keys.weightData)))throw new Error("In local storage, the binary weight values of model '"+this.modelPath+"' are missing.");return t.weightData=base64StringToArrayBuffer(o),[2,t]})})},e.URL_SCHEME="localstorage://",e}(),localStorageRouter=function(e){return ENV.get("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(BrowserLocalStorage.URL_SCHEME)?browserLocalStorage(e.slice(BrowserLocalStorage.URL_SCHEME.length)):null};function browserLocalStorage(e){return new BrowserLocalStorage(e)}IORouterRegistry.registerSaveRouter(localStorageRouter),IORouterRegistry.registerLoadRouter(localStorageRouter);var BrowserLocalStorageManager=function(){function e(){assert(ENV.get("IS_BROWSER"),"Current environment is not a web browser"),assert(void 0!==window.localStorage,"Current browser does not appear to support localStorage"),this.LS=window.localStorage;}return e.prototype.listModels=function(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o,a;return __generator(this,function(i){for(e={},t=PATH_PREFIX+PATH_SEPARATOR,n=PATH_SEPARATOR+INFO_SUFFIX,r=0;r<this.LS.length;++r)(o=this.LS.key(r)).startsWith(t)&&o.endsWith(n)&&(a=getModelPathFromKey(o),e[a]=JSON.parse(this.LS.getItem(o)));return [2,e]})})},e.prototype.removeModel=function(e){return __awaiter(this,void 0,void 0,function(){var t,n;return __generator(this,function(r){if(e=maybeStripScheme$1(e),t=getModelKeys(e),null==this.LS.getItem(t.info))throw new Error("Cannot find model at path '"+e+"'");return n=JSON.parse(this.LS.getItem(t.info)),this.LS.removeItem(t.info),this.LS.removeItem(t.topology),this.LS.removeItem(t.weightSpecs),this.LS.removeItem(t.weightData),[2,n]})})},e}();if(ENV.get("IS_BROWSER"))try{ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME,new BrowserLocalStorageManager);}catch(e){}var DEFAULT_FILE_NAME_PREFIX="model",DEFAULT_JSON_EXTENSION_NAME=".json",DEFAULT_WEIGHT_DATA_EXTENSION_NAME=".weights.bin",BrowserDownloads=function(){function e(t){if(!ENV.get("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");t.startsWith(e.URL_SCHEME)&&(t=t.slice(e.URL_SCHEME.length)),null!=t&&0!==t.length||(t=DEFAULT_FILE_NAME_PREFIX),this.modelTopologyFileName=t+DEFAULT_JSON_EXTENSION_NAME,this.weightDataFileName=t+DEFAULT_WEIGHT_DATA_EXTENSION_NAME;}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o,a,i;return __generator(this,function(s){if(t=window.URL.createObjectURL(new Blob([e.weightData],{type:"application/octet-stream"})),e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");return n=[{paths:["./"+this.weightDataFileName],weights:e.weightSpecs}],r={modelTopology:e.modelTopology,weightsManifest:n},o=window.URL.createObjectURL(new Blob([JSON.stringify(r)],{type:"application/json"})),(a=null==this.jsonAnchor?document.createElement("a"):this.jsonAnchor).download=this.modelTopologyFileName,a.href=o,a.click(),null!=e.weightData&&((i=null==this.weightDataAnchor?document.createElement("a"):this.weightDataAnchor).download=this.weightDataFileName,i.href=t,i.click()),[2,{modelArtifactsInfo:getModelArtifactsInfoForJSON(e)}]})})},e.URL_SCHEME="downloads://",e}(),BrowserFiles=function(){function e(e){if(null==e||e.length<1)throw new Error("When calling browserFiles, at least 1 file is required, but received "+e);this.files=e;}return e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){var e,t,n=this;return __generator(this,function(r){return e=this.files[0],t=this.files.slice(1),[2,new Promise(function(r,o){var a=new FileReader;a.onload=function(a){var i=JSON.parse(a.target.result),s=i.modelTopology;if(null!=s){0===t.length&&r({modelTopology:s});var u=i.weightsManifest;if(null!=u){var l;try{l=n.checkManifestAndWeightFiles(u,t);}catch(e){return void o(e)}var c=[],p=[],d=[];u.forEach(function(e){e.paths.forEach(function(e){p.push(e),d.push(null);}),c.push.apply(c,e.weights);}),u.forEach(function(e){e.paths.forEach(function(e){var t=new FileReader;t.onload=function(t){var n=t.target.result,o=p.indexOf(e);d[o]=n,-1===d.indexOf(null)&&r({modelTopology:s,weightSpecs:c,weightData:concatenateArrayBuffers(d)});},t.onerror=function(t){return o("Failed to weights data from file of path '"+e+"'.")},t.readAsArrayBuffer(l[e]);});});}else o(new Error("weightManifest field is missing from file "+e.name));}else o(new Error("modelTopology field is missing from file "+e.name));},a.onerror=function(t){return o("Failed to read model topology and weights manifest JSON from file '"+e.name+"'. BrowserFiles supports loading Keras-style tf.Model artifacts only.")},a.readAsText(e);})]})})},e.prototype.checkManifestAndWeightFiles=function(e,t){for(var n=[],r=t.map(function(e){return basename(e.name)}),o={},a=0,i=e;a<i.length;a++){i[a].paths.forEach(function(e){var a=basename(e);if(-1!==n.indexOf(a))throw new Error("Duplicate file basename found in weights manifest: '"+a+"'");if(n.push(a),-1===r.indexOf(a))throw new Error("Weight file with basename '"+a+"' is not provided.");o[e]=t[r.indexOf(a)];});}if(n.length!==t.length)throw new Error("Mismatch in the number of files in weights manifest ("+n.length+") and the number of weight files provided ("+t.length+").");return o},e}(),browserDownloadsRouter=function(e){return ENV.get("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(BrowserDownloads.URL_SCHEME)?browserDownloads(e.slice(BrowserDownloads.URL_SCHEME.length)):null};function browserDownloads(e){return void 0===e&&(e="model"),new BrowserDownloads(e)}function browserFiles(e){return new BrowserFiles(e)}function loadWeightsAsArrayBuffer(e,t,n){return __awaiter(this,void 0,void 0,function(){var r,o;return __generator(this,function(a){switch(a.label){case 0:return null==n&&(n=fetch),r=e.map(function(e){return n(e,t)}),[4,Promise.all(r)];case 1:return o=a.sent(),[4,Promise.all(o.map(function(e){return e.arrayBuffer()}))];case 2:return [2,a.sent()]}})})}function loadWeights(e,t,n,r){return void 0===t&&(t=""),__awaiter(this,void 0,void 0,function(){return __generator(this,function(o){return [2,weightsLoaderFactory(function(e){return loadWeightsAsArrayBuffer(e,r)})(e,t,n)]})})}function weightsLoaderFactory(e){var t=this;return function(n,r,o){return void 0===r&&(r=""),__awaiter(t,void 0,void 0,function(){var t,a,i,s,u,l,c,p,d,h;return __generator(this,function(f){switch(f.label){case 0:if(t=n.map(function(){return !1}),a={},i=null!=o?o.map(function(){return !1}):[],s=[],n.forEach(function(e,n){var r=0;e.weights.forEach(function(e){var u="quantization"in e?e.quantization.dtype:e.dtype,l=DTYPE_VALUE_SIZE_MAP[u]*sizeFromShape(e.shape),c=function(){t[n]=!0,null==a[n]&&(a[n]=[]),a[n].push({manifestEntry:e,groupOffset:r,sizeBytes:l});};null!=o?o.forEach(function(t,n){t===e.name&&(c(),i[n]=!0);}):c(),s.push(e.name),r+=l;});}),!i.every(function(e){return e}))throw u=o.filter(function(e,t){return !i[t]}),new Error("Could not find weights in manifest with names: "+u.join(", ")+". \nManifest JSON has weights with names: "+s.join(", ")+".");return l=t.reduce(function(e,t,n){return t&&e.push(n),e},[]),c=[],l.forEach(function(e){n[e].paths.forEach(function(e){var t=r+(r.endsWith("/")?"":"/")+e;c.push(t);});}),[4,e(c)];case 1:return p=f.sent(),d={},h=0,l.forEach(function(e){for(var t=n[e].paths.length,r=0,o=0;o<t;o++)r+=p[h+o].byteLength;for(var i=new ArrayBuffer(r),s=new Uint8Array(i),u=0,l=0;l<t;l++){var c=new Uint8Array(p[h+l]);s.set(c,u),u+=c.byteLength;}a[e].forEach(function(e){var t=decodeWeights(i.slice(e.groupOffset,e.groupOffset+e.sizeBytes),[e.manifestEntry]);for(var n in t)d[n]=t[n];}),h+=t;}),[2,d]}})})}}IORouterRegistry.registerSaveRouter(browserDownloadsRouter);var BrowserHTTPRequest=function(){function e(e,t,n,r){if(this.weightPathPrefix=n,this.DEFAULT_METHOD="POST",null==r){if("undefined"==typeof fetch)throw new Error("browserHTTPRequest is not supported outside the web browser without a fetch polyfill.");this.fetchFunc=fetch.bind("undefined"==typeof window?null:window);}else assert("function"==typeof r,"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetchFunc=r;if(assert(null!=e&&e.length>0,"URL path for browserHTTPRequest must not be null, undefined or empty."),Array.isArray(e)&&assert(2===e.length,"URL paths for browserHTTPRequest must have a length of 2, (actual length is "+e.length+")."),this.path=e,null!=t&&null!=t.body)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=t||{};}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o;return __generator(this,function(a){switch(a.label){case 0:if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");return (t=Object.assign({method:this.DEFAULT_METHOD},this.requestInit)).body=new FormData,n=[{paths:["./model.weights.bin"],weights:e.weightSpecs}],r={modelTopology:e.modelTopology,weightsManifest:n},t.body.append("model.json",new Blob([JSON.stringify(r)],{type:"application/json"}),"model.json"),null!=e.weightData&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:"application/octet-stream"}),"model.weights.bin"),[4,this.getFetchFunc()(this.path,t)];case 1:if((o=a.sent()).ok)return [2,{modelArtifactsInfo:getModelArtifactsInfoForJSON(e),responses:[o]}];throw new Error("BrowserHTTPRequest.save() failed due to HTTP response status "+o.status+".")}})})},e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(e){return [2,Array.isArray(this.path)?this.loadBinaryModel():this.loadJSONModel()]})})},e.prototype.loadBinaryTopology=function(){return __awaiter(this,void 0,void 0,function(){var e,t;return __generator(this,function(n){switch(n.label){case 0:return n.trys.push([0,3,,4]),[4,this.getFetchFunc()(this.path[0],this.requestInit)];case 1:if(!(e=n.sent()).ok)throw new Error("BrowserHTTPRequest.load() failed due to HTTP response: "+e.statusText);return [4,e.arrayBuffer()];case 2:return [2,n.sent()];case 3:throw t=n.sent(),new Error(this.path[0]+" not found. "+t);case 4:return [2]}})})},e.prototype.loadBinaryModel=function(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o,a,i,s;return __generator(this,function(u){switch(u.label){case 0:return e=this.loadBinaryTopology(),[4,this.getFetchFunc()(this.path[1],this.requestInit)];case 1:if(!(t=u.sent()).ok)throw new Error("BrowserHTTPRequest.load() failed due to HTTP response: "+t.statusText);return [4,Promise.all([e,t])];case 2:return n=u.sent(),r=n[0],[4,n[1].json()];case 3:return null==(o=u.sent())?[3,5]:[4,this.loadWeights(o)];case 4:s=u.sent(),a=s[0],i=s[1],u.label=5;case 5:return [2,{modelTopology:r,weightSpecs:a,weightData:i}]}})})},e.prototype.loadJSONModel=function(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o,a,i,s;return __generator(this,function(u){switch(u.label){case 0:return [4,this.getFetchFunc()(this.path,this.requestInit)];case 1:if(!(e=u.sent()).ok)throw new Error("BrowserHTTPRequest.load() failed due to HTTP response: "+e.statusText);return [4,e.json()];case 2:if(t=u.sent(),n=t.modelTopology,r=t.weightsManifest,null==n&&null==r)throw new Error("The JSON from HTTP path "+this.path+" contains neither model topology or manifest for weights.");return null==r?[3,4]:(i=t.weightsManifest,[4,this.loadWeights(i)]);case 3:s=u.sent(),o=s[0],a=s[1],u.label=4;case 4:return [2,{modelTopology:n,weightSpecs:o,weightData:a}]}})})},e.prototype.loadWeights=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o,a,i,s,u,l,c,p,d;return __generator(this,function(h){switch(h.label){case 0:for(t=Array.isArray(this.path)?this.path[1]:this.path,n=parseUrl(t),r=n[0],o=n[1],a=this.weightPathPrefix||r,i=[],s=0,u=e;s<u.length;s++)l=u[s],i.push.apply(i,l.weights);return c=[],e.forEach(function(e){e.paths.forEach(function(e){c.push(a+e+o);});}),p=[i],d=concatenateArrayBuffers,[4,loadWeightsAsArrayBuffer(c,this.requestInit,this.getFetchFunc())];case 1:return [2,p.concat([d.apply(void 0,[h.sent()])])]}})})},e.prototype.getFetchFunc=function(){return this.fetchFunc},e.URL_SCHEME_REGEX=/^https?:\/\//,e}();function parseUrl(e){var t=e.lastIndexOf("/"),n=e.lastIndexOf("?");return [e.substring(0,t)+"/",n>t?e.substring(n):""]}function isHTTPScheme(e){return null!=e.match(BrowserHTTPRequest.URL_SCHEME_REGEX)}var httpRequestRouter=function(e){if("undefined"==typeof fetch)return null;return (Array.isArray(e)?e.every(function(e){return isHTTPScheme(e)}):isHTTPScheme(e))?browserHTTPRequest(e):null};function browserHTTPRequest(e,t,n,r){return new BrowserHTTPRequest(e,t,n,r)}IORouterRegistry.registerSaveRouter(httpRequestRouter),IORouterRegistry.registerLoadRouter(httpRequestRouter);var PassthroughLoader=function(){function e(e,t,n){this.modelTopology=e,this.weightSpecs=t,this.weightData=n;}return e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){var e;return __generator(this,function(t){return e={},null!=this.modelTopology&&(e=__assign({modelTopology:this.modelTopology},e)),null!=this.weightSpecs&&this.weightSpecs.length>0&&(e=__assign({weightSpecs:this.weightSpecs},e)),null!=this.weightData&&this.weightData.byteLength>0&&(e=__assign({weightData:this.weightData},e)),[2,e]})})},e}(),PassthroughSaver=function(){function e(e){this.saveHandler=e;}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(t){return [2,this.saveHandler(e)]})})},e}();function fromMemory(e,t,n){return new PassthroughLoader(e,t,n)}function withSaveHandler(e){return new PassthroughSaver(e)}var registerSaveRouter=IORouterRegistry.registerSaveRouter,registerLoadRouter=IORouterRegistry.registerLoadRouter,getSaveHandlers=IORouterRegistry.getSaveHandlers,getLoadHandlers=IORouterRegistry.getLoadHandlers,io=Object.freeze({browserFiles:browserFiles,browserHTTPRequest:browserHTTPRequest,concatenateArrayBuffers:concatenateArrayBuffers,decodeWeights:decodeWeights,encodeWeights:encodeWeights,fromMemory:fromMemory,getLoadHandlers:getLoadHandlers,getModelArtifactsInfoForJSON:getModelArtifactsInfoForJSON,getSaveHandlers:getSaveHandlers,isHTTPScheme:isHTTPScheme,loadWeights:loadWeights,registerLoadRouter:registerLoadRouter,registerSaveRouter:registerSaveRouter,weightsLoaderFactory:weightsLoaderFactory,withSaveHandler:withSaveHandler,copyModel:copyModel,listModels:listModels,moveModel:moveModel,removeModel:removeModel});function confusionMatrix_(e,t,n){var r=convertToTensor(e,"labels","confusionMatrix"),o=convertToTensor(t,"predictions","confusionMatrix");assert(null==n||n>0&&Number.isInteger(n),"If provided, numClasses must be a positive integer, but got "+n),assert(1===r.rank,"Expected the rank of labels to be 1, but got "+r.rank),assert(1===o.rank,"Expected the rank of predictions to be 1, but got "+o.rank),assert(r.shape[0]===o.shape[0],"Mismatch in the number of examples: "+r.shape[0]+" vs. "+o.shape[0]+". Labels and predictions should have the same number of elements."),assert(n>0&&Number.isInteger(n),"numClasses is required to be a positive integer, but got "+n);var a=oneHot(r.asType("int32"),n),i=oneHot(o.asType("int32"),n);return a.transpose().matMul(i).asType("int32")}var confusionMatrix=op({confusionMatrix_:confusionMatrix_}),math=Object.freeze({confusionMatrix:confusionMatrix}),Serializable=function(){function e(){}return e.prototype.getClassName=function(){return this.constructor.className},e.fromConfig=function(e,t){return new e(t)},e}(),SerializationMap=function(){function e(){this.classNameMap={};}return e.getMap=function(){return null==e.instance&&(e.instance=new e),e.instance},e.register=function(t){e.getMap().classNameMap[t.className]=[t,t.fromConfig];},e}();function registerClass(e){assert(null!=e.className,"Class being registered does not have the static className property defined."),assert("string"==typeof e.className,"className is required to be a string, but got type "+typeof e.className),assert(e.className.length>0,"Class being registered has an empty-string as its className, which is disallowed."),SerializationMap.register(e);}var serialization=Object.freeze({Serializable:Serializable,SerializationMap:SerializationMap,registerClass:registerClass}),WEBGL_ENVS={HAS_WEBGL:!0},NODE_ENVS={IS_NODE:!0},CHROME_ENVS={IS_CHROME:!0},BROWSER_ENVS={IS_BROWSER:!0},CPU_ENVS={HAS_WEBGL:!1},ALL_ENVS={};function expectArraysClose(e,t,n){return null==n&&(n=ENV.get("TEST_EPSILON")),expectArraysPredicate(e,t,function(e,t){return areClose(e,Number(t),n)})}function expectArraysPredicate(e,t,n){if(e instanceof Tensor||t instanceof Tensor){if(e instanceof Tensor&&t instanceof Tensor){if(e.dtype!==t.dtype)throw new Error("Arrays are of different type actual: "+e.dtype+" vs expected: "+t.dtype+".");if(!arraysEqual(e.shape,t.shape))throw new Error("Arrays are of different shape actual: "+e.shape+" vs expected: "+t.shape+".")}}else{var r=e.constructor.name,o=t.constructor.name;if(r!==o)throw new Error("Arrays are of different type actual: "+r+" vs expected: "+o)}var a,i;if(a=e instanceof Tensor?e.dataSync():e,i=t instanceof Tensor?t.dataSync():t,a.length!==i.length)throw new Error("Arrays have different lengths actual: "+a.length+" vs expected: "+i.length+".\nActual:   "+a+".\nExpected: "+i+".");for(var s=0;s<i.length;++s){var u=a[s],l=i[s];if(!n(u,l))throw new Error("Arrays differ: actual["+s+"] = "+u+", expected["+s+"] = "+l+".\nActual:   "+a+".\nExpected: "+i+".")}}function expectPromiseToFail(e,t){e().then(function(){return t.fail()},function(){return t()});}function expectArraysEqual(e,t){return e instanceof Tensor&&"string"===e.dtype||t instanceof Tensor&&"string"===t.dtype||e instanceof Array&&isString(e[0])||t instanceof Array&&isString(t[0])?expectArraysPredicate(e,t,function(e,t){return e==t}):expectArraysClose(e,t,0)}function expectNumbersClose(e,t,n){if(null==n&&(n=ENV.get("TEST_EPSILON")),!areClose(e,t,n))throw new Error("Numbers differ: actual === "+e+", expected === "+t)}function areClose(e,t,n){return !(!isNaN(e)||!isNaN(t))||!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function expectValuesInRange(e,t,n){var r;r=e instanceof Tensor?e.dataSync():e;for(var o=0;o<r.length;o++)if(r[o]<t||r[o]>n)throw new Error("Value out of range:"+r[o]+" low: "+t+", high: "+n)}function expectArrayBuffersEqual(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t));}var test_util=Object.freeze({WEBGL_ENVS:WEBGL_ENVS,NODE_ENVS:NODE_ENVS,CHROME_ENVS:CHROME_ENVS,BROWSER_ENVS:BROWSER_ENVS,CPU_ENVS:CPU_ENVS,ALL_ENVS:ALL_ENVS,expectArraysClose:expectArraysClose,expectPromiseToFail:expectPromiseToFail,expectArraysEqual:expectArraysEqual,expectNumbersClose:expectNumbersClose,expectValuesInRange:expectValuesInRange,expectArrayBuffersEqual:expectArrayBuffersEqual}),version="0.14.2",webgl=Object.freeze({gpgpu_util:gpgpu_util,webgl_util:webgl_util,MathBackendWebGL:MathBackendWebGL,GPGPUContext:GPGPUContext}),Optimizer=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends(t,e),t.prototype.minimize=function(e,t,n){void 0===t&&(t=!1);var r=this.computeGradients(e,n),o=r.value,a=r.grads;return this.applyGradients(a),Object.keys(a).forEach(function(e){return a[e].dispose()}),t?o:(o.dispose(),null)},t.prototype.computeGradients=function(e,t){return variableGrads(e,t)},t}(Serializable),AdadeltaOptimizer=function(e){function t(t,n,r){void 0===r&&(r=null);var o=e.call(this)||this;return o.learningRate=t,o.rho=n,o.epsilon=r,o.accumulatedGrads={},o.accumulatedUpdates={},o.c=keep(scalar(-t)),o.rhoScalar=keep(scalar(n)),o.oneMinusRho=keep(scalar(1-n)),null===r&&(r=ENV.get("EPSILON")),o.epsilonScalar=keep(scalar(r)),o}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulatedGrads[n]){tidy(function(){t.accumulatedGrads[n]=zerosLike(o).variable(!1);});}if(null==r.accumulatedUpdates[n]){tidy(function(){t.accumulatedUpdates[n]=zerosLike(o).variable(!1);});}var a=e[n],i=r.accumulatedGrads[n],s=r.accumulatedUpdates[n];tidy(function(){var e=t.rhoScalar.mul(i).add(t.oneMinusRho.mul(a.square())),r=s.add(t.epsilonScalar).sqrt().div(i.add(t.epsilonScalar).sqrt()).mul(a),u=t.rhoScalar.mul(s).add(t.oneMinusRho.mul(r.square()));t.accumulatedGrads[n].assign(e),t.accumulatedUpdates[n].assign(u);var l=t.c.mul(r).add(o);o.assign(l);});},r=this;for(var o in e)n(o);},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsilonScalar.dispose(),this.rhoScalar.dispose(),this.oneMinusRho.dispose(),null!=this.accumulatedUpdates&&(Object.keys(this.accumulatedUpdates).forEach(function(t){return e.accumulatedUpdates[t].dispose()}),Object.keys(this.accumulatedGrads).forEach(function(t){return e.accumulatedGrads[t].dispose()}));},t.prototype.getConfig=function(){return {learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}},t.fromConfig=function(e,t){return new e(t.learningRate,t.rho,t.epsilon)},t.className="AdadeltaOptimizer",t}(Optimizer);registerClass(AdadeltaOptimizer);var AdagradOptimizer=function(e){function t(t,n){void 0===n&&(n=.1);var r=e.call(this)||this;return r.learningRate=t,r.initialAccumulatorValue=n,r.accumulatedGrads={},r.c=keep(scalar(-t)),r.epsilon=keep(scalar(ENV.get("EPSILON"))),r}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulatedGrads[n]){tidy(function(){t.accumulatedGrads[n]=fill(o.shape,t.initialAccumulatorValue).variable(!1);});}var a=e[n],i=r.accumulatedGrads[n];tidy(function(){var e=i.add(a.square());t.accumulatedGrads[n].assign(e);var r=t.c.mul(a.div(e.add(t.epsilon).sqrt())).add(o);o.assign(r);});},r=this;for(var o in e)n(o);},t.prototype.dispose=function(){var e=this;this.epsilon.dispose(),this.c.dispose(),null!=this.accumulatedGrads&&Object.keys(this.accumulatedGrads).forEach(function(t){return e.accumulatedGrads[t].dispose()});},t.prototype.getConfig=function(){return {learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}},t.fromConfig=function(e,t){return new e(t.learningRate,t.initialAccumulatorValue)},t.className="AdagradOptimizer",t}(Optimizer);registerClass(AdagradOptimizer);var AdamOptimizer=function(e){function t(t,n,r,o){void 0===o&&(o=null);var a=e.call(this)||this;return a.learningRate=t,a.beta1=n,a.beta2=r,a.epsilon=o,a.accumulatedFirstMoment={},a.accumulatedSecondMoment={},a.c=keep(scalar(-t)),a.beta1Scalar=keep(scalar(n)),a.beta2Scalar=keep(scalar(r)),tidy(function(){a.accBeta1=scalar(n).variable(),a.accBeta2=scalar(r).variable();}),a.oneMinusBeta1=keep(scalar(1-n)),a.oneMinusBeta2=keep(scalar(1-r)),a.one=keep(scalar(1)),null===o&&(o=ENV.get("EPSILON")),a.epsScalar=keep(scalar(o)),a}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this;tidy(function(){var n=t.one.sub(t.accBeta1),r=t.one.sub(t.accBeta2);for(var o in e){var a=ENV.engine.registeredVariables[o];if(null==t.accumulatedFirstMoment[o]){var i=!1;t.accumulatedFirstMoment[o]=zerosLike(a).variable(i);}if(null==t.accumulatedSecondMoment[o]){i=!1;t.accumulatedSecondMoment[o]=zerosLike(a).variable(i);}var s=e[o],u=t.accumulatedFirstMoment[o],l=t.accumulatedSecondMoment[o],c=t.beta1Scalar.mul(u).add(t.oneMinusBeta1.mul(s)),p=t.beta2Scalar.mul(l).add(t.oneMinusBeta2.mul(s.square())),d=c.div(n),h=p.div(r);t.accumulatedFirstMoment[o].assign(c),t.accumulatedSecondMoment[o].assign(p);var f=t.c.mul(d.div(t.epsScalar.add(h.sqrt()))).add(a);a.assign(f);}t.accBeta1.assign(t.accBeta1.mul(t.beta1Scalar)),t.accBeta2.assign(t.accBeta2.mul(t.beta2Scalar));});},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsScalar.dispose(),this.beta1Scalar.dispose(),this.beta2Scalar.dispose(),this.accBeta1.dispose(),this.accBeta2.dispose(),this.oneMinusBeta1.dispose(),this.oneMinusBeta2.dispose(),this.one.dispose(),null!=this.accumulatedFirstMoment&&Object.keys(this.accumulatedFirstMoment).forEach(function(t){return e.accumulatedFirstMoment[t].dispose()}),null!=this.accumulatedSecondMoment&&Object.keys(this.accumulatedSecondMoment).forEach(function(t){return e.accumulatedSecondMoment[t].dispose()});},t.prototype.getConfig=function(){return {learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}},t.fromConfig=function(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)},t.className="AdamOptimizer",t}(Optimizer);registerClass(AdamOptimizer);var AdamaxOptimizer=function(e){function t(t,n,r,o,a){void 0===o&&(o=null),void 0===a&&(a=0);var i=e.call(this)||this;return i.learningRate=t,i.beta1=n,i.beta2=r,i.epsilon=o,i.decay=a,i.accumulatedFirstMoment={},i.accumulatedWeightedInfNorm={},i.c=keep(scalar(-t)),i.beta1Scalar=keep(scalar(n)),i.beta2Scalar=keep(scalar(r)),i.decayScalar=keep(scalar(a)),tidy(function(){i.iteration=scalar(0).variable(),i.accBeta1=scalar(n).variable();}),i.oneMinusBeta1=keep(scalar(1-n)),i.one=keep(scalar(1)),null===o&&(o=ENV.get("EPSILON")),i.epsScalar=keep(scalar(o)),i}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this;tidy(function(){var n=t.one.sub(t.accBeta1),r=t.c.div(t.one.add(t.decayScalar.mul(t.iteration)));for(var o in e){var a=ENV.engine.registeredVariables[o];if(null==t.accumulatedFirstMoment[o]){var i=!1;t.accumulatedFirstMoment[o]=zerosLike(a).variable(i);}if(null==t.accumulatedWeightedInfNorm[o]){i=!1;t.accumulatedWeightedInfNorm[o]=zerosLike(a).variable(i);}var s=e[o],u=t.accumulatedFirstMoment[o],l=t.accumulatedWeightedInfNorm[o],c=t.beta1Scalar.mul(u).add(t.oneMinusBeta1.mul(s)),p=t.beta2Scalar.mul(l),d=s.abs(),h=p.maximum(d);t.accumulatedFirstMoment[o].assign(c),t.accumulatedWeightedInfNorm[o].assign(h);var f=r.div(n).mul(c.div(t.epsScalar.add(h))).add(a);a.assign(f);}t.iteration.assign(t.iteration.add(t.one)),t.accBeta1.assign(t.accBeta1.mul(t.beta1Scalar));});},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsScalar.dispose(),this.accBeta1.dispose(),this.beta1Scalar.dispose(),this.beta2Scalar.dispose(),this.oneMinusBeta1.dispose(),this.decayScalar.dispose(),this.iteration.dispose(),this.one.dispose(),null!=this.accumulatedFirstMoment&&Object.keys(this.accumulatedFirstMoment).forEach(function(t){return e.accumulatedFirstMoment[t].dispose()}),null!=this.accumulatedWeightedInfNorm&&Object.keys(this.accumulatedWeightedInfNorm).forEach(function(t){return e.accumulatedWeightedInfNorm[t].dispose()});},t.prototype.getConfig=function(){return {learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}},t.fromConfig=function(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)},t.className="AdamaxOptimizer",t}(Optimizer);registerClass(AdamaxOptimizer);var SGDOptimizer=function(e){function t(t){var n=e.call(this)||this;return n.learningRate=t,n.setLearningRate(t),n}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this;Object.keys(e).forEach(function(n){var r=e[n],o=ENV.engine.registeredVariables[n];tidy(function(){var e=t.c.mul(r).add(o);o.assign(e);});});},t.prototype.setLearningRate=function(e){this.learningRate=e,null!=this.c&&this.c.dispose(),this.c=keep(scalar(-e));},t.prototype.dispose=function(){this.c.dispose();},t.prototype.getConfig=function(){return {learningRate:this.learningRate}},t.fromConfig=function(e,t){return new e(t.learningRate)},t.className="SGDOptimizer",t}(Optimizer);registerClass(SGDOptimizer);var MomentumOptimizer=function(e){function t(t,n,r){void 0===r&&(r=!1);var o=e.call(this,t)||this;return o.learningRate=t,o.momentum=n,o.useNesterov=r,o.m=scalar(o.momentum),o.accumulations={},o}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulations[n]){tidy(function(){t.accumulations[n]=zerosLike(o).variable(!1);});}var a=r.accumulations[n],i=e[n];tidy(function(){var e,r=t.m.mul(a).add(i);e=t.useNesterov?t.c.mul(i.add(r.mul(t.m))).add(o):t.c.mul(r).add(o),t.accumulations[n].assign(r),o.assign(e);});},r=this;for(var o in e)n(o);},t.prototype.dispose=function(){if(e.prototype.dispose.call(this),this.m.dispose(),null!=this.accumulations)for(var t in this.accumulations)this.accumulations[t].dispose();},t.prototype.setMomentum=function(e){this.momentum=e;},t.prototype.getConfig=function(){return {learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}},t.fromConfig=function(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)},t.className="MomentumOptimizer",t}(SGDOptimizer);registerClass(MomentumOptimizer);var RMSPropOptimizer=function(e){function t(t,n,r,o,a){void 0===n&&(n=.9),void 0===r&&(r=0),void 0===o&&(o=null),void 0===a&&(a=!1);var i=e.call(this)||this;return i.learningRate=t,i.decay=n,i.momentum=r,i.epsilon=o,i.accumulatedMeanSquares={},i.accumulatedMeanGrads={},i.accumulatedMoments={},i.c=keep(scalar(t)),i.decayScalar=keep(scalar(n)),i.momentumScalar=keep(scalar(r)),i.oneMinusDecay=keep(scalar(1-n)),i.centered=a,null===o&&(o=ENV.get("EPSILON")),i.epsilonScalar=keep(scalar(o)),i}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulatedMeanSquares[n]){tidy(function(){t.accumulatedMeanSquares[n]=zerosLike(o).variable(!1);});}if(null==r.accumulatedMeanGrads[n]&&r.centered){tidy(function(){t.accumulatedMeanGrads[n]=zerosLike(o).variable(!1);});}if(null==r.accumulatedMoments[n]){tidy(function(){t.accumulatedMoments[n]=zerosLike(o).variable(!1);});}var a=r.accumulatedMeanSquares[n],i=r.accumulatedMeanGrads[n],s=r.accumulatedMoments[n],u=e[n];tidy(function(){var e=t.decayScalar.mul(a).add(t.oneMinusDecay.mul(u.square()));if(t.centered){var r=t.decayScalar.mul(i).add(t.oneMinusDecay.mul(u)),l=t.momentumScalar.mul(s).add(t.c.mul(u).div(e.sub(r.square().add(t.epsilonScalar)).sqrt()));t.accumulatedMeanSquares[n].assign(e),t.accumulatedMeanGrads[n].assign(r),t.accumulatedMoments[n].assign(l);var c=o.sub(l);o.assign(c);}else{var p=t.decayScalar.mul(a).add(t.oneMinusDecay.mul(u.square()));l=t.momentumScalar.mul(s).add(t.c.mul(u).div(p.add(t.epsilonScalar).sqrt()));t.accumulatedMeanSquares[n].assign(p),t.accumulatedMoments[n].assign(l);c=o.sub(l);o.assign(c);}});},r=this;for(var o in e)n(o);},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsilonScalar.dispose(),this.decayScalar.dispose(),this.momentumScalar.dispose(),this.oneMinusDecay.dispose(),null!=this.accumulatedMeanSquares&&Object.keys(this.accumulatedMeanSquares).forEach(function(t){return e.accumulatedMeanSquares[t].dispose()}),null!=this.accumulatedMeanGrads&&this.centered&&Object.keys(this.accumulatedMeanGrads).forEach(function(t){return e.accumulatedMeanGrads[t].dispose()}),null!=this.accumulatedMoments&&Object.keys(this.accumulatedMoments).forEach(function(t){return e.accumulatedMoments[t].dispose()});},t.prototype.getConfig=function(){return {learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}},t.fromConfig=function(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)},t.className="RMSPropOptimizer",t}(Optimizer);registerClass(RMSPropOptimizer);var OptimizerConstructors=function(){function e(){}return e.sgd=function(e){return new SGDOptimizer(e)},e.momentum=function(e,t,n){return void 0===n&&(n=!1),new MomentumOptimizer(e,t,n)},e.rmsprop=function(e,t,n,r,o){return void 0===t&&(t=.9),void 0===n&&(n=0),void 0===r&&(r=null),void 0===o&&(o=!1),new RMSPropOptimizer(e,t,n,r,o)},e.adam=function(e,t,n,r){return void 0===e&&(e=.001),void 0===t&&(t=.9),void 0===n&&(n=.999),void 0===r&&(r=null),new AdamOptimizer(e,t,n,r)},e.adadelta=function(e,t,n){return void 0===e&&(e=.001),void 0===t&&(t=.95),void 0===n&&(n=null),new AdadeltaOptimizer(e,t,n)},e.adamax=function(e,t,n,r,o){return void 0===e&&(e=.002),void 0===t&&(t=.9),void 0===n&&(n=.999),void 0===r&&(r=null),void 0===o&&(o=0),new AdamaxOptimizer(e,t,n,r,o)},e.adagrad=function(e,t){return void 0===t&&(t=.1),new AdagradOptimizer(e,t)},e}(),train={sgd:OptimizerConstructors.sgd,momentum:OptimizerConstructors.momentum,adadelta:OptimizerConstructors.adadelta,adagrad:OptimizerConstructors.adagrad,rmsprop:OptimizerConstructors.rmsprop,adamax:OptimizerConstructors.adamax,adam:OptimizerConstructors.adam},setBackend=Environment.setBackend,getBackend=Environment.getBackend,disposeVariables=Environment.disposeVariables,memory=Environment.memory;setOpHandler(ops);
+
+    var tfCore_esm = /*#__PURE__*/Object.freeze({
+        setBackend: setBackend,
+        getBackend: getBackend,
+        disposeVariables: disposeVariables,
+        memory: memory,
+        version_core: version,
+        nextFrame: nextFrame,
+        environment: environment,
+        io: io,
+        math: math,
+        serialization: serialization,
+        test_util: test_util,
+        util: util,
+        webgl: webgl,
+        AdadeltaOptimizer: AdadeltaOptimizer,
+        AdagradOptimizer: AdagradOptimizer,
+        AdamOptimizer: AdamOptimizer,
+        AdamaxOptimizer: AdamaxOptimizer,
+        MomentumOptimizer: MomentumOptimizer,
+        Optimizer: Optimizer,
+        RMSPropOptimizer: RMSPropOptimizer,
+        SGDOptimizer: SGDOptimizer,
+        Tensor: Tensor,
+        TensorBuffer: TensorBuffer,
+        variable: variable,
+        Variable: Variable,
+        get Rank () { return Rank; },
+        get Reduction () { return Reduction; },
+        ENV: ENV,
+        Environment: Environment,
+        KernelBackend: KernelBackend,
+        DataStorage: DataStorage,
+        image: image_ops,
+        linalg: linalg_ops,
+        losses: loss_ops,
+        spectral: spectral_ops,
+        op: op,
+        batchNormalization2d: batchNormalization2d,
+        batchNormalization3d: batchNormalization3d,
+        batchNormalization4d: batchNormalization4d,
+        batchNormalization: batchNormalization,
+        complex: complex,
+        real: real,
+        imag: imag,
+        concat: concat,
+        concat1d: concat1d,
+        concat2d: concat2d,
+        concat3d: concat3d,
+        concat4d: concat4d,
+        split: split$1,
+        conv1d: conv1d,
+        conv2d: conv2d,
+        conv3d: conv3d,
+        conv2dDerFilter: conv2dDerFilter,
+        depthwiseConv2d: depthwiseConv2d,
+        separableConv2d: separableConv2d,
+        conv2dTranspose: conv2dTranspose,
+        matMul: matMul,
+        dot: dot,
+        outerProduct: outerProduct,
+        reverse: reverse,
+        reverse1d: reverse1d,
+        reverse2d: reverse2d,
+        reverse3d: reverse3d,
+        reverse4d: reverse4d,
+        maxPool: maxPool,
+        avgPool: avgPool,
+        pool: pool,
+        slice: slice,
+        slice1d: slice1d,
+        slice2d: slice2d,
+        slice3d: slice3d,
+        slice4d: slice4d,
+        abs: abs,
+        acos: acos,
+        acosh: acosh,
+        asin: asin,
+        asinh: asinh,
+        atan: atan,
+        atanh: atanh,
+        ceil: ceil,
+        clipByValue: clipByValue,
+        cos: cos,
+        cosh: cosh,
+        erf: erf,
+        exp: exp,
+        expm1: expm1,
+        floor: floor,
+        log: log$1,
+        log1p: log1p,
+        logSigmoid: logSigmoid,
+        neg: neg,
+        reciprocal: reciprocal,
+        round: round,
+        rsqrt: rsqrt,
+        sigmoid: sigmoid,
+        sign: sign,
+        sin: sin,
+        sinh: sinh,
+        softplus: softplus,
+        sqrt: sqrt,
+        square: square,
+        step: step,
+        tan: tan,
+        tanh: tanh$1,
+        all: all,
+        any: any,
+        argMax: argMax,
+        argMin: argMin,
+        logSumExp: logSumExp,
+        max: max,
+        mean: mean,
+        min: min,
+        moments: moments,
+        sum: sum$1,
+        prod: prod,
+        equal: equal,
+        equalStrict: equalStrict,
+        greater: greater,
+        greaterEqual: greaterEqual,
+        greaterEqualStrict: greaterEqualStrict,
+        greaterStrict: greaterStrict,
+        less: less,
+        lessEqual: lessEqual,
+        lessEqualStrict: lessEqualStrict,
+        lessStrict: lessStrict,
+        notEqual: notEqual,
+        notEqualStrict: notEqualStrict,
+        add: add,
+        addN: addN,
+        addStrict: addStrict,
+        atan2: atan2,
+        div: div,
+        divStrict: divStrict,
+        floorDiv: floorDiv,
+        maximum: maximum,
+        maximumStrict: maximumStrict,
+        minimum: minimum,
+        minimumStrict: minimumStrict,
+        mod: mod,
+        modStrict: modStrict,
+        mul: mul,
+        mulStrict: mulStrict,
+        pow: pow,
+        powStrict: powStrict,
+        squaredDifference: squaredDifference,
+        squaredDifferenceStrict: squaredDifferenceStrict,
+        sub: sub,
+        subStrict: subStrict,
+        elu: elu,
+        leakyRelu: leakyRelu,
+        prelu: prelu,
+        relu: relu,
+        selu: selu,
+        logicalAnd: logicalAnd,
+        logicalNot: logicalNot,
+        logicalOr: logicalOr,
+        logicalXor: logicalXor,
+        where: where,
+        whereAsync: whereAsync,
+        buffer: buffer,
+        toPixels: toPixels,
+        print: print,
+        batchToSpaceND: batchToSpaceND,
+        cast: cast,
+        clone: clone,
+        cumsum: cumsum,
+        depthToSpace: depthToSpace,
+        expandDims: expandDims,
+        eye: eye,
+        fromPixels: fromPixels,
+        multinomial: multinomial,
+        oneHot: oneHot,
+        pad: pad,
+        pad1d: pad1d,
+        pad2d: pad2d,
+        pad3d: pad3d,
+        pad4d: pad4d,
+        rand: rand,
+        randomNormal: randomNormal,
+        randomUniform: randomUniform,
+        reshape: reshape,
+        spaceToBatchND: spaceToBatchND,
+        squeeze: squeeze,
+        stack: stack,
+        tile: tile,
+        truncatedNormal: truncatedNormal,
+        unstack: unstack,
+        setdiff1dAsync: setdiff1dAsync,
+        fill: fill,
+        linspace: linspace,
+        ones: ones$1,
+        range: range,
+        scalar: scalar,
+        tensor: tensor,
+        tensor1d: tensor1d,
+        tensor2d: tensor2d,
+        tensor3d: tensor3d,
+        tensor4d: tensor4d,
+        tensor5d: tensor5d,
+        tensor6d: tensor6d,
+        zeros: zeros,
+        onesLike: onesLike,
+        zerosLike: zerosLike,
+        transpose: transpose,
+        softmax: softmax,
+        logSoftmax: logSoftmax,
+        localResponseNormalization: localResponseNormalization,
+        norm: norm,
+        gather: gather,
+        unsortedSegmentSum: unsortedSegmentSum,
+        basicLSTMCell: basicLSTMCell,
+        multiRNNCell: multiRNNCell,
+        movingAverage: movingAverage,
+        stridedSlice: stridedSlice,
+        topk: topk,
+        scatterND: scatterND,
+        fft: fft,
+        ifft: ifft,
+        rfft: rfft,
+        irfft: irfft,
+        sparseToDense: sparseToDense,
+        gatherND: gatherND,
+        train: train,
+        tidy: tidy,
+        keep: keep,
+        dispose: dispose,
+        time: time,
+        profile: profile,
+        customGrad: customGrad,
+        grad: grad,
+        grads: grads,
+        valueAndGrad: valueAndGrad,
+        valueAndGrads: valueAndGrads,
+        variableGrads: variableGrads
+    });
+
+    /*! *****************************************************************************
+    Copyright (c) Microsoft Corporation. All rights reserved.
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use
+    this file except in compliance with the License. You may obtain a copy of the
+    License at http://www.apache.org/licenses/LICENSE-2.0
+
+    THIS CODE IS PROVIDED ON AN *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
+    WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
+    MERCHANTABLITY OR NON-INFRINGEMENT.
+
+    See the Apache Version 2.0 License for specific language governing permissions
+    and limitations under the License.
+    ***************************************************************************** */
+    /* global Reflect, Promise */
+
+    var extendStatics$1 = function(d, b) {
+        extendStatics$1 = Object.setPrototypeOf ||
+            ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
+            function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
+        return extendStatics$1(d, b);
+    };
+
+    function __extends$1(d, b) {
+        extendStatics$1(d, b);
+        function __() { this.constructor = d; }
+        d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
+    }
+
+    var __assign$1 = function() {
+        __assign$1 = Object.assign || function __assign(t) {
+            for (var s, i = 1, n = arguments.length; i < n; i++) {
+                s = arguments[i];
+                for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];
+            }
+            return t;
+        };
+        return __assign$1.apply(this, arguments);
+    };
+
+    function __awaiter$1(thisArg, _arguments, P, generator) {
+        return new (P || (P = Promise))(function (resolve, reject) {
+            function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+            function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+            function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+            step((generator = generator.apply(thisArg, _arguments || [])).next());
+        });
+    }
+
+    function __generator$1(thisArg, body) {
+        var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+        return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+        function verb(n) { return function (v) { return step([n, v]); }; }
+        function step(op) {
+            if (f) throw new TypeError("Generator is already executing.");
+            while (_) try {
+                if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+                if (y = 0, t) op = [op[0] & 2, t.value];
+                switch (op[0]) {
+                    case 0: case 1: t = op; break;
+                    case 4: _.label++; return { value: op[1], done: false };
+                    case 5: _.label++; y = op[1]; op = [0]; continue;
+                    case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                    default:
+                        if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                        if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                        if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                        if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                        if (t[2]) _.ops.pop();
+                        _.trys.pop(); continue;
+                }
+                op = body.call(thisArg, _);
+            } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+            if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+        }
+    }
+
+    var Dimensions = /** @class */ (function () {
+        function Dimensions(width, height) {
+            if (!isValidNumber(width) || !isValidNumber(height)) {
+                throw new Error("Dimensions.constructor - expected width and height to be valid numbers, instead have " + JSON.stringify({ width: width, height: height }));
+            }
+            this._width = width;
+            this._height = height;
+        }
+        Object.defineProperty(Dimensions.prototype, "width", {
+            get: function () { return this._width; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(Dimensions.prototype, "height", {
+            get: function () { return this._height; },
+            enumerable: true,
+            configurable: true
+        });
+        Dimensions.prototype.reverse = function () {
+            return new Dimensions(1 / this.width, 1 / this.height);
+        };
+        return Dimensions;
+    }());
+
+    var Point = /** @class */ (function () {
+        function Point(x, y) {
+            this._x = x;
+            this._y = y;
+        }
+        Object.defineProperty(Point.prototype, "x", {
+            get: function () { return this._x; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(Point.prototype, "y", {
+            get: function () { return this._y; },
+            enumerable: true,
+            configurable: true
+        });
+        Point.prototype.add = function (pt) {
+            return new Point(this.x + pt.x, this.y + pt.y);
+        };
+        Point.prototype.sub = function (pt) {
+            return new Point(this.x - pt.x, this.y - pt.y);
+        };
+        Point.prototype.mul = function (pt) {
+            return new Point(this.x * pt.x, this.y * pt.y);
+        };
+        Point.prototype.div = function (pt) {
+            return new Point(this.x / pt.x, this.y / pt.y);
+        };
+        Point.prototype.abs = function () {
+            return new Point(Math.abs(this.x), Math.abs(this.y));
+        };
+        Point.prototype.magnitude = function () {
+            return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2));
+        };
+        Point.prototype.floor = function () {
+            return new Point(Math.floor(this.x), Math.floor(this.y));
+        };
+        return Point;
+    }());
+
+    function isTensor(tensor$$1, dim) {
+        return tensor$$1 instanceof Tensor && tensor$$1.shape.length === dim;
+    }
+    function isTensor1D(tensor$$1) {
+        return isTensor(tensor$$1, 1);
+    }
+    function isTensor2D(tensor$$1) {
+        return isTensor(tensor$$1, 2);
+    }
+    function isTensor3D(tensor$$1) {
+        return isTensor(tensor$$1, 3);
+    }
+    function isTensor4D(tensor$$1) {
+        return isTensor(tensor$$1, 4);
+    }
+    function isFloat(num) {
+        return num % 1 !== 0;
+    }
+    function isEven$1(num) {
+        return num % 2 === 0;
+    }
+    function round$1(num, prec) {
+        if (prec === void 0) { prec = 2; }
+        var f = Math.pow(10, prec);
+        return Math.floor(num * f) / f;
+    }
+    function isDimensions(obj) {
+        return obj && obj.width && obj.height;
+    }
+    function computeReshapedDimensions(_a, inputSize) {
+        var width = _a.width, height = _a.height;
+        var scale = inputSize / Math.max(height, width);
+        return new Dimensions(Math.round(width * scale), Math.round(height * scale));
+    }
+    function getCenterPoint(pts) {
+        return pts.reduce(function (sum, pt) { return sum.add(pt); }, new Point(0, 0))
+            .div(new Point(pts.length, pts.length));
+    }
+    function range$1(num, start, step$$1) {
+        return Array(num).fill(0).map(function (_, i) { return start + (i * step$$1); });
+    }
+    function isValidNumber(num) {
+        return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0;
+    }
+    function isValidProbablitiy(num) {
+        return isValidNumber(num) && 0 <= num && num <= 1.0;
+    }
+
+    var Box = /** @class */ (function () {
+        // TODO: MTCNN boxes sometimes have negative widths or heights, figure out why and remove
+        // allowNegativeDimensions flag again
+        function Box(_box, allowNegativeDimensions) {
+            if (allowNegativeDimensions === void 0) { allowNegativeDimensions = false; }
+            var box = (_box || {});
+            var isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);
+            var isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);
+            if (!isRect && !isBbox) {
+                throw new Error("Box.constructor - expected box to be IBoundingBox | IRect, instead have " + JSON.stringify(box));
+            }
+            var _a = isRect
+                ? [box.x, box.y, box.width, box.height]
+                : [box.left, box.top, box.right - box.left, box.bottom - box.top], x = _a[0], y = _a[1], width = _a[2], height = _a[3];
+            Box.assertIsValidBox({ x: x, y: y, width: width, height: height }, 'Box.constructor', allowNegativeDimensions);
+            this._x = x;
+            this._y = y;
+            this._width = width;
+            this._height = height;
+        }
+        Box.isRect = function (rect) {
+            return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);
+        };
+        Box.assertIsValidBox = function (box, callee, allowNegativeDimensions) {
+            if (allowNegativeDimensions === void 0) { allowNegativeDimensions = false; }
+            if (!Box.isRect(box)) {
+                throw new Error(callee + " - invalid box: " + JSON.stringify(box) + ", expected object with properties x, y, width, height");
+            }
+            if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {
+                throw new Error(callee + " - width (" + box.width + ") and height (" + box.height + ") must be positive numbers");
+            }
+        };
+        Object.defineProperty(Box.prototype, "x", {
+            get: function () { return this._x; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(Box.prototype, "y", {
+            get: function () { return this._y; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(Box.prototype, "width", {
+            get: function () { return this._width; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(Box.prototype, "height", {
+            get: function () { return this._height; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(Box.prototype, "left", {
+            get: function () { return this.x; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(Box.prototype, "top", {
+            get: function () { return this.y; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(Box.prototype, "right", {
+            get: function () { return this.x + this.width; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(Box.prototype, "bottom", {
+            get: function () { return this.y + this.height; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(Box.prototype, "area", {
+            get: function () { return this.width * this.height; },
+            enumerable: true,
+            configurable: true
+        });
+        Box.prototype.round = function () {
+            var _a = [this.x, this.y, this.width, this.height]
+                .map(function (val) { return Math.round(val); }), x = _a[0], y = _a[1], width = _a[2], height = _a[3];
+            return new Box({ x: x, y: y, width: width, height: height });
+        };
+        Box.prototype.floor = function () {
+            var _a = [this.x, this.y, this.width, this.height]
+                .map(function (val) { return Math.floor(val); }), x = _a[0], y = _a[1], width = _a[2], height = _a[3];
+            return new Box({ x: x, y: y, width: width, height: height });
+        };
+        Box.prototype.toSquare = function () {
+            var _a = this, x = _a.x, y = _a.y, width = _a.width, height = _a.height;
+            var diff = Math.abs(width - height);
+            if (width < height) {
+                x -= (diff / 2);
+                width += diff;
+            }
+            if (height < width) {
+                y -= (diff / 2);
+                height += diff;
+            }
+            return new Box({ x: x, y: y, width: width, height: height });
+        };
+        Box.prototype.rescale = function (s) {
+            var scaleX = isDimensions(s) ? s.width : s;
+            var scaleY = isDimensions(s) ? s.height : s;
+            return new Box({
+                x: this.x * scaleX,
+                y: this.y * scaleY,
+                width: this.width * scaleX,
+                height: this.height * scaleY
+            });
+        };
+        Box.prototype.pad = function (padX, padY) {
+            var _a = [
+                this.x - (padX / 2),
+                this.y - (padY / 2),
+                this.width + padX,
+                this.height + padY
+            ], x = _a[0], y = _a[1], width = _a[2], height = _a[3];
+            return new Box({ x: x, y: y, width: width, height: height });
+        };
+        Box.prototype.clipAtImageBorders = function (imgWidth, imgHeight) {
+            var _a = this, x = _a.x, y = _a.y, right = _a.right, bottom = _a.bottom;
+            var clippedX = Math.max(x, 0);
+            var clippedY = Math.max(y, 0);
+            var newWidth = right - clippedX;
+            var newHeight = bottom - clippedY;
+            var clippedWidth = Math.min(newWidth, imgWidth - clippedX);
+            var clippedHeight = Math.min(newHeight, imgHeight - clippedY);
+            return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight })).floor();
+        };
+        Box.prototype.shift = function (sx, sy) {
+            var _a = this, width = _a.width, height = _a.height;
+            var x = this.x + sx;
+            var y = this.y + sy;
+            return new Box({ x: x, y: y, width: width, height: height });
+        };
+        Box.prototype.padAtBorders = function (imageHeight, imageWidth) {
+            var w = this.width + 1;
+            var h = this.height + 1;
+            var dx = 1;
+            var dy = 1;
+            var edx = w;
+            var edy = h;
+            var x = this.left;
+            var y = this.top;
+            var ex = this.right;
+            var ey = this.bottom;
+            if (ex > imageWidth) {
+                edx = -ex + imageWidth + w;
+                ex = imageWidth;
+            }
+            if (ey > imageHeight) {
+                edy = -ey + imageHeight + h;
+                ey = imageHeight;
+            }
+            if (x < 1) {
+                edy = 2 - x;
+                x = 1;
+            }
+            if (y < 1) {
+                edy = 2 - y;
+                y = 1;
+            }
+            return { dy: dy, edy: edy, dx: dx, edx: edx, y: y, ey: ey, x: x, ex: ex, w: w, h: h };
+        };
+        Box.prototype.calibrate = function (region) {
+            return new Box({
+                left: this.left + (region.left * this.width),
+                top: this.top + (region.top * this.height),
+                right: this.right + (region.right * this.width),
+                bottom: this.bottom + (region.bottom * this.height)
+            }).toSquare().round();
+        };
+        return Box;
+    }());
+
+    var BoundingBox = /** @class */ (function (_super) {
+        __extends$1(BoundingBox, _super);
+        function BoundingBox(left, top, right, bottom) {
+            return _super.call(this, { left: left, top: top, right: right, bottom: bottom }) || this;
+        }
+        return BoundingBox;
+    }(Box));
+
+    var BoxWithText = /** @class */ (function (_super) {
+        __extends$1(BoxWithText, _super);
+        function BoxWithText(box, text) {
+            var _this = _super.call(this, box) || this;
+            _this._text = text;
+            return _this;
+        }
+        Object.defineProperty(BoxWithText.prototype, "text", {
+            get: function () { return this._text; },
+            enumerable: true,
+            configurable: true
+        });
+        return BoxWithText;
+    }(Box));
+
+    var LabeledBox = /** @class */ (function (_super) {
+        __extends$1(LabeledBox, _super);
+        function LabeledBox(box, label) {
+            var _this = _super.call(this, box) || this;
+            _this._label = label;
+            return _this;
+        }
+        LabeledBox.assertIsValidLabeledBox = function (box, callee) {
+            Box.assertIsValidBox(box, callee);
+            if (!isValidNumber(box.label)) {
+                throw new Error(callee + " - expected property label (" + box.label + ") to be a number");
+            }
+        };
+        Object.defineProperty(LabeledBox.prototype, "label", {
+            get: function () { return this._label; },
+            enumerable: true,
+            configurable: true
+        });
+        return LabeledBox;
+    }(Box));
+
+    var ObjectDetection = /** @class */ (function () {
+        function ObjectDetection(score, classScore, className, relativeBox, imageDims) {
+            this._imageDims = new Dimensions(imageDims.width, imageDims.height);
+            this._score = score;
+            this._classScore = classScore;
+            this._className = className;
+            this._box = new Box(relativeBox).rescale(this._imageDims);
+        }
+        Object.defineProperty(ObjectDetection.prototype, "score", {
+            get: function () { return this._score; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(ObjectDetection.prototype, "classScore", {
+            get: function () { return this._classScore; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(ObjectDetection.prototype, "className", {
+            get: function () { return this._className; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(ObjectDetection.prototype, "box", {
+            get: function () { return this._box; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(ObjectDetection.prototype, "imageDims", {
+            get: function () { return this._imageDims; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(ObjectDetection.prototype, "imageWidth", {
+            get: function () { return this.imageDims.width; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(ObjectDetection.prototype, "imageHeight", {
+            get: function () { return this.imageDims.height; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(ObjectDetection.prototype, "relativeBox", {
+            get: function () { return new Box(this._box).rescale(this.imageDims.reverse()); },
+            enumerable: true,
+            configurable: true
+        });
+        ObjectDetection.prototype.forSize = function (width, height) {
+            return new ObjectDetection(this.score, this.classScore, this.className, this.relativeBox, { width: width, height: height });
+        };
+        return ObjectDetection;
+    }());
+
+    var PredictedBox = /** @class */ (function (_super) {
+        __extends$1(PredictedBox, _super);
+        function PredictedBox(box, label, score, classScore) {
+            var _this = _super.call(this, box, label) || this;
+            _this._score = score;
+            _this._classScore = classScore;
+            return _this;
+        }
+        PredictedBox.assertIsValidPredictedBox = function (box, callee) {
+            LabeledBox.assertIsValidLabeledBox(box, callee);
+            if (!isValidProbablitiy(box.score)
+                || !isValidProbablitiy(box.classScore)) {
+                throw new Error(callee + " - expected properties score (" + box.score + ") and (" + box.classScore + ") to be a number between [0, 1]");
+            }
+        };
+        Object.defineProperty(PredictedBox.prototype, "score", {
+            get: function () { return this._score; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(PredictedBox.prototype, "classScore", {
+            get: function () { return this._classScore; },
+            enumerable: true,
+            configurable: true
+        });
+        return PredictedBox;
+    }(LabeledBox));
+
+    var Rect = /** @class */ (function (_super) {
+        __extends$1(Rect, _super);
+        function Rect(x, y, width, height) {
+            return _super.call(this, { x: x, y: y, width: width, height: height }) || this;
+        }
+        return Rect;
+    }(Box));
+
+    function disposeUnusedWeightTensors(weightMap, paramMappings) {
+        Object.keys(weightMap).forEach(function (path) {
+            if (!paramMappings.some(function (pm) { return pm.originalPath === path; })) {
+                weightMap[path].dispose();
+            }
+        });
+    }
+
+    function extractWeightEntryFactory(weightMap, paramMappings) {
+        return function (originalPath, paramRank, mappedPath) {
+            var tensor = weightMap[originalPath];
+            if (!isTensor(tensor, paramRank)) {
+                throw new Error("expected weightMap[" + originalPath + "] to be a Tensor" + paramRank + "D, instead have " + tensor);
+            }
+            paramMappings.push({ originalPath: originalPath, paramPath: mappedPath || originalPath });
+            return tensor;
+        };
+    }
+
+    function extractWeightsFactory(weights) {
+        var remainingWeights = weights;
+        function extractWeights(numWeights) {
+            var ret = remainingWeights.slice(0, numWeights);
+            remainingWeights = remainingWeights.slice(numWeights);
+            return ret;
+        }
+        function getRemainingWeights() {
+            return remainingWeights;
+        }
+        return {
+            extractWeights: extractWeights,
+            getRemainingWeights: getRemainingWeights
+        };
+    }
+
+    function getModelUris(uri, defaultModelName) {
+        var defaultManifestFilename = defaultModelName + "-weights_manifest.json";
+        if (!uri) {
+            return {
+                modelBaseUri: '',
+                manifestUri: defaultManifestFilename
+            };
+        }
+        if (uri === '/') {
+            return {
+                modelBaseUri: '/',
+                manifestUri: "/" + defaultManifestFilename
+            };
+        }
+        var protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';
+        uri = uri.replace(protocol, '');
+        var parts = uri.split('/').filter(function (s) { return s; });
+        var manifestFile = uri.endsWith('.json')
+            ? parts[parts.length - 1]
+            : defaultManifestFilename;
+        var modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');
+        modelBaseUri = uri.startsWith('/') ? "/" + modelBaseUri : modelBaseUri;
+        return {
+            modelBaseUri: modelBaseUri,
+            manifestUri: modelBaseUri === '/' ? "/" + manifestFile : modelBaseUri + "/" + manifestFile
+        };
+    }
+
+    function createBrowserEnv() {
+        var fetch = window['fetch'] || function () {
+            throw new Error('fetch - missing fetch implementation for browser environment');
+        };
+        var readFile = function () {
+            throw new Error('readFile - filesystem not available for browser environment');
+        };
+        return {
+            Canvas: HTMLCanvasElement,
+            Image: HTMLImageElement,
+            ImageData: ImageData,
+            Video: HTMLVideoElement,
+            createCanvasElement: function () { return document.createElement('canvas'); },
+            createImageElement: function () { return document.createElement('img'); },
+            fetch: fetch,
+            readFile: readFile
+        };
+    }
+
+    function createFileSystem(fs) {
+        var requireFsError = '';
+        if (!fs) {
+            try {
+                fs = require('fs');
+            }
+            catch (err) {
+                requireFsError = err.toString();
+            }
+        }
+        var readFile = fs
+            ? function (filePath) {
+                return new Promise(function (res, rej) {
+                    fs.readFile(filePath, function (err, buffer) {
+                        return err ? rej(err) : res(buffer);
+                    });
+                });
+            }
+            : function () {
+                throw new Error("readFile - failed to require fs in nodejs environment with error: " + requireFsError);
+            };
+        return {
+            readFile: readFile
+        };
+    }
+
+    function createNodejsEnv() {
+        var Canvas = global['Canvas'] || global['HTMLCanvasElement'];
+        var Image = global['Image'] || global['HTMLImageElement'];
+        var createCanvasElement = function () {
+            if (Canvas) {
+                return new Canvas();
+            }
+            throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');
+        };
+        var createImageElement = function () {
+            if (Image) {
+                return new Image();
+            }
+            throw new Error('createImageElement - missing Image implementation for nodejs environment');
+        };
+        var fetch = global['fetch'] || function () {
+            throw new Error('fetch - missing fetch implementation for nodejs environment');
+        };
+        var fileSystem = createFileSystem();
+        return __assign$1({ Canvas: Canvas || /** @class */ (function () {
+                function class_1() {
+                }
+                return class_1;
+            }()), Image: Image || /** @class */ (function () {
+                function class_2() {
+                }
+                return class_2;
+            }()), ImageData: global['ImageData'] || /** @class */ (function () {
+                function class_3() {
+                }
+                return class_3;
+            }()), Video: global['HTMLVideoElement'] || /** @class */ (function () {
+                function class_4() {
+                }
+                return class_4;
+            }()), createCanvasElement: createCanvasElement,
+            createImageElement: createImageElement,
+            fetch: fetch }, fileSystem);
+    }
+
+    function isBrowser() {
+        return typeof window === 'object'
+            && typeof document !== 'undefined'
+            && typeof HTMLImageElement !== 'undefined'
+            && typeof HTMLCanvasElement !== 'undefined'
+            && typeof HTMLVideoElement !== 'undefined'
+            && typeof ImageData !== 'undefined';
+    }
+
+    function isNodejs() {
+        return typeof global === 'object'
+            && typeof require === 'function'
+            && typeof module !== 'undefined'
+            // issues with gatsby.js: module.exports is undefined
+            // && !!module.exports
+            && typeof process !== 'undefined' && !!process.version;
+    }
+
+    var environment$1;
+    function getEnv() {
+        if (!environment$1) {
+            throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');
+        }
+        return environment$1;
+    }
+    function setEnv(env) {
+        environment$1 = env;
+    }
+    function initialize() {
+        // check for isBrowser() first to prevent electron renderer process
+        // to be initialized with wrong environment due to isNodejs() returning true
+        if (isBrowser()) {
+            setEnv(createBrowserEnv());
+        }
+        if (isNodejs()) {
+            setEnv(createNodejsEnv());
+        }
+    }
+    function monkeyPatch(env) {
+        if (!environment$1) {
+            initialize();
+        }
+        if (!environment$1) {
+            throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');
+        }
+        var _a = env.Canvas, Canvas = _a === void 0 ? environment$1.Canvas : _a, _b = env.Image, Image = _b === void 0 ? environment$1.Image : _b;
+        environment$1.Canvas = Canvas;
+        environment$1.Image = Image;
+        environment$1.createCanvasElement = env.createCanvasElement || (function () { return new Canvas(); });
+        environment$1.createImageElement = env.createImageElement || (function () { return new Image(); });
+        environment$1.ImageData = env.ImageData || environment$1.ImageData;
+        environment$1.Video = env.Video || environment$1.Video;
+        environment$1.fetch = env.fetch || environment$1.fetch;
+        environment$1.readFile = env.readFile || environment$1.readFile;
+    }
+    var env = {
+        getEnv: getEnv,
+        setEnv: setEnv,
+        initialize: initialize,
+        createBrowserEnv: createBrowserEnv,
+        createFileSystem: createFileSystem,
+        createNodejsEnv: createNodejsEnv,
+        monkeyPatch: monkeyPatch,
+        isBrowser: isBrowser,
+        isNodejs: isNodejs
+    };
+    initialize();
+
+    function isMediaLoaded(media) {
+        var _a = env.getEnv(), Image = _a.Image, Video = _a.Video;
+        return (media instanceof Image && media.complete)
+            || (media instanceof Video && media.readyState >= 3);
+    }
+
+    function awaitMediaLoaded(media) {
+        return new Promise(function (resolve, reject) {
+            if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {
+                return resolve();
+            }
+            function onLoad(e) {
+                if (!e.currentTarget)
+                    return;
+                e.currentTarget.removeEventListener('load', onLoad);
+                e.currentTarget.removeEventListener('error', onError);
+                resolve(e);
+            }
+            function onError(e) {
+                if (!e.currentTarget)
+                    return;
+                e.currentTarget.removeEventListener('load', onLoad);
+                e.currentTarget.removeEventListener('error', onError);
+                reject(e);
+            }
+            media.addEventListener('load', onLoad);
+            media.addEventListener('error', onError);
+        });
+    }
+
+    function bufferToImage(buf) {
+        return new Promise(function (resolve, reject) {
+            if (!(buf instanceof Blob)) {
+                return reject('bufferToImage - expected buf to be of type: Blob');
+            }
+            var reader = new FileReader();
+            reader.onload = function () {
+                if (typeof reader.result !== 'string') {
+                    return reject('bufferToImage - expected reader.result to be a string, in onload');
+                }
+                var img = env.getEnv().createImageElement();
+                img.onload = function () { return resolve(img); };
+                img.onerror = reject;
+                img.src = reader.result;
+            };
+            reader.onerror = reject;
+            reader.readAsDataURL(buf);
+        });
+    }
+
+    function getContext2dOrThrow(canvas) {
+        var ctx = canvas.getContext('2d');
+        if (!ctx) {
+            throw new Error('canvas 2d context is null');
+        }
+        return ctx;
+    }
+
+    function getMediaDimensions(input) {
+        var _a = env.getEnv(), Image = _a.Image, Video = _a.Video;
+        if (input instanceof Image) {
+            return new Dimensions(input.naturalWidth, input.naturalHeight);
+        }
+        if (input instanceof Video) {
+            return new Dimensions(input.videoWidth, input.videoHeight);
+        }
+        return new Dimensions(input.width, input.height);
+    }
+
+    function createCanvas(_a) {
+        var width = _a.width, height = _a.height;
+        var createCanvasElement = env.getEnv().createCanvasElement;
+        var canvas = createCanvasElement();
+        canvas.width = width;
+        canvas.height = height;
+        return canvas;
+    }
+    function createCanvasFromMedia(media, dims) {
+        var ImageData = env.getEnv().ImageData;
+        if (!(media instanceof ImageData) && !isMediaLoaded(media)) {
+            throw new Error('createCanvasFromMedia - media has not finished loading yet');
+        }
+        var _a = dims || getMediaDimensions(media), width = _a.width, height = _a.height;
+        var canvas = createCanvas({ width: width, height: height });
+        if (media instanceof ImageData) {
+            getContext2dOrThrow(canvas).putImageData(media, 0, 0);
+        }
+        else {
+            getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);
+        }
+        return canvas;
+    }
+
+    function getDefaultDrawOptions(options) {
+        if (options === void 0) { options = {}; }
+        return Object.assign({}, {
+            boxColor: 'blue',
+            textColor: 'red',
+            lineWidth: 2,
+            fontSize: 20,
+            fontStyle: 'Georgia',
+            withScore: true,
+            withClassName: true
+        }, options);
+    }
+
+    function drawBox(ctx, x, y, w, h, options) {
+        var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
+        ctx.strokeStyle = drawOptions.boxColor;
+        ctx.lineWidth = drawOptions.lineWidth;
+        ctx.strokeRect(x, y, w, h);
+    }
+
+    function drawText(ctx, x, y, text, options) {
+        if (options === void 0) { options = {}; }
+        var drawOptions = Object.assign(getDefaultDrawOptions(), options);
+        var padText = 2 + drawOptions.lineWidth;
+        ctx.fillStyle = drawOptions.textColor;
+        ctx.font = drawOptions.fontSize + "px " + drawOptions.fontStyle;
+        ctx.fillText(text, x + padText, y + padText + (drawOptions.fontSize * 0.6));
+    }
+
+    function resolveInput(arg) {
+        if (!env.isNodejs() && typeof arg === 'string') {
+            return document.getElementById(arg);
+        }
+        return arg;
+    }
+
+    function drawDetection(canvasArg, detection, options) {
+        var Canvas = env.getEnv().Canvas;
+        var canvas = resolveInput(canvasArg);
+        if (!(canvas instanceof Canvas)) {
+            throw new Error('drawDetection - expected canvas to be of type: HTMLCanvasElement');
+        }
+        var detectionArray = Array.isArray(detection)
+            ? detection
+            : [detection];
+        detectionArray.forEach(function (det) {
+            var _a = det instanceof ObjectDetection ? det.box : det, x = _a.x, y = _a.y, width = _a.width, height = _a.height;
+            var drawOptions = getDefaultDrawOptions(options);
+            var ctx = getContext2dOrThrow(canvas);
+            drawBox(ctx, x, y, width, height, drawOptions);
+            var withScore = drawOptions.withScore;
+            var text = det instanceof BoxWithText
+                ? det.text
+                : ((withScore && det instanceof PredictedBox)
+                    ? "" + round$1(det.score)
+                    : (det instanceof ObjectDetection
+                        ? "" + det.className + (withScore ? " (" + round$1(det.score) + ")" : '')
+                        : ''));
+            if (text) {
+                drawText(ctx, x, y + height, text, drawOptions);
+            }
+        });
+    }
+
+    function fetchOrThrow(url, init) {
+        return __awaiter$1(this, void 0, void 0, function () {
+            var fetch, res;
+            return __generator$1(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        fetch = env.getEnv().fetch;
+                        return [4 /*yield*/, fetch(url, init)];
+                    case 1:
+                        res = _a.sent();
+                        if (!(res.status < 400)) {
+                            throw new Error("failed to fetch: (" + res.status + ") " + res.statusText + ", from url: " + res.url);
+                        }
+                        return [2 /*return*/, res];
+                }
+            });
+        });
+    }
+
+    function fetchImage(uri) {
+        return __awaiter$1(this, void 0, void 0, function () {
+            var res, blob;
+            return __generator$1(this, function (_a) {
+                switch (_a.label) {
+                    case 0: return [4 /*yield*/, fetchOrThrow(uri)];
+                    case 1:
+                        res = _a.sent();
+                        return [4 /*yield*/, (res).blob()];
+                    case 2:
+                        blob = _a.sent();
+                        if (!blob.type.startsWith('image/')) {
+                            throw new Error("fetchImage - expected blob type to be of type image/*, instead have: " + blob.type + ", for url: " + res.url);
+                        }
+                        return [2 /*return*/, bufferToImage(blob)];
+                }
+            });
+        });
+    }
+
+    function fetchJson(uri) {
+        return __awaiter$1(this, void 0, void 0, function () {
+            return __generator$1(this, function (_a) {
+                switch (_a.label) {
+                    case 0: return [4 /*yield*/, fetchOrThrow(uri)];
+                    case 1: return [2 /*return*/, (_a.sent()).json()];
+                }
+            });
+        });
+    }
+
+    function fetchNetWeights(uri) {
+        return __awaiter$1(this, void 0, void 0, function () {
+            var _a;
+            return __generator$1(this, function (_b) {
+                switch (_b.label) {
+                    case 0:
+                        _a = Float32Array.bind;
+                        return [4 /*yield*/, fetchOrThrow(uri)];
+                    case 1: return [4 /*yield*/, (_b.sent()).arrayBuffer()];
+                    case 2: return [2 /*return*/, new (_a.apply(Float32Array, [void 0, _b.sent()]))()];
+                }
+            });
+        });
+    }
+
+    function imageTensorToCanvas(imgTensor, canvas) {
+        return __awaiter$1(this, void 0, void 0, function () {
+            var targetCanvas, _a, height, width, numChannels, imgTensor3D;
+            return __generator$1(this, function (_b) {
+                switch (_b.label) {
+                    case 0:
+                        targetCanvas = canvas || env.getEnv().createCanvasElement();
+                        _a = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0), height = _a[0], width = _a[1], numChannels = _a[2];
+                        imgTensor3D = tidy(function () { return imgTensor.as3D(height, width, numChannels).toInt(); });
+                        return [4 /*yield*/, toPixels(imgTensor3D, targetCanvas)];
+                    case 1:
+                        _b.sent();
+                        imgTensor3D.dispose();
+                        return [2 /*return*/, targetCanvas];
+                }
+            });
+        });
+    }
+
+    function imageToSquare(input, inputSize, centerImage) {
+        if (centerImage === void 0) { centerImage = false; }
+        var _a = env.getEnv(), Image = _a.Image, Canvas = _a.Canvas;
+        if (!(input instanceof Image || input instanceof Canvas)) {
+            throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');
+        }
+        var dims = getMediaDimensions(input);
+        var scale = inputSize / Math.max(dims.height, dims.width);
+        var width = scale * dims.width;
+        var height = scale * dims.height;
+        var targetCanvas = createCanvas({ width: inputSize, height: inputSize });
+        var inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);
+        var offset = Math.abs(width - height) / 2;
+        var dx = centerImage && width < height ? offset : 0;
+        var dy = centerImage && height < width ? offset : 0;
+        getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);
+        return targetCanvas;
+    }
+
+    function isMediaElement(input) {
+        var _a = env.getEnv(), Image = _a.Image, Canvas = _a.Canvas, Video = _a.Video;
+        return input instanceof Image
+            || input instanceof Canvas
+            || input instanceof Video;
+    }
+
+    function loadWeightMap(uri, defaultModelName) {
+        return __awaiter$1(this, void 0, void 0, function () {
+            var _a, manifestUri, modelBaseUri, manifest;
+            return __generator$1(this, function (_b) {
+                switch (_b.label) {
+                    case 0:
+                        _a = getModelUris(uri, defaultModelName), manifestUri = _a.manifestUri, modelBaseUri = _a.modelBaseUri;
+                        return [4 /*yield*/, fetchJson(manifestUri)];
+                    case 1:
+                        manifest = _b.sent();
+                        return [2 /*return*/, io.loadWeights(manifest, modelBaseUri)];
+                }
+            });
+        });
+    }
+
+    /**
+     * Pads the smaller dimension of an image tensor with zeros, such that width === height.
+     *
+     * @param imgTensor The image tensor.
+     * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on
+     * both sides of the minor dimension oof the image.
+     * @returns The padded tensor with width === height.
+     */
+    function padToSquare(imgTensor, isCenterImage) {
+        if (isCenterImage === void 0) { isCenterImage = false; }
+        return tidy(function () {
+            var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
+            if (height === width) {
+                return imgTensor;
+            }
+            var dimDiff = Math.abs(height - width);
+            var paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));
+            var paddingAxis = height > width ? 2 : 1;
+            var createPaddingTensor = function (paddingAmount) {
+                var paddingTensorShape = imgTensor.shape.slice();
+                paddingTensorShape[paddingAxis] = paddingAmount;
+                return fill(paddingTensorShape, 0);
+            };
+            var paddingTensorAppend = createPaddingTensor(paddingAmount);
+            var remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];
+            var paddingTensorPrepend = isCenterImage && remainingPaddingAmount
+                ? createPaddingTensor(remainingPaddingAmount)
+                : null;
+            var tensorsToStack = [
+                paddingTensorPrepend,
+                imgTensor,
+                paddingTensorAppend
+            ]
+                .filter(function (t) { return !!t; })
+                .map(function (t) { return t.toFloat(); });
+            return concat(tensorsToStack, paddingAxis);
+        });
+    }
+
+    var NetInput = /** @class */ (function () {
+        function NetInput(inputs, treatAsBatchInput) {
+            if (treatAsBatchInput === void 0) { treatAsBatchInput = false; }
+            var _this = this;
+            this._imageTensors = [];
+            this._canvases = [];
+            this._treatAsBatchInput = false;
+            this._inputDimensions = [];
+            if (!Array.isArray(inputs)) {
+                throw new Error("NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have " + inputs);
+            }
+            this._treatAsBatchInput = treatAsBatchInput;
+            this._batchSize = inputs.length;
+            inputs.forEach(function (input, idx) {
+                if (isTensor3D(input)) {
+                    _this._imageTensors[idx] = input;
+                    _this._inputDimensions[idx] = input.shape;
+                    return;
+                }
+                if (isTensor4D(input)) {
+                    var batchSize = input.shape[0];
+                    if (batchSize !== 1) {
+                        throw new Error("NetInput - tf.Tensor4D with batchSize " + batchSize + " passed, but not supported in input array");
+                    }
+                    _this._imageTensors[idx] = input;
+                    _this._inputDimensions[idx] = input.shape.slice(1);
+                    return;
+                }
+                var canvas = input instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);
+                _this._canvases[idx] = canvas;
+                _this._inputDimensions[idx] = [canvas.height, canvas.width, 3];
+            });
+        }
+        Object.defineProperty(NetInput.prototype, "imageTensors", {
+            get: function () {
+                return this._imageTensors;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(NetInput.prototype, "canvases", {
+            get: function () {
+                return this._canvases;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(NetInput.prototype, "isBatchInput", {
+            get: function () {
+                return this.batchSize > 1 || this._treatAsBatchInput;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(NetInput.prototype, "batchSize", {
+            get: function () {
+                return this._batchSize;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(NetInput.prototype, "inputDimensions", {
+            get: function () {
+                return this._inputDimensions;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(NetInput.prototype, "inputSize", {
+            get: function () {
+                return this._inputSize;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(NetInput.prototype, "reshapedInputDimensions", {
+            get: function () {
+                var _this = this;
+                return range$1(this.batchSize, 0, 1).map(function (_, batchIdx) { return _this.getReshapedInputDimensions(batchIdx); });
+            },
+            enumerable: true,
+            configurable: true
+        });
+        NetInput.prototype.getInput = function (batchIdx) {
+            return this.canvases[batchIdx] || this.imageTensors[batchIdx];
+        };
+        NetInput.prototype.getInputDimensions = function (batchIdx) {
+            return this._inputDimensions[batchIdx];
+        };
+        NetInput.prototype.getInputHeight = function (batchIdx) {
+            return this._inputDimensions[batchIdx][0];
+        };
+        NetInput.prototype.getInputWidth = function (batchIdx) {
+            return this._inputDimensions[batchIdx][1];
+        };
+        NetInput.prototype.getReshapedInputDimensions = function (batchIdx) {
+            if (typeof this.inputSize !== 'number') {
+                throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');
+            }
+            var width = this.getInputWidth(batchIdx);
+            var height = this.getInputHeight(batchIdx);
+            return computeReshapedDimensions({ width: width, height: height }, this.inputSize);
+        };
+        /**
+         * Create a batch tensor from all input canvases and tensors
+         * with size [batchSize, inputSize, inputSize, 3].
+         *
+         * @param inputSize Height and width of the tensor.
+         * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on
+         * both sides of the minor dimension oof the image.
+         * @returns The batch tensor.
+         */
+        NetInput.prototype.toBatchTensor = function (inputSize, isCenterInputs) {
+            var _this = this;
+            if (isCenterInputs === void 0) { isCenterInputs = true; }
+            this._inputSize = inputSize;
+            return tidy(function () {
+                var inputTensors = range$1(_this.batchSize, 0, 1).map(function (batchIdx) {
+                    var input = _this.getInput(batchIdx);
+                    if (input instanceof Tensor) {
+                        var imgTensor = isTensor4D(input) ? input : input.expandDims();
+                        imgTensor = padToSquare(imgTensor, isCenterInputs);
+                        if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {
+                            imgTensor = image_ops.resizeBilinear(imgTensor, [inputSize, inputSize]);
+                        }
+                        return imgTensor.as3D(inputSize, inputSize, 3);
+                    }
+                    if (input instanceof env.getEnv().Canvas) {
+                        return fromPixels(imageToSquare(input, inputSize, isCenterInputs));
+                    }
+                    throw new Error("toBatchTensor - at batchIdx " + batchIdx + ", expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have " + input);
+                });
+                var batchTensor = stack(inputTensors.map(function (t) { return t.toFloat(); })).as4D(_this.batchSize, inputSize, inputSize, 3);
+                return batchTensor;
+            });
+        };
+        return NetInput;
+    }());
+
+    /**
+     * Validates the input to make sure, they are valid net inputs and awaits all media elements
+     * to be finished loading.
+     *
+     * @param input The input, which can be a media element or an array of different media elements.
+     * @returns A NetInput instance, which can be passed into one of the neural networks.
+     */
+    function toNetInput(inputs) {
+        return __awaiter$1(this, void 0, void 0, function () {
+            var inputArgArray, getIdxHint, inputArray;
+            return __generator$1(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        if (inputs instanceof NetInput) {
+                            return [2 /*return*/, inputs];
+                        }
+                        inputArgArray = Array.isArray(inputs)
+                            ? inputs
+                            : [inputs];
+                        if (!inputArgArray.length) {
+                            throw new Error('toNetInput - empty array passed as input');
+                        }
+                        getIdxHint = function (idx) { return Array.isArray(inputs) ? " at input index " + idx + ":" : ''; };
+                        inputArray = inputArgArray.map(resolveInput);
+                        inputArray.forEach(function (input, i) {
+                            if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {
+                                if (typeof inputArgArray[i] === 'string') {
+                                    throw new Error("toNetInput -" + getIdxHint(i) + " string passed, but could not resolve HTMLElement for element id " + inputArgArray[i]);
+                                }
+                                throw new Error("toNetInput -" + getIdxHint(i) + " expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id");
+                            }
+                            if (isTensor4D(input)) {
+                                // if tf.Tensor4D is passed in the input array, the batch size has to be 1
+                                var batchSize = input.shape[0];
+                                if (batchSize !== 1) {
+                                    throw new Error("toNetInput -" + getIdxHint(i) + " tf.Tensor4D with batchSize " + batchSize + " passed, but not supported in input array");
+                                }
+                            }
+                        });
+                        // wait for all media elements being loaded
+                        return [4 /*yield*/, Promise.all(inputArray.map(function (input) { return isMediaElement(input) && awaitMediaLoaded(input); }))];
+                    case 1:
+                        // wait for all media elements being loaded
+                        _a.sent();
+                        return [2 /*return*/, new NetInput(inputArray, Array.isArray(inputs))];
+                }
+            });
+        });
+    }
+
+    function iou(box1, box2, isIOU) {
+        if (isIOU === void 0) { isIOU = true; }
+        var width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));
+        var height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));
+        var interSection = width * height;
+        return isIOU
+            ? interSection / (box1.area + box2.area - interSection)
+            : interSection / Math.min(box1.area, box2.area);
+    }
+
+    function nonMaxSuppression$1(boxes, scores, iouThreshold, isIOU) {
+        if (isIOU === void 0) { isIOU = true; }
+        var indicesSortedByScore = scores
+            .map(function (score, boxIndex) { return ({ score: score, boxIndex: boxIndex }); })
+            .sort(function (c1, c2) { return c1.score - c2.score; })
+            .map(function (c) { return c.boxIndex; });
+        var pick = [];
+        var _loop_1 = function () {
+            var curr = indicesSortedByScore.pop();
+            pick.push(curr);
+            var indices = indicesSortedByScore;
+            var outputs = [];
+            for (var i = 0; i < indices.length; i++) {
+                var idx = indices[i];
+                var currBox = boxes[curr];
+                var idxBox = boxes[idx];
+                outputs.push(iou(currBox, idxBox, isIOU));
+            }
+            indicesSortedByScore = indicesSortedByScore.filter(function (_, j) { return outputs[j] <= iouThreshold; });
+        };
+        while (indicesSortedByScore.length > 0) {
+            _loop_1();
+        }
+        return pick;
+    }
+
+    function normalize(x, meanRgb) {
+        return tidy(function () {
+            var r = meanRgb[0], g = meanRgb[1], b = meanRgb[2];
+            var avg_r = fill(x.shape.slice(0, 3).concat([1]), r);
+            var avg_g = fill(x.shape.slice(0, 3).concat([1]), g);
+            var avg_b = fill(x.shape.slice(0, 3).concat([1]), b);
+            var avg_rgb = concat([avg_r, avg_g, avg_b], 3);
+            return sub(x, avg_rgb);
+        });
+    }
+
+    function shuffleArray(inputArray) {
+        var array = inputArray.slice();
+        for (var i = array.length - 1; i > 0; i--) {
+            var j = Math.floor(Math.random() * (i + 1));
+            var x = array[i];
+            array[i] = array[j];
+            array[j] = x;
+        }
+        return array;
+    }
+
+    function sigmoid$1(x) {
+        return 1 / (1 + Math.exp(-x));
+    }
+    function inverseSigmoid(x) {
+        return Math.log(x / (1 - x));
+    }
+
+    var NeuralNetwork = /** @class */ (function () {
+        function NeuralNetwork(_name) {
+            this._name = _name;
+            this._params = undefined;
+            this._paramMappings = [];
+        }
+        Object.defineProperty(NeuralNetwork.prototype, "params", {
+            get: function () { return this._params; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(NeuralNetwork.prototype, "paramMappings", {
+            get: function () { return this._paramMappings; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(NeuralNetwork.prototype, "isLoaded", {
+            get: function () { return !!this.params; },
+            enumerable: true,
+            configurable: true
+        });
+        NeuralNetwork.prototype.getParamFromPath = function (paramPath) {
+            var _a = this.traversePropertyPath(paramPath), obj = _a.obj, objProp = _a.objProp;
+            return obj[objProp];
+        };
+        NeuralNetwork.prototype.reassignParamFromPath = function (paramPath, tensor$$1) {
+            var _a = this.traversePropertyPath(paramPath), obj = _a.obj, objProp = _a.objProp;
+            obj[objProp].dispose();
+            obj[objProp] = tensor$$1;
+        };
+        NeuralNetwork.prototype.getParamList = function () {
+            var _this = this;
+            return this._paramMappings.map(function (_a) {
+                var paramPath = _a.paramPath;
+                return ({
+                    path: paramPath,
+                    tensor: _this.getParamFromPath(paramPath)
+                });
+            });
+        };
+        NeuralNetwork.prototype.getTrainableParams = function () {
+            return this.getParamList().filter(function (param) { return param.tensor instanceof Variable; });
+        };
+        NeuralNetwork.prototype.getFrozenParams = function () {
+            return this.getParamList().filter(function (param) { return !(param.tensor instanceof Variable); });
+        };
+        NeuralNetwork.prototype.variable = function () {
+            var _this = this;
+            this.getFrozenParams().forEach(function (_a) {
+                var path = _a.path, tensor$$1 = _a.tensor;
+                _this.reassignParamFromPath(path, tensor$$1.variable());
+            });
+        };
+        NeuralNetwork.prototype.freeze = function () {
+            var _this = this;
+            this.getTrainableParams().forEach(function (_a) {
+                var path = _a.path, variable$$1 = _a.tensor;
+                var tensor$$1 = tensor(variable$$1.dataSync());
+                variable$$1.dispose();
+                _this.reassignParamFromPath(path, tensor$$1);
+            });
+        };
+        NeuralNetwork.prototype.dispose = function (throwOnRedispose) {
+            if (throwOnRedispose === void 0) { throwOnRedispose = true; }
+            this.getParamList().forEach(function (param) {
+                if (throwOnRedispose && param.tensor.isDisposed) {
+                    throw new Error("param tensor has already been disposed for path " + param.path);
+                }
+                param.tensor.dispose();
+            });
+            this._params = undefined;
+        };
+        NeuralNetwork.prototype.serializeParams = function () {
+            return new Float32Array(this.getParamList()
+                .map(function (_a) {
+                var tensor$$1 = _a.tensor;
+                return Array.from(tensor$$1.dataSync());
+            })
+                .reduce(function (flat, arr) { return flat.concat(arr); }));
+        };
+        NeuralNetwork.prototype.load = function (weightsOrUrl) {
+            return __awaiter$1(this, void 0, void 0, function () {
+                return __generator$1(this, function (_a) {
+                    switch (_a.label) {
+                        case 0:
+                            if (weightsOrUrl instanceof Float32Array) {
+                                this.extractWeights(weightsOrUrl);
+                                return [2 /*return*/];
+                            }
+                            return [4 /*yield*/, this.loadFromUri(weightsOrUrl)];
+                        case 1:
+                            _a.sent();
+                            return [2 /*return*/];
+                    }
+                });
+            });
+        };
+        NeuralNetwork.prototype.loadFromUri = function (uri) {
+            return __awaiter$1(this, void 0, void 0, function () {
+                var weightMap;
+                return __generator$1(this, function (_a) {
+                    switch (_a.label) {
+                        case 0:
+                            if (uri && typeof uri !== 'string') {
+                                throw new Error(this._name + ".loadFromUri - expected model uri");
+                            }
+                            return [4 /*yield*/, loadWeightMap(uri, this.getDefaultModelName())];
+                        case 1:
+                            weightMap = _a.sent();
+                            this.loadFromWeightMap(weightMap);
+                            return [2 /*return*/];
+                    }
+                });
+            });
+        };
+        NeuralNetwork.prototype.loadFromDisk = function (filePath) {
+            return __awaiter$1(this, void 0, void 0, function () {
+                var readFile, _a, manifestUri, modelBaseUri, fetchWeightsFromDisk, loadWeights, manifest, _b, _c, weightMap;
+                return __generator$1(this, function (_d) {
+                    switch (_d.label) {
+                        case 0:
+                            if (filePath && typeof filePath !== 'string') {
+                                throw new Error(this._name + ".loadFromDisk - expected model file path");
+                            }
+                            readFile = env.getEnv().readFile;
+                            _a = getModelUris(filePath, this.getDefaultModelName()), manifestUri = _a.manifestUri, modelBaseUri = _a.modelBaseUri;
+                            fetchWeightsFromDisk = function (filePaths) { return Promise.all(filePaths.map(function (filePath) { return readFile(filePath).then(function (buf) { return buf.buffer; }); })); };
+                            loadWeights = io.weightsLoaderFactory(fetchWeightsFromDisk);
+                            _c = (_b = JSON).parse;
+                            return [4 /*yield*/, readFile(manifestUri)];
+                        case 1:
+                            manifest = _c.apply(_b, [(_d.sent()).toString()]);
+                            return [4 /*yield*/, loadWeights(manifest, modelBaseUri)];
+                        case 2:
+                            weightMap = _d.sent();
+                            this.loadFromWeightMap(weightMap);
+                            return [2 /*return*/];
+                    }
+                });
+            });
+        };
+        NeuralNetwork.prototype.loadFromWeightMap = function (weightMap) {
+            var _a = this.extractParamsFromWeigthMap(weightMap), paramMappings = _a.paramMappings, params = _a.params;
+            this._paramMappings = paramMappings;
+            this._params = params;
+        };
+        NeuralNetwork.prototype.extractWeights = function (weights) {
+            var _a = this.extractParams(weights), paramMappings = _a.paramMappings, params = _a.params;
+            this._paramMappings = paramMappings;
+            this._params = params;
+        };
+        NeuralNetwork.prototype.traversePropertyPath = function (paramPath) {
+            if (!this.params) {
+                throw new Error("traversePropertyPath - model has no loaded params");
+            }
+            var result = paramPath.split('/').reduce(function (res, objProp) {
+                if (!res.nextObj.hasOwnProperty(objProp)) {
+                    throw new Error("traversePropertyPath - object does not have property " + objProp + ", for path " + paramPath);
+                }
+                return { obj: res.nextObj, objProp: objProp, nextObj: res.nextObj[objProp] };
+            }, { nextObj: this.params });
+            var obj = result.obj, objProp = result.objProp;
+            if (!obj || !objProp || !(obj[objProp] instanceof Tensor)) {
+                throw new Error("traversePropertyPath - parameter is not a tensor, for path " + paramPath);
+            }
+            return { obj: obj, objProp: objProp };
+        };
+        return NeuralNetwork;
+    }());
+
+    var FaceDetection = /** @class */ (function (_super) {
+        __extends$1(FaceDetection, _super);
+        function FaceDetection(score, relativeBox, imageDims) {
+            return _super.call(this, score, score, '', relativeBox, imageDims) || this;
+        }
+        FaceDetection.prototype.forSize = function (width, height) {
+            return _super.prototype.forSize.call(this, width, height);
+        };
+        return FaceDetection;
+    }(ObjectDetection));
+
+    // face alignment constants
+    var relX = 0.5;
+    var relY = 0.43;
+    var relScale = 0.45;
+    var FaceLandmarks = /** @class */ (function () {
+        function FaceLandmarks(relativeFaceLandmarkPositions, imgDims, shift) {
+            if (shift === void 0) { shift = new Point(0, 0); }
+            var width = imgDims.width, height = imgDims.height;
+            this._imgDims = new Dimensions(width, height);
+            this._shift = shift;
+            this._positions = relativeFaceLandmarkPositions.map(function (pt) { return pt.mul(new Point(width, height)).add(shift); });
+        }
+        Object.defineProperty(FaceLandmarks.prototype, "shift", {
+            get: function () { return new Point(this._shift.x, this._shift.y); },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(FaceLandmarks.prototype, "imageWidth", {
+            get: function () { return this._imgDims.width; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(FaceLandmarks.prototype, "imageHeight", {
+            get: function () { return this._imgDims.height; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(FaceLandmarks.prototype, "positions", {
+            get: function () { return this._positions; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(FaceLandmarks.prototype, "relativePositions", {
+            get: function () {
+                var _this = this;
+                return this._positions.map(function (pt) { return pt.sub(_this._shift).div(new Point(_this.imageWidth, _this.imageHeight)); });
+            },
+            enumerable: true,
+            configurable: true
+        });
+        FaceLandmarks.prototype.forSize = function (width, height) {
+            return new this.constructor(this.relativePositions, { width: width, height: height });
+        };
+        FaceLandmarks.prototype.shiftBy = function (x, y) {
+            return new this.constructor(this.relativePositions, this._imgDims, new Point(x, y));
+        };
+        FaceLandmarks.prototype.shiftByPoint = function (pt) {
+            return this.shiftBy(pt.x, pt.y);
+        };
+        /**
+         * Aligns the face landmarks after face detection from the relative positions of the faces
+         * bounding box, or it's current shift. This function should be used to align the face images
+         * after face detection has been performed, before they are passed to the face recognition net.
+         * This will make the computed face descriptor more accurate.
+         *
+         * @param detection (optional) The bounding box of the face or the face detection result. If
+         * no argument was passed the position of the face landmarks are assumed to be relative to
+         * it's current shift.
+         * @returns The bounding box of the aligned face.
+         */
+        FaceLandmarks.prototype.align = function (detection) {
+            if (detection) {
+                var box = detection instanceof FaceDetection
+                    ? detection.box.floor()
+                    : detection;
+                return this.shiftBy(box.x, box.y).align();
+            }
+            var centers = this.getRefPointsForAlignment();
+            var leftEyeCenter = centers[0], rightEyeCenter = centers[1], mouthCenter = centers[2];
+            var distToMouth = function (pt) { return mouthCenter.sub(pt).magnitude(); };
+            var eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2;
+            var size = Math.floor(eyeToMouthDist / relScale);
+            var refPoint = getCenterPoint(centers);
+            // TODO: pad in case rectangle is out of image bounds
+            var x = Math.floor(Math.max(0, refPoint.x - (relX * size)));
+            var y = Math.floor(Math.max(0, refPoint.y - (relY * size)));
+            return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y));
+        };
+        FaceLandmarks.prototype.getRefPointsForAlignment = function () {
+            throw new Error('getRefPointsForAlignment not implemented by base class');
+        };
+        return FaceLandmarks;
+    }());
+
+    var FaceLandmarks5 = /** @class */ (function (_super) {
+        __extends$1(FaceLandmarks5, _super);
+        function FaceLandmarks5() {
+            return _super !== null && _super.apply(this, arguments) || this;
+        }
+        FaceLandmarks5.prototype.getRefPointsForAlignment = function () {
+            var pts = this.positions;
+            return [
+                pts[0],
+                pts[1],
+                getCenterPoint([pts[3], pts[4]])
+            ];
+        };
+        return FaceLandmarks5;
+    }(FaceLandmarks));
+
+    var FaceLandmarks68 = /** @class */ (function (_super) {
+        __extends$1(FaceLandmarks68, _super);
+        function FaceLandmarks68() {
+            return _super !== null && _super.apply(this, arguments) || this;
+        }
+        FaceLandmarks68.prototype.getJawOutline = function () {
+            return this.positions.slice(0, 17);
+        };
+        FaceLandmarks68.prototype.getLeftEyeBrow = function () {
+            return this.positions.slice(17, 22);
+        };
+        FaceLandmarks68.prototype.getRightEyeBrow = function () {
+            return this.positions.slice(22, 27);
+        };
+        FaceLandmarks68.prototype.getNose = function () {
+            return this.positions.slice(27, 36);
+        };
+        FaceLandmarks68.prototype.getLeftEye = function () {
+            return this.positions.slice(36, 42);
+        };
+        FaceLandmarks68.prototype.getRightEye = function () {
+            return this.positions.slice(42, 48);
+        };
+        FaceLandmarks68.prototype.getMouth = function () {
+            return this.positions.slice(48, 68);
+        };
+        FaceLandmarks68.prototype.getRefPointsForAlignment = function () {
+            return [
+                this.getLeftEye(),
+                this.getRightEye(),
+                this.getMouth()
+            ].map(getCenterPoint);
+        };
+        return FaceLandmarks68;
+    }(FaceLandmarks));
+
+    var FaceMatch = /** @class */ (function () {
+        function FaceMatch(label, distance) {
+            this._label = label;
+            this._distance = distance;
+        }
+        Object.defineProperty(FaceMatch.prototype, "label", {
+            get: function () { return this._label; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(FaceMatch.prototype, "distance", {
+            get: function () { return this._distance; },
+            enumerable: true,
+            configurable: true
+        });
+        FaceMatch.prototype.toString = function (withDistance) {
+            if (withDistance === void 0) { withDistance = true; }
+            return "" + this.label + (withDistance ? " (" + round$1(this.distance) + ")" : '');
+        };
+        return FaceMatch;
+    }());
+
+    var LabeledFaceDescriptors = /** @class */ (function () {
+        function LabeledFaceDescriptors(label, descriptors) {
+            if (!(typeof label === 'string')) {
+                throw new Error('LabeledFaceDescriptors - constructor expected label to be a string');
+            }
+            if (!Array.isArray(descriptors) || descriptors.some(function (desc) { return !(desc instanceof Float32Array); })) {
+                throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array');
+            }
+            this._label = label;
+            this._descriptors = descriptors;
+        }
+        Object.defineProperty(LabeledFaceDescriptors.prototype, "label", {
+            get: function () { return this._label; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(LabeledFaceDescriptors.prototype, "descriptors", {
+            get: function () { return this._descriptors; },
+            enumerable: true,
+            configurable: true
+        });
+        return LabeledFaceDescriptors;
+    }());
+
+    function drawContour(ctx, points, isClosed) {
+        if (isClosed === void 0) { isClosed = false; }
+        ctx.beginPath();
+        points.slice(1).forEach(function (_a, prevIdx) {
+            var x = _a.x, y = _a.y;
+            var from = points[prevIdx];
+            ctx.moveTo(from.x, from.y);
+            ctx.lineTo(x, y);
+        });
+        if (isClosed) {
+            var from = points[points.length - 1];
+            var to = points[0];
+            if (!from || !to) {
+                return;
+            }
+            ctx.moveTo(from.x, from.y);
+            ctx.lineTo(to.x, to.y);
+        }
+        ctx.stroke();
+    }
+
+    function drawLandmarks(canvasArg, faceLandmarks, options) {
+        var canvas = resolveInput(canvasArg);
+        if (!(canvas instanceof env.getEnv().Canvas)) {
+            throw new Error('drawLandmarks - expected canvas to be of type: HTMLCanvasElement');
+        }
+        var drawOptions = Object.assign(getDefaultDrawOptions(options), (options || {}));
+        var drawLines = Object.assign({ drawLines: false }, (options || {})).drawLines;
+        var ctx = getContext2dOrThrow(canvas);
+        var lineWidth = drawOptions.lineWidth, _a = drawOptions.color, color = _a === void 0 ? 'blue' : _a;
+        var faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks];
+        faceLandmarksArray.forEach(function (landmarks) {
+            if (drawLines && landmarks instanceof FaceLandmarks68) {
+                ctx.strokeStyle = color;
+                ctx.lineWidth = lineWidth;
+                drawContour(ctx, landmarks.getJawOutline());
+                drawContour(ctx, landmarks.getLeftEyeBrow());
+                drawContour(ctx, landmarks.getRightEyeBrow());
+                drawContour(ctx, landmarks.getNose());
+                drawContour(ctx, landmarks.getLeftEye(), true);
+                drawContour(ctx, landmarks.getRightEye(), true);
+                drawContour(ctx, landmarks.getMouth(), true);
+                return;
+            }
+            // else draw points
+            var ptOffset = lineWidth / 2;
+            ctx.fillStyle = color;
+            landmarks.positions.forEach(function (pt) { return ctx.fillRect(pt.x - ptOffset, pt.y - ptOffset, lineWidth, lineWidth); });
+        });
+    }
+
+    function drawFaceExpressions(canvasArg, faceExpressions, options) {
+        var canvas = resolveInput(canvasArg);
+        if (!(canvas instanceof env.getEnv().Canvas)) {
+            throw new Error('drawFaceExpressions - expected canvas to be of type: HTMLCanvasElement');
+        }
+        var drawOptions = Object.assign(getDefaultDrawOptions(options), (options || {}));
+        var ctx = getContext2dOrThrow(canvas);
+        var _a = drawOptions.primaryColor, primaryColor = _a === void 0 ? 'red' : _a, _b = drawOptions.secondaryColor, secondaryColor = _b === void 0 ? 'blue' : _b, _c = drawOptions.primaryFontSize, primaryFontSize = _c === void 0 ? 22 : _c, _d = drawOptions.secondaryFontSize, secondaryFontSize = _d === void 0 ? 16 : _d, _e = drawOptions.minConfidence, minConfidence = _e === void 0 ? 0.2 : _e;
+        var faceExpressionsArray = Array.isArray(faceExpressions)
+            ? faceExpressions
+            : [faceExpressions];
+        faceExpressionsArray.forEach(function (_a) {
+            var position = _a.position, expressions = _a.expressions;
+            var x = position.x, y = position.y;
+            var height = position.height || 0;
+            var sorted = expressions.sort(function (a, b) { return b.probability - a.probability; });
+            var resultsToDisplay = sorted.filter(function (expr) { return expr.probability > minConfidence; });
+            var offset = (y + height + resultsToDisplay.length * primaryFontSize) > canvas.height
+                ? -(resultsToDisplay.length * primaryFontSize)
+                : 0;
+            resultsToDisplay.forEach(function (expr, i) {
+                var text = expr.expression + " (" + round$1(expr.probability) + ")";
+                drawText(ctx, x, y + height + (i * primaryFontSize) + offset, text, {
+                    textColor: i === 0 ? primaryColor : secondaryColor,
+                    fontSize: i === 0 ? primaryFontSize : secondaryFontSize
+                });
+            });
+        });
+    }
+
+    /**
+     * Extracts the image regions containing the detected faces.
+     *
+     * @param input The image that face detection has been performed on.
+     * @param detections The face detection results or face bounding boxes for that image.
+     * @returns The Canvases of the corresponding image region for each detected face.
+     */
+    function extractFaces(input, detections) {
+        return __awaiter$1(this, void 0, void 0, function () {
+            var Canvas, canvas, netInput, tensorOrCanvas, _a, ctx, boxes;
+            return __generator$1(this, function (_b) {
+                switch (_b.label) {
+                    case 0:
+                        Canvas = env.getEnv().Canvas;
+                        canvas = input;
+                        if (!!(input instanceof Canvas)) return [3 /*break*/, 5];
+                        return [4 /*yield*/, toNetInput(input)];
+                    case 1:
+                        netInput = _b.sent();
+                        if (netInput.batchSize > 1) {
+                            throw new Error('extractFaces - batchSize > 1 not supported');
+                        }
+                        tensorOrCanvas = netInput.getInput(0);
+                        if (!(tensorOrCanvas instanceof Canvas)) return [3 /*break*/, 2];
+                        _a = tensorOrCanvas;
+                        return [3 /*break*/, 4];
+                    case 2: return [4 /*yield*/, imageTensorToCanvas(tensorOrCanvas)];
+                    case 3:
+                        _a = _b.sent();
+                        _b.label = 4;
+                    case 4:
+                        canvas = _a;
+                        _b.label = 5;
+                    case 5:
+                        ctx = getContext2dOrThrow(canvas);
+                        boxes = detections.map(function (det) { return det instanceof FaceDetection
+                            ? det.forSize(canvas.width, canvas.height).box.floor()
+                            : det; })
+                            .map(function (box) { return box.clipAtImageBorders(canvas.width, canvas.height); });
+                        return [2 /*return*/, boxes.map(function (_a) {
+                                var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
+                                var faceImg = createCanvas({ width: width, height: height });
+                                getContext2dOrThrow(faceImg)
+                                    .putImageData(ctx.getImageData(x, y, width, height), 0, 0);
+                                return faceImg;
+                            })];
+                }
+            });
+        });
+    }
+
+    /**
+     * Extracts the tensors of the image regions containing the detected faces.
+     * Useful if you want to compute the face descriptors for the face images.
+     * Using this method is faster then extracting a canvas for each face and
+     * converting them to tensors individually.
+     *
+     * @param imageTensor The image tensor that face detection has been performed on.
+     * @param detections The face detection results or face bounding boxes for that image.
+     * @returns Tensors of the corresponding image region for each detected face.
+     */
+    function extractFaceTensors(imageTensor, detections) {
+        return __awaiter$1(this, void 0, void 0, function () {
+            return __generator$1(this, function (_a) {
+                if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {
+                    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D');
+                }
+                if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {
+                    throw new Error('extractFaceTensors - batchSize > 1 not supported');
+                }
+                return [2 /*return*/, tidy(function () {
+                        var _a = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0), imgHeight = _a[0], imgWidth = _a[1], numChannels = _a[2];
+                        var boxes = detections.map(function (det) { return det instanceof FaceDetection
+                            ? det.forSize(imgWidth, imgHeight).box
+                            : det; })
+                            .map(function (box) { return box.clipAtImageBorders(imgWidth, imgHeight); });
+                        var faceTensors = boxes.map(function (_a) {
+                            var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
+                            return slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels]);
+                        });
+                        return faceTensors;
+                    })];
+            });
+        });
+    }
+
+    function depthwiseSeparableConv(x, params, stride) {
+        return tidy(function () {
+            var out = separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same');
+            out = add(out, params.bias);
+            return out;
+        });
+    }
+
+    function denseBlock3(x, denseBlockParams, isFirstLayer) {
+        if (isFirstLayer === void 0) { isFirstLayer = false; }
+        return tidy(function () {
+            var out1 = relu(isFirstLayer
+                ? add(conv2d(x, denseBlockParams.conv0.filters, [2, 2], 'same'), denseBlockParams.conv0.bias)
+                : depthwiseSeparableConv(x, denseBlockParams.conv0, [2, 2]));
+            var out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);
+            var in3 = relu(add(out1, out2));
+            var out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);
+            return relu(add(out1, add(out2, out3)));
+        });
+    }
+    function denseBlock4(x, denseBlockParams, isFirstLayer, isScaleDown) {
+        if (isFirstLayer === void 0) { isFirstLayer = false; }
+        if (isScaleDown === void 0) { isScaleDown = true; }
+        return tidy(function () {
+            var out1 = relu(isFirstLayer
+                ? add(conv2d(x, denseBlockParams.conv0.filters, isScaleDown ? [2, 2] : [1, 1], 'same'), denseBlockParams.conv0.bias)
+                : depthwiseSeparableConv(x, denseBlockParams.conv0, isScaleDown ? [2, 2] : [1, 1]));
+            var out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1]);
+            var in3 = relu(add(out1, out2));
+            var out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1]);
+            var in4 = relu(add(out1, add(out2, out3)));
+            var out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1]);
+            return relu(add(out1, add(out2, add(out3, out4))));
+        });
+    }
+
+    function convLayer(x, params, padding, withRelu) {
+        if (padding === void 0) { padding = 'same'; }
+        if (withRelu === void 0) { withRelu = false; }
+        return tidy(function () {
+            var out = add(conv2d(x, params.filters, [1, 1], padding), params.bias);
+            return withRelu ? relu(out) : out;
+        });
+    }
+
+    function extractConvParamsFactory(extractWeights, paramMappings) {
+        return function (channelsIn, channelsOut, filterSize, mappedPrefix) {
+            var filters = tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
+            var bias = tensor1d(extractWeights(channelsOut));
+            paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/bias" });
+            return { filters: filters, bias: bias };
+        };
+    }
+
+    function extractFCParamsFactory(extractWeights, paramMappings) {
+        return function (channelsIn, channelsOut, mappedPrefix) {
+            var fc_weights = tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
+            var fc_bias = tensor1d(extractWeights(channelsOut));
+            paramMappings.push({ paramPath: mappedPrefix + "/weights" }, { paramPath: mappedPrefix + "/bias" });
+            return {
+                weights: fc_weights,
+                bias: fc_bias
+            };
+        };
+    }
+
+    var SeparableConvParams = /** @class */ (function () {
+        function SeparableConvParams(depthwise_filter, pointwise_filter, bias) {
+            this.depthwise_filter = depthwise_filter;
+            this.pointwise_filter = pointwise_filter;
+            this.bias = bias;
+        }
+        return SeparableConvParams;
+    }());
+
+    function extractSeparableConvParamsFactory(extractWeights, paramMappings) {
+        return function (channelsIn, channelsOut, mappedPrefix) {
+            var depthwise_filter = tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
+            var pointwise_filter = tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
+            var bias = tensor1d(extractWeights(channelsOut));
+            paramMappings.push({ paramPath: mappedPrefix + "/depthwise_filter" }, { paramPath: mappedPrefix + "/pointwise_filter" }, { paramPath: mappedPrefix + "/bias" });
+            return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
+        };
+    }
+    function loadSeparableConvParamsFactory(extractWeightEntry) {
+        return function (prefix) {
+            var depthwise_filter = extractWeightEntry(prefix + "/depthwise_filter", 4);
+            var pointwise_filter = extractWeightEntry(prefix + "/pointwise_filter", 4);
+            var bias = extractWeightEntry(prefix + "/bias", 1);
+            return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
+        };
+    }
+
+    var isNumber$1 = function (arg) { return typeof arg === 'number'; };
+    function validateConfig(config) {
+        if (!config) {
+            throw new Error("invalid config: " + config);
+        }
+        if (typeof config.withSeparableConvs !== 'boolean') {
+            throw new Error("config.withSeparableConvs has to be a boolean, have: " + config.withSeparableConvs);
+        }
+        if (!isNumber$1(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {
+            throw new Error("config.iouThreshold has to be a number between [0, 1], have: " + config.iouThreshold);
+        }
+        if (!Array.isArray(config.classes)
+            || !config.classes.length
+            || !config.classes.every(function (c) { return typeof c === 'string'; })) {
+            throw new Error("config.classes has to be an array class names: string[], have: " + JSON.stringify(config.classes));
+        }
+        if (!Array.isArray(config.anchors)
+            || !config.anchors.length
+            || !config.anchors.map(function (a) { return a || {}; }).every(function (a) { return isNumber$1(a.x) && isNumber$1(a.y); })) {
+            throw new Error("config.anchors has to be an array of { x: number, y: number }, have: " + JSON.stringify(config.anchors));
+        }
+        if (config.meanRgb && (!Array.isArray(config.meanRgb)
+            || config.meanRgb.length !== 3
+            || !config.meanRgb.every(isNumber$1))) {
+            throw new Error("config.meanRgb has to be an array of shape [number, number, number], have: " + JSON.stringify(config.meanRgb));
+        }
+    }
+    function validateTrainConfig(config) {
+        if (![config.noObjectScale, config.objectScale, config.coordScale, config.classScale].every(isNumber$1)) {
+            throw new Error("for training you have to specify noObjectScale, objectScale, coordScale, classScale parameters in your config.json file");
+        }
+        return config;
+    }
+
+    var CELL_SIZE = 32;
+    var DEFAULT_FILTER_SIZES = [
+        3, 16, 32, 64, 128, 256, 512, 1024, 1024
+    ];
+
+    function leaky(x) {
+        return tidy(function () {
+            var min$$1 = mul(x, scalar(0.10000000149011612));
+            return add(relu(sub(x, min$$1)), min$$1);
+            //return tf.maximum(x, min)
+        });
+    }
+
+    function convWithBatchNorm(x, params) {
+        return tidy(function () {
+            var out = pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
+            out = conv2d(out, params.conv.filters, [1, 1], 'valid');
+            out = sub(out, params.bn.sub);
+            out = mul(out, params.bn.truediv);
+            out = add(out, params.conv.bias);
+            return leaky(out);
+        });
+    }
+
+    function depthwiseSeparableConv$1(x, params) {
+        return tidy(function () {
+            var out = pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
+            out = separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');
+            out = add(out, params.bias);
+            return leaky(out);
+        });
+    }
+
+    function extractorsFactory(extractWeights, paramMappings) {
+        var extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
+        function extractBatchNormParams(size, mappedPrefix) {
+            var sub$$1 = tensor1d(extractWeights(size));
+            var truediv = tensor1d(extractWeights(size));
+            paramMappings.push({ paramPath: mappedPrefix + "/sub" }, { paramPath: mappedPrefix + "/truediv" });
+            return { sub: sub$$1, truediv: truediv };
+        }
+        function extractConvWithBatchNormParams(channelsIn, channelsOut, mappedPrefix) {
+            var conv = extractConvParams(channelsIn, channelsOut, 3, mappedPrefix + "/conv");
+            var bn = extractBatchNormParams(channelsOut, mappedPrefix + "/bn");
+            return { conv: conv, bn: bn };
+        }
+        var extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);
+        return {
+            extractConvParams: extractConvParams,
+            extractConvWithBatchNormParams: extractConvWithBatchNormParams,
+            extractSeparableConvParams: extractSeparableConvParams
+        };
+    }
+    function extractParams(weights, config, boxEncodingSize, filterSizes) {
+        var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
+        var paramMappings = [];
+        var _b = extractorsFactory(extractWeights, paramMappings), extractConvParams = _b.extractConvParams, extractConvWithBatchNormParams = _b.extractConvWithBatchNormParams, extractSeparableConvParams = _b.extractSeparableConvParams;
+        var params;
+        if (config.withSeparableConvs) {
+            var s0 = filterSizes[0], s1 = filterSizes[1], s2 = filterSizes[2], s3 = filterSizes[3], s4 = filterSizes[4], s5 = filterSizes[5], s6 = filterSizes[6], s7 = filterSizes[7], s8 = filterSizes[8];
+            var conv0 = config.isFirstLayerConv2d
+                ? extractConvParams(s0, s1, 3, 'conv0')
+                : extractSeparableConvParams(s0, s1, 'conv0');
+            var conv1 = extractSeparableConvParams(s1, s2, 'conv1');
+            var conv2 = extractSeparableConvParams(s2, s3, 'conv2');
+            var conv3 = extractSeparableConvParams(s3, s4, 'conv3');
+            var conv4 = extractSeparableConvParams(s4, s5, 'conv4');
+            var conv5 = extractSeparableConvParams(s5, s6, 'conv5');
+            var conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;
+            var conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;
+            var conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');
+            params = { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3, conv4: conv4, conv5: conv5, conv6: conv6, conv7: conv7, conv8: conv8 };
+        }
+        else {
+            var s0 = filterSizes[0], s1 = filterSizes[1], s2 = filterSizes[2], s3 = filterSizes[3], s4 = filterSizes[4], s5 = filterSizes[5], s6 = filterSizes[6], s7 = filterSizes[7], s8 = filterSizes[8];
+            var conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');
+            var conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');
+            var conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');
+            var conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');
+            var conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');
+            var conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');
+            var conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');
+            var conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');
+            var conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');
+            params = { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3, conv4: conv4, conv5: conv5, conv6: conv6, conv7: conv7, conv8: conv8 };
+        }
+        if (getRemainingWeights().length !== 0) {
+            throw new Error("weights remaing after extract: " + getRemainingWeights().length);
+        }
+        return { params: params, paramMappings: paramMappings };
+    }
+
+    function extractorsFactory$1(weightMap, paramMappings) {
+        var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
+        function extractBatchNormParams(prefix) {
+            var sub = extractWeightEntry(prefix + "/sub", 1);
+            var truediv = extractWeightEntry(prefix + "/truediv", 1);
+            return { sub: sub, truediv: truediv };
+        }
+        function extractConvParams(prefix) {
+            var filters = extractWeightEntry(prefix + "/filters", 4);
+            var bias = extractWeightEntry(prefix + "/bias", 1);
+            return { filters: filters, bias: bias };
+        }
+        function extractConvWithBatchNormParams(prefix) {
+            var conv = extractConvParams(prefix + "/conv");
+            var bn = extractBatchNormParams(prefix + "/bn");
+            return { conv: conv, bn: bn };
+        }
+        var extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);
+        return {
+            extractConvParams: extractConvParams,
+            extractConvWithBatchNormParams: extractConvWithBatchNormParams,
+            extractSeparableConvParams: extractSeparableConvParams
+        };
+    }
+    function extractParamsFromWeigthMap(weightMap, config) {
+        var paramMappings = [];
+        var _a = extractorsFactory$1(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractConvWithBatchNormParams = _a.extractConvWithBatchNormParams, extractSeparableConvParams = _a.extractSeparableConvParams;
+        var params;
+        if (config.withSeparableConvs) {
+            var numFilters = (config.filterSizes && config.filterSizes.length || 9);
+            params = {
+                conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),
+                conv1: extractSeparableConvParams('conv1'),
+                conv2: extractSeparableConvParams('conv2'),
+                conv3: extractSeparableConvParams('conv3'),
+                conv4: extractSeparableConvParams('conv4'),
+                conv5: extractSeparableConvParams('conv5'),
+                conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,
+                conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,
+                conv8: extractConvParams('conv8')
+            };
+        }
+        else {
+            params = {
+                conv0: extractConvWithBatchNormParams('conv0'),
+                conv1: extractConvWithBatchNormParams('conv1'),
+                conv2: extractConvWithBatchNormParams('conv2'),
+                conv3: extractConvWithBatchNormParams('conv3'),
+                conv4: extractConvWithBatchNormParams('conv4'),
+                conv5: extractConvWithBatchNormParams('conv5'),
+                conv6: extractConvWithBatchNormParams('conv6'),
+                conv7: extractConvWithBatchNormParams('conv7'),
+                conv8: extractConvParams('conv8')
+            };
+        }
+        disposeUnusedWeightTensors(weightMap, paramMappings);
+        return { params: params, paramMappings: paramMappings };
+    }
+
+    var TinyYolov2SizeType;
+    (function (TinyYolov2SizeType) {
+        TinyYolov2SizeType[TinyYolov2SizeType["XS"] = 224] = "XS";
+        TinyYolov2SizeType[TinyYolov2SizeType["SM"] = 320] = "SM";
+        TinyYolov2SizeType[TinyYolov2SizeType["MD"] = 416] = "MD";
+        TinyYolov2SizeType[TinyYolov2SizeType["LG"] = 608] = "LG";
+    })(TinyYolov2SizeType || (TinyYolov2SizeType = {}));
+    var TinyYolov2Options = /** @class */ (function () {
+        function TinyYolov2Options(_a) {
+            var _b = _a === void 0 ? {} : _a, inputSize = _b.inputSize, scoreThreshold = _b.scoreThreshold;
+            this._name = 'TinyYolov2Options';
+            this._inputSize = inputSize || 416;
+            this._scoreThreshold = scoreThreshold || 0.5;
+            if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {
+                throw new Error(this._name + " - expected inputSize to be a number divisible by 32");
+            }
+            if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {
+                throw new Error(this._name + " - expected scoreThreshold to be a number between 0 and 1");
+            }
+        }
+        Object.defineProperty(TinyYolov2Options.prototype, "inputSize", {
+            get: function () { return this._inputSize; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2Options.prototype, "scoreThreshold", {
+            get: function () { return this._scoreThreshold; },
+            enumerable: true,
+            configurable: true
+        });
+        return TinyYolov2Options;
+    }());
+
+    var TinyYolov2 = /** @class */ (function (_super) {
+        __extends$1(TinyYolov2, _super);
+        function TinyYolov2(config) {
+            var _this = _super.call(this, 'TinyYolov2') || this;
+            validateConfig(config);
+            _this._config = config;
+            return _this;
+        }
+        Object.defineProperty(TinyYolov2.prototype, "config", {
+            get: function () {
+                return this._config;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2.prototype, "withClassScores", {
+            get: function () {
+                return this.config.withClassScores || this.config.classes.length > 1;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2.prototype, "boxEncodingSize", {
+            get: function () {
+                return 5 + (this.withClassScores ? this.config.classes.length : 0);
+            },
+            enumerable: true,
+            configurable: true
+        });
+        TinyYolov2.prototype.runTinyYolov2 = function (x, params) {
+            var out = convWithBatchNorm(x, params.conv0);
+            out = maxPool(out, [2, 2], [2, 2], 'same');
+            out = convWithBatchNorm(out, params.conv1);
+            out = maxPool(out, [2, 2], [2, 2], 'same');
+            out = convWithBatchNorm(out, params.conv2);
+            out = maxPool(out, [2, 2], [2, 2], 'same');
+            out = convWithBatchNorm(out, params.conv3);
+            out = maxPool(out, [2, 2], [2, 2], 'same');
+            out = convWithBatchNorm(out, params.conv4);
+            out = maxPool(out, [2, 2], [2, 2], 'same');
+            out = convWithBatchNorm(out, params.conv5);
+            out = maxPool(out, [2, 2], [1, 1], 'same');
+            out = convWithBatchNorm(out, params.conv6);
+            out = convWithBatchNorm(out, params.conv7);
+            return convLayer(out, params.conv8, 'valid', false);
+        };
+        TinyYolov2.prototype.runMobilenet = function (x, params) {
+            var out = this.config.isFirstLayerConv2d
+                ? leaky(convLayer(x, params.conv0, 'valid', false))
+                : depthwiseSeparableConv$1(x, params.conv0);
+            out = maxPool(out, [2, 2], [2, 2], 'same');
+            out = depthwiseSeparableConv$1(out, params.conv1);
+            out = maxPool(out, [2, 2], [2, 2], 'same');
+            out = depthwiseSeparableConv$1(out, params.conv2);
+            out = maxPool(out, [2, 2], [2, 2], 'same');
+            out = depthwiseSeparableConv$1(out, params.conv3);
+            out = maxPool(out, [2, 2], [2, 2], 'same');
+            out = depthwiseSeparableConv$1(out, params.conv4);
+            out = maxPool(out, [2, 2], [2, 2], 'same');
+            out = depthwiseSeparableConv$1(out, params.conv5);
+            out = maxPool(out, [2, 2], [1, 1], 'same');
+            out = params.conv6 ? depthwiseSeparableConv$1(out, params.conv6) : out;
+            out = params.conv7 ? depthwiseSeparableConv$1(out, params.conv7) : out;
+            return convLayer(out, params.conv8, 'valid', false);
+        };
+        TinyYolov2.prototype.forwardInput = function (input, inputSize) {
+            var _this = this;
+            var params = this.params;
+            if (!params) {
+                throw new Error('TinyYolov2 - load model before inference');
+            }
+            return tidy(function () {
+                var batchTensor = input.toBatchTensor(inputSize, false).toFloat();
+                batchTensor = _this.config.meanRgb
+                    ? normalize(batchTensor, _this.config.meanRgb)
+                    : batchTensor;
+                batchTensor = batchTensor.div(scalar(256));
+                return _this.config.withSeparableConvs
+                    ? _this.runMobilenet(batchTensor, params)
+                    : _this.runTinyYolov2(batchTensor, params);
+            });
+        };
+        TinyYolov2.prototype.forward = function (input, inputSize) {
+            return __awaiter$1(this, void 0, void 0, function () {
+                var _a;
+                return __generator$1(this, function (_b) {
+                    switch (_b.label) {
+                        case 0:
+                            _a = this.forwardInput;
+                            return [4 /*yield*/, toNetInput(input)];
+                        case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(), inputSize])];
+                        case 2: return [2 /*return*/, _b.sent()];
+                    }
+                });
+            });
+        };
+        TinyYolov2.prototype.detect = function (input, forwardParams) {
+            if (forwardParams === void 0) { forwardParams = {}; }
+            return __awaiter$1(this, void 0, void 0, function () {
+                var _this = this;
+                var _a, inputSize, scoreThreshold, netInput, out, out0, inputDimensions, results, boxes, scores, classScores, classNames, indices, detections;
+                return __generator$1(this, function (_b) {
+                    switch (_b.label) {
+                        case 0:
+                            _a = new TinyYolov2Options(forwardParams), inputSize = _a.inputSize, scoreThreshold = _a.scoreThreshold;
+                            return [4 /*yield*/, toNetInput(input)];
+                        case 1:
+                            netInput = _b.sent();
+                            return [4 /*yield*/, this.forwardInput(netInput, inputSize)];
+                        case 2:
+                            out = _b.sent();
+                            out0 = tidy(function () { return unstack(out)[0].expandDims(); });
+                            inputDimensions = {
+                                width: netInput.getInputWidth(0),
+                                height: netInput.getInputHeight(0)
+                            };
+                            results = this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);
+                            out.dispose();
+                            out0.dispose();
+                            boxes = results.map(function (res) { return res.box; });
+                            scores = results.map(function (res) { return res.score; });
+                            classScores = results.map(function (res) { return res.classScore; });
+                            classNames = results.map(function (res) { return _this.config.classes[res.label]; });
+                            indices = nonMaxSuppression$1(boxes.map(function (box) { return box.rescale(inputSize); }), scores, this.config.iouThreshold, true);
+                            detections = indices.map(function (idx) {
+                                return new ObjectDetection(scores[idx], classScores[idx], classNames[idx], boxes[idx], inputDimensions);
+                            });
+                            return [2 /*return*/, detections];
+                    }
+                });
+            });
+        };
+        TinyYolov2.prototype.getDefaultModelName = function () {
+            return '';
+        };
+        TinyYolov2.prototype.extractParamsFromWeigthMap = function (weightMap) {
+            return extractParamsFromWeigthMap(weightMap, this.config);
+        };
+        TinyYolov2.prototype.extractParams = function (weights) {
+            var filterSizes = this.config.filterSizes || DEFAULT_FILTER_SIZES;
+            var numFilters = filterSizes ? filterSizes.length : undefined;
+            if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {
+                throw new Error("TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found " + numFilters + " filterSizes in config");
+            }
+            return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);
+        };
+        TinyYolov2.prototype.extractBoxes = function (outputTensor, inputBlobDimensions, scoreThreshold) {
+            var _this = this;
+            var width = inputBlobDimensions.width, height = inputBlobDimensions.height;
+            var inputSize = Math.max(width, height);
+            var correctionFactorX = inputSize / width;
+            var correctionFactorY = inputSize / height;
+            var numCells = outputTensor.shape[1];
+            var numBoxes = this.config.anchors.length;
+            var _a = tidy(function () {
+                var reshaped = outputTensor.reshape([numCells, numCells, numBoxes, _this.boxEncodingSize]);
+                var boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);
+                var scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);
+                var classScores = _this.withClassScores
+                    ? softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, _this.config.classes.length]), 3)
+                    : scalar(0);
+                return [boxes, scores, classScores];
+            }), boxesTensor = _a[0], scoresTensor = _a[1], classScoresTensor = _a[2];
+            var results = [];
+            for (var row = 0; row < numCells; row++) {
+                for (var col = 0; col < numCells; col++) {
+                    for (var anchor = 0; anchor < numBoxes; anchor++) {
+                        var score = sigmoid$1(scoresTensor.get(row, col, anchor, 0));
+                        if (!scoreThreshold || score > scoreThreshold) {
+                            var ctX = ((col + sigmoid$1(boxesTensor.get(row, col, anchor, 0))) / numCells) * correctionFactorX;
+                            var ctY = ((row + sigmoid$1(boxesTensor.get(row, col, anchor, 1))) / numCells) * correctionFactorY;
+                            var width_1 = ((Math.exp(boxesTensor.get(row, col, anchor, 2)) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;
+                            var height_1 = ((Math.exp(boxesTensor.get(row, col, anchor, 3)) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;
+                            var x = (ctX - (width_1 / 2));
+                            var y = (ctY - (height_1 / 2));
+                            var pos = { row: row, col: col, anchor: anchor };
+                            var _b = this.withClassScores
+                                ? this.extractPredictedClass(classScoresTensor, pos)
+                                : { classScore: 1, label: 0 }, classScore = _b.classScore, label = _b.label;
+                            results.push(__assign$1({ box: new BoundingBox(x, y, x + width_1, y + height_1), score: score, classScore: score * classScore, label: label }, pos));
+                        }
+                    }
+                }
+            }
+            boxesTensor.dispose();
+            scoresTensor.dispose();
+            classScoresTensor.dispose();
+            return results;
+        };
+        TinyYolov2.prototype.extractPredictedClass = function (classesTensor, pos) {
+            var row = pos.row, col = pos.col, anchor = pos.anchor;
+            return Array(this.config.classes.length).fill(0)
+                .map(function (_, i) { return classesTensor.get(row, col, anchor, i); })
+                .map(function (classScore, label) { return ({
+                classScore: classScore,
+                label: label
+            }); })
+                .reduce(function (max$$1, curr) { return max$$1.classScore > curr.classScore ? max$$1 : curr; });
+        };
+        return TinyYolov2;
+    }(NeuralNetwork));
+
+    var TinyYolov2LossFunction = /** @class */ (function () {
+        function TinyYolov2LossFunction(outputTensor, groundTruth, predictedBoxes, reshapedImgDims, config) {
+            this._config = config;
+            this._reshapedImgDims = new Dimensions(reshapedImgDims.width, reshapedImgDims.height);
+            this._outputTensor = outputTensor;
+            this._predictedBoxes = predictedBoxes;
+            this.validateGroundTruthBoxes(groundTruth);
+            this._groundTruth = this.assignGroundTruthToAnchors(groundTruth);
+            var groundTruthMask = this.createGroundTruthMask();
+            var _a = this.createCoordAndScoreMasks(), coordBoxOffsetMask = _a.coordBoxOffsetMask, coordBoxSizeMask = _a.coordBoxSizeMask, scoreMask = _a.scoreMask;
+            this.noObjectLossMask = tidy(function () { return mul(scoreMask, sub(scalar(1), groundTruthMask)); });
+            this.objectLossMask = tidy(function () { return mul(scoreMask, groundTruthMask); });
+            this.coordBoxOffsetMask = tidy(function () { return mul(coordBoxOffsetMask, groundTruthMask); });
+            this.coordBoxSizeMask = tidy(function () { return mul(coordBoxSizeMask, groundTruthMask); });
+            var classScoresMask = tidy(function () { return sub(scalar(1), coordBoxOffsetMask.add(coordBoxSizeMask).add(scoreMask)); });
+            this.groundTruthClassScoresMask = tidy(function () { return mul(classScoresMask, groundTruthMask); });
+        }
+        Object.defineProperty(TinyYolov2LossFunction.prototype, "config", {
+            get: function () {
+                return this._config;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2LossFunction.prototype, "reshapedImgDims", {
+            get: function () {
+                return this._reshapedImgDims;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2LossFunction.prototype, "outputTensor", {
+            get: function () {
+                return this._outputTensor;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2LossFunction.prototype, "groundTruth", {
+            get: function () {
+                return this._groundTruth;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2LossFunction.prototype, "predictedBoxes", {
+            get: function () {
+                return this._predictedBoxes;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2LossFunction.prototype, "inputSize", {
+            get: function () {
+                return Math.max(this.reshapedImgDims.width, this.reshapedImgDims.height);
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2LossFunction.prototype, "withClassScores", {
+            get: function () {
+                return this._config.withClassScores || this._config.classes.length > 1;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2LossFunction.prototype, "boxEncodingSize", {
+            get: function () {
+                return 5 + (this.withClassScores ? this._config.classes.length : 0);
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2LossFunction.prototype, "anchors", {
+            get: function () {
+                return this._config.anchors;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2LossFunction.prototype, "numBoxes", {
+            get: function () {
+                return this.anchors.length;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2LossFunction.prototype, "numCells", {
+            get: function () {
+                return this.inputSize / CELL_SIZE;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2LossFunction.prototype, "gridCellEncodingSize", {
+            get: function () {
+                return this.boxEncodingSize * this.numBoxes;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        TinyYolov2LossFunction.prototype.toOutputTensorShape = function (tensor$$1) {
+            var _this = this;
+            return tidy(function () { return tensor$$1.reshape([1, _this.numCells, _this.numCells, _this.gridCellEncodingSize]); });
+        };
+        TinyYolov2LossFunction.prototype.computeLoss = function () {
+            var _this = this;
+            return tidy(function () {
+                var noObjectLoss = _this.computeNoObjectLoss();
+                var objectLoss = _this.computeObjectLoss();
+                var coordLoss = _this.computeCoordLoss();
+                var classLoss = _this.withClassScores
+                    ? _this.computeClassLoss()
+                    : scalar(0);
+                var totalLoss = tidy(function () { return noObjectLoss.add(objectLoss).add(coordLoss).add(classLoss); });
+                return {
+                    noObjectLoss: noObjectLoss,
+                    objectLoss: objectLoss,
+                    coordLoss: coordLoss,
+                    classLoss: classLoss,
+                    totalLoss: totalLoss
+                };
+            });
+        };
+        TinyYolov2LossFunction.prototype.computeNoObjectLoss = function () {
+            var _this = this;
+            return tidy(function () {
+                return _this.computeLossTerm(_this.config.noObjectScale, _this.toOutputTensorShape(_this.noObjectLossMask), sigmoid(_this.outputTensor));
+            });
+        };
+        TinyYolov2LossFunction.prototype.computeObjectLoss = function () {
+            var _this = this;
+            return tidy(function () {
+                return _this.computeLossTerm(_this.config.objectScale, _this.toOutputTensorShape(_this.objectLossMask), sub(_this.toOutputTensorShape(_this.computeIous()), sigmoid(_this.outputTensor)));
+            });
+        };
+        TinyYolov2LossFunction.prototype.computeClassLoss = function () {
+            var _this = this;
+            return tidy(function () {
+                var classLossTensor = tidy(function () {
+                    var predClassScores = mul(softmax(_this.outputTensor.reshape([_this.numCells, _this.numCells, _this.numBoxes, _this.boxEncodingSize]), 3), _this.groundTruthClassScoresMask);
+                    var gtClassScores = _this.createOneHotClassScoreMask();
+                    return sub(gtClassScores, predClassScores);
+                });
+                return _this.computeLossTerm(_this.config.classScale, scalar(1), classLossTensor);
+            });
+        };
+        TinyYolov2LossFunction.prototype.computeCoordLoss = function () {
+            var _this = this;
+            return tidy(function () {
+                return _this.computeLossTerm(_this.config.coordScale, scalar(1), add(_this.computeCoordBoxOffsetError(), _this.computeCoordBoxSizeError()));
+            });
+        };
+        TinyYolov2LossFunction.prototype.computeCoordBoxOffsetError = function () {
+            var _this = this;
+            return tidy(function () {
+                var mask = _this.toOutputTensorShape(_this.coordBoxOffsetMask);
+                var gtBoxOffsets = mul(mask, _this.toOutputTensorShape(_this.computeCoordBoxOffsets()));
+                var predBoxOffsets = mul(mask, sigmoid(_this.outputTensor));
+                return sub(gtBoxOffsets, predBoxOffsets);
+            });
+        };
+        TinyYolov2LossFunction.prototype.computeCoordBoxSizeError = function () {
+            var _this = this;
+            return tidy(function () {
+                var mask = _this.toOutputTensorShape(_this.coordBoxSizeMask);
+                var gtBoxSizes = mul(mask, _this.toOutputTensorShape(_this.computeCoordBoxSizes()));
+                var predBoxSizes = mul(mask, _this.outputTensor);
+                return sub(gtBoxSizes, predBoxSizes);
+            });
+        };
+        TinyYolov2LossFunction.prototype.computeLossTerm = function (scale, mask, lossTensor) {
+            var _this = this;
+            return tidy(function () { return mul(scalar(scale), _this.squaredSumOverMask(mask, lossTensor)); });
+        };
+        TinyYolov2LossFunction.prototype.squaredSumOverMask = function (mask, lossTensor) {
+            return tidy(function () { return sum$1(square(mul(mask, lossTensor))); });
+        };
+        TinyYolov2LossFunction.prototype.validateGroundTruthBoxes = function (groundTruth) {
+            var _this = this;
+            groundTruth.forEach(function (_a) {
+                var x = _a.x, y = _a.y, width = _a.width, height = _a.height, label = _a.label;
+                if (typeof label !== 'number' || label < 0 || label > (_this.config.classes.length - 1)) {
+                    throw new Error("invalid ground truth data, expected label to be a number in [0, " + (_this.config.classes.length - 1) + "]");
+                }
+                if (x < 0 || x > 1 || y < 0 || y > 1 || width < 0 || (x + width) > 1 || height < 0 || (y + height) > 1) {
+                    throw new Error("invalid ground truth data, box is out of image boundaries " + JSON.stringify({ x: x, y: y, width: width, height: height }));
+                }
+            });
+        };
+        TinyYolov2LossFunction.prototype.assignGroundTruthToAnchors = function (groundTruth) {
+            var _this = this;
+            var groundTruthBoxes = groundTruth
+                .map(function (_a) {
+                var x = _a.x, y = _a.y, width = _a.width, height = _a.height, label = _a.label;
+                return ({
+                    box: new Rect(x, y, width, height),
+                    label: label
+                });
+            });
+            return groundTruthBoxes.map(function (_a) {
+                var box = _a.box, label = _a.label;
+                var _b = box.rescale(_this.reshapedImgDims), left = _b.left, top = _b.top, bottom = _b.bottom, right = _b.right, x = _b.x, y = _b.y, width = _b.width, height = _b.height;
+                var ctX = left + (width / 2);
+                var ctY = top + (height / 2);
+                var col = Math.floor((ctX / _this.inputSize) * _this.numCells);
+                var row = Math.floor((ctY / _this.inputSize) * _this.numCells);
+                var anchorsByIou = _this.anchors.map(function (anchor, idx) { return ({
+                    idx: idx,
+                    iou: iou(new BoundingBox(0, 0, anchor.x * CELL_SIZE, anchor.y * CELL_SIZE), new BoundingBox(0, 0, width, height))
+                }); }).sort(function (a1, a2) { return a2.iou - a1.iou; });
+                var anchor = anchorsByIou[0].idx;
+                return { row: row, col: col, anchor: anchor, box: box, label: label };
+            });
+        };
+        TinyYolov2LossFunction.prototype.createGroundTruthMask = function () {
+            var _this = this;
+            var mask = zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize]);
+            var buf = mask.buffer();
+            this.groundTruth.forEach(function (_a) {
+                var row = _a.row, col = _a.col, anchor = _a.anchor;
+                for (var i = 0; i < _this.boxEncodingSize; i++) {
+                    buf.set(1, row, col, anchor, i);
+                }
+            });
+            return mask;
+        };
+        TinyYolov2LossFunction.prototype.createCoordAndScoreMasks = function () {
+            var _this = this;
+            return tidy(function () {
+                var coordBoxOffsetMask = zeros([_this.numCells, _this.numCells, _this.numBoxes, _this.boxEncodingSize]);
+                var coordBoxSizeMask = zeros([_this.numCells, _this.numCells, _this.numBoxes, _this.boxEncodingSize]);
+                var scoreMask = zeros([_this.numCells, _this.numCells, _this.numBoxes, _this.boxEncodingSize]);
+                var coordBoxOffsetBuf = coordBoxOffsetMask.buffer();
+                var coordBoxSizeBuf = coordBoxSizeMask.buffer();
+                var scoreBuf = scoreMask.buffer();
+                for (var row = 0; row < _this.numCells; row++) {
+                    for (var col = 0; col < _this.numCells; col++) {
+                        for (var anchor = 0; anchor < _this.numBoxes; anchor++) {
+                            coordBoxOffsetBuf.set(1, row, col, anchor, 0);
+                            coordBoxOffsetBuf.set(1, row, col, anchor, 1);
+                            coordBoxSizeBuf.set(1, row, col, anchor, 2);
+                            coordBoxSizeBuf.set(1, row, col, anchor, 3);
+                            scoreBuf.set(1, row, col, anchor, 4);
+                        }
+                    }
+                }
+                return { coordBoxOffsetMask: coordBoxOffsetMask, coordBoxSizeMask: coordBoxSizeMask, scoreMask: scoreMask };
+            });
+        };
+        TinyYolov2LossFunction.prototype.createOneHotClassScoreMask = function () {
+            var mask = zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize]);
+            var buf = mask.buffer();
+            var classValuesOffset = 5;
+            this.groundTruth.forEach(function (_a) {
+                var row = _a.row, col = _a.col, anchor = _a.anchor, label = _a.label;
+                buf.set(1, row, col, anchor, classValuesOffset + label);
+            });
+            return mask;
+        };
+        TinyYolov2LossFunction.prototype.computeIous = function () {
+            var _this = this;
+            var isSameAnchor = function (p1) { return function (p2) {
+                return p1.row === p2.row
+                    && p1.col === p2.col
+                    && p1.anchor === p2.anchor;
+            }; };
+            var ious = zeros([this.numCells, this.numCells, this.gridCellEncodingSize]);
+            var buf = ious.buffer();
+            this.groundTruth.forEach(function (_a) {
+                var row = _a.row, col = _a.col, anchor = _a.anchor, box = _a.box;
+                var predBox = _this.predictedBoxes.find(isSameAnchor({ row: row, col: col, anchor: anchor }));
+                if (!predBox) {
+                    throw new Error("no output box found for: row " + row + ", col " + col + ", anchor " + anchor);
+                }
+                var boxIou = iou(box.rescale(_this.reshapedImgDims), predBox.box.rescale(_this.reshapedImgDims));
+                var anchorOffset = _this.boxEncodingSize * anchor;
+                var scoreValueOffset = 4;
+                buf.set(boxIou, row, col, anchorOffset + scoreValueOffset);
+            });
+            return ious;
+        };
+        TinyYolov2LossFunction.prototype.computeCoordBoxOffsets = function () {
+            var _this = this;
+            var offsets = zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize]);
+            var buf = offsets.buffer();
+            this.groundTruth.forEach(function (_a) {
+                var row = _a.row, col = _a.col, anchor = _a.anchor, box = _a.box;
+                var _b = box.rescale(_this.reshapedImgDims), left = _b.left, top = _b.top, right = _b.right, bottom = _b.bottom;
+                var centerX = (left + right) / 2;
+                var centerY = (top + bottom) / 2;
+                var dCenterX = centerX - (col * CELL_SIZE);
+                var dCenterY = centerY - (row * CELL_SIZE);
+                // inverseSigmoid(0) === -Infinity, inverseSigmoid(1) === Infinity
+                //const dx = inverseSigmoid(Math.min(0.999, Math.max(0.001, dCenterX / CELL_SIZE)))
+                //const dy = inverseSigmoid(Math.min(0.999, Math.max(0.001, dCenterY / CELL_SIZE)))
+                var dx = dCenterX / CELL_SIZE;
+                var dy = dCenterY / CELL_SIZE;
+                buf.set(dx, row, col, anchor, 0);
+                buf.set(dy, row, col, anchor, 1);
+            });
+            return offsets;
+        };
+        TinyYolov2LossFunction.prototype.computeCoordBoxSizes = function () {
+            var _this = this;
+            var sizes = zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize]);
+            var buf = sizes.buffer();
+            this.groundTruth.forEach(function (_a) {
+                var row = _a.row, col = _a.col, anchor = _a.anchor, box = _a.box;
+                var _b = box.rescale(_this.reshapedImgDims), width = _b.width, height = _b.height;
+                var dw = Math.log(width / (_this.anchors[anchor].x * CELL_SIZE));
+                var dh = Math.log(height / (_this.anchors[anchor].y * CELL_SIZE));
+                buf.set(dw, row, col, anchor, 2);
+                buf.set(dh, row, col, anchor, 3);
+            });
+            return sizes;
+        };
+        return TinyYolov2LossFunction;
+    }());
+
+    function getDefaultBackwardOptions(options) {
+        return Object.assign({}, {
+            minBoxSize: CELL_SIZE
+        }, options);
+    }
+
+    var TinyYolov2Trainable = /** @class */ (function (_super) {
+        __extends$1(TinyYolov2Trainable, _super);
+        function TinyYolov2Trainable(trainableConfig, optimizer) {
+            var _this = _super.call(this, trainableConfig) || this;
+            _this._trainableConfig = validateTrainConfig(trainableConfig);
+            _this._optimizer = optimizer;
+            return _this;
+        }
+        Object.defineProperty(TinyYolov2Trainable.prototype, "trainableConfig", {
+            get: function () {
+                return this._trainableConfig;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2Trainable.prototype, "optimizer", {
+            get: function () {
+                return this._optimizer;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        TinyYolov2Trainable.prototype.backward = function (img, groundTruth, inputSize, options) {
+            if (options === void 0) { options = {}; }
+            return __awaiter$1(this, void 0, void 0, function () {
+                var _this = this;
+                var _a, minBoxSize, reportLosses, reshapedImgDims, filteredGroundTruthBoxes, netInput, loss;
+                return __generator$1(this, function (_b) {
+                    switch (_b.label) {
+                        case 0:
+                            _a = getDefaultBackwardOptions(options), minBoxSize = _a.minBoxSize, reportLosses = _a.reportLosses;
+                            reshapedImgDims = computeReshapedDimensions(getMediaDimensions(img), inputSize);
+                            filteredGroundTruthBoxes = this.filterGroundTruthBoxes(groundTruth, reshapedImgDims, minBoxSize);
+                            if (!filteredGroundTruthBoxes.length) {
+                                return [2 /*return*/, null];
+                            }
+                            return [4 /*yield*/, toNetInput(imageToSquare(img, inputSize))];
+                        case 1:
+                            netInput = _b.sent();
+                            loss = this.optimizer.minimize(function () {
+                                var _a = _this.computeLoss(_this.forwardInput(netInput, inputSize), filteredGroundTruthBoxes, reshapedImgDims), noObjectLoss = _a.noObjectLoss, objectLoss = _a.objectLoss, coordLoss = _a.coordLoss, classLoss = _a.classLoss, totalLoss = _a.totalLoss;
+                                if (reportLosses) {
+                                    var losses = {
+                                        totalLoss: totalLoss.dataSync()[0],
+                                        noObjectLoss: noObjectLoss.dataSync()[0],
+                                        objectLoss: objectLoss.dataSync()[0],
+                                        coordLoss: coordLoss.dataSync()[0],
+                                        classLoss: classLoss.dataSync()[0]
+                                    };
+                                    var report = {
+                                        losses: losses,
+                                        numBoxes: filteredGroundTruthBoxes.length,
+                                        inputSize: inputSize
+                                    };
+                                    reportLosses(report);
+                                }
+                                return totalLoss;
+                            }, true);
+                            return [2 /*return*/, loss];
+                    }
+                });
+            });
+        };
+        TinyYolov2Trainable.prototype.computeLoss = function (outputTensor, groundTruth, reshapedImgDims) {
+            var config = validateTrainConfig(this.config);
+            var inputSize = Math.max(reshapedImgDims.width, reshapedImgDims.height);
+            if (!inputSize) {
+                throw new Error("computeLoss - invalid inputSize: " + inputSize);
+            }
+            var predictedBoxes = this.extractBoxes(outputTensor, reshapedImgDims);
+            return tidy(function () {
+                var lossFunction = new TinyYolov2LossFunction(outputTensor, groundTruth, predictedBoxes, reshapedImgDims, config);
+                return lossFunction.computeLoss();
+            });
+        };
+        TinyYolov2Trainable.prototype.filterGroundTruthBoxes = function (groundTruth, imgDims, minBoxSize) {
+            var imgHeight = imgDims.height, imgWidth = imgDims.width;
+            return groundTruth.filter(function (_a) {
+                var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
+                var box = (new Rect(x, y, width, height))
+                    .rescale({ height: imgHeight, width: imgWidth });
+                var isTooTiny = box.width < minBoxSize || box.height < minBoxSize;
+                return !isTooTiny;
+            });
+        };
+        TinyYolov2Trainable.prototype.load = function (weightsOrUrl) {
+            return __awaiter$1(this, void 0, void 0, function () {
+                return __generator$1(this, function (_a) {
+                    switch (_a.label) {
+                        case 0: return [4 /*yield*/, _super.prototype.load.call(this, weightsOrUrl)];
+                        case 1:
+                            _a.sent();
+                            this.variable();
+                            return [2 /*return*/];
+                    }
+                });
+            });
+        };
+        return TinyYolov2Trainable;
+    }(TinyYolov2));
+
+    function extractorsFactory$2(extractWeights, paramMappings) {
+        function extractSeparableConvParams(channelsIn, channelsOut, mappedPrefix) {
+            var depthwise_filter = tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
+            var pointwise_filter = tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
+            var bias = tensor1d(extractWeights(channelsOut));
+            paramMappings.push({ paramPath: mappedPrefix + "/depthwise_filter" }, { paramPath: mappedPrefix + "/pointwise_filter" }, { paramPath: mappedPrefix + "/bias" });
+            return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
+        }
+        var extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
+        function extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer) {
+            if (isFirstLayer === void 0) { isFirstLayer = false; }
+            var conv0 = isFirstLayer
+                ? extractConvParams(channelsIn, channelsOut, 3, mappedPrefix + "/conv0")
+                : extractSeparableConvParams(channelsIn, channelsOut, mappedPrefix + "/conv0");
+            var conv1 = extractSeparableConvParams(channelsOut, channelsOut, mappedPrefix + "/conv1");
+            var conv2 = extractSeparableConvParams(channelsOut, channelsOut, mappedPrefix + "/conv2");
+            return { conv0: conv0, conv1: conv1, conv2: conv2 };
+        }
+        function extractDenseBlock4Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer) {
+            if (isFirstLayer === void 0) { isFirstLayer = false; }
+            var _a = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer), conv0 = _a.conv0, conv1 = _a.conv1, conv2 = _a.conv2;
+            var conv3 = extractSeparableConvParams(channelsOut, channelsOut, mappedPrefix + "/conv3");
+            return { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3 };
+        }
+        return {
+            extractDenseBlock3Params: extractDenseBlock3Params,
+            extractDenseBlock4Params: extractDenseBlock4Params
+        };
+    }
+
+    function extractParams$1(weights) {
+        var paramMappings = [];
+        var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
+        var extractDenseBlock4Params = extractorsFactory$2(extractWeights, paramMappings).extractDenseBlock4Params;
+        var dense0 = extractDenseBlock4Params(3, 32, 'dense0', true);
+        var dense1 = extractDenseBlock4Params(32, 64, 'dense1');
+        var dense2 = extractDenseBlock4Params(64, 128, 'dense2');
+        var dense3 = extractDenseBlock4Params(128, 256, 'dense3');
+        if (getRemainingWeights().length !== 0) {
+            throw new Error("weights remaing after extract: " + getRemainingWeights().length);
+        }
+        return {
+            paramMappings: paramMappings,
+            params: { dense0: dense0, dense1: dense1, dense2: dense2, dense3: dense3 }
+        };
+    }
+
+    function loadParamsFactory(weightMap, paramMappings) {
+        var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
+        function extractConvParams(prefix) {
+            var filters = extractWeightEntry(prefix + "/filters", 4);
+            var bias = extractWeightEntry(prefix + "/bias", 1);
+            return { filters: filters, bias: bias };
+        }
+        function extractSeparableConvParams(prefix) {
+            var depthwise_filter = extractWeightEntry(prefix + "/depthwise_filter", 4);
+            var pointwise_filter = extractWeightEntry(prefix + "/pointwise_filter", 4);
+            var bias = extractWeightEntry(prefix + "/bias", 1);
+            return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
+        }
+        function extractDenseBlock3Params(prefix, isFirstLayer) {
+            if (isFirstLayer === void 0) { isFirstLayer = false; }
+            var conv0 = isFirstLayer
+                ? extractConvParams(prefix + "/conv0")
+                : extractSeparableConvParams(prefix + "/conv0");
+            var conv1 = extractSeparableConvParams(prefix + "/conv1");
+            var conv2 = extractSeparableConvParams(prefix + "/conv2");
+            return { conv0: conv0, conv1: conv1, conv2: conv2 };
+        }
+        function extractDenseBlock4Params(prefix, isFirstLayer) {
+            if (isFirstLayer === void 0) { isFirstLayer = false; }
+            var conv0 = isFirstLayer
+                ? extractConvParams(prefix + "/conv0")
+                : extractSeparableConvParams(prefix + "/conv0");
+            var conv1 = extractSeparableConvParams(prefix + "/conv1");
+            var conv2 = extractSeparableConvParams(prefix + "/conv2");
+            var conv3 = extractSeparableConvParams(prefix + "/conv3");
+            return { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3 };
+        }
+        return {
+            extractDenseBlock3Params: extractDenseBlock3Params,
+            extractDenseBlock4Params: extractDenseBlock4Params
+        };
+    }
+
+    function extractParamsFromWeigthMap$1(weightMap) {
+        var paramMappings = [];
+        var extractDenseBlock4Params = loadParamsFactory(weightMap, paramMappings).extractDenseBlock4Params;
+        var params = {
+            dense0: extractDenseBlock4Params('dense0', true),
+            dense1: extractDenseBlock4Params('dense1'),
+            dense2: extractDenseBlock4Params('dense2'),
+            dense3: extractDenseBlock4Params('dense3')
+        };
+        disposeUnusedWeightTensors(weightMap, paramMappings);
+        return { params: params, paramMappings: paramMappings };
+    }
+
+    var FaceFeatureExtractor = /** @class */ (function (_super) {
+        __extends$1(FaceFeatureExtractor, _super);
+        function FaceFeatureExtractor() {
+            return _super.call(this, 'FaceFeatureExtractor') || this;
+        }
+        FaceFeatureExtractor.prototype.forwardInput = function (input) {
+            var params = this.params;
+            if (!params) {
+                throw new Error('FaceFeatureExtractor - load model before inference');
+            }
+            return tidy(function () {
+                var batchTensor = input.toBatchTensor(112, true);
+                var meanRgb = [122.782, 117.001, 104.298];
+                var normalized = normalize(batchTensor, meanRgb).div(scalar(255));
+                var out = denseBlock4(normalized, params.dense0, true);
+                out = denseBlock4(out, params.dense1);
+                out = denseBlock4(out, params.dense2);
+                out = denseBlock4(out, params.dense3);
+                out = avgPool(out, [7, 7], [2, 2], 'valid');
+                return out;
+            });
+        };
+        FaceFeatureExtractor.prototype.forward = function (input) {
+            return __awaiter$1(this, void 0, void 0, function () {
+                var _a;
+                return __generator$1(this, function (_b) {
+                    switch (_b.label) {
+                        case 0:
+                            _a = this.forwardInput;
+                            return [4 /*yield*/, toNetInput(input)];
+                        case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
+                    }
+                });
+            });
+        };
+        FaceFeatureExtractor.prototype.getDefaultModelName = function () {
+            return 'face_feature_extractor_model';
+        };
+        FaceFeatureExtractor.prototype.extractParamsFromWeigthMap = function (weightMap) {
+            return extractParamsFromWeigthMap$1(weightMap);
+        };
+        FaceFeatureExtractor.prototype.extractParams = function (weights) {
+            return extractParams$1(weights);
+        };
+        return FaceFeatureExtractor;
+    }(NeuralNetwork));
+
+    function fullyConnectedLayer(x, params) {
+        return tidy(function () {
+            return add(matMul(x, params.weights), params.bias);
+        });
+    }
+
+    function extractParams$2(weights, channelsIn, channelsOut) {
+        var paramMappings = [];
+        var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
+        var extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);
+        var fc = extractFCParams(channelsIn, channelsOut, 'fc');
+        if (getRemainingWeights().length !== 0) {
+            throw new Error("weights remaing after extract: " + getRemainingWeights().length);
+        }
+        return {
+            paramMappings: paramMappings,
+            params: { fc: fc }
+        };
+    }
+
+    function extractParamsFromWeigthMap$2(weightMap) {
+        var paramMappings = [];
+        var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
+        function extractFcParams(prefix) {
+            var weights = extractWeightEntry(prefix + "/weights", 2);
+            var bias = extractWeightEntry(prefix + "/bias", 1);
+            return { weights: weights, bias: bias };
+        }
+        var params = {
+            fc: extractFcParams('fc')
+        };
+        disposeUnusedWeightTensors(weightMap, paramMappings);
+        return { params: params, paramMappings: paramMappings };
+    }
+
+    function seperateWeightMaps(weightMap) {
+        var featureExtractorMap = {};
+        var classifierMap = {};
+        Object.keys(weightMap).forEach(function (key) {
+            var map = key.startsWith('fc') ? classifierMap : featureExtractorMap;
+            map[key] = weightMap[key];
+        });
+        return { featureExtractorMap: featureExtractorMap, classifierMap: classifierMap };
+    }
+
+    var FaceProcessor = /** @class */ (function (_super) {
+        __extends$1(FaceProcessor, _super);
+        function FaceProcessor(_name, faceFeatureExtractor) {
+            var _this = _super.call(this, _name) || this;
+            _this._faceFeatureExtractor = faceFeatureExtractor;
+            return _this;
+        }
+        Object.defineProperty(FaceProcessor.prototype, "faceFeatureExtractor", {
+            get: function () {
+                return this._faceFeatureExtractor;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        FaceProcessor.prototype.runNet = function (input) {
+            var _this = this;
+            var params = this.params;
+            if (!params) {
+                throw new Error(this._name + " - load model before inference");
+            }
+            return tidy(function () {
+                var bottleneckFeatures = input instanceof NetInput
+                    ? _this.faceFeatureExtractor.forwardInput(input)
+                    : input;
+                return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc);
+            });
+        };
+        FaceProcessor.prototype.dispose = function (throwOnRedispose) {
+            if (throwOnRedispose === void 0) { throwOnRedispose = true; }
+            this.faceFeatureExtractor.dispose(throwOnRedispose);
+            _super.prototype.dispose.call(this, throwOnRedispose);
+        };
+        FaceProcessor.prototype.loadClassifierParams = function (weights) {
+            var _a = this.extractClassifierParams(weights), params = _a.params, paramMappings = _a.paramMappings;
+            this._params = params;
+            this._paramMappings = paramMappings;
+        };
+        FaceProcessor.prototype.extractClassifierParams = function (weights) {
+            return extractParams$2(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut());
+        };
+        FaceProcessor.prototype.extractParamsFromWeigthMap = function (weightMap) {
+            var _a = seperateWeightMaps(weightMap), featureExtractorMap = _a.featureExtractorMap, classifierMap = _a.classifierMap;
+            this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap);
+            return extractParamsFromWeigthMap$2(classifierMap);
+        };
+        FaceProcessor.prototype.extractParams = function (weights) {
+            var cIn = this.getClassifierChannelsIn();
+            var cOut = this.getClassifierChannelsOut();
+            var classifierWeightSize = (cOut * cIn) + cOut;
+            var featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize);
+            var classifierWeights = weights.slice(weights.length - classifierWeightSize);
+            this.faceFeatureExtractor.extractWeights(featureExtractorWeights);
+            return this.extractClassifierParams(classifierWeights);
+        };
+        return FaceProcessor;
+    }(NeuralNetwork));
+
+    var faceExpressionLabels = {
+        neutral: 0,
+        happy: 1,
+        sad: 2,
+        angry: 3,
+        fearful: 4,
+        disgusted: 5,
+        surprised: 6
+    };
+
+    var FaceExpressionNet = /** @class */ (function (_super) {
+        __extends$1(FaceExpressionNet, _super);
+        function FaceExpressionNet(faceFeatureExtractor) {
+            if (faceFeatureExtractor === void 0) { faceFeatureExtractor = new FaceFeatureExtractor(); }
+            return _super.call(this, 'FaceExpressionNet', faceFeatureExtractor) || this;
+        }
+        FaceExpressionNet.getFaceExpressionLabel = function (faceExpression) {
+            var label = faceExpressionLabels[faceExpression];
+            if (typeof label !== 'number') {
+                throw new Error("getFaceExpressionLabel - no label for faceExpression: " + faceExpression);
+            }
+            return label;
+        };
+        FaceExpressionNet.decodeProbabilites = function (probabilities) {
+            if (probabilities.length !== 7) {
+                throw new Error("decodeProbabilites - expected probabilities.length to be 7, have: " + probabilities.length);
+            }
+            return Object.keys(faceExpressionLabels)
+                .map(function (expression) { return ({ expression: expression, probability: probabilities[faceExpressionLabels[expression]] }); });
+        };
+        FaceExpressionNet.prototype.forwardInput = function (input) {
+            var _this = this;
+            return tidy(function () { return softmax(_this.runNet(input)); });
+        };
+        FaceExpressionNet.prototype.forward = function (input) {
+            return __awaiter$1(this, void 0, void 0, function () {
+                var _a;
+                return __generator$1(this, function (_b) {
+                    switch (_b.label) {
+                        case 0:
+                            _a = this.forwardInput;
+                            return [4 /*yield*/, toNetInput(input)];
+                        case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
+                    }
+                });
+            });
+        };
+        FaceExpressionNet.prototype.predictExpressions = function (input) {
+            return __awaiter$1(this, void 0, void 0, function () {
+                var _this = this;
+                var netInput, out, probabilitesByBatch, predictionsByBatch;
+                return __generator$1(this, function (_a) {
+                    switch (_a.label) {
+                        case 0: return [4 /*yield*/, toNetInput(input)];
+                        case 1:
+                            netInput = _a.sent();
+                            return [4 /*yield*/, this.forwardInput(netInput)];
+                        case 2:
+                            out = _a.sent();
+                            return [4 /*yield*/, Promise.all(unstack(out).map(function (t) { return __awaiter$1(_this, void 0, void 0, function () {
+                                    var data;
+                                    return __generator$1(this, function (_a) {
+                                        switch (_a.label) {
+                                            case 0: return [4 /*yield*/, t.data()];
+                                            case 1:
+                                                data = _a.sent();
+                                                t.dispose();
+                                                return [2 /*return*/, data];
+                                        }
+                                    });
+                                }); }))];
+                        case 3:
+                            probabilitesByBatch = _a.sent();
+                            out.dispose();
+                            predictionsByBatch = probabilitesByBatch
+                                .map(function (propablities) { return FaceExpressionNet.decodeProbabilites(propablities); });
+                            return [2 /*return*/, netInput.isBatchInput
+                                    ? predictionsByBatch
+                                    : predictionsByBatch[0]];
+                    }
+                });
+            });
+        };
+        FaceExpressionNet.prototype.getDefaultModelName = function () {
+            return 'face_expression_model';
+        };
+        FaceExpressionNet.prototype.getClassifierChannelsIn = function () {
+            return 256;
+        };
+        FaceExpressionNet.prototype.getClassifierChannelsOut = function () {
+            return 7;
+        };
+        return FaceExpressionNet;
+    }(FaceProcessor));
+
+    var FaceLandmark68NetBase = /** @class */ (function (_super) {
+        __extends$1(FaceLandmark68NetBase, _super);
+        function FaceLandmark68NetBase() {
+            return _super !== null && _super.apply(this, arguments) || this;
+        }
+        FaceLandmark68NetBase.prototype.postProcess = function (output, inputSize, originalDimensions) {
+            var inputDimensions = originalDimensions.map(function (_a) {
+                var width = _a.width, height = _a.height;
+                var scale = inputSize / Math.max(height, width);
+                return {
+                    width: width * scale,
+                    height: height * scale
+                };
+            });
+            var batchSize = inputDimensions.length;
+            return tidy(function () {
+                var createInterleavedTensor = function (fillX, fillY) {
+                    return stack([
+                        fill([68], fillX),
+                        fill([68], fillY)
+                    ], 1).as2D(1, 136).as1D();
+                };
+                var getPadding = function (batchIdx, cond) {
+                    var _a = inputDimensions[batchIdx], width = _a.width, height = _a.height;
+                    return cond(width, height) ? Math.abs(width - height) / 2 : 0;
+                };
+                var getPaddingX = function (batchIdx) { return getPadding(batchIdx, function (w, h) { return w < h; }); };
+                var getPaddingY = function (batchIdx) { return getPadding(batchIdx, function (w, h) { return h < w; }); };
+                var landmarkTensors = output
+                    .mul(fill([batchSize, 136], inputSize))
+                    .sub(stack(Array.from(Array(batchSize), function (_, batchIdx) {
+                    return createInterleavedTensor(getPaddingX(batchIdx), getPaddingY(batchIdx));
+                })))
+                    .div(stack(Array.from(Array(batchSize), function (_, batchIdx) {
+                    return createInterleavedTensor(inputDimensions[batchIdx].width, inputDimensions[batchIdx].height);
+                })));
+                return landmarkTensors;
+            });
+        };
+        FaceLandmark68NetBase.prototype.forwardInput = function (input) {
+            var _this = this;
+            return tidy(function () {
+                var out = _this.runNet(input);
+                return _this.postProcess(out, input.inputSize, input.inputDimensions.map(function (_a) {
+                    var height = _a[0], width = _a[1];
+                    return ({ height: height, width: width });
+                }));
+            });
+        };
+        FaceLandmark68NetBase.prototype.forward = function (input) {
+            return __awaiter$1(this, void 0, void 0, function () {
+                var _a;
+                return __generator$1(this, function (_b) {
+                    switch (_b.label) {
+                        case 0:
+                            _a = this.forwardInput;
+                            return [4 /*yield*/, toNetInput(input)];
+                        case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
+                    }
+                });
+            });
+        };
+        FaceLandmark68NetBase.prototype.detectLandmarks = function (input) {
+            return __awaiter$1(this, void 0, void 0, function () {
+                var _this = this;
+                var netInput, landmarkTensors, landmarksForBatch;
+                return __generator$1(this, function (_a) {
+                    switch (_a.label) {
+                        case 0: return [4 /*yield*/, toNetInput(input)];
+                        case 1:
+                            netInput = _a.sent();
+                            landmarkTensors = tidy(function () { return unstack(_this.forwardInput(netInput)); });
+                            return [4 /*yield*/, Promise.all(landmarkTensors.map(function (landmarkTensor, batchIdx) { return __awaiter$1(_this, void 0, void 0, function () {
+                                    var landmarksArray, _a, _b, xCoords, yCoords;
+                                    return __generator$1(this, function (_c) {
+                                        switch (_c.label) {
+                                            case 0:
+                                                _b = (_a = Array).from;
+                                                return [4 /*yield*/, landmarkTensor.data()];
+                                            case 1:
+                                                landmarksArray = _b.apply(_a, [_c.sent()]);
+                                                xCoords = landmarksArray.filter(function (_, i) { return isEven$1(i); });
+                                                yCoords = landmarksArray.filter(function (_, i) { return !isEven$1(i); });
+                                                return [2 /*return*/, new FaceLandmarks68(Array(68).fill(0).map(function (_, i) { return new Point(xCoords[i], yCoords[i]); }), {
+                                                        height: netInput.getInputHeight(batchIdx),
+                                                        width: netInput.getInputWidth(batchIdx),
+                                                    })];
+                                        }
+                                    });
+                                }); }))];
+                        case 2:
+                            landmarksForBatch = _a.sent();
+                            landmarkTensors.forEach(function (t) { return t.dispose(); });
+                            return [2 /*return*/, netInput.isBatchInput
+                                    ? landmarksForBatch
+                                    : landmarksForBatch[0]];
+                    }
+                });
+            });
+        };
+        FaceLandmark68NetBase.prototype.getClassifierChannelsOut = function () {
+            return 136;
+        };
+        return FaceLandmark68NetBase;
+    }(FaceProcessor));
+
+    var FaceLandmark68Net = /** @class */ (function (_super) {
+        __extends$1(FaceLandmark68Net, _super);
+        function FaceLandmark68Net(faceFeatureExtractor) {
+            if (faceFeatureExtractor === void 0) { faceFeatureExtractor = new FaceFeatureExtractor(); }
+            return _super.call(this, 'FaceLandmark68Net', faceFeatureExtractor) || this;
+        }
+        FaceLandmark68Net.prototype.getDefaultModelName = function () {
+            return 'face_landmark_68_model';
+        };
+        FaceLandmark68Net.prototype.getClassifierChannelsIn = function () {
+            return 256;
+        };
+        return FaceLandmark68Net;
+    }(FaceLandmark68NetBase));
+
+    function extractParamsFromWeigthMapTiny(weightMap) {
+        var paramMappings = [];
+        var extractDenseBlock3Params = loadParamsFactory(weightMap, paramMappings).extractDenseBlock3Params;
+        var params = {
+            dense0: extractDenseBlock3Params('dense0', true),
+            dense1: extractDenseBlock3Params('dense1'),
+            dense2: extractDenseBlock3Params('dense2')
+        };
+        disposeUnusedWeightTensors(weightMap, paramMappings);
+        return { params: params, paramMappings: paramMappings };
+    }
+
+    function extractParamsTiny(weights) {
+        var paramMappings = [];
+        var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
+        var extractDenseBlock3Params = extractorsFactory$2(extractWeights, paramMappings).extractDenseBlock3Params;
+        var dense0 = extractDenseBlock3Params(3, 32, 'dense0', true);
+        var dense1 = extractDenseBlock3Params(32, 64, 'dense1');
+        var dense2 = extractDenseBlock3Params(64, 128, 'dense2');
+        if (getRemainingWeights().length !== 0) {
+            throw new Error("weights remaing after extract: " + getRemainingWeights().length);
+        }
+        return {
+            paramMappings: paramMappings,
+            params: { dense0: dense0, dense1: dense1, dense2: dense2 }
+        };
+    }
+
+    var TinyFaceFeatureExtractor = /** @class */ (function (_super) {
+        __extends$1(TinyFaceFeatureExtractor, _super);
+        function TinyFaceFeatureExtractor() {
+            return _super.call(this, 'TinyFaceFeatureExtractor') || this;
+        }
+        TinyFaceFeatureExtractor.prototype.forwardInput = function (input) {
+            var params = this.params;
+            if (!params) {
+                throw new Error('TinyFaceFeatureExtractor - load model before inference');
+            }
+            return tidy(function () {
+                var batchTensor = input.toBatchTensor(112, true);
+                var meanRgb = [122.782, 117.001, 104.298];
+                var normalized = normalize(batchTensor, meanRgb).div(scalar(255));
+                var out = denseBlock3(normalized, params.dense0, true);
+                out = denseBlock3(out, params.dense1);
+                out = denseBlock3(out, params.dense2);
+                out = avgPool(out, [14, 14], [2, 2], 'valid');
+                return out;
+            });
+        };
+        TinyFaceFeatureExtractor.prototype.forward = function (input) {
+            return __awaiter$1(this, void 0, void 0, function () {
+                var _a;
+                return __generator$1(this, function (_b) {
+                    switch (_b.label) {
+                        case 0:
+                            _a = this.forwardInput;
+                            return [4 /*yield*/, toNetInput(input)];
+                        case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
+                    }
+                });
+            });
+        };
+        TinyFaceFeatureExtractor.prototype.getDefaultModelName = function () {
+            return 'face_feature_extractor_tiny_model';
+        };
+        TinyFaceFeatureExtractor.prototype.extractParamsFromWeigthMap = function (weightMap) {
+            return extractParamsFromWeigthMapTiny(weightMap);
+        };
+        TinyFaceFeatureExtractor.prototype.extractParams = function (weights) {
+            return extractParamsTiny(weights);
+        };
+        return TinyFaceFeatureExtractor;
+    }(NeuralNetwork));
+
+    var FaceLandmark68TinyNet = /** @class */ (function (_super) {
+        __extends$1(FaceLandmark68TinyNet, _super);
+        function FaceLandmark68TinyNet(faceFeatureExtractor) {
+            if (faceFeatureExtractor === void 0) { faceFeatureExtractor = new TinyFaceFeatureExtractor(); }
+            return _super.call(this, 'FaceLandmark68TinyNet', faceFeatureExtractor) || this;
+        }
+        FaceLandmark68TinyNet.prototype.getDefaultModelName = function () {
+            return 'face_landmark_68_tiny_model';
+        };
+        FaceLandmark68TinyNet.prototype.getClassifierChannelsIn = function () {
+            return 128;
+        };
+        return FaceLandmark68TinyNet;
+    }(FaceLandmark68NetBase));
+
+    var FaceLandmarkNet = /** @class */ (function (_super) {
+        __extends$1(FaceLandmarkNet, _super);
+        function FaceLandmarkNet() {
+            return _super !== null && _super.apply(this, arguments) || this;
+        }
+        return FaceLandmarkNet;
+    }(FaceLandmark68Net));
+
+    function scale(x, params) {
+        return add(mul(x, params.weights), params.biases);
+    }
+
+    function convLayer$1(x, params, strides, withRelu, padding) {
+        if (padding === void 0) { padding = 'same'; }
+        var _a = params.conv, filters = _a.filters, bias = _a.bias;
+        var out = conv2d(x, filters, strides, padding);
+        out = add(out, bias);
+        out = scale(out, params.scale);
+        return withRelu ? relu(out) : out;
+    }
+    function conv(x, params) {
+        return convLayer$1(x, params, [1, 1], true);
+    }
+    function convNoRelu(x, params) {
+        return convLayer$1(x, params, [1, 1], false);
+    }
+    function convDown(x, params) {
+        return convLayer$1(x, params, [2, 2], true, 'valid');
+    }
+
+    function extractorsFactory$3(extractWeights, paramMappings) {
+        function extractFilterValues(numFilterValues, numFilters, filterSize) {
+            var weights = extractWeights(numFilterValues);
+            var depth = weights.length / (numFilters * filterSize * filterSize);
+            if (isFloat(depth)) {
+                throw new Error("depth has to be an integer: " + depth + ", weights.length: " + weights.length + ", numFilters: " + numFilters + ", filterSize: " + filterSize);
+            }
+            return tidy(function () { return transpose(tensor4d(weights, [numFilters, depth, filterSize, filterSize]), [2, 3, 1, 0]); });
+        }
+        function extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
+            var filters = extractFilterValues(numFilterValues, numFilters, filterSize);
+            var bias = tensor1d(extractWeights(numFilters));
+            paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/bias" });
+            return { filters: filters, bias: bias };
+        }
+        function extractScaleLayerParams(numWeights, mappedPrefix) {
+            var weights = tensor1d(extractWeights(numWeights));
+            var biases = tensor1d(extractWeights(numWeights));
+            paramMappings.push({ paramPath: mappedPrefix + "/weights" }, { paramPath: mappedPrefix + "/biases" });
+            return {
+                weights: weights,
+                biases: biases
+            };
+        }
+        function extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix) {
+            var conv = extractConvParams(numFilterValues, numFilters, filterSize, mappedPrefix + "/conv");
+            var scale = extractScaleLayerParams(numFilters, mappedPrefix + "/scale");
+            return { conv: conv, scale: scale };
+        }
+        function extractResidualLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix, isDown) {
+            if (isDown === void 0) { isDown = false; }
+            var conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, mappedPrefix + "/conv1");
+            var conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, mappedPrefix + "/conv2");
+            return { conv1: conv1, conv2: conv2 };
+        }
+        return {
+            extractConvLayerParams: extractConvLayerParams,
+            extractResidualLayerParams: extractResidualLayerParams
+        };
+    }
+    function extractParams$3(weights) {
+        var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
+        var paramMappings = [];
+        var _b = extractorsFactory$3(extractWeights, paramMappings), extractConvLayerParams = _b.extractConvLayerParams, extractResidualLayerParams = _b.extractResidualLayerParams;
+        var conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down');
+        var conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1');
+        var conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2');
+        var conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3');
+        var conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true);
+        var conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1');
+        var conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2');
+        var conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3');
+        var conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true);
+        var conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1');
+        var conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2');
+        var conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true);
+        var conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1');
+        var conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2');
+        var conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out');
+        var fc = tidy(function () { return transpose(tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0]); });
+        paramMappings.push({ paramPath: "fc" });
+        if (getRemainingWeights().length !== 0) {
+            throw new Error("weights remaing after extract: " + getRemainingWeights().length);
+        }
+        var params = {
+            conv32_down: conv32_down,
+            conv32_1: conv32_1,
+            conv32_2: conv32_2,
+            conv32_3: conv32_3,
+            conv64_down: conv64_down,
+            conv64_1: conv64_1,
+            conv64_2: conv64_2,
+            conv64_3: conv64_3,
+            conv128_down: conv128_down,
+            conv128_1: conv128_1,
+            conv128_2: conv128_2,
+            conv256_down: conv256_down,
+            conv256_1: conv256_1,
+            conv256_2: conv256_2,
+            conv256_down_out: conv256_down_out,
+            fc: fc
+        };
+        return { params: params, paramMappings: paramMappings };
+    }
+
+    function extractorsFactory$4(weightMap, paramMappings) {
+        var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
+        function extractScaleLayerParams(prefix) {
+            var weights = extractWeightEntry(prefix + "/scale/weights", 1);
+            var biases = extractWeightEntry(prefix + "/scale/biases", 1);
+            return { weights: weights, biases: biases };
+        }
+        function extractConvLayerParams(prefix) {
+            var filters = extractWeightEntry(prefix + "/conv/filters", 4);
+            var bias = extractWeightEntry(prefix + "/conv/bias", 1);
+            var scale = extractScaleLayerParams(prefix);
+            return { conv: { filters: filters, bias: bias }, scale: scale };
+        }
+        function extractResidualLayerParams(prefix) {
+            return {
+                conv1: extractConvLayerParams(prefix + "/conv1"),
+                conv2: extractConvLayerParams(prefix + "/conv2")
+            };
+        }
+        return {
+            extractConvLayerParams: extractConvLayerParams,
+            extractResidualLayerParams: extractResidualLayerParams
+        };
+    }
+    function extractParamsFromWeigthMap$3(weightMap) {
+        var paramMappings = [];
+        var _a = extractorsFactory$4(weightMap, paramMappings), extractConvLayerParams = _a.extractConvLayerParams, extractResidualLayerParams = _a.extractResidualLayerParams;
+        var conv32_down = extractConvLayerParams('conv32_down');
+        var conv32_1 = extractResidualLayerParams('conv32_1');
+        var conv32_2 = extractResidualLayerParams('conv32_2');
+        var conv32_3 = extractResidualLayerParams('conv32_3');
+        var conv64_down = extractResidualLayerParams('conv64_down');
+        var conv64_1 = extractResidualLayerParams('conv64_1');
+        var conv64_2 = extractResidualLayerParams('conv64_2');
+        var conv64_3 = extractResidualLayerParams('conv64_3');
+        var conv128_down = extractResidualLayerParams('conv128_down');
+        var conv128_1 = extractResidualLayerParams('conv128_1');
+        var conv128_2 = extractResidualLayerParams('conv128_2');
+        var conv256_down = extractResidualLayerParams('conv256_down');
+        var conv256_1 = extractResidualLayerParams('conv256_1');
+        var conv256_2 = extractResidualLayerParams('conv256_2');
+        var conv256_down_out = extractResidualLayerParams('conv256_down_out');
+        var fc = weightMap['fc'];
+        paramMappings.push({ originalPath: 'fc', paramPath: 'fc' });
+        if (!isTensor2D(fc)) {
+            throw new Error("expected weightMap[fc] to be a Tensor2D, instead have " + fc);
+        }
+        var params = {
+            conv32_down: conv32_down,
+            conv32_1: conv32_1,
+            conv32_2: conv32_2,
+            conv32_3: conv32_3,
+            conv64_down: conv64_down,
+            conv64_1: conv64_1,
+            conv64_2: conv64_2,
+            conv64_3: conv64_3,
+            conv128_down: conv128_down,
+            conv128_1: conv128_1,
+            conv128_2: conv128_2,
+            conv256_down: conv256_down,
+            conv256_1: conv256_1,
+            conv256_2: conv256_2,
+            conv256_down_out: conv256_down_out,
+            fc: fc
+        };
+        disposeUnusedWeightTensors(weightMap, paramMappings);
+        return { params: params, paramMappings: paramMappings };
+    }
+
+    function residual(x, params) {
+        var out = conv(x, params.conv1);
+        out = convNoRelu(out, params.conv2);
+        out = add(out, x);
+        out = relu(out);
+        return out;
+    }
+    function residualDown(x, params) {
+        var out = convDown(x, params.conv1);
+        out = convNoRelu(out, params.conv2);
+        var pooled = avgPool(x, 2, 2, 'valid');
+        var zeros$$1 = zeros(pooled.shape);
+        var isPad = pooled.shape[3] !== out.shape[3];
+        var isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2];
+        if (isAdjustShape) {
+            var padShapeX = out.shape.slice();
+            padShapeX[1] = 1;
+            var zerosW = zeros(padShapeX);
+            out = concat([out, zerosW], 1);
+            var padShapeY = out.shape.slice();
+            padShapeY[2] = 1;
+            var zerosH = zeros(padShapeY);
+            out = concat([out, zerosH], 2);
+        }
+        pooled = isPad ? concat([pooled, zeros$$1], 3) : pooled;
+        out = add(pooled, out);
+        out = relu(out);
+        return out;
+    }
+
+    var FaceRecognitionNet = /** @class */ (function (_super) {
+        __extends$1(FaceRecognitionNet, _super);
+        function FaceRecognitionNet() {
+            return _super.call(this, 'FaceRecognitionNet') || this;
+        }
+        FaceRecognitionNet.prototype.forwardInput = function (input) {
+            var params = this.params;
+            if (!params) {
+                throw new Error('FaceRecognitionNet - load model before inference');
+            }
+            return tidy(function () {
+                var batchTensor = input.toBatchTensor(150, true).toFloat();
+                var meanRgb = [122.782, 117.001, 104.298];
+                var normalized = normalize(batchTensor, meanRgb).div(scalar(256));
+                var out = convDown(normalized, params.conv32_down);
+                out = maxPool(out, 3, 2, 'valid');
+                out = residual(out, params.conv32_1);
+                out = residual(out, params.conv32_2);
+                out = residual(out, params.conv32_3);
+                out = residualDown(out, params.conv64_down);
+                out = residual(out, params.conv64_1);
+                out = residual(out, params.conv64_2);
+                out = residual(out, params.conv64_3);
+                out = residualDown(out, params.conv128_down);
+                out = residual(out, params.conv128_1);
+                out = residual(out, params.conv128_2);
+                out = residualDown(out, params.conv256_down);
+                out = residual(out, params.conv256_1);
+                out = residual(out, params.conv256_2);
+                out = residualDown(out, params.conv256_down_out);
+                var globalAvg = out.mean([1, 2]);
+                var fullyConnected = matMul(globalAvg, params.fc);
+                return fullyConnected;
+            });
+        };
+        FaceRecognitionNet.prototype.forward = function (input) {
+            return __awaiter$1(this, void 0, void 0, function () {
+                var _a;
+                return __generator$1(this, function (_b) {
+                    switch (_b.label) {
+                        case 0:
+                            _a = this.forwardInput;
+                            return [4 /*yield*/, toNetInput(input)];
+                        case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
+                    }
+                });
+            });
+        };
+        FaceRecognitionNet.prototype.computeFaceDescriptor = function (input) {
+            return __awaiter$1(this, void 0, void 0, function () {
+                var _this = this;
+                var netInput, faceDescriptorTensors, faceDescriptorsForBatch;
+                return __generator$1(this, function (_a) {
+                    switch (_a.label) {
+                        case 0: return [4 /*yield*/, toNetInput(input)];
+                        case 1:
+                            netInput = _a.sent();
+                            faceDescriptorTensors = tidy(function () { return unstack(_this.forwardInput(netInput)); });
+                            return [4 /*yield*/, Promise.all(faceDescriptorTensors.map(function (t) { return t.data(); }))];
+                        case 2:
+                            faceDescriptorsForBatch = _a.sent();
+                            faceDescriptorTensors.forEach(function (t) { return t.dispose(); });
+                            return [2 /*return*/, netInput.isBatchInput
+                                    ? faceDescriptorsForBatch
+                                    : faceDescriptorsForBatch[0]];
+                    }
+                });
+            });
+        };
+        FaceRecognitionNet.prototype.getDefaultModelName = function () {
+            return 'face_recognition_model';
+        };
+        FaceRecognitionNet.prototype.extractParamsFromWeigthMap = function (weightMap) {
+            return extractParamsFromWeigthMap$3(weightMap);
+        };
+        FaceRecognitionNet.prototype.extractParams = function (weights) {
+            return extractParams$3(weights);
+        };
+        return FaceRecognitionNet;
+    }(NeuralNetwork));
+
+    function createFaceRecognitionNet(weights) {
+        var net = new FaceRecognitionNet();
+        net.extractWeights(weights);
+        return net;
+    }
+
+    function extendWithFaceDescriptor(sourceObj, descriptor) {
+        var extension = { descriptor: descriptor };
+        return Object.assign({}, sourceObj, extension);
+    }
+
+    function extendWithFaceDetection(sourceObj, detection) {
+        var extension = { detection: detection };
+        return Object.assign({}, sourceObj, extension);
+    }
+
+    function extendWithFaceExpressions(sourceObj, expressions) {
+        var extension = { expressions: expressions };
+        return Object.assign({}, sourceObj, extension);
+    }
+
+    function extendWithFaceLandmarks(sourceObj, unshiftedLandmarks) {
+        var shift = sourceObj.detection.box;
+        var landmarks = unshiftedLandmarks.shiftBy(shift.x, shift.y);
+        var rect = landmarks.align();
+        var imageDims = sourceObj.detection.imageDims;
+        var alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims);
+        var extension = {
+            landmarks: landmarks,
+            unshiftedLandmarks: unshiftedLandmarks,
+            alignedRect: alignedRect
+        };
+        return Object.assign({}, sourceObj, extension);
+    }
+
+    var MtcnnOptions = /** @class */ (function () {
+        function MtcnnOptions(_a) {
+            var _b = _a === void 0 ? {} : _a, minFaceSize = _b.minFaceSize, scaleFactor = _b.scaleFactor, maxNumScales = _b.maxNumScales, scoreThresholds = _b.scoreThresholds, scaleSteps = _b.scaleSteps;
+            this._name = 'MtcnnOptions';
+            this._minFaceSize = minFaceSize || 20;
+            this._scaleFactor = scaleFactor || 0.709;
+            this._maxNumScales = maxNumScales || 10;
+            this._scoreThresholds = scoreThresholds || [0.6, 0.7, 0.7];
+            this._scaleSteps = scaleSteps;
+            if (typeof this._minFaceSize !== 'number' || this._minFaceSize < 0) {
+                throw new Error(this._name + " - expected minFaceSize to be a number > 0");
+            }
+            if (typeof this._scaleFactor !== 'number' || this._scaleFactor <= 0 || this._scaleFactor >= 1) {
+                throw new Error(this._name + " - expected scaleFactor to be a number between 0 and 1");
+            }
+            if (typeof this._maxNumScales !== 'number' || this._maxNumScales < 0) {
+                throw new Error(this._name + " - expected maxNumScales to be a number > 0");
+            }
+            if (!Array.isArray(this._scoreThresholds)
+                || this._scoreThresholds.length !== 3
+                || this._scoreThresholds.some(function (th) { return typeof th !== 'number'; })) {
+                throw new Error(this._name + " - expected scoreThresholds to be an array of numbers of length 3");
+            }
+            if (this._scaleSteps
+                && (!Array.isArray(this._scaleSteps) || this._scaleSteps.some(function (th) { return typeof th !== 'number'; }))) {
+                throw new Error(this._name + " - expected scaleSteps to be an array of numbers");
+            }
+        }
+        Object.defineProperty(MtcnnOptions.prototype, "minFaceSize", {
+            get: function () { return this._minFaceSize; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(MtcnnOptions.prototype, "scaleFactor", {
+            get: function () { return this._scaleFactor; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(MtcnnOptions.prototype, "maxNumScales", {
+            get: function () { return this._maxNumScales; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(MtcnnOptions.prototype, "scoreThresholds", {
+            get: function () { return this._scoreThresholds; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(MtcnnOptions.prototype, "scaleSteps", {
+            get: function () { return this._scaleSteps; },
+            enumerable: true,
+            configurable: true
+        });
+        return MtcnnOptions;
+    }());
+
+    function extractorsFactory$5(extractWeights, paramMappings) {
+        function extractDepthwiseConvParams(numChannels, mappedPrefix) {
+            var filters = tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1]);
+            var batch_norm_scale = tensor1d(extractWeights(numChannels));
+            var batch_norm_offset = tensor1d(extractWeights(numChannels));
+            var batch_norm_mean = tensor1d(extractWeights(numChannels));
+            var batch_norm_variance = tensor1d(extractWeights(numChannels));
+            paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/batch_norm_scale" }, { paramPath: mappedPrefix + "/batch_norm_offset" }, { paramPath: mappedPrefix + "/batch_norm_mean" }, { paramPath: mappedPrefix + "/batch_norm_variance" });
+            return {
+                filters: filters,
+                batch_norm_scale: batch_norm_scale,
+                batch_norm_offset: batch_norm_offset,
+                batch_norm_mean: batch_norm_mean,
+                batch_norm_variance: batch_norm_variance
+            };
+        }
+        function extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, isPointwiseConv) {
+            var filters = tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
+            var bias = tensor1d(extractWeights(channelsOut));
+            paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/" + (isPointwiseConv ? 'batch_norm_offset' : 'bias') });
+            return { filters: filters, bias: bias };
+        }
+        function extractPointwiseConvParams(channelsIn, channelsOut, filterSize, mappedPrefix) {
+            var _a = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true), filters = _a.filters, bias = _a.bias;
+            return {
+                filters: filters,
+                batch_norm_offset: bias
+            };
+        }
+        function extractConvPairParams(channelsIn, channelsOut, mappedPrefix) {
+            var depthwise_conv = extractDepthwiseConvParams(channelsIn, mappedPrefix + "/depthwise_conv");
+            var pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, mappedPrefix + "/pointwise_conv");
+            return { depthwise_conv: depthwise_conv, pointwise_conv: pointwise_conv };
+        }
+        function extractMobilenetV1Params() {
+            var conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0');
+            var conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1');
+            var conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2');
+            var conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3');
+            var conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4');
+            var conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5');
+            var conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6');
+            var conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7');
+            var conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8');
+            var conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9');
+            var conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10');
+            var conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11');
+            var conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12');
+            var conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13');
+            return {
+                conv_0: conv_0,
+                conv_1: conv_1,
+                conv_2: conv_2,
+                conv_3: conv_3,
+                conv_4: conv_4,
+                conv_5: conv_5,
+                conv_6: conv_6,
+                conv_7: conv_7,
+                conv_8: conv_8,
+                conv_9: conv_9,
+                conv_10: conv_10,
+                conv_11: conv_11,
+                conv_12: conv_12,
+                conv_13: conv_13
+            };
+        }
+        function extractPredictionLayerParams() {
+            var conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0');
+            var conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1');
+            var conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2');
+            var conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3');
+            var conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4');
+            var conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5');
+            var conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6');
+            var conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7');
+            var box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor');
+            var class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor');
+            var box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor');
+            var class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor');
+            var box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor');
+            var class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor');
+            var box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor');
+            var class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor');
+            var box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor');
+            var class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor');
+            var box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor');
+            var class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor');
+            var box_predictor_0 = {
+                box_encoding_predictor: box_encoding_0_predictor,
+                class_predictor: class_predictor_0
+            };
+            var box_predictor_1 = {
+                box_encoding_predictor: box_encoding_1_predictor,
+                class_predictor: class_predictor_1
+            };
+            var box_predictor_2 = {
+                box_encoding_predictor: box_encoding_2_predictor,
+                class_predictor: class_predictor_2
+            };
+            var box_predictor_3 = {
+                box_encoding_predictor: box_encoding_3_predictor,
+                class_predictor: class_predictor_3
+            };
+            var box_predictor_4 = {
+                box_encoding_predictor: box_encoding_4_predictor,
+                class_predictor: class_predictor_4
+            };
+            var box_predictor_5 = {
+                box_encoding_predictor: box_encoding_5_predictor,
+                class_predictor: class_predictor_5
+            };
+            return {
+                conv_0: conv_0,
+                conv_1: conv_1,
+                conv_2: conv_2,
+                conv_3: conv_3,
+                conv_4: conv_4,
+                conv_5: conv_5,
+                conv_6: conv_6,
+                conv_7: conv_7,
+                box_predictor_0: box_predictor_0,
+                box_predictor_1: box_predictor_1,
+                box_predictor_2: box_predictor_2,
+                box_predictor_3: box_predictor_3,
+                box_predictor_4: box_predictor_4,
+                box_predictor_5: box_predictor_5
+            };
+        }
+        return {
+            extractMobilenetV1Params: extractMobilenetV1Params,
+            extractPredictionLayerParams: extractPredictionLayerParams
+        };
+    }
+    function extractParams$4(weights) {
+        var paramMappings = [];
+        var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
+        var _b = extractorsFactory$5(extractWeights, paramMappings), extractMobilenetV1Params = _b.extractMobilenetV1Params, extractPredictionLayerParams = _b.extractPredictionLayerParams;
+        var mobilenetv1 = extractMobilenetV1Params();
+        var prediction_layer = extractPredictionLayerParams();
+        var extra_dim = tensor3d(extractWeights(5118 * 4), [1, 5118, 4]);
+        var output_layer = {
+            extra_dim: extra_dim
+        };
+        paramMappings.push({ paramPath: 'output_layer/extra_dim' });
+        if (getRemainingWeights().length !== 0) {
+            throw new Error("weights remaing after extract: " + getRemainingWeights().length);
+        }
+        return {
+            params: {
+                mobilenetv1: mobilenetv1,
+                prediction_layer: prediction_layer,
+                output_layer: output_layer
+            },
+            paramMappings: paramMappings
+        };
+    }
+
+    function extractorsFactory$6(weightMap, paramMappings) {
+        var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
+        function extractPointwiseConvParams(prefix, idx, mappedPrefix) {
+            var filters = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/weights", 4, mappedPrefix + "/filters");
+            var batch_norm_offset = extractWeightEntry(prefix + "/Conv2d_" + idx + "_pointwise/convolution_bn_offset", 1, mappedPrefix + "/batch_norm_offset");
+            return { filters: filters, batch_norm_offset: batch_norm_offset };
+        }
+        function extractConvPairParams(idx) {
+            var mappedPrefix = "mobilenetv1/conv_" + idx;
+            var prefixDepthwiseConv = "MobilenetV1/Conv2d_" + idx + "_depthwise";
+            var mappedPrefixDepthwiseConv = mappedPrefix + "/depthwise_conv";
+            var mappedPrefixPointwiseConv = mappedPrefix + "/pointwise_conv";
+            var filters = extractWeightEntry(prefixDepthwiseConv + "/depthwise_weights", 4, mappedPrefixDepthwiseConv + "/filters");
+            var batch_norm_scale = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/gamma", 1, mappedPrefixDepthwiseConv + "/batch_norm_scale");
+            var batch_norm_offset = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/beta", 1, mappedPrefixDepthwiseConv + "/batch_norm_offset");
+            var batch_norm_mean = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/moving_mean", 1, mappedPrefixDepthwiseConv + "/batch_norm_mean");
+            var batch_norm_variance = extractWeightEntry(prefixDepthwiseConv + "/BatchNorm/moving_variance", 1, mappedPrefixDepthwiseConv + "/batch_norm_variance");
+            return {
+                depthwise_conv: {
+                    filters: filters,
+                    batch_norm_scale: batch_norm_scale,
+                    batch_norm_offset: batch_norm_offset,
+                    batch_norm_mean: batch_norm_mean,
+                    batch_norm_variance: batch_norm_variance
+                },
+                pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)
+            };
+        }
+        function extractMobilenetV1Params() {
+            return {
+                conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),
+                conv_1: extractConvPairParams(1),
+                conv_2: extractConvPairParams(2),
+                conv_3: extractConvPairParams(3),
+                conv_4: extractConvPairParams(4),
+                conv_5: extractConvPairParams(5),
+                conv_6: extractConvPairParams(6),
+                conv_7: extractConvPairParams(7),
+                conv_8: extractConvPairParams(8),
+                conv_9: extractConvPairParams(9),
+                conv_10: extractConvPairParams(10),
+                conv_11: extractConvPairParams(11),
+                conv_12: extractConvPairParams(12),
+                conv_13: extractConvPairParams(13)
+            };
+        }
+        function extractConvParams(prefix, mappedPrefix) {
+            var filters = extractWeightEntry(prefix + "/weights", 4, mappedPrefix + "/filters");
+            var bias = extractWeightEntry(prefix + "/biases", 1, mappedPrefix + "/bias");
+            return { filters: filters, bias: bias };
+        }
+        function extractBoxPredictorParams(idx) {
+            var box_encoding_predictor = extractConvParams("Prediction/BoxPredictor_" + idx + "/BoxEncodingPredictor", "prediction_layer/box_predictor_" + idx + "/box_encoding_predictor");
+            var class_predictor = extractConvParams("Prediction/BoxPredictor_" + idx + "/ClassPredictor", "prediction_layer/box_predictor_" + idx + "/class_predictor");
+            return { box_encoding_predictor: box_encoding_predictor, class_predictor: class_predictor };
+        }
+        function extractPredictionLayerParams() {
+            return {
+                conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),
+                conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),
+                conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),
+                conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),
+                conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),
+                conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),
+                conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),
+                conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),
+                box_predictor_0: extractBoxPredictorParams(0),
+                box_predictor_1: extractBoxPredictorParams(1),
+                box_predictor_2: extractBoxPredictorParams(2),
+                box_predictor_3: extractBoxPredictorParams(3),
+                box_predictor_4: extractBoxPredictorParams(4),
+                box_predictor_5: extractBoxPredictorParams(5)
+            };
+        }
+        return {
+            extractMobilenetV1Params: extractMobilenetV1Params,
+            extractPredictionLayerParams: extractPredictionLayerParams
+        };
+    }
+    function extractParamsFromWeigthMap$4(weightMap) {
+        var paramMappings = [];
+        var _a = extractorsFactory$6(weightMap, paramMappings), extractMobilenetV1Params = _a.extractMobilenetV1Params, extractPredictionLayerParams = _a.extractPredictionLayerParams;
+        var extra_dim = weightMap['Output/extra_dim'];
+        paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' });
+        if (!isTensor3D(extra_dim)) {
+            throw new Error("expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have " + extra_dim);
+        }
+        var params = {
+            mobilenetv1: extractMobilenetV1Params(),
+            prediction_layer: extractPredictionLayerParams(),
+            output_layer: {
+                extra_dim: extra_dim
+            }
+        };
+        disposeUnusedWeightTensors(weightMap, paramMappings);
+        return { params: params, paramMappings: paramMappings };
+    }
+
+    function pointwiseConvLayer(x, params, strides) {
+        return tidy(function () {
+            var out = conv2d(x, params.filters, strides, 'same');
+            out = add(out, params.batch_norm_offset);
+            return clipByValue(out, 0, 6);
+        });
+    }
+
+    var epsilon = 0.0010000000474974513;
+    function depthwiseConvLayer(x, params, strides) {
+        return tidy(function () {
+            var out = depthwiseConv2d(x, params.filters, strides, 'same');
+            out = batchNormalization(out, params.batch_norm_mean, params.batch_norm_variance, epsilon, params.batch_norm_scale, params.batch_norm_offset);
+            return clipByValue(out, 0, 6);
+        });
+    }
+    function getStridesForLayerIdx(layerIdx) {
+        return [2, 4, 6, 12].some(function (idx) { return idx === layerIdx; }) ? [2, 2] : [1, 1];
+    }
+    function mobileNetV1(x, params) {
+        return tidy(function () {
+            var conv11 = null;
+            var out = pointwiseConvLayer(x, params.conv_0, [2, 2]);
+            var convPairParams = [
+                params.conv_1,
+                params.conv_2,
+                params.conv_3,
+                params.conv_4,
+                params.conv_5,
+                params.conv_6,
+                params.conv_7,
+                params.conv_8,
+                params.conv_9,
+                params.conv_10,
+                params.conv_11,
+                params.conv_12,
+                params.conv_13
+            ];
+            convPairParams.forEach(function (param, i) {
+                var layerIdx = i + 1;
+                var depthwiseConvStrides = getStridesForLayerIdx(layerIdx);
+                out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides);
+                out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1]);
+                if (layerIdx === 11) {
+                    conv11 = out;
+                }
+            });
+            if (conv11 === null) {
+                throw new Error('mobileNetV1 - output of conv layer 11 is null');
+            }
+            return {
+                out: out,
+                conv11: conv11
+            };
+        });
+    }
+
+    function nonMaxSuppression$2(boxes, scores, maxOutputSize, iouThreshold, scoreThreshold) {
+        var numBoxes = boxes.shape[0];
+        var outputSize = Math.min(maxOutputSize, numBoxes);
+        var candidates = scores
+            .map(function (score, boxIndex) { return ({ score: score, boxIndex: boxIndex }); })
+            .filter(function (c) { return c.score > scoreThreshold; })
+            .sort(function (c1, c2) { return c2.score - c1.score; });
+        var suppressFunc = function (x) { return x <= iouThreshold ? 1 : 0; };
+        var selected = [];
+        candidates.forEach(function (c) {
+            if (selected.length >= outputSize) {
+                return;
+            }
+            var originalScore = c.score;
+            for (var j = selected.length - 1; j >= 0; --j) {
+                var iou = IOU(boxes, c.boxIndex, selected[j]);
+                if (iou === 0.0) {
+                    continue;
+                }
+                c.score *= suppressFunc(iou);
+                if (c.score <= scoreThreshold) {
+                    break;
+                }
+            }
+            if (originalScore === c.score) {
+                selected.push(c.boxIndex);
+            }
+        });
+        return selected;
+    }
+    function IOU(boxes, i, j) {
+        var yminI = Math.min(boxes.get(i, 0), boxes.get(i, 2));
+        var xminI = Math.min(boxes.get(i, 1), boxes.get(i, 3));
+        var ymaxI = Math.max(boxes.get(i, 0), boxes.get(i, 2));
+        var xmaxI = Math.max(boxes.get(i, 1), boxes.get(i, 3));
+        var yminJ = Math.min(boxes.get(j, 0), boxes.get(j, 2));
+        var xminJ = Math.min(boxes.get(j, 1), boxes.get(j, 3));
+        var ymaxJ = Math.max(boxes.get(j, 0), boxes.get(j, 2));
+        var xmaxJ = Math.max(boxes.get(j, 1), boxes.get(j, 3));
+        var areaI = (ymaxI - yminI) * (xmaxI - xminI);
+        var areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ);
+        if (areaI <= 0 || areaJ <= 0) {
+            return 0.0;
+        }
+        var intersectionYmin = Math.max(yminI, yminJ);
+        var intersectionXmin = Math.max(xminI, xminJ);
+        var intersectionYmax = Math.min(ymaxI, ymaxJ);
+        var intersectionXmax = Math.min(xmaxI, xmaxJ);
+        var intersectionArea = Math.max(intersectionYmax - intersectionYmin, 0.0) *
+            Math.max(intersectionXmax - intersectionXmin, 0.0);
+        return intersectionArea / (areaI + areaJ - intersectionArea);
+    }
+
+    function getCenterCoordinatesAndSizesLayer(x) {
+        var vec = unstack(transpose(x, [1, 0]));
+        var sizes = [
+            sub(vec[2], vec[0]),
+            sub(vec[3], vec[1])
+        ];
+        var centers = [
+            add(vec[0], div(sizes[0], scalar(2))),
+            add(vec[1], div(sizes[1], scalar(2)))
+        ];
+        return {
+            sizes: sizes,
+            centers: centers
+        };
+    }
+    function decodeBoxesLayer(x0, x1) {
+        var _a = getCenterCoordinatesAndSizesLayer(x0), sizes = _a.sizes, centers = _a.centers;
+        var vec = unstack(transpose(x1, [1, 0]));
+        var div0_out = div(mul(exp(div(vec[2], scalar(5))), sizes[0]), scalar(2));
+        var add0_out = add(mul(div(vec[0], scalar(10)), sizes[0]), centers[0]);
+        var div1_out = div(mul(exp(div(vec[3], scalar(5))), sizes[1]), scalar(2));
+        var add1_out = add(mul(div(vec[1], scalar(10)), sizes[1]), centers[1]);
+        return transpose(stack([
+            sub(add0_out, div0_out),
+            sub(add1_out, div1_out),
+            add(add0_out, div0_out),
+            add(add1_out, div1_out)
+        ]), [1, 0]);
+    }
+    function outputLayer(boxPredictions, classPredictions, params) {
+        return tidy(function () {
+            var batchSize = boxPredictions.shape[0];
+            var boxes = decodeBoxesLayer(reshape(tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]), reshape(boxPredictions, [-1, 4]));
+            boxes = reshape(boxes, [batchSize, (boxes.shape[0] / batchSize), 4]);
+            var scoresAndClasses = sigmoid(slice(classPredictions, [0, 0, 1], [-1, -1, -1]));
+            var scores = slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]);
+            scores = reshape(scores, [batchSize, scores.shape[1]]);
+            var boxesByBatch = unstack(boxes);
+            var scoresByBatch = unstack(scores);
+            return {
+                boxes: boxesByBatch,
+                scores: scoresByBatch
+            };
+        });
+    }
+
+    function boxPredictionLayer(x, params) {
+        return tidy(function () {
+            var batchSize = x.shape[0];
+            var boxPredictionEncoding = reshape(convLayer(x, params.box_encoding_predictor), [batchSize, -1, 1, 4]);
+            var classPrediction = reshape(convLayer(x, params.class_predictor), [batchSize, -1, 3]);
+            return {
+                boxPredictionEncoding: boxPredictionEncoding,
+                classPrediction: classPrediction
+            };
+        });
+    }
+
+    function predictionLayer(x, conv11, params) {
+        return tidy(function () {
+            var conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1]);
+            var conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2]);
+            var conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1]);
+            var conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2]);
+            var conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1]);
+            var conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2]);
+            var conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1]);
+            var conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2]);
+            var boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0);
+            var boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1);
+            var boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2);
+            var boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3);
+            var boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4);
+            var boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5);
+            var boxPredictions = concat([
+                boxPrediction0.boxPredictionEncoding,
+                boxPrediction1.boxPredictionEncoding,
+                boxPrediction2.boxPredictionEncoding,
+                boxPrediction3.boxPredictionEncoding,
+                boxPrediction4.boxPredictionEncoding,
+                boxPrediction5.boxPredictionEncoding
+            ], 1);
+            var classPredictions = concat([
+                boxPrediction0.classPrediction,
+                boxPrediction1.classPrediction,
+                boxPrediction2.classPrediction,
+                boxPrediction3.classPrediction,
+                boxPrediction4.classPrediction,
+                boxPrediction5.classPrediction
+            ], 1);
+            return {
+                boxPredictions: boxPredictions,
+                classPredictions: classPredictions
+            };
+        });
+    }
+
+    var SsdMobilenetv1Options = /** @class */ (function () {
+        function SsdMobilenetv1Options(_a) {
+            var _b = _a === void 0 ? {} : _a, minConfidence = _b.minConfidence, maxResults = _b.maxResults;
+            this._name = 'SsdMobilenetv1Options';
+            this._minConfidence = minConfidence || 0.5;
+            this._maxResults = maxResults || 100;
+            if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {
+                throw new Error(this._name + " - expected minConfidence to be a number between 0 and 1");
+            }
+            if (typeof this._maxResults !== 'number') {
+                throw new Error(this._name + " - expected maxResults to be a number");
+            }
+        }
+        Object.defineProperty(SsdMobilenetv1Options.prototype, "minConfidence", {
+            get: function () { return this._minConfidence; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(SsdMobilenetv1Options.prototype, "maxResults", {
+            get: function () { return this._maxResults; },
+            enumerable: true,
+            configurable: true
+        });
+        return SsdMobilenetv1Options;
+    }());
+
+    var SsdMobilenetv1 = /** @class */ (function (_super) {
+        __extends$1(SsdMobilenetv1, _super);
+        function SsdMobilenetv1() {
+            return _super.call(this, 'SsdMobilenetv1') || this;
+        }
+        SsdMobilenetv1.prototype.forwardInput = function (input) {
+            var params = this.params;
+            if (!params) {
+                throw new Error('SsdMobilenetv1 - load model before inference');
+            }
+            return tidy(function () {
+                var batchTensor = input.toBatchTensor(512, false).toFloat();
+                var x = sub(mul(batchTensor, scalar(0.007843137718737125)), scalar(1));
+                var features = mobileNetV1(x, params.mobilenetv1);
+                var _a = predictionLayer(features.out, features.conv11, params.prediction_layer), boxPredictions = _a.boxPredictions, classPredictions = _a.classPredictions;
+                return outputLayer(boxPredictions, classPredictions, params.output_layer);
+            });
+        };
+        SsdMobilenetv1.prototype.forward = function (input) {
+            return __awaiter$1(this, void 0, void 0, function () {
+                var _a;
+                return __generator$1(this, function (_b) {
+                    switch (_b.label) {
+                        case 0:
+                            _a = this.forwardInput;
+                            return [4 /*yield*/, toNetInput(input)];
+                        case 1: return [2 /*return*/, _a.apply(this, [_b.sent()])];
+                    }
+                });
+            });
+        };
+        SsdMobilenetv1.prototype.locateFaces = function (input, options) {
+            if (options === void 0) { options = {}; }
+            return __awaiter$1(this, void 0, void 0, function () {
+                var _a, maxResults, minConfidence, netInput, _b, _boxes, _scores, boxes, scores, i, scoresData, _c, _d, iouThreshold, indices, reshapedDims, inputSize, padX, padY, results;
+                return __generator$1(this, function (_e) {
+                    switch (_e.label) {
+                        case 0:
+                            _a = new SsdMobilenetv1Options(options), maxResults = _a.maxResults, minConfidence = _a.minConfidence;
+                            return [4 /*yield*/, toNetInput(input)];
+                        case 1:
+                            netInput = _e.sent();
+                            _b = this.forwardInput(netInput), _boxes = _b.boxes, _scores = _b.scores;
+                            boxes = _boxes[0];
+                            scores = _scores[0];
+                            for (i = 1; i < _boxes.length; i++) {
+                                _boxes[i].dispose();
+                                _scores[i].dispose();
+                            }
+                            _d = (_c = Array).from;
+                            return [4 /*yield*/, scores.data()];
+                        case 2:
+                            scoresData = _d.apply(_c, [_e.sent()]);
+                            iouThreshold = 0.5;
+                            indices = nonMaxSuppression$2(boxes, scoresData, maxResults, iouThreshold, minConfidence);
+                            reshapedDims = netInput.getReshapedInputDimensions(0);
+                            inputSize = netInput.inputSize;
+                            padX = inputSize / reshapedDims.width;
+                            padY = inputSize / reshapedDims.height;
+                            results = indices
+                                .map(function (idx) {
+                                var _a = [
+                                    Math.max(0, boxes.get(idx, 0)),
+                                    Math.min(1.0, boxes.get(idx, 2))
+                                ].map(function (val) { return val * padY; }), top = _a[0], bottom = _a[1];
+                                var _b = [
+                                    Math.max(0, boxes.get(idx, 1)),
+                                    Math.min(1.0, boxes.get(idx, 3))
+                                ].map(function (val) { return val * padX; }), left = _b[0], right = _b[1];
+                                return new FaceDetection(scoresData[idx], new Rect(left, top, right - left, bottom - top), {
+                                    height: netInput.getInputHeight(0),
+                                    width: netInput.getInputWidth(0)
+                                });
+                            });
+                            boxes.dispose();
+                            scores.dispose();
+                            return [2 /*return*/, results];
+                    }
+                });
+            });
+        };
+        SsdMobilenetv1.prototype.getDefaultModelName = function () {
+            return 'ssd_mobilenetv1_model';
+        };
+        SsdMobilenetv1.prototype.extractParamsFromWeigthMap = function (weightMap) {
+            return extractParamsFromWeigthMap$4(weightMap);
+        };
+        SsdMobilenetv1.prototype.extractParams = function (weights) {
+            return extractParams$4(weights);
+        };
+        return SsdMobilenetv1;
+    }(NeuralNetwork));
+
+    function createSsdMobilenetv1(weights) {
+        var net = new SsdMobilenetv1();
+        net.extractWeights(weights);
+        return net;
+    }
+    function createFaceDetectionNet(weights) {
+        return createSsdMobilenetv1(weights);
+    }
+    // alias for backward compatibily
+    var FaceDetectionNet = /** @class */ (function (_super) {
+        __extends$1(FaceDetectionNet, _super);
+        function FaceDetectionNet() {
+            return _super !== null && _super.apply(this, arguments) || this;
+        }
+        return FaceDetectionNet;
+    }(SsdMobilenetv1));
+
+    var TinyFaceDetectorOptions = /** @class */ (function (_super) {
+        __extends$1(TinyFaceDetectorOptions, _super);
+        function TinyFaceDetectorOptions() {
+            var _this = _super !== null && _super.apply(this, arguments) || this;
+            _this._name = 'TinyFaceDetectorOptions';
+            return _this;
+        }
+        return TinyFaceDetectorOptions;
+    }(TinyYolov2Options));
+
+    var ComposableTask = /** @class */ (function () {
+        function ComposableTask() {
+        }
+        ComposableTask.prototype.then = function (onfulfilled) {
+            return __awaiter$1(this, void 0, void 0, function () {
+                var _a;
+                return __generator$1(this, function (_b) {
+                    switch (_b.label) {
+                        case 0:
+                            _a = onfulfilled;
+                            return [4 /*yield*/, this.run()];
+                        case 1: return [2 /*return*/, _a.apply(void 0, [_b.sent()])];
+                    }
+                });
+            });
+        };
+        ComposableTask.prototype.run = function () {
+            return __awaiter$1(this, void 0, void 0, function () {
+                return __generator$1(this, function (_a) {
+                    throw new Error('ComposableTask - run is not implemented');
+                });
+            });
+        };
+        return ComposableTask;
+    }());
+
+    function bgrToRgbTensor(tensor$$1) {
+        return tidy(function () { return stack(unstack(tensor$$1, 3).reverse(), 3); });
+    }
+
+    var CELL_STRIDE = 2;
+    var CELL_SIZE$1 = 12;
+
+    function extractorsFactory$7(extractWeights, paramMappings) {
+        var extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
+        var extractFCParams = extractFCParamsFactory(extractWeights, paramMappings);
+        function extractPReluParams(size, paramPath) {
+            var alpha = tensor1d(extractWeights(size));
+            paramMappings.push({ paramPath: paramPath });
+            return alpha;
+        }
+        function extractSharedParams(numFilters, mappedPrefix, isRnet) {
+            if (isRnet === void 0) { isRnet = false; }
+            var conv1 = extractConvParams(numFilters[0], numFilters[1], 3, mappedPrefix + "/conv1");
+            var prelu1_alpha = extractPReluParams(numFilters[1], mappedPrefix + "/prelu1_alpha");
+            var conv2 = extractConvParams(numFilters[1], numFilters[2], 3, mappedPrefix + "/conv2");
+            var prelu2_alpha = extractPReluParams(numFilters[2], mappedPrefix + "/prelu2_alpha");
+            var conv3 = extractConvParams(numFilters[2], numFilters[3], isRnet ? 2 : 3, mappedPrefix + "/conv3");
+            var prelu3_alpha = extractPReluParams(numFilters[3], mappedPrefix + "/prelu3_alpha");
+            return { conv1: conv1, prelu1_alpha: prelu1_alpha, conv2: conv2, prelu2_alpha: prelu2_alpha, conv3: conv3, prelu3_alpha: prelu3_alpha };
+        }
+        function extractPNetParams() {
+            var sharedParams = extractSharedParams([3, 10, 16, 32], 'pnet');
+            var conv4_1 = extractConvParams(32, 2, 1, 'pnet/conv4_1');
+            var conv4_2 = extractConvParams(32, 4, 1, 'pnet/conv4_2');
+            return __assign$1({}, sharedParams, { conv4_1: conv4_1, conv4_2: conv4_2 });
+        }
+        function extractRNetParams() {
+            var sharedParams = extractSharedParams([3, 28, 48, 64], 'rnet', true);
+            var fc1 = extractFCParams(576, 128, 'rnet/fc1');
+            var prelu4_alpha = extractPReluParams(128, 'rnet/prelu4_alpha');
+            var fc2_1 = extractFCParams(128, 2, 'rnet/fc2_1');
+            var fc2_2 = extractFCParams(128, 4, 'rnet/fc2_2');
+            return __assign$1({}, sharedParams, { fc1: fc1, prelu4_alpha: prelu4_alpha, fc2_1: fc2_1, fc2_2: fc2_2 });
+        }
+        function extractONetParams() {
+            var sharedParams = extractSharedParams([3, 32, 64, 64], 'onet');
+            var conv4 = extractConvParams(64, 128, 2, 'onet/conv4');
+            var prelu4_alpha = extractPReluParams(128, 'onet/prelu4_alpha');
+            var fc1 = extractFCParams(1152, 256, 'onet/fc1');
+            var prelu5_alpha = extractPReluParams(256, 'onet/prelu5_alpha');
+            var fc2_1 = extractFCParams(256, 2, 'onet/fc2_1');
+            var fc2_2 = extractFCParams(256, 4, 'onet/fc2_2');
+            var fc2_3 = extractFCParams(256, 10, 'onet/fc2_3');
+            return __assign$1({}, sharedParams, { conv4: conv4, prelu4_alpha: prelu4_alpha, fc1: fc1, prelu5_alpha: prelu5_alpha, fc2_1: fc2_1, fc2_2: fc2_2, fc2_3: fc2_3 });
+        }
+        return {
+            extractPNetParams: extractPNetParams,
+            extractRNetParams: extractRNetParams,
+            extractONetParams: extractONetParams
+        };
+    }
+    function extractParams$5(weights) {
+        var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
+        var paramMappings = [];
+        var _b = extractorsFactory$7(extractWeights, paramMappings), extractPNetParams = _b.extractPNetParams, extractRNetParams = _b.extractRNetParams, extractONetParams = _b.extractONetParams;
+        var pnet = extractPNetParams();
+        var rnet = extractRNetParams();
+        var onet = extractONetParams();
+        if (getRemainingWeights().length !== 0) {
+            throw new Error("weights remaing after extract: " + getRemainingWeights().length);
+        }
+        return { params: { pnet: pnet, rnet: rnet, onet: onet }, paramMappings: paramMappings };
+    }
+
+    function extractorsFactory$8(weightMap, paramMappings) {
+        var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
+        function extractConvParams(prefix) {
+            var filters = extractWeightEntry(prefix + "/weights", 4, prefix + "/filters");
+            var bias = extractWeightEntry(prefix + "/bias", 1);
+            return { filters: filters, bias: bias };
+        }
+        function extractFCParams(prefix) {
+            var weights = extractWeightEntry(prefix + "/weights", 2);
+            var bias = extractWeightEntry(prefix + "/bias", 1);
+            return { weights: weights, bias: bias };
+        }
+        function extractPReluParams(paramPath) {
+            return extractWeightEntry(paramPath, 1);
+        }
+        function extractSharedParams(prefix) {
+            var conv1 = extractConvParams(prefix + "/conv1");
+            var prelu1_alpha = extractPReluParams(prefix + "/prelu1_alpha");
+            var conv2 = extractConvParams(prefix + "/conv2");
+            var prelu2_alpha = extractPReluParams(prefix + "/prelu2_alpha");
+            var conv3 = extractConvParams(prefix + "/conv3");
+            var prelu3_alpha = extractPReluParams(prefix + "/prelu3_alpha");
+            return { conv1: conv1, prelu1_alpha: prelu1_alpha, conv2: conv2, prelu2_alpha: prelu2_alpha, conv3: conv3, prelu3_alpha: prelu3_alpha };
+        }
+        function extractPNetParams() {
+            var sharedParams = extractSharedParams('pnet');
+            var conv4_1 = extractConvParams('pnet/conv4_1');
+            var conv4_2 = extractConvParams('pnet/conv4_2');
+            return __assign$1({}, sharedParams, { conv4_1: conv4_1, conv4_2: conv4_2 });
+        }
+        function extractRNetParams() {
+            var sharedParams = extractSharedParams('rnet');
+            var fc1 = extractFCParams('rnet/fc1');
+            var prelu4_alpha = extractPReluParams('rnet/prelu4_alpha');
+            var fc2_1 = extractFCParams('rnet/fc2_1');
+            var fc2_2 = extractFCParams('rnet/fc2_2');
+            return __assign$1({}, sharedParams, { fc1: fc1, prelu4_alpha: prelu4_alpha, fc2_1: fc2_1, fc2_2: fc2_2 });
+        }
+        function extractONetParams() {
+            var sharedParams = extractSharedParams('onet');
+            var conv4 = extractConvParams('onet/conv4');
+            var prelu4_alpha = extractPReluParams('onet/prelu4_alpha');
+            var fc1 = extractFCParams('onet/fc1');
+            var prelu5_alpha = extractPReluParams('onet/prelu5_alpha');
+            var fc2_1 = extractFCParams('onet/fc2_1');
+            var fc2_2 = extractFCParams('onet/fc2_2');
+            var fc2_3 = extractFCParams('onet/fc2_3');
+            return __assign$1({}, sharedParams, { conv4: conv4, prelu4_alpha: prelu4_alpha, fc1: fc1, prelu5_alpha: prelu5_alpha, fc2_1: fc2_1, fc2_2: fc2_2, fc2_3: fc2_3 });
+        }
+        return {
+            extractPNetParams: extractPNetParams,
+            extractRNetParams: extractRNetParams,
+            extractONetParams: extractONetParams
+        };
+    }
+    function extractParamsFromWeigthMap$5(weightMap) {
+        var paramMappings = [];
+        var _a = extractorsFactory$8(weightMap, paramMappings), extractPNetParams = _a.extractPNetParams, extractRNetParams = _a.extractRNetParams, extractONetParams = _a.extractONetParams;
+        var pnet = extractPNetParams();
+        var rnet = extractRNetParams();
+        var onet = extractONetParams();
+        disposeUnusedWeightTensors(weightMap, paramMappings);
+        return { params: { pnet: pnet, rnet: rnet, onet: onet }, paramMappings: paramMappings };
+    }
+
+    function getSizesForScale(scale, _a) {
+        var height = _a[0], width = _a[1];
+        return {
+            height: Math.floor(height * scale),
+            width: Math.floor(width * scale)
+        };
+    }
+
+    function pyramidDown(minFaceSize, scaleFactor, dims) {
+        var height = dims[0], width = dims[1];
+        var m = CELL_SIZE$1 / minFaceSize;
+        var scales = [];
+        var minLayer = Math.min(height, width) * m;
+        var exp = 0;
+        while (minLayer >= 12) {
+            scales.push(m * Math.pow(scaleFactor, exp));
+            minLayer = minLayer * scaleFactor;
+            exp += 1;
+        }
+        return scales;
+    }
+
+    var MtcnnBox = /** @class */ (function (_super) {
+        __extends$1(MtcnnBox, _super);
+        function MtcnnBox(left, top, right, bottom) {
+            return _super.call(this, { left: left, top: top, right: right, bottom: bottom }, true) || this;
+        }
+        return MtcnnBox;
+    }(Box));
+
+    function normalize$1(x) {
+        return tidy(function () { return mul(sub(x, scalar(127.5)), scalar(0.0078125)); });
+    }
+
+    function prelu$1(x, alpha) {
+        return tidy(function () {
+            return add(relu(x), mul(alpha, neg(relu(neg(x)))));
+        });
+    }
+
+    function sharedLayer(x, params, isPnet) {
+        if (isPnet === void 0) { isPnet = false; }
+        return tidy(function () {
+            var out = convLayer(x, params.conv1, 'valid');
+            out = prelu$1(out, params.prelu1_alpha);
+            out = maxPool(out, isPnet ? [2, 2] : [3, 3], [2, 2], 'same');
+            out = convLayer(out, params.conv2, 'valid');
+            out = prelu$1(out, params.prelu2_alpha);
+            out = isPnet ? out : maxPool(out, [3, 3], [2, 2], 'valid');
+            out = convLayer(out, params.conv3, 'valid');
+            out = prelu$1(out, params.prelu3_alpha);
+            return out;
+        });
+    }
+
+    function PNet(x, params) {
+        return tidy(function () {
+            var out = sharedLayer(x, params, true);
+            var conv = convLayer(out, params.conv4_1, 'valid');
+            var max$$1 = expandDims(max(conv, 3), 3);
+            var prob = softmax(sub(conv, max$$1), 3);
+            var regions = convLayer(out, params.conv4_2, 'valid');
+            return { prob: prob, regions: regions };
+        });
+    }
+
+    function rescaleAndNormalize(x, scale) {
+        return tidy(function () {
+            var _a = getSizesForScale(scale, x.shape.slice(1)), height = _a.height, width = _a.width;
+            var resized = image_ops.resizeBilinear(x, [height, width]);
+            var normalized = normalize$1(resized);
+            return transpose(normalized, [0, 2, 1, 3]);
+        });
+    }
+    function extractBoundingBoxes(scoresTensor, regionsTensor, scale, scoreThreshold) {
+        // TODO: fix this!, maybe better to use tf.gather here
+        var indices = [];
+        for (var y = 0; y < scoresTensor.shape[0]; y++) {
+            for (var x = 0; x < scoresTensor.shape[1]; x++) {
+                if (scoresTensor.get(y, x) >= scoreThreshold) {
+                    indices.push(new Point(x, y));
+                }
+            }
+        }
+        var boundingBoxes = indices.map(function (idx) {
+            var cell = new BoundingBox(Math.round((idx.y * CELL_STRIDE + 1) / scale), Math.round((idx.x * CELL_STRIDE + 1) / scale), Math.round((idx.y * CELL_STRIDE + CELL_SIZE$1) / scale), Math.round((idx.x * CELL_STRIDE + CELL_SIZE$1) / scale));
+            var score = scoresTensor.get(idx.y, idx.x);
+            var region = new MtcnnBox(regionsTensor.get(idx.y, idx.x, 0), regionsTensor.get(idx.y, idx.x, 1), regionsTensor.get(idx.y, idx.x, 2), regionsTensor.get(idx.y, idx.x, 3));
+            return {
+                cell: cell,
+                score: score,
+                region: region
+            };
+        });
+        return boundingBoxes;
+    }
+    function stage1(imgTensor, scales, scoreThreshold, params, stats) {
+        stats.stage1 = [];
+        var pnetOutputs = scales.map(function (scale) { return tidy(function () {
+            var statsForScale = { scale: scale };
+            var resized = rescaleAndNormalize(imgTensor, scale);
+            var ts = Date.now();
+            var _a = PNet(resized, params), prob = _a.prob, regions = _a.regions;
+            statsForScale.pnet = Date.now() - ts;
+            var scoresTensor = unstack(unstack(prob, 3)[1])[0];
+            var regionsTensor = unstack(regions)[0];
+            return {
+                scoresTensor: scoresTensor,
+                regionsTensor: regionsTensor,
+                scale: scale,
+                statsForScale: statsForScale
+            };
+        }); });
+        var boxesForScale = pnetOutputs.map(function (_a) {
+            var scoresTensor = _a.scoresTensor, regionsTensor = _a.regionsTensor, scale = _a.scale, statsForScale = _a.statsForScale;
+            var boundingBoxes = extractBoundingBoxes(scoresTensor, regionsTensor, scale, scoreThreshold);
+            scoresTensor.dispose();
+            regionsTensor.dispose();
+            if (!boundingBoxes.length) {
+                stats.stage1.push(statsForScale);
+                return [];
+            }
+            var ts = Date.now();
+            var indices = nonMaxSuppression$1(boundingBoxes.map(function (bbox) { return bbox.cell; }), boundingBoxes.map(function (bbox) { return bbox.score; }), 0.5);
+            statsForScale.nms = Date.now() - ts;
+            statsForScale.numBoxes = indices.length;
+            stats.stage1.push(statsForScale);
+            return indices.map(function (boxIdx) { return boundingBoxes[boxIdx]; });
+        });
+        var allBoxes = boxesForScale.reduce(function (all$$1, boxes) { return all$$1.concat(boxes); }, []);
+        var finalBoxes = [];
+        var finalScores = [];
+        if (allBoxes.length > 0) {
+            var ts = Date.now();
+            var indices = nonMaxSuppression$1(allBoxes.map(function (bbox) { return bbox.cell; }), allBoxes.map(function (bbox) { return bbox.score; }), 0.7);
+            stats.stage1_nms = Date.now() - ts;
+            finalScores = indices.map(function (idx) { return allBoxes[idx].score; });
+            finalBoxes = indices
+                .map(function (idx) { return allBoxes[idx]; })
+                .map(function (_a) {
+                var cell = _a.cell, region = _a.region;
+                return new BoundingBox(cell.left + (region.left * cell.width), cell.top + (region.top * cell.height), cell.right + (region.right * cell.width), cell.bottom + (region.bottom * cell.height)).toSquare().round();
+            });
+        }
+        return {
+            boxes: finalBoxes,
+            scores: finalScores
+        };
+    }
+
+    function extractImagePatches(img, boxes, _a) {
+        var width = _a.width, height = _a.height;
+        return __awaiter$1(this, void 0, void 0, function () {
+            var _this = this;
+            var imgCtx, bitmaps, imagePatchesDatas;
+            return __generator$1(this, function (_b) {
+                switch (_b.label) {
+                    case 0:
+                        imgCtx = getContext2dOrThrow(img);
+                        return [4 /*yield*/, Promise.all(boxes.map(function (box) { return __awaiter$1(_this, void 0, void 0, function () {
+                                var _a, y, ey, x, ex, fromX, fromY, imgData;
+                                return __generator$1(this, function (_b) {
+                                    _a = box.padAtBorders(img.height, img.width), y = _a.y, ey = _a.ey, x = _a.x, ex = _a.ex;
+                                    fromX = x - 1;
+                                    fromY = y - 1;
+                                    imgData = imgCtx.getImageData(fromX, fromY, (ex - fromX), (ey - fromY));
+                                    return [2 /*return*/, env.isNodejs() ? createCanvasFromMedia(imgData) : createImageBitmap(imgData)];
+                                });
+                            }); }))];
+                    case 1:
+                        bitmaps = _b.sent();
+                        imagePatchesDatas = [];
+                        bitmaps.forEach(function (bmp) {
+                            var patch = createCanvas({ width: width, height: height });
+                            var patchCtx = getContext2dOrThrow(patch);
+                            patchCtx.drawImage(bmp, 0, 0, width, height);
+                            var data = patchCtx.getImageData(0, 0, width, height).data;
+                            var currData = [];
+                            // RGBA -> BGR
+                            for (var i = 0; i < data.length; i += 4) {
+                                currData.push(data[i + 2]);
+                                currData.push(data[i + 1]);
+                                currData.push(data[i]);
+                            }
+                            imagePatchesDatas.push(currData);
+                        });
+                        return [2 /*return*/, imagePatchesDatas.map(function (data) {
+                                var t = tidy(function () {
+                                    var imagePatchTensor = transpose(tensor4d(data, [1, width, height, 3]), [0, 2, 1, 3]).toFloat();
+                                    return normalize$1(imagePatchTensor);
+                                });
+                                return t;
+                            })];
+                }
+            });
+        });
+    }
+
+    function RNet(x, params) {
+        return tidy(function () {
+            var convOut = sharedLayer(x, params);
+            var vectorized = reshape(convOut, [convOut.shape[0], params.fc1.weights.shape[0]]);
+            var fc1 = fullyConnectedLayer(vectorized, params.fc1);
+            var prelu4 = prelu$1(fc1, params.prelu4_alpha);
+            var fc2_1 = fullyConnectedLayer(prelu4, params.fc2_1);
+            var max$$1 = expandDims(max(fc2_1, 1), 1);
+            var prob = softmax(sub(fc2_1, max$$1), 1);
+            var regions = fullyConnectedLayer(prelu4, params.fc2_2);
+            var scores = unstack(prob, 1)[1];
+            return { scores: scores, regions: regions };
+        });
+    }
+
+    function stage2(img, inputBoxes, scoreThreshold, params, stats) {
+        return __awaiter$1(this, void 0, void 0, function () {
+            var ts, rnetInputs, rnetOuts, scoresTensor, scores, _a, _b, indices, filteredBoxes, filteredScores, finalBoxes, finalScores, indicesNms, regions_1;
+            return __generator$1(this, function (_c) {
+                switch (_c.label) {
+                    case 0:
+                        ts = Date.now();
+                        return [4 /*yield*/, extractImagePatches(img, inputBoxes, { width: 24, height: 24 })];
+                    case 1:
+                        rnetInputs = _c.sent();
+                        stats.stage2_extractImagePatches = Date.now() - ts;
+                        ts = Date.now();
+                        rnetOuts = rnetInputs.map(function (rnetInput) {
+                            var out = RNet(rnetInput, params);
+                            rnetInput.dispose();
+                            return out;
+                        });
+                        stats.stage2_rnet = Date.now() - ts;
+                        scoresTensor = rnetOuts.length > 1
+                            ? concat(rnetOuts.map(function (out) { return out.scores; }))
+                            : rnetOuts[0].scores;
+                        _b = (_a = Array).from;
+                        return [4 /*yield*/, scoresTensor.data()];
+                    case 2:
+                        scores = _b.apply(_a, [_c.sent()]);
+                        scoresTensor.dispose();
+                        indices = scores
+                            .map(function (score, idx) { return ({ score: score, idx: idx }); })
+                            .filter(function (c) { return c.score > scoreThreshold; })
+                            .map(function (_a) {
+                            var idx = _a.idx;
+                            return idx;
+                        });
+                        filteredBoxes = indices.map(function (idx) { return inputBoxes[idx]; });
+                        filteredScores = indices.map(function (idx) { return scores[idx]; });
+                        finalBoxes = [];
+                        finalScores = [];
+                        if (filteredBoxes.length > 0) {
+                            ts = Date.now();
+                            indicesNms = nonMaxSuppression$1(filteredBoxes, filteredScores, 0.7);
+                            stats.stage2_nms = Date.now() - ts;
+                            regions_1 = indicesNms.map(function (idx) {
+                                return new MtcnnBox(rnetOuts[indices[idx]].regions.get(0, 0), rnetOuts[indices[idx]].regions.get(0, 1), rnetOuts[indices[idx]].regions.get(0, 2), rnetOuts[indices[idx]].regions.get(0, 3));
+                            });
+                            finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
+                            finalBoxes = indicesNms.map(function (idx, i) { return filteredBoxes[idx].calibrate(regions_1[i]); });
+                        }
+                        rnetOuts.forEach(function (t) {
+                            t.regions.dispose();
+                            t.scores.dispose();
+                        });
+                        return [2 /*return*/, {
+                                boxes: finalBoxes,
+                                scores: finalScores
+                            }];
+                }
+            });
+        });
+    }
+
+    function ONet(x, params) {
+        return tidy(function () {
+            var out = sharedLayer(x, params);
+            out = maxPool(out, [2, 2], [2, 2], 'same');
+            out = convLayer(out, params.conv4, 'valid');
+            out = prelu$1(out, params.prelu4_alpha);
+            var vectorized = reshape(out, [out.shape[0], params.fc1.weights.shape[0]]);
+            var fc1 = fullyConnectedLayer(vectorized, params.fc1);
+            var prelu5 = prelu$1(fc1, params.prelu5_alpha);
+            var fc2_1 = fullyConnectedLayer(prelu5, params.fc2_1);
+            var max$$1 = expandDims(max(fc2_1, 1), 1);
+            var prob = softmax(sub(fc2_1, max$$1), 1);
+            var regions = fullyConnectedLayer(prelu5, params.fc2_2);
+            var points = fullyConnectedLayer(prelu5, params.fc2_3);
+            var scores = unstack(prob, 1)[1];
+            return { scores: scores, regions: regions, points: points };
+        });
+    }
+
+    function stage3(img, inputBoxes, scoreThreshold, params, stats) {
+        return __awaiter$1(this, void 0, void 0, function () {
+            var ts, onetInputs, onetOuts, scoresTensor, scores, _a, _b, indices, filteredRegions, filteredBoxes, filteredScores, finalBoxes, finalScores, points, indicesNms;
+            return __generator$1(this, function (_c) {
+                switch (_c.label) {
+                    case 0:
+                        ts = Date.now();
+                        return [4 /*yield*/, extractImagePatches(img, inputBoxes, { width: 48, height: 48 })];
+                    case 1:
+                        onetInputs = _c.sent();
+                        stats.stage3_extractImagePatches = Date.now() - ts;
+                        ts = Date.now();
+                        onetOuts = onetInputs.map(function (onetInput) {
+                            var out = ONet(onetInput, params);
+                            onetInput.dispose();
+                            return out;
+                        });
+                        stats.stage3_onet = Date.now() - ts;
+                        scoresTensor = onetOuts.length > 1
+                            ? concat(onetOuts.map(function (out) { return out.scores; }))
+                            : onetOuts[0].scores;
+                        _b = (_a = Array).from;
+                        return [4 /*yield*/, scoresTensor.data()];
+                    case 2:
+                        scores = _b.apply(_a, [_c.sent()]);
+                        scoresTensor.dispose();
+                        indices = scores
+                            .map(function (score, idx) { return ({ score: score, idx: idx }); })
+                            .filter(function (c) { return c.score > scoreThreshold; })
+                            .map(function (_a) {
+                            var idx = _a.idx;
+                            return idx;
+                        });
+                        filteredRegions = indices.map(function (idx) { return new MtcnnBox(onetOuts[idx].regions.get(0, 0), onetOuts[idx].regions.get(0, 1), onetOuts[idx].regions.get(0, 2), onetOuts[idx].regions.get(0, 3)); });
+                        filteredBoxes = indices
+                            .map(function (idx, i) { return inputBoxes[idx].calibrate(filteredRegions[i]); });
+                        filteredScores = indices.map(function (idx) { return scores[idx]; });
+                        finalBoxes = [];
+                        finalScores = [];
+                        points = [];
+                        if (filteredBoxes.length > 0) {
+                            ts = Date.now();
+                            indicesNms = nonMaxSuppression$1(filteredBoxes, filteredScores, 0.7, false);
+                            stats.stage3_nms = Date.now() - ts;
+                            finalBoxes = indicesNms.map(function (idx) { return filteredBoxes[idx]; });
+                            finalScores = indicesNms.map(function (idx) { return filteredScores[idx]; });
+                            points = indicesNms.map(function (idx, i) {
+                                return Array(5).fill(0).map(function (_, ptIdx) {
+                                    return new Point(((onetOuts[idx].points.get(0, ptIdx) * (finalBoxes[i].width + 1)) + finalBoxes[i].left), ((onetOuts[idx].points.get(0, ptIdx + 5) * (finalBoxes[i].height + 1)) + finalBoxes[i].top));
+                                });
+                            });
+                        }
+                        onetOuts.forEach(function (t) {
+                            t.regions.dispose();
+                            t.scores.dispose();
+                            t.points.dispose();
+                        });
+                        return [2 /*return*/, {
+                                boxes: finalBoxes,
+                                scores: finalScores,
+                                points: points
+                            }];
+                }
+            });
+        });
+    }
+
+    var Mtcnn = /** @class */ (function (_super) {
+        __extends$1(Mtcnn, _super);
+        function Mtcnn() {
+            return _super.call(this, 'Mtcnn') || this;
+        }
+        Mtcnn.prototype.forwardInput = function (input, forwardParams) {
+            if (forwardParams === void 0) { forwardParams = {}; }
+            return __awaiter$1(this, void 0, void 0, function () {
+                var params, inputCanvas, stats, tsTotal, imgTensor, onReturn, _a, height, width, _b, minFaceSize, scaleFactor, maxNumScales, scoreThresholds, scaleSteps, scales, ts, out1, out2, out3, results;
+                return __generator$1(this, function (_c) {
+                    switch (_c.label) {
+                        case 0:
+                            params = this.params;
+                            if (!params) {
+                                throw new Error('Mtcnn - load model before inference');
+                            }
+                            inputCanvas = input.canvases[0];
+                            if (!inputCanvas) {
+                                throw new Error('Mtcnn - inputCanvas is not defined, note that passing tensors into Mtcnn.forwardInput is not supported yet.');
+                            }
+                            stats = {};
+                            tsTotal = Date.now();
+                            imgTensor = tidy(function () {
+                                return bgrToRgbTensor(expandDims(fromPixels(inputCanvas)).toFloat());
+                            });
+                            onReturn = function (results) {
+                                // dispose tensors on return
+                                imgTensor.dispose();
+                                stats.total = Date.now() - tsTotal;
+                                return results;
+                            };
+                            _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
+                            _b = new MtcnnOptions(forwardParams), minFaceSize = _b.minFaceSize, scaleFactor = _b.scaleFactor, maxNumScales = _b.maxNumScales, scoreThresholds = _b.scoreThresholds, scaleSteps = _b.scaleSteps;
+                            scales = (scaleSteps || pyramidDown(minFaceSize, scaleFactor, [height, width]))
+                                .filter(function (scale) {
+                                var sizes = getSizesForScale(scale, [height, width]);
+                                return Math.min(sizes.width, sizes.height) > CELL_SIZE$1;
+                            })
+                                .slice(0, maxNumScales);
+                            stats.scales = scales;
+                            stats.pyramid = scales.map(function (scale) { return getSizesForScale(scale, [height, width]); });
+                            ts = Date.now();
+                            return [4 /*yield*/, stage1(imgTensor, scales, scoreThresholds[0], params.pnet, stats)];
+                        case 1:
+                            out1 = _c.sent();
+                            stats.total_stage1 = Date.now() - ts;
+                            if (!out1.boxes.length) {
+                                return [2 /*return*/, onReturn({ results: [], stats: stats })];
+                            }
+                            stats.stage2_numInputBoxes = out1.boxes.length;
+                            // using the inputCanvas to extract and resize the image patches, since it is faster
+                            // than doing this on the gpu
+                            ts = Date.now();
+                            return [4 /*yield*/, stage2(inputCanvas, out1.boxes, scoreThresholds[1], params.rnet, stats)];
+                        case 2:
+                            out2 = _c.sent();
+                            stats.total_stage2 = Date.now() - ts;
+                            if (!out2.boxes.length) {
+                                return [2 /*return*/, onReturn({ results: [], stats: stats })];
+                            }
+                            stats.stage3_numInputBoxes = out2.boxes.length;
+                            ts = Date.now();
+                            return [4 /*yield*/, stage3(inputCanvas, out2.boxes, scoreThresholds[2], params.onet, stats)];
+                        case 3:
+                            out3 = _c.sent();
+                            stats.total_stage3 = Date.now() - ts;
+                            results = out3.boxes.map(function (box, idx) { return extendWithFaceLandmarks(extendWithFaceDetection({}, new FaceDetection(out3.scores[idx], new Rect(box.left / width, box.top / height, box.width / width, box.height / height), {
+                                height: height,
+                                width: width
+                            })), new FaceLandmarks5(out3.points[idx].map(function (pt) { return pt.sub(new Point(box.left, box.top)).div(new Point(box.width, box.height)); }), { width: box.width, height: box.height })); });
+                            return [2 /*return*/, onReturn({ results: results, stats: stats })];
+                    }
+                });
+            });
+        };
+        Mtcnn.prototype.forward = function (input, forwardParams) {
+            if (forwardParams === void 0) { forwardParams = {}; }
+            return __awaiter$1(this, void 0, void 0, function () {
+                var _a;
+                return __generator$1(this, function (_b) {
+                    switch (_b.label) {
+                        case 0:
+                            _a = this.forwardInput;
+                            return [4 /*yield*/, toNetInput(input)];
+                        case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(),
+                                forwardParams])];
+                        case 2: return [2 /*return*/, (_b.sent()).results];
+                    }
+                });
+            });
+        };
+        Mtcnn.prototype.forwardWithStats = function (input, forwardParams) {
+            if (forwardParams === void 0) { forwardParams = {}; }
+            return __awaiter$1(this, void 0, void 0, function () {
+                var _a;
+                return __generator$1(this, function (_b) {
+                    switch (_b.label) {
+                        case 0:
+                            _a = this.forwardInput;
+                            return [4 /*yield*/, toNetInput(input)];
+                        case 1: return [2 /*return*/, _a.apply(this, [_b.sent(),
+                                forwardParams])];
+                    }
+                });
+            });
+        };
+        Mtcnn.prototype.getDefaultModelName = function () {
+            return 'mtcnn_model';
+        };
+        Mtcnn.prototype.extractParamsFromWeigthMap = function (weightMap) {
+            return extractParamsFromWeigthMap$5(weightMap);
+        };
+        Mtcnn.prototype.extractParams = function (weights) {
+            return extractParams$5(weights);
+        };
+        return Mtcnn;
+    }(NeuralNetwork));
+
+    var IOU_THRESHOLD = 0.4;
+    var BOX_ANCHORS = [
+        new Point(1.603231, 2.094468),
+        new Point(6.041143, 7.080126),
+        new Point(2.882459, 3.518061),
+        new Point(4.266906, 5.178857),
+        new Point(9.041765, 10.66308)
+    ];
+    var MEAN_RGB = [117.001, 114.697, 97.404];
+
+    var TinyFaceDetector = /** @class */ (function (_super) {
+        __extends$1(TinyFaceDetector, _super);
+        function TinyFaceDetector() {
+            var _this = this;
+            var config = {
+                withSeparableConvs: true,
+                iouThreshold: IOU_THRESHOLD,
+                classes: ['face'],
+                anchors: BOX_ANCHORS,
+                meanRgb: MEAN_RGB,
+                isFirstLayerConv2d: true,
+                filterSizes: [3, 16, 32, 64, 128, 256, 512]
+            };
+            _this = _super.call(this, config) || this;
+            return _this;
+        }
+        Object.defineProperty(TinyFaceDetector.prototype, "anchors", {
+            get: function () {
+                return this.config.anchors;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        TinyFaceDetector.prototype.locateFaces = function (input, forwardParams) {
+            return __awaiter$1(this, void 0, void 0, function () {
+                var objectDetections;
+                return __generator$1(this, function (_a) {
+                    switch (_a.label) {
+                        case 0: return [4 /*yield*/, this.detect(input, forwardParams)];
+                        case 1:
+                            objectDetections = _a.sent();
+                            return [2 /*return*/, objectDetections.map(function (det) { return new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }); })];
+                    }
+                });
+            });
+        };
+        TinyFaceDetector.prototype.getDefaultModelName = function () {
+            return 'tiny_face_detector_model';
+        };
+        TinyFaceDetector.prototype.extractParamsFromWeigthMap = function (weightMap) {
+            return _super.prototype.extractParamsFromWeigthMap.call(this, weightMap);
+        };
+        return TinyFaceDetector;
+    }(TinyYolov2));
+
+    var IOU_THRESHOLD$1 = 0.4;
+    var BOX_ANCHORS$1 = [
+        new Point(0.738768, 0.874946),
+        new Point(2.42204, 2.65704),
+        new Point(4.30971, 7.04493),
+        new Point(10.246, 4.59428),
+        new Point(12.6868, 11.8741)
+    ];
+    var BOX_ANCHORS_SEPARABLE = [
+        new Point(1.603231, 2.094468),
+        new Point(6.041143, 7.080126),
+        new Point(2.882459, 3.518061),
+        new Point(4.266906, 5.178857),
+        new Point(9.041765, 10.66308)
+    ];
+    var MEAN_RGB_SEPARABLE = [117.001, 114.697, 97.404];
+    var DEFAULT_MODEL_NAME = 'tiny_yolov2_model';
+    var DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model';
+
+    var TinyYolov2$1 = /** @class */ (function (_super) {
+        __extends$1(TinyYolov2$$1, _super);
+        function TinyYolov2$$1(withSeparableConvs) {
+            if (withSeparableConvs === void 0) { withSeparableConvs = true; }
+            var _this = this;
+            var config = Object.assign({}, {
+                withSeparableConvs: withSeparableConvs,
+                iouThreshold: IOU_THRESHOLD$1,
+                classes: ['face']
+            }, withSeparableConvs
+                ? {
+                    anchors: BOX_ANCHORS_SEPARABLE,
+                    meanRgb: MEAN_RGB_SEPARABLE
+                }
+                : {
+                    anchors: BOX_ANCHORS$1,
+                    withClassScores: true
+                });
+            _this = _super.call(this, config) || this;
+            return _this;
+        }
+        Object.defineProperty(TinyYolov2$$1.prototype, "withSeparableConvs", {
+            get: function () {
+                return this.config.withSeparableConvs;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2$$1.prototype, "anchors", {
+            get: function () {
+                return this.config.anchors;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        TinyYolov2$$1.prototype.locateFaces = function (input, forwardParams) {
+            return __awaiter$1(this, void 0, void 0, function () {
+                var objectDetections;
+                return __generator$1(this, function (_a) {
+                    switch (_a.label) {
+                        case 0: return [4 /*yield*/, this.detect(input, forwardParams)];
+                        case 1:
+                            objectDetections = _a.sent();
+                            return [2 /*return*/, objectDetections.map(function (det) { return new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }); })];
+                    }
+                });
+            });
+        };
+        TinyYolov2$$1.prototype.getDefaultModelName = function () {
+            return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME;
+        };
+        TinyYolov2$$1.prototype.extractParamsFromWeigthMap = function (weightMap) {
+            return _super.prototype.extractParamsFromWeigthMap.call(this, weightMap);
+        };
+        return TinyYolov2$$1;
+    }(TinyYolov2));
+
+    var nets = {
+        ssdMobilenetv1: new SsdMobilenetv1(),
+        tinyFaceDetector: new TinyFaceDetector(),
+        tinyYolov2: new TinyYolov2$1(),
+        mtcnn: new Mtcnn(),
+        faceLandmark68Net: new FaceLandmark68Net(),
+        faceLandmark68TinyNet: new FaceLandmark68TinyNet(),
+        faceRecognitionNet: new FaceRecognitionNet(),
+        faceExpressionNet: new FaceExpressionNet()
+    };
+    /**
+     * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.
+     *
+     * @param input The input image.
+     * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).
+     * @returns Bounding box of each face with score.
+     */
+    var ssdMobilenetv1 = function (input, options) {
+        return nets.ssdMobilenetv1.locateFaces(input, options);
+    };
+    /**
+     * Attempts to detect all faces in an image using the Tiny Face Detector.
+     *
+     * @param input The input image.
+     * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).
+     * @returns Bounding box of each face with score.
+     */
+    var tinyFaceDetector = function (input, options) {
+        return nets.tinyFaceDetector.locateFaces(input, options);
+    };
+    /**
+     * Attempts to detect all faces in an image using the Tiny Yolov2 Network.
+     *
+     * @param input The input image.
+     * @param options (optional, default: see TinyYolov2Options constructor for default parameters).
+     * @returns Bounding box of each face with score.
+     */
+    var tinyYolov2 = function (input, options) {
+        return nets.tinyYolov2.locateFaces(input, options);
+    };
+    /**
+     * Attempts to detect all faces in an image and the 5 point face landmarks
+     * of each detected face using the MTCNN Network.
+     *
+     * @param input The input image.
+     * @param options (optional, default: see MtcnnOptions constructor for default parameters).
+     * @returns Bounding box of each face with score and 5 point face landmarks.
+     */
+    var mtcnn = function (input, options) {
+        return nets.mtcnn.forward(input, options);
+    };
+    /**
+     * Detects the 68 point face landmark positions of the face shown in an image.
+     *
+     * @param inputs The face image extracted from the bounding box of a face. Can
+     * also be an array of input images, which will be batch processed.
+     * @returns 68 point face landmarks or array thereof in case of batch input.
+     */
+    var detectFaceLandmarks = function (input) {
+        return nets.faceLandmark68Net.detectLandmarks(input);
+    };
+    /**
+     * Detects the 68 point face landmark positions of the face shown in an image
+     * using a tinier version of the 68 point face landmark model, which is slightly
+     * faster at inference, but also slightly less accurate.
+     *
+     * @param inputs The face image extracted from the bounding box of a face. Can
+     * also be an array of input images, which will be batch processed.
+     * @returns 68 point face landmarks or array thereof in case of batch input.
+     */
+    var detectFaceLandmarksTiny = function (input) {
+        return nets.faceLandmark68TinyNet.detectLandmarks(input);
+    };
+    /**
+     * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,
+     * which uniquely represents the features of that persons face. The computed face descriptor can
+     * be used to measure the similarity between faces, by computing the euclidean distance of two
+     * face descriptors.
+     *
+     * @param inputs The face image extracted from the aligned bounding box of a face. Can
+     * also be an array of input images, which will be batch processed.
+     * @returns Face descriptor with 128 entries or array thereof in case of batch input.
+     */
+    var computeFaceDescriptor = function (input) {
+        return nets.faceRecognitionNet.computeFaceDescriptor(input);
+    };
+    /**
+     * Recognizes the facial expressions of a face and returns the likelyhood of
+     * each facial expression.
+     *
+     * @param inputs The face image extracted from the bounding box of a face. Can
+     * also be an array of input images, which will be batch processed.
+     * @returns An array of facial expressions with corresponding probabilities or array thereof in case of batch input.
+     */
+    var recognizeFaceExpressions = function (input) {
+        return nets.faceExpressionNet.predictExpressions(input);
+    };
+    var loadSsdMobilenetv1Model = function (url) { return nets.ssdMobilenetv1.load(url); };
+    var loadTinyFaceDetectorModel = function (url) { return nets.tinyFaceDetector.load(url); };
+    var loadMtcnnModel = function (url) { return nets.mtcnn.load(url); };
+    var loadTinyYolov2Model = function (url) { return nets.tinyYolov2.load(url); };
+    var loadFaceLandmarkModel = function (url) { return nets.faceLandmark68Net.load(url); };
+    var loadFaceLandmarkTinyModel = function (url) { return nets.faceLandmark68TinyNet.load(url); };
+    var loadFaceRecognitionModel = function (url) { return nets.faceRecognitionNet.load(url); };
+    var loadFaceExpressionModel = function (url) { return nets.faceExpressionNet.load(url); };
+    // backward compatibility
+    var loadFaceDetectionModel = loadSsdMobilenetv1Model;
+    var locateFaces = ssdMobilenetv1;
+    var detectLandmarks = detectFaceLandmarks;
+
+    var ComputeFaceDescriptorsTaskBase = /** @class */ (function (_super) {
+        __extends$1(ComputeFaceDescriptorsTaskBase, _super);
+        function ComputeFaceDescriptorsTaskBase(parentTask, input) {
+            var _this = _super.call(this) || this;
+            _this.parentTask = parentTask;
+            _this.input = input;
+            return _this;
+        }
+        return ComputeFaceDescriptorsTaskBase;
+    }(ComposableTask));
+    var ComputeAllFaceDescriptorsTask = /** @class */ (function (_super) {
+        __extends$1(ComputeAllFaceDescriptorsTask, _super);
+        function ComputeAllFaceDescriptorsTask() {
+            return _super !== null && _super.apply(this, arguments) || this;
+        }
+        ComputeAllFaceDescriptorsTask.prototype.run = function () {
+            return __awaiter$1(this, void 0, void 0, function () {
+                var _this = this;
+                var parentResults, alignedRects, alignedFaces, _a, results;
+                return __generator$1(this, function (_b) {
+                    switch (_b.label) {
+                        case 0: return [4 /*yield*/, this.parentTask];
+                        case 1:
+                            parentResults = _b.sent();
+                            alignedRects = parentResults.map(function (_a) {
+                                var alignedRect = _a.alignedRect;
+                                return alignedRect;
+                            });
+                            if (!(this.input instanceof Tensor)) return [3 /*break*/, 3];
+                            return [4 /*yield*/, extractFaceTensors(this.input, alignedRects)];
+                        case 2:
+                            _a = _b.sent();
+                            return [3 /*break*/, 5];
+                        case 3: return [4 /*yield*/, extractFaces(this.input, alignedRects)];
+                        case 4:
+                            _a = _b.sent();
+                            _b.label = 5;
+                        case 5:
+                            alignedFaces = _a;
+                            return [4 /*yield*/, Promise.all(parentResults.map(function (parentResult, i) { return __awaiter$1(_this, void 0, void 0, function () {
+                                    var descriptor;
+                                    return __generator$1(this, function (_a) {
+                                        switch (_a.label) {
+                                            case 0: return [4 /*yield*/, nets.faceRecognitionNet.computeFaceDescriptor(alignedFaces[i])];
+                                            case 1:
+                                                descriptor = _a.sent();
+                                                return [2 /*return*/, extendWithFaceDescriptor(parentResult, descriptor)];
+                                        }
+                                    });
+                                }); }))];
+                        case 6:
+                            results = _b.sent();
+                            alignedFaces.forEach(function (f) { return f instanceof Tensor && f.dispose(); });
+                            return [2 /*return*/, results];
+                    }
+                });
+            });
+        };
+        return ComputeAllFaceDescriptorsTask;
+    }(ComputeFaceDescriptorsTaskBase));
+    var ComputeSingleFaceDescriptorTask = /** @class */ (function (_super) {
+        __extends$1(ComputeSingleFaceDescriptorTask, _super);
+        function ComputeSingleFaceDescriptorTask() {
+            return _super !== null && _super.apply(this, arguments) || this;
+        }
+        ComputeSingleFaceDescriptorTask.prototype.run = function () {
+            return __awaiter$1(this, void 0, void 0, function () {
+                var parentResult, alignedRect, alignedFaces, _a, descriptor;
+                return __generator$1(this, function (_b) {
+                    switch (_b.label) {
+                        case 0: return [4 /*yield*/, this.parentTask];
+                        case 1:
+                            parentResult = _b.sent();
+                            if (!parentResult) {
+                                return [2 /*return*/];
+                            }
+                            alignedRect = parentResult.alignedRect;
+                            if (!(this.input instanceof Tensor)) return [3 /*break*/, 3];
+                            return [4 /*yield*/, extractFaceTensors(this.input, [alignedRect])];
+                        case 2:
+                            _a = _b.sent();
+                            return [3 /*break*/, 5];
+                        case 3: return [4 /*yield*/, extractFaces(this.input, [alignedRect])];
+                        case 4:
+                            _a = _b.sent();
+                            _b.label = 5;
+                        case 5:
+                            alignedFaces = _a;
+                            return [4 /*yield*/, nets.faceRecognitionNet.computeFaceDescriptor(alignedFaces[0])];
+                        case 6:
+                            descriptor = _b.sent();
+                            alignedFaces.forEach(function (f) { return f instanceof Tensor && f.dispose(); });
+                            return [2 /*return*/, extendWithFaceDescriptor(parentResult, descriptor)];
+                    }
+                });
+            });
+        };
+        return ComputeSingleFaceDescriptorTask;
+    }(ComputeFaceDescriptorsTaskBase));
+
+    var DetectFaceLandmarksTaskBase = /** @class */ (function (_super) {
+        __extends$1(DetectFaceLandmarksTaskBase, _super);
+        function DetectFaceLandmarksTaskBase(parentTask, input, useTinyLandmarkNet) {
+            var _this = _super.call(this) || this;
+            _this.parentTask = parentTask;
+            _this.input = input;
+            _this.useTinyLandmarkNet = useTinyLandmarkNet;
+            return _this;
+        }
+        Object.defineProperty(DetectFaceLandmarksTaskBase.prototype, "landmarkNet", {
+            get: function () {
+                return this.useTinyLandmarkNet
+                    ? nets.faceLandmark68TinyNet
+                    : nets.faceLandmark68Net;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        return DetectFaceLandmarksTaskBase;
+    }(ComposableTask));
+    var DetectAllFaceLandmarksTask = /** @class */ (function (_super) {
+        __extends$1(DetectAllFaceLandmarksTask, _super);
+        function DetectAllFaceLandmarksTask() {
+            return _super !== null && _super.apply(this, arguments) || this;
+        }
+        DetectAllFaceLandmarksTask.prototype.run = function () {
+            return __awaiter$1(this, void 0, void 0, function () {
+                var _this = this;
+                var parentResults, detections, faces, _a, faceLandmarksByFace;
+                return __generator$1(this, function (_b) {
+                    switch (_b.label) {
+                        case 0: return [4 /*yield*/, this.parentTask];
+                        case 1:
+                            parentResults = _b.sent();
+                            detections = parentResults.map(function (res) { return res.detection; });
+                            if (!(this.input instanceof Tensor)) return [3 /*break*/, 3];
+                            return [4 /*yield*/, extractFaceTensors(this.input, detections)];
+                        case 2:
+                            _a = _b.sent();
+                            return [3 /*break*/, 5];
+                        case 3: return [4 /*yield*/, extractFaces(this.input, detections)];
+                        case 4:
+                            _a = _b.sent();
+                            _b.label = 5;
+                        case 5:
+                            faces = _a;
+                            return [4 /*yield*/, Promise.all(faces.map(function (face) { return _this.landmarkNet.detectLandmarks(face); }))];
+                        case 6:
+                            faceLandmarksByFace = _b.sent();
+                            faces.forEach(function (f) { return f instanceof Tensor && f.dispose(); });
+                            return [2 /*return*/, parentResults.map(function (parentResult, i) {
+                                    return extendWithFaceLandmarks(parentResult, faceLandmarksByFace[i]);
+                                })];
+                    }
+                });
+            });
+        };
+        DetectAllFaceLandmarksTask.prototype.withFaceDescriptors = function () {
+            return new ComputeAllFaceDescriptorsTask(this, this.input);
+        };
+        return DetectAllFaceLandmarksTask;
+    }(DetectFaceLandmarksTaskBase));
+    var DetectSingleFaceLandmarksTask = /** @class */ (function (_super) {
+        __extends$1(DetectSingleFaceLandmarksTask, _super);
+        function DetectSingleFaceLandmarksTask() {
+            return _super !== null && _super.apply(this, arguments) || this;
+        }
+        DetectSingleFaceLandmarksTask.prototype.run = function () {
+            return __awaiter$1(this, void 0, void 0, function () {
+                var parentResult, detection, faces, _a, landmarks;
+                return __generator$1(this, function (_b) {
+                    switch (_b.label) {
+                        case 0: return [4 /*yield*/, this.parentTask];
+                        case 1:
+                            parentResult = _b.sent();
+                            if (!parentResult) {
+                                return [2 /*return*/];
+                            }
+                            detection = parentResult.detection;
+                            if (!(this.input instanceof Tensor)) return [3 /*break*/, 3];
+                            return [4 /*yield*/, extractFaceTensors(this.input, [detection])];
+                        case 2:
+                            _a = _b.sent();
+                            return [3 /*break*/, 5];
+                        case 3: return [4 /*yield*/, extractFaces(this.input, [detection])];
+                        case 4:
+                            _a = _b.sent();
+                            _b.label = 5;
+                        case 5:
+                            faces = _a;
+                            return [4 /*yield*/, this.landmarkNet.detectLandmarks(faces[0])];
+                        case 6:
+                            landmarks = _b.sent();
+                            faces.forEach(function (f) { return f instanceof Tensor && f.dispose(); });
+                            return [2 /*return*/, extendWithFaceLandmarks(parentResult, landmarks)];
+                    }
+                });
+            });
+        };
+        DetectSingleFaceLandmarksTask.prototype.withFaceDescriptor = function () {
+            return new ComputeSingleFaceDescriptorTask(this, this.input);
+        };
+        return DetectSingleFaceLandmarksTask;
+    }(DetectFaceLandmarksTaskBase));
+
+    var PredictFaceExpressionsTaskBase = /** @class */ (function (_super) {
+        __extends$1(PredictFaceExpressionsTaskBase, _super);
+        function PredictFaceExpressionsTaskBase(parentTask, input) {
+            var _this = _super.call(this) || this;
+            _this.parentTask = parentTask;
+            _this.input = input;
+            return _this;
+        }
+        return PredictFaceExpressionsTaskBase;
+    }(ComposableTask));
+    var PredictAllFaceExpressionsTask = /** @class */ (function (_super) {
+        __extends$1(PredictAllFaceExpressionsTask, _super);
+        function PredictAllFaceExpressionsTask() {
+            return _super !== null && _super.apply(this, arguments) || this;
+        }
+        PredictAllFaceExpressionsTask.prototype.run = function () {
+            return __awaiter$1(this, void 0, void 0, function () {
+                var parentResults, detections, faces, _a, faceExpressionsByFace;
+                return __generator$1(this, function (_b) {
+                    switch (_b.label) {
+                        case 0: return [4 /*yield*/, this.parentTask];
+                        case 1:
+                            parentResults = _b.sent();
+                            detections = parentResults.map(function (parentResult) { return parentResult.detection; });
+                            if (!(this.input instanceof Tensor)) return [3 /*break*/, 3];
+                            return [4 /*yield*/, extractFaceTensors(this.input, detections)];
+                        case 2:
+                            _a = _b.sent();
+                            return [3 /*break*/, 5];
+                        case 3: return [4 /*yield*/, extractFaces(this.input, detections)];
+                        case 4:
+                            _a = _b.sent();
+                            _b.label = 5;
+                        case 5:
+                            faces = _a;
+                            return [4 /*yield*/, Promise.all(faces.map(function (face) { return nets.faceExpressionNet.predictExpressions(face); }))];
+                        case 6:
+                            faceExpressionsByFace = _b.sent();
+                            faces.forEach(function (f) { return f instanceof Tensor && f.dispose(); });
+                            return [2 /*return*/, parentResults.map(function (parentResult, i) { return extendWithFaceExpressions(parentResult, faceExpressionsByFace[i]); })];
+                    }
+                });
+            });
+        };
+        PredictAllFaceExpressionsTask.prototype.withFaceLandmarks = function () {
+            return new DetectAllFaceLandmarksTask(this, this.input, false);
+        };
+        return PredictAllFaceExpressionsTask;
+    }(PredictFaceExpressionsTaskBase));
+    var PredictSingleFaceExpressionTask = /** @class */ (function (_super) {
+        __extends$1(PredictSingleFaceExpressionTask, _super);
+        function PredictSingleFaceExpressionTask() {
+            return _super !== null && _super.apply(this, arguments) || this;
+        }
+        PredictSingleFaceExpressionTask.prototype.run = function () {
+            return __awaiter$1(this, void 0, void 0, function () {
+                var parentResult, detection, faces, _a, faceExpressions;
+                return __generator$1(this, function (_b) {
+                    switch (_b.label) {
+                        case 0: return [4 /*yield*/, this.parentTask];
+                        case 1:
+                            parentResult = _b.sent();
+                            if (!parentResult) {
+                                return [2 /*return*/];
+                            }
+                            detection = parentResult.detection;
+                            if (!(this.input instanceof Tensor)) return [3 /*break*/, 3];
+                            return [4 /*yield*/, extractFaceTensors(this.input, [detection])];
+                        case 2:
+                            _a = _b.sent();
+                            return [3 /*break*/, 5];
+                        case 3: return [4 /*yield*/, extractFaces(this.input, [detection])];
+                        case 4:
+                            _a = _b.sent();
+                            _b.label = 5;
+                        case 5:
+                            faces = _a;
+                            return [4 /*yield*/, nets.faceExpressionNet.predictExpressions(faces[0])];
+                        case 6:
+                            faceExpressions = _b.sent();
+                            faces.forEach(function (f) { return f instanceof Tensor && f.dispose(); });
+                            return [2 /*return*/, extendWithFaceExpressions(parentResult, faceExpressions)];
+                    }
+                });
+            });
+        };
+        PredictSingleFaceExpressionTask.prototype.withFaceLandmarks = function () {
+            return new DetectSingleFaceLandmarksTask(this, this.input, false);
+        };
+        return PredictSingleFaceExpressionTask;
+    }(PredictFaceExpressionsTaskBase));
+
+    var DetectFacesTaskBase = /** @class */ (function (_super) {
+        __extends$1(DetectFacesTaskBase, _super);
+        function DetectFacesTaskBase(input, options) {
+            if (options === void 0) { options = new SsdMobilenetv1Options(); }
+            var _this = _super.call(this) || this;
+            _this.input = input;
+            _this.options = options;
+            return _this;
+        }
+        return DetectFacesTaskBase;
+    }(ComposableTask));
+    var DetectAllFacesTask = /** @class */ (function (_super) {
+        __extends$1(DetectAllFacesTask, _super);
+        function DetectAllFacesTask() {
+            return _super !== null && _super.apply(this, arguments) || this;
+        }
+        DetectAllFacesTask.prototype.run = function () {
+            return __awaiter$1(this, void 0, void 0, function () {
+                var _a, input, options, faceDetectionFunction;
+                return __generator$1(this, function (_b) {
+                    switch (_b.label) {
+                        case 0:
+                            _a = this, input = _a.input, options = _a.options;
+                            if (!(options instanceof MtcnnOptions)) return [3 /*break*/, 2];
+                            return [4 /*yield*/, nets.mtcnn.forward(input, options)];
+                        case 1: return [2 /*return*/, (_b.sent())
+                                .map(function (result) { return result.detection; })];
+                        case 2:
+                            faceDetectionFunction = options instanceof TinyFaceDetectorOptions
+                                ? function (input) { return nets.tinyFaceDetector.locateFaces(input, options); }
+                                : (options instanceof SsdMobilenetv1Options
+                                    ? function (input) { return nets.ssdMobilenetv1.locateFaces(input, options); }
+                                    : (options instanceof TinyYolov2Options
+                                        ? function (input) { return nets.tinyYolov2.locateFaces(input, options); }
+                                        : null));
+                            if (!faceDetectionFunction) {
+                                throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options');
+                            }
+                            return [2 /*return*/, faceDetectionFunction(input)];
+                    }
+                });
+            });
+        };
+        DetectAllFacesTask.prototype.runAndExtendWithFaceDetections = function () {
+            var _this = this;
+            return new Promise(function (res) { return __awaiter$1(_this, void 0, void 0, function () {
+                var detections;
+                return __generator$1(this, function (_a) {
+                    switch (_a.label) {
+                        case 0: return [4 /*yield*/, this.run()];
+                        case 1:
+                            detections = _a.sent();
+                            return [2 /*return*/, res(detections.map(function (detection) { return extendWithFaceDetection({}, detection); }))];
+                    }
+                });
+            }); });
+        };
+        DetectAllFacesTask.prototype.withFaceLandmarks = function (useTinyLandmarkNet) {
+            if (useTinyLandmarkNet === void 0) { useTinyLandmarkNet = false; }
+            return new DetectAllFaceLandmarksTask(this.runAndExtendWithFaceDetections(), this.input, useTinyLandmarkNet);
+        };
+        DetectAllFacesTask.prototype.withFaceExpressions = function () {
+            return new PredictAllFaceExpressionsTask(this.runAndExtendWithFaceDetections(), this.input);
+        };
+        return DetectAllFacesTask;
+    }(DetectFacesTaskBase));
+    var DetectSingleFaceTask = /** @class */ (function (_super) {
+        __extends$1(DetectSingleFaceTask, _super);
+        function DetectSingleFaceTask() {
+            return _super !== null && _super.apply(this, arguments) || this;
+        }
+        DetectSingleFaceTask.prototype.run = function () {
+            return __awaiter$1(this, void 0, void 0, function () {
+                var faceDetections, faceDetectionWithHighestScore;
+                return __generator$1(this, function (_a) {
+                    switch (_a.label) {
+                        case 0: return [4 /*yield*/, new DetectAllFacesTask(this.input, this.options)];
+                        case 1:
+                            faceDetections = _a.sent();
+                            faceDetectionWithHighestScore = faceDetections[0];
+                            faceDetections.forEach(function (faceDetection) {
+                                if (faceDetection.score > faceDetectionWithHighestScore.score) {
+                                    faceDetectionWithHighestScore = faceDetection;
+                                }
+                            });
+                            return [2 /*return*/, faceDetectionWithHighestScore];
+                    }
+                });
+            });
+        };
+        DetectSingleFaceTask.prototype.runAndExtendWithFaceDetection = function () {
+            var _this = this;
+            return new Promise(function (res) { return __awaiter$1(_this, void 0, void 0, function () {
+                var detection;
+                return __generator$1(this, function (_a) {
+                    switch (_a.label) {
+                        case 0: return [4 /*yield*/, this.run()];
+                        case 1:
+                            detection = _a.sent();
+                            return [2 /*return*/, res(detection ? extendWithFaceDetection({}, detection) : undefined)];
+                    }
+                });
+            }); });
+        };
+        DetectSingleFaceTask.prototype.withFaceLandmarks = function (useTinyLandmarkNet) {
+            if (useTinyLandmarkNet === void 0) { useTinyLandmarkNet = false; }
+            return new DetectSingleFaceLandmarksTask(this.runAndExtendWithFaceDetection(), this.input, useTinyLandmarkNet);
+        };
+        DetectSingleFaceTask.prototype.withFaceExpressions = function () {
+            return new PredictSingleFaceExpressionTask(this.runAndExtendWithFaceDetection(), this.input);
+        };
+        return DetectSingleFaceTask;
+    }(DetectFacesTaskBase));
+
+    function detectSingleFace(input, options) {
+        if (options === void 0) { options = new SsdMobilenetv1Options(); }
+        return new DetectSingleFaceTask(input, options);
+    }
+    function detectAllFaces(input, options) {
+        if (options === void 0) { options = new SsdMobilenetv1Options(); }
+        return new DetectAllFacesTask(input, options);
+    }
+
+    // export allFaces API for backward compatibility
+    function allFacesSsdMobilenetv1(input, minConfidence) {
+        return __awaiter$1(this, void 0, void 0, function () {
+            return __generator$1(this, function (_a) {
+                switch (_a.label) {
+                    case 0: return [4 /*yield*/, detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence: minConfidence } : {}))
+                            .withFaceLandmarks()
+                            .withFaceDescriptors()];
+                    case 1: return [2 /*return*/, _a.sent()];
+                }
+            });
+        });
+    }
+    function allFacesTinyYolov2(input, forwardParams) {
+        if (forwardParams === void 0) { forwardParams = {}; }
+        return __awaiter$1(this, void 0, void 0, function () {
+            return __generator$1(this, function (_a) {
+                switch (_a.label) {
+                    case 0: return [4 /*yield*/, detectAllFaces(input, new TinyYolov2Options(forwardParams))
+                            .withFaceLandmarks()
+                            .withFaceDescriptors()];
+                    case 1: return [2 /*return*/, _a.sent()];
+                }
+            });
+        });
+    }
+    function allFacesMtcnn(input, forwardParams) {
+        if (forwardParams === void 0) { forwardParams = {}; }
+        return __awaiter$1(this, void 0, void 0, function () {
+            return __generator$1(this, function (_a) {
+                switch (_a.label) {
+                    case 0: return [4 /*yield*/, detectAllFaces(input, new MtcnnOptions(forwardParams))
+                            .withFaceLandmarks()
+                            .withFaceDescriptors()];
+                    case 1: return [2 /*return*/, _a.sent()];
+                }
+            });
+        });
+    }
+    var allFaces = allFacesSsdMobilenetv1;
+
+    function euclideanDistance(arr1, arr2) {
+        if (arr1.length !== arr2.length)
+            throw new Error('euclideanDistance: arr1.length !== arr2.length');
+        var desc1 = Array.from(arr1);
+        var desc2 = Array.from(arr2);
+        return Math.sqrt(desc1
+            .map(function (val, i) { return val - desc2[i]; })
+            .reduce(function (res, diff) { return res + Math.pow(diff, 2); }, 0));
+    }
+
+    var FaceMatcher = /** @class */ (function () {
+        function FaceMatcher(inputs, distanceThreshold) {
+            if (distanceThreshold === void 0) { distanceThreshold = 0.6; }
+            this._distanceThreshold = distanceThreshold;
+            var inputArray = Array.isArray(inputs) ? inputs : [inputs];
+            if (!inputArray.length) {
+                throw new Error("FaceRecognizer.constructor - expected atleast one input");
+            }
+            var count = 1;
+            var createUniqueLabel = function () { return "person " + count++; };
+            this._labeledDescriptors = inputArray.map(function (desc) {
+                if (desc instanceof LabeledFaceDescriptors) {
+                    return desc;
+                }
+                if (desc instanceof Float32Array) {
+                    return new LabeledFaceDescriptors(createUniqueLabel(), [desc]);
+                }
+                if (desc.descriptor && desc.descriptor instanceof Float32Array) {
+                    return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor]);
+                }
+                throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>");
+            });
+        }
+        Object.defineProperty(FaceMatcher.prototype, "labeledDescriptors", {
+            get: function () { return this._labeledDescriptors; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(FaceMatcher.prototype, "distanceThreshold", {
+            get: function () { return this._distanceThreshold; },
+            enumerable: true,
+            configurable: true
+        });
+        FaceMatcher.prototype.computeMeanDistance = function (queryDescriptor, descriptors) {
+            return descriptors
+                .map(function (d) { return euclideanDistance(d, queryDescriptor); })
+                .reduce(function (d1, d2) { return d1 + d2; }, 0)
+                / (descriptors.length || 1);
+        };
+        FaceMatcher.prototype.matchDescriptor = function (queryDescriptor) {
+            var _this = this;
+            return this.labeledDescriptors
+                .map(function (_a) {
+                var descriptors = _a.descriptors, label = _a.label;
+                return new FaceMatch(label, _this.computeMeanDistance(queryDescriptor, descriptors));
+            })
+                .reduce(function (best, curr) { return best.distance < curr.distance ? best : curr; });
+        };
+        FaceMatcher.prototype.findBestMatch = function (queryDescriptor) {
+            var bestMatch = this.matchDescriptor(queryDescriptor);
+            return bestMatch.distance < this.distanceThreshold
+                ? bestMatch
+                : new FaceMatch('unknown', bestMatch.distance);
+        };
+        return FaceMatcher;
+    }());
+
+    function createMtcnn(weights) {
+        var net = new Mtcnn();
+        net.extractWeights(weights);
+        return net;
+    }
+
+    function createTinyFaceDetector(weights) {
+        var net = new TinyFaceDetector();
+        net.extractWeights(weights);
+        return net;
+    }
+
+    function createTinyYolov2(weights, withSeparableConvs) {
+        if (withSeparableConvs === void 0) { withSeparableConvs = true; }
+        var net = new TinyYolov2$1(withSeparableConvs);
+        net.extractWeights(weights);
+        return net;
+    }
+
+    function resizeResults(results, _a) {
+        var width = _a.width, height = _a.height;
+        if (Array.isArray(results)) {
+            return results.map(function (obj) { return resizeResults(obj, { width: width, height: height }); });
+        }
+        var hasLandmarks = results['unshiftedLandmarks'] && results['unshiftedLandmarks'] instanceof FaceLandmarks;
+        var hasDetection = results['detection'] && results['detection'] instanceof FaceDetection;
+        if (hasLandmarks) {
+            var resizedDetection = results['detection'].forSize(width, height);
+            var resizedLandmarks = results['unshiftedLandmarks'].forSize(resizedDetection.box.width, resizedDetection.box.height);
+            return extendWithFaceLandmarks(extendWithFaceDetection(results, resizedDetection), resizedLandmarks);
+        }
+        if (hasDetection) {
+            return extendWithFaceDetection(results, results['detection'].forSize(width, height));
+        }
+        if (results instanceof FaceLandmarks || results instanceof FaceDetection) {
+            return results.forSize(width, height);
+        }
+        return results;
+    }
+
+    exports.tf = tfCore_esm;
+    exports.BoundingBox = BoundingBox;
+    exports.Box = Box;
+    exports.BoxWithText = BoxWithText;
+    exports.Dimensions = Dimensions;
+    exports.LabeledBox = LabeledBox;
+    exports.ObjectDetection = ObjectDetection;
+    exports.Point = Point;
+    exports.PredictedBox = PredictedBox;
+    exports.Rect = Rect;
+    exports.disposeUnusedWeightTensors = disposeUnusedWeightTensors;
+    exports.extractWeightEntryFactory = extractWeightEntryFactory;
+    exports.extractWeightsFactory = extractWeightsFactory;
+    exports.getModelUris = getModelUris;
+    exports.awaitMediaLoaded = awaitMediaLoaded;
+    exports.bufferToImage = bufferToImage;
+    exports.createCanvas = createCanvas;
+    exports.createCanvasFromMedia = createCanvasFromMedia;
+    exports.drawBox = drawBox;
+    exports.drawDetection = drawDetection;
+    exports.drawText = drawText;
+    exports.fetchImage = fetchImage;
+    exports.fetchJson = fetchJson;
+    exports.fetchNetWeights = fetchNetWeights;
+    exports.fetchOrThrow = fetchOrThrow;
+    exports.getContext2dOrThrow = getContext2dOrThrow;
+    exports.getDefaultDrawOptions = getDefaultDrawOptions;
+    exports.getMediaDimensions = getMediaDimensions;
+    exports.imageTensorToCanvas = imageTensorToCanvas;
+    exports.imageToSquare = imageToSquare;
+    exports.isMediaElement = isMediaElement;
+    exports.isMediaLoaded = isMediaLoaded;
+    exports.loadWeightMap = loadWeightMap;
+    exports.NetInput = NetInput;
+    exports.resolveInput = resolveInput;
+    exports.toNetInput = toNetInput;
+    exports.env = env;
+    exports.sigmoid = sigmoid$1;
+    exports.inverseSigmoid = inverseSigmoid;
+    exports.iou = iou;
+    exports.nonMaxSuppression = nonMaxSuppression$1;
+    exports.normalize = normalize;
+    exports.padToSquare = padToSquare;
+    exports.shuffleArray = shuffleArray;
+    exports.isTensor = isTensor;
+    exports.isTensor1D = isTensor1D;
+    exports.isTensor2D = isTensor2D;
+    exports.isTensor3D = isTensor3D;
+    exports.isTensor4D = isTensor4D;
+    exports.isFloat = isFloat;
+    exports.isEven = isEven$1;
+    exports.round = round$1;
+    exports.isDimensions = isDimensions;
+    exports.computeReshapedDimensions = computeReshapedDimensions;
+    exports.getCenterPoint = getCenterPoint;
+    exports.range = range$1;
+    exports.isValidNumber = isValidNumber;
+    exports.isValidProbablitiy = isValidProbablitiy;
+    exports.NeuralNetwork = NeuralNetwork;
+    exports.FaceDetection = FaceDetection;
+    exports.FaceLandmarks = FaceLandmarks;
+    exports.FaceLandmarks5 = FaceLandmarks5;
+    exports.FaceLandmarks68 = FaceLandmarks68;
+    exports.FaceMatch = FaceMatch;
+    exports.LabeledFaceDescriptors = LabeledFaceDescriptors;
+    exports.drawContour = drawContour;
+    exports.drawLandmarks = drawLandmarks;
+    exports.drawFaceExpressions = drawFaceExpressions;
+    exports.extractFaces = extractFaces;
+    exports.extractFaceTensors = extractFaceTensors;
+    exports.FaceExpressionNet = FaceExpressionNet;
+    exports.faceExpressionLabels = faceExpressionLabels;
+    exports.FaceLandmarkNet = FaceLandmarkNet;
+    exports.FaceLandmark68Net = FaceLandmark68Net;
+    exports.FaceLandmark68TinyNet = FaceLandmark68TinyNet;
+    exports.createFaceRecognitionNet = createFaceRecognitionNet;
+    exports.FaceRecognitionNet = FaceRecognitionNet;
+    exports.extendWithFaceDescriptor = extendWithFaceDescriptor;
+    exports.extendWithFaceDetection = extendWithFaceDetection;
+    exports.extendWithFaceExpressions = extendWithFaceExpressions;
+    exports.extendWithFaceLandmarks = extendWithFaceLandmarks;
+    exports.allFacesSsdMobilenetv1 = allFacesSsdMobilenetv1;
+    exports.allFacesTinyYolov2 = allFacesTinyYolov2;
+    exports.allFacesMtcnn = allFacesMtcnn;
+    exports.allFaces = allFaces;
+    exports.ComposableTask = ComposableTask;
+    exports.ComputeFaceDescriptorsTaskBase = ComputeFaceDescriptorsTaskBase;
+    exports.ComputeAllFaceDescriptorsTask = ComputeAllFaceDescriptorsTask;
+    exports.ComputeSingleFaceDescriptorTask = ComputeSingleFaceDescriptorTask;
+    exports.detectSingleFace = detectSingleFace;
+    exports.detectAllFaces = detectAllFaces;
+    exports.DetectFacesTaskBase = DetectFacesTaskBase;
+    exports.DetectAllFacesTask = DetectAllFacesTask;
+    exports.DetectSingleFaceTask = DetectSingleFaceTask;
+    exports.DetectFaceLandmarksTaskBase = DetectFaceLandmarksTaskBase;
+    exports.DetectAllFaceLandmarksTask = DetectAllFaceLandmarksTask;
+    exports.DetectSingleFaceLandmarksTask = DetectSingleFaceLandmarksTask;
+    exports.FaceMatcher = FaceMatcher;
+    exports.nets = nets;
+    exports.ssdMobilenetv1 = ssdMobilenetv1;
+    exports.tinyFaceDetector = tinyFaceDetector;
+    exports.tinyYolov2 = tinyYolov2;
+    exports.mtcnn = mtcnn;
+    exports.detectFaceLandmarks = detectFaceLandmarks;
+    exports.detectFaceLandmarksTiny = detectFaceLandmarksTiny;
+    exports.computeFaceDescriptor = computeFaceDescriptor;
+    exports.recognizeFaceExpressions = recognizeFaceExpressions;
+    exports.loadSsdMobilenetv1Model = loadSsdMobilenetv1Model;
+    exports.loadTinyFaceDetectorModel = loadTinyFaceDetectorModel;
+    exports.loadMtcnnModel = loadMtcnnModel;
+    exports.loadTinyYolov2Model = loadTinyYolov2Model;
+    exports.loadFaceLandmarkModel = loadFaceLandmarkModel;
+    exports.loadFaceLandmarkTinyModel = loadFaceLandmarkTinyModel;
+    exports.loadFaceRecognitionModel = loadFaceRecognitionModel;
+    exports.loadFaceExpressionModel = loadFaceExpressionModel;
+    exports.loadFaceDetectionModel = loadFaceDetectionModel;
+    exports.locateFaces = locateFaces;
+    exports.detectLandmarks = detectLandmarks;
+    exports.createMtcnn = createMtcnn;
+    exports.Mtcnn = Mtcnn;
+    exports.MtcnnOptions = MtcnnOptions;
+    exports.createSsdMobilenetv1 = createSsdMobilenetv1;
+    exports.createFaceDetectionNet = createFaceDetectionNet;
+    exports.FaceDetectionNet = FaceDetectionNet;
+    exports.SsdMobilenetv1 = SsdMobilenetv1;
+    exports.SsdMobilenetv1Options = SsdMobilenetv1Options;
+    exports.createTinyFaceDetector = createTinyFaceDetector;
+    exports.TinyFaceDetector = TinyFaceDetector;
+    exports.TinyFaceDetectorOptions = TinyFaceDetectorOptions;
+    exports.createTinyYolov2 = createTinyYolov2;
+    exports.TinyYolov2 = TinyYolov2$1;
+    exports.euclideanDistance = euclideanDistance;
+    exports.resizeResults = resizeResults;
+
+    Object.defineProperty(exports, '__esModule', { value: true });
+
+})));
+//# sourceMappingURL=face-api.js.map
diff --git a/node_modules/face-api.js/dist/face-api.js.map b/node_modules/face-api.js/dist/face-api.js.map
new file mode 100644
index 0000000..e3d10f1
--- /dev/null
+++ b/node_modules/face-api.js/dist/face-api.js.map
@@ -0,0 +1 @@
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Got \"+t+\" / \"+n);var a=e.slice();return a[r]=t/n,a}function squeezeShape(e,t){for(var n=[],r=[],o=0,a=0;a<e.length;++a){if(null!=t){if(t[o]===a&&1!==e[a])throw new Error(\"Can't squeeze axis \"+a+\" since its dim '\"+e[a]+\"' is not 1\");(null==t[o]||t[o]>a)&&1===e[a]&&(n.push(e[a]),r.push(a)),t[o]<=a&&o++}1!==e[a]&&(n.push(e[a]),r.push(a))}return{newShape:n,keptDims:r}}function getTypedArrayFromDType(e,t){var n=null;if(null==e||\"float32\"===e)n=new Float32Array(t);else if(\"int32\"===e)n=new Int32Array(t);else{if(\"bool\"!==e)throw new Error(\"Unknown data type \"+e);n=new Uint8Array(t)}return n}function getArrayFromDType(e,t){var n=null;if(null==e||\"float32\"===e)n=new Float32Array(t);else if(\"int32\"===e)n=new Int32Array(t);else if(\"bool\"===e)n=new Uint8Array(t);else{if(\"string\"!==e)throw new Error(\"Unknown data type \"+e);n=new Array(t)}return n}function checkComputationForNaN(e,t,n){if(\"float32\"===t)for(var r=0;r<e.length;r++)if(isNaN(e[r]))throw Error(\"The result of the '\"+n+\"' has NaNs.\")}function checkConversionForNaN(e,t){if(\"float32\"!==t)for(var n=0;n<e.length;n++)if(isNaN(e[n]))throw Error(\"NaN is not a valid value for dtype: '\"+t+\"'.\")}function hasEncodingLoss(e,t){return\"complex64\"!==t&&((\"float32\"!==t||\"complex64\"===e)&&((\"int32\"!==t||\"float32\"===e||\"complex64\"===e)&&(\"bool\"!==t||\"bool\"!==e)))}function isTypedArray(e){return e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array}function bytesPerElement(e){if(\"float32\"===e||\"int32\"===e)return 4;if(\"complex64\"===e)return 8;if(\"bool\"===e)return 1;throw new Error(\"Unknown dtype \"+e)}function bytesFromStringArray(e){if(null==e)return 0;var t=0;return e.forEach(function(e){return t+=2*e.length}),t}function isString(e){return\"string\"==typeof e||e instanceof String}function isBoolean(e){return\"boolean\"==typeof e}function isNumber(e){return\"number\"==typeof e}function inferDtype(e){return e instanceof Array?inferDtype(e[0]):e instanceof Float32Array?\"float32\":e instanceof Int32Array||e instanceof Uint8Array?\"int32\":isNumber(e)?\"float32\":isString(e)?\"string\":isBoolean(e)?\"bool\":\"float32\"}function isFunction(e){return!!(e&&e.constructor&&e.call&&e.apply)}function nearestDivisor(e,t){for(var n=t;n<e;++n)if(e%n==0)return n;return e}function computeStrides(e){var t=e.length;if(t<2)return[];var n=new Array(t-1);n[t-2]=e[t-1];for(var r=t-3;r>=0;--r)n[r]=n[r+1]*e[r+1];return n}function toTypedArray(e,t,n){if(\"string\"===t)throw new Error(\"Cannot convert a string[] to a TypedArray\");if(noConversionNeeded(e,t))return e;if(Array.isArray(e)&&(e=flatten(e)),null==t||\"float32\"===t||\"complex64\"===t)return new Float32Array(e);if(\"int32\"===t)return n&&checkConversionForNaN(e,t),new Int32Array(e);if(\"bool\"===t){for(var r=new Uint8Array(e.length),o=0;o<r.length;++o)0!==Math.round(e[o])&&(r[o]=1);return r}throw new Error(\"Unknown data type \"+t)}function noConversionNeeded(e,t){return e instanceof Float32Array&&\"float32\"===t||e instanceof Int32Array&&\"int32\"===t||e instanceof Uint8Array&&\"bool\"===t}function makeOnesTypedArray(e,t){for(var n=makeZerosTypedArray(e,t),r=0;r<n.length;r++)n[r]=1;return n}function makeZerosTypedArray(e,t){if(null==t||\"float32\"===t||\"complex64\"===t)return new Float32Array(e);if(\"int32\"===t)return new Int32Array(e);if(\"bool\"===t)return new Uint8Array(e);throw new Error(\"Unknown data type \"+t)}function now(){if(\"undefined\"!=typeof performance)return performance.now();if(\"undefined\"!=typeof process){var e=process.hrtime();return 1e3*e[0]+e[1]/1e6}throw new Error(\"Cannot measure time in this environment. You should run tf.js in the browser or in Node.js\")}var util=Object.freeze({shuffle:shuffle,clamp:clamp,nearestLargerEven:nearestLargerEven,sum:sum,randUniform:randUniform,distSquared:distSquared,assert:assert,assertShapesMatch:assertShapesMatch,assertNonNull:assertNonNull,flatten:flatten,sizeFromShape:sizeFromShape,isScalarShape:isScalarShape,arraysEqual:arraysEqual,isInt:isInt,tanh:tanh,sizeToSquarishShape:sizeToSquarishShape,createShuffledIndices:createShuffledIndices,rightPad:rightPad,repeatedTry:repeatedTry,inferFromImplicitShape:inferFromImplicitShape,squeezeShape:squeezeShape,getTypedArrayFromDType:getTypedArrayFromDType,getArrayFromDType:getArrayFromDType,checkComputationForNaN:checkComputationForNaN,checkConversionForNaN:checkConversionForNaN,hasEncodingLoss:hasEncodingLoss,isTypedArray:isTypedArray,bytesPerElement:bytesPerElement,bytesFromStringArray:bytesFromStringArray,isString:isString,isBoolean:isBoolean,isNumber:isNumber,inferDtype:inferDtype,isFunction:isFunction,nearestDivisor:nearestDivisor,computeStrides:computeStrides,toTypedArray:toTypedArray,makeOnesTypedArray:makeOnesTypedArray,makeZerosTypedArray:makeZerosTypedArray,now:now}),Profiler=function(){function e(e,t){this.backendTimer=e,this.logger=t,null==t&&(this.logger=new Logger)}return e.prototype.profileKernel=function(e,t){var n,r=this,o=this.backendTimer.time(function(){n=t()});return(Array.isArray(n)?n:[n]).forEach(function(t){var n=t.dataSync();checkComputationForNaN(n,t.dtype,e),o.then(function(o){var a=\"\";null!=o.getExtraProfileInfo&&(a=o.getExtraProfileInfo()),r.logger.logKernelProfile(e,t,n,o.kernelMs,a)})}),n},e}(),Logger=function(){function e(){}return e.prototype.logKernelProfile=function(e,t,n,r,o){var a=rightPad(r+\"ms\",9),i=rightPad(e,25),s=t.rank,u=t.size,l=rightPad(t.shape.toString(),14);console.log(\"%c\"+i+\"\\t%c\"+a+\"\\t%c\"+s+\"D \"+l+\"\\t%c\"+u+\"\\t%c\"+o,\"font-weight:bold\",\"color:red\",\"color:blue\",\"color: orange\",\"color: green\")},e}(),FORMAT_LIMIT_NUM_VALS=20,FORMAT_NUM_FIRST_LAST_VALS=3,FORMAT_NUM_SIG_DIGITS=7;function tensorToString(e,t,n,r){var o=computeStrides(t),a=computeMaxSizePerColumn(e,t,n,o),i=t.length,s=subTensorToString(e,t,n,o,a),u=[\"Tensor\"];return r&&(u.push(\"  dtype: \"+n),u.push(\"  rank: \"+i),u.push(\"  shape: [\"+t+\"]\"),u.push(\"  values:\")),u.push(s.map(function(e){return\"    \"+e}).join(\"\\n\")),u.join(\"\\n\")}function computeMaxSizePerColumn(e,t,n,r){var o=sizeFromShape(t),a=r[r.length-1],i=new Array(a).fill(0),s=t.length,u=\"complex64\"===n?createComplexTuples(e):e;if(s>1)for(var l=0;l<o/a;l++)for(var c=l*a,p=0;p<a;p++)i[p]=Math.max(i[p],valToString(u[c+p],0).length);return i}function valToString(e,t){return rightPad(Array.isArray(e)?parseFloat(e[0].toFixed(FORMAT_NUM_SIG_DIGITS))+\" + \"+parseFloat(e[1].toFixed(FORMAT_NUM_SIG_DIGITS))+\"j\":isString(e)?\"'\"+e+\"'\":parseFloat(e.toFixed(FORMAT_NUM_SIG_DIGITS)).toString(),t)}function subTensorToString(e,t,n,r,o,a){void 0===a&&(a=!0);var i=\"complex64\"===n?2:1,s=t[0],u=t.length;if(0===u)return\"complex64\"===n?[valToString(createComplexTuples(e)[0],0)]:[e[0].toString()];if(1===u){if(s>FORMAT_LIMIT_NUM_VALS){var l=FORMAT_NUM_FIRST_LAST_VALS*i,c=Array.from(e.slice(0,l)),p=Array.from(e.slice(s-FORMAT_NUM_FIRST_LAST_VALS*i,s));return\"complex64\"===n&&(c=createComplexTuples(c),p=createComplexTuples(p)),[\"[\"+c.map(function(e,t){return valToString(e,o[t])}).join(\", \")+\", ..., \"+p.map(function(e,t){return valToString(e,o[s-FORMAT_NUM_FIRST_LAST_VALS+t])}).join(\", \")+\"]\"]}return[\"[\"+(\"complex64\"===n?createComplexTuples(e):Array.from(e)).map(function(e,t){return valToString(e,o[t])}).join(\", \")+\"]\"]}var d=t.slice(1),h=r.slice(1),f=r[0]*i,m=[];if(s>FORMAT_LIMIT_NUM_VALS){for(var g=0;g<FORMAT_NUM_FIRST_LAST_VALS;g++){var v=(y=g*f)+f;m.push.apply(m,subTensorToString(e.slice(y,v),d,n,h,o,!1))}m.push(\"...\");for(g=s-FORMAT_NUM_FIRST_LAST_VALS;g<s;g++){v=(y=g*f)+f;m.push.apply(m,subTensorToString(e.slice(y,v),d,n,h,o,g===s-1))}}else for(g=0;g<s;g++){var y;v=(y=g*f)+f;m.push.apply(m,subTensorToString(e.slice(y,v),d,n,h,o,g===s-1))}var x=2===u?\",\":\"\";m[0]=\"[\"+m[0]+x;for(g=1;g<m.length-1;g++)m[g]=\" \"+m[g]+x;var T=\",\\n\";for(g=2;g<u;g++)T+=\"\\n\";return m[m.length-1]=\" \"+m[m.length-1]+\"]\"+(a?\"\":T),m}function createComplexTuples(e){for(var t=[],n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var TensorBuffer=function(){function e(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=sizeFromShape(e),null!=n){var r=n.length;assert(r===this.size,\"Length of values '\"+r+\"' does not match the size inferred by the shape '\"+this.size+\"'.\")}if(\"complex64\"===t)throw new Error(\"complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).\");this.values=n||getArrayFromDType(t,sizeFromShape(this.shape)),this.strides=computeStrides(e)}return e.prototype.set=function(e){for(var t=[],n=1;n<arguments.length;n++)t[n-1]=arguments[n];0===t.length&&(t=[0]),assert(t.length===this.rank,\"The number of provided coordinates (\"+t.length+\") must match the rank (\"+this.rank+\")\");var r=this.locToIndex(t);this.values[r]=e},e.prototype.get=function(){for(var e=[],t=0;t<arguments.length;t++)e[t]=arguments[t];0===e.length&&(e=[0]);for(var n=e[e.length-1],r=0;r<e.length-1;++r)n+=this.strides[r]*e[r];return this.values[n]},e.prototype.locToIndex=function(e){if(0===this.rank)return 0;if(1===this.rank)return e[0];for(var t=e[e.length-1],n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t},e.prototype.indexToLoc=function(e){if(0===this.rank)return[];if(1===this.rank)return[e];for(var t=new Array(this.shape.length),n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t},Object.defineProperty(e.prototype,\"rank\",{get:function(){return this.shape.length},enumerable:!0,configurable:!0}),e.prototype.toTensor=function(){return Tensor.make(this.shape,{values:this.values},this.dtype)},e}(),trackerFn=null,opHandler=null;function setTensorTracker(e){trackerFn=e}function setOpHandler(e){opHandler=e}var Tensor=function(){function e(e,t,n,r){this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||\"float32\",this.size=sizeFromShape(e),this.strides=computeStrides(e),this.dataId=null!=r?r:{},this.id=trackerFn().nextTensorId(),this.rankType=this.rank<5?this.rank.toString():\"higher\",trackerFn().registerTensor(this),null!=n&&trackerFn().write(this.dataId,n)}return e.make=function(t,n,r){return new e(t,r,n.values,n.dataId)},e.prototype.flatten=function(){return this.throwIfDisposed(),this.as1D()},e.prototype.asScalar=function(){return this.throwIfDisposed(),assert(1===this.size,\"The array must have only 1 element.\"),this.reshape([])},e.prototype.as1D=function(){return this.throwIfDisposed(),this.reshape([this.size])},e.prototype.as2D=function(e,t){return this.throwIfDisposed(),this.reshape([e,t])},e.prototype.as3D=function(e,t,n){return this.throwIfDisposed(),this.reshape([e,t,n])},e.prototype.as4D=function(e,t,n,r){return this.throwIfDisposed(),this.reshape([e,t,n,r])},e.prototype.as5D=function(e,t,n,r,o){return this.throwIfDisposed(),this.reshape([e,t,n,r,o])},e.prototype.asType=function(e){return this.throwIfDisposed(),opHandler.cast(this,e)},Object.defineProperty(e.prototype,\"rank\",{get:function(){return this.shape.length},enumerable:!0,configurable:!0}),e.prototype.get=function(){for(var e=[],t=0;t<arguments.length;t++)e[t]=arguments[t];assert(e.length===this.rank,\"Number of coordinates in get() must match the rank of the tensor\"),assert(\"complex64\"!==this.dtype,\"Tensor.get() is not supported for complex64 tensors yet.\"),this.throwIfDisposed(),0===e.length&&(e=[0]);for(var n=e[e.length-1],r=0;r<e.length-1;++r)n+=this.strides[r]*e[r];return this.dataSync()[n]},e.prototype.buffer=function(){return opHandler.buffer(this.shape,this.dtype,this.dataSync())},e.prototype.data=function(){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(e){return this.throwIfDisposed(),[2,trackerFn().read(this.dataId)]})})},e.prototype.dataSync=function(){return this.throwIfDisposed(),trackerFn().readSync(this.dataId)},e.prototype.dispose=function(){this.isDisposed||(trackerFn().disposeTensor(this),this.isDisposedInternal=!0)},Object.defineProperty(e.prototype,\"isDisposed\",{get:function(){return this.isDisposedInternal},enumerable:!0,configurable:!0}),e.prototype.throwIfDisposed=function(){if(this.isDisposed)throw new Error(\"Tensor is disposed.\")},e.prototype.toFloat=function(){return this.asType(\"float32\")},e.prototype.toInt=function(){return this.asType(\"int32\")},e.prototype.toBool=function(){return this.asType(\"bool\")},e.prototype.print=function(e){return void 0===e&&(e=!1),opHandler.print(this,e)},e.prototype.reshape=function(e){return this.throwIfDisposed(),opHandler.reshape(this,e)},e.prototype.reshapeAs=function(e){return this.throwIfDisposed(),this.reshape(e.shape)},e.prototype.expandDims=function(e){return void 0===e&&(e=0),opHandler.expandDims(this,e)},e.prototype.cumsum=function(e,t,n){return void 0===e&&(e=0),void 0===t&&(t=!1),void 0===n&&(n=!1),opHandler.cumsum(this,e,t,n)},e.prototype.squeeze=function(e){return this.throwIfDisposed(),opHandler.squeeze(this,e)},e.prototype.clone=function(){return this.throwIfDisposed(),opHandler.clone(this)},e.prototype.toString=function(e){return void 0===e&&(e=!1),tensorToString(this.dataSync(),this.shape,this.dtype,e)},e.prototype.tile=function(e){return this.throwIfDisposed(),opHandler.tile(this,e)},e.prototype.gather=function(e,t){return void 0===t&&(t=0),this.throwIfDisposed(),opHandler.gather(this,e,t)},e.prototype.matMul=function(e,t,n){return void 0===t&&(t=!1),void 0===n&&(n=!1),this.throwIfDisposed(),opHandler.matMul(this,e,t,n)},e.prototype.dot=function(e){return this.throwIfDisposed(),opHandler.dot(this,e)},e.prototype.norm=function(e,t,n){return void 0===e&&(e=\"euclidean\"),void 0===t&&(t=null),void 0===n&&(n=!1),this.throwIfDisposed(),opHandler.norm(this,e,t,n)},e.prototype.slice=function(e,t){return this.throwIfDisposed(),opHandler.slice(this,e,t)},e.prototype.reverse=function(e){return this.throwIfDisposed(),opHandler.reverse(this,e)},e.prototype.concat=function(t,n){return void 0===n&&(n=0),this.throwIfDisposed(),t instanceof e&&(t=[t]),opHandler.concat([this].concat(t),n)},e.prototype.split=function(e,t){return void 0===t&&(t=0),this.throwIfDisposed(),opHandler.split(this,e,t)},e.prototype.stack=function(e,t){return void 0===t&&(t=0),opHandler.stack([this,e],t)},e.prototype.unstack=function(e,t){return void 0===t&&(t=0),opHandler.unstack(this,t)},e.prototype.pad=function(e,t){return void 0===t&&(t=0),opHandler.pad(this,e,t)},e.prototype.batchNormalization=function(e,t,n,r,o){return void 0===n&&(n=.001),this.throwIfDisposed(),opHandler.batchNormalization(this,e,t,n,r,o)},e.prototype.all=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.all(this,e,t)},e.prototype.any=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.any(this,e,t)},e.prototype.logSumExp=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.logSumExp(this,e,t)},e.prototype.sum=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.sum(this,e,t)},e.prototype.prod=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.prod(this,e,t)},e.prototype.mean=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.mean(this,e,t)},e.prototype.min=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.min(this,e,t)},e.prototype.max=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.max(this,e,t)},e.prototype.argMin=function(e){return void 0===e&&(e=null),this.throwIfDisposed(),opHandler.argMin(this,e)},e.prototype.argMax=function(e){return void 0===e&&(e=null),this.throwIfDisposed(),opHandler.argMax(this,e)},e.prototype.cast=function(e){return this.throwIfDisposed(),opHandler.cast(this,e)},e.prototype.add=function(e){return this.throwIfDisposed(),opHandler.add(this,e)},e.prototype.addStrict=function(e){return this.throwIfDisposed(),opHandler.addStrict(this,e)},e.prototype.atan2=function(e){return this.throwIfDisposed(),opHandler.atan2(this,e)},e.prototype.sub=function(e){return this.throwIfDisposed(),opHandler.sub(this,e)},e.prototype.subStrict=function(e){return this.throwIfDisposed(),opHandler.subStrict(this,e)},e.prototype.pow=function(e){return this.throwIfDisposed(),opHandler.pow(this,e)},e.prototype.powStrict=function(e){return this.throwIfDisposed(),opHandler.powStrict(this,e)},e.prototype.mul=function(e){return this.throwIfDisposed(),opHandler.mul(this,e)},e.prototype.mulStrict=function(e){return this.throwIfDisposed(),opHandler.mulStrict(this,e)},e.prototype.div=function(e){return this.throwIfDisposed(),opHandler.div(this,e)},e.prototype.floorDiv=function(e){return this.throwIfDisposed(),opHandler.floorDiv(this,e)},e.prototype.divStrict=function(e){return this.throwIfDisposed(),opHandler.divStrict(this,e)},e.prototype.minimum=function(e){return this.throwIfDisposed(),opHandler.minimum(this,e)},e.prototype.minimumStrict=function(e){return this.throwIfDisposed(),opHandler.minimumStrict(this,e)},e.prototype.maximum=function(e){return this.throwIfDisposed(),opHandler.maximum(this,e)},e.prototype.maximumStrict=function(e){return this.throwIfDisposed(),opHandler.maximumStrict(this,e)},e.prototype.mod=function(e){return this.throwIfDisposed(),opHandler.mod(this,e)},e.prototype.modStrict=function(e){return this.throwIfDisposed(),opHandler.modStrict(this,e)},e.prototype.squaredDifference=function(e){return this.throwIfDisposed(),opHandler.squaredDifference(this,e)},e.prototype.squaredDifferenceStrict=function(e){return this.throwIfDisposed(),opHandler.squaredDifferenceStrict(this,e)},e.prototype.transpose=function(e){return this.throwIfDisposed(),opHandler.transpose(this,e)},e.prototype.notEqual=function(e){return this.throwIfDisposed(),opHandler.notEqual(this,e)},e.prototype.notEqualStrict=function(e){return this.throwIfDisposed(),opHandler.notEqualStrict(this,e)},e.prototype.less=function(e){return this.throwIfDisposed(),opHandler.less(this,e)},e.prototype.lessStrict=function(e){return this.throwIfDisposed(),opHandler.lessStrict(this,e)},e.prototype.equal=function(e){return this.throwIfDisposed(),opHandler.equal(this,e)},e.prototype.equalStrict=function(e){return this.throwIfDisposed(),opHandler.equalStrict(this,e)},e.prototype.lessEqual=function(e){return this.throwIfDisposed(),opHandler.lessEqual(this,e)},e.prototype.lessEqualStrict=function(e){return this.throwIfDisposed(),opHandler.lessEqualStrict(this,e)},e.prototype.greater=function(e){return this.throwIfDisposed(),opHandler.greater(this,e)},e.prototype.greaterStrict=function(e){return this.throwIfDisposed(),opHandler.greaterStrict(this,e)},e.prototype.greaterEqual=function(e){return this.throwIfDisposed(),opHandler.greaterEqual(this,e)},e.prototype.greaterEqualStrict=function(e){return this.throwIfDisposed(),opHandler.greaterEqualStrict(this,e)},e.prototype.logicalAnd=function(e){return this.throwIfDisposed(),opHandler.logicalAnd(this,e)},e.prototype.logicalOr=function(e){return this.throwIfDisposed(),opHandler.logicalOr(this,e)},e.prototype.logicalNot=function(){return this.throwIfDisposed(),opHandler.logicalNot(this)},e.prototype.logicalXor=function(e){return this.throwIfDisposed(),opHandler.logicalXor(this,e)},e.prototype.where=function(e,t){return this.throwIfDisposed(),opHandler.where(e,this,t)},e.prototype.neg=function(){return this.throwIfDisposed(),opHandler.neg(this)},e.prototype.ceil=function(){return this.throwIfDisposed(),opHandler.ceil(this)},e.prototype.floor=function(){return this.throwIfDisposed(),opHandler.floor(this)},e.prototype.sign=function(){return this.throwIfDisposed(),opHandler.sign(this)},e.prototype.exp=function(){return this.throwIfDisposed(),opHandler.exp(this)},e.prototype.expm1=function(){return this.throwIfDisposed(),opHandler.expm1(this)},e.prototype.log=function(){return this.throwIfDisposed(),opHandler.log(this)},e.prototype.log1p=function(){return this.throwIfDisposed(),opHandler.log1p(this)},e.prototype.sqrt=function(){return this.throwIfDisposed(),opHandler.sqrt(this)},e.prototype.rsqrt=function(){return this.throwIfDisposed(),opHandler.rsqrt(this)},e.prototype.square=function(){return this.throwIfDisposed(),opHandler.square(this)},e.prototype.reciprocal=function(){return this.throwIfDisposed(),opHandler.reciprocal(this)},e.prototype.abs=function(){return this.throwIfDisposed(),opHandler.abs(this)},e.prototype.clipByValue=function(e,t){return this.throwIfDisposed(),opHandler.clipByValue(this,e,t)},e.prototype.relu=function(){return this.throwIfDisposed(),opHandler.relu(this)},e.prototype.elu=function(){return this.throwIfDisposed(),opHandler.elu(this)},e.prototype.selu=function(){return this.throwIfDisposed(),opHandler.selu(this)},e.prototype.leakyRelu=function(e){return void 0===e&&(e=.2),this.throwIfDisposed(),opHandler.leakyRelu(this,e)},e.prototype.prelu=function(e){return this.throwIfDisposed(),opHandler.prelu(this,e)},e.prototype.sigmoid=function(){return this.throwIfDisposed(),opHandler.sigmoid(this)},e.prototype.logSigmoid=function(){return this.throwIfDisposed(),opHandler.logSigmoid(this)},e.prototype.softplus=function(){return this.throwIfDisposed(),opHandler.softplus(this)},e.prototype.zerosLike=function(){return this.throwIfDisposed(),opHandler.zerosLike(this)},e.prototype.onesLike=function(){return this.throwIfDisposed(),opHandler.onesLike(this)},e.prototype.sin=function(){return this.throwIfDisposed(),opHandler.sin(this)},e.prototype.cos=function(){return this.throwIfDisposed(),opHandler.cos(this)},e.prototype.tan=function(){return this.throwIfDisposed(),opHandler.tan(this)},e.prototype.asin=function(){return this.throwIfDisposed(),opHandler.asin(this)},e.prototype.acos=function(){return this.throwIfDisposed(),opHandler.acos(this)},e.prototype.atan=function(){return this.throwIfDisposed(),opHandler.atan(this)},e.prototype.sinh=function(){return this.throwIfDisposed(),opHandler.sinh(this)},e.prototype.cosh=function(){return this.throwIfDisposed(),opHandler.cosh(this)},e.prototype.tanh=function(){return this.throwIfDisposed(),opHandler.tanh(this)},e.prototype.asinh=function(){return this.throwIfDisposed(),opHandler.asinh(this)},e.prototype.acosh=function(){return this.throwIfDisposed(),opHandler.acosh(this)},e.prototype.atanh=function(){return this.throwIfDisposed(),opHandler.atanh(this)},e.prototype.erf=function(){return this.throwIfDisposed(),opHandler.erf(this)},e.prototype.round=function(){return this.throwIfDisposed(),opHandler.round(this)},e.prototype.step=function(e){return void 0===e&&(e=0),this.throwIfDisposed(),opHandler.step(this,e)},e.prototype.softmax=function(e){return void 0===e&&(e=-1),this.throwIfDisposed(),opHandler.softmax(this,e)},e.prototype.logSoftmax=function(e){return void 0===e&&(e=-1),this.throwIfDisposed(),opHandler.logSoftmax(this,e)},e.prototype.resizeBilinear=function(e,t){return void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.image.resizeBilinear(this,e,t)},e.prototype.resizeNearestNeighbor=function(e,t){return void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.image.resizeNearestNeighbor(this,e,t)},e.prototype.conv1d=function(e,t,n,r,o,a){return void 0===r&&(r=\"NWC\"),void 0===o&&(o=1),this.throwIfDisposed(),opHandler.conv1d(this,e,t,n,r,o,a)},e.prototype.conv2d=function(e,t,n,r,o,a){return void 0===r&&(r=\"NHWC\"),void 0===o&&(o=[1,1]),this.throwIfDisposed(),opHandler.conv2d(this,e,t,n,r,o,a)},e.prototype.conv2dTranspose=function(e,t,n,r,o){return this.throwIfDisposed(),opHandler.conv2dTranspose(this,e,t,n,r,o)},e.prototype.depthwiseConv2D=function(e,t,n,r,o,a){return void 0===r&&(r=\"NHWC\"),void 0===o&&(o=[1,1]),this.throwIfDisposed(),opHandler.depthwiseConv2d(this,e,t,n,r,o,a)},e.prototype.separableConv2d=function(e,t,n,r,o,a){return void 0===o&&(o=[1,1]),void 0===a&&(a=\"NHWC\"),this.throwIfDisposed(),opHandler.separableConv2d(this,e,t,n,r,o,a)},e.prototype.avgPool=function(e,t,n,r){return this.throwIfDisposed(),opHandler.avgPool(this,e,t,n,r)},e.prototype.maxPool=function(e,t,n,r){return this.throwIfDisposed(),opHandler.maxPool(this,e,t,n,r)},e.prototype.localResponseNormalization=function(e,t,n,r){return void 0===e&&(e=5),void 0===t&&(t=1),void 0===n&&(n=1),void 0===r&&(r=.5),opHandler.localResponseNormalization(this,e,t,n,r)},e.prototype.pool=function(e,t,n,r,o){return this.throwIfDisposed(),opHandler.pool(this,e,t,n,r,o)},e.prototype.variable=function(e,t,n){return void 0===e&&(e=!0),this.throwIfDisposed(),Variable.variable(this,e,t,n)},e.prototype.unsortedSegmentSum=function(e,t){return this.throwIfDisposed(),opHandler.unsortedSegmentSum(this,e,t)},e.prototype.batchToSpaceND=function(e,t){return this.throwIfDisposed(),opHandler.batchToSpaceND(this,e,t)},e.prototype.spaceToBatchND=function(e,t){return this.throwIfDisposed(),opHandler.spaceToBatchND(this,e,t)},e.prototype.topk=function(e,t){return void 0===e&&(e=1),void 0===t&&(t=!0),this.throwIfDisposed(),opHandler.topk(this,e,t)},e.prototype.stridedSlice=function(e,t,n,r,o){return void 0===r&&(r=0),void 0===o&&(o=0),this.throwIfDisposed(),opHandler.stridedSlice(this,e,t,n,r,o)},e.prototype.depthToSpace=function(e,t){return this.throwIfDisposed(),opHandler.depthToSpace(this,e,t)},e.prototype.fft=function(){return this.throwIfDisposed(),opHandler.spectral.fft(this)},e.prototype.ifft=function(){return this.throwIfDisposed(),opHandler.spectral.ifft(this)},e}();Object.defineProperty(Tensor,Symbol.hasInstance,{value:function(e){return!!e&&null!=e.shape&&null!=e.dtype}});var Variable=function(e){function t(t,n,r){void 0===n&&(n=!0);var o=e.call(this,t.shape,t.dtype,null,t.dataId)||this;o.trainable=n,o.name=r,null==o.name&&(o.name=trackerFn().nextVariableId().toString());try{trackerFn().registerVariable(o)}catch(e){throw trackerFn().disposeTensor(o),e}return o}return __extends(t,e),t.variable=function(e,n,r,o){return void 0===n&&(n=!0),null!=o&&o!==e.dtype&&(e=e.asType(o)),new t(e,n,r)},t.prototype.assign=function(e){if(e.dtype!==this.dtype)throw new Error(\"dtype of the new value (\"+e.dtype+\") and previous value (\"+this.dtype+\") must match\");if(!arraysEqual(e.shape,this.shape))throw new Error(\"shape of the new value (\"+e.shape+\") and previous value (\"+this.shape+\") must match\");trackerFn().disposeTensor(this),this.dataId=e.dataId,trackerFn().registerTensor(this)},t}(Tensor);Object.defineProperty(Variable,Symbol.hasInstance,{value:function(e){return e instanceof Tensor&&null!=e.assign&&e.assign instanceof Function}});var Rank,UpcastInt32AndMap,UpcastBoolAndMap,UpcastFloat32AndMap,UpcastComplex64AndMap,variable=Variable.variable;function getFilteredNodesXToY(e,t,n){for(var r={},o={},a=0;a<t.length;a++)r[t[a].id]=!0;for(a=0;a<e.length;a++){var i=(f=e[a]).inputs;for(var s in i){for(var u=i[s],l=!1,c=0;c<t.length;c++)if(r[u.id]){f.outputs.forEach(function(e){return r[e.id]=!0}),l=!0,o[f.id]=!0;break}if(l)break}}var p={};p[n.id]=!0;var d={};for(a=e.length-1;a>=0;a--)for(i=(f=e[a]).inputs,c=0;c<f.outputs.length;c++)if(p[f.outputs[c].id]){for(var s in i)p[i[s].id]=!0,d[f.id]=!0;break}var h=[];for(a=0;a<e.length;a++){var f;if(o[(f=e[a]).id]&&d[f.id]){var m={};for(var s in f.inputs){var g=f.inputs[s];r[g.id]&&(m[s]=g)}var v=Object.assign({},f);v.inputs=m,v.outputs=f.outputs,h.push(v)}}return h}function backpropagateGradients(e,t){for(var n=function(n){var r=t[n],o=[];if(r.outputs.forEach(function(t){var n=e[t.id];if(null!=n)o.push(n);else{var r=Tensor.make(t.shape,{values:makeZerosTypedArray(t.size,t.dtype)},t.dtype);o.push(r)}}),null==r.gradient)throw new Error(\"Cannot compute gradient: gradient function not found for \"+r.name+\".\");var a=r.gradient(1===r.outputs.length?o[0]:o);for(var i in r.inputs){if(!(i in a))throw new Error(\"Cannot backprop through input \"+i+\". Available gradients found: \"+Object.keys(a)+\".\");var s=a[i]();if(\"float32\"!==s.dtype)throw new Error(\"Error in gradient for op \"+r.name+\". The gradient of input \"+i+\" must have 'float32' dtype, but has '\"+s.dtype+\"'\");var u=r.inputs[i];if(!arraysEqual(s.shape,u.shape))throw new Error(\"Error in gradient for op \"+r.name+\". The gradient of input '\"+i+\"' has shape '\"+s.shape+\"', which does not match the shape of the input '\"+u.shape+\"'\");if(null==e[u.id])e[u.id]=s;else{var l=e[u.id];e[u.id]=l.add(s),l.dispose()}}},r=t.length-1;r>=0;r--)n(r)}!function(e){e.R0=\"R0\",e.R1=\"R1\",e.R2=\"R2\",e.R3=\"R3\",e.R4=\"R4\",e.R5=\"R5\",e.R6=\"R6\"}(Rank||(Rank={})),function(e){e.float32=\"float32\",e.int32=\"int32\",e.bool=\"int32\",e.complex64=\"complex64\"}(UpcastInt32AndMap||(UpcastInt32AndMap={})),function(e){e.float32=\"float32\",e.int32=\"int32\",e.bool=\"bool\",e.complex64=\"complex64\"}(UpcastBoolAndMap||(UpcastBoolAndMap={})),function(e){e.float32=\"float32\",e.int32=\"float32\",e.bool=\"float32\",e.complex64=\"complex64\"}(UpcastFloat32AndMap||(UpcastFloat32AndMap={})),function(e){e.float32=\"complex64\",e.int32=\"complex64\",e.bool=\"complex64\",e.complex64=\"complex64\"}(UpcastComplex64AndMap||(UpcastComplex64AndMap={}));var upcastTypeMap={float32:UpcastFloat32AndMap,int32:UpcastInt32AndMap,bool:UpcastBoolAndMap,complex64:UpcastComplex64AndMap};function upcastType(e,t){if(\"string\"===e||\"string\"===t){if(\"string\"===e&&\"string\"===t)return\"string\";throw new Error(\"Can not upcast \"+e+\" with \"+t)}return upcastTypeMap[e][t]}function sumOutType(e){return upcastType(e,\"int32\")}function makeTypesMatch(e,t){if(e.dtype===t.dtype)return[e,t];var n=upcastType(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function assertTypesMatch(e,t){assert(e.dtype===t.dtype,\"The dtypes of the first(\"+e.dtype+\") and second(\"+t.dtype+\") input must match\")}function isTensorInList(e,t){for(var n=0;n<t.length;n++)if(t[n].id===e.id)return!0;return!1}function getTensorsInContainer(e){var t=[];return walkTensorContainer(e,t,new Set),t}function walkTensorContainer(e,t,n){if(null!=e)if(e instanceof Tensor)t.push(e);else if(isIterable(e)){var r=e;for(var o in r){var a=r[o];n.has(a)||(n.add(a),walkTensorContainer(a,t,n))}}}function isIterable(e){return Array.isArray(e)||\"object\"==typeof e}var Type,Engine=function(){function e(e,t,n){this.backend=e,this.safeMode=t,this.debugMode=n,this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.profiling=!1,this.gradientScopeCount=0,this.customGradientDepth=0,this.scopeStack=[],this.keepTensors=new Set,this.tensorInfo=new WeakMap,this.profiler=new Profiler(e),this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}return e.prototype.moveData=function(e){this.write(e,this.readSync(e))},e.prototype.tidy=function(e,t,n){var r=this;void 0===n&&(n=!1);var o,a=null;if(null==t){if(\"function\"!=typeof e)throw new Error(\"Please provide a function to tidy()\");t=e}else{if(\"string\"!=typeof e&&!(e instanceof String))throw new Error(\"When calling with two arguments, the first argument to tidy() must be a string\");if(\"function\"!=typeof t)throw new Error(\"When calling with two arguments, the 2nd argument to tidy() must be a function\");a=e}return this.scopedRun(function(){return r.startScope(a,n)},function(){return r.endScope(o,n)},function(){return(o=t())instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),o})},e.prototype.scopedRun=function(e,t,n){e();try{var r=n();return t(),r}catch(e){throw t(),e}},e.prototype.nextTensorId=function(){return e.nextTensorId++},e.prototype.nextVariableId=function(){return e.nextVariableId++},e.prototype.runKernel=function(e,t,n){var r,o=this,a=[],i=function(e){return a.push(e),e},s=this.activeScope.name,u=this.numBytes,l=this.numTensors;if(this.scopedRun(function(){return o.customGradientDepth++},function(){return o.customGradientDepth--},function(){r=o.debugMode()?o.profiler.profileKernel(s,function(){return e(o.backend,i)}):e(o.backend,i)}),this.shouldRecord()){var c={id:this.nextTapeNodeId++,name:s,inputs:t,outputs:Array.isArray(r)?r:[r]};null!=n&&(c.gradient=function(e){return n(e,a)}),this.activeTape.push(c)}return this.profiling&&this.activeProfile.kernels.push({name:s,bytesAdded:this.numBytes-u,totalBytesSnapshot:this.numBytes,tensorsAdded:this.numTensors-l,totalTensorsSnapshot:this.numTensors,inputShapes:Object.keys(t).map(function(e){return t[e].shape}),outputShape:Array.isArray(r)?r.map(function(e){return e.shape}):r.shape}),r},e.prototype.registerTensor=function(e){var t=this.tensorInfo.has(e.dataId)?this.tensorInfo.get(e.dataId).refCount:0;if(this.numTensors++,\"string\"===e.dtype&&this.numStringTensors++,0===t){this.numDataBuffers++;var n=0;\"complex64\"!==e.dtype&&\"string\"!==e.dtype&&(n=sizeFromShape(e.shape)*bytesPerElement(e.dtype)),this.tensorInfo.set(e.dataId,{backend:this.backend,dtype:e.dtype,shape:e.shape,bytes:n,refCount:0}),this.numBytes+=n,this.backend.register(e.dataId,e.shape,e.dtype)}this.tensorInfo.get(e.dataId).refCount++,e instanceof Variable||this.track(e)},e.prototype.registerVariable=function(e){if(null!=this.registeredVariables[e.name])throw new Error(\"Variable with name \"+e.name+\" was already registered\");this.registeredVariables[e.name]=e},e.prototype.disposeTensor=function(e){if(this.tensorInfo.has(e.dataId)){this.keepTensors.has(e.id)&&this.keepTensors.delete(e.id),this.numTensors--,\"string\"===e.dtype&&this.numStringTensors--;var t=this.tensorInfo.get(e.dataId);t.refCount<=1?(\"complex64\"!==e.dtype&&(this.numBytes-=t.bytes),this.numDataBuffers--,t.backend.disposeData(e.dataId),this.tensorInfo.delete(e.dataId)):this.tensorInfo.get(e.dataId).refCount--}},e.prototype.disposeVariables=function(){for(var e in this.registeredVariables){var t=this.registeredVariables[e];this.disposeTensor(t),delete this.registeredVariables[e]}},e.prototype.memory=function(){var e=this.backend.memory();return e.numTensors=this.numTensors,e.numDataBuffers=this.numDataBuffers,e.numBytes=this.numBytes,this.numStringTensors>0&&(e.unreliable=!0,null==e.reasons&&(e.reasons=[]),e.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\")),e},e.prototype.profile=function(e){return __awaiter(this,void 0,void 0,function(){var t,n;return __generator(this,function(r){return this.profiling=!0,t=this.numBytes,n=this.numTensors,this.activeProfile.kernels=[],this.activeProfile.result=e(),this.profiling=!1,this.activeProfile.peakBytes=Math.max.apply(Math,this.activeProfile.kernels.map(function(e){return e.totalBytesSnapshot})),this.activeProfile.newBytes=this.numBytes-t,this.activeProfile.newTensors=this.numTensors-n,[2,this.activeProfile]})})},e.prototype.shouldRecord=function(){return null!=this.activeTape&&0===this.customGradientDepth},e.prototype.addTapeNode=function(e,t,n){var r={};e.forEach(function(e,t){r[t]=e});var o={id:this.nextTapeNodeId++,name:this.activeScope.name,inputs:r,outputs:[t],gradient:function(e){var t={};return n(e).forEach(function(e,n){t[n]=function(){return e}}),t}};this.activeTape.push(o)},e.prototype.keep=function(e){if(1===this.scopeStack.length&&this.safeMode)throw new Error(\"Safe mode is ON. Enclose all tensor operations inside tf.tidy(): tf.tidy(() => {...}) to avoid memory leaks.\");return this.keepTensors.add(e.id),e},e.prototype.startScope=function(e,t){void 0===t&&(t=!1),t&&0===this.gradientScopeCount&&(this.activeTape=[]),t&&this.gradientScopeCount++;var n={track:[],name:\"unnamed scope\"};e&&(n.name=e),this.scopeStack.push(n),this.activeScope=n},e.prototype.endScope=function(e,t){var n=this;void 0===t&&(t=!1),t&&(this.gradientScopeCount--,0===this.gradientScopeCount&&(this.activeTape=null));var r=new Set(this.keepTensors),o=getTensorsInContainer(e);o.forEach(function(e){return r.add(e.id)});for(var a=0;a<this.activeScope.track.length;a++){var i=this.activeScope.track[a];r.has(i.id)||(null!=this.activeTape?o.push(i):i.dispose())}var s=this.scopeStack.pop();this.activeScope=0===this.scopeStack.length?null:this.scopeStack[this.scopeStack.length-1],o.forEach(function(e){!n.keepTensors.has(e.id)&&isTensorInList(e,s.track)&&n.track(e)})},e.prototype.gradients=function(e,t,n,r){var o=this;if(void 0===r&&(r=!1),assert(t.length>0,\"gradients() received an empty list of xs.\"),null!=n&&\"float32\"!==n.dtype)throw new Error(\"dy must have 'float32' dtype, but has '\"+n.dtype+\"'\");return this.tidy(\"gradients\",function(){var a=e();assert(a instanceof Tensor,\"The result y returned by f() must be a tensor.\");var i=getFilteredNodesXToY(o.activeTape,t,a);if(!r&&0===i.length&&t.length>0)throw new Error(\"Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.\");var s={};return s[a.id]=null==n?ones(a.shape):n,backpropagateGradients(s,i),{value:a,grads:t.map(function(e){return s[e.id]})}},!0)},e.prototype.customGrad=function(e){var t=this;return assert(isFunction(e),\"The f passed in customGrad(f) must be a function.\"),function(){for(var n,r,o=[],a=0;a<arguments.length;a++)o[a]=arguments[a];if(assert(o.every(function(e){return e instanceof Tensor}),\"The args passed in customGrad(f)(x1, x2,...) must all be tensors\"),t.scopedRun(function(){return t.customGradientDepth++},function(){return t.customGradientDepth--},function(){r=t.tidy(e.name,function(){var t=e.apply(void 0,o),r=t.value,a=t.gradFunc;return assert(r instanceof Tensor,\"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor\"),assert(isFunction(a),\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.\"),n=a,r},!0)}),t.shouldRecord()){t.addTapeNode(o,r,function(e){var t=n(e),r=Array.isArray(t)?t:[t];return assert(r.length===o.length,\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).\"),assert(r.every(function(e){return e instanceof Tensor}),\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.\"),r})}return r}},e.prototype.write=function(e,t){var n=this.tensorInfo.get(e);if(\"string\"===n.dtype){var r=bytesFromStringArray(t);this.numBytes+=r-n.bytes,n.bytes=r}this.backend!==n.backend&&(n.backend.disposeData(e),n.backend=this.backend,this.backend.register(e,n.shape,n.dtype)),this.backend.write(e,t)},e.prototype.readSync=function(e){return this.tensorInfo.get(e).backend.readSync(e)},e.prototype.read=function(e){return this.tensorInfo.get(e).backend.read(e)},e.prototype.fromPixels=function(e,t){return this.backend.fromPixels(e,t)},e.prototype.time=function(e){return __awaiter(this,void 0,void 0,function(){var t,n;return __generator(this,function(r){switch(r.label){case 0:return t=now(),[4,this.backend.time(e)];case 1:return(n=r.sent()).wallMs=now()-t,[2,n]}})})},e.prototype.track=function(e){if(1===this.scopeStack.length&&this.safeMode)throw new Error(\"Safe mode is ON. Enclose all tensor operations inside tf.tidy(): tf.tidy(() => {op();...}); to avoid memory leaks.\");return null!=this.activeScope&&this.activeScope.track.push(e),e},e.nextTensorId=0,e.nextVariableId=0,e}();function ones(e){var t=makeOnesTypedArray(sizeFromShape(e),\"float32\");return Tensor.make(e,{values:t})}!function(e){e[e.NUMBER=0]=\"NUMBER\",e[e.BOOLEAN=1]=\"BOOLEAN\",e[e.STRING=2]=\"STRING\"}(Type||(Type={}));var MAX_TEXTURE_SIZE,URL_PROPERTIES=[{name:\"DEBUG\",type:Type.BOOLEAN},{name:\"IS_BROWSER\",type:Type.BOOLEAN},{name:\"WEBGL_LAZILY_UNPACK\",type:Type.BOOLEAN},{name:\"WEBGL_CPU_FORWARD\",type:Type.BOOLEAN},{name:\"WEBGL_PACK\",type:Type.BOOLEAN},{name:\"WEBGL_PACK_BATCHNORMALIZATION\",type:Type.BOOLEAN},{name:\"WEBGL_PACK_CLIP\",type:Type.BOOLEAN},{name:\"WEBGL_PACK_DEPTHWISECONV\",type:Type.BOOLEAN},{name:\"WEBGL_CONV_IM2COL\",type:Type.BOOLEAN},{name:\"WEBGL_MAX_TEXTURE_SIZE\",type:Type.NUMBER},{name:\"WEBGL_PAGING_ENABLED\",type:Type.BOOLEAN},{name:\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\",type:Type.NUMBER},{name:\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\",type:Type.BOOLEAN},{name:\"WEBGL_VERSION\",type:Type.NUMBER},{name:\"WEBGL_RENDER_FLOAT32_ENABLED\",type:Type.BOOLEAN},{name:\"WEBGL_DOWNLOAD_FLOAT_ENABLED\",type:Type.BOOLEAN},{name:\"WEBGL_FENCE_API_ENABLED\",type:Type.BOOLEAN},{name:\"WEBGL_SIZE_UPLOAD_UNIFORM\",type:Type.NUMBER},{name:\"BACKEND\",type:Type.STRING},{name:\"EPSILON\",type:Type.NUMBER},{name:\"PROD\",type:Type.BOOLEAN},{name:\"TENSORLIKE_CHECK_SHAPE_CONSISTENCY\",type:Type.BOOLEAN}];function isWebGLVersionEnabled(e){try{if(null!=getWebGLContext(e))return!0}catch(e){return!1}return!1}function getWebGLMaxTextureSize(e){if(null==MAX_TEXTURE_SIZE){var t=getWebGLContext(e);MAX_TEXTURE_SIZE=t.getParameter(t.MAX_TEXTURE_SIZE)}return MAX_TEXTURE_SIZE}function getWebGLDisjointQueryTimerVersion(e){if(0===e)return 0;var t=getWebGLContext(e);return hasExtension(t,\"EXT_disjoint_timer_query_webgl2\")&&2===e?2:hasExtension(t,\"EXT_disjoint_timer_query\")?1:0}function isRenderToFloatTextureEnabled(e){if(0===e)return!1;var t=getWebGLContext(e);if(1===e){if(!hasExtension(t,\"OES_texture_float\"))return!1}else if(!hasExtension(t,\"EXT_color_buffer_float\"))return!1;return createFloatTextureAndBindToFramebuffer(t,e)}function isDownloadFloatTextureEnabled(e){if(0===e)return!1;var t=getWebGLContext(e);if(1===e){if(!hasExtension(t,\"OES_texture_float\"))return!1;if(!hasExtension(t,\"WEBGL_color_buffer_float\"))return!1}else if(!hasExtension(t,\"EXT_color_buffer_float\"))return!1;return createFloatTextureAndBindToFramebuffer(t,e)}function isWebGLFenceEnabled(e){return 2===e&&null!=getWebGLContext(e).fenceSync}function isChrome(){return\"undefined\"!=typeof navigator&&null!=navigator&&null!=navigator.userAgent&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor)}var TENSORFLOWJS_FLAGS_PREFIX=\"tfjsflags\";function getFeaturesFromURL(){var e={};if(\"undefined\"==typeof window||void 0===window.location||void 0===window.location.search)return e;var t=getQueryParams(window.location.search);if(TENSORFLOWJS_FLAGS_PREFIX in t){var n={};t[TENSORFLOWJS_FLAGS_PREFIX].split(\",\").forEach(function(e){var t=e.split(\":\"),r=t[0],o=t[1];n[r]=o}),URL_PROPERTIES.forEach(function(t){t.name in n&&(console.log(\"Setting feature override from URL \"+t.name+\": \"+n[t.name]),t.type===Type.NUMBER?e[t.name]=+n[t.name]:t.type===Type.BOOLEAN?e[t.name]=\"true\"===n[t.name]:t.type===Type.STRING?e[t.name]=n[t.name]:console.warn(\"Unknown URL param: \"+t.name+\".\"))})}return e}function hasExtension(e,t){return null!=e.getExtension(t)}function createFloatTextureAndBindToFramebuffer(e,t){var n=e.createFramebuffer(),r=e.createTexture();e.bindTexture(e.TEXTURE_2D,r);var o=2===t?e.RGBA32F:e.RGBA;e.texImage2D(e.TEXTURE_2D,0,o,1,1,0,e.RGBA,e.FLOAT,null),e.bindFramebuffer(e.FRAMEBUFFER,n),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,r,0);var a=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(r),e.deleteFramebuffer(n),a}function getQueryParams(e){var t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,function(e){for(var n=[],r=1;r<arguments.length;r++)n[r-1]=arguments[r];return decodeParam(t,n[0],n[1]),n.join(\"=\")}),t}function decodeParam(e,t,n){e[decodeURIComponent(t)]=decodeURIComponent(n||\"\")}var EPSILON_FLOAT16=1e-4,TEST_EPSILON_FLOAT16=.1,EPSILON_FLOAT32=1e-7,TEST_EPSILON_FLOAT32=.001,Environment=function(){function e(e){this.features={},this.registry={},null!=e&&(this.features=e),this.get(\"DEBUG\")&&console.warn(\"Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.\")}return e.setBackend=function(e,t){if(void 0===t&&(t=!1),!(e in ENV.registry))throw new Error(\"Backend name '\"+e+\"' not found in registry\");ENV.engine.backend=ENV.findBackend(e),ENV.backendName=e},e.getBackend=function(){return ENV.initEngine(),ENV.backendName},e.disposeVariables=function(){ENV.engine.disposeVariables()},e.memory=function(){return ENV.engine.memory()},e.profile=function(e){return ENV.engine.profile(e)},e.tidy=function(e,t){return ENV.engine.tidy(e,t)},e.dispose=function(e){getTensorsInContainer(e).forEach(function(e){return e.dispose()})},e.keep=function(e){return ENV.engine.keep(e)},e.time=function(e){return ENV.engine.time(e)},e.prototype.get=function(e){return e in this.features?this.features[e]:(this.features[e]=this.evaluateFeature(e),this.features[e])},e.prototype.getFeatures=function(){return this.features},e.prototype.set=function(e,t){this.features[e]=t},e.prototype.getBestBackendName=function(){var e=this;if(0===Object.keys(this.registry).length)throw new Error(\"No backend found in registry.\");return Object.keys(this.registry).map(function(t){return{name:t,entry:e.registry[t]}}).sort(function(e,t){return t.entry.priority-e.entry.priority})[0].name},e.prototype.evaluateFeature=function(e){if(\"DEBUG\"===e)return!1;if(\"IS_BROWSER\"===e)return\"undefined\"!=typeof window;if(\"IS_NODE\"===e)return\"undefined\"!=typeof process&&void 0!==process.versions&&void 0!==process.versions.node;if(\"IS_CHROME\"===e)return isChrome();if(\"WEBGL_CPU_FORWARD\"===e)return!0;if(\"WEBGL_PACK\"===e)return!1;if(\"WEBGL_PACK_BATCHNORMALIZATION\"===e)return this.get(\"WEBGL_PACK\");if(\"WEBGL_PACK_CLIP\"===e)return this.get(\"WEBGL_PACK\");if(\"WEBGL_PACK_DEPTHWISECONV\"===e)return this.get(\"WEBGL_PACK\");if(\"WEBGL_LAZILY_UNPACK\"===e)return this.get(\"WEBGL_PACK\");if(\"WEBGL_CONV_IM2COL\"===e)return this.get(\"WEBGL_PACK\");if(\"WEBGL_PAGING_ENABLED\"===e)return this.get(\"IS_BROWSER\")&&!this.get(\"PROD\");if(\"WEBGL_MAX_TEXTURE_SIZE\"===e)return getWebGLMaxTextureSize(this.get(\"WEBGL_VERSION\"));if(\"IS_TEST\"===e)return!1;if(\"BACKEND\"===e)return this.getBestBackendName();if(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"===e){var t=this.get(\"WEBGL_VERSION\");return 0===t?0:getWebGLDisjointQueryTimerVersion(t)}if(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\"===e)return this.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0&&!isMobile();if(\"HAS_WEBGL\"===e)return this.get(\"WEBGL_VERSION\")>0;if(\"WEBGL_VERSION\"===e)return isWebGLVersionEnabled(2)?2:isWebGLVersionEnabled(1)?1:0;if(\"WEBGL_RENDER_FLOAT32_ENABLED\"===e)return isRenderToFloatTextureEnabled(this.get(\"WEBGL_VERSION\"));if(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\"===e)return isDownloadFloatTextureEnabled(this.get(\"WEBGL_VERSION\"));if(\"WEBGL_FENCE_API_ENABLED\"===e)return isWebGLFenceEnabled(this.get(\"WEBGL_VERSION\"));if(\"WEBGL_SIZE_UPLOAD_UNIFORM\"===e)return this.get(\"WEBGL_RENDER_FLOAT32_ENABLED\")?4:0;if(\"TEST_EPSILON\"===e)return 32===this.backend.floatPrecision()?TEST_EPSILON_FLOAT32:TEST_EPSILON_FLOAT16;if(\"EPSILON\"===e)return 32===this.backend.floatPrecision()?EPSILON_FLOAT32:EPSILON_FLOAT16;if(\"PROD\"===e)return!1;if(\"TENSORLIKE_CHECK_SHAPE_CONSISTENCY\"===e)return!this.get(\"PROD\");throw new Error(\"Unknown feature \"+e+\".\")},e.prototype.setFeatures=function(e){this.features=Object.assign({},e)},e.prototype.reset=function(){this.features=getFeaturesFromURL(),null!=this.globalEngine&&(this.globalEngine=null)},Object.defineProperty(e.prototype,\"backend\",{get:function(){return this.engine.backend},enumerable:!0,configurable:!0}),e.prototype.findBackend=function(e){return e in this.registry?this.registry[e].backend:null},e.prototype.registerBackend=function(e,t,n,r){var o=this;if(void 0===n&&(n=1),e in this.registry)return console.warn(e+\" backend was already registered. Reusing existing backend\"),null!=r&&r(function(){return o.engine}),!1;try{var a=t();return a.setDataMover({moveData:function(e){return o.engine.moveData(e)}}),this.registry[e]={backend:a,priority:n},!0}catch(t){return console.warn(\"Registration of backend \"+e+\" failed\"),console.warn(t.stack||t.message),!1}},e.prototype.removeBackend=function(e){if(!(e in this.registry))throw new Error(e+\" backend not found in registry\");this.registry[e].backend.dispose(),delete this.registry[e]},Object.defineProperty(e.prototype,\"engine\",{get:function(){return this.initEngine(),this.globalEngine},enumerable:!0,configurable:!0}),e.prototype.initEngine=function(){var e=this;if(null==this.globalEngine){this.backendName=this.get(\"BACKEND\");var t=this.findBackend(this.backendName);this.globalEngine=new Engine(t,!1,function(){return e.get(\"DEBUG\")})}},e}();function getGlobalNamespace(){var e;if(\"undefined\"!=typeof window)e=window;else{if(\"undefined\"==typeof process)throw new Error(\"Could not find a global object\");e=process}return e}function getOrMakeEnvironment(){var e=getGlobalNamespace();return null==e.ENV&&(e.ENV=new Environment(getFeaturesFromURL()),setTensorTracker(function(){return e.ENV.engine})),e.ENV}var ENV=getOrMakeEnvironment(),environment=Object.freeze({EPSILON_FLOAT16:EPSILON_FLOAT16,EPSILON_FLOAT32:EPSILON_FLOAT32,Environment:Environment,ENV:ENV});function grad(e){return assert(isFunction(e),\"The f passed in grad(f) must be a function\"),function(t,n){return assert(t instanceof Tensor,\"The x passed in grad(f)(x) must be a tensor\"),assert(null==n||n instanceof Tensor,\"The dy passed in grad(f)(x, dy) must be a tensor\"),ENV.engine.tidy(function(){var r=ENV.engine.gradients(function(){return e(t)},[t],n),o=r.value,a=r.grads;return null!=n&&assertShapesMatch(o.shape,n.shape,\"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)\"),checkGrads(a),a[0]})}}function grads(e){return assert(isFunction(e),\"The f passed in grads(f) must be a function\"),function(t,n){return assert(Array.isArray(t)&&t.every(function(e){return e instanceof Tensor}),\"The args passed in grads(f)(args) must be an array of tensors\"),assert(null==n||n instanceof Tensor,\"The dy passed in grads(f)(args, dy) must be a tensor\"),ENV.engine.tidy(function(){var r=ENV.engine.gradients(function(){return e.apply(void 0,t)},t,n),o=r.value,a=r.grads;return null!=n&&assertShapesMatch(o.shape,n.shape,\"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])\"),checkGrads(a),a})}}function valueAndGrad(e){return assert(isFunction(e),\"The f passed in valueAndGrad(f) must be a function\"),function(t,n){assert(t instanceof Tensor,\"The x passed in valueAndGrad(f)(x) must be a tensor\"),assert(null==n||n instanceof Tensor,\"The dy passed in valueAndGrad(f)(x, dy) must be a tensor\");var r=ENV.engine.gradients(function(){return e(t)},[t],n),o=r.grads,a=r.value;return checkGrads(o),{grad:o[0],value:a}}}function valueAndGrads(e){return assert(isFunction(e),\"The f passed in valueAndGrads(f) must be a function\"),function(t,n){assert(Array.isArray(t)&&t.every(function(e){return e instanceof Tensor}),\"The args passed in valueAndGrads(f)(args) must be array of tensors\"),assert(null==n||n instanceof Tensor,\"The dy passed in valueAndGrads(f)(args, dy) must be a tensor\");var r=ENV.engine.gradients(function(){return e.apply(void 0,t)},t,n);return null!=n&&assertShapesMatch(r.value.shape,n.shape,\"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])\"),checkGrads(r.grads),r}}function variableGrads(e,t){if(assert(isFunction(e),\"The f passed in variableGrads(f) must be a function\"),assert(null==t||Array.isArray(t)&&t.every(function(e){return e instanceof Variable}),\"The varList passed in variableGrads(f, varList) must be an array of variables\"),null==t)for(var n in t=[],ENV.engine.registeredVariables)t.push(ENV.engine.registeredVariables[n]);var r=t.length;assert((t=t.filter(function(e){return e.trainable})).length>0,\"variableGrads() expects at least one of the input variables to be trainable, but none of the \"+r+\" variables is trainable.\");var o=ENV.engine.gradients(e,t,null,!0),a=o.value,i=o.grads;assert(i.some(function(e){return null!=e}),\"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize().\"),assert(0===a.rank,\"The f passed in variableGrads(f) must return a scalar, but it returned a rank-\"+a.rank+\" tensor\");var s={};return t.forEach(function(e,t){null!=i[t]&&(s[e.name]=i[t])}),{value:a,grads:s}}function customGrad(e){return ENV.engine.customGrad(e)}function checkGrads(e){if(e.filter(function(e){return null==e}).length>0)throw new Error(\"Cannot compute gradient of y=f(x) with respect to x. Make sure that\\n    the f you passed encloses all operations that lead from x to y.\")}var tidy=Environment.tidy,keep=Environment.keep,dispose=Environment.dispose,time=Environment.time,profile=Environment.profile;function warn(){for(var e=[],t=0;t<arguments.length;t++)e[t]=arguments[t];ENV.get(\"IS_TEST\")||console.warn.apply(console,e)}function getReshaped(e,t,n,r){void 0===r&&(r=!0);var o=[];if(r)(o=o.concat(t.slice(0))).push(e[0]/n),o=o.concat(e.slice(1));else{o=o.concat(e[0]);for(var a=t.length,i=0;i<a;++i)o=o.concat([e[i+1]/t[i],t[i]]);o=o.concat(e.slice(a+1))}return o}function getPermuted(e,t,n){void 0===n&&(n=!0);var r=[];if(n){r.push(t);for(var o=t+1;o<e;++o)o<=2*t?(r.push(o),r.push(o-(t+1))):r.push(o)}else{var a=[],i=[];for(o=1;o<e;++o)o>=2*t+1||o%2==1?i.push(o):a.push(o);r.push.apply(r,a),r.push(0),r.push.apply(r,i)}return r}function getReshapedPermuted(e,t,n,r){void 0===r&&(r=!0);var o=[];r?o.push(e[0]/n):o.push(e[0]*n);for(var a=1;a<e.length;++a)a<=t.length?r?o.push(t[a-1]*e[a]):o.push(e[a]/t[a-1]):o.push(e[a]);return o}function getSliceBeginCoords(e,t){for(var n=[0],r=0;r<t;++r)n.push(e[r][0]);return n}function getSliceSize(e,t,n){for(var r=e.slice(0,1),o=0;o<n;++o)r.push(e[o+1]-t[o][0]-t[o][1]);return r}function axesAreInnerMostDims(e,t){for(var n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return!1;return!0}function combineLocations(e,t,n){for(var r=e.length+t.length,o=[],a=0,i=0,s=0;s<r;s++)-1===n.indexOf(s)?o.push(e[a++]):o.push(t[i++]);return o}function computeOutAndReduceShapes(e,t){for(var n=[],r=e.length,o=0;o<r;o++)-1===t.indexOf(o)&&n.push(e[o]);return[n,t.map(function(t){return e[t]})]}function expandShapeToKeepDim(e,t){return combineLocations(e,t.map(function(e){return 1}),t)}function parseAxisParam(e,t){var n=t.length;return assert((e=null==e?t.map(function(e,t){return t}):[].concat(e)).every(function(e){return e>=-n&&e<n}),\"All values in axis param must be in range [-\"+n+\", \"+n+\") but got axis \"+e),assert(e.every(function(e){return isInt(e)}),\"All values in axis param must be integers but got axis \"+e),e.map(function(e){return e<0?n+e:e})}function assertAxesAreInnerMostDims(e,t,n){assert(axesAreInnerMostDims(t,n),e+\" supports only inner-most axes for now. Got axes \"+t+\" and rank-\"+n+\" input.\")}function getAxesPermutation(e,t){if(axesAreInnerMostDims(e,t))return null;for(var n=[],r=0;r<t;++r)-1===e.indexOf(r)&&n.push(r);return e.forEach(function(e){return n.push(e)}),n}function getUndoAxesPermutation(e){return e.map(function(e,t){return[t,e]}).sort(function(e,t){return e[1]-t[1]}).map(function(e){return e[0]})}function getInnerMostAxes(e,t){for(var n=[],r=t-e;r<t;++r)n.push(r);return n}function assertParamsConsistent(e,t){var n=e[0].length;e.forEach(function(e,t){assert(e.length===n,\"Error in concat\"+n+\"D: rank of tensors[\"+t+\"] must be the same as the rank of the rest (\"+n+\")\")}),assert(t>=0&&t<n,\"Error in concat\"+n+\"D: axis must be between 0 and \"+(n-1)+\".\");var r=e[0];e.forEach(function(e,o){for(var a=0;a<n;a++)assert(a===t||e[a]===r[a],\"Error in concat\"+n+\"D: Shape of tensors[\"+o+\"] (\"+e+\") does not match the shape of the rest (\"+r+\") along the non-concatenated axis \"+o+\".\")})}function computeOutShape(e,t){for(var n=e[0].slice(),r=1;r<e.length;r++)n[t]+=e[r][t];return n}function prepareAndValidate(e,t){if(e.rank<1)throw new Error(\"tf.gatherND() expects the input to be rank 1 or higher, but the rank was \"+e.rank+\".\");if(t.rank<1)throw new Error(\"tf.gatherND() expects the indices to be rank 1 or higher, but the rank was \"+t.rank+\".\");if(\"int32\"!==t.dtype)throw new Error(\"tf.gatherND() expects the indices to be int32 type, but the dtype was \"+t.dtype+\".\");if(t.shape[t.rank-1]>e.rank)throw new Error(\"index innermost dimension length must be <= tensor rank; saw: \"+t.shape[t.rank-1]+\" vs. \"+e.rank);if(0===e.size)throw new Error(\"Requested more than 0 entries, but input is empty. Input shape: \"+e.shape+\".\");for(var n=t.shape,r=n[n.length-1],o=1,a=0;a<n.length-1;++a)o*=n[a];var i=e.shape,s=n.slice();s.pop();var u=1;for(a=r;a<e.rank;++a)u*=i[a],s.push(i[a]);var l=computeStrides(e.shape).map(function(e){return e/u}).concat([1]).slice(0,r);return[s,o,u,l]}var PARALLELIZE_THRESHOLD=30;function computeOptimalWindowSize(e){return e<=PARALLELIZE_THRESHOLD?e:nearestDivisor(e,Math.floor(Math.sqrt(e)))}function validateUpdateShape(e,t,n){var r=t.rank>1?t.shape[t.rank-1]:1,o=t.rank>1?t.rank-1:1,a=\"Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: \"+n.shape+\", indices.shape: \"+t.shape+\", shape: \"+e+\", sliceDim: \"+r+\", and batchDim: \"+o+\".\";if(n.rank<o)throw new Error(a+\" update.rank < \"+o+\". \");if(e.length<r+(n.rank-o))throw new Error(a+\" Output shape length < \"+(r+(n.rank-o)));if(n.rank!==o+e.length-r)throw new Error(a+\" update.rank != \"+(o+e.length-r));for(var i=0;i<o;++i)if(n.shape[i]!==t.shape[i])throw new Error(a+\" updates.shape[\"+i+\"] (\"+n.shape[i]+\") != indices.shape[\"+i+\"] (\"+t.shape[i]+\").\");for(i=0;i<n.rank-o;++i)if(n.shape[i+o]!==e[i+r])throw new Error(a+\" updates.shape[\"+(i+o)+\"] (\"+n.shape[i+o]+\") != shape[\"+(i+o)+\"] (\"+e[i+o]+\")\")}function validateInput(e,t,n){if(t.rank<1)throw new Error(\"tf.scatterND() expects the indices to be rank 1 or higher, but the rank was \"+t.rank+\".\");if(e.rank<1)throw new Error(\"tf.scatterND() expects the updates to be rank 1 or higher, but the rank was \"+e.rank+\".\");if(\"int32\"!==t.dtype)throw new Error(\"The dtype of 'indices' should be int32, but got dtype: \"+t.dtype);if(n.length<1)throw new Error(\"Output rank must be greater or equal to 1, but got shape: \"+n);if(0===n.length){if(0===t.size)throw new Error(\"Indices specified for empty output. indices shape: \"+t.shape);if(0===e.size)throw new Error(\"Updates specified for empty output. updates shape: \"+e.shape)}validateUpdateShape(n,t,e)}function calculateShapes(e,t,n){for(var r=t.rank>1?t.shape[t.rank-1]:1,o=n.length,a=1,i=r;i<o;++i)a*=n[i];var s=r<1?1:r,u=t.size/s,l=computeStrides(n).concat([1]);return{sliceRank:r,numUpdates:u,sliceSize:a,strides:l.slice(l.length-r,l.length),outputSize:sizeFromShape(n)}}function segOpComputeOptimalWindowSize(e,t){var n,r=!1;for(e<=PARALLELIZE_THRESHOLD?(n=e,r=!0):n=nearestDivisor(e,Math.floor(Math.sqrt(e)));!r;){if(n>t||n===e){r=!0;break}n=nearestDivisor(e,n+1)}return n}function computeOutShape$1(e,t,n){for(var r=[],o=e.length,a=0;a<o;a++)a!==t?r.push(e[a]):r.push(n);return r}function assertParamsValid(e,t,n){assert(e.rank===t.length,\"Error in slice\"+e.rank+\"D: Length of begin \"+t+\" must match the rank of the array (\"+e.rank+\").\"),assert(e.rank===n.length,\"Error in slice\"+e.rank+\"D: Length of size \"+n+\" must match the rank of the array (\"+e.rank+\").\");for(var r=0;r<e.rank;++r)assert(t[r]+n[r]<=e.shape[r],\"Error in slice\"+e.rank+\"D: begin[\"+r+\"] + size[\"+r+\"] (\"+(t[r]+n[r])+\") would overflow input.shape[\"+r+\"] (\"+e.shape[r]+\")\")}function getStridedSlicedInfo(e,t,n,r,o,a,i,s,u){if(void 0===o&&(o=0),void 0===a&&(a=0),void 0===i&&(i=0),void 0===s&&(s=0),void 0===u&&(u=0),0!==i)throw new Error(\"ellipsis mask is not yet supported\");if(0!==s)throw new Error(\"new axis mask is not yet supported\");for(var l=[],c=[],p=[],d=0;d<e.length;d++)l[d]=startForAxis(o,t,r,e,d),c[d]=stopForAxis(a,n,r,e,d),u&1<<d&&(c[d]=l[d]+1,p.push(d));var h=new Array(e.length).fill(0);return h=h.map(function(e,t){for(var n=0,o=l[t];!(r[t]>0?o>=c[t]:o<=c[t]);o+=r[t])n+=1;return n}),[l,h,p]}function startForAxis(e,t,n,r,o){var a=t[o];e&1<<o&&(a=n[o]>0?Number.MIN_SAFE_INTEGER:Number.MAX_SAFE_INTEGER);var i=r[o];return a<0&&(a+=i),a=clamp(0,a,i-1)}function stopForAxis(e,t,n,r,o){var a=t[o];e&1<<o&&(a=n[o]>0?Number.MAX_SAFE_INTEGER:Number.MIN_SAFE_INTEGER);var i=r[o];return a<0&&(a+=i),a=n[o]>0?clamp(0,a,i):clamp(-1,a,i-1)}function inferShape(e){var t=e;if(isTypedArray(e))return[e.length];if(!Array.isArray(e))return[];for(var n=[];t instanceof Array||isTypedArray(t);)n.push(t.length),t=t[0];return e instanceof Array&&ENV.get(\"TENSORLIKE_CHECK_SHAPE_CONSISTENCY\")&&deepAssertShapeConsistency(e,n,[]),n}function deepAssertShapeConsistency(e,t,n){if(n=n||[],e instanceof Array||isTypedArray(e)){assert(t.length>0,function(){return\"Element arr[\"+n.join(\"][\")+\"] should be a primitive, but is an array of \"+e.length+\" elements\"}),assert(e.length===t[0],function(){return\"Element arr[\"+n.join(\"][\")+\"] should have \"+t[0]+\" elements, but has \"+e.length+\" elements\"});for(var r=t.slice(1),o=0;o<e.length;++o)deepAssertShapeConsistency(e[o],r,n.concat(o))}else assert(0===t.length,function(){return\"Element arr[\"+n.join(\"][\")+\"] is a primitive, but should be an array/TypedArray of \"+t[0]+\" elements\"})}function assertDtype(e,t,n,r){if(null!=e&&(\"numeric\"!==e&&e!==t||\"numeric\"===e&&\"string\"===t))throw new Error(\"Argument '\"+n+\"' passed to '\"+r+\"' must be \"+e+\" tensor, but got \"+t+\" tensor\")}function convertToTensor(e,t,n,r){if(void 0===r&&(r=\"numeric\"),e instanceof Tensor)return assertDtype(r,e.dtype,t,n),e;var o=inferDtype(e);if(\"string\"!==o&&[\"bool\",\"int32\",\"float32\"].indexOf(r)>=0&&(o=r),assertDtype(r,o,t,n),!isTypedArray(e)&&!Array.isArray(e)&&\"number\"!=typeof e&&\"boolean\"!=typeof e&&\"string\"!=typeof e)throw new Error(\"Argument '\"+t+\"' passed to '\"+n+\"' must be a Tensor or TensorLike, but got '\"+e.constructor.name+\"'\");var a=inferShape(e);isTypedArray(e)||Array.isArray(e)||(e=[e]);var i=\"string\"!==o?toTypedArray(e,o,ENV.get(\"DEBUG\")):flatten(e);return Tensor.make(a,{values:i},o)}function convertToTensorArray(e,t,n){if(!Array.isArray(e))throw new Error(\"Argument \"+t+\" passed to \"+n+\" must be a `Tensor[]` or `TensorLike[]`\");return e.map(function(e,r){return convertToTensor(e,t+\"[\"+r+\"]\",n)})}function op(e){var t=Object.keys(e);if(1!==t.length)throw new Error(\"Please provide an object with a single key (operation name) mapping to a function. Got an object with \"+t.length+\" keys.\");var n=t[0],r=e[n];n.endsWith(\"_\")&&(n=n.substring(0,n.length-1));var o=function(){for(var e=[],t=0;t<arguments.length;t++)e[t]=arguments[t];ENV.engine.startScope(n);try{var o=r.apply(void 0,e);return o instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),ENV.engine.endScope(o),o}catch(e){throw ENV.engine.endScope(null),e}};return Object.defineProperty(o,\"name\",{value:n,configurable:!0}),o}function softmax_(e,t){void 0===t&&(t=-1);var n=convertToTensor(e,\"logits\",\"softmax\");if(-1===t&&(t=n.rank-1),t!==n.rank-1)throw Error(\"Softmax along a non-last dimension is not yet supported. Logits was rank \"+n.rank+\" and dim was \"+t);return customGrad(function(e){var n=e.logSumExp([t],!0),r=e.toFloat().sub(n).exp();return{value:r,gradFunc:function(e){var n=e.mul(r);return n.sub(n.sum([t],!0).mul(r))}}})(n)}function logSoftmax_(e,t){void 0===t&&(t=-1);var n=convertToTensor(e,\"logits\",\"logSoftmax\");if(-1===t&&(t=n.rank-1),t!==n.rank-1)throw Error(\"Log Softmax along a non-last dimension is not yet supported. Logits was rank \"+n.rank+\" and axis was \"+t);return customGrad(function(e){var n=e.max(t,!0),r=e.sub(n),o=r.toFloat().sub(r.exp().sum(t,!0).log());return{value:o,gradFunc:function(e){var n=o.exp();return e.sub(e.sum(t,!0).mul(n))}}})(n)}var softmax=op({softmax_:softmax_}),logSoftmax=op({logSoftmax_:logSoftmax_});function complex_(e,t){var n=convertToTensor(e,\"real\",\"complex\"),r=convertToTensor(t,\"imag\",\"complex\");return assertShapesMatch(n.shape,r.shape,\"real and imag shapes, \"+n.shape+\" and \"+r.shape+\", must match in call to tf.complex().\"),ENV.engine.runKernel(function(e){return e.complex(n,r)},{$real:n,$imag:r})}function real_(e){var t=convertToTensor(e,\"input\",\"real\");return ENV.engine.runKernel(function(e){return e.real(t)},{$input:t})}function imag_(e){var t=convertToTensor(e,\"input\",\"imag\");return ENV.engine.runKernel(function(e){return e.imag(t)},{$input:t})}var complex=op({complex_:complex_}),real=op({real_:real_}),imag=op({imag_:imag_});function tensor(e,t,n){if(null==n&&(n=inferDtype(e)),\"complex64\"===n)throw new Error(\"Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).\");if(!isTypedArray(e)&&!Array.isArray(e)&&\"number\"!=typeof e&&\"boolean\"!=typeof e&&\"string\"!=typeof e)throw new Error(\"values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray\");var r=inferShape(e);if(null!=t){var o=sizeFromShape(t),a=sizeFromShape(r);assert(o===a,function(){return\"Based on the provided shape, [\"+t+\"], the tensor should have \"+o+\" values but has \"+a});for(var i=0;i<r.length;++i){var s=r[i],u=i!==r.length-1||s!==sizeFromShape(t.slice(i));assert(r[i]===t[i]||!u,function(){return\"Error creating a new Tensor. Inferred shape (\"+r+\") does not match the provided shape (\"+t+\"). \"})}}return isTypedArray(e)||Array.isArray(e)||(e=[e]),t=t||r,e=\"string\"!==n?toTypedArray(e,n,ENV.get(\"DEBUG\")):flatten(e),Tensor.make(t,{values:e},n)}function scalar(e,t){if((isTypedArray(e)||Array.isArray(e))&&\"complex64\"!==t)throw new Error(\"Error creating a new Scalar: value must be a primitive (number|boolean|string)\");return tensor(e,[],t)}function tensor1d(e,t){assertNonNull(e);var n=inferShape(e);if(1!==n.length)throw new Error(\"tensor1d() requires values to be a flat/TypedArray\");return tensor(e,n,t)}function tensor2d(e,t,n){if(assertNonNull(e),null!=t&&2!==t.length)throw new Error(\"tensor2d() requires shape to have two numbers\");var r=inferShape(e);if(2!==r.length&&1!==r.length)throw new Error(\"tensor2d() requires values to be number[][] or flat/TypedArray\");if(1===r.length&&null==t)throw new Error(\"tensor2d() requires shape to be provided when `values` are a flat/TypedArray\");return tensor(e,t=t||r,n)}function tensor3d(e,t,n){if(assertNonNull(e),null!=t&&3!==t.length)throw new Error(\"tensor3d() requires shape to have three numbers\");var r=inferShape(e);if(3!==r.length&&1!==r.length)throw new Error(\"tensor3d() requires values to be number[][][] or flat/TypedArray\");if(1===r.length&&null==t)throw new Error(\"tensor3d() requires shape to be provided when `values` are a flat array\");return tensor(e,t=t||r,n)}function tensor4d(e,t,n){if(assertNonNull(e),null!=t&&4!==t.length)throw new Error(\"tensor4d() requires shape to have four numbers\");var r=inferShape(e);if(4!==r.length&&1!==r.length)throw new Error(\"tensor4d() requires values to be number[][][][] or flat/TypedArray\");if(1===r.length&&null==t)throw new Error(\"tensor4d() requires shape to be provided when `values` are a flat array\");return tensor(e,t=t||r,n)}function tensor5d(e,t,n){if(assertNonNull(e),null!=t&&5!==t.length)throw new Error(\"tensor5d() requires shape to have five numbers\");var r=inferShape(e);if(5!==r.length&&1!==r.length)throw new Error(\"tensor5d() requires values to be number[][][][][] or flat/TypedArray\");if(1===r.length&&null==t)throw new Error(\"tensor5d() requires shape to be provided when `values` are a flat array\");return tensor(e,t=t||r,n)}function tensor6d(e,t,n){if(assertNonNull(e),null!=t&&6!==t.length)throw new Error(\"tensor6d() requires shape to have six numbers\");var r=inferShape(e);if(6!==r.length&&1!==r.length)throw new Error(\"tensor6d() requires values to be number[][][][] or flat/TypedArray\");if(1===r.length&&null==t)throw new Error(\"tensor6d() requires shape to be provided when `values` are a flat array\");return tensor(e,t=t||r,n)}function ones$1(e,t){if(void 0===t&&(t=\"float32\"),\"complex64\"===t){var n=ones$1(e,\"float32\"),r=ones$1(e,\"float32\");return complex(n,r)}var o=makeOnesTypedArray(sizeFromShape(e),t);return Tensor.make(e,{values:o},t)}function zeros(e,t){if(void 0===t&&(t=\"float32\"),\"complex64\"===t){var n=zeros(e,\"float32\"),r=zeros(e,\"float32\");return complex(n,r)}var o=makeZerosTypedArray(sizeFromShape(e),t);return Tensor.make(e,{values:o},t)}function fill(e,t,n){var r=getArrayFromDType(n=n||inferDtype(t),sizeFromShape(e));return r.fill(t),Tensor.make(e,{values:r},n)}function onesLike_(e){var t=convertToTensor(e,\"x\",\"onesLike\");return ones$1(t.shape,t.dtype)}function zerosLike_(e){var t=convertToTensor(e,\"x\",\"zerosLike\");return zeros(t.shape,t.dtype)}function linspace(e,t,n){if(0===n)throw new Error(\"Cannot request zero samples\");var r=(t-e)/(n-1),o=makeZerosTypedArray(n,\"float32\");o[0]=e;for(var a=1;a<o.length;a++)o[a]=o[a-1]+r;return tensor1d(o,\"float32\")}function range(e,t,n,r){if(void 0===n&&(n=1),void 0===r&&(r=\"float32\"),0===n)throw new Error(\"Cannot have a step of zero\");if(e===t||e<t&&n<0||t<e&&n>1)return zeros([0],r);var o=makeZerosTypedArray(Math.abs(Math.ceil((t-e)/n)),r);t<e&&1===n&&(n=-1),o[0]=e;for(var a=1;a<o.length;a++)o[a]=o[a-1]+n;return tensor1d(o,r)}var onesLike=op({onesLike_:onesLike_}),zerosLike=op({zerosLike_:zerosLike_}),DataStorage=function(){function e(e){this.dataMover=e,this.data=new WeakMap}return e.prototype.get=function(e){return this.data.has(e)||this.dataMover.moveData(e),this.data.get(e)},e.prototype.set=function(e,t){this.data.set(e,t)},e.prototype.has=function(e){return this.data.has(e)},e.prototype.delete=function(e){return this.data.delete(e)},e}(),KernelBackend=function(){function e(){}return e.prototype.time=function(e){throw new Error(\"Not yet implemented.\")},e.prototype.read=function(e){throw new Error(\"Not yet implemented.\")},e.prototype.readSync=function(e){throw new Error(\"Not yet implemented.\")},e.prototype.disposeData=function(e){throw new Error(\"Not yet implemented.\")},e.prototype.write=function(e,t){throw new Error(\"Not yet implemented.\")},e.prototype.fromPixels=function(e,t){throw new Error(\"Not yet implemented.\")},e.prototype.register=function(e,t,n){throw new Error(\"Not yet implemented.\")},e.prototype.memory=function(){throw new Error(\"Not yet implemented.\")},e.prototype.floatPrecision=function(){throw new Error(\"Not yet implemented\")},e.prototype.batchMatMul=function(e,t,n,r){throw new Error(\"Not yet implemented\")},e.prototype.slice=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.stridedSlice=function(e,t,n,r,o,a,i,s,u){throw new Error(\"Not yet implemented\")},e.prototype.reverse=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.concat=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.neg=function(e){throw new Error(\"Not yet implemented\")},e.prototype.add=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.addN=function(e){throw new Error(\"Not yet implemented\")},e.prototype.subtract=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.multiply=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.realDivide=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.floorDiv=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.sum=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.prod=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.unsortedSegmentSum=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.argMin=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.argMax=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.equal=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.notEqual=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.less=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.lessEqual=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.greater=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.greaterEqual=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.logicalNot=function(e){throw new Error(\"Not yet implemented\")},e.prototype.logicalAnd=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.logicalOr=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.where=function(e){throw new Error(\"Not yet implemented\")},e.prototype.select=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.topk=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.min=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.minimum=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.mod=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.max=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.maximum=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.all=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.any=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.squaredDifference=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.ceil=function(e){throw new Error(\"Not yet implemented\")},e.prototype.floor=function(e){throw new Error(\"Not yet implemented\")},e.prototype.round=function(e){throw new Error(\"Not yet implemented\")},e.prototype.sign=function(e){throw new Error(\"Not yet implemented\")},e.prototype.pow=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.exp=function(e){throw new Error(\"Not yet implemented\")},e.prototype.expm1=function(e){throw new Error(\"Not yet implemented\")},e.prototype.log=function(e){throw new Error(\"Not yet implemented\")},e.prototype.log1p=function(e){throw new Error(\"Not yet implemented\")},e.prototype.sqrt=function(e){throw new Error(\"Not yet implemented\")},e.prototype.rsqrt=function(e){throw new Error(\"Not yet implemented\")},e.prototype.square=function(e){throw new Error(\"Not yet implemented\")},e.prototype.reciprocal=function(e){throw new Error(\"Not yet implemented\")},e.prototype.relu=function(e){throw new Error(\"Not yet implemented\")},e.prototype.elu=function(e){throw new Error(\"Not yet implemented\")},e.prototype.eluDer=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.selu=function(e){throw new Error(\"Not yet implemented\")},e.prototype.int=function(e){throw new Error(\"Not yet implemented\")},e.prototype.clip=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.abs=function(e){throw new Error(\"Not yet implemented\")},e.prototype.complexAbs=function(e){throw new Error(\"Not yet implemented\")},e.prototype.sigmoid=function(e){throw new Error(\"Not yet implemented\")},e.prototype.softplus=function(e){throw new Error(\"Not yet implemented\")},e.prototype.sin=function(e){throw new Error(\"Not yet implemented\")},e.prototype.cos=function(e){throw new Error(\"Not yet implemented\")},e.prototype.tan=function(e){throw new Error(\"Not yet implemented\")},e.prototype.asin=function(e){throw new Error(\"Not yet implemented\")},e.prototype.acos=function(e){throw new Error(\"Not yet implemented\")},e.prototype.atan=function(e){throw new Error(\"Not yet implemented\")},e.prototype.atan2=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.sinh=function(e){throw new Error(\"Not yet implemented\")},e.prototype.cosh=function(e){throw new Error(\"Not yet implemented\")},e.prototype.tanh=function(e){throw new Error(\"Not yet implemented\")},e.prototype.asinh=function(e){throw new Error(\"Not yet implemented\")},e.prototype.acosh=function(e){throw new Error(\"Not yet implemented\")},e.prototype.atanh=function(e){throw new Error(\"Not yet implemented\")},e.prototype.erf=function(e){throw new Error(\"Not yet implemented\")},e.prototype.step=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.conv2d=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.conv2dDerInput=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.conv2dDerFilter=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.depthwiseConv2D=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.depthwiseConv2DDerInput=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.depthwiseConv2DDerFilter=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.conv3d=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.conv3dDerInput=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.conv3dDerFilter=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.maxPool=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.maxPoolBackprop=function(e,t,n,r){throw new Error(\"Not yet implemented\")},e.prototype.avgPool=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.avgPoolBackprop=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.reshape=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.cast=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.tile=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.pad=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.transpose=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.gather=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.gatherND=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.scatterND=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.batchToSpaceND=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.spaceToBatchND=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.resizeBilinear=function(e,t,n,r){throw new Error(\"Not yet implemented\")},e.prototype.resizeBilinearBackprop=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.resizeNearestNeighbor=function(e,t,n,r){throw new Error(\"Not yet implemented\")},e.prototype.resizeNearestNeighborBackprop=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.batchNormalization=function(e,t,n,r,o,a){throw new Error(\"Not yet implemented\")},e.prototype.localResponseNormalization4D=function(e,t,n,r,o){throw new Error(\"Not yet implemented\")},e.prototype.LRNGrad=function(e,t,n,r,o,a,i){throw new Error(\"Not yet implemented\")},e.prototype.multinomial=function(e,t,n,r){throw new Error(\"Not yet implemented\")},e.prototype.oneHot=function(e,t,n,r){throw new Error(\"Not yet implemented\")},e.prototype.cumsum=function(e,t,n,r){throw new Error(\"Not yet implemented\")},e.prototype.nonMaxSuppression=function(e,t,n,r,o){throw new Error(\"Not yet implemented\")},e.prototype.fft=function(e){throw new Error(\"Not yet implemented\")},e.prototype.ifft=function(e){throw new Error(\"Not yet implemented\")},e.prototype.complex=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.real=function(e){throw new Error(\"Not yet implemented\")},e.prototype.imag=function(e){throw new Error(\"Not yet implemented\")},e.prototype.cropAndResize=function(e,t,n,r,o,a){throw new Error(\"Not yet implemented\")},e.prototype.depthToSpace=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.split=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.sparseToDense=function(e,t,n,r){throw new Error(\"Not yet implemented\")},e.prototype.setDataMover=function(e){throw new Error(\"Not yet implemented\")},e.prototype.dispose=function(){throw new Error(\"Not yet implemented\")},e}();function castTensor(e,t,n){if(\"complex64\"===t){if(\"complex64\"===e.dtype)return e.clone();var r=zeros(e.shape),o=e.toFloat(),a=n.complex(o,r);return r.dispose(),o.dispose(),a}if(!hasEncodingLoss(e.dtype,t))return Tensor.make(e.shape,{dataId:e.dataId},t);if(\"complex64\"===e.dtype){var i=n.real(e);a=i.cast(t);return i.dispose(),a}if(\"int32\"===t)return n.int(e);if(\"bool\"===t){var s=scalar(0,e.dtype);a=n.notEqual(e,s);return s.dispose(),a}throw new Error(\"Error in Cast: unknown dtype argument (\"+t+\")\")}function reshapeTensor(e,t){return Tensor.make(t,{dataId:e.dataId},e.dtype)}function mergeRealAndImagArrays(e,t){if(e.length!==t.length)throw new Error(\"Cannot merge real and imag arrays of different lengths. real:\"+e.length+\", imag: \"+t.length+\".\");for(var n=new Float32Array(2*e.length),r=0;r<n.length;r+=2)n[r]=e[r/2],n[r+1]=t[r/2];return n}function splitRealAndImagArrays(e){for(var t=new Float32Array(e.length/2),n=new Float32Array(e.length/2),r=0;r<e.length;r+=2)t[r/2]=e[r],n[r/2]=e[r+1];return{real:t,imag:n}}function complexWithEvenIndex(e){for(var t=Math.ceil(e.length/4),n=new Float32Array(t),r=new Float32Array(t),o=0;o<e.length;o+=4)n[Math.floor(o/4)]=e[o],r[Math.floor(o/4)]=e[o+1];return{real:n,imag:r}}function complexWithOddIndex(e){for(var t=Math.floor(e.length/4),n=new Float32Array(t),r=new Float32Array(t),o=2;o<e.length;o+=4)n[Math.floor(o/4)]=e[o],r[Math.floor(o/4)]=e[o+1];return{real:n,imag:r}}function getComplexWithIndex(e,t){return{real:e[2*t],imag:e[2*t+1]}}function assignToTypedArray(e,t,n,r){e[2*r]=t,e[2*r+1]=n}function exponents(e,t){for(var n=new Float32Array(e/2),r=new Float32Array(e/2),o=0;o<Math.ceil(e/2);o++){var a=(t?2:-2)*Math.PI*(o/e);n[o]=Math.cos(a),r[o]=Math.sin(a)}return{real:n,imag:r}}function exponent(e,t,n){var r=(n?2:-2)*Math.PI*(e/t);return{real:Math.cos(r),imag:Math.sin(r)}}function nonMaxSuppressionImpl(e,t,n,r,o){for(var a=Array.from(t).map(function(e,t){return{score:e,boxIndex:t}}).filter(function(e){return e.score>o}).sort(function(e,t){return t.score-e.score}),i=[],s=0;s<a.length;s++){var u=a[s],l=u.score,c=u.boxIndex;if(l<o)break;for(var p=!1,d=i.length-1;d>=0;--d){if(intersectionOverUnion(e,c,i[d])>=r){p=!0;break}}if(!p&&(i.push(c),i.length>=n))break}return tensor1d(i,\"int32\")}function intersectionOverUnion(e,t,n){var r=e.subarray(4*t,4*t+4),o=e.subarray(4*n,4*n+4),a=Math.min(r[0],r[2]),i=Math.min(r[1],r[3]),s=Math.max(r[0],r[2]),u=Math.max(r[1],r[3]),l=Math.min(o[0],o[2]),c=Math.min(o[1],o[3]),p=Math.max(o[0],o[2]),d=Math.max(o[1],o[3]),h=(s-a)*(u-i),f=(p-l)*(d-c);if(h<=0||f<=0)return 0;var m=Math.max(a,l),g=Math.max(i,c),v=Math.min(s,p),y=Math.min(u,d),x=Math.max(v-m,0)*Math.max(y-g,0);return x/(h+f-x)}function split(e,t,n){var r=Array(e.rank).fill(0),o=e.shape.slice();return t.map(function(t){o[n]=t;var a=e.slice(r,o);return r[n]+=t,a})}function topkImpl(e,t,n,r,o){for(var a=t[t.length-1],i=[e.length/a,a],s=i[0],u=i[1],l=getTypedArrayFromDType(n,s*r),c=getTypedArrayFromDType(\"int32\",s*r),p=0;p<s;p++){for(var d=p*u,h=e.subarray(d,d+u),f=[],m=0;m<h.length;m++)f.push({value:h[m],index:m});f.sort(function(e,t){return t.value-e.value});var g=p*r,v=l.subarray(g,g+r),y=c.subarray(g,g+r);for(m=0;m<r;m++)v[m]=f[m].value,y[m]=f[m].index}var x=t.slice();return x[x.length-1]=r,[tensor(l,x,n),tensor(c,x,\"int32\")]}var ArgMinMaxProgram=function(){return function(e,t,n){this.variableNames=[\"A\"];var r=e.windowSize,o=e.batchSize,a=e.inSize,i=Math.ceil(a/r);n||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[o,i];var s=\"max\"===t?\">\":\"<\",u=n?\"inOffset + i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=\"\\n      void main() {\\n        ivec2 coords = getOutputCoords();\\n        int batch = coords[0];\\n        int outIdx = coords[1];\\n        int inOffset = outIdx * \"+r+\";\\n\\n        int bestIndex = inOffset;\\n        float bestValue = getA(batch, bestIndex);\\n\\n        for (int i = 0; i < \"+r+\"; i++) {\\n          int inIdx = \"+u+\";\\n          float candidate = getA(batch, inIdx);\\n          if (candidate \"+s+\" bestValue) {\\n            bestValue = candidate;\\n            bestIndex = inIdx;\\n          }\\n        }\\n        setOutput(float(bestIndex));\\n      }\\n    \"}}(),AvgPool2DBackpropProgram=function(){return function(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;var t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,o=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,s=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=s-1-e.padInfo.top,c=u-1-e.padInfo.left,p=1/(t*n);this.userCode=\"\\n      const ivec2 pads = ivec2(\"+l+\", \"+c+\");\\n      const float avgMultiplier = float(\"+p+\");\\n\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int b = coords[0];\\n        int d = coords[3];\\n\\n        ivec2 dyRCCorner = coords.yz - pads;\\n        int dyRCorner = dyRCCorner.x;\\n        int dyCCorner = dyRCCorner.y;\\n\\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\\n        // ? = to be determined. : = across all values in that axis.\\n        float dotProd = 0.0;\\n        for (int wR = 0; wR < \"+s+\";\\n            wR += \"+a+\") {\\n          float dyR = float(dyRCorner + wR) / \"+r+\".0;\\n\\n          if (dyR < 0.0 || dyR >= \"+e.outHeight+\".0 || fract(dyR) > 0.0) {\\n            continue;\\n          }\\n          int idyR = int(dyR);\\n\\n          for (int wC = 0; wC < \"+u+\";\\n            wC+= \"+i+\") {\\n            float dyC = float(dyCCorner + wC) / \"+o+\".0;\\n\\n            if (dyC < 0.0 || dyC >= \"+e.outWidth+\".0 ||\\n                fract(dyC) > 0.0) {\\n              continue;\\n            }\\n            int idyC = int(dyC);\\n\\n            float dyValue = getDy(b, idyR, idyC, d);\\n\\n            dotProd += dyValue * avgMultiplier;\\n          }\\n        }\\n        setOutput(dotProd);\\n      }\\n    \"}}();function getBroadcastDims(e,t){for(var n=e.length,r=[],o=0;o<n;o++){var a=n-1-o,i=e[a]||1;(t[t.length-1-o]||1)>1&&1===i&&r.unshift(a)}return r}function getReductionAxes(e,t){for(var n=[],r=0;r<t.length;r++){var o=e[e.length-r-1],a=t.length-r-1,i=t[a];(null==o||1===o&&i>1)&&n.unshift(a)}return n}function broadcastDimsAreOuter(e){for(var t=0;t<e.length;t++)if(e[t]!==t)return!1;return!0}function assertAndGetBroadcastShape(e,t){for(var n=[],r=Math.max(e.length,t.length),o=0;o<r;o++){var a=e[e.length-o-1];null==a&&(a=1);var i=t[t.length-o-1];if(null==i&&(i=1),1===a)n.unshift(i);else if(1===i)n.unshift(a);else{if(a!==i)throw Error(\"Operands could not be broadcast together with shapes \"+e+\" and \"+t+\".\");n.unshift(a)}}return n}var BatchNormProgram=function(){return function(e,t,n,r,o,a){this.outputShape=[],this.supportsBroadcasting=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],assertAndGetBroadcastShape(e,t),assertAndGetBroadcastShape(e,n);var i=\"0.0\";null!=r&&(assertAndGetBroadcastShape(e,r),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");var s=\"1.0\";null!=o&&(assertAndGetBroadcastShape(e,o),this.variableNames.push(\"scale\"),s=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=\"\\n      void main() {\\n        float x = getXAtOutCoords();\\n        float mean = getMeanAtOutCoords();\\n        float variance = getVarianceAtOutCoords();\\n        float offset = \"+i+\";\\n        float scale = \"+s+\";\\n        float inv = scale * inversesqrt(variance + float(\"+a+\"));\\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\\n      }\\n    \"}}(),BatchNormPackedProgram=function(){return function(e,t,n,r,o,a){this.supportsBroadcasting=!0,this.usesPackedTextures=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],assertAndGetBroadcastShape(e,t),assertAndGetBroadcastShape(e,n);var i=\"vec4(0.0)\";null!=r&&(assertAndGetBroadcastShape(e,r),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");var s=\"vec4(1.0)\";null!=o&&(assertAndGetBroadcastShape(e,o),this.variableNames.push(\"scale\"),s=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=\"\\n      void main() {\\n        vec4 offset = \"+i+\";\\n        vec4 scale = \"+s+\";\\n\\n        vec4 x = getXAtOutCoords();\\n        vec4 mean = getMeanAtOutCoords();\\n        vec4 variance = getVarianceAtOutCoords();\\n\\n        vec4 inv = scale * inversesqrt(variance + vec4(\"+a+\"));\\n\\n        setOutput((x - mean) * inv + offset);\\n      }\\n    \"}}(),COMPLEX_MULTIPLY={REAL:\"return areal * breal - aimag * bimag;\",IMAG:\"return areal * bimag + aimag * breal;\"},BinaryOpComplexProgram=function(){return function(e,t,n){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.supportsBroadcasting=!0,this.outputShape=assertAndGetBroadcastShape(t,n),this.userCode=\"\\n      float binaryOpComplex(\\n          float areal, float aimag, float breal, float bimag) {\\n        \"+e+\"\\n      }\\n\\n      void main() {\\n        float areal = getARealAtOutCoords();\\n        float aimag = getAImagAtOutCoords();\\n        float breal = getBRealAtOutCoords();\\n        float bimag = getBImagAtOutCoords();\\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\\n      }\\n    \"}}(),CHECK_NAN_SNIPPET=\"\\n  if (isNaN(a)) return a;\\n  if (isNaN(b)) return b;\\n\",ADD=\"return a + b;\",SUB=\"return a - b;\",MUL=\"return a * b;\",DIV=\"if (a == b) return 1.0;\\n  return a / b;\",INT_DIV=\"\\n  float resultSign = sign(a) * sign(b);\\n  int ia = round(a);\\n  int ib = round(b);\\n  int result = ia / ib;\\n  int amodb = ia - ib * result;\\n\\n  if (resultSign < 0.0 && amodb != 0) {\\n    result -= 1;\\n  }\\n  return float(result);\\n\",POW=\"\\nif(a < 0.0 && floor(b) < b){\\n  return NAN;\\n}\\nreturn (round(mod(b, 2.0)) == 0 || round(mod(b, 2.0)) == 2) ?\\n    pow(abs(a), b) : sign(a) * pow(abs(a), b);\\n\",SQUARED_DIFFERENCE=\"return (a - b) * (a - b);\",EQUAL=\"return float(a == b);\",NOT_EQUAL=\"return float(a != b);\",LESS=\"return float(a < b);\",LESS_EQUAL=\"return float(a <= b);\",GREATER=\"return float(a > b);\",GREATER_EQUAL=\"return float(a >= b);\",LOGICAL_AND=\"return float(a >= 1.0 && b >= 1.0);\",LOGICAL_OR=\"return float(a >= 1.0 || b >= 1.0);\",MAX=CHECK_NAN_SNIPPET+\"\\n  return max(a, b);\\n\",MIN=CHECK_NAN_SNIPPET+\"\\n  return min(a, b);\\n\",MOD=\"if (b == 0.0) return NAN;\\n  return mod(a, b);\",ATAN2=CHECK_NAN_SNIPPET+\"\\n  return atan(a, b);\\n\",ELU_DER=\"return (b >= 1.0) ? a : a * (b + 1.0);\",BinaryOpProgram=function(){function e(e,t,n){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.outputShape=assertAndGetBroadcastShape(t,n),this.userCode=\"\\n      uniform float NAN;\\n      float binaryOperation(float a, float b) {\\n        \"+e+\"\\n      }\\n\\n      void main() {\\n        float a = getAAtOutCoords();\\n        float b = getBAtOutCoords();\\n        setOutput(binaryOperation(a, b));\\n      }\\n    \"}return e.prototype.getCustomSetupFunc=function(){var e=this;return function(t,n){null==e.startLoc&&(e.startLoc=t.getUniformLocationNoThrow(n,\"NAN\"),null==e.startLoc)||t.gl.uniform1f(e.startLoc,NaN)}},e}(),ClipProgram=function(){return function(e,t,n){this.variableNames=[\"A\"],this.outputShape=e,this.userCode=\"\\n      void main() {\\n        float value = getAAtOutCoords();\\n        if (isNaN(value)) {\\n          setOutput(value);\\n          return;\\n        }\\n\\n        setOutput(clamp(value, float(\"+t+\"), float(\"+n+\")));\\n      }\\n    \"}}(),ClipPackedProgram=function(){return function(e,t,n){this.variableNames=[\"A\"],this.usesPackedTextures=!0,this.outputShape=e,this.userCode=\"\\n      void main() {\\n        vec4 value = getAAtOutCoords();\\n\\n        if (hasNaN(value)) {\\n          setOutput(value);\\n          return;\\n        }\\n\\n        setOutput(clamp(value, vec4(\"+t+\"), vec4(\"+n+\")));\\n      }\\n    \"}}(),ComplexAbsProgram=function(){return function(e){this.variableNames=[\"real\",\"imag\"],this.outputShape=e,this.userCode=\"\\n      void main() {\\n        float re = abs(getRealAtOutCoords());\\n        float im = abs(getImagAtOutCoords());\\n        float mx = max(re, im);\\n\\n        // sadly the length function in glsl is not underflow-safe\\n        // (at least not on Intel GPUs). So the safe solution is\\n        // to ensure underflow-safety in all cases.\\n        setOutput(\\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\\n        );\\n      }\\n    \"}}(),ConcatProgram=function(){return function(e,t){this.variableNames=[\"A\",\"B\"],this.outputShape=[],this.outputShape=computeOutShape([e,t],1),this.userCode=\"\\n      void main() {\\n        ivec2 coords = getOutputCoords();\\n        int yR = coords.x;\\n        int yC = coords.y;\\n\\n        float value = 0.0;\\n        if (yC < \"+e[1]+\") {\\n          value = getA(yR, yC);\\n        } else {\\n          yC -= \"+e[1]+\";\\n          value = getB(yR, yC);\\n        }\\n\\n        setOutput(value);\\n      }\\n    \"}}(),Conv2DDerFilterProgram=function(){return function(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;var t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,o=e.padInfo.left;this.userCode=\"\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int wR = coords.x;\\n        int wC = coords.y;\\n        int d1 = coords.z;\\n        int d2 = coords.w;\\n\\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\\n        // ? = to be determined. : = across all values in that axis.\\n        float dotProd = 0.0;\\n\\n        for (int b = 0; b < \"+e.batchSize+\"; b++) {\\n          for (int yR = 0; yR < \"+e.outHeight+\"; yR++) {\\n            int xR = wR + yR * \"+t+\" - \"+r+\";\\n\\n            if (xR < 0 || xR >= \"+e.inHeight+\") {\\n              continue;\\n            }\\n\\n            for (int yC = 0; yC < \"+e.outWidth+\"; yC++) {\\n              int xC = wC + yC * \"+n+\" - \"+o+\";\\n\\n              if (xC < 0 || xC >= \"+e.inWidth+\") {\\n                continue;\\n              }\\n\\n              float dyValue = getDy(b, yR, yC, d2);\\n              float xValue = getX(b, xR, xC, d1);\\n              dotProd += (xValue * dyValue);\\n            }\\n          }\\n        }\\n        setOutput(dotProd);\\n      }\\n    \"}}(),Conv2DDerInputProgram=function(){return function(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;var t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,o=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left;this.userCode=\"\\n      const ivec2 pads = ivec2(\"+a+\", \"+i+\");\\n\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int batch = coords[0];\\n        int d1 = coords[3];\\n\\n        ivec2 dyCorner = coords.yz - pads;\\n        int dyRCorner = dyCorner.x;\\n        int dyCCorner = dyCorner.y;\\n\\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\\n        // ? = to be determined. : = across all values in that axis.\\n        float dotProd = 0.0;\\n        for (int wR = 0; wR < \"+t+\"; wR++) {\\n          float dyR = float(dyRCorner + wR) / \"+r+\".0;\\n\\n          if (dyR < 0.0 || dyR >= \"+e.outHeight+\".0 || fract(dyR) > 0.0) {\\n            continue;\\n          }\\n          int idyR = int(dyR);\\n\\n          int wRPerm = \"+t+\" - 1 - wR;\\n\\n          for (int wC = 0; wC < \"+n+\"; wC++) {\\n            float dyC = float(dyCCorner + wC) / \"+o+\".0;\\n\\n            if (dyC < 0.0 || dyC >= \"+e.outWidth+\".0 ||\\n                fract(dyC) > 0.0) {\\n              continue;\\n            }\\n            int idyC = int(dyC);\\n\\n            int wCPerm = \"+n+\" - 1 - wC;\\n\\n            for (int d2 = 0; d2 < \"+e.outChannels+\"; d2++) {\\n              float xValue = getDy(batch, idyR, idyC, d2);\\n              float wValue = getW(wRPerm, wCPerm, d1, d2);\\n              dotProd += xValue * wValue;\\n            }\\n          }\\n        }\\n        setOutput(dotProd);\\n      }\\n    \"}}(),Conv3DDerFilterProgram=function(){return function(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;var t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,o=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=\"\\n      void main() {\\n        ivec5 coords = getOutputCoords();\\n        int wF = coords.x;\\n        int wR = coords.y;\\n        int wC = coords.z;\\n        int d1 = coords.w;\\n        int d2 = coords.u;\\n\\n        float dotProd = 0.0;\\n\\n        for (int b = 0; b < \"+e.batchSize+\"; b++) {\\n          for (int yF = 0; yF < \"+e.outDepth+\"; yF++) {\\n            int xF = wF + yF * \"+t+\" - \"+o+\";\\n\\n            if (xF < 0 || xF >= \"+e.inDepth+\") {\\n              continue;\\n            }\\n\\n            for (int yR = 0; yR < \"+e.outHeight+\"; yR++) {\\n              int xR = wR + yR * \"+n+\" - \"+a+\";\\n\\n              if (xR < 0 || xR >= \"+e.inHeight+\") {\\n                continue;\\n              }\\n\\n              for (int yC = 0; yC < \"+e.outWidth+\"; yC++) {\\n                int xC = wC + yC * \"+r+\" - \"+i+\";\\n\\n                if (xC < 0 || xC >= \"+e.inWidth+\") {\\n                  continue;\\n                }\\n\\n                float dyValue = getDy(b, yF, yR, yC, d2);\\n                float xValue = getX(b, xF, xR, xC, d1);\\n                dotProd += (xValue * dyValue);\\n              }\\n            }\\n          }\\n        }\\n        setOutput(dotProd);\\n      }\\n    \"}}(),Conv3DDerInputProgram=function(){return function(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;var t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,o=e.strideDepth,a=e.strideHeight,i=e.strideWidth,s=t-1-e.padInfo.front,u=n-1-e.padInfo.top,l=r-1-e.padInfo.left;this.userCode=\"\\n      const ivec3 pads = ivec3(\"+s+\", \"+u+\", \"+l+\");\\n\\n      void main() {\\n        ivec5 coords = getOutputCoords();\\n        int batch = coords.x;\\n        int d1 = coords.u;\\n\\n\\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\\n        int dyFCorner = dyCorner.x;\\n        int dyRCorner = dyCorner.y;\\n        int dyCCorner = dyCorner.z;\\n\\n        float dotProd = 0.0;\\n        for (int wF = 0; wF < \"+t+\"; wF++) {\\n          float dyF = float(dyFCorner + wF) / \"+o+\".0;\\n\\n          if (dyF < 0.0 || dyF >= \"+e.outDepth+\".0 || fract(dyF) > 0.0) {\\n            continue;\\n          }\\n          int idyF = int(dyF);\\n\\n          int wFPerm = \"+t+\" - 1 - wF;\\n\\n          for (int wR = 0; wR < \"+n+\"; wR++) {\\n            float dyR = float(dyRCorner + wR) / \"+a+\".0;\\n\\n            if (dyR < 0.0 || dyR >= \"+e.outHeight+\".0 ||\\n              fract(dyR) > 0.0) {\\n              continue;\\n            }\\n            int idyR = int(dyR);\\n\\n            int wRPerm = \"+n+\" - 1 - wR;\\n\\n            for (int wC = 0; wC < \"+r+\"; wC++) {\\n              float dyC = float(dyCCorner + wC) / \"+i+\".0;\\n\\n              if (dyC < 0.0 || dyC >= \"+e.outWidth+\".0 ||\\n                  fract(dyC) > 0.0) {\\n                continue;\\n              }\\n              int idyC = int(dyC);\\n\\n              int wCPerm = \"+r+\" - 1 - wC;\\n\\n              for (int d2 = 0; d2 < \"+e.outChannels+\"; d2++) {\\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\\n                dotProd += xValue * wValue;\\n              }\\n            }\\n          }\\n        }\\n        setOutput(dotProd);\\n      }\\n    \"}}(),DepthwiseConv2DDerFilterProgram=function(){return function(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;var t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,o=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=\"\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int wR = coords.x;\\n        int wC = coords.y;\\n        int d1 = coords.z;\\n        int dm = coords.w;\\n        int d2 = d1 * \"+a+\" + dm;\\n\\n        float dotProd = 0.0;\\n\\n        // TODO: Vec4 over the batch size\\n        for (int b = 0; b < \"+e.batchSize+\"; b++) {\\n          for (int yR = 0; yR < \"+e.outHeight+\"; yR++) {\\n            int xR = wR + yR * \"+t+\" - \"+r+\";\\n\\n            if (xR < 0 || xR >= \"+e.inHeight+\") {\\n              continue;\\n            }\\n\\n            for (int yC = 0; yC < \"+e.outWidth+\"; yC++) {\\n              int xC = wC + yC * \"+n+\" - \"+o+\";\\n\\n              if (xC < 0 || xC >= \"+e.inWidth+\") {\\n                continue;\\n              }\\n\\n              float dyValue = getDy(b, yR, yC, d2);\\n              float xValue = getX(b, xR, xC, d1);\\n              dotProd += (xValue * dyValue);\\n            }\\n          }\\n        }\\n        setOutput(dotProd);\\n      }\\n    \"}}(),DepthwiseConv2DDerInputProgram=function(){return function(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;var t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,o=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=\"\\n      const ivec2 pads = ivec2(\"+a+\", \"+i+\");\\n\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int batch = coords[0];\\n        int d1 = coords[3];\\n        ivec2 dyCorner = coords.yz - pads;\\n        int dyRCorner = dyCorner.x;\\n        int dyCCorner = dyCorner.y;\\n\\n        float dotProd = 0.0;\\n\\n        for (int wR = 0; wR < \"+t+\"; wR++) {\\n          float dyR = float(dyRCorner + wR) / \"+r+\".0;\\n\\n          if (dyR < 0.0 || dyR >= \"+e.outHeight+\".0 || fract(dyR) > 0.0) {\\n            continue;\\n          }\\n          int idyR = int(dyR);\\n\\n          int wRPerm = \"+t+\" - 1 - wR;\\n\\n          for (int wC = 0; wC < \"+n+\"; wC++) {\\n            float dyC = float(dyCCorner + wC) / \"+o+\".0;\\n\\n            if (dyC < 0.0 || dyC >= \"+e.outWidth+\".0 ||\\n                fract(dyC) > 0.0) {\\n              continue;\\n            }\\n            int idyC = int(dyC);\\n\\n            int wCPerm = \"+n+\" - 1 - wC;\\n\\n            // TODO: Vec4 over the channelMul\\n            for (int dm = 0; dm < \"+s+\"; dm++) {\\n              int d2 = d1 * \"+s+\" + dm;\\n              float xValue = getDy(batch, idyR, idyC, d2);\\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\\n              dotProd += xValue * wValue;\\n            }\\n          }\\n        }\\n        setOutput(dotProd);\\n      }\\n    \"}}(),Conv2DProgram=function(){return function(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;var t=e.padInfo.top,n=e.padInfo.left,r=e.strideHeight,o=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,s=e.filterHeight,u=e.filterWidth,l=4*Math.floor(e.inChannels/4),c=e.inChannels%4;this.userCode=\"\\n      const ivec2 strides = ivec2(\"+r+\", \"+o+\");\\n      const ivec2 pads = ivec2(\"+t+\", \"+n+\");\\n\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int batch = coords[0];\\n        int d2 = coords[3];\\n\\n        ivec2 xRCCorner = coords.yz * strides - pads;\\n        int xRCorner = xRCCorner.x;\\n        int xCCorner = xRCCorner.y;\\n\\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\\n        // ? = to be determined. : = across all values in that axis.\\n        float dotProd = 0.0;\\n        for (int wR = 0; wR < \"+s+\"; wR++) {\\n          int xR = xRCorner + wR * \"+a+\";\\n\\n          if (xR < 0 || xR >= \"+e.inHeight+\") {\\n            continue;\\n          }\\n\\n          for (int wC = 0; wC < \"+u+\"; wC++) {\\n            int xC = xCCorner + wC * \"+i+\";\\n\\n            if (xC < 0 || xC >= \"+e.inWidth+\") {\\n              continue;\\n            }\\n\\n            for (int d1 = 0; d1 < \"+l+\"; d1 += 4) {\\n              vec4 xValues = vec4(\\n                getX(batch, xR, xC, d1),\\n                getX(batch, xR, xC, d1 + 1),\\n                getX(batch, xR, xC, d1 + 2),\\n                getX(batch, xR, xC, d1 + 3)\\n              );\\n              vec4 wValues = vec4(\\n                getW(wR, wC, d1, d2),\\n                getW(wR, wC, d1 + 1, d2),\\n                getW(wR, wC, d1 + 2, d2),\\n                getW(wR, wC, d1 + 3, d2)\\n              );\\n\\n              dotProd += dot(xValues, wValues);\\n            }\\n\\n            if (\"+(1===c)+\") {\\n              dotProd +=\\n                getX(batch, xR, xC, \"+l+\") *\\n                getW(wR, wC, \"+l+\", d2);\\n            } else if (\"+(2===c)+\") {\\n              vec2 xValues = vec2(\\n                getX(batch, xR, xC, \"+l+\"),\\n                getX(batch, xR, xC, \"+l+\" + 1)\\n              );\\n              vec2 wValues = vec2(\\n                getW(wR, wC, \"+l+\", d2),\\n                getW(wR, wC, \"+l+\" + 1, d2)\\n              );\\n              dotProd += dot(xValues, wValues);\\n            } else if (\"+(3===c)+\") {\\n              vec3 xValues = vec3(\\n                getX(batch, xR, xC, \"+l+\"),\\n                getX(batch, xR, xC, \"+l+\" + 1),\\n                getX(batch, xR, xC, \"+l+\" + 2)\\n              );\\n              vec3 wValues = vec3(\\n                getW(wR, wC, \"+l+\", d2),\\n                getW(wR, wC, \"+l+\" + 1, d2),\\n                getW(wR, wC, \"+l+\" + 2, d2)\\n              );\\n              dotProd += dot(xValues, wValues);\\n            }\\n          }\\n        }\\n        setOutput(dotProd);\\n      }\\n    \"}}(),Conv3DProgram=function(){return function(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;var t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,o=e.strideDepth,a=e.strideHeight,i=e.strideWidth,s=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=4*Math.floor(e.inChannels/4),f=e.inChannels%4;this.userCode=\"\\n      const ivec3 strides = ivec3(\"+o+\", \"+a+\", \"+i+\");\\n      const ivec3 pads = ivec3(\"+t+\", \"+n+\", \"+r+\");\\n\\n      void main() {\\n        ivec5 coords = getOutputCoords();\\n        int batch = coords.x;\\n        int d2 = coords.u;\\n\\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\\n        int xFCorner = xFRCCorner.x;\\n        int xRCorner = xFRCCorner.y;\\n        int xCCorner = xFRCCorner.z;\\n\\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\\n        // values in that axis.\\n        float dotProd = 0.0;\\n        for (int wF = 0; wF < \"+c+\"; wF++) {\\n          int xF = xFCorner + wF * \"+s+\";\\n\\n          if (xF < 0 || xF >= \"+e.inDepth+\") {\\n            continue;\\n          }\\n\\n          for (int wR = 0; wR < \"+p+\"; wR++) {\\n            int xR = xRCorner + wR * \"+u+\";\\n\\n            if (xR < 0 || xR >= \"+e.inHeight+\") {\\n              continue;\\n            }\\n\\n            for (int wC = 0; wC < \"+d+\"; wC++) {\\n              int xC = xCCorner + wC * \"+l+\";\\n\\n              if (xC < 0 || xC >= \"+e.inWidth+\") {\\n                continue;\\n              }\\n\\n              for (int d1 = 0; d1 < \"+h+\"; d1 += 4) {\\n                vec4 xValues = vec4(\\n                  getX(batch, xF, xR, xC, d1),\\n                  getX(batch, xF, xR, xC, d1 + 1),\\n                  getX(batch, xF, xR, xC, d1 + 2),\\n                  getX(batch, xF, xR, xC, d1 + 3)\\n                );\\n                vec4 wValues = vec4(\\n                  getW(wF, wR, wC, d1, d2),\\n                  getW(wF, wR, wC, d1 + 1, d2),\\n                  getW(wF, wR, wC, d1 + 2, d2),\\n                  getW(wF, wR, wC, d1 + 3, d2)\\n                );\\n\\n                dotProd += dot(xValues, wValues);\\n              }\\n\\n              if (\"+(1===f)+\") {\\n                dotProd +=\\n                  getX(batch, xF, xR, xC, \"+h+\") *\\n                  getW(wF, wR, wC, \"+h+\", d2);\\n              } else if (\"+(2===f)+\") {\\n                vec2 xValues = vec2(\\n                  getX(batch, xF, xR, xC, \"+h+\"),\\n                  getX(batch, xF, xR, xC, \"+h+\" + 1)\\n                );\\n                vec2 wValues = vec2(\\n                  getW(wF, wR, wC, \"+h+\", d2),\\n                  getW(wF, wR, wC, \"+h+\" + 1, d2)\\n                );\\n                dotProd += dot(xValues, wValues);\\n              } else if (\"+(3===f)+\") {\\n                vec3 xValues = vec3(\\n                  getX(batch, xF, xR, xC, \"+h+\"),\\n                  getX(batch, xF, xR, xC, \"+h+\" + 1),\\n                  getX(batch, xF, xR, xC, \"+h+\" + 2)\\n                );\\n                vec3 wValues = vec3(\\n                  getW(wF, wR, wC, \"+h+\", d2),\\n                  getW(wF, wR, wC, \"+h+\" + 1, d2),\\n                  getW(wF, wR, wC, \"+h+\" + 2, d2)\\n                );\\n                dotProd += dot(xValues, wValues);\\n              }\\n            }\\n          }\\n        }\\n        setOutput(dotProd);\\n      }\\n    \"}}(),DepthwiseConv2DProgram=function(){return function(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;var t=e.inHeight,n=e.inWidth,r=e.padInfo.top,o=e.padInfo.left,a=e.strideHeight,i=e.strideWidth,s=e.dilationHeight,u=e.dilationWidth,l=e.filterHeight,c=e.filterWidth,p=e.outChannels/e.inChannels;this.userCode=\"\\n      const ivec2 strides = ivec2(\"+a+\", \"+i+\");\\n      const ivec2 pads = ivec2(\"+r+\", \"+o+\");\\n\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int batch = coords.x;\\n        ivec2 xRCCorner = coords.yz * strides - pads;\\n        int d2 = coords.w;\\n        int d1 = d2 / \"+p+\";\\n        int q = d2 - d1 * \"+p+\";\\n\\n        int xRCorner = xRCCorner.x;\\n        int xCCorner = xRCCorner.y;\\n\\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\\n        // ? = to be determined. : = across all values in that axis.\\n        float dotProd = 0.0;\\n        // TODO(dsmilkov): Flatten the two for loops and vec4 the operations.\\n        for (int wR = 0; wR < \"+l+\"; wR++) {\\n          int xR = xRCorner + wR * \"+s+\";\\n\\n          if (xR < 0 || xR >= \"+t+\") {\\n            continue;\\n          }\\n\\n          for (int wC = 0; wC < \"+c+\"; wC++) {\\n            int xC = xCCorner + wC * \"+u+\";\\n\\n            if (xC < 0 || xC >= \"+n+\") {\\n              continue;\\n            }\\n\\n            float xVal = getX(batch, xR, xC, d1);\\n            float wVal = getW(wR, wC, d1, q);\\n            dotProd += xVal * wVal;\\n          }\\n        }\\n        setOutput(dotProd);\\n      }\\n    \"}}(),DepthwiseConvPacked2DProgram=function(){return function(e){this.variableNames=[\"x\",\"W\"],this.usesPackedTextures=!0,this.outputShape=e.outShape;for(var t=e.inHeight,n=e.inWidth,r=e.padInfo.top,o=e.padInfo.left,a=e.strideHeight,i=e.strideWidth,s=e.filterHeight,u=e.filterWidth,l=Math.ceil((u+1)/2),c=\"int xR; int xC;\",p=0;p<s;p++){for(var d=-o;d<2*l;d++)c+=\"vec4 \"+xTexelName(p,d)+\" = vec4(0.);\";for(d=0;d<u;d++)c+=\"\\n          vec4 wR\"+p+\"C\"+d+\" = vec4(0.);\\n          vec4 xR\"+p+\"C\"+d+\" = vec4(0.);\"}for(p=0;p<s;p++)for(d=0;d<l;d++){var h=2*d,f=2*d+o;c+=\"\\n          xR = xRCorner + \"+p+\";\\n          xC = xCCorner + \"+f+\";\\n\\n          if(xR >= 0 && xR < \"+t+\" && xC >= 0 && xC < \"+n+\") {\\n            \"+xTexelName(p,f)+\" = getX(batch, xR, xC, d1);\\n          }\",0===o?h<u&&d===l-1&&(i>1&&(c+=\"\\n                vec4 \"+xTexelName(p,f+2)+\" = vec4(0.);\\n\\n                if(xR >= 0 && xR < \"+t+\" && xC + 2 < \"+n+\") {\\n                  \"+xTexelName(p,f+2)+\" = getX(batch, xR, xC + 2, d1);\\n                }\"),c+=\"\\n              xR\"+p+\"C\"+f+\" = \"+constructTexel(p,f,i,o)+\";\\n            \"):0===d&&(c+=\"\\n            if(xR >= 0 && xR < \"+t+\" && xC - 2 >= 0) {\\n              \"+xTexelName(p,f-2)+\" = getX(batch, xR, xC - 2, d1);\\n            }\"),h>0&&(c+=\"xR\"+p+\"C\"+(f-2)+\" =\\n            \"+constructTexel(p,f-2,i,o)+\";\"),f-1>=0&&f-1<u&&(c+=\"xR\"+p+\"C\"+(f-1)+\" =\\n              \"+constructTexel(p,f-1,i,o)+\";\"),h<u&&(c+=\"\\n            vec4 wTexel\"+p+\"C\"+h+\" = getW(\"+p+\", \"+h+\", d1, q);\\n            wR\"+p+\"C\"+h+\" = vec4(wTexel\"+p+\"C\"+h+\".xz, wTexel\"+p+\"C\"+h+\".xz);\\n          \",h+1<u&&(c+=\"\\n              vec4 wTexelR\"+p+\"C\"+(h+1)+\" = getW(\"+p+\", \"+(h+1)+\", d1, q);\\n              wR\"+p+\"C\"+(h+1)+\" =\\n                vec4(wTexelR\"+p+\"C\"+(h+1)+\".xz, wTexelR\"+p+\"C\"+(h+1)+\".xz);\"))}for(p=0;p<s;p++)for(d=0;d<u;d++)c+=\"result += xR\"+p+\"C\"+d+\" * wR\"+p+\"C\"+d+\";\";this.userCode=\"\\n      const ivec2 strides = ivec2(\"+a+\", \"+i+\");\\n      const ivec2 pads = ivec2(\"+r+\", \"+o+\");\\n\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int batch = coords.x;\\n        ivec2 xRCCorner = coords.yz * strides - pads;\\n        int d2 = coords.w;\\n        int d1 = d2;\\n        int q = 0;\\n        int xRCorner = xRCCorner.x;\\n        int xCCorner = xRCCorner.y;\\n\\n        vec4 result = vec4(0.);\\n\\n        \"+c+\"\\n\\n        setOutput(result);\\n      }\\n    \"}}();function xTexelName(e,t){return\"xTexelR\"+e+\"C\"+(t<0?\"minus\"+Math.abs(t).toString():t)}function constructTexel(e,t,n,r){return 1===n?r%2==t%2?xTexelName(e,t):\"vec4(\"+xTexelName(e,t-1)+\".zw, \"+xTexelName(e,t+1)+\".xy)\":r%2==t%2?\"vec4(\"+xTexelName(e,t)+\".xy, \"+xTexelName(e,t+2)+\".xy)\":\"vec4(\"+xTexelName(e,t-1)+\".zw, \"+xTexelName(e,t+1)+\".zw)\"}var CropAndResizeProgram=function(){return function(e,t,n,r,o){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];var a=e[0],i=e[1],s=e[2],u=e[3],l=t[0],c=n[0],p=n[1];this.outputShape=[l,c,p,u];var d=\"bilinear\"===r?1:0,h=[i-1+\".0\",s-1+\".0\"],f=h[0],m=h[1],g=c>1?[\"\"+(i-1)/(c-1),\"(y2-y1) * height_ratio\",\"y1*\"+f+\" + float(y)*(height_scale)\"]:[\"0.0\",\"0.0\",\"0.5 * (y1+y2) * \"+f],v=g[0],y=g[1],x=g[2],T=p>1?[\"\"+(s-1)/(p-1),\"(x2-x1) * width_ratio\",\"x1*\"+m+\" + float(x)*(width_scale)\"]:[\"0.0\",\"0.0\",\"0.5 * (x1+x2) * \"+m],w=T[0],E=T[1],S=T[2];this.userCode=\"\\n      const float height_ratio = float(\"+v+\");\\n      const float width_ratio = float(\"+w+\");\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int b = coords[0];\\n        int y = coords[1];\\n        int x = coords[2];\\n        int d = coords[3];\\n\\n        // get box vals\\n        float y1 = getBoxes(b,0);\\n        float x1 = getBoxes(b,1);\\n        float y2 = getBoxes(b,2);\\n        float x2 = getBoxes(b,3);\\n\\n        // get image in batch index\\n        int bInd = round(getBoxInd(b));\\n        if(bInd < 0 || bInd >= \"+a+\") {\\n          return;\\n        }\\n\\n        float height_scale = \"+y+\";\\n        float width_scale = \"+E+\";\\n\\n        float in_y = \"+x+\";\\n        if( in_y < 0.0 || in_y > \"+f+\" ) {\\n          setOutput(float(\"+o+\"));\\n          return;\\n        }\\n        float in_x = \"+S+\";\\n        if( in_x < 0.0 || in_x > \"+m+\" ) {\\n          setOutput(float(\"+o+\"));\\n          return;\\n        }\\n\\n        vec2 sourceFracIndexRC = vec2(in_y,in_x);\\n        if(\"+d+\" == 1) {\\n          // Compute the four integer indices.\\n          ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\\n          ivec2 sourceCeilRC = ivec2(ceil(sourceFracIndexRC));\\n\\n          float topLeft = getImage(b, sourceFloorRC.x, sourceFloorRC.y, d);\\n          float bottomLeft = getImage(b, sourceCeilRC.x, sourceFloorRC.y, d);\\n          float topRight = getImage(b, sourceFloorRC.x, sourceCeilRC.y, d);\\n          float bottomRight = getImage(b, sourceCeilRC.x, sourceCeilRC.y, d);\\n\\n          vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\\n\\n          float top = topLeft + (topRight - topLeft) * fracRC.y;\\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\\n          float newValue = top + (bottom - top) * fracRC.x;\\n          setOutput(newValue);\\n        } else {\\n          // Compute the coordinators of nearest neighbor point.\\n          ivec2 sourceNearestRC = ivec2(floor(\\n            sourceFracIndexRC + vec2(0.5,0.5)));\\n          float newValue = getImage(b, sourceNearestRC.x, sourceNearestRC.y, d);\\n          setOutput(newValue);\\n        }\\n      }\\n    \"}}();function getLogicalCoordinatesFromFlatIndex(e,t,n){void 0===n&&(n=\"index\");var r=computeStrides(t);return r.map(function(t,o){return\"int \"+e[o]+\" = \"+n+\" / \"+t+\"; \"+(o===r.length-1?\"int \"+e[o+1]+\" = \"+n+\" - \"+e[o]+\" * \"+t:\"index -= \"+e[o]+\" * \"+t)+\";\"}).join(\"\")}function buildVec(e){return 1===e.length?\"\"+e[0]:\"vec\"+e.length+\"(\"+e.join(\",\")+\")\"}function dotify(e,t){if(e.length!==t.length)throw new Error(\"Vectors to be dotted must be of the same length -got \"+e.length+\" and \"+t.length);for(var n=[],r=Math.floor(e.length/4),o=e.length%4,a=0;a<r;a++){var i=e.slice(4*a,4*a+4),s=t.slice(4*a,4*a+4);n.push(buildVec(i)+\", \"+buildVec(s))}if(0!==o){i=e.slice(4*r),s=t.slice(4*r);1===i.length&&(i=i.map(function(e){return\"float(\"+e+\")\"}),s=s.map(function(e){return\"float(\"+e+\")\"})),n.push(buildVec(i)+\", \"+buildVec(s))}return n.map(function(e,t){return\"dot(\"+e+\")\"}).join(\"+\")}function makeShader(e,t,n,r,o){var a=e.map(function(e){var t=sizeFromShape(e.shapeInfo.logicalShape);return e.shapeInfo.isUniform?\"uniform float \"+e.name+(t>1?\"[\"+t+\"]\":\"\")+\";\":\"uniform sampler2D \"+e.name+\";\"});a=a.join(\"\\n\");var i,s,u=e.map(function(e){return getInputSamplingSnippet(e,t,r,o)}).join(\"\\n\"),l=t.texShape,c=SHADER_PREFIX;return t.isPacked?(i=getPackedOutputSamplingSnippet(t.logicalShape,l),s=FLOAT_TEXTURE_SET_RGBA_SNIPPET):(i=getOutputSamplingSnippet(t.logicalShape,l),s=FLOAT_TEXTURE_SET_R_SNIPPET),o&&(c+=SHADER_PACKED_PREFIX),[c,FLOAT_TEXTURE_SAMPLE_SNIPPET,s,a,i,u,n].join(\"\\n\")}function getSamplerFromInInfo(e){var t=e.shapeInfo.logicalShape;switch(t.length){case 0:return getSamplerScalar(e);case 1:return getSampler1D(e);case 2:return getSampler2D(e);case 3:return getSampler3D(e);case 4:return getSampler4D(e);case 5:return getSampler5D(e);case 6:return getSampler6D(e);default:throw new Error(t.length+\"-D input sampling is not yet supported\")}}function getPackedSamplerFromInInfo(e){var t=e.shapeInfo.logicalShape;switch(t.length){case 0:return getPackedSamplerScalar(e);case 1:return getPackedSampler1D(e);case 2:return getPackedSampler2D(e);case 3:return getPackedSampler3D(e);case 4:return getPackedSampler4D(e);default:throw new Error(\"Packed \"+t.length+\"-D input sampling is not yet supported\")}}function getInputSamplingSnippet(e,t,n,r){void 0===r&&(r=!1);var o=getSamplerFlat(e);return o+=r?getPackedSamplerFromInInfo(e):getSamplerFromInInfo(e),(n||arraysEqual(e.shapeInfo.logicalShape,t.logicalShape))&&(o+=r?getPackedSamplerAtOutputCoords(e,t,n):getSamplerAtOutputCoords(e,t,n)),o}function getPackedOutputSamplingSnippet(e,t){switch(e.length){case 0:return getOutputScalarCoords();case 1:return getOutputPacked1DCoords(e,t);case 2:return getOutputPacked2DCoords(e,t);case 3:return getOutputPacked3DCoords(e,t);case 4:return getOutputPacked4DCoords(e,t);default:throw new Error(e.length+\"-D packed output coordinate fetching is not yet supported\")}}function getOutputSamplingSnippet(e,t){switch(e.length){case 0:return getOutputScalarCoords();case 1:return getOutput1DCoords(e,t);case 2:return getOutput2DCoords(e,t);case 3:return getOutput3DCoords(e,t);case 4:return getOutput4DCoords(e,t);case 5:return getOutput5DCoords(e,t);case 6:return getOutput6DCoords(e,t);default:throw new Error(e.length+\"-D output sampling is not yet supported\")}}var SAMPLE_1D_SNIPPET=\"\\nvec2 UVfrom1D(int texNumR, int texNumC, int index) {\\n  int texR = index / texNumC;\\n  int texC = index - texR * texNumC;\\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\\n  int texelIndex = index / 2;\\n  int texR = texelIndex / texNumC;\\n  int texC = texelIndex - texR * texNumC;\\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\n\",SAMPLE_2D_SNIPPET=\"\\nvec2 UVfrom2D(int texNumR, int texNumC, int numC, int row, int col) {\\n  int index = row * numC + col;\\n  int texR = index / texNumC;\\n  int texC = index - texR * texNumC;\\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\\n  int texNumC, int row, int col) {\\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\\n  int texR = texelIndex / texNumC;\\n  int texC = texelIndex - texR * texNumC;\\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\n\",SAMPLE_3D_SNIPPET=\"\\nvec2 UVfrom3D(int texNumR, int texNumC, int stride0,\\n    int stride1, int row, int col, int depth) {\\n  // Explicitly use integer operations as dot() only works on floats.\\n  int index = row * stride0 + col * stride1 + depth;\\n  int texR = index / texNumC;\\n  int texC = index - texR * texNumC;\\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\nvec2 packedUVfrom3D(int texNumR, int texNumC,\\n    int texelsInBatch, int texelsInLogicalRow, int b,\\n    int row, int col) {\\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\\n  int texR = index / texNumC;\\n  int texC = index - texR * texNumC;\\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\n\",SAMPLE_4D_SNIPPET=\"\\nvec2 UVfrom4D(int texNumR, int texNumC, int stride0,\\n    int stride1, int stride2, int row, int col, int depth,\\n    int depth2) {\\n  // Explicitly use integer operations as dot() only works on floats.\\n  int index = row * stride0 + col * stride1 + depth * stride2 + depth2;\\n  int texR = index / texNumC;\\n  int texC = index - texR * texNumC;\\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\nvec2 packedUVfrom4D(int texNumR, int texNumC, int texelsInBatch2,\\n    int texelsInBatch, int texelsInLogicalRow, int b2, int b,\\n    int row, int col) {\\n  int index = b2 * texelsInBatch2 + b * texelsInBatch +\\n    (row / 2) * texelsInLogicalRow + (col / 2);\\n  int texR = index / texNumC;\\n  int texC = index - texR * texNumC;\\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\n\",SAMPLE_5D_SNIPPET=\"\\nvec2 UVfrom5D(int texNumR, int texNumC, int stride0,\\n    int stride1, int stride2, int stride3, int row, int col, int depth,\\n    int depth2, int depth3) {\\n  // Explicitly use integer operations as dot() only works on floats.\\n  int index = row * stride0 + col * stride1 +\\n              depth * stride2 + depth2 * stride3 + depth3;\\n  int texR = index / texNumC;\\n  int texC = index - texR * texNumC;\\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\n\",SAMPLE_6D_SNIPPET=\"\\nvec2 UVfrom6D(int texNumR, int texNumC, int stride0,\\n    int stride1, int stride2, int stride3, int stride4,\\n    int row, int col, int depth, int depth2, int depth3, int depth4) {\\n  // Explicitly use integer operations as dot() only works on floats.\\n  int index = row * stride0 + col * stride1 + depth * stride2 + depth2 *\\n    stride3 + depth3 * stride4 + depth4;\\n  int texR = index / texNumC;\\n  int texC = index - texR * texNumC;\\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\n\",FLOAT_TEXTURE_SAMPLE_SNIPPET=\"\\n  float sampleTexture(sampler2D textureSampler, vec2 uv) {\\n    return texture2D(textureSampler, uv).r;\\n  }\\n\",FLOAT_TEXTURE_SET_R_SNIPPET=\"\\n  void setOutput(float val) {\\n    gl_FragColor = vec4(val, 0, 0, 0);\\n  }\\n\",FLOAT_TEXTURE_SET_RGBA_SNIPPET=\"\\n  void setOutput(vec4 val) {\\n    gl_FragColor = val;\\n  }\\n\",NAN_CHECKS=\"\",SHADER_PREFIX=\"\\n  precision highp float;\\n  precision highp int;\\n  varying vec2 resultUV;\\n  const vec2 halfCR = vec2(0.5, 0.5);\\n\\n  struct ivec5\\n  {\\n    int x;\\n    int y;\\n    int z;\\n    int w;\\n    int u;\\n  };\\n\\n  struct ivec6\\n  {\\n    int x;\\n    int y;\\n    int z;\\n    int w;\\n    int u;\\n    int v;\\n  };\\n\\n  \"+(NAN_CHECKS=ENV.get(\"PROD\")?\"\\n    bool isNaN(float val) {\\n      return false;\\n    }\\n\\n    bool hasNaN(vec4 values) {\\n      return false;\\n    }\\n  \":\"\\n    bool isNaN(float val) {\\n      return (val < 1.0 || 0.0 < val || val == 0.0) ? false : true;\\n    }\\n\\n    bool hasNaN(vec4 values) {\\n      return any(bvec4(\\n        isNaN(values.x),\\n        isNaN(values.y),\\n        isNaN(values.z),\\n        isNaN(values.w)\\n      ));\\n    }\\n  \")+\"\\n\\n  float getNaN(vec4 values) {\\n    return dot(vec4(1), values);\\n  }\\n\\n  int round(float value) {\\n    return int(floor(value + 0.5));\\n  }\\n\\n  int imod(int x, int y) {\\n    return x - y * (x / y);\\n  }\\n\\n  //Based on the work of Dave Hoskins\\n  //https://www.shadertoy.com/view/4djSRW\\n  #define HASHSCALE1 443.8975\\n  float random(float seed){\\n    vec2 p = resultUV * seed;\\n    vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\\n    p3 += dot(p3, p3.yzx + 19.19);\\n    return fract((p3.x + p3.y) * p3.z);\\n  }\\n\\n  \"+SAMPLE_1D_SNIPPET+\"\\n  \"+SAMPLE_2D_SNIPPET+\"\\n  \"+SAMPLE_3D_SNIPPET+\"\\n  \"+SAMPLE_4D_SNIPPET+\"\\n  \"+SAMPLE_5D_SNIPPET+\"\\n  \"+SAMPLE_6D_SNIPPET+\"\\n\",SHADER_PACKED_PREFIX=\"\\n  float getChannel(vec4 frag, vec2 innerDims) {\\n    vec2 modCoord = mod(innerDims, 2.);\\n    return modCoord.x == 0. ?\\n      (modCoord.y == 0. ? frag.r : frag.g) :\\n      (modCoord.y == 0. ? frag.b : frag.a);\\n  }\\n  float getChannel(vec4 frag, int dim) {\\n    float modCoord = mod(float(dim), 2.);\\n    return modCoord == 0. ? frag.r : frag.g;\\n  }\\n\";function getOutputScalarCoords(){return\"\\n    int getOutputCoords() {\\n      return 0;\\n    }\\n  \"}function getOutputPacked1DCoords(e,t){var n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return 1===t[0]?\"\\n      int getOutputCoords() {\\n        return 2 * int(resultUV.x * \"+n[1]+\".0);\\n      }\\n    \":1===t[1]?\"\\n      int getOutputCoords() {\\n        return 2 * int(resultUV.y * \"+n[0]+\".0);\\n      }\\n    \":\"\\n    int getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n                             vec2(\"+n[0]+\", \"+n[1]+\"));\\n      return resTexRC.x * \"+n[1]+\" + resTexRC.y;\\n    }\\n  \"}function getOutput1DCoords(e,t){return 1===t[0]?\"\\n      int getOutputCoords() {\\n        return int(resultUV.x * \"+t[1]+\".0);\\n      }\\n    \":1===t[1]?\"\\n      int getOutputCoords() {\\n        return int(resultUV.y * \"+t[0]+\".0);\\n      }\\n    \":\"\\n    int getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n                             vec2(\"+t[0]+\", \"+t[1]+\"));\\n      return resTexRC.x * \"+t[1]+\" + resTexRC.y;\\n    }\\n  \"}function getOutputPacked3DCoords(e,t){var n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),o=r*Math.ceil(e[1]/2);return\"\\n    ivec3 getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n                             vec2(\"+n[0]+\", \"+n[1]+\"));\\n      int index = resTexRC.x * \"+n[1]+\" + resTexRC.y;\\n\\n      int b = index / \"+o+\";\\n      index -= b * \"+o+\";\\n\\n      int r = 2 * (index / \"+r+\");\\n      int c = imod(index, \"+r+\") * 2;\\n\\n      return ivec3(b, r, c);\\n    }\\n  \"}function getOutput3DCoords(e,t){var n=getLogicalCoordinatesFromFlatIndex([\"r\",\"c\",\"d\"],e);return\"\\n    ivec3 getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n                             vec2(\"+t[0]+\", \"+t[1]+\"));\\n      int index = resTexRC.x * \"+t[1]+\" + resTexRC.y;\\n      \"+n+\"\\n      return ivec3(r, c, d);\\n    }\\n  \"}function getOutputPacked4DCoords(e,t){var n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[3]/2),o=r*Math.ceil(e[2]/2),a=o*e[1];return\"\\n    ivec4 getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n                             vec2(\"+n[0]+\", \"+n[1]+\"));\\n      int index = resTexRC.x * \"+n[1]+\" + resTexRC.y;\\n\\n      int b2 = index / \"+a+\";\\n      index -= b2 * \"+a+\";\\n\\n      int b = index / \"+o+\";\\n      index -= b * \"+o+\";\\n\\n      int r = 2 * (index / \"+r+\");\\n      int c = imod(index, \"+r+\") * 2;\\n\\n      return ivec4(b2, b, r, c);\\n    }\\n  \"}function getOutput4DCoords(e,t){var n=getLogicalCoordinatesFromFlatIndex([\"r\",\"c\",\"d\",\"d2\"],e);return\"\\n    ivec4 getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n        vec2(\"+t[0]+\", \"+t[1]+\"));\\n      int index = resTexRC.x * \"+t[1]+\" + resTexRC.y;\\n      \"+n+\"\\n      return ivec4(r, c, d, d2);\\n    }\\n  \"}function getOutput5DCoords(e,t){var n=getLogicalCoordinatesFromFlatIndex([\"r\",\"c\",\"d\",\"d2\",\"d3\"],e);return\"\\n    ivec5 getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(\"+t[0]+\",\\n                             \"+t[1]+\"));\\n\\n      int index = resTexRC.x * \"+t[1]+\" + resTexRC.y;\\n\\n      \"+n+\"\\n\\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\\n      return outShape;\\n    }\\n  \"}function getOutput6DCoords(e,t){var n=getLogicalCoordinatesFromFlatIndex([\"r\",\"c\",\"d\",\"d2\",\"d3\",\"d4\"],e);return\"\\n    ivec6 getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n        vec2(\"+t[0]+\", \"+t[1]+\"));\\n      int index = resTexRC.x * \"+t[1]+\" + resTexRC.y;\\n\\n      \"+n+\"\\n\\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\\n      return result;\\n    }\\n  \"}function getOutputPacked2DCoords(e,t){var n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(arraysEqual(e,t))return\"\\n      ivec2 getOutputCoords() {\\n        return 2 * ivec2(resultUV.yx * vec2(\"+n[0]+\", \"+n[1]+\"));\\n      }\\n    \";var r=Math.ceil(e[1]/2);return\"\\n    ivec2 getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n                             vec2(\"+n[0]+\", \"+n[1]+\"));\\n\\n      int index = resTexRC.x * \"+n[1]+\" + resTexRC.y;\\n      int r = 2 * (index / \"+r+\");\\n      int c = imod(index, \"+r+\") * 2;\\n\\n      return ivec2(r, c);\\n    }\\n  \"}function getOutput2DCoords(e,t){return arraysEqual(e,t)?\"\\n      ivec2 getOutputCoords() {\\n        return ivec2(resultUV.yx * vec2(\"+t[0]+\", \"+t[1]+\"));\\n      }\\n    \":1===e[1]?\"\\n      ivec2 getOutputCoords() {\\n        ivec2 resTexRC = ivec2(resultUV.yx *\\n                               vec2(\"+t[0]+\", \"+t[1]+\"));\\n        int index = resTexRC.x * \"+t[1]+\" + resTexRC.y;\\n        return ivec2(index, 0);\\n      }\\n    \":1===e[0]?\"\\n      ivec2 getOutputCoords() {\\n        ivec2 resTexRC = ivec2(resultUV.yx *\\n                               vec2(\"+t[0]+\", \"+t[1]+\"));\\n        int index = resTexRC.x * \"+t[1]+\" + resTexRC.y;\\n        return ivec2(0, index);\\n      }\\n    \":\"\\n    ivec2 getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n                             vec2(\"+t[0]+\", \"+t[1]+\"));\\n      int index = resTexRC.x * \"+t[1]+\" + resTexRC.y;\\n      int r = index / \"+e[1]+\";\\n      int c = index - r * \"+e[1]+\";\\n      return ivec2(r, c);\\n    }\\n  \"}function getPackedSamplerScalar(e){var t=e.name;return\"\\n    vec4 \"+(\"get\"+t.charAt(0).toUpperCase()+t.slice(1))+\"() {\\n      return texture2D(\"+t+\", halfCR);\\n    }\\n  \"}function getSamplerScalar(e){var t=e.name,n=\"get\"+t.charAt(0).toUpperCase()+t.slice(1);return e.shapeInfo.isUniform?\"float \"+n+\"() {return \"+t+\";}\":\"\\n    float \"+n+\"() {\\n      return sampleTexture(\"+t+\", halfCR);\\n    }\\n  \"}function getPackedSampler1D(e){var t=e.name,n=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),r=e.shapeInfo.texShape,o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return\"\\n    vec4 \"+n+\"(int index) {\\n      vec2 uv = packedUVfrom1D(\\n        \"+o[0]+\", \"+o[1]+\", index);\\n      return texture2D(\"+t+\", uv);\\n    }\\n  \"}function getSampler1D(e){var t=e.name,n=\"get\"+t.charAt(0).toUpperCase()+t.slice(1);return\"\\n    float \"+n+\"(int index) {\\n      return \"+n+\"Flat(index);\\n    }\\n  \"}function getPackedSampler2D(e){var t=e.shapeInfo.logicalShape,n=e.name,r=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),o=e.shapeInfo.texShape,a=o[0],i=o[1];if(null!=o&&arraysEqual(t,o))return\"\\n      vec4 \"+r+\"(int row, int col) {\\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(\"+i+\".0, \"+a+\".0);\\n\\n        return texture2D(\"+n+\", uv);\\n      }\\n    \";var s=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];return\"\\n    vec4 \"+r+\"(int row, int col) {\\n      vec2 uv = packedUVfrom2D(\"+Math.ceil(t[1]/2)+\", \"+s[0]+\", \"+s[1]+\", row, col);\\n      return texture2D(\"+n+\", uv);\\n    }\\n  \"}function getSampler2D(e){var t=e.shapeInfo.logicalShape,n=e.name,r=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),o=e.shapeInfo.texShape;if(null!=o&&arraysEqual(t,o)){var a=o[0];return\"\\n    float \"+r+\"(int row, int col) {\\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(\"+o[1]+\".0, \"+a+\".0);\\n      return sampleTexture(\"+n+\", uv);\\n    }\\n  \"}var i=squeezeShape(t),s=i.newShape,u=i.keptDims,l=s;if(l.length<t.length){return\"\\n      \"+getSamplerFromInInfo(squeezeInputInfo(e,l))+\"\\n      float \"+r+\"(int row, int col) {\\n        return \"+r+\"(\"+getSqueezedParams([\"row\",\"col\"],u)+\");\\n      }\\n    \"}if(e.shapeInfo.isUniform)return\"\\n      float \"+r+\"(int row, int col) {\\n        float index = dot(vec2(row, col), vec2(\"+t[1]+\", 1));\\n        return \"+r+\"Flat(round(index));\\n      }\\n    \";var c=o[0],p=o[1];return 1===p?\"\\n    float \"+r+\"(int row, int col) {\\n      float index = dot(vec2(row, col), vec2(\"+t[1]+\", 1));\\n      vec2 uv = vec2(0.5, (index + 0.5) / \"+c+\".0);\\n      return sampleTexture(\"+n+\", uv);\\n    }\\n  \":1===c?\"\\n    float \"+r+\"(int row, int col) {\\n      float index = dot(vec2(row, col), vec2(\"+t[1]+\", 1));\\n      vec2 uv = vec2((index + 0.5) / \"+p+\".0, 0.5);\\n      return sampleTexture(\"+n+\", uv);\\n    }\\n  \":\"\\n  float \"+r+\"(int row, int col) {\\n    vec2 uv = UVfrom2D(\"+c+\", \"+p+\", \"+t[1]+\", row, col);\\n    return sampleTexture(\"+n+\", uv);\\n  }\\n\"}function getPackedSampler3D(e){var t=e.shapeInfo.logicalShape,n=e.name,r=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),o=e.shapeInfo.texShape,a=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];if(1===t[0]){return\"\\n        \"+getPackedSamplerFromInInfo(squeezeInputInfo(e,t.slice(1)))+\"\\n        vec4 \"+r+\"(int b, int row, int col) {\\n          return \"+r+\"(\"+getSqueezedParams([\"b\",\"row\",\"col\"],[1,2])+\");\\n        }\\n      \"}var i=a[0],s=a[1],u=Math.ceil(t[2]/2);return\"\\n    vec4 \"+r+\"(int b, int row, int col) {\\n      vec2 uv = packedUVfrom3D(\\n        \"+i+\", \"+s+\", \"+u*Math.ceil(t[1]/2)+\", \"+u+\", b, row, col);\\n      return texture2D(\"+n+\", uv);\\n    }\\n  \"}function getSampler3D(e){var t=e.shapeInfo.logicalShape,n=e.name,r=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),o=t[1]*t[2],a=t[2],i=squeezeShape(t),s=i.newShape,u=i.keptDims,l=s;if(l.length<t.length){return\"\\n        \"+getSamplerFromInInfo(squeezeInputInfo(e,l))+\"\\n        float \"+r+\"(int row, int col, int depth) {\\n          return \"+r+\"(\"+getSqueezedParams([\"row\",\"col\",\"depth\"],u)+\");\\n        }\\n      \"}if(e.shapeInfo.isUniform)return\"\\n      float \"+r+\"(int row, int col, int depth) {\\n        float index = dot(vec3(row, col, depth),\\n                          vec3(\"+o+\", \"+a+\", 1));\\n        return \"+r+\"Flat(round(index));\\n      }\\n    \";var c=e.shapeInfo.texShape,p=c[0],d=c[1];return d===o?\"\\n        float \"+r+\"(int row, int col, int depth) {\\n          float texR = float(row);\\n          float texC = dot(vec2(col, depth), vec2(\"+a+\", 1));\\n          vec2 uv = (vec2(texC, texR) + halfCR) /\\n                     vec2(\"+d+\".0, \"+p+\".0);\\n          return sampleTexture(\"+n+\", uv);\\n        }\\n      \":d===a?\"\\n    float \"+r+\"(int row, int col, int depth) {\\n      float texR = dot(vec2(row, col), vec2(\"+t[1]+\", 1));\\n      float texC = float(depth);\\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(\"+d+\".0, \"+p+\".0);\\n      return sampleTexture(\"+n+\", uv);\\n    }\\n  \":\"\\n      float \"+r+\"(int row, int col, int depth) {\\n        vec2 uv = UVfrom3D(\\n            \"+p+\", \"+d+\", \"+o+\", \"+a+\", row, col, depth);\\n        return sampleTexture(\"+n+\", uv);\\n      }\\n  \"}function getPackedSampler4D(e){var t=e.shapeInfo.logicalShape,n=e.name,r=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),o=e.shapeInfo.texShape,a=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],i=a[0],s=a[1],u=Math.ceil(t[3]/2),l=u*Math.ceil(t[2]/2);return\"\\n    vec4 \"+r+\"(int b2, int b, int row, int col) {\\n      vec2 uv = packedUVfrom4D(\\n        \"+i+\", \"+s+\", \"+l*t[1]+\",\\n        \"+l+\", \"+u+\", b2, b, row, col);\\n      return texture2D(\"+n+\", uv);\\n    }\\n  \"}function getSampler4D(e){var t=e.shapeInfo.logicalShape,n=e.name,r=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),o=t[3],a=t[2]*o,i=t[1]*a,s=squeezeShape(t),u=s.newShape,l=s.keptDims;if(u.length<t.length){return\"\\n      \"+getSamplerFromInInfo(squeezeInputInfo(e,u))+\"\\n      float \"+r+\"(int row, int col, int depth, int depth2) {\\n        return \"+r+\"(\"+getSqueezedParams([\"row\",\"col\",\"depth\",\"depth2\"],l)+\");\\n      }\\n    \"}if(e.shapeInfo.isUniform)return\"\\n      float \"+r+\"(int row, int col, int depth, int depth2) {\\n        float index = dot(vec4(row, col, depth, depth2),\\n                          vec4(\"+i+\", \"+a+\", \"+o+\", 1));\\n        return \"+r+\"Flat(round(index));\\n      }\\n    \";var c=e.shapeInfo.texShape,p=c[0],d=c[1];return d===i?\"\\n      float \"+r+\"(int row, int col, int depth, int depth2) {\\n        float texR = float(row);\\n        float texC =\\n            dot(vec3(col, depth, depth2), vec3(\"+a+\", \"+o+\", 1));\\n        vec2 uv = (vec2(texC, texR) + halfCR) /\\n                   vec2(\"+d+\".0, \"+p+\".0);\\n        return sampleTexture(\"+n+\", uv);\\n      }\\n    \":d===o?\"\\n      float \"+r+\"(int row, int col, int depth, int depth2) {\\n        float texR = dot(vec3(row, col, depth),\\n                         vec3(\"+t[1]*t[2]+\", \"+t[2]+\", 1));\\n        float texC = float(depth2);\\n        vec2 uv = (vec2(texC, texR) + halfCR) /\\n                  vec2(\"+d+\".0, \"+p+\".0);\\n        return sampleTexture(\"+n+\", uv);\\n      }\\n    \":\"\\n    float \"+r+\"(int row, int col, int depth, int depth2) {\\n      vec2 uv = UVfrom4D(\"+p+\", \"+d+\", \"+i+\", \"+a+\",\\n          \"+o+\", row, col, depth, depth2);\\n      return sampleTexture(\"+n+\", uv);\\n    }\\n  \"}function getSampler5D(e){var t=e.shapeInfo.logicalShape,n=e.name,r=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),o=t[4],a=t[3]*o,i=t[2]*a,s=t[1]*i,u=squeezeShape(t),l=u.newShape,c=u.keptDims;if(l.length<t.length){return\"\\n      \"+getSamplerFromInInfo(squeezeInputInfo(e,l))+\"\\n      float \"+r+\"(int row, int col, int depth, int depth2, int depth3) {\\n        return \"+r+\"(\"+getSqueezedParams([\"row\",\"col\",\"depth\",\"depth2\",\"depth3\"],c)+\");\\n      }\\n    \"}if(e.shapeInfo.isUniform)return\"\\n      float \"+r+\"(int row, int col, int depth, int depth2, int depth3) {\\n        float index = dot(\\n          vec4(row, col, depth, depth2),\\n          vec4(\"+s+\", \"+i+\", \"+a+\", \"+o+\")) +\\n          depth3;\\n        return \"+r+\"Flat(index);\\n      }\\n    \";var p=e.shapeInfo.texShape,d=p[0],h=p[1];return h===s?\"\\n      float \"+r+\"(int row, int col, int depth, int depth2, int depth3) {\\n        int texR = row;\\n        float texC = dot(\\n          vec4(col, depth, depth2, depth3),\\n          vec4(\"+i+\", \"+a+\", \"+o+\", 1));\\n        vec2 uv = (vec2(texC, texR) + halfCR) /\\n                   vec2(\"+h+\".0, \"+d+\".0);\\n        return sampleTexture(\"+n+\", uv);\\n      }\\n    \":h===o?\"\\n      float \"+r+\"(int row, int col, int depth, int depth2, int depth3) {\\n        float texR = dot(\\n          vec4(row, col, depth, depth2),\\n          vec4(\"+t[1]*t[2]*t[3]+\", \"+t[2]*t[3]+\",\\n            \"+t[3]+\", 1));\\n        int texC = depth3;\\n        vec2 uv = (vec2(texC, texR) + halfCR) /\\n                  vec2(\"+h+\".0, \"+d+\".0);\\n        return sampleTexture(\"+n+\", uv);\\n      }\\n    \":\"\\n    float \"+r+\"(int row, int col, int depth, int depth2, int depth3) {\\n      vec2 uv = UVfrom5D(\"+d+\", \"+h+\", \"+s+\", \"+i+\",\\n          \"+a+\", \"+o+\", row, col, depth, depth2, depth3);\\n      return sampleTexture(\"+n+\", uv);\\n    }\\n  \"}function getSampler6D(e){var t=e.shapeInfo.logicalShape,n=e.name,r=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),o=t[5],a=t[4]*o,i=t[3]*a,s=t[2]*i,u=t[1]*s,l=squeezeShape(t),c=l.newShape,p=l.keptDims;if(c.length<t.length){return\"\\n      \"+getSamplerFromInInfo(squeezeInputInfo(e,c))+\"\\n      float \"+r+\"(int row, int col, int depth,\\n                    int depth2, int depth3, int depth4) {\\n        return \"+r+\"(\"+getSqueezedParams([\"row\",\"col\",\"depth\",\"depth2\",\"depth3\",\"depth4\"],p)+\");\\n      }\\n    \"}if(e.shapeInfo.isUniform)return\"\\n      float \"+r+\"(int row, int col, int depth,\\n                  int depth2, int depth3, int depth4) {\\n        float index = dot(\\n          vec4(row, col, depth, depth2),\\n          vec4(\"+u+\", \"+s+\", \"+i+\", \"+a+\")) +\\n          dot(\\n            vec2(depth3, depth4),\\n            vec2(\"+o+\", 1));\\n        return \"+r+\"Flat(index);\\n      }\\n    \";var d=e.shapeInfo.texShape,h=d[0],f=d[1];return f===u?\"\\n      float \"+r+\"(int row, int col, int depth,\\n                    int depth2, int depth3, int depth4) {\\n        int texR = row;\\n        float texC = dot(\\n          vec4(col, depth, depth2, depth3),\\n          vec4(\"+s+\", \"+i+\", \"+a+\", \"+o+\")) + depth4;\\n        vec2 uv = (vec2(texC, texR) + halfCR) /\\n                   vec2(\"+f+\".0, \"+h+\".0);\\n        return sampleTexture(\"+n+\", uv);\\n      }\\n    \":f===o?\"\\n      float \"+r+\"(int row, int col, int depth,\\n                    int depth2, int depth3, int depth4) {\\n        float texR = dot(\\n          vec4(row, col, depth, depth2),\\n          vec4(\"+t[1]*t[2]*t[3]*t[4]+\",\\n               \"+t[2]*t[3]*t[4]+\",\\n               \"+t[3]*t[4]+\",\\n               \"+t[4]+\")) + depth3;\\n        int texC = depth4;\\n        vec2 uv = (vec2(texC, texR) + halfCR) /\\n                  vec2(\"+f+\".0, \"+h+\".0);\\n        return sampleTexture(\"+n+\", uv);\\n      }\\n    \":\"\\n    float \"+r+\"(int row, int col, int depth,\\n                  int depth2, int depth3, int depth4) {\\n      vec2 uv = UVfrom6D(\"+h+\", \"+f+\", \"+u+\", \"+s+\",\\n          \"+i+\", \"+a+\", \"+o+\"\\n          ,row, col, depth, depth2, depth3, depth4);\\n      return sampleTexture(\"+n+\", uv);\\n    }\\n  \"}function getSamplerFlat(e){var t=e.name,n=\"get\"+t.charAt(0).toUpperCase()+t.slice(1)+\"Flat\",r=sizeFromShape(e.shapeInfo.logicalShape);if(e.shapeInfo.isUniform)return 1===r?\"float \"+n+\"(int index) {return \"+t+\";}\":\"\\n      float \"+n+\"(int index) {\\n        for (int i = 0; i < \"+r+\"; i++) {\\n          if (i == index) {\\n            return \"+t+\"[i];\\n          }\\n        }\\n      }\\n    \";var o=e.shapeInfo.texShape,a=o[0],i=o[1];return 1===i&&1===a?\"\\n      float \"+n+\"(int index) {\\n        return sampleTexture(\"+t+\", halfCR);\\n      }\\n    \":1===i?\"\\n      float \"+n+\"(int index) {\\n        vec2 uv = vec2(0.5, (float(index) + 0.5) / \"+a+\".0);\\n        return sampleTexture(\"+t+\", uv);\\n      }\\n    \":1===a?\"\\n      float \"+n+\"(int index) {\\n        vec2 uv = vec2((float(index) + 0.5) / \"+i+\".0, 0.5);\\n        return sampleTexture(\"+t+\", uv);\\n      }\\n    \":\"\\n    float \"+n+\"(int index) {\\n      vec2 uv = UVfrom1D(\"+a+\", \"+i+\", index);\\n      return sampleTexture(\"+t+\", uv);\\n    }\\n  \"}function getBroadcastOutputCoordsSampler(e,t,n,r){var o=e.shapeInfo.logicalShape.length,a=t.logicalShape.length,i=\"int\";2===a?i=\"ivec2\":3===a?i=\"ivec3\":4===a&&(i=\"ivec4\");var s=getBroadcastDims(e.shapeInfo.logicalShape,t.logicalShape),u=a-o;return\"\\n    float \"+r+\"() {\\n      \"+i+\" coords = getOutputCoords();\\n      \"+(0===o?\"\":a<2&&s.length>=1?\"coords = 0;\":s.map(function(e){return\"coords[\"+(e+u)+\"] = 0;\"}).join(\"\\n\"))+\"\\n      return get\"+n+\"(\"+(a<2&&o>0?\"coords\":e.shapeInfo.logicalShape.map(function(e,t){return\"coords[\"+(t+u)+\"]\"}).join(\", \"))+\");\\n    }\\n  \"}function getPackedSamplerAtOutputCoords(e,t,n){var r=e.name,o=r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape,i=\"get\"+o+\"AtOutCoords\",s=t.texShape,u=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],l=u[0],c=u[1],p=getBroadcastDims(e.shapeInfo.logicalShape,t.logicalShape),d=e.shapeInfo.logicalShape.length,h=t.logicalShape.length;if(p.length)throw Error(\"Packed broadcast sampling is not implemented yet.\");if(arraysEqual(e.shapeInfo.texShape,s))return\"\\n      vec4 \"+i+\"() {\\n        return texture2D(\"+r+\", resultUV);\\n      }\\n    \";var f=\"return texture2D(\"+r+\", uv)\";return 1===d&&h>1?f=\"\\n      vec4 sample = texture2D(\"+r+\", uv);\\n      return vec4(sample.xy, sample.xy);\\n    \":0===d&&h>0&&(f=1===h?\"\\n        vec4 sample = texture2D(\"+r+\", uv);\\n        return vec4(sample.x, sample.x, 0., 0.);\\n      \":\"\\n        vec4 sample = texture2D(\"+r+\", uv);\\n        return vec4(sample.x);\\n      \"),\"\\n    vec4 \"+i+\"() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n                             vec2(\"+u[0]+\", \"+u[1]+\"));\\n      int index = resTexRC.x * \"+u[1]+\" + resTexRC.y;\\n\\n      int texR = index / \"+c+\";\\n      int texC = index - texR * \"+c+\";\\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(\"+c+\", \"+l+\");\\n\\n      \"+f+\";\\n    }\\n  \"}function getSamplerAtOutputCoords(e,t,n){var r=e.name,o=r.charAt(0).toUpperCase()+r.slice(1),a=\"get\"+o+\"AtOutCoords\",i=getBroadcastDims(e.shapeInfo.logicalShape,t.logicalShape),s=e.shapeInfo.logicalShape.length,u=t.logicalShape.length,l=n&&(u>s||i.length>0),c=broadcastDimsAreOuter(i),p=e.shapeInfo.isUniform;if(l&&!c)return getBroadcastOutputCoordsSampler(e,t,o,a);var d=sizeFromShape(e.shapeInfo.logicalShape),h=\"\";l&&c&&(h=\"\\n        int mainPart = index / \"+d+\";\\n        index -= mainPart * \"+d+\";\\n      \");var f=t.texShape;if(p)return 1===d?\"float \"+a+\"() {return \"+r+\";}\":\"\\n      float \"+a+\"() {\\n        ivec2 resTexRC = ivec2(resultUV.yx *\\n                              vec2(\"+f[0]+\", \"+f[1]+\"));\\n        int index = resTexRC.x * \"+f[1]+\" + resTexRC.y;\\n        \"+h+\"\\n        return get\"+o+\"Flat(index);\\n      }\\n    \";var m=e.shapeInfo.texShape;return arraysEqual(m,f)?\"\\n      float \"+a+\"() {\\n        return sampleTexture(\"+r+\", resultUV);\\n      }\\n    \":\"\\n    float \"+a+\"() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n                             vec2(\"+f[0]+\", \"+f[1]+\"));\\n      int index = resTexRC.x * \"+f[1]+\" + resTexRC.y;\\n      \"+h+\"\\n      int texR = index / \"+m[1]+\";\\n      int texC = index - texR * \"+m[1]+\";\\n      vec2 uv = (vec2(texC, texR) + halfCR) /\\n                 vec2(\"+m[1]+\".0, \"+m[0]+\".0);\\n\\n      return sampleTexture(\"+r+\", uv);\\n    }\\n  \"}function getCoordsDataType(e){if(e<=1)return\"int\";if(2===e)return\"ivec2\";if(3===e)return\"ivec3\";if(4===e)return\"ivec4\";if(5===e)return\"ivec5\";if(6===e)return\"ivec6\";throw Error(\"GPU for rank \"+e+\" is not yet supported\")}function squeezeInputInfo(e,t){var n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function getSqueezedParams(e,t){return t.map(function(t){return e[t]}).join(\", \")}var CumSumProgram=function(){return function(e,t,n){this.variableNames=[\"x\"],this.outputShape=e;var r=e.length,o=e[e.length-1],a=n?\"<\":\">\";this.userCode=\"\\n      int getIndex(int i) {\\n        \"+(n?\"return \"+o+\" -i - 1;\":\"return i;\")+\"\\n      }\\n\\n      void main() {\\n        \"+getCoordsDataType(r)+\" coords = getOutputCoords();\\n        int end = \"+getFinalCoord(r,\"coords\")+\";\\n        float val = 0.0;\\n        for (int i = \"+o+\" - 1; i >= 0; i -= 1) {\\n          int idx = getIndex(i);\\n          if (idx \"+a+\" end) {\\n            continue;\\n          }\\n          if (idx == end && \"+t+\") {\\n            continue;\\n          }\\n          \"+getFinalCoord(r,\"coords\")+\" = idx;\\n          val += getX(\"+getCoords(r,\"coords\")+\");\\n        }\\n        setOutput(val);\\n      }\\n    \"}}();function getCoords(e,t){if(1===e)return\"\"+t;if(2===e)return t+\".x, \"+t+\".y\";if(3===e)return t+\".x, \"+t+\".y, \"+t+\".z\";if(4===e)return t+\".x, \"+t+\".y, \"+t+\".z, \"+t+\".w\";throw Error(\"Cumulative sum for rank \"+e+\" is not yet supported\")}function getFinalCoord(e,t){if(1===e)return\"\"+t;if(2===e)return t+\".y\";if(3===e)return t+\".z\";if(4===e)return t+\".w\";throw Error(\"Cumulative sum for rank \"+e+\" is not yet supported\")}var DepthToSpaceProgram=function(){function e(e,t,n){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=\"\\n    void main() {\\n      ivec4 coords = getOutputCoords();\\n      int b = coords[0];\\n      int h = \"+this.getHeightCoordString()+\";\\n      int w = \"+this.getWidthCoordString()+\";\\n      int d = \"+this.getDepthCoordString()+\";\\n\\n      int in_h = h / \"+t+\";\\n      int offset_h = imod(h, \"+t+\");\\n      int in_w = w / \"+t+\";\\n      int offset_w = imod(w, \"+t+\");\\n      int offset_d = (offset_h * \"+t+\" + offset_w) *\\n        \"+this.getOutputDepthSize()+\";\\n      int in_d = d + offset_d;\\n\\n      float result = \"+this.getInputSamplingString()+\";\\n      setOutput(result);\\n    }\\n  \"}return e.prototype.getHeightCoordString=function(){return\"NHWC\"===this.dataFormat?\"coords[1]\":\"coords[2]\"},e.prototype.getWidthCoordString=function(){return\"NHWC\"===this.dataFormat?\"coords[2]\":\"coords[3]\"},e.prototype.getDepthCoordString=function(){return\"NHWC\"===this.dataFormat?\"coords[3]\":\"coords[1]\"},e.prototype.getOutputDepthSize=function(){return\"NHWC\"===this.dataFormat?this.outputShape[3]:this.outputShape[1]},e.prototype.getInputSamplingString=function(){return\"NHWC\"===this.dataFormat?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"},e}(),EncodeFloatProgram=function(){return function(e){this.variableNames=[\"A\"],this.outputShape=e,this.userCode=\"\\n      const float FLOAT_MAX = 1.70141184e38;\\n      const float FLOAT_MIN = 1.17549435e-38;\\n\\n      lowp vec4 encode_float(highp float v) {\\n        if (isNaN(v)) {\\n          return vec4(255, 255, 255, 255);\\n        }\\n\\n        highp float av = abs(v);\\n\\n        if(av < FLOAT_MIN) {\\n          return vec4(0.0, 0.0, 0.0, 0.0);\\n        } else if(v > FLOAT_MAX) {\\n          return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\\n        } else if(v < -FLOAT_MAX) {\\n          return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\\n        }\\n\\n        highp vec4 c = vec4(0,0,0,0);\\n\\n        highp float e = floor(log2(av));\\n        highp float m = exp2(fract(log2(av))) - 1.0;\\n\\n        c[2] = floor(128.0 * m);\\n        m -= c[2] / 128.0;\\n        c[1] = floor(32768.0 * m);\\n        m -= c[1] / 32768.0;\\n        c[0] = floor(8388608.0 * m);\\n\\n        highp float ebias = e + 127.0;\\n        c[3] = floor(ebias / 2.0);\\n        ebias -= c[3] * 2.0;\\n        c[2] += floor(ebias) * 128.0;\\n\\n        c[3] += 128.0 * step(0.0, -v);\\n\\n        return c / 255.0;\\n      }\\n\\n      void main() {\\n        float x = getAAtOutCoords();\\n        gl_FragColor = encode_float(x);\\n      }\\n    \"}}(),COMPLEX_FFT={REAL:\"return real * expR - imag * expI;\",IMAG:\"return real * expI + imag * expR;\"},FFTProgram=function(){return function(e,t,n){this.variableNames=[\"real\",\"imag\"];var r=t[1];this.outputShape=t;var o=n?\"2.0 * \"+Math.PI:\"-2.0 * \"+Math.PI,a=n?r+\".0\":\"1.0\";this.userCode=\"\\n      const float exponentMultiplier = \"+o+\";\\n\\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\\n        \"+e+\"\\n      }\\n\\n      float mulMatDFT(int batch, int index) {\\n        float indexRatio = float(index) / float(\"+r+\");\\n        float exponentMultiplierTimesIndexRatio =\\n            exponentMultiplier * indexRatio;\\n\\n        float result = 0.0;\\n\\n        for (int i = 0; i < \"+r+\"; i++) {\\n          // x = (-2|2 * PI / N) * index * i;\\n          float x = exponentMultiplierTimesIndexRatio * float(i);\\n          float expR = cos(x);\\n          float expI = sin(x);\\n          float real = getReal(batch, i);\\n          float imag = getImag(batch, i);\\n\\n          result +=\\n              unaryOpComplex(real, expR, imag, expI) / \"+a+\";\\n        }\\n\\n        return result;\\n      }\\n\\n      void main() {\\n        ivec2 coords = getOutputCoords();\\n        setOutput(mulMatDFT(coords[0], coords[1]));\\n      }\\n    \"}}(),FromPixelsProgram=function(){return function(e){this.variableNames=[\"A\"];var t=e[0],n=e[1];this.outputShape=e,this.userCode=\"\\n      void main() {\\n        ivec3 coords = getOutputCoords();\\n        int texR = coords[0];\\n        int texC = coords[1];\\n        int depth = coords[2];\\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(\"+n+\".0, \"+t+\".0);\\n\\n        vec4 values = texture2D(A, uv);\\n        float value;\\n        if (depth == 0) {\\n          value = values.r;\\n        } else if (depth == 1) {\\n          value = values.g;\\n        } else if (depth == 2) {\\n          value = values.b;\\n        } else if (depth == 3) {\\n          value = values.a;\\n        }\\n\\n        setOutput(floor(value * 255.0 + 0.5));\\n      }\\n    \"}}(),GatherProgram=function(){return function(e,t,n){this.variableNames=[\"A\",\"indices\"];var r=e.slice();r[n]=t,this.outputShape=r,this.rank=r.length;var o=getCoordsDataType(this.rank),a=getSourceCoords(e,n);this.userCode=\"\\n      void main() {\\n        \"+o+\" resRC = getOutputCoords();\\n        setOutput(getA(\"+a+\"));\\n      }\\n    \"}}();function getSourceCoords(e,t){var n=e.length;if(n>4)throw Error(\"Gather for rank \"+n+\" is not yet supported\");if(1===n)return\"int(getIndices(resRC))\";for(var r=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[],a=0;a<e.length;a++)a===t?o.push(\"int(getIndices(\"+r[a]+\"))\"):o.push(\"\"+r[a]);return o.join()}var TextureUsage,PhysicalTextureType,GatherNDProgram=function(){return function(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=[\"x\",\"indices\"],this.outputShape=n;var r=getCoordsDataType(t.length),o=getCoordsDataType(n.length),a=this.sliceDim>1?\"strides[j]\":\"strides\";this.userCode=\"\\n        \"+r+\" strides = \"+r+\"(\"+this.strides+\");\\n         void main() {\\n          \"+o+\" coords = getOutputCoords();\\n          int flattenIndex = 0;\\n          for (int j = 0; j < \"+this.sliceDim+\"; j++) {\\n            int index = round(getIndices(coords[0], j));\\n            flattenIndex += index * \"+a+\";\\n          }\\n          setOutput(getX(flattenIndex, coords[1]));\\n        }\\n      \"}}();function getUnpackedMatrixTextureShapeWidthHeight(e,t){return[t,e]}function getUnpackedArraySizeFromMatrixSize(e,t){return e*t}function getMatrixSizeFromUnpackedArraySize(e,t){if(e%t!=0)throw new Error(\"unpackedSize (\"+e+\") must be a multiple of \"+t);return e/t}function encodeMatrixToUnpackedArray(e,t,n){var r=getUnpackedArraySizeFromMatrixSize(e.length,n);if(t.length<r)throw new Error(\"unpackedArray length (\"+t.length+\") must be >= \"+r);for(var o=0,a=0;a<e.length;++a)t[o]=e[a],o+=n}function decodeMatrixFromUnpackedArray(e,t,n){var r=getMatrixSizeFromUnpackedArraySize(e.length,n);if(t.length<r)throw new Error(\"matrix length (\"+t.length+\") must be >= \"+r);for(var o=0,a=0;a<e.length;a+=n)t[o++]=e[a]}function getPackedMatrixTextureShapeWidthHeight(e,t){return[Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function getPackedRGBAArraySizeFromMatrixShape(e,t){var n=getPackedMatrixTextureShapeWidthHeight(e,t);return n[0]*n[1]*4}function encodeMatrixToPackedRGBA(e,t,n,r,o){var a=getPackedRGBAArraySizeFromMatrixShape(n,r);if(o.length<a)throw new Error(\"packedRGBA length (\"+o.length+\") must be >=\\n        \"+a);for(var i=r%2==1,s=n%2==1,u=Math.floor(r/2),l=Math.floor(n/2),c=Math.ceil(r/2),p=c*Math.ceil(n/2),d=nearestLargerEven(n)*nearestLargerEven(r),h=0;h<t;h++){for(var f=h*n*r,m=h*d,g=i?4:0,v=r,y=m,x=0;x<l;++x){for(var T=2*x*r,w=0;w<u;++w){var E=f+T+2*w;o[y]=e[E],o[y+1]=e[E+1],o[y+2]=e[E+v],o[y+3]=e[E+v+1],y+=4}y+=g}if(i){E=f+r-1,y=m+4*(c-1);var S=2*r;for(g=4*c,x=0;x<l;++x)o[y]=e[E],o[y+2]=e[E+r],E+=S,y+=g}if(s){for(E=f+(n-1)*r,y=m+4*(p-c),w=0;w<u;++w)o[y++]=e[E++],o[y++]=e[E++],y+=2;i&&s&&(o[m+d-4]=e[E])}}return o}function decodeMatrixFromPackedRGBA(e,t,n,r,o){var a=n*r;if(o.length<a)throw new Error(\"matrix length (\"+o.length+\") must be >= \"+a);for(var i=r%2==1,s=n%2==1,u=Math.floor(r/2),l=Math.floor(n/2),c=Math.ceil(r/2),p=c*Math.ceil(n/2),d=nearestLargerEven(n)*nearestLargerEven(r),h=0;h<t;h++){for(var f=h*n*r,m=h*d,g=i?4:0,v=r+(i?1:0),y=m,x=f,T=f+r,w=0;w<l;++w){for(var E=0;E<u;++E)o[x++]=e[y++],o[x++]=e[y++],o[T++]=e[y++],o[T++]=e[y++];y+=g,x+=v,T+=v}if(i){y=m+4*(c-1);var S=f+r-1;for(g=4*c,v=2*r,w=0;w<l;++w)o[S]=e[y],o[S+r]=e[y+2],y+=g,S+=v}if(s){for(y=m+4*(p-c),S=f+(n-1)*r,E=0;E<u;++E)o[S++]=e[y++],o[S++]=e[y++],y+=2;i&&(o[f+n*r-1]=e[y])}}return o}function callAndCheck(e,t){var n=t();return checkWebGLError(e),n}!function(e){e[e.RENDER=0]=\"RENDER\",e[e.UPLOAD=1]=\"UPLOAD\",e[e.PIXELS=2]=\"PIXELS\",e[e.DOWNLOAD=3]=\"DOWNLOAD\"}(TextureUsage||(TextureUsage={})),function(e){e[e.UNPACKED_FLOAT16=0]=\"UNPACKED_FLOAT16\",e[e.UNPACKED_FLOAT32=1]=\"UNPACKED_FLOAT32\",e[e.PACKED_4X1_UNSIGNED_BYTE=2]=\"PACKED_4X1_UNSIGNED_BYTE\",e[e.PACKED_2X2_FLOAT32=3]=\"PACKED_2X2_FLOAT32\",e[e.PACKED_2X2_FLOAT16=4]=\"PACKED_2X2_FLOAT16\"}(PhysicalTextureType||(PhysicalTextureType={}));var webGLDebugErrorCheckingEnabled=!1;function enableDebugWebGLErrorChecking(e){webGLDebugErrorCheckingEnabled=e}function checkWebGLError(e){if(webGLDebugErrorCheckingEnabled){var t=e.getError();if(t!==e.NO_ERROR)throw new Error(\"WebGL Error: \"+getWebGLErrorMessage(e,t))}}function getWebGLErrorMessage(e,t){switch(t){case e.NO_ERROR:return\"NO_ERROR\";case e.INVALID_ENUM:return\"INVALID_ENUM\";case e.INVALID_VALUE:return\"INVALID_VALUE\";case e.INVALID_OPERATION:return\"INVALID_OPERATION\";case e.INVALID_FRAMEBUFFER_OPERATION:return\"INVALID_FRAMEBUFFER_OPERATION\";case e.OUT_OF_MEMORY:return\"OUT_OF_MEMORY\";case e.CONTEXT_LOST_WEBGL:return\"CONTEXT_LOST_WEBGL\";default:return\"Unknown error code \"+t}}function getExtensionOrThrow(e,t){return throwIfNull(e,function(){return e.getExtension(t)},'Extension \"'+t+'\" not supported on this browser.')}function createVertexShader(e,t){var n=throwIfNull(e,function(){return e.createShader(e.VERTEX_SHADER)},\"Unable to create vertex WebGLShader.\");if(callAndCheck(e,function(){return e.shaderSource(n,t)}),callAndCheck(e,function(){return e.compileShader(n)}),!1===e.getShaderParameter(n,e.COMPILE_STATUS))throw console.log(e.getShaderInfoLog(n)),new Error(\"Failed to compile vertex shader.\");return n}function createFragmentShader(e,t){var n=throwIfNull(e,function(){return e.createShader(e.FRAGMENT_SHADER)},\"Unable to create fragment WebGLShader.\");if(callAndCheck(e,function(){return e.shaderSource(n,t)}),callAndCheck(e,function(){return e.compileShader(n)}),!1===e.getShaderParameter(n,e.COMPILE_STATUS))throw logShaderSourceAndInfoLog(t,e.getShaderInfoLog(n)),new Error(\"Failed to compile fragment shader.\");return n}var lineNumberRegex=/ERROR: [0-9]+:([0-9]+):/g;function logShaderSourceAndInfoLog(e,t){var n=lineNumberRegex.exec(t);if(null==n)return console.log(\"Couldn't parse line number in error: \"+t),void console.log(e);for(var r=+n[1],o=e.split(\"\\n\"),a=o.length.toString().length+2,i=o.map(function(e,t){return rightPad((t+1).toString(),a)+e}),s=0,u=0;u<i.length;u++)s=Math.max(i[u].length,s);var l=i.slice(0,r-1),c=i.slice(r-1,r),p=i.slice(r);console.log(l.join(\"\\n\")),console.log(t.split(\"\\n\")[0]),console.log(\"%c \"+rightPad(c[0],s),\"border:1px solid red; background-color:#e3d2d2; color:#a61717\"),console.log(p.join(\"\\n\"))}function createProgram(e){return throwIfNull(e,function(){return e.createProgram()},\"Unable to create WebGLProgram.\")}function linkProgram(e,t){if(callAndCheck(e,function(){return e.linkProgram(t)}),!1===e.getProgramParameter(t,e.LINK_STATUS))throw console.log(e.getProgramInfoLog(t)),new Error(\"Failed to link vertex and fragment shaders.\")}function validateProgram(e,t){if(callAndCheck(e,function(){return e.validateProgram(t)}),!1===e.getProgramParameter(t,e.VALIDATE_STATUS))throw console.log(e.getProgramInfoLog(t)),new Error(\"Shader program validation failed.\")}function createStaticVertexBuffer(e,t){var n=throwIfNull(e,function(){return e.createBuffer()},\"Unable to create WebGLBuffer\");return callAndCheck(e,function(){return e.bindBuffer(e.ARRAY_BUFFER,n)}),callAndCheck(e,function(){return e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)}),n}function createStaticIndexBuffer(e,t){var n=throwIfNull(e,function(){return e.createBuffer()},\"Unable to create WebGLBuffer\");return callAndCheck(e,function(){return e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)}),callAndCheck(e,function(){return e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)}),n}function getNumChannels(){return 2===ENV.get(\"WEBGL_VERSION\")?1:4}function createTexture(e){return throwIfNull(e,function(){return e.createTexture()},\"Unable to create WebGLTexture.\")}function validateTextureSize(e,t){var n=ENV.get(\"WEBGL_MAX_TEXTURE_SIZE\");if(e<=0||t<=0){var r=\"[\"+e+\"x\"+t+\"]\";throw new Error(\"Requested texture size \"+r+\" is invalid.\")}if(e>n||t>n){r=\"[\"+e+\"x\"+t+\"]\";throw new Error(\"Requested texture size \"+r+\" greater than WebGL maximum on this browser / GPU \"+(\"[\"+n+\"x\"+n+\"]\")+\".\")}}function createFramebuffer(e){return throwIfNull(e,function(){return e.createFramebuffer()},\"Unable to create WebGLFramebuffer.\")}function bindVertexBufferToProgramAttribute(e,t,n,r,o,a,i){var s=e.getAttribLocation(t,n);return-1!==s&&(callAndCheck(e,function(){return e.bindBuffer(e.ARRAY_BUFFER,r)}),callAndCheck(e,function(){return e.vertexAttribPointer(s,o,e.FLOAT,!1,a,i)}),callAndCheck(e,function(){return e.enableVertexAttribArray(s)}),!0)}function bindTextureUnit(e,t,n){validateTextureUnit(e,n),callAndCheck(e,function(){return e.activeTexture(e.TEXTURE0+n)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,t)})}function unbindTextureUnit(e,t){validateTextureUnit(e,t),callAndCheck(e,function(){return e.activeTexture(e.TEXTURE0+t)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)})}function getProgramUniformLocationOrThrow(e,t,n){return throwIfNull(e,function(){return e.getUniformLocation(t,n)},'uniform \"'+n+'\" not present in program.')}function getProgramUniformLocation(e,t,n){return e.getUniformLocation(t,n)}function bindTextureToProgramUniformSampler(e,t,n,r,o){callAndCheck(e,function(){return bindTextureUnit(e,n,o)}),callAndCheck(e,function(){return e.uniform1i(r,o)})}function bindCanvasToFramebuffer(e){callAndCheck(e,function(){return e.bindFramebuffer(e.FRAMEBUFFER,null)}),callAndCheck(e,function(){return e.viewport(0,0,e.canvas.width,e.canvas.height)}),callAndCheck(e,function(){return e.scissor(0,0,e.canvas.width,e.canvas.height)})}function bindColorTextureToFramebuffer(e,t,n){callAndCheck(e,function(){return e.bindFramebuffer(e.FRAMEBUFFER,n)}),callAndCheck(e,function(){return e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0)})}function unbindColorTextureFromFramebuffer(e,t){callAndCheck(e,function(){return e.bindFramebuffer(e.FRAMEBUFFER,t)}),callAndCheck(e,function(){return e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0)})}function validateFramebuffer(e){var t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error(\"Error binding framebuffer: \"+getFramebufferErrorMessage(e,t))}function getFramebufferErrorMessage(e,t){switch(t){case e.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return\"FRAMEBUFFER_INCOMPLETE_ATTACHMENT\";case e.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return\"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT\";case e.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return\"FRAMEBUFFER_INCOMPLETE_DIMENSIONS\";case e.FRAMEBUFFER_UNSUPPORTED:return\"FRAMEBUFFER_UNSUPPORTED\";default:return\"unknown error \"+t}}function throwIfNull(e,t,n){var r=callAndCheck(e,function(){return t()});if(null==r)throw new Error(n);return r}function validateTextureUnit(e,t){var n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,r=t+e.TEXTURE0;if(r<e.TEXTURE0||r>n)throw new Error(\"textureUnit must be in \"+(\"[gl.TEXTURE0, gl.TEXTURE\"+n+\"]\")+\".\")}function getTextureShapeFromLogicalShape(e,t){void 0===t&&(t=!1);var n=ENV.get(\"WEBGL_MAX_TEXTURE_SIZE\");if(t&&(n*=2,e=e.map(function(t,n){return n>=e.length-2?nearestLargerEven(e[n]):e[n]})),2!==e.length){var r=squeezeShape(e);e=r.newShape}var o=sizeFromShape(e);return e.length<=1&&o<=n?[1,o]:2===e.length&&e[0]<=n&&e[1]<=n?e:3===e.length&&e[0]*e[1]<=n&&e[2]<=n?[e[0]*e[1],e[2]]:3===e.length&&e[0]<=n&&e[1]*e[2]<=n?[e[0],e[1]*e[2]]:4===e.length&&e[0]*e[1]*e[2]<=n&&e[3]<=n?[e[0]*e[1]*e[2],e[3]]:4===e.length&&e[0]<=n&&e[1]*e[2]*e[3]<=n?[e[0],e[1]*e[2]*e[3]]:sizeToSquarishShape(o)}function isEven(e){return e%2==0}function isReshapeFree(e,t){if(arraysEqual(e=e.slice(-2),t=t.slice(-2)))return!0;if(!e.length||!t.length)return!0;if(0===e[0]||0===e[1]||0===t[0]||0===t[1])return!0;if(e.length!==t.length){var n=e.slice(-1)[0],r=t.slice(-1)[0];if(n===r)return!0;if(isEven(n)&&isEven(r)&&(1===e[0]||1===t[0]))return!0}else if(isEven(e[0])&&isEven(t[0])){if(isEven(e[1])&&isEven(t[1]))return!0;if(e[1]===t[1])return!0}return!1}var webgl_util=Object.freeze({callAndCheck:callAndCheck,enableDebugWebGLErrorChecking:enableDebugWebGLErrorChecking,checkWebGLError:checkWebGLError,getWebGLErrorMessage:getWebGLErrorMessage,getExtensionOrThrow:getExtensionOrThrow,createVertexShader:createVertexShader,createFragmentShader:createFragmentShader,createProgram:createProgram,linkProgram:linkProgram,validateProgram:validateProgram,createStaticVertexBuffer:createStaticVertexBuffer,createStaticIndexBuffer:createStaticIndexBuffer,getNumChannels:getNumChannels,createTexture:createTexture,validateTextureSize:validateTextureSize,createFramebuffer:createFramebuffer,bindVertexBufferToProgramAttribute:bindVertexBufferToProgramAttribute,bindTextureUnit:bindTextureUnit,unbindTextureUnit:unbindTextureUnit,getProgramUniformLocationOrThrow:getProgramUniformLocationOrThrow,getProgramUniformLocation:getProgramUniformLocation,bindTextureToProgramUniformSampler:bindTextureToProgramUniformSampler,bindCanvasToFramebuffer:bindCanvasToFramebuffer,bindColorTextureToFramebuffer:bindColorTextureToFramebuffer,unbindColorTextureFromFramebuffer:unbindColorTextureFromFramebuffer,validateFramebuffer:validateFramebuffer,getFramebufferErrorMessage:getFramebufferErrorMessage,getTextureShapeFromLogicalShape:getTextureShapeFromLogicalShape,isReshapeFree:isReshapeFree});function createVertexShader$1(e){return createVertexShader(e,\"\\n    precision highp float;\\n    attribute vec3 clipSpacePos;\\n    attribute vec2 uv;\\n    varying vec2 resultUV;\\n\\n    void main() {\\n      gl_Position = vec4(clipSpacePos, 1);\\n      resultUV = uv;\\n    }\")}function createVertexBuffer(e){return createStaticVertexBuffer(e,new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]))}function createIndexBuffer(e){return createStaticIndexBuffer(e,new Uint16Array([0,1,2,2,1,3]))}function getTextureConfig(e,t){var n,r,o,a,i,s,u,l=e;return 2===ENV.get(\"WEBGL_VERSION\")?(n=l.R32F,r=l.R16F,o=l.RGBA32F,a=l.RED,i=4,s=1,u=l.HALF_FLOAT):(n=e.RGBA,r=e.RGBA,o=l.RGBA,a=e.RGBA,i=4,s=4,u=null!=t?t.HALF_FLOAT_OES:null),{internalFormatFloat:n,internalFormatHalfFloat:r,internalFormatPackedFloat:o,textureFormatFloat:a,downloadTextureFormat:e.RGBA,downloadUnpackNumChannels:i,defaultNumChannels:s,textureTypeHalfFloat:u}}function createAndConfigureTexture(e,t,n,r,o,a){validateTextureSize(t,n);var i=createTexture(e),s=e.TEXTURE_2D;return callAndCheck(e,function(){return e.bindTexture(s,i)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_MIN_FILTER,e.NEAREST)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_MAG_FILTER,e.NEAREST)}),callAndCheck(e,function(){return e.texImage2D(s,0,r,t,n,0,o,a,null)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)}),i}function createFloat32MatrixTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],r.internalFormatFloat,r.textureFormatFloat,e.FLOAT)}function createFloat16MatrixTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],r.internalFormatFloat,r.textureFormatFloat,r.textureTypeHalfFloat)}function createUnsignedBytesMatrixTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],e.RGBA,e.RGBA,e.UNSIGNED_BYTE)}function createPackedMatrixTexture(e,t,n,r){var o=getPackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],r.internalFormatPackedFloat,e.RGBA,e.FLOAT)}function createFloat16PackedMatrixTexture(e,t,n,r){var o=getPackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],r.internalFormatHalfFloat,e.RGBA,r.textureTypeHalfFloat)}function bindVertexProgramAttributeStreams(e,t,n){return callAndCheck(e,function(){return e.bindBuffer(e.ARRAY_BUFFER,n)}),bindVertexBufferToProgramAttribute(e,t,\"clipSpacePos\",n,3,20,0)&&bindVertexBufferToProgramAttribute(e,t,\"uv\",n,2,20,12)}function uploadPixelDataToTexture(e,t,n){callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,t)}),callAndCheck(e,function(){return e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)})}function uploadDataToTexture(e,t,n,r,o,a){validateTextureSize(n,r),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,t)}),callAndCheck(e,function(){return e.texSubImage2D(e.TEXTURE_2D,0,0,0,n,r,a,e.FLOAT,o)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)})}function uploadMatrixToTexture(e,t,n,r,o,a,i){var s,u=getUnpackedMatrixTextureShapeWidthHeight(n,r),l=u[0],c=u[1];1===i.defaultNumChannels?s=o:encodeMatrixToUnpackedArray(o,s=new Float32Array(getUnpackedArraySizeFromMatrixSize(o.length,a)),a),uploadDataToTexture(e,t,l,c,s,i.textureFormatFloat)}function uploadMatrixToPackedTexture(e,t,n,r,o,a,i,s,u){var l=getPackedMatrixTextureShapeWidthHeight(a,i),c=l[0],p=l[1],d=new Float32Array(n*getPackedRGBAArraySizeFromMatrixShape(r,o));encodeMatrixToPackedRGBA(s,n,r,o,d),uploadDataToTexture(e,t,c,p,d,e.RGBA)}function maybeCreateBufferFromOutputTexture(e,t,n,r,o){var a=t;if(2===ENV.get(\"WEBGL_VERSION\")){var i=e,s=i.createBuffer();callAndCheck(e,function(){return e.bindBuffer(i.PIXEL_PACK_BUFFER,s)});var u=4*getUnpackedArraySizeFromMatrixSize(n*r,o.downloadUnpackNumChannels);callAndCheck(e,function(){return e.bufferData(i.PIXEL_PACK_BUFFER,u,e.STATIC_DRAW)}),callAndCheck(e,function(){return i.readPixels(0,0,r,n,e.RGBA,e.FLOAT,0)}),callAndCheck(e,function(){return e.bindBuffer(i.PIXEL_PACK_BUFFER,null)}),a=s}return a}function downloadFloat32MatrixFromBuffer(e,t,n,r,o){var a=e,i=new Float32Array(getUnpackedArraySizeFromMatrixSize(n*r,o.downloadUnpackNumChannels));a.bindBuffer(e.ARRAY_BUFFER,t),a.getBufferSubData(e.ARRAY_BUFFER,0,i),a.bindBuffer(e.ARRAY_BUFFER,null);var s=new Float32Array(n*r);return decodeMatrixFromUnpackedArray(i,s,o.downloadUnpackNumChannels),s}function downloadFloat32MatrixFromOutputTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n),a=o[0],i=o[1],s=new Float32Array(getUnpackedArraySizeFromMatrixSize(t*n,r.downloadUnpackNumChannels));callAndCheck(e,function(){return e.readPixels(0,0,a,i,r.downloadTextureFormat,e.FLOAT,s)});var u=new Float32Array(t*n);return decodeMatrixFromUnpackedArray(s,u,r.downloadUnpackNumChannels),u}function downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n),a=o[0],i=o[1],s=new Uint8Array(getUnpackedArraySizeFromMatrixSize(t*n,4));return callAndCheck(e,function(){return e.readPixels(0,0,a,i,r.downloadTextureFormat,e.UNSIGNED_BYTE,s)}),new Float32Array(s.buffer)}function downloadPackedMatrixFromBuffer(e,t,n,r,o,a,i,s){var u=e,l=new Float32Array(getPackedRGBAArraySizeFromMatrixShape(a,i));u.bindBuffer(e.ARRAY_BUFFER,t),u.getBufferSubData(e.ARRAY_BUFFER,0,l),u.bindBuffer(e.ARRAY_BUFFER,null);var c=new Float32Array(sizeFromShape([n,r,o]));return decodeMatrixFromPackedRGBA(l,n,r,o,c),c}function downloadMatrixFromPackedOutputTexture(e,t,n,r,o,a,i){var s=getPackedMatrixTextureShapeWidthHeight(o,a),u=s[0],l=s[1],c=new Float32Array(getPackedRGBAArraySizeFromMatrixShape(o,a));callAndCheck(e,function(){return e.readPixels(0,0,u,l,e.RGBA,e.FLOAT,c)});var p=new Float32Array(sizeFromShape([t,n,r]));return decodeMatrixFromPackedRGBA(c,t,n,r,p)}var gpgpu_util=Object.freeze({createVertexShader:createVertexShader$1,createVertexBuffer:createVertexBuffer,createIndexBuffer:createIndexBuffer,getTextureConfig:getTextureConfig,createFloat32MatrixTexture:createFloat32MatrixTexture,createFloat16MatrixTexture:createFloat16MatrixTexture,createUnsignedBytesMatrixTexture:createUnsignedBytesMatrixTexture,createPackedMatrixTexture:createPackedMatrixTexture,createFloat16PackedMatrixTexture:createFloat16PackedMatrixTexture,bindVertexProgramAttributeStreams:bindVertexProgramAttributeStreams,uploadPixelDataToTexture:uploadPixelDataToTexture,uploadMatrixToTexture:uploadMatrixToTexture,uploadMatrixToPackedTexture:uploadMatrixToPackedTexture,maybeCreateBufferFromOutputTexture:maybeCreateBufferFromOutputTexture,downloadFloat32MatrixFromBuffer:downloadFloat32MatrixFromBuffer,downloadFloat32MatrixFromOutputTexture:downloadFloat32MatrixFromOutputTexture,downloadByteEncodedFloatMatrixFromOutputTexture:downloadByteEncodedFloatMatrixFromOutputTexture,downloadPackedMatrixFromBuffer:downloadPackedMatrixFromBuffer,downloadMatrixFromPackedOutputTexture:downloadMatrixFromPackedOutputTexture}),GPGPUContext=function(){function e(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.autoDebugValidate=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[],this.gl=null!=e?e:getWebGLContext(ENV.get(\"WEBGL_VERSION\")),1===ENV.get(\"WEBGL_VERSION\")?(this.textureFloatExtension=getExtensionOrThrow(this.gl,\"OES_texture_float\"),this.colorBufferFloatExtension=this.gl.getExtension(\"WEBGL_color_buffer_float\"),ENV.get(\"WEBGL_RENDER_FLOAT32_ENABLED\")||(this.textureHalfFloatExtension=getExtensionOrThrow(this.gl,\"OES_texture_half_float\"),this.colorBufferHalfFloatExtension=this.gl.getExtension(\"EXT_color_buffer_half_float\"))):this.colorBufferFloatExtension=getExtensionOrThrow(this.gl,\"EXT_color_buffer_float\"),this.vertexBuffer=createVertexBuffer(this.gl),this.indexBuffer=createIndexBuffer(this.gl),this.framebuffer=createFramebuffer(this.gl),this.textureConfig=getTextureConfig(this.gl,this.textureHalfFloatExtension)}return e.prototype.dispose=function(){var e=this;if(!this.disposed){null!=this.program&&console.warn(\"Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing.\"),null!=this.outputTexture&&console.warn(\"Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.\");var t=this.gl;callAndCheck(t,function(){return t.finish()}),callAndCheck(t,function(){return t.bindFramebuffer(t.FRAMEBUFFER,null)}),callAndCheck(t,function(){return t.deleteFramebuffer(e.framebuffer)}),callAndCheck(t,function(){return t.bindBuffer(t.ARRAY_BUFFER,null)}),callAndCheck(t,function(){return t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)}),callAndCheck(t,function(){return t.deleteBuffer(e.indexBuffer)}),this.disposed=!0}},e.prototype.enableAutomaticDebugValidation=function(e){this.autoDebugValidate=e,enableDebugWebGLErrorChecking(e)},e.prototype.createFloat32MatrixTexture=function(e,t){return this.throwIfDisposed(),createFloat32MatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.createFloat16MatrixTexture=function(e,t){return this.throwIfDisposed(),createFloat16MatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.createUnsignedBytesMatrixTexture=function(e,t){return this.throwIfDisposed(),createUnsignedBytesMatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.uploadPixelDataToTexture=function(e,t){this.throwIfDisposed(),uploadPixelDataToTexture(this.gl,e,t)},e.prototype.createFloat16PackedMatrixTexture=function(e,t){return this.throwIfDisposed(),createFloat16PackedMatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.createPackedMatrixTexture=function(e,t){return this.throwIfDisposed(),createPackedMatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.deleteMatrixTexture=function(e){var t=this;this.throwIfDisposed(),this.outputTexture===e&&(unbindColorTextureFromFramebuffer(this.gl,this.framebuffer),this.outputTexture=null),callAndCheck(this.gl,function(){return t.gl.deleteTexture(e)})},e.prototype.uploadMatrixToTexture=function(e,t,n,r){this.throwIfDisposed();var o=getNumChannels();return uploadMatrixToTexture(this.gl,e,t,n,r,o,this.textureConfig)},e.prototype.uploadMatrixToPackedTexture=function(e,t,n,r,o,a,i){return this.throwIfDisposed(),uploadMatrixToPackedTexture(this.gl,e,t,n,r,o,a,i,this.textureConfig)},e.prototype.downloadFloat32MatrixFromOutputTexture=function(e,t,n){var r=this;return this.downloadMatrixDriver(e,function(){return downloadFloat32MatrixFromOutputTexture(r.gl,t,n,r.textureConfig)})},e.prototype.downloadByteEncodedFloatMatrixFromOutputTexture=function(e,t,n){var r=this;return this.downloadMatrixDriver(e,function(){return downloadByteEncodedFloatMatrixFromOutputTexture(r.gl,t,n,r.textureConfig)})},e.prototype.downloadPackedMatrixFromBuffer=function(e,t,n,r,o,a){return downloadPackedMatrixFromBuffer(this.gl,e,t,n,r,o,a,this.textureConfig)},e.prototype.downloadFloat32MatrixFromBuffer=function(e,t,n){return downloadFloat32MatrixFromBuffer(this.gl,e,t,n,this.textureConfig)},e.prototype.maybeCreateBufferFromTexture=function(e,t,n){this.bindTextureToFrameBuffer(e);var r=maybeCreateBufferFromOutputTexture(this.gl,e,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r},e.prototype.createAndWaitForFence=function(){var e=this.createFence(this.gl);return this.pollFence(e)},e.prototype.createFence=function(e){var t,n,r=this;if(ENV.get(\"WEBGL_FENCE_API_ENABLED\")){var o=e,a=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=function(){var e=o.clientWaitSync(a,0,0);return e===o.ALREADY_SIGNALED||e===o.CONDITION_SATISFIED},t=a}else ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?(t=this.beginQuery(),this.endQuery(),n=function(){return r.isQueryAvailable(t,ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))}):n=function(){return!0};return{query:t,isFencePassed:n}},e.prototype.downloadMatrixFromPackedTexture=function(e,t,n,r,o,a){var i=this;return this.downloadMatrixDriver(e,function(){return downloadMatrixFromPackedOutputTexture(i.gl,t,n,r,o,a,i.textureConfig)})},e.prototype.createProgram=function(e){this.throwIfDisposed();var t=this.gl,n=createFragmentShader(t,e),r=createVertexShader$1(t),o=createProgram(t);return callAndCheck(t,function(){return t.attachShader(o,r)}),callAndCheck(t,function(){return t.attachShader(o,n)}),linkProgram(t,o),this.autoDebugValidate&&validateProgram(t,o),this.vertexAttrsAreBound||(this.setProgram(o),this.vertexAttrsAreBound=bindVertexProgramAttributeStreams(t,this.program,this.vertexBuffer)),o},e.prototype.deleteProgram=function(e){var t=this;this.throwIfDisposed(),e===this.program&&(this.program=null),null!=e&&callAndCheck(this.gl,function(){return t.gl.deleteProgram(e)})},e.prototype.setProgram=function(e){var t=this;this.throwIfDisposed(),this.program=e,null!=this.program&&this.autoDebugValidate&&validateProgram(this.gl,this.program),callAndCheck(this.gl,function(){return t.gl.useProgram(e)})},e.prototype.getUniformLocation=function(e,t,n){return void 0===n&&(n=!0),this.throwIfDisposed(),n?getProgramUniformLocationOrThrow(this.gl,e,t):getProgramUniformLocation(this.gl,e,t)},e.prototype.getAttributeLocation=function(e,t){var n=this;return this.throwIfDisposed(),callAndCheck(this.gl,function(){return n.gl.getAttribLocation(e,t)})},e.prototype.getUniformLocationNoThrow=function(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)},e.prototype.setInputMatrixTexture=function(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),bindTextureToProgramUniformSampler(this.gl,this.program,e,t,n)},e.prototype.setOutputMatrixTexture=function(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)},e.prototype.setOutputPackedMatrixTexture=function(e,t,n){this.throwIfDisposed();var r=getPackedMatrixTextureShapeWidthHeight(t,n),o=r[0],a=r[1];this.setOutputMatrixTextureDriver(e,o,a)},e.prototype.setOutputMatrixWriteRegion=function(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)},e.prototype.setOutputPackedMatrixWriteRegion=function(e,t,n,r){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")},e.prototype.debugValidate=function(){null!=this.program&&validateProgram(this.gl,this.program),validateFramebuffer(this.gl)},e.prototype.executeProgram=function(){this.throwIfDisposed(),this.throwIfNoProgram();var e=this.gl;this.autoDebugValidate&&this.debugValidate(),callAndCheck(e,function(){return e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0)})},e.prototype.blockUntilAllProgramsCompleted=function(){var e=this;this.throwIfDisposed(),callAndCheck(this.gl,function(){return e.gl.finish()})},e.prototype.getQueryTimerExtension=function(){return null==this.disjointQueryTimerExtension&&(this.disjointQueryTimerExtension=getExtensionOrThrow(this.gl,2===ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")?\"EXT_disjoint_timer_query_webgl2\":\"EXT_disjoint_timer_query\")),this.disjointQueryTimerExtension},e.prototype.getQueryTimerExtensionWebGL2=function(){return this.getQueryTimerExtension()},e.prototype.getQueryTimerExtensionWebGL1=function(){return this.getQueryTimerExtension()},e.prototype.beginQuery=function(){if(2===ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")){var e=this.gl,t=this.getQueryTimerExtensionWebGL2(),n=e.createQuery();return e.beginQuery(t.TIME_ELAPSED_EXT,n),n}var r=this.getQueryTimerExtensionWebGL1(),o=r.createQueryEXT();return r.beginQueryEXT(r.TIME_ELAPSED_EXT,o),o},e.prototype.endQuery=function(){if(2!==ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")){var e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}else{var t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT)}},e.prototype.waitForQueryAndGetTime=function(e){return __awaiter(this,void 0,void 0,function(){var t=this;return __generator(this,function(n){switch(n.label){case 0:return[4,repeatedTry(function(){return t.disposed||t.isQueryAvailable(e,ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))})];case 1:return n.sent(),[2,this.getQueryTime(e,ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))]}})})},e.prototype.getQueryTime=function(e,t){if(0===t)return null;if(2===t){var n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}var r=this.getQueryTimerExtensionWebGL1();return r.getQueryObjectEXT(e,r.QUERY_RESULT_EXT)/1e6},e.prototype.isQueryAvailable=function(e,t){if(0===t)return!0;if(2===t){var n=this.gl,r=this.getQueryTimerExtensionWebGL2(),o=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint}o=(r=this.getQueryTimerExtensionWebGL1()).getQueryObjectEXT(e,r.QUERY_RESULT_AVAILABLE_EXT);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint},e.prototype.pollFence=function(e){var t=this;return new Promise(function(n){t.addItemToPoll(function(){return e.isFencePassed()},function(){return n()})})},e.prototype.pollItems=function(){for(var e=binSearchLastTrue(this.itemsToPoll.map(function(e){return e.isDoneFn})),t=0;t<=e;++t){(0,this.itemsToPoll[t].resolveFn)()}this.itemsToPoll=this.itemsToPoll.slice(e+1)},e.prototype.addItemToPoll=function(e,t){var n=this;this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1||repeatedTry(function(){return n.pollItems(),0===n.itemsToPoll.length})},e.prototype.bindTextureToFrameBuffer=function(e){this.throwIfDisposed(),bindColorTextureToFramebuffer(this.gl,e,this.framebuffer),this.autoDebugValidate&&validateFramebuffer(this.gl)},e.prototype.unbindTextureToFrameBuffer=function(){null!=this.outputTexture?(bindColorTextureToFramebuffer(this.gl,this.outputTexture,this.framebuffer),this.autoDebugValidate&&validateFramebuffer(this.gl)):unbindColorTextureFromFramebuffer(this.gl,this.framebuffer)},e.prototype.downloadMatrixDriver=function(e,t){this.bindTextureToFrameBuffer(e);var n=t();return this.unbindTextureToFrameBuffer(),n},e.prototype.setOutputMatrixTextureDriver=function(e,t,n){this.throwIfDisposed();var r=this.gl;bindColorTextureToFramebuffer(r,e,this.framebuffer),this.autoDebugValidate&&validateFramebuffer(r),this.outputTexture=e,callAndCheck(r,function(){return r.viewport(0,0,t,n)}),callAndCheck(r,function(){return r.scissor(0,0,t,n)})},e.prototype.setOutputMatrixWriteRegionDriver=function(e,t,n,r){var o=this;this.throwIfDisposed(),callAndCheck(this.gl,function(){return o.gl.scissor(e,t,n,r)})},e.prototype.throwIfDisposed=function(){if(this.disposed)throw new Error(\"Attempted to use disposed GPGPUContext.\")},e.prototype.throwIfNoProgram=function(){if(null==this.program)throw new Error(\"No GPU program is currently set.\")},e}();function binSearchLastTrue(e){for(var t=0,n=e.length-1,r=-1;t<=n;){var o=t+n>>1;e[o]()?(r=o,t=o+1):n=o-1}return r}function compileProgram(e,t,n,r){for(var o=t.userCode,a=n.map(function(e,n){var r={logicalShape:e.shape,texShape:e.isUniform?null:e.texData.texShape,isUniform:e.isUniform,isPacked:!e.isUniform&&e.texData.isPacked};return{name:t.variableNames[n],shapeInfo:r}}),i=a.map(function(e){return e.shapeInfo}),s={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked},u=makeShader(a,s,o,!0===t.supportsBroadcasting,t.usesPackedTextures),l=e.createProgram(u),c={},p=0;p<t.variableNames.length;p++){var d=t.variableNames[p];c[d]=e.getUniformLocation(l,d,!1)}return{program:t,source:u,webGLProgram:l,uniformLocations:c,gpgpu:e,inShapeInfos:i,outShapeInfo:s}}function validateBinaryAndProgram(e,t){if(e.length!==t.length)throw Error(\"Binary was compiled with \"+e.length+\" inputs, but was executed with \"+t.length+\" inputs\");e.forEach(function(e,n){var r=e.logicalShape,o=t[n],a=o.shape;if(!arraysEqual(r,a))throw Error(\"Binary was compiled with different shapes than the current args. Shapes \"+r+\" and \"+a+\" must match\");if(!e.isUniform||!o.isUniform){var i=e.texShape,s=o.isUniform?null:o.texData.texShape;if(!arraysEqual(i,s))throw Error(\"Binary was compiled with different texture shapes than the current args. Shape \"+i+\" and \"+s+\" must match\")}})}function runProgram(e,t,n,r){validateBinaryAndProgram(e.inShapeInfos,t),validateBinaryAndProgram([e.outShapeInfo],[n]);var o=n.texData.texture,a=n.texData.texShape,i=e.gpgpu;n.texData.isPacked?i.setOutputPackedMatrixTexture(o,a[0],a[1]):i.setOutputMatrixTexture(o,a[0],a[1]),i.setProgram(e.webGLProgram),t.forEach(function(t,n){var r=e.program.variableNames[n],o=e.uniformLocations[r];if(null!=o){if(t.isUniform){if(1===sizeFromShape(t.shape))i.gl.uniform1f(o,t.uniformValues[0]);else{var a=t.uniformValues;a instanceof Float32Array||(a=new Float32Array(a)),i.gl.uniform1fv(o,a)}return}var s=t.texData.texture;i.setInputMatrixTexture(s,o,n)}}),null!=r&&r(i,e.webGLProgram),i.executeProgram()}function makeShaderKey(e,t,n){var r=\"\";t.concat(n).forEach(function(e){r+=e.shape+\"_\"+(e.isUniform?\"uniform\":e.texData.texShape)});var o=e.userCode,a=(!0===e.supportsBroadcasting).toString(),i=e.constructor.name;return i+=\"_\"+a+\"_\"+r+\"_\"+o}var Im2ColProgram=function(){return function(e,t,n){this.variableNames=[\"A\"],this.outputShape=e;var r=n.filterWidth,o=n.inChannels,a=n.strideWidth,i=n.strideHeight,s=n.padInfo,u=n.outWidth,l=n.dilationWidth,c=n.dilationHeight,p=s.left,d=s.top,h=o*r;this.userCode=\"\\n      void main() {\\n        ivec2 rc = getOutputCoords();\\n\\n        vec4 result = vec4(0);\\n\\n        for(int row=0; row<=1; row++) {\\n          for(int col=0; col<=1; col++) {\\n            int blockIndex = rc.y + col;\\n            int pos = rc.x + row;\\n\\n            if(blockIndex >= \"+e[1]+\" || pos >= \"+e[0]+\") continue;\\n\\n            int offsetY = int(blockIndex / (\"+u+\")) * \"+i+\" - \"+d+\";\\n            int d0 = offsetY + \"+c+\" * (pos / \"+h+\");\\n\\n            if(d0 >= \"+t[0]+\" || d0 < 0) continue;\\n\\n            int offsetX = int(mod(float(blockIndex), \"+u+\".) * \"+a+\". - \"+p+\".);\\n            int d1 = offsetX + \"+l+\" * (int(mod(float(pos), \"+h+\".) / \"+o+\".));\\n\\n            if(d1 >= \"+t[1]+\" || d1 < 0) continue;\\n\\n            result[row * 2 + col] = getA(d0, d1, int(mod(float(pos), \"+o+\".)));\\n          }\\n        }\\n\\n        gl_FragColor = result;\\n      }\\n    \"}}(),LRNProgram=function(){return function(e,t,n,r,o){this.variableNames=[\"x\"],this.outputShape=[];var a,i=t,s=e[3]-1;this.outputShape=e;var u=\"float(\"+n+\") + float(\"+r+\") * sum\";a=.5===o?\"inversesqrt(\"+u+\")\":1===o?\"1.0/(\"+u+\")\":\"exp(log(\"+u+\") * float(-\"+o+\"));\",this.userCode=\"\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int b = coords[0];\\n        int r = coords[1];\\n        int c = coords[2];\\n        int d = coords[3];\\n        float x = getX(b, r, c, d);\\n        float sum = 0.0;\\n        for (int j = -\"+i+\"; j <= \"+i+\"; j++) {\\n          int idx = d + j;\\n          if (idx >= 0 && idx <=  \"+s+\") {\\n            float z = getX(b, r, c, idx);\\n            sum += z * z;\\n          }\\n        }\\n        float val = x * \"+a+\";\\n        setOutput(val);\\n      }\\n    \"}}(),LRNGradProgram=function(){return function(e,t,n,r,o){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=o,this.userCode=\"\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int b = coords[0];\\n        int r = coords[1];\\n        int c = coords[2];\\n\\n        float result = 0.0;\\n        for (int d = 0; d < \"+this.depth+\"; ++d) {\\n          int depthBegin = int(max(0.0, float(d - \"+t+\")));\\n          int depthEnd = int(min(float(\"+this.depth+\"),\\n              float(d + \"+t+\" + 1)));\\n\\n          const int MIN_DEPTH_BEGIN = 0;\\n          const int MAX_DEPTH_END = \"+this.depth+\";\\n\\n          float norm = 0.0;\\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\\n            if (k < depthBegin){\\n              continue;\\n            }\\n            else if (k >= depthBegin && k < depthEnd) {\\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\\n            }\\n            else {\\n              break;\\n            }\\n          }\\n\\n          norm = float(\"+r+\") * norm + float(\"+n+\");\\n\\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\\n            if (k < depthBegin){\\n              continue;\\n            }\\n            else if (k >= depthBegin && k < depthEnd){\\n              float dyi = -2.0 * float(\"+r+\")\\n                * float(\"+o+\")\\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\\n                / norm;\\n              if (k == d) {\\n                dyi += pow(norm, -1.0 * \"+o+\");\\n              }\\n              if (k == coords[3]) {\\n                dyi *= getDy(b, r, c, d);\\n                result += dyi;\\n              }\\n            }\\n            else {\\n              break;\\n            }\\n          }\\n      }\\n      setOutput(result);\\n      }\\n    \"}}(),MaxPool2DBackpropProgram=function(){return function(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;var t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,o=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=o-1-e.padInfo.top,s=a-1-e.padInfo.left,u=o*a-1;this.userCode=\"\\n      const ivec2 pads = ivec2(\"+i+\", \"+s+\");\\n\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int b = coords[0];\\n        int d = coords[3];\\n\\n        ivec2 dyRCCorner = coords.yz - pads;\\n        int dyRCorner = dyRCCorner.x;\\n        int dyCCorner = dyRCCorner.y;\\n\\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\\n        // ? = to be determined. : = across all values in that axis.\\n        float dotProd = 0.0;\\n        for (int wR = 0; wR < \"+o+\";\\n          wR += \"+r+\") {\\n          float dyR = float(dyRCorner + wR) / \"+t+\".0;\\n\\n          if (dyR < 0.0 || dyR >= \"+e.outHeight+\".0 || fract(dyR) > 0.0) {\\n            continue;\\n          }\\n          int idyR = int(dyR);\\n\\n          for (int wC = 0; wC < \"+a+\"; wC++) {\\n            float dyC = float(dyCCorner + wC) / \"+n+\".0;\\n\\n            if (dyC < 0.0 || dyC >= \"+e.outWidth+\".0 ||\\n                fract(dyC) > 0.0) {\\n              continue;\\n            }\\n            int idyC = int(dyC);\\n\\n            float dyValue = getDy(b, idyR, idyC, d);\\n            int maxPosValue = \"+u+\" - int(getMaxPos(b, idyR, idyC, d));\\n\\n            // Get the current value, check it against the value from the\\n            // position matrix.\\n            int curPosValue = wR * \"+a+\" + wC;\\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\\n\\n            dotProd += dyValue * mask;\\n          }\\n        }\\n        setOutput(dotProd);\\n      }\\n    \"}}(),MatMulProgram=function(){return function(e,t,n,r){void 0===n&&(n=!1),void 0===r&&(r=!1),this.variableNames=[\"matrixA\",\"matrixB\"];var o=e[0],a=n?e[2]:e[1],i=r?t[1]:t[2],s=n?e[1]:e[2];this.outputShape=[o,a,i];var u=function(e,t){return n?\"batch, \"+t+\" + \"+e+\", aRow\":\"batch, aRow, \"+t+\" + \"+e},l=function(e,t){return r?\"batch, bCol, \"+t+\" + \"+e:\"batch, \"+t+\" + \"+e+\", bCol\"},c=4*Math.floor(s/4),p=s%4;this.userCode=\" float dotARowBCol(int batch, int aRow, int bCol) {\\n      float result = 0.0;\\n      for (int i = 0; i < \"+c+\"; i += 4) {\\n        vec4 a = vec4(\\n          getMatrixA(\"+u(0,\"i\")+\"),\\n          getMatrixA(\"+u(1,\"i\")+\"),\\n          getMatrixA(\"+u(2,\"i\")+\"),\\n          getMatrixA(\"+u(3,\"i\")+\")\\n        );\\n        vec4 b = vec4(\\n          getMatrixB(\"+l(0,\"i\")+\"),\\n          getMatrixB(\"+l(1,\"i\")+\"),\\n          getMatrixB(\"+l(2,\"i\")+\"),\\n          getMatrixB(\"+l(3,\"i\")+\")\\n        );\\n\\n        result += dot(a, b);\\n      }\\n\\n      if (\"+(1===p)+\") {\\n        result += getMatrixA(\"+u(0,c)+\") *\\n          getMatrixB(\"+l(0,c)+\");\\n      } else if (\"+(2===p)+\") {\\n        vec2 a = vec2(\\n          getMatrixA(\"+u(0,c)+\"),\\n          getMatrixA(\"+u(1,c)+\")\\n        );\\n        vec2 b = vec2(\\n          getMatrixB(\"+l(0,c)+\"),\\n          getMatrixB(\"+l(1,c)+\")\\n        );\\n        result += dot(a, b);\\n      } else if (\"+(3===p)+\") {\\n        vec3 a = vec3(\\n          getMatrixA(\"+u(0,c)+\"),\\n          getMatrixA(\"+u(1,c)+\"),\\n          getMatrixA(\"+u(2,c)+\")\\n        );\\n        vec3 b = vec3(\\n          getMatrixB(\"+l(0,c)+\"),\\n          getMatrixB(\"+l(1,c)+\"),\\n          getMatrixB(\"+l(2,c)+\")\\n        );\\n        result += dot(a, b);\\n      }\\n\\n      return result;\\n    }\\n\\n    void main() {\\n      ivec3 resBRC = getOutputCoords();\\n      setOutput(dotARowBCol(resBRC.x, resBRC.y, resBRC.z));\\n    }\\n    \"}}(),MatMulPackedProgram=function(){return function(e,t,n,r,o){void 0===r&&(r=!1),void 0===o&&(o=!1),this.variableNames=[\"matrixA\",\"matrixB\"],this.usesPackedTextures=!0,this.outputShape=n;var a=r?e[0]:e[1],i=Math.ceil(a/2),s=r?\"i * 2, rc.x\":\"rc.x, i * 2\",u=o?\"rc.y, i * 2\":\"i * 2, rc.y\",l=r?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],c=o?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"];this.userCode=\"\\n      const float sharedDimension = \"+i+\".0;\\n\\n      vec4 dot2x2ARowBCol(ivec2 rc) {\\n        vec4 result = vec4(0);\\n        for (int i = 0; i < \"+i+\"; i++) {\\n          vec4 a = getMatrixA(\"+s+\");\\n          vec4 b = getMatrixB(\"+u+\");\\n\\n          result += (\"+l[0]+\" * \"+c[0]+\") + (\"+l[1]+\" * \"+c[1]+\");\\n        }\\n        return result;\\n      }\\n\\n      void main() {\\n        ivec2 rc = getOutputCoords();\\n        setOutput(dot2x2ARowBCol(rc));\\n      }\\n    \"}}(),MultinomialProgram=function(){function e(e,t,n){this.variableNames=[\"probs\"],this.outputShape=[e,n],this.userCode=\"\\n      uniform float seed;\\n\\n      void main() {\\n        ivec2 coords = getOutputCoords();\\n        int batch = coords[0];\\n\\n        float r = random(seed);\\n        float cdf = 0.0;\\n\\n        for (int i = 0; i < \"+(t-1)+\"; i++) {\\n          cdf += getProbs(batch, i);\\n\\n          if (r < cdf) {\\n            setOutput(float(i));\\n            return;\\n          }\\n        }\\n\\n        // If no other event happened, last event happened.\\n        setOutput(float(\"+(t-1)+\"));\\n      }\\n    \"}return e.prototype.getCustomSetupFunc=function(e){var t=this;return function(n,r){null==t.seedLoc&&(t.seedLoc=n.getUniformLocation(r,\"seed\")),n.gl.uniform1f(t.seedLoc,e)}},e}(),OneHotProgram=function(){return function(e,t,n,r){this.variableNames=[\"indices\"],this.outputShape=[e,t],this.userCode=\"\\n      void main() {\\n        ivec2 coords = getOutputCoords();\\n        int index = round(getIndices(coords.x));\\n        setOutput(mix(float(\"+r+\"), float(\"+n+\"),\\n                      float(index == coords.y)));\\n      }\\n    \"}}();function getVecChannels(e,t){return[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,t).map(function(t){return e+\".\"+t})}function getChannels(e,t){return 1===t?[e]:getVecChannels(e,t)}function getSourceCoords$1(e,t){if(1===e)return\"rc\";for(var n=\"\",r=0;r<e;r++)n+=t[r],r<e-1&&(n+=\",\");return n}var PackProgram=function(){return function(e){this.variableNames=[\"A\"],this.isPackShader=!0,this.outputShape=e;var t=e.length;if(0===t)this.userCode=\"\\n        void main() {\\n          setOutput(vec4(getA(), 0., 0., 0.));\\n        }\\n      \";else{var n=getChannels(\"rc\",t),r=getCoordsDataType(t),o=getOutOfBoundsCondition(t,e,n),a=getSetup(t,e[e.length-1],e[e.length-2],n),i=getOutput(e,n);this.userCode=\"\\n        void main() {\\n          \"+r+\" rc = getOutputCoords();\\n\\n          if(\"+o+\") {\\n            setOutput(vec4(0));\\n          } else {\\n            \"+a+\"\\n\\n            setOutput(vec4(\"+i+\"));\\n          }\\n        }\\n      \"}}}();function getSourceCoordsArr(e,t){for(var n=[],r=0;r<=1;r++)for(var o=0;o<=1;o++){for(var a=(0===r?\"r\":\"rp1\")+\", \"+(0===o?\"c\":\"cp1\"),i=2;i<e;i++)a=t[t.length-1-i]+\",\"+a;n.push(a)}return n}function getOutOfBoundsCondition(e,t,n){if(1===e)return\"rc > \"+t[0];for(var r=\"\",o=e-2;o<e;o++)r+=n[o]+\" >= \"+t[o],o<e-1&&(r+=\"||\");return r}function getSetup(e,t,n,r){if(1===e)return\"\";var o=r.slice(-2);return\"\\n    int r = \"+o[0]+\";\\n    int c = \"+o[1]+\";\\n    int rp1 = r + 1;\\n    int cp1 = c + 1;\\n\\n    bool cEdge = cp1 >= \"+t+\";\\n    bool rEdge = rp1 >= \"+n+\";\\n  \"}function getOutput(e,t){var n=e.length,r=getSourceCoordsArr(n,t);return 1===n?\"getA(rc),\\n            rc + 1 >= \"+e[0]+\" ? 0. : getA(rc + 1),\\n            0, 0\":\"getA(\"+r[0]+\"),\\n          cEdge ? 0. : getA(\"+r[1]+\"),\\n          rEdge ? 0. : getA(\"+r[2]+\"),\\n          rEdge || cEdge ? 0. : getA(\"+r[3]+\")\"}var PadProgram=function(){return function(e,t,n){this.variableNames=[\"x\"],this.outputShape=t.map(function(t,n){return t[0]+e[n]+t[1]});var r=e.length,o=getCoordsDataType(r),a=t.map(function(e){return e[0]}).join(\",\"),i=t.map(function(t,n){return t[0]+e[n]}).join(\",\"),s=[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,r);this.userCode=1!==r?\"\\n      \"+o+\" start = \"+o+\"(\"+a+\");\\n      \"+o+\" end = \"+o+\"(\"+i+\");\\n\\n      void main() {\\n        \"+o+\" outC = getOutputCoords();\\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\\n          setOutput(float(\"+n+\"));\\n        } else {\\n          \"+o+\" coords = outC - start;\\n          setOutput(getX(\"+s+\"));\\n        }\\n      }\\n    \":\"\\n        int start = \"+a+\";\\n        int end = \"+i+\";\\n\\n        void main() {\\n          int outC = getOutputCoords();\\n          if (outC < start || outC >= end) {\\n            setOutput(float(\"+n+\"));\\n          } else {\\n            setOutput(getX(outC - start));\\n          }\\n        }\\n      \"}}(),Pool2DProgram=function(){return function(e,t,n){if(this.variableNames=[\"x\"],\"avg\"===t&&n)throw new Error(\"Cannot compute positions for average pool.\");var r=e.filterWidth,o=e.strideHeight,a=e.strideWidth,i=e.dilationHeight,s=e.dilationWidth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;var d=\"avg\"===t,h=\"0.0\";if(d||(h=\"-1.0 / 0.0\"),n)this.userCode=\"\\n        const ivec2 strides = ivec2(\"+o+\", \"+a+\");\\n        const ivec2 pads = ivec2(\"+c+\", \"+p+\");\\n\\n        void main() {\\n          ivec4 coords = getOutputCoords();\\n          int batch = coords[0];\\n          int d = coords[3];\\n\\n          ivec2 xRCCorner = coords.yz * strides - pads;\\n          int xRCorner = xRCCorner.x;\\n          int xCCorner = xRCCorner.y;\\n\\n          // max/min x(?, ?, d) to get y(yR, yC, d).\\n          // ? = to be determined\\n          float minMaxValue = 0.0;\\n          float minMaxValueFound = 0.0;\\n          int minMaxPosition = 0;\\n          float avgValue = 0.0;\\n\\n          for (int wR = 0; wR < \"+u+\";\\n              wR += \"+i+\") {\\n            int xR = xRCorner + wR;\\n\\n            if (xR < 0 || xR >= \"+e.inHeight+\") {\\n              continue;\\n            }\\n\\n            for (int wC = 0; wC < \"+l+\";\\n                wC += \"+s+\") {\\n              int xC = xCCorner + wC;\\n\\n              if (xC < 0 || xC >= \"+e.inWidth+\") {\\n                continue;\\n              }\\n\\n              float value = getX(batch, xR, xC, d);\\n\\n              // If a min / max value has already been found, use it. If not,\\n              // use the current value.\\n              float currMinMaxValue = mix(\\n                  value, minMaxValue, minMaxValueFound);\\n              if (value >= currMinMaxValue) {\\n                minMaxValue = value;\\n                minMaxValueFound = 1.0;\\n                minMaxPosition = wR * \"+l+\" + wC;\\n              }\\n            }\\n          }\\n          setOutput(float(minMaxPosition));\\n        }\\n      \";else{var f=t+\"(\"+t+\"(\"+t+\"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])\";\"avg\"===t&&(f=\"avgValue / count\");var m=4*Math.floor(r/4),g=r%4,v=\"\\n      if (\"+d+\") {\\n        avgValue += dot(values, ones);\\n      } else {\\n        minMaxValue = max(values, minMaxValue);\\n      }\\n    \";this.userCode=\"\\n      const ivec2 strides = ivec2(\"+o+\", \"+a+\");\\n      const ivec2 pads = ivec2(\"+c+\", \"+p+\");\\n      const float initializationValue = \"+h+\";\\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\\n\\n      float count = 0.0;\\n\\n      float getValue(int batch, int xR, int xC, int d) {\\n        if (xC < 0 || xC >= \"+e.inWidth+\") {\\n          return initializationValue;\\n        }\\n        count += 1.0;\\n        return getX(batch, xR, xC, d);\\n      }\\n\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int batch = coords[0];\\n        int d = coords[3];\\n\\n        ivec2 xRCCorner = coords.yz * strides - pads;\\n        int xRCorner = xRCCorner.x;\\n        int xCCorner = xRCCorner.y;\\n\\n        // max/min x(?, ?, d) to get y(yR, yC, d).\\n        // ? = to be determined\\n        vec4 minMaxValue = vec4(\"+h+\");\\n        float avgValue = 0.0;\\n        count = 0.0;\\n\\n        for (int wR = 0; wR < \"+u+\";\\n            wR += \"+i+\") {\\n          int xR = xRCorner + wR;\\n\\n          if (xR < 0 || xR >= \"+e.inHeight+\") {\\n            continue;\\n          }\\n\\n          for (int wC = 0; wC < \"+m+\"; wC += 4) {\\n            int xC = xCCorner + wC * \"+s+\";\\n\\n            vec4 values = vec4(\\n              getValue(batch, xR, xC, d),\\n              getValue(batch, xR, xC + \"+s+\", d),\\n              getValue(batch, xR, xC + 2 * \"+s+\", d),\\n              getValue(batch, xR, xC + 3 * \"+s+\", d)\\n            );\\n\\n            \"+v+\"\\n          }\\n\\n          int xC = xCCorner + \"+m+\";\\n          if (\"+(1===g)+\") {\\n            vec4 values = vec4(\\n              getValue(batch, xR, xC, d),\\n              initializationValue,\\n              initializationValue,\\n              initializationValue\\n            );\\n\\n            \"+v+\"\\n          } else if (\"+(2===g)+\") {\\n            vec4 values = vec4(\\n              getValue(batch, xR, xC, d),\\n              getValue(batch, xR, xC + \"+s+\", d),\\n              initializationValue,\\n              initializationValue\\n            );\\n\\n            \"+v+\"\\n          } else if (\"+(3===g)+\") {\\n            vec4 values = vec4(\\n              getValue(batch, xR, xC, d),\\n              getValue(batch, xR, xC + \"+s+\", d),\\n              getValue(batch, xR, xC + 2 * \"+s+\", d),\\n              initializationValue\\n            );\\n\\n            \"+v+\"\\n          }\\n        }\\n        setOutput(\"+f+\");\\n      }\\n    \"}}}(),ReduceProgram=function(){return function(e,t){this.variableNames=[\"x\"];var n=e.windowSize,r=e.batchSize,o=e.inSize,a=Math.ceil(o/n);this.outputShape=[r,a];var i=\"0.0\",s=\"\";\"prod\"===t?i=\"1.0\":\"min\"===t?(i=\"1.0 / 0.0\",s=\"min\"):\"max\"===t&&(i=\"-1.0 / 0.0\",s=\"max\");var u=t+\"(\"+t+\"(\"+t+\"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])\";\"sum\"===t?u=\"sumValue\":\"prod\"===t?u=\"prodValue\":\"all\"===t?u=\"allValue\":\"any\"===t&&(u=\"anyValue\");var l=4*Math.floor(n/4),c=n%4,p=\"\\n      if (\"+(\"sum\"===t)+\") {\\n        sumValue += dot(values, ones);\\n      } else if (\"+(\"prod\"===t)+\") {\\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\\n        prodValue *= tmp[0] * tmp[1];\\n      } else {\\n        minMaxValue = \"+s+\"(values, minMaxValue);\\n      }\\n    \",d=\"vec4\";\"all\"===t?(i=\"1.0\",p=\"\\n        bool reducedAllValue = all(values);\\n        float floatedReducedAllValue = float(reducedAllValue);\\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\\n      \",d=\"bvec4\"):\"any\"===t&&(i=\"0.0\",p=\"\\n        bool reducedAnyValue = any(values);\\n        float floatedReducedAnyValue = float(reducedAnyValue);\\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\\n      \",d=\"bvec4\");var h=\"\";o%n>0&&(h=\"\\n        if (inIdx < 0 || inIdx >= \"+o+\") {\\n          return initializationValue;\\n        }\\n      \"),this.userCode=\"\\n      const float initializationValue = \"+i+\";\\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\\n\\n      float getValue(int batch, int inIdx) {\\n        \"+h+\"\\n        return getX(batch, inIdx);\\n      }\\n\\n      void main() {\\n        ivec2 coords = getOutputCoords();\\n        int batch = coords[0];\\n        int outIdx = coords[1];\\n        int inOffset = outIdx * \"+n+\";\\n\\n        vec4 minMaxValue = vec4(\"+i+\");\\n        float prodValue = 1.0;\\n        float sumValue = 0.0;\\n        float allValue = 1.0;\\n        float anyValue = 0.0;\\n\\n        for (int i = 0; i < \"+l+\"; i += 4) {\\n          int inIdx = inOffset + i;\\n          \"+d+\" values = \"+d+\"(\\n            getValue(batch, inIdx),\\n            getValue(batch, inIdx + 1),\\n            getValue(batch, inIdx + 2),\\n            getValue(batch, inIdx + 3)\\n          );\\n\\n          \"+p+\"\\n        }\\n\\n        int inIdx = inOffset + \"+l+\";\\n        if (\"+(1===c)+\") {\\n          \"+d+\" values = \"+d+\"(\\n            getValue(batch, inIdx),\\n            initializationValue,\\n            initializationValue,\\n            initializationValue\\n          );\\n\\n          \"+p+\"\\n        } else if (\"+(2===c)+\") {\\n          \"+d+\" values = \"+d+\"(\\n            getValue(batch, inIdx),\\n            getValue(batch, inIdx + 1),\\n            initializationValue,\\n            initializationValue\\n          );\\n\\n          \"+p+\"\\n        } else if (\"+(3===c)+\") {\\n          \"+d+\" values = \"+d+\"(\\n            getValue(batch, inIdx),\\n            getValue(batch, inIdx + 1),\\n            getValue(batch, inIdx + 2),\\n            initializationValue\\n          );\\n\\n          \"+p+\"\\n        }\\n        setOutput(\"+u+\");\\n      }\\n    \"}}(),ReshapePackedProgram=function(){return function(e,t){this.variableNames=[\"A\"],this.usesPackedTextures=!0,this.outputShape=e;for(var n=\"\",r=0;r<4;r++){var o=\"thisRC = rc;\";r%2==1&&(o+=\"thisRC.z += 1;\"),r>1&&(o+=\"thisRC.y += 1;\"),n+=\"\\n        \"+o+\"\\n        \"+(r>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\")+\"\\n          int flatIndex = getFlatIndex(thisRC);\\n\\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\\n\\n          result[\"+r+\"] =\\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\\n        \"+(r>0?\"}\":\"\")+\"\\n      \"}this.userCode=\"\\n      \"+getReshapedInputCoords(t)+\"\\n      \"+getFlatIndex(e)+\"\\n\\n      void main() {\\n        ivec3 rc = getOutputCoords();\\n\\n        vec4 result = vec4(0.);\\n\\n        ivec3 thisRC;\\n        int rows = \"+e[1]+\";\\n        int cols = \"+e[2]+\";\\n\\n        \"+n+\"\\n\\n        setOutput(result);\\n      }\\n    \"}}();function getFlatIndex(e){return\"\\n    int getFlatIndex(ivec3 coords) {\\n      return round(\"+dotify([\"coords.x\",\"coords.y\",\"coords.z\"],computeStrides(e).map(function(e){return e.toString()}).concat([\"1.\"]))+\");\\n    }\\n  \"}function getReshapedInputCoords(e){return\"\\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\\n      \"+getLogicalCoordinatesFromFlatIndex([\"r\",\"c\",\"d\"],e)+\"\\n      return ivec3(r, c, d);\\n    }\\n  \"}var ResizeBilinearBackpropProgram=function(){return function(e,t,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t.shape;var r=t.shape,o=r[1],a=r[2],i=e.shape,s=i[1],u=i[2],l=[n&&s>1?o-1:o,n&&u>1?a-1:a],c=[n&&s>1?s-1:s,n&&u>1?u-1:u],p=l[0]/c[0],d=l[1]/c[1],h=1/p,f=1/d,m=2*Math.ceil(h)+2,g=2*Math.ceil(f)+2;this.userCode=\"\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int b = coords[0];\\n        int d = coords[3];\\n        int r = coords[1];\\n        int c = coords[2];\\n\\n        float accumulator = 0.0;\\n\\n        const float heightScale = float(\"+p+\");\\n        const float widthScale = float(\"+d+\");\\n\\n        const float invHeightScale = float(\"+h+\");\\n        const float invWidthScale = float(\"+f+\");\\n\\n        const int winHeight = int(\"+m+\");\\n        const int winWidth = int(\"+g+\");\\n\\n        // Compute bounds for where in dy we will look\\n        float startRLerp = floor(float(r) * invHeightScale);\\n        int startDyR = int(startRLerp - float(winHeight / 2));\\n\\n        float startCLerp = floor(float(c) * invWidthScale);\\n        int startDyC = int(startCLerp - float(winWidth / 2));\\n\\n        // Loop over dy\\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\\n          int dyR = dyROffset + startDyR;\\n\\n          // Guard against the window exceeding the bounds of dy\\n          if (dyR < 0 || dyR >= \"+s+\") {\\n            continue;\\n          }\\n\\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\\n            int dyC = dyCOffset + startDyC;\\n\\n            // Guard against the window exceeding the bounds of dy\\n            if (dyC < 0 || dyC >= \"+u+\") {\\n              continue;\\n            }\\n\\n            float dxR = float(dyR) * heightScale;\\n            int topDxRIndex = int(floor(dxR));\\n            int bottomDxRIndex = int(min(ceil(dxR), \"+(o-1)+\".0));\\n            float dxRLerp = dxR - float(topDxRIndex);\\n            float inverseDxRLerp = 1.0 - dxRLerp;\\n\\n            float dxC = float(dyC) * widthScale;\\n            int leftDxCIndex = int(floor(dxC));\\n            int rightDxCIndex = int(min(ceil(dxC), \"+(a-1)+\".0));\\n            float dxCLerp = dxC - float(leftDxCIndex);\\n            float inverseDxCLerp = 1.0 - dxCLerp;\\n\\n            if (r == topDxRIndex && c == leftDxCIndex) {\\n              // topLeft\\n              accumulator +=\\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\\n            }\\n\\n            if (r == topDxRIndex && c == rightDxCIndex) {\\n              // topRight\\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\\n            }\\n\\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\\n              // bottomLeft\\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\\n            }\\n\\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\\n              // bottomRight\\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\\n            }\\n          }\\n        }\\n        // End loop over dy\\n\\n        setOutput(accumulator);\\n      }\\n    \"}}(),ResizeBilinearProgram=function(){return function(e,t,n,r){this.variableNames=[\"A\"],this.outputShape=[];var o=e[0],a=e[1],i=e[2],s=e[3];this.outputShape=[o,t,n,s];var u=[r&&t>1?a-1:a,r&&n>1?i-1:i],l=[r&&t>1?t-1:t,r&&n>1?n-1:n];this.userCode=\"\\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\\n          \"+u[0]/l[0]+\",\\n          \"+u[1]/l[1]+\");\\n      const vec2 inputShapeRC = vec2(\"+a+\".0, \"+i+\".0);\\n\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int b = coords[0];\\n        int d = coords[3];\\n        ivec2 yRC = coords.yz;\\n\\n        // Fractional source index.\\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\\n\\n        // Compute the four integer indices.\\n        ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\\n        ivec2 sourceCeilRC = ivec2(\\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\\n\\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\\n\\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\\n\\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\\n        float newValue = top + (bottom - top) * fracRC.x;\\n\\n        setOutput(newValue);\\n      }\\n    \"}}(),ResizeNearestNeigborBackpropProgram=function(){return function(e,t,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t.shape;var r=t.shape,o=r[1],a=r[2],i=e.shape,s=i[1],u=i[2],l=[n&&s>1?o-1:o,n&&u>1?a-1:a],c=[n&&s>1?s-1:s,n&&u>1?u-1:u],p=l[0]/c[0],d=l[1]/c[1],h=1/p,f=1/d,m=2*Math.ceil(h)+2,g=2*Math.ceil(f)+2;this.userCode=\"\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int b = coords[0];\\n        int d = coords[3];\\n        int r = coords[1];\\n        int c = coords[2];\\n\\n        float accumulator = 0.0;\\n\\n        const float heightScale = float(\"+p+\");\\n        const float widthScale = float(\"+d+\");\\n\\n        const float invHeightScale = float(\"+h+\");\\n        const float invWidthScale = float(\"+f+\");\\n\\n        const int winHeight = int(\"+m+\");\\n        const int winWidth = int(\"+g+\");\\n\\n        // Compute bounds for where in dy we will look\\n        float startRLerp = floor(float(r) * invHeightScale);\\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\\n\\n        float startCLerp = floor(float(c) * invWidthScale);\\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\\n\\n        // Loop over dy\\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\\n          int dyR = dyROffset + startDyR;\\n\\n          // Guard against the window exceeding the bounds of dy\\n          if (dyR < 0 || dyR >= \"+s+\") {\\n            continue;\\n          }\\n\\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\\n            int dyC = dyCOffset + startDyC;\\n\\n            // Guard against the window exceeding the bounds of dy\\n            if (dyC < 0 || dyC >= \"+u+\") {\\n              continue;\\n            }\\n\\n            float sourceFracRow =\\n              float(\"+l[0]+\") *\\n                (float(dyR) / float(\"+c[0]+\"));\\n\\n            float sourceFracCol =\\n                float(\"+l[1]+\") *\\n                  (float(dyC) / float(\"+c[1]+\"));\\n\\n            int sourceNearestRow = int(min(\\n                float(int(\"+o+\") - 1),\\n                \"+n+\" ? float(round(sourceFracRow)) :\\n                                  float(floor(sourceFracRow))));\\n\\n            int sourceNearestCol = int(min(\\n                float(int(\"+a+\") - 1),\\n                \"+n+\" ? float(round(sourceFracCol)) :\\n                                  float(floor(sourceFracCol))));\\n\\n            if (r == sourceNearestRow && c == sourceNearestCol) {\\n              accumulator += getDy(b, dyR, dyC, d);\\n            }\\n          }\\n        }\\n        // End loop over dy\\n\\n        setOutput(accumulator);\\n      }\\n    \"}}(),ResizeNearestNeighborProgram=function(){return function(e,t,n,r){this.variableNames=[\"A\"],this.outputShape=[];var o=e[0],a=e[1],i=e[2],s=e[3];this.outputShape=[o,t,n,s];var u=[r&&t>1?a-1:a,r&&n>1?i-1:i],l=[r&&t>1?t-1:t,r&&n>1?n-1:n],c=r?\"0.5\":\"0.0\";this.userCode=\"\\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\\n          \"+u[0]/l[0]+\",\\n          \"+u[1]/l[1]+\");\\n      const vec2 inputShapeRC = vec2(\"+a+\".0, \"+i+\".0);\\n\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int b = coords[0];\\n        int d = coords[3];\\n        ivec2 yRC = coords.yz;\\n\\n        // Fractional source index.\\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\\n\\n        // Compute the coordinators of nearest neighbor point.\\n        ivec2 sourceNearestRC = ivec2(\\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + \"+c+\")));\\n\\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\\n\\n        setOutput(newValue);\\n      }\\n    \"}}(),ReverseProgram=function(){return function(e,t){this.variableNames=[\"x\"];var n=e.length;if(n>4)throw new Error(\"WebGL backend: Reverse of rank-\"+n+\" tensor is not yet supported\");if(this.outputShape=e,1!==n){var r=e.map(function(n,r){return function(n){return-1!==t.indexOf(n)&&1!==e[n]?e[n]+\" - coords[\"+n+\"] - 1\":\"coords[\"+n+\"]\"}(r)}).join(\",\"),o=getCoordsDataType(n);this.userCode=\"\\n      void main() {\\n        \"+o+\" coords = getOutputCoords();\\n        setOutput(getX(\"+r+\"));\\n      }\\n    \"}else this.userCode=\"\\n        void main() {\\n          int coord = getOutputCoords();\\n          setOutput(getX(\"+e[0]+\" - coord - 1));\\n        }\\n      \"}}(),ScatterProgram=function(){return function(e,t,n,r,o,a,i){void 0===i&&(i=!0),this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.outputShape=a;var s=getCoordsDataType(o.length),u=getCoordsDataType(a.length),l=\"\";1===n?l=\"i\":2===n&&(l=\"i, j\");var c=\"getIndices(\"+l+\")\",p=\"\";1===r?p=\"i\":2===r&&(p=\"i, coords[1]\");var d=\"getUpdates(\"+p+\")\",h=t>1?\"strides[j]\":\"strides\";this.userCode=\"\\n        \"+s+\" strides = \"+s+\"(\"+o+\");\\n\\n        void main() {\\n          \"+u+\" coords = getOutputCoords();\\n          float sum = 0.0;\\n          bool found = false;\\n          for (int i = 0; i < \"+e+\"; i++) {\\n            int flattenedIndex = 0;\\n            for (int j = 0; j < \"+t+\"; j++) {\\n              int index = round(\"+c+\");\\n              flattenedIndex += index * \"+h+\";\\n            }\\n            if (flattenedIndex == coords[0]) {\\n              sum += \"+d+\";\\n              found = true;\\n            }\\n          }\\n          setOutput(mix(getDefaultValue(), sum, float(found)));\\n        }\\n      \"}}(),SegmentOpProgram=function(){return function(e,t){this.variableNames=[\"x\",\"segmentIds\"];var n=e.windowSize,r=e.batchSize,o=e.inSize,a=e.numSegments,i=a*Math.ceil(o/n);this.outputShape=[r,i];var s=4*Math.floor(n/4),u=n%4,l=\"\\n        sumValue += dot(values, filter);\\n    \",c=\"\";o%n>0&&(c=\"\\n        if (inIdx < 0 || inIdx >= \"+o+\") {\\n          return initializationValue;\\n        }\\n      \");var p=\"\";o%n>0&&(p=\"\\n        if (inIdx < 0 || inIdx >= \"+o+\") {\\n          return -1.0;\\n        }\\n      \"),this.userCode=\"\\n      const float initializationValue = 0.0;\\n\\n      float getValue(int batch, int inIdx) {\\n        \"+c+\"\\n        return getX(batch, inIdx);\\n      }\\n\\n      float getSegmentIdAtIndex(int inIdx) {\\n        \"+p+\"\\n        return getSegmentIds(inIdx);\\n      }\\n\\n      void main() {\\n        ivec2 coords = getOutputCoords();\\n        int batch = coords[0];\\n        int outIdx = coords[1];\\n        int inOffset = int(floor(float(outIdx) / float(\\n          \"+a+\")) * float(\"+n+\"));\\n        int currentSeg = int(mod(float(outIdx), float(\"+a+\")));\\n\\n        float sumValue = 0.0;\\n\\n        for (int i = 0; i < \"+s+\"; i += 4) {\\n          int inIdx = inOffset + i;\\n          vec4 values = vec4(\\n            getValue(batch, inIdx),\\n            getValue(batch, inIdx + 1),\\n            getValue(batch, inIdx + 2),\\n            getValue(batch, inIdx + 3)\\n          );\\n\\n          vec4 filter = vec4(\\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\\n          );\\n\\n          \"+l+\"\\n        }\\n\\n        int inIdx = inOffset + \"+s+\";\\n        if (\"+(1===u)+\") {\\n          vec4 values = vec4(\\n            getValue(batch, inIdx),\\n            initializationValue,\\n            initializationValue,\\n            initializationValue\\n          );\\n\\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\\n\\n          vec4 filter = vec4(\\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\\n            0,\\n            0,\\n            0\\n          );\\n\\n          \"+l+\"\\n        } else if (\"+(2===u)+\") {\\n          vec4 values = vec4(\\n            getValue(batch, inIdx),\\n            getValue(batch, inIdx + 1),\\n            initializationValue,\\n            initializationValue\\n          );\\n\\n          vec4 filter = vec4(\\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\\n              0,\\n              0\\n          );\\n\\n          \"+l+\"\\n        } else if (\"+(3===u)+\") {\\n          vec4 values = vec4(\\n            getValue(batch, inIdx),\\n            getValue(batch, inIdx + 1),\\n            getValue(batch, inIdx + 2),\\n            initializationValue\\n          );\\n\\n          vec4 filter = vec4(\\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\\n            0\\n          );\\n\\n          \"+l+\"\\n        }\\n        setOutput(sumValue);\\n      }\\n    \"}}(),SelectProgram=function(){return function(e,t,n){var r,o;if(this.variableNames=[\"c\",\"a\",\"b\"],this.outputShape=t,n>4)throw Error(\"Where for rank \"+n+\" is not yet supported\");if(1===n)o=\"resRC\",r=\"resRC\";else{for(var a=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],i=[],s=[],u=0;u<t.length;u++)s.push(\"\"+a[u]),u<e&&i.push(\"\"+a[u]);r=i.join(),o=s.join()}var l=getCoordsDataType(n);this.userCode=\"\\n      void main() {\\n        \"+l+\" resRC = getOutputCoords();\\n        float cVal = getC(\"+r+\");\\n        if (cVal >= 1.0) {\\n          setOutput(getA(\"+o+\"));\\n        } else {\\n          setOutput(getB(\"+o+\"));\\n        }\\n      }\\n    \"}}(),SliceProgram=function(){function e(e){this.variableNames=[\"source\"],this.outputShape=e,this.rank=e.length;var t=getCoordsDataType(this.rank),n=getCoords$1(this.rank);this.userCode=\"\\n      uniform \"+t+\" start;\\n\\n      void main() {\\n        \"+t+\" sourceLoc = start + getOutputCoords();\\n        setOutput(getSource(\"+n+\"));\\n      }\\n    \"}return e.prototype.getCustomSetupFunc=function(e){var t=this;if(e.length!==this.rank)throw Error(\"The rank (\"+this.rank+\") of the program must match the length of start (\"+e.length+\")\");return function(n,r){if(null!=t.startLoc||(t.startLoc=n.getUniformLocationNoThrow(r,\"start\"),null!=t.startLoc))if(1===t.rank)n.gl.uniform1i(t.startLoc,e[0]);else if(2===t.rank)n.gl.uniform2i(t.startLoc,e[0],e[1]);else if(3===t.rank)n.gl.uniform3i(t.startLoc,e[0],e[1],e[2]);else{if(4!==t.rank)throw Error(\"Slicing for rank \"+t.rank+\" is not yet supported\");n.gl.uniform4i(t.startLoc,e[0],e[1],e[2],e[3])}}},e}();function getCoords$1(e){if(1===e)return\"sourceLoc\";if(2===e)return\"sourceLoc.x, sourceLoc.y\";if(3===e)return\"sourceLoc.x, sourceLoc.y, sourceLoc.z\";if(4===e)return\"sourceLoc.x, sourceLoc.y, sourceLoc.z, sourceLoc.w\";throw Error(\"Slicing for rank \"+e+\" is not yet supported\")}var StridedSliceProgram=function(){return function(e,t,n,r){this.variableNames=[\"x\"];var o=n.filter(function(e,t){return-1===r.indexOf(t)});this.outputShape=o;var a=n.length,i=getCoordsDataType(n.length),s=getCoordsDataType(o.length),u=\"\";if(1===a)u=\"coords * strides + begin\";else{var l=0;u=n.map(function(e,t){return-1===r.indexOf(t)?(l++,1===o.length?\"coords * strides[\"+t+\"] + begin[\"+t+\"]\":\"coords[\"+(l-1)+\"] * strides[\"+t+\"] + begin[\"+t+\"]\"):\"begin[\"+t+\"]\"}).join(\",\")}this.userCode=\"\\n      \"+i+\" begin = \"+i+\"(\"+e+\");\\n      \"+i+\" strides = \"+i+\"(\"+t+\");\\n\\n      void main() {\\n        \"+s+\" coords = getOutputCoords();\\n        setOutput(getX(\"+u+\"));\\n      }\\n    \"}}(),TextureManager=function(){function e(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}return e.prototype.acquireTexture=function(e,t,n){var r,o=getPhysicalFromLogicalTextureType(t,n),a=getKeyFromTextureShape(e,o,n);if(a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]),this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this.log();var i=this.freeTextures[a].shift();return this.usedTextures[a].push(i),i}return this.numUsedTextures++,this.log(),o===PhysicalTextureType.PACKED_2X2_FLOAT32?r=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):o===PhysicalTextureType.PACKED_2X2_FLOAT16?r=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):o===PhysicalTextureType.UNPACKED_FLOAT32?r=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):o===PhysicalTextureType.UNPACKED_FLOAT16?r=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):o===PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE&&(r=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(r),r},e.prototype.releaseTexture=function(e,t,n,r){if(null!=this.freeTextures){var o=getKeyFromTextureShape(t,getPhysicalFromLogicalTextureType(n,r),r);o in this.freeTextures||(this.freeTextures[o]=[]),this.freeTextures[o].push(e),this.numFreeTextures++,this.numUsedTextures--;var a=this.usedTextures[o],i=a.indexOf(e);if(i<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");a.splice(i,1),this.log()}},e.prototype.log=function(){if(this.logEnabled){var e=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",this.numFreeTextures+\" / \"+this.numUsedTextures,\"(\"+e+\")\")}},e.prototype.getNumUsedTextures=function(){return this.numUsedTextures},e.prototype.getNumFreeTextures=function(){return this.numFreeTextures},e.prototype.dispose=function(){var e=this;if(null!=this.freeTextures){for(var t in this.freeTextures)this.freeTextures[t].forEach(function(t){e.gpgpu.deleteMatrixTexture(t)});for(var t in this.usedTextures)this.usedTextures[t].forEach(function(t){e.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0}},e}();function getPhysicalFromLogicalTextureType(e,t){if(e===TextureUsage.UPLOAD)return t?PhysicalTextureType.PACKED_2X2_FLOAT32:PhysicalTextureType.UNPACKED_FLOAT32;if(e===TextureUsage.RENDER||null==e)return t?ENV.get(\"WEBGL_RENDER_FLOAT32_ENABLED\")?PhysicalTextureType.PACKED_2X2_FLOAT32:PhysicalTextureType.PACKED_2X2_FLOAT16:ENV.get(\"WEBGL_RENDER_FLOAT32_ENABLED\")?PhysicalTextureType.UNPACKED_FLOAT32:PhysicalTextureType.UNPACKED_FLOAT16;if(e===TextureUsage.DOWNLOAD||e===TextureUsage.PIXELS)return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;throw new Error(\"Unknown logical texture type \"+e)}function getKeyFromTextureShape(e,t,n){return e[0]+\"_\"+e[1]+\"_\"+t+\"_\"+n}var TileProgram=function(){return function(e,t){this.variableNames=[\"A\"];for(var n=new Array(e.length),r=0;r<n.length;r++)n[r]=e[r]*t[r];this.outputShape=n,this.rank=n.length;var o=getCoordsDataType(this.rank),a=getSourceCoords$2(e);this.userCode=\"\\n      void main() {\\n        \"+o+\" resRC = getOutputCoords();\\n        setOutput(getA(\"+a+\"));\\n      }\\n    \"}}();function getSourceCoords$2(e){var t=e.length;if(t>5)throw Error(\"Tile for rank \"+t+\" is not yet supported\");if(1===t)return\"imod(resRC, \"+e[0]+\")\";for(var n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\"],r=[],o=0;o<e.length;o++)r.push(\"imod(\"+n[o]+\", \"+e[o]+\")\");return r.join()}var TransposeProgram=function(){return function(e,t){this.variableNames=[\"A\"];for(var n=new Array(e.length),r=0;r<n.length;r++)n[r]=e[t[r]];this.outputShape=n,this.rank=n.length;var o=getCoordsDataType(this.rank),a=getSwitchedCoords(t);this.userCode=\"\\n    void main() {\\n      \"+o+\" resRC = getOutputCoords();\\n      setOutput(getA(\"+a+\"));\\n    }\\n    \"}}();function getSwitchedCoords(e){var t=e.length;if(t>6)throw Error(\"Transpose for rank \"+t+\" is not yet supported\");for(var n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],r=new Array(t),o=0;o<e.length;o++)r[e[o]]=n[o];return r.join()}var ERF_P=.3275911,ERF_A1=.254829592,ERF_A2=-.284496736,ERF_A3=1.421413741,ERF_A4=-1.453152027,ERF_A5=1.061405429,SELU_SCALEALPHA=1.7580993408473768,SELU_SCALE=1.0507009873554805,UnaryOpProgram=function(){function e(e,t){this.variableNames=[\"A\"],this.outputShape=e,this.userCode=\"\\n      uniform float NAN;\\n      float unaryOperation(float x) {\\n        \"+t+\"\\n      }\\n\\n      void main() {\\n        float x = getAAtOutCoords();\\n        float y = unaryOperation(x);\\n\\n        setOutput(y);\\n      }\\n    \"}return e.prototype.getCustomSetupFunc=function(){var e=this;return function(t,n){null==e.startLoc&&(e.startLoc=t.getUniformLocationNoThrow(n,\"NAN\"),null==e.startLoc)||t.gl.uniform1f(e.startLoc,NaN)}},e}(),CHECK_NAN_SNIPPET$1=\"if (isNaN(x)) return x;\",ABS=\"return abs(x);\",RELU=CHECK_NAN_SNIPPET$1+\"\\n  return (x < 0.0) ? 0.0 : x;\\n\",ELU=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",SELU=\"\\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\\n  // see: https://arxiv.org/abs/1706.02515\\n  float scaleAlpha = \"+SELU_SCALEALPHA+\";\\n  float scale = \"+SELU_SCALE+\";\\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\\n\";function STEP(e){return void 0===e&&(e=0),CHECK_NAN_SNIPPET$1+\"\\n    return x > 0.0 ? 1.0 : float(\"+e+\");\\n  \"}var NEG=\"return -x;\",CEIL=\"return ceil(x);\",FLOOR=\"return floor(x);\",SIGN=\"\\n  if (isNaN(x)) { return 0.0; }\\n  return sign(x);\\n\",ROUND=\"\\n  // OpenGL ES does not support round function.\\n  // The algorithm is based on banker's rounding.\\n  float base = floor(x);\\n  if ((x - base) < 0.5) {\\n    return floor(x);\\n  } else if ((x - base) > 0.5) {\\n    return ceil(x);\\n  } else {\\n    if (mod(base, 2.0) == 0.0) {\\n      return base;\\n    } else {\\n      return base + 1.0;\\n    }\\n  }\\n\",EXP=\"return exp(x);\",EXPM1=\"return exp(x) - 1.0;\",LOG=\"if (x < 0.0) return NAN;\\n  return log(x);\",LOG1P=\"return log(1.0 + x);\",SQRT=\"return sqrt(x);\",RSQRT=\"return inversesqrt(x);\",SIGMOID=\"return 1.0 / (1.0 + exp(-1.0 * x));\",SOFTPLUS=\"\\n  float epsilon = 1.1920928955078125e-7;\\n  float threshold = log(epsilon) + 2.0;\\n\\n  bool too_large = x > -threshold;\\n  bool too_small = x < threshold;\\n\\n  float result;\\n  float exp_x = exp(x);\\n\\n  if (too_large){\\n    result = x;\\n  }\\n  else if (too_small){\\n    result = exp_x;\\n  }\\n  else{\\n    result = log(exp_x + 1.0);\\n  }\\n  return result;\\n\",SIN=CHECK_NAN_SNIPPET$1+\"\\n  return sin(x);\\n\",COS=CHECK_NAN_SNIPPET$1+\"\\n  return cos(x);\\n\",TAN=\"return tan(x);\",ASIN=\"return asin(x);\",ACOS=\"return acos(x);\",ATAN=CHECK_NAN_SNIPPET$1+\"\\n  return atan(x);\\n\",SINH=\"\\n  float e2x = exp(x);\\n  return (e2x - 1.0 / e2x) / 2.0;\\n\",COSH=\"\\n  float e2x = exp(-x);\\n  return (e2x + 1.0 / e2x) / 2.0;\\n\",TANH=\"\\n  float e2x = exp(-2.0 * abs(x));\\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\\n\",ASINH=\"return log(x + sqrt(x * x + 1.0));\",ACOSH=CHECK_NAN_SNIPPET$1+\"\\n  if (x < 1.0) return NAN;\\n  return log(x + sqrt(x * x - 1.0));\",ATANH=CHECK_NAN_SNIPPET$1+\"\\n  if ((x < -1.0) || (x > 1.0)) return NAN;\\n  return (log(1.0 + x) - log(1.0 - x)) / 2.0;\",ERF='\\n  // Error function is calculated approximately with elementary function.\\n  // See \"Handbook of Mathematical Functions with Formulas,\\n  // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\\n  float p = '+ERF_P+\";\\n  float a1 = \"+ERF_A1+\";\\n  float a2 = \"+ERF_A2+\";\\n  float a3 = \"+ERF_A3+\";\\n  float a4 = \"+ERF_A4+\";\\n  float a5 = \"+ERF_A5+\";\\n\\n  float t = 1.0 / (1.0 + p * x);\\n  return 1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x);\\n\",SQUARE=\"return x * x;\",RECIPROCAL=\"return 1.0 / x;\",LOGICAL_NOT=\"return float(!(x >= 1.0));\",TO_INT=\"return float(int(x));\",UnpackProgram=function(){return function(e){this.variableNames=[\"A\"],this.usesPackedTextures=!0,this.outputShape=e;var t=e.length,n=getChannels(\"rc\",t),r=getCoordsDataType(t),o=getSourceCoords$1(t,n),a=n.slice(-2),i=t<=1?\"rc\":\"vec2(\"+a.join(\",\")+\")\";this.userCode=\"\\n      void main() {\\n        \"+r+\" rc = getOutputCoords();\\n        vec4 packedInput = getA(\"+o+\");\\n\\n        setOutput(getChannel(packedInput, \"+i+\"));\\n      }\\n    \"}}();function concat1d_(e){return concat(e,0)}function concat2d_(e,t){return concat(e,t)}function concat3d_(e,t){return concat(e,t)}function concat4d_(e,t){return concat(e,t)}function concat_(e,t){void 0===t&&(t=0),assert(e.length>=1,\"Pass at least one tensor to concat\");var n=convertToTensorArray(e,\"tensors\",\"concat\");t=parseAxisParam(t,n[0].shape)[0];var r=computeOutShape(n.map(function(e){return e.shape}),t);if(0===sizeFromShape(r))return tensor([],r);if(1===(n=n.filter(function(e){return e.size>0})).length)return n[0];var o=n.map(function(e){return e.shape});assertParamsConsistent(o,t);var a=n;return ENV.engine.runKernel(function(e){return e.concat(n,t)},a,function(e){var n=o.map(function(e){return e[t]});return split$1(e,n,t).map(function(e){return function(){return e}})})}function split_(e,t,n){void 0===n&&(n=0);var r,o=convertToTensor(e,\"x\",\"split\");n=parseAxisParam(n,o.shape)[0],\"number\"==typeof t?(assert(o.shape[n]%t==0,\"Number of splits must evenly divide the axis.\"),r=Array(t).fill(o.shape[n]/t)):(assert(o.shape[n]===t.reduce(function(e,t){return e+t}),\"The sum of sizes must match the size of the axis dimension.\"),r=t);return ENV.engine.runKernel(function(e){return e.split(o,r,n)},{$x:o},function(e){return{$x:function(){return concat(e,n)}}})}var concat=op({concat_:concat_}),concat1d=op({concat1d_:concat1d_}),concat2d=op({concat2d_:concat2d_}),concat3d=op({concat3d_:concat3d_}),concat4d=op({concat4d_:concat4d_}),split$1=op({split_:split_}),commonjsGlobal=\"undefined\"!=typeof window?window:\"undefined\"!=typeof global?global:\"undefined\"!=typeof self?self:{};function createCommonjsModule(e,t){return e(t={exports:{}},t.exports),t.exports}var alea=createCommonjsModule(function(e){!function(e,t,n){function r(e,t){return t.c=e.c,t.s0=e.s0,t.s1=e.s1,t.s2=e.s2,t}function o(e,t){var n=new function(e){var t,n=this,r=(t=4022871197,function(e){e=e.toString();for(var n=0;n<e.length;n++){var r=.02519603282416938*(t+=e.charCodeAt(n));r-=t=r>>>0,t=(r*=t)>>>0,t+=4294967296*(r-=t)}return 2.3283064365386963e-10*(t>>>0)});n.next=function(){var e=2091639*n.s0+2.3283064365386963e-10*n.c;return n.s0=n.s1,n.s1=n.s2,n.s2=e-(n.c=0|e)},n.c=1,n.s0=r(\" \"),n.s1=r(\" \"),n.s2=r(\" \"),n.s0-=r(e),n.s0<0&&(n.s0+=1),n.s1-=r(e),n.s1<0&&(n.s1+=1),n.s2-=r(e),n.s2<0&&(n.s2+=1),r=null}(e),o=t&&t.state,a=n.next;return a.int32=function(){return 4294967296*n.next()|0},a.double=function(){return a()+1.1102230246251565e-16*(2097152*a()|0)},a.quick=a,o&&(\"object\"==typeof o&&r(o,n),a.state=function(){return r(n,{})}),a}t&&t.exports?t.exports=o:n&&n.amd?n(function(){return 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Pass non-scalar to pad\");var o=t.map(function(e){return e[0]});return ENV.engine.runKernel(function(e){return e.pad(r,t,n)},{$x:r},function(e){return{$x:function(){return e.slice(o,r.shape)}}})}function stack_(e,t){void 0===t&&(t=0);var n=convertToTensorArray(e,\"tensors\",\"stack\");if(assert(n.length>=1,\"Pass at least one tensor to tf.stack\"),1===n.length)return n[0].expandDims(t);var r=n[0].rank,o=n[0].shape,a=n[0].dtype;assert(t<=r,\"Axis must be <= rank of the tensor\"),n.forEach(function(e){assertShapesMatch(o,e.shape,\"All tensors passed to stack must have matching shapes\")}),n.forEach(function(e){assert(a===e.dtype,\"All tensors passed to stack must have matching dtypes\")});var i=n.map(function(e){return e.expandDims(t)});return concat(i,t)}function batchToSpaceND_(e,t,n){var r=convertToTensor(e,\"x\",\"batchToSpaceND\"),o=t.reduce(function(e,t){return e*t});assert(r.rank>=1+t.length,\"input rank is \"+r.rank+\" but should be > than blockShape.length \"+t.length),assert(n.length===t.length,\"crops.length is \"+n.length+\" but should be equal to blockShape.length  \"+t.length),assert(r.shape[0]%o==0,\"input tensor batch is \"+r.shape[0]+\" but is not divisible by the product of the elements of blockShape \"+t.join(\" * \")+\" === \"+o);return ENV.engine.runKernel(function(e){return e.batchToSpaceND(r,t,n)},{$x:r},function(e){return{$x:function(){return e.spaceToBatchND(t,n)}}})}function spaceToBatchND_(e,t,n){var r=convertToTensor(e,\"x\",\"spaceToBatchND\");assert(r.rank>=1+t.length,\"input rank \"+r.rank+\" should be > than [blockShape] \"+t.length),assert(n.length===t.length,\"paddings.shape[0] \"+n.length+\" must be equal to [blockShape] \"+t.length),assert(r.shape.reduce(function(e,r,o){return o>0&&o<=t.length?e&&(r+n[o-1][0]+n[o-1][1])%t[o-1]==0:e},!0),\"input spatial dimensions \"+r.shape.slice(1)+\" with paddings \"+n.toString()+\" must be divisible by blockShapes \"+t.toString());return ENV.engine.runKernel(function(e){return e.spaceToBatchND(r,t,n)},{$x:r},function(e){return{$x:function(){return e.batchToSpaceND(t,n)}}})}function unstack_(e,t){void 0===t&&(t=0);for(var n,r=convertToTensor(e,\"x\",\"unstack\"),o=r.shape[t],a=Array(r.rank-1).fill(0),i=0,s=0;s<r.rank;s++)s!==t&&(a[i]=r.shape[s],i++);n=Array(o).fill(1);var u=Array(r.rank).fill(0),l=r.shape.slice();return n.map(function(e){l[t]=e;var n=r.slice(u,l);return u[t]+=e,n.reshape(a)})}function cumsum_(e,t,n,r){void 0===t&&(t=0),void 0===n&&(n=!1),void 0===r&&(r=!1);var o=convertToTensor(e,\"x\",\"cumsum\"),a=getAxesPermutation([t|=0],o.rank),i=o;null!=a&&(i=o.transpose(a));var s=getInnerMostAxes(1,o.rank)[0],u=ENV.engine.runKernel(function(e){return e.cumsum(i,s,n,r)},{permutedX:i},function(e){return{permutedX:function(){return e.cumsum(t,n,!r)}}});return null!=a&&(u=u.transpose(a)),u}function expandDims_(e,t){void 0===t&&(t=0);var n=convertToTensor(e,\"x\",\"expandDims\");assert(t<=n.rank,\"Axis must be <= rank of the tensor\");var r=n.shape.slice();return t<0&&(assert(-(n.rank+1)<=t,\"Axis must be in the interval [\"+-(n.rank+1)+\", \"+n.rank+\"]\"),t=n.rank+t+1),r.splice(t,0,1),reshape(n,r)}function depthToSpace_(e,t,n){void 0===n&&(n=\"NHWC\");var r=convertToTensor(e,\"x\",\"depthToSpace\"),o=\"NHWC\"===n?r.shape[1]:r.shape[2],a=\"NHWC\"===n?r.shape[2]:r.shape[3],i=\"NHWC\"===n?r.shape[3]:r.shape[1];return assert(o*t>=0,\"Negative dimension size caused by overflow when multiplying\\n      \"+o+\" and \"+t+\"  for depthToSpace with input shape\\n      \"+r.shape),assert(a*t>=0,\"Negative dimension size caused by overflow when multiplying\\n      \"+a+\" and \"+t+\" for depthToSpace with input shape\\n          \"+r.shape),assert(i%(t*t)==0,\"Dimension size must be evenly divisible by \"+t*t+\" but is \"+i+\" for depthToSpace with input shape \"+r.shape),ENV.engine.runKernel(function(e){return e.depthToSpace(r,t,n)},{$x:r})}function setdiff1dAsync_(e,t){return __awaiter(this,void 0,void 0,function(){var n,r,o,a,i,s,u,l,c,p;return __generator(this,function(d){switch(d.label){case 0:return n=convertToTensor(e,\"x\",\"setdiff1d\"),r=convertToTensor(t,\"y\",\"setdiff1d\"),assert(n.dtype===r.dtype,\"x and y should have the same dtype, but got x (\"+n.dtype+\") and y (\"+r.dtype+\").\"),assert(1===n.rank,\"x should be 1D tensor, but got x (\"+n.shape+\").\"),assert(1===r.rank,\"y should be 1D tensor, but got y (\"+r.shape+\").\"),[4,n.data()];case 1:return o=d.sent(),[4,r.data()];case 2:for(a=d.sent(),i=new Set(a),s=0,c=0;c<o.length;c++)i.has(o[c])||s++;for(u=new TensorBuffer([s],n.dtype),l=new TensorBuffer([s],\"int32\"),c=0,p=0;c<o.length;c++)i.has(o[c])||(u.values[p]=o[c],l.values[p]=c,p++);return[2,[u.toTensor(),l.toTensor()]]}})})}function buffer(e,t,n){return void 0===t&&(t=\"float32\"),new TensorBuffer(e,t=t||\"float32\",n)}function print(e,t){void 0===t&&(t=!1),console.log(e.toString(t))}var batchToSpaceND=op({batchToSpaceND_:batchToSpaceND_}),cast=op({cast_:cast_}),clone=op({clone_:clone_}),cumsum=op({cumsum_:cumsum_}),depthToSpace=op({depthToSpace_:depthToSpace_}),expandDims=op({expandDims_:expandDims_}),eye=op({eye_:eye_}),fromPixels=op({fromPixels_:fromPixels_}),multinomial=op({multinomial_:multinomial_}),oneHot=op({oneHot_:oneHot_}),pad=op({pad_:pad_}),pad1d=op({pad1d_:pad1d_}),pad2d=op({pad2d_:pad2d_}),pad3d=op({pad3d_:pad3d_}),pad4d=op({pad4d_:pad4d_}),rand=op({rand_:rand_}),randomNormal=op({randomNormal_:randomNormal_}),randomUniform=op({randomUniform_:randomUniform_}),reshape=op({reshape_:reshape_}),spaceToBatchND=op({spaceToBatchND_:spaceToBatchND_}),squeeze=op({squeeze_:squeeze_}),stack=op({stack_:stack_}),tile=op({tile_:tile_}),truncatedNormal=op({truncatedNormal_:truncatedNormal_}),unstack=op({unstack_:unstack_}),setdiff1dAsync=setdiff1dAsync_;function whereImpl(e,t){for(var n=[],r=0;r<t.length;r++)t[r]&&n.push(r);var o=buffer(e,\"int32\"),a=buffer([n.length,e.length],\"int32\");for(r=0;r<n.length;r++){var i=o.indexToLoc(n[r]),s=r*e.length;a.values.set(i,s)}return a.toTensor()}var CPU_HANDOFF_SIZE_THRESHOLD=10,BEFORE_PAGING_CONSTANT=300,MATMUL_SHARED_DIM_THRESHOLD=1e3,MathBackendWebGL=function(){function e(e,t){if(void 0===t&&(t=!0),this.gpgpu=e,this.delayedStorage=t,this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.lruDataGPU=[],this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.binaryCache={},this.disposed=!1,ENV.get(\"WEBGL_VERSION\")<1)throw new Error(\"WebGL is not supported on this device\");if(null==e){var n=getWebGLContext(ENV.get(\"WEBGL_VERSION\"));this.gpgpu=new GPGPUContext(n),this.canvas=n.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;ENV.get(\"WEBGL_PAGING_ENABLED\")&&(this.NUM_BYTES_BEFORE_PAGING=window.screen.height*window.screen.width*window.devicePixelRatio*BEFORE_PAGING_CONSTANT),this.textureManager=new TextureManager(this.gpgpu)}return e.prototype.register=function(e,t,n){if(this.texData.has(e))throw new Error(\"Data buffer is already registered\");this.texData.set(e,{shape:t,dtype:n})},e.prototype.setDataMover=function(e){this.texData=new DataStorage(e)},e.prototype.fromPixels=function(e,t){if(null==e)throw new Error(\"pixels passed to tf.fromPixels() can not be null\");var n=[e.height,e.width],r=[e.height,e.width,t];if(!(e instanceof HTMLVideoElement||e instanceof HTMLImageElement||e instanceof HTMLCanvasElement||e instanceof ImageData))throw new Error(\"pixels passed to tf.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ImageData, but was \"+e.constructor.name);if(e instanceof HTMLVideoElement){if(null==this.fromPixels2DContext){if(!ENV.get(\"IS_BROWSER\"))throw new Error(\"Can't read pixels from HTMLImageElement outside the browser.\");if(\"complete\"!==document.readyState)throw new Error(\"The DOM is not ready yet. Please call tf.fromPixels() once the DOM is ready. One way to do that is to add an event listener for `DOMContentLoaded` on the document object\");this.fromPixels2DContext=document.createElement(\"canvas\").getContext(\"2d\")}this.fromPixels2DContext.canvas.width=e.width,this.fromPixels2DContext.canvas.height=e.height,this.fromPixels2DContext.drawImage(e,0,0,e.width,e.height),e=this.fromPixels2DContext.canvas}var o=this.makeTensorHandle(n,\"int32\");this.texData.get(o.dataId).usage=TextureUsage.PIXELS,this.gpgpu.uploadPixelDataToTexture(this.getTexture(o.dataId),e);var a=new FromPixelsProgram(r),i=this.compileAndRun(a,[o]);return this.disposeData(o.dataId),i},e.prototype.makeTensorHandle=function(e,t){var n={};return this.register(n,e,t),{dataId:n,shape:e,dtype:t}},e.prototype.write=function(e,t){if(null==t)throw new Error(\"MathBackendWebGL.write(): values can not be null\");var n=this.texData.get(e),r=n.texture,o=n.texShape,a=n.usage,i=n.dtype,s=n.isPacked;if(\"complex64\"===i)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");null!=r&&(this.releaseTexture(e,r,o,a,s),n.texture=null,n.texShape=null),n.usage=TextureUsage.UPLOAD,n.values=t,this.delayedStorage||this.uploadToGPU(e)},e.prototype.readSync=function(e){var t=this.texData.get(e),n=t.values,r=t.dtype,o=t.complexTensors;if(null!=n)return this.convertAndCacheOnCPU(e);if(\"string\"===r)return n;var a,i,s=null!=this.activeTimers;(s&&(a=performance.now()),\"complex64\"===r)?i=mergeRealAndImagArrays(o.real.dataSync(),o.imag.dataSync()):i=this.getValuesFromTexture(e);return s&&(this.downloadWaitMs+=performance.now()-a),this.convertAndCacheOnCPU(e,i)},e.prototype.read=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o,a,i,s,u,l,c,p,d,h,f,m,g,v,y;return __generator(this,function(x){switch(x.label){case 0:if(this.pendingRead.has(e))return r=this.pendingRead.get(e),[2,new Promise(function(e){return r.push(e)})];if(o=this.texData.get(e),a=o.texture,i=o.values,s=o.texShape,u=o.isPacked,l=o.shape,null!=i)return[2,this.convertAndCacheOnCPU(e)];if(this.pendingRead.set(e,[]),!ENV.get(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&2===ENV.get(\"WEBGL_VERSION\"))throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");return c=s[1],p=s[0],u&&(t=getPackedMatrixTextureShapeWidthHeight(s[0],s[1]),c=t[0],p=t[1]),d=this.gpgpu.maybeCreateBufferFromTexture(a,p,c),[4,this.gpgpu.createAndWaitForFence()];case 1:return x.sent(),d instanceof WebGLTexture?h=this.getValuesFromTexture(e):u?(f=this.getBatchDim(l),m=1,g=1,l.length&&(n=this.getRowsCols(l),m=n[0],g=n[1]),h=this.gpgpu.downloadPackedMatrixFromBuffer(d,f,m,g,s[0],s[1])):h=this.gpgpu.downloadFloat32MatrixFromBuffer(d,s[0],s[1]),v=this.convertAndCacheOnCPU(e,h),y=this.pendingRead.get(e),this.pendingRead.delete(e),y.forEach(function(e){return e(v)}),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)),[2,v]}})})},e.prototype.getValuesFromTexture=function(e){var t,n=this.texData.get(e),r=n.shape,o=n.dtype,a=n.texture,i=n.texShape;if(ENV.get(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")){if(this.texData.get(e).isPacked){var s=this.getBatchDim(r),u=1,l=1;return r.length&&(u=(t=this.getRowsCols(r))[0],l=t[1]),this.gpgpu.downloadMatrixFromPackedTexture(a,s,u,l,i[0],i[1])}return this.gpgpu.downloadFloat32MatrixFromOutputTexture(a,i[0],i[1])}var c=this.makeTensorHandle(r,\"float32\");c.size=sizeFromShape(r),this.texData.get(c.dataId).usage=TextureUsage.DOWNLOAD;var p=new EncodeFloatProgram(r);this.compileAndRun(p,[{shape:r,dtype:o,dataId:e}],c,null,!1);var d=this.texData.get(c.dataId),h=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(d.texture,d.texShape[0],d.texShape[1]);return this.disposeData(c.dataId),h},e.prototype.time=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o,a,i,s;return __generator(this,function(u){switch(u.label){case 0:return t=this.activeTimers,n=[],r=!1,null==this.programTimersStack?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e(),o=flatten(this.activeTimers.map(function(e){return e.query})).filter(function(e){return null!=e}),a=flatten(this.activeTimers.map(function(e){return e.name})).filter(function(e){return null!=e}),this.activeTimers=t,r&&(this.programTimersStack=null),[4,Promise.all(o)];case 1:return i=u.sent(),s={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:sum(i),getExtraProfileInfo:function(){return i.map(function(e,t){return{name:a[t],ms:e}}).map(function(e){return e.name+\": \"+e.ms}).join(\", \")},wallMs:null},this.uploadWaitMs=0,this.downloadWaitMs=0,[2,s]}})})},e.prototype.memory=function(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU}},e.prototype.startTimer=function(){return ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?this.gpgpu.beginQuery():{startMs:performance.now(),endMs:null}},e.prototype.endTimer=function(e){return ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?(this.gpgpu.endQuery(),e):(e.endMs=performance.now(),e)},e.prototype.getQueryTime=function(e){return __awaiter(this,void 0,void 0,function(){var t;return __generator(this,function(n){return ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?[2,this.gpgpu.waitForQueryAndGetTime(e)]:[2,(t=e).endMs-t.startMs]})})},e.prototype.disposeData=function(e){if(!this.pendingDisposal.has(e))if(this.pendingRead.has(e))this.pendingDisposal.add(e);else if(this.texData.has(e)){var t=this.texData.get(e),n=t.texture,r=t.texShape,o=t.usage,a=t.complexTensors,i=t.isPacked;null!=n&&this.releaseTexture(e,n,r,o,i),null!=a&&(a.real.dispose(),a.imag.dispose()),this.texData.delete(e)}},e.prototype.getTexture=function(e){return this.uploadToGPU(e),this.texData.get(e).texture},e.prototype.getCPUBackend=function(){return ENV.get(\"WEBGL_CPU_FORWARD\")?(null==this.cpuBackend&&(this.cpuBackend=ENV.findBackend(\"cpu\")),this.cpuBackend):null},e.prototype.shouldExecuteOnCPU=function(e,t){var n=this;return void 0===t&&(t=CPU_HANDOFF_SIZE_THRESHOLD),null!=this.getCPUBackend()&&e.every(function(e){return null==n.texData.get(e.dataId).texture&&e.size<t})},e.prototype.getGPGPUContext=function(){return this.gpgpu},e.prototype.getCanvas=function(){return this.canvas},e.prototype.complex=function(e,t){var n=this.makeOutputArray(e.shape,\"complex64\");return this.texData.get(n.dataId).complexTensors={real:ENV.engine.keep(e.clone()),imag:ENV.engine.keep(t.clone())},n},e.prototype.real=function(e){return this.texData.get(e.dataId).complexTensors.real.clone()},e.prototype.imag=function(e){return this.texData.get(e.dataId).complexTensors.imag.clone()},e.prototype.slice=function(e,t,n){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.slice(e,t,n);var r=new SliceProgram(n),o=r.getCustomSetupFunc(t);return this.compileAndRun(r,[e],null,o)},e.prototype.stridedSlice=function(e,t,n,r,o,a,i,s,u){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.stridedSlice(e,t,n,r,o,a,i,s,u);var l=getStridedSlicedInfo(e.shape,t,n,r,o,a,i,s,u),c=l[0],p=l[1],d=l[2],h=p.filter(function(e,t){return-1===d.indexOf(t)});if(h.some(function(e){return 0===e}))return tensor([],h);var f=new StridedSliceProgram(c,r,p,d);return this.compileAndRun(f,[e])},e.prototype.reverse=function(e,t){var n=new ReverseProgram(e.shape,t);return this.compileAndRun(n,[e])},e.prototype.concat2Tensors=function(e,t,n){var r=computeOutShape([e.shape,t.shape],n),o=e.as2D(-1,sizeFromShape(e.shape.slice(n))),a=t.as2D(-1,sizeFromShape(t.shape.slice(n))),i=new ConcatProgram(o.shape,a.shape);return this.compileAndRun(i,[o,a]).reshape(r)},e.prototype.concat=function(e,t){if(this.shouldExecuteOnCPU(e))return this.cpuBackend.concat(e,t);if(1===e.length)return e[0];for(var n=e[0],r=1;r<e.length;++r)n=this.concat2Tensors(n,e[r],t);return n},e.prototype.neg=function(e){var t=new UnaryOpProgram(e.shape,NEG);return this.compileAndRun(t,[e])},e.prototype.batchMatMul=function(e,t,n,r){var o=n?e.shape[2]:e.shape[1],a=r?t.shape[1]:t.shape[2],i=n?e.shape[1]:e.shape[2],s=e.shape[0];if((1===o||1===a)&&i>MATMUL_SHARED_DIM_THRESHOLD){n&&(e=e.transpose([0,2,1])),r&&(t=t.transpose([0,2,1]));var u=1===a?e:e.as3D(s,i,1),l=1===a?2:1,c=1===a?t.as3D(s,1,i):t;return this.multiply(u,c).sum(l,!0)}var p=upcastType(e.dtype,t.dtype);if(1===s){var d=e.as2D(e.shape[1],e.shape[2]),h=t.as2D(t.shape[1],t.shape[2]),f=new MatMulPackedProgram(d.shape,h.shape,[o,a],n,r),m=this.makePackedTensor(f.outputShape,p),g=this.compileAndRun(f,[d,h],m);return g.reshape([1,g.shape[0],g.shape[1]])}f=new MatMulProgram(e.shape,t.shape,n,r),m=this.makeOutputArray(f.outputShape,p);return this.compileAndRun(f,[e,t],m)},e.prototype.multiply=function(e,t){if(\"complex64\"===e.dtype){var n=this.texData.get(e.dataId),r=this.texData.get(t.dataId),o=new BinaryOpComplexProgram(COMPLEX_MULTIPLY.REAL,e.shape,t.shape),a=new BinaryOpComplexProgram(COMPLEX_MULTIPLY.IMAG,e.shape,t.shape),i=[this.makeComplexComponentTensorHandle(e,n.complexTensors.real),this.makeComplexComponentTensorHandle(e,n.complexTensors.imag),this.makeComplexComponentTensorHandle(t,r.complexTensors.real),this.makeComplexComponentTensorHandle(t,r.complexTensors.imag)],s=this.compileAndRun(o,i),u=this.compileAndRun(a,i),l=this.complex(s,u);return s.dispose(),u.dispose(),l}if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.multiply(e,t);var c=new BinaryOpProgram(MUL,e.shape,t.shape),p=this.makeOutputArray(c.outputShape,e.dtype);return this.compileAndRun(c,[e,t],p)},e.prototype.batchNormalization=function(e,t,n,r,o,a){var i=[e,t,n],s=null;null!=a&&(s=a.shape,i.push(a));var u=null;if(null!=o&&(u=o.shape,i.push(o)),ENV.get(\"WEBGL_PACK_BATCHNORMALIZATION\")){var l=new BatchNormPackedProgram(e.shape,t.shape,n.shape,s,u,r);return this.compileAndRun(l,i)}var c=new BatchNormProgram(e.shape,t.shape,n.shape,s,u,r);return this.compileAndRun(c,i)},e.prototype.localResponseNormalization4D=function(e,t,n,r,o){var a=new LRNProgram(e.shape,t,n,r,o);return this.compileAndRun(a,[e])},e.prototype.LRNGrad=function(e,t,n,r,o,a,i){var s=new LRNGradProgram(t.shape,r,o,a,i);return this.compileAndRun(s,[t,n,e])},e.prototype.tile=function(e,t){var n=new TileProgram(e.shape,t);return this.compileAndRun(n,[e])},e.prototype.pad=function(e,t,n){var r=new PadProgram(e.shape,t,n);return this.compileAndRun(r,[e])},e.prototype.transpose=function(e,t){var n=new TransposeProgram(e.shape,t);return this.compileAndRun(n,[e])},e.prototype.gather=function(e,t,n){var r=new GatherProgram(e.shape,t.size,n);return this.compileAndRun(r,[e,t])},e.prototype.batchToSpaceND=function(e,t,n){assert(e.rank<=4,\"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");var r=t.reduce(function(e,t){return e*t}),o=getReshaped(e.shape,t,r),a=getPermuted(o.length,t.length),i=getReshapedPermuted(e.shape,t,r),s=getSliceBeginCoords(n,t.length),u=getSliceSize(i,n,t.length);return e.reshape(o).transpose(a).reshape(i).slice(s,u)},e.prototype.spaceToBatchND=function(e,t,n){assert(e.rank<=4,\"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\");var r=t.reduce(function(e,t){return e*t}),o=[[0,0]];o.push.apply(o,n);for(var a=1+t.length;a<e.shape.length;++a)o.push([0,0]);var i=e.pad(o),s=getReshaped(i.shape,t,r,!1),u=getPermuted(s.length,t.length,!1),l=getReshapedPermuted(i.shape,t,r,!1);return i.reshape(s).transpose(u).reshape(l)},e.prototype.reduce=function(e,t,n){var r=e.shape[0],o=e.shape[1],a=computeOptimalWindowSize(o),i=new ReduceProgram({windowSize:a,inSize:o,batchSize:r},t),s=i.outputShape,u=s[0],l=s[1],c=this.makeOutputArray([u,l],n);return this.compileAndRun(i,[e],c),1===c.shape[1]?c:this.reduce(c,t,n)},e.prototype.argReduce=function(e,t,n){void 0===n&&(n=null);var r=e.shape[0],o=e.shape[1];null!=n&&(r=n.shape[0],o=n.shape[1]);var a=computeOptimalWindowSize(o),i=new ArgMinMaxProgram({windowSize:a,inSize:o,batchSize:r},t,null==n),s=i.outputShape,u=s[0],l=s[1],c=this.makeOutputArray([u,l],\"int32\"),p=[e];return null!=n&&p.push(n),this.compileAndRun(i,p,c),1===c.shape[1]?c:this.argReduce(e,t,c)},e.prototype.sum=function(e,t){assertAxesAreInnerMostDims(\"sum\",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o),i=sumOutType(e.dtype);return this.reduce(a,\"sum\",i).reshape(r)},e.prototype.prod=function(e,t){var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o),i=sumOutType(e.dtype);return this.reduce(a,\"prod\",i).reshape(r)},e.prototype.unsortedSegmentSum=function(e,t,n){var r=0,o=getAxesPermutation([r],e.rank),a=e;null!=o&&(a=e.transpose(o),r=getInnerMostAxes(1,e.rank)[0]);var i=computeOutShape$1(a.shape,r,n),s=sizeFromShape([a.shape[r]]),u=a.as2D(-1,s),l=sumOutType(e.dtype),c=this.segOpCompute(u,\"unsortedSegmentSum\",t,l,n).reshape(i);return null!=o&&(c=c.transpose(getUndoAxesPermutation(o))),c},e.prototype.segOpCompute=function(e,t,n,r,o){var a=e.shape[0],i=e.shape[1],s=segOpComputeOptimalWindowSize(i,o),u=new SegmentOpProgram({windowSize:s,inSize:i,batchSize:a,numSegments:o},t),l=u.outputShape,c=l[0],p=l[1],d=this.makeOutputArray([c,p],r);return this.compileAndRun(u,[e,n],d),d.shape[1]===o?d:(n=range(0,o).tile([i/s]),this.segOpCompute(d,t,n,r,o))},e.prototype.argMin=function(e,t){var n=[t];assertAxesAreInnerMostDims(\"argMin\",n,e.rank);var r=computeOutAndReduceShapes(e.shape,n),o=r[0],a=sizeFromShape(r[1]),i=e.as2D(-1,a);return this.argReduce(i,\"min\").reshape(o)},e.prototype.argMax=function(e,t){var n=[t];assertAxesAreInnerMostDims(\"argMax\",n,e.rank);var r=computeOutAndReduceShapes(e.shape,n),o=r[0],a=sizeFromShape(r[1]),i=e.as2D(-1,a);return this.argReduce(i,\"max\").reshape(o)},e.prototype.cumsum=function(e,t,n,r){if(t!==e.rank-1)throw new Error(\"WebGL cumsum shader expects an inner-most axis=\"+(e.rank-1)+\" but got axis=\"+t);var o=new CumSumProgram(e.shape,n,r);return this.compileAndRun(o,[e])},e.prototype.equal=function(e,t){var n=new BinaryOpProgram(EQUAL,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,\"bool\");return this.compileAndRun(n,[e,t],r)},e.prototype.notEqual=function(e,t){var n=new BinaryOpProgram(NOT_EQUAL,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,\"bool\");return this.compileAndRun(n,[e,t],r)},e.prototype.less=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.less(e,t);var n=new BinaryOpProgram(LESS,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,\"bool\");return this.compileAndRun(n,[e,t],r)},e.prototype.lessEqual=function(e,t){var n=new BinaryOpProgram(LESS_EQUAL,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,\"bool\");return this.compileAndRun(n,[e,t],r)},e.prototype.greater=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.greater(e,t);var n=new BinaryOpProgram(GREATER,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,\"bool\");return this.compileAndRun(n,[e,t],r)},e.prototype.greaterEqual=function(e,t){var n=new BinaryOpProgram(GREATER_EQUAL,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,\"bool\");return this.compileAndRun(n,[e,t],r)},e.prototype.logicalNot=function(e){var t=new UnaryOpProgram(e.shape,LOGICAL_NOT);return this.compileAndRun(t,[e])},e.prototype.logicalAnd=function(e,t){var n=new BinaryOpProgram(LOGICAL_AND,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,\"bool\");return this.compileAndRun(n,[e,t],r)},e.prototype.logicalOr=function(e,t){var n=new BinaryOpProgram(LOGICAL_OR,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,\"bool\");return this.compileAndRun(n,[e,t],r)},e.prototype.select=function(e,t,n){var r=new SelectProgram(e.rank,t.shape,t.rank),o=this.makeOutputArray(r.outputShape,upcastType(t.dtype,n.dtype));return this.compileAndRun(r,[e,t,n],o)},e.prototype.where=function(e){warn(\"tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead\");var t=e.dataSync();return whereImpl(e.shape,t)},e.prototype.topk=function(e,t,n){return topkImpl(e.dataSync(),e.shape,e.dtype,t,n)},e.prototype.min=function(e,t){assertAxesAreInnerMostDims(\"min\",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o);return this.reduce(a,\"min\",a.dtype).reshape(r)},e.prototype.minimum=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.minimum(e,t);var n=new BinaryOpProgram(MIN,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.mod=function(e,t){var n=new BinaryOpProgram(MOD,e.shape,t.shape),r=n.getCustomSetupFunc();return this.compileAndRun(n,[e,t],null,r)},e.prototype.max=function(e,t){assertAxesAreInnerMostDims(\"max\",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o);return this.reduce(a,\"max\",a.dtype).reshape(r)},e.prototype.maximum=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.maximum(e,t);var n=new BinaryOpProgram(MAX,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.all=function(e,t){assertAxesAreInnerMostDims(\"all\",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o);return this.reduce(a,\"all\",a.dtype).reshape(r)},e.prototype.any=function(e,t){assertAxesAreInnerMostDims(\"any\",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o);return this.reduce(a,\"any\",a.dtype).reshape(r)},e.prototype.squaredDifference=function(e,t){var n=new BinaryOpProgram(SQUARED_DIFFERENCE,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.realDivide=function(e,t){var n=new BinaryOpProgram(DIV,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,\"float32\");return this.compileAndRun(n,[e,t],r)},e.prototype.floorDiv=function(e,t){var n=new BinaryOpProgram(INT_DIV,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,\"int32\");return this.compileAndRun(n,[e,t],r)},e.prototype.add=function(e,t){if(\"complex64\"===e.dtype&&\"complex64\"===t.dtype)return this.complexSeparableBinaryOp(e,t,ADD);var n=new BinaryOpProgram(ADD,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,upcastType(e.dtype,t.dtype));return this.compileAndRun(n,[e,t],r)},e.prototype.complexSeparableBinaryOp=function(e,t,n){var r=this,o=this.texData.get(e.dataId),a=this.texData.get(t.dataId),i=[[o.complexTensors.real,a.complexTensors.real],[o.complexTensors.imag,a.complexTensors.imag]].map(function(o){var a=o[0],i=o[1],s=new BinaryOpProgram(n,e.shape,t.shape),u=r.makeOutputArray(s.outputShape,upcastType(a.dtype,i.dtype)),l=r.makeComplexComponentTensorHandle(e,a),c=r.makeComplexComponentTensorHandle(t,i);return r.compileAndRun(s,[l,c],u)}),s=i[0],u=i[1],l=this.complex(s,u);return s.dispose(),u.dispose(),l},e.prototype.makeComplexComponentTensorHandle=function(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}},e.prototype.addN=function(e){for(var t=e[0],n=1;n<e.length;n++)t=this.add(t,e[n]);return t},e.prototype.subtract=function(e,t){if(\"complex64\"===e.dtype&&\"complex64\"===t.dtype)return this.complexSeparableBinaryOp(e,t,SUB);if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.subtract(e,t);var n=new BinaryOpProgram(SUB,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,upcastType(e.dtype,t.dtype));return this.compileAndRun(n,[e,t],r)},e.prototype.pow=function(e,t){var n=new BinaryOpProgram(POW,e.shape,t.shape),r=n.getCustomSetupFunc(),o=this.makeOutputArray(n.outputShape,upcastType(e.dtype,t.dtype));return this.compileAndRun(n,[e,t],o,r)},e.prototype.ceil=function(e){var t=new UnaryOpProgram(e.shape,CEIL);return this.compileAndRun(t,[e])},e.prototype.floor=function(e){var t=new UnaryOpProgram(e.shape,FLOOR);return this.compileAndRun(t,[e])},e.prototype.sign=function(e){var t=new UnaryOpProgram(e.shape,SIGN);return this.compileAndRun(t,[e])},e.prototype.round=function(e){var t=new UnaryOpProgram(e.shape,ROUND);return this.compileAndRun(t,[e])},e.prototype.exp=function(e){var t=new UnaryOpProgram(e.shape,EXP);return this.compileAndRun(t,[e])},e.prototype.expm1=function(e){var t=new UnaryOpProgram(e.shape,EXPM1);return this.compileAndRun(t,[e])},e.prototype.log=function(e){var t=new UnaryOpProgram(e.shape,LOG),n=t.getCustomSetupFunc();return this.compileAndRun(t,[e],null,n)},e.prototype.log1p=function(e){var t=new UnaryOpProgram(e.shape,LOG1P);return this.compileAndRun(t,[e])},e.prototype.sqrt=function(e){var t=new UnaryOpProgram(e.shape,SQRT);return this.compileAndRun(t,[e])},e.prototype.rsqrt=function(e){var t=new UnaryOpProgram(e.shape,RSQRT);return this.compileAndRun(t,[e])},e.prototype.square=function(e){var t=new UnaryOpProgram(e.shape,SQUARE);return this.compileAndRun(t,[e])},e.prototype.reciprocal=function(e){var t=new UnaryOpProgram(e.shape,RECIPROCAL);return this.compileAndRun(t,[e])},e.prototype.relu=function(e){var t=new UnaryOpProgram(e.shape,RELU);return this.compileAndRun(t,[e])},e.prototype.elu=function(e){var t=new UnaryOpProgram(e.shape,ELU);return this.compileAndRun(t,[e])},e.prototype.eluDer=function(e,t){var n=new BinaryOpProgram(ELU_DER,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.selu=function(e){var t=new UnaryOpProgram(e.shape,SELU);return this.compileAndRun(t,[e])},e.prototype.int=function(e){var t=new UnaryOpProgram(e.shape,TO_INT),n=this.makeOutputArray(t.outputShape,\"int32\");return this.compileAndRun(t,[e],n)},e.prototype.clip=function(e,t,n){var r;return r=ENV.get(\"WEBGL_PACK_CLIP\")?new ClipPackedProgram(e.shape,t,n):new ClipProgram(e.shape,t,n),this.compileAndRun(r,[e])},e.prototype.abs=function(e){var t=new UnaryOpProgram(e.shape,ABS);return this.compileAndRun(t,[e])},e.prototype.complexAbs=function(e){var t=this.texData.get(e.dataId),n=new ComplexAbsProgram(e.shape),r=[this.makeComplexComponentTensorHandle(e,t.complexTensors.real),this.makeComplexComponentTensorHandle(e,t.complexTensors.imag)];return this.compileAndRun(n,r)},e.prototype.sigmoid=function(e){var t=new UnaryOpProgram(e.shape,SIGMOID);return this.compileAndRun(t,[e])},e.prototype.softplus=function(e){var t=new UnaryOpProgram(e.shape,SOFTPLUS);return this.compileAndRun(t,[e])},e.prototype.sin=function(e){var t=new UnaryOpProgram(e.shape,SIN);return this.compileAndRun(t,[e])},e.prototype.cos=function(e){var t=new UnaryOpProgram(e.shape,COS);return this.compileAndRun(t,[e])},e.prototype.tan=function(e){var t=new UnaryOpProgram(e.shape,TAN);return this.compileAndRun(t,[e])},e.prototype.asin=function(e){var t=new UnaryOpProgram(e.shape,ASIN);return this.compileAndRun(t,[e])},e.prototype.acos=function(e){var t=new UnaryOpProgram(e.shape,ACOS);return this.compileAndRun(t,[e])},e.prototype.atan=function(e){var t=new UnaryOpProgram(e.shape,ATAN);return this.compileAndRun(t,[e])},e.prototype.atan2=function(e,t){var n=new BinaryOpProgram(ATAN2,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.sinh=function(e){var t=new UnaryOpProgram(e.shape,SINH);return this.compileAndRun(t,[e])},e.prototype.cosh=function(e){var t=new UnaryOpProgram(e.shape,COSH);return this.compileAndRun(t,[e])},e.prototype.tanh=function(e){var t=new UnaryOpProgram(e.shape,TANH);return this.compileAndRun(t,[e])},e.prototype.asinh=function(e){var t=new UnaryOpProgram(e.shape,ASINH);return this.compileAndRun(t,[e])},e.prototype.acosh=function(e){var t=new UnaryOpProgram(e.shape,ACOSH),n=t.getCustomSetupFunc();return this.compileAndRun(t,[e],null,n)},e.prototype.atanh=function(e){var t=new UnaryOpProgram(e.shape,ATANH),n=t.getCustomSetupFunc();return this.compileAndRun(t,[e],null,n)},e.prototype.erf=function(e){var t=new UnaryOpProgram(e.shape,ERF);return this.compileAndRun(t,[e])},e.prototype.step=function(e,t){var n=new UnaryOpProgram(e.shape,STEP(t));return this.compileAndRun(n,[e])},e.prototype.conv2dWithIm2Row=function(e,t,n){var r=n.filterWidth,o=n.filterHeight,a=n.inChannels,i=n.outWidth,s=n.outHeight,u=r*o*a,l=s*i,c=[u,l],p=e.squeeze([0]),d=t.reshape([u,-1]),h=new Im2ColProgram(c,p.shape,n),f=this.compileAndRun(h,[p]),m=new MatMulPackedProgram(f.shape,d.shape,[l,n.outChannels],!0,!1);return this.compileAndRun(m,[f,d]).reshape([1,s,i,n.outChannels])},e.prototype.conv2d=function(e,t,n){if(ENV.get(\"WEBGL_CONV_IM2COL\")&&1===e.shape[0])return this.conv2dWithIm2Row(e,t,n);var r=new Conv2DProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv2dDerInput=function(e,t,n){var r=new Conv2DDerInputProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv2dDerFilter=function(e,t,n){var r=new Conv2DDerFilterProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.depthwiseConv2D=function(e,t,n){var r;return ENV.get(\"WEBGL_PACK_DEPTHWISECONV\")&&1===n.dilationWidth&&1===n.dilationHeight&&n.padInfo.left<=1&&n.strideWidth<=2&&n.outChannels/n.inChannels==1?(r=new DepthwiseConvPacked2DProgram(n),this.compileAndRun(r,[e,t],this.makePackedTensor(n.outShape,e.dtype))):(r=new DepthwiseConv2DProgram(n),this.compileAndRun(r,[e,t]))},e.prototype.depthwiseConv2DDerInput=function(e,t,n){var r=new DepthwiseConv2DDerInputProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.depthwiseConv2DDerFilter=function(e,t,n){var r=new DepthwiseConv2DDerFilterProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv3d=function(e,t,n){var r=new Conv3DProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv3dDerInput=function(e,t,n){var r=new Conv3DDerInputProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv3dDerFilter=function(e,t,n){var r=new Conv3DDerFilterProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.maxPool=function(e,t){var n=new Pool2DProgram(t,\"max\",!1),r=this.makeOutputArray(n.outputShape,e.dtype);return this.compileAndRun(n,[e],r)},e.prototype.avgPool=function(e,t){var n=new Pool2DProgram(t,\"avg\",!1),r=this.makeOutputArray(n.outputShape,\"float32\");return this.compileAndRun(n,[e],r)},e.prototype.maxPoolBackprop=function(e,t,n,r){var o=new Pool2DProgram(r,\"max\",!0),a=this.compileAndRun(o,[t]),i=new MaxPool2DBackpropProgram(r),s=this.makeOutputArray(i.outputShape,t.dtype),u=this.compileAndRun(i,[e,a],s);return a.dispose(),u},e.prototype.avgPoolBackprop=function(e,t,n){var r=new AvgPool2DBackpropProgram(n),o=this.makeOutputArray(r.outputShape,t.dtype);return this.compileAndRun(r,[e],o)},e.prototype.cast=function(e,t){return castTensor(e,t,this)},e.prototype.reshape=function(e,t){return this.texData.get(e.dataId).isPacked&&!isReshapeFree(e.shape,t)?this.packedReshape(e,t):reshapeTensor(e,t)},e.prototype.resizeBilinear=function(e,t,n,r){var o=new ResizeBilinearProgram(e.shape,t,n,r);return this.compileAndRun(o,[e])},e.prototype.resizeBilinearBackprop=function(e,t,n){var r=new ResizeBilinearBackpropProgram(e,t,n);return this.compileAndRun(r,[e])},e.prototype.resizeNearestNeighbor=function(e,t,n,r){var o=new ResizeNearestNeighborProgram(e.shape,t,n,r);return this.compileAndRun(o,[e])},e.prototype.resizeNearestNeighborBackprop=function(e,t,n){var r=new ResizeNearestNeigborBackpropProgram(e,t,n);return this.compileAndRun(r,[e])},e.prototype.multinomial=function(e,t,n,r){var o=t?e:softmax(e),a=o.shape[0],i=o.shape[1],s=new MultinomialProgram(a,i,n),u=this.makeOutputArray(s.outputShape,\"int32\"),l=s.getCustomSetupFunc(r);return this.compileAndRun(s,[o],u,l)},e.prototype.oneHot=function(e,t,n,r){var o=new OneHotProgram(e.size,t,n,r);return this.compileAndRun(o,[e])},e.prototype.nonMaxSuppression=function(e,t,n,r,o){return warn(\"tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\"),nonMaxSuppressionImpl(e.dataSync(),t.dataSync(),n,r,o)},e.prototype.cropAndResize=function(e,t,n,r,o,a){var i=new CropAndResizeProgram(e.shape,t.shape,r,o,a);return this.compileAndRun(i,[e,t,n])},e.prototype.depthToSpace=function(e,t,n){assert(t>1,\"blockSize should be > 1 for depthToSpace, but was: \"+t);var r=e.shape[0],o=(\"NHWC\"===n?e.shape[1]:e.shape[2])*t,a=(\"NHWC\"===n?e.shape[2]:e.shape[3])*t,i=(\"NHWC\"===n?e.shape[3]:e.shape[1])/(t*t),s=new DepthToSpaceProgram(\"NHWC\"===n?[r,o,a,i]:[r,i,o,a],t,n);return this.compileAndRun(s,[e])},e.prototype.split=function(e,t,n){return split(e,t,n)},e.prototype.scatterND=function(e,t,n){var r=calculateShapes(t,e,n),o=r.sliceRank,a=r.numUpdates,i=r.sliceSize,s=r.strides,u=r.outputSize,l=[u/i,i],c=e.reshape([a,o]),p=t.reshape([a,i]);if(0===u)return reshapeTensor(tensor([]),n);var d=scalar(0),h=new ScatterProgram(a,o,c.rank,p.rank,s,l);return this.compileAndRun(h,[p,c,d]).reshape(n)},e.prototype.sparseToDense=function(e,t,n,r){var o=calculateShapes(t,e,n),a=o.sliceRank,i=o.numUpdates,s=o.strides,u=o.outputSize,l=new ScatterProgram(i,a,e.rank,t.rank,s,[u,1],!1);return this.compileAndRun(l,[t,e,r]).reshape(n)},e.prototype.fft=function(e){return this.fftImpl(e,!1)},e.prototype.ifft=function(e){return this.fftImpl(e,!0)},e.prototype.fftImpl=function(e,t){var n=this.texData.get(e.dataId),r=new FFTProgram(COMPLEX_FFT.REAL,e.shape,t),o=new FFTProgram(COMPLEX_FFT.IMAG,e.shape,t),a=[this.makeComplexComponentTensorHandle(e,n.complexTensors.real),this.makeComplexComponentTensorHandle(e,n.complexTensors.imag)],i=this.compileAndRun(r,a),s=this.compileAndRun(o,a),u=this.complex(i,s).as2D(e.shape[0],e.shape[1]);return i.dispose(),s.dispose(),u},e.prototype.gatherND=function(e,t){var n=t.shape,r=n[n.length-1],o=prepareAndValidate(e,t),a=o[0],i=o[1],s=o[2],u=o[3],l=t.reshape([i,r]),c=e.reshape([e.size/s,s]),p=new GatherNDProgram(r,u,[i,s]);return this.compileAndRun(p,[c,l]).reshape(a)},e.prototype.makeOutputArray=function(e,t){return Tensor.make(e,{},t)},e.prototype.makePackedTensor=function(e,t){var n=Tensor.make(e,{},t);return this.texData.get(n.dataId).isPacked=!0,n},e.prototype.unpackTensor=function(e){var t=new UnpackProgram(e.shape);return this.compileAndRun(t,[e],Tensor.make(t.outputShape,{},e.dtype))},e.prototype.getBatchDim=function(e,t){return void 0===t&&(t=2),sizeFromShape(e.slice(0,e.length-t))},e.prototype.getRowsCols=function(e){if(0===e.length)throw Error(\"Cannot get rows and columns of an empty shape array.\");return[e.length>1?e[e.length-2]:1,e[e.length-1]]},e.prototype.packedReshape=function(e,t){var n=e.reshape([this.getBatchDim(e.shape)].concat(this.getRowsCols(e.shape))),r=[this.getBatchDim(t)].concat(this.getRowsCols(t)),o=new ReshapePackedProgram(r,n.shape);return this.compileAndRun(o,[n]).reshape(t)},e.prototype.compileAndRun=function(e,t,n,r,o){var a=this;if(void 0===o&&(o=!0),null==n&&(n=e.usesPackedTextures?this.makePackedTensor(e.outputShape,t[0].dtype):this.makeOutputArray(e.outputShape,t[0].dtype)),0===n.size)return this.texData.get(n.dataId).values=getTypedArrayFromDType(n.dtype,0),n;var i=t.map(function(t){if(\"complex64\"===t.dtype)throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");var n=a.texData.get(t.dataId);if(null==n.texture){if(!e.usesPackedTextures&&sizeFromShape(t.shape)<=ENV.get(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:t.shape,texData:null,isUniform:!0,uniformValues:a.readSync(t.dataId)};e.usesPackedTextures&&(n.isPacked=!0,n.shape=t.shape)}else if(!!n.isPacked!=!!e.usesPackedTextures){var r=void 0,o=void 0;n.isPacked?(r=new UnpackProgram(t.shape),o=a.compileAndRun(r,[t],Tensor.make(r.outputShape,{},t.dtype))):(r=new PackProgram(t.shape),o=a.compileAndRun(r,[t],a.makePackedTensor(t.shape,t.dtype))),n=a.texData.get(o.dataId),t=o}else if(n.isPacked&&!isReshapeFree(n.shape,t.shape)){a.delayedStorage=!1;var i=t.dataSync();a.delayedStorage=!0,t=Tensor.make(t.shape,{values:i},t.dtype),(n=a.texData.get(t.dataId)).isPacked=!0}return a.uploadToGPU(t.dataId),{shape:t.shape,texData:n,isUniform:!1}});this.uploadToGPU(n.dataId);var s,u={shape:n.shape,texData:this.texData.get(n.dataId),isUniform:!1},l=makeShaderKey(e,i,u),c=this.getAndSaveBinary(l,function(){return compileProgram(a.gpgpu,e,i,u)}),p=null!=this.activeTimers;if(p&&(s=this.startTimer()),runProgram(c,i,u,r),ENV.get(\"WEBGL_PAGING_ENABLED\")&&o&&this.numBytesInGPU>this.NUM_BYTES_BEFORE_PAGING)for(var d=this.numBytesInGPU-this.NUM_BYTES_BEFORE_PAGING;d>0&&this.lruDataGPU.length>0;){var h=this.lruDataGPU.shift(),f=this.texData.get(h),m=f.shape,g=f.dtype;d-=this.computeBytes(m,g),this.read(h)}return p&&(s=this.endTimer(s),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(s)})),ENV.get(\"WEBGL_LAZILY_UNPACK\")||!this.texData.get(n.dataId).isPacked||e.isPackShader?n:this.unpackTensor(n)},e.prototype.getAndSaveBinary=function(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]},e.prototype.getTextureManager=function(){return this.textureManager},e.prototype.dispose=function(){if(!this.disposed){for(var e in this.binaryCache)this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram);this.textureManager.dispose(),this.canvas.remove(),null!=this.fromPixels2DContext&&this.fromPixels2DContext.canvas.remove(),this.gpgpuCreatedLocally&&this.gpgpu.dispose(),this.disposed=!0}},e.prototype.floatPrecision=function(){var e=this;return tidy(function(){return e.abs(scalar(1e-8)).get()>0?32:16})},e.prototype.uploadToGPU=function(e){var t,n=this.texData.get(e),r=n.shape,o=n.values,a=n.texture,i=n.usage,s=n.isPacked;if(null==a){var u,l=null!=this.activeTimers;l&&(u=performance.now());var c=getTextureShapeFromLogicalShape(r,s);n.texShape=c;var p=this.acquireTexture(e,c,i,s);if(n.texture=p,null!=o){if(s){var d=this.getBatchDim(r),h=1,f=1;r.length&&(h=(t=this.getRowsCols(r))[0],f=t[1]),this.gpgpu.uploadMatrixToPackedTexture(p,d,h,f,c[0],c[1],typedArrayToFloat32(o))}else this.gpgpu.uploadMatrixToTexture(p,c[0],c[1],typedArrayToFloat32(o));n.values=null,l&&(this.uploadWaitMs+=performance.now()-u)}}else{ENV.get(\"WEBGL_PAGING_ENABLED\")&&this.lruDataGPU.indexOf(e)>=0&&(this.lruDataGPU.splice(this.lruDataGPU.indexOf(e),1),this.lruDataGPU.push(e))}},e.prototype.convertAndCacheOnCPU=function(e,t){var n=this.delayedStorage,r=this.texData.get(e),o=r.texture,a=r.texShape,i=r.dtype,s=r.usage,u=r.isPacked;return n&&null!=o&&(this.releaseTexture(e,o,a,s,u),r.texture=null,r.texShape=null),r.usage=TextureUsage.UPLOAD,null!=t&&(r.values=float32ToTypedArray(t,i)),r.values},e.prototype.releaseTexture=function(e,t,n,r,o){var a=this.texData.get(e),i=a.shape,s=a.dtype;if(ENV.get(\"WEBGL_PAGING_ENABLED\")){var u=this.lruDataGPU.indexOf(e);u>=0&&this.lruDataGPU.splice(u,1)}this.numBytesInGPU-=this.computeBytes(i,s),this.textureManager.releaseTexture(t,n,r,o)},e.prototype.acquireTexture=function(e,t,n,r){var o=this.texData.get(e),a=o.shape,i=o.dtype;return ENV.get(\"WEBGL_PAGING_ENABLED\")&&this.lruDataGPU.push(e),this.numBytesInGPU+=this.computeBytes(a,i),this.textureManager.acquireTexture(t,n,r)},e.prototype.computeBytes=function(e,t){return sizeFromShape(e)*bytesPerElement(t)},e}();function float32ToTypedArray(e,t){if(\"float32\"===t||\"complex64\"===t)return e;if(\"int32\"===t||\"bool\"===t){for(var n=\"int32\"===t?new Int32Array(e.length):new Uint8Array(e.length),r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}throw new Error(\"Unknown dtype \"+t)}function typedArrayToFloat32(e){return e instanceof Float32Array?e:new Float32Array(e)}function neg_(e){var t=convertToTensor(e,\"x\",\"neg\");return ENV.engine.runKernel(function(e){return e.neg(t)},{$x:t},function(e){return{$x:function(){return e.neg()}}})}function ceil_(e){var t=convertToTensor(e,\"x\",\"ceil\");return ENV.engine.runKernel(function(e){return e.ceil(t)},{$x:t},function(e){return{$x:function(){return zerosLike(e)}}})}function floor_(e){var t=convertToTensor(e,\"x\",\"floor\");return ENV.engine.runKernel(function(e){return e.floor(t)},{$x:t},function(e){return{$x:function(){return zerosLike(e)}}})}function sign_(e){var t=convertToTensor(e,\"x\",\"sign\");return ENV.engine.runKernel(function(e){return e.sign(t)},{$x:t},function(e){return{$x:function(){return zerosLike(e)}}})}function round_(e){var t=convertToTensor(e,\"x\",\"round\");return ENV.engine.runKernel(function(e){return e.round(t)},{$x:t},function(e){return{$x:function(){return zerosLike(e)}}})}function exp_(e){var t=convertToTensor(e,\"x\",\"exp\");return ENV.engine.runKernel(function(e,n){return n(e.exp(t))},{$x:t},function(e,t){var n=t[0];return{$x:function(){return e.mulStrict(n)}}})}function expm1_(e){var t=convertToTensor(e,\"x\",\"expm1\");return ENV.engine.runKernel(function(e){return e.expm1(t)},{$x:t},function(e){return{$x:function(){return e.mulStrict(t.exp())}}})}function log_(e){var t=convertToTensor(e,\"x\",\"log\");return ENV.engine.runKernel(function(e){return e.log(t)},{$x:t},function(e){return{$x:function(){return e.divStrict(t.toFloat())}}})}function log1p_(e){var t=convertToTensor(e,\"x\",\"log1p\");return ENV.engine.runKernel(function(e){return e.log1p(t)},{$x:t},function(e){return{$x:function(){return e.div(t.add(1))}}})}function sqrt_(e){var t=convertToTensor(e,\"x\",\"sqrt\");return ENV.engine.runKernel(function(e){return e.sqrt(t)},{$x:t},function(e){return{$x:function(){return e.div(t.toFloat().sqrt().mul(2))}}})}function rsqrt_(e){var t=convertToTensor(e,\"x\",\"rsqrt\");return ENV.engine.runKernel(function(e){return e.rsqrt(t)},{$x:t},function(e){return{$x:function(){return e.div(t.pow(1.5).mul(2)).neg()}}})}function square_(e){var t=convertToTensor(e,\"x\",\"square\");return ENV.engine.runKernel(function(e){return e.square(t)},{$x:t},function(e){return{$x:function(){return e.mul(t.toFloat().mul(2))}}})}function reciprocal_(e){var t=convertToTensor(e,\"x\",\"reciprocal\");return ENV.engine.runKernel(function(e){return e.reciprocal(t)},{$x:t},function(e){return{$x:function(){return e.divStrict(t.square().neg())}}})}function abs_(e){var t=convertToTensor(e,\"x\",\"abs\");if(\"complex64\"===t.dtype)return ENV.engine.runKernel(function(e){return e.complexAbs(t)},{$x:t});return ENV.engine.runKernel(function(e){return e.abs(t)},{$x:t},function(e){return{$x:function(){return e.mulStrict(t.toFloat().step(-1))}}})}function clipByValue_(e,t,n){var r=convertToTensor(e,\"x\",\"clipByValue\");assert(t<=n,\"Error in clip: min (\"+t+\") must be less than or equal to max (\"+n+\").\");return ENV.engine.runKernel(function(e){return e.clip(r,t,n)},{$x:r},function(e){return{$x:function(){return e.where(r.greaterEqual(t).logicalAnd(r.lessEqual(n)),zerosLike(e))}}})}function sigmoid_(e){var t=convertToTensor(e,\"x\",\"sigmoid\");return ENV.engine.runKernel(function(e,n){return n(e.sigmoid(t))},{$x:t},function(e,t){var n=t[0];return{$x:function(){return e.mul(n.mul(scalar(1).sub(n)))}}})}function logSigmoid_(e){var t=convertToTensor(e,\"x\",\"logSigmoid\");return ENV.engine.runKernel(function(e){return e.softplus(t.neg()).neg()},{$x:t},function(e){return{$x:function(){return e.mulStrict(t.neg().sigmoid())}}})}function softplus_(e){var t=convertToTensor(e,\"x\",\"softplus\");return ENV.engine.runKernel(function(e){return e.softplus(t)},{$x:t},function(e){return{$x:function(){return e.mulStrict(t.sigmoid())}}})}function sin_(e){var t=convertToTensor(e,\"x\",\"sin\");return ENV.engine.runKernel(function(e){return e.sin(t)},{$x:t},function(e){return{$x:function(){return t.toFloat().cos().mulStrict(e)}}})}function cos_(e){var t=convertToTensor(e,\"x\",\"cos\");return ENV.engine.runKernel(function(e){return e.cos(t)},{$x:t},function(e){return{$x:function(){return t.toFloat().sin().neg().mulStrict(e)}}})}function tan_(e){var t=convertToTensor(e,\"x\",\"tan\");return ENV.engine.runKernel(function(e){return e.tan(t)},{$x:t},function(e){return{$x:function(){return e.divStrict(t.cos().square())}}})}function asin_(e){var t=convertToTensor(e,\"x\",\"asin\");return ENV.engine.runKernel(function(e){return e.asin(t)},{$x:t},function(e){return{$x:function(){return e.divStrict(scalar(1).sub(t.toFloat().square()).sqrt())}}})}function acos_(e){var t=convertToTensor(e,\"x\",\"acos\");return ENV.engine.runKernel(function(e){return e.acos(t)},{$x:t},function(e){return{$x:function(){return e.divStrict(scalar(1).sub(t.toFloat().square()).sqrt()).neg()}}})}function atan_(e){var t=convertToTensor(e,\"x\",\"atan\");return ENV.engine.runKernel(function(e){return e.atan(t)},{$x:t},function(e){return{$x:function(){return e.div(t.toFloat().square().add(1))}}})}function sinh_(e){var t=convertToTensor(e,\"x\",\"sinh\");return ENV.engine.runKernel(function(e){return e.sinh(t)},{$x:t},function(e){return{$x:function(){return t.toFloat().cosh().mulStrict(e)}}})}function cosh_(e){var t=convertToTensor(e,\"x\",\"cosh\");return ENV.engine.runKernel(function(e){return e.cosh(t)},{$x:t},function(e){return{$x:function(){return t.toFloat().sinh().mulStrict(e)}}})}function tanh_(e){var t=convertToTensor(e,\"x\",\"tanh\");return ENV.engine.runKernel(function(e,n){return n(e.tanh(t))},{$x:t},function(e,t){var n=t[0];return{$x:function(){return scalar(1).sub(n.square()).mulStrict(e)}}})}function asinh_(e){var t=convertToTensor(e,\"x\",\"asinh\");return ENV.engine.runKernel(function(e){return e.asinh(t)},{$x:t},function(e){return{$x:function(){return e.divStrict(scalar(1).add(t.toFloat().square()).sqrt())}}})}function acosh_(e){var t=convertToTensor(e,\"x\",\"acosh\");return ENV.engine.runKernel(function(e){return e.acosh(t)},{$x:t},function(e){return{$x:function(){return e.divStrict(t.toFloat().square().sub(1).sqrt())}}})}function atanh_(e){var t=convertToTensor(e,\"x\",\"atanh\");return ENV.engine.runKernel(function(e){return e.atanh(t)},{$x:t},function(e){return{$x:function(){return e.div(scalar(1).sub(t.toFloat().square()))}}})}function erf_(e){var t=convertToTensor(e,\"x\",\"erf\");assert(\"int32\"===t.dtype||\"float32\"===t.dtype,\"Input dtype must be `int32` or `float32`.\"),\"int32\"===t.dtype&&(t=t.toFloat());return ENV.engine.runKernel(function(e){return e.erf(t)},{$x:t},function(e){return{$x:function(){return e.mul(t.square().neg().exp().mul(2/Math.sqrt(Math.PI)))}}})}function step_(e,t){void 0===t&&(t=0);var n=convertToTensor(e,\"x\",\"step\");return ENV.engine.runKernel(function(e){return e.step(n,t)},{$x:n},function(e){return{$x:function(){return zerosLike(e)}}})}ENV.get(\"IS_BROWSER\")&&ENV.registerBackend(\"webgl\",function(){return new MathBackendWebGL},2,setTensorTracker);var abs=op({abs_:abs_}),acos=op({acos_:acos_}),acosh=op({acosh_:acosh_}),asin=op({asin_:asin_}),asinh=op({asinh_:asinh_}),atan=op({atan_:atan_}),atanh=op({atanh_:atanh_}),ceil=op({ceil_:ceil_}),clipByValue=op({clipByValue_:clipByValue_}),cos=op({cos_:cos_}),cosh=op({cosh_:cosh_}),erf=op({erf_:erf_}),exp=op({exp_:exp_}),expm1=op({expm1_:expm1_}),floor=op({floor_:floor_}),log$1=op({log_:log_}),log1p=op({log1p_:log1p_}),logSigmoid=op({logSigmoid_:logSigmoid_}),neg=op({neg_:neg_}),reciprocal=op({reciprocal_:reciprocal_}),round=op({round_:round_}),rsqrt=op({rsqrt_:rsqrt_}),sigmoid=op({sigmoid_:sigmoid_}),sign=op({sign_:sign_}),sin=op({sin_:sin_}),sinh=op({sinh_:sinh_}),softplus=op({softplus_:softplus_}),sqrt=op({sqrt_:sqrt_}),square=op({square_:square_}),step=op({step_:step_}),tan=op({tan_:tan_}),tanh$1=op({tanh_:tanh_});function batchNormalization2d_(e,t,n,r,o,a){void 0===r&&(r=.001);var i,s,u=convertToTensor(e,\"x\",\"batchNormalization\"),l=convertToTensor(t,\"mean\",\"batchNormalization\"),c=convertToTensor(n,\"variance\",\"batchNormalization\");return null!=o&&(i=convertToTensor(o,\"scale\",\"batchNormalization\")),null!=a&&(s=convertToTensor(a,\"offset\",\"batchNormalization\")),assert(2===u.rank,\"Error in batchNormalization3D: x must be rank 3 but got rank \"+u.rank+\".\"),assert(2===l.rank||1===l.rank,\"Error in batchNormalization2D: mean must be rank 2 or rank 1 but got rank \"+l.rank+\".\"),assert(2===c.rank||1===c.rank,\"Error in batchNormalization2D: variance must be rank 2 or rank 1 but got rank \"+c.rank+\".\"),null!=i&&assert(2===i.rank||1===i.rank,\"Error in batchNormalization2D: scale must be rank 2 or rank 1 but got rank \"+i.rank+\".\"),null!=s&&assert(2===s.rank||1===s.rank,\"Error in batchNormalization2D: offset must be rank 2 or rank 1 but got rank \"+s.rank+\".\"),batchNormalization(u,l,c,r,i,s)}function batchNormalization3d_(e,t,n,r,o,a){void 0===r&&(r=.001);var i,s,u=convertToTensor(e,\"x\",\"batchNormalization\"),l=convertToTensor(t,\"mean\",\"batchNormalization\"),c=convertToTensor(n,\"variance\",\"batchNormalization\");return null!=o&&(i=convertToTensor(o,\"scale\",\"batchNormalization\")),null!=a&&(s=convertToTensor(a,\"offset\",\"batchNormalization\")),assert(3===u.rank,\"Error in batchNormalization3D: x must be rank 3 but got rank \"+u.rank+\".\"),assert(3===l.rank||1===l.rank,\"Error in batchNormalization3D: mean must be rank 3 or rank 1 but got rank \"+l.rank+\".\"),assert(3===c.rank||1===c.rank,\"Error in batchNormalization3D: variance must be rank 3 or rank 1 but got rank \"+c.rank+\".\"),null!=i&&assert(3===i.rank||1===i.rank,\"Error in batchNormalization3D: scale must be rank 3 or rank 1 but got rank \"+i.rank+\".\"),null!=s&&assert(3===s.rank||1===s.rank,\"Error in batchNormalization3D: offset must be rank 3 or rank 1 but got rank \"+s.rank+\".\"),batchNormalization(u,l,c,r,i,s)}function batchNormalization4d_(e,t,n,r,o,a){void 0===r&&(r=.001);var i,s,u=convertToTensor(e,\"x\",\"batchNormalization\"),l=convertToTensor(t,\"mean\",\"batchNormalization\"),c=convertToTensor(n,\"variance\",\"batchNormalization\");return null!=o&&(i=convertToTensor(o,\"scale\",\"batchNormalization\")),null!=a&&(s=convertToTensor(a,\"offset\",\"batchNormalization\")),assert(4===u.rank,\"Error in batchNormalization4D: x must be rank 4 but got rank \"+u.rank+\".\"),assert(4===l.rank||1===l.rank,\"Error in batchNormalization4D: mean must be rank 4 or rank 1 but got rank \"+l.rank+\".\"),assert(4===c.rank||1===c.rank,\"Error in batchNormalization4D: variance must be rank 4 or rank 1 but got rank \"+c.rank+\".\"),null!=i&&assert(4===i.rank||1===i.rank,\"Error in batchNormalization4D: scale must be rank 4 or rank 1 but got rank \"+i.rank+\".\"),null!=s&&assert(4===s.rank||1===s.rank,\"Error in batchNormalization4D: offset must be rank 4 or rank 1 but got rank \"+s.rank+\".\"),batchNormalization(u,l,c,r,i,s)}function batchNormalization_(e,t,n,r,o,a){void 0===r&&(r=.001);var i,s,u,l=convertToTensor(e,\"x\",\"batchNormalization\"),c=convertToTensor(t,\"mean\",\"batchNormalization\"),p=convertToTensor(n,\"variance\",\"batchNormalization\");null!=o&&(i=convertToTensor(o,\"scale\",\"batchNormalization\")),null!=a&&(s=convertToTensor(a,\"offset\",\"batchNormalization\")),assert(c.rank===p.rank,\"Batch normalization gradient requires mean and variance to have equal ranks.\"),assert(null==s||c.rank===s.rank,\"Batch normalization gradient requires mean and offset to have equal ranks.\"),assert(null==i||c.rank===i.rank,\"Batch normalization gradient requires mean and scale to have equal ranks.\"),u=0===l.rank||1===l.rank?l.as4D(1,1,1,l.size):2===l.rank?l.as4D(1,1,l.shape[0],l.shape[1]):3===l.rank?l.as4D(1,l.shape[0],l.shape[1],l.shape[2]):l;return ENV.engine.runKernel(function(e){return e.batchNormalization(u,batchnormReshape4D(c),batchnormReshape4D(p),r,batchnormReshape4D(i),batchnormReshape4D(s))},{$x:l,$mean:c,$variance:p,$scale:i,$offset:s},function(e){var t=null==i?scalar(1):i,n=getReductionAxes(c.shape,u.shape),o=[];if(1===c.rank){for(var a=0;a<u.shape.length-1;++a)o.push(u.shape[a]);o.push(1)}var s=l.sub(c),d=e.mul(t),h=rsqrt(p.add(scalar(r))),f=h.mul(h).mul(h).mul(scalar(-.5));return{$x:function(){return 1===c.rank?e.mul(tile(h.as4D(1,1,1,c.shape[0]),o)).mul(t).reshape(l.shape):e.mul(h).mul(t).reshape(l.shape)},$mean:function(){var e=h.mul(scalar(-1)).mul(d);return 1===c.rank&&(e=e.sum(n)),e.reshape(c.shape)},$variance:function(){var e=f.mul(s).mul(d);return 1===c.rank&&(e=e.sum(n)),e.reshape(c.shape)},$scale:function(){var t=s.mul(h),r=e.mul(t);return 1===c.rank&&(r=r.sum(n)),r.reshape(c.shape)},$offset:function(){var t=e;return 1===c.rank&&(t=t.sum(n)),t.reshape(c.shape)}}}).reshape(l.shape)}function batchnormReshape4D(e){return null==e?null:0===e.rank?e.as1D():1===e.rank?e:2===e.rank?e.as4D(1,1,e.shape[0],e.shape[1]):3===e.rank?e.as4D(1,e.shape[0],e.shape[1],e.shape[2]):e}var batchNormalization2d=op({batchNormalization2d_:batchNormalization2d_}),batchNormalization3d=op({batchNormalization3d_:batchNormalization3d_}),batchNormalization4d=op({batchNormalization4d_:batchNormalization4d_}),batchNormalization=op({batchNormalization_:batchNormalization_});function computePool2DInfo(e,t,n,r,o,a,i){void 0===i&&(i=\"channelsLast\");var s,u=parseTupleParam(t),l=u[0],c=u[1];if(\"channelsLast\"===i)s=[l,c,e[3],e[3]];else{if(\"channelsFirst\"!==i)throw new Error(\"Unknown dataFormat \"+i);s=[l,c,e[1],e[1]]}return computeConv2DInfo(e,s,n,r,o,a,!1,i)}function computeConv2DInfo(e,t,n,r,o,a,i,s){void 0===i&&(i=!1),void 0===s&&(s=\"channelsLast\");var u=[-1,-1,-1,-1],l=u[0],c=u[1],p=u[2],d=u[3];if(\"channelsLast\"===s)l=e[0],c=e[1],p=e[2],d=e[3];else{if(\"channelsFirst\"!==s)throw new Error(\"Unknown dataFormat \"+s);l=e[0],d=e[1],c=e[2],p=e[3]}var h,f=t[0],m=t[1],g=t[3],v=parseTupleParam(n),y=v[0],x=v[1],T=parseTupleParam(r),w=T[0],E=T[1],S=getEffectiveFilterSize(f,w),b=getEffectiveFilterSize(m,E),C=getPadAndOutInfo(o,c,p,y,x,S,b,a),A=C.padInfo,_=C.outHeight,N=C.outWidth,R=i?g*d:g;return\"channelsFirst\"===s?h=[l,R,_,N]:\"channelsLast\"===s&&(h=[l,_,N,R]),{batchSize:l,dataFormat:s,inHeight:c,inWidth:p,inChannels:d,outHeight:_,outWidth:N,outChannels:R,padInfo:A,strideHeight:y,strideWidth:x,filterHeight:f,filterWidth:m,effectiveFilterHeight:S,effectiveFilterWidth:b,dilationHeight:w,dilationWidth:E,inShape:e,outShape:h,filterShape:t}}function computeConv3DInfo(e,t,n,r,o,a,i){void 0===a&&(a=!1),void 0===i&&(i=\"channelsLast\");var s=[-1,-1,-1,-1,-1],u=s[0],l=s[1],c=s[2],p=s[3],d=s[4];if(\"channelsLast\"===i)u=e[0],l=e[1],c=e[2],p=e[3],d=e[4];else{if(\"channelsFirst\"!==i)throw new Error(\"Unknown dataFormat \"+i);u=e[0],d=e[1],l=e[2],c=e[3],p=e[4]}var h,f=t[0],m=t[1],g=t[2],v=t[4],y=parse3TupleParam(n),x=y[0],T=y[1],w=y[2],E=parse3TupleParam(r),S=E[0],b=E[1],C=E[2],A=get3DPadAndOutInfo(o,l,c,p,x,T,w,getEffectiveFilterSize(f,S),getEffectiveFilterSize(m,b),getEffectiveFilterSize(g,C)),_=A.padInfo,N=A.outDepth,R=A.outHeight,k=A.outWidth,I=a?v*d:v;return\"channelsFirst\"===i?h=[u,I,N,R,k]:\"channelsLast\"===i&&(h=[u,N,R,k,I]),{batchSize:u,dataFormat:i,inDepth:l,inHeight:c,inWidth:p,inChannels:d,outDepth:N,outHeight:R,outWidth:k,outChannels:I,padInfo:_,strideDepth:x,strideHeight:T,strideWidth:w,filterDepth:f,filterHeight:m,filterWidth:g,dilationDepth:S,dilationHeight:b,dilationWidth:C,inShape:e,outShape:h,filterShape:t}}function computeOutputShape3D(e,t,n,r,o,a){null==o&&(o=computeDefaultPad(e,t,r));var i=e[0],s=e[1],u=conditionalRound((i-t+2*o)/r+1,a);assert(isInt(u),\"The output # of rows (\"+u+\") must be an integer. Change the stride and/or zero pad parameters\");var l=conditionalRound((s-t+2*o)/r+1,a);return assert(isInt(l),\"The output # of columns (\"+l+\") must be an integer. Change the stride and/or zero pad parameters\"),[u,l,n]}function computeDefaultPad(e,t,n,r){void 0===r&&(r=1);var o=getEffectiveFilterSize(t,r);return Math.floor((e[0]*(n-1)-n+o)/2)}function parseTupleParam(e){return\"number\"==typeof e?[e,e]:e}function parse3TupleParam(e){return\"number\"==typeof e?[e,e,e]:e}function getEffectiveFilterSize(e,t){return t<=1?e:e+(e-1)*(t-1)}function getPadAndOutInfo(e,t,n,r,o,a,i,s){var u,l,c;if(\"number\"==typeof e){u={top:e,bottom:e,left:e,right:e,type:0===e?\"VALID\":\"NUMBER\"};var p=computeOutputShape3D([t,n,1],a,1,r,e,s);l=p[0],c=p[1]}else if(\"same\"===e){var d=((l=Math.ceil(t/r))-1)*r+a-t,h=((c=Math.ceil(n/o))-1)*o+i-n,f=Math.floor(d/2),m=d-f,g=Math.floor(h/2);u={top:f,bottom:m,left:g,right:h-g,type:\"SAME\"}}else{if(\"valid\"!==e)throw Error(\"Unknown padding parameter: \"+e);u={top:0,bottom:0,left:0,right:0,type:\"VALID\"},l=Math.ceil((t-a+1)/r),c=Math.ceil((n-i+1)/o)}return{padInfo:u,outHeight:l,outWidth:c}}function get3DPadAndOutInfo(e,t,n,r,o,a,i,s,u,l){var c,p,d,h;if(\"same\"===e){var f=((p=Math.ceil(t/o))-1)*o+s-t,m=((d=Math.ceil(n/a))-1)*a+u-n,g=((h=Math.ceil(r/i))-1)*i+l-r,v=Math.floor(f/2),y=f-v,x=Math.floor(m/2),T=m-x,w=Math.floor(g/2);c={top:x,bottom:T,left:w,right:g-w,front:v,back:y,type:\"SAME\"}}else{if(\"valid\"!==e)throw Error(\"Unknown padding parameter: \"+e);c={top:0,bottom:0,left:0,right:0,front:0,back:0,type:\"VALID\"},p=Math.ceil((t-s+1)/o),d=Math.ceil((n-u+1)/a),h=Math.ceil((r-l+1)/i)}return{padInfo:c,outDepth:p,outHeight:d,outWidth:h}}function conditionalRound(e,t){if(!t)return e;switch(t){case\"round\":return Math.round(e);case\"ceil\":return Math.ceil(e);case\"floor\":return Math.floor(e);default:throw new Error(\"Unknown roundingMode \"+t)}}function tupleValuesAreOne(e){var t=parseTupleParam(e),n=t[0],r=t[1];return 1===n&&1===r}function eitherStridesOrDilationsAreOne(e,t){return tupleValuesAreOne(e)||tupleValuesAreOne(t)}function matMul_(e,t,n,r){var o;void 0===n&&(n=!1),void 0===r&&(r=!1);var a=convertToTensor(e,\"a\",\"matMul\"),i=convertToTensor(t,\"b\",\"matMul\");a=(o=makeTypesMatch(a,i))[0],i=o[1];var s=n?a.shape[a.rank-2]:a.shape[a.rank-1],u=r?i.shape[i.rank-1]:i.shape[i.rank-2],l=n?a.shape[a.rank-1]:a.shape[a.rank-2],c=r?i.shape[i.rank-2]:i.shape[i.rank-1],p=a.shape.slice(0,-2),d=i.shape.slice(0,-2),h=sizeFromShape(p),f=sizeFromShape(d);assert(a.rank>=2&&i.rank>=2&&a.rank===i.rank,\"Error in matMul: inputs must have the same rank of at least 2, got ranks \"+a.rank+\" and \"+i.rank+\".\"),assert(arraysEqual(p,d),\"Error in matMul: outer dimensions (\"+p+\") and (\"+d+\") of Tensors with shapes \"+a.shape+\" and \"+i.shape+\" must match.\"),assert(s===u,\"Error in matMul: inner shapes (\"+s+\") and (\"+u+\") of Tensors with shapes \"+a.shape+\" and \"+i.shape+\" and transposeA=\"+n+\" and transposeB=\"+r+\" must match.\");var m=a.shape.slice(0,-2).concat([l,c]),g=n?a.as3D(h,s,l):a.as3D(h,l,s),v=r?i.as3D(f,c,u):i.as3D(f,u,c);return ENV.engine.runKernel(function(e){return e.batchMatMul(g,v,n,r)},{$a:g,$b:v},function(e){return n||r?!n&&r?{$a:function(){return e.matMul(v,!1,!1)},$b:function(){return e.matMul(g,!0,!1)}}:n&&!r?{$a:function(){return v.matMul(e,!1,!0)},$b:function(){return g.matMul(e,!1,!1)}}:{$a:function(){return v.matMul(e,!0,!0)},$b:function(){return e.matMul(g,!0,!0)}}:{$a:function(){return e.matMul(v,!1,!0)},$b:function(){return g.matMul(e,!0,!1)}}}).reshape(m)}function outerProduct_(e,t){var n=convertToTensor(e,\"v1\",\"outerProduct\"),r=convertToTensor(t,\"v2\",\"outerProduct\");return assert(1===n.rank&&1===r.rank,\"Error in outerProduct: inputs must be rank 1, but got ranks \"+n.rank+\" and \"+r.rank+\".\"),n.as2D(-1,1).matMul(r.as2D(1,-1))}function dot_(e,t){var n=convertToTensor(e,\"t1\",\"dot\"),r=convertToTensor(t,\"t2\",\"dot\");assert(!(1!==n.rank&&2!==n.rank||1!==r.rank&&2!==r.rank),\"Error in dot: inputs must all be rank 1 or 2, but got ranks \"+n.rank+\" and \"+r.rank+\".\");var o=1===n.rank?n.size:n.shape[1],a=1===r.rank?r.size:r.shape[0];return assert(o===a,\"Error in dot: inner dimensions of inputs must match, but got \"+o+\" and \"+a+\".\"),1===n.rank&&1===r.rank?n.as2D(1,-1).matMul(r.as2D(-1,1)).asScalar():1===n.rank&&2===r.rank?n.as2D(1,-1).matMul(r.as2D(r.shape[0],r.shape[1])).as1D():2===n.rank&&1===r.rank?n.matMul(r.as2D(-1,1)).as1D():n.matMul(r.as2D(r.shape[0],r.shape[1]))}var matMul=op({matMul_:matMul_}),dot=op({dot_:dot_}),outerProduct=op({outerProduct_:outerProduct_});function conv1d_(e,t,n,r,o,a,i){void 0===o&&(o=\"NWC\"),void 0===a&&(a=1);var s=convertToTensor(e,\"x\",\"conv1d\"),u=convertToTensor(t,\"filter\",\"conv1d\"),l=s,c=!1;2===s.rank&&(c=!0,l=s.as3D(1,s.shape[0],s.shape[1])),assert(3===l.rank,\"Error in conv1d: input must be rank 3, but got rank \"+l.rank+\".\"),assert(3===u.rank,\"Error in conv1d: filter must be rank 3, but got rank \"+u.rank+\".\"),null!=i&&assert(isInt(r),\"Error in conv1d: pad must be an integer when using, dimRoundingMode \"+i+\" but got pad \"+r+\".\"),assert(l.shape[2]===u.shape[1],\"Error in conv1d: depth of input (\"+l.shape[2]+\") must match input depth for filter \"+u.shape[1]+\".\"),assert(eitherStridesOrDilationsAreOne(n,a),\"Error in conv1D: Either stride or dilation must be 1. Got stride \"+n+\" and dilation '\"+a+\"'\"),assert(\"NWC\"===o,\"Error in conv1d: got dataFormat of \"+o+\" but only NWC is currently supported.\");var p=u.as4D(1,u.shape[0],u.shape[1],u.shape[2]),d=l.as4D(l.shape[0],1,l.shape[1],l.shape[2]),h=conv2d(d,p,[1,n],r,\"NHWC\",[1,a],i);return c?h.as2D(h.shape[2],h.shape[3]):h.as3D(h.shape[0],h.shape[2],h.shape[3])}function conv2d_(e,t,n,r,o,a,i){void 0===o&&(o=\"NHWC\"),void 0===a&&(a=[1,1]);var s=convertToTensor(e,\"x\",\"conv2d\"),u=convertToTensor(t,\"filter\",\"conv2d\"),l=s,c=!1;3===s.rank&&(c=!0,l=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),assert(4===l.rank,\"Error in conv2d: input must be rank 4, but got rank \"+l.rank+\".\"),assert(4===u.rank,\"Error in conv2d: filter must be rank 4, but got rank \"+u.rank+\".\"),null!=i&&assert(isInt(r),\"Error in conv2d: pad must be an integer when using, dimRoundingMode \"+i+\" but got pad \"+r+\".\"),assert(l.shape[3]===u.shape[2],\"Error in conv2d: depth of input (\"+l.shape[3]+\") must match input depth for filter \"+u.shape[2]+\".\"),assert(eitherStridesOrDilationsAreOne(n,a),\"Error in conv2D: Either strides or dilations must be 1. Got strides \"+n+\" and dilations '\"+a+\"'\"),assert(\"NHWC\"===o,\"Error in conv2d: got dataFormat of \"+o+\" but only NHWC is currently supported.\");var p,d=computeConv2DInfo(l.shape,u.shape,n,a,r,i);if(1!==d.filterHeight||1!==d.filterWidth||1!==d.dilationHeight||1!==d.dilationWidth||1!==d.strideHeight||1!==d.strideWidth||\"SAME\"!==d.padInfo.type&&\"VALID\"!==d.padInfo.type){p=ENV.engine.runKernel(function(e){return e.conv2d(l,u,d)},{x:l,$filter:u},function(e){return assert(tupleValuesAreOne(a),\"Error in gradient of conv2D: dilation rates greater than 1 are notyet supported in gradients. Got dilations '\"+a+\"'\"),{x:function(){return conv2dDerInput_(l.shape,e,u,n,r)},$filter:function(){return conv2dDerFilter_(l,e,u.shape,n,r)}}})}else{var h=l.reshape([-1,d.inChannels]),f=u.reshape([d.inChannels,d.outChannels]);p=matMul(h,f).reshape(d.outShape)}return c?p.as3D(p.shape[1],p.shape[2],p.shape[3]):p}function conv2dDerInput_(e,t,n,r,o,a){assert(e.length===t.rank,\"Length of inShape (\"+e.length+\") and rank of dy (\"+t.rank+\") must match\");var i=e,s=t,u=!1;3===t.rank&&(u=!0,s=t.as4D(1,t.shape[0],t.shape[1],t.shape[2]),i=[1,e[0],e[1],e[2]]);var l=i[3],c=s.shape[3];assert(4===i.length,\"Error in conv2dDerInput: inShape must be length 4, but got length \"+i.length+\".\"),assert(4===s.rank,\"Error in conv2dDerInput: dy must be rank 4, but got rank \"+s.rank),assert(4===n.rank,\"Error in conv2dDerInput: filter must be rank 4, but got rank \"+n.rank),assert(l===n.shape[2],\"Error in conv2dDerInput: depth of input (\"+l+\") must match input depth for filter \"+n.shape[2]+\".\"),assert(c===n.shape[3],\"Error in conv2dDerInput: depth of output (\"+c+\") must match output depth for filter \"+n.shape[3]+\".\"),null!=a&&assert(isInt(o),\"Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode \"+a+\" but got pad \"+o+\".\");var p=computeConv2DInfo(i,n.shape,r,1,o,a),d=ENV.engine.runKernel(function(e){return e.conv2dDerInput(s,n,p)},{dy4D:s,filter:n},function(e){return{dy4D:function(){return conv2d(e,n,r,o,\"NHWC\",1,a)},filter:function(){return conv2dDerFilter(e,s,n.shape,r,o,a)}}});return u?d.as3D(d.shape[1],d.shape[2],d.shape[3]):d}function conv2dDerFilter_(e,t,n,r,o,a){var i=e;3===e.rank&&(i=e.as4D(1,e.shape[0],e.shape[1],e.shape[2]));var s=t;3===s.rank&&(s=t.as4D(1,t.shape[0],t.shape[1],t.shape[2])),assert(4===i.rank,\"Error in conv2dDerFilter: input must be rank 4, but got shape \"+i.shape+\".\"),assert(4===s.rank,\"Error in conv2dDerFilter: dy must be rank 4, but got shape \"+s.shape+\".\"),assert(4===n.length,\"Error in conv2dDerFilter: filterShape must be length 4, but got \"+n+\".\"),assert(i.shape[3]===n[2],\"Error in conv2dDerFilter: depth of input \"+i.shape[3]+\") must match input depth in filter (\"+n[2]+\".\"),assert(s.shape[3]===n[3],\"Error in conv2dDerFilter: depth of dy (\"+s.shape[3]+\") must match output depth for filter (\"+n[3]+\").\"),null!=a&&assert(isInt(o),\"Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode \"+a+\" but got pad \"+o+\".\");var u=computeConv2DInfo(i.shape,n,r,1,o,a);return ENV.engine.runKernel(function(e){return e.conv2dDerFilter(i,s,u)},{x4D:i,dy4D:s})}function conv2dTranspose_(e,t,n,r,o,a){return conv2dDerInput_(n,convertToTensor(e,\"x\",\"conv2dTranspose\"),convertToTensor(t,\"filter\",\"conv2dTranspose\"),r,o,a)}function depthwiseConv2d_(e,t,n,r,o,a,i){void 0===o&&(o=\"NHWC\"),void 0===a&&(a=[1,1]);var s=convertToTensor(e,\"x\",\"depthwiseConv2d\"),u=convertToTensor(t,\"filter\",\"depthwiseConv2d\"),l=s,c=!1;3===s.rank&&(c=!0,l=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),assert(4===l.rank,\"Error in depthwiseConv2d: input must be rank 4, but got rank \"+l.rank+\".\"),assert(4===u.rank,\"Error in depthwiseConv2d: filter must be rank 4, but got rank \"+u.rank+\".\"),assert(l.shape[3]===u.shape[2],\"Error in depthwiseConv2d: number of input channels (\"+l.shape[3]+\") must match the inChannels dimension in filter \"+u.shape[2]+\".\"),null==a&&(a=[1,1]),assert(eitherStridesOrDilationsAreOne(n,a),\"Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides \"+n+\" and dilations '\"+a+\"'\"),null!=i&&assert(isInt(r),\"Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode \"+i+\" but got pad \"+r+\".\");var p=computeConv2DInfo(l.shape,u.shape,n,a,r,i,!0),d=ENV.engine.runKernel(function(e){return e.depthwiseConv2D(l,u,p)},{x:l,$filter:u},function(e){return assert(tupleValuesAreOne(a),\"Error in gradient of depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '\"+a+\"'\"),{x:function(){return depthwiseConv2dDerInput(l.shape,e,u,p)},$filter:function(){return depthwiseConv2dDerFilter(l,e,u.shape,p)}}});return c?d.as3D(d.shape[1],d.shape[2],d.shape[3]):d}function separableConv2d_(e,t,n,r,o,a,i){void 0===a&&(a=[1,1]),void 0===i&&(i=\"NHWC\");var s=convertToTensor(e,\"x\",\"separableConv2d\"),u=convertToTensor(t,\"depthwiseFilter\",\"separableConv2d\"),l=convertToTensor(n,\"pointwiseFilter\",\"separableConv2d\"),c=s,p=!1;if(3===s.rank&&(p=!0,c=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),\"NCHW\"===i)throw new Error(\"separableConv2d currently does not support dataFormat NCHW; only NHWC is supported\");assert(4===c.rank,\"Error in separableConv2d: input must be rank 4, but got rank \"+c.rank+\".\"),assert(4===u.rank,\"Error in separableConv2d: depthwise filter must be rank 4, but got rank \"+u.rank+\".\"),assert(4===l.rank,\"Error in separableConv2d: pointwise filter must be rank 4, but got rank \"+u.rank+\".\"),assert(1===l.shape[0],\"Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got \"+l.shape[0]+\".\"),assert(1===l.shape[1],\"Error in separableConv2d: the second dimension of pointwise filter  must be 1, but got \"+l.shape[1]+\".\");var d=u.shape[2],h=u.shape[3];assert(l.shape[2]===d*h,\"Error in separableConv2d: the third dimension of pointwise filter must be \"+d*h+\", but got \"+l.shape[2]+\".\");var f=depthwiseConv2d(c,u,r,o,i,a),m=conv2d(f,l,1,\"valid\",i);return p?m.as3D(m.shape[1],m.shape[2],m.shape[3]):m}function parseTupleParam$1(e){return\"number\"==typeof e?[e,e,e]:2===e.length?[e[0],e[1],1]:e}function tupleValuesAreOne$1(e){var t=parseTupleParam$1(e),n=t[0],r=t[1],o=t[2];return 1===n&&1===r&&1===o}function eitherStridesOrDilationsAreOne$1(e,t){return tupleValuesAreOne$1(e)||tupleValuesAreOne$1(t)}function depthwiseConv2dDerInput(e,t,n,r){var o=t,a=!1;3===t.rank&&(a=!0,o=t.as4D(1,t.shape[0],t.shape[1],t.shape[2]));var i=ENV.engine.runKernel(function(e){return e.depthwiseConv2DDerInput(o,n,r)},{dy4D:o});return a?i.as3D(i.shape[1],i.shape[2],i.shape[3]):i}function depthwiseConv2dDerFilter(e,t,n,r){var o=e;3===e.rank&&(o=e.as4D(1,e.shape[0],e.shape[1],e.shape[2]));var a=t;return 3===a.rank&&(a=t.as4D(1,t.shape[0],t.shape[1],t.shape[2])),ENV.engine.runKernel(function(e){return e.depthwiseConv2DDerFilter(o,a,r)},{x4D:o,dy4D:a})}function conv3d_(e,t,n,r,o,a){void 0===o&&(o=\"NHWC\"),void 0===a&&(a=[1,1,1]);var i=convertToTensor(e,\"x\",\"conv3d\"),s=convertToTensor(t,\"filter\",\"conv3d\"),u=i,l=!1;4===i.rank&&(l=!0,u=i.as5D(1,i.shape[0],i.shape[1],i.shape[2],i.shape[3])),assert(5===u.rank,\"Error in conv3d: input must be rank 5, but got rank \"+u.rank+\".\"),assert(5===s.rank,\"Error in conv3d: filter must be rank 5, but got rank \"+s.rank+\".\"),assert(u.shape[4]===s.shape[3],\"Error in conv3d: depth of input (\"+u.shape[4]+\") must match input depth for filter \"+s.shape[3]+\".\"),assert(eitherStridesOrDilationsAreOne$1(n,a),\"Error in conv3D: Either strides or dilations must be 1. Got strides \"+n+\" and dilations '\"+a+\"'\"),assert(\"NHWC\"===o,\"Error in conv3d: got dataFormat of \"+o+\" but only NHWC is currently supported.\");var c=computeConv3DInfo(u.shape,s.shape,n,a,r),p=ENV.engine.runKernel(function(e){return e.conv3d(u,s,c)},{x:u,$filter:s},function(e){return assert(tupleValuesAreOne$1(a),\"Error in gradient of conv3D: dilation rates greater than 1 are notyet supported in gradients. Got dilations '\"+a+\"'\"),{x:function(){return conv3dDerInput_(u.shape,e,s,n,r)},$filter:function(){return conv3dDerFilter_(u,e,s.shape,n,r)}}});return l?p.as4D(p.shape[1],p.shape[2],p.shape[3],p.shape[4]):p}function conv3dDerInput_(e,t,n,r,o){assert(e.length===t.rank,\"Length of inShape (\"+e.length+\") and rank of dy (\"+t.rank+\") must match\");var a=e,i=t,s=!1;4===t.rank&&(s=!0,i=t.as5D(1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]),a=[1,e[0],e[1],e[2],e[3]]);var u=a[4],l=i.shape[4];assert(5===a.length,\"Error in conv3dDerInput: inShape must be length 5, but got length \"+a.length+\".\"),assert(5===i.rank,\"Error in conv3dDerInput: dy must be rank 5, but got rank \"+i.rank),assert(5===n.rank,\"Error in conv3dDerInput: filter must be rank 5, but got rank \"+n.rank),assert(u===n.shape[3],\"Error in conv3dDerInput: depth of input (\"+u+\") must match input depth for filter \"+n.shape[3]+\".\"),assert(l===n.shape[4],\"Error in conv3dDerInput: depth of output (\"+l+\") must match output depth for filter \"+n.shape[4]+\".\");var c=computeConv3DInfo(a,n.shape,r,1,o),p=ENV.engine.runKernel(function(e){return e.conv3dDerInput(i,n,c)},{dy5D:i});return s?p.as4D(p.shape[1],p.shape[2],p.shape[3],p.shape[4]):p}function conv3dDerFilter_(e,t,n,r,o){var a=e;4===e.rank&&(a=e.as5D(1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]));var i=t;4===i.rank&&(i=t.as5D(1,t.shape[0],t.shape[1],t.shape[2],t.shape[3])),assert(5===a.rank,\"Error in conv3dDerFilter: input must be rank 5, but got shape \"+a.shape+\".\"),assert(5===i.rank,\"Error in conv3dDerFilter: dy must be rank 5, but got shape \"+i.shape+\".\"),assert(5===n.length,\"Error in conv3dDerFilter: filterShape must be length 5, but got \"+n+\".\"),assert(a.shape[4]===n[3],\"Error in conv3dDerFilter: depth of input \"+a.shape[4]+\") must match input depth in filter (\"+n[3]+\".\"),assert(i.shape[4]===n[4],\"Error in conv3dDerFilter: depth of dy (\"+i.shape[4]+\") must match output depth for filter (\"+n[4]+\").\");var s=computeConv3DInfo(a.shape,n,r,1,o);return ENV.engine.runKernel(function(e){return e.conv3dDerFilter(a,i,s)},{x5D:a,dy5D:i})}var conv1d=op({conv1d_:conv1d_}),conv2d=op({conv2d_:conv2d_}),conv3d=op({conv3d_:conv3d_}),conv2dDerFilter=op({conv2dDerFilter_:conv2dDerFilter_}),depthwiseConv2d=op({depthwiseConv2d_:depthwiseConv2d_}),separableConv2d=op({separableConv2d_:separableConv2d_}),conv2dTranspose=op({conv2dTranspose_:conv2dTranspose_});function reverse1d_(e){var t=convertToTensor(e,\"x\",\"reverse\");return assert(1===t.rank,\"Error in reverse1D: x must be rank 1 but got\\n             rank \"+t.rank+\".\"),reverse(t,0)}function reverse2d_(e,t){var n=convertToTensor(e,\"x\",\"reverse\");return assert(2===n.rank,\"Error in reverse2D: x must be rank 2 but got\\n             rank \"+n.rank+\".\"),reverse(n,t)}function reverse3d_(e,t){var n=convertToTensor(e,\"x\",\"reverse\");return assert(3===n.rank,\"Error in reverse3D: x must be rank 3 but got\\n             rank \"+n.rank+\".\"),reverse(n,t)}function reverse4d_(e,t){var n=convertToTensor(e,\"x\",\"reverse\");return assert(4===n.rank,\"Error in reverse4D: x must be rank 4 but got\\n             rank \"+n.rank+\".\"),reverse(n,t)}function reverse_(e,t){var n=convertToTensor(e,\"x\",\"reverse\");if(0===n.rank)return n.clone();var r=parseAxisParam(t,n.shape);return ENV.engine.runKernel(function(e){return e.reverse(n,r)},{$x:n},function(e){return{$x:function(){return e.reverse(r)}}}).reshapeAs(n)}var reverse=op({reverse_:reverse_}),reverse1d=op({reverse1d_:reverse1d_}),reverse2d=op({reverse2d_:reverse2d_}),reverse3d=op({reverse3d_:reverse3d_}),reverse4d=op({reverse4d_:reverse4d_});function maxPoolImpl_(e,t,n,r,o,a){var i=convertToTensor(e,\"x\",\"maxPool\"),s=i,u=!1;3===i.rank&&(u=!0,s=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),null==r&&(r=[1,1]),assert(4===s.rank,\"Error in maxPool: input must be rank 4 but got rank \"+s.rank+\".\"),assert(eitherStridesOrDilationsAreOne(n,r),\"Error in maxPool: Either strides or dilations must be 1. Got strides \"+n+\" and dilations '\"+r+\"'\"),null!=a&&assert(isInt(o),\"Error in maxPool: pad must be an integer when using, dimRoundingMode \"+a+\" but got pad \"+o+\".\");var l=computePool2DInfo(s.shape,t,n,r,o,a),c=ENV.engine.runKernel(function(e,t){return t(e.maxPool(s,l))},{x:s},function(e,a){var i=a[0];return{x:function(){return maxPoolBackprop(e,s,i,t,n,r,o)}}});return u?c.as3D(c.shape[1],c.shape[2],c.shape[3]):c}function maxPool_(e,t,n,r,o){return maxPoolImpl_(e,t,n,1,r,o)}function avgPoolImpl_(e,t,n,r,o,a){var i=convertToTensor(e,\"x\",\"avgPool\",\"float32\");null==r&&(r=[1,1]),assert(eitherStridesOrDilationsAreOne(n,r),\"Error in avgPool: Either strides or dilations must be 1. Got strides \"+n+\" and dilations '\"+r+\"'\");var s=i,u=!1;3===i.rank&&(u=!0,s=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),assert(4===s.rank,\"Error in avgPool: x must be rank 4 but got rank \"+s.rank+\".\"),null!=a&&assert(isInt(o),\"Error in avgPool: pad must be an integer when using, dimRoundingMode \"+a+\" but got pad \"+o+\".\");var l=computePool2DInfo(s.shape,t,n,r,o),c=ENV.engine.runKernel(function(e){return e.avgPool(s,l)},{x:s},function(e){return{x:function(){return avgPoolBackprop(e,s,t,n,r,o)}}});return c=c.cast(i.dtype),u?c.as3D(c.shape[1],c.shape[2],c.shape[3]):c}function avgPool_(e,t,n,r,o){return avgPoolImpl_(e,t,n,1,r,o)}function pool_(e,t,n,r,o,a){null==o&&(o=[1,1]),null==a&&(a=1),0===r&&(r=\"valid\");var i=convertToTensor(e,\"x\",\"maxPool\"),s=i,u=!1;3===i.rank&&(u=!0,s=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),assert(eitherStridesOrDilationsAreOne(a,o),\"Error in pool: Either strides or dilations must be 1. Got strides \"+a+\" and dilations '\"+o+\"'\");var l,c=computePool2DInfo(s.shape,t,a,o,r),p=[c.dilationHeight,c.dilationWidth];l=\"same\"===r?withSpaceToBatchBasePaddings([c.filterHeight,c.filterWidth],p):[[0,0],[0,0]];var d=1===p[0]&&1===p[1],h=requiredSpaceToBatchPaddings([c.inHeight,c.inWidth],p,l),f=h[0],m=h[1],g=d?r:\"valid\",v=d?s:spaceToBatchND(s,p,f),y=(\"avg\"===n?function(){return avgPoolImpl_(v,t,a,1,g)}:function(){return maxPoolImpl_(v,t,a,1,g)})(),x=d?y:batchToSpaceND(y,p,m);return u?x.as3D(x.shape[1],x.shape[2],x.shape[3]):x}function maxPoolBackprop(e,t,n,r,o,a,i,s){var u=convertToTensor(e,\"dy\",\"maxPoolBackprop\"),l=convertToTensor(t,\"input\",\"maxPoolBackprop\"),c=convertToTensor(n,\"output\",\"maxPoolBackprop\");assert(l.rank===u.rank,\"Rank of input (\"+l.rank+\") does not match rank of dy (\"+u.rank+\")\"),null==a&&(a=[1,1]),assert(eitherStridesOrDilationsAreOne(o,a),\"Error in maxPoolBackProp: Either strides or dilations must be 1. Got strides \"+o+\" and dilations '\"+a+\"'\"),assert(4===u.rank,\"Error in maxPoolBackprop: dy must be rank 4 but got rank \"+u.rank+\".\"),assert(4===l.rank,\"Error in maxPoolBackprop: input must be rank 4 but got rank \"+l.rank+\".\"),null!=s&&assert(isInt(i),\"Error in maxPoolBackprop: pad must be an integer when using, dimRoundingMode \"+s+\" but got pad \"+i+\".\");var p=computePool2DInfo(l.shape,r,o,a,i,s);return ENV.engine.runKernel(function(e){return e.maxPoolBackprop(u,l,c,p)},{$dy:u,$input:l})}function avgPoolBackprop(e,t,n,r,o,a){var i=convertToTensor(e,\"dy\",\"avgPoolBackprop\"),s=convertToTensor(t,\"input\",\"avgPoolBackprop\");assert(s.rank===i.rank,\"Rank of input (\"+s.rank+\") does not match rank of dy (\"+i.rank+\")\"),null==o&&(o=[1,1]),assert(eitherStridesOrDilationsAreOne(r,o),\"Error in avgPoolBackprop: Either strides or dilations must be 1. Got strides \"+r+\" and dilations '\"+o+\"'\");var u=s,l=i,c=!1;3===s.rank&&(c=!0,u=s.as4D(1,s.shape[0],s.shape[1],s.shape[2]),l=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),assert(4===l.rank,\"Error in avgPoolBackprop: dy must be rank 4 but got rank \"+l.rank+\".\"),assert(4===u.rank,\"Error in avgPoolBackprop: input must be rank 4 but got rank \"+u.rank+\".\");var p=computePool2DInfo(u.shape,n,r,o,a),d=ENV.engine.runKernel(function(e){return e.avgPoolBackprop(l,u,p)},{dy4D:l,input4D:u});return c?d.as3D(d.shape[1],d.shape[2],d.shape[3]):d}function requiredSpaceToBatchPaddings(e,t,n){var r=n.map(function(e){return e[0]}),o=n.map(function(e){return e[1]}),a=e.concat(r,o),i=t.map(function(e,t){return(e-a[t]%e)%e}),s=o.map(function(e,t){return e+i[t]});return[t.map(function(e,t){return[r[t],s[t]]}),t.map(function(e,t){return[0,i[t]]})]}function withSpaceToBatchBasePaddings(e,t){var n=e.map(function(e,n){return e+(e-1)*(t[n]-1)}).map(function(e){return e-1}),r=n.map(function(e){return Math.floor(e/2)}),o=n.map(function(e,t){return e-r[t]});return n.map(function(e,t){return[r[t],o[t]]})}var maxPool=op({maxPool_:maxPool_}),avgPool=op({avgPool_:avgPool_}),pool=op({pool_:pool_});function slice1d_(e,t,n){var r=convertToTensor(e,\"x\",\"slice1d\");return assert(1===r.rank,\"slice1d expects a rank-1 tensor, but got a rank-\"+r.rank+\" tensor\"),slice(r,[t],[n])}function slice2d_(e,t,n){var r=convertToTensor(e,\"x\",\"slice2d\");return assert(2===r.rank,\"slice2d expects a rank-2 tensor, but got a rank-\"+r.rank+\" tensor\"),slice(r,t,n)}function slice3d_(e,t,n){var r=convertToTensor(e,\"x\",\"slice3d\");return assert(3===r.rank,\"slice3d expects a rank-3 tensor, but got a rank-\"+r.rank+\" tensor\"),slice(r,t,n)}function slice4d_(e,t,n){var r=convertToTensor(e,\"x\",\"slice4d\");return assert(4===r.rank,\"slice4d expects a rank-4 tensor, but got a rank-\"+r.rank+\" tensor\"),slice(r,t,n)}function slice_(e,t,n){var r,o,a=convertToTensor(e,\"x\",\"slice\");if(0===a.rank)throw new Error(\"Slicing scalar is not possible\");r=\"number\"==typeof t?[t].concat(new Array(a.rank-1).fill(0)):t.length<a.rank?t.concat(new Array(a.rank-t.length).fill(0)):t.slice(),o=(o=null==n?new Array(a.rank).fill(-1):\"number\"==typeof n?[n].concat(new Array(a.rank-1).fill(-1)):n.length<a.rank?n.concat(new Array(a.rank-n.length).fill(-1)):n).map(function(e,t){return e>=0?e:(assert(-1===e,\"Bad value in size\"),a.shape[t]-r[t])}),assertParamsValid(a,r,o);var i=a.shape;return ENV.engine.runKernel(function(e){return e.slice(a,r,o)},{$x:a},function(e){for(var t=[],n=0;n<e.rank;n++)t.push([r[n],i[n]-r[n]-o[n]]);return{$x:function(){return e.pad(t)}}})}var slice=op({slice_:slice_}),slice1d=op({slice1d_:slice1d_}),slice2d=op({slice2d_:slice2d_}),slice3d=op({slice3d_:slice3d_}),slice4d=op({slice4d_:slice4d_});function logSumExp_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,\"x\",\"logSumExp\"),o=parseAxisParam(t,r.shape),a=r.max(o,!0),i=r.sub(a).exp().sum(o).log(),s=a.reshape(i.shape).add(i);if(n){var u=expandShapeToKeepDim(s.shape,o);return s.reshape(u)}return s}function sum_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,\"x\",\"sum\");\"bool\"===r.dtype&&(r=r.toInt());var o=parseAxisParam(t,r.shape);return customGrad(function(e){var t=getAxesPermutation(o,e.rank),r=o,a=e;null!=t&&(a=e.transpose(t),r=getInnerMostAxes(r.length,e.rank));var i=ENV.engine.runKernel(function(e){return e.sum(a,r)},{permutedX:a});if(n){var s=expandShapeToKeepDim(i.shape,o);i=i.reshape(s)}return{value:i,gradFunc:function(t){var n=e.shape.slice();return o.forEach(function(e){n[e]=1}),t.reshape(n).mul(ones$1(e.shape,\"float32\"))}}})(r)}function prod_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,\"x\",\"prod\");\"bool\"===r.dtype&&(r=r.toInt());var o=parseAxisParam(t,r.shape),a=getAxesPermutation(o,r.rank),i=o,s=r;null!=a&&(s=r.transpose(a),i=getInnerMostAxes(i.length,r.rank));var u=ENV.engine.runKernel(function(e){return e.prod(s,i)},{permutedX:s});if(n){var l=expandShapeToKeepDim(u.shape,o);u=u.reshape(l)}return u}function mean_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,\"x\",\"mean\"),o=parseAxisParam(t,r.shape),a=sizeFromShape(computeOutAndReduceShapes(r.shape,o)[1]);return customGrad(function(e){var r=scalar(a);return{value:(r.dtype===e.dtype?e:e.cast(r.dtype)).div(r).sum(t,n),gradFunc:function(t){var n=e.shape.slice();return o.forEach(function(e){n[e]=1}),t.reshape(n).mul(ones$1(e.shape,\"float32\")).div(r)}}})(r)}function gradForMinAndMax(e,t,n,r,o){var a=t[0];return a.rank<n.rank&&(a=a.reshape(expandShapeToKeepDim(a.shape,r))),e.rank<n.rank&&(e=e.reshape(expandShapeToKeepDim(e.shape,r))),{$x:function(){var t=e.mul(n.equal(a).cast(e.dtype));return null==o?t:t.transpose(o)}}}function min_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,\"x\",\"min\"),o=r,a=parseAxisParam(t,r.shape),i=a,s=getAxesPermutation(i,r.rank);null!=s&&(r=r.transpose(s),i=getInnerMostAxes(i.length,r.rank));var u=ENV.engine.runKernel(function(e,t){return t(e.min(r,i))},{$x:r},function(e,t){return gradForMinAndMax(e,t,o,a,s)});if(n){var l=expandShapeToKeepDim(u.shape,a);u=u.reshape(l)}return u}function max_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,\"x\",\"max\"),o=r,a=parseAxisParam(t,r.shape),i=a,s=getAxesPermutation(i,r.rank);null!=s&&(r=r.transpose(s),i=getInnerMostAxes(i.length,r.rank));var u=ENV.engine.runKernel(function(e,t){return t(e.max(r,i))},{$x:r},function(e,t){return gradForMinAndMax(e,t,o,a,s)});if(n){var l=expandShapeToKeepDim(u.shape,a);u=u.reshape(l)}return u}function argMin_(e,t){void 0===t&&(t=0);var n=convertToTensor(e,\"x\",\"argMin\");null==t&&(t=0);var r=parseAxisParam(t,n.shape),o=getAxesPermutation(r,n.rank);null!=o&&(n=n.transpose(o),r=getInnerMostAxes(r.length,n.rank));return ENV.engine.runKernel(function(e){return e.argMin(n,r[0])},{$x:n},function(e){return{$x:function(){return zerosLike(n)}}})}function argMax_(e,t){void 0===t&&(t=0);var n=convertToTensor(e,\"x\",\"argMax\");null==t&&(t=0);var r=parseAxisParam(t,n.shape),o=getAxesPermutation(r,n.rank);null!=o&&(n=n.transpose(o),r=getInnerMostAxes(r.length,n.rank));return ENV.engine.runKernel(function(e){return e.argMax(n,r[0])},{$x:n},function(e){return{$x:function(){return zerosLike(n)}}})}function all_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,\"x\",\"all\",\"bool\"),o=parseAxisParam(t,r.shape),a=o,i=getAxesPermutation(a,r.rank);null!=i&&(r=r.transpose(i),a=getInnerMostAxes(a.length,r.rank));var s=ENV.engine.runKernel(function(e){return e.all(r,a)},{$x:r});if(n){var u=expandShapeToKeepDim(s.shape,o);return s.reshape(u)}return s}function any_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,\"x\",\"any\",\"bool\"),o=parseAxisParam(t,r.shape),a=o,i=getAxesPermutation(a,r.rank);null!=i&&(r=r.transpose(i),a=getInnerMostAxes(a.length,r.rank));var s=ENV.engine.runKernel(function(e){return e.any(r,a)},{$x:r});if(n){var u=expandShapeToKeepDim(s.shape,o);return s.reshape(u)}return s}function moments_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=parseAxisParam(t,(e=convertToTensor(e,\"x\",\"moments\")).shape),o=e.mean(r,n),a=o.shape;return n||(a=expandShapeToKeepDim(o.shape,r)),{mean:o,variance:e.toFloat().sub(o.reshape(a)).square().mean(r,n)}}var all=op({all_:all_}),any=op({any_:any_}),argMax=op({argMax_:argMax_}),argMin=op({argMin_:argMin_}),logSumExp=op({logSumExp_:logSumExp_}),max=op({max_:max_}),mean=op({mean_:mean_}),min=op({min_:min_}),moments=op({moments_:moments_}),sum$1=op({sum_:sum_}),prod=op({prod_:prod_});function notEqual_(e,t){var n,r=convertToTensor(e,\"a\",\"notEqual\"),o=convertToTensor(t,\"b\",\"notEqual\");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.notEqual(r,o)},{$a:r,$b:o})}function notEqualStrict_(e,t){var n=convertToTensor(e,\"a\",\"notEqualStrict\"),r=convertToTensor(t,\"b\",\"notEqualStrict\");return assertShapesMatch(n.shape,r.shape,\"Error in notEqualStrict: \"),n.notEqual(r)}function less_(e,t){var n,r=convertToTensor(e,\"a\",\"less\"),o=convertToTensor(t,\"b\",\"less\");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.less(r,o)},{$a:r,$b:o})}function lessStrict_(e,t){var n=convertToTensor(e,\"a\",\"lessStrict\"),r=convertToTensor(t,\"b\",\"lessStrict\");return assertShapesMatch(n.shape,r.shape,\"Error in lessStrict: \"),n.less(r)}function equal_(e,t){var n,r=convertToTensor(e,\"a\",\"equal\"),o=convertToTensor(t,\"b\",\"equal\");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.equal(r,o)},{$a:r,$b:o})}function equalStrict_(e,t){var n=convertToTensor(e,\"a\",\"equalStrict\"),r=convertToTensor(t,\"b\",\"equalStrict\");return assertShapesMatch(n.shape,r.shape,\"Error in equalStrict: \"),n.equal(r)}function lessEqual_(e,t){var n,r=convertToTensor(e,\"a\",\"lessEqual\"),o=convertToTensor(t,\"b\",\"lessEqual\");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.lessEqual(r,o)},{$a:r,$b:o})}function lessEqualStrict_(e,t){var n=convertToTensor(e,\"a\",\"lessEqualStrict\"),r=convertToTensor(t,\"b\",\"lessEqualStrict\");return assertShapesMatch(n.shape,r.shape,\"Error in lessEqualStrict: \"),n.lessEqual(r)}function greater_(e,t){var n,r=convertToTensor(e,\"a\",\"greater\"),o=convertToTensor(t,\"b\",\"greater\");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.greater(r,o)},{$a:r,$b:o})}function greaterStrict_(e,t){var n=convertToTensor(e,\"a\",\"greaterStrict\"),r=convertToTensor(t,\"b\",\"greaterStrict\");return assertShapesMatch(n.shape,r.shape,\"Error in greaterStrict: \"),n.greater(r)}function greaterEqual_(e,t){var n,r=convertToTensor(e,\"a\",\"greaterEqual\"),o=convertToTensor(t,\"b\",\"greaterEqual\");n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.greaterEqual(r,o)},{$a:r,$b:o},function(e){return{$a:function(){return zerosLike(r)},$b:function(){return zerosLike(o)}}})}function greaterEqualStrict_(e,t){var n=convertToTensor(e,\"a\",\"greaterEqualStrict\"),r=convertToTensor(t,\"b\",\"greaterEqualStrict\");return assertShapesMatch(n.shape,r.shape,\"Error in greaterEqualStrict: \"),n.greaterEqual(r)}var equal=op({equal_:equal_}),equalStrict=op({equalStrict_:equalStrict_}),greater=op({greater_:greater_}),greaterEqual=op({greaterEqual_:greaterEqual_}),greaterEqualStrict=op({greaterEqualStrict_:greaterEqualStrict_}),greaterStrict=op({greaterStrict_:greaterStrict_}),less=op({less_:less_}),lessEqual=op({lessEqual_:lessEqual_}),lessEqualStrict=op({lessEqualStrict_:lessEqualStrict_}),lessStrict=op({lessStrict_:lessStrict_}),notEqual=op({notEqual_:notEqual_}),notEqualStrict=op({notEqualStrict_:notEqualStrict_});function add_(e,t){var n,r=convertToTensor(e,\"a\",\"add\"),o=convertToTensor(t,\"b\",\"add\");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.add(r,o)},{$a:r,$b:o},function(e){return{$a:function(){var t=e,n=getReductionAxes(r.shape,a);return n.length>0&&(t=t.sum(n)),t.reshape(r.shape)},$b:function(){var t=e,n=getReductionAxes(o.shape,a);return n.length>0&&(t=t.sum(n)),t.reshape(o.shape)}}})}function addN_(e){assert(Array.isArray(e),function(){return\"The argument passed to tf.addN() must be a list of tensors\"}),assert(e.length>=1,function(){return\"Must pass at least one tensor to tf.addN(), but got \"+e.length});var t=e.map(function(e,t){return convertToTensor(e,\"tensors\"+t,\"addN\")}),n=t[0];t.forEach(function(e){if(e.dtype!==n.dtype)throw new Error(\"All tensors passed to tf.addN() must have the same dtype\")}),t.forEach(function(e){if(!arraysEqual(e.shape,n.shape))throw new Error(\"All tensors passed to tf.addN() must have the same shape\")});var r=t;return ENV.engine.runKernel(function(e){return e.addN(t)},r,function(e){var n={};return t.forEach(function(t,r){n[r]=function(){return e.clone()}}),n})}function addStrict_(e,t){var n=convertToTensor(e,\"a\",\"addStrict\"),r=convertToTensor(t,\"b\",\"addStrict\");return assertShapesMatch(n.shape,r.shape,\"Error in addStrict: \"),n.add(r)}function sub_(e,t){var n,r=convertToTensor(e,\"a\",\"sub\"),o=convertToTensor(t,\"b\",\"sub\");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.subtract(r,o)},{$a:r,$b:o},function(e){return{$a:function(){var t=e,n=getReductionAxes(r.shape,a);return n.length>0&&(t=t.sum(n)),t.reshape(r.shape)},$b:function(){var t=e,n=getReductionAxes(o.shape,a);return n.length>0&&(t=t.sum(n)),t.neg().reshape(o.shape)}}})}function subStrict_(e,t){var n=convertToTensor(e,\"a\",\"subStrict\"),r=convertToTensor(t,\"b\",\"subStrict\");return assertShapesMatch(n.shape,r.shape,\"Error in subStrict: \"),n.sub(r)}function pow_(e,t){var n=convertToTensor(e,\"base\",\"pow\"),r=convertToTensor(t,\"exp\",\"pow\"),o=assertAndGetBroadcastShape(n.shape,r.shape);e=n.cast(upcastType(n.dtype,r.dtype)),t=r.cast(upcastType(n.dtype,r.dtype));return ENV.engine.runKernel(function(e,t){return t(e.pow(n,r))},{$base:n,$exp:r},function(e,t){var a=t[0];return{$base:function(){var t=r.toFloat(),a=e.mul(t.mul(n.pow(t.sub(scalar(1))))),i=getReductionAxes(n.shape,o);return i.length>0&&(a=a.sum(i)),a.reshape(n.shape)},$exp:function(){var t=e.mul(a.mul(n.log()).toFloat()),i=getReductionAxes(r.shape,o);return i.length>0&&(t=t.sum(i)),t.reshape(r.shape)}}})}function powStrict_(e,t){return assertShapesMatch(e.shape,t.shape,\"Error in powStrict: \"),e.pow(t)}function mul_(e,t){var n,r=convertToTensor(e,\"a\",\"mul\"),o=convertToTensor(t,\"b\",\"mul\");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.multiply(r,o)},{$a:r,$b:o},function(e){return{$a:function(){var t=e.mul(o.toFloat()),n=getReductionAxes(r.shape,a);return n.length>0?t.sum(n).reshape(r.shape):t},$b:function(){var t=e.mul(r.toFloat()),n=getReductionAxes(o.shape,a);return n.length>0?t.sum(n).reshape(o.shape):t}}})}function mulStrict_(e,t){var n=convertToTensor(e,\"a\",\"mul\"),r=convertToTensor(t,\"b\",\"mul\");return assertShapesMatch(n.shape,r.shape,\"Error in multiplyStrict: \"),n.mul(r)}function div_(e,t){var n,r,o=convertToTensor(e,\"a\",\"div\"),a=convertToTensor(t,\"b\",\"div\");if(n=makeTypesMatch(o,a),o=n[0],a=n[1],\"int32\"===o.dtype&&\"int32\"===a.dtype)return floorDiv(o,a);r=function(e){return e.realDivide(o,a)};var i=assertAndGetBroadcastShape(o.shape,a.shape);return ENV.engine.runKernel(r,{$a:o,$b:a},function(e){return{$a:function(){var t=e.div(a.toFloat()),n=getReductionAxes(o.shape,i);return n.length>0?t.sum(n).reshape(o.shape):t},$b:function(){var t=e.mul(o.toFloat()),n=getReductionAxes(a.shape,i);n.length>0&&(t=t.sum(n).reshape(a.shape));var r=a.square();return t.div(r.toFloat()).neg()}}})}function floorDiv_(e,t){var n,r=convertToTensor(e,\"a\",\"floorDiv\"),o=convertToTensor(t,\"b\",\"floorDiv\");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.floorDiv(r,o)},{$a:r,$b:o},function(e){return{$a:function(){var t=e.div(o.toFloat()),n=getReductionAxes(r.shape,a);return n.length>0?t.sum(n).reshape(r.shape):t},$b:function(){var t=e.mul(r.toFloat()),n=getReductionAxes(o.shape,a);n.length>0&&(t=t.sum(n).reshape(o.shape));var i=o.square();return t.div(i.toFloat()).neg()}}})}function divStrict_(e,t){var n=convertToTensor(e,\"a\",\"div\"),r=convertToTensor(t,\"b\",\"div\");return assertShapesMatch(n.shape,r.shape,\"Error in divideStrict: \"),n.div(r)}function mod_(e,t){var n,r=convertToTensor(e,\"a\",\"mod\"),o=convertToTensor(t,\"b\",\"mod\");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.mod(r,o)},{$a:r,$b:o},function(e){return{$a:function(){var t=getReductionAxes(r.shape,a);return t.length>0?e.sum(t).reshape(r.shape):e},$b:function(){var t=e.mul(r.div(o).floor().neg()),n=getReductionAxes(o.shape,a);return n.length>0?t.sum(n).reshape(o.shape):t}}})}function modStrict_(e,t){var n=convertToTensor(e,\"a\",\"modStrict\"),r=convertToTensor(t,\"b\",\"modStrict\");return assertShapesMatch(n.shape,r.shape,\"Error in modStrict: \"),n.mod(r)}function minimum_(e,t){var n,r=convertToTensor(e,\"a\",\"minimum\"),o=convertToTensor(t,\"b\",\"minimum\");n=makeTypesMatch(r,o),r=n[0],o=n[1],\"bool\"===r.dtype&&(r=r.toInt(),o=o.toInt()),assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.minimum(r,o)},{$a:r,$b:o},function(e){return{$a:function(){return e.mul(r.lessEqual(o).toFloat())},$b:function(){return e.mul(r.greater(o).toFloat())}}})}function minimumStrict_(e,t){var n=convertToTensor(e,\"a\",\"minimumStrict\"),r=convertToTensor(t,\"b\",\"minimumStrict\");return assertShapesMatch(n.shape,r.shape,\"Error in minimumStrict: \"),n.minimum(r)}function maximum_(e,t){var n,r=convertToTensor(e,\"a\",\"maximum\"),o=convertToTensor(t,\"b\",\"maximum\");n=makeTypesMatch(r,o),r=n[0],o=n[1],\"bool\"===r.dtype&&(r=r.toInt(),o=o.toInt()),assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.maximum(r,o)},{$a:r,$b:o},function(e){return{$a:function(){return e.mul(r.greaterEqual(o).toFloat())},$b:function(){return e.mul(r.less(o).toFloat())}}})}function maximumStrict_(e,t){var n=convertToTensor(e,\"a\",\"maximumStrict\"),r=convertToTensor(t,\"b\",\"maximumStrict\");return assertShapesMatch(n.shape,r.shape,\"Error in maximumStrict: \"),n.maximum(r)}function squaredDifference_(e,t){var n,r=convertToTensor(e,\"a\",\"squaredDifference\"),o=convertToTensor(t,\"b\",\"squaredDifference\");n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.squaredDifference(r,o)},{$a:r,$b:o},function(e){var t=scalar(2);return{$a:function(){return e.mul(r.sub(o).mul(t))},$b:function(){return e.mul(o.sub(r).mul(t))}}})}function squaredDifferenceStrict_(e,t){var n=convertToTensor(e,\"a\",\"squaredDifferenceStrict\"),r=convertToTensor(t,\"b\",\"squaredDifferenceStrict\");return assertShapesMatch(n.shape,r.shape,\"Error in squaredDifferenceStrict: \"),n.squaredDifference(r)}function atan2_(e,t){var n,r=convertToTensor(e,\"a\",\"atan2\"),o=convertToTensor(t,\"b\",\"atan2\");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.atan2(r,o)},{$a:r,$b:o},function(e){return{$a:function(){var t=add(r.square(),o.square()),n=e.mul(o.div(t)),i=getReductionAxes(r.shape,a);return i.length>0&&(n=n.sum(i)),n.reshape(r.shape)},$b:function(){var t=add(r.square(),o.square()),n=neg(e.mul(r.div(t))),i=getReductionAxes(o.shape,a);return i.length>0&&(n=n.sum(i)),n.reshape(o.shape)}}})}var add=op({add_:add_}),addN=op({addN_:addN_}),addStrict=op({addStrict_:addStrict_}),atan2=op({atan2_:atan2_}),div=op({div_:div_}),divStrict=op({divStrict_:divStrict_}),floorDiv=op({floorDiv_:floorDiv_}),maximum=op({maximum_:maximum_}),maximumStrict=op({maximumStrict_:maximumStrict_}),minimum=op({minimum_:minimum_}),minimumStrict=op({minimumStrict_:minimumStrict_}),mod=op({mod_:mod_}),modStrict=op({modStrict_:modStrict_}),mul=op({mul_:mul_}),mulStrict=op({mulStrict_:mulStrict_}),pow=op({pow_:pow_}),powStrict=op({powStrict_:powStrict_}),squaredDifference=op({squaredDifference_:squaredDifference_}),squaredDifferenceStrict=op({squaredDifferenceStrict_:squaredDifferenceStrict_}),sub=op({sub_:sub_}),subStrict=op({subStrict_:subStrict_});function logicalNot_(e){var t=convertToTensor(e,\"x\",\"logicalNot\",\"bool\");return ENV.engine.runKernel(function(e){return e.logicalNot(t)},{$x:t})}function logicalAnd_(e,t){var n=convertToTensor(e,\"a\",\"logicalAnd\",\"bool\"),r=convertToTensor(t,\"b\",\"logicalAnd\",\"bool\");return assertAndGetBroadcastShape(n.shape,r.shape),ENV.engine.runKernel(function(e){return e.logicalAnd(n,r)},{$a:n,$b:r})}function logicalOr_(e,t){var n=convertToTensor(e,\"a\",\"logicalOr\",\"bool\"),r=convertToTensor(t,\"b\",\"logicalOr\",\"bool\");return assertAndGetBroadcastShape(n.shape,r.shape),ENV.engine.runKernel(function(e){return e.logicalOr(n,r)},{$a:n,$b:r})}function logicalXor_(e,t){var n=convertToTensor(e,\"a\",\"logicalXor\",\"bool\"),r=convertToTensor(t,\"b\",\"logicalXor\",\"bool\");return assertAndGetBroadcastShape(n.shape,r.shape),logicalOr(e,t).logicalAnd(logicalAnd(e,t).logicalNot())}function where_(e,t,n){var r=convertToTensor(t,\"a\",\"where\"),o=convertToTensor(n,\"b\",\"where\"),a=convertToTensor(e,\"condition\",\"where\",\"bool\");assertShapesMatch(r.shape,o.shape,\"Error in where: \"),1===a.rank?assert(a.shape[0]===r.shape[0],\"The first dimension of `a` must match the size of `condition`.\"):assertShapesMatch(a.shape,o.shape,\"Error in where: \");return ENV.engine.runKernel(function(e){return e.select(a,r,o)},{$condition:a,$a:r,$b:o},function(e){return{$condition:function(){return zerosLike(a).toFloat()},$a:function(){return e.mul(a.cast(e.dtype))},$b:function(){return e.mul(a.logicalNot().cast(e.dtype))}}})}function whereAsync_(e){return __awaiter(this,void 0,void 0,function(){var t,n,r;return __generator(this,function(o){switch(o.label){case 0:return[4,(t=convertToTensor(e,\"condition\",\"whereAsync\",\"bool\")).data()];case 1:return n=o.sent(),r=whereImpl(t.shape,n),e!==t&&t.dispose(),[2,r]}})})}var logicalAnd=op({logicalAnd_:logicalAnd_}),logicalNot=op({logicalNot_:logicalNot_}),logicalOr=op({logicalOr_:logicalOr_}),logicalXor=op({logicalXor_:logicalXor_}),where=op({where_:where_}),whereAsync=whereAsync_;function relu_(e){var t=convertToTensor(e,\"x\",\"relu\");if(\"bool\"===t.dtype)return t.toInt();return ENV.engine.runKernel(function(e){return e.relu(t)},{$x:t},function(e){var n=t.step();return{$x:function(){return e.mulStrict(n.toFloat())}}})}function elu_(e){var t=convertToTensor(e,\"x\",\"elu\");return ENV.engine.runKernel(function(e,n){return n(e.elu(t))},{$x:t},function(e,t){var n=t[0];return{$x:function(){return ENV.engine.runKernel(function(t){return t.eluDer(e,n)},{dy:e,y:n})}}})}function selu_(e){var t=convertToTensor(e,\"x\",\"selu\");return ENV.engine.runKernel(function(e){return e.selu(t)},{$x:t},function(e){return{$x:function(){var n=t.greater(scalar(0)),r=scalar(SELU_SCALEALPHA),o=scalar(SELU_SCALE),a=e.mul(o),i=e.mul(r).mul(t.toFloat().exp());return where(n,a,i)}}})}function leakyRelu_(e,t){void 0===t&&(t=.2);var n=convertToTensor(e,\"x\",\"leakyRelu\");return maximum(scalar(t).mul(n),n)}function prelu_(e,t){var n=convertToTensor(e,\"x\",\"prelu\"),r=convertToTensor(t,\"alpha\",\"prelu\"),o=scalar(0);return maximum(o,n).add(r.mul(minimum(o,n)))}var elu=op({elu_:elu_}),leakyRelu=op({leakyRelu_:leakyRelu_}),prelu=op({prelu_:prelu_}),relu=op({relu_:relu_}),selu=op({selu_:selu_});function transpose_(e,t){var n=convertToTensor(e,\"x\",\"transpose\");if(null==t&&(t=n.shape.map(function(e,t){return t}).reverse()),assert(n.rank===t.length,\"Error in transpose: rank of input \"+n.rank+\" must match length of perm \"+t+\".\"),t.forEach(function(e){assert(e>=0&&e<n.rank,\"All entries in 'perm' must be between 0 and \"+(n.rank-1)+\" but got \"+t)}),n.rank<=1)return n.clone();return ENV.engine.runKernel(function(e){return e.transpose(n,t)},{$x:n},function(e){var n=getUndoAxesPermutation(t);return{$x:function(){return e.transpose(n)}}})}var transpose=op({transpose_:transpose_});function localResponseNormalization_(e,t,n,r,o){void 0===t&&(t=5),void 0===n&&(n=1),void 0===r&&(r=1),void 0===o&&(o=.5);var a=convertToTensor(e,\"x\",\"localResponseNormalization\");assert(4===a.rank||3===a.rank,\"Error in localResponseNormalization: x must be rank 3 or 4 but got\\n               rank \"+a.rank+\".\"),assert(isInt(t),\"Error in localResponseNormalization: depthRadius must be an integer\\n                     but got depthRadius \"+t+\".\");var i=a,s=!1;3===a.rank&&(s=!0,i=a.as4D(1,a.shape[0],a.shape[1],a.shape[2]));var u=ENV.engine.runKernel(function(e,a){return a(e.localResponseNormalization4D(i,t,n,r,o))},{x4D:i},function(e,a){var s=a[0];return{x4D:function(){return ENV.engine.runKernel(function(a){return a.LRNGrad(e,i,s,t,n,r,o)},{})}}});return s?u.as3D(u.shape[1],u.shape[2],u.shape[3]):u}var localResponseNormalization=op({localResponseNormalization_:localResponseNormalization_});function norm_(e,t,n,r){void 0===t&&(t=\"euclidean\"),void 0===n&&(n=null),void 0===r&&(r=!1);var o=normImpl(e=convertToTensor(e,\"x\",\"norm\"),t,n),a=o.shape;if(r){var i=parseAxisParam(n,e.shape);a=expandShapeToKeepDim(o.shape,i)}return o.reshape(a)}function normImpl(e,t,n){if(void 0===n&&(n=null),0===e.rank)return e.abs();if(1!==e.rank&&null===n)return normImpl(e.reshape([-1]),t,n);if(1===e.rank||\"number\"==typeof n||n instanceof Array&&1===n.length){if(1===t)return e.abs().sum(n);if(t===1/0)return e.abs().max(n);if(t===-1/0)return e.abs().min(n);if(\"euclidean\"===t||2===t)return e.abs().pow(scalar(2,\"int32\")).sum(n).sqrt();throw new Error(\"Error in norm: invalid ord value: \"+t)}if(n instanceof Array&&2===n.length){if(1===t)return e.abs().sum(n[0]).max(n[1]-1);if(t===1/0)return e.abs().sum(n[1]).max(n[0]);if(t===-1/0)return e.abs().sum(n[1]).min(n[0]);if(\"fro\"===t||\"euclidean\"===t)return e.square().sum(n).sqrt();throw new Error(\"Error in norm: invalid ord value: \"+t)}throw new Error(\"Error in norm: invalid axis: \"+n)}var norm=op({norm_:norm_});function unsortedSegmentSum_(e,t,n){var r=convertToTensor(e,\"x\",\"unsortedSegmentSum\"),o=convertToTensor(t,\"segmentIds\",\"unsortedSegmentSum\",\"int32\");assert(isInt(n),\"numSegments must be of dtype int\");return ENV.engine.runKernel(function(e){return e.unsortedSegmentSum(r,o,n)},{$x:r},function(e){return{$x:function(){return gatherDropNegatives(e,o)}}})}function gather_(e,t,n){void 0===n&&(n=0);var r=convertToTensor(e,\"x\",\"gather\"),o=convertToTensor(t,\"indices\",\"gather\",\"int32\");n=parseAxisParam(n,r.shape)[0];return ENV.engine.runKernel(function(e){return e.gather(r,o,n)},{$x:r},function(e){return{$x:function(){if(0===n)return unsortedSegmentSum(e,o,r.shape[n]);var t=r.shape,a=o.size,i=t.slice(0,n),s=i.length,u=t.slice(n,t.length).slice(1),l=u.length,c=arrayRange(0,s),p=arrayRange(s+1,s+1+l),d=arrayConcat([i,[a],u]),h=e.reshape(d),f=o.reshape([a]),m=arrayConcat([[s],c,p]),g=h.transpose(m),v=unsortedSegmentSum(g,f,r.shape[n]),y=getUndoAxesPermutation(m);return v=v.transpose(y)}}})}function arrayRange(e,t){for(var n=[],r=e;r<t;++r)n.push(r);return n}function arrayConcat(e){for(var t=[],n=0;n<e.length;++n)for(var r=0;r<e[n].length;++r)t.push(e[n][r]);return t}function gatherDropNegatives(e,t){for(var n=maximum(t,zerosLike(t)),r=gather(e,n),o=greaterEqual(t,scalar(0,\"int32\")),a=r.rank-o.rank,i=0;i<a;++i)o=expandDims(o,i+1);o=logicalAnd(o,ones$1(r.shape,\"bool\"));var s=zerosLike(r);return where(o,r,s)}var gather=op({gather_:gather_}),unsortedSegmentSum=op({unsortedSegmentSum_:unsortedSegmentSum_});function multiRNNCell_(e,t,n,r){for(var o=convertToTensor(t,\"data\",\"multiRNNCell\"),a=convertToTensorArray(n,\"c\",\"multiRNNCell\"),i=convertToTensorArray(r,\"h\",\"multiRNNCell\"),s=o,u=[],l=0;l<e.length;l++){var c=e[l](s,a[l],i[l]);u.push(c[0]),u.push(c[1]),s=c[1]}var p=[],d=[];for(l=0;l<u.length;l+=2)p.push(u[l]),d.push(u[l+1]);return[p,d]}function basicLSTMCell_(e,t,n,r,o,a){var i=convertToTensor(e,\"forgetBias\",\"basicLSTMCell\"),s=convertToTensor(t,\"lstmKernel\",\"basicLSTMCell\"),u=convertToTensor(n,\"lstmBias\",\"basicLSTMCell\"),l=convertToTensor(r,\"data\",\"basicLSTMCell\"),c=convertToTensor(o,\"c\",\"basicLSTMCell\"),p=convertToTensor(a,\"h\",\"basicLSTMCell\"),d=l.concat(p,1).matMul(s).add(u),h=d.shape[0],f=d.shape[1]/4,m=[h,f],g=d.slice([0,0],m),v=d.slice([0,f],m),y=d.slice([0,2*f],m),x=d.slice([0,3*f],m),T=g.sigmoid().mulStrict(v.tanh()).addStrict(c.mulStrict(i.add(y).sigmoid()));return[T,T.tanh().mulStrict(x.sigmoid())]}var basicLSTMCell=op({basicLSTMCell_:basicLSTMCell_}),multiRNNCell=op({multiRNNCell_:multiRNNCell_});function movingAverage_(e,t,n,r,o){void 0===o&&(o=!0);var a=convertToTensor(e,\"v\",\"movingAverage\"),i=convertToTensor(t,\"x\",\"movingAverage\"),s=convertToTensor(n,\"decay\",\"movingAverage\");assertTypesMatch(a,i),assert(arraysEqual(a.shape,i.shape),\"Shape mismatch in v and x\");var u=scalar(1),l=u.sub(s),c=i.sub(a).mul(l);if(o){assert(null!=r,\"When using zeroDebias: true, step is required.\");var p=convertToTensor(r,\"step\",\"movingAverage\");c=c.div(u.sub(pow(s,p)))}return a.add(c)}var movingAverage=op({movingAverage_:movingAverage_});function stridedSlice_(e,t,n,r,o,a,i,s,u){if(void 0===o&&(o=0),void 0===a&&(a=0),void 0===i&&(i=0),void 0===s&&(s=0),void 0===u&&(u=0),0!==i)throw new Error(\"ellipsis mask is not yet supported\");if(0!==s)throw new Error(\"new axis mask is not yet supported\");var l=convertToTensor(e,\"x\",\"stridedSlice\");return ENV.engine.runKernel(function(e){return e.stridedSlice(l,t,n,r,o,a,i,s,u)},{$x:l})}var stridedSlice=op({stridedSlice_:stridedSlice_});function topk_(e,t,n){void 0===t&&(t=1),void 0===n&&(n=!0);var r=convertToTensor(e,\"x\",\"topk\");if(0===r.rank)throw new Error(\"topk() expects the input to be of rank 1 or higher\");var o=r.shape[r.shape.length-1];if(t>o)throw new Error(\"'k' passed to topk() must be <= the last dimension (\"+o+\") but got \"+t);var a=ENV.engine.runKernel(function(e){return e.topk(r,t,n)},{$x:r});return{values:a[0],indices:a[1]}}var topk=op({topk_:topk_});function scatterND_(e,t,n){var r=convertToTensor(e,\"indices\",\"scatterND\",\"int32\"),o=convertToTensor(t,\"updates\",\"scatterND\");return validateInput(o,r,n),ENV.engine.runKernel(function(e){return e.scatterND(r,o,n)},{$indices:r,$updates:o})}var scatterND=op({scatterND_:scatterND_});function fft_(e){assert(\"complex64\"===e.dtype,\"The dtype for tf.spectral.fft() must be complex64 but got \"+e.dtype+\".\");var t=e.shape[e.shape.length-1],n=e.size/t,r=e.as2D(n,t);return ENV.engine.runKernel(function(e){return e.fft(r)},{input:e}).reshape(e.shape)}function ifft_(e){assert(\"complex64\"===e.dtype,\"The dtype for tf.spectral.ifft() must be complex64 but got \"+e.dtype+\".\");var t=e.shape[e.shape.length-1],n=e.size/t,r=e.as2D(n,t);return ENV.engine.runKernel(function(e){return e.ifft(r)},{input:e}).reshape(e.shape)}function rfft_(e){assert(\"float32\"===e.dtype,\"The dtype for rfft() must be real value but\\n    got \"+e.dtype);var t=e.shape[e.shape.length-1],n=e.size/t,r=e.zerosLike(),o=complex(e,r).as2D(n,t),a=fft(o),i=Math.floor(t/2)+1,s=real(a),u=imag(a),l=s.split([i,t-i],s.shape.length-1),c=u.split([i,t-i],u.shape.length-1),p=e.shape.slice();return p[e.shape.length-1]=i,complex(l[0],c[0]).reshape(p)}function irfft_(e){var t=e.shape[e.shape.length-1],n=e.size/t;if(t<=2){var r=e.as2D(n,t),o=ifft(r);return real(o)}var a=[n,2*(t-1)],i=real(e).as2D(n,t),s=imag(e).as2D(n,t),u=i.slice([0,1],[n,t-2]).reverse(1),l=s.slice([0,1],[n,t-2]).reverse(1).mul(scalar(-1)),c=i.concat(u,1),p=s.concat(l,1);r=complex(c,p).as2D(a[0],a[1]),o=ifft(r);return real(o)}var fft=op({fft_:fft_}),ifft=op({ifft_:ifft_}),rfft=op({rfft_:rfft_}),irfft=op({irfft_:irfft_}),spectral_ops=Object.freeze({fft:fft,ifft:ifft,rfft:rfft,irfft:irfft});function validateInput$1(e,t,n,r){if(\"int32\"!==e.dtype)throw new Error(\"tf.sparseToDense() expects the indices to be int32 type, but the dtype was \"+e.dtype+\".\");if(e.rank>2)throw new Error(\"sparseIndices should be a scalar, vector, or matrix, but got shape \"+e.shape+\".\");var o=e.rank>0?e.shape[0]:1,a=e.rank>1?e.shape[1]:1;if(n.length!==a)throw new Error(\"outputShape has incorrect number of elements:, \"+n.length+\", should be: \"+a+\".\");var i=t.size;if(0!==t.rank&&(1!==t.rank||i!==o))throw new Error(\"sparseValues has incorrect shape \"+t.shape+\", should be [] or [\"+o+\"]\");if(t.dtype!==r.dtype)throw new Error(\"sparseValues.dtype must match defaultValues.dtype\")}function sparseToDense_(e,t,n,r){var o=convertToTensor(e,\"sparseIndices\",\"sparseToDense\",\"int32\"),a=convertToTensor(t,\"sparseValues\",\"sparseToDense\"),i=convertToTensor(r,\"defaultValue\",\"sparseToDense\",a.dtype);return validateInput$1(o,a,n,i),ENV.engine.runKernel(function(e){return e.sparseToDense(o,a,n,i)},{$sparseIndices:o,$sparseValues:a,$defaultValue:i})}var sparseToDense=op({sparseToDense_:sparseToDense_});function gatherND_(e,t){var n=convertToTensor(t,\"indices\",\"gatherND\",\"int32\"),r=convertToTensor(e,\"x\",\"gatherND\");return ENV.engine.runKernel(function(e){return e.gatherND(r,n)},{$x:r,$indices:n})}var Reduction,gatherND=op({gatherND_:gatherND_});function computeWeightedLoss_(e,t,n){void 0===n&&(n=Reduction.SUM_BY_NONZERO_WEIGHTS);var r=convertToTensor(e,\"losses\",\"computeWeightedLoss\"),o=null;null!=t&&(o=convertToTensor(t,\"weights\",\"computeWeightedLoss\"));var a=null==o?r:r.mul(o);if(n===Reduction.NONE)return a;if(n===Reduction.SUM)return a.sum();if(n===Reduction.MEAN){if(null==o)return a.mean();var i=sizeFromShape(r.shape)/sizeFromShape(o.shape),s=a.sum().div(o.sum());return i>1?s.div(scalar(i)):s}if(n===Reduction.SUM_BY_NONZERO_WEIGHTS){if(null==o)return a.sum().div(scalar(r.size));var u=o.mul(ones$1(r.shape)).notEqual(scalar(0)).sum().toFloat();return a.sum().div(u)}throw Error(\"Unknown reduction: \"+n)}function absoluteDifference_(e,t,n,r){void 0===r&&(r=Reduction.SUM_BY_NONZERO_WEIGHTS);var o=convertToTensor(e,\"labels\",\"absoluteDifference\"),a=convertToTensor(t,\"predictions\",\"absoluteDifference\"),i=null;null!=n&&(i=convertToTensor(n,\"weights\",\"absoluteDifference\")),assertShapesMatch(o.shape,a.shape,\"Error in absoluteDifference: \");var s=o.sub(a).abs();return computeWeightedLoss(s,i,r)}function meanSquaredError_(e,t,n,r){void 0===r&&(r=Reduction.SUM_BY_NONZERO_WEIGHTS);var o=convertToTensor(e,\"labels\",\"meanSquaredError\"),a=convertToTensor(t,\"predictions\",\"meanSquaredError\"),i=null;null!=n&&(i=convertToTensor(n,\"weights\",\"meanSquaredError\")),assertShapesMatch(o.shape,a.shape,\"Error in meanSquaredError: \");var s=o.squaredDifference(a);return computeWeightedLoss(s,i,r)}function cosineDistance_(e,t,n,r,o){void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,\"labels\",\"cosineDistance\"),i=convertToTensor(t,\"predictions\",\"cosineDistance\"),s=null;null!=r&&(s=convertToTensor(r,\"weights\",\"cosineDistance\")),assertShapesMatch(a.shape,i.shape,\"Error in cosineDistance: \");var u=scalar(1).sub(a.mul(i).sum(n,!0));return computeWeightedLoss(u,s,o)}function hingeLoss_(e,t,n,r){void 0===r&&(r=Reduction.SUM_BY_NONZERO_WEIGHTS);var o=convertToTensor(e,\"labels\",\"hingeLoss\"),a=convertToTensor(t,\"predictions\",\"hingeLoss\"),i=null;null!=n&&(i=convertToTensor(n,\"weights\",\"hingeLoss\")),assertShapesMatch(o.shape,a.shape,\"Error in hingeLoss: \");var s=scalar(1);o=scalar(2).mul(o).sub(s);var u=s.sub(o.mul(a)).relu();return computeWeightedLoss(u,i,r)}function logLoss_(e,t,n,r,o){void 0===r&&(r=1e-7),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,\"labels\",\"logLoss\"),i=convertToTensor(t,\"predictions\",\"logLoss\"),s=null;null!=n&&(s=convertToTensor(n,\"weights\",\"logLoss\")),assertShapesMatch(a.shape,i.shape,\"Error in logLoss: \");var u=scalar(1),l=scalar(r),c=a.mul(i.add(l).log()).neg().sub(u.sub(a).mul(u.sub(i).add(l).log()));return computeWeightedLoss(c,s,o)}function sigmoidCrossEntropyWithLogits_(e,t){var n=convertToTensor(e,\"labels\",\"sigmoidCrossEntropyWithLogits\"),r=convertToTensor(t,\"logits\",\"sigmoidCrossEntropyWithLogits\");assertShapesMatch(n.shape,r.shape,\"Error in sigmoidCrossEntropyWithLogits: \");var o=r.relu(),a=r.mul(n),i=r.abs().neg().exp().log1p();return o.sub(a).add(i)}function sigmoidCrossEntropy_(e,t,n,r,o){void 0===r&&(r=0),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,\"multiClassLabels\",\"sigmoidCrossEntropy\"),i=convertToTensor(t,\"logits\",\"sigmoidCrossEntropy\"),s=null;if(null!=n&&(s=convertToTensor(n,\"weights\",\"sigmoidCrossEntropy\")),assertShapesMatch(a.shape,i.shape,\"Error in sigmoidCrossEntropy: \"),r>0){var u=scalar(r),l=scalar(1),c=scalar(.5);a=a.mul(l.sub(u)).add(c.mul(u))}var p=sigmoidCrossEntropyWithLogits_(a,i);return computeWeightedLoss(p,s,o)}function huberLoss_(e,t,n,r,o){void 0===r&&(r=1),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,\"labels\",\"huberLoss\"),i=convertToTensor(t,\"predictions\",\"huberLoss\"),s=null;null!=n&&(s=convertToTensor(n,\"weights\",\"huberLoss\")),assertShapesMatch(a.shape,i.shape,\"Error in huberLoss: \");var u=scalar(r),l=i.sub(a).abs(),c=minimum(l,u),p=l.sub(c),d=scalar(.5).mul(c.square()).add(u.mul(p));return computeWeightedLoss(d,s,o)}function softmaxCrossEntropyWithLogits_(e,t,n){if(void 0===n&&(n=-1),-1===n&&(n=t.rank-1),n!==t.rank-1)throw Error(\"Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank \"+t.rank+\" and dim was \"+n);return customGrad(function(e,t){var r=t.logSumExp([n],!0),o=t.toFloat().sub(r);return{value:o.mul(e).neg().sum([n]),gradFunc:function(t){var r=expandShapeToKeepDim(t.shape,[n]);return[t.reshape(r).mul(e.toFloat().sub(o.exp())),t.reshape(r).mul(o.exp().sub(e.toFloat()))]}}})(e,t)}function softmaxCrossEntropy_(e,t,n,r,o){void 0===r&&(r=0),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,\"onehotLabels\",\"softmaxCrossEntropy\"),i=convertToTensor(t,\"logits\",\"softmaxCrossEntropy\"),s=null;if(null!=n&&(s=convertToTensor(n,\"weights\",\"softmaxCrossEntropy\")),assertShapesMatch(a.shape,i.shape,\"Error in softmaxCrossEntropy: \"),r>0){var u=scalar(r),l=scalar(1),c=scalar(a.shape[1]);a=a.mul(l.sub(u)).add(u.div(c))}var p=softmaxCrossEntropyWithLogits_(a,i);return computeWeightedLoss(p,s,o)}!function(e){e[e.NONE=0]=\"NONE\",e[e.MEAN=1]=\"MEAN\",e[e.SUM=2]=\"SUM\",e[e.SUM_BY_NONZERO_WEIGHTS=3]=\"SUM_BY_NONZERO_WEIGHTS\"}(Reduction||(Reduction={}));var absoluteDifference=op({absoluteDifference_:absoluteDifference_}),computeWeightedLoss=op({computeWeightedLoss_:computeWeightedLoss_}),cosineDistance=op({cosineDistance_:cosineDistance_}),hingeLoss=op({hingeLoss_:hingeLoss_}),huberLoss=op({huberLoss_:huberLoss_}),logLoss=op({logLoss_:logLoss_}),meanSquaredError=op({meanSquaredError_:meanSquaredError_}),sigmoidCrossEntropy=op({sigmoidCrossEntropy_:sigmoidCrossEntropy_}),softmaxCrossEntropy=op({softmaxCrossEntropy_:softmaxCrossEntropy_}),loss_ops=Object.freeze({get Reduction(){return Reduction},absoluteDifference:absoluteDifference,computeWeightedLoss:computeWeightedLoss,cosineDistance:cosineDistance,hingeLoss:hingeLoss,huberLoss:huberLoss,logLoss:logLoss,meanSquaredError:meanSquaredError,sigmoidCrossEntropy:sigmoidCrossEntropy,softmaxCrossEntropy:softmaxCrossEntropy});function gramSchmidt_(e){var t;if(Array.isArray(e)){t=!1,assert(null!=e&&e.length>0,\"Gram-Schmidt process: input must not be null, undefined, or empty\");for(var n=e[0].shape[0],r=1;r<e.length;++r)assert(e[r].shape[0]===n,\"Gram-Schmidt: Non-unique lengths found in the input vectors: (\"+e[r].shape[0]+\" vs. \"+n+\")\")}else t=!0,e=split$1(e,e.shape[0],0).map(function(e){return squeeze(e,[0])});assert(e.length<=e[0].shape[0],\"Gram-Schmidt: Number of vectors (\"+e.length+\") exceeds number of dimensions (\"+e[0].shape[0]+\").\");var o=[],a=e,i=function(e){o.push(ENV.engine.tidy(function(){var t=a[e];if(e>0)for(var n=0;n<e;++n){var r=sum$1(o[n].mulStrict(t)).mul(o[n]);t=t.sub(r)}return t.div(norm(t,\"euclidean\"))}))};for(r=0;r<e.length;++r)i(r);return t?stack(o,0):o}function qr_(e,t){if(void 0===t&&(t=!1),e.rank<2)throw new Error(\"qr() requires input tensor to have a rank >= 2, but got rank \"+e.rank);if(2===e.rank)return qr2d(e,t);var n=e.shape.slice(0,e.shape.length-2).reduce(function(e,t){return e*t}),r=[],o=[];return unstack(e.reshape([n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0).forEach(function(e){var n=qr2d(e,t),a=n[0],i=n[1];r.push(a),o.push(i)}),[stack(r,0).reshape(e.shape),stack(o,0).reshape(e.shape)]}function qr2d(e,t){return void 0===t&&(t=!1),ENV.engine.tidy(function(){if(2!==e.shape.length)throw new Error(\"qr2d() requires a 2D Tensor, but got a \"+e.shape.length+\"D Tensor.\");for(var n=e.shape[0],r=e.shape[1],o=eye(n),a=e.clone(),i=tensor2d([[1]],[1,1]),s=i.clone(),u=n>=r?r:n,l=function(e){var t,u=a,l=s,c=o;t=ENV.engine.tidy(function(){var t=a.slice([e,e],[n-e,1]),u=t.norm(),l=a.slice([e,e],[1,1]),c=l.sign().neg(),p=l.sub(c.mul(u)),d=t.div(p);s=1===d.shape[0]?i.clone():i.concat(d.slice([1,0],[d.shape[0]-1,d.shape[1]]),0);var h=c.matMul(p).div(u).neg(),f=a.slice([e,0],[n-e,r]),m=h.mul(s);a=0===e?f.sub(m.matMul(s.transpose().matMul(f))):a.slice([0,0],[e,r]).concat(f.sub(m.matMul(s.transpose().matMul(f))),0);var g=o.slice([0,e],[n,o.shape[1]-e]);return o=0===e?g.sub(g.matMul(s).matMul(m.transpose())):o.slice([0,0],[n,e]).concat(g.sub(g.matMul(s).matMul(m.transpose())),1),[s,a,o]}),s=t[0],a=t[1],o=t[2],dispose([u,l,c])},c=0;c<u;++c)l(c);return!t&&n>r&&(o=o.slice([0,0],[n,r]),a=a.slice([0,0],[r,r])),[o,a]})}var gramSchmidt=op({gramSchmidt_:gramSchmidt_}),qr=op({qr_:qr_}),linalg_ops=Object.freeze({gramSchmidt:gramSchmidt,qr:qr});function resizeBilinear_(e,t,n){void 0===n&&(n=!1);var r=convertToTensor(e,\"images\",\"resizeBilinear\");assert(3===r.rank||4===r.rank,\"Error in resizeBilinear: x must be rank 3 or 4, but got rank \"+r.rank+\".\"),assert(2===t.length,\"Error in resizeBilinear: new shape must 2D, but got shape \"+t+\".\");var o=r,a=!1;3===r.rank&&(a=!0,o=r.as4D(1,r.shape[0],r.shape[1],r.shape[2]));var i=t[0],s=t[1],u=ENV.engine.runKernel(function(e,t){return e.resizeBilinear(o,i,s,n)},{batchImages:o},function(e,t){return{batchImages:function(){return ENV.engine.runKernel(function(t){return t.resizeBilinearBackprop(e,o,n)},{})}}});return a?u.as3D(u.shape[1],u.shape[2],u.shape[3]):u}function resizeNearestNeighbor_(e,t,n){void 0===n&&(n=!1);var r=convertToTensor(e,\"images\",\"resizeNearestNeighbor\");assert(3===r.rank||4===r.rank,\"Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank \"+r.rank+\".\"),assert(2===t.length,\"Error in resizeNearestNeighbor: new shape must 2D, but got shape \"+t+\".\"),assert(\"float32\"===r.dtype||\"int32\"===r.dtype,\"`images` must have `int32` or `float32` as dtype\");var o=r,a=!1;3===r.rank&&(a=!0,o=r.as4D(1,r.shape[0],r.shape[1],r.shape[2]));var i=t[0],s=t[1],u=ENV.engine.runKernel(function(e,t){return e.resizeNearestNeighbor(o,i,s,n)},{batchImages:o},function(e,t){return{batchImages:function(){return ENV.engine.runKernel(function(t){return t.resizeNearestNeighborBackprop(e,o,n)},{})}}});return a?u.as3D(u.shape[1],u.shape[2],u.shape[3]):u}function nonMaxSuppression_(e,t,n,r,o){void 0===r&&(r=.5),void 0===o&&(o=Number.NEGATIVE_INFINITY);var a=convertToTensor(e,\"boxes\",\"nonMaxSuppression\"),i=convertToTensor(t,\"scores\",\"nonMaxSuppression\"),s=nonMaxSuppSanityCheck(a,i,n,r,o);return n=s.maxOutputSize,r=s.iouThreshold,o=s.scoreThreshold,ENV.engine.runKernel(function(e){return e.nonMaxSuppression(a,i,n,r,o)},{$boxes:a})}function nonMaxSuppressionAsync_(e,t,n,r,o){return void 0===r&&(r=.5),void 0===o&&(o=Number.NEGATIVE_INFINITY),__awaiter(this,void 0,void 0,function(){var a,i,s,u,l,c;return __generator(this,function(p){switch(p.label){case 0:return a=convertToTensor(e,\"boxes\",\"nonMaxSuppressionAsync\"),i=convertToTensor(t,\"scores\",\"nonMaxSuppressionAsync\"),s=nonMaxSuppSanityCheck(a,i,n,r,o),n=s.maxOutputSize,r=s.iouThreshold,o=s.scoreThreshold,[4,a.data()];case 1:return u=p.sent(),[4,i.data()];case 2:return l=p.sent(),c=nonMaxSuppressionImpl(u,l,n,r,o),a!==e&&a.dispose(),i!==t&&i.dispose(),[2,c]}})})}function nonMaxSuppSanityCheck(e,t,n,r,o){null==r&&(r=.5),null==o&&(o=Number.NEGATIVE_INFINITY);var a=e.shape[0];return n=Math.min(n,a),assert(0<=r&&r<=1,\"iouThreshold must be in [0, 1], but was '\"+r+\"'\"),assert(2===e.rank,\"boxes must be a 2D tensor, but was of rank '\"+e.rank+\"'\"),assert(4===e.shape[1],\"boxes must have 4 columns, but 2nd dimension was \"+e.shape[1]),assert(1===t.rank,\"scores must be a 1D tensor\"),assert(t.shape[0]===a,\"scores has incompatible shape with boxes. Expected \"+a+\", but was \"+t.shape[0]),{maxOutputSize:n,iouThreshold:r,scoreThreshold:o}}function cropAndResize_(e,t,n,r,o,a){var i=convertToTensor(e,\"image\",\"cropAndResize\",\"float32\"),s=convertToTensor(t,\"boxes\",\"cropAndResize\",\"float32\"),u=convertToTensor(n,\"boxInd\",\"cropAndResize\",\"int32\");o=o||\"bilinear\",a=a||0;var l=s.shape[0];assert(4===i.rank,\"Error in cropAndResize: image must be rank 4,but got rank \"+i.rank+\".\"),assert(2===s.rank&&4===s.shape[1],\"Error in cropAndResize: boxes must be have size [\"+l+\",4] but had shape \"+s.shape+\".\"),assert(1===u.rank&&u.shape[0]===l,\"Error in cropAndResize: boxInd must be have size [\"+l+\"] but had shape \"+s.shape+\".\"),assert(2===r.length,\"Error in cropAndResize: cropSize must be of length 2, but got length \"+r.length+\".\"),assert(r[0]>=1&&r[1]>=1,\"cropSize must be atleast [1,1], but was \"+r),assert(\"bilinear\"===o||\"nearest\"===o,\"method must be bilinear or nearest, but was \"+o);return ENV.engine.runKernel(function(e,t){return e.cropAndResize(i,s,u,r,o,a)},{$image:i,$boxes:s})}var resizeBilinear=op({resizeBilinear_:resizeBilinear_}),resizeNearestNeighbor=op({resizeNearestNeighbor_:resizeNearestNeighbor_}),nonMaxSuppression=op({nonMaxSuppression_:nonMaxSuppression_}),nonMaxSuppressionAsync=nonMaxSuppressionAsync_,cropAndResize=cropAndResize_,image_ops=Object.freeze({resizeBilinear:resizeBilinear,resizeNearestNeighbor:resizeNearestNeighbor,nonMaxSuppression:nonMaxSuppression,nonMaxSuppressionAsync:nonMaxSuppressionAsync,cropAndResize:cropAndResize}),ops=Object.freeze({image:image_ops,linalg:linalg_ops,losses:loss_ops,spectral:spectral_ops,op:op,batchNormalization2d:batchNormalization2d,batchNormalization3d:batchNormalization3d,batchNormalization4d:batchNormalization4d,batchNormalization:batchNormalization,complex:complex,real:real,imag:imag,concat:concat,concat1d:concat1d,concat2d:concat2d,concat3d:concat3d,concat4d:concat4d,split:split$1,conv1d:conv1d,conv2d:conv2d,conv3d:conv3d,conv2dDerFilter:conv2dDerFilter,depthwiseConv2d:depthwiseConv2d,separableConv2d:separableConv2d,conv2dTranspose:conv2dTranspose,matMul:matMul,dot:dot,outerProduct:outerProduct,reverse:reverse,reverse1d:reverse1d,reverse2d:reverse2d,reverse3d:reverse3d,reverse4d:reverse4d,maxPool:maxPool,avgPool:avgPool,pool:pool,slice:slice,slice1d:slice1d,slice2d:slice2d,slice3d:slice3d,slice4d:slice4d,abs:abs,acos:acos,acosh:acosh,asin:asin,asinh:asinh,atan:atan,atanh:atanh,ceil:ceil,clipByValue:clipByValue,cos:cos,cosh:cosh,erf:erf,exp:exp,expm1:expm1,floor:floor,log:log$1,log1p:log1p,logSigmoid:logSigmoid,neg:neg,reciprocal:reciprocal,round:round,rsqrt:rsqrt,sigmoid:sigmoid,sign:sign,sin:sin,sinh:sinh,softplus:softplus,sqrt:sqrt,square:square,step:step,tan:tan,tanh:tanh$1,all:all,any:any,argMax:argMax,argMin:argMin,logSumExp:logSumExp,max:max,mean:mean,min:min,moments:moments,sum:sum$1,prod:prod,equal:equal,equalStrict:equalStrict,greater:greater,greaterEqual:greaterEqual,greaterEqualStrict:greaterEqualStrict,greaterStrict:greaterStrict,less:less,lessEqual:lessEqual,lessEqualStrict:lessEqualStrict,lessStrict:lessStrict,notEqual:notEqual,notEqualStrict:notEqualStrict,add:add,addN:addN,addStrict:addStrict,atan2:atan2,div:div,divStrict:divStrict,floorDiv:floorDiv,maximum:maximum,maximumStrict:maximumStrict,minimum:minimum,minimumStrict:minimumStrict,mod:mod,modStrict:modStrict,mul:mul,mulStrict:mulStrict,pow:pow,powStrict:powStrict,squaredDifference:squaredDifference,squaredDifferenceStrict:squaredDifferenceStrict,sub:sub,subStrict:subStrict,elu:elu,leakyRelu:leakyRelu,prelu:prelu,relu:relu,selu:selu,logicalAnd:logicalAnd,logicalNot:logicalNot,logicalOr:logicalOr,logicalXor:logicalXor,where:where,whereAsync:whereAsync,buffer:buffer,toPixels:toPixels,print:print,batchToSpaceND:batchToSpaceND,cast:cast,clone:clone,cumsum:cumsum,depthToSpace:depthToSpace,expandDims:expandDims,eye:eye,fromPixels:fromPixels,multinomial:multinomial,oneHot:oneHot,pad:pad,pad1d:pad1d,pad2d:pad2d,pad3d:pad3d,pad4d:pad4d,rand:rand,randomNormal:randomNormal,randomUniform:randomUniform,reshape:reshape,spaceToBatchND:spaceToBatchND,squeeze:squeeze,stack:stack,tile:tile,truncatedNormal:truncatedNormal,unstack:unstack,setdiff1dAsync:setdiff1dAsync,fill:fill,linspace:linspace,ones:ones$1,range:range,scalar:scalar,tensor:tensor,tensor1d:tensor1d,tensor2d:tensor2d,tensor3d:tensor3d,tensor4d:tensor4d,tensor5d:tensor5d,tensor6d:tensor6d,zeros:zeros,onesLike:onesLike,zerosLike:zerosLike,transpose:transpose,softmax:softmax,logSoftmax:logSoftmax,localResponseNormalization:localResponseNormalization,norm:norm,gather:gather,unsortedSegmentSum:unsortedSegmentSum,basicLSTMCell:basicLSTMCell,multiRNNCell:multiRNNCell,movingAverage:movingAverage,stridedSlice:stridedSlice,topk:topk,scatterND:scatterND,fft:fft,ifft:ifft,rfft:rfft,irfft:irfft,sparseToDense:sparseToDense,gatherND:gatherND}),MathBackendCPU=function(){function e(){this.blockSize=48,this.firstUse=!0,ENV.get(\"IS_BROWSER\")&&(this.fromPixels2DContext=document.createElement(\"canvas\").getContext(\"2d\"))}return e.prototype.setDataMover=function(e){this.data=new DataStorage(e)},e.prototype.register=function(e,t,n){if(this.firstUse&&(this.firstUse=!1,ENV.get(\"IS_NODE\")&&warn(\"\\n============================\\nHi there 👋. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.\\n============================\\n\")),this.data.has(e))throw new Error(\"Data buffer is already registered\");this.data.set(e,{dtype:n})},e.prototype.write=function(e,t){if(null==t)throw new Error(\"MathBackendCPU.write(): values can not be null\");this.data.get(e).values=t},e.prototype.fromPixels=function(e,t){if(null==e)throw new Error(\"pixels passed to tf.fromPixels() can not be null\");var n,r;if(ENV.get(\"IS_NODE\")&&null==e.getContext)throw new Error(\"When running in node, pixels must be an HTMLCanvasElement like the one returned by the `canvas` npm package\");if(null!=e.getContext)n=e.getContext(\"2d\").getImageData(0,0,e.width,e.height).data;else if(e instanceof ImageData)n=e.data;else{if(!(e instanceof HTMLImageElement||e instanceof HTMLVideoElement))throw new Error(\"pixels passed to tf.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ImageData, but was \"+e.constructor.name);if(null==this.fromPixels2DContext)throw new Error(\"Can't read pixels from HTMLImageElement outside the browser.\");this.fromPixels2DContext.canvas.width=e.width,this.fromPixels2DContext.canvas.height=e.height,this.fromPixels2DContext.drawImage(e,0,0,e.width,e.height),n=this.fromPixels2DContext.getImageData(0,0,e.width,e.height).data}if(4===t)r=new Int32Array(n);else{var o=e.width*e.height;r=new Int32Array(o*t);for(var a=0;a<o;a++)for(var i=0;i<t;++i)r[a*t+i]=n[4*a+i]}return tensor3d(r,[e.height,e.width,t],\"int32\")},e.prototype.read=function(e){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(t){return[2,this.readSync(e)]})})},e.prototype.readSync=function(e){var t=this.data.get(e),n=t.dtype,r=t.complexTensors;return\"complex64\"===n?mergeRealAndImagArrays(r.real.dataSync(),r.imag.dataSync()):this.data.get(e).values},e.prototype.disposeData=function(e){if(this.data.has(e)){var t=this.data.get(e).complexTensors;null!=t&&(t.real.dispose(),t.imag.dispose()),this.data.delete(e)}},e.prototype.time=function(e){return __awaiter(this,void 0,void 0,function(){var t;return __generator(this,function(n){return t=now(),e(),[2,{kernelMs:now()-t}]})})},e.prototype.memory=function(){return{unreliable:!0,reasons:[\"The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less.\"]}},e.prototype.complex=function(e,t){var n=Tensor.make(e.shape,{},\"complex64\");return this.data.get(n.dataId).complexTensors={real:ENV.engine.keep(e.clone()),imag:ENV.engine.keep(t.clone())},n},e.prototype.real=function(e){return this.data.get(e.dataId).complexTensors.real.clone()},e.prototype.imag=function(e){return this.data.get(e.dataId).complexTensors.imag.clone()},e.prototype.assertNotComplex=function(e,t){Array.isArray(e)||(e=[e]),e.forEach(function(e){null!=e&&assert(\"complex64\"!==e.dtype,t+\" does not support complex64 tensors.\")})},e.prototype.slice=function(e,t,n){this.assertNotComplex(e,\"slice\");for(var r=buffer(n,e.dtype),o=0;o<r.size;++o){var a=r.indexToLoc(o),i=a.map(function(e,n){return e+t[n]});r.set.apply(r,[e.get.apply(e,i)].concat(a))}return r.toTensor()},e.prototype.stridedSlice=function(e,t,n,r,o,a,i,s,u){this.assertNotComplex(e,\"stridedSlice\");var l=getStridedSlicedInfo(e.shape,t,n,r,o,a,i,s,u),c=l[0],p=l[1],d=l[2],h=p.filter(function(e,t){return-1===d.indexOf(t)});if(h.some(function(e){return 0===e}))return tensor([],h);for(var f=buffer(p,e.dtype),m=0;m<f.size;m++){for(var g=f.indexToLoc(m),v=new Array(g.length),y=0;y<v.length;y++)v[y]=g[y]*r[y]+c[y];f.set.apply(f,[e.get.apply(e,v)].concat(g))}return f.toTensor().reshape(h)},e.prototype.reverse=function(e,t){this.assertNotComplex(e,\"reverse\");for(var n=buffer(e.shape,e.dtype),r=e.buffer(),o=function(o){var a=n.indexToLoc(o),i=a.slice();t.forEach(function(t){return i[t]=e.shape[t]-1-i[t]}),n.set.apply(n,[r.get.apply(r,i)].concat(a))},a=0;a<n.size;a++)o(a);return n.toTensor()},e.prototype.concat=function(e,t){this.assertNotComplex(e,\"concat\");var n=e.map(function(e){var n=sizeFromShape(e.shape.slice(t));return e.as2D(-1,n)}),r=computeOutShape(n.map(function(e){return e.shape}),1),o=buffer(r,e[0].dtype).values;if(1===n[0].shape[0]){var a=0;n.forEach(function(e){o.set(e.dataSync(),a),a+=e.size})}else{var i=0;n.forEach(function(e){for(var t=e.dataSync(),n=0,a=0;a<e.shape[0];++a)for(var s=a*r[1]+i,u=0;u<e.shape[1];++u)o[s+u]=t[n++];i+=e.shape[1]})}var s=computeOutShape(e.map(function(e){return e.shape}),t);return tensor(o,s,e[0].dtype)},e.prototype.neg=function(e){return this.assertNotComplex(e,\"neg\"),this.multiply(scalar(-1),e)},e.prototype.add=function(e,t){return\"complex64\"===e.dtype||\"complex64\"===t.dtype?this.broadcastedBinaryComplexOp(e.cast(\"complex64\"),t.cast(\"complex64\"),function(e,t,n,r){return{real:e+n,imag:t+r}}):this.broadcastedBinaryOp(e,t,upcastType(e.dtype,t.dtype),function(e,t){return e+t})},e.prototype.addN=function(e){this.assertNotComplex(e,\"addN\");for(var t=e.map(function(e){return e.dataSync()}),n=buffer(e[0].shape,e[0].dtype),r=n.values,o=0;o<e.length;o++)for(var a=t[o],i=0;i<r.length;i++)r[i]+=a[i];return n.toTensor()},e.prototype.subtract=function(e,t){return\"complex64\"===e.dtype||\"complex64\"===t.dtype?this.broadcastedBinaryComplexOp(e.cast(\"complex64\"),t.cast(\"complex64\"),function(e,t,n,r){return{real:e-n,imag:t-r}}):this.broadcastedBinaryOp(e,t,upcastType(e.dtype,t.dtype),function(e,t){return e-t})},e.prototype.pow=function(e,t){return this.assertNotComplex([e,t],\"pow\"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){return Math.pow(e,t)})},e.prototype.batchMatMul=function(e,t,n,r){this.assertNotComplex([e,t],\"matMul\");for(var o=n?e.shape[1]:e.shape[2],a=n?e.shape[2]:e.shape[1],i=r?t.shape[1]:t.shape[2],s=e.shape[0],u=e.dataSync(),l=t.dataSync(),c=n?[e.strides[0],1,e.strides[1]]:[e.strides[0],e.strides[1],1],p=c[0],d=c[1],h=c[2],f=r?[1,t.strides[1],t.strides[0]]:[t.strides[1],1,t.strides[0]],m=f[0],g=f[1],v=f[2],y=a*i,x=buffer([s,a,i],e.dtype),T=x.values,w=this.blockSize,E=0;E<s;E++)for(var S=0;S<a;S+=w)for(var b=0;b<i;b+=w)for(var C=0;C<o;C+=w)for(var 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n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,upcastType(e.dtype,\"int32\")),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),l=0;l<s.length;++l){for(var c=l*i,p=0,d=0;d<i;++d)p+=u[c+d];s[l]=p}return a},e.prototype.prod=function(e,t){this.assertNotComplex(e,\"sum\");for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,upcastType(e.dtype,\"int32\")),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),l=0;l<s.length;++l){for(var c=l*i,p=1,d=0;d<i;++d)p*=u[c+d];s[l]=p}return a},e.prototype.unsortedSegmentSum=function(e,t,n){this.assertNotComplex(e,\"unsortedSegmentSum\");for(var r=[],o=e.rank-t.rank,a=0;a<o;++a)t=t.expandDims(a+1);for(a=0;a<n;++a){var i=scalar(a,\"int32\"),s=equal(i,t).asType(\"float32\").mul(e).sum(0);r.push(s)}return stack(r)},e.prototype.argMin=function(e,t){this.assertNotComplex(e,\"argMin\");var n=[t];assertAxesAreInnerMostDims(\"argMin\",n,e.rank);for(var r=computeOutAndReduceShapes(e.shape,n),o=r[0],a=r[1],i=zeros(o,\"int32\"),s=sizeFromShape(a),u=i.dataSync(),l=e.dataSync(),c=0;c<u.length;++c){for(var p=c*s,d=l[p],h=0,f=0;f<s;++f){var m=l[p+f];m<d&&(d=m,h=f)}u[c]=h}return i},e.prototype.argMax=function(e,t){this.assertNotComplex(e,\"argMax\");var n=[t];assertAxesAreInnerMostDims(\"argMax\",n,e.rank);for(var r=computeOutAndReduceShapes(e.shape,n),o=r[0],a=r[1],i=zeros(o,\"int32\"),s=sizeFromShape(a),u=i.dataSync(),l=e.dataSync(),c=0;c<u.length;++c){for(var p=c*s,d=l[p],h=0,f=0;f<s;++f){var m=l[p+f];m>d&&(d=m,h=f)}u[c]=h}return i},e.prototype.cumsum=function(e,t,n,r){if(this.assertNotComplex(e,\"cumsum\"),t!==e.rank-1)throw new Error(\"backend.cumsum in CPU expects an inner-most axis=\"+(e.rank-1)+\" but got axis=\"+t);for(var o=upcastType(e.dtype,\"int32\"),a=zeros(e.shape,o),i=a.dataSync(),s=e.dataSync(),u=e.shape[e.rank-1],l=r?function(e,t){return e+u-t-1}:function(e,t){return e+t},c=0;c<s.length;c+=u)for(var p=0;p<u;p++){var 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n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,e.dtype),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),l=0;l<s.length;++l){for(var c=l*i,p=u[c],d=0;d<i;++d){var h=u[c+d];p=p||h}s[l]=p}return a},e.prototype.squaredDifference=function(e,t){return this.assertNotComplex([e,t],\"squaredDifference\"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){var n=e-t;return n*n})},e.prototype.ceil=function(e){this.assertNotComplex(e,\"ceil\");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)n[r]=Math.ceil(t[r]);return Tensor.make(e.shape,{values:n})},e.prototype.floor=function(e){this.assertNotComplex(e,\"floor\");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)n[r]=Math.floor(t[r]);return Tensor.make(e.shape,{values:n})},e.prototype.sign=function(e){this.assertNotComplex(e,\"x\");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)t[r]<0?n[r]=-1:t[r]>0?n[r]=1:n[r]=0;return 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Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=Math.log1p(o)}return Tensor.make(e.shape,{values:n})},e.prototype.sqrt=function(e){this.assertNotComplex(e,\"sqrt\");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=Math.sqrt(o)}return Tensor.make(e.shape,{values:n})},e.prototype.rsqrt=function(e){this.assertNotComplex(e,\"rsqrt\");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=1/Math.sqrt(o)}return Tensor.make(e.shape,{values:n})},e.prototype.square=function(e){this.assertNotComplex(e,\"square\");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=o*o}return Tensor.make(e.shape,{values:n})},e.prototype.reciprocal=function(e){this.assertNotComplex(e,\"reciprocal\");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)n[r]=1/t[r];return Tensor.make(e.shape,{values:n})},e.prototype.relu=function(e){this.assertNotComplex(e,\"relu\");for(var t=zeros(e.shape,e.dtype),n=t.dataSync(),r=e.dataSync(),o=0;o<r.length;++o)n[o]=Math.max(0,r[o]);return t},e.prototype.elu=function(e){this.assertNotComplex(e,\"elu\");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r){var o=n[r];t[r]=o>=0?o:Math.exp(o)-1}return Tensor.make(e.shape,{values:t})},e.prototype.eluDer=function(e,t){this.assertNotComplex([e,t],\"eluDer\");for(var n=new Float32Array(t.size),r=t.dataSync(),o=e.dataSync(),a=0;a<r.length;++a){var i=r[a];n[a]=i>=1?o[a]:o[a]*(i+1)}return Tensor.make(t.shape,{values:n})},e.prototype.selu=function(e){this.assertNotComplex(e,\"selu\");for(var t=SELU_SCALEALPHA,n=SELU_SCALE,r=new Float32Array(e.size),o=e.dataSync(),a=0;a<o.length;++a){var i=o[a];r[a]=i>=0?n*i:t*(Math.exp(i)-1)}return Tensor.make(e.shape,{values:r})},e.prototype.clip=function(e,t,n){this.assertNotComplex(e,\"clip\");for(var r=new Float32Array(e.size),o=e.dataSync(),a=0;a<o.length;++a){var i=o[a];r[a]=i>n?n:i<t?t:i}return 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Tensor.make(e.shape,{values:t})},e.prototype.asinh=function(e){this.assertNotComplex(e,\"asinh\");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.asinh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.acosh=function(e){this.assertNotComplex(e,\"acosh\");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.acosh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.atanh=function(e){this.assertNotComplex(e,\"atanh\");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.atanh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.erf=function(e){this.assertNotComplex(e,\"erf\");for(var t=new Float32Array(e.size),n=e.dataSync(),r=ERF_P,o=ERF_A1,a=ERF_A2,i=ERF_A3,s=ERF_A4,u=ERF_A5,l=0;l<n.length;++l){var c=n[l],p=1/(1+r*c);t[l]=1-((((u*p+s)*p+i)*p+a)*p+o)*p*Math.exp(-c*c)}return Tensor.make(e.shape,{values:t})},e.prototype.step=function(e,t){void 0===t&&(t=0),this.assertNotComplex(e,\"step\");for(var n=new Float32Array(e.size),r=e.dataSync(),o=0;o<r.length;++o){var a=r[o];isNaN(a)?n[o]=NaN:n[o]=a>0?1:t}return Tensor.make(e.shape,{values:n})},e.prototype.conv2d=function(e,t,n){this.assertNotComplex([e,t],\"conv2d\");for(var r=n.filterHeight,o=n.filterWidth,a=n.dilationHeight,i=n.dilationWidth,s=n.padInfo.left,u=n.padInfo.top,l=buffer(n.outShape,e.dtype),c=e.dataSync(),p=t.dataSync(),d=l.values,h=0;h<n.batchSize;++h)for(var f=h*e.strides[0],m=h*l.strides[0],g=0;g<n.outHeight;++g)for(var v=m+g*l.strides[1],y=g*n.strideHeight-s,x=0;x<r;x++){var T=y+x*a;if(!(T<0||T>=n.inHeight))for(var w=x*t.strides[0],E=f+T*e.strides[1],S=0;S<n.outWidth;++S)for(var b=v+S*n.outChannels,C=S*n.strideWidth-u,A=0;A<o;A++){var _=C+A*i;if(!(_<0||_>=n.inWidth))for(var N=w+A*t.strides[1],R=E+_*n.inChannels,k=N,I=0;I<n.inChannels;++I){for(var D=c[R+I],M=0;M<n.outChannels;++M)d[b+M]+=D*p[k+M];k+=n.outChannels}}}return l.toTensor()},e.prototype.conv3d=function(e,t,n){for(var r=n.filterDepth,o=n.filterHeight,a=n.filterWidth,i=n.dilationDepth,s=n.dilationHeight,u=n.dilationWidth,l=n.padInfo.front,c=n.padInfo.left,p=n.padInfo.top,d=buffer(n.outShape,e.dtype),h=e.dataSync(),f=t.dataSync(),m=d.values,g=0;g<n.batchSize;++g)for(var v=g*e.strides[0],y=g*d.strides[0],x=0;x<n.outDepth;++x)for(var T=y+x*d.strides[1],w=x*n.strideDepth-l,E=0;E<r;E++){var S=w+E*i;if(!(S<0||S>=n.inDepth))for(var b=E*t.strides[0],C=v+S*e.strides[1],A=0;A<n.outHeight;++A)for(var _=T+A*d.strides[2],N=A*n.strideHeight-p,R=0;R<o;R++){var k=N+R*s;if(!(k<0||k>=n.inHeight))for(var I=b+R*t.strides[1],D=C+k*e.strides[2],M=0;M<n.outWidth;++M)for(var P=_+M*n.outChannels,O=M*n.strideWidth-c,F=0;F<a;F++){var L=O+F*u;if(!(L<0||L>=n.inWidth))for(var B=I+F*t.strides[2],U=D+L*n.inChannels,V=B,z=0;z<n.inChannels;++z){for(var H=h[U+z],W=0;W<n.outChannels;++W)m[P+W]+=H*f[V+W];V+=n.outChannels}}}}return d.toTensor()},e.prototype.conv2dDerInput=function(e,t,n){this.assertNotComplex([e,t],\"conv2dDerInput\");for(var r=buffer(n.inShape,\"float32\"),o=r.values,a=r.strides,i=a[0],s=a[1],u=a[2],l=e.dataSync(),c=e.strides,p=c[0],d=c[1],h=c[2],f=t.dataSync(),m=t.strides,g=m[0],v=m[1],y=m[2],x=n.batchSize,T=n.filterHeight,w=n.filterWidth,E=n.inChannels,S=n.inHeight,b=n.inWidth,C=n.outChannels,A=n.outHeight,_=n.outWidth,N=n.strideHeight,R=n.strideWidth,k=T-1-n.padInfo.top,I=w-1-n.padInfo.left,D=0;D<x;++D)for(var M=0;M<E;++M)for(var P=0;P<S;++P)for(var O=P-k,F=Math.max(0,Math.ceil(O/N)),L=Math.min(A,(T+O)/N),B=0;B<b;++B){for(var U=B-I,V=Math.max(0,Math.ceil(U/R)),z=Math.min(_,(w+U)/R),H=0,W=F;W<L;++W)for(var G=W*N-O,q=V;q<z;++q)for(var $=p*D+d*W+h*q,X=g*(T-1-G)+v*(w-1-(q*R-U))+y*M,K=0;K<C;++K){H+=l[$+K]*f[X+K]}o[i*D+s*P+u*B+M]=H}return r.toTensor()},e.prototype.conv3dDerInput=function(e,t,n){for(var r=buffer(n.inShape,\"float32\"),o=r.values,a=r.strides,i=a[0],s=a[1],u=a[2],l=a[3],c=e.dataSync(),p=e.strides,d=p[0],h=p[1],f=p[2],m=p[3],g=t.dataSync(),v=t.strides,y=v[0],x=v[1],T=v[2],w=v[3],E=n.batchSize,S=n.filterDepth,b=n.filterHeight,C=n.filterWidth,A=n.inChannels,_=n.inDepth,N=n.inHeight,R=n.inWidth,k=n.outChannels,I=n.outDepth,D=n.outHeight,M=n.outWidth,P=n.strideDepth,O=n.strideHeight,F=n.strideWidth,L=S-1-n.padInfo.front,B=b-1-n.padInfo.top,U=C-1-n.padInfo.left,V=0;V<E;++V)for(var z=0;z<A;++z)for(var H=0;H<_;++H)for(var W=H-L,G=Math.max(0,Math.ceil(W/P)),q=Math.min(I,(S+W)/P),$=0;$<N;++$)for(var X=$-B,K=Math.max(0,Math.ceil(X/O)),j=Math.min(D,(b+X)/O),Y=0;Y<R;++Y){for(var Q=Y-U,J=Math.max(0,Math.ceil(Q/F)),Z=Math.min(M,(C+Q)/F),ee=0,te=G;te<q;++te)for(var ne=te*P-W,re=K;re<j;++re)for(var oe=re*O-X,ae=J;ae<Z;++ae)for(var ie=d*V+h*te+f*re+m*ae,se=y*(S-1-ne)+x*(b-1-oe)+T*(C-1-(ae*F-Q))+w*z,ue=0;ue<k;++ue){ee+=c[ie+ue]*g[se+ue]}o[i*V+s*H+u*$+l*Y+z]=ee}return r.toTensor()},e.prototype.conv2dDerFilter=function(e,t,n){this.assertNotComplex([e,t],\"conv2dDerFilter\");for(var r=n.strideHeight,o=n.strideWidth,a=n.filterHeight,i=n.filterWidth,s=buffer(n.filterShape,\"float32\"),u=n.padInfo.left,l=n.padInfo.top,c=0;c<a;++c)for(var p=Math.max(0,Math.ceil((l-c)/r)),d=Math.min(n.outHeight,(n.inHeight+l-c)/r),h=0;h<i;++h)for(var f=Math.max(0,Math.ceil((u-h)/o)),m=Math.min(n.outWidth,(n.inWidth+u-h)/o),g=0;g<n.inChannels;++g)for(var v=0;v<n.outChannels;++v){for(var y=0,x=0;x<n.batchSize;++x)for(var T=p;T<d;++T)for(var w=c+T*r-l,E=f;E<m;++E){var S=h+E*o-u;y+=e.get(x,w,S,g)*t.get(x,T,E,v)}s.set(y,c,h,g,v)}return s.toTensor()},e.prototype.conv3dDerFilter=function(e,t,n){for(var r=n.strideDepth,o=n.strideHeight,a=n.strideWidth,i=n.filterDepth,s=n.filterHeight,u=n.filterWidth,l=buffer(n.filterShape,\"float32\"),c=l.values,p=l.strides,d=p[0],h=p[1],f=p[2],m=p[3],g=t.dataSync(),v=t.strides,y=v[0],x=v[1],T=v[2],w=v[3],E=e.dataSync(),S=e.strides,b=S[0],C=S[1],A=S[2],_=S[3],N=n.padInfo.front,R=n.padInfo.left,k=n.padInfo.top,I=0;I<i;++I)for(var D=Math.max(0,Math.ceil((N-I)/r)),M=Math.min(n.outDepth,(n.inDepth+N-I)/r),P=I*d,O=0;O<s;++O)for(var F=Math.max(0,Math.ceil((k-O)/o)),L=Math.min(n.outHeight,(n.inHeight+k-O)/o),B=O*h+P,U=0;U<u;++U)for(var V=Math.max(0,Math.ceil((R-U)/a)),z=Math.min(n.outWidth,(n.inWidth+R-U)/a),H=U*f+B,W=0;W<n.inChannels;++W)for(var G=W*m+H,q=0;q<n.outChannels;++q){for(var $=0,X=0;X<n.batchSize;++X)for(var K=X*b,j=X*y,Y=D;Y<M;++Y)for(var Q=(I+Y*r-N)*C+K,J=Y*x+j,Z=F;Z<L;++Z)for(var ee=(O+Z*o-k)*A+Q,te=Z*T+J,ne=V;ne<z;++ne){var re=ne*w+te;$+=E[(U+ne*a-R)*_+ee+W]*g[re+q]}c[G+q]=$}return l.toTensor()},e.prototype.depthwiseConv2D=function(e,t,n){this.assertNotComplex([e,t],\"depthwiseConv2D\");for(var r=n.filterHeight,o=n.filterWidth,a=n.dilationHeight,i=n.dilationWidth,s=n.padInfo.left,u=n.padInfo.top,l=n.outChannels/n.inChannels,c=buffer(n.outShape,e.dtype),p=e.dataSync(),d=t.dataSync(),h=c.values,f=0;f<n.batchSize;++f)for(var m=f*e.strides[0],g=f*c.strides[0],v=0;v<n.outHeight;++v)for(var y=g+v*c.strides[1],x=v*n.strideHeight-s,T=0;T<r;++T){var w=x+T*a;if(!(w<0||w>=n.inHeight))for(var E=T*t.strides[0],S=m+w*e.strides[1],b=0;b<n.outWidth;++b)for(var C=y+b*c.strides[2],A=b*n.strideWidth-u,_=0;_<o;++_){var N=A+_*i;if(!(N<0||N>=n.inWidth))for(var R=E+_*t.strides[1],k=S+N*n.inChannels,I=C,D=R,M=0;M<n.inChannels;++M){for(var P=p[k+M],O=0;O<l;++O)h[I+O]+=P*d[D+O];I+=l,D+=l}}}return c.toTensor()},e.prototype.depthwiseConv2DDerInput=function(e,t,n){this.assertNotComplex([e,t],\"depthwiseConv2DDerInput\");for(var r=buffer(n.inShape,\"float32\"),o=r.values,a=r.strides,i=a[0],s=a[1],u=a[2],l=e.dataSync(),c=e.strides,p=c[0],d=c[1],h=c[2],f=t.dataSync(),m=t.strides,g=m[0],v=m[1],y=m[2],x=n.batchSize,T=n.filterHeight,w=n.filterWidth,E=n.inChannels,S=n.inHeight,b=n.inWidth,C=n.outChannels,A=n.outHeight,_=n.outWidth,N=n.strideHeight,R=n.strideWidth,k=T-1-n.padInfo.top,I=w-1-n.padInfo.left,D=C/E,M=0;M<x;++M)for(var P=0;P<E;++P)for(var O=0;O<S;++O)for(var F=O-k,L=Math.max(0,Math.ceil(F/N)),B=Math.min(A,(T+F)/N),U=0;U<b;++U){for(var V=U-I,z=Math.max(0,Math.ceil(V/R)),H=Math.min(_,(w+V)/R),W=0,G=L;G<B;++G)for(var q=G*N-F,$=z;$<H;++$)for(var X=p*M+d*G+h*$,K=g*(T-1-q)+v*(w-1-($*R-V))+y*P,j=0;j<D;++j){W+=l[X+(P*D+j)]*f[K+j]}o[i*M+s*O+u*U+P]=W}return r.toTensor()},e.prototype.depthwiseConv2DDerFilter=function(e,t,n){this.assertNotComplex([e,t],\"depthwiseConv2DDerFilter\");for(var r=n.strideHeight,o=n.strideWidth,a=n.filterHeight,i=n.filterWidth,s=buffer(n.filterShape,\"float32\"),u=n.padInfo.left,l=n.padInfo.top,c=n.outChannels/n.inChannels,p=0;p<a;++p)for(var d=Math.max(0,Math.ceil((l-p)/r)),h=Math.min(n.outHeight,(n.inHeight+l-p)/r),f=0;f<i;++f)for(var m=Math.max(0,Math.ceil((u-f)/o)),g=Math.min(n.outWidth,(n.inWidth+u-f)/o),v=0;v<n.outChannels;++v){for(var y=Math.trunc(v/c),x=v%c,T=0,w=0;w<n.batchSize;++w)for(var E=d;E<h;++E)for(var S=p+E*r-l,b=m;b<g;++b){var C=f+b*o-u;T+=e.get(w,S,C,y)*t.get(w,E,b,v)}s.set(T,p,f,y,x)}return s.toTensor()},e.prototype.tile=function(e,t){this.assertNotComplex(e,\"tile\");for(var n=new Array(e.rank),r=0;r<n.length;r++)n[r]=e.shape[r]*t[r];var o=buffer(n,e.dtype),a=e.buffer();for(r=0;r<o.values.length;++r){for(var i=o.indexToLoc(r),s=new Array(e.rank),u=0;u<s.length;u++)s[u]=i[u]%e.shape[u];var l=a.locToIndex(s);o.values[r]=a.values[l]}return o.toTensor()},e.prototype.pad=function(e,t,n){this.assertNotComplex(e,\"pad\");var r=t.map(function(t,n){return t[0]+e.shape[n]+t[1]}),o=t.map(function(e){return e[0]}),a=e.buffer(),i=buffer(r,e.dtype);0!==n&&i.values.fill(n);for(var s=0;s<e.size;s++){var u=a.indexToLoc(s),l=u.map(function(e,t){return e+o[t]});i.set.apply(i,[e.get.apply(e,u)].concat(l))}return i.toTensor()},e.prototype.transpose=function(e,t){this.assertNotComplex(e,\"transpose\");for(var n=new Array(e.rank),r=0;r<n.length;r++)n[r]=e.shape[t[r]];var o=e.dataSync(),a=buffer(n,e.dtype),i=e.buffer();for(r=0;r<e.size;++r){for(var s=i.indexToLoc(r),u=new Array(s.length),l=0;l<u.length;l++)u[l]=s[t[l]];var c=a.locToIndex(u);a.values[c]=o[r]}return a.toTensor()},e.prototype.gather=function(e,t,n){this.assertNotComplex([e,t],\"gather\");var r=e.shape.slice(),o=t.dataSync();r[n]=o.length;for(var a=buffer(r,e.dtype),i=e.buffer(),s=0;s<a.size;++s){var u=a.indexToLoc(s),l=u.slice();l[n]=o[u[n]];var c=i.locToIndex(l);a.values[s]=i.values[c]}return a.toTensor()},e.prototype.batchToSpaceND=function(e,t,n){this.assertNotComplex([e],\"batchToSpaceND\");var r=t.reduce(function(e,t){return e*t}),o=getReshaped(e.shape,t,r),a=getPermuted(o.length,t.length),i=getReshapedPermuted(e.shape,t,r),s=getSliceBeginCoords(n,t.length),u=getSliceSize(i,n,t.length);return e.reshape(o).transpose(a).reshape(i).slice(s,u)},e.prototype.spaceToBatchND=function(e,t,n){this.assertNotComplex([e],\"spaceToBatchND\");var r=t.reduce(function(e,t){return e*t}),o=[[0,0]];o.push.apply(o,n);for(var a=1+t.length;a<e.shape.length;++a)o.push([0,0]);var i=e.pad(o),s=getReshaped(i.shape,t,r,!1),u=getPermuted(s.length,t.length,!1),l=getReshapedPermuted(i.shape,t,r,!1);return i.reshape(s).transpose(u).reshape(l)},e.prototype.pool=function(e,t,n){this.assertNotComplex(e,\"pool\");for(var r=t.strideHeight,o=t.strideWidth,a=t.dilationHeight,i=t.dilationWidth,s=t.effectiveFilterHeight,u=t.effectiveFilterWidth,l=t.padInfo.top,c=t.padInfo.left,p=\"max\"===n?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,d=e.dataSync(),h=buffer(t.outShape,e.dtype),f=h.values,m=t.outShape[1]*t.outShape[2]*t.outShape[3],g=t.outShape[2]*t.outShape[3],v=t.outShape[3],y=0;y<t.batchSize;++y)for(var x=y*m,T=y*e.strides[0],w=0;w<t.inChannels;++w)for(var E=0;E<t.outHeight;++E)for(var S=E*r-l,b=Math.max(0,S),C=Math.min(t.inHeight,s+S),A=x+E*g,_=0;_<t.outWidth;++_){for(var N=_*o-c,R=Math.max(0,N),k=Math.min(t.inWidth,u+N),I=p,D=0,M=0,P=b;P<C;P+=a){for(var O=T+P*e.strides[1],F=R;F<k;F+=i){var L=d[O+F*e.strides[2]+w];\"max\"===n&&L>I?I=L:\"avg\"===n&&(D+=L,M++)}if(isNaN(I))break}f[A+_*v+w]=\"avg\"===n?D/M:I}return h.toTensor()},e.prototype.maxPool=function(e,t){return this.pool(e,t,\"max\")},e.prototype.maxPoolPositions=function(e,t){for(var n=buffer(t.outShape,\"int32\"),r=t.strideHeight,o=t.strideWidth,a=t.dilationHeight,i=t.dilationWidth,s=t.effectiveFilterHeight,u=t.effectiveFilterWidth,l=t.padInfo.top,c=t.padInfo.left,p=0;p<t.batchSize;++p)for(var d=0;d<t.inChannels;++d)for(var h=0;h<t.outHeight;++h){for(var f=h*r-l,m=f;m<0;)m+=a;for(var g=Math.min(t.inHeight,s+f),v=0;v<t.outWidth;++v){for(var y=v*o-c,x=y;x<0;)x+=i;for(var T=Math.min(t.inWidth,u+y),w=Number.NEGATIVE_INFINITY,E=-1,S=m;S<g;S+=a)for(var b=S-f,C=x;C<T;C+=i){var A=C-y,_=e.get(p,S,C,d);_>w&&(w=_,E=b*u+A)}n.set(E,p,h,v,d)}}return n.toTensor()},e.prototype.maxPoolBackprop=function(e,t,n,r){this.assertNotComplex([t,n],\"maxPoolBackprop\");for(var o=this.maxPoolPositions(t,r),a=r.strideHeight,i=r.strideWidth,s=r.dilationHeight,u=r.dilationWidth,l=r.effectiveFilterHeight,c=r.effectiveFilterWidth,p=c-1-r.padInfo.left,d=l-1-r.padInfo.top,h=buffer(t.shape,\"float32\"),f=0;f<r.batchSize;++f)for(var m=0;m<r.inChannels;++m)for(var g=0;g<r.inHeight;++g)for(var v=0;v<r.inWidth;++v){for(var y=g-d,x=v-p,T=0,w=0;w<l;w+=s){var E=(y+w)/a;if(!(E<0||E>=r.outHeight||Math.floor(E)!==E))for(var S=0;S<c;S+=u){var b=(x+S)/i;if(!(b<0||b>=r.outWidth||Math.floor(b)!==b)){var C=l*c-1-o.get(f,E,b,m)===w*c+S?1:0;if(0!==C)T+=e.get(f,E,b,m)*C}}}h.set(T,f,g,v,m)}return h.toTensor()},e.prototype.avgPoolBackprop=function(e,t,n){this.assertNotComplex([e,t],\"avgPoolBackprop\");for(var r=n.strideHeight,o=n.strideWidth,a=n.filterHeight,i=n.filterWidth,s=n.dilationHeight,u=n.dilationWidth,l=n.effectiveFilterHeight,c=n.effectiveFilterWidth,p=c-1-n.padInfo.left,d=l-1-n.padInfo.top,h=buffer(t.shape,\"float32\"),f=1/(a*i),m=0;m<n.batchSize;++m)for(var g=0;g<n.inChannels;++g)for(var v=0;v<n.inHeight;++v)for(var y=0;y<n.inWidth;++y){for(var x=v-d,T=y-p,w=0,E=0;E<l;E+=s){var S=(x+E)/r;if(!(S<0||S>=n.outHeight||Math.floor(S)!==S))for(var b=0;b<c;b+=u){var C=(T+b)/o;if(!(C<0||C>=n.outWidth||Math.floor(C)!==C))w+=e.get(m,S,C,g)}}h.set(w*f,m,v,y,g)}return h.toTensor()},e.prototype.cast=function(e,t){return castTensor(e,t,this)},e.prototype.reshape=function(e,t){return reshapeTensor(e,t)},e.prototype.avgPool=function(e,t){return this.assertNotComplex(e,\"avgPool\"),this.pool(e,t,\"avg\").toFloat()},e.prototype.resizeBilinear=function(e,t,n,r){this.assertNotComplex(e,\"resizeBilinear\");for(var o=e.shape,a=o[0],i=o[1],s=o[2],u=o[3],l=e.dataSync(),c=new Float32Array(sizeFromShape([a,t,n,u])),p=[r&&t>1?i-1:i,r&&n>1?s-1:s],d=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=0,f=p[0]/d[0],m=p[1]/d[1],g=0;g<a;g++)for(var v=0;v<t;v++)for(var y=f*v,x=Math.floor(y),T=y-x,w=Math.min(i-1,Math.ceil(y)),E=g*e.strides[0]+x*e.strides[1],S=g*e.strides[0]+w*e.strides[1],b=0;b<n;b++)for(var C=m*b,A=Math.floor(C),_=C-A,N=Math.min(s-1,Math.ceil(C)),R=E+A*e.strides[2],k=S+A*e.strides[2],I=E+ +N*e.strides[2],D=S+N*e.strides[2],M=0;M<u;M++){var P=l[R+M],O=l[k+M],F=P+(l[I+M]-P)*_,L=F+(O+(l[D+M]-O)*_-F)*T;c[h++]=L}return tensor(c,[a,t,n,u])},e.prototype.resizeBilinearBackprop=function(e,t,n){this.assertNotComplex([e,t],\"resizeBilinearBackprop\");for(var r=t.shape,o=r[0],a=r[1],i=r[2],s=r[3],u=e.shape,l=u[1],c=u[2],p=new Float32Array(o*a*i*s),d=[n&&l>1?a-1:a,n&&c>1?i-1:i],h=[n&&l>1?l-1:l,n&&c>1?c-1:c],f=d[0]/h[0],m=d[1]/h[1],g=e.dataSync(),v=0,y=0;y<o;y++)for(var x=y*t.strides[0],T=0;T<l;T++)for(var w=T*f,E=Math.floor(w),S=Math.min(Math.ceil(w),a-1),b=x+E*t.strides[1],C=x+S*t.strides[1],A=w-E,_=1-A,N=0;N<c;N++)for(var R=N*m,k=Math.floor(R),I=Math.min(Math.ceil(R),i-1),D=R-k,M=1-D,P=b+k*t.strides[2],O=b+I*t.strides[2],F=C+k*t.strides[2],L=C+I*t.strides[2],B=_*M,U=_*D,V=A*M,z=A*D,H=0;H<s;H++){var W=g[v++];p[P+H]+=W*B,p[O+H]+=W*U,p[F+H]+=W*V,p[L+H]+=W*z}return tensor4d(p,[o,i,a,s],t.dtype)},e.prototype.resizeNearestNeighbor=function(e,t,n,r){this.assertNotComplex(e,\"resizeNearestNeighbor\");for(var o=e.shape,a=o[0],i=o[1],s=o[2],u=o[3],l=e.dataSync(),c=new Float32Array(a*t*n*u),p=[r&&t>1?i-1:i,r&&n>1?s-1:s],d=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=p[0]/d[0],f=p[1]/d[1],m=0,g=0;g<a;g++)for(var v=g*e.strides[0],y=0;y<t;y++)for(var x=h*y,T=v+Math.min(i-1,r?Math.round(x):Math.floor(x))*e.strides[1],w=0;w<n;w++)for(var E=f*w,S=T+Math.min(s-1,r?Math.round(E):Math.floor(E))*e.strides[2],b=0;b<u;b++){var C=l[S+b];c[m++]=C}return tensor(c,[a,t,n,u],e.dtype)},e.prototype.resizeNearestNeighborBackprop=function(e,t,n){this.assertNotComplex([e,t],\"resizeNearestNeighborBackprop\");for(var r=t.shape,o=r[0],a=r[1],i=r[2],s=r[3],u=e.shape,l=u[1],c=u[2],p=new Float32Array(o*a*i*s),d=e.dataSync(),h=[n&&l>1?a-1:a,n&&c>1?i-1:i],f=[n&&l>1?l-1:l,n&&c>1?c-1:c],m=h[0]/f[0],g=h[1]/f[1],v=1/m,y=1/g,x=2*Math.ceil(v)+2,T=2*Math.ceil(y)+2,w=0;w<o;w++)for(var E=w*t.strides[0],S=0;S<a;S++)for(var b=E+S*t.strides[1],C=Math.floor(S*v),A=Math.floor(C-x/2),_=0;_<i;_++)for(var N=b+_*t.strides[2],R=Math.floor(_*y),k=Math.floor(R-T/2),I=0;I<s;I++){for(var D=0,M=0;M<x;M++){var P=M+A;if(!(P<0||P>=l)){var O=E+P*e.strides[1],F=P*m;if(S===Math.min(a-1,n?Math.round(F):Math.floor(F)))for(var L=0;L<T;L++){var B=L+k;if(!(B<0||B>=c)){var U=O+B*e.strides[2],V=B*g;_===Math.min(i-1,n?Math.round(V):Math.floor(V))&&(D+=d[U+I])}}}}p[N+I]=D}return tensor4d(p,t.shape,t.dtype)},e.prototype.batchNormalization=function(e,t,n,r,o,a){this.assertNotComplex([e,t,n,o,a],\"batchNormalization\");for(var i=e.dataSync(),s=t.dataSync(),u=n.dataSync(),l=o?o.dataSync():new Float32Array([1]),c=a?a.dataSync():new Float32Array([0]),p=new Float32Array(i.length),d=c.length,h=l.length,f=u.length,m=s.length,g=0,v=0,y=0,x=0,T=0;T<i.length;++T)p[T]=c[g++]+(i[T]-s[v++])*l[y++]/Math.sqrt(u[x++]+r),g>=d&&(g=0),v>=m&&(v=0),y>=h&&(y=0),x>=f&&(x=0);return tensor4d(p,e.shape)},e.prototype.localResponseNormalization4D=function(e,t,n,r,o){this.assertNotComplex(e,\"localResponseNormalization4D\");var a=e.shape[3],i=a-1,s=e.dataSync(),u=sizeFromShape(e.shape),l=new Float32Array(u);function c(e){for(var n=e%a,r=e-n+Math.max(0,n-t),o=e-n+Math.min(n+t,i),u=0;r<=o;r++){var l=s[r];u+=l*l}return u}for(var p=0;p<u;p++){var d=c(p),h=s[p]*Math.pow(n+r*d,-o);l[p]=h}return tensor4d(l,e.shape)},e.prototype.LRNGrad=function(e,t,n,r,o,a,i){this.assertNotComplex(e,\"LRNGrad\");for(var s=e.shape[3],u=e.dataSync(),l=t.dataSync(),c=n.dataSync(),p=new Float32Array(sizeFromShape(e.shape)),d=sizeFromShape(e.shape),h=0;h<d;h++){for(var f=h%s,m=h-f+Math.max(0,f-r),g=h-f+Math.min(s,f+r+1),v=0,y=m;y<g;y++)v+=Math.pow(l[y],2);v=a*v+o;for(y=m;y<g;y++){var x=-2*a*i*l[y]*c[h]/v;h===y&&(x+=Math.pow(v,-i)),x*=u[h],p[y]+=x}}return tensor4d(p,e.shape)},e.prototype.multinomial=function(e,t,n,r){this.assertNotComplex(e,\"multinomial\");for(var o=t?e:softmax(e),a=o.shape[0],i=o.shape[1],s=zeros([a,n],\"int32\"),u=s.dataSync(),l=o.dataSync(),c=0;c<a;++c){var p=c*i,d=new Float32Array(i-1);d[0]=l[p];for(var h=1;h<d.length;++h)d[h]=d[h-1]+l[p+h];for(var f=seedrandom_1(r.toString()),m=c*n,g=0;g<n;++g){var v=f();u[m+g]=d.length;for(var y=0;y<d.length;y++)if(v<d[y]){u[m+g]=y;break}}}return s},e.prototype.oneHot=function(e,t,n,r){this.assertNotComplex(e,\"oneHot\");var o=new Float32Array(e.size*t);o.fill(r);for(var a=0;a<e.size;++a)e.get(a)>=0&&e.get(a)<t&&(o[a*t+e.get(a)]=n);return tensor2d(o,[e.size,t],\"int32\")},e.prototype.nonMaxSuppression=function(e,t,n,r,o){return this.assertNotComplex(e,\"nonMaxSuppression\"),nonMaxSuppressionImpl(e.dataSync(),t.dataSync(),n,r,o)},e.prototype.fft=function(e){return this.fftBatch(e,!1)},e.prototype.ifft=function(e){return this.fftBatch(e,!0)},e.prototype.fftBatch=function(e,t){for(var n=e.shape[0],r=e.shape[1],o=buffer(e.shape,\"float32\"),a=buffer(e.shape,\"float32\"),i=real(e).as2D(n,r),s=imag(e).as2D(n,r),u=0;u<n;u++)for(var l=i.slice([u,0],[1,r]),c=s.slice([u,0],[1,r]),p=complex(l,c),d=this.fftImpl(p,t).dataSync(),h=0;h<r;h++){var f=getComplexWithIndex(d,h);o.values[u*r+h]=f.real,a.values[u*r+h]=f.imag}return complex(o.toTensor(),a.toTensor()).as2D(n,r)},e.prototype.fftImpl=function(e,t){var n=e.as1D(),r=n.size;if(this.isExponentOf2(r)){var o=this.fftRadix2(n,r,t).as2D(e.shape[0],e.shape[1]);return t&&(o=complex(real(o).div(scalar(r)),imag(o).div(scalar(r)))),o}var a=e.dataSync(),i=splitRealAndImagArrays(this.fourierTransformByMatmul(a,r,t));return complex(i.real,i.imag).as2D(e.shape[0],e.shape[1])},e.prototype.isExponentOf2=function(e){return 0==(e&e-1)},e.prototype.fftRadix2=function(e,t,n){if(1===t)return e;var r=e.dataSync(),o=t/2,a=complexWithEvenIndex(r),i=complex(a.real,a.imag).as1D(),s=complexWithOddIndex(r),u=complex(s.real,s.imag).as1D();i=this.fftRadix2(i,o,n),u=this.fftRadix2(u,o,n);var l=exponents(t,n),c=complex(l.real,l.imag).mul(u),p=i.add(c),d=i.sub(c),h=real(p).concat(real(d)),f=imag(p).concat(imag(d));return complex(h,f).as1D()},e.prototype.fourierTransformByMatmul=function(e,t,n){for(var r=new Float32Array(2*t),o=0;o<t;o++){for(var a=0,i=0,s=0;s<t;s++){var u=exponent(o*s,t,n),l=getComplexWithIndex(e,s);a+=l.real*u.real-l.imag*u.imag,i+=l.real*u.imag+l.imag*u.real}n&&(a/=t,i/=t),assignToTypedArray(r,a,i,o)}return r},e.prototype.depthToSpace=function(e,t,n){assert(\"NHWC\"===n,\"Only NHWC dataFormat supported on CPU for depthToSpace. Got \"+n),assert(t>1,\"blockSize should be > 1 for depthToSpace, but was: \"+t);for(var r=e.shape[0],o=e.shape[1],a=e.shape[2],i=e.shape[3],s=o*t,u=a*t,l=i/(t*t),c=e.dataSync(),p=new Float32Array(r*s*u*l),d=0,h=0;h<r;++h)for(var f=0;f<s;++f)for(var m=Math.floor(f/t),g=f%t,v=0;v<u;++v)for(var y=Math.floor(v/t),x=(g*t+v%t)*l,T=0;T<l;++T){var w=T+x+i*(y+a*(m+o*h));p[d++]=c[w]}return tensor4d(p,[r,s,u,l])},e.prototype.broadcastedBinaryOp=function(e,t,n,r){var o=assertAndGetBroadcastShape(e.shape,t.shape),a=buffer(o,n),i=e.dataSync(),s=t.dataSync(),u=getBroadcastDims(e.shape,o),l=getBroadcastDims(t.shape,o),c=a.values;if(u.length+l.length===0)for(var p=0;p<c.length;++p)c[p]=r(i[p%i.length],s[p%s.length]);else{var d=e.buffer(),h=t.buffer(),f=function(n){var o=a.indexToLoc(n),p=o.slice(-e.rank);u.forEach(function(e){return p[e]=0});var f=d.locToIndex(p),m=o.slice(-t.rank);l.forEach(function(e){return m[e]=0});var g=h.locToIndex(m);c[n]=r(i[f],s[g])};for(p=0;p<c.length;++p)f(p)}return a.toTensor()},e.prototype.broadcastedBinaryComplexOp=function(e,t,n){var r=assertAndGetBroadcastShape(e.shape,t.shape),o=buffer(r,\"float32\"),a=buffer(r,\"float32\"),i=e.dataSync(),s=t.dataSync(),u=getBroadcastDims(e.shape,r),l=getBroadcastDims(t.shape,r),c=o.values,p=a.values;if(u.length+l.length===0)for(var d=0;d<c.length;d++){var h=d%i.length,f=d%s.length,m=n(i[2*h],i[2*h+1],s[2*f],s[2*f+1]);c[d]=m.real,p[d]=m.imag}else{var g=this.data.get(e.dataId).complexTensors.real.buffer(),v=this.data.get(t.dataId).complexTensors.real.buffer(),y=function(r){var a=o.indexToLoc(r),d=a.slice(-e.rank);u.forEach(function(e){return d[e]=0});var h=g.locToIndex(d),f=a.slice(-t.rank);l.forEach(function(e){return f[e]=0});var m=v.locToIndex(f),y=n(i[2*h],i[2*h+1],s[2*m],s[2*m+1]);c[r]=y.real,p[r]=y.imag};for(d=0;d<c.length;d++)y(d)}return this.complex(o.toTensor(),a.toTensor())},e.prototype.split=function(e,t,n){return split(e,t,n)},e.prototype.dispose=function(){},e.prototype.floatPrecision=function(){return 32},e.prototype.cropAndResize=function(e,t,n,r,o,a){for(var i=e.shape,s=i[0],u=i[1],l=i[2],c=i[3],p=t.shape[0],d=r[0],h=r[1],f=buffer([p,d,h,c],e.dtype),m=t.dataSync(),g=n.dataSync(),v=e.dataSync(),y=e.strides,x=f.strides,T=0;T<p;T++){var w=4*T,E=m[w],S=m[w+1],b=m[w+2],C=m[w+3],A=g[T];if(!(A>=s))for(var _=d>1?(b-E)*(u-1)/(d-1):0,N=h>1?(C-S)*(l-1)/(h-1):0,R=0;R<d;R++){var k=d>1?E*(u-1)+R*_:.5*(E+b)*(u-1);if(k<0||k>u-1)for(var I=0;I<h;I++)for(var D=0;D<c;D++){var M=D+I*x[2]+R*x[1]+T*x[0];f.values[M]=a}else if(\"bilinear\"===o){var P=Math.floor(k),O=Math.ceil(k),F=k-P;for(I=0;I<h;I++){if((q=h>1?S*(l-1)+I*N:.5*(S+C)*(l-1))<0||q>l-1)for(D=0;D<c;D++){M=D+I*x[2]+R*x[1]+T*x[0];f.values[M]=a}else{var L=Math.floor(q),B=Math.ceil(q),U=q-L;for(D=0;D<c;D++){var V=v[M=D+L*y[2]+P*y[1]+A*y[0]],z=v[M=D+B*y[2]+P*y[1]+A*y[0]],H=v[M=D+L*y[2]+O*y[1]+A*y[0]],W=V+(z-V)*U,G=H+(v[M=D+B*y[2]+O*y[1]+A*y[0]]-H)*U;M=D+I*x[2]+R*x[1]+T*x[0],f.values[M]=W+(G-W)*F}}}}else for(I=0;I<h;++I){var q;if((q=h>1?S*(l-1)+I*N:.5*(S+C)*(l-1))<0||q>l-1)for(D=0;D<c;D++){M=D+I*x[2]+R*x[1]+T*x[0];f.values[M]=a}else{var $=Math.round(q),X=Math.round(k);for(D=0;D<c;D++){var K=D+$*y[2]+X*y[1]+A*y[0],j=D+I*x[2]+R*x[1]+T*x[0];f.values[j]=v[K]}}}}}return f.toTensor()},e.prototype.sparseToDense=function(e,t,n,r){var o=calculateShapes(t,e,n),a=o.sliceRank,i=o.numUpdates,s=o.sliceSize,u=o.strides,l=o.outputSize;return this.scatter(e,t,n,l,s,i,a,u,r,!1)},e.prototype.gatherND=function(e,t){var n=t.shape,r=n[n.length-1],o=prepareAndValidate(e,t),a=o[0],i=o[1],s=o[2],u=o[3];if(0===i)return tensor([],a,e.dtype);for(var l=new TensorBuffer([i,s],e.dtype),c=t.dataSync(),p=e.dataSync(),d=0;d<i;d++){for(var h=[],f=0,m=0;m<r;m++){var g=c[d*r+m];f+=g*u[m],h.push(g)}if(f<0||f>=e.size/s)throw new Error(\"Invalid indices: \"+h+\" does not index into \"+e.shape);for(var v=0;v<s;v++)l.values[d*s+v]=p[f*s+v]}return l.toTensor().reshape(a)},e.prototype.scatterND=function(e,t,n){var r=calculateShapes(t,e,n),o=r.sliceRank,a=r.numUpdates,i=r.sliceSize,s=r.strides,u=r.outputSize,l=scalar(0);return this.scatter(e,t,n,u,i,a,o,s,l,!0)},e.prototype.scatter=function(e,t,n,r,o,a,i,s,u,l){var c=[r/o,o],p=e.dataSync(),d=t.dataSync();if(0===r)return tensor([],n,t.dtype);var h=new TensorBuffer(c,t.dtype);h.values.fill(u.dataSync()[0]);for(var f=0;f<a;f++){for(var m=[],g=0,v=0;v<i;v++){var y=p[f*i+v];m.push(y),g+=y*s[v]}if(g<0||g>=r/o)throw new Error(\"Invalid indices: \"+m+\" does not index into \"+n);for(var x=0;x<o;x++)l?h.values[g*o+x]+=d[f*o+x]:h.values[g*o+x]=0===t.rank?d[0]:d[f*o+x]}return h.toTensor().reshape(n)},e}();ENV.registerBackend(\"cpu\",function(){return new MathBackendCPU},1,setTensorTracker);var delayCallback=\"undefined\"!=typeof requestAnimationFrame?requestAnimationFrame:setImmediate;function nextFrame(){return new Promise(function(e){return delayCallback(function(){return e()})})}var DTYPE_VALUE_SIZE_MAP={float32:4,int32:4,uint16:2,uint8:1,bool:1};function encodeWeights(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o;return __generator(this,function(a){switch(a.label){case 0:for(r in t=[],n=[],e){if(\"float32\"!==(o=e[r]).dtype&&\"int32\"!==o.dtype&&\"bool\"!==o.dtype)throw new Error(\"Unsupported dtype in weight '\"+r+\"': \"+o.dtype);t.push({name:r,shape:o.shape,dtype:o.dtype}),n.push(o.data())}return[4,Promise.all(n)];case 1:return[2,{data:concatenateTypedArrays(a.sent()),specs:t}]}})})}function decodeWeights(e,t){for(var n={},r=0,o=function(t){var o=t.name,a=t.dtype,i=t.shape,s=sizeFromShape(i),u=void 0;if(\"quantization\"in t){var l=t.quantization;if(\"uint8\"!==l.dtype&&\"uint16\"!==l.dtype)throw new Error(\"Weight \"+t.name+\" has unknown quantization dtype \"+l.dtype+\". Supported quantization dtypes are: 'uint8' and 'uint16'.\");var c=DTYPE_VALUE_SIZE_MAP[l.dtype],p=e.slice(r,r+s*c),d=\"uint8\"===l.dtype?new Uint8Array(p):new Uint16Array(p);if(\"float32\"===a)u=Float32Array.from(d,function(e){return e*l.scale+l.min});else{if(\"int32\"!==a)throw new Error(\"Unsupported dtype in weight '\"+o+\"': \"+a);u=Int32Array.from(d,function(e){return Math.round(e*l.scale+l.min)})}r+=s*c}else{var h=DTYPE_VALUE_SIZE_MAP[a];p=e.slice(r,r+s*h);if(\"float32\"===a)u=new Float32Array(p);else if(\"int32\"===a)u=new Int32Array(p);else{if(\"bool\"!==a)throw new Error(\"Unsupported dtype in weight '\"+o+\"': \"+a);u=new Uint8Array(p)}r+=s*h}var f=void 0;if(\"float32\"===a)f=tensor(u,i,\"float32\");else if(\"int32\"===a)f=tensor(u,i,\"int32\");else{if(\"bool\"!==a)throw new Error(\"Unsupported dtype in weight '\"+o+\"': \"+a);f=tensor(u,i,\"bool\")}n[o]=f},a=0,i=t;a<i.length;a++){o(i[a])}return n}function concatenateTypedArrays(e){if(null===e)throw new Error(\"Invalid input value: \"+JSON.stringify(e));var t=0,n=[];e.forEach(function(e){if(t+=e.byteLength,n.push(e.byteLength===e.buffer.byteLength?e:new e.constructor(e)),!(e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array))throw new Error(\"Unsupported TypedArray subtype: \"+e.constructor.name)});var r=new Uint8Array(t),o=0;return n.forEach(function(e){r.set(new Uint8Array(e.buffer),o),o+=e.byteLength}),r.buffer}var useNodeBuffer=\"undefined\"!=typeof Buffer&&(\"undefined\"==typeof Blob||\"undefined\"==typeof atob||\"undefined\"==typeof btoa);function stringByteLength(e){return useNodeBuffer?Buffer.byteLength(e):new Blob([e]).size}function arrayBufferToBase64String(e){return useNodeBuffer?Buffer.from(e).toString(\"base64\"):btoa(String.fromCharCode.apply(null,new Uint8Array(e)))}function base64StringToArrayBuffer(e){if(useNodeBuffer){var t=Buffer.from(e,\"base64\");return t.buffer.slice(t.byteOffset,t.byteOffset+t.byteLength)}for(var n=atob(e),r=new Uint8Array(n.length),o=0;o<n.length;++o)r.set([n.charCodeAt(o)],o);return r.buffer}function concatenateArrayBuffers(e){var t=0;e.forEach(function(e){t+=e.byteLength});var n=new Uint8Array(t),r=0;return e.forEach(function(e){n.set(new Uint8Array(e),r),r+=e.byteLength}),n.buffer}function basename(e){for(e=e.trim();e.endsWith(\"/\");)e=e.slice(0,e.length-1);var t=e.split(\"/\");return t[t.length-1]}function getModelArtifactsInfoForJSON(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error(\"Expected JSON model topology, received ArrayBuffer.\");return{dateSaved:new Date,modelTopologyType:\"JSON\",modelTopologyBytes:null==e.modelTopology?0:stringByteLength(JSON.stringify(e.modelTopology)),weightSpecsBytes:null==e.weightSpecs?0:stringByteLength(JSON.stringify(e.weightSpecs)),weightDataBytes:null==e.weightData?0:e.weightData.byteLength}}var IORouterRegistry=function(){function e(){this.saveRouters=[],this.loadRouters=[]}return e.getInstance=function(){return null==e.instance&&(e.instance=new e),e.instance},e.registerSaveRouter=function(t){e.getInstance().saveRouters.push(t)},e.registerLoadRouter=function(t){e.getInstance().loadRouters.push(t)},e.getSaveHandlers=function(t){return e.getHandlers(t,\"save\")},e.getLoadHandlers=function(t){return e.getHandlers(t,\"load\")},e.getHandlers=function(e,t){var n=[];return(\"load\"===t?this.getInstance().loadRouters:this.getInstance().saveRouters).forEach(function(t){var r=t(e);null!==r&&n.push(r)}),n},e}(),URL_SCHEME_SUFFIX=\"://\",ModelStoreManagerRegistry=function(){function e(){this.managers={}}return e.getInstance=function(){return null==e.instance&&(e.instance=new e),e.instance},e.registerManager=function(t,n){assert(null!=t,\"scheme must not be undefined or null.\"),t.endsWith(URL_SCHEME_SUFFIX)&&(t=t.slice(0,t.indexOf(URL_SCHEME_SUFFIX))),assert(t.length>0,\"scheme must not be an empty string.\");var r=e.getInstance();assert(null==r.managers[t],\"A model store manager is already registered for scheme '\"+t+\"'.\"),r.managers[t]=n},e.getManager=function(e){var t=this.getInstance().managers[e];if(null==t)throw new Error(\"Cannot find model manager for scheme '\"+e+\"'\");return t},e.getSchemes=function(){return Object.keys(this.getInstance().managers)},e}();function parseURL(e){if(-1===e.indexOf(URL_SCHEME_SUFFIX))throw new Error(\"The url string provided does not contain a scheme. Supported schemes are: \"+ModelStoreManagerRegistry.getSchemes().join(\",\"));return{scheme:e.split(URL_SCHEME_SUFFIX)[0],path:e.split(URL_SCHEME_SUFFIX)[1]}}function cloneModelInternal(e,t,n){return void 0===n&&(n=!1),__awaiter(this,void 0,void 0,function(){var r,o,a,i,s,u,l,c,p;return __generator(this,function(d){switch(d.label){case 0:return assert(e!==t,\"Old path and new path are the same: '\"+e+\"'\"),assert((r=IORouterRegistry.getLoadHandlers(e)).length>0,\"Copying failed because no load handler is found for source URL \"+e+\".\"),assert(r.length<2,\"Copying failed because more than one (\"+r.length+\") load handlers for source URL \"+e+\".\"),o=r[0],assert((a=IORouterRegistry.getSaveHandlers(t)).length>0,\"Copying failed because no save handler is found for destination URL \"+t+\".\"),assert(a.length<2,\"Copying failed because more than one (\"+r.length+\") save handlers for destination URL \"+t+\".\"),i=a[0],s=parseURL(e).scheme,u=parseURL(e).path,l=s===parseURL(e).scheme,[4,o.load()];case 1:return c=d.sent(),n&&l?[4,ModelStoreManagerRegistry.getManager(s).removeModel(u)]:[3,3];case 2:d.sent(),d.label=3;case 3:return[4,i.save(c)];case 4:return p=d.sent(),!n||l?[3,6]:[4,ModelStoreManagerRegistry.getManager(s).removeModel(u)];case 5:d.sent(),d.label=6;case 6:return[2,p.modelArtifactsInfo]}})})}function listModels(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o,a,i;return __generator(this,function(s){switch(s.label){case 0:e=ModelStoreManagerRegistry.getSchemes(),t={},n=0,r=e,s.label=1;case 1:return n<r.length?(o=r[n],[4,ModelStoreManagerRegistry.getManager(o).listModels()]):[3,4];case 2:for(i in a=s.sent())t[o+URL_SCHEME_SUFFIX+i]=a[i];s.label=3;case 3:return n++,[3,1];case 4:return[2,t]}})})}function removeModel(e){return __awaiter(this,void 0,void 0,function(){var t;return __generator(this,function(n){switch(n.label){case 0:return t=parseURL(e),[4,ModelStoreManagerRegistry.getManager(t.scheme).removeModel(t.path)];case 1:return[2,n.sent()]}})})}function copyModel(e,t){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(n){switch(n.label){case 0:return[4,cloneModelInternal(e,t,!1)];case 1:return[2,n.sent()]}})})}function moveModel(e,t){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(n){switch(n.label){case 0:return[4,cloneModelInternal(e,t,!0)];case 1:return[2,n.sent()]}})})}var DATABASE_NAME=\"tensorflowjs\",DATABASE_VERSION=1,MODEL_STORE_NAME=\"models_store\",INFO_STORE_NAME=\"model_info_store\";function getIndexedDBFactory(){if(!ENV.get(\"IS_BROWSER\"))throw new Error(\"Failed to obtain IndexedDB factory because the current environmentis not a web browser.\");var e=window,t=e.indexedDB||e.mozIndexedDB||e.webkitIndexedDB||e.msIndexedDB||e.shimIndexedDB;if(null==t)throw new Error(\"The current browser does not appear to support IndexedDB.\");return t}function setUpDatabase(e){var t=e.result;t.createObjectStore(MODEL_STORE_NAME,{keyPath:\"modelPath\"}),t.createObjectStore(INFO_STORE_NAME,{keyPath:\"modelPath\"})}var BrowserIndexedDB=function(){function e(e){if(this.indexedDB=getIndexedDBFactory(),null==e||!e)throw new Error(\"For IndexedDB, modelPath must not be null, undefined or empty.\");this.modelPath=e}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(t){if(e.modelTopology instanceof ArrayBuffer)throw new Error(\"BrowserLocalStorage.save() does not support saving model topology in binary formats yet.\");return[2,this.databaseAction(this.modelPath,e)]})})},e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(e){return[2,this.databaseAction(this.modelPath)]})})},e.prototype.databaseAction=function(e,t){var n=this;return new Promise(function(e,r){var o=n.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);o.onupgradeneeded=function(){return setUpDatabase(o)},o.onsuccess=function(){var a=o.result;if(null==t){var i=a.transaction(MODEL_STORE_NAME,\"readonly\"),s=i.objectStore(MODEL_STORE_NAME).get(n.modelPath);s.onsuccess=function(){if(null==s.result)return a.close(),r(new Error(\"Cannot find model with path '\"+n.modelPath+\"' in IndexedDB.\"));e(s.result.modelArtifacts)},s.onerror=function(e){return a.close(),r(s.error)},i.oncomplete=function(){return a.close()}}else{var u,l=getModelArtifactsInfoForJSON(t),c=a.transaction(INFO_STORE_NAME,\"readwrite\"),p=c.objectStore(INFO_STORE_NAME),d=p.put({modelPath:n.modelPath,modelArtifactsInfo:l});d.onsuccess=function(){var o=(u=a.transaction(MODEL_STORE_NAME,\"readwrite\")).objectStore(MODEL_STORE_NAME).put({modelPath:n.modelPath,modelArtifacts:t,modelArtifactsInfo:l});o.onsuccess=function(){return e({modelArtifactsInfo:l})},o.onerror=function(e){var t=(p=c.objectStore(INFO_STORE_NAME)).delete(n.modelPath);t.onsuccess=function(){return a.close(),r(o.error)},t.onerror=function(e){return a.close(),r(o.error)}}},d.onerror=function(e){return a.close(),r(d.error)},c.oncomplete=function(){null==u?a.close():u.oncomplete=function(){return a.close()}}}},o.onerror=function(e){return r(o.error)}})},e.URL_SCHEME=\"indexeddb://\",e}(),indexedDBRouter=function(e){return ENV.get(\"IS_BROWSER\")&&!Array.isArray(e)&&e.startsWith(BrowserIndexedDB.URL_SCHEME)?browserIndexedDB(e.slice(BrowserIndexedDB.URL_SCHEME.length)):null};function browserIndexedDB(e){return new BrowserIndexedDB(e)}function maybeStripScheme(e){return e.startsWith(BrowserIndexedDB.URL_SCHEME)?e.slice(BrowserIndexedDB.URL_SCHEME.length):e}IORouterRegistry.registerSaveRouter(indexedDBRouter),IORouterRegistry.registerLoadRouter(indexedDBRouter);var BrowserIndexedDBManager=function(){function e(){this.indexedDB=getIndexedDBFactory()}return e.prototype.listModels=function(){return __awaiter(this,void 0,void 0,function(){var e=this;return __generator(this,function(t){return[2,new Promise(function(t,n){var r=e.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);r.onupgradeneeded=function(){return setUpDatabase(r)},r.onsuccess=function(){var e=r.result,o=e.transaction(INFO_STORE_NAME,\"readonly\"),a=o.objectStore(INFO_STORE_NAME).getAll();a.onsuccess=function(){for(var e={},n=0,r=a.result;n<r.length;n++){var o=r[n];e[o.modelPath]=o.modelArtifactsInfo}t(e)},a.onerror=function(t){return e.close(),n(a.error)},o.oncomplete=function(){return e.close()}},r.onerror=function(e){return n(r.error)}})]})})},e.prototype.removeModel=function(e){return __awaiter(this,void 0,void 0,function(){var t=this;return __generator(this,function(n){return e=maybeStripScheme(e),[2,new Promise(function(n,r){var o=t.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);o.onupgradeneeded=function(){return setUpDatabase(o)},o.onsuccess=function(){var t,a=o.result,i=a.transaction(INFO_STORE_NAME,\"readwrite\"),s=i.objectStore(INFO_STORE_NAME),u=s.get(e);u.onsuccess=function(){if(null==u.result)return a.close(),r(new Error(\"Cannot find model with path '\"+e+\"' in IndexedDB.\"));var o=s.delete(e),i=function(){var o=(t=a.transaction(MODEL_STORE_NAME,\"readwrite\")).objectStore(MODEL_STORE_NAME).delete(e);o.onsuccess=function(){return n(u.result.modelArtifactsInfo)},o.onerror=function(e){return r(u.error)}};o.onsuccess=i,o.onerror=function(e){return i(),a.close(),r(u.error)}},u.onerror=function(e){return a.close(),r(u.error)},i.oncomplete=function(){null==t?a.close():t.oncomplete=function(){return a.close()}}},o.onerror=function(e){return r(o.error)}})]})})},e}();if(ENV.get(\"IS_BROWSER\"))try{ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME,new BrowserIndexedDBManager)}catch(e){}var PATH_SEPARATOR=\"/\",PATH_PREFIX=\"tensorflowjs_models\",INFO_SUFFIX=\"info\",MODEL_TOPOLOGY_SUFFIX=\"model_topology\",WEIGHT_SPECS_SUFFIX=\"weight_specs\",WEIGHT_DATA_SUFFIX=\"weight_data\";function getModelKeys(e){return{info:[PATH_PREFIX,e,INFO_SUFFIX].join(PATH_SEPARATOR),topology:[PATH_PREFIX,e,MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),weightSpecs:[PATH_PREFIX,e,WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),weightData:[PATH_PREFIX,e,WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR)}}function getModelPathFromKey(e){var t=e.split(PATH_SEPARATOR);if(t.length<3)throw new Error(\"Invalid key format: \"+e);return t.slice(1,t.length-1).join(PATH_SEPARATOR)}function maybeStripScheme$1(e){return e.startsWith(BrowserLocalStorage.URL_SCHEME)?e.slice(BrowserLocalStorage.URL_SCHEME.length):e}var BrowserLocalStorage=function(){function e(e){if(!ENV.get(\"IS_BROWSER\")||void 0===window.localStorage)throw new Error(\"The current environment does not support local storage.\");if(this.LS=window.localStorage,null==e||!e)throw new Error(\"For local storage, modelPath must not be null, undefined or empty.\");this.modelPath=e,this.keys=getModelKeys(this.modelPath)}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o;return __generator(this,function(a){if(e.modelTopology instanceof ArrayBuffer)throw new Error(\"BrowserLocalStorage.save() does not support saving model topology in binary formats yet.\");t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),r=getModelArtifactsInfoForJSON(e);try{return this.LS.setItem(this.keys.info,JSON.stringify(r)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,arrayBufferToBase64String(e.weightData)),[2,{modelArtifactsInfo:r}]}catch(e){for(o in this.keys)this.LS.removeItem(this.keys[o]);throw new Error(\"Failed to save model '\"+this.modelPath+\"' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=\"+r.modelTopologyBytes+\", weightSpecsBytes=\"+r.weightSpecsBytes+\", weightDataBytes=\"+r.weightDataBytes+\".\")}return[2]})})},e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o;return __generator(this,function(a){if(null==(e=JSON.parse(this.LS.getItem(this.keys.info))))throw new Error(\"In local storage, there is no model with name '\"+this.modelPath+\"'\");if(\"JSON\"!==e.modelTopologyType)throw new Error(\"BrowserLocalStorage does not support loading non-JSON model topology yet.\");if(t={},null==(n=JSON.parse(this.LS.getItem(this.keys.topology))))throw new Error(\"In local storage, the topology of model '\"+this.modelPath+\"' is missing.\");if(t.modelTopology=n,null==(r=JSON.parse(this.LS.getItem(this.keys.weightSpecs))))throw new Error(\"In local storage, the weight specs of model '\"+this.modelPath+\"' are missing.\");if(t.weightSpecs=r,null==(o=this.LS.getItem(this.keys.weightData)))throw new Error(\"In local storage, the binary weight values of model '\"+this.modelPath+\"' are missing.\");return t.weightData=base64StringToArrayBuffer(o),[2,t]})})},e.URL_SCHEME=\"localstorage://\",e}(),localStorageRouter=function(e){return ENV.get(\"IS_BROWSER\")&&!Array.isArray(e)&&e.startsWith(BrowserLocalStorage.URL_SCHEME)?browserLocalStorage(e.slice(BrowserLocalStorage.URL_SCHEME.length)):null};function browserLocalStorage(e){return new BrowserLocalStorage(e)}IORouterRegistry.registerSaveRouter(localStorageRouter),IORouterRegistry.registerLoadRouter(localStorageRouter);var BrowserLocalStorageManager=function(){function e(){assert(ENV.get(\"IS_BROWSER\"),\"Current environment is not a web browser\"),assert(void 0!==window.localStorage,\"Current browser does not appear to support localStorage\"),this.LS=window.localStorage}return e.prototype.listModels=function(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o,a;return __generator(this,function(i){for(e={},t=PATH_PREFIX+PATH_SEPARATOR,n=PATH_SEPARATOR+INFO_SUFFIX,r=0;r<this.LS.length;++r)(o=this.LS.key(r)).startsWith(t)&&o.endsWith(n)&&(a=getModelPathFromKey(o),e[a]=JSON.parse(this.LS.getItem(o)));return[2,e]})})},e.prototype.removeModel=function(e){return __awaiter(this,void 0,void 0,function(){var t,n;return __generator(this,function(r){if(e=maybeStripScheme$1(e),t=getModelKeys(e),null==this.LS.getItem(t.info))throw new Error(\"Cannot find model at path '\"+e+\"'\");return n=JSON.parse(this.LS.getItem(t.info)),this.LS.removeItem(t.info),this.LS.removeItem(t.topology),this.LS.removeItem(t.weightSpecs),this.LS.removeItem(t.weightData),[2,n]})})},e}();if(ENV.get(\"IS_BROWSER\"))try{ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME,new BrowserLocalStorageManager)}catch(e){}var DEFAULT_FILE_NAME_PREFIX=\"model\",DEFAULT_JSON_EXTENSION_NAME=\".json\",DEFAULT_WEIGHT_DATA_EXTENSION_NAME=\".weights.bin\",BrowserDownloads=function(){function e(t){if(!ENV.get(\"IS_BROWSER\"))throw new Error(\"browserDownloads() cannot proceed because the current environment is not a browser.\");t.startsWith(e.URL_SCHEME)&&(t=t.slice(e.URL_SCHEME.length)),null!=t&&0!==t.length||(t=DEFAULT_FILE_NAME_PREFIX),this.modelTopologyFileName=t+DEFAULT_JSON_EXTENSION_NAME,this.weightDataFileName=t+DEFAULT_WEIGHT_DATA_EXTENSION_NAME}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o,a,i;return __generator(this,function(s){if(t=window.URL.createObjectURL(new Blob([e.weightData],{type:\"application/octet-stream\"})),e.modelTopology instanceof ArrayBuffer)throw new Error(\"BrowserDownloads.save() does not support saving model topology in binary formats yet.\");return n=[{paths:[\"./\"+this.weightDataFileName],weights:e.weightSpecs}],r={modelTopology:e.modelTopology,weightsManifest:n},o=window.URL.createObjectURL(new Blob([JSON.stringify(r)],{type:\"application/json\"})),(a=null==this.jsonAnchor?document.createElement(\"a\"):this.jsonAnchor).download=this.modelTopologyFileName,a.href=o,a.click(),null!=e.weightData&&((i=null==this.weightDataAnchor?document.createElement(\"a\"):this.weightDataAnchor).download=this.weightDataFileName,i.href=t,i.click()),[2,{modelArtifactsInfo:getModelArtifactsInfoForJSON(e)}]})})},e.URL_SCHEME=\"downloads://\",e}(),BrowserFiles=function(){function e(e){if(null==e||e.length<1)throw new Error(\"When calling browserFiles, at least 1 file is required, but received \"+e);this.files=e}return e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){var e,t,n=this;return __generator(this,function(r){return e=this.files[0],t=this.files.slice(1),[2,new Promise(function(r,o){var a=new FileReader;a.onload=function(a){var i=JSON.parse(a.target.result),s=i.modelTopology;if(null!=s){0===t.length&&r({modelTopology:s});var u=i.weightsManifest;if(null!=u){var l;try{l=n.checkManifestAndWeightFiles(u,t)}catch(e){return void o(e)}var c=[],p=[],d=[];u.forEach(function(e){e.paths.forEach(function(e){p.push(e),d.push(null)}),c.push.apply(c,e.weights)}),u.forEach(function(e){e.paths.forEach(function(e){var t=new FileReader;t.onload=function(t){var n=t.target.result,o=p.indexOf(e);d[o]=n,-1===d.indexOf(null)&&r({modelTopology:s,weightSpecs:c,weightData:concatenateArrayBuffers(d)})},t.onerror=function(t){return o(\"Failed to weights data from file of path '\"+e+\"'.\")},t.readAsArrayBuffer(l[e])})})}else o(new Error(\"weightManifest field is missing from file \"+e.name))}else o(new Error(\"modelTopology field is missing from file \"+e.name))},a.onerror=function(t){return o(\"Failed to read model topology and weights manifest JSON from file '\"+e.name+\"'. BrowserFiles supports loading Keras-style tf.Model artifacts only.\")},a.readAsText(e)})]})})},e.prototype.checkManifestAndWeightFiles=function(e,t){for(var n=[],r=t.map(function(e){return basename(e.name)}),o={},a=0,i=e;a<i.length;a++){i[a].paths.forEach(function(e){var a=basename(e);if(-1!==n.indexOf(a))throw new Error(\"Duplicate file basename found in weights manifest: '\"+a+\"'\");if(n.push(a),-1===r.indexOf(a))throw new Error(\"Weight file with basename '\"+a+\"' is not provided.\");o[e]=t[r.indexOf(a)]})}if(n.length!==t.length)throw new Error(\"Mismatch in the number of files in weights manifest (\"+n.length+\") and the number of weight files provided (\"+t.length+\").\");return o},e}(),browserDownloadsRouter=function(e){return ENV.get(\"IS_BROWSER\")&&!Array.isArray(e)&&e.startsWith(BrowserDownloads.URL_SCHEME)?browserDownloads(e.slice(BrowserDownloads.URL_SCHEME.length)):null};function browserDownloads(e){return void 0===e&&(e=\"model\"),new BrowserDownloads(e)}function browserFiles(e){return new BrowserFiles(e)}function loadWeightsAsArrayBuffer(e,t,n){return __awaiter(this,void 0,void 0,function(){var r,o;return __generator(this,function(a){switch(a.label){case 0:return null==n&&(n=fetch),r=e.map(function(e){return n(e,t)}),[4,Promise.all(r)];case 1:return o=a.sent(),[4,Promise.all(o.map(function(e){return e.arrayBuffer()}))];case 2:return[2,a.sent()]}})})}function loadWeights(e,t,n,r){return void 0===t&&(t=\"\"),__awaiter(this,void 0,void 0,function(){return __generator(this,function(o){return[2,weightsLoaderFactory(function(e){return loadWeightsAsArrayBuffer(e,r)})(e,t,n)]})})}function weightsLoaderFactory(e){var t=this;return function(n,r,o){return void 0===r&&(r=\"\"),__awaiter(t,void 0,void 0,function(){var t,a,i,s,u,l,c,p,d,h;return __generator(this,function(f){switch(f.label){case 0:if(t=n.map(function(){return!1}),a={},i=null!=o?o.map(function(){return!1}):[],s=[],n.forEach(function(e,n){var r=0;e.weights.forEach(function(e){var u=\"quantization\"in e?e.quantization.dtype:e.dtype,l=DTYPE_VALUE_SIZE_MAP[u]*sizeFromShape(e.shape),c=function(){t[n]=!0,null==a[n]&&(a[n]=[]),a[n].push({manifestEntry:e,groupOffset:r,sizeBytes:l})};null!=o?o.forEach(function(t,n){t===e.name&&(c(),i[n]=!0)}):c(),s.push(e.name),r+=l})}),!i.every(function(e){return e}))throw u=o.filter(function(e,t){return!i[t]}),new Error(\"Could not find weights in manifest with names: \"+u.join(\", \")+\". \\nManifest JSON has weights with names: \"+s.join(\", \")+\".\");return l=t.reduce(function(e,t,n){return t&&e.push(n),e},[]),c=[],l.forEach(function(e){n[e].paths.forEach(function(e){var t=r+(r.endsWith(\"/\")?\"\":\"/\")+e;c.push(t)})}),[4,e(c)];case 1:return p=f.sent(),d={},h=0,l.forEach(function(e){for(var t=n[e].paths.length,r=0,o=0;o<t;o++)r+=p[h+o].byteLength;for(var i=new ArrayBuffer(r),s=new Uint8Array(i),u=0,l=0;l<t;l++){var c=new Uint8Array(p[h+l]);s.set(c,u),u+=c.byteLength}a[e].forEach(function(e){var t=decodeWeights(i.slice(e.groupOffset,e.groupOffset+e.sizeBytes),[e.manifestEntry]);for(var n in t)d[n]=t[n]}),h+=t}),[2,d]}})})}}IORouterRegistry.registerSaveRouter(browserDownloadsRouter);var BrowserHTTPRequest=function(){function e(e,t,n,r){if(this.weightPathPrefix=n,this.DEFAULT_METHOD=\"POST\",null==r){if(\"undefined\"==typeof fetch)throw new Error(\"browserHTTPRequest is not supported outside the web browser without a fetch polyfill.\");this.fetchFunc=fetch.bind(\"undefined\"==typeof window?null:window)}else assert(\"function\"==typeof r,\"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)\"),this.fetchFunc=r;if(assert(null!=e&&e.length>0,\"URL path for browserHTTPRequest must not be null, undefined or empty.\"),Array.isArray(e)&&assert(2===e.length,\"URL paths for browserHTTPRequest must have a length of 2, (actual length is \"+e.length+\").\"),this.path=e,null!=t&&null!=t.body)throw new Error(\"requestInit is expected to have no pre-existing body, but has one.\");this.requestInit=t||{}}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o;return __generator(this,function(a){switch(a.label){case 0:if(e.modelTopology instanceof ArrayBuffer)throw new Error(\"BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.\");return(t=Object.assign({method:this.DEFAULT_METHOD},this.requestInit)).body=new FormData,n=[{paths:[\"./model.weights.bin\"],weights:e.weightSpecs}],r={modelTopology:e.modelTopology,weightsManifest:n},t.body.append(\"model.json\",new Blob([JSON.stringify(r)],{type:\"application/json\"}),\"model.json\"),null!=e.weightData&&t.body.append(\"model.weights.bin\",new Blob([e.weightData],{type:\"application/octet-stream\"}),\"model.weights.bin\"),[4,this.getFetchFunc()(this.path,t)];case 1:if((o=a.sent()).ok)return[2,{modelArtifactsInfo:getModelArtifactsInfoForJSON(e),responses:[o]}];throw new Error(\"BrowserHTTPRequest.save() failed due to HTTP response status \"+o.status+\".\")}})})},e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(e){return[2,Array.isArray(this.path)?this.loadBinaryModel():this.loadJSONModel()]})})},e.prototype.loadBinaryTopology=function(){return __awaiter(this,void 0,void 0,function(){var e,t;return __generator(this,function(n){switch(n.label){case 0:return n.trys.push([0,3,,4]),[4,this.getFetchFunc()(this.path[0],this.requestInit)];case 1:if(!(e=n.sent()).ok)throw new Error(\"BrowserHTTPRequest.load() failed due to HTTP response: \"+e.statusText);return[4,e.arrayBuffer()];case 2:return[2,n.sent()];case 3:throw t=n.sent(),new Error(this.path[0]+\" not found. \"+t);case 4:return[2]}})})},e.prototype.loadBinaryModel=function(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o,a,i,s;return __generator(this,function(u){switch(u.label){case 0:return e=this.loadBinaryTopology(),[4,this.getFetchFunc()(this.path[1],this.requestInit)];case 1:if(!(t=u.sent()).ok)throw new Error(\"BrowserHTTPRequest.load() failed due to HTTP response: \"+t.statusText);return[4,Promise.all([e,t])];case 2:return n=u.sent(),r=n[0],[4,n[1].json()];case 3:return null==(o=u.sent())?[3,5]:[4,this.loadWeights(o)];case 4:s=u.sent(),a=s[0],i=s[1],u.label=5;case 5:return[2,{modelTopology:r,weightSpecs:a,weightData:i}]}})})},e.prototype.loadJSONModel=function(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o,a,i,s;return __generator(this,function(u){switch(u.label){case 0:return[4,this.getFetchFunc()(this.path,this.requestInit)];case 1:if(!(e=u.sent()).ok)throw new Error(\"BrowserHTTPRequest.load() failed due to HTTP response: \"+e.statusText);return[4,e.json()];case 2:if(t=u.sent(),n=t.modelTopology,r=t.weightsManifest,null==n&&null==r)throw new Error(\"The JSON from HTTP path \"+this.path+\" contains neither model topology or manifest for weights.\");return null==r?[3,4]:(i=t.weightsManifest,[4,this.loadWeights(i)]);case 3:s=u.sent(),o=s[0],a=s[1],u.label=4;case 4:return[2,{modelTopology:n,weightSpecs:o,weightData:a}]}})})},e.prototype.loadWeights=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o,a,i,s,u,l,c,p,d;return __generator(this,function(h){switch(h.label){case 0:for(t=Array.isArray(this.path)?this.path[1]:this.path,n=parseUrl(t),r=n[0],o=n[1],a=this.weightPathPrefix||r,i=[],s=0,u=e;s<u.length;s++)l=u[s],i.push.apply(i,l.weights);return c=[],e.forEach(function(e){e.paths.forEach(function(e){c.push(a+e+o)})}),p=[i],d=concatenateArrayBuffers,[4,loadWeightsAsArrayBuffer(c,this.requestInit,this.getFetchFunc())];case 1:return[2,p.concat([d.apply(void 0,[h.sent()])])]}})})},e.prototype.getFetchFunc=function(){return this.fetchFunc},e.URL_SCHEME_REGEX=/^https?:\\/\\//,e}();function parseUrl(e){var t=e.lastIndexOf(\"/\"),n=e.lastIndexOf(\"?\");return[e.substring(0,t)+\"/\",n>t?e.substring(n):\"\"]}function isHTTPScheme(e){return null!=e.match(BrowserHTTPRequest.URL_SCHEME_REGEX)}var httpRequestRouter=function(e){if(\"undefined\"==typeof fetch)return null;return(Array.isArray(e)?e.every(function(e){return isHTTPScheme(e)}):isHTTPScheme(e))?browserHTTPRequest(e):null};function browserHTTPRequest(e,t,n,r){return new BrowserHTTPRequest(e,t,n,r)}IORouterRegistry.registerSaveRouter(httpRequestRouter),IORouterRegistry.registerLoadRouter(httpRequestRouter);var PassthroughLoader=function(){function e(e,t,n){this.modelTopology=e,this.weightSpecs=t,this.weightData=n}return e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){var e;return __generator(this,function(t){return e={},null!=this.modelTopology&&(e=__assign({modelTopology:this.modelTopology},e)),null!=this.weightSpecs&&this.weightSpecs.length>0&&(e=__assign({weightSpecs:this.weightSpecs},e)),null!=this.weightData&&this.weightData.byteLength>0&&(e=__assign({weightData:this.weightData},e)),[2,e]})})},e}(),PassthroughSaver=function(){function e(e){this.saveHandler=e}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(t){return[2,this.saveHandler(e)]})})},e}();function fromMemory(e,t,n){return new PassthroughLoader(e,t,n)}function withSaveHandler(e){return new PassthroughSaver(e)}var registerSaveRouter=IORouterRegistry.registerSaveRouter,registerLoadRouter=IORouterRegistry.registerLoadRouter,getSaveHandlers=IORouterRegistry.getSaveHandlers,getLoadHandlers=IORouterRegistry.getLoadHandlers,io=Object.freeze({browserFiles:browserFiles,browserHTTPRequest:browserHTTPRequest,concatenateArrayBuffers:concatenateArrayBuffers,decodeWeights:decodeWeights,encodeWeights:encodeWeights,fromMemory:fromMemory,getLoadHandlers:getLoadHandlers,getModelArtifactsInfoForJSON:getModelArtifactsInfoForJSON,getSaveHandlers:getSaveHandlers,isHTTPScheme:isHTTPScheme,loadWeights:loadWeights,registerLoadRouter:registerLoadRouter,registerSaveRouter:registerSaveRouter,weightsLoaderFactory:weightsLoaderFactory,withSaveHandler:withSaveHandler,copyModel:copyModel,listModels:listModels,moveModel:moveModel,removeModel:removeModel});function confusionMatrix_(e,t,n){var r=convertToTensor(e,\"labels\",\"confusionMatrix\"),o=convertToTensor(t,\"predictions\",\"confusionMatrix\");assert(null==n||n>0&&Number.isInteger(n),\"If provided, numClasses must be a positive integer, but got \"+n),assert(1===r.rank,\"Expected the rank of labels to be 1, but got \"+r.rank),assert(1===o.rank,\"Expected the rank of predictions to be 1, but got \"+o.rank),assert(r.shape[0]===o.shape[0],\"Mismatch in the number of examples: \"+r.shape[0]+\" vs. \"+o.shape[0]+\". Labels and predictions should have the same number of elements.\"),assert(n>0&&Number.isInteger(n),\"numClasses is required to be a positive integer, but got \"+n);var a=oneHot(r.asType(\"int32\"),n),i=oneHot(o.asType(\"int32\"),n);return a.transpose().matMul(i).asType(\"int32\")}var confusionMatrix=op({confusionMatrix_:confusionMatrix_}),math=Object.freeze({confusionMatrix:confusionMatrix}),Serializable=function(){function e(){}return e.prototype.getClassName=function(){return this.constructor.className},e.fromConfig=function(e,t){return new e(t)},e}(),SerializationMap=function(){function e(){this.classNameMap={}}return e.getMap=function(){return null==e.instance&&(e.instance=new e),e.instance},e.register=function(t){e.getMap().classNameMap[t.className]=[t,t.fromConfig]},e}();function registerClass(e){assert(null!=e.className,\"Class being registered does not have the static className property defined.\"),assert(\"string\"==typeof e.className,\"className is required to be a string, but got type \"+typeof e.className),assert(e.className.length>0,\"Class being registered has an empty-string as its className, which is disallowed.\"),SerializationMap.register(e)}var serialization=Object.freeze({Serializable:Serializable,SerializationMap:SerializationMap,registerClass:registerClass}),WEBGL_ENVS={HAS_WEBGL:!0},NODE_ENVS={IS_NODE:!0},CHROME_ENVS={IS_CHROME:!0},BROWSER_ENVS={IS_BROWSER:!0},CPU_ENVS={HAS_WEBGL:!1},ALL_ENVS={};function expectArraysClose(e,t,n){return null==n&&(n=ENV.get(\"TEST_EPSILON\")),expectArraysPredicate(e,t,function(e,t){return areClose(e,Number(t),n)})}function expectArraysPredicate(e,t,n){if(e instanceof Tensor||t instanceof Tensor){if(e instanceof Tensor&&t instanceof Tensor){if(e.dtype!==t.dtype)throw new Error(\"Arrays are of different type actual: \"+e.dtype+\" vs expected: \"+t.dtype+\".\");if(!arraysEqual(e.shape,t.shape))throw new Error(\"Arrays are of different shape actual: \"+e.shape+\" vs expected: \"+t.shape+\".\")}}else{var r=e.constructor.name,o=t.constructor.name;if(r!==o)throw new Error(\"Arrays are of different type actual: \"+r+\" vs expected: \"+o)}var a,i;if(a=e instanceof Tensor?e.dataSync():e,i=t instanceof Tensor?t.dataSync():t,a.length!==i.length)throw new Error(\"Arrays have different lengths actual: \"+a.length+\" vs expected: \"+i.length+\".\\nActual:   \"+a+\".\\nExpected: \"+i+\".\");for(var s=0;s<i.length;++s){var u=a[s],l=i[s];if(!n(u,l))throw new Error(\"Arrays differ: actual[\"+s+\"] = \"+u+\", expected[\"+s+\"] = \"+l+\".\\nActual:   \"+a+\".\\nExpected: \"+i+\".\")}}function expectPromiseToFail(e,t){e().then(function(){return t.fail()},function(){return t()})}function expectArraysEqual(e,t){return e instanceof Tensor&&\"string\"===e.dtype||t instanceof Tensor&&\"string\"===t.dtype||e instanceof Array&&isString(e[0])||t instanceof Array&&isString(t[0])?expectArraysPredicate(e,t,function(e,t){return e==t}):expectArraysClose(e,t,0)}function expectNumbersClose(e,t,n){if(null==n&&(n=ENV.get(\"TEST_EPSILON\")),!areClose(e,t,n))throw new Error(\"Numbers differ: actual === \"+e+\", expected === \"+t)}function areClose(e,t,n){return!(!isNaN(e)||!isNaN(t))||!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function expectValuesInRange(e,t,n){var r;r=e instanceof Tensor?e.dataSync():e;for(var o=0;o<r.length;o++)if(r[o]<t||r[o]>n)throw new Error(\"Value out of range:\"+r[o]+\" low: \"+t+\", high: \"+n)}function expectArrayBuffersEqual(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t))}var test_util=Object.freeze({WEBGL_ENVS:WEBGL_ENVS,NODE_ENVS:NODE_ENVS,CHROME_ENVS:CHROME_ENVS,BROWSER_ENVS:BROWSER_ENVS,CPU_ENVS:CPU_ENVS,ALL_ENVS:ALL_ENVS,expectArraysClose:expectArraysClose,expectPromiseToFail:expectPromiseToFail,expectArraysEqual:expectArraysEqual,expectNumbersClose:expectNumbersClose,expectValuesInRange:expectValuesInRange,expectArrayBuffersEqual:expectArrayBuffersEqual}),version=\"0.14.2\",webgl=Object.freeze({gpgpu_util:gpgpu_util,webgl_util:webgl_util,MathBackendWebGL:MathBackendWebGL,GPGPUContext:GPGPUContext}),Optimizer=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends(t,e),t.prototype.minimize=function(e,t,n){void 0===t&&(t=!1);var r=this.computeGradients(e,n),o=r.value,a=r.grads;return this.applyGradients(a),Object.keys(a).forEach(function(e){return a[e].dispose()}),t?o:(o.dispose(),null)},t.prototype.computeGradients=function(e,t){return variableGrads(e,t)},t}(Serializable),AdadeltaOptimizer=function(e){function t(t,n,r){void 0===r&&(r=null);var o=e.call(this)||this;return o.learningRate=t,o.rho=n,o.epsilon=r,o.accumulatedGrads={},o.accumulatedUpdates={},o.c=keep(scalar(-t)),o.rhoScalar=keep(scalar(n)),o.oneMinusRho=keep(scalar(1-n)),null===r&&(r=ENV.get(\"EPSILON\")),o.epsilonScalar=keep(scalar(r)),o}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulatedGrads[n]){tidy(function(){t.accumulatedGrads[n]=zerosLike(o).variable(!1)})}if(null==r.accumulatedUpdates[n]){tidy(function(){t.accumulatedUpdates[n]=zerosLike(o).variable(!1)})}var a=e[n],i=r.accumulatedGrads[n],s=r.accumulatedUpdates[n];tidy(function(){var e=t.rhoScalar.mul(i).add(t.oneMinusRho.mul(a.square())),r=s.add(t.epsilonScalar).sqrt().div(i.add(t.epsilonScalar).sqrt()).mul(a),u=t.rhoScalar.mul(s).add(t.oneMinusRho.mul(r.square()));t.accumulatedGrads[n].assign(e),t.accumulatedUpdates[n].assign(u);var l=t.c.mul(r).add(o);o.assign(l)})},r=this;for(var o in e)n(o)},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsilonScalar.dispose(),this.rhoScalar.dispose(),this.oneMinusRho.dispose(),null!=this.accumulatedUpdates&&(Object.keys(this.accumulatedUpdates).forEach(function(t){return e.accumulatedUpdates[t].dispose()}),Object.keys(this.accumulatedGrads).forEach(function(t){return e.accumulatedGrads[t].dispose()}))},t.prototype.getConfig=function(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}},t.fromConfig=function(e,t){return new e(t.learningRate,t.rho,t.epsilon)},t.className=\"AdadeltaOptimizer\",t}(Optimizer);registerClass(AdadeltaOptimizer);var AdagradOptimizer=function(e){function t(t,n){void 0===n&&(n=.1);var r=e.call(this)||this;return r.learningRate=t,r.initialAccumulatorValue=n,r.accumulatedGrads={},r.c=keep(scalar(-t)),r.epsilon=keep(scalar(ENV.get(\"EPSILON\"))),r}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulatedGrads[n]){tidy(function(){t.accumulatedGrads[n]=fill(o.shape,t.initialAccumulatorValue).variable(!1)})}var a=e[n],i=r.accumulatedGrads[n];tidy(function(){var e=i.add(a.square());t.accumulatedGrads[n].assign(e);var r=t.c.mul(a.div(e.add(t.epsilon).sqrt())).add(o);o.assign(r)})},r=this;for(var o in e)n(o)},t.prototype.dispose=function(){var e=this;this.epsilon.dispose(),this.c.dispose(),null!=this.accumulatedGrads&&Object.keys(this.accumulatedGrads).forEach(function(t){return e.accumulatedGrads[t].dispose()})},t.prototype.getConfig=function(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}},t.fromConfig=function(e,t){return new e(t.learningRate,t.initialAccumulatorValue)},t.className=\"AdagradOptimizer\",t}(Optimizer);registerClass(AdagradOptimizer);var AdamOptimizer=function(e){function t(t,n,r,o){void 0===o&&(o=null);var a=e.call(this)||this;return a.learningRate=t,a.beta1=n,a.beta2=r,a.epsilon=o,a.accumulatedFirstMoment={},a.accumulatedSecondMoment={},a.c=keep(scalar(-t)),a.beta1Scalar=keep(scalar(n)),a.beta2Scalar=keep(scalar(r)),tidy(function(){a.accBeta1=scalar(n).variable(),a.accBeta2=scalar(r).variable()}),a.oneMinusBeta1=keep(scalar(1-n)),a.oneMinusBeta2=keep(scalar(1-r)),a.one=keep(scalar(1)),null===o&&(o=ENV.get(\"EPSILON\")),a.epsScalar=keep(scalar(o)),a}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this;tidy(function(){var n=t.one.sub(t.accBeta1),r=t.one.sub(t.accBeta2);for(var o in e){var a=ENV.engine.registeredVariables[o];if(null==t.accumulatedFirstMoment[o]){var i=!1;t.accumulatedFirstMoment[o]=zerosLike(a).variable(i)}if(null==t.accumulatedSecondMoment[o]){i=!1;t.accumulatedSecondMoment[o]=zerosLike(a).variable(i)}var s=e[o],u=t.accumulatedFirstMoment[o],l=t.accumulatedSecondMoment[o],c=t.beta1Scalar.mul(u).add(t.oneMinusBeta1.mul(s)),p=t.beta2Scalar.mul(l).add(t.oneMinusBeta2.mul(s.square())),d=c.div(n),h=p.div(r);t.accumulatedFirstMoment[o].assign(c),t.accumulatedSecondMoment[o].assign(p);var f=t.c.mul(d.div(t.epsScalar.add(h.sqrt()))).add(a);a.assign(f)}t.accBeta1.assign(t.accBeta1.mul(t.beta1Scalar)),t.accBeta2.assign(t.accBeta2.mul(t.beta2Scalar))})},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsScalar.dispose(),this.beta1Scalar.dispose(),this.beta2Scalar.dispose(),this.accBeta1.dispose(),this.accBeta2.dispose(),this.oneMinusBeta1.dispose(),this.oneMinusBeta2.dispose(),this.one.dispose(),null!=this.accumulatedFirstMoment&&Object.keys(this.accumulatedFirstMoment).forEach(function(t){return e.accumulatedFirstMoment[t].dispose()}),null!=this.accumulatedSecondMoment&&Object.keys(this.accumulatedSecondMoment).forEach(function(t){return e.accumulatedSecondMoment[t].dispose()})},t.prototype.getConfig=function(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}},t.fromConfig=function(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)},t.className=\"AdamOptimizer\",t}(Optimizer);registerClass(AdamOptimizer);var AdamaxOptimizer=function(e){function t(t,n,r,o,a){void 0===o&&(o=null),void 0===a&&(a=0);var i=e.call(this)||this;return i.learningRate=t,i.beta1=n,i.beta2=r,i.epsilon=o,i.decay=a,i.accumulatedFirstMoment={},i.accumulatedWeightedInfNorm={},i.c=keep(scalar(-t)),i.beta1Scalar=keep(scalar(n)),i.beta2Scalar=keep(scalar(r)),i.decayScalar=keep(scalar(a)),tidy(function(){i.iteration=scalar(0).variable(),i.accBeta1=scalar(n).variable()}),i.oneMinusBeta1=keep(scalar(1-n)),i.one=keep(scalar(1)),null===o&&(o=ENV.get(\"EPSILON\")),i.epsScalar=keep(scalar(o)),i}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this;tidy(function(){var n=t.one.sub(t.accBeta1),r=t.c.div(t.one.add(t.decayScalar.mul(t.iteration)));for(var o in e){var a=ENV.engine.registeredVariables[o];if(null==t.accumulatedFirstMoment[o]){var i=!1;t.accumulatedFirstMoment[o]=zerosLike(a).variable(i)}if(null==t.accumulatedWeightedInfNorm[o]){i=!1;t.accumulatedWeightedInfNorm[o]=zerosLike(a).variable(i)}var s=e[o],u=t.accumulatedFirstMoment[o],l=t.accumulatedWeightedInfNorm[o],c=t.beta1Scalar.mul(u).add(t.oneMinusBeta1.mul(s)),p=t.beta2Scalar.mul(l),d=s.abs(),h=p.maximum(d);t.accumulatedFirstMoment[o].assign(c),t.accumulatedWeightedInfNorm[o].assign(h);var f=r.div(n).mul(c.div(t.epsScalar.add(h))).add(a);a.assign(f)}t.iteration.assign(t.iteration.add(t.one)),t.accBeta1.assign(t.accBeta1.mul(t.beta1Scalar))})},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsScalar.dispose(),this.accBeta1.dispose(),this.beta1Scalar.dispose(),this.beta2Scalar.dispose(),this.oneMinusBeta1.dispose(),this.decayScalar.dispose(),this.iteration.dispose(),this.one.dispose(),null!=this.accumulatedFirstMoment&&Object.keys(this.accumulatedFirstMoment).forEach(function(t){return e.accumulatedFirstMoment[t].dispose()}),null!=this.accumulatedWeightedInfNorm&&Object.keys(this.accumulatedWeightedInfNorm).forEach(function(t){return e.accumulatedWeightedInfNorm[t].dispose()})},t.prototype.getConfig=function(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}},t.fromConfig=function(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)},t.className=\"AdamaxOptimizer\",t}(Optimizer);registerClass(AdamaxOptimizer);var SGDOptimizer=function(e){function t(t){var n=e.call(this)||this;return n.learningRate=t,n.setLearningRate(t),n}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this;Object.keys(e).forEach(function(n){var r=e[n],o=ENV.engine.registeredVariables[n];tidy(function(){var e=t.c.mul(r).add(o);o.assign(e)})})},t.prototype.setLearningRate=function(e){this.learningRate=e,null!=this.c&&this.c.dispose(),this.c=keep(scalar(-e))},t.prototype.dispose=function(){this.c.dispose()},t.prototype.getConfig=function(){return{learningRate:this.learningRate}},t.fromConfig=function(e,t){return new e(t.learningRate)},t.className=\"SGDOptimizer\",t}(Optimizer);registerClass(SGDOptimizer);var MomentumOptimizer=function(e){function t(t,n,r){void 0===r&&(r=!1);var o=e.call(this,t)||this;return o.learningRate=t,o.momentum=n,o.useNesterov=r,o.m=scalar(o.momentum),o.accumulations={},o}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulations[n]){tidy(function(){t.accumulations[n]=zerosLike(o).variable(!1)})}var a=r.accumulations[n],i=e[n];tidy(function(){var e,r=t.m.mul(a).add(i);e=t.useNesterov?t.c.mul(i.add(r.mul(t.m))).add(o):t.c.mul(r).add(o),t.accumulations[n].assign(r),o.assign(e)})},r=this;for(var o in e)n(o)},t.prototype.dispose=function(){if(e.prototype.dispose.call(this),this.m.dispose(),null!=this.accumulations)for(var t in this.accumulations)this.accumulations[t].dispose()},t.prototype.setMomentum=function(e){this.momentum=e},t.prototype.getConfig=function(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}},t.fromConfig=function(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)},t.className=\"MomentumOptimizer\",t}(SGDOptimizer);registerClass(MomentumOptimizer);var RMSPropOptimizer=function(e){function t(t,n,r,o,a){void 0===n&&(n=.9),void 0===r&&(r=0),void 0===o&&(o=null),void 0===a&&(a=!1);var i=e.call(this)||this;return i.learningRate=t,i.decay=n,i.momentum=r,i.epsilon=o,i.accumulatedMeanSquares={},i.accumulatedMeanGrads={},i.accumulatedMoments={},i.c=keep(scalar(t)),i.decayScalar=keep(scalar(n)),i.momentumScalar=keep(scalar(r)),i.oneMinusDecay=keep(scalar(1-n)),i.centered=a,null===o&&(o=ENV.get(\"EPSILON\")),i.epsilonScalar=keep(scalar(o)),i}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulatedMeanSquares[n]){tidy(function(){t.accumulatedMeanSquares[n]=zerosLike(o).variable(!1)})}if(null==r.accumulatedMeanGrads[n]&&r.centered){tidy(function(){t.accumulatedMeanGrads[n]=zerosLike(o).variable(!1)})}if(null==r.accumulatedMoments[n]){tidy(function(){t.accumulatedMoments[n]=zerosLike(o).variable(!1)})}var a=r.accumulatedMeanSquares[n],i=r.accumulatedMeanGrads[n],s=r.accumulatedMoments[n],u=e[n];tidy(function(){var e=t.decayScalar.mul(a).add(t.oneMinusDecay.mul(u.square()));if(t.centered){var r=t.decayScalar.mul(i).add(t.oneMinusDecay.mul(u)),l=t.momentumScalar.mul(s).add(t.c.mul(u).div(e.sub(r.square().add(t.epsilonScalar)).sqrt()));t.accumulatedMeanSquares[n].assign(e),t.accumulatedMeanGrads[n].assign(r),t.accumulatedMoments[n].assign(l);var c=o.sub(l);o.assign(c)}else{var p=t.decayScalar.mul(a).add(t.oneMinusDecay.mul(u.square()));l=t.momentumScalar.mul(s).add(t.c.mul(u).div(p.add(t.epsilonScalar).sqrt()));t.accumulatedMeanSquares[n].assign(p),t.accumulatedMoments[n].assign(l);c=o.sub(l);o.assign(c)}})},r=this;for(var o in e)n(o)},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsilonScalar.dispose(),this.decayScalar.dispose(),this.momentumScalar.dispose(),this.oneMinusDecay.dispose(),null!=this.accumulatedMeanSquares&&Object.keys(this.accumulatedMeanSquares).forEach(function(t){return e.accumulatedMeanSquares[t].dispose()}),null!=this.accumulatedMeanGrads&&this.centered&&Object.keys(this.accumulatedMeanGrads).forEach(function(t){return e.accumulatedMeanGrads[t].dispose()}),null!=this.accumulatedMoments&&Object.keys(this.accumulatedMoments).forEach(function(t){return e.accumulatedMoments[t].dispose()})},t.prototype.getConfig=function(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}},t.fromConfig=function(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)},t.className=\"RMSPropOptimizer\",t}(Optimizer);registerClass(RMSPropOptimizer);var OptimizerConstructors=function(){function e(){}return e.sgd=function(e){return new SGDOptimizer(e)},e.momentum=function(e,t,n){return void 0===n&&(n=!1),new MomentumOptimizer(e,t,n)},e.rmsprop=function(e,t,n,r,o){return void 0===t&&(t=.9),void 0===n&&(n=0),void 0===r&&(r=null),void 0===o&&(o=!1),new RMSPropOptimizer(e,t,n,r,o)},e.adam=function(e,t,n,r){return void 0===e&&(e=.001),void 0===t&&(t=.9),void 0===n&&(n=.999),void 0===r&&(r=null),new AdamOptimizer(e,t,n,r)},e.adadelta=function(e,t,n){return void 0===e&&(e=.001),void 0===t&&(t=.95),void 0===n&&(n=null),new AdadeltaOptimizer(e,t,n)},e.adamax=function(e,t,n,r,o){return void 0===e&&(e=.002),void 0===t&&(t=.9),void 0===n&&(n=.999),void 0===r&&(r=null),void 0===o&&(o=0),new AdamaxOptimizer(e,t,n,r,o)},e.adagrad=function(e,t){return void 0===t&&(t=.1),new AdagradOptimizer(e,t)},e}(),train={sgd:OptimizerConstructors.sgd,momentum:OptimizerConstructors.momentum,adadelta:OptimizerConstructors.adadelta,adagrad:OptimizerConstructors.adagrad,rmsprop:OptimizerConstructors.rmsprop,adamax:OptimizerConstructors.adamax,adam:OptimizerConstructors.adam},setBackend=Environment.setBackend,getBackend=Environment.getBackend,disposeVariables=Environment.disposeVariables,memory=Environment.memory;setOpHandler(ops);export{setBackend,getBackend,disposeVariables,memory,version as version_core,nextFrame,environment,io,math,serialization,test_util,util,webgl,AdadeltaOptimizer,AdagradOptimizer,AdamOptimizer,AdamaxOptimizer,MomentumOptimizer,Optimizer,RMSPropOptimizer,SGDOptimizer,Tensor,TensorBuffer,variable,Variable,Rank,Reduction,ENV,Environment,KernelBackend,DataStorage,image_ops as image,linalg_ops as linalg,loss_ops as losses,spectral_ops as spectral,op,batchNormalization2d,batchNormalization3d,batchNormalization4d,batchNormalization,complex,real,imag,concat,concat1d,concat2d,concat3d,concat4d,split$1 as split,conv1d,conv2d,conv3d,conv2dDerFilter,depthwiseConv2d,separableConv2d,conv2dTranspose,matMul,dot,outerProduct,reverse,reverse1d,reverse2d,reverse3d,reverse4d,maxPool,avgPool,pool,slice,slice1d,slice2d,slice3d,slice4d,abs,acos,acosh,asin,asinh,atan,atanh,ceil,clipByValue,cos,cosh,erf,exp,expm1,floor,log$1 as log,log1p,logSigmoid,neg,reciprocal,round,rsqrt,sigmoid,sign,sin,sinh,softplus,sqrt,square,step,tan,tanh$1 as tanh,all,any,argMax,argMin,logSumExp,max,mean,min,moments,sum$1 as sum,prod,equal,equalStrict,greater,greaterEqual,greaterEqualStrict,greaterStrict,less,lessEqual,lessEqualStrict,lessStrict,notEqual,notEqualStrict,add,addN,addStrict,atan2,div,divStrict,floorDiv,maximum,maximumStrict,minimum,minimumStrict,mod,modStrict,mul,mulStrict,pow,powStrict,squaredDifference,squaredDifferenceStrict,sub,subStrict,elu,leakyRelu,prelu,relu,selu,logicalAnd,logicalNot,logicalOr,logicalXor,where,whereAsync,buffer,toPixels,print,batchToSpaceND,cast,clone,cumsum,depthToSpace,expandDims,eye,fromPixels,multinomial,oneHot,pad,pad1d,pad2d,pad3d,pad4d,rand,randomNormal,randomUniform,reshape,spaceToBatchND,squeeze,stack,tile,truncatedNormal,unstack,setdiff1dAsync,fill,linspace,ones$1 as ones,range,scalar,tensor,tensor1d,tensor2d,tensor3d,tensor4d,tensor5d,tensor6d,zeros,onesLike,zerosLike,transpose,softmax,logSoftmax,localResponseNormalization,norm,gather,unsortedSegmentSum,basicLSTMCell,multiRNNCell,movingAverage,stridedSlice,topk,scatterND,fft,ifft,rfft,irfft,sparseToDense,gatherND,train,tidy,keep,dispose,time,profile,customGrad,grad,grads,valueAndGrad,valueAndGrads,variableGrads};\n//# sourceMappingURL=tf-core.esm.js.map\n","import { isValidNumber } from '../utils';\r\nvar Dimensions = /** @class */ (function () {\r\n    function Dimensions(width, height) {\r\n        if (!isValidNumber(width) || !isValidNumber(height)) {\r\n            throw new Error(\"Dimensions.constructor - expected width and height to be valid numbers, instead have \" + JSON.stringify({ width: width, height: height }));\r\n        }\r\n        this._width = width;\r\n        this._height = height;\r\n    }\r\n    Object.defineProperty(Dimensions.prototype, \"width\", {\r\n        get: function () { return this._width; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Dimensions.prototype, \"height\", {\r\n        get: function () { return this._height; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Dimensions.prototype.reverse = function () {\r\n        return new Dimensions(1 / this.width, 1 / this.height);\r\n    };\r\n    return Dimensions;\r\n}());\r\nexport { Dimensions };\r\n//# sourceMappingURL=Dimensions.js.map","var Point = /** @class */ (function () {\r\n    function Point(x, y) {\r\n        this._x = x;\r\n        this._y = y;\r\n    }\r\n    Object.defineProperty(Point.prototype, \"x\", {\r\n        get: function () { return this._x; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Point.prototype, \"y\", {\r\n        get: function () { return this._y; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Point.prototype.add = function (pt) {\r\n        return new Point(this.x + pt.x, this.y + pt.y);\r\n    };\r\n    Point.prototype.sub = function (pt) {\r\n        return new Point(this.x - pt.x, this.y - pt.y);\r\n    };\r\n    Point.prototype.mul = function (pt) {\r\n        return new Point(this.x * pt.x, this.y * pt.y);\r\n    };\r\n    Point.prototype.div = function (pt) {\r\n        return new Point(this.x / pt.x, this.y / pt.y);\r\n    };\r\n    Point.prototype.abs = function () {\r\n        return new Point(Math.abs(this.x), Math.abs(this.y));\r\n    };\r\n    Point.prototype.magnitude = function () {\r\n        return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2));\r\n    };\r\n    Point.prototype.floor = function () {\r\n        return new Point(Math.floor(this.x), Math.floor(this.y));\r\n    };\r\n    return Point;\r\n}());\r\nexport { Point };\r\n//# sourceMappingURL=Point.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { Dimensions } from '../classes/Dimensions';\r\nimport { Point } from '../classes/Point';\r\nexport function isTensor(tensor, dim) {\r\n    return tensor instanceof tf.Tensor && tensor.shape.length === dim;\r\n}\r\nexport function isTensor1D(tensor) {\r\n    return isTensor(tensor, 1);\r\n}\r\nexport function isTensor2D(tensor) {\r\n    return isTensor(tensor, 2);\r\n}\r\nexport function isTensor3D(tensor) {\r\n    return isTensor(tensor, 3);\r\n}\r\nexport function isTensor4D(tensor) {\r\n    return isTensor(tensor, 4);\r\n}\r\nexport function isFloat(num) {\r\n    return num % 1 !== 0;\r\n}\r\nexport function isEven(num) {\r\n    return num % 2 === 0;\r\n}\r\nexport function round(num, prec) {\r\n    if (prec === void 0) { prec = 2; }\r\n    var f = Math.pow(10, prec);\r\n    return Math.floor(num * f) / f;\r\n}\r\nexport function isDimensions(obj) {\r\n    return obj && obj.width && obj.height;\r\n}\r\nexport function computeReshapedDimensions(_a, inputSize) {\r\n    var width = _a.width, height = _a.height;\r\n    var scale = inputSize / Math.max(height, width);\r\n    return new Dimensions(Math.round(width * scale), Math.round(height * scale));\r\n}\r\nexport function getCenterPoint(pts) {\r\n    return pts.reduce(function (sum, pt) { return sum.add(pt); }, new Point(0, 0))\r\n        .div(new Point(pts.length, pts.length));\r\n}\r\nexport function range(num, start, step) {\r\n    return Array(num).fill(0).map(function (_, i) { return start + (i * step); });\r\n}\r\nexport function isValidNumber(num) {\r\n    return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0;\r\n}\r\nexport function isValidProbablitiy(num) {\r\n    return isValidNumber(num) && 0 <= num && num <= 1.0;\r\n}\r\n//# sourceMappingURL=index.js.map","import { isDimensions, isValidNumber } from '../utils';\r\nvar Box = /** @class */ (function () {\r\n    // TODO: MTCNN boxes sometimes have negative widths or heights, figure out why and remove\r\n    // allowNegativeDimensions flag again\r\n    function Box(_box, allowNegativeDimensions) {\r\n        if (allowNegativeDimensions === void 0) { allowNegativeDimensions = false; }\r\n        var box = (_box || {});\r\n        var isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);\r\n        var isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);\r\n        if (!isRect && !isBbox) {\r\n            throw new Error(\"Box.constructor - expected box to be IBoundingBox | IRect, instead have \" + JSON.stringify(box));\r\n        }\r\n        var _a = isRect\r\n            ? [box.x, box.y, box.width, box.height]\r\n            : [box.left, box.top, box.right - box.left, box.bottom - box.top], x = _a[0], y = _a[1], width = _a[2], height = _a[3];\r\n        Box.assertIsValidBox({ x: x, y: y, width: width, height: height }, 'Box.constructor', allowNegativeDimensions);\r\n        this._x = x;\r\n        this._y = y;\r\n        this._width = width;\r\n        this._height = height;\r\n    }\r\n    Box.isRect = function (rect) {\r\n        return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\r\n    };\r\n    Box.assertIsValidBox = function (box, callee, allowNegativeDimensions) {\r\n        if (allowNegativeDimensions === void 0) { allowNegativeDimensions = false; }\r\n        if (!Box.isRect(box)) {\r\n            throw new Error(callee + \" - invalid box: \" + JSON.stringify(box) + \", expected object with properties x, y, width, height\");\r\n        }\r\n        if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\r\n            throw new Error(callee + \" - width (\" + box.width + \") and height (\" + box.height + \") must be positive numbers\");\r\n        }\r\n    };\r\n    Object.defineProperty(Box.prototype, \"x\", {\r\n        get: function () { return this._x; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"y\", {\r\n        get: function () { return this._y; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"width\", {\r\n        get: function () { return this._width; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"height\", {\r\n        get: function () { return this._height; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"left\", {\r\n        get: function () { return this.x; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"top\", {\r\n        get: function () { return this.y; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"right\", {\r\n        get: function () { return this.x + this.width; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"bottom\", {\r\n        get: function () { return this.y + this.height; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"area\", {\r\n        get: function () { return this.width * this.height; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Box.prototype.round = function () {\r\n        var _a = [this.x, this.y, this.width, this.height]\r\n            .map(function (val) { return Math.round(val); }), x = _a[0], y = _a[1], width = _a[2], height = _a[3];\r\n        return new Box({ x: x, y: y, width: width, height: height });\r\n    };\r\n    Box.prototype.floor = function () {\r\n        var _a = [this.x, this.y, this.width, this.height]\r\n            .map(function (val) { return Math.floor(val); }), x = _a[0], y = _a[1], width = _a[2], height = _a[3];\r\n        return new Box({ x: x, y: y, width: width, height: height });\r\n    };\r\n    Box.prototype.toSquare = function () {\r\n        var _a = this, x = _a.x, y = _a.y, width = _a.width, height = _a.height;\r\n        var diff = Math.abs(width - height);\r\n        if (width < height) {\r\n            x -= (diff / 2);\r\n            width += diff;\r\n        }\r\n        if (height < width) {\r\n            y -= (diff / 2);\r\n            height += diff;\r\n        }\r\n        return new Box({ x: x, y: y, width: width, height: height });\r\n    };\r\n    Box.prototype.rescale = function (s) {\r\n        var scaleX = isDimensions(s) ? s.width : s;\r\n        var scaleY = isDimensions(s) ? s.height : s;\r\n        return new Box({\r\n            x: this.x * scaleX,\r\n            y: this.y * scaleY,\r\n            width: this.width * scaleX,\r\n            height: this.height * scaleY\r\n        });\r\n    };\r\n    Box.prototype.pad = function (padX, padY) {\r\n        var _a = [\r\n            this.x - (padX / 2),\r\n            this.y - (padY / 2),\r\n            this.width + padX,\r\n            this.height + padY\r\n        ], x = _a[0], y = _a[1], width = _a[2], height = _a[3];\r\n        return new Box({ x: x, y: y, width: width, height: height });\r\n    };\r\n    Box.prototype.clipAtImageBorders = function (imgWidth, imgHeight) {\r\n        var _a = this, x = _a.x, y = _a.y, right = _a.right, bottom = _a.bottom;\r\n        var clippedX = Math.max(x, 0);\r\n        var clippedY = Math.max(y, 0);\r\n        var newWidth = right - clippedX;\r\n        var newHeight = bottom - clippedY;\r\n        var clippedWidth = Math.min(newWidth, imgWidth - clippedX);\r\n        var clippedHeight = Math.min(newHeight, imgHeight - clippedY);\r\n        return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight })).floor();\r\n    };\r\n    Box.prototype.shift = function (sx, sy) {\r\n        var _a = this, width = _a.width, height = _a.height;\r\n        var x = this.x + sx;\r\n        var y = this.y + sy;\r\n        return new Box({ x: x, y: y, width: width, height: height });\r\n    };\r\n    Box.prototype.padAtBorders = function (imageHeight, imageWidth) {\r\n        var w = this.width + 1;\r\n        var h = this.height + 1;\r\n        var dx = 1;\r\n        var dy = 1;\r\n        var edx = w;\r\n        var edy = h;\r\n        var x = this.left;\r\n        var y = this.top;\r\n        var ex = this.right;\r\n        var ey = this.bottom;\r\n        if (ex > imageWidth) {\r\n            edx = -ex + imageWidth + w;\r\n            ex = imageWidth;\r\n        }\r\n        if (ey > imageHeight) {\r\n            edy = -ey + imageHeight + h;\r\n            ey = imageHeight;\r\n        }\r\n        if (x < 1) {\r\n            edy = 2 - x;\r\n            x = 1;\r\n        }\r\n        if (y < 1) {\r\n            edy = 2 - y;\r\n            y = 1;\r\n        }\r\n        return { dy: dy, edy: edy, dx: dx, edx: edx, y: y, ey: ey, x: x, ex: ex, w: w, h: h };\r\n    };\r\n    Box.prototype.calibrate = function (region) {\r\n        return new Box({\r\n            left: this.left + (region.left * this.width),\r\n            top: this.top + (region.top * this.height),\r\n            right: this.right + (region.right * this.width),\r\n            bottom: this.bottom + (region.bottom * this.height)\r\n        }).toSquare().round();\r\n    };\r\n    return Box;\r\n}());\r\nexport { Box };\r\n//# sourceMappingURL=Box.js.map","import * as tslib_1 from \"tslib\";\r\nimport { Box } from './Box';\r\nvar BoundingBox = /** @class */ (function (_super) {\r\n    tslib_1.__extends(BoundingBox, _super);\r\n    function BoundingBox(left, top, right, bottom) {\r\n        return _super.call(this, { left: left, top: top, right: right, bottom: bottom }) || this;\r\n    }\r\n    return BoundingBox;\r\n}(Box));\r\nexport { BoundingBox };\r\n//# sourceMappingURL=BoundingBox.js.map","import * as tslib_1 from \"tslib\";\r\nimport { Box } from './Box';\r\nvar BoxWithText = /** @class */ (function (_super) {\r\n    tslib_1.__extends(BoxWithText, _super);\r\n    function BoxWithText(box, text) {\r\n        var _this = _super.call(this, box) || this;\r\n        _this._text = text;\r\n        return _this;\r\n    }\r\n    Object.defineProperty(BoxWithText.prototype, \"text\", {\r\n        get: function () { return this._text; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    return BoxWithText;\r\n}(Box));\r\nexport { BoxWithText };\r\n//# sourceMappingURL=BoxWithText.js.map","import * as tslib_1 from \"tslib\";\r\nimport { Box } from './Box';\r\nimport { isValidNumber } from '../utils/index';\r\nvar LabeledBox = /** @class */ (function (_super) {\r\n    tslib_1.__extends(LabeledBox, _super);\r\n    function LabeledBox(box, label) {\r\n        var _this = _super.call(this, box) || this;\r\n        _this._label = label;\r\n        return _this;\r\n    }\r\n    LabeledBox.assertIsValidLabeledBox = function (box, callee) {\r\n        Box.assertIsValidBox(box, callee);\r\n        if (!isValidNumber(box.label)) {\r\n            throw new Error(callee + \" - expected property label (\" + box.label + \") to be a number\");\r\n        }\r\n    };\r\n    Object.defineProperty(LabeledBox.prototype, \"label\", {\r\n        get: function () { return this._label; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    return LabeledBox;\r\n}(Box));\r\nexport { LabeledBox };\r\n//# sourceMappingURL=LabeledBox.js.map","import { Box } from './Box';\r\nimport { Dimensions } from './Dimensions';\r\nvar ObjectDetection = /** @class */ (function () {\r\n    function ObjectDetection(score, classScore, className, relativeBox, imageDims) {\r\n        this._imageDims = new Dimensions(imageDims.width, imageDims.height);\r\n        this._score = score;\r\n        this._classScore = classScore;\r\n        this._className = className;\r\n        this._box = new Box(relativeBox).rescale(this._imageDims);\r\n    }\r\n    Object.defineProperty(ObjectDetection.prototype, \"score\", {\r\n        get: function () { return this._score; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(ObjectDetection.prototype, \"classScore\", {\r\n        get: function () { return this._classScore; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(ObjectDetection.prototype, \"className\", {\r\n        get: function () { return this._className; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(ObjectDetection.prototype, \"box\", {\r\n        get: function () { return this._box; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(ObjectDetection.prototype, \"imageDims\", {\r\n        get: function () { return this._imageDims; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(ObjectDetection.prototype, \"imageWidth\", {\r\n        get: function () { return this.imageDims.width; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(ObjectDetection.prototype, \"imageHeight\", {\r\n        get: function () { return this.imageDims.height; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(ObjectDetection.prototype, \"relativeBox\", {\r\n        get: function () { return new Box(this._box).rescale(this.imageDims.reverse()); },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    ObjectDetection.prototype.forSize = function (width, height) {\r\n        return new ObjectDetection(this.score, this.classScore, this.className, this.relativeBox, { width: width, height: height });\r\n    };\r\n    return ObjectDetection;\r\n}());\r\nexport { ObjectDetection };\r\n//# sourceMappingURL=ObjectDetection.js.map","import * as tslib_1 from \"tslib\";\r\nimport { isValidProbablitiy } from '../utils';\r\nimport { LabeledBox } from './LabeledBox';\r\nvar PredictedBox = /** @class */ (function (_super) {\r\n    tslib_1.__extends(PredictedBox, _super);\r\n    function PredictedBox(box, label, score, classScore) {\r\n        var _this = _super.call(this, box, label) || this;\r\n        _this._score = score;\r\n        _this._classScore = classScore;\r\n        return _this;\r\n    }\r\n    PredictedBox.assertIsValidPredictedBox = function (box, callee) {\r\n        LabeledBox.assertIsValidLabeledBox(box, callee);\r\n        if (!isValidProbablitiy(box.score)\r\n            || !isValidProbablitiy(box.classScore)) {\r\n            throw new Error(callee + \" - expected properties score (\" + box.score + \") and (\" + box.classScore + \") to be a number between [0, 1]\");\r\n        }\r\n    };\r\n    Object.defineProperty(PredictedBox.prototype, \"score\", {\r\n        get: function () { return this._score; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(PredictedBox.prototype, \"classScore\", {\r\n        get: function () { return this._classScore; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    return PredictedBox;\r\n}(LabeledBox));\r\nexport { PredictedBox };\r\n//# sourceMappingURL=PredictedBox.js.map","import * as tslib_1 from \"tslib\";\r\nimport { Box } from './Box';\r\nvar Rect = /** @class */ (function (_super) {\r\n    tslib_1.__extends(Rect, _super);\r\n    function Rect(x, y, width, height) {\r\n        return _super.call(this, { x: x, y: y, width: width, height: height }) || this;\r\n    }\r\n    return Rect;\r\n}(Box));\r\nexport { Rect };\r\n//# sourceMappingURL=Rect.js.map","export function disposeUnusedWeightTensors(weightMap, paramMappings) {\r\n    Object.keys(weightMap).forEach(function (path) {\r\n        if (!paramMappings.some(function (pm) { return pm.originalPath === path; })) {\r\n            weightMap[path].dispose();\r\n        }\r\n    });\r\n}\r\n//# sourceMappingURL=disposeUnusedWeightTensors.js.map","import { isTensor } from '../utils';\r\nexport function extractWeightEntryFactory(weightMap, paramMappings) {\r\n    return function (originalPath, paramRank, mappedPath) {\r\n        var tensor = weightMap[originalPath];\r\n        if (!isTensor(tensor, paramRank)) {\r\n            throw new Error(\"expected weightMap[\" + originalPath + \"] to be a Tensor\" + paramRank + \"D, instead have \" + tensor);\r\n        }\r\n        paramMappings.push({ originalPath: originalPath, paramPath: mappedPath || originalPath });\r\n        return tensor;\r\n    };\r\n}\r\n//# sourceMappingURL=extractWeightEntryFactory.js.map","export function extractWeightsFactory(weights) {\r\n    var remainingWeights = weights;\r\n    function extractWeights(numWeights) {\r\n        var ret = remainingWeights.slice(0, numWeights);\r\n        remainingWeights = remainingWeights.slice(numWeights);\r\n        return ret;\r\n    }\r\n    function getRemainingWeights() {\r\n        return remainingWeights;\r\n    }\r\n    return {\r\n        extractWeights: extractWeights,\r\n        getRemainingWeights: getRemainingWeights\r\n    };\r\n}\r\n//# sourceMappingURL=extractWeightsFactory.js.map","export function getModelUris(uri, defaultModelName) {\r\n    var defaultManifestFilename = defaultModelName + \"-weights_manifest.json\";\r\n    if (!uri) {\r\n        return {\r\n            modelBaseUri: '',\r\n            manifestUri: defaultManifestFilename\r\n        };\r\n    }\r\n    if (uri === '/') {\r\n        return {\r\n            modelBaseUri: '/',\r\n            manifestUri: \"/\" + defaultManifestFilename\r\n        };\r\n    }\r\n    var protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\r\n    uri = uri.replace(protocol, '');\r\n    var parts = uri.split('/').filter(function (s) { return s; });\r\n    var manifestFile = uri.endsWith('.json')\r\n        ? parts[parts.length - 1]\r\n        : defaultManifestFilename;\r\n    var modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');\r\n    modelBaseUri = uri.startsWith('/') ? \"/\" + modelBaseUri : modelBaseUri;\r\n    return {\r\n        modelBaseUri: modelBaseUri,\r\n        manifestUri: modelBaseUri === '/' ? \"/\" + manifestFile : modelBaseUri + \"/\" + manifestFile\r\n    };\r\n}\r\n//# sourceMappingURL=getModelUris.js.map","export function createBrowserEnv() {\r\n    var fetch = window['fetch'] || function () {\r\n        throw new Error('fetch - missing fetch implementation for browser environment');\r\n    };\r\n    var readFile = function () {\r\n        throw new Error('readFile - filesystem not available for browser environment');\r\n    };\r\n    return {\r\n        Canvas: HTMLCanvasElement,\r\n        Image: HTMLImageElement,\r\n        ImageData: ImageData,\r\n        Video: HTMLVideoElement,\r\n        createCanvasElement: function () { return document.createElement('canvas'); },\r\n        createImageElement: function () { return document.createElement('img'); },\r\n        fetch: fetch,\r\n        readFile: readFile\r\n    };\r\n}\r\n//# sourceMappingURL=createBrowserEnv.js.map","export function createFileSystem(fs) {\r\n    var requireFsError = '';\r\n    if (!fs) {\r\n        try {\r\n            fs = require('fs');\r\n        }\r\n        catch (err) {\r\n            requireFsError = err.toString();\r\n        }\r\n    }\r\n    var readFile = fs\r\n        ? function (filePath) {\r\n            return new Promise(function (res, rej) {\r\n                fs.readFile(filePath, function (err, buffer) {\r\n                    return err ? rej(err) : res(buffer);\r\n                });\r\n            });\r\n        }\r\n        : function () {\r\n            throw new Error(\"readFile - failed to require fs in nodejs environment with error: \" + requireFsError);\r\n        };\r\n    return {\r\n        readFile: readFile\r\n    };\r\n}\r\n//# sourceMappingURL=createFileSystem.js.map","import * as tslib_1 from \"tslib\";\r\nimport { createFileSystem } from './createFileSystem';\r\nexport function createNodejsEnv() {\r\n    var Canvas = global['Canvas'] || global['HTMLCanvasElement'];\r\n    var Image = global['Image'] || global['HTMLImageElement'];\r\n    var createCanvasElement = function () {\r\n        if (Canvas) {\r\n            return new Canvas();\r\n        }\r\n        throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');\r\n    };\r\n    var createImageElement = function () {\r\n        if (Image) {\r\n            return new Image();\r\n        }\r\n        throw new Error('createImageElement - missing Image implementation for nodejs environment');\r\n    };\r\n    var fetch = global['fetch'] || function () {\r\n        throw new Error('fetch - missing fetch implementation for nodejs environment');\r\n    };\r\n    var fileSystem = createFileSystem();\r\n    return tslib_1.__assign({ Canvas: Canvas || /** @class */ (function () {\r\n            function class_1() {\r\n            }\r\n            return class_1;\r\n        }()), Image: Image || /** @class */ (function () {\r\n            function class_2() {\r\n            }\r\n            return class_2;\r\n        }()), ImageData: global['ImageData'] || /** @class */ (function () {\r\n            function class_3() {\r\n            }\r\n            return class_3;\r\n        }()), Video: global['HTMLVideoElement'] || /** @class */ (function () {\r\n            function class_4() {\r\n            }\r\n            return class_4;\r\n        }()), createCanvasElement: createCanvasElement,\r\n        createImageElement: createImageElement,\r\n        fetch: fetch }, fileSystem);\r\n}\r\n//# sourceMappingURL=createNodejsEnv.js.map","export function isBrowser() {\r\n    return typeof window === 'object'\r\n        && typeof document !== 'undefined'\r\n        && typeof HTMLImageElement !== 'undefined'\r\n        && typeof HTMLCanvasElement !== 'undefined'\r\n        && typeof HTMLVideoElement !== 'undefined'\r\n        && typeof ImageData !== 'undefined';\r\n}\r\n//# sourceMappingURL=isBrowser.js.map","export function isNodejs() {\r\n    return typeof global === 'object'\r\n        && typeof require === 'function'\r\n        && typeof module !== 'undefined'\r\n        // issues with gatsby.js: module.exports is undefined\r\n        // && !!module.exports\r\n        && typeof process !== 'undefined' && !!process.version;\r\n}\r\n//# sourceMappingURL=isNodejs.js.map","import { createBrowserEnv } from './createBrowserEnv';\r\nimport { createFileSystem } from './createFileSystem';\r\nimport { createNodejsEnv } from './createNodejsEnv';\r\nimport { isBrowser } from './isBrowser';\r\nimport { isNodejs } from './isNodejs';\r\nvar environment;\r\nfunction getEnv() {\r\n    if (!environment) {\r\n        throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');\r\n    }\r\n    return environment;\r\n}\r\nfunction setEnv(env) {\r\n    environment = env;\r\n}\r\nfunction initialize() {\r\n    // check for isBrowser() first to prevent electron renderer process\r\n    // to be initialized with wrong environment due to isNodejs() returning true\r\n    if (isBrowser()) {\r\n        setEnv(createBrowserEnv());\r\n    }\r\n    if (isNodejs()) {\r\n        setEnv(createNodejsEnv());\r\n    }\r\n}\r\nfunction monkeyPatch(env) {\r\n    if (!environment) {\r\n        initialize();\r\n    }\r\n    if (!environment) {\r\n        throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');\r\n    }\r\n    var _a = env.Canvas, Canvas = _a === void 0 ? environment.Canvas : _a, _b = env.Image, Image = _b === void 0 ? environment.Image : _b;\r\n    environment.Canvas = Canvas;\r\n    environment.Image = Image;\r\n    environment.createCanvasElement = env.createCanvasElement || (function () { return new Canvas(); });\r\n    environment.createImageElement = env.createImageElement || (function () { return new Image(); });\r\n    environment.ImageData = env.ImageData || environment.ImageData;\r\n    environment.Video = env.Video || environment.Video;\r\n    environment.fetch = env.fetch || environment.fetch;\r\n    environment.readFile = env.readFile || environment.readFile;\r\n}\r\nexport var env = {\r\n    getEnv: getEnv,\r\n    setEnv: setEnv,\r\n    initialize: initialize,\r\n    createBrowserEnv: createBrowserEnv,\r\n    createFileSystem: createFileSystem,\r\n    createNodejsEnv: createNodejsEnv,\r\n    monkeyPatch: monkeyPatch,\r\n    isBrowser: isBrowser,\r\n    isNodejs: isNodejs\r\n};\r\ninitialize();\r\n//# sourceMappingURL=index.js.map","import { env } from '../env';\r\nexport function isMediaLoaded(media) {\r\n    var _a = env.getEnv(), Image = _a.Image, Video = _a.Video;\r\n    return (media instanceof Image && media.complete)\r\n        || (media instanceof Video && media.readyState >= 3);\r\n}\r\n//# sourceMappingURL=isMediaLoaded.js.map","import { env } from '../env';\r\nimport { isMediaLoaded } from './isMediaLoaded';\r\nexport function awaitMediaLoaded(media) {\r\n    return new Promise(function (resolve, reject) {\r\n        if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\r\n            return resolve();\r\n        }\r\n        function onLoad(e) {\r\n            if (!e.currentTarget)\r\n                return;\r\n            e.currentTarget.removeEventListener('load', onLoad);\r\n            e.currentTarget.removeEventListener('error', onError);\r\n            resolve(e);\r\n        }\r\n        function onError(e) {\r\n            if (!e.currentTarget)\r\n                return;\r\n            e.currentTarget.removeEventListener('load', onLoad);\r\n            e.currentTarget.removeEventListener('error', onError);\r\n            reject(e);\r\n        }\r\n        media.addEventListener('load', onLoad);\r\n        media.addEventListener('error', onError);\r\n    });\r\n}\r\n//# sourceMappingURL=awaitMediaLoaded.js.map","import { env } from '../env';\r\nexport function bufferToImage(buf) {\r\n    return new Promise(function (resolve, reject) {\r\n        if (!(buf instanceof Blob)) {\r\n            return reject('bufferToImage - expected buf to be of type: Blob');\r\n        }\r\n        var reader = new FileReader();\r\n        reader.onload = function () {\r\n            if (typeof reader.result !== 'string') {\r\n                return reject('bufferToImage - expected reader.result to be a string, in onload');\r\n            }\r\n            var img = env.getEnv().createImageElement();\r\n            img.onload = function () { return resolve(img); };\r\n            img.onerror = reject;\r\n            img.src = reader.result;\r\n        };\r\n        reader.onerror = reject;\r\n        reader.readAsDataURL(buf);\r\n    });\r\n}\r\n//# sourceMappingURL=bufferToImage.js.map","export function getContext2dOrThrow(canvas) {\r\n    var ctx = canvas.getContext('2d');\r\n    if (!ctx) {\r\n        throw new Error('canvas 2d context is null');\r\n    }\r\n    return ctx;\r\n}\r\n//# sourceMappingURL=getContext2dOrThrow.js.map","import { Dimensions } from '../classes/Dimensions';\r\nimport { env } from '../env';\r\nexport function getMediaDimensions(input) {\r\n    var _a = env.getEnv(), Image = _a.Image, Video = _a.Video;\r\n    if (input instanceof Image) {\r\n        return new Dimensions(input.naturalWidth, input.naturalHeight);\r\n    }\r\n    if (input instanceof Video) {\r\n        return new Dimensions(input.videoWidth, input.videoHeight);\r\n    }\r\n    return new Dimensions(input.width, input.height);\r\n}\r\n//# sourceMappingURL=getMediaDimensions.js.map","import { env } from '../env';\r\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\r\nimport { getMediaDimensions } from './getMediaDimensions';\r\nimport { isMediaLoaded } from './isMediaLoaded';\r\nexport function createCanvas(_a) {\r\n    var width = _a.width, height = _a.height;\r\n    var createCanvasElement = env.getEnv().createCanvasElement;\r\n    var canvas = createCanvasElement();\r\n    canvas.width = width;\r\n    canvas.height = height;\r\n    return canvas;\r\n}\r\nexport function createCanvasFromMedia(media, dims) {\r\n    var ImageData = env.getEnv().ImageData;\r\n    if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\r\n        throw new Error('createCanvasFromMedia - media has not finished loading yet');\r\n    }\r\n    var _a = dims || getMediaDimensions(media), width = _a.width, height = _a.height;\r\n    var canvas = createCanvas({ width: width, height: height });\r\n    if (media instanceof ImageData) {\r\n        getContext2dOrThrow(canvas).putImageData(media, 0, 0);\r\n    }\r\n    else {\r\n        getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);\r\n    }\r\n    return canvas;\r\n}\r\n//# sourceMappingURL=createCanvas.js.map","export function getDefaultDrawOptions(options) {\r\n    if (options === void 0) { options = {}; }\r\n    return Object.assign({}, {\r\n        boxColor: 'blue',\r\n        textColor: 'red',\r\n        lineWidth: 2,\r\n        fontSize: 20,\r\n        fontStyle: 'Georgia',\r\n        withScore: true,\r\n        withClassName: true\r\n    }, options);\r\n}\r\n//# sourceMappingURL=getDefaultDrawOptions.js.map","import { getDefaultDrawOptions } from './getDefaultDrawOptions';\r\nexport function drawBox(ctx, x, y, w, h, options) {\r\n    var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));\r\n    ctx.strokeStyle = drawOptions.boxColor;\r\n    ctx.lineWidth = drawOptions.lineWidth;\r\n    ctx.strokeRect(x, y, w, h);\r\n}\r\n//# sourceMappingURL=drawBox.js.map","import { getDefaultDrawOptions } from './getDefaultDrawOptions';\r\nexport function drawText(ctx, x, y, text, options) {\r\n    if (options === void 0) { options = {}; }\r\n    var drawOptions = Object.assign(getDefaultDrawOptions(), options);\r\n    var padText = 2 + drawOptions.lineWidth;\r\n    ctx.fillStyle = drawOptions.textColor;\r\n    ctx.font = drawOptions.fontSize + \"px \" + drawOptions.fontStyle;\r\n    ctx.fillText(text, x + padText, y + padText + (drawOptions.fontSize * 0.6));\r\n}\r\n//# sourceMappingURL=drawText.js.map","import { env } from '../env';\r\nexport function resolveInput(arg) {\r\n    if (!env.isNodejs() && typeof arg === 'string') {\r\n        return document.getElementById(arg);\r\n    }\r\n    return arg;\r\n}\r\n//# sourceMappingURL=resolveInput.js.map","import { BoxWithText } from '../classes/BoxWithText';\r\nimport { ObjectDetection } from '../classes/ObjectDetection';\r\nimport { PredictedBox } from '../classes/PredictedBox';\r\nimport { env } from '../env';\r\nimport { round } from '../utils';\r\nimport { drawBox } from './drawBox';\r\nimport { drawText } from './drawText';\r\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\r\nimport { getDefaultDrawOptions } from './getDefaultDrawOptions';\r\nimport { resolveInput } from './resolveInput';\r\nexport function drawDetection(canvasArg, detection, options) {\r\n    var Canvas = env.getEnv().Canvas;\r\n    var canvas = resolveInput(canvasArg);\r\n    if (!(canvas instanceof Canvas)) {\r\n        throw new Error('drawDetection - expected canvas to be of type: HTMLCanvasElement');\r\n    }\r\n    var detectionArray = Array.isArray(detection)\r\n        ? detection\r\n        : [detection];\r\n    detectionArray.forEach(function (det) {\r\n        var _a = det instanceof ObjectDetection ? det.box : det, x = _a.x, y = _a.y, width = _a.width, height = _a.height;\r\n        var drawOptions = getDefaultDrawOptions(options);\r\n        var ctx = getContext2dOrThrow(canvas);\r\n        drawBox(ctx, x, y, width, height, drawOptions);\r\n        var withScore = drawOptions.withScore;\r\n        var text = det instanceof BoxWithText\r\n            ? det.text\r\n            : ((withScore && det instanceof PredictedBox)\r\n                ? \"\" + round(det.score)\r\n                : (det instanceof ObjectDetection\r\n                    ? \"\" + det.className + (withScore ? \" (\" + round(det.score) + \")\" : '')\r\n                    : ''));\r\n        if (text) {\r\n            drawText(ctx, x, y + height, text, drawOptions);\r\n        }\r\n    });\r\n}\r\n//# sourceMappingURL=drawDetection.js.map","import * as tslib_1 from \"tslib\";\r\nimport { env } from '../env';\r\nexport function fetchOrThrow(url, init) {\r\n    return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n        var fetch, res;\r\n        return tslib_1.__generator(this, function (_a) {\r\n            switch (_a.label) {\r\n                case 0:\r\n                    fetch = env.getEnv().fetch;\r\n                    return [4 /*yield*/, fetch(url, init)];\r\n                case 1:\r\n                    res = _a.sent();\r\n                    if (!(res.status < 400)) {\r\n                        throw new Error(\"failed to fetch: (\" + res.status + \") \" + res.statusText + \", from url: \" + res.url);\r\n                    }\r\n                    return [2 /*return*/, res];\r\n            }\r\n        });\r\n    });\r\n}\r\n//# sourceMappingURL=fetchOrThrow.js.map","import * as tslib_1 from \"tslib\";\r\nimport { bufferToImage } from './bufferToImage';\r\nimport { fetchOrThrow } from './fetchOrThrow';\r\nexport function fetchImage(uri) {\r\n    return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n        var res, blob;\r\n        return tslib_1.__generator(this, function (_a) {\r\n            switch (_a.label) {\r\n                case 0: return [4 /*yield*/, fetchOrThrow(uri)];\r\n                case 1:\r\n                    res = _a.sent();\r\n                    return [4 /*yield*/, (res).blob()];\r\n                case 2:\r\n                    blob = _a.sent();\r\n                    if (!blob.type.startsWith('image/')) {\r\n                        throw new Error(\"fetchImage - expected blob type to be of type image/*, instead have: \" + blob.type + \", for url: \" + res.url);\r\n                    }\r\n                    return [2 /*return*/, bufferToImage(blob)];\r\n            }\r\n        });\r\n    });\r\n}\r\n//# sourceMappingURL=fetchImage.js.map","import * as tslib_1 from \"tslib\";\r\nimport { fetchOrThrow } from './fetchOrThrow';\r\nexport function fetchJson(uri) {\r\n    return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n        return tslib_1.__generator(this, function (_a) {\r\n            switch (_a.label) {\r\n                case 0: return [4 /*yield*/, fetchOrThrow(uri)];\r\n                case 1: return [2 /*return*/, (_a.sent()).json()];\r\n            }\r\n        });\r\n    });\r\n}\r\n//# sourceMappingURL=fetchJson.js.map","import * as tslib_1 from \"tslib\";\r\nimport { fetchOrThrow } from './fetchOrThrow';\r\nexport function fetchNetWeights(uri) {\r\n    return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n        var _a;\r\n        return tslib_1.__generator(this, function (_b) {\r\n            switch (_b.label) {\r\n                case 0:\r\n                    _a = Float32Array.bind;\r\n                    return [4 /*yield*/, fetchOrThrow(uri)];\r\n                case 1: return [4 /*yield*/, (_b.sent()).arrayBuffer()];\r\n                case 2: return [2 /*return*/, new (_a.apply(Float32Array, [void 0, _b.sent()]))()];\r\n            }\r\n        });\r\n    });\r\n}\r\n//# sourceMappingURL=fetchNetWeights.js.map","import * as tslib_1 from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { env } from '../env';\r\nimport { isTensor4D } from '../utils';\r\nexport function imageTensorToCanvas(imgTensor, canvas) {\r\n    return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n        var targetCanvas, _a, height, width, numChannels, imgTensor3D;\r\n        return tslib_1.__generator(this, function (_b) {\r\n            switch (_b.label) {\r\n                case 0:\r\n                    targetCanvas = canvas || env.getEnv().createCanvasElement();\r\n                    _a = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0), height = _a[0], width = _a[1], numChannels = _a[2];\r\n                    imgTensor3D = tf.tidy(function () { return imgTensor.as3D(height, width, numChannels).toInt(); });\r\n                    return [4 /*yield*/, tf.toPixels(imgTensor3D, targetCanvas)];\r\n                case 1:\r\n                    _b.sent();\r\n                    imgTensor3D.dispose();\r\n                    return [2 /*return*/, targetCanvas];\r\n            }\r\n        });\r\n    });\r\n}\r\n//# sourceMappingURL=imageTensorToCanvas.js.map","import { env } from '../env';\r\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\r\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\r\nimport { getMediaDimensions } from './getMediaDimensions';\r\nexport function imageToSquare(input, inputSize, centerImage) {\r\n    if (centerImage === void 0) { centerImage = false; }\r\n    var _a = env.getEnv(), Image = _a.Image, Canvas = _a.Canvas;\r\n    if (!(input instanceof Image || input instanceof Canvas)) {\r\n        throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');\r\n    }\r\n    var dims = getMediaDimensions(input);\r\n    var scale = inputSize / Math.max(dims.height, dims.width);\r\n    var width = scale * dims.width;\r\n    var height = scale * dims.height;\r\n    var targetCanvas = createCanvas({ width: inputSize, height: inputSize });\r\n    var inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);\r\n    var offset = Math.abs(width - height) / 2;\r\n    var dx = centerImage && width < height ? offset : 0;\r\n    var dy = centerImage && height < width ? offset : 0;\r\n    getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);\r\n    return targetCanvas;\r\n}\r\n//# sourceMappingURL=imageToSquare.js.map","import { env } from '../env';\r\nexport function isMediaElement(input) {\r\n    var _a = env.getEnv(), Image = _a.Image, Canvas = _a.Canvas, Video = _a.Video;\r\n    return input instanceof Image\r\n        || input instanceof Canvas\r\n        || input instanceof Video;\r\n}\r\n//# sourceMappingURL=isMediaElement.js.map","import * as tslib_1 from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { getModelUris } from '../common/getModelUris';\r\nimport { fetchJson } from './fetchJson';\r\nexport function loadWeightMap(uri, defaultModelName) {\r\n    return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n        var _a, manifestUri, modelBaseUri, manifest;\r\n        return tslib_1.__generator(this, function (_b) {\r\n            switch (_b.label) {\r\n                case 0:\r\n                    _a = getModelUris(uri, defaultModelName), manifestUri = _a.manifestUri, modelBaseUri = _a.modelBaseUri;\r\n                    return [4 /*yield*/, fetchJson(manifestUri)];\r\n                case 1:\r\n                    manifest = _b.sent();\r\n                    return [2 /*return*/, tf.io.loadWeights(manifest, modelBaseUri)];\r\n            }\r\n        });\r\n    });\r\n}\r\n//# sourceMappingURL=loadWeightMap.js.map","import * as tf from '@tensorflow/tfjs-core';\r\n/**\r\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\r\n *\r\n * @param imgTensor The image tensor.\r\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\r\n * both sides of the minor dimension oof the image.\r\n * @returns The padded tensor with width === height.\r\n */\r\nexport function padToSquare(imgTensor, isCenterImage) {\r\n    if (isCenterImage === void 0) { isCenterImage = false; }\r\n    return tf.tidy(function () {\r\n        var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];\r\n        if (height === width) {\r\n            return imgTensor;\r\n        }\r\n        var dimDiff = Math.abs(height - width);\r\n        var paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));\r\n        var paddingAxis = height > width ? 2 : 1;\r\n        var createPaddingTensor = function (paddingAmount) {\r\n            var paddingTensorShape = imgTensor.shape.slice();\r\n            paddingTensorShape[paddingAxis] = paddingAmount;\r\n            return tf.fill(paddingTensorShape, 0);\r\n        };\r\n        var paddingTensorAppend = createPaddingTensor(paddingAmount);\r\n        var remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];\r\n        var paddingTensorPrepend = isCenterImage && remainingPaddingAmount\r\n            ? createPaddingTensor(remainingPaddingAmount)\r\n            : null;\r\n        var tensorsToStack = [\r\n            paddingTensorPrepend,\r\n            imgTensor,\r\n            paddingTensorAppend\r\n        ]\r\n            .filter(function (t) { return !!t; })\r\n            .map(function (t) { return t.toFloat(); });\r\n        return tf.concat(tensorsToStack, paddingAxis);\r\n    });\r\n}\r\n//# sourceMappingURL=padToSquare.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { env } from '../env';\r\nimport { padToSquare } from '../ops/padToSquare';\r\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\r\nimport { createCanvasFromMedia } from './createCanvas';\r\nimport { imageToSquare } from './imageToSquare';\r\nvar NetInput = /** @class */ (function () {\r\n    function NetInput(inputs, treatAsBatchInput) {\r\n        if (treatAsBatchInput === void 0) { treatAsBatchInput = false; }\r\n        var _this = this;\r\n        this._imageTensors = [];\r\n        this._canvases = [];\r\n        this._treatAsBatchInput = false;\r\n        this._inputDimensions = [];\r\n        if (!Array.isArray(inputs)) {\r\n            throw new Error(\"NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have \" + inputs);\r\n        }\r\n        this._treatAsBatchInput = treatAsBatchInput;\r\n        this._batchSize = inputs.length;\r\n        inputs.forEach(function (input, idx) {\r\n            if (isTensor3D(input)) {\r\n                _this._imageTensors[idx] = input;\r\n                _this._inputDimensions[idx] = input.shape;\r\n                return;\r\n            }\r\n            if (isTensor4D(input)) {\r\n                var batchSize = input.shape[0];\r\n                if (batchSize !== 1) {\r\n                    throw new Error(\"NetInput - tf.Tensor4D with batchSize \" + batchSize + \" passed, but not supported in input array\");\r\n                }\r\n                _this._imageTensors[idx] = input;\r\n                _this._inputDimensions[idx] = input.shape.slice(1);\r\n                return;\r\n            }\r\n            var canvas = input instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\r\n            _this._canvases[idx] = canvas;\r\n            _this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\r\n        });\r\n    }\r\n    Object.defineProperty(NetInput.prototype, \"imageTensors\", {\r\n        get: function () {\r\n            return this._imageTensors;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(NetInput.prototype, \"canvases\", {\r\n        get: function () {\r\n            return this._canvases;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(NetInput.prototype, \"isBatchInput\", {\r\n        get: function () {\r\n            return this.batchSize > 1 || this._treatAsBatchInput;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(NetInput.prototype, \"batchSize\", {\r\n        get: function () {\r\n            return this._batchSize;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(NetInput.prototype, \"inputDimensions\", {\r\n        get: function () {\r\n            return this._inputDimensions;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(NetInput.prototype, \"inputSize\", {\r\n        get: function () {\r\n            return this._inputSize;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(NetInput.prototype, \"reshapedInputDimensions\", {\r\n        get: function () {\r\n            var _this = this;\r\n            return range(this.batchSize, 0, 1).map(function (_, batchIdx) { return _this.getReshapedInputDimensions(batchIdx); });\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    NetInput.prototype.getInput = function (batchIdx) {\r\n        return this.canvases[batchIdx] || this.imageTensors[batchIdx];\r\n    };\r\n    NetInput.prototype.getInputDimensions = function (batchIdx) {\r\n        return this._inputDimensions[batchIdx];\r\n    };\r\n    NetInput.prototype.getInputHeight = function (batchIdx) {\r\n        return this._inputDimensions[batchIdx][0];\r\n    };\r\n    NetInput.prototype.getInputWidth = function (batchIdx) {\r\n        return this._inputDimensions[batchIdx][1];\r\n    };\r\n    NetInput.prototype.getReshapedInputDimensions = function (batchIdx) {\r\n        if (typeof this.inputSize !== 'number') {\r\n            throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');\r\n        }\r\n        var width = this.getInputWidth(batchIdx);\r\n        var height = this.getInputHeight(batchIdx);\r\n        return computeReshapedDimensions({ width: width, height: height }, this.inputSize);\r\n    };\r\n    /**\r\n     * Create a batch tensor from all input canvases and tensors\r\n     * with size [batchSize, inputSize, inputSize, 3].\r\n     *\r\n     * @param inputSize Height and width of the tensor.\r\n     * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\r\n     * both sides of the minor dimension oof the image.\r\n     * @returns The batch tensor.\r\n     */\r\n    NetInput.prototype.toBatchTensor = function (inputSize, isCenterInputs) {\r\n        var _this = this;\r\n        if (isCenterInputs === void 0) { isCenterInputs = true; }\r\n        this._inputSize = inputSize;\r\n        return tf.tidy(function () {\r\n            var inputTensors = range(_this.batchSize, 0, 1).map(function (batchIdx) {\r\n                var input = _this.getInput(batchIdx);\r\n                if (input instanceof tf.Tensor) {\r\n                    var imgTensor = isTensor4D(input) ? input : input.expandDims();\r\n                    imgTensor = padToSquare(imgTensor, isCenterInputs);\r\n                    if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\r\n                        imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize]);\r\n                    }\r\n                    return imgTensor.as3D(inputSize, inputSize, 3);\r\n                }\r\n                if (input instanceof env.getEnv().Canvas) {\r\n                    return tf.fromPixels(imageToSquare(input, inputSize, isCenterInputs));\r\n                }\r\n                throw new Error(\"toBatchTensor - at batchIdx \" + batchIdx + \", expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have \" + input);\r\n            });\r\n            var batchTensor = tf.stack(inputTensors.map(function (t) { return t.toFloat(); })).as4D(_this.batchSize, inputSize, inputSize, 3);\r\n            return batchTensor;\r\n        });\r\n    };\r\n    return NetInput;\r\n}());\r\nexport { NetInput };\r\n//# sourceMappingURL=NetInput.js.map","import * as tslib_1 from \"tslib\";\r\nimport { isTensor3D, isTensor4D } from '../utils';\r\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\r\nimport { isMediaElement } from './isMediaElement';\r\nimport { NetInput } from './NetInput';\r\nimport { resolveInput } from './resolveInput';\r\n/**\r\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\r\n * to be finished loading.\r\n *\r\n * @param input The input, which can be a media element or an array of different media elements.\r\n * @returns A NetInput instance, which can be passed into one of the neural networks.\r\n */\r\nexport function toNetInput(inputs) {\r\n    return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n        var inputArgArray, getIdxHint, inputArray;\r\n        return tslib_1.__generator(this, function (_a) {\r\n            switch (_a.label) {\r\n                case 0:\r\n                    if (inputs instanceof NetInput) {\r\n                        return [2 /*return*/, inputs];\r\n                    }\r\n                    inputArgArray = Array.isArray(inputs)\r\n                        ? inputs\r\n                        : [inputs];\r\n                    if (!inputArgArray.length) {\r\n                        throw new Error('toNetInput - empty array passed as input');\r\n                    }\r\n                    getIdxHint = function (idx) { return Array.isArray(inputs) ? \" at input index \" + idx + \":\" : ''; };\r\n                    inputArray = inputArgArray.map(resolveInput);\r\n                    inputArray.forEach(function (input, i) {\r\n                        if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\r\n                            if (typeof inputArgArray[i] === 'string') {\r\n                                throw new Error(\"toNetInput -\" + getIdxHint(i) + \" string passed, but could not resolve HTMLElement for element id \" + inputArgArray[i]);\r\n                            }\r\n                            throw new Error(\"toNetInput -\" + getIdxHint(i) + \" expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id\");\r\n                        }\r\n                        if (isTensor4D(input)) {\r\n                            // if tf.Tensor4D is passed in the input array, the batch size has to be 1\r\n                            var batchSize = input.shape[0];\r\n                            if (batchSize !== 1) {\r\n                                throw new Error(\"toNetInput -\" + getIdxHint(i) + \" tf.Tensor4D with batchSize \" + batchSize + \" passed, but not supported in input array\");\r\n                            }\r\n                        }\r\n                    });\r\n                    // wait for all media elements being loaded\r\n                    return [4 /*yield*/, Promise.all(inputArray.map(function (input) { return isMediaElement(input) && awaitMediaLoaded(input); }))];\r\n                case 1:\r\n                    // wait for all media elements being loaded\r\n                    _a.sent();\r\n                    return [2 /*return*/, new NetInput(inputArray, Array.isArray(inputs))];\r\n            }\r\n        });\r\n    });\r\n}\r\n//# sourceMappingURL=toNetInput.js.map","export function iou(box1, box2, isIOU) {\r\n    if (isIOU === void 0) { isIOU = true; }\r\n    var width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));\r\n    var height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));\r\n    var interSection = width * height;\r\n    return isIOU\r\n        ? interSection / (box1.area + box2.area - interSection)\r\n        : interSection / Math.min(box1.area, box2.area);\r\n}\r\n//# sourceMappingURL=iou.js.map","import { iou } from './iou';\r\nexport function nonMaxSuppression(boxes, scores, iouThreshold, isIOU) {\r\n    if (isIOU === void 0) { isIOU = true; }\r\n    var indicesSortedByScore = scores\r\n        .map(function (score, boxIndex) { return ({ score: score, boxIndex: boxIndex }); })\r\n        .sort(function (c1, c2) { return c1.score - c2.score; })\r\n        .map(function (c) { return c.boxIndex; });\r\n    var pick = [];\r\n    var _loop_1 = function () {\r\n        var curr = indicesSortedByScore.pop();\r\n        pick.push(curr);\r\n        var indices = indicesSortedByScore;\r\n        var outputs = [];\r\n        for (var i = 0; i < indices.length; i++) {\r\n            var idx = indices[i];\r\n            var currBox = boxes[curr];\r\n            var idxBox = boxes[idx];\r\n            outputs.push(iou(currBox, idxBox, isIOU));\r\n        }\r\n        indicesSortedByScore = indicesSortedByScore.filter(function (_, j) { return outputs[j] <= iouThreshold; });\r\n    };\r\n    while (indicesSortedByScore.length > 0) {\r\n        _loop_1();\r\n    }\r\n    return pick;\r\n}\r\n//# sourceMappingURL=nonMaxSuppression.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nexport function normalize(x, meanRgb) {\r\n    return tf.tidy(function () {\r\n        var r = meanRgb[0], g = meanRgb[1], b = meanRgb[2];\r\n        var avg_r = tf.fill(x.shape.slice(0, 3).concat([1]), r);\r\n        var avg_g = tf.fill(x.shape.slice(0, 3).concat([1]), g);\r\n        var avg_b = tf.fill(x.shape.slice(0, 3).concat([1]), b);\r\n        var avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);\r\n        return tf.sub(x, avg_rgb);\r\n    });\r\n}\r\n//# sourceMappingURL=normalize.js.map","export function shuffleArray(inputArray) {\r\n    var array = inputArray.slice();\r\n    for (var i = array.length - 1; i > 0; i--) {\r\n        var j = Math.floor(Math.random() * (i + 1));\r\n        var x = array[i];\r\n        array[i] = array[j];\r\n        array[j] = x;\r\n    }\r\n    return array;\r\n}\r\n//# sourceMappingURL=shuffleArray.js.map","export * from './iou';\r\nexport * from './nonMaxSuppression';\r\nexport * from './normalize';\r\nexport * from './padToSquare';\r\nexport * from './shuffleArray';\r\nexport function sigmoid(x) {\r\n    return 1 / (1 + Math.exp(-x));\r\n}\r\nexport function inverseSigmoid(x) {\r\n    return Math.log(x / (1 - x));\r\n}\r\n//# sourceMappingURL=index.js.map","import * as tslib_1 from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { getModelUris } from './common/getModelUris';\r\nimport { loadWeightMap } from './dom';\r\nimport { env } from './env';\r\nvar NeuralNetwork = /** @class */ (function () {\r\n    function NeuralNetwork(_name) {\r\n        this._name = _name;\r\n        this._params = undefined;\r\n        this._paramMappings = [];\r\n    }\r\n    Object.defineProperty(NeuralNetwork.prototype, \"params\", {\r\n        get: function () { return this._params; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(NeuralNetwork.prototype, \"paramMappings\", {\r\n        get: function () { return this._paramMappings; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(NeuralNetwork.prototype, \"isLoaded\", {\r\n        get: function () { return !!this.params; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    NeuralNetwork.prototype.getParamFromPath = function (paramPath) {\r\n        var _a = this.traversePropertyPath(paramPath), obj = _a.obj, objProp = _a.objProp;\r\n        return obj[objProp];\r\n    };\r\n    NeuralNetwork.prototype.reassignParamFromPath = function (paramPath, tensor) {\r\n        var _a = this.traversePropertyPath(paramPath), obj = _a.obj, objProp = _a.objProp;\r\n        obj[objProp].dispose();\r\n        obj[objProp] = tensor;\r\n    };\r\n    NeuralNetwork.prototype.getParamList = function () {\r\n        var _this = this;\r\n        return this._paramMappings.map(function (_a) {\r\n            var paramPath = _a.paramPath;\r\n            return ({\r\n                path: paramPath,\r\n                tensor: _this.getParamFromPath(paramPath)\r\n            });\r\n        });\r\n    };\r\n    NeuralNetwork.prototype.getTrainableParams = function () {\r\n        return this.getParamList().filter(function (param) { return param.tensor instanceof tf.Variable; });\r\n    };\r\n    NeuralNetwork.prototype.getFrozenParams = function () {\r\n        return this.getParamList().filter(function (param) { return !(param.tensor instanceof tf.Variable); });\r\n    };\r\n    NeuralNetwork.prototype.variable = function () {\r\n        var _this = this;\r\n        this.getFrozenParams().forEach(function (_a) {\r\n            var path = _a.path, tensor = _a.tensor;\r\n            _this.reassignParamFromPath(path, tensor.variable());\r\n        });\r\n    };\r\n    NeuralNetwork.prototype.freeze = function () {\r\n        var _this = this;\r\n        this.getTrainableParams().forEach(function (_a) {\r\n            var path = _a.path, variable = _a.tensor;\r\n            var tensor = tf.tensor(variable.dataSync());\r\n            variable.dispose();\r\n            _this.reassignParamFromPath(path, tensor);\r\n        });\r\n    };\r\n    NeuralNetwork.prototype.dispose = function (throwOnRedispose) {\r\n        if (throwOnRedispose === void 0) { throwOnRedispose = true; }\r\n        this.getParamList().forEach(function (param) {\r\n            if (throwOnRedispose && param.tensor.isDisposed) {\r\n                throw new Error(\"param tensor has already been disposed for path \" + param.path);\r\n            }\r\n            param.tensor.dispose();\r\n        });\r\n        this._params = undefined;\r\n    };\r\n    NeuralNetwork.prototype.serializeParams = function () {\r\n        return new Float32Array(this.getParamList()\r\n            .map(function (_a) {\r\n            var tensor = _a.tensor;\r\n            return Array.from(tensor.dataSync());\r\n        })\r\n            .reduce(function (flat, arr) { return flat.concat(arr); }));\r\n    };\r\n    NeuralNetwork.prototype.load = function (weightsOrUrl) {\r\n        return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n            return tslib_1.__generator(this, function (_a) {\r\n                switch (_a.label) {\r\n                    case 0:\r\n                        if (weightsOrUrl instanceof Float32Array) {\r\n                            this.extractWeights(weightsOrUrl);\r\n                            return [2 /*return*/];\r\n                        }\r\n                        return [4 /*yield*/, this.loadFromUri(weightsOrUrl)];\r\n                    case 1:\r\n                        _a.sent();\r\n                        return [2 /*return*/];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    NeuralNetwork.prototype.loadFromUri = function (uri) {\r\n        return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n            var weightMap;\r\n            return tslib_1.__generator(this, function (_a) {\r\n                switch (_a.label) {\r\n                    case 0:\r\n                        if (uri && typeof uri !== 'string') {\r\n                            throw new Error(this._name + \".loadFromUri - expected model uri\");\r\n                        }\r\n                        return [4 /*yield*/, loadWeightMap(uri, this.getDefaultModelName())];\r\n                    case 1:\r\n                        weightMap = _a.sent();\r\n                        this.loadFromWeightMap(weightMap);\r\n                        return [2 /*return*/];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    NeuralNetwork.prototype.loadFromDisk = function (filePath) {\r\n        return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n            var readFile, _a, manifestUri, modelBaseUri, fetchWeightsFromDisk, loadWeights, manifest, _b, _c, weightMap;\r\n            return tslib_1.__generator(this, function (_d) {\r\n                switch (_d.label) {\r\n                    case 0:\r\n                        if (filePath && typeof filePath !== 'string') {\r\n                            throw new Error(this._name + \".loadFromDisk - expected model file path\");\r\n                        }\r\n                        readFile = env.getEnv().readFile;\r\n                        _a = getModelUris(filePath, this.getDefaultModelName()), manifestUri = _a.manifestUri, modelBaseUri = _a.modelBaseUri;\r\n                        fetchWeightsFromDisk = function (filePaths) { return Promise.all(filePaths.map(function (filePath) { return readFile(filePath).then(function (buf) { return buf.buffer; }); })); };\r\n                        loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk);\r\n                        _c = (_b = JSON).parse;\r\n                        return [4 /*yield*/, readFile(manifestUri)];\r\n                    case 1:\r\n                        manifest = _c.apply(_b, [(_d.sent()).toString()]);\r\n                        return [4 /*yield*/, loadWeights(manifest, modelBaseUri)];\r\n                    case 2:\r\n                        weightMap = _d.sent();\r\n                        this.loadFromWeightMap(weightMap);\r\n                        return [2 /*return*/];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    NeuralNetwork.prototype.loadFromWeightMap = function (weightMap) {\r\n        var _a = this.extractParamsFromWeigthMap(weightMap), paramMappings = _a.paramMappings, params = _a.params;\r\n        this._paramMappings = paramMappings;\r\n        this._params = params;\r\n    };\r\n    NeuralNetwork.prototype.extractWeights = function (weights) {\r\n        var _a = this.extractParams(weights), paramMappings = _a.paramMappings, params = _a.params;\r\n        this._paramMappings = paramMappings;\r\n        this._params = params;\r\n    };\r\n    NeuralNetwork.prototype.traversePropertyPath = function (paramPath) {\r\n        if (!this.params) {\r\n            throw new Error(\"traversePropertyPath - model has no loaded params\");\r\n        }\r\n        var result = paramPath.split('/').reduce(function (res, objProp) {\r\n            if (!res.nextObj.hasOwnProperty(objProp)) {\r\n                throw new Error(\"traversePropertyPath - object does not have property \" + objProp + \", for path \" + paramPath);\r\n            }\r\n            return { obj: res.nextObj, objProp: objProp, nextObj: res.nextObj[objProp] };\r\n        }, { nextObj: this.params });\r\n        var obj = result.obj, objProp = result.objProp;\r\n        if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\r\n            throw new Error(\"traversePropertyPath - parameter is not a tensor, for path \" + paramPath);\r\n        }\r\n        return { obj: obj, objProp: objProp };\r\n    };\r\n    return NeuralNetwork;\r\n}());\r\nexport { NeuralNetwork };\r\n//# sourceMappingURL=NeuralNetwork.js.map","import { Box, IDimensions, ObjectDetection, Rect } from 'tfjs-image-recognition-base';\r\n\r\nexport interface IFaceDetecion {\r\n  score: number\r\n  box: Box\r\n}\r\n\r\nexport class FaceDetection extends ObjectDetection implements IFaceDetecion {\r\n  constructor(\r\n    score: number,\r\n    relativeBox: Rect,\r\n    imageDims: IDimensions\r\n  ) {\r\n    super(score, score, '', relativeBox, imageDims)\r\n  }\r\n\r\n  public forSize(width: number, height: number): FaceDetection {\r\n    return super.forSize(width, height)\r\n  }\r\n}","import { Dimensions, getCenterPoint, IDimensions, Point, Rect } from 'tfjs-image-recognition-base';\r\n\r\nimport { FaceDetection } from './FaceDetection';\r\n\r\n// face alignment constants\r\nconst relX = 0.5\r\nconst relY = 0.43\r\nconst relScale = 0.45\r\n\r\nexport interface IFaceLandmarks {\r\n  positions: Point[]\r\n  shift: Point\r\n}\r\n\r\nexport class FaceLandmarks implements IFaceLandmarks {\r\n  protected _shift: Point\r\n  protected _positions: Point[]\r\n  protected _imgDims: Dimensions\r\n\r\n  constructor(\r\n    relativeFaceLandmarkPositions: Point[],\r\n    imgDims: IDimensions,\r\n    shift: Point = new Point(0, 0)\r\n  ) {\r\n    const { width, height } = imgDims\r\n    this._imgDims = new Dimensions(width, height)\r\n    this._shift = shift\r\n    this._positions = relativeFaceLandmarkPositions.map(\r\n      pt => pt.mul(new Point(width, height)).add(shift)\r\n    )\r\n  }\r\n\r\n  public get shift(): Point { return new Point(this._shift.x, this._shift.y) }\r\n  public get imageWidth(): number { return this._imgDims.width }\r\n  public get imageHeight(): number { return this._imgDims.height }\r\n  public get positions(): Point[] { return this._positions }\r\n  public get relativePositions(): Point[] {\r\n    return this._positions.map(\r\n      pt => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight))\r\n    )\r\n  }\r\n\r\n  public forSize<T extends FaceLandmarks>(width: number, height: number): T {\r\n    return new (this.constructor as any)(\r\n      this.relativePositions,\r\n      { width, height }\r\n    )\r\n  }\r\n\r\n  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {\r\n    return new (this.constructor as any)(\r\n      this.relativePositions,\r\n      this._imgDims,\r\n      new Point(x, y)\r\n    )\r\n  }\r\n\r\n  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {\r\n    return this.shiftBy(pt.x, pt.y)\r\n  }\r\n\r\n  /**\r\n   * Aligns the face landmarks after face detection from the relative positions of the faces\r\n   * bounding box, or it's current shift. This function should be used to align the face images\r\n   * after face detection has been performed, before they are passed to the face recognition net.\r\n   * This will make the computed face descriptor more accurate.\r\n   *\r\n   * @param detection (optional) The bounding box of the face or the face detection result. If\r\n   * no argument was passed the position of the face landmarks are assumed to be relative to\r\n   * it's current shift.\r\n   * @returns The bounding box of the aligned face.\r\n   */\r\n  public align(\r\n    detection?: FaceDetection | Rect\r\n  ): Rect {\r\n    if (detection) {\r\n      const box = detection instanceof FaceDetection\r\n        ? detection.box.floor()\r\n        : detection\r\n\r\n      return this.shiftBy(box.x, box.y).align()\r\n    }\r\n\r\n    const centers = this.getRefPointsForAlignment()\r\n\r\n    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers\r\n    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude()\r\n    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2\r\n\r\n    const size = Math.floor(eyeToMouthDist / relScale)\r\n\r\n    const refPoint = getCenterPoint(centers)\r\n    // TODO: pad in case rectangle is out of image bounds\r\n    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)))\r\n    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)))\r\n\r\n    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y))\r\n  }\r\n\r\n  protected getRefPointsForAlignment(): Point[] {\r\n    throw new Error('getRefPointsForAlignment not implemented by base class')\r\n  }\r\n}","import { getCenterPoint, Point } from 'tfjs-image-recognition-base';\r\n\r\nimport { FaceLandmarks } from './FaceLandmarks';\r\n\r\nexport class FaceLandmarks5 extends FaceLandmarks {\r\n\r\n  protected getRefPointsForAlignment(): Point[] {\r\n    const pts = this.positions\r\n    return [\r\n      pts[0],\r\n      pts[1],\r\n      getCenterPoint([pts[3], pts[4]])\r\n    ]\r\n  }\r\n}","import { getCenterPoint, Point } from 'tfjs-image-recognition-base';\r\n\r\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\r\n\r\nexport class FaceLandmarks68 extends FaceLandmarks {\r\n  public getJawOutline(): Point[] {\r\n    return this.positions.slice(0, 17)\r\n  }\r\n\r\n  public getLeftEyeBrow(): Point[] {\r\n    return this.positions.slice(17, 22)\r\n  }\r\n\r\n  public getRightEyeBrow(): Point[] {\r\n    return this.positions.slice(22, 27)\r\n  }\r\n\r\n  public getNose(): Point[] {\r\n    return this.positions.slice(27, 36)\r\n  }\r\n\r\n  public getLeftEye(): Point[] {\r\n    return this.positions.slice(36, 42)\r\n  }\r\n\r\n  public getRightEye(): Point[] {\r\n    return this.positions.slice(42, 48)\r\n  }\r\n\r\n  public getMouth(): Point[] {\r\n    return this.positions.slice(48, 68)\r\n  }\r\n\r\n  protected getRefPointsForAlignment(): Point[] {\r\n    return [\r\n      this.getLeftEye(),\r\n      this.getRightEye(),\r\n      this.getMouth()\r\n    ].map(getCenterPoint)\r\n  }\r\n}","import { round } from 'tfjs-image-recognition-base';\r\n\r\nexport interface IFaceMatch {\r\n  label: string\r\n  distance: number\r\n}\r\n\r\nexport class FaceMatch implements IFaceMatch {\r\n  private _label: string\r\n  private _distance: number\r\n\r\n  constructor(label: string, distance: number) {\r\n    this._label = label\r\n    this._distance = distance\r\n  }\r\n\r\n  public get label(): string { return this._label }\r\n  public get distance(): number { return this._distance }\r\n\r\n  public toString(withDistance: boolean = true): string {\r\n    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`\r\n  }\r\n}","export class LabeledFaceDescriptors {\r\n  private _label: string\r\n  private _descriptors: Float32Array[]\r\n\r\n  constructor(label: string, descriptors: Float32Array[]) {\r\n    if (!(typeof label === 'string')) {\r\n      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string')\r\n    }\r\n\r\n    if (!Array.isArray(descriptors) || descriptors.some(desc => !(desc instanceof Float32Array))) {\r\n      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array')\r\n    }\r\n\r\n    this._label = label\r\n    this._descriptors = descriptors\r\n  }\r\n\r\n  public get label(): string { return this._label }\r\n  public get descriptors(): Float32Array[] { return this._descriptors }\r\n}","import { Point } from 'tfjs-image-recognition-base';\r\n\r\nexport function drawContour(\r\n  ctx: CanvasRenderingContext2D,\r\n  points: Point[],\r\n  isClosed: boolean = false\r\n) {\r\n  ctx.beginPath()\r\n\r\n  points.slice(1).forEach(({ x, y }, prevIdx) => {\r\n    const from = points[prevIdx]\r\n    ctx.moveTo(from.x, from.y)\r\n    ctx.lineTo(x, y)\r\n  })\r\n\r\n  if (isClosed) {\r\n    const from = points[points.length - 1]\r\n    const to = points[0]\r\n    if (!from || !to) {\r\n      return\r\n    }\r\n\r\n    ctx.moveTo(from.x, from.y)\r\n    ctx.lineTo(to.x, to.y)\r\n  }\r\n\r\n  ctx.stroke()\r\n}","import { env, getContext2dOrThrow, getDefaultDrawOptions, resolveInput } from 'tfjs-image-recognition-base';\r\n\r\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\r\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\r\nimport { drawContour } from './drawContour';\r\nimport { DrawLandmarksOptions } from './types';\r\n\r\nexport function drawLandmarks(\r\n  canvasArg: string | HTMLCanvasElement,\r\n  faceLandmarks: FaceLandmarks | FaceLandmarks[],\r\n  options?: DrawLandmarksOptions\r\n) {\r\n  const canvas = resolveInput(canvasArg)\r\n  if (!(canvas instanceof env.getEnv().Canvas)) {\r\n    throw new Error('drawLandmarks - expected canvas to be of type: HTMLCanvasElement')\r\n  }\r\n\r\n  const drawOptions = Object.assign(\r\n    getDefaultDrawOptions(options),\r\n    (options || {})\r\n  )\r\n\r\n  const { drawLines } = Object.assign({ drawLines: false }, (options || {}))\r\n\r\n  const ctx = getContext2dOrThrow(canvas)\r\n  const { lineWidth, color = 'blue' } = drawOptions\r\n\r\n  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]\r\n\r\n  faceLandmarksArray.forEach(landmarks => {\r\n    if (drawLines && landmarks instanceof FaceLandmarks68) {\r\n      ctx.strokeStyle = color\r\n      ctx.lineWidth = lineWidth\r\n      drawContour(ctx, landmarks.getJawOutline())\r\n      drawContour(ctx, landmarks.getLeftEyeBrow())\r\n      drawContour(ctx, landmarks.getRightEyeBrow())\r\n      drawContour(ctx, landmarks.getNose())\r\n      drawContour(ctx, landmarks.getLeftEye(), true)\r\n      drawContour(ctx, landmarks.getRightEye(), true)\r\n      drawContour(ctx, landmarks.getMouth(), true)\r\n      return\r\n    }\r\n\r\n    // else draw points\r\n    const ptOffset = lineWidth / 2\r\n    ctx.fillStyle = color\r\n    landmarks.positions.forEach(pt => ctx.fillRect(pt.x - ptOffset, pt.y - ptOffset, lineWidth, lineWidth))\r\n  })\r\n}","import { drawText, env, getContext2dOrThrow, getDefaultDrawOptions, resolveInput, round } from 'tfjs-image-recognition-base';\r\nimport { IRect } from 'tfjs-tiny-yolov2';\r\n\r\nimport { DrawFaceExpressionsInput, DrawFaceExpressionsOptions } from './types';\r\n\r\nexport function drawFaceExpressions(\r\n  canvasArg: string | HTMLCanvasElement,\r\n  faceExpressions: DrawFaceExpressionsInput | DrawFaceExpressionsInput[],\r\n  options?: DrawFaceExpressionsOptions\r\n) {\r\n  const canvas = resolveInput(canvasArg)\r\n  if (!(canvas instanceof env.getEnv().Canvas)) {\r\n    throw new Error('drawFaceExpressions - expected canvas to be of type: HTMLCanvasElement')\r\n  }\r\n\r\n  const drawOptions = Object.assign(\r\n    getDefaultDrawOptions(options),\r\n    (options || {})\r\n  )\r\n\r\n  const ctx = getContext2dOrThrow(canvas)\r\n  const {\r\n    primaryColor = 'red',\r\n    secondaryColor = 'blue',\r\n    primaryFontSize = 22,\r\n    secondaryFontSize = 16,\r\n    minConfidence = 0.2\r\n  } = drawOptions\r\n\r\n  const faceExpressionsArray = Array.isArray(faceExpressions)\r\n    ? faceExpressions\r\n    : [faceExpressions]\r\n\r\n  faceExpressionsArray.forEach(({ position, expressions }) => {\r\n    const { x, y } = position\r\n    const height = (position as IRect).height || 0\r\n    const sorted = expressions.sort((a, b) => b.probability - a.probability)\r\n    const resultsToDisplay = sorted.filter(expr => expr.probability > minConfidence)\r\n\r\n    let offset = (y + height + resultsToDisplay.length * primaryFontSize) > canvas.height\r\n      ? -(resultsToDisplay.length * primaryFontSize)\r\n      : 0\r\n    resultsToDisplay.forEach((expr, i) => {\r\n      const text = `${expr.expression} (${round(expr.probability)})`\r\n      drawText(\r\n        ctx,\r\n        x,\r\n        y + height + (i * primaryFontSize) + offset,\r\n        text,\r\n        {\r\n          textColor: i === 0 ? primaryColor : secondaryColor,\r\n          fontSize: i === 0 ? primaryFontSize : secondaryFontSize\r\n        }\r\n      )\r\n    })\r\n  })\r\n\r\n\r\n}","import {\r\n  createCanvas,\r\n  env,\r\n  getContext2dOrThrow,\r\n  imageTensorToCanvas,\r\n  Rect,\r\n  TNetInput,\r\n  toNetInput,\r\n} from 'tfjs-image-recognition-base';\r\n\r\nimport { FaceDetection } from '../classes/FaceDetection';\r\n\r\n/**\r\n * Extracts the image regions containing the detected faces.\r\n *\r\n * @param input The image that face detection has been performed on.\r\n * @param detections The face detection results or face bounding boxes for that image.\r\n * @returns The Canvases of the corresponding image region for each detected face.\r\n */\r\nexport async function extractFaces(\r\n  input: TNetInput,\r\n  detections: Array<FaceDetection | Rect>\r\n): Promise<HTMLCanvasElement[]> {\r\n\r\n  const { Canvas } = env.getEnv()\r\n\r\n  let canvas = input as HTMLCanvasElement\r\n\r\n  if (!(input instanceof Canvas)) {\r\n    const netInput = await toNetInput(input)\r\n\r\n    if (netInput.batchSize > 1) {\r\n      throw new Error('extractFaces - batchSize > 1 not supported')\r\n    }\r\n\r\n    const tensorOrCanvas = netInput.getInput(0)\r\n    canvas = tensorOrCanvas instanceof Canvas\r\n      ? tensorOrCanvas\r\n      : await imageTensorToCanvas(tensorOrCanvas)\r\n  }\r\n\r\n  const ctx = getContext2dOrThrow(canvas)\r\n  const boxes = detections.map(\r\n    det => det instanceof FaceDetection\r\n      ? det.forSize(canvas.width, canvas.height).box.floor()\r\n      : det\r\n  )\r\n    .map(box => box.clipAtImageBorders(canvas.width, canvas.height))\r\n\r\n  return boxes.map(({ x, y, width, height }) => {\r\n    const faceImg = createCanvas({ width, height })\r\n    getContext2dOrThrow(faceImg)\r\n      .putImageData(ctx.getImageData(x, y, width, height), 0, 0)\r\n    return faceImg\r\n  })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { isTensor4D, Rect, isTensor3D } from 'tfjs-image-recognition-base';\r\n\r\nimport { FaceDetection } from '../classes/FaceDetection';\r\n\r\n/**\r\n * Extracts the tensors of the image regions containing the detected faces.\r\n * Useful if you want to compute the face descriptors for the face images.\r\n * Using this method is faster then extracting a canvas for each face and\r\n * converting them to tensors individually.\r\n *\r\n * @param imageTensor The image tensor that face detection has been performed on.\r\n * @param detections The face detection results or face bounding boxes for that image.\r\n * @returns Tensors of the corresponding image region for each detected face.\r\n */\r\nexport async function extractFaceTensors(\r\n  imageTensor: tf.Tensor3D | tf.Tensor4D,\r\n  detections: Array<FaceDetection | Rect>\r\n): Promise<tf.Tensor3D[]> {\r\n\r\n  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {\r\n    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')\r\n  }\r\n\r\n  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {\r\n    throw new Error('extractFaceTensors - batchSize > 1 not supported')\r\n  }\r\n\r\n  return tf.tidy(() => {\r\n    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)\r\n\r\n    const boxes = detections.map(\r\n      det => det instanceof FaceDetection\r\n        ? det.forSize(imgWidth, imgHeight).box\r\n        : det\r\n    )\r\n      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))\r\n\r\n    const faceTensors = boxes.map(({ x, y, width, height }) =>\r\n      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])\r\n    )\r\n\r\n    return faceTensors\r\n  })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { SeparableConvParams } from 'tfjs-tiny-yolov2';\r\n\r\nexport function depthwiseSeparableConv(\r\n  x: tf.Tensor4D,\r\n  params: SeparableConvParams,\r\n  stride: [number, number]\r\n): tf.Tensor4D {\r\n  return tf.tidy(() => {\r\n    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same')\r\n    out = tf.add(out, params.bias)\r\n    return out\r\n  })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { ConvParams, SeparableConvParams } from 'tfjs-tiny-yolov2';\r\n\r\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\r\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\r\n\r\nexport function denseBlock3(\r\n  x: tf.Tensor4D,\r\n  denseBlockParams: DenseBlock3Params,\r\n  isFirstLayer: boolean = false\r\n): tf.Tensor4D {\r\n  return tf.tidy(() => {\r\n    const out1 = tf.relu(\r\n      isFirstLayer\r\n        ? tf.add(\r\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),\r\n          denseBlockParams.conv0.bias\r\n        )\r\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])\r\n    ) as tf.Tensor4D\r\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\r\n\r\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\r\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\r\n\r\n    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\r\n  })\r\n}\r\n\r\nexport function denseBlock4(\r\n  x: tf.Tensor4D,\r\n  denseBlockParams: DenseBlock4Params,\r\n  isFirstLayer: boolean = false,\r\n  isScaleDown: boolean = true\r\n): tf.Tensor4D {\r\n  return tf.tidy(() => {\r\n    const out1 = tf.relu(\r\n      isFirstLayer\r\n        ? tf.add(\r\n          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),\r\n          denseBlockParams.conv0.bias\r\n        )\r\n        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])\r\n    ) as tf.Tensor4D\r\n    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])\r\n\r\n    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D\r\n    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])\r\n\r\n    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D\r\n    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])\r\n\r\n    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D\r\n  })\r\n}\r\n","import * as tf from '@tensorflow/tfjs-core';\r\nexport function convLayer(x, params, padding, withRelu) {\r\n    if (padding === void 0) { padding = 'same'; }\r\n    if (withRelu === void 0) { withRelu = false; }\r\n    return tf.tidy(function () {\r\n        var out = tf.add(tf.conv2d(x, params.filters, [1, 1], padding), params.bias);\r\n        return withRelu ? tf.relu(out) : out;\r\n    });\r\n}\r\n//# sourceMappingURL=convLayer.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nexport function extractConvParamsFactory(extractWeights, paramMappings) {\r\n    return function (channelsIn, channelsOut, filterSize, mappedPrefix) {\r\n        var filters = tf.tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);\r\n        var bias = tf.tensor1d(extractWeights(channelsOut));\r\n        paramMappings.push({ paramPath: mappedPrefix + \"/filters\" }, { paramPath: mappedPrefix + \"/bias\" });\r\n        return { filters: filters, bias: bias };\r\n    };\r\n}\r\n//# sourceMappingURL=extractConvParamsFactory.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nexport function extractFCParamsFactory(extractWeights, paramMappings) {\r\n    return function (channelsIn, channelsOut, mappedPrefix) {\r\n        var fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);\r\n        var fc_bias = tf.tensor1d(extractWeights(channelsOut));\r\n        paramMappings.push({ paramPath: mappedPrefix + \"/weights\" }, { paramPath: mappedPrefix + \"/bias\" });\r\n        return {\r\n            weights: fc_weights,\r\n            bias: fc_bias\r\n        };\r\n    };\r\n}\r\n//# sourceMappingURL=extractFCParamsFactory.js.map","var SeparableConvParams = /** @class */ (function () {\r\n    function SeparableConvParams(depthwise_filter, pointwise_filter, bias) {\r\n        this.depthwise_filter = depthwise_filter;\r\n        this.pointwise_filter = pointwise_filter;\r\n        this.bias = bias;\r\n    }\r\n    return SeparableConvParams;\r\n}());\r\nexport { SeparableConvParams };\r\n//# sourceMappingURL=types.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { SeparableConvParams } from './types';\r\nexport function extractSeparableConvParamsFactory(extractWeights, paramMappings) {\r\n    return function (channelsIn, channelsOut, mappedPrefix) {\r\n        var depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);\r\n        var pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);\r\n        var bias = tf.tensor1d(extractWeights(channelsOut));\r\n        paramMappings.push({ paramPath: mappedPrefix + \"/depthwise_filter\" }, { paramPath: mappedPrefix + \"/pointwise_filter\" }, { paramPath: mappedPrefix + \"/bias\" });\r\n        return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);\r\n    };\r\n}\r\nexport function loadSeparableConvParamsFactory(extractWeightEntry) {\r\n    return function (prefix) {\r\n        var depthwise_filter = extractWeightEntry(prefix + \"/depthwise_filter\", 4);\r\n        var pointwise_filter = extractWeightEntry(prefix + \"/pointwise_filter\", 4);\r\n        var bias = extractWeightEntry(prefix + \"/bias\", 1);\r\n        return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);\r\n    };\r\n}\r\n//# sourceMappingURL=extractSeparableConvParamsFactory.js.map","var isNumber = function (arg) { return typeof arg === 'number'; };\r\nexport function validateConfig(config) {\r\n    if (!config) {\r\n        throw new Error(\"invalid config: \" + config);\r\n    }\r\n    if (typeof config.withSeparableConvs !== 'boolean') {\r\n        throw new Error(\"config.withSeparableConvs has to be a boolean, have: \" + config.withSeparableConvs);\r\n    }\r\n    if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\r\n        throw new Error(\"config.iouThreshold has to be a number between [0, 1], have: \" + config.iouThreshold);\r\n    }\r\n    if (!Array.isArray(config.classes)\r\n        || !config.classes.length\r\n        || !config.classes.every(function (c) { return typeof c === 'string'; })) {\r\n        throw new Error(\"config.classes has to be an array class names: string[], have: \" + JSON.stringify(config.classes));\r\n    }\r\n    if (!Array.isArray(config.anchors)\r\n        || !config.anchors.length\r\n        || !config.anchors.map(function (a) { return a || {}; }).every(function (a) { return isNumber(a.x) && isNumber(a.y); })) {\r\n        throw new Error(\"config.anchors has to be an array of { x: number, y: number }, have: \" + JSON.stringify(config.anchors));\r\n    }\r\n    if (config.meanRgb && (!Array.isArray(config.meanRgb)\r\n        || config.meanRgb.length !== 3\r\n        || !config.meanRgb.every(isNumber))) {\r\n        throw new Error(\"config.meanRgb has to be an array of shape [number, number, number], have: \" + JSON.stringify(config.meanRgb));\r\n    }\r\n}\r\nexport function validateTrainConfig(config) {\r\n    if (![config.noObjectScale, config.objectScale, config.coordScale, config.classScale].every(isNumber)) {\r\n        throw new Error(\"for training you have to specify noObjectScale, objectScale, coordScale, classScale parameters in your config.json file\");\r\n    }\r\n    return config;\r\n}\r\n//# sourceMappingURL=config.js.map","export var CELL_SIZE = 32;\r\nexport var DEFAULT_FILTER_SIZES = [\r\n    3, 16, 32, 64, 128, 256, 512, 1024, 1024\r\n];\r\n//# sourceMappingURL=const.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nexport function leaky(x) {\r\n    return tf.tidy(function () {\r\n        var min = tf.mul(x, tf.scalar(0.10000000149011612));\r\n        return tf.add(tf.relu(tf.sub(x, min)), min);\r\n        //return tf.maximum(x, min)\r\n    });\r\n}\r\n//# sourceMappingURL=leaky.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { leaky } from './leaky';\r\nexport function convWithBatchNorm(x, params) {\r\n    return tf.tidy(function () {\r\n        var out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);\r\n        out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid');\r\n        out = tf.sub(out, params.bn.sub);\r\n        out = tf.mul(out, params.bn.truediv);\r\n        out = tf.add(out, params.conv.bias);\r\n        return leaky(out);\r\n    });\r\n}\r\n//# sourceMappingURL=convWithBatchNorm.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { leaky } from './leaky';\r\nexport function depthwiseSeparableConv(x, params) {\r\n    return tf.tidy(function () {\r\n        var out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);\r\n        out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');\r\n        out = tf.add(out, params.bias);\r\n        return leaky(out);\r\n    });\r\n}\r\n//# sourceMappingURL=depthwiseSeparableConv.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { extractWeightsFactory } from 'tfjs-image-recognition-base';\r\nimport { extractConvParamsFactory } from '../common';\r\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\r\nfunction extractorsFactory(extractWeights, paramMappings) {\r\n    var extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);\r\n    function extractBatchNormParams(size, mappedPrefix) {\r\n        var sub = tf.tensor1d(extractWeights(size));\r\n        var truediv = tf.tensor1d(extractWeights(size));\r\n        paramMappings.push({ paramPath: mappedPrefix + \"/sub\" }, { paramPath: mappedPrefix + \"/truediv\" });\r\n        return { sub: sub, truediv: truediv };\r\n    }\r\n    function extractConvWithBatchNormParams(channelsIn, channelsOut, mappedPrefix) {\r\n        var conv = extractConvParams(channelsIn, channelsOut, 3, mappedPrefix + \"/conv\");\r\n        var bn = extractBatchNormParams(channelsOut, mappedPrefix + \"/bn\");\r\n        return { conv: conv, bn: bn };\r\n    }\r\n    var extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);\r\n    return {\r\n        extractConvParams: extractConvParams,\r\n        extractConvWithBatchNormParams: extractConvWithBatchNormParams,\r\n        extractSeparableConvParams: extractSeparableConvParams\r\n    };\r\n}\r\nexport function extractParams(weights, config, boxEncodingSize, filterSizes) {\r\n    var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;\r\n    var paramMappings = [];\r\n    var _b = extractorsFactory(extractWeights, paramMappings), extractConvParams = _b.extractConvParams, extractConvWithBatchNormParams = _b.extractConvWithBatchNormParams, extractSeparableConvParams = _b.extractSeparableConvParams;\r\n    var params;\r\n    if (config.withSeparableConvs) {\r\n        var s0 = filterSizes[0], s1 = filterSizes[1], s2 = filterSizes[2], s3 = filterSizes[3], s4 = filterSizes[4], s5 = filterSizes[5], s6 = filterSizes[6], s7 = filterSizes[7], s8 = filterSizes[8];\r\n        var conv0 = config.isFirstLayerConv2d\r\n            ? extractConvParams(s0, s1, 3, 'conv0')\r\n            : extractSeparableConvParams(s0, s1, 'conv0');\r\n        var conv1 = extractSeparableConvParams(s1, s2, 'conv1');\r\n        var conv2 = extractSeparableConvParams(s2, s3, 'conv2');\r\n        var conv3 = extractSeparableConvParams(s3, s4, 'conv3');\r\n        var conv4 = extractSeparableConvParams(s4, s5, 'conv4');\r\n        var conv5 = extractSeparableConvParams(s5, s6, 'conv5');\r\n        var conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;\r\n        var conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;\r\n        var conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');\r\n        params = { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3, conv4: conv4, conv5: conv5, conv6: conv6, conv7: conv7, conv8: conv8 };\r\n    }\r\n    else {\r\n        var s0 = filterSizes[0], s1 = filterSizes[1], s2 = filterSizes[2], s3 = filterSizes[3], s4 = filterSizes[4], s5 = filterSizes[5], s6 = filterSizes[6], s7 = filterSizes[7], s8 = filterSizes[8];\r\n        var conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');\r\n        var conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');\r\n        var conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');\r\n        var conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');\r\n        var conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');\r\n        var conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');\r\n        var conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');\r\n        var conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');\r\n        var conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');\r\n        params = { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3, conv4: conv4, conv5: conv5, conv6: conv6, conv7: conv7, conv8: conv8 };\r\n    }\r\n    if (getRemainingWeights().length !== 0) {\r\n        throw new Error(\"weights remaing after extract: \" + getRemainingWeights().length);\r\n    }\r\n    return { params: params, paramMappings: paramMappings };\r\n}\r\n//# sourceMappingURL=extractParams.js.map","import { disposeUnusedWeightTensors, extractWeightEntryFactory } from 'tfjs-image-recognition-base';\r\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\r\nfunction extractorsFactory(weightMap, paramMappings) {\r\n    var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);\r\n    function extractBatchNormParams(prefix) {\r\n        var sub = extractWeightEntry(prefix + \"/sub\", 1);\r\n        var truediv = extractWeightEntry(prefix + \"/truediv\", 1);\r\n        return { sub: sub, truediv: truediv };\r\n    }\r\n    function extractConvParams(prefix) {\r\n        var filters = extractWeightEntry(prefix + \"/filters\", 4);\r\n        var bias = extractWeightEntry(prefix + \"/bias\", 1);\r\n        return { filters: filters, bias: bias };\r\n    }\r\n    function extractConvWithBatchNormParams(prefix) {\r\n        var conv = extractConvParams(prefix + \"/conv\");\r\n        var bn = extractBatchNormParams(prefix + \"/bn\");\r\n        return { conv: conv, bn: bn };\r\n    }\r\n    var extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);\r\n    return {\r\n        extractConvParams: extractConvParams,\r\n        extractConvWithBatchNormParams: extractConvWithBatchNormParams,\r\n        extractSeparableConvParams: extractSeparableConvParams\r\n    };\r\n}\r\nexport function extractParamsFromWeigthMap(weightMap, config) {\r\n    var paramMappings = [];\r\n    var _a = extractorsFactory(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractConvWithBatchNormParams = _a.extractConvWithBatchNormParams, extractSeparableConvParams = _a.extractSeparableConvParams;\r\n    var params;\r\n    if (config.withSeparableConvs) {\r\n        var numFilters = (config.filterSizes && config.filterSizes.length || 9);\r\n        params = {\r\n            conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\r\n            conv1: extractSeparableConvParams('conv1'),\r\n            conv2: extractSeparableConvParams('conv2'),\r\n            conv3: extractSeparableConvParams('conv3'),\r\n            conv4: extractSeparableConvParams('conv4'),\r\n            conv5: extractSeparableConvParams('conv5'),\r\n            conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\r\n            conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\r\n            conv8: extractConvParams('conv8')\r\n        };\r\n    }\r\n    else {\r\n        params = {\r\n            conv0: extractConvWithBatchNormParams('conv0'),\r\n            conv1: extractConvWithBatchNormParams('conv1'),\r\n            conv2: extractConvWithBatchNormParams('conv2'),\r\n            conv3: extractConvWithBatchNormParams('conv3'),\r\n            conv4: extractConvWithBatchNormParams('conv4'),\r\n            conv5: extractConvWithBatchNormParams('conv5'),\r\n            conv6: extractConvWithBatchNormParams('conv6'),\r\n            conv7: extractConvWithBatchNormParams('conv7'),\r\n            conv8: extractConvParams('conv8')\r\n        };\r\n    }\r\n    disposeUnusedWeightTensors(weightMap, paramMappings);\r\n    return { params: params, paramMappings: paramMappings };\r\n}\r\n//# sourceMappingURL=extractParamsFromWeigthMap.js.map","export var TinyYolov2SizeType;\r\n(function (TinyYolov2SizeType) {\r\n    TinyYolov2SizeType[TinyYolov2SizeType[\"XS\"] = 224] = \"XS\";\r\n    TinyYolov2SizeType[TinyYolov2SizeType[\"SM\"] = 320] = \"SM\";\r\n    TinyYolov2SizeType[TinyYolov2SizeType[\"MD\"] = 416] = \"MD\";\r\n    TinyYolov2SizeType[TinyYolov2SizeType[\"LG\"] = 608] = \"LG\";\r\n})(TinyYolov2SizeType || (TinyYolov2SizeType = {}));\r\nvar TinyYolov2Options = /** @class */ (function () {\r\n    function TinyYolov2Options(_a) {\r\n        var _b = _a === void 0 ? {} : _a, inputSize = _b.inputSize, scoreThreshold = _b.scoreThreshold;\r\n        this._name = 'TinyYolov2Options';\r\n        this._inputSize = inputSize || 416;\r\n        this._scoreThreshold = scoreThreshold || 0.5;\r\n        if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\r\n            throw new Error(this._name + \" - expected inputSize to be a number divisible by 32\");\r\n        }\r\n        if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\r\n            throw new Error(this._name + \" - expected scoreThreshold to be a number between 0 and 1\");\r\n        }\r\n    }\r\n    Object.defineProperty(TinyYolov2Options.prototype, \"inputSize\", {\r\n        get: function () { return this._inputSize; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(TinyYolov2Options.prototype, \"scoreThreshold\", {\r\n        get: function () { return this._scoreThreshold; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    return TinyYolov2Options;\r\n}());\r\nexport { TinyYolov2Options };\r\n//# sourceMappingURL=TinyYolov2Options.js.map","import * as tslib_1 from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { BoundingBox, NeuralNetwork, nonMaxSuppression, normalize, ObjectDetection, sigmoid, toNetInput, } from 'tfjs-image-recognition-base';\r\nimport { convLayer } from '../common';\r\nimport { validateConfig } from './config';\r\nimport { DEFAULT_FILTER_SIZES } from './const';\r\nimport { convWithBatchNorm } from './convWithBatchNorm';\r\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\r\nimport { extractParams } from './extractParams';\r\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\r\nimport { leaky } from './leaky';\r\nimport { TinyYolov2Options } from './TinyYolov2Options';\r\nvar TinyYolov2 = /** @class */ (function (_super) {\r\n    tslib_1.__extends(TinyYolov2, _super);\r\n    function TinyYolov2(config) {\r\n        var _this = _super.call(this, 'TinyYolov2') || this;\r\n        validateConfig(config);\r\n        _this._config = config;\r\n        return _this;\r\n    }\r\n    Object.defineProperty(TinyYolov2.prototype, \"config\", {\r\n        get: function () {\r\n            return this._config;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(TinyYolov2.prototype, \"withClassScores\", {\r\n        get: function () {\r\n            return this.config.withClassScores || this.config.classes.length > 1;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(TinyYolov2.prototype, \"boxEncodingSize\", {\r\n        get: function () {\r\n            return 5 + (this.withClassScores ? this.config.classes.length : 0);\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    TinyYolov2.prototype.runTinyYolov2 = function (x, params) {\r\n        var out = convWithBatchNorm(x, params.conv0);\r\n        out = tf.maxPool(out, [2, 2], [2, 2], 'same');\r\n        out = convWithBatchNorm(out, params.conv1);\r\n        out = tf.maxPool(out, [2, 2], [2, 2], 'same');\r\n        out = convWithBatchNorm(out, params.conv2);\r\n        out = tf.maxPool(out, [2, 2], [2, 2], 'same');\r\n        out = convWithBatchNorm(out, params.conv3);\r\n        out = tf.maxPool(out, [2, 2], [2, 2], 'same');\r\n        out = convWithBatchNorm(out, params.conv4);\r\n        out = tf.maxPool(out, [2, 2], [2, 2], 'same');\r\n        out = convWithBatchNorm(out, params.conv5);\r\n        out = tf.maxPool(out, [2, 2], [1, 1], 'same');\r\n        out = convWithBatchNorm(out, params.conv6);\r\n        out = convWithBatchNorm(out, params.conv7);\r\n        return convLayer(out, params.conv8, 'valid', false);\r\n    };\r\n    TinyYolov2.prototype.runMobilenet = function (x, params) {\r\n        var out = this.config.isFirstLayerConv2d\r\n            ? leaky(convLayer(x, params.conv0, 'valid', false))\r\n            : depthwiseSeparableConv(x, params.conv0);\r\n        out = tf.maxPool(out, [2, 2], [2, 2], 'same');\r\n        out = depthwiseSeparableConv(out, params.conv1);\r\n        out = tf.maxPool(out, [2, 2], [2, 2], 'same');\r\n        out = depthwiseSeparableConv(out, params.conv2);\r\n        out = tf.maxPool(out, [2, 2], [2, 2], 'same');\r\n        out = depthwiseSeparableConv(out, params.conv3);\r\n        out = tf.maxPool(out, [2, 2], [2, 2], 'same');\r\n        out = depthwiseSeparableConv(out, params.conv4);\r\n        out = tf.maxPool(out, [2, 2], [2, 2], 'same');\r\n        out = depthwiseSeparableConv(out, params.conv5);\r\n        out = tf.maxPool(out, [2, 2], [1, 1], 'same');\r\n        out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;\r\n        out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out;\r\n        return convLayer(out, params.conv8, 'valid', false);\r\n    };\r\n    TinyYolov2.prototype.forwardInput = function (input, inputSize) {\r\n        var _this = this;\r\n        var params = this.params;\r\n        if (!params) {\r\n            throw new Error('TinyYolov2 - load model before inference');\r\n        }\r\n        return tf.tidy(function () {\r\n            var batchTensor = input.toBatchTensor(inputSize, false).toFloat();\r\n            batchTensor = _this.config.meanRgb\r\n                ? normalize(batchTensor, _this.config.meanRgb)\r\n                : batchTensor;\r\n            batchTensor = batchTensor.div(tf.scalar(256));\r\n            return _this.config.withSeparableConvs\r\n                ? _this.runMobilenet(batchTensor, params)\r\n                : _this.runTinyYolov2(batchTensor, params);\r\n        });\r\n    };\r\n    TinyYolov2.prototype.forward = function (input, inputSize) {\r\n        return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n            var _a;\r\n            return tslib_1.__generator(this, function (_b) {\r\n                switch (_b.label) {\r\n                    case 0:\r\n                        _a = this.forwardInput;\r\n                        return [4 /*yield*/, toNetInput(input)];\r\n                    case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(), inputSize])];\r\n                    case 2: return [2 /*return*/, _b.sent()];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    TinyYolov2.prototype.detect = function (input, forwardParams) {\r\n        if (forwardParams === void 0) { forwardParams = {}; }\r\n        return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n            var _this = this;\r\n            var _a, inputSize, scoreThreshold, netInput, out, out0, inputDimensions, results, boxes, scores, classScores, classNames, indices, detections;\r\n            return tslib_1.__generator(this, function (_b) {\r\n                switch (_b.label) {\r\n                    case 0:\r\n                        _a = new TinyYolov2Options(forwardParams), inputSize = _a.inputSize, scoreThreshold = _a.scoreThreshold;\r\n                        return [4 /*yield*/, toNetInput(input)];\r\n                    case 1:\r\n                        netInput = _b.sent();\r\n                        return [4 /*yield*/, this.forwardInput(netInput, inputSize)];\r\n                    case 2:\r\n                        out = _b.sent();\r\n                        out0 = tf.tidy(function () { return tf.unstack(out)[0].expandDims(); });\r\n                        inputDimensions = {\r\n                            width: netInput.getInputWidth(0),\r\n                            height: netInput.getInputHeight(0)\r\n                        };\r\n                        results = this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);\r\n                        out.dispose();\r\n                        out0.dispose();\r\n                        boxes = results.map(function (res) { return res.box; });\r\n                        scores = results.map(function (res) { return res.score; });\r\n                        classScores = results.map(function (res) { return res.classScore; });\r\n                        classNames = results.map(function (res) { return _this.config.classes[res.label]; });\r\n                        indices = nonMaxSuppression(boxes.map(function (box) { return box.rescale(inputSize); }), scores, this.config.iouThreshold, true);\r\n                        detections = indices.map(function (idx) {\r\n                            return new ObjectDetection(scores[idx], classScores[idx], classNames[idx], boxes[idx], inputDimensions);\r\n                        });\r\n                        return [2 /*return*/, detections];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    TinyYolov2.prototype.getDefaultModelName = function () {\r\n        return '';\r\n    };\r\n    TinyYolov2.prototype.extractParamsFromWeigthMap = function (weightMap) {\r\n        return extractParamsFromWeigthMap(weightMap, this.config);\r\n    };\r\n    TinyYolov2.prototype.extractParams = function (weights) {\r\n        var filterSizes = this.config.filterSizes || DEFAULT_FILTER_SIZES;\r\n        var numFilters = filterSizes ? filterSizes.length : undefined;\r\n        if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\r\n            throw new Error(\"TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found \" + numFilters + \" filterSizes in config\");\r\n        }\r\n        return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);\r\n    };\r\n    TinyYolov2.prototype.extractBoxes = function (outputTensor, inputBlobDimensions, scoreThreshold) {\r\n        var _this = this;\r\n        var width = inputBlobDimensions.width, height = inputBlobDimensions.height;\r\n        var inputSize = Math.max(width, height);\r\n        var correctionFactorX = inputSize / width;\r\n        var correctionFactorY = inputSize / height;\r\n        var numCells = outputTensor.shape[1];\r\n        var numBoxes = this.config.anchors.length;\r\n        var _a = tf.tidy(function () {\r\n            var reshaped = outputTensor.reshape([numCells, numCells, numBoxes, _this.boxEncodingSize]);\r\n            var boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);\r\n            var scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);\r\n            var classScores = _this.withClassScores\r\n                ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, _this.config.classes.length]), 3)\r\n                : tf.scalar(0);\r\n            return [boxes, scores, classScores];\r\n        }), boxesTensor = _a[0], scoresTensor = _a[1], classScoresTensor = _a[2];\r\n        var results = [];\r\n        for (var row = 0; row < numCells; row++) {\r\n            for (var col = 0; col < numCells; col++) {\r\n                for (var anchor = 0; anchor < numBoxes; anchor++) {\r\n                    var score = sigmoid(scoresTensor.get(row, col, anchor, 0));\r\n                    if (!scoreThreshold || score > scoreThreshold) {\r\n                        var ctX = ((col + sigmoid(boxesTensor.get(row, col, anchor, 0))) / numCells) * correctionFactorX;\r\n                        var ctY = ((row + sigmoid(boxesTensor.get(row, col, anchor, 1))) / numCells) * correctionFactorY;\r\n                        var width_1 = ((Math.exp(boxesTensor.get(row, col, anchor, 2)) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;\r\n                        var height_1 = ((Math.exp(boxesTensor.get(row, col, anchor, 3)) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;\r\n                        var x = (ctX - (width_1 / 2));\r\n                        var y = (ctY - (height_1 / 2));\r\n                        var pos = { row: row, col: col, anchor: anchor };\r\n                        var _b = this.withClassScores\r\n                            ? this.extractPredictedClass(classScoresTensor, pos)\r\n                            : { classScore: 1, label: 0 }, classScore = _b.classScore, label = _b.label;\r\n                        results.push(tslib_1.__assign({ box: new BoundingBox(x, y, x + width_1, y + height_1), score: score, classScore: score * classScore, label: label }, pos));\r\n                    }\r\n                }\r\n            }\r\n        }\r\n        boxesTensor.dispose();\r\n        scoresTensor.dispose();\r\n        classScoresTensor.dispose();\r\n        return results;\r\n    };\r\n    TinyYolov2.prototype.extractPredictedClass = function (classesTensor, pos) {\r\n        var row = pos.row, col = pos.col, anchor = pos.anchor;\r\n        return Array(this.config.classes.length).fill(0)\r\n            .map(function (_, i) { return classesTensor.get(row, col, anchor, i); })\r\n            .map(function (classScore, label) { return ({\r\n            classScore: classScore,\r\n            label: label\r\n        }); })\r\n            .reduce(function (max, curr) { return max.classScore > curr.classScore ? max : curr; });\r\n    };\r\n    return TinyYolov2;\r\n}(NeuralNetwork));\r\nexport { TinyYolov2 };\r\n//# sourceMappingURL=TinyYolov2.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { BoundingBox, Dimensions, iou, Rect } from 'tfjs-image-recognition-base';\r\nimport { CELL_SIZE } from './const';\r\nvar TinyYolov2LossFunction = /** @class */ (function () {\r\n    function TinyYolov2LossFunction(outputTensor, groundTruth, predictedBoxes, reshapedImgDims, config) {\r\n        this._config = config;\r\n        this._reshapedImgDims = new Dimensions(reshapedImgDims.width, reshapedImgDims.height);\r\n        this._outputTensor = outputTensor;\r\n        this._predictedBoxes = predictedBoxes;\r\n        this.validateGroundTruthBoxes(groundTruth);\r\n        this._groundTruth = this.assignGroundTruthToAnchors(groundTruth);\r\n        var groundTruthMask = this.createGroundTruthMask();\r\n        var _a = this.createCoordAndScoreMasks(), coordBoxOffsetMask = _a.coordBoxOffsetMask, coordBoxSizeMask = _a.coordBoxSizeMask, scoreMask = _a.scoreMask;\r\n        this.noObjectLossMask = tf.tidy(function () { return tf.mul(scoreMask, tf.sub(tf.scalar(1), groundTruthMask)); });\r\n        this.objectLossMask = tf.tidy(function () { return tf.mul(scoreMask, groundTruthMask); });\r\n        this.coordBoxOffsetMask = tf.tidy(function () { return tf.mul(coordBoxOffsetMask, groundTruthMask); });\r\n        this.coordBoxSizeMask = tf.tidy(function () { return tf.mul(coordBoxSizeMask, groundTruthMask); });\r\n        var classScoresMask = tf.tidy(function () { return tf.sub(tf.scalar(1), coordBoxOffsetMask.add(coordBoxSizeMask).add(scoreMask)); });\r\n        this.groundTruthClassScoresMask = tf.tidy(function () { return tf.mul(classScoresMask, groundTruthMask); });\r\n    }\r\n    Object.defineProperty(TinyYolov2LossFunction.prototype, \"config\", {\r\n        get: function () {\r\n            return this._config;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(TinyYolov2LossFunction.prototype, \"reshapedImgDims\", {\r\n        get: function () {\r\n            return this._reshapedImgDims;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(TinyYolov2LossFunction.prototype, \"outputTensor\", {\r\n        get: function () {\r\n            return this._outputTensor;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(TinyYolov2LossFunction.prototype, \"groundTruth\", {\r\n        get: function () {\r\n            return this._groundTruth;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(TinyYolov2LossFunction.prototype, \"predictedBoxes\", {\r\n        get: function () {\r\n            return this._predictedBoxes;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(TinyYolov2LossFunction.prototype, \"inputSize\", {\r\n        get: function () {\r\n            return Math.max(this.reshapedImgDims.width, this.reshapedImgDims.height);\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(TinyYolov2LossFunction.prototype, \"withClassScores\", {\r\n        get: function () {\r\n            return this._config.withClassScores || this._config.classes.length > 1;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(TinyYolov2LossFunction.prototype, \"boxEncodingSize\", {\r\n        get: function () {\r\n            return 5 + (this.withClassScores ? this._config.classes.length : 0);\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(TinyYolov2LossFunction.prototype, \"anchors\", {\r\n        get: function () {\r\n            return this._config.anchors;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(TinyYolov2LossFunction.prototype, \"numBoxes\", {\r\n        get: function () {\r\n            return this.anchors.length;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(TinyYolov2LossFunction.prototype, \"numCells\", {\r\n        get: function () {\r\n            return this.inputSize / CELL_SIZE;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(TinyYolov2LossFunction.prototype, \"gridCellEncodingSize\", {\r\n        get: function () {\r\n            return this.boxEncodingSize * this.numBoxes;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    TinyYolov2LossFunction.prototype.toOutputTensorShape = function (tensor) {\r\n        var _this = this;\r\n        return tf.tidy(function () { return tensor.reshape([1, _this.numCells, _this.numCells, _this.gridCellEncodingSize]); });\r\n    };\r\n    TinyYolov2LossFunction.prototype.computeLoss = function () {\r\n        var _this = this;\r\n        return tf.tidy(function () {\r\n            var noObjectLoss = _this.computeNoObjectLoss();\r\n            var objectLoss = _this.computeObjectLoss();\r\n            var coordLoss = _this.computeCoordLoss();\r\n            var classLoss = _this.withClassScores\r\n                ? _this.computeClassLoss()\r\n                : tf.scalar(0);\r\n            var totalLoss = tf.tidy(function () { return noObjectLoss.add(objectLoss).add(coordLoss).add(classLoss); });\r\n            return {\r\n                noObjectLoss: noObjectLoss,\r\n                objectLoss: objectLoss,\r\n                coordLoss: coordLoss,\r\n                classLoss: classLoss,\r\n                totalLoss: totalLoss\r\n            };\r\n        });\r\n    };\r\n    TinyYolov2LossFunction.prototype.computeNoObjectLoss = function () {\r\n        var _this = this;\r\n        return tf.tidy(function () {\r\n            return _this.computeLossTerm(_this.config.noObjectScale, _this.toOutputTensorShape(_this.noObjectLossMask), tf.sigmoid(_this.outputTensor));\r\n        });\r\n    };\r\n    TinyYolov2LossFunction.prototype.computeObjectLoss = function () {\r\n        var _this = this;\r\n        return tf.tidy(function () {\r\n            return _this.computeLossTerm(_this.config.objectScale, _this.toOutputTensorShape(_this.objectLossMask), tf.sub(_this.toOutputTensorShape(_this.computeIous()), tf.sigmoid(_this.outputTensor)));\r\n        });\r\n    };\r\n    TinyYolov2LossFunction.prototype.computeClassLoss = function () {\r\n        var _this = this;\r\n        return tf.tidy(function () {\r\n            var classLossTensor = tf.tidy(function () {\r\n                var predClassScores = tf.mul(tf.softmax(_this.outputTensor.reshape([_this.numCells, _this.numCells, _this.numBoxes, _this.boxEncodingSize]), 3), _this.groundTruthClassScoresMask);\r\n                var gtClassScores = _this.createOneHotClassScoreMask();\r\n                return tf.sub(gtClassScores, predClassScores);\r\n            });\r\n            return _this.computeLossTerm(_this.config.classScale, tf.scalar(1), classLossTensor);\r\n        });\r\n    };\r\n    TinyYolov2LossFunction.prototype.computeCoordLoss = function () {\r\n        var _this = this;\r\n        return tf.tidy(function () {\r\n            return _this.computeLossTerm(_this.config.coordScale, tf.scalar(1), tf.add(_this.computeCoordBoxOffsetError(), _this.computeCoordBoxSizeError()));\r\n        });\r\n    };\r\n    TinyYolov2LossFunction.prototype.computeCoordBoxOffsetError = function () {\r\n        var _this = this;\r\n        return tf.tidy(function () {\r\n            var mask = _this.toOutputTensorShape(_this.coordBoxOffsetMask);\r\n            var gtBoxOffsets = tf.mul(mask, _this.toOutputTensorShape(_this.computeCoordBoxOffsets()));\r\n            var predBoxOffsets = tf.mul(mask, tf.sigmoid(_this.outputTensor));\r\n            return tf.sub(gtBoxOffsets, predBoxOffsets);\r\n        });\r\n    };\r\n    TinyYolov2LossFunction.prototype.computeCoordBoxSizeError = function () {\r\n        var _this = this;\r\n        return tf.tidy(function () {\r\n            var mask = _this.toOutputTensorShape(_this.coordBoxSizeMask);\r\n            var gtBoxSizes = tf.mul(mask, _this.toOutputTensorShape(_this.computeCoordBoxSizes()));\r\n            var predBoxSizes = tf.mul(mask, _this.outputTensor);\r\n            return tf.sub(gtBoxSizes, predBoxSizes);\r\n        });\r\n    };\r\n    TinyYolov2LossFunction.prototype.computeLossTerm = function (scale, mask, lossTensor) {\r\n        var _this = this;\r\n        return tf.tidy(function () { return tf.mul(tf.scalar(scale), _this.squaredSumOverMask(mask, lossTensor)); });\r\n    };\r\n    TinyYolov2LossFunction.prototype.squaredSumOverMask = function (mask, lossTensor) {\r\n        return tf.tidy(function () { return tf.sum(tf.square(tf.mul(mask, lossTensor))); });\r\n    };\r\n    TinyYolov2LossFunction.prototype.validateGroundTruthBoxes = function (groundTruth) {\r\n        var _this = this;\r\n        groundTruth.forEach(function (_a) {\r\n            var x = _a.x, y = _a.y, width = _a.width, height = _a.height, label = _a.label;\r\n            if (typeof label !== 'number' || label < 0 || label > (_this.config.classes.length - 1)) {\r\n                throw new Error(\"invalid ground truth data, expected label to be a number in [0, \" + (_this.config.classes.length - 1) + \"]\");\r\n            }\r\n            if (x < 0 || x > 1 || y < 0 || y > 1 || width < 0 || (x + width) > 1 || height < 0 || (y + height) > 1) {\r\n                throw new Error(\"invalid ground truth data, box is out of image boundaries \" + JSON.stringify({ x: x, y: y, width: width, height: height }));\r\n            }\r\n        });\r\n    };\r\n    TinyYolov2LossFunction.prototype.assignGroundTruthToAnchors = function (groundTruth) {\r\n        var _this = this;\r\n        var groundTruthBoxes = groundTruth\r\n            .map(function (_a) {\r\n            var x = _a.x, y = _a.y, width = _a.width, height = _a.height, label = _a.label;\r\n            return ({\r\n                box: new Rect(x, y, width, height),\r\n                label: label\r\n            });\r\n        });\r\n        return groundTruthBoxes.map(function (_a) {\r\n            var box = _a.box, label = _a.label;\r\n            var _b = box.rescale(_this.reshapedImgDims), left = _b.left, top = _b.top, bottom = _b.bottom, right = _b.right, x = _b.x, y = _b.y, width = _b.width, height = _b.height;\r\n            var ctX = left + (width / 2);\r\n            var ctY = top + (height / 2);\r\n            var col = Math.floor((ctX / _this.inputSize) * _this.numCells);\r\n            var row = Math.floor((ctY / _this.inputSize) * _this.numCells);\r\n            var anchorsByIou = _this.anchors.map(function (anchor, idx) { return ({\r\n                idx: idx,\r\n                iou: iou(new BoundingBox(0, 0, anchor.x * CELL_SIZE, anchor.y * CELL_SIZE), new BoundingBox(0, 0, width, height))\r\n            }); }).sort(function (a1, a2) { return a2.iou - a1.iou; });\r\n            var anchor = anchorsByIou[0].idx;\r\n            return { row: row, col: col, anchor: anchor, box: box, label: label };\r\n        });\r\n    };\r\n    TinyYolov2LossFunction.prototype.createGroundTruthMask = function () {\r\n        var _this = this;\r\n        var mask = tf.zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize]);\r\n        var buf = mask.buffer();\r\n        this.groundTruth.forEach(function (_a) {\r\n            var row = _a.row, col = _a.col, anchor = _a.anchor;\r\n            for (var i = 0; i < _this.boxEncodingSize; i++) {\r\n                buf.set(1, row, col, anchor, i);\r\n            }\r\n        });\r\n        return mask;\r\n    };\r\n    TinyYolov2LossFunction.prototype.createCoordAndScoreMasks = function () {\r\n        var _this = this;\r\n        return tf.tidy(function () {\r\n            var coordBoxOffsetMask = tf.zeros([_this.numCells, _this.numCells, _this.numBoxes, _this.boxEncodingSize]);\r\n            var coordBoxSizeMask = tf.zeros([_this.numCells, _this.numCells, _this.numBoxes, _this.boxEncodingSize]);\r\n            var scoreMask = tf.zeros([_this.numCells, _this.numCells, _this.numBoxes, _this.boxEncodingSize]);\r\n            var coordBoxOffsetBuf = coordBoxOffsetMask.buffer();\r\n            var coordBoxSizeBuf = coordBoxSizeMask.buffer();\r\n            var scoreBuf = scoreMask.buffer();\r\n            for (var row = 0; row < _this.numCells; row++) {\r\n                for (var col = 0; col < _this.numCells; col++) {\r\n                    for (var anchor = 0; anchor < _this.numBoxes; anchor++) {\r\n                        coordBoxOffsetBuf.set(1, row, col, anchor, 0);\r\n                        coordBoxOffsetBuf.set(1, row, col, anchor, 1);\r\n                        coordBoxSizeBuf.set(1, row, col, anchor, 2);\r\n                        coordBoxSizeBuf.set(1, row, col, anchor, 3);\r\n                        scoreBuf.set(1, row, col, anchor, 4);\r\n                    }\r\n                }\r\n            }\r\n            return { coordBoxOffsetMask: coordBoxOffsetMask, coordBoxSizeMask: coordBoxSizeMask, scoreMask: scoreMask };\r\n        });\r\n    };\r\n    TinyYolov2LossFunction.prototype.createOneHotClassScoreMask = function () {\r\n        var mask = tf.zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize]);\r\n        var buf = mask.buffer();\r\n        var classValuesOffset = 5;\r\n        this.groundTruth.forEach(function (_a) {\r\n            var row = _a.row, col = _a.col, anchor = _a.anchor, label = _a.label;\r\n            buf.set(1, row, col, anchor, classValuesOffset + label);\r\n        });\r\n        return mask;\r\n    };\r\n    TinyYolov2LossFunction.prototype.computeIous = function () {\r\n        var _this = this;\r\n        var isSameAnchor = function (p1) { return function (p2) {\r\n            return p1.row === p2.row\r\n                && p1.col === p2.col\r\n                && p1.anchor === p2.anchor;\r\n        }; };\r\n        var ious = tf.zeros([this.numCells, this.numCells, this.gridCellEncodingSize]);\r\n        var buf = ious.buffer();\r\n        this.groundTruth.forEach(function (_a) {\r\n            var row = _a.row, col = _a.col, anchor = _a.anchor, box = _a.box;\r\n            var predBox = _this.predictedBoxes.find(isSameAnchor({ row: row, col: col, anchor: anchor }));\r\n            if (!predBox) {\r\n                throw new Error(\"no output box found for: row \" + row + \", col \" + col + \", anchor \" + anchor);\r\n            }\r\n            var boxIou = iou(box.rescale(_this.reshapedImgDims), predBox.box.rescale(_this.reshapedImgDims));\r\n            var anchorOffset = _this.boxEncodingSize * anchor;\r\n            var scoreValueOffset = 4;\r\n            buf.set(boxIou, row, col, anchorOffset + scoreValueOffset);\r\n        });\r\n        return ious;\r\n    };\r\n    TinyYolov2LossFunction.prototype.computeCoordBoxOffsets = function () {\r\n        var _this = this;\r\n        var offsets = tf.zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize]);\r\n        var buf = offsets.buffer();\r\n        this.groundTruth.forEach(function (_a) {\r\n            var row = _a.row, col = _a.col, anchor = _a.anchor, box = _a.box;\r\n            var _b = box.rescale(_this.reshapedImgDims), left = _b.left, top = _b.top, right = _b.right, bottom = _b.bottom;\r\n            var centerX = (left + right) / 2;\r\n            var centerY = (top + bottom) / 2;\r\n            var dCenterX = centerX - (col * CELL_SIZE);\r\n            var dCenterY = centerY - (row * CELL_SIZE);\r\n            // inverseSigmoid(0) === -Infinity, inverseSigmoid(1) === Infinity\r\n            //const dx = inverseSigmoid(Math.min(0.999, Math.max(0.001, dCenterX / CELL_SIZE)))\r\n            //const dy = inverseSigmoid(Math.min(0.999, Math.max(0.001, dCenterY / CELL_SIZE)))\r\n            var dx = dCenterX / CELL_SIZE;\r\n            var dy = dCenterY / CELL_SIZE;\r\n            buf.set(dx, row, col, anchor, 0);\r\n            buf.set(dy, row, col, anchor, 1);\r\n        });\r\n        return offsets;\r\n    };\r\n    TinyYolov2LossFunction.prototype.computeCoordBoxSizes = function () {\r\n        var _this = this;\r\n        var sizes = tf.zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize]);\r\n        var buf = sizes.buffer();\r\n        this.groundTruth.forEach(function (_a) {\r\n            var row = _a.row, col = _a.col, anchor = _a.anchor, box = _a.box;\r\n            var _b = box.rescale(_this.reshapedImgDims), width = _b.width, height = _b.height;\r\n            var dw = Math.log(width / (_this.anchors[anchor].x * CELL_SIZE));\r\n            var dh = Math.log(height / (_this.anchors[anchor].y * CELL_SIZE));\r\n            buf.set(dw, row, col, anchor, 2);\r\n            buf.set(dh, row, col, anchor, 3);\r\n        });\r\n        return sizes;\r\n    };\r\n    return TinyYolov2LossFunction;\r\n}());\r\nexport { TinyYolov2LossFunction };\r\n//# sourceMappingURL=TinyYolov2LossFunction.js.map","import { CELL_SIZE } from './const';\r\nexport function getDefaultBackwardOptions(options) {\r\n    return Object.assign({}, {\r\n        minBoxSize: CELL_SIZE\r\n    }, options);\r\n}\r\n//# sourceMappingURL=getDefaultBackwardOptions.js.map","import * as tslib_1 from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { computeReshapedDimensions, getMediaDimensions, imageToSquare, Rect, toNetInput, } from 'tfjs-image-recognition-base';\r\nimport { validateTrainConfig } from './config';\r\nimport { getDefaultBackwardOptions } from './getDefaultBackwardOptions';\r\nimport { TinyYolov2 } from './TinyYolov2';\r\nimport { TinyYolov2LossFunction } from './TinyYolov2LossFunction';\r\nvar TinyYolov2Trainable = /** @class */ (function (_super) {\r\n    tslib_1.__extends(TinyYolov2Trainable, _super);\r\n    function TinyYolov2Trainable(trainableConfig, optimizer) {\r\n        var _this = _super.call(this, trainableConfig) || this;\r\n        _this._trainableConfig = validateTrainConfig(trainableConfig);\r\n        _this._optimizer = optimizer;\r\n        return _this;\r\n    }\r\n    Object.defineProperty(TinyYolov2Trainable.prototype, \"trainableConfig\", {\r\n        get: function () {\r\n            return this._trainableConfig;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(TinyYolov2Trainable.prototype, \"optimizer\", {\r\n        get: function () {\r\n            return this._optimizer;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    TinyYolov2Trainable.prototype.backward = function (img, groundTruth, inputSize, options) {\r\n        if (options === void 0) { options = {}; }\r\n        return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n            var _this = this;\r\n            var _a, minBoxSize, reportLosses, reshapedImgDims, filteredGroundTruthBoxes, netInput, loss;\r\n            return tslib_1.__generator(this, function (_b) {\r\n                switch (_b.label) {\r\n                    case 0:\r\n                        _a = getDefaultBackwardOptions(options), minBoxSize = _a.minBoxSize, reportLosses = _a.reportLosses;\r\n                        reshapedImgDims = computeReshapedDimensions(getMediaDimensions(img), inputSize);\r\n                        filteredGroundTruthBoxes = this.filterGroundTruthBoxes(groundTruth, reshapedImgDims, minBoxSize);\r\n                        if (!filteredGroundTruthBoxes.length) {\r\n                            return [2 /*return*/, null];\r\n                        }\r\n                        return [4 /*yield*/, toNetInput(imageToSquare(img, inputSize))];\r\n                    case 1:\r\n                        netInput = _b.sent();\r\n                        loss = this.optimizer.minimize(function () {\r\n                            var _a = _this.computeLoss(_this.forwardInput(netInput, inputSize), filteredGroundTruthBoxes, reshapedImgDims), noObjectLoss = _a.noObjectLoss, objectLoss = _a.objectLoss, coordLoss = _a.coordLoss, classLoss = _a.classLoss, totalLoss = _a.totalLoss;\r\n                            if (reportLosses) {\r\n                                var losses = {\r\n                                    totalLoss: totalLoss.dataSync()[0],\r\n                                    noObjectLoss: noObjectLoss.dataSync()[0],\r\n                                    objectLoss: objectLoss.dataSync()[0],\r\n                                    coordLoss: coordLoss.dataSync()[0],\r\n                                    classLoss: classLoss.dataSync()[0]\r\n                                };\r\n                                var report = {\r\n                                    losses: losses,\r\n                                    numBoxes: filteredGroundTruthBoxes.length,\r\n                                    inputSize: inputSize\r\n                                };\r\n                                reportLosses(report);\r\n                            }\r\n                            return totalLoss;\r\n                        }, true);\r\n                        return [2 /*return*/, loss];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    TinyYolov2Trainable.prototype.computeLoss = function (outputTensor, groundTruth, reshapedImgDims) {\r\n        var config = validateTrainConfig(this.config);\r\n        var inputSize = Math.max(reshapedImgDims.width, reshapedImgDims.height);\r\n        if (!inputSize) {\r\n            throw new Error(\"computeLoss - invalid inputSize: \" + inputSize);\r\n        }\r\n        var predictedBoxes = this.extractBoxes(outputTensor, reshapedImgDims);\r\n        return tf.tidy(function () {\r\n            var lossFunction = new TinyYolov2LossFunction(outputTensor, groundTruth, predictedBoxes, reshapedImgDims, config);\r\n            return lossFunction.computeLoss();\r\n        });\r\n    };\r\n    TinyYolov2Trainable.prototype.filterGroundTruthBoxes = function (groundTruth, imgDims, minBoxSize) {\r\n        var imgHeight = imgDims.height, imgWidth = imgDims.width;\r\n        return groundTruth.filter(function (_a) {\r\n            var x = _a.x, y = _a.y, width = _a.width, height = _a.height;\r\n            var box = (new Rect(x, y, width, height))\r\n                .rescale({ height: imgHeight, width: imgWidth });\r\n            var isTooTiny = box.width < minBoxSize || box.height < minBoxSize;\r\n            return !isTooTiny;\r\n        });\r\n    };\r\n    TinyYolov2Trainable.prototype.load = function (weightsOrUrl) {\r\n        return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n            return tslib_1.__generator(this, function (_a) {\r\n                switch (_a.label) {\r\n                    case 0: return [4 /*yield*/, _super.prototype.load.call(this, weightsOrUrl)];\r\n                    case 1:\r\n                        _a.sent();\r\n                        this.variable();\r\n                        return [2 /*return*/];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    return TinyYolov2Trainable;\r\n}(TinyYolov2));\r\nexport { TinyYolov2Trainable };\r\n//# sourceMappingURL=TinyYolov2Trainable.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { ExtractWeightsFunction, ParamMapping } from 'tfjs-image-recognition-base';\r\nimport { extractConvParamsFactory, FCParams, SeparableConvParams } from 'tfjs-tiny-yolov2';\r\n\r\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\r\n\r\nexport function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\r\n\r\n  function extractSeparableConvParams(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\r\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\r\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\r\n    const bias = tf.tensor1d(extractWeights(channelsOut))\r\n\r\n    paramMappings.push(\r\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\r\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\r\n      { paramPath: `${mappedPrefix}/bias` }\r\n    )\r\n\r\n    return new SeparableConvParams(\r\n      depthwise_filter,\r\n      pointwise_filter,\r\n      bias\r\n    )\r\n  }\r\n\r\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\r\n\r\n  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\r\n\r\n    const conv0 = isFirstLayer\r\n      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)\r\n      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`)\r\n    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`)\r\n    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`)\r\n\r\n    return { conv0, conv1, conv2 }\r\n  }\r\n\r\n  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\r\n\r\n    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer)\r\n    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`)\r\n\r\n    return { conv0, conv1, conv2, conv3 }\r\n  }\r\n\r\n  return {\r\n    extractDenseBlock3Params,\r\n    extractDenseBlock4Params\r\n  }\r\n\r\n}","import { extractWeightsFactory, ParamMapping } from 'tfjs-image-recognition-base';\r\n\r\nimport { extractorsFactory } from './extractorsFactory';\r\nimport { FaceFeatureExtractorParams } from './types';\r\n\r\nexport function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\r\n\r\n  const paramMappings: ParamMapping[] = []\r\n\r\n  const {\r\n    extractWeights,\r\n    getRemainingWeights\r\n  } = extractWeightsFactory(weights)\r\n\r\n  const {\r\n    extractDenseBlock4Params\r\n  } = extractorsFactory(extractWeights, paramMappings)\r\n\r\n  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true)\r\n  const dense1 = extractDenseBlock4Params(32, 64, 'dense1')\r\n  const dense2 = extractDenseBlock4Params(64, 128, 'dense2')\r\n  const dense3 = extractDenseBlock4Params(128, 256, 'dense3')\r\n\r\n  if (getRemainingWeights().length !== 0) {\r\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\r\n  }\r\n\r\n  return {\r\n    paramMappings,\r\n    params: { dense0, dense1, dense2, dense3 }\r\n  }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { extractWeightEntryFactory, ParamMapping } from 'tfjs-image-recognition-base';\r\nimport { ConvParams, SeparableConvParams } from 'tfjs-tiny-yolov2';\r\n\r\nimport { DenseBlock3Params, DenseBlock4Params } from './types';\r\n\r\nexport function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {\r\n\r\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\r\n\r\n  function extractConvParams(prefix: string): ConvParams {\r\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\r\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\r\n\r\n    return { filters, bias }\r\n  }\r\n\r\n  function extractSeparableConvParams(prefix: string): SeparableConvParams {\r\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\r\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\r\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\r\n\r\n    return new SeparableConvParams(\r\n      depthwise_filter,\r\n      pointwise_filter,\r\n      bias\r\n    )\r\n  }\r\n\r\n  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {\r\n    const conv0 = isFirstLayer\r\n      ? extractConvParams(`${prefix}/conv0`)\r\n      : extractSeparableConvParams(`${prefix}/conv0`)\r\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\r\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\r\n\r\n    return { conv0, conv1, conv2 }\r\n  }\r\n\r\n  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {\r\n    const conv0 = isFirstLayer\r\n      ? extractConvParams(`${prefix}/conv0`)\r\n      : extractSeparableConvParams(`${prefix}/conv0`)\r\n    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)\r\n    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)\r\n    const conv3 = extractSeparableConvParams(`${prefix}/conv3`)\r\n\r\n    return { conv0, conv1, conv2, conv3 }\r\n  }\r\n\r\n  return {\r\n    extractDenseBlock3Params,\r\n    extractDenseBlock4Params\r\n  }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { disposeUnusedWeightTensors, ParamMapping } from 'tfjs-image-recognition-base';\r\n\r\nimport { loadParamsFactory } from './loadParamsFactory';\r\nimport { FaceFeatureExtractorParams } from './types';\r\n\r\nexport function extractParamsFromWeigthMap(\r\n  weightMap: tf.NamedTensorMap\r\n): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\r\n\r\n  const paramMappings: ParamMapping[] = []\r\n\r\n  const {\r\n    extractDenseBlock4Params\r\n  } = loadParamsFactory(weightMap, paramMappings)\r\n\r\n  const params = {\r\n    dense0: extractDenseBlock4Params('dense0', true),\r\n    dense1: extractDenseBlock4Params('dense1'),\r\n    dense2: extractDenseBlock4Params('dense2'),\r\n    dense3: extractDenseBlock4Params('dense3')\r\n  }\r\n\r\n  disposeUnusedWeightTensors(weightMap, paramMappings)\r\n\r\n  return { params, paramMappings }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { NetInput, NeuralNetwork, normalize, TNetInput, toNetInput } from 'tfjs-image-recognition-base';\r\n\r\nimport { denseBlock4 } from './denseBlock';\r\nimport { extractParams } from './extractParams';\r\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\r\nimport { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';\r\n\r\nexport class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {\r\n\r\n  constructor() {\r\n    super('FaceFeatureExtractor')\r\n  }\r\n\r\n  public forwardInput(input: NetInput): tf.Tensor4D {\r\n\r\n    const { params } = this\r\n\r\n    if (!params) {\r\n      throw new Error('FaceFeatureExtractor - load model before inference')\r\n    }\r\n\r\n    return tf.tidy(() => {\r\n      const batchTensor = input.toBatchTensor(112, true)\r\n      const meanRgb = [122.782, 117.001, 104.298]\r\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\r\n\r\n      let out = denseBlock4(normalized, params.dense0, true)\r\n      out = denseBlock4(out, params.dense1)\r\n      out = denseBlock4(out, params.dense2)\r\n      out = denseBlock4(out, params.dense3)\r\n      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')\r\n\r\n      return out\r\n    })\r\n  }\r\n\r\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\r\n    return this.forwardInput(await toNetInput(input))\r\n  }\r\n\r\n  protected getDefaultModelName(): string {\r\n    return 'face_feature_extractor_model'\r\n  }\r\n\r\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\r\n    return extractParamsFromWeigthMap(weightMap)\r\n  }\r\n\r\n  protected extractParams(weights: Float32Array) {\r\n    return extractParams(weights)\r\n  }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { FCParams } from 'tfjs-tiny-yolov2';\r\n\r\nexport function fullyConnectedLayer(\r\n  x: tf.Tensor2D,\r\n  params: FCParams\r\n): tf.Tensor2D {\r\n  return tf.tidy(() =>\r\n    tf.add(\r\n      tf.matMul(x, params.weights),\r\n      params.bias\r\n    )\r\n  )\r\n}","import { extractWeightsFactory, ParamMapping } from 'tfjs-image-recognition-base';\r\nimport { extractFCParamsFactory } from 'tfjs-tiny-yolov2';\r\n\r\nimport { NetParams } from './types';\r\n\r\nexport function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {\r\n\r\n  const paramMappings: ParamMapping[] = []\r\n\r\n  const {\r\n    extractWeights,\r\n    getRemainingWeights\r\n  } = extractWeightsFactory(weights)\r\n\r\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\r\n\r\n  const fc = extractFCParams(channelsIn, channelsOut, 'fc')\r\n\r\n  if (getRemainingWeights().length !== 0) {\r\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\r\n  }\r\n\r\n  return {\r\n    paramMappings,\r\n    params: { fc }\r\n  }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from 'tfjs-image-recognition-base';\r\nimport { FCParams } from 'tfjs-tiny-yolov2';\r\n\r\nimport { NetParams } from './types';\r\n\r\nexport function extractParamsFromWeigthMap(\r\n  weightMap: tf.NamedTensorMap\r\n): { params: NetParams, paramMappings: ParamMapping[] } {\r\n\r\n  const paramMappings: ParamMapping[] = []\r\n\r\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\r\n\r\n  function extractFcParams(prefix: string): FCParams {\r\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\r\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\r\n    return { weights, bias }\r\n  }\r\n\r\n  const params = {\r\n    fc: extractFcParams('fc')\r\n  }\r\n\r\n  disposeUnusedWeightTensors(weightMap, paramMappings)\r\n\r\n  return { params, paramMappings }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nexport function seperateWeightMaps(weightMap: tf.NamedTensorMap) {\r\n\r\n  const featureExtractorMap: tf.NamedTensorMap = {}\r\n  const classifierMap: tf.NamedTensorMap = {}\r\n\r\n  Object.keys(weightMap).forEach(key => {\r\n    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap\r\n    map[key] = weightMap[key]\r\n  })\r\n\r\n  return { featureExtractorMap, classifierMap }\r\n\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { NetInput, NeuralNetwork } from 'tfjs-image-recognition-base';\r\n\r\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\r\nimport {\r\n  FaceFeatureExtractorParams,\r\n  IFaceFeatureExtractor,\r\n  TinyFaceFeatureExtractorParams,\r\n} from '../faceFeatureExtractor/types';\r\nimport { extractParams } from './extractParams';\r\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\r\nimport { NetParams } from './types';\r\nimport { seperateWeightMaps } from './util';\r\n\r\nexport abstract class FaceProcessor<\r\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\r\n>\r\n  extends NeuralNetwork<NetParams> {\r\n\r\n  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>\r\n\r\n  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {\r\n    super(_name)\r\n    this._faceFeatureExtractor = faceFeatureExtractor\r\n  }\r\n\r\n  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {\r\n    return this._faceFeatureExtractor\r\n  }\r\n\r\n  protected abstract getDefaultModelName(): string\r\n  protected abstract getClassifierChannelsIn(): number\r\n  protected abstract getClassifierChannelsOut(): number\r\n\r\n  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {\r\n\r\n    const { params } = this\r\n\r\n    if (!params) {\r\n      throw new Error(`${this._name} - load model before inference`)\r\n    }\r\n\r\n    return tf.tidy(() => {\r\n      const bottleneckFeatures = input instanceof NetInput\r\n        ? this.faceFeatureExtractor.forwardInput(input)\r\n        : input\r\n      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)\r\n    })\r\n  }\r\n\r\n  public dispose(throwOnRedispose: boolean = true) {\r\n    this.faceFeatureExtractor.dispose(throwOnRedispose)\r\n    super.dispose(throwOnRedispose)\r\n  }\r\n\r\n  public loadClassifierParams(weights: Float32Array) {\r\n    const { params, paramMappings } = this.extractClassifierParams(weights)\r\n    this._params = params\r\n    this._paramMappings = paramMappings\r\n  }\r\n\r\n  public extractClassifierParams(weights: Float32Array) {\r\n    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())\r\n  }\r\n\r\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\r\n\r\n    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)\r\n\r\n    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)\r\n\r\n    return extractParamsFromWeigthMap(classifierMap)\r\n  }\r\n\r\n  protected extractParams(weights: Float32Array) {\r\n\r\n    const cIn = this.getClassifierChannelsIn()\r\n    const cOut = this.getClassifierChannelsOut()\r\n    const classifierWeightSize = (cOut * cIn ) + cOut\r\n\r\n    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)\r\n    const classifierWeights = weights.slice(weights.length - classifierWeightSize)\r\n\r\n    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)\r\n    return this.extractClassifierParams(classifierWeights)\r\n  }\r\n}","export const faceExpressionLabels = {\r\n  neutral: 0,\r\n  happy: 1,\r\n  sad: 2,\r\n  angry: 3,\r\n  fearful: 4,\r\n  disgusted: 5,\r\n  surprised:6\r\n}\r\n\r\nexport type FaceExpression = 'neutral' | 'happy' | 'sad' | 'angry' | 'fearful' | 'disgusted' | 'surprised'\r\n\r\nexport type FaceExpressionPrediction = {\r\n  expression: FaceExpression,\r\n  probability: number\r\n}\r\n","import * as tf from '@tensorflow/tfjs-core';\r\nimport { NetInput, TNetInput, toNetInput } from 'tfjs-image-recognition-base';\r\n\r\nimport { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\r\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\r\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\r\nimport { FaceExpression, faceExpressionLabels, FaceExpressionPrediction } from './types';\r\n\r\nexport class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {\r\n\r\n  public static getFaceExpressionLabel(faceExpression: string) {\r\n    const label = faceExpressionLabels[faceExpression]\r\n\r\n    if (typeof label !== 'number') {\r\n      throw new Error(`getFaceExpressionLabel - no label for faceExpression: ${faceExpression}`)\r\n    }\r\n\r\n    return label\r\n  }\r\n\r\n  public static decodeProbabilites(probabilities: number[] | Float32Array): FaceExpressionPrediction[] {\r\n    if (probabilities.length !== 7) {\r\n      throw new Error(`decodeProbabilites - expected probabilities.length to be 7, have: ${probabilities.length}`)\r\n    }\r\n\r\n    return (Object.keys(faceExpressionLabels) as FaceExpression[])\r\n      .map(expression => ({ expression, probability: probabilities[faceExpressionLabels[expression]] }))\r\n  }\r\n\r\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\r\n    super('FaceExpressionNet', faceFeatureExtractor)\r\n  }\r\n\r\n  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {\r\n    return tf.tidy(() => tf.softmax(this.runNet(input)))\r\n  }\r\n\r\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\r\n    return this.forwardInput(await toNetInput(input))\r\n  }\r\n\r\n  public async predictExpressions(input: TNetInput) {\r\n    const netInput = await toNetInput(input)\r\n    const out = await this.forwardInput(netInput)\r\n    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {\r\n      const data = await t.data()\r\n      t.dispose()\r\n      return data\r\n    }))\r\n    out.dispose()\r\n\r\n    const predictionsByBatch = probabilitesByBatch\r\n      .map(propablities => FaceExpressionNet.decodeProbabilites(propablities as Float32Array))\r\n\r\n    return netInput.isBatchInput\r\n      ? predictionsByBatch\r\n      : predictionsByBatch[0]\r\n  }\r\n\r\n  protected getDefaultModelName(): string {\r\n    return 'face_expression_model'\r\n  }\r\n\r\n  protected getClassifierChannelsIn(): number {\r\n    return 256\r\n  }\r\n\r\n  protected getClassifierChannelsOut(): number {\r\n    return 7\r\n  }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { IDimensions, isEven, NetInput, Point, TNetInput, toNetInput } from 'tfjs-image-recognition-base';\r\n\r\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\r\nimport { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\r\nimport { FaceProcessor } from '../faceProcessor/FaceProcessor';\r\n\r\nexport abstract class FaceLandmark68NetBase<\r\n  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams\r\n>\r\n  extends FaceProcessor<TExtractorParams> {\r\n\r\n  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {\r\n\r\n    const inputDimensions = originalDimensions.map(({ width, height }) => {\r\n      const scale = inputSize / Math.max(height, width)\r\n      return {\r\n        width: width * scale,\r\n        height: height * scale\r\n      }\r\n    })\r\n\r\n    const batchSize = inputDimensions.length\r\n\r\n    return tf.tidy(() => {\r\n      const createInterleavedTensor = (fillX: number, fillY: number) =>\r\n        tf.stack([\r\n          tf.fill([68], fillX),\r\n          tf.fill([68], fillY)\r\n        ], 1).as2D(1, 136).as1D()\r\n\r\n      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {\r\n        const { width, height } = inputDimensions[batchIdx]\r\n        return cond(width, height) ? Math.abs(width - height) / 2 : 0\r\n      }\r\n      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)\r\n      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)\r\n\r\n      const landmarkTensors = output\r\n        .mul(tf.fill([batchSize, 136], inputSize))\r\n        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\r\n          createInterleavedTensor(\r\n            getPaddingX(batchIdx),\r\n            getPaddingY(batchIdx)\r\n          )\r\n        )))\r\n        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>\r\n          createInterleavedTensor(\r\n            inputDimensions[batchIdx].width,\r\n            inputDimensions[batchIdx].height\r\n          )\r\n        )))\r\n\r\n      return landmarkTensors as tf.Tensor2D\r\n    })\r\n  }\r\n\r\n  public forwardInput(input: NetInput): tf.Tensor2D {\r\n    return tf.tidy(() => {\r\n      const out = this.runNet(input)\r\n      return this.postProcess(\r\n        out,\r\n        input.inputSize as number,\r\n        input.inputDimensions.map(([height, width]) => ({ height, width }))\r\n      )\r\n    })\r\n  }\r\n\r\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\r\n    return this.forwardInput(await toNetInput(input))\r\n  }\r\n\r\n  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {\r\n    const netInput = await toNetInput(input)\r\n    const landmarkTensors = tf.tidy(\r\n      () => tf.unstack(this.forwardInput(netInput))\r\n    )\r\n\r\n    const landmarksForBatch = await Promise.all(landmarkTensors.map(\r\n      async (landmarkTensor, batchIdx) => {\r\n        const landmarksArray = Array.from(await landmarkTensor.data())\r\n        const xCoords = landmarksArray.filter((_, i) => isEven(i))\r\n        const yCoords = landmarksArray.filter((_, i) => !isEven(i))\r\n\r\n        return new FaceLandmarks68(\r\n          Array(68).fill(0).map((_, i) => new Point(xCoords[i], yCoords[i])),\r\n          {\r\n            height: netInput.getInputHeight(batchIdx),\r\n            width : netInput.getInputWidth(batchIdx),\r\n          }\r\n        )\r\n      }\r\n    ))\r\n\r\n    landmarkTensors.forEach(t => t.dispose())\r\n\r\n    return netInput.isBatchInput\r\n      ? landmarksForBatch\r\n      : landmarksForBatch[0]\r\n  }\r\n\r\n  protected getClassifierChannelsOut(): number {\r\n    return 136\r\n  }\r\n}","import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';\r\nimport { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\r\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\r\n\r\nexport class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {\r\n\r\n  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {\r\n    super('FaceLandmark68Net', faceFeatureExtractor)\r\n  }\r\n\r\n  protected getDefaultModelName(): string {\r\n    return 'face_landmark_68_model'\r\n  }\r\n\r\n  protected getClassifierChannelsIn(): number {\r\n    return 256\r\n  }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { disposeUnusedWeightTensors, ParamMapping } from 'tfjs-image-recognition-base';\r\n\r\nimport { loadParamsFactory } from './loadParamsFactory';\r\nimport { TinyFaceFeatureExtractorParams } from './types';\r\n\r\nexport function extractParamsFromWeigthMapTiny(\r\n  weightMap: tf.NamedTensorMap\r\n): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\r\n\r\n  const paramMappings: ParamMapping[] = []\r\n\r\n  const {\r\n    extractDenseBlock3Params\r\n  } = loadParamsFactory(weightMap, paramMappings)\r\n\r\n  const params = {\r\n    dense0: extractDenseBlock3Params('dense0', true),\r\n    dense1: extractDenseBlock3Params('dense1'),\r\n    dense2: extractDenseBlock3Params('dense2')\r\n  }\r\n\r\n  disposeUnusedWeightTensors(weightMap, paramMappings)\r\n\r\n  return { params, paramMappings }\r\n}","import { extractWeightsFactory, ParamMapping } from 'tfjs-image-recognition-base';\r\n\r\nimport { extractorsFactory } from './extractorsFactory';\r\nimport { TinyFaceFeatureExtractorParams } from './types';\r\n\r\nexport function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {\r\n\r\n  const paramMappings: ParamMapping[] = []\r\n\r\n  const {\r\n    extractWeights,\r\n    getRemainingWeights\r\n  } = extractWeightsFactory(weights)\r\n\r\n  const {\r\n    extractDenseBlock3Params\r\n  } = extractorsFactory(extractWeights, paramMappings)\r\n\r\n  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true)\r\n  const dense1 = extractDenseBlock3Params(32, 64, 'dense1')\r\n  const dense2 = extractDenseBlock3Params(64, 128, 'dense2')\r\n\r\n  if (getRemainingWeights().length !== 0) {\r\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\r\n  }\r\n\r\n  return {\r\n    paramMappings,\r\n    params: { dense0, dense1, dense2 }\r\n  }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { NetInput, NeuralNetwork, normalize, TNetInput, toNetInput } from 'tfjs-image-recognition-base';\r\n\r\nimport { denseBlock3 } from './denseBlock';\r\nimport { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';\r\nimport { extractParamsTiny } from './extractParamsTiny';\r\nimport { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';\r\n\r\nexport class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {\r\n\r\n  constructor() {\r\n    super('TinyFaceFeatureExtractor')\r\n  }\r\n\r\n  public forwardInput(input: NetInput): tf.Tensor4D {\r\n\r\n    const { params } = this\r\n\r\n    if (!params) {\r\n      throw new Error('TinyFaceFeatureExtractor - load model before inference')\r\n    }\r\n\r\n    return tf.tidy(() => {\r\n      const batchTensor = input.toBatchTensor(112, true)\r\n      const meanRgb = [122.782, 117.001, 104.298]\r\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D\r\n\r\n      let out = denseBlock3(normalized, params.dense0, true)\r\n      out = denseBlock3(out, params.dense1)\r\n      out = denseBlock3(out, params.dense2)\r\n      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')\r\n\r\n      return out\r\n    })\r\n  }\r\n\r\n  public async forward(input: TNetInput): Promise<tf.Tensor4D> {\r\n    return this.forwardInput(await toNetInput(input))\r\n  }\r\n\r\n  protected getDefaultModelName(): string {\r\n    return 'face_feature_extractor_tiny_model'\r\n  }\r\n\r\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\r\n    return extractParamsFromWeigthMapTiny(weightMap)\r\n  }\r\n\r\n  protected extractParams(weights: Float32Array) {\r\n    return extractParamsTiny(weights)\r\n  }\r\n}","import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';\r\nimport { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';\r\nimport { FaceLandmark68NetBase } from './FaceLandmark68NetBase';\r\n\r\nexport class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {\r\n\r\n  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {\r\n    super('FaceLandmark68TinyNet', faceFeatureExtractor)\r\n  }\r\n\r\n  protected getDefaultModelName(): string {\r\n    return 'face_landmark_68_tiny_model'\r\n  }\r\n\r\n  protected getClassifierChannelsIn(): number {\r\n    return 128\r\n  }\r\n}","import { FaceLandmark68Net } from './FaceLandmark68Net';\r\n\r\nexport * from './FaceLandmark68Net';\r\nexport * from './FaceLandmark68TinyNet';\r\n\r\nexport class FaceLandmarkNet extends FaceLandmark68Net {}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { ScaleLayerParams } from './types';\r\n\r\nexport function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {\r\n  return tf.add(tf.mul(x, params.weights), params.biases)\r\n}\r\n","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { scale } from './scaleLayer';\r\nimport { ConvLayerParams } from './types';\r\n\r\n\r\nfunction convLayer(\r\n  x: tf.Tensor4D,\r\n  params: ConvLayerParams,\r\n  strides: [number, number],\r\n  withRelu: boolean,\r\n  padding: 'valid' | 'same' = 'same'\r\n): tf.Tensor4D {\r\n  const { filters, bias } = params.conv\r\n\r\n  let out = tf.conv2d(x, filters, strides, padding)\r\n  out = tf.add(out, bias)\r\n  out = scale(out, params.scale)\r\n  return withRelu ? tf.relu(out) : out\r\n}\r\n\r\nexport function conv(x: tf.Tensor4D, params: ConvLayerParams) {\r\n  return convLayer(x, params, [1, 1], true)\r\n}\r\n\r\nexport function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {\r\n  return convLayer(x, params, [1, 1], false)\r\n}\r\n\r\nexport function convDown(x: tf.Tensor4D, params: ConvLayerParams) {\r\n  return convLayer(x, params, [2, 2], true, 'valid')\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { extractWeightsFactory, ExtractWeightsFunction, isFloat, ParamMapping } from 'tfjs-image-recognition-base';\r\nimport { ConvParams } from 'tfjs-tiny-yolov2';\r\n\r\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\r\n\r\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\r\n\r\n  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {\r\n    const weights = extractWeights(numFilterValues)\r\n    const depth = weights.length / (numFilters * filterSize * filterSize)\r\n\r\n    if (isFloat(depth)) {\r\n      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)\r\n    }\r\n\r\n    return tf.tidy(\r\n      () => tf.transpose(\r\n        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),\r\n        [2, 3, 1, 0]\r\n      )\r\n    )\r\n  }\r\n\r\n  function extractConvParams(\r\n    numFilterValues: number,\r\n    numFilters: number,\r\n    filterSize: number,\r\n    mappedPrefix: string\r\n  ): ConvParams {\r\n\r\n    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)\r\n    const bias = tf.tensor1d(extractWeights(numFilters))\r\n\r\n    paramMappings.push(\r\n      { paramPath: `${mappedPrefix}/filters` },\r\n      { paramPath: `${mappedPrefix}/bias` }\r\n    )\r\n\r\n    return { filters, bias }\r\n  }\r\n\r\n  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {\r\n\r\n    const weights = tf.tensor1d(extractWeights(numWeights))\r\n    const biases = tf.tensor1d(extractWeights(numWeights))\r\n\r\n    paramMappings.push(\r\n      { paramPath: `${mappedPrefix}/weights` },\r\n      { paramPath: `${mappedPrefix}/biases` }\r\n    )\r\n\r\n    return {\r\n      weights,\r\n      biases\r\n    }\r\n  }\r\n\r\n  function extractConvLayerParams(\r\n    numFilterValues: number,\r\n    numFilters: number,\r\n    filterSize: number,\r\n    mappedPrefix: string\r\n  ): ConvLayerParams {\r\n\r\n    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)\r\n    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)\r\n\r\n    return { conv, scale }\r\n  }\r\n\r\n  function extractResidualLayerParams(\r\n    numFilterValues: number,\r\n    numFilters: number,\r\n    filterSize: number,\r\n    mappedPrefix: string,\r\n    isDown: boolean = false\r\n  ): ResidualLayerParams {\r\n\r\n    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)\r\n    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)\r\n\r\n    return { conv1, conv2 }\r\n  }\r\n\r\n  return {\r\n    extractConvLayerParams,\r\n    extractResidualLayerParams\r\n  }\r\n\r\n}\r\n\r\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\r\n\r\n  const {\r\n    extractWeights,\r\n    getRemainingWeights\r\n  } = extractWeightsFactory(weights)\r\n\r\n  const paramMappings: ParamMapping[] = []\r\n\r\n  const {\r\n    extractConvLayerParams,\r\n    extractResidualLayerParams\r\n  } = extractorsFactory(extractWeights, paramMappings)\r\n\r\n  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')\r\n  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')\r\n  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')\r\n  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')\r\n\r\n  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)\r\n  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')\r\n  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')\r\n  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')\r\n\r\n  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)\r\n  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')\r\n  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')\r\n\r\n  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)\r\n  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')\r\n  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')\r\n  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')\r\n\r\n  const fc = tf.tidy(\r\n    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])\r\n  )\r\n  paramMappings.push({ paramPath: `fc` })\r\n\r\n  if (getRemainingWeights().length !== 0) {\r\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\r\n  }\r\n\r\n  const params = {\r\n    conv32_down,\r\n    conv32_1,\r\n    conv32_2,\r\n    conv32_3,\r\n    conv64_down,\r\n    conv64_1,\r\n    conv64_2,\r\n    conv64_3,\r\n    conv128_down,\r\n    conv128_1,\r\n    conv128_2,\r\n    conv256_down,\r\n    conv256_1,\r\n    conv256_2,\r\n    conv256_down_out,\r\n    fc\r\n  }\r\n\r\n  return { params, paramMappings }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport {\r\n  disposeUnusedWeightTensors,\r\n  extractWeightEntryFactory,\r\n  isTensor2D,\r\n  loadWeightMap,\r\n  ParamMapping,\r\n} from 'tfjs-image-recognition-base';\r\n\r\nimport { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';\r\n\r\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\r\n\r\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\r\n\r\n  function extractScaleLayerParams(prefix: string): ScaleLayerParams {\r\n\r\n    const weights = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/weights`, 1)\r\n    const biases = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/biases`, 1)\r\n\r\n    return { weights, biases }\r\n  }\r\n\r\n  function extractConvLayerParams(prefix: string): ConvLayerParams {\r\n\r\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/conv/filters`, 4)\r\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/conv/bias`, 1)\r\n    const scale = extractScaleLayerParams(prefix)\r\n\r\n    return { conv: { filters, bias }, scale }\r\n  }\r\n\r\n  function extractResidualLayerParams(prefix: string): ResidualLayerParams {\r\n    return {\r\n      conv1: extractConvLayerParams(`${prefix}/conv1`),\r\n      conv2: extractConvLayerParams(`${prefix}/conv2`)\r\n    }\r\n  }\r\n\r\n  return {\r\n    extractConvLayerParams,\r\n    extractResidualLayerParams\r\n  }\r\n\r\n}\r\n\r\nexport function extractParamsFromWeigthMap(\r\n  weightMap: tf.NamedTensorMap\r\n): { params: NetParams, paramMappings: ParamMapping[] } {\r\n\r\n  const paramMappings: ParamMapping[] = []\r\n\r\n  const {\r\n    extractConvLayerParams,\r\n    extractResidualLayerParams\r\n  } = extractorsFactory(weightMap, paramMappings)\r\n\r\n  const conv32_down = extractConvLayerParams('conv32_down')\r\n  const conv32_1 = extractResidualLayerParams('conv32_1')\r\n  const conv32_2 = extractResidualLayerParams('conv32_2')\r\n  const conv32_3 = extractResidualLayerParams('conv32_3')\r\n\r\n  const conv64_down = extractResidualLayerParams('conv64_down')\r\n  const conv64_1 = extractResidualLayerParams('conv64_1')\r\n  const conv64_2 = extractResidualLayerParams('conv64_2')\r\n  const conv64_3 = extractResidualLayerParams('conv64_3')\r\n\r\n  const conv128_down = extractResidualLayerParams('conv128_down')\r\n  const conv128_1 = extractResidualLayerParams('conv128_1')\r\n  const conv128_2 = extractResidualLayerParams('conv128_2')\r\n\r\n  const conv256_down = extractResidualLayerParams('conv256_down')\r\n  const conv256_1 = extractResidualLayerParams('conv256_1')\r\n  const conv256_2 = extractResidualLayerParams('conv256_2')\r\n  const conv256_down_out = extractResidualLayerParams('conv256_down_out')\r\n\r\n  const fc = weightMap['fc']\r\n  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' })\r\n\r\n  if (!isTensor2D(fc)) {\r\n    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`)\r\n  }\r\n\r\n  const params = {\r\n    conv32_down,\r\n    conv32_1,\r\n    conv32_2,\r\n    conv32_3,\r\n    conv64_down,\r\n    conv64_1,\r\n    conv64_2,\r\n    conv64_3,\r\n    conv128_down,\r\n    conv128_1,\r\n    conv128_2,\r\n    conv256_down,\r\n    conv256_1,\r\n    conv256_2,\r\n    conv256_down_out,\r\n    fc\r\n  }\r\n\r\n  disposeUnusedWeightTensors(weightMap, paramMappings)\r\n\r\n  return { params, paramMappings }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { conv, convDown, convNoRelu } from './convLayer';\r\nimport { ResidualLayerParams } from './types';\r\n\r\nexport function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\r\n  let out = conv(x, params.conv1)\r\n  out = convNoRelu(out, params.conv2)\r\n  out = tf.add(out, x)\r\n  out = tf.relu(out)\r\n  return out\r\n}\r\n\r\nexport function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {\r\n  let out = convDown(x, params.conv1)\r\n  out = convNoRelu(out, params.conv2)\r\n\r\n  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D\r\n  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)\r\n  const isPad = pooled.shape[3] !== out.shape[3]\r\n  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]\r\n\r\n  if (isAdjustShape) {\r\n    const padShapeX = [...out.shape] as [number, number, number, number]\r\n    padShapeX[1] = 1\r\n    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)\r\n    out = tf.concat([out, zerosW], 1)\r\n\r\n    const padShapeY = [...out.shape] as [number, number, number, number]\r\n    padShapeY[2] = 1\r\n    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)\r\n    out = tf.concat([out, zerosH], 2)\r\n  }\r\n\r\n  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled\r\n  out = tf.add(pooled, out) as tf.Tensor4D\r\n\r\n  out = tf.relu(out)\r\n  return out\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { NetInput, NeuralNetwork, normalize, TNetInput, toNetInput } from 'tfjs-image-recognition-base';\r\n\r\nimport { convDown } from './convLayer';\r\nimport { extractParams } from './extractParams';\r\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\r\nimport { residual, residualDown } from './residualLayer';\r\nimport { NetParams } from './types';\r\n\r\n\r\nexport class FaceRecognitionNet extends NeuralNetwork<NetParams> {\r\n\r\n  constructor() {\r\n    super('FaceRecognitionNet')\r\n  }\r\n\r\n  public forwardInput(input: NetInput): tf.Tensor2D {\r\n\r\n    const { params } = this\r\n\r\n    if (!params) {\r\n      throw new Error('FaceRecognitionNet - load model before inference')\r\n    }\r\n\r\n    return tf.tidy(() => {\r\n      const batchTensor = input.toBatchTensor(150, true).toFloat()\r\n\r\n      const meanRgb = [122.782, 117.001, 104.298]\r\n      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D\r\n\r\n      let out = convDown(normalized, params.conv32_down)\r\n      out = tf.maxPool(out, 3, 2, 'valid')\r\n\r\n      out = residual(out, params.conv32_1)\r\n      out = residual(out, params.conv32_2)\r\n      out = residual(out, params.conv32_3)\r\n\r\n      out = residualDown(out, params.conv64_down)\r\n      out = residual(out, params.conv64_1)\r\n      out = residual(out, params.conv64_2)\r\n      out = residual(out, params.conv64_3)\r\n\r\n      out = residualDown(out, params.conv128_down)\r\n      out = residual(out, params.conv128_1)\r\n      out = residual(out, params.conv128_2)\r\n\r\n      out = residualDown(out, params.conv256_down)\r\n      out = residual(out, params.conv256_1)\r\n      out = residual(out, params.conv256_2)\r\n      out = residualDown(out, params.conv256_down_out)\r\n\r\n      const globalAvg = out.mean([1, 2]) as tf.Tensor2D\r\n      const fullyConnected = tf.matMul(globalAvg, params.fc)\r\n\r\n      return fullyConnected\r\n    })\r\n  }\r\n\r\n  public async forward(input: TNetInput): Promise<tf.Tensor2D> {\r\n    return this.forwardInput(await toNetInput(input))\r\n  }\r\n\r\n  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {\r\n    const netInput = await toNetInput(input)\r\n\r\n    const faceDescriptorTensors = tf.tidy(\r\n      () => tf.unstack(this.forwardInput(netInput))\r\n    )\r\n\r\n    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(\r\n      t => t.data()\r\n    )) as Float32Array[]\r\n\r\n    faceDescriptorTensors.forEach(t => t.dispose())\r\n\r\n    return netInput.isBatchInput\r\n      ? faceDescriptorsForBatch\r\n      : faceDescriptorsForBatch[0]\r\n  }\r\n\r\n  protected getDefaultModelName(): string {\r\n    return 'face_recognition_model'\r\n  }\r\n\r\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\r\n    return extractParamsFromWeigthMap(weightMap)\r\n  }\r\n\r\n  protected extractParams(weights: Float32Array) {\r\n    return extractParams(weights)\r\n  }\r\n}","import { FaceRecognitionNet } from './FaceRecognitionNet';\r\n\r\nexport * from './FaceRecognitionNet';\r\n\r\nexport function createFaceRecognitionNet(weights: Float32Array) {\r\n  const net = new FaceRecognitionNet()\r\n  net.extractWeights(weights)\r\n  return net\r\n}","export type WithFaceDescriptor<TSource> = TSource & {\r\n  descriptor: Float32Array\r\n}\r\n\r\nexport function extendWithFaceDescriptor<\r\n  TSource\r\n> (\r\n  sourceObj: TSource,\r\n  descriptor: Float32Array\r\n): WithFaceDescriptor<TSource> {\r\n\r\n  const extension = { descriptor }\r\n  return Object.assign({}, sourceObj, extension)\r\n}\r\n\r\n","import { FaceDetection } from '../classes/FaceDetection';\r\n\r\nexport type WithFaceDetection<TSource> = TSource & {\r\n  detection: FaceDetection\r\n}\r\n\r\nexport function extendWithFaceDetection<\r\n  TSource\r\n> (\r\n  sourceObj: TSource,\r\n  detection: FaceDetection\r\n): WithFaceDetection<TSource> {\r\n\r\n  const extension = { detection }\r\n  return Object.assign({}, sourceObj, extension)\r\n}\r\n","import { FaceExpressionPrediction } from '../faceExpressionNet/types';\r\n\r\nexport type WithFaceExpressions<TSource> = TSource & {\r\n  expressions: FaceExpressionPrediction[]\r\n}\r\n\r\nexport function extendWithFaceExpressions<\r\n  TSource\r\n> (\r\n  sourceObj: TSource,\r\n  expressions: FaceExpressionPrediction[]\r\n): WithFaceExpressions<TSource> {\r\n\r\n  const extension = { expressions }\r\n  return Object.assign({}, sourceObj, extension)\r\n}","import { FaceDetection } from '../classes/FaceDetection';\r\nimport { FaceLandmarks } from '../classes/FaceLandmarks';\r\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\r\nimport { WithFaceDetection } from './WithFaceDetection';\r\n\r\nexport type WithFaceLandmarks<\r\n  TSource extends WithFaceDetection<{}>,\r\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\r\n> = TSource & {\r\n  landmarks: TFaceLandmarks\r\n  unshiftedLandmarks: TFaceLandmarks\r\n  alignedRect: FaceDetection\r\n}\r\n\r\nexport function extendWithFaceLandmarks<\r\n  TSource extends WithFaceDetection<{}>,\r\n  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68\r\n> (\r\n  sourceObj: TSource,\r\n  unshiftedLandmarks: TFaceLandmarks\r\n): WithFaceLandmarks<TSource, TFaceLandmarks> {\r\n\r\n  const { box: shift } = sourceObj.detection\r\n  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y)\r\n\r\n  const rect = landmarks.align()\r\n  const { imageDims } = sourceObj.detection\r\n  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims)\r\n\r\n  const extension = {\r\n    landmarks,\r\n    unshiftedLandmarks,\r\n    alignedRect\r\n  }\r\n\r\n  return Object.assign({}, sourceObj, extension)\r\n}","export interface IMtcnnOptions {\r\n  minFaceSize?: number\r\n  scaleFactor?: number\r\n  maxNumScales?: number\r\n  scoreThresholds?: number[]\r\n  scaleSteps?: number[]\r\n}\r\n\r\nexport class MtcnnOptions {\r\n  protected _name: string = 'MtcnnOptions'\r\n\r\n  private _minFaceSize: number\r\n  private _scaleFactor: number\r\n  private _maxNumScales: number\r\n  private _scoreThresholds: number[]\r\n  private _scaleSteps: number[] | undefined\r\n\r\n  constructor({ minFaceSize, scaleFactor, maxNumScales, scoreThresholds, scaleSteps }: IMtcnnOptions = {}) {\r\n    this._minFaceSize = minFaceSize || 20\r\n    this._scaleFactor = scaleFactor || 0.709\r\n    this._maxNumScales = maxNumScales || 10\r\n    this._scoreThresholds = scoreThresholds || [0.6, 0.7, 0.7]\r\n    this._scaleSteps = scaleSteps\r\n\r\n    if (typeof this._minFaceSize !== 'number' || this._minFaceSize < 0) {\r\n      throw new Error(`${this._name} - expected minFaceSize to be a number > 0`)\r\n    }\r\n\r\n    if (typeof this._scaleFactor !== 'number' || this._scaleFactor <= 0 || this._scaleFactor >= 1) {\r\n      throw new Error(`${this._name} - expected scaleFactor to be a number between 0 and 1`)\r\n    }\r\n\r\n    if (typeof this._maxNumScales !== 'number' || this._maxNumScales < 0) {\r\n      throw new Error(`${this._name} - expected maxNumScales to be a number > 0`)\r\n    }\r\n\r\n    if (\r\n      !Array.isArray(this._scoreThresholds)\r\n        || this._scoreThresholds.length !== 3\r\n        || this._scoreThresholds.some(th => typeof th !== 'number')\r\n    ) {\r\n      throw new Error(`${this._name} - expected scoreThresholds to be an array of numbers of length 3`)\r\n    }\r\n\r\n    if (\r\n      this._scaleSteps\r\n        && (!Array.isArray(this._scaleSteps) || this._scaleSteps.some(th => typeof th !== 'number'))\r\n    ) {\r\n      throw new Error(`${this._name} - expected scaleSteps to be an array of numbers`)\r\n    }\r\n  }\r\n\r\n  get minFaceSize(): number { return this._minFaceSize }\r\n  get scaleFactor(): number { return this._scaleFactor }\r\n  get maxNumScales(): number { return this._maxNumScales }\r\n  get scoreThresholds(): number[] { return this._scoreThresholds }\r\n  get scaleSteps(): number[] | undefined { return this._scaleSteps }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from 'tfjs-image-recognition-base';\r\nimport { ConvParams } from 'tfjs-tiny-yolov2';\r\n\r\nimport { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\r\n\r\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\r\n\r\n  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {\r\n\r\n    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])\r\n    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))\r\n    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))\r\n    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))\r\n    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))\r\n\r\n    paramMappings.push(\r\n      { paramPath: `${mappedPrefix}/filters` },\r\n      { paramPath: `${mappedPrefix}/batch_norm_scale` },\r\n      { paramPath: `${mappedPrefix}/batch_norm_offset` },\r\n      { paramPath: `${mappedPrefix}/batch_norm_mean` },\r\n      { paramPath: `${mappedPrefix}/batch_norm_variance` }\r\n    )\r\n\r\n    return {\r\n      filters,\r\n      batch_norm_scale,\r\n      batch_norm_offset,\r\n      batch_norm_mean,\r\n      batch_norm_variance\r\n    }\r\n  }\r\n\r\n  function extractConvParams(\r\n    channelsIn: number,\r\n    channelsOut: number,\r\n    filterSize: number,\r\n    mappedPrefix: string,\r\n    isPointwiseConv?: boolean\r\n  ): ConvParams {\r\n\r\n    const filters = tf.tensor4d(\r\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\r\n      [filterSize, filterSize, channelsIn, channelsOut]\r\n    )\r\n    const bias = tf.tensor1d(extractWeights(channelsOut))\r\n\r\n    paramMappings.push(\r\n      { paramPath: `${mappedPrefix}/filters` },\r\n      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }\r\n    )\r\n\r\n    return { filters, bias }\r\n  }\r\n\r\n  function extractPointwiseConvParams(\r\n    channelsIn: number,\r\n    channelsOut: number,\r\n    filterSize: number,\r\n    mappedPrefix: string\r\n  ): PointwiseConvParams {\r\n\r\n    const {\r\n      filters,\r\n      bias\r\n    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)\r\n\r\n    return {\r\n      filters,\r\n      batch_norm_offset: bias\r\n    }\r\n  }\r\n\r\n  function extractConvPairParams(\r\n    channelsIn: number,\r\n    channelsOut: number,\r\n    mappedPrefix: string\r\n  ): MobileNetV1.ConvPairParams {\r\n\r\n    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)\r\n    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)\r\n\r\n    return { depthwise_conv, pointwise_conv }\r\n  }\r\n\r\n  function extractMobilenetV1Params(): MobileNetV1.Params {\r\n\r\n    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')\r\n\r\n    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')\r\n    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')\r\n    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')\r\n    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')\r\n    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')\r\n    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')\r\n    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')\r\n    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')\r\n    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')\r\n    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')\r\n    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')\r\n    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')\r\n    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')\r\n\r\n    return {\r\n      conv_0,\r\n      conv_1,\r\n      conv_2,\r\n      conv_3,\r\n      conv_4,\r\n      conv_5,\r\n      conv_6,\r\n      conv_7,\r\n      conv_8,\r\n      conv_9,\r\n      conv_10,\r\n      conv_11,\r\n      conv_12,\r\n      conv_13\r\n    }\r\n  }\r\n\r\n  function extractPredictionLayerParams(): PredictionLayerParams {\r\n    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')\r\n    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')\r\n    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')\r\n    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')\r\n    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')\r\n    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')\r\n    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')\r\n    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')\r\n\r\n    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')\r\n    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')\r\n    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')\r\n    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')\r\n    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')\r\n    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')\r\n    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')\r\n    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')\r\n    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')\r\n    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')\r\n    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')\r\n    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')\r\n\r\n    const box_predictor_0 = {\r\n      box_encoding_predictor: box_encoding_0_predictor,\r\n      class_predictor: class_predictor_0\r\n    }\r\n    const box_predictor_1 = {\r\n      box_encoding_predictor: box_encoding_1_predictor,\r\n      class_predictor: class_predictor_1\r\n    }\r\n    const box_predictor_2 = {\r\n      box_encoding_predictor: box_encoding_2_predictor,\r\n      class_predictor: class_predictor_2\r\n    }\r\n    const box_predictor_3 = {\r\n      box_encoding_predictor: box_encoding_3_predictor,\r\n      class_predictor: class_predictor_3\r\n    }\r\n    const box_predictor_4 = {\r\n      box_encoding_predictor: box_encoding_4_predictor,\r\n      class_predictor: class_predictor_4\r\n    }\r\n    const box_predictor_5 = {\r\n      box_encoding_predictor: box_encoding_5_predictor,\r\n      class_predictor: class_predictor_5\r\n    }\r\n\r\n    return {\r\n      conv_0,\r\n      conv_1,\r\n      conv_2,\r\n      conv_3,\r\n      conv_4,\r\n      conv_5,\r\n      conv_6,\r\n      conv_7,\r\n      box_predictor_0,\r\n      box_predictor_1,\r\n      box_predictor_2,\r\n      box_predictor_3,\r\n      box_predictor_4,\r\n      box_predictor_5\r\n    }\r\n  }\r\n\r\n  return {\r\n    extractMobilenetV1Params,\r\n    extractPredictionLayerParams\r\n  }\r\n\r\n}\r\n\r\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\r\n\r\n  const paramMappings: ParamMapping[] = []\r\n\r\n  const {\r\n    extractWeights,\r\n    getRemainingWeights\r\n  } = extractWeightsFactory(weights)\r\n\r\n  const {\r\n    extractMobilenetV1Params,\r\n    extractPredictionLayerParams\r\n  } = extractorsFactory(extractWeights, paramMappings)\r\n\r\n  const mobilenetv1 = extractMobilenetV1Params()\r\n  const prediction_layer = extractPredictionLayerParams()\r\n  const extra_dim = tf.tensor3d(\r\n    extractWeights(5118 * 4),\r\n    [1, 5118, 4]\r\n  )\r\n  const output_layer = {\r\n    extra_dim\r\n  }\r\n\r\n  paramMappings.push({ paramPath: 'output_layer/extra_dim' })\r\n\r\n  if (getRemainingWeights().length !== 0) {\r\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\r\n  }\r\n\r\n  return {\r\n    params: {\r\n      mobilenetv1,\r\n      prediction_layer,\r\n      output_layer\r\n    },\r\n    paramMappings\r\n  }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport {\r\n  disposeUnusedWeightTensors,\r\n  extractWeightEntryFactory,\r\n  isTensor3D,\r\n  ParamMapping,\r\n} from 'tfjs-image-recognition-base';\r\nimport { ConvParams } from 'tfjs-tiny-yolov2';\r\n\r\nimport { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';\r\n\r\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\r\n\r\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\r\n\r\n  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {\r\n\r\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`)\r\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`)\r\n\r\n    return { filters, batch_norm_offset }\r\n  }\r\n\r\n  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {\r\n\r\n    const mappedPrefix = `mobilenetv1/conv_${idx}`\r\n    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`\r\n    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`\r\n    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`\r\n\r\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`)\r\n    const batch_norm_scale = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`)\r\n    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`)\r\n    const batch_norm_mean = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`)\r\n    const batch_norm_variance = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`)\r\n\r\n    return {\r\n      depthwise_conv: {\r\n        filters,\r\n        batch_norm_scale,\r\n        batch_norm_offset,\r\n        batch_norm_mean,\r\n        batch_norm_variance\r\n      },\r\n      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)\r\n    }\r\n  }\r\n\r\n  function extractMobilenetV1Params(): MobileNetV1.Params {\r\n    return {\r\n      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),\r\n      conv_1: extractConvPairParams(1),\r\n      conv_2: extractConvPairParams(2),\r\n      conv_3: extractConvPairParams(3),\r\n      conv_4: extractConvPairParams(4),\r\n      conv_5: extractConvPairParams(5),\r\n      conv_6: extractConvPairParams(6),\r\n      conv_7: extractConvPairParams(7),\r\n      conv_8: extractConvPairParams(8),\r\n      conv_9: extractConvPairParams(9),\r\n      conv_10: extractConvPairParams(10),\r\n      conv_11: extractConvPairParams(11),\r\n      conv_12: extractConvPairParams(12),\r\n      conv_13: extractConvPairParams(13)\r\n    }\r\n  }\r\n\r\n  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {\r\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/weights`, 4, `${mappedPrefix}/filters`)\r\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/biases`, 1, `${mappedPrefix}/bias`)\r\n\r\n    return { filters, bias }\r\n  }\r\n\r\n  function extractBoxPredictorParams(idx: number): BoxPredictionParams {\r\n\r\n    const box_encoding_predictor = extractConvParams(\r\n      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,\r\n      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`\r\n    )\r\n    const class_predictor = extractConvParams(\r\n      `Prediction/BoxPredictor_${idx}/ClassPredictor`,\r\n      `prediction_layer/box_predictor_${idx}/class_predictor`\r\n    )\r\n\r\n    return { box_encoding_predictor, class_predictor }\r\n  }\r\n\r\n  function extractPredictionLayerParams(): PredictionLayerParams {\r\n    return {\r\n      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),\r\n      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),\r\n      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),\r\n      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),\r\n      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),\r\n      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),\r\n      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),\r\n      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),\r\n      box_predictor_0: extractBoxPredictorParams(0),\r\n      box_predictor_1: extractBoxPredictorParams(1),\r\n      box_predictor_2: extractBoxPredictorParams(2),\r\n      box_predictor_3: extractBoxPredictorParams(3),\r\n      box_predictor_4: extractBoxPredictorParams(4),\r\n      box_predictor_5: extractBoxPredictorParams(5)\r\n    }\r\n  }\r\n\r\n  return {\r\n    extractMobilenetV1Params,\r\n    extractPredictionLayerParams\r\n  }\r\n}\r\n\r\nexport function extractParamsFromWeigthMap(\r\n  weightMap: tf.NamedTensorMap\r\n): { params: NetParams, paramMappings: ParamMapping[] } {\r\n\r\n  const paramMappings: ParamMapping[] = []\r\n\r\n  const {\r\n    extractMobilenetV1Params,\r\n    extractPredictionLayerParams\r\n  } = extractorsFactory(weightMap, paramMappings)\r\n\r\n  const extra_dim = weightMap['Output/extra_dim']\r\n  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' })\r\n\r\n  if (!isTensor3D(extra_dim)) {\r\n    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`)\r\n  }\r\n\r\n  const params = {\r\n    mobilenetv1: extractMobilenetV1Params(),\r\n    prediction_layer: extractPredictionLayerParams(),\r\n    output_layer: {\r\n      extra_dim\r\n    }\r\n  }\r\n\r\n  disposeUnusedWeightTensors(weightMap, paramMappings)\r\n\r\n  return { params, paramMappings }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { PointwiseConvParams } from './types';\r\n\r\nexport function pointwiseConvLayer(\r\n  x: tf.Tensor4D,\r\n  params: PointwiseConvParams,\r\n  strides: [number, number]\r\n) {\r\n  return tf.tidy(() => {\r\n\r\n    let out = tf.conv2d(x, params.filters, strides, 'same')\r\n    out = tf.add(out, params.batch_norm_offset)\r\n    return tf.clipByValue(out, 0, 6)\r\n\r\n  })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\r\nimport { MobileNetV1 } from './types';\r\n\r\nconst epsilon = 0.0010000000474974513\r\n\r\nfunction depthwiseConvLayer(\r\n  x: tf.Tensor4D,\r\n  params: MobileNetV1.DepthwiseConvParams,\r\n  strides: [number, number]\r\n) {\r\n  return tf.tidy(() => {\r\n\r\n    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')\r\n    out = tf.batchNormalization<tf.Rank.R4>(\r\n      out,\r\n      params.batch_norm_mean,\r\n      params.batch_norm_variance,\r\n      epsilon,\r\n      params.batch_norm_scale,\r\n      params.batch_norm_offset\r\n    )\r\n    return tf.clipByValue(out, 0, 6)\r\n\r\n  })\r\n}\r\n\r\nfunction getStridesForLayerIdx(layerIdx: number): [number, number] {\r\n  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]\r\n}\r\n\r\nexport function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {\r\n  return tf.tidy(() => {\r\n\r\n    let conv11 = null\r\n    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])\r\n\r\n    const convPairParams = [\r\n      params.conv_1,\r\n      params.conv_2,\r\n      params.conv_3,\r\n      params.conv_4,\r\n      params.conv_5,\r\n      params.conv_6,\r\n      params.conv_7,\r\n      params.conv_8,\r\n      params.conv_9,\r\n      params.conv_10,\r\n      params.conv_11,\r\n      params.conv_12,\r\n      params.conv_13\r\n    ]\r\n\r\n    convPairParams.forEach((param, i) => {\r\n      const layerIdx = i + 1\r\n      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)\r\n      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)\r\n      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])\r\n      if (layerIdx === 11) {\r\n        conv11 = out\r\n      }\r\n    })\r\n\r\n    if (conv11 === null) {\r\n      throw new Error('mobileNetV1 - output of conv layer 11 is null')\r\n    }\r\n\r\n    return {\r\n      out,\r\n      conv11: conv11 as any\r\n    }\r\n\r\n  })\r\n}","import * as tf from '@tensorflow/tfjs-core'\r\n\r\nexport function nonMaxSuppression(\r\n  boxes: tf.Tensor2D,\r\n  scores: number[],\r\n  maxOutputSize: number,\r\n  iouThreshold: number,\r\n  scoreThreshold: number\r\n): number[] {\r\n\r\n  const numBoxes = boxes.shape[0]\r\n  const outputSize = Math.min(\r\n    maxOutputSize,\r\n    numBoxes\r\n  )\r\n\r\n  const candidates = scores\r\n    .map((score, boxIndex) => ({ score, boxIndex }))\r\n    .filter(c => c.score > scoreThreshold)\r\n    .sort((c1, c2) => c2.score - c1.score)\r\n\r\n  const suppressFunc = (x: number) => x <= iouThreshold ? 1 : 0\r\n\r\n  const selected: number[] = []\r\n\r\n  candidates.forEach(c => {\r\n    if (selected.length >= outputSize) {\r\n      return\r\n    }\r\n    const originalScore = c.score\r\n\r\n    for (let j = selected.length - 1; j >= 0; --j) {\r\n      const iou = IOU(boxes, c.boxIndex, selected[j])\r\n      if (iou === 0.0) {\r\n        continue\r\n      }\r\n      c.score *= suppressFunc(iou)\r\n      if (c.score <= scoreThreshold) {\r\n        break\r\n      }\r\n    }\r\n\r\n    if (originalScore === c.score) {\r\n      selected.push(c.boxIndex)\r\n    }\r\n  })\r\n\r\n  return selected\r\n}\r\n\r\nfunction IOU(boxes: tf.Tensor2D, i: number, j: number) {\r\n  const yminI = Math.min(boxes.get(i, 0), boxes.get(i, 2))\r\n  const xminI = Math.min(boxes.get(i, 1), boxes.get(i, 3))\r\n  const ymaxI = Math.max(boxes.get(i, 0), boxes.get(i, 2))\r\n  const xmaxI = Math.max(boxes.get(i, 1), boxes.get(i, 3))\r\n  const yminJ = Math.min(boxes.get(j, 0), boxes.get(j, 2))\r\n  const xminJ = Math.min(boxes.get(j, 1), boxes.get(j, 3))\r\n  const ymaxJ = Math.max(boxes.get(j, 0), boxes.get(j, 2))\r\n  const xmaxJ = Math.max(boxes.get(j, 1), boxes.get(j, 3))\r\n  const areaI = (ymaxI - yminI) * (xmaxI - xminI)\r\n  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ)\r\n  if (areaI <= 0 || areaJ <= 0) {\r\n    return 0.0\r\n  }\r\n  const intersectionYmin = Math.max(yminI, yminJ)\r\n  const intersectionXmin = Math.max(xminI, xminJ)\r\n  const intersectionYmax = Math.min(ymaxI, ymaxJ)\r\n  const intersectionXmax = Math.min(xmaxI, xmaxJ)\r\n  const intersectionArea =\r\n      Math.max(intersectionYmax - intersectionYmin, 0.0) *\r\n      Math.max(intersectionXmax - intersectionXmin, 0.0)\r\n  return intersectionArea / (areaI + areaJ - intersectionArea)\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { OutputLayerParams } from './types';\r\n\r\n\r\nfunction getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {\r\n  const vec = tf.unstack(tf.transpose(x, [1, 0]))\r\n\r\n  const sizes = [\r\n    tf.sub(vec[2], vec[0]),\r\n    tf.sub(vec[3], vec[1])\r\n  ]\r\n\r\n  const centers = [\r\n    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),\r\n    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))\r\n  ]\r\n\r\n  return {\r\n    sizes,\r\n    centers\r\n  }\r\n}\r\n\r\nfunction decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {\r\n  const {\r\n    sizes,\r\n    centers\r\n  } = getCenterCoordinatesAndSizesLayer(x0)\r\n\r\n  const vec = tf.unstack(tf.transpose(x1, [1, 0]))\r\n\r\n  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2))\r\n  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0])\r\n\r\n  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2))\r\n  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1])\r\n\r\n  return tf.transpose(\r\n    tf.stack([\r\n      tf.sub(add0_out, div0_out),\r\n      tf.sub(add1_out, div1_out),\r\n      tf.add(add0_out, div0_out),\r\n      tf.add(add1_out, div1_out)\r\n    ]),\r\n    [1, 0]\r\n  )\r\n}\r\n\r\nexport function outputLayer(\r\n  boxPredictions: tf.Tensor4D,\r\n  classPredictions: tf.Tensor4D,\r\n  params: OutputLayerParams\r\n) {\r\n  return tf.tidy(() => {\r\n\r\n    const batchSize = boxPredictions.shape[0]\r\n\r\n    let boxes = decodeBoxesLayer(\r\n      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,\r\n      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D\r\n    )\r\n    boxes = tf.reshape(\r\n      boxes,\r\n      [batchSize, (boxes.shape[0] / batchSize), 4]\r\n    )\r\n\r\n    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]))\r\n    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor\r\n\r\n    scores = tf.reshape(\r\n      scores,\r\n      [batchSize, scores.shape[1]]\r\n    )\r\n\r\n    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[]\r\n    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[]\r\n\r\n    return {\r\n      boxes: boxesByBatch,\r\n      scores: scoresByBatch\r\n    }\r\n\r\n  })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { convLayer } from 'tfjs-tiny-yolov2';\r\n\r\nimport { BoxPredictionParams } from './types';\r\n\r\n\r\nexport function boxPredictionLayer(\r\n  x: tf.Tensor4D,\r\n  params: BoxPredictionParams\r\n) {\r\n  return tf.tidy(() => {\r\n\r\n    const batchSize = x.shape[0]\r\n\r\n    const boxPredictionEncoding = tf.reshape(\r\n      convLayer(x, params.box_encoding_predictor),\r\n      [batchSize, -1, 1, 4]\r\n    )\r\n    const classPrediction = tf.reshape(\r\n      convLayer(x, params.class_predictor),\r\n      [batchSize, -1, 3]\r\n    )\r\n\r\n    return {\r\n      boxPredictionEncoding,\r\n      classPrediction\r\n    }\r\n  })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { boxPredictionLayer } from './boxPredictionLayer';\r\nimport { pointwiseConvLayer } from './pointwiseConvLayer';\r\nimport { PredictionLayerParams } from './types';\r\n\r\nexport function predictionLayer(\r\n  x: tf.Tensor4D,\r\n  conv11: tf.Tensor4D,\r\n  params: PredictionLayerParams\r\n) {\r\n  return tf.tidy(() => {\r\n\r\n    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])\r\n    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])\r\n    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])\r\n    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])\r\n    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])\r\n    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])\r\n    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])\r\n    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])\r\n\r\n    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)\r\n    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)\r\n    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)\r\n    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)\r\n    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)\r\n    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)\r\n\r\n    const boxPredictions = tf.concat([\r\n      boxPrediction0.boxPredictionEncoding,\r\n      boxPrediction1.boxPredictionEncoding,\r\n      boxPrediction2.boxPredictionEncoding,\r\n      boxPrediction3.boxPredictionEncoding,\r\n      boxPrediction4.boxPredictionEncoding,\r\n      boxPrediction5.boxPredictionEncoding\r\n    ], 1) as tf.Tensor4D\r\n\r\n    const classPredictions = tf.concat([\r\n      boxPrediction0.classPrediction,\r\n      boxPrediction1.classPrediction,\r\n      boxPrediction2.classPrediction,\r\n      boxPrediction3.classPrediction,\r\n      boxPrediction4.classPrediction,\r\n      boxPrediction5.classPrediction\r\n    ], 1) as tf.Tensor4D\r\n\r\n    return {\r\n      boxPredictions,\r\n      classPredictions\r\n    }\r\n  })\r\n}","export interface ISsdMobilenetv1Options {\r\n  minConfidence?: number\r\n  maxResults?: number\r\n}\r\n\r\nexport class SsdMobilenetv1Options {\r\n  protected _name: string = 'SsdMobilenetv1Options'\r\n\r\n  private _minConfidence: number\r\n  private _maxResults: number\r\n\r\n  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {\r\n    this._minConfidence = minConfidence || 0.5\r\n    this._maxResults = maxResults || 100\r\n\r\n    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {\r\n      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`)\r\n    }\r\n\r\n    if (typeof this._maxResults !== 'number') {\r\n      throw new Error(`${this._name} - expected maxResults to be a number`)\r\n    }\r\n  }\r\n\r\n  get minConfidence(): number { return this._minConfidence }\r\n  get maxResults(): number { return this._maxResults }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { NetInput, NeuralNetwork, Rect, TNetInput, toNetInput } from 'tfjs-image-recognition-base';\r\n\r\nimport { FaceDetection } from '../classes/FaceDetection';\r\nimport { extractParams } from './extractParams';\r\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\r\nimport { mobileNetV1 } from './mobileNetV1';\r\nimport { nonMaxSuppression } from './nonMaxSuppression';\r\nimport { outputLayer } from './outputLayer';\r\nimport { predictionLayer } from './predictionLayer';\r\nimport { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';\r\nimport { NetParams } from './types';\r\n\r\n\r\nexport class SsdMobilenetv1 extends NeuralNetwork<NetParams> {\r\n\r\n  constructor() {\r\n    super('SsdMobilenetv1')\r\n  }\r\n\r\n  public forwardInput(input: NetInput) {\r\n\r\n    const { params } = this\r\n\r\n    if (!params) {\r\n      throw new Error('SsdMobilenetv1 - load model before inference')\r\n    }\r\n\r\n    return tf.tidy(() => {\r\n      const batchTensor = input.toBatchTensor(512, false).toFloat()\r\n\r\n      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D\r\n      const features = mobileNetV1(x, params.mobilenetv1)\r\n\r\n      const {\r\n        boxPredictions,\r\n        classPredictions\r\n      } = predictionLayer(features.out, features.conv11, params.prediction_layer)\r\n\r\n      return outputLayer(boxPredictions, classPredictions, params.output_layer)\r\n    })\r\n  }\r\n\r\n  public async forward(input: TNetInput) {\r\n    return this.forwardInput(await toNetInput(input))\r\n  }\r\n\r\n  public async locateFaces(\r\n    input: TNetInput,\r\n    options: ISsdMobilenetv1Options = {}\r\n  ): Promise<FaceDetection[]> {\r\n\r\n    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)\r\n\r\n    const netInput = await toNetInput(input)\r\n\r\n    const {\r\n      boxes: _boxes,\r\n      scores: _scores\r\n    } = this.forwardInput(netInput)\r\n\r\n\r\n    // TODO batches\r\n    const boxes = _boxes[0]\r\n    const scores = _scores[0]\r\n    for (let i = 1; i < _boxes.length; i++) {\r\n      _boxes[i].dispose()\r\n      _scores[i].dispose()\r\n    }\r\n\r\n    // TODO find a better way to filter by minConfidence\r\n    const scoresData = Array.from(await scores.data())\r\n\r\n    const iouThreshold = 0.5\r\n    const indices = nonMaxSuppression(\r\n      boxes,\r\n      scoresData,\r\n      maxResults,\r\n      iouThreshold,\r\n      minConfidence\r\n    )\r\n\r\n    const reshapedDims = netInput.getReshapedInputDimensions(0)\r\n    const inputSize = netInput.inputSize as number\r\n    const padX = inputSize / reshapedDims.width\r\n    const padY = inputSize / reshapedDims.height\r\n\r\n    const results = indices\r\n      .map(idx => {\r\n        const [top, bottom] = [\r\n          Math.max(0, boxes.get(idx, 0)),\r\n          Math.min(1.0, boxes.get(idx, 2))\r\n        ].map(val => val * padY)\r\n        const [left, right] = [\r\n          Math.max(0, boxes.get(idx, 1)),\r\n          Math.min(1.0, boxes.get(idx, 3))\r\n        ].map(val => val * padX)\r\n        return new FaceDetection(\r\n          scoresData[idx],\r\n          new Rect(\r\n            left,\r\n            top,\r\n            right - left,\r\n            bottom - top\r\n          ),\r\n          {\r\n            height: netInput.getInputHeight(0),\r\n            width: netInput.getInputWidth(0)\r\n          }\r\n        )\r\n      })\r\n\r\n    boxes.dispose()\r\n    scores.dispose()\r\n\r\n    return results\r\n  }\r\n\r\n  protected getDefaultModelName(): string {\r\n    return 'ssd_mobilenetv1_model'\r\n  }\r\n\r\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\r\n    return extractParamsFromWeigthMap(weightMap)\r\n  }\r\n\r\n  protected extractParams(weights: Float32Array) {\r\n    return extractParams(weights)\r\n  }\r\n}","import { SsdMobilenetv1 } from './SsdMobilenetv1';\r\n\r\nexport * from './SsdMobilenetv1';\r\nexport * from './SsdMobilenetv1Options';\r\n\r\nexport function createSsdMobilenetv1(weights: Float32Array) {\r\n  const net = new SsdMobilenetv1()\r\n  net.extractWeights(weights)\r\n  return net\r\n}\r\n\r\nexport function createFaceDetectionNet(weights: Float32Array) {\r\n  return createSsdMobilenetv1(weights)\r\n}\r\n\r\n// alias for backward compatibily\r\nexport class FaceDetectionNet extends SsdMobilenetv1 {}","import { ITinyYolov2Options, TinyYolov2Options } from 'tfjs-tiny-yolov2';\r\n\r\nexport interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}\r\n\r\nexport class TinyFaceDetectorOptions extends TinyYolov2Options {\r\n  protected _name: string = 'TinyFaceDetectorOptions'\r\n}","export class ComposableTask<T> {\r\n\r\n  public async then(\r\n    onfulfilled: (value: T) => T | PromiseLike<T>\r\n  ): Promise<T> {\r\n    return onfulfilled(await this.run())\r\n  }\r\n\r\n  public async run(): Promise<T> {\r\n    throw new Error('ComposableTask - run is not implemented')\r\n  }\r\n}\r\n","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nexport function bgrToRgbTensor(tensor: tf.Tensor4D): tf.Tensor4D {\r\n  return tf.tidy(\r\n    () => tf.stack(tf.unstack(tensor, 3).reverse(), 3)\r\n  ) as tf.Tensor4D\r\n}","export const CELL_STRIDE = 2\r\nexport const CELL_SIZE = 12","import * as tf from '@tensorflow/tfjs-core';\r\nimport { extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from 'tfjs-image-recognition-base';\r\nimport { extractConvParamsFactory, extractFCParamsFactory } from 'tfjs-tiny-yolov2';\r\n\r\nimport { NetParams, ONetParams, PNetParams, RNetParams, SharedParams } from './types';\r\n\r\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\r\n\r\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\r\n  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)\r\n\r\n  function extractPReluParams(size: number, paramPath: string): tf.Tensor1D {\r\n    const alpha = tf.tensor1d(extractWeights(size))\r\n    paramMappings.push({ paramPath })\r\n    return alpha\r\n  }\r\n\r\n  function extractSharedParams(numFilters: number[], mappedPrefix: string, isRnet: boolean = false): SharedParams {\r\n\r\n    const conv1 = extractConvParams(numFilters[0], numFilters[1], 3, `${mappedPrefix}/conv1`)\r\n    const prelu1_alpha = extractPReluParams(numFilters[1], `${mappedPrefix}/prelu1_alpha`)\r\n    const conv2 = extractConvParams(numFilters[1], numFilters[2], 3, `${mappedPrefix}/conv2`)\r\n    const prelu2_alpha = extractPReluParams(numFilters[2], `${mappedPrefix}/prelu2_alpha`)\r\n    const conv3 = extractConvParams(numFilters[2], numFilters[3], isRnet ? 2 : 3, `${mappedPrefix}/conv3`)\r\n    const prelu3_alpha = extractPReluParams(numFilters[3], `${mappedPrefix}/prelu3_alpha`)\r\n\r\n    return { conv1, prelu1_alpha, conv2, prelu2_alpha, conv3, prelu3_alpha }\r\n  }\r\n\r\n  function extractPNetParams(): PNetParams {\r\n\r\n    const sharedParams = extractSharedParams([3, 10, 16, 32], 'pnet')\r\n    const conv4_1 = extractConvParams(32, 2, 1, 'pnet/conv4_1')\r\n    const conv4_2 = extractConvParams(32, 4, 1, 'pnet/conv4_2')\r\n\r\n    return { ...sharedParams, conv4_1, conv4_2 }\r\n  }\r\n\r\n  function extractRNetParams(): RNetParams {\r\n\r\n    const sharedParams = extractSharedParams([3, 28, 48, 64], 'rnet', true)\r\n    const fc1 = extractFCParams(576, 128, 'rnet/fc1')\r\n    const prelu4_alpha = extractPReluParams(128, 'rnet/prelu4_alpha')\r\n    const fc2_1 = extractFCParams(128, 2, 'rnet/fc2_1')\r\n    const fc2_2 = extractFCParams(128, 4, 'rnet/fc2_2')\r\n\r\n    return { ...sharedParams, fc1, prelu4_alpha, fc2_1, fc2_2 }\r\n  }\r\n\r\n  function extractONetParams(): ONetParams {\r\n\r\n    const sharedParams = extractSharedParams([3, 32, 64, 64], 'onet')\r\n    const conv4 = extractConvParams(64, 128, 2, 'onet/conv4')\r\n    const prelu4_alpha = extractPReluParams(128, 'onet/prelu4_alpha')\r\n    const fc1 = extractFCParams(1152, 256, 'onet/fc1')\r\n    const prelu5_alpha = extractPReluParams(256, 'onet/prelu5_alpha')\r\n    const fc2_1 = extractFCParams(256, 2, 'onet/fc2_1')\r\n    const fc2_2 = extractFCParams(256, 4, 'onet/fc2_2')\r\n    const fc2_3 = extractFCParams(256, 10, 'onet/fc2_3')\r\n\r\n    return { ...sharedParams, conv4, prelu4_alpha, fc1, prelu5_alpha, fc2_1, fc2_2, fc2_3 }\r\n  }\r\n\r\n  return {\r\n    extractPNetParams,\r\n    extractRNetParams,\r\n    extractONetParams\r\n  }\r\n\r\n}\r\n\r\nexport function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {\r\n\r\n  const {\r\n    extractWeights,\r\n    getRemainingWeights\r\n  } = extractWeightsFactory(weights)\r\n\r\n  const paramMappings: ParamMapping[] = []\r\n\r\n  const {\r\n    extractPNetParams,\r\n    extractRNetParams,\r\n    extractONetParams\r\n  } = extractorsFactory(extractWeights, paramMappings)\r\n\r\n  const pnet = extractPNetParams()\r\n  const rnet = extractRNetParams()\r\n  const onet = extractONetParams()\r\n\r\n  if (getRemainingWeights().length !== 0) {\r\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\r\n  }\r\n\r\n  return { params: { pnet, rnet, onet }, paramMappings }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from 'tfjs-image-recognition-base';\r\nimport { ConvParams, FCParams } from 'tfjs-tiny-yolov2';\r\n\r\nimport { NetParams, ONetParams, PNetParams, RNetParams, SharedParams } from './types';\r\n\r\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\r\n\r\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\r\n\r\n  function extractConvParams(prefix: string): ConvParams {\r\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/weights`, 4, `${prefix}/filters`)\r\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\r\n\r\n    return { filters, bias }\r\n  }\r\n\r\n  function extractFCParams(prefix: string): FCParams {\r\n    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)\r\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\r\n\r\n    return { weights, bias }\r\n  }\r\n\r\n  function extractPReluParams(paramPath: string): tf.Tensor1D {\r\n    return extractWeightEntry<tf.Tensor1D>(paramPath, 1)\r\n  }\r\n\r\n  function extractSharedParams(prefix: string): SharedParams {\r\n\r\n    const conv1 = extractConvParams(`${prefix}/conv1`)\r\n    const prelu1_alpha = extractPReluParams(`${prefix}/prelu1_alpha`)\r\n    const conv2 = extractConvParams(`${prefix}/conv2`)\r\n    const prelu2_alpha = extractPReluParams(`${prefix}/prelu2_alpha`)\r\n    const conv3 = extractConvParams(`${prefix}/conv3`)\r\n    const prelu3_alpha = extractPReluParams(`${prefix}/prelu3_alpha`)\r\n\r\n    return { conv1, prelu1_alpha, conv2, prelu2_alpha, conv3, prelu3_alpha }\r\n  }\r\n\r\n  function extractPNetParams(): PNetParams {\r\n\r\n    const sharedParams = extractSharedParams('pnet')\r\n    const conv4_1 = extractConvParams('pnet/conv4_1')\r\n    const conv4_2 = extractConvParams('pnet/conv4_2')\r\n\r\n    return { ...sharedParams, conv4_1, conv4_2 }\r\n  }\r\n\r\n  function extractRNetParams(): RNetParams {\r\n\r\n    const sharedParams = extractSharedParams('rnet')\r\n    const fc1 = extractFCParams('rnet/fc1')\r\n    const prelu4_alpha = extractPReluParams('rnet/prelu4_alpha')\r\n    const fc2_1 = extractFCParams('rnet/fc2_1')\r\n    const fc2_2 = extractFCParams('rnet/fc2_2')\r\n\r\n    return { ...sharedParams, fc1, prelu4_alpha, fc2_1, fc2_2 }\r\n  }\r\n\r\n  function extractONetParams(): ONetParams {\r\n\r\n    const sharedParams = extractSharedParams('onet')\r\n    const conv4 = extractConvParams('onet/conv4')\r\n    const prelu4_alpha = extractPReluParams('onet/prelu4_alpha')\r\n    const fc1 = extractFCParams('onet/fc1')\r\n    const prelu5_alpha = extractPReluParams('onet/prelu5_alpha')\r\n    const fc2_1 = extractFCParams('onet/fc2_1')\r\n    const fc2_2 = extractFCParams('onet/fc2_2')\r\n    const fc2_3 = extractFCParams('onet/fc2_3')\r\n\r\n    return { ...sharedParams, conv4, prelu4_alpha, fc1, prelu5_alpha, fc2_1, fc2_2, fc2_3 }\r\n  }\r\n\r\n  return {\r\n    extractPNetParams,\r\n    extractRNetParams,\r\n    extractONetParams\r\n  }\r\n\r\n}\r\n\r\nexport function extractParamsFromWeigthMap(\r\n  weightMap: tf.NamedTensorMap\r\n): { params: NetParams, paramMappings: ParamMapping[] } {\r\n\r\n  const paramMappings: ParamMapping[] = []\r\n\r\n  const {\r\n    extractPNetParams,\r\n    extractRNetParams,\r\n    extractONetParams\r\n  } = extractorsFactory(weightMap, paramMappings)\r\n\r\n  const pnet = extractPNetParams()\r\n  const rnet = extractRNetParams()\r\n  const onet = extractONetParams()\r\n\r\n  disposeUnusedWeightTensors(weightMap, paramMappings)\r\n\r\n  return { params: { pnet, rnet, onet }, paramMappings }\r\n}","export function getSizesForScale(scale: number, [height, width]: number[]) {\r\n  return {\r\n    height: Math.floor(height * scale),\r\n    width: Math.floor(width * scale)\r\n  }\r\n}","import { CELL_SIZE } from './config';\r\n\r\nexport function pyramidDown(minFaceSize: number, scaleFactor: number, dims: number[]): number[] {\r\n\r\n  const [height, width] = dims\r\n  const m = CELL_SIZE / minFaceSize\r\n\r\n  const scales = []\r\n\r\n  let minLayer = Math.min(height, width) * m\r\n  let exp = 0\r\n  while (minLayer >= 12) {\r\n    scales.push(m * Math.pow(scaleFactor, exp))\r\n    minLayer = minLayer * scaleFactor\r\n    exp += 1\r\n  }\r\n\r\n  return scales\r\n}","import { Box } from 'tfjs-image-recognition-base';\r\n\r\nexport class MtcnnBox extends Box<MtcnnBox> {\r\n  constructor(left: number, top: number, right: number, bottom: number) {\r\n    super({ left, top, right, bottom }, true)\r\n  }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nexport function normalize(x: tf.Tensor4D): tf.Tensor4D {\r\n  return tf.tidy(\r\n    () => tf.mul(tf.sub(x, tf.scalar(127.5)), tf.scalar(0.0078125))\r\n  )\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nexport function prelu<T extends tf.Tensor>(x: T, alpha: tf.Tensor1D): T {\r\n  return tf.tidy(() =>\r\n    tf.add(\r\n      tf.relu(x),\r\n      tf.mul(alpha, tf.neg(tf.relu(tf.neg(x))))\r\n    )\r\n  )\r\n}\r\n","import * as tf from '@tensorflow/tfjs-core';\r\nimport { convLayer } from 'tfjs-tiny-yolov2';\r\n\r\nimport { prelu } from './prelu';\r\nimport { SharedParams } from './types';\r\n\r\nexport function sharedLayer(x: tf.Tensor4D, params: SharedParams, isPnet: boolean = false) {\r\n  return tf.tidy(() => {\r\n\r\n    let out = convLayer(x, params.conv1, 'valid')\r\n    out = prelu<tf.Tensor4D>(out, params.prelu1_alpha)\r\n    out = tf.maxPool(out, isPnet ? [2, 2]: [3, 3], [2, 2], 'same')\r\n    out = convLayer(out, params.conv2, 'valid')\r\n    out = prelu<tf.Tensor4D>(out, params.prelu2_alpha)\r\n    out = isPnet ? out : tf.maxPool(out, [3, 3], [2, 2], 'valid')\r\n    out = convLayer(out, params.conv3, 'valid')\r\n    out = prelu<tf.Tensor4D>(out, params.prelu3_alpha)\r\n\r\n    return out\r\n  })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { convLayer } from 'tfjs-tiny-yolov2';\r\n\r\nimport { sharedLayer } from './sharedLayers';\r\nimport { PNetParams } from './types';\r\n\r\nexport function PNet(x: tf.Tensor4D, params: PNetParams): { prob: tf.Tensor4D, regions: tf.Tensor4D } {\r\n  return tf.tidy(() => {\r\n\r\n    let out = sharedLayer(x, params, true)\r\n    const conv = convLayer(out, params.conv4_1, 'valid')\r\n    const max = tf.expandDims(tf.max(conv, 3), 3)\r\n    const prob = tf.softmax(tf.sub(conv, max), 3) as tf.Tensor4D\r\n    const regions = convLayer(out, params.conv4_2, 'valid')\r\n\r\n    return { prob, regions }\r\n  })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { BoundingBox, nonMaxSuppression, Point } from 'tfjs-image-recognition-base';\r\n\r\nimport { CELL_SIZE, CELL_STRIDE } from './config';\r\nimport { getSizesForScale } from './getSizesForScale';\r\nimport { MtcnnBox } from './MtcnnBox';\r\nimport { normalize } from './normalize';\r\nimport { PNet } from './PNet';\r\nimport { PNetParams } from './types';\r\n\r\nfunction rescaleAndNormalize(x: tf.Tensor4D, scale: number): tf.Tensor4D {\r\n  return tf.tidy(() => {\r\n\r\n    const { height, width } = getSizesForScale(scale, x.shape.slice(1))\r\n    const resized = tf.image.resizeBilinear(x, [height, width])\r\n    const normalized = normalize(resized)\r\n\r\n    return (tf.transpose(normalized, [0, 2, 1, 3]) as tf.Tensor4D)\r\n  })\r\n}\r\n\r\nfunction extractBoundingBoxes(\r\n  scoresTensor: tf.Tensor2D,\r\n  regionsTensor: tf.Tensor3D,\r\n  scale: number,\r\n  scoreThreshold: number\r\n) {\r\n\r\n  // TODO: fix this!, maybe better to use tf.gather here\r\n  const indices: Point[] = []\r\n  for (let y = 0; y < scoresTensor.shape[0]; y++) {\r\n    for (let x = 0; x < scoresTensor.shape[1]; x++) {\r\n      if (scoresTensor.get(y, x) >= scoreThreshold) {\r\n        indices.push(new Point(x, y))\r\n      }\r\n    }\r\n  }\r\n\r\n  const boundingBoxes = indices.map(idx => {\r\n    const cell = new BoundingBox(\r\n      Math.round((idx.y * CELL_STRIDE + 1) / scale),\r\n      Math.round((idx.x * CELL_STRIDE + 1) / scale),\r\n      Math.round((idx.y * CELL_STRIDE + CELL_SIZE) / scale),\r\n      Math.round((idx.x * CELL_STRIDE + CELL_SIZE) / scale)\r\n    )\r\n\r\n    const score = scoresTensor.get(idx.y, idx.x)\r\n\r\n    const region = new MtcnnBox(\r\n      regionsTensor.get(idx.y, idx.x, 0),\r\n      regionsTensor.get(idx.y, idx.x, 1),\r\n      regionsTensor.get(idx.y, idx.x, 2),\r\n      regionsTensor.get(idx.y, idx.x, 3)\r\n    )\r\n\r\n    return {\r\n      cell,\r\n      score,\r\n      region\r\n    }\r\n  })\r\n\r\n  return boundingBoxes\r\n}\r\n\r\nexport function stage1(\r\n  imgTensor: tf.Tensor4D,\r\n  scales: number[],\r\n  scoreThreshold: number,\r\n  params: PNetParams,\r\n  stats: any\r\n) {\r\n  stats.stage1 = []\r\n\r\n  const pnetOutputs = scales.map((scale) => tf.tidy(() => {\r\n    const statsForScale: any = { scale }\r\n    const resized = rescaleAndNormalize(imgTensor, scale)\r\n\r\n    let ts = Date.now()\r\n    const { prob, regions } = PNet(resized, params)\r\n    statsForScale.pnet = Date.now() - ts\r\n\r\n    const scoresTensor = tf.unstack(tf.unstack(prob, 3)[1])[0] as tf.Tensor2D\r\n    const regionsTensor = tf.unstack(regions)[0] as tf.Tensor3D\r\n\r\n    return {\r\n      scoresTensor,\r\n      regionsTensor,\r\n      scale,\r\n      statsForScale\r\n    }\r\n  }))\r\n\r\n  const boxesForScale = pnetOutputs.map(({ scoresTensor, regionsTensor, scale, statsForScale }) => {\r\n    const boundingBoxes = extractBoundingBoxes(\r\n      scoresTensor,\r\n      regionsTensor,\r\n      scale,\r\n      scoreThreshold\r\n    )\r\n\r\n    scoresTensor.dispose()\r\n    regionsTensor.dispose()\r\n\r\n    if (!boundingBoxes.length) {\r\n      stats.stage1.push(statsForScale)\r\n      return []\r\n    }\r\n\r\n    let ts = Date.now()\r\n    const indices = nonMaxSuppression(\r\n      boundingBoxes.map(bbox => bbox.cell),\r\n      boundingBoxes.map(bbox => bbox.score),\r\n      0.5\r\n    )\r\n    statsForScale.nms = Date.now() - ts\r\n    statsForScale.numBoxes = indices.length\r\n\r\n    stats.stage1.push(statsForScale)\r\n    return indices.map(boxIdx => boundingBoxes[boxIdx])\r\n  })\r\n\r\n  const allBoxes = boxesForScale.reduce(\r\n    (all, boxes) => all.concat(boxes), []\r\n  )\r\n\r\n  let finalBoxes: BoundingBox[] = []\r\n  let finalScores: number[] = []\r\n\r\n  if (allBoxes.length > 0) {\r\n    let ts = Date.now()\r\n    const indices = nonMaxSuppression(\r\n      allBoxes.map(bbox => bbox.cell),\r\n      allBoxes.map(bbox => bbox.score),\r\n      0.7\r\n    )\r\n    stats.stage1_nms = Date.now() - ts\r\n\r\n    finalScores = indices.map(idx => allBoxes[idx].score)\r\n    finalBoxes = indices\r\n      .map(idx => allBoxes[idx])\r\n      .map(({ cell, region }) =>\r\n        new BoundingBox(\r\n          cell.left + (region.left * cell.width),\r\n          cell.top + (region.top * cell.height),\r\n          cell.right + (region.right * cell.width),\r\n          cell.bottom + (region.bottom * cell.height)\r\n        ).toSquare().round()\r\n      )\r\n\r\n  }\r\n\r\n  return {\r\n    boxes: finalBoxes,\r\n    scores: finalScores\r\n  }\r\n\r\n}\r\n","import * as tf from '@tensorflow/tfjs-core';\r\nimport {\r\n  Box,\r\n  createCanvas,\r\n  createCanvasFromMedia,\r\n  env,\r\n  getContext2dOrThrow,\r\n  IDimensions,\r\n} from 'tfjs-image-recognition-base';\r\n\r\nimport { normalize } from './normalize';\r\n\r\nexport async function extractImagePatches(\r\n  img: HTMLCanvasElement,\r\n  boxes: Box[],\r\n  { width, height }: IDimensions\r\n): Promise<tf.Tensor4D[]> {\r\n\r\n\r\n  const imgCtx = getContext2dOrThrow(img)\r\n\r\n  const bitmaps = await Promise.all(boxes.map(async box => {\r\n    // TODO: correct padding\r\n    const { y, ey, x, ex } = box.padAtBorders(img.height, img.width)\r\n\r\n    const fromX = x - 1\r\n    const fromY = y - 1\r\n    const imgData = imgCtx.getImageData(fromX, fromY, (ex - fromX), (ey - fromY))\r\n\r\n    return env.isNodejs() ? createCanvasFromMedia(imgData) : createImageBitmap(imgData)\r\n  }))\r\n\r\n  const imagePatchesDatas: number[][] = []\r\n\r\n  bitmaps.forEach(bmp => {\r\n    const patch = createCanvas({ width, height })\r\n    const patchCtx = getContext2dOrThrow(patch)\r\n    patchCtx.drawImage(bmp, 0, 0, width, height)\r\n    const { data } = patchCtx.getImageData(0, 0, width, height)\r\n\r\n    const currData = []\r\n    // RGBA -> BGR\r\n    for(let i = 0; i < data.length; i+=4) {\r\n      currData.push(data[i + 2])\r\n      currData.push(data[i + 1])\r\n      currData.push(data[i])\r\n    }\r\n    imagePatchesDatas.push(currData)\r\n  })\r\n\r\n\r\n  return imagePatchesDatas.map(data => {\r\n    const t = tf.tidy(() => {\r\n      const imagePatchTensor = tf.transpose(\r\n        tf.tensor4d(data, [1, width, height, 3]),\r\n        [0, 2, 1, 3]\r\n      ).toFloat() as tf.Tensor4D\r\n\r\n      return normalize(imagePatchTensor)\r\n    })\r\n    return t\r\n  })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\r\nimport { prelu } from './prelu';\r\nimport { sharedLayer } from './sharedLayers';\r\nimport { RNetParams } from './types';\r\n\r\nexport function RNet(x: tf.Tensor4D, params: RNetParams): { scores: tf.Tensor1D, regions: tf.Tensor2D } {\r\n  return tf.tidy(() => {\r\n\r\n    const convOut = sharedLayer(x, params)\r\n    const vectorized = tf.reshape(convOut, [convOut.shape[0], params.fc1.weights.shape[0]]) as tf.Tensor2D\r\n    const fc1 = fullyConnectedLayer(vectorized, params.fc1)\r\n    const prelu4 = prelu<tf.Tensor2D>(fc1, params.prelu4_alpha)\r\n    const fc2_1 = fullyConnectedLayer(prelu4, params.fc2_1)\r\n    const max = tf.expandDims(tf.max(fc2_1, 1), 1)\r\n    const prob = tf.softmax(tf.sub(fc2_1, max), 1) as tf.Tensor2D\r\n    const regions = fullyConnectedLayer(prelu4, params.fc2_2)\r\n\r\n    const scores = tf.unstack(prob, 1)[1] as tf.Tensor1D\r\n    return { scores, regions }\r\n  })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { Box, nonMaxSuppression } from 'tfjs-image-recognition-base';\r\n\r\nimport { extractImagePatches } from './extractImagePatches';\r\nimport { MtcnnBox } from './MtcnnBox';\r\nimport { RNet } from './RNet';\r\nimport { RNetParams } from './types';\r\n\r\nexport async function stage2(\r\n  img: HTMLCanvasElement,\r\n  inputBoxes: Box[],\r\n  scoreThreshold: number,\r\n  params: RNetParams,\r\n  stats: any\r\n) {\r\n\r\n  let ts = Date.now()\r\n  const rnetInputs = await extractImagePatches(img, inputBoxes, { width: 24, height: 24 })\r\n  stats.stage2_extractImagePatches = Date.now() - ts\r\n\r\n  ts = Date.now()\r\n  const rnetOuts = rnetInputs.map(\r\n    rnetInput => {\r\n      const out = RNet(rnetInput, params)\r\n      rnetInput.dispose()\r\n      return out\r\n    }\r\n  )\r\n  stats.stage2_rnet = Date.now() - ts\r\n\r\n  const scoresTensor = rnetOuts.length > 1\r\n    ? tf.concat(rnetOuts.map(out => out.scores))\r\n    : rnetOuts[0].scores\r\n  const scores = Array.from(await scoresTensor.data())\r\n  scoresTensor.dispose()\r\n\r\n  const indices = scores\r\n    .map((score, idx) => ({ score, idx }))\r\n    .filter(c => c.score > scoreThreshold)\r\n    .map(({ idx }) => idx)\r\n\r\n  const filteredBoxes = indices.map(idx => inputBoxes[idx])\r\n  const filteredScores = indices.map(idx => scores[idx])\r\n\r\n  let finalBoxes: Box[] = []\r\n  let finalScores: number[] = []\r\n\r\n  if (filteredBoxes.length > 0) {\r\n    ts = Date.now()\r\n    const indicesNms = nonMaxSuppression(\r\n      filteredBoxes,\r\n      filteredScores,\r\n      0.7\r\n    )\r\n    stats.stage2_nms = Date.now() - ts\r\n\r\n    const regions = indicesNms.map(idx =>\r\n      new MtcnnBox(\r\n        rnetOuts[indices[idx]].regions.get(0, 0),\r\n        rnetOuts[indices[idx]].regions.get(0, 1),\r\n        rnetOuts[indices[idx]].regions.get(0, 2),\r\n        rnetOuts[indices[idx]].regions.get(0, 3)\r\n      )\r\n    )\r\n\r\n    finalScores = indicesNms.map(idx => filteredScores[idx])\r\n    finalBoxes = indicesNms.map((idx, i) => filteredBoxes[idx].calibrate(regions[i]))\r\n  }\r\n\r\n  rnetOuts.forEach(t => {\r\n    t.regions.dispose()\r\n    t.scores.dispose()\r\n  })\r\n\r\n  return {\r\n    boxes: finalBoxes,\r\n    scores: finalScores\r\n  }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { convLayer } from 'tfjs-tiny-yolov2';\r\n\r\nimport { fullyConnectedLayer } from '../common/fullyConnectedLayer';\r\nimport { prelu } from './prelu';\r\nimport { sharedLayer } from './sharedLayers';\r\nimport { ONetParams } from './types';\r\n\r\nexport function ONet(x: tf.Tensor4D, params: ONetParams): { scores: tf.Tensor1D, regions: tf.Tensor2D, points: tf.Tensor2D } {\r\n  return tf.tidy(() => {\r\n\r\n    let out = sharedLayer(x, params)\r\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\r\n    out = convLayer(out, params.conv4, 'valid')\r\n    out = prelu<tf.Tensor4D>(out, params.prelu4_alpha)\r\n\r\n    const vectorized = tf.reshape(out, [out.shape[0], params.fc1.weights.shape[0]]) as tf.Tensor2D\r\n    const fc1 = fullyConnectedLayer(vectorized, params.fc1)\r\n    const prelu5 = prelu<tf.Tensor2D>(fc1, params.prelu5_alpha)\r\n    const fc2_1 = fullyConnectedLayer(prelu5, params.fc2_1)\r\n    const max = tf.expandDims(tf.max(fc2_1, 1), 1)\r\n\r\n    const prob = tf.softmax(tf.sub(fc2_1, max), 1) as tf.Tensor2D\r\n    const regions = fullyConnectedLayer(prelu5, params.fc2_2)\r\n    const points = fullyConnectedLayer(prelu5, params.fc2_3)\r\n\r\n    const scores = tf.unstack(prob, 1)[1] as tf.Tensor1D\r\n    return { scores, regions, points }\r\n  })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { BoundingBox, Box, nonMaxSuppression, Point } from 'tfjs-image-recognition-base';\r\n\r\nimport { extractImagePatches } from './extractImagePatches';\r\nimport { MtcnnBox } from './MtcnnBox';\r\nimport { ONet } from './ONet';\r\nimport { ONetParams } from './types';\r\n\r\nexport async function stage3(\r\n  img: HTMLCanvasElement,\r\n  inputBoxes: BoundingBox[],\r\n  scoreThreshold: number,\r\n  params: ONetParams,\r\n  stats: any\r\n) {\r\n\r\n  let ts = Date.now()\r\n  const onetInputs = await extractImagePatches(img, inputBoxes, { width: 48, height: 48 })\r\n  stats.stage3_extractImagePatches = Date.now() - ts\r\n\r\n  ts = Date.now()\r\n  const onetOuts = onetInputs.map(\r\n    onetInput => {\r\n      const out = ONet(onetInput, params)\r\n      onetInput.dispose()\r\n      return out\r\n    }\r\n  )\r\n  stats.stage3_onet = Date.now() - ts\r\n\r\n  const scoresTensor = onetOuts.length > 1\r\n    ? tf.concat(onetOuts.map(out => out.scores))\r\n    : onetOuts[0].scores\r\n  const scores = Array.from(await scoresTensor.data())\r\n  scoresTensor.dispose()\r\n\r\n  const indices = scores\r\n    .map((score, idx) => ({ score, idx }))\r\n    .filter(c => c.score > scoreThreshold)\r\n    .map(({ idx }) => idx)\r\n\r\n  const filteredRegions = indices.map(idx => new MtcnnBox(\r\n    onetOuts[idx].regions.get(0, 0),\r\n    onetOuts[idx].regions.get(0, 1),\r\n    onetOuts[idx].regions.get(0, 2),\r\n    onetOuts[idx].regions.get(0, 3)\r\n  ))\r\n  const filteredBoxes = indices\r\n    .map((idx, i) => inputBoxes[idx].calibrate(filteredRegions[i]))\r\n  const filteredScores = indices.map(idx => scores[idx])\r\n\r\n  let finalBoxes: Box[] = []\r\n  let finalScores: number[] = []\r\n  let points: Point[][] = []\r\n\r\n  if (filteredBoxes.length > 0) {\r\n\r\n    ts = Date.now()\r\n    const indicesNms = nonMaxSuppression(\r\n      filteredBoxes,\r\n      filteredScores,\r\n      0.7,\r\n      false\r\n    )\r\n    stats.stage3_nms = Date.now() - ts\r\n\r\n    finalBoxes = indicesNms.map(idx => filteredBoxes[idx])\r\n    finalScores = indicesNms.map(idx => filteredScores[idx])\r\n    points = indicesNms.map((idx, i) =>\r\n      Array(5).fill(0).map((_, ptIdx) =>\r\n        new Point(\r\n          ((onetOuts[idx].points.get(0, ptIdx) * (finalBoxes[i].width + 1)) + finalBoxes[i].left) ,\r\n          ((onetOuts[idx].points.get(0, ptIdx + 5) * (finalBoxes[i].height + 1)) + finalBoxes[i].top)\r\n        )\r\n      )\r\n    )\r\n  }\r\n\r\n  onetOuts.forEach(t => {\r\n    t.regions.dispose()\r\n    t.scores.dispose()\r\n    t.points.dispose()\r\n  })\r\n\r\n  return {\r\n    boxes: finalBoxes,\r\n    scores: finalScores,\r\n    points\r\n  }\r\n\r\n}\r\n","import * as tf from '@tensorflow/tfjs-core';\r\nimport { NetInput, NeuralNetwork, Point, Rect, TNetInput, toNetInput } from 'tfjs-image-recognition-base';\r\n\r\nimport { FaceDetection } from '../classes/FaceDetection';\r\nimport { FaceLandmarks5 } from '../classes/FaceLandmarks5';\r\nimport { extendWithFaceDetection, extendWithFaceLandmarks } from '../factories';\r\nimport { bgrToRgbTensor } from './bgrToRgbTensor';\r\nimport { CELL_SIZE } from './config';\r\nimport { extractParams } from './extractParams';\r\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\r\nimport { getSizesForScale } from './getSizesForScale';\r\nimport { IMtcnnOptions, MtcnnOptions } from './MtcnnOptions';\r\nimport { pyramidDown } from './pyramidDown';\r\nimport { stage1 } from './stage1';\r\nimport { stage2 } from './stage2';\r\nimport { stage3 } from './stage3';\r\nimport { MtcnnResult, NetParams } from './types';\r\n\r\nexport class Mtcnn extends NeuralNetwork<NetParams> {\r\n\r\n  constructor() {\r\n    super('Mtcnn')\r\n  }\r\n\r\n  public async forwardInput(\r\n    input: NetInput,\r\n    forwardParams: IMtcnnOptions = {}\r\n  ): Promise<{ results: MtcnnResult[], stats: any }> {\r\n\r\n    const { params } = this\r\n\r\n    if (!params) {\r\n      throw new Error('Mtcnn - load model before inference')\r\n    }\r\n\r\n    const inputCanvas = input.canvases[0]\r\n\r\n    if (!inputCanvas) {\r\n      throw new Error('Mtcnn - inputCanvas is not defined, note that passing tensors into Mtcnn.forwardInput is not supported yet.')\r\n    }\r\n\r\n    const stats: any = {}\r\n\r\n    const tsTotal = Date.now()\r\n\r\n    const imgTensor = tf.tidy(() =>\r\n      bgrToRgbTensor(\r\n        tf.expandDims(tf.fromPixels(inputCanvas)).toFloat() as tf.Tensor4D\r\n      )\r\n    )\r\n\r\n    const onReturn = (results: any) => {\r\n      // dispose tensors on return\r\n      imgTensor.dispose()\r\n      stats.total = Date.now() - tsTotal\r\n      return results\r\n    }\r\n\r\n    const [height, width] = imgTensor.shape.slice(1)\r\n\r\n    const {\r\n      minFaceSize,\r\n      scaleFactor,\r\n      maxNumScales,\r\n      scoreThresholds,\r\n      scaleSteps\r\n    } = new MtcnnOptions(forwardParams)\r\n\r\n    const scales = (scaleSteps || pyramidDown(minFaceSize, scaleFactor, [height, width]))\r\n      .filter(scale => {\r\n        const sizes = getSizesForScale(scale, [height, width])\r\n        return Math.min(sizes.width, sizes.height) > CELL_SIZE\r\n      })\r\n      .slice(0, maxNumScales)\r\n\r\n    stats.scales = scales\r\n    stats.pyramid = scales.map(scale => getSizesForScale(scale, [height, width]))\r\n\r\n    let ts = Date.now()\r\n    const out1 = await stage1(imgTensor, scales, scoreThresholds[0], params.pnet, stats)\r\n    stats.total_stage1 = Date.now() - ts\r\n\r\n    if (!out1.boxes.length) {\r\n      return onReturn({ results: [], stats })\r\n    }\r\n\r\n    stats.stage2_numInputBoxes = out1.boxes.length\r\n    // using the inputCanvas to extract and resize the image patches, since it is faster\r\n    // than doing this on the gpu\r\n    ts = Date.now()\r\n    const out2 = await stage2(inputCanvas, out1.boxes, scoreThresholds[1], params.rnet, stats)\r\n    stats.total_stage2 = Date.now() - ts\r\n\r\n    if (!out2.boxes.length) {\r\n      return onReturn({ results: [], stats })\r\n    }\r\n\r\n    stats.stage3_numInputBoxes = out2.boxes.length\r\n\r\n    ts = Date.now()\r\n    const out3 = await stage3(inputCanvas, out2.boxes, scoreThresholds[2], params.onet, stats)\r\n    stats.total_stage3 = Date.now() - ts\r\n\r\n    const results = out3.boxes.map((box, idx) => extendWithFaceLandmarks(\r\n      extendWithFaceDetection<{}>(\r\n        {},\r\n        new FaceDetection(\r\n          out3.scores[idx],\r\n          new Rect(\r\n            box.left / width,\r\n            box.top / height,\r\n            box.width / width,\r\n            box.height / height\r\n          ),\r\n          {\r\n            height,\r\n            width\r\n          }\r\n        )\r\n      ),\r\n      new FaceLandmarks5(\r\n        out3.points[idx].map(pt => pt.sub(new Point(box.left, box.top)).div(new Point(box.width, box.height))),\r\n        { width: box.width, height: box.height }\r\n      )\r\n    ))\r\n\r\n    return onReturn({ results, stats })\r\n  }\r\n\r\n  public async forward(\r\n    input: TNetInput,\r\n    forwardParams: IMtcnnOptions = {}\r\n  ): Promise<MtcnnResult[]> {\r\n    return (\r\n      await this.forwardInput(\r\n        await toNetInput(input),\r\n        forwardParams\r\n      )\r\n    ).results\r\n  }\r\n\r\n  public async forwardWithStats(\r\n    input: TNetInput,\r\n    forwardParams: IMtcnnOptions = {}\r\n  ): Promise<{ results: MtcnnResult[], stats: any }> {\r\n    return this.forwardInput(\r\n      await toNetInput(input),\r\n      forwardParams\r\n    )\r\n  }\r\n\r\n  protected getDefaultModelName(): string {\r\n    return 'mtcnn_model'\r\n  }\r\n\r\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\r\n    return extractParamsFromWeigthMap(weightMap)\r\n  }\r\n\r\n  protected extractParams(weights: Float32Array) {\r\n    return extractParams(weights)\r\n  }\r\n}","import { Point } from 'tfjs-image-recognition-base';\r\n\r\nexport const IOU_THRESHOLD = 0.4\r\n\r\nexport const BOX_ANCHORS = [\r\n  new Point(1.603231, 2.094468),\r\n  new Point(6.041143, 7.080126),\r\n  new Point(2.882459, 3.518061),\r\n  new Point(4.266906, 5.178857),\r\n  new Point(9.041765, 10.66308)\r\n]\r\n\r\nexport const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404]","import * as tf from '@tensorflow/tfjs-core';\r\nimport { ParamMapping, Point, TNetInput } from 'tfjs-image-recognition-base';\r\nimport { ITinyYolov2Options, TinyYolov2 as TinyYolov2Base } from 'tfjs-tiny-yolov2';\r\nimport { TinyYolov2NetParams } from 'tfjs-tiny-yolov2/build/commonjs/tinyYolov2/types';\r\n\r\nimport { FaceDetection } from '../classes';\r\nimport { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';\r\n\r\nexport class TinyFaceDetector extends TinyYolov2Base {\r\n\r\n  constructor() {\r\n    const config = {\r\n      withSeparableConvs: true,\r\n      iouThreshold: IOU_THRESHOLD,\r\n      classes: ['face'],\r\n      anchors: BOX_ANCHORS,\r\n      meanRgb: MEAN_RGB,\r\n      isFirstLayerConv2d: true,\r\n      filterSizes: [3, 16, 32, 64, 128, 256, 512]\r\n    }\r\n\r\n    super(config)\r\n  }\r\n\r\n  public get anchors(): Point[] {\r\n    return this.config.anchors\r\n  }\r\n\r\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\r\n    const objectDetections = await this.detect(input, forwardParams)\r\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\r\n  }\r\n\r\n  protected getDefaultModelName(): string {\r\n    return 'tiny_face_detector_model'\r\n  }\r\n\r\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\r\n    return super.extractParamsFromWeigthMap(weightMap)\r\n  }\r\n}","import { Point } from 'tfjs-image-recognition-base';\r\n\r\nexport const IOU_THRESHOLD = 0.4\r\n\r\nexport const BOX_ANCHORS = [\r\n  new Point(0.738768, 0.874946),\r\n  new Point(2.42204, 2.65704),\r\n  new Point(4.30971, 7.04493),\r\n  new Point(10.246, 4.59428),\r\n  new Point(12.6868, 11.8741)\r\n]\r\n\r\nexport const BOX_ANCHORS_SEPARABLE = [\r\n  new Point(1.603231, 2.094468),\r\n  new Point(6.041143, 7.080126),\r\n  new Point(2.882459, 3.518061),\r\n  new Point(4.266906, 5.178857),\r\n  new Point(9.041765, 10.66308)\r\n]\r\n\r\nexport const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404]\r\n\r\nexport const DEFAULT_MODEL_NAME = 'tiny_yolov2_model'\r\nexport const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model'","import * as tf from '@tensorflow/tfjs-core';\r\nimport { ParamMapping, Point, TNetInput } from 'tfjs-image-recognition-base';\r\nimport { ITinyYolov2Options, TinyYolov2 as TinyYolov2Base, TinyYolov2NetParams } from 'tfjs-tiny-yolov2';\r\n\r\nimport { FaceDetection } from '../classes';\r\nimport {\r\n  BOX_ANCHORS,\r\n  BOX_ANCHORS_SEPARABLE,\r\n  DEFAULT_MODEL_NAME,\r\n  DEFAULT_MODEL_NAME_SEPARABLE_CONV,\r\n  IOU_THRESHOLD,\r\n  MEAN_RGB_SEPARABLE,\r\n} from './const';\r\n\r\nexport class TinyYolov2 extends TinyYolov2Base {\r\n\r\n  constructor(withSeparableConvs: boolean = true) {\r\n    const config = Object.assign({}, {\r\n      withSeparableConvs,\r\n      iouThreshold: IOU_THRESHOLD,\r\n      classes: ['face']\r\n    },\r\n    withSeparableConvs\r\n      ? {\r\n        anchors: BOX_ANCHORS_SEPARABLE,\r\n        meanRgb: MEAN_RGB_SEPARABLE\r\n      }\r\n      : {\r\n        anchors: BOX_ANCHORS,\r\n        withClassScores: true\r\n      }\r\n    )\r\n\r\n    super(config)\r\n  }\r\n\r\n  public get withSeparableConvs(): boolean {\r\n    return this.config.withSeparableConvs\r\n  }\r\n\r\n  public get anchors(): Point[] {\r\n    return this.config.anchors\r\n  }\r\n\r\n  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {\r\n    const objectDetections = await this.detect(input, forwardParams)\r\n    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))\r\n  }\r\n\r\n  protected getDefaultModelName(): string {\r\n    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME\r\n  }\r\n\r\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\r\n    return super.extractParamsFromWeigthMap(weightMap)\r\n  }\r\n}","import { TNetInput } from 'tfjs-image-recognition-base';\r\nimport { ITinyYolov2Options } from 'tfjs-tiny-yolov2';\r\n\r\nimport { FaceDetection } from '../classes/FaceDetection';\r\nimport { FaceLandmarks5 } from '../classes/FaceLandmarks5';\r\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\r\nimport { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';\r\nimport { FaceExpressionPrediction } from '../faceExpressionNet/types';\r\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\r\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\r\nimport { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';\r\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\r\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\r\nimport { Mtcnn } from '../mtcnn/Mtcnn';\r\nimport { MtcnnOptions } from '../mtcnn/MtcnnOptions';\r\nimport { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';\r\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\r\nimport { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';\r\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\r\nimport { TinyYolov2 } from '../tinyYolov2/TinyYolov2';\r\n\r\nexport const nets = {\r\n  ssdMobilenetv1: new SsdMobilenetv1(),\r\n  tinyFaceDetector: new TinyFaceDetector(),\r\n  tinyYolov2: new TinyYolov2(),\r\n  mtcnn: new Mtcnn(),\r\n  faceLandmark68Net: new FaceLandmark68Net(),\r\n  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),\r\n  faceRecognitionNet: new FaceRecognitionNet(),\r\n  faceExpressionNet: new FaceExpressionNet()\r\n}\r\n\r\n/**\r\n * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.\r\n *\r\n * @param input The input image.\r\n * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).\r\n * @returns Bounding box of each face with score.\r\n */\r\nexport const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> =>\r\n  nets.ssdMobilenetv1.locateFaces(input, options)\r\n\r\n/**\r\n * Attempts to detect all faces in an image using the Tiny Face Detector.\r\n *\r\n * @param input The input image.\r\n * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).\r\n * @returns Bounding box of each face with score.\r\n */\r\nexport const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> =>\r\n  nets.tinyFaceDetector.locateFaces(input, options)\r\n\r\n/**\r\n * Attempts to detect all faces in an image using the Tiny Yolov2 Network.\r\n *\r\n * @param input The input image.\r\n * @param options (optional, default: see TinyYolov2Options constructor for default parameters).\r\n * @returns Bounding box of each face with score.\r\n */\r\nexport const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> =>\r\n  nets.tinyYolov2.locateFaces(input, options)\r\n\r\n/**\r\n * Attempts to detect all faces in an image and the 5 point face landmarks\r\n * of each detected face using the MTCNN Network.\r\n *\r\n * @param input The input image.\r\n * @param options (optional, default: see MtcnnOptions constructor for default parameters).\r\n * @returns Bounding box of each face with score and 5 point face landmarks.\r\n */\r\nexport const mtcnn = (input: TNetInput, options: MtcnnOptions): Promise<WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks5>[]> =>\r\n  nets.mtcnn.forward(input, options)\r\n\r\n/**\r\n * Detects the 68 point face landmark positions of the face shown in an image.\r\n *\r\n * @param inputs The face image extracted from the bounding box of a face. Can\r\n * also be an array of input images, which will be batch processed.\r\n * @returns 68 point face landmarks or array thereof in case of batch input.\r\n */\r\nexport const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> =>\r\n  nets.faceLandmark68Net.detectLandmarks(input)\r\n\r\n/**\r\n * Detects the 68 point face landmark positions of the face shown in an image\r\n * using a tinier version of the 68 point face landmark model, which is slightly\r\n * faster at inference, but also slightly less accurate.\r\n *\r\n * @param inputs The face image extracted from the bounding box of a face. Can\r\n * also be an array of input images, which will be batch processed.\r\n * @returns 68 point face landmarks or array thereof in case of batch input.\r\n */\r\nexport const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>  =>\r\n  nets.faceLandmark68TinyNet.detectLandmarks(input)\r\n\r\n/**\r\n * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,\r\n * which uniquely represents the features of that persons face. The computed face descriptor can\r\n * be used to measure the similarity between faces, by computing the euclidean distance of two\r\n * face descriptors.\r\n *\r\n * @param inputs The face image extracted from the aligned bounding box of a face. Can\r\n * also be an array of input images, which will be batch processed.\r\n * @returns Face descriptor with 128 entries or array thereof in case of batch input.\r\n */\r\nexport const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]>  =>\r\n  nets.faceRecognitionNet.computeFaceDescriptor(input)\r\n\r\n\r\n/**\r\n * Recognizes the facial expressions of a face and returns the likelyhood of\r\n * each facial expression.\r\n *\r\n * @param inputs The face image extracted from the bounding box of a face. Can\r\n * also be an array of input images, which will be batch processed.\r\n * @returns An array of facial expressions with corresponding probabilities or array thereof in case of batch input.\r\n */\r\nexport const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressionPrediction[] | FaceExpressionPrediction[][]> =>\r\n  nets.faceExpressionNet.predictExpressions(input)\r\n\r\nexport const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url)\r\nexport const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url)\r\nexport const loadMtcnnModel = (url: string) => nets.mtcnn.load(url)\r\nexport const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url)\r\nexport const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url)\r\nexport const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url)\r\nexport const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url)\r\nexport const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url)\r\n\r\n// backward compatibility\r\nexport const loadFaceDetectionModel = loadSsdMobilenetv1Model\r\nexport const locateFaces = ssdMobilenetv1\r\nexport const detectLandmarks = detectFaceLandmarks","import * as tf from '@tensorflow/tfjs-core';\r\nimport { TNetInput } from 'tfjs-image-recognition-base';\r\n\r\nimport { extractFaces, extractFaceTensors } from '../dom';\r\nimport { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';\r\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\r\nimport { WithFaceLandmarks } from '../factories/WithFaceLandmarks';\r\nimport { ComposableTask } from './ComposableTask';\r\nimport { nets } from './nets';\r\n\r\nexport class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\r\n  constructor(\r\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\r\n    protected input: TNetInput\r\n  ) {\r\n    super()\r\n  }\r\n}\r\n\r\nexport class ComputeAllFaceDescriptorsTask<\r\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\r\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {\r\n\r\n  public async run(): Promise<WithFaceDescriptor<TSource>[]> {\r\n\r\n    const parentResults = await this.parentTask\r\n\r\n    const alignedRects = parentResults.map(({ alignedRect }) => alignedRect)\r\n    const alignedFaces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\r\n      ? await extractFaceTensors(this.input, alignedRects)\r\n      : await extractFaces(this.input, alignedRects)\r\n\r\n    const results = await Promise.all(parentResults.map(async (parentResult, i) => {\r\n      const descriptor = await nets.faceRecognitionNet.computeFaceDescriptor(alignedFaces[i]) as Float32Array\r\n      return extendWithFaceDescriptor<TSource>(parentResult, descriptor)\r\n    }))\r\n\r\n    alignedFaces.forEach(f => f instanceof tf.Tensor && f.dispose())\r\n\r\n    return results\r\n  }\r\n}\r\n\r\nexport class ComputeSingleFaceDescriptorTask<\r\n  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>\r\n> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {\r\n\r\n  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {\r\n\r\n    const parentResult = await this.parentTask\r\n    if (!parentResult) {\r\n      return\r\n    }\r\n\r\n    const { alignedRect } = parentResult\r\n    const alignedFaces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\r\n      ? await extractFaceTensors(this.input, [alignedRect])\r\n      : await extractFaces(this.input, [alignedRect])\r\n    const descriptor = await nets.faceRecognitionNet.computeFaceDescriptor(alignedFaces[0]) as Float32Array\r\n\r\n    alignedFaces.forEach(f => f instanceof tf.Tensor && f.dispose())\r\n\r\n    return extendWithFaceDescriptor(parentResult, descriptor)\r\n  }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { TNetInput } from 'tfjs-image-recognition-base';\r\n\r\nimport { FaceLandmarks68 } from '../classes/FaceLandmarks68';\r\nimport { extractFaces, extractFaceTensors } from '../dom';\r\nimport { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';\r\nimport { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';\r\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\r\nimport { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';\r\nimport { ComposableTask } from './ComposableTask';\r\nimport { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';\r\nimport { nets } from './nets';\r\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionTask } from './PredictFaceExpressionsTask';\r\n\r\nexport class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\r\n  constructor(\r\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\r\n    protected input: TNetInput,\r\n    protected useTinyLandmarkNet: boolean\r\n  ) {\r\n    super()\r\n  }\r\n\r\n  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {\r\n    return this.useTinyLandmarkNet\r\n      ? nets.faceLandmark68TinyNet\r\n      : nets.faceLandmark68Net\r\n  }\r\n}\r\n\r\nexport class DetectAllFaceLandmarksTask<\r\n  TSource extends WithFaceDetection<{}>\r\n> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {\r\n\r\n  public async run(): Promise<WithFaceLandmarks<TSource>[]> {\r\n\r\n    const parentResults = await this.parentTask\r\n    const detections = parentResults.map(res => res.detection)\r\n\r\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\r\n      ? await extractFaceTensors(this.input, detections)\r\n      : await extractFaces(this.input, detections)\r\n\r\n    const faceLandmarksByFace = await Promise.all(faces.map(\r\n      face => this.landmarkNet.detectLandmarks(face)\r\n    )) as FaceLandmarks68[]\r\n\r\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\r\n\r\n    return parentResults.map((parentResult, i) =>\r\n      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])\r\n    )\r\n  }\r\n\r\n  withFaceDescriptors(): ComputeAllFaceDescriptorsTask<WithFaceLandmarks<TSource>> {\r\n    return new ComputeAllFaceDescriptorsTask<WithFaceLandmarks<TSource>>(this, this.input)\r\n  }\r\n}\r\n\r\nexport class DetectSingleFaceLandmarksTask<\r\n  TSource extends WithFaceDetection<{}>\r\n>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {\r\n\r\n  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {\r\n\r\n    const parentResult = await this.parentTask\r\n    if (!parentResult) {\r\n      return\r\n    }\r\n\r\n    const { detection } = parentResult\r\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\r\n      ? await extractFaceTensors(this.input, [detection])\r\n      : await extractFaces(this.input, [detection])\r\n\r\n    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68\r\n\r\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\r\n\r\n    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)\r\n  }\r\n\r\n  withFaceDescriptor(): ComputeSingleFaceDescriptorTask<WithFaceLandmarks<TSource>> {\r\n    return new ComputeSingleFaceDescriptorTask<WithFaceLandmarks<TSource>>(this, this.input)\r\n  }\r\n}","import { TNetInput } from 'tfjs-image-recognition-base';\r\nimport { tf } from 'tfjs-tiny-yolov2';\r\n\r\nimport { extractFaces, extractFaceTensors } from '../dom';\r\nimport { FaceExpressionPrediction } from '../faceExpressionNet/types';\r\nimport { WithFaceDetection } from '../factories/WithFaceDetection';\r\nimport { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';\r\nimport { ComposableTask } from './ComposableTask';\r\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\r\nimport { nets } from './nets';\r\n\r\nexport class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {\r\n  constructor(\r\n    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,\r\n    protected input: TNetInput\r\n  ) {\r\n    super()\r\n  }\r\n}\r\n\r\nexport class PredictAllFaceExpressionsTask<\r\n  TSource extends WithFaceDetection<{}>\r\n> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {\r\n\r\n  public async run(): Promise<WithFaceExpressions<TSource>[]> {\r\n\r\n    const parentResults = await this.parentTask\r\n\r\n    const detections = parentResults.map(parentResult => parentResult.detection)\r\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\r\n      ? await extractFaceTensors(this.input, detections)\r\n      : await extractFaces(this.input, detections)\r\n\r\n    const faceExpressionsByFace = await Promise.all(faces.map(\r\n      face => nets.faceExpressionNet.predictExpressions(face)\r\n    )) as FaceExpressionPrediction[][]\r\n\r\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\r\n\r\n    return parentResults.map(\r\n      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i])\r\n    )\r\n  }\r\n\r\n  withFaceLandmarks(): DetectAllFaceLandmarksTask<WithFaceExpressions<TSource>> {\r\n    return new DetectAllFaceLandmarksTask(this, this.input, false)\r\n  }\r\n}\r\n\r\nexport class PredictSingleFaceExpressionTask<\r\n  TSource extends WithFaceDetection<{}>\r\n>  extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {\r\n\r\n  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {\r\n\r\n    const parentResult = await this.parentTask\r\n    if (!parentResult) {\r\n      return\r\n    }\r\n\r\n    const { detection } = parentResult\r\n    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor\r\n      ? await extractFaceTensors(this.input, [detection])\r\n      : await extractFaces(this.input, [detection])\r\n\r\n    const faceExpressions = await nets.faceExpressionNet.predictExpressions(faces[0]) as FaceExpressionPrediction[]\r\n\r\n    faces.forEach(f => f instanceof tf.Tensor && f.dispose())\r\n\r\n    return extendWithFaceExpressions(parentResult, faceExpressions)\r\n  }\r\n\r\n  withFaceLandmarks(): DetectSingleFaceLandmarksTask<WithFaceExpressions<TSource>> {\r\n    return new DetectSingleFaceLandmarksTask(this, this.input, false)\r\n  }\r\n}","import { TNetInput } from 'tfjs-image-recognition-base';\r\nimport { TinyYolov2Options } from 'tfjs-tiny-yolov2';\r\n\r\nimport { FaceDetection } from '../classes/FaceDetection';\r\nimport { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';\r\nimport { MtcnnOptions } from '../mtcnn/MtcnnOptions';\r\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\r\nimport { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';\r\nimport { ComposableTask } from './ComposableTask';\r\nimport { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';\r\nimport { nets } from './nets';\r\nimport { FaceDetectionOptions } from './types';\r\nimport { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionTask } from './PredictFaceExpressionsTask';\r\n\r\nexport class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {\r\n  constructor(\r\n    protected input: TNetInput,\r\n    protected options: FaceDetectionOptions = new SsdMobilenetv1Options()\r\n  ) {\r\n    super()\r\n  }\r\n}\r\n\r\nexport class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {\r\n\r\n  public async run(): Promise<FaceDetection[]> {\r\n\r\n    const { input, options } = this\r\n\r\n    if (options instanceof MtcnnOptions) {\r\n      return (await nets.mtcnn.forward(input, options))\r\n        .map(result => result.detection)\r\n    }\r\n\r\n    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions\r\n      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)\r\n      : (\r\n        options instanceof SsdMobilenetv1Options\r\n          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)\r\n          : (\r\n            options instanceof TinyYolov2Options\r\n              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)\r\n              : null\r\n          )\r\n      )\r\n\r\n    if (!faceDetectionFunction) {\r\n      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options')\r\n    }\r\n\r\n    return faceDetectionFunction(input)\r\n  }\r\n\r\n  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {\r\n    return new Promise<WithFaceDetection<{}>[]>(async res => {\r\n      const detections = await this.run()\r\n      return res(detections.map(detection => extendWithFaceDetection({}, detection)))\r\n    })\r\n  }\r\n\r\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false): DetectAllFaceLandmarksTask<WithFaceDetection<{}>> {\r\n    return new DetectAllFaceLandmarksTask<WithFaceDetection<{}>>(\r\n      this.runAndExtendWithFaceDetections(),\r\n      this.input,\r\n      useTinyLandmarkNet\r\n    )\r\n  }\r\n\r\n  withFaceExpressions(): PredictAllFaceExpressionsTask<WithFaceDetection<{}>> {\r\n    return new PredictAllFaceExpressionsTask<WithFaceDetection<{}>>(\r\n      this.runAndExtendWithFaceDetections(),\r\n      this.input\r\n    )\r\n  }\r\n}\r\n\r\nexport class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {\r\n\r\n  public async run(): Promise<FaceDetection | undefined> {\r\n    const faceDetections = await new DetectAllFacesTask(this.input, this.options);\r\n    let faceDetectionWithHighestScore = faceDetections[0];\r\n    faceDetections.forEach(faceDetection => {\r\n      if (faceDetection.score > faceDetectionWithHighestScore.score) {\r\n        faceDetectionWithHighestScore = faceDetection;\r\n      }\r\n    });\r\n    return faceDetectionWithHighestScore;\r\n  }\r\n\r\n  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}>> {\r\n    return new Promise<WithFaceDetection<{}>>(async res => {\r\n      const detection = await this.run()\r\n      return res(detection ? extendWithFaceDetection<{}>({}, detection) : undefined)\r\n    })\r\n  }\r\n\r\n  withFaceLandmarks(useTinyLandmarkNet: boolean = false): DetectSingleFaceLandmarksTask<WithFaceDetection<{}>> {\r\n    return new DetectSingleFaceLandmarksTask<WithFaceDetection<{}>>(\r\n      this.runAndExtendWithFaceDetection(),\r\n      this.input,\r\n      useTinyLandmarkNet\r\n    )\r\n  }\r\n\r\n  withFaceExpressions(): PredictSingleFaceExpressionTask<WithFaceDetection<{}>> {\r\n    return new PredictSingleFaceExpressionTask<WithFaceDetection<{}>>(\r\n      this.runAndExtendWithFaceDetection(),\r\n      this.input\r\n    )\r\n  }\r\n\r\n}","import { TNetInput } from 'tfjs-image-recognition-base';\r\n\r\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';\r\nimport { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';\r\nimport { FaceDetectionOptions } from './types';\r\n\r\nexport function detectSingleFace(\r\n  input: TNetInput,\r\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\r\n): DetectSingleFaceTask {\r\n  return new DetectSingleFaceTask(input, options)\r\n}\r\n\r\nexport function detectAllFaces(\r\n  input: TNetInput,\r\n  options: FaceDetectionOptions = new SsdMobilenetv1Options()\r\n): DetectAllFacesTask {\r\n  return new DetectAllFacesTask(input, options)\r\n}","import { TNetInput } from 'tfjs-image-recognition-base';\r\nimport { ITinyYolov2Options, TinyYolov2Options } from 'tfjs-tiny-yolov2';\r\n\r\nimport { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories';\r\nimport { IMtcnnOptions, MtcnnOptions } from '../mtcnn/MtcnnOptions';\r\nimport { SsdMobilenetv1Options } from '../ssdMobilenetv1';\r\nimport { detectAllFaces } from './detectFaces';\r\n\r\n// export allFaces API for backward compatibility\r\n\r\nexport async function allFacesSsdMobilenetv1(\r\n  input: TNetInput,\r\n  minConfidence?: number\r\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\r\n  return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))\r\n    .withFaceLandmarks()\r\n    .withFaceDescriptors()\r\n}\r\n\r\nexport async function allFacesTinyYolov2(\r\n  input: TNetInput,\r\n  forwardParams: ITinyYolov2Options = {}\r\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\r\n  return await detectAllFaces(input, new TinyYolov2Options(forwardParams))\r\n    .withFaceLandmarks()\r\n    .withFaceDescriptors()\r\n}\r\n\r\nexport async function allFacesMtcnn(\r\n  input: TNetInput,\r\n  forwardParams: IMtcnnOptions = {}\r\n): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {\r\n  return await detectAllFaces(input, new MtcnnOptions(forwardParams))\r\n    .withFaceLandmarks()\r\n    .withFaceDescriptors()\r\n}\r\n\r\nexport const allFaces = allFacesSsdMobilenetv1\r\n","export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {\r\n  if (arr1.length !== arr2.length)\r\n    throw new Error('euclideanDistance: arr1.length !== arr2.length')\r\n\r\n  const desc1 = Array.from(arr1)\r\n  const desc2 = Array.from(arr2)\r\n\r\n  return Math.sqrt(\r\n    desc1\r\n      .map((val, i) => val - desc2[i])\r\n      .reduce((res, diff) => res + Math.pow(diff, 2), 0)\r\n  )\r\n}","import { FaceMatch } from '../classes/FaceMatch';\r\nimport { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';\r\nimport { euclideanDistance } from '../euclideanDistance';\r\nimport { WithFaceDescriptor } from '../factories';\r\n\r\nexport class FaceMatcher {\r\n\r\n  private _labeledDescriptors: LabeledFaceDescriptors[]\r\n  private _distanceThreshold: number\r\n\r\n  constructor(\r\n    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,\r\n    distanceThreshold: number = 0.6\r\n  ) {\r\n\r\n    this._distanceThreshold = distanceThreshold\r\n\r\n    const inputArray = Array.isArray(inputs) ? inputs : [inputs]\r\n\r\n    if (!inputArray.length) {\r\n      throw new Error(`FaceRecognizer.constructor - expected atleast one input`)\r\n    }\r\n\r\n    let count = 1\r\n    const createUniqueLabel = () => `person ${count++}`\r\n\r\n    this._labeledDescriptors = inputArray.map((desc) => {\r\n      if (desc instanceof LabeledFaceDescriptors) {\r\n        return desc\r\n      }\r\n\r\n      if (desc instanceof Float32Array) {\r\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc])\r\n      }\r\n\r\n      if (desc.descriptor && desc.descriptor instanceof Float32Array) {\r\n        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor])\r\n      }\r\n\r\n      throw new Error(`FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>`)\r\n    })\r\n  }\r\n\r\n  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors }\r\n  public get distanceThreshold(): number { return this._distanceThreshold }\r\n\r\n  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {\r\n    return descriptors\r\n      .map(d => euclideanDistance(d, queryDescriptor))\r\n      .reduce((d1, d2) => d1 + d2, 0)\r\n        / (descriptors.length || 1)\r\n  }\r\n\r\n  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {\r\n    return this.labeledDescriptors\r\n      .map(({ descriptors, label }) => new FaceMatch(\r\n          label,\r\n          this.computeMeanDistance(queryDescriptor, descriptors)\r\n      ))\r\n      .reduce((best, curr) => best.distance < curr.distance ? best : curr)\r\n  }\r\n\r\n  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {\r\n    const bestMatch = this.matchDescriptor(queryDescriptor)\r\n    return bestMatch.distance < this.distanceThreshold\r\n      ? bestMatch\r\n      : new FaceMatch('unknown', bestMatch.distance)\r\n  }\r\n\r\n}","import { Mtcnn } from './Mtcnn';\r\n\r\nexport * from './Mtcnn';\r\nexport * from './MtcnnOptions';\r\n\r\nexport function createMtcnn(weights: Float32Array) {\r\n  const net = new Mtcnn()\r\n  net.extractWeights(weights)\r\n  return net\r\n}","import { TinyFaceDetector } from './TinyFaceDetector';\r\n\r\nexport * from './TinyFaceDetector';\r\nexport * from './TinyFaceDetectorOptions';\r\n\r\nexport function createTinyFaceDetector(weights: Float32Array) {\r\n  const net = new TinyFaceDetector()\r\n  net.extractWeights(weights)\r\n  return net\r\n}","import { TinyYolov2 } from './TinyYolov2';\r\n\r\nexport * from './TinyYolov2';\r\n\r\nexport function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {\r\n  const net = new TinyYolov2(withSeparableConvs)\r\n  net.extractWeights(weights)\r\n  return net\r\n}","import { IDimensions } from 'tfjs-image-recognition-base';\r\n\r\nimport { FaceDetection } from './classes/FaceDetection';\r\nimport { FaceLandmarks } from './classes/FaceLandmarks';\r\nimport { extendWithFaceDetection } from './factories/WithFaceDetection';\r\nimport { extendWithFaceLandmarks } from './factories/WithFaceLandmarks';\r\n\r\nexport function resizeResults<T>(results: T, { width, height }: IDimensions): T {\r\n\r\n  if (Array.isArray(results)) {\r\n    return results.map(obj => resizeResults(obj, { width, height })) as any as T\r\n  }\r\n\r\n  const hasLandmarks = results['unshiftedLandmarks'] && results['unshiftedLandmarks'] instanceof FaceLandmarks\r\n  const hasDetection = results['detection'] && results['detection'] instanceof FaceDetection\r\n\r\n  if (hasLandmarks) {\r\n    const resizedDetection = results['detection'].forSize(width, height)\r\n    const resizedLandmarks = results['unshiftedLandmarks'].forSize(resizedDetection.box.width, resizedDetection.box.height)\r\n\r\n    return extendWithFaceLandmarks(extendWithFaceDetection(results as any, resizedDetection), resizedLandmarks)\r\n  }\r\n\r\n  if (hasDetection) {\r\n    return extendWithFaceDetection(results as any, results['detection'].forSize(width, height))\r\n  }\r\n\r\n  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {\r\n    return (results as any).forSize(width, height)\r\n  }\r\n\r\n  return 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\ No newline at end of file
diff --git a/node_modules/face-api.js/dist/face-api.min.js b/node_modules/face-api.js/dist/face-api.min.js
new file mode 100644
index 0000000..27cd426
--- /dev/null
+++ b/node_modules/face-api.js/dist/face-api.min.js
@@ -0,0 +1 @@
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this.throwIfDisposed(),rt.batchToSpaceND(this,t,e)},r.prototype.spaceToBatchND=function(t,e){return this.throwIfDisposed(),rt.spaceToBatchND(this,t,e)},r.prototype.topk=function(t,e){return void 0===t&&(t=1),void 0===e&&(e=!0),this.throwIfDisposed(),rt.topk(this,t,e)},r.prototype.stridedSlice=function(t,e,n,r,o){return void 0===r&&(r=0),void 0===o&&(o=0),this.throwIfDisposed(),rt.stridedSlice(this,t,e,n,r,o)},r.prototype.depthToSpace=function(t,e){return this.throwIfDisposed(),rt.depthToSpace(this,t,e)},r.prototype.fft=function(){return this.throwIfDisposed(),rt.spectral.fft(this)},r.prototype.ifft=function(){return this.throwIfDisposed(),rt.spectral.ifft(this)},r}();Object.defineProperty(it,Symbol.hasInstance,{value:function(t){return!!t&&null!=t.shape&&null!=t.dtype}});var at=function(o){function i(t,e,n){void 0===e&&(e=!0);var r=o.call(this,t.shape,t.dtype,null,t.dataId)||this;r.trainable=e,r.name=n,null==r.name&&(r.name=nt().nextVariableId().toString());try{nt().registerVariable(r)}catch(t){throw nt().disposeTensor(r),t}return r}return s(i,o),i.variable=function(t,e,n,r){return void 0===e&&(e=!0),null!=r&&r!==t.dtype&&(t=t.asType(r)),new i(t,e,n)},i.prototype.assign=function(t){if(t.dtype!==this.dtype)throw new Error("dtype of the new value ("+t.dtype+") and previous value ("+this.dtype+") must match");if(!y(t.shape,this.shape))throw new Error("shape of the new value ("+t.shape+") and previous value ("+this.shape+") must match");nt().disposeTensor(this),this.dataId=t.dataId,nt().registerTensor(this)},i}(it);Object.defineProperty(at,Symbol.hasInstance,{value:function(t){return t instanceof it&&null!=t.assign&&t.assign instanceof Function}});var 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l={id:this.nextTapeNodeId++,name:s,inputs:e,outputs:Array.isArray(r)?r:[r]};null!=n&&(l.gradient=function(t){return n(t,i)}),this.activeTape.push(l)}return this.profiling&&this.activeProfile.kernels.push({name:s,bytesAdded:this.numBytes-u,totalBytesSnapshot:this.numBytes,tensorsAdded:this.numTensors-c,totalTensorsSnapshot:this.numTensors,inputShapes:Object.keys(e).map(function(t){return e[t].shape}),outputShape:Array.isArray(r)?r.map(function(t){return t.shape}):r.shape}),r},t.prototype.registerTensor=function(t){var e=this.tensorInfo.has(t.dataId)?this.tensorInfo.get(t.dataId).refCount:0;if(this.numTensors++,"string"===t.dtype&&this.numStringTensors++,0===e){this.numDataBuffers++;var n=0;"complex64"!==t.dtype&&"string"!==t.dtype&&(n=L(t.shape)*M(t.dtype)),this.tensorInfo.set(t.dataId,{backend:this.backend,dtype:t.dtype,shape:t.shape,bytes:n,refCount:0}),this.numBytes+=n,this.backend.register(t.dataId,t.shape,t.dtype)}this.tensorInfo.get(t.dataId).refCount++,t instanceof at||this.track(t)},t.prototype.registerVariable=function(t){if(null!=this.registeredVariables[t.name])throw new Error("Variable with name "+t.name+" was already registered");this.registeredVariables[t.name]=t},t.prototype.disposeTensor=function(t){if(this.tensorInfo.has(t.dataId)){this.keepTensors.has(t.id)&&this.keepTensors.delete(t.id),this.numTensors--,"string"===t.dtype&&this.numStringTensors--;var e=this.tensorInfo.get(t.dataId);e.refCount<=1?("complex64"!==t.dtype&&(this.numBytes-=e.bytes),this.numDataBuffers--,e.backend.disposeData(t.dataId),this.tensorInfo.delete(t.dataId)):this.tensorInfo.get(t.dataId).refCount--}},t.prototype.disposeVariables=function(){for(var t in this.registeredVariables){var e=this.registeredVariables[t];this.disposeTensor(e),delete this.registeredVariables[t]}},t.prototype.memory=function(){var t=this.backend.memory();return t.numTensors=this.numTensors,t.numDataBuffers=this.numDataBuffers,t.numBytes=this.numBytes,0<this.numStringTensors&&(t.unreliable=!0,null==t.reasons&&(t.reasons=[]),t.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),t},t.prototype.profile=function(r){return m(this,void 0,void 0,function(){var e,n;return C(this,function(t){return this.profiling=!0,e=this.numBytes,n=this.numTensors,this.activeProfile.kernels=[],this.activeProfile.result=r(),this.profiling=!1,this.activeProfile.peakBytes=Math.max.apply(Math,this.activeProfile.kernels.map(function(t){return t.totalBytesSnapshot})),this.activeProfile.newBytes=this.numBytes-e,this.activeProfile.newTensors=this.numTensors-n,[2,this.activeProfile]})})},t.prototype.shouldRecord=function(){return null!=this.activeTape&&0===this.customGradientDepth},t.prototype.addTapeNode=function(t,e,r){var n={};t.forEach(function(t,e){n[e]=t});var o={id:this.nextTapeNodeId++,name:this.activeScope.name,inputs:n,outputs:[e],gradient:function(t){var n={};return r(t).forEach(function(t,e){n[e]=function(){return t}}),n}};this.activeTape.push(o)},t.prototype.keep=function(t){if(1===this.scopeStack.length&&this.safeMode)throw new Error("Safe mode is ON. 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implemented")},t.prototype.conv2d=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.conv2dDerInput=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.conv2dDerFilter=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.depthwiseConv2D=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.depthwiseConv2DDerInput=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.depthwiseConv2DDerFilter=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.conv3d=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.conv3dDerInput=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.conv3dDerFilter=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.maxPool=function(t,e){throw new Error("Not yet implemented")},t.prototype.maxPoolBackprop=function(t,e,n,r){throw new Error("Not yet implemented")},t.prototype.avgPool=function(t,e){throw new Error("Not yet implemented")},t.prototype.avgPoolBackprop=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.reshape=function(t,e){throw new Error("Not yet implemented")},t.prototype.cast=function(t,e){throw new Error("Not yet implemented")},t.prototype.tile=function(t,e){throw new Error("Not yet implemented")},t.prototype.pad=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.transpose=function(t,e){throw new Error("Not yet implemented")},t.prototype.gather=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.gatherND=function(t,e){throw new Error("Not yet implemented")},t.prototype.scatterND=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.batchToSpaceND=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.spaceToBatchND=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.resizeBilinear=function(t,e,n,r){throw new Error("Not yet implemented")},t.prototype.resizeBilinearBackprop=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.resizeNearestNeighbor=function(t,e,n,r){throw new Error("Not yet implemented")},t.prototype.resizeNearestNeighborBackprop=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.batchNormalization=function(t,e,n,r,o,i){throw new Error("Not yet implemented")},t.prototype.localResponseNormalization4D=function(t,e,n,r,o){throw new Error("Not yet implemented")},t.prototype.LRNGrad=function(t,e,n,r,o,i,a){throw new Error("Not yet implemented")},t.prototype.multinomial=function(t,e,n,r){throw new Error("Not yet implemented")},t.prototype.oneHot=function(t,e,n,r){throw new Error("Not yet implemented")},t.prototype.cumsum=function(t,e,n,r){throw new Error("Not yet implemented")},t.prototype.nonMaxSuppression=function(t,e,n,r,o){throw new Error("Not yet implemented")},t.prototype.fft=function(t){throw new Error("Not yet implemented")},t.prototype.ifft=function(t){throw new Error("Not yet implemented")},t.prototype.complex=function(t,e){throw new Error("Not yet implemented")},t.prototype.real=function(t){throw new Error("Not yet implemented")},t.prototype.imag=function(t){throw new Error("Not yet implemented")},t.prototype.cropAndResize=function(t,e,n,r,o,i){throw new Error("Not yet implemented")},t.prototype.depthToSpace=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.split=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.sparseToDense=function(t,e,n,r){throw new Error("Not yet implemented")},t.prototype.setDataMover=function(t){throw new Error("Not yet implemented")},t.prototype.dispose=function(){throw new Error("Not yet implemented")},t}();function ze(t,e,n){if("complex64"===e){if("complex64"===t.dtype)return t.clone();var r=Ae(t.shape),o=t.toFloat(),i=n.complex(o,r);return r.dispose(),o.dispose(),i}if(!T(t.dtype,e))return it.make(t.shape,{dataId:t.dataId},e);if("complex64"===t.dtype){var a=n.real(t);return i=a.cast(e),a.dispose(),i}if("int32"===e)return n.int(t);if("bool"!==e)throw new Error("Error in Cast: unknown dtype argument ("+e+")");var s=Se(0,t.dtype);return i=n.notEqual(t,s),s.dispose(),i}function Ue(t,e){return it.make(e,{dataId:t.dataId},t.dtype)}function Ve(t,e){if(t.length!==e.length)throw new Error("Cannot merge real and imag arrays of different lengths. real:"+t.length+", imag: "+e.length+".");for(var n=new Float32Array(2*t.length),r=0;r<n.length;r+=2)n[r]=t[r/2],n[r+1]=e[r/2];return n}function Ge(t,e){return{real:t[2*e],imag:t[2*e+1]}}function He(t,e,n,r,o){for(var i=Array.from(e).map(function(t,e){return{score:t,boxIndex:e}}).filter(function(t){return t.score>o}).sort(function(t,e){return e.score-t.score}),a=[],s=0;s<i.length;s++){var u=i[s],c=u.score,l=u.boxIndex;if(c<o)break;for(var h=!1,p=a.length-1;0<=p;--p)if(qe(t,l,a[p])>=r){h=!0;break}if(!h&&(a.push(l),a.length>=n))break}return _e(a,"int32")}function qe(t,e,n){var r=t.subarray(4*e,4*e+4),o=t.subarray(4*n,4*n+4),i=Math.min(r[0],r[2]),a=Math.min(r[1],r[3]),s=Math.max(r[0],r[2]),u=Math.max(r[1],r[3]),c=Math.min(o[0],o[2]),l=Math.min(o[1],o[3]),h=Math.max(o[0],o[2]),p=Math.max(o[1],o[3]),f=(s-i)*(u-a),d=(h-c)*(p-l);if(f<=0||d<=0)return 0;var m=Math.max(i,c),v=Math.max(a,l),g=Math.min(s,h),y=Math.min(u,p),x=Math.max(g-m,0)*Math.max(y-v,0);return x/(f+d-x)}function je(n,t,r){var o=Array(n.rank).fill(0),i=n.shape.slice();return t.map(function(t){i[r]=t;var e=n.slice(o,i);return o[r]+=t,e})}function $e(t,e,n,r,o){for(var i=e[e.length-1],a=[t.length/i,i],s=a[0],u=a[1],c=_(n,s*r),l=_("int32",s*r),h=0;h<s;h++){for(var p=h*u,f=t.subarray(p,p+u),d=[],m=0;m<f.length;m++)d.push({value:f[m],index:m});d.sort(function(t,e){return e.value-t.value});var v=h*r,g=c.subarray(v,v+r),y=l.subarray(v,v+r);for(m=0;m<r;m++)g[m]=d[m].value,y[m]=d[m].index}var x=e.slice();return x[x.length-1]=r,[Ce(c,x,n),Ce(l,x,"int32")]}var Ke=function(t,e,n){this.variableNames=["A"];var r=t.windowSize,o=t.batchSize,i=t.inSize,a=Math.ceil(i/r);n||this.variableNames.push("bestIndicesA"),this.outputShape=[o,a];var s="max"===e?">":"<",u=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode="\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * "+r+";\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < "+r+"; i++) {\n          int inIdx = "+u+";\n          float candidate = getA(batch, inIdx);\n          if (candidate "+s+" bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    "},Xe=function(t){this.variableNames=["dy"],this.outputShape=t.inShape;var e=t.filterHeight,n=t.filterWidth,r=t.strideHeight,o=t.strideWidth,i=t.dilationHeight,a=t.dilationWidth,s=t.effectiveFilterHeight,u=t.effectiveFilterWidth,c=s-1-t.padInfo.top,l=u-1-t.padInfo.left,h=1/(e*n);this.userCode="\n      const ivec2 pads = ivec2("+c+", "+l+");\n      const float avgMultiplier = float("+h+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+s+";\n            wR += "+i+") {\n          float dyR = float(dyRCorner + wR) / "+r+".0;\n\n          if (dyR < 0.0 || dyR >= "+t.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < "+u+";\n            wC+= "+a+") {\n            float dyC = float(dyCCorner + wC) / "+o+".0;\n\n            if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    "};function Ye(t,e){for(var n=t.length,r=[],o=0;o<n;o++){var i=n-1-o,a=t[i]||1;1<(e[e.length-1-o]||1)&&1===a&&r.unshift(i)}return r}function Qe(t,e){for(var n=[],r=0;r<e.length;r++){var o=t[t.length-r-1],i=e.length-r-1,a=e[i];(null==o||1===o&&1<a)&&n.unshift(i)}return n}function Je(t,e){for(var n=[],r=Math.max(t.length,e.length),o=0;o<r;o++){var i=t[t.length-o-1];null==i&&(i=1);var a=e[e.length-o-1];if(null==a&&(a=1),1===i)n.unshift(a);else if(1===a)n.unshift(i);else{if(i!==a)throw Error("Operands could not be broadcast together with shapes "+t+" and "+e+".");n.unshift(i)}}return n}var Ze=function(t,e,n,r,o,i){this.outputShape=[],this.supportsBroadcasting=!0,this.variableNames=["x","mean","variance"],Je(t,e),Je(t,n);var a="0.0";null!=r&&(Je(t,r),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");var s="1.0";null!=o&&(Je(t,o),this.variableNames.push("scale"),s="getScaleAtOutCoords()"),this.outputShape=t,this.userCode="\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = "+a+";\n        float scale = "+s+";\n        float inv = scale * inversesqrt(variance + float("+i+"));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    "},tn=function(t,e,n,r,o,i){this.supportsBroadcasting=!0,this.usesPackedTextures=!0,this.variableNames=["x","mean","variance"],Je(t,e),Je(t,n);var a="vec4(0.0)";null!=r&&(Je(t,r),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");var s="vec4(1.0)";null!=o&&(Je(t,o),this.variableNames.push("scale"),s="getScaleAtOutCoords()"),this.outputShape=t,this.userCode="\n      void main() {\n        vec4 offset = "+a+";\n        vec4 scale = "+s+";\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4("+i+"));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    "},en="return areal * breal - aimag * bimag;",nn="return areal * bimag + aimag * breal;",rn=function(t,e,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.supportsBroadcasting=!0,this.outputShape=Je(e,n),this.userCode="\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        "+t+"\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    "},on="return a + b;",an="return a - b;",sn=function(){function t(t,e,n){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.outputShape=Je(e,n),this.userCode="\n      uniform float NAN;\n      float binaryOperation(float a, float b) {\n        "+t+"\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    "}return t.prototype.getCustomSetupFunc=function(){var n=this;return function(t,e){null==n.startLoc&&(n.startLoc=t.getUniformLocationNoThrow(e,"NAN"),null==n.startLoc)||t.gl.uniform1f(n.startLoc,NaN)}},t}(),un=function(t,e,n){this.variableNames=["A"],this.outputShape=t,this.userCode="\n      void main() {\n        float value = getAAtOutCoords();\n        if (isNaN(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, float("+e+"), float("+n+")));\n      }\n    "},cn=function(t,e,n){this.variableNames=["A"],this.usesPackedTextures=!0,this.outputShape=t,this.userCode="\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (hasNaN(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4("+e+"), vec4("+n+")));\n      }\n    "},ln=function(t){this.variableNames=["real","imag"],this.outputShape=t,this.userCode="\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    "},hn=function(t,e){this.variableNames=["A","B"],this.outputShape=[],this.outputShape=ie([t,e],1),this.userCode="\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        float value = 0.0;\n        if (yC < "+t[1]+") {\n          value = getA(yR, yC);\n        } else {\n          yC -= "+t[1]+";\n          value = getB(yR, yC);\n        }\n\n        setOutput(value);\n      }\n    "},pn=function(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;var e=t.strideHeight,n=t.strideWidth,r=t.padInfo.top,o=t.padInfo.left;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < "+t.batchSize+"; b++) {\n          for (int yR = 0; yR < "+t.outHeight+"; yR++) {\n            int xR = wR + yR * "+e+" - "+r+";\n\n            if (xR < 0 || xR >= "+t.inHeight+") {\n              continue;\n            }\n\n            for (int yC = 0; yC < "+t.outWidth+"; yC++) {\n              int xC = wC + yC * "+n+" - "+o+";\n\n              if (xC < 0 || xC >= "+t.inWidth+") {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},fn=function(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;var e=t.filterHeight,n=t.filterWidth,r=t.strideHeight,o=t.strideWidth,i=e-1-t.padInfo.top,a=n-1-t.padInfo.left;this.userCode="\n      const ivec2 pads = ivec2("+i+", "+a+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+e+"; wR++) {\n          float dyR = float(dyRCorner + wR) / "+r+".0;\n\n          if (dyR < 0.0 || dyR >= "+t.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = "+e+" - 1 - wR;\n\n          for (int wC = 0; wC < "+n+"; wC++) {\n            float dyC = float(dyCCorner + wC) / "+o+".0;\n\n            if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = "+n+" - 1 - wC;\n\n            for (int d2 = 0; d2 < "+t.outChannels+"; d2++) {\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, d2);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},dn=function(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;var e=t.strideDepth,n=t.strideHeight,r=t.strideWidth,o=t.padInfo.front,i=t.padInfo.top,a=t.padInfo.left;this.userCode="\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < "+t.batchSize+"; b++) {\n          for (int yF = 0; yF < "+t.outDepth+"; yF++) {\n            int xF = wF + yF * "+e+" - "+o+";\n\n            if (xF < 0 || xF >= "+t.inDepth+") {\n              continue;\n            }\n\n            for (int yR = 0; yR < "+t.outHeight+"; yR++) {\n              int xR = wR + yR * "+n+" - "+i+";\n\n              if (xR < 0 || xR >= "+t.inHeight+") {\n                continue;\n              }\n\n              for (int yC = 0; yC < "+t.outWidth+"; yC++) {\n                int xC = wC + yC * "+r+" - "+a+";\n\n                if (xC < 0 || xC >= "+t.inWidth+") {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},mn=function(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;var e=t.filterDepth,n=t.filterHeight,r=t.filterWidth,o=t.strideDepth,i=t.strideHeight,a=t.strideWidth,s=e-1-t.padInfo.front,u=n-1-t.padInfo.top,c=r-1-t.padInfo.left;this.userCode="\n      const ivec3 pads = ivec3("+s+", "+u+", "+c+");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < "+e+"; wF++) {\n          float dyF = float(dyFCorner + wF) / "+o+".0;\n\n          if (dyF < 0.0 || dyF >= "+t.outDepth+".0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = "+e+" - 1 - wF;\n\n          for (int wR = 0; wR < "+n+"; wR++) {\n            float dyR = float(dyRCorner + wR) / "+i+".0;\n\n            if (dyR < 0.0 || dyR >= "+t.outHeight+".0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = "+n+" - 1 - wR;\n\n            for (int wC = 0; wC < "+r+"; wC++) {\n              float dyC = float(dyCCorner + wC) / "+a+".0;\n\n              if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = "+r+" - 1 - wC;\n\n              for (int d2 = 0; d2 < "+t.outChannels+"; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},vn=function(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;var e=t.strideHeight,n=t.strideWidth,r=t.padInfo.top,o=t.padInfo.left,i=t.outChannels/t.inChannels;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * "+i+" + dm;\n\n        float dotProd = 0.0;\n\n        // TODO: Vec4 over the batch size\n        for (int b = 0; b < "+t.batchSize+"; b++) {\n          for (int yR = 0; yR < "+t.outHeight+"; yR++) {\n            int xR = wR + yR * "+e+" - "+r+";\n\n            if (xR < 0 || xR >= "+t.inHeight+") {\n              continue;\n            }\n\n            for (int yC = 0; yC < "+t.outWidth+"; yC++) {\n              int xC = wC + yC * "+n+" - "+o+";\n\n              if (xC < 0 || xC >= "+t.inWidth+") {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},gn=function(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;var e=t.filterHeight,n=t.filterWidth,r=t.strideHeight,o=t.strideWidth,i=e-1-t.padInfo.top,a=n-1-t.padInfo.left,s=t.outChannels/t.inChannels;this.userCode="\n      const ivec2 pads = ivec2("+i+", "+a+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < "+e+"; wR++) {\n          float dyR = float(dyRCorner + wR) / "+r+".0;\n\n          if (dyR < 0.0 || dyR >= "+t.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = "+e+" - 1 - wR;\n\n          for (int wC = 0; wC < "+n+"; wC++) {\n            float dyC = float(dyCCorner + wC) / "+o+".0;\n\n            if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = "+n+" - 1 - wC;\n\n            // TODO: Vec4 over the channelMul\n            for (int dm = 0; dm < "+s+"; dm++) {\n              int d2 = d1 * "+s+" + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},yn=function(t){this.variableNames=["x","W"],this.outputShape=t.outShape;var e=t.padInfo.top,n=t.padInfo.left,r=t.strideHeight,o=t.strideWidth,i=t.dilationHeight,a=t.dilationWidth,s=t.filterHeight,u=t.filterWidth,c=4*Math.floor(t.inChannels/4),l=t.inChannels%4;this.userCode="\n      const ivec2 strides = ivec2("+r+", "+o+");\n      const ivec2 pads = ivec2("+e+", "+n+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+s+"; wR++) {\n          int xR = xRCorner + wR * "+i+";\n\n          if (xR < 0 || xR >= "+t.inHeight+") {\n            continue;\n          }\n\n          for (int wC = 0; wC < "+u+"; wC++) {\n            int xC = xCCorner + wC * "+a+";\n\n            if (xC < 0 || xC >= "+t.inWidth+") {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < "+c+"; d1 += 4) {\n              vec4 xValues = vec4(\n                getX(batch, xR, xC, d1),\n                getX(batch, xR, xC, d1 + 1),\n                getX(batch, xR, xC, d1 + 2),\n                getX(batch, xR, xC, d1 + 3)\n              );\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              dotProd += dot(xValues, wValues);\n            }\n\n            if ("+(1===l)+") {\n              dotProd +=\n                getX(batch, xR, xC, "+c+") *\n                getW(wR, wC, "+c+", d2);\n            } else if ("+(2===l)+") {\n              vec2 xValues = vec2(\n                getX(batch, xR, xC, "+c+"),\n                getX(batch, xR, xC, "+c+" + 1)\n              );\n              vec2 wValues = vec2(\n                getW(wR, wC, "+c+", d2),\n                getW(wR, wC, "+c+" + 1, d2)\n              );\n              dotProd += dot(xValues, wValues);\n            } else if ("+(3===l)+") {\n              vec3 xValues = vec3(\n                getX(batch, xR, xC, "+c+"),\n                getX(batch, xR, xC, "+c+" + 1),\n                getX(batch, xR, xC, "+c+" + 2)\n              );\n              vec3 wValues = vec3(\n                getW(wR, wC, "+c+", d2),\n                getW(wR, wC, "+c+" + 1, d2),\n                getW(wR, wC, "+c+" + 2, d2)\n              );\n              dotProd += dot(xValues, wValues);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},xn=function(t){this.variableNames=["x","W"],this.outputShape=t.outShape;var e=t.padInfo.front,n=t.padInfo.top,r=t.padInfo.left,o=t.strideDepth,i=t.strideHeight,a=t.strideWidth,s=t.dilationDepth,u=t.dilationHeight,c=t.dilationWidth,l=t.filterDepth,h=t.filterHeight,p=t.filterWidth,f=4*Math.floor(t.inChannels/4),d=t.inChannels%4;this.userCode="\n      const ivec3 strides = ivec3("+o+", "+i+", "+a+");\n      const ivec3 pads = ivec3("+e+", "+n+", "+r+");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < "+l+"; wF++) {\n          int xF = xFCorner + wF * "+s+";\n\n          if (xF < 0 || xF >= "+t.inDepth+") {\n            continue;\n          }\n\n          for (int wR = 0; wR < "+h+"; wR++) {\n            int xR = xRCorner + wR * "+u+";\n\n            if (xR < 0 || xR >= "+t.inHeight+") {\n              continue;\n            }\n\n            for (int wC = 0; wC < "+p+"; wC++) {\n              int xC = xCCorner + wC * "+c+";\n\n              if (xC < 0 || xC >= "+t.inWidth+") {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < "+f+"; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if ("+(1===d)+") {\n                dotProd +=\n                  getX(batch, xF, xR, xC, "+f+") *\n                  getW(wF, wR, wC, "+f+", d2);\n              } else if ("+(2===d)+") {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, "+f+"),\n                  getX(batch, xF, xR, xC, "+f+" + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, "+f+", d2),\n                  getW(wF, wR, wC, "+f+" + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if ("+(3===d)+") {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, "+f+"),\n                  getX(batch, xF, xR, xC, "+f+" + 1),\n                  getX(batch, xF, xR, xC, "+f+" + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, "+f+", d2),\n                  getW(wF, wR, wC, "+f+" + 1, d2),\n                  getW(wF, wR, wC, "+f+" + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},wn=function(t){this.variableNames=["x","W"],this.outputShape=t.outShape;var e=t.inHeight,n=t.inWidth,r=t.padInfo.top,o=t.padInfo.left,i=t.strideHeight,a=t.strideWidth,s=t.dilationHeight,u=t.dilationWidth,c=t.filterHeight,l=t.filterWidth,h=t.outChannels/t.inChannels;this.userCode="\n      const ivec2 strides = ivec2("+i+", "+a+");\n      const ivec2 pads = ivec2("+r+", "+o+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / "+h+";\n        int q = d2 - d1 * "+h+";\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TODO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < "+c+"; wR++) {\n          int xR = xRCorner + wR * "+s+";\n\n          if (xR < 0 || xR >= "+e+") {\n            continue;\n          }\n\n          for (int wC = 0; wC < "+l+"; wC++) {\n            int xC = xCCorner + wC * "+u+";\n\n            if (xC < 0 || xC >= "+n+") {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},bn=function(t){this.variableNames=["x","W"],this.usesPackedTextures=!0,this.outputShape=t.outShape;for(var e=t.inHeight,n=t.inWidth,r=t.padInfo.top,o=t.padInfo.left,i=t.strideHeight,a=t.strideWidth,s=t.filterHeight,u=t.filterWidth,c=Math.ceil((u+1)/2),l="int xR; int xC;",h=0;h<s;h++){for(var p=-o;p<2*c;p++)l+="vec4 "+En(h,p)+" = vec4(0.);";for(p=0;p<u;p++)l+="\n          vec4 wR"+h+"C"+p+" = vec4(0.);\n          vec4 xR"+h+"C"+p+" = vec4(0.);"}for(h=0;h<s;h++)for(p=0;p<c;p++){var f=2*p,d=2*p+o;l+="\n          xR = xRCorner + "+h+";\n          xC = xCCorner + "+d+";\n\n          if(xR >= 0 && xR < "+e+" && xC >= 0 && xC < "+n+") {\n            "+En(h,d)+" = getX(batch, xR, xC, d1);\n          }",0===o?f<u&&p===c-1&&(1<a&&(l+="\n                vec4 "+En(h,d+2)+" = vec4(0.);\n\n                if(xR >= 0 && xR < "+e+" && xC + 2 < "+n+") {\n                  "+En(h,d+2)+" = getX(batch, xR, xC + 2, d1);\n                }"),l+="\n              xR"+h+"C"+d+" = "+Cn(h,d,a,o)+";\n            "):0===p&&(l+="\n            if(xR >= 0 && xR < "+e+" && xC - 2 >= 0) {\n              "+En(h,d-2)+" = getX(batch, xR, xC - 2, d1);\n            }"),0<f&&(l+="xR"+h+"C"+(d-2)+" =\n            "+Cn(h,d-2,a,o)+";"),0<=d-1&&d-1<u&&(l+="xR"+h+"C"+(d-1)+" =\n              "+Cn(h,d-1,a,o)+";"),f<u&&(l+="\n            vec4 wTexel"+h+"C"+f+" = getW("+h+", "+f+", d1, q);\n            wR"+h+"C"+f+" = vec4(wTexel"+h+"C"+f+".xz, wTexel"+h+"C"+f+".xz);\n          ",f+1<u&&(l+="\n              vec4 wTexelR"+h+"C"+(f+1)+" = getW("+h+", "+(f+1)+", d1, q);\n              wR"+h+"C"+(f+1)+" =\n                vec4(wTexelR"+h+"C"+(f+1)+".xz, wTexelR"+h+"C"+(f+1)+".xz);"))}for(h=0;h<s;h++)for(p=0;p<u;p++)l+="result += xR"+h+"C"+p+" * wR"+h+"C"+p+";";this.userCode="\n      const ivec2 strides = ivec2("+i+", "+a+");\n      const ivec2 pads = ivec2("+r+", "+o+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2;\n        int q = 0;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        vec4 result = vec4(0.);\n\n        "+l+"\n\n        setOutput(result);\n      }\n    "};function En(t,e){return"xTexelR"+t+"C"+(e<0?"minus"+Math.abs(e).toString():e)}function Cn(t,e,n,r){return 1===n?r%2==e%2?En(t,e):"vec4("+En(t,e-1)+".zw, "+En(t,e+1)+".xy)":r%2==e%2?"vec4("+En(t,e)+".xy, "+En(t,e+2)+".xy)":"vec4("+En(t,e-1)+".zw, "+En(t,e+1)+".zw)"}var Sn=function(t,e,n,r,o){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];var i=t[0],a=t[1],s=t[2],u=t[3],c=e[0],l=n[0],h=n[1];this.outputShape=[c,l,h,u];var p="bilinear"===r?1:0,f=[a-1+".0",s-1+".0"],d=f[0],m=f[1],v=1<l?[""+(a-1)/(l-1),"(y2-y1) * height_ratio","y1*"+d+" + float(y)*(height_scale)"]:["0.0","0.0","0.5 * (y1+y2) * "+d],g=v[0],y=v[1],x=v[2],w=1<h?[""+(s-1)/(h-1),"(x2-x1) * width_ratio","x1*"+m+" + float(x)*(width_scale)"]:["0.0","0.0","0.5 * (x1+x2) * "+m],b=w[0],E=w[1],C=w[2];this.userCode="\n      const float height_ratio = float("+g+");\n      const float width_ratio = float("+b+");\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= "+i+") {\n          return;\n        }\n\n        float height_scale = "+y+";\n        float width_scale = "+E+";\n\n        float in_y = "+x+";\n        if( in_y < 0.0 || in_y > "+d+" ) {\n          setOutput(float("+o+"));\n          return;\n        }\n        float in_x = "+C+";\n        if( in_x < 0.0 || in_x > "+m+" ) {\n          setOutput(float("+o+"));\n          return;\n        }\n\n        vec2 sourceFracIndexRC = vec2(in_y,in_x);\n        if("+p+" == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n          ivec2 sourceCeilRC = ivec2(ceil(sourceFracIndexRC));\n\n          float topLeft = getImage(b, sourceFloorRC.x, sourceFloorRC.y, d);\n          float bottomLeft = getImage(b, sourceCeilRC.x, sourceFloorRC.y, d);\n          float topRight = getImage(b, sourceFloorRC.x, sourceCeilRC.y, d);\n          float bottomRight = getImage(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n          vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n          float top = topLeft + (topRight - topLeft) * fracRC.y;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n          float newValue = top + (bottom - top) * fracRC.x;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestRC = ivec2(floor(\n            sourceFracIndexRC + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestRC.x, sourceNearestRC.y, d);\n          setOutput(newValue);\n        }\n      }\n    "};function _n(n,t,r){void 0===r&&(r="index");var o=U(t);return o.map(function(t,e){return"int "+n[e]+" = "+r+" / "+t+"; "+(e===o.length-1?"int "+n[e+1]+" = "+r+" - "+n[e]+" * "+t:"index -= "+n[e]+" * "+t)+";"}).join("")}function Rn(t){return 1===t.length?""+t[0]:"vec"+t.length+"("+t.join(",")+")"}function Nn(t,e,n,r,o){var i=t.map(function(t){var e=L(t.shapeInfo.logicalShape);return t.shapeInfo.isUniform?"uniform float "+t.name+(1<e?"["+e+"]":"")+";":"uniform sampler2D "+t.name+";"});i=i.join("\n");var a,s,u=t.map(function(t){return function(t,e,n,r){void 0===r&&(r=!1);var o=function(t){var e=t.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1)+"Flat",r=L(t.shapeInfo.logicalShape);if(t.shapeInfo.isUniform)return 1===r?"float "+n+"(int index) {return "+e+";}":"\n      float "+n+"(int index) {\n        for (int i = 0; i < "+r+"; i++) {\n          if (i == index) {\n            return "+e+"[i];\n          }\n        }\n      }\n    ";var o=t.shapeInfo.texShape,i=o[0],a=o[1];return 1===a&&1===i?"\n      float "+n+"(int index) {\n        return sampleTexture("+e+", halfCR);\n      }\n    ":1===a?"\n      float "+n+"(int index) {\n        vec2 uv = vec2(0.5, (float(index) + 0.5) / "+i+".0);\n        return sampleTexture("+e+", uv);\n      }\n    ":1===i?"\n      float "+n+"(int index) {\n        vec2 uv = vec2((float(index) + 0.5) / "+a+".0, 0.5);\n        return sampleTexture("+e+", uv);\n      }\n    ":"\n    float "+n+"(int index) {\n      vec2 uv = UVfrom1D("+i+", "+a+", index);\n      return sampleTexture("+e+", uv);\n    }\n  "}(t);return o+=r?Tn(t):kn(t),(n||y(t.shapeInfo.logicalShape,e.logicalShape))&&(o+=r?function(t,e,n){var r=t.name,o=r.charAt(0).toUpperCase()+r.slice(1),i=t.shapeInfo.texShape,a="get"+o+"AtOutCoords",s=e.texShape,u=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],c=u[0],l=u[1],h=Ye(t.shapeInfo.logicalShape,e.logicalShape),p=t.shapeInfo.logicalShape.length,f=e.logicalShape.length;if(h.length)throw Error("Packed broadcast sampling is not implemented yet.");if(y(t.shapeInfo.texShape,s))return"\n      vec4 "+a+"() {\n        return texture2D("+r+", resultUV);\n      }\n    ";var d="return texture2D("+r+", uv)";return 1===p&&1<f?d="\n      vec4 sample = texture2D("+r+", uv);\n      return vec4(sample.xy, sample.xy);\n    ":0===p&&0<f&&(d=1===f?"\n        vec4 sample = texture2D("+r+", uv);\n        return vec4(sample.x, sample.x, 0., 0.);\n      ":"\n        vec4 sample = texture2D("+r+", uv);\n        return vec4(sample.x);\n      "),"\n    vec4 "+a+"() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+u[0]+", "+u[1]+"));\n      int index = resTexRC.x * "+u[1]+" + resTexRC.y;\n\n      int texR = index / "+l+";\n      int texC = index - texR * "+l+";\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2("+l+", "+c+");\n\n      "+d+";\n    }\n  "}(t,e):function(t,e,n){var r=t.name,o=r.charAt(0).toUpperCase()+r.slice(1),i="get"+o+"AtOutCoords",a=Ye(t.shapeInfo.logicalShape,e.logicalShape),s=t.shapeInfo.logicalShape.length,u=e.logicalShape.length,c=n&&(s<u||0<a.length),l=function(t){for(var e=0;e<t.length;e++)if(t[e]!==e)return!1;return!0}(a),h=t.shapeInfo.isUniform;if(c&&!l)return function(t,e,n,r){var o=t.shapeInfo.logicalShape.length,i=e.logicalShape.length,a="int";2===i?a="ivec2":3===i?a="ivec3":4===i&&(a="ivec4");var s=Ye(t.shapeInfo.logicalShape,e.logicalShape),u=i-o;return"\n    float "+r+"() {\n      "+a+" coords = getOutputCoords();\n      "+(0===o?"":i<2&&1<=s.length?"coords = 0;":s.map(function(t){return"coords["+(t+u)+"] = 0;"}).join("\n"))+"\n      return get"+n+"("+(i<2&&0<o?"coords":t.shapeInfo.logicalShape.map(function(t,e){return"coords["+(e+u)+"]"}).join(", "))+");\n    }\n  "}(t,e,o,i);var p=L(t.shapeInfo.logicalShape),f="";c&&l&&(f="\n        int mainPart = index / "+p+";\n        index -= mainPart * "+p+";\n      ");var d=e.texShape;if(h)return 1===p?"float "+i+"() {return "+r+";}":"\n      float "+i+"() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                              vec2("+d[0]+", "+d[1]+"));\n        int index = resTexRC.x * "+d[1]+" + resTexRC.y;\n        "+f+"\n        return get"+o+"Flat(index);\n      }\n    ";var m=t.shapeInfo.texShape;return y(m,d)?"\n      float "+i+"() {\n        return sampleTexture("+r+", resultUV);\n      }\n    ":"\n    float "+i+"() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+d[0]+", "+d[1]+"));\n      int index = resTexRC.x * "+d[1]+" + resTexRC.y;\n      "+f+"\n      int texR = index / "+m[1]+";\n      int texC = index - texR * "+m[1]+";\n      vec2 uv = (vec2(texC, texR) + halfCR) /\n                 vec2("+m[1]+".0, "+m[0]+".0);\n\n      return sampleTexture("+r+", uv);\n    }\n  "}(t,e,n)),o}(t,e,r,o)}).join("\n"),c=e.texShape,l=Mn;return e.isPacked?(a=function(t,e){switch(t.length){case 0:return"\n    int getOutputCoords() {\n      return 0;\n    }\n  ";case 1:return f=e,d=[Math.ceil(f[0]/2),Math.ceil(f[1]/2)],1===f[0]?"\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * "+d[1]+".0);\n      }\n    ":1===f[1]?"\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * "+d[0]+".0);\n      }\n    ":"\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+d[0]+", "+d[1]+"));\n      return resTexRC.x * "+d[1]+" + resTexRC.y;\n    }\n  ";case 2:return function(t,e){var n=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(y(t,e))return"\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2("+n[0]+", "+n[1]+"));\n      }\n    ";var r=Math.ceil(t[1]/2);return"\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+n[0]+", "+n[1]+"));\n\n      int index = resTexRC.x * "+n[1]+" + resTexRC.y;\n      int r = 2 * (index / "+r+");\n      int c = imod(index, "+r+") * 2;\n\n      return ivec2(r, c);\n    }\n  "}(t,e);case 3:return u=t,c=e,l=[Math.ceil(c[0]/2),Math.ceil(c[1]/2)],h=Math.ceil(u[2]/2),p=h*Math.ceil(u[1]/2),"\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+l[0]+", "+l[1]+"));\n      int index = resTexRC.x * "+l[1]+" + resTexRC.y;\n\n      int b = index / "+p+";\n      index -= b * "+p+";\n\n      int r = 2 * (index / "+h+");\n      int c = imod(index, "+h+") * 2;\n\n      return ivec3(b, r, c);\n    }\n  ";case 4:return n=t,r=e,o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],i=Math.ceil(n[3]/2),a=i*Math.ceil(n[2]/2),s=a*n[1],"\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+o[0]+", "+o[1]+"));\n      int index = resTexRC.x * "+o[1]+" + resTexRC.y;\n\n      int b2 = index / "+s+";\n      index -= b2 * "+s+";\n\n      int b = index / "+a+";\n      index -= b * "+a+";\n\n      int r = 2 * (index / "+i+");\n      int c = imod(index, "+i+") * 2;\n\n      return ivec4(b2, b, r, c);\n    }\n  ";default:throw new Error(t.length+"-D packed output coordinate fetching is not yet supported")}var n,r,o,i,a,s;var u,c,l,h,p;var f,d}(e.logicalShape,c),s=An):(a=function(t,e){switch(t.length){case 0:return"\n    int getOutputCoords() {\n      return 0;\n    }\n  ";case 1:return 1===(p=e)[0]?"\n      int getOutputCoords() {\n        return int(resultUV.x * "+p[1]+".0);\n      }\n    ":1===p[1]?"\n      int getOutputCoords() {\n        return int(resultUV.y * "+p[0]+".0);\n      }\n    ":"\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+p[0]+", "+p[1]+"));\n      return resTexRC.x * "+p[1]+" + resTexRC.y;\n    }\n  ";case 2:return y(l=t,h=e)?"\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2("+h[0]+", "+h[1]+"));\n      }\n    ":1===l[1]?"\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2("+h[0]+", "+h[1]+"));\n        int index = resTexRC.x * "+h[1]+" + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    ":1===l[0]?"\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2("+h[0]+", "+h[1]+"));\n        int index = resTexRC.x * "+h[1]+" + resTexRC.y;\n        return ivec2(0, index);\n      }\n    ":"\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+h[0]+", "+h[1]+"));\n      int index = resTexRC.x * "+h[1]+" + resTexRC.y;\n      int r = index / "+l[1]+";\n      int c = index - r * "+l[1]+";\n      return ivec2(r, c);\n    }\n  ";case 3:return u=e,c=_n(["r","c","d"],t),"\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+u[0]+", "+u[1]+"));\n      int index = resTexRC.x * "+u[1]+" + resTexRC.y;\n      "+c+"\n      return ivec3(r, c, d);\n    }\n  ";case 4:return a=e,s=_n(["r","c","d","d2"],t),"\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2("+a[0]+", "+a[1]+"));\n      int index = resTexRC.x * "+a[1]+" + resTexRC.y;\n      "+s+"\n      return ivec4(r, c, d, d2);\n    }\n  ";case 5:return o=e,i=_n(["r","c","d","d2","d3"],t),"\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2("+o[0]+",\n                             "+o[1]+"));\n\n      int index = resTexRC.x * "+o[1]+" + resTexRC.y;\n\n      "+i+"\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  ";case 6:return n=e,r=_n(["r","c","d","d2","d3","d4"],t),"\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2("+n[0]+", "+n[1]+"));\n      int index = resTexRC.x * "+n[1]+" + resTexRC.y;\n\n      "+r+"\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  ";default:throw new Error(t.length+"-D output sampling is not yet supported")}var n,r;var o,i;var a,s;var u,c;var l,h;var p}(e.logicalShape,c),s=In),o&&(l+=On),[l,Dn,s,i,a,u,n].join("\n")}function kn(t){var e,n,r,o,i,a=t.shapeInfo.logicalShape;switch(a.length){case 0:return o=(r=t).name,i="get"+o.charAt(0).toUpperCase()+o.slice(1),r.shapeInfo.isUniform?"float "+i+"() {return "+o+";}":"\n    float "+i+"() {\n      return sampleTexture("+o+", halfCR);\n    }\n  ";case 1:return e=t.name,"\n    float "+(n="get"+e.charAt(0).toUpperCase()+e.slice(1))+"(int index) {\n      return "+n+"Flat(index);\n    }\n  ";case 2:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=t.shapeInfo.texShape;if(null!=o&&y(e,o)){var i=o[0];return"\n    float "+r+"(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2("+o[1]+".0, "+i+".0);\n      return sampleTexture("+n+", uv);\n    }\n  "}var a=E(e),s=a.newShape,u=a.keptDims,c=s;if(c.length<e.length)return"\n      "+kn(Pn(t,c))+"\n      float "+r+"(int row, int col) {\n        return "+r+"("+Bn(["row","col"],u)+");\n      }\n    ";if(t.shapeInfo.isUniform)return"\n      float "+r+"(int row, int col) {\n        float index = dot(vec2(row, col), vec2("+e[1]+", 1));\n        return "+r+"Flat(round(index));\n      }\n    ";var l=o[0],h=o[1];return 1===h?"\n    float "+r+"(int row, int col) {\n      float index = dot(vec2(row, col), vec2("+e[1]+", 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / "+l+".0);\n      return sampleTexture("+n+", uv);\n    }\n  ":1===l?"\n    float "+r+"(int row, int col) {\n      float index = dot(vec2(row, col), vec2("+e[1]+", 1));\n      vec2 uv = vec2((index + 0.5) / "+h+".0, 0.5);\n      return sampleTexture("+n+", uv);\n    }\n  ":"\n  float "+r+"(int row, int col) {\n    vec2 uv = UVfrom2D("+l+", "+h+", "+e[1]+", row, col);\n    return sampleTexture("+n+", uv);\n  }\n"}(t);case 3:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e[1]*e[2],i=e[2],a=E(e),s=a.newShape,u=a.keptDims,c=s;if(c.length<e.length)return"\n        "+kn(Pn(t,c))+"\n        float "+r+"(int row, int col, int depth) {\n          return "+r+"("+Bn(["row","col","depth"],u)+");\n        }\n      ";if(t.shapeInfo.isUniform)return"\n      float "+r+"(int row, int col, int depth) {\n        float index = dot(vec3(row, col, depth),\n                          vec3("+o+", "+i+", 1));\n        return "+r+"Flat(round(index));\n      }\n    ";var l=t.shapeInfo.texShape,h=l[0],p=l[1];return p===o?"\n        float "+r+"(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2("+i+", 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2("+p+".0, "+h+".0);\n          return sampleTexture("+n+", uv);\n        }\n      ":p===i?"\n    float "+r+"(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2("+e[1]+", 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2("+p+".0, "+h+".0);\n      return sampleTexture("+n+", uv);\n    }\n  ":"\n      float "+r+"(int row, int col, int depth) {\n        vec2 uv = UVfrom3D(\n            "+h+", "+p+", "+o+", "+i+", row, col, depth);\n        return sampleTexture("+n+", uv);\n      }\n  "}(t);case 4:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e[3],i=e[2]*o,a=e[1]*i,s=E(e),u=s.newShape,c=s.keptDims;if(u.length<e.length)return"\n      "+kn(Pn(t,u))+"\n      float "+r+"(int row, int col, int depth, int depth2) {\n        return "+r+"("+Bn(["row","col","depth","depth2"],c)+");\n      }\n    ";if(t.shapeInfo.isUniform)return"\n      float "+r+"(int row, int col, int depth, int depth2) {\n        float index = dot(vec4(row, col, depth, depth2),\n                          vec4("+a+", "+i+", "+o+", 1));\n        return "+r+"Flat(round(index));\n      }\n    ";var l=t.shapeInfo.texShape,h=l[0],p=l[1];return p===a?"\n      float "+r+"(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2), vec3("+i+", "+o+", 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2("+p+".0, "+h+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":p===o?"\n      float "+r+"(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3("+e[1]*e[2]+", "+e[2]+", 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2("+p+".0, "+h+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":"\n    float "+r+"(int row, int col, int depth, int depth2) {\n      vec2 uv = UVfrom4D("+h+", "+p+", "+a+", "+i+",\n          "+o+", row, col, depth, depth2);\n      return sampleTexture("+n+", uv);\n    }\n  "}(t);case 5:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e[4],i=e[3]*o,a=e[2]*i,s=e[1]*a,u=E(e),c=u.newShape,l=u.keptDims;if(c.length<e.length)return"\n      "+kn(Pn(t,c))+"\n      float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n        return "+r+"("+Bn(["row","col","depth","depth2","depth3"],l)+");\n      }\n    ";if(t.shapeInfo.isUniform)return"\n      float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4("+s+", "+a+", "+i+", "+o+")) +\n          depth3;\n        return "+r+"Flat(index);\n      }\n    ";var h=t.shapeInfo.texShape,p=h[0],f=h[1];return f===s?"\n      float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(\n          vec4(col, depth, depth2, depth3),\n          vec4("+a+", "+i+", "+o+", 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2("+f+".0, "+p+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":f===o?"\n      float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4("+e[1]*e[2]*e[3]+", "+e[2]*e[3]+",\n            "+e[3]+", 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2("+f+".0, "+p+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":"\n    float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n      vec2 uv = UVfrom5D("+p+", "+f+", "+s+", "+a+",\n          "+i+", "+o+", row, col, depth, depth2, depth3);\n      return sampleTexture("+n+", uv);\n    }\n  "}(t);case 6:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e[5],i=e[4]*o,a=e[3]*i,s=e[2]*a,u=e[1]*s,c=E(e),l=c.newShape,h=c.keptDims;if(l.length<e.length)return"\n      "+kn(Pn(t,l))+"\n      float "+r+"(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return "+r+"("+Bn(["row","col","depth","depth2","depth3","depth4"],h)+");\n      }\n    ";if(t.shapeInfo.isUniform)return"\n      float "+r+"(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4("+u+", "+s+", "+a+", "+i+")) +\n          dot(\n            vec2(depth3, depth4),\n            vec2("+o+", 1));\n        return "+r+"Flat(index);\n      }\n    ";var p=t.shapeInfo.texShape,f=p[0],d=p[1];return d===u?"\n      float "+r+"(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(\n          vec4(col, depth, depth2, depth3),\n          vec4("+s+", "+a+", "+i+", "+o+")) + depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2("+d+".0, "+f+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":d===o?"\n      float "+r+"(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4("+e[1]*e[2]*e[3]*e[4]+",\n               "+e[2]*e[3]*e[4]+",\n               "+e[3]*e[4]+",\n               "+e[4]+")) + depth3;\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2("+d+".0, "+f+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":"\n    float "+r+"(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      vec2 uv = UVfrom6D("+f+", "+d+", "+u+", "+s+",\n          "+a+", "+i+", "+o+"\n          ,row, col, depth, depth2, depth3, depth4);\n      return sampleTexture("+n+", uv);\n    }\n  "}(t);default:throw new Error(a.length+"-D input sampling is not yet supported")}}function Tn(t){var e,n,r,o,i,a,s,u,c,l,h,p,f,d,m,v,g=t.shapeInfo.logicalShape;switch(g.length){case 0:return"\n    vec4 get"+(v=t.name).charAt(0).toUpperCase()+v.slice(1)+"() {\n      return texture2D("+v+", halfCR);\n    }\n  ";case 1:return p=(h=t).name,f="get"+p.charAt(0).toUpperCase()+p.slice(1),d=h.shapeInfo.texShape,m=[Math.ceil(d[0]/2),Math.ceil(d[1]/2)],"\n    vec4 "+f+"(int index) {\n      vec2 uv = packedUVfrom1D(\n        "+m[0]+", "+m[1]+", index);\n      return texture2D("+p+", uv);\n    }\n  ";case 2:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=t.shapeInfo.texShape,i=o[0],a=o[1];if(null!=o&&y(e,o))return"\n      vec4 "+r+"(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2("+a+".0, "+i+".0);\n\n        return texture2D("+n+", uv);\n      }\n    ";var s=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];return"\n    vec4 "+r+"(int row, int col) {\n      vec2 uv = packedUVfrom2D("+Math.ceil(e[1]/2)+", "+s[0]+", "+s[1]+", row, col);\n      return texture2D("+n+", uv);\n    }\n  "}(t);case 3:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=t.shapeInfo.texShape,i=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];if(1===e[0])return"\n        "+Tn(Pn(t,e.slice(1)))+"\n        vec4 "+r+"(int b, int row, int col) {\n          return "+r+"("+Bn(["b","row","col"],[1,2])+");\n        }\n      ";var a=i[0],s=i[1],u=Math.ceil(e[2]/2);return"\n    vec4 "+r+"(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        "+a+", "+s+", "+u*Math.ceil(e[1]/2)+", "+u+", b, row, col);\n      return texture2D("+n+", uv);\n    }\n  "}(t);case 4:return n=(e=t).shapeInfo.logicalShape,r=e.name,o="get"+r.charAt(0).toUpperCase()+r.slice(1),i=e.shapeInfo.texShape,a=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],s=a[0],u=a[1],c=Math.ceil(n[3]/2),l=c*Math.ceil(n[2]/2),"\n    vec4 "+o+"(int b2, int b, int row, int col) {\n      vec2 uv = packedUVfrom4D(\n        "+s+", "+u+", "+l*n[1]+",\n        "+l+", "+c+", b2, b, row, col);\n      return texture2D("+r+", uv);\n    }\n  ";default:throw new Error("Packed "+g.length+"-D input sampling is not yet supported")}}var Dn="\n  float sampleTexture(sampler2D textureSampler, vec2 uv) {\n    return texture2D(textureSampler, uv).r;\n  }\n",In="\n  void setOutput(float val) {\n    gl_FragColor = vec4(val, 0, 0, 0);\n  }\n",An="\n  void setOutput(vec4 val) {\n    gl_FragColor = val;\n  }\n",Mn="\n  precision highp float;\n  precision highp int;\n  varying vec2 resultUV;\n  const vec2 halfCR = vec2(0.5, 0.5);\n\n  struct ivec5\n  {\n    int x;\n    int y;\n    int z;\n    int w;\n    int u;\n  };\n\n  struct ivec6\n  {\n    int x;\n    int y;\n    int z;\n    int w;\n    int u;\n    int v;\n  };\n\n  "+(Ft.get("PROD")?"\n    bool isNaN(float val) {\n      return false;\n    }\n\n    bool hasNaN(vec4 values) {\n      return false;\n    }\n  ":"\n    bool isNaN(float val) {\n      return (val < 1.0 || 0.0 < val || val == 0.0) ? false : true;\n    }\n\n    bool hasNaN(vec4 values) {\n      return any(bvec4(\n        isNaN(values.x),\n        isNaN(values.y),\n        isNaN(values.z),\n        isNaN(values.w)\n      ));\n    }\n  ")+"\n\n  float getNaN(vec4 values) {\n    return dot(vec4(1), values);\n  }\n\n  int round(float value) {\n    return int(floor(value + 0.5));\n  }\n\n  int imod(int x, int y) {\n    return x - y * (x / y);\n  }\n\n  //Based on the work of Dave Hoskins\n  //https://www.shadertoy.com/view/4djSRW\n  #define HASHSCALE1 443.8975\n  float random(float seed){\n    vec2 p = resultUV * seed;\n    vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n    p3 += dot(p3, p3.yzx + 19.19);\n    return fract((p3.x + p3.y) * p3.z);\n  }\n\n  \nvec2 UVfrom1D(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n\n  \nvec2 UVfrom2D(int texNumR, int texNumC, int numC, int row, int col) {\n  int index = row * numC + col;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n\n  \nvec2 UVfrom3D(int texNumR, int texNumC, int stride0,\n    int stride1, int row, int col, int depth) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n\n  \nvec2 UVfrom4D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int row, int col, int depth,\n    int depth2) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth * stride2 + depth2;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom4D(int texNumR, int texNumC, int texelsInBatch2,\n    int texelsInBatch, int texelsInLogicalRow, int b2, int b,\n    int row, int col) {\n  int index = b2 * texelsInBatch2 + b * texelsInBatch +\n    (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n\n  \nvec2 UVfrom5D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int stride3, int row, int col, int depth,\n    int depth2, int depth3) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 +\n              depth * stride2 + depth2 * stride3 + depth3;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n\n  \nvec2 UVfrom6D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int stride3, int stride4,\n    int row, int col, int depth, int depth2, int depth3, int depth4) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth * stride2 + depth2 *\n    stride3 + depth3 * stride4 + depth4;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n\n",On="\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n";function Fn(t){if(t<=1)return"int";if(2===t)return"ivec2";if(3===t)return"ivec3";if(4===t)return"ivec4";if(5===t)return"ivec5";if(6===t)return"ivec6";throw Error("GPU for rank "+t+" is not yet supported")}function Pn(t,e){var n=JSON.parse(JSON.stringify(t));return n.shapeInfo.logicalShape=e,n}function Bn(e,t){return t.map(function(t){return e[t]}).join(", ")}var Ln=function(t,e,n){this.variableNames=["x"];var r=(this.outputShape=t).length,o=t[t.length-1],i=n?"<":">";this.userCode="\n      int getIndex(int i) {\n        "+(n?"return "+o+" -i - 1;":"return i;")+"\n      }\n\n      void main() {\n        "+Fn(r)+" coords = getOutputCoords();\n        int end = "+Wn(r,"coords")+";\n        float val = 0.0;\n        for (int i = "+o+" - 1; i >= 0; i -= 1) {\n          int idx = getIndex(i);\n          if (idx "+i+" end) {\n            continue;\n          }\n          if (idx == end && "+e+") {\n            continue;\n          }\n          "+Wn(r,"coords")+" = idx;\n          val += getX("+function(t,e){if(1===t)return""+e;if(2===t)return e+".x, "+e+".y";if(3===t)return e+".x, "+e+".y, "+e+".z";if(4===t)return e+".x, "+e+".y, "+e+".z, "+e+".w";throw Error("Cumulative sum for rank "+t+" is not yet supported")}(r,"coords")+");\n        }\n        setOutput(val);\n      }\n    "};function Wn(t,e){if(1===t)return""+e;if(2===t)return e+".y";if(3===t)return e+".z";if(4===t)return e+".w";throw Error("Cumulative sum for rank "+t+" is not yet supported")}var zn=function(){function t(t,e,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=n,this.userCode="\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = "+this.getHeightCoordString()+";\n      int w = "+this.getWidthCoordString()+";\n      int d = "+this.getDepthCoordString()+";\n\n      int in_h = h / "+e+";\n      int offset_h = imod(h, "+e+");\n      int in_w = w / "+e+";\n      int offset_w = imod(w, "+e+");\n      int offset_d = (offset_h * "+e+" + offset_w) *\n        "+this.getOutputDepthSize()+";\n      int in_d = d + offset_d;\n\n      float result = "+this.getInputSamplingString()+";\n      setOutput(result);\n    }\n  "}return t.prototype.getHeightCoordString=function(){return"NHWC"===this.dataFormat?"coords[1]":"coords[2]"},t.prototype.getWidthCoordString=function(){return"NHWC"===this.dataFormat?"coords[2]":"coords[3]"},t.prototype.getDepthCoordString=function(){return"NHWC"===this.dataFormat?"coords[3]":"coords[1]"},t.prototype.getOutputDepthSize=function(){return"NHWC"===this.dataFormat?this.outputShape[3]:this.outputShape[1]},t.prototype.getInputSamplingString=function(){return"NHWC"===this.dataFormat?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"},t}(),Un=function(t){this.variableNames=["A"],this.outputShape=t,this.userCode="\n      const float FLOAT_MAX = 1.70141184e38;\n      const float FLOAT_MIN = 1.17549435e-38;\n\n      lowp vec4 encode_float(highp float v) {\n        if (isNaN(v)) {\n          return vec4(255, 255, 255, 255);\n        }\n\n        highp float av = abs(v);\n\n        if(av < FLOAT_MIN) {\n          return vec4(0.0, 0.0, 0.0, 0.0);\n        } else if(v > FLOAT_MAX) {\n          return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n        } else if(v < -FLOAT_MAX) {\n          return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n        }\n\n        highp vec4 c = vec4(0,0,0,0);\n\n        highp float e = floor(log2(av));\n        highp float m = exp2(fract(log2(av))) - 1.0;\n\n        c[2] = floor(128.0 * m);\n        m -= c[2] / 128.0;\n        c[1] = floor(32768.0 * m);\n        m -= c[1] / 32768.0;\n        c[0] = floor(8388608.0 * m);\n\n        highp float ebias = e + 127.0;\n        c[3] = floor(ebias / 2.0);\n        ebias -= c[3] * 2.0;\n        c[2] += floor(ebias) * 128.0;\n\n        c[3] += 128.0 * step(0.0, -v);\n\n        return c / 255.0;\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        gl_FragColor = encode_float(x);\n      }\n    "},Vn="return real * expR - imag * expI;",Gn="return real * expI + imag * expR;",Hn=function(t,e,n){this.variableNames=["real","imag"];var r=e[1];this.outputShape=e;var o=n?"2.0 * "+Math.PI:"-2.0 * "+Math.PI,i=n?r+".0":"1.0";this.userCode="\n      const float exponentMultiplier = "+o+";\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        "+t+"\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float("+r+");\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < "+r+"; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / "+i+";\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    "},qn=function(t){this.variableNames=["A"];var e=t[0],n=t[1];this.outputShape=t,this.userCode="\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2("+n+".0, "+e+".0);\n\n        vec4 values = texture2D(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    "},jn=function(t,e,n){this.variableNames=["A","indices"];var r=t.slice();r[n]=e,this.outputShape=r,this.rank=r.length;var o=Fn(this.rank),i=function(t,e){var n=t.length;if(4<n)throw Error("Gather for rank "+n+" is not yet supported");if(1===n)return"int(getIndices(resRC))";for(var r=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],i=0;i<t.length;i++)i===e?o.push("int(getIndices("+r[i]+"))"):o.push(""+r[i]);return o.join()}(t,n);this.userCode="\n      void main() {\n        "+o+" resRC = getOutputCoords();\n        setOutput(getA("+i+"));\n      }\n    "};var $n,Kn,Xn,Yn,Qn=function(t,e,n){this.sliceDim=t,this.strides=e,this.variableNames=["x","indices"],this.outputShape=n;var r=Fn(e.length),o=Fn(n.length),i=1<this.sliceDim?"strides[j]":"strides";this.userCode="\n        "+r+" strides = "+r+"("+this.strides+");\n         void main() {\n          "+o+" coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < "+this.sliceDim+"; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * "+i+";\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      "};function Jn(t,e){return[e,t]}function Zn(t,e){return t*e}function tr(t,e,n){var r=function(t,e){if(t%e!=0)throw new Error("unpackedSize ("+t+") must be a multiple of "+e);return t/e}(t.length,n);if(e.length<r)throw new Error("matrix length ("+e.length+") must be >= "+r);for(var o=0,i=0;i<t.length;i+=n)e[o++]=t[i]}function er(t,e){return[Math.max(1,Math.ceil(e/2)),Math.max(1,Math.ceil(t/2))]}function nr(t,e){var n=er(t,e);return n[0]*n[1]*4}function rr(t,e,n,r,o){var i=n*r;if(o.length<i)throw new Error("matrix length ("+o.length+") must be >= "+i);for(var 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e.gl.deleteProgram(t)})},t.prototype.setProgram=function(t){var e=this;this.throwIfDisposed(),this.program=t,null!=this.program&&this.autoDebugValidate&&mr(this.gl,this.program),or(this.gl,function(){return e.gl.useProgram(t)})},t.prototype.getUniformLocation=function(t,e,n){return void 0===n&&(n=!0),this.throwIfDisposed(),n?Sr(this.gl,t,e):_r(this.gl,t,e)},t.prototype.getAttributeLocation=function(t,e){var n=this;return this.throwIfDisposed(),or(this.gl,function(){return n.gl.getAttribLocation(t,e)})},t.prototype.getUniformLocationNoThrow=function(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)},t.prototype.setInputMatrixTexture=function(t,e,n){this.throwIfDisposed(),this.throwIfNoProgram(),Rr(this.gl,this.program,t,e,n)},t.prototype.setOutputMatrixTexture=function(t,e,n){this.setOutputMatrixTextureDriver(t,n,e)},t.prototype.setOutputPackedMatrixTexture=function(t,e,n){this.throwIfDisposed();var r=er(e,n),o=r[0],i=r[1];this.setOutputMatrixTextureDriver(t,o,i)},t.prototype.setOutputMatrixWriteRegion=function(t,e,n,r){this.setOutputMatrixWriteRegionDriver(n,t,r,e)},t.prototype.setOutputPackedMatrixWriteRegion=function(t,e,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")},t.prototype.debugValidate=function(){null!=this.program&&mr(this.gl,this.program),Tr(this.gl)},t.prototype.executeProgram=function(){this.throwIfDisposed(),this.throwIfNoProgram();var t=this.gl;this.autoDebugValidate&&this.debugValidate(),or(t,function(){return t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0)})},t.prototype.blockUntilAllProgramsCompleted=function(){var t=this;this.throwIfDisposed(),or(this.gl,function(){return t.gl.finish()})},t.prototype.getQueryTimerExtension=function(){return null==this.disjointQueryTimerExtension&&(this.disjointQueryTimerExtension=cr(this.gl,2===Ft.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension},t.prototype.getQueryTimerExtensionWebGL2=function(){return this.getQueryTimerExtension()},t.prototype.getQueryTimerExtensionWebGL1=function(){return this.getQueryTimerExtension()},t.prototype.beginQuery=function(){if(2===Ft.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){var t=this.gl,e=this.getQueryTimerExtensionWebGL2(),n=t.createQuery();return t.beginQuery(e.TIME_ELAPSED_EXT,n),n}var r=this.getQueryTimerExtensionWebGL1(),o=r.createQueryEXT();return r.beginQueryEXT(r.TIME_ELAPSED_EXT,o),o},t.prototype.endQuery=function(){if(2!==Ft.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){var t=this.getQueryTimerExtensionWebGL1();t.endQueryEXT(t.TIME_ELAPSED_EXT)}else{var e=this.gl,n=this.getQueryTimerExtensionWebGL2();e.endQuery(n.TIME_ELAPSED_EXT)}},t.prototype.waitForQueryAndGetTime=function(n){return m(this,void 0,void 0,function(){var e=this;return C(this,function(t){switch(t.label){case 0:return[4,w(function(){return e.disposed||e.isQueryAvailable(n,Ft.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))})];case 1:return t.sent(),[2,this.getQueryTime(n,Ft.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))]}})})},t.prototype.getQueryTime=function(t,e){if(0===e)return null;if(2===e){var n=this.gl;return n.getQueryParameter(t,n.QUERY_RESULT)/1e6}var r=this.getQueryTimerExtensionWebGL1();return r.getQueryObjectEXT(t,r.QUERY_RESULT_EXT)/1e6},t.prototype.isQueryAvailable=function(t,e){if(0===e)return!0;if(2!==e)return o=(r=this.getQueryTimerExtensionWebGL1()).getQueryObjectEXT(t,r.QUERY_RESULT_AVAILABLE_EXT),null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint;var n=this.gl,r=this.getQueryTimerExtensionWebGL2(),o=n.getQueryParameter(t,n.QUERY_RESULT_AVAILABLE);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint},t.prototype.pollFence=function(e){var n=this;return new Promise(function(t){n.addItemToPoll(function(){return e.isFencePassed()},function(){return t()})})},t.prototype.pollItems=function(){for(var t=function(t){for(var e=0,n=t.length-1,r=-1;e<=n;){var o=e+n>>1;t[o]()?e=(r=o)+1:n=o-1}return r}(this.itemsToPoll.map(function(t){return t.isDoneFn})),e=0;e<=t;++e)(0,this.itemsToPoll[e].resolveFn)();this.itemsToPoll=this.itemsToPoll.slice(t+1)},t.prototype.addItemToPoll=function(t,e){var n=this;this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),1<this.itemsToPoll.length||w(function(){return n.pollItems(),0===n.itemsToPoll.length})},t.prototype.bindTextureToFrameBuffer=function(t){this.throwIfDisposed(),Nr(this.gl,t,this.framebuffer),this.autoDebugValidate&&Tr(this.gl)},t.prototype.unbindTextureToFrameBuffer=function(){null!=this.outputTexture?(Nr(this.gl,this.outputTexture,this.framebuffer),this.autoDebugValidate&&Tr(this.gl)):kr(this.gl,this.framebuffer)},t.prototype.downloadMatrixDriver=function(t,e){this.bindTextureToFrameBuffer(t);var n=e();return this.unbindTextureToFrameBuffer(),n},t.prototype.setOutputMatrixTextureDriver=function(t,e,n){this.throwIfDisposed();var r=this.gl;Nr(r,t,this.framebuffer),this.autoDebugValidate&&Tr(r),this.outputTexture=t,or(r,function(){return r.viewport(0,0,e,n)}),or(r,function(){return r.scissor(0,0,e,n)})},t.prototype.setOutputMatrixWriteRegionDriver=function(t,e,n,r){var o=this;this.throwIfDisposed(),or(this.gl,function(){return o.gl.scissor(t,e,n,r)})},t.prototype.throwIfDisposed=function(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")},t.prototype.throwIfNoProgram=function(){if(null==this.program)throw new Error("No GPU program is currently set.")},t}();function ao(t,s){if(t.length!==s.length)throw Error("Binary was compiled with "+t.length+" inputs, but was executed with "+s.length+" inputs");t.forEach(function(t,e){var n=t.logicalShape,r=s[e],o=r.shape;if(!y(n,o))throw Error("Binary was compiled with different shapes than the current args. Shapes "+n+" and "+o+" must match");if(!t.isUniform||!r.isUniform){var i=t.texShape,a=r.isUniform?null:r.texData.texShape;if(!y(i,a))throw Error("Binary was compiled with different texture shapes than the current args. Shape "+i+" and "+a+" must match")}})}var so=function(t,e,n){this.variableNames=["A"],this.outputShape=t;var r=n.filterWidth,o=n.inChannels,i=n.strideWidth,a=n.strideHeight,s=n.padInfo,u=n.outWidth,c=n.dilationWidth,l=n.dilationHeight,h=s.left,p=s.top,f=o*r;this.userCode="\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            int blockIndex = rc.y + col;\n            int pos = rc.x + row;\n\n            if(blockIndex >= "+t[1]+" || pos >= "+t[0]+") continue;\n\n            int offsetY = int(blockIndex / ("+u+")) * "+a+" - "+p+";\n            int d0 = offsetY + "+l+" * (pos / "+f+");\n\n            if(d0 >= "+e[0]+" || d0 < 0) continue;\n\n            int offsetX = int(mod(float(blockIndex), "+u+".) * "+i+". - "+h+".);\n            int d1 = offsetX + "+c+" * (int(mod(float(pos), "+f+".) / "+o+".));\n\n            if(d1 >= "+e[1]+" || d1 < 0) continue;\n\n            result[row * 2 + col] = getA(d0, d1, int(mod(float(pos), "+o+".)));\n          }\n        }\n\n        gl_FragColor = result;\n      }\n    "},uo=function(t,e,n,r,o){this.variableNames=["x"],this.outputShape=[];var i,a=e,s=t[3]-1;this.outputShape=t;var u="float("+n+") + float("+r+") * sum";i=.5===o?"inversesqrt("+u+")":1===o?"1.0/("+u+")":"exp(log("+u+") * float(-"+o+"));",this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -"+a+"; j <= "+a+"; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  "+s+") {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * "+i+";\n        setOutput(val);\n      }\n    "},co=function(t,e,n,r,o){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=n,this.alpha=r,this.beta=o,this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < "+this.depth+"; ++d) {\n          int depthBegin = int(max(0.0, float(d - "+e+")));\n          int depthEnd = int(min(float("+this.depth+"),\n              float(d + "+e+" + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = "+this.depth+";\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float("+r+") * norm + float("+n+");\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float("+r+")\n                * float("+o+")\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * "+o+");\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    "},lo=function(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;var e=t.strideHeight,n=t.strideWidth,r=t.dilationHeight,o=t.effectiveFilterHeight,i=t.effectiveFilterWidth,a=o-1-t.padInfo.top,s=i-1-t.padInfo.left,u=o*i-1;this.userCode="\n      const ivec2 pads = ivec2("+a+", "+s+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+o+";\n          wR += "+r+") {\n          float dyR = float(dyRCorner + wR) / "+e+".0;\n\n          if (dyR < 0.0 || dyR >= "+t.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < "+i+"; wC++) {\n            float dyC = float(dyCCorner + wC) / "+n+".0;\n\n            if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = "+u+" - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * "+i+" + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},ho=function(t,e,n,r){void 0===n&&(n=!1),void 0===r&&(r=!1),this.variableNames=["matrixA","matrixB"];var o=t[0],i=n?t[2]:t[1],a=r?e[1]:e[2],s=n?t[1]:t[2];this.outputShape=[o,i,a];var u=function(t,e){return n?"batch, "+e+" + "+t+", aRow":"batch, aRow, "+e+" + "+t},c=function(t,e){return r?"batch, bCol, "+e+" + "+t:"batch, "+e+" + "+t+", bCol"},l=4*Math.floor(s/4),h=s%4;this.userCode=" float dotARowBCol(int batch, int aRow, int bCol) {\n      float result = 0.0;\n      for (int i = 0; i < "+l+"; i += 4) {\n        vec4 a = vec4(\n          getMatrixA("+u(0,"i")+"),\n          getMatrixA("+u(1,"i")+"),\n          getMatrixA("+u(2,"i")+"),\n          getMatrixA("+u(3,"i")+")\n        );\n        vec4 b = vec4(\n          getMatrixB("+c(0,"i")+"),\n          getMatrixB("+c(1,"i")+"),\n          getMatrixB("+c(2,"i")+"),\n          getMatrixB("+c(3,"i")+")\n        );\n\n        result += dot(a, b);\n      }\n\n      if ("+(1===h)+") {\n        result += getMatrixA("+u(0,l)+") *\n          getMatrixB("+c(0,l)+");\n      } else if ("+(2===h)+") {\n        vec2 a = vec2(\n          getMatrixA("+u(0,l)+"),\n          getMatrixA("+u(1,l)+")\n        );\n        vec2 b = vec2(\n          getMatrixB("+c(0,l)+"),\n          getMatrixB("+c(1,l)+")\n        );\n        result += dot(a, b);\n      } else if ("+(3===h)+") {\n        vec3 a = vec3(\n          getMatrixA("+u(0,l)+"),\n          getMatrixA("+u(1,l)+"),\n          getMatrixA("+u(2,l)+")\n        );\n        vec3 b = vec3(\n          getMatrixB("+c(0,l)+"),\n          getMatrixB("+c(1,l)+"),\n          getMatrixB("+c(2,l)+")\n        );\n        result += dot(a, b);\n      }\n\n      return result;\n    }\n\n    void main() {\n      ivec3 resBRC = getOutputCoords();\n      setOutput(dotARowBCol(resBRC.x, resBRC.y, resBRC.z));\n    }\n    "},po=function(t,e,n,r,o){void 0===r&&(r=!1),void 0===o&&(o=!1),this.variableNames=["matrixA","matrixB"],this.usesPackedTextures=!0,this.outputShape=n;var i=r?t[0]:t[1],a=Math.ceil(i/2),s=r?"i * 2, rc.x":"rc.x, i * 2",u=o?"rc.y, i * 2":"i * 2, rc.y",c=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],l=o?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"];this.userCode="\n      const float sharedDimension = "+a+".0;\n\n      vec4 dot2x2ARowBCol(ivec2 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < "+a+"; i++) {\n          vec4 a = getMatrixA("+s+");\n          vec4 b = getMatrixB("+u+");\n\n          result += ("+c[0]+" * "+l[0]+") + ("+c[1]+" * "+l[1]+");\n        }\n        return result;\n      }\n\n      void main() {\n        ivec2 rc = getOutputCoords();\n        setOutput(dot2x2ARowBCol(rc));\n      }\n    "},fo=function(){function t(t,e,n){this.variableNames=["probs"],this.outputShape=[t,n],this.userCode="\n      uniform float seed;\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < "+(e-1)+"; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float("+(e-1)+"));\n      }\n    "}return t.prototype.getCustomSetupFunc=function(n){var r=this;return function(t,e){null==r.seedLoc&&(r.seedLoc=t.getUniformLocation(e,"seed")),t.gl.uniform1f(r.seedLoc,n)}},t}(),mo=function(t,e,n,r){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode="\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float("+r+"), float("+n+"),\n                      float(index == coords.y)));\n      }\n    "};function vo(t,e){return 1===e?[t]:(n=t,["x","y","z","w","u","v"].slice(0,e).map(function(t){return n+"."+t}));var n}var go=function(t){this.variableNames=["A"],this.isPackShader=!0;var e,n,r,o,i=(this.outputShape=t).length;if(0===i)this.userCode="\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      ";else{var a=vo("rc",i),s=Fn(i),u=function(t,e,n){if(1===t)return"rc > "+e[0];for(var r="",o=t-2;o<t;o++)r+=n[o]+" >= "+e[o],o<t-1&&(r+="||");return r}(i,t,a),c=function(t,e,n,r){if(1===t)return"";var o=r.slice(-2);return"\n    int r = "+o[0]+";\n    int c = "+o[1]+";\n    int rp1 = r + 1;\n    int cp1 = c + 1;\n\n    bool cEdge = cp1 >= "+e+";\n    bool rEdge = rp1 >= "+n+";\n  "}(i,t[t.length-1],t[t.length-2],a),l=(n=a,r=(e=t).length,o=function(t,e){for(var n=[],r=0;r<=1;r++)for(var o=0;o<=1;o++){for(var i=(0===r?"r":"rp1")+", "+(0===o?"c":"cp1"),a=2;a<t;a++)i=e[e.length-1-a]+","+i;n.push(i)}return n}(r,n),1===r?"getA(rc),\n            rc + 1 >= "+e[0]+" ? 0. : getA(rc + 1),\n            0, 0":"getA("+o[0]+"),\n          cEdge ? 0. : getA("+o[1]+"),\n          rEdge ? 0. : getA("+o[2]+"),\n          rEdge || cEdge ? 0. : getA("+o[3]+")");this.userCode="\n        void main() {\n          "+s+" rc = getOutputCoords();\n\n          if("+u+") {\n            setOutput(vec4(0));\n          } else {\n            "+c+"\n\n            setOutput(vec4("+l+"));\n          }\n        }\n      "}};var yo=function(n,t,e){this.variableNames=["x"],this.outputShape=t.map(function(t,e){return t[0]+n[e]+t[1]});var r=n.length,o=Fn(r),i=t.map(function(t){return t[0]}).join(","),a=t.map(function(t,e){return t[0]+n[e]}).join(","),s=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);this.userCode=1!==r?"\n      "+o+" start = "+o+"("+i+");\n      "+o+" end = "+o+"("+a+");\n\n      void main() {\n        "+o+" outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(float("+e+"));\n        } else {\n          "+o+" coords = outC - start;\n          setOutput(getX("+s+"));\n        }\n      }\n    ":"\n        int start = "+i+";\n        int end = "+a+";\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(float("+e+"));\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      "},xo=function(t,e,n){if(this.variableNames=["x"],"avg"===e&&n)throw new Error("Cannot compute positions for average pool.");var r=t.filterWidth,o=t.strideHeight,i=t.strideWidth,a=t.dilationHeight,s=t.dilationWidth,u=t.effectiveFilterHeight,c=t.effectiveFilterWidth,l=t.padInfo.top,h=t.padInfo.left;this.outputShape=t.outShape;var p="avg"===e,f="0.0";if(p||(f="-1.0 / 0.0"),n)this.userCode="\n        const ivec2 strides = ivec2("+o+", "+i+");\n        const ivec2 pads = ivec2("+l+", "+h+");\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < "+u+";\n              wR += "+a+") {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= "+t.inHeight+") {\n              continue;\n            }\n\n            for (int wC = 0; wC < "+c+";\n                wC += "+s+") {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= "+t.inWidth+") {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value >= currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = wR * "+c+" + wC;\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      ";else{var d=e+"("+e+"("+e+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"avg"===e&&(d="avgValue / count");var m=4*Math.floor(r/4),v=r%4,g="\n      if ("+p+") {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = max(values, minMaxValue);\n      }\n    ";this.userCode="\n      const ivec2 strides = ivec2("+o+", "+i+");\n      const ivec2 pads = ivec2("+l+", "+h+");\n      const float initializationValue = "+f+";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= "+t.inWidth+") {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4("+f+");\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < "+u+";\n            wR += "+a+") {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= "+t.inHeight+") {\n            continue;\n          }\n\n          for (int wC = 0; wC < "+m+"; wC += 4) {\n            int xC = xCCorner + wC * "+s+";\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + "+s+", d),\n              getValue(batch, xR, xC + 2 * "+s+", d),\n              getValue(batch, xR, xC + 3 * "+s+", d)\n            );\n\n            "+g+"\n          }\n\n          int xC = xCCorner + "+m+";\n          if ("+(1===v)+") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            "+g+"\n          } else if ("+(2===v)+") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + "+s+", d),\n              initializationValue,\n              initializationValue\n            );\n\n            "+g+"\n          } else if ("+(3===v)+") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + "+s+", d),\n              getValue(batch, xR, xC + 2 * "+s+", d),\n              initializationValue\n            );\n\n            "+g+"\n          }\n        }\n        setOutput("+d+");\n      }\n    "}},wo=function(t,e){this.variableNames=["x"];var n=t.windowSize,r=t.batchSize,o=t.inSize,i=Math.ceil(o/n);this.outputShape=[r,i];var a="0.0",s="";"prod"===e?a="1.0":"min"===e?(a="1.0 / 0.0",s="min"):"max"===e&&(a="-1.0 / 0.0",s="max");var u=e+"("+e+"("+e+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"sum"===e?u="sumValue":"prod"===e?u="prodValue":"all"===e?u="allValue":"any"===e&&(u="anyValue");var c=4*Math.floor(n/4),l=n%4,h="\n      if ("+("sum"===e)+") {\n        sumValue += dot(values, ones);\n      } else if ("+("prod"===e)+") {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = "+s+"(values, minMaxValue);\n      }\n    ",p="vec4";"all"===e?(a="1.0",h="\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      ",p="bvec4"):"any"===e&&(a="0.0",h="\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      ",p="bvec4");var f="";0<o%n&&(f="\n        if (inIdx < 0 || inIdx >= "+o+") {\n          return initializationValue;\n        }\n      "),this.userCode="\n      const float initializationValue = "+a+";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        "+f+"\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * "+n+";\n\n        vec4 minMaxValue = vec4("+a+");\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < "+c+"; i += 4) {\n          int inIdx = inOffset + i;\n          "+p+" values = "+p+"(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          "+h+"\n        }\n\n        int inIdx = inOffset + "+c+";\n        if ("+(1===l)+") {\n          "+p+" values = "+p+"(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          "+h+"\n        } else if ("+(2===l)+") {\n          "+p+" values = "+p+"(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          "+h+"\n        } else if ("+(3===l)+") {\n          "+p+" values = "+p+"(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          "+h+"\n        }\n        setOutput("+u+");\n      }\n    "},bo=function(t,e){this.variableNames=["A"],this.usesPackedTextures=!0,this.outputShape=t;for(var n="",r=0;r<4;r++){var o="thisRC = rc;";r%2==1&&(o+="thisRC.z += 1;"),1<r&&(o+="thisRC.y += 1;"),n+="\n        "+o+"\n        "+(0<r?"if(thisRC.y < rows && thisRC.z < cols){":"")+"\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result["+r+"] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        "+(0<r?"}":"")+"\n      "}this.userCode="\n      \n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      "+_n(["r","c","d"],e)+"\n      return ivec3(r, c, d);\n    }\n  \n      \n    int getFlatIndex(ivec3 coords) {\n      return round("+function(t,e){if(t.length!==e.length)throw new Error("Vectors to be dotted must be of the same length -got "+t.length+" and "+e.length);for(var n=[],r=Math.floor(t.length/4),o=t.length%4,i=0;i<r;i++){var a=t.slice(4*i,4*i+4),s=e.slice(4*i,4*i+4);n.push(Rn(a)+", "+Rn(s))}return 0!==o&&(a=t.slice(4*r),s=e.slice(4*r),1===a.length&&(a=a.map(function(t){return"float("+t+")"}),s=s.map(function(t){return"float("+t+")"})),n.push(Rn(a)+", "+Rn(s))),n.map(function(t,e){return"dot("+t+")"}).join("+")}(["coords.x","coords.y","coords.z"],U(t).map(function(t){return t.toString()}).concat(["1."]))+");\n    }\n  \n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = "+t[1]+";\n        int cols = "+t[2]+";\n\n        "+n+"\n\n        setOutput(result);\n      }\n    "};var Eo=function(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e.shape;var r=e.shape,o=r[1],i=r[2],a=t.shape,s=a[1],u=a[2],c=[n&&1<s?o-1:o,n&&1<u?i-1:i],l=[n&&1<s?s-1:s,n&&1<u?u-1:u],h=c[0]/l[0],p=c[1]/l[1],f=1/h,d=1/p,m=2*Math.ceil(f)+2,v=2*Math.ceil(d)+2;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float("+h+");\n        const float widthScale = float("+p+");\n\n        const float invHeightScale = float("+f+");\n        const float invWidthScale = float("+d+");\n\n        const int winHeight = int("+m+");\n        const int winWidth = int("+v+");\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= "+s+") {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= "+u+") {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), "+(o-1)+".0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), "+(i-1)+".0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    "},Co=function(t,e,n,r){this.variableNames=["A"],this.outputShape=[];var o=t[0],i=t[1],a=t[2],s=t[3];this.outputShape=[o,e,n,s];var u=[r&&1<e?i-1:i,r&&1<n?a-1:a],c=[r&&1<e?e-1:e,r&&1<n?n-1:n];this.userCode="\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          "+u[0]/c[0]+",\n          "+u[1]/c[1]+");\n      const vec2 inputShapeRC = vec2("+i+".0, "+a+".0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    "},So=function(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e.shape;var r=e.shape,o=r[1],i=r[2],a=t.shape,s=a[1],u=a[2],c=[n&&1<s?o-1:o,n&&1<u?i-1:i],l=[n&&1<s?s-1:s,n&&1<u?u-1:u],h=c[0]/l[0],p=c[1]/l[1],f=1/h,d=1/p,m=2*Math.ceil(f)+2,v=2*Math.ceil(d)+2;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float("+h+");\n        const float widthScale = float("+p+");\n\n        const float invHeightScale = float("+f+");\n        const float invWidthScale = float("+d+");\n\n        const int winHeight = int("+m+");\n        const int winWidth = int("+v+");\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= "+s+") {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= "+u+") {\n              continue;\n            }\n\n            float sourceFracRow =\n              float("+c[0]+") *\n                (float(dyR) / float("+l[0]+"));\n\n            float sourceFracCol =\n                float("+c[1]+") *\n                  (float(dyC) / float("+l[1]+"));\n\n            int sourceNearestRow = int(min(\n                float(int("+o+") - 1),\n                "+n+" ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int("+i+") - 1),\n                "+n+" ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    "},_o=function(t,e,n,r){this.variableNames=["A"],this.outputShape=[];var o=t[0],i=t[1],a=t[2],s=t[3];this.outputShape=[o,e,n,s];var u=[r&&1<e?i-1:i,r&&1<n?a-1:a],c=[r&&1<e?e-1:e,r&&1<n?n-1:n],l=r?"0.5":"0.0";this.userCode="\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          "+u[0]/c[0]+",\n          "+u[1]/c[1]+");\n      const vec2 inputShapeRC = vec2("+i+".0, "+a+".0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + "+l+")));\n\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    "},Ro=function(r,o){this.variableNames=["x"];var t=r.length;if(4<t)throw new Error("WebGL backend: Reverse of rank-"+t+" tensor is not yet supported");if(this.outputShape=r,1!==t){var e=r.map(function(t,e){return n=e,-1!==o.indexOf(n)&&1!==r[n]?r[n]+" - coords["+n+"] - 1":"coords["+n+"]";var n}).join(","),n=Fn(t);this.userCode="\n      void main() {\n        "+n+" coords = getOutputCoords();\n        setOutput(getX("+e+"));\n      }\n    "}else this.userCode="\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX("+r[0]+" - coord - 1));\n        }\n      "},No=function(t,e,n,r,o,i,a){void 0===a&&(a=!0),this.variableNames=["updates","indices","defaultValue"],this.outputShape=i;var s=Fn(o.length),u=Fn(i.length),c="";1===n?c="i":2===n&&(c="i, j");var l="getIndices("+c+")",h="";1===r?h="i":2===r&&(h="i, coords[1]");var p="getUpdates("+h+")",f=1<e?"strides[j]":"strides";this.userCode="\n        "+s+" strides = "+s+"("+o+");\n\n        void main() {\n          "+u+" coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < "+t+"; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < "+e+"; j++) {\n              int index = round("+l+");\n              flattenedIndex += index * "+f+";\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += "+p+";\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      "},ko=function(t,e){this.variableNames=["x","segmentIds"];var n=t.windowSize,r=t.batchSize,o=t.inSize,i=t.numSegments,a=i*Math.ceil(o/n);this.outputShape=[r,a];var s=4*Math.floor(n/4),u=n%4,c="\n        sumValue += dot(values, filter);\n    ",l="";0<o%n&&(l="\n        if (inIdx < 0 || inIdx >= "+o+") {\n          return initializationValue;\n        }\n      ");var h="";0<o%n&&(h="\n        if (inIdx < 0 || inIdx >= "+o+") {\n          return -1.0;\n        }\n      "),this.userCode="\n      const float initializationValue = 0.0;\n\n      float getValue(int batch, int inIdx) {\n        "+l+"\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        "+h+"\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          "+i+")) * float("+n+"));\n        int currentSeg = int(mod(float(outIdx), float("+i+")));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < "+s+"; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          "+c+"\n        }\n\n        int inIdx = inOffset + "+s+";\n        if ("+(1===u)+") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          "+c+"\n        } else if ("+(2===u)+") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          "+c+"\n        } else if ("+(3===u)+") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          "+c+"\n        }\n        setOutput(sumValue);\n      }\n    "},To=function(t,e,n){var r,o;if(this.variableNames=["c","a","b"],this.outputShape=e,4<n)throw Error("Where for rank "+n+" is not yet supported");if(1===n)r=o="resRC";else{for(var i=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[],s=[],u=0;u<e.length;u++)s.push(""+i[u]),u<t&&a.push(""+i[u]);r=a.join(),o=s.join()}var c=Fn(n);this.userCode="\n      void main() {\n        "+c+" resRC = getOutputCoords();\n        float cVal = getC("+r+");\n        if (cVal >= 1.0) {\n          setOutput(getA("+o+"));\n        } else {\n          setOutput(getB("+o+"));\n        }\n      }\n    "},Do=function(){function t(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;var e=Fn(this.rank),n=function(t){if(1===t)return"sourceLoc";if(2===t)return"sourceLoc.x, sourceLoc.y";if(3===t)return"sourceLoc.x, sourceLoc.y, sourceLoc.z";if(4===t)return"sourceLoc.x, sourceLoc.y, sourceLoc.z, sourceLoc.w";throw Error("Slicing for rank "+t+" is not yet supported")}(this.rank);this.userCode="\n      uniform "+e+" start;\n\n     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this.truncated&&!this.isValidTruncated(n)||(this.nextVal=this.convertValue(n)),this.convertValue(e)},t.prototype.convertValue=function(t){return null==this.dtype||"float32"===this.dtype?t:Math.round(t)},t.prototype.isValidTruncated=function(t){return t<=this.upper&&t>=this.lower},t}();function oi(b,E){return m(this,void 0,void 0,function(){var e,n,r,o,i,a,s,u,c,l,h,p,f,d,m,v,g,y,x,w;return C(this,function(t){switch(t.label){case 0:if(e=me(b,"img","toPixels"),b instanceof it||(e=e.toInt()),2!==e.rank&&3!==e.rank)throw new Error("toPixels only supports rank 2 or 3 tensors, got rank "+e.rank+".");if(n=e.shape.slice(0,2),r=n[0],o=n[1],4<(i=2===e.rank?1:e.shape[2])||2===i)throw new Error("toPixels only supports depth of size 1, 3 or 4 but got "+i);return a=e.min(),s=e.max(),[4,a.data()];case 1:return u=t.sent()[0],[4,s.data()];case 2:if(c=t.sent()[0],a.dispose(),s.dispose(),"float32"===e.dtype){if(u<0||1<c)throw new Error("Tensor values for a float32 Tensor must be in the range [0 - 1] but got range ["+u+" - "+c+"].")}else{if("int32"!==e.dtype)throw new Error("Unsupported type for toPixels: "+e.dtype+". Please use float32 or int32 tensors.");if(u<0||255<c)throw new Error("Tensor values for a int32 Tensor must be in the range [0 - 255] but got range ["+u+" - "+c+"].")}return[4,e.data()];case 3:for(l=t.sent(),h="float32"===e.dtype?255:1,p=new Uint8ClampedArray(o*r*4),f=0;f<r*o;++f)g=v=m=d=void 0,1===i?(d=l[f]*h,m=l[f]*h,v=l[f]*h,g=255):3===i?(d=l[3*f]*h,m=l[3*f+1]*h,v=l[3*f+2]*h,g=255):4===i&&(d=l[4*f]*h,m=l[4*f+1]*h,v=l[4*f+2]*h,g=l[4*f+3]*h),p[0+(y=4*f)]=Math.round(d),p[y+1]=Math.round(m),p[y+2]=Math.round(v),p[y+3]=Math.round(g);return null!=E&&(E.width=o,E.height=r,x=E.getContext("2d"),w=new ImageData(p,o,r),x.putImageData(w,0,0)),e!==b&&e.dispose(),[2,p]}})})}function ii(t,e,n){return void 0===e&&(e="float32"),new et(t,e=e||"float32",n)}function ai(t,e){void 0===e&&(e=!1),console.log(t.toString(e))}var si=ge({batchToSpaceND_:function(t,e,n){var r=me(t,"x","batchToSpaceND"),o=e.reduce(function(t,e){return t*e});return A(r.rank>=1+e.length,"input rank is "+r.rank+" but should be > than blockShape.length "+e.length),A(n.length===e.length,"crops.length is "+n.length+" but should be equal to blockShape.length  "+e.length),A(r.shape[0]%o==0,"input tensor batch is "+r.shape[0]+" but is not divisible by the product of the elements of blockShape "+e.join(" * ")+" === "+o),Ft.engine.runKernel(function(t){return t.batchToSpaceND(r,e,n)},{$x:r},function(t){return{$x:function(){return t.spaceToBatchND(e,n)}}})}}),ui=ge({cast_:function(t,e){var n=me(t,"x","cast");return Ft.engine.runKernel(function(t){return t.cast(n,e)},{$x:n},function(t){return{$x:function(){return t.clone()}}})}}),ci=ge({clone_:function(t){var e=me(t,"x","clone",null);return Ft.engine.runKernel(function(t){return it.make(e.shape,{dataId:e.dataId},e.dtype)},{$x:e},function(t){return{$x:function(){return t.toFloat()}}})}}),li=ge({cumsum_:function(t,e,n,r){void 0===e&&(e=0),void 0===n&&(n=!1),void 0===r&&(r=!1);var o=me(t,"x","cumsum"),i=ne([e|=0],o.rank),a=o;null!=i&&(a=o.transpose(i));var s=oe(1,o.rank)[0],u=Ft.engine.runKernel(function(t){return t.cumsum(a,s,n,r)},{permutedX:a},function(t){return{permutedX:function(){return t.cumsum(e,n,!r)}}});return null!=i&&(u=u.transpose(i)),u}}),hi=ge({depthToSpace_:function(t,e,n){void 0===n&&(n="NHWC");var r=me(t,"x","depthToSpace"),o="NHWC"===n?r.shape[1]:r.shape[2],i="NHWC"===n?r.shape[2]:r.shape[3],a="NHWC"===n?r.shape[3]:r.shape[1];return A(0<=o*e,"Negative dimension size caused by overflow when multiplying\n      "+o+" and "+e+"  for depthToSpace with input shape\n      "+r.shape),A(0<=i*e,"Negative dimension size caused by overflow when multiplying\n      "+i+" and "+e+" for depthToSpace with input shape\n          "+r.shape),A(a%(e*e)==0,"Dimension size must be evenly divisible by "+e*e+" but is "+a+" for depthToSpace with input shape "+r.shape),Ft.engine.runKernel(function(t){return t.depthToSpace(r,e,n)},{$x:r})}}),pi=ge({expandDims_:function(t,e){void 0===e&&(e=0);var n=me(t,"x","expandDims");A(e<=n.rank,"Axis must be <= rank of the tensor");var r=n.shape.slice();return e<0&&(A(-(n.rank+1)<=e,"Axis must be in the interval ["+-(n.rank+1)+", "+n.rank+"]"),e=n.rank+e+1),r.splice(e,0,1),_i(n,r)}}),fi=ge({eye_:function(t,e,n,r){void 0===r&&(r="float32"),null==e&&(e=t);for(var o=ii([t,e],r),i=t<=e?t:e,a=0;a<i;++a)o.set(1,a,a);var s=o.toTensor().as2D(t,e);if(null==n)return s;if(1===n.length)return Ti(pi(s,0),[n[0],1,1]);if(2===n.length)return Ti(pi(pi(s,0),0),[n[0],n[1],1,1]);if(3===n.length)return Ti(pi(pi(pi(s,0),0),0),[n[0],n[1],n[2],1,1]);throw new Error("eye() currently supports only 1D and 2D batchShapes, but received "+n.length+"D.")}}),di=ge({fromPixels_:function(t,e){if(void 0===e&&(e=3),4<e)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");return Ft.engine.fromPixels(t,e)}}),mi=ge({multinomial_:function(t,e,n,r){void 0===r&&(r=!1);var o=me(t,"logits","multinomial"),i=o.size,a=o.rank;if(i<2)throw new Error("Error in multinomial: you need at least 2 outcomes, but got "+i+".");if(2<a)throw new Error("Rank of probabilities must be 1 or 2, but is "+a);n=n||Math.random();var s=1===a?o.as2D(1,-1):o,u=Ft.engine.runKernel(function(t){return t.multinomial(s,r,e,n)},{logits2D:s});return 1===a?u.as1D():u}}),vi=ge({oneHot_:function(t,e,n,r){void 0===n&&(n=1),void 0===r&&(r=0);var o=me(t,"indices","oneHot","int32");if(e<2)throw new Error("Error in oneHot: depth must be >=2, but it is "+e);return Ft.engine.runKernel(function(t){return t.oneHot(o,e,n,r)},{$indices:o},function(t){return{$indices:function(){return Ae(o.shape,"float32")}}})}}),gi=ge({pad_:function(t,e,n){void 0===n&&(n=0);var r=me(t,"x","pad");if(0===r.rank)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");var o=e.map(function(t){return t[0]});return Ft.engine.runKernel(function(t){return t.pad(r,e,n)},{$x:r},function(t){return{$x:function(){return t.slice(o,r.shape)}}})}}),yi=ge({pad1d_:function(t,e,n){return void 0===n&&(n=0),A(2===e.length,"Invalid number of paddings. Must be length of 2."),gi(t,[e],n)}}),xi=ge({pad2d_:function(t,e,n){return void 0===n&&(n=0),A(2===e.length&&2===e[0].length&&2===e[1].length,"Invalid number of paddings. Must be length of 2 each."),gi(t,e,n)}}),wi=ge({pad3d_:function(t,e,n){return void 0===n&&(n=0),A(3===e.length&&2===e[0].length&&2===e[1].length&&2===e[2].length,"Invalid number of paddings. Must be length of 2 each."),gi(t,e,n)}}),bi=ge({pad4d_:function(t,e,n){return void 0===n&&(n=0),A(4===e.length&&2===e[0].length&&2===e[1].length&&2===e[2].length&&2===e[3].length,"Invalid number of paddings. Must be length of 2 each."),gi(t,e,n)}}),Ei=ge({rand_:function(t,e,n){var r=L(t),o=null;if(null==n||"float32"===n)o=new Float32Array(r);else if("int32"===n)o=new Int32Array(r);else{if("bool"!==n)throw new Error("Unknown data type "+n);o=new Uint8Array(r)}for(var i=0;i<r;i++)o[i]=e();return it.make(t,{values:o},n)}}),Ci=ge({randomNormal_:function(t,e,n,r,o){if(void 0===e&&(e=0),void 0===n&&(n=1),null!=r&&"bool"===r)throw new Error("Unsupported data type "+r);for(var i=new ri(e,n,r,!1,o),a=ii(t,r),s=0;s<a.values.length;s++)a.values[s]=i.nextValue();return a.toTensor()}}),Si=ge({randomUniform_:function(t,e,n,r){void 0===e&&(e=0),void 0===n&&(n=1),void 0===r&&(r="float32");for(var o=ii(t,r),i=0;i<o.values.length;i++)o.values[i]=h(e,n);return o.toTensor()}}),_i=ge({reshape_:function(t,e){var n=me(t,"x","reshape",null);return e=b(e,n.size),A(n.size===L(e),"new shape and old shape must have the same number of elements."),Ft.engine.runKernel(function(t){return t.reshape(n,e)},{$x:n},function(t){return{$x:function(){return t.reshape(n.shape)}}})}}),Ri=ge({spaceToBatchND_:function(t,r,o){var e=me(t,"x","spaceToBatchND");return A(e.rank>=1+r.length,"input rank "+e.rank+" should be > than [blockShape] "+r.length),A(o.length===r.length,"paddings.shape[0] "+o.length+" must be equal to [blockShape] "+r.length),A(e.shape.reduce(function(t,e,n){return 0<n&&n<=r.length?t&&(e+o[n-1][0]+o[n-1][1])%r[n-1]==0:t},!0),"input spatial dimensions "+e.shape.slice(1)+" with paddings "+o.toString()+" must be divisible by blockShapes "+r.toString()),Ft.engine.runKernel(function(t){return t.spaceToBatchND(e,r,o)},{$x:e},function(t){return{$x:function(){return t.batchToSpaceND(r,o)}}})}}),Ni=ge({squeeze_:function(t,e){var n=me(t,"x","squeeze");return _i(n,E(n.shape,e).newShape)}}),ki=ge({stack_:function(t,e){void 0===e&&(e=0);var n=ve(t,"tensors","stack");if(A(1<=n.length,"Pass at least one tensor to tf.stack"),1===n.length)return n[0].expandDims(e);var r=n[0].rank,o=n[0].shape,i=n[0].dtype;A(e<=r,"Axis must be <= rank of the tensor"),n.forEach(function(t){f(o,t.shape,"All tensors passed to stack must have matching shapes")}),n.forEach(function(t){A(i===t.dtype,"All tensors passed to stack must have matching dtypes")});var a=n.map(function(t){return t.expandDims(e)});return Vo(a,e)}}),Ti=ge({tile_:function(t,a){var s=me(t,"x","tile");return A(s.rank===a.length,"Error in transpose: rank of input "+s.rank+" must match length of reps "+a+"."),Ft.engine.runKernel(function(t){return t.tile(s,a)},{$x:s},function(i){return{$x:function(){var t=Be(s);if(1===s.rank)for(var e=0;e<a[0];++e)t=t.add(i.slice([e*s.shape[0]],[s.shape[0]]));else if(2===s.rank)for(e=0;e<a[0];++e)for(var n=0;n<a[1];++n)t=t.add(i.slice([e*s.shape[0],n*s.shape[1]],[s.shape[0],s.shape[1]]));else if(3===s.rank)for(e=0;e<a[0];++e)for(n=0;n<a[1];++n)for(var r=0;r<a[2];++r)t=t.add(i.slice([e*s.shape[0],n*s.shape[1],r*s.shape[2]],[s.shape[0],s.shape[1],s.shape[2]]));else{if(4!==s.rank)throw new Error("Gradient for tile operation is not implemented for rank-"+s.rank+" tensors yet.");for(e=0;e<a[0];++e)for(n=0;n<a[1];++n)for(r=0;r<a[2];++r)for(var o=0;o<a[3];++o)t=t.add(i.slice([e*s.shape[0],n*s.shape[1],r*s.shape[2],o*s.shape[3]],[s.shape[0],s.shape[1],s.shape[2],s.shape[3]]))}return t}}})}}),Di=ge({truncatedNormal_:function(t,e,n,r,o){if(void 0===e&&(e=0),void 0===n&&(n=1),null!=r&&"bool"===r)throw new Error("Unsupported data type "+r);for(var i=new ri(e,n,r,!0,o),a=ii(t,r),s=0;s<a.values.length;s++)a.values[s]=i.nextValue();return a.toTensor()}}),Ii=ge({unstack_:function(t,n){void 0===n&&(n=0);for(var e,r=me(t,"x","unstack"),o=r.shape[n],i=Array(r.rank-1).fill(0),a=0,s=0;s<r.rank;s++)s!==n&&(i[a]=r.shape[s],a++);e=Array(o).fill(1);var u=Array(r.rank).fill(0),c=r.shape.slice();return e.map(function(t){c[n]=t;var e=r.slice(u,c);return u[n]+=t,e.reshape(i)})}}),Ai=function(h,p){return m(this,void 0,void 0,function(){var e,n,r,o,i,a,s,u,c,l;return C(this,function(t){switch(t.label){case 0:return e=me(h,"x","setdiff1d"),n=me(p,"y","setdiff1d"),A(e.dtype===n.dtype,"x and y should have the same dtype, but got x ("+e.dtype+") and y ("+n.dtype+")."),A(1===e.rank,"x should be 1D tensor, but got x ("+e.shape+")."),A(1===n.rank,"y should be 1D tensor, but got y ("+n.shape+")."),[4,e.data()];case 1:return r=t.sent(),[4,n.data()];case 2:for(o=t.sent(),i=new Set(o),c=a=0;c<r.length;c++)i.has(r[c])||a++;for(s=new et([a],e.dtype),u=new et([a],"int32"),l=c=0;c<r.length;c++)i.has(r[c])||(s.values[l]=r[c],u.values[l]=c,l++);return[2,[s.toTensor(),u.toTensor()]]}})})};function Mi(t,e){for(var n=[],r=0;r<e.length;r++)e[r]&&n.push(r);var o=ii(t,"int32"),i=ii([n.length,t.length],"int32");for(r=0;r<n.length;r++){var a=o.indexToLoc(n[r]),s=r*t.length;i.values.set(a,s)}return i.toTensor()}var Oi=function(){function t(t,e){if(void 0===e&&(e=!0),this.gpgpu=t,this.delayedStorage=e,this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.lruDataGPU=[],this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.binaryCache={},this.disposed=!1,Ft.get("WEBGL_VERSION")<1)throw new Error("WebGL is not supported on this device");if(null==t){var n=a(Ft.get("WEBGL_VERSION"));this.gpgpu=new io(n),this.canvas=n.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpuCreatedLocally=!1,this.canvas=t.gl.canvas;Ft.get("WEBGL_PAGING_ENABLED")&&(this.NUM_BYTES_BEFORE_PAGING=window.screen.height*window.screen.width*window.devicePixelRatio*300),this.textureManager=new Ao(this.gpgpu)}return t.prototype.register=function(t,e,n){if(this.texData.has(t))throw new Error("Data buffer is already registered");this.texData.set(t,{shape:e,dtype:n})},t.prototype.setDataMover=function(t){this.texData=new Le(t)},t.prototype.fromPixels=function(t,e){if(null==t)throw new Error("pixels passed to tf.fromPixels() can not be null");var n=[t.height,t.width],r=[t.height,t.width,e];if(!(t instanceof HTMLVideoElement||t instanceof HTMLImageElement||t instanceof HTMLCanvasElement||t instanceof ImageData))throw new Error("pixels passed to tf.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ImageData, but was "+t.constructor.name);if(t instanceof HTMLVideoElement){if(null==this.fromPixels2DContext){if(!Ft.get("IS_BROWSER"))throw new Error("Can't read pixels from HTMLImageElement outside the browser.");if("complete"!==document.readyState)throw new Error("The DOM is not ready yet. Please call tf.fromPixels() once the DOM is ready. One way to do that is to add an event listener for `DOMContentLoaded` on the document object");this.fromPixels2DContext=document.createElement("canvas").getContext("2d")}this.fromPixels2DContext.canvas.width=t.width,this.fromPixels2DContext.canvas.height=t.height,this.fromPixels2DContext.drawImage(t,0,0,t.width,t.height),t=this.fromPixels2DContext.canvas}var o=this.makeTensorHandle(n,"int32");this.texData.get(o.dataId).usage=$n.PIXELS,this.gpgpu.uploadPixelDataToTexture(this.getTexture(o.dataId),t);var i=new qn(r),a=this.compileAndRun(i,[o]);return this.disposeData(o.dataId),a},t.prototype.makeTensorHandle=function(t,e){var n={};return this.register(n,t,e),{dataId:n,shape:t,dtype:e}},t.prototype.write=function(t,e){if(null==e)throw new Error("MathBackendWebGL.write(): values can not be null");var n=this.texData.get(t),r=n.texture,o=n.texShape,i=n.usage,a=n.dtype,s=n.isPacked;if("complex64"===a)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");null!=r&&(this.releaseTexture(t,r,o,i,s),n.texture=null,n.texShape=null),n.usage=$n.UPLOAD,n.values=e,this.delayedStorage||this.uploadToGPU(t)},t.prototype.readSync=function(t){var e=this.texData.get(t),n=e.values,r=e.dtype,o=e.complexTensors;if(null!=n)return this.convertAndCacheOnCPU(t);if("string"===r)return n;var i,a,s=null!=this.activeTimers;return s&&(i=performance.now()),a="complex64"===r?Ve(o.real.dataSync(),o.imag.dataSync()):this.getValuesFromTexture(t),s&&(this.downloadWaitMs+=performance.now()-i),this.convertAndCacheOnCPU(t,a)},t.prototype.read=function(x){return m(this,void 0,void 0,function(){var e,n,r,o,i,a,s,u,c,l,h,p,f,d,m,v,g,y;return C(this,function(t){switch(t.label){case 0:if(this.pendingRead.has(x))return r=this.pendingRead.get(x),[2,new Promise(function(t){return r.push(t)})];if(o=this.texData.get(x),i=o.texture,a=o.values,s=o.texShape,u=o.isPacked,c=o.shape,null!=a)return[2,this.convertAndCacheOnCPU(x)];if(this.pendingRead.set(x,[]),!Ft.get("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&2===Ft.get("WEBGL_VERSION"))throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");return l=s[1],h=s[0],u&&(e=er(s[0],s[1]),l=e[0],h=e[1]),p=this.gpgpu.maybeCreateBufferFromTexture(i,h,l),[4,this.gpgpu.createAndWaitForFence()];case 1:return t.sent(),p instanceof WebGLTexture?f=this.getValuesFromTexture(x):u?(d=this.getBatchDim(c),v=m=1,c.length&&(n=this.getRowsCols(c),m=n[0],v=n[1]),f=this.gpgpu.downloadPackedMatrixFromBuffer(p,d,m,v,s[0],s[1])):f=this.gpgpu.downloadFloat32MatrixFromBuffer(p,s[0],s[1]),g=this.convertAndCacheOnCPU(x,f),y=this.pendingRead.get(x),this.pendingRead.delete(x),y.forEach(function(t){return t(g)}),this.pendingDisposal.has(x)&&(this.pendingDisposal.delete(x),this.disposeData(x)),[2,g]}})})},t.prototype.getValuesFromTexture=function(t){var e,n=this.texData.get(t),r=n.shape,o=n.dtype,i=n.texture,a=n.texShape;if(Ft.get("WEBGL_DOWNLOAD_FLOAT_ENABLED")){if(this.texData.get(t).isPacked){var s=this.getBatchDim(r),u=1,c=1;return r.length&&(u=(e=this.getRowsCols(r))[0],c=e[1]),this.gpgpu.downloadMatrixFromPackedTexture(i,s,u,c,a[0],a[1])}return this.gpgpu.downloadFloat32MatrixFromOutputTexture(i,a[0],a[1])}var l=this.makeTensorHandle(r,"float32");l.size=L(r),this.texData.get(l.dataId).usage=$n.DOWNLOAD;var h=new Un(r);this.compileAndRun(h,[{shape:r,dtype:o,dataId:t}],l,null,!1);var p=this.texData.get(l.dataId),f=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(p.texture,p.texShape[0],p.texShape[1]);return this.disposeData(l.dataId),f},t.prototype.time=function(u){return m(this,void 0,void 0,function(){var e,n,r,o,i,a,s;return C(this,function(t){switch(t.label){case 0:return e=this.activeTimers,r=!(n=[]),null==this.programTimersStack?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,u(),o=d(this.activeTimers.map(function(t){return t.query})).filter(function(t){return null!=t}),i=d(this.activeTimers.map(function(t){return t.name})).filter(function(t){return null!=t}),this.activeTimers=e,r&&(this.programTimersStack=null),[4,Promise.all(o)];case 1:return a=t.sent(),s={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:l(a),getExtraProfileInfo:function(){return a.map(function(t,e){return{name:i[e],ms:t}}).map(function(t){return t.name+": "+t.ms}).join(", ")},wallMs:null},this.uploadWaitMs=0,this.downloadWaitMs=0,[2,s]}})})},t.prototype.memory=function(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU}},t.prototype.startTimer=function(){return 0<Ft.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")?this.gpgpu.beginQuery():{startMs:performance.now(),endMs:null}},t.prototype.endTimer=function(t){return 0<Ft.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")?this.gpgpu.endQuery():t.endMs=performance.now(),t},t.prototype.getQueryTime=function(n){return m(this,void 0,void 0,function(){var e;return C(this,function(t){return 0<Ft.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")?[2,this.gpgpu.waitForQueryAndGetTime(n)]:[2,(e=n).endMs-e.startMs]})})},t.prototype.disposeData=function(t){if(!this.pendingDisposal.has(t))if(this.pendingRead.has(t))this.pendingDisposal.add(t);else if(this.texData.has(t)){var e=this.texData.get(t),n=e.texture,r=e.texShape,o=e.usage,i=e.complexTensors,a=e.isPacked;null!=n&&this.releaseTexture(t,n,r,o,a),null!=i&&(i.real.dispose(),i.imag.dispose()),this.texData.delete(t)}},t.prototype.getTexture=function(t){return this.uploadToGPU(t),this.texData.get(t).texture},t.prototype.getCPUBackend=function(){return Ft.get("WEBGL_CPU_FORWARD")?(null==this.cpuBackend&&(this.cpuBackend=Ft.findBackend("cpu")),this.cpuBackend):null},t.prototype.shouldExecuteOnCPU=function(t,e){var n=this;return void 0===e&&(e=10),null!=this.getCPUBackend()&&t.every(function(t){return null==n.texData.get(t.dataId).texture&&t.size<e})},t.prototype.getGPGPUContext=function(){return this.gpgpu},t.prototype.getCanvas=function(){return this.canvas},t.prototype.complex=function(t,e){var n=this.makeOutputArray(t.shape,"complex64");return this.texData.get(n.dataId).complexTensors={real:Ft.engine.keep(t.clone()),imag:Ft.engine.keep(e.clone())},n},t.prototype.real=function(t){return this.texData.get(t.dataId).complexTensors.real.clone()},t.prototype.imag=function(t){return this.texData.get(t.dataId).complexTensors.imag.clone()},t.prototype.slice=function(t,e,n){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.slice(t,e,n);var r=new Do(n),o=r.getCustomSetupFunc(e);return this.compileAndRun(r,[t],null,o)},t.prototype.stridedSlice=function(t,e,n,r,o,i,a,s,u){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.stridedSlice(t,e,n,r,o,i,a,s,u);var c=le(t.shape,e,n,r,o,i,a,s,u),l=c[0],h=c[1],p=c[2],f=h.filter(function(t,e){return-1===p.indexOf(e)});if(f.some(function(t){return 0===t}))return Ce([],f);var d=new Io(l,r,h,p);return this.compileAndRun(d,[t])},t.prototype.reverse=function(t,e){var n=new Ro(t.shape,e);return this.compileAndRun(n,[t])},t.prototype.concat2Tensors=function(t,e,n){var r=ie([t.shape,e.shape],n),o=t.as2D(-1,L(t.shape.slice(n))),i=e.as2D(-1,L(e.shape.slice(n))),a=new hn(o.shape,i.shape);return this.compileAndRun(a,[o,i]).reshape(r)},t.prototype.concat=function(t,e){if(this.shouldExecuteOnCPU(t))return this.cpuBackend.concat(t,e);if(1===t.length)return t[0];for(var n=t[0],r=1;r<t.length;++r)n=this.concat2Tensors(n,t[r],e);return n},t.prototype.neg=function(t){var e=new Wo(t.shape,"return -x;");return this.compileAndRun(e,[t])},t.prototype.batchMatMul=function(t,e,n,r){var o=n?t.shape[2]:t.shape[1],i=r?e.shape[1]:e.shape[2],a=n?t.shape[1]:t.shape[2],s=t.shape[0];if((1===o||1===i)&&1e3<a){n&&(t=t.transpose([0,2,1])),r&&(e=e.transpose([0,2,1]));var u=1===i?t:t.as3D(s,a,1),c=1===i?2:1,l=1===i?e.as3D(s,1,a):e;return this.multiply(u,l).sum(c,!0)}var h=xt(t.dtype,e.dtype);if(1!==s)return d=new ho(t.shape,e.shape,n,r),m=this.makeOutputArray(d.outputShape,h),this.compileAndRun(d,[t,e],m);var p=t.as2D(t.shape[1],t.shape[2]),f=e.as2D(e.shape[1],e.shape[2]),d=new po(p.shape,f.shape,[o,i],n,r),m=this.makePackedTensor(d.outputShape,h),v=this.compileAndRun(d,[p,f],m);return v.reshape([1,v.shape[0],v.shape[1]])},t.prototype.multiply=function(t,e){if("complex64"===t.dtype){var n=this.texData.get(t.dataId),r=this.texData.get(e.dataId),o=new rn(en,t.shape,e.shape),i=new rn(nn,t.shape,e.shape),a=[this.makeComplexComponentTensorHandle(t,n.complexTensors.real),this.makeComplexComponentTensorHandle(t,n.complexTensors.imag),this.makeComplexComponentTensorHandle(e,r.complexTensors.real),this.makeComplexComponentTensorHandle(e,r.complexTensors.imag)],s=this.compileAndRun(o,a),u=this.compileAndRun(i,a),c=this.complex(s,u);return s.dispose(),u.dispose(),c}if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.multiply(t,e);var l=new sn("return a * b;",t.shape,e.shape),h=this.makeOutputArray(l.outputShape,t.dtype);return this.compileAndRun(l,[t,e],h)},t.prototype.batchNormalization=function(t,e,n,r,o,i){var a=[t,e,n],s=null;null!=i&&(s=i.shape,a.push(i));var u=null;if(null!=o&&(u=o.shape,a.push(o)),Ft.get("WEBGL_PACK_BATCHNORMALIZATION")){var c=new tn(t.shape,e.shape,n.shape,s,u,r);return this.compileAndRun(c,a)}var l=new Ze(t.shape,e.shape,n.shape,s,u,r);return this.compileAndRun(l,a)},t.prototype.localResponseNormalization4D=function(t,e,n,r,o){var i=new uo(t.shape,e,n,r,o);return this.compileAndRun(i,[t])},t.prototype.LRNGrad=function(t,e,n,r,o,i,a){var s=new co(e.shape,r,o,i,a);return this.compileAndRun(s,[e,n,t])},t.prototype.tile=function(t,e){var n=new Fo(t.shape,e);return this.compileAndRun(n,[t])},t.prototype.pad=function(t,e,n){var r=new yo(t.shape,e,n);return this.compileAndRun(r,[t])},t.prototype.transpose=function(t,e){var n=new Po(t.shape,e);return this.compileAndRun(n,[t])},t.prototype.gather=function(t,e,n){var r=new jn(t.shape,e.size,n);return this.compileAndRun(r,[t,e])},t.prototype.batchToSpaceND=function(t,e,n){A(t.rank<=4,"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");var r=e.reduce(function(t,e){return t*e}),o=jt(t.shape,e,r),i=$t(o.length,e.length),a=Kt(t.shape,e,r),s=Xt(n,e.length),u=Yt(a,n,e.length);return t.reshape(o).transpose(i).reshape(a).slice(s,u)},t.prototype.spaceToBatchND=function(t,e,n){A(t.rank<=4,"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");var r=e.reduce(function(t,e){return t*e}),o=[[0,0]];o.push.apply(o,n);for(var i=1+e.length;i<t.shape.length;++i)o.push([0,0]);var a=t.pad(o),s=jt(a.shape,e,r,!1),u=$t(s.length,e.length,!1),c=Kt(a.shape,e,r,!1);return a.reshape(s).transpose(u).reshape(c)},t.prototype.reduce=function(t,e,n){var r=t.shape[0],o=t.shape[1],i=se(o),a=new wo({windowSize:i,inSize:o,batchSize:r},e),s=a.outputShape,u=s[0],c=s[1],l=this.makeOutputArray([u,c],n);return this.compileAndRun(a,[t],l),1===l.shape[1]?l:this.reduce(l,e,n)},t.prototype.argReduce=function(t,e,n){void 0===n&&(n=null);var r=t.shape[0],o=t.shape[1];null!=n&&(r=n.shape[0],o=n.shape[1]);var i=se(o),a=new Ke({windowSize:i,inSize:o,batchSize:r},e,null==n),s=a.outputShape,u=s[0],c=s[1],l=this.makeOutputArray([u,c],"int32"),h=[t];return null!=n&&h.push(n),this.compileAndRun(a,h,l),1===l.shape[1]?l:this.argReduce(t,e,l)},t.prototype.sum=function(t,e){ee("sum",e,t.rank);var n=Jt(t.shape,e),r=n[0],o=L(n[1]),i=t.as2D(-1,o),a=wt(t.dtype);return this.reduce(i,"sum",a).reshape(r)},t.prototype.prod=function(t,e){var n=Jt(t.shape,e),r=n[0],o=L(n[1]),i=t.as2D(-1,o),a=wt(t.dtype);return this.reduce(i,"prod",a).reshape(r)},t.prototype.unsortedSegmentSum=function(t,e,n){var r=0,o=ne([r],t.rank),i=t;null!=o&&(i=t.transpose(o),r=oe(1,t.rank)[0]);var a=function(t,e,n){for(var r=[],o=t.length,i=0;i<o;i++)i!==e?r.push(t[i]):r.push(n);return r}(i.shape,r,n),s=L([i.shape[r]]),u=i.as2D(-1,s),c=wt(t.dtype),l=this.segOpCompute(u,"unsortedSegmentSum",e,c,n).reshape(a);return null!=o&&(l=l.transpose(re(o))),l},t.prototype.segOpCompute=function(t,e,n,r,o){var i=t.shape[0],a=t.shape[1],s=function(t,e){var n,r=!1;for(t<=30?(n=t,r=!0):n=z(t,Math.floor(Math.sqrt(t)));!r;){if(e<n||n===t){r=!0;break}n=z(t,n+1)}return n}(a,o),u=new ko({windowSize:s,inSize:a,batchSize:i,numSegments:o},e),c=u.outputShape,l=c[0],h=c[1],p=this.makeOutputArray([l,h],r);return this.compileAndRun(u,[t,n],p),p.shape[1]===o?p:(n=Fe(0,o).tile([a/s]),this.segOpCompute(p,e,n,r,o))},t.prototype.argMin=function(t,e){var n=[e];ee("argMin",n,t.rank);var r=Jt(t.shape,n),o=r[0],i=L(r[1]),a=t.as2D(-1,i);return this.argReduce(a,"min").reshape(o)},t.prototype.argMax=function(t,e){var n=[e];ee("argMax",n,t.rank);var r=Jt(t.shape,n),o=r[0],i=L(r[1]),a=t.as2D(-1,i);return this.argReduce(a,"max").reshape(o)},t.prototype.cumsum=function(t,e,n,r){if(e!==t.rank-1)throw new Error("WebGL cumsum shader expects an inner-most axis="+(t.rank-1)+" but got axis="+e);var o=new Ln(t.shape,n,r);return this.compileAndRun(o,[t])},t.prototype.equal=function(t,e){var n=new sn("return float(a == b);",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[t,e],r)},t.prototype.notEqual=function(t,e){var n=new sn("return float(a != b);",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[t,e],r)},t.prototype.less=function(t,e){if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.less(t,e);var n=new sn("return float(a < b);",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[t,e],r)},t.prototype.lessEqual=function(t,e){var n=new sn("return float(a <= b);",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[t,e],r)},t.prototype.greater=function(t,e){if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.greater(t,e);var n=new sn("return float(a > b);",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[t,e],r)},t.prototype.greaterEqual=function(t,e){var n=new sn("return float(a >= b);",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[t,e],r)},t.prototype.logicalNot=function(t){var e=new Wo(t.shape,"return float(!(x >= 1.0));");return this.compileAndRun(e,[t])},t.prototype.logicalAnd=function(t,e){var n=new sn("return float(a >= 1.0 && b >= 1.0);",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[t,e],r)},t.prototype.logicalOr=function(t,e){var n=new sn("return float(a >= 1.0 || b >= 1.0);",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[t,e],r)},t.prototype.select=function(t,e,n){var r=new To(t.rank,e.shape,e.rank),o=this.makeOutputArray(r.outputShape,xt(e.dtype,n.dtype));return this.compileAndRun(r,[t,e,n],o)},t.prototype.where=function(t){qt("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");var e=t.dataSync();return Mi(t.shape,e)},t.prototype.topk=function(t,e,n){return $e(t.dataSync(),t.shape,t.dtype,e)},t.prototype.min=function(t,e){ee("min",e,t.rank);var n=Jt(t.shape,e),r=n[0],o=L(n[1]),i=t.as2D(-1,o);return this.reduce(i,"min",i.dtype).reshape(r)},t.prototype.minimum=function(t,e){if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.minimum(t,e);var n=new sn("\n  if (isNaN(a)) return a;\n  if (isNaN(b)) return b;\n\n  return min(a, b);\n",t.shape,e.shape);return this.compileAndRun(n,[t,e])},t.prototype.mod=function(t,e){var n=new sn("if (b == 0.0) return NAN;\n  return mod(a, b);",t.shape,e.shape),r=n.getCustomSetupFunc();return this.compileAndRun(n,[t,e],null,r)},t.prototype.max=function(t,e){ee("max",e,t.rank);var n=Jt(t.shape,e),r=n[0],o=L(n[1]),i=t.as2D(-1,o);return this.reduce(i,"max",i.dtype).reshape(r)},t.prototype.maximum=function(t,e){if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.maximum(t,e);var n=new sn("\n  if (isNaN(a)) return a;\n  if (isNaN(b)) return b;\n\n  return max(a, b);\n",t.shape,e.shape);return this.compileAndRun(n,[t,e])},t.prototype.all=function(t,e){ee("all",e,t.rank);var n=Jt(t.shape,e),r=n[0],o=L(n[1]),i=t.as2D(-1,o);return this.reduce(i,"all",i.dtype).reshape(r)},t.prototype.any=function(t,e){ee("any",e,t.rank);var n=Jt(t.shape,e),r=n[0],o=L(n[1]),i=t.as2D(-1,o);return this.reduce(i,"any",i.dtype).reshape(r)},t.prototype.squaredDifference=function(t,e){var n=new sn("return (a - b) * (a - b);",t.shape,e.shape);return this.compileAndRun(n,[t,e])},t.prototype.realDivide=function(t,e){var n=new sn("if (a == b) return 1.0;\n  return a / b;",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"float32");return this.compileAndRun(n,[t,e],r)},t.prototype.floorDiv=function(t,e){var n=new sn("\n  float resultSign = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  int result = ia / ib;\n  int amodb = ia - ib * result;\n\n  if (resultSign < 0.0 && amodb != 0) {\n    result -= 1;\n  }\n  return float(result);\n",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"int32");return this.compileAndRun(n,[t,e],r)},t.prototype.add=function(t,e){if("complex64"===t.dtype&&"complex64"===e.dtype)return this.complexSeparableBinaryOp(t,e,on);var n=new sn(on,t.shape,e.shape),r=this.makeOutputArray(n.outputShape,xt(t.dtype,e.dtype));return this.compileAndRun(n,[t,e],r)},t.prototype.complexSeparableBinaryOp=function(s,u,c){var l=this,t=this.texData.get(s.dataId),e=this.texData.get(u.dataId),n=[[t.complexTensors.real,e.complexTensors.real],[t.complexTensors.imag,e.complexTensors.imag]].map(function(t){var e=t[0],n=t[1],r=new sn(c,s.shape,u.shape),o=l.makeOutputArray(r.outputShape,xt(e.dtype,n.dtype)),i=l.makeComplexComponentTensorHandle(s,e),a=l.makeComplexComponentTensorHandle(u,n);return l.compileAndRun(r,[i,a],o)}),r=n[0],o=n[1],i=this.complex(r,o);return r.dispose(),o.dispose(),i},t.prototype.makeComplexComponentTensorHandle=function(t,e){return{dataId:e.dataId,dtype:e.dtype,shape:t.shape}},t.prototype.addN=function(t){for(var e=t[0],n=1;n<t.length;n++)e=this.add(e,t[n]);return e},t.prototype.subtract=function(t,e){if("complex64"===t.dtype&&"complex64"===e.dtype)return this.complexSeparableBinaryOp(t,e,an);if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.subtract(t,e);var n=new sn(an,t.shape,e.shape),r=this.makeOutputArray(n.outputShape,xt(t.dtype,e.dtype));return this.compileAndRun(n,[t,e],r)},t.prototype.pow=function(t,e){var n=new sn("\nif(a < 0.0 && floor(b) < b){\n  return NAN;\n}\nreturn (round(mod(b, 2.0)) == 0 || round(mod(b, 2.0)) == 2) ?\n    pow(abs(a), b) : sign(a) * pow(abs(a), b);\n",t.shape,e.shape),r=n.getCustomSetupFunc(),o=this.makeOutputArray(n.outputShape,xt(t.dtype,e.dtype));return this.compileAndRun(n,[t,e],o,r)},t.prototype.ceil=function(t){var e=new Wo(t.shape,"return ceil(x);");return this.compileAndRun(e,[t])},t.prototype.floor=function(t){var e=new Wo(t.shape,"return floor(x);");return this.compileAndRun(e,[t])},t.prototype.sign=function(t){var e=new Wo(t.shape,"\n  if (isNaN(x)) { return 0.0; }\n  return sign(x);\n");return this.compileAndRun(e,[t])},t.prototype.round=function(t){var e=new Wo(t.shape,"\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n");return this.compileAndRun(e,[t])},t.prototype.exp=function(t){var e=new Wo(t.shape,"return exp(x);");return this.compileAndRun(e,[t])},t.prototype.expm1=function(t){var e=new Wo(t.shape,"return exp(x) - 1.0;");return this.compileAndRun(e,[t])},t.prototype.log=function(t){var e=new Wo(t.shape,"if (x < 0.0) return NAN;\n  return log(x);"),n=e.getCustomSetupFunc();return this.compileAndRun(e,[t],null,n)},t.prototype.log1p=function(t){var e=new Wo(t.shape,"return log(1.0 + x);");return this.compileAndRun(e,[t])},t.prototype.sqrt=function(t){var e=new Wo(t.shape,"return sqrt(x);");return this.compileAndRun(e,[t])},t.prototype.rsqrt=function(t){var e=new Wo(t.shape,"return inversesqrt(x);");return this.compileAndRun(e,[t])},t.prototype.square=function(t){var e=new Wo(t.shape,"return x * x;");return this.compileAndRun(e,[t])},t.prototype.reciprocal=function(t){var e=new Wo(t.shape,"return 1.0 / x;");return this.compileAndRun(e,[t])},t.prototype.relu=function(t){var e=new Wo(t.shape,"if (isNaN(x)) return x;\n  return (x < 0.0) ? 0.0 : x;\n");return this.compileAndRun(e,[t])},t.prototype.elu=function(t){var e=new Wo(t.shape,"return (x >= 0.0) ? x : (exp(x) - 1.0);");return this.compileAndRun(e,[t])},t.prototype.eluDer=function(t,e){var n=new sn("return (b >= 1.0) ? a : a * (b + 1.0);",t.shape,e.shape);return this.compileAndRun(n,[t,e])},t.prototype.selu=function(t){var e=new Wo(t.shape,"\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = 1.7580993408473768;\n  float scale = 1.0507009873554805;\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n");return this.compileAndRun(e,[t])},t.prototype.int=function(t){var e=new Wo(t.shape,"return float(int(x));"),n=this.makeOutputArray(e.outputShape,"int32");return this.compileAndRun(e,[t],n)},t.prototype.clip=function(t,e,n){var r;return r=Ft.get("WEBGL_PACK_CLIP")?new cn(t.shape,e,n):new un(t.shape,e,n),this.compileAndRun(r,[t])},t.prototype.abs=function(t){var e=new Wo(t.shape,"return abs(x);");return this.compileAndRun(e,[t])},t.prototype.complexAbs=function(t){var e=this.texData.get(t.dataId),n=new ln(t.shape),r=[this.makeComplexComponentTensorHandle(t,e.complexTensors.real),this.makeComplexComponentTensorHandle(t,e.complexTensors.imag)];return this.compileAndRun(n,r)},t.prototype.sigmoid=function(t){var e=new Wo(t.shape,"return 1.0 / (1.0 + exp(-1.0 * x));");return this.compileAndRun(e,[t])},t.prototype.softplus=function(t){var e=new Wo(t.shape,"\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n");return this.compileAndRun(e,[t])},t.prototype.sin=function(t){var e=new Wo(t.shape,"if (isNaN(x)) return x;\n  return sin(x);\n");return this.compileAndRun(e,[t])},t.prototype.cos=function(t){var e=new Wo(t.shape,"if (isNaN(x)) return x;\n  return cos(x);\n");return this.compileAndRun(e,[t])},t.prototype.tan=function(t){var e=new Wo(t.shape,"return tan(x);");return this.compileAndRun(e,[t])},t.prototype.asin=function(t){var e=new Wo(t.shape,"return asin(x);");return this.compileAndRun(e,[t])},t.prototype.acos=function(t){var e=new Wo(t.shape,"return acos(x);");return this.compileAndRun(e,[t])},t.prototype.atan=function(t){var e=new Wo(t.shape,"if (isNaN(x)) return x;\n  return atan(x);\n");return this.compileAndRun(e,[t])},t.prototype.atan2=function(t,e){var n=new sn("\n  if (isNaN(a)) return a;\n  if (isNaN(b)) return b;\n\n  return atan(a, b);\n",t.shape,e.shape);return this.compileAndRun(n,[t,e])},t.prototype.sinh=function(t){var e=new Wo(t.shape,"\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n");return this.compileAndRun(e,[t])},t.prototype.cosh=function(t){var e=new Wo(t.shape,"\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n");return this.compileAndRun(e,[t])},t.prototype.tanh=function(t){var e=new Wo(t.shape,"\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n");return this.compileAndRun(e,[t])},t.prototype.asinh=function(t){var e=new Wo(t.shape,"return log(x + sqrt(x * x + 1.0));");return this.compileAndRun(e,[t])},t.prototype.acosh=function(t){var e=new Wo(t.shape,"if (isNaN(x)) return x;\n  if (x < 1.0) return NAN;\n  return log(x + sqrt(x * x - 1.0));"),n=e.getCustomSetupFunc();return this.compileAndRun(e,[t],null,n)},t.prototype.atanh=function(t){var e=new Wo(t.shape,"if (isNaN(x)) return x;\n  if ((x < -1.0) || (x > 1.0)) return NAN;\n  return (log(1.0 + x) - log(1.0 - x)) / 2.0;"),n=e.getCustomSetupFunc();return this.compileAndRun(e,[t],null,n)},t.prototype.erf=function(t){var e=new Wo(t.shape,'\n  // Error function is calculated approximately with elementary function.\n  // See "Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables", Abramowitz and Stegun.\n  float p = 0.3275911;\n  float a1 = 0.254829592;\n  float a2 = -0.284496736;\n  float a3 = 1.421413741;\n  float a4 = -1.453152027;\n  float a5 = 1.061405429;\n\n  float t = 1.0 / (1.0 + p * x);\n  return 1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x);\n');return this.compileAndRun(e,[t])},t.prototype.step=function(t,e){var n,r=new Wo(t.shape,(void 0===(n=e)&&(n=0),zo+"\n    return x > 0.0 ? 1.0 : float("+n+");\n  "));return this.compileAndRun(r,[t])},t.prototype.conv2dWithIm2Row=function(t,e,n){var r=n.filterWidth,o=n.filterHeight,i=n.inChannels,a=n.outWidth,s=n.outHeight,u=r*o*i,c=s*a,l=[u,c],h=t.squeeze([0]),p=e.reshape([u,-1]),f=new so(l,h.shape,n),d=this.compileAndRun(f,[h]),m=new po(d.shape,p.shape,[c,n.outChannels],!0,!1);return this.compileAndRun(m,[d,p]).reshape([1,s,a,n.outChannels])},t.prototype.conv2d=function(t,e,n){if(Ft.get("WEBGL_CONV_IM2COL")&&1===t.shape[0])return this.conv2dWithIm2Row(t,e,n);var r=new yn(n);return this.compileAndRun(r,[t,e])},t.prototype.conv2dDerInput=function(t,e,n){var r=new fn(n);return this.compileAndRun(r,[t,e])},t.prototype.conv2dDerFilter=function(t,e,n){var r=new pn(n);return this.compileAndRun(r,[t,e])},t.prototype.depthwiseConv2D=function(t,e,n){var r;return Ft.get("WEBGL_PACK_DEPTHWISECONV")&&1===n.dilationWidth&&1===n.dilationHeight&&n.padInfo.left<=1&&n.strideWidth<=2&&n.outChannels/n.inChannels==1?(r=new bn(n),this.compileAndRun(r,[t,e],this.makePackedTensor(n.outShape,t.dtype))):(r=new wn(n),this.compileAndRun(r,[t,e]))},t.prototype.depthwiseConv2DDerInput=function(t,e,n){var r=new gn(n);return this.compileAndRun(r,[t,e])},t.prototype.depthwiseConv2DDerFilter=function(t,e,n){var r=new vn(n);return this.compileAndRun(r,[t,e])},t.prototype.conv3d=function(t,e,n){var r=new xn(n);return this.compileAndRun(r,[t,e])},t.prototype.conv3dDerInput=function(t,e,n){var r=new mn(n);return this.compileAndRun(r,[t,e])},t.prototype.conv3dDerFilter=function(t,e,n){var r=new dn(n);return this.compileAndRun(r,[t,e])},t.prototype.maxPool=function(t,e){var n=new xo(e,"max",!1),r=this.makeOutputArray(n.outputShape,t.dtype);return this.compileAndRun(n,[t],r)},t.prototype.avgPool=function(t,e){var n=new xo(e,"avg",!1),r=this.makeOutputArray(n.outputShape,"float32");return this.compileAndRun(n,[t],r)},t.prototype.maxPoolBackprop=function(t,e,n,r){var o=new xo(r,"max",!0),i=this.compileAndRun(o,[e]),a=new lo(r),s=this.makeOutputArray(a.outputShape,e.dtype),u=this.compileAndRun(a,[t,i],s);return i.dispose(),u},t.prototype.avgPoolBackprop=function(t,e,n){var r=new Xe(n),o=this.makeOutputArray(r.outputShape,e.dtype);return this.compileAndRun(r,[t],o)},t.prototype.cast=function(t,e){return ze(t,e,this)},t.prototype.reshape=function(t,e){return this.texData.get(t.dataId).isPacked&&!Fr(t.shape,e)?this.packedReshape(t,e):Ue(t,e)},t.prototype.resizeBilinear=function(t,e,n,r){var o=new Co(t.shape,e,n,r);return this.compileAndRun(o,[t])},t.prototype.resizeBilinearBackprop=function(t,e,n){var r=new Eo(t,e,n);return this.compileAndRun(r,[t])},t.prototype.resizeNearestNeighbor=function(t,e,n,r){var o=new _o(t.shape,e,n,r);return this.compileAndRun(o,[t])},t.prototype.resizeNearestNeighborBackprop=function(t,e,n){var r=new So(t,e,n);return this.compileAndRun(r,[t])},t.prototype.multinomial=function(t,e,n,r){var o=e?t:ye(t),i=o.shape[0],a=o.shape[1],s=new fo(i,a,n),u=this.makeOutputArray(s.outputShape,"int32"),c=s.getCustomSetupFunc(r);return this.compileAndRun(s,[o],u,c)},t.prototype.oneHot=function(t,e,n,r){var o=new mo(t.size,e,n,r);return this.compileAndRun(o,[t])},t.prototype.nonMaxSuppression=function(t,e,n,r,o){return qt("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"),He(t.dataSync(),e.dataSync(),n,r,o)},t.prototype.cropAndResize=function(t,e,n,r,o,i){var a=new Sn(t.shape,e.shape,r,o,i);return this.compileAndRun(a,[t,e,n])},t.prototype.depthToSpace=function(t,e,n){A(1<e,"blockSize should be > 1 for depthToSpace, but was: "+e);var r=t.shape[0],o=("NHWC"===n?t.shape[1]:t.shape[2])*e,i=("NHWC"===n?t.shape[2]:t.shape[3])*e,a=("NHWC"===n?t.shape[3]:t.shape[1])/(e*e),s=new zn("NHWC"===n?[r,o,i,a]:[r,a,o,i],e,n);return this.compileAndRun(s,[t])},t.prototype.split=function(t,e,n){return je(t,e,n)},t.prototype.scatterND=function(t,e,n){var r=ce(0,t,n),o=r.sliceRank,i=r.numUpdates,a=r.sliceSize,s=r.strides,u=r.outputSize,c=[u/a,a],l=t.reshape([i,o]),h=e.reshape([i,a]);if(0===u)return Ue(Ce([]),n);var p=Se(0),f=new No(i,o,l.rank,h.rank,s,c);return this.compileAndRun(f,[h,l,p]).reshape(n)},t.prototype.sparseToDense=function(t,e,n,r){var o=ce(0,t,n),i=o.sliceRank,a=o.numUpdates,s=o.strides,u=o.outputSize,c=new No(a,i,t.rank,e.rank,s,[u,1],!1);return this.compileAndRun(c,[e,t,r]).reshape(n)},t.prototype.fft=function(t){return this.fftImpl(t,!1)},t.prototype.ifft=function(t){return this.fftImpl(t,!0)},t.prototype.fftImpl=function(t,e){var n=this.texData.get(t.dataId),r=new Hn(Vn,t.shape,e),o=new Hn(Gn,t.shape,e),i=[this.makeComplexComponentTensorHandle(t,n.complexTensors.real),this.makeComplexComponentTensorHandle(t,n.complexTensors.imag)],a=this.compileAndRun(r,i),s=this.compileAndRun(o,i),u=this.complex(a,s).as2D(t.shape[0],t.shape[1]);return a.dispose(),s.dispose(),u},t.prototype.gatherND=function(t,e){var n=e.shape,r=n[n.length-1],o=ae(t,e),i=o[0],a=o[1],s=o[2],u=o[3],c=e.reshape([a,r]),l=t.reshape([t.size/s,s]),h=new Qn(r,u,[a,s]);return this.compileAndRun(h,[l,c]).reshape(i)},t.prototype.makeOutputArray=function(t,e){return it.make(t,{},e)},t.prototype.makePackedTensor=function(t,e){var n=it.make(t,{},e);return this.texData.get(n.dataId).isPacked=!0,n},t.prototype.unpackTensor=function(t){var e=new Uo(t.shape);return this.compileAndRun(e,[t],it.make(e.outputShape,{},t.dtype))},t.prototype.getBatchDim=function(t,e){return void 0===e&&(e=2),L(t.slice(0,t.length-e))},t.prototype.getRowsCols=function(t){if(0===t.length)throw Error("Cannot get rows and columns of an empty shape array.");return[1<t.length?t[t.length-2]:1,t[t.length-1]]},t.prototype.packedReshape=function(t,e){var n=t.reshape([this.getBatchDim(t.shape)].concat(this.getRowsCols(t.shape))),r=[this.getBatchDim(e)].concat(this.getRowsCols(e)),o=new bo(r,n.shape);return this.compileAndRun(o,[n]).reshape(e)},t.prototype.compileAndRun=function(i,t,e,n,r){var a=this;if(void 0===r&&(r=!0),null==e&&(e=i.usesPackedTextures?this.makePackedTensor(i.outputShape,t[0].dtype):this.makeOutputArray(i.outputShape,t[0].dtype)),0===e.size)return this.texData.get(e.dataId).values=_(e.dtype,0),e;var o=t.map(function(t){if("complex64"===t.dtype)throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");var e=a.texData.get(t.dataId);if(null==e.texture){if(!i.usesPackedTextures&&L(t.shape)<=Ft.get("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:t.shape,texData:null,isUniform:!0,uniformValues:a.readSync(t.dataId)};i.usesPackedTextures&&(e.isPacked=!0,e.shape=t.shape)}else if(!!e.isPacked!=!!i.usesPackedTextures){var n=void 0,r=void 0;e.isPacked?(n=new Uo(t.shape),r=a.compileAndRun(n,[t],it.make(n.outputShape,{},t.dtype))):(n=new go(t.shape),r=a.compileAndRun(n,[t],a.makePackedTensor(t.shape,t.dtype))),e=a.texData.get(r.dataId),t=r}else if(e.isPacked&&!Fr(e.shape,t.shape)){a.delayedStorage=!1;var o=t.dataSync();a.delayedStorage=!0,t=it.make(t.shape,{values:o},t.dtype),(e=a.texData.get(t.dataId)).isPacked=!0}return a.uploadToGPU(t.dataId),{shape:t.shape,texData:e,isUniform:!1}});this.uploadToGPU(e.dataId);var s,u={shape:e.shape,texData:this.texData.get(e.dataId),isUniform:!1},c=function(t,e,n){var r="";e.concat(n).forEach(function(t){r+=t.shape+"_"+(t.isUniform?"uniform":t.texData.texShape)});var o=t.userCode,i=(!0===t.supportsBroadcasting).toString();return t.constructor.name+"_"+i+"_"+r+"_"+o}(i,o,u),l=this.getAndSaveBinary(c,function(){return function(t,r,e,n){for(var o=r.userCode,i=e.map(function(t,e){var n={logicalShape:t.shape,texShape:t.isUniform?null:t.texData.texShape,isUniform:t.isUniform,isPacked:!t.isUniform&&t.texData.isPacked};return{name:r.variableNames[e],shapeInfo:n}}),a=i.map(function(t){return t.shapeInfo}),s={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked},u=Nn(i,s,o,!0===r.supportsBroadcasting,r.usesPackedTextures),c=t.createProgram(u),l={},h=0;h<r.variableNames.length;h++){var p=r.variableNames[h];l[p]=t.getUniformLocation(c,p,!1)}return{program:r,source:u,webGLProgram:c,uniformLocations:l,gpgpu:t,inShapeInfos:a,outShapeInfo:s}}(a.gpgpu,i,o,u)}),h=null!=this.activeTimers;if(h&&(s=this.startTimer()),function(a,t,e,n){ao(a.inShapeInfos,t),ao([a.outShapeInfo],[e]);var r=e.texData.texture,o=e.texData.texShape,s=a.gpgpu;e.texData.isPacked?s.setOutputPackedMatrixTexture(r,o[0],o[1]):s.setOutputMatrixTexture(r,o[0],o[1]),s.setProgram(a.webGLProgram),t.forEach(function(t,e){var n=a.program.variableNames[e],r=a.uniformLocations[n];if(null!=r){if(t.isUniform){if(1===L(t.shape))s.gl.uniform1f(r,t.uniformValues[0]);else{var o=t.uniformValues;o instanceof Float32Array||(o=new Float32Array(o)),s.gl.uniform1fv(r,o)}return}var i=t.texData.texture;s.setInputMatrixTexture(i,r,e)}}),null!=n&&n(s,a.webGLProgram),s.executeProgram()}(l,o,u,n),Ft.get("WEBGL_PAGING_ENABLED")&&r&&this.numBytesInGPU>this.NUM_BYTES_BEFORE_PAGING)for(var p=this.numBytesInGPU-this.NUM_BYTES_BEFORE_PAGING;0<p&&0<this.lruDataGPU.length;){var f=this.lruDataGPU.shift(),d=this.texData.get(f),m=d.shape,v=d.dtype;p-=this.computeBytes(m,v),this.read(f)}return h&&(s=this.endTimer(s),this.activeTimers.push({name:i.constructor.name,query:this.getQueryTime(s)})),Ft.get("WEBGL_LAZILY_UNPACK")||!this.texData.get(e.dataId).isPacked||i.isPackShader?e:this.unpackTensor(e)},t.prototype.getAndSaveBinary=function(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]},t.prototype.getTextureManager=function(){return this.textureManager},t.prototype.dispose=function(){if(!this.disposed){for(var t in this.binaryCache)this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram);this.textureManager.dispose(),this.canvas.remove(),null!=this.fromPixels2DContext&&this.fromPixels2DContext.canvas.remove(),this.gpgpuCreatedLocally&&this.gpgpu.dispose(),this.disposed=!0}},t.prototype.floatPrecision=function(){var t=this;return zt(function(){return 0<t.abs(Se(1e-8)).get()?32:16})},t.prototype.uploadToGPU=function(t){var e,n=this.texData.get(t),r=n.shape,o=n.values,i=n.texture,a=n.usage,s=n.isPacked;if(null==i){var u,c=null!=this.activeTimers;c&&(u=performance.now());var l=Mr(r,s);n.texShape=l;var h=this.acquireTexture(t,l,a,s);if(n.texture=h,null!=o){if(s){var p=this.getBatchDim(r),f=1,d=1;r.length&&(f=(e=this.getRowsCols(r))[0],d=e[1]),this.gpgpu.uploadMatrixToPackedTexture(h,p,f,d,l[0],l[1],Fi(o))}else this.gpgpu.uploadMatrixToTexture(h,l[0],l[1],Fi(o));n.values=null,c&&(this.uploadWaitMs+=performance.now()-u)}}else Ft.get("WEBGL_PAGING_ENABLED")&&0<=this.lruDataGPU.indexOf(t)&&(this.lruDataGPU.splice(this.lruDataGPU.indexOf(t),1),this.lruDataGPU.push(t))},t.prototype.convertAndCacheOnCPU=function(t,e){var n=this.delayedStorage,r=this.texData.get(t),o=r.texture,i=r.texShape,a=r.dtype,s=r.usage,u=r.isPacked;return n&&null!=o&&(this.releaseTexture(t,o,i,s,u),r.texture=null,r.texShape=null),r.usage=$n.UPLOAD,null!=e&&(r.values=function(t,e){if("float32"===e||"complex64"===e)return t;if("int32"!==e&&"bool"!==e)throw new Error("Unknown dtype "+e);for(var n="int32"===e?new Int32Array(t.length):new Uint8Array(t.length),r=0;r<n.length;++r)n[r]=Math.round(t[r]);return n}(e,a)),r.values},t.prototype.releaseTexture=function(t,e,n,r,o){var i=this.texData.get(t),a=i.shape,s=i.dtype;if(Ft.get("WEBGL_PAGING_ENABLED")){var u=this.lruDataGPU.indexOf(t);0<=u&&this.lruDataGPU.splice(u,1)}this.numBytesInGPU-=this.computeBytes(a,s),this.textureManager.releaseTexture(e,n,r,o)},t.prototype.acquireTexture=function(t,e,n,r){var o=this.texData.get(t),i=o.shape,a=o.dtype;return Ft.get("WEBGL_PAGING_ENABLED")&&this.lruDataGPU.push(t),this.numBytesInGPU+=this.computeBytes(i,a),this.textureManager.acquireTexture(e,n,r)},t.prototype.computeBytes=function(t,e){return L(t)*M(e)},t}();function Fi(t){return t instanceof Float32Array?t:new Float32Array(t)}Ft.get("IS_BROWSER")&&Ft.registerBackend("webgl",function(){return new Oi},2,ot);var Pi=ge({abs_:function(t){var e=me(t,"x","abs");return"complex64"===e.dtype?Ft.engine.runKernel(function(t){return t.complexAbs(e)},{$x:e}):Ft.engine.runKernel(function(t){return t.abs(e)},{$x:e},function(t){return{$x:function(){return t.mulStrict(e.toFloat().step(-1))}}})}}),Bi=ge({acos_:function(t){var e=me(t,"x","acos");return Ft.engine.runKernel(function(t){return t.acos(e)},{$x:e},function(t){return{$x:function(){return t.divStrict(Se(1).sub(e.toFloat().square()).sqrt()).neg()}}})}}),Li=ge({acosh_:function(t){var e=me(t,"x","acosh");return Ft.engine.runKernel(function(t){return t.acosh(e)},{$x:e},function(t){return{$x:function(){return t.divStrict(e.toFloat().square().sub(1).sqrt())}}})}}),Wi=ge({asin_:function(t){var e=me(t,"x","asin");return Ft.engine.runKernel(function(t){return t.asin(e)},{$x:e},function(t){return{$x:function(){return t.divStrict(Se(1).sub(e.toFloat().square()).sqrt())}}})}}),zi=ge({asinh_:function(t){var e=me(t,"x","asinh");return Ft.engine.runKernel(function(t){return t.asinh(e)},{$x:e},function(t){return{$x:function(){return t.divStrict(Se(1).add(e.toFloat().square()).sqrt())}}})}}),Ui=ge({atan_:function(t){var e=me(t,"x","atan");return Ft.engine.runKernel(function(t){return t.atan(e)},{$x:e},function(t){return{$x:function(){return t.div(e.toFloat().square().add(1))}}})}}),Vi=ge({atanh_:function(t){var e=me(t,"x","atanh");return Ft.engine.runKernel(function(t){return t.atanh(e)},{$x:e},function(t){return{$x:function(){return t.div(Se(1).sub(e.toFloat().square()))}}})}}),Gi=ge({ceil_:function(t){var e=me(t,"x","ceil");return Ft.engine.runKernel(function(t){return t.ceil(e)},{$x:e},function(t){return{$x:function(){return Be(t)}}})}}),Hi=ge({clipByValue_:function(t,e,n){var r=me(t,"x","clipByValue");return A(e<=n,"Error in clip: min ("+e+") must be less than or equal to max ("+n+")."),Ft.engine.runKernel(function(t){return t.clip(r,e,n)},{$x:r},function(t){return{$x:function(){return t.where(r.greaterEqual(e).logicalAnd(r.lessEqual(n)),Be(t))}}})}}),qi=ge({cos_:function(t){var e=me(t,"x","cos");return Ft.engine.runKernel(function(t){return t.cos(e)},{$x:e},function(t){return{$x:function(){return e.toFloat().sin().neg().mulStrict(t)}}})}}),ji=ge({cosh_:function(t){var e=me(t,"x","cosh");return Ft.engine.runKernel(function(t){return t.cosh(e)},{$x:e},function(t){return{$x:function(){return e.toFloat().sinh().mulStrict(t)}}})}}),$i=ge({erf_:function(t){var e=me(t,"x","erf");return A("int32"===e.dtype||"float32"===e.dtype,"Input dtype must be `int32` or `float32`."),"int32"===e.dtype&&(e=e.toFloat()),Ft.engine.runKernel(function(t){return t.erf(e)},{$x:e},function(t){return{$x:function(){return t.mul(e.square().neg().exp().mul(2/Math.sqrt(Math.PI)))}}})}}),Ki=ge({exp_:function(t){var n=me(t,"x","exp");return Ft.engine.runKernel(function(t,e){return e(t.exp(n))},{$x:n},function(t,e){var n=e[0];return{$x:function(){return t.mulStrict(n)}}})}}),Xi=ge({expm1_:function(t){var e=me(t,"x","expm1");return Ft.engine.runKernel(function(t){return t.expm1(e)},{$x:e},function(t){return{$x:function(){return t.mulStrict(e.exp())}}})}}),Yi=ge({floor_:function(t){var e=me(t,"x","floor");return Ft.engine.runKernel(function(t){return t.floor(e)},{$x:e},function(t){return{$x:function(){return Be(t)}}})}}),Qi=ge({log_:function(t){var e=me(t,"x","log");return Ft.engine.runKernel(function(t){return t.log(e)},{$x:e},function(t){return{$x:function(){return t.divStrict(e.toFloat())}}})}}),Ji=ge({log1p_:function(t){var e=me(t,"x","log1p");return Ft.engine.runKernel(function(t){return t.log1p(e)},{$x:e},function(t){return{$x:function(){return t.div(e.add(1))}}})}}),Zi=ge({logSigmoid_:function(t){var e=me(t,"x","logSigmoid");return Ft.engine.runKernel(function(t){return t.softplus(e.neg()).neg()},{$x:e},function(t){return{$x:function(){return t.mulStrict(e.neg().sigmoid())}}})}}),ta=ge({neg_:function(t){var e=me(t,"x","neg");return Ft.engine.runKernel(function(t){return t.neg(e)},{$x:e},function(t){return{$x:function(){return t.neg()}}})}}),ea=ge({reciprocal_:function(t){var e=me(t,"x","reciprocal");return Ft.engine.runKernel(function(t){return t.reciprocal(e)},{$x:e},function(t){return{$x:function(){return t.divStrict(e.square().neg())}}})}}),na=ge({round_:function(t){var e=me(t,"x","round");return Ft.engine.runKernel(function(t){return t.round(e)},{$x:e},function(t){return{$x:function(){return Be(t)}}})}}),ra=ge({rsqrt_:function(t){var e=me(t,"x","rsqrt");return Ft.engine.runKernel(function(t){return t.rsqrt(e)},{$x:e},function(t){return{$x:function(){return t.div(e.pow(1.5).mul(2)).neg()}}})}}),oa=ge({sigmoid_:function(t){var n=me(t,"x","sigmoid");return Ft.engine.runKernel(function(t,e){return e(t.sigmoid(n))},{$x:n},function(t,e){var n=e[0];return{$x:function(){return t.mul(n.mul(Se(1).sub(n)))}}})}}),ia=ge({sign_:function(t){var e=me(t,"x","sign");return Ft.engine.runKernel(function(t){return t.sign(e)},{$x:e},function(t){return{$x:function(){return Be(t)}}})}}),aa=ge({sin_:function(t){var e=me(t,"x","sin");return Ft.engine.runKernel(function(t){return t.sin(e)},{$x:e},function(t){return{$x:function(){return e.toFloat().cos().mulStrict(t)}}})}}),sa=ge({sinh_:function(t){var e=me(t,"x","sinh");return Ft.engine.runKernel(function(t){return t.sinh(e)},{$x:e},function(t){return{$x:function(){return e.toFloat().cosh().mulStrict(t)}}})}}),ua=ge({softplus_:function(t){var e=me(t,"x","softplus");return Ft.engine.runKernel(function(t){return t.softplus(e)},{$x:e},function(t){return{$x:function(){return t.mulStrict(e.sigmoid())}}})}}),ca=ge({sqrt_:function(t){var e=me(t,"x","sqrt");return Ft.engine.runKernel(function(t){return t.sqrt(e)},{$x:e},function(t){return{$x:function(){return t.div(e.toFloat().sqrt().mul(2))}}})}}),la=ge({square_:function(t){var e=me(t,"x","square");return Ft.engine.runKernel(function(t){return t.square(e)},{$x:e},function(t){return{$x:function(){return t.mul(e.toFloat().mul(2))}}})}}),ha=ge({step_:function(t,e){void 0===e&&(e=0);var n=me(t,"x","step");return Ft.engine.runKernel(function(t){return t.step(n,e)},{$x:n},function(t){return{$x:function(){return Be(t)}}})}}),pa=ge({tan_:function(t){var e=me(t,"x","tan");return Ft.engine.runKernel(function(t){return t.tan(e)},{$x:e},function(t){return{$x:function(){return t.divStrict(e.cos().square())}}})}}),fa=ge({tanh_:function(t){var n=me(t,"x","tanh");return Ft.engine.runKernel(function(t,e){return e(t.tanh(n))},{$x:n},function(t,e){var n=e[0];return{$x:function(){return Se(1).sub(n.square()).mulStrict(t)}}})}});function da(t){return null==t?null:0===t.rank?t.as1D():1===t.rank?t:2===t.rank?t.as4D(1,1,t.shape[0],t.shape[1]):3===t.rank?t.as4D(1,t.shape[0],t.shape[1],t.shape[2]):t}var ma=ge({batchNormalization2d_:function(t,e,n,r,o,i){void 0===r&&(r=.001);var a,s,u=me(t,"x","batchNormalization"),c=me(e,"mean","batchNormalization"),l=me(n,"variance","batchNormalization");return null!=o&&(a=me(o,"scale","batchNormalization")),null!=i&&(s=me(i,"offset","batchNormalization")),A(2===u.rank,"Error in batchNormalization3D: x must be rank 3 but got rank "+u.rank+"."),A(2===c.rank||1===c.rank,"Error in batchNormalization2D: mean must be rank 2 or rank 1 but got rank "+c.rank+"."),A(2===l.rank||1===l.rank,"Error in batchNormalization2D: variance must be rank 2 or rank 1 but got rank "+l.rank+"."),null!=a&&A(2===a.rank||1===a.rank,"Error in batchNormalization2D: scale must be rank 2 or rank 1 but got rank "+a.rank+"."),null!=s&&A(2===s.rank||1===s.rank,"Error in batchNormalization2D: offset must be rank 2 or rank 1 but got rank "+s.rank+"."),ya(u,c,l,r,a,s)}}),va=ge({batchNormalization3d_:function(t,e,n,r,o,i){void 0===r&&(r=.001);var a,s,u=me(t,"x","batchNormalization"),c=me(e,"mean","batchNormalization"),l=me(n,"variance","batchNormalization");return null!=o&&(a=me(o,"scale","batchNormalization")),null!=i&&(s=me(i,"offset","batchNormalization")),A(3===u.rank,"Error in batchNormalization3D: x must be rank 3 but got rank "+u.rank+"."),A(3===c.rank||1===c.rank,"Error in batchNormalization3D: mean must be rank 3 or rank 1 but got rank "+c.rank+"."),A(3===l.rank||1===l.rank,"Error in batchNormalization3D: variance must be rank 3 or rank 1 but got rank "+l.rank+"."),null!=a&&A(3===a.rank||1===a.rank,"Error in batchNormalization3D: scale must be rank 3 or rank 1 but got rank "+a.rank+"."),null!=s&&A(3===s.rank||1===s.rank,"Error in batchNormalization3D: offset must be rank 3 or rank 1 but got rank "+s.rank+"."),ya(u,c,l,r,a,s)}}),ga=ge({batchNormalization4d_:function(t,e,n,r,o,i){void 0===r&&(r=.001);var a,s,u=me(t,"x","batchNormalization"),c=me(e,"mean","batchNormalization"),l=me(n,"variance","batchNormalization");return null!=o&&(a=me(o,"scale","batchNormalization")),null!=i&&(s=me(i,"offset","batchNormalization")),A(4===u.rank,"Error in batchNormalization4D: x must be rank 4 but got rank "+u.rank+"."),A(4===c.rank||1===c.rank,"Error in batchNormalization4D: mean must be rank 4 or rank 1 but got rank "+c.rank+"."),A(4===l.rank||1===l.rank,"Error in batchNormalization4D: variance must be rank 4 or rank 1 but got rank "+l.rank+"."),null!=a&&A(4===a.rank||1===a.rank,"Error in batchNormalization4D: scale must be rank 4 or rank 1 but got rank "+a.rank+"."),null!=s&&A(4===s.rank||1===s.rank,"Error in batchNormalization4D: offset must be rank 4 or rank 1 but got rank "+s.rank+"."),ya(u,c,l,r,a,s)}}),ya=ge({batchNormalization_:function(t,e,n,c,r,o){void 0===c&&(c=.001);var l,i,h,p=me(t,"x","batchNormalization"),f=me(e,"mean","batchNormalization"),d=me(n,"variance","batchNormalization");return null!=r&&(l=me(r,"scale","batchNormalization")),null!=o&&(i=me(o,"offset","batchNormalization")),A(f.rank===d.rank,"Batch normalization gradient requires mean and variance to have equal ranks."),A(null==i||f.rank===i.rank,"Batch normalization gradient requires mean and offset to have equal ranks."),A(null==l||f.rank===l.rank,"Batch normalization gradient requires mean and scale to have equal ranks."),h=0===p.rank||1===p.rank?p.as4D(1,1,1,p.size):2===p.rank?p.as4D(1,1,p.shape[0],p.shape[1]):3===p.rank?p.as4D(1,p.shape[0],p.shape[1],p.shape[2]):p,Ft.engine.runKernel(function(t){return t.batchNormalization(h,da(f),da(d),c,da(l),da(i))},{$x:p,$mean:f,$variance:d,$scale:l,$offset:i},function(n){var t=null==l?Se(1):l,r=Qe(f.shape,h.shape),e=[];if(1===f.rank){for(var o=0;o<h.shape.length-1;++o)e.push(h.shape[o]);e.push(1)}var i=p.sub(f),a=n.mul(t),s=ra(d.add(Se(c))),u=s.mul(s).mul(s).mul(Se(-.5));return{$x:function(){return 1===f.rank?n.mul(Ti(s.as4D(1,1,1,f.shape[0]),e)).mul(t).reshape(p.shape):n.mul(s).mul(t).reshape(p.shape)},$mean:function(){var t=s.mul(Se(-1)).mul(a);return 1===f.rank&&(t=t.sum(r)),t.reshape(f.shape)},$variance:function(){var t=u.mul(i).mul(a);return 1===f.rank&&(t=t.sum(r)),t.reshape(f.shape)},$scale:function(){var t=i.mul(s),e=n.mul(t);return 1===f.rank&&(e=e.sum(r)),e.reshape(f.shape)},$offset:function(){var t=n;return 1===f.rank&&(t=t.sum(r)),t.reshape(f.shape)}}}).reshape(p.shape)}});function xa(t,e,n,r,o,i,a){void 0===a&&(a="channelsLast");var s,u=Ea(e),c=u[0],l=u[1];if("channelsLast"===a)s=[c,l,t[3],t[3]];else{if("channelsFirst"!==a)throw new Error("Unknown dataFormat "+a);s=[c,l,t[1],t[1]]}return wa(t,s,n,r,o,i,!1,a)}function wa(t,e,n,r,o,i,a,s){void 0===a&&(a=!1),void 0===s&&(s="channelsLast");var u=[-1,-1,-1,-1],c=u[0],l=u[1],h=u[2],p=u[3];if("channelsLast"===s)c=t[0],l=t[1],h=t[2],p=t[3];else{if("channelsFirst"!==s)throw new Error("Unknown dataFormat "+s);c=t[0],p=t[1],l=t[2],h=t[3]}var f,d=e[0],m=e[1],v=e[3],g=Ea(n),y=g[0],x=g[1],w=Ea(r),b=w[0],E=w[1],C=Sa(d,b),S=Sa(m,E),_=function(t,e,n,r,o,i,a,s){var u,c,l;if("number"==typeof t){u={top:t,bottom:t,left:t,right:t,type:0===t?"VALID":"NUMBER"};var h=function(t,e,n,r,o,i){null==o&&(o=function(t,e,n,r){void 0===r&&(r=1);var o=Sa(e,r);return Math.floor((t[0]*(n-1)-n+o)/2)}(t,e,r));var a=t[0],s=t[1],u=_a((a-e+2*o)/r+1,i);A(D(u),"The output # of rows ("+u+") must be an integer. Change the stride and/or zero pad parameters");var c=_a((s-e+2*o)/r+1,i);return A(D(c),"The output # of columns ("+c+") must be an integer. Change the stride and/or zero pad parameters"),[u,c,n]}([e,n,1],i,1,r,t,s);c=h[0],l=h[1]}else if("same"===t){var p=((c=Math.ceil(e/r))-1)*r+i-e,f=((l=Math.ceil(n/o))-1)*o+a-n,d=Math.floor(p/2),m=p-d,v=Math.floor(f/2);u={top:d,bottom:m,left:v,right:f-v,type:"SAME"}}else{if("valid"!==t)throw Error("Unknown padding parameter: "+t);u={top:0,bottom:0,left:0,right:0,type:"VALID"},c=Math.ceil((e-i+1)/r),l=Math.ceil((n-a+1)/o)}return{padInfo:u,outHeight:c,outWidth:l}}(o,l,h,y,x,C,S,i),R=_.padInfo,N=_.outHeight,k=_.outWidth,T=a?v*p:v;return"channelsFirst"===s?f=[c,T,N,k]:"channelsLast"===s&&(f=[c,N,k,T]),{batchSize:c,dataFormat:s,inHeight:l,inWidth:h,inChannels:p,outHeight:N,outWidth:k,outChannels:T,padInfo:R,strideHeight:y,strideWidth:x,filterHeight:d,filterWidth:m,effectiveFilterHeight:C,effectiveFilterWidth:S,dilationHeight:b,dilationWidth:E,inShape:t,outShape:f,filterShape:e}}function ba(t,e,n,r,o,i,a){void 0===i&&(i=!1),void 0===a&&(a="channelsLast");var s=[-1,-1,-1,-1,-1],u=s[0],c=s[1],l=s[2],h=s[3],p=s[4];if("channelsLast"===a)u=t[0],c=t[1],l=t[2],h=t[3],p=t[4];else{if("channelsFirst"!==a)throw new Error("Unknown dataFormat "+a);u=t[0],p=t[1],c=t[2],l=t[3],h=t[4]}var f,d=e[0],m=e[1],v=e[2],g=e[4],y=Ca(n),x=y[0],w=y[1],b=y[2],E=Ca(r),C=E[0],S=E[1],_=E[2],R=function(t,e,n,r,o,i,a,s,u,c){var l,h,p,f;if("same"===t){var d=((h=Math.ceil(e/o))-1)*o+s-e,m=((p=Math.ceil(n/i))-1)*i+u-n,v=((f=Math.ceil(r/a))-1)*a+c-r,g=Math.floor(d/2),y=d-g,x=Math.floor(m/2),w=m-x,b=Math.floor(v/2);l={top:x,bottom:w,left:b,right:v-b,front:g,back:y,type:"SAME"}}else{if("valid"!==t)throw Error("Unknown padding parameter: "+t);l={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},h=Math.ceil((e-s+1)/o),p=Math.ceil((n-u+1)/i),f=Math.ceil((r-c+1)/a)}return{padInfo:l,outDepth:h,outHeight:p,outWidth:f}}(o,c,l,h,x,w,b,Sa(d,C),Sa(m,S),Sa(v,_)),N=R.padInfo,k=R.outDepth,T=R.outHeight,D=R.outWidth,I=i?g*p:g;return"channelsFirst"===a?f=[u,I,k,T,D]:"channelsLast"===a&&(f=[u,k,T,D,I]),{batchSize:u,dataFormat:a,inDepth:c,inHeight:l,inWidth:h,inChannels:p,outDepth:k,outHeight:T,outWidth:D,outChannels:I,padInfo:N,strideDepth:x,strideHeight:w,strideWidth:b,filterDepth:d,filterHeight:m,filterWidth:v,dilationDepth:C,dilationHeight:S,dilationWidth:_,inShape:t,outShape:f,filterShape:e}}function Ea(t){return"number"==typeof t?[t,t]:t}function Ca(t){return"number"==typeof t?[t,t,t]:t}function Sa(t,e){return e<=1?t:t+(t-1)*(e-1)}function _a(t,e){if(!e)return t;switch(e){case"round":return Math.round(t);case"ceil":return Math.ceil(t);case"floor":return Math.floor(t);default:throw new Error("Unknown roundingMode "+e)}}function Ra(t){var e=Ea(t),n=e[0],r=e[1];return 1===n&&1===r}function Na(t,e){return Ra(t)||Ra(e)}var ka=ge({matMul_:function(t,e,n,r){var o;void 0===n&&(n=!1),void 0===r&&(r=!1);var i=me(t,"a","matMul"),a=me(e,"b","matMul");i=(o=bt(i,a))[0],a=o[1];var s=n?i.shape[i.rank-2]:i.shape[i.rank-1],u=r?a.shape[a.rank-1]:a.shape[a.rank-2],c=n?i.shape[i.rank-1]:i.shape[i.rank-2],l=r?a.shape[a.rank-2]:a.shape[a.rank-1],h=i.shape.slice(0,-2),p=a.shape.slice(0,-2),f=L(h),d=L(p);A(2<=i.rank&&2<=a.rank&&i.rank===a.rank,"Error in matMul: inputs must have the same rank of at least 2, got ranks "+i.rank+" and "+a.rank+"."),A(y(h,p),"Error in matMul: outer dimensions ("+h+") and ("+p+") of Tensors with shapes "+i.shape+" and "+a.shape+" must match."),A(s===u,"Error in matMul: inner shapes ("+s+") and ("+u+") of Tensors with shapes "+i.shape+" and "+a.shape+" and transposeA="+n+" and transposeB="+r+" must match.");var m=i.shape.slice(0,-2).concat([c,l]),v=n?i.as3D(f,s,c):i.as3D(f,c,s),g=r?a.as3D(d,l,u):a.as3D(d,u,l);return Ft.engine.runKernel(function(t){return t.batchMatMul(v,g,n,r)},{$a:v,$b:g},function(t){return n||r?!n&&r?{$a:function(){return t.matMul(g,!1,!1)},$b:function(){return t.matMul(v,!0,!1)}}:n&&!r?{$a:function(){return g.matMul(t,!1,!0)},$b:function(){return v.matMul(t,!1,!1)}}:{$a:function(){return g.matMul(t,!0,!0)},$b:function(){return t.matMul(v,!0,!0)}}:{$a:function(){return t.matMul(g,!1,!0)},$b:function(){return v.matMul(t,!0,!1)}}}).reshape(m)}}),Ta=ge({dot_:function(t,e){var n=me(t,"t1","dot"),r=me(e,"t2","dot");A(!(1!==n.rank&&2!==n.rank||1!==r.rank&&2!==r.rank),"Error in dot: inputs must all be rank 1 or 2, but got ranks "+n.rank+" and "+r.rank+".");var o=1===n.rank?n.size:n.shape[1],i=1===r.rank?r.size:r.shape[0];return A(o===i,"Error in dot: inner dimensions of inputs must match, but got "+o+" and "+i+"."),1===n.rank&&1===r.rank?n.as2D(1,-1).matMul(r.as2D(-1,1)).asScalar():1===n.rank&&2===r.rank?n.as2D(1,-1).matMul(r.as2D(r.shape[0],r.shape[1])).as1D():2===n.rank&&1===r.rank?n.matMul(r.as2D(-1,1)).as1D():n.matMul(r.as2D(r.shape[0],r.shape[1]))}}),Da=ge({outerProduct_:function(t,e){var n=me(t,"v1","outerProduct"),r=me(e,"v2","outerProduct");return A(1===n.rank&&1===r.rank,"Error in outerProduct: inputs must be rank 1, but got ranks "+n.rank+" and "+r.rank+"."),n.as2D(-1,1).matMul(r.as2D(1,-1))}});function Ia(t,e,n,r,o,i){A(t.length===e.rank,"Length of inShape ("+t.length+") and rank of dy ("+e.rank+") must match");var a=t,s=e,u=!1;3===e.rank&&(u=!0,s=e.as4D(1,e.shape[0],e.shape[1],e.shape[2]),a=[1,t[0],t[1],t[2]]);var c=a[3],l=s.shape[3];A(4===a.length,"Error in conv2dDerInput: inShape must be length 4, but got length "+a.length+"."),A(4===s.rank,"Error in conv2dDerInput: dy must be rank 4, but got rank "+s.rank),A(4===n.rank,"Error in conv2dDerInput: filter must be rank 4, but got rank "+n.rank),A(c===n.shape[2],"Error in conv2dDerInput: depth of input ("+c+") must match input depth for filter "+n.shape[2]+"."),A(l===n.shape[3],"Error in conv2dDerInput: depth of output ("+l+") must match output depth for filter "+n.shape[3]+"."),null!=i&&A(D(o),"Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode "+i+" but got pad "+o+".");var h=wa(a,n.shape,r,1,o,i),p=Ft.engine.runKernel(function(t){return t.conv2dDerInput(s,n,h)},{dy4D:s,filter:n},function(t){return{dy4D:function(){return Fa(t,n,r,o,"NHWC",1,i)},filter:function(){return Ba(t,s,n.shape,r,o,i)}}});return u?p.as3D(p.shape[1],p.shape[2],p.shape[3]):p}function Aa(t,e,n,r,o,i){var a=t;3===t.rank&&(a=t.as4D(1,t.shape[0],t.shape[1],t.shape[2]));var s=e;3===s.rank&&(s=e.as4D(1,e.shape[0],e.shape[1],e.shape[2])),A(4===a.rank,"Error in conv2dDerFilter: input must be rank 4, but got shape "+a.shape+"."),A(4===s.rank,"Error in conv2dDerFilter: dy must be rank 4, but got shape "+s.shape+"."),A(4===n.length,"Error in conv2dDerFilter: filterShape must be length 4, but got "+n+"."),A(a.shape[3]===n[2],"Error in conv2dDerFilter: depth of input "+a.shape[3]+") must match input depth in filter ("+n[2]+"."),A(s.shape[3]===n[3],"Error in conv2dDerFilter: depth of dy ("+s.shape[3]+") must match output depth for filter ("+n[3]+")."),null!=i&&A(D(o),"Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode "+i+" but got pad "+o+".");var u=wa(a.shape,n,r,1,o,i);return Ft.engine.runKernel(function(t){return t.conv2dDerFilter(a,s,u)},{x4D:a,dy4D:s})}function Ma(t){var e,n="number"==typeof(e=t)?[e,e,e]:2===e.length?[e[0],e[1],1]:e,r=n[0],o=n[1],i=n[2];return 1===r&&1===o&&1===i}var Oa=ge({conv1d_:function(t,e,n,r,o,i,a){void 0===o&&(o="NWC"),void 0===i&&(i=1);var s=me(t,"x","conv1d"),u=me(e,"filter","conv1d"),c=s,l=!1;2===s.rank&&(l=!0,c=s.as3D(1,s.shape[0],s.shape[1])),A(3===c.rank,"Error in conv1d: input must be rank 3, but got rank "+c.rank+"."),A(3===u.rank,"Error in conv1d: filter must be rank 3, but got rank "+u.rank+"."),null!=a&&A(D(r),"Error in conv1d: pad must be an integer when using, dimRoundingMode "+a+" but got pad "+r+"."),A(c.shape[2]===u.shape[1],"Error in conv1d: depth of input ("+c.shape[2]+") must match input depth for filter "+u.shape[1]+"."),A(Na(n,i),"Error in conv1D: Either stride or dilation must be 1. Got stride "+n+" and dilation '"+i+"'"),A("NWC"===o,"Error in conv1d: got dataFormat of "+o+" but only NWC is currently supported.");var h=u.as4D(1,u.shape[0],u.shape[1],u.shape[2]),p=c.as4D(c.shape[0],1,c.shape[1],c.shape[2]),f=Fa(p,h,[1,n],r,"NHWC",[1,i],a);return l?f.as2D(f.shape[2],f.shape[3]):f.as3D(f.shape[0],f.shape[2],f.shape[3])}}),Fa=ge({conv2d_:function(t,e,n,r,o,i,a){void 0===o&&(o="NHWC"),void 0===i&&(i=[1,1]);var s=me(t,"x","conv2d"),u=me(e,"filter","conv2d"),c=s,l=!1;3===s.rank&&(l=!0,c=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),A(4===c.rank,"Error in conv2d: input must be rank 4, but got rank "+c.rank+"."),A(4===u.rank,"Error in conv2d: filter must be rank 4, but got rank "+u.rank+"."),null!=a&&A(D(r),"Error in conv2d: pad must be an integer when using, dimRoundingMode "+a+" but got pad "+r+"."),A(c.shape[3]===u.shape[2],"Error in conv2d: depth of input ("+c.shape[3]+") must match input depth for filter "+u.shape[2]+"."),A(Na(n,i),"Error in conv2D: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+i+"'"),A("NHWC"===o,"Error in conv2d: got dataFormat of "+o+" but only NHWC is currently supported.");var h,p=wa(c.shape,u.shape,n,i,r,a);if(1!==p.filterHeight||1!==p.filterWidth||1!==p.dilationHeight||1!==p.dilationWidth||1!==p.strideHeight||1!==p.strideWidth||"SAME"!==p.padInfo.type&&"VALID"!==p.padInfo.type)h=Ft.engine.runKernel(function(t){return t.conv2d(c,u,p)},{x:c,$filter:u},function(t){return A(Ra(i),"Error in gradient of conv2D: dilation rates greater than 1 are notyet supported in gradients. Got dilations '"+i+"'"),{x:function(){return Ia(c.shape,t,u,n,r)},$filter:function(){return Aa(c,t,u.shape,n,r)}}});else{var f=c.reshape([-1,p.inChannels]),d=u.reshape([p.inChannels,p.outChannels]);h=ka(f,d).reshape(p.outShape)}return l?h.as3D(h.shape[1],h.shape[2],h.shape[3]):h}}),Pa=ge({conv3d_:function(t,e,n,r,o,i){void 0===o&&(o="NHWC"),void 0===i&&(i=[1,1,1]);var a,s=me(t,"x","conv3d"),u=me(e,"filter","conv3d"),c=s,l=!1;4===s.rank&&(l=!0,c=s.as5D(1,s.shape[0],s.shape[1],s.shape[2],s.shape[3])),A(5===c.rank,"Error in conv3d: input must be rank 5, but got rank "+c.rank+"."),A(5===u.rank,"Error in conv3d: filter must be rank 5, but got rank "+u.rank+"."),A(c.shape[4]===u.shape[3],"Error in conv3d: depth of input ("+c.shape[4]+") must match input depth for filter "+u.shape[3]+"."),A((a=i,Ma(n)||Ma(a)),"Error in conv3D: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+i+"'"),A("NHWC"===o,"Error in conv3d: got dataFormat of "+o+" but only NHWC is currently supported.");var h=ba(c.shape,u.shape,n,i,r),p=Ft.engine.runKernel(function(t){return t.conv3d(c,u,h)},{x:c,$filter:u},function(t){return A(Ma(i),"Error in gradient of conv3D: dilation rates greater than 1 are notyet supported in gradients. Got dilations '"+i+"'"),{x:function(){return function(t,e,n,r,o){A(t.length===e.rank,"Length of inShape ("+t.length+") and rank of dy ("+e.rank+") must match");var i=t,a=e,s=!1;4===e.rank&&(s=!0,a=e.as5D(1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]),i=[1,t[0],t[1],t[2],t[3]]);var u=i[4],c=a.shape[4];A(5===i.length,"Error in conv3dDerInput: inShape must be length 5, but got length "+i.length+"."),A(5===a.rank,"Error in conv3dDerInput: dy must be rank 5, but got rank "+a.rank),A(5===n.rank,"Error in conv3dDerInput: filter must be rank 5, but got rank "+n.rank),A(u===n.shape[3],"Error in conv3dDerInput: depth of input ("+u+") must match input depth for filter "+n.shape[3]+"."),A(c===n.shape[4],"Error in conv3dDerInput: depth of output ("+c+") must match output depth for filter "+n.shape[4]+".");var l=ba(i,n.shape,r,1,o),h=Ft.engine.runKernel(function(t){return t.conv3dDerInput(a,n,l)},{dy5D:a});return s?h.as4D(h.shape[1],h.shape[2],h.shape[3],h.shape[4]):h}(c.shape,t,u,n,r)},$filter:function(){return function(t,e,n,r,o){var i=t;4===t.rank&&(i=t.as5D(1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]));var a=e;4===a.rank&&(a=e.as5D(1,e.shape[0],e.shape[1],e.shape[2],e.shape[3])),A(5===i.rank,"Error in conv3dDerFilter: input must be rank 5, but got shape "+i.shape+"."),A(5===a.rank,"Error in conv3dDerFilter: dy must be rank 5, but got shape "+a.shape+"."),A(5===n.length,"Error in conv3dDerFilter: filterShape must be length 5, but got "+n+"."),A(i.shape[4]===n[3],"Error in conv3dDerFilter: depth of input "+i.shape[4]+") must match input depth in filter ("+n[3]+"."),A(a.shape[4]===n[4],"Error in conv3dDerFilter: depth of dy ("+a.shape[4]+") must match output depth for filter ("+n[4]+").");var s=ba(i.shape,n,r,1,o);return Ft.engine.runKernel(function(t){return t.conv3dDerFilter(i,a,s)},{x5D:i,dy5D:a})}(c,t,u.shape,n,r)}}});return l?p.as4D(p.shape[1],p.shape[2],p.shape[3],p.shape[4]):p}}),Ba=ge({conv2dDerFilter_:Aa}),La=ge({depthwiseConv2d_:function(t,e,n,r,o,i,a){void 0===o&&(o="NHWC"),void 0===i&&(i=[1,1]);var s=me(t,"x","depthwiseConv2d"),u=me(e,"filter","depthwiseConv2d"),c=s,l=!1;3===s.rank&&(l=!0,c=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),A(4===c.rank,"Error in depthwiseConv2d: input must be rank 4, but got rank "+c.rank+"."),A(4===u.rank,"Error in depthwiseConv2d: filter must be rank 4, but got rank "+u.rank+"."),A(c.shape[3]===u.shape[2],"Error in depthwiseConv2d: number of input channels ("+c.shape[3]+") must match the inChannels dimension in filter "+u.shape[2]+"."),null==i&&(i=[1,1]),A(Na(n,i),"Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+i+"'"),null!=a&&A(D(r),"Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode "+a+" but got pad "+r+".");var h=wa(c.shape,u.shape,n,i,r,a,!0),p=Ft.engine.runKernel(function(t){return t.depthwiseConv2D(c,u,h)},{x:c,$filter:u},function(t){return A(Ra(i),"Error in gradient of depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '"+i+"'"),{x:function(){return function(t,e,n,r){var o=e,i=!1;3===e.rank&&(i=!0,o=e.as4D(1,e.shape[0],e.shape[1],e.shape[2]));var a=Ft.engine.runKernel(function(t){return t.depthwiseConv2DDerInput(o,n,r)},{dy4D:o});return i?a.as3D(a.shape[1],a.shape[2],a.shape[3]):a}(c.shape,t,u,h)},$filter:function(){return function(t,e,n,r){var o=t;3===t.rank&&(o=t.as4D(1,t.shape[0],t.shape[1],t.shape[2]));var i=e;return 3===i.rank&&(i=e.as4D(1,e.shape[0],e.shape[1],e.shape[2])),Ft.engine.runKernel(function(t){return t.depthwiseConv2DDerFilter(o,i,r)},{x4D:o,dy4D:i})}(c,t,u.shape,h)}}});return l?p.as3D(p.shape[1],p.shape[2],p.shape[3]):p}}),Wa=ge({separableConv2d_:function(t,e,n,r,o,i,a){void 0===i&&(i=[1,1]),void 0===a&&(a="NHWC");var s=me(t,"x","separableConv2d"),u=me(e,"depthwiseFilter","separableConv2d"),c=me(n,"pointwiseFilter","separableConv2d"),l=s,h=!1;if(3===s.rank&&(h=!0,l=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),"NCHW"===a)throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");A(4===l.rank,"Error in separableConv2d: input must be rank 4, but got rank "+l.rank+"."),A(4===u.rank,"Error in separableConv2d: depthwise filter must be rank 4, but got rank "+u.rank+"."),A(4===c.rank,"Error in separableConv2d: pointwise filter must be rank 4, but got rank "+u.rank+"."),A(1===c.shape[0],"Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got "+c.shape[0]+"."),A(1===c.shape[1],"Error in separableConv2d: the second dimension of pointwise filter  must be 1, but got "+c.shape[1]+".");var p=u.shape[2],f=u.shape[3];A(c.shape[2]===p*f,"Error in separableConv2d: the third dimension of pointwise filter must be "+p*f+", but got "+c.shape[2]+".");var d=La(l,u,r,o,a,i),m=Fa(d,c,1,"valid",a);return h?m.as3D(m.shape[1],m.shape[2],m.shape[3]):m}}),za=ge({conv2dTranspose_:function(t,e,n,r,o,i){return Ia(n,me(t,"x","conv2dTranspose"),me(e,"filter","conv2dTranspose"),r,o,i)}});var Ua=ge({reverse_:function(t,e){var n=me(t,"x","reverse");if(0===n.rank)return n.clone();var r=te(e,n.shape);return Ft.engine.runKernel(function(t){return t.reverse(n,r)},{$x:n},function(t){return{$x:function(){return t.reverse(r)}}}).reshapeAs(n)}}),Va=ge({reverse1d_:function(t){var e=me(t,"x","reverse");return A(1===e.rank,"Error in reverse1D: x must be rank 1 but got\n             rank "+e.rank+"."),Ua(e,0)}}),Ga=ge({reverse2d_:function(t,e){var n=me(t,"x","reverse");return A(2===n.rank,"Error in reverse2D: x must be rank 2 but got\n             rank "+n.rank+"."),Ua(n,e)}}),Ha=ge({reverse3d_:function(t,e){var n=me(t,"x","reverse");return A(3===n.rank,"Error in reverse3D: x must be rank 3 but got\n             rank "+n.rank+"."),Ua(n,e)}}),qa=ge({reverse4d_:function(t,e){var n=me(t,"x","reverse");return A(4===n.rank,"Error in reverse4D: x must be rank 4 but got\n             rank "+n.rank+"."),Ua(n,e)}});function ja(t,r,o,i,a,e){var n=me(t,"x","maxPool"),s=n,u=!1;3===n.rank&&(u=!0,s=n.as4D(1,n.shape[0],n.shape[1],n.shape[2])),null==i&&(i=[1,1]),A(4===s.rank,"Error in maxPool: input must be rank 4 but got rank "+s.rank+"."),A(Na(o,i),"Error in maxPool: Either strides or dilations must be 1. Got strides "+o+" and dilations '"+i+"'"),null!=e&&A(D(a),"Error in maxPool: pad must be an integer when using, dimRoundingMode "+e+" but got pad "+a+".");var c=xa(s.shape,r,o,i,a,e),l=Ft.engine.runKernel(function(t,e){return e(t.maxPool(s,c))},{x:s},function(t,e){var n=e[0];return{x:function(){return function(t,e,n,r,o,i,a,s){var u=me(t,"dy","maxPoolBackprop"),c=me(e,"input","maxPoolBackprop"),l=me(n,"output","maxPoolBackprop");A(c.rank===u.rank,"Rank of input ("+c.rank+") does not match rank of dy ("+u.rank+")"),null==i&&(i=[1,1]),A(Na(o,i),"Error in maxPoolBackProp: Either strides or dilations must be 1. Got strides "+o+" and dilations '"+i+"'"),A(4===u.rank,"Error in maxPoolBackprop: dy must be rank 4 but got rank "+u.rank+"."),A(4===c.rank,"Error in maxPoolBackprop: input must be rank 4 but got rank "+c.rank+"."),null!=s&&A(D(a),"Error in maxPoolBackprop: pad must be an integer when using, dimRoundingMode "+s+" but got pad "+a+".");var h=xa(c.shape,r,o,i,a,s);return Ft.engine.runKernel(function(t){return t.maxPoolBackprop(u,c,l,h)},{$dy:u,$input:c})}(t,s,n,r,o,i,a)}}});return u?l.as3D(l.shape[1],l.shape[2],l.shape[3]):l}function $a(t,e,n,r,o,i){var a=me(t,"x","avgPool","float32");null==r&&(r=[1,1]),A(Na(n,r),"Error in avgPool: Either strides or dilations must be 1. 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hu=ge({basicLSTMCell_:function(t,e,n,r,o,i){var a=me(t,"forgetBias","basicLSTMCell"),s=me(e,"lstmKernel","basicLSTMCell"),u=me(n,"lstmBias","basicLSTMCell"),c=me(r,"data","basicLSTMCell"),l=me(o,"c","basicLSTMCell"),h=me(i,"h","basicLSTMCell"),p=c.concat(h,1).matMul(s).add(u),f=p.shape[0],d=p.shape[1]/4,m=[f,d],v=p.slice([0,0],m),g=p.slice([0,d],m),y=p.slice([0,2*d],m),x=p.slice([0,3*d],m),w=v.sigmoid().mulStrict(g.tanh()).addStrict(l.mulStrict(a.add(y).sigmoid()));return[w,w.tanh().mulStrict(x.sigmoid())]}}),pu=ge({multiRNNCell_:function(t,e,n,r){for(var o=me(e,"data","multiRNNCell"),i=ve(n,"c","multiRNNCell"),a=ve(r,"h","multiRNNCell"),s=o,u=[],c=0;c<t.length;c++){var l=t[c](s,i[c],a[c]);u.push(l[0]),u.push(l[1]),s=l[1]}var h=[],p=[];for(c=0;c<u.length;c+=2)h.push(u[c]),p.push(u[c+1]);return[h,p]}});var fu=ge({movingAverage_:function(t,e,n,r,o){void 0===o&&(o=!0);var 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n=me(e,"indices","gatherND","int32"),r=me(t,"x","gatherND");return Ft.engine.runKernel(function(t){return t.gatherND(r,n)},{$x:r,$indices:n})}});(Su=Cu||(Cu={}))[Su.NONE=0]="NONE",Su[Su.MEAN=1]="MEAN",Su[Su.SUM=2]="SUM",Su[Su.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS";var Ru=ge({absoluteDifference_:function(t,e,n,r){void 0===r&&(r=Cu.SUM_BY_NONZERO_WEIGHTS);var o=me(t,"labels","absoluteDifference"),i=me(e,"predictions","absoluteDifference"),a=null;null!=n&&(a=me(n,"weights","absoluteDifference")),f(o.shape,i.shape,"Error in absoluteDifference: ");var s=o.sub(i).abs();return Nu(s,a,r)}}),Nu=ge({computeWeightedLoss_:function(t,e,n){void 0===n&&(n=Cu.SUM_BY_NONZERO_WEIGHTS);var r=me(t,"losses","computeWeightedLoss"),o=null;null!=e&&(o=me(e,"weights","computeWeightedLoss"));var i=null==o?r:r.mul(o);if(n===Cu.NONE)return i;if(n===Cu.SUM)return i.sum();if(n===Cu.MEAN){if(null==o)return i.mean();var a=L(r.shape)/L(o.shape),s=i.sum().div(o.sum());return 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Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=Math.acos(n[r]);return it.make(t.shape,{values:e})},t.prototype.atan=function(t){this.assertNotComplex(t,"atan");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=Math.atan(n[r]);return it.make(t.shape,{values:e})},t.prototype.atan2=function(t,e){return this.assertNotComplex([t,e],"atan2"),this.broadcastedBinaryOp(t,e,t.dtype,function(t,e){return Math.atan2(t,e)})},t.prototype.sinh=function(t){this.assertNotComplex(t,"sinh");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=Math.sinh(n[r]);return it.make(t.shape,{values:e})},t.prototype.cosh=function(t){this.assertNotComplex(t,"cosh");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=Math.cosh(n[r]);return it.make(t.shape,{values:e})},t.prototype.tanh=function(t){this.assertNotComplex(t,"tanh");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=v(n[r]);return it.make(t.shape,{values:e})},t.prototype.asinh=function(t){this.assertNotComplex(t,"asinh");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=Math.asinh(n[r]);return it.make(t.shape,{values:e})},t.prototype.acosh=function(t){this.assertNotComplex(t,"acosh");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=Math.acosh(n[r]);return it.make(t.shape,{values:e})},t.prototype.atanh=function(t){this.assertNotComplex(t,"atanh");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=Math.atanh(n[r]);return it.make(t.shape,{values:e})},t.prototype.erf=function(t){this.assertNotComplex(t,"erf");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r){var o=n[r],i=1/(1+.3275911*o);e[r]=1-((((1.061405429*i-1.453152027)*i+1.421413741)*i-.284496736)*i+.254829592)*i*Math.exp(-o*o)}return it.make(t.shape,{values:e})},t.prototype.step=function(t,e){void 0===e&&(e=0),this.assertNotComplex(t,"step");for(var n=new Float32Array(t.size),r=t.dataSync(),o=0;o<r.length;++o){var i=r[o];isNaN(i)?n[o]=NaN:n[o]=0<i?1:e}return it.make(t.shape,{values:n})},t.prototype.conv2d=function(t,e,n){this.assertNotComplex([t,e],"conv2d");for(var r=n.filterHeight,o=n.filterWidth,i=n.dilationHeight,a=n.dilationWidth,s=n.padInfo.left,u=n.padInfo.top,c=ii(n.outShape,t.dtype),l=t.dataSync(),h=e.dataSync(),p=c.values,f=0;f<n.batchSize;++f)for(var d=f*t.strides[0],m=f*c.strides[0],v=0;v<n.outHeight;++v)for(var g=m+v*c.strides[1],y=v*n.strideHeight-s,x=0;x<r;x++){var w=y+x*i;if(!(w<0||w>=n.inHeight))for(var b=x*e.strides[0],E=d+w*t.strides[1],C=0;C<n.outWidth;++C)for(var S=g+C*n.outChannels,_=C*n.strideWidth-u,R=0;R<o;R++){var N=_+R*a;if(!(N<0||N>=n.inWidth))for(var k=b+R*e.strides[1],T=E+N*n.inChannels,D=k,I=0;I<n.inChannels;++I){for(var A=l[T+I],M=0;M<n.outChannels;++M)p[S+M]+=A*h[D+M];D+=n.outChannels}}}return c.toTensor()},t.prototype.conv3d=function(t,e,n){for(var r=n.filterDepth,o=n.filterHeight,i=n.filterWidth,a=n.dilationDepth,s=n.dilationHeight,u=n.dilationWidth,c=n.padInfo.front,l=n.padInfo.left,h=n.padInfo.top,p=ii(n.outShape,t.dtype),f=t.dataSync(),d=e.dataSync(),m=p.values,v=0;v<n.batchSize;++v)for(var g=v*t.strides[0],y=v*p.strides[0],x=0;x<n.outDepth;++x)for(var w=y+x*p.strides[1],b=x*n.strideDepth-c,E=0;E<r;E++){var C=b+E*a;if(!(C<0||C>=n.inDepth))for(var S=E*e.strides[0],_=g+C*t.strides[1],R=0;R<n.outHeight;++R)for(var N=w+R*p.strides[2],k=R*n.strideHeight-h,T=0;T<o;T++){var D=k+T*s;if(!(D<0||D>=n.inHeight))for(var I=S+T*e.strides[1],A=_+D*t.strides[2],M=0;M<n.outWidth;++M)for(var O=N+M*n.outChannels,F=M*n.strideWidth-l,P=0;P<i;P++){var B=F+P*u;if(!(B<0||B>=n.inWidth))for(var L=I+P*e.strides[2],W=A+B*n.inChannels,z=L,U=0;U<n.inChannels;++U){for(var V=f[W+U],G=0;G<n.outChannels;++G)m[O+G]+=V*d[z+G];z+=n.outChannels}}}}return p.toTensor()},t.prototype.conv2dDerInput=function(t,e,n){this.assertNotComplex([t,e],"conv2dDerInput");for(var r=ii(n.inShape,"float32"),o=r.values,i=r.strides,a=i[0],s=i[1],u=i[2],c=t.dataSync(),l=t.strides,h=l[0],p=l[1],f=l[2],d=e.dataSync(),m=e.strides,v=m[0],g=m[1],y=m[2],x=n.batchSize,w=n.filterHeight,b=n.filterWidth,E=n.inChannels,C=n.inHeight,S=n.inWidth,_=n.outChannels,R=n.outHeight,N=n.outWidth,k=n.strideHeight,T=n.strideWidth,D=w-1-n.padInfo.top,I=b-1-n.padInfo.left,A=0;A<x;++A)for(var M=0;M<E;++M)for(var O=0;O<C;++O)for(var F=O-D,P=Math.max(0,Math.ceil(F/k)),B=Math.min(R,(w+F)/k),L=0;L<S;++L){for(var W=L-I,z=Math.max(0,Math.ceil(W/T)),U=Math.min(N,(b+W)/T),V=0,G=P;G<B;++G)for(var H=G*k-F,q=z;q<U;++q)for(var j=h*A+p*G+f*q,$=v*(w-1-H)+g*(b-1-(q*T-W))+y*M,K=0;K<_;++K)V+=c[j+K]*d[$+K];o[a*A+s*O+u*L+M]=V}return r.toTensor()},t.prototype.conv3dDerInput=function(t,e,n){for(var r=ii(n.inShape,"float32"),o=r.values,i=r.strides,a=i[0],s=i[1],u=i[2],c=i[3],l=t.dataSync(),h=t.strides,p=h[0],f=h[1],d=h[2],m=h[3],v=e.dataSync(),g=e.strides,y=g[0],x=g[1],w=g[2],b=g[3],E=n.batchSize,C=n.filterDepth,S=n.filterHeight,_=n.filterWidth,R=n.inChannels,N=n.inDepth,k=n.inHeight,T=n.inWidth,D=n.outChannels,I=n.outDepth,A=n.outHeight,M=n.outWidth,O=n.strideDepth,F=n.strideHeight,P=n.strideWidth,B=C-1-n.padInfo.front,L=S-1-n.padInfo.top,W=_-1-n.padInfo.left,z=0;z<E;++z)for(var U=0;U<R;++U)for(var V=0;V<N;++V)for(var G=V-B,H=Math.max(0,Math.ceil(G/O)),q=Math.min(I,(C+G)/O),j=0;j<k;++j)for(var $=j-L,K=Math.max(0,Math.ceil($/F)),X=Math.min(A,(S+$)/F),Y=0;Y<T;++Y){for(var Q=Y-W,J=Math.max(0,Math.ceil(Q/P)),Z=Math.min(M,(_+Q)/P),tt=0,et=H;et<q;++et)for(var nt=et*O-G,rt=K;rt<X;++rt)for(var ot=rt*F-$,it=J;it<Z;++it)for(var at=p*z+f*et+d*rt+m*it,st=y*(C-1-nt)+x*(S-1-ot)+w*(_-1-(it*P-Q))+b*U,ut=0;ut<D;++ut)tt+=l[at+ut]*v[st+ut];o[a*z+s*V+u*j+c*Y+U]=tt}return r.toTensor()},t.prototype.conv2dDerFilter=function(t,e,n){this.assertNotComplex([t,e],"conv2dDerFilter");for(var r=n.strideHeight,o=n.strideWidth,i=n.filterHeight,a=n.filterWidth,s=ii(n.filterShape,"float32"),u=n.padInfo.left,c=n.padInfo.top,l=0;l<i;++l)for(var h=Math.max(0,Math.ceil((c-l)/r)),p=Math.min(n.outHeight,(n.inHeight+c-l)/r),f=0;f<a;++f)for(var d=Math.max(0,Math.ceil((u-f)/o)),m=Math.min(n.outWidth,(n.inWidth+u-f)/o),v=0;v<n.inChannels;++v)for(var g=0;g<n.outChannels;++g){for(var y=0,x=0;x<n.batchSize;++x)for(var w=h;w<p;++w)for(var b=l+w*r-c,E=d;E<m;++E){var C=f+E*o-u;y+=t.get(x,b,C,v)*e.get(x,w,E,g)}s.set(y,l,f,v,g)}return s.toTensor()},t.prototype.conv3dDerFilter=function(t,e,n){for(var r=n.strideDepth,o=n.strideHeight,i=n.strideWidth,a=n.filterDepth,s=n.filterHeight,u=n.filterWidth,c=ii(n.filterShape,"float32"),l=c.values,h=c.strides,p=h[0],f=h[1],d=h[2],m=h[3],v=e.dataSync(),g=e.strides,y=g[0],x=g[1],w=g[2],b=g[3],E=t.dataSync(),C=t.strides,S=C[0],_=C[1],R=C[2],N=C[3],k=n.padInfo.front,T=n.padInfo.left,D=n.padInfo.top,I=0;I<a;++I)for(var A=Math.max(0,Math.ceil((k-I)/r)),M=Math.min(n.outDepth,(n.inDepth+k-I)/r),O=I*p,F=0;F<s;++F)for(var P=Math.max(0,Math.ceil((D-F)/o)),B=Math.min(n.outHeight,(n.inHeight+D-F)/o),L=F*f+O,W=0;W<u;++W)for(var z=Math.max(0,Math.ceil((T-W)/i)),U=Math.min(n.outWidth,(n.inWidth+T-W)/i),V=W*d+L,G=0;G<n.inChannels;++G)for(var H=G*m+V,q=0;q<n.outChannels;++q){for(var j=0,$=0;$<n.batchSize;++$)for(var K=$*S,X=$*y,Y=A;Y<M;++Y)for(var Q=(I+Y*r-k)*_+K,J=Y*x+X,Z=P;Z<B;++Z)for(var tt=(F+Z*o-D)*R+Q,et=Z*w+J,nt=z;nt<U;++nt){var rt=nt*b+et;j+=E[(W+nt*i-T)*N+tt+G]*v[rt+q]}l[H+q]=j}return c.toTensor()},t.prototype.depthwiseConv2D=function(t,e,n){this.assertNotComplex([t,e],"depthwiseConv2D");for(var r=n.filterHeight,o=n.filterWidth,i=n.dilationHeight,a=n.dilationWidth,s=n.padInfo.left,u=n.padInfo.top,c=n.outChannels/n.inChannels,l=ii(n.outShape,t.dtype),h=t.dataSync(),p=e.dataSync(),f=l.values,d=0;d<n.batchSize;++d)for(var m=d*t.strides[0],v=d*l.strides[0],g=0;g<n.outHeight;++g)for(var y=v+g*l.strides[1],x=g*n.strideHeight-s,w=0;w<r;++w){var b=x+w*i;if(!(b<0||b>=n.inHeight))for(var E=w*e.strides[0],C=m+b*t.strides[1],S=0;S<n.outWidth;++S)for(var _=y+S*l.strides[2],R=S*n.strideWidth-u,N=0;N<o;++N){var k=R+N*a;if(!(k<0||k>=n.inWidth))for(var T=E+N*e.strides[1],D=C+k*n.inChannels,I=_,A=T,M=0;M<n.inChannels;++M){for(var O=h[D+M],F=0;F<c;++F)f[I+F]+=O*p[A+F];I+=c,A+=c}}}return l.toTensor()},t.prototype.depthwiseConv2DDerInput=function(t,e,n){this.assertNotComplex([t,e],"depthwiseConv2DDerInput");for(var r=ii(n.inShape,"float32"),o=r.values,i=r.strides,a=i[0],s=i[1],u=i[2],c=t.dataSync(),l=t.strides,h=l[0],p=l[1],f=l[2],d=e.dataSync(),m=e.strides,v=m[0],g=m[1],y=m[2],x=n.batchSize,w=n.filterHeight,b=n.filterWidth,E=n.inChannels,C=n.inHeight,S=n.inWidth,_=n.outChannels,R=n.outHeight,N=n.outWidth,k=n.strideHeight,T=n.strideWidth,D=w-1-n.padInfo.top,I=b-1-n.padInfo.left,A=_/E,M=0;M<x;++M)for(var O=0;O<E;++O)for(var F=0;F<C;++F)for(var P=F-D,B=Math.max(0,Math.ceil(P/k)),L=Math.min(R,(w+P)/k),W=0;W<S;++W){for(var z=W-I,U=Math.max(0,Math.ceil(z/T)),V=Math.min(N,(b+z)/T),G=0,H=B;H<L;++H)for(var q=H*k-P,j=U;j<V;++j)for(var $=h*M+p*H+f*j,K=v*(w-1-q)+g*(b-1-(j*T-z))+y*O,X=0;X<A;++X)G+=c[$+(O*A+X)]*d[K+X];o[a*M+s*F+u*W+O]=G}return r.toTensor()},t.prototype.depthwiseConv2DDerFilter=function(t,e,n){this.assertNotComplex([t,e],"depthwiseConv2DDerFilter");for(var r=n.strideHeight,o=n.strideWidth,i=n.filterHeight,a=n.filterWidth,s=ii(n.filterShape,"float32"),u=n.padInfo.left,c=n.padInfo.top,l=n.outChannels/n.inChannels,h=0;h<i;++h)for(var p=Math.max(0,Math.ceil((c-h)/r)),f=Math.min(n.outHeight,(n.inHeight+c-h)/r),d=0;d<a;++d)for(var m=Math.max(0,Math.ceil((u-d)/o)),v=Math.min(n.outWidth,(n.inWidth+u-d)/o),g=0;g<n.outChannels;++g){for(var y=Math.trunc(g/l),x=g%l,w=0,b=0;b<n.batchSize;++b)for(var E=p;E<f;++E)for(var C=h+E*r-c,S=m;S<v;++S){var _=d+S*o-u;w+=t.get(b,C,_,y)*e.get(b,E,S,g)}s.set(w,h,d,y,x)}return s.toTensor()},t.prototype.tile=function(t,e){this.assertNotComplex(t,"tile");for(var n=new Array(t.rank),r=0;r<n.length;r++)n[r]=t.shape[r]*e[r];var o=ii(n,t.dtype),i=t.buffer();for(r=0;r<o.values.length;++r){for(var a=o.indexToLoc(r),s=new Array(t.rank),u=0;u<s.length;u++)s[u]=a[u]%t.shape[u];var c=i.locToIndex(s);o.values[r]=i.values[c]}return o.toTensor()},t.prototype.pad=function(n,t,e){this.assertNotComplex(n,"pad");var r=t.map(function(t,e){return t[0]+n.shape[e]+t[1]}),o=t.map(function(t){return t[0]}),i=n.buffer(),a=ii(r,n.dtype);0!==e&&a.values.fill(e);for(var s=0;s<n.size;s++){var u=i.indexToLoc(s),c=u.map(function(t,e){return t+o[e]});a.set.apply(a,[n.get.apply(n,u)].concat(c))}return a.toTensor()},t.prototype.transpose=function(t,e){this.assertNotComplex(t,"transpose");for(var n=new Array(t.rank),r=0;r<n.length;r++)n[r]=t.shape[e[r]];var o=t.dataSync(),i=ii(n,t.dtype),a=t.buffer();for(r=0;r<t.size;++r){for(var s=a.indexToLoc(r),u=new Array(s.length),c=0;c<u.length;c++)u[c]=s[e[c]];var l=i.locToIndex(u);i.values[l]=o[r]}return i.toTensor()},t.prototype.gather=function(t,e,n){this.assertNotComplex([t,e],"gather");var r=t.shape.slice(),o=e.dataSync();r[n]=o.length;for(var i=ii(r,t.dtype),a=t.buffer(),s=0;s<i.size;++s){var u=i.indexToLoc(s),c=u.slice();c[n]=o[u[n]];var l=a.locToIndex(c);i.values[s]=a.values[l]}return i.toTensor()},t.prototype.batchToSpaceND=function(t,e,n){this.assertNotComplex([t],"batchToSpaceND");var r=e.reduce(function(t,e){return t*e}),o=jt(t.shape,e,r),i=$t(o.length,e.length),a=Kt(t.shape,e,r),s=Xt(n,e.length),u=Yt(a,n,e.length);return t.reshape(o).transpose(i).reshape(a).slice(s,u)},t.prototype.spaceToBatchND=function(t,e,n){this.assertNotComplex([t],"spaceToBatchND");var r=e.reduce(function(t,e){return t*e}),o=[[0,0]];o.push.apply(o,n);for(var i=1+e.length;i<t.shape.length;++i)o.push([0,0]);var a=t.pad(o),s=jt(a.shape,e,r,!1),u=$t(s.length,e.length,!1),c=Kt(a.shape,e,r,!1);return a.reshape(s).transpose(u).reshape(c)},t.prototype.pool=function(t,e,n){this.assertNotComplex(t,"pool");for(var r=e.strideHeight,o=e.strideWidth,i=e.dilationHeight,a=e.dilationWidth,s=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=e.padInfo.top,l=e.padInfo.left,h="max"===n?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,p=t.dataSync(),f=ii(e.outShape,t.dtype),d=f.values,m=e.outShape[1]*e.outShape[2]*e.outShape[3],v=e.outShape[2]*e.outShape[3],g=e.outShape[3],y=0;y<e.batchSize;++y)for(var x=y*m,w=y*t.strides[0],b=0;b<e.inChannels;++b)for(var E=0;E<e.outHeight;++E)for(var C=E*r-c,S=Math.max(0,C),_=Math.min(e.inHeight,s+C),R=x+E*v,N=0;N<e.outWidth;++N){for(var k=N*o-l,T=Math.max(0,k),D=Math.min(e.inWidth,u+k),I=h,A=0,M=0,O=S;O<_;O+=i){for(var F=w+O*t.strides[1],P=T;P<D;P+=a){var B=p[F+P*t.strides[2]+b];"max"===n&&I<B?I=B:"avg"===n&&(A+=B,M++)}if(isNaN(I))break}d[R+N*g+b]="avg"===n?A/M:I}return f.toTensor()},t.prototype.maxPool=function(t,e){return this.pool(t,e,"max")},t.prototype.maxPoolPositions=function(t,e){for(var n=ii(e.outShape,"int32"),r=e.strideHeight,o=e.strideWidth,i=e.dilationHeight,a=e.dilationWidth,s=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=e.padInfo.top,l=e.padInfo.left,h=0;h<e.batchSize;++h)for(var p=0;p<e.inChannels;++p)for(var f=0;f<e.outHeight;++f){for(var d=f*r-c,m=d;m<0;)m+=i;for(var v=Math.min(e.inHeight,s+d),g=0;g<e.outWidth;++g){for(var y=g*o-l,x=y;x<0;)x+=a;for(var w=Math.min(e.inWidth,u+y),b=Number.NEGATIVE_INFINITY,E=-1,C=m;C<v;C+=i)for(var S=C-d,_=x;_<w;_+=a){var R=_-y,N=t.get(h,C,_,p);b<N&&(b=N,E=S*u+R)}n.set(E,h,f,g,p)}}return n.toTensor()},t.prototype.maxPoolBackprop=function(t,e,n,r){this.assertNotComplex([e,n],"maxPoolBackprop");for(var o=this.maxPoolPositions(e,r),i=r.strideHeight,a=r.strideWidth,s=r.dilationHeight,u=r.dilationWidth,c=r.effectiveFilterHeight,l=r.effectiveFilterWidth,h=l-1-r.padInfo.left,p=c-1-r.padInfo.top,f=ii(e.shape,"float32"),d=0;d<r.batchSize;++d)for(var m=0;m<r.inChannels;++m)for(var v=0;v<r.inHeight;++v)for(var g=0;g<r.inWidth;++g){for(var y=v-p,x=g-h,w=0,b=0;b<c;b+=s){var E=(y+b)/i;if(!(E<0||E>=r.outHeight||Math.floor(E)!==E))for(var C=0;C<l;C+=u){var S=(x+C)/a;if(!(S<0||S>=r.outWidth||Math.floor(S)!==S)){var _=c*l-1-o.get(d,E,S,m)===b*l+C?1:0;0!==_&&(w+=t.get(d,E,S,m)*_)}}}f.set(w,d,v,g,m)}return f.toTensor()},t.prototype.avgPoolBackprop=function(t,e,n){this.assertNotComplex([t,e],"avgPoolBackprop");for(var r=n.strideHeight,o=n.strideWidth,i=n.filterHeight,a=n.filterWidth,s=n.dilationHeight,u=n.dilationWidth,c=n.effectiveFilterHeight,l=n.effectiveFilterWidth,h=l-1-n.padInfo.left,p=c-1-n.padInfo.top,f=ii(e.shape,"float32"),d=1/(i*a),m=0;m<n.batchSize;++m)for(var v=0;v<n.inChannels;++v)for(var g=0;g<n.inHeight;++g)for(var y=0;y<n.inWidth;++y){for(var x=g-p,w=y-h,b=0,E=0;E<c;E+=s){var C=(x+E)/r;if(!(C<0||C>=n.outHeight||Math.floor(C)!==C))for(var S=0;S<l;S+=u){var _=(w+S)/o;_<0||_>=n.outWidth||Math.floor(_)!==_||(b+=t.get(m,C,_,v))}}f.set(b*d,m,g,y,v)}return f.toTensor()},t.prototype.cast=function(t,e){return ze(t,e,this)},t.prototype.reshape=function(t,e){return Ue(t,e)},t.prototype.avgPool=function(t,e){return this.assertNotComplex(t,"avgPool"),this.pool(t,e,"avg").toFloat()},t.prototype.resizeBilinear=function(t,e,n,r){this.assertNotComplex(t,"resizeBilinear");for(var o=t.shape,i=o[0],a=o[1],s=o[2],u=o[3],c=t.dataSync(),l=new Float32Array(L([i,e,n,u])),h=[r&&1<e?a-1:a,r&&1<n?s-1:s],p=[r&&1<e?e-1:e,r&&1<n?n-1:n],f=0,d=h[0]/p[0],m=h[1]/p[1],v=0;v<i;v++)for(var g=0;g<e;g++)for(var y=d*g,x=Math.floor(y),w=y-x,b=Math.min(a-1,Math.ceil(y)),E=v*t.strides[0]+x*t.strides[1],C=v*t.strides[0]+b*t.strides[1],S=0;S<n;S++)for(var _=m*S,R=Math.floor(_),N=_-R,k=Math.min(s-1,Math.ceil(_)),T=E+R*t.strides[2],D=C+R*t.strides[2],I=E+ +k*t.strides[2],A=C+k*t.strides[2],M=0;M<u;M++){var O=c[T+M],F=c[D+M],P=O+(c[I+M]-O)*N,B=P+(F+(c[A+M]-F)*N-P)*w;l[f++]=B}return Ce(l,[i,e,n,u])},t.prototype.resizeBilinearBackprop=function(t,e,n){this.assertNotComplex([t,e],"resizeBilinearBackprop");for(var r=e.shape,o=r[0],i=r[1],a=r[2],s=r[3],u=t.shape,c=u[1],l=u[2],h=new Float32Array(o*i*a*s),p=[n&&1<c?i-1:i,n&&1<l?a-1:a],f=[n&&1<c?c-1:c,n&&1<l?l-1:l],d=p[0]/f[0],m=p[1]/f[1],v=t.dataSync(),g=0,y=0;y<o;y++)for(var x=y*e.strides[0],w=0;w<c;w++)for(var b=w*d,E=Math.floor(b),C=Math.min(Math.ceil(b),i-1),S=x+E*e.strides[1],_=x+C*e.strides[1],R=b-E,N=1-R,k=0;k<l;k++)for(var T=k*m,D=Math.floor(T),I=Math.min(Math.ceil(T),a-1),A=T-D,M=1-A,O=S+D*e.strides[2],F=S+I*e.strides[2],P=_+D*e.strides[2],B=_+I*e.strides[2],L=N*M,W=N*A,z=R*M,U=R*A,V=0;V<s;V++){var G=v[g++];h[O+V]+=G*L,h[F+V]+=G*W,h[P+V]+=G*z,h[B+V]+=G*U}return ke(h,[o,a,i,s],e.dtype)},t.prototype.resizeNearestNeighbor=function(t,e,n,r){this.assertNotComplex(t,"resizeNearestNeighbor");for(var o=t.shape,i=o[0],a=o[1],s=o[2],u=o[3],c=t.dataSync(),l=new Float32Array(i*e*n*u),h=[r&&1<e?a-1:a,r&&1<n?s-1:s],p=[r&&1<e?e-1:e,r&&1<n?n-1:n],f=h[0]/p[0],d=h[1]/p[1],m=0,v=0;v<i;v++)for(var g=v*t.strides[0],y=0;y<e;y++)for(var x=f*y,w=g+Math.min(a-1,r?Math.round(x):Math.floor(x))*t.strides[1],b=0;b<n;b++)for(var E=d*b,C=w+Math.min(s-1,r?Math.round(E):Math.floor(E))*t.strides[2],S=0;S<u;S++){var _=c[C+S];l[m++]=_}return Ce(l,[i,e,n,u],t.dtype)},t.prototype.resizeNearestNeighborBackprop=function(t,e,n){this.assertNotComplex([t,e],"resizeNearestNeighborBackprop");for(var r=e.shape,o=r[0],i=r[1],a=r[2],s=r[3],u=t.shape,c=u[1],l=u[2],h=new Float32Array(o*i*a*s),p=t.dataSync(),f=[n&&1<c?i-1:i,n&&1<l?a-1:a],d=[n&&1<c?c-1:c,n&&1<l?l-1:l],m=f[0]/d[0],v=f[1]/d[1],g=1/m,y=1/v,x=2*Math.ceil(g)+2,w=2*Math.ceil(y)+2,b=0;b<o;b++)for(var E=b*e.strides[0],C=0;C<i;C++)for(var S=E+C*e.strides[1],_=Math.floor(C*g),R=Math.floor(_-x/2),N=0;N<a;N++)for(var k=S+N*e.strides[2],T=Math.floor(N*y),D=Math.floor(T-w/2),I=0;I<s;I++){for(var A=0,M=0;M<x;M++){var O=M+R;if(!(O<0||c<=O)){var F=E+O*t.strides[1],P=O*m;if(C===Math.min(i-1,n?Math.round(P):Math.floor(P)))for(var B=0;B<w;B++){var L=B+D;if(!(L<0||l<=L)){var W=F+L*t.strides[2],z=L*v;N===Math.min(a-1,n?Math.round(z):Math.floor(z))&&(A+=p[W+I])}}}}h[k+I]=A}return ke(h,e.shape,e.dtype)},t.prototype.batchNormalization=function(t,e,n,r,o,i){this.assertNotComplex([t,e,n,o,i],"batchNormalization");for(var a=t.dataSync(),s=e.dataSync(),u=n.dataSync(),c=o?o.dataSync():new Float32Array([1]),l=i?i.dataSync():new Float32Array([0]),h=new Float32Array(a.length),p=l.length,f=c.length,d=u.length,m=s.length,v=0,g=0,y=0,x=0,w=0;w<a.length;++w)h[w]=l[v++]+(a[w]-s[g++])*c[y++]/Math.sqrt(u[x++]+r),p<=v&&(v=0),m<=g&&(g=0),f<=y&&(y=0),d<=x&&(x=0);return ke(h,t.shape)},t.prototype.localResponseNormalization4D=function(t,a,e,n,r){this.assertNotComplex(t,"localResponseNormalization4D");var s=t.shape[3],u=s-1,c=t.dataSync(),o=L(t.shape),i=new Float32Array(o);function l(t){for(var e=t%s,n=t-e+Math.max(0,e-a),r=t-e+Math.min(e+a,u),o=0;n<=r;n++){var i=c[n];o+=i*i}return o}for(var h=0;h<o;h++){var p=l(h),f=c[h]*Math.pow(e+n*p,-r);i[h]=f}return ke(i,t.shape)},t.prototype.LRNGrad=function(t,e,n,r,o,i,a){this.assertNotComplex(t,"LRNGrad");for(var s=t.shape[3],u=t.dataSync(),c=e.dataSync(),l=n.dataSync(),h=new Float32Array(L(t.shape)),p=L(t.shape),f=0;f<p;f++){for(var d=f%s,m=f-d+Math.max(0,d-r),v=f-d+Math.min(s,d+r+1),g=0,y=m;y<v;y++)g+=Math.pow(c[y],2);for(g=i*g+o,y=m;y<v;y++){var x=-2*i*a*c[y]*l[f]/g;f===y&&(x+=Math.pow(g,-a)),x*=u[f],h[y]+=x}}return ke(h,t.shape)},t.prototype.multinomial=function(t,e,n,r){this.assertNotComplex(t,"multinomial");for(var o=e?t:ye(t),i=o.shape[0],a=o.shape[1],s=Ae([i,n],"int32"),u=s.dataSync(),c=o.dataSync(),l=0;l<i;++l){var h=l*a,p=new Float32Array(a-1);p[0]=c[h];for(var f=1;f<p.length;++f)p[f]=p[f-1]+c[h+f];for(var d=ni(r.toString()),m=l*n,v=0;v<n;++v){var g=d();u[m+v]=p.length;for(var y=0;y<p.length;y++)if(g<p[y]){u[m+v]=y;break}}}return s},t.prototype.oneHot=function(t,e,n,r){this.assertNotComplex(t,"oneHot");var o=new Float32Array(t.size*e);o.fill(r);for(var i=0;i<t.size;++i)0<=t.get(i)&&t.get(i)<e&&(o[i*e+t.get(i)]=n);return Re(o,[t.size,e],"int32")},t.prototype.nonMaxSuppression=function(t,e,n,r,o){return this.assertNotComplex(t,"nonMaxSuppression"),He(t.dataSync(),e.dataSync(),n,r,o)},t.prototype.fft=function(t){return this.fftBatch(t,!1)},t.prototype.ifft=function(t){return this.fftBatch(t,!0)},t.prototype.fftBatch=function(t,e){for(var n=t.shape[0],r=t.shape[1],o=ii(t.shape,"float32"),i=ii(t.shape,"float32"),a=be(t).as2D(n,r),s=Ee(t).as2D(n,r),u=0;u<n;u++)for(var c=a.slice([u,0],[1,r]),l=s.slice([u,0],[1,r]),h=we(c,l),p=this.fftImpl(h,e).dataSync(),f=0;f<r;f++){var d=Ge(p,f);o.values[u*r+f]=d.real,i.values[u*r+f]=d.imag}return we(o.toTensor(),i.toTensor()).as2D(n,r)},t.prototype.fftImpl=function(t,e){var n=t.as1D(),r=n.size;if(this.isExponentOf2(r)){var o=this.fftRadix2(n,r,e).as2D(t.shape[0],t.shape[1]);return e&&(o=we(be(o).div(Se(r)),Ee(o).div(Se(r)))),o}var i=t.dataSync(),a=function(t){for(var e=new Float32Array(t.length/2),n=new Float32Array(t.length/2),r=0;r<t.length;r+=2)e[r/2]=t[r],n[r/2]=t[r+1];return{real:e,imag:n}}(this.fourierTransformByMatmul(i,r,e));return we(a.real,a.imag).as2D(t.shape[0],t.shape[1])},t.prototype.isExponentOf2=function(t){return 0==(t&t-1)},t.prototype.fftRadix2=function(t,e,n){if(1===e)return t;var r=t.dataSync(),o=e/2,i=function(t){for(var e=Math.ceil(t.length/4),n=new Float32Array(e),r=new Float32Array(e),o=0;o<t.length;o+=4)n[Math.floor(o/4)]=t[o],r[Math.floor(o/4)]=t[o+1];return{real:n,imag:r}}(r),a=we(i.real,i.imag).as1D(),s=function(t){for(var e=Math.floor(t.length/4),n=new Float32Array(e),r=new Float32Array(e),o=2;o<t.length;o+=4)n[Math.floor(o/4)]=t[o],r[Math.floor(o/4)]=t[o+1];return{real:n,imag:r}}(r),u=we(s.real,s.imag).as1D();a=this.fftRadix2(a,o,n),u=this.fftRadix2(u,o,n);var c=function(t,e){for(var n=new Float32Array(t/2),r=new Float32Array(t/2),o=0;o<Math.ceil(t/2);o++){var i=(e?2:-2)*Math.PI*(o/t);n[o]=Math.cos(i),r[o]=Math.sin(i)}return{real:n,imag:r}}(e,n),l=we(c.real,c.imag).mul(u),h=a.add(l),p=a.sub(l),f=be(h).concat(be(p)),d=Ee(h).concat(Ee(p));return we(f,d).as1D()},t.prototype.fourierTransformByMatmul=function(t,e,n){for(var r=new Float32Array(2*e),o=0;o<e;o++){for(var i=0,a=0,s=0;s<e;s++){var u=(d=o*s,m=e,void 0,v=(n?2:-2)*Math.PI*(d/m),{real:Math.cos(v),imag:Math.sin(v)}),c=Ge(t,s);i+=c.real*u.real-c.imag*u.imag,a+=c.real*u.imag+c.imag*u.real}n&&(i/=e,a/=e),h=i,p=a,(l=r)[2*(f=o)]=h,l[2*f+1]=p}var l,h,p,f,d,m,v;return r},t.prototype.depthToSpace=function(t,e,n){A("NHWC"===n,"Only NHWC dataFormat supported on CPU for depthToSpace. Got "+n),A(1<e,"blockSize should be > 1 for depthToSpace, but was: "+e);for(var r=t.shape[0],o=t.shape[1],i=t.shape[2],a=t.shape[3],s=o*e,u=i*e,c=a/(e*e),l=t.dataSync(),h=new Float32Array(r*s*u*c),p=0,f=0;f<r;++f)for(var d=0;d<s;++d)for(var m=Math.floor(d/e),v=d%e,g=0;g<u;++g)for(var y=Math.floor(g/e),x=(v*e+g%e)*c,w=0;w<c;++w){var b=w+x+a*(y+i*(m+o*f));h[p++]=l[b]}return ke(h,[r,s,u,c])},t.prototype.broadcastedBinaryOp=function(a,s,t,u){var e=Je(a.shape,s.shape),c=ii(e,t),l=a.dataSync(),h=s.dataSync(),p=Ye(a.shape,e),f=Ye(s.shape,e),d=c.values;if(p.length+f.length===0)for(var n=0;n<d.length;++n)d[n]=u(l[n%l.length],h[n%h.length]);else{var m=a.buffer(),v=s.buffer(),r=function(t){var e=c.indexToLoc(t),n=e.slice(-a.rank);p.forEach(function(t){return n[t]=0});var r=m.locToIndex(n),o=e.slice(-s.rank);f.forEach(function(t){return o[t]=0});var i=v.locToIndex(o);d[t]=u(l[r],h[i])};for(n=0;n<d.length;++n)r(n)}return c.toTensor()},t.prototype.broadcastedBinaryComplexOp=function(s,u,c){var t=Je(s.shape,u.shape),l=ii(t,"float32"),e=ii(t,"float32"),h=s.dataSync(),p=u.dataSync(),f=Ye(s.shape,t),d=Ye(u.shape,t),m=l.values,v=e.values;if(f.length+d.length===0)for(var n=0;n<m.length;n++){var r=n%h.length,o=n%p.length,i=c(h[2*r],h[2*r+1],p[2*o],p[2*o+1]);m[n]=i.real,v[n]=i.imag}else{var g=this.data.get(s.dataId).complexTensors.real.buffer(),y=this.data.get(u.dataId).complexTensors.real.buffer(),a=function(t){var e=l.indexToLoc(t),n=e.slice(-s.rank);f.forEach(function(t){return n[t]=0});var r=g.locToIndex(n),o=e.slice(-u.rank);d.forEach(function(t){return o[t]=0});var i=y.locToIndex(o),a=c(h[2*r],h[2*r+1],p[2*i],p[2*i+1]);m[t]=a.real,v[t]=a.imag};for(n=0;n<m.length;n++)a(n)}return this.complex(l.toTensor(),e.toTensor())},t.prototype.split=function(t,e,n){return je(t,e,n)},t.prototype.dispose=function(){},t.prototype.floatPrecision=function(){return 32},t.prototype.cropAndResize=function(t,e,n,r,o,i){for(var a=t.shape,s=a[0],u=a[1],c=a[2],l=a[3],h=e.shape[0],p=r[0],f=r[1],d=ii([h,p,f,l],t.dtype),m=e.dataSync(),v=n.dataSync(),g=t.dataSync(),y=t.strides,x=d.strides,w=0;w<h;w++){var b=4*w,E=m[b],C=m[b+1],S=m[b+2],_=m[b+3],R=v[w];if(!(s<=R))for(var N=1<p?(S-E)*(u-1)/(p-1):0,k=1<f?(_-C)*(c-1)/(f-1):0,T=0;T<p;T++){var D=1<p?E*(u-1)+T*N:.5*(E+S)*(u-1);if(D<0||u-1<D)for(var I=0;I<f;I++)for(var A=0;A<l;A++){var M=A+I*x[2]+T*x[1]+w*x[0];d.values[M]=i}else if("bilinear"===o){var O=Math.floor(D),F=Math.ceil(D),P=D-O;for(I=0;I<f;I++)if((q=1<f?C*(c-1)+I*k:.5*(C+_)*(c-1))<0||c-1<q)for(A=0;A<l;A++)M=A+I*x[2]+T*x[1]+w*x[0],d.values[M]=i;else{var B=Math.floor(q),L=Math.ceil(q),W=q-B;for(A=0;A<l;A++){var z=g[M=A+B*y[2]+O*y[1]+R*y[0]],U=g[M=A+L*y[2]+O*y[1]+R*y[0]],V=g[M=A+B*y[2]+F*y[1]+R*y[0]],G=z+(U-z)*W,H=V+(g[M=A+L*y[2]+F*y[1]+R*y[0]]-V)*W;M=A+I*x[2]+T*x[1]+w*x[0],d.values[M]=G+(H-G)*P}}}else for(I=0;I<f;++I){var q;if((q=1<f?C*(c-1)+I*k:.5*(C+_)*(c-1))<0||c-1<q)for(A=0;A<l;A++)M=A+I*x[2]+T*x[1]+w*x[0],d.values[M]=i;else{var j=Math.round(q),$=Math.round(D);for(A=0;A<l;A++){var K=A+j*y[2]+$*y[1]+R*y[0],X=A+I*x[2]+T*x[1]+w*x[0];d.values[X]=g[K]}}}}}return d.toTensor()},t.prototype.sparseToDense=function(t,e,n,r){var o=ce(0,t,n),i=o.sliceRank,a=o.numUpdates,s=o.sliceSize,u=o.strides,c=o.outputSize;return this.scatter(t,e,n,c,s,a,i,u,r,!1)},t.prototype.gatherND=function(t,e){var n=e.shape,r=n[n.length-1],o=ae(t,e),i=o[0],a=o[1],s=o[2],u=o[3];if(0===a)return Ce([],i,t.dtype);for(var c=new et([a,s],t.dtype),l=e.dataSync(),h=t.dataSync(),p=0;p<a;p++){for(var f=[],d=0,m=0;m<r;m++){var v=l[p*r+m];d+=v*u[m],f.push(v)}if(d<0||d>=t.size/s)throw new Error("Invalid indices: "+f+" does not index into "+t.shape);for(var g=0;g<s;g++)c.values[p*s+g]=h[d*s+g]}return c.toTensor().reshape(i)},t.prototype.scatterND=function(t,e,n){var r=ce(0,t,n),o=r.sliceRank,i=r.numUpdates,a=r.sliceSize,s=r.strides,u=r.outputSize,c=Se(0);return this.scatter(t,e,n,u,a,i,o,s,c,!0)},t.prototype.scatter=function(t,e,n,r,o,i,a,s,u,c){var l=[r/o,o],h=t.dataSync(),p=e.dataSync();if(0===r)return Ce([],n,e.dtype);var f=new et(l,e.dtype);f.values.fill(u.dataSync()[0]);for(var d=0;d<i;d++){for(var m=[],v=0,g=0;g<a;g++){var y=h[d*a+g];m.push(y),v+=y*s[g]}if(v<0||r/o<=v)throw new Error("Invalid indices: "+m+" does not index into "+n);for(var x=0;x<o;x++)c?f.values[v*o+x]+=p[d*o+x]:f.values[v*o+x]=0===e.rank?p[0]:p[d*o+x]}return f.toTensor().reshape(n)},t}();Ft.registerBackend("cpu",function(){return new Ku},1,ot);var Xu="undefined"!=typeof requestAnimationFrame?requestAnimationFrame:setImmediate;var Yu={float32:4,int32:4,uint16:2,uint8:1,bool:1};function Qu(p,t){for(var f={},d=0,e=function(t){var e=t.name,n=t.dtype,r=t.shape,o=L(r),i=void 0;if("quantization"in t){var a=t.quantization;if("uint8"!==a.dtype&&"uint16"!==a.dtype)throw new Error("Weight "+t.name+" has unknown quantization dtype "+a.dtype+". 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e=t.paramPath;return{path:e,tensor:n.getParamFromPath(e)}})},t.prototype.getTrainableParams=function(){return this.getParamList().filter(function(t){return t.tensor instanceof at})},t.prototype.getFrozenParams=function(){return this.getParamList().filter(function(t){return!(t.tensor instanceof at)})},t.prototype.variable=function(){var r=this;this.getFrozenParams().forEach(function(t){var e=t.path,n=t.tensor;r.reassignParamFromPath(e,n.variable())})},t.prototype.freeze=function(){var o=this;this.getTrainableParams().forEach(function(t){var e=t.path,n=t.tensor,r=Ce(n.dataSync());n.dispose(),o.reassignParamFromPath(e,r)})},t.prototype.dispose=function(e){void 0===e&&(e=!0),this.getParamList().forEach(function(t){if(e&&t.tensor.isDisposed)throw new Error("param tensor has already been disposed for path "+t.path);t.tensor.dispose()}),this._params=void 0},t.prototype.serializeParams=function(){return new Float32Array(this.getParamList().map(function(t){var e=t.tensor;return 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function(t,e,n,r){var o=ke(a(t*e*n*n),[n,n,t,e]),i=_e(a(e));return s.push({paramPath:r+"/filters"},{paramPath:r+"/bias"}),{filters:o,bias:i}}}function Wh(i,a){return function(t,e,n){var r=Re(i(t*e),[t,e]),o=_e(i(e));return a.push({paramPath:n+"/weights"},{paramPath:n+"/bias"}),{weights:r,bias:o}}}var zh=function(t,e,n){this.depthwise_filter=t,this.pointwise_filter=e,this.bias=n};var Uh=function(t){return"number"==typeof t};function Vh(t){if(![t.noObjectScale,t.objectScale,t.coordScale,t.classScale].every(Uh))throw new Error("for training you have to specify noObjectScale, objectScale, coordScale, classScale parameters in your config.json file");return t}var Gh,Hh,qh=[3,16,32,64,128,256,512,1024,1024];function jh(e){return zt(function(){var t=Ws(e,Se(.10000000149011612));return Rs(nu(qs(e,t)),t)})}function $h(e,n){return zt(function(){var t=gi(e,[[0,0],[1,1],[1,1],[0,0]]);return t=Fa(t,n.conv.filters,[1,1],"valid"),t=qs(t,n.bn.sub),t=Ws(t,n.bn.truediv),jh(t=Rs(t,n.conv.bias))})}function Kh(e,n){return zt(function(){var t=gi(e,[[0,0],[1,1],[1,1],[0,0]]);return t=Wa(t,n.depthwise_filter,n.pointwise_filter,[1,1],"valid"),jh(t=Rs(t,n.bias))})}function Xh(s,u){var c=Lh(s,u);var a,l,t=(a=s,l=u,function(t,e,n){var r=ke(a(9*t),[3,3,t,1]),o=ke(a(t*e),[1,1,t,e]),i=_e(a(e));return l.push({paramPath:n+"/depthwise_filter"},{paramPath:n+"/pointwise_filter"},{paramPath:n+"/bias"}),new zh(r,o,i)});return{extractConvParams:c,extractConvWithBatchNormParams:function(t,e,n){var r,o,i,a;return{conv:c(t,e,3,n+"/conv"),bn:(o=n+"/bn",i=_e(s(r=e)),a=_e(s(r)),u.push({paramPath:o+"/sub"},{paramPath:o+"/truediv"}),{sub:i,truediv:a})}},extractSeparableConvParams:t}}function Yh(t,e){var o,n=Gl(t,e);function r(t){return{filters:n(t+"/filters",4),bias:n(t+"/bias",1)}}return{extractConvParams:r,extractConvWithBatchNormParams:function(t){var e;return{conv:r(t+"/conv"),bn:{sub:n((e=t+"/bn")+"/sub",1),truediv:n(e+"/truediv",1)}}},extractSeparableConvParams:(o=n,function(t){var e=o(t+"/depthwise_filter",4),n=o(t+"/pointwise_filter",4),r=o(t+"/bias",1);return new zh(e,n,r)})}}(Hh=Gh||(Gh={}))[Hh.XS=224]="XS",Hh[Hh.SM=320]="SM",Hh[Hh.MD=416]="MD",Hh[Hh.LG=608]="LG";var Qh=function(){function t(t){var e=void 0===t?{}:t,n=e.inputSize,r=e.scoreThreshold;if(this._name="TinyYolov2Options",this._inputSize=n||416,this._scoreThreshold=r||.5,"number"!=typeof this._inputSize||this._inputSize%32!=0)throw new Error(this._name+" - expected inputSize to be a number divisible by 32");if("number"!=typeof this._scoreThreshold||this._scoreThreshold<=0||1<=this._scoreThreshold)throw new Error(this._name+" - expected scoreThreshold to be a number between 0 and 1")}return Object.defineProperty(t.prototype,"inputSize",{get:function(){return this._inputSize},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"scoreThreshold",{get:function(){return this._scoreThreshold},enumerable:!0,configurable:!0}),t}(),Jh=function(n){function t(t){var e=n.call(this,"TinyYolov2")||this;return function(t){if(!t)throw new Error("invalid config: "+t);if("boolean"!=typeof t.withSeparableConvs)throw new Error("config.withSeparableConvs has to be a boolean, have: "+t.withSeparableConvs);if(!Uh(t.iouThreshold)||t.iouThreshold<0||1<t.iouThreshold)throw new Error("config.iouThreshold has to be a number between [0, 1], have: "+t.iouThreshold);if(!Array.isArray(t.classes)||!t.classes.length||!t.classes.every(function(t){return"string"==typeof t}))throw new Error("config.classes has to be an array class names: string[], have: "+JSON.stringify(t.classes));if(!Array.isArray(t.anchors)||!t.anchors.length||!t.anchors.map(function(t){return t||{}}).every(function(t){return Uh(t.x)&&Uh(t.y)}))throw new Error("config.anchors has to be an array of { x: number, y: number }, have: "+JSON.stringify(t.anchors));if(t.meanRgb&&(!Array.isArray(t.meanRgb)||3!==t.meanRgb.length||!t.meanRgb.every(Uh)))throw new Error("config.meanRgb has to be an array of shape 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n=this.config.isFirstLayerConv2d?jh(Bh(t,e.conv0,"valid",!1)):Kh(t,e.conv0);return n=Kh(n=Ka(n,[2,2],[2,2],"same"),e.conv1),n=Kh(n=Ka(n,[2,2],[2,2],"same"),e.conv2),n=Kh(n=Ka(n,[2,2],[2,2],"same"),e.conv3),n=Kh(n=Ka(n,[2,2],[2,2],"same"),e.conv4),n=Kh(n=Ka(n,[2,2],[2,2],"same"),e.conv5),n=Ka(n,[2,2],[1,1],"same"),n=e.conv6?Kh(n,e.conv6):n,Bh(n=e.conv7?Kh(n,e.conv7):n,e.conv8,"valid",!1)},t.prototype.forwardInput=function(e,n){var r=this,o=this.params;if(!o)throw new Error("TinyYolov2 - load model before inference");return zt(function(){var t=e.toBatchTensor(n,!1).toFloat();return t=(t=r.config.meanRgb?Eh(t,r.config.meanRgb):t).div(Se(256)),r.config.withSeparableConvs?r.runMobilenet(t,o):r.runTinyYolov2(t,o)})},t.prototype.forward=function(n,r){return gl(this,void 0,void 0,function(){var e;return yl(this,function(t){switch(t.label){case 0:return e=this.forwardInput,[4,xh(n)];case 1:return[4,e.apply(this,[t.sent(),r])];case 2:return[2,t.sent()]}})})},t.prototype.detect=function(m,v){return void 0===v&&(v={}),gl(this,void 0,void 0,function(){var e,n,r,o,i,a,s,u,c,l,h,p,f,d=this;return yl(this,function(t){switch(t.label){case 0:return e=new Qh(v),n=e.inputSize,r=e.scoreThreshold,[4,xh(m)];case 1:return o=t.sent(),[4,this.forwardInput(o,n)];case 2:return i=t.sent(),a=zt(function(){return Ii(i)[0].expandDims()}),s={width:o.getInputWidth(0),height:o.getInputHeight(0)},u=this.extractBoxes(a,o.getReshapedInputDimensions(0),r),i.dispose(),a.dispose(),c=u.map(function(t){return t.box}),l=u.map(function(t){return t.score}),h=u.map(function(t){return t.classScore}),p=u.map(function(t){return d.config.classes[t.label]}),f=bh(c.map(function(t){return t.rescale(n)}),l,this.config.iouThreshold,!0),[2,f.map(function(t){return new Wl(l[t],h[t],p[t],c[t],s)})]}})})},t.prototype.getDefaultModelName=function(){return""},t.prototype.extractParamsFromWeigthMap=function(t){return function(t,e){var n,r=[],o=Yh(t,r),i=o.extractConvParams,a=o.extractConvWithBatchNormParams,s=o.extractSeparableConvParams;if(e.withSeparableConvs){var u=e.filterSizes&&e.filterSizes.length||9;n={conv0:e.isFirstLayerConv2d?i("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:7<u?s("conv6"):void 0,conv7:8<u?s("conv7"):void 0,conv8:i("conv8")}}else n={conv0:a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:a("conv6"),conv7:a("conv7"),conv8:i("conv8")};return Vl(t,r),{params:n,paramMappings:r}}(t,this.config)},t.prototype.extractParams=function(t){var e=this.config.filterSizes||qh,n=e?e.length:void 0;if(7!==n&&8!==n&&9!==n)throw new Error("TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found "+n+" filterSizes in config");return function(t,e,n,r){var o,i=Hl(t),a=i.extractWeights,s=i.getRemainingWeights,u=[],c=Xh(a,u),l=c.extractConvParams,h=c.extractConvWithBatchNormParams,p=c.extractSeparableConvParams;if(e.withSeparableConvs){var f=r[0],d=r[1],m=r[2],v=r[3],g=r[4],y=r[5],x=r[6],w=r[7],b=r[8];o={conv0:e.isFirstLayerConv2d?l(f,d,3,"conv0"):p(f,d,"conv0"),conv1:p(d,m,"conv1"),conv2:p(m,v,"conv2"),conv3:p(v,g,"conv3"),conv4:p(g,y,"conv4"),conv5:p(y,x,"conv5"),conv6:w?p(x,w,"conv6"):void 0,conv7:b?p(w,b,"conv7"):void 0,conv8:l(b||w||x,5*n,1,"conv8")}}else f=r[0],d=r[1],m=r[2],v=r[3],g=r[4],y=r[5],x=r[6],w=r[7],b=r[8],o={conv0:h(f,d,"conv0"),conv1:h(d,m,"conv1"),conv2:h(m,v,"conv2"),conv3:h(v,g,"conv3"),conv4:h(g,y,"conv4"),conv5:h(y,x,"conv5"),conv6:h(x,w,"conv6"),conv7:h(w,b,"conv7"),conv8:l(b,5*n,1,"conv8")};if(0!==s().length)throw new Error("weights remaing after extract: "+s().length);return{params:o,paramMappings:u}}(t,this.config,this.boxEncodingSize,e)},t.prototype.extractBoxes=function(e,t,n){for(var r=this,o=t.width,i=t.height,a=Math.max(o,i),s=a/o,u=a/i,c=e.shape[1],l=this.config.anchors.length,h=zt(function(){var t=e.reshape([c,c,l,r.boxEncodingSize]);return[t.slice([0,0,0,0],[c,c,l,4]),t.slice([0,0,0,4],[c,c,l,1]),r.withClassScores?ye(t.slice([0,0,0,5],[c,c,l,r.config.classes.length]),3):Se(0)]}),p=h[0],f=h[1],d=h[2],m=[],v=0;v<c;v++)for(var g=0;g<c;g++)for(var y=0;y<l;y++){var x=Ch(f.get(v,g,y,0));if(!n||n<x){var w=(g+Ch(p.get(v,g,y,0)))/c*s,b=(v+Ch(p.get(v,g,y,1)))/c*u,E=Math.exp(p.get(v,g,y,2))*this.config.anchors[y].x/c*s,C=Math.exp(p.get(v,g,y,3))*this.config.anchors[y].y/c*u,S=w-E/2,_=b-C/2,R={row:v,col:g,anchor:y},N=this.withClassScores?this.extractPredictedClass(d,R):{classScore:1,label:0},k=N.classScore,T=N.label;m.push(vl({box:new Pl(S,_,S+E,_+C),score:x,classScore:x*k,label:T},R))}}return p.dispose(),f.dispose(),d.dispose(),m},t.prototype.extractPredictedClass=function(n,t){var r=t.row,o=t.col,i=t.anchor;return Array(this.config.classes.length).fill(0).map(function(t,e){return n.get(r,o,i,e)}).map(function(t,e){return{classScore:t,label:e}}).reduce(function(t,e){return t.classScore>e.classScore?t:e})},t}(Sh),Zh=function(){function t(t,e,n,r,o){this._config=o,this._reshapedImgDims=new xl(r.width,r.height),this._outputTensor=t,this._predictedBoxes=n,this.validateGroundTruthBoxes(e),this._groundTruth=this.assignGroundTruthToAnchors(e);var i=this.createGroundTruthMask(),a=this.createCoordAndScoreMasks(),s=a.coordBoxOffsetMask,u=a.coordBoxSizeMask,c=a.scoreMask;this.noObjectLossMask=zt(function(){return Ws(c,qs(Se(1),i))}),this.objectLossMask=zt(function(){return Ws(c,i)}),this.coordBoxOffsetMask=zt(function(){return Ws(s,i)}),this.coordBoxSizeMask=zt(function(){return Ws(u,i)});var l=zt(function(){return qs(Se(1),s.add(u).add(c))});this.groundTruthClassScoresMask=zt(function(){return Ws(l,i)})}return Object.defineProperty(t.prototype,"config",{get:function(){return this._config},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"reshapedImgDims",{get:function(){return this._reshapedImgDims},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"outputTensor",{get:function(){return this._outputTensor},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"groundTruth",{get:function(){return this._groundTruth},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"predictedBoxes",{get:function(){return this._predictedBoxes},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"inputSize",{get:function(){return Math.max(this.reshapedImgDims.width,this.reshapedImgDims.height)},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"withClassScores",{get:function(){return this._config.withClassScores||1<this._config.classes.length},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"boxEncodingSize",{get:function(){return 5+(this.withClassScores?this._config.classes.length:0)},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"anchors",{get:function(){return this._config.anchors},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"numBoxes",{get:function(){return this.anchors.length},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"numCells",{get:function(){return this.inputSize/32},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"gridCellEncodingSize",{get:function(){return this.boxEncodingSize*this.numBoxes},enumerable:!0,configurable:!0}),t.prototype.toOutputTensorShape=function(t){var e=this;return zt(function(){return t.reshape([1,e.numCells,e.numCells,e.gridCellEncodingSize])})},t.prototype.computeLoss=function(){var i=this;return zt(function(){var t=i.computeNoObjectLoss(),e=i.computeObjectLoss(),n=i.computeCoordLoss(),r=i.withClassScores?i.computeClassLoss():Se(0),o=zt(function(){return t.add(e).add(n).add(r)});return{noObjectLoss:t,objectLoss:e,coordLoss:n,classLoss:r,totalLoss:o}})},t.prototype.computeNoObjectLoss=function(){var t=this;return zt(function(){return t.computeLossTerm(t.config.noObjectScale,t.toOutputTensorShape(t.noObjectLossMask),oa(t.outputTensor))})},t.prototype.computeObjectLoss=function(){var t=this;return zt(function(){return t.computeLossTerm(t.config.objectScale,t.toOutputTensorShape(t.objectLossMask),qs(t.toOutputTensorShape(t.computeIous()),oa(t.outputTensor)))})},t.prototype.computeClassLoss=function(){var n=this;return zt(function(){var t=zt(function(){var t=Ws(ye(n.outputTensor.reshape([n.numCells,n.numCells,n.numBoxes,n.boxEncodingSize]),3),n.groundTruthClassScoresMask),e=n.createOneHotClassScoreMask();return qs(e,t)});return n.computeLossTerm(n.config.classScale,Se(1),t)})},t.prototype.computeCoordLoss=function(){var t=this;return zt(function(){return t.computeLossTerm(t.config.coordScale,Se(1),Rs(t.computeCoordBoxOffsetError(),t.computeCoordBoxSizeError()))})},t.prototype.computeCoordBoxOffsetError=function(){var r=this;return zt(function(){var t=r.toOutputTensorShape(r.coordBoxOffsetMask),e=Ws(t,r.toOutputTensorShape(r.computeCoordBoxOffsets())),n=Ws(t,oa(r.outputTensor));return qs(e,n)})},t.prototype.computeCoordBoxSizeError=function(){var r=this;return zt(function(){var t=r.toOutputTensorShape(r.coordBoxSizeMask),e=Ws(t,r.toOutputTensorShape(r.computeCoordBoxSizes())),n=Ws(t,r.outputTensor);return qs(e,n)})},t.prototype.computeLossTerm=function(t,e,n){var r=this;return zt(function(){return Ws(Se(t),r.squaredSumOverMask(e,n))})},t.prototype.squaredSumOverMask=function(t,e){return zt(function(){return ps(la(Ws(t,e)))})},t.prototype.validateGroundTruthBoxes=function(t){var a=this;t.forEach(function(t){var e=t.x,n=t.y,r=t.width,o=t.height,i=t.label;if("number"!=typeof i||i<0||i>a.config.classes.length-1)throw new Error("invalid ground truth data, expected label to be a number in [0, "+(a.config.classes.length-1)+"]");if(e<0||1<e||n<0||1<n||r<0||1<e+r||o<0||1<n+o)throw new Error("invalid ground truth data, box is out of image boundaries "+JSON.stringify({x:e,y:n,width:r,height:o}))})},t.prototype.assignGroundTruthToAnchors=function(t){var h=this;return t.map(function(t){var e=t.x,n=t.y,r=t.width,o=t.height,i=t.label;return{box:new Ul(e,n,r,o),label:i}}).map(function(t){var e=t.box,n=t.label,r=e.rescale(h.reshapedImgDims),o=r.left,i=r.top,a=(r.bottom,r.right,r.x,r.y,r.width),s=r.height,u=o+a/2,c=i+s/2,l=Math.floor(u/h.inputSize*h.numCells);return{row:Math.floor(c/h.inputSize*h.numCells),col:l,anchor:h.anchors.map(function(t,e){return{idx:e,iou:wh(new Pl(0,0,32*t.x,32*t.y),new Pl(0,0,a,s))}}).sort(function(t,e){return e.iou-t.iou})[0].idx,box:e,label:n}})},t.prototype.createGroundTruthMask=function(){var i=this,t=Ae([this.numCells,this.numCells,this.numBoxes,this.boxEncodingSize]),a=t.buffer();return this.groundTruth.forEach(function(t){for(var e=t.row,n=t.col,r=t.anchor,o=0;o<i.boxEncodingSize;o++)a.set(1,e,n,r,o)}),t},t.prototype.createCoordAndScoreMasks=function(){var c=this;return zt(function(){for(var t=Ae([c.numCells,c.numCells,c.numBoxes,c.boxEncodingSize]),e=Ae([c.numCells,c.numCells,c.numBoxes,c.boxEncodingSize]),n=Ae([c.numCells,c.numCells,c.numBoxes,c.boxEncodingSize]),r=t.buffer(),o=e.buffer(),i=n.buffer(),a=0;a<c.numCells;a++)for(var s=0;s<c.numCells;s++)for(var u=0;u<c.numBoxes;u++)r.set(1,a,s,u,0),r.set(1,a,s,u,1),o.set(1,a,s,u,2),o.set(1,a,s,u,3),i.set(1,a,s,u,4);return{coordBoxOffsetMask:t,coordBoxSizeMask:e,scoreMask:n}})},t.prototype.createOneHotClassScoreMask=function(){var t=Ae([this.numCells,this.numCells,this.numBoxes,this.boxEncodingSize]),i=t.buffer();return this.groundTruth.forEach(function(t){var e=t.row,n=t.col,r=t.anchor,o=t.label;i.set(1,e,n,r,5+o)}),t},t.prototype.computeIous=function(){var c=this,t=Ae([this.numCells,this.numCells,this.gridCellEncodingSize]),l=t.buffer();return this.groundTruth.forEach(function(t){var e,n=t.row,r=t.col,o=t.anchor,i=t.box,a=c.predictedBoxes.find((e={row:n,col:r,anchor:o},function(t){return e.row===t.row&&e.col===t.col&&e.anchor===t.anchor}));if(!a)throw new Error("no output box found for: row "+n+", col "+r+", anchor "+o);var s=wh(i.rescale(c.reshapedImgDims),a.box.rescale(c.reshapedImgDims)),u=c.boxEncodingSize*o;l.set(s,n,r,u+4)}),t},t.prototype.computeCoordBoxOffsets=function(){var c=this,t=Ae([this.numCells,this.numCells,this.numBoxes,this.boxEncodingSize]),l=t.buffer();return this.groundTruth.forEach(function(t){var e=t.row,n=t.col,r=t.anchor,o=t.box.rescale(c.reshapedImgDims),i=o.left,a=o.top,s=((i+o.right)/2-32*n)/32,u=((a+o.bottom)/2-32*e)/32;l.set(s,e,n,r,0),l.set(u,e,n,r,1)}),t},t.prototype.computeCoordBoxSizes=function(){var c=this,t=Ae([this.numCells,this.numCells,this.numBoxes,this.boxEncodingSize]),l=t.buffer();return this.groundTruth.forEach(function(t){var e=t.row,n=t.col,r=t.anchor,o=t.box.rescale(c.reshapedImgDims),i=o.width,a=o.height,s=Math.log(i/(32*c.anchors[r].x)),u=Math.log(a/(32*c.anchors[r].y));l.set(s,e,n,r,2),l.set(u,e,n,r,3)}),t},t}();!function(r){function t(t,e){var n=r.call(this,t)||this;return n._trainableConfig=Vh(t),n._optimizer=e,n}ml(t,r),Object.defineProperty(t.prototype,"trainableConfig",{get:function(){return this._trainableConfig},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"optimizer",{get:function(){return this._optimizer},enumerable:!0,configurable:!0}),t.prototype.backward=function(o,i,p,a){return void 0===a&&(a={}),gl(this,void 0,void 0,function(){var n,r,s,u,c,l,h=this;return yl(this,function(t){switch(t.label){case 0:return e=a,n=Object.assign({},{minBoxSize:32},e),r=n.minBoxSize,s=n.reportLosses,u=Tl(oh(o),p),(c=this.filterGroundTruthBoxes(i,u,r)).length?[4,xh(dh(o,p))]:[2,null];case 1:return l=t.sent(),[2,this.optimizer.minimize(function(){var t=h.computeLoss(h.forwardInput(l,p),c,u),e=t.noObjectLoss,n=t.objectLoss,r=t.coordLoss,o=t.classLoss,i=t.totalLoss;if(s){var a={losses:{totalLoss:i.dataSync()[0],noObjectLoss:e.dataSync()[0],objectLoss:n.dataSync()[0],coordLoss:r.dataSync()[0],classLoss:o.dataSync()[0]},numBoxes:c.length,inputSize:p};s(a)}return i},!0)]}var e})})},t.prototype.computeLoss=function(t,e,n){var r=Vh(this.config),o=Math.max(n.width,n.height);if(!o)throw new Error("computeLoss - invalid inputSize: "+o);var i=this.extractBoxes(t,n);return zt(function(){return new Zh(t,e,i,n,r).computeLoss()})},t.prototype.filterGroundTruthBoxes=function(t,e,a){var s=e.height,u=e.width;return t.filter(function(t){var e=t.x,n=t.y,r=t.width,o=t.height,i=new Ul(e,n,r,o).rescale({height:s,width:u});return!(i.width<a||i.height<a)})},t.prototype.load=function(e){return gl(this,void 0,void 0,function(){return yl(this,function(t){switch(t.label){case 0:return[4,r.prototype.load.call(this,e)];case 1:return t.sent(),this.variable(),[2]}})})}}(Jh);function tp(a,s){function i(t,e,n){var r=ke(a(9*t),[3,3,t,1]),o=ke(a(t*e),[1,1,t,e]),i=_e(a(e));return s.push({paramPath:n+"/depthwise_filter"},{paramPath:n+"/pointwise_filter"},{paramPath:n+"/bias"}),new zh(r,o,i)}var o=Lh(a,s);function u(t,e,n,r){return void 0===r&&(r=!1),{conv0:r?o(t,e,3,n+"/conv0"):i(t,e,n+"/conv0"),conv1:i(e,e,n+"/conv1"),conv2:i(e,e,n+"/conv2")}}return{extractDenseBlock3Params:u,extractDenseBlock4Params:function(t,e,n,r){void 0===r&&(r=!1);var o=u(t,e,n,r);return{conv0:o.conv0,conv1:o.conv1,conv2:o.conv2,conv3:i(e,e,n+"/conv3")}}}}function ep(t,e){var o=Gl(t,e);function n(t){return{filters:o(t+"/filters",4),bias:o(t+"/bias",1)}}function r(t){var e=o(t+"/depthwise_filter",4),n=o(t+"/pointwise_filter",4),r=o(t+"/bias",1);return new zh(e,n,r)}return{extractDenseBlock3Params:function(t,e){return void 0===e&&(e=!1),{conv0:e?n(t+"/conv0"):r(t+"/conv0"),conv1:r(t+"/conv1"),conv2:r(t+"/conv2")}},extractDenseBlock4Params:function(t,e){return void 0===e&&(e=!1),{conv0:e?n(t+"/conv0"):r(t+"/conv0"),conv1:r(t+"/conv1"),conv2:r(t+"/conv2"),conv3:r(t+"/conv3")}}}}var np=function(t){function e(){return t.call(this,"FaceFeatureExtractor")||this}return ml(e,t),e.prototype.forwardInput=function(e){var n=this.params;if(!n)throw new Error("FaceFeatureExtractor - load model before inference");return zt(function(){var t=Ph(Eh(e.toBatchTensor(112,!0),[122.782,117.001,104.298]).div(Se(255)),n.dense0,!0);return t=Ph(t=Ph(t=Ph(t,n.dense1),n.dense2),n.dense3),t=Xa(t,[7,7],[2,2],"valid")})},e.prototype.forward=function(n){return gl(this,void 0,void 0,function(){var e;return yl(this,function(t){switch(t.label){case 0:return e=this.forwardInput,[4,xh(n)];case 1:return[2,e.apply(this,[t.sent()])]}})})},e.prototype.getDefaultModelName=function(){return"face_feature_extractor_model"},e.prototype.extractParamsFromWeigthMap=function(t){return r=ep(e=t,n=[]).extractDenseBlock4Params,o={dense0:r("dense0",!0),dense1:r("dense1"),dense2:r("dense2"),dense3:r("dense3")},Vl(e,n),{params:o,paramMappings:n};var e,n,r,o},e.prototype.extractParams=function(t){return function(t){var e=[],n=Hl(t),r=n.extractWeights,o=n.getRemainingWeights,i=tp(r,e).extractDenseBlock4Params,a=i(3,32,"dense0",!0),s=i(32,64,"dense1"),u=i(64,128,"dense2"),c=i(128,256,"dense3");if(0!==o().length)throw new Error("weights remaing after extract: "+o().length);return{paramMappings:e,params:{dense0:a,dense1:s,dense2:u,dense3:c}}}(t)},e}(Sh);function rp(t,e){return zt(function(){return Rs(ka(t,e.weights),e.bias)})}var op=function(r){function t(t,e){var n=r.call(this,t)||this;return n._faceFeatureExtractor=e,n}return ml(t,r),Object.defineProperty(t.prototype,"faceFeatureExtractor",{get:function(){return this._faceFeatureExtractor},enumerable:!0,configurable:!0}),t.prototype.runNet=function(e){var n=this,r=this.params;if(!r)throw new Error(this._name+" - load model before inference");return zt(function(){var t=e instanceof yh?n.faceFeatureExtractor.forwardInput(e):e;return rp(t.as2D(t.shape[0],-1),r.fc)})},t.prototype.dispose=function(t){void 0===t&&(t=!0),this.faceFeatureExtractor.dispose(t),r.prototype.dispose.call(this,t)},t.prototype.loadClassifierParams=function(t){var e=this.extractClassifierParams(t),n=e.params,r=e.paramMappings;this._params=n,this._paramMappings=r},t.prototype.extractClassifierParams=function(t){return function(t,e,n){var r=[],o=Hl(t),i=o.extractWeights,a=o.getRemainingWeights,s=Wh(i,r)(e,n,"fc");if(0!==a().length)throw new Error("weights remaing after extract: "+a().length);return{paramMappings:r,params:{fc:s}}}(t,this.getClassifierChannelsIn(),this.getClassifierChannelsOut())},t.prototype.extractParamsFromWeigthMap=function(t){var e,n,r,o,i,a,s,u,c=(e=t,n={},r={},Object.keys(e).forEach(function(t){(t.startsWith("fc")?r:n)[t]=e[t]}),{featureExtractorMap:n,classifierMap:r}),l=c.featureExtractorMap,h=c.classifierMap;return this.faceFeatureExtractor.loadFromWeightMap(l),s=Gl(o=h,a=[]),u={fc:(i="fc",{weights:s(i+"/weights",2),bias:s(i+"/bias",1)})},Vl(o,a),{params:u,paramMappings:a}},t.prototype.extractParams=function(t){var e=this.getClassifierChannelsIn(),n=this.getClassifierChannelsOut(),r=n*e+n,o=t.slice(0,t.length-r),i=t.slice(t.length-r);return this.faceFeatureExtractor.extractWeights(o),this.extractClassifierParams(i)},t}(Sh),ip={neutral:0,happy:1,sad:2,angry:3,fearful:4,disgusted:5,surprised:6},ap=function(e){function s(t){return void 0===t&&(t=new np),e.call(this,"FaceExpressionNet",t)||this}return ml(s,e),s.getFaceExpressionLabel=function(t){var e=ip[t];if("number"!=typeof e)throw new Error("getFaceExpressionLabel - no label for faceExpression: "+t);return e},s.decodeProbabilites=function(e){if(7!==e.length)throw new Error("decodeProbabilites - expected probabilities.length to be 7, have: "+e.length);return Object.keys(ip).map(function(t){return{expression:t,probability:e[ip[t]]}})},s.prototype.forwardInput=function(t){var e=this;return zt(function(){return ye(e.runNet(t))})},s.prototype.forward=function(n){return gl(this,void 0,void 0,function(){var e;return yl(this,function(t){switch(t.label){case 0:return e=this.forwardInput,[4,xh(n)];case 1:return[2,e.apply(this,[t.sent()])]}})})},s.prototype.predictExpressions=function(a){return gl(this,void 0,void 0,function(){var e,n,r,o,i=this;return yl(this,function(t){switch(t.label){case 0:return[4,xh(a)];case 1:return e=t.sent(),[4,this.forwardInput(e)];case 2:return n=t.sent(),[4,Promise.all(Ii(n).map(function(n){return gl(i,void 0,void 0,function(){var e;return yl(this,function(t){switch(t.label){case 0:return[4,n.data()];case 1:return e=t.sent(),n.dispose(),[2,e]}})})}))];case 3:return r=t.sent(),n.dispose(),o=r.map(function(t){return s.decodeProbabilites(t)}),[2,e.isBatchInput?o:o[0]]}})})},s.prototype.getDefaultModelName=function(){return"face_expression_model"},s.prototype.getClassifierChannelsIn=function(){return 256},s.prototype.getClassifierChannelsOut=function(){return 7},s}(op),sp=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return ml(e,t),e.prototype.postProcess=function(t,o,e){var i=e.map(function(t){var e=t.width,n=t.height,r=o/Math.max(n,e);return{width:e*r,height:n*r}}),a=i.length;return zt(function(){var n=function(t,e){return ki([Me([68],t),Me([68],e)],1).as2D(1,136).as1D()},r=function(t,e){var n=i[t],r=n.width,o=n.height;return e(r,o)?Math.abs(r-o)/2:0};return t.mul(Me([a,136],o)).sub(ki(Array.from(Array(a),function(t,e){return n(r(e,function(t,e){return t<e}),r(e,function(t,e){return e<t}))}))).div(ki(Array.from(Array(a),function(t,e){return n(i[e].width,i[e].height)})))})},e.prototype.forwardInput=function(e){var n=this;return zt(function(){var t=n.runNet(e);return n.postProcess(t,e.inputSize,e.inputDimensions.map(function(t){return{height:t[0],width:t[1]}}))})},e.prototype.forward=function(n){return gl(this,void 0,void 0,function(){var e;return yl(this,function(t){switch(t.label){case 0:return e=this.forwardInput,[4,xh(n)];case 1:return[2,e.apply(this,[t.sent()])]}})})},e.prototype.detectLandmarks=function(o){return gl(this,void 0,void 0,function(){var u,e,n,r=this;return yl(this,function(t){switch(t.label){case 0:return[4,xh(o)];case 1:return u=t.sent(),e=zt(function(){return Ii(r.forwardInput(u))}),[4,Promise.all(e.map(function(a,s){return gl(r,void 0,void 0,function(){var e,n,r,o,i;return yl(this,function(t){switch(t.label){case 0:return r=(n=Array).from,[4,a.data()];case 1:return e=r.apply(n,[t.sent()]),o=e.filter(function(t,e){return Rl(e)}),i=e.filter(function(t,e){return!Rl(e)}),[2,new kh(Array(68).fill(0).map(function(t,e){return new wl(o[e],i[e])}),{height:u.getInputHeight(s),width:u.getInputWidth(s)})]}})})}))];case 2:return n=t.sent(),e.forEach(function(t){return t.dispose()}),[2,u.isBatchInput?n:n[0]]}})})},e.prototype.getClassifierChannelsOut=function(){return 136},e}(op),up=function(e){function t(t){return void 0===t&&(t=new np),e.call(this,"FaceLandmark68Net",t)||this}return ml(t,e),t.prototype.getDefaultModelName=function(){return"face_landmark_68_model"},t.prototype.getClassifierChannelsIn=function(){return 256},t}(sp);var cp=function(t){function e(){return t.call(this,"TinyFaceFeatureExtractor")||this}return ml(e,t),e.prototype.forwardInput=function(e){var n=this.params;if(!n)throw new Error("TinyFaceFeatureExtractor - load model before inference");return zt(function(){var t=Fh(Eh(e.toBatchTensor(112,!0),[122.782,117.001,104.298]).div(Se(255)),n.dense0,!0);return t=Fh(t=Fh(t,n.dense1),n.dense2),t=Xa(t,[14,14],[2,2],"valid")})},e.prototype.forward=function(n){return gl(this,void 0,void 0,function(){var e;return yl(this,function(t){switch(t.label){case 0:return e=this.forwardInput,[4,xh(n)];case 1:return[2,e.apply(this,[t.sent()])]}})})},e.prototype.getDefaultModelName=function(){return"face_feature_extractor_tiny_model"},e.prototype.extractParamsFromWeigthMap=function(t){return r=ep(e=t,n=[]).extractDenseBlock3Params,o={dense0:r("dense0",!0),dense1:r("dense1"),dense2:r("dense2")},Vl(e,n),{params:o,paramMappings:n};var e,n,r,o},e.prototype.extractParams=function(t){return function(t){var e=[],n=Hl(t),r=n.extractWeights,o=n.getRemainingWeights,i=tp(r,e).extractDenseBlock3Params,a=i(3,32,"dense0",!0),s=i(32,64,"dense1"),u=i(64,128,"dense2");if(0!==o().length)throw new Error("weights remaing after extract: "+o().length);return{paramMappings:e,params:{dense0:a,dense1:s,dense2:u}}}(t)},e}(Sh),lp=function(e){function t(t){return void 0===t&&(t=new cp),e.call(this,"FaceLandmark68TinyNet",t)||this}return ml(t,e),t.prototype.getDefaultModelName=function(){return"face_landmark_68_tiny_model"},t.prototype.getClassifierChannelsIn=function(){return 128},t}(sp),hp=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return ml(e,t),e}(up);function pp(t,e,n,r,o){void 0===o&&(o="same");var i,a,s=e.conv,u=s.filters,c=s.bias,l=Fa(t,u,n,o);return l=Rs(l,c),i=l,a=e.scale,l=Rs(Ws(i,a.weights),a.biases),r?nu(l):l}function fp(t,e){return pp(t,e,[1,1],!1)}function dp(t,e){return pp(t,e,[2,2],!0,"valid")}function mp(u,c){function l(t,e,n,r){var o=function(t,e,n){var r=u(t),o=r.length/(e*n*n);if(_l(o))throw new Error("depth has to be an integer: "+o+", weights.length: "+r.length+", numFilters: "+e+", filterSize: "+n);return zt(function(){return ou(ke(r,[e,o,n,n]),[2,3,1,0])})}(t,e,n),i=_e(u(e));return c.push({paramPath:r+"/filters"},{paramPath:r+"/bias"}),{filters:o,bias:i}}function i(t,e,n,r){var o,i,a,s;return{conv:l(t,e,n,r+"/conv"),scale:(i=r+"/scale",a=_e(u(o=e)),s=_e(u(o)),c.push({paramPath:i+"/weights"},{paramPath:i+"/biases"}),{weights:a,biases:s})}}return{extractConvLayerParams:i,extractResidualLayerParams:function(t,e,n,r,o){return void 0===o&&(o=!1),{conv1:i((o?.5:1)*t,e,n,r+"/conv1"),conv2:i(t,e,n,r+"/conv2")}}}}function vp(t,e){var n=Gl(t,e);function r(t){var e;return{conv:{filters:n(t+"/conv/filters",4),bias:n(t+"/conv/bias",1)},scale:{weights:n((e=t)+"/scale/weights",1),biases:n(e+"/scale/biases",1)}}}return{extractConvLayerParams:r,extractResidualLayerParams:function(t){return{conv1:r(t+"/conv1"),conv2:r(t+"/conv2")}}}}function gp(t,e){var n,r,o=(n=t,r=e.conv1,pp(n,r,[1,1],!0));return o=fp(o,e.conv2),o=Rs(o,t),o=nu(o)}function yp(t,e){var n=dp(t,e.conv1);n=fp(n,e.conv2);var r=Xa(t,2,2,"valid"),o=Ae(r.shape),i=r.shape[3]!==n.shape[3];if(r.shape[1]!==n.shape[1]||r.shape[2]!==n.shape[2]){var a=n.shape.slice();a[1]=1;var s=Ae(a),u=(n=Vo([n,s],1)).shape.slice();u[2]=1;var c=Ae(u);n=Vo([n,c],2)}return r=i?Vo([r,o],3):r,n=Rs(r,n),n=nu(n)}var xp=function(t){function e(){return t.call(this,"FaceRecognitionNet")||this}return ml(e,t),e.prototype.forwardInput=function(n){var r=this.params;if(!r)throw new Error("FaceRecognitionNet - load model before inference");return zt(function(){var t=dp(Eh(n.toBatchTensor(150,!0).toFloat(),[122.782,117.001,104.298]).div(Se(256)),r.conv32_down),e=(t=yp(t=gp(t=gp(t=yp(t=gp(t=gp(t=yp(t=gp(t=gp(t=gp(t=yp(t=gp(t=gp(t=gp(t=Ka(t,3,2,"valid"),r.conv32_1),r.conv32_2),r.conv32_3),r.conv64_down),r.conv64_1),r.conv64_2),r.conv64_3),r.conv128_down),r.conv128_1),r.conv128_2),r.conv256_down),r.conv256_1),r.conv256_2),r.conv256_down_out)).mean([1,2]);return ka(e,r.fc)})},e.prototype.forward=function(n){return gl(this,void 0,void 0,function(){var e;return yl(this,function(t){switch(t.label){case 0:return e=this.forwardInput,[4,xh(n)];case 1:return[2,e.apply(this,[t.sent()])]}})})},e.prototype.computeFaceDescriptor=function(i){return gl(this,void 0,void 0,function(){var e,n,r,o=this;return yl(this,function(t){switch(t.label){case 0:return[4,xh(i)];case 1:return e=t.sent(),n=zt(function(){return Ii(o.forwardInput(e))}),[4,Promise.all(n.map(function(t){return t.data()}))];case 2:return r=t.sent(),n.forEach(function(t){return t.dispose()}),[2,e.isBatchInput?r:r[0]]}})})},e.prototype.getDefaultModelName=function(){return"face_recognition_model"},e.prototype.extractParamsFromWeigthMap=function(t){return function(t){var e=[],n=vp(t,e),r=n.extractConvLayerParams,o=n.extractResidualLayerParams,i=r("conv32_down"),a=o("conv32_1"),s=o("conv32_2"),u=o("conv32_3"),c=o("conv64_down"),l=o("conv64_1"),h=o("conv64_2"),p=o("conv64_3"),f=o("conv128_down"),d=o("conv128_1"),m=o("conv128_2"),v=o("conv256_down"),g=o("conv256_1"),y=o("conv256_2"),x=o("conv256_down_out"),w=t.fc;if(e.push({originalPath:"fc",paramPath:"fc"}),!El(w))throw new Error("expected weightMap[fc] to be a Tensor2D, instead have "+w);var b={conv32_down:i,conv32_1:a,conv32_2:s,conv32_3:u,conv64_down:c,conv64_1:l,conv64_2:h,conv64_3:p,conv128_down:f,conv128_1:d,conv128_2:m,conv256_down:v,conv256_1:g,conv256_2:y,conv256_down_out:x,fc:w};return Vl(t,e),{params:b,paramMappings:e}}(t)},e.prototype.extractParams=function(t){return function(t){var e=Hl(t),n=e.extractWeights,r=e.getRemainingWeights,o=[],i=mp(n,o),a=i.extractConvLayerParams,s=i.extractResidualLayerParams,u=a(4704,32,7,"conv32_down"),c=s(9216,32,3,"conv32_1"),l=s(9216,32,3,"conv32_2"),h=s(9216,32,3,"conv32_3"),p=s(36864,64,3,"conv64_down",!0),f=s(36864,64,3,"conv64_1"),d=s(36864,64,3,"conv64_2"),m=s(36864,64,3,"conv64_3"),v=s(147456,128,3,"conv128_down",!0),g=s(147456,128,3,"conv128_1"),y=s(147456,128,3,"conv128_2"),x=s(589824,256,3,"conv256_down",!0),w=s(589824,256,3,"conv256_1"),b=s(589824,256,3,"conv256_2"),E=s(589824,256,3,"conv256_down_out"),C=zt(function(){return ou(Re(n(32768),[128,256]),[1,0])});if(o.push({paramPath:"fc"}),0!==r().length)throw new Error("weights remaing after extract: "+r().length);return{params:{conv32_down:u,conv32_1:c,conv32_2:l,conv32_3:h,conv64_down:p,conv64_1:f,conv64_2:d,conv64_3:m,conv128_down:v,conv128_1:g,conv128_2:y,conv256_down:x,conv256_1:w,conv256_2:b,conv256_down_out:E,fc:C},paramMappings:o}}(t)},e}(Sh);function wp(t,e){var n={descriptor:e};return Object.assign({},t,n)}function bp(t,e){var n={detection:e};return Object.assign({},t,n)}function Ep(t,e){var n={expressions:e};return Object.assign({},t,n)}function Cp(t,e){var n=t.detection.box,r=e.shiftBy(n.x,n.y),o=r.align(),i=t.detection.imageDims,a={landmarks:r,unshiftedLandmarks:e,alignedRect:new _h(t.detection.score,o.rescale(i.reverse()),i)};return Object.assign({},t,a)}var Sp=function(){function t(t){var e=void 0===t?{}:t,n=e.minFaceSize,r=e.scaleFactor,o=e.maxNumScales,i=e.scoreThresholds,a=e.scaleSteps;if(this._name="MtcnnOptions",this._minFaceSize=n||20,this._scaleFactor=r||.709,this._maxNumScales=o||10,this._scoreThresholds=i||[.6,.7,.7],this._scaleSteps=a,"number"!=typeof this._minFaceSize||this._minFaceSize<0)throw new Error(this._name+" - expected minFaceSize to be a number > 0");if("number"!=typeof this._scaleFactor||this._scaleFactor<=0||1<=this._scaleFactor)throw new Error(this._name+" - expected scaleFactor to be a number between 0 and 1");if("number"!=typeof this._maxNumScales||this._maxNumScales<0)throw new Error(this._name+" - expected maxNumScales to be a number > 0");if(!Array.isArray(this._scoreThresholds)||3!==this._scoreThresholds.length||this._scoreThresholds.some(function(t){return"number"!=typeof t}))throw new Error(this._name+" - expected scoreThresholds to be an array of numbers of length 3");if(this._scaleSteps&&(!Array.isArray(this._scaleSteps)||this._scaleSteps.some(function(t){return"number"!=typeof t})))throw new Error(this._name+" - expected scaleSteps to be an array of numbers")}return Object.defineProperty(t.prototype,"minFaceSize",{get:function(){return this._minFaceSize},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"scaleFactor",{get:function(){return this._scaleFactor},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"maxNumScales",{get:function(){return this._maxNumScales},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"scoreThresholds",{get:function(){return this._scoreThresholds},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"scaleSteps",{get:function(){return this._scaleSteps},enumerable:!0,configurable:!0}),t}();function _p(l,h){function i(t,e,n,r,o){var i=ke(l(t*e*n*n),[n,n,t,e]),a=_e(l(e));return h.push({paramPath:r+"/filters"},{paramPath:r+"/"+(o?"batch_norm_offset":"bias")}),{filters:i,bias:a}}function p(t,e,n,r){var o=i(t,e,n,r,!0);return{filters:o.filters,batch_norm_offset:o.bias}}function t(t,e,n){var r,o,i,a,s,u,c;return{depthwise_conv:(o=n+"/depthwise_conv",i=ke(l(9*(r=t)),[3,3,r,1]),a=_e(l(r)),s=_e(l(r)),u=_e(l(r)),c=_e(l(r)),h.push({paramPath:o+"/filters"},{paramPath:o+"/batch_norm_scale"},{paramPath:o+"/batch_norm_offset"},{paramPath:o+"/batch_norm_mean"},{paramPath:o+"/batch_norm_variance"}),{filters:i,batch_norm_scale:a,batch_norm_offset:s,batch_norm_mean:u,batch_norm_variance:c}),pointwise_conv:p(t,e,1,n+"/pointwise_conv")}}return{extractMobilenetV1Params:function(){return{conv_0:p(3,32,3,"mobilenetv1/conv_0"),conv_1:t(32,64,"mobilenetv1/conv_1"),conv_2:t(64,128,"mobilenetv1/conv_2"),conv_3:t(128,128,"mobilenetv1/conv_3"),conv_4:t(128,256,"mobilenetv1/conv_4"),conv_5:t(256,256,"mobilenetv1/conv_5"),conv_6:t(256,512,"mobilenetv1/conv_6"),conv_7:t(512,512,"mobilenetv1/conv_7"),conv_8:t(512,512,"mobilenetv1/conv_8"),conv_9:t(512,512,"mobilenetv1/conv_9"),conv_10:t(512,512,"mobilenetv1/conv_10"),conv_11:t(512,512,"mobilenetv1/conv_11"),conv_12:t(512,1024,"mobilenetv1/conv_12"),conv_13:t(1024,1024,"mobilenetv1/conv_13")}},extractPredictionLayerParams:function(){return{conv_0:p(1024,256,1,"prediction_layer/conv_0"),conv_1:p(256,512,3,"prediction_layer/conv_1"),conv_2:p(512,128,1,"prediction_layer/conv_2"),conv_3:p(128,256,3,"prediction_layer/conv_3"),conv_4:p(256,128,1,"prediction_layer/conv_4"),conv_5:p(128,256,3,"prediction_layer/conv_5"),conv_6:p(256,64,1,"prediction_layer/conv_6"),conv_7:p(64,128,3,"prediction_layer/conv_7"),box_predictor_0:{box_encoding_predictor:i(512,12,1,"prediction_layer/box_predictor_0/box_encoding_predictor"),class_predictor:i(512,9,1,"prediction_layer/box_predictor_0/class_predictor")},box_predictor_1:{box_encoding_predictor:i(1024,24,1,"prediction_layer/box_predictor_1/box_encoding_predictor"),class_predictor:i(1024,18,1,"prediction_layer/box_predictor_1/class_predictor")},box_predictor_2:{box_encoding_predictor:i(512,24,1,"prediction_layer/box_predictor_2/box_encoding_predictor"),class_predictor:i(512,18,1,"prediction_layer/box_predictor_2/class_predictor")},box_predictor_3:{box_encoding_predictor:i(256,24,1,"prediction_layer/box_predictor_3/box_encoding_predictor"),class_predictor:i(256,18,1,"prediction_layer/box_predictor_3/class_predictor")},box_predictor_4:{box_encoding_predictor:i(256,24,1,"prediction_layer/box_predictor_4/box_encoding_predictor"),class_predictor:i(256,18,1,"prediction_layer/box_predictor_4/class_predictor")},box_predictor_5:{box_encoding_predictor:i(128,24,1,"prediction_layer/box_predictor_5/box_encoding_predictor"),class_predictor:i(128,18,1,"prediction_layer/box_predictor_5/class_predictor")}}}}}function Rp(t){var e=[],n=function(t,e){var i=Gl(t,e);function a(t,e,n){return{filters:i(t+"/Conv2d_"+e+"_pointwise/weights",4,n+"/filters"),batch_norm_offset:i(t+"/Conv2d_"+e+"_pointwise/convolution_bn_offset",1,n+"/batch_norm_offset")}}function n(t){var e="mobilenetv1/conv_"+t,n="MobilenetV1/Conv2d_"+t+"_depthwise",r=e+"/depthwise_conv",o=e+"/pointwise_conv";return{depthwise_conv:{filters:i(n+"/depthwise_weights",4,r+"/filters"),batch_norm_scale:i(n+"/BatchNorm/gamma",1,r+"/batch_norm_scale"),batch_norm_offset:i(n+"/BatchNorm/beta",1,r+"/batch_norm_offset"),batch_norm_mean:i(n+"/BatchNorm/moving_mean",1,r+"/batch_norm_mean"),batch_norm_variance:i(n+"/BatchNorm/moving_variance",1,r+"/batch_norm_variance")},pointwise_conv:a("MobilenetV1",t,o)}}function r(t,e){return{filters:i(t+"/weights",4,e+"/filters"),bias:i(t+"/biases",1,e+"/bias")}}function o(t){return{box_encoding_predictor:r("Prediction/BoxPredictor_"+t+"/BoxEncodingPredictor","prediction_layer/box_predictor_"+t+"/box_encoding_predictor"),class_predictor:r("Prediction/BoxPredictor_"+t+"/ClassPredictor","prediction_layer/box_predictor_"+t+"/class_predictor")}}return{extractMobilenetV1Params:function(){return{conv_0:a("MobilenetV1",0,"mobilenetv1/conv_0"),conv_1:n(1),conv_2:n(2),conv_3:n(3),conv_4:n(4),conv_5:n(5),conv_6:n(6),conv_7:n(7),conv_8:n(8),conv_9:n(9),conv_10:n(10),conv_11:n(11),conv_12:n(12),conv_13:n(13)}},extractPredictionLayerParams:function(){return{conv_0:a("Prediction",0,"prediction_layer/conv_0"),conv_1:a("Prediction",1,"prediction_layer/conv_1"),conv_2:a("Prediction",2,"prediction_layer/conv_2"),conv_3:a("Prediction",3,"prediction_layer/conv_3"),conv_4:a("Prediction",4,"prediction_layer/conv_4"),conv_5:a("Prediction",5,"prediction_layer/conv_5"),conv_6:a("Prediction",6,"prediction_layer/conv_6"),conv_7:a("Prediction",7,"prediction_layer/conv_7"),box_predictor_0:o(0),box_predictor_1:o(1),box_predictor_2:o(2),box_predictor_3:o(3),box_predictor_4:o(4),box_predictor_5:o(5)}}}}(t,e),r=n.extractMobilenetV1Params,o=n.extractPredictionLayerParams,i=t["Output/extra_dim"];if(e.push({originalPath:"Output/extra_dim",paramPath:"output_layer/extra_dim"}),!Cl(i))throw new Error("expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have "+i);var a={mobilenetv1:r(),prediction_layer:o(),output_layer:{extra_dim:i}};return Vl(t,e),{params:a,paramMappings:e}}function Np(e,n,r){return zt(function(){var t=Fa(e,n.filters,r,"same");return t=Rs(t,n.batch_norm_offset),Hi(t,0,6)})}var kp=.0010000000474974513;function Tp(t,e){return zt(function(){var u=null,c=Np(t,e.conv_0,[2,2]);if([e.conv_1,e.conv_2,e.conv_3,e.conv_4,e.conv_5,e.conv_6,e.conv_7,e.conv_8,e.conv_9,e.conv_10,e.conv_11,e.conv_12,e.conv_13].forEach(function(t,e){var n,r,o,i,a=e+1,s=(n=a,[2,4,6,12].some(function(t){return t===n})?[2,2]:[1,1]);r=c,o=t.depthwise_conv,i=s,c=Np(c=zt(function(){var t=La(r,o.filters,i,"same");return t=ya(t,o.batch_norm_mean,o.batch_norm_variance,kp,o.batch_norm_scale,o.batch_norm_offset),Hi(t,0,6)}),t.pointwise_conv,[1,1]),11===a&&(u=c)}),null===u)throw new Error("mobileNetV1 - output of conv layer 11 is null");return{out:c,conv11:u}})}function Dp(t,e,n){var r=Math.min(t.get(e,0),t.get(e,2)),o=Math.min(t.get(e,1),t.get(e,3)),i=Math.max(t.get(e,0),t.get(e,2)),a=Math.max(t.get(e,1),t.get(e,3)),s=Math.min(t.get(n,0),t.get(n,2)),u=Math.min(t.get(n,1),t.get(n,3)),c=Math.max(t.get(n,0),t.get(n,2)),l=Math.max(t.get(n,1),t.get(n,3)),h=(i-r)*(a-o),p=(c-s)*(l-u);if(h<=0||p<=0)return 0;var f=Math.max(r,s),d=Math.max(o,u),m=Math.min(i,c),v=Math.min(a,l),g=Math.max(m-f,0)*Math.max(v-d,0);return g/(h+p-g)}function Ip(t,e){var n,r,o=(n=Ii(ou(t,[1,0])),{sizes:r=[qs(n[2],n[0]),qs(n[3],n[1])],centers:[Rs(n[0],Ds(r[0],Se(2))),Rs(n[1],Ds(r[1],Se(2)))]}),i=o.sizes,a=o.centers,s=Ii(ou(e,[1,0])),u=Ds(Ws(Ki(Ds(s[2],Se(5))),i[0]),Se(2)),c=Rs(Ws(Ds(s[0],Se(10)),i[0]),a[0]),l=Ds(Ws(Ki(Ds(s[3],Se(5))),i[1]),Se(2)),h=Rs(Ws(Ds(s[1],Se(10)),i[1]),a[1]);return ou(ki([qs(c,u),qs(h,l),Rs(c,u),Rs(h,l)]),[1,0])}function Ap(e,n){return zt(function(){var t=e.shape[0];return{boxPredictionEncoding:_i(Bh(e,n.box_encoding_predictor),[t,-1,1,4]),classPrediction:_i(Bh(e,n.class_predictor),[t,-1,3])}})}var Mp=function(){function t(t){var e=void 0===t?{}:t,n=e.minConfidence,r=e.maxResults;if(this._name="SsdMobilenetv1Options",this._minConfidence=n||.5,this._maxResults=r||100,"number"!=typeof this._minConfidence||this._minConfidence<=0||1<=this._minConfidence)throw new Error(this._name+" - expected minConfidence to be a number between 0 and 1");if("number"!=typeof this._maxResults)throw new Error(this._name+" - expected maxResults to be a number")}return Object.defineProperty(t.prototype,"minConfidence",{get:function(){return this._minConfidence},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"maxResults",{get:function(){return this._maxResults},enumerable:!0,configurable:!0}),t}(),Op=function(t){function e(){return t.call(this,"SsdMobilenetv1")||this}return ml(e,t),e.prototype.forwardInput=function(u){var c=this.params;if(!c)throw new Error("SsdMobilenetv1 - load model before inference");return zt(function(){var l,h,p,o,i,a,t=u.toBatchTensor(512,!1).toFloat(),e=Tp(qs(Ws(t,Se(.007843137718737125)),Se(1)),c.mobilenetv1),n=(l=e.out,h=e.conv11,p=c.prediction_layer,zt(function(){var t=Np(Np(l,p.conv_0,[1,1]),p.conv_1,[2,2]),e=Np(Np(t,p.conv_2,[1,1]),p.conv_3,[2,2]),n=Np(Np(e,p.conv_4,[1,1]),p.conv_5,[2,2]),r=Np(Np(n,p.conv_6,[1,1]),p.conv_7,[2,2]),o=Ap(h,p.box_predictor_0),i=Ap(l,p.box_predictor_1),a=Ap(t,p.box_predictor_2),s=Ap(e,p.box_predictor_3),u=Ap(n,p.box_predictor_4),c=Ap(r,p.box_predictor_5);return{boxPredictions:Vo([o.boxPredictionEncoding,i.boxPredictionEncoding,a.boxPredictionEncoding,s.boxPredictionEncoding,u.boxPredictionEncoding,c.boxPredictionEncoding],1),classPredictions:Vo([o.classPrediction,i.classPrediction,a.classPrediction,s.classPrediction,u.classPrediction,c.classPrediction],1)}})),r=n.boxPredictions,s=n.classPredictions;return o=r,i=s,a=c.output_layer,zt(function(){var t=o.shape[0],e=Ip(_i(Ti(a.extra_dim,[t,1,1]),[-1,4]),_i(o,[-1,4]));e=_i(e,[t,e.shape[0]/t,4]);var n=oa(Qa(i,[0,0,1],[-1,-1,-1])),r=Qa(n,[0,0,0],[-1,-1,1]);return r=_i(r,[t,r.shape[1]]),{boxes:Ii(e),scores:Ii(r)}})})},e.prototype.forward=function(n){return gl(this,void 0,void 0,function(){var e;return yl(this,function(t){switch(t.label){case 0:return e=this.forwardInput,[4,xh(n)];case 1:return[2,e.apply(this,[t.sent()])]}})})},e.prototype.locateFaces=function(T,D){return void 0===D&&(D={}),gl(this,void 0,void 0,function(){var l,h,p,f,d,m,v,g,y,x,w,b,E,C,S,_,R,N,k;return yl(this,function(t){switch(t.label){case 0:return l=new Mp(D),h=l.maxResults,p=l.minConfidence,[4,xh(T)];case 1:for(f=t.sent(),d=this.forwardInput(f),m=d.boxes,v=d.scores,g=m[0],y=v[0],x=1;x<m.length;x++)m[x].dispose(),v[x].dispose();return E=(b=Array).from,[4,y.data()];case 2:return w=E.apply(b,[t.sent()]),e=w,n=h,i=.5,a=p,r=(o=g).shape[0],s=Math.min(n,r),u=e.map(function(t,e){return{score:t,boxIndex:e}}).filter(function(t){return t.score>a}).sort(function(t,e){return e.score-t.score}),c=[],u.forEach(function(t){if(!(c.length>=s)){for(var e=t.score,n=c.length-1;0<=n;--n){var r=Dp(o,t.boxIndex,c[n]);if(0!==r&&(t.score*=r<=i?1:0,t.score<=a))break}e===t.score&&c.push(t.boxIndex)}}),C=c,S=f.getReshapedInputDimensions(0),_=f.inputSize,R=_/S.width,N=_/S.height,k=C.map(function(t){var e=[Math.max(0,g.get(t,0)),Math.min(1,g.get(t,2))].map(function(t){return t*N}),n=e[0],r=e[1],o=[Math.max(0,g.get(t,1)),Math.min(1,g.get(t,3))].map(function(t){return t*R}),i=o[0],a=o[1];return new _h(w[t],new Ul(i,n,a-i,r-n),{height:f.getInputHeight(0),width:f.getInputWidth(0)})}),g.dispose(),y.dispose(),[2,k]}var o,e,n,i,a,r,s,u,c})})},e.prototype.getDefaultModelName=function(){return"ssd_mobilenetv1_model"},e.prototype.extractParamsFromWeigthMap=function(t){return Rp(t)},e.prototype.extractParams=function(t){return function(t){var e=[],n=Hl(t),r=n.extractWeights,o=n.getRemainingWeights,i=_p(r,e),a=i.extractMobilenetV1Params,s=i.extractPredictionLayerParams,u=a(),c=s(),l={extra_dim:Ne(r(20472),[1,5118,4])};if(e.push({paramPath:"output_layer/extra_dim"}),0!==o().length)throw new Error("weights remaing after extract: "+o().length);return{params:{mobilenetv1:u,prediction_layer:c,output_layer:l},paramMappings:e}}(t)},e}(Sh);function Fp(t){var e=new Op;return e.extractWeights(t),e}var Pp=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return ml(e,t),e}(Op),Bp=function(e){function t(){var t=null!==e&&e.apply(this,arguments)||this;return t._name="TinyFaceDetectorOptions",t}return ml(t,e),t}(Qh),Lp=function(){function t(){}return t.prototype.then=function(n){return gl(this,void 0,void 0,function(){var e;return yl(this,function(t){switch(t.label){case 0:return e=n,[4,this.run()];case 1:return[2,e.apply(void 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vl({},t,{fc1:e,prelu4_alpha:n,fc2_1:r,fc2_2:o})},extractONetParams:function(){var t=h([3,32,64,64],"onet"),e=u(64,128,2,"onet/conv4"),n=l(128,"onet/prelu4_alpha"),r=c(1152,256,"onet/fc1"),o=l(256,"onet/prelu5_alpha"),i=c(256,2,"onet/fc2_1"),a=c(256,4,"onet/fc2_2"),s=c(256,10,"onet/fc2_3");return vl({},t,{conv4:e,prelu4_alpha:n,fc1:r,prelu5_alpha:o,fc2_1:i,fc2_2:a,fc2_3:s})}}}(n,o),a=i.extractPNetParams,s=i.extractRNetParams,u=i.extractONetParams,c=a(),l=s(),h=u();if(0!==r().length)throw new Error("weights remaing after extract: "+r().length);return{params:{pnet:c,rnet:l,onet:h},paramMappings:o}}function Vp(t){var e=[],n=function(t,e){var n=Gl(t,e);function u(t){return{filters:n(t+"/weights",4,t+"/filters"),bias:n(t+"/bias",1)}}function c(t){return{weights:n(t+"/weights",2),bias:n(t+"/bias",1)}}function l(t){return n(t,1)}function h(t){return{conv1:u(t+"/conv1"),prelu1_alpha:l(t+"/prelu1_alpha"),conv2:u(t+"/conv2"),prelu2_alpha:l(t+"/prelu2_alpha"),conv3:u(t+"/conv3"),prelu3_alpha:l(t+"/prelu3_alpha")}}return{extractPNetParams:function(){var t=h("pnet"),e=u("pnet/conv4_1"),n=u("pnet/conv4_2");return vl({},t,{conv4_1:e,conv4_2:n})},extractRNetParams:function(){var t=h("rnet"),e=c("rnet/fc1"),n=l("rnet/prelu4_alpha"),r=c("rnet/fc2_1"),o=c("rnet/fc2_2");return vl({},t,{fc1:e,prelu4_alpha:n,fc2_1:r,fc2_2:o})},extractONetParams:function(){var t=h("onet"),e=u("onet/conv4"),n=l("onet/prelu4_alpha"),r=c("onet/fc1"),o=l("onet/prelu5_alpha"),i=c("onet/fc2_1"),a=c("onet/fc2_2"),s=c("onet/fc2_3");return vl({},t,{conv4:e,prelu4_alpha:n,fc1:r,prelu5_alpha:o,fc2_1:i,fc2_2:a,fc2_3:s})}}}(t,e),r=n.extractPNetParams,o=n.extractRNetParams,i=n.extractONetParams,a=r(),s=o(),u=i();return Vl(t,e),{params:{pnet:a,rnet:s,onet:u},paramMappings:e}}function Gp(t,e){var n=e[0],r=e[1];return{height:Math.floor(n*t),width:Math.floor(r*t)}}var Hp=function(o){function t(t,e,n,r){return o.call(this,{left:t,top:e,right:n,bottom:r},!0)||this}return ml(t,o),t}(Fl);function qp(t){return zt(function(){return Ws(qs(t,Se(127.5)),Se(.0078125))})}function jp(t,e){return zt(function(){return Rs(nu(t),Ws(e,ta(nu(ta(t)))))})}function $p(e,n,r){return void 0===r&&(r=!1),zt(function(){var t=Bh(e,n.conv1,"valid");return t=jp(t,n.prelu1_alpha),t=jp(t=Bh(t=Ka(t,r?[2,2]:[3,3],[2,2],"same"),n.conv2,"valid"),n.prelu2_alpha),t=jp(t=Bh(t=r?t:Ka(t,[3,3],[2,2],"valid"),n.conv3,"valid"),n.prelu3_alpha)})}function Kp(h,t,u,p,c){c.stage1=[];var e=t.map(function(l){return zt(function(){var o,i,r,a,t={scale:l},e=(o=h,i=l,zt(function(){var t=Gp(i,o.shape.slice(1)),e=t.height,n=t.width,r=qp(ju.resizeBilinear(o,[e,n]));return ou(r,[0,2,1,3])})),n=Date.now(),s=(r=e,a=p,zt(function(){var t=$p(r,a,!0),e=Bh(t,a.conv4_1,"valid"),n=pi(us(e,3),3);return{prob:ye(qs(e,n),3),regions:Bh(t,a.conv4_2,"valid")}})),u=s.prob,c=s.regions;return t.pnet=Date.now()-n,{scoresTensor:Ii(Ii(u,3)[1])[0],regionsTensor:Ii(c)[0],scale:l,statsForScale:t}})}).map(function(t){var e=t.scoresTensor,n=t.regionsTensor,r=t.scale,o=t.statsForScale,i=function(e,n,r,t){for(var o=[],i=0;i<e.shape[0];i++)for(var a=0;a<e.shape[1];a++)e.get(i,a)>=t&&o.push(new wl(a,i));return o.map(function(t){return{cell:new Pl(Math.round((t.y*Wp+1)/r),Math.round((t.x*Wp+1)/r),Math.round((t.y*Wp+zp)/r),Math.round((t.x*Wp+zp)/r)),score:e.get(t.y,t.x),region:new Hp(n.get(t.y,t.x,0),n.get(t.y,t.x,1),n.get(t.y,t.x,2),n.get(t.y,t.x,3))}})}(e,n,r,u);if(e.dispose(),n.dispose(),!i.length)return c.stage1.push(o),[];var a=Date.now(),s=bh(i.map(function(t){return t.cell}),i.map(function(t){return t.score}),.5);return o.nms=Date.now()-a,o.numBoxes=s.length,c.stage1.push(o),s.map(function(t){return i[t]})}).reduce(function(t,e){return t.concat(e)},[]),n=[],r=[];if(0<e.length){var o=Date.now(),i=bh(e.map(function(t){return t.cell}),e.map(function(t){return t.score}),.7);c.stage1_nms=Date.now()-o,r=i.map(function(t){return e[t].score}),n=i.map(function(t){return e[t]}).map(function(t){var e=t.cell,n=t.region;return new Pl(e.left+n.left*e.width,e.top+n.top*e.height,e.right+n.right*e.width,e.bottom+n.bottom*e.height).toSquare().round()})}return{boxes:n,scores:r}}function Xp(h,r,t){var a=t.width,s=t.height;return gl(this,void 0,void 0,function(){var l,e,i,n=this;return yl(this,function(t){switch(t.label){case 0:return l=rh(h),[4,Promise.all(r.map(function(c){return gl(n,void 0,void 0,function(){var e,n,r,o,i,a,s,u;return yl(this,function(t){return e=c.padAtBorders(h.height,h.width),n=e.y,r=e.ey,o=e.x,i=e.ex,a=o-1,s=n-1,u=l.getImageData(a,s,i-a,r-s),[2,Zl.isNodejs()?ah(u):createImageBitmap(u)]})})}))];case 1:return e=t.sent(),i=[],e.forEach(function(t){var e=rh(ih({width:a,height:s}));e.drawImage(t,0,0,a,s);for(var n=e.getImageData(0,0,a,s).data,r=[],o=0;o<n.length;o+=4)r.push(n[o+2]),r.push(n[o+1]),r.push(n[o]);i.push(r)}),[2,i.map(function(t){return zt(function(){return qp(ou(ke(t,[1,a,s,3]),[0,2,1,3]).toFloat())})})]}})})}function Yp(m,v,g,y,x){return gl(this,void 0,void 0,function(){var e,n,r,o,i,a,s,u,c,l,h,p,f,d;return yl(this,function(t){switch(t.label){case 0:return e=Date.now(),[4,Xp(m,v,{width:24,height:24})];case 1:return n=t.sent(),x.stage2_extractImagePatches=Date.now()-e,e=Date.now(),r=n.map(function(t){var a,s,e=(a=t,s=y,zt(function(){var t=$p(a,s),e=jp(rp(_i(t,[t.shape[0],s.fc1.weights.shape[0]]),s.fc1),s.prelu4_alpha),n=rp(e,s.fc2_1),r=pi(us(n,1),1),o=ye(qs(n,r),1),i=rp(e,s.fc2_2);return{scores:Ii(o,1)[1],regions:i}}));return t.dispose(),e}),x.stage2_rnet=Date.now()-e,o=1<r.length?Vo(r.map(function(t){return t.scores})):r[0].scores,s=(a=Array).from,[4,o.data()];case 2:return i=s.apply(a,[t.sent()]),o.dispose(),u=i.map(function(t,e){return{score:t,idx:e}}).filter(function(t){return t.score>g}).map(function(t){return t.idx}),c=u.map(function(t){return v[t]}),l=u.map(function(t){return i[t]}),h=[],p=[],0<c.length&&(e=Date.now(),f=bh(c,l,.7),x.stage2_nms=Date.now()-e,d=f.map(function(t){return new Hp(r[u[t]].regions.get(0,0),r[u[t]].regions.get(0,1),r[u[t]].regions.get(0,2),r[u[t]].regions.get(0,3))}),p=f.map(function(t){return l[t]}),h=f.map(function(t,e){return c[t].calibrate(d[e])})),r.forEach(function(t){t.regions.dispose(),t.scores.dispose()}),[2,{boxes:h,scores:p}]}})})}function Qp(v,g,y,x,w){return gl(this,void 0,void 0,function(){var e,n,o,r,i,a,s,u,c,l,h,p,f,d,m;return yl(this,function(t){switch(t.label){case 0:return e=Date.now(),[4,Xp(v,g,{width:48,height:48})];case 1:return n=t.sent(),w.stage3_extractImagePatches=Date.now()-e,e=Date.now(),o=n.map(function(t){var s,u,e=(s=t,u=x,zt(function(){var t=$p(s,u);t=jp(t=Bh(t=Ka(t,[2,2],[2,2],"same"),u.conv4,"valid"),u.prelu4_alpha);var e=jp(rp(_i(t,[t.shape[0],u.fc1.weights.shape[0]]),u.fc1),u.prelu5_alpha),n=rp(e,u.fc2_1),r=pi(us(n,1),1),o=ye(qs(n,r),1),i=rp(e,u.fc2_2),a=rp(e,u.fc2_3);return{scores:Ii(o,1)[1],regions:i,points:a}}));return t.dispose(),e}),w.stage3_onet=Date.now()-e,r=1<o.length?Vo(o.map(function(t){return t.scores})):o[0].scores,s=(a=Array).from,[4,r.data()];case 2:return i=s.apply(a,[t.sent()]),r.dispose(),u=i.map(function(t,e){return{score:t,idx:e}}).filter(function(t){return t.score>y}).map(function(t){return t.idx}),c=u.map(function(t){return new Hp(o[t].regions.get(0,0),o[t].regions.get(0,1),o[t].regions.get(0,2),o[t].regions.get(0,3))}),l=u.map(function(t,e){return g[t].calibrate(c[e])}),h=u.map(function(t){return i[t]}),p=[],f=[],d=[],0<l.length&&(e=Date.now(),m=bh(l,h,.7,!1),w.stage3_nms=Date.now()-e,p=m.map(function(t){return l[t]}),f=m.map(function(t){return h[t]}),d=m.map(function(n,r){return Array(5).fill(0).map(function(t,e){return new wl(o[n].points.get(0,e)*(p[r].width+1)+p[r].left,o[n].points.get(0,e+5)*(p[r].height+1)+p[r].top)})})),o.forEach(function(t){t.regions.dispose(),t.scores.dispose(),t.points.dispose()}),[2,{boxes:p,scores:f,points:d}]}})})}var Jp=function(t){function e(){return t.call(this,"Mtcnn")||this}return ml(e,t),e.prototype.forwardInput=function(E,C){return void 0===C&&(C={}),gl(this,void 0,void 0,function(){var e,n,r,o,i,a,s,u,c,l,h,p,f,d,m,v,g,y,x,w,b;return yl(this,function(t){switch(t.label){case 0:if(!(e=this.params))throw new Error("Mtcnn - load model before inference");if(!(n=E.canvases[0]))throw new Error("Mtcnn - inputCanvas is not defined, note that passing tensors into Mtcnn.forwardInput is not supported yet.");return r={},o=Date.now(),i=zt(function(){return t=pi(di(n)).toFloat(),zt(function(){return ki(Ii(t,3).reverse(),3)});var t}),a=function(t){return i.dispose(),r.total=Date.now()-o,t},s=i.shape.slice(1),u=s[0],c=s[1],l=new Sp(C),h=l.minFaceSize,p=l.scaleFactor,f=l.maxNumScales,d=l.scoreThresholds,m=l.scaleSteps,v=(m||function(t,e,n){for(var r=n[0],o=n[1],i=zp/t,a=[],s=Math.min(r,o)*i,u=0;12<=s;)a.push(i*Math.pow(e,u)),s*=e,u+=1;return a}(h,p,[u,c])).filter(function(t){var e=Gp(t,[u,c]);return Math.min(e.width,e.height)>zp}).slice(0,f),r.scales=v,r.pyramid=v.map(function(t){return Gp(t,[u,c])}),g=Date.now(),[4,Kp(i,v,d[0],e.pnet,r)];case 1:return y=t.sent(),r.total_stage1=Date.now()-g,y.boxes.length?(r.stage2_numInputBoxes=y.boxes.length,g=Date.now(),[4,Yp(n,y.boxes,d[1],e.rnet,r)]):[2,a({results:[],stats:r})];case 2:return x=t.sent(),r.total_stage2=Date.now()-g,x.boxes.length?(r.stage3_numInputBoxes=x.boxes.length,g=Date.now(),[4,Qp(n,x.boxes,d[2],e.onet,r)]):[2,a({results:[],stats:r})];case 3:return w=t.sent(),r.total_stage3=Date.now()-g,b=w.boxes.map(function(e,t){return Cp(bp({},new _h(w.scores[t],new Ul(e.left/c,e.top/u,e.width/c,e.height/u),{height:u,width:c})),new Nh(w.points[t].map(function(t){return t.sub(new wl(e.left,e.top)).div(new wl(e.width,e.height))}),{width:e.width,height:e.height}))}),[2,a({results:b,stats:r})]}})})},e.prototype.forward=function(n,r){return void 0===r&&(r={}),gl(this,void 0,void 0,function(){var e;return yl(this,function(t){switch(t.label){case 0:return e=this.forwardInput,[4,xh(n)];case 1:return[4,e.apply(this,[t.sent(),r])];case 2:return[2,t.sent().results]}})})},e.prototype.forwardWithStats=function(n,r){return void 0===r&&(r={}),gl(this,void 0,void 0,function(){var e;return yl(this,function(t){switch(t.label){case 0:return e=this.forwardInput,[4,xh(n)];case 1:return[2,e.apply(this,[t.sent(),r])]}})})},e.prototype.getDefaultModelName=function(){return"mtcnn_model"},e.prototype.extractParamsFromWeigthMap=function(t){return Vp(t)},e.prototype.extractParams=function(t){return Up(t)},e}(Sh),Zp=[new wl(1.603231,2.094468),new wl(6.041143,7.080126),new wl(2.882459,3.518061),new wl(4.266906,5.178857),new wl(9.041765,10.66308)],tf=[117.001,114.697,97.404],ef=function(e){function t(){var t={withSeparableConvs:!0,iouThreshold:.4,classes:["face"],anchors:Zp,meanRgb:tf,isFirstLayerConv2d:!0,filterSizes:[3,16,32,64,128,256,512]};return e.call(this,t)||this}return ml(t,e),Object.defineProperty(t.prototype,"anchors",{get:function(){return this.config.anchors},enumerable:!0,configurable:!0}),t.prototype.locateFaces=function(e,n){return gl(this,void 0,void 0,function(){return yl(this,function(t){switch(t.label){case 0:return[4,this.detect(e,n)];case 1:return[2,t.sent().map(function(t){return new _h(t.score,t.relativeBox,{width:t.imageWidth,height:t.imageHeight})})]}})})},t.prototype.getDefaultModelName=function(){return"tiny_face_detector_model"},t.prototype.extractParamsFromWeigthMap=function(t){return e.prototype.extractParamsFromWeigthMap.call(this,t)},t}(Jh),nf=[new wl(.738768,.874946),new wl(2.42204,2.65704),new wl(4.30971,7.04493),new wl(10.246,4.59428),new wl(12.6868,11.8741)],rf=[new wl(1.603231,2.094468),new wl(6.041143,7.080126),new wl(2.882459,3.518061),new wl(4.266906,5.178857),new wl(9.041765,10.66308)],of=[117.001,114.697,97.404],af=function(n){function t(t){void 0===t&&(t=!0);var e=Object.assign({},{withSeparableConvs:t,iouThreshold:.4,classes:["face"]},t?{anchors:rf,meanRgb:of}:{anchors:nf,withClassScores:!0});return n.call(this,e)||this}return ml(t,n),Object.defineProperty(t.prototype,"withSeparableConvs",{get:function(){return this.config.withSeparableConvs},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"anchors",{get:function(){return this.config.anchors},enumerable:!0,configurable:!0}),t.prototype.locateFaces=function(e,n){return gl(this,void 0,void 0,function(){return yl(this,function(t){switch(t.label){case 0:return[4,this.detect(e,n)];case 1:return[2,t.sent().map(function(t){return new _h(t.score,t.relativeBox,{width:t.imageWidth,height:t.imageHeight})})]}})})},t.prototype.getDefaultModelName=function(){return this.withSeparableConvs?"tiny_yolov2_separable_conv_model":"tiny_yolov2_model"},t.prototype.extractParamsFromWeigthMap=function(t){return n.prototype.extractParamsFromWeigthMap.call(this,t)},t}(Jh),sf={ssdMobilenetv1:new Op,tinyFaceDetector:new ef,tinyYolov2:new af,mtcnn:new Jp,faceLandmark68Net:new up,faceLandmark68TinyNet:new lp,faceRecognitionNet:new xp,faceExpressionNet:new ap},uf=function(t,e){return sf.ssdMobilenetv1.locateFaces(t,e)},cf=function(t){return sf.faceLandmark68Net.detectLandmarks(t)},lf=function(t){return sf.ssdMobilenetv1.load(t)},hf=lf,pf=uf,ff=cf,df=function(r){function t(t,e){var n=r.call(this)||this;return n.parentTask=t,n.input=e,n}return ml(t,r),t}(Lp),mf=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return ml(e,t),e.prototype.run=function(){return gl(this,void 0,void 0,function(){var e,n,o,r,i,a=this;return yl(this,function(t){switch(t.label){case 0:return[4,this.parentTask];case 1:return e=t.sent(),n=e.map(function(t){return t.alignedRect}),this.input instanceof it?[4,Mh(this.input,n)]:[3,3];case 2:return r=t.sent(),[3,5];case 3:return[4,Ah(this.input,n)];case 4:r=t.sent(),t.label=5;case 5:return o=r,[4,Promise.all(e.map(function(n,r){return gl(a,void 0,void 0,function(){var e;return yl(this,function(t){switch(t.label){case 0:return[4,sf.faceRecognitionNet.computeFaceDescriptor(o[r])];case 1:return e=t.sent(),[2,wp(n,e)]}})})}))];case 6:return i=t.sent(),o.forEach(function(t){return t instanceof it&&t.dispose()}),[2,i]}})})},e}(df),vf=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return ml(e,t),e.prototype.run=function(){return gl(this,void 0,void 0,function(){var e,n,r,o,i;return yl(this,function(t){switch(t.label){case 0:return[4,this.parentTask];case 1:return(e=t.sent())?(n=e.alignedRect,this.input instanceof it?[4,Mh(this.input,[n])]:[3,3]):[2];case 2:return o=t.sent(),[3,5];case 3:return[4,Ah(this.input,[n])];case 4:o=t.sent(),t.label=5;case 5:return r=o,[4,sf.faceRecognitionNet.computeFaceDescriptor(r[0])];case 6:return i=t.sent(),r.forEach(function(t){return t instanceof it&&t.dispose()}),[2,wp(e,i)]}})})},e}(df),gf=function(o){function t(t,e,n){var r=o.call(this)||this;return r.parentTask=t,r.input=e,r.useTinyLandmarkNet=n,r}return ml(t,o),Object.defineProperty(t.prototype,"landmarkNet",{get:function(){return this.useTinyLandmarkNet?sf.faceLandmark68TinyNet:sf.faceLandmark68Net},enumerable:!0,configurable:!0}),t}(Lp),yf=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return ml(e,t),e.prototype.run=function(){return gl(this,void 0,void 0,function(){var e,n,r,o,i,a=this;return yl(this,function(t){switch(t.label){case 0:return[4,this.parentTask];case 1:return e=t.sent(),n=e.map(function(t){return t.detection}),this.input instanceof it?[4,Mh(this.input,n)]:[3,3];case 2:return o=t.sent(),[3,5];case 3:return[4,Ah(this.input,n)];case 4:o=t.sent(),t.label=5;case 5:return r=o,[4,Promise.all(r.map(function(t){return a.landmarkNet.detectLandmarks(t)}))];case 6:return i=t.sent(),r.forEach(function(t){return t instanceof it&&t.dispose()}),[2,e.map(function(t,e){return Cp(t,i[e])})]}})})},e.prototype.withFaceDescriptors=function(){return new mf(this,this.input)},e}(gf),xf=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return ml(e,t),e.prototype.run=function(){return gl(this,void 0,void 0,function(){var e,n,r,o,i;return yl(this,function(t){switch(t.label){case 0:return[4,this.parentTask];case 1:return(e=t.sent())?(n=e.detection,this.input instanceof it?[4,Mh(this.input,[n])]:[3,3]):[2];case 2:return o=t.sent(),[3,5];case 3:return[4,Ah(this.input,[n])];case 4:o=t.sent(),t.label=5;case 5:return r=o,[4,this.landmarkNet.detectLandmarks(r[0])];case 6:return i=t.sent(),r.forEach(function(t){return t instanceof it&&t.dispose()}),[2,Cp(e,i)]}})})},e.prototype.withFaceDescriptor=function(){return new vf(this,this.input)},e}(gf),wf=function(r){function t(t,e){var n=r.call(this)||this;return n.parentTask=t,n.input=e,n}return ml(t,r),t}(Lp),bf=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return ml(e,t),e.prototype.run=function(){return gl(this,void 0,void 0,function(){var e,n,r,o,i;return yl(this,function(t){switch(t.label){case 0:return[4,this.parentTask];case 1:return e=t.sent(),n=e.map(function(t){return t.detection}),this.input instanceof it?[4,Mh(this.input,n)]:[3,3];case 2:return o=t.sent(),[3,5];case 3:return[4,Ah(this.input,n)];case 4:o=t.sent(),t.label=5;case 5:return r=o,[4,Promise.all(r.map(function(t){return sf.faceExpressionNet.predictExpressions(t)}))];case 6:return i=t.sent(),r.forEach(function(t){return t instanceof it&&t.dispose()}),[2,e.map(function(t,e){return Ep(t,i[e])})]}})})},e.prototype.withFaceLandmarks=function(){return new yf(this,this.input,!1)},e}(wf),Ef=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return ml(e,t),e.prototype.run=function(){return gl(this,void 0,void 0,function(){var e,n,r,o,i;return yl(this,function(t){switch(t.label){case 0:return[4,this.parentTask];case 1:return(e=t.sent())?(n=e.detection,this.input instanceof it?[4,Mh(this.input,[n])]:[3,3]):[2];case 2:return o=t.sent(),[3,5];case 3:return[4,Ah(this.input,[n])];case 4:o=t.sent(),t.label=5;case 5:return r=o,[4,sf.faceExpressionNet.predictExpressions(r[0])];case 6:return i=t.sent(),r.forEach(function(t){return t instanceof it&&t.dispose()}),[2,Ep(e,i)]}})})},e.prototype.withFaceLandmarks=function(){return new xf(this,this.input,!1)},e}(wf),Cf=function(r){function t(t,e){void 0===e&&(e=new Mp);var n=r.call(this)||this;return n.input=t,n.options=e,n}return ml(t,r),t}(Lp),Sf=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return ml(e,t),e.prototype.run=function(){return gl(this,void 0,void 0,function(){var e,n,r,o;return yl(this,function(t){switch(t.label){case 0:return n=(e=this).input,(r=e.options)instanceof Sp?[4,sf.mtcnn.forward(n,r)]:[3,2];case 1:return[2,t.sent().map(function(t){return t.detection})];case 2:if(!(o=r instanceof Bp?function(t){return sf.tinyFaceDetector.locateFaces(t,r)}:r instanceof Mp?function(t){return sf.ssdMobilenetv1.locateFaces(t,r)}:r instanceof Qh?function(t){return sf.tinyYolov2.locateFaces(t,r)}:null))throw new Error("detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options");return[2,o(n)]}})})},e.prototype.runAndExtendWithFaceDetections=function(){var t=this;return new Promise(function(n){return gl(t,void 0,void 0,function(){var e;return yl(this,function(t){switch(t.label){case 0:return[4,this.run()];case 1:return e=t.sent(),[2,n(e.map(function(t){return bp({},t)}))]}})})})},e.prototype.withFaceLandmarks=function(t){return void 0===t&&(t=!1),new yf(this.runAndExtendWithFaceDetections(),this.input,t)},e.prototype.withFaceExpressions=function(){return new bf(this.runAndExtendWithFaceDetections(),this.input)},e}(Cf),_f=function(t){function e(){return null!==t&&t.apply(this,arguments)||this}return ml(e,t),e.prototype.run=function(){return gl(this,void 0,void 0,function(){var e,n;return yl(this,function(t){switch(t.label){case 0:return[4,new Sf(this.input,this.options)];case 1:return e=t.sent(),n=e[0],e.forEach(function(t){t.score>n.score&&(n=t)}),[2,n]}})})},e.prototype.runAndExtendWithFaceDetection=function(){var t=this;return new Promise(function(n){return gl(t,void 0,void 0,function(){var e;return yl(this,function(t){switch(t.label){case 0:return[4,this.run()];case 1:return e=t.sent(),[2,n(e?bp({},e):void 0)]}})})})},e.prototype.withFaceLandmarks=function(t){return void 0===t&&(t=!1),new xf(this.runAndExtendWithFaceDetection(),this.input,t)},e.prototype.withFaceExpressions=function(){return new Ef(this.runAndExtendWithFaceDetection(),this.input)},e}(Cf);function Rf(t,e){return void 0===e&&(e=new Mp),new Sf(t,e)}function Nf(e,n){return gl(this,void 0,void 0,function(){return yl(this,function(t){switch(t.label){case 0:return[4,Rf(e,new Mp(n?{minConfidence:n}:{})).withFaceLandmarks().withFaceDescriptors()];case 1:return[2,t.sent()]}})})}var kf=Nf;function Tf(t,e){if(t.length!==e.length)throw new Error("euclideanDistance: arr1.length !== arr2.length");var n=Array.from(t),r=Array.from(e);return Math.sqrt(n.map(function(t,e){return t-r[e]}).reduce(function(t,e){return t+Math.pow(e,2)},0))}var Df=function(){function t(t,e){void 0===e&&(e=.6),this._distanceThreshold=e;var n=Array.isArray(t)?t:[t];if(!n.length)throw new Error("FaceRecognizer.constructor - expected atleast one input");var r=1,o=function(){return"person "+r++};this._labeledDescriptors=n.map(function(t){if(t instanceof Dh)return t;if(t instanceof Float32Array)return new Dh(o(),[t]);if(t.descriptor&&t.descriptor instanceof Float32Array)return new Dh(o(),[t.descriptor]);throw new Error("FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>")})}return Object.defineProperty(t.prototype,"labeledDescriptors",{get:function(){return this._labeledDescriptors},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"distanceThreshold",{get:function(){return this._distanceThreshold},enumerable:!0,configurable:!0}),t.prototype.computeMeanDistance=function(e,t){return t.map(function(t){return Tf(t,e)}).reduce(function(t,e){return t+e},0)/(t.length||1)},t.prototype.matchDescriptor=function(r){var o=this;return this.labeledDescriptors.map(function(t){var e=t.descriptors,n=t.label;return new Th(n,o.computeMeanDistance(r,e))}).reduce(function(t,e){return t.distance<e.distance?t:e})},t.prototype.findBestMatch=function(t){var e=this.matchDescriptor(t);return e.distance<this.distanceThreshold?e:new Th("unknown",e.distance)},t}();t.tf=fl,t.BoundingBox=Pl,t.Box=Fl,t.BoxWithText=Bl,t.Dimensions=xl,t.LabeledBox=Ll,t.ObjectDetection=Wl,t.Point=wl,t.PredictedBox=zl,t.Rect=Ul,t.disposeUnusedWeightTensors=Vl,t.extractWeightEntryFactory=Gl,t.extractWeightsFactory=Hl,t.getModelUris=ql,t.awaitMediaLoaded=eh,t.bufferToImage=nh,t.createCanvas=ih,t.createCanvasFromMedia=ah,t.drawBox=uh,t.drawDetection=function(t,e,l){var n=Zl.getEnv().Canvas,h=lh(t);if(!(h instanceof n))throw new Error("drawDetection - expected canvas to be of type: HTMLCanvasElement");(Array.isArray(e)?e:[e]).forEach(function(t){var e=t instanceof Wl?t.box:t,n=e.x,r=e.y,o=e.width,i=e.height,a=sh(l),s=rh(h);uh(s,n,r,o,i,a);var u=a.withScore,c=t instanceof Bl?t.text:u&&t instanceof zl?""+Nl(t.score):t instanceof Wl?t.className+(u?" ("+Nl(t.score)+")":""):"";c&&ch(s,n,r+i,c,a)})},t.drawText=ch,t.fetchImage=function(r){return gl(this,void 0,void 0,function(){var e,n;return yl(this,function(t){switch(t.label){case 0:return[4,hh(r)];case 1:return[4,(e=t.sent()).blob()];case 2:if(!(n=t.sent()).type.startsWith("image/"))throw new Error("fetchImage - expected blob type to be of type image/*, instead have: "+n.type+", for url: "+e.url);return[2,nh(n)]}})})},t.fetchJson=ph,t.fetchNetWeights=function(n){return gl(this,void 0,void 0,function(){var e;return yl(this,function(t){switch(t.label){case 0:return e=Float32Array.bind,[4,hh(n)];case 1:return[4,t.sent().arrayBuffer()];case 2:return[2,new(e.apply(Float32Array,[void 0,t.sent()]))]}})})},t.fetchOrThrow=hh,t.getContext2dOrThrow=rh,t.getDefaultDrawOptions=sh,t.getMediaDimensions=oh,t.imageTensorToCanvas=fh,t.imageToSquare=dh,t.isMediaElement=mh,t.isMediaLoaded=th,t.loadWeightMap=vh,t.NetInput=yh,t.resolveInput=lh,t.toNetInput=xh,t.env=Zl,t.sigmoid=Ch,t.inverseSigmoid=function(t){return Math.log(t/(1-t))},t.iou=wh,t.nonMaxSuppression=bh,t.normalize=Eh,t.padToSquare=gh,t.shuffleArray=function(t){for(var e=t.slice(),n=e.length-1;0<n;n--){var r=Math.floor(Math.random()*(n+1)),o=e[n];e[n]=e[r],e[r]=o}return e},t.isTensor=bl,t.isTensor1D=function(t){return bl(t,1)},t.isTensor2D=El,t.isTensor3D=Cl,t.isTensor4D=Sl,t.isFloat=_l,t.isEven=Rl,t.round=Nl,t.isDimensions=kl,t.computeReshapedDimensions=Tl,t.getCenterPoint=Dl,t.range=Il,t.isValidNumber=Al,t.isValidProbablitiy=Ml,t.NeuralNetwork=Sh,t.FaceDetection=_h,t.FaceLandmarks=Rh,t.FaceLandmarks5=Nh,t.FaceLandmarks68=kh,t.FaceMatch=Th,t.LabeledFaceDescriptors=Dh,t.drawContour=Ih,t.drawLandmarks=function(t,e,n){var r=lh(t);if(!(r instanceof Zl.getEnv().Canvas))throw new Error("drawLandmarks - expected canvas to be of type: HTMLCanvasElement");var o=Object.assign(sh(n),n||{}),i=Object.assign({drawLines:!1},n||{}).drawLines,a=rh(r),s=o.lineWidth,u=o.color,c=void 0===u?"blue":u;(Array.isArray(e)?e:[e]).forEach(function(t){if(i&&t instanceof kh)return a.strokeStyle=c,a.lineWidth=s,Ih(a,t.getJawOutline()),Ih(a,t.getLeftEyeBrow()),Ih(a,t.getRightEyeBrow()),Ih(a,t.getNose()),Ih(a,t.getLeftEye(),!0),Ih(a,t.getRightEye(),!0),void Ih(a,t.getMouth(),!0);var e=s/2;a.fillStyle=c,t.positions.forEach(function(t){return a.fillRect(t.x-e,t.y-e,s,s)})})},t.drawFaceExpressions=function(t,e,n){var u=lh(t);if(!(u instanceof Zl.getEnv().Canvas))throw new Error("drawFaceExpressions - expected canvas to be of type: HTMLCanvasElement");var r=Object.assign(sh(n),n||{}),c=rh(u),o=r.primaryColor,l=void 0===o?"red":o,i=r.secondaryColor,h=void 0===i?"blue":i,a=r.primaryFontSize,p=void 0===a?22:a,s=r.secondaryFontSize,f=void 0===s?16:s,d=r.minConfidence,m=void 0===d?.2:d;(Array.isArray(e)?e:[e]).forEach(function(t){var e=t.position,n=t.expressions,r=e.x,o=e.y,i=e.height||0,a=n.sort(function(t,e){return e.probability-t.probability}).filter(function(t){return t.probability>m}),s=o+i+a.length*p>u.height?-a.length*p:0;a.forEach(function(t,e){var n=t.expression+" ("+Nl(t.probability)+")";ch(c,r,o+i+e*p+s,n,{textColor:0===e?l:h,fontSize:0===e?p:f})})})},t.extractFaces=Ah,t.extractFaceTensors=Mh,t.FaceExpressionNet=ap,t.faceExpressionLabels=ip,t.FaceLandmarkNet=hp,t.FaceLandmark68Net=up,t.FaceLandmark68TinyNet=lp,t.createFaceRecognitionNet=function(t){var e=new xp;return e.extractWeights(t),e},t.FaceRecognitionNet=xp,t.extendWithFaceDescriptor=wp,t.extendWithFaceDetection=bp,t.extendWithFaceExpressions=Ep,t.extendWithFaceLandmarks=Cp,t.allFacesSsdMobilenetv1=Nf,t.allFacesTinyYolov2=function(e,n){return void 0===n&&(n={}),gl(this,void 0,void 0,function(){return yl(this,function(t){switch(t.label){case 0:return[4,Rf(e,new Qh(n)).withFaceLandmarks().withFaceDescriptors()];case 1:return[2,t.sent()]}})})},t.allFacesMtcnn=function(e,n){return void 0===n&&(n={}),gl(this,void 0,void 0,function(){return yl(this,function(t){switch(t.label){case 0:return[4,Rf(e,new Sp(n)).withFaceLandmarks().withFaceDescriptors()];case 1:return[2,t.sent()]}})})},t.allFaces=kf,t.ComposableTask=Lp,t.ComputeFaceDescriptorsTaskBase=df,t.ComputeAllFaceDescriptorsTask=mf,t.ComputeSingleFaceDescriptorTask=vf,t.detectSingleFace=function(t,e){return void 0===e&&(e=new Mp),new _f(t,e)},t.detectAllFaces=Rf,t.DetectFacesTaskBase=Cf,t.DetectAllFacesTask=Sf,t.DetectSingleFaceTask=_f,t.DetectFaceLandmarksTaskBase=gf,t.DetectAllFaceLandmarksTask=yf,t.DetectSingleFaceLandmarksTask=xf,t.FaceMatcher=Df,t.nets=sf,t.ssdMobilenetv1=uf,t.tinyFaceDetector=function(t,e){return sf.tinyFaceDetector.locateFaces(t,e)},t.tinyYolov2=function(t,e){return sf.tinyYolov2.locateFaces(t,e)},t.mtcnn=function(t,e){return sf.mtcnn.forward(t,e)},t.detectFaceLandmarks=cf,t.detectFaceLandmarksTiny=function(t){return sf.faceLandmark68TinyNet.detectLandmarks(t)},t.computeFaceDescriptor=function(t){return sf.faceRecognitionNet.computeFaceDescriptor(t)},t.recognizeFaceExpressions=function(t){return sf.faceExpressionNet.predictExpressions(t)},t.loadSsdMobilenetv1Model=lf,t.loadTinyFaceDetectorModel=function(t){return sf.tinyFaceDetector.load(t)},t.loadMtcnnModel=function(t){return sf.mtcnn.load(t)},t.loadTinyYolov2Model=function(t){return sf.tinyYolov2.load(t)},t.loadFaceLandmarkModel=function(t){return sf.faceLandmark68Net.load(t)},t.loadFaceLandmarkTinyModel=function(t){return sf.faceLandmark68TinyNet.load(t)},t.loadFaceRecognitionModel=function(t){return sf.faceRecognitionNet.load(t)},t.loadFaceExpressionModel=function(t){return sf.faceExpressionNet.load(t)},t.loadFaceDetectionModel=hf,t.locateFaces=pf,t.detectLandmarks=ff,t.createMtcnn=function(t){var e=new Jp;return e.extractWeights(t),e},t.Mtcnn=Jp,t.MtcnnOptions=Sp,t.createSsdMobilenetv1=Fp,t.createFaceDetectionNet=function(t){return Fp(t)},t.FaceDetectionNet=Pp,t.SsdMobilenetv1=Op,t.SsdMobilenetv1Options=Mp,t.createTinyFaceDetector=function(t){var e=new ef;return e.extractWeights(t),e},t.TinyFaceDetector=ef,t.TinyFaceDetectorOptions=Bp,t.createTinyYolov2=function(t,e){void 0===e&&(e=!0);var n=new af(e);return n.extractWeights(t),n},t.TinyYolov2=af,t.euclideanDistance=Tf,t.resizeResults=function e(t,n){var r=n.width,o=n.height;if(Array.isArray(t))return t.map(function(t){return e(t,{width:r,height:o})});var i=t.unshiftedLandmarks&&t.unshiftedLandmarks instanceof Rh,a=t.detection&&t.detection instanceof _h;if(i){var s=t.detection.forSize(r,o),u=t.unshiftedLandmarks.forSize(s.box.width,s.box.height);return Cp(bp(t,s),u)}return a?bp(t,t.detection.forSize(r,o)):t instanceof Rh||t instanceof _h?t.forSize(r,o):t},Object.defineProperty(t,"__esModule",{value:!0})});
diff --git a/node_modules/face-api.js/jasmine-node.js b/node_modules/face-api.js/jasmine-node.js
new file mode 100644
index 0000000..40e1017
--- /dev/null
+++ b/node_modules/face-api.js/jasmine-node.js
@@ -0,0 +1,15 @@
+
+let spec_files = ['**/*.test.ts'].concat(
+  process.env.EXCLUDE_UNCOMPRESSED
+    ? ['!**/*.uncompressed.test.ts']
+    : []
+)
+
+// exclude browser tests
+spec_files = spec_files.concat(['!**/*.browser.test.ts'])
+
+module.exports = {
+  spec_dir: 'test',
+  spec_files,
+  random: false
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/karma.conf.js b/node_modules/face-api.js/karma.conf.js
new file mode 100644
index 0000000..5279d53
--- /dev/null
+++ b/node_modules/face-api.js/karma.conf.js
@@ -0,0 +1,74 @@
+const dataFiles = [
+  'test/images/*.jpg',
+  'test/images/*.png',
+  'test/data/*.json',
+  'test/media/*.mp4',
+  'weights/**/*',
+  'weights_uncompressed/**/*',
+  'weights_unused/**/*'
+].map(pattern => ({
+  pattern,
+  watched: false,
+  included: false,
+  served: true,
+  nocache: false
+}))
+
+let exclude = (
+  process.env.UUT
+    ? [
+        'dom',
+        'faceLandmarkNet',
+        'faceRecognitionNet',
+        'ssdMobilenetv1',
+        'tinyFaceDetector',
+        'mtcnn'
+      ]
+    : []
+  )
+    .filter(ex => ex !== process.env.UUT)
+    .map(ex => `test/tests/${ex}/*.ts`)
+
+
+exclude = exclude.concat(
+  process.env.EXCLUDE_UNCOMPRESSED
+    ? ['**/*.uncompressed.test.ts']
+    : []
+)
+
+// exclude nodejs tests
+exclude = exclude.concat(['**/*.node.test.ts'])
+
+
+module.exports = function(config) {
+  const args = []
+  if (process.env.BACKEND_CPU) {
+    args.push('backend_cpu')
+  }
+
+  config.set({
+    frameworks: ['jasmine', 'karma-typescript'],
+    files: [
+      'src/**/*.ts',
+      'test/**/*.ts'
+    ].concat(dataFiles),
+    exclude,
+    preprocessors: {
+      '**/*.ts': ['karma-typescript']
+    },
+    karmaTypescriptConfig: {
+      tsconfig: 'tsconfig.test.json'
+    },
+    browsers: ['Chrome'],
+    browserNoActivityTimeout: 120000,
+    browserDisconnectTolerance: 3,
+    browserDisconnectTimeout : 120000,
+    captureTimeout: 60000,
+    client: {
+      jasmine: {
+        timeoutInterval: 60000,
+        args
+      }
+    }
+  })
+}
diff --git a/node_modules/face-api.js/package.json b/node_modules/face-api.js/package.json
new file mode 100644
index 0000000..b1f780c
--- /dev/null
+++ b/node_modules/face-api.js/package.json
@@ -0,0 +1,92 @@
+{
+  "_args": [
+    [
+      "face-api.js@0.17.1",
+      "/Users/zamua/Dropbox/workspace/madify"
+    ]
+  ],
+  "_from": "face-api.js@0.17.1",
+  "_id": "face-api.js@0.17.1",
+  "_inBundle": false,
+  "_integrity": "sha512-iSkDnukKd0eZAdQzBXltLe0gG35hrS8NZXq76giVRSk7DR1eJGJtkNoPAdGfLveS82qazU8/gns4tgHU2uMtNQ==",
+  "_location": "/face-api.js",
+  "_phantomChildren": {},
+  "_requested": {
+    "type": "version",
+    "registry": true,
+    "raw": "face-api.js@0.17.1",
+    "name": "face-api.js",
+    "escapedName": "face-api.js",
+    "rawSpec": "0.17.1",
+    "saveSpec": null,
+    "fetchSpec": "0.17.1"
+  },
+  "_requiredBy": [
+    "/"
+  ],
+  "_resolved": "https://registry.npmjs.org/face-api.js/-/face-api.js-0.17.1.tgz",
+  "_spec": "0.17.1",
+  "_where": "/Users/zamua/Dropbox/workspace/madify",
+  "author": {
+    "name": "justadudewhohacks"
+  },
+  "dependencies": {
+    "@tensorflow/tfjs-core": "0.14.2",
+    "tfjs-image-recognition-base": "^0.3.1",
+    "tfjs-tiny-yolov2": "^0.4.0",
+    "tslib": "^1.9.3"
+  },
+  "description": "JavaScript API for face detection and face recognition in the browser with tensorflow.js",
+  "devDependencies": {
+    "@tensorflow/tfjs-node": "^0.1.21",
+    "@types/jasmine": "^2.8.8",
+    "@types/node": "^10.12.12",
+    "canvas": "^2.0.1",
+    "jasmine": "^3.3.0",
+    "jasmine-core": "^3.2.1",
+    "karma": "^3.0.0",
+    "karma-chrome-launcher": "^2.2.0",
+    "karma-jasmine": "^1.1.2",
+    "karma-typescript": "^3.0.12",
+    "rollup": "^0.65.0",
+    "rollup-plugin-commonjs": "^9.1.6",
+    "rollup-plugin-node-resolve": "^3.3.0",
+    "rollup-plugin-typescript2": "^0.16.1",
+    "rollup-plugin-uglify": "^4.0.0",
+    "ts-node": "^7.0.1",
+    "typescript": "2.8.4"
+  },
+  "keywords": [
+    "face",
+    "detection",
+    "recognition",
+    "tensorflow",
+    "tf"
+  ],
+  "license": "MIT",
+  "main": "./build/commonjs/index.js",
+  "module": "./build/es6/index.js",
+  "name": "face-api.js",
+  "scripts": {
+    "build": "rm -rf ./build && rm -rf ./dist && npm run rollup && npm run rollup-min && npm run tsc && npm run tsc-es6",
+    "docs": "typedoc --options ./typedoc.config.js ./src",
+    "rollup": "rollup -c rollup.config.js",
+    "rollup-min": "rollup -c rollup.config.js --environment minify:true",
+    "test": "karma start",
+    "test-all": "npm run test-browser && npm run test-node",
+    "test-browser": "karma start --single-run",
+    "test-cpu": "set BACKEND_CPU=true&& karma start",
+    "test-exclude-uncompressed": "set EXCLUDE_UNCOMPRESSED=true&& karma start",
+    "test-facelandmarknets": "set UUT=faceLandmarkNet&& karma start",
+    "test-facerecognitionnet": "set UUT=faceRecognitionNet&& karma start",
+    "test-mtcnn": "set UUT=mtcnn&& karma start",
+    "test-node": "ts-node node_modules/jasmine/bin/jasmine --config=jasmine-node.js",
+    "test-node-exclude-uncompressed": "set EXCLUDE_UNCOMPRESSED=true&& ts-node node_modules/jasmine/bin/jasmine --config=jasmine-node.js",
+    "test-ssdmobilenetv1": "set UUT=ssdMobilenetv1&& karma start",
+    "test-tinyfacedetector": "set UUT=tinyFaceDetector&& karma start",
+    "tsc": "tsc",
+    "tsc-es6": "tsc --p tsconfig.es6.json"
+  },
+  "typings": "./build/commonjs/index.d.ts",
+  "version": "0.17.1"
+}
diff --git a/node_modules/face-api.js/rollup.config.js b/node_modules/face-api.js/rollup.config.js
new file mode 100644
index 0000000..f77b72d
--- /dev/null
+++ b/node_modules/face-api.js/rollup.config.js
@@ -0,0 +1,46 @@
+import commonjs from 'rollup-plugin-commonjs';
+import node from 'rollup-plugin-node-resolve';
+import typescript from 'rollup-plugin-typescript2';
+import { uglify } from 'rollup-plugin-uglify';
+import path from 'path';
+
+const { minify } = process.env
+
+export default {
+  input: 'src/index.ts',
+  plugins: [
+    typescript({
+      tsconfigOverride: {
+        compilerOptions: {
+          module: 'ES2015',
+          declaration: false
+        }
+      }
+    }),
+    node(),
+    commonjs({
+      include: 'node_modules/**'
+    })
+  ].concat(minify ? uglify() : []),
+  output: {
+    extend: true,
+    file: `dist/face-api${minify ? '.min' : ''}.js`,
+    format: 'umd',
+    name: 'faceapi',
+    globals: {
+      'crypto': 'crypto'
+    },
+    sourcemap: minify ? false : true
+  },
+  external: ['crypto'],
+  onwarn: (warning) => {
+    const ignoreWarnings = ['CIRCULAR_DEPENDENCY', 'CIRCULAR', 'THIS_IS_UNDEFINED']
+    if (ignoreWarnings.some(w => w === warning.code))
+      return
+
+    if (warning.missing === 'alea')
+      return
+
+    console.warn(warning.message)
+  }
+}
diff --git a/node_modules/face-api.js/src/classes/FaceDetection.ts b/node_modules/face-api.js/src/classes/FaceDetection.ts
new file mode 100644
index 0000000..f01a430
--- /dev/null
+++ b/node_modules/face-api.js/src/classes/FaceDetection.ts
@@ -0,0 +1,20 @@
+import { Box, IDimensions, ObjectDetection, Rect } from 'tfjs-image-recognition-base';
+
+export interface IFaceDetecion {
+  score: number
+  box: Box
+}
+
+export class FaceDetection extends ObjectDetection implements IFaceDetecion {
+  constructor(
+    score: number,
+    relativeBox: Rect,
+    imageDims: IDimensions
+  ) {
+    super(score, score, '', relativeBox, imageDims)
+  }
+
+  public forSize(width: number, height: number): FaceDetection {
+    return super.forSize(width, height)
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/classes/FaceLandmarks.ts b/node_modules/face-api.js/src/classes/FaceLandmarks.ts
new file mode 100644
index 0000000..9aac3e0
--- /dev/null
+++ b/node_modules/face-api.js/src/classes/FaceLandmarks.ts
@@ -0,0 +1,103 @@
+import { Dimensions, getCenterPoint, IDimensions, Point, Rect } from 'tfjs-image-recognition-base';
+
+import { FaceDetection } from './FaceDetection';
+
+// face alignment constants
+const relX = 0.5
+const relY = 0.43
+const relScale = 0.45
+
+export interface IFaceLandmarks {
+  positions: Point[]
+  shift: Point
+}
+
+export class FaceLandmarks implements IFaceLandmarks {
+  protected _shift: Point
+  protected _positions: Point[]
+  protected _imgDims: Dimensions
+
+  constructor(
+    relativeFaceLandmarkPositions: Point[],
+    imgDims: IDimensions,
+    shift: Point = new Point(0, 0)
+  ) {
+    const { width, height } = imgDims
+    this._imgDims = new Dimensions(width, height)
+    this._shift = shift
+    this._positions = relativeFaceLandmarkPositions.map(
+      pt => pt.mul(new Point(width, height)).add(shift)
+    )
+  }
+
+  public get shift(): Point { return new Point(this._shift.x, this._shift.y) }
+  public get imageWidth(): number { return this._imgDims.width }
+  public get imageHeight(): number { return this._imgDims.height }
+  public get positions(): Point[] { return this._positions }
+  public get relativePositions(): Point[] {
+    return this._positions.map(
+      pt => pt.sub(this._shift).div(new Point(this.imageWidth, this.imageHeight))
+    )
+  }
+
+  public forSize<T extends FaceLandmarks>(width: number, height: number): T {
+    return new (this.constructor as any)(
+      this.relativePositions,
+      { width, height }
+    )
+  }
+
+  public shiftBy<T extends FaceLandmarks>(x: number, y: number): T {
+    return new (this.constructor as any)(
+      this.relativePositions,
+      this._imgDims,
+      new Point(x, y)
+    )
+  }
+
+  public shiftByPoint<T extends FaceLandmarks>(pt: Point): T {
+    return this.shiftBy(pt.x, pt.y)
+  }
+
+  /**
+   * Aligns the face landmarks after face detection from the relative positions of the faces
+   * bounding box, or it's current shift. This function should be used to align the face images
+   * after face detection has been performed, before they are passed to the face recognition net.
+   * This will make the computed face descriptor more accurate.
+   *
+   * @param detection (optional) The bounding box of the face or the face detection result. If
+   * no argument was passed the position of the face landmarks are assumed to be relative to
+   * it's current shift.
+   * @returns The bounding box of the aligned face.
+   */
+  public align(
+    detection?: FaceDetection | Rect
+  ): Rect {
+    if (detection) {
+      const box = detection instanceof FaceDetection
+        ? detection.box.floor()
+        : detection
+
+      return this.shiftBy(box.x, box.y).align()
+    }
+
+    const centers = this.getRefPointsForAlignment()
+
+    const [leftEyeCenter, rightEyeCenter, mouthCenter] = centers
+    const distToMouth = (pt: Point) => mouthCenter.sub(pt).magnitude()
+    const eyeToMouthDist = (distToMouth(leftEyeCenter) + distToMouth(rightEyeCenter)) / 2
+
+    const size = Math.floor(eyeToMouthDist / relScale)
+
+    const refPoint = getCenterPoint(centers)
+    // TODO: pad in case rectangle is out of image bounds
+    const x = Math.floor(Math.max(0, refPoint.x - (relX * size)))
+    const y = Math.floor(Math.max(0, refPoint.y - (relY * size)))
+
+    return new Rect(x, y, Math.min(size, this.imageWidth + x), Math.min(size, this.imageHeight + y))
+  }
+
+  protected getRefPointsForAlignment(): Point[] {
+    throw new Error('getRefPointsForAlignment not implemented by base class')
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/classes/FaceLandmarks5.ts b/node_modules/face-api.js/src/classes/FaceLandmarks5.ts
new file mode 100644
index 0000000..f76c45c
--- /dev/null
+++ b/node_modules/face-api.js/src/classes/FaceLandmarks5.ts
@@ -0,0 +1,15 @@
+import { getCenterPoint, Point } from 'tfjs-image-recognition-base';
+
+import { FaceLandmarks } from './FaceLandmarks';
+
+export class FaceLandmarks5 extends FaceLandmarks {
+
+  protected getRefPointsForAlignment(): Point[] {
+    const pts = this.positions
+    return [
+      pts[0],
+      pts[1],
+      getCenterPoint([pts[3], pts[4]])
+    ]
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/classes/FaceLandmarks68.ts b/node_modules/face-api.js/src/classes/FaceLandmarks68.ts
new file mode 100644
index 0000000..311e567
--- /dev/null
+++ b/node_modules/face-api.js/src/classes/FaceLandmarks68.ts
@@ -0,0 +1,41 @@
+import { getCenterPoint, Point } from 'tfjs-image-recognition-base';
+
+import { FaceLandmarks } from '../classes/FaceLandmarks';
+
+export class FaceLandmarks68 extends FaceLandmarks {
+  public getJawOutline(): Point[] {
+    return this.positions.slice(0, 17)
+  }
+
+  public getLeftEyeBrow(): Point[] {
+    return this.positions.slice(17, 22)
+  }
+
+  public getRightEyeBrow(): Point[] {
+    return this.positions.slice(22, 27)
+  }
+
+  public getNose(): Point[] {
+    return this.positions.slice(27, 36)
+  }
+
+  public getLeftEye(): Point[] {
+    return this.positions.slice(36, 42)
+  }
+
+  public getRightEye(): Point[] {
+    return this.positions.slice(42, 48)
+  }
+
+  public getMouth(): Point[] {
+    return this.positions.slice(48, 68)
+  }
+
+  protected getRefPointsForAlignment(): Point[] {
+    return [
+      this.getLeftEye(),
+      this.getRightEye(),
+      this.getMouth()
+    ].map(getCenterPoint)
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/classes/FaceMatch.ts b/node_modules/face-api.js/src/classes/FaceMatch.ts
new file mode 100644
index 0000000..84d6f6f
--- /dev/null
+++ b/node_modules/face-api.js/src/classes/FaceMatch.ts
@@ -0,0 +1,23 @@
+import { round } from 'tfjs-image-recognition-base';
+
+export interface IFaceMatch {
+  label: string
+  distance: number
+}
+
+export class FaceMatch implements IFaceMatch {
+  private _label: string
+  private _distance: number
+
+  constructor(label: string, distance: number) {
+    this._label = label
+    this._distance = distance
+  }
+
+  public get label(): string { return this._label }
+  public get distance(): number { return this._distance }
+
+  public toString(withDistance: boolean = true): string {
+    return `${this.label}${withDistance ? ` (${round(this.distance)})` : ''}`
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/classes/LabeledFaceDescriptors.ts b/node_modules/face-api.js/src/classes/LabeledFaceDescriptors.ts
new file mode 100644
index 0000000..afcc54c
--- /dev/null
+++ b/node_modules/face-api.js/src/classes/LabeledFaceDescriptors.ts
@@ -0,0 +1,20 @@
+export class LabeledFaceDescriptors {
+  private _label: string
+  private _descriptors: Float32Array[]
+
+  constructor(label: string, descriptors: Float32Array[]) {
+    if (!(typeof label === 'string')) {
+      throw new Error('LabeledFaceDescriptors - constructor expected label to be a string')
+    }
+
+    if (!Array.isArray(descriptors) || descriptors.some(desc => !(desc instanceof Float32Array))) {
+      throw new Error('LabeledFaceDescriptors - constructor expected descriptors to be an array of Float32Array')
+    }
+
+    this._label = label
+    this._descriptors = descriptors
+  }
+
+  public get label(): string { return this._label }
+  public get descriptors(): Float32Array[] { return this._descriptors }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/classes/index.ts b/node_modules/face-api.js/src/classes/index.ts
new file mode 100644
index 0000000..12b5bf2
--- /dev/null
+++ b/node_modules/face-api.js/src/classes/index.ts
@@ -0,0 +1,6 @@
+export * from './FaceDetection';
+export * from './FaceLandmarks';
+export * from './FaceLandmarks5';
+export * from './FaceLandmarks68';
+export * from './FaceMatch';
+export * from './LabeledFaceDescriptors';
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/common/fullyConnectedLayer.ts b/node_modules/face-api.js/src/common/fullyConnectedLayer.ts
new file mode 100644
index 0000000..fd24ecb
--- /dev/null
+++ b/node_modules/face-api.js/src/common/fullyConnectedLayer.ts
@@ -0,0 +1,14 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { FCParams } from 'tfjs-tiny-yolov2';
+
+export function fullyConnectedLayer(
+  x: tf.Tensor2D,
+  params: FCParams
+): tf.Tensor2D {
+  return tf.tidy(() =>
+    tf.add(
+      tf.matMul(x, params.weights),
+      params.bias
+    )
+  )
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/dom/drawContour.ts b/node_modules/face-api.js/src/dom/drawContour.ts
new file mode 100644
index 0000000..1b2e501
--- /dev/null
+++ b/node_modules/face-api.js/src/dom/drawContour.ts
@@ -0,0 +1,28 @@
+import { Point } from 'tfjs-image-recognition-base';
+
+export function drawContour(
+  ctx: CanvasRenderingContext2D,
+  points: Point[],
+  isClosed: boolean = false
+) {
+  ctx.beginPath()
+
+  points.slice(1).forEach(({ x, y }, prevIdx) => {
+    const from = points[prevIdx]
+    ctx.moveTo(from.x, from.y)
+    ctx.lineTo(x, y)
+  })
+
+  if (isClosed) {
+    const from = points[points.length - 1]
+    const to = points[0]
+    if (!from || !to) {
+      return
+    }
+
+    ctx.moveTo(from.x, from.y)
+    ctx.lineTo(to.x, to.y)
+  }
+
+  ctx.stroke()
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/dom/drawFaceExpressions.ts b/node_modules/face-api.js/src/dom/drawFaceExpressions.ts
new file mode 100644
index 0000000..1d31bbc
--- /dev/null
+++ b/node_modules/face-api.js/src/dom/drawFaceExpressions.ts
@@ -0,0 +1,59 @@
+import { drawText, env, getContext2dOrThrow, getDefaultDrawOptions, resolveInput, round } from 'tfjs-image-recognition-base';
+import { IRect } from 'tfjs-tiny-yolov2';
+
+import { DrawFaceExpressionsInput, DrawFaceExpressionsOptions } from './types';
+
+export function drawFaceExpressions(
+  canvasArg: string | HTMLCanvasElement,
+  faceExpressions: DrawFaceExpressionsInput | DrawFaceExpressionsInput[],
+  options?: DrawFaceExpressionsOptions
+) {
+  const canvas = resolveInput(canvasArg)
+  if (!(canvas instanceof env.getEnv().Canvas)) {
+    throw new Error('drawFaceExpressions - expected canvas to be of type: HTMLCanvasElement')
+  }
+
+  const drawOptions = Object.assign(
+    getDefaultDrawOptions(options),
+    (options || {})
+  )
+
+  const ctx = getContext2dOrThrow(canvas)
+  const {
+    primaryColor = 'red',
+    secondaryColor = 'blue',
+    primaryFontSize = 22,
+    secondaryFontSize = 16,
+    minConfidence = 0.2
+  } = drawOptions
+
+  const faceExpressionsArray = Array.isArray(faceExpressions)
+    ? faceExpressions
+    : [faceExpressions]
+
+  faceExpressionsArray.forEach(({ position, expressions }) => {
+    const { x, y } = position
+    const height = (position as IRect).height || 0
+    const sorted = expressions.sort((a, b) => b.probability - a.probability)
+    const resultsToDisplay = sorted.filter(expr => expr.probability > minConfidence)
+
+    let offset = (y + height + resultsToDisplay.length * primaryFontSize) > canvas.height
+      ? -(resultsToDisplay.length * primaryFontSize)
+      : 0
+    resultsToDisplay.forEach((expr, i) => {
+      const text = `${expr.expression} (${round(expr.probability)})`
+      drawText(
+        ctx,
+        x,
+        y + height + (i * primaryFontSize) + offset,
+        text,
+        {
+          textColor: i === 0 ? primaryColor : secondaryColor,
+          fontSize: i === 0 ? primaryFontSize : secondaryFontSize
+        }
+      )
+    })
+  })
+
+
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/dom/drawLandmarks.ts b/node_modules/face-api.js/src/dom/drawLandmarks.ts
new file mode 100644
index 0000000..1b0eeeb
--- /dev/null
+++ b/node_modules/face-api.js/src/dom/drawLandmarks.ts
@@ -0,0 +1,49 @@
+import { env, getContext2dOrThrow, getDefaultDrawOptions, resolveInput } from 'tfjs-image-recognition-base';
+
+import { FaceLandmarks } from '../classes/FaceLandmarks';
+import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
+import { drawContour } from './drawContour';
+import { DrawLandmarksOptions } from './types';
+
+export function drawLandmarks(
+  canvasArg: string | HTMLCanvasElement,
+  faceLandmarks: FaceLandmarks | FaceLandmarks[],
+  options?: DrawLandmarksOptions
+) {
+  const canvas = resolveInput(canvasArg)
+  if (!(canvas instanceof env.getEnv().Canvas)) {
+    throw new Error('drawLandmarks - expected canvas to be of type: HTMLCanvasElement')
+  }
+
+  const drawOptions = Object.assign(
+    getDefaultDrawOptions(options),
+    (options || {})
+  )
+
+  const { drawLines } = Object.assign({ drawLines: false }, (options || {}))
+
+  const ctx = getContext2dOrThrow(canvas)
+  const { lineWidth, color = 'blue' } = drawOptions
+
+  const faceLandmarksArray = Array.isArray(faceLandmarks) ? faceLandmarks : [faceLandmarks]
+
+  faceLandmarksArray.forEach(landmarks => {
+    if (drawLines && landmarks instanceof FaceLandmarks68) {
+      ctx.strokeStyle = color
+      ctx.lineWidth = lineWidth
+      drawContour(ctx, landmarks.getJawOutline())
+      drawContour(ctx, landmarks.getLeftEyeBrow())
+      drawContour(ctx, landmarks.getRightEyeBrow())
+      drawContour(ctx, landmarks.getNose())
+      drawContour(ctx, landmarks.getLeftEye(), true)
+      drawContour(ctx, landmarks.getRightEye(), true)
+      drawContour(ctx, landmarks.getMouth(), true)
+      return
+    }
+
+    // else draw points
+    const ptOffset = lineWidth / 2
+    ctx.fillStyle = color
+    landmarks.positions.forEach(pt => ctx.fillRect(pt.x - ptOffset, pt.y - ptOffset, lineWidth, lineWidth))
+  })
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/dom/extractFaceTensors.ts b/node_modules/face-api.js/src/dom/extractFaceTensors.ts
new file mode 100644
index 0000000..6237013
--- /dev/null
+++ b/node_modules/face-api.js/src/dom/extractFaceTensors.ts
@@ -0,0 +1,45 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { isTensor4D, Rect, isTensor3D } from 'tfjs-image-recognition-base';
+
+import { FaceDetection } from '../classes/FaceDetection';
+
+/**
+ * Extracts the tensors of the image regions containing the detected faces.
+ * Useful if you want to compute the face descriptors for the face images.
+ * Using this method is faster then extracting a canvas for each face and
+ * converting them to tensors individually.
+ *
+ * @param imageTensor The image tensor that face detection has been performed on.
+ * @param detections The face detection results or face bounding boxes for that image.
+ * @returns Tensors of the corresponding image region for each detected face.
+ */
+export async function extractFaceTensors(
+  imageTensor: tf.Tensor3D | tf.Tensor4D,
+  detections: Array<FaceDetection | Rect>
+): Promise<tf.Tensor3D[]> {
+
+  if (!isTensor3D(imageTensor) && !isTensor4D(imageTensor)) {
+    throw new Error('extractFaceTensors - expected image tensor to be 3D or 4D')
+  }
+
+  if (isTensor4D(imageTensor) && imageTensor.shape[0] > 1) {
+    throw new Error('extractFaceTensors - batchSize > 1 not supported')
+  }
+
+  return tf.tidy(() => {
+    const [imgHeight, imgWidth, numChannels] = imageTensor.shape.slice(isTensor4D(imageTensor) ? 1 : 0)
+
+    const boxes = detections.map(
+      det => det instanceof FaceDetection
+        ? det.forSize(imgWidth, imgHeight).box
+        : det
+    )
+      .map(box => box.clipAtImageBorders(imgWidth, imgHeight))
+
+    const faceTensors = boxes.map(({ x, y, width, height }) =>
+      tf.slice3d(imageTensor.as3D(imgHeight, imgWidth, numChannels), [y, x, 0], [height, width, numChannels])
+    )
+
+    return faceTensors
+  })
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/dom/extractFaces.ts b/node_modules/face-api.js/src/dom/extractFaces.ts
new file mode 100644
index 0000000..9289407
--- /dev/null
+++ b/node_modules/face-api.js/src/dom/extractFaces.ts
@@ -0,0 +1,56 @@
+import {
+  createCanvas,
+  env,
+  getContext2dOrThrow,
+  imageTensorToCanvas,
+  Rect,
+  TNetInput,
+  toNetInput,
+} from 'tfjs-image-recognition-base';
+
+import { FaceDetection } from '../classes/FaceDetection';
+
+/**
+ * Extracts the image regions containing the detected faces.
+ *
+ * @param input The image that face detection has been performed on.
+ * @param detections The face detection results or face bounding boxes for that image.
+ * @returns The Canvases of the corresponding image region for each detected face.
+ */
+export async function extractFaces(
+  input: TNetInput,
+  detections: Array<FaceDetection | Rect>
+): Promise<HTMLCanvasElement[]> {
+
+  const { Canvas } = env.getEnv()
+
+  let canvas = input as HTMLCanvasElement
+
+  if (!(input instanceof Canvas)) {
+    const netInput = await toNetInput(input)
+
+    if (netInput.batchSize > 1) {
+      throw new Error('extractFaces - batchSize > 1 not supported')
+    }
+
+    const tensorOrCanvas = netInput.getInput(0)
+    canvas = tensorOrCanvas instanceof Canvas
+      ? tensorOrCanvas
+      : await imageTensorToCanvas(tensorOrCanvas)
+  }
+
+  const ctx = getContext2dOrThrow(canvas)
+  const boxes = detections.map(
+    det => det instanceof FaceDetection
+      ? det.forSize(canvas.width, canvas.height).box.floor()
+      : det
+  )
+    .map(box => box.clipAtImageBorders(canvas.width, canvas.height))
+
+  return boxes.map(({ x, y, width, height }) => {
+    const faceImg = createCanvas({ width, height })
+    getContext2dOrThrow(faceImg)
+      .putImageData(ctx.getImageData(x, y, width, height), 0, 0)
+    return faceImg
+  })
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/dom/index.ts b/node_modules/face-api.js/src/dom/index.ts
new file mode 100644
index 0000000..eecc0ae
--- /dev/null
+++ b/node_modules/face-api.js/src/dom/index.ts
@@ -0,0 +1,6 @@
+export * from './drawContour'
+export * from './drawLandmarks'
+export * from './drawFaceExpressions'
+export * from './extractFaces'
+export * from './extractFaceTensors'
+export * from './types'
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/dom/types.ts b/node_modules/face-api.js/src/dom/types.ts
new file mode 100644
index 0000000..02bb81b
--- /dev/null
+++ b/node_modules/face-api.js/src/dom/types.ts
@@ -0,0 +1,21 @@
+import { IPoint, IRect } from 'tfjs-tiny-yolov2';
+
+import { WithFaceExpressions } from '../factories/WithFaceExpressions';
+
+export type DrawLandmarksOptions = {
+  lineWidth?: number
+  color?: string
+  drawLines?: boolean
+}
+
+export type DrawFaceExpressionsOptions = {
+  primaryColor?: string
+  secondaryColor?: string
+  primaryFontSize?: number
+  secondaryFontSize?: number
+  minConfidence?: number
+}
+
+export type DrawFaceExpressionsInput = WithFaceExpressions<{
+  position: IPoint | IRect
+}>
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/euclideanDistance.ts b/node_modules/face-api.js/src/euclideanDistance.ts
new file mode 100644
index 0000000..bae75e8
--- /dev/null
+++ b/node_modules/face-api.js/src/euclideanDistance.ts
@@ -0,0 +1,13 @@
+export function euclideanDistance(arr1: number[] | Float32Array, arr2: number[] | Float32Array) {
+  if (arr1.length !== arr2.length)
+    throw new Error('euclideanDistance: arr1.length !== arr2.length')
+
+  const desc1 = Array.from(arr1)
+  const desc2 = Array.from(arr2)
+
+  return Math.sqrt(
+    desc1
+      .map((val, i) => val - desc2[i])
+      .reduce((res, diff) => res + Math.pow(diff, 2), 0)
+  )
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceExpressionNet/FaceExpressionNet.ts b/node_modules/face-api.js/src/faceExpressionNet/FaceExpressionNet.ts
new file mode 100644
index 0000000..ce2712d
--- /dev/null
+++ b/node_modules/face-api.js/src/faceExpressionNet/FaceExpressionNet.ts
@@ -0,0 +1,71 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { NetInput, TNetInput, toNetInput } from 'tfjs-image-recognition-base';
+
+import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';
+import { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';
+import { FaceProcessor } from '../faceProcessor/FaceProcessor';
+import { FaceExpression, faceExpressionLabels, FaceExpressionPrediction } from './types';
+
+export class FaceExpressionNet extends FaceProcessor<FaceFeatureExtractorParams> {
+
+  public static getFaceExpressionLabel(faceExpression: string) {
+    const label = faceExpressionLabels[faceExpression]
+
+    if (typeof label !== 'number') {
+      throw new Error(`getFaceExpressionLabel - no label for faceExpression: ${faceExpression}`)
+    }
+
+    return label
+  }
+
+  public static decodeProbabilites(probabilities: number[] | Float32Array): FaceExpressionPrediction[] {
+    if (probabilities.length !== 7) {
+      throw new Error(`decodeProbabilites - expected probabilities.length to be 7, have: ${probabilities.length}`)
+    }
+
+    return (Object.keys(faceExpressionLabels) as FaceExpression[])
+      .map(expression => ({ expression, probability: probabilities[faceExpressionLabels[expression]] }))
+  }
+
+  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {
+    super('FaceExpressionNet', faceFeatureExtractor)
+  }
+
+  public forwardInput(input: NetInput | tf.Tensor4D): tf.Tensor2D {
+    return tf.tidy(() => tf.softmax(this.runNet(input)))
+  }
+
+  public async forward(input: TNetInput): Promise<tf.Tensor2D> {
+    return this.forwardInput(await toNetInput(input))
+  }
+
+  public async predictExpressions(input: TNetInput) {
+    const netInput = await toNetInput(input)
+    const out = await this.forwardInput(netInput)
+    const probabilitesByBatch = await Promise.all(tf.unstack(out).map(async t => {
+      const data = await t.data()
+      t.dispose()
+      return data
+    }))
+    out.dispose()
+
+    const predictionsByBatch = probabilitesByBatch
+      .map(propablities => FaceExpressionNet.decodeProbabilites(propablities as Float32Array))
+
+    return netInput.isBatchInput
+      ? predictionsByBatch
+      : predictionsByBatch[0]
+  }
+
+  protected getDefaultModelName(): string {
+    return 'face_expression_model'
+  }
+
+  protected getClassifierChannelsIn(): number {
+    return 256
+  }
+
+  protected getClassifierChannelsOut(): number {
+    return 7
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceExpressionNet/index.ts b/node_modules/face-api.js/src/faceExpressionNet/index.ts
new file mode 100644
index 0000000..5ac0738
--- /dev/null
+++ b/node_modules/face-api.js/src/faceExpressionNet/index.ts
@@ -0,0 +1,2 @@
+export * from './FaceExpressionNet';
+export * from './types';
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceExpressionNet/types.ts b/node_modules/face-api.js/src/faceExpressionNet/types.ts
new file mode 100644
index 0000000..58ac277
--- /dev/null
+++ b/node_modules/face-api.js/src/faceExpressionNet/types.ts
@@ -0,0 +1,16 @@
+export const faceExpressionLabels = {
+  neutral: 0,
+  happy: 1,
+  sad: 2,
+  angry: 3,
+  fearful: 4,
+  disgusted: 5,
+  surprised:6
+}
+
+export type FaceExpression = 'neutral' | 'happy' | 'sad' | 'angry' | 'fearful' | 'disgusted' | 'surprised'
+
+export type FaceExpressionPrediction = {
+  expression: FaceExpression,
+  probability: number
+}
diff --git a/node_modules/face-api.js/src/faceFeatureExtractor/FaceFeatureExtractor.ts b/node_modules/face-api.js/src/faceFeatureExtractor/FaceFeatureExtractor.ts
new file mode 100644
index 0000000..67b6d38
--- /dev/null
+++ b/node_modules/face-api.js/src/faceFeatureExtractor/FaceFeatureExtractor.ts
@@ -0,0 +1,53 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { NetInput, NeuralNetwork, normalize, TNetInput, toNetInput } from 'tfjs-image-recognition-base';
+
+import { denseBlock4 } from './denseBlock';
+import { extractParams } from './extractParams';
+import { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';
+import { FaceFeatureExtractorParams, IFaceFeatureExtractor } from './types';
+
+export class FaceFeatureExtractor extends NeuralNetwork<FaceFeatureExtractorParams> implements IFaceFeatureExtractor<FaceFeatureExtractorParams> {
+
+  constructor() {
+    super('FaceFeatureExtractor')
+  }
+
+  public forwardInput(input: NetInput): tf.Tensor4D {
+
+    const { params } = this
+
+    if (!params) {
+      throw new Error('FaceFeatureExtractor - load model before inference')
+    }
+
+    return tf.tidy(() => {
+      const batchTensor = input.toBatchTensor(112, true)
+      const meanRgb = [122.782, 117.001, 104.298]
+      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D
+
+      let out = denseBlock4(normalized, params.dense0, true)
+      out = denseBlock4(out, params.dense1)
+      out = denseBlock4(out, params.dense2)
+      out = denseBlock4(out, params.dense3)
+      out = tf.avgPool(out, [7, 7], [2, 2], 'valid')
+
+      return out
+    })
+  }
+
+  public async forward(input: TNetInput): Promise<tf.Tensor4D> {
+    return this.forwardInput(await toNetInput(input))
+  }
+
+  protected getDefaultModelName(): string {
+    return 'face_feature_extractor_model'
+  }
+
+  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {
+    return extractParamsFromWeigthMap(weightMap)
+  }
+
+  protected extractParams(weights: Float32Array) {
+    return extractParams(weights)
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts b/node_modules/face-api.js/src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts
new file mode 100644
index 0000000..58fd1c5
--- /dev/null
+++ b/node_modules/face-api.js/src/faceFeatureExtractor/TinyFaceFeatureExtractor.ts
@@ -0,0 +1,52 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { NetInput, NeuralNetwork, normalize, TNetInput, toNetInput } from 'tfjs-image-recognition-base';
+
+import { denseBlock3 } from './denseBlock';
+import { extractParamsFromWeigthMapTiny } from './extractParamsFromWeigthMapTiny';
+import { extractParamsTiny } from './extractParamsTiny';
+import { IFaceFeatureExtractor, TinyFaceFeatureExtractorParams } from './types';
+
+export class TinyFaceFeatureExtractor extends NeuralNetwork<TinyFaceFeatureExtractorParams> implements IFaceFeatureExtractor<TinyFaceFeatureExtractorParams> {
+
+  constructor() {
+    super('TinyFaceFeatureExtractor')
+  }
+
+  public forwardInput(input: NetInput): tf.Tensor4D {
+
+    const { params } = this
+
+    if (!params) {
+      throw new Error('TinyFaceFeatureExtractor - load model before inference')
+    }
+
+    return tf.tidy(() => {
+      const batchTensor = input.toBatchTensor(112, true)
+      const meanRgb = [122.782, 117.001, 104.298]
+      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(255)) as tf.Tensor4D
+
+      let out = denseBlock3(normalized, params.dense0, true)
+      out = denseBlock3(out, params.dense1)
+      out = denseBlock3(out, params.dense2)
+      out = tf.avgPool(out, [14, 14], [2, 2], 'valid')
+
+      return out
+    })
+  }
+
+  public async forward(input: TNetInput): Promise<tf.Tensor4D> {
+    return this.forwardInput(await toNetInput(input))
+  }
+
+  protected getDefaultModelName(): string {
+    return 'face_feature_extractor_tiny_model'
+  }
+
+  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {
+    return extractParamsFromWeigthMapTiny(weightMap)
+  }
+
+  protected extractParams(weights: Float32Array) {
+    return extractParamsTiny(weights)
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceFeatureExtractor/denseBlock.ts b/node_modules/face-api.js/src/faceFeatureExtractor/denseBlock.ts
new file mode 100644
index 0000000..f626a88
--- /dev/null
+++ b/node_modules/face-api.js/src/faceFeatureExtractor/denseBlock.ts
@@ -0,0 +1,55 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { ConvParams, SeparableConvParams } from 'tfjs-tiny-yolov2';
+
+import { depthwiseSeparableConv } from './depthwiseSeparableConv';
+import { DenseBlock3Params, DenseBlock4Params } from './types';
+
+export function denseBlock3(
+  x: tf.Tensor4D,
+  denseBlockParams: DenseBlock3Params,
+  isFirstLayer: boolean = false
+): tf.Tensor4D {
+  return tf.tidy(() => {
+    const out1 = tf.relu(
+      isFirstLayer
+        ? tf.add(
+          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, [2, 2], 'same'),
+          denseBlockParams.conv0.bias
+        )
+        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, [2, 2])
+    ) as tf.Tensor4D
+    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])
+
+    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D
+    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])
+
+    return tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D
+  })
+}
+
+export function denseBlock4(
+  x: tf.Tensor4D,
+  denseBlockParams: DenseBlock4Params,
+  isFirstLayer: boolean = false,
+  isScaleDown: boolean = true
+): tf.Tensor4D {
+  return tf.tidy(() => {
+    const out1 = tf.relu(
+      isFirstLayer
+        ? tf.add(
+          tf.conv2d(x, (denseBlockParams.conv0 as ConvParams).filters, isScaleDown ? [2, 2] : [1, 1], 'same'),
+          denseBlockParams.conv0.bias
+        )
+        : depthwiseSeparableConv(x, denseBlockParams.conv0 as SeparableConvParams, isScaleDown ? [2, 2] : [1, 1])
+    ) as tf.Tensor4D
+    const out2 = depthwiseSeparableConv(out1, denseBlockParams.conv1, [1, 1])
+
+    const in3 = tf.relu(tf.add(out1, out2)) as tf.Tensor4D
+    const out3 = depthwiseSeparableConv(in3, denseBlockParams.conv2, [1, 1])
+
+    const in4 = tf.relu(tf.add(out1, tf.add(out2, out3))) as tf.Tensor4D
+    const out4 = depthwiseSeparableConv(in4, denseBlockParams.conv3, [1, 1])
+
+    return tf.relu(tf.add(out1, tf.add(out2, tf.add(out3, out4)))) as tf.Tensor4D
+  })
+}
diff --git a/node_modules/face-api.js/src/faceFeatureExtractor/depthwiseSeparableConv.ts b/node_modules/face-api.js/src/faceFeatureExtractor/depthwiseSeparableConv.ts
new file mode 100644
index 0000000..80135eb
--- /dev/null
+++ b/node_modules/face-api.js/src/faceFeatureExtractor/depthwiseSeparableConv.ts
@@ -0,0 +1,14 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { SeparableConvParams } from 'tfjs-tiny-yolov2';
+
+export function depthwiseSeparableConv(
+  x: tf.Tensor4D,
+  params: SeparableConvParams,
+  stride: [number, number]
+): tf.Tensor4D {
+  return tf.tidy(() => {
+    let out = tf.separableConv2d(x, params.depthwise_filter, params.pointwise_filter, stride, 'same')
+    out = tf.add(out, params.bias)
+    return out
+  })
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceFeatureExtractor/extractParams.ts b/node_modules/face-api.js/src/faceFeatureExtractor/extractParams.ts
new file mode 100644
index 0000000..7926abe
--- /dev/null
+++ b/node_modules/face-api.js/src/faceFeatureExtractor/extractParams.ts
@@ -0,0 +1,32 @@
+import { extractWeightsFactory, ParamMapping } from 'tfjs-image-recognition-base';
+
+import { extractorsFactory } from './extractorsFactory';
+import { FaceFeatureExtractorParams } from './types';
+
+export function extractParams(weights: Float32Array): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {
+
+  const paramMappings: ParamMapping[] = []
+
+  const {
+    extractWeights,
+    getRemainingWeights
+  } = extractWeightsFactory(weights)
+
+  const {
+    extractDenseBlock4Params
+  } = extractorsFactory(extractWeights, paramMappings)
+
+  const dense0 = extractDenseBlock4Params(3, 32, 'dense0', true)
+  const dense1 = extractDenseBlock4Params(32, 64, 'dense1')
+  const dense2 = extractDenseBlock4Params(64, 128, 'dense2')
+  const dense3 = extractDenseBlock4Params(128, 256, 'dense3')
+
+  if (getRemainingWeights().length !== 0) {
+    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)
+  }
+
+  return {
+    paramMappings,
+    params: { dense0, dense1, dense2, dense3 }
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceFeatureExtractor/extractParamsFromWeigthMap.ts b/node_modules/face-api.js/src/faceFeatureExtractor/extractParamsFromWeigthMap.ts
new file mode 100644
index 0000000..6e979c1
--- /dev/null
+++ b/node_modules/face-api.js/src/faceFeatureExtractor/extractParamsFromWeigthMap.ts
@@ -0,0 +1,27 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { disposeUnusedWeightTensors, ParamMapping } from 'tfjs-image-recognition-base';
+
+import { loadParamsFactory } from './loadParamsFactory';
+import { FaceFeatureExtractorParams } from './types';
+
+export function extractParamsFromWeigthMap(
+  weightMap: tf.NamedTensorMap
+): { params: FaceFeatureExtractorParams, paramMappings: ParamMapping[] } {
+
+  const paramMappings: ParamMapping[] = []
+
+  const {
+    extractDenseBlock4Params
+  } = loadParamsFactory(weightMap, paramMappings)
+
+  const params = {
+    dense0: extractDenseBlock4Params('dense0', true),
+    dense1: extractDenseBlock4Params('dense1'),
+    dense2: extractDenseBlock4Params('dense2'),
+    dense3: extractDenseBlock4Params('dense3')
+  }
+
+  disposeUnusedWeightTensors(weightMap, paramMappings)
+
+  return { params, paramMappings }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts b/node_modules/face-api.js/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts
new file mode 100644
index 0000000..c7f3047
--- /dev/null
+++ b/node_modules/face-api.js/src/faceFeatureExtractor/extractParamsFromWeigthMapTiny.ts
@@ -0,0 +1,26 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { disposeUnusedWeightTensors, ParamMapping } from 'tfjs-image-recognition-base';
+
+import { loadParamsFactory } from './loadParamsFactory';
+import { TinyFaceFeatureExtractorParams } from './types';
+
+export function extractParamsFromWeigthMapTiny(
+  weightMap: tf.NamedTensorMap
+): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {
+
+  const paramMappings: ParamMapping[] = []
+
+  const {
+    extractDenseBlock3Params
+  } = loadParamsFactory(weightMap, paramMappings)
+
+  const params = {
+    dense0: extractDenseBlock3Params('dense0', true),
+    dense1: extractDenseBlock3Params('dense1'),
+    dense2: extractDenseBlock3Params('dense2')
+  }
+
+  disposeUnusedWeightTensors(weightMap, paramMappings)
+
+  return { params, paramMappings }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceFeatureExtractor/extractParamsTiny.ts b/node_modules/face-api.js/src/faceFeatureExtractor/extractParamsTiny.ts
new file mode 100644
index 0000000..f603713
--- /dev/null
+++ b/node_modules/face-api.js/src/faceFeatureExtractor/extractParamsTiny.ts
@@ -0,0 +1,31 @@
+import { extractWeightsFactory, ParamMapping } from 'tfjs-image-recognition-base';
+
+import { extractorsFactory } from './extractorsFactory';
+import { TinyFaceFeatureExtractorParams } from './types';
+
+export function extractParamsTiny(weights: Float32Array): { params: TinyFaceFeatureExtractorParams, paramMappings: ParamMapping[] } {
+
+  const paramMappings: ParamMapping[] = []
+
+  const {
+    extractWeights,
+    getRemainingWeights
+  } = extractWeightsFactory(weights)
+
+  const {
+    extractDenseBlock3Params
+  } = extractorsFactory(extractWeights, paramMappings)
+
+  const dense0 = extractDenseBlock3Params(3, 32, 'dense0', true)
+  const dense1 = extractDenseBlock3Params(32, 64, 'dense1')
+  const dense2 = extractDenseBlock3Params(64, 128, 'dense2')
+
+  if (getRemainingWeights().length !== 0) {
+    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)
+  }
+
+  return {
+    paramMappings,
+    params: { dense0, dense1, dense2 }
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceFeatureExtractor/extractorsFactory.ts b/node_modules/face-api.js/src/faceFeatureExtractor/extractorsFactory.ts
new file mode 100644
index 0000000..af3ca91
--- /dev/null
+++ b/node_modules/face-api.js/src/faceFeatureExtractor/extractorsFactory.ts
@@ -0,0 +1,53 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { ExtractWeightsFunction, ParamMapping } from 'tfjs-image-recognition-base';
+import { extractConvParamsFactory, FCParams, SeparableConvParams } from 'tfjs-tiny-yolov2';
+
+import { DenseBlock3Params, DenseBlock4Params } from './types';
+
+export function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {
+
+  function extractSeparableConvParams(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {
+    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])
+    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])
+    const bias = tf.tensor1d(extractWeights(channelsOut))
+
+    paramMappings.push(
+      { paramPath: `${mappedPrefix}/depthwise_filter` },
+      { paramPath: `${mappedPrefix}/pointwise_filter` },
+      { paramPath: `${mappedPrefix}/bias` }
+    )
+
+    return new SeparableConvParams(
+      depthwise_filter,
+      pointwise_filter,
+      bias
+    )
+  }
+
+  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)
+
+  function extractDenseBlock3Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock3Params {
+
+    const conv0 = isFirstLayer
+      ? extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv0`)
+      : extractSeparableConvParams(channelsIn, channelsOut, `${mappedPrefix}/conv0`)
+    const conv1 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv1`)
+    const conv2 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv2`)
+
+    return { conv0, conv1, conv2 }
+  }
+
+  function extractDenseBlock4Params(channelsIn: number, channelsOut: number, mappedPrefix: string, isFirstLayer: boolean = false): DenseBlock4Params {
+
+    const { conv0, conv1, conv2 } = extractDenseBlock3Params(channelsIn, channelsOut, mappedPrefix, isFirstLayer)
+    const conv3 = extractSeparableConvParams(channelsOut, channelsOut, `${mappedPrefix}/conv3`)
+
+    return { conv0, conv1, conv2, conv3 }
+  }
+
+  return {
+    extractDenseBlock3Params,
+    extractDenseBlock4Params
+  }
+
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceFeatureExtractor/index.ts b/node_modules/face-api.js/src/faceFeatureExtractor/index.ts
new file mode 100644
index 0000000..18f738c
--- /dev/null
+++ b/node_modules/face-api.js/src/faceFeatureExtractor/index.ts
@@ -0,0 +1,2 @@
+export * from './FaceFeatureExtractor';
+export * from './TinyFaceFeatureExtractor';
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceFeatureExtractor/loadParamsFactory.ts b/node_modules/face-api.js/src/faceFeatureExtractor/loadParamsFactory.ts
new file mode 100644
index 0000000..f701281
--- /dev/null
+++ b/node_modules/face-api.js/src/faceFeatureExtractor/loadParamsFactory.ts
@@ -0,0 +1,55 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { extractWeightEntryFactory, ParamMapping } from 'tfjs-image-recognition-base';
+import { ConvParams, SeparableConvParams } from 'tfjs-tiny-yolov2';
+
+import { DenseBlock3Params, DenseBlock4Params } from './types';
+
+export function loadParamsFactory(weightMap: any, paramMappings: ParamMapping[]) {
+
+  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)
+
+  function extractConvParams(prefix: string): ConvParams {
+    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)
+    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)
+
+    return { filters, bias }
+  }
+
+  function extractSeparableConvParams(prefix: string): SeparableConvParams {
+    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)
+    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)
+    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)
+
+    return new SeparableConvParams(
+      depthwise_filter,
+      pointwise_filter,
+      bias
+    )
+  }
+
+  function extractDenseBlock3Params(prefix: string, isFirstLayer: boolean = false): DenseBlock3Params {
+    const conv0 = isFirstLayer
+      ? extractConvParams(`${prefix}/conv0`)
+      : extractSeparableConvParams(`${prefix}/conv0`)
+    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)
+    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)
+
+    return { conv0, conv1, conv2 }
+  }
+
+  function extractDenseBlock4Params(prefix: string, isFirstLayer: boolean = false): DenseBlock4Params {
+    const conv0 = isFirstLayer
+      ? extractConvParams(`${prefix}/conv0`)
+      : extractSeparableConvParams(`${prefix}/conv0`)
+    const conv1 = extractSeparableConvParams(`${prefix}/conv1`)
+    const conv2 = extractSeparableConvParams(`${prefix}/conv2`)
+    const conv3 = extractSeparableConvParams(`${prefix}/conv3`)
+
+    return { conv0, conv1, conv2, conv3 }
+  }
+
+  return {
+    extractDenseBlock3Params,
+    extractDenseBlock4Params
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceFeatureExtractor/types.ts b/node_modules/face-api.js/src/faceFeatureExtractor/types.ts
new file mode 100644
index 0000000..085cf1f
--- /dev/null
+++ b/node_modules/face-api.js/src/faceFeatureExtractor/types.ts
@@ -0,0 +1,47 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { NetInput, NeuralNetwork, TNetInput } from 'tfjs-image-recognition-base';
+import { ConvParams, SeparableConvParams } from 'tfjs-tiny-yolov2';
+
+export type ConvWithBatchNormParams = BatchNormParams & {
+  filter: tf.Tensor4D
+}
+
+export type BatchNormParams = {
+  mean: tf.Tensor1D
+  variance: tf.Tensor1D
+  scale: tf.Tensor1D
+  offset: tf.Tensor1D
+}
+
+export type SeparableConvWithBatchNormParams = {
+  depthwise: ConvWithBatchNormParams
+  pointwise: ConvWithBatchNormParams
+}
+
+export type DenseBlock3Params = {
+  conv0: SeparableConvParams | ConvParams
+  conv1: SeparableConvParams
+  conv2: SeparableConvParams
+}
+
+export type DenseBlock4Params = DenseBlock3Params & {
+  conv3: SeparableConvParams
+}
+
+export type TinyFaceFeatureExtractorParams = {
+  dense0: DenseBlock3Params
+  dense1: DenseBlock3Params
+  dense2: DenseBlock3Params
+}
+
+export type FaceFeatureExtractorParams = {
+  dense0: DenseBlock4Params
+  dense1: DenseBlock4Params
+  dense2: DenseBlock4Params
+  dense3: DenseBlock4Params
+}
+
+export interface IFaceFeatureExtractor<TNetParams extends TinyFaceFeatureExtractorParams | FaceFeatureExtractorParams> extends NeuralNetwork<TNetParams> {
+  forwardInput(input: NetInput): tf.Tensor4D
+  forward(input: TNetInput): Promise<tf.Tensor4D>
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceLandmarkNet/FaceLandmark68Net.ts b/node_modules/face-api.js/src/faceLandmarkNet/FaceLandmark68Net.ts
new file mode 100644
index 0000000..1c8022f
--- /dev/null
+++ b/node_modules/face-api.js/src/faceLandmarkNet/FaceLandmark68Net.ts
@@ -0,0 +1,18 @@
+import { FaceFeatureExtractor } from '../faceFeatureExtractor/FaceFeatureExtractor';
+import { FaceFeatureExtractorParams } from '../faceFeatureExtractor/types';
+import { FaceLandmark68NetBase } from './FaceLandmark68NetBase';
+
+export class FaceLandmark68Net extends FaceLandmark68NetBase<FaceFeatureExtractorParams> {
+
+  constructor(faceFeatureExtractor: FaceFeatureExtractor = new FaceFeatureExtractor()) {
+    super('FaceLandmark68Net', faceFeatureExtractor)
+  }
+
+  protected getDefaultModelName(): string {
+    return 'face_landmark_68_model'
+  }
+
+  protected getClassifierChannelsIn(): number {
+    return 256
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceLandmarkNet/FaceLandmark68NetBase.ts b/node_modules/face-api.js/src/faceLandmarkNet/FaceLandmark68NetBase.ts
new file mode 100644
index 0000000..2c032f1
--- /dev/null
+++ b/node_modules/face-api.js/src/faceLandmarkNet/FaceLandmark68NetBase.ts
@@ -0,0 +1,105 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { IDimensions, isEven, NetInput, Point, TNetInput, toNetInput } from 'tfjs-image-recognition-base';
+
+import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
+import { FaceFeatureExtractorParams, TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';
+import { FaceProcessor } from '../faceProcessor/FaceProcessor';
+
+export abstract class FaceLandmark68NetBase<
+  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams
+>
+  extends FaceProcessor<TExtractorParams> {
+
+  public postProcess(output: tf.Tensor2D, inputSize: number, originalDimensions: IDimensions[]): tf.Tensor2D {
+
+    const inputDimensions = originalDimensions.map(({ width, height }) => {
+      const scale = inputSize / Math.max(height, width)
+      return {
+        width: width * scale,
+        height: height * scale
+      }
+    })
+
+    const batchSize = inputDimensions.length
+
+    return tf.tidy(() => {
+      const createInterleavedTensor = (fillX: number, fillY: number) =>
+        tf.stack([
+          tf.fill([68], fillX),
+          tf.fill([68], fillY)
+        ], 1).as2D(1, 136).as1D()
+
+      const getPadding = (batchIdx: number, cond: (w: number, h: number) => boolean): number => {
+        const { width, height } = inputDimensions[batchIdx]
+        return cond(width, height) ? Math.abs(width - height) / 2 : 0
+      }
+      const getPaddingX = (batchIdx: number) => getPadding(batchIdx, (w, h) => w < h)
+      const getPaddingY = (batchIdx: number) => getPadding(batchIdx, (w, h) => h < w)
+
+      const landmarkTensors = output
+        .mul(tf.fill([batchSize, 136], inputSize))
+        .sub(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>
+          createInterleavedTensor(
+            getPaddingX(batchIdx),
+            getPaddingY(batchIdx)
+          )
+        )))
+        .div(tf.stack(Array.from(Array(batchSize), (_, batchIdx) =>
+          createInterleavedTensor(
+            inputDimensions[batchIdx].width,
+            inputDimensions[batchIdx].height
+          )
+        )))
+
+      return landmarkTensors as tf.Tensor2D
+    })
+  }
+
+  public forwardInput(input: NetInput): tf.Tensor2D {
+    return tf.tidy(() => {
+      const out = this.runNet(input)
+      return this.postProcess(
+        out,
+        input.inputSize as number,
+        input.inputDimensions.map(([height, width]) => ({ height, width }))
+      )
+    })
+  }
+
+  public async forward(input: TNetInput): Promise<tf.Tensor2D> {
+    return this.forwardInput(await toNetInput(input))
+  }
+
+  public async detectLandmarks(input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> {
+    const netInput = await toNetInput(input)
+    const landmarkTensors = tf.tidy(
+      () => tf.unstack(this.forwardInput(netInput))
+    )
+
+    const landmarksForBatch = await Promise.all(landmarkTensors.map(
+      async (landmarkTensor, batchIdx) => {
+        const landmarksArray = Array.from(await landmarkTensor.data())
+        const xCoords = landmarksArray.filter((_, i) => isEven(i))
+        const yCoords = landmarksArray.filter((_, i) => !isEven(i))
+
+        return new FaceLandmarks68(
+          Array(68).fill(0).map((_, i) => new Point(xCoords[i], yCoords[i])),
+          {
+            height: netInput.getInputHeight(batchIdx),
+            width : netInput.getInputWidth(batchIdx),
+          }
+        )
+      }
+    ))
+
+    landmarkTensors.forEach(t => t.dispose())
+
+    return netInput.isBatchInput
+      ? landmarksForBatch
+      : landmarksForBatch[0]
+  }
+
+  protected getClassifierChannelsOut(): number {
+    return 136
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceLandmarkNet/FaceLandmark68TinyNet.ts b/node_modules/face-api.js/src/faceLandmarkNet/FaceLandmark68TinyNet.ts
new file mode 100644
index 0000000..96b11c1
--- /dev/null
+++ b/node_modules/face-api.js/src/faceLandmarkNet/FaceLandmark68TinyNet.ts
@@ -0,0 +1,18 @@
+import { TinyFaceFeatureExtractor } from '../faceFeatureExtractor/TinyFaceFeatureExtractor';
+import { TinyFaceFeatureExtractorParams } from '../faceFeatureExtractor/types';
+import { FaceLandmark68NetBase } from './FaceLandmark68NetBase';
+
+export class FaceLandmark68TinyNet extends FaceLandmark68NetBase<TinyFaceFeatureExtractorParams> {
+
+  constructor(faceFeatureExtractor: TinyFaceFeatureExtractor = new TinyFaceFeatureExtractor()) {
+    super('FaceLandmark68TinyNet', faceFeatureExtractor)
+  }
+
+  protected getDefaultModelName(): string {
+    return 'face_landmark_68_tiny_model'
+  }
+
+  protected getClassifierChannelsIn(): number {
+    return 128
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceLandmarkNet/index.ts b/node_modules/face-api.js/src/faceLandmarkNet/index.ts
new file mode 100644
index 0000000..44f78b0
--- /dev/null
+++ b/node_modules/face-api.js/src/faceLandmarkNet/index.ts
@@ -0,0 +1,6 @@
+import { FaceLandmark68Net } from './FaceLandmark68Net';
+
+export * from './FaceLandmark68Net';
+export * from './FaceLandmark68TinyNet';
+
+export class FaceLandmarkNet extends FaceLandmark68Net {}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceProcessor/FaceProcessor.ts b/node_modules/face-api.js/src/faceProcessor/FaceProcessor.ts
new file mode 100644
index 0000000..517c252
--- /dev/null
+++ b/node_modules/face-api.js/src/faceProcessor/FaceProcessor.ts
@@ -0,0 +1,87 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { NetInput, NeuralNetwork } from 'tfjs-image-recognition-base';
+
+import { fullyConnectedLayer } from '../common/fullyConnectedLayer';
+import {
+  FaceFeatureExtractorParams,
+  IFaceFeatureExtractor,
+  TinyFaceFeatureExtractorParams,
+} from '../faceFeatureExtractor/types';
+import { extractParams } from './extractParams';
+import { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';
+import { NetParams } from './types';
+import { seperateWeightMaps } from './util';
+
+export abstract class FaceProcessor<
+  TExtractorParams extends FaceFeatureExtractorParams | TinyFaceFeatureExtractorParams
+>
+  extends NeuralNetwork<NetParams> {
+
+  protected _faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>
+
+  constructor(_name: string, faceFeatureExtractor: IFaceFeatureExtractor<TExtractorParams>) {
+    super(_name)
+    this._faceFeatureExtractor = faceFeatureExtractor
+  }
+
+  public get faceFeatureExtractor(): IFaceFeatureExtractor<TExtractorParams> {
+    return this._faceFeatureExtractor
+  }
+
+  protected abstract getDefaultModelName(): string
+  protected abstract getClassifierChannelsIn(): number
+  protected abstract getClassifierChannelsOut(): number
+
+  public runNet(input: NetInput | tf.Tensor4D): tf.Tensor2D {
+
+    const { params } = this
+
+    if (!params) {
+      throw new Error(`${this._name} - load model before inference`)
+    }
+
+    return tf.tidy(() => {
+      const bottleneckFeatures = input instanceof NetInput
+        ? this.faceFeatureExtractor.forwardInput(input)
+        : input
+      return fullyConnectedLayer(bottleneckFeatures.as2D(bottleneckFeatures.shape[0], -1), params.fc)
+    })
+  }
+
+  public dispose(throwOnRedispose: boolean = true) {
+    this.faceFeatureExtractor.dispose(throwOnRedispose)
+    super.dispose(throwOnRedispose)
+  }
+
+  public loadClassifierParams(weights: Float32Array) {
+    const { params, paramMappings } = this.extractClassifierParams(weights)
+    this._params = params
+    this._paramMappings = paramMappings
+  }
+
+  public extractClassifierParams(weights: Float32Array) {
+    return extractParams(weights, this.getClassifierChannelsIn(), this.getClassifierChannelsOut())
+  }
+
+  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {
+
+    const { featureExtractorMap, classifierMap } = seperateWeightMaps(weightMap)
+
+    this.faceFeatureExtractor.loadFromWeightMap(featureExtractorMap)
+
+    return extractParamsFromWeigthMap(classifierMap)
+  }
+
+  protected extractParams(weights: Float32Array) {
+
+    const cIn = this.getClassifierChannelsIn()
+    const cOut = this.getClassifierChannelsOut()
+    const classifierWeightSize = (cOut * cIn ) + cOut
+
+    const featureExtractorWeights = weights.slice(0, weights.length - classifierWeightSize)
+    const classifierWeights = weights.slice(weights.length - classifierWeightSize)
+
+    this.faceFeatureExtractor.extractWeights(featureExtractorWeights)
+    return this.extractClassifierParams(classifierWeights)
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceProcessor/extractParams.ts b/node_modules/face-api.js/src/faceProcessor/extractParams.ts
new file mode 100644
index 0000000..478d5d2
--- /dev/null
+++ b/node_modules/face-api.js/src/faceProcessor/extractParams.ts
@@ -0,0 +1,27 @@
+import { extractWeightsFactory, ParamMapping } from 'tfjs-image-recognition-base';
+import { extractFCParamsFactory } from 'tfjs-tiny-yolov2';
+
+import { NetParams } from './types';
+
+export function extractParams(weights: Float32Array, channelsIn: number, channelsOut: number): { params: NetParams, paramMappings: ParamMapping[] } {
+
+  const paramMappings: ParamMapping[] = []
+
+  const {
+    extractWeights,
+    getRemainingWeights
+  } = extractWeightsFactory(weights)
+
+  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)
+
+  const fc = extractFCParams(channelsIn, channelsOut, 'fc')
+
+  if (getRemainingWeights().length !== 0) {
+    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)
+  }
+
+  return {
+    paramMappings,
+    params: { fc }
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceProcessor/extractParamsFromWeigthMap.ts b/node_modules/face-api.js/src/faceProcessor/extractParamsFromWeigthMap.ts
new file mode 100644
index 0000000..6feb1f9
--- /dev/null
+++ b/node_modules/face-api.js/src/faceProcessor/extractParamsFromWeigthMap.ts
@@ -0,0 +1,28 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from 'tfjs-image-recognition-base';
+import { FCParams } from 'tfjs-tiny-yolov2';
+
+import { NetParams } from './types';
+
+export function extractParamsFromWeigthMap(
+  weightMap: tf.NamedTensorMap
+): { params: NetParams, paramMappings: ParamMapping[] } {
+
+  const paramMappings: ParamMapping[] = []
+
+  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)
+
+  function extractFcParams(prefix: string): FCParams {
+    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)
+    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)
+    return { weights, bias }
+  }
+
+  const params = {
+    fc: extractFcParams('fc')
+  }
+
+  disposeUnusedWeightTensors(weightMap, paramMappings)
+
+  return { params, paramMappings }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceProcessor/index.ts b/node_modules/face-api.js/src/faceProcessor/index.ts
new file mode 100644
index 0000000..943ea08
--- /dev/null
+++ b/node_modules/face-api.js/src/faceProcessor/index.ts
@@ -0,0 +1 @@
+export * from './FaceProcessor';
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceProcessor/types.ts b/node_modules/face-api.js/src/faceProcessor/types.ts
new file mode 100644
index 0000000..a234f76
--- /dev/null
+++ b/node_modules/face-api.js/src/faceProcessor/types.ts
@@ -0,0 +1,6 @@
+import { FCParams } from 'tfjs-tiny-yolov2';
+
+export type NetParams = {
+  fc: FCParams
+}
+
diff --git a/node_modules/face-api.js/src/faceProcessor/util.ts b/node_modules/face-api.js/src/faceProcessor/util.ts
new file mode 100644
index 0000000..6671a06
--- /dev/null
+++ b/node_modules/face-api.js/src/faceProcessor/util.ts
@@ -0,0 +1,15 @@
+import * as tf from '@tensorflow/tfjs-core';
+
+export function seperateWeightMaps(weightMap: tf.NamedTensorMap) {
+
+  const featureExtractorMap: tf.NamedTensorMap = {}
+  const classifierMap: tf.NamedTensorMap = {}
+
+  Object.keys(weightMap).forEach(key => {
+    const map = key.startsWith('fc') ? classifierMap : featureExtractorMap
+    map[key] = weightMap[key]
+  })
+
+  return { featureExtractorMap, classifierMap }
+
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceRecognitionNet/FaceRecognitionNet.ts b/node_modules/face-api.js/src/faceRecognitionNet/FaceRecognitionNet.ts
new file mode 100644
index 0000000..acbbcef
--- /dev/null
+++ b/node_modules/face-api.js/src/faceRecognitionNet/FaceRecognitionNet.ts
@@ -0,0 +1,92 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { NetInput, NeuralNetwork, normalize, TNetInput, toNetInput } from 'tfjs-image-recognition-base';
+
+import { convDown } from './convLayer';
+import { extractParams } from './extractParams';
+import { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';
+import { residual, residualDown } from './residualLayer';
+import { NetParams } from './types';
+
+
+export class FaceRecognitionNet extends NeuralNetwork<NetParams> {
+
+  constructor() {
+    super('FaceRecognitionNet')
+  }
+
+  public forwardInput(input: NetInput): tf.Tensor2D {
+
+    const { params } = this
+
+    if (!params) {
+      throw new Error('FaceRecognitionNet - load model before inference')
+    }
+
+    return tf.tidy(() => {
+      const batchTensor = input.toBatchTensor(150, true).toFloat()
+
+      const meanRgb = [122.782, 117.001, 104.298]
+      const normalized = normalize(batchTensor, meanRgb).div(tf.scalar(256)) as tf.Tensor4D
+
+      let out = convDown(normalized, params.conv32_down)
+      out = tf.maxPool(out, 3, 2, 'valid')
+
+      out = residual(out, params.conv32_1)
+      out = residual(out, params.conv32_2)
+      out = residual(out, params.conv32_3)
+
+      out = residualDown(out, params.conv64_down)
+      out = residual(out, params.conv64_1)
+      out = residual(out, params.conv64_2)
+      out = residual(out, params.conv64_3)
+
+      out = residualDown(out, params.conv128_down)
+      out = residual(out, params.conv128_1)
+      out = residual(out, params.conv128_2)
+
+      out = residualDown(out, params.conv256_down)
+      out = residual(out, params.conv256_1)
+      out = residual(out, params.conv256_2)
+      out = residualDown(out, params.conv256_down_out)
+
+      const globalAvg = out.mean([1, 2]) as tf.Tensor2D
+      const fullyConnected = tf.matMul(globalAvg, params.fc)
+
+      return fullyConnected
+    })
+  }
+
+  public async forward(input: TNetInput): Promise<tf.Tensor2D> {
+    return this.forwardInput(await toNetInput(input))
+  }
+
+  public async computeFaceDescriptor(input: TNetInput): Promise<Float32Array|Float32Array[]> {
+    const netInput = await toNetInput(input)
+
+    const faceDescriptorTensors = tf.tidy(
+      () => tf.unstack(this.forwardInput(netInput))
+    )
+
+    const faceDescriptorsForBatch = await Promise.all(faceDescriptorTensors.map(
+      t => t.data()
+    )) as Float32Array[]
+
+    faceDescriptorTensors.forEach(t => t.dispose())
+
+    return netInput.isBatchInput
+      ? faceDescriptorsForBatch
+      : faceDescriptorsForBatch[0]
+  }
+
+  protected getDefaultModelName(): string {
+    return 'face_recognition_model'
+  }
+
+  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {
+    return extractParamsFromWeigthMap(weightMap)
+  }
+
+  protected extractParams(weights: Float32Array) {
+    return extractParams(weights)
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceRecognitionNet/convLayer.ts b/node_modules/face-api.js/src/faceRecognitionNet/convLayer.ts
new file mode 100644
index 0000000..cdae895
--- /dev/null
+++ b/node_modules/face-api.js/src/faceRecognitionNet/convLayer.ts
@@ -0,0 +1,32 @@
+import * as tf from '@tensorflow/tfjs-core';
+
+import { scale } from './scaleLayer';
+import { ConvLayerParams } from './types';
+
+
+function convLayer(
+  x: tf.Tensor4D,
+  params: ConvLayerParams,
+  strides: [number, number],
+  withRelu: boolean,
+  padding: 'valid' | 'same' = 'same'
+): tf.Tensor4D {
+  const { filters, bias } = params.conv
+
+  let out = tf.conv2d(x, filters, strides, padding)
+  out = tf.add(out, bias)
+  out = scale(out, params.scale)
+  return withRelu ? tf.relu(out) : out
+}
+
+export function conv(x: tf.Tensor4D, params: ConvLayerParams) {
+  return convLayer(x, params, [1, 1], true)
+}
+
+export function convNoRelu(x: tf.Tensor4D, params: ConvLayerParams) {
+  return convLayer(x, params, [1, 1], false)
+}
+
+export function convDown(x: tf.Tensor4D, params: ConvLayerParams) {
+  return convLayer(x, params, [2, 2], true, 'valid')
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceRecognitionNet/extractParams.ts b/node_modules/face-api.js/src/faceRecognitionNet/extractParams.ts
new file mode 100644
index 0000000..853c1aa
--- /dev/null
+++ b/node_modules/face-api.js/src/faceRecognitionNet/extractParams.ts
@@ -0,0 +1,155 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { extractWeightsFactory, ExtractWeightsFunction, isFloat, ParamMapping } from 'tfjs-image-recognition-base';
+import { ConvParams } from 'tfjs-tiny-yolov2';
+
+import { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';
+
+function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {
+
+  function extractFilterValues(numFilterValues: number, numFilters: number, filterSize: number): tf.Tensor4D {
+    const weights = extractWeights(numFilterValues)
+    const depth = weights.length / (numFilters * filterSize * filterSize)
+
+    if (isFloat(depth)) {
+      throw new Error(`depth has to be an integer: ${depth}, weights.length: ${weights.length}, numFilters: ${numFilters}, filterSize: ${filterSize}`)
+    }
+
+    return tf.tidy(
+      () => tf.transpose(
+        tf.tensor4d(weights, [numFilters, depth, filterSize, filterSize]),
+        [2, 3, 1, 0]
+      )
+    )
+  }
+
+  function extractConvParams(
+    numFilterValues: number,
+    numFilters: number,
+    filterSize: number,
+    mappedPrefix: string
+  ): ConvParams {
+
+    const filters = extractFilterValues(numFilterValues, numFilters, filterSize)
+    const bias = tf.tensor1d(extractWeights(numFilters))
+
+    paramMappings.push(
+      { paramPath: `${mappedPrefix}/filters` },
+      { paramPath: `${mappedPrefix}/bias` }
+    )
+
+    return { filters, bias }
+  }
+
+  function extractScaleLayerParams(numWeights: number, mappedPrefix: string): ScaleLayerParams {
+
+    const weights = tf.tensor1d(extractWeights(numWeights))
+    const biases = tf.tensor1d(extractWeights(numWeights))
+
+    paramMappings.push(
+      { paramPath: `${mappedPrefix}/weights` },
+      { paramPath: `${mappedPrefix}/biases` }
+    )
+
+    return {
+      weights,
+      biases
+    }
+  }
+
+  function extractConvLayerParams(
+    numFilterValues: number,
+    numFilters: number,
+    filterSize: number,
+    mappedPrefix: string
+  ): ConvLayerParams {
+
+    const conv = extractConvParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv`)
+    const scale = extractScaleLayerParams(numFilters, `${mappedPrefix}/scale`)
+
+    return { conv, scale }
+  }
+
+  function extractResidualLayerParams(
+    numFilterValues: number,
+    numFilters: number,
+    filterSize: number,
+    mappedPrefix: string,
+    isDown: boolean = false
+  ): ResidualLayerParams {
+
+    const conv1 = extractConvLayerParams((isDown ? 0.5 : 1) * numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv1`)
+    const conv2 = extractConvLayerParams(numFilterValues, numFilters, filterSize, `${mappedPrefix}/conv2`)
+
+    return { conv1, conv2 }
+  }
+
+  return {
+    extractConvLayerParams,
+    extractResidualLayerParams
+  }
+
+}
+
+export function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {
+
+  const {
+    extractWeights,
+    getRemainingWeights
+  } = extractWeightsFactory(weights)
+
+  const paramMappings: ParamMapping[] = []
+
+  const {
+    extractConvLayerParams,
+    extractResidualLayerParams
+  } = extractorsFactory(extractWeights, paramMappings)
+
+  const conv32_down = extractConvLayerParams(4704, 32, 7, 'conv32_down')
+  const conv32_1 = extractResidualLayerParams(9216, 32, 3, 'conv32_1')
+  const conv32_2 = extractResidualLayerParams(9216, 32, 3, 'conv32_2')
+  const conv32_3 = extractResidualLayerParams(9216, 32, 3, 'conv32_3')
+
+  const conv64_down = extractResidualLayerParams(36864, 64, 3, 'conv64_down', true)
+  const conv64_1 = extractResidualLayerParams(36864, 64, 3, 'conv64_1')
+  const conv64_2 = extractResidualLayerParams(36864, 64, 3, 'conv64_2')
+  const conv64_3 = extractResidualLayerParams(36864, 64, 3, 'conv64_3')
+
+  const conv128_down = extractResidualLayerParams(147456, 128, 3, 'conv128_down', true)
+  const conv128_1 = extractResidualLayerParams(147456, 128, 3, 'conv128_1')
+  const conv128_2 = extractResidualLayerParams(147456, 128, 3, 'conv128_2')
+
+  const conv256_down = extractResidualLayerParams(589824, 256, 3, 'conv256_down', true)
+  const conv256_1 = extractResidualLayerParams(589824, 256, 3, 'conv256_1')
+  const conv256_2 = extractResidualLayerParams(589824, 256, 3, 'conv256_2')
+  const conv256_down_out = extractResidualLayerParams(589824, 256, 3, 'conv256_down_out')
+
+  const fc = tf.tidy(
+    () => tf.transpose(tf.tensor2d(extractWeights(256 * 128), [128, 256]), [1, 0])
+  )
+  paramMappings.push({ paramPath: `fc` })
+
+  if (getRemainingWeights().length !== 0) {
+    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)
+  }
+
+  const params = {
+    conv32_down,
+    conv32_1,
+    conv32_2,
+    conv32_3,
+    conv64_down,
+    conv64_1,
+    conv64_2,
+    conv64_3,
+    conv128_down,
+    conv128_1,
+    conv128_2,
+    conv256_down,
+    conv256_1,
+    conv256_2,
+    conv256_down_out,
+    fc
+  }
+
+  return { params, paramMappings }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceRecognitionNet/extractParamsFromWeigthMap.ts b/node_modules/face-api.js/src/faceRecognitionNet/extractParamsFromWeigthMap.ts
new file mode 100644
index 0000000..0afd3e3
--- /dev/null
+++ b/node_modules/face-api.js/src/faceRecognitionNet/extractParamsFromWeigthMap.ts
@@ -0,0 +1,106 @@
+import * as tf from '@tensorflow/tfjs-core';
+import {
+  disposeUnusedWeightTensors,
+  extractWeightEntryFactory,
+  isTensor2D,
+  loadWeightMap,
+  ParamMapping,
+} from 'tfjs-image-recognition-base';
+
+import { ConvLayerParams, NetParams, ResidualLayerParams, ScaleLayerParams } from './types';
+
+function extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {
+
+  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)
+
+  function extractScaleLayerParams(prefix: string): ScaleLayerParams {
+
+    const weights = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/weights`, 1)
+    const biases = extractWeightEntry<tf.Tensor1D>(`${prefix}/scale/biases`, 1)
+
+    return { weights, biases }
+  }
+
+  function extractConvLayerParams(prefix: string): ConvLayerParams {
+
+    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/conv/filters`, 4)
+    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/conv/bias`, 1)
+    const scale = extractScaleLayerParams(prefix)
+
+    return { conv: { filters, bias }, scale }
+  }
+
+  function extractResidualLayerParams(prefix: string): ResidualLayerParams {
+    return {
+      conv1: extractConvLayerParams(`${prefix}/conv1`),
+      conv2: extractConvLayerParams(`${prefix}/conv2`)
+    }
+  }
+
+  return {
+    extractConvLayerParams,
+    extractResidualLayerParams
+  }
+
+}
+
+export function extractParamsFromWeigthMap(
+  weightMap: tf.NamedTensorMap
+): { params: NetParams, paramMappings: ParamMapping[] } {
+
+  const paramMappings: ParamMapping[] = []
+
+  const {
+    extractConvLayerParams,
+    extractResidualLayerParams
+  } = extractorsFactory(weightMap, paramMappings)
+
+  const conv32_down = extractConvLayerParams('conv32_down')
+  const conv32_1 = extractResidualLayerParams('conv32_1')
+  const conv32_2 = extractResidualLayerParams('conv32_2')
+  const conv32_3 = extractResidualLayerParams('conv32_3')
+
+  const conv64_down = extractResidualLayerParams('conv64_down')
+  const conv64_1 = extractResidualLayerParams('conv64_1')
+  const conv64_2 = extractResidualLayerParams('conv64_2')
+  const conv64_3 = extractResidualLayerParams('conv64_3')
+
+  const conv128_down = extractResidualLayerParams('conv128_down')
+  const conv128_1 = extractResidualLayerParams('conv128_1')
+  const conv128_2 = extractResidualLayerParams('conv128_2')
+
+  const conv256_down = extractResidualLayerParams('conv256_down')
+  const conv256_1 = extractResidualLayerParams('conv256_1')
+  const conv256_2 = extractResidualLayerParams('conv256_2')
+  const conv256_down_out = extractResidualLayerParams('conv256_down_out')
+
+  const fc = weightMap['fc']
+  paramMappings.push({ originalPath: 'fc', paramPath: 'fc' })
+
+  if (!isTensor2D(fc)) {
+    throw new Error(`expected weightMap[fc] to be a Tensor2D, instead have ${fc}`)
+  }
+
+  const params = {
+    conv32_down,
+    conv32_1,
+    conv32_2,
+    conv32_3,
+    conv64_down,
+    conv64_1,
+    conv64_2,
+    conv64_3,
+    conv128_down,
+    conv128_1,
+    conv128_2,
+    conv256_down,
+    conv256_1,
+    conv256_2,
+    conv256_down_out,
+    fc
+  }
+
+  disposeUnusedWeightTensors(weightMap, paramMappings)
+
+  return { params, paramMappings }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceRecognitionNet/index.ts b/node_modules/face-api.js/src/faceRecognitionNet/index.ts
new file mode 100644
index 0000000..221249e
--- /dev/null
+++ b/node_modules/face-api.js/src/faceRecognitionNet/index.ts
@@ -0,0 +1,9 @@
+import { FaceRecognitionNet } from './FaceRecognitionNet';
+
+export * from './FaceRecognitionNet';
+
+export function createFaceRecognitionNet(weights: Float32Array) {
+  const net = new FaceRecognitionNet()
+  net.extractWeights(weights)
+  return net
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceRecognitionNet/residualLayer.ts b/node_modules/face-api.js/src/faceRecognitionNet/residualLayer.ts
new file mode 100644
index 0000000..d0efeaa
--- /dev/null
+++ b/node_modules/face-api.js/src/faceRecognitionNet/residualLayer.ts
@@ -0,0 +1,40 @@
+import * as tf from '@tensorflow/tfjs-core';
+
+import { conv, convDown, convNoRelu } from './convLayer';
+import { ResidualLayerParams } from './types';
+
+export function residual(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {
+  let out = conv(x, params.conv1)
+  out = convNoRelu(out, params.conv2)
+  out = tf.add(out, x)
+  out = tf.relu(out)
+  return out
+}
+
+export function residualDown(x: tf.Tensor4D, params: ResidualLayerParams): tf.Tensor4D {
+  let out = convDown(x, params.conv1)
+  out = convNoRelu(out, params.conv2)
+
+  let pooled = tf.avgPool(x, 2, 2, 'valid') as tf.Tensor4D
+  const zeros = tf.zeros<tf.Rank.R4>(pooled.shape)
+  const isPad = pooled.shape[3] !== out.shape[3]
+  const isAdjustShape = pooled.shape[1] !== out.shape[1] || pooled.shape[2] !== out.shape[2]
+
+  if (isAdjustShape) {
+    const padShapeX = [...out.shape] as [number, number, number, number]
+    padShapeX[1] = 1
+    const zerosW = tf.zeros<tf.Rank.R4>(padShapeX)
+    out = tf.concat([out, zerosW], 1)
+
+    const padShapeY = [...out.shape] as [number, number, number, number]
+    padShapeY[2] = 1
+    const zerosH = tf.zeros<tf.Rank.R4>(padShapeY)
+    out = tf.concat([out, zerosH], 2)
+  }
+
+  pooled = isPad ? tf.concat([pooled, zeros], 3) : pooled
+  out = tf.add(pooled, out) as tf.Tensor4D
+
+  out = tf.relu(out)
+  return out
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/faceRecognitionNet/scaleLayer.ts b/node_modules/face-api.js/src/faceRecognitionNet/scaleLayer.ts
new file mode 100644
index 0000000..1f9902a
--- /dev/null
+++ b/node_modules/face-api.js/src/faceRecognitionNet/scaleLayer.ts
@@ -0,0 +1,7 @@
+import * as tf from '@tensorflow/tfjs-core';
+
+import { ScaleLayerParams } from './types';
+
+export function scale(x: tf.Tensor4D, params: ScaleLayerParams): tf.Tensor4D {
+  return tf.add(tf.mul(x, params.weights), params.biases)
+}
diff --git a/node_modules/face-api.js/src/faceRecognitionNet/types.ts b/node_modules/face-api.js/src/faceRecognitionNet/types.ts
new file mode 100644
index 0000000..e40ce40
--- /dev/null
+++ b/node_modules/face-api.js/src/faceRecognitionNet/types.ts
@@ -0,0 +1,35 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { ConvParams } from 'tfjs-tiny-yolov2';
+
+export type ScaleLayerParams = {
+  weights: tf.Tensor1D
+  biases: tf.Tensor1D
+}
+export type ResidualLayerParams = {
+  conv1: ConvLayerParams
+  conv2: ConvLayerParams
+}
+
+export type ConvLayerParams = {
+  conv: ConvParams
+  scale: ScaleLayerParams
+}
+
+export type NetParams = {
+  conv32_down: ConvLayerParams
+  conv32_1: ResidualLayerParams
+  conv32_2: ResidualLayerParams
+  conv32_3: ResidualLayerParams
+  conv64_down: ResidualLayerParams
+  conv64_1: ResidualLayerParams
+  conv64_2: ResidualLayerParams
+  conv64_3: ResidualLayerParams
+  conv128_down: ResidualLayerParams
+  conv128_1: ResidualLayerParams
+  conv128_2: ResidualLayerParams
+  conv256_down: ResidualLayerParams
+  conv256_1: ResidualLayerParams
+  conv256_2: ResidualLayerParams
+  conv256_down_out: ResidualLayerParams
+  fc: tf.Tensor2D
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/factories/WithFaceDescriptor.ts b/node_modules/face-api.js/src/factories/WithFaceDescriptor.ts
new file mode 100644
index 0000000..bb7f313
--- /dev/null
+++ b/node_modules/face-api.js/src/factories/WithFaceDescriptor.ts
@@ -0,0 +1,15 @@
+export type WithFaceDescriptor<TSource> = TSource & {
+  descriptor: Float32Array
+}
+
+export function extendWithFaceDescriptor<
+  TSource
+> (
+  sourceObj: TSource,
+  descriptor: Float32Array
+): WithFaceDescriptor<TSource> {
+
+  const extension = { descriptor }
+  return Object.assign({}, sourceObj, extension)
+}
+
diff --git a/node_modules/face-api.js/src/factories/WithFaceDetection.ts b/node_modules/face-api.js/src/factories/WithFaceDetection.ts
new file mode 100644
index 0000000..ff6c2bd
--- /dev/null
+++ b/node_modules/face-api.js/src/factories/WithFaceDetection.ts
@@ -0,0 +1,16 @@
+import { FaceDetection } from '../classes/FaceDetection';
+
+export type WithFaceDetection<TSource> = TSource & {
+  detection: FaceDetection
+}
+
+export function extendWithFaceDetection<
+  TSource
+> (
+  sourceObj: TSource,
+  detection: FaceDetection
+): WithFaceDetection<TSource> {
+
+  const extension = { detection }
+  return Object.assign({}, sourceObj, extension)
+}
diff --git a/node_modules/face-api.js/src/factories/WithFaceExpressions.ts b/node_modules/face-api.js/src/factories/WithFaceExpressions.ts
new file mode 100644
index 0000000..e02d920
--- /dev/null
+++ b/node_modules/face-api.js/src/factories/WithFaceExpressions.ts
@@ -0,0 +1,16 @@
+import { FaceExpressionPrediction } from '../faceExpressionNet/types';
+
+export type WithFaceExpressions<TSource> = TSource & {
+  expressions: FaceExpressionPrediction[]
+}
+
+export function extendWithFaceExpressions<
+  TSource
+> (
+  sourceObj: TSource,
+  expressions: FaceExpressionPrediction[]
+): WithFaceExpressions<TSource> {
+
+  const extension = { expressions }
+  return Object.assign({}, sourceObj, extension)
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/factories/WithFaceLandmarks.ts b/node_modules/face-api.js/src/factories/WithFaceLandmarks.ts
new file mode 100644
index 0000000..877f7b1
--- /dev/null
+++ b/node_modules/face-api.js/src/factories/WithFaceLandmarks.ts
@@ -0,0 +1,37 @@
+import { FaceDetection } from '../classes/FaceDetection';
+import { FaceLandmarks } from '../classes/FaceLandmarks';
+import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
+import { WithFaceDetection } from './WithFaceDetection';
+
+export type WithFaceLandmarks<
+  TSource extends WithFaceDetection<{}>,
+  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68
+> = TSource & {
+  landmarks: TFaceLandmarks
+  unshiftedLandmarks: TFaceLandmarks
+  alignedRect: FaceDetection
+}
+
+export function extendWithFaceLandmarks<
+  TSource extends WithFaceDetection<{}>,
+  TFaceLandmarks extends FaceLandmarks = FaceLandmarks68
+> (
+  sourceObj: TSource,
+  unshiftedLandmarks: TFaceLandmarks
+): WithFaceLandmarks<TSource, TFaceLandmarks> {
+
+  const { box: shift } = sourceObj.detection
+  const landmarks = unshiftedLandmarks.shiftBy<TFaceLandmarks>(shift.x, shift.y)
+
+  const rect = landmarks.align()
+  const { imageDims } = sourceObj.detection
+  const alignedRect = new FaceDetection(sourceObj.detection.score, rect.rescale(imageDims.reverse()), imageDims)
+
+  const extension = {
+    landmarks,
+    unshiftedLandmarks,
+    alignedRect
+  }
+
+  return Object.assign({}, sourceObj, extension)
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/factories/index.ts b/node_modules/face-api.js/src/factories/index.ts
new file mode 100644
index 0000000..01bbcb7
--- /dev/null
+++ b/node_modules/face-api.js/src/factories/index.ts
@@ -0,0 +1,4 @@
+export * from './WithFaceDescriptor'
+export * from './WithFaceDetection'
+export * from './WithFaceExpressions'
+export * from './WithFaceLandmarks'
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/globalApi/ComposableTask.ts b/node_modules/face-api.js/src/globalApi/ComposableTask.ts
new file mode 100644
index 0000000..1531a3e
--- /dev/null
+++ b/node_modules/face-api.js/src/globalApi/ComposableTask.ts
@@ -0,0 +1,12 @@
+export class ComposableTask<T> {
+
+  public async then(
+    onfulfilled: (value: T) => T | PromiseLike<T>
+  ): Promise<T> {
+    return onfulfilled(await this.run())
+  }
+
+  public async run(): Promise<T> {
+    throw new Error('ComposableTask - run is not implemented')
+  }
+}
diff --git a/node_modules/face-api.js/src/globalApi/ComputeFaceDescriptorsTasks.ts b/node_modules/face-api.js/src/globalApi/ComputeFaceDescriptorsTasks.ts
new file mode 100644
index 0000000..d5388b8
--- /dev/null
+++ b/node_modules/face-api.js/src/globalApi/ComputeFaceDescriptorsTasks.ts
@@ -0,0 +1,65 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { TNetInput } from 'tfjs-image-recognition-base';
+
+import { extractFaces, extractFaceTensors } from '../dom';
+import { extendWithFaceDescriptor, WithFaceDescriptor } from '../factories/WithFaceDescriptor';
+import { WithFaceDetection } from '../factories/WithFaceDetection';
+import { WithFaceLandmarks } from '../factories/WithFaceLandmarks';
+import { ComposableTask } from './ComposableTask';
+import { nets } from './nets';
+
+export class ComputeFaceDescriptorsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {
+  constructor(
+    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,
+    protected input: TNetInput
+  ) {
+    super()
+  }
+}
+
+export class ComputeAllFaceDescriptorsTask<
+  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>
+> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource>[], TSource[]> {
+
+  public async run(): Promise<WithFaceDescriptor<TSource>[]> {
+
+    const parentResults = await this.parentTask
+
+    const alignedRects = parentResults.map(({ alignedRect }) => alignedRect)
+    const alignedFaces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor
+      ? await extractFaceTensors(this.input, alignedRects)
+      : await extractFaces(this.input, alignedRects)
+
+    const results = await Promise.all(parentResults.map(async (parentResult, i) => {
+      const descriptor = await nets.faceRecognitionNet.computeFaceDescriptor(alignedFaces[i]) as Float32Array
+      return extendWithFaceDescriptor<TSource>(parentResult, descriptor)
+    }))
+
+    alignedFaces.forEach(f => f instanceof tf.Tensor && f.dispose())
+
+    return results
+  }
+}
+
+export class ComputeSingleFaceDescriptorTask<
+  TSource extends WithFaceLandmarks<WithFaceDetection<{}>>
+> extends ComputeFaceDescriptorsTaskBase<WithFaceDescriptor<TSource> | undefined, TSource | undefined> {
+
+  public async run(): Promise<WithFaceDescriptor<TSource> | undefined> {
+
+    const parentResult = await this.parentTask
+    if (!parentResult) {
+      return
+    }
+
+    const { alignedRect } = parentResult
+    const alignedFaces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor
+      ? await extractFaceTensors(this.input, [alignedRect])
+      : await extractFaces(this.input, [alignedRect])
+    const descriptor = await nets.faceRecognitionNet.computeFaceDescriptor(alignedFaces[0]) as Float32Array
+
+    alignedFaces.forEach(f => f instanceof tf.Tensor && f.dispose())
+
+    return extendWithFaceDescriptor(parentResult, descriptor)
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/globalApi/DetectFaceLandmarksTasks.ts b/node_modules/face-api.js/src/globalApi/DetectFaceLandmarksTasks.ts
new file mode 100644
index 0000000..d88bb35
--- /dev/null
+++ b/node_modules/face-api.js/src/globalApi/DetectFaceLandmarksTasks.ts
@@ -0,0 +1,86 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { TNetInput } from 'tfjs-image-recognition-base';
+
+import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
+import { extractFaces, extractFaceTensors } from '../dom';
+import { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';
+import { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';
+import { WithFaceDetection } from '../factories/WithFaceDetection';
+import { extendWithFaceLandmarks, WithFaceLandmarks } from '../factories/WithFaceLandmarks';
+import { ComposableTask } from './ComposableTask';
+import { ComputeAllFaceDescriptorsTask, ComputeSingleFaceDescriptorTask } from './ComputeFaceDescriptorsTasks';
+import { nets } from './nets';
+import { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionTask } from './PredictFaceExpressionsTask';
+
+export class DetectFaceLandmarksTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {
+  constructor(
+    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,
+    protected input: TNetInput,
+    protected useTinyLandmarkNet: boolean
+  ) {
+    super()
+  }
+
+  protected get landmarkNet(): FaceLandmark68Net | FaceLandmark68TinyNet {
+    return this.useTinyLandmarkNet
+      ? nets.faceLandmark68TinyNet
+      : nets.faceLandmark68Net
+  }
+}
+
+export class DetectAllFaceLandmarksTask<
+  TSource extends WithFaceDetection<{}>
+> extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource>[], TSource[]> {
+
+  public async run(): Promise<WithFaceLandmarks<TSource>[]> {
+
+    const parentResults = await this.parentTask
+    const detections = parentResults.map(res => res.detection)
+
+    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor
+      ? await extractFaceTensors(this.input, detections)
+      : await extractFaces(this.input, detections)
+
+    const faceLandmarksByFace = await Promise.all(faces.map(
+      face => this.landmarkNet.detectLandmarks(face)
+    )) as FaceLandmarks68[]
+
+    faces.forEach(f => f instanceof tf.Tensor && f.dispose())
+
+    return parentResults.map((parentResult, i) =>
+      extendWithFaceLandmarks<TSource>(parentResult, faceLandmarksByFace[i])
+    )
+  }
+
+  withFaceDescriptors(): ComputeAllFaceDescriptorsTask<WithFaceLandmarks<TSource>> {
+    return new ComputeAllFaceDescriptorsTask<WithFaceLandmarks<TSource>>(this, this.input)
+  }
+}
+
+export class DetectSingleFaceLandmarksTask<
+  TSource extends WithFaceDetection<{}>
+>  extends DetectFaceLandmarksTaskBase<WithFaceLandmarks<TSource> | undefined, TSource | undefined> {
+
+  public async run(): Promise<WithFaceLandmarks<TSource> | undefined> {
+
+    const parentResult = await this.parentTask
+    if (!parentResult) {
+      return
+    }
+
+    const { detection } = parentResult
+    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor
+      ? await extractFaceTensors(this.input, [detection])
+      : await extractFaces(this.input, [detection])
+
+    const landmarks = await this.landmarkNet.detectLandmarks(faces[0]) as FaceLandmarks68
+
+    faces.forEach(f => f instanceof tf.Tensor && f.dispose())
+
+    return extendWithFaceLandmarks<TSource>(parentResult, landmarks)
+  }
+
+  withFaceDescriptor(): ComputeSingleFaceDescriptorTask<WithFaceLandmarks<TSource>> {
+    return new ComputeSingleFaceDescriptorTask<WithFaceLandmarks<TSource>>(this, this.input)
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/globalApi/DetectFacesTasks.ts b/node_modules/face-api.js/src/globalApi/DetectFacesTasks.ts
new file mode 100644
index 0000000..695ab76
--- /dev/null
+++ b/node_modules/face-api.js/src/globalApi/DetectFacesTasks.ts
@@ -0,0 +1,112 @@
+import { TNetInput } from 'tfjs-image-recognition-base';
+import { TinyYolov2Options } from 'tfjs-tiny-yolov2';
+
+import { FaceDetection } from '../classes/FaceDetection';
+import { extendWithFaceDetection, WithFaceDetection } from '../factories/WithFaceDetection';
+import { MtcnnOptions } from '../mtcnn/MtcnnOptions';
+import { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';
+import { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';
+import { ComposableTask } from './ComposableTask';
+import { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';
+import { nets } from './nets';
+import { FaceDetectionOptions } from './types';
+import { PredictAllFaceExpressionsTask, PredictSingleFaceExpressionTask } from './PredictFaceExpressionsTask';
+
+export class DetectFacesTaskBase<TReturn> extends ComposableTask<TReturn> {
+  constructor(
+    protected input: TNetInput,
+    protected options: FaceDetectionOptions = new SsdMobilenetv1Options()
+  ) {
+    super()
+  }
+}
+
+export class DetectAllFacesTask extends DetectFacesTaskBase<FaceDetection[]> {
+
+  public async run(): Promise<FaceDetection[]> {
+
+    const { input, options } = this
+
+    if (options instanceof MtcnnOptions) {
+      return (await nets.mtcnn.forward(input, options))
+        .map(result => result.detection)
+    }
+
+    const faceDetectionFunction = options instanceof TinyFaceDetectorOptions
+      ? (input: TNetInput) => nets.tinyFaceDetector.locateFaces(input, options)
+      : (
+        options instanceof SsdMobilenetv1Options
+          ? (input: TNetInput) => nets.ssdMobilenetv1.locateFaces(input, options)
+          : (
+            options instanceof TinyYolov2Options
+              ? (input: TNetInput) => nets.tinyYolov2.locateFaces(input, options)
+              : null
+          )
+      )
+
+    if (!faceDetectionFunction) {
+      throw new Error('detectFaces - expected options to be instance of TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options')
+    }
+
+    return faceDetectionFunction(input)
+  }
+
+  private runAndExtendWithFaceDetections(): Promise<WithFaceDetection<{}>[]> {
+    return new Promise<WithFaceDetection<{}>[]>(async res => {
+      const detections = await this.run()
+      return res(detections.map(detection => extendWithFaceDetection({}, detection)))
+    })
+  }
+
+  withFaceLandmarks(useTinyLandmarkNet: boolean = false): DetectAllFaceLandmarksTask<WithFaceDetection<{}>> {
+    return new DetectAllFaceLandmarksTask<WithFaceDetection<{}>>(
+      this.runAndExtendWithFaceDetections(),
+      this.input,
+      useTinyLandmarkNet
+    )
+  }
+
+  withFaceExpressions(): PredictAllFaceExpressionsTask<WithFaceDetection<{}>> {
+    return new PredictAllFaceExpressionsTask<WithFaceDetection<{}>>(
+      this.runAndExtendWithFaceDetections(),
+      this.input
+    )
+  }
+}
+
+export class DetectSingleFaceTask extends DetectFacesTaskBase<FaceDetection | undefined> {
+
+  public async run(): Promise<FaceDetection | undefined> {
+    const faceDetections = await new DetectAllFacesTask(this.input, this.options);
+    let faceDetectionWithHighestScore = faceDetections[0];
+    faceDetections.forEach(faceDetection => {
+      if (faceDetection.score > faceDetectionWithHighestScore.score) {
+        faceDetectionWithHighestScore = faceDetection;
+      }
+    });
+    return faceDetectionWithHighestScore;
+  }
+
+  private runAndExtendWithFaceDetection(): Promise<WithFaceDetection<{}>> {
+    return new Promise<WithFaceDetection<{}>>(async res => {
+      const detection = await this.run()
+      return res(detection ? extendWithFaceDetection<{}>({}, detection) : undefined)
+    })
+  }
+
+  withFaceLandmarks(useTinyLandmarkNet: boolean = false): DetectSingleFaceLandmarksTask<WithFaceDetection<{}>> {
+    return new DetectSingleFaceLandmarksTask<WithFaceDetection<{}>>(
+      this.runAndExtendWithFaceDetection(),
+      this.input,
+      useTinyLandmarkNet
+    )
+  }
+
+  withFaceExpressions(): PredictSingleFaceExpressionTask<WithFaceDetection<{}>> {
+    return new PredictSingleFaceExpressionTask<WithFaceDetection<{}>>(
+      this.runAndExtendWithFaceDetection(),
+      this.input
+    )
+  }
+
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/globalApi/FaceMatcher.ts b/node_modules/face-api.js/src/globalApi/FaceMatcher.ts
new file mode 100644
index 0000000..fc078f2
--- /dev/null
+++ b/node_modules/face-api.js/src/globalApi/FaceMatcher.ts
@@ -0,0 +1,70 @@
+import { FaceMatch } from '../classes/FaceMatch';
+import { LabeledFaceDescriptors } from '../classes/LabeledFaceDescriptors';
+import { euclideanDistance } from '../euclideanDistance';
+import { WithFaceDescriptor } from '../factories';
+
+export class FaceMatcher {
+
+  private _labeledDescriptors: LabeledFaceDescriptors[]
+  private _distanceThreshold: number
+
+  constructor(
+    inputs: LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>,
+    distanceThreshold: number = 0.6
+  ) {
+
+    this._distanceThreshold = distanceThreshold
+
+    const inputArray = Array.isArray(inputs) ? inputs : [inputs]
+
+    if (!inputArray.length) {
+      throw new Error(`FaceRecognizer.constructor - expected atleast one input`)
+    }
+
+    let count = 1
+    const createUniqueLabel = () => `person ${count++}`
+
+    this._labeledDescriptors = inputArray.map((desc) => {
+      if (desc instanceof LabeledFaceDescriptors) {
+        return desc
+      }
+
+      if (desc instanceof Float32Array) {
+        return new LabeledFaceDescriptors(createUniqueLabel(), [desc])
+      }
+
+      if (desc.descriptor && desc.descriptor instanceof Float32Array) {
+        return new LabeledFaceDescriptors(createUniqueLabel(), [desc.descriptor])
+      }
+
+      throw new Error(`FaceRecognizer.constructor - expected inputs to be of type LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array | Array<LabeledFaceDescriptors | WithFaceDescriptor<any> | Float32Array>`)
+    })
+  }
+
+  public get labeledDescriptors(): LabeledFaceDescriptors[] { return this._labeledDescriptors }
+  public get distanceThreshold(): number { return this._distanceThreshold }
+
+  public computeMeanDistance(queryDescriptor: Float32Array, descriptors: Float32Array[]): number {
+    return descriptors
+      .map(d => euclideanDistance(d, queryDescriptor))
+      .reduce((d1, d2) => d1 + d2, 0)
+        / (descriptors.length || 1)
+  }
+
+  public matchDescriptor(queryDescriptor: Float32Array): FaceMatch {
+    return this.labeledDescriptors
+      .map(({ descriptors, label }) => new FaceMatch(
+          label,
+          this.computeMeanDistance(queryDescriptor, descriptors)
+      ))
+      .reduce((best, curr) => best.distance < curr.distance ? best : curr)
+  }
+
+  public findBestMatch(queryDescriptor: Float32Array): FaceMatch {
+    const bestMatch = this.matchDescriptor(queryDescriptor)
+    return bestMatch.distance < this.distanceThreshold
+      ? bestMatch
+      : new FaceMatch('unknown', bestMatch.distance)
+  }
+
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/globalApi/PredictFaceExpressionsTask.ts b/node_modules/face-api.js/src/globalApi/PredictFaceExpressionsTask.ts
new file mode 100644
index 0000000..20ba367
--- /dev/null
+++ b/node_modules/face-api.js/src/globalApi/PredictFaceExpressionsTask.ts
@@ -0,0 +1,76 @@
+import { TNetInput } from 'tfjs-image-recognition-base';
+import { tf } from 'tfjs-tiny-yolov2';
+
+import { extractFaces, extractFaceTensors } from '../dom';
+import { FaceExpressionPrediction } from '../faceExpressionNet/types';
+import { WithFaceDetection } from '../factories/WithFaceDetection';
+import { extendWithFaceExpressions, WithFaceExpressions } from '../factories/WithFaceExpressions';
+import { ComposableTask } from './ComposableTask';
+import { DetectAllFaceLandmarksTask, DetectSingleFaceLandmarksTask } from './DetectFaceLandmarksTasks';
+import { nets } from './nets';
+
+export class PredictFaceExpressionsTaskBase<TReturn, TParentReturn> extends ComposableTask<TReturn> {
+  constructor(
+    protected parentTask: ComposableTask<TParentReturn> | Promise<TParentReturn>,
+    protected input: TNetInput
+  ) {
+    super()
+  }
+}
+
+export class PredictAllFaceExpressionsTask<
+  TSource extends WithFaceDetection<{}>
+> extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource>[], TSource[]> {
+
+  public async run(): Promise<WithFaceExpressions<TSource>[]> {
+
+    const parentResults = await this.parentTask
+
+    const detections = parentResults.map(parentResult => parentResult.detection)
+    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor
+      ? await extractFaceTensors(this.input, detections)
+      : await extractFaces(this.input, detections)
+
+    const faceExpressionsByFace = await Promise.all(faces.map(
+      face => nets.faceExpressionNet.predictExpressions(face)
+    )) as FaceExpressionPrediction[][]
+
+    faces.forEach(f => f instanceof tf.Tensor && f.dispose())
+
+    return parentResults.map(
+      (parentResult, i) => extendWithFaceExpressions<TSource>(parentResult, faceExpressionsByFace[i])
+    )
+  }
+
+  withFaceLandmarks(): DetectAllFaceLandmarksTask<WithFaceExpressions<TSource>> {
+    return new DetectAllFaceLandmarksTask(this, this.input, false)
+  }
+}
+
+export class PredictSingleFaceExpressionTask<
+  TSource extends WithFaceDetection<{}>
+>  extends PredictFaceExpressionsTaskBase<WithFaceExpressions<TSource> | undefined, TSource | undefined> {
+
+  public async run(): Promise<WithFaceExpressions<TSource> | undefined> {
+
+    const parentResult = await this.parentTask
+    if (!parentResult) {
+      return
+    }
+
+    const { detection } = parentResult
+    const faces: Array<HTMLCanvasElement | tf.Tensor3D> = this.input instanceof tf.Tensor
+      ? await extractFaceTensors(this.input, [detection])
+      : await extractFaces(this.input, [detection])
+
+    const faceExpressions = await nets.faceExpressionNet.predictExpressions(faces[0]) as FaceExpressionPrediction[]
+
+    faces.forEach(f => f instanceof tf.Tensor && f.dispose())
+
+    return extendWithFaceExpressions(parentResult, faceExpressions)
+  }
+
+  withFaceLandmarks(): DetectSingleFaceLandmarksTask<WithFaceExpressions<TSource>> {
+    return new DetectSingleFaceLandmarksTask(this, this.input, false)
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/globalApi/allFaces.ts b/node_modules/face-api.js/src/globalApi/allFaces.ts
new file mode 100644
index 0000000..646e009
--- /dev/null
+++ b/node_modules/face-api.js/src/globalApi/allFaces.ts
@@ -0,0 +1,38 @@
+import { TNetInput } from 'tfjs-image-recognition-base';
+import { ITinyYolov2Options, TinyYolov2Options } from 'tfjs-tiny-yolov2';
+
+import { WithFaceDescriptor, WithFaceDetection, WithFaceLandmarks } from '../factories';
+import { IMtcnnOptions, MtcnnOptions } from '../mtcnn/MtcnnOptions';
+import { SsdMobilenetv1Options } from '../ssdMobilenetv1';
+import { detectAllFaces } from './detectFaces';
+
+// export allFaces API for backward compatibility
+
+export async function allFacesSsdMobilenetv1(
+  input: TNetInput,
+  minConfidence?: number
+): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {
+  return await detectAllFaces(input, new SsdMobilenetv1Options(minConfidence ? { minConfidence } : {}))
+    .withFaceLandmarks()
+    .withFaceDescriptors()
+}
+
+export async function allFacesTinyYolov2(
+  input: TNetInput,
+  forwardParams: ITinyYolov2Options = {}
+): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {
+  return await detectAllFaces(input, new TinyYolov2Options(forwardParams))
+    .withFaceLandmarks()
+    .withFaceDescriptors()
+}
+
+export async function allFacesMtcnn(
+  input: TNetInput,
+  forwardParams: IMtcnnOptions = {}
+): Promise<WithFaceDescriptor<WithFaceLandmarks<WithFaceDetection<{}>>>[]> {
+  return await detectAllFaces(input, new MtcnnOptions(forwardParams))
+    .withFaceLandmarks()
+    .withFaceDescriptors()
+}
+
+export const allFaces = allFacesSsdMobilenetv1
diff --git a/node_modules/face-api.js/src/globalApi/detectFaces.ts b/node_modules/face-api.js/src/globalApi/detectFaces.ts
new file mode 100644
index 0000000..da565e5
--- /dev/null
+++ b/node_modules/face-api.js/src/globalApi/detectFaces.ts
@@ -0,0 +1,19 @@
+import { TNetInput } from 'tfjs-image-recognition-base';
+
+import { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';
+import { DetectAllFacesTask, DetectSingleFaceTask } from './DetectFacesTasks';
+import { FaceDetectionOptions } from './types';
+
+export function detectSingleFace(
+  input: TNetInput,
+  options: FaceDetectionOptions = new SsdMobilenetv1Options()
+): DetectSingleFaceTask {
+  return new DetectSingleFaceTask(input, options)
+}
+
+export function detectAllFaces(
+  input: TNetInput,
+  options: FaceDetectionOptions = new SsdMobilenetv1Options()
+): DetectAllFacesTask {
+  return new DetectAllFacesTask(input, options)
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/globalApi/index.ts b/node_modules/face-api.js/src/globalApi/index.ts
new file mode 100644
index 0000000..5663285
--- /dev/null
+++ b/node_modules/face-api.js/src/globalApi/index.ts
@@ -0,0 +1,10 @@
+export * from './allFaces'
+export * from './ComposableTask'
+export * from './ComputeFaceDescriptorsTasks'
+export * from './detectFaces'
+export * from './DetectFacesTasks'
+export * from './DetectFaceLandmarksTasks'
+export * from './FaceMatcher'
+export * from './nets'
+export * from './types'
+
diff --git a/node_modules/face-api.js/src/globalApi/nets.ts b/node_modules/face-api.js/src/globalApi/nets.ts
new file mode 100644
index 0000000..c9e4a04
--- /dev/null
+++ b/node_modules/face-api.js/src/globalApi/nets.ts
@@ -0,0 +1,133 @@
+import { TNetInput } from 'tfjs-image-recognition-base';
+import { ITinyYolov2Options } from 'tfjs-tiny-yolov2';
+
+import { FaceDetection } from '../classes/FaceDetection';
+import { FaceLandmarks5 } from '../classes/FaceLandmarks5';
+import { FaceLandmarks68 } from '../classes/FaceLandmarks68';
+import { FaceExpressionNet } from '../faceExpressionNet/FaceExpressionNet';
+import { FaceExpressionPrediction } from '../faceExpressionNet/types';
+import { FaceLandmark68Net } from '../faceLandmarkNet/FaceLandmark68Net';
+import { FaceLandmark68TinyNet } from '../faceLandmarkNet/FaceLandmark68TinyNet';
+import { FaceRecognitionNet } from '../faceRecognitionNet/FaceRecognitionNet';
+import { WithFaceDetection } from '../factories/WithFaceDetection';
+import { WithFaceLandmarks } from '../factories/WithFaceLandmarks';
+import { Mtcnn } from '../mtcnn/Mtcnn';
+import { MtcnnOptions } from '../mtcnn/MtcnnOptions';
+import { SsdMobilenetv1 } from '../ssdMobilenetv1/SsdMobilenetv1';
+import { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';
+import { TinyFaceDetector } from '../tinyFaceDetector/TinyFaceDetector';
+import { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';
+import { TinyYolov2 } from '../tinyYolov2/TinyYolov2';
+
+export const nets = {
+  ssdMobilenetv1: new SsdMobilenetv1(),
+  tinyFaceDetector: new TinyFaceDetector(),
+  tinyYolov2: new TinyYolov2(),
+  mtcnn: new Mtcnn(),
+  faceLandmark68Net: new FaceLandmark68Net(),
+  faceLandmark68TinyNet: new FaceLandmark68TinyNet(),
+  faceRecognitionNet: new FaceRecognitionNet(),
+  faceExpressionNet: new FaceExpressionNet()
+}
+
+/**
+ * Attempts to detect all faces in an image using SSD Mobilenetv1 Network.
+ *
+ * @param input The input image.
+ * @param options (optional, default: see SsdMobilenetv1Options constructor for default parameters).
+ * @returns Bounding box of each face with score.
+ */
+export const ssdMobilenetv1 = (input: TNetInput, options: SsdMobilenetv1Options): Promise<FaceDetection[]> =>
+  nets.ssdMobilenetv1.locateFaces(input, options)
+
+/**
+ * Attempts to detect all faces in an image using the Tiny Face Detector.
+ *
+ * @param input The input image.
+ * @param options (optional, default: see TinyFaceDetectorOptions constructor for default parameters).
+ * @returns Bounding box of each face with score.
+ */
+export const tinyFaceDetector = (input: TNetInput, options: TinyFaceDetectorOptions): Promise<FaceDetection[]> =>
+  nets.tinyFaceDetector.locateFaces(input, options)
+
+/**
+ * Attempts to detect all faces in an image using the Tiny Yolov2 Network.
+ *
+ * @param input The input image.
+ * @param options (optional, default: see TinyYolov2Options constructor for default parameters).
+ * @returns Bounding box of each face with score.
+ */
+export const tinyYolov2 = (input: TNetInput, options: ITinyYolov2Options): Promise<FaceDetection[]> =>
+  nets.tinyYolov2.locateFaces(input, options)
+
+/**
+ * Attempts to detect all faces in an image and the 5 point face landmarks
+ * of each detected face using the MTCNN Network.
+ *
+ * @param input The input image.
+ * @param options (optional, default: see MtcnnOptions constructor for default parameters).
+ * @returns Bounding box of each face with score and 5 point face landmarks.
+ */
+export const mtcnn = (input: TNetInput, options: MtcnnOptions): Promise<WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks5>[]> =>
+  nets.mtcnn.forward(input, options)
+
+/**
+ * Detects the 68 point face landmark positions of the face shown in an image.
+ *
+ * @param inputs The face image extracted from the bounding box of a face. Can
+ * also be an array of input images, which will be batch processed.
+ * @returns 68 point face landmarks or array thereof in case of batch input.
+ */
+export const detectFaceLandmarks = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]> =>
+  nets.faceLandmark68Net.detectLandmarks(input)
+
+/**
+ * Detects the 68 point face landmark positions of the face shown in an image
+ * using a tinier version of the 68 point face landmark model, which is slightly
+ * faster at inference, but also slightly less accurate.
+ *
+ * @param inputs The face image extracted from the bounding box of a face. Can
+ * also be an array of input images, which will be batch processed.
+ * @returns 68 point face landmarks or array thereof in case of batch input.
+ */
+export const detectFaceLandmarksTiny = (input: TNetInput): Promise<FaceLandmarks68 | FaceLandmarks68[]>  =>
+  nets.faceLandmark68TinyNet.detectLandmarks(input)
+
+/**
+ * Computes a 128 entry vector (face descriptor / face embeddings) from the face shown in an image,
+ * which uniquely represents the features of that persons face. The computed face descriptor can
+ * be used to measure the similarity between faces, by computing the euclidean distance of two
+ * face descriptors.
+ *
+ * @param inputs The face image extracted from the aligned bounding box of a face. Can
+ * also be an array of input images, which will be batch processed.
+ * @returns Face descriptor with 128 entries or array thereof in case of batch input.
+ */
+export const computeFaceDescriptor = (input: TNetInput): Promise<Float32Array | Float32Array[]>  =>
+  nets.faceRecognitionNet.computeFaceDescriptor(input)
+
+
+/**
+ * Recognizes the facial expressions of a face and returns the likelyhood of
+ * each facial expression.
+ *
+ * @param inputs The face image extracted from the bounding box of a face. Can
+ * also be an array of input images, which will be batch processed.
+ * @returns An array of facial expressions with corresponding probabilities or array thereof in case of batch input.
+ */
+export const recognizeFaceExpressions = (input: TNetInput): Promise<FaceExpressionPrediction[] | FaceExpressionPrediction[][]> =>
+  nets.faceExpressionNet.predictExpressions(input)
+
+export const loadSsdMobilenetv1Model = (url: string) => nets.ssdMobilenetv1.load(url)
+export const loadTinyFaceDetectorModel = (url: string) => nets.tinyFaceDetector.load(url)
+export const loadMtcnnModel = (url: string) => nets.mtcnn.load(url)
+export const loadTinyYolov2Model = (url: string) => nets.tinyYolov2.load(url)
+export const loadFaceLandmarkModel = (url: string) => nets.faceLandmark68Net.load(url)
+export const loadFaceLandmarkTinyModel = (url: string) => nets.faceLandmark68TinyNet.load(url)
+export const loadFaceRecognitionModel = (url: string) => nets.faceRecognitionNet.load(url)
+export const loadFaceExpressionModel = (url: string) => nets.faceExpressionNet.load(url)
+
+// backward compatibility
+export const loadFaceDetectionModel = loadSsdMobilenetv1Model
+export const locateFaces = ssdMobilenetv1
+export const detectLandmarks = detectFaceLandmarks
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/globalApi/types.ts b/node_modules/face-api.js/src/globalApi/types.ts
new file mode 100644
index 0000000..46d70d8
--- /dev/null
+++ b/node_modules/face-api.js/src/globalApi/types.ts
@@ -0,0 +1,11 @@
+import { TNetInput } from 'tfjs-image-recognition-base';
+import { TinyYolov2Options } from 'tfjs-tiny-yolov2';
+
+import { FaceDetection } from '../classes/FaceDetection';
+import { MtcnnOptions } from '../mtcnn/MtcnnOptions';
+import { SsdMobilenetv1Options } from '../ssdMobilenetv1/SsdMobilenetv1Options';
+import { TinyFaceDetectorOptions } from '../tinyFaceDetector/TinyFaceDetectorOptions';
+
+export type FaceDetectionOptions = TinyFaceDetectorOptions | SsdMobilenetv1Options | MtcnnOptions | TinyYolov2Options
+
+export type FaceDetectionFunction = (input: TNetInput) => Promise<FaceDetection[]>
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/index.ts b/node_modules/face-api.js/src/index.ts
new file mode 100644
index 0000000..5f1007e
--- /dev/null
+++ b/node_modules/face-api.js/src/index.ts
@@ -0,0 +1,22 @@
+import * as tf from '@tensorflow/tfjs-core';
+
+export {
+  tf
+}
+
+export * from 'tfjs-image-recognition-base';
+
+export * from './classes/index';
+export * from './dom/index'
+export * from './faceExpressionNet/index';
+export * from './faceLandmarkNet/index';
+export * from './faceRecognitionNet/index';
+export * from './factories/index';
+export * from './globalApi/index';
+export * from './mtcnn/index';
+export * from './ssdMobilenetv1/index';
+export * from './tinyFaceDetector/index';
+export * from './tinyYolov2/index';
+
+export * from './euclideanDistance';
+export * from './resizeResults';
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/mtcnn/Mtcnn.ts b/node_modules/face-api.js/src/mtcnn/Mtcnn.ts
new file mode 100644
index 0000000..4ae2aa6
--- /dev/null
+++ b/node_modules/face-api.js/src/mtcnn/Mtcnn.ts
@@ -0,0 +1,163 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { NetInput, NeuralNetwork, Point, Rect, TNetInput, toNetInput } from 'tfjs-image-recognition-base';
+
+import { FaceDetection } from '../classes/FaceDetection';
+import { FaceLandmarks5 } from '../classes/FaceLandmarks5';
+import { extendWithFaceDetection, extendWithFaceLandmarks } from '../factories';
+import { bgrToRgbTensor } from './bgrToRgbTensor';
+import { CELL_SIZE } from './config';
+import { extractParams } from './extractParams';
+import { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';
+import { getSizesForScale } from './getSizesForScale';
+import { IMtcnnOptions, MtcnnOptions } from './MtcnnOptions';
+import { pyramidDown } from './pyramidDown';
+import { stage1 } from './stage1';
+import { stage2 } from './stage2';
+import { stage3 } from './stage3';
+import { MtcnnResult, NetParams } from './types';
+
+export class Mtcnn extends NeuralNetwork<NetParams> {
+
+  constructor() {
+    super('Mtcnn')
+  }
+
+  public async forwardInput(
+    input: NetInput,
+    forwardParams: IMtcnnOptions = {}
+  ): Promise<{ results: MtcnnResult[], stats: any }> {
+
+    const { params } = this
+
+    if (!params) {
+      throw new Error('Mtcnn - load model before inference')
+    }
+
+    const inputCanvas = input.canvases[0]
+
+    if (!inputCanvas) {
+      throw new Error('Mtcnn - inputCanvas is not defined, note that passing tensors into Mtcnn.forwardInput is not supported yet.')
+    }
+
+    const stats: any = {}
+
+    const tsTotal = Date.now()
+
+    const imgTensor = tf.tidy(() =>
+      bgrToRgbTensor(
+        tf.expandDims(tf.fromPixels(inputCanvas)).toFloat() as tf.Tensor4D
+      )
+    )
+
+    const onReturn = (results: any) => {
+      // dispose tensors on return
+      imgTensor.dispose()
+      stats.total = Date.now() - tsTotal
+      return results
+    }
+
+    const [height, width] = imgTensor.shape.slice(1)
+
+    const {
+      minFaceSize,
+      scaleFactor,
+      maxNumScales,
+      scoreThresholds,
+      scaleSteps
+    } = new MtcnnOptions(forwardParams)
+
+    const scales = (scaleSteps || pyramidDown(minFaceSize, scaleFactor, [height, width]))
+      .filter(scale => {
+        const sizes = getSizesForScale(scale, [height, width])
+        return Math.min(sizes.width, sizes.height) > CELL_SIZE
+      })
+      .slice(0, maxNumScales)
+
+    stats.scales = scales
+    stats.pyramid = scales.map(scale => getSizesForScale(scale, [height, width]))
+
+    let ts = Date.now()
+    const out1 = await stage1(imgTensor, scales, scoreThresholds[0], params.pnet, stats)
+    stats.total_stage1 = Date.now() - ts
+
+    if (!out1.boxes.length) {
+      return onReturn({ results: [], stats })
+    }
+
+    stats.stage2_numInputBoxes = out1.boxes.length
+    // using the inputCanvas to extract and resize the image patches, since it is faster
+    // than doing this on the gpu
+    ts = Date.now()
+    const out2 = await stage2(inputCanvas, out1.boxes, scoreThresholds[1], params.rnet, stats)
+    stats.total_stage2 = Date.now() - ts
+
+    if (!out2.boxes.length) {
+      return onReturn({ results: [], stats })
+    }
+
+    stats.stage3_numInputBoxes = out2.boxes.length
+
+    ts = Date.now()
+    const out3 = await stage3(inputCanvas, out2.boxes, scoreThresholds[2], params.onet, stats)
+    stats.total_stage3 = Date.now() - ts
+
+    const results = out3.boxes.map((box, idx) => extendWithFaceLandmarks(
+      extendWithFaceDetection<{}>(
+        {},
+        new FaceDetection(
+          out3.scores[idx],
+          new Rect(
+            box.left / width,
+            box.top / height,
+            box.width / width,
+            box.height / height
+          ),
+          {
+            height,
+            width
+          }
+        )
+      ),
+      new FaceLandmarks5(
+        out3.points[idx].map(pt => pt.sub(new Point(box.left, box.top)).div(new Point(box.width, box.height))),
+        { width: box.width, height: box.height }
+      )
+    ))
+
+    return onReturn({ results, stats })
+  }
+
+  public async forward(
+    input: TNetInput,
+    forwardParams: IMtcnnOptions = {}
+  ): Promise<MtcnnResult[]> {
+    return (
+      await this.forwardInput(
+        await toNetInput(input),
+        forwardParams
+      )
+    ).results
+  }
+
+  public async forwardWithStats(
+    input: TNetInput,
+    forwardParams: IMtcnnOptions = {}
+  ): Promise<{ results: MtcnnResult[], stats: any }> {
+    return this.forwardInput(
+      await toNetInput(input),
+      forwardParams
+    )
+  }
+
+  protected getDefaultModelName(): string {
+    return 'mtcnn_model'
+  }
+
+  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {
+    return extractParamsFromWeigthMap(weightMap)
+  }
+
+  protected extractParams(weights: Float32Array) {
+    return extractParams(weights)
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/mtcnn/MtcnnBox.ts b/node_modules/face-api.js/src/mtcnn/MtcnnBox.ts
new file mode 100644
index 0000000..ff1c7d5
--- /dev/null
+++ b/node_modules/face-api.js/src/mtcnn/MtcnnBox.ts
@@ -0,0 +1,7 @@
+import { Box } from 'tfjs-image-recognition-base';
+
+export class MtcnnBox extends Box<MtcnnBox> {
+  constructor(left: number, top: number, right: number, bottom: number) {
+    super({ left, top, right, bottom }, true)
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/mtcnn/MtcnnOptions.ts b/node_modules/face-api.js/src/mtcnn/MtcnnOptions.ts
new file mode 100644
index 0000000..e507df6
--- /dev/null
+++ b/node_modules/face-api.js/src/mtcnn/MtcnnOptions.ts
@@ -0,0 +1,58 @@
+export interface IMtcnnOptions {
+  minFaceSize?: number
+  scaleFactor?: number
+  maxNumScales?: number
+  scoreThresholds?: number[]
+  scaleSteps?: number[]
+}
+
+export class MtcnnOptions {
+  protected _name: string = 'MtcnnOptions'
+
+  private _minFaceSize: number
+  private _scaleFactor: number
+  private _maxNumScales: number
+  private _scoreThresholds: number[]
+  private _scaleSteps: number[] | undefined
+
+  constructor({ minFaceSize, scaleFactor, maxNumScales, scoreThresholds, scaleSteps }: IMtcnnOptions = {}) {
+    this._minFaceSize = minFaceSize || 20
+    this._scaleFactor = scaleFactor || 0.709
+    this._maxNumScales = maxNumScales || 10
+    this._scoreThresholds = scoreThresholds || [0.6, 0.7, 0.7]
+    this._scaleSteps = scaleSteps
+
+    if (typeof this._minFaceSize !== 'number' || this._minFaceSize < 0) {
+      throw new Error(`${this._name} - expected minFaceSize to be a number > 0`)
+    }
+
+    if (typeof this._scaleFactor !== 'number' || this._scaleFactor <= 0 || this._scaleFactor >= 1) {
+      throw new Error(`${this._name} - expected scaleFactor to be a number between 0 and 1`)
+    }
+
+    if (typeof this._maxNumScales !== 'number' || this._maxNumScales < 0) {
+      throw new Error(`${this._name} - expected maxNumScales to be a number > 0`)
+    }
+
+    if (
+      !Array.isArray(this._scoreThresholds)
+        || this._scoreThresholds.length !== 3
+        || this._scoreThresholds.some(th => typeof th !== 'number')
+    ) {
+      throw new Error(`${this._name} - expected scoreThresholds to be an array of numbers of length 3`)
+    }
+
+    if (
+      this._scaleSteps
+        && (!Array.isArray(this._scaleSteps) || this._scaleSteps.some(th => typeof th !== 'number'))
+    ) {
+      throw new Error(`${this._name} - expected scaleSteps to be an array of numbers`)
+    }
+  }
+
+  get minFaceSize(): number { return this._minFaceSize }
+  get scaleFactor(): number { return this._scaleFactor }
+  get maxNumScales(): number { return this._maxNumScales }
+  get scoreThresholds(): number[] { return this._scoreThresholds }
+  get scaleSteps(): number[] | undefined { return this._scaleSteps }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/mtcnn/ONet.ts b/node_modules/face-api.js/src/mtcnn/ONet.ts
new file mode 100644
index 0000000..9c06e02
--- /dev/null
+++ b/node_modules/face-api.js/src/mtcnn/ONet.ts
@@ -0,0 +1,30 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { convLayer } from 'tfjs-tiny-yolov2';
+
+import { fullyConnectedLayer } from '../common/fullyConnectedLayer';
+import { prelu } from './prelu';
+import { sharedLayer } from './sharedLayers';
+import { ONetParams } from './types';
+
+export function ONet(x: tf.Tensor4D, params: ONetParams): { scores: tf.Tensor1D, regions: tf.Tensor2D, points: tf.Tensor2D } {
+  return tf.tidy(() => {
+
+    let out = sharedLayer(x, params)
+    out = tf.maxPool(out, [2, 2], [2, 2], 'same')
+    out = convLayer(out, params.conv4, 'valid')
+    out = prelu<tf.Tensor4D>(out, params.prelu4_alpha)
+
+    const vectorized = tf.reshape(out, [out.shape[0], params.fc1.weights.shape[0]]) as tf.Tensor2D
+    const fc1 = fullyConnectedLayer(vectorized, params.fc1)
+    const prelu5 = prelu<tf.Tensor2D>(fc1, params.prelu5_alpha)
+    const fc2_1 = fullyConnectedLayer(prelu5, params.fc2_1)
+    const max = tf.expandDims(tf.max(fc2_1, 1), 1)
+
+    const prob = tf.softmax(tf.sub(fc2_1, max), 1) as tf.Tensor2D
+    const regions = fullyConnectedLayer(prelu5, params.fc2_2)
+    const points = fullyConnectedLayer(prelu5, params.fc2_3)
+
+    const scores = tf.unstack(prob, 1)[1] as tf.Tensor1D
+    return { scores, regions, points }
+  })
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/mtcnn/PNet.ts b/node_modules/face-api.js/src/mtcnn/PNet.ts
new file mode 100644
index 0000000..a49bc91
--- /dev/null
+++ b/node_modules/face-api.js/src/mtcnn/PNet.ts
@@ -0,0 +1,18 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { convLayer } from 'tfjs-tiny-yolov2';
+
+import { sharedLayer } from './sharedLayers';
+import { PNetParams } from './types';
+
+export function PNet(x: tf.Tensor4D, params: PNetParams): { prob: tf.Tensor4D, regions: tf.Tensor4D } {
+  return tf.tidy(() => {
+
+    let out = sharedLayer(x, params, true)
+    const conv = convLayer(out, params.conv4_1, 'valid')
+    const max = tf.expandDims(tf.max(conv, 3), 3)
+    const prob = tf.softmax(tf.sub(conv, max), 3) as tf.Tensor4D
+    const regions = convLayer(out, params.conv4_2, 'valid')
+
+    return { prob, regions }
+  })
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/mtcnn/RNet.ts b/node_modules/face-api.js/src/mtcnn/RNet.ts
new file mode 100644
index 0000000..99cf326
--- /dev/null
+++ b/node_modules/face-api.js/src/mtcnn/RNet.ts
@@ -0,0 +1,23 @@
+import * as tf from '@tensorflow/tfjs-core';
+
+import { fullyConnectedLayer } from '../common/fullyConnectedLayer';
+import { prelu } from './prelu';
+import { sharedLayer } from './sharedLayers';
+import { RNetParams } from './types';
+
+export function RNet(x: tf.Tensor4D, params: RNetParams): { scores: tf.Tensor1D, regions: tf.Tensor2D } {
+  return tf.tidy(() => {
+
+    const convOut = sharedLayer(x, params)
+    const vectorized = tf.reshape(convOut, [convOut.shape[0], params.fc1.weights.shape[0]]) as tf.Tensor2D
+    const fc1 = fullyConnectedLayer(vectorized, params.fc1)
+    const prelu4 = prelu<tf.Tensor2D>(fc1, params.prelu4_alpha)
+    const fc2_1 = fullyConnectedLayer(prelu4, params.fc2_1)
+    const max = tf.expandDims(tf.max(fc2_1, 1), 1)
+    const prob = tf.softmax(tf.sub(fc2_1, max), 1) as tf.Tensor2D
+    const regions = fullyConnectedLayer(prelu4, params.fc2_2)
+
+    const scores = tf.unstack(prob, 1)[1] as tf.Tensor1D
+    return { scores, regions }
+  })
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/mtcnn/bgrToRgbTensor.ts b/node_modules/face-api.js/src/mtcnn/bgrToRgbTensor.ts
new file mode 100644
index 0000000..25f2452
--- /dev/null
+++ b/node_modules/face-api.js/src/mtcnn/bgrToRgbTensor.ts
@@ -0,0 +1,7 @@
+import * as tf from '@tensorflow/tfjs-core';
+
+export function bgrToRgbTensor(tensor: tf.Tensor4D): tf.Tensor4D {
+  return tf.tidy(
+    () => tf.stack(tf.unstack(tensor, 3).reverse(), 3)
+  ) as tf.Tensor4D
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/mtcnn/config.ts b/node_modules/face-api.js/src/mtcnn/config.ts
new file mode 100644
index 0000000..560bf0e
--- /dev/null
+++ b/node_modules/face-api.js/src/mtcnn/config.ts
@@ -0,0 +1,2 @@
+export const CELL_STRIDE = 2
+export const CELL_SIZE = 12
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/mtcnn/extractImagePatches.ts b/node_modules/face-api.js/src/mtcnn/extractImagePatches.ts
new file mode 100644
index 0000000..6c34aa2
--- /dev/null
+++ b/node_modules/face-api.js/src/mtcnn/extractImagePatches.ts
@@ -0,0 +1,63 @@
+import * as tf from '@tensorflow/tfjs-core';
+import {
+  Box,
+  createCanvas,
+  createCanvasFromMedia,
+  env,
+  getContext2dOrThrow,
+  IDimensions,
+} from 'tfjs-image-recognition-base';
+
+import { normalize } from './normalize';
+
+export async function extractImagePatches(
+  img: HTMLCanvasElement,
+  boxes: Box[],
+  { width, height }: IDimensions
+): Promise<tf.Tensor4D[]> {
+
+
+  const imgCtx = getContext2dOrThrow(img)
+
+  const bitmaps = await Promise.all(boxes.map(async box => {
+    // TODO: correct padding
+    const { y, ey, x, ex } = box.padAtBorders(img.height, img.width)
+
+    const fromX = x - 1
+    const fromY = y - 1
+    const imgData = imgCtx.getImageData(fromX, fromY, (ex - fromX), (ey - fromY))
+
+    return env.isNodejs() ? createCanvasFromMedia(imgData) : createImageBitmap(imgData)
+  }))
+
+  const imagePatchesDatas: number[][] = []
+
+  bitmaps.forEach(bmp => {
+    const patch = createCanvas({ width, height })
+    const patchCtx = getContext2dOrThrow(patch)
+    patchCtx.drawImage(bmp, 0, 0, width, height)
+    const { data } = patchCtx.getImageData(0, 0, width, height)
+
+    const currData = []
+    // RGBA -> BGR
+    for(let i = 0; i < data.length; i+=4) {
+      currData.push(data[i + 2])
+      currData.push(data[i + 1])
+      currData.push(data[i])
+    }
+    imagePatchesDatas.push(currData)
+  })
+
+
+  return imagePatchesDatas.map(data => {
+    const t = tf.tidy(() => {
+      const imagePatchTensor = tf.transpose(
+        tf.tensor4d(data, [1, width, height, 3]),
+        [0, 2, 1, 3]
+      ).toFloat() as tf.Tensor4D
+
+      return normalize(imagePatchTensor)
+    })
+    return t
+  })
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/mtcnn/extractParams.ts b/node_modules/face-api.js/src/mtcnn/extractParams.ts
new file mode 100644
index 0000000..f071e16
--- /dev/null
+++ b/node_modules/face-api.js/src/mtcnn/extractParams.ts
@@ -0,0 +1,96 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from 'tfjs-image-recognition-base';
+import { extractConvParamsFactory, extractFCParamsFactory } from 'tfjs-tiny-yolov2';
+
+import { NetParams, ONetParams, PNetParams, RNetParams, SharedParams } from './types';
+
+function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {
+
+  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)
+  const extractFCParams = extractFCParamsFactory(extractWeights, paramMappings)
+
+  function extractPReluParams(size: number, paramPath: string): tf.Tensor1D {
+    const alpha = tf.tensor1d(extractWeights(size))
+    paramMappings.push({ paramPath })
+    return alpha
+  }
+
+  function extractSharedParams(numFilters: number[], mappedPrefix: string, isRnet: boolean = false): SharedParams {
+
+    const conv1 = extractConvParams(numFilters[0], numFilters[1], 3, `${mappedPrefix}/conv1`)
+    const prelu1_alpha = extractPReluParams(numFilters[1], `${mappedPrefix}/prelu1_alpha`)
+    const conv2 = extractConvParams(numFilters[1], numFilters[2], 3, `${mappedPrefix}/conv2`)
+    const prelu2_alpha = extractPReluParams(numFilters[2], `${mappedPrefix}/prelu2_alpha`)
+    const conv3 = extractConvParams(numFilters[2], numFilters[3], isRnet ? 2 : 3, `${mappedPrefix}/conv3`)
+    const prelu3_alpha = extractPReluParams(numFilters[3], `${mappedPrefix}/prelu3_alpha`)
+
+    return { conv1, prelu1_alpha, conv2, prelu2_alpha, conv3, prelu3_alpha }
+  }
+
+  function extractPNetParams(): PNetParams {
+
+    const sharedParams = extractSharedParams([3, 10, 16, 32], 'pnet')
+    const conv4_1 = extractConvParams(32, 2, 1, 'pnet/conv4_1')
+    const conv4_2 = extractConvParams(32, 4, 1, 'pnet/conv4_2')
+
+    return { ...sharedParams, conv4_1, conv4_2 }
+  }
+
+  function extractRNetParams(): RNetParams {
+
+    const sharedParams = extractSharedParams([3, 28, 48, 64], 'rnet', true)
+    const fc1 = extractFCParams(576, 128, 'rnet/fc1')
+    const prelu4_alpha = extractPReluParams(128, 'rnet/prelu4_alpha')
+    const fc2_1 = extractFCParams(128, 2, 'rnet/fc2_1')
+    const fc2_2 = extractFCParams(128, 4, 'rnet/fc2_2')
+
+    return { ...sharedParams, fc1, prelu4_alpha, fc2_1, fc2_2 }
+  }
+
+  function extractONetParams(): ONetParams {
+
+    const sharedParams = extractSharedParams([3, 32, 64, 64], 'onet')
+    const conv4 = extractConvParams(64, 128, 2, 'onet/conv4')
+    const prelu4_alpha = extractPReluParams(128, 'onet/prelu4_alpha')
+    const fc1 = extractFCParams(1152, 256, 'onet/fc1')
+    const prelu5_alpha = extractPReluParams(256, 'onet/prelu5_alpha')
+    const fc2_1 = extractFCParams(256, 2, 'onet/fc2_1')
+    const fc2_2 = extractFCParams(256, 4, 'onet/fc2_2')
+    const fc2_3 = extractFCParams(256, 10, 'onet/fc2_3')
+
+    return { ...sharedParams, conv4, prelu4_alpha, fc1, prelu5_alpha, fc2_1, fc2_2, fc2_3 }
+  }
+
+  return {
+    extractPNetParams,
+    extractRNetParams,
+    extractONetParams
+  }
+
+}
+
+export function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {
+
+  const {
+    extractWeights,
+    getRemainingWeights
+  } = extractWeightsFactory(weights)
+
+  const paramMappings: ParamMapping[] = []
+
+  const {
+    extractPNetParams,
+    extractRNetParams,
+    extractONetParams
+  } = extractorsFactory(extractWeights, paramMappings)
+
+  const pnet = extractPNetParams()
+  const rnet = extractRNetParams()
+  const onet = extractONetParams()
+
+  if (getRemainingWeights().length !== 0) {
+    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)
+  }
+
+  return { params: { pnet, rnet, onet }, paramMappings }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/mtcnn/extractParamsFromWeigthMap.ts b/node_modules/face-api.js/src/mtcnn/extractParamsFromWeigthMap.ts
new file mode 100644
index 0000000..406f543
--- /dev/null
+++ b/node_modules/face-api.js/src/mtcnn/extractParamsFromWeigthMap.ts
@@ -0,0 +1,102 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from 'tfjs-image-recognition-base';
+import { ConvParams, FCParams } from 'tfjs-tiny-yolov2';
+
+import { NetParams, ONetParams, PNetParams, RNetParams, SharedParams } from './types';
+
+function extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {
+
+  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)
+
+  function extractConvParams(prefix: string): ConvParams {
+    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/weights`, 4, `${prefix}/filters`)
+    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)
+
+    return { filters, bias }
+  }
+
+  function extractFCParams(prefix: string): FCParams {
+    const weights = extractWeightEntry<tf.Tensor2D>(`${prefix}/weights`, 2)
+    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)
+
+    return { weights, bias }
+  }
+
+  function extractPReluParams(paramPath: string): tf.Tensor1D {
+    return extractWeightEntry<tf.Tensor1D>(paramPath, 1)
+  }
+
+  function extractSharedParams(prefix: string): SharedParams {
+
+    const conv1 = extractConvParams(`${prefix}/conv1`)
+    const prelu1_alpha = extractPReluParams(`${prefix}/prelu1_alpha`)
+    const conv2 = extractConvParams(`${prefix}/conv2`)
+    const prelu2_alpha = extractPReluParams(`${prefix}/prelu2_alpha`)
+    const conv3 = extractConvParams(`${prefix}/conv3`)
+    const prelu3_alpha = extractPReluParams(`${prefix}/prelu3_alpha`)
+
+    return { conv1, prelu1_alpha, conv2, prelu2_alpha, conv3, prelu3_alpha }
+  }
+
+  function extractPNetParams(): PNetParams {
+
+    const sharedParams = extractSharedParams('pnet')
+    const conv4_1 = extractConvParams('pnet/conv4_1')
+    const conv4_2 = extractConvParams('pnet/conv4_2')
+
+    return { ...sharedParams, conv4_1, conv4_2 }
+  }
+
+  function extractRNetParams(): RNetParams {
+
+    const sharedParams = extractSharedParams('rnet')
+    const fc1 = extractFCParams('rnet/fc1')
+    const prelu4_alpha = extractPReluParams('rnet/prelu4_alpha')
+    const fc2_1 = extractFCParams('rnet/fc2_1')
+    const fc2_2 = extractFCParams('rnet/fc2_2')
+
+    return { ...sharedParams, fc1, prelu4_alpha, fc2_1, fc2_2 }
+  }
+
+  function extractONetParams(): ONetParams {
+
+    const sharedParams = extractSharedParams('onet')
+    const conv4 = extractConvParams('onet/conv4')
+    const prelu4_alpha = extractPReluParams('onet/prelu4_alpha')
+    const fc1 = extractFCParams('onet/fc1')
+    const prelu5_alpha = extractPReluParams('onet/prelu5_alpha')
+    const fc2_1 = extractFCParams('onet/fc2_1')
+    const fc2_2 = extractFCParams('onet/fc2_2')
+    const fc2_3 = extractFCParams('onet/fc2_3')
+
+    return { ...sharedParams, conv4, prelu4_alpha, fc1, prelu5_alpha, fc2_1, fc2_2, fc2_3 }
+  }
+
+  return {
+    extractPNetParams,
+    extractRNetParams,
+    extractONetParams
+  }
+
+}
+
+export function extractParamsFromWeigthMap(
+  weightMap: tf.NamedTensorMap
+): { params: NetParams, paramMappings: ParamMapping[] } {
+
+  const paramMappings: ParamMapping[] = []
+
+  const {
+    extractPNetParams,
+    extractRNetParams,
+    extractONetParams
+  } = extractorsFactory(weightMap, paramMappings)
+
+  const pnet = extractPNetParams()
+  const rnet = extractRNetParams()
+  const onet = extractONetParams()
+
+  disposeUnusedWeightTensors(weightMap, paramMappings)
+
+  return { params: { pnet, rnet, onet }, paramMappings }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/mtcnn/getSizesForScale.ts b/node_modules/face-api.js/src/mtcnn/getSizesForScale.ts
new file mode 100644
index 0000000..e6b0181
--- /dev/null
+++ b/node_modules/face-api.js/src/mtcnn/getSizesForScale.ts
@@ -0,0 +1,6 @@
+export function getSizesForScale(scale: number, [height, width]: number[]) {
+  return {
+    height: Math.floor(height * scale),
+    width: Math.floor(width * scale)
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/mtcnn/index.ts b/node_modules/face-api.js/src/mtcnn/index.ts
new file mode 100644
index 0000000..dedd19a
--- /dev/null
+++ b/node_modules/face-api.js/src/mtcnn/index.ts
@@ -0,0 +1,10 @@
+import { Mtcnn } from './Mtcnn';
+
+export * from './Mtcnn';
+export * from './MtcnnOptions';
+
+export function createMtcnn(weights: Float32Array) {
+  const net = new Mtcnn()
+  net.extractWeights(weights)
+  return net
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/mtcnn/normalize.ts b/node_modules/face-api.js/src/mtcnn/normalize.ts
new file mode 100644
index 0000000..f3f1ce5
--- /dev/null
+++ b/node_modules/face-api.js/src/mtcnn/normalize.ts
@@ -0,0 +1,7 @@
+import * as tf from '@tensorflow/tfjs-core';
+
+export function normalize(x: tf.Tensor4D): tf.Tensor4D {
+  return tf.tidy(
+    () => tf.mul(tf.sub(x, tf.scalar(127.5)), tf.scalar(0.0078125))
+  )
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/mtcnn/prelu.ts b/node_modules/face-api.js/src/mtcnn/prelu.ts
new file mode 100644
index 0000000..d8a0d2f
--- /dev/null
+++ b/node_modules/face-api.js/src/mtcnn/prelu.ts
@@ -0,0 +1,10 @@
+import * as tf from '@tensorflow/tfjs-core';
+
+export function prelu<T extends tf.Tensor>(x: T, alpha: tf.Tensor1D): T {
+  return tf.tidy(() =>
+    tf.add(
+      tf.relu(x),
+      tf.mul(alpha, tf.neg(tf.relu(tf.neg(x))))
+    )
+  )
+}
diff --git a/node_modules/face-api.js/src/mtcnn/pyramidDown.ts b/node_modules/face-api.js/src/mtcnn/pyramidDown.ts
new file mode 100644
index 0000000..a283126
--- /dev/null
+++ b/node_modules/face-api.js/src/mtcnn/pyramidDown.ts
@@ -0,0 +1,19 @@
+import { CELL_SIZE } from './config';
+
+export function pyramidDown(minFaceSize: number, scaleFactor: number, dims: number[]): number[] {
+
+  const [height, width] = dims
+  const m = CELL_SIZE / minFaceSize
+
+  const scales = []
+
+  let minLayer = Math.min(height, width) * m
+  let exp = 0
+  while (minLayer >= 12) {
+    scales.push(m * Math.pow(scaleFactor, exp))
+    minLayer = minLayer * scaleFactor
+    exp += 1
+  }
+
+  return scales
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/mtcnn/sharedLayers.ts b/node_modules/face-api.js/src/mtcnn/sharedLayers.ts
new file mode 100644
index 0000000..83916cb
--- /dev/null
+++ b/node_modules/face-api.js/src/mtcnn/sharedLayers.ts
@@ -0,0 +1,21 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { convLayer } from 'tfjs-tiny-yolov2';
+
+import { prelu } from './prelu';
+import { SharedParams } from './types';
+
+export function sharedLayer(x: tf.Tensor4D, params: SharedParams, isPnet: boolean = false) {
+  return tf.tidy(() => {
+
+    let out = convLayer(x, params.conv1, 'valid')
+    out = prelu<tf.Tensor4D>(out, params.prelu1_alpha)
+    out = tf.maxPool(out, isPnet ? [2, 2]: [3, 3], [2, 2], 'same')
+    out = convLayer(out, params.conv2, 'valid')
+    out = prelu<tf.Tensor4D>(out, params.prelu2_alpha)
+    out = isPnet ? out : tf.maxPool(out, [3, 3], [2, 2], 'valid')
+    out = convLayer(out, params.conv3, 'valid')
+    out = prelu<tf.Tensor4D>(out, params.prelu3_alpha)
+
+    return out
+  })
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/mtcnn/stage1.ts b/node_modules/face-api.js/src/mtcnn/stage1.ts
new file mode 100644
index 0000000..b361ece
--- /dev/null
+++ b/node_modules/face-api.js/src/mtcnn/stage1.ts
@@ -0,0 +1,158 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { BoundingBox, nonMaxSuppression, Point } from 'tfjs-image-recognition-base';
+
+import { CELL_SIZE, CELL_STRIDE } from './config';
+import { getSizesForScale } from './getSizesForScale';
+import { MtcnnBox } from './MtcnnBox';
+import { normalize } from './normalize';
+import { PNet } from './PNet';
+import { PNetParams } from './types';
+
+function rescaleAndNormalize(x: tf.Tensor4D, scale: number): tf.Tensor4D {
+  return tf.tidy(() => {
+
+    const { height, width } = getSizesForScale(scale, x.shape.slice(1))
+    const resized = tf.image.resizeBilinear(x, [height, width])
+    const normalized = normalize(resized)
+
+    return (tf.transpose(normalized, [0, 2, 1, 3]) as tf.Tensor4D)
+  })
+}
+
+function extractBoundingBoxes(
+  scoresTensor: tf.Tensor2D,
+  regionsTensor: tf.Tensor3D,
+  scale: number,
+  scoreThreshold: number
+) {
+
+  // TODO: fix this!, maybe better to use tf.gather here
+  const indices: Point[] = []
+  for (let y = 0; y < scoresTensor.shape[0]; y++) {
+    for (let x = 0; x < scoresTensor.shape[1]; x++) {
+      if (scoresTensor.get(y, x) >= scoreThreshold) {
+        indices.push(new Point(x, y))
+      }
+    }
+  }
+
+  const boundingBoxes = indices.map(idx => {
+    const cell = new BoundingBox(
+      Math.round((idx.y * CELL_STRIDE + 1) / scale),
+      Math.round((idx.x * CELL_STRIDE + 1) / scale),
+      Math.round((idx.y * CELL_STRIDE + CELL_SIZE) / scale),
+      Math.round((idx.x * CELL_STRIDE + CELL_SIZE) / scale)
+    )
+
+    const score = scoresTensor.get(idx.y, idx.x)
+
+    const region = new MtcnnBox(
+      regionsTensor.get(idx.y, idx.x, 0),
+      regionsTensor.get(idx.y, idx.x, 1),
+      regionsTensor.get(idx.y, idx.x, 2),
+      regionsTensor.get(idx.y, idx.x, 3)
+    )
+
+    return {
+      cell,
+      score,
+      region
+    }
+  })
+
+  return boundingBoxes
+}
+
+export function stage1(
+  imgTensor: tf.Tensor4D,
+  scales: number[],
+  scoreThreshold: number,
+  params: PNetParams,
+  stats: any
+) {
+  stats.stage1 = []
+
+  const pnetOutputs = scales.map((scale) => tf.tidy(() => {
+    const statsForScale: any = { scale }
+    const resized = rescaleAndNormalize(imgTensor, scale)
+
+    let ts = Date.now()
+    const { prob, regions } = PNet(resized, params)
+    statsForScale.pnet = Date.now() - ts
+
+    const scoresTensor = tf.unstack(tf.unstack(prob, 3)[1])[0] as tf.Tensor2D
+    const regionsTensor = tf.unstack(regions)[0] as tf.Tensor3D
+
+    return {
+      scoresTensor,
+      regionsTensor,
+      scale,
+      statsForScale
+    }
+  }))
+
+  const boxesForScale = pnetOutputs.map(({ scoresTensor, regionsTensor, scale, statsForScale }) => {
+    const boundingBoxes = extractBoundingBoxes(
+      scoresTensor,
+      regionsTensor,
+      scale,
+      scoreThreshold
+    )
+
+    scoresTensor.dispose()
+    regionsTensor.dispose()
+
+    if (!boundingBoxes.length) {
+      stats.stage1.push(statsForScale)
+      return []
+    }
+
+    let ts = Date.now()
+    const indices = nonMaxSuppression(
+      boundingBoxes.map(bbox => bbox.cell),
+      boundingBoxes.map(bbox => bbox.score),
+      0.5
+    )
+    statsForScale.nms = Date.now() - ts
+    statsForScale.numBoxes = indices.length
+
+    stats.stage1.push(statsForScale)
+    return indices.map(boxIdx => boundingBoxes[boxIdx])
+  })
+
+  const allBoxes = boxesForScale.reduce(
+    (all, boxes) => all.concat(boxes), []
+  )
+
+  let finalBoxes: BoundingBox[] = []
+  let finalScores: number[] = []
+
+  if (allBoxes.length > 0) {
+    let ts = Date.now()
+    const indices = nonMaxSuppression(
+      allBoxes.map(bbox => bbox.cell),
+      allBoxes.map(bbox => bbox.score),
+      0.7
+    )
+    stats.stage1_nms = Date.now() - ts
+
+    finalScores = indices.map(idx => allBoxes[idx].score)
+    finalBoxes = indices
+      .map(idx => allBoxes[idx])
+      .map(({ cell, region }) =>
+        new BoundingBox(
+          cell.left + (region.left * cell.width),
+          cell.top + (region.top * cell.height),
+          cell.right + (region.right * cell.width),
+          cell.bottom + (region.bottom * cell.height)
+        ).toSquare().round()
+      )
+
+  }
+
+  return {
+    boxes: finalBoxes,
+    scores: finalScores
+  }
+
+}
diff --git a/node_modules/face-api.js/src/mtcnn/stage2.ts b/node_modules/face-api.js/src/mtcnn/stage2.ts
new file mode 100644
index 0000000..eadab7a
--- /dev/null
+++ b/node_modules/face-api.js/src/mtcnn/stage2.ts
@@ -0,0 +1,79 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { Box, nonMaxSuppression } from 'tfjs-image-recognition-base';
+
+import { extractImagePatches } from './extractImagePatches';
+import { MtcnnBox } from './MtcnnBox';
+import { RNet } from './RNet';
+import { RNetParams } from './types';
+
+export async function stage2(
+  img: HTMLCanvasElement,
+  inputBoxes: Box[],
+  scoreThreshold: number,
+  params: RNetParams,
+  stats: any
+) {
+
+  let ts = Date.now()
+  const rnetInputs = await extractImagePatches(img, inputBoxes, { width: 24, height: 24 })
+  stats.stage2_extractImagePatches = Date.now() - ts
+
+  ts = Date.now()
+  const rnetOuts = rnetInputs.map(
+    rnetInput => {
+      const out = RNet(rnetInput, params)
+      rnetInput.dispose()
+      return out
+    }
+  )
+  stats.stage2_rnet = Date.now() - ts
+
+  const scoresTensor = rnetOuts.length > 1
+    ? tf.concat(rnetOuts.map(out => out.scores))
+    : rnetOuts[0].scores
+  const scores = Array.from(await scoresTensor.data())
+  scoresTensor.dispose()
+
+  const indices = scores
+    .map((score, idx) => ({ score, idx }))
+    .filter(c => c.score > scoreThreshold)
+    .map(({ idx }) => idx)
+
+  const filteredBoxes = indices.map(idx => inputBoxes[idx])
+  const filteredScores = indices.map(idx => scores[idx])
+
+  let finalBoxes: Box[] = []
+  let finalScores: number[] = []
+
+  if (filteredBoxes.length > 0) {
+    ts = Date.now()
+    const indicesNms = nonMaxSuppression(
+      filteredBoxes,
+      filteredScores,
+      0.7
+    )
+    stats.stage2_nms = Date.now() - ts
+
+    const regions = indicesNms.map(idx =>
+      new MtcnnBox(
+        rnetOuts[indices[idx]].regions.get(0, 0),
+        rnetOuts[indices[idx]].regions.get(0, 1),
+        rnetOuts[indices[idx]].regions.get(0, 2),
+        rnetOuts[indices[idx]].regions.get(0, 3)
+      )
+    )
+
+    finalScores = indicesNms.map(idx => filteredScores[idx])
+    finalBoxes = indicesNms.map((idx, i) => filteredBoxes[idx].calibrate(regions[i]))
+  }
+
+  rnetOuts.forEach(t => {
+    t.regions.dispose()
+    t.scores.dispose()
+  })
+
+  return {
+    boxes: finalBoxes,
+    scores: finalScores
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/mtcnn/stage3.ts b/node_modules/face-api.js/src/mtcnn/stage3.ts
new file mode 100644
index 0000000..fa5bbb3
--- /dev/null
+++ b/node_modules/face-api.js/src/mtcnn/stage3.ts
@@ -0,0 +1,91 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { BoundingBox, Box, nonMaxSuppression, Point } from 'tfjs-image-recognition-base';
+
+import { extractImagePatches } from './extractImagePatches';
+import { MtcnnBox } from './MtcnnBox';
+import { ONet } from './ONet';
+import { ONetParams } from './types';
+
+export async function stage3(
+  img: HTMLCanvasElement,
+  inputBoxes: BoundingBox[],
+  scoreThreshold: number,
+  params: ONetParams,
+  stats: any
+) {
+
+  let ts = Date.now()
+  const onetInputs = await extractImagePatches(img, inputBoxes, { width: 48, height: 48 })
+  stats.stage3_extractImagePatches = Date.now() - ts
+
+  ts = Date.now()
+  const onetOuts = onetInputs.map(
+    onetInput => {
+      const out = ONet(onetInput, params)
+      onetInput.dispose()
+      return out
+    }
+  )
+  stats.stage3_onet = Date.now() - ts
+
+  const scoresTensor = onetOuts.length > 1
+    ? tf.concat(onetOuts.map(out => out.scores))
+    : onetOuts[0].scores
+  const scores = Array.from(await scoresTensor.data())
+  scoresTensor.dispose()
+
+  const indices = scores
+    .map((score, idx) => ({ score, idx }))
+    .filter(c => c.score > scoreThreshold)
+    .map(({ idx }) => idx)
+
+  const filteredRegions = indices.map(idx => new MtcnnBox(
+    onetOuts[idx].regions.get(0, 0),
+    onetOuts[idx].regions.get(0, 1),
+    onetOuts[idx].regions.get(0, 2),
+    onetOuts[idx].regions.get(0, 3)
+  ))
+  const filteredBoxes = indices
+    .map((idx, i) => inputBoxes[idx].calibrate(filteredRegions[i]))
+  const filteredScores = indices.map(idx => scores[idx])
+
+  let finalBoxes: Box[] = []
+  let finalScores: number[] = []
+  let points: Point[][] = []
+
+  if (filteredBoxes.length > 0) {
+
+    ts = Date.now()
+    const indicesNms = nonMaxSuppression(
+      filteredBoxes,
+      filteredScores,
+      0.7,
+      false
+    )
+    stats.stage3_nms = Date.now() - ts
+
+    finalBoxes = indicesNms.map(idx => filteredBoxes[idx])
+    finalScores = indicesNms.map(idx => filteredScores[idx])
+    points = indicesNms.map((idx, i) =>
+      Array(5).fill(0).map((_, ptIdx) =>
+        new Point(
+          ((onetOuts[idx].points.get(0, ptIdx) * (finalBoxes[i].width + 1)) + finalBoxes[i].left) ,
+          ((onetOuts[idx].points.get(0, ptIdx + 5) * (finalBoxes[i].height + 1)) + finalBoxes[i].top)
+        )
+      )
+    )
+  }
+
+  onetOuts.forEach(t => {
+    t.regions.dispose()
+    t.scores.dispose()
+    t.points.dispose()
+  })
+
+  return {
+    boxes: finalBoxes,
+    scores: finalScores,
+    points
+  }
+
+}
diff --git a/node_modules/face-api.js/src/mtcnn/types.ts b/node_modules/face-api.js/src/mtcnn/types.ts
new file mode 100644
index 0000000..9ff977e
--- /dev/null
+++ b/node_modules/face-api.js/src/mtcnn/types.ts
@@ -0,0 +1,44 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { ConvParams, FCParams } from 'tfjs-tiny-yolov2';
+
+import { FaceLandmarks5 } from '../classes/FaceLandmarks5';
+import { WithFaceDetection, WithFaceLandmarks } from '../factories';
+
+export type SharedParams = {
+  conv1: ConvParams
+  prelu1_alpha: tf.Tensor1D
+  conv2: ConvParams
+  prelu2_alpha: tf.Tensor1D
+  conv3: ConvParams
+  prelu3_alpha: tf.Tensor1D
+}
+
+export type PNetParams = SharedParams & {
+  conv4_1: ConvParams
+  conv4_2: ConvParams
+}
+
+export type RNetParams = SharedParams & {
+  fc1: FCParams
+  prelu4_alpha: tf.Tensor1D
+  fc2_1: FCParams
+  fc2_2: FCParams
+}
+
+export type ONetParams = SharedParams & {
+  conv4: ConvParams
+  prelu4_alpha: tf.Tensor1D
+  fc1: FCParams
+  prelu5_alpha: tf.Tensor1D
+  fc2_1: FCParams
+  fc2_2: FCParams
+  fc2_3: FCParams
+}
+
+export type NetParams = {
+  pnet: PNetParams
+  rnet: RNetParams
+  onet: ONetParams
+}
+
+export type MtcnnResult = WithFaceLandmarks<WithFaceDetection<{}>, FaceLandmarks5>
diff --git a/node_modules/face-api.js/src/resizeResults.ts b/node_modules/face-api.js/src/resizeResults.ts
new file mode 100644
index 0000000..ee17e88
--- /dev/null
+++ b/node_modules/face-api.js/src/resizeResults.ts
@@ -0,0 +1,33 @@
+import { IDimensions } from 'tfjs-image-recognition-base';
+
+import { FaceDetection } from './classes/FaceDetection';
+import { FaceLandmarks } from './classes/FaceLandmarks';
+import { extendWithFaceDetection } from './factories/WithFaceDetection';
+import { extendWithFaceLandmarks } from './factories/WithFaceLandmarks';
+
+export function resizeResults<T>(results: T, { width, height }: IDimensions): T {
+
+  if (Array.isArray(results)) {
+    return results.map(obj => resizeResults(obj, { width, height })) as any as T
+  }
+
+  const hasLandmarks = results['unshiftedLandmarks'] && results['unshiftedLandmarks'] instanceof FaceLandmarks
+  const hasDetection = results['detection'] && results['detection'] instanceof FaceDetection
+
+  if (hasLandmarks) {
+    const resizedDetection = results['detection'].forSize(width, height)
+    const resizedLandmarks = results['unshiftedLandmarks'].forSize(resizedDetection.box.width, resizedDetection.box.height)
+
+    return extendWithFaceLandmarks(extendWithFaceDetection(results as any, resizedDetection), resizedLandmarks)
+  }
+
+  if (hasDetection) {
+    return extendWithFaceDetection(results as any, results['detection'].forSize(width, height))
+  }
+
+  if (results instanceof FaceLandmarks || results instanceof FaceDetection) {
+    return (results as any).forSize(width, height)
+  }
+
+  return results
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/ssdMobilenetv1/SsdMobilenetv1.ts b/node_modules/face-api.js/src/ssdMobilenetv1/SsdMobilenetv1.ts
new file mode 100644
index 0000000..d32dc78
--- /dev/null
+++ b/node_modules/face-api.js/src/ssdMobilenetv1/SsdMobilenetv1.ts
@@ -0,0 +1,130 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { NetInput, NeuralNetwork, Rect, TNetInput, toNetInput } from 'tfjs-image-recognition-base';
+
+import { FaceDetection } from '../classes/FaceDetection';
+import { extractParams } from './extractParams';
+import { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';
+import { mobileNetV1 } from './mobileNetV1';
+import { nonMaxSuppression } from './nonMaxSuppression';
+import { outputLayer } from './outputLayer';
+import { predictionLayer } from './predictionLayer';
+import { ISsdMobilenetv1Options, SsdMobilenetv1Options } from './SsdMobilenetv1Options';
+import { NetParams } from './types';
+
+
+export class SsdMobilenetv1 extends NeuralNetwork<NetParams> {
+
+  constructor() {
+    super('SsdMobilenetv1')
+  }
+
+  public forwardInput(input: NetInput) {
+
+    const { params } = this
+
+    if (!params) {
+      throw new Error('SsdMobilenetv1 - load model before inference')
+    }
+
+    return tf.tidy(() => {
+      const batchTensor = input.toBatchTensor(512, false).toFloat()
+
+      const x = tf.sub(tf.mul(batchTensor, tf.scalar(0.007843137718737125)), tf.scalar(1)) as tf.Tensor4D
+      const features = mobileNetV1(x, params.mobilenetv1)
+
+      const {
+        boxPredictions,
+        classPredictions
+      } = predictionLayer(features.out, features.conv11, params.prediction_layer)
+
+      return outputLayer(boxPredictions, classPredictions, params.output_layer)
+    })
+  }
+
+  public async forward(input: TNetInput) {
+    return this.forwardInput(await toNetInput(input))
+  }
+
+  public async locateFaces(
+    input: TNetInput,
+    options: ISsdMobilenetv1Options = {}
+  ): Promise<FaceDetection[]> {
+
+    const { maxResults, minConfidence } = new SsdMobilenetv1Options(options)
+
+    const netInput = await toNetInput(input)
+
+    const {
+      boxes: _boxes,
+      scores: _scores
+    } = this.forwardInput(netInput)
+
+
+    // TODO batches
+    const boxes = _boxes[0]
+    const scores = _scores[0]
+    for (let i = 1; i < _boxes.length; i++) {
+      _boxes[i].dispose()
+      _scores[i].dispose()
+    }
+
+    // TODO find a better way to filter by minConfidence
+    const scoresData = Array.from(await scores.data())
+
+    const iouThreshold = 0.5
+    const indices = nonMaxSuppression(
+      boxes,
+      scoresData,
+      maxResults,
+      iouThreshold,
+      minConfidence
+    )
+
+    const reshapedDims = netInput.getReshapedInputDimensions(0)
+    const inputSize = netInput.inputSize as number
+    const padX = inputSize / reshapedDims.width
+    const padY = inputSize / reshapedDims.height
+
+    const results = indices
+      .map(idx => {
+        const [top, bottom] = [
+          Math.max(0, boxes.get(idx, 0)),
+          Math.min(1.0, boxes.get(idx, 2))
+        ].map(val => val * padY)
+        const [left, right] = [
+          Math.max(0, boxes.get(idx, 1)),
+          Math.min(1.0, boxes.get(idx, 3))
+        ].map(val => val * padX)
+        return new FaceDetection(
+          scoresData[idx],
+          new Rect(
+            left,
+            top,
+            right - left,
+            bottom - top
+          ),
+          {
+            height: netInput.getInputHeight(0),
+            width: netInput.getInputWidth(0)
+          }
+        )
+      })
+
+    boxes.dispose()
+    scores.dispose()
+
+    return results
+  }
+
+  protected getDefaultModelName(): string {
+    return 'ssd_mobilenetv1_model'
+  }
+
+  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {
+    return extractParamsFromWeigthMap(weightMap)
+  }
+
+  protected extractParams(weights: Float32Array) {
+    return extractParams(weights)
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/ssdMobilenetv1/SsdMobilenetv1Options.ts b/node_modules/face-api.js/src/ssdMobilenetv1/SsdMobilenetv1Options.ts
new file mode 100644
index 0000000..c81dcb7
--- /dev/null
+++ b/node_modules/face-api.js/src/ssdMobilenetv1/SsdMobilenetv1Options.ts
@@ -0,0 +1,27 @@
+export interface ISsdMobilenetv1Options {
+  minConfidence?: number
+  maxResults?: number
+}
+
+export class SsdMobilenetv1Options {
+  protected _name: string = 'SsdMobilenetv1Options'
+
+  private _minConfidence: number
+  private _maxResults: number
+
+  constructor({ minConfidence, maxResults }: ISsdMobilenetv1Options = {}) {
+    this._minConfidence = minConfidence || 0.5
+    this._maxResults = maxResults || 100
+
+    if (typeof this._minConfidence !== 'number' || this._minConfidence <= 0 || this._minConfidence >= 1) {
+      throw new Error(`${this._name} - expected minConfidence to be a number between 0 and 1`)
+    }
+
+    if (typeof this._maxResults !== 'number') {
+      throw new Error(`${this._name} - expected maxResults to be a number`)
+    }
+  }
+
+  get minConfidence(): number { return this._minConfidence }
+  get maxResults(): number { return this._maxResults }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/ssdMobilenetv1/boxPredictionLayer.ts b/node_modules/face-api.js/src/ssdMobilenetv1/boxPredictionLayer.ts
new file mode 100644
index 0000000..e8927d4
--- /dev/null
+++ b/node_modules/face-api.js/src/ssdMobilenetv1/boxPredictionLayer.ts
@@ -0,0 +1,29 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { convLayer } from 'tfjs-tiny-yolov2';
+
+import { BoxPredictionParams } from './types';
+
+
+export function boxPredictionLayer(
+  x: tf.Tensor4D,
+  params: BoxPredictionParams
+) {
+  return tf.tidy(() => {
+
+    const batchSize = x.shape[0]
+
+    const boxPredictionEncoding = tf.reshape(
+      convLayer(x, params.box_encoding_predictor),
+      [batchSize, -1, 1, 4]
+    )
+    const classPrediction = tf.reshape(
+      convLayer(x, params.class_predictor),
+      [batchSize, -1, 3]
+    )
+
+    return {
+      boxPredictionEncoding,
+      classPrediction
+    }
+  })
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/ssdMobilenetv1/extractParams.ts b/node_modules/face-api.js/src/ssdMobilenetv1/extractParams.ts
new file mode 100644
index 0000000..0404006
--- /dev/null
+++ b/node_modules/face-api.js/src/ssdMobilenetv1/extractParams.ts
@@ -0,0 +1,233 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from 'tfjs-image-recognition-base';
+import { ConvParams } from 'tfjs-tiny-yolov2';
+
+import { MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';
+
+function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {
+
+  function extractDepthwiseConvParams(numChannels: number, mappedPrefix: string): MobileNetV1.DepthwiseConvParams {
+
+    const filters = tf.tensor4d(extractWeights(3 * 3 * numChannels), [3, 3, numChannels, 1])
+    const batch_norm_scale = tf.tensor1d(extractWeights(numChannels))
+    const batch_norm_offset = tf.tensor1d(extractWeights(numChannels))
+    const batch_norm_mean = tf.tensor1d(extractWeights(numChannels))
+    const batch_norm_variance = tf.tensor1d(extractWeights(numChannels))
+
+    paramMappings.push(
+      { paramPath: `${mappedPrefix}/filters` },
+      { paramPath: `${mappedPrefix}/batch_norm_scale` },
+      { paramPath: `${mappedPrefix}/batch_norm_offset` },
+      { paramPath: `${mappedPrefix}/batch_norm_mean` },
+      { paramPath: `${mappedPrefix}/batch_norm_variance` }
+    )
+
+    return {
+      filters,
+      batch_norm_scale,
+      batch_norm_offset,
+      batch_norm_mean,
+      batch_norm_variance
+    }
+  }
+
+  function extractConvParams(
+    channelsIn: number,
+    channelsOut: number,
+    filterSize: number,
+    mappedPrefix: string,
+    isPointwiseConv?: boolean
+  ): ConvParams {
+
+    const filters = tf.tensor4d(
+      extractWeights(channelsIn * channelsOut * filterSize * filterSize),
+      [filterSize, filterSize, channelsIn, channelsOut]
+    )
+    const bias = tf.tensor1d(extractWeights(channelsOut))
+
+    paramMappings.push(
+      { paramPath: `${mappedPrefix}/filters` },
+      { paramPath: `${mappedPrefix}/${isPointwiseConv ? 'batch_norm_offset' : 'bias'}` }
+    )
+
+    return { filters, bias }
+  }
+
+  function extractPointwiseConvParams(
+    channelsIn: number,
+    channelsOut: number,
+    filterSize: number,
+    mappedPrefix: string
+  ): PointwiseConvParams {
+
+    const {
+      filters,
+      bias
+    } = extractConvParams(channelsIn, channelsOut, filterSize, mappedPrefix, true)
+
+    return {
+      filters,
+      batch_norm_offset: bias
+    }
+  }
+
+  function extractConvPairParams(
+    channelsIn: number,
+    channelsOut: number,
+    mappedPrefix: string
+  ): MobileNetV1.ConvPairParams {
+
+    const depthwise_conv = extractDepthwiseConvParams(channelsIn, `${mappedPrefix}/depthwise_conv`)
+    const pointwise_conv = extractPointwiseConvParams(channelsIn, channelsOut, 1, `${mappedPrefix}/pointwise_conv`)
+
+    return { depthwise_conv, pointwise_conv }
+  }
+
+  function extractMobilenetV1Params(): MobileNetV1.Params {
+
+    const conv_0 = extractPointwiseConvParams(3, 32, 3, 'mobilenetv1/conv_0')
+
+    const conv_1 = extractConvPairParams(32, 64, 'mobilenetv1/conv_1')
+    const conv_2 = extractConvPairParams(64, 128, 'mobilenetv1/conv_2')
+    const conv_3 = extractConvPairParams(128, 128, 'mobilenetv1/conv_3')
+    const conv_4 = extractConvPairParams(128, 256, 'mobilenetv1/conv_4')
+    const conv_5 = extractConvPairParams(256, 256, 'mobilenetv1/conv_5')
+    const conv_6 = extractConvPairParams(256, 512, 'mobilenetv1/conv_6')
+    const conv_7 = extractConvPairParams(512, 512, 'mobilenetv1/conv_7')
+    const conv_8 = extractConvPairParams(512, 512, 'mobilenetv1/conv_8')
+    const conv_9 = extractConvPairParams(512, 512, 'mobilenetv1/conv_9')
+    const conv_10 = extractConvPairParams(512, 512, 'mobilenetv1/conv_10')
+    const conv_11 = extractConvPairParams(512, 512, 'mobilenetv1/conv_11')
+    const conv_12 = extractConvPairParams(512, 1024, 'mobilenetv1/conv_12')
+    const conv_13 = extractConvPairParams(1024, 1024, 'mobilenetv1/conv_13')
+
+    return {
+      conv_0,
+      conv_1,
+      conv_2,
+      conv_3,
+      conv_4,
+      conv_5,
+      conv_6,
+      conv_7,
+      conv_8,
+      conv_9,
+      conv_10,
+      conv_11,
+      conv_12,
+      conv_13
+    }
+  }
+
+  function extractPredictionLayerParams(): PredictionLayerParams {
+    const conv_0 = extractPointwiseConvParams(1024, 256, 1, 'prediction_layer/conv_0')
+    const conv_1 = extractPointwiseConvParams(256, 512, 3, 'prediction_layer/conv_1')
+    const conv_2 = extractPointwiseConvParams(512, 128, 1, 'prediction_layer/conv_2')
+    const conv_3 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_3')
+    const conv_4 = extractPointwiseConvParams(256, 128, 1, 'prediction_layer/conv_4')
+    const conv_5 = extractPointwiseConvParams(128, 256, 3, 'prediction_layer/conv_5')
+    const conv_6 = extractPointwiseConvParams(256, 64, 1, 'prediction_layer/conv_6')
+    const conv_7 = extractPointwiseConvParams(64, 128, 3, 'prediction_layer/conv_7')
+
+    const box_encoding_0_predictor = extractConvParams(512, 12, 1, 'prediction_layer/box_predictor_0/box_encoding_predictor')
+    const class_predictor_0 = extractConvParams(512, 9, 1, 'prediction_layer/box_predictor_0/class_predictor')
+    const box_encoding_1_predictor = extractConvParams(1024, 24, 1, 'prediction_layer/box_predictor_1/box_encoding_predictor')
+    const class_predictor_1 = extractConvParams(1024, 18, 1, 'prediction_layer/box_predictor_1/class_predictor')
+    const box_encoding_2_predictor = extractConvParams(512, 24, 1, 'prediction_layer/box_predictor_2/box_encoding_predictor')
+    const class_predictor_2 = extractConvParams(512, 18, 1, 'prediction_layer/box_predictor_2/class_predictor')
+    const box_encoding_3_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_3/box_encoding_predictor')
+    const class_predictor_3 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_3/class_predictor')
+    const box_encoding_4_predictor = extractConvParams(256, 24, 1, 'prediction_layer/box_predictor_4/box_encoding_predictor')
+    const class_predictor_4 = extractConvParams(256, 18, 1, 'prediction_layer/box_predictor_4/class_predictor')
+    const box_encoding_5_predictor = extractConvParams(128, 24, 1, 'prediction_layer/box_predictor_5/box_encoding_predictor')
+    const class_predictor_5 = extractConvParams(128, 18, 1, 'prediction_layer/box_predictor_5/class_predictor')
+
+    const box_predictor_0 = {
+      box_encoding_predictor: box_encoding_0_predictor,
+      class_predictor: class_predictor_0
+    }
+    const box_predictor_1 = {
+      box_encoding_predictor: box_encoding_1_predictor,
+      class_predictor: class_predictor_1
+    }
+    const box_predictor_2 = {
+      box_encoding_predictor: box_encoding_2_predictor,
+      class_predictor: class_predictor_2
+    }
+    const box_predictor_3 = {
+      box_encoding_predictor: box_encoding_3_predictor,
+      class_predictor: class_predictor_3
+    }
+    const box_predictor_4 = {
+      box_encoding_predictor: box_encoding_4_predictor,
+      class_predictor: class_predictor_4
+    }
+    const box_predictor_5 = {
+      box_encoding_predictor: box_encoding_5_predictor,
+      class_predictor: class_predictor_5
+    }
+
+    return {
+      conv_0,
+      conv_1,
+      conv_2,
+      conv_3,
+      conv_4,
+      conv_5,
+      conv_6,
+      conv_7,
+      box_predictor_0,
+      box_predictor_1,
+      box_predictor_2,
+      box_predictor_3,
+      box_predictor_4,
+      box_predictor_5
+    }
+  }
+
+  return {
+    extractMobilenetV1Params,
+    extractPredictionLayerParams
+  }
+
+}
+
+export function extractParams(weights: Float32Array): { params: NetParams, paramMappings: ParamMapping[] } {
+
+  const paramMappings: ParamMapping[] = []
+
+  const {
+    extractWeights,
+    getRemainingWeights
+  } = extractWeightsFactory(weights)
+
+  const {
+    extractMobilenetV1Params,
+    extractPredictionLayerParams
+  } = extractorsFactory(extractWeights, paramMappings)
+
+  const mobilenetv1 = extractMobilenetV1Params()
+  const prediction_layer = extractPredictionLayerParams()
+  const extra_dim = tf.tensor3d(
+    extractWeights(5118 * 4),
+    [1, 5118, 4]
+  )
+  const output_layer = {
+    extra_dim
+  }
+
+  paramMappings.push({ paramPath: 'output_layer/extra_dim' })
+
+  if (getRemainingWeights().length !== 0) {
+    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)
+  }
+
+  return {
+    params: {
+      mobilenetv1,
+      prediction_layer,
+      output_layer
+    },
+    paramMappings
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/ssdMobilenetv1/extractParamsFromWeigthMap.ts b/node_modules/face-api.js/src/ssdMobilenetv1/extractParamsFromWeigthMap.ts
new file mode 100644
index 0000000..85c33ea
--- /dev/null
+++ b/node_modules/face-api.js/src/ssdMobilenetv1/extractParamsFromWeigthMap.ts
@@ -0,0 +1,143 @@
+import * as tf from '@tensorflow/tfjs-core';
+import {
+  disposeUnusedWeightTensors,
+  extractWeightEntryFactory,
+  isTensor3D,
+  ParamMapping,
+} from 'tfjs-image-recognition-base';
+import { ConvParams } from 'tfjs-tiny-yolov2';
+
+import { BoxPredictionParams, MobileNetV1, NetParams, PointwiseConvParams, PredictionLayerParams } from './types';
+
+function extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {
+
+  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)
+
+  function extractPointwiseConvParams(prefix: string, idx: number, mappedPrefix: string): PointwiseConvParams {
+
+    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/Conv2d_${idx}_pointwise/weights`, 4, `${mappedPrefix}/filters`)
+    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefix}/Conv2d_${idx}_pointwise/convolution_bn_offset`, 1, `${mappedPrefix}/batch_norm_offset`)
+
+    return { filters, batch_norm_offset }
+  }
+
+  function extractConvPairParams(idx: number): MobileNetV1.ConvPairParams {
+
+    const mappedPrefix = `mobilenetv1/conv_${idx}`
+    const prefixDepthwiseConv = `MobilenetV1/Conv2d_${idx}_depthwise`
+    const mappedPrefixDepthwiseConv = `${mappedPrefix}/depthwise_conv`
+    const mappedPrefixPointwiseConv = `${mappedPrefix}/pointwise_conv`
+
+    const filters = extractWeightEntry<tf.Tensor4D>(`${prefixDepthwiseConv}/depthwise_weights`, 4, `${mappedPrefixDepthwiseConv}/filters`)
+    const batch_norm_scale = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/gamma`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_scale`)
+    const batch_norm_offset = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/beta`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_offset`)
+    const batch_norm_mean = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_mean`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_mean`)
+    const batch_norm_variance = extractWeightEntry<tf.Tensor1D>(`${prefixDepthwiseConv}/BatchNorm/moving_variance`, 1, `${mappedPrefixDepthwiseConv}/batch_norm_variance`)
+
+    return {
+      depthwise_conv: {
+        filters,
+        batch_norm_scale,
+        batch_norm_offset,
+        batch_norm_mean,
+        batch_norm_variance
+      },
+      pointwise_conv: extractPointwiseConvParams('MobilenetV1', idx, mappedPrefixPointwiseConv)
+    }
+  }
+
+  function extractMobilenetV1Params(): MobileNetV1.Params {
+    return {
+      conv_0: extractPointwiseConvParams('MobilenetV1', 0, 'mobilenetv1/conv_0'),
+      conv_1: extractConvPairParams(1),
+      conv_2: extractConvPairParams(2),
+      conv_3: extractConvPairParams(3),
+      conv_4: extractConvPairParams(4),
+      conv_5: extractConvPairParams(5),
+      conv_6: extractConvPairParams(6),
+      conv_7: extractConvPairParams(7),
+      conv_8: extractConvPairParams(8),
+      conv_9: extractConvPairParams(9),
+      conv_10: extractConvPairParams(10),
+      conv_11: extractConvPairParams(11),
+      conv_12: extractConvPairParams(12),
+      conv_13: extractConvPairParams(13)
+    }
+  }
+
+  function extractConvParams(prefix: string, mappedPrefix: string): ConvParams {
+    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/weights`, 4, `${mappedPrefix}/filters`)
+    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/biases`, 1, `${mappedPrefix}/bias`)
+
+    return { filters, bias }
+  }
+
+  function extractBoxPredictorParams(idx: number): BoxPredictionParams {
+
+    const box_encoding_predictor = extractConvParams(
+      `Prediction/BoxPredictor_${idx}/BoxEncodingPredictor`,
+      `prediction_layer/box_predictor_${idx}/box_encoding_predictor`
+    )
+    const class_predictor = extractConvParams(
+      `Prediction/BoxPredictor_${idx}/ClassPredictor`,
+      `prediction_layer/box_predictor_${idx}/class_predictor`
+    )
+
+    return { box_encoding_predictor, class_predictor }
+  }
+
+  function extractPredictionLayerParams(): PredictionLayerParams {
+    return {
+      conv_0: extractPointwiseConvParams('Prediction', 0, 'prediction_layer/conv_0'),
+      conv_1: extractPointwiseConvParams('Prediction', 1, 'prediction_layer/conv_1'),
+      conv_2: extractPointwiseConvParams('Prediction', 2, 'prediction_layer/conv_2'),
+      conv_3: extractPointwiseConvParams('Prediction', 3, 'prediction_layer/conv_3'),
+      conv_4: extractPointwiseConvParams('Prediction', 4, 'prediction_layer/conv_4'),
+      conv_5: extractPointwiseConvParams('Prediction', 5, 'prediction_layer/conv_5'),
+      conv_6: extractPointwiseConvParams('Prediction', 6, 'prediction_layer/conv_6'),
+      conv_7: extractPointwiseConvParams('Prediction', 7, 'prediction_layer/conv_7'),
+      box_predictor_0: extractBoxPredictorParams(0),
+      box_predictor_1: extractBoxPredictorParams(1),
+      box_predictor_2: extractBoxPredictorParams(2),
+      box_predictor_3: extractBoxPredictorParams(3),
+      box_predictor_4: extractBoxPredictorParams(4),
+      box_predictor_5: extractBoxPredictorParams(5)
+    }
+  }
+
+  return {
+    extractMobilenetV1Params,
+    extractPredictionLayerParams
+  }
+}
+
+export function extractParamsFromWeigthMap(
+  weightMap: tf.NamedTensorMap
+): { params: NetParams, paramMappings: ParamMapping[] } {
+
+  const paramMappings: ParamMapping[] = []
+
+  const {
+    extractMobilenetV1Params,
+    extractPredictionLayerParams
+  } = extractorsFactory(weightMap, paramMappings)
+
+  const extra_dim = weightMap['Output/extra_dim']
+  paramMappings.push({ originalPath: 'Output/extra_dim', paramPath: 'output_layer/extra_dim' })
+
+  if (!isTensor3D(extra_dim)) {
+    throw new Error(`expected weightMap['Output/extra_dim'] to be a Tensor3D, instead have ${extra_dim}`)
+  }
+
+  const params = {
+    mobilenetv1: extractMobilenetV1Params(),
+    prediction_layer: extractPredictionLayerParams(),
+    output_layer: {
+      extra_dim
+    }
+  }
+
+  disposeUnusedWeightTensors(weightMap, paramMappings)
+
+  return { params, paramMappings }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/ssdMobilenetv1/index.ts b/node_modules/face-api.js/src/ssdMobilenetv1/index.ts
new file mode 100644
index 0000000..bd1a0c6
--- /dev/null
+++ b/node_modules/face-api.js/src/ssdMobilenetv1/index.ts
@@ -0,0 +1,17 @@
+import { SsdMobilenetv1 } from './SsdMobilenetv1';
+
+export * from './SsdMobilenetv1';
+export * from './SsdMobilenetv1Options';
+
+export function createSsdMobilenetv1(weights: Float32Array) {
+  const net = new SsdMobilenetv1()
+  net.extractWeights(weights)
+  return net
+}
+
+export function createFaceDetectionNet(weights: Float32Array) {
+  return createSsdMobilenetv1(weights)
+}
+
+// alias for backward compatibily
+export class FaceDetectionNet extends SsdMobilenetv1 {}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/ssdMobilenetv1/mobileNetV1.ts b/node_modules/face-api.js/src/ssdMobilenetv1/mobileNetV1.ts
new file mode 100644
index 0000000..0fb0a94
--- /dev/null
+++ b/node_modules/face-api.js/src/ssdMobilenetv1/mobileNetV1.ts
@@ -0,0 +1,75 @@
+import * as tf from '@tensorflow/tfjs-core';
+
+import { pointwiseConvLayer } from './pointwiseConvLayer';
+import { MobileNetV1 } from './types';
+
+const epsilon = 0.0010000000474974513
+
+function depthwiseConvLayer(
+  x: tf.Tensor4D,
+  params: MobileNetV1.DepthwiseConvParams,
+  strides: [number, number]
+) {
+  return tf.tidy(() => {
+
+    let out = tf.depthwiseConv2d(x, params.filters, strides, 'same')
+    out = tf.batchNormalization<tf.Rank.R4>(
+      out,
+      params.batch_norm_mean,
+      params.batch_norm_variance,
+      epsilon,
+      params.batch_norm_scale,
+      params.batch_norm_offset
+    )
+    return tf.clipByValue(out, 0, 6)
+
+  })
+}
+
+function getStridesForLayerIdx(layerIdx: number): [number, number] {
+  return [2, 4, 6, 12].some(idx => idx === layerIdx) ? [2, 2] : [1, 1]
+}
+
+export function mobileNetV1(x: tf.Tensor4D, params: MobileNetV1.Params) {
+  return tf.tidy(() => {
+
+    let conv11 = null
+    let out = pointwiseConvLayer(x, params.conv_0, [2, 2])
+
+    const convPairParams = [
+      params.conv_1,
+      params.conv_2,
+      params.conv_3,
+      params.conv_4,
+      params.conv_5,
+      params.conv_6,
+      params.conv_7,
+      params.conv_8,
+      params.conv_9,
+      params.conv_10,
+      params.conv_11,
+      params.conv_12,
+      params.conv_13
+    ]
+
+    convPairParams.forEach((param, i) => {
+      const layerIdx = i + 1
+      const depthwiseConvStrides = getStridesForLayerIdx(layerIdx)
+      out = depthwiseConvLayer(out, param.depthwise_conv, depthwiseConvStrides)
+      out = pointwiseConvLayer(out, param.pointwise_conv, [1, 1])
+      if (layerIdx === 11) {
+        conv11 = out
+      }
+    })
+
+    if (conv11 === null) {
+      throw new Error('mobileNetV1 - output of conv layer 11 is null')
+    }
+
+    return {
+      out,
+      conv11: conv11 as any
+    }
+
+  })
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/ssdMobilenetv1/nonMaxSuppression.ts b/node_modules/face-api.js/src/ssdMobilenetv1/nonMaxSuppression.ts
new file mode 100644
index 0000000..0032037
--- /dev/null
+++ b/node_modules/face-api.js/src/ssdMobilenetv1/nonMaxSuppression.ts
@@ -0,0 +1,73 @@
+import * as tf from '@tensorflow/tfjs-core'
+
+export function nonMaxSuppression(
+  boxes: tf.Tensor2D,
+  scores: number[],
+  maxOutputSize: number,
+  iouThreshold: number,
+  scoreThreshold: number
+): number[] {
+
+  const numBoxes = boxes.shape[0]
+  const outputSize = Math.min(
+    maxOutputSize,
+    numBoxes
+  )
+
+  const candidates = scores
+    .map((score, boxIndex) => ({ score, boxIndex }))
+    .filter(c => c.score > scoreThreshold)
+    .sort((c1, c2) => c2.score - c1.score)
+
+  const suppressFunc = (x: number) => x <= iouThreshold ? 1 : 0
+
+  const selected: number[] = []
+
+  candidates.forEach(c => {
+    if (selected.length >= outputSize) {
+      return
+    }
+    const originalScore = c.score
+
+    for (let j = selected.length - 1; j >= 0; --j) {
+      const iou = IOU(boxes, c.boxIndex, selected[j])
+      if (iou === 0.0) {
+        continue
+      }
+      c.score *= suppressFunc(iou)
+      if (c.score <= scoreThreshold) {
+        break
+      }
+    }
+
+    if (originalScore === c.score) {
+      selected.push(c.boxIndex)
+    }
+  })
+
+  return selected
+}
+
+function IOU(boxes: tf.Tensor2D, i: number, j: number) {
+  const yminI = Math.min(boxes.get(i, 0), boxes.get(i, 2))
+  const xminI = Math.min(boxes.get(i, 1), boxes.get(i, 3))
+  const ymaxI = Math.max(boxes.get(i, 0), boxes.get(i, 2))
+  const xmaxI = Math.max(boxes.get(i, 1), boxes.get(i, 3))
+  const yminJ = Math.min(boxes.get(j, 0), boxes.get(j, 2))
+  const xminJ = Math.min(boxes.get(j, 1), boxes.get(j, 3))
+  const ymaxJ = Math.max(boxes.get(j, 0), boxes.get(j, 2))
+  const xmaxJ = Math.max(boxes.get(j, 1), boxes.get(j, 3))
+  const areaI = (ymaxI - yminI) * (xmaxI - xminI)
+  const areaJ = (ymaxJ - yminJ) * (xmaxJ - xminJ)
+  if (areaI <= 0 || areaJ <= 0) {
+    return 0.0
+  }
+  const intersectionYmin = Math.max(yminI, yminJ)
+  const intersectionXmin = Math.max(xminI, xminJ)
+  const intersectionYmax = Math.min(ymaxI, ymaxJ)
+  const intersectionXmax = Math.min(xmaxI, xmaxJ)
+  const intersectionArea =
+      Math.max(intersectionYmax - intersectionYmin, 0.0) *
+      Math.max(intersectionXmax - intersectionXmin, 0.0)
+  return intersectionArea / (areaI + areaJ - intersectionArea)
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/ssdMobilenetv1/outputLayer.ts b/node_modules/face-api.js/src/ssdMobilenetv1/outputLayer.ts
new file mode 100644
index 0000000..f8da930
--- /dev/null
+++ b/node_modules/face-api.js/src/ssdMobilenetv1/outputLayer.ts
@@ -0,0 +1,85 @@
+import * as tf from '@tensorflow/tfjs-core';
+
+import { OutputLayerParams } from './types';
+
+
+function getCenterCoordinatesAndSizesLayer(x: tf.Tensor2D) {
+  const vec = tf.unstack(tf.transpose(x, [1, 0]))
+
+  const sizes = [
+    tf.sub(vec[2], vec[0]),
+    tf.sub(vec[3], vec[1])
+  ]
+
+  const centers = [
+    tf.add(vec[0], tf.div(sizes[0], tf.scalar(2))),
+    tf.add(vec[1], tf.div(sizes[1], tf.scalar(2)))
+  ]
+
+  return {
+    sizes,
+    centers
+  }
+}
+
+function decodeBoxesLayer(x0: tf.Tensor2D, x1: tf.Tensor2D) {
+  const {
+    sizes,
+    centers
+  } = getCenterCoordinatesAndSizesLayer(x0)
+
+  const vec = tf.unstack(tf.transpose(x1, [1, 0]))
+
+  const div0_out = tf.div(tf.mul(tf.exp(tf.div(vec[2], tf.scalar(5))), sizes[0]), tf.scalar(2))
+  const add0_out = tf.add(tf.mul(tf.div(vec[0], tf.scalar(10)), sizes[0]), centers[0])
+
+  const div1_out = tf.div(tf.mul(tf.exp(tf.div(vec[3], tf.scalar(5))), sizes[1]), tf.scalar(2))
+  const add1_out = tf.add(tf.mul(tf.div(vec[1], tf.scalar(10)), sizes[1]), centers[1])
+
+  return tf.transpose(
+    tf.stack([
+      tf.sub(add0_out, div0_out),
+      tf.sub(add1_out, div1_out),
+      tf.add(add0_out, div0_out),
+      tf.add(add1_out, div1_out)
+    ]),
+    [1, 0]
+  )
+}
+
+export function outputLayer(
+  boxPredictions: tf.Tensor4D,
+  classPredictions: tf.Tensor4D,
+  params: OutputLayerParams
+) {
+  return tf.tidy(() => {
+
+    const batchSize = boxPredictions.shape[0]
+
+    let boxes = decodeBoxesLayer(
+      tf.reshape(tf.tile(params.extra_dim, [batchSize, 1, 1]), [-1, 4]) as tf.Tensor2D,
+      tf.reshape(boxPredictions, [-1, 4]) as tf.Tensor2D
+    )
+    boxes = tf.reshape(
+      boxes,
+      [batchSize, (boxes.shape[0] / batchSize), 4]
+    )
+
+    const scoresAndClasses = tf.sigmoid(tf.slice(classPredictions, [0, 0, 1], [-1, -1, -1]))
+    let scores = tf.slice(scoresAndClasses, [0, 0, 0], [-1, -1, 1]) as tf.Tensor
+
+    scores = tf.reshape(
+      scores,
+      [batchSize, scores.shape[1]]
+    )
+
+    const boxesByBatch = tf.unstack(boxes) as tf.Tensor2D[]
+    const scoresByBatch = tf.unstack(scores) as tf.Tensor1D[]
+
+    return {
+      boxes: boxesByBatch,
+      scores: scoresByBatch
+    }
+
+  })
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/ssdMobilenetv1/pointwiseConvLayer.ts b/node_modules/face-api.js/src/ssdMobilenetv1/pointwiseConvLayer.ts
new file mode 100644
index 0000000..6613143
--- /dev/null
+++ b/node_modules/face-api.js/src/ssdMobilenetv1/pointwiseConvLayer.ts
@@ -0,0 +1,17 @@
+import * as tf from '@tensorflow/tfjs-core';
+
+import { PointwiseConvParams } from './types';
+
+export function pointwiseConvLayer(
+  x: tf.Tensor4D,
+  params: PointwiseConvParams,
+  strides: [number, number]
+) {
+  return tf.tidy(() => {
+
+    let out = tf.conv2d(x, params.filters, strides, 'same')
+    out = tf.add(out, params.batch_norm_offset)
+    return tf.clipByValue(out, 0, 6)
+
+  })
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/ssdMobilenetv1/predictionLayer.ts b/node_modules/face-api.js/src/ssdMobilenetv1/predictionLayer.ts
new file mode 100644
index 0000000..b038786
--- /dev/null
+++ b/node_modules/face-api.js/src/ssdMobilenetv1/predictionLayer.ts
@@ -0,0 +1,53 @@
+import * as tf from '@tensorflow/tfjs-core';
+
+import { boxPredictionLayer } from './boxPredictionLayer';
+import { pointwiseConvLayer } from './pointwiseConvLayer';
+import { PredictionLayerParams } from './types';
+
+export function predictionLayer(
+  x: tf.Tensor4D,
+  conv11: tf.Tensor4D,
+  params: PredictionLayerParams
+) {
+  return tf.tidy(() => {
+
+    const conv0 = pointwiseConvLayer(x, params.conv_0, [1, 1])
+    const conv1 = pointwiseConvLayer(conv0, params.conv_1, [2, 2])
+    const conv2 = pointwiseConvLayer(conv1, params.conv_2, [1, 1])
+    const conv3 = pointwiseConvLayer(conv2, params.conv_3, [2, 2])
+    const conv4 = pointwiseConvLayer(conv3, params.conv_4, [1, 1])
+    const conv5 = pointwiseConvLayer(conv4, params.conv_5, [2, 2])
+    const conv6 = pointwiseConvLayer(conv5, params.conv_6, [1, 1])
+    const conv7 = pointwiseConvLayer(conv6, params.conv_7, [2, 2])
+
+    const boxPrediction0 = boxPredictionLayer(conv11, params.box_predictor_0)
+    const boxPrediction1 = boxPredictionLayer(x, params.box_predictor_1)
+    const boxPrediction2 = boxPredictionLayer(conv1, params.box_predictor_2)
+    const boxPrediction3 = boxPredictionLayer(conv3, params.box_predictor_3)
+    const boxPrediction4 = boxPredictionLayer(conv5, params.box_predictor_4)
+    const boxPrediction5 = boxPredictionLayer(conv7, params.box_predictor_5)
+
+    const boxPredictions = tf.concat([
+      boxPrediction0.boxPredictionEncoding,
+      boxPrediction1.boxPredictionEncoding,
+      boxPrediction2.boxPredictionEncoding,
+      boxPrediction3.boxPredictionEncoding,
+      boxPrediction4.boxPredictionEncoding,
+      boxPrediction5.boxPredictionEncoding
+    ], 1) as tf.Tensor4D
+
+    const classPredictions = tf.concat([
+      boxPrediction0.classPrediction,
+      boxPrediction1.classPrediction,
+      boxPrediction2.classPrediction,
+      boxPrediction3.classPrediction,
+      boxPrediction4.classPrediction,
+      boxPrediction5.classPrediction
+    ], 1) as tf.Tensor4D
+
+    return {
+      boxPredictions,
+      classPredictions
+    }
+  })
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/ssdMobilenetv1/types.ts b/node_modules/face-api.js/src/ssdMobilenetv1/types.ts
new file mode 100644
index 0000000..24647d2
--- /dev/null
+++ b/node_modules/face-api.js/src/ssdMobilenetv1/types.ts
@@ -0,0 +1,73 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { ConvParams } from 'tfjs-tiny-yolov2';
+
+export type PointwiseConvParams = {
+  filters: tf.Tensor4D
+  batch_norm_offset: tf.Tensor1D
+}
+
+export namespace MobileNetV1 {
+
+  export type DepthwiseConvParams = {
+    filters: tf.Tensor4D
+    batch_norm_scale: tf.Tensor1D
+    batch_norm_offset: tf.Tensor1D
+    batch_norm_mean: tf.Tensor1D
+    batch_norm_variance: tf.Tensor1D
+  }
+
+  export type ConvPairParams = {
+    depthwise_conv: DepthwiseConvParams
+    pointwise_conv: PointwiseConvParams
+  }
+
+  export type Params = {
+    conv_0: PointwiseConvParams
+    conv_1: ConvPairParams
+    conv_2: ConvPairParams
+    conv_3: ConvPairParams
+    conv_4: ConvPairParams
+    conv_5: ConvPairParams
+    conv_6: ConvPairParams
+    conv_7: ConvPairParams
+    conv_8: ConvPairParams
+    conv_9: ConvPairParams
+    conv_10: ConvPairParams
+    conv_11: ConvPairParams
+    conv_12: ConvPairParams
+    conv_13: ConvPairParams
+  }
+
+}
+
+export type BoxPredictionParams = {
+  box_encoding_predictor: ConvParams
+  class_predictor: ConvParams
+}
+
+export type PredictionLayerParams = {
+  conv_0: PointwiseConvParams
+  conv_1: PointwiseConvParams
+  conv_2: PointwiseConvParams
+  conv_3: PointwiseConvParams
+  conv_4: PointwiseConvParams
+  conv_5: PointwiseConvParams
+  conv_6: PointwiseConvParams
+  conv_7: PointwiseConvParams
+  box_predictor_0: BoxPredictionParams
+  box_predictor_1: BoxPredictionParams
+  box_predictor_2: BoxPredictionParams
+  box_predictor_3: BoxPredictionParams
+  box_predictor_4: BoxPredictionParams
+  box_predictor_5: BoxPredictionParams
+}
+
+export type OutputLayerParams = {
+  extra_dim: tf.Tensor3D
+}
+
+export type NetParams = {
+  mobilenetv1: MobileNetV1.Params,
+  prediction_layer: PredictionLayerParams,
+  output_layer: OutputLayerParams
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/tinyFaceDetector/TinyFaceDetector.ts b/node_modules/face-api.js/src/tinyFaceDetector/TinyFaceDetector.ts
new file mode 100644
index 0000000..73466a6
--- /dev/null
+++ b/node_modules/face-api.js/src/tinyFaceDetector/TinyFaceDetector.ts
@@ -0,0 +1,41 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { ParamMapping, Point, TNetInput } from 'tfjs-image-recognition-base';
+import { ITinyYolov2Options, TinyYolov2 as TinyYolov2Base } from 'tfjs-tiny-yolov2';
+import { TinyYolov2NetParams } from 'tfjs-tiny-yolov2/build/commonjs/tinyYolov2/types';
+
+import { FaceDetection } from '../classes';
+import { BOX_ANCHORS, IOU_THRESHOLD, MEAN_RGB } from './const';
+
+export class TinyFaceDetector extends TinyYolov2Base {
+
+  constructor() {
+    const config = {
+      withSeparableConvs: true,
+      iouThreshold: IOU_THRESHOLD,
+      classes: ['face'],
+      anchors: BOX_ANCHORS,
+      meanRgb: MEAN_RGB,
+      isFirstLayerConv2d: true,
+      filterSizes: [3, 16, 32, 64, 128, 256, 512]
+    }
+
+    super(config)
+  }
+
+  public get anchors(): Point[] {
+    return this.config.anchors
+  }
+
+  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {
+    const objectDetections = await this.detect(input, forwardParams)
+    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))
+  }
+
+  protected getDefaultModelName(): string {
+    return 'tiny_face_detector_model'
+  }
+
+  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {
+    return super.extractParamsFromWeigthMap(weightMap)
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/tinyFaceDetector/TinyFaceDetectorOptions.ts b/node_modules/face-api.js/src/tinyFaceDetector/TinyFaceDetectorOptions.ts
new file mode 100644
index 0000000..da1fa17
--- /dev/null
+++ b/node_modules/face-api.js/src/tinyFaceDetector/TinyFaceDetectorOptions.ts
@@ -0,0 +1,7 @@
+import { ITinyYolov2Options, TinyYolov2Options } from 'tfjs-tiny-yolov2';
+
+export interface ITinyFaceDetectorOptions extends ITinyYolov2Options {}
+
+export class TinyFaceDetectorOptions extends TinyYolov2Options {
+  protected _name: string = 'TinyFaceDetectorOptions'
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/tinyFaceDetector/const.ts b/node_modules/face-api.js/src/tinyFaceDetector/const.ts
new file mode 100644
index 0000000..e601e01
--- /dev/null
+++ b/node_modules/face-api.js/src/tinyFaceDetector/const.ts
@@ -0,0 +1,13 @@
+import { Point } from 'tfjs-image-recognition-base';
+
+export const IOU_THRESHOLD = 0.4
+
+export const BOX_ANCHORS = [
+  new Point(1.603231, 2.094468),
+  new Point(6.041143, 7.080126),
+  new Point(2.882459, 3.518061),
+  new Point(4.266906, 5.178857),
+  new Point(9.041765, 10.66308)
+]
+
+export const MEAN_RGB: [number, number, number] = [117.001, 114.697, 97.404]
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/tinyFaceDetector/index.ts b/node_modules/face-api.js/src/tinyFaceDetector/index.ts
new file mode 100644
index 0000000..4ea1953
--- /dev/null
+++ b/node_modules/face-api.js/src/tinyFaceDetector/index.ts
@@ -0,0 +1,10 @@
+import { TinyFaceDetector } from './TinyFaceDetector';
+
+export * from './TinyFaceDetector';
+export * from './TinyFaceDetectorOptions';
+
+export function createTinyFaceDetector(weights: Float32Array) {
+  const net = new TinyFaceDetector()
+  net.extractWeights(weights)
+  return net
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/tinyYolov2/TinyYolov2.ts b/node_modules/face-api.js/src/tinyYolov2/TinyYolov2.ts
new file mode 100644
index 0000000..bdd7794
--- /dev/null
+++ b/node_modules/face-api.js/src/tinyYolov2/TinyYolov2.ts
@@ -0,0 +1,57 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { ParamMapping, Point, TNetInput } from 'tfjs-image-recognition-base';
+import { ITinyYolov2Options, TinyYolov2 as TinyYolov2Base, TinyYolov2NetParams } from 'tfjs-tiny-yolov2';
+
+import { FaceDetection } from '../classes';
+import {
+  BOX_ANCHORS,
+  BOX_ANCHORS_SEPARABLE,
+  DEFAULT_MODEL_NAME,
+  DEFAULT_MODEL_NAME_SEPARABLE_CONV,
+  IOU_THRESHOLD,
+  MEAN_RGB_SEPARABLE,
+} from './const';
+
+export class TinyYolov2 extends TinyYolov2Base {
+
+  constructor(withSeparableConvs: boolean = true) {
+    const config = Object.assign({}, {
+      withSeparableConvs,
+      iouThreshold: IOU_THRESHOLD,
+      classes: ['face']
+    },
+    withSeparableConvs
+      ? {
+        anchors: BOX_ANCHORS_SEPARABLE,
+        meanRgb: MEAN_RGB_SEPARABLE
+      }
+      : {
+        anchors: BOX_ANCHORS,
+        withClassScores: true
+      }
+    )
+
+    super(config)
+  }
+
+  public get withSeparableConvs(): boolean {
+    return this.config.withSeparableConvs
+  }
+
+  public get anchors(): Point[] {
+    return this.config.anchors
+  }
+
+  public async locateFaces(input: TNetInput, forwardParams: ITinyYolov2Options): Promise<FaceDetection[]> {
+    const objectDetections = await this.detect(input, forwardParams)
+    return objectDetections.map(det => new FaceDetection(det.score, det.relativeBox, { width: det.imageWidth, height: det.imageHeight }))
+  }
+
+  protected getDefaultModelName(): string {
+    return this.withSeparableConvs ? DEFAULT_MODEL_NAME_SEPARABLE_CONV : DEFAULT_MODEL_NAME
+  }
+
+  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {
+    return super.extractParamsFromWeigthMap(weightMap)
+  }
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/tinyYolov2/const.ts b/node_modules/face-api.js/src/tinyYolov2/const.ts
new file mode 100644
index 0000000..fdf8c44
--- /dev/null
+++ b/node_modules/face-api.js/src/tinyYolov2/const.ts
@@ -0,0 +1,24 @@
+import { Point } from 'tfjs-image-recognition-base';
+
+export const IOU_THRESHOLD = 0.4
+
+export const BOX_ANCHORS = [
+  new Point(0.738768, 0.874946),
+  new Point(2.42204, 2.65704),
+  new Point(4.30971, 7.04493),
+  new Point(10.246, 4.59428),
+  new Point(12.6868, 11.8741)
+]
+
+export const BOX_ANCHORS_SEPARABLE = [
+  new Point(1.603231, 2.094468),
+  new Point(6.041143, 7.080126),
+  new Point(2.882459, 3.518061),
+  new Point(4.266906, 5.178857),
+  new Point(9.041765, 10.66308)
+]
+
+export const MEAN_RGB_SEPARABLE: [number, number, number] = [117.001, 114.697, 97.404]
+
+export const DEFAULT_MODEL_NAME = 'tiny_yolov2_model'
+export const DEFAULT_MODEL_NAME_SEPARABLE_CONV = 'tiny_yolov2_separable_conv_model'
\ No newline at end of file
diff --git a/node_modules/face-api.js/src/tinyYolov2/index.ts b/node_modules/face-api.js/src/tinyYolov2/index.ts
new file mode 100644
index 0000000..87e85ea
--- /dev/null
+++ b/node_modules/face-api.js/src/tinyYolov2/index.ts
@@ -0,0 +1,9 @@
+import { TinyYolov2 } from './TinyYolov2';
+
+export * from './TinyYolov2';
+
+export function createTinyYolov2(weights: Float32Array, withSeparableConvs: boolean = true) {
+  const net = new TinyYolov2(withSeparableConvs)
+  net.extractWeights(weights)
+  return net
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/tsconfig.es6.json b/node_modules/face-api.js/tsconfig.es6.json
new file mode 100644
index 0000000..621477b
--- /dev/null
+++ b/node_modules/face-api.js/tsconfig.es6.json
@@ -0,0 +1,11 @@
+{
+  "extends": "./tsconfig.json",
+  "compilerOptions": {
+    "outDir": "build/es6",
+    "module": "es6",
+    "target": "es5"
+  },
+  "include": [
+    "src"
+  ]
+}
diff --git a/node_modules/face-api.js/tsconfig.json b/node_modules/face-api.js/tsconfig.json
new file mode 100644
index 0000000..12734d7
--- /dev/null
+++ b/node_modules/face-api.js/tsconfig.json
@@ -0,0 +1,42 @@
+{
+  "compilerOptions": {
+    //"allowUnreachableCode": true,
+    //"noUnusedLocals": false,
+    //"noUnusedParameters": false,
+
+
+    "removeComments": false,
+    "preserveConstEnums": true,
+    "emitDecoratorMetadata": true,
+    "experimentalDecorators": true,
+    "sourceMap": true,
+    "declaration": true,
+    "noImplicitAny": true,
+    "noImplicitReturns": true,
+    "noImplicitThis": true,
+    "noFallthroughCasesInSwitch": true,
+    "suppressImplicitAnyIndexErrors": true,
+    "strictNullChecks": true,
+    "importHelpers": true,
+    "outDir": "build/commonjs",
+    "module": "commonjs",
+    "target": "es5",
+    "moduleResolution": "node",
+    "lib": ["es2015", "dom"],
+    "typeRoots": [
+      "typings",
+      "node_modules/@types"
+    ]
+  },
+  "formatCodeOptions": {
+    "indentSize": 2,
+    "tabSize": 2
+  },
+  "exclude": [
+    "node_modules",
+    "dist"
+  ],
+  "include": [
+    "src"
+  ]
+}
\ No newline at end of file
diff --git a/node_modules/face-api.js/tsconfig.test.json b/node_modules/face-api.js/tsconfig.test.json
new file mode 100644
index 0000000..f885fbc
--- /dev/null
+++ b/node_modules/face-api.js/tsconfig.test.json
@@ -0,0 +1,7 @@
+{
+  "extends": "./tsconfig.json",
+  "include": [
+    "src",
+    "test"
+  ]
+}
diff --git a/node_modules/face-api.js/typedoc.config.js b/node_modules/face-api.js/typedoc.config.js
new file mode 100644
index 0000000..f8fef62
--- /dev/null
+++ b/node_modules/face-api.js/typedoc.config.js
@@ -0,0 +1,33 @@
+const path = require('path')
+const fs = require('fs')
+
+const excludes = [
+  { dir: 'faceLandmarkNet', exceptions: ['index.ts', 'FaceLandmark68Net.ts', 'FaceLandmark68TinyNet.ts'] },
+  { dir: 'faceRecognitionNet', exceptions: ['index.ts', 'FaceRecognitionNet.ts'] },
+  { dir: 'mtcnn', exceptions: ['index.ts', 'Mtcnn.ts', 'MtcnnOptions.ts'] },
+  { dir: 'ssdMobilenetv1', exceptions: ['index.ts', 'SsdMobilenetv1.ts', 'SsdMobilenetv1Options.ts'] },
+  { dir: 'tinyFaceDetector', exceptions: ['index.ts', 'TinyFaceDetector.ts', 'TinyFaceDetectorOptions.ts'] },
+  { dir: 'tinyYolov2', exceptions: ['index.ts', 'TinyYolov2.ts'] }
+]
+
+const exclude = excludes.map(({ dir, exceptions }) => {
+  const files = fs.readdirSync(path.resolve('src', dir))
+    .filter(file => !exceptions.some(ex => ex === file))
+
+  return files.map(file => `**/${dir}/${file}`)
+}).reduce((flat, arr) => flat.concat(arr), [])
+
+module.exports = {
+  mode: 'file',
+  out: 'docs',
+  module: 'commonjs',
+  target: 'es5',
+  theme: 'default',
+  excludeExternals: true,
+  includeDeclarations: true,
+  excludePrivate: true,
+  excludeNotExported: true,
+  stripInternal: true,
+  externalPattern: 'node_modules/@tensorflow',
+  exclude
+}
\ No newline at end of file
diff --git a/node_modules/seedrandom/.coveralls.yml b/node_modules/seedrandom/.coveralls.yml
new file mode 100644
index 0000000..9160059
--- /dev/null
+++ b/node_modules/seedrandom/.coveralls.yml
@@ -0,0 +1 @@
+service_name: travis-ci
diff --git a/node_modules/seedrandom/.npmignore b/node_modules/seedrandom/.npmignore
new file mode 100644
index 0000000..a088b6f
--- /dev/null
+++ b/node_modules/seedrandom/.npmignore
@@ -0,0 +1,2 @@
+node_modules
+bower_components
diff --git a/node_modules/seedrandom/.nvmrc b/node_modules/seedrandom/.nvmrc
new file mode 100644
index 0000000..1e8b314
--- /dev/null
+++ b/node_modules/seedrandom/.nvmrc
@@ -0,0 +1 @@
+6
diff --git a/node_modules/seedrandom/.travis.yml b/node_modules/seedrandom/.travis.yml
new file mode 100644
index 0000000..205850f
--- /dev/null
+++ b/node_modules/seedrandom/.travis.yml
@@ -0,0 +1,9 @@
+language: node_js
+sudo: false
+node_js:
+- '0.10'
+- '0.12'
+- '4'
+- '5'
+- '6'
+- '7'
diff --git a/node_modules/seedrandom/Gruntfile.js b/node_modules/seedrandom/Gruntfile.js
new file mode 100644
index 0000000..c24df6e
--- /dev/null
+++ b/node_modules/seedrandom/Gruntfile.js
@@ -0,0 +1,108 @@
+module.exports = function(grunt) {
+  "use strict";
+
+  grunt.initConfig({
+    pkg: grunt.file.readJSON("package.json"),
+    bowercopy: {
+      options: {
+        clean: true
+      },
+      test: {
+        options: {
+          destPrefix: "test/lib"
+        },
+        files: {
+          "qunit.js" : "qunit/qunit/qunit.js",
+          "qunit.css" : "qunit/qunit/qunit.css",
+          "require.js" : "requirejs/require.js"
+        }
+      }
+    },
+    uglify: {
+      all: {
+        files: {
+          "<%= pkg.name %>.min.js": [ "<%= pkg.name %>.js" ],
+          "lib/alea.min.js": [ "lib/alea.js" ],
+          "lib/tychei.min.js": [ "lib/tychei.js" ],
+          "lib/xor4096.min.js": [ "lib/xor4096.js" ],
+          "lib/xorshift7.min.js": [ "lib/xorshift7.js" ],
+          "lib/xorwow.min.js": [ "lib/xorwow.js" ],
+          "lib/xor128.min.js": [ "lib/xor128.js" ]
+        },
+        options: {
+          preserveComments: false,
+          report: "min",
+          beautify: {
+            ascii_only: true
+          }
+        }
+      }
+    },
+    qunit: {
+      options: {
+        noGlobals: true,
+        httpBase: 'http://localhost:8192'
+      },
+      all: ["test/*.html"]
+    },
+    connect: {
+      server: {
+        options: {
+          port: 8192,
+          base: '.'
+        }
+      }
+    },
+    browserify: {
+      test: {
+        files: {
+          'test/browserified.js': ['test/nodetest.js'],
+        },
+        options: {
+          ignore: ['requirejs', 'process'],
+          alias: {
+            'assert': './test/qunitassert.js'
+          }
+        }
+      }
+    },
+    mochacov: {
+      options: {
+        files: [
+          'test/cryptotest.js',
+          'test/nodetest.js',
+          'test/prngtest.js'
+       ]
+      },
+      coverage: {
+        options: {
+          coveralls: true
+        }
+      },
+      test: {
+        options: {
+          reporter: 'dot'
+        }
+      }
+    },
+    release: {
+      options: {
+        bump: false
+      }
+    }
+  });
+
+  grunt.loadNpmTasks('grunt-bowercopy');
+  grunt.loadNpmTasks('grunt-contrib-connect');
+  grunt.loadNpmTasks('grunt-contrib-qunit');
+  grunt.loadNpmTasks('grunt-contrib-uglify');
+  grunt.loadNpmTasks('grunt-mocha-cov');
+  grunt.loadNpmTasks('grunt-release');
+  grunt.loadNpmTasks('grunt-browserify');
+
+  grunt.registerTask("test",
+      ["browserify", "connect", "qunit", "mochacov:test"]);
+  grunt.registerTask("default", ["uglify", "test"]);
+  grunt.registerTask("travis", ["default", "mochacov:coverage"]);
+};
+
diff --git a/node_modules/seedrandom/README.md b/node_modules/seedrandom/README.md
new file mode 100644
index 0000000..3645bcc
--- /dev/null
+++ b/node_modules/seedrandom/README.md
@@ -0,0 +1,298 @@
+seedrandom.js
+=============
+[![Build Status](https://travis-ci.org/davidbau/seedrandom.svg?branch=master)](https://travis-ci.org/davidbau/seedrandom)
+[![NPM version](https://badge.fury.io/js/seedrandom.svg)](http://badge.fury.io/js/seedrandom)
+[![Bower version](https://badge.fury.io/bo/seedrandom.svg)](http://badge.fury.io/bo/seedrandom)
+
+Seeded random number generator for JavaScript.
+
+Version 2.4.3
+
+Author: David Bau
+
+Date: 2015-07-04
+
+Can be used as a plain script, a Node.js module or an AMD module.
+
+
+Script tag usage
+----------------
+
+```html
+<script src="//cdnjs.cloudflare.com/ajax/libs/seedrandom/2.4.3/seedrandom.min.js">
+</script>
+```
+
+```js
+// Sets Math.random to a PRNG initialized using the given explicit seed.
+Math.seedrandom('hello.');
+console.log(Math.random());          // Always 0.9282578795792454
+console.log(Math.random());          // Always 0.3752569768646784
+
+// Sets Math.random to an ARC4-based PRNG that is autoseeded using the
+// current time, dom state, and other accumulated local entropy.
+// The generated seed string is returned.
+Math.seedrandom();
+console.log(Math.random());          // Reasonably unpredictable.
+
+// Seeds using the given explicit seed mixed with accumulated entropy.
+Math.seedrandom('added entropy.', { entropy: true });
+console.log(Math.random());          // As unpredictable as added entropy.
+
+// Use "new" to create a local prng without altering Math.random.
+var myrng = new Math.seedrandom('hello.');
+console.log(myrng());                // Always 0.9282578795792454
+
+// Use "quick" to get only 32 bits of randomness in a float.
+console.log(myrng.quick());          // Always 0.3752569768112153
+
+// Use "int32" to get a 32 bit (signed) integer
+console.log(myrng.int32());          // Always 986220731
+
+```
+
+Other Fast PRNG Algorithms
+--------------------------
+
+The package includes some other fast PRNGs.  To use Johannes Baagøe's
+extremely fast Alea PRNG:
+
+
+```html
+<script src="//cdnjs.cloudflare.com/ajax/libs/seedrandom/2.4.3/lib/alea.min.js">
+</script>
+```
+
+```js
+// Use xor4096 for Richard Brent's xorgens-4096 algorithm.
+var xorgen = new xor4096('hello.');
+
+// By default provides 32 bits of randomness in a float.
+console.log(xorgen());               // Always 0.9798525865189731
+
+// Use "double" to get 56 bits of randomness.
+console.log(xorgen.double());        // Always 0.03583478477375346
+
+// Use "int32" to get a 32 bit (signed) integer.
+console.log(xorgen.int32());         // Always 1341429986
+````
+
+Besides xor4096, there are several other faster PRNGs available.
+
+|PRNG name  | Time vs native | Period      | Author               |
+|-----------|----------------|-------------|----------------------|
+|`alea`     |  1.95 ns, 0.9x | ~2^116      | Baagøe               |
+|`xor128`   |  2.04 ns, 0.9x | 2^128-1     | Marsaglia            |
+|`tychei`   |  2.32 ns, 1.1x | ~2^127      | Neves/Araujo (ChaCha)|
+|`xorwow`   |  2.40 ns, 1.1x | 2^192-2^32  | Marsaglia            |
+|`xor4096`  |  2.40 ns, 1.1x | 2^4096-2^32 | Brent (xorgens)      |
+|`xorshift7`|  2.64 ns, 1.3x | 2^256-1     | Panneton/L'ecuyer    |
+|`quick`    |  3.80 ns, 1.8x | ~2^1600     | Bau (ARC4)           |
+
+(Timings were done on node v0.12.2 on a single-core Google Compute Engine
+instance.  `quick` is just the 32-bit version of the RC4-based PRNG
+originally packaged with seedrandom.)
+
+
+Node.js usage
+-------------
+
+```
+npm install seedrandom
+```
+
+```js
+// Local PRNG: does not affect Math.random.
+var seedrandom = require('seedrandom');
+var rng = seedrandom('hello.');
+console.log(rng());                  // Always 0.9282578795792454
+
+// Global PRNG: set Math.random.
+seedrandom('hello.', { global: true });
+console.log(Math.random());          // Always 0.9282578795792454
+
+// Autoseeded ARC4-based PRNG.
+rng = seedrandom();
+console.log(rng());                  // Reasonably unpredictable.
+
+// Mixing accumulated entropy.
+rng = seedrandom('added entropy.', { entropy: true });
+console.log(rng());                  // As unpredictable as added entropy.
+
+// Using alternate algorithms, as listed above.
+var rng2 = seedrandom.xor4096('hello.')
+console.log(rng2());
+```
+
+
+Require.js usage
+----------------
+
+Similar to Node.js usage:
+
+```
+bower install seedrandom
+```
+
+```
+require(['seedrandom'], function(seedrandom) {
+  var rng = seedrandom('hello.');
+  console.log(rng());                  // Always 0.9282578795792454
+});
+```
+
+
+Network seeding
+---------------
+
+```html
+<script src=//cdnjs.cloudflare.com/ajax/libs/seedrandom/2.4.3/seedrandom.min.js>
+</script>
+<!-- Seeds using urandom bits from a server. -->
+<script src=//jsonlib.appspot.com/urandom?callback=Math.seedrandom>
+</script>
+
+<!-- Seeds mixing in random.org bits -->
+<script>
+(function(x, u, s){
+  try {
+    // Make a synchronous request to random.org.
+    x.open('GET', u, false);
+    x.send();
+    s = unescape(x.response.trim().replace(/^|\s/g, '%'));
+  } finally {
+    // Seed with the response, or autoseed on failure.
+    Math.seedrandom(s, !!s);
+  }
+})(new XMLHttpRequest, 'https://www.random.org/integers/' +
+  '?num=256&min=0&max=255&col=1&base=16&format=plain&rnd=new');
+</script>
+```
+
+Reseeding using user input
+--------------------------
+
+```js
+var seed = Math.seedrandom();        // Use prng with an automatic seed.
+document.write(Math.random());       // Pretty much unpredictable x.
+
+var rng = new Math.seedrandom(seed); // A new prng with the same seed.
+document.write(rng());               // Repeat the 'unpredictable' x.
+
+function reseed(event, count) {      // Define a custom entropy collector.
+  var t = [];
+  function w(e) {
+    t.push([e.pageX, e.pageY, +new Date]);
+    if (t.length < count) { return; }
+    document.removeEventListener(event, w);
+    Math.seedrandom(t, { entropy: true });
+  }
+  document.addEventListener(event, w);
+}
+reseed('mousemove', 100);            // Reseed after 100 mouse moves.
+```
+
+The "pass" option can be used to get both the prng and the seed.
+The following returns both an autoseeded prng and the seed as an object,
+without mutating Math.random:
+
+```js
+var obj = Math.seedrandom(null, { pass: function(prng, seed) {
+  return { random: prng, seed: seed };
+}});
+```
+
+
+Saving and Restoring PRNG state
+-------------------------------
+
+```js
+var seedrandom = Math.seedrandom;
+var saveable = seedrandom("secret-seed", {state: true});
+for (var j = 0; j < 1e5; ++j) saveable();
+var saved = saveable.state();
+var replica = seedrandom("", {state: saved});
+assert(replica() == saveable());
+```
+
+In normal use the prng is opaque and its internal state cannot be accessed.
+However, if the "state" option is specified, the prng gets a state() method
+that returns a plain object the can be used to reconstruct a prng later in
+the same state (by passing that saved object back as the state option).
+
+
+Version notes
+-------------
+
+The random number sequence is the same as version 1.0 for string seeds.
+
+* Version 2.0 changed the sequence for non-string seeds.
+* Version 2.1 speeds seeding and uses window.crypto to autoseed if present.
+* Version 2.2 alters non-crypto autoseeding to sweep up entropy from plugins.
+* Version 2.3 adds support for "new", module loading, and a null seed arg.
+* Version 2.3.1 adds a build environment, module packaging, and tests.
+* Version 2.3.4 fixes bugs on IE8, and switches to MIT license.
+* Version 2.3.6 adds a readable options object argument.
+* Version 2.3.10 adds support for node.js crypto (contributed by ctd1500).
+* Version 2.3.11 adds an option to load and save internal state.
+* Version 2.4.0 adds implementations of several other fast PRNGs.
+* Version 2.4.2 adds an implementation of Baagoe's very fast Alea PRNG.
+* Version 2.4.3 ignores nodejs crypto when under browserify.
+
+The standard ARC4 key scheduler cycles short keys, which means that
+seedrandom('ab') is equivalent to seedrandom('abab') and 'ababab'.
+Therefore it is a good idea to add a terminator to avoid trivial
+equivalences on short string seeds, e.g., Math.seedrandom(str + '\0').
+Starting with version 2.0, a terminator is added automatically for
+non-string seeds, so seeding with the number 111 is the same as seeding
+with '111\0'.
+
+When seedrandom() is called with zero args or a null seed, it uses a
+seed drawn from the browser crypto object if present.  If there is no
+crypto support, seedrandom() uses the current time, the native rng,
+and a walk of several DOM objects to collect a few bits of entropy.
+
+Each time the one- or two-argument forms of seedrandom are called,
+entropy from the passed seed is accumulated in a pool to help generate
+future seeds for the zero- and two-argument forms of seedrandom.
+
+On speed - This javascript implementation of Math.random() is several
+times slower than the built-in Math.random() because it is not native
+code, but that is typically fast enough.  Some details (timings on
+Chrome 25 on a 2010 vintage macbook):
+
+* seeded Math.random()          - avg less than 0.0002 milliseconds per call
+* seedrandom('explicit.')       - avg less than 0.2 milliseconds per call
+* seedrandom('explicit.', true) - avg less than 0.2 milliseconds per call
+* seedrandom() with crypto      - avg less than 0.2 milliseconds per call
+
+Autoseeding without crypto is somewhat slow, about 20-30 milliseconds on
+a 2012 windows 7 1.5ghz i5 laptop, as seen on Firefox 19, IE 10, and Opera.
+Seeded rng calls themselves are fast across these browsers, with slowest
+numbers on Opera at about 0.0005 ms per seeded Math.random().
+
+
+LICENSE (MIT)
+-------------
+
+Copyright 2015 David Bau.
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
diff --git a/node_modules/seedrandom/bower.json b/node_modules/seedrandom/bower.json
new file mode 100644
index 0000000..8e62b3a
--- /dev/null
+++ b/node_modules/seedrandom/bower.json
@@ -0,0 +1,16 @@
+{
+  "name": "seedrandom",
+  "description": "Seeded random number generator for Javascript.",
+  "main": "seedrandom.js",
+  "license": "MIT",
+  "keywords": [
+    "random",
+    "seed",
+    "crypto"
+  ],
+  "ignore": [],
+  "devDependencies": {
+    "qunit": "latest",
+    "requirejs": "latest"
+  }
+}
diff --git a/node_modules/seedrandom/component.json b/node_modules/seedrandom/component.json
new file mode 100644
index 0000000..fe1aa60
--- /dev/null
+++ b/node_modules/seedrandom/component.json
@@ -0,0 +1,10 @@
+{
+  "name": "seedrandom",
+  "version": "2.4.0",
+  "description": "Seeded random number generator for Javascript",
+  "repository": "davidbau/seedrandom",
+  "main": "seedrandom.js",
+  "scripts": [ "seedrandom.js" ],
+  "keywords": [ "random", "seed", "crypto" ],
+  "license": "MIT"
+}
diff --git a/node_modules/seedrandom/index.js b/node_modules/seedrandom/index.js
new file mode 100644
index 0000000..513669a
--- /dev/null
+++ b/node_modules/seedrandom/index.js
@@ -0,0 +1,60 @@
+// A library of seedable RNGs implemented in Javascript.
+//
+// Usage:
+//
+// var seedrandom = require('seedrandom');
+// var random = seedrandom(1); // or any seed.
+// var x = random();       // 0 <= x < 1.  Every bit is random.
+// var x = random.quick(); // 0 <= x < 1.  32 bits of randomness.
+
+// alea, a 53-bit multiply-with-carry generator by Johannes Baagøe.
+// Period: ~2^116
+// Reported to pass all BigCrush tests.
+var alea = require('./lib/alea');
+
+// xor128, a pure xor-shift generator by George Marsaglia.
+// Period: 2^128-1.
+// Reported to fail: MatrixRank and LinearComp.
+var xor128 = require('./lib/xor128');
+
+// xorwow, George Marsaglia's 160-bit xor-shift combined plus weyl.
+// Period: 2^192-2^32
+// Reported to fail: CollisionOver, SimpPoker, and LinearComp.
+var xorwow = require('./lib/xorwow');
+
+// xorshift7, by François Panneton and Pierre L'ecuyer, takes
+// a different approach: it adds robustness by allowing more shifts
+// than Marsaglia's original three.  It is a 7-shift generator
+// with 256 bits, that passes BigCrush with no systmatic failures.
+// Period 2^256-1.
+// No systematic BigCrush failures reported.
+var xorshift7 = require('./lib/xorshift7');
+
+// xor4096, by Richard Brent, is a 4096-bit xor-shift with a
+// very long period that also adds a Weyl generator. It also passes
+// BigCrush with no systematic failures.  Its long period may
+// be useful if you have many generators and need to avoid
+// collisions.
+// Period: 2^4128-2^32.
+// No systematic BigCrush failures reported.
+var xor4096 = require('./lib/xor4096');
+
+// Tyche-i, by Samuel Neves and Filipe Araujo, is a bit-shifting random
+// number generator derived from ChaCha, a modern stream cipher.
+// https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf
+// Period: ~2^127
+// No systematic BigCrush failures reported.
+var tychei = require('./lib/tychei');
+
+// The original ARC4-based prng included in this library.
+// Period: ~2^1600
+var sr = require('./seedrandom');
+
+sr.alea = alea;
+sr.xor128 = xor128;
+sr.xorwow = xorwow;
+sr.xorshift7 = xorshift7;
+sr.xor4096 = xor4096;
+sr.tychei = tychei;
+
+module.exports = sr;
diff --git a/node_modules/seedrandom/lib/alea.js b/node_modules/seedrandom/lib/alea.js
new file mode 100644
index 0000000..f912ffd
--- /dev/null
+++ b/node_modules/seedrandom/lib/alea.js
@@ -0,0 +1,114 @@
+// A port of an algorithm by Johannes Baagøe <baagoe@baagoe.com>, 2010
+// http://baagoe.com/en/RandomMusings/javascript/
+// https://github.com/nquinlan/better-random-numbers-for-javascript-mirror
+// Original work is under MIT license -
+
+// Copyright (C) 2010 by Johannes Baagøe <baagoe@baagoe.org>
+//
+// Permission is hereby granted, free of charge, to any person obtaining a copy
+// of this software and associated documentation files (the "Software"), to deal
+// in the Software without restriction, including without limitation the rights
+// to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+// copies of the Software, and to permit persons to whom the Software is
+// furnished to do so, subject to the following conditions:
+// 
+// The above copyright notice and this permission notice shall be included in
+// all copies or substantial portions of the Software.
+// 
+// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+// AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+// LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+// OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
+// THE SOFTWARE.
+
+
+
+(function(global, module, define) {
+
+function Alea(seed) {
+  var me = this, mash = Mash();
+
+  me.next = function() {
+    var t = 2091639 * me.s0 + me.c * 2.3283064365386963e-10; // 2^-32
+    me.s0 = me.s1;
+    me.s1 = me.s2;
+    return me.s2 = t - (me.c = t | 0);
+  };
+
+  // Apply the seeding algorithm from Baagoe.
+  me.c = 1;
+  me.s0 = mash(' ');
+  me.s1 = mash(' ');
+  me.s2 = mash(' ');
+  me.s0 -= mash(seed);
+  if (me.s0 < 0) { me.s0 += 1; }
+  me.s1 -= mash(seed);
+  if (me.s1 < 0) { me.s1 += 1; }
+  me.s2 -= mash(seed);
+  if (me.s2 < 0) { me.s2 += 1; }
+  mash = null;
+}
+
+function copy(f, t) {
+  t.c = f.c;
+  t.s0 = f.s0;
+  t.s1 = f.s1;
+  t.s2 = f.s2;
+  return t;
+}
+
+function impl(seed, opts) {
+  var xg = new Alea(seed),
+      state = opts && opts.state,
+      prng = xg.next;
+  prng.int32 = function() { return (xg.next() * 0x100000000) | 0; }
+  prng.double = function() {
+    return prng() + (prng() * 0x200000 | 0) * 1.1102230246251565e-16; // 2^-53
+  };
+  prng.quick = prng;
+  if (state) {
+    if (typeof(state) == 'object') copy(state, xg);
+    prng.state = function() { return copy(xg, {}); }
+  }
+  return prng;
+}
+
+function Mash() {
+  var n = 0xefc8249d;
+
+  var mash = function(data) {
+    data = data.toString();
+    for (var i = 0; i < data.length; i++) {
+      n += data.charCodeAt(i);
+      var h = 0.02519603282416938 * n;
+      n = h >>> 0;
+      h -= n;
+      h *= n;
+      n = h >>> 0;
+      h -= n;
+      n += h * 0x100000000; // 2^32
+    }
+    return (n >>> 0) * 2.3283064365386963e-10; // 2^-32
+  };
+
+  return mash;
+}
+
+
+if (module && module.exports) {
+  module.exports = impl;
+} else if (define && define.amd) {
+  define(function() { return impl; });
+} else {
+  this.alea = impl;
+}
+
+})(
+  this,
+  (typeof module) == 'object' && module,    // present in node.js
+  (typeof define) == 'function' && define   // present with an AMD loader
+);
+
+
diff --git a/node_modules/seedrandom/lib/alea.min.js b/node_modules/seedrandom/lib/alea.min.js
new file mode 100644
index 0000000..51b0219
--- /dev/null
+++ b/node_modules/seedrandom/lib/alea.min.js
@@ -0,0 +1 @@
+!function(a,b,c){function d(a){var b=this,c=g();b.next=function(){var a=2091639*b.s0+2.3283064365386963e-10*b.c;return b.s0=b.s1,b.s1=b.s2,b.s2=a-(b.c=0|a)},b.c=1,b.s0=c(" "),b.s1=c(" "),b.s2=c(" "),b.s0-=c(a),b.s0<0&&(b.s0+=1),b.s1-=c(a),b.s1<0&&(b.s1+=1),b.s2-=c(a),b.s2<0&&(b.s2+=1),c=null}function e(a,b){return b.c=a.c,b.s0=a.s0,b.s1=a.s1,b.s2=a.s2,b}function f(a,b){var c=new d(a),f=b&&b.state,g=c.next;return g.int32=function(){return 4294967296*c.next()|0},g.double=function(){return g()+1.1102230246251565e-16*(2097152*g()|0)},g.quick=g,f&&("object"==typeof f&&e(f,c),g.state=function(){return e(c,{})}),g}function g(){var a=4022871197,b=function(b){b=b.toString();for(var c=0;c<b.length;c++){a+=b.charCodeAt(c);var d=.02519603282416938*a;a=d>>>0,d-=a,d*=a,a=d>>>0,d-=a,a+=4294967296*d}return 2.3283064365386963e-10*(a>>>0)};return b}b&&b.exports?b.exports=f:c&&c.amd?c(function(){return f}):this.alea=f}(this,"object"==typeof module&&module,"function"==typeof define&&define);
\ No newline at end of file
diff --git a/node_modules/seedrandom/lib/crypto.js b/node_modules/seedrandom/lib/crypto.js
new file mode 100644
index 0000000..18ee91f
--- /dev/null
+++ b/node_modules/seedrandom/lib/crypto.js
@@ -0,0 +1,11 @@
+// mimic a subset of node's crypto API for the browser
+
+function randomBytes(width) {
+  var out = new Uint8Array(width);
+  (global.crypto || global.msCrypto).getRandomValues(out);
+  return out;
+}
+
+module.exports = {
+  randomBytes: randomBytes
+}
diff --git a/node_modules/seedrandom/lib/tychei.js b/node_modules/seedrandom/lib/tychei.js
new file mode 100644
index 0000000..3d0ddd4
--- /dev/null
+++ b/node_modules/seedrandom/lib/tychei.js
@@ -0,0 +1,103 @@
+// A Javascript implementaion of the "Tyche-i" prng algorithm by
+// Samuel Neves and Filipe Araujo.
+// See https://eden.dei.uc.pt/~sneves/pubs/2011-snfa2.pdf
+
+(function(global, module, define) {
+
+function XorGen(seed) {
+  var me = this, strseed = '';
+
+  // Set up generator function.
+  me.next = function() {
+    var b = me.b, c = me.c, d = me.d, a = me.a;
+    b = (b << 25) ^ (b >>> 7) ^ c;
+    c = (c - d) | 0;
+    d = (d << 24) ^ (d >>> 8) ^ a;
+    a = (a - b) | 0;
+    me.b = b = (b << 20) ^ (b >>> 12) ^ c;
+    me.c = c = (c - d) | 0;
+    me.d = (d << 16) ^ (c >>> 16) ^ a;
+    return me.a = (a - b) | 0;
+  };
+
+  /* The following is non-inverted tyche, which has better internal
+   * bit diffusion, but which is about 25% slower than tyche-i in JS.
+  me.next = function() {
+    var a = me.a, b = me.b, c = me.c, d = me.d;
+    a = (me.a + me.b | 0) >>> 0;
+    d = me.d ^ a; d = d << 16 ^ d >>> 16;
+    c = me.c + d | 0;
+    b = me.b ^ c; b = b << 12 ^ d >>> 20;
+    me.a = a = a + b | 0;
+    d = d ^ a; me.d = d = d << 8 ^ d >>> 24;
+    me.c = c = c + d | 0;
+    b = b ^ c;
+    return me.b = (b << 7 ^ b >>> 25);
+  }
+  */
+
+  me.a = 0;
+  me.b = 0;
+  me.c = 2654435769 | 0;
+  me.d = 1367130551;
+
+  if (seed === Math.floor(seed)) {
+    // Integer seed.
+    me.a = (seed / 0x100000000) | 0;
+    me.b = seed | 0;
+  } else {
+    // String seed.
+    strseed += seed;
+  }
+
+  // Mix in string seed, then discard an initial batch of 64 values.
+  for (var k = 0; k < strseed.length + 20; k++) {
+    me.b ^= strseed.charCodeAt(k) | 0;
+    me.next();
+  }
+}
+
+function copy(f, t) {
+  t.a = f.a;
+  t.b = f.b;
+  t.c = f.c;
+  t.d = f.d;
+  return t;
+};
+
+function impl(seed, opts) {
+  var xg = new XorGen(seed),
+      state = opts && opts.state,
+      prng = function() { return (xg.next() >>> 0) / 0x100000000; };
+  prng.double = function() {
+    do {
+      var top = xg.next() >>> 11,
+          bot = (xg.next() >>> 0) / 0x100000000,
+          result = (top + bot) / (1 << 21);
+    } while (result === 0);
+    return result;
+  };
+  prng.int32 = xg.next;
+  prng.quick = prng;
+  if (state) {
+    if (typeof(state) == 'object') copy(state, xg);
+    prng.state = function() { return copy(xg, {}); }
+  }
+  return prng;
+}
+
+if (module && module.exports) {
+  module.exports = impl;
+} else if (define && define.amd) {
+  define(function() { return impl; });
+} else {
+  this.tychei = impl;
+}
+
+})(
+  this,
+  (typeof module) == 'object' && module,    // present in node.js
+  (typeof define) == 'function' && define   // present with an AMD loader
+);
+
+
diff --git a/node_modules/seedrandom/lib/tychei.min.js b/node_modules/seedrandom/lib/tychei.min.js
new file mode 100644
index 0000000..12849dc
--- /dev/null
+++ b/node_modules/seedrandom/lib/tychei.min.js
@@ -0,0 +1 @@
+!function(a,b,c){function d(a){var b=this,c="";b.next=function(){var a=b.b,c=b.c,d=b.d,e=b.a;return a=a<<25^a>>>7^c,c=c-d|0,d=d<<24^d>>>8^e,e=e-a|0,b.b=a=a<<20^a>>>12^c,b.c=c=c-d|0,b.d=d<<16^c>>>16^e,b.a=e-a|0},b.a=0,b.b=0,b.c=-1640531527,b.d=1367130551,a===Math.floor(a)?(b.a=a/4294967296|0,b.b=0|a):c+=a;for(var d=0;d<c.length+20;d++)b.b^=0|c.charCodeAt(d),b.next()}function e(a,b){return b.a=a.a,b.b=a.b,b.c=a.c,b.d=a.d,b}function f(a,b){var c=new d(a),f=b&&b.state,g=function(){return(c.next()>>>0)/4294967296};return g.double=function(){do var a=c.next()>>>11,b=(c.next()>>>0)/4294967296,d=(a+b)/(1<<21);while(0===d);return d},g.int32=c.next,g.quick=g,f&&("object"==typeof f&&e(f,c),g.state=function(){return e(c,{})}),g}b&&b.exports?b.exports=f:c&&c.amd?c(function(){return f}):this.tychei=f}(this,"object"==typeof module&&module,"function"==typeof define&&define);
\ No newline at end of file
diff --git a/node_modules/seedrandom/lib/xor128.js b/node_modules/seedrandom/lib/xor128.js
new file mode 100644
index 0000000..c461934
--- /dev/null
+++ b/node_modules/seedrandom/lib/xor128.js
@@ -0,0 +1,81 @@
+// A Javascript implementaion of the "xor128" prng algorithm by
+// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper
+
+(function(global, module, define) {
+
+function XorGen(seed) {
+  var me = this, strseed = '';
+
+  me.x = 0;
+  me.y = 0;
+  me.z = 0;
+  me.w = 0;
+
+  // Set up generator function.
+  me.next = function() {
+    var t = me.x ^ (me.x << 11);
+    me.x = me.y;
+    me.y = me.z;
+    me.z = me.w;
+    return me.w ^= (me.w >>> 19) ^ t ^ (t >>> 8);
+  };
+
+  if (seed === (seed | 0)) {
+    // Integer seed.
+    me.x = seed;
+  } else {
+    // String seed.
+    strseed += seed;
+  }
+
+  // Mix in string seed, then discard an initial batch of 64 values.
+  for (var k = 0; k < strseed.length + 64; k++) {
+    me.x ^= strseed.charCodeAt(k) | 0;
+    me.next();
+  }
+}
+
+function copy(f, t) {
+  t.x = f.x;
+  t.y = f.y;
+  t.z = f.z;
+  t.w = f.w;
+  return t;
+}
+
+function impl(seed, opts) {
+  var xg = new XorGen(seed),
+      state = opts && opts.state,
+      prng = function() { return (xg.next() >>> 0) / 0x100000000; };
+  prng.double = function() {
+    do {
+      var top = xg.next() >>> 11,
+          bot = (xg.next() >>> 0) / 0x100000000,
+          result = (top + bot) / (1 << 21);
+    } while (result === 0);
+    return result;
+  };
+  prng.int32 = xg.next;
+  prng.quick = prng;
+  if (state) {
+    if (typeof(state) == 'object') copy(state, xg);
+    prng.state = function() { return copy(xg, {}); }
+  }
+  return prng;
+}
+
+if (module && module.exports) {
+  module.exports = impl;
+} else if (define && define.amd) {
+  define(function() { return impl; });
+} else {
+  this.xor128 = impl;
+}
+
+})(
+  this,
+  (typeof module) == 'object' && module,    // present in node.js
+  (typeof define) == 'function' && define   // present with an AMD loader
+);
+
+
diff --git a/node_modules/seedrandom/lib/xor128.min.js b/node_modules/seedrandom/lib/xor128.min.js
new file mode 100644
index 0000000..dbd1cba
--- /dev/null
+++ b/node_modules/seedrandom/lib/xor128.min.js
@@ -0,0 +1 @@
+!function(a,b,c){function d(a){var b=this,c="";b.x=0,b.y=0,b.z=0,b.w=0,b.next=function(){var a=b.x^b.x<<11;return b.x=b.y,b.y=b.z,b.z=b.w,b.w^=b.w>>>19^a^a>>>8},a===(0|a)?b.x=a:c+=a;for(var d=0;d<c.length+64;d++)b.x^=0|c.charCodeAt(d),b.next()}function e(a,b){return b.x=a.x,b.y=a.y,b.z=a.z,b.w=a.w,b}function f(a,b){var c=new d(a),f=b&&b.state,g=function(){return(c.next()>>>0)/4294967296};return g.double=function(){do var a=c.next()>>>11,b=(c.next()>>>0)/4294967296,d=(a+b)/(1<<21);while(0===d);return d},g.int32=c.next,g.quick=g,f&&("object"==typeof f&&e(f,c),g.state=function(){return e(c,{})}),g}b&&b.exports?b.exports=f:c&&c.amd?c(function(){return f}):this.xor128=f}(this,"object"==typeof module&&module,"function"==typeof define&&define);
\ No newline at end of file
diff --git a/node_modules/seedrandom/lib/xor4096.js b/node_modules/seedrandom/lib/xor4096.js
new file mode 100644
index 0000000..6adf19f
--- /dev/null
+++ b/node_modules/seedrandom/lib/xor4096.js
@@ -0,0 +1,146 @@
+// A Javascript implementaion of Richard Brent's Xorgens xor4096 algorithm.
+//
+// This fast non-cryptographic random number generator is designed for
+// use in Monte-Carlo algorithms. It combines a long-period xorshift
+// generator with a Weyl generator, and it passes all common batteries
+// of stasticial tests for randomness while consuming only a few nanoseconds
+// for each prng generated.  For background on the generator, see Brent's
+// paper: "Some long-period random number generators using shifts and xors."
+// http://arxiv.org/pdf/1004.3115v1.pdf
+//
+// Usage:
+//
+// var xor4096 = require('xor4096');
+// random = xor4096(1);                        // Seed with int32 or string.
+// assert.equal(random(), 0.1520436450538547); // (0, 1) range, 53 bits.
+// assert.equal(random.int32(), 1806534897);   // signed int32, 32 bits.
+//
+// For nonzero numeric keys, this impelementation provides a sequence
+// identical to that by Brent's xorgens 3 implementaion in C.  This
+// implementation also provides for initalizing the generator with
+// string seeds, or for saving and restoring the state of the generator.
+//
+// On Chrome, this prng benchmarks about 2.1 times slower than
+// Javascript's built-in Math.random().
+
+(function(global, module, define) {
+
+function XorGen(seed) {
+  var me = this;
+
+  // Set up generator function.
+  me.next = function() {
+    var w = me.w,
+        X = me.X, i = me.i, t, v;
+    // Update Weyl generator.
+    me.w = w = (w + 0x61c88647) | 0;
+    // Update xor generator.
+    v = X[(i + 34) & 127];
+    t = X[i = ((i + 1) & 127)];
+    v ^= v << 13;
+    t ^= t << 17;
+    v ^= v >>> 15;
+    t ^= t >>> 12;
+    // Update Xor generator array state.
+    v = X[i] = v ^ t;
+    me.i = i;
+    // Result is the combination.
+    return (v + (w ^ (w >>> 16))) | 0;
+  };
+
+  function init(me, seed) {
+    var t, v, i, j, w, X = [], limit = 128;
+    if (seed === (seed | 0)) {
+      // Numeric seeds initialize v, which is used to generates X.
+      v = seed;
+      seed = null;
+    } else {
+      // String seeds are mixed into v and X one character at a time.
+      seed = seed + '\0';
+      v = 0;
+      limit = Math.max(limit, seed.length);
+    }
+    // Initialize circular array and weyl value.
+    for (i = 0, j = -32; j < limit; ++j) {
+      // Put the unicode characters into the array, and shuffle them.
+      if (seed) v ^= seed.charCodeAt((j + 32) % seed.length);
+      // After 32 shuffles, take v as the starting w value.
+      if (j === 0) w = v;
+      v ^= v << 10;
+      v ^= v >>> 15;
+      v ^= v << 4;
+      v ^= v >>> 13;
+      if (j >= 0) {
+        w = (w + 0x61c88647) | 0;     // Weyl.
+        t = (X[j & 127] ^= (v + w));  // Combine xor and weyl to init array.
+        i = (0 == t) ? i + 1 : 0;     // Count zeroes.
+      }
+    }
+    // We have detected all zeroes; make the key nonzero.
+    if (i >= 128) {
+      X[(seed && seed.length || 0) & 127] = -1;
+    }
+    // Run the generator 512 times to further mix the state before using it.
+    // Factoring this as a function slows the main generator, so it is just
+    // unrolled here.  The weyl generator is not advanced while warming up.
+    i = 127;
+    for (j = 4 * 128; j > 0; --j) {
+      v = X[(i + 34) & 127];
+      t = X[i = ((i + 1) & 127)];
+      v ^= v << 13;
+      t ^= t << 17;
+      v ^= v >>> 15;
+      t ^= t >>> 12;
+      X[i] = v ^ t;
+    }
+    // Storing state as object members is faster than using closure variables.
+    me.w = w;
+    me.X = X;
+    me.i = i;
+  }
+
+  init(me, seed);
+}
+
+function copy(f, t) {
+  t.i = f.i;
+  t.w = f.w;
+  t.X = f.X.slice();
+  return t;
+};
+
+function impl(seed, opts) {
+  if (seed == null) seed = +(new Date);
+  var xg = new XorGen(seed),
+      state = opts && opts.state,
+      prng = function() { return (xg.next() >>> 0) / 0x100000000; };
+  prng.double = function() {
+    do {
+      var top = xg.next() >>> 11,
+          bot = (xg.next() >>> 0) / 0x100000000,
+          result = (top + bot) / (1 << 21);
+    } while (result === 0);
+    return result;
+  };
+  prng.int32 = xg.next;
+  prng.quick = prng;
+  if (state) {
+    if (state.X) copy(state, xg);
+    prng.state = function() { return copy(xg, {}); }
+  }
+  return prng;
+}
+
+if (module && module.exports) {
+  module.exports = impl;
+} else if (define && define.amd) {
+  define(function() { return impl; });
+} else {
+  this.xor4096 = impl;
+}
+
+})(
+  this,                                     // window object or global
+  (typeof module) == 'object' && module,    // present in node.js
+  (typeof define) == 'function' && define   // present with an AMD loader
+);
diff --git a/node_modules/seedrandom/lib/xor4096.min.js b/node_modules/seedrandom/lib/xor4096.min.js
new file mode 100644
index 0000000..a7d37c5
--- /dev/null
+++ b/node_modules/seedrandom/lib/xor4096.min.js
@@ -0,0 +1 @@
+!function(a,b,c){function d(a){function b(a,b){var c,d,e,f,g,h=[],i=128;for(b===(0|b)?(d=b,b=null):(b+="\0",d=0,i=Math.max(i,b.length)),e=0,f=-32;f<i;++f)b&&(d^=b.charCodeAt((f+32)%b.length)),0===f&&(g=d),d^=d<<10,d^=d>>>15,d^=d<<4,d^=d>>>13,f>=0&&(g=g+1640531527|0,c=h[127&f]^=d+g,e=0==c?e+1:0);for(e>=128&&(h[127&(b&&b.length||0)]=-1),e=127,f=512;f>0;--f)d=h[e+34&127],c=h[e=e+1&127],d^=d<<13,c^=c<<17,d^=d>>>15,c^=c>>>12,h[e]=d^c;a.w=g,a.X=h,a.i=e}var c=this;c.next=function(){var a,b,d=c.w,e=c.X,f=c.i;return c.w=d=d+1640531527|0,b=e[f+34&127],a=e[f=f+1&127],b^=b<<13,a^=a<<17,b^=b>>>15,a^=a>>>12,b=e[f]=b^a,c.i=f,b+(d^d>>>16)|0},b(c,a)}function e(a,b){return b.i=a.i,b.w=a.w,b.X=a.X.slice(),b}function f(a,b){null==a&&(a=+new Date);var c=new d(a),f=b&&b.state,g=function(){return(c.next()>>>0)/4294967296};return g.double=function(){do var a=c.next()>>>11,b=(c.next()>>>0)/4294967296,d=(a+b)/(1<<21);while(0===d);return d},g.int32=c.next,g.quick=g,f&&(f.X&&e(f,c),g.state=function(){return e(c,{})}),g}b&&b.exports?b.exports=f:c&&c.amd?c(function(){return f}):this.xor4096=f}(this,"object"==typeof module&&module,"function"==typeof define&&define);
\ No newline at end of file
diff --git a/node_modules/seedrandom/lib/xorshift7.js b/node_modules/seedrandom/lib/xorshift7.js
new file mode 100644
index 0000000..b7e5151
--- /dev/null
+++ b/node_modules/seedrandom/lib/xorshift7.js
@@ -0,0 +1,97 @@
+// A Javascript implementaion of the "xorshift7" algorithm by
+// François Panneton and Pierre L'ecuyer:
+// "On the Xorgshift Random Number Generators"
+// http://saluc.engr.uconn.edu/refs/crypto/rng/panneton05onthexorshift.pdf
+
+(function(global, module, define) {
+
+function XorGen(seed) {
+  var me = this;
+
+  // Set up generator function.
+  me.next = function() {
+    // Update xor generator.
+    var X = me.x, i = me.i, t, v, w;
+    t = X[i]; t ^= (t >>> 7); v = t ^ (t << 24);
+    t = X[(i + 1) & 7]; v ^= t ^ (t >>> 10);
+    t = X[(i + 3) & 7]; v ^= t ^ (t >>> 3);
+    t = X[(i + 4) & 7]; v ^= t ^ (t << 7);
+    t = X[(i + 7) & 7]; t = t ^ (t << 13); v ^= t ^ (t << 9);
+    X[i] = v;
+    me.i = (i + 1) & 7;
+    return v;
+  };
+
+  function init(me, seed) {
+    var j, w, X = [];
+
+    if (seed === (seed | 0)) {
+      // Seed state array using a 32-bit integer.
+      w = X[0] = seed;
+    } else {
+      // Seed state using a string.
+      seed = '' + seed;
+      for (j = 0; j < seed.length; ++j) {
+        X[j & 7] = (X[j & 7] << 15) ^
+            (seed.charCodeAt(j) + X[(j + 1) & 7] << 13);
+      }
+    }
+    // Enforce an array length of 8, not all zeroes.
+    while (X.length < 8) X.push(0);
+    for (j = 0; j < 8 && X[j] === 0; ++j);
+    if (j == 8) w = X[7] = -1; else w = X[j];
+
+    me.x = X;
+    me.i = 0;
+
+    // Discard an initial 256 values.
+    for (j = 256; j > 0; --j) {
+      me.next();
+    }
+  }
+
+  init(me, seed);
+}
+
+function copy(f, t) {
+  t.x = f.x.slice();
+  t.i = f.i;
+  return t;
+}
+
+function impl(seed, opts) {
+  if (seed == null) seed = +(new Date);
+  var xg = new XorGen(seed),
+      state = opts && opts.state,
+      prng = function() { return (xg.next() >>> 0) / 0x100000000; };
+  prng.double = function() {
+    do {
+      var top = xg.next() >>> 11,
+          bot = (xg.next() >>> 0) / 0x100000000,
+          result = (top + bot) / (1 << 21);
+    } while (result === 0);
+    return result;
+  };
+  prng.int32 = xg.next;
+  prng.quick = prng;
+  if (state) {
+    if (state.x) copy(state, xg);
+    prng.state = function() { return copy(xg, {}); }
+  }
+  return prng;
+}
+
+if (module && module.exports) {
+  module.exports = impl;
+} else if (define && define.amd) {
+  define(function() { return impl; });
+} else {
+  this.xorshift7 = impl;
+}
+
+})(
+  this,
+  (typeof module) == 'object' && module,    // present in node.js
+  (typeof define) == 'function' && define   // present with an AMD loader
+);
+
diff --git a/node_modules/seedrandom/lib/xorshift7.min.js b/node_modules/seedrandom/lib/xorshift7.min.js
new file mode 100644
index 0000000..cf534d4
--- /dev/null
+++ b/node_modules/seedrandom/lib/xorshift7.min.js
@@ -0,0 +1 @@
+!function(a,b,c){function d(a){function b(a,b){var c,d,e=[];if(b===(0|b))d=e[0]=b;else for(b=""+b,c=0;c<b.length;++c)e[7&c]=e[7&c]<<15^b.charCodeAt(c)+e[c+1&7]<<13;for(;e.length<8;)e.push(0);for(c=0;c<8&&0===e[c];++c);for(d=8==c?e[7]=-1:e[c],a.x=e,a.i=0,c=256;c>0;--c)a.next()}var c=this;c.next=function(){var a,b,d=c.x,e=c.i;return a=d[e],a^=a>>>7,b=a^a<<24,a=d[e+1&7],b^=a^a>>>10,a=d[e+3&7],b^=a^a>>>3,a=d[e+4&7],b^=a^a<<7,a=d[e+7&7],a^=a<<13,b^=a^a<<9,d[e]=b,c.i=e+1&7,b},b(c,a)}function e(a,b){return b.x=a.x.slice(),b.i=a.i,b}function f(a,b){null==a&&(a=+new Date);var c=new d(a),f=b&&b.state,g=function(){return(c.next()>>>0)/4294967296};return g.double=function(){do var a=c.next()>>>11,b=(c.next()>>>0)/4294967296,d=(a+b)/(1<<21);while(0===d);return d},g.int32=c.next,g.quick=g,f&&(f.x&&e(f,c),g.state=function(){return e(c,{})}),g}b&&b.exports?b.exports=f:c&&c.amd?c(function(){return f}):this.xorshift7=f}(this,"object"==typeof module&&module,"function"==typeof define&&define);
\ No newline at end of file
diff --git a/node_modules/seedrandom/lib/xorwow.js b/node_modules/seedrandom/lib/xorwow.js
new file mode 100644
index 0000000..79d5e44
--- /dev/null
+++ b/node_modules/seedrandom/lib/xorwow.js
@@ -0,0 +1,86 @@
+// A Javascript implementaion of the "xorwow" prng algorithm by
+// George Marsaglia.  See http://www.jstatsoft.org/v08/i14/paper
+
+(function(global, module, define) {
+
+function XorGen(seed) {
+  var me = this, strseed = '';
+
+  // Set up generator function.
+  me.next = function() {
+    var t = (me.x ^ (me.x >>> 2));
+    me.x = me.y; me.y = me.z; me.z = me.w; me.w = me.v;
+    return (me.d = (me.d + 362437 | 0)) +
+       (me.v = (me.v ^ (me.v << 4)) ^ (t ^ (t << 1))) | 0;
+  };
+
+  me.x = 0;
+  me.y = 0;
+  me.z = 0;
+  me.w = 0;
+  me.v = 0;
+
+  if (seed === (seed | 0)) {
+    // Integer seed.
+    me.x = seed;
+  } else {
+    // String seed.
+    strseed += seed;
+  }
+
+  // Mix in string seed, then discard an initial batch of 64 values.
+  for (var k = 0; k < strseed.length + 64; k++) {
+    me.x ^= strseed.charCodeAt(k) | 0;
+    if (k == strseed.length) {
+      me.d = me.x << 10 ^ me.x >>> 4;
+    }
+    me.next();
+  }
+}
+
+function copy(f, t) {
+  t.x = f.x;
+  t.y = f.y;
+  t.z = f.z;
+  t.w = f.w;
+  t.v = f.v;
+  t.d = f.d;
+  return t;
+}
+
+function impl(seed, opts) {
+  var xg = new XorGen(seed),
+      state = opts && opts.state,
+      prng = function() { return (xg.next() >>> 0) / 0x100000000; };
+  prng.double = function() {
+    do {
+      var top = xg.next() >>> 11,
+          bot = (xg.next() >>> 0) / 0x100000000,
+          result = (top + bot) / (1 << 21);
+    } while (result === 0);
+    return result;
+  };
+  prng.int32 = xg.next;
+  prng.quick = prng;
+  if (state) {
+    if (typeof(state) == 'object') copy(state, xg);
+    prng.state = function() { return copy(xg, {}); }
+  }
+  return prng;
+}
+
+if (module && module.exports) {
+  module.exports = impl;
+} else if (define && define.amd) {
+  define(function() { return impl; });
+} else {
+  this.xorwow = impl;
+}
+
+})(
+  this,
+  (typeof module) == 'object' && module,    // present in node.js
+  (typeof define) == 'function' && define   // present with an AMD loader
+);
+
+
diff --git a/node_modules/seedrandom/lib/xorwow.min.js b/node_modules/seedrandom/lib/xorwow.min.js
new file mode 100644
index 0000000..877a481
--- /dev/null
+++ b/node_modules/seedrandom/lib/xorwow.min.js
@@ -0,0 +1 @@
+!function(a,b,c){function d(a){var b=this,c="";b.next=function(){var a=b.x^b.x>>>2;return b.x=b.y,b.y=b.z,b.z=b.w,b.w=b.v,(b.d=b.d+362437|0)+(b.v=b.v^b.v<<4^(a^a<<1))|0},b.x=0,b.y=0,b.z=0,b.w=0,b.v=0,a===(0|a)?b.x=a:c+=a;for(var d=0;d<c.length+64;d++)b.x^=0|c.charCodeAt(d),d==c.length&&(b.d=b.x<<10^b.x>>>4),b.next()}function e(a,b){return b.x=a.x,b.y=a.y,b.z=a.z,b.w=a.w,b.v=a.v,b.d=a.d,b}function f(a,b){var c=new d(a),f=b&&b.state,g=function(){return(c.next()>>>0)/4294967296};return g.double=function(){do var a=c.next()>>>11,b=(c.next()>>>0)/4294967296,d=(a+b)/(1<<21);while(0===d);return d},g.int32=c.next,g.quick=g,f&&("object"==typeof f&&e(f,c),g.state=function(){return e(c,{})}),g}b&&b.exports?b.exports=f:c&&c.amd?c(function(){return f}):this.xorwow=f}(this,"object"==typeof module&&module,"function"==typeof define&&define);
\ No newline at end of file
diff --git a/node_modules/seedrandom/package.json b/node_modules/seedrandom/package.json
new file mode 100644
index 0000000..13c9087
--- /dev/null
+++ b/node_modules/seedrandom/package.json
@@ -0,0 +1,85 @@
+{
+  "_args": [
+    [
+      "seedrandom@2.4.3",
+      "/Users/zamua/Dropbox/workspace/madify"
+    ]
+  ],
+  "_from": "seedrandom@2.4.3",
+  "_id": "seedrandom@2.4.3",
+  "_inBundle": false,
+  "_integrity": "sha1-JDhQTa0zkXMUv/GKxNeU8W1qrsw=",
+  "_location": "/seedrandom",
+  "_phantomChildren": {},
+  "_requested": {
+    "type": "version",
+    "registry": true,
+    "raw": "seedrandom@2.4.3",
+    "name": "seedrandom",
+    "escapedName": "seedrandom",
+    "rawSpec": "2.4.3",
+    "saveSpec": null,
+    "fetchSpec": "2.4.3"
+  },
+  "_requiredBy": [
+    "/@tensorflow/tfjs-core"
+  ],
+  "_resolved": "https://registry.npmjs.org/seedrandom/-/seedrandom-2.4.3.tgz",
+  "_spec": "2.4.3",
+  "_where": "/Users/zamua/Dropbox/workspace/madify",
+  "author": {
+    "name": "David Bau"
+  },
+  "browser": {
+    "crypto": false
+  },
+  "bugs": {
+    "url": "https://github.com/davidbau/seedrandom/issues"
+  },
+  "config": {
+    "blanket": {
+      "pattern": [
+        "seedrandom.js",
+        "lib/alea.js",
+        "lib/xor128.js",
+        "lib/xorwow.js",
+        "lib/xorshift7.js",
+        "lib/tychei.js",
+        "lib/xor4096.js"
+      ]
+    }
+  },
+  "description": "Seeded random number generator for Javascript.",
+  "devDependencies": {
+    "blanket": "latest",
+    "grunt": "latest",
+    "grunt-bowercopy": "latest",
+    "grunt-browserify": "latest",
+    "grunt-cli": "latest",
+    "grunt-contrib-connect": "latest",
+    "grunt-contrib-qunit": "latest",
+    "grunt-contrib-uglify": "latest",
+    "grunt-mocha-cov": "latest",
+    "grunt-release": "latest",
+    "phantomjs-prebuilt": "latest",
+    "proxyquire": "latest",
+    "requirejs": "latest"
+  },
+  "homepage": "http://davidbau.com/archives/2010/01/30/random_seeds_coded_hints_and_quintillions.html",
+  "keywords": [
+    "seed",
+    "random",
+    "crypto"
+  ],
+  "license": "MIT",
+  "main": "index.js",
+  "name": "seedrandom",
+  "repository": {
+    "type": "git",
+    "url": "git://github.com/davidbau/seedrandom.git"
+  },
+  "scripts": {
+    "test": "grunt travis"
+  },
+  "version": "2.4.3"
+}
diff --git a/node_modules/seedrandom/seedrandom.js b/node_modules/seedrandom/seedrandom.js
new file mode 100644
index 0000000..83ea6e8
--- /dev/null
+++ b/node_modules/seedrandom/seedrandom.js
@@ -0,0 +1,247 @@
+/*
+Copyright 2014 David Bau.
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+*/
+
+(function (pool, math) {
+//
+// The following constants are related to IEEE 754 limits.
+//
+var global = this,
+    width = 256,        // each RC4 output is 0 <= x < 256
+    chunks = 6,         // at least six RC4 outputs for each double
+    digits = 52,        // there are 52 significant digits in a double
+    rngname = 'random', // rngname: name for Math.random and Math.seedrandom
+    startdenom = math.pow(width, chunks),
+    significance = math.pow(2, digits),
+    overflow = significance * 2,
+    mask = width - 1,
+    nodecrypto;         // node.js crypto module, initialized at the bottom.
+
+//
+// seedrandom()
+// This is the seedrandom function described above.
+//
+function seedrandom(seed, options, callback) {
+  var key = [];
+  options = (options == true) ? { entropy: true } : (options || {});
+
+  // Flatten the seed string or build one from local entropy if needed.
+  var shortseed = mixkey(flatten(
+    options.entropy ? [seed, tostring(pool)] :
+    (seed == null) ? autoseed() : seed, 3), key);
+
+  // Use the seed to initialize an ARC4 generator.
+  var arc4 = new ARC4(key);
+
+  // This function returns a random double in [0, 1) that contains
+  // randomness in every bit of the mantissa of the IEEE 754 value.
+  var prng = function() {
+    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48
+        d = startdenom,                 //   and denominator d = 2 ^ 48.
+        x = 0;                          //   and no 'extra last byte'.
+    while (n < significance) {          // Fill up all significant digits by
+      n = (n + x) * width;              //   shifting numerator and
+      d *= width;                       //   denominator and generating a
+      x = arc4.g(1);                    //   new least-significant-byte.
+    }
+    while (n >= overflow) {             // To avoid rounding up, before adding
+      n /= 2;                           //   last byte, shift everything
+      d /= 2;                           //   right using integer math until
+      x >>>= 1;                         //   we have exactly the desired bits.
+    }
+    return (n + x) / d;                 // Form the number within [0, 1).
+  };
+
+  prng.int32 = function() { return arc4.g(4) | 0; }
+  prng.quick = function() { return arc4.g(4) / 0x100000000; }
+  prng.double = prng;
+
+  // Mix the randomness into accumulated entropy.
+  mixkey(tostring(arc4.S), pool);
+
+  // Calling convention: what to return as a function of prng, seed, is_math.
+  return (options.pass || callback ||
+      function(prng, seed, is_math_call, state) {
+        if (state) {
+          // Load the arc4 state from the given state if it has an S array.
+          if (state.S) { copy(state, arc4); }
+          // Only provide the .state method if requested via options.state.
+          prng.state = function() { return copy(arc4, {}); }
+        }
+
+        // If called as a method of Math (Math.seedrandom()), mutate
+        // Math.random because that is how seedrandom.js has worked since v1.0.
+        if (is_math_call) { math[rngname] = prng; return seed; }
+
+        // Otherwise, it is a newer calling convention, so return the
+        // prng directly.
+        else return prng;
+      })(
+  prng,
+  shortseed,
+  'global' in options ? options.global : (this == math),
+  options.state);
+}
+math['seed' + rngname] = seedrandom;
+
+//
+// ARC4
+//
+// An ARC4 implementation.  The constructor takes a key in the form of
+// an array of at most (width) integers that should be 0 <= x < (width).
+//
+// The g(count) method returns a pseudorandom integer that concatenates
+// the next (count) outputs from ARC4.  Its return value is a number x
+// that is in the range 0 <= x < (width ^ count).
+//
+function ARC4(key) {
+  var t, keylen = key.length,
+      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];
+
+  // The empty key [] is treated as [0].
+  if (!keylen) { key = [keylen++]; }
+
+  // Set up S using the standard key scheduling algorithm.
+  while (i < width) {
+    s[i] = i++;
+  }
+  for (i = 0; i < width; i++) {
+    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];
+    s[j] = t;
+  }
+
+  // The "g" method returns the next (count) outputs as one number.
+  (me.g = function(count) {
+    // Using instance members instead of closure state nearly doubles speed.
+    var t, r = 0,
+        i = me.i, j = me.j, s = me.S;
+    while (count--) {
+      t = s[i = mask & (i + 1)];
+      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];
+    }
+    me.i = i; me.j = j;
+    return r;
+    // For robust unpredictability, the function call below automatically
+    // discards an initial batch of values.  This is called RC4-drop[256].
+    // See http://google.com/search?q=rsa+fluhrer+response&btnI
+  })(width);
+}
+
+//
+// copy()
+// Copies internal state of ARC4 to or from a plain object.
+//
+function copy(f, t) {
+  t.i = f.i;
+  t.j = f.j;
+  t.S = f.S.slice();
+  return t;
+};
+
+//
+// flatten()
+// Converts an object tree to nested arrays of strings.
+//
+function flatten(obj, depth) {
+  var result = [], typ = (typeof obj), prop;
+  if (depth && typ == 'object') {
+    for (prop in obj) {
+      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}
+    }
+  }
+  return (result.length ? result : typ == 'string' ? obj : obj + '\0');
+}
+
+//
+// mixkey()
+// Mixes a string seed into a key that is an array of integers, and
+// returns a shortened string seed that is equivalent to the result key.
+//
+function mixkey(seed, key) {
+  var stringseed = seed + '', smear, j = 0;
+  while (j < stringseed.length) {
+    key[mask & j] =
+      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));
+  }
+  return tostring(key);
+}
+
+//
+// autoseed()
+// Returns an object for autoseeding, using window.crypto and Node crypto
+// module if available.
+//
+function autoseed() {
+  try {
+    var out;
+    if (nodecrypto && (out = nodecrypto.randomBytes)) {
+      // The use of 'out' to remember randomBytes makes tight minified code.
+      out = out(width);
+    } else {
+      out = new Uint8Array(width);
+      (global.crypto || global.msCrypto).getRandomValues(out);
+    }
+    return tostring(out);
+  } catch (e) {
+    var browser = global.navigator,
+        plugins = browser && browser.plugins;
+    return [+new Date, global, plugins, global.screen, tostring(pool)];
+  }
+}
+
+//
+// tostring()
+// Converts an array of charcodes to a string
+//
+function tostring(a) {
+  return String.fromCharCode.apply(0, a);
+}
+
+//
+// When seedrandom.js is loaded, we immediately mix a few bits
+// from the built-in RNG into the entropy pool.  Because we do
+// not want to interfere with deterministic PRNG state later,
+// seedrandom will not call math.random on its own again after
+// initialization.
+//
+mixkey(math.random(), pool);
+
+//
+// Nodejs and AMD support: export the implementation as a module using
+// either convention.
+//
+if ((typeof module) == 'object' && module.exports) {
+  module.exports = seedrandom;
+  // When in node.js, try using crypto package for autoseeding.
+  try {
+    nodecrypto = require('crypto');
+  } catch (ex) {}
+} else if ((typeof define) == 'function' && define.amd) {
+  define(function() { return seedrandom; });
+}
+
+// End anonymous scope, and pass initial values.
+})(
+  [],     // pool: entropy pool starts empty
+  Math    // math: package containing random, pow, and seedrandom
+);
diff --git a/node_modules/seedrandom/seedrandom.min.js b/node_modules/seedrandom/seedrandom.min.js
new file mode 100644
index 0000000..401b4bf
--- /dev/null
+++ b/node_modules/seedrandom/seedrandom.min.js
@@ -0,0 +1 @@
+!function(a,b){function c(c,j,k){var n=[];j=1==j?{entropy:!0}:j||{};var s=g(f(j.entropy?[c,i(a)]:null==c?h():c,3),n),t=new d(n),u=function(){for(var a=t.g(m),b=p,c=0;a<q;)a=(a+c)*l,b*=l,c=t.g(1);for(;a>=r;)a/=2,b/=2,c>>>=1;return(a+c)/b};return u.int32=function(){return 0|t.g(4)},u.quick=function(){return t.g(4)/4294967296},u.double=u,g(i(t.S),a),(j.pass||k||function(a,c,d,f){return f&&(f.S&&e(f,t),a.state=function(){return e(t,{})}),d?(b[o]=a,c):a})(u,s,"global"in j?j.global:this==b,j.state)}function d(a){var b,c=a.length,d=this,e=0,f=d.i=d.j=0,g=d.S=[];for(c||(a=[c++]);e<l;)g[e]=e++;for(e=0;e<l;e++)g[e]=g[f=s&f+a[e%c]+(b=g[e])],g[f]=b;(d.g=function(a){for(var b,c=0,e=d.i,f=d.j,g=d.S;a--;)b=g[e=s&e+1],c=c*l+g[s&(g[e]=g[f=s&f+b])+(g[f]=b)];return d.i=e,d.j=f,c})(l)}function e(a,b){return b.i=a.i,b.j=a.j,b.S=a.S.slice(),b}function f(a,b){var c,d=[],e=typeof a;if(b&&"object"==e)for(c in a)try{d.push(f(a[c],b-1))}catch(a){}return d.length?d:"string"==e?a:a+"\0"}function g(a,b){for(var c,d=a+"",e=0;e<d.length;)b[s&e]=s&(c^=19*b[s&e])+d.charCodeAt(e++);return i(b)}function h(){try{var b;return j&&(b=j.randomBytes)?b=b(l):(b=new Uint8Array(l),(k.crypto||k.msCrypto).getRandomValues(b)),i(b)}catch(b){var c=k.navigator,d=c&&c.plugins;return[+new Date,k,d,k.screen,i(a)]}}function i(a){return String.fromCharCode.apply(0,a)}var j,k=this,l=256,m=6,n=52,o="random",p=b.pow(l,m),q=b.pow(2,n),r=2*q,s=l-1;if(b["seed"+o]=c,g(b.random(),a),"object"==typeof module&&module.exports){module.exports=c;try{j=require("crypto")}catch(a){}}else"function"==typeof define&&define.amd&&define(function(){return c})}([],Math);
\ No newline at end of file
diff --git a/node_modules/seedrandom/test/altprng.html b/node_modules/seedrandom/test/altprng.html
new file mode 100644
index 0000000..781679b
--- /dev/null
+++ b/node_modules/seedrandom/test/altprng.html
@@ -0,0 +1,30 @@
+<html>
+<head>
+<link rel="stylesheet" href="lib/qunit.css">
+</head>
+<body>
+<div id="qunit"></div>
+<div id="qunit-fixture"></div>
+<script src="../lib/xor4096.min.js"></script>
+<script src="lib/qunit.js"></script>
+<script>
+QUnit.module("Alternative PRNG Test");
+
+QUnit.test("Verify that we can use xor4096", function(assert) {
+// Use xor4096 for Richard Brent's xorgens-4096 algorithm.
+var xorgen = new xor4096('hello.');
+
+// By default provides 32 bits of randomness in a float.
+assert.equal(xorgen(), 0.9798525865189731);
+
+// Use "double" to get 56 bits of randomness.
+assert.equal(xorgen.double(), 0.03583478477375346);
+
+// Use "int32" to get a 32 bit (signed) integer.
+assert.equal(xorgen.int32(), 1341429986);
+
+});
+</script>
+</html>
+
+
diff --git a/node_modules/seedrandom/test/autoseed.html b/node_modules/seedrandom/test/autoseed.html
new file mode 100644
index 0000000..2738f24
--- /dev/null
+++ b/node_modules/seedrandom/test/autoseed.html
@@ -0,0 +1,46 @@
+<html>
+<head>
+<link rel="stylesheet" href="lib/qunit.css">
+</head>
+<body>
+<div id="qunit"></div>
+<div id="qunit-fixture"></div>
+<script src="lib/qunit.js"></script>
+<script src="../seedrandom.js"></script>
+<script>
+QUnit.module("Simple Test");
+
+QUnit.test("Check that we can reproduce a seed", function(assert) {
+  var seed;
+  var time = new Date().getTime();
+  var seediter = 50;
+  for (var k = 0; k < seediter; ++k) {
+    seed = Math.seedrandom();
+  }
+  var seedtime = (new Date().getTime() - time) / seediter;
+
+  time = new Date().getTime();
+  var vals = [];
+  var iters = 1000;
+  var j;
+  for (j = 0; j < iters; ++j) {
+    var saw = Math.random();
+    vals.push(saw);
+  }
+  time = new Date().getTime() - time;
+  var errors = 0;
+  Math.seedrandom(seed);
+  for (j = 0; j < vals.length; ++j) {
+    var saw = vals[j];
+    var got = Math.random();
+    assert.equal(saw, got, saw + " vs " + got);
+  }
+
+  assert.ok(true, '' +
+     'Seeding took ' + seedtime + ' ms per seedrandom' +
+     ' in ' + time + ' ms for ' + iters +
+     ' calls, ' + (time / iters) + ' ms per random()' + '');
+});
+</script>
+</body>
+</html>
diff --git a/node_modules/seedrandom/test/bitgen.js b/node_modules/seedrandom/test/bitgen.js
new file mode 100755
index 0000000..c3563d5
--- /dev/null
+++ b/node_modules/seedrandom/test/bitgen.js
@@ -0,0 +1,45 @@
+#!/usr/bin/env node
+var seedrandom = require('../seedrandom');
+
+// process.on('SIGPIPE', process.exit);
+function epipeBomb(stream, callback) {
+  if (stream == null) stream = process.stdout;
+  if (callback == null) callback = process.exit;
+
+  function epipeFilter(err) {
+    if (err.code === 'EPIPE') return callback();
+
+    // If there's more than one error handler (ie, us),
+    // then the error won't be bubbled up anyway
+    if (stream.listeners('error').length <= 1) {
+      stream.removeAllListeners();    // Pretend we were never here
+      stream.emit('error', err);      // Then emit as if we were never here
+      stream.on('error', epipeFilter);// Reattach, ready for the next error!
+    }
+  }
+
+  stream.on('error', epipeFilter);
+}
+
+epipeBomb();
+
+var bufsize = 1024 * 256,
+    buf = new Buffer(bufsize * 4),
+    prng = seedrandom(0),
+    count = parseInt(process.argv[2]) || Infinity;
+function dowrite() {
+  while (count > 0) {
+    for (var j = 0; j < bufsize; ++j) {
+      buf.writeUInt32BE(Math.floor(
+          prng() * 256 * 256 * 256 * 256
+      ), j * 4);
+    }
+    count -= bufsize * 32;
+    if (!process.stdout.write(buf)) {
+      process.stdout.once('drain', function() { setTimeout(dowrite, 0) });
+      return;
+    }
+  }
+}
+
+dowrite();
diff --git a/node_modules/seedrandom/test/browserified.html b/node_modules/seedrandom/test/browserified.html
new file mode 100644
index 0000000..724654e
--- /dev/null
+++ b/node_modules/seedrandom/test/browserified.html
@@ -0,0 +1,23 @@
+<html>
+<head>
+<link rel="stylesheet" href="lib/qunit.css">
+</head>
+<body>
+<div id="qunit"></div>
+<div id="qunit-fixture"></div>
+<script src="lib/qunit.js"></script>
+<script>
+function describe(name, fn) {
+  QUnit.module(name);
+  fn();
+}
+function it(desc, fn) {
+  QUnit.test(desc, function(a) {
+    fn();
+    a.ok(true);
+  });
+}
+</script>
+<script src="browserified.js"></script>
+</body>
+</html>
diff --git a/node_modules/seedrandom/test/browserified.js b/node_modules/seedrandom/test/browserified.js
new file mode 100644
index 0000000..518be87
--- /dev/null
+++ b/node_modules/seedrandom/test/browserified.js
@@ -0,0 +1,489 @@
+require=(function e(t,n,r){function s(o,u){if(!n[o]){if(!t[o]){var a=typeof require=="function"&&require;if(!u&&a)return a(o,!0);if(i)return i(o,!0);var f=new Error("Cannot find module '"+o+"'");throw f.code="MODULE_NOT_FOUND",f}var l=n[o]={exports:{}};t[o][0].call(l.exports,function(e){var n=t[o][1][e];return s(n?n:e)},l,l.exports,e,t,n,r)}return n[o].exports}var i=typeof require=="function"&&require;for(var o=0;o<r.length;o++)s(r[o]);return s})({1:[function(require,module,exports){
+
+},{}],2:[function(require,module,exports){
+arguments[4][1][0].apply(exports,arguments)
+},{"dup":1}],3:[function(require,module,exports){
+/*
+Copyright 2014 David Bau.
+
+Permission is hereby granted, free of charge, to any person obtaining
+a copy of this software and associated documentation files (the
+"Software"), to deal in the Software without restriction, including
+without limitation the rights to use, copy, modify, merge, publish,
+distribute, sublicense, and/or sell copies of the Software, and to
+permit persons to whom the Software is furnished to do so, subject to
+the following conditions:
+
+The above copyright notice and this permission notice shall be
+included in all copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND,
+EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF
+MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT.
+IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY
+CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT,
+TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE
+SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.
+
+*/
+
+(function (pool, math) {
+//
+// The following constants are related to IEEE 754 limits.
+//
+var global = this,
+    width = 256,        // each RC4 output is 0 <= x < 256
+    chunks = 6,         // at least six RC4 outputs for each double
+    digits = 52,        // there are 52 significant digits in a double
+    rngname = 'random', // rngname: name for Math.random and Math.seedrandom
+    startdenom = math.pow(width, chunks),
+    significance = math.pow(2, digits),
+    overflow = significance * 2,
+    mask = width - 1,
+    nodecrypto;         // node.js crypto module, initialized at the bottom.
+
+//
+// seedrandom()
+// This is the seedrandom function described above.
+//
+function seedrandom(seed, options, callback) {
+  var key = [];
+  options = (options == true) ? { entropy: true } : (options || {});
+
+  // Flatten the seed string or build one from local entropy if needed.
+  var shortseed = mixkey(flatten(
+    options.entropy ? [seed, tostring(pool)] :
+    (seed == null) ? autoseed() : seed, 3), key);
+
+  // Use the seed to initialize an ARC4 generator.
+  var arc4 = new ARC4(key);
+
+  // This function returns a random double in [0, 1) that contains
+  // randomness in every bit of the mantissa of the IEEE 754 value.
+  var prng = function() {
+    var n = arc4.g(chunks),             // Start with a numerator n < 2 ^ 48
+        d = startdenom,                 //   and denominator d = 2 ^ 48.
+        x = 0;                          //   and no 'extra last byte'.
+    while (n < significance) {          // Fill up all significant digits by
+      n = (n + x) * width;              //   shifting numerator and
+      d *= width;                       //   denominator and generating a
+      x = arc4.g(1);                    //   new least-significant-byte.
+    }
+    while (n >= overflow) {             // To avoid rounding up, before adding
+      n /= 2;                           //   last byte, shift everything
+      d /= 2;                           //   right using integer math until
+      x >>>= 1;                         //   we have exactly the desired bits.
+    }
+    return (n + x) / d;                 // Form the number within [0, 1).
+  };
+
+  prng.int32 = function() { return arc4.g(4) | 0; }
+  prng.quick = function() { return arc4.g(4) / 0x100000000; }
+  prng.double = prng;
+
+  // Mix the randomness into accumulated entropy.
+  mixkey(tostring(arc4.S), pool);
+
+  // Calling convention: what to return as a function of prng, seed, is_math.
+  return (options.pass || callback ||
+      function(prng, seed, is_math_call, state) {
+        if (state) {
+          // Load the arc4 state from the given state if it has an S array.
+          if (state.S) { copy(state, arc4); }
+          // Only provide the .state method if requested via options.state.
+          prng.state = function() { return copy(arc4, {}); }
+        }
+
+        // If called as a method of Math (Math.seedrandom()), mutate
+        // Math.random because that is how seedrandom.js has worked since v1.0.
+        if (is_math_call) { math[rngname] = prng; return seed; }
+
+        // Otherwise, it is a newer calling convention, so return the
+        // prng directly.
+        else return prng;
+      })(
+  prng,
+  shortseed,
+  'global' in options ? options.global : (this == math),
+  options.state);
+}
+math['seed' + rngname] = seedrandom;
+
+//
+// ARC4
+//
+// An ARC4 implementation.  The constructor takes a key in the form of
+// an array of at most (width) integers that should be 0 <= x < (width).
+//
+// The g(count) method returns a pseudorandom integer that concatenates
+// the next (count) outputs from ARC4.  Its return value is a number x
+// that is in the range 0 <= x < (width ^ count).
+//
+function ARC4(key) {
+  var t, keylen = key.length,
+      me = this, i = 0, j = me.i = me.j = 0, s = me.S = [];
+
+  // The empty key [] is treated as [0].
+  if (!keylen) { key = [keylen++]; }
+
+  // Set up S using the standard key scheduling algorithm.
+  while (i < width) {
+    s[i] = i++;
+  }
+  for (i = 0; i < width; i++) {
+    s[i] = s[j = mask & (j + key[i % keylen] + (t = s[i]))];
+    s[j] = t;
+  }
+
+  // The "g" method returns the next (count) outputs as one number.
+  (me.g = function(count) {
+    // Using instance members instead of closure state nearly doubles speed.
+    var t, r = 0,
+        i = me.i, j = me.j, s = me.S;
+    while (count--) {
+      t = s[i = mask & (i + 1)];
+      r = r * width + s[mask & ((s[i] = s[j = mask & (j + t)]) + (s[j] = t))];
+    }
+    me.i = i; me.j = j;
+    return r;
+    // For robust unpredictability, the function call below automatically
+    // discards an initial batch of values.  This is called RC4-drop[256].
+    // See http://google.com/search?q=rsa+fluhrer+response&btnI
+  })(width);
+}
+
+//
+// copy()
+// Copies internal state of ARC4 to or from a plain object.
+//
+function copy(f, t) {
+  t.i = f.i;
+  t.j = f.j;
+  t.S = f.S.slice();
+  return t;
+};
+
+//
+// flatten()
+// Converts an object tree to nested arrays of strings.
+//
+function flatten(obj, depth) {
+  var result = [], typ = (typeof obj), prop;
+  if (depth && typ == 'object') {
+    for (prop in obj) {
+      try { result.push(flatten(obj[prop], depth - 1)); } catch (e) {}
+    }
+  }
+  return (result.length ? result : typ == 'string' ? obj : obj + '\0');
+}
+
+//
+// mixkey()
+// Mixes a string seed into a key that is an array of integers, and
+// returns a shortened string seed that is equivalent to the result key.
+//
+function mixkey(seed, key) {
+  var stringseed = seed + '', smear, j = 0;
+  while (j < stringseed.length) {
+    key[mask & j] =
+      mask & ((smear ^= key[mask & j] * 19) + stringseed.charCodeAt(j++));
+  }
+  return tostring(key);
+}
+
+//
+// autoseed()
+// Returns an object for autoseeding, using window.crypto and Node crypto
+// module if available.
+//
+function autoseed() {
+  try {
+    var out;
+    if (nodecrypto && (out = nodecrypto.randomBytes)) {
+      // The use of 'out' to remember randomBytes makes tight minified code.
+      out = out(width);
+    } else {
+      out = new Uint8Array(width);
+      (global.crypto || global.msCrypto).getRandomValues(out);
+    }
+    return tostring(out);
+  } catch (e) {
+    var browser = global.navigator,
+        plugins = browser && browser.plugins;
+    return [+new Date, global, plugins, global.screen, tostring(pool)];
+  }
+}
+
+//
+// tostring()
+// Converts an array of charcodes to a string
+//
+function tostring(a) {
+  return String.fromCharCode.apply(0, a);
+}
+
+//
+// When seedrandom.js is loaded, we immediately mix a few bits
+// from the built-in RNG into the entropy pool.  Because we do
+// not want to interfere with deterministic PRNG state later,
+// seedrandom will not call math.random on its own again after
+// initialization.
+//
+mixkey(math.random(), pool);
+
+//
+// Nodejs and AMD support: export the implementation as a module using
+// either convention.
+//
+if ((typeof module) == 'object' && module.exports) {
+  module.exports = seedrandom;
+  // When in node.js, try using crypto package for autoseeding.
+  try {
+    nodecrypto = require('crypto');
+  } catch (ex) {}
+} else if ((typeof define) == 'function' && define.amd) {
+  define(function() { return seedrandom; });
+}
+
+// End anonymous scope, and pass initial values.
+})(
+  [],     // pool: entropy pool starts empty
+  Math    // math: package containing random, pow, and seedrandom
+);
+
+},{"crypto":1}],4:[function(require,module,exports){
+(function (__dirname){
+var assert = require("assert");
+var seedrandom = require("../seedrandom");
+var requirejs = require("requirejs");
+
+// Stub out requirejs if in the browser via browserify.
+if (!requirejs.config) {
+  requirejs = require;
+} else {
+  requirejs.config({
+    baseUrl: __dirname
+  });
+}
+
+describe("Nodejs API Test", function() {
+
+it('should pass basic tests.', function() {
+  var original = Math.random,
+      result, r, xprng, obj, as2, as3, autoseed1, myrng,
+      firstprng, secondprng, thirdprng, rng;
+
+  result = Math.seedrandom('hello.');
+  firstprng = Math.random;
+  assert.ok(original !== firstprng, "Should change Math.random.");
+  assert.equal(result, "hello.", "Should return short seed.");
+  r = Math.random();
+  assert.equal(r, 0.9282578795792454, "Should be 'hello.'#1");
+  r = Math.random();
+  assert.equal(r, 0.3752569768646784, "Should be 'hello.'#2");
+
+  // should be able to autoseed
+  result = Math.seedrandom();
+  secondprng = Math.random;
+  assert.ok(original !== secondprng, "Should change Math.random.");
+  assert.ok(firstprng !== secondprng, "Should change Math.random.");
+  assert.equal(result.length, 256, "Should return short seed.");
+  r = Math.random();
+  assert.ok(r > 0, "Should be posititive.");
+  assert.ok(r < 1, "Should be less than 1.");
+  assert.ok(r != 0.9282578795792454, "Should not be 'hello.'#1");
+  assert.ok(r != 0.3752569768646784, "Should not be 'hello.'#2");
+  assert.ok(r != 0.7316977468919549, "Should not be 'hello.'#3");
+  autoseed1 = r;
+
+  // should be able to add entropy.
+  result = Math.seedrandom('added entropy.', { entropy:true });
+  assert.equal(result.length, 256, "Should return short seed.");
+  thirdprng = Math.random;
+  assert.ok(thirdprng !== secondprng, "Should change Math.random.");
+  r = Math.random();
+  assert.ok(r != 0.597067214994467, "Should not be 'added entropy.'#1");
+
+  // Reset to original Math.random.
+  Math.random = original;
+  // should be able to use new Math.seedrandom('hello.')
+  myrng = new Math.seedrandom('hello.');
+  assert.ok(original === Math.random, "Should not change Math.random.");
+  assert.ok(original !== myrng, "PRNG should not be Math.random.");
+  r = myrng();
+  assert.equal(r, 0.9282578795792454, "Should be 'hello.'#1");
+
+  // should be able to use seedrandom('hello.')"
+  rng = seedrandom('hello.');
+  assert.equal(typeof(rng), 'function', "Should return a function.");
+  r = rng();
+  assert.equal(r, 0.9282578795792454, "Should be 'hello.'#1");
+  assert.ok(original === Math.random, "Should not change Math.random.");
+  assert.ok(original !== rng, "PRNG should not be Math.random.");
+
+  // Global PRNG: set Math.random.
+  // should be able to use seedrandom('hello.', { global: true })
+  result = seedrandom('hello.', { global: true });
+  assert.equal(result, 'hello.', "Should return short seed.");
+  assert.ok(original != Math.random, "Should change Math.random.");
+  r = Math.random();
+  assert.equal(r, 0.9282578795792454, "Should be 'hello.'#1");
+
+  // Autoseeded non-global
+  Math.random = original;
+  // should be able to use seedrandom()
+  result = seedrandom();
+  assert.equal(typeof(result), 'function', "Should return function.");
+  assert.ok(original === Math.random, "Should not change Math.random.");
+  r = result();
+  // got " + r);
+  assert.ok(r != autoseed1, "Should not repeat previous autoseed.");
+  assert.ok(r != 0.9282578795792454, "Should not be 'hello.'#1");
+  assert.ok(r != 0.7316977468919549, "Should not be 'hello.'#3");
+
+  // Mixing accumulated entropy.
+  // should be able to use seedrandom('added entropy.', { entropy: true })
+  rng = seedrandom('added entropy.', { entropy: true });
+  r = result();
+  // got " + r);
+  assert.ok(r != autoseed1, "Should not repeat previous autoseed.");
+  assert.ok(r != 0.597067214994467, "Should not be 'added entropy.'#1");
+
+  // Legacy calling convention for mixing accumulated entropy.
+  // should be able to use seedrandom('added entropy.', true)
+  rng = seedrandom('added entropy.', true);
+  r = result();
+  // got " + r);
+  assert.ok(r != autoseed1, "Should not repeat previous autoseed.");
+  assert.ok(r != 0.597067214994467, "Should not be 'added entropy.'#1");
+
+  // The pass option
+  // should be able to use Math.seedrandom(null, { pass: ...
+  obj = Math.seedrandom(null, { pass: function(prng, seed) {
+    return { random: prng, seed: seed };
+  }});
+  assert.ok(original === Math.random, "Should not change Math.random.");
+  assert.ok(original !== obj.random, "Should be different from Math.random.");
+  assert.equal(typeof(obj.random), 'function', "Should return a PRNG function.");
+  assert.equal(typeof(obj.seed), 'string', "Should return a seed.");
+  as2 = obj.random();
+  assert.ok(as2 != 0.9282578795792454, "Should not be 'hello.'#1");
+  rng = seedrandom(obj.seed);
+  as3 = rng();
+  assert.equal(as2, as3, "Should be reproducible when using the seed.");
+
+  // Exercise pass again, with explicit seed and global
+  // should be able to use Math.seedrandom('hello.', { pass: ...
+  result = Math.seedrandom('hello.', {
+    global: 'abc',
+    pass: function(prng, seed, global) {
+      assert.equal(typeof(prng), 'function', "Callback arg #1 assert");
+      assert.equal(seed, 'hello.', "Callback arg #2 assert");
+      assert.equal(global, 'abc', "Callback arg #3 passed through.");
+      assert.equal(prng(), 0.9282578795792454, "Should be 'hello.'#1");
+      return 'def';
+  }});
+  assert.equal(result, 'def', "Should return value from callback.");
+  assert.ok(original === Math.random, "Should not change Math.random.");
+
+  // Legacy third argument callback argument:
+  // should be able to use Math.seedrandom('hello.', { global: 50 }, callback)
+  result = Math.seedrandom('hello.', { global: 50 },
+    function(prng, seed, global) {
+      assert.equal(typeof(prng), 'function', "Callback arg #1 assert");
+      assert.equal(seed, 'hello.', "Callback arg #2 assert");
+      assert.equal(global, 50, "Callback arg #3 assert");
+      assert.equal(prng(), 0.9282578795792454, "Should be 'hello.'#1");
+      return 'zzz';
+  });
+  assert.equal(result, 'zzz', "Should return value from callback.");
+  assert.ok(original === Math.random, "Should not change Math.random.");
+
+  // Global: false.
+  // should be able to use new Math.seedrandom('hello.', {global: false})
+  myrng = new Math.seedrandom('hello.', {global:false});
+  assert.equal(typeof(myrng), 'function', "Should return a PRNG funciton.");
+  assert.ok(original === Math.random, "Should not change Math.random.");
+  assert.ok(original !== myrng, "PRNG should not be Math.random.");
+  r = myrng();
+  assert.equal(r, 0.9282578795792454, "Should be 'hello.'#1");
+
+  // options = {} when a method of Math.
+  // should be able to use Math.seedrandom('hello.', {})
+  result = Math.seedrandom('hello.');
+  xprng = Math.random;
+  assert.ok(original !== xprng, "Should change Math.random.");
+  assert.equal(result, "hello.", "Should return short seed.");
+  r = Math.random();
+  assert.equal(r, 0.9282578795792454, "Should be 'hello.'#1");
+  r = Math.random();
+  assert.equal(r, 0.3752569768646784, "Should be 'hello.'#2");
+  Math.random = original;
+
+  // options = {} when not a method of Math
+  // should be able to use seedrandom('hello.', {})
+  rng = seedrandom('hello.', {});
+  assert.equal(typeof(rng), 'function', "Should return a function.");
+  r = rng();
+  assert.equal(r, 0.9282578795792454, "Should be 'hello.'#1");
+  assert.ok(original === Math.random, "Should not change Math.random.");
+  assert.ok(original !== rng, "PRNG should not be Math.random.");
+});
+
+it('should support state api.', function() {
+  // Verify that there is no state method
+  var dummy = seedrandom('hello');
+  var unexpected = -1;
+  var expected = -1;
+  try {
+    unexpected = dummy.state();
+  } catch(e) {
+    expected = 1;
+  }
+  assert.equal(unexpected, -1);
+  assert.equal(expected, 1);
+  var count = 0;
+  for (var x in dummy) {
+    if (x == 'state') count += 1;
+  }
+  assert.equal(count, 0);
+
+  // Verify that a state method can be added
+  var saveable = seedrandom("secret-seed", {state: true});
+  var ordinary = seedrandom("secret-seed");
+  for (var j = 0; j < 1e2; ++j) {
+    assert.equal(ordinary(), saveable());
+  }
+  var virgin = seedrandom("secret-seed");
+  var saved = saveable.state();
+  var replica = seedrandom("", {state: saved});
+  for (var j = 0; j < 1e2; ++j) {
+    var r = replica();
+    assert.equal(r, saveable());
+    assert.equal(r, ordinary());
+    assert.ok(r != virgin());
+  }
+});
+
+it('should support requirejs in node.', function() {
+  var original = Math.random;
+  var rsr = requirejs('../seedrandom');
+  var rng = rsr('hello.');
+  assert.equal(typeof(rng), 'function', "Should return a function.");
+  var r = rng();
+  assert.equal(r, 0.9282578795792454, "Should be 'hello.'#1");
+  assert.ok(original === Math.random, "Should not change Math.random.");
+  assert.ok(original !== rng, "PRNG should not be Math.random.");
+});
+
+// End of test.
+
+});
+
+}).call(this,"/test")
+},{"../seedrandom":3,"assert":"assert","requirejs":2}],"assert":[function(require,module,exports){
+// Use QUnit.assert to mimic node.assert.
+
+module.exports = QUnit.assert;
+
+},{}]},{},[4]);
diff --git a/node_modules/seedrandom/test/cryptotest.js b/node_modules/seedrandom/test/cryptotest.js
new file mode 100644
index 0000000..1686e59
--- /dev/null
+++ b/node_modules/seedrandom/test/cryptotest.js
@@ -0,0 +1,57 @@
+var proxyquire = require('proxyquire');
+var assert = require("assert");
+var Module = require("module");
+
+describe("Crypto API Test", function() {
+
+var crypto_stub = {};
+var original = Math.random;
+var rng, r;
+
+// Load seedrandom in absence of any crypto package.
+it("should be able to seed without crypto", function() {
+  var noncrypto_sr = proxyquire('../seedrandom', { crypto: null });
+  rng = noncrypto_sr('hello.');
+  assert.equal(typeof(rng), 'function', "Should return a function.");
+  r = rng();
+  assert.equal(r, 0.9282578795792454, "Should be 'hello.'#1");
+  assert(original === Math.random, "Should not change Math.random.");
+  assert(original !== rng, "PRNG should not be Math.random.");
+  var noncrypto_sr = proxyquire('../seedrandom', { crypto: null });
+  result = noncrypto_sr();
+  assert.equal(typeof(result), 'function', "Should return function.");
+  assert(original === Math.random, "Should not change Math.random.");
+  r = result();
+  assert(r != 0.9282578795792454, "Should not be 'hello.'#1");
+  assert(r != 0.7316977468919549, "Should not be 'hello.'#3");
+  assert(r != 0.23144008215179881, "Should not be '\\0'#1");
+  assert(r != 0.7220382488550928, "Should not be '\\1'#1");
+  // Recache original seedrandom.
+  proxyquire('../seedrandom', {});
+});
+
+// Load seedrandom with zeroed-out crypto package.
+it("should be able to seed ('hello.') with zero crypto", function() {
+  var zerocrypto_sr = proxyquire('../seedrandom', {
+    crypto: { randomBytes: function(n) {
+      result = []; while (n-- > 0) { result.push(1); } return result; } }
+  });
+  rng = zerocrypto_sr('hello.');
+  assert.equal(typeof(rng), 'function', "Should return a function.");
+  r = rng();
+  assert.equal(r, 0.9282578795792454 , "Should be 'hello.'#1");
+  assert(original === Math.random, "Should not change Math.random.");
+  assert(original !== rng, "PRNG should not be Math.random.");
+  rng = zerocrypto_sr();
+  assert.equal(typeof(rng), 'function', "Should return function.");
+  assert(original === Math.random, "Should not change Math.random.");
+  r = rng();
+  assert.equal(r, 0.7220382488550928, "Should be '\\1'#1");
+  r = rng();
+  assert.equal(r, 0.0259971860493045, "Should be '\\1'#2");
+  // Recache original seedrandom.
+  proxyquire('../seedrandom', {});
+});
+
+
+});
diff --git a/node_modules/seedrandom/test/lib/qunit.css b/node_modules/seedrandom/test/lib/qunit.css
new file mode 100644
index 0000000..593a65f
--- /dev/null
+++ b/node_modules/seedrandom/test/lib/qunit.css
@@ -0,0 +1,415 @@
+/*!
+ * QUnit 2.1.0
+ * https://qunitjs.com/
+ *
+ * Copyright jQuery Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2016-12-06T04:45Z
+ */
+
+/** Font Family and Sizes */
+
+#qunit-tests, #qunit-header, #qunit-banner, #qunit-testrunner-toolbar, #qunit-filteredTest, #qunit-userAgent, #qunit-testresult {
+	font-family: "Helvetica Neue Light", "HelveticaNeue-Light", "Helvetica Neue", Calibri, Helvetica, Arial, sans-serif;
+}
+
+#qunit-testrunner-toolbar, #qunit-filteredTest, #qunit-userAgent, #qunit-testresult, #qunit-tests li { font-size: small; }
+#qunit-tests { font-size: smaller; }
+
+
+/** Resets */
+
+#qunit-tests, #qunit-header, #qunit-banner, #qunit-filteredTest, #qunit-userAgent, #qunit-testresult, #qunit-modulefilter {
+	margin: 0;
+	padding: 0;
+}
+
+
+/** Header (excluding toolbar) */
+
+#qunit-header {
+	padding: 0.5em 0 0.5em 1em;
+
+	color: #8699A4;
+	background-color: #0D3349;
+
+	font-size: 1.5em;
+	line-height: 1em;
+	font-weight: 400;
+
+	border-radius: 5px 5px 0 0;
+}
+
+#qunit-header a {
+	text-decoration: none;
+	color: #C2CCD1;
+}
+
+#qunit-header a:hover,
+#qunit-header a:focus {
+	color: #FFF;
+}
+
+#qunit-banner {
+	height: 5px;
+}
+
+#qunit-filteredTest {
+	padding: 0.5em 1em 0.5em 1em;
+	color: #366097;
+	background-color: #F4FF77;
+}
+
+#qunit-userAgent {
+	padding: 0.5em 1em 0.5em 1em;
+	color: #FFF;
+	background-color: #2B81AF;
+	text-shadow: rgba(0, 0, 0, 0.5) 2px 2px 1px;
+}
+
+
+/** Toolbar */
+
+#qunit-testrunner-toolbar {
+	padding: 0.5em 1em 0.5em 1em;
+	color: #5E740B;
+	background-color: #EEE;
+}
+
+#qunit-testrunner-toolbar .clearfix {
+	height: 0;
+	clear: both;
+}
+
+#qunit-testrunner-toolbar label {
+	display: inline-block;
+}
+
+#qunit-testrunner-toolbar input[type=checkbox],
+#qunit-testrunner-toolbar input[type=radio] {
+	margin: 3px;
+	vertical-align: -2px;
+}
+
+#qunit-testrunner-toolbar input[type=text] {
+	box-sizing: border-box;
+	height: 1.6em;
+}
+
+.qunit-url-config,
+.qunit-filter,
+#qunit-modulefilter {
+	display: inline-block;
+	line-height: 2.1em;
+}
+
+.qunit-filter,
+#qunit-modulefilter {
+	float: right;
+	position: relative;
+	margin-left: 1em;
+}
+
+.qunit-url-config label {
+	margin-right: 0.5em;
+}
+
+#qunit-modulefilter-search {
+	box-sizing: border-box;
+	width: 400px;
+}
+
+#qunit-modulefilter-search-container:after {
+	position: absolute;
+	right: 0.3em;
+	content: "\25bc";
+	color: black;
+}
+
+#qunit-modulefilter-dropdown {
+	/* align with #qunit-modulefilter-search */
+	box-sizing: border-box;
+	width: 400px;
+	position: absolute;
+	right: 0;
+	top: 50%;
+	margin-top: 0.8em;
+
+	border: 1px solid #D3D3D3;
+	border-top: none;
+	border-radius: 0 0 .25em .25em;
+	color: #000;
+	background-color: #F5F5F5;
+	z-index: 99;
+}
+
+#qunit-modulefilter-dropdown a {
+	color: inherit;
+	text-decoration: none;
+}
+
+#qunit-modulefilter-dropdown .clickable.checked {
+	font-weight: bold;
+	color: #000;
+	background-color: #D2E0E6;
+}
+
+#qunit-modulefilter-dropdown .clickable:hover {
+	color: #FFF;
+	background-color: #0D3349;
+}
+
+#qunit-modulefilter-actions {
+	display: block;
+	overflow: auto;
+
+	/* align with #qunit-modulefilter-dropdown-list */
+	font: smaller/1.5em sans-serif;
+}
+
+#qunit-modulefilter-dropdown #qunit-modulefilter-actions > * {
+	box-sizing: border-box;
+	max-height: 2.8em;
+	display: block;
+	padding: 0.4em;
+}
+
+#qunit-modulefilter-dropdown #qunit-modulefilter-actions > button {
+	float: right;
+	font: inherit;
+}
+
+#qunit-modulefilter-dropdown #qunit-modulefilter-actions > :last-child {
+	/* insert padding to align with checkbox margins */
+	padding-left: 3px;
+}
+
+#qunit-modulefilter-dropdown-list {
+	max-height: 200px;
+	overflow-y: auto;
+	margin: 0;
+	border-top: 2px groove threedhighlight;
+	padding: 0.4em 0 0;
+	font: smaller/1.5em sans-serif;
+}
+
+#qunit-modulefilter-dropdown-list li {
+	white-space: nowrap;
+	overflow: hidden;
+	text-overflow: ellipsis;
+}
+
+#qunit-modulefilter-dropdown-list .clickable {
+	display: block;
+	padding-left: 0.15em;
+}
+
+
+/** Tests: Pass/Fail */
+
+#qunit-tests {
+	list-style-position: inside;
+}
+
+#qunit-tests li {
+	padding: 0.4em 1em 0.4em 1em;
+	border-bottom: 1px solid #FFF;
+	list-style-position: inside;
+}
+
+#qunit-tests > li {
+	display: none;
+}
+
+#qunit-tests li.running,
+#qunit-tests li.pass,
+#qunit-tests li.fail,
+#qunit-tests li.skipped {
+	display: list-item;
+}
+
+#qunit-tests.hidepass {
+	position: relative;
+}
+
+#qunit-tests.hidepass li.running,
+#qunit-tests.hidepass li.pass {
+	visibility: hidden;
+	position: absolute;
+	width:   0;
+	height:  0;
+	padding: 0;
+	border:  0;
+	margin:  0;
+}
+
+#qunit-tests li strong {
+	cursor: pointer;
+}
+
+#qunit-tests li.skipped strong {
+	cursor: default;
+}
+
+#qunit-tests li a {
+	padding: 0.5em;
+	color: #C2CCD1;
+	text-decoration: none;
+}
+
+#qunit-tests li p a {
+	padding: 0.25em;
+	color: #6B6464;
+}
+#qunit-tests li a:hover,
+#qunit-tests li a:focus {
+	color: #000;
+}
+
+#qunit-tests li .runtime {
+	float: right;
+	font-size: smaller;
+}
+
+.qunit-assert-list {
+	margin-top: 0.5em;
+	padding: 0.5em;
+
+	background-color: #FFF;
+
+	border-radius: 5px;
+}
+
+.qunit-source {
+	margin: 0.6em 0 0.3em;
+}
+
+.qunit-collapsed {
+	display: none;
+}
+
+#qunit-tests table {
+	border-collapse: collapse;
+	margin-top: 0.2em;
+}
+
+#qunit-tests th {
+	text-align: right;
+	vertical-align: top;
+	padding: 0 0.5em 0 0;
+}
+
+#qunit-tests td {
+	vertical-align: top;
+}
+
+#qunit-tests pre {
+	margin: 0;
+	white-space: pre-wrap;
+	word-wrap: break-word;
+}
+
+#qunit-tests del {
+	color: #374E0C;
+	background-color: #E0F2BE;
+	text-decoration: none;
+}
+
+#qunit-tests ins {
+	color: #500;
+	background-color: #FFCACA;
+	text-decoration: none;
+}
+
+/*** Test Counts */
+
+#qunit-tests b.counts                       { color: #000; }
+#qunit-tests b.passed                       { color: #5E740B; }
+#qunit-tests b.failed                       { color: #710909; }
+
+#qunit-tests li li {
+	padding: 5px;
+	background-color: #FFF;
+	border-bottom: none;
+	list-style-position: inside;
+}
+
+/*** Passing Styles */
+
+#qunit-tests li li.pass {
+	color: #3C510C;
+	background-color: #FFF;
+	border-left: 10px solid #C6E746;
+}
+
+#qunit-tests .pass                          { color: #528CE0; background-color: #D2E0E6; }
+#qunit-tests .pass .test-name               { color: #366097; }
+
+#qunit-tests .pass .test-actual,
+#qunit-tests .pass .test-expected           { color: #999; }
+
+#qunit-banner.qunit-pass                    { background-color: #C6E746; }
+
+/*** Failing Styles */
+
+#qunit-tests li li.fail {
+	color: #710909;
+	background-color: #FFF;
+	border-left: 10px solid #EE5757;
+	white-space: pre;
+}
+
+#qunit-tests > li:last-child {
+	border-radius: 0 0 5px 5px;
+}
+
+#qunit-tests .fail                          { color: #000; background-color: #EE5757; }
+#qunit-tests .fail .test-name,
+#qunit-tests .fail .module-name             { color: #000; }
+
+#qunit-tests .fail .test-actual             { color: #EE5757; }
+#qunit-tests .fail .test-expected           { color: #008000; }
+
+#qunit-banner.qunit-fail                    { background-color: #EE5757; }
+
+/*** Skipped tests */
+
+#qunit-tests .skipped {
+	background-color: #EBECE9;
+}
+
+#qunit-tests .qunit-skipped-label {
+	background-color: #F4FF77;
+	display: inline-block;
+	font-style: normal;
+	color: #366097;
+	line-height: 1.8em;
+	padding: 0 0.5em;
+	margin: -0.4em 0.4em -0.4em 0;
+}
+
+/** Result */
+
+#qunit-testresult {
+	padding: 0.5em 1em 0.5em 1em;
+
+	color: #2B81AF;
+	background-color: #D2E0E6;
+
+	border-bottom: 1px solid #FFF;
+}
+#qunit-testresult .module-name {
+	font-weight: 700;
+}
+
+/** Fixture */
+
+#qunit-fixture {
+	position: absolute;
+	top: -10000px;
+	left: -10000px;
+	width: 1000px;
+	height: 1000px;
+}
diff --git a/node_modules/seedrandom/test/lib/qunit.js b/node_modules/seedrandom/test/lib/qunit.js
new file mode 100644
index 0000000..8b5e69e
--- /dev/null
+++ b/node_modules/seedrandom/test/lib/qunit.js
@@ -0,0 +1,4349 @@
+/*!
+ * QUnit 2.1.0
+ * https://qunitjs.com/
+ *
+ * Copyright jQuery Foundation and other contributors
+ * Released under the MIT license
+ * https://jquery.org/license
+ *
+ * Date: 2016-12-06T04:45Z
+ */
+(function (global$1) {
+  'use strict';
+
+  global$1 = 'default' in global$1 ? global$1['default'] : global$1;
+
+  var window = global$1.window;
+  var console = global$1.console;
+  var setTimeout = global$1.setTimeout;
+  var clearTimeout = global$1.clearTimeout;
+
+  var document = window && window.document;
+  var navigator = window && window.navigator;
+  var sessionStorage = window && window.sessionStorage;
+
+  var _typeof = typeof Symbol === "function" && typeof Symbol.iterator === "symbol" ? function (obj) {
+    return typeof obj;
+  } : function (obj) {
+    return obj && typeof Symbol === "function" && obj.constructor === Symbol && obj !== Symbol.prototype ? "symbol" : typeof obj;
+  };
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  var get$1 = function get$1(object, property, receiver) {
+    if (object === null) object = Function.prototype;
+    var desc = Object.getOwnPropertyDescriptor(object, property);
+
+    if (desc === undefined) {
+      var parent = Object.getPrototypeOf(object);
+
+      if (parent === null) {
+        return undefined;
+      } else {
+        return get$1(parent, property, receiver);
+      }
+    } else if ("value" in desc) {
+      return desc.value;
+    } else {
+      var getter = desc.get;
+
+      if (getter === undefined) {
+        return undefined;
+      }
+
+      return getter.call(receiver);
+    }
+  };
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  var set$1 = function set$1(object, property, value, receiver) {
+    var desc = Object.getOwnPropertyDescriptor(object, property);
+
+    if (desc === undefined) {
+      var parent = Object.getPrototypeOf(object);
+
+      if (parent !== null) {
+        set$1(parent, property, value, receiver);
+      }
+    } else if ("value" in desc && desc.writable) {
+      desc.value = value;
+    } else {
+      var setter = desc.set;
+
+      if (setter !== undefined) {
+        setter.call(receiver, value);
+      }
+    }
+
+    return value;
+  };
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+
+  var toConsumableArray = function (arr) {
+    if (Array.isArray(arr)) {
+      for (var i = 0, arr2 = Array(arr.length); i < arr.length; i++) arr2[i] = arr[i];
+
+      return arr2;
+    } else {
+      return Array.from(arr);
+    }
+  };
+
+  var toString = Object.prototype.toString;
+  var hasOwn = Object.prototype.hasOwnProperty;
+  var now = Date.now || function () {
+  	return new Date().getTime();
+  };
+
+  var defined = {
+  	document: window && window.document !== undefined,
+  	setTimeout: setTimeout !== undefined
+  };
+
+  // Returns a new Array with the elements that are in a but not in b
+  function diff(a, b) {
+  	var i,
+  	    j,
+  	    result = a.slice();
+
+  	for (i = 0; i < result.length; i++) {
+  		for (j = 0; j < b.length; j++) {
+  			if (result[i] === b[j]) {
+  				result.splice(i, 1);
+  				i--;
+  				break;
+  			}
+  		}
+  	}
+  	return result;
+  }
+
+  // From jquery.js
+  function inArray(elem, array) {
+  	if (array.indexOf) {
+  		return array.indexOf(elem);
+  	}
+
+  	for (var i = 0, length = array.length; i < length; i++) {
+  		if (array[i] === elem) {
+  			return i;
+  		}
+  	}
+
+  	return -1;
+  }
+
+  /**
+   * Makes a clone of an object using only Array or Object as base,
+   * and copies over the own enumerable properties.
+   *
+   * @param {Object} obj
+   * @return {Object} New object with only the own properties (recursively).
+   */
+  function objectValues(obj) {
+  	var key,
+  	    val,
+  	    vals = is("array", obj) ? [] : {};
+  	for (key in obj) {
+  		if (hasOwn.call(obj, key)) {
+  			val = obj[key];
+  			vals[key] = val === Object(val) ? objectValues(val) : val;
+  		}
+  	}
+  	return vals;
+  }
+
+  function extend(a, b, undefOnly) {
+  	for (var prop in b) {
+  		if (hasOwn.call(b, prop)) {
+  			if (b[prop] === undefined) {
+  				delete a[prop];
+  			} else if (!(undefOnly && typeof a[prop] !== "undefined")) {
+  				a[prop] = b[prop];
+  			}
+  		}
+  	}
+
+  	return a;
+  }
+
+  function objectType(obj) {
+  	if (typeof obj === "undefined") {
+  		return "undefined";
+  	}
+
+  	// Consider: typeof null === object
+  	if (obj === null) {
+  		return "null";
+  	}
+
+  	var match = toString.call(obj).match(/^\[object\s(.*)\]$/),
+  	    type = match && match[1];
+
+  	switch (type) {
+  		case "Number":
+  			if (isNaN(obj)) {
+  				return "nan";
+  			}
+  			return "number";
+  		case "String":
+  		case "Boolean":
+  		case "Array":
+  		case "Set":
+  		case "Map":
+  		case "Date":
+  		case "RegExp":
+  		case "Function":
+  		case "Symbol":
+  			return type.toLowerCase();
+  	}
+
+  	if ((typeof obj === "undefined" ? "undefined" : _typeof(obj)) === "object") {
+  		return "object";
+  	}
+  }
+
+  // Safe object type checking
+  function is(type, obj) {
+  	return objectType(obj) === type;
+  }
+
+  // Test for equality any JavaScript type.
+  // Author: Philippe Rathé <prathe@gmail.com>
+  var equiv = (function () {
+
+  	// Stack to decide between skip/abort functions
+  	var callers = [];
+
+  	// Stack to avoiding loops from circular referencing
+  	var parents = [];
+  	var parentsB = [];
+
+  	var getProto = Object.getPrototypeOf || function (obj) {
+  		return obj.__proto__;
+  	};
+
+  	function useStrictEquality(b, a) {
+
+  		// To catch short annotation VS 'new' annotation of a declaration. e.g.:
+  		// `var i = 1;`
+  		// `var j = new Number(1);`
+  		if ((typeof a === "undefined" ? "undefined" : _typeof(a)) === "object") {
+  			a = a.valueOf();
+  		}
+  		if ((typeof b === "undefined" ? "undefined" : _typeof(b)) === "object") {
+  			b = b.valueOf();
+  		}
+
+  		return a === b;
+  	}
+
+  	function compareConstructors(a, b) {
+  		var protoA = getProto(a);
+  		var protoB = getProto(b);
+
+  		// Comparing constructors is more strict than using `instanceof`
+  		if (a.constructor === b.constructor) {
+  			return true;
+  		}
+
+  		// Ref #851
+  		// If the obj prototype descends from a null constructor, treat it
+  		// as a null prototype.
+  		if (protoA && protoA.constructor === null) {
+  			protoA = null;
+  		}
+  		if (protoB && protoB.constructor === null) {
+  			protoB = null;
+  		}
+
+  		// Allow objects with no prototype to be equivalent to
+  		// objects with Object as their constructor.
+  		if (protoA === null && protoB === Object.prototype || protoB === null && protoA === Object.prototype) {
+  			return true;
+  		}
+
+  		return false;
+  	}
+
+  	function getRegExpFlags(regexp) {
+  		return "flags" in regexp ? regexp.flags : regexp.toString().match(/[gimuy]*$/)[0];
+  	}
+
+  	var callbacks = {
+  		"string": useStrictEquality,
+  		"boolean": useStrictEquality,
+  		"number": useStrictEquality,
+  		"null": useStrictEquality,
+  		"undefined": useStrictEquality,
+  		"symbol": useStrictEquality,
+  		"date": useStrictEquality,
+
+  		"nan": function nan() {
+  			return true;
+  		},
+
+  		"regexp": function regexp(b, a) {
+  			return a.source === b.source &&
+
+  			// Include flags in the comparison
+  			getRegExpFlags(a) === getRegExpFlags(b);
+  		},
+
+  		// - skip when the property is a method of an instance (OOP)
+  		// - abort otherwise,
+  		// initial === would have catch identical references anyway
+  		"function": function _function(b, a) {
+
+  			var caller = callers[callers.length - 1];
+  			return caller !== Object && typeof caller !== "undefined" && a.toString() === b.toString();
+  		},
+
+  		"array": function array(b, a) {
+  			var i, j, len, loop, aCircular, bCircular;
+
+  			len = a.length;
+  			if (len !== b.length) {
+
+  				// Safe and faster
+  				return false;
+  			}
+
+  			// Track reference to avoid circular references
+  			parents.push(a);
+  			parentsB.push(b);
+  			for (i = 0; i < len; i++) {
+  				loop = false;
+  				for (j = 0; j < parents.length; j++) {
+  					aCircular = parents[j] === a[i];
+  					bCircular = parentsB[j] === b[i];
+  					if (aCircular || bCircular) {
+  						if (a[i] === b[i] || aCircular && bCircular) {
+  							loop = true;
+  						} else {
+  							parents.pop();
+  							parentsB.pop();
+  							return false;
+  						}
+  					}
+  				}
+  				if (!loop && !innerEquiv(a[i], b[i])) {
+  					parents.pop();
+  					parentsB.pop();
+  					return false;
+  				}
+  			}
+  			parents.pop();
+  			parentsB.pop();
+  			return true;
+  		},
+
+  		"set": function set(b, a) {
+  			var innerEq,
+  			    outerEq = true;
+
+  			if (a.size !== b.size) {
+  				return false;
+  			}
+
+  			a.forEach(function (aVal) {
+  				innerEq = false;
+
+  				b.forEach(function (bVal) {
+  					if (innerEquiv(bVal, aVal)) {
+  						innerEq = true;
+  					}
+  				});
+
+  				if (!innerEq) {
+  					outerEq = false;
+  				}
+  			});
+
+  			return outerEq;
+  		},
+
+  		"map": function map(b, a) {
+  			var innerEq,
+  			    outerEq = true;
+
+  			if (a.size !== b.size) {
+  				return false;
+  			}
+
+  			a.forEach(function (aVal, aKey) {
+  				innerEq = false;
+
+  				b.forEach(function (bVal, bKey) {
+  					if (innerEquiv([bVal, bKey], [aVal, aKey])) {
+  						innerEq = true;
+  					}
+  				});
+
+  				if (!innerEq) {
+  					outerEq = false;
+  				}
+  			});
+
+  			return outerEq;
+  		},
+
+  		"object": function object(b, a) {
+  			var i, j, loop, aCircular, bCircular;
+
+  			// Default to true
+  			var eq = true;
+  			var aProperties = [];
+  			var bProperties = [];
+
+  			if (compareConstructors(a, b) === false) {
+  				return false;
+  			}
+
+  			// Stack constructor before traversing properties
+  			callers.push(a.constructor);
+
+  			// Track reference to avoid circular references
+  			parents.push(a);
+  			parentsB.push(b);
+
+  			// Be strict: don't ensure hasOwnProperty and go deep
+  			for (i in a) {
+  				loop = false;
+  				for (j = 0; j < parents.length; j++) {
+  					aCircular = parents[j] === a[i];
+  					bCircular = parentsB[j] === b[i];
+  					if (aCircular || bCircular) {
+  						if (a[i] === b[i] || aCircular && bCircular) {
+  							loop = true;
+  						} else {
+  							eq = false;
+  							break;
+  						}
+  					}
+  				}
+  				aProperties.push(i);
+  				if (!loop && !innerEquiv(a[i], b[i])) {
+  					eq = false;
+  					break;
+  				}
+  			}
+
+  			parents.pop();
+  			parentsB.pop();
+
+  			// Unstack, we are done
+  			callers.pop();
+
+  			for (i in b) {
+
+  				// Collect b's properties
+  				bProperties.push(i);
+  			}
+
+  			// Ensures identical properties name
+  			return eq && innerEquiv(aProperties.sort(), bProperties.sort());
+  		}
+  	};
+
+  	function typeEquiv(a, b) {
+  		var type = objectType(a);
+  		return objectType(b) === type && callbacks[type](b, a);
+  	}
+
+  	// The real equiv function
+  	function innerEquiv(a, b) {
+
+  		// We're done when there's nothing more to compare
+  		if (arguments.length < 2) {
+  			return true;
+  		}
+
+  		// Require type-specific equality
+  		return (a === b || typeEquiv(a, b)) && (
+
+  		// ...across all consecutive argument pairs
+  		arguments.length === 2 || innerEquiv.apply(this, [].slice.call(arguments, 1)));
+  	}
+
+  	return innerEquiv;
+  })();
+
+  /**
+   * Config object: Maintain internal state
+   * Later exposed as QUnit.config
+   * `config` initialized at top of scope
+   */
+  var config = {
+
+  	// The queue of tests to run
+  	queue: [],
+
+  	// Block until document ready
+  	blocking: true,
+
+  	// By default, run previously failed tests first
+  	// very useful in combination with "Hide passed tests" checked
+  	reorder: true,
+
+  	// By default, modify document.title when suite is done
+  	altertitle: true,
+
+  	// HTML Reporter: collapse every test except the first failing test
+  	// If false, all failing tests will be expanded
+  	collapse: true,
+
+  	// By default, scroll to top of the page when suite is done
+  	scrolltop: true,
+
+  	// Depth up-to which object will be dumped
+  	maxDepth: 5,
+
+  	// When enabled, all tests must call expect()
+  	requireExpects: false,
+
+  	// Placeholder for user-configurable form-exposed URL parameters
+  	urlConfig: [],
+
+  	// Set of all modules.
+  	modules: [],
+
+  	// Stack of nested modules
+  	moduleStack: [],
+
+  	// The first unnamed module
+  	currentModule: {
+  		name: "",
+  		tests: [],
+  		childModules: [],
+  		testsRun: 0
+  	},
+
+  	callbacks: {},
+
+  	// The storage module to use for reordering tests
+  	storage: sessionStorage
+  };
+
+  // take a predefined QUnit.config and extend the defaults
+  var globalConfig = window && window.QUnit && window.QUnit.config;
+
+  // only extend the global config if there is no QUnit overload
+  if (window && window.QUnit && !window.QUnit.version) {
+  	extend(config, globalConfig);
+  }
+
+  // Push a loose unnamed module to the modules collection
+  config.modules.push(config.currentModule);
+
+  // Based on jsDump by Ariel Flesler
+  // http://flesler.blogspot.com/2008/05/jsdump-pretty-dump-of-any-javascript.html
+  var dump = (function () {
+  	function quote(str) {
+  		return "\"" + str.toString().replace(/\\/g, "\\\\").replace(/"/g, "\\\"") + "\"";
+  	}
+  	function literal(o) {
+  		return o + "";
+  	}
+  	function join(pre, arr, post) {
+  		var s = dump.separator(),
+  		    base = dump.indent(),
+  		    inner = dump.indent(1);
+  		if (arr.join) {
+  			arr = arr.join("," + s + inner);
+  		}
+  		if (!arr) {
+  			return pre + post;
+  		}
+  		return [pre, inner + arr, base + post].join(s);
+  	}
+  	function array(arr, stack) {
+  		var i = arr.length,
+  		    ret = new Array(i);
+
+  		if (dump.maxDepth && dump.depth > dump.maxDepth) {
+  			return "[object Array]";
+  		}
+
+  		this.up();
+  		while (i--) {
+  			ret[i] = this.parse(arr[i], undefined, stack);
+  		}
+  		this.down();
+  		return join("[", ret, "]");
+  	}
+
+  	function isArray(obj) {
+  		return (
+
+  			//Native Arrays
+  			toString.call(obj) === "[object Array]" ||
+
+  			// NodeList objects
+  			typeof obj.length === "number" && obj.item !== undefined && (obj.length ? obj.item(0) === obj[0] : obj.item(0) === null && obj[0] === undefined)
+  		);
+  	}
+
+  	var reName = /^function (\w+)/,
+  	    dump = {
+
+  		// The objType is used mostly internally, you can fix a (custom) type in advance
+  		parse: function parse(obj, objType, stack) {
+  			stack = stack || [];
+  			var res,
+  			    parser,
+  			    parserType,
+  			    inStack = inArray(obj, stack);
+
+  			if (inStack !== -1) {
+  				return "recursion(" + (inStack - stack.length) + ")";
+  			}
+
+  			objType = objType || this.typeOf(obj);
+  			parser = this.parsers[objType];
+  			parserType = typeof parser === "undefined" ? "undefined" : _typeof(parser);
+
+  			if (parserType === "function") {
+  				stack.push(obj);
+  				res = parser.call(this, obj, stack);
+  				stack.pop();
+  				return res;
+  			}
+  			return parserType === "string" ? parser : this.parsers.error;
+  		},
+  		typeOf: function typeOf(obj) {
+  			var type;
+
+  			if (obj === null) {
+  				type = "null";
+  			} else if (typeof obj === "undefined") {
+  				type = "undefined";
+  			} else if (is("regexp", obj)) {
+  				type = "regexp";
+  			} else if (is("date", obj)) {
+  				type = "date";
+  			} else if (is("function", obj)) {
+  				type = "function";
+  			} else if (obj.setInterval !== undefined && obj.document !== undefined && obj.nodeType === undefined) {
+  				type = "window";
+  			} else if (obj.nodeType === 9) {
+  				type = "document";
+  			} else if (obj.nodeType) {
+  				type = "node";
+  			} else if (isArray(obj)) {
+  				type = "array";
+  			} else if (obj.constructor === Error.prototype.constructor) {
+  				type = "error";
+  			} else {
+  				type = typeof obj === "undefined" ? "undefined" : _typeof(obj);
+  			}
+  			return type;
+  		},
+
+  		separator: function separator() {
+  			if (this.multiline) {
+  				return this.HTML ? "<br />" : "\n";
+  			} else {
+  				return this.HTML ? "&#160;" : " ";
+  			}
+  		},
+
+  		// Extra can be a number, shortcut for increasing-calling-decreasing
+  		indent: function indent(extra) {
+  			if (!this.multiline) {
+  				return "";
+  			}
+  			var chr = this.indentChar;
+  			if (this.HTML) {
+  				chr = chr.replace(/\t/g, "   ").replace(/ /g, "&#160;");
+  			}
+  			return new Array(this.depth + (extra || 0)).join(chr);
+  		},
+  		up: function up(a) {
+  			this.depth += a || 1;
+  		},
+  		down: function down(a) {
+  			this.depth -= a || 1;
+  		},
+  		setParser: function setParser(name, parser) {
+  			this.parsers[name] = parser;
+  		},
+
+  		// The next 3 are exposed so you can use them
+  		quote: quote,
+  		literal: literal,
+  		join: join,
+  		depth: 1,
+  		maxDepth: config.maxDepth,
+
+  		// This is the list of parsers, to modify them, use dump.setParser
+  		parsers: {
+  			window: "[Window]",
+  			document: "[Document]",
+  			error: function error(_error) {
+  				return "Error(\"" + _error.message + "\")";
+  			},
+  			unknown: "[Unknown]",
+  			"null": "null",
+  			"undefined": "undefined",
+  			"function": function _function(fn) {
+  				var ret = "function",
+
+
+  				// Functions never have name in IE
+  				name = "name" in fn ? fn.name : (reName.exec(fn) || [])[1];
+
+  				if (name) {
+  					ret += " " + name;
+  				}
+  				ret += "(";
+
+  				ret = [ret, dump.parse(fn, "functionArgs"), "){"].join("");
+  				return join(ret, dump.parse(fn, "functionCode"), "}");
+  			},
+  			array: array,
+  			nodelist: array,
+  			"arguments": array,
+  			object: function object(map, stack) {
+  				var keys,
+  				    key,
+  				    val,
+  				    i,
+  				    nonEnumerableProperties,
+  				    ret = [];
+
+  				if (dump.maxDepth && dump.depth > dump.maxDepth) {
+  					return "[object Object]";
+  				}
+
+  				dump.up();
+  				keys = [];
+  				for (key in map) {
+  					keys.push(key);
+  				}
+
+  				// Some properties are not always enumerable on Error objects.
+  				nonEnumerableProperties = ["message", "name"];
+  				for (i in nonEnumerableProperties) {
+  					key = nonEnumerableProperties[i];
+  					if (key in map && inArray(key, keys) < 0) {
+  						keys.push(key);
+  					}
+  				}
+  				keys.sort();
+  				for (i = 0; i < keys.length; i++) {
+  					key = keys[i];
+  					val = map[key];
+  					ret.push(dump.parse(key, "key") + ": " + dump.parse(val, undefined, stack));
+  				}
+  				dump.down();
+  				return join("{", ret, "}");
+  			},
+  			node: function node(_node) {
+  				var len,
+  				    i,
+  				    val,
+  				    open = dump.HTML ? "&lt;" : "<",
+  				    close = dump.HTML ? "&gt;" : ">",
+  				    tag = _node.nodeName.toLowerCase(),
+  				    ret = open + tag,
+  				    attrs = _node.attributes;
+
+  				if (attrs) {
+  					for (i = 0, len = attrs.length; i < len; i++) {
+  						val = attrs[i].nodeValue;
+
+  						// IE6 includes all attributes in .attributes, even ones not explicitly
+  						// set. Those have values like undefined, null, 0, false, "" or
+  						// "inherit".
+  						if (val && val !== "inherit") {
+  							ret += " " + attrs[i].nodeName + "=" + dump.parse(val, "attribute");
+  						}
+  					}
+  				}
+  				ret += close;
+
+  				// Show content of TextNode or CDATASection
+  				if (_node.nodeType === 3 || _node.nodeType === 4) {
+  					ret += _node.nodeValue;
+  				}
+
+  				return ret + open + "/" + tag + close;
+  			},
+
+  			// Function calls it internally, it's the arguments part of the function
+  			functionArgs: function functionArgs(fn) {
+  				var args,
+  				    l = fn.length;
+
+  				if (!l) {
+  					return "";
+  				}
+
+  				args = new Array(l);
+  				while (l--) {
+
+  					// 97 is 'a'
+  					args[l] = String.fromCharCode(97 + l);
+  				}
+  				return " " + args.join(", ") + " ";
+  			},
+
+  			// Object calls it internally, the key part of an item in a map
+  			key: quote,
+
+  			// Function calls it internally, it's the content of the function
+  			functionCode: "[code]",
+
+  			// Node calls it internally, it's a html attribute value
+  			attribute: quote,
+  			string: quote,
+  			date: quote,
+  			regexp: literal,
+  			number: literal,
+  			"boolean": literal,
+  			symbol: function symbol(sym) {
+  				return sym.toString();
+  			}
+  		},
+
+  		// If true, entities are escaped ( <, >, \t, space and \n )
+  		HTML: false,
+
+  		// Indentation unit
+  		indentChar: "  ",
+
+  		// If true, items in a collection, are separated by a \n, else just a space.
+  		multiline: true
+  	};
+
+  	return dump;
+  })();
+
+  // Register logging callbacks
+  function registerLoggingCallbacks(obj) {
+  	var i,
+  	    l,
+  	    key,
+  	    callbackNames = ["begin", "done", "log", "testStart", "testDone", "moduleStart", "moduleDone"];
+
+  	function registerLoggingCallback(key) {
+  		var loggingCallback = function loggingCallback(callback) {
+  			if (objectType(callback) !== "function") {
+  				throw new Error("QUnit logging methods require a callback function as their first parameters.");
+  			}
+
+  			config.callbacks[key].push(callback);
+  		};
+
+  		return loggingCallback;
+  	}
+
+  	for (i = 0, l = callbackNames.length; i < l; i++) {
+  		key = callbackNames[i];
+
+  		// Initialize key collection of logging callback
+  		if (objectType(config.callbacks[key]) === "undefined") {
+  			config.callbacks[key] = [];
+  		}
+
+  		obj[key] = registerLoggingCallback(key);
+  	}
+  }
+
+  function runLoggingCallbacks(key, args) {
+  	var i, l, callbacks;
+
+  	callbacks = config.callbacks[key];
+  	for (i = 0, l = callbacks.length; i < l; i++) {
+  		callbacks[i](args);
+  	}
+  }
+
+  // Doesn't support IE9, it will return undefined on these browsers
+  // See also https://developer.mozilla.org/en/JavaScript/Reference/Global_Objects/Error/Stack
+  var fileName = (sourceFromStacktrace(0) || "").replace(/(:\d+)+\)?/, "").replace(/.+\//, "");
+
+  function extractStacktrace(e, offset) {
+  	offset = offset === undefined ? 4 : offset;
+
+  	var stack, include, i;
+
+  	if (e && e.stack) {
+  		stack = e.stack.split("\n");
+  		if (/^error$/i.test(stack[0])) {
+  			stack.shift();
+  		}
+  		if (fileName) {
+  			include = [];
+  			for (i = offset; i < stack.length; i++) {
+  				if (stack[i].indexOf(fileName) !== -1) {
+  					break;
+  				}
+  				include.push(stack[i]);
+  			}
+  			if (include.length) {
+  				return include.join("\n");
+  			}
+  		}
+  		return stack[offset];
+  	}
+  }
+
+  function sourceFromStacktrace(offset) {
+  	var error = new Error();
+
+  	// Support: Safari <=7 only, IE <=10 - 11 only
+  	// Not all browsers generate the `stack` property for `new Error()`, see also #636
+  	if (!error.stack) {
+  		try {
+  			throw error;
+  		} catch (err) {
+  			error = err;
+  		}
+  	}
+
+  	return extractStacktrace(error, offset);
+  }
+
+  var unitSampler;
+  var focused = false;
+  var priorityCount = 0;
+
+  function Test(settings) {
+  	var i, l;
+
+  	++Test.count;
+
+  	this.expected = null;
+  	extend(this, settings);
+  	this.assertions = [];
+  	this.semaphore = 0;
+  	this.usedAsync = false;
+  	this.module = config.currentModule;
+  	this.stack = sourceFromStacktrace(3);
+
+  	// Register unique strings
+  	for (i = 0, l = this.module.tests; i < l.length; i++) {
+  		if (this.module.tests[i].name === this.testName) {
+  			this.testName += " ";
+  		}
+  	}
+
+  	this.testId = generateHash(this.module.name, this.testName);
+
+  	this.module.tests.push({
+  		name: this.testName,
+  		testId: this.testId
+  	});
+
+  	if (settings.skip) {
+
+  		// Skipped tests will fully ignore any sent callback
+  		this.callback = function () {};
+  		this.async = false;
+  		this.expected = 0;
+  	} else {
+  		this.assert = new Assert(this);
+  	}
+  }
+
+  Test.count = 0;
+
+  function getNotStartedModules(startModule) {
+  	var module = startModule,
+  	    modules = [];
+
+  	while (module && module.testsRun === 0) {
+  		modules.push(module);
+  		module = module.parentModule;
+  	}
+
+  	return modules;
+  }
+
+  Test.prototype = {
+  	before: function before() {
+  		var i,
+  		    startModule,
+  		    module = this.module,
+  		    notStartedModules = getNotStartedModules(module);
+
+  		for (i = notStartedModules.length - 1; i >= 0; i--) {
+  			startModule = notStartedModules[i];
+  			startModule.stats = { all: 0, bad: 0, started: now() };
+  			runLoggingCallbacks("moduleStart", {
+  				name: startModule.name,
+  				tests: startModule.tests
+  			});
+  		}
+
+  		config.current = this;
+
+  		if (module.testEnvironment) {
+  			delete module.testEnvironment.before;
+  			delete module.testEnvironment.beforeEach;
+  			delete module.testEnvironment.afterEach;
+  			delete module.testEnvironment.after;
+  		}
+  		this.testEnvironment = extend({}, module.testEnvironment);
+
+  		this.started = now();
+  		runLoggingCallbacks("testStart", {
+  			name: this.testName,
+  			module: module.name,
+  			testId: this.testId,
+  			previousFailure: this.previousFailure
+  		});
+
+  		if (!config.pollution) {
+  			saveGlobal();
+  		}
+  	},
+
+  	run: function run() {
+  		var promise;
+
+  		config.current = this;
+
+  		this.callbackStarted = now();
+
+  		if (config.notrycatch) {
+  			runTest(this);
+  			return;
+  		}
+
+  		try {
+  			runTest(this);
+  		} catch (e) {
+  			this.pushFailure("Died on test #" + (this.assertions.length + 1) + " " + this.stack + ": " + (e.message || e), extractStacktrace(e, 0));
+
+  			// Else next test will carry the responsibility
+  			saveGlobal();
+
+  			// Restart the tests if they're blocking
+  			if (config.blocking) {
+  				internalRecover(this);
+  			}
+  		}
+
+  		function runTest(test) {
+  			promise = test.callback.call(test.testEnvironment, test.assert);
+  			test.resolvePromise(promise);
+  		}
+  	},
+
+  	after: function after() {
+  		checkPollution();
+  	},
+
+  	queueHook: function queueHook(hook, hookName, hookOwner) {
+  		var promise,
+  		    test = this;
+  		return function runHook() {
+  			if (hookName === "before") {
+  				if (hookOwner.testsRun !== 0) {
+  					return;
+  				}
+
+  				test.preserveEnvironment = true;
+  			}
+
+  			if (hookName === "after" && hookOwner.testsRun !== numberOfTests(hookOwner) - 1) {
+  				return;
+  			}
+
+  			config.current = test;
+  			if (config.notrycatch) {
+  				callHook();
+  				return;
+  			}
+  			try {
+  				callHook();
+  			} catch (error) {
+  				test.pushFailure(hookName + " failed on " + test.testName + ": " + (error.message || error), extractStacktrace(error, 0));
+  			}
+
+  			function callHook() {
+  				promise = hook.call(test.testEnvironment, test.assert);
+  				test.resolvePromise(promise, hookName);
+  			}
+  		};
+  	},
+
+  	// Currently only used for module level hooks, can be used to add global level ones
+  	hooks: function hooks(handler) {
+  		var hooks = [];
+
+  		function processHooks(test, module) {
+  			if (module.parentModule) {
+  				processHooks(test, module.parentModule);
+  			}
+  			if (module.testEnvironment && objectType(module.testEnvironment[handler]) === "function") {
+  				hooks.push(test.queueHook(module.testEnvironment[handler], handler, module));
+  			}
+  		}
+
+  		// Hooks are ignored on skipped tests
+  		if (!this.skip) {
+  			processHooks(this, this.module);
+  		}
+  		return hooks;
+  	},
+
+  	finish: function finish() {
+  		config.current = this;
+  		if (config.requireExpects && this.expected === null) {
+  			this.pushFailure("Expected number of assertions to be defined, but expect() was " + "not called.", this.stack);
+  		} else if (this.expected !== null && this.expected !== this.assertions.length) {
+  			this.pushFailure("Expected " + this.expected + " assertions, but " + this.assertions.length + " were run", this.stack);
+  		} else if (this.expected === null && !this.assertions.length) {
+  			this.pushFailure("Expected at least one assertion, but none were run - call " + "expect(0) to accept zero assertions.", this.stack);
+  		}
+
+  		var i,
+  		    module = this.module,
+  		    moduleName = module.name,
+  		    testName = this.testName,
+  		    skipped = !!this.skip,
+  		    bad = 0,
+  		    storage = config.storage;
+
+  		this.runtime = now() - this.started;
+
+  		config.stats.all += this.assertions.length;
+  		module.stats.all += this.assertions.length;
+
+  		for (i = 0; i < this.assertions.length; i++) {
+  			if (!this.assertions[i].result) {
+  				bad++;
+  				config.stats.bad++;
+  				module.stats.bad++;
+  			}
+  		}
+
+  		notifyTestsRan(module);
+
+  		// Store result when possible
+  		if (storage) {
+  			if (bad) {
+  				storage.setItem("qunit-test-" + moduleName + "-" + testName, bad);
+  			} else {
+  				storage.removeItem("qunit-test-" + moduleName + "-" + testName);
+  			}
+  		}
+
+  		runLoggingCallbacks("testDone", {
+  			name: testName,
+  			module: moduleName,
+  			skipped: skipped,
+  			failed: bad,
+  			passed: this.assertions.length - bad,
+  			total: this.assertions.length,
+  			runtime: skipped ? 0 : this.runtime,
+
+  			// HTML Reporter use
+  			assertions: this.assertions,
+  			testId: this.testId,
+
+  			// Source of Test
+  			source: this.stack
+  		});
+
+  		if (module.testsRun === numberOfTests(module)) {
+  			runLoggingCallbacks("moduleDone", {
+  				name: module.name,
+  				tests: module.tests,
+  				failed: module.stats.bad,
+  				passed: module.stats.all - module.stats.bad,
+  				total: module.stats.all,
+  				runtime: now() - module.stats.started
+  			});
+  		}
+
+  		config.current = undefined;
+  	},
+
+  	preserveTestEnvironment: function preserveTestEnvironment() {
+  		if (this.preserveEnvironment) {
+  			this.module.testEnvironment = this.testEnvironment;
+  			this.testEnvironment = extend({}, this.module.testEnvironment);
+  		}
+  	},
+
+  	queue: function queue() {
+  		var priority,
+  		    previousFailCount,
+  		    test = this;
+
+  		if (!this.valid()) {
+  			return;
+  		}
+
+  		function run() {
+
+  			// Each of these can by async
+  			synchronize([function () {
+  				test.before();
+  			}, test.hooks("before"), function () {
+  				test.preserveTestEnvironment();
+  			}, test.hooks("beforeEach"), function () {
+  				test.run();
+  			}, test.hooks("afterEach").reverse(), test.hooks("after").reverse(), function () {
+  				test.after();
+  			}, function () {
+  				test.finish();
+  			}]);
+  		}
+
+  		previousFailCount = config.storage && +config.storage.getItem("qunit-test-" + this.module.name + "-" + this.testName);
+
+  		// Prioritize previously failed tests, detected from storage
+  		priority = config.reorder && previousFailCount;
+
+  		this.previousFailure = !!previousFailCount;
+
+  		return synchronize(run, priority, config.seed);
+  	},
+
+  	pushResult: function pushResult(resultInfo) {
+
+  		// Destructure of resultInfo = { result, actual, expected, message, negative }
+  		var source,
+  		    details = {
+  			module: this.module.name,
+  			name: this.testName,
+  			result: resultInfo.result,
+  			message: resultInfo.message,
+  			actual: resultInfo.actual,
+  			expected: resultInfo.expected,
+  			testId: this.testId,
+  			negative: resultInfo.negative || false,
+  			runtime: now() - this.started
+  		};
+
+  		if (!resultInfo.result) {
+  			source = sourceFromStacktrace();
+
+  			if (source) {
+  				details.source = source;
+  			}
+  		}
+
+  		runLoggingCallbacks("log", details);
+
+  		this.assertions.push({
+  			result: !!resultInfo.result,
+  			message: resultInfo.message
+  		});
+  	},
+
+  	pushFailure: function pushFailure(message, source, actual) {
+  		if (!(this instanceof Test)) {
+  			throw new Error("pushFailure() assertion outside test context, was " + sourceFromStacktrace(2));
+  		}
+
+  		var details = {
+  			module: this.module.name,
+  			name: this.testName,
+  			result: false,
+  			message: message || "error",
+  			actual: actual || null,
+  			testId: this.testId,
+  			runtime: now() - this.started
+  		};
+
+  		if (source) {
+  			details.source = source;
+  		}
+
+  		runLoggingCallbacks("log", details);
+
+  		this.assertions.push({
+  			result: false,
+  			message: message
+  		});
+  	},
+
+  	resolvePromise: function resolvePromise(promise, phase) {
+  		var then,
+  		    resume,
+  		    message,
+  		    test = this;
+  		if (promise != null) {
+  			then = promise.then;
+  			if (objectType(then) === "function") {
+  				resume = internalStop(test);
+  				then.call(promise, function () {
+  					resume();
+  				}, function (error) {
+  					message = "Promise rejected " + (!phase ? "during" : phase.replace(/Each$/, "")) + " \"" + test.testName + "\": " + (error && error.message || error);
+  					test.pushFailure(message, extractStacktrace(error, 0));
+
+  					// Else next test will carry the responsibility
+  					saveGlobal();
+
+  					// Unblock
+  					resume();
+  				});
+  			}
+  		}
+  	},
+
+  	valid: function valid() {
+  		var filter = config.filter,
+  		    regexFilter = /^(!?)\/([\w\W]*)\/(i?$)/.exec(filter),
+  		    module = config.module && config.module.toLowerCase(),
+  		    fullName = this.module.name + ": " + this.testName;
+
+  		function moduleChainNameMatch(testModule) {
+  			var testModuleName = testModule.name ? testModule.name.toLowerCase() : null;
+  			if (testModuleName === module) {
+  				return true;
+  			} else if (testModule.parentModule) {
+  				return moduleChainNameMatch(testModule.parentModule);
+  			} else {
+  				return false;
+  			}
+  		}
+
+  		function moduleChainIdMatch(testModule) {
+  			return inArray(testModule.moduleId, config.moduleId) > -1 || testModule.parentModule && moduleChainIdMatch(testModule.parentModule);
+  		}
+
+  		// Internally-generated tests are always valid
+  		if (this.callback && this.callback.validTest) {
+  			return true;
+  		}
+
+  		if (config.moduleId && config.moduleId.length > 0 && !moduleChainIdMatch(this.module)) {
+
+  			return false;
+  		}
+
+  		if (config.testId && config.testId.length > 0 && inArray(this.testId, config.testId) < 0) {
+
+  			return false;
+  		}
+
+  		if (module && !moduleChainNameMatch(this.module)) {
+  			return false;
+  		}
+
+  		if (!filter) {
+  			return true;
+  		}
+
+  		return regexFilter ? this.regexFilter(!!regexFilter[1], regexFilter[2], regexFilter[3], fullName) : this.stringFilter(filter, fullName);
+  	},
+
+  	regexFilter: function regexFilter(exclude, pattern, flags, fullName) {
+  		var regex = new RegExp(pattern, flags);
+  		var match = regex.test(fullName);
+
+  		return match !== exclude;
+  	},
+
+  	stringFilter: function stringFilter(filter, fullName) {
+  		filter = filter.toLowerCase();
+  		fullName = fullName.toLowerCase();
+
+  		var include = filter.charAt(0) !== "!";
+  		if (!include) {
+  			filter = filter.slice(1);
+  		}
+
+  		// If the filter matches, we need to honour include
+  		if (fullName.indexOf(filter) !== -1) {
+  			return include;
+  		}
+
+  		// Otherwise, do the opposite
+  		return !include;
+  	}
+  };
+
+  function pushFailure() {
+  	if (!config.current) {
+  		throw new Error("pushFailure() assertion outside test context, in " + sourceFromStacktrace(2));
+  	}
+
+  	// Gets current test obj
+  	var currentTest = config.current;
+
+  	return currentTest.pushFailure.apply(currentTest, arguments);
+  }
+
+  // Based on Java's String.hashCode, a simple but not
+  // rigorously collision resistant hashing function
+  function generateHash(module, testName) {
+  	var hex,
+  	    i = 0,
+  	    hash = 0,
+  	    str = module + "\x1C" + testName,
+  	    len = str.length;
+
+  	for (; i < len; i++) {
+  		hash = (hash << 5) - hash + str.charCodeAt(i);
+  		hash |= 0;
+  	}
+
+  	// Convert the possibly negative integer hash code into an 8 character hex string, which isn't
+  	// strictly necessary but increases user understanding that the id is a SHA-like hash
+  	hex = (0x100000000 + hash).toString(16);
+  	if (hex.length < 8) {
+  		hex = "0000000" + hex;
+  	}
+
+  	return hex.slice(-8);
+  }
+
+  function synchronize(callback, priority, seed) {
+  	var last = !priority,
+  	    index;
+
+  	if (objectType(callback) === "array") {
+  		while (callback.length) {
+  			synchronize(callback.shift());
+  		}
+  		return;
+  	}
+
+  	if (priority) {
+  		config.queue.splice(priorityCount++, 0, callback);
+  	} else if (seed) {
+  		if (!unitSampler) {
+  			unitSampler = unitSamplerGenerator(seed);
+  		}
+
+  		// Insert into a random position after all priority items
+  		index = Math.floor(unitSampler() * (config.queue.length - priorityCount + 1));
+  		config.queue.splice(priorityCount + index, 0, callback);
+  	} else {
+  		config.queue.push(callback);
+  	}
+
+  	if (internalState.autorun && !config.blocking) {
+  		process(last);
+  	}
+  }
+
+  function unitSamplerGenerator(seed) {
+
+  	// 32-bit xorshift, requires only a nonzero seed
+  	// http://excamera.com/sphinx/article-xorshift.html
+  	var sample = parseInt(generateHash(seed), 16) || -1;
+  	return function () {
+  		sample ^= sample << 13;
+  		sample ^= sample >>> 17;
+  		sample ^= sample << 5;
+
+  		// ECMAScript has no unsigned number type
+  		if (sample < 0) {
+  			sample += 0x100000000;
+  		}
+
+  		return sample / 0x100000000;
+  	};
+  }
+
+  function saveGlobal() {
+  	config.pollution = [];
+
+  	if (config.noglobals) {
+  		for (var key in global$1) {
+  			if (hasOwn.call(global$1, key)) {
+
+  				// In Opera sometimes DOM element ids show up here, ignore them
+  				if (/^qunit-test-output/.test(key)) {
+  					continue;
+  				}
+  				config.pollution.push(key);
+  			}
+  		}
+  	}
+  }
+
+  function checkPollution() {
+  	var newGlobals,
+  	    deletedGlobals,
+  	    old = config.pollution;
+
+  	saveGlobal();
+
+  	newGlobals = diff(config.pollution, old);
+  	if (newGlobals.length > 0) {
+  		pushFailure("Introduced global variable(s): " + newGlobals.join(", "));
+  	}
+
+  	deletedGlobals = diff(old, config.pollution);
+  	if (deletedGlobals.length > 0) {
+  		pushFailure("Deleted global variable(s): " + deletedGlobals.join(", "));
+  	}
+  }
+
+  // Will be exposed as QUnit.test
+  function test(testName, callback) {
+  	if (focused) {
+  		return;
+  	}
+
+  	var newTest;
+
+  	newTest = new Test({
+  		testName: testName,
+  		callback: callback
+  	});
+
+  	newTest.queue();
+  }
+
+  // Will be exposed as QUnit.skip
+  function skip(testName) {
+  	if (focused) {
+  		return;
+  	}
+
+  	var test = new Test({
+  		testName: testName,
+  		skip: true
+  	});
+
+  	test.queue();
+  }
+
+  // Will be exposed as QUnit.only
+  function only(testName, callback) {
+  	var newTest;
+
+  	if (focused) {
+  		return;
+  	}
+
+  	config.queue.length = 0;
+  	focused = true;
+
+  	newTest = new Test({
+  		testName: testName,
+  		callback: callback
+  	});
+
+  	newTest.queue();
+  }
+
+  // Put a hold on processing and return a function that will release it.
+  function internalStop(test) {
+  	var released = false;
+
+  	test.semaphore += 1;
+  	config.blocking = true;
+
+  	// Set a recovery timeout, if so configured.
+  	if (config.testTimeout && defined.setTimeout) {
+  		clearTimeout(config.timeout);
+  		config.timeout = setTimeout(function () {
+  			pushFailure("Test timed out", sourceFromStacktrace(2));
+  			internalRecover(test);
+  		}, config.testTimeout);
+  	}
+
+  	return function resume() {
+  		if (released) {
+  			return;
+  		}
+
+  		released = true;
+  		test.semaphore -= 1;
+  		internalStart(test);
+  	};
+  }
+
+  // Forcefully release all processing holds.
+  function internalRecover(test) {
+  	test.semaphore = 0;
+  	internalStart(test);
+  }
+
+  // Release a processing hold, scheduling a resumption attempt if no holds remain.
+  function internalStart(test) {
+
+  	// If semaphore is non-numeric, throw error
+  	if (isNaN(test.semaphore)) {
+  		test.semaphore = 0;
+
+  		pushFailure("Invalid value on test.semaphore", sourceFromStacktrace(2));
+  		return;
+  	}
+
+  	// Don't start until equal number of stop-calls
+  	if (test.semaphore > 0) {
+  		return;
+  	}
+
+  	// Throw an Error if start is called more often than stop
+  	if (test.semaphore < 0) {
+  		test.semaphore = 0;
+
+  		pushFailure("Tried to restart test while already started (test's semaphore was 0 already)", sourceFromStacktrace(2));
+  		return;
+  	}
+
+  	// Add a slight delay to allow more assertions etc.
+  	if (defined.setTimeout) {
+  		if (config.timeout) {
+  			clearTimeout(config.timeout);
+  		}
+  		config.timeout = setTimeout(function () {
+  			if (test.semaphore > 0) {
+  				return;
+  			}
+
+  			if (config.timeout) {
+  				clearTimeout(config.timeout);
+  			}
+
+  			begin();
+  		}, 13);
+  	} else {
+  		begin();
+  	}
+  }
+
+  function numberOfTests(module) {
+  	var count = module.tests.length,
+  	    modules = [].concat(toConsumableArray(module.childModules));
+
+  	// Do a breadth-first traversal of the child modules
+  	while (modules.length) {
+  		var nextModule = modules.shift();
+  		count += nextModule.tests.length;
+  		modules.push.apply(modules, toConsumableArray(nextModule.childModules));
+  	}
+
+  	return count;
+  }
+
+  function notifyTestsRan(module) {
+  	module.testsRun++;
+  	while (module = module.parentModule) {
+  		module.testsRun++;
+  	}
+  }
+
+  function Assert(testContext) {
+  	this.test = testContext;
+  }
+
+  // Assert helpers
+  Assert.prototype = {
+
+  	// Specify the number of expected assertions to guarantee that failed test
+  	// (no assertions are run at all) don't slip through.
+  	expect: function expect(asserts) {
+  		if (arguments.length === 1) {
+  			this.test.expected = asserts;
+  		} else {
+  			return this.test.expected;
+  		}
+  	},
+
+  	// Put a hold on processing and return a function that will release it a maximum of once.
+  	async: function async(count) {
+  		var resume,
+  		    test$$1 = this.test,
+  		    popped = false,
+  		    acceptCallCount = count;
+
+  		if (typeof acceptCallCount === "undefined") {
+  			acceptCallCount = 1;
+  		}
+
+  		test$$1.usedAsync = true;
+  		resume = internalStop(test$$1);
+
+  		return function done() {
+
+  			if (popped) {
+  				test$$1.pushFailure("Too many calls to the `assert.async` callback", sourceFromStacktrace(2));
+  				return;
+  			}
+  			acceptCallCount -= 1;
+  			if (acceptCallCount > 0) {
+  				return;
+  			}
+
+  			popped = true;
+  			resume();
+  		};
+  	},
+
+  	// Exports test.push() to the user API
+  	// Alias of pushResult.
+  	push: function push(result, actual, expected, message, negative) {
+  		var currentAssert = this instanceof Assert ? this : config.current.assert;
+  		return currentAssert.pushResult({
+  			result: result,
+  			actual: actual,
+  			expected: expected,
+  			message: message,
+  			negative: negative
+  		});
+  	},
+
+  	pushResult: function pushResult(resultInfo) {
+
+  		// Destructure of resultInfo = { result, actual, expected, message, negative }
+  		var assert = this,
+  		    currentTest = assert instanceof Assert && assert.test || config.current;
+
+  		// Backwards compatibility fix.
+  		// Allows the direct use of global exported assertions and QUnit.assert.*
+  		// Although, it's use is not recommended as it can leak assertions
+  		// to other tests from async tests, because we only get a reference to the current test,
+  		// not exactly the test where assertion were intended to be called.
+  		if (!currentTest) {
+  			throw new Error("assertion outside test context, in " + sourceFromStacktrace(2));
+  		}
+
+  		if (currentTest.usedAsync === true && currentTest.semaphore === 0) {
+  			currentTest.pushFailure("Assertion after the final `assert.async` was resolved", sourceFromStacktrace(2));
+
+  			// Allow this assertion to continue running anyway...
+  		}
+
+  		if (!(assert instanceof Assert)) {
+  			assert = currentTest.assert;
+  		}
+
+  		return assert.test.pushResult(resultInfo);
+  	},
+
+  	ok: function ok(result, message) {
+  		message = message || (result ? "okay" : "failed, expected argument to be truthy, was: " + dump.parse(result));
+  		this.pushResult({
+  			result: !!result,
+  			actual: result,
+  			expected: true,
+  			message: message
+  		});
+  	},
+
+  	notOk: function notOk(result, message) {
+  		message = message || (!result ? "okay" : "failed, expected argument to be falsy, was: " + dump.parse(result));
+  		this.pushResult({
+  			result: !result,
+  			actual: result,
+  			expected: false,
+  			message: message
+  		});
+  	},
+
+  	equal: function equal(actual, expected, message) {
+
+  		// eslint-disable-next-line eqeqeq
+  		var result = expected == actual;
+
+  		this.pushResult({
+  			result: result,
+  			actual: actual,
+  			expected: expected,
+  			message: message
+  		});
+  	},
+
+  	notEqual: function notEqual(actual, expected, message) {
+
+  		// eslint-disable-next-line eqeqeq
+  		var result = expected != actual;
+
+  		this.pushResult({
+  			result: result,
+  			actual: actual,
+  			expected: expected,
+  			message: message,
+  			negative: true
+  		});
+  	},
+
+  	propEqual: function propEqual(actual, expected, message) {
+  		actual = objectValues(actual);
+  		expected = objectValues(expected);
+  		this.pushResult({
+  			result: equiv(actual, expected),
+  			actual: actual,
+  			expected: expected,
+  			message: message
+  		});
+  	},
+
+  	notPropEqual: function notPropEqual(actual, expected, message) {
+  		actual = objectValues(actual);
+  		expected = objectValues(expected);
+  		this.pushResult({
+  			result: !equiv(actual, expected),
+  			actual: actual,
+  			expected: expected,
+  			message: message,
+  			negative: true
+  		});
+  	},
+
+  	deepEqual: function deepEqual(actual, expected, message) {
+  		this.pushResult({
+  			result: equiv(actual, expected),
+  			actual: actual,
+  			expected: expected,
+  			message: message
+  		});
+  	},
+
+  	notDeepEqual: function notDeepEqual(actual, expected, message) {
+  		this.pushResult({
+  			result: !equiv(actual, expected),
+  			actual: actual,
+  			expected: expected,
+  			message: message,
+  			negative: true
+  		});
+  	},
+
+  	strictEqual: function strictEqual(actual, expected, message) {
+  		this.pushResult({
+  			result: expected === actual,
+  			actual: actual,
+  			expected: expected,
+  			message: message
+  		});
+  	},
+
+  	notStrictEqual: function notStrictEqual(actual, expected, message) {
+  		this.pushResult({
+  			result: expected !== actual,
+  			actual: actual,
+  			expected: expected,
+  			message: message,
+  			negative: true
+  		});
+  	},
+
+  	"throws": function throws(block, expected, message) {
+  		var actual,
+  		    expectedType,
+  		    expectedOutput = expected,
+  		    ok = false,
+  		    currentTest = this instanceof Assert && this.test || config.current;
+
+  		// 'expected' is optional unless doing string comparison
+  		if (objectType(expected) === "string") {
+  			if (message == null) {
+  				message = expected;
+  				expected = null;
+  			} else {
+  				throw new Error("throws/raises does not accept a string value for the expected argument.\n" + "Use a non-string object value (e.g. regExp) instead if it's necessary." + "Details in our upgrade guide at https://qunitjs.com/upgrade-guide-2.x/");
+  			}
+  		}
+
+  		currentTest.ignoreGlobalErrors = true;
+  		try {
+  			block.call(currentTest.testEnvironment);
+  		} catch (e) {
+  			actual = e;
+  		}
+  		currentTest.ignoreGlobalErrors = false;
+
+  		if (actual) {
+  			expectedType = objectType(expected);
+
+  			// We don't want to validate thrown error
+  			if (!expected) {
+  				ok = true;
+  				expectedOutput = null;
+
+  				// Expected is a regexp
+  			} else if (expectedType === "regexp") {
+  				ok = expected.test(errorString(actual));
+
+  				// Expected is a constructor, maybe an Error constructor
+  			} else if (expectedType === "function" && actual instanceof expected) {
+  				ok = true;
+
+  				// Expected is an Error object
+  			} else if (expectedType === "object") {
+  				ok = actual instanceof expected.constructor && actual.name === expected.name && actual.message === expected.message;
+
+  				// Expected is a validation function which returns true if validation passed
+  			} else if (expectedType === "function" && expected.call({}, actual) === true) {
+  				expectedOutput = null;
+  				ok = true;
+  			}
+  		}
+
+  		currentTest.assert.pushResult({
+  			result: ok,
+  			actual: actual,
+  			expected: expectedOutput,
+  			message: message
+  		});
+  	}
+  };
+
+  // Provide an alternative to assert.throws(), for environments that consider throws a reserved word
+  // Known to us are: Closure Compiler, Narwhal
+  (function () {
+
+  	// eslint-disable-next-line dot-notation
+  	Assert.prototype.raises = Assert.prototype["throws"];
+  })();
+
+  function errorString(error) {
+  	var name,
+  	    message,
+  	    resultErrorString = error.toString();
+  	if (resultErrorString.substring(0, 7) === "[object") {
+  		name = error.name ? error.name.toString() : "Error";
+  		message = error.message ? error.message.toString() : "";
+  		if (name && message) {
+  			return name + ": " + message;
+  		} else if (name) {
+  			return name;
+  		} else if (message) {
+  			return message;
+  		} else {
+  			return "Error";
+  		}
+  	} else {
+  		return resultErrorString;
+  	}
+  }
+
+  /* global module, exports, define */
+  function applyDeprecated(name) {
+  	return function () {
+  		throw new Error(name + " is removed in QUnit 2.0.\n" + "Details in our upgrade guide at https://qunitjs.com/upgrade-guide-2.x/");
+  	};
+  }
+
+  function exportQUnit(QUnit) {
+
+  	Object.keys(Assert.prototype).forEach(function (key) {
+  		QUnit[key] = applyDeprecated("`QUnit." + key + "`");
+  	});
+
+  	QUnit.asyncTest = function () {
+  		throw new Error("asyncTest is removed in QUnit 2.0, use QUnit.test() with assert.async() instead.\n" + "Details in our upgrade guide at https://qunitjs.com/upgrade-guide-2.x/");
+  	};
+
+  	QUnit.stop = function () {
+  		throw new Error("QUnit.stop is removed in QUnit 2.0, use QUnit.test() with assert.async() instead.\n" + "Details in our upgrade guide at https://qunitjs.com/upgrade-guide-2.x/");
+  	};
+
+  	function resetThrower() {
+  		throw new Error("QUnit.reset is removed in QUnit 2.0 without replacement.\n" + "Details in our upgrade guide at https://qunitjs.com/upgrade-guide-2.x/");
+  	}
+
+  	Object.defineProperty(QUnit, "reset", {
+  		get: function get() {
+  			return resetThrower;
+  		},
+  		set: resetThrower
+  	});
+
+  	if (defined.document) {
+
+  		// QUnit may be defined when it is preconfigured but then only QUnit and QUnit.config may be defined.
+  		if (window.QUnit && window.QUnit.version) {
+  			throw new Error("QUnit has already been defined.");
+  		}
+
+  		["test", "module", "expect", "start", "ok", "notOk", "equal", "notEqual", "propEqual", "notPropEqual", "deepEqual", "notDeepEqual", "strictEqual", "notStrictEqual", "throws", "raises"].forEach(function (key) {
+  			window[key] = applyDeprecated("The global `" + key + "`");
+  		});
+
+  		window.QUnit = QUnit;
+  	}
+
+  	// For nodejs
+  	if (typeof module !== "undefined" && module && module.exports) {
+  		module.exports = QUnit;
+
+  		// For consistency with CommonJS environments' exports
+  		module.exports.QUnit = QUnit;
+  	}
+
+  	// For CommonJS with exports, but without module.exports, like Rhino
+  	if (typeof exports !== "undefined" && exports) {
+  		exports.QUnit = QUnit;
+  	}
+
+  	if (typeof define === "function" && define.amd) {
+  		define(function () {
+  			return QUnit;
+  		});
+  		QUnit.config.autostart = false;
+  	}
+  }
+
+  (function () {
+  	if (!defined.document) {
+  		return;
+  	}
+
+  	// `onErrorFnPrev` initialized at top of scope
+  	// Preserve other handlers
+  	var onErrorFnPrev = window.onerror;
+
+  	// Cover uncaught exceptions
+  	// Returning true will suppress the default browser handler,
+  	// returning false will let it run.
+  	window.onerror = function (error, filePath, linerNr) {
+  		var ret = false;
+  		if (onErrorFnPrev) {
+  			ret = onErrorFnPrev(error, filePath, linerNr);
+  		}
+
+  		// Treat return value as window.onerror itself does,
+  		// Only do our handling if not suppressed.
+  		if (ret !== true) {
+  			if (config.current) {
+  				if (config.current.ignoreGlobalErrors) {
+  					return true;
+  				}
+  				pushFailure(error, filePath + ":" + linerNr);
+  			} else {
+  				test("global failure", extend(function () {
+  					pushFailure(error, filePath + ":" + linerNr);
+  				}, { validTest: true }));
+  			}
+  			return false;
+  		}
+
+  		return ret;
+  	};
+  })();
+
+  var QUnit = {};
+
+  var globalStartCalled = false;
+  var runStarted = false;
+
+  var internalState = {
+  	autorun: false
+  };
+
+  // Figure out if we're running the tests from a server or not
+  QUnit.isLocal = !(defined.document && window.location.protocol !== "file:");
+
+  // Expose the current QUnit version
+  QUnit.version = "2.1.0";
+
+  extend(QUnit, {
+
+  	// Call on start of module test to prepend name to all tests
+  	module: function module(name, testEnvironment, executeNow) {
+  		var module, moduleFns;
+  		var currentModule = config.currentModule;
+
+  		if (arguments.length === 2) {
+  			if (objectType(testEnvironment) === "function") {
+  				executeNow = testEnvironment;
+  				testEnvironment = undefined;
+  			}
+  		}
+
+  		module = createModule();
+
+  		if (testEnvironment && (testEnvironment.setup || testEnvironment.teardown)) {
+  			console.warn("Module's `setup` and `teardown` are not hooks anymore on QUnit 2.0, use " + "`beforeEach` and `afterEach` instead\n" + "Details in our upgrade guide at https://qunitjs.com/upgrade-guide-2.x/");
+  		}
+
+  		moduleFns = {
+  			before: setHook(module, "before"),
+  			beforeEach: setHook(module, "beforeEach"),
+  			afterEach: setHook(module, "afterEach"),
+  			after: setHook(module, "after")
+  		};
+
+  		if (objectType(executeNow) === "function") {
+  			config.moduleStack.push(module);
+  			setCurrentModule(module);
+  			executeNow.call(module.testEnvironment, moduleFns);
+  			config.moduleStack.pop();
+  			module = module.parentModule || currentModule;
+  		}
+
+  		setCurrentModule(module);
+
+  		function createModule() {
+  			var parentModule = config.moduleStack.length ? config.moduleStack.slice(-1)[0] : null;
+  			var moduleName = parentModule !== null ? [parentModule.name, name].join(" > ") : name;
+  			var module = {
+  				name: moduleName,
+  				parentModule: parentModule,
+  				tests: [],
+  				moduleId: generateHash(moduleName),
+  				testsRun: 0,
+  				childModules: []
+  			};
+
+  			var env = {};
+  			if (parentModule) {
+  				parentModule.childModules.push(module);
+  				extend(env, parentModule.testEnvironment);
+  				delete env.beforeEach;
+  				delete env.afterEach;
+  			}
+  			extend(env, testEnvironment);
+  			module.testEnvironment = env;
+
+  			config.modules.push(module);
+  			return module;
+  		}
+
+  		function setCurrentModule(module) {
+  			config.currentModule = module;
+  		}
+  	},
+
+  	test: test,
+
+  	skip: skip,
+
+  	only: only,
+
+  	start: function start(count) {
+  		var globalStartAlreadyCalled = globalStartCalled;
+
+  		if (!config.current) {
+  			globalStartCalled = true;
+
+  			if (runStarted) {
+  				throw new Error("Called start() while test already started running");
+  			} else if (globalStartAlreadyCalled || count > 1) {
+  				throw new Error("Called start() outside of a test context too many times");
+  			} else if (config.autostart) {
+  				throw new Error("Called start() outside of a test context when " + "QUnit.config.autostart was true");
+  			} else if (!config.pageLoaded) {
+
+  				// The page isn't completely loaded yet, so bail out and let `QUnit.load` handle it
+  				config.autostart = true;
+  				return;
+  			}
+  		} else {
+  			throw new Error("QUnit.start cannot be called inside a test context. This feature is removed in " + "QUnit 2.0. For async tests, use QUnit.test() with assert.async() instead.\n" + "Details in our upgrade guide at https://qunitjs.com/upgrade-guide-2.x/");
+  		}
+
+  		scheduleBegin();
+  	},
+
+  	config: config,
+
+  	is: is,
+
+  	objectType: objectType,
+
+  	extend: extend,
+
+  	load: function load() {
+  		config.pageLoaded = true;
+
+  		// Initialize the configuration options
+  		extend(config, {
+  			stats: { all: 0, bad: 0 },
+  			started: 0,
+  			updateRate: 1000,
+  			autostart: true,
+  			filter: ""
+  		}, true);
+
+  		if (!runStarted) {
+  			config.blocking = false;
+
+  			if (config.autostart) {
+  				scheduleBegin();
+  			}
+  		}
+  	},
+
+  	stack: function stack(offset) {
+  		offset = (offset || 0) + 2;
+  		return sourceFromStacktrace(offset);
+  	}
+  });
+
+  QUnit.pushFailure = pushFailure;
+  QUnit.assert = Assert.prototype;
+  QUnit.equiv = equiv;
+  QUnit.dump = dump;
+
+  // 3.0 TODO: Remove
+  function jsDumpThrower() {
+  	throw new Error("QUnit.jsDump is removed in QUnit 2.0, use QUnit.dump instead.\n" + "Details in our upgrade guide at https://qunitjs.com/upgrade-guide-2.x/");
+  }
+
+  Object.defineProperty(QUnit, "jsDump", {
+  	get: jsDumpThrower,
+  	set: jsDumpThrower
+  });
+
+  registerLoggingCallbacks(QUnit);
+
+  function scheduleBegin() {
+
+  	runStarted = true;
+
+  	// Add a slight delay to allow definition of more modules and tests.
+  	if (defined.setTimeout) {
+  		setTimeout(function () {
+  			begin();
+  		}, 13);
+  	} else {
+  		begin();
+  	}
+  }
+
+  function begin() {
+  	var i,
+  	    l,
+  	    modulesLog = [];
+
+  	// If the test run hasn't officially begun yet
+  	if (!config.started) {
+
+  		// Record the time of the test run's beginning
+  		config.started = now();
+
+  		// Delete the loose unnamed module if unused.
+  		if (config.modules[0].name === "" && config.modules[0].tests.length === 0) {
+  			config.modules.shift();
+  		}
+
+  		// Avoid unnecessary information by not logging modules' test environments
+  		for (i = 0, l = config.modules.length; i < l; i++) {
+  			modulesLog.push({
+  				name: config.modules[i].name,
+  				tests: config.modules[i].tests
+  			});
+  		}
+
+  		// The test run is officially beginning now
+  		runLoggingCallbacks("begin", {
+  			totalTests: Test.count,
+  			modules: modulesLog
+  		});
+  	}
+
+  	config.blocking = false;
+  	process(true);
+  }
+
+  function process(last) {
+  	function next() {
+  		process(last);
+  	}
+  	var start = now();
+  	config.depth = (config.depth || 0) + 1;
+
+  	while (config.queue.length && !config.blocking) {
+  		if (!defined.setTimeout || config.updateRate <= 0 || now() - start < config.updateRate) {
+  			if (config.current) {
+
+  				// Reset async tracking for each phase of the Test lifecycle
+  				config.current.usedAsync = false;
+  			}
+  			config.queue.shift()();
+  		} else {
+  			setTimeout(next, 13);
+  			break;
+  		}
+  	}
+  	config.depth--;
+  	if (last && !config.blocking && !config.queue.length && config.depth === 0) {
+  		done$1();
+  	}
+  }
+
+  function done$1() {
+  	var runtime,
+  	    passed,
+  	    i,
+  	    key,
+  	    storage = config.storage;
+
+  	internalState.autorun = true;
+
+  	runtime = now() - config.started;
+  	passed = config.stats.all - config.stats.bad;
+
+  	runLoggingCallbacks("done", {
+  		failed: config.stats.bad,
+  		passed: passed,
+  		total: config.stats.all,
+  		runtime: runtime
+  	});
+
+  	// Clear own storage items if all tests passed
+  	if (storage && config.stats.bad === 0) {
+  		for (i = storage.length - 1; i >= 0; i--) {
+  			key = storage.key(i);
+  			if (key.indexOf("qunit-test-") === 0) {
+  				storage.removeItem(key);
+  			}
+  		}
+  	}
+  }
+
+  function setHook(module, hookName) {
+  	if (module.testEnvironment === undefined) {
+  		module.testEnvironment = {};
+  	}
+
+  	return function (callback) {
+  		module.testEnvironment[hookName] = callback;
+  	};
+  }
+
+  exportQUnit(QUnit);
+
+  (function () {
+
+  	if (typeof window === "undefined" || typeof document === "undefined") {
+  		return;
+  	}
+
+  	var config = QUnit.config,
+  	    hasOwn = Object.prototype.hasOwnProperty;
+
+  	// Stores fixture HTML for resetting later
+  	function storeFixture() {
+
+  		// Avoid overwriting user-defined values
+  		if (hasOwn.call(config, "fixture")) {
+  			return;
+  		}
+
+  		var fixture = document.getElementById("qunit-fixture");
+  		if (fixture) {
+  			config.fixture = fixture.innerHTML;
+  		}
+  	}
+
+  	QUnit.begin(storeFixture);
+
+  	// Resets the fixture DOM element if available.
+  	function resetFixture() {
+  		if (config.fixture == null) {
+  			return;
+  		}
+
+  		var fixture = document.getElementById("qunit-fixture");
+  		if (fixture) {
+  			fixture.innerHTML = config.fixture;
+  		}
+  	}
+
+  	QUnit.testStart(resetFixture);
+  })();
+
+  // Escape text for attribute or text content.
+  function escapeText(s) {
+  	if (!s) {
+  		return "";
+  	}
+  	s = s + "";
+
+  	// Both single quotes and double quotes (for attributes)
+  	return s.replace(/['"<>&]/g, function (s) {
+  		switch (s) {
+  			case "'":
+  				return "&#039;";
+  			case "\"":
+  				return "&quot;";
+  			case "<":
+  				return "&lt;";
+  			case ">":
+  				return "&gt;";
+  			case "&":
+  				return "&amp;";
+  		}
+  	});
+  }
+
+  (function () {
+
+  	// Don't load the HTML Reporter on non-browser environments
+  	if (typeof window === "undefined" || !window.document) {
+  		return;
+  	}
+
+  	QUnit.init = function () {
+  		throw new Error("QUnit.init is removed in QUnit 2.0, use QUnit.test() with assert.async() instead.\n" + "Details in our upgrade guide at https://qunitjs.com/upgrade-guide-2.x/");
+  	};
+
+  	var config = QUnit.config,
+  	    document$$1 = window.document,
+  	    collapseNext = false,
+  	    hasOwn = Object.prototype.hasOwnProperty,
+  	    unfilteredUrl = setUrl({ filter: undefined, module: undefined,
+  		moduleId: undefined, testId: undefined }),
+  	    modulesList = [];
+
+  	function addEvent(elem, type, fn) {
+  		elem.addEventListener(type, fn, false);
+  	}
+
+  	function removeEvent(elem, type, fn) {
+  		elem.removeEventListener(type, fn, false);
+  	}
+
+  	function addEvents(elems, type, fn) {
+  		var i = elems.length;
+  		while (i--) {
+  			addEvent(elems[i], type, fn);
+  		}
+  	}
+
+  	function hasClass(elem, name) {
+  		return (" " + elem.className + " ").indexOf(" " + name + " ") >= 0;
+  	}
+
+  	function addClass(elem, name) {
+  		if (!hasClass(elem, name)) {
+  			elem.className += (elem.className ? " " : "") + name;
+  		}
+  	}
+
+  	function toggleClass(elem, name, force) {
+  		if (force || typeof force === "undefined" && !hasClass(elem, name)) {
+  			addClass(elem, name);
+  		} else {
+  			removeClass(elem, name);
+  		}
+  	}
+
+  	function removeClass(elem, name) {
+  		var set = " " + elem.className + " ";
+
+  		// Class name may appear multiple times
+  		while (set.indexOf(" " + name + " ") >= 0) {
+  			set = set.replace(" " + name + " ", " ");
+  		}
+
+  		// Trim for prettiness
+  		elem.className = typeof set.trim === "function" ? set.trim() : set.replace(/^\s+|\s+$/g, "");
+  	}
+
+  	function id(name) {
+  		return document$$1.getElementById && document$$1.getElementById(name);
+  	}
+
+  	function interceptNavigation(ev) {
+  		applyUrlParams();
+
+  		if (ev && ev.preventDefault) {
+  			ev.preventDefault();
+  		}
+
+  		return false;
+  	}
+
+  	function getUrlConfigHtml() {
+  		var i,
+  		    j,
+  		    val,
+  		    escaped,
+  		    escapedTooltip,
+  		    selection = false,
+  		    urlConfig = config.urlConfig,
+  		    urlConfigHtml = "";
+
+  		for (i = 0; i < urlConfig.length; i++) {
+
+  			// Options can be either strings or objects with nonempty "id" properties
+  			val = config.urlConfig[i];
+  			if (typeof val === "string") {
+  				val = {
+  					id: val,
+  					label: val
+  				};
+  			}
+
+  			escaped = escapeText(val.id);
+  			escapedTooltip = escapeText(val.tooltip);
+
+  			if (!val.value || typeof val.value === "string") {
+  				urlConfigHtml += "<label for='qunit-urlconfig-" + escaped + "' title='" + escapedTooltip + "'><input id='qunit-urlconfig-" + escaped + "' name='" + escaped + "' type='checkbox'" + (val.value ? " value='" + escapeText(val.value) + "'" : "") + (config[val.id] ? " checked='checked'" : "") + " title='" + escapedTooltip + "' />" + escapeText(val.label) + "</label>";
+  			} else {
+  				urlConfigHtml += "<label for='qunit-urlconfig-" + escaped + "' title='" + escapedTooltip + "'>" + val.label + ": </label><select id='qunit-urlconfig-" + escaped + "' name='" + escaped + "' title='" + escapedTooltip + "'><option></option>";
+
+  				if (QUnit.is("array", val.value)) {
+  					for (j = 0; j < val.value.length; j++) {
+  						escaped = escapeText(val.value[j]);
+  						urlConfigHtml += "<option value='" + escaped + "'" + (config[val.id] === val.value[j] ? (selection = true) && " selected='selected'" : "") + ">" + escaped + "</option>";
+  					}
+  				} else {
+  					for (j in val.value) {
+  						if (hasOwn.call(val.value, j)) {
+  							urlConfigHtml += "<option value='" + escapeText(j) + "'" + (config[val.id] === j ? (selection = true) && " selected='selected'" : "") + ">" + escapeText(val.value[j]) + "</option>";
+  						}
+  					}
+  				}
+  				if (config[val.id] && !selection) {
+  					escaped = escapeText(config[val.id]);
+  					urlConfigHtml += "<option value='" + escaped + "' selected='selected' disabled='disabled'>" + escaped + "</option>";
+  				}
+  				urlConfigHtml += "</select>";
+  			}
+  		}
+
+  		return urlConfigHtml;
+  	}
+
+  	// Handle "click" events on toolbar checkboxes and "change" for select menus.
+  	// Updates the URL with the new state of `config.urlConfig` values.
+  	function toolbarChanged() {
+  		var updatedUrl,
+  		    value,
+  		    tests,
+  		    field = this,
+  		    params = {};
+
+  		// Detect if field is a select menu or a checkbox
+  		if ("selectedIndex" in field) {
+  			value = field.options[field.selectedIndex].value || undefined;
+  		} else {
+  			value = field.checked ? field.defaultValue || true : undefined;
+  		}
+
+  		params[field.name] = value;
+  		updatedUrl = setUrl(params);
+
+  		// Check if we can apply the change without a page refresh
+  		if ("hidepassed" === field.name && "replaceState" in window.history) {
+  			QUnit.urlParams[field.name] = value;
+  			config[field.name] = value || false;
+  			tests = id("qunit-tests");
+  			if (tests) {
+  				toggleClass(tests, "hidepass", value || false);
+  			}
+  			window.history.replaceState(null, "", updatedUrl);
+  		} else {
+  			window.location = updatedUrl;
+  		}
+  	}
+
+  	function setUrl(params) {
+  		var key,
+  		    arrValue,
+  		    i,
+  		    querystring = "?",
+  		    location = window.location;
+
+  		params = QUnit.extend(QUnit.extend({}, QUnit.urlParams), params);
+
+  		for (key in params) {
+
+  			// Skip inherited or undefined properties
+  			if (hasOwn.call(params, key) && params[key] !== undefined) {
+
+  				// Output a parameter for each value of this key (but usually just one)
+  				arrValue = [].concat(params[key]);
+  				for (i = 0; i < arrValue.length; i++) {
+  					querystring += encodeURIComponent(key);
+  					if (arrValue[i] !== true) {
+  						querystring += "=" + encodeURIComponent(arrValue[i]);
+  					}
+  					querystring += "&";
+  				}
+  			}
+  		}
+  		return location.protocol + "//" + location.host + location.pathname + querystring.slice(0, -1);
+  	}
+
+  	function applyUrlParams() {
+  		var i,
+  		    selectedModules = [],
+  		    modulesList = id("qunit-modulefilter-dropdown-list").getElementsByTagName("input"),
+  		    filter = id("qunit-filter-input").value;
+
+  		for (i = 0; i < modulesList.length; i++) {
+  			if (modulesList[i].checked) {
+  				selectedModules.push(modulesList[i].value);
+  			}
+  		}
+
+  		window.location = setUrl({
+  			filter: filter === "" ? undefined : filter,
+  			moduleId: selectedModules.length === 0 ? undefined : selectedModules,
+
+  			// Remove module and testId filter
+  			module: undefined,
+  			testId: undefined
+  		});
+  	}
+
+  	function toolbarUrlConfigContainer() {
+  		var urlConfigContainer = document$$1.createElement("span");
+
+  		urlConfigContainer.innerHTML = getUrlConfigHtml();
+  		addClass(urlConfigContainer, "qunit-url-config");
+
+  		addEvents(urlConfigContainer.getElementsByTagName("input"), "change", toolbarChanged);
+  		addEvents(urlConfigContainer.getElementsByTagName("select"), "change", toolbarChanged);
+
+  		return urlConfigContainer;
+  	}
+
+  	function toolbarLooseFilter() {
+  		var filter = document$$1.createElement("form"),
+  		    label = document$$1.createElement("label"),
+  		    input = document$$1.createElement("input"),
+  		    button = document$$1.createElement("button");
+
+  		addClass(filter, "qunit-filter");
+
+  		label.innerHTML = "Filter: ";
+
+  		input.type = "text";
+  		input.value = config.filter || "";
+  		input.name = "filter";
+  		input.id = "qunit-filter-input";
+
+  		button.innerHTML = "Go";
+
+  		label.appendChild(input);
+
+  		filter.appendChild(label);
+  		filter.appendChild(document$$1.createTextNode(" "));
+  		filter.appendChild(button);
+  		addEvent(filter, "submit", interceptNavigation);
+
+  		return filter;
+  	}
+
+  	function moduleListHtml() {
+  		var i,
+  		    checked,
+  		    html = "";
+
+  		for (i = 0; i < config.modules.length; i++) {
+  			if (config.modules[i].name !== "") {
+  				checked = config.moduleId.indexOf(config.modules[i].moduleId) > -1;
+  				html += "<li><label class='clickable" + (checked ? " checked" : "") + "'><input type='checkbox' " + "value='" + config.modules[i].moduleId + "'" + (checked ? " checked='checked'" : "") + " />" + escapeText(config.modules[i].name) + "</label></li>";
+  			}
+  		}
+
+  		return html;
+  	}
+
+  	function toolbarModuleFilter() {
+  		var allCheckbox,
+  		    commit,
+  		    reset,
+  		    moduleFilter = document$$1.createElement("form"),
+  		    label = document$$1.createElement("label"),
+  		    moduleSearch = document$$1.createElement("input"),
+  		    dropDown = document$$1.createElement("div"),
+  		    actions = document$$1.createElement("span"),
+  		    dropDownList = document$$1.createElement("ul"),
+  		    dirty = false;
+
+  		moduleSearch.id = "qunit-modulefilter-search";
+  		addEvent(moduleSearch, "input", searchInput);
+  		addEvent(moduleSearch, "input", searchFocus);
+  		addEvent(moduleSearch, "focus", searchFocus);
+  		addEvent(moduleSearch, "click", searchFocus);
+
+  		label.id = "qunit-modulefilter-search-container";
+  		label.innerHTML = "Module: ";
+  		label.appendChild(moduleSearch);
+
+  		actions.id = "qunit-modulefilter-actions";
+  		actions.innerHTML = "<button style='display:none'>Apply</button>" + "<button type='reset' style='display:none'>Reset</button>" + "<label class='clickable" + (config.moduleId.length ? "" : " checked") + "'><input type='checkbox'" + (config.moduleId.length ? "" : " checked='checked'") + ">All modules</label>";
+  		allCheckbox = actions.lastChild.firstChild;
+  		commit = actions.firstChild;
+  		reset = commit.nextSibling;
+  		addEvent(commit, "click", applyUrlParams);
+
+  		dropDownList.id = "qunit-modulefilter-dropdown-list";
+  		dropDownList.innerHTML = moduleListHtml();
+
+  		dropDown.id = "qunit-modulefilter-dropdown";
+  		dropDown.style.display = "none";
+  		dropDown.appendChild(actions);
+  		dropDown.appendChild(dropDownList);
+  		addEvent(dropDown, "change", selectionChange);
+  		selectionChange();
+
+  		moduleFilter.id = "qunit-modulefilter";
+  		moduleFilter.appendChild(label);
+  		moduleFilter.appendChild(dropDown);
+  		addEvent(moduleFilter, "submit", interceptNavigation);
+  		addEvent(moduleFilter, "reset", function () {
+
+  			// Let the reset happen, then update styles
+  			window.setTimeout(selectionChange);
+  		});
+
+  		// Enables show/hide for the dropdown
+  		function searchFocus() {
+  			if (dropDown.style.display !== "none") {
+  				return;
+  			}
+
+  			dropDown.style.display = "block";
+  			addEvent(document$$1, "click", hideHandler);
+  			addEvent(document$$1, "keydown", hideHandler);
+
+  			// Hide on Escape keydown or outside-container click
+  			function hideHandler(e) {
+  				var inContainer = moduleFilter.contains(e.target);
+
+  				if (e.keyCode === 27 || !inContainer) {
+  					if (e.keyCode === 27 && inContainer) {
+  						moduleSearch.focus();
+  					}
+  					dropDown.style.display = "none";
+  					removeEvent(document$$1, "click", hideHandler);
+  					removeEvent(document$$1, "keydown", hideHandler);
+  					moduleSearch.value = "";
+  					searchInput();
+  				}
+  			}
+  		}
+
+  		// Processes module search box input
+  		function searchInput() {
+  			var i,
+  			    item,
+  			    searchText = moduleSearch.value.toLowerCase(),
+  			    listItems = dropDownList.children;
+
+  			for (i = 0; i < listItems.length; i++) {
+  				item = listItems[i];
+  				if (!searchText || item.textContent.toLowerCase().indexOf(searchText) > -1) {
+  					item.style.display = "";
+  				} else {
+  					item.style.display = "none";
+  				}
+  			}
+  		}
+
+  		// Processes selection changes
+  		function selectionChange(evt) {
+  			var i,
+  			    item,
+  			    checkbox = evt && evt.target || allCheckbox,
+  			    modulesList = dropDownList.getElementsByTagName("input"),
+  			    selectedNames = [];
+
+  			toggleClass(checkbox.parentNode, "checked", checkbox.checked);
+
+  			dirty = false;
+  			if (checkbox.checked && checkbox !== allCheckbox) {
+  				allCheckbox.checked = false;
+  				removeClass(allCheckbox.parentNode, "checked");
+  			}
+  			for (i = 0; i < modulesList.length; i++) {
+  				item = modulesList[i];
+  				if (!evt) {
+  					toggleClass(item.parentNode, "checked", item.checked);
+  				} else if (checkbox === allCheckbox && checkbox.checked) {
+  					item.checked = false;
+  					removeClass(item.parentNode, "checked");
+  				}
+  				dirty = dirty || item.checked !== item.defaultChecked;
+  				if (item.checked) {
+  					selectedNames.push(item.parentNode.textContent);
+  				}
+  			}
+
+  			commit.style.display = reset.style.display = dirty ? "" : "none";
+  			moduleSearch.placeholder = selectedNames.join(", ") || allCheckbox.parentNode.textContent;
+  			moduleSearch.title = "Type to filter list. Current selection:\n" + (selectedNames.join("\n") || allCheckbox.parentNode.textContent);
+  		}
+
+  		return moduleFilter;
+  	}
+
+  	function appendToolbar() {
+  		var toolbar = id("qunit-testrunner-toolbar");
+
+  		if (toolbar) {
+  			toolbar.appendChild(toolbarUrlConfigContainer());
+  			toolbar.appendChild(toolbarModuleFilter());
+  			toolbar.appendChild(toolbarLooseFilter());
+  			toolbar.appendChild(document$$1.createElement("div")).className = "clearfix";
+  		}
+  	}
+
+  	function appendHeader() {
+  		var header = id("qunit-header");
+
+  		if (header) {
+  			header.innerHTML = "<a href='" + escapeText(unfilteredUrl) + "'>" + header.innerHTML + "</a> ";
+  		}
+  	}
+
+  	function appendBanner() {
+  		var banner = id("qunit-banner");
+
+  		if (banner) {
+  			banner.className = "";
+  		}
+  	}
+
+  	function appendTestResults() {
+  		var tests = id("qunit-tests"),
+  		    result = id("qunit-testresult");
+
+  		if (result) {
+  			result.parentNode.removeChild(result);
+  		}
+
+  		if (tests) {
+  			tests.innerHTML = "";
+  			result = document$$1.createElement("p");
+  			result.id = "qunit-testresult";
+  			result.className = "result";
+  			tests.parentNode.insertBefore(result, tests);
+  			result.innerHTML = "Running...<br />&#160;";
+  		}
+  	}
+
+  	function appendFilteredTest() {
+  		var testId = QUnit.config.testId;
+  		if (!testId || testId.length <= 0) {
+  			return "";
+  		}
+  		return "<div id='qunit-filteredTest'>Rerunning selected tests: " + escapeText(testId.join(", ")) + " <a id='qunit-clearFilter' href='" + escapeText(unfilteredUrl) + "'>Run all tests</a></div>";
+  	}
+
+  	function appendUserAgent() {
+  		var userAgent = id("qunit-userAgent");
+
+  		if (userAgent) {
+  			userAgent.innerHTML = "";
+  			userAgent.appendChild(document$$1.createTextNode("QUnit " + QUnit.version + "; " + navigator.userAgent));
+  		}
+  	}
+
+  	function appendInterface() {
+  		var qunit = id("qunit");
+
+  		if (qunit) {
+  			qunit.innerHTML = "<h1 id='qunit-header'>" + escapeText(document$$1.title) + "</h1>" + "<h2 id='qunit-banner'></h2>" + "<div id='qunit-testrunner-toolbar'></div>" + appendFilteredTest() + "<h2 id='qunit-userAgent'></h2>" + "<ol id='qunit-tests'></ol>";
+  		}
+
+  		appendHeader();
+  		appendBanner();
+  		appendTestResults();
+  		appendUserAgent();
+  		appendToolbar();
+  	}
+
+  	function appendTestsList(modules) {
+  		var i, l, x, z, test, moduleObj;
+
+  		for (i = 0, l = modules.length; i < l; i++) {
+  			moduleObj = modules[i];
+
+  			for (x = 0, z = moduleObj.tests.length; x < z; x++) {
+  				test = moduleObj.tests[x];
+
+  				appendTest(test.name, test.testId, moduleObj.name);
+  			}
+  		}
+  	}
+
+  	function appendTest(name, testId, moduleName) {
+  		var title,
+  		    rerunTrigger,
+  		    testBlock,
+  		    assertList,
+  		    tests = id("qunit-tests");
+
+  		if (!tests) {
+  			return;
+  		}
+
+  		title = document$$1.createElement("strong");
+  		title.innerHTML = getNameHtml(name, moduleName);
+
+  		rerunTrigger = document$$1.createElement("a");
+  		rerunTrigger.innerHTML = "Rerun";
+  		rerunTrigger.href = setUrl({ testId: testId });
+
+  		testBlock = document$$1.createElement("li");
+  		testBlock.appendChild(title);
+  		testBlock.appendChild(rerunTrigger);
+  		testBlock.id = "qunit-test-output-" + testId;
+
+  		assertList = document$$1.createElement("ol");
+  		assertList.className = "qunit-assert-list";
+
+  		testBlock.appendChild(assertList);
+
+  		tests.appendChild(testBlock);
+  	}
+
+  	// HTML Reporter initialization and load
+  	QUnit.begin(function (details) {
+  		var i, moduleObj, tests;
+
+  		// Sort modules by name for the picker
+  		for (i = 0; i < details.modules.length; i++) {
+  			moduleObj = details.modules[i];
+  			if (moduleObj.name) {
+  				modulesList.push(moduleObj.name);
+  			}
+  		}
+  		modulesList.sort(function (a, b) {
+  			return a.localeCompare(b);
+  		});
+
+  		// Initialize QUnit elements
+  		appendInterface();
+  		appendTestsList(details.modules);
+  		tests = id("qunit-tests");
+  		if (tests && config.hidepassed) {
+  			addClass(tests, "hidepass");
+  		}
+  	});
+
+  	QUnit.done(function (details) {
+  		var banner = id("qunit-banner"),
+  		    tests = id("qunit-tests"),
+  		    html = ["Tests completed in ", details.runtime, " milliseconds.<br />", "<span class='passed'>", details.passed, "</span> assertions of <span class='total'>", details.total, "</span> passed, <span class='failed'>", details.failed, "</span> failed."].join("");
+
+  		if (banner) {
+  			banner.className = details.failed ? "qunit-fail" : "qunit-pass";
+  		}
+
+  		if (tests) {
+  			id("qunit-testresult").innerHTML = html;
+  		}
+
+  		if (config.altertitle && document$$1.title) {
+
+  			// Show ✖ for good, ✔ for bad suite result in title
+  			// use escape sequences in case file gets loaded with non-utf-8-charset
+  			document$$1.title = [details.failed ? "\u2716" : "\u2714", document$$1.title.replace(/^[\u2714\u2716] /i, "")].join(" ");
+  		}
+
+  		// Scroll back to top to show results
+  		if (config.scrolltop && window.scrollTo) {
+  			window.scrollTo(0, 0);
+  		}
+  	});
+
+  	function getNameHtml(name, module) {
+  		var nameHtml = "";
+
+  		if (module) {
+  			nameHtml = "<span class='module-name'>" + escapeText(module) + "</span>: ";
+  		}
+
+  		nameHtml += "<span class='test-name'>" + escapeText(name) + "</span>";
+
+  		return nameHtml;
+  	}
+
+  	QUnit.testStart(function (details) {
+  		var running, testBlock, bad;
+
+  		testBlock = id("qunit-test-output-" + details.testId);
+  		if (testBlock) {
+  			testBlock.className = "running";
+  		} else {
+
+  			// Report later registered tests
+  			appendTest(details.name, details.testId, details.module);
+  		}
+
+  		running = id("qunit-testresult");
+  		if (running) {
+  			bad = QUnit.config.reorder && details.previousFailure;
+
+  			running.innerHTML = (bad ? "Rerunning previously failed test: <br />" : "Running: <br />") + getNameHtml(details.name, details.module);
+  		}
+  	});
+
+  	function stripHtml(string) {
+
+  		// Strip tags, html entity and whitespaces
+  		return string.replace(/<\/?[^>]+(>|$)/g, "").replace(/\&quot;/g, "").replace(/\s+/g, "");
+  	}
+
+  	QUnit.log(function (details) {
+  		var assertList,
+  		    assertLi,
+  		    message,
+  		    expected,
+  		    actual,
+  		    diff,
+  		    showDiff = false,
+  		    testItem = id("qunit-test-output-" + details.testId);
+
+  		if (!testItem) {
+  			return;
+  		}
+
+  		message = escapeText(details.message) || (details.result ? "okay" : "failed");
+  		message = "<span class='test-message'>" + message + "</span>";
+  		message += "<span class='runtime'>@ " + details.runtime + " ms</span>";
+
+  		// The pushFailure doesn't provide details.expected
+  		// when it calls, it's implicit to also not show expected and diff stuff
+  		// Also, we need to check details.expected existence, as it can exist and be undefined
+  		if (!details.result && hasOwn.call(details, "expected")) {
+  			if (details.negative) {
+  				expected = "NOT " + QUnit.dump.parse(details.expected);
+  			} else {
+  				expected = QUnit.dump.parse(details.expected);
+  			}
+
+  			actual = QUnit.dump.parse(details.actual);
+  			message += "<table><tr class='test-expected'><th>Expected: </th><td><pre>" + escapeText(expected) + "</pre></td></tr>";
+
+  			if (actual !== expected) {
+
+  				message += "<tr class='test-actual'><th>Result: </th><td><pre>" + escapeText(actual) + "</pre></td></tr>";
+
+  				// Don't show diff if actual or expected are booleans
+  				if (!/^(true|false)$/.test(actual) && !/^(true|false)$/.test(expected)) {
+  					diff = QUnit.diff(expected, actual);
+  					showDiff = stripHtml(diff).length !== stripHtml(expected).length + stripHtml(actual).length;
+  				}
+
+  				// Don't show diff if expected and actual are totally different
+  				if (showDiff) {
+  					message += "<tr class='test-diff'><th>Diff: </th><td><pre>" + diff + "</pre></td></tr>";
+  				}
+  			} else if (expected.indexOf("[object Array]") !== -1 || expected.indexOf("[object Object]") !== -1) {
+  				message += "<tr class='test-message'><th>Message: </th><td>" + "Diff suppressed as the depth of object is more than current max depth (" + QUnit.config.maxDepth + ").<p>Hint: Use <code>QUnit.dump.maxDepth</code> to " + " run with a higher max depth or <a href='" + escapeText(setUrl({ maxDepth: -1 })) + "'>" + "Rerun</a> without max depth.</p></td></tr>";
+  			} else {
+  				message += "<tr class='test-message'><th>Message: </th><td>" + "Diff suppressed as the expected and actual results have an equivalent" + " serialization</td></tr>";
+  			}
+
+  			if (details.source) {
+  				message += "<tr class='test-source'><th>Source: </th><td><pre>" + escapeText(details.source) + "</pre></td></tr>";
+  			}
+
+  			message += "</table>";
+
+  			// This occurs when pushFailure is set and we have an extracted stack trace
+  		} else if (!details.result && details.source) {
+  			message += "<table>" + "<tr class='test-source'><th>Source: </th><td><pre>" + escapeText(details.source) + "</pre></td></tr>" + "</table>";
+  		}
+
+  		assertList = testItem.getElementsByTagName("ol")[0];
+
+  		assertLi = document$$1.createElement("li");
+  		assertLi.className = details.result ? "pass" : "fail";
+  		assertLi.innerHTML = message;
+  		assertList.appendChild(assertLi);
+  	});
+
+  	QUnit.testDone(function (details) {
+  		var testTitle,
+  		    time,
+  		    testItem,
+  		    assertList,
+  		    good,
+  		    bad,
+  		    testCounts,
+  		    skipped,
+  		    sourceName,
+  		    tests = id("qunit-tests");
+
+  		if (!tests) {
+  			return;
+  		}
+
+  		testItem = id("qunit-test-output-" + details.testId);
+
+  		assertList = testItem.getElementsByTagName("ol")[0];
+
+  		good = details.passed;
+  		bad = details.failed;
+
+  		if (bad === 0) {
+
+  			// Collapse the passing tests
+  			addClass(assertList, "qunit-collapsed");
+  		} else if (config.collapse) {
+  			if (!collapseNext) {
+
+  				// Skip collapsing the first failing test
+  				collapseNext = true;
+  			} else {
+
+  				// Collapse remaining tests
+  				addClass(assertList, "qunit-collapsed");
+  			}
+  		}
+
+  		// The testItem.firstChild is the test name
+  		testTitle = testItem.firstChild;
+
+  		testCounts = bad ? "<b class='failed'>" + bad + "</b>, " + "<b class='passed'>" + good + "</b>, " : "";
+
+  		testTitle.innerHTML += " <b class='counts'>(" + testCounts + details.assertions.length + ")</b>";
+
+  		if (details.skipped) {
+  			testItem.className = "skipped";
+  			skipped = document$$1.createElement("em");
+  			skipped.className = "qunit-skipped-label";
+  			skipped.innerHTML = "skipped";
+  			testItem.insertBefore(skipped, testTitle);
+  		} else {
+  			addEvent(testTitle, "click", function () {
+  				toggleClass(assertList, "qunit-collapsed");
+  			});
+
+  			testItem.className = bad ? "fail" : "pass";
+
+  			time = document$$1.createElement("span");
+  			time.className = "runtime";
+  			time.innerHTML = details.runtime + " ms";
+  			testItem.insertBefore(time, assertList);
+  		}
+
+  		// Show the source of the test when showing assertions
+  		if (details.source) {
+  			sourceName = document$$1.createElement("p");
+  			sourceName.innerHTML = "<strong>Source: </strong>" + details.source;
+  			addClass(sourceName, "qunit-source");
+  			if (bad === 0) {
+  				addClass(sourceName, "qunit-collapsed");
+  			}
+  			addEvent(testTitle, "click", function () {
+  				toggleClass(sourceName, "qunit-collapsed");
+  			});
+  			testItem.appendChild(sourceName);
+  		}
+  	});
+
+  	// Avoid readyState issue with phantomjs
+  	// Ref: #818
+  	var notPhantom = function (p) {
+  		return !(p && p.version && p.version.major > 0);
+  	}(window.phantom);
+
+  	if (notPhantom && document$$1.readyState === "complete") {
+  		QUnit.load();
+  	} else {
+  		addEvent(window, "load", QUnit.load);
+  	}
+  })();
+
+  /*
+   * This file is a modified version of google-diff-match-patch's JavaScript implementation
+   * (https://code.google.com/p/google-diff-match-patch/source/browse/trunk/javascript/diff_match_patch_uncompressed.js),
+   * modifications are licensed as more fully set forth in LICENSE.txt.
+   *
+   * The original source of google-diff-match-patch is attributable and licensed as follows:
+   *
+   * Copyright 2006 Google Inc.
+   * https://code.google.com/p/google-diff-match-patch/
+   *
+   * Licensed under the Apache License, Version 2.0 (the "License");
+   * you may not use this file except in compliance with the License.
+   * You may obtain a copy of the License at
+   *
+   * https://www.apache.org/licenses/LICENSE-2.0
+   *
+   * Unless required by applicable law or agreed to in writing, software
+   * distributed under the License is distributed on an "AS IS" BASIS,
+   * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   * See the License for the specific language governing permissions and
+   * limitations under the License.
+   *
+   * More Info:
+   *  https://code.google.com/p/google-diff-match-patch/
+   *
+   * Usage: QUnit.diff(expected, actual)
+   *
+   */
+  QUnit.diff = function () {
+  	function DiffMatchPatch() {}
+
+  	//  DIFF FUNCTIONS
+
+  	/**
+    * The data structure representing a diff is an array of tuples:
+    * [[DIFF_DELETE, 'Hello'], [DIFF_INSERT, 'Goodbye'], [DIFF_EQUAL, ' world.']]
+    * which means: delete 'Hello', add 'Goodbye' and keep ' world.'
+    */
+  	var DIFF_DELETE = -1,
+  	    DIFF_INSERT = 1,
+  	    DIFF_EQUAL = 0;
+
+  	/**
+    * Find the differences between two texts.  Simplifies the problem by stripping
+    * any common prefix or suffix off the texts before diffing.
+    * @param {string} text1 Old string to be diffed.
+    * @param {string} text2 New string to be diffed.
+    * @param {boolean=} optChecklines Optional speedup flag. If present and false,
+    *     then don't run a line-level diff first to identify the changed areas.
+    *     Defaults to true, which does a faster, slightly less optimal diff.
+    * @return {!Array.<!DiffMatchPatch.Diff>} Array of diff tuples.
+    */
+  	DiffMatchPatch.prototype.DiffMain = function (text1, text2, optChecklines) {
+  		var deadline, checklines, commonlength, commonprefix, commonsuffix, diffs;
+
+  		// The diff must be complete in up to 1 second.
+  		deadline = new Date().getTime() + 1000;
+
+  		// Check for null inputs.
+  		if (text1 === null || text2 === null) {
+  			throw new Error("Null input. (DiffMain)");
+  		}
+
+  		// Check for equality (speedup).
+  		if (text1 === text2) {
+  			if (text1) {
+  				return [[DIFF_EQUAL, text1]];
+  			}
+  			return [];
+  		}
+
+  		if (typeof optChecklines === "undefined") {
+  			optChecklines = true;
+  		}
+
+  		checklines = optChecklines;
+
+  		// Trim off common prefix (speedup).
+  		commonlength = this.diffCommonPrefix(text1, text2);
+  		commonprefix = text1.substring(0, commonlength);
+  		text1 = text1.substring(commonlength);
+  		text2 = text2.substring(commonlength);
+
+  		// Trim off common suffix (speedup).
+  		commonlength = this.diffCommonSuffix(text1, text2);
+  		commonsuffix = text1.substring(text1.length - commonlength);
+  		text1 = text1.substring(0, text1.length - commonlength);
+  		text2 = text2.substring(0, text2.length - commonlength);
+
+  		// Compute the diff on the middle block.
+  		diffs = this.diffCompute(text1, text2, checklines, deadline);
+
+  		// Restore the prefix and suffix.
+  		if (commonprefix) {
+  			diffs.unshift([DIFF_EQUAL, commonprefix]);
+  		}
+  		if (commonsuffix) {
+  			diffs.push([DIFF_EQUAL, commonsuffix]);
+  		}
+  		this.diffCleanupMerge(diffs);
+  		return diffs;
+  	};
+
+  	/**
+    * Reduce the number of edits by eliminating operationally trivial equalities.
+    * @param {!Array.<!DiffMatchPatch.Diff>} diffs Array of diff tuples.
+    */
+  	DiffMatchPatch.prototype.diffCleanupEfficiency = function (diffs) {
+  		var changes, equalities, equalitiesLength, lastequality, pointer, preIns, preDel, postIns, postDel;
+  		changes = false;
+  		equalities = []; // Stack of indices where equalities are found.
+  		equalitiesLength = 0; // Keeping our own length var is faster in JS.
+  		/** @type {?string} */
+  		lastequality = null;
+
+  		// Always equal to diffs[equalities[equalitiesLength - 1]][1]
+  		pointer = 0; // Index of current position.
+
+  		// Is there an insertion operation before the last equality.
+  		preIns = false;
+
+  		// Is there a deletion operation before the last equality.
+  		preDel = false;
+
+  		// Is there an insertion operation after the last equality.
+  		postIns = false;
+
+  		// Is there a deletion operation after the last equality.
+  		postDel = false;
+  		while (pointer < diffs.length) {
+
+  			// Equality found.
+  			if (diffs[pointer][0] === DIFF_EQUAL) {
+  				if (diffs[pointer][1].length < 4 && (postIns || postDel)) {
+
+  					// Candidate found.
+  					equalities[equalitiesLength++] = pointer;
+  					preIns = postIns;
+  					preDel = postDel;
+  					lastequality = diffs[pointer][1];
+  				} else {
+
+  					// Not a candidate, and can never become one.
+  					equalitiesLength = 0;
+  					lastequality = null;
+  				}
+  				postIns = postDel = false;
+
+  				// An insertion or deletion.
+  			} else {
+
+  				if (diffs[pointer][0] === DIFF_DELETE) {
+  					postDel = true;
+  				} else {
+  					postIns = true;
+  				}
+
+  				/*
+       * Five types to be split:
+       * <ins>A</ins><del>B</del>XY<ins>C</ins><del>D</del>
+       * <ins>A</ins>X<ins>C</ins><del>D</del>
+       * <ins>A</ins><del>B</del>X<ins>C</ins>
+       * <ins>A</del>X<ins>C</ins><del>D</del>
+       * <ins>A</ins><del>B</del>X<del>C</del>
+       */
+  				if (lastequality && (preIns && preDel && postIns && postDel || lastequality.length < 2 && preIns + preDel + postIns + postDel === 3)) {
+
+  					// Duplicate record.
+  					diffs.splice(equalities[equalitiesLength - 1], 0, [DIFF_DELETE, lastequality]);
+
+  					// Change second copy to insert.
+  					diffs[equalities[equalitiesLength - 1] + 1][0] = DIFF_INSERT;
+  					equalitiesLength--; // Throw away the equality we just deleted;
+  					lastequality = null;
+  					if (preIns && preDel) {
+
+  						// No changes made which could affect previous entry, keep going.
+  						postIns = postDel = true;
+  						equalitiesLength = 0;
+  					} else {
+  						equalitiesLength--; // Throw away the previous equality.
+  						pointer = equalitiesLength > 0 ? equalities[equalitiesLength - 1] : -1;
+  						postIns = postDel = false;
+  					}
+  					changes = true;
+  				}
+  			}
+  			pointer++;
+  		}
+
+  		if (changes) {
+  			this.diffCleanupMerge(diffs);
+  		}
+  	};
+
+  	/**
+    * Convert a diff array into a pretty HTML report.
+    * @param {!Array.<!DiffMatchPatch.Diff>} diffs Array of diff tuples.
+    * @param {integer} string to be beautified.
+    * @return {string} HTML representation.
+    */
+  	DiffMatchPatch.prototype.diffPrettyHtml = function (diffs) {
+  		var op,
+  		    data,
+  		    x,
+  		    html = [];
+  		for (x = 0; x < diffs.length; x++) {
+  			op = diffs[x][0]; // Operation (insert, delete, equal)
+  			data = diffs[x][1]; // Text of change.
+  			switch (op) {
+  				case DIFF_INSERT:
+  					html[x] = "<ins>" + escapeText(data) + "</ins>";
+  					break;
+  				case DIFF_DELETE:
+  					html[x] = "<del>" + escapeText(data) + "</del>";
+  					break;
+  				case DIFF_EQUAL:
+  					html[x] = "<span>" + escapeText(data) + "</span>";
+  					break;
+  			}
+  		}
+  		return html.join("");
+  	};
+
+  	/**
+    * Determine the common prefix of two strings.
+    * @param {string} text1 First string.
+    * @param {string} text2 Second string.
+    * @return {number} The number of characters common to the start of each
+    *     string.
+    */
+  	DiffMatchPatch.prototype.diffCommonPrefix = function (text1, text2) {
+  		var pointermid, pointermax, pointermin, pointerstart;
+
+  		// Quick check for common null cases.
+  		if (!text1 || !text2 || text1.charAt(0) !== text2.charAt(0)) {
+  			return 0;
+  		}
+
+  		// Binary search.
+  		// Performance analysis: https://neil.fraser.name/news/2007/10/09/
+  		pointermin = 0;
+  		pointermax = Math.min(text1.length, text2.length);
+  		pointermid = pointermax;
+  		pointerstart = 0;
+  		while (pointermin < pointermid) {
+  			if (text1.substring(pointerstart, pointermid) === text2.substring(pointerstart, pointermid)) {
+  				pointermin = pointermid;
+  				pointerstart = pointermin;
+  			} else {
+  				pointermax = pointermid;
+  			}
+  			pointermid = Math.floor((pointermax - pointermin) / 2 + pointermin);
+  		}
+  		return pointermid;
+  	};
+
+  	/**
+    * Determine the common suffix of two strings.
+    * @param {string} text1 First string.
+    * @param {string} text2 Second string.
+    * @return {number} The number of characters common to the end of each string.
+    */
+  	DiffMatchPatch.prototype.diffCommonSuffix = function (text1, text2) {
+  		var pointermid, pointermax, pointermin, pointerend;
+
+  		// Quick check for common null cases.
+  		if (!text1 || !text2 || text1.charAt(text1.length - 1) !== text2.charAt(text2.length - 1)) {
+  			return 0;
+  		}
+
+  		// Binary search.
+  		// Performance analysis: https://neil.fraser.name/news/2007/10/09/
+  		pointermin = 0;
+  		pointermax = Math.min(text1.length, text2.length);
+  		pointermid = pointermax;
+  		pointerend = 0;
+  		while (pointermin < pointermid) {
+  			if (text1.substring(text1.length - pointermid, text1.length - pointerend) === text2.substring(text2.length - pointermid, text2.length - pointerend)) {
+  				pointermin = pointermid;
+  				pointerend = pointermin;
+  			} else {
+  				pointermax = pointermid;
+  			}
+  			pointermid = Math.floor((pointermax - pointermin) / 2 + pointermin);
+  		}
+  		return pointermid;
+  	};
+
+  	/**
+    * Find the differences between two texts.  Assumes that the texts do not
+    * have any common prefix or suffix.
+    * @param {string} text1 Old string to be diffed.
+    * @param {string} text2 New string to be diffed.
+    * @param {boolean} checklines Speedup flag.  If false, then don't run a
+    *     line-level diff first to identify the changed areas.
+    *     If true, then run a faster, slightly less optimal diff.
+    * @param {number} deadline Time when the diff should be complete by.
+    * @return {!Array.<!DiffMatchPatch.Diff>} Array of diff tuples.
+    * @private
+    */
+  	DiffMatchPatch.prototype.diffCompute = function (text1, text2, checklines, deadline) {
+  		var diffs, longtext, shorttext, i, hm, text1A, text2A, text1B, text2B, midCommon, diffsA, diffsB;
+
+  		if (!text1) {
+
+  			// Just add some text (speedup).
+  			return [[DIFF_INSERT, text2]];
+  		}
+
+  		if (!text2) {
+
+  			// Just delete some text (speedup).
+  			return [[DIFF_DELETE, text1]];
+  		}
+
+  		longtext = text1.length > text2.length ? text1 : text2;
+  		shorttext = text1.length > text2.length ? text2 : text1;
+  		i = longtext.indexOf(shorttext);
+  		if (i !== -1) {
+
+  			// Shorter text is inside the longer text (speedup).
+  			diffs = [[DIFF_INSERT, longtext.substring(0, i)], [DIFF_EQUAL, shorttext], [DIFF_INSERT, longtext.substring(i + shorttext.length)]];
+
+  			// Swap insertions for deletions if diff is reversed.
+  			if (text1.length > text2.length) {
+  				diffs[0][0] = diffs[2][0] = DIFF_DELETE;
+  			}
+  			return diffs;
+  		}
+
+  		if (shorttext.length === 1) {
+
+  			// Single character string.
+  			// After the previous speedup, the character can't be an equality.
+  			return [[DIFF_DELETE, text1], [DIFF_INSERT, text2]];
+  		}
+
+  		// Check to see if the problem can be split in two.
+  		hm = this.diffHalfMatch(text1, text2);
+  		if (hm) {
+
+  			// A half-match was found, sort out the return data.
+  			text1A = hm[0];
+  			text1B = hm[1];
+  			text2A = hm[2];
+  			text2B = hm[3];
+  			midCommon = hm[4];
+
+  			// Send both pairs off for separate processing.
+  			diffsA = this.DiffMain(text1A, text2A, checklines, deadline);
+  			diffsB = this.DiffMain(text1B, text2B, checklines, deadline);
+
+  			// Merge the results.
+  			return diffsA.concat([[DIFF_EQUAL, midCommon]], diffsB);
+  		}
+
+  		if (checklines && text1.length > 100 && text2.length > 100) {
+  			return this.diffLineMode(text1, text2, deadline);
+  		}
+
+  		return this.diffBisect(text1, text2, deadline);
+  	};
+
+  	/**
+    * Do the two texts share a substring which is at least half the length of the
+    * longer text?
+    * This speedup can produce non-minimal diffs.
+    * @param {string} text1 First string.
+    * @param {string} text2 Second string.
+    * @return {Array.<string>} Five element Array, containing the prefix of
+    *     text1, the suffix of text1, the prefix of text2, the suffix of
+    *     text2 and the common middle.  Or null if there was no match.
+    * @private
+    */
+  	DiffMatchPatch.prototype.diffHalfMatch = function (text1, text2) {
+  		var longtext, shorttext, dmp, text1A, text2B, text2A, text1B, midCommon, hm1, hm2, hm;
+
+  		longtext = text1.length > text2.length ? text1 : text2;
+  		shorttext = text1.length > text2.length ? text2 : text1;
+  		if (longtext.length < 4 || shorttext.length * 2 < longtext.length) {
+  			return null; // Pointless.
+  		}
+  		dmp = this; // 'this' becomes 'window' in a closure.
+
+  		/**
+     * Does a substring of shorttext exist within longtext such that the substring
+     * is at least half the length of longtext?
+     * Closure, but does not reference any external variables.
+     * @param {string} longtext Longer string.
+     * @param {string} shorttext Shorter string.
+     * @param {number} i Start index of quarter length substring within longtext.
+     * @return {Array.<string>} Five element Array, containing the prefix of
+     *     longtext, the suffix of longtext, the prefix of shorttext, the suffix
+     *     of shorttext and the common middle.  Or null if there was no match.
+     * @private
+     */
+  		function diffHalfMatchI(longtext, shorttext, i) {
+  			var seed, j, bestCommon, prefixLength, suffixLength, bestLongtextA, bestLongtextB, bestShorttextA, bestShorttextB;
+
+  			// Start with a 1/4 length substring at position i as a seed.
+  			seed = longtext.substring(i, i + Math.floor(longtext.length / 4));
+  			j = -1;
+  			bestCommon = "";
+  			while ((j = shorttext.indexOf(seed, j + 1)) !== -1) {
+  				prefixLength = dmp.diffCommonPrefix(longtext.substring(i), shorttext.substring(j));
+  				suffixLength = dmp.diffCommonSuffix(longtext.substring(0, i), shorttext.substring(0, j));
+  				if (bestCommon.length < suffixLength + prefixLength) {
+  					bestCommon = shorttext.substring(j - suffixLength, j) + shorttext.substring(j, j + prefixLength);
+  					bestLongtextA = longtext.substring(0, i - suffixLength);
+  					bestLongtextB = longtext.substring(i + prefixLength);
+  					bestShorttextA = shorttext.substring(0, j - suffixLength);
+  					bestShorttextB = shorttext.substring(j + prefixLength);
+  				}
+  			}
+  			if (bestCommon.length * 2 >= longtext.length) {
+  				return [bestLongtextA, bestLongtextB, bestShorttextA, bestShorttextB, bestCommon];
+  			} else {
+  				return null;
+  			}
+  		}
+
+  		// First check if the second quarter is the seed for a half-match.
+  		hm1 = diffHalfMatchI(longtext, shorttext, Math.ceil(longtext.length / 4));
+
+  		// Check again based on the third quarter.
+  		hm2 = diffHalfMatchI(longtext, shorttext, Math.ceil(longtext.length / 2));
+  		if (!hm1 && !hm2) {
+  			return null;
+  		} else if (!hm2) {
+  			hm = hm1;
+  		} else if (!hm1) {
+  			hm = hm2;
+  		} else {
+
+  			// Both matched.  Select the longest.
+  			hm = hm1[4].length > hm2[4].length ? hm1 : hm2;
+  		}
+
+  		// A half-match was found, sort out the return data.
+  		if (text1.length > text2.length) {
+  			text1A = hm[0];
+  			text1B = hm[1];
+  			text2A = hm[2];
+  			text2B = hm[3];
+  		} else {
+  			text2A = hm[0];
+  			text2B = hm[1];
+  			text1A = hm[2];
+  			text1B = hm[3];
+  		}
+  		midCommon = hm[4];
+  		return [text1A, text1B, text2A, text2B, midCommon];
+  	};
+
+  	/**
+    * Do a quick line-level diff on both strings, then rediff the parts for
+    * greater accuracy.
+    * This speedup can produce non-minimal diffs.
+    * @param {string} text1 Old string to be diffed.
+    * @param {string} text2 New string to be diffed.
+    * @param {number} deadline Time when the diff should be complete by.
+    * @return {!Array.<!DiffMatchPatch.Diff>} Array of diff tuples.
+    * @private
+    */
+  	DiffMatchPatch.prototype.diffLineMode = function (text1, text2, deadline) {
+  		var a, diffs, linearray, pointer, countInsert, countDelete, textInsert, textDelete, j;
+
+  		// Scan the text on a line-by-line basis first.
+  		a = this.diffLinesToChars(text1, text2);
+  		text1 = a.chars1;
+  		text2 = a.chars2;
+  		linearray = a.lineArray;
+
+  		diffs = this.DiffMain(text1, text2, false, deadline);
+
+  		// Convert the diff back to original text.
+  		this.diffCharsToLines(diffs, linearray);
+
+  		// Eliminate freak matches (e.g. blank lines)
+  		this.diffCleanupSemantic(diffs);
+
+  		// Rediff any replacement blocks, this time character-by-character.
+  		// Add a dummy entry at the end.
+  		diffs.push([DIFF_EQUAL, ""]);
+  		pointer = 0;
+  		countDelete = 0;
+  		countInsert = 0;
+  		textDelete = "";
+  		textInsert = "";
+  		while (pointer < diffs.length) {
+  			switch (diffs[pointer][0]) {
+  				case DIFF_INSERT:
+  					countInsert++;
+  					textInsert += diffs[pointer][1];
+  					break;
+  				case DIFF_DELETE:
+  					countDelete++;
+  					textDelete += diffs[pointer][1];
+  					break;
+  				case DIFF_EQUAL:
+
+  					// Upon reaching an equality, check for prior redundancies.
+  					if (countDelete >= 1 && countInsert >= 1) {
+
+  						// Delete the offending records and add the merged ones.
+  						diffs.splice(pointer - countDelete - countInsert, countDelete + countInsert);
+  						pointer = pointer - countDelete - countInsert;
+  						a = this.DiffMain(textDelete, textInsert, false, deadline);
+  						for (j = a.length - 1; j >= 0; j--) {
+  							diffs.splice(pointer, 0, a[j]);
+  						}
+  						pointer = pointer + a.length;
+  					}
+  					countInsert = 0;
+  					countDelete = 0;
+  					textDelete = "";
+  					textInsert = "";
+  					break;
+  			}
+  			pointer++;
+  		}
+  		diffs.pop(); // Remove the dummy entry at the end.
+
+  		return diffs;
+  	};
+
+  	/**
+    * Find the 'middle snake' of a diff, split the problem in two
+    * and return the recursively constructed diff.
+    * See Myers 1986 paper: An O(ND) Difference Algorithm and Its Variations.
+    * @param {string} text1 Old string to be diffed.
+    * @param {string} text2 New string to be diffed.
+    * @param {number} deadline Time at which to bail if not yet complete.
+    * @return {!Array.<!DiffMatchPatch.Diff>} Array of diff tuples.
+    * @private
+    */
+  	DiffMatchPatch.prototype.diffBisect = function (text1, text2, deadline) {
+  		var text1Length, text2Length, maxD, vOffset, vLength, v1, v2, x, delta, front, k1start, k1end, k2start, k2end, k2Offset, k1Offset, x1, x2, y1, y2, d, k1, k2;
+
+  		// Cache the text lengths to prevent multiple calls.
+  		text1Length = text1.length;
+  		text2Length = text2.length;
+  		maxD = Math.ceil((text1Length + text2Length) / 2);
+  		vOffset = maxD;
+  		vLength = 2 * maxD;
+  		v1 = new Array(vLength);
+  		v2 = new Array(vLength);
+
+  		// Setting all elements to -1 is faster in Chrome & Firefox than mixing
+  		// integers and undefined.
+  		for (x = 0; x < vLength; x++) {
+  			v1[x] = -1;
+  			v2[x] = -1;
+  		}
+  		v1[vOffset + 1] = 0;
+  		v2[vOffset + 1] = 0;
+  		delta = text1Length - text2Length;
+
+  		// If the total number of characters is odd, then the front path will collide
+  		// with the reverse path.
+  		front = delta % 2 !== 0;
+
+  		// Offsets for start and end of k loop.
+  		// Prevents mapping of space beyond the grid.
+  		k1start = 0;
+  		k1end = 0;
+  		k2start = 0;
+  		k2end = 0;
+  		for (d = 0; d < maxD; d++) {
+
+  			// Bail out if deadline is reached.
+  			if (new Date().getTime() > deadline) {
+  				break;
+  			}
+
+  			// Walk the front path one step.
+  			for (k1 = -d + k1start; k1 <= d - k1end; k1 += 2) {
+  				k1Offset = vOffset + k1;
+  				if (k1 === -d || k1 !== d && v1[k1Offset - 1] < v1[k1Offset + 1]) {
+  					x1 = v1[k1Offset + 1];
+  				} else {
+  					x1 = v1[k1Offset - 1] + 1;
+  				}
+  				y1 = x1 - k1;
+  				while (x1 < text1Length && y1 < text2Length && text1.charAt(x1) === text2.charAt(y1)) {
+  					x1++;
+  					y1++;
+  				}
+  				v1[k1Offset] = x1;
+  				if (x1 > text1Length) {
+
+  					// Ran off the right of the graph.
+  					k1end += 2;
+  				} else if (y1 > text2Length) {
+
+  					// Ran off the bottom of the graph.
+  					k1start += 2;
+  				} else if (front) {
+  					k2Offset = vOffset + delta - k1;
+  					if (k2Offset >= 0 && k2Offset < vLength && v2[k2Offset] !== -1) {
+
+  						// Mirror x2 onto top-left coordinate system.
+  						x2 = text1Length - v2[k2Offset];
+  						if (x1 >= x2) {
+
+  							// Overlap detected.
+  							return this.diffBisectSplit(text1, text2, x1, y1, deadline);
+  						}
+  					}
+  				}
+  			}
+
+  			// Walk the reverse path one step.
+  			for (k2 = -d + k2start; k2 <= d - k2end; k2 += 2) {
+  				k2Offset = vOffset + k2;
+  				if (k2 === -d || k2 !== d && v2[k2Offset - 1] < v2[k2Offset + 1]) {
+  					x2 = v2[k2Offset + 1];
+  				} else {
+  					x2 = v2[k2Offset - 1] + 1;
+  				}
+  				y2 = x2 - k2;
+  				while (x2 < text1Length && y2 < text2Length && text1.charAt(text1Length - x2 - 1) === text2.charAt(text2Length - y2 - 1)) {
+  					x2++;
+  					y2++;
+  				}
+  				v2[k2Offset] = x2;
+  				if (x2 > text1Length) {
+
+  					// Ran off the left of the graph.
+  					k2end += 2;
+  				} else if (y2 > text2Length) {
+
+  					// Ran off the top of the graph.
+  					k2start += 2;
+  				} else if (!front) {
+  					k1Offset = vOffset + delta - k2;
+  					if (k1Offset >= 0 && k1Offset < vLength && v1[k1Offset] !== -1) {
+  						x1 = v1[k1Offset];
+  						y1 = vOffset + x1 - k1Offset;
+
+  						// Mirror x2 onto top-left coordinate system.
+  						x2 = text1Length - x2;
+  						if (x1 >= x2) {
+
+  							// Overlap detected.
+  							return this.diffBisectSplit(text1, text2, x1, y1, deadline);
+  						}
+  					}
+  				}
+  			}
+  		}
+
+  		// Diff took too long and hit the deadline or
+  		// number of diffs equals number of characters, no commonality at all.
+  		return [[DIFF_DELETE, text1], [DIFF_INSERT, text2]];
+  	};
+
+  	/**
+    * Given the location of the 'middle snake', split the diff in two parts
+    * and recurse.
+    * @param {string} text1 Old string to be diffed.
+    * @param {string} text2 New string to be diffed.
+    * @param {number} x Index of split point in text1.
+    * @param {number} y Index of split point in text2.
+    * @param {number} deadline Time at which to bail if not yet complete.
+    * @return {!Array.<!DiffMatchPatch.Diff>} Array of diff tuples.
+    * @private
+    */
+  	DiffMatchPatch.prototype.diffBisectSplit = function (text1, text2, x, y, deadline) {
+  		var text1a, text1b, text2a, text2b, diffs, diffsb;
+  		text1a = text1.substring(0, x);
+  		text2a = text2.substring(0, y);
+  		text1b = text1.substring(x);
+  		text2b = text2.substring(y);
+
+  		// Compute both diffs serially.
+  		diffs = this.DiffMain(text1a, text2a, false, deadline);
+  		diffsb = this.DiffMain(text1b, text2b, false, deadline);
+
+  		return diffs.concat(diffsb);
+  	};
+
+  	/**
+    * Reduce the number of edits by eliminating semantically trivial equalities.
+    * @param {!Array.<!DiffMatchPatch.Diff>} diffs Array of diff tuples.
+    */
+  	DiffMatchPatch.prototype.diffCleanupSemantic = function (diffs) {
+  		var changes, equalities, equalitiesLength, lastequality, pointer, lengthInsertions2, lengthDeletions2, lengthInsertions1, lengthDeletions1, deletion, insertion, overlapLength1, overlapLength2;
+  		changes = false;
+  		equalities = []; // Stack of indices where equalities are found.
+  		equalitiesLength = 0; // Keeping our own length var is faster in JS.
+  		/** @type {?string} */
+  		lastequality = null;
+
+  		// Always equal to diffs[equalities[equalitiesLength - 1]][1]
+  		pointer = 0; // Index of current position.
+
+  		// Number of characters that changed prior to the equality.
+  		lengthInsertions1 = 0;
+  		lengthDeletions1 = 0;
+
+  		// Number of characters that changed after the equality.
+  		lengthInsertions2 = 0;
+  		lengthDeletions2 = 0;
+  		while (pointer < diffs.length) {
+  			if (diffs[pointer][0] === DIFF_EQUAL) {
+  				// Equality found.
+  				equalities[equalitiesLength++] = pointer;
+  				lengthInsertions1 = lengthInsertions2;
+  				lengthDeletions1 = lengthDeletions2;
+  				lengthInsertions2 = 0;
+  				lengthDeletions2 = 0;
+  				lastequality = diffs[pointer][1];
+  			} else {
+  				// An insertion or deletion.
+  				if (diffs[pointer][0] === DIFF_INSERT) {
+  					lengthInsertions2 += diffs[pointer][1].length;
+  				} else {
+  					lengthDeletions2 += diffs[pointer][1].length;
+  				}
+
+  				// Eliminate an equality that is smaller or equal to the edits on both
+  				// sides of it.
+  				if (lastequality && lastequality.length <= Math.max(lengthInsertions1, lengthDeletions1) && lastequality.length <= Math.max(lengthInsertions2, lengthDeletions2)) {
+
+  					// Duplicate record.
+  					diffs.splice(equalities[equalitiesLength - 1], 0, [DIFF_DELETE, lastequality]);
+
+  					// Change second copy to insert.
+  					diffs[equalities[equalitiesLength - 1] + 1][0] = DIFF_INSERT;
+
+  					// Throw away the equality we just deleted.
+  					equalitiesLength--;
+
+  					// Throw away the previous equality (it needs to be reevaluated).
+  					equalitiesLength--;
+  					pointer = equalitiesLength > 0 ? equalities[equalitiesLength - 1] : -1;
+
+  					// Reset the counters.
+  					lengthInsertions1 = 0;
+  					lengthDeletions1 = 0;
+  					lengthInsertions2 = 0;
+  					lengthDeletions2 = 0;
+  					lastequality = null;
+  					changes = true;
+  				}
+  			}
+  			pointer++;
+  		}
+
+  		// Normalize the diff.
+  		if (changes) {
+  			this.diffCleanupMerge(diffs);
+  		}
+
+  		// Find any overlaps between deletions and insertions.
+  		// e.g: <del>abcxxx</del><ins>xxxdef</ins>
+  		//   -> <del>abc</del>xxx<ins>def</ins>
+  		// e.g: <del>xxxabc</del><ins>defxxx</ins>
+  		//   -> <ins>def</ins>xxx<del>abc</del>
+  		// Only extract an overlap if it is as big as the edit ahead or behind it.
+  		pointer = 1;
+  		while (pointer < diffs.length) {
+  			if (diffs[pointer - 1][0] === DIFF_DELETE && diffs[pointer][0] === DIFF_INSERT) {
+  				deletion = diffs[pointer - 1][1];
+  				insertion = diffs[pointer][1];
+  				overlapLength1 = this.diffCommonOverlap(deletion, insertion);
+  				overlapLength2 = this.diffCommonOverlap(insertion, deletion);
+  				if (overlapLength1 >= overlapLength2) {
+  					if (overlapLength1 >= deletion.length / 2 || overlapLength1 >= insertion.length / 2) {
+
+  						// Overlap found.  Insert an equality and trim the surrounding edits.
+  						diffs.splice(pointer, 0, [DIFF_EQUAL, insertion.substring(0, overlapLength1)]);
+  						diffs[pointer - 1][1] = deletion.substring(0, deletion.length - overlapLength1);
+  						diffs[pointer + 1][1] = insertion.substring(overlapLength1);
+  						pointer++;
+  					}
+  				} else {
+  					if (overlapLength2 >= deletion.length / 2 || overlapLength2 >= insertion.length / 2) {
+
+  						// Reverse overlap found.
+  						// Insert an equality and swap and trim the surrounding edits.
+  						diffs.splice(pointer, 0, [DIFF_EQUAL, deletion.substring(0, overlapLength2)]);
+
+  						diffs[pointer - 1][0] = DIFF_INSERT;
+  						diffs[pointer - 1][1] = insertion.substring(0, insertion.length - overlapLength2);
+  						diffs[pointer + 1][0] = DIFF_DELETE;
+  						diffs[pointer + 1][1] = deletion.substring(overlapLength2);
+  						pointer++;
+  					}
+  				}
+  				pointer++;
+  			}
+  			pointer++;
+  		}
+  	};
+
+  	/**
+    * Determine if the suffix of one string is the prefix of another.
+    * @param {string} text1 First string.
+    * @param {string} text2 Second string.
+    * @return {number} The number of characters common to the end of the first
+    *     string and the start of the second string.
+    * @private
+    */
+  	DiffMatchPatch.prototype.diffCommonOverlap = function (text1, text2) {
+  		var text1Length, text2Length, textLength, best, length, pattern, found;
+
+  		// Cache the text lengths to prevent multiple calls.
+  		text1Length = text1.length;
+  		text2Length = text2.length;
+
+  		// Eliminate the null case.
+  		if (text1Length === 0 || text2Length === 0) {
+  			return 0;
+  		}
+
+  		// Truncate the longer string.
+  		if (text1Length > text2Length) {
+  			text1 = text1.substring(text1Length - text2Length);
+  		} else if (text1Length < text2Length) {
+  			text2 = text2.substring(0, text1Length);
+  		}
+  		textLength = Math.min(text1Length, text2Length);
+
+  		// Quick check for the worst case.
+  		if (text1 === text2) {
+  			return textLength;
+  		}
+
+  		// Start by looking for a single character match
+  		// and increase length until no match is found.
+  		// Performance analysis: https://neil.fraser.name/news/2010/11/04/
+  		best = 0;
+  		length = 1;
+  		while (true) {
+  			pattern = text1.substring(textLength - length);
+  			found = text2.indexOf(pattern);
+  			if (found === -1) {
+  				return best;
+  			}
+  			length += found;
+  			if (found === 0 || text1.substring(textLength - length) === text2.substring(0, length)) {
+  				best = length;
+  				length++;
+  			}
+  		}
+  	};
+
+  	/**
+    * Split two texts into an array of strings.  Reduce the texts to a string of
+    * hashes where each Unicode character represents one line.
+    * @param {string} text1 First string.
+    * @param {string} text2 Second string.
+    * @return {{chars1: string, chars2: string, lineArray: !Array.<string>}}
+    *     An object containing the encoded text1, the encoded text2 and
+    *     the array of unique strings.
+    *     The zeroth element of the array of unique strings is intentionally blank.
+    * @private
+    */
+  	DiffMatchPatch.prototype.diffLinesToChars = function (text1, text2) {
+  		var lineArray, lineHash, chars1, chars2;
+  		lineArray = []; // E.g. lineArray[4] === 'Hello\n'
+  		lineHash = {}; // E.g. lineHash['Hello\n'] === 4
+
+  		// '\x00' is a valid character, but various debuggers don't like it.
+  		// So we'll insert a junk entry to avoid generating a null character.
+  		lineArray[0] = "";
+
+  		/**
+     * Split a text into an array of strings.  Reduce the texts to a string of
+     * hashes where each Unicode character represents one line.
+     * Modifies linearray and linehash through being a closure.
+     * @param {string} text String to encode.
+     * @return {string} Encoded string.
+     * @private
+     */
+  		function diffLinesToCharsMunge(text) {
+  			var chars, lineStart, lineEnd, lineArrayLength, line;
+  			chars = "";
+
+  			// Walk the text, pulling out a substring for each line.
+  			// text.split('\n') would would temporarily double our memory footprint.
+  			// Modifying text would create many large strings to garbage collect.
+  			lineStart = 0;
+  			lineEnd = -1;
+
+  			// Keeping our own length variable is faster than looking it up.
+  			lineArrayLength = lineArray.length;
+  			while (lineEnd < text.length - 1) {
+  				lineEnd = text.indexOf("\n", lineStart);
+  				if (lineEnd === -1) {
+  					lineEnd = text.length - 1;
+  				}
+  				line = text.substring(lineStart, lineEnd + 1);
+  				lineStart = lineEnd + 1;
+
+  				if (lineHash.hasOwnProperty ? lineHash.hasOwnProperty(line) : lineHash[line] !== undefined) {
+  					chars += String.fromCharCode(lineHash[line]);
+  				} else {
+  					chars += String.fromCharCode(lineArrayLength);
+  					lineHash[line] = lineArrayLength;
+  					lineArray[lineArrayLength++] = line;
+  				}
+  			}
+  			return chars;
+  		}
+
+  		chars1 = diffLinesToCharsMunge(text1);
+  		chars2 = diffLinesToCharsMunge(text2);
+  		return {
+  			chars1: chars1,
+  			chars2: chars2,
+  			lineArray: lineArray
+  		};
+  	};
+
+  	/**
+    * Rehydrate the text in a diff from a string of line hashes to real lines of
+    * text.
+    * @param {!Array.<!DiffMatchPatch.Diff>} diffs Array of diff tuples.
+    * @param {!Array.<string>} lineArray Array of unique strings.
+    * @private
+    */
+  	DiffMatchPatch.prototype.diffCharsToLines = function (diffs, lineArray) {
+  		var x, chars, text, y;
+  		for (x = 0; x < diffs.length; x++) {
+  			chars = diffs[x][1];
+  			text = [];
+  			for (y = 0; y < chars.length; y++) {
+  				text[y] = lineArray[chars.charCodeAt(y)];
+  			}
+  			diffs[x][1] = text.join("");
+  		}
+  	};
+
+  	/**
+    * Reorder and merge like edit sections.  Merge equalities.
+    * Any edit section can move as long as it doesn't cross an equality.
+    * @param {!Array.<!DiffMatchPatch.Diff>} diffs Array of diff tuples.
+    */
+  	DiffMatchPatch.prototype.diffCleanupMerge = function (diffs) {
+  		var pointer, countDelete, countInsert, textInsert, textDelete, commonlength, changes, diffPointer, position;
+  		diffs.push([DIFF_EQUAL, ""]); // Add a dummy entry at the end.
+  		pointer = 0;
+  		countDelete = 0;
+  		countInsert = 0;
+  		textDelete = "";
+  		textInsert = "";
+
+  		while (pointer < diffs.length) {
+  			switch (diffs[pointer][0]) {
+  				case DIFF_INSERT:
+  					countInsert++;
+  					textInsert += diffs[pointer][1];
+  					pointer++;
+  					break;
+  				case DIFF_DELETE:
+  					countDelete++;
+  					textDelete += diffs[pointer][1];
+  					pointer++;
+  					break;
+  				case DIFF_EQUAL:
+
+  					// Upon reaching an equality, check for prior redundancies.
+  					if (countDelete + countInsert > 1) {
+  						if (countDelete !== 0 && countInsert !== 0) {
+
+  							// Factor out any common prefixes.
+  							commonlength = this.diffCommonPrefix(textInsert, textDelete);
+  							if (commonlength !== 0) {
+  								if (pointer - countDelete - countInsert > 0 && diffs[pointer - countDelete - countInsert - 1][0] === DIFF_EQUAL) {
+  									diffs[pointer - countDelete - countInsert - 1][1] += textInsert.substring(0, commonlength);
+  								} else {
+  									diffs.splice(0, 0, [DIFF_EQUAL, textInsert.substring(0, commonlength)]);
+  									pointer++;
+  								}
+  								textInsert = textInsert.substring(commonlength);
+  								textDelete = textDelete.substring(commonlength);
+  							}
+
+  							// Factor out any common suffixies.
+  							commonlength = this.diffCommonSuffix(textInsert, textDelete);
+  							if (commonlength !== 0) {
+  								diffs[pointer][1] = textInsert.substring(textInsert.length - commonlength) + diffs[pointer][1];
+  								textInsert = textInsert.substring(0, textInsert.length - commonlength);
+  								textDelete = textDelete.substring(0, textDelete.length - commonlength);
+  							}
+  						}
+
+  						// Delete the offending records and add the merged ones.
+  						if (countDelete === 0) {
+  							diffs.splice(pointer - countInsert, countDelete + countInsert, [DIFF_INSERT, textInsert]);
+  						} else if (countInsert === 0) {
+  							diffs.splice(pointer - countDelete, countDelete + countInsert, [DIFF_DELETE, textDelete]);
+  						} else {
+  							diffs.splice(pointer - countDelete - countInsert, countDelete + countInsert, [DIFF_DELETE, textDelete], [DIFF_INSERT, textInsert]);
+  						}
+  						pointer = pointer - countDelete - countInsert + (countDelete ? 1 : 0) + (countInsert ? 1 : 0) + 1;
+  					} else if (pointer !== 0 && diffs[pointer - 1][0] === DIFF_EQUAL) {
+
+  						// Merge this equality with the previous one.
+  						diffs[pointer - 1][1] += diffs[pointer][1];
+  						diffs.splice(pointer, 1);
+  					} else {
+  						pointer++;
+  					}
+  					countInsert = 0;
+  					countDelete = 0;
+  					textDelete = "";
+  					textInsert = "";
+  					break;
+  			}
+  		}
+  		if (diffs[diffs.length - 1][1] === "") {
+  			diffs.pop(); // Remove the dummy entry at the end.
+  		}
+
+  		// Second pass: look for single edits surrounded on both sides by equalities
+  		// which can be shifted sideways to eliminate an equality.
+  		// e.g: A<ins>BA</ins>C -> <ins>AB</ins>AC
+  		changes = false;
+  		pointer = 1;
+
+  		// Intentionally ignore the first and last element (don't need checking).
+  		while (pointer < diffs.length - 1) {
+  			if (diffs[pointer - 1][0] === DIFF_EQUAL && diffs[pointer + 1][0] === DIFF_EQUAL) {
+
+  				diffPointer = diffs[pointer][1];
+  				position = diffPointer.substring(diffPointer.length - diffs[pointer - 1][1].length);
+
+  				// This is a single edit surrounded by equalities.
+  				if (position === diffs[pointer - 1][1]) {
+
+  					// Shift the edit over the previous equality.
+  					diffs[pointer][1] = diffs[pointer - 1][1] + diffs[pointer][1].substring(0, diffs[pointer][1].length - diffs[pointer - 1][1].length);
+  					diffs[pointer + 1][1] = diffs[pointer - 1][1] + diffs[pointer + 1][1];
+  					diffs.splice(pointer - 1, 1);
+  					changes = true;
+  				} else if (diffPointer.substring(0, diffs[pointer + 1][1].length) === diffs[pointer + 1][1]) {
+
+  					// Shift the edit over the next equality.
+  					diffs[pointer - 1][1] += diffs[pointer + 1][1];
+  					diffs[pointer][1] = diffs[pointer][1].substring(diffs[pointer + 1][1].length) + diffs[pointer + 1][1];
+  					diffs.splice(pointer + 1, 1);
+  					changes = true;
+  				}
+  			}
+  			pointer++;
+  		}
+
+  		// If shifts were made, the diff needs reordering and another shift sweep.
+  		if (changes) {
+  			this.diffCleanupMerge(diffs);
+  		}
+  	};
+
+  	return function (o, n) {
+  		var diff, output, text;
+  		diff = new DiffMatchPatch();
+  		output = diff.DiffMain(o, n);
+  		diff.diffCleanupEfficiency(output);
+  		text = diff.diffPrettyHtml(output);
+
+  		return text;
+  	};
+  }();
+
+  (function () {
+
+  	// Only interact with URLs via window.location
+  	var location = typeof window !== "undefined" && window.location;
+  	if (!location) {
+  		return;
+  	}
+
+  	var urlParams = getUrlParams();
+
+  	QUnit.urlParams = urlParams;
+
+  	// Match module/test by inclusion in an array
+  	QUnit.config.moduleId = [].concat(urlParams.moduleId || []);
+  	QUnit.config.testId = [].concat(urlParams.testId || []);
+
+  	// Exact case-insensitive match of the module name
+  	QUnit.config.module = urlParams.module;
+
+  	// Regular expression or case-insenstive substring match against "moduleName: testName"
+  	QUnit.config.filter = urlParams.filter;
+
+  	// Test order randomization
+  	if (urlParams.seed === true) {
+
+  		// Generate a random seed if the option is specified without a value
+  		QUnit.config.seed = Math.random().toString(36).slice(2);
+  	} else if (urlParams.seed) {
+  		QUnit.config.seed = urlParams.seed;
+  	}
+
+  	// Add URL-parameter-mapped config values with UI form rendering data
+  	QUnit.config.urlConfig.push({
+  		id: "hidepassed",
+  		label: "Hide passed tests",
+  		tooltip: "Only show tests and assertions that fail. Stored as query-strings."
+  	}, {
+  		id: "noglobals",
+  		label: "Check for Globals",
+  		tooltip: "Enabling this will test if any test introduces new properties on the " + "global object (`window` in Browsers). Stored as query-strings."
+  	}, {
+  		id: "notrycatch",
+  		label: "No try-catch",
+  		tooltip: "Enabling this will run tests outside of a try-catch block. Makes debugging " + "exceptions in IE reasonable. Stored as query-strings."
+  	});
+
+  	QUnit.begin(function () {
+  		var i,
+  		    option,
+  		    urlConfig = QUnit.config.urlConfig;
+
+  		for (i = 0; i < urlConfig.length; i++) {
+
+  			// Options can be either strings or objects with nonempty "id" properties
+  			option = QUnit.config.urlConfig[i];
+  			if (typeof option !== "string") {
+  				option = option.id;
+  			}
+
+  			if (QUnit.config[option] === undefined) {
+  				QUnit.config[option] = urlParams[option];
+  			}
+  		}
+  	});
+
+  	function getUrlParams() {
+  		var i, param, name, value;
+  		var urlParams = Object.create(null);
+  		var params = location.search.slice(1).split("&");
+  		var length = params.length;
+
+  		for (i = 0; i < length; i++) {
+  			if (params[i]) {
+  				param = params[i].split("=");
+  				name = decodeQueryParam(param[0]);
+
+  				// Allow just a key to turn on a flag, e.g., test.html?noglobals
+  				value = param.length === 1 || decodeQueryParam(param.slice(1).join("="));
+  				if (name in urlParams) {
+  					urlParams[name] = [].concat(urlParams[name], value);
+  				} else {
+  					urlParams[name] = value;
+  				}
+  			}
+  		}
+
+  		return urlParams;
+  	}
+
+  	function decodeQueryParam(param) {
+  		return decodeURIComponent(param.replace(/\+/g, "%20"));
+  	}
+  })();
+
+}((function() { return this; }())));
diff --git a/node_modules/seedrandom/test/lib/require.js b/node_modules/seedrandom/test/lib/require.js
new file mode 100644
index 0000000..4db1424
--- /dev/null
+++ b/node_modules/seedrandom/test/lib/require.js
@@ -0,0 +1,2142 @@
+/** vim: et:ts=4:sw=4:sts=4
+ * @license RequireJS 2.3.2 Copyright jQuery Foundation and other contributors.
+ * Released under MIT license, https://github.com/requirejs/requirejs/blob/master/LICENSE
+ */
+//Not using strict: uneven strict support in browsers, #392, and causes
+//problems with requirejs.exec()/transpiler plugins that may not be strict.
+/*jslint regexp: true, nomen: true, sloppy: true */
+/*global window, navigator, document, importScripts, setTimeout, opera */
+
+var requirejs, require, define;
+(function (global, setTimeout) {
+    var req, s, head, baseElement, dataMain, src,
+        interactiveScript, currentlyAddingScript, mainScript, subPath,
+        version = '2.3.2',
+        commentRegExp = /\/\*[\s\S]*?\*\/|([^:"'=]|^)\/\/.*$/mg,
+        cjsRequireRegExp = /[^.]\s*require\s*\(\s*["']([^'"\s]+)["']\s*\)/g,
+        jsSuffixRegExp = /\.js$/,
+        currDirRegExp = /^\.\//,
+        op = Object.prototype,
+        ostring = op.toString,
+        hasOwn = op.hasOwnProperty,
+        isBrowser = !!(typeof window !== 'undefined' && typeof navigator !== 'undefined' && window.document),
+        isWebWorker = !isBrowser && typeof importScripts !== 'undefined',
+        //PS3 indicates loaded and complete, but need to wait for complete
+        //specifically. Sequence is 'loading', 'loaded', execution,
+        // then 'complete'. The UA check is unfortunate, but not sure how
+        //to feature test w/o causing perf issues.
+        readyRegExp = isBrowser && navigator.platform === 'PLAYSTATION 3' ?
+                      /^complete$/ : /^(complete|loaded)$/,
+        defContextName = '_',
+        //Oh the tragedy, detecting opera. See the usage of isOpera for reason.
+        isOpera = typeof opera !== 'undefined' && opera.toString() === '[object Opera]',
+        contexts = {},
+        cfg = {},
+        globalDefQueue = [],
+        useInteractive = false;
+
+    //Could match something like ')//comment', do not lose the prefix to comment.
+    function commentReplace(match, singlePrefix) {
+        return singlePrefix || '';
+    }
+
+    function isFunction(it) {
+        return ostring.call(it) === '[object Function]';
+    }
+
+    function isArray(it) {
+        return ostring.call(it) === '[object Array]';
+    }
+
+    /**
+     * Helper function for iterating over an array. If the func returns
+     * a true value, it will break out of the loop.
+     */
+    function each(ary, func) {
+        if (ary) {
+            var i;
+            for (i = 0; i < ary.length; i += 1) {
+                if (ary[i] && func(ary[i], i, ary)) {
+                    break;
+                }
+            }
+        }
+    }
+
+    /**
+     * Helper function for iterating over an array backwards. If the func
+     * returns a true value, it will break out of the loop.
+     */
+    function eachReverse(ary, func) {
+        if (ary) {
+            var i;
+            for (i = ary.length - 1; i > -1; i -= 1) {
+                if (ary[i] && func(ary[i], i, ary)) {
+                    break;
+                }
+            }
+        }
+    }
+
+    function hasProp(obj, prop) {
+        return hasOwn.call(obj, prop);
+    }
+
+    function getOwn(obj, prop) {
+        return hasProp(obj, prop) && obj[prop];
+    }
+
+    /**
+     * Cycles over properties in an object and calls a function for each
+     * property value. If the function returns a truthy value, then the
+     * iteration is stopped.
+     */
+    function eachProp(obj, func) {
+        var prop;
+        for (prop in obj) {
+            if (hasProp(obj, prop)) {
+                if (func(obj[prop], prop)) {
+                    break;
+                }
+            }
+        }
+    }
+
+    /**
+     * Simple function to mix in properties from source into target,
+     * but only if target does not already have a property of the same name.
+     */
+    function mixin(target, source, force, deepStringMixin) {
+        if (source) {
+            eachProp(source, function (value, prop) {
+                if (force || !hasProp(target, prop)) {
+                    if (deepStringMixin && typeof value === 'object' && value &&
+                        !isArray(value) && !isFunction(value) &&
+                        !(value instanceof RegExp)) {
+
+                        if (!target[prop]) {
+                            target[prop] = {};
+                        }
+                        mixin(target[prop], value, force, deepStringMixin);
+                    } else {
+                        target[prop] = value;
+                    }
+                }
+            });
+        }
+        return target;
+    }
+
+    //Similar to Function.prototype.bind, but the 'this' object is specified
+    //first, since it is easier to read/figure out what 'this' will be.
+    function bind(obj, fn) {
+        return function () {
+            return fn.apply(obj, arguments);
+        };
+    }
+
+    function scripts() {
+        return document.getElementsByTagName('script');
+    }
+
+    function defaultOnError(err) {
+        throw err;
+    }
+
+    //Allow getting a global that is expressed in
+    //dot notation, like 'a.b.c'.
+    function getGlobal(value) {
+        if (!value) {
+            return value;
+        }
+        var g = global;
+        each(value.split('.'), function (part) {
+            g = g[part];
+        });
+        return g;
+    }
+
+    /**
+     * Constructs an error with a pointer to an URL with more information.
+     * @param {String} id the error ID that maps to an ID on a web page.
+     * @param {String} message human readable error.
+     * @param {Error} [err] the original error, if there is one.
+     *
+     * @returns {Error}
+     */
+    function makeError(id, msg, err, requireModules) {
+        var e = new Error(msg + '\nhttp://requirejs.org/docs/errors.html#' + id);
+        e.requireType = id;
+        e.requireModules = requireModules;
+        if (err) {
+            e.originalError = err;
+        }
+        return e;
+    }
+
+    if (typeof define !== 'undefined') {
+        //If a define is already in play via another AMD loader,
+        //do not overwrite.
+        return;
+    }
+
+    if (typeof requirejs !== 'undefined') {
+        if (isFunction(requirejs)) {
+            //Do not overwrite an existing requirejs instance.
+            return;
+        }
+        cfg = requirejs;
+        requirejs = undefined;
+    }
+
+    //Allow for a require config object
+    if (typeof require !== 'undefined' && !isFunction(require)) {
+        //assume it is a config object.
+        cfg = require;
+        require = undefined;
+    }
+
+    function newContext(contextName) {
+        var inCheckLoaded, Module, context, handlers,
+            checkLoadedTimeoutId,
+            config = {
+                //Defaults. Do not set a default for map
+                //config to speed up normalize(), which
+                //will run faster if there is no default.
+                waitSeconds: 7,
+                baseUrl: './',
+                paths: {},
+                bundles: {},
+                pkgs: {},
+                shim: {},
+                config: {}
+            },
+            registry = {},
+            //registry of just enabled modules, to speed
+            //cycle breaking code when lots of modules
+            //are registered, but not activated.
+            enabledRegistry = {},
+            undefEvents = {},
+            defQueue = [],
+            defined = {},
+            urlFetched = {},
+            bundlesMap = {},
+            requireCounter = 1,
+            unnormalizedCounter = 1;
+
+        /**
+         * Trims the . and .. from an array of path segments.
+         * It will keep a leading path segment if a .. will become
+         * the first path segment, to help with module name lookups,
+         * which act like paths, but can be remapped. But the end result,
+         * all paths that use this function should look normalized.
+         * NOTE: this method MODIFIES the input array.
+         * @param {Array} ary the array of path segments.
+         */
+        function trimDots(ary) {
+            var i, part;
+            for (i = 0; i < ary.length; i++) {
+                part = ary[i];
+                if (part === '.') {
+                    ary.splice(i, 1);
+                    i -= 1;
+                } else if (part === '..') {
+                    // If at the start, or previous value is still ..,
+                    // keep them so that when converted to a path it may
+                    // still work when converted to a path, even though
+                    // as an ID it is less than ideal. In larger point
+                    // releases, may be better to just kick out an error.
+                    if (i === 0 || (i === 1 && ary[2] === '..') || ary[i - 1] === '..') {
+                        continue;
+                    } else if (i > 0) {
+                        ary.splice(i - 1, 2);
+                        i -= 2;
+                    }
+                }
+            }
+        }
+
+        /**
+         * Given a relative module name, like ./something, normalize it to
+         * a real name that can be mapped to a path.
+         * @param {String} name the relative name
+         * @param {String} baseName a real name that the name arg is relative
+         * to.
+         * @param {Boolean} applyMap apply the map config to the value. Should
+         * only be done if this normalization is for a dependency ID.
+         * @returns {String} normalized name
+         */
+        function normalize(name, baseName, applyMap) {
+            var pkgMain, mapValue, nameParts, i, j, nameSegment, lastIndex,
+                foundMap, foundI, foundStarMap, starI, normalizedBaseParts,
+                baseParts = (baseName && baseName.split('/')),
+                map = config.map,
+                starMap = map && map['*'];
+
+            //Adjust any relative paths.
+            if (name) {
+                name = name.split('/');
+                lastIndex = name.length - 1;
+
+                // If wanting node ID compatibility, strip .js from end
+                // of IDs. Have to do this here, and not in nameToUrl
+                // because node allows either .js or non .js to map
+                // to same file.
+                if (config.nodeIdCompat && jsSuffixRegExp.test(name[lastIndex])) {
+                    name[lastIndex] = name[lastIndex].replace(jsSuffixRegExp, '');
+                }
+
+                // Starts with a '.' so need the baseName
+                if (name[0].charAt(0) === '.' && baseParts) {
+                    //Convert baseName to array, and lop off the last part,
+                    //so that . matches that 'directory' and not name of the baseName's
+                    //module. For instance, baseName of 'one/two/three', maps to
+                    //'one/two/three.js', but we want the directory, 'one/two' for
+                    //this normalization.
+                    normalizedBaseParts = baseParts.slice(0, baseParts.length - 1);
+                    name = normalizedBaseParts.concat(name);
+                }
+
+                trimDots(name);
+                name = name.join('/');
+            }
+
+            //Apply map config if available.
+            if (applyMap && map && (baseParts || starMap)) {
+                nameParts = name.split('/');
+
+                outerLoop: for (i = nameParts.length; i > 0; i -= 1) {
+                    nameSegment = nameParts.slice(0, i).join('/');
+
+                    if (baseParts) {
+                        //Find the longest baseName segment match in the config.
+                        //So, do joins on the biggest to smallest lengths of baseParts.
+                        for (j = baseParts.length; j > 0; j -= 1) {
+                            mapValue = getOwn(map, baseParts.slice(0, j).join('/'));
+
+                            //baseName segment has config, find if it has one for
+                            //this name.
+                            if (mapValue) {
+                                mapValue = getOwn(mapValue, nameSegment);
+                                if (mapValue) {
+                                    //Match, update name to the new value.
+                                    foundMap = mapValue;
+                                    foundI = i;
+                                    break outerLoop;
+                                }
+                            }
+                        }
+                    }
+
+                    //Check for a star map match, but just hold on to it,
+                    //if there is a shorter segment match later in a matching
+                    //config, then favor over this star map.
+                    if (!foundStarMap && starMap && getOwn(starMap, nameSegment)) {
+                        foundStarMap = getOwn(starMap, nameSegment);
+                        starI = i;
+                    }
+                }
+
+                if (!foundMap && foundStarMap) {
+                    foundMap = foundStarMap;
+                    foundI = starI;
+                }
+
+                if (foundMap) {
+                    nameParts.splice(0, foundI, foundMap);
+                    name = nameParts.join('/');
+                }
+            }
+
+            // If the name points to a package's name, use
+            // the package main instead.
+            pkgMain = getOwn(config.pkgs, name);
+
+            return pkgMain ? pkgMain : name;
+        }
+
+        function removeScript(name) {
+            if (isBrowser) {
+                each(scripts(), function (scriptNode) {
+                    if (scriptNode.getAttribute('data-requiremodule') === name &&
+                            scriptNode.getAttribute('data-requirecontext') === context.contextName) {
+                        scriptNode.parentNode.removeChild(scriptNode);
+                        return true;
+                    }
+                });
+            }
+        }
+
+        function hasPathFallback(id) {
+            var pathConfig = getOwn(config.paths, id);
+            if (pathConfig && isArray(pathConfig) && pathConfig.length > 1) {
+                //Pop off the first array value, since it failed, and
+                //retry
+                pathConfig.shift();
+                context.require.undef(id);
+
+                //Custom require that does not do map translation, since
+                //ID is "absolute", already mapped/resolved.
+                context.makeRequire(null, {
+                    skipMap: true
+                })([id]);
+
+                return true;
+            }
+        }
+
+        //Turns a plugin!resource to [plugin, resource]
+        //with the plugin being undefined if the name
+        //did not have a plugin prefix.
+        function splitPrefix(name) {
+            var prefix,
+                index = name ? name.indexOf('!') : -1;
+            if (index > -1) {
+                prefix = name.substring(0, index);
+                name = name.substring(index + 1, name.length);
+            }
+            return [prefix, name];
+        }
+
+        /**
+         * Creates a module mapping that includes plugin prefix, module
+         * name, and path. If parentModuleMap is provided it will
+         * also normalize the name via require.normalize()
+         *
+         * @param {String} name the module name
+         * @param {String} [parentModuleMap] parent module map
+         * for the module name, used to resolve relative names.
+         * @param {Boolean} isNormalized: is the ID already normalized.
+         * This is true if this call is done for a define() module ID.
+         * @param {Boolean} applyMap: apply the map config to the ID.
+         * Should only be true if this map is for a dependency.
+         *
+         * @returns {Object}
+         */
+        function makeModuleMap(name, parentModuleMap, isNormalized, applyMap) {
+            var url, pluginModule, suffix, nameParts,
+                prefix = null,
+                parentName = parentModuleMap ? parentModuleMap.name : null,
+                originalName = name,
+                isDefine = true,
+                normalizedName = '';
+
+            //If no name, then it means it is a require call, generate an
+            //internal name.
+            if (!name) {
+                isDefine = false;
+                name = '_@r' + (requireCounter += 1);
+            }
+
+            nameParts = splitPrefix(name);
+            prefix = nameParts[0];
+            name = nameParts[1];
+
+            if (prefix) {
+                prefix = normalize(prefix, parentName, applyMap);
+                pluginModule = getOwn(defined, prefix);
+            }
+
+            //Account for relative paths if there is a base name.
+            if (name) {
+                if (prefix) {
+                    if (pluginModule && pluginModule.normalize) {
+                        //Plugin is loaded, use its normalize method.
+                        normalizedName = pluginModule.normalize(name, function (name) {
+                            return normalize(name, parentName, applyMap);
+                        });
+                    } else {
+                        // If nested plugin references, then do not try to
+                        // normalize, as it will not normalize correctly. This
+                        // places a restriction on resourceIds, and the longer
+                        // term solution is not to normalize until plugins are
+                        // loaded and all normalizations to allow for async
+                        // loading of a loader plugin. But for now, fixes the
+                        // common uses. Details in #1131
+                        normalizedName = name.indexOf('!') === -1 ?
+                                         normalize(name, parentName, applyMap) :
+                                         name;
+                    }
+                } else {
+                    //A regular module.
+                    normalizedName = normalize(name, parentName, applyMap);
+
+                    //Normalized name may be a plugin ID due to map config
+                    //application in normalize. The map config values must
+                    //already be normalized, so do not need to redo that part.
+                    nameParts = splitPrefix(normalizedName);
+                    prefix = nameParts[0];
+                    normalizedName = nameParts[1];
+                    isNormalized = true;
+
+                    url = context.nameToUrl(normalizedName);
+                }
+            }
+
+            //If the id is a plugin id that cannot be determined if it needs
+            //normalization, stamp it with a unique ID so two matching relative
+            //ids that may conflict can be separate.
+            suffix = prefix && !pluginModule && !isNormalized ?
+                     '_unnormalized' + (unnormalizedCounter += 1) :
+                     '';
+
+            return {
+                prefix: prefix,
+                name: normalizedName,
+                parentMap: parentModuleMap,
+                unnormalized: !!suffix,
+                url: url,
+                originalName: originalName,
+                isDefine: isDefine,
+                id: (prefix ?
+                        prefix + '!' + normalizedName :
+                        normalizedName) + suffix
+            };
+        }
+
+        function getModule(depMap) {
+            var id = depMap.id,
+                mod = getOwn(registry, id);
+
+            if (!mod) {
+                mod = registry[id] = new context.Module(depMap);
+            }
+
+            return mod;
+        }
+
+        function on(depMap, name, fn) {
+            var id = depMap.id,
+                mod = getOwn(registry, id);
+
+            if (hasProp(defined, id) &&
+                    (!mod || mod.defineEmitComplete)) {
+                if (name === 'defined') {
+                    fn(defined[id]);
+                }
+            } else {
+                mod = getModule(depMap);
+                if (mod.error && name === 'error') {
+                    fn(mod.error);
+                } else {
+                    mod.on(name, fn);
+                }
+            }
+        }
+
+        function onError(err, errback) {
+            var ids = err.requireModules,
+                notified = false;
+
+            if (errback) {
+                errback(err);
+            } else {
+                each(ids, function (id) {
+                    var mod = getOwn(registry, id);
+                    if (mod) {
+                        //Set error on module, so it skips timeout checks.
+                        mod.error = err;
+                        if (mod.events.error) {
+                            notified = true;
+                            mod.emit('error', err);
+                        }
+                    }
+                });
+
+                if (!notified) {
+                    req.onError(err);
+                }
+            }
+        }
+
+        /**
+         * Internal method to transfer globalQueue items to this context's
+         * defQueue.
+         */
+        function takeGlobalQueue() {
+            //Push all the globalDefQueue items into the context's defQueue
+            if (globalDefQueue.length) {
+                each(globalDefQueue, function(queueItem) {
+                    var id = queueItem[0];
+                    if (typeof id === 'string') {
+                        context.defQueueMap[id] = true;
+                    }
+                    defQueue.push(queueItem);
+                });
+                globalDefQueue = [];
+            }
+        }
+
+        handlers = {
+            'require': function (mod) {
+                if (mod.require) {
+                    return mod.require;
+                } else {
+                    return (mod.require = context.makeRequire(mod.map));
+                }
+            },
+            'exports': function (mod) {
+                mod.usingExports = true;
+                if (mod.map.isDefine) {
+                    if (mod.exports) {
+                        return (defined[mod.map.id] = mod.exports);
+                    } else {
+                        return (mod.exports = defined[mod.map.id] = {});
+                    }
+                }
+            },
+            'module': function (mod) {
+                if (mod.module) {
+                    return mod.module;
+                } else {
+                    return (mod.module = {
+                        id: mod.map.id,
+                        uri: mod.map.url,
+                        config: function () {
+                            return getOwn(config.config, mod.map.id) || {};
+                        },
+                        exports: mod.exports || (mod.exports = {})
+                    });
+                }
+            }
+        };
+
+        function cleanRegistry(id) {
+            //Clean up machinery used for waiting modules.
+            delete registry[id];
+            delete enabledRegistry[id];
+        }
+
+        function breakCycle(mod, traced, processed) {
+            var id = mod.map.id;
+
+            if (mod.error) {
+                mod.emit('error', mod.error);
+            } else {
+                traced[id] = true;
+                each(mod.depMaps, function (depMap, i) {
+                    var depId = depMap.id,
+                        dep = getOwn(registry, depId);
+
+                    //Only force things that have not completed
+                    //being defined, so still in the registry,
+                    //and only if it has not been matched up
+                    //in the module already.
+                    if (dep && !mod.depMatched[i] && !processed[depId]) {
+                        if (getOwn(traced, depId)) {
+                            mod.defineDep(i, defined[depId]);
+                            mod.check(); //pass false?
+                        } else {
+                            breakCycle(dep, traced, processed);
+                        }
+                    }
+                });
+                processed[id] = true;
+            }
+        }
+
+        function checkLoaded() {
+            var err, usingPathFallback,
+                waitInterval = config.waitSeconds * 1000,
+                //It is possible to disable the wait interval by using waitSeconds of 0.
+                expired = waitInterval && (context.startTime + waitInterval) < new Date().getTime(),
+                noLoads = [],
+                reqCalls = [],
+                stillLoading = false,
+                needCycleCheck = true;
+
+            //Do not bother if this call was a result of a cycle break.
+            if (inCheckLoaded) {
+                return;
+            }
+
+            inCheckLoaded = true;
+
+            //Figure out the state of all the modules.
+            eachProp(enabledRegistry, function (mod) {
+                var map = mod.map,
+                    modId = map.id;
+
+                //Skip things that are not enabled or in error state.
+                if (!mod.enabled) {
+                    return;
+                }
+
+                if (!map.isDefine) {
+                    reqCalls.push(mod);
+                }
+
+                if (!mod.error) {
+                    //If the module should be executed, and it has not
+                    //been inited and time is up, remember it.
+                    if (!mod.inited && expired) {
+                        if (hasPathFallback(modId)) {
+                            usingPathFallback = true;
+                            stillLoading = true;
+                        } else {
+                            noLoads.push(modId);
+                            removeScript(modId);
+                        }
+                    } else if (!mod.inited && mod.fetched && map.isDefine) {
+                        stillLoading = true;
+                        if (!map.prefix) {
+                            //No reason to keep looking for unfinished
+                            //loading. If the only stillLoading is a
+                            //plugin resource though, keep going,
+                            //because it may be that a plugin resource
+                            //is waiting on a non-plugin cycle.
+                            return (needCycleCheck = false);
+                        }
+                    }
+                }
+            });
+
+            if (expired && noLoads.length) {
+                //If wait time expired, throw error of unloaded modules.
+                err = makeError('timeout', 'Load timeout for modules: ' + noLoads, null, noLoads);
+                err.contextName = context.contextName;
+                return onError(err);
+            }
+
+            //Not expired, check for a cycle.
+            if (needCycleCheck) {
+                each(reqCalls, function (mod) {
+                    breakCycle(mod, {}, {});
+                });
+            }
+
+            //If still waiting on loads, and the waiting load is something
+            //other than a plugin resource, or there are still outstanding
+            //scripts, then just try back later.
+            if ((!expired || usingPathFallback) && stillLoading) {
+                //Something is still waiting to load. Wait for it, but only
+                //if a timeout is not already in effect.
+                if ((isBrowser || isWebWorker) && !checkLoadedTimeoutId) {
+                    checkLoadedTimeoutId = setTimeout(function () {
+                        checkLoadedTimeoutId = 0;
+                        checkLoaded();
+                    }, 50);
+                }
+            }
+
+            inCheckLoaded = false;
+        }
+
+        Module = function (map) {
+            this.events = getOwn(undefEvents, map.id) || {};
+            this.map = map;
+            this.shim = getOwn(config.shim, map.id);
+            this.depExports = [];
+            this.depMaps = [];
+            this.depMatched = [];
+            this.pluginMaps = {};
+            this.depCount = 0;
+
+            /* this.exports this.factory
+               this.depMaps = [],
+               this.enabled, this.fetched
+            */
+        };
+
+        Module.prototype = {
+            init: function (depMaps, factory, errback, options) {
+                options = options || {};
+
+                //Do not do more inits if already done. Can happen if there
+                //are multiple define calls for the same module. That is not
+                //a normal, common case, but it is also not unexpected.
+                if (this.inited) {
+                    return;
+                }
+
+                this.factory = factory;
+
+                if (errback) {
+                    //Register for errors on this module.
+                    this.on('error', errback);
+                } else if (this.events.error) {
+                    //If no errback already, but there are error listeners
+                    //on this module, set up an errback to pass to the deps.
+                    errback = bind(this, function (err) {
+                        this.emit('error', err);
+                    });
+                }
+
+                //Do a copy of the dependency array, so that
+                //source inputs are not modified. For example
+                //"shim" deps are passed in here directly, and
+                //doing a direct modification of the depMaps array
+                //would affect that config.
+                this.depMaps = depMaps && depMaps.slice(0);
+
+                this.errback = errback;
+
+                //Indicate this module has be initialized
+                this.inited = true;
+
+                this.ignore = options.ignore;
+
+                //Could have option to init this module in enabled mode,
+                //or could have been previously marked as enabled. However,
+                //the dependencies are not known until init is called. So
+                //if enabled previously, now trigger dependencies as enabled.
+                if (options.enabled || this.enabled) {
+                    //Enable this module and dependencies.
+                    //Will call this.check()
+                    this.enable();
+                } else {
+                    this.check();
+                }
+            },
+
+            defineDep: function (i, depExports) {
+                //Because of cycles, defined callback for a given
+                //export can be called more than once.
+                if (!this.depMatched[i]) {
+                    this.depMatched[i] = true;
+                    this.depCount -= 1;
+                    this.depExports[i] = depExports;
+                }
+            },
+
+            fetch: function () {
+                if (this.fetched) {
+                    return;
+                }
+                this.fetched = true;
+
+                context.startTime = (new Date()).getTime();
+
+                var map = this.map;
+
+                //If the manager is for a plugin managed resource,
+                //ask the plugin to load it now.
+                if (this.shim) {
+                    context.makeRequire(this.map, {
+                        enableBuildCallback: true
+                    })(this.shim.deps || [], bind(this, function () {
+                        return map.prefix ? this.callPlugin() : this.load();
+                    }));
+                } else {
+                    //Regular dependency.
+                    return map.prefix ? this.callPlugin() : this.load();
+                }
+            },
+
+            load: function () {
+                var url = this.map.url;
+
+                //Regular dependency.
+                if (!urlFetched[url]) {
+                    urlFetched[url] = true;
+                    context.load(this.map.id, url);
+                }
+            },
+
+            /**
+             * Checks if the module is ready to define itself, and if so,
+             * define it.
+             */
+            check: function () {
+                if (!this.enabled || this.enabling) {
+                    return;
+                }
+
+                var err, cjsModule,
+                    id = this.map.id,
+                    depExports = this.depExports,
+                    exports = this.exports,
+                    factory = this.factory;
+
+                if (!this.inited) {
+                    // Only fetch if not already in the defQueue.
+                    if (!hasProp(context.defQueueMap, id)) {
+                        this.fetch();
+                    }
+                } else if (this.error) {
+                    this.emit('error', this.error);
+                } else if (!this.defining) {
+                    //The factory could trigger another require call
+                    //that would result in checking this module to
+                    //define itself again. If already in the process
+                    //of doing that, skip this work.
+                    this.defining = true;
+
+                    if (this.depCount < 1 && !this.defined) {
+                        if (isFunction(factory)) {
+                            //If there is an error listener, favor passing
+                            //to that instead of throwing an error. However,
+                            //only do it for define()'d  modules. require
+                            //errbacks should not be called for failures in
+                            //their callbacks (#699). However if a global
+                            //onError is set, use that.
+                            if ((this.events.error && this.map.isDefine) ||
+                                req.onError !== defaultOnError) {
+                                try {
+                                    exports = context.execCb(id, factory, depExports, exports);
+                                } catch (e) {
+                                    err = e;
+                                }
+                            } else {
+                                exports = context.execCb(id, factory, depExports, exports);
+                            }
+
+                            // Favor return value over exports. If node/cjs in play,
+                            // then will not have a return value anyway. Favor
+                            // module.exports assignment over exports object.
+                            if (this.map.isDefine && exports === undefined) {
+                                cjsModule = this.module;
+                                if (cjsModule) {
+                                    exports = cjsModule.exports;
+                                } else if (this.usingExports) {
+                                    //exports already set the defined value.
+                                    exports = this.exports;
+                                }
+                            }
+
+                            if (err) {
+                                err.requireMap = this.map;
+                                err.requireModules = this.map.isDefine ? [this.map.id] : null;
+                                err.requireType = this.map.isDefine ? 'define' : 'require';
+                                return onError((this.error = err));
+                            }
+
+                        } else {
+                            //Just a literal value
+                            exports = factory;
+                        }
+
+                        this.exports = exports;
+
+                        if (this.map.isDefine && !this.ignore) {
+                            defined[id] = exports;
+
+                            if (req.onResourceLoad) {
+                                var resLoadMaps = [];
+                                each(this.depMaps, function (depMap) {
+                                    resLoadMaps.push(depMap.normalizedMap || depMap);
+                                });
+                                req.onResourceLoad(context, this.map, resLoadMaps);
+                            }
+                        }
+
+                        //Clean up
+                        cleanRegistry(id);
+
+                        this.defined = true;
+                    }
+
+                    //Finished the define stage. Allow calling check again
+                    //to allow define notifications below in the case of a
+                    //cycle.
+                    this.defining = false;
+
+                    if (this.defined && !this.defineEmitted) {
+                        this.defineEmitted = true;
+                        this.emit('defined', this.exports);
+                        this.defineEmitComplete = true;
+                    }
+
+                }
+            },
+
+            callPlugin: function () {
+                var map = this.map,
+                    id = map.id,
+                    //Map already normalized the prefix.
+                    pluginMap = makeModuleMap(map.prefix);
+
+                //Mark this as a dependency for this plugin, so it
+                //can be traced for cycles.
+                this.depMaps.push(pluginMap);
+
+                on(pluginMap, 'defined', bind(this, function (plugin) {
+                    var load, normalizedMap, normalizedMod,
+                        bundleId = getOwn(bundlesMap, this.map.id),
+                        name = this.map.name,
+                        parentName = this.map.parentMap ? this.map.parentMap.name : null,
+                        localRequire = context.makeRequire(map.parentMap, {
+                            enableBuildCallback: true
+                        });
+
+                    //If current map is not normalized, wait for that
+                    //normalized name to load instead of continuing.
+                    if (this.map.unnormalized) {
+                        //Normalize the ID if the plugin allows it.
+                        if (plugin.normalize) {
+                            name = plugin.normalize(name, function (name) {
+                                return normalize(name, parentName, true);
+                            }) || '';
+                        }
+
+                        //prefix and name should already be normalized, no need
+                        //for applying map config again either.
+                        normalizedMap = makeModuleMap(map.prefix + '!' + name,
+                                                      this.map.parentMap);
+                        on(normalizedMap,
+                            'defined', bind(this, function (value) {
+                                this.map.normalizedMap = normalizedMap;
+                                this.init([], function () { return value; }, null, {
+                                    enabled: true,
+                                    ignore: true
+                                });
+                            }));
+
+                        normalizedMod = getOwn(registry, normalizedMap.id);
+                        if (normalizedMod) {
+                            //Mark this as a dependency for this plugin, so it
+                            //can be traced for cycles.
+                            this.depMaps.push(normalizedMap);
+
+                            if (this.events.error) {
+                                normalizedMod.on('error', bind(this, function (err) {
+                                    this.emit('error', err);
+                                }));
+                            }
+                            normalizedMod.enable();
+                        }
+
+                        return;
+                    }
+
+                    //If a paths config, then just load that file instead to
+                    //resolve the plugin, as it is built into that paths layer.
+                    if (bundleId) {
+                        this.map.url = context.nameToUrl(bundleId);
+                        this.load();
+                        return;
+                    }
+
+                    load = bind(this, function (value) {
+                        this.init([], function () { return value; }, null, {
+                            enabled: true
+                        });
+                    });
+
+                    load.error = bind(this, function (err) {
+                        this.inited = true;
+                        this.error = err;
+                        err.requireModules = [id];
+
+                        //Remove temp unnormalized modules for this module,
+                        //since they will never be resolved otherwise now.
+                        eachProp(registry, function (mod) {
+                            if (mod.map.id.indexOf(id + '_unnormalized') === 0) {
+                                cleanRegistry(mod.map.id);
+                            }
+                        });
+
+                        onError(err);
+                    });
+
+                    //Allow plugins to load other code without having to know the
+                    //context or how to 'complete' the load.
+                    load.fromText = bind(this, function (text, textAlt) {
+                        /*jslint evil: true */
+                        var moduleName = map.name,
+                            moduleMap = makeModuleMap(moduleName),
+                            hasInteractive = useInteractive;
+
+                        //As of 2.1.0, support just passing the text, to reinforce
+                        //fromText only being called once per resource. Still
+                        //support old style of passing moduleName but discard
+                        //that moduleName in favor of the internal ref.
+                        if (textAlt) {
+                            text = textAlt;
+                        }
+
+                        //Turn off interactive script matching for IE for any define
+                        //calls in the text, then turn it back on at the end.
+                        if (hasInteractive) {
+                            useInteractive = false;
+                        }
+
+                        //Prime the system by creating a module instance for
+                        //it.
+                        getModule(moduleMap);
+
+                        //Transfer any config to this other module.
+                        if (hasProp(config.config, id)) {
+                            config.config[moduleName] = config.config[id];
+                        }
+
+                        try {
+                            req.exec(text);
+                        } catch (e) {
+                            return onError(makeError('fromtexteval',
+                                             'fromText eval for ' + id +
+                                            ' failed: ' + e,
+                                             e,
+                                             [id]));
+                        }
+
+                        if (hasInteractive) {
+                            useInteractive = true;
+                        }
+
+                        //Mark this as a dependency for the plugin
+                        //resource
+                        this.depMaps.push(moduleMap);
+
+                        //Support anonymous modules.
+                        context.completeLoad(moduleName);
+
+                        //Bind the value of that module to the value for this
+                        //resource ID.
+                        localRequire([moduleName], load);
+                    });
+
+                    //Use parentName here since the plugin's name is not reliable,
+                    //could be some weird string with no path that actually wants to
+                    //reference the parentName's path.
+                    plugin.load(map.name, localRequire, load, config);
+                }));
+
+                context.enable(pluginMap, this);
+                this.pluginMaps[pluginMap.id] = pluginMap;
+            },
+
+            enable: function () {
+                enabledRegistry[this.map.id] = this;
+                this.enabled = true;
+
+                //Set flag mentioning that the module is enabling,
+                //so that immediate calls to the defined callbacks
+                //for dependencies do not trigger inadvertent load
+                //with the depCount still being zero.
+                this.enabling = true;
+
+                //Enable each dependency
+                each(this.depMaps, bind(this, function (depMap, i) {
+                    var id, mod, handler;
+
+                    if (typeof depMap === 'string') {
+                        //Dependency needs to be converted to a depMap
+                        //and wired up to this module.
+                        depMap = makeModuleMap(depMap,
+                                               (this.map.isDefine ? this.map : this.map.parentMap),
+                                               false,
+                                               !this.skipMap);
+                        this.depMaps[i] = depMap;
+
+                        handler = getOwn(handlers, depMap.id);
+
+                        if (handler) {
+                            this.depExports[i] = handler(this);
+                            return;
+                        }
+
+                        this.depCount += 1;
+
+                        on(depMap, 'defined', bind(this, function (depExports) {
+                            if (this.undefed) {
+                                return;
+                            }
+                            this.defineDep(i, depExports);
+                            this.check();
+                        }));
+
+                        if (this.errback) {
+                            on(depMap, 'error', bind(this, this.errback));
+                        } else if (this.events.error) {
+                            // No direct errback on this module, but something
+                            // else is listening for errors, so be sure to
+                            // propagate the error correctly.
+                            on(depMap, 'error', bind(this, function(err) {
+                                this.emit('error', err);
+                            }));
+                        }
+                    }
+
+                    id = depMap.id;
+                    mod = registry[id];
+
+                    //Skip special modules like 'require', 'exports', 'module'
+                    //Also, don't call enable if it is already enabled,
+                    //important in circular dependency cases.
+                    if (!hasProp(handlers, id) && mod && !mod.enabled) {
+                        context.enable(depMap, this);
+                    }
+                }));
+
+                //Enable each plugin that is used in
+                //a dependency
+                eachProp(this.pluginMaps, bind(this, function (pluginMap) {
+                    var mod = getOwn(registry, pluginMap.id);
+                    if (mod && !mod.enabled) {
+                        context.enable(pluginMap, this);
+                    }
+                }));
+
+                this.enabling = false;
+
+                this.check();
+            },
+
+            on: function (name, cb) {
+                var cbs = this.events[name];
+                if (!cbs) {
+                    cbs = this.events[name] = [];
+                }
+                cbs.push(cb);
+            },
+
+            emit: function (name, evt) {
+                each(this.events[name], function (cb) {
+                    cb(evt);
+                });
+                if (name === 'error') {
+                    //Now that the error handler was triggered, remove
+                    //the listeners, since this broken Module instance
+                    //can stay around for a while in the registry.
+                    delete this.events[name];
+                }
+            }
+        };
+
+        function callGetModule(args) {
+            //Skip modules already defined.
+            if (!hasProp(defined, args[0])) {
+                getModule(makeModuleMap(args[0], null, true)).init(args[1], args[2]);
+            }
+        }
+
+        function removeListener(node, func, name, ieName) {
+            //Favor detachEvent because of IE9
+            //issue, see attachEvent/addEventListener comment elsewhere
+            //in this file.
+            if (node.detachEvent && !isOpera) {
+                //Probably IE. If not it will throw an error, which will be
+                //useful to know.
+                if (ieName) {
+                    node.detachEvent(ieName, func);
+                }
+            } else {
+                node.removeEventListener(name, func, false);
+            }
+        }
+
+        /**
+         * Given an event from a script node, get the requirejs info from it,
+         * and then removes the event listeners on the node.
+         * @param {Event} evt
+         * @returns {Object}
+         */
+        function getScriptData(evt) {
+            //Using currentTarget instead of target for Firefox 2.0's sake. Not
+            //all old browsers will be supported, but this one was easy enough
+            //to support and still makes sense.
+            var node = evt.currentTarget || evt.srcElement;
+
+            //Remove the listeners once here.
+            removeListener(node, context.onScriptLoad, 'load', 'onreadystatechange');
+            removeListener(node, context.onScriptError, 'error');
+
+            return {
+                node: node,
+                id: node && node.getAttribute('data-requiremodule')
+            };
+        }
+
+        function intakeDefines() {
+            var args;
+
+            //Any defined modules in the global queue, intake them now.
+            takeGlobalQueue();
+
+            //Make sure any remaining defQueue items get properly processed.
+            while (defQueue.length) {
+                args = defQueue.shift();
+                if (args[0] === null) {
+                    return onError(makeError('mismatch', 'Mismatched anonymous define() module: ' +
+                        args[args.length - 1]));
+                } else {
+                    //args are id, deps, factory. Should be normalized by the
+                    //define() function.
+                    callGetModule(args);
+                }
+            }
+            context.defQueueMap = {};
+        }
+
+        context = {
+            config: config,
+            contextName: contextName,
+            registry: registry,
+            defined: defined,
+            urlFetched: urlFetched,
+            defQueue: defQueue,
+            defQueueMap: {},
+            Module: Module,
+            makeModuleMap: makeModuleMap,
+            nextTick: req.nextTick,
+            onError: onError,
+
+            /**
+             * Set a configuration for the context.
+             * @param {Object} cfg config object to integrate.
+             */
+            configure: function (cfg) {
+                //Make sure the baseUrl ends in a slash.
+                if (cfg.baseUrl) {
+                    if (cfg.baseUrl.charAt(cfg.baseUrl.length - 1) !== '/') {
+                        cfg.baseUrl += '/';
+                    }
+                }
+
+                // Convert old style urlArgs string to a function.
+                if (typeof cfg.urlArgs === 'string') {
+                    var urlArgs = cfg.urlArgs;
+                    cfg.urlArgs = function(id, url) {
+                        return (url.indexOf('?') === -1 ? '?' : '&') + urlArgs;
+                    };
+                }
+
+                //Save off the paths since they require special processing,
+                //they are additive.
+                var shim = config.shim,
+                    objs = {
+                        paths: true,
+                        bundles: true,
+                        config: true,
+                        map: true
+                    };
+
+                eachProp(cfg, function (value, prop) {
+                    if (objs[prop]) {
+                        if (!config[prop]) {
+                            config[prop] = {};
+                        }
+                        mixin(config[prop], value, true, true);
+                    } else {
+                        config[prop] = value;
+                    }
+                });
+
+                //Reverse map the bundles
+                if (cfg.bundles) {
+                    eachProp(cfg.bundles, function (value, prop) {
+                        each(value, function (v) {
+                            if (v !== prop) {
+                                bundlesMap[v] = prop;
+                            }
+                        });
+                    });
+                }
+
+                //Merge shim
+                if (cfg.shim) {
+                    eachProp(cfg.shim, function (value, id) {
+                        //Normalize the structure
+                        if (isArray(value)) {
+                            value = {
+                                deps: value
+                            };
+                        }
+                        if ((value.exports || value.init) && !value.exportsFn) {
+                            value.exportsFn = context.makeShimExports(value);
+                        }
+                        shim[id] = value;
+                    });
+                    config.shim = shim;
+                }
+
+                //Adjust packages if necessary.
+                if (cfg.packages) {
+                    each(cfg.packages, function (pkgObj) {
+                        var location, name;
+
+                        pkgObj = typeof pkgObj === 'string' ? {name: pkgObj} : pkgObj;
+
+                        name = pkgObj.name;
+                        location = pkgObj.location;
+                        if (location) {
+                            config.paths[name] = pkgObj.location;
+                        }
+
+                        //Save pointer to main module ID for pkg name.
+                        //Remove leading dot in main, so main paths are normalized,
+                        //and remove any trailing .js, since different package
+                        //envs have different conventions: some use a module name,
+                        //some use a file name.
+                        config.pkgs[name] = pkgObj.name + '/' + (pkgObj.main || 'main')
+                                     .replace(currDirRegExp, '')
+                                     .replace(jsSuffixRegExp, '');
+                    });
+                }
+
+                //If there are any "waiting to execute" modules in the registry,
+                //update the maps for them, since their info, like URLs to load,
+                //may have changed.
+                eachProp(registry, function (mod, id) {
+                    //If module already has init called, since it is too
+                    //late to modify them, and ignore unnormalized ones
+                    //since they are transient.
+                    if (!mod.inited && !mod.map.unnormalized) {
+                        mod.map = makeModuleMap(id, null, true);
+                    }
+                });
+
+                //If a deps array or a config callback is specified, then call
+                //require with those args. This is useful when require is defined as a
+                //config object before require.js is loaded.
+                if (cfg.deps || cfg.callback) {
+                    context.require(cfg.deps || [], cfg.callback);
+                }
+            },
+
+            makeShimExports: function (value) {
+                function fn() {
+                    var ret;
+                    if (value.init) {
+                        ret = value.init.apply(global, arguments);
+                    }
+                    return ret || (value.exports && getGlobal(value.exports));
+                }
+                return fn;
+            },
+
+            makeRequire: function (relMap, options) {
+                options = options || {};
+
+                function localRequire(deps, callback, errback) {
+                    var id, map, requireMod;
+
+                    if (options.enableBuildCallback && callback && isFunction(callback)) {
+                        callback.__requireJsBuild = true;
+                    }
+
+                    if (typeof deps === 'string') {
+                        if (isFunction(callback)) {
+                            //Invalid call
+                            return onError(makeError('requireargs', 'Invalid require call'), errback);
+                        }
+
+                        //If require|exports|module are requested, get the
+                        //value for them from the special handlers. Caveat:
+                        //this only works while module is being defined.
+                        if (relMap && hasProp(handlers, deps)) {
+                            return handlers[deps](registry[relMap.id]);
+                        }
+
+                        //Synchronous access to one module. If require.get is
+                        //available (as in the Node adapter), prefer that.
+                        if (req.get) {
+                            return req.get(context, deps, relMap, localRequire);
+                        }
+
+                        //Normalize module name, if it contains . or ..
+                        map = makeModuleMap(deps, relMap, false, true);
+                        id = map.id;
+
+                        if (!hasProp(defined, id)) {
+                            return onError(makeError('notloaded', 'Module name "' +
+                                        id +
+                                        '" has not been loaded yet for context: ' +
+                                        contextName +
+                                        (relMap ? '' : '. Use require([])')));
+                        }
+                        return defined[id];
+                    }
+
+                    //Grab defines waiting in the global queue.
+                    intakeDefines();
+
+                    //Mark all the dependencies as needing to be loaded.
+                    context.nextTick(function () {
+                        //Some defines could have been added since the
+                        //require call, collect them.
+                        intakeDefines();
+
+                        requireMod = getModule(makeModuleMap(null, relMap));
+
+                        //Store if map config should be applied to this require
+                        //call for dependencies.
+                        requireMod.skipMap = options.skipMap;
+
+                        requireMod.init(deps, callback, errback, {
+                            enabled: true
+                        });
+
+                        checkLoaded();
+                    });
+
+                    return localRequire;
+                }
+
+                mixin(localRequire, {
+                    isBrowser: isBrowser,
+
+                    /**
+                     * Converts a module name + .extension into an URL path.
+                     * *Requires* the use of a module name. It does not support using
+                     * plain URLs like nameToUrl.
+                     */
+                    toUrl: function (moduleNamePlusExt) {
+                        var ext,
+                            index = moduleNamePlusExt.lastIndexOf('.'),
+                            segment = moduleNamePlusExt.split('/')[0],
+                            isRelative = segment === '.' || segment === '..';
+
+                        //Have a file extension alias, and it is not the
+                        //dots from a relative path.
+                        if (index !== -1 && (!isRelative || index > 1)) {
+                            ext = moduleNamePlusExt.substring(index, moduleNamePlusExt.length);
+                            moduleNamePlusExt = moduleNamePlusExt.substring(0, index);
+                        }
+
+                        return context.nameToUrl(normalize(moduleNamePlusExt,
+                                                relMap && relMap.id, true), ext,  true);
+                    },
+
+                    defined: function (id) {
+                        return hasProp(defined, makeModuleMap(id, relMap, false, true).id);
+                    },
+
+                    specified: function (id) {
+                        id = makeModuleMap(id, relMap, false, true).id;
+                        return hasProp(defined, id) || hasProp(registry, id);
+                    }
+                });
+
+                //Only allow undef on top level require calls
+                if (!relMap) {
+                    localRequire.undef = function (id) {
+                        //Bind any waiting define() calls to this context,
+                        //fix for #408
+                        takeGlobalQueue();
+
+                        var map = makeModuleMap(id, relMap, true),
+                            mod = getOwn(registry, id);
+
+                        mod.undefed = true;
+                        removeScript(id);
+
+                        delete defined[id];
+                        delete urlFetched[map.url];
+                        delete undefEvents[id];
+
+                        //Clean queued defines too. Go backwards
+                        //in array so that the splices do not
+                        //mess up the iteration.
+                        eachReverse(defQueue, function(args, i) {
+                            if (args[0] === id) {
+                                defQueue.splice(i, 1);
+                            }
+                        });
+                        delete context.defQueueMap[id];
+
+                        if (mod) {
+                            //Hold on to listeners in case the
+                            //module will be attempted to be reloaded
+                            //using a different config.
+                            if (mod.events.defined) {
+                                undefEvents[id] = mod.events;
+                            }
+
+                            cleanRegistry(id);
+                        }
+                    };
+                }
+
+                return localRequire;
+            },
+
+            /**
+             * Called to enable a module if it is still in the registry
+             * awaiting enablement. A second arg, parent, the parent module,
+             * is passed in for context, when this method is overridden by
+             * the optimizer. Not shown here to keep code compact.
+             */
+            enable: function (depMap) {
+                var mod = getOwn(registry, depMap.id);
+                if (mod) {
+                    getModule(depMap).enable();
+                }
+            },
+
+            /**
+             * Internal method used by environment adapters to complete a load event.
+             * A load event could be a script load or just a load pass from a synchronous
+             * load call.
+             * @param {String} moduleName the name of the module to potentially complete.
+             */
+            completeLoad: function (moduleName) {
+                var found, args, mod,
+                    shim = getOwn(config.shim, moduleName) || {},
+                    shExports = shim.exports;
+
+                takeGlobalQueue();
+
+                while (defQueue.length) {
+                    args = defQueue.shift();
+                    if (args[0] === null) {
+                        args[0] = moduleName;
+                        //If already found an anonymous module and bound it
+                        //to this name, then this is some other anon module
+                        //waiting for its completeLoad to fire.
+                        if (found) {
+                            break;
+                        }
+                        found = true;
+                    } else if (args[0] === moduleName) {
+                        //Found matching define call for this script!
+                        found = true;
+                    }
+
+                    callGetModule(args);
+                }
+                context.defQueueMap = {};
+
+                //Do this after the cycle of callGetModule in case the result
+                //of those calls/init calls changes the registry.
+                mod = getOwn(registry, moduleName);
+
+                if (!found && !hasProp(defined, moduleName) && mod && !mod.inited) {
+                    if (config.enforceDefine && (!shExports || !getGlobal(shExports))) {
+                        if (hasPathFallback(moduleName)) {
+                            return;
+                        } else {
+                            return onError(makeError('nodefine',
+                                             'No define call for ' + moduleName,
+                                             null,
+                                             [moduleName]));
+                        }
+                    } else {
+                        //A script that does not call define(), so just simulate
+                        //the call for it.
+                        callGetModule([moduleName, (shim.deps || []), shim.exportsFn]);
+                    }
+                }
+
+                checkLoaded();
+            },
+
+            /**
+             * Converts a module name to a file path. Supports cases where
+             * moduleName may actually be just an URL.
+             * Note that it **does not** call normalize on the moduleName,
+             * it is assumed to have already been normalized. This is an
+             * internal API, not a public one. Use toUrl for the public API.
+             */
+            nameToUrl: function (moduleName, ext, skipExt) {
+                var paths, syms, i, parentModule, url,
+                    parentPath, bundleId,
+                    pkgMain = getOwn(config.pkgs, moduleName);
+
+                if (pkgMain) {
+                    moduleName = pkgMain;
+                }
+
+                bundleId = getOwn(bundlesMap, moduleName);
+
+                if (bundleId) {
+                    return context.nameToUrl(bundleId, ext, skipExt);
+                }
+
+                //If a colon is in the URL, it indicates a protocol is used and it is just
+                //an URL to a file, or if it starts with a slash, contains a query arg (i.e. ?)
+                //or ends with .js, then assume the user meant to use an url and not a module id.
+                //The slash is important for protocol-less URLs as well as full paths.
+                if (req.jsExtRegExp.test(moduleName)) {
+                    //Just a plain path, not module name lookup, so just return it.
+                    //Add extension if it is included. This is a bit wonky, only non-.js things pass
+                    //an extension, this method probably needs to be reworked.
+                    url = moduleName + (ext || '');
+                } else {
+                    //A module that needs to be converted to a path.
+                    paths = config.paths;
+
+                    syms = moduleName.split('/');
+                    //For each module name segment, see if there is a path
+                    //registered for it. Start with most specific name
+                    //and work up from it.
+                    for (i = syms.length; i > 0; i -= 1) {
+                        parentModule = syms.slice(0, i).join('/');
+
+                        parentPath = getOwn(paths, parentModule);
+                        if (parentPath) {
+                            //If an array, it means there are a few choices,
+                            //Choose the one that is desired
+                            if (isArray(parentPath)) {
+                                parentPath = parentPath[0];
+                            }
+                            syms.splice(0, i, parentPath);
+                            break;
+                        }
+                    }
+
+                    //Join the path parts together, then figure out if baseUrl is needed.
+                    url = syms.join('/');
+                    url += (ext || (/^data\:|^blob\:|\?/.test(url) || skipExt ? '' : '.js'));
+                    url = (url.charAt(0) === '/' || url.match(/^[\w\+\.\-]+:/) ? '' : config.baseUrl) + url;
+                }
+
+                return config.urlArgs && !/^blob\:/.test(url) ?
+                       url + config.urlArgs(moduleName, url) : url;
+            },
+
+            //Delegates to req.load. Broken out as a separate function to
+            //allow overriding in the optimizer.
+            load: function (id, url) {
+                req.load(context, id, url);
+            },
+
+            /**
+             * Executes a module callback function. Broken out as a separate function
+             * solely to allow the build system to sequence the files in the built
+             * layer in the right sequence.
+             *
+             * @private
+             */
+            execCb: function (name, callback, args, exports) {
+                return callback.apply(exports, args);
+            },
+
+            /**
+             * callback for script loads, used to check status of loading.
+             *
+             * @param {Event} evt the event from the browser for the script
+             * that was loaded.
+             */
+            onScriptLoad: function (evt) {
+                //Using currentTarget instead of target for Firefox 2.0's sake. Not
+                //all old browsers will be supported, but this one was easy enough
+                //to support and still makes sense.
+                if (evt.type === 'load' ||
+                        (readyRegExp.test((evt.currentTarget || evt.srcElement).readyState))) {
+                    //Reset interactive script so a script node is not held onto for
+                    //to long.
+                    interactiveScript = null;
+
+                    //Pull out the name of the module and the context.
+                    var data = getScriptData(evt);
+                    context.completeLoad(data.id);
+                }
+            },
+
+            /**
+             * Callback for script errors.
+             */
+            onScriptError: function (evt) {
+                var data = getScriptData(evt);
+                if (!hasPathFallback(data.id)) {
+                    var parents = [];
+                    eachProp(registry, function(value, key) {
+                        if (key.indexOf('_@r') !== 0) {
+                            each(value.depMaps, function(depMap) {
+                                if (depMap.id === data.id) {
+                                    parents.push(key);
+                                    return true;
+                                }
+                            });
+                        }
+                    });
+                    return onError(makeError('scripterror', 'Script error for "' + data.id +
+                                             (parents.length ?
+                                             '", needed by: ' + parents.join(', ') :
+                                             '"'), evt, [data.id]));
+                }
+            }
+        };
+
+        context.require = context.makeRequire();
+        return context;
+    }
+
+    /**
+     * Main entry point.
+     *
+     * If the only argument to require is a string, then the module that
+     * is represented by that string is fetched for the appropriate context.
+     *
+     * If the first argument is an array, then it will be treated as an array
+     * of dependency string names to fetch. An optional function callback can
+     * be specified to execute when all of those dependencies are available.
+     *
+     * Make a local req variable to help Caja compliance (it assumes things
+     * on a require that are not standardized), and to give a short
+     * name for minification/local scope use.
+     */
+    req = requirejs = function (deps, callback, errback, optional) {
+
+        //Find the right context, use default
+        var context, config,
+            contextName = defContextName;
+
+        // Determine if have config object in the call.
+        if (!isArray(deps) && typeof deps !== 'string') {
+            // deps is a config object
+            config = deps;
+            if (isArray(callback)) {
+                // Adjust args if there are dependencies
+                deps = callback;
+                callback = errback;
+                errback = optional;
+            } else {
+                deps = [];
+            }
+        }
+
+        if (config && config.context) {
+            contextName = config.context;
+        }
+
+        context = getOwn(contexts, contextName);
+        if (!context) {
+            context = contexts[contextName] = req.s.newContext(contextName);
+        }
+
+        if (config) {
+            context.configure(config);
+        }
+
+        return context.require(deps, callback, errback);
+    };
+
+    /**
+     * Support require.config() to make it easier to cooperate with other
+     * AMD loaders on globally agreed names.
+     */
+    req.config = function (config) {
+        return req(config);
+    };
+
+    /**
+     * Execute something after the current tick
+     * of the event loop. Override for other envs
+     * that have a better solution than setTimeout.
+     * @param  {Function} fn function to execute later.
+     */
+    req.nextTick = typeof setTimeout !== 'undefined' ? function (fn) {
+        setTimeout(fn, 4);
+    } : function (fn) { fn(); };
+
+    /**
+     * Export require as a global, but only if it does not already exist.
+     */
+    if (!require) {
+        require = req;
+    }
+
+    req.version = version;
+
+    //Used to filter out dependencies that are already paths.
+    req.jsExtRegExp = /^\/|:|\?|\.js$/;
+    req.isBrowser = isBrowser;
+    s = req.s = {
+        contexts: contexts,
+        newContext: newContext
+    };
+
+    //Create default context.
+    req({});
+
+    //Exports some context-sensitive methods on global require.
+    each([
+        'toUrl',
+        'undef',
+        'defined',
+        'specified'
+    ], function (prop) {
+        //Reference from contexts instead of early binding to default context,
+        //so that during builds, the latest instance of the default context
+        //with its config gets used.
+        req[prop] = function () {
+            var ctx = contexts[defContextName];
+            return ctx.require[prop].apply(ctx, arguments);
+        };
+    });
+
+    if (isBrowser) {
+        head = s.head = document.getElementsByTagName('head')[0];
+        //If BASE tag is in play, using appendChild is a problem for IE6.
+        //When that browser dies, this can be removed. Details in this jQuery bug:
+        //http://dev.jquery.com/ticket/2709
+        baseElement = document.getElementsByTagName('base')[0];
+        if (baseElement) {
+            head = s.head = baseElement.parentNode;
+        }
+    }
+
+    /**
+     * Any errors that require explicitly generates will be passed to this
+     * function. Intercept/override it if you want custom error handling.
+     * @param {Error} err the error object.
+     */
+    req.onError = defaultOnError;
+
+    /**
+     * Creates the node for the load command. Only used in browser envs.
+     */
+    req.createNode = function (config, moduleName, url) {
+        var node = config.xhtml ?
+                document.createElementNS('http://www.w3.org/1999/xhtml', 'html:script') :
+                document.createElement('script');
+        node.type = config.scriptType || 'text/javascript';
+        node.charset = 'utf-8';
+        node.async = true;
+        return node;
+    };
+
+    /**
+     * Does the request to load a module for the browser case.
+     * Make this a separate function to allow other environments
+     * to override it.
+     *
+     * @param {Object} context the require context to find state.
+     * @param {String} moduleName the name of the module.
+     * @param {Object} url the URL to the module.
+     */
+    req.load = function (context, moduleName, url) {
+        var config = (context && context.config) || {},
+            node;
+        if (isBrowser) {
+            //In the browser so use a script tag
+            node = req.createNode(config, moduleName, url);
+
+            node.setAttribute('data-requirecontext', context.contextName);
+            node.setAttribute('data-requiremodule', moduleName);
+
+            //Set up load listener. Test attachEvent first because IE9 has
+            //a subtle issue in its addEventListener and script onload firings
+            //that do not match the behavior of all other browsers with
+            //addEventListener support, which fire the onload event for a
+            //script right after the script execution. See:
+            //https://connect.microsoft.com/IE/feedback/details/648057/script-onload-event-is-not-fired-immediately-after-script-execution
+            //UNFORTUNATELY Opera implements attachEvent but does not follow the script
+            //script execution mode.
+            if (node.attachEvent &&
+                    //Check if node.attachEvent is artificially added by custom script or
+                    //natively supported by browser
+                    //read https://github.com/requirejs/requirejs/issues/187
+                    //if we can NOT find [native code] then it must NOT natively supported.
+                    //in IE8, node.attachEvent does not have toString()
+                    //Note the test for "[native code" with no closing brace, see:
+                    //https://github.com/requirejs/requirejs/issues/273
+                    !(node.attachEvent.toString && node.attachEvent.toString().indexOf('[native code') < 0) &&
+                    !isOpera) {
+                //Probably IE. IE (at least 6-8) do not fire
+                //script onload right after executing the script, so
+                //we cannot tie the anonymous define call to a name.
+                //However, IE reports the script as being in 'interactive'
+                //readyState at the time of the define call.
+                useInteractive = true;
+
+                node.attachEvent('onreadystatechange', context.onScriptLoad);
+                //It would be great to add an error handler here to catch
+                //404s in IE9+. However, onreadystatechange will fire before
+                //the error handler, so that does not help. If addEventListener
+                //is used, then IE will fire error before load, but we cannot
+                //use that pathway given the connect.microsoft.com issue
+                //mentioned above about not doing the 'script execute,
+                //then fire the script load event listener before execute
+                //next script' that other browsers do.
+                //Best hope: IE10 fixes the issues,
+                //and then destroys all installs of IE 6-9.
+                //node.attachEvent('onerror', context.onScriptError);
+            } else {
+                node.addEventListener('load', context.onScriptLoad, false);
+                node.addEventListener('error', context.onScriptError, false);
+            }
+            node.src = url;
+
+            //Calling onNodeCreated after all properties on the node have been
+            //set, but before it is placed in the DOM.
+            if (config.onNodeCreated) {
+                config.onNodeCreated(node, config, moduleName, url);
+            }
+
+            //For some cache cases in IE 6-8, the script executes before the end
+            //of the appendChild execution, so to tie an anonymous define
+            //call to the module name (which is stored on the node), hold on
+            //to a reference to this node, but clear after the DOM insertion.
+            currentlyAddingScript = node;
+            if (baseElement) {
+                head.insertBefore(node, baseElement);
+            } else {
+                head.appendChild(node);
+            }
+            currentlyAddingScript = null;
+
+            return node;
+        } else if (isWebWorker) {
+            try {
+                //In a web worker, use importScripts. This is not a very
+                //efficient use of importScripts, importScripts will block until
+                //its script is downloaded and evaluated. However, if web workers
+                //are in play, the expectation is that a build has been done so
+                //that only one script needs to be loaded anyway. This may need
+                //to be reevaluated if other use cases become common.
+
+                // Post a task to the event loop to work around a bug in WebKit
+                // where the worker gets garbage-collected after calling
+                // importScripts(): https://webkit.org/b/153317
+                setTimeout(function() {}, 0);
+                importScripts(url);
+
+                //Account for anonymous modules
+                context.completeLoad(moduleName);
+            } catch (e) {
+                context.onError(makeError('importscripts',
+                                'importScripts failed for ' +
+                                    moduleName + ' at ' + url,
+                                e,
+                                [moduleName]));
+            }
+        }
+    };
+
+    function getInteractiveScript() {
+        if (interactiveScript && interactiveScript.readyState === 'interactive') {
+            return interactiveScript;
+        }
+
+        eachReverse(scripts(), function (script) {
+            if (script.readyState === 'interactive') {
+                return (interactiveScript = script);
+            }
+        });
+        return interactiveScript;
+    }
+
+    //Look for a data-main script attribute, which could also adjust the baseUrl.
+    if (isBrowser && !cfg.skipDataMain) {
+        //Figure out baseUrl. Get it from the script tag with require.js in it.
+        eachReverse(scripts(), function (script) {
+            //Set the 'head' where we can append children by
+            //using the script's parent.
+            if (!head) {
+                head = script.parentNode;
+            }
+
+            //Look for a data-main attribute to set main script for the page
+            //to load. If it is there, the path to data main becomes the
+            //baseUrl, if it is not already set.
+            dataMain = script.getAttribute('data-main');
+            if (dataMain) {
+                //Preserve dataMain in case it is a path (i.e. contains '?')
+                mainScript = dataMain;
+
+                //Set final baseUrl if there is not already an explicit one,
+                //but only do so if the data-main value is not a loader plugin
+                //module ID.
+                if (!cfg.baseUrl && mainScript.indexOf('!') === -1) {
+                    //Pull off the directory of data-main for use as the
+                    //baseUrl.
+                    src = mainScript.split('/');
+                    mainScript = src.pop();
+                    subPath = src.length ? src.join('/')  + '/' : './';
+
+                    cfg.baseUrl = subPath;
+                }
+
+                //Strip off any trailing .js since mainScript is now
+                //like a module name.
+                mainScript = mainScript.replace(jsSuffixRegExp, '');
+
+                //If mainScript is still a path, fall back to dataMain
+                if (req.jsExtRegExp.test(mainScript)) {
+                    mainScript = dataMain;
+                }
+
+                //Put the data-main script in the files to load.
+                cfg.deps = cfg.deps ? cfg.deps.concat(mainScript) : [mainScript];
+
+                return true;
+            }
+        });
+    }
+
+    /**
+     * The function that handles definitions of modules. Differs from
+     * require() in that a string for the module should be the first argument,
+     * and the function to execute after dependencies are loaded should
+     * return a value to define the module corresponding to the first argument's
+     * name.
+     */
+    define = function (name, deps, callback) {
+        var node, context;
+
+        //Allow for anonymous modules
+        if (typeof name !== 'string') {
+            //Adjust args appropriately
+            callback = deps;
+            deps = name;
+            name = null;
+        }
+
+        //This module may not have dependencies
+        if (!isArray(deps)) {
+            callback = deps;
+            deps = null;
+        }
+
+        //If no name, and callback is a function, then figure out if it a
+        //CommonJS thing with dependencies.
+        if (!deps && isFunction(callback)) {
+            deps = [];
+            //Remove comments from the callback string,
+            //look for require calls, and pull them into the dependencies,
+            //but only if there are function args.
+            if (callback.length) {
+                callback
+                    .toString()
+                    .replace(commentRegExp, commentReplace)
+                    .replace(cjsRequireRegExp, function (match, dep) {
+                        deps.push(dep);
+                    });
+
+                //May be a CommonJS thing even without require calls, but still
+                //could use exports, and module. Avoid doing exports and module
+                //work though if it just needs require.
+                //REQUIRES the function to expect the CommonJS variables in the
+                //order listed below.
+                deps = (callback.length === 1 ? ['require'] : ['require', 'exports', 'module']).concat(deps);
+            }
+        }
+
+        //If in IE 6-8 and hit an anonymous define() call, do the interactive
+        //work.
+        if (useInteractive) {
+            node = currentlyAddingScript || getInteractiveScript();
+            if (node) {
+                if (!name) {
+                    name = node.getAttribute('data-requiremodule');
+                }
+                context = contexts[node.getAttribute('data-requirecontext')];
+            }
+        }
+
+        //Always save off evaluating the def call until the script onload handler.
+        //This allows multiple modules to be in a file without prematurely
+        //tracing dependencies, and allows for anonymous module support,
+        //where the module name is not known until the script onload event
+        //occurs. If no context, use the global queue, and get it processed
+        //in the onscript load callback.
+        if (context) {
+            context.defQueue.push([name, deps, callback]);
+            context.defQueueMap[name] = true;
+        } else {
+            globalDefQueue.push([name, deps, callback]);
+        }
+    };
+
+    define.amd = {
+        jQuery: true
+    };
+
+    /**
+     * Executes the text. Normally just uses eval, but can be modified
+     * to use a better, environment-specific call. Only used for transpiling
+     * loader plugins, not for plain JS modules.
+     * @param {String} text the text to execute/evaluate.
+     */
+    req.exec = function (text) {
+        /*jslint evil: true */
+        return eval(text);
+    };
+
+    //Set up with config info.
+    req(cfg);
+}(this, (typeof setTimeout === 'undefined' ? undefined : setTimeout)));
diff --git a/node_modules/seedrandom/test/newapi.html b/node_modules/seedrandom/test/newapi.html
new file mode 100644
index 0000000..0767be1
--- /dev/null
+++ b/node_modules/seedrandom/test/newapi.html
@@ -0,0 +1,44 @@
+<html>
+<head>
+<link rel="stylesheet" href="lib/qunit.css">
+</head>
+<body>
+<div id="qunit"></div>
+<div id="qunit-fixture"></div>
+<script src="lib/qunit.js"></script>
+<script src="../seedrandom.min.js"></script>
+<script>
+QUnit.module("New API Test");
+
+QUnit.test("Check that we can use new", function(assert) {
+
+assert.ok(true, "Seeded random created with new:");
+var check = [];
+var prng = new Math.seedrandom(1);
+var r;
+for (var j = 0; j < 5; ++j) {
+  r = prng();
+  assert.ok(true, r);
+  check.push(r);
+}
+assert.ok(true, "Native random:");
+for (var j = 0; j < 5; ++j) {
+  r = Math.random();
+  assert.ok(true, r);
+  check.push(r);
+}
+var seed = Math.seedrandom(1);
+assert.ok(true, "Overridden random without new " +
+  "(return value " + seed + "):");
+for (var j = 0; j < 10; ++j) {
+  r = Math.random();
+  if (j < 5) {
+    assert.equal(check[j], r, r + " vs " + check[j]);
+  } else {
+    assert.ok(check[j] != r, "unequal: " + r + " vs " + check[j]);
+  }
+}
+});
+</script>
+</body>
+</html>
diff --git a/node_modules/seedrandom/test/nodetest.js b/node_modules/seedrandom/test/nodetest.js
new file mode 100644
index 0000000..ab90184
--- /dev/null
+++ b/node_modules/seedrandom/test/nodetest.js
@@ -0,0 +1,226 @@
+var assert = require("assert");
+var seedrandom = require("../seedrandom");
+var requirejs = require("requirejs");
+
+// Stub out requirejs if in the browser via browserify.
+if (!requirejs.config) {
+  requirejs = require;
+} else {
+  requirejs.config({
+    baseUrl: __dirname
+  });
+}
+
+describe("Nodejs API Test", function() {
+
+it('should pass basic tests.', function() {
+  var original = Math.random,
+      result, r, xprng, obj, as2, as3, autoseed1, myrng,
+      firstprng, secondprng, thirdprng, rng;
+
+  result = Math.seedrandom('hello.');
+  firstprng = Math.random;
+  assert.ok(original !== firstprng, "Should change Math.random.");
+  assert.equal(result, "hello.", "Should return short seed.");
+  r = Math.random();
+  assert.equal(r, 0.9282578795792454, "Should be 'hello.'#1");
+  r = Math.random();
+  assert.equal(r, 0.3752569768646784, "Should be 'hello.'#2");
+
+  // should be able to autoseed
+  result = Math.seedrandom();
+  secondprng = Math.random;
+  assert.ok(original !== secondprng, "Should change Math.random.");
+  assert.ok(firstprng !== secondprng, "Should change Math.random.");
+  assert.equal(result.length, 256, "Should return short seed.");
+  r = Math.random();
+  assert.ok(r > 0, "Should be posititive.");
+  assert.ok(r < 1, "Should be less than 1.");
+  assert.ok(r != 0.9282578795792454, "Should not be 'hello.'#1");
+  assert.ok(r != 0.3752569768646784, "Should not be 'hello.'#2");
+  assert.ok(r != 0.7316977468919549, "Should not be 'hello.'#3");
+  autoseed1 = r;
+
+  // should be able to add entropy.
+  result = Math.seedrandom('added entropy.', { entropy:true });
+  assert.equal(result.length, 256, "Should return short seed.");
+  thirdprng = Math.random;
+  assert.ok(thirdprng !== secondprng, "Should change Math.random.");
+  r = Math.random();
+  assert.ok(r != 0.597067214994467, "Should not be 'added entropy.'#1");
+
+  // Reset to original Math.random.
+  Math.random = original;
+  // should be able to use new Math.seedrandom('hello.')
+  myrng = new Math.seedrandom('hello.');
+  assert.ok(original === Math.random, "Should not change Math.random.");
+  assert.ok(original !== myrng, "PRNG should not be Math.random.");
+  r = myrng();
+  assert.equal(r, 0.9282578795792454, "Should be 'hello.'#1");
+
+  // should be able to use seedrandom('hello.')"
+  rng = seedrandom('hello.');
+  assert.equal(typeof(rng), 'function', "Should return a function.");
+  r = rng();
+  assert.equal(r, 0.9282578795792454, "Should be 'hello.'#1");
+  assert.ok(original === Math.random, "Should not change Math.random.");
+  assert.ok(original !== rng, "PRNG should not be Math.random.");
+
+  // Global PRNG: set Math.random.
+  // should be able to use seedrandom('hello.', { global: true })
+  result = seedrandom('hello.', { global: true });
+  assert.equal(result, 'hello.', "Should return short seed.");
+  assert.ok(original != Math.random, "Should change Math.random.");
+  r = Math.random();
+  assert.equal(r, 0.9282578795792454, "Should be 'hello.'#1");
+
+  // Autoseeded non-global
+  Math.random = original;
+  // should be able to use seedrandom()
+  result = seedrandom();
+  assert.equal(typeof(result), 'function', "Should return function.");
+  assert.ok(original === Math.random, "Should not change Math.random.");
+  r = result();
+  // got " + r);
+  assert.ok(r != autoseed1, "Should not repeat previous autoseed.");
+  assert.ok(r != 0.9282578795792454, "Should not be 'hello.'#1");
+  assert.ok(r != 0.7316977468919549, "Should not be 'hello.'#3");
+
+  // Mixing accumulated entropy.
+  // should be able to use seedrandom('added entropy.', { entropy: true })
+  rng = seedrandom('added entropy.', { entropy: true });
+  r = result();
+  // got " + r);
+  assert.ok(r != autoseed1, "Should not repeat previous autoseed.");
+  assert.ok(r != 0.597067214994467, "Should not be 'added entropy.'#1");
+
+  // Legacy calling convention for mixing accumulated entropy.
+  // should be able to use seedrandom('added entropy.', true)
+  rng = seedrandom('added entropy.', true);
+  r = result();
+  // got " + r);
+  assert.ok(r != autoseed1, "Should not repeat previous autoseed.");
+  assert.ok(r != 0.597067214994467, "Should not be 'added entropy.'#1");
+
+  // The pass option
+  // should be able to use Math.seedrandom(null, { pass: ...
+  obj = Math.seedrandom(null, { pass: function(prng, seed) {
+    return { random: prng, seed: seed };
+  }});
+  assert.ok(original === Math.random, "Should not change Math.random.");
+  assert.ok(original !== obj.random, "Should be different from Math.random.");
+  assert.equal(typeof(obj.random), 'function', "Should return a PRNG function.");
+  assert.equal(typeof(obj.seed), 'string', "Should return a seed.");
+  as2 = obj.random();
+  assert.ok(as2 != 0.9282578795792454, "Should not be 'hello.'#1");
+  rng = seedrandom(obj.seed);
+  as3 = rng();
+  assert.equal(as2, as3, "Should be reproducible when using the seed.");
+
+  // Exercise pass again, with explicit seed and global
+  // should be able to use Math.seedrandom('hello.', { pass: ...
+  result = Math.seedrandom('hello.', {
+    global: 'abc',
+    pass: function(prng, seed, global) {
+      assert.equal(typeof(prng), 'function', "Callback arg #1 assert");
+      assert.equal(seed, 'hello.', "Callback arg #2 assert");
+      assert.equal(global, 'abc', "Callback arg #3 passed through.");
+      assert.equal(prng(), 0.9282578795792454, "Should be 'hello.'#1");
+      return 'def';
+  }});
+  assert.equal(result, 'def', "Should return value from callback.");
+  assert.ok(original === Math.random, "Should not change Math.random.");
+
+  // Legacy third argument callback argument:
+  // should be able to use Math.seedrandom('hello.', { global: 50 }, callback)
+  result = Math.seedrandom('hello.', { global: 50 },
+    function(prng, seed, global) {
+      assert.equal(typeof(prng), 'function', "Callback arg #1 assert");
+      assert.equal(seed, 'hello.', "Callback arg #2 assert");
+      assert.equal(global, 50, "Callback arg #3 assert");
+      assert.equal(prng(), 0.9282578795792454, "Should be 'hello.'#1");
+      return 'zzz';
+  });
+  assert.equal(result, 'zzz', "Should return value from callback.");
+  assert.ok(original === Math.random, "Should not change Math.random.");
+
+  // Global: false.
+  // should be able to use new Math.seedrandom('hello.', {global: false})
+  myrng = new Math.seedrandom('hello.', {global:false});
+  assert.equal(typeof(myrng), 'function', "Should return a PRNG funciton.");
+  assert.ok(original === Math.random, "Should not change Math.random.");
+  assert.ok(original !== myrng, "PRNG should not be Math.random.");
+  r = myrng();
+  assert.equal(r, 0.9282578795792454, "Should be 'hello.'#1");
+
+  // options = {} when a method of Math.
+  // should be able to use Math.seedrandom('hello.', {})
+  result = Math.seedrandom('hello.');
+  xprng = Math.random;
+  assert.ok(original !== xprng, "Should change Math.random.");
+  assert.equal(result, "hello.", "Should return short seed.");
+  r = Math.random();
+  assert.equal(r, 0.9282578795792454, "Should be 'hello.'#1");
+  r = Math.random();
+  assert.equal(r, 0.3752569768646784, "Should be 'hello.'#2");
+  Math.random = original;
+
+  // options = {} when not a method of Math
+  // should be able to use seedrandom('hello.', {})
+  rng = seedrandom('hello.', {});
+  assert.equal(typeof(rng), 'function', "Should return a function.");
+  r = rng();
+  assert.equal(r, 0.9282578795792454, "Should be 'hello.'#1");
+  assert.ok(original === Math.random, "Should not change Math.random.");
+  assert.ok(original !== rng, "PRNG should not be Math.random.");
+});
+
+it('should support state api.', function() {
+  // Verify that there is no state method
+  var dummy = seedrandom('hello');
+  var unexpected = -1;
+  var expected = -1;
+  try {
+    unexpected = dummy.state();
+  } catch(e) {
+    expected = 1;
+  }
+  assert.equal(unexpected, -1);
+  assert.equal(expected, 1);
+  var count = 0;
+  for (var x in dummy) {
+    if (x == 'state') count += 1;
+  }
+  assert.equal(count, 0);
+
+  // Verify that a state method can be added
+  var saveable = seedrandom("secret-seed", {state: true});
+  var ordinary = seedrandom("secret-seed");
+  for (var j = 0; j < 1e2; ++j) {
+    assert.equal(ordinary(), saveable());
+  }
+  var virgin = seedrandom("secret-seed");
+  var saved = saveable.state();
+  var replica = seedrandom("", {state: saved});
+  for (var j = 0; j < 1e2; ++j) {
+    var r = replica();
+    assert.equal(r, saveable());
+    assert.equal(r, ordinary());
+    assert.ok(r != virgin());
+  }
+});
+
+it('should support requirejs in node.', function() {
+  var original = Math.random;
+  var rsr = requirejs('../seedrandom');
+  var rng = rsr('hello.');
+  assert.equal(typeof(rng), 'function', "Should return a function.");
+  var r = rng();
+  assert.equal(r, 0.9282578795792454, "Should be 'hello.'#1");
+  assert.ok(original === Math.random, "Should not change Math.random.");
+  assert.ok(original !== rng, "PRNG should not be Math.random.");
+});
+
+// End of test.
+
+});
diff --git a/node_modules/seedrandom/test/options.html b/node_modules/seedrandom/test/options.html
new file mode 100644
index 0000000..d8ca26b
--- /dev/null
+++ b/node_modules/seedrandom/test/options.html
@@ -0,0 +1,195 @@
+<html>
+<head>
+<link rel="stylesheet" href="lib/qunit.css">
+</head>
+<body>
+<div id="qunit"></div>
+<div id="qunit-fixture"></div>
+<script src="../seedrandom.min.js"></script>
+<script src="lib/qunit.js"></script>
+<script>
+QUnit.module("Options API Test");
+
+QUnit.test("Verify that we can use documented options", function(assert) {
+assert.ok(true, "Seeded random created with new:");
+
+var original = Math.random;
+
+assert.ok(true, "Using Math.seedrandom('hello.')");
+var result = Math.seedrandom('hello.');
+var firstprng = Math.random;
+assert.ok(original !== firstprng, "Should change Math.random.");
+assert.equal(result, "hello.", "Should return short seed.");
+var r = Math.random();
+assert.ok(true, "Got " + r);
+assert.equal(r, 0.9282578795792454, "Should be 'hello.'#1");
+r = Math.random();
+assert.ok(true, "Got " + r);
+assert.equal(r, 0.3752569768646784, "Should be 'hello.'#2");
+
+assert.ok(true, "Using Math.seedrandom()");
+result = Math.seedrandom();
+var secondprng = Math.random;
+assert.ok(original !== secondprng, "Should change Math.random.");
+assert.ok(firstprng !== secondprng, "Should change Math.random.");
+assert.equal(result.length, 256, "Should return short seed.");
+r = Math.random();
+assert.ok(true, "Got " + r);
+assert.ok(r != 0.9282578795792454, "Should not be 'hello.'#1");
+assert.ok(r != 0.7316977468919549, "Should not be 'hello.'#3");
+var autoseed1 = r;
+
+assert.ok(true, "Using Math.seedrandom('added entropy.', { entropy:true })");
+result = Math.seedrandom('added entropy.', { entropy:true });
+assert.equal(result.length, 256, "Should return short seed.");
+var thirdprng = Math.random;
+assert.ok(thirdprng !== secondprng, "Should change Math.random.");
+r = Math.random();
+assert.ok(true, "Got " + r);
+assert.ok(r != 0.597067214994467, "Should not be 'added entropy.'#1");
+
+// Reset to original Math.random.
+Math.random = original;
+assert.ok(true, "Using new Math.seedrandom('hello.')");
+var myrng = new Math.seedrandom('hello.');
+assert.ok(original === Math.random, "Should not change Math.random.");
+assert.ok(original !== myrng, "PRNG should not be Math.random.");
+r = myrng();
+assert.ok(true, "Got " + r);
+assert.equal(r, 0.9282578795792454, "Should be 'hello.'#1");
+
+// Use "quick" to get only 32 bits of randomness in a float.
+assert.equal(myrng.quick(), 0.3752569768112153, "Should be quick #1.1");
+
+// Use "int32" to get a 32 bit (signed) integer
+assert.equal(myrng.int32(), 986220731, "Should be int32 #1.2");
+
+// As if brought in by node.js
+var seedrandom = Math.seedrandom;
+
+assert.ok(true, "Using seedrandom('hello.')");
+var rng = seedrandom('hello.');
+assert.equal(typeof(rng), 'function', "Should return a function.");
+r = rng();
+assert.ok(true, "Got " + r);
+assert.equal(r, 0.9282578795792454, "Should be 'hello.'#1");
+assert.ok(original === Math.random, "Should not change Math.random.");
+assert.ok(original !== rng, "PRNG should not be Math.random.");
+
+// Global PRNG: set Math.random.
+assert.ok(true, "Using seedrandom('hello.', { global: true })");
+result = seedrandom('hello.', { global: true });
+assert.equal(result, 'hello.', "Should return short seed.");
+assert.ok(original != Math.random, "Should change Math.random.");
+r = Math.random();
+assert.ok(true, "Got " + r);
+assert.equal(r, 0.9282578795792454, "Should be 'hello.'#1");
+
+// Autoseeded non-global
+Math.random = original;
+assert.ok(true, "Using seedrandom()");
+result = seedrandom();
+assert.equal(typeof(result), 'function', "Should return function.");
+assert.ok(original === Math.random, "Should not change Math.random.");
+r = result();
+assert.ok(true, "Got " + r);
+assert.ok(r != autoseed1, "Should not repeat previous autoseed.");
+assert.ok(r != 0.9282578795792454, "Should not be 'hello.'#1");
+assert.ok(r != 0.7316977468919549, "Should not be 'hello.'#3");
+
+// Mixing accumulated entropy.
+assert.ok(true, "Using seedrandom('added entropy.', { entropy: true })");
+rng = seedrandom('added entropy.', { entropy: true });
+r = result();
+assert.ok(true, "Got " + r);
+assert.ok(r != autoseed1, "Should not repeat previous autoseed.");
+assert.ok(r != 0.597067214994467, "Should not be 'added entropy.'#1");
+
+// Legacy calling convention for mixing accumulated entropy.
+assert.ok(true, "Using seedrandom('added entropy.', true)");
+rng = seedrandom('added entropy.', true);
+r = result();
+assert.ok(true, "Got " + r);
+assert.ok(r != autoseed1, "Should not repeat previous autoseed.");
+assert.ok(r != 0.597067214994467, "Should not be 'added entropy.'#1");
+
+// The pass option
+assert.ok(true, "Using Math.seedrandom(null, { pass: ...");
+var obj = Math.seedrandom(null, { pass: function(prng, seed) {
+  return { random: prng, seed: seed };
+}});
+assert.ok(original === Math.random, "Should not change Math.random.");
+assert.ok(original !== obj.random, "Should be different from Math.random.");
+assert.equal(typeof(obj.random), 'function', "Should return a PRNG function.");
+assert.equal(typeof(obj.seed), 'string', "Should return a seed.");
+var as2 = obj.random();
+assert.ok(as2 != 0.9282578795792454, "Should not be 'hello.'#1");
+rng = seedrandom(obj.seed);
+var as3 = rng();
+assert.equal(as2, as3, "Should be reproducible when using the seed.");
+
+// Exercise pass again, with explicit seed and global
+assert.ok(true, "Using Math.seedrandom('hello.', { pass: ...");
+result = Math.seedrandom('hello.', {
+  global: 'abc',
+  pass: function(prng, seed, global) {
+    assert.equal(typeof(prng), 'function', "Callback arg #1 assert.ok");
+    assert.equal(seed, 'hello.', "Callback arg #2 assert.ok");
+    assert.equal(global, 'abc', "Callback arg #3 passed through.");
+    assert.equal(prng(), 0.9282578795792454, "Should be 'hello.'#1");
+    return 'def';
+}});
+assert.equal(result, 'def', "Should return value from callback.");
+assert.ok(original === Math.random, "Should not change Math.random.");
+
+// Legacy third argument callback argument:
+assert.ok(true, "Using Math.seedrandom('hello.', { global: 50 }, callback)");
+result = Math.seedrandom('hello.', { global: 50 },
+  function(prng, seed, global) {
+    assert.equal(typeof(prng), 'function', "Callback arg #1 assert.ok");
+    assert.equal(seed, 'hello.', "Callback arg #2 assert.ok");
+    assert.equal(global, 50, "Callback arg #3 assert.ok");
+    assert.equal(prng(), 0.9282578795792454, "Should be 'hello.'#1");
+    return 'zzz';
+});
+assert.equal(result, 'zzz', "Should return value from callback.");
+assert.ok(original === Math.random, "Should not change Math.random.");
+
+// Global: false.
+assert.ok(true, "Using new Math.seedrandom('hello.', {global: false})");
+myrng = new Math.seedrandom('hello.', {global:false});
+assert.equal(typeof(myrng), 'function', "Should return a PRNG funciton.");
+assert.ok(original === Math.random, "Should not change Math.random.");
+assert.ok(original !== myrng, "PRNG should not be Math.random.");
+r = myrng();
+assert.ok(true, "Got " + r);
+assert.equal(r, 0.9282578795792454, "Should be 'hello.'#1");
+
+// options = {} when a method of Math.
+assert.ok(true, "Using Math.seedrandom('hello.', {})");
+var result = Math.seedrandom('hello.');
+var xprng = Math.random;
+assert.ok(original !== xprng, "Should change Math.random.");
+assert.equal(result, "hello.", "Should return short seed.");
+var r = Math.random();
+assert.ok(true, "Got " + r);
+assert.equal(r, 0.9282578795792454, "Should be 'hello.'#1");
+r = Math.random();
+assert.ok(true, "Got " + r);
+assert.equal(r, 0.3752569768646784, "Should be 'hello.'#2");
+Math.random = original;
+
+// options = {} when not a method of Math
+assert.ok(true, "Using seedrandom('hello.', {})");
+rng = seedrandom('hello.', {});
+assert.equal(typeof(rng), 'function', "Should return a function.");
+r = rng();
+assert.ok(true, "Got " + r);
+assert.equal(r, 0.9282578795792454, "Should be 'hello.'#1");
+assert.ok(original === Math.random, "Should not change Math.random.");
+assert.ok(original !== rng, "PRNG should not be Math.random.");
+
+});
+</script>
+</body>
+</html>
diff --git a/node_modules/seedrandom/test/out/dieharder-report.txt b/node_modules/seedrandom/test/out/dieharder-report.txt
new file mode 100644
index 0000000..9e94668
--- /dev/null
+++ b/node_modules/seedrandom/test/out/dieharder-report.txt
@@ -0,0 +1,125 @@
+#=============================================================================#
+#            dieharder version 3.31.1 Copyright 2003 Robert G. Brown          #
+#=============================================================================#
+   rng_name    |rands/second|   Seed   |
+stdin_input_raw|  8.07e+05  | 254212981|
+#=============================================================================#
+        test_name   |ntup| tsamples |psamples|  p-value |Assessment
+#=============================================================================#
+   diehard_birthdays|   0|       100|     100|0.55960061|  PASSED  
+      diehard_operm5|   0|   1000000|     100|0.10062010|  PASSED  
+  diehard_rank_32x32|   0|     40000|     100|0.70957907|  PASSED  
+    diehard_rank_6x8|   0|    100000|     100|0.55953143|  PASSED  
+   diehard_bitstream|   0|   2097152|     100|0.22843857|  PASSED  
+        diehard_opso|   0|   2097152|     100|0.72251086|  PASSED  
+        diehard_oqso|   0|   2097152|     100|0.30642074|  PASSED  
+         diehard_dna|   0|   2097152|     100|0.30448899|  PASSED  
+diehard_count_1s_str|   0|    256000|     100|0.23557064|  PASSED  
+diehard_count_1s_byt|   0|    256000|     100|0.71300874|  PASSED  
+ diehard_parking_lot|   0|     12000|     100|0.88217430|  PASSED  
+    diehard_2dsphere|   2|      8000|     100|0.32366362|  PASSED  
+    diehard_3dsphere|   3|      4000|     100|0.30804215|  PASSED  
+     diehard_squeeze|   0|    100000|     100|0.97856588|  PASSED  
+        diehard_sums|   0|       100|     100|0.01512286|  PASSED  
+        diehard_runs|   0|    100000|     100|0.39065054|  PASSED  
+        diehard_runs|   0|    100000|     100|0.10369088|  PASSED  
+       diehard_craps|   0|    200000|     100|0.57875078|  PASSED  
+       diehard_craps|   0|    200000|     100|0.96945165|  PASSED  
+ marsaglia_tsang_gcd|   0|  10000000|     100|0.58793715|  PASSED  
+ marsaglia_tsang_gcd|   0|  10000000|     100|0.59724594|  PASSED  
+         sts_monobit|   1|    100000|     100|0.49155110|  PASSED  
+            sts_runs|   2|    100000|     100|0.01880740|  PASSED  
+          sts_serial|   1|    100000|     100|0.79060354|  PASSED  
+          sts_serial|   2|    100000|     100|0.91055644|  PASSED  
+          sts_serial|   3|    100000|     100|0.15984346|  PASSED  
+          sts_serial|   3|    100000|     100|0.04426690|  PASSED  
+          sts_serial|   4|    100000|     100|0.31589675|  PASSED  
+          sts_serial|   4|    100000|     100|0.65789943|  PASSED  
+          sts_serial|   5|    100000|     100|0.84228003|  PASSED  
+          sts_serial|   5|    100000|     100|0.28748169|  PASSED  
+          sts_serial|   6|    100000|     100|0.00092985|   WEAK   
+          sts_serial|   6|    100000|     100|0.06074248|  PASSED  
+          sts_serial|   7|    100000|     100|0.54740122|  PASSED  
+          sts_serial|   7|    100000|     100|0.30286638|  PASSED  
+          sts_serial|   8|    100000|     100|0.56593073|  PASSED  
+          sts_serial|   8|    100000|     100|0.48256390|  PASSED  
+          sts_serial|   9|    100000|     100|0.70850963|  PASSED  
+          sts_serial|   9|    100000|     100|0.63732762|  PASSED  
+          sts_serial|  10|    100000|     100|0.70209276|  PASSED  
+          sts_serial|  10|    100000|     100|0.99576606|   WEAK   
+          sts_serial|  11|    100000|     100|0.96272563|  PASSED  
+          sts_serial|  11|    100000|     100|0.76682898|  PASSED  
+          sts_serial|  12|    100000|     100|0.51522677|  PASSED  
+          sts_serial|  12|    100000|     100|0.49381681|  PASSED  
+          sts_serial|  13|    100000|     100|0.24260261|  PASSED  
+          sts_serial|  13|    100000|     100|0.41953338|  PASSED  
+          sts_serial|  14|    100000|     100|0.34770045|  PASSED  
+          sts_serial|  14|    100000|     100|0.16117277|  PASSED  
+          sts_serial|  15|    100000|     100|0.27319992|  PASSED  
+          sts_serial|  15|    100000|     100|0.61170864|  PASSED  
+          sts_serial|  16|    100000|     100|0.90638742|  PASSED  
+          sts_serial|  16|    100000|     100|0.60186602|  PASSED  
+         rgb_bitdist|   1|    100000|     100|0.72630216|  PASSED  
+         rgb_bitdist|   2|    100000|     100|0.61396597|  PASSED  
+         rgb_bitdist|   3|    100000|     100|0.40711729|  PASSED  
+         rgb_bitdist|   4|    100000|     100|0.94730365|  PASSED  
+         rgb_bitdist|   5|    100000|     100|0.95781972|  PASSED  
+         rgb_bitdist|   6|    100000|     100|0.15054771|  PASSED  
+         rgb_bitdist|   7|    100000|     100|0.25022202|  PASSED  
+         rgb_bitdist|   8|    100000|     100|0.66027754|  PASSED  
+         rgb_bitdist|   9|    100000|     100|0.86566842|  PASSED  
+         rgb_bitdist|  10|    100000|     100|0.55219204|  PASSED  
+         rgb_bitdist|  11|    100000|     100|0.73446935|  PASSED  
+         rgb_bitdist|  12|    100000|     100|0.99761332|   WEAK   
+rgb_minimum_distance|   2|     10000|    1000|0.42222311|  PASSED  
+rgb_minimum_distance|   3|     10000|    1000|0.81566417|  PASSED  
+rgb_minimum_distance|   4|     10000|    1000|0.84024874|  PASSED  
+rgb_minimum_distance|   5|     10000|    1000|0.37732026|  PASSED  
+    rgb_permutations|   2|    100000|     100|0.10996142|  PASSED  
+    rgb_permutations|   3|    100000|     100|0.64682554|  PASSED  
+    rgb_permutations|   4|    100000|     100|0.75843859|  PASSED  
+    rgb_permutations|   5|    100000|     100|0.95248930|  PASSED  
+      rgb_lagged_sum|   0|   1000000|     100|0.57812648|  PASSED  
+      rgb_lagged_sum|   1|   1000000|     100|0.67863162|  PASSED  
+      rgb_lagged_sum|   2|   1000000|     100|0.70003210|  PASSED  
+      rgb_lagged_sum|   3|   1000000|     100|0.50312943|  PASSED  
+      rgb_lagged_sum|   4|   1000000|     100|0.80009739|  PASSED  
+      rgb_lagged_sum|   5|   1000000|     100|0.66383600|  PASSED  
+      rgb_lagged_sum|   6|   1000000|     100|0.65640392|  PASSED  
+      rgb_lagged_sum|   7|   1000000|     100|0.10710511|  PASSED  
+      rgb_lagged_sum|   8|   1000000|     100|0.70414014|  PASSED  
+      rgb_lagged_sum|   9|   1000000|     100|0.63857571|  PASSED  
+      rgb_lagged_sum|  10|   1000000|     100|0.25221229|  PASSED  
+      rgb_lagged_sum|  11|   1000000|     100|0.04199433|  PASSED  
+      rgb_lagged_sum|  12|   1000000|     100|0.82738115|  PASSED  
+      rgb_lagged_sum|  13|   1000000|     100|0.28316509|  PASSED  
+      rgb_lagged_sum|  14|   1000000|     100|0.21184422|  PASSED  
+      rgb_lagged_sum|  15|   1000000|     100|0.35537687|  PASSED  
+      rgb_lagged_sum|  16|   1000000|     100|0.40157319|  PASSED  
+      rgb_lagged_sum|  17|   1000000|     100|0.98108259|  PASSED  
+      rgb_lagged_sum|  18|   1000000|     100|0.65892868|  PASSED  
+      rgb_lagged_sum|  19|   1000000|     100|0.61659671|  PASSED  
+      rgb_lagged_sum|  20|   1000000|     100|0.21462018|  PASSED  
+      rgb_lagged_sum|  21|   1000000|     100|0.94489214|  PASSED  
+      rgb_lagged_sum|  22|   1000000|     100|0.76019491|  PASSED  
+      rgb_lagged_sum|  23|   1000000|     100|0.21024028|  PASSED  
+      rgb_lagged_sum|  24|   1000000|     100|0.25249456|  PASSED  
+      rgb_lagged_sum|  25|   1000000|     100|0.87385459|  PASSED  
+      rgb_lagged_sum|  26|   1000000|     100|0.81553540|  PASSED  
+      rgb_lagged_sum|  27|   1000000|     100|0.94450657|  PASSED  
+      rgb_lagged_sum|  28|   1000000|     100|0.82470366|  PASSED  
+      rgb_lagged_sum|  29|   1000000|     100|0.93993722|  PASSED  
+      rgb_lagged_sum|  30|   1000000|     100|0.52675692|  PASSED  
+      rgb_lagged_sum|  31|   1000000|     100|0.59975558|  PASSED  
+      rgb_lagged_sum|  32|   1000000|     100|0.27951088|  PASSED  
+     rgb_kstest_test|   0|     10000|    1000|0.49707175|  PASSED  
+     dab_bytedistrib|   0|  51200000|       1|0.73019918|  PASSED  
+             dab_dct| 256|     50000|       1|0.33856081|  PASSED  
+Preparing to run test 207.  ntuple = 0
+        dab_filltree|  32|  15000000|       1|0.92953640|  PASSED  
+        dab_filltree|  32|  15000000|       1|0.10970674|  PASSED  
+Preparing to run test 208.  ntuple = 0
+       dab_filltree2|   0|   5000000|       1|0.88411850|  PASSED  
+       dab_filltree2|   1|   5000000|       1|0.56835378|  PASSED  
+Preparing to run test 209.  ntuple = 0
+        dab_monobit2|  12|  65000000|       1|0.07987839|  PASSED  
diff --git a/node_modules/seedrandom/test/prngtest.js b/node_modules/seedrandom/test/prngtest.js
new file mode 100644
index 0000000..c5ae03a
--- /dev/null
+++ b/node_modules/seedrandom/test/prngtest.js
@@ -0,0 +1,122 @@
+// A simple smoke test and benchmark for the generators.
+
+var assert = require('assert');
+var xor128 = require('../lib/xor128');
+var xorwow = require('../lib/xorwow');
+var xs7 = require('../lib/xorshift7');
+var xor4096 = require('../lib/xor4096');
+var tychei = require('../lib/tychei');
+var alea = require('../lib/alea');
+var sr = require('../seedrandom');
+
+describe("XOR-Shift generator test", function() {
+
+var benchmarks = { native: { rand: Math.random, times: [] } };
+
+function test(label, alg, double1, float3, int4, hc, qc, ec, e2c) {
+  var fn = alg(1);
+  var fn2 = alg('hello.', { state: true });
+  benchmarks[label] = {rand: fn.quick, times: []};
+  it("should use " + label + " correctly", function() {
+    if (double1 != null) assert.equal(fn.double(), double1);
+    if (float3 != null) assert.equal(fn.quick(), float3);
+    if (int4 != null) assert.equal(fn.int32(), int4);
+    assert(fn() > 0);
+    assert(fn() < 1);
+    assert(fn2() > 0);
+    // Internal state is visible only if requested.
+    assert(!('state' in fn));
+    assert('state' in fn2);
+    var ss = fn2.state();
+    var rs = fn2();
+    assert(rs < 1);
+    var j, h = 0, q = 0, e = 0, r, p, e2 = 0;
+    for (j = 0; j < 1024; ++j) {
+      r = fn();
+      if (r < 0.5) h += 1;
+      if (r < 0.25) q += 1;
+      if (r < 0.125) e += 1;
+      r2 = fn2();
+      if (r2 < 0.125) e2 += 1;
+    }
+    if (hc != null) {
+      assert.equal(h, hc);
+      assert.equal(q, qc);
+      assert.equal(e, ec);
+      assert.equal(e2, e2c);
+      h = q = e = p = 0;
+      for (j = 0; j < 1024; ++j) {
+        r = fn.double();
+        if (r < 0.5) h += 1;
+        if (r < 0.25) q += 1;
+        if (r < 0.125) e += 1;
+        if (fn.int32() >= 0) p += 1;
+      }
+      // Sanity-check double() and int32.
+      assert(h >= 480 && h <= 543, h);
+      assert(q >= 226 && q <= 286, q);
+      assert(e >= 100 && e <= 156, e);
+      assert(e2 >= 100 && e2 <= 156, e2);
+      assert(p >= 482 && p <= 543, p);
+    }
+    var fn3 = alg(0, { state: ss });
+    assert.equal(fn3(), rs);
+  });
+}
+
+test("xor128", xor128,
+    0.7963797148975774, 0.22171171731315553, 317177041, 498, 236, 110, 115);
+test("xorwow", xorwow,
+    0.8178000247146859, 0.8407576507888734, 533150816, 519, 228, 121, 123);
+test("xorshift7", xs7,
+    0.21241471533241418, 0.9957620368804783, -1678071207, 510, 261, 143, 124);
+test("tychei", tychei,
+    0.42331440041340196, 0.9365617581643164, -884984569, 521, 242, 116, 126);
+test("seedrandom", sr,
+    0.1776348083296759, 0.2160690303426236, 1397712774, 526, 282, 131, 137);
+test("xor4096", xor4096,
+    0.1520436450538547, 0.4206166828516871, 1312695376, 496, 241, 113, 142);
+test("alea", alea,
+    0.5260470956849501, 0.47771977609954774, -1625913352, 494, 246, 125, 122);
+
+it("runs benchmarks", function() {
+  var n = 4;
+  var trials = 10;
+  var top = 4;
+  this.timeout(200 * n * trials);
+  this.slow(30 * n * trials);
+  var fn, k, start, end, j, t;
+  for (k in benchmarks) {
+    fn = benchmarks[k].rand;
+    // warmup.
+    for (j = 0; j < 1e5; ++j) fn();
+  }
+  for (t = 0; t < trials; ++t) {
+    for (k in benchmarks) {
+      fn = benchmarks[k].rand;
+      start = +new Date;
+      // benchmark.
+      for (j = 0; j < n * 1e5; ++j) fn();
+      end = +new Date;
+      benchmarks[k].times.push(end - start);
+    }
+  }
+  for (k in benchmarks) {
+    benchmarks[k].times.sort();
+  }
+  function fastest(array) {
+    var sum = 0;
+    for (var j = 0; j < top; ++j) {
+       sum += array[j];
+    }
+    return sum / top;
+  }
+  var nativetime = fastest(benchmarks.native.times);
+  for (k in benchmarks) {
+    var time = fastest(benchmarks[k].times);
+    console.log(k+ ': ' + time / n + ' nanoseconds per call, ' +
+       (time / nativetime).toFixed(1) + 'x native random.');
+  }
+});
+
+});
diff --git a/node_modules/seedrandom/test/qunitassert.js b/node_modules/seedrandom/test/qunitassert.js
new file mode 100644
index 0000000..9b72d6e
--- /dev/null
+++ b/node_modules/seedrandom/test/qunitassert.js
@@ -0,0 +1,3 @@
+// Use QUnit.assert to mimic node.assert.
+
+module.exports = QUnit.assert;
diff --git a/node_modules/seedrandom/test/require.html b/node_modules/seedrandom/test/require.html
new file mode 100644
index 0000000..b8aac32
--- /dev/null
+++ b/node_modules/seedrandom/test/require.html
@@ -0,0 +1,57 @@
+<!DOCTYPE html>
+<html>
+<head>
+<link rel="stylesheet" href="lib/qunit.css">
+</head>
+<body>
+<div id="qunit"></div>
+<div id="qunit-fixture"></div>
+<script src="lib/require.js"></script>
+
+<script>
+
+require.config({
+  paths: {
+    'qunit': 'lib/qunit',
+    'seedrandom': '/seedrandom.min'
+  }
+});
+
+require(['qunit', 'seedrandom'], function(QUnit, seedrandom) {
+  QUnit.start();
+  QUnit.module('require.js test');
+  QUnit.test('some normal test', function(assert) {
+    assert.ok(true, "Seeded random created using module:");
+    var check = [];
+    var prng = seedrandom('predictable.');
+    var r;
+    for (var j = 0; j < 5; ++j) {
+      r = prng();
+      assert.ok(true, r + "");
+      check.push(r);
+    }
+    assert.ok(true, "Native random:");
+    for (var j = 0; j < 5; ++j) {
+      r = Math.random();
+      assert.ok(true, r + "");
+      check.push(r);
+    }
+    // Verify against Math.seedrandom.
+    var seed = Math.seedrandom('predictable.');
+    assert.equal(seed, 'predictable.',
+        "Seed should be returned from Math.seedrandom.");
+    for (var j = 0; j < 10; ++j) {
+      r = Math.random();
+      if (j < 5) {
+        assert.equal(check[j], r, "Equal: " + r + " vs " + check[j]);
+      } else {
+        assert.ok(check[j] != r, "Unqual: " + r + " vs " + check[j]);
+      }
+    }
+    document.close();
+  });
+});
+
+</script>
+</body>
+</html>
diff --git a/node_modules/seedrandom/test/run_dieharder.sh b/node_modules/seedrandom/test/run_dieharder.sh
new file mode 100755
index 0000000..89fb5e1
--- /dev/null
+++ b/node_modules/seedrandom/test/run_dieharder.sh
@@ -0,0 +1,5 @@
+#!/bin/sh
+# http://www.phy.duke.edu/~rgb/General/dieharder.php
+# How to run dieharder against seedrandom:
+
+node bitgen.js | dieharder -g 200 -a | tee out/dieharder-report.txt
diff --git a/node_modules/seedrandom/test/state.html b/node_modules/seedrandom/test/state.html
new file mode 100644
index 0000000..609b4f0
--- /dev/null
+++ b/node_modules/seedrandom/test/state.html
@@ -0,0 +1,56 @@
+<html>
+<head>
+<link rel="stylesheet" href="lib/qunit.css">
+</head>
+<body>
+<div id="qunit"></div>
+<div id="qunit-fixture"></div>
+<script src="../seedrandom.min.js"></script>
+<script src="lib/qunit.js"></script>
+<script>
+QUnit.module("Options API Test");
+
+QUnit.test("Verify that state method is not normally present",
+function(assert) {
+
+var seedrandom = Math.seedrandom;
+var dummy = seedrandom('hello');
+var unexpected = -1;
+var expected = -1;
+try {
+  unexpected = dummy.state();
+} catch(e) {
+  expected = 1;
+}
+assert.equal(unexpected, -1);
+assert.equal(expected, 1);
+var count = 0;
+for (var x in dummy) {
+  if (x == 'state') count += 1;
+}
+assert.equal(count, 0);
+
+});
+
+QUnit.test("Verify that state option works as advertised", function(assert) {
+
+var seedrandom = Math.seedrandom;
+var saveable = seedrandom("secret-seed", {state: true});
+var ordinary = seedrandom("secret-seed");
+for (var j = 0; j < 1e3; ++j) {
+  assert.equal(ordinary(), saveable());
+}
+var virgin = seedrandom("secret-seed");
+var saved = saveable.state();
+var replica = seedrandom("", {state: saved});
+for (var j = 0; j < 1e2; ++j) {
+  var r = replica();
+  assert.equal(r, saveable());
+  assert.equal(r, ordinary());
+  assert.ok(r != virgin());
+}
+
+});
+</script>
+</body>
+</html>
diff --git a/node_modules/tfjs-image-recognition-base/.travis.yml b/node_modules/tfjs-image-recognition-base/.travis.yml
new file mode 100644
index 0000000..d7da92f
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/.travis.yml
@@ -0,0 +1,19 @@
+sudo: required
+language: node_js
+node_js:
+  - "10"
+env:
+  global:
+    - BACKEND_CPU=true
+  matrix:
+    - ENV=browser
+    - ENV=node
+addons:
+  chrome: stable
+before_install:
+ - export DISPLAY=:99.0
+ - sh -e /etc/init.d/xvfb start
+script:
+  - if [ $ENV == 'browser' ]; then npm run test-browser; fi
+  - if [ $ENV == 'node' ]; then npm run test-node; fi
+  - npm run build
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/LICENSE b/node_modules/tfjs-image-recognition-base/LICENSE
new file mode 100644
index 0000000..2fafbb1
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2018 Vincent Mühler
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/node_modules/tfjs-image-recognition-base/README.md b/node_modules/tfjs-image-recognition-base/README.md
new file mode 100644
index 0000000..d214478
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/README.md
@@ -0,0 +1 @@
+[![Build Status](https://travis-ci.org/justadudewhohacks/tfjs-image-recognition-base.svg?branch=master)](https://travis-ci.org/justadudewhohacks/tfjs-image-recognition-base)
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/jasmine-node.json b/node_modules/tfjs-image-recognition-base/jasmine-node.json
new file mode 100644
index 0000000..bcf9c99
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/jasmine-node.json
@@ -0,0 +1,8 @@
+{
+  "spec_dir": "test",
+  "spec_files": [
+    "**/*.test.ts",
+    "!**/*.browser.test.ts"
+  ],
+  "random": false
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/karma.conf.js b/node_modules/tfjs-image-recognition-base/karma.conf.js
new file mode 100644
index 0000000..e6437a3
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/karma.conf.js
@@ -0,0 +1,35 @@
+const dataFiles = [
+  'test/*.png',
+  'test/*.json',
+  'test/*.weights'
+].map(pattern => ({
+  pattern,
+  watched: false,
+  included: false,
+  served: true,
+  nocache: false
+}))
+
+module.exports = function(config) {
+  config.set({
+    frameworks: ['jasmine', 'karma-typescript'],
+    files: [
+      'src/**/*.ts',
+      'test/**/*.ts'
+    ].concat(dataFiles),
+    preprocessors: {
+      '**/*.ts': ['karma-typescript']
+    },
+    karmaTypescriptConfig: {
+      tsconfig: 'tsconfig.test.json'
+    },
+    browsers: ['Chrome'],
+    browserNoActivityTimeout: 120000,
+    captureTimeout: 60000,
+    client: {
+      jasmine: {
+        timeoutInterval: 60000
+      }
+    }
+  })
+}
diff --git a/node_modules/tfjs-image-recognition-base/package.json b/node_modules/tfjs-image-recognition-base/package.json
new file mode 100644
index 0000000..e30f1ea
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/package.json
@@ -0,0 +1,68 @@
+{
+  "_args": [
+    [
+      "tfjs-image-recognition-base@0.3.1",
+      "/Users/zamua/Dropbox/workspace/madify"
+    ]
+  ],
+  "_from": "tfjs-image-recognition-base@0.3.1",
+  "_id": "tfjs-image-recognition-base@0.3.1",
+  "_inBundle": false,
+  "_integrity": "sha512-58PNmCNwPx0AsQ7qfPn6iOiabIqzMuQymKFW4eZHXmsWW0fGlZBvO8AXLyuqW1ZbtH0LQUnyEETfWNFAYQmkwA==",
+  "_location": "/tfjs-image-recognition-base",
+  "_phantomChildren": {},
+  "_requested": {
+    "type": "version",
+    "registry": true,
+    "raw": "tfjs-image-recognition-base@0.3.1",
+    "name": "tfjs-image-recognition-base",
+    "escapedName": "tfjs-image-recognition-base",
+    "rawSpec": "0.3.1",
+    "saveSpec": null,
+    "fetchSpec": "0.3.1"
+  },
+  "_requiredBy": [
+    "/face-api.js",
+    "/tfjs-tiny-yolov2"
+  ],
+  "_resolved": "https://registry.npmjs.org/tfjs-image-recognition-base/-/tfjs-image-recognition-base-0.3.1.tgz",
+  "_spec": "0.3.1",
+  "_where": "/Users/zamua/Dropbox/workspace/madify",
+  "author": {
+    "name": "justadudewhohacks"
+  },
+  "dependencies": {
+    "@tensorflow/tfjs-core": "0.14.2",
+    "tslib": "^1.9.3"
+  },
+  "description": "A shared codebase for face-api.js and tfjs-tiny-yolo-v2.",
+  "devDependencies": {
+    "@tensorflow/tfjs-node": "^0.1.21",
+    "@types/jasmine": "^2.8.8",
+    "@types/node": "^10.12.12",
+    "canvas": "^2.0.1",
+    "jasmine": "^3.3.0",
+    "jasmine-core": "^3.2.1",
+    "karma": "^3.0.0",
+    "karma-chrome-launcher": "^2.2.0",
+    "karma-jasmine": "^1.1.2",
+    "karma-typescript": "^3.0.12",
+    "ts-node": "^7.0.1",
+    "typescript": "2.8.4"
+  },
+  "license": "MIT",
+  "main": "./build/commonjs/index.js",
+  "module": "./build/es6/index.js",
+  "name": "tfjs-image-recognition-base",
+  "scripts": {
+    "build": "rm -rf ./build && npm run tsc && npm run tsc-es6",
+    "test": "karma start",
+    "test-all": "npm run test-browser && npm run test-node",
+    "test-browser": "karma start --single-run",
+    "test-node": "ts-node node_modules/jasmine/bin/jasmine --config=jasmine-node.json",
+    "tsc": "tsc",
+    "tsc-es6": "tsc --p tsconfig.es6.json"
+  },
+  "typings": "./build/commonjs/index.d.ts",
+  "version": "0.3.1"
+}
diff --git a/node_modules/tfjs-image-recognition-base/src/NeuralNetwork.ts b/node_modules/tfjs-image-recognition-base/src/NeuralNetwork.ts
new file mode 100644
index 0000000..653cf4f
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/NeuralNetwork.ts
@@ -0,0 +1,159 @@
+import * as tf from '@tensorflow/tfjs-core';
+
+import { ParamMapping } from './common';
+import { getModelUris } from './common/getModelUris';
+import { loadWeightMap } from './dom';
+import { env } from './env';
+
+export abstract class NeuralNetwork<TNetParams> {
+
+  protected _params: TNetParams | undefined = undefined
+  protected _paramMappings: ParamMapping[] = []
+
+  constructor(protected _name: string) {}
+
+  public get params(): TNetParams | undefined { return this._params }
+  public get paramMappings(): ParamMapping[] { return this._paramMappings }
+  public get isLoaded(): boolean { return !!this.params }
+
+  public getParamFromPath(paramPath: string): tf.Tensor {
+    const { obj, objProp } = this.traversePropertyPath(paramPath)
+    return obj[objProp]
+  }
+
+  public reassignParamFromPath(paramPath: string, tensor: tf.Tensor) {
+    const { obj, objProp } = this.traversePropertyPath(paramPath)
+    obj[objProp].dispose()
+    obj[objProp] = tensor
+  }
+
+  public getParamList() {
+    return this._paramMappings.map(({ paramPath }) => ({
+      path: paramPath,
+      tensor: this.getParamFromPath(paramPath)
+    }))
+  }
+
+  public getTrainableParams() {
+    return this.getParamList().filter(param => param.tensor instanceof tf.Variable)
+  }
+
+  public getFrozenParams() {
+    return this.getParamList().filter(param => !(param.tensor instanceof tf.Variable))
+  }
+
+  public variable() {
+    this.getFrozenParams().forEach(({ path, tensor }) => {
+      this.reassignParamFromPath(path, tensor.variable())
+    })
+  }
+
+  public freeze() {
+    this.getTrainableParams().forEach(({ path, tensor: variable }) => {
+      const tensor = tf.tensor(variable.dataSync())
+      variable.dispose()
+      this.reassignParamFromPath(path, tensor)
+    })
+  }
+
+  public dispose(throwOnRedispose: boolean = true) {
+    this.getParamList().forEach(param => {
+      if (throwOnRedispose && param.tensor.isDisposed) {
+        throw new Error(`param tensor has already been disposed for path ${param.path}`)
+      }
+      param.tensor.dispose()
+    })
+    this._params = undefined
+  }
+
+  public serializeParams(): Float32Array {
+    return new Float32Array(
+      this.getParamList()
+        .map(({ tensor }) => Array.from(tensor.dataSync()))
+        .reduce((flat, arr) => flat.concat(arr))
+    )
+  }
+
+  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {
+    if (weightsOrUrl instanceof Float32Array) {
+      this.extractWeights(weightsOrUrl)
+      return
+    }
+
+    await this.loadFromUri(weightsOrUrl)
+  }
+
+  public async loadFromUri(uri: string | undefined) {
+    if (uri && typeof uri !== 'string') {
+      throw new Error(`${this._name}.loadFromUri - expected model uri`)
+    }
+
+    const weightMap = await loadWeightMap(uri, this.getDefaultModelName())
+    this.loadFromWeightMap(weightMap)
+  }
+
+  public async loadFromDisk(filePath: string | undefined) {
+    if (filePath && typeof filePath !== 'string') {
+      throw new Error(`${this._name}.loadFromDisk - expected model file path`)
+    }
+
+    const { readFile } = env.getEnv()
+
+    const { manifestUri, modelBaseUri } = getModelUris(filePath, this.getDefaultModelName())
+
+    const fetchWeightsFromDisk = (filePaths: string[]) => Promise.all(
+      filePaths.map(filePath => readFile(filePath).then(buf => buf.buffer))
+    )
+    const loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk)
+
+    const manifest = JSON.parse((await readFile(manifestUri)).toString())
+    const weightMap = await loadWeights(manifest, modelBaseUri)
+
+    this.loadFromWeightMap(weightMap)
+  }
+
+  public loadFromWeightMap(weightMap: tf.NamedTensorMap) {
+    const {
+      paramMappings,
+      params
+    } = this.extractParamsFromWeigthMap(weightMap)
+
+    this._paramMappings = paramMappings
+    this._params = params
+  }
+
+  public extractWeights(weights: Float32Array) {
+    const {
+      paramMappings,
+      params
+    } = this.extractParams(weights)
+
+    this._paramMappings = paramMappings
+    this._params = params
+  }
+
+  private traversePropertyPath(paramPath: string) {
+    if (!this.params) {
+      throw new Error(`traversePropertyPath - model has no loaded params`)
+    }
+
+    const result = paramPath.split('/').reduce((res: { nextObj: any, obj?: any, objProp?: string }, objProp) => {
+      if (!res.nextObj.hasOwnProperty(objProp)) {
+        throw new Error(`traversePropertyPath - object does not have property ${objProp}, for path ${paramPath}`)
+      }
+
+      return { obj: res.nextObj, objProp, nextObj: res.nextObj[objProp] }
+    }, { nextObj: this.params })
+
+    const { obj, objProp } = result
+    if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {
+      throw new Error(`traversePropertyPath - parameter is not a tensor, for path ${paramPath}`)
+    }
+
+    return { obj, objProp }
+  }
+
+  protected abstract getDefaultModelName(): string
+  protected abstract extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap): { params: TNetParams, paramMappings: ParamMapping[] }
+  protected abstract extractParams(weights: Float32Array): { params: TNetParams, paramMappings: ParamMapping[] }
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/classes/BoundingBox.ts b/node_modules/tfjs-image-recognition-base/src/classes/BoundingBox.ts
new file mode 100644
index 0000000..c23049e
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/classes/BoundingBox.ts
@@ -0,0 +1,14 @@
+import { Box } from './Box';
+
+export interface IBoundingBox {
+  left: number
+  top: number
+  right: number
+  bottom: number
+}
+
+export class BoundingBox extends Box<BoundingBox> implements IBoundingBox {
+  constructor(left: number, top: number, right: number, bottom: number) {
+    super({ left, top, right, bottom })
+  }
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/classes/Box.ts b/node_modules/tfjs-image-recognition-base/src/classes/Box.ts
new file mode 100644
index 0000000..04c8791
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/classes/Box.ts
@@ -0,0 +1,172 @@
+import { isDimensions, isValidNumber } from '../utils';
+import { IBoundingBox } from './BoundingBox';
+import { IRect } from './Rect';
+import { IDimensions } from './Dimensions';
+
+export class Box<BoxType = any> implements IBoundingBox, IRect {
+
+  public static isRect(rect: any): boolean {
+    return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber)
+  }
+
+  public static assertIsValidBox(box: any, callee: string, allowNegativeDimensions: boolean = false) {
+    if (!Box.isRect(box)) {
+      throw new Error(`${callee} - invalid box: ${JSON.stringify(box)}, expected object with properties x, y, width, height`)
+    }
+
+    if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {
+      throw new Error(`${callee} - width (${box.width}) and height (${box.height}) must be positive numbers`)
+    }
+  }
+
+  private _x: number
+  private _y: number
+  private _width: number
+  private _height: number
+
+  // TODO: MTCNN boxes sometimes have negative widths or heights, figure out why and remove
+  // allowNegativeDimensions flag again
+  constructor(_box: IBoundingBox | IRect, allowNegativeDimensions: boolean = false) {
+    const box = (_box || {}) as any
+
+    const isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber)
+    const isRect = [box.x, box.y, box.width, box.height].every(isValidNumber)
+
+    if (!isRect && !isBbox) {
+      throw new Error(`Box.constructor - expected box to be IBoundingBox | IRect, instead have ${JSON.stringify(box)}`)
+    }
+
+    const [x, y, width, height] = isRect
+      ? [box.x, box.y, box.width, box.height]
+      : [box.left, box.top, box.right - box.left, box.bottom - box.top]
+
+    Box.assertIsValidBox({ x, y, width, height }, 'Box.constructor', allowNegativeDimensions)
+
+    this._x = x
+    this._y = y
+    this._width = width
+    this._height = height
+  }
+
+  public get x(): number { return this._x }
+  public get y(): number { return this._y }
+  public get width(): number { return this._width }
+  public get height(): number { return this._height }
+  public get left(): number { return this.x }
+  public get top(): number { return this.y }
+  public get right(): number { return this.x + this.width }
+  public get bottom(): number { return this.y + this.height }
+  public get area(): number { return this.width * this.height }
+
+  public round(): Box<BoxType> {
+    const [x, y, width, height] = [this.x, this.y, this.width, this.height]
+      .map(val => Math.round(val))
+    return new Box({ x, y, width, height })
+  }
+
+  public floor(): Box<BoxType> {
+    const [x, y, width, height] = [this.x, this.y, this.width, this.height]
+      .map(val => Math.floor(val))
+    return new Box({ x, y, width, height })
+  }
+
+  public toSquare(): Box<BoxType> {
+    let { x, y, width, height } = this
+    const diff = Math.abs(width - height)
+    if (width < height) {
+      x -= (diff / 2)
+      width += diff
+    }
+    if (height < width) {
+      y -= (diff / 2)
+      height += diff
+    }
+
+    return new Box({ x, y, width, height })
+  }
+
+  public rescale(s: IDimensions | number): Box<BoxType> {
+    const scaleX = isDimensions(s) ? (s as IDimensions).width : s as number
+    const scaleY = isDimensions(s) ? (s as IDimensions).height : s as number
+    return new Box({
+      x: this.x * scaleX,
+      y: this.y * scaleY,
+      width: this.width * scaleX,
+      height: this.height * scaleY
+    })
+  }
+
+  public pad(padX: number, padY: number): Box<BoxType> {
+    let [x, y, width, height] = [
+      this.x - (padX / 2),
+      this.y - (padY / 2),
+      this.width + padX,
+      this.height + padY
+    ]
+    return new Box({ x, y, width, height })
+  }
+
+  public clipAtImageBorders(imgWidth: number, imgHeight: number): Box<BoxType> {
+    const { x, y, right, bottom } = this
+    const clippedX = Math.max(x, 0)
+    const clippedY = Math.max(y, 0)
+
+    const newWidth = right - clippedX
+    const newHeight = bottom - clippedY
+    const clippedWidth = Math.min(newWidth, imgWidth - clippedX)
+    const clippedHeight = Math.min(newHeight, imgHeight - clippedY)
+
+    return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight})).floor()
+  }
+
+  public shift(sx: number, sy: number): Box<BoxType> {
+    const { width, height } = this
+    const x = this.x + sx
+    const y = this.y + sy
+
+    return new Box({ x, y, width, height })
+  }
+
+  public padAtBorders(imageHeight: number, imageWidth: number) {
+    const w = this.width + 1
+    const h = this.height + 1
+
+    let dx = 1
+    let dy = 1
+    let edx = w
+    let edy = h
+
+    let x = this.left
+    let y = this.top
+    let ex = this.right
+    let ey = this.bottom
+
+    if (ex > imageWidth) {
+      edx = -ex + imageWidth + w
+      ex = imageWidth
+    }
+    if (ey > imageHeight) {
+      edy = -ey + imageHeight + h
+      ey = imageHeight
+    }
+    if (x < 1) {
+      edy = 2 - x
+      x = 1
+    }
+    if (y < 1) {
+      edy = 2 - y
+      y = 1
+    }
+
+    return { dy, edy, dx, edx, y, ey, x, ex, w, h }
+  }
+
+  public calibrate(region: Box) {
+    return new Box({
+      left: this.left + (region.left * this.width),
+      top: this.top + (region.top * this.height),
+      right: this.right + (region.right * this.width),
+      bottom: this.bottom + (region.bottom * this.height)
+    }).toSquare().round()
+  }
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/classes/BoxWithText.ts b/node_modules/tfjs-image-recognition-base/src/classes/BoxWithText.ts
new file mode 100644
index 0000000..39f0eab
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/classes/BoxWithText.ts
@@ -0,0 +1,15 @@
+import { IBoundingBox } from './BoundingBox';
+import { Box } from './Box';
+import { IRect } from './Rect';
+
+export class BoxWithText extends Box<BoxWithText> {
+
+  private _text: string
+
+  constructor(box: IRect | IBoundingBox, text: string) {
+    super(box)
+    this._text = text
+  }
+
+  public get text(): string { return this._text }
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/classes/Dimensions.ts b/node_modules/tfjs-image-recognition-base/src/classes/Dimensions.ts
new file mode 100644
index 0000000..f20a900
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/classes/Dimensions.ts
@@ -0,0 +1,28 @@
+import { isValidNumber } from '../utils';
+
+export interface IDimensions {
+  width: number
+  height: number
+}
+
+export class Dimensions implements IDimensions {
+
+  private _width: number
+  private _height: number
+
+  constructor(width: number, height: number) {
+    if (!isValidNumber(width) || !isValidNumber(height)) {
+      throw new Error(`Dimensions.constructor - expected width and height to be valid numbers, instead have ${JSON.stringify({ width, height })}`)
+    }
+
+    this._width = width
+    this._height = height
+  }
+
+  public get width(): number { return this._width }
+  public get height(): number { return this._height }
+
+  public reverse(): Dimensions {
+    return new Dimensions(1 / this.width, 1 / this.height)
+  }
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/classes/LabeledBox.ts b/node_modules/tfjs-image-recognition-base/src/classes/LabeledBox.ts
new file mode 100644
index 0000000..27c7395
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/classes/LabeledBox.ts
@@ -0,0 +1,25 @@
+import { IBoundingBox } from './BoundingBox';
+import { Box } from './Box';
+import { IRect } from './Rect';
+import { isValidNumber } from '../utils/index';
+
+export class LabeledBox extends Box<LabeledBox> {
+
+  public static assertIsValidLabeledBox(box: any, callee: string) {
+    Box.assertIsValidBox(box, callee)
+
+    if (!isValidNumber(box.label)) {
+      throw new Error(`${callee} - expected property label (${box.label}) to be a number`)
+    }
+  }
+
+  private _label: number
+
+  constructor(box: IBoundingBox | IRect | any, label: number) {
+    super(box)
+    this._label = label
+  }
+
+  public get label(): number { return this._label }
+
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/classes/ObjectDetection.ts b/node_modules/tfjs-image-recognition-base/src/classes/ObjectDetection.ts
new file mode 100644
index 0000000..ee42368
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/classes/ObjectDetection.ts
@@ -0,0 +1,44 @@
+import { Box } from './Box';
+import { Dimensions, IDimensions } from './Dimensions';
+import { IRect, Rect } from './Rect';
+
+export class ObjectDetection {
+  private _score: number
+  private _classScore: number
+  private _className: string
+  private _box: Rect
+  private _imageDims: Dimensions
+
+  constructor(
+    score: number,
+    classScore: number,
+    className: string,
+    relativeBox: IRect,
+    imageDims: IDimensions
+  ) {
+    this._imageDims = new Dimensions(imageDims.width, imageDims.height)
+    this._score = score
+    this._classScore = classScore
+    this._className = className
+    this._box = new Box(relativeBox).rescale(this._imageDims)
+  }
+
+  public get score(): number { return this._score }
+  public get classScore(): number { return this._classScore }
+  public get className(): string { return this._className }
+  public get box(): Box { return this._box }
+  public get imageDims(): Dimensions { return this._imageDims }
+  public get imageWidth(): number { return this.imageDims.width }
+  public get imageHeight(): number { return this.imageDims.height }
+  public get relativeBox(): Box { return new Box(this._box).rescale(this.imageDims.reverse()) }
+
+  public forSize(width: number, height: number): ObjectDetection {
+    return new ObjectDetection(
+      this.score,
+      this.classScore,
+      this.className,
+      this.relativeBox,
+      { width, height}
+    )
+  }
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/classes/Point.ts b/node_modules/tfjs-image-recognition-base/src/classes/Point.ts
new file mode 100644
index 0000000..8b9e9aa
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/classes/Point.ts
@@ -0,0 +1,45 @@
+export interface IPoint {
+  x: number
+  y: number
+}
+
+export class Point implements IPoint {
+  private _x: number
+  private _y: number
+
+  constructor(x: number, y: number) {
+    this._x = x
+    this._y = y
+  }
+
+  get x(): number { return this._x }
+  get y(): number { return this._y }
+
+  public add(pt: IPoint): Point {
+    return new Point(this.x + pt.x, this.y + pt.y)
+  }
+
+  public sub(pt: IPoint): Point {
+    return new Point(this.x - pt.x, this.y - pt.y)
+  }
+
+  public mul(pt: IPoint): Point {
+    return new Point(this.x * pt.x, this.y * pt.y)
+  }
+
+  public div(pt: IPoint): Point {
+    return new Point(this.x / pt.x, this.y / pt.y)
+  }
+
+  public abs(): Point {
+    return new Point(Math.abs(this.x), Math.abs(this.y))
+  }
+
+  public magnitude(): number {
+    return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2))
+  }
+
+  public floor(): Point {
+    return new Point(Math.floor(this.x), Math.floor(this.y))
+  }
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/classes/PredictedBox.ts b/node_modules/tfjs-image-recognition-base/src/classes/PredictedBox.ts
new file mode 100644
index 0000000..fe12c58
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/classes/PredictedBox.ts
@@ -0,0 +1,31 @@
+import { isValidProbablitiy } from '../utils';
+import { IBoundingBox } from './BoundingBox';
+import { LabeledBox } from './LabeledBox';
+import { IRect } from './Rect';
+
+export class PredictedBox extends LabeledBox {
+
+  public static assertIsValidPredictedBox(box: any, callee: string) {
+    LabeledBox.assertIsValidLabeledBox(box, callee)
+
+    if (
+      !isValidProbablitiy(box.score)
+      || !isValidProbablitiy(box.classScore)
+    ) {
+      throw new Error(`${callee} - expected properties score (${box.score}) and (${box.classScore}) to be a number between [0, 1]`)
+    }
+  }
+
+  private _score: number
+  private _classScore: number
+
+  constructor(box: IBoundingBox | IRect | any, label: number, score: number, classScore: number) {
+    super(box, label)
+    this._score = score
+    this._classScore = classScore
+  }
+
+  public get score(): number { return this._score }
+  public get classScore(): number { return this._classScore }
+
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/classes/Rect.ts b/node_modules/tfjs-image-recognition-base/src/classes/Rect.ts
new file mode 100644
index 0000000..5a65a7e
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/classes/Rect.ts
@@ -0,0 +1,14 @@
+import { Box } from './Box';
+
+export interface IRect {
+  x: number
+  y: number
+  width: number
+  height: number
+}
+
+export class Rect extends Box<Rect> implements IRect {
+  constructor(x: number, y: number, width: number, height: number) {
+    super({ x, y, width, height })
+  }
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/classes/index.ts b/node_modules/tfjs-image-recognition-base/src/classes/index.ts
new file mode 100644
index 0000000..eeb5dfa
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/classes/index.ts
@@ -0,0 +1,9 @@
+export * from './BoundingBox'
+export * from './Box'
+export * from './BoxWithText'
+export * from './Dimensions'
+export * from './LabeledBox'
+export * from './ObjectDetection'
+export * from './Point'
+export * from './PredictedBox'
+export * from './Rect'
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/common/disposeUnusedWeightTensors.ts b/node_modules/tfjs-image-recognition-base/src/common/disposeUnusedWeightTensors.ts
new file mode 100644
index 0000000..a9d23ad
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/common/disposeUnusedWeightTensors.ts
@@ -0,0 +1,9 @@
+import { ParamMapping } from './types';
+
+export function disposeUnusedWeightTensors(weightMap: any, paramMappings: ParamMapping[]) {
+  Object.keys(weightMap).forEach(path => {
+    if (!paramMappings.some(pm => pm.originalPath === path)) {
+      weightMap[path].dispose()
+    }
+  })
+}
diff --git a/node_modules/tfjs-image-recognition-base/src/common/extractWeightEntryFactory.ts b/node_modules/tfjs-image-recognition-base/src/common/extractWeightEntryFactory.ts
new file mode 100644
index 0000000..50e4f10
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/common/extractWeightEntryFactory.ts
@@ -0,0 +1,20 @@
+import { isTensor } from '../utils';
+import { ParamMapping } from './types';
+
+export function extractWeightEntryFactory(weightMap: any, paramMappings: ParamMapping[]) {
+
+  return function<T> (originalPath: string, paramRank: number, mappedPath?: string): T {
+    const tensor = weightMap[originalPath]
+
+    if (!isTensor(tensor, paramRank)) {
+      throw new Error(`expected weightMap[${originalPath}] to be a Tensor${paramRank}D, instead have ${tensor}`)
+    }
+
+    paramMappings.push(
+      { originalPath, paramPath: mappedPath || originalPath }
+    )
+
+    return tensor
+  }
+
+}
diff --git a/node_modules/tfjs-image-recognition-base/src/common/extractWeightsFactory.ts b/node_modules/tfjs-image-recognition-base/src/common/extractWeightsFactory.ts
new file mode 100644
index 0000000..b86f213
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/common/extractWeightsFactory.ts
@@ -0,0 +1,18 @@
+export function extractWeightsFactory(weights: Float32Array) {
+  let remainingWeights = weights
+
+  function extractWeights(numWeights: number): Float32Array {
+    const ret = remainingWeights.slice(0, numWeights)
+    remainingWeights = remainingWeights.slice(numWeights)
+    return ret
+  }
+
+  function getRemainingWeights(): Float32Array {
+    return remainingWeights
+  }
+
+  return {
+    extractWeights,
+    getRemainingWeights
+  }
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/common/getModelUris.ts b/node_modules/tfjs-image-recognition-base/src/common/getModelUris.ts
new file mode 100644
index 0000000..1ca1dec
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/common/getModelUris.ts
@@ -0,0 +1,33 @@
+export function getModelUris(uri: string | undefined, defaultModelName: string) {
+  const defaultManifestFilename = `${defaultModelName}-weights_manifest.json`
+
+  if (!uri) {
+    return {
+      modelBaseUri: '',
+      manifestUri: defaultManifestFilename
+    }
+  }
+
+  if (uri === '/') {
+    return {
+      modelBaseUri: '/',
+      manifestUri: `/${defaultManifestFilename}`
+    }
+  }
+  const protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';
+  uri = uri.replace(protocol, '');
+
+  const parts = uri.split('/').filter(s => s)
+
+  const manifestFile = uri.endsWith('.json')
+    ? parts[parts.length - 1]
+    : defaultManifestFilename
+
+  let modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/')
+  modelBaseUri = uri.startsWith('/') ? `/${modelBaseUri}` : modelBaseUri
+
+  return {
+    modelBaseUri,
+    manifestUri: modelBaseUri === '/' ? `/${manifestFile}` : `${modelBaseUri}/${manifestFile}`
+  }
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/common/index.ts b/node_modules/tfjs-image-recognition-base/src/common/index.ts
new file mode 100644
index 0000000..8f57482
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/common/index.ts
@@ -0,0 +1,5 @@
+export * from './disposeUnusedWeightTensors'
+export * from './extractWeightEntryFactory'
+export * from './extractWeightsFactory'
+export * from './getModelUris'
+export * from './types'
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/common/types.ts b/node_modules/tfjs-image-recognition-base/src/common/types.ts
new file mode 100644
index 0000000..1efc6b7
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/common/types.ts
@@ -0,0 +1,6 @@
+export type ExtractWeightsFunction = (numWeights: number) => Float32Array
+
+export type ParamMapping = {
+  originalPath?: string
+  paramPath: string
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/dom/NetInput.ts b/node_modules/tfjs-image-recognition-base/src/dom/NetInput.ts
new file mode 100644
index 0000000..939b4be
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/dom/NetInput.ts
@@ -0,0 +1,153 @@
+import * as tf from '@tensorflow/tfjs-core';
+
+import { Dimensions } from '../classes/Dimensions';
+import { env } from '../env';
+import { padToSquare } from '../ops/padToSquare';
+import { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';
+import { createCanvasFromMedia } from './createCanvas';
+import { imageToSquare } from './imageToSquare';
+import { TResolvedNetInput } from './types';
+
+export class NetInput {
+  private _imageTensors: Array<tf.Tensor3D | tf.Tensor4D> = []
+  private _canvases: HTMLCanvasElement[] = []
+  private _batchSize: number
+  private _treatAsBatchInput: boolean = false
+
+  private _inputDimensions: number[][] = []
+  private _inputSize: number
+
+  constructor(
+    inputs: Array<TResolvedNetInput>,
+    treatAsBatchInput: boolean = false
+  ) {
+    if (!Array.isArray(inputs)) {
+      throw new Error(`NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have ${inputs}`)
+    }
+
+    this._treatAsBatchInput = treatAsBatchInput
+    this._batchSize = inputs.length
+
+    inputs.forEach((input, idx) => {
+
+      if (isTensor3D(input)) {
+        this._imageTensors[idx] = input
+        this._inputDimensions[idx] = input.shape
+        return
+      }
+
+      if (isTensor4D(input)) {
+        const batchSize = input.shape[0]
+        if (batchSize !== 1) {
+          throw new Error(`NetInput - tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)
+        }
+
+        this._imageTensors[idx] = input
+        this._inputDimensions[idx] = input.shape.slice(1)
+        return
+      }
+
+      const canvas = input instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input)
+      this._canvases[idx] = canvas
+      this._inputDimensions[idx] = [canvas.height, canvas.width, 3]
+    })
+  }
+
+  public get imageTensors(): Array<tf.Tensor3D | tf.Tensor4D> {
+    return this._imageTensors
+  }
+
+  public get canvases(): HTMLCanvasElement[] {
+    return this._canvases
+  }
+
+  public get isBatchInput(): boolean {
+    return this.batchSize > 1 || this._treatAsBatchInput
+  }
+
+  public get batchSize(): number {
+    return this._batchSize
+  }
+
+  public get inputDimensions(): number[][] {
+    return this._inputDimensions
+  }
+
+  public get inputSize(): number | undefined {
+    return this._inputSize
+  }
+
+  public get reshapedInputDimensions(): Dimensions[] {
+    return range(this.batchSize, 0, 1).map(
+      (_, batchIdx) => this.getReshapedInputDimensions(batchIdx)
+    )
+  }
+
+  public getInput(batchIdx: number): tf.Tensor3D  | tf.Tensor4D | HTMLCanvasElement {
+    return this.canvases[batchIdx] || this.imageTensors[batchIdx]
+  }
+
+  public getInputDimensions(batchIdx: number): number[] {
+    return this._inputDimensions[batchIdx]
+  }
+
+  public getInputHeight(batchIdx: number): number {
+    return this._inputDimensions[batchIdx][0]
+  }
+
+  public getInputWidth(batchIdx: number): number {
+    return this._inputDimensions[batchIdx][1]
+  }
+
+  public getReshapedInputDimensions(batchIdx: number): Dimensions {
+    if (typeof this.inputSize !== 'number') {
+      throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet')
+    }
+
+    const width = this.getInputWidth(batchIdx)
+    const height = this.getInputHeight(batchIdx)
+    return computeReshapedDimensions({ width, height }, this.inputSize)
+  }
+
+  /**
+   * Create a batch tensor from all input canvases and tensors
+   * with size [batchSize, inputSize, inputSize, 3].
+   *
+   * @param inputSize Height and width of the tensor.
+   * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on
+   * both sides of the minor dimension oof the image.
+   * @returns The batch tensor.
+   */
+  public toBatchTensor(inputSize: number, isCenterInputs: boolean = true): tf.Tensor4D {
+
+    this._inputSize = inputSize
+
+    return tf.tidy(() => {
+
+      const inputTensors = range(this.batchSize, 0, 1).map(batchIdx => {
+        const input = this.getInput(batchIdx)
+
+        if (input instanceof tf.Tensor) {
+          let imgTensor = isTensor4D(input) ? input : input.expandDims<tf.Rank.R4>()
+          imgTensor = padToSquare(imgTensor, isCenterInputs)
+
+          if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {
+            imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize])
+          }
+
+          return imgTensor.as3D(inputSize, inputSize, 3)
+        }
+
+        if (input instanceof env.getEnv().Canvas) {
+          return tf.fromPixels(imageToSquare(input, inputSize, isCenterInputs))
+        }
+
+        throw new Error(`toBatchTensor - at batchIdx ${batchIdx}, expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have ${input}`)
+      })
+
+      const batchTensor = tf.stack(inputTensors.map(t => t.toFloat())).as4D(this.batchSize, inputSize, inputSize, 3)
+
+      return batchTensor
+    })
+  }
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/dom/awaitMediaLoaded.ts b/node_modules/tfjs-image-recognition-base/src/dom/awaitMediaLoaded.ts
new file mode 100644
index 0000000..54d4229
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/dom/awaitMediaLoaded.ts
@@ -0,0 +1,28 @@
+import { env } from '../env';
+import { isMediaLoaded } from './isMediaLoaded';
+
+export function awaitMediaLoaded(media: HTMLImageElement | HTMLVideoElement | HTMLCanvasElement) {
+
+  return new Promise((resolve, reject) => {
+    if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {
+      return resolve()
+    }
+
+    function onLoad(e: Event) {
+      if (!e.currentTarget) return
+      e.currentTarget.removeEventListener('load', onLoad)
+      e.currentTarget.removeEventListener('error', onError)
+      resolve(e)
+    }
+
+    function onError(e: Event) {
+      if (!e.currentTarget) return
+      e.currentTarget.removeEventListener('load', onLoad)
+      e.currentTarget.removeEventListener('error', onError)
+      reject(e)
+    }
+
+    media.addEventListener('load', onLoad)
+    media.addEventListener('error', onError)
+  })
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/dom/bufferToImage.ts b/node_modules/tfjs-image-recognition-base/src/dom/bufferToImage.ts
new file mode 100644
index 0000000..de2a42e
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/dom/bufferToImage.ts
@@ -0,0 +1,23 @@
+import { env } from '../env';
+
+export function bufferToImage(buf: Blob): Promise<HTMLImageElement> {
+  return new Promise((resolve, reject) => {
+    if (!(buf instanceof Blob)) {
+      return reject('bufferToImage - expected buf to be of type: Blob')
+    }
+
+    const reader = new FileReader()
+    reader.onload = () => {
+      if (typeof reader.result !== 'string') {
+        return reject('bufferToImage - expected reader.result to be a string, in onload')
+      }
+
+      const img = env.getEnv().createImageElement()
+      img.onload = () => resolve(img)
+      img.onerror = reject
+      img.src = reader.result
+    }
+    reader.onerror = reject
+    reader.readAsDataURL(buf)
+  })
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/dom/createCanvas.ts b/node_modules/tfjs-image-recognition-base/src/dom/createCanvas.ts
new file mode 100644
index 0000000..937d58c
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/dom/createCanvas.ts
@@ -0,0 +1,33 @@
+import { IDimensions } from '../classes/Dimensions';
+import { env } from '../env';
+import { getContext2dOrThrow } from './getContext2dOrThrow';
+import { getMediaDimensions } from './getMediaDimensions';
+import { isMediaLoaded } from './isMediaLoaded';
+
+export function createCanvas({ width, height }: IDimensions): HTMLCanvasElement {
+
+  const { createCanvasElement } = env.getEnv()
+  const canvas = createCanvasElement()
+  canvas.width = width
+  canvas.height = height
+  return canvas
+}
+
+export function createCanvasFromMedia(media: HTMLImageElement | HTMLVideoElement | ImageData, dims?: IDimensions): HTMLCanvasElement {
+
+  const { ImageData } = env.getEnv()
+
+  if (!(media instanceof ImageData) && !isMediaLoaded(media)) {
+    throw new Error('createCanvasFromMedia - media has not finished loading yet')
+  }
+
+  const { width, height } = dims || getMediaDimensions(media)
+  const canvas = createCanvas({ width, height })
+
+  if (media instanceof ImageData) {
+    getContext2dOrThrow(canvas).putImageData(media, 0, 0)
+  } else {
+    getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height)
+  }
+  return canvas
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/dom/drawBox.ts b/node_modules/tfjs-image-recognition-base/src/dom/drawBox.ts
new file mode 100644
index 0000000..7feb7e6
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/dom/drawBox.ts
@@ -0,0 +1,20 @@
+import { getDefaultDrawOptions } from './getDefaultDrawOptions';
+import { DrawBoxOptions } from './types';
+
+export function drawBox(
+  ctx: CanvasRenderingContext2D,
+  x: number,
+  y: number,
+  w: number,
+  h: number,
+  options: DrawBoxOptions
+) {
+  const drawOptions = Object.assign(
+    getDefaultDrawOptions(),
+    (options || {})
+  )
+
+  ctx.strokeStyle = drawOptions.boxColor
+  ctx.lineWidth = drawOptions.lineWidth
+  ctx.strokeRect(x, y, w, h)
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/dom/drawDetection.ts b/node_modules/tfjs-image-recognition-base/src/dom/drawDetection.ts
new file mode 100644
index 0000000..082609c
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/dom/drawDetection.ts
@@ -0,0 +1,66 @@
+import { BoxWithText } from '../classes/BoxWithText';
+import { ObjectDetection } from '../classes/ObjectDetection';
+import { PredictedBox } from '../classes/PredictedBox';
+import { Rect } from '../classes/Rect';
+import { env } from '../env';
+import { round } from '../utils';
+import { drawBox } from './drawBox';
+import { drawText } from './drawText';
+import { getContext2dOrThrow } from './getContext2dOrThrow';
+import { getDefaultDrawOptions } from './getDefaultDrawOptions';
+import { resolveInput } from './resolveInput';
+import { DrawDetectionOptions } from './types';
+
+export function drawDetection(
+  canvasArg: string | HTMLCanvasElement,
+  detection: Rect | PredictedBox | ObjectDetection | BoxWithText | Array<Rect | PredictedBox | ObjectDetection | BoxWithText>,
+  options?: DrawDetectionOptions
+) {
+
+  const { Canvas } = env.getEnv()
+
+  const canvas = resolveInput(canvasArg)
+  if (!(canvas instanceof Canvas)) {
+    throw new Error('drawDetection - expected canvas to be of type: HTMLCanvasElement')
+  }
+
+  const detectionArray = Array.isArray(detection)
+    ? detection
+    : [detection]
+
+  detectionArray.forEach((det) => {
+    const {
+      x,
+      y,
+      width,
+      height
+    } = det instanceof ObjectDetection ? det.box : det
+
+    const drawOptions = getDefaultDrawOptions(options)
+
+    const ctx = getContext2dOrThrow(canvas)
+    drawBox(
+      ctx,
+      x,
+      y,
+      width,
+      height,
+      drawOptions
+    )
+
+    const { withScore } = drawOptions
+    let text = det instanceof BoxWithText
+      ? det.text
+      : ((withScore && det instanceof PredictedBox)
+        ? `${round(det.score)}`
+        : (det instanceof ObjectDetection
+          ? `${det.className}${withScore ? ` (${round(det.score)})` : ''}`
+          : ''
+        )
+      )
+
+    if (text) {
+      drawText(ctx, x, y + height, text, drawOptions)
+    }
+  })
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/dom/drawText.ts b/node_modules/tfjs-image-recognition-base/src/dom/drawText.ts
new file mode 100644
index 0000000..5df2466
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/dom/drawText.ts
@@ -0,0 +1,21 @@
+import { getDefaultDrawOptions } from './getDefaultDrawOptions';
+import { DrawTextOptions } from './types';
+
+export function drawText(
+  ctx: CanvasRenderingContext2D,
+  x: number,
+  y: number,
+  text: string,
+  options: DrawTextOptions = {}
+) {
+  const drawOptions = Object.assign(
+    getDefaultDrawOptions(),
+    options
+  )
+
+  const padText = 2 + drawOptions.lineWidth
+
+  ctx.fillStyle = drawOptions.textColor
+  ctx.font = `${drawOptions.fontSize}px ${drawOptions.fontStyle}`
+  ctx.fillText(text, x + padText, y + padText + (drawOptions.fontSize * 0.6))
+}
diff --git a/node_modules/tfjs-image-recognition-base/src/dom/fetchImage.ts b/node_modules/tfjs-image-recognition-base/src/dom/fetchImage.ts
new file mode 100644
index 0000000..5a783c9
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/dom/fetchImage.ts
@@ -0,0 +1,12 @@
+import { bufferToImage } from './bufferToImage';
+import { fetchOrThrow } from './fetchOrThrow';
+
+export async function fetchImage(uri: string): Promise<HTMLImageElement> {
+  const res = await fetchOrThrow(uri)
+  const blob = await (res).blob()
+
+  if (!blob.type.startsWith('image/')) {
+    throw new Error(`fetchImage - expected blob type to be of type image/*, instead have: ${blob.type}, for url: ${res.url}`)
+  }
+  return bufferToImage(blob)
+}
diff --git a/node_modules/tfjs-image-recognition-base/src/dom/fetchJson.ts b/node_modules/tfjs-image-recognition-base/src/dom/fetchJson.ts
new file mode 100644
index 0000000..a9692e7
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/dom/fetchJson.ts
@@ -0,0 +1,5 @@
+import { fetchOrThrow } from './fetchOrThrow';
+
+export async function fetchJson<T>(uri: string): Promise<T> {
+  return (await fetchOrThrow(uri)).json()
+}
diff --git a/node_modules/tfjs-image-recognition-base/src/dom/fetchNetWeights.ts b/node_modules/tfjs-image-recognition-base/src/dom/fetchNetWeights.ts
new file mode 100644
index 0000000..e9361df
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/dom/fetchNetWeights.ts
@@ -0,0 +1,5 @@
+import { fetchOrThrow } from './fetchOrThrow';
+
+export async function fetchNetWeights(uri: string): Promise<Float32Array> {
+  return new Float32Array(await (await fetchOrThrow(uri)).arrayBuffer())
+}
diff --git a/node_modules/tfjs-image-recognition-base/src/dom/fetchOrThrow.ts b/node_modules/tfjs-image-recognition-base/src/dom/fetchOrThrow.ts
new file mode 100644
index 0000000..9bdac35
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/dom/fetchOrThrow.ts
@@ -0,0 +1,14 @@
+import { env } from '../env';
+
+export async function fetchOrThrow(
+  url: string,
+  init?: RequestInit
+): Promise<Response> {
+
+  const fetch = env.getEnv().fetch
+  const res = await fetch(url, init)
+  if (!(res.status < 400)) {
+    throw new Error(`failed to fetch: (${res.status}) ${res.statusText}, from url: ${res.url}`)
+  }
+  return res
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/dom/getContext2dOrThrow.ts b/node_modules/tfjs-image-recognition-base/src/dom/getContext2dOrThrow.ts
new file mode 100644
index 0000000..940468f
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/dom/getContext2dOrThrow.ts
@@ -0,0 +1,7 @@
+export function getContext2dOrThrow(canvas: HTMLCanvasElement): CanvasRenderingContext2D {
+  const ctx = canvas.getContext('2d')
+  if (!ctx) {
+    throw new Error('canvas 2d context is null')
+  }
+  return ctx
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/dom/getDefaultDrawOptions.ts b/node_modules/tfjs-image-recognition-base/src/dom/getDefaultDrawOptions.ts
new file mode 100644
index 0000000..6e2b203
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/dom/getDefaultDrawOptions.ts
@@ -0,0 +1,17 @@
+import { DrawOptions } from './types';
+
+export function getDefaultDrawOptions(options: any = {}): DrawOptions {
+  return Object.assign(
+    {},
+    {
+      boxColor: 'blue',
+      textColor: 'red',
+      lineWidth: 2,
+      fontSize: 20,
+      fontStyle: 'Georgia',
+      withScore: true,
+      withClassName: true
+    },
+    options
+  )
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/dom/getMediaDimensions.ts b/node_modules/tfjs-image-recognition-base/src/dom/getMediaDimensions.ts
new file mode 100644
index 0000000..7a076f3
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/dom/getMediaDimensions.ts
@@ -0,0 +1,15 @@
+import { Dimensions, IDimensions } from '../classes/Dimensions';
+import { env } from '../env';
+
+export function getMediaDimensions(input: HTMLImageElement | HTMLCanvasElement | HTMLVideoElement | IDimensions): Dimensions {
+
+  const { Image, Video } = env.getEnv()
+
+  if (input instanceof Image) {
+    return new Dimensions(input.naturalWidth, input.naturalHeight)
+  }
+  if (input instanceof Video) {
+    return new Dimensions(input.videoWidth, input.videoHeight)
+  }
+  return new Dimensions(input.width, input.height)
+}
diff --git a/node_modules/tfjs-image-recognition-base/src/dom/imageTensorToCanvas.ts b/node_modules/tfjs-image-recognition-base/src/dom/imageTensorToCanvas.ts
new file mode 100644
index 0000000..530630b
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/dom/imageTensorToCanvas.ts
@@ -0,0 +1,20 @@
+import * as tf from '@tensorflow/tfjs-core';
+
+import { env } from '../env';
+import { isTensor4D } from '../utils';
+
+export async function imageTensorToCanvas(
+  imgTensor: tf.Tensor,
+  canvas?: HTMLCanvasElement
+): Promise<HTMLCanvasElement> {
+
+  const targetCanvas = canvas || env.getEnv().createCanvasElement()
+
+  const [height, width, numChannels] = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0)
+  const imgTensor3D = tf.tidy(() => imgTensor.as3D(height, width, numChannels).toInt())
+  await tf.toPixels(imgTensor3D, targetCanvas)
+
+  imgTensor3D.dispose()
+
+  return targetCanvas
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/dom/imageToSquare.ts b/node_modules/tfjs-image-recognition-base/src/dom/imageToSquare.ts
new file mode 100644
index 0000000..dcd1256
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/dom/imageToSquare.ts
@@ -0,0 +1,28 @@
+import { env } from '../env';
+import { createCanvas, createCanvasFromMedia } from './createCanvas';
+import { getContext2dOrThrow } from './getContext2dOrThrow';
+import { getMediaDimensions } from './getMediaDimensions';
+
+export function imageToSquare(input: HTMLImageElement | HTMLCanvasElement, inputSize: number, centerImage: boolean = false) {
+
+  const { Image, Canvas } = env.getEnv()
+
+  if (!(input instanceof Image || input instanceof Canvas)) {
+    throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement')
+  }
+
+  const dims = getMediaDimensions(input)
+  const scale = inputSize / Math.max(dims.height, dims.width)
+  const width = scale * dims.width
+  const height = scale * dims.height
+
+  const targetCanvas = createCanvas({ width: inputSize, height: inputSize })
+  const inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input)
+
+  const offset = Math.abs(width - height) / 2
+  const dx = centerImage && width < height ? offset : 0
+  const dy = centerImage && height < width ? offset : 0
+  getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height)
+
+  return targetCanvas
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/dom/index.ts b/node_modules/tfjs-image-recognition-base/src/dom/index.ts
new file mode 100644
index 0000000..8756e60
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/dom/index.ts
@@ -0,0 +1,22 @@
+export * from './awaitMediaLoaded'
+export * from './bufferToImage'
+export * from './createCanvas'
+export * from './drawBox'
+export * from './drawDetection'
+export * from './drawText'
+export * from './fetchImage'
+export * from './fetchJson'
+export * from './fetchNetWeights'
+export * from './fetchOrThrow'
+export * from './getContext2dOrThrow'
+export * from './getDefaultDrawOptions'
+export * from './getMediaDimensions'
+export * from './imageTensorToCanvas'
+export * from './imageToSquare'
+export * from './isMediaElement'
+export * from './isMediaLoaded'
+export * from './loadWeightMap'
+export * from './NetInput'
+export * from './resolveInput'
+export * from './toNetInput'
+export * from './types'
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/dom/isMediaElement.ts b/node_modules/tfjs-image-recognition-base/src/dom/isMediaElement.ts
new file mode 100644
index 0000000..4223121
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/dom/isMediaElement.ts
@@ -0,0 +1,10 @@
+import { env } from '../env';
+
+export function isMediaElement(input: any) {
+
+  const { Image, Canvas, Video } = env.getEnv()
+
+  return input instanceof Image
+    || input instanceof Canvas
+    || input instanceof Video
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/dom/isMediaLoaded.ts b/node_modules/tfjs-image-recognition-base/src/dom/isMediaLoaded.ts
new file mode 100644
index 0000000..bf83678
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/dom/isMediaLoaded.ts
@@ -0,0 +1,9 @@
+import { env } from '../env';
+
+export function isMediaLoaded(media: HTMLImageElement | HTMLVideoElement) : boolean {
+
+  const { Image, Video } = env.getEnv()
+
+  return (media instanceof Image && media.complete)
+    || (media instanceof Video && media.readyState >= 3)
+}
diff --git a/node_modules/tfjs-image-recognition-base/src/dom/loadWeightMap.ts b/node_modules/tfjs-image-recognition-base/src/dom/loadWeightMap.ts
new file mode 100644
index 0000000..27e4331
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/dom/loadWeightMap.ts
@@ -0,0 +1,15 @@
+import * as tf from '@tensorflow/tfjs-core';
+
+import { getModelUris } from '../common/getModelUris';
+import { fetchJson } from './fetchJson';
+
+export async function loadWeightMap(
+  uri: string | undefined,
+  defaultModelName: string,
+): Promise<tf.NamedTensorMap> {
+  const { manifestUri, modelBaseUri } = getModelUris(uri, defaultModelName)
+
+  const manifest = await fetchJson<tf.io.WeightsManifestConfig>(manifestUri)
+
+  return tf.io.loadWeights(manifest, modelBaseUri)
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/dom/resolveInput.ts b/node_modules/tfjs-image-recognition-base/src/dom/resolveInput.ts
new file mode 100644
index 0000000..47bd149
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/dom/resolveInput.ts
@@ -0,0 +1,8 @@
+import { env } from '../env';
+
+export function resolveInput(arg: string | any) {
+  if (!env.isNodejs() && typeof arg === 'string') {
+    return document.getElementById(arg)
+  }
+  return arg
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/dom/toNetInput.ts b/node_modules/tfjs-image-recognition-base/src/dom/toNetInput.ts
new file mode 100644
index 0000000..893a469
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/dom/toNetInput.ts
@@ -0,0 +1,57 @@
+import { isTensor3D, isTensor4D } from '../utils';
+import { awaitMediaLoaded } from './awaitMediaLoaded';
+import { isMediaElement } from './isMediaElement';
+import { NetInput } from './NetInput';
+import { resolveInput } from './resolveInput';
+import { TNetInput } from './types';
+
+/**
+ * Validates the input to make sure, they are valid net inputs and awaits all media elements
+ * to be finished loading.
+ *
+ * @param input The input, which can be a media element or an array of different media elements.
+ * @returns A NetInput instance, which can be passed into one of the neural networks.
+ */
+export async function toNetInput(inputs: TNetInput): Promise<NetInput> {
+  if (inputs instanceof NetInput) {
+    return inputs
+  }
+
+  let inputArgArray = Array.isArray(inputs)
+      ? inputs
+      : [inputs]
+
+  if (!inputArgArray.length) {
+    throw new Error('toNetInput - empty array passed as input')
+  }
+
+  const getIdxHint = (idx: number) => Array.isArray(inputs) ? ` at input index ${idx}:` : ''
+
+  const inputArray = inputArgArray.map(resolveInput)
+
+  inputArray.forEach((input, i) => {
+    if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {
+
+      if (typeof inputArgArray[i] === 'string') {
+        throw new Error(`toNetInput -${getIdxHint(i)} string passed, but could not resolve HTMLElement for element id ${inputArgArray[i]}`)
+      }
+
+      throw new Error(`toNetInput -${getIdxHint(i)} expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id`)
+    }
+
+    if (isTensor4D(input)) {
+      // if tf.Tensor4D is passed in the input array, the batch size has to be 1
+      const batchSize = input.shape[0]
+      if (batchSize !== 1) {
+        throw new Error(`toNetInput -${getIdxHint(i)} tf.Tensor4D with batchSize ${batchSize} passed, but not supported in input array`)
+      }
+    }
+  })
+
+  // wait for all media elements being loaded
+  await Promise.all(
+    inputArray.map(input => isMediaElement(input) && awaitMediaLoaded(input))
+  )
+
+  return new NetInput(inputArray, Array.isArray(inputs))
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/dom/types.ts b/node_modules/tfjs-image-recognition-base/src/dom/types.ts
new file mode 100644
index 0000000..13c48af
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/dom/types.ts
@@ -0,0 +1,43 @@
+import * as tf from '@tensorflow/tfjs-core';
+
+import { NetInput } from './NetInput';
+
+export type TMediaElement = HTMLImageElement | HTMLVideoElement | HTMLCanvasElement
+
+export type TResolvedNetInput = TMediaElement | tf.Tensor3D | tf.Tensor4D
+
+export type TNetInputArg = string | TResolvedNetInput
+
+export type TNetInput = TNetInputArg | Array<TNetInputArg> | NetInput | tf.Tensor4D
+
+export type DrawBoxOptions = {
+  lineWidth?: number
+  color?: string
+}
+
+export type DrawTextOptions = {
+  lineWidth?: number
+  fontSize?: number
+  fontStyle?: string
+  textColor?: string
+}
+
+export type DrawDetectionOptions = {
+  lineWidth?: number
+  fontSize?: number
+  fontStyle?: string
+  textColor?: string
+  boxColor?: string,
+  withScore?: boolean,
+  withClassName?: boolean
+}
+
+export type DrawOptions = {
+  lineWidth: number
+  fontSize: number
+  fontStyle: string
+  textColor: string
+  boxColor: string,
+  withScore: boolean,
+  withClassName: boolean
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/env/createBrowserEnv.ts b/node_modules/tfjs-image-recognition-base/src/env/createBrowserEnv.ts
new file mode 100644
index 0000000..39e090e
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/env/createBrowserEnv.ts
@@ -0,0 +1,23 @@
+import { Environment } from './types';
+
+export function createBrowserEnv(): Environment {
+
+  const fetch = window['fetch'] || function() {
+    throw new Error('fetch - missing fetch implementation for browser environment')
+  }
+
+  const readFile = function() {
+    throw new Error('readFile - filesystem not available for browser environment')
+  }
+
+  return {
+    Canvas: HTMLCanvasElement,
+    Image: HTMLImageElement,
+    ImageData: ImageData,
+    Video: HTMLVideoElement,
+    createCanvasElement: () => document.createElement('canvas'),
+    createImageElement: () => document.createElement('img'),
+    fetch,
+    readFile
+  }
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/env/createFileSystem.ts b/node_modules/tfjs-image-recognition-base/src/env/createFileSystem.ts
new file mode 100644
index 0000000..b2c272b
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/env/createFileSystem.ts
@@ -0,0 +1,30 @@
+import { FileSystem } from './types';
+
+export function createFileSystem(fs?: any): FileSystem {
+
+  let requireFsError = ''
+
+  if (!fs) {
+    try {
+      fs = require('fs')
+    } catch (err) {
+      requireFsError = err.toString()
+    }
+  }
+
+  const readFile = fs
+    ? function(filePath: string) {
+      return new Promise<Buffer>((res, rej) => {
+        fs.readFile(filePath, function(err: any, buffer: Buffer) {
+          return err ? rej(err) : res(buffer)
+        })
+      })
+    }
+    : function() {
+      throw new Error(`readFile - failed to require fs in nodejs environment with error: ${requireFsError}`)
+    }
+
+  return {
+    readFile
+  }
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/env/createNodejsEnv.ts b/node_modules/tfjs-image-recognition-base/src/env/createNodejsEnv.ts
new file mode 100644
index 0000000..86c4162
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/env/createNodejsEnv.ts
@@ -0,0 +1,39 @@
+import { createFileSystem } from './createFileSystem';
+import { Environment } from './types';
+
+export function createNodejsEnv(): Environment {
+
+  const Canvas = global['Canvas'] || global['HTMLCanvasElement']
+  const Image = global['Image'] || global['HTMLImageElement']
+
+  const createCanvasElement = function() {
+    if (Canvas) {
+      return new Canvas()
+    }
+    throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment')
+  }
+
+  const createImageElement = function() {
+    if (Image) {
+      return new Image()
+    }
+    throw new Error('createImageElement - missing Image implementation for nodejs environment')
+  }
+
+  const fetch = global['fetch'] || function() {
+    throw new Error('fetch - missing fetch implementation for nodejs environment')
+  }
+
+  const fileSystem = createFileSystem()
+
+  return {
+    Canvas: Canvas || class {},
+    Image: Image || class {},
+    ImageData: global['ImageData'] || class {},
+    Video: global['HTMLVideoElement'] || class {},
+    createCanvasElement,
+    createImageElement,
+    fetch,
+    ...fileSystem
+  }
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/env/index.ts b/node_modules/tfjs-image-recognition-base/src/env/index.ts
new file mode 100644
index 0000000..47f2e53
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/env/index.ts
@@ -0,0 +1,67 @@
+import { createBrowserEnv } from './createBrowserEnv';
+import { createFileSystem } from './createFileSystem';
+import { createNodejsEnv } from './createNodejsEnv';
+import { isBrowser } from './isBrowser';
+import { isNodejs } from './isNodejs';
+import { Environment } from './types';
+
+let environment: Environment | null
+
+function getEnv(): Environment {
+  if (!environment) {
+    throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()')
+  }
+  return environment
+}
+
+function setEnv(env: Environment) {
+  environment = env
+}
+
+function initialize() {
+  // check for isBrowser() first to prevent electron renderer process
+  // to be initialized with wrong environment due to isNodejs() returning true
+  if (isBrowser()) {
+    setEnv(createBrowserEnv())
+  }
+  if (isNodejs()) {
+    setEnv(createNodejsEnv())
+  }
+}
+
+function monkeyPatch(env: Partial<Environment>) {
+  if (!environment) {
+    initialize()
+  }
+
+  if (!environment) {
+    throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()')
+  }
+
+  const { Canvas = environment.Canvas, Image = environment.Image } = env
+  environment.Canvas = Canvas
+  environment.Image = Image
+  environment.createCanvasElement = env.createCanvasElement || (() => new Canvas())
+  environment.createImageElement = env.createImageElement || (() => new Image())
+
+  environment.ImageData = env.ImageData || environment.ImageData
+  environment.Video = env.Video || environment.Video
+  environment.fetch = env.fetch || environment.fetch
+  environment.readFile = env.readFile || environment.readFile
+}
+
+export const env = {
+  getEnv,
+  setEnv,
+  initialize,
+  createBrowserEnv,
+  createFileSystem,
+  createNodejsEnv,
+  monkeyPatch,
+  isBrowser,
+  isNodejs
+}
+
+initialize()
+
+export * from './types'
diff --git a/node_modules/tfjs-image-recognition-base/src/env/isBrowser.ts b/node_modules/tfjs-image-recognition-base/src/env/isBrowser.ts
new file mode 100644
index 0000000..329bae2
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/env/isBrowser.ts
@@ -0,0 +1,8 @@
+export function isBrowser(): boolean {
+  return typeof window === 'object'
+    && typeof document !== 'undefined'
+    && typeof HTMLImageElement !== 'undefined'
+    && typeof HTMLCanvasElement !== 'undefined'
+    && typeof HTMLVideoElement !== 'undefined'
+    && typeof ImageData !== 'undefined'
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/env/isNodejs.ts b/node_modules/tfjs-image-recognition-base/src/env/isNodejs.ts
new file mode 100644
index 0000000..f5f0c8b
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/env/isNodejs.ts
@@ -0,0 +1,8 @@
+export function isNodejs(): boolean {
+  return typeof global === 'object'
+    && typeof require === 'function'
+    && typeof module !== 'undefined'
+    // issues with gatsby.js: module.exports is undefined
+    // && !!module.exports
+    && typeof process !== 'undefined' && !!process.version
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/env/types.ts b/node_modules/tfjs-image-recognition-base/src/env/types.ts
new file mode 100644
index 0000000..e233307
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/env/types.ts
@@ -0,0 +1,13 @@
+export type FileSystem = {
+  readFile: (filePath: string) => Promise<Buffer>
+}
+
+export type Environment = FileSystem & {
+  Canvas: typeof HTMLCanvasElement
+  Image: typeof HTMLImageElement
+  ImageData: typeof ImageData
+  Video: typeof HTMLVideoElement
+  createCanvasElement: () => HTMLCanvasElement
+  createImageElement: () => HTMLImageElement
+  fetch: (url: string, init?: RequestInit) => Promise<Response>
+}
diff --git a/node_modules/tfjs-image-recognition-base/src/index.ts b/node_modules/tfjs-image-recognition-base/src/index.ts
new file mode 100644
index 0000000..4df333a
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/index.ts
@@ -0,0 +1,7 @@
+export * from './classes';
+export * from './common';
+export * from './dom';
+export * from './env';
+export * from './ops';
+export * from './utils';
+export * from './NeuralNetwork';
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/metrics/assignFalseAndTruePositives.ts b/node_modules/tfjs-image-recognition-base/src/metrics/assignFalseAndTruePositives.ts
new file mode 100644
index 0000000..54b8d3b
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/metrics/assignFalseAndTruePositives.ts
@@ -0,0 +1,47 @@
+import { Box } from '../classes/Box';
+import { iou } from '../ops/iou';
+
+/**
+ * Categorize predicted boxes of an image as true or false positives based on IOU to ground truth boxes.
+ *
+ * @param groundTruth Ground truth boxes for that image.
+ * @param predictions Predicted boxes and for that image.
+ * @param iouThreshold IOU threshold at which a predicted box is considered to be a true positive.
+ * @returns AP
+ */
+export function assignFalseAndTruePositives<T extends Box, S extends Box>(
+  groundTruth: T[],
+  predictions: S[],
+  iouThreshold: number
+) {
+  // sort descending by iou to ensure predicted box with highest iou
+  // is considered to be true positive
+  const sortedIouPairs = createSortedIouPairs(groundTruth, predictions)
+    .filter(pair => pair.iou > iouThreshold)
+
+  const assignedGtBoxes = new Set<T>()
+  const assignedPredBoxes = new Set<S>()
+
+  sortedIouPairs.forEach(({ gt, pred }) => {
+    if (assignedGtBoxes.has(gt) || assignedPredBoxes.has(pred)) {
+      return
+    }
+    assignedGtBoxes.add(gt)
+    assignedPredBoxes.add(pred)
+  })
+
+  const truePositives = Array.from(assignedPredBoxes.values())
+  const falsePositives = predictions.filter(pred => !assignedPredBoxes.has(pred))
+
+  return { truePositives, falsePositives }
+}
+
+export function createSortedIouPairs<T extends Box, S extends Box>(groundTruth: T[], predictions: S[]) {
+  return predictions.map(pred => groundTruth.map(gt => ({
+    pred,
+    gt,
+    iou: iou(gt, pred)
+  })))
+    .reduce((flat, arr) => flat.concat(arr), [])
+    .sort((p1, p2) => p2.iou - p1.iou)
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/metrics/averagePrecision.ts b/node_modules/tfjs-image-recognition-base/src/metrics/averagePrecision.ts
new file mode 100644
index 0000000..5a2b2d8
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/metrics/averagePrecision.ts
@@ -0,0 +1,67 @@
+import { PredictedBox } from '../classes/PredictedBox';
+import { range } from '../utils';
+import { assignFalseAndTruePositives } from './assignFalseAndTruePositives';
+import { GroundTruthsAndPredictions } from './types';
+
+/**
+ * Calculates the average precision (AP) over 11 recall levels between [0, 1].
+ *
+ * @param inputs Array of ground truth and predicted boxes of each image.
+ * @param iouThreshold IOU threshold at which a predicted box is considered to be a true positive.
+ * @returns AP
+ */
+export function averagePrecision(inputs: GroundTruthsAndPredictions[], iouThreshold: number) {
+
+  const sortedDetections = inputs
+    .map(input => categorizeBoxes(input, iouThreshold))
+    .reduce((flat, arr) => flat.concat(arr))
+    .sort((b1, b2) => b2.score - b1.score)
+    .map(b => b.isTruePositive)
+
+  const numGroundTruth = inputs
+    .map(input => input.groundTruth.length)
+    .reduce((sum, val) => sum + val, 0)
+
+  const computeRecall = (tp: number) => tp / numGroundTruth
+  const computePrecision = (tp: number, fp: number) => tp / (tp + fp)
+
+  let accumulatedTps = 0, accumulatedFps = 0
+  const precisionRecallPairs = sortedDetections.map((isTruePositive) => {
+    accumulatedTps += (isTruePositive ? 1 : 0)
+    accumulatedFps += (isTruePositive ? 0 : 1)
+
+    const recall = computeRecall(accumulatedTps)
+    const precision = computePrecision(accumulatedTps, accumulatedFps)
+
+    return { recall, precision }
+  })
+
+  const numIntervals = 11
+  const precisionsForRecallInterval = range(numIntervals, 0, 0.1).map(
+    recallThreshold =>
+      precisionRecallPairs
+        .filter(pair => pair.recall >= recallThreshold)
+        .map(pair => pair.precision)
+        .reduce((max, val) => max > val ? max : val, 0)
+  )
+
+  return {
+    averagePrec: precisionsForRecallInterval.reduce((sum, val) => sum + val, 0) / numIntervals,
+    accumulatedTps,
+    accumulatedFps
+  }
+}
+
+function categorizeBoxes(input: GroundTruthsAndPredictions, iouThreshold: number) {
+  const {
+    truePositives,
+    falsePositives
+  } = assignFalseAndTruePositives(input.groundTruth, input.predictions, iouThreshold)
+
+
+  return truePositives
+    .map(categorize(true))
+    .concat(falsePositives.map(categorize(false)))
+}
+
+const categorize = (isTruePositive: boolean) => ({ score }: PredictedBox) => ({ score, isTruePositive })
diff --git a/node_modules/tfjs-image-recognition-base/src/metrics/meanAveragePrecision.ts b/node_modules/tfjs-image-recognition-base/src/metrics/meanAveragePrecision.ts
new file mode 100644
index 0000000..f853f6f
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/metrics/meanAveragePrecision.ts
@@ -0,0 +1,76 @@
+import { LabeledBox } from '../classes/LabeledBox';
+import { PredictedBox } from '../classes/PredictedBox';
+import { averagePrecision } from './averagePrecision';
+import { GroundTruthsAndPredictions } from './types';
+
+/**
+ * Calculates the mean average precision (mAP) over all classes.
+ *
+ * @param inputs Array ground truth and predicted boxes of each image labeled by class.
+ * @param iouThreshold IOU threshold at which a predicted box is considered to be a true positive.
+ * @returns mAP
+ */
+export function meanAveragePrecision(
+  inputs: GroundTruthsAndPredictions[],
+  iouThreshold: number = 0.5,
+  classScoreThreshold: number = 0.5
+) {
+
+  inputs.forEach(input => {
+    input.groundTruth.forEach(box => {
+      if (!(box instanceof LabeledBox)) {
+        throw new Error('meanAveragePrecision - expected ground truth boxes to be instanceof LabeledBox')
+      }
+    })
+    input.predictions.forEach(box => {
+      if (!(box instanceof PredictedBox)) {
+        throw new Error('meanAveragePrecision - expected predicted boxes to be instanceof PredictedBox')
+      }
+    })
+  })
+
+  const unique = (arr: number[]) => Array.from(new Set<number>(arr).values())
+
+  const classLabels = unique(
+    inputs
+      .map(input =>
+        input.groundTruth.map(gt => gt.label)
+          .concat(input.predictions.map(pred => pred.label))
+      )
+      .reduce((flat, arr) => flat.concat(arr), [])
+  )
+
+  const classLabelFilter = (classLabel: number) => <T extends LabeledBox>(boxes: T[]) =>
+    boxes.filter(box => box.label === classLabel)
+
+  const filterByClassConfidence = (boxes: PredictedBox[]) =>
+    boxes.filter(box => box.classScore >= classScoreThreshold)
+
+  const inputsByClass = classLabels.map(classLabel => {
+
+    const filterByClassLabel = classLabelFilter(classLabel)
+    classScoreThreshold
+    return {
+      classLabel,
+      inputs: inputs.map(input => {
+        return {
+          groundTruth: filterByClassLabel(input.groundTruth),
+          predictions: filterByClassConfidence(filterByClassLabel(input.predictions))
+        }
+      })
+    }
+  })
+
+  const averagePrecisionsByClass = inputsByClass.map(({ classLabel, inputs }) => {
+    return {
+      classLabel,
+      ...averagePrecision(inputs, iouThreshold)
+    }
+  })
+
+  return {
+    meanAveragePrec: averagePrecisionsByClass
+      .reduce((sum, { averagePrec }) => sum + averagePrec, 0) / classLabels.length,
+    averagePrecisionsByClass
+  }
+}
diff --git a/node_modules/tfjs-image-recognition-base/src/metrics/types.ts b/node_modules/tfjs-image-recognition-base/src/metrics/types.ts
new file mode 100644
index 0000000..2b49e62
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/metrics/types.ts
@@ -0,0 +1,7 @@
+import { LabeledBox } from '../classes/LabeledBox';
+import { PredictedBox } from '../classes/PredictedBox';
+
+export type GroundTruthsAndPredictions = {
+  groundTruth: LabeledBox[]
+  predictions: PredictedBox[]
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/ops/index.ts b/node_modules/tfjs-image-recognition-base/src/ops/index.ts
new file mode 100644
index 0000000..059adaf
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/ops/index.ts
@@ -0,0 +1,13 @@
+export * from './iou'
+export * from './nonMaxSuppression'
+export * from './normalize'
+export * from './padToSquare'
+export * from './shuffleArray'
+
+export function sigmoid(x: number) {
+  return 1 / (1 + Math.exp(-x))
+}
+
+export function inverseSigmoid(x: number) {
+  return Math.log(x / (1 - x))
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/ops/iou.ts b/node_modules/tfjs-image-recognition-base/src/ops/iou.ts
new file mode 100644
index 0000000..7523832
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/ops/iou.ts
@@ -0,0 +1,11 @@
+import { Box } from '../classes/Box';
+
+export function iou(box1: Box, box2: Box, isIOU: boolean = true) {
+  const width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left))
+  const height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top))
+  const interSection = width * height
+
+  return isIOU
+    ? interSection / (box1.area + box2.area - interSection)
+    : interSection / Math.min(box1.area, box2.area)
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/ops/nonMaxSuppression.ts b/node_modules/tfjs-image-recognition-base/src/ops/nonMaxSuppression.ts
new file mode 100644
index 0000000..6292f28
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/ops/nonMaxSuppression.ts
@@ -0,0 +1,41 @@
+import { Box } from '../classes/Box';
+import { iou } from './iou';
+
+export function nonMaxSuppression(
+  boxes: Box[],
+  scores: number[],
+  iouThreshold: number,
+  isIOU: boolean = true
+): number[] {
+
+  let indicesSortedByScore = scores
+    .map((score, boxIndex) => ({ score, boxIndex }))
+    .sort((c1, c2) => c1.score - c2.score)
+    .map(c => c.boxIndex)
+
+  const pick: number[] = []
+
+  while(indicesSortedByScore.length > 0) {
+    const curr = indicesSortedByScore.pop() as number
+    pick.push(curr)
+
+    const indices = indicesSortedByScore
+
+    const outputs: number[] = []
+    for (let i = 0; i < indices.length; i++) {
+      const idx = indices[i]
+
+      const currBox = boxes[curr]
+      const idxBox = boxes[idx]
+
+      outputs.push(iou(currBox, idxBox, isIOU))
+    }
+
+    indicesSortedByScore = indicesSortedByScore.filter(
+      (_, j) => outputs[j] <= iouThreshold
+    )
+  }
+
+  return pick
+
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/ops/normalize.ts b/node_modules/tfjs-image-recognition-base/src/ops/normalize.ts
new file mode 100644
index 0000000..de55ed5
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/ops/normalize.ts
@@ -0,0 +1,13 @@
+import * as tf from '@tensorflow/tfjs-core';
+
+export function normalize(x: tf.Tensor4D, meanRgb: number[]): tf.Tensor4D {
+  return tf.tidy(() => {
+    const [r, g, b] = meanRgb
+    const avg_r = tf.fill([...x.shape.slice(0, 3), 1], r)
+    const avg_g = tf.fill([...x.shape.slice(0, 3), 1], g)
+    const avg_b = tf.fill([...x.shape.slice(0, 3), 1], b)
+    const avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3)
+
+    return tf.sub(x, avg_rgb)
+  })
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/ops/padToSquare.ts b/node_modules/tfjs-image-recognition-base/src/ops/padToSquare.ts
new file mode 100644
index 0000000..615a84d
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/ops/padToSquare.ts
@@ -0,0 +1,48 @@
+import * as tf from '@tensorflow/tfjs-core';
+
+/**
+ * Pads the smaller dimension of an image tensor with zeros, such that width === height.
+ *
+ * @param imgTensor The image tensor.
+ * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on
+ * both sides of the minor dimension oof the image.
+ * @returns The padded tensor with width === height.
+ */
+export function padToSquare(
+  imgTensor: tf.Tensor4D,
+  isCenterImage: boolean = false
+): tf.Tensor4D {
+  return tf.tidy(() => {
+
+    const [height, width] = imgTensor.shape.slice(1)
+    if (height === width) {
+      return imgTensor
+    }
+
+    const dimDiff = Math.abs(height - width)
+    const paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1))
+    const paddingAxis = height > width ? 2 : 1
+
+    const createPaddingTensor = (paddingAmount: number): tf.Tensor => {
+      const paddingTensorShape = imgTensor.shape.slice()
+      paddingTensorShape[paddingAxis] = paddingAmount
+      return tf.fill(paddingTensorShape, 0)
+    }
+
+    const paddingTensorAppend = createPaddingTensor(paddingAmount)
+    const remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis]
+
+    const paddingTensorPrepend = isCenterImage && remainingPaddingAmount
+      ? createPaddingTensor(remainingPaddingAmount)
+      : null
+
+    const tensorsToStack = [
+      paddingTensorPrepend,
+      imgTensor,
+      paddingTensorAppend
+    ]
+      .filter(t => !!t)
+      .map((t: tf.Tensor) => t.toFloat()) as tf.Tensor4D[]
+    return tf.concat(tensorsToStack, paddingAxis)
+  })
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/ops/shuffleArray.ts b/node_modules/tfjs-image-recognition-base/src/ops/shuffleArray.ts
new file mode 100644
index 0000000..bb1df31
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/ops/shuffleArray.ts
@@ -0,0 +1,10 @@
+export function shuffleArray(inputArray: any[]) {
+  const array = inputArray.slice()
+  for (let i = array.length - 1; i > 0; i--) {
+      const j = Math.floor(Math.random() * (i + 1))
+      const x = array[i]
+      array[i] = array[j]
+      array[j] = x
+  }
+  return array
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/src/utils/index.ts b/node_modules/tfjs-image-recognition-base/src/utils/index.ts
new file mode 100644
index 0000000..55c3e1f
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/src/utils/index.ts
@@ -0,0 +1,63 @@
+import * as tf from '@tensorflow/tfjs-core';
+
+import { Dimensions, IDimensions } from '../classes/Dimensions';
+import { Point } from '../classes/Point';
+
+export function isTensor(tensor: any, dim: number) {
+  return tensor instanceof tf.Tensor && tensor.shape.length === dim
+}
+
+export function isTensor1D(tensor: any): tensor is tf.Tensor1D {
+  return isTensor(tensor, 1)
+}
+
+export function isTensor2D(tensor: any): tensor is tf.Tensor2D {
+  return isTensor(tensor, 2)
+}
+
+export function isTensor3D(tensor: any): tensor is tf.Tensor3D {
+  return isTensor(tensor, 3)
+}
+
+export function isTensor4D(tensor: any): tensor is tf.Tensor4D {
+  return isTensor(tensor, 4)
+}
+
+export function isFloat(num: number) {
+  return num % 1 !== 0
+}
+
+export function isEven(num: number) {
+  return num % 2 === 0
+}
+
+export function round(num: number, prec: number = 2) {
+  const f = Math.pow(10, prec)
+  return Math.floor(num * f) / f
+}
+
+export function isDimensions(obj: any): boolean {
+  return obj && obj.width && obj.height
+}
+
+export function computeReshapedDimensions({ width, height }: IDimensions, inputSize: number) {
+  const scale = inputSize / Math.max(height, width)
+  return new Dimensions(Math.round(width * scale), Math.round(height * scale))
+}
+
+export function getCenterPoint(pts: Point[]): Point {
+  return pts.reduce((sum, pt) => sum.add(pt), new Point(0, 0))
+    .div(new Point(pts.length, pts.length))
+}
+
+export function range(num: number, start: number, step: number): number[] {
+  return Array(num).fill(0).map((_, i) => start + (i * step))
+}
+
+export function isValidNumber(num: any) {
+  return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0
+}
+
+export function isValidProbablitiy(num: any) {
+  return isValidNumber(num) && 0 <= num && num <= 1.0
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/tsconfig.es6.json b/node_modules/tfjs-image-recognition-base/tsconfig.es6.json
new file mode 100644
index 0000000..621477b
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/tsconfig.es6.json
@@ -0,0 +1,11 @@
+{
+  "extends": "./tsconfig.json",
+  "compilerOptions": {
+    "outDir": "build/es6",
+    "module": "es6",
+    "target": "es5"
+  },
+  "include": [
+    "src"
+  ]
+}
diff --git a/node_modules/tfjs-image-recognition-base/tsconfig.json b/node_modules/tfjs-image-recognition-base/tsconfig.json
new file mode 100644
index 0000000..dfbd7ca
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/tsconfig.json
@@ -0,0 +1,35 @@
+{
+  "compilerOptions": {
+
+    "removeComments": false,
+    "preserveConstEnums": true,
+    "emitDecoratorMetadata": true,
+    "experimentalDecorators": true,
+    "sourceMap": true,
+    "declaration": true,
+    "noImplicitAny": true,
+    "noImplicitReturns": true,
+    "noImplicitThis": true,
+    "noFallthroughCasesInSwitch": true,
+    "suppressImplicitAnyIndexErrors": true,
+    "strictNullChecks": true,
+    "importHelpers": true,
+    "outDir": "build/commonjs",
+    "module": "commonjs",
+    "target": "es5",
+    "moduleResolution": "node",
+    "lib": ["es2015", "dom"],
+    "typeRoots": [
+      "typings",
+      "node_modules/@types"
+    ],
+    "baseUrl": "."
+  },
+  "formatCodeOptions": {
+    "indentSize": 2,
+    "tabSize": 2
+  },
+  "include": [
+    "src"
+  ]
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-image-recognition-base/tsconfig.test.json b/node_modules/tfjs-image-recognition-base/tsconfig.test.json
new file mode 100644
index 0000000..f885fbc
--- /dev/null
+++ b/node_modules/tfjs-image-recognition-base/tsconfig.test.json
@@ -0,0 +1,7 @@
+{
+  "extends": "./tsconfig.json",
+  "include": [
+    "src",
+    "test"
+  ]
+}
diff --git a/node_modules/tfjs-tiny-yolov2/.travis.yml b/node_modules/tfjs-tiny-yolov2/.travis.yml
new file mode 100644
index 0000000..589c5fd
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/.travis.yml
@@ -0,0 +1,14 @@
+sudo: required
+language: node_js
+node_js:
+  - "10"
+before_script:
+  - npm install
+  - npm run build
+  - sudo chown root /opt/google/chrome/chrome-sandbox
+  - sudo chmod 4755 /opt/google/chrome/chrome-sandbox
+  - export CHROME_BIN=chromium-browser
+  - export DISPLAY=:99.0
+  - sh -e /etc/init.d/xvfb start
+script:
+  - npm run test-travis
\ No newline at end of file
diff --git a/node_modules/tfjs-tiny-yolov2/LICENSE b/node_modules/tfjs-tiny-yolov2/LICENSE
new file mode 100644
index 0000000..2fafbb1
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2018 Vincent Mühler
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/node_modules/tfjs-tiny-yolov2/README.md b/node_modules/tfjs-tiny-yolov2/README.md
new file mode 100644
index 0000000..cb43d1c
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/README.md
@@ -0,0 +1,288 @@
+# tfjs-tiny-yolov2
+
+[![Build Status](https://travis-ci.org/justadudewhohacks/tfjs-tiny-yolov2.svg?branch=master)](https://travis-ci.org/justadudewhohacks/tfjs-tiny-yolov2)
+
+**JavaScript object detection in the browser based on a tensorflow.js implementation of tiny yolov2.**
+
+Table of Contents:
+
+* **[Pre Trained Models](#pre-trained-models)**
+* **[Running the Examples](#running-the-examples)**
+* **[Usage](#usage)**
+* **[Training your own Object Detector](#training-your-own-object-detector)**
+  * **[Defining your Model Config](#defining-your-model-config)**
+  * **[Labeling your Data with Ground Truth Boxes](#labeling-your-data-with-ground-truth-boxes)**
+  * **[Computing Box Anchors](#computing-box-anchors)**
+  * **[Yolo Loss Function](#yolo-loss-function)**
+  * **[Initializing the Model Weights](#initializing-the-model-weights)**
+  * **[Start Training](#start-training)**
+  * **[Overfit first!](#overfit-first)**
+
+<a name="pre-trained-models"></a>
+
+## Pre Trained Models
+
+The VOC and COCO models correspond to the quantized weights from the official [darknet](https://github.com/pjreddie/darknet) repo. The face detector uses depthwise separable convolutions instead of regular convolutions allowing for much faster prediction and a tiny model size, which is well suited for object detection on mobile devices as well. I trained the face detection model from scratch. Have a look at the **[Training your own Object Detector](#training-your-own-object-detector)** section if you want to train such a model for your own dataset!
+
+### Pascal VOC
+
+![voc1](https://user-images.githubusercontent.com/31125521/44733258-7ca2a000-aae7-11e8-93f3-07be9943e222.jpg)
+![voc2](https://user-images.githubusercontent.com/31125521/44733259-7d3b3680-aae7-11e8-9794-ab00edef9a48.jpg)
+
+### COCO
+
+![coco1](https://user-images.githubusercontent.com/31125521/44733254-7ca2a000-aae7-11e8-9113-28eaea552093.jpg)
+![coco2](https://user-images.githubusercontent.com/31125521/44733256-7ca2a000-aae7-11e8-98f6-26853a12248a.jpg)
+
+### Face Detection
+
+The face detection model is one of the models available in [face-api.js](https://github.com/justadudewhohacks/face-api.js).
+
+![face](https://user-images.githubusercontent.com/31125521/44733257-7ca2a000-aae7-11e8-9ede-1a38f20e36be.jpg)
+
+<a name="running-the-examples"></a>
+
+## Running the Examples
+
+``` bash
+cd examples
+npm i
+npm start
+```
+
+Browse to http://localhost:3000/.
+
+<a name="usage"></a>
+
+## Usage
+
+Get the latest build from dist/tiny-yolov2.js or dist/tiny-yolov2.min.js and include the script:
+
+``` html
+<script src="tiny-yolov2.js"></script>
+```
+
+Simply load the model:
+
+``` javascript
+const config = // yolo config
+const net = new yolo.TinyYolov2(config)
+await net.load(`voc_model-weights_manifest.json`)
+```
+
+The config file of the VOC model looks as follows:
+
+``` js
+{
+  // the pre trained VOC model uses regular convolutions
+  "withSeparableConvs": false,
+  // iou threshold for nonMaxSuppression
+  "iouThreshold": 0.4,
+  // anchor box dimensions, relative to cell size (32px)
+  "anchors": [
+    { "x": 1.08, "y": 1.19 },
+    { "x": 3.42, "y": 4.41 },
+    { "x": 6.63, "y": 11.38 },
+    { "x": 9.42, "y": 5.11 },
+    { "x": 16.62, "y": 10.52 }
+  ],
+  // class labels in correct order
+  "classes": [
+    "aeroplane", "bicycle", "bird", "boat", "bottle",
+    "bus", "car", "cat", "chair", "cow",
+    "diningtable", "dog", "horse", "motorbike", "person",
+    "pottedplant", "sheep", "sofa", "train", "tvmonitor"
+  ]
+}
+```
+
+Inference and drawing the results:
+
+``` javascript
+const forwardParams = {
+  inputSize: 416,
+  scoreThreshold: 0.8
+}
+
+const detections = await net.detect('myInputImage', forwardParams)
+yolo.drawDetection('myCanvas', detections)
+```
+
+Also check out the examples.
+
+<a name="training-your-own-object-detector"></a>
+
+## Training your own Object Detector
+
+If you want to train your own object detector, I would suggest training a model using separable convolutions, as it will allow for much faster inference times and the training process will converge much faster, as there are significantly less parameters to train.
+
+Training a multiclass detector will take quite some time, depending on how much classes you are training your object detector on. However, training a single class detector it is possible to get already pretty good results after training for only a few epochs.
+
+<a name="defining-your-model-config"></a>
+
+### Defining your Model Config
+
+``` js
+{
+  // use separable convolutions over regular convolutions
+  "withSeparableConvs": true,
+  // iou threshold for nonMaxSuppression
+  "iouThreshold": 0.4,
+  // instructions for how to determine anchors is given below
+  "anchors": [...],
+  // whatever kind of objects you are training your object detector on
+  "classes": ["cat"],
+  // optionally you can compute the mean RGB value for your dataset and
+  // pass it in the config for performing mean value subtraction on your
+  // input images
+  "meanRgb": [...],
+  // scale factors for each loss term (only required for training),
+  // explained below
+  "objectScale": 5,
+  "noObjectScale": 1,
+  "coordScale": 1,
+  "classScale": 1
+}
+```
+
+<a name="labeling-your-data-with-ground-truth-boxes"></a>
+
+### Labeling your Data with Ground Truth Boxes
+
+For each image in your training set, you should create a corresponding json file, containing the bounding boxes and class labels of each of the instance of objects located in that image. The bounding box dimensions should be relative to the image dimensions.
+
+Consider an image with a width and height of 400px, showing a single cat, which is spanned by the bounding box at x = 50px, y = 100px (upper left corner) with a box size of width = 200px and height = 100px. The corresponding json file should look as follows (note, it is an array of all bounding boxes for that image):
+
+``` json
+[
+  {
+    "x": 0.125,
+    "y": 0.25,
+    "width": 0.5,
+    "height": 0.25,
+    "label": "cat"
+  }
+]
+```
+
+<a name="computing-box-anchors"></a>
+
+### Computing Box Anchors
+
+Before training your detector, you want to compute 5 anchor boxes over your training set. An anchor box is basically an object of shape { "x": boxWidth / 32, "y": boxHeight / 32 } where x and y are the anchor box sizes relative to the grid cell size (32px).
+
+To determine the 5 anchor boxes, you want to simply perform kmeans clustering with 5 clusters over the width and height of each ground truth box of your training set. There should be plenty of options out there, which you can use for kmeans clustering, but I will provide a script for that, coming soon...
+
+<a name="yolo-loss-function"></a>
+
+### Yolo Loss Function
+
+The Yolo loss function computes the sum of the coordinate, object, class and no object loss. You can tune the weight of each loss term contributing to the totoal loss by adjusting the corresponding scale parameters in your config file, as mentioned above.
+
+The no object loss term penalizes the scores of the bounding box of all the box anchors in the grid, which do not have a corresponding ground truth bounding box. In other words, they should optimally predict a score of 0, if there is no object of interest at that position.
+
+On the other hand, the object, class and coordinate loss terms refer to the accuracy of the prediction at each anchor position where there is a ground truth bounding box. The coordinate loss simply penalizes the difference between predicted bounding box coordinates and ground truth box coordinates, the object loss penalizes the difference of the predicted confidence score to the box IOU.
+
+The class loss penalizes the confidence score of the predicted score. Note, that training a single class object detector you can simply ignore that parameter, as the class loss is always 0 in that case.
+
+PS: You can simply go with the default values in the above shown config example.
+
+<a name="initializing-the-model-weights"></a>
+
+### Initializing the Model Weights
+
+Training a model from scratch, you need some weights to begin with. Simply open initWeights.html located in the /train folder of the repo in your browser. Enter the number of classes, hit save and use the saved file as the initial checkpoint weight file.
+
+<a name="start-training"></a>
+
+### Start Training
+
+For a complete example, also check out the /train folder at the root of this repo, which also contains some tooling to save intermediary checkpoints of your model weights as well as statistics of the average loss after each epoch.
+
+Set up the model for training:
+
+``` javascript
+const config = // your config
+
+// simply use any of the optimizer provided by tfjs (I usually use adam)
+const learningRate = 0.001
+const optimizer = tf.train.adam(learningRate, 0.9, 0.999, 1e-8)
+
+// initialize a trainable TinyYolov2
+const net = new yolo.TinyYolov2Trainable(config, optimizer)
+
+// load initial weights or the weights of any checkpoint
+const checkpointUri = 'checkpoints/initial_glorot_1_classes.weights'
+const weights = new Float32Array(await (await fetch(checkpointUri)).arrayBuffer())
+await net.load(weights)
+```
+
+What I usually do is naming the json files the same as the corresponding image, e.g. *img1.jpg* and *img1.json* and provide an endpoint to retrieve the json file names as an array:
+
+``` javascript
+const boxJsonUris = (await fetch('/boxJsonUris')).json()
+```
+
+Furthermore you can choose to train your model on a fixed input size or you can perform multi scale training, which is a good way to improve the accuracy of your model at different scales. This can also be helpful to augment your data, in case you only have a limited number of training samples:
+
+``` javascript
+// should be multiples of 32 (grid cell size)
+const trainingSizes = [160, 224, 320, 416]
+```
+
+Then we can actually train it:
+
+``` javascript
+for (let epoch = startEpoch; epoch < maxEpoch; epoch++) {
+
+  // always shuffle your inputs for each epoch
+  const shuffledInputs = yolo.shuffleArray(boxJsonUris)
+
+  // loop through shuffled inputs
+  for (let dataIdx = 0; dataIdx < shuffledInputs.length; dataIdx++) {
+
+    // fetch image and corresponding ground truth bounding boxes
+    const boxJsonUri = shuffledInputs[dataIdx]
+    const imgUri = boxJsonUri.replace('.json', '.jpg')
+
+    const groundTruth = await (await fetch(boxJsonUri)).json()
+    const img = await yolo.bufferToImage(await (await fetch(imgUri)).blob())
+
+    // rescale and backward pass input image for each input size
+    for (let sizeIdx = 0; sizeIdx < trainSizes.length; sizeIdx++) {
+
+      const inputSize = trainSizes[sizeIdx]
+
+      const backwardOptions = {
+        // filter boxes with width < 32 or height < 32
+        minBoxSize: 32,
+        // log computed losses
+        reportLosses: function({ losses, numBoxes, inputSize }) {
+          console.log(`ground truth boxes: ${numBoxes} (${inputSize})`)
+          console.log(`noObjectLoss[${dataIdx}]: ${yolo.round(losses.noObjectLoss, 4)}`)
+          console.log(`objectLoss[${dataIdx}]: ${yolo.round(losses.objectLoss, 4)}`)
+          console.log(`coordLoss[${dataIdx}]: ${yolo.round(losses.coordLoss, 4)}`)
+          console.log(`classLoss[${dataIdx}]: ${yolo.round(losses.classLoss, 4)}`)
+          console.log(`totalLoss[${dataIdx}]: ${yolo.round(losses.totalLoss, 4)}`)
+        }
+      }
+
+      const loss = await net.backward(img, groundTruth, inputSize, backwardOptions)
+
+      if (loss) {
+        // don't forget to free the loss tensor
+        loss.dispose()
+      } else {
+        console.log('no boxes remaining after filtering')
+      }
+
+    }
+  }
+}
+```
+
+<a name="overfit-first"></a>
+
+### Overfit first!
+
+Generally it's a good idea, to overfit on a small subset of your training data, to verify, that the loss is converging and that your detector is actually learning something. Therefore, you can simply train your detector on 10 - 20 images of your training data for some epochs. Once the loss converges, save the model, run inference on these 10 - 20 images to view the predicted bounding boxes and compare them to the ground truth boxes.
\ No newline at end of file
diff --git a/node_modules/tfjs-tiny-yolov2/dist/tiny-yolov2.js b/node_modules/tfjs-tiny-yolov2/dist/tiny-yolov2.js
new file mode 100644
index 0000000..ea25cb5
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/dist/tiny-yolov2.js
@@ -0,0 +1,2691 @@
+(function (global, factory) {
+    typeof exports === 'object' && typeof module !== 'undefined' ? factory(exports) :
+    typeof define === 'function' && define.amd ? define(['exports'], factory) :
+    (factory((global.yolo = global.yolo || {})));
+}(this, (function (exports) { 'use strict';
+
+    /**
+     * @license
+     * Copyright 2018 Google LLC. All Rights Reserved.
+     * Licensed under the Apache License, Version 2.0 (the "License");
+     * you may not use this file except in compliance with the License.
+     * You may obtain a copy of the License at
+     *
+     * http://www.apache.org/licenses/LICENSE-2.0
+     *
+     * Unless required by applicable law or agreed to in writing, software
+     * distributed under the License is distributed on an "AS IS" BASIS,
+     * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+     * See the License for the specific language governing permissions and
+     * limitations under the License.
+     * =============================================================================
+     */
+    var extendStatics=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t;}||function(e,t){for(var n in t)t.hasOwnProperty(n)&&(e[n]=t[n]);};function __extends(e,t){function n(){this.constructor=e;}extendStatics(e,t),e.prototype=null===t?Object.create(t):(n.prototype=t.prototype,new n);}var __assign=Object.assign||function(e){for(var t,n=1,r=arguments.length;n<r;n++)for(var o in t=arguments[n])Object.prototype.hasOwnProperty.call(t,o)&&(e[o]=t[o]);return e};function __awaiter(e,t,n,r){return new(n||(n=Promise))(function(o,a){function i(e){try{u(r.next(e));}catch(e){a(e);}}function s(e){try{u(r.throw(e));}catch(e){a(e);}}function u(e){e.done?o(e.value):new n(function(t){t(e.value);}).then(i,s);}u((r=r.apply(e,t||[])).next());})}function __generator(e,t){var n,r,o,a,i={label:0,sent:function(){if(1&o[0])throw o[1];return o[1]},trys:[],ops:[]};return a={next:s(0),throw:s(1),return:s(2)},"function"==typeof Symbol&&(a[Symbol.iterator]=function(){return this}),a;function s(a){return function(s){return function(a){if(n)throw new TypeError("Generator is already executing.");for(;i;)try{if(n=1,r&&(o=2&a[0]?r.return:a[0]?r.throw||((o=r.return)&&o.call(r),0):r.next)&&!(o=o.call(r,a[1])).done)return o;switch(r=0,o&&(a=[2&a[0],o.value]),a[0]){case 0:case 1:o=a;break;case 4:return i.label++,{value:a[1],done:!1};case 5:i.label++,r=a[1],a=[0];continue;case 7:a=i.ops.pop(),i.trys.pop();continue;default:if(!(o=(o=i.trys).length>0&&o[o.length-1])&&(6===a[0]||2===a[0])){i=0;continue}if(3===a[0]&&(!o||a[1]>o[0]&&a[1]<o[3])){i.label=a[1];break}if(6===a[0]&&i.label<o[1]){i.label=o[1],o=a;break}if(o&&i.label<o[2]){i.label=o[2],i.ops.push(a);break}o[2]&&i.ops.pop(),i.trys.pop();continue}a=t.call(e,i);}catch(e){a=[6,e],r=0;}finally{n=o=0;}if(5&a[0])throw a[1];return {value:a[0]?a[1]:void 0,done:!0}}([a,s])}}}var 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isMobile(){var e=navigator.userAgent||navigator.vendor||window.opera;return /(android|bb\d+|meego).+mobile|avantgo|bada\/|blackberry|blazer|compal|elaine|fennec|hiptop|iemobile|ip(hone|od)|iris|kindle|lge |maemo|midp|mmp|mobile.+firefox|netfront|opera m(ob|in)i|palm( os)?|phone|p(ixi|re)\/|plucker|pocket|psp|series(4|6)0|symbian|treo|up\.(browser|link)|vodafone|wap|windows ce|xda|xiino/i.test(e)||/1207|6310|6590|3gso|4thp|50[1-6]i|770s|802s|a wa|abac|ac(er|oo|s\-)|ai(ko|rn)|al(av|ca|co)|amoi|an(ex|ny|yw)|aptu|ar(ch|go)|as(te|us)|attw|au(di|\-m|r |s )|avan|be(ck|ll|nq)|bi(lb|rd)|bl(ac|az)|br(e|v)w|bumb|bw\-(n|u)|c55\/|capi|ccwa|cdm\-|cell|chtm|cldc|cmd\-|co(mp|nd)|craw|da(it|ll|ng)|dbte|dc\-s|devi|dica|dmob|do(c|p)o|ds(12|\-d)|el(49|ai)|em(l2|ul)|er(ic|k0)|esl8|ez([4-7]0|os|wa|ze)|fetc|fly(\-|_)|g1 u|g560|gene|gf\-5|g\-mo|go(\.w|od)|gr(ad|un)|haie|hcit|hd\-(m|p|t)|hei\-|hi(pt|ta)|hp( i|ip)|hs\-c|ht(c(\-| |_|a|g|p|s|t)|tp)|hu(aw|tc)|i\-(20|go|ma)|i230|iac( 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|nc|nw)|wmlb|wonu|x700|yas\-|your|zeto|zte\-/i.test(e.substr(0,4))}function shuffle(e){for(var t=e.length,n=0,r=0;t>0;)r=Math.random()*t|0,n=e[--t],e[t]=e[r],e[r]=n;}function clamp(e,t,n){return Math.max(e,Math.min(t,n))}function nearestLargerEven(e){return e%2==0?e:e+1}function sum(e){for(var t=0,n=0;n<e.length;n++)t+=e[n];return t}function randUniform(e,t){var n=Math.random();return t*n+(1-n)*e}function distSquared(e,t){for(var n=0,r=0;r<e.length;r++){var o=Number(e[r])-Number(t[r]);n+=o*o;}return n}function assert(e,t){if(!e)throw new Error("string"==typeof t?t:t())}function assertShapesMatch(e,t,n){void 0===n&&(n=""),assert(arraysEqual(e,t),n+" Shapes "+e+" and "+t+" must match");}function assertNonNull(e){assert(null!=e,"The input to the tensor constructor must be a non-null value.");}function flatten(e,t){if(void 0===t&&(t=[]),Array.isArray(e)||isTypedArray(e))for(var n=0;n<e.length;++n)flatten(e[n],t);else t.push(e);return t}function sizeFromShape(e){if(0===e.length)return 1;for(var t=e[0],n=1;n<e.length;n++)t*=e[n];return t}function isScalarShape(e){return 0===e.length}function arraysEqual(e,t){if(e===t)return !0;if(null==e||null==t)return !1;if(e.length!==t.length)return !1;for(var n=0;n<e.length;n++)if(e[n]!==t[n])return !1;return !0}function isInt(e){return e%1==0}function tanh(e){if(null!=Math.tanh)return Math.tanh(e);if(e===1/0)return 1;if(e===-1/0)return -1;var t=Math.exp(2*e);return (t-1)/(t+1)}function sizeToSquarishShape(e){for(var t=Math.floor(Math.sqrt(e));t>1;--t)if(e%t==0)return [t,e/t];return [1,e]}function createShuffledIndices(e){for(var t=new Uint32Array(e),n=0;n<e;++n)t[n]=n;return shuffle(t),t}function rightPad(e,t){return t<=e.length?e:e+" ".repeat(t-e.length)}function repeatedTry(e,t,n){return void 0===t&&(t=function(e){return 0}),new Promise(function(r,o){var a=0,i=function(){if(e())r();else{var s=t(++a);null!=n&&a>=n?o():setTimeout(i,s);}};i();})}function inferFromImplicitShape(e,t){for(var n=1,r=-1,o=0;o<e.length;++o)if(e[o]>=0)n*=e[o];else if(-1===e[o]){if(-1!==r)throw Error("Shapes can only have 1 implicit size. Found -1 at dim "+r+" and dim "+o);r=o;}else if(e[o]<0)throw Error("Shapes can not be < 0. Found "+e[o]+" at dim "+o);if(-1===r){if(t>0&&t!==n)throw Error("Size("+t+") must match the product of shape "+e);return e}if(0===n)throw Error("Cannot infer the missing size in ["+e+"] when there are 0 elements");if(t%n!=0)throw Error("The implicit shape can't be a fractional number. Got "+t+" / "+n);var a=e.slice();return a[r]=t/n,a}function squeezeShape(e,t){for(var n=[],r=[],o=0,a=0;a<e.length;++a){if(null!=t){if(t[o]===a&&1!==e[a])throw new Error("Can't squeeze axis "+a+" since its dim '"+e[a]+"' is not 1");(null==t[o]||t[o]>a)&&1===e[a]&&(n.push(e[a]),r.push(a)),t[o]<=a&&o++;}1!==e[a]&&(n.push(e[a]),r.push(a));}return {newShape:n,keptDims:r}}function getTypedArrayFromDType(e,t){var n=null;if(null==e||"float32"===e)n=new Float32Array(t);else if("int32"===e)n=new Int32Array(t);else{if("bool"!==e)throw new Error("Unknown data type "+e);n=new Uint8Array(t);}return n}function getArrayFromDType(e,t){var n=null;if(null==e||"float32"===e)n=new Float32Array(t);else if("int32"===e)n=new Int32Array(t);else if("bool"===e)n=new Uint8Array(t);else{if("string"!==e)throw new Error("Unknown data type "+e);n=new Array(t);}return n}function checkComputationForNaN(e,t,n){if("float32"===t)for(var r=0;r<e.length;r++)if(isNaN(e[r]))throw Error("The result of the '"+n+"' has NaNs.")}function checkConversionForNaN(e,t){if("float32"!==t)for(var n=0;n<e.length;n++)if(isNaN(e[n]))throw Error("NaN is not a valid value for dtype: '"+t+"'.")}function hasEncodingLoss(e,t){return "complex64"!==t&&(("float32"!==t||"complex64"===e)&&(("int32"!==t||"float32"===e||"complex64"===e)&&("bool"!==t||"bool"!==e)))}function isTypedArray(e){return e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array}function bytesPerElement(e){if("float32"===e||"int32"===e)return 4;if("complex64"===e)return 8;if("bool"===e)return 1;throw new Error("Unknown dtype "+e)}function bytesFromStringArray(e){if(null==e)return 0;var t=0;return e.forEach(function(e){return t+=2*e.length}),t}function isString(e){return "string"==typeof e||e instanceof String}function isBoolean(e){return "boolean"==typeof e}function isNumber(e){return "number"==typeof e}function inferDtype(e){return e instanceof Array?inferDtype(e[0]):e instanceof Float32Array?"float32":e instanceof Int32Array||e instanceof Uint8Array?"int32":isNumber(e)?"float32":isString(e)?"string":isBoolean(e)?"bool":"float32"}function isFunction(e){return !!(e&&e.constructor&&e.call&&e.apply)}function nearestDivisor(e,t){for(var n=t;n<e;++n)if(e%n==0)return n;return e}function computeStrides(e){var t=e.length;if(t<2)return [];var n=new Array(t-1);n[t-2]=e[t-1];for(var r=t-3;r>=0;--r)n[r]=n[r+1]*e[r+1];return n}function toTypedArray(e,t,n){if("string"===t)throw new Error("Cannot convert a string[] to a TypedArray");if(noConversionNeeded(e,t))return e;if(Array.isArray(e)&&(e=flatten(e)),null==t||"float32"===t||"complex64"===t)return new Float32Array(e);if("int32"===t)return n&&checkConversionForNaN(e,t),new Int32Array(e);if("bool"===t){for(var r=new Uint8Array(e.length),o=0;o<r.length;++o)0!==Math.round(e[o])&&(r[o]=1);return r}throw new Error("Unknown data type "+t)}function noConversionNeeded(e,t){return e instanceof Float32Array&&"float32"===t||e instanceof Int32Array&&"int32"===t||e instanceof Uint8Array&&"bool"===t}function makeOnesTypedArray(e,t){for(var n=makeZerosTypedArray(e,t),r=0;r<n.length;r++)n[r]=1;return n}function makeZerosTypedArray(e,t){if(null==t||"float32"===t||"complex64"===t)return new Float32Array(e);if("int32"===t)return new Int32Array(e);if("bool"===t)return new Uint8Array(e);throw new Error("Unknown data type "+t)}function now(){if("undefined"!=typeof performance)return performance.now();if("undefined"!=typeof process){var e=process.hrtime();return 1e3*e[0]+e[1]/1e6}throw new Error("Cannot measure time in this environment. You should run tf.js in the browser or in Node.js")}var util=Object.freeze({shuffle:shuffle,clamp:clamp,nearestLargerEven:nearestLargerEven,sum:sum,randUniform:randUniform,distSquared:distSquared,assert:assert,assertShapesMatch:assertShapesMatch,assertNonNull:assertNonNull,flatten:flatten,sizeFromShape:sizeFromShape,isScalarShape:isScalarShape,arraysEqual:arraysEqual,isInt:isInt,tanh:tanh,sizeToSquarishShape:sizeToSquarishShape,createShuffledIndices:createShuffledIndices,rightPad:rightPad,repeatedTry:repeatedTry,inferFromImplicitShape:inferFromImplicitShape,squeezeShape:squeezeShape,getTypedArrayFromDType:getTypedArrayFromDType,getArrayFromDType:getArrayFromDType,checkComputationForNaN:checkComputationForNaN,checkConversionForNaN:checkConversionForNaN,hasEncodingLoss:hasEncodingLoss,isTypedArray:isTypedArray,bytesPerElement:bytesPerElement,bytesFromStringArray:bytesFromStringArray,isString:isString,isBoolean:isBoolean,isNumber:isNumber,inferDtype:inferDtype,isFunction:isFunction,nearestDivisor:nearestDivisor,computeStrides:computeStrides,toTypedArray:toTypedArray,makeOnesTypedArray:makeOnesTypedArray,makeZerosTypedArray:makeZerosTypedArray,now:now}),Profiler=function(){function e(e,t){this.backendTimer=e,this.logger=t,null==t&&(this.logger=new Logger);}return e.prototype.profileKernel=function(e,t){var n,r=this,o=this.backendTimer.time(function(){n=t();});return (Array.isArray(n)?n:[n]).forEach(function(t){var n=t.dataSync();checkComputationForNaN(n,t.dtype,e),o.then(function(o){var a="";null!=o.getExtraProfileInfo&&(a=o.getExtraProfileInfo()),r.logger.logKernelProfile(e,t,n,o.kernelMs,a);});}),n},e}(),Logger=function(){function e(){}return e.prototype.logKernelProfile=function(e,t,n,r,o){var a=rightPad(r+"ms",9),i=rightPad(e,25),s=t.rank,u=t.size,l=rightPad(t.shape.toString(),14);console.log("%c"+i+"\t%c"+a+"\t%c"+s+"D "+l+"\t%c"+u+"\t%c"+o,"font-weight:bold","color:red","color:blue","color: orange","color: green");},e}(),FORMAT_LIMIT_NUM_VALS=20,FORMAT_NUM_FIRST_LAST_VALS=3,FORMAT_NUM_SIG_DIGITS=7;function tensorToString(e,t,n,r){var o=computeStrides(t),a=computeMaxSizePerColumn(e,t,n,o),i=t.length,s=subTensorToString(e,t,n,o,a),u=["Tensor"];return r&&(u.push("  dtype: "+n),u.push("  rank: "+i),u.push("  shape: ["+t+"]"),u.push("  values:")),u.push(s.map(function(e){return "    "+e}).join("\n")),u.join("\n")}function computeMaxSizePerColumn(e,t,n,r){var o=sizeFromShape(t),a=r[r.length-1],i=new Array(a).fill(0),s=t.length,u="complex64"===n?createComplexTuples(e):e;if(s>1)for(var l=0;l<o/a;l++)for(var c=l*a,p=0;p<a;p++)i[p]=Math.max(i[p],valToString(u[c+p],0).length);return i}function valToString(e,t){return rightPad(Array.isArray(e)?parseFloat(e[0].toFixed(FORMAT_NUM_SIG_DIGITS))+" + "+parseFloat(e[1].toFixed(FORMAT_NUM_SIG_DIGITS))+"j":isString(e)?"'"+e+"'":parseFloat(e.toFixed(FORMAT_NUM_SIG_DIGITS)).toString(),t)}function subTensorToString(e,t,n,r,o,a){void 0===a&&(a=!0);var i="complex64"===n?2:1,s=t[0],u=t.length;if(0===u)return "complex64"===n?[valToString(createComplexTuples(e)[0],0)]:[e[0].toString()];if(1===u){if(s>FORMAT_LIMIT_NUM_VALS){var l=FORMAT_NUM_FIRST_LAST_VALS*i,c=Array.from(e.slice(0,l)),p=Array.from(e.slice(s-FORMAT_NUM_FIRST_LAST_VALS*i,s));return "complex64"===n&&(c=createComplexTuples(c),p=createComplexTuples(p)),["["+c.map(function(e,t){return valToString(e,o[t])}).join(", ")+", ..., "+p.map(function(e,t){return valToString(e,o[s-FORMAT_NUM_FIRST_LAST_VALS+t])}).join(", ")+"]"]}return ["["+("complex64"===n?createComplexTuples(e):Array.from(e)).map(function(e,t){return valToString(e,o[t])}).join(", ")+"]"]}var d=t.slice(1),h=r.slice(1),f=r[0]*i,m=[];if(s>FORMAT_LIMIT_NUM_VALS){for(var g=0;g<FORMAT_NUM_FIRST_LAST_VALS;g++){var v=(y=g*f)+f;m.push.apply(m,subTensorToString(e.slice(y,v),d,n,h,o,!1));}m.push("...");for(g=s-FORMAT_NUM_FIRST_LAST_VALS;g<s;g++){v=(y=g*f)+f;m.push.apply(m,subTensorToString(e.slice(y,v),d,n,h,o,g===s-1));}}else for(g=0;g<s;g++){var y;v=(y=g*f)+f;m.push.apply(m,subTensorToString(e.slice(y,v),d,n,h,o,g===s-1));}var x=2===u?",":"";m[0]="["+m[0]+x;for(g=1;g<m.length-1;g++)m[g]=" "+m[g]+x;var T=",\n";for(g=2;g<u;g++)T+="\n";return m[m.length-1]=" "+m[m.length-1]+"]"+(a?"":T),m}function createComplexTuples(e){for(var t=[],n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var TensorBuffer=function(){function e(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=sizeFromShape(e),null!=n){var r=n.length;assert(r===this.size,"Length of values '"+r+"' does not match the size inferred by the shape '"+this.size+"'.");}if("complex64"===t)throw new Error("complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).");this.values=n||getArrayFromDType(t,sizeFromShape(this.shape)),this.strides=computeStrides(e);}return e.prototype.set=function(e){for(var t=[],n=1;n<arguments.length;n++)t[n-1]=arguments[n];0===t.length&&(t=[0]),assert(t.length===this.rank,"The number of provided coordinates ("+t.length+") must match the rank ("+this.rank+")");var r=this.locToIndex(t);this.values[r]=e;},e.prototype.get=function(){for(var e=[],t=0;t<arguments.length;t++)e[t]=arguments[t];0===e.length&&(e=[0]);for(var n=e[e.length-1],r=0;r<e.length-1;++r)n+=this.strides[r]*e[r];return this.values[n]},e.prototype.locToIndex=function(e){if(0===this.rank)return 0;if(1===this.rank)return e[0];for(var t=e[e.length-1],n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t},e.prototype.indexToLoc=function(e){if(0===this.rank)return [];if(1===this.rank)return [e];for(var t=new Array(this.shape.length),n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t},Object.defineProperty(e.prototype,"rank",{get:function(){return this.shape.length},enumerable:!0,configurable:!0}),e.prototype.toTensor=function(){return Tensor.make(this.shape,{values:this.values},this.dtype)},e}(),trackerFn=null,opHandler=null;function setTensorTracker(e){trackerFn=e;}function setOpHandler(e){opHandler=e;}var Tensor=function(){function e(e,t,n,r){this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||"float32",this.size=sizeFromShape(e),this.strides=computeStrides(e),this.dataId=null!=r?r:{},this.id=trackerFn().nextTensorId(),this.rankType=this.rank<5?this.rank.toString():"higher",trackerFn().registerTensor(this),null!=n&&trackerFn().write(this.dataId,n);}return e.make=function(t,n,r){return new e(t,r,n.values,n.dataId)},e.prototype.flatten=function(){return this.throwIfDisposed(),this.as1D()},e.prototype.asScalar=function(){return this.throwIfDisposed(),assert(1===this.size,"The array must have only 1 element."),this.reshape([])},e.prototype.as1D=function(){return this.throwIfDisposed(),this.reshape([this.size])},e.prototype.as2D=function(e,t){return this.throwIfDisposed(),this.reshape([e,t])},e.prototype.as3D=function(e,t,n){return this.throwIfDisposed(),this.reshape([e,t,n])},e.prototype.as4D=function(e,t,n,r){return this.throwIfDisposed(),this.reshape([e,t,n,r])},e.prototype.as5D=function(e,t,n,r,o){return this.throwIfDisposed(),this.reshape([e,t,n,r,o])},e.prototype.asType=function(e){return this.throwIfDisposed(),opHandler.cast(this,e)},Object.defineProperty(e.prototype,"rank",{get:function(){return this.shape.length},enumerable:!0,configurable:!0}),e.prototype.get=function(){for(var e=[],t=0;t<arguments.length;t++)e[t]=arguments[t];assert(e.length===this.rank,"Number of coordinates in get() must match the rank of the tensor"),assert("complex64"!==this.dtype,"Tensor.get() is not supported for complex64 tensors yet."),this.throwIfDisposed(),0===e.length&&(e=[0]);for(var n=e[e.length-1],r=0;r<e.length-1;++r)n+=this.strides[r]*e[r];return this.dataSync()[n]},e.prototype.buffer=function(){return opHandler.buffer(this.shape,this.dtype,this.dataSync())},e.prototype.data=function(){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(e){return this.throwIfDisposed(),[2,trackerFn().read(this.dataId)]})})},e.prototype.dataSync=function(){return this.throwIfDisposed(),trackerFn().readSync(this.dataId)},e.prototype.dispose=function(){this.isDisposed||(trackerFn().disposeTensor(this),this.isDisposedInternal=!0);},Object.defineProperty(e.prototype,"isDisposed",{get:function(){return this.isDisposedInternal},enumerable:!0,configurable:!0}),e.prototype.throwIfDisposed=function(){if(this.isDisposed)throw new Error("Tensor is disposed.")},e.prototype.toFloat=function(){return this.asType("float32")},e.prototype.toInt=function(){return this.asType("int32")},e.prototype.toBool=function(){return this.asType("bool")},e.prototype.print=function(e){return void 0===e&&(e=!1),opHandler.print(this,e)},e.prototype.reshape=function(e){return this.throwIfDisposed(),opHandler.reshape(this,e)},e.prototype.reshapeAs=function(e){return this.throwIfDisposed(),this.reshape(e.shape)},e.prototype.expandDims=function(e){return void 0===e&&(e=0),opHandler.expandDims(this,e)},e.prototype.cumsum=function(e,t,n){return void 0===e&&(e=0),void 0===t&&(t=!1),void 0===n&&(n=!1),opHandler.cumsum(this,e,t,n)},e.prototype.squeeze=function(e){return this.throwIfDisposed(),opHandler.squeeze(this,e)},e.prototype.clone=function(){return this.throwIfDisposed(),opHandler.clone(this)},e.prototype.toString=function(e){return void 0===e&&(e=!1),tensorToString(this.dataSync(),this.shape,this.dtype,e)},e.prototype.tile=function(e){return this.throwIfDisposed(),opHandler.tile(this,e)},e.prototype.gather=function(e,t){return void 0===t&&(t=0),this.throwIfDisposed(),opHandler.gather(this,e,t)},e.prototype.matMul=function(e,t,n){return void 0===t&&(t=!1),void 0===n&&(n=!1),this.throwIfDisposed(),opHandler.matMul(this,e,t,n)},e.prototype.dot=function(e){return this.throwIfDisposed(),opHandler.dot(this,e)},e.prototype.norm=function(e,t,n){return void 0===e&&(e="euclidean"),void 0===t&&(t=null),void 0===n&&(n=!1),this.throwIfDisposed(),opHandler.norm(this,e,t,n)},e.prototype.slice=function(e,t){return this.throwIfDisposed(),opHandler.slice(this,e,t)},e.prototype.reverse=function(e){return this.throwIfDisposed(),opHandler.reverse(this,e)},e.prototype.concat=function(t,n){return void 0===n&&(n=0),this.throwIfDisposed(),t instanceof e&&(t=[t]),opHandler.concat([this].concat(t),n)},e.prototype.split=function(e,t){return void 0===t&&(t=0),this.throwIfDisposed(),opHandler.split(this,e,t)},e.prototype.stack=function(e,t){return void 0===t&&(t=0),opHandler.stack([this,e],t)},e.prototype.unstack=function(e,t){return void 0===t&&(t=0),opHandler.unstack(this,t)},e.prototype.pad=function(e,t){return void 0===t&&(t=0),opHandler.pad(this,e,t)},e.prototype.batchNormalization=function(e,t,n,r,o){return void 0===n&&(n=.001),this.throwIfDisposed(),opHandler.batchNormalization(this,e,t,n,r,o)},e.prototype.all=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.all(this,e,t)},e.prototype.any=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.any(this,e,t)},e.prototype.logSumExp=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.logSumExp(this,e,t)},e.prototype.sum=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.sum(this,e,t)},e.prototype.prod=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.prod(this,e,t)},e.prototype.mean=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.mean(this,e,t)},e.prototype.min=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.min(this,e,t)},e.prototype.max=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.max(this,e,t)},e.prototype.argMin=function(e){return void 0===e&&(e=null),this.throwIfDisposed(),opHandler.argMin(this,e)},e.prototype.argMax=function(e){return void 0===e&&(e=null),this.throwIfDisposed(),opHandler.argMax(this,e)},e.prototype.cast=function(e){return this.throwIfDisposed(),opHandler.cast(this,e)},e.prototype.add=function(e){return this.throwIfDisposed(),opHandler.add(this,e)},e.prototype.addStrict=function(e){return this.throwIfDisposed(),opHandler.addStrict(this,e)},e.prototype.atan2=function(e){return this.throwIfDisposed(),opHandler.atan2(this,e)},e.prototype.sub=function(e){return this.throwIfDisposed(),opHandler.sub(this,e)},e.prototype.subStrict=function(e){return this.throwIfDisposed(),opHandler.subStrict(this,e)},e.prototype.pow=function(e){return this.throwIfDisposed(),opHandler.pow(this,e)},e.prototype.powStrict=function(e){return this.throwIfDisposed(),opHandler.powStrict(this,e)},e.prototype.mul=function(e){return this.throwIfDisposed(),opHandler.mul(this,e)},e.prototype.mulStrict=function(e){return this.throwIfDisposed(),opHandler.mulStrict(this,e)},e.prototype.div=function(e){return this.throwIfDisposed(),opHandler.div(this,e)},e.prototype.floorDiv=function(e){return this.throwIfDisposed(),opHandler.floorDiv(this,e)},e.prototype.divStrict=function(e){return this.throwIfDisposed(),opHandler.divStrict(this,e)},e.prototype.minimum=function(e){return this.throwIfDisposed(),opHandler.minimum(this,e)},e.prototype.minimumStrict=function(e){return this.throwIfDisposed(),opHandler.minimumStrict(this,e)},e.prototype.maximum=function(e){return this.throwIfDisposed(),opHandler.maximum(this,e)},e.prototype.maximumStrict=function(e){return this.throwIfDisposed(),opHandler.maximumStrict(this,e)},e.prototype.mod=function(e){return this.throwIfDisposed(),opHandler.mod(this,e)},e.prototype.modStrict=function(e){return this.throwIfDisposed(),opHandler.modStrict(this,e)},e.prototype.squaredDifference=function(e){return this.throwIfDisposed(),opHandler.squaredDifference(this,e)},e.prototype.squaredDifferenceStrict=function(e){return this.throwIfDisposed(),opHandler.squaredDifferenceStrict(this,e)},e.prototype.transpose=function(e){return this.throwIfDisposed(),opHandler.transpose(this,e)},e.prototype.notEqual=function(e){return this.throwIfDisposed(),opHandler.notEqual(this,e)},e.prototype.notEqualStrict=function(e){return this.throwIfDisposed(),opHandler.notEqualStrict(this,e)},e.prototype.less=function(e){return this.throwIfDisposed(),opHandler.less(this,e)},e.prototype.lessStrict=function(e){return this.throwIfDisposed(),opHandler.lessStrict(this,e)},e.prototype.equal=function(e){return this.throwIfDisposed(),opHandler.equal(this,e)},e.prototype.equalStrict=function(e){return this.throwIfDisposed(),opHandler.equalStrict(this,e)},e.prototype.lessEqual=function(e){return this.throwIfDisposed(),opHandler.lessEqual(this,e)},e.prototype.lessEqualStrict=function(e){return this.throwIfDisposed(),opHandler.lessEqualStrict(this,e)},e.prototype.greater=function(e){return this.throwIfDisposed(),opHandler.greater(this,e)},e.prototype.greaterStrict=function(e){return this.throwIfDisposed(),opHandler.greaterStrict(this,e)},e.prototype.greaterEqual=function(e){return this.throwIfDisposed(),opHandler.greaterEqual(this,e)},e.prototype.greaterEqualStrict=function(e){return this.throwIfDisposed(),opHandler.greaterEqualStrict(this,e)},e.prototype.logicalAnd=function(e){return this.throwIfDisposed(),opHandler.logicalAnd(this,e)},e.prototype.logicalOr=function(e){return this.throwIfDisposed(),opHandler.logicalOr(this,e)},e.prototype.logicalNot=function(){return this.throwIfDisposed(),opHandler.logicalNot(this)},e.prototype.logicalXor=function(e){return this.throwIfDisposed(),opHandler.logicalXor(this,e)},e.prototype.where=function(e,t){return this.throwIfDisposed(),opHandler.where(e,this,t)},e.prototype.neg=function(){return this.throwIfDisposed(),opHandler.neg(this)},e.prototype.ceil=function(){return this.throwIfDisposed(),opHandler.ceil(this)},e.prototype.floor=function(){return this.throwIfDisposed(),opHandler.floor(this)},e.prototype.sign=function(){return this.throwIfDisposed(),opHandler.sign(this)},e.prototype.exp=function(){return this.throwIfDisposed(),opHandler.exp(this)},e.prototype.expm1=function(){return this.throwIfDisposed(),opHandler.expm1(this)},e.prototype.log=function(){return this.throwIfDisposed(),opHandler.log(this)},e.prototype.log1p=function(){return this.throwIfDisposed(),opHandler.log1p(this)},e.prototype.sqrt=function(){return this.throwIfDisposed(),opHandler.sqrt(this)},e.prototype.rsqrt=function(){return this.throwIfDisposed(),opHandler.rsqrt(this)},e.prototype.square=function(){return this.throwIfDisposed(),opHandler.square(this)},e.prototype.reciprocal=function(){return this.throwIfDisposed(),opHandler.reciprocal(this)},e.prototype.abs=function(){return this.throwIfDisposed(),opHandler.abs(this)},e.prototype.clipByValue=function(e,t){return this.throwIfDisposed(),opHandler.clipByValue(this,e,t)},e.prototype.relu=function(){return this.throwIfDisposed(),opHandler.relu(this)},e.prototype.elu=function(){return this.throwIfDisposed(),opHandler.elu(this)},e.prototype.selu=function(){return this.throwIfDisposed(),opHandler.selu(this)},e.prototype.leakyRelu=function(e){return void 0===e&&(e=.2),this.throwIfDisposed(),opHandler.leakyRelu(this,e)},e.prototype.prelu=function(e){return this.throwIfDisposed(),opHandler.prelu(this,e)},e.prototype.sigmoid=function(){return this.throwIfDisposed(),opHandler.sigmoid(this)},e.prototype.logSigmoid=function(){return this.throwIfDisposed(),opHandler.logSigmoid(this)},e.prototype.softplus=function(){return this.throwIfDisposed(),opHandler.softplus(this)},e.prototype.zerosLike=function(){return this.throwIfDisposed(),opHandler.zerosLike(this)},e.prototype.onesLike=function(){return this.throwIfDisposed(),opHandler.onesLike(this)},e.prototype.sin=function(){return this.throwIfDisposed(),opHandler.sin(this)},e.prototype.cos=function(){return this.throwIfDisposed(),opHandler.cos(this)},e.prototype.tan=function(){return this.throwIfDisposed(),opHandler.tan(this)},e.prototype.asin=function(){return this.throwIfDisposed(),opHandler.asin(this)},e.prototype.acos=function(){return this.throwIfDisposed(),opHandler.acos(this)},e.prototype.atan=function(){return this.throwIfDisposed(),opHandler.atan(this)},e.prototype.sinh=function(){return this.throwIfDisposed(),opHandler.sinh(this)},e.prototype.cosh=function(){return this.throwIfDisposed(),opHandler.cosh(this)},e.prototype.tanh=function(){return this.throwIfDisposed(),opHandler.tanh(this)},e.prototype.asinh=function(){return this.throwIfDisposed(),opHandler.asinh(this)},e.prototype.acosh=function(){return this.throwIfDisposed(),opHandler.acosh(this)},e.prototype.atanh=function(){return this.throwIfDisposed(),opHandler.atanh(this)},e.prototype.erf=function(){return this.throwIfDisposed(),opHandler.erf(this)},e.prototype.round=function(){return this.throwIfDisposed(),opHandler.round(this)},e.prototype.step=function(e){return void 0===e&&(e=0),this.throwIfDisposed(),opHandler.step(this,e)},e.prototype.softmax=function(e){return void 0===e&&(e=-1),this.throwIfDisposed(),opHandler.softmax(this,e)},e.prototype.logSoftmax=function(e){return void 0===e&&(e=-1),this.throwIfDisposed(),opHandler.logSoftmax(this,e)},e.prototype.resizeBilinear=function(e,t){return void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.image.resizeBilinear(this,e,t)},e.prototype.resizeNearestNeighbor=function(e,t){return void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.image.resizeNearestNeighbor(this,e,t)},e.prototype.conv1d=function(e,t,n,r,o,a){return void 0===r&&(r="NWC"),void 0===o&&(o=1),this.throwIfDisposed(),opHandler.conv1d(this,e,t,n,r,o,a)},e.prototype.conv2d=function(e,t,n,r,o,a){return void 0===r&&(r="NHWC"),void 0===o&&(o=[1,1]),this.throwIfDisposed(),opHandler.conv2d(this,e,t,n,r,o,a)},e.prototype.conv2dTranspose=function(e,t,n,r,o){return this.throwIfDisposed(),opHandler.conv2dTranspose(this,e,t,n,r,o)},e.prototype.depthwiseConv2D=function(e,t,n,r,o,a){return void 0===r&&(r="NHWC"),void 0===o&&(o=[1,1]),this.throwIfDisposed(),opHandler.depthwiseConv2d(this,e,t,n,r,o,a)},e.prototype.separableConv2d=function(e,t,n,r,o,a){return void 0===o&&(o=[1,1]),void 0===a&&(a="NHWC"),this.throwIfDisposed(),opHandler.separableConv2d(this,e,t,n,r,o,a)},e.prototype.avgPool=function(e,t,n,r){return this.throwIfDisposed(),opHandler.avgPool(this,e,t,n,r)},e.prototype.maxPool=function(e,t,n,r){return this.throwIfDisposed(),opHandler.maxPool(this,e,t,n,r)},e.prototype.localResponseNormalization=function(e,t,n,r){return void 0===e&&(e=5),void 0===t&&(t=1),void 0===n&&(n=1),void 0===r&&(r=.5),opHandler.localResponseNormalization(this,e,t,n,r)},e.prototype.pool=function(e,t,n,r,o){return this.throwIfDisposed(),opHandler.pool(this,e,t,n,r,o)},e.prototype.variable=function(e,t,n){return void 0===e&&(e=!0),this.throwIfDisposed(),Variable.variable(this,e,t,n)},e.prototype.unsortedSegmentSum=function(e,t){return this.throwIfDisposed(),opHandler.unsortedSegmentSum(this,e,t)},e.prototype.batchToSpaceND=function(e,t){return this.throwIfDisposed(),opHandler.batchToSpaceND(this,e,t)},e.prototype.spaceToBatchND=function(e,t){return this.throwIfDisposed(),opHandler.spaceToBatchND(this,e,t)},e.prototype.topk=function(e,t){return void 0===e&&(e=1),void 0===t&&(t=!0),this.throwIfDisposed(),opHandler.topk(this,e,t)},e.prototype.stridedSlice=function(e,t,n,r,o){return void 0===r&&(r=0),void 0===o&&(o=0),this.throwIfDisposed(),opHandler.stridedSlice(this,e,t,n,r,o)},e.prototype.depthToSpace=function(e,t){return this.throwIfDisposed(),opHandler.depthToSpace(this,e,t)},e.prototype.fft=function(){return this.throwIfDisposed(),opHandler.spectral.fft(this)},e.prototype.ifft=function(){return this.throwIfDisposed(),opHandler.spectral.ifft(this)},e}();Object.defineProperty(Tensor,Symbol.hasInstance,{value:function(e){return !!e&&null!=e.shape&&null!=e.dtype}});var Variable=function(e){function t(t,n,r){void 0===n&&(n=!0);var o=e.call(this,t.shape,t.dtype,null,t.dataId)||this;o.trainable=n,o.name=r,null==o.name&&(o.name=trackerFn().nextVariableId().toString());try{trackerFn().registerVariable(o);}catch(e){throw trackerFn().disposeTensor(o),e}return o}return __extends(t,e),t.variable=function(e,n,r,o){return void 0===n&&(n=!0),null!=o&&o!==e.dtype&&(e=e.asType(o)),new t(e,n,r)},t.prototype.assign=function(e){if(e.dtype!==this.dtype)throw new Error("dtype of the new value ("+e.dtype+") and previous value ("+this.dtype+") must match");if(!arraysEqual(e.shape,this.shape))throw new Error("shape of the new value ("+e.shape+") and previous value ("+this.shape+") must match");trackerFn().disposeTensor(this),this.dataId=e.dataId,trackerFn().registerTensor(this);},t}(Tensor);Object.defineProperty(Variable,Symbol.hasInstance,{value:function(e){return e instanceof Tensor&&null!=e.assign&&e.assign instanceof Function}});var Rank,UpcastInt32AndMap,UpcastBoolAndMap,UpcastFloat32AndMap,UpcastComplex64AndMap,variable=Variable.variable;function getFilteredNodesXToY(e,t,n){for(var r={},o={},a=0;a<t.length;a++)r[t[a].id]=!0;for(a=0;a<e.length;a++){var i=(f=e[a]).inputs;for(var s in i){for(var u=i[s],l=!1,c=0;c<t.length;c++)if(r[u.id]){f.outputs.forEach(function(e){return r[e.id]=!0}),l=!0,o[f.id]=!0;break}if(l)break}}var p={};p[n.id]=!0;var d={};for(a=e.length-1;a>=0;a--)for(i=(f=e[a]).inputs,c=0;c<f.outputs.length;c++)if(p[f.outputs[c].id]){for(var s in i)p[i[s].id]=!0,d[f.id]=!0;break}var h=[];for(a=0;a<e.length;a++){var f;if(o[(f=e[a]).id]&&d[f.id]){var m={};for(var s in f.inputs){var g=f.inputs[s];r[g.id]&&(m[s]=g);}var v=Object.assign({},f);v.inputs=m,v.outputs=f.outputs,h.push(v);}}return h}function backpropagateGradients(e,t){for(var n=function(n){var r=t[n],o=[];if(r.outputs.forEach(function(t){var n=e[t.id];if(null!=n)o.push(n);else{var r=Tensor.make(t.shape,{values:makeZerosTypedArray(t.size,t.dtype)},t.dtype);o.push(r);}}),null==r.gradient)throw new Error("Cannot compute gradient: gradient function not found for "+r.name+".");var a=r.gradient(1===r.outputs.length?o[0]:o);for(var i in r.inputs){if(!(i in a))throw new Error("Cannot backprop through input "+i+". Available gradients found: "+Object.keys(a)+".");var s=a[i]();if("float32"!==s.dtype)throw new Error("Error in gradient for op "+r.name+". The gradient of input "+i+" must have 'float32' dtype, but has '"+s.dtype+"'");var u=r.inputs[i];if(!arraysEqual(s.shape,u.shape))throw new Error("Error in gradient for op "+r.name+". The gradient of input '"+i+"' has shape '"+s.shape+"', which does not match the shape of the input '"+u.shape+"'");if(null==e[u.id])e[u.id]=s;else{var l=e[u.id];e[u.id]=l.add(s),l.dispose();}}},r=t.length-1;r>=0;r--)n(r);}!function(e){e.R0="R0",e.R1="R1",e.R2="R2",e.R3="R3",e.R4="R4",e.R5="R5",e.R6="R6";}(Rank||(Rank={})),function(e){e.float32="float32",e.int32="int32",e.bool="int32",e.complex64="complex64";}(UpcastInt32AndMap||(UpcastInt32AndMap={})),function(e){e.float32="float32",e.int32="int32",e.bool="bool",e.complex64="complex64";}(UpcastBoolAndMap||(UpcastBoolAndMap={})),function(e){e.float32="float32",e.int32="float32",e.bool="float32",e.complex64="complex64";}(UpcastFloat32AndMap||(UpcastFloat32AndMap={})),function(e){e.float32="complex64",e.int32="complex64",e.bool="complex64",e.complex64="complex64";}(UpcastComplex64AndMap||(UpcastComplex64AndMap={}));var upcastTypeMap={float32:UpcastFloat32AndMap,int32:UpcastInt32AndMap,bool:UpcastBoolAndMap,complex64:UpcastComplex64AndMap};function upcastType(e,t){if("string"===e||"string"===t){if("string"===e&&"string"===t)return "string";throw new Error("Can not upcast "+e+" with "+t)}return upcastTypeMap[e][t]}function sumOutType(e){return upcastType(e,"int32")}function makeTypesMatch(e,t){if(e.dtype===t.dtype)return [e,t];var n=upcastType(e.dtype,t.dtype);return [e.cast(n),t.cast(n)]}function assertTypesMatch(e,t){assert(e.dtype===t.dtype,"The dtypes of the first("+e.dtype+") and second("+t.dtype+") input must match");}function isTensorInList(e,t){for(var n=0;n<t.length;n++)if(t[n].id===e.id)return !0;return !1}function getTensorsInContainer(e){var t=[];return walkTensorContainer(e,t,new Set),t}function walkTensorContainer(e,t,n){if(null!=e)if(e instanceof Tensor)t.push(e);else if(isIterable(e)){var r=e;for(var o in r){var a=r[o];n.has(a)||(n.add(a),walkTensorContainer(a,t,n));}}}function isIterable(e){return Array.isArray(e)||"object"==typeof e}var Type,Engine=function(){function e(e,t,n){this.backend=e,this.safeMode=t,this.debugMode=n,this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.profiling=!1,this.gradientScopeCount=0,this.customGradientDepth=0,this.scopeStack=[],this.keepTensors=new Set,this.tensorInfo=new WeakMap,this.profiler=new Profiler(e),this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null};}return e.prototype.moveData=function(e){this.write(e,this.readSync(e));},e.prototype.tidy=function(e,t,n){var r=this;void 0===n&&(n=!1);var o,a=null;if(null==t){if("function"!=typeof e)throw new Error("Please provide a function to tidy()");t=e;}else{if("string"!=typeof e&&!(e instanceof String))throw new Error("When calling with two arguments, the first argument to tidy() must be a string");if("function"!=typeof t)throw new Error("When calling with two arguments, the 2nd argument to tidy() must be a function");a=e;}return this.scopedRun(function(){return r.startScope(a,n)},function(){return r.endScope(o,n)},function(){return (o=t())instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),o})},e.prototype.scopedRun=function(e,t,n){e();try{var r=n();return t(),r}catch(e){throw t(),e}},e.prototype.nextTensorId=function(){return e.nextTensorId++},e.prototype.nextVariableId=function(){return e.nextVariableId++},e.prototype.runKernel=function(e,t,n){var r,o=this,a=[],i=function(e){return a.push(e),e},s=this.activeScope.name,u=this.numBytes,l=this.numTensors;if(this.scopedRun(function(){return o.customGradientDepth++},function(){return o.customGradientDepth--},function(){r=o.debugMode()?o.profiler.profileKernel(s,function(){return e(o.backend,i)}):e(o.backend,i);}),this.shouldRecord()){var c={id:this.nextTapeNodeId++,name:s,inputs:t,outputs:Array.isArray(r)?r:[r]};null!=n&&(c.gradient=function(e){return n(e,a)}),this.activeTape.push(c);}return this.profiling&&this.activeProfile.kernels.push({name:s,bytesAdded:this.numBytes-u,totalBytesSnapshot:this.numBytes,tensorsAdded:this.numTensors-l,totalTensorsSnapshot:this.numTensors,inputShapes:Object.keys(t).map(function(e){return t[e].shape}),outputShape:Array.isArray(r)?r.map(function(e){return e.shape}):r.shape}),r},e.prototype.registerTensor=function(e){var t=this.tensorInfo.has(e.dataId)?this.tensorInfo.get(e.dataId).refCount:0;if(this.numTensors++,"string"===e.dtype&&this.numStringTensors++,0===t){this.numDataBuffers++;var n=0;"complex64"!==e.dtype&&"string"!==e.dtype&&(n=sizeFromShape(e.shape)*bytesPerElement(e.dtype)),this.tensorInfo.set(e.dataId,{backend:this.backend,dtype:e.dtype,shape:e.shape,bytes:n,refCount:0}),this.numBytes+=n,this.backend.register(e.dataId,e.shape,e.dtype);}this.tensorInfo.get(e.dataId).refCount++,e instanceof Variable||this.track(e);},e.prototype.registerVariable=function(e){if(null!=this.registeredVariables[e.name])throw new Error("Variable with name "+e.name+" was already registered");this.registeredVariables[e.name]=e;},e.prototype.disposeTensor=function(e){if(this.tensorInfo.has(e.dataId)){this.keepTensors.has(e.id)&&this.keepTensors.delete(e.id),this.numTensors--,"string"===e.dtype&&this.numStringTensors--;var t=this.tensorInfo.get(e.dataId);t.refCount<=1?("complex64"!==e.dtype&&(this.numBytes-=t.bytes),this.numDataBuffers--,t.backend.disposeData(e.dataId),this.tensorInfo.delete(e.dataId)):this.tensorInfo.get(e.dataId).refCount--;}},e.prototype.disposeVariables=function(){for(var e in this.registeredVariables){var t=this.registeredVariables[e];this.disposeTensor(t),delete this.registeredVariables[e];}},e.prototype.memory=function(){var e=this.backend.memory();return e.numTensors=this.numTensors,e.numDataBuffers=this.numDataBuffers,e.numBytes=this.numBytes,this.numStringTensors>0&&(e.unreliable=!0,null==e.reasons&&(e.reasons=[]),e.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),e},e.prototype.profile=function(e){return __awaiter(this,void 0,void 0,function(){var t,n;return __generator(this,function(r){return this.profiling=!0,t=this.numBytes,n=this.numTensors,this.activeProfile.kernels=[],this.activeProfile.result=e(),this.profiling=!1,this.activeProfile.peakBytes=Math.max.apply(Math,this.activeProfile.kernels.map(function(e){return e.totalBytesSnapshot})),this.activeProfile.newBytes=this.numBytes-t,this.activeProfile.newTensors=this.numTensors-n,[2,this.activeProfile]})})},e.prototype.shouldRecord=function(){return null!=this.activeTape&&0===this.customGradientDepth},e.prototype.addTapeNode=function(e,t,n){var r={};e.forEach(function(e,t){r[t]=e;});var o={id:this.nextTapeNodeId++,name:this.activeScope.name,inputs:r,outputs:[t],gradient:function(e){var t={};return n(e).forEach(function(e,n){t[n]=function(){return e};}),t}};this.activeTape.push(o);},e.prototype.keep=function(e){if(1===this.scopeStack.length&&this.safeMode)throw new Error("Safe mode is ON. Enclose all tensor operations inside tf.tidy(): tf.tidy(() => {...}) to avoid memory leaks.");return this.keepTensors.add(e.id),e},e.prototype.startScope=function(e,t){void 0===t&&(t=!1),t&&0===this.gradientScopeCount&&(this.activeTape=[]),t&&this.gradientScopeCount++;var n={track:[],name:"unnamed scope"};e&&(n.name=e),this.scopeStack.push(n),this.activeScope=n;},e.prototype.endScope=function(e,t){var n=this;void 0===t&&(t=!1),t&&(this.gradientScopeCount--,0===this.gradientScopeCount&&(this.activeTape=null));var r=new Set(this.keepTensors),o=getTensorsInContainer(e);o.forEach(function(e){return r.add(e.id)});for(var a=0;a<this.activeScope.track.length;a++){var i=this.activeScope.track[a];r.has(i.id)||(null!=this.activeTape?o.push(i):i.dispose());}var s=this.scopeStack.pop();this.activeScope=0===this.scopeStack.length?null:this.scopeStack[this.scopeStack.length-1],o.forEach(function(e){!n.keepTensors.has(e.id)&&isTensorInList(e,s.track)&&n.track(e);});},e.prototype.gradients=function(e,t,n,r){var o=this;if(void 0===r&&(r=!1),assert(t.length>0,"gradients() received an empty list of xs."),null!=n&&"float32"!==n.dtype)throw new Error("dy must have 'float32' dtype, but has '"+n.dtype+"'");return this.tidy("gradients",function(){var a=e();assert(a instanceof Tensor,"The result y returned by f() must be a tensor.");var i=getFilteredNodesXToY(o.activeTape,t,a);if(!r&&0===i.length&&t.length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.");var s={};return s[a.id]=null==n?ones(a.shape):n,backpropagateGradients(s,i),{value:a,grads:t.map(function(e){return s[e.id]})}},!0)},e.prototype.customGrad=function(e){var t=this;return assert(isFunction(e),"The f passed in customGrad(f) must be a function."),function(){for(var n,r,o=[],a=0;a<arguments.length;a++)o[a]=arguments[a];if(assert(o.every(function(e){return e instanceof Tensor}),"The args passed in customGrad(f)(x1, x2,...) must all be tensors"),t.scopedRun(function(){return t.customGradientDepth++},function(){return t.customGradientDepth--},function(){r=t.tidy(e.name,function(){var t=e.apply(void 0,o),r=t.value,a=t.gradFunc;return assert(r instanceof Tensor,"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor"),assert(isFunction(a),"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function."),n=a,r},!0);}),t.shouldRecord()){t.addTapeNode(o,r,function(e){var t=n(e),r=Array.isArray(t)?t:[t];return assert(r.length===o.length,"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...)."),assert(r.every(function(e){return e instanceof Tensor}),"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors."),r});}return r}},e.prototype.write=function(e,t){var n=this.tensorInfo.get(e);if("string"===n.dtype){var r=bytesFromStringArray(t);this.numBytes+=r-n.bytes,n.bytes=r;}this.backend!==n.backend&&(n.backend.disposeData(e),n.backend=this.backend,this.backend.register(e,n.shape,n.dtype)),this.backend.write(e,t);},e.prototype.readSync=function(e){return this.tensorInfo.get(e).backend.readSync(e)},e.prototype.read=function(e){return this.tensorInfo.get(e).backend.read(e)},e.prototype.fromPixels=function(e,t){return this.backend.fromPixels(e,t)},e.prototype.time=function(e){return __awaiter(this,void 0,void 0,function(){var t,n;return __generator(this,function(r){switch(r.label){case 0:return t=now(),[4,this.backend.time(e)];case 1:return (n=r.sent()).wallMs=now()-t,[2,n]}})})},e.prototype.track=function(e){if(1===this.scopeStack.length&&this.safeMode)throw new Error("Safe mode is ON. Enclose all tensor operations inside tf.tidy(): tf.tidy(() => {op();...}); to avoid memory leaks.");return null!=this.activeScope&&this.activeScope.track.push(e),e},e.nextTensorId=0,e.nextVariableId=0,e}();function ones(e){var t=makeOnesTypedArray(sizeFromShape(e),"float32");return Tensor.make(e,{values:t})}!function(e){e[e.NUMBER=0]="NUMBER",e[e.BOOLEAN=1]="BOOLEAN",e[e.STRING=2]="STRING";}(Type||(Type={}));var MAX_TEXTURE_SIZE,URL_PROPERTIES=[{name:"DEBUG",type:Type.BOOLEAN},{name:"IS_BROWSER",type:Type.BOOLEAN},{name:"WEBGL_LAZILY_UNPACK",type:Type.BOOLEAN},{name:"WEBGL_CPU_FORWARD",type:Type.BOOLEAN},{name:"WEBGL_PACK",type:Type.BOOLEAN},{name:"WEBGL_PACK_BATCHNORMALIZATION",type:Type.BOOLEAN},{name:"WEBGL_PACK_CLIP",type:Type.BOOLEAN},{name:"WEBGL_PACK_DEPTHWISECONV",type:Type.BOOLEAN},{name:"WEBGL_CONV_IM2COL",type:Type.BOOLEAN},{name:"WEBGL_MAX_TEXTURE_SIZE",type:Type.NUMBER},{name:"WEBGL_PAGING_ENABLED",type:Type.BOOLEAN},{name:"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION",type:Type.NUMBER},{name:"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE",type:Type.BOOLEAN},{name:"WEBGL_VERSION",type:Type.NUMBER},{name:"WEBGL_RENDER_FLOAT32_ENABLED",type:Type.BOOLEAN},{name:"WEBGL_DOWNLOAD_FLOAT_ENABLED",type:Type.BOOLEAN},{name:"WEBGL_FENCE_API_ENABLED",type:Type.BOOLEAN},{name:"WEBGL_SIZE_UPLOAD_UNIFORM",type:Type.NUMBER},{name:"BACKEND",type:Type.STRING},{name:"EPSILON",type:Type.NUMBER},{name:"PROD",type:Type.BOOLEAN},{name:"TENSORLIKE_CHECK_SHAPE_CONSISTENCY",type:Type.BOOLEAN}];function isWebGLVersionEnabled(e){try{if(null!=getWebGLContext(e))return !0}catch(e){return !1}return !1}function getWebGLMaxTextureSize(e){if(null==MAX_TEXTURE_SIZE){var t=getWebGLContext(e);MAX_TEXTURE_SIZE=t.getParameter(t.MAX_TEXTURE_SIZE);}return MAX_TEXTURE_SIZE}function getWebGLDisjointQueryTimerVersion(e){if(0===e)return 0;var t=getWebGLContext(e);return hasExtension(t,"EXT_disjoint_timer_query_webgl2")&&2===e?2:hasExtension(t,"EXT_disjoint_timer_query")?1:0}function isRenderToFloatTextureEnabled(e){if(0===e)return !1;var t=getWebGLContext(e);if(1===e){if(!hasExtension(t,"OES_texture_float"))return !1}else if(!hasExtension(t,"EXT_color_buffer_float"))return !1;return createFloatTextureAndBindToFramebuffer(t,e)}function isDownloadFloatTextureEnabled(e){if(0===e)return !1;var t=getWebGLContext(e);if(1===e){if(!hasExtension(t,"OES_texture_float"))return !1;if(!hasExtension(t,"WEBGL_color_buffer_float"))return !1}else if(!hasExtension(t,"EXT_color_buffer_float"))return !1;return createFloatTextureAndBindToFramebuffer(t,e)}function isWebGLFenceEnabled(e){return 2===e&&null!=getWebGLContext(e).fenceSync}function isChrome(){return "undefined"!=typeof navigator&&null!=navigator&&null!=navigator.userAgent&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor)}var TENSORFLOWJS_FLAGS_PREFIX="tfjsflags";function getFeaturesFromURL(){var e={};if("undefined"==typeof window||void 0===window.location||void 0===window.location.search)return e;var t=getQueryParams(window.location.search);if(TENSORFLOWJS_FLAGS_PREFIX in t){var n={};t[TENSORFLOWJS_FLAGS_PREFIX].split(",").forEach(function(e){var t=e.split(":"),r=t[0],o=t[1];n[r]=o;}),URL_PROPERTIES.forEach(function(t){t.name in n&&(console.log("Setting feature override from URL "+t.name+": "+n[t.name]),t.type===Type.NUMBER?e[t.name]=+n[t.name]:t.type===Type.BOOLEAN?e[t.name]="true"===n[t.name]:t.type===Type.STRING?e[t.name]=n[t.name]:console.warn("Unknown URL param: "+t.name+"."));});}return e}function hasExtension(e,t){return null!=e.getExtension(t)}function createFloatTextureAndBindToFramebuffer(e,t){var n=e.createFramebuffer(),r=e.createTexture();e.bindTexture(e.TEXTURE_2D,r);var o=2===t?e.RGBA32F:e.RGBA;e.texImage2D(e.TEXTURE_2D,0,o,1,1,0,e.RGBA,e.FLOAT,null),e.bindFramebuffer(e.FRAMEBUFFER,n),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,r,0);var a=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(r),e.deleteFramebuffer(n),a}function getQueryParams(e){var t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,function(e){for(var n=[],r=1;r<arguments.length;r++)n[r-1]=arguments[r];return decodeParam(t,n[0],n[1]),n.join("=")}),t}function decodeParam(e,t,n){e[decodeURIComponent(t)]=decodeURIComponent(n||"");}var EPSILON_FLOAT16=1e-4,TEST_EPSILON_FLOAT16=.1,EPSILON_FLOAT32=1e-7,TEST_EPSILON_FLOAT32=.001,Environment=function(){function e(e){this.features={},this.registry={},null!=e&&(this.features=e),this.get("DEBUG")&&console.warn("Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.");}return e.setBackend=function(e,t){if(void 0===t&&(t=!1),!(e in ENV.registry))throw new Error("Backend name '"+e+"' not found in registry");ENV.engine.backend=ENV.findBackend(e),ENV.backendName=e;},e.getBackend=function(){return ENV.initEngine(),ENV.backendName},e.disposeVariables=function(){ENV.engine.disposeVariables();},e.memory=function(){return ENV.engine.memory()},e.profile=function(e){return ENV.engine.profile(e)},e.tidy=function(e,t){return ENV.engine.tidy(e,t)},e.dispose=function(e){getTensorsInContainer(e).forEach(function(e){return e.dispose()});},e.keep=function(e){return ENV.engine.keep(e)},e.time=function(e){return ENV.engine.time(e)},e.prototype.get=function(e){return e in this.features?this.features[e]:(this.features[e]=this.evaluateFeature(e),this.features[e])},e.prototype.getFeatures=function(){return this.features},e.prototype.set=function(e,t){this.features[e]=t;},e.prototype.getBestBackendName=function(){var e=this;if(0===Object.keys(this.registry).length)throw new Error("No backend found in registry.");return Object.keys(this.registry).map(function(t){return {name:t,entry:e.registry[t]}}).sort(function(e,t){return t.entry.priority-e.entry.priority})[0].name},e.prototype.evaluateFeature=function(e){if("DEBUG"===e)return !1;if("IS_BROWSER"===e)return "undefined"!=typeof window;if("IS_NODE"===e)return "undefined"!=typeof process&&void 0!==process.versions&&void 0!==process.versions.node;if("IS_CHROME"===e)return isChrome();if("WEBGL_CPU_FORWARD"===e)return !0;if("WEBGL_PACK"===e)return !1;if("WEBGL_PACK_BATCHNORMALIZATION"===e)return this.get("WEBGL_PACK");if("WEBGL_PACK_CLIP"===e)return this.get("WEBGL_PACK");if("WEBGL_PACK_DEPTHWISECONV"===e)return this.get("WEBGL_PACK");if("WEBGL_LAZILY_UNPACK"===e)return this.get("WEBGL_PACK");if("WEBGL_CONV_IM2COL"===e)return this.get("WEBGL_PACK");if("WEBGL_PAGING_ENABLED"===e)return this.get("IS_BROWSER")&&!this.get("PROD");if("WEBGL_MAX_TEXTURE_SIZE"===e)return getWebGLMaxTextureSize(this.get("WEBGL_VERSION"));if("IS_TEST"===e)return !1;if("BACKEND"===e)return this.getBestBackendName();if("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"===e){var t=this.get("WEBGL_VERSION");return 0===t?0:getWebGLDisjointQueryTimerVersion(t)}if("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE"===e)return this.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0&&!isMobile();if("HAS_WEBGL"===e)return this.get("WEBGL_VERSION")>0;if("WEBGL_VERSION"===e)return isWebGLVersionEnabled(2)?2:isWebGLVersionEnabled(1)?1:0;if("WEBGL_RENDER_FLOAT32_ENABLED"===e)return isRenderToFloatTextureEnabled(this.get("WEBGL_VERSION"));if("WEBGL_DOWNLOAD_FLOAT_ENABLED"===e)return isDownloadFloatTextureEnabled(this.get("WEBGL_VERSION"));if("WEBGL_FENCE_API_ENABLED"===e)return isWebGLFenceEnabled(this.get("WEBGL_VERSION"));if("WEBGL_SIZE_UPLOAD_UNIFORM"===e)return this.get("WEBGL_RENDER_FLOAT32_ENABLED")?4:0;if("TEST_EPSILON"===e)return 32===this.backend.floatPrecision()?TEST_EPSILON_FLOAT32:TEST_EPSILON_FLOAT16;if("EPSILON"===e)return 32===this.backend.floatPrecision()?EPSILON_FLOAT32:EPSILON_FLOAT16;if("PROD"===e)return !1;if("TENSORLIKE_CHECK_SHAPE_CONSISTENCY"===e)return !this.get("PROD");throw new Error("Unknown feature "+e+".")},e.prototype.setFeatures=function(e){this.features=Object.assign({},e);},e.prototype.reset=function(){this.features=getFeaturesFromURL(),null!=this.globalEngine&&(this.globalEngine=null);},Object.defineProperty(e.prototype,"backend",{get:function(){return this.engine.backend},enumerable:!0,configurable:!0}),e.prototype.findBackend=function(e){return e in this.registry?this.registry[e].backend:null},e.prototype.registerBackend=function(e,t,n,r){var o=this;if(void 0===n&&(n=1),e in this.registry)return console.warn(e+" backend was already registered. Reusing existing backend"),null!=r&&r(function(){return o.engine}),!1;try{var a=t();return a.setDataMover({moveData:function(e){return o.engine.moveData(e)}}),this.registry[e]={backend:a,priority:n},!0}catch(t){return console.warn("Registration of backend "+e+" failed"),console.warn(t.stack||t.message),!1}},e.prototype.removeBackend=function(e){if(!(e in this.registry))throw new Error(e+" backend not found in registry");this.registry[e].backend.dispose(),delete this.registry[e];},Object.defineProperty(e.prototype,"engine",{get:function(){return this.initEngine(),this.globalEngine},enumerable:!0,configurable:!0}),e.prototype.initEngine=function(){var e=this;if(null==this.globalEngine){this.backendName=this.get("BACKEND");var t=this.findBackend(this.backendName);this.globalEngine=new Engine(t,!1,function(){return e.get("DEBUG")});}},e}();function getGlobalNamespace(){var e;if("undefined"!=typeof window)e=window;else{if("undefined"==typeof process)throw new Error("Could not find a global object");e=process;}return e}function getOrMakeEnvironment(){var e=getGlobalNamespace();return null==e.ENV&&(e.ENV=new Environment(getFeaturesFromURL()),setTensorTracker(function(){return e.ENV.engine})),e.ENV}var ENV=getOrMakeEnvironment(),environment=Object.freeze({EPSILON_FLOAT16:EPSILON_FLOAT16,EPSILON_FLOAT32:EPSILON_FLOAT32,Environment:Environment,ENV:ENV});function grad(e){return assert(isFunction(e),"The f passed in grad(f) must be a function"),function(t,n){return assert(t instanceof Tensor,"The x passed in grad(f)(x) must be a tensor"),assert(null==n||n instanceof Tensor,"The dy passed in grad(f)(x, dy) must be a tensor"),ENV.engine.tidy(function(){var r=ENV.engine.gradients(function(){return e(t)},[t],n),o=r.value,a=r.grads;return null!=n&&assertShapesMatch(o.shape,n.shape,"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)"),checkGrads(a),a[0]})}}function grads(e){return assert(isFunction(e),"The f passed in grads(f) must be a function"),function(t,n){return assert(Array.isArray(t)&&t.every(function(e){return e instanceof Tensor}),"The args passed in grads(f)(args) must be an array of tensors"),assert(null==n||n instanceof Tensor,"The dy passed in grads(f)(args, dy) must be a tensor"),ENV.engine.tidy(function(){var r=ENV.engine.gradients(function(){return e.apply(void 0,t)},t,n),o=r.value,a=r.grads;return null!=n&&assertShapesMatch(o.shape,n.shape,"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(a),a})}}function valueAndGrad(e){return assert(isFunction(e),"The f passed in valueAndGrad(f) must be a function"),function(t,n){assert(t instanceof Tensor,"The x passed in valueAndGrad(f)(x) must be a tensor"),assert(null==n||n instanceof Tensor,"The dy passed in valueAndGrad(f)(x, dy) must be a tensor");var r=ENV.engine.gradients(function(){return e(t)},[t],n),o=r.grads,a=r.value;return checkGrads(o),{grad:o[0],value:a}}}function valueAndGrads(e){return assert(isFunction(e),"The f passed in valueAndGrads(f) must be a function"),function(t,n){assert(Array.isArray(t)&&t.every(function(e){return e instanceof Tensor}),"The args passed in valueAndGrads(f)(args) must be array of tensors"),assert(null==n||n instanceof Tensor,"The dy passed in valueAndGrads(f)(args, dy) must be a tensor");var r=ENV.engine.gradients(function(){return e.apply(void 0,t)},t,n);return null!=n&&assertShapesMatch(r.value.shape,n.shape,"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])"),checkGrads(r.grads),r}}function variableGrads(e,t){if(assert(isFunction(e),"The f passed in variableGrads(f) must be a function"),assert(null==t||Array.isArray(t)&&t.every(function(e){return e instanceof Variable}),"The varList passed in variableGrads(f, varList) must be an array of variables"),null==t)for(var n in t=[],ENV.engine.registeredVariables)t.push(ENV.engine.registeredVariables[n]);var r=t.length;assert((t=t.filter(function(e){return e.trainable})).length>0,"variableGrads() expects at least one of the input variables to be trainable, but none of the "+r+" variables is trainable.");var o=ENV.engine.gradients(e,t,null,!0),a=o.value,i=o.grads;assert(i.some(function(e){return null!=e}),"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize()."),assert(0===a.rank,"The f passed in variableGrads(f) must return a scalar, but it returned a rank-"+a.rank+" tensor");var s={};return t.forEach(function(e,t){null!=i[t]&&(s[e.name]=i[t]);}),{value:a,grads:s}}function customGrad(e){return ENV.engine.customGrad(e)}function checkGrads(e){if(e.filter(function(e){return null==e}).length>0)throw new Error("Cannot compute gradient of y=f(x) with respect to x. Make sure that\n    the f you passed encloses all operations that lead from x to y.")}var tidy=Environment.tidy,keep=Environment.keep,dispose=Environment.dispose,time=Environment.time,profile=Environment.profile;function warn(){for(var e=[],t=0;t<arguments.length;t++)e[t]=arguments[t];ENV.get("IS_TEST")||console.warn.apply(console,e);}function getReshaped(e,t,n,r){void 0===r&&(r=!0);var o=[];if(r)(o=o.concat(t.slice(0))).push(e[0]/n),o=o.concat(e.slice(1));else{o=o.concat(e[0]);for(var a=t.length,i=0;i<a;++i)o=o.concat([e[i+1]/t[i],t[i]]);o=o.concat(e.slice(a+1));}return o}function getPermuted(e,t,n){void 0===n&&(n=!0);var r=[];if(n){r.push(t);for(var o=t+1;o<e;++o)o<=2*t?(r.push(o),r.push(o-(t+1))):r.push(o);}else{var a=[],i=[];for(o=1;o<e;++o)o>=2*t+1||o%2==1?i.push(o):a.push(o);r.push.apply(r,a),r.push(0),r.push.apply(r,i);}return r}function getReshapedPermuted(e,t,n,r){void 0===r&&(r=!0);var o=[];r?o.push(e[0]/n):o.push(e[0]*n);for(var a=1;a<e.length;++a)a<=t.length?r?o.push(t[a-1]*e[a]):o.push(e[a]/t[a-1]):o.push(e[a]);return o}function getSliceBeginCoords(e,t){for(var n=[0],r=0;r<t;++r)n.push(e[r][0]);return n}function getSliceSize(e,t,n){for(var r=e.slice(0,1),o=0;o<n;++o)r.push(e[o+1]-t[o][0]-t[o][1]);return r}function axesAreInnerMostDims(e,t){for(var n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return !1;return !0}function combineLocations(e,t,n){for(var r=e.length+t.length,o=[],a=0,i=0,s=0;s<r;s++)-1===n.indexOf(s)?o.push(e[a++]):o.push(t[i++]);return o}function computeOutAndReduceShapes(e,t){for(var n=[],r=e.length,o=0;o<r;o++)-1===t.indexOf(o)&&n.push(e[o]);return [n,t.map(function(t){return e[t]})]}function expandShapeToKeepDim(e,t){return combineLocations(e,t.map(function(e){return 1}),t)}function parseAxisParam(e,t){var n=t.length;return assert((e=null==e?t.map(function(e,t){return t}):[].concat(e)).every(function(e){return e>=-n&&e<n}),"All values in axis param must be in range [-"+n+", "+n+") but got axis "+e),assert(e.every(function(e){return isInt(e)}),"All values in axis param must be integers but got axis "+e),e.map(function(e){return e<0?n+e:e})}function assertAxesAreInnerMostDims(e,t,n){assert(axesAreInnerMostDims(t,n),e+" supports only inner-most axes for now. Got axes "+t+" and rank-"+n+" input.");}function getAxesPermutation(e,t){if(axesAreInnerMostDims(e,t))return null;for(var n=[],r=0;r<t;++r)-1===e.indexOf(r)&&n.push(r);return e.forEach(function(e){return n.push(e)}),n}function getUndoAxesPermutation(e){return e.map(function(e,t){return [t,e]}).sort(function(e,t){return e[1]-t[1]}).map(function(e){return e[0]})}function getInnerMostAxes(e,t){for(var n=[],r=t-e;r<t;++r)n.push(r);return n}function assertParamsConsistent(e,t){var n=e[0].length;e.forEach(function(e,t){assert(e.length===n,"Error in concat"+n+"D: rank of tensors["+t+"] must be the same as the rank of the rest ("+n+")");}),assert(t>=0&&t<n,"Error in concat"+n+"D: axis must be between 0 and "+(n-1)+".");var r=e[0];e.forEach(function(e,o){for(var a=0;a<n;a++)assert(a===t||e[a]===r[a],"Error in concat"+n+"D: Shape of tensors["+o+"] ("+e+") does not match the shape of the rest ("+r+") along the non-concatenated axis "+o+".");});}function computeOutShape(e,t){for(var n=e[0].slice(),r=1;r<e.length;r++)n[t]+=e[r][t];return n}function prepareAndValidate(e,t){if(e.rank<1)throw new Error("tf.gatherND() expects the input to be rank 1 or higher, but the rank was "+e.rank+".");if(t.rank<1)throw new Error("tf.gatherND() expects the indices to be rank 1 or higher, but the rank was "+t.rank+".");if("int32"!==t.dtype)throw new Error("tf.gatherND() expects the indices to be int32 type, but the dtype was "+t.dtype+".");if(t.shape[t.rank-1]>e.rank)throw new Error("index innermost dimension length must be <= tensor rank; saw: "+t.shape[t.rank-1]+" vs. "+e.rank);if(0===e.size)throw new Error("Requested more than 0 entries, but input is empty. Input shape: "+e.shape+".");for(var n=t.shape,r=n[n.length-1],o=1,a=0;a<n.length-1;++a)o*=n[a];var i=e.shape,s=n.slice();s.pop();var u=1;for(a=r;a<e.rank;++a)u*=i[a],s.push(i[a]);var l=computeStrides(e.shape).map(function(e){return e/u}).concat([1]).slice(0,r);return [s,o,u,l]}var PARALLELIZE_THRESHOLD=30;function computeOptimalWindowSize(e){return e<=PARALLELIZE_THRESHOLD?e:nearestDivisor(e,Math.floor(Math.sqrt(e)))}function validateUpdateShape(e,t,n){var r=t.rank>1?t.shape[t.rank-1]:1,o=t.rank>1?t.rank-1:1,a="Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: "+n.shape+", indices.shape: "+t.shape+", shape: "+e+", sliceDim: "+r+", and batchDim: "+o+".";if(n.rank<o)throw new Error(a+" update.rank < "+o+". ");if(e.length<r+(n.rank-o))throw new Error(a+" Output shape length < "+(r+(n.rank-o)));if(n.rank!==o+e.length-r)throw new Error(a+" update.rank != "+(o+e.length-r));for(var i=0;i<o;++i)if(n.shape[i]!==t.shape[i])throw new Error(a+" updates.shape["+i+"] ("+n.shape[i]+") != indices.shape["+i+"] ("+t.shape[i]+").");for(i=0;i<n.rank-o;++i)if(n.shape[i+o]!==e[i+r])throw new Error(a+" updates.shape["+(i+o)+"] ("+n.shape[i+o]+") != shape["+(i+o)+"] ("+e[i+o]+")")}function validateInput(e,t,n){if(t.rank<1)throw new Error("tf.scatterND() expects the indices to be rank 1 or higher, but the rank was "+t.rank+".");if(e.rank<1)throw new Error("tf.scatterND() expects the updates to be rank 1 or higher, but the rank was "+e.rank+".");if("int32"!==t.dtype)throw new Error("The dtype of 'indices' should be int32, but got dtype: "+t.dtype);if(n.length<1)throw new Error("Output rank must be greater or equal to 1, but got shape: "+n);if(0===n.length){if(0===t.size)throw new Error("Indices specified for empty output. indices shape: "+t.shape);if(0===e.size)throw new Error("Updates specified for empty output. updates shape: "+e.shape)}validateUpdateShape(n,t,e);}function calculateShapes(e,t,n){for(var r=t.rank>1?t.shape[t.rank-1]:1,o=n.length,a=1,i=r;i<o;++i)a*=n[i];var s=r<1?1:r,u=t.size/s,l=computeStrides(n).concat([1]);return {sliceRank:r,numUpdates:u,sliceSize:a,strides:l.slice(l.length-r,l.length),outputSize:sizeFromShape(n)}}function segOpComputeOptimalWindowSize(e,t){var n,r=!1;for(e<=PARALLELIZE_THRESHOLD?(n=e,r=!0):n=nearestDivisor(e,Math.floor(Math.sqrt(e)));!r;){if(n>t||n===e){r=!0;break}n=nearestDivisor(e,n+1);}return n}function computeOutShape$1(e,t,n){for(var r=[],o=e.length,a=0;a<o;a++)a!==t?r.push(e[a]):r.push(n);return r}function assertParamsValid(e,t,n){assert(e.rank===t.length,"Error in slice"+e.rank+"D: Length of begin "+t+" must match the rank of the array ("+e.rank+")."),assert(e.rank===n.length,"Error in slice"+e.rank+"D: Length of size "+n+" must match the rank of the array ("+e.rank+").");for(var r=0;r<e.rank;++r)assert(t[r]+n[r]<=e.shape[r],"Error in slice"+e.rank+"D: begin["+r+"] + size["+r+"] ("+(t[r]+n[r])+") would overflow input.shape["+r+"] ("+e.shape[r]+")");}function getStridedSlicedInfo(e,t,n,r,o,a,i,s,u){if(void 0===o&&(o=0),void 0===a&&(a=0),void 0===i&&(i=0),void 0===s&&(s=0),void 0===u&&(u=0),0!==i)throw new Error("ellipsis mask is not yet supported");if(0!==s)throw new Error("new axis mask is not yet supported");for(var l=[],c=[],p=[],d=0;d<e.length;d++)l[d]=startForAxis(o,t,r,e,d),c[d]=stopForAxis(a,n,r,e,d),u&1<<d&&(c[d]=l[d]+1,p.push(d));var h=new Array(e.length).fill(0);return h=h.map(function(e,t){for(var n=0,o=l[t];!(r[t]>0?o>=c[t]:o<=c[t]);o+=r[t])n+=1;return n}),[l,h,p]}function startForAxis(e,t,n,r,o){var a=t[o];e&1<<o&&(a=n[o]>0?Number.MIN_SAFE_INTEGER:Number.MAX_SAFE_INTEGER);var i=r[o];return a<0&&(a+=i),a=clamp(0,a,i-1)}function stopForAxis(e,t,n,r,o){var a=t[o];e&1<<o&&(a=n[o]>0?Number.MAX_SAFE_INTEGER:Number.MIN_SAFE_INTEGER);var i=r[o];return a<0&&(a+=i),a=n[o]>0?clamp(0,a,i):clamp(-1,a,i-1)}function inferShape(e){var t=e;if(isTypedArray(e))return [e.length];if(!Array.isArray(e))return [];for(var n=[];t instanceof Array||isTypedArray(t);)n.push(t.length),t=t[0];return e instanceof Array&&ENV.get("TENSORLIKE_CHECK_SHAPE_CONSISTENCY")&&deepAssertShapeConsistency(e,n,[]),n}function deepAssertShapeConsistency(e,t,n){if(n=n||[],e instanceof Array||isTypedArray(e)){assert(t.length>0,function(){return "Element arr["+n.join("][")+"] should be a primitive, but is an array of "+e.length+" elements"}),assert(e.length===t[0],function(){return "Element arr["+n.join("][")+"] should have "+t[0]+" elements, but has "+e.length+" elements"});for(var r=t.slice(1),o=0;o<e.length;++o)deepAssertShapeConsistency(e[o],r,n.concat(o));}else assert(0===t.length,function(){return "Element arr["+n.join("][")+"] is a primitive, but should be an array/TypedArray of "+t[0]+" elements"});}function assertDtype(e,t,n,r){if(null!=e&&("numeric"!==e&&e!==t||"numeric"===e&&"string"===t))throw new Error("Argument '"+n+"' passed to '"+r+"' must be "+e+" tensor, but got "+t+" tensor")}function convertToTensor(e,t,n,r){if(void 0===r&&(r="numeric"),e instanceof Tensor)return assertDtype(r,e.dtype,t,n),e;var o=inferDtype(e);if("string"!==o&&["bool","int32","float32"].indexOf(r)>=0&&(o=r),assertDtype(r,o,t,n),!isTypedArray(e)&&!Array.isArray(e)&&"number"!=typeof e&&"boolean"!=typeof e&&"string"!=typeof e)throw new Error("Argument '"+t+"' passed to '"+n+"' must be a Tensor or TensorLike, but got '"+e.constructor.name+"'");var a=inferShape(e);isTypedArray(e)||Array.isArray(e)||(e=[e]);var i="string"!==o?toTypedArray(e,o,ENV.get("DEBUG")):flatten(e);return Tensor.make(a,{values:i},o)}function convertToTensorArray(e,t,n){if(!Array.isArray(e))throw new Error("Argument "+t+" passed to "+n+" must be a `Tensor[]` or `TensorLike[]`");return e.map(function(e,r){return convertToTensor(e,t+"["+r+"]",n)})}function op(e){var t=Object.keys(e);if(1!==t.length)throw new Error("Please provide an object with a single key (operation name) mapping to a function. Got an object with "+t.length+" keys.");var n=t[0],r=e[n];n.endsWith("_")&&(n=n.substring(0,n.length-1));var o=function(){for(var e=[],t=0;t<arguments.length;t++)e[t]=arguments[t];ENV.engine.startScope(n);try{var o=r.apply(void 0,e);return o instanceof Promise&&console.error("Cannot return a Promise inside of tidy."),ENV.engine.endScope(o),o}catch(e){throw ENV.engine.endScope(null),e}};return Object.defineProperty(o,"name",{value:n,configurable:!0}),o}function softmax_(e,t){void 0===t&&(t=-1);var n=convertToTensor(e,"logits","softmax");if(-1===t&&(t=n.rank-1),t!==n.rank-1)throw Error("Softmax along a non-last dimension is not yet supported. Logits was rank "+n.rank+" and dim was "+t);return customGrad(function(e){var n=e.logSumExp([t],!0),r=e.toFloat().sub(n).exp();return {value:r,gradFunc:function(e){var n=e.mul(r);return n.sub(n.sum([t],!0).mul(r))}}})(n)}function logSoftmax_(e,t){void 0===t&&(t=-1);var n=convertToTensor(e,"logits","logSoftmax");if(-1===t&&(t=n.rank-1),t!==n.rank-1)throw Error("Log Softmax along a non-last dimension is not yet supported. Logits was rank "+n.rank+" and axis was "+t);return customGrad(function(e){var n=e.max(t,!0),r=e.sub(n),o=r.toFloat().sub(r.exp().sum(t,!0).log());return {value:o,gradFunc:function(e){var n=o.exp();return e.sub(e.sum(t,!0).mul(n))}}})(n)}var softmax=op({softmax_:softmax_}),logSoftmax=op({logSoftmax_:logSoftmax_});function complex_(e,t){var n=convertToTensor(e,"real","complex"),r=convertToTensor(t,"imag","complex");return assertShapesMatch(n.shape,r.shape,"real and imag shapes, "+n.shape+" and "+r.shape+", must match in call to tf.complex()."),ENV.engine.runKernel(function(e){return e.complex(n,r)},{$real:n,$imag:r})}function real_(e){var t=convertToTensor(e,"input","real");return ENV.engine.runKernel(function(e){return e.real(t)},{$input:t})}function imag_(e){var t=convertToTensor(e,"input","imag");return ENV.engine.runKernel(function(e){return e.imag(t)},{$input:t})}var complex=op({complex_:complex_}),real=op({real_:real_}),imag=op({imag_:imag_});function tensor(e,t,n){if(null==n&&(n=inferDtype(e)),"complex64"===n)throw new Error("Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).");if(!isTypedArray(e)&&!Array.isArray(e)&&"number"!=typeof e&&"boolean"!=typeof e&&"string"!=typeof e)throw new Error("values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray");var r=inferShape(e);if(null!=t){var o=sizeFromShape(t),a=sizeFromShape(r);assert(o===a,function(){return "Based on the provided shape, ["+t+"], the tensor should have "+o+" values but has "+a});for(var i=0;i<r.length;++i){var s=r[i],u=i!==r.length-1||s!==sizeFromShape(t.slice(i));assert(r[i]===t[i]||!u,function(){return "Error creating a new Tensor. Inferred shape ("+r+") does not match the provided shape ("+t+"). "});}}return isTypedArray(e)||Array.isArray(e)||(e=[e]),t=t||r,e="string"!==n?toTypedArray(e,n,ENV.get("DEBUG")):flatten(e),Tensor.make(t,{values:e},n)}function scalar(e,t){if((isTypedArray(e)||Array.isArray(e))&&"complex64"!==t)throw new Error("Error creating a new Scalar: value must be a primitive (number|boolean|string)");return tensor(e,[],t)}function tensor1d(e,t){assertNonNull(e);var n=inferShape(e);if(1!==n.length)throw new Error("tensor1d() requires values to be a flat/TypedArray");return tensor(e,n,t)}function tensor2d(e,t,n){if(assertNonNull(e),null!=t&&2!==t.length)throw new Error("tensor2d() requires shape to have two numbers");var r=inferShape(e);if(2!==r.length&&1!==r.length)throw new Error("tensor2d() requires values to be number[][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor2d() requires shape to be provided when `values` are a flat/TypedArray");return tensor(e,t=t||r,n)}function tensor3d(e,t,n){if(assertNonNull(e),null!=t&&3!==t.length)throw new Error("tensor3d() requires shape to have three numbers");var r=inferShape(e);if(3!==r.length&&1!==r.length)throw new Error("tensor3d() requires values to be number[][][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor3d() requires shape to be provided when `values` are a flat array");return tensor(e,t=t||r,n)}function tensor4d(e,t,n){if(assertNonNull(e),null!=t&&4!==t.length)throw new Error("tensor4d() requires shape to have four numbers");var r=inferShape(e);if(4!==r.length&&1!==r.length)throw new Error("tensor4d() requires values to be number[][][][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor4d() requires shape to be provided when `values` are a flat array");return tensor(e,t=t||r,n)}function tensor5d(e,t,n){if(assertNonNull(e),null!=t&&5!==t.length)throw new Error("tensor5d() requires shape to have five numbers");var r=inferShape(e);if(5!==r.length&&1!==r.length)throw new Error("tensor5d() requires values to be number[][][][][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor5d() requires shape to be provided when `values` are a flat array");return tensor(e,t=t||r,n)}function tensor6d(e,t,n){if(assertNonNull(e),null!=t&&6!==t.length)throw new Error("tensor6d() requires shape to have six numbers");var r=inferShape(e);if(6!==r.length&&1!==r.length)throw new Error("tensor6d() requires values to be number[][][][] or flat/TypedArray");if(1===r.length&&null==t)throw new Error("tensor6d() requires shape to be provided when `values` are a flat array");return tensor(e,t=t||r,n)}function ones$1(e,t){if(void 0===t&&(t="float32"),"complex64"===t){var n=ones$1(e,"float32"),r=ones$1(e,"float32");return complex(n,r)}var o=makeOnesTypedArray(sizeFromShape(e),t);return Tensor.make(e,{values:o},t)}function zeros(e,t){if(void 0===t&&(t="float32"),"complex64"===t){var n=zeros(e,"float32"),r=zeros(e,"float32");return complex(n,r)}var o=makeZerosTypedArray(sizeFromShape(e),t);return Tensor.make(e,{values:o},t)}function fill(e,t,n){var r=getArrayFromDType(n=n||inferDtype(t),sizeFromShape(e));return r.fill(t),Tensor.make(e,{values:r},n)}function onesLike_(e){var t=convertToTensor(e,"x","onesLike");return ones$1(t.shape,t.dtype)}function zerosLike_(e){var t=convertToTensor(e,"x","zerosLike");return zeros(t.shape,t.dtype)}function linspace(e,t,n){if(0===n)throw new Error("Cannot request zero samples");var r=(t-e)/(n-1),o=makeZerosTypedArray(n,"float32");o[0]=e;for(var a=1;a<o.length;a++)o[a]=o[a-1]+r;return tensor1d(o,"float32")}function range(e,t,n,r){if(void 0===n&&(n=1),void 0===r&&(r="float32"),0===n)throw new Error("Cannot have a step of zero");if(e===t||e<t&&n<0||t<e&&n>1)return zeros([0],r);var o=makeZerosTypedArray(Math.abs(Math.ceil((t-e)/n)),r);t<e&&1===n&&(n=-1),o[0]=e;for(var a=1;a<o.length;a++)o[a]=o[a-1]+n;return tensor1d(o,r)}var onesLike=op({onesLike_:onesLike_}),zerosLike=op({zerosLike_:zerosLike_}),DataStorage=function(){function e(e){this.dataMover=e,this.data=new WeakMap;}return e.prototype.get=function(e){return this.data.has(e)||this.dataMover.moveData(e),this.data.get(e)},e.prototype.set=function(e,t){this.data.set(e,t);},e.prototype.has=function(e){return this.data.has(e)},e.prototype.delete=function(e){return this.data.delete(e)},e}(),KernelBackend=function(){function e(){}return e.prototype.time=function(e){throw new Error("Not yet implemented.")},e.prototype.read=function(e){throw new Error("Not yet implemented.")},e.prototype.readSync=function(e){throw new Error("Not yet implemented.")},e.prototype.disposeData=function(e){throw new Error("Not yet implemented.")},e.prototype.write=function(e,t){throw new Error("Not yet implemented.")},e.prototype.fromPixels=function(e,t){throw new Error("Not yet implemented.")},e.prototype.register=function(e,t,n){throw new Error("Not yet implemented.")},e.prototype.memory=function(){throw new Error("Not yet implemented.")},e.prototype.floatPrecision=function(){throw new Error("Not yet implemented")},e.prototype.batchMatMul=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.slice=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.stridedSlice=function(e,t,n,r,o,a,i,s,u){throw new Error("Not yet implemented")},e.prototype.reverse=function(e,t){throw new Error("Not yet implemented")},e.prototype.concat=function(e,t){throw new Error("Not yet implemented")},e.prototype.neg=function(e){throw new Error("Not yet implemented")},e.prototype.add=function(e,t){throw new Error("Not yet implemented")},e.prototype.addN=function(e){throw new Error("Not yet implemented")},e.prototype.subtract=function(e,t){throw new Error("Not yet implemented")},e.prototype.multiply=function(e,t){throw new Error("Not yet implemented")},e.prototype.realDivide=function(e,t){throw new Error("Not yet implemented")},e.prototype.floorDiv=function(e,t){throw new Error("Not yet implemented")},e.prototype.sum=function(e,t){throw new Error("Not yet implemented")},e.prototype.prod=function(e,t){throw new Error("Not yet implemented")},e.prototype.unsortedSegmentSum=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.argMin=function(e,t){throw new Error("Not yet implemented")},e.prototype.argMax=function(e,t){throw new Error("Not yet implemented")},e.prototype.equal=function(e,t){throw new Error("Not yet implemented")},e.prototype.notEqual=function(e,t){throw new Error("Not yet implemented")},e.prototype.less=function(e,t){throw new Error("Not yet implemented")},e.prototype.lessEqual=function(e,t){throw new Error("Not yet implemented")},e.prototype.greater=function(e,t){throw new Error("Not yet implemented")},e.prototype.greaterEqual=function(e,t){throw new Error("Not yet implemented")},e.prototype.logicalNot=function(e){throw new Error("Not yet implemented")},e.prototype.logicalAnd=function(e,t){throw new Error("Not yet implemented")},e.prototype.logicalOr=function(e,t){throw new Error("Not yet implemented")},e.prototype.where=function(e){throw new Error("Not yet implemented")},e.prototype.select=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.topk=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.min=function(e,t){throw new Error("Not yet implemented")},e.prototype.minimum=function(e,t){throw new Error("Not yet implemented")},e.prototype.mod=function(e,t){throw new Error("Not yet implemented")},e.prototype.max=function(e,t){throw new Error("Not yet implemented")},e.prototype.maximum=function(e,t){throw new Error("Not yet implemented")},e.prototype.all=function(e,t){throw new Error("Not yet implemented")},e.prototype.any=function(e,t){throw new Error("Not yet implemented")},e.prototype.squaredDifference=function(e,t){throw new Error("Not yet implemented")},e.prototype.ceil=function(e){throw new Error("Not yet implemented")},e.prototype.floor=function(e){throw new Error("Not yet implemented")},e.prototype.round=function(e){throw new Error("Not yet implemented")},e.prototype.sign=function(e){throw new Error("Not yet implemented")},e.prototype.pow=function(e,t){throw new Error("Not yet implemented")},e.prototype.exp=function(e){throw new Error("Not yet implemented")},e.prototype.expm1=function(e){throw new Error("Not yet implemented")},e.prototype.log=function(e){throw new Error("Not yet implemented")},e.prototype.log1p=function(e){throw new Error("Not yet implemented")},e.prototype.sqrt=function(e){throw new Error("Not yet implemented")},e.prototype.rsqrt=function(e){throw new Error("Not yet implemented")},e.prototype.square=function(e){throw new Error("Not yet implemented")},e.prototype.reciprocal=function(e){throw new Error("Not yet implemented")},e.prototype.relu=function(e){throw new Error("Not yet implemented")},e.prototype.elu=function(e){throw new Error("Not yet implemented")},e.prototype.eluDer=function(e,t){throw new Error("Not yet implemented")},e.prototype.selu=function(e){throw new Error("Not yet implemented")},e.prototype.int=function(e){throw new Error("Not yet implemented")},e.prototype.clip=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.abs=function(e){throw new Error("Not yet implemented")},e.prototype.complexAbs=function(e){throw new Error("Not yet implemented")},e.prototype.sigmoid=function(e){throw new Error("Not yet implemented")},e.prototype.softplus=function(e){throw new Error("Not yet implemented")},e.prototype.sin=function(e){throw new Error("Not yet implemented")},e.prototype.cos=function(e){throw new Error("Not yet implemented")},e.prototype.tan=function(e){throw new Error("Not yet implemented")},e.prototype.asin=function(e){throw new Error("Not yet implemented")},e.prototype.acos=function(e){throw new Error("Not yet implemented")},e.prototype.atan=function(e){throw new Error("Not yet implemented")},e.prototype.atan2=function(e,t){throw new Error("Not yet implemented")},e.prototype.sinh=function(e){throw new Error("Not yet implemented")},e.prototype.cosh=function(e){throw new Error("Not yet implemented")},e.prototype.tanh=function(e){throw new Error("Not yet implemented")},e.prototype.asinh=function(e){throw new Error("Not yet implemented")},e.prototype.acosh=function(e){throw new Error("Not yet implemented")},e.prototype.atanh=function(e){throw new Error("Not yet implemented")},e.prototype.erf=function(e){throw new Error("Not yet implemented")},e.prototype.step=function(e,t){throw new Error("Not yet implemented")},e.prototype.conv2d=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.conv2dDerInput=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.conv2dDerFilter=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.depthwiseConv2D=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.depthwiseConv2DDerInput=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.depthwiseConv2DDerFilter=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.conv3d=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.conv3dDerInput=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.conv3dDerFilter=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.maxPool=function(e,t){throw new Error("Not yet implemented")},e.prototype.maxPoolBackprop=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.avgPool=function(e,t){throw new Error("Not yet implemented")},e.prototype.avgPoolBackprop=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.reshape=function(e,t){throw new Error("Not yet implemented")},e.prototype.cast=function(e,t){throw new Error("Not yet implemented")},e.prototype.tile=function(e,t){throw new Error("Not yet implemented")},e.prototype.pad=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.transpose=function(e,t){throw new Error("Not yet implemented")},e.prototype.gather=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.gatherND=function(e,t){throw new Error("Not yet implemented")},e.prototype.scatterND=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.batchToSpaceND=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.spaceToBatchND=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.resizeBilinear=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.resizeBilinearBackprop=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.resizeNearestNeighbor=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.resizeNearestNeighborBackprop=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.batchNormalization=function(e,t,n,r,o,a){throw new Error("Not yet implemented")},e.prototype.localResponseNormalization4D=function(e,t,n,r,o){throw new Error("Not yet implemented")},e.prototype.LRNGrad=function(e,t,n,r,o,a,i){throw new Error("Not yet implemented")},e.prototype.multinomial=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.oneHot=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.cumsum=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.nonMaxSuppression=function(e,t,n,r,o){throw new Error("Not yet implemented")},e.prototype.fft=function(e){throw new Error("Not yet implemented")},e.prototype.ifft=function(e){throw new Error("Not yet implemented")},e.prototype.complex=function(e,t){throw new Error("Not yet implemented")},e.prototype.real=function(e){throw new Error("Not yet implemented")},e.prototype.imag=function(e){throw new Error("Not yet implemented")},e.prototype.cropAndResize=function(e,t,n,r,o,a){throw new Error("Not yet implemented")},e.prototype.depthToSpace=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.split=function(e,t,n){throw new Error("Not yet implemented")},e.prototype.sparseToDense=function(e,t,n,r){throw new Error("Not yet implemented")},e.prototype.setDataMover=function(e){throw new Error("Not yet implemented")},e.prototype.dispose=function(){throw new Error("Not yet implemented")},e}();function castTensor(e,t,n){if("complex64"===t){if("complex64"===e.dtype)return e.clone();var r=zeros(e.shape),o=e.toFloat(),a=n.complex(o,r);return r.dispose(),o.dispose(),a}if(!hasEncodingLoss(e.dtype,t))return Tensor.make(e.shape,{dataId:e.dataId},t);if("complex64"===e.dtype){var i=n.real(e);a=i.cast(t);return i.dispose(),a}if("int32"===t)return n.int(e);if("bool"===t){var s=scalar(0,e.dtype);a=n.notEqual(e,s);return s.dispose(),a}throw new Error("Error in Cast: unknown dtype argument ("+t+")")}function reshapeTensor(e,t){return Tensor.make(t,{dataId:e.dataId},e.dtype)}function mergeRealAndImagArrays(e,t){if(e.length!==t.length)throw new Error("Cannot merge real and imag arrays of different lengths. real:"+e.length+", imag: "+t.length+".");for(var n=new Float32Array(2*e.length),r=0;r<n.length;r+=2)n[r]=e[r/2],n[r+1]=t[r/2];return n}function splitRealAndImagArrays(e){for(var t=new Float32Array(e.length/2),n=new Float32Array(e.length/2),r=0;r<e.length;r+=2)t[r/2]=e[r],n[r/2]=e[r+1];return {real:t,imag:n}}function complexWithEvenIndex(e){for(var t=Math.ceil(e.length/4),n=new Float32Array(t),r=new Float32Array(t),o=0;o<e.length;o+=4)n[Math.floor(o/4)]=e[o],r[Math.floor(o/4)]=e[o+1];return {real:n,imag:r}}function complexWithOddIndex(e){for(var t=Math.floor(e.length/4),n=new Float32Array(t),r=new Float32Array(t),o=2;o<e.length;o+=4)n[Math.floor(o/4)]=e[o],r[Math.floor(o/4)]=e[o+1];return {real:n,imag:r}}function getComplexWithIndex(e,t){return {real:e[2*t],imag:e[2*t+1]}}function assignToTypedArray(e,t,n,r){e[2*r]=t,e[2*r+1]=n;}function exponents(e,t){for(var n=new Float32Array(e/2),r=new Float32Array(e/2),o=0;o<Math.ceil(e/2);o++){var a=(t?2:-2)*Math.PI*(o/e);n[o]=Math.cos(a),r[o]=Math.sin(a);}return {real:n,imag:r}}function exponent(e,t,n){var r=(n?2:-2)*Math.PI*(e/t);return {real:Math.cos(r),imag:Math.sin(r)}}function nonMaxSuppressionImpl(e,t,n,r,o){for(var a=Array.from(t).map(function(e,t){return {score:e,boxIndex:t}}).filter(function(e){return e.score>o}).sort(function(e,t){return t.score-e.score}),i=[],s=0;s<a.length;s++){var u=a[s],l=u.score,c=u.boxIndex;if(l<o)break;for(var p=!1,d=i.length-1;d>=0;--d){if(intersectionOverUnion(e,c,i[d])>=r){p=!0;break}}if(!p&&(i.push(c),i.length>=n))break}return tensor1d(i,"int32")}function intersectionOverUnion(e,t,n){var r=e.subarray(4*t,4*t+4),o=e.subarray(4*n,4*n+4),a=Math.min(r[0],r[2]),i=Math.min(r[1],r[3]),s=Math.max(r[0],r[2]),u=Math.max(r[1],r[3]),l=Math.min(o[0],o[2]),c=Math.min(o[1],o[3]),p=Math.max(o[0],o[2]),d=Math.max(o[1],o[3]),h=(s-a)*(u-i),f=(p-l)*(d-c);if(h<=0||f<=0)return 0;var m=Math.max(a,l),g=Math.max(i,c),v=Math.min(s,p),y=Math.min(u,d),x=Math.max(v-m,0)*Math.max(y-g,0);return x/(h+f-x)}function split(e,t,n){var r=Array(e.rank).fill(0),o=e.shape.slice();return t.map(function(t){o[n]=t;var a=e.slice(r,o);return r[n]+=t,a})}function topkImpl(e,t,n,r,o){for(var a=t[t.length-1],i=[e.length/a,a],s=i[0],u=i[1],l=getTypedArrayFromDType(n,s*r),c=getTypedArrayFromDType("int32",s*r),p=0;p<s;p++){for(var d=p*u,h=e.subarray(d,d+u),f=[],m=0;m<h.length;m++)f.push({value:h[m],index:m});f.sort(function(e,t){return t.value-e.value});var g=p*r,v=l.subarray(g,g+r),y=c.subarray(g,g+r);for(m=0;m<r;m++)v[m]=f[m].value,y[m]=f[m].index;}var x=t.slice();return x[x.length-1]=r,[tensor(l,x,n),tensor(c,x,"int32")]}var ArgMinMaxProgram=function(){return function(e,t,n){this.variableNames=["A"];var r=e.windowSize,o=e.batchSize,a=e.inSize,i=Math.ceil(a/r);n||this.variableNames.push("bestIndicesA"),this.outputShape=[o,i];var s="max"===t?">":"<",u=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode="\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * "+r+";\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < "+r+"; i++) {\n          int inIdx = "+u+";\n          float candidate = getA(batch, inIdx);\n          if (candidate "+s+" bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    ";}}(),AvgPool2DBackpropProgram=function(){return function(e){this.variableNames=["dy"],this.outputShape=e.inShape;var t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,o=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,s=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=s-1-e.padInfo.top,c=u-1-e.padInfo.left,p=1/(t*n);this.userCode="\n      const ivec2 pads = ivec2("+l+", "+c+");\n      const float avgMultiplier = float("+p+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+s+";\n            wR += "+a+") {\n          float dyR = float(dyRCorner + wR) / "+r+".0;\n\n          if (dyR < 0.0 || dyR >= "+e.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < "+u+";\n            wC+= "+i+") {\n            float dyC = float(dyCCorner + wC) / "+o+".0;\n\n            if (dyC < 0.0 || dyC >= "+e.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";}}();function getBroadcastDims(e,t){for(var n=e.length,r=[],o=0;o<n;o++){var a=n-1-o,i=e[a]||1;(t[t.length-1-o]||1)>1&&1===i&&r.unshift(a);}return r}function getReductionAxes(e,t){for(var n=[],r=0;r<t.length;r++){var o=e[e.length-r-1],a=t.length-r-1,i=t[a];(null==o||1===o&&i>1)&&n.unshift(a);}return n}function broadcastDimsAreOuter(e){for(var t=0;t<e.length;t++)if(e[t]!==t)return !1;return !0}function assertAndGetBroadcastShape(e,t){for(var n=[],r=Math.max(e.length,t.length),o=0;o<r;o++){var a=e[e.length-o-1];null==a&&(a=1);var i=t[t.length-o-1];if(null==i&&(i=1),1===a)n.unshift(i);else if(1===i)n.unshift(a);else{if(a!==i)throw Error("Operands could not be broadcast together with shapes "+e+" and "+t+".");n.unshift(a);}}return n}var BatchNormProgram=function(){return function(e,t,n,r,o,a){this.outputShape=[],this.supportsBroadcasting=!0,this.variableNames=["x","mean","variance"],assertAndGetBroadcastShape(e,t),assertAndGetBroadcastShape(e,n);var i="0.0";null!=r&&(assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");var s="1.0";null!=o&&(assertAndGetBroadcastShape(e,o),this.variableNames.push("scale"),s="getScaleAtOutCoords()"),this.outputShape=e,this.userCode="\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = "+i+";\n        float scale = "+s+";\n        float inv = scale * inversesqrt(variance + float("+a+"));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    ";}}(),BatchNormPackedProgram=function(){return function(e,t,n,r,o,a){this.supportsBroadcasting=!0,this.usesPackedTextures=!0,this.variableNames=["x","mean","variance"],assertAndGetBroadcastShape(e,t),assertAndGetBroadcastShape(e,n);var i="vec4(0.0)";null!=r&&(assertAndGetBroadcastShape(e,r),this.variableNames.push("offset"),i="getOffsetAtOutCoords()");var s="vec4(1.0)";null!=o&&(assertAndGetBroadcastShape(e,o),this.variableNames.push("scale"),s="getScaleAtOutCoords()"),this.outputShape=e,this.userCode="\n      void main() {\n        vec4 offset = "+i+";\n        vec4 scale = "+s+";\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4("+a+"));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    ";}}(),COMPLEX_MULTIPLY={REAL:"return areal * breal - aimag * bimag;",IMAG:"return areal * bimag + aimag * breal;"},BinaryOpComplexProgram=function(){return function(e,t,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.supportsBroadcasting=!0,this.outputShape=assertAndGetBroadcastShape(t,n),this.userCode="\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        "+e+"\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    ";}}(),CHECK_NAN_SNIPPET="\n  if (isNaN(a)) return a;\n  if (isNaN(b)) return b;\n",ADD="return a + b;",SUB="return a - b;",MUL="return a * b;",DIV="if (a == b) return 1.0;\n  return a / b;",INT_DIV="\n  float resultSign = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  int result = ia / ib;\n  int amodb = ia - ib * result;\n\n  if (resultSign < 0.0 && amodb != 0) {\n    result -= 1;\n  }\n  return float(result);\n",POW="\nif(a < 0.0 && floor(b) < b){\n  return NAN;\n}\nreturn (round(mod(b, 2.0)) == 0 || round(mod(b, 2.0)) == 2) ?\n    pow(abs(a), b) : sign(a) * pow(abs(a), b);\n",SQUARED_DIFFERENCE="return (a - b) * (a - b);",EQUAL="return float(a == b);",NOT_EQUAL="return float(a != b);",LESS="return float(a < b);",LESS_EQUAL="return float(a <= b);",GREATER="return float(a > b);",GREATER_EQUAL="return float(a >= b);",LOGICAL_AND="return float(a >= 1.0 && b >= 1.0);",LOGICAL_OR="return float(a >= 1.0 || b >= 1.0);",MAX=CHECK_NAN_SNIPPET+"\n  return max(a, b);\n",MIN=CHECK_NAN_SNIPPET+"\n  return min(a, b);\n",MOD="if (b == 0.0) return NAN;\n  return mod(a, b);",ATAN2=CHECK_NAN_SNIPPET+"\n  return atan(a, b);\n",ELU_DER="return (b >= 1.0) ? a : a * (b + 1.0);",BinaryOpProgram=function(){function e(e,t,n){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.outputShape=assertAndGetBroadcastShape(t,n),this.userCode="\n      uniform float NAN;\n      float binaryOperation(float a, float b) {\n        "+e+"\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    ";}return e.prototype.getCustomSetupFunc=function(){var e=this;return function(t,n){null==e.startLoc&&(e.startLoc=t.getUniformLocationNoThrow(n,"NAN"),null==e.startLoc)||t.gl.uniform1f(e.startLoc,NaN);}},e}(),ClipProgram=function(){return function(e,t,n){this.variableNames=["A"],this.outputShape=e,this.userCode="\n      void main() {\n        float value = getAAtOutCoords();\n        if (isNaN(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, float("+t+"), float("+n+")));\n      }\n    ";}}(),ClipPackedProgram=function(){return function(e,t,n){this.variableNames=["A"],this.usesPackedTextures=!0,this.outputShape=e,this.userCode="\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (hasNaN(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4("+t+"), vec4("+n+")));\n      }\n    ";}}(),ComplexAbsProgram=function(){return function(e){this.variableNames=["real","imag"],this.outputShape=e,this.userCode="\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    ";}}(),ConcatProgram=function(){return function(e,t){this.variableNames=["A","B"],this.outputShape=[],this.outputShape=computeOutShape([e,t],1),this.userCode="\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        float value = 0.0;\n        if (yC < "+e[1]+") {\n          value = getA(yR, yC);\n        } else {\n          yC -= "+e[1]+";\n          value = getB(yR, yC);\n        }\n\n        setOutput(value);\n      }\n    ";}}(),Conv2DDerFilterProgram=function(){return function(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;var t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,o=e.padInfo.left;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < "+e.batchSize+"; b++) {\n          for (int yR = 0; yR < "+e.outHeight+"; yR++) {\n            int xR = wR + yR * "+t+" - "+r+";\n\n            if (xR < 0 || xR >= "+e.inHeight+") {\n              continue;\n            }\n\n            for (int yC = 0; yC < "+e.outWidth+"; yC++) {\n              int xC = wC + yC * "+n+" - "+o+";\n\n              if (xC < 0 || xC >= "+e.inWidth+") {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";}}(),Conv2DDerInputProgram=function(){return function(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;var t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,o=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left;this.userCode="\n      const ivec2 pads = ivec2("+a+", "+i+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+t+"; wR++) {\n          float dyR = float(dyRCorner + wR) / "+r+".0;\n\n          if (dyR < 0.0 || dyR >= "+e.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = "+t+" - 1 - wR;\n\n          for (int wC = 0; wC < "+n+"; wC++) {\n            float dyC = float(dyCCorner + wC) / "+o+".0;\n\n            if (dyC < 0.0 || dyC >= "+e.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = "+n+" - 1 - wC;\n\n            for (int d2 = 0; d2 < "+e.outChannels+"; d2++) {\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, d2);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";}}(),Conv3DDerFilterProgram=function(){return function(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;var t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,o=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode="\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < "+e.batchSize+"; b++) {\n          for (int yF = 0; yF < "+e.outDepth+"; yF++) {\n            int xF = wF + yF * "+t+" - "+o+";\n\n            if (xF < 0 || xF >= "+e.inDepth+") {\n              continue;\n            }\n\n            for (int yR = 0; yR < "+e.outHeight+"; yR++) {\n              int xR = wR + yR * "+n+" - "+a+";\n\n              if (xR < 0 || xR >= "+e.inHeight+") {\n                continue;\n              }\n\n              for (int yC = 0; yC < "+e.outWidth+"; yC++) {\n                int xC = wC + yC * "+r+" - "+i+";\n\n                if (xC < 0 || xC >= "+e.inWidth+") {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";}}(),Conv3DDerInputProgram=function(){return function(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;var t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,o=e.strideDepth,a=e.strideHeight,i=e.strideWidth,s=t-1-e.padInfo.front,u=n-1-e.padInfo.top,l=r-1-e.padInfo.left;this.userCode="\n      const ivec3 pads = ivec3("+s+", "+u+", "+l+");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < "+t+"; wF++) {\n          float dyF = float(dyFCorner + wF) / "+o+".0;\n\n          if (dyF < 0.0 || dyF >= "+e.outDepth+".0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = "+t+" - 1 - wF;\n\n          for (int wR = 0; wR < "+n+"; wR++) {\n            float dyR = float(dyRCorner + wR) / "+a+".0;\n\n            if (dyR < 0.0 || dyR >= "+e.outHeight+".0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = "+n+" - 1 - wR;\n\n            for (int wC = 0; wC < "+r+"; wC++) {\n              float dyC = float(dyCCorner + wC) / "+i+".0;\n\n              if (dyC < 0.0 || dyC >= "+e.outWidth+".0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = "+r+" - 1 - wC;\n\n              for (int d2 = 0; d2 < "+e.outChannels+"; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";}}(),DepthwiseConv2DDerFilterProgram=function(){return function(e){this.variableNames=["x","dy"],this.outputShape=e.filterShape;var t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,o=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * "+a+" + dm;\n\n        float dotProd = 0.0;\n\n        // TODO: Vec4 over the batch size\n        for (int b = 0; b < "+e.batchSize+"; b++) {\n          for (int yR = 0; yR < "+e.outHeight+"; yR++) {\n            int xR = wR + yR * "+t+" - "+r+";\n\n            if (xR < 0 || xR >= "+e.inHeight+") {\n              continue;\n            }\n\n            for (int yC = 0; yC < "+e.outWidth+"; yC++) {\n              int xC = wC + yC * "+n+" - "+o+";\n\n              if (xC < 0 || xC >= "+e.inWidth+") {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";}}(),DepthwiseConv2DDerInputProgram=function(){return function(e){this.variableNames=["dy","W"],this.outputShape=e.inShape;var t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,o=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode="\n      const ivec2 pads = ivec2("+a+", "+i+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < "+t+"; wR++) {\n          float dyR = float(dyRCorner + wR) / "+r+".0;\n\n          if (dyR < 0.0 || dyR >= "+e.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = "+t+" - 1 - wR;\n\n          for (int wC = 0; wC < "+n+"; wC++) {\n            float dyC = float(dyCCorner + wC) / "+o+".0;\n\n            if (dyC < 0.0 || dyC >= "+e.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = "+n+" - 1 - wC;\n\n            // TODO: Vec4 over the channelMul\n            for (int dm = 0; dm < "+s+"; dm++) {\n              int d2 = d1 * "+s+" + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";}}(),Conv2DProgram=function(){return function(e){this.variableNames=["x","W"],this.outputShape=e.outShape;var t=e.padInfo.top,n=e.padInfo.left,r=e.strideHeight,o=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,s=e.filterHeight,u=e.filterWidth,l=4*Math.floor(e.inChannels/4),c=e.inChannels%4;this.userCode="\n      const ivec2 strides = ivec2("+r+", "+o+");\n      const ivec2 pads = ivec2("+t+", "+n+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+s+"; wR++) {\n          int xR = xRCorner + wR * "+a+";\n\n          if (xR < 0 || xR >= "+e.inHeight+") {\n            continue;\n          }\n\n          for (int wC = 0; wC < "+u+"; wC++) {\n            int xC = xCCorner + wC * "+i+";\n\n            if (xC < 0 || xC >= "+e.inWidth+") {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < "+l+"; d1 += 4) {\n              vec4 xValues = vec4(\n                getX(batch, xR, xC, d1),\n                getX(batch, xR, xC, d1 + 1),\n                getX(batch, xR, xC, d1 + 2),\n                getX(batch, xR, xC, d1 + 3)\n              );\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              dotProd += dot(xValues, wValues);\n            }\n\n            if ("+(1===c)+") {\n              dotProd +=\n                getX(batch, xR, xC, "+l+") *\n                getW(wR, wC, "+l+", d2);\n            } else if ("+(2===c)+") {\n              vec2 xValues = vec2(\n                getX(batch, xR, xC, "+l+"),\n                getX(batch, xR, xC, "+l+" + 1)\n              );\n              vec2 wValues = vec2(\n                getW(wR, wC, "+l+", d2),\n                getW(wR, wC, "+l+" + 1, d2)\n              );\n              dotProd += dot(xValues, wValues);\n            } else if ("+(3===c)+") {\n              vec3 xValues = vec3(\n                getX(batch, xR, xC, "+l+"),\n                getX(batch, xR, xC, "+l+" + 1),\n                getX(batch, xR, xC, "+l+" + 2)\n              );\n              vec3 wValues = vec3(\n                getW(wR, wC, "+l+", d2),\n                getW(wR, wC, "+l+" + 1, d2),\n                getW(wR, wC, "+l+" + 2, d2)\n              );\n              dotProd += dot(xValues, wValues);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";}}(),Conv3DProgram=function(){return function(e){this.variableNames=["x","W"],this.outputShape=e.outShape;var t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,o=e.strideDepth,a=e.strideHeight,i=e.strideWidth,s=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=4*Math.floor(e.inChannels/4),f=e.inChannels%4;this.userCode="\n      const ivec3 strides = ivec3("+o+", "+a+", "+i+");\n      const ivec3 pads = ivec3("+t+", "+n+", "+r+");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < "+c+"; wF++) {\n          int xF = xFCorner + wF * "+s+";\n\n          if (xF < 0 || xF >= "+e.inDepth+") {\n            continue;\n          }\n\n          for (int wR = 0; wR < "+p+"; wR++) {\n            int xR = xRCorner + wR * "+u+";\n\n            if (xR < 0 || xR >= "+e.inHeight+") {\n              continue;\n            }\n\n            for (int wC = 0; wC < "+d+"; wC++) {\n              int xC = xCCorner + wC * "+l+";\n\n              if (xC < 0 || xC >= "+e.inWidth+") {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < "+h+"; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if ("+(1===f)+") {\n                dotProd +=\n                  getX(batch, xF, xR, xC, "+h+") *\n                  getW(wF, wR, wC, "+h+", d2);\n              } else if ("+(2===f)+") {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, "+h+"),\n                  getX(batch, xF, xR, xC, "+h+" + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, "+h+", d2),\n                  getW(wF, wR, wC, "+h+" + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if ("+(3===f)+") {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, "+h+"),\n                  getX(batch, xF, xR, xC, "+h+" + 1),\n                  getX(batch, xF, xR, xC, "+h+" + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, "+h+", d2),\n                  getW(wF, wR, wC, "+h+" + 1, d2),\n                  getW(wF, wR, wC, "+h+" + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";}}(),DepthwiseConv2DProgram=function(){return function(e){this.variableNames=["x","W"],this.outputShape=e.outShape;var t=e.inHeight,n=e.inWidth,r=e.padInfo.top,o=e.padInfo.left,a=e.strideHeight,i=e.strideWidth,s=e.dilationHeight,u=e.dilationWidth,l=e.filterHeight,c=e.filterWidth,p=e.outChannels/e.inChannels;this.userCode="\n      const ivec2 strides = ivec2("+a+", "+i+");\n      const ivec2 pads = ivec2("+r+", "+o+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / "+p+";\n        int q = d2 - d1 * "+p+";\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TODO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < "+l+"; wR++) {\n          int xR = xRCorner + wR * "+s+";\n\n          if (xR < 0 || xR >= "+t+") {\n            continue;\n          }\n\n          for (int wC = 0; wC < "+c+"; wC++) {\n            int xC = xCCorner + wC * "+u+";\n\n            if (xC < 0 || xC >= "+n+") {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";}}(),DepthwiseConvPacked2DProgram=function(){return function(e){this.variableNames=["x","W"],this.usesPackedTextures=!0,this.outputShape=e.outShape;for(var t=e.inHeight,n=e.inWidth,r=e.padInfo.top,o=e.padInfo.left,a=e.strideHeight,i=e.strideWidth,s=e.filterHeight,u=e.filterWidth,l=Math.ceil((u+1)/2),c="int xR; int xC;",p=0;p<s;p++){for(var d=-o;d<2*l;d++)c+="vec4 "+xTexelName(p,d)+" = vec4(0.);";for(d=0;d<u;d++)c+="\n          vec4 wR"+p+"C"+d+" = vec4(0.);\n          vec4 xR"+p+"C"+d+" = vec4(0.);";}for(p=0;p<s;p++)for(d=0;d<l;d++){var h=2*d,f=2*d+o;c+="\n          xR = xRCorner + "+p+";\n          xC = xCCorner + "+f+";\n\n          if(xR >= 0 && xR < "+t+" && xC >= 0 && xC < "+n+") {\n            "+xTexelName(p,f)+" = getX(batch, xR, xC, d1);\n          }",0===o?h<u&&d===l-1&&(i>1&&(c+="\n                vec4 "+xTexelName(p,f+2)+" = vec4(0.);\n\n                if(xR >= 0 && xR < "+t+" && xC + 2 < "+n+") {\n                  "+xTexelName(p,f+2)+" = getX(batch, xR, xC + 2, d1);\n                }"),c+="\n              xR"+p+"C"+f+" = "+constructTexel(p,f,i,o)+";\n            "):0===d&&(c+="\n            if(xR >= 0 && xR < "+t+" && xC - 2 >= 0) {\n              "+xTexelName(p,f-2)+" = getX(batch, xR, xC - 2, d1);\n            }"),h>0&&(c+="xR"+p+"C"+(f-2)+" =\n            "+constructTexel(p,f-2,i,o)+";"),f-1>=0&&f-1<u&&(c+="xR"+p+"C"+(f-1)+" =\n              "+constructTexel(p,f-1,i,o)+";"),h<u&&(c+="\n            vec4 wTexel"+p+"C"+h+" = getW("+p+", "+h+", d1, q);\n            wR"+p+"C"+h+" = vec4(wTexel"+p+"C"+h+".xz, wTexel"+p+"C"+h+".xz);\n          ",h+1<u&&(c+="\n              vec4 wTexelR"+p+"C"+(h+1)+" = getW("+p+", "+(h+1)+", d1, q);\n              wR"+p+"C"+(h+1)+" =\n                vec4(wTexelR"+p+"C"+(h+1)+".xz, wTexelR"+p+"C"+(h+1)+".xz);"));}for(p=0;p<s;p++)for(d=0;d<u;d++)c+="result += xR"+p+"C"+d+" * wR"+p+"C"+d+";";this.userCode="\n      const ivec2 strides = ivec2("+a+", "+i+");\n      const ivec2 pads = ivec2("+r+", "+o+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2;\n        int q = 0;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        vec4 result = vec4(0.);\n\n        "+c+"\n\n        setOutput(result);\n      }\n    ";}}();function xTexelName(e,t){return "xTexelR"+e+"C"+(t<0?"minus"+Math.abs(t).toString():t)}function constructTexel(e,t,n,r){return 1===n?r%2==t%2?xTexelName(e,t):"vec4("+xTexelName(e,t-1)+".zw, "+xTexelName(e,t+1)+".xy)":r%2==t%2?"vec4("+xTexelName(e,t)+".xy, "+xTexelName(e,t+2)+".xy)":"vec4("+xTexelName(e,t-1)+".zw, "+xTexelName(e,t+1)+".zw)"}var CropAndResizeProgram=function(){return function(e,t,n,r,o){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];var a=e[0],i=e[1],s=e[2],u=e[3],l=t[0],c=n[0],p=n[1];this.outputShape=[l,c,p,u];var d="bilinear"===r?1:0,h=[i-1+".0",s-1+".0"],f=h[0],m=h[1],g=c>1?[""+(i-1)/(c-1),"(y2-y1) * height_ratio","y1*"+f+" + float(y)*(height_scale)"]:["0.0","0.0","0.5 * (y1+y2) * "+f],v=g[0],y=g[1],x=g[2],T=p>1?[""+(s-1)/(p-1),"(x2-x1) * width_ratio","x1*"+m+" + float(x)*(width_scale)"]:["0.0","0.0","0.5 * (x1+x2) * "+m],w=T[0],E=T[1],S=T[2];this.userCode="\n      const float height_ratio = float("+v+");\n      const float width_ratio = float("+w+");\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= "+a+") {\n          return;\n        }\n\n        float height_scale = "+y+";\n        float width_scale = "+E+";\n\n        float in_y = "+x+";\n        if( in_y < 0.0 || in_y > "+f+" ) {\n          setOutput(float("+o+"));\n          return;\n        }\n        float in_x = "+S+";\n        if( in_x < 0.0 || in_x > "+m+" ) {\n          setOutput(float("+o+"));\n          return;\n        }\n\n        vec2 sourceFracIndexRC = vec2(in_y,in_x);\n        if("+d+" == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n          ivec2 sourceCeilRC = ivec2(ceil(sourceFracIndexRC));\n\n          float topLeft = getImage(b, sourceFloorRC.x, sourceFloorRC.y, d);\n          float bottomLeft = getImage(b, sourceCeilRC.x, sourceFloorRC.y, d);\n          float topRight = getImage(b, sourceFloorRC.x, sourceCeilRC.y, d);\n          float bottomRight = getImage(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n          vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n          float top = topLeft + (topRight - topLeft) * fracRC.y;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n          float newValue = top + (bottom - top) * fracRC.x;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestRC = ivec2(floor(\n            sourceFracIndexRC + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestRC.x, sourceNearestRC.y, d);\n          setOutput(newValue);\n        }\n      }\n    ";}}();function getLogicalCoordinatesFromFlatIndex(e,t,n){void 0===n&&(n="index");var r=computeStrides(t);return r.map(function(t,o){return "int "+e[o]+" = "+n+" / "+t+"; "+(o===r.length-1?"int "+e[o+1]+" = "+n+" - "+e[o]+" * "+t:"index -= "+e[o]+" * "+t)+";"}).join("")}function buildVec(e){return 1===e.length?""+e[0]:"vec"+e.length+"("+e.join(",")+")"}function dotify(e,t){if(e.length!==t.length)throw new Error("Vectors to be dotted must be of the same length -got "+e.length+" and "+t.length);for(var n=[],r=Math.floor(e.length/4),o=e.length%4,a=0;a<r;a++){var i=e.slice(4*a,4*a+4),s=t.slice(4*a,4*a+4);n.push(buildVec(i)+", "+buildVec(s));}if(0!==o){i=e.slice(4*r),s=t.slice(4*r);1===i.length&&(i=i.map(function(e){return "float("+e+")"}),s=s.map(function(e){return "float("+e+")"})),n.push(buildVec(i)+", "+buildVec(s));}return n.map(function(e,t){return "dot("+e+")"}).join("+")}function makeShader(e,t,n,r,o){var a=e.map(function(e){var t=sizeFromShape(e.shapeInfo.logicalShape);return e.shapeInfo.isUniform?"uniform float "+e.name+(t>1?"["+t+"]":"")+";":"uniform sampler2D "+e.name+";"});a=a.join("\n");var i,s,u=e.map(function(e){return getInputSamplingSnippet(e,t,r,o)}).join("\n"),l=t.texShape,c=SHADER_PREFIX;return t.isPacked?(i=getPackedOutputSamplingSnippet(t.logicalShape,l),s=FLOAT_TEXTURE_SET_RGBA_SNIPPET):(i=getOutputSamplingSnippet(t.logicalShape,l),s=FLOAT_TEXTURE_SET_R_SNIPPET),o&&(c+=SHADER_PACKED_PREFIX),[c,FLOAT_TEXTURE_SAMPLE_SNIPPET,s,a,i,u,n].join("\n")}function getSamplerFromInInfo(e){var t=e.shapeInfo.logicalShape;switch(t.length){case 0:return getSamplerScalar(e);case 1:return getSampler1D(e);case 2:return getSampler2D(e);case 3:return getSampler3D(e);case 4:return getSampler4D(e);case 5:return getSampler5D(e);case 6:return getSampler6D(e);default:throw new Error(t.length+"-D input sampling is not yet supported")}}function getPackedSamplerFromInInfo(e){var t=e.shapeInfo.logicalShape;switch(t.length){case 0:return getPackedSamplerScalar(e);case 1:return getPackedSampler1D(e);case 2:return getPackedSampler2D(e);case 3:return getPackedSampler3D(e);case 4:return getPackedSampler4D(e);default:throw new Error("Packed "+t.length+"-D input sampling is not yet supported")}}function getInputSamplingSnippet(e,t,n,r){void 0===r&&(r=!1);var o=getSamplerFlat(e);return o+=r?getPackedSamplerFromInInfo(e):getSamplerFromInInfo(e),(n||arraysEqual(e.shapeInfo.logicalShape,t.logicalShape))&&(o+=r?getPackedSamplerAtOutputCoords(e,t,n):getSamplerAtOutputCoords(e,t,n)),o}function getPackedOutputSamplingSnippet(e,t){switch(e.length){case 0:return getOutputScalarCoords();case 1:return getOutputPacked1DCoords(e,t);case 2:return getOutputPacked2DCoords(e,t);case 3:return getOutputPacked3DCoords(e,t);case 4:return getOutputPacked4DCoords(e,t);default:throw new Error(e.length+"-D packed output coordinate fetching is not yet supported")}}function getOutputSamplingSnippet(e,t){switch(e.length){case 0:return getOutputScalarCoords();case 1:return getOutput1DCoords(e,t);case 2:return getOutput2DCoords(e,t);case 3:return getOutput3DCoords(e,t);case 4:return getOutput4DCoords(e,t);case 5:return getOutput5DCoords(e,t);case 6:return getOutput6DCoords(e,t);default:throw new Error(e.length+"-D output sampling is not yet supported")}}var SAMPLE_1D_SNIPPET="\nvec2 UVfrom1D(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",SAMPLE_2D_SNIPPET="\nvec2 UVfrom2D(int texNumR, int texNumC, int numC, int row, int col) {\n  int index = row * numC + col;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",SAMPLE_3D_SNIPPET="\nvec2 UVfrom3D(int texNumR, int texNumC, int stride0,\n    int stride1, int row, int col, int depth) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",SAMPLE_4D_SNIPPET="\nvec2 UVfrom4D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int row, int col, int depth,\n    int depth2) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth * stride2 + depth2;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom4D(int texNumR, int texNumC, int texelsInBatch2,\n    int texelsInBatch, int texelsInLogicalRow, int b2, int b,\n    int row, int col) {\n  int index = b2 * texelsInBatch2 + b * texelsInBatch +\n    (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",SAMPLE_5D_SNIPPET="\nvec2 UVfrom5D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int stride3, int row, int col, int depth,\n    int depth2, int depth3) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 +\n              depth * stride2 + depth2 * stride3 + depth3;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",SAMPLE_6D_SNIPPET="\nvec2 UVfrom6D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int stride3, int stride4,\n    int row, int col, int depth, int depth2, int depth3, int depth4) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth * stride2 + depth2 *\n    stride3 + depth3 * stride4 + depth4;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n",FLOAT_TEXTURE_SAMPLE_SNIPPET="\n  float sampleTexture(sampler2D textureSampler, vec2 uv) {\n    return texture2D(textureSampler, uv).r;\n  }\n",FLOAT_TEXTURE_SET_R_SNIPPET="\n  void setOutput(float val) {\n    gl_FragColor = vec4(val, 0, 0, 0);\n  }\n",FLOAT_TEXTURE_SET_RGBA_SNIPPET="\n  void setOutput(vec4 val) {\n    gl_FragColor = val;\n  }\n",NAN_CHECKS="",SHADER_PREFIX="\n  precision highp float;\n  precision highp int;\n  varying vec2 resultUV;\n  const vec2 halfCR = vec2(0.5, 0.5);\n\n  struct ivec5\n  {\n    int x;\n    int y;\n    int z;\n    int w;\n    int u;\n  };\n\n  struct ivec6\n  {\n    int x;\n    int y;\n    int z;\n    int w;\n    int u;\n    int v;\n  };\n\n  "+(NAN_CHECKS=ENV.get("PROD")?"\n    bool isNaN(float val) {\n      return false;\n    }\n\n    bool hasNaN(vec4 values) {\n      return false;\n    }\n  ":"\n    bool isNaN(float val) {\n      return (val < 1.0 || 0.0 < val || val == 0.0) ? false : true;\n    }\n\n    bool hasNaN(vec4 values) {\n      return any(bvec4(\n        isNaN(values.x),\n        isNaN(values.y),\n        isNaN(values.z),\n        isNaN(values.w)\n      ));\n    }\n  ")+"\n\n  float getNaN(vec4 values) {\n    return dot(vec4(1), values);\n  }\n\n  int round(float value) {\n    return int(floor(value + 0.5));\n  }\n\n  int imod(int x, int y) {\n    return x - y * (x / y);\n  }\n\n  //Based on the work of Dave Hoskins\n  //https://www.shadertoy.com/view/4djSRW\n  #define HASHSCALE1 443.8975\n  float random(float seed){\n    vec2 p = resultUV * seed;\n    vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n    p3 += dot(p3, p3.yzx + 19.19);\n    return fract((p3.x + p3.y) * p3.z);\n  }\n\n  "+SAMPLE_1D_SNIPPET+"\n  "+SAMPLE_2D_SNIPPET+"\n  "+SAMPLE_3D_SNIPPET+"\n  "+SAMPLE_4D_SNIPPET+"\n  "+SAMPLE_5D_SNIPPET+"\n  "+SAMPLE_6D_SNIPPET+"\n",SHADER_PACKED_PREFIX="\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n";function getOutputScalarCoords(){return "\n    int getOutputCoords() {\n      return 0;\n    }\n  "}function getOutputPacked1DCoords(e,t){var n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return 1===t[0]?"\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * "+n[1]+".0);\n      }\n    ":1===t[1]?"\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * "+n[0]+".0);\n      }\n    ":"\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+n[0]+", "+n[1]+"));\n      return resTexRC.x * "+n[1]+" + resTexRC.y;\n    }\n  "}function getOutput1DCoords(e,t){return 1===t[0]?"\n      int getOutputCoords() {\n        return int(resultUV.x * "+t[1]+".0);\n      }\n    ":1===t[1]?"\n      int getOutputCoords() {\n        return int(resultUV.y * "+t[0]+".0);\n      }\n    ":"\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+t[0]+", "+t[1]+"));\n      return resTexRC.x * "+t[1]+" + resTexRC.y;\n    }\n  "}function getOutputPacked3DCoords(e,t){var n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),o=r*Math.ceil(e[1]/2);return "\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+n[0]+", "+n[1]+"));\n      int index = resTexRC.x * "+n[1]+" + resTexRC.y;\n\n      int b = index / "+o+";\n      index -= b * "+o+";\n\n      int r = 2 * (index / "+r+");\n      int c = imod(index, "+r+") * 2;\n\n      return ivec3(b, r, c);\n    }\n  "}function getOutput3DCoords(e,t){var n=getLogicalCoordinatesFromFlatIndex(["r","c","d"],e);return "\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+t[0]+", "+t[1]+"));\n      int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n      "+n+"\n      return ivec3(r, c, d);\n    }\n  "}function getOutputPacked4DCoords(e,t){var n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[3]/2),o=r*Math.ceil(e[2]/2),a=o*e[1];return "\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+n[0]+", "+n[1]+"));\n      int index = resTexRC.x * "+n[1]+" + resTexRC.y;\n\n      int b2 = index / "+a+";\n      index -= b2 * "+a+";\n\n      int b = index / "+o+";\n      index -= b * "+o+";\n\n      int r = 2 * (index / "+r+");\n      int c = imod(index, "+r+") * 2;\n\n      return ivec4(b2, b, r, c);\n    }\n  "}function getOutput4DCoords(e,t){var n=getLogicalCoordinatesFromFlatIndex(["r","c","d","d2"],e);return "\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2("+t[0]+", "+t[1]+"));\n      int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n      "+n+"\n      return ivec4(r, c, d, d2);\n    }\n  "}function getOutput5DCoords(e,t){var n=getLogicalCoordinatesFromFlatIndex(["r","c","d","d2","d3"],e);return "\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2("+t[0]+",\n                             "+t[1]+"));\n\n      int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n\n      "+n+"\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  "}function getOutput6DCoords(e,t){var n=getLogicalCoordinatesFromFlatIndex(["r","c","d","d2","d3","d4"],e);return "\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2("+t[0]+", "+t[1]+"));\n      int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n\n      "+n+"\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  "}function getOutputPacked2DCoords(e,t){var n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(arraysEqual(e,t))return "\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2("+n[0]+", "+n[1]+"));\n      }\n    ";var r=Math.ceil(e[1]/2);return "\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+n[0]+", "+n[1]+"));\n\n      int index = resTexRC.x * "+n[1]+" + resTexRC.y;\n      int r = 2 * (index / "+r+");\n      int c = imod(index, "+r+") * 2;\n\n      return ivec2(r, c);\n    }\n  "}function getOutput2DCoords(e,t){return arraysEqual(e,t)?"\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2("+t[0]+", "+t[1]+"));\n      }\n    ":1===e[1]?"\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2("+t[0]+", "+t[1]+"));\n        int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    ":1===e[0]?"\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2("+t[0]+", "+t[1]+"));\n        int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n        return ivec2(0, index);\n      }\n    ":"\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+t[0]+", "+t[1]+"));\n      int index = resTexRC.x * "+t[1]+" + resTexRC.y;\n      int r = index / "+e[1]+";\n      int c = index - r * "+e[1]+";\n      return ivec2(r, c);\n    }\n  "}function getPackedSamplerScalar(e){var t=e.name;return "\n    vec4 "+("get"+t.charAt(0).toUpperCase()+t.slice(1))+"() {\n      return texture2D("+t+", halfCR);\n    }\n  "}function getSamplerScalar(e){var t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);return e.shapeInfo.isUniform?"float "+n+"() {return "+t+";}":"\n    float "+n+"() {\n      return sampleTexture("+t+", halfCR);\n    }\n  "}function getPackedSampler1D(e){var t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1),r=e.shapeInfo.texShape,o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return "\n    vec4 "+n+"(int index) {\n      vec2 uv = packedUVfrom1D(\n        "+o[0]+", "+o[1]+", index);\n      return texture2D("+t+", uv);\n    }\n  "}function getSampler1D(e){var t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1);return "\n    float "+n+"(int index) {\n      return "+n+"Flat(index);\n    }\n  "}function getPackedSampler2D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e.shapeInfo.texShape,a=o[0],i=o[1];if(null!=o&&arraysEqual(t,o))return "\n      vec4 "+r+"(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2("+i+".0, "+a+".0);\n\n        return texture2D("+n+", uv);\n      }\n    ";var s=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];return "\n    vec4 "+r+"(int row, int col) {\n      vec2 uv = packedUVfrom2D("+Math.ceil(t[1]/2)+", "+s[0]+", "+s[1]+", row, col);\n      return texture2D("+n+", uv);\n    }\n  "}function getSampler2D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e.shapeInfo.texShape;if(null!=o&&arraysEqual(t,o)){var a=o[0];return "\n    float "+r+"(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2("+o[1]+".0, "+a+".0);\n      return sampleTexture("+n+", uv);\n    }\n  "}var i=squeezeShape(t),s=i.newShape,u=i.keptDims,l=s;if(l.length<t.length){return "\n      "+getSamplerFromInInfo(squeezeInputInfo(e,l))+"\n      float "+r+"(int row, int col) {\n        return "+r+"("+getSqueezedParams(["row","col"],u)+");\n      }\n    "}if(e.shapeInfo.isUniform)return "\n      float "+r+"(int row, int col) {\n        float index = dot(vec2(row, col), vec2("+t[1]+", 1));\n        return "+r+"Flat(round(index));\n      }\n    ";var c=o[0],p=o[1];return 1===p?"\n    float "+r+"(int row, int col) {\n      float index = dot(vec2(row, col), vec2("+t[1]+", 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / "+c+".0);\n      return sampleTexture("+n+", uv);\n    }\n  ":1===c?"\n    float "+r+"(int row, int col) {\n      float index = dot(vec2(row, col), vec2("+t[1]+", 1));\n      vec2 uv = vec2((index + 0.5) / "+p+".0, 0.5);\n      return sampleTexture("+n+", uv);\n    }\n  ":"\n  float "+r+"(int row, int col) {\n    vec2 uv = UVfrom2D("+c+", "+p+", "+t[1]+", row, col);\n    return sampleTexture("+n+", uv);\n  }\n"}function getPackedSampler3D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e.shapeInfo.texShape,a=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];if(1===t[0]){return "\n        "+getPackedSamplerFromInInfo(squeezeInputInfo(e,t.slice(1)))+"\n        vec4 "+r+"(int b, int row, int col) {\n          return "+r+"("+getSqueezedParams(["b","row","col"],[1,2])+");\n        }\n      "}var i=a[0],s=a[1],u=Math.ceil(t[2]/2);return "\n    vec4 "+r+"(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        "+i+", "+s+", "+u*Math.ceil(t[1]/2)+", "+u+", b, row, col);\n      return texture2D("+n+", uv);\n    }\n  "}function getSampler3D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=t[1]*t[2],a=t[2],i=squeezeShape(t),s=i.newShape,u=i.keptDims,l=s;if(l.length<t.length){return "\n        "+getSamplerFromInInfo(squeezeInputInfo(e,l))+"\n        float "+r+"(int row, int col, int depth) {\n          return "+r+"("+getSqueezedParams(["row","col","depth"],u)+");\n        }\n      "}if(e.shapeInfo.isUniform)return "\n      float "+r+"(int row, int col, int depth) {\n        float index = dot(vec3(row, col, depth),\n                          vec3("+o+", "+a+", 1));\n        return "+r+"Flat(round(index));\n      }\n    ";var c=e.shapeInfo.texShape,p=c[0],d=c[1];return d===o?"\n        float "+r+"(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2("+a+", 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2("+d+".0, "+p+".0);\n          return sampleTexture("+n+", uv);\n        }\n      ":d===a?"\n    float "+r+"(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2("+t[1]+", 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2("+d+".0, "+p+".0);\n      return sampleTexture("+n+", uv);\n    }\n  ":"\n      float "+r+"(int row, int col, int depth) {\n        vec2 uv = UVfrom3D(\n            "+p+", "+d+", "+o+", "+a+", row, col, depth);\n        return sampleTexture("+n+", uv);\n      }\n  "}function getPackedSampler4D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e.shapeInfo.texShape,a=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],i=a[0],s=a[1],u=Math.ceil(t[3]/2),l=u*Math.ceil(t[2]/2);return "\n    vec4 "+r+"(int b2, int b, int row, int col) {\n      vec2 uv = packedUVfrom4D(\n        "+i+", "+s+", "+l*t[1]+",\n        "+l+", "+u+", b2, b, row, col);\n      return texture2D("+n+", uv);\n    }\n  "}function getSampler4D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=t[3],a=t[2]*o,i=t[1]*a,s=squeezeShape(t),u=s.newShape,l=s.keptDims;if(u.length<t.length){return "\n      "+getSamplerFromInInfo(squeezeInputInfo(e,u))+"\n      float "+r+"(int row, int col, int depth, int depth2) {\n        return "+r+"("+getSqueezedParams(["row","col","depth","depth2"],l)+");\n      }\n    "}if(e.shapeInfo.isUniform)return "\n      float "+r+"(int row, int col, int depth, int depth2) {\n        float index = dot(vec4(row, col, depth, depth2),\n                          vec4("+i+", "+a+", "+o+", 1));\n        return "+r+"Flat(round(index));\n      }\n    ";var c=e.shapeInfo.texShape,p=c[0],d=c[1];return d===i?"\n      float "+r+"(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2), vec3("+a+", "+o+", 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2("+d+".0, "+p+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":d===o?"\n      float "+r+"(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3("+t[1]*t[2]+", "+t[2]+", 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2("+d+".0, "+p+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":"\n    float "+r+"(int row, int col, int depth, int depth2) {\n      vec2 uv = UVfrom4D("+p+", "+d+", "+i+", "+a+",\n          "+o+", row, col, depth, depth2);\n      return sampleTexture("+n+", uv);\n    }\n  "}function getSampler5D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=t[4],a=t[3]*o,i=t[2]*a,s=t[1]*i,u=squeezeShape(t),l=u.newShape,c=u.keptDims;if(l.length<t.length){return "\n      "+getSamplerFromInInfo(squeezeInputInfo(e,l))+"\n      float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n        return "+r+"("+getSqueezedParams(["row","col","depth","depth2","depth3"],c)+");\n      }\n    "}if(e.shapeInfo.isUniform)return "\n      float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4("+s+", "+i+", "+a+", "+o+")) +\n          depth3;\n        return "+r+"Flat(index);\n      }\n    ";var p=e.shapeInfo.texShape,d=p[0],h=p[1];return h===s?"\n      float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(\n          vec4(col, depth, depth2, depth3),\n          vec4("+i+", "+a+", "+o+", 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2("+h+".0, "+d+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":h===o?"\n      float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4("+t[1]*t[2]*t[3]+", "+t[2]*t[3]+",\n            "+t[3]+", 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2("+h+".0, "+d+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":"\n    float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n      vec2 uv = UVfrom5D("+d+", "+h+", "+s+", "+i+",\n          "+a+", "+o+", row, col, depth, depth2, depth3);\n      return sampleTexture("+n+", uv);\n    }\n  "}function getSampler6D(e){var t=e.shapeInfo.logicalShape,n=e.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=t[5],a=t[4]*o,i=t[3]*a,s=t[2]*i,u=t[1]*s,l=squeezeShape(t),c=l.newShape,p=l.keptDims;if(c.length<t.length){return "\n      "+getSamplerFromInInfo(squeezeInputInfo(e,c))+"\n      float "+r+"(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return "+r+"("+getSqueezedParams(["row","col","depth","depth2","depth3","depth4"],p)+");\n      }\n    "}if(e.shapeInfo.isUniform)return "\n      float "+r+"(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4("+u+", "+s+", "+i+", "+a+")) +\n          dot(\n            vec2(depth3, depth4),\n            vec2("+o+", 1));\n        return "+r+"Flat(index);\n      }\n    ";var d=e.shapeInfo.texShape,h=d[0],f=d[1];return f===u?"\n      float "+r+"(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(\n          vec4(col, depth, depth2, depth3),\n          vec4("+s+", "+i+", "+a+", "+o+")) + depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2("+f+".0, "+h+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":f===o?"\n      float "+r+"(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4("+t[1]*t[2]*t[3]*t[4]+",\n               "+t[2]*t[3]*t[4]+",\n               "+t[3]*t[4]+",\n               "+t[4]+")) + depth3;\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2("+f+".0, "+h+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":"\n    float "+r+"(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      vec2 uv = UVfrom6D("+h+", "+f+", "+u+", "+s+",\n          "+i+", "+a+", "+o+"\n          ,row, col, depth, depth2, depth3, depth4);\n      return sampleTexture("+n+", uv);\n    }\n  "}function getSamplerFlat(e){var t=e.name,n="get"+t.charAt(0).toUpperCase()+t.slice(1)+"Flat",r=sizeFromShape(e.shapeInfo.logicalShape);if(e.shapeInfo.isUniform)return 1===r?"float "+n+"(int index) {return "+t+";}":"\n      float "+n+"(int index) {\n        for (int i = 0; i < "+r+"; i++) {\n          if (i == index) {\n            return "+t+"[i];\n          }\n        }\n      }\n    ";var o=e.shapeInfo.texShape,a=o[0],i=o[1];return 1===i&&1===a?"\n      float "+n+"(int index) {\n        return sampleTexture("+t+", halfCR);\n      }\n    ":1===i?"\n      float "+n+"(int index) {\n        vec2 uv = vec2(0.5, (float(index) + 0.5) / "+a+".0);\n        return sampleTexture("+t+", uv);\n      }\n    ":1===a?"\n      float "+n+"(int index) {\n        vec2 uv = vec2((float(index) + 0.5) / "+i+".0, 0.5);\n        return sampleTexture("+t+", uv);\n      }\n    ":"\n    float "+n+"(int index) {\n      vec2 uv = UVfrom1D("+a+", "+i+", index);\n      return sampleTexture("+t+", uv);\n    }\n  "}function getBroadcastOutputCoordsSampler(e,t,n,r){var o=e.shapeInfo.logicalShape.length,a=t.logicalShape.length,i="int";2===a?i="ivec2":3===a?i="ivec3":4===a&&(i="ivec4");var s=getBroadcastDims(e.shapeInfo.logicalShape,t.logicalShape),u=a-o;return "\n    float "+r+"() {\n      "+i+" coords = getOutputCoords();\n      "+(0===o?"":a<2&&s.length>=1?"coords = 0;":s.map(function(e){return "coords["+(e+u)+"] = 0;"}).join("\n"))+"\n      return get"+n+"("+(a<2&&o>0?"coords":e.shapeInfo.logicalShape.map(function(e,t){return "coords["+(t+u)+"]"}).join(", "))+");\n    }\n  "}function getPackedSamplerAtOutputCoords(e,t,n){var r=e.name,o=r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape,i="get"+o+"AtOutCoords",s=t.texShape,u=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],l=u[0],c=u[1],p=getBroadcastDims(e.shapeInfo.logicalShape,t.logicalShape),d=e.shapeInfo.logicalShape.length,h=t.logicalShape.length;if(p.length)throw Error("Packed broadcast sampling is not implemented yet.");if(arraysEqual(e.shapeInfo.texShape,s))return "\n      vec4 "+i+"() {\n        return texture2D("+r+", resultUV);\n      }\n    ";var f="return texture2D("+r+", uv)";return 1===d&&h>1?f="\n      vec4 sample = texture2D("+r+", uv);\n      return vec4(sample.xy, sample.xy);\n    ":0===d&&h>0&&(f=1===h?"\n        vec4 sample = texture2D("+r+", uv);\n        return vec4(sample.x, sample.x, 0., 0.);\n      ":"\n        vec4 sample = texture2D("+r+", uv);\n        return vec4(sample.x);\n      "),"\n    vec4 "+i+"() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+u[0]+", "+u[1]+"));\n      int index = resTexRC.x * "+u[1]+" + resTexRC.y;\n\n      int texR = index / "+c+";\n      int texC = index - texR * "+c+";\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2("+c+", "+l+");\n\n      "+f+";\n    }\n  "}function getSamplerAtOutputCoords(e,t,n){var r=e.name,o=r.charAt(0).toUpperCase()+r.slice(1),a="get"+o+"AtOutCoords",i=getBroadcastDims(e.shapeInfo.logicalShape,t.logicalShape),s=e.shapeInfo.logicalShape.length,u=t.logicalShape.length,l=n&&(u>s||i.length>0),c=broadcastDimsAreOuter(i),p=e.shapeInfo.isUniform;if(l&&!c)return getBroadcastOutputCoordsSampler(e,t,o,a);var d=sizeFromShape(e.shapeInfo.logicalShape),h="";l&&c&&(h="\n        int mainPart = index / "+d+";\n        index -= mainPart * "+d+";\n      ");var f=t.texShape;if(p)return 1===d?"float "+a+"() {return "+r+";}":"\n      float "+a+"() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                              vec2("+f[0]+", "+f[1]+"));\n        int index = resTexRC.x * "+f[1]+" + resTexRC.y;\n        "+h+"\n        return get"+o+"Flat(index);\n      }\n    ";var m=e.shapeInfo.texShape;return arraysEqual(m,f)?"\n      float "+a+"() {\n        return sampleTexture("+r+", resultUV);\n      }\n    ":"\n    float "+a+"() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+f[0]+", "+f[1]+"));\n      int index = resTexRC.x * "+f[1]+" + resTexRC.y;\n      "+h+"\n      int texR = index / "+m[1]+";\n      int texC = index - texR * "+m[1]+";\n      vec2 uv = (vec2(texC, texR) + halfCR) /\n                 vec2("+m[1]+".0, "+m[0]+".0);\n\n      return sampleTexture("+r+", uv);\n    }\n  "}function getCoordsDataType(e){if(e<=1)return "int";if(2===e)return "ivec2";if(3===e)return "ivec3";if(4===e)return "ivec4";if(5===e)return "ivec5";if(6===e)return "ivec6";throw Error("GPU for rank "+e+" is not yet supported")}function squeezeInputInfo(e,t){var n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function getSqueezedParams(e,t){return t.map(function(t){return e[t]}).join(", ")}var CumSumProgram=function(){return function(e,t,n){this.variableNames=["x"],this.outputShape=e;var r=e.length,o=e[e.length-1],a=n?"<":">";this.userCode="\n      int getIndex(int i) {\n        "+(n?"return "+o+" -i - 1;":"return i;")+"\n      }\n\n      void main() {\n        "+getCoordsDataType(r)+" coords = getOutputCoords();\n        int end = "+getFinalCoord(r,"coords")+";\n        float val = 0.0;\n        for (int i = "+o+" - 1; i >= 0; i -= 1) {\n          int idx = getIndex(i);\n          if (idx "+a+" end) {\n            continue;\n          }\n          if (idx == end && "+t+") {\n            continue;\n          }\n          "+getFinalCoord(r,"coords")+" = idx;\n          val += getX("+getCoords(r,"coords")+");\n        }\n        setOutput(val);\n      }\n    ";}}();function getCoords(e,t){if(1===e)return ""+t;if(2===e)return t+".x, "+t+".y";if(3===e)return t+".x, "+t+".y, "+t+".z";if(4===e)return t+".x, "+t+".y, "+t+".z, "+t+".w";throw Error("Cumulative sum for rank "+e+" is not yet supported")}function getFinalCoord(e,t){if(1===e)return ""+t;if(2===e)return t+".y";if(3===e)return t+".z";if(4===e)return t+".w";throw Error("Cumulative sum for rank "+e+" is not yet supported")}var DepthToSpaceProgram=function(){function e(e,t,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode="\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = "+this.getHeightCoordString()+";\n      int w = "+this.getWidthCoordString()+";\n      int d = "+this.getDepthCoordString()+";\n\n      int in_h = h / "+t+";\n      int offset_h = imod(h, "+t+");\n      int in_w = w / "+t+";\n      int offset_w = imod(w, "+t+");\n      int offset_d = (offset_h * "+t+" + offset_w) *\n        "+this.getOutputDepthSize()+";\n      int in_d = d + offset_d;\n\n      float result = "+this.getInputSamplingString()+";\n      setOutput(result);\n    }\n  ";}return e.prototype.getHeightCoordString=function(){return "NHWC"===this.dataFormat?"coords[1]":"coords[2]"},e.prototype.getWidthCoordString=function(){return "NHWC"===this.dataFormat?"coords[2]":"coords[3]"},e.prototype.getDepthCoordString=function(){return "NHWC"===this.dataFormat?"coords[3]":"coords[1]"},e.prototype.getOutputDepthSize=function(){return "NHWC"===this.dataFormat?this.outputShape[3]:this.outputShape[1]},e.prototype.getInputSamplingString=function(){return "NHWC"===this.dataFormat?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"},e}(),EncodeFloatProgram=function(){return function(e){this.variableNames=["A"],this.outputShape=e,this.userCode="\n      const float FLOAT_MAX = 1.70141184e38;\n      const float FLOAT_MIN = 1.17549435e-38;\n\n      lowp vec4 encode_float(highp float v) {\n        if (isNaN(v)) {\n          return vec4(255, 255, 255, 255);\n        }\n\n        highp float av = abs(v);\n\n        if(av < FLOAT_MIN) {\n          return vec4(0.0, 0.0, 0.0, 0.0);\n        } else if(v > FLOAT_MAX) {\n          return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n        } else if(v < -FLOAT_MAX) {\n          return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n        }\n\n        highp vec4 c = vec4(0,0,0,0);\n\n        highp float e = floor(log2(av));\n        highp float m = exp2(fract(log2(av))) - 1.0;\n\n        c[2] = floor(128.0 * m);\n        m -= c[2] / 128.0;\n        c[1] = floor(32768.0 * m);\n        m -= c[1] / 32768.0;\n        c[0] = floor(8388608.0 * m);\n\n        highp float ebias = e + 127.0;\n        c[3] = floor(ebias / 2.0);\n        ebias -= c[3] * 2.0;\n        c[2] += floor(ebias) * 128.0;\n\n        c[3] += 128.0 * step(0.0, -v);\n\n        return c / 255.0;\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        gl_FragColor = encode_float(x);\n      }\n    ";}}(),COMPLEX_FFT={REAL:"return real * expR - imag * expI;",IMAG:"return real * expI + imag * expR;"},FFTProgram=function(){return function(e,t,n){this.variableNames=["real","imag"];var r=t[1];this.outputShape=t;var o=n?"2.0 * "+Math.PI:"-2.0 * "+Math.PI,a=n?r+".0":"1.0";this.userCode="\n      const float exponentMultiplier = "+o+";\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        "+e+"\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float("+r+");\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < "+r+"; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / "+a+";\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    ";}}(),FromPixelsProgram=function(){return function(e){this.variableNames=["A"];var t=e[0],n=e[1];this.outputShape=e,this.userCode="\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2("+n+".0, "+t+".0);\n\n        vec4 values = texture2D(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    ";}}(),GatherProgram=function(){return function(e,t,n){this.variableNames=["A","indices"];var r=e.slice();r[n]=t,this.outputShape=r,this.rank=r.length;var o=getCoordsDataType(this.rank),a=getSourceCoords(e,n);this.userCode="\n      void main() {\n        "+o+" resRC = getOutputCoords();\n        setOutput(getA("+a+"));\n      }\n    ";}}();function getSourceCoords(e,t){var n=e.length;if(n>4)throw Error("Gather for rank "+n+" is not yet supported");if(1===n)return "int(getIndices(resRC))";for(var r=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],a=0;a<e.length;a++)a===t?o.push("int(getIndices("+r[a]+"))"):o.push(""+r[a]);return o.join()}var TextureUsage,PhysicalTextureType,GatherNDProgram=function(){return function(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=["x","indices"],this.outputShape=n;var r=getCoordsDataType(t.length),o=getCoordsDataType(n.length),a=this.sliceDim>1?"strides[j]":"strides";this.userCode="\n        "+r+" strides = "+r+"("+this.strides+");\n         void main() {\n          "+o+" coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < "+this.sliceDim+"; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * "+a+";\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      ";}}();function getUnpackedMatrixTextureShapeWidthHeight(e,t){return [t,e]}function getUnpackedArraySizeFromMatrixSize(e,t){return e*t}function getMatrixSizeFromUnpackedArraySize(e,t){if(e%t!=0)throw new Error("unpackedSize ("+e+") must be a multiple of "+t);return e/t}function encodeMatrixToUnpackedArray(e,t,n){var r=getUnpackedArraySizeFromMatrixSize(e.length,n);if(t.length<r)throw new Error("unpackedArray length ("+t.length+") must be >= "+r);for(var o=0,a=0;a<e.length;++a)t[o]=e[a],o+=n;}function decodeMatrixFromUnpackedArray(e,t,n){var r=getMatrixSizeFromUnpackedArraySize(e.length,n);if(t.length<r)throw new Error("matrix length ("+t.length+") must be >= "+r);for(var o=0,a=0;a<e.length;a+=n)t[o++]=e[a];}function getPackedMatrixTextureShapeWidthHeight(e,t){return [Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function getPackedRGBAArraySizeFromMatrixShape(e,t){var n=getPackedMatrixTextureShapeWidthHeight(e,t);return n[0]*n[1]*4}function encodeMatrixToPackedRGBA(e,t,n,r,o){var a=getPackedRGBAArraySizeFromMatrixShape(n,r);if(o.length<a)throw new Error("packedRGBA length ("+o.length+") must be >=\n        "+a);for(var i=r%2==1,s=n%2==1,u=Math.floor(r/2),l=Math.floor(n/2),c=Math.ceil(r/2),p=c*Math.ceil(n/2),d=nearestLargerEven(n)*nearestLargerEven(r),h=0;h<t;h++){for(var f=h*n*r,m=h*d,g=i?4:0,v=r,y=m,x=0;x<l;++x){for(var T=2*x*r,w=0;w<u;++w){var E=f+T+2*w;o[y]=e[E],o[y+1]=e[E+1],o[y+2]=e[E+v],o[y+3]=e[E+v+1],y+=4;}y+=g;}if(i){E=f+r-1,y=m+4*(c-1);var S=2*r;for(g=4*c,x=0;x<l;++x)o[y]=e[E],o[y+2]=e[E+r],E+=S,y+=g;}if(s){for(E=f+(n-1)*r,y=m+4*(p-c),w=0;w<u;++w)o[y++]=e[E++],o[y++]=e[E++],y+=2;i&&s&&(o[m+d-4]=e[E]);}}return o}function decodeMatrixFromPackedRGBA(e,t,n,r,o){var a=n*r;if(o.length<a)throw new Error("matrix length ("+o.length+") must be >= "+a);for(var i=r%2==1,s=n%2==1,u=Math.floor(r/2),l=Math.floor(n/2),c=Math.ceil(r/2),p=c*Math.ceil(n/2),d=nearestLargerEven(n)*nearestLargerEven(r),h=0;h<t;h++){for(var f=h*n*r,m=h*d,g=i?4:0,v=r+(i?1:0),y=m,x=f,T=f+r,w=0;w<l;++w){for(var E=0;E<u;++E)o[x++]=e[y++],o[x++]=e[y++],o[T++]=e[y++],o[T++]=e[y++];y+=g,x+=v,T+=v;}if(i){y=m+4*(c-1);var S=f+r-1;for(g=4*c,v=2*r,w=0;w<l;++w)o[S]=e[y],o[S+r]=e[y+2],y+=g,S+=v;}if(s){for(y=m+4*(p-c),S=f+(n-1)*r,E=0;E<u;++E)o[S++]=e[y++],o[S++]=e[y++],y+=2;i&&(o[f+n*r-1]=e[y]);}}return o}function callAndCheck(e,t){var n=t();return checkWebGLError(e),n}!function(e){e[e.RENDER=0]="RENDER",e[e.UPLOAD=1]="UPLOAD",e[e.PIXELS=2]="PIXELS",e[e.DOWNLOAD=3]="DOWNLOAD";}(TextureUsage||(TextureUsage={})),function(e){e[e.UNPACKED_FLOAT16=0]="UNPACKED_FLOAT16",e[e.UNPACKED_FLOAT32=1]="UNPACKED_FLOAT32",e[e.PACKED_4X1_UNSIGNED_BYTE=2]="PACKED_4X1_UNSIGNED_BYTE",e[e.PACKED_2X2_FLOAT32=3]="PACKED_2X2_FLOAT32",e[e.PACKED_2X2_FLOAT16=4]="PACKED_2X2_FLOAT16";}(PhysicalTextureType||(PhysicalTextureType={}));var webGLDebugErrorCheckingEnabled=!1;function enableDebugWebGLErrorChecking(e){webGLDebugErrorCheckingEnabled=e;}function checkWebGLError(e){if(webGLDebugErrorCheckingEnabled){var t=e.getError();if(t!==e.NO_ERROR)throw new Error("WebGL Error: "+getWebGLErrorMessage(e,t))}}function getWebGLErrorMessage(e,t){switch(t){case e.NO_ERROR:return "NO_ERROR";case e.INVALID_ENUM:return "INVALID_ENUM";case e.INVALID_VALUE:return "INVALID_VALUE";case e.INVALID_OPERATION:return "INVALID_OPERATION";case e.INVALID_FRAMEBUFFER_OPERATION:return "INVALID_FRAMEBUFFER_OPERATION";case e.OUT_OF_MEMORY:return "OUT_OF_MEMORY";case e.CONTEXT_LOST_WEBGL:return "CONTEXT_LOST_WEBGL";default:return "Unknown error code "+t}}function getExtensionOrThrow(e,t){return throwIfNull(e,function(){return e.getExtension(t)},'Extension "'+t+'" not supported on this browser.')}function createVertexShader(e,t){var n=throwIfNull(e,function(){return e.createShader(e.VERTEX_SHADER)},"Unable to create vertex WebGLShader.");if(callAndCheck(e,function(){return e.shaderSource(n,t)}),callAndCheck(e,function(){return e.compileShader(n)}),!1===e.getShaderParameter(n,e.COMPILE_STATUS))throw console.log(e.getShaderInfoLog(n)),new Error("Failed to compile vertex shader.");return n}function createFragmentShader(e,t){var n=throwIfNull(e,function(){return e.createShader(e.FRAGMENT_SHADER)},"Unable to create fragment WebGLShader.");if(callAndCheck(e,function(){return e.shaderSource(n,t)}),callAndCheck(e,function(){return e.compileShader(n)}),!1===e.getShaderParameter(n,e.COMPILE_STATUS))throw logShaderSourceAndInfoLog(t,e.getShaderInfoLog(n)),new Error("Failed to compile fragment shader.");return n}var lineNumberRegex=/ERROR: [0-9]+:([0-9]+):/g;function logShaderSourceAndInfoLog(e,t){var n=lineNumberRegex.exec(t);if(null==n)return console.log("Couldn't parse line number in error: "+t),void console.log(e);for(var r=+n[1],o=e.split("\n"),a=o.length.toString().length+2,i=o.map(function(e,t){return rightPad((t+1).toString(),a)+e}),s=0,u=0;u<i.length;u++)s=Math.max(i[u].length,s);var l=i.slice(0,r-1),c=i.slice(r-1,r),p=i.slice(r);console.log(l.join("\n")),console.log(t.split("\n")[0]),console.log("%c "+rightPad(c[0],s),"border:1px solid red; background-color:#e3d2d2; color:#a61717"),console.log(p.join("\n"));}function createProgram(e){return throwIfNull(e,function(){return e.createProgram()},"Unable to create WebGLProgram.")}function linkProgram(e,t){if(callAndCheck(e,function(){return e.linkProgram(t)}),!1===e.getProgramParameter(t,e.LINK_STATUS))throw console.log(e.getProgramInfoLog(t)),new Error("Failed to link vertex and fragment shaders.")}function validateProgram(e,t){if(callAndCheck(e,function(){return e.validateProgram(t)}),!1===e.getProgramParameter(t,e.VALIDATE_STATUS))throw console.log(e.getProgramInfoLog(t)),new Error("Shader program validation failed.")}function createStaticVertexBuffer(e,t){var n=throwIfNull(e,function(){return e.createBuffer()},"Unable to create WebGLBuffer");return callAndCheck(e,function(){return e.bindBuffer(e.ARRAY_BUFFER,n)}),callAndCheck(e,function(){return e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)}),n}function createStaticIndexBuffer(e,t){var n=throwIfNull(e,function(){return e.createBuffer()},"Unable to create WebGLBuffer");return callAndCheck(e,function(){return e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)}),callAndCheck(e,function(){return e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)}),n}function getNumChannels(){return 2===ENV.get("WEBGL_VERSION")?1:4}function createTexture(e){return throwIfNull(e,function(){return e.createTexture()},"Unable to create WebGLTexture.")}function validateTextureSize(e,t){var n=ENV.get("WEBGL_MAX_TEXTURE_SIZE");if(e<=0||t<=0){var r="["+e+"x"+t+"]";throw new Error("Requested texture size "+r+" is invalid.")}if(e>n||t>n){r="["+e+"x"+t+"]";throw new Error("Requested texture size "+r+" greater than WebGL maximum on this browser / GPU "+("["+n+"x"+n+"]")+".")}}function createFramebuffer(e){return throwIfNull(e,function(){return e.createFramebuffer()},"Unable to create WebGLFramebuffer.")}function bindVertexBufferToProgramAttribute(e,t,n,r,o,a,i){var s=e.getAttribLocation(t,n);return -1!==s&&(callAndCheck(e,function(){return e.bindBuffer(e.ARRAY_BUFFER,r)}),callAndCheck(e,function(){return e.vertexAttribPointer(s,o,e.FLOAT,!1,a,i)}),callAndCheck(e,function(){return e.enableVertexAttribArray(s)}),!0)}function bindTextureUnit(e,t,n){validateTextureUnit(e,n),callAndCheck(e,function(){return e.activeTexture(e.TEXTURE0+n)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,t)});}function unbindTextureUnit(e,t){validateTextureUnit(e,t),callAndCheck(e,function(){return e.activeTexture(e.TEXTURE0+t)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)});}function getProgramUniformLocationOrThrow(e,t,n){return throwIfNull(e,function(){return e.getUniformLocation(t,n)},'uniform "'+n+'" not present in program.')}function getProgramUniformLocation(e,t,n){return e.getUniformLocation(t,n)}function bindTextureToProgramUniformSampler(e,t,n,r,o){callAndCheck(e,function(){return bindTextureUnit(e,n,o)}),callAndCheck(e,function(){return e.uniform1i(r,o)});}function bindCanvasToFramebuffer(e){callAndCheck(e,function(){return e.bindFramebuffer(e.FRAMEBUFFER,null)}),callAndCheck(e,function(){return e.viewport(0,0,e.canvas.width,e.canvas.height)}),callAndCheck(e,function(){return e.scissor(0,0,e.canvas.width,e.canvas.height)});}function bindColorTextureToFramebuffer(e,t,n){callAndCheck(e,function(){return e.bindFramebuffer(e.FRAMEBUFFER,n)}),callAndCheck(e,function(){return e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0)});}function unbindColorTextureFromFramebuffer(e,t){callAndCheck(e,function(){return e.bindFramebuffer(e.FRAMEBUFFER,t)}),callAndCheck(e,function(){return e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0)});}function validateFramebuffer(e){var t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error("Error binding framebuffer: "+getFramebufferErrorMessage(e,t))}function getFramebufferErrorMessage(e,t){switch(t){case e.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return "FRAMEBUFFER_INCOMPLETE_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return "FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT";case e.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return "FRAMEBUFFER_INCOMPLETE_DIMENSIONS";case e.FRAMEBUFFER_UNSUPPORTED:return "FRAMEBUFFER_UNSUPPORTED";default:return "unknown error "+t}}function throwIfNull(e,t,n){var r=callAndCheck(e,function(){return t()});if(null==r)throw new Error(n);return r}function validateTextureUnit(e,t){var n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,r=t+e.TEXTURE0;if(r<e.TEXTURE0||r>n)throw new Error("textureUnit must be in "+("[gl.TEXTURE0, gl.TEXTURE"+n+"]")+".")}function getTextureShapeFromLogicalShape(e,t){void 0===t&&(t=!1);var n=ENV.get("WEBGL_MAX_TEXTURE_SIZE");if(t&&(n*=2,e=e.map(function(t,n){return n>=e.length-2?nearestLargerEven(e[n]):e[n]})),2!==e.length){var r=squeezeShape(e);e=r.newShape;}var o=sizeFromShape(e);return e.length<=1&&o<=n?[1,o]:2===e.length&&e[0]<=n&&e[1]<=n?e:3===e.length&&e[0]*e[1]<=n&&e[2]<=n?[e[0]*e[1],e[2]]:3===e.length&&e[0]<=n&&e[1]*e[2]<=n?[e[0],e[1]*e[2]]:4===e.length&&e[0]*e[1]*e[2]<=n&&e[3]<=n?[e[0]*e[1]*e[2],e[3]]:4===e.length&&e[0]<=n&&e[1]*e[2]*e[3]<=n?[e[0],e[1]*e[2]*e[3]]:sizeToSquarishShape(o)}function isEven(e){return e%2==0}function isReshapeFree(e,t){if(arraysEqual(e=e.slice(-2),t=t.slice(-2)))return !0;if(!e.length||!t.length)return !0;if(0===e[0]||0===e[1]||0===t[0]||0===t[1])return !0;if(e.length!==t.length){var n=e.slice(-1)[0],r=t.slice(-1)[0];if(n===r)return !0;if(isEven(n)&&isEven(r)&&(1===e[0]||1===t[0]))return !0}else if(isEven(e[0])&&isEven(t[0])){if(isEven(e[1])&&isEven(t[1]))return !0;if(e[1]===t[1])return !0}return !1}var webgl_util=Object.freeze({callAndCheck:callAndCheck,enableDebugWebGLErrorChecking:enableDebugWebGLErrorChecking,checkWebGLError:checkWebGLError,getWebGLErrorMessage:getWebGLErrorMessage,getExtensionOrThrow:getExtensionOrThrow,createVertexShader:createVertexShader,createFragmentShader:createFragmentShader,createProgram:createProgram,linkProgram:linkProgram,validateProgram:validateProgram,createStaticVertexBuffer:createStaticVertexBuffer,createStaticIndexBuffer:createStaticIndexBuffer,getNumChannels:getNumChannels,createTexture:createTexture,validateTextureSize:validateTextureSize,createFramebuffer:createFramebuffer,bindVertexBufferToProgramAttribute:bindVertexBufferToProgramAttribute,bindTextureUnit:bindTextureUnit,unbindTextureUnit:unbindTextureUnit,getProgramUniformLocationOrThrow:getProgramUniformLocationOrThrow,getProgramUniformLocation:getProgramUniformLocation,bindTextureToProgramUniformSampler:bindTextureToProgramUniformSampler,bindCanvasToFramebuffer:bindCanvasToFramebuffer,bindColorTextureToFramebuffer:bindColorTextureToFramebuffer,unbindColorTextureFromFramebuffer:unbindColorTextureFromFramebuffer,validateFramebuffer:validateFramebuffer,getFramebufferErrorMessage:getFramebufferErrorMessage,getTextureShapeFromLogicalShape:getTextureShapeFromLogicalShape,isReshapeFree:isReshapeFree});function createVertexShader$1(e){return createVertexShader(e,"\n    precision highp float;\n    attribute vec3 clipSpacePos;\n    attribute vec2 uv;\n    varying vec2 resultUV;\n\n    void main() {\n      gl_Position = vec4(clipSpacePos, 1);\n      resultUV = uv;\n    }")}function createVertexBuffer(e){return createStaticVertexBuffer(e,new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]))}function createIndexBuffer(e){return createStaticIndexBuffer(e,new Uint16Array([0,1,2,2,1,3]))}function getTextureConfig(e,t){var n,r,o,a,i,s,u,l=e;return 2===ENV.get("WEBGL_VERSION")?(n=l.R32F,r=l.R16F,o=l.RGBA32F,a=l.RED,i=4,s=1,u=l.HALF_FLOAT):(n=e.RGBA,r=e.RGBA,o=l.RGBA,a=e.RGBA,i=4,s=4,u=null!=t?t.HALF_FLOAT_OES:null),{internalFormatFloat:n,internalFormatHalfFloat:r,internalFormatPackedFloat:o,textureFormatFloat:a,downloadTextureFormat:e.RGBA,downloadUnpackNumChannels:i,defaultNumChannels:s,textureTypeHalfFloat:u}}function createAndConfigureTexture(e,t,n,r,o,a){validateTextureSize(t,n);var i=createTexture(e),s=e.TEXTURE_2D;return callAndCheck(e,function(){return e.bindTexture(s,i)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_MIN_FILTER,e.NEAREST)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_MAG_FILTER,e.NEAREST)}),callAndCheck(e,function(){return e.texImage2D(s,0,r,t,n,0,o,a,null)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)}),i}function createFloat32MatrixTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],r.internalFormatFloat,r.textureFormatFloat,e.FLOAT)}function createFloat16MatrixTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],r.internalFormatFloat,r.textureFormatFloat,r.textureTypeHalfFloat)}function createUnsignedBytesMatrixTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],e.RGBA,e.RGBA,e.UNSIGNED_BYTE)}function createPackedMatrixTexture(e,t,n,r){var o=getPackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],r.internalFormatPackedFloat,e.RGBA,e.FLOAT)}function createFloat16PackedMatrixTexture(e,t,n,r){var o=getPackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],r.internalFormatHalfFloat,e.RGBA,r.textureTypeHalfFloat)}function bindVertexProgramAttributeStreams(e,t,n){return callAndCheck(e,function(){return e.bindBuffer(e.ARRAY_BUFFER,n)}),bindVertexBufferToProgramAttribute(e,t,"clipSpacePos",n,3,20,0)&&bindVertexBufferToProgramAttribute(e,t,"uv",n,2,20,12)}function uploadPixelDataToTexture(e,t,n){callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,t)}),callAndCheck(e,function(){return e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)});}function uploadDataToTexture(e,t,n,r,o,a){validateTextureSize(n,r),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,t)}),callAndCheck(e,function(){return e.texSubImage2D(e.TEXTURE_2D,0,0,0,n,r,a,e.FLOAT,o)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)});}function uploadMatrixToTexture(e,t,n,r,o,a,i){var s,u=getUnpackedMatrixTextureShapeWidthHeight(n,r),l=u[0],c=u[1];1===i.defaultNumChannels?s=o:encodeMatrixToUnpackedArray(o,s=new Float32Array(getUnpackedArraySizeFromMatrixSize(o.length,a)),a),uploadDataToTexture(e,t,l,c,s,i.textureFormatFloat);}function uploadMatrixToPackedTexture(e,t,n,r,o,a,i,s,u){var l=getPackedMatrixTextureShapeWidthHeight(a,i),c=l[0],p=l[1],d=new Float32Array(n*getPackedRGBAArraySizeFromMatrixShape(r,o));encodeMatrixToPackedRGBA(s,n,r,o,d),uploadDataToTexture(e,t,c,p,d,e.RGBA);}function maybeCreateBufferFromOutputTexture(e,t,n,r,o){var a=t;if(2===ENV.get("WEBGL_VERSION")){var i=e,s=i.createBuffer();callAndCheck(e,function(){return e.bindBuffer(i.PIXEL_PACK_BUFFER,s)});var u=4*getUnpackedArraySizeFromMatrixSize(n*r,o.downloadUnpackNumChannels);callAndCheck(e,function(){return e.bufferData(i.PIXEL_PACK_BUFFER,u,e.STATIC_DRAW)}),callAndCheck(e,function(){return i.readPixels(0,0,r,n,e.RGBA,e.FLOAT,0)}),callAndCheck(e,function(){return e.bindBuffer(i.PIXEL_PACK_BUFFER,null)}),a=s;}return a}function downloadFloat32MatrixFromBuffer(e,t,n,r,o){var a=e,i=new Float32Array(getUnpackedArraySizeFromMatrixSize(n*r,o.downloadUnpackNumChannels));a.bindBuffer(e.ARRAY_BUFFER,t),a.getBufferSubData(e.ARRAY_BUFFER,0,i),a.bindBuffer(e.ARRAY_BUFFER,null);var s=new Float32Array(n*r);return decodeMatrixFromUnpackedArray(i,s,o.downloadUnpackNumChannels),s}function downloadFloat32MatrixFromOutputTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n),a=o[0],i=o[1],s=new Float32Array(getUnpackedArraySizeFromMatrixSize(t*n,r.downloadUnpackNumChannels));callAndCheck(e,function(){return e.readPixels(0,0,a,i,r.downloadTextureFormat,e.FLOAT,s)});var u=new Float32Array(t*n);return decodeMatrixFromUnpackedArray(s,u,r.downloadUnpackNumChannels),u}function downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n),a=o[0],i=o[1],s=new Uint8Array(getUnpackedArraySizeFromMatrixSize(t*n,4));return callAndCheck(e,function(){return e.readPixels(0,0,a,i,r.downloadTextureFormat,e.UNSIGNED_BYTE,s)}),new Float32Array(s.buffer)}function downloadPackedMatrixFromBuffer(e,t,n,r,o,a,i,s){var u=e,l=new Float32Array(getPackedRGBAArraySizeFromMatrixShape(a,i));u.bindBuffer(e.ARRAY_BUFFER,t),u.getBufferSubData(e.ARRAY_BUFFER,0,l),u.bindBuffer(e.ARRAY_BUFFER,null);var c=new Float32Array(sizeFromShape([n,r,o]));return decodeMatrixFromPackedRGBA(l,n,r,o,c),c}function downloadMatrixFromPackedOutputTexture(e,t,n,r,o,a,i){var s=getPackedMatrixTextureShapeWidthHeight(o,a),u=s[0],l=s[1],c=new Float32Array(getPackedRGBAArraySizeFromMatrixShape(o,a));callAndCheck(e,function(){return e.readPixels(0,0,u,l,e.RGBA,e.FLOAT,c)});var p=new Float32Array(sizeFromShape([t,n,r]));return decodeMatrixFromPackedRGBA(c,t,n,r,p)}var gpgpu_util=Object.freeze({createVertexShader:createVertexShader$1,createVertexBuffer:createVertexBuffer,createIndexBuffer:createIndexBuffer,getTextureConfig:getTextureConfig,createFloat32MatrixTexture:createFloat32MatrixTexture,createFloat16MatrixTexture:createFloat16MatrixTexture,createUnsignedBytesMatrixTexture:createUnsignedBytesMatrixTexture,createPackedMatrixTexture:createPackedMatrixTexture,createFloat16PackedMatrixTexture:createFloat16PackedMatrixTexture,bindVertexProgramAttributeStreams:bindVertexProgramAttributeStreams,uploadPixelDataToTexture:uploadPixelDataToTexture,uploadMatrixToTexture:uploadMatrixToTexture,uploadMatrixToPackedTexture:uploadMatrixToPackedTexture,maybeCreateBufferFromOutputTexture:maybeCreateBufferFromOutputTexture,downloadFloat32MatrixFromBuffer:downloadFloat32MatrixFromBuffer,downloadFloat32MatrixFromOutputTexture:downloadFloat32MatrixFromOutputTexture,downloadByteEncodedFloatMatrixFromOutputTexture:downloadByteEncodedFloatMatrixFromOutputTexture,downloadPackedMatrixFromBuffer:downloadPackedMatrixFromBuffer,downloadMatrixFromPackedOutputTexture:downloadMatrixFromPackedOutputTexture}),GPGPUContext=function(){function e(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.autoDebugValidate=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[],this.gl=null!=e?e:getWebGLContext(ENV.get("WEBGL_VERSION")),1===ENV.get("WEBGL_VERSION")?(this.textureFloatExtension=getExtensionOrThrow(this.gl,"OES_texture_float"),this.colorBufferFloatExtension=this.gl.getExtension("WEBGL_color_buffer_float"),ENV.get("WEBGL_RENDER_FLOAT32_ENABLED")||(this.textureHalfFloatExtension=getExtensionOrThrow(this.gl,"OES_texture_half_float"),this.colorBufferHalfFloatExtension=this.gl.getExtension("EXT_color_buffer_half_float"))):this.colorBufferFloatExtension=getExtensionOrThrow(this.gl,"EXT_color_buffer_float"),this.vertexBuffer=createVertexBuffer(this.gl),this.indexBuffer=createIndexBuffer(this.gl),this.framebuffer=createFramebuffer(this.gl),this.textureConfig=getTextureConfig(this.gl,this.textureHalfFloatExtension);}return e.prototype.dispose=function(){var e=this;if(!this.disposed){null!=this.program&&console.warn("Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing."),null!=this.outputTexture&&console.warn("Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.");var t=this.gl;callAndCheck(t,function(){return t.finish()}),callAndCheck(t,function(){return t.bindFramebuffer(t.FRAMEBUFFER,null)}),callAndCheck(t,function(){return t.deleteFramebuffer(e.framebuffer)}),callAndCheck(t,function(){return t.bindBuffer(t.ARRAY_BUFFER,null)}),callAndCheck(t,function(){return t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)}),callAndCheck(t,function(){return t.deleteBuffer(e.indexBuffer)}),this.disposed=!0;}},e.prototype.enableAutomaticDebugValidation=function(e){this.autoDebugValidate=e,enableDebugWebGLErrorChecking(e);},e.prototype.createFloat32MatrixTexture=function(e,t){return this.throwIfDisposed(),createFloat32MatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.createFloat16MatrixTexture=function(e,t){return this.throwIfDisposed(),createFloat16MatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.createUnsignedBytesMatrixTexture=function(e,t){return this.throwIfDisposed(),createUnsignedBytesMatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.uploadPixelDataToTexture=function(e,t){this.throwIfDisposed(),uploadPixelDataToTexture(this.gl,e,t);},e.prototype.createFloat16PackedMatrixTexture=function(e,t){return this.throwIfDisposed(),createFloat16PackedMatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.createPackedMatrixTexture=function(e,t){return this.throwIfDisposed(),createPackedMatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.deleteMatrixTexture=function(e){var t=this;this.throwIfDisposed(),this.outputTexture===e&&(unbindColorTextureFromFramebuffer(this.gl,this.framebuffer),this.outputTexture=null),callAndCheck(this.gl,function(){return t.gl.deleteTexture(e)});},e.prototype.uploadMatrixToTexture=function(e,t,n,r){this.throwIfDisposed();var o=getNumChannels();return uploadMatrixToTexture(this.gl,e,t,n,r,o,this.textureConfig)},e.prototype.uploadMatrixToPackedTexture=function(e,t,n,r,o,a,i){return this.throwIfDisposed(),uploadMatrixToPackedTexture(this.gl,e,t,n,r,o,a,i,this.textureConfig)},e.prototype.downloadFloat32MatrixFromOutputTexture=function(e,t,n){var r=this;return this.downloadMatrixDriver(e,function(){return downloadFloat32MatrixFromOutputTexture(r.gl,t,n,r.textureConfig)})},e.prototype.downloadByteEncodedFloatMatrixFromOutputTexture=function(e,t,n){var r=this;return this.downloadMatrixDriver(e,function(){return downloadByteEncodedFloatMatrixFromOutputTexture(r.gl,t,n,r.textureConfig)})},e.prototype.downloadPackedMatrixFromBuffer=function(e,t,n,r,o,a){return downloadPackedMatrixFromBuffer(this.gl,e,t,n,r,o,a,this.textureConfig)},e.prototype.downloadFloat32MatrixFromBuffer=function(e,t,n){return downloadFloat32MatrixFromBuffer(this.gl,e,t,n,this.textureConfig)},e.prototype.maybeCreateBufferFromTexture=function(e,t,n){this.bindTextureToFrameBuffer(e);var r=maybeCreateBufferFromOutputTexture(this.gl,e,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r},e.prototype.createAndWaitForFence=function(){var e=this.createFence(this.gl);return this.pollFence(e)},e.prototype.createFence=function(e){var t,n,r=this;if(ENV.get("WEBGL_FENCE_API_ENABLED")){var o=e,a=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=function(){var e=o.clientWaitSync(a,0,0);return e===o.ALREADY_SIGNALED||e===o.CONDITION_SATISFIED},t=a;}else ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(t=this.beginQuery(),this.endQuery(),n=function(){return r.isQueryAvailable(t,ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))}):n=function(){return !0};return {query:t,isFencePassed:n}},e.prototype.downloadMatrixFromPackedTexture=function(e,t,n,r,o,a){var i=this;return this.downloadMatrixDriver(e,function(){return downloadMatrixFromPackedOutputTexture(i.gl,t,n,r,o,a,i.textureConfig)})},e.prototype.createProgram=function(e){this.throwIfDisposed();var t=this.gl,n=createFragmentShader(t,e),r=createVertexShader$1(t),o=createProgram(t);return callAndCheck(t,function(){return t.attachShader(o,r)}),callAndCheck(t,function(){return t.attachShader(o,n)}),linkProgram(t,o),this.autoDebugValidate&&validateProgram(t,o),this.vertexAttrsAreBound||(this.setProgram(o),this.vertexAttrsAreBound=bindVertexProgramAttributeStreams(t,this.program,this.vertexBuffer)),o},e.prototype.deleteProgram=function(e){var t=this;this.throwIfDisposed(),e===this.program&&(this.program=null),null!=e&&callAndCheck(this.gl,function(){return t.gl.deleteProgram(e)});},e.prototype.setProgram=function(e){var t=this;this.throwIfDisposed(),this.program=e,null!=this.program&&this.autoDebugValidate&&validateProgram(this.gl,this.program),callAndCheck(this.gl,function(){return t.gl.useProgram(e)});},e.prototype.getUniformLocation=function(e,t,n){return void 0===n&&(n=!0),this.throwIfDisposed(),n?getProgramUniformLocationOrThrow(this.gl,e,t):getProgramUniformLocation(this.gl,e,t)},e.prototype.getAttributeLocation=function(e,t){var n=this;return this.throwIfDisposed(),callAndCheck(this.gl,function(){return n.gl.getAttribLocation(e,t)})},e.prototype.getUniformLocationNoThrow=function(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)},e.prototype.setInputMatrixTexture=function(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),bindTextureToProgramUniformSampler(this.gl,this.program,e,t,n);},e.prototype.setOutputMatrixTexture=function(e,t,n){this.setOutputMatrixTextureDriver(e,n,t);},e.prototype.setOutputPackedMatrixTexture=function(e,t,n){this.throwIfDisposed();var r=getPackedMatrixTextureShapeWidthHeight(t,n),o=r[0],a=r[1];this.setOutputMatrixTextureDriver(e,o,a);},e.prototype.setOutputMatrixWriteRegion=function(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t);},e.prototype.setOutputPackedMatrixWriteRegion=function(e,t,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")},e.prototype.debugValidate=function(){null!=this.program&&validateProgram(this.gl,this.program),validateFramebuffer(this.gl);},e.prototype.executeProgram=function(){this.throwIfDisposed(),this.throwIfNoProgram();var e=this.gl;this.autoDebugValidate&&this.debugValidate(),callAndCheck(e,function(){return e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0)});},e.prototype.blockUntilAllProgramsCompleted=function(){var e=this;this.throwIfDisposed(),callAndCheck(this.gl,function(){return e.gl.finish()});},e.prototype.getQueryTimerExtension=function(){return null==this.disjointQueryTimerExtension&&(this.disjointQueryTimerExtension=getExtensionOrThrow(this.gl,2===ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension},e.prototype.getQueryTimerExtensionWebGL2=function(){return this.getQueryTimerExtension()},e.prototype.getQueryTimerExtensionWebGL1=function(){return this.getQueryTimerExtension()},e.prototype.beginQuery=function(){if(2===ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){var e=this.gl,t=this.getQueryTimerExtensionWebGL2(),n=e.createQuery();return e.beginQuery(t.TIME_ELAPSED_EXT,n),n}var r=this.getQueryTimerExtensionWebGL1(),o=r.createQueryEXT();return r.beginQueryEXT(r.TIME_ELAPSED_EXT,o),o},e.prototype.endQuery=function(){if(2!==ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){var e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT);}else{var t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT);}},e.prototype.waitForQueryAndGetTime=function(e){return __awaiter(this,void 0,void 0,function(){var t=this;return __generator(this,function(n){switch(n.label){case 0:return [4,repeatedTry(function(){return t.disposed||t.isQueryAvailable(e,ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))})];case 1:return n.sent(),[2,this.getQueryTime(e,ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))]}})})},e.prototype.getQueryTime=function(e,t){if(0===t)return null;if(2===t){var n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}var r=this.getQueryTimerExtensionWebGL1();return r.getQueryObjectEXT(e,r.QUERY_RESULT_EXT)/1e6},e.prototype.isQueryAvailable=function(e,t){if(0===t)return !0;if(2===t){var n=this.gl,r=this.getQueryTimerExtensionWebGL2(),o=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint}o=(r=this.getQueryTimerExtensionWebGL1()).getQueryObjectEXT(e,r.QUERY_RESULT_AVAILABLE_EXT);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint},e.prototype.pollFence=function(e){var t=this;return new Promise(function(n){t.addItemToPoll(function(){return e.isFencePassed()},function(){return n()});})},e.prototype.pollItems=function(){for(var e=binSearchLastTrue(this.itemsToPoll.map(function(e){return e.isDoneFn})),t=0;t<=e;++t){(0, this.itemsToPoll[t].resolveFn)();}this.itemsToPoll=this.itemsToPoll.slice(e+1);},e.prototype.addItemToPoll=function(e,t){var n=this;this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1||repeatedTry(function(){return n.pollItems(),0===n.itemsToPoll.length});},e.prototype.bindTextureToFrameBuffer=function(e){this.throwIfDisposed(),bindColorTextureToFramebuffer(this.gl,e,this.framebuffer),this.autoDebugValidate&&validateFramebuffer(this.gl);},e.prototype.unbindTextureToFrameBuffer=function(){null!=this.outputTexture?(bindColorTextureToFramebuffer(this.gl,this.outputTexture,this.framebuffer),this.autoDebugValidate&&validateFramebuffer(this.gl)):unbindColorTextureFromFramebuffer(this.gl,this.framebuffer);},e.prototype.downloadMatrixDriver=function(e,t){this.bindTextureToFrameBuffer(e);var n=t();return this.unbindTextureToFrameBuffer(),n},e.prototype.setOutputMatrixTextureDriver=function(e,t,n){this.throwIfDisposed();var r=this.gl;bindColorTextureToFramebuffer(r,e,this.framebuffer),this.autoDebugValidate&&validateFramebuffer(r),this.outputTexture=e,callAndCheck(r,function(){return r.viewport(0,0,t,n)}),callAndCheck(r,function(){return r.scissor(0,0,t,n)});},e.prototype.setOutputMatrixWriteRegionDriver=function(e,t,n,r){var o=this;this.throwIfDisposed(),callAndCheck(this.gl,function(){return o.gl.scissor(e,t,n,r)});},e.prototype.throwIfDisposed=function(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")},e.prototype.throwIfNoProgram=function(){if(null==this.program)throw new Error("No GPU program is currently set.")},e}();function binSearchLastTrue(e){for(var t=0,n=e.length-1,r=-1;t<=n;){var o=t+n>>1;e[o]()?(r=o,t=o+1):n=o-1;}return r}function compileProgram(e,t,n,r){for(var o=t.userCode,a=n.map(function(e,n){var r={logicalShape:e.shape,texShape:e.isUniform?null:e.texData.texShape,isUniform:e.isUniform,isPacked:!e.isUniform&&e.texData.isPacked};return {name:t.variableNames[n],shapeInfo:r}}),i=a.map(function(e){return e.shapeInfo}),s={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked},u=makeShader(a,s,o,!0===t.supportsBroadcasting,t.usesPackedTextures),l=e.createProgram(u),c={},p=0;p<t.variableNames.length;p++){var d=t.variableNames[p];c[d]=e.getUniformLocation(l,d,!1);}return {program:t,source:u,webGLProgram:l,uniformLocations:c,gpgpu:e,inShapeInfos:i,outShapeInfo:s}}function validateBinaryAndProgram(e,t){if(e.length!==t.length)throw Error("Binary was compiled with "+e.length+" inputs, but was executed with "+t.length+" inputs");e.forEach(function(e,n){var r=e.logicalShape,o=t[n],a=o.shape;if(!arraysEqual(r,a))throw Error("Binary was compiled with different shapes than the current args. Shapes "+r+" and "+a+" must match");if(!e.isUniform||!o.isUniform){var i=e.texShape,s=o.isUniform?null:o.texData.texShape;if(!arraysEqual(i,s))throw Error("Binary was compiled with different texture shapes than the current args. Shape "+i+" and "+s+" must match")}});}function runProgram(e,t,n,r){validateBinaryAndProgram(e.inShapeInfos,t),validateBinaryAndProgram([e.outShapeInfo],[n]);var o=n.texData.texture,a=n.texData.texShape,i=e.gpgpu;n.texData.isPacked?i.setOutputPackedMatrixTexture(o,a[0],a[1]):i.setOutputMatrixTexture(o,a[0],a[1]),i.setProgram(e.webGLProgram),t.forEach(function(t,n){var r=e.program.variableNames[n],o=e.uniformLocations[r];if(null!=o){if(t.isUniform){if(1===sizeFromShape(t.shape))i.gl.uniform1f(o,t.uniformValues[0]);else{var a=t.uniformValues;a instanceof Float32Array||(a=new Float32Array(a)),i.gl.uniform1fv(o,a);}return}var s=t.texData.texture;i.setInputMatrixTexture(s,o,n);}}),null!=r&&r(i,e.webGLProgram),i.executeProgram();}function makeShaderKey(e,t,n){var r="";t.concat(n).forEach(function(e){r+=e.shape+"_"+(e.isUniform?"uniform":e.texData.texShape);});var o=e.userCode,a=(!0===e.supportsBroadcasting).toString(),i=e.constructor.name;return i+="_"+a+"_"+r+"_"+o}var Im2ColProgram=function(){return function(e,t,n){this.variableNames=["A"],this.outputShape=e;var r=n.filterWidth,o=n.inChannels,a=n.strideWidth,i=n.strideHeight,s=n.padInfo,u=n.outWidth,l=n.dilationWidth,c=n.dilationHeight,p=s.left,d=s.top,h=o*r;this.userCode="\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            int blockIndex = rc.y + col;\n            int pos = rc.x + row;\n\n            if(blockIndex >= "+e[1]+" || pos >= "+e[0]+") continue;\n\n            int offsetY = int(blockIndex / ("+u+")) * "+i+" - "+d+";\n            int d0 = offsetY + "+c+" * (pos / "+h+");\n\n            if(d0 >= "+t[0]+" || d0 < 0) continue;\n\n            int offsetX = int(mod(float(blockIndex), "+u+".) * "+a+". - "+p+".);\n            int d1 = offsetX + "+l+" * (int(mod(float(pos), "+h+".) / "+o+".));\n\n            if(d1 >= "+t[1]+" || d1 < 0) continue;\n\n            result[row * 2 + col] = getA(d0, d1, int(mod(float(pos), "+o+".)));\n          }\n        }\n\n        gl_FragColor = result;\n      }\n    ";}}(),LRNProgram=function(){return function(e,t,n,r,o){this.variableNames=["x"],this.outputShape=[];var a,i=t,s=e[3]-1;this.outputShape=e;var u="float("+n+") + float("+r+") * sum";a=.5===o?"inversesqrt("+u+")":1===o?"1.0/("+u+")":"exp(log("+u+") * float(-"+o+"));",this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -"+i+"; j <= "+i+"; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  "+s+") {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * "+a+";\n        setOutput(val);\n      }\n    ";}}(),LRNGradProgram=function(){return function(e,t,n,r,o){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=o,this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < "+this.depth+"; ++d) {\n          int depthBegin = int(max(0.0, float(d - "+t+")));\n          int depthEnd = int(min(float("+this.depth+"),\n              float(d + "+t+" + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = "+this.depth+";\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float("+r+") * norm + float("+n+");\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float("+r+")\n                * float("+o+")\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * "+o+");\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    ";}}(),MaxPool2DBackpropProgram=function(){return function(e){this.variableNames=["dy","maxPos"],this.outputShape=e.inShape;var t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,o=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=o-1-e.padInfo.top,s=a-1-e.padInfo.left,u=o*a-1;this.userCode="\n      const ivec2 pads = ivec2("+i+", "+s+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+o+";\n          wR += "+r+") {\n          float dyR = float(dyRCorner + wR) / "+t+".0;\n\n          if (dyR < 0.0 || dyR >= "+e.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < "+a+"; wC++) {\n            float dyC = float(dyCCorner + wC) / "+n+".0;\n\n            if (dyC < 0.0 || dyC >= "+e.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = "+u+" - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * "+a+" + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    ";}}(),MatMulProgram=function(){return function(e,t,n,r){void 0===n&&(n=!1),void 0===r&&(r=!1),this.variableNames=["matrixA","matrixB"];var o=e[0],a=n?e[2]:e[1],i=r?t[1]:t[2],s=n?e[1]:e[2];this.outputShape=[o,a,i];var u=function(e,t){return n?"batch, "+t+" + "+e+", aRow":"batch, aRow, "+t+" + "+e},l=function(e,t){return r?"batch, bCol, "+t+" + "+e:"batch, "+t+" + "+e+", bCol"},c=4*Math.floor(s/4),p=s%4;this.userCode=" float dotARowBCol(int batch, int aRow, int bCol) {\n      float result = 0.0;\n      for (int i = 0; i < "+c+"; i += 4) {\n        vec4 a = vec4(\n          getMatrixA("+u(0,"i")+"),\n          getMatrixA("+u(1,"i")+"),\n          getMatrixA("+u(2,"i")+"),\n          getMatrixA("+u(3,"i")+")\n        );\n        vec4 b = vec4(\n          getMatrixB("+l(0,"i")+"),\n          getMatrixB("+l(1,"i")+"),\n          getMatrixB("+l(2,"i")+"),\n          getMatrixB("+l(3,"i")+")\n        );\n\n        result += dot(a, b);\n      }\n\n      if ("+(1===p)+") {\n        result += getMatrixA("+u(0,c)+") *\n          getMatrixB("+l(0,c)+");\n      } else if ("+(2===p)+") {\n        vec2 a = vec2(\n          getMatrixA("+u(0,c)+"),\n          getMatrixA("+u(1,c)+")\n        );\n        vec2 b = vec2(\n          getMatrixB("+l(0,c)+"),\n          getMatrixB("+l(1,c)+")\n        );\n        result += dot(a, b);\n      } else if ("+(3===p)+") {\n        vec3 a = vec3(\n          getMatrixA("+u(0,c)+"),\n          getMatrixA("+u(1,c)+"),\n          getMatrixA("+u(2,c)+")\n        );\n        vec3 b = vec3(\n          getMatrixB("+l(0,c)+"),\n          getMatrixB("+l(1,c)+"),\n          getMatrixB("+l(2,c)+")\n        );\n        result += dot(a, b);\n      }\n\n      return result;\n    }\n\n    void main() {\n      ivec3 resBRC = getOutputCoords();\n      setOutput(dotARowBCol(resBRC.x, resBRC.y, resBRC.z));\n    }\n    ";}}(),MatMulPackedProgram=function(){return function(e,t,n,r,o){void 0===r&&(r=!1),void 0===o&&(o=!1),this.variableNames=["matrixA","matrixB"],this.usesPackedTextures=!0,this.outputShape=n;var a=r?e[0]:e[1],i=Math.ceil(a/2),s=r?"i * 2, rc.x":"rc.x, i * 2",u=o?"rc.y, i * 2":"i * 2, rc.y",l=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],c=o?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"];this.userCode="\n      const float sharedDimension = "+i+".0;\n\n      vec4 dot2x2ARowBCol(ivec2 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < "+i+"; i++) {\n          vec4 a = getMatrixA("+s+");\n          vec4 b = getMatrixB("+u+");\n\n          result += ("+l[0]+" * "+c[0]+") + ("+l[1]+" * "+c[1]+");\n        }\n        return result;\n      }\n\n      void main() {\n        ivec2 rc = getOutputCoords();\n        setOutput(dot2x2ARowBCol(rc));\n      }\n    ";}}(),MultinomialProgram=function(){function e(e,t,n){this.variableNames=["probs"],this.outputShape=[e,n],this.userCode="\n      uniform float seed;\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < "+(t-1)+"; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float("+(t-1)+"));\n      }\n    ";}return e.prototype.getCustomSetupFunc=function(e){var t=this;return function(n,r){null==t.seedLoc&&(t.seedLoc=n.getUniformLocation(r,"seed")),n.gl.uniform1f(t.seedLoc,e);}},e}(),OneHotProgram=function(){return function(e,t,n,r){this.variableNames=["indices"],this.outputShape=[e,t],this.userCode="\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float("+r+"), float("+n+"),\n                      float(index == coords.y)));\n      }\n    ";}}();function getVecChannels(e,t){return ["x","y","z","w","u","v"].slice(0,t).map(function(t){return e+"."+t})}function getChannels(e,t){return 1===t?[e]:getVecChannels(e,t)}function getSourceCoords$1(e,t){if(1===e)return "rc";for(var n="",r=0;r<e;r++)n+=t[r],r<e-1&&(n+=",");return n}var PackProgram=function(){return function(e){this.variableNames=["A"],this.isPackShader=!0,this.outputShape=e;var t=e.length;if(0===t)this.userCode="\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      ";else{var n=getChannels("rc",t),r=getCoordsDataType(t),o=getOutOfBoundsCondition(t,e,n),a=getSetup(t,e[e.length-1],e[e.length-2],n),i=getOutput(e,n);this.userCode="\n        void main() {\n          "+r+" rc = getOutputCoords();\n\n          if("+o+") {\n            setOutput(vec4(0));\n          } else {\n            "+a+"\n\n            setOutput(vec4("+i+"));\n          }\n        }\n      ";}}}();function getSourceCoordsArr(e,t){for(var n=[],r=0;r<=1;r++)for(var o=0;o<=1;o++){for(var a=(0===r?"r":"rp1")+", "+(0===o?"c":"cp1"),i=2;i<e;i++)a=t[t.length-1-i]+","+a;n.push(a);}return n}function getOutOfBoundsCondition(e,t,n){if(1===e)return "rc > "+t[0];for(var r="",o=e-2;o<e;o++)r+=n[o]+" >= "+t[o],o<e-1&&(r+="||");return r}function getSetup(e,t,n,r){if(1===e)return "";var o=r.slice(-2);return "\n    int r = "+o[0]+";\n    int c = "+o[1]+";\n    int rp1 = r + 1;\n    int cp1 = c + 1;\n\n    bool cEdge = cp1 >= "+t+";\n    bool rEdge = rp1 >= "+n+";\n  "}function getOutput(e,t){var n=e.length,r=getSourceCoordsArr(n,t);return 1===n?"getA(rc),\n            rc + 1 >= "+e[0]+" ? 0. : getA(rc + 1),\n            0, 0":"getA("+r[0]+"),\n          cEdge ? 0. : getA("+r[1]+"),\n          rEdge ? 0. : getA("+r[2]+"),\n          rEdge || cEdge ? 0. : getA("+r[3]+")"}var PadProgram=function(){return function(e,t,n){this.variableNames=["x"],this.outputShape=t.map(function(t,n){return t[0]+e[n]+t[1]});var r=e.length,o=getCoordsDataType(r),a=t.map(function(e){return e[0]}).join(","),i=t.map(function(t,n){return t[0]+e[n]}).join(","),s=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);this.userCode=1!==r?"\n      "+o+" start = "+o+"("+a+");\n      "+o+" end = "+o+"("+i+");\n\n      void main() {\n        "+o+" outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(float("+n+"));\n        } else {\n          "+o+" coords = outC - start;\n          setOutput(getX("+s+"));\n        }\n      }\n    ":"\n        int start = "+a+";\n        int end = "+i+";\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(float("+n+"));\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      ";}}(),Pool2DProgram=function(){return function(e,t,n){if(this.variableNames=["x"],"avg"===t&&n)throw new Error("Cannot compute positions for average pool.");var r=e.filterWidth,o=e.strideHeight,a=e.strideWidth,i=e.dilationHeight,s=e.dilationWidth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;var d="avg"===t,h="0.0";if(d||(h="-1.0 / 0.0"),n)this.userCode="\n        const ivec2 strides = ivec2("+o+", "+a+");\n        const ivec2 pads = ivec2("+c+", "+p+");\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < "+u+";\n              wR += "+i+") {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= "+e.inHeight+") {\n              continue;\n            }\n\n            for (int wC = 0; wC < "+l+";\n                wC += "+s+") {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= "+e.inWidth+") {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value >= currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = wR * "+l+" + wC;\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      ";else{var f=t+"("+t+"("+t+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"avg"===t&&(f="avgValue / count");var m=4*Math.floor(r/4),g=r%4,v="\n      if ("+d+") {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = max(values, minMaxValue);\n      }\n    ";this.userCode="\n      const ivec2 strides = ivec2("+o+", "+a+");\n      const ivec2 pads = ivec2("+c+", "+p+");\n      const float initializationValue = "+h+";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= "+e.inWidth+") {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4("+h+");\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < "+u+";\n            wR += "+i+") {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= "+e.inHeight+") {\n            continue;\n          }\n\n          for (int wC = 0; wC < "+m+"; wC += 4) {\n            int xC = xCCorner + wC * "+s+";\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + "+s+", d),\n              getValue(batch, xR, xC + 2 * "+s+", d),\n              getValue(batch, xR, xC + 3 * "+s+", d)\n            );\n\n            "+v+"\n          }\n\n          int xC = xCCorner + "+m+";\n          if ("+(1===g)+") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            "+v+"\n          } else if ("+(2===g)+") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + "+s+", d),\n              initializationValue,\n              initializationValue\n            );\n\n            "+v+"\n          } else if ("+(3===g)+") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + "+s+", d),\n              getValue(batch, xR, xC + 2 * "+s+", d),\n              initializationValue\n            );\n\n            "+v+"\n          }\n        }\n        setOutput("+f+");\n      }\n    ";}}}(),ReduceProgram=function(){return function(e,t){this.variableNames=["x"];var n=e.windowSize,r=e.batchSize,o=e.inSize,a=Math.ceil(o/n);this.outputShape=[r,a];var i="0.0",s="";"prod"===t?i="1.0":"min"===t?(i="1.0 / 0.0",s="min"):"max"===t&&(i="-1.0 / 0.0",s="max");var u=t+"("+t+"("+t+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"sum"===t?u="sumValue":"prod"===t?u="prodValue":"all"===t?u="allValue":"any"===t&&(u="anyValue");var l=4*Math.floor(n/4),c=n%4,p="\n      if ("+("sum"===t)+") {\n        sumValue += dot(values, ones);\n      } else if ("+("prod"===t)+") {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = "+s+"(values, minMaxValue);\n      }\n    ",d="vec4";"all"===t?(i="1.0",p="\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      ",d="bvec4"):"any"===t&&(i="0.0",p="\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      ",d="bvec4");var h="";o%n>0&&(h="\n        if (inIdx < 0 || inIdx >= "+o+") {\n          return initializationValue;\n        }\n      "),this.userCode="\n      const float initializationValue = "+i+";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        "+h+"\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * "+n+";\n\n        vec4 minMaxValue = vec4("+i+");\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < "+l+"; i += 4) {\n          int inIdx = inOffset + i;\n          "+d+" values = "+d+"(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          "+p+"\n        }\n\n        int inIdx = inOffset + "+l+";\n        if ("+(1===c)+") {\n          "+d+" values = "+d+"(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          "+p+"\n        } else if ("+(2===c)+") {\n          "+d+" values = "+d+"(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          "+p+"\n        } else if ("+(3===c)+") {\n          "+d+" values = "+d+"(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          "+p+"\n        }\n        setOutput("+u+");\n      }\n    ";}}(),ReshapePackedProgram=function(){return function(e,t){this.variableNames=["A"],this.usesPackedTextures=!0,this.outputShape=e;for(var n="",r=0;r<4;r++){var o="thisRC = rc;";r%2==1&&(o+="thisRC.z += 1;"),r>1&&(o+="thisRC.y += 1;"),n+="\n        "+o+"\n        "+(r>0?"if(thisRC.y < rows && thisRC.z < cols){":"")+"\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result["+r+"] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        "+(r>0?"}":"")+"\n      ";}this.userCode="\n      "+getReshapedInputCoords(t)+"\n      "+getFlatIndex(e)+"\n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = "+e[1]+";\n        int cols = "+e[2]+";\n\n        "+n+"\n\n        setOutput(result);\n      }\n    ";}}();function getFlatIndex(e){return "\n    int getFlatIndex(ivec3 coords) {\n      return round("+dotify(["coords.x","coords.y","coords.z"],computeStrides(e).map(function(e){return e.toString()}).concat(["1."]))+");\n    }\n  "}function getReshapedInputCoords(e){return "\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      "+getLogicalCoordinatesFromFlatIndex(["r","c","d"],e)+"\n      return ivec3(r, c, d);\n    }\n  "}var ResizeBilinearBackpropProgram=function(){return function(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t.shape;var r=t.shape,o=r[1],a=r[2],i=e.shape,s=i[1],u=i[2],l=[n&&s>1?o-1:o,n&&u>1?a-1:a],c=[n&&s>1?s-1:s,n&&u>1?u-1:u],p=l[0]/c[0],d=l[1]/c[1],h=1/p,f=1/d,m=2*Math.ceil(h)+2,g=2*Math.ceil(f)+2;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float("+p+");\n        const float widthScale = float("+d+");\n\n        const float invHeightScale = float("+h+");\n        const float invWidthScale = float("+f+");\n\n        const int winHeight = int("+m+");\n        const int winWidth = int("+g+");\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= "+s+") {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= "+u+") {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), "+(o-1)+".0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), "+(a-1)+".0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    ";}}(),ResizeBilinearProgram=function(){return function(e,t,n,r){this.variableNames=["A"],this.outputShape=[];var o=e[0],a=e[1],i=e[2],s=e[3];this.outputShape=[o,t,n,s];var u=[r&&t>1?a-1:a,r&&n>1?i-1:i],l=[r&&t>1?t-1:t,r&&n>1?n-1:n];this.userCode="\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          "+u[0]/l[0]+",\n          "+u[1]/l[1]+");\n      const vec2 inputShapeRC = vec2("+a+".0, "+i+".0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    ";}}(),ResizeNearestNeigborBackpropProgram=function(){return function(e,t,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=t.shape;var r=t.shape,o=r[1],a=r[2],i=e.shape,s=i[1],u=i[2],l=[n&&s>1?o-1:o,n&&u>1?a-1:a],c=[n&&s>1?s-1:s,n&&u>1?u-1:u],p=l[0]/c[0],d=l[1]/c[1],h=1/p,f=1/d,m=2*Math.ceil(h)+2,g=2*Math.ceil(f)+2;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float("+p+");\n        const float widthScale = float("+d+");\n\n        const float invHeightScale = float("+h+");\n        const float invWidthScale = float("+f+");\n\n        const int winHeight = int("+m+");\n        const int winWidth = int("+g+");\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= "+s+") {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= "+u+") {\n              continue;\n            }\n\n            float sourceFracRow =\n              float("+l[0]+") *\n                (float(dyR) / float("+c[0]+"));\n\n            float sourceFracCol =\n                float("+l[1]+") *\n                  (float(dyC) / float("+c[1]+"));\n\n            int sourceNearestRow = int(min(\n                float(int("+o+") - 1),\n                "+n+" ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int("+a+") - 1),\n                "+n+" ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    ";}}(),ResizeNearestNeighborProgram=function(){return function(e,t,n,r){this.variableNames=["A"],this.outputShape=[];var o=e[0],a=e[1],i=e[2],s=e[3];this.outputShape=[o,t,n,s];var u=[r&&t>1?a-1:a,r&&n>1?i-1:i],l=[r&&t>1?t-1:t,r&&n>1?n-1:n],c=r?"0.5":"0.0";this.userCode="\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          "+u[0]/l[0]+",\n          "+u[1]/l[1]+");\n      const vec2 inputShapeRC = vec2("+a+".0, "+i+".0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + "+c+")));\n\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    ";}}(),ReverseProgram=function(){return function(e,t){this.variableNames=["x"];var n=e.length;if(n>4)throw new Error("WebGL backend: Reverse of rank-"+n+" tensor is not yet supported");if(this.outputShape=e,1!==n){var r=e.map(function(n,r){return function(n){return -1!==t.indexOf(n)&&1!==e[n]?e[n]+" - coords["+n+"] - 1":"coords["+n+"]"}(r)}).join(","),o=getCoordsDataType(n);this.userCode="\n      void main() {\n        "+o+" coords = getOutputCoords();\n        setOutput(getX("+r+"));\n      }\n    ";}else this.userCode="\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX("+e[0]+" - coord - 1));\n        }\n      ";}}(),ScatterProgram=function(){return function(e,t,n,r,o,a,i){void 0===i&&(i=!0),this.variableNames=["updates","indices","defaultValue"],this.outputShape=a;var s=getCoordsDataType(o.length),u=getCoordsDataType(a.length),l="";1===n?l="i":2===n&&(l="i, j");var c="getIndices("+l+")",p="";1===r?p="i":2===r&&(p="i, coords[1]");var d="getUpdates("+p+")",h=t>1?"strides[j]":"strides";this.userCode="\n        "+s+" strides = "+s+"("+o+");\n\n        void main() {\n          "+u+" coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < "+e+"; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < "+t+"; j++) {\n              int index = round("+c+");\n              flattenedIndex += index * "+h+";\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += "+d+";\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      ";}}(),SegmentOpProgram=function(){return function(e,t){this.variableNames=["x","segmentIds"];var n=e.windowSize,r=e.batchSize,o=e.inSize,a=e.numSegments,i=a*Math.ceil(o/n);this.outputShape=[r,i];var s=4*Math.floor(n/4),u=n%4,l="\n        sumValue += dot(values, filter);\n    ",c="";o%n>0&&(c="\n        if (inIdx < 0 || inIdx >= "+o+") {\n          return initializationValue;\n        }\n      ");var p="";o%n>0&&(p="\n        if (inIdx < 0 || inIdx >= "+o+") {\n          return -1.0;\n        }\n      "),this.userCode="\n      const float initializationValue = 0.0;\n\n      float getValue(int batch, int inIdx) {\n        "+c+"\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        "+p+"\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          "+a+")) * float("+n+"));\n        int currentSeg = int(mod(float(outIdx), float("+a+")));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < "+s+"; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          "+l+"\n        }\n\n        int inIdx = inOffset + "+s+";\n        if ("+(1===u)+") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          "+l+"\n        } else if ("+(2===u)+") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          "+l+"\n        } else if ("+(3===u)+") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          "+l+"\n        }\n        setOutput(sumValue);\n      }\n    ";}}(),SelectProgram=function(){return function(e,t,n){var r,o;if(this.variableNames=["c","a","b"],this.outputShape=t,n>4)throw Error("Where for rank "+n+" is not yet supported");if(1===n)o="resRC",r="resRC";else{for(var a=["resRC.x","resRC.y","resRC.z","resRC.w"],i=[],s=[],u=0;u<t.length;u++)s.push(""+a[u]),u<e&&i.push(""+a[u]);r=i.join(),o=s.join();}var l=getCoordsDataType(n);this.userCode="\n      void main() {\n        "+l+" resRC = getOutputCoords();\n        float cVal = getC("+r+");\n        if (cVal >= 1.0) {\n          setOutput(getA("+o+"));\n        } else {\n          setOutput(getB("+o+"));\n        }\n      }\n    ";}}(),SliceProgram=function(){function e(e){this.variableNames=["source"],this.outputShape=e,this.rank=e.length;var t=getCoordsDataType(this.rank),n=getCoords$1(this.rank);this.userCode="\n      uniform "+t+" start;\n\n      void main() {\n        "+t+" sourceLoc = start + getOutputCoords();\n        setOutput(getSource("+n+"));\n      }\n    ";}return e.prototype.getCustomSetupFunc=function(e){var t=this;if(e.length!==this.rank)throw Error("The rank ("+this.rank+") of the program must match the length of start ("+e.length+")");return function(n,r){if(null!=t.startLoc||(t.startLoc=n.getUniformLocationNoThrow(r,"start"),null!=t.startLoc))if(1===t.rank)n.gl.uniform1i(t.startLoc,e[0]);else if(2===t.rank)n.gl.uniform2i(t.startLoc,e[0],e[1]);else if(3===t.rank)n.gl.uniform3i(t.startLoc,e[0],e[1],e[2]);else{if(4!==t.rank)throw Error("Slicing for rank "+t.rank+" is not yet supported");n.gl.uniform4i(t.startLoc,e[0],e[1],e[2],e[3]);}}},e}();function getCoords$1(e){if(1===e)return "sourceLoc";if(2===e)return "sourceLoc.x, sourceLoc.y";if(3===e)return "sourceLoc.x, sourceLoc.y, sourceLoc.z";if(4===e)return "sourceLoc.x, sourceLoc.y, sourceLoc.z, sourceLoc.w";throw Error("Slicing for rank "+e+" is not yet supported")}var StridedSliceProgram=function(){return function(e,t,n,r){this.variableNames=["x"];var o=n.filter(function(e,t){return -1===r.indexOf(t)});this.outputShape=o;var a=n.length,i=getCoordsDataType(n.length),s=getCoordsDataType(o.length),u="";if(1===a)u="coords * strides + begin";else{var l=0;u=n.map(function(e,t){return -1===r.indexOf(t)?(l++,1===o.length?"coords * strides["+t+"] + begin["+t+"]":"coords["+(l-1)+"] * strides["+t+"] + begin["+t+"]"):"begin["+t+"]"}).join(",");}this.userCode="\n      "+i+" begin = "+i+"("+e+");\n      "+i+" strides = "+i+"("+t+");\n\n      void main() {\n        "+s+" coords = getOutputCoords();\n        setOutput(getX("+u+"));\n      }\n    ";}}(),TextureManager=function(){function e(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={};}return e.prototype.acquireTexture=function(e,t,n){var r,o=getPhysicalFromLogicalTextureType(t,n),a=getKeyFromTextureShape(e,o,n);if(a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]),this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this.log();var i=this.freeTextures[a].shift();return this.usedTextures[a].push(i),i}return this.numUsedTextures++,this.log(),o===PhysicalTextureType.PACKED_2X2_FLOAT32?r=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):o===PhysicalTextureType.PACKED_2X2_FLOAT16?r=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):o===PhysicalTextureType.UNPACKED_FLOAT32?r=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):o===PhysicalTextureType.UNPACKED_FLOAT16?r=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):o===PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE&&(r=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(r),r},e.prototype.releaseTexture=function(e,t,n,r){if(null!=this.freeTextures){var o=getKeyFromTextureShape(t,getPhysicalFromLogicalTextureType(n,r),r);o in this.freeTextures||(this.freeTextures[o]=[]),this.freeTextures[o].push(e),this.numFreeTextures++,this.numUsedTextures--;var a=this.usedTextures[o],i=a.indexOf(e);if(i<0)throw new Error("Cannot release a texture that was never provided by this texture manager");a.splice(i,1),this.log();}},e.prototype.log=function(){if(this.logEnabled){var e=this.numFreeTextures+this.numUsedTextures;console.log("Free/Used",this.numFreeTextures+" / "+this.numUsedTextures,"("+e+")");}},e.prototype.getNumUsedTextures=function(){return this.numUsedTextures},e.prototype.getNumFreeTextures=function(){return this.numFreeTextures},e.prototype.dispose=function(){var e=this;if(null!=this.freeTextures){for(var t in this.freeTextures)this.freeTextures[t].forEach(function(t){e.gpgpu.deleteMatrixTexture(t);});for(var t in this.usedTextures)this.usedTextures[t].forEach(function(t){e.gpgpu.deleteMatrixTexture(t);});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0;}},e}();function getPhysicalFromLogicalTextureType(e,t){if(e===TextureUsage.UPLOAD)return t?PhysicalTextureType.PACKED_2X2_FLOAT32:PhysicalTextureType.UNPACKED_FLOAT32;if(e===TextureUsage.RENDER||null==e)return t?ENV.get("WEBGL_RENDER_FLOAT32_ENABLED")?PhysicalTextureType.PACKED_2X2_FLOAT32:PhysicalTextureType.PACKED_2X2_FLOAT16:ENV.get("WEBGL_RENDER_FLOAT32_ENABLED")?PhysicalTextureType.UNPACKED_FLOAT32:PhysicalTextureType.UNPACKED_FLOAT16;if(e===TextureUsage.DOWNLOAD||e===TextureUsage.PIXELS)return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;throw new Error("Unknown logical texture type "+e)}function getKeyFromTextureShape(e,t,n){return e[0]+"_"+e[1]+"_"+t+"_"+n}var TileProgram=function(){return function(e,t){this.variableNames=["A"];for(var n=new Array(e.length),r=0;r<n.length;r++)n[r]=e[r]*t[r];this.outputShape=n,this.rank=n.length;var o=getCoordsDataType(this.rank),a=getSourceCoords$2(e);this.userCode="\n      void main() {\n        "+o+" resRC = getOutputCoords();\n        setOutput(getA("+a+"));\n      }\n    ";}}();function getSourceCoords$2(e){var t=e.length;if(t>5)throw Error("Tile for rank "+t+" is not yet supported");if(1===t)return "imod(resRC, "+e[0]+")";for(var n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u"],r=[],o=0;o<e.length;o++)r.push("imod("+n[o]+", "+e[o]+")");return r.join()}var TransposeProgram=function(){return function(e,t){this.variableNames=["A"];for(var n=new Array(e.length),r=0;r<n.length;r++)n[r]=e[t[r]];this.outputShape=n,this.rank=n.length;var o=getCoordsDataType(this.rank),a=getSwitchedCoords(t);this.userCode="\n    void main() {\n      "+o+" resRC = getOutputCoords();\n      setOutput(getA("+a+"));\n    }\n    ";}}();function getSwitchedCoords(e){var t=e.length;if(t>6)throw Error("Transpose for rank "+t+" is not yet supported");for(var n=["resRC.x","resRC.y","resRC.z","resRC.w","resRC.u","resRC.v"],r=new Array(t),o=0;o<e.length;o++)r[e[o]]=n[o];return r.join()}var ERF_P=.3275911,ERF_A1=.254829592,ERF_A2=-.284496736,ERF_A3=1.421413741,ERF_A4=-1.453152027,ERF_A5=1.061405429,SELU_SCALEALPHA=1.7580993408473768,SELU_SCALE=1.0507009873554805,UnaryOpProgram=function(){function e(e,t){this.variableNames=["A"],this.outputShape=e,this.userCode="\n      uniform float NAN;\n      float unaryOperation(float x) {\n        "+t+"\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        float y = unaryOperation(x);\n\n        setOutput(y);\n      }\n    ";}return e.prototype.getCustomSetupFunc=function(){var e=this;return function(t,n){null==e.startLoc&&(e.startLoc=t.getUniformLocationNoThrow(n,"NAN"),null==e.startLoc)||t.gl.uniform1f(e.startLoc,NaN);}},e}(),CHECK_NAN_SNIPPET$1="if (isNaN(x)) return x;",ABS="return abs(x);",RELU=CHECK_NAN_SNIPPET$1+"\n  return (x < 0.0) ? 0.0 : x;\n",ELU="return (x >= 0.0) ? x : (exp(x) - 1.0);",SELU="\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = "+SELU_SCALEALPHA+";\n  float scale = "+SELU_SCALE+";\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n";function STEP(e){return void 0===e&&(e=0),CHECK_NAN_SNIPPET$1+"\n    return x > 0.0 ? 1.0 : float("+e+");\n  "}var NEG="return -x;",CEIL="return ceil(x);",FLOOR="return floor(x);",SIGN="\n  if (isNaN(x)) { return 0.0; }\n  return sign(x);\n",ROUND="\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n",EXP="return exp(x);",EXPM1="return exp(x) - 1.0;",LOG="if (x < 0.0) return NAN;\n  return log(x);",LOG1P="return log(1.0 + x);",SQRT="return sqrt(x);",RSQRT="return inversesqrt(x);",SIGMOID="return 1.0 / (1.0 + exp(-1.0 * x));",SOFTPLUS="\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n",SIN=CHECK_NAN_SNIPPET$1+"\n  return sin(x);\n",COS=CHECK_NAN_SNIPPET$1+"\n  return cos(x);\n",TAN="return tan(x);",ASIN="return asin(x);",ACOS="return acos(x);",ATAN=CHECK_NAN_SNIPPET$1+"\n  return atan(x);\n",SINH="\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n",COSH="\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n",TANH="\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n",ASINH="return log(x + sqrt(x * x + 1.0));",ACOSH=CHECK_NAN_SNIPPET$1+"\n  if (x < 1.0) return NAN;\n  return log(x + sqrt(x * x - 1.0));",ATANH=CHECK_NAN_SNIPPET$1+"\n  if ((x < -1.0) || (x > 1.0)) return NAN;\n  return (log(1.0 + x) - log(1.0 - x)) / 2.0;",ERF='\n  // Error function is calculated approximately with elementary function.\n  // See "Handbook of Mathematical Functions with 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s=1===i?o.as2D(1,-1):o,u=ENV.engine.runKernel(function(e){return e.multinomial(s,r,t,n)},{logits2D:s});return 1===i?u.as1D():u}function oneHot_(e,t,n,r){void 0===n&&(n=1),void 0===r&&(r=0);var o=convertToTensor(e,"indices","oneHot","int32");if(t<2)throw new Error("Error in oneHot: depth must be >=2, but it is "+t);return ENV.engine.runKernel(function(e){return e.oneHot(o,t,n,r)},{$indices:o},function(e){return {$indices:function(){return zeros(o.shape,"float32")}}})}function fromPixels_(e,t){if(void 0===t&&(t=3),t>4)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");return ENV.engine.fromPixels(e,t)}function toPixels(e,t){return __awaiter(this,void 0,void 0,function(){var n,r,o,a,i,s,u,l,c,p,d,h,f,m,g,v,y,x,T,w;return __generator(this,function(E){switch(E.label){case 0:if(n=convertToTensor(e,"img","toPixels"),e instanceof Tensor||(n=n.toInt()),2!==n.rank&&3!==n.rank)throw new Error("toPixels only supports rank 2 or 3 tensors, got rank "+n.rank+".");if(r=n.shape.slice(0,2),o=r[0],a=r[1],(i=2===n.rank?1:n.shape[2])>4||2===i)throw new Error("toPixels only supports depth of size 1, 3 or 4 but got "+i);return s=n.min(),u=n.max(),[4,s.data()];case 1:return l=E.sent()[0],[4,u.data()];case 2:if(c=E.sent()[0],s.dispose(),u.dispose(),"float32"===n.dtype){if(l<0||c>1)throw new Error("Tensor values for a float32 Tensor must be in the range [0 - 1] but got range ["+l+" - "+c+"].")}else{if("int32"!==n.dtype)throw new Error("Unsupported type for toPixels: "+n.dtype+". Please use float32 or int32 tensors.");if(l<0||c>255)throw new Error("Tensor values for a int32 Tensor must be in the range [0 - 255] but got range ["+l+" - "+c+"].")}return [4,n.data()];case 3:for(p=E.sent(),d="float32"===n.dtype?255:1,h=new Uint8ClampedArray(a*o*4),f=0;f<o*a;++f)m=void 0,g=void 0,v=void 0,y=void 0,1===i?(m=p[f]*d,g=p[f]*d,v=p[f]*d,y=255):3===i?(m=p[3*f]*d,g=p[3*f+1]*d,v=p[3*f+2]*d,y=255):4===i&&(m=p[4*f]*d,g=p[4*f+1]*d,v=p[4*f+2]*d,y=p[4*f+3]*d),h[(x=4*f)+0]=Math.round(m),h[x+1]=Math.round(g),h[x+2]=Math.round(v),h[x+3]=Math.round(y);return null!=t&&(t.width=a,t.height=o,T=t.getContext("2d"),w=new ImageData(h,a,o),T.putImageData(w,0,0)),n!==e&&n.dispose(),[2,h]}})})}function reshape_(e,t){var n=convertToTensor(e,"x","reshape",null);t=inferFromImplicitShape(t,n.size),assert(n.size===sizeFromShape(t),"new shape and old shape must have the same number of elements.");return ENV.engine.runKernel(function(e){return e.reshape(n,t)},{$x:n},function(e){return {$x:function(){return e.reshape(n.shape)}}})}function squeeze_(e,t){var n=convertToTensor(e,"x","squeeze");return reshape(n,squeezeShape(n.shape,t).newShape)}function cast_(e,t){var n=convertToTensor(e,"x","cast");return ENV.engine.runKernel(function(e){return e.cast(n,t)},{$x:n},function(e){return {$x:function(){return e.clone()}}})}function tile_(e,t){var n=convertToTensor(e,"x","tile");assert(n.rank===t.length,"Error in transpose: rank of input "+n.rank+" must match length of reps "+t+".");return ENV.engine.runKernel(function(e){return e.tile(n,t)},{$x:n},function(e){return {$x:function(){var r=zerosLike(n);if(1===n.rank)for(var o=0;o<t[0];++o)r=r.add(e.slice([o*n.shape[0]],[n.shape[0]]));else if(2===n.rank)for(o=0;o<t[0];++o)for(var a=0;a<t[1];++a)r=r.add(e.slice([o*n.shape[0],a*n.shape[1]],[n.shape[0],n.shape[1]]));else if(3===n.rank)for(o=0;o<t[0];++o)for(a=0;a<t[1];++a)for(var i=0;i<t[2];++i)r=r.add(e.slice([o*n.shape[0],a*n.shape[1],i*n.shape[2]],[n.shape[0],n.shape[1],n.shape[2]]));else{if(4!==n.rank)throw new Error("Gradient for tile operation is not implemented for rank-"+n.rank+" tensors yet.");for(o=0;o<t[0];++o)for(a=0;a<t[1];++a)for(i=0;i<t[2];++i)for(var s=0;s<t[3];++s)r=r.add(e.slice([o*n.shape[0],a*n.shape[1],i*n.shape[2],s*n.shape[3]],[n.shape[0],n.shape[1],n.shape[2],n.shape[3]]));}return r}}})}function pad1d_(e,t,n){return void 0===n&&(n=0),assert(2===t.length,"Invalid number of paddings. Must be length of 2."),pad(e,[t],n)}function pad2d_(e,t,n){return void 0===n&&(n=0),assert(2===t.length&&2===t[0].length&&2===t[1].length,"Invalid number of paddings. Must be length of 2 each."),pad(e,t,n)}function pad3d_(e,t,n){return void 0===n&&(n=0),assert(3===t.length&&2===t[0].length&&2===t[1].length&&2===t[2].length,"Invalid number of paddings. Must be length of 2 each."),pad(e,t,n)}function pad4d_(e,t,n){return void 0===n&&(n=0),assert(4===t.length&&2===t[0].length&&2===t[1].length&&2===t[2].length&&2===t[3].length,"Invalid number of paddings. Must be length of 2 each."),pad(e,t,n)}function pad_(e,t,n){void 0===n&&(n=0);var r=convertToTensor(e,"x","pad");if(0===r.rank)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");var o=t.map(function(e){return e[0]});return ENV.engine.runKernel(function(e){return e.pad(r,t,n)},{$x:r},function(e){return {$x:function(){return e.slice(o,r.shape)}}})}function stack_(e,t){void 0===t&&(t=0);var n=convertToTensorArray(e,"tensors","stack");if(assert(n.length>=1,"Pass at least one tensor to tf.stack"),1===n.length)return n[0].expandDims(t);var r=n[0].rank,o=n[0].shape,a=n[0].dtype;assert(t<=r,"Axis must be <= rank of the tensor"),n.forEach(function(e){assertShapesMatch(o,e.shape,"All tensors passed to stack must have matching shapes");}),n.forEach(function(e){assert(a===e.dtype,"All tensors passed to stack must have matching dtypes");});var i=n.map(function(e){return e.expandDims(t)});return concat(i,t)}function batchToSpaceND_(e,t,n){var r=convertToTensor(e,"x","batchToSpaceND"),o=t.reduce(function(e,t){return e*t});assert(r.rank>=1+t.length,"input rank is "+r.rank+" but should be > than blockShape.length "+t.length),assert(n.length===t.length,"crops.length is "+n.length+" but should be equal to blockShape.length  "+t.length),assert(r.shape[0]%o==0,"input tensor batch is "+r.shape[0]+" but is not divisible by the product of the elements of blockShape "+t.join(" * ")+" === "+o);return ENV.engine.runKernel(function(e){return e.batchToSpaceND(r,t,n)},{$x:r},function(e){return {$x:function(){return e.spaceToBatchND(t,n)}}})}function spaceToBatchND_(e,t,n){var r=convertToTensor(e,"x","spaceToBatchND");assert(r.rank>=1+t.length,"input rank "+r.rank+" should be > than [blockShape] "+t.length),assert(n.length===t.length,"paddings.shape[0] "+n.length+" must be equal to [blockShape] "+t.length),assert(r.shape.reduce(function(e,r,o){return o>0&&o<=t.length?e&&(r+n[o-1][0]+n[o-1][1])%t[o-1]==0:e},!0),"input spatial dimensions "+r.shape.slice(1)+" with paddings "+n.toString()+" must be divisible by blockShapes "+t.toString());return ENV.engine.runKernel(function(e){return e.spaceToBatchND(r,t,n)},{$x:r},function(e){return {$x:function(){return e.batchToSpaceND(t,n)}}})}function unstack_(e,t){void 0===t&&(t=0);for(var n,r=convertToTensor(e,"x","unstack"),o=r.shape[t],a=Array(r.rank-1).fill(0),i=0,s=0;s<r.rank;s++)s!==t&&(a[i]=r.shape[s],i++);n=Array(o).fill(1);var u=Array(r.rank).fill(0),l=r.shape.slice();return n.map(function(e){l[t]=e;var n=r.slice(u,l);return u[t]+=e,n.reshape(a)})}function cumsum_(e,t,n,r){void 0===t&&(t=0),void 0===n&&(n=!1),void 0===r&&(r=!1);var o=convertToTensor(e,"x","cumsum"),a=getAxesPermutation([t|=0],o.rank),i=o;null!=a&&(i=o.transpose(a));var s=getInnerMostAxes(1,o.rank)[0],u=ENV.engine.runKernel(function(e){return e.cumsum(i,s,n,r)},{permutedX:i},function(e){return {permutedX:function(){return e.cumsum(t,n,!r)}}});return null!=a&&(u=u.transpose(a)),u}function expandDims_(e,t){void 0===t&&(t=0);var n=convertToTensor(e,"x","expandDims");assert(t<=n.rank,"Axis must be <= rank of the tensor");var r=n.shape.slice();return t<0&&(assert(-(n.rank+1)<=t,"Axis must be in the interval ["+-(n.rank+1)+", "+n.rank+"]"),t=n.rank+t+1),r.splice(t,0,1),reshape(n,r)}function depthToSpace_(e,t,n){void 0===n&&(n="NHWC");var r=convertToTensor(e,"x","depthToSpace"),o="NHWC"===n?r.shape[1]:r.shape[2],a="NHWC"===n?r.shape[2]:r.shape[3],i="NHWC"===n?r.shape[3]:r.shape[1];return assert(o*t>=0,"Negative dimension size caused by overflow when multiplying\n      "+o+" and "+t+"  for depthToSpace with input shape\n      "+r.shape),assert(a*t>=0,"Negative dimension size caused by overflow when multiplying\n      "+a+" and "+t+" for depthToSpace with input shape\n          "+r.shape),assert(i%(t*t)==0,"Dimension size must be evenly divisible by "+t*t+" but is "+i+" for depthToSpace with input shape "+r.shape),ENV.engine.runKernel(function(e){return e.depthToSpace(r,t,n)},{$x:r})}function setdiff1dAsync_(e,t){return __awaiter(this,void 0,void 0,function(){var n,r,o,a,i,s,u,l,c,p;return __generator(this,function(d){switch(d.label){case 0:return n=convertToTensor(e,"x","setdiff1d"),r=convertToTensor(t,"y","setdiff1d"),assert(n.dtype===r.dtype,"x and y should have the same dtype, but got x ("+n.dtype+") and y ("+r.dtype+")."),assert(1===n.rank,"x should be 1D tensor, but got x ("+n.shape+")."),assert(1===r.rank,"y should be 1D tensor, but got y ("+r.shape+")."),[4,n.data()];case 1:return o=d.sent(),[4,r.data()];case 2:for(a=d.sent(),i=new Set(a),s=0,c=0;c<o.length;c++)i.has(o[c])||s++;for(u=new TensorBuffer([s],n.dtype),l=new TensorBuffer([s],"int32"),c=0,p=0;c<o.length;c++)i.has(o[c])||(u.values[p]=o[c],l.values[p]=c,p++);return [2,[u.toTensor(),l.toTensor()]]}})})}function buffer(e,t,n){return void 0===t&&(t="float32"),new TensorBuffer(e,t=t||"float32",n)}function print(e,t){void 0===t&&(t=!1),console.log(e.toString(t));}var batchToSpaceND=op({batchToSpaceND_:batchToSpaceND_}),cast=op({cast_:cast_}),clone=op({clone_:clone_}),cumsum=op({cumsum_:cumsum_}),depthToSpace=op({depthToSpace_:depthToSpace_}),expandDims=op({expandDims_:expandDims_}),eye=op({eye_:eye_}),fromPixels=op({fromPixels_:fromPixels_}),multinomial=op({multinomial_:multinomial_}),oneHot=op({oneHot_:oneHot_}),pad=op({pad_:pad_}),pad1d=op({pad1d_:pad1d_}),pad2d=op({pad2d_:pad2d_}),pad3d=op({pad3d_:pad3d_}),pad4d=op({pad4d_:pad4d_}),rand=op({rand_:rand_}),randomNormal=op({randomNormal_:randomNormal_}),randomUniform=op({randomUniform_:randomUniform_}),reshape=op({reshape_:reshape_}),spaceToBatchND=op({spaceToBatchND_:spaceToBatchND_}),squeeze=op({squeeze_:squeeze_}),stack=op({stack_:stack_}),tile=op({tile_:tile_}),truncatedNormal=op({truncatedNormal_:truncatedNormal_}),unstack=op({unstack_:unstack_}),setdiff1dAsync=setdiff1dAsync_;function whereImpl(e,t){for(var n=[],r=0;r<t.length;r++)t[r]&&n.push(r);var o=buffer(e,"int32"),a=buffer([n.length,e.length],"int32");for(r=0;r<n.length;r++){var i=o.indexToLoc(n[r]),s=r*e.length;a.values.set(i,s);}return a.toTensor()}var CPU_HANDOFF_SIZE_THRESHOLD=10,BEFORE_PAGING_CONSTANT=300,MATMUL_SHARED_DIM_THRESHOLD=1e3,MathBackendWebGL=function(){function e(e,t){if(void 0===t&&(t=!0),this.gpgpu=e,this.delayedStorage=t,this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.lruDataGPU=[],this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.binaryCache={},this.disposed=!1,ENV.get("WEBGL_VERSION")<1)throw new Error("WebGL is not supported on this device");if(null==e){var n=getWebGLContext(ENV.get("WEBGL_VERSION"));this.gpgpu=new GPGPUContext(n),this.canvas=n.canvas,this.gpgpuCreatedLocally=!0;}else this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;ENV.get("WEBGL_PAGING_ENABLED")&&(this.NUM_BYTES_BEFORE_PAGING=window.screen.height*window.screen.width*window.devicePixelRatio*BEFORE_PAGING_CONSTANT),this.textureManager=new TextureManager(this.gpgpu);}return e.prototype.register=function(e,t,n){if(this.texData.has(e))throw new Error("Data buffer is already registered");this.texData.set(e,{shape:t,dtype:n});},e.prototype.setDataMover=function(e){this.texData=new DataStorage(e);},e.prototype.fromPixels=function(e,t){if(null==e)throw new Error("pixels passed to tf.fromPixels() can not be null");var n=[e.height,e.width],r=[e.height,e.width,t];if(!(e instanceof HTMLVideoElement||e instanceof HTMLImageElement||e instanceof HTMLCanvasElement||e instanceof ImageData))throw new Error("pixels passed to tf.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ImageData, but was "+e.constructor.name);if(e instanceof HTMLVideoElement){if(null==this.fromPixels2DContext){if(!ENV.get("IS_BROWSER"))throw new Error("Can't read pixels from HTMLImageElement outside the browser.");if("complete"!==document.readyState)throw new Error("The DOM is not ready yet. Please call tf.fromPixels() once the DOM is ready. One way to do that is to add an event listener for `DOMContentLoaded` on the document object");this.fromPixels2DContext=document.createElement("canvas").getContext("2d");}this.fromPixels2DContext.canvas.width=e.width,this.fromPixels2DContext.canvas.height=e.height,this.fromPixels2DContext.drawImage(e,0,0,e.width,e.height),e=this.fromPixels2DContext.canvas;}var o=this.makeTensorHandle(n,"int32");this.texData.get(o.dataId).usage=TextureUsage.PIXELS,this.gpgpu.uploadPixelDataToTexture(this.getTexture(o.dataId),e);var a=new FromPixelsProgram(r),i=this.compileAndRun(a,[o]);return this.disposeData(o.dataId),i},e.prototype.makeTensorHandle=function(e,t){var n={};return this.register(n,e,t),{dataId:n,shape:e,dtype:t}},e.prototype.write=function(e,t){if(null==t)throw new Error("MathBackendWebGL.write(): values can not be null");var n=this.texData.get(e),r=n.texture,o=n.texShape,a=n.usage,i=n.dtype,s=n.isPacked;if("complex64"===i)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");null!=r&&(this.releaseTexture(e,r,o,a,s),n.texture=null,n.texShape=null),n.usage=TextureUsage.UPLOAD,n.values=t,this.delayedStorage||this.uploadToGPU(e);},e.prototype.readSync=function(e){var t=this.texData.get(e),n=t.values,r=t.dtype,o=t.complexTensors;if(null!=n)return this.convertAndCacheOnCPU(e);if("string"===r)return n;var a,i,s=null!=this.activeTimers;(s&&(a=performance.now()),"complex64"===r)?i=mergeRealAndImagArrays(o.real.dataSync(),o.imag.dataSync()):i=this.getValuesFromTexture(e);return s&&(this.downloadWaitMs+=performance.now()-a),this.convertAndCacheOnCPU(e,i)},e.prototype.read=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o,a,i,s,u,l,c,p,d,h,f,m,g,v,y;return __generator(this,function(x){switch(x.label){case 0:if(this.pendingRead.has(e))return r=this.pendingRead.get(e),[2,new Promise(function(e){return r.push(e)})];if(o=this.texData.get(e),a=o.texture,i=o.values,s=o.texShape,u=o.isPacked,l=o.shape,null!=i)return [2,this.convertAndCacheOnCPU(e)];if(this.pendingRead.set(e,[]),!ENV.get("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&2===ENV.get("WEBGL_VERSION"))throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");return c=s[1],p=s[0],u&&(t=getPackedMatrixTextureShapeWidthHeight(s[0],s[1]),c=t[0],p=t[1]),d=this.gpgpu.maybeCreateBufferFromTexture(a,p,c),[4,this.gpgpu.createAndWaitForFence()];case 1:return x.sent(),d instanceof WebGLTexture?h=this.getValuesFromTexture(e):u?(f=this.getBatchDim(l),m=1,g=1,l.length&&(n=this.getRowsCols(l),m=n[0],g=n[1]),h=this.gpgpu.downloadPackedMatrixFromBuffer(d,f,m,g,s[0],s[1])):h=this.gpgpu.downloadFloat32MatrixFromBuffer(d,s[0],s[1]),v=this.convertAndCacheOnCPU(e,h),y=this.pendingRead.get(e),this.pendingRead.delete(e),y.forEach(function(e){return e(v)}),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)),[2,v]}})})},e.prototype.getValuesFromTexture=function(e){var t,n=this.texData.get(e),r=n.shape,o=n.dtype,a=n.texture,i=n.texShape;if(ENV.get("WEBGL_DOWNLOAD_FLOAT_ENABLED")){if(this.texData.get(e).isPacked){var s=this.getBatchDim(r),u=1,l=1;return r.length&&(u=(t=this.getRowsCols(r))[0],l=t[1]),this.gpgpu.downloadMatrixFromPackedTexture(a,s,u,l,i[0],i[1])}return this.gpgpu.downloadFloat32MatrixFromOutputTexture(a,i[0],i[1])}var c=this.makeTensorHandle(r,"float32");c.size=sizeFromShape(r),this.texData.get(c.dataId).usage=TextureUsage.DOWNLOAD;var p=new EncodeFloatProgram(r);this.compileAndRun(p,[{shape:r,dtype:o,dataId:e}],c,null,!1);var d=this.texData.get(c.dataId),h=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(d.texture,d.texShape[0],d.texShape[1]);return this.disposeData(c.dataId),h},e.prototype.time=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o,a,i,s;return __generator(this,function(u){switch(u.label){case 0:return t=this.activeTimers,n=[],r=!1,null==this.programTimersStack?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e(),o=flatten(this.activeTimers.map(function(e){return e.query})).filter(function(e){return null!=e}),a=flatten(this.activeTimers.map(function(e){return e.name})).filter(function(e){return null!=e}),this.activeTimers=t,r&&(this.programTimersStack=null),[4,Promise.all(o)];case 1:return i=u.sent(),s={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:sum(i),getExtraProfileInfo:function(){return i.map(function(e,t){return {name:a[t],ms:e}}).map(function(e){return e.name+": "+e.ms}).join(", ")},wallMs:null},this.uploadWaitMs=0,this.downloadWaitMs=0,[2,s]}})})},e.prototype.memory=function(){return {unreliable:!1,numBytesInGPU:this.numBytesInGPU}},e.prototype.startTimer=function(){return ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?this.gpgpu.beginQuery():{startMs:performance.now(),endMs:null}},e.prototype.endTimer=function(e){return ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?(this.gpgpu.endQuery(),e):(e.endMs=performance.now(),e)},e.prototype.getQueryTime=function(e){return __awaiter(this,void 0,void 0,function(){var t;return __generator(this,function(n){return ENV.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")>0?[2,this.gpgpu.waitForQueryAndGetTime(e)]:[2,(t=e).endMs-t.startMs]})})},e.prototype.disposeData=function(e){if(!this.pendingDisposal.has(e))if(this.pendingRead.has(e))this.pendingDisposal.add(e);else if(this.texData.has(e)){var t=this.texData.get(e),n=t.texture,r=t.texShape,o=t.usage,a=t.complexTensors,i=t.isPacked;null!=n&&this.releaseTexture(e,n,r,o,i),null!=a&&(a.real.dispose(),a.imag.dispose()),this.texData.delete(e);}},e.prototype.getTexture=function(e){return this.uploadToGPU(e),this.texData.get(e).texture},e.prototype.getCPUBackend=function(){return ENV.get("WEBGL_CPU_FORWARD")?(null==this.cpuBackend&&(this.cpuBackend=ENV.findBackend("cpu")),this.cpuBackend):null},e.prototype.shouldExecuteOnCPU=function(e,t){var n=this;return void 0===t&&(t=CPU_HANDOFF_SIZE_THRESHOLD),null!=this.getCPUBackend()&&e.every(function(e){return null==n.texData.get(e.dataId).texture&&e.size<t})},e.prototype.getGPGPUContext=function(){return this.gpgpu},e.prototype.getCanvas=function(){return this.canvas},e.prototype.complex=function(e,t){var n=this.makeOutputArray(e.shape,"complex64");return this.texData.get(n.dataId).complexTensors={real:ENV.engine.keep(e.clone()),imag:ENV.engine.keep(t.clone())},n},e.prototype.real=function(e){return this.texData.get(e.dataId).complexTensors.real.clone()},e.prototype.imag=function(e){return this.texData.get(e.dataId).complexTensors.imag.clone()},e.prototype.slice=function(e,t,n){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.slice(e,t,n);var r=new SliceProgram(n),o=r.getCustomSetupFunc(t);return this.compileAndRun(r,[e],null,o)},e.prototype.stridedSlice=function(e,t,n,r,o,a,i,s,u){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.stridedSlice(e,t,n,r,o,a,i,s,u);var l=getStridedSlicedInfo(e.shape,t,n,r,o,a,i,s,u),c=l[0],p=l[1],d=l[2],h=p.filter(function(e,t){return -1===d.indexOf(t)});if(h.some(function(e){return 0===e}))return tensor([],h);var f=new StridedSliceProgram(c,r,p,d);return this.compileAndRun(f,[e])},e.prototype.reverse=function(e,t){var n=new ReverseProgram(e.shape,t);return this.compileAndRun(n,[e])},e.prototype.concat2Tensors=function(e,t,n){var r=computeOutShape([e.shape,t.shape],n),o=e.as2D(-1,sizeFromShape(e.shape.slice(n))),a=t.as2D(-1,sizeFromShape(t.shape.slice(n))),i=new ConcatProgram(o.shape,a.shape);return this.compileAndRun(i,[o,a]).reshape(r)},e.prototype.concat=function(e,t){if(this.shouldExecuteOnCPU(e))return this.cpuBackend.concat(e,t);if(1===e.length)return e[0];for(var n=e[0],r=1;r<e.length;++r)n=this.concat2Tensors(n,e[r],t);return n},e.prototype.neg=function(e){var t=new UnaryOpProgram(e.shape,NEG);return this.compileAndRun(t,[e])},e.prototype.batchMatMul=function(e,t,n,r){var o=n?e.shape[2]:e.shape[1],a=r?t.shape[1]:t.shape[2],i=n?e.shape[1]:e.shape[2],s=e.shape[0];if((1===o||1===a)&&i>MATMUL_SHARED_DIM_THRESHOLD){n&&(e=e.transpose([0,2,1])),r&&(t=t.transpose([0,2,1]));var u=1===a?e:e.as3D(s,i,1),l=1===a?2:1,c=1===a?t.as3D(s,1,i):t;return this.multiply(u,c).sum(l,!0)}var p=upcastType(e.dtype,t.dtype);if(1===s){var d=e.as2D(e.shape[1],e.shape[2]),h=t.as2D(t.shape[1],t.shape[2]),f=new MatMulPackedProgram(d.shape,h.shape,[o,a],n,r),m=this.makePackedTensor(f.outputShape,p),g=this.compileAndRun(f,[d,h],m);return g.reshape([1,g.shape[0],g.shape[1]])}f=new MatMulProgram(e.shape,t.shape,n,r),m=this.makeOutputArray(f.outputShape,p);return this.compileAndRun(f,[e,t],m)},e.prototype.multiply=function(e,t){if("complex64"===e.dtype){var n=this.texData.get(e.dataId),r=this.texData.get(t.dataId),o=new BinaryOpComplexProgram(COMPLEX_MULTIPLY.REAL,e.shape,t.shape),a=new BinaryOpComplexProgram(COMPLEX_MULTIPLY.IMAG,e.shape,t.shape),i=[this.makeComplexComponentTensorHandle(e,n.complexTensors.real),this.makeComplexComponentTensorHandle(e,n.complexTensors.imag),this.makeComplexComponentTensorHandle(t,r.complexTensors.real),this.makeComplexComponentTensorHandle(t,r.complexTensors.imag)],s=this.compileAndRun(o,i),u=this.compileAndRun(a,i),l=this.complex(s,u);return s.dispose(),u.dispose(),l}if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.multiply(e,t);var c=new BinaryOpProgram(MUL,e.shape,t.shape),p=this.makeOutputArray(c.outputShape,e.dtype);return this.compileAndRun(c,[e,t],p)},e.prototype.batchNormalization=function(e,t,n,r,o,a){var i=[e,t,n],s=null;null!=a&&(s=a.shape,i.push(a));var u=null;if(null!=o&&(u=o.shape,i.push(o)),ENV.get("WEBGL_PACK_BATCHNORMALIZATION")){var l=new BatchNormPackedProgram(e.shape,t.shape,n.shape,s,u,r);return this.compileAndRun(l,i)}var c=new BatchNormProgram(e.shape,t.shape,n.shape,s,u,r);return this.compileAndRun(c,i)},e.prototype.localResponseNormalization4D=function(e,t,n,r,o){var a=new LRNProgram(e.shape,t,n,r,o);return this.compileAndRun(a,[e])},e.prototype.LRNGrad=function(e,t,n,r,o,a,i){var s=new LRNGradProgram(t.shape,r,o,a,i);return this.compileAndRun(s,[t,n,e])},e.prototype.tile=function(e,t){var n=new TileProgram(e.shape,t);return this.compileAndRun(n,[e])},e.prototype.pad=function(e,t,n){var r=new PadProgram(e.shape,t,n);return this.compileAndRun(r,[e])},e.prototype.transpose=function(e,t){var n=new TransposeProgram(e.shape,t);return this.compileAndRun(n,[e])},e.prototype.gather=function(e,t,n){var r=new GatherProgram(e.shape,t.size,n);return this.compileAndRun(r,[e,t])},e.prototype.batchToSpaceND=function(e,t,n){assert(e.rank<=4,"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");var r=t.reduce(function(e,t){return e*t}),o=getReshaped(e.shape,t,r),a=getPermuted(o.length,t.length),i=getReshapedPermuted(e.shape,t,r),s=getSliceBeginCoords(n,t.length),u=getSliceSize(i,n,t.length);return e.reshape(o).transpose(a).reshape(i).slice(s,u)},e.prototype.spaceToBatchND=function(e,t,n){assert(e.rank<=4,"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");var r=t.reduce(function(e,t){return e*t}),o=[[0,0]];o.push.apply(o,n);for(var a=1+t.length;a<e.shape.length;++a)o.push([0,0]);var i=e.pad(o),s=getReshaped(i.shape,t,r,!1),u=getPermuted(s.length,t.length,!1),l=getReshapedPermuted(i.shape,t,r,!1);return i.reshape(s).transpose(u).reshape(l)},e.prototype.reduce=function(e,t,n){var r=e.shape[0],o=e.shape[1],a=computeOptimalWindowSize(o),i=new ReduceProgram({windowSize:a,inSize:o,batchSize:r},t),s=i.outputShape,u=s[0],l=s[1],c=this.makeOutputArray([u,l],n);return this.compileAndRun(i,[e],c),1===c.shape[1]?c:this.reduce(c,t,n)},e.prototype.argReduce=function(e,t,n){void 0===n&&(n=null);var r=e.shape[0],o=e.shape[1];null!=n&&(r=n.shape[0],o=n.shape[1]);var a=computeOptimalWindowSize(o),i=new ArgMinMaxProgram({windowSize:a,inSize:o,batchSize:r},t,null==n),s=i.outputShape,u=s[0],l=s[1],c=this.makeOutputArray([u,l],"int32"),p=[e];return null!=n&&p.push(n),this.compileAndRun(i,p,c),1===c.shape[1]?c:this.argReduce(e,t,c)},e.prototype.sum=function(e,t){assertAxesAreInnerMostDims("sum",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o),i=sumOutType(e.dtype);return this.reduce(a,"sum",i).reshape(r)},e.prototype.prod=function(e,t){var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o),i=sumOutType(e.dtype);return this.reduce(a,"prod",i).reshape(r)},e.prototype.unsortedSegmentSum=function(e,t,n){var r=0,o=getAxesPermutation([r],e.rank),a=e;null!=o&&(a=e.transpose(o),r=getInnerMostAxes(1,e.rank)[0]);var i=computeOutShape$1(a.shape,r,n),s=sizeFromShape([a.shape[r]]),u=a.as2D(-1,s),l=sumOutType(e.dtype),c=this.segOpCompute(u,"unsortedSegmentSum",t,l,n).reshape(i);return null!=o&&(c=c.transpose(getUndoAxesPermutation(o))),c},e.prototype.segOpCompute=function(e,t,n,r,o){var a=e.shape[0],i=e.shape[1],s=segOpComputeOptimalWindowSize(i,o),u=new SegmentOpProgram({windowSize:s,inSize:i,batchSize:a,numSegments:o},t),l=u.outputShape,c=l[0],p=l[1],d=this.makeOutputArray([c,p],r);return this.compileAndRun(u,[e,n],d),d.shape[1]===o?d:(n=range(0,o).tile([i/s]),this.segOpCompute(d,t,n,r,o))},e.prototype.argMin=function(e,t){var n=[t];assertAxesAreInnerMostDims("argMin",n,e.rank);var r=computeOutAndReduceShapes(e.shape,n),o=r[0],a=sizeFromShape(r[1]),i=e.as2D(-1,a);return this.argReduce(i,"min").reshape(o)},e.prototype.argMax=function(e,t){var n=[t];assertAxesAreInnerMostDims("argMax",n,e.rank);var r=computeOutAndReduceShapes(e.shape,n),o=r[0],a=sizeFromShape(r[1]),i=e.as2D(-1,a);return this.argReduce(i,"max").reshape(o)},e.prototype.cumsum=function(e,t,n,r){if(t!==e.rank-1)throw new Error("WebGL cumsum shader expects an inner-most axis="+(e.rank-1)+" but got axis="+t);var o=new CumSumProgram(e.shape,n,r);return this.compileAndRun(o,[e])},e.prototype.equal=function(e,t){var n=new BinaryOpProgram(EQUAL,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.notEqual=function(e,t){var n=new BinaryOpProgram(NOT_EQUAL,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.less=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.less(e,t);var n=new BinaryOpProgram(LESS,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.lessEqual=function(e,t){var n=new BinaryOpProgram(LESS_EQUAL,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.greater=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.greater(e,t);var n=new BinaryOpProgram(GREATER,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.greaterEqual=function(e,t){var n=new BinaryOpProgram(GREATER_EQUAL,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.logicalNot=function(e){var t=new UnaryOpProgram(e.shape,LOGICAL_NOT);return this.compileAndRun(t,[e])},e.prototype.logicalAnd=function(e,t){var n=new BinaryOpProgram(LOGICAL_AND,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.logicalOr=function(e,t){var n=new BinaryOpProgram(LOGICAL_OR,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[e,t],r)},e.prototype.select=function(e,t,n){var r=new SelectProgram(e.rank,t.shape,t.rank),o=this.makeOutputArray(r.outputShape,upcastType(t.dtype,n.dtype));return this.compileAndRun(r,[e,t,n],o)},e.prototype.where=function(e){warn("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");var t=e.dataSync();return whereImpl(e.shape,t)},e.prototype.topk=function(e,t,n){return topkImpl(e.dataSync(),e.shape,e.dtype,t,n)},e.prototype.min=function(e,t){assertAxesAreInnerMostDims("min",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o);return this.reduce(a,"min",a.dtype).reshape(r)},e.prototype.minimum=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.minimum(e,t);var n=new BinaryOpProgram(MIN,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.mod=function(e,t){var n=new BinaryOpProgram(MOD,e.shape,t.shape),r=n.getCustomSetupFunc();return this.compileAndRun(n,[e,t],null,r)},e.prototype.max=function(e,t){assertAxesAreInnerMostDims("max",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o);return this.reduce(a,"max",a.dtype).reshape(r)},e.prototype.maximum=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.maximum(e,t);var n=new BinaryOpProgram(MAX,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.all=function(e,t){assertAxesAreInnerMostDims("all",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o);return this.reduce(a,"all",a.dtype).reshape(r)},e.prototype.any=function(e,t){assertAxesAreInnerMostDims("any",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o);return this.reduce(a,"any",a.dtype).reshape(r)},e.prototype.squaredDifference=function(e,t){var n=new BinaryOpProgram(SQUARED_DIFFERENCE,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.realDivide=function(e,t){var n=new BinaryOpProgram(DIV,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"float32");return this.compileAndRun(n,[e,t],r)},e.prototype.floorDiv=function(e,t){var n=new BinaryOpProgram(INT_DIV,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,"int32");return this.compileAndRun(n,[e,t],r)},e.prototype.add=function(e,t){if("complex64"===e.dtype&&"complex64"===t.dtype)return this.complexSeparableBinaryOp(e,t,ADD);var n=new BinaryOpProgram(ADD,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,upcastType(e.dtype,t.dtype));return this.compileAndRun(n,[e,t],r)},e.prototype.complexSeparableBinaryOp=function(e,t,n){var r=this,o=this.texData.get(e.dataId),a=this.texData.get(t.dataId),i=[[o.complexTensors.real,a.complexTensors.real],[o.complexTensors.imag,a.complexTensors.imag]].map(function(o){var a=o[0],i=o[1],s=new BinaryOpProgram(n,e.shape,t.shape),u=r.makeOutputArray(s.outputShape,upcastType(a.dtype,i.dtype)),l=r.makeComplexComponentTensorHandle(e,a),c=r.makeComplexComponentTensorHandle(t,i);return r.compileAndRun(s,[l,c],u)}),s=i[0],u=i[1],l=this.complex(s,u);return s.dispose(),u.dispose(),l},e.prototype.makeComplexComponentTensorHandle=function(e,t){return {dataId:t.dataId,dtype:t.dtype,shape:e.shape}},e.prototype.addN=function(e){for(var t=e[0],n=1;n<e.length;n++)t=this.add(t,e[n]);return t},e.prototype.subtract=function(e,t){if("complex64"===e.dtype&&"complex64"===t.dtype)return this.complexSeparableBinaryOp(e,t,SUB);if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.subtract(e,t);var n=new BinaryOpProgram(SUB,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,upcastType(e.dtype,t.dtype));return this.compileAndRun(n,[e,t],r)},e.prototype.pow=function(e,t){var n=new BinaryOpProgram(POW,e.shape,t.shape),r=n.getCustomSetupFunc(),o=this.makeOutputArray(n.outputShape,upcastType(e.dtype,t.dtype));return this.compileAndRun(n,[e,t],o,r)},e.prototype.ceil=function(e){var t=new UnaryOpProgram(e.shape,CEIL);return this.compileAndRun(t,[e])},e.prototype.floor=function(e){var t=new UnaryOpProgram(e.shape,FLOOR);return this.compileAndRun(t,[e])},e.prototype.sign=function(e){var t=new UnaryOpProgram(e.shape,SIGN);return this.compileAndRun(t,[e])},e.prototype.round=function(e){var t=new UnaryOpProgram(e.shape,ROUND);return this.compileAndRun(t,[e])},e.prototype.exp=function(e){var t=new UnaryOpProgram(e.shape,EXP);return this.compileAndRun(t,[e])},e.prototype.expm1=function(e){var t=new UnaryOpProgram(e.shape,EXPM1);return this.compileAndRun(t,[e])},e.prototype.log=function(e){var t=new UnaryOpProgram(e.shape,LOG),n=t.getCustomSetupFunc();return this.compileAndRun(t,[e],null,n)},e.prototype.log1p=function(e){var t=new UnaryOpProgram(e.shape,LOG1P);return this.compileAndRun(t,[e])},e.prototype.sqrt=function(e){var t=new UnaryOpProgram(e.shape,SQRT);return this.compileAndRun(t,[e])},e.prototype.rsqrt=function(e){var t=new UnaryOpProgram(e.shape,RSQRT);return this.compileAndRun(t,[e])},e.prototype.square=function(e){var t=new UnaryOpProgram(e.shape,SQUARE);return this.compileAndRun(t,[e])},e.prototype.reciprocal=function(e){var t=new UnaryOpProgram(e.shape,RECIPROCAL);return this.compileAndRun(t,[e])},e.prototype.relu=function(e){var t=new UnaryOpProgram(e.shape,RELU);return this.compileAndRun(t,[e])},e.prototype.elu=function(e){var t=new UnaryOpProgram(e.shape,ELU);return this.compileAndRun(t,[e])},e.prototype.eluDer=function(e,t){var n=new BinaryOpProgram(ELU_DER,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.selu=function(e){var t=new UnaryOpProgram(e.shape,SELU);return this.compileAndRun(t,[e])},e.prototype.int=function(e){var t=new UnaryOpProgram(e.shape,TO_INT),n=this.makeOutputArray(t.outputShape,"int32");return this.compileAndRun(t,[e],n)},e.prototype.clip=function(e,t,n){var r;return r=ENV.get("WEBGL_PACK_CLIP")?new ClipPackedProgram(e.shape,t,n):new ClipProgram(e.shape,t,n),this.compileAndRun(r,[e])},e.prototype.abs=function(e){var t=new UnaryOpProgram(e.shape,ABS);return this.compileAndRun(t,[e])},e.prototype.complexAbs=function(e){var t=this.texData.get(e.dataId),n=new ComplexAbsProgram(e.shape),r=[this.makeComplexComponentTensorHandle(e,t.complexTensors.real),this.makeComplexComponentTensorHandle(e,t.complexTensors.imag)];return this.compileAndRun(n,r)},e.prototype.sigmoid=function(e){var t=new UnaryOpProgram(e.shape,SIGMOID);return this.compileAndRun(t,[e])},e.prototype.softplus=function(e){var t=new UnaryOpProgram(e.shape,SOFTPLUS);return this.compileAndRun(t,[e])},e.prototype.sin=function(e){var t=new UnaryOpProgram(e.shape,SIN);return this.compileAndRun(t,[e])},e.prototype.cos=function(e){var t=new UnaryOpProgram(e.shape,COS);return this.compileAndRun(t,[e])},e.prototype.tan=function(e){var t=new UnaryOpProgram(e.shape,TAN);return this.compileAndRun(t,[e])},e.prototype.asin=function(e){var t=new UnaryOpProgram(e.shape,ASIN);return this.compileAndRun(t,[e])},e.prototype.acos=function(e){var t=new UnaryOpProgram(e.shape,ACOS);return this.compileAndRun(t,[e])},e.prototype.atan=function(e){var t=new UnaryOpProgram(e.shape,ATAN);return this.compileAndRun(t,[e])},e.prototype.atan2=function(e,t){var n=new BinaryOpProgram(ATAN2,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.sinh=function(e){var t=new UnaryOpProgram(e.shape,SINH);return this.compileAndRun(t,[e])},e.prototype.cosh=function(e){var t=new UnaryOpProgram(e.shape,COSH);return this.compileAndRun(t,[e])},e.prototype.tanh=function(e){var t=new UnaryOpProgram(e.shape,TANH);return this.compileAndRun(t,[e])},e.prototype.asinh=function(e){var t=new UnaryOpProgram(e.shape,ASINH);return this.compileAndRun(t,[e])},e.prototype.acosh=function(e){var t=new UnaryOpProgram(e.shape,ACOSH),n=t.getCustomSetupFunc();return this.compileAndRun(t,[e],null,n)},e.prototype.atanh=function(e){var t=new UnaryOpProgram(e.shape,ATANH),n=t.getCustomSetupFunc();return this.compileAndRun(t,[e],null,n)},e.prototype.erf=function(e){var t=new UnaryOpProgram(e.shape,ERF);return this.compileAndRun(t,[e])},e.prototype.step=function(e,t){var n=new UnaryOpProgram(e.shape,STEP(t));return this.compileAndRun(n,[e])},e.prototype.conv2dWithIm2Row=function(e,t,n){var r=n.filterWidth,o=n.filterHeight,a=n.inChannels,i=n.outWidth,s=n.outHeight,u=r*o*a,l=s*i,c=[u,l],p=e.squeeze([0]),d=t.reshape([u,-1]),h=new Im2ColProgram(c,p.shape,n),f=this.compileAndRun(h,[p]),m=new MatMulPackedProgram(f.shape,d.shape,[l,n.outChannels],!0,!1);return this.compileAndRun(m,[f,d]).reshape([1,s,i,n.outChannels])},e.prototype.conv2d=function(e,t,n){if(ENV.get("WEBGL_CONV_IM2COL")&&1===e.shape[0])return this.conv2dWithIm2Row(e,t,n);var r=new Conv2DProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv2dDerInput=function(e,t,n){var r=new Conv2DDerInputProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv2dDerFilter=function(e,t,n){var r=new Conv2DDerFilterProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.depthwiseConv2D=function(e,t,n){var r;return ENV.get("WEBGL_PACK_DEPTHWISECONV")&&1===n.dilationWidth&&1===n.dilationHeight&&n.padInfo.left<=1&&n.strideWidth<=2&&n.outChannels/n.inChannels==1?(r=new DepthwiseConvPacked2DProgram(n),this.compileAndRun(r,[e,t],this.makePackedTensor(n.outShape,e.dtype))):(r=new DepthwiseConv2DProgram(n),this.compileAndRun(r,[e,t]))},e.prototype.depthwiseConv2DDerInput=function(e,t,n){var r=new DepthwiseConv2DDerInputProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.depthwiseConv2DDerFilter=function(e,t,n){var r=new DepthwiseConv2DDerFilterProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv3d=function(e,t,n){var r=new Conv3DProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv3dDerInput=function(e,t,n){var r=new Conv3DDerInputProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv3dDerFilter=function(e,t,n){var r=new Conv3DDerFilterProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.maxPool=function(e,t){var n=new Pool2DProgram(t,"max",!1),r=this.makeOutputArray(n.outputShape,e.dtype);return this.compileAndRun(n,[e],r)},e.prototype.avgPool=function(e,t){var n=new Pool2DProgram(t,"avg",!1),r=this.makeOutputArray(n.outputShape,"float32");return this.compileAndRun(n,[e],r)},e.prototype.maxPoolBackprop=function(e,t,n,r){var o=new Pool2DProgram(r,"max",!0),a=this.compileAndRun(o,[t]),i=new MaxPool2DBackpropProgram(r),s=this.makeOutputArray(i.outputShape,t.dtype),u=this.compileAndRun(i,[e,a],s);return a.dispose(),u},e.prototype.avgPoolBackprop=function(e,t,n){var r=new AvgPool2DBackpropProgram(n),o=this.makeOutputArray(r.outputShape,t.dtype);return this.compileAndRun(r,[e],o)},e.prototype.cast=function(e,t){return castTensor(e,t,this)},e.prototype.reshape=function(e,t){return this.texData.get(e.dataId).isPacked&&!isReshapeFree(e.shape,t)?this.packedReshape(e,t):reshapeTensor(e,t)},e.prototype.resizeBilinear=function(e,t,n,r){var o=new ResizeBilinearProgram(e.shape,t,n,r);return this.compileAndRun(o,[e])},e.prototype.resizeBilinearBackprop=function(e,t,n){var r=new ResizeBilinearBackpropProgram(e,t,n);return this.compileAndRun(r,[e])},e.prototype.resizeNearestNeighbor=function(e,t,n,r){var o=new ResizeNearestNeighborProgram(e.shape,t,n,r);return this.compileAndRun(o,[e])},e.prototype.resizeNearestNeighborBackprop=function(e,t,n){var r=new ResizeNearestNeigborBackpropProgram(e,t,n);return this.compileAndRun(r,[e])},e.prototype.multinomial=function(e,t,n,r){var o=t?e:softmax(e),a=o.shape[0],i=o.shape[1],s=new MultinomialProgram(a,i,n),u=this.makeOutputArray(s.outputShape,"int32"),l=s.getCustomSetupFunc(r);return this.compileAndRun(s,[o],u,l)},e.prototype.oneHot=function(e,t,n,r){var o=new OneHotProgram(e.size,t,n,r);return this.compileAndRun(o,[e])},e.prototype.nonMaxSuppression=function(e,t,n,r,o){return warn("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"),nonMaxSuppressionImpl(e.dataSync(),t.dataSync(),n,r,o)},e.prototype.cropAndResize=function(e,t,n,r,o,a){var i=new CropAndResizeProgram(e.shape,t.shape,r,o,a);return this.compileAndRun(i,[e,t,n])},e.prototype.depthToSpace=function(e,t,n){assert(t>1,"blockSize should be > 1 for depthToSpace, but was: "+t);var r=e.shape[0],o=("NHWC"===n?e.shape[1]:e.shape[2])*t,a=("NHWC"===n?e.shape[2]:e.shape[3])*t,i=("NHWC"===n?e.shape[3]:e.shape[1])/(t*t),s=new DepthToSpaceProgram("NHWC"===n?[r,o,a,i]:[r,i,o,a],t,n);return this.compileAndRun(s,[e])},e.prototype.split=function(e,t,n){return split(e,t,n)},e.prototype.scatterND=function(e,t,n){var r=calculateShapes(t,e,n),o=r.sliceRank,a=r.numUpdates,i=r.sliceSize,s=r.strides,u=r.outputSize,l=[u/i,i],c=e.reshape([a,o]),p=t.reshape([a,i]);if(0===u)return reshapeTensor(tensor([]),n);var d=scalar(0),h=new ScatterProgram(a,o,c.rank,p.rank,s,l);return this.compileAndRun(h,[p,c,d]).reshape(n)},e.prototype.sparseToDense=function(e,t,n,r){var o=calculateShapes(t,e,n),a=o.sliceRank,i=o.numUpdates,s=o.strides,u=o.outputSize,l=new ScatterProgram(i,a,e.rank,t.rank,s,[u,1],!1);return this.compileAndRun(l,[t,e,r]).reshape(n)},e.prototype.fft=function(e){return this.fftImpl(e,!1)},e.prototype.ifft=function(e){return this.fftImpl(e,!0)},e.prototype.fftImpl=function(e,t){var n=this.texData.get(e.dataId),r=new FFTProgram(COMPLEX_FFT.REAL,e.shape,t),o=new FFTProgram(COMPLEX_FFT.IMAG,e.shape,t),a=[this.makeComplexComponentTensorHandle(e,n.complexTensors.real),this.makeComplexComponentTensorHandle(e,n.complexTensors.imag)],i=this.compileAndRun(r,a),s=this.compileAndRun(o,a),u=this.complex(i,s).as2D(e.shape[0],e.shape[1]);return i.dispose(),s.dispose(),u},e.prototype.gatherND=function(e,t){var n=t.shape,r=n[n.length-1],o=prepareAndValidate(e,t),a=o[0],i=o[1],s=o[2],u=o[3],l=t.reshape([i,r]),c=e.reshape([e.size/s,s]),p=new GatherNDProgram(r,u,[i,s]);return this.compileAndRun(p,[c,l]).reshape(a)},e.prototype.makeOutputArray=function(e,t){return Tensor.make(e,{},t)},e.prototype.makePackedTensor=function(e,t){var n=Tensor.make(e,{},t);return this.texData.get(n.dataId).isPacked=!0,n},e.prototype.unpackTensor=function(e){var t=new UnpackProgram(e.shape);return this.compileAndRun(t,[e],Tensor.make(t.outputShape,{},e.dtype))},e.prototype.getBatchDim=function(e,t){return void 0===t&&(t=2),sizeFromShape(e.slice(0,e.length-t))},e.prototype.getRowsCols=function(e){if(0===e.length)throw Error("Cannot get rows and columns of an empty shape array.");return [e.length>1?e[e.length-2]:1,e[e.length-1]]},e.prototype.packedReshape=function(e,t){var n=e.reshape([this.getBatchDim(e.shape)].concat(this.getRowsCols(e.shape))),r=[this.getBatchDim(t)].concat(this.getRowsCols(t)),o=new ReshapePackedProgram(r,n.shape);return this.compileAndRun(o,[n]).reshape(t)},e.prototype.compileAndRun=function(e,t,n,r,o){var a=this;if(void 0===o&&(o=!0),null==n&&(n=e.usesPackedTextures?this.makePackedTensor(e.outputShape,t[0].dtype):this.makeOutputArray(e.outputShape,t[0].dtype)),0===n.size)return this.texData.get(n.dataId).values=getTypedArrayFromDType(n.dtype,0),n;var i=t.map(function(t){if("complex64"===t.dtype)throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");var n=a.texData.get(t.dataId);if(null==n.texture){if(!e.usesPackedTextures&&sizeFromShape(t.shape)<=ENV.get("WEBGL_SIZE_UPLOAD_UNIFORM"))return {shape:t.shape,texData:null,isUniform:!0,uniformValues:a.readSync(t.dataId)};e.usesPackedTextures&&(n.isPacked=!0,n.shape=t.shape);}else if(!!n.isPacked!=!!e.usesPackedTextures){var r=void 0,o=void 0;n.isPacked?(r=new UnpackProgram(t.shape),o=a.compileAndRun(r,[t],Tensor.make(r.outputShape,{},t.dtype))):(r=new PackProgram(t.shape),o=a.compileAndRun(r,[t],a.makePackedTensor(t.shape,t.dtype))),n=a.texData.get(o.dataId),t=o;}else if(n.isPacked&&!isReshapeFree(n.shape,t.shape)){a.delayedStorage=!1;var i=t.dataSync();a.delayedStorage=!0,t=Tensor.make(t.shape,{values:i},t.dtype),(n=a.texData.get(t.dataId)).isPacked=!0;}return a.uploadToGPU(t.dataId),{shape:t.shape,texData:n,isUniform:!1}});this.uploadToGPU(n.dataId);var s,u={shape:n.shape,texData:this.texData.get(n.dataId),isUniform:!1},l=makeShaderKey(e,i,u),c=this.getAndSaveBinary(l,function(){return compileProgram(a.gpgpu,e,i,u)}),p=null!=this.activeTimers;if(p&&(s=this.startTimer()),runProgram(c,i,u,r),ENV.get("WEBGL_PAGING_ENABLED")&&o&&this.numBytesInGPU>this.NUM_BYTES_BEFORE_PAGING)for(var d=this.numBytesInGPU-this.NUM_BYTES_BEFORE_PAGING;d>0&&this.lruDataGPU.length>0;){var h=this.lruDataGPU.shift(),f=this.texData.get(h),m=f.shape,g=f.dtype;d-=this.computeBytes(m,g),this.read(h);}return p&&(s=this.endTimer(s),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(s)})),ENV.get("WEBGL_LAZILY_UNPACK")||!this.texData.get(n.dataId).isPacked||e.isPackShader?n:this.unpackTensor(n)},e.prototype.getAndSaveBinary=function(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]},e.prototype.getTextureManager=function(){return this.textureManager},e.prototype.dispose=function(){if(!this.disposed){for(var e in this.binaryCache)this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram);this.textureManager.dispose(),this.canvas.remove(),null!=this.fromPixels2DContext&&this.fromPixels2DContext.canvas.remove(),this.gpgpuCreatedLocally&&this.gpgpu.dispose(),this.disposed=!0;}},e.prototype.floatPrecision=function(){var e=this;return tidy(function(){return e.abs(scalar(1e-8)).get()>0?32:16})},e.prototype.uploadToGPU=function(e){var t,n=this.texData.get(e),r=n.shape,o=n.values,a=n.texture,i=n.usage,s=n.isPacked;if(null==a){var u,l=null!=this.activeTimers;l&&(u=performance.now());var c=getTextureShapeFromLogicalShape(r,s);n.texShape=c;var p=this.acquireTexture(e,c,i,s);if(n.texture=p,null!=o){if(s){var d=this.getBatchDim(r),h=1,f=1;r.length&&(h=(t=this.getRowsCols(r))[0],f=t[1]),this.gpgpu.uploadMatrixToPackedTexture(p,d,h,f,c[0],c[1],typedArrayToFloat32(o));}else this.gpgpu.uploadMatrixToTexture(p,c[0],c[1],typedArrayToFloat32(o));n.values=null,l&&(this.uploadWaitMs+=performance.now()-u);}}else{ENV.get("WEBGL_PAGING_ENABLED")&&this.lruDataGPU.indexOf(e)>=0&&(this.lruDataGPU.splice(this.lruDataGPU.indexOf(e),1),this.lruDataGPU.push(e));}},e.prototype.convertAndCacheOnCPU=function(e,t){var n=this.delayedStorage,r=this.texData.get(e),o=r.texture,a=r.texShape,i=r.dtype,s=r.usage,u=r.isPacked;return n&&null!=o&&(this.releaseTexture(e,o,a,s,u),r.texture=null,r.texShape=null),r.usage=TextureUsage.UPLOAD,null!=t&&(r.values=float32ToTypedArray(t,i)),r.values},e.prototype.releaseTexture=function(e,t,n,r,o){var a=this.texData.get(e),i=a.shape,s=a.dtype;if(ENV.get("WEBGL_PAGING_ENABLED")){var u=this.lruDataGPU.indexOf(e);u>=0&&this.lruDataGPU.splice(u,1);}this.numBytesInGPU-=this.computeBytes(i,s),this.textureManager.releaseTexture(t,n,r,o);},e.prototype.acquireTexture=function(e,t,n,r){var o=this.texData.get(e),a=o.shape,i=o.dtype;return ENV.get("WEBGL_PAGING_ENABLED")&&this.lruDataGPU.push(e),this.numBytesInGPU+=this.computeBytes(a,i),this.textureManager.acquireTexture(t,n,r)},e.prototype.computeBytes=function(e,t){return sizeFromShape(e)*bytesPerElement(t)},e}();function float32ToTypedArray(e,t){if("float32"===t||"complex64"===t)return e;if("int32"===t||"bool"===t){for(var n="int32"===t?new Int32Array(e.length):new Uint8Array(e.length),r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}throw new Error("Unknown dtype "+t)}function typedArrayToFloat32(e){return e instanceof Float32Array?e:new Float32Array(e)}function neg_(e){var t=convertToTensor(e,"x","neg");return ENV.engine.runKernel(function(e){return e.neg(t)},{$x:t},function(e){return {$x:function(){return e.neg()}}})}function ceil_(e){var t=convertToTensor(e,"x","ceil");return ENV.engine.runKernel(function(e){return e.ceil(t)},{$x:t},function(e){return {$x:function(){return zerosLike(e)}}})}function floor_(e){var t=convertToTensor(e,"x","floor");return ENV.engine.runKernel(function(e){return e.floor(t)},{$x:t},function(e){return {$x:function(){return zerosLike(e)}}})}function sign_(e){var t=convertToTensor(e,"x","sign");return ENV.engine.runKernel(function(e){return e.sign(t)},{$x:t},function(e){return {$x:function(){return zerosLike(e)}}})}function round_(e){var t=convertToTensor(e,"x","round");return ENV.engine.runKernel(function(e){return e.round(t)},{$x:t},function(e){return {$x:function(){return zerosLike(e)}}})}function exp_(e){var t=convertToTensor(e,"x","exp");return ENV.engine.runKernel(function(e,n){return n(e.exp(t))},{$x:t},function(e,t){var n=t[0];return {$x:function(){return e.mulStrict(n)}}})}function expm1_(e){var t=convertToTensor(e,"x","expm1");return ENV.engine.runKernel(function(e){return e.expm1(t)},{$x:t},function(e){return {$x:function(){return e.mulStrict(t.exp())}}})}function log_(e){var t=convertToTensor(e,"x","log");return ENV.engine.runKernel(function(e){return e.log(t)},{$x:t},function(e){return {$x:function(){return e.divStrict(t.toFloat())}}})}function log1p_(e){var t=convertToTensor(e,"x","log1p");return ENV.engine.runKernel(function(e){return e.log1p(t)},{$x:t},function(e){return {$x:function(){return e.div(t.add(1))}}})}function sqrt_(e){var t=convertToTensor(e,"x","sqrt");return ENV.engine.runKernel(function(e){return e.sqrt(t)},{$x:t},function(e){return {$x:function(){return e.div(t.toFloat().sqrt().mul(2))}}})}function rsqrt_(e){var t=convertToTensor(e,"x","rsqrt");return ENV.engine.runKernel(function(e){return e.rsqrt(t)},{$x:t},function(e){return {$x:function(){return e.div(t.pow(1.5).mul(2)).neg()}}})}function square_(e){var t=convertToTensor(e,"x","square");return ENV.engine.runKernel(function(e){return e.square(t)},{$x:t},function(e){return {$x:function(){return e.mul(t.toFloat().mul(2))}}})}function reciprocal_(e){var t=convertToTensor(e,"x","reciprocal");return ENV.engine.runKernel(function(e){return e.reciprocal(t)},{$x:t},function(e){return {$x:function(){return e.divStrict(t.square().neg())}}})}function abs_(e){var t=convertToTensor(e,"x","abs");if("complex64"===t.dtype)return ENV.engine.runKernel(function(e){return e.complexAbs(t)},{$x:t});return ENV.engine.runKernel(function(e){return e.abs(t)},{$x:t},function(e){return {$x:function(){return e.mulStrict(t.toFloat().step(-1))}}})}function clipByValue_(e,t,n){var r=convertToTensor(e,"x","clipByValue");assert(t<=n,"Error in clip: min ("+t+") must be less than or equal to max ("+n+").");return ENV.engine.runKernel(function(e){return e.clip(r,t,n)},{$x:r},function(e){return {$x:function(){return e.where(r.greaterEqual(t).logicalAnd(r.lessEqual(n)),zerosLike(e))}}})}function sigmoid_(e){var t=convertToTensor(e,"x","sigmoid");return ENV.engine.runKernel(function(e,n){return n(e.sigmoid(t))},{$x:t},function(e,t){var n=t[0];return {$x:function(){return e.mul(n.mul(scalar(1).sub(n)))}}})}function logSigmoid_(e){var t=convertToTensor(e,"x","logSigmoid");return ENV.engine.runKernel(function(e){return e.softplus(t.neg()).neg()},{$x:t},function(e){return {$x:function(){return e.mulStrict(t.neg().sigmoid())}}})}function softplus_(e){var t=convertToTensor(e,"x","softplus");return ENV.engine.runKernel(function(e){return e.softplus(t)},{$x:t},function(e){return {$x:function(){return e.mulStrict(t.sigmoid())}}})}function sin_(e){var t=convertToTensor(e,"x","sin");return ENV.engine.runKernel(function(e){return e.sin(t)},{$x:t},function(e){return {$x:function(){return t.toFloat().cos().mulStrict(e)}}})}function cos_(e){var t=convertToTensor(e,"x","cos");return ENV.engine.runKernel(function(e){return e.cos(t)},{$x:t},function(e){return {$x:function(){return t.toFloat().sin().neg().mulStrict(e)}}})}function tan_(e){var t=convertToTensor(e,"x","tan");return ENV.engine.runKernel(function(e){return e.tan(t)},{$x:t},function(e){return {$x:function(){return e.divStrict(t.cos().square())}}})}function asin_(e){var t=convertToTensor(e,"x","asin");return ENV.engine.runKernel(function(e){return e.asin(t)},{$x:t},function(e){return {$x:function(){return e.divStrict(scalar(1).sub(t.toFloat().square()).sqrt())}}})}function acos_(e){var t=convertToTensor(e,"x","acos");return ENV.engine.runKernel(function(e){return e.acos(t)},{$x:t},function(e){return {$x:function(){return e.divStrict(scalar(1).sub(t.toFloat().square()).sqrt()).neg()}}})}function atan_(e){var t=convertToTensor(e,"x","atan");return ENV.engine.runKernel(function(e){return e.atan(t)},{$x:t},function(e){return {$x:function(){return e.div(t.toFloat().square().add(1))}}})}function sinh_(e){var t=convertToTensor(e,"x","sinh");return ENV.engine.runKernel(function(e){return e.sinh(t)},{$x:t},function(e){return {$x:function(){return t.toFloat().cosh().mulStrict(e)}}})}function cosh_(e){var t=convertToTensor(e,"x","cosh");return ENV.engine.runKernel(function(e){return e.cosh(t)},{$x:t},function(e){return {$x:function(){return t.toFloat().sinh().mulStrict(e)}}})}function tanh_(e){var t=convertToTensor(e,"x","tanh");return ENV.engine.runKernel(function(e,n){return n(e.tanh(t))},{$x:t},function(e,t){var n=t[0];return {$x:function(){return scalar(1).sub(n.square()).mulStrict(e)}}})}function asinh_(e){var t=convertToTensor(e,"x","asinh");return ENV.engine.runKernel(function(e){return e.asinh(t)},{$x:t},function(e){return {$x:function(){return e.divStrict(scalar(1).add(t.toFloat().square()).sqrt())}}})}function acosh_(e){var t=convertToTensor(e,"x","acosh");return ENV.engine.runKernel(function(e){return e.acosh(t)},{$x:t},function(e){return {$x:function(){return e.divStrict(t.toFloat().square().sub(1).sqrt())}}})}function atanh_(e){var t=convertToTensor(e,"x","atanh");return ENV.engine.runKernel(function(e){return e.atanh(t)},{$x:t},function(e){return {$x:function(){return e.div(scalar(1).sub(t.toFloat().square()))}}})}function erf_(e){var t=convertToTensor(e,"x","erf");assert("int32"===t.dtype||"float32"===t.dtype,"Input dtype must be `int32` or `float32`."),"int32"===t.dtype&&(t=t.toFloat());return ENV.engine.runKernel(function(e){return e.erf(t)},{$x:t},function(e){return {$x:function(){return e.mul(t.square().neg().exp().mul(2/Math.sqrt(Math.PI)))}}})}function step_(e,t){void 0===t&&(t=0);var n=convertToTensor(e,"x","step");return ENV.engine.runKernel(function(e){return e.step(n,t)},{$x:n},function(e){return {$x:function(){return zerosLike(e)}}})}ENV.get("IS_BROWSER")&&ENV.registerBackend("webgl",function(){return new MathBackendWebGL},2,setTensorTracker);var abs=op({abs_:abs_}),acos=op({acos_:acos_}),acosh=op({acosh_:acosh_}),asin=op({asin_:asin_}),asinh=op({asinh_:asinh_}),atan=op({atan_:atan_}),atanh=op({atanh_:atanh_}),ceil=op({ceil_:ceil_}),clipByValue=op({clipByValue_:clipByValue_}),cos=op({cos_:cos_}),cosh=op({cosh_:cosh_}),erf=op({erf_:erf_}),exp=op({exp_:exp_}),expm1=op({expm1_:expm1_}),floor=op({floor_:floor_}),log$1=op({log_:log_}),log1p=op({log1p_:log1p_}),logSigmoid=op({logSigmoid_:logSigmoid_}),neg=op({neg_:neg_}),reciprocal=op({reciprocal_:reciprocal_}),round=op({round_:round_}),rsqrt=op({rsqrt_:rsqrt_}),sigmoid=op({sigmoid_:sigmoid_}),sign=op({sign_:sign_}),sin=op({sin_:sin_}),sinh=op({sinh_:sinh_}),softplus=op({softplus_:softplus_}),sqrt=op({sqrt_:sqrt_}),square=op({square_:square_}),step=op({step_:step_}),tan=op({tan_:tan_}),tanh$1=op({tanh_:tanh_});function batchNormalization2d_(e,t,n,r,o,a){void 0===r&&(r=.001);var i,s,u=convertToTensor(e,"x","batchNormalization"),l=convertToTensor(t,"mean","batchNormalization"),c=convertToTensor(n,"variance","batchNormalization");return null!=o&&(i=convertToTensor(o,"scale","batchNormalization")),null!=a&&(s=convertToTensor(a,"offset","batchNormalization")),assert(2===u.rank,"Error in batchNormalization3D: x must be rank 3 but got rank "+u.rank+"."),assert(2===l.rank||1===l.rank,"Error in batchNormalization2D: mean must be rank 2 or rank 1 but got rank "+l.rank+"."),assert(2===c.rank||1===c.rank,"Error in batchNormalization2D: variance must be rank 2 or rank 1 but got rank "+c.rank+"."),null!=i&&assert(2===i.rank||1===i.rank,"Error in batchNormalization2D: scale must be rank 2 or rank 1 but got rank "+i.rank+"."),null!=s&&assert(2===s.rank||1===s.rank,"Error in batchNormalization2D: offset must be rank 2 or rank 1 but got rank "+s.rank+"."),batchNormalization(u,l,c,r,i,s)}function batchNormalization3d_(e,t,n,r,o,a){void 0===r&&(r=.001);var i,s,u=convertToTensor(e,"x","batchNormalization"),l=convertToTensor(t,"mean","batchNormalization"),c=convertToTensor(n,"variance","batchNormalization");return null!=o&&(i=convertToTensor(o,"scale","batchNormalization")),null!=a&&(s=convertToTensor(a,"offset","batchNormalization")),assert(3===u.rank,"Error in batchNormalization3D: x must be rank 3 but got rank "+u.rank+"."),assert(3===l.rank||1===l.rank,"Error in batchNormalization3D: mean must be rank 3 or rank 1 but got rank "+l.rank+"."),assert(3===c.rank||1===c.rank,"Error in batchNormalization3D: variance must be rank 3 or rank 1 but got rank "+c.rank+"."),null!=i&&assert(3===i.rank||1===i.rank,"Error in batchNormalization3D: scale must be rank 3 or rank 1 but got rank "+i.rank+"."),null!=s&&assert(3===s.rank||1===s.rank,"Error in batchNormalization3D: offset must be rank 3 or rank 1 but got rank "+s.rank+"."),batchNormalization(u,l,c,r,i,s)}function batchNormalization4d_(e,t,n,r,o,a){void 0===r&&(r=.001);var i,s,u=convertToTensor(e,"x","batchNormalization"),l=convertToTensor(t,"mean","batchNormalization"),c=convertToTensor(n,"variance","batchNormalization");return null!=o&&(i=convertToTensor(o,"scale","batchNormalization")),null!=a&&(s=convertToTensor(a,"offset","batchNormalization")),assert(4===u.rank,"Error in batchNormalization4D: x must be rank 4 but got rank "+u.rank+"."),assert(4===l.rank||1===l.rank,"Error in batchNormalization4D: mean must be rank 4 or rank 1 but got rank "+l.rank+"."),assert(4===c.rank||1===c.rank,"Error in batchNormalization4D: variance must be rank 4 or rank 1 but got rank "+c.rank+"."),null!=i&&assert(4===i.rank||1===i.rank,"Error in batchNormalization4D: scale must be rank 4 or rank 1 but got rank "+i.rank+"."),null!=s&&assert(4===s.rank||1===s.rank,"Error in batchNormalization4D: offset must be rank 4 or rank 1 but got rank "+s.rank+"."),batchNormalization(u,l,c,r,i,s)}function batchNormalization_(e,t,n,r,o,a){void 0===r&&(r=.001);var i,s,u,l=convertToTensor(e,"x","batchNormalization"),c=convertToTensor(t,"mean","batchNormalization"),p=convertToTensor(n,"variance","batchNormalization");null!=o&&(i=convertToTensor(o,"scale","batchNormalization")),null!=a&&(s=convertToTensor(a,"offset","batchNormalization")),assert(c.rank===p.rank,"Batch normalization gradient requires mean and variance to have equal ranks."),assert(null==s||c.rank===s.rank,"Batch normalization gradient requires mean and offset to have equal ranks."),assert(null==i||c.rank===i.rank,"Batch normalization gradient requires mean and scale to have equal ranks."),u=0===l.rank||1===l.rank?l.as4D(1,1,1,l.size):2===l.rank?l.as4D(1,1,l.shape[0],l.shape[1]):3===l.rank?l.as4D(1,l.shape[0],l.shape[1],l.shape[2]):l;return ENV.engine.runKernel(function(e){return e.batchNormalization(u,batchnormReshape4D(c),batchnormReshape4D(p),r,batchnormReshape4D(i),batchnormReshape4D(s))},{$x:l,$mean:c,$variance:p,$scale:i,$offset:s},function(e){var t=null==i?scalar(1):i,n=getReductionAxes(c.shape,u.shape),o=[];if(1===c.rank){for(var a=0;a<u.shape.length-1;++a)o.push(u.shape[a]);o.push(1);}var s=l.sub(c),d=e.mul(t),h=rsqrt(p.add(scalar(r))),f=h.mul(h).mul(h).mul(scalar(-.5));return {$x:function(){return 1===c.rank?e.mul(tile(h.as4D(1,1,1,c.shape[0]),o)).mul(t).reshape(l.shape):e.mul(h).mul(t).reshape(l.shape)},$mean:function(){var e=h.mul(scalar(-1)).mul(d);return 1===c.rank&&(e=e.sum(n)),e.reshape(c.shape)},$variance:function(){var e=f.mul(s).mul(d);return 1===c.rank&&(e=e.sum(n)),e.reshape(c.shape)},$scale:function(){var t=s.mul(h),r=e.mul(t);return 1===c.rank&&(r=r.sum(n)),r.reshape(c.shape)},$offset:function(){var t=e;return 1===c.rank&&(t=t.sum(n)),t.reshape(c.shape)}}}).reshape(l.shape)}function batchnormReshape4D(e){return null==e?null:0===e.rank?e.as1D():1===e.rank?e:2===e.rank?e.as4D(1,1,e.shape[0],e.shape[1]):3===e.rank?e.as4D(1,e.shape[0],e.shape[1],e.shape[2]):e}var batchNormalization2d=op({batchNormalization2d_:batchNormalization2d_}),batchNormalization3d=op({batchNormalization3d_:batchNormalization3d_}),batchNormalization4d=op({batchNormalization4d_:batchNormalization4d_}),batchNormalization=op({batchNormalization_:batchNormalization_});function computePool2DInfo(e,t,n,r,o,a,i){void 0===i&&(i="channelsLast");var s,u=parseTupleParam(t),l=u[0],c=u[1];if("channelsLast"===i)s=[l,c,e[3],e[3]];else{if("channelsFirst"!==i)throw new Error("Unknown dataFormat "+i);s=[l,c,e[1],e[1]];}return computeConv2DInfo(e,s,n,r,o,a,!1,i)}function computeConv2DInfo(e,t,n,r,o,a,i,s){void 0===i&&(i=!1),void 0===s&&(s="channelsLast");var u=[-1,-1,-1,-1],l=u[0],c=u[1],p=u[2],d=u[3];if("channelsLast"===s)l=e[0],c=e[1],p=e[2],d=e[3];else{if("channelsFirst"!==s)throw new Error("Unknown dataFormat "+s);l=e[0],d=e[1],c=e[2],p=e[3];}var h,f=t[0],m=t[1],g=t[3],v=parseTupleParam(n),y=v[0],x=v[1],T=parseTupleParam(r),w=T[0],E=T[1],S=getEffectiveFilterSize(f,w),b=getEffectiveFilterSize(m,E),C=getPadAndOutInfo(o,c,p,y,x,S,b,a),A=C.padInfo,_=C.outHeight,N=C.outWidth,R=i?g*d:g;return "channelsFirst"===s?h=[l,R,_,N]:"channelsLast"===s&&(h=[l,_,N,R]),{batchSize:l,dataFormat:s,inHeight:c,inWidth:p,inChannels:d,outHeight:_,outWidth:N,outChannels:R,padInfo:A,strideHeight:y,strideWidth:x,filterHeight:f,filterWidth:m,effectiveFilterHeight:S,effectiveFilterWidth:b,dilationHeight:w,dilationWidth:E,inShape:e,outShape:h,filterShape:t}}function computeConv3DInfo(e,t,n,r,o,a,i){void 0===a&&(a=!1),void 0===i&&(i="channelsLast");var s=[-1,-1,-1,-1,-1],u=s[0],l=s[1],c=s[2],p=s[3],d=s[4];if("channelsLast"===i)u=e[0],l=e[1],c=e[2],p=e[3],d=e[4];else{if("channelsFirst"!==i)throw new Error("Unknown dataFormat "+i);u=e[0],d=e[1],l=e[2],c=e[3],p=e[4];}var h,f=t[0],m=t[1],g=t[2],v=t[4],y=parse3TupleParam(n),x=y[0],T=y[1],w=y[2],E=parse3TupleParam(r),S=E[0],b=E[1],C=E[2],A=get3DPadAndOutInfo(o,l,c,p,x,T,w,getEffectiveFilterSize(f,S),getEffectiveFilterSize(m,b),getEffectiveFilterSize(g,C)),_=A.padInfo,N=A.outDepth,R=A.outHeight,k=A.outWidth,I=a?v*d:v;return "channelsFirst"===i?h=[u,I,N,R,k]:"channelsLast"===i&&(h=[u,N,R,k,I]),{batchSize:u,dataFormat:i,inDepth:l,inHeight:c,inWidth:p,inChannels:d,outDepth:N,outHeight:R,outWidth:k,outChannels:I,padInfo:_,strideDepth:x,strideHeight:T,strideWidth:w,filterDepth:f,filterHeight:m,filterWidth:g,dilationDepth:S,dilationHeight:b,dilationWidth:C,inShape:e,outShape:h,filterShape:t}}function computeOutputShape3D(e,t,n,r,o,a){null==o&&(o=computeDefaultPad(e,t,r));var i=e[0],s=e[1],u=conditionalRound((i-t+2*o)/r+1,a);assert(isInt(u),"The output # of rows ("+u+") must be an integer. Change the stride and/or zero pad parameters");var l=conditionalRound((s-t+2*o)/r+1,a);return assert(isInt(l),"The output # of columns ("+l+") must be an integer. Change the stride and/or zero pad parameters"),[u,l,n]}function computeDefaultPad(e,t,n,r){void 0===r&&(r=1);var o=getEffectiveFilterSize(t,r);return Math.floor((e[0]*(n-1)-n+o)/2)}function parseTupleParam(e){return "number"==typeof e?[e,e]:e}function parse3TupleParam(e){return "number"==typeof e?[e,e,e]:e}function getEffectiveFilterSize(e,t){return t<=1?e:e+(e-1)*(t-1)}function getPadAndOutInfo(e,t,n,r,o,a,i,s){var u,l,c;if("number"==typeof e){u={top:e,bottom:e,left:e,right:e,type:0===e?"VALID":"NUMBER"};var p=computeOutputShape3D([t,n,1],a,1,r,e,s);l=p[0],c=p[1];}else if("same"===e){var d=((l=Math.ceil(t/r))-1)*r+a-t,h=((c=Math.ceil(n/o))-1)*o+i-n,f=Math.floor(d/2),m=d-f,g=Math.floor(h/2);u={top:f,bottom:m,left:g,right:h-g,type:"SAME"};}else{if("valid"!==e)throw Error("Unknown padding parameter: "+e);u={top:0,bottom:0,left:0,right:0,type:"VALID"},l=Math.ceil((t-a+1)/r),c=Math.ceil((n-i+1)/o);}return {padInfo:u,outHeight:l,outWidth:c}}function get3DPadAndOutInfo(e,t,n,r,o,a,i,s,u,l){var c,p,d,h;if("same"===e){var f=((p=Math.ceil(t/o))-1)*o+s-t,m=((d=Math.ceil(n/a))-1)*a+u-n,g=((h=Math.ceil(r/i))-1)*i+l-r,v=Math.floor(f/2),y=f-v,x=Math.floor(m/2),T=m-x,w=Math.floor(g/2);c={top:x,bottom:T,left:w,right:g-w,front:v,back:y,type:"SAME"};}else{if("valid"!==e)throw Error("Unknown padding parameter: "+e);c={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},p=Math.ceil((t-s+1)/o),d=Math.ceil((n-u+1)/a),h=Math.ceil((r-l+1)/i);}return {padInfo:c,outDepth:p,outHeight:d,outWidth:h}}function conditionalRound(e,t){if(!t)return e;switch(t){case"round":return Math.round(e);case"ceil":return Math.ceil(e);case"floor":return Math.floor(e);default:throw new Error("Unknown roundingMode "+t)}}function tupleValuesAreOne(e){var t=parseTupleParam(e),n=t[0],r=t[1];return 1===n&&1===r}function eitherStridesOrDilationsAreOne(e,t){return tupleValuesAreOne(e)||tupleValuesAreOne(t)}function matMul_(e,t,n,r){var o;void 0===n&&(n=!1),void 0===r&&(r=!1);var a=convertToTensor(e,"a","matMul"),i=convertToTensor(t,"b","matMul");a=(o=makeTypesMatch(a,i))[0],i=o[1];var s=n?a.shape[a.rank-2]:a.shape[a.rank-1],u=r?i.shape[i.rank-1]:i.shape[i.rank-2],l=n?a.shape[a.rank-1]:a.shape[a.rank-2],c=r?i.shape[i.rank-2]:i.shape[i.rank-1],p=a.shape.slice(0,-2),d=i.shape.slice(0,-2),h=sizeFromShape(p),f=sizeFromShape(d);assert(a.rank>=2&&i.rank>=2&&a.rank===i.rank,"Error in matMul: inputs must have the same rank of at least 2, got ranks "+a.rank+" and "+i.rank+"."),assert(arraysEqual(p,d),"Error in matMul: outer dimensions ("+p+") and ("+d+") of Tensors with shapes "+a.shape+" and "+i.shape+" must match."),assert(s===u,"Error in matMul: inner shapes ("+s+") and ("+u+") of Tensors with shapes "+a.shape+" and "+i.shape+" and transposeA="+n+" and transposeB="+r+" must match.");var m=a.shape.slice(0,-2).concat([l,c]),g=n?a.as3D(h,s,l):a.as3D(h,l,s),v=r?i.as3D(f,c,u):i.as3D(f,u,c);return ENV.engine.runKernel(function(e){return e.batchMatMul(g,v,n,r)},{$a:g,$b:v},function(e){return n||r?!n&&r?{$a:function(){return e.matMul(v,!1,!1)},$b:function(){return e.matMul(g,!0,!1)}}:n&&!r?{$a:function(){return v.matMul(e,!1,!0)},$b:function(){return g.matMul(e,!1,!1)}}:{$a:function(){return v.matMul(e,!0,!0)},$b:function(){return e.matMul(g,!0,!0)}}:{$a:function(){return e.matMul(v,!1,!0)},$b:function(){return g.matMul(e,!0,!1)}}}).reshape(m)}function outerProduct_(e,t){var n=convertToTensor(e,"v1","outerProduct"),r=convertToTensor(t,"v2","outerProduct");return assert(1===n.rank&&1===r.rank,"Error in outerProduct: inputs must be rank 1, but got ranks "+n.rank+" and "+r.rank+"."),n.as2D(-1,1).matMul(r.as2D(1,-1))}function dot_(e,t){var n=convertToTensor(e,"t1","dot"),r=convertToTensor(t,"t2","dot");assert(!(1!==n.rank&&2!==n.rank||1!==r.rank&&2!==r.rank),"Error in dot: inputs must all be rank 1 or 2, but got ranks "+n.rank+" and "+r.rank+".");var o=1===n.rank?n.size:n.shape[1],a=1===r.rank?r.size:r.shape[0];return assert(o===a,"Error in dot: inner dimensions of inputs must match, but got "+o+" and "+a+"."),1===n.rank&&1===r.rank?n.as2D(1,-1).matMul(r.as2D(-1,1)).asScalar():1===n.rank&&2===r.rank?n.as2D(1,-1).matMul(r.as2D(r.shape[0],r.shape[1])).as1D():2===n.rank&&1===r.rank?n.matMul(r.as2D(-1,1)).as1D():n.matMul(r.as2D(r.shape[0],r.shape[1]))}var matMul=op({matMul_:matMul_}),dot=op({dot_:dot_}),outerProduct=op({outerProduct_:outerProduct_});function conv1d_(e,t,n,r,o,a,i){void 0===o&&(o="NWC"),void 0===a&&(a=1);var s=convertToTensor(e,"x","conv1d"),u=convertToTensor(t,"filter","conv1d"),l=s,c=!1;2===s.rank&&(c=!0,l=s.as3D(1,s.shape[0],s.shape[1])),assert(3===l.rank,"Error in conv1d: input must be rank 3, but got rank "+l.rank+"."),assert(3===u.rank,"Error in conv1d: filter must be rank 3, but got rank "+u.rank+"."),null!=i&&assert(isInt(r),"Error in conv1d: pad must be an integer when using, dimRoundingMode "+i+" but got pad "+r+"."),assert(l.shape[2]===u.shape[1],"Error in conv1d: depth of input ("+l.shape[2]+") must match input depth for filter "+u.shape[1]+"."),assert(eitherStridesOrDilationsAreOne(n,a),"Error in conv1D: Either stride or dilation must be 1. Got stride "+n+" and dilation '"+a+"'"),assert("NWC"===o,"Error in conv1d: got dataFormat of "+o+" but only NWC is currently supported.");var p=u.as4D(1,u.shape[0],u.shape[1],u.shape[2]),d=l.as4D(l.shape[0],1,l.shape[1],l.shape[2]),h=conv2d(d,p,[1,n],r,"NHWC",[1,a],i);return c?h.as2D(h.shape[2],h.shape[3]):h.as3D(h.shape[0],h.shape[2],h.shape[3])}function conv2d_(e,t,n,r,o,a,i){void 0===o&&(o="NHWC"),void 0===a&&(a=[1,1]);var s=convertToTensor(e,"x","conv2d"),u=convertToTensor(t,"filter","conv2d"),l=s,c=!1;3===s.rank&&(c=!0,l=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),assert(4===l.rank,"Error in conv2d: input must be rank 4, but got rank "+l.rank+"."),assert(4===u.rank,"Error in conv2d: filter must be rank 4, but got rank "+u.rank+"."),null!=i&&assert(isInt(r),"Error in conv2d: pad must be an integer when using, dimRoundingMode "+i+" but got pad "+r+"."),assert(l.shape[3]===u.shape[2],"Error in conv2d: depth of input ("+l.shape[3]+") must match input depth for filter "+u.shape[2]+"."),assert(eitherStridesOrDilationsAreOne(n,a),"Error in conv2D: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+a+"'"),assert("NHWC"===o,"Error in conv2d: got dataFormat of "+o+" but only NHWC is currently supported.");var p,d=computeConv2DInfo(l.shape,u.shape,n,a,r,i);if(1!==d.filterHeight||1!==d.filterWidth||1!==d.dilationHeight||1!==d.dilationWidth||1!==d.strideHeight||1!==d.strideWidth||"SAME"!==d.padInfo.type&&"VALID"!==d.padInfo.type){p=ENV.engine.runKernel(function(e){return e.conv2d(l,u,d)},{x:l,$filter:u},function(e){return assert(tupleValuesAreOne(a),"Error in gradient of conv2D: dilation rates greater than 1 are notyet supported in gradients. Got dilations '"+a+"'"),{x:function(){return conv2dDerInput_(l.shape,e,u,n,r)},$filter:function(){return conv2dDerFilter_(l,e,u.shape,n,r)}}});}else{var h=l.reshape([-1,d.inChannels]),f=u.reshape([d.inChannels,d.outChannels]);p=matMul(h,f).reshape(d.outShape);}return c?p.as3D(p.shape[1],p.shape[2],p.shape[3]):p}function conv2dDerInput_(e,t,n,r,o,a){assert(e.length===t.rank,"Length of inShape ("+e.length+") and rank of dy ("+t.rank+") must match");var i=e,s=t,u=!1;3===t.rank&&(u=!0,s=t.as4D(1,t.shape[0],t.shape[1],t.shape[2]),i=[1,e[0],e[1],e[2]]);var l=i[3],c=s.shape[3];assert(4===i.length,"Error in conv2dDerInput: inShape must be length 4, but got length "+i.length+"."),assert(4===s.rank,"Error in conv2dDerInput: dy must be rank 4, but got rank "+s.rank),assert(4===n.rank,"Error in conv2dDerInput: filter must be rank 4, but got rank "+n.rank),assert(l===n.shape[2],"Error in conv2dDerInput: depth of input ("+l+") must match input depth for filter "+n.shape[2]+"."),assert(c===n.shape[3],"Error in conv2dDerInput: depth of output ("+c+") must match output depth for filter "+n.shape[3]+"."),null!=a&&assert(isInt(o),"Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode "+a+" but got pad "+o+".");var p=computeConv2DInfo(i,n.shape,r,1,o,a),d=ENV.engine.runKernel(function(e){return e.conv2dDerInput(s,n,p)},{dy4D:s,filter:n},function(e){return {dy4D:function(){return conv2d(e,n,r,o,"NHWC",1,a)},filter:function(){return conv2dDerFilter(e,s,n.shape,r,o,a)}}});return u?d.as3D(d.shape[1],d.shape[2],d.shape[3]):d}function conv2dDerFilter_(e,t,n,r,o,a){var i=e;3===e.rank&&(i=e.as4D(1,e.shape[0],e.shape[1],e.shape[2]));var s=t;3===s.rank&&(s=t.as4D(1,t.shape[0],t.shape[1],t.shape[2])),assert(4===i.rank,"Error in conv2dDerFilter: input must be rank 4, but got shape "+i.shape+"."),assert(4===s.rank,"Error in conv2dDerFilter: dy must be rank 4, but got shape "+s.shape+"."),assert(4===n.length,"Error in conv2dDerFilter: filterShape must be length 4, but got "+n+"."),assert(i.shape[3]===n[2],"Error in conv2dDerFilter: depth of input "+i.shape[3]+") must match input depth in filter ("+n[2]+"."),assert(s.shape[3]===n[3],"Error in conv2dDerFilter: depth of dy ("+s.shape[3]+") must match output depth for filter ("+n[3]+")."),null!=a&&assert(isInt(o),"Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode "+a+" but got pad "+o+".");var u=computeConv2DInfo(i.shape,n,r,1,o,a);return ENV.engine.runKernel(function(e){return e.conv2dDerFilter(i,s,u)},{x4D:i,dy4D:s})}function conv2dTranspose_(e,t,n,r,o,a){return conv2dDerInput_(n,convertToTensor(e,"x","conv2dTranspose"),convertToTensor(t,"filter","conv2dTranspose"),r,o,a)}function depthwiseConv2d_(e,t,n,r,o,a,i){void 0===o&&(o="NHWC"),void 0===a&&(a=[1,1]);var s=convertToTensor(e,"x","depthwiseConv2d"),u=convertToTensor(t,"filter","depthwiseConv2d"),l=s,c=!1;3===s.rank&&(c=!0,l=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),assert(4===l.rank,"Error in depthwiseConv2d: input must be rank 4, but got rank "+l.rank+"."),assert(4===u.rank,"Error in depthwiseConv2d: filter must be rank 4, but got rank "+u.rank+"."),assert(l.shape[3]===u.shape[2],"Error in depthwiseConv2d: number of input channels ("+l.shape[3]+") must match the inChannels dimension in filter "+u.shape[2]+"."),null==a&&(a=[1,1]),assert(eitherStridesOrDilationsAreOne(n,a),"Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+a+"'"),null!=i&&assert(isInt(r),"Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode "+i+" but got pad "+r+".");var p=computeConv2DInfo(l.shape,u.shape,n,a,r,i,!0),d=ENV.engine.runKernel(function(e){return e.depthwiseConv2D(l,u,p)},{x:l,$filter:u},function(e){return assert(tupleValuesAreOne(a),"Error in gradient of depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '"+a+"'"),{x:function(){return depthwiseConv2dDerInput(l.shape,e,u,p)},$filter:function(){return depthwiseConv2dDerFilter(l,e,u.shape,p)}}});return c?d.as3D(d.shape[1],d.shape[2],d.shape[3]):d}function separableConv2d_(e,t,n,r,o,a,i){void 0===a&&(a=[1,1]),void 0===i&&(i="NHWC");var s=convertToTensor(e,"x","separableConv2d"),u=convertToTensor(t,"depthwiseFilter","separableConv2d"),l=convertToTensor(n,"pointwiseFilter","separableConv2d"),c=s,p=!1;if(3===s.rank&&(p=!0,c=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),"NCHW"===i)throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");assert(4===c.rank,"Error in separableConv2d: input must be rank 4, but got rank "+c.rank+"."),assert(4===u.rank,"Error in separableConv2d: depthwise filter must be rank 4, but got rank "+u.rank+"."),assert(4===l.rank,"Error in separableConv2d: pointwise filter must be rank 4, but got rank "+u.rank+"."),assert(1===l.shape[0],"Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got "+l.shape[0]+"."),assert(1===l.shape[1],"Error in separableConv2d: the second dimension of pointwise filter  must be 1, but got "+l.shape[1]+".");var d=u.shape[2],h=u.shape[3];assert(l.shape[2]===d*h,"Error in separableConv2d: the third dimension of pointwise filter must be "+d*h+", but got "+l.shape[2]+".");var f=depthwiseConv2d(c,u,r,o,i,a),m=conv2d(f,l,1,"valid",i);return p?m.as3D(m.shape[1],m.shape[2],m.shape[3]):m}function parseTupleParam$1(e){return "number"==typeof e?[e,e,e]:2===e.length?[e[0],e[1],1]:e}function tupleValuesAreOne$1(e){var t=parseTupleParam$1(e),n=t[0],r=t[1],o=t[2];return 1===n&&1===r&&1===o}function eitherStridesOrDilationsAreOne$1(e,t){return tupleValuesAreOne$1(e)||tupleValuesAreOne$1(t)}function depthwiseConv2dDerInput(e,t,n,r){var o=t,a=!1;3===t.rank&&(a=!0,o=t.as4D(1,t.shape[0],t.shape[1],t.shape[2]));var i=ENV.engine.runKernel(function(e){return e.depthwiseConv2DDerInput(o,n,r)},{dy4D:o});return a?i.as3D(i.shape[1],i.shape[2],i.shape[3]):i}function depthwiseConv2dDerFilter(e,t,n,r){var o=e;3===e.rank&&(o=e.as4D(1,e.shape[0],e.shape[1],e.shape[2]));var a=t;return 3===a.rank&&(a=t.as4D(1,t.shape[0],t.shape[1],t.shape[2])),ENV.engine.runKernel(function(e){return e.depthwiseConv2DDerFilter(o,a,r)},{x4D:o,dy4D:a})}function conv3d_(e,t,n,r,o,a){void 0===o&&(o="NHWC"),void 0===a&&(a=[1,1,1]);var i=convertToTensor(e,"x","conv3d"),s=convertToTensor(t,"filter","conv3d"),u=i,l=!1;4===i.rank&&(l=!0,u=i.as5D(1,i.shape[0],i.shape[1],i.shape[2],i.shape[3])),assert(5===u.rank,"Error in conv3d: input must be rank 5, but got rank "+u.rank+"."),assert(5===s.rank,"Error in conv3d: filter must be rank 5, but got rank "+s.rank+"."),assert(u.shape[4]===s.shape[3],"Error in conv3d: depth of input ("+u.shape[4]+") must match input depth for filter "+s.shape[3]+"."),assert(eitherStridesOrDilationsAreOne$1(n,a),"Error in conv3D: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+a+"'"),assert("NHWC"===o,"Error in conv3d: got dataFormat of "+o+" but only NHWC is currently supported.");var c=computeConv3DInfo(u.shape,s.shape,n,a,r),p=ENV.engine.runKernel(function(e){return e.conv3d(u,s,c)},{x:u,$filter:s},function(e){return assert(tupleValuesAreOne$1(a),"Error in gradient of conv3D: dilation rates greater than 1 are notyet supported in gradients. Got dilations '"+a+"'"),{x:function(){return conv3dDerInput_(u.shape,e,s,n,r)},$filter:function(){return conv3dDerFilter_(u,e,s.shape,n,r)}}});return l?p.as4D(p.shape[1],p.shape[2],p.shape[3],p.shape[4]):p}function conv3dDerInput_(e,t,n,r,o){assert(e.length===t.rank,"Length of inShape ("+e.length+") and rank of dy ("+t.rank+") must match");var a=e,i=t,s=!1;4===t.rank&&(s=!0,i=t.as5D(1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]),a=[1,e[0],e[1],e[2],e[3]]);var u=a[4],l=i.shape[4];assert(5===a.length,"Error in conv3dDerInput: inShape must be length 5, but got length "+a.length+"."),assert(5===i.rank,"Error in conv3dDerInput: dy must be rank 5, but got rank "+i.rank),assert(5===n.rank,"Error in conv3dDerInput: filter must be rank 5, but got rank "+n.rank),assert(u===n.shape[3],"Error in conv3dDerInput: depth of input ("+u+") must match input depth for filter "+n.shape[3]+"."),assert(l===n.shape[4],"Error in conv3dDerInput: depth of output ("+l+") must match output depth for filter "+n.shape[4]+".");var c=computeConv3DInfo(a,n.shape,r,1,o),p=ENV.engine.runKernel(function(e){return e.conv3dDerInput(i,n,c)},{dy5D:i});return s?p.as4D(p.shape[1],p.shape[2],p.shape[3],p.shape[4]):p}function conv3dDerFilter_(e,t,n,r,o){var a=e;4===e.rank&&(a=e.as5D(1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]));var i=t;4===i.rank&&(i=t.as5D(1,t.shape[0],t.shape[1],t.shape[2],t.shape[3])),assert(5===a.rank,"Error in conv3dDerFilter: input must be rank 5, but got shape "+a.shape+"."),assert(5===i.rank,"Error in conv3dDerFilter: dy must be rank 5, but got shape "+i.shape+"."),assert(5===n.length,"Error in conv3dDerFilter: filterShape must be length 5, but got "+n+"."),assert(a.shape[4]===n[3],"Error in conv3dDerFilter: depth of input "+a.shape[4]+") must match input depth in filter ("+n[3]+"."),assert(i.shape[4]===n[4],"Error in conv3dDerFilter: depth of dy ("+i.shape[4]+") must match output depth for filter ("+n[4]+").");var s=computeConv3DInfo(a.shape,n,r,1,o);return ENV.engine.runKernel(function(e){return e.conv3dDerFilter(a,i,s)},{x5D:a,dy5D:i})}var conv1d=op({conv1d_:conv1d_}),conv2d=op({conv2d_:conv2d_}),conv3d=op({conv3d_:conv3d_}),conv2dDerFilter=op({conv2dDerFilter_:conv2dDerFilter_}),depthwiseConv2d=op({depthwiseConv2d_:depthwiseConv2d_}),separableConv2d=op({separableConv2d_:separableConv2d_}),conv2dTranspose=op({conv2dTranspose_:conv2dTranspose_});function reverse1d_(e){var t=convertToTensor(e,"x","reverse");return assert(1===t.rank,"Error in reverse1D: x must be rank 1 but got\n             rank "+t.rank+"."),reverse(t,0)}function reverse2d_(e,t){var n=convertToTensor(e,"x","reverse");return assert(2===n.rank,"Error in reverse2D: x must be rank 2 but got\n             rank "+n.rank+"."),reverse(n,t)}function reverse3d_(e,t){var n=convertToTensor(e,"x","reverse");return assert(3===n.rank,"Error in reverse3D: x must be rank 3 but got\n             rank "+n.rank+"."),reverse(n,t)}function reverse4d_(e,t){var n=convertToTensor(e,"x","reverse");return assert(4===n.rank,"Error in reverse4D: x must be rank 4 but got\n             rank "+n.rank+"."),reverse(n,t)}function reverse_(e,t){var n=convertToTensor(e,"x","reverse");if(0===n.rank)return n.clone();var r=parseAxisParam(t,n.shape);return ENV.engine.runKernel(function(e){return e.reverse(n,r)},{$x:n},function(e){return {$x:function(){return e.reverse(r)}}}).reshapeAs(n)}var reverse=op({reverse_:reverse_}),reverse1d=op({reverse1d_:reverse1d_}),reverse2d=op({reverse2d_:reverse2d_}),reverse3d=op({reverse3d_:reverse3d_}),reverse4d=op({reverse4d_:reverse4d_});function maxPoolImpl_(e,t,n,r,o,a){var i=convertToTensor(e,"x","maxPool"),s=i,u=!1;3===i.rank&&(u=!0,s=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),null==r&&(r=[1,1]),assert(4===s.rank,"Error in maxPool: input must be rank 4 but got rank "+s.rank+"."),assert(eitherStridesOrDilationsAreOne(n,r),"Error in maxPool: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+r+"'"),null!=a&&assert(isInt(o),"Error in maxPool: pad must be an integer when using, dimRoundingMode "+a+" but got pad "+o+".");var l=computePool2DInfo(s.shape,t,n,r,o,a),c=ENV.engine.runKernel(function(e,t){return t(e.maxPool(s,l))},{x:s},function(e,a){var i=a[0];return {x:function(){return maxPoolBackprop(e,s,i,t,n,r,o)}}});return u?c.as3D(c.shape[1],c.shape[2],c.shape[3]):c}function maxPool_(e,t,n,r,o){return maxPoolImpl_(e,t,n,1,r,o)}function avgPoolImpl_(e,t,n,r,o,a){var i=convertToTensor(e,"x","avgPool","float32");null==r&&(r=[1,1]),assert(eitherStridesOrDilationsAreOne(n,r),"Error in avgPool: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+r+"'");var s=i,u=!1;3===i.rank&&(u=!0,s=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),assert(4===s.rank,"Error in avgPool: x must be rank 4 but got rank "+s.rank+"."),null!=a&&assert(isInt(o),"Error in avgPool: pad must be an integer when using, dimRoundingMode "+a+" but got pad "+o+".");var l=computePool2DInfo(s.shape,t,n,r,o),c=ENV.engine.runKernel(function(e){return e.avgPool(s,l)},{x:s},function(e){return {x:function(){return avgPoolBackprop(e,s,t,n,r,o)}}});return c=c.cast(i.dtype),u?c.as3D(c.shape[1],c.shape[2],c.shape[3]):c}function avgPool_(e,t,n,r,o){return avgPoolImpl_(e,t,n,1,r,o)}function pool_(e,t,n,r,o,a){null==o&&(o=[1,1]),null==a&&(a=1),0===r&&(r="valid");var i=convertToTensor(e,"x","maxPool"),s=i,u=!1;3===i.rank&&(u=!0,s=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),assert(eitherStridesOrDilationsAreOne(a,o),"Error in pool: Either strides or dilations must be 1. Got strides "+a+" and dilations '"+o+"'");var l,c=computePool2DInfo(s.shape,t,a,o,r),p=[c.dilationHeight,c.dilationWidth];l="same"===r?withSpaceToBatchBasePaddings([c.filterHeight,c.filterWidth],p):[[0,0],[0,0]];var d=1===p[0]&&1===p[1],h=requiredSpaceToBatchPaddings([c.inHeight,c.inWidth],p,l),f=h[0],m=h[1],g=d?r:"valid",v=d?s:spaceToBatchND(s,p,f),y=("avg"===n?function(){return avgPoolImpl_(v,t,a,1,g)}:function(){return maxPoolImpl_(v,t,a,1,g)})(),x=d?y:batchToSpaceND(y,p,m);return u?x.as3D(x.shape[1],x.shape[2],x.shape[3]):x}function maxPoolBackprop(e,t,n,r,o,a,i,s){var u=convertToTensor(e,"dy","maxPoolBackprop"),l=convertToTensor(t,"input","maxPoolBackprop"),c=convertToTensor(n,"output","maxPoolBackprop");assert(l.rank===u.rank,"Rank of input ("+l.rank+") does not match rank of dy ("+u.rank+")"),null==a&&(a=[1,1]),assert(eitherStridesOrDilationsAreOne(o,a),"Error in maxPoolBackProp: Either strides or dilations must be 1. Got strides "+o+" and dilations '"+a+"'"),assert(4===u.rank,"Error in maxPoolBackprop: dy must be rank 4 but got rank "+u.rank+"."),assert(4===l.rank,"Error in maxPoolBackprop: input must be rank 4 but got rank "+l.rank+"."),null!=s&&assert(isInt(i),"Error in maxPoolBackprop: pad must be an integer when using, dimRoundingMode "+s+" but got pad "+i+".");var p=computePool2DInfo(l.shape,r,o,a,i,s);return ENV.engine.runKernel(function(e){return e.maxPoolBackprop(u,l,c,p)},{$dy:u,$input:l})}function avgPoolBackprop(e,t,n,r,o,a){var i=convertToTensor(e,"dy","avgPoolBackprop"),s=convertToTensor(t,"input","avgPoolBackprop");assert(s.rank===i.rank,"Rank of input ("+s.rank+") does not match rank of dy ("+i.rank+")"),null==o&&(o=[1,1]),assert(eitherStridesOrDilationsAreOne(r,o),"Error in avgPoolBackprop: Either strides or dilations must be 1. Got strides "+r+" and dilations '"+o+"'");var u=s,l=i,c=!1;3===s.rank&&(c=!0,u=s.as4D(1,s.shape[0],s.shape[1],s.shape[2]),l=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),assert(4===l.rank,"Error in avgPoolBackprop: dy must be rank 4 but got rank "+l.rank+"."),assert(4===u.rank,"Error in avgPoolBackprop: input must be rank 4 but got rank "+u.rank+".");var p=computePool2DInfo(u.shape,n,r,o,a),d=ENV.engine.runKernel(function(e){return e.avgPoolBackprop(l,u,p)},{dy4D:l,input4D:u});return c?d.as3D(d.shape[1],d.shape[2],d.shape[3]):d}function requiredSpaceToBatchPaddings(e,t,n){var r=n.map(function(e){return e[0]}),o=n.map(function(e){return e[1]}),a=e.concat(r,o),i=t.map(function(e,t){return (e-a[t]%e)%e}),s=o.map(function(e,t){return e+i[t]});return [t.map(function(e,t){return [r[t],s[t]]}),t.map(function(e,t){return [0,i[t]]})]}function withSpaceToBatchBasePaddings(e,t){var n=e.map(function(e,n){return e+(e-1)*(t[n]-1)}).map(function(e){return e-1}),r=n.map(function(e){return Math.floor(e/2)}),o=n.map(function(e,t){return e-r[t]});return n.map(function(e,t){return [r[t],o[t]]})}var maxPool=op({maxPool_:maxPool_}),avgPool=op({avgPool_:avgPool_}),pool=op({pool_:pool_});function slice1d_(e,t,n){var r=convertToTensor(e,"x","slice1d");return assert(1===r.rank,"slice1d expects a rank-1 tensor, but got a rank-"+r.rank+" tensor"),slice(r,[t],[n])}function slice2d_(e,t,n){var r=convertToTensor(e,"x","slice2d");return assert(2===r.rank,"slice2d expects a rank-2 tensor, but got a rank-"+r.rank+" tensor"),slice(r,t,n)}function slice3d_(e,t,n){var r=convertToTensor(e,"x","slice3d");return assert(3===r.rank,"slice3d expects a rank-3 tensor, but got a rank-"+r.rank+" tensor"),slice(r,t,n)}function slice4d_(e,t,n){var r=convertToTensor(e,"x","slice4d");return assert(4===r.rank,"slice4d expects a rank-4 tensor, but got a rank-"+r.rank+" tensor"),slice(r,t,n)}function slice_(e,t,n){var r,o,a=convertToTensor(e,"x","slice");if(0===a.rank)throw new Error("Slicing scalar is not possible");r="number"==typeof t?[t].concat(new Array(a.rank-1).fill(0)):t.length<a.rank?t.concat(new Array(a.rank-t.length).fill(0)):t.slice(),o=(o=null==n?new Array(a.rank).fill(-1):"number"==typeof n?[n].concat(new Array(a.rank-1).fill(-1)):n.length<a.rank?n.concat(new Array(a.rank-n.length).fill(-1)):n).map(function(e,t){return e>=0?e:(assert(-1===e,"Bad value in size"),a.shape[t]-r[t])}),assertParamsValid(a,r,o);var i=a.shape;return ENV.engine.runKernel(function(e){return e.slice(a,r,o)},{$x:a},function(e){for(var t=[],n=0;n<e.rank;n++)t.push([r[n],i[n]-r[n]-o[n]]);return {$x:function(){return e.pad(t)}}})}var slice=op({slice_:slice_}),slice1d=op({slice1d_:slice1d_}),slice2d=op({slice2d_:slice2d_}),slice3d=op({slice3d_:slice3d_}),slice4d=op({slice4d_:slice4d_});function logSumExp_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","logSumExp"),o=parseAxisParam(t,r.shape),a=r.max(o,!0),i=r.sub(a).exp().sum(o).log(),s=a.reshape(i.shape).add(i);if(n){var u=expandShapeToKeepDim(s.shape,o);return s.reshape(u)}return s}function sum_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","sum");"bool"===r.dtype&&(r=r.toInt());var o=parseAxisParam(t,r.shape);return customGrad(function(e){var t=getAxesPermutation(o,e.rank),r=o,a=e;null!=t&&(a=e.transpose(t),r=getInnerMostAxes(r.length,e.rank));var i=ENV.engine.runKernel(function(e){return e.sum(a,r)},{permutedX:a});if(n){var s=expandShapeToKeepDim(i.shape,o);i=i.reshape(s);}return {value:i,gradFunc:function(t){var n=e.shape.slice();return o.forEach(function(e){n[e]=1;}),t.reshape(n).mul(ones$1(e.shape,"float32"))}}})(r)}function prod_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","prod");"bool"===r.dtype&&(r=r.toInt());var o=parseAxisParam(t,r.shape),a=getAxesPermutation(o,r.rank),i=o,s=r;null!=a&&(s=r.transpose(a),i=getInnerMostAxes(i.length,r.rank));var u=ENV.engine.runKernel(function(e){return e.prod(s,i)},{permutedX:s});if(n){var l=expandShapeToKeepDim(u.shape,o);u=u.reshape(l);}return u}function mean_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","mean"),o=parseAxisParam(t,r.shape),a=sizeFromShape(computeOutAndReduceShapes(r.shape,o)[1]);return customGrad(function(e){var r=scalar(a);return {value:(r.dtype===e.dtype?e:e.cast(r.dtype)).div(r).sum(t,n),gradFunc:function(t){var n=e.shape.slice();return o.forEach(function(e){n[e]=1;}),t.reshape(n).mul(ones$1(e.shape,"float32")).div(r)}}})(r)}function gradForMinAndMax(e,t,n,r,o){var a=t[0];return a.rank<n.rank&&(a=a.reshape(expandShapeToKeepDim(a.shape,r))),e.rank<n.rank&&(e=e.reshape(expandShapeToKeepDim(e.shape,r))),{$x:function(){var t=e.mul(n.equal(a).cast(e.dtype));return null==o?t:t.transpose(o)}}}function min_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","min"),o=r,a=parseAxisParam(t,r.shape),i=a,s=getAxesPermutation(i,r.rank);null!=s&&(r=r.transpose(s),i=getInnerMostAxes(i.length,r.rank));var u=ENV.engine.runKernel(function(e,t){return t(e.min(r,i))},{$x:r},function(e,t){return gradForMinAndMax(e,t,o,a,s)});if(n){var l=expandShapeToKeepDim(u.shape,a);u=u.reshape(l);}return u}function max_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","max"),o=r,a=parseAxisParam(t,r.shape),i=a,s=getAxesPermutation(i,r.rank);null!=s&&(r=r.transpose(s),i=getInnerMostAxes(i.length,r.rank));var u=ENV.engine.runKernel(function(e,t){return t(e.max(r,i))},{$x:r},function(e,t){return gradForMinAndMax(e,t,o,a,s)});if(n){var l=expandShapeToKeepDim(u.shape,a);u=u.reshape(l);}return u}function argMin_(e,t){void 0===t&&(t=0);var n=convertToTensor(e,"x","argMin");null==t&&(t=0);var r=parseAxisParam(t,n.shape),o=getAxesPermutation(r,n.rank);null!=o&&(n=n.transpose(o),r=getInnerMostAxes(r.length,n.rank));return ENV.engine.runKernel(function(e){return e.argMin(n,r[0])},{$x:n},function(e){return {$x:function(){return zerosLike(n)}}})}function argMax_(e,t){void 0===t&&(t=0);var n=convertToTensor(e,"x","argMax");null==t&&(t=0);var r=parseAxisParam(t,n.shape),o=getAxesPermutation(r,n.rank);null!=o&&(n=n.transpose(o),r=getInnerMostAxes(r.length,n.rank));return ENV.engine.runKernel(function(e){return e.argMax(n,r[0])},{$x:n},function(e){return {$x:function(){return zerosLike(n)}}})}function all_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","all","bool"),o=parseAxisParam(t,r.shape),a=o,i=getAxesPermutation(a,r.rank);null!=i&&(r=r.transpose(i),a=getInnerMostAxes(a.length,r.rank));var s=ENV.engine.runKernel(function(e){return e.all(r,a)},{$x:r});if(n){var u=expandShapeToKeepDim(s.shape,o);return s.reshape(u)}return s}function any_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,"x","any","bool"),o=parseAxisParam(t,r.shape),a=o,i=getAxesPermutation(a,r.rank);null!=i&&(r=r.transpose(i),a=getInnerMostAxes(a.length,r.rank));var s=ENV.engine.runKernel(function(e){return e.any(r,a)},{$x:r});if(n){var u=expandShapeToKeepDim(s.shape,o);return s.reshape(u)}return s}function moments_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=parseAxisParam(t,(e=convertToTensor(e,"x","moments")).shape),o=e.mean(r,n),a=o.shape;return n||(a=expandShapeToKeepDim(o.shape,r)),{mean:o,variance:e.toFloat().sub(o.reshape(a)).square().mean(r,n)}}var all=op({all_:all_}),any=op({any_:any_}),argMax=op({argMax_:argMax_}),argMin=op({argMin_:argMin_}),logSumExp=op({logSumExp_:logSumExp_}),max=op({max_:max_}),mean=op({mean_:mean_}),min=op({min_:min_}),moments=op({moments_:moments_}),sum$1=op({sum_:sum_}),prod=op({prod_:prod_});function notEqual_(e,t){var n,r=convertToTensor(e,"a","notEqual"),o=convertToTensor(t,"b","notEqual");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.notEqual(r,o)},{$a:r,$b:o})}function notEqualStrict_(e,t){var n=convertToTensor(e,"a","notEqualStrict"),r=convertToTensor(t,"b","notEqualStrict");return assertShapesMatch(n.shape,r.shape,"Error in notEqualStrict: "),n.notEqual(r)}function less_(e,t){var n,r=convertToTensor(e,"a","less"),o=convertToTensor(t,"b","less");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.less(r,o)},{$a:r,$b:o})}function lessStrict_(e,t){var n=convertToTensor(e,"a","lessStrict"),r=convertToTensor(t,"b","lessStrict");return assertShapesMatch(n.shape,r.shape,"Error in lessStrict: "),n.less(r)}function equal_(e,t){var n,r=convertToTensor(e,"a","equal"),o=convertToTensor(t,"b","equal");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.equal(r,o)},{$a:r,$b:o})}function equalStrict_(e,t){var n=convertToTensor(e,"a","equalStrict"),r=convertToTensor(t,"b","equalStrict");return assertShapesMatch(n.shape,r.shape,"Error in equalStrict: "),n.equal(r)}function lessEqual_(e,t){var n,r=convertToTensor(e,"a","lessEqual"),o=convertToTensor(t,"b","lessEqual");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.lessEqual(r,o)},{$a:r,$b:o})}function lessEqualStrict_(e,t){var n=convertToTensor(e,"a","lessEqualStrict"),r=convertToTensor(t,"b","lessEqualStrict");return assertShapesMatch(n.shape,r.shape,"Error in lessEqualStrict: "),n.lessEqual(r)}function greater_(e,t){var n,r=convertToTensor(e,"a","greater"),o=convertToTensor(t,"b","greater");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.greater(r,o)},{$a:r,$b:o})}function greaterStrict_(e,t){var n=convertToTensor(e,"a","greaterStrict"),r=convertToTensor(t,"b","greaterStrict");return assertShapesMatch(n.shape,r.shape,"Error in greaterStrict: "),n.greater(r)}function greaterEqual_(e,t){var n,r=convertToTensor(e,"a","greaterEqual"),o=convertToTensor(t,"b","greaterEqual");n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.greaterEqual(r,o)},{$a:r,$b:o},function(e){return {$a:function(){return zerosLike(r)},$b:function(){return zerosLike(o)}}})}function greaterEqualStrict_(e,t){var n=convertToTensor(e,"a","greaterEqualStrict"),r=convertToTensor(t,"b","greaterEqualStrict");return assertShapesMatch(n.shape,r.shape,"Error in greaterEqualStrict: "),n.greaterEqual(r)}var equal=op({equal_:equal_}),equalStrict=op({equalStrict_:equalStrict_}),greater=op({greater_:greater_}),greaterEqual=op({greaterEqual_:greaterEqual_}),greaterEqualStrict=op({greaterEqualStrict_:greaterEqualStrict_}),greaterStrict=op({greaterStrict_:greaterStrict_}),less=op({less_:less_}),lessEqual=op({lessEqual_:lessEqual_}),lessEqualStrict=op({lessEqualStrict_:lessEqualStrict_}),lessStrict=op({lessStrict_:lessStrict_}),notEqual=op({notEqual_:notEqual_}),notEqualStrict=op({notEqualStrict_:notEqualStrict_});function add_(e,t){var n,r=convertToTensor(e,"a","add"),o=convertToTensor(t,"b","add");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.add(r,o)},{$a:r,$b:o},function(e){return {$a:function(){var t=e,n=getReductionAxes(r.shape,a);return n.length>0&&(t=t.sum(n)),t.reshape(r.shape)},$b:function(){var t=e,n=getReductionAxes(o.shape,a);return n.length>0&&(t=t.sum(n)),t.reshape(o.shape)}}})}function addN_(e){assert(Array.isArray(e),function(){return "The argument passed to tf.addN() must be a list of tensors"}),assert(e.length>=1,function(){return "Must pass at least one tensor to tf.addN(), but got "+e.length});var t=e.map(function(e,t){return convertToTensor(e,"tensors"+t,"addN")}),n=t[0];t.forEach(function(e){if(e.dtype!==n.dtype)throw new Error("All tensors passed to tf.addN() must have the same dtype")}),t.forEach(function(e){if(!arraysEqual(e.shape,n.shape))throw new Error("All tensors passed to tf.addN() must have the same shape")});var r=t;return ENV.engine.runKernel(function(e){return e.addN(t)},r,function(e){var n={};return t.forEach(function(t,r){n[r]=function(){return e.clone()};}),n})}function addStrict_(e,t){var n=convertToTensor(e,"a","addStrict"),r=convertToTensor(t,"b","addStrict");return assertShapesMatch(n.shape,r.shape,"Error in addStrict: "),n.add(r)}function sub_(e,t){var n,r=convertToTensor(e,"a","sub"),o=convertToTensor(t,"b","sub");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.subtract(r,o)},{$a:r,$b:o},function(e){return {$a:function(){var t=e,n=getReductionAxes(r.shape,a);return n.length>0&&(t=t.sum(n)),t.reshape(r.shape)},$b:function(){var t=e,n=getReductionAxes(o.shape,a);return n.length>0&&(t=t.sum(n)),t.neg().reshape(o.shape)}}})}function subStrict_(e,t){var n=convertToTensor(e,"a","subStrict"),r=convertToTensor(t,"b","subStrict");return assertShapesMatch(n.shape,r.shape,"Error in subStrict: "),n.sub(r)}function pow_(e,t){var n=convertToTensor(e,"base","pow"),r=convertToTensor(t,"exp","pow"),o=assertAndGetBroadcastShape(n.shape,r.shape);e=n.cast(upcastType(n.dtype,r.dtype)),t=r.cast(upcastType(n.dtype,r.dtype));return ENV.engine.runKernel(function(e,t){return t(e.pow(n,r))},{$base:n,$exp:r},function(e,t){var a=t[0];return {$base:function(){var t=r.toFloat(),a=e.mul(t.mul(n.pow(t.sub(scalar(1))))),i=getReductionAxes(n.shape,o);return i.length>0&&(a=a.sum(i)),a.reshape(n.shape)},$exp:function(){var t=e.mul(a.mul(n.log()).toFloat()),i=getReductionAxes(r.shape,o);return i.length>0&&(t=t.sum(i)),t.reshape(r.shape)}}})}function powStrict_(e,t){return assertShapesMatch(e.shape,t.shape,"Error in powStrict: "),e.pow(t)}function mul_(e,t){var n,r=convertToTensor(e,"a","mul"),o=convertToTensor(t,"b","mul");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.multiply(r,o)},{$a:r,$b:o},function(e){return {$a:function(){var t=e.mul(o.toFloat()),n=getReductionAxes(r.shape,a);return n.length>0?t.sum(n).reshape(r.shape):t},$b:function(){var t=e.mul(r.toFloat()),n=getReductionAxes(o.shape,a);return n.length>0?t.sum(n).reshape(o.shape):t}}})}function mulStrict_(e,t){var n=convertToTensor(e,"a","mul"),r=convertToTensor(t,"b","mul");return assertShapesMatch(n.shape,r.shape,"Error in multiplyStrict: "),n.mul(r)}function div_(e,t){var n,r,o=convertToTensor(e,"a","div"),a=convertToTensor(t,"b","div");if(n=makeTypesMatch(o,a),o=n[0],a=n[1],"int32"===o.dtype&&"int32"===a.dtype)return floorDiv(o,a);r=function(e){return e.realDivide(o,a)};var i=assertAndGetBroadcastShape(o.shape,a.shape);return ENV.engine.runKernel(r,{$a:o,$b:a},function(e){return {$a:function(){var t=e.div(a.toFloat()),n=getReductionAxes(o.shape,i);return n.length>0?t.sum(n).reshape(o.shape):t},$b:function(){var t=e.mul(o.toFloat()),n=getReductionAxes(a.shape,i);n.length>0&&(t=t.sum(n).reshape(a.shape));var r=a.square();return t.div(r.toFloat()).neg()}}})}function floorDiv_(e,t){var n,r=convertToTensor(e,"a","floorDiv"),o=convertToTensor(t,"b","floorDiv");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.floorDiv(r,o)},{$a:r,$b:o},function(e){return {$a:function(){var t=e.div(o.toFloat()),n=getReductionAxes(r.shape,a);return n.length>0?t.sum(n).reshape(r.shape):t},$b:function(){var t=e.mul(r.toFloat()),n=getReductionAxes(o.shape,a);n.length>0&&(t=t.sum(n).reshape(o.shape));var i=o.square();return t.div(i.toFloat()).neg()}}})}function divStrict_(e,t){var n=convertToTensor(e,"a","div"),r=convertToTensor(t,"b","div");return assertShapesMatch(n.shape,r.shape,"Error in divideStrict: "),n.div(r)}function mod_(e,t){var n,r=convertToTensor(e,"a","mod"),o=convertToTensor(t,"b","mod");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.mod(r,o)},{$a:r,$b:o},function(e){return {$a:function(){var t=getReductionAxes(r.shape,a);return t.length>0?e.sum(t).reshape(r.shape):e},$b:function(){var t=e.mul(r.div(o).floor().neg()),n=getReductionAxes(o.shape,a);return n.length>0?t.sum(n).reshape(o.shape):t}}})}function modStrict_(e,t){var n=convertToTensor(e,"a","modStrict"),r=convertToTensor(t,"b","modStrict");return assertShapesMatch(n.shape,r.shape,"Error in modStrict: "),n.mod(r)}function minimum_(e,t){var n,r=convertToTensor(e,"a","minimum"),o=convertToTensor(t,"b","minimum");n=makeTypesMatch(r,o),r=n[0],o=n[1],"bool"===r.dtype&&(r=r.toInt(),o=o.toInt()),assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.minimum(r,o)},{$a:r,$b:o},function(e){return {$a:function(){return e.mul(r.lessEqual(o).toFloat())},$b:function(){return e.mul(r.greater(o).toFloat())}}})}function minimumStrict_(e,t){var n=convertToTensor(e,"a","minimumStrict"),r=convertToTensor(t,"b","minimumStrict");return assertShapesMatch(n.shape,r.shape,"Error in minimumStrict: "),n.minimum(r)}function maximum_(e,t){var n,r=convertToTensor(e,"a","maximum"),o=convertToTensor(t,"b","maximum");n=makeTypesMatch(r,o),r=n[0],o=n[1],"bool"===r.dtype&&(r=r.toInt(),o=o.toInt()),assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.maximum(r,o)},{$a:r,$b:o},function(e){return {$a:function(){return e.mul(r.greaterEqual(o).toFloat())},$b:function(){return e.mul(r.less(o).toFloat())}}})}function maximumStrict_(e,t){var n=convertToTensor(e,"a","maximumStrict"),r=convertToTensor(t,"b","maximumStrict");return assertShapesMatch(n.shape,r.shape,"Error in maximumStrict: "),n.maximum(r)}function squaredDifference_(e,t){var n,r=convertToTensor(e,"a","squaredDifference"),o=convertToTensor(t,"b","squaredDifference");n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.squaredDifference(r,o)},{$a:r,$b:o},function(e){var t=scalar(2);return {$a:function(){return e.mul(r.sub(o).mul(t))},$b:function(){return e.mul(o.sub(r).mul(t))}}})}function squaredDifferenceStrict_(e,t){var n=convertToTensor(e,"a","squaredDifferenceStrict"),r=convertToTensor(t,"b","squaredDifferenceStrict");return assertShapesMatch(n.shape,r.shape,"Error in squaredDifferenceStrict: "),n.squaredDifference(r)}function atan2_(e,t){var n,r=convertToTensor(e,"a","atan2"),o=convertToTensor(t,"b","atan2");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.atan2(r,o)},{$a:r,$b:o},function(e){return {$a:function(){var t=add(r.square(),o.square()),n=e.mul(o.div(t)),i=getReductionAxes(r.shape,a);return i.length>0&&(n=n.sum(i)),n.reshape(r.shape)},$b:function(){var t=add(r.square(),o.square()),n=neg(e.mul(r.div(t))),i=getReductionAxes(o.shape,a);return i.length>0&&(n=n.sum(i)),n.reshape(o.shape)}}})}var add=op({add_:add_}),addN=op({addN_:addN_}),addStrict=op({addStrict_:addStrict_}),atan2=op({atan2_:atan2_}),div=op({div_:div_}),divStrict=op({divStrict_:divStrict_}),floorDiv=op({floorDiv_:floorDiv_}),maximum=op({maximum_:maximum_}),maximumStrict=op({maximumStrict_:maximumStrict_}),minimum=op({minimum_:minimum_}),minimumStrict=op({minimumStrict_:minimumStrict_}),mod=op({mod_:mod_}),modStrict=op({modStrict_:modStrict_}),mul=op({mul_:mul_}),mulStrict=op({mulStrict_:mulStrict_}),pow=op({pow_:pow_}),powStrict=op({powStrict_:powStrict_}),squaredDifference=op({squaredDifference_:squaredDifference_}),squaredDifferenceStrict=op({squaredDifferenceStrict_:squaredDifferenceStrict_}),sub=op({sub_:sub_}),subStrict=op({subStrict_:subStrict_});function logicalNot_(e){var t=convertToTensor(e,"x","logicalNot","bool");return ENV.engine.runKernel(function(e){return e.logicalNot(t)},{$x:t})}function logicalAnd_(e,t){var n=convertToTensor(e,"a","logicalAnd","bool"),r=convertToTensor(t,"b","logicalAnd","bool");return assertAndGetBroadcastShape(n.shape,r.shape),ENV.engine.runKernel(function(e){return e.logicalAnd(n,r)},{$a:n,$b:r})}function logicalOr_(e,t){var n=convertToTensor(e,"a","logicalOr","bool"),r=convertToTensor(t,"b","logicalOr","bool");return assertAndGetBroadcastShape(n.shape,r.shape),ENV.engine.runKernel(function(e){return e.logicalOr(n,r)},{$a:n,$b:r})}function logicalXor_(e,t){var n=convertToTensor(e,"a","logicalXor","bool"),r=convertToTensor(t,"b","logicalXor","bool");return assertAndGetBroadcastShape(n.shape,r.shape),logicalOr(e,t).logicalAnd(logicalAnd(e,t).logicalNot())}function where_(e,t,n){var r=convertToTensor(t,"a","where"),o=convertToTensor(n,"b","where"),a=convertToTensor(e,"condition","where","bool");assertShapesMatch(r.shape,o.shape,"Error in where: "),1===a.rank?assert(a.shape[0]===r.shape[0],"The first dimension of `a` must match the size of `condition`."):assertShapesMatch(a.shape,o.shape,"Error in where: ");return ENV.engine.runKernel(function(e){return e.select(a,r,o)},{$condition:a,$a:r,$b:o},function(e){return {$condition:function(){return zerosLike(a).toFloat()},$a:function(){return e.mul(a.cast(e.dtype))},$b:function(){return e.mul(a.logicalNot().cast(e.dtype))}}})}function whereAsync_(e){return __awaiter(this,void 0,void 0,function(){var t,n,r;return __generator(this,function(o){switch(o.label){case 0:return [4,(t=convertToTensor(e,"condition","whereAsync","bool")).data()];case 1:return n=o.sent(),r=whereImpl(t.shape,n),e!==t&&t.dispose(),[2,r]}})})}var logicalAnd=op({logicalAnd_:logicalAnd_}),logicalNot=op({logicalNot_:logicalNot_}),logicalOr=op({logicalOr_:logicalOr_}),logicalXor=op({logicalXor_:logicalXor_}),where=op({where_:where_}),whereAsync=whereAsync_;function relu_(e){var t=convertToTensor(e,"x","relu");if("bool"===t.dtype)return t.toInt();return ENV.engine.runKernel(function(e){return e.relu(t)},{$x:t},function(e){var n=t.step();return {$x:function(){return e.mulStrict(n.toFloat())}}})}function elu_(e){var t=convertToTensor(e,"x","elu");return ENV.engine.runKernel(function(e,n){return n(e.elu(t))},{$x:t},function(e,t){var n=t[0];return {$x:function(){return ENV.engine.runKernel(function(t){return t.eluDer(e,n)},{dy:e,y:n})}}})}function selu_(e){var t=convertToTensor(e,"x","selu");return ENV.engine.runKernel(function(e){return e.selu(t)},{$x:t},function(e){return {$x:function(){var n=t.greater(scalar(0)),r=scalar(SELU_SCALEALPHA),o=scalar(SELU_SCALE),a=e.mul(o),i=e.mul(r).mul(t.toFloat().exp());return where(n,a,i)}}})}function leakyRelu_(e,t){void 0===t&&(t=.2);var n=convertToTensor(e,"x","leakyRelu");return maximum(scalar(t).mul(n),n)}function prelu_(e,t){var n=convertToTensor(e,"x","prelu"),r=convertToTensor(t,"alpha","prelu"),o=scalar(0);return maximum(o,n).add(r.mul(minimum(o,n)))}var elu=op({elu_:elu_}),leakyRelu=op({leakyRelu_:leakyRelu_}),prelu=op({prelu_:prelu_}),relu=op({relu_:relu_}),selu=op({selu_:selu_});function transpose_(e,t){var n=convertToTensor(e,"x","transpose");if(null==t&&(t=n.shape.map(function(e,t){return t}).reverse()),assert(n.rank===t.length,"Error in transpose: rank of input "+n.rank+" must match length of perm "+t+"."),t.forEach(function(e){assert(e>=0&&e<n.rank,"All entries in 'perm' must be between 0 and "+(n.rank-1)+" but got "+t);}),n.rank<=1)return n.clone();return ENV.engine.runKernel(function(e){return e.transpose(n,t)},{$x:n},function(e){var n=getUndoAxesPermutation(t);return {$x:function(){return e.transpose(n)}}})}var transpose=op({transpose_:transpose_});function localResponseNormalization_(e,t,n,r,o){void 0===t&&(t=5),void 0===n&&(n=1),void 0===r&&(r=1),void 0===o&&(o=.5);var a=convertToTensor(e,"x","localResponseNormalization");assert(4===a.rank||3===a.rank,"Error in localResponseNormalization: x must be rank 3 or 4 but got\n               rank "+a.rank+"."),assert(isInt(t),"Error in localResponseNormalization: depthRadius must be an integer\n                     but got depthRadius "+t+".");var i=a,s=!1;3===a.rank&&(s=!0,i=a.as4D(1,a.shape[0],a.shape[1],a.shape[2]));var u=ENV.engine.runKernel(function(e,a){return a(e.localResponseNormalization4D(i,t,n,r,o))},{x4D:i},function(e,a){var s=a[0];return {x4D:function(){return ENV.engine.runKernel(function(a){return a.LRNGrad(e,i,s,t,n,r,o)},{})}}});return s?u.as3D(u.shape[1],u.shape[2],u.shape[3]):u}var localResponseNormalization=op({localResponseNormalization_:localResponseNormalization_});function norm_(e,t,n,r){void 0===t&&(t="euclidean"),void 0===n&&(n=null),void 0===r&&(r=!1);var o=normImpl(e=convertToTensor(e,"x","norm"),t,n),a=o.shape;if(r){var i=parseAxisParam(n,e.shape);a=expandShapeToKeepDim(o.shape,i);}return o.reshape(a)}function normImpl(e,t,n){if(void 0===n&&(n=null),0===e.rank)return e.abs();if(1!==e.rank&&null===n)return normImpl(e.reshape([-1]),t,n);if(1===e.rank||"number"==typeof n||n instanceof Array&&1===n.length){if(1===t)return e.abs().sum(n);if(t===1/0)return e.abs().max(n);if(t===-1/0)return e.abs().min(n);if("euclidean"===t||2===t)return e.abs().pow(scalar(2,"int32")).sum(n).sqrt();throw new Error("Error in norm: invalid ord value: "+t)}if(n instanceof Array&&2===n.length){if(1===t)return e.abs().sum(n[0]).max(n[1]-1);if(t===1/0)return e.abs().sum(n[1]).max(n[0]);if(t===-1/0)return e.abs().sum(n[1]).min(n[0]);if("fro"===t||"euclidean"===t)return e.square().sum(n).sqrt();throw new Error("Error in norm: invalid ord value: "+t)}throw new Error("Error in norm: invalid axis: "+n)}var norm=op({norm_:norm_});function unsortedSegmentSum_(e,t,n){var r=convertToTensor(e,"x","unsortedSegmentSum"),o=convertToTensor(t,"segmentIds","unsortedSegmentSum","int32");assert(isInt(n),"numSegments must be of dtype int");return ENV.engine.runKernel(function(e){return e.unsortedSegmentSum(r,o,n)},{$x:r},function(e){return {$x:function(){return gatherDropNegatives(e,o)}}})}function gather_(e,t,n){void 0===n&&(n=0);var r=convertToTensor(e,"x","gather"),o=convertToTensor(t,"indices","gather","int32");n=parseAxisParam(n,r.shape)[0];return ENV.engine.runKernel(function(e){return e.gather(r,o,n)},{$x:r},function(e){return {$x:function(){if(0===n)return unsortedSegmentSum(e,o,r.shape[n]);var t=r.shape,a=o.size,i=t.slice(0,n),s=i.length,u=t.slice(n,t.length).slice(1),l=u.length,c=arrayRange(0,s),p=arrayRange(s+1,s+1+l),d=arrayConcat([i,[a],u]),h=e.reshape(d),f=o.reshape([a]),m=arrayConcat([[s],c,p]),g=h.transpose(m),v=unsortedSegmentSum(g,f,r.shape[n]),y=getUndoAxesPermutation(m);return v=v.transpose(y)}}})}function arrayRange(e,t){for(var n=[],r=e;r<t;++r)n.push(r);return n}function arrayConcat(e){for(var t=[],n=0;n<e.length;++n)for(var r=0;r<e[n].length;++r)t.push(e[n][r]);return t}function gatherDropNegatives(e,t){for(var n=maximum(t,zerosLike(t)),r=gather(e,n),o=greaterEqual(t,scalar(0,"int32")),a=r.rank-o.rank,i=0;i<a;++i)o=expandDims(o,i+1);o=logicalAnd(o,ones$1(r.shape,"bool"));var s=zerosLike(r);return where(o,r,s)}var gather=op({gather_:gather_}),unsortedSegmentSum=op({unsortedSegmentSum_:unsortedSegmentSum_});function multiRNNCell_(e,t,n,r){for(var o=convertToTensor(t,"data","multiRNNCell"),a=convertToTensorArray(n,"c","multiRNNCell"),i=convertToTensorArray(r,"h","multiRNNCell"),s=o,u=[],l=0;l<e.length;l++){var c=e[l](s,a[l],i[l]);u.push(c[0]),u.push(c[1]),s=c[1];}var p=[],d=[];for(l=0;l<u.length;l+=2)p.push(u[l]),d.push(u[l+1]);return [p,d]}function basicLSTMCell_(e,t,n,r,o,a){var i=convertToTensor(e,"forgetBias","basicLSTMCell"),s=convertToTensor(t,"lstmKernel","basicLSTMCell"),u=convertToTensor(n,"lstmBias","basicLSTMCell"),l=convertToTensor(r,"data","basicLSTMCell"),c=convertToTensor(o,"c","basicLSTMCell"),p=convertToTensor(a,"h","basicLSTMCell"),d=l.concat(p,1).matMul(s).add(u),h=d.shape[0],f=d.shape[1]/4,m=[h,f],g=d.slice([0,0],m),v=d.slice([0,f],m),y=d.slice([0,2*f],m),x=d.slice([0,3*f],m),T=g.sigmoid().mulStrict(v.tanh()).addStrict(c.mulStrict(i.add(y).sigmoid()));return [T,T.tanh().mulStrict(x.sigmoid())]}var basicLSTMCell=op({basicLSTMCell_:basicLSTMCell_}),multiRNNCell=op({multiRNNCell_:multiRNNCell_});function movingAverage_(e,t,n,r,o){void 0===o&&(o=!0);var a=convertToTensor(e,"v","movingAverage"),i=convertToTensor(t,"x","movingAverage"),s=convertToTensor(n,"decay","movingAverage");assertTypesMatch(a,i),assert(arraysEqual(a.shape,i.shape),"Shape mismatch in v and x");var u=scalar(1),l=u.sub(s),c=i.sub(a).mul(l);if(o){assert(null!=r,"When using zeroDebias: true, step is required.");var p=convertToTensor(r,"step","movingAverage");c=c.div(u.sub(pow(s,p)));}return a.add(c)}var movingAverage=op({movingAverage_:movingAverage_});function stridedSlice_(e,t,n,r,o,a,i,s,u){if(void 0===o&&(o=0),void 0===a&&(a=0),void 0===i&&(i=0),void 0===s&&(s=0),void 0===u&&(u=0),0!==i)throw new Error("ellipsis mask is not yet supported");if(0!==s)throw new Error("new axis mask is not yet supported");var l=convertToTensor(e,"x","stridedSlice");return ENV.engine.runKernel(function(e){return e.stridedSlice(l,t,n,r,o,a,i,s,u)},{$x:l})}var stridedSlice=op({stridedSlice_:stridedSlice_});function topk_(e,t,n){void 0===t&&(t=1),void 0===n&&(n=!0);var r=convertToTensor(e,"x","topk");if(0===r.rank)throw new Error("topk() expects the input to be of rank 1 or higher");var o=r.shape[r.shape.length-1];if(t>o)throw new Error("'k' passed to topk() must be <= the last dimension ("+o+") but got "+t);var a=ENV.engine.runKernel(function(e){return e.topk(r,t,n)},{$x:r});return {values:a[0],indices:a[1]}}var topk=op({topk_:topk_});function scatterND_(e,t,n){var r=convertToTensor(e,"indices","scatterND","int32"),o=convertToTensor(t,"updates","scatterND");return validateInput(o,r,n),ENV.engine.runKernel(function(e){return e.scatterND(r,o,n)},{$indices:r,$updates:o})}var scatterND=op({scatterND_:scatterND_});function fft_(e){assert("complex64"===e.dtype,"The dtype for tf.spectral.fft() must be complex64 but got "+e.dtype+".");var t=e.shape[e.shape.length-1],n=e.size/t,r=e.as2D(n,t);return ENV.engine.runKernel(function(e){return e.fft(r)},{input:e}).reshape(e.shape)}function ifft_(e){assert("complex64"===e.dtype,"The dtype for tf.spectral.ifft() must be complex64 but got "+e.dtype+".");var t=e.shape[e.shape.length-1],n=e.size/t,r=e.as2D(n,t);return ENV.engine.runKernel(function(e){return e.ifft(r)},{input:e}).reshape(e.shape)}function rfft_(e){assert("float32"===e.dtype,"The dtype for rfft() must be real value but\n    got "+e.dtype);var t=e.shape[e.shape.length-1],n=e.size/t,r=e.zerosLike(),o=complex(e,r).as2D(n,t),a=fft(o),i=Math.floor(t/2)+1,s=real(a),u=imag(a),l=s.split([i,t-i],s.shape.length-1),c=u.split([i,t-i],u.shape.length-1),p=e.shape.slice();return p[e.shape.length-1]=i,complex(l[0],c[0]).reshape(p)}function irfft_(e){var t=e.shape[e.shape.length-1],n=e.size/t;if(t<=2){var r=e.as2D(n,t),o=ifft(r);return real(o)}var a=[n,2*(t-1)],i=real(e).as2D(n,t),s=imag(e).as2D(n,t),u=i.slice([0,1],[n,t-2]).reverse(1),l=s.slice([0,1],[n,t-2]).reverse(1).mul(scalar(-1)),c=i.concat(u,1),p=s.concat(l,1);r=complex(c,p).as2D(a[0],a[1]),o=ifft(r);return real(o)}var fft=op({fft_:fft_}),ifft=op({ifft_:ifft_}),rfft=op({rfft_:rfft_}),irfft=op({irfft_:irfft_}),spectral_ops=Object.freeze({fft:fft,ifft:ifft,rfft:rfft,irfft:irfft});function validateInput$1(e,t,n,r){if("int32"!==e.dtype)throw new Error("tf.sparseToDense() expects the indices to be int32 type, but the dtype was "+e.dtype+".");if(e.rank>2)throw new Error("sparseIndices should be a scalar, vector, or matrix, but got shape "+e.shape+".");var o=e.rank>0?e.shape[0]:1,a=e.rank>1?e.shape[1]:1;if(n.length!==a)throw new Error("outputShape has incorrect number of elements:, "+n.length+", should be: "+a+".");var i=t.size;if(0!==t.rank&&(1!==t.rank||i!==o))throw new Error("sparseValues has incorrect shape "+t.shape+", should be [] or ["+o+"]");if(t.dtype!==r.dtype)throw new Error("sparseValues.dtype must match defaultValues.dtype")}function sparseToDense_(e,t,n,r){var o=convertToTensor(e,"sparseIndices","sparseToDense","int32"),a=convertToTensor(t,"sparseValues","sparseToDense"),i=convertToTensor(r,"defaultValue","sparseToDense",a.dtype);return validateInput$1(o,a,n,i),ENV.engine.runKernel(function(e){return e.sparseToDense(o,a,n,i)},{$sparseIndices:o,$sparseValues:a,$defaultValue:i})}var sparseToDense=op({sparseToDense_:sparseToDense_});function gatherND_(e,t){var n=convertToTensor(t,"indices","gatherND","int32"),r=convertToTensor(e,"x","gatherND");return ENV.engine.runKernel(function(e){return e.gatherND(r,n)},{$x:r,$indices:n})}var Reduction,gatherND=op({gatherND_:gatherND_});function computeWeightedLoss_(e,t,n){void 0===n&&(n=Reduction.SUM_BY_NONZERO_WEIGHTS);var r=convertToTensor(e,"losses","computeWeightedLoss"),o=null;null!=t&&(o=convertToTensor(t,"weights","computeWeightedLoss"));var a=null==o?r:r.mul(o);if(n===Reduction.NONE)return a;if(n===Reduction.SUM)return a.sum();if(n===Reduction.MEAN){if(null==o)return a.mean();var i=sizeFromShape(r.shape)/sizeFromShape(o.shape),s=a.sum().div(o.sum());return i>1?s.div(scalar(i)):s}if(n===Reduction.SUM_BY_NONZERO_WEIGHTS){if(null==o)return a.sum().div(scalar(r.size));var u=o.mul(ones$1(r.shape)).notEqual(scalar(0)).sum().toFloat();return a.sum().div(u)}throw Error("Unknown reduction: "+n)}function absoluteDifference_(e,t,n,r){void 0===r&&(r=Reduction.SUM_BY_NONZERO_WEIGHTS);var o=convertToTensor(e,"labels","absoluteDifference"),a=convertToTensor(t,"predictions","absoluteDifference"),i=null;null!=n&&(i=convertToTensor(n,"weights","absoluteDifference")),assertShapesMatch(o.shape,a.shape,"Error in absoluteDifference: ");var s=o.sub(a).abs();return computeWeightedLoss(s,i,r)}function meanSquaredError_(e,t,n,r){void 0===r&&(r=Reduction.SUM_BY_NONZERO_WEIGHTS);var o=convertToTensor(e,"labels","meanSquaredError"),a=convertToTensor(t,"predictions","meanSquaredError"),i=null;null!=n&&(i=convertToTensor(n,"weights","meanSquaredError")),assertShapesMatch(o.shape,a.shape,"Error in meanSquaredError: ");var s=o.squaredDifference(a);return computeWeightedLoss(s,i,r)}function cosineDistance_(e,t,n,r,o){void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,"labels","cosineDistance"),i=convertToTensor(t,"predictions","cosineDistance"),s=null;null!=r&&(s=convertToTensor(r,"weights","cosineDistance")),assertShapesMatch(a.shape,i.shape,"Error in cosineDistance: ");var u=scalar(1).sub(a.mul(i).sum(n,!0));return computeWeightedLoss(u,s,o)}function hingeLoss_(e,t,n,r){void 0===r&&(r=Reduction.SUM_BY_NONZERO_WEIGHTS);var o=convertToTensor(e,"labels","hingeLoss"),a=convertToTensor(t,"predictions","hingeLoss"),i=null;null!=n&&(i=convertToTensor(n,"weights","hingeLoss")),assertShapesMatch(o.shape,a.shape,"Error in hingeLoss: ");var s=scalar(1);o=scalar(2).mul(o).sub(s);var u=s.sub(o.mul(a)).relu();return computeWeightedLoss(u,i,r)}function logLoss_(e,t,n,r,o){void 0===r&&(r=1e-7),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,"labels","logLoss"),i=convertToTensor(t,"predictions","logLoss"),s=null;null!=n&&(s=convertToTensor(n,"weights","logLoss")),assertShapesMatch(a.shape,i.shape,"Error in logLoss: ");var u=scalar(1),l=scalar(r),c=a.mul(i.add(l).log()).neg().sub(u.sub(a).mul(u.sub(i).add(l).log()));return computeWeightedLoss(c,s,o)}function sigmoidCrossEntropyWithLogits_(e,t){var n=convertToTensor(e,"labels","sigmoidCrossEntropyWithLogits"),r=convertToTensor(t,"logits","sigmoidCrossEntropyWithLogits");assertShapesMatch(n.shape,r.shape,"Error in sigmoidCrossEntropyWithLogits: ");var o=r.relu(),a=r.mul(n),i=r.abs().neg().exp().log1p();return o.sub(a).add(i)}function sigmoidCrossEntropy_(e,t,n,r,o){void 0===r&&(r=0),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,"multiClassLabels","sigmoidCrossEntropy"),i=convertToTensor(t,"logits","sigmoidCrossEntropy"),s=null;if(null!=n&&(s=convertToTensor(n,"weights","sigmoidCrossEntropy")),assertShapesMatch(a.shape,i.shape,"Error in sigmoidCrossEntropy: "),r>0){var u=scalar(r),l=scalar(1),c=scalar(.5);a=a.mul(l.sub(u)).add(c.mul(u));}var p=sigmoidCrossEntropyWithLogits_(a,i);return computeWeightedLoss(p,s,o)}function huberLoss_(e,t,n,r,o){void 0===r&&(r=1),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,"labels","huberLoss"),i=convertToTensor(t,"predictions","huberLoss"),s=null;null!=n&&(s=convertToTensor(n,"weights","huberLoss")),assertShapesMatch(a.shape,i.shape,"Error in huberLoss: ");var u=scalar(r),l=i.sub(a).abs(),c=minimum(l,u),p=l.sub(c),d=scalar(.5).mul(c.square()).add(u.mul(p));return computeWeightedLoss(d,s,o)}function softmaxCrossEntropyWithLogits_(e,t,n){if(void 0===n&&(n=-1),-1===n&&(n=t.rank-1),n!==t.rank-1)throw Error("Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank "+t.rank+" and dim was "+n);return customGrad(function(e,t){var r=t.logSumExp([n],!0),o=t.toFloat().sub(r);return {value:o.mul(e).neg().sum([n]),gradFunc:function(t){var r=expandShapeToKeepDim(t.shape,[n]);return [t.reshape(r).mul(e.toFloat().sub(o.exp())),t.reshape(r).mul(o.exp().sub(e.toFloat()))]}}})(e,t)}function softmaxCrossEntropy_(e,t,n,r,o){void 0===r&&(r=0),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,"onehotLabels","softmaxCrossEntropy"),i=convertToTensor(t,"logits","softmaxCrossEntropy"),s=null;if(null!=n&&(s=convertToTensor(n,"weights","softmaxCrossEntropy")),assertShapesMatch(a.shape,i.shape,"Error in softmaxCrossEntropy: "),r>0){var u=scalar(r),l=scalar(1),c=scalar(a.shape[1]);a=a.mul(l.sub(u)).add(u.div(c));}var p=softmaxCrossEntropyWithLogits_(a,i);return computeWeightedLoss(p,s,o)}!function(e){e[e.NONE=0]="NONE",e[e.MEAN=1]="MEAN",e[e.SUM=2]="SUM",e[e.SUM_BY_NONZERO_WEIGHTS=3]="SUM_BY_NONZERO_WEIGHTS";}(Reduction||(Reduction={}));var absoluteDifference=op({absoluteDifference_:absoluteDifference_}),computeWeightedLoss=op({computeWeightedLoss_:computeWeightedLoss_}),cosineDistance=op({cosineDistance_:cosineDistance_}),hingeLoss=op({hingeLoss_:hingeLoss_}),huberLoss=op({huberLoss_:huberLoss_}),logLoss=op({logLoss_:logLoss_}),meanSquaredError=op({meanSquaredError_:meanSquaredError_}),sigmoidCrossEntropy=op({sigmoidCrossEntropy_:sigmoidCrossEntropy_}),softmaxCrossEntropy=op({softmaxCrossEntropy_:softmaxCrossEntropy_}),loss_ops=Object.freeze({get Reduction(){return Reduction},absoluteDifference:absoluteDifference,computeWeightedLoss:computeWeightedLoss,cosineDistance:cosineDistance,hingeLoss:hingeLoss,huberLoss:huberLoss,logLoss:logLoss,meanSquaredError:meanSquaredError,sigmoidCrossEntropy:sigmoidCrossEntropy,softmaxCrossEntropy:softmaxCrossEntropy});function gramSchmidt_(e){var t;if(Array.isArray(e)){t=!1,assert(null!=e&&e.length>0,"Gram-Schmidt process: input must not be null, undefined, or empty");for(var n=e[0].shape[0],r=1;r<e.length;++r)assert(e[r].shape[0]===n,"Gram-Schmidt: Non-unique lengths found in the input vectors: ("+e[r].shape[0]+" vs. "+n+")");}else t=!0,e=split$1(e,e.shape[0],0).map(function(e){return squeeze(e,[0])});assert(e.length<=e[0].shape[0],"Gram-Schmidt: Number of vectors ("+e.length+") exceeds number of dimensions ("+e[0].shape[0]+").");var o=[],a=e,i=function(e){o.push(ENV.engine.tidy(function(){var t=a[e];if(e>0)for(var n=0;n<e;++n){var r=sum$1(o[n].mulStrict(t)).mul(o[n]);t=t.sub(r);}return t.div(norm(t,"euclidean"))}));};for(r=0;r<e.length;++r)i(r);return t?stack(o,0):o}function qr_(e,t){if(void 0===t&&(t=!1),e.rank<2)throw new Error("qr() requires input tensor to have a rank >= 2, but got rank "+e.rank);if(2===e.rank)return qr2d(e,t);var n=e.shape.slice(0,e.shape.length-2).reduce(function(e,t){return e*t}),r=[],o=[];return unstack(e.reshape([n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0).forEach(function(e){var n=qr2d(e,t),a=n[0],i=n[1];r.push(a),o.push(i);}),[stack(r,0).reshape(e.shape),stack(o,0).reshape(e.shape)]}function qr2d(e,t){return void 0===t&&(t=!1),ENV.engine.tidy(function(){if(2!==e.shape.length)throw new Error("qr2d() requires a 2D Tensor, but got a "+e.shape.length+"D Tensor.");for(var n=e.shape[0],r=e.shape[1],o=eye(n),a=e.clone(),i=tensor2d([[1]],[1,1]),s=i.clone(),u=n>=r?r:n,l=function(e){var t,u=a,l=s,c=o;t=ENV.engine.tidy(function(){var t=a.slice([e,e],[n-e,1]),u=t.norm(),l=a.slice([e,e],[1,1]),c=l.sign().neg(),p=l.sub(c.mul(u)),d=t.div(p);s=1===d.shape[0]?i.clone():i.concat(d.slice([1,0],[d.shape[0]-1,d.shape[1]]),0);var h=c.matMul(p).div(u).neg(),f=a.slice([e,0],[n-e,r]),m=h.mul(s);a=0===e?f.sub(m.matMul(s.transpose().matMul(f))):a.slice([0,0],[e,r]).concat(f.sub(m.matMul(s.transpose().matMul(f))),0);var g=o.slice([0,e],[n,o.shape[1]-e]);return o=0===e?g.sub(g.matMul(s).matMul(m.transpose())):o.slice([0,0],[n,e]).concat(g.sub(g.matMul(s).matMul(m.transpose())),1),[s,a,o]}),s=t[0],a=t[1],o=t[2],dispose([u,l,c]);},c=0;c<u;++c)l(c);return !t&&n>r&&(o=o.slice([0,0],[n,r]),a=a.slice([0,0],[r,r])),[o,a]})}var gramSchmidt=op({gramSchmidt_:gramSchmidt_}),qr=op({qr_:qr_}),linalg_ops=Object.freeze({gramSchmidt:gramSchmidt,qr:qr});function resizeBilinear_(e,t,n){void 0===n&&(n=!1);var r=convertToTensor(e,"images","resizeBilinear");assert(3===r.rank||4===r.rank,"Error in resizeBilinear: x must be rank 3 or 4, but got rank "+r.rank+"."),assert(2===t.length,"Error in resizeBilinear: new shape must 2D, but got shape "+t+".");var o=r,a=!1;3===r.rank&&(a=!0,o=r.as4D(1,r.shape[0],r.shape[1],r.shape[2]));var i=t[0],s=t[1],u=ENV.engine.runKernel(function(e,t){return e.resizeBilinear(o,i,s,n)},{batchImages:o},function(e,t){return {batchImages:function(){return ENV.engine.runKernel(function(t){return t.resizeBilinearBackprop(e,o,n)},{})}}});return a?u.as3D(u.shape[1],u.shape[2],u.shape[3]):u}function resizeNearestNeighbor_(e,t,n){void 0===n&&(n=!1);var r=convertToTensor(e,"images","resizeNearestNeighbor");assert(3===r.rank||4===r.rank,"Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank "+r.rank+"."),assert(2===t.length,"Error in resizeNearestNeighbor: new shape must 2D, but got shape "+t+"."),assert("float32"===r.dtype||"int32"===r.dtype,"`images` must have `int32` or `float32` as dtype");var o=r,a=!1;3===r.rank&&(a=!0,o=r.as4D(1,r.shape[0],r.shape[1],r.shape[2]));var i=t[0],s=t[1],u=ENV.engine.runKernel(function(e,t){return e.resizeNearestNeighbor(o,i,s,n)},{batchImages:o},function(e,t){return {batchImages:function(){return ENV.engine.runKernel(function(t){return t.resizeNearestNeighborBackprop(e,o,n)},{})}}});return a?u.as3D(u.shape[1],u.shape[2],u.shape[3]):u}function nonMaxSuppression_(e,t,n,r,o){void 0===r&&(r=.5),void 0===o&&(o=Number.NEGATIVE_INFINITY);var a=convertToTensor(e,"boxes","nonMaxSuppression"),i=convertToTensor(t,"scores","nonMaxSuppression"),s=nonMaxSuppSanityCheck(a,i,n,r,o);return n=s.maxOutputSize,r=s.iouThreshold,o=s.scoreThreshold,ENV.engine.runKernel(function(e){return e.nonMaxSuppression(a,i,n,r,o)},{$boxes:a})}function nonMaxSuppressionAsync_(e,t,n,r,o){return void 0===r&&(r=.5),void 0===o&&(o=Number.NEGATIVE_INFINITY),__awaiter(this,void 0,void 0,function(){var a,i,s,u,l,c;return __generator(this,function(p){switch(p.label){case 0:return a=convertToTensor(e,"boxes","nonMaxSuppressionAsync"),i=convertToTensor(t,"scores","nonMaxSuppressionAsync"),s=nonMaxSuppSanityCheck(a,i,n,r,o),n=s.maxOutputSize,r=s.iouThreshold,o=s.scoreThreshold,[4,a.data()];case 1:return u=p.sent(),[4,i.data()];case 2:return l=p.sent(),c=nonMaxSuppressionImpl(u,l,n,r,o),a!==e&&a.dispose(),i!==t&&i.dispose(),[2,c]}})})}function nonMaxSuppSanityCheck(e,t,n,r,o){null==r&&(r=.5),null==o&&(o=Number.NEGATIVE_INFINITY);var a=e.shape[0];return n=Math.min(n,a),assert(0<=r&&r<=1,"iouThreshold must be in [0, 1], but was '"+r+"'"),assert(2===e.rank,"boxes must be a 2D tensor, but was of rank '"+e.rank+"'"),assert(4===e.shape[1],"boxes must have 4 columns, but 2nd dimension was "+e.shape[1]),assert(1===t.rank,"scores must be a 1D tensor"),assert(t.shape[0]===a,"scores has incompatible shape with boxes. Expected "+a+", but was "+t.shape[0]),{maxOutputSize:n,iouThreshold:r,scoreThreshold:o}}function cropAndResize_(e,t,n,r,o,a){var i=convertToTensor(e,"image","cropAndResize","float32"),s=convertToTensor(t,"boxes","cropAndResize","float32"),u=convertToTensor(n,"boxInd","cropAndResize","int32");o=o||"bilinear",a=a||0;var l=s.shape[0];assert(4===i.rank,"Error in cropAndResize: image must be rank 4,but got rank "+i.rank+"."),assert(2===s.rank&&4===s.shape[1],"Error in cropAndResize: boxes must be have size ["+l+",4] but had shape "+s.shape+"."),assert(1===u.rank&&u.shape[0]===l,"Error in cropAndResize: boxInd must be have size ["+l+"] but had shape "+s.shape+"."),assert(2===r.length,"Error in cropAndResize: cropSize must be of length 2, but got length "+r.length+"."),assert(r[0]>=1&&r[1]>=1,"cropSize must be atleast [1,1], but was "+r),assert("bilinear"===o||"nearest"===o,"method must be bilinear or nearest, but was "+o);return ENV.engine.runKernel(function(e,t){return e.cropAndResize(i,s,u,r,o,a)},{$image:i,$boxes:s})}var resizeBilinear=op({resizeBilinear_:resizeBilinear_}),resizeNearestNeighbor=op({resizeNearestNeighbor_:resizeNearestNeighbor_}),nonMaxSuppression=op({nonMaxSuppression_:nonMaxSuppression_}),nonMaxSuppressionAsync=nonMaxSuppressionAsync_,cropAndResize=cropAndResize_,image_ops=Object.freeze({resizeBilinear:resizeBilinear,resizeNearestNeighbor:resizeNearestNeighbor,nonMaxSuppression:nonMaxSuppression,nonMaxSuppressionAsync:nonMaxSuppressionAsync,cropAndResize:cropAndResize}),ops=Object.freeze({image:image_ops,linalg:linalg_ops,losses:loss_ops,spectral:spectral_ops,op:op,batchNormalization2d:batchNormalization2d,batchNormalization3d:batchNormalization3d,batchNormalization4d:batchNormalization4d,batchNormalization:batchNormalization,complex:complex,real:real,imag:imag,concat:concat,concat1d:concat1d,concat2d:concat2d,concat3d:concat3d,concat4d:concat4d,split:split$1,conv1d:conv1d,conv2d:conv2d,conv3d:conv3d,conv2dDerFilter:conv2dDerFilter,depthwiseConv2d:depthwiseConv2d,separableConv2d:separableConv2d,conv2dTranspose:conv2dTranspose,matMul:matMul,dot:dot,outerProduct:outerProduct,reverse:reverse,reverse1d:reverse1d,reverse2d:reverse2d,reverse3d:reverse3d,reverse4d:reverse4d,maxPool:maxPool,avgPool:avgPool,pool:pool,slice:slice,slice1d:slice1d,slice2d:slice2d,slice3d:slice3d,slice4d:slice4d,abs:abs,acos:acos,acosh:acosh,asin:asin,asinh:asinh,atan:atan,atanh:atanh,ceil:ceil,clipByValue:clipByValue,cos:cos,cosh:cosh,erf:erf,exp:exp,expm1:expm1,floor:floor,log:log$1,log1p:log1p,logSigmoid:logSigmoid,neg:neg,reciprocal:reciprocal,round:round,rsqrt:rsqrt,sigmoid:sigmoid,sign:sign,sin:sin,sinh:sinh,softplus:softplus,sqrt:sqrt,square:square,step:step,tan:tan,tanh:tanh$1,all:all,any:any,argMax:argMax,argMin:argMin,logSumExp:logSumExp,max:max,mean:mean,min:min,moments:moments,sum:sum$1,prod:prod,equal:equal,equalStrict:equalStrict,greater:greater,greaterEqual:greaterEqual,greaterEqualStrict:greaterEqualStrict,greaterStrict:greaterStrict,less:less,lessEqual:lessEqual,lessEqualStrict:lessEqualStrict,lessStrict:lessStrict,notEqual:notEqual,notEqualStrict:notEqualStrict,add:add,addN:addN,addStrict:addStrict,atan2:atan2,div:div,divStrict:divStrict,floorDiv:floorDiv,maximum:maximum,maximumStrict:maximumStrict,minimum:minimum,minimumStrict:minimumStrict,mod:mod,modStrict:modStrict,mul:mul,mulStrict:mulStrict,pow:pow,powStrict:powStrict,squaredDifference:squaredDifference,squaredDifferenceStrict:squaredDifferenceStrict,sub:sub,subStrict:subStrict,elu:elu,leakyRelu:leakyRelu,prelu:prelu,relu:relu,selu:selu,logicalAnd:logicalAnd,logicalNot:logicalNot,logicalOr:logicalOr,logicalXor:logicalXor,where:where,whereAsync:whereAsync,buffer:buffer,toPixels:toPixels,print:print,batchToSpaceND:batchToSpaceND,cast:cast,clone:clone,cumsum:cumsum,depthToSpace:depthToSpace,expandDims:expandDims,eye:eye,fromPixels:fromPixels,multinomial:multinomial,oneHot:oneHot,pad:pad,pad1d:pad1d,pad2d:pad2d,pad3d:pad3d,pad4d:pad4d,rand:rand,randomNormal:randomNormal,randomUniform:randomUniform,reshape:reshape,spaceToBatchND:spaceToBatchND,squeeze:squeeze,stack:stack,tile:tile,truncatedNormal:truncatedNormal,unstack:unstack,setdiff1dAsync:setdiff1dAsync,fill:fill,linspace:linspace,ones:ones$1,range:range,scalar:scalar,tensor:tensor,tensor1d:tensor1d,tensor2d:tensor2d,tensor3d:tensor3d,tensor4d:tensor4d,tensor5d:tensor5d,tensor6d:tensor6d,zeros:zeros,onesLike:onesLike,zerosLike:zerosLike,transpose:transpose,softmax:softmax,logSoftmax:logSoftmax,localResponseNormalization:localResponseNormalization,norm:norm,gather:gather,unsortedSegmentSum:unsortedSegmentSum,basicLSTMCell:basicLSTMCell,multiRNNCell:multiRNNCell,movingAverage:movingAverage,stridedSlice:stridedSlice,topk:topk,scatterND:scatterND,fft:fft,ifft:ifft,rfft:rfft,irfft:irfft,sparseToDense:sparseToDense,gatherND:gatherND}),MathBackendCPU=function(){function e(){this.blockSize=48,this.firstUse=!0,ENV.get("IS_BROWSER")&&(this.fromPixels2DContext=document.createElement("canvas").getContext("2d"));}return e.prototype.setDataMover=function(e){this.data=new DataStorage(e);},e.prototype.register=function(e,t,n){if(this.firstUse&&(this.firstUse=!1,ENV.get("IS_NODE")&&warn("\n============================\nHi there 👋. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.\n============================\n")),this.data.has(e))throw new Error("Data buffer is already registered");this.data.set(e,{dtype:n});},e.prototype.write=function(e,t){if(null==t)throw new Error("MathBackendCPU.write(): values can not be null");this.data.get(e).values=t;},e.prototype.fromPixels=function(e,t){if(null==e)throw new Error("pixels passed to tf.fromPixels() can not be null");var n,r;if(ENV.get("IS_NODE")&&null==e.getContext)throw new Error("When running in node, pixels must be an HTMLCanvasElement like the one returned by the `canvas` npm package");if(null!=e.getContext)n=e.getContext("2d").getImageData(0,0,e.width,e.height).data;else if(e instanceof ImageData)n=e.data;else{if(!(e instanceof HTMLImageElement||e instanceof HTMLVideoElement))throw new Error("pixels passed to tf.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ImageData, but was "+e.constructor.name);if(null==this.fromPixels2DContext)throw new Error("Can't read pixels from HTMLImageElement outside the browser.");this.fromPixels2DContext.canvas.width=e.width,this.fromPixels2DContext.canvas.height=e.height,this.fromPixels2DContext.drawImage(e,0,0,e.width,e.height),n=this.fromPixels2DContext.getImageData(0,0,e.width,e.height).data;}if(4===t)r=new Int32Array(n);else{var o=e.width*e.height;r=new Int32Array(o*t);for(var a=0;a<o;a++)for(var i=0;i<t;++i)r[a*t+i]=n[4*a+i];}return tensor3d(r,[e.height,e.width,t],"int32")},e.prototype.read=function(e){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(t){return [2,this.readSync(e)]})})},e.prototype.readSync=function(e){var t=this.data.get(e),n=t.dtype,r=t.complexTensors;return "complex64"===n?mergeRealAndImagArrays(r.real.dataSync(),r.imag.dataSync()):this.data.get(e).values},e.prototype.disposeData=function(e){if(this.data.has(e)){var t=this.data.get(e).complexTensors;null!=t&&(t.real.dispose(),t.imag.dispose()),this.data.delete(e);}},e.prototype.time=function(e){return __awaiter(this,void 0,void 0,function(){var t;return __generator(this,function(n){return t=now(),e(),[2,{kernelMs:now()-t}]})})},e.prototype.memory=function(){return {unreliable:!0,reasons:["The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less."]}},e.prototype.complex=function(e,t){var n=Tensor.make(e.shape,{},"complex64");return this.data.get(n.dataId).complexTensors={real:ENV.engine.keep(e.clone()),imag:ENV.engine.keep(t.clone())},n},e.prototype.real=function(e){return this.data.get(e.dataId).complexTensors.real.clone()},e.prototype.imag=function(e){return this.data.get(e.dataId).complexTensors.imag.clone()},e.prototype.assertNotComplex=function(e,t){Array.isArray(e)||(e=[e]),e.forEach(function(e){null!=e&&assert("complex64"!==e.dtype,t+" does not support complex64 tensors.");});},e.prototype.slice=function(e,t,n){this.assertNotComplex(e,"slice");for(var r=buffer(n,e.dtype),o=0;o<r.size;++o){var a=r.indexToLoc(o),i=a.map(function(e,n){return e+t[n]});r.set.apply(r,[e.get.apply(e,i)].concat(a));}return r.toTensor()},e.prototype.stridedSlice=function(e,t,n,r,o,a,i,s,u){this.assertNotComplex(e,"stridedSlice");var l=getStridedSlicedInfo(e.shape,t,n,r,o,a,i,s,u),c=l[0],p=l[1],d=l[2],h=p.filter(function(e,t){return -1===d.indexOf(t)});if(h.some(function(e){return 0===e}))return tensor([],h);for(var f=buffer(p,e.dtype),m=0;m<f.size;m++){for(var g=f.indexToLoc(m),v=new Array(g.length),y=0;y<v.length;y++)v[y]=g[y]*r[y]+c[y];f.set.apply(f,[e.get.apply(e,v)].concat(g));}return f.toTensor().reshape(h)},e.prototype.reverse=function(e,t){this.assertNotComplex(e,"reverse");for(var n=buffer(e.shape,e.dtype),r=e.buffer(),o=function(o){var a=n.indexToLoc(o),i=a.slice();t.forEach(function(t){return i[t]=e.shape[t]-1-i[t]}),n.set.apply(n,[r.get.apply(r,i)].concat(a));},a=0;a<n.size;a++)o(a);return n.toTensor()},e.prototype.concat=function(e,t){this.assertNotComplex(e,"concat");var n=e.map(function(e){var n=sizeFromShape(e.shape.slice(t));return e.as2D(-1,n)}),r=computeOutShape(n.map(function(e){return e.shape}),1),o=buffer(r,e[0].dtype).values;if(1===n[0].shape[0]){var a=0;n.forEach(function(e){o.set(e.dataSync(),a),a+=e.size;});}else{var i=0;n.forEach(function(e){for(var t=e.dataSync(),n=0,a=0;a<e.shape[0];++a)for(var s=a*r[1]+i,u=0;u<e.shape[1];++u)o[s+u]=t[n++];i+=e.shape[1];});}var s=computeOutShape(e.map(function(e){return e.shape}),t);return tensor(o,s,e[0].dtype)},e.prototype.neg=function(e){return this.assertNotComplex(e,"neg"),this.multiply(scalar(-1),e)},e.prototype.add=function(e,t){return "complex64"===e.dtype||"complex64"===t.dtype?this.broadcastedBinaryComplexOp(e.cast("complex64"),t.cast("complex64"),function(e,t,n,r){return {real:e+n,imag:t+r}}):this.broadcastedBinaryOp(e,t,upcastType(e.dtype,t.dtype),function(e,t){return e+t})},e.prototype.addN=function(e){this.assertNotComplex(e,"addN");for(var t=e.map(function(e){return e.dataSync()}),n=buffer(e[0].shape,e[0].dtype),r=n.values,o=0;o<e.length;o++)for(var a=t[o],i=0;i<r.length;i++)r[i]+=a[i];return n.toTensor()},e.prototype.subtract=function(e,t){return "complex64"===e.dtype||"complex64"===t.dtype?this.broadcastedBinaryComplexOp(e.cast("complex64"),t.cast("complex64"),function(e,t,n,r){return {real:e-n,imag:t-r}}):this.broadcastedBinaryOp(e,t,upcastType(e.dtype,t.dtype),function(e,t){return e-t})},e.prototype.pow=function(e,t){return this.assertNotComplex([e,t],"pow"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){return Math.pow(e,t)})},e.prototype.batchMatMul=function(e,t,n,r){this.assertNotComplex([e,t],"matMul");for(var o=n?e.shape[1]:e.shape[2],a=n?e.shape[2]:e.shape[1],i=r?t.shape[1]:t.shape[2],s=e.shape[0],u=e.dataSync(),l=t.dataSync(),c=n?[e.strides[0],1,e.strides[1]]:[e.strides[0],e.strides[1],1],p=c[0],d=c[1],h=c[2],f=r?[1,t.strides[1],t.strides[0]]:[t.strides[1],1,t.strides[0]],m=f[0],g=f[1],v=f[2],y=a*i,x=buffer([s,a,i],e.dtype),T=x.values,w=this.blockSize,E=0;E<s;E++)for(var S=0;S<a;S+=w)for(var b=0;b<i;b+=w)for(var C=0;C<o;C+=w)for(var A=Math.min(S+w,a),_=Math.min(b+w,i),N=Math.min(C+w,o),R=S;R<A;R++)for(var k=b;k<_;k++){for(var I=0,D=C;D<N;D++)I+=u[E*p+R*d+D*h]*l[D*m+k*g+E*v];T[E*y+(R*i+k)]+=I;}return x.toTensor()},e.prototype.multiply=function(e,t){return "complex64"===e.dtype||"complex64"===t.dtype?this.broadcastedBinaryComplexOp(e.cast("complex64"),t.cast("complex64"),function(e,t,n,r){return {real:e*n-t*r,imag:e*r+t*n}}):this.broadcastedBinaryOp(e,t,upcastType(e.dtype,t.dtype),function(e,t){return e*t})},e.prototype.realDivide=function(e,t){this.assertNotComplex([e,t],"realDivide");return this.broadcastedBinaryOp(e,t,"float32",function(e,t){return e/t})},e.prototype.floorDiv=function(e,t){this.assertNotComplex([e,t],"floorDiv");return this.broadcastedBinaryOp(e,t,"int32",function(e,t){return Math.floor(e/t)})},e.prototype.sum=function(e,t){this.assertNotComplex(e,"sum"),assertAxesAreInnerMostDims("sum",t,e.rank);for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,upcastType(e.dtype,"int32")),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),l=0;l<s.length;++l){for(var c=l*i,p=0,d=0;d<i;++d)p+=u[c+d];s[l]=p;}return a},e.prototype.prod=function(e,t){this.assertNotComplex(e,"sum");for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,upcastType(e.dtype,"int32")),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),l=0;l<s.length;++l){for(var c=l*i,p=1,d=0;d<i;++d)p*=u[c+d];s[l]=p;}return a},e.prototype.unsortedSegmentSum=function(e,t,n){this.assertNotComplex(e,"unsortedSegmentSum");for(var r=[],o=e.rank-t.rank,a=0;a<o;++a)t=t.expandDims(a+1);for(a=0;a<n;++a){var i=scalar(a,"int32"),s=equal(i,t).asType("float32").mul(e).sum(0);r.push(s);}return stack(r)},e.prototype.argMin=function(e,t){this.assertNotComplex(e,"argMin");var n=[t];assertAxesAreInnerMostDims("argMin",n,e.rank);for(var r=computeOutAndReduceShapes(e.shape,n),o=r[0],a=r[1],i=zeros(o,"int32"),s=sizeFromShape(a),u=i.dataSync(),l=e.dataSync(),c=0;c<u.length;++c){for(var p=c*s,d=l[p],h=0,f=0;f<s;++f){var m=l[p+f];m<d&&(d=m,h=f);}u[c]=h;}return i},e.prototype.argMax=function(e,t){this.assertNotComplex(e,"argMax");var n=[t];assertAxesAreInnerMostDims("argMax",n,e.rank);for(var r=computeOutAndReduceShapes(e.shape,n),o=r[0],a=r[1],i=zeros(o,"int32"),s=sizeFromShape(a),u=i.dataSync(),l=e.dataSync(),c=0;c<u.length;++c){for(var p=c*s,d=l[p],h=0,f=0;f<s;++f){var m=l[p+f];m>d&&(d=m,h=f);}u[c]=h;}return i},e.prototype.cumsum=function(e,t,n,r){if(this.assertNotComplex(e,"cumsum"),t!==e.rank-1)throw new Error("backend.cumsum in CPU expects an inner-most axis="+(e.rank-1)+" but got axis="+t);for(var o=upcastType(e.dtype,"int32"),a=zeros(e.shape,o),i=a.dataSync(),s=e.dataSync(),u=e.shape[e.rank-1],l=r?function(e,t){return e+u-t-1}:function(e,t){return e+t},c=0;c<s.length;c+=u)for(var p=0;p<u;p++){var d=l(c,p);if(0===p)i[d]=n?0:s[d];else{var h=l(c,p-1);i[d]=n?s[h]+i[h]:s[d]+i[h];}}return a},e.prototype.equal=function(e,t){return this.assertNotComplex([e,t],"equal"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e===t?1:0})},e.prototype.notEqual=function(e,t){return this.assertNotComplex([e,t],"notEqual"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e!==t?1:0})},e.prototype.less=function(e,t){return this.assertNotComplex([e,t],"less"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e<t?1:0})},e.prototype.lessEqual=function(e,t){return this.assertNotComplex([e,t],"lessEqual"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e<=t?1:0})},e.prototype.greater=function(e,t){return this.assertNotComplex([e,t],"greater"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e>t?1:0})},e.prototype.greaterEqual=function(e,t){return this.assertNotComplex([e,t],"greaterEqual"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e>=t?1:0})},e.prototype.logicalNot=function(e){this.assertNotComplex(e,"logicalNot");for(var t=e.dataSync(),n=new Uint8Array(t.length),r=0;r<t.length;++r)n[r]=t[r]?0:1;return Tensor.make(e.shape,{values:n},"bool")},e.prototype.logicalAnd=function(e,t){return this.assertNotComplex([e,t],"logicalAnd"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e&&t})},e.prototype.logicalOr=function(e,t){return this.assertNotComplex([e,t],"logicalOr"),this.broadcastedBinaryOp(e,t,"bool",function(e,t){return e||t})},e.prototype.select=function(e,t,n){this.assertNotComplex([e,t,n],"select");for(var r=e.dataSync(),o=t.dataSync(),a=n.dataSync(),i=zeros(t.shape,upcastType(t.dtype,n.dtype)),s=i.dataSync(),u=0,l=0===e.rank||e.rank>1||1===t.rank?1:t.shape[1],c=0;c<r.length;c++)for(var p=0;p<l;p++)1===r[c]?s[u++]=o[c]:s[u++]=a[c];return i},e.prototype.where=function(e){this.assertNotComplex([e],"where");var t=e.dataSync();return whereImpl(e.shape,t)},e.prototype.topk=function(e,t,n){return this.assertNotComplex(e,"topk"),topkImpl(e.dataSync(),e.shape,e.dtype,t,n)},e.prototype.min=function(e,t){this.assertNotComplex(e,"min"),assertAxesAreInnerMostDims("min",t,e.rank);for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,e.dtype),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),l=0;l<s.length;++l){for(var c=l*i,p=u[c],d=0;d<i;++d){var h=u[c+d];h<p&&(p=h);}s[l]=p;}return a},e.prototype.minimum=function(e,t){return this.assertNotComplex([e,t],"minimum"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){return Math.min(e,t)})},e.prototype.mod=function(e,t){return this.assertNotComplex([e,t],"mod"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){var n=e%t;return e<0&&t<0||e>=0&&t>=0?n:(n+t)%t})},e.prototype.max=function(e,t){this.assertNotComplex(e,"max"),assertAxesAreInnerMostDims("max",t,e.rank);for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,e.dtype),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),l=0;l<s.length;++l){for(var c=l*i,p=u[c],d=0;d<i;++d){var h=u[c+d];h>p&&(p=h);}s[l]=p;}return a},e.prototype.maximum=function(e,t){return this.assertNotComplex([e,t],"maximum"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){return Math.max(e,t)})},e.prototype.all=function(e,t){this.assertNotComplex(e,"all"),assertAxesAreInnerMostDims("all",t,e.rank);for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,e.dtype),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),l=0;l<s.length;++l){for(var c=l*i,p=u[c],d=0;d<i;++d){var h=u[c+d];p=p&&h;}s[l]=p;}return a},e.prototype.any=function(e,t){this.assertNotComplex(e,"any"),assertAxesAreInnerMostDims("any",t,e.rank);for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,e.dtype),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),l=0;l<s.length;++l){for(var c=l*i,p=u[c],d=0;d<i;++d){var h=u[c+d];p=p||h;}s[l]=p;}return a},e.prototype.squaredDifference=function(e,t){return this.assertNotComplex([e,t],"squaredDifference"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){var n=e-t;return n*n})},e.prototype.ceil=function(e){this.assertNotComplex(e,"ceil");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)n[r]=Math.ceil(t[r]);return Tensor.make(e.shape,{values:n})},e.prototype.floor=function(e){this.assertNotComplex(e,"floor");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)n[r]=Math.floor(t[r]);return Tensor.make(e.shape,{values:n})},e.prototype.sign=function(e){this.assertNotComplex(e,"x");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)t[r]<0?n[r]=-1:t[r]>0?n[r]=1:n[r]=0;return Tensor.make(e.shape,{values:n})},e.prototype.round=function(e){this.assertNotComplex(e,"round");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=Math.floor(t[r]);t[r]-o<.5?n[r]=Math.floor(t[r]):t[r]-o>.5?n[r]=Math.ceil(t[r]):n[r]=o%2==0?o:o+1;}return Tensor.make(e.shape,{values:n})},e.prototype.exp=function(e){this.assertNotComplex(e,"exp");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)n[r]=Math.exp(t[r]);return Tensor.make(e.shape,{values:n})},e.prototype.expm1=function(e){this.assertNotComplex(e,"expm1");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)n[r]=Math.expm1(t[r]);return Tensor.make(e.shape,{values:n})},e.prototype.log=function(e){this.assertNotComplex(e,"log");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=Math.log(o);}return Tensor.make(e.shape,{values:n})},e.prototype.log1p=function(e){this.assertNotComplex(e,"log1p");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=Math.log1p(o);}return Tensor.make(e.shape,{values:n})},e.prototype.sqrt=function(e){this.assertNotComplex(e,"sqrt");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=Math.sqrt(o);}return Tensor.make(e.shape,{values:n})},e.prototype.rsqrt=function(e){this.assertNotComplex(e,"rsqrt");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=1/Math.sqrt(o);}return Tensor.make(e.shape,{values:n})},e.prototype.square=function(e){this.assertNotComplex(e,"square");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=o*o;}return Tensor.make(e.shape,{values:n})},e.prototype.reciprocal=function(e){this.assertNotComplex(e,"reciprocal");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)n[r]=1/t[r];return Tensor.make(e.shape,{values:n})},e.prototype.relu=function(e){this.assertNotComplex(e,"relu");for(var t=zeros(e.shape,e.dtype),n=t.dataSync(),r=e.dataSync(),o=0;o<r.length;++o)n[o]=Math.max(0,r[o]);return t},e.prototype.elu=function(e){this.assertNotComplex(e,"elu");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r){var o=n[r];t[r]=o>=0?o:Math.exp(o)-1;}return Tensor.make(e.shape,{values:t})},e.prototype.eluDer=function(e,t){this.assertNotComplex([e,t],"eluDer");for(var n=new Float32Array(t.size),r=t.dataSync(),o=e.dataSync(),a=0;a<r.length;++a){var i=r[a];n[a]=i>=1?o[a]:o[a]*(i+1);}return Tensor.make(t.shape,{values:n})},e.prototype.selu=function(e){this.assertNotComplex(e,"selu");for(var t=SELU_SCALEALPHA,n=SELU_SCALE,r=new Float32Array(e.size),o=e.dataSync(),a=0;a<o.length;++a){var i=o[a];r[a]=i>=0?n*i:t*(Math.exp(i)-1);}return Tensor.make(e.shape,{values:r})},e.prototype.clip=function(e,t,n){this.assertNotComplex(e,"clip");for(var r=new Float32Array(e.size),o=e.dataSync(),a=0;a<o.length;++a){var i=o[a];r[a]=i>n?n:i<t?t:i;}return Tensor.make(e.shape,{values:r})},e.prototype.abs=function(e){for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.abs(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.complexAbs=function(e){for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<e.size;++r){var o=n[2*r],a=n[2*r+1];t[r]=Math.hypot(o,a);}return Tensor.make(e.shape,{values:t})},e.prototype.int=function(e){this.assertNotComplex(e,"int");for(var t=new Int32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=n[r];return Tensor.make(e.shape,{values:t},"int32")},e.prototype.sigmoid=function(e){this.assertNotComplex(e,"sigmoid");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=1/(1+Math.exp(-n[r]));return Tensor.make(e.shape,{values:t})},e.prototype.softplus=function(e){this.assertNotComplex(e,"softplus");for(var t=Math.log(1.1920928955078125e-7)+2,n=new Float32Array(e.size),r=e.dataSync(),o=0;o<r.length;++o){var a=r[o]>-t,i=r[o]<t,s=Math.exp(r[o]),u=void 0;u=i?s:a?r[o]:Math.log(1+s),n[o]=u;}return Tensor.make(e.shape,{values:n})},e.prototype.sin=function(e){this.assertNotComplex(e,"sin");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.sin(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.cos=function(e){this.assertNotComplex(e,"cos");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.cos(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.tan=function(e){this.assertNotComplex(e,"tan");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.tan(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.asin=function(e){this.assertNotComplex(e,"asin");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.asin(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.acos=function(e){this.assertNotComplex(e,"acos");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.acos(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.atan=function(e){this.assertNotComplex(e,"atan");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.atan(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.atan2=function(e,t){return this.assertNotComplex([e,t],"atan2"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){return Math.atan2(e,t)})},e.prototype.sinh=function(e){this.assertNotComplex(e,"sinh");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.sinh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.cosh=function(e){this.assertNotComplex(e,"cosh");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.cosh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.tanh=function(e){this.assertNotComplex(e,"tanh");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=tanh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.asinh=function(e){this.assertNotComplex(e,"asinh");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.asinh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.acosh=function(e){this.assertNotComplex(e,"acosh");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.acosh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.atanh=function(e){this.assertNotComplex(e,"atanh");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.atanh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.erf=function(e){this.assertNotComplex(e,"erf");for(var t=new Float32Array(e.size),n=e.dataSync(),r=ERF_P,o=ERF_A1,a=ERF_A2,i=ERF_A3,s=ERF_A4,u=ERF_A5,l=0;l<n.length;++l){var c=n[l],p=1/(1+r*c);t[l]=1-((((u*p+s)*p+i)*p+a)*p+o)*p*Math.exp(-c*c);}return Tensor.make(e.shape,{values:t})},e.prototype.step=function(e,t){void 0===t&&(t=0),this.assertNotComplex(e,"step");for(var n=new Float32Array(e.size),r=e.dataSync(),o=0;o<r.length;++o){var a=r[o];isNaN(a)?n[o]=NaN:n[o]=a>0?1:t;}return Tensor.make(e.shape,{values:n})},e.prototype.conv2d=function(e,t,n){this.assertNotComplex([e,t],"conv2d");for(var r=n.filterHeight,o=n.filterWidth,a=n.dilationHeight,i=n.dilationWidth,s=n.padInfo.left,u=n.padInfo.top,l=buffer(n.outShape,e.dtype),c=e.dataSync(),p=t.dataSync(),d=l.values,h=0;h<n.batchSize;++h)for(var f=h*e.strides[0],m=h*l.strides[0],g=0;g<n.outHeight;++g)for(var v=m+g*l.strides[1],y=g*n.strideHeight-s,x=0;x<r;x++){var T=y+x*a;if(!(T<0||T>=n.inHeight))for(var w=x*t.strides[0],E=f+T*e.strides[1],S=0;S<n.outWidth;++S)for(var b=v+S*n.outChannels,C=S*n.strideWidth-u,A=0;A<o;A++){var _=C+A*i;if(!(_<0||_>=n.inWidth))for(var N=w+A*t.strides[1],R=E+_*n.inChannels,k=N,I=0;I<n.inChannels;++I){for(var D=c[R+I],M=0;M<n.outChannels;++M)d[b+M]+=D*p[k+M];k+=n.outChannels;}}}return l.toTensor()},e.prototype.conv3d=function(e,t,n){for(var r=n.filterDepth,o=n.filterHeight,a=n.filterWidth,i=n.dilationDepth,s=n.dilationHeight,u=n.dilationWidth,l=n.padInfo.front,c=n.padInfo.left,p=n.padInfo.top,d=buffer(n.outShape,e.dtype),h=e.dataSync(),f=t.dataSync(),m=d.values,g=0;g<n.batchSize;++g)for(var v=g*e.strides[0],y=g*d.strides[0],x=0;x<n.outDepth;++x)for(var T=y+x*d.strides[1],w=x*n.strideDepth-l,E=0;E<r;E++){var S=w+E*i;if(!(S<0||S>=n.inDepth))for(var b=E*t.strides[0],C=v+S*e.strides[1],A=0;A<n.outHeight;++A)for(var _=T+A*d.strides[2],N=A*n.strideHeight-p,R=0;R<o;R++){var k=N+R*s;if(!(k<0||k>=n.inHeight))for(var I=b+R*t.strides[1],D=C+k*e.strides[2],M=0;M<n.outWidth;++M)for(var P=_+M*n.outChannels,O=M*n.strideWidth-c,F=0;F<a;F++){var L=O+F*u;if(!(L<0||L>=n.inWidth))for(var B=I+F*t.strides[2],U=D+L*n.inChannels,V=B,z=0;z<n.inChannels;++z){for(var H=h[U+z],W=0;W<n.outChannels;++W)m[P+W]+=H*f[V+W];V+=n.outChannels;}}}}return d.toTensor()},e.prototype.conv2dDerInput=function(e,t,n){this.assertNotComplex([e,t],"conv2dDerInput");for(var r=buffer(n.inShape,"float32"),o=r.values,a=r.strides,i=a[0],s=a[1],u=a[2],l=e.dataSync(),c=e.strides,p=c[0],d=c[1],h=c[2],f=t.dataSync(),m=t.strides,g=m[0],v=m[1],y=m[2],x=n.batchSize,T=n.filterHeight,w=n.filterWidth,E=n.inChannels,S=n.inHeight,b=n.inWidth,C=n.outChannels,A=n.outHeight,_=n.outWidth,N=n.strideHeight,R=n.strideWidth,k=T-1-n.padInfo.top,I=w-1-n.padInfo.left,D=0;D<x;++D)for(var M=0;M<E;++M)for(var P=0;P<S;++P)for(var O=P-k,F=Math.max(0,Math.ceil(O/N)),L=Math.min(A,(T+O)/N),B=0;B<b;++B){for(var U=B-I,V=Math.max(0,Math.ceil(U/R)),z=Math.min(_,(w+U)/R),H=0,W=F;W<L;++W)for(var G=W*N-O,q=V;q<z;++q)for(var $=p*D+d*W+h*q,X=g*(T-1-G)+v*(w-1-(q*R-U))+y*M,K=0;K<C;++K){H+=l[$+K]*f[X+K];}o[i*D+s*P+u*B+M]=H;}return r.toTensor()},e.prototype.conv3dDerInput=function(e,t,n){for(var r=buffer(n.inShape,"float32"),o=r.values,a=r.strides,i=a[0],s=a[1],u=a[2],l=a[3],c=e.dataSync(),p=e.strides,d=p[0],h=p[1],f=p[2],m=p[3],g=t.dataSync(),v=t.strides,y=v[0],x=v[1],T=v[2],w=v[3],E=n.batchSize,S=n.filterDepth,b=n.filterHeight,C=n.filterWidth,A=n.inChannels,_=n.inDepth,N=n.inHeight,R=n.inWidth,k=n.outChannels,I=n.outDepth,D=n.outHeight,M=n.outWidth,P=n.strideDepth,O=n.strideHeight,F=n.strideWidth,L=S-1-n.padInfo.front,B=b-1-n.padInfo.top,U=C-1-n.padInfo.left,V=0;V<E;++V)for(var z=0;z<A;++z)for(var H=0;H<_;++H)for(var W=H-L,G=Math.max(0,Math.ceil(W/P)),q=Math.min(I,(S+W)/P),$=0;$<N;++$)for(var X=$-B,K=Math.max(0,Math.ceil(X/O)),j=Math.min(D,(b+X)/O),Y=0;Y<R;++Y){for(var Q=Y-U,J=Math.max(0,Math.ceil(Q/F)),Z=Math.min(M,(C+Q)/F),ee=0,te=G;te<q;++te)for(var ne=te*P-W,re=K;re<j;++re)for(var oe=re*O-X,ae=J;ae<Z;++ae)for(var ie=d*V+h*te+f*re+m*ae,se=y*(S-1-ne)+x*(b-1-oe)+T*(C-1-(ae*F-Q))+w*z,ue=0;ue<k;++ue){ee+=c[ie+ue]*g[se+ue];}o[i*V+s*H+u*$+l*Y+z]=ee;}return r.toTensor()},e.prototype.conv2dDerFilter=function(e,t,n){this.assertNotComplex([e,t],"conv2dDerFilter");for(var r=n.strideHeight,o=n.strideWidth,a=n.filterHeight,i=n.filterWidth,s=buffer(n.filterShape,"float32"),u=n.padInfo.left,l=n.padInfo.top,c=0;c<a;++c)for(var p=Math.max(0,Math.ceil((l-c)/r)),d=Math.min(n.outHeight,(n.inHeight+l-c)/r),h=0;h<i;++h)for(var f=Math.max(0,Math.ceil((u-h)/o)),m=Math.min(n.outWidth,(n.inWidth+u-h)/o),g=0;g<n.inChannels;++g)for(var v=0;v<n.outChannels;++v){for(var y=0,x=0;x<n.batchSize;++x)for(var T=p;T<d;++T)for(var w=c+T*r-l,E=f;E<m;++E){var S=h+E*o-u;y+=e.get(x,w,S,g)*t.get(x,T,E,v);}s.set(y,c,h,g,v);}return s.toTensor()},e.prototype.conv3dDerFilter=function(e,t,n){for(var r=n.strideDepth,o=n.strideHeight,a=n.strideWidth,i=n.filterDepth,s=n.filterHeight,u=n.filterWidth,l=buffer(n.filterShape,"float32"),c=l.values,p=l.strides,d=p[0],h=p[1],f=p[2],m=p[3],g=t.dataSync(),v=t.strides,y=v[0],x=v[1],T=v[2],w=v[3],E=e.dataSync(),S=e.strides,b=S[0],C=S[1],A=S[2],_=S[3],N=n.padInfo.front,R=n.padInfo.left,k=n.padInfo.top,I=0;I<i;++I)for(var D=Math.max(0,Math.ceil((N-I)/r)),M=Math.min(n.outDepth,(n.inDepth+N-I)/r),P=I*d,O=0;O<s;++O)for(var F=Math.max(0,Math.ceil((k-O)/o)),L=Math.min(n.outHeight,(n.inHeight+k-O)/o),B=O*h+P,U=0;U<u;++U)for(var V=Math.max(0,Math.ceil((R-U)/a)),z=Math.min(n.outWidth,(n.inWidth+R-U)/a),H=U*f+B,W=0;W<n.inChannels;++W)for(var G=W*m+H,q=0;q<n.outChannels;++q){for(var $=0,X=0;X<n.batchSize;++X)for(var K=X*b,j=X*y,Y=D;Y<M;++Y)for(var Q=(I+Y*r-N)*C+K,J=Y*x+j,Z=F;Z<L;++Z)for(var ee=(O+Z*o-k)*A+Q,te=Z*T+J,ne=V;ne<z;++ne){var re=ne*w+te;$+=E[(U+ne*a-R)*_+ee+W]*g[re+q];}c[G+q]=$;}return l.toTensor()},e.prototype.depthwiseConv2D=function(e,t,n){this.assertNotComplex([e,t],"depthwiseConv2D");for(var r=n.filterHeight,o=n.filterWidth,a=n.dilationHeight,i=n.dilationWidth,s=n.padInfo.left,u=n.padInfo.top,l=n.outChannels/n.inChannels,c=buffer(n.outShape,e.dtype),p=e.dataSync(),d=t.dataSync(),h=c.values,f=0;f<n.batchSize;++f)for(var m=f*e.strides[0],g=f*c.strides[0],v=0;v<n.outHeight;++v)for(var y=g+v*c.strides[1],x=v*n.strideHeight-s,T=0;T<r;++T){var w=x+T*a;if(!(w<0||w>=n.inHeight))for(var E=T*t.strides[0],S=m+w*e.strides[1],b=0;b<n.outWidth;++b)for(var C=y+b*c.strides[2],A=b*n.strideWidth-u,_=0;_<o;++_){var N=A+_*i;if(!(N<0||N>=n.inWidth))for(var R=E+_*t.strides[1],k=S+N*n.inChannels,I=C,D=R,M=0;M<n.inChannels;++M){for(var P=p[k+M],O=0;O<l;++O)h[I+O]+=P*d[D+O];I+=l,D+=l;}}}return c.toTensor()},e.prototype.depthwiseConv2DDerInput=function(e,t,n){this.assertNotComplex([e,t],"depthwiseConv2DDerInput");for(var r=buffer(n.inShape,"float32"),o=r.values,a=r.strides,i=a[0],s=a[1],u=a[2],l=e.dataSync(),c=e.strides,p=c[0],d=c[1],h=c[2],f=t.dataSync(),m=t.strides,g=m[0],v=m[1],y=m[2],x=n.batchSize,T=n.filterHeight,w=n.filterWidth,E=n.inChannels,S=n.inHeight,b=n.inWidth,C=n.outChannels,A=n.outHeight,_=n.outWidth,N=n.strideHeight,R=n.strideWidth,k=T-1-n.padInfo.top,I=w-1-n.padInfo.left,D=C/E,M=0;M<x;++M)for(var P=0;P<E;++P)for(var O=0;O<S;++O)for(var F=O-k,L=Math.max(0,Math.ceil(F/N)),B=Math.min(A,(T+F)/N),U=0;U<b;++U){for(var V=U-I,z=Math.max(0,Math.ceil(V/R)),H=Math.min(_,(w+V)/R),W=0,G=L;G<B;++G)for(var q=G*N-F,$=z;$<H;++$)for(var X=p*M+d*G+h*$,K=g*(T-1-q)+v*(w-1-($*R-V))+y*P,j=0;j<D;++j){W+=l[X+(P*D+j)]*f[K+j];}o[i*M+s*O+u*U+P]=W;}return r.toTensor()},e.prototype.depthwiseConv2DDerFilter=function(e,t,n){this.assertNotComplex([e,t],"depthwiseConv2DDerFilter");for(var r=n.strideHeight,o=n.strideWidth,a=n.filterHeight,i=n.filterWidth,s=buffer(n.filterShape,"float32"),u=n.padInfo.left,l=n.padInfo.top,c=n.outChannels/n.inChannels,p=0;p<a;++p)for(var d=Math.max(0,Math.ceil((l-p)/r)),h=Math.min(n.outHeight,(n.inHeight+l-p)/r),f=0;f<i;++f)for(var m=Math.max(0,Math.ceil((u-f)/o)),g=Math.min(n.outWidth,(n.inWidth+u-f)/o),v=0;v<n.outChannels;++v){for(var y=Math.trunc(v/c),x=v%c,T=0,w=0;w<n.batchSize;++w)for(var E=d;E<h;++E)for(var S=p+E*r-l,b=m;b<g;++b){var C=f+b*o-u;T+=e.get(w,S,C,y)*t.get(w,E,b,v);}s.set(T,p,f,y,x);}return s.toTensor()},e.prototype.tile=function(e,t){this.assertNotComplex(e,"tile");for(var n=new Array(e.rank),r=0;r<n.length;r++)n[r]=e.shape[r]*t[r];var o=buffer(n,e.dtype),a=e.buffer();for(r=0;r<o.values.length;++r){for(var i=o.indexToLoc(r),s=new Array(e.rank),u=0;u<s.length;u++)s[u]=i[u]%e.shape[u];var l=a.locToIndex(s);o.values[r]=a.values[l];}return o.toTensor()},e.prototype.pad=function(e,t,n){this.assertNotComplex(e,"pad");var r=t.map(function(t,n){return t[0]+e.shape[n]+t[1]}),o=t.map(function(e){return e[0]}),a=e.buffer(),i=buffer(r,e.dtype);0!==n&&i.values.fill(n);for(var s=0;s<e.size;s++){var u=a.indexToLoc(s),l=u.map(function(e,t){return e+o[t]});i.set.apply(i,[e.get.apply(e,u)].concat(l));}return i.toTensor()},e.prototype.transpose=function(e,t){this.assertNotComplex(e,"transpose");for(var n=new Array(e.rank),r=0;r<n.length;r++)n[r]=e.shape[t[r]];var o=e.dataSync(),a=buffer(n,e.dtype),i=e.buffer();for(r=0;r<e.size;++r){for(var s=i.indexToLoc(r),u=new Array(s.length),l=0;l<u.length;l++)u[l]=s[t[l]];var c=a.locToIndex(u);a.values[c]=o[r];}return a.toTensor()},e.prototype.gather=function(e,t,n){this.assertNotComplex([e,t],"gather");var r=e.shape.slice(),o=t.dataSync();r[n]=o.length;for(var a=buffer(r,e.dtype),i=e.buffer(),s=0;s<a.size;++s){var u=a.indexToLoc(s),l=u.slice();l[n]=o[u[n]];var c=i.locToIndex(l);a.values[s]=i.values[c];}return a.toTensor()},e.prototype.batchToSpaceND=function(e,t,n){this.assertNotComplex([e],"batchToSpaceND");var r=t.reduce(function(e,t){return e*t}),o=getReshaped(e.shape,t,r),a=getPermuted(o.length,t.length),i=getReshapedPermuted(e.shape,t,r),s=getSliceBeginCoords(n,t.length),u=getSliceSize(i,n,t.length);return e.reshape(o).transpose(a).reshape(i).slice(s,u)},e.prototype.spaceToBatchND=function(e,t,n){this.assertNotComplex([e],"spaceToBatchND");var r=t.reduce(function(e,t){return e*t}),o=[[0,0]];o.push.apply(o,n);for(var a=1+t.length;a<e.shape.length;++a)o.push([0,0]);var i=e.pad(o),s=getReshaped(i.shape,t,r,!1),u=getPermuted(s.length,t.length,!1),l=getReshapedPermuted(i.shape,t,r,!1);return i.reshape(s).transpose(u).reshape(l)},e.prototype.pool=function(e,t,n){this.assertNotComplex(e,"pool");for(var r=t.strideHeight,o=t.strideWidth,a=t.dilationHeight,i=t.dilationWidth,s=t.effectiveFilterHeight,u=t.effectiveFilterWidth,l=t.padInfo.top,c=t.padInfo.left,p="max"===n?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,d=e.dataSync(),h=buffer(t.outShape,e.dtype),f=h.values,m=t.outShape[1]*t.outShape[2]*t.outShape[3],g=t.outShape[2]*t.outShape[3],v=t.outShape[3],y=0;y<t.batchSize;++y)for(var x=y*m,T=y*e.strides[0],w=0;w<t.inChannels;++w)for(var E=0;E<t.outHeight;++E)for(var S=E*r-l,b=Math.max(0,S),C=Math.min(t.inHeight,s+S),A=x+E*g,_=0;_<t.outWidth;++_){for(var N=_*o-c,R=Math.max(0,N),k=Math.min(t.inWidth,u+N),I=p,D=0,M=0,P=b;P<C;P+=a){for(var O=T+P*e.strides[1],F=R;F<k;F+=i){var L=d[O+F*e.strides[2]+w];"max"===n&&L>I?I=L:"avg"===n&&(D+=L,M++);}if(isNaN(I))break}f[A+_*v+w]="avg"===n?D/M:I;}return h.toTensor()},e.prototype.maxPool=function(e,t){return this.pool(e,t,"max")},e.prototype.maxPoolPositions=function(e,t){for(var n=buffer(t.outShape,"int32"),r=t.strideHeight,o=t.strideWidth,a=t.dilationHeight,i=t.dilationWidth,s=t.effectiveFilterHeight,u=t.effectiveFilterWidth,l=t.padInfo.top,c=t.padInfo.left,p=0;p<t.batchSize;++p)for(var d=0;d<t.inChannels;++d)for(var h=0;h<t.outHeight;++h){for(var f=h*r-l,m=f;m<0;)m+=a;for(var g=Math.min(t.inHeight,s+f),v=0;v<t.outWidth;++v){for(var y=v*o-c,x=y;x<0;)x+=i;for(var T=Math.min(t.inWidth,u+y),w=Number.NEGATIVE_INFINITY,E=-1,S=m;S<g;S+=a)for(var b=S-f,C=x;C<T;C+=i){var A=C-y,_=e.get(p,S,C,d);_>w&&(w=_,E=b*u+A);}n.set(E,p,h,v,d);}}return n.toTensor()},e.prototype.maxPoolBackprop=function(e,t,n,r){this.assertNotComplex([t,n],"maxPoolBackprop");for(var o=this.maxPoolPositions(t,r),a=r.strideHeight,i=r.strideWidth,s=r.dilationHeight,u=r.dilationWidth,l=r.effectiveFilterHeight,c=r.effectiveFilterWidth,p=c-1-r.padInfo.left,d=l-1-r.padInfo.top,h=buffer(t.shape,"float32"),f=0;f<r.batchSize;++f)for(var m=0;m<r.inChannels;++m)for(var g=0;g<r.inHeight;++g)for(var v=0;v<r.inWidth;++v){for(var y=g-d,x=v-p,T=0,w=0;w<l;w+=s){var E=(y+w)/a;if(!(E<0||E>=r.outHeight||Math.floor(E)!==E))for(var S=0;S<c;S+=u){var b=(x+S)/i;if(!(b<0||b>=r.outWidth||Math.floor(b)!==b)){var C=l*c-1-o.get(f,E,b,m)===w*c+S?1:0;if(0!==C)T+=e.get(f,E,b,m)*C;}}}h.set(T,f,g,v,m);}return h.toTensor()},e.prototype.avgPoolBackprop=function(e,t,n){this.assertNotComplex([e,t],"avgPoolBackprop");for(var r=n.strideHeight,o=n.strideWidth,a=n.filterHeight,i=n.filterWidth,s=n.dilationHeight,u=n.dilationWidth,l=n.effectiveFilterHeight,c=n.effectiveFilterWidth,p=c-1-n.padInfo.left,d=l-1-n.padInfo.top,h=buffer(t.shape,"float32"),f=1/(a*i),m=0;m<n.batchSize;++m)for(var g=0;g<n.inChannels;++g)for(var v=0;v<n.inHeight;++v)for(var y=0;y<n.inWidth;++y){for(var x=v-d,T=y-p,w=0,E=0;E<l;E+=s){var S=(x+E)/r;if(!(S<0||S>=n.outHeight||Math.floor(S)!==S))for(var b=0;b<c;b+=u){var C=(T+b)/o;if(!(C<0||C>=n.outWidth||Math.floor(C)!==C))w+=e.get(m,S,C,g);}}h.set(w*f,m,v,y,g);}return h.toTensor()},e.prototype.cast=function(e,t){return castTensor(e,t,this)},e.prototype.reshape=function(e,t){return reshapeTensor(e,t)},e.prototype.avgPool=function(e,t){return this.assertNotComplex(e,"avgPool"),this.pool(e,t,"avg").toFloat()},e.prototype.resizeBilinear=function(e,t,n,r){this.assertNotComplex(e,"resizeBilinear");for(var o=e.shape,a=o[0],i=o[1],s=o[2],u=o[3],l=e.dataSync(),c=new Float32Array(sizeFromShape([a,t,n,u])),p=[r&&t>1?i-1:i,r&&n>1?s-1:s],d=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=0,f=p[0]/d[0],m=p[1]/d[1],g=0;g<a;g++)for(var v=0;v<t;v++)for(var y=f*v,x=Math.floor(y),T=y-x,w=Math.min(i-1,Math.ceil(y)),E=g*e.strides[0]+x*e.strides[1],S=g*e.strides[0]+w*e.strides[1],b=0;b<n;b++)for(var C=m*b,A=Math.floor(C),_=C-A,N=Math.min(s-1,Math.ceil(C)),R=E+A*e.strides[2],k=S+A*e.strides[2],I=E+ +N*e.strides[2],D=S+N*e.strides[2],M=0;M<u;M++){var P=l[R+M],O=l[k+M],F=P+(l[I+M]-P)*_,L=F+(O+(l[D+M]-O)*_-F)*T;c[h++]=L;}return tensor(c,[a,t,n,u])},e.prototype.resizeBilinearBackprop=function(e,t,n){this.assertNotComplex([e,t],"resizeBilinearBackprop");for(var r=t.shape,o=r[0],a=r[1],i=r[2],s=r[3],u=e.shape,l=u[1],c=u[2],p=new Float32Array(o*a*i*s),d=[n&&l>1?a-1:a,n&&c>1?i-1:i],h=[n&&l>1?l-1:l,n&&c>1?c-1:c],f=d[0]/h[0],m=d[1]/h[1],g=e.dataSync(),v=0,y=0;y<o;y++)for(var x=y*t.strides[0],T=0;T<l;T++)for(var w=T*f,E=Math.floor(w),S=Math.min(Math.ceil(w),a-1),b=x+E*t.strides[1],C=x+S*t.strides[1],A=w-E,_=1-A,N=0;N<c;N++)for(var R=N*m,k=Math.floor(R),I=Math.min(Math.ceil(R),i-1),D=R-k,M=1-D,P=b+k*t.strides[2],O=b+I*t.strides[2],F=C+k*t.strides[2],L=C+I*t.strides[2],B=_*M,U=_*D,V=A*M,z=A*D,H=0;H<s;H++){var W=g[v++];p[P+H]+=W*B,p[O+H]+=W*U,p[F+H]+=W*V,p[L+H]+=W*z;}return tensor4d(p,[o,i,a,s],t.dtype)},e.prototype.resizeNearestNeighbor=function(e,t,n,r){this.assertNotComplex(e,"resizeNearestNeighbor");for(var o=e.shape,a=o[0],i=o[1],s=o[2],u=o[3],l=e.dataSync(),c=new Float32Array(a*t*n*u),p=[r&&t>1?i-1:i,r&&n>1?s-1:s],d=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=p[0]/d[0],f=p[1]/d[1],m=0,g=0;g<a;g++)for(var v=g*e.strides[0],y=0;y<t;y++)for(var x=h*y,T=v+Math.min(i-1,r?Math.round(x):Math.floor(x))*e.strides[1],w=0;w<n;w++)for(var E=f*w,S=T+Math.min(s-1,r?Math.round(E):Math.floor(E))*e.strides[2],b=0;b<u;b++){var C=l[S+b];c[m++]=C;}return tensor(c,[a,t,n,u],e.dtype)},e.prototype.resizeNearestNeighborBackprop=function(e,t,n){this.assertNotComplex([e,t],"resizeNearestNeighborBackprop");for(var r=t.shape,o=r[0],a=r[1],i=r[2],s=r[3],u=e.shape,l=u[1],c=u[2],p=new Float32Array(o*a*i*s),d=e.dataSync(),h=[n&&l>1?a-1:a,n&&c>1?i-1:i],f=[n&&l>1?l-1:l,n&&c>1?c-1:c],m=h[0]/f[0],g=h[1]/f[1],v=1/m,y=1/g,x=2*Math.ceil(v)+2,T=2*Math.ceil(y)+2,w=0;w<o;w++)for(var E=w*t.strides[0],S=0;S<a;S++)for(var b=E+S*t.strides[1],C=Math.floor(S*v),A=Math.floor(C-x/2),_=0;_<i;_++)for(var N=b+_*t.strides[2],R=Math.floor(_*y),k=Math.floor(R-T/2),I=0;I<s;I++){for(var D=0,M=0;M<x;M++){var P=M+A;if(!(P<0||P>=l)){var O=E+P*e.strides[1],F=P*m;if(S===Math.min(a-1,n?Math.round(F):Math.floor(F)))for(var L=0;L<T;L++){var B=L+k;if(!(B<0||B>=c)){var U=O+B*e.strides[2],V=B*g;_===Math.min(i-1,n?Math.round(V):Math.floor(V))&&(D+=d[U+I]);}}}}p[N+I]=D;}return tensor4d(p,t.shape,t.dtype)},e.prototype.batchNormalization=function(e,t,n,r,o,a){this.assertNotComplex([e,t,n,o,a],"batchNormalization");for(var i=e.dataSync(),s=t.dataSync(),u=n.dataSync(),l=o?o.dataSync():new Float32Array([1]),c=a?a.dataSync():new Float32Array([0]),p=new Float32Array(i.length),d=c.length,h=l.length,f=u.length,m=s.length,g=0,v=0,y=0,x=0,T=0;T<i.length;++T)p[T]=c[g++]+(i[T]-s[v++])*l[y++]/Math.sqrt(u[x++]+r),g>=d&&(g=0),v>=m&&(v=0),y>=h&&(y=0),x>=f&&(x=0);return tensor4d(p,e.shape)},e.prototype.localResponseNormalization4D=function(e,t,n,r,o){this.assertNotComplex(e,"localResponseNormalization4D");var a=e.shape[3],i=a-1,s=e.dataSync(),u=sizeFromShape(e.shape),l=new Float32Array(u);function c(e){for(var n=e%a,r=e-n+Math.max(0,n-t),o=e-n+Math.min(n+t,i),u=0;r<=o;r++){var l=s[r];u+=l*l;}return u}for(var p=0;p<u;p++){var d=c(p),h=s[p]*Math.pow(n+r*d,-o);l[p]=h;}return tensor4d(l,e.shape)},e.prototype.LRNGrad=function(e,t,n,r,o,a,i){this.assertNotComplex(e,"LRNGrad");for(var s=e.shape[3],u=e.dataSync(),l=t.dataSync(),c=n.dataSync(),p=new Float32Array(sizeFromShape(e.shape)),d=sizeFromShape(e.shape),h=0;h<d;h++){for(var f=h%s,m=h-f+Math.max(0,f-r),g=h-f+Math.min(s,f+r+1),v=0,y=m;y<g;y++)v+=Math.pow(l[y],2);v=a*v+o;for(y=m;y<g;y++){var x=-2*a*i*l[y]*c[h]/v;h===y&&(x+=Math.pow(v,-i)),x*=u[h],p[y]+=x;}}return tensor4d(p,e.shape)},e.prototype.multinomial=function(e,t,n,r){this.assertNotComplex(e,"multinomial");for(var o=t?e:softmax(e),a=o.shape[0],i=o.shape[1],s=zeros([a,n],"int32"),u=s.dataSync(),l=o.dataSync(),c=0;c<a;++c){var p=c*i,d=new Float32Array(i-1);d[0]=l[p];for(var h=1;h<d.length;++h)d[h]=d[h-1]+l[p+h];for(var f=seedrandom_1(r.toString()),m=c*n,g=0;g<n;++g){var v=f();u[m+g]=d.length;for(var y=0;y<d.length;y++)if(v<d[y]){u[m+g]=y;break}}}return s},e.prototype.oneHot=function(e,t,n,r){this.assertNotComplex(e,"oneHot");var o=new Float32Array(e.size*t);o.fill(r);for(var a=0;a<e.size;++a)e.get(a)>=0&&e.get(a)<t&&(o[a*t+e.get(a)]=n);return tensor2d(o,[e.size,t],"int32")},e.prototype.nonMaxSuppression=function(e,t,n,r,o){return this.assertNotComplex(e,"nonMaxSuppression"),nonMaxSuppressionImpl(e.dataSync(),t.dataSync(),n,r,o)},e.prototype.fft=function(e){return this.fftBatch(e,!1)},e.prototype.ifft=function(e){return this.fftBatch(e,!0)},e.prototype.fftBatch=function(e,t){for(var n=e.shape[0],r=e.shape[1],o=buffer(e.shape,"float32"),a=buffer(e.shape,"float32"),i=real(e).as2D(n,r),s=imag(e).as2D(n,r),u=0;u<n;u++)for(var l=i.slice([u,0],[1,r]),c=s.slice([u,0],[1,r]),p=complex(l,c),d=this.fftImpl(p,t).dataSync(),h=0;h<r;h++){var f=getComplexWithIndex(d,h);o.values[u*r+h]=f.real,a.values[u*r+h]=f.imag;}return complex(o.toTensor(),a.toTensor()).as2D(n,r)},e.prototype.fftImpl=function(e,t){var n=e.as1D(),r=n.size;if(this.isExponentOf2(r)){var o=this.fftRadix2(n,r,t).as2D(e.shape[0],e.shape[1]);return t&&(o=complex(real(o).div(scalar(r)),imag(o).div(scalar(r)))),o}var a=e.dataSync(),i=splitRealAndImagArrays(this.fourierTransformByMatmul(a,r,t));return complex(i.real,i.imag).as2D(e.shape[0],e.shape[1])},e.prototype.isExponentOf2=function(e){return 0==(e&e-1)},e.prototype.fftRadix2=function(e,t,n){if(1===t)return e;var r=e.dataSync(),o=t/2,a=complexWithEvenIndex(r),i=complex(a.real,a.imag).as1D(),s=complexWithOddIndex(r),u=complex(s.real,s.imag).as1D();i=this.fftRadix2(i,o,n),u=this.fftRadix2(u,o,n);var l=exponents(t,n),c=complex(l.real,l.imag).mul(u),p=i.add(c),d=i.sub(c),h=real(p).concat(real(d)),f=imag(p).concat(imag(d));return complex(h,f).as1D()},e.prototype.fourierTransformByMatmul=function(e,t,n){for(var r=new Float32Array(2*t),o=0;o<t;o++){for(var a=0,i=0,s=0;s<t;s++){var u=exponent(o*s,t,n),l=getComplexWithIndex(e,s);a+=l.real*u.real-l.imag*u.imag,i+=l.real*u.imag+l.imag*u.real;}n&&(a/=t,i/=t),assignToTypedArray(r,a,i,o);}return r},e.prototype.depthToSpace=function(e,t,n){assert("NHWC"===n,"Only NHWC dataFormat supported on CPU for depthToSpace. Got "+n),assert(t>1,"blockSize should be > 1 for depthToSpace, but was: "+t);for(var r=e.shape[0],o=e.shape[1],a=e.shape[2],i=e.shape[3],s=o*t,u=a*t,l=i/(t*t),c=e.dataSync(),p=new Float32Array(r*s*u*l),d=0,h=0;h<r;++h)for(var f=0;f<s;++f)for(var m=Math.floor(f/t),g=f%t,v=0;v<u;++v)for(var y=Math.floor(v/t),x=(g*t+v%t)*l,T=0;T<l;++T){var w=T+x+i*(y+a*(m+o*h));p[d++]=c[w];}return tensor4d(p,[r,s,u,l])},e.prototype.broadcastedBinaryOp=function(e,t,n,r){var o=assertAndGetBroadcastShape(e.shape,t.shape),a=buffer(o,n),i=e.dataSync(),s=t.dataSync(),u=getBroadcastDims(e.shape,o),l=getBroadcastDims(t.shape,o),c=a.values;if(u.length+l.length===0)for(var p=0;p<c.length;++p)c[p]=r(i[p%i.length],s[p%s.length]);else{var d=e.buffer(),h=t.buffer(),f=function(n){var o=a.indexToLoc(n),p=o.slice(-e.rank);u.forEach(function(e){return p[e]=0});var f=d.locToIndex(p),m=o.slice(-t.rank);l.forEach(function(e){return m[e]=0});var g=h.locToIndex(m);c[n]=r(i[f],s[g]);};for(p=0;p<c.length;++p)f(p);}return a.toTensor()},e.prototype.broadcastedBinaryComplexOp=function(e,t,n){var r=assertAndGetBroadcastShape(e.shape,t.shape),o=buffer(r,"float32"),a=buffer(r,"float32"),i=e.dataSync(),s=t.dataSync(),u=getBroadcastDims(e.shape,r),l=getBroadcastDims(t.shape,r),c=o.values,p=a.values;if(u.length+l.length===0)for(var d=0;d<c.length;d++){var h=d%i.length,f=d%s.length,m=n(i[2*h],i[2*h+1],s[2*f],s[2*f+1]);c[d]=m.real,p[d]=m.imag;}else{var g=this.data.get(e.dataId).complexTensors.real.buffer(),v=this.data.get(t.dataId).complexTensors.real.buffer(),y=function(r){var a=o.indexToLoc(r),d=a.slice(-e.rank);u.forEach(function(e){return d[e]=0});var h=g.locToIndex(d),f=a.slice(-t.rank);l.forEach(function(e){return f[e]=0});var m=v.locToIndex(f),y=n(i[2*h],i[2*h+1],s[2*m],s[2*m+1]);c[r]=y.real,p[r]=y.imag;};for(d=0;d<c.length;d++)y(d);}return this.complex(o.toTensor(),a.toTensor())},e.prototype.split=function(e,t,n){return split(e,t,n)},e.prototype.dispose=function(){},e.prototype.floatPrecision=function(){return 32},e.prototype.cropAndResize=function(e,t,n,r,o,a){for(var i=e.shape,s=i[0],u=i[1],l=i[2],c=i[3],p=t.shape[0],d=r[0],h=r[1],f=buffer([p,d,h,c],e.dtype),m=t.dataSync(),g=n.dataSync(),v=e.dataSync(),y=e.strides,x=f.strides,T=0;T<p;T++){var w=4*T,E=m[w],S=m[w+1],b=m[w+2],C=m[w+3],A=g[T];if(!(A>=s))for(var _=d>1?(b-E)*(u-1)/(d-1):0,N=h>1?(C-S)*(l-1)/(h-1):0,R=0;R<d;R++){var k=d>1?E*(u-1)+R*_:.5*(E+b)*(u-1);if(k<0||k>u-1)for(var I=0;I<h;I++)for(var D=0;D<c;D++){var M=D+I*x[2]+R*x[1]+T*x[0];f.values[M]=a;}else if("bilinear"===o){var P=Math.floor(k),O=Math.ceil(k),F=k-P;for(I=0;I<h;I++){if((q=h>1?S*(l-1)+I*N:.5*(S+C)*(l-1))<0||q>l-1)for(D=0;D<c;D++){M=D+I*x[2]+R*x[1]+T*x[0];f.values[M]=a;}else{var L=Math.floor(q),B=Math.ceil(q),U=q-L;for(D=0;D<c;D++){var V=v[M=D+L*y[2]+P*y[1]+A*y[0]],z=v[M=D+B*y[2]+P*y[1]+A*y[0]],H=v[M=D+L*y[2]+O*y[1]+A*y[0]],W=V+(z-V)*U,G=H+(v[M=D+B*y[2]+O*y[1]+A*y[0]]-H)*U;M=D+I*x[2]+R*x[1]+T*x[0],f.values[M]=W+(G-W)*F;}}}}else for(I=0;I<h;++I){var q;if((q=h>1?S*(l-1)+I*N:.5*(S+C)*(l-1))<0||q>l-1)for(D=0;D<c;D++){M=D+I*x[2]+R*x[1]+T*x[0];f.values[M]=a;}else{var $=Math.round(q),X=Math.round(k);for(D=0;D<c;D++){var K=D+$*y[2]+X*y[1]+A*y[0],j=D+I*x[2]+R*x[1]+T*x[0];f.values[j]=v[K];}}}}}return f.toTensor()},e.prototype.sparseToDense=function(e,t,n,r){var o=calculateShapes(t,e,n),a=o.sliceRank,i=o.numUpdates,s=o.sliceSize,u=o.strides,l=o.outputSize;return this.scatter(e,t,n,l,s,i,a,u,r,!1)},e.prototype.gatherND=function(e,t){var n=t.shape,r=n[n.length-1],o=prepareAndValidate(e,t),a=o[0],i=o[1],s=o[2],u=o[3];if(0===i)return tensor([],a,e.dtype);for(var l=new TensorBuffer([i,s],e.dtype),c=t.dataSync(),p=e.dataSync(),d=0;d<i;d++){for(var h=[],f=0,m=0;m<r;m++){var g=c[d*r+m];f+=g*u[m],h.push(g);}if(f<0||f>=e.size/s)throw new Error("Invalid indices: "+h+" does not index into "+e.shape);for(var v=0;v<s;v++)l.values[d*s+v]=p[f*s+v];}return l.toTensor().reshape(a)},e.prototype.scatterND=function(e,t,n){var r=calculateShapes(t,e,n),o=r.sliceRank,a=r.numUpdates,i=r.sliceSize,s=r.strides,u=r.outputSize,l=scalar(0);return this.scatter(e,t,n,u,i,a,o,s,l,!0)},e.prototype.scatter=function(e,t,n,r,o,a,i,s,u,l){var c=[r/o,o],p=e.dataSync(),d=t.dataSync();if(0===r)return tensor([],n,t.dtype);var h=new TensorBuffer(c,t.dtype);h.values.fill(u.dataSync()[0]);for(var f=0;f<a;f++){for(var m=[],g=0,v=0;v<i;v++){var y=p[f*i+v];m.push(y),g+=y*s[v];}if(g<0||g>=r/o)throw new Error("Invalid indices: "+m+" does not index into "+n);for(var x=0;x<o;x++)l?h.values[g*o+x]+=d[f*o+x]:h.values[g*o+x]=0===t.rank?d[0]:d[f*o+x];}return h.toTensor().reshape(n)},e}();ENV.registerBackend("cpu",function(){return new MathBackendCPU},1,setTensorTracker);var delayCallback="undefined"!=typeof requestAnimationFrame?requestAnimationFrame:setImmediate;function nextFrame(){return new Promise(function(e){return delayCallback(function(){return e()})})}var DTYPE_VALUE_SIZE_MAP={float32:4,int32:4,uint16:2,uint8:1,bool:1};function encodeWeights(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o;return __generator(this,function(a){switch(a.label){case 0:for(r in t=[],n=[],e){if("float32"!==(o=e[r]).dtype&&"int32"!==o.dtype&&"bool"!==o.dtype)throw new Error("Unsupported dtype in weight '"+r+"': "+o.dtype);t.push({name:r,shape:o.shape,dtype:o.dtype}),n.push(o.data());}return [4,Promise.all(n)];case 1:return [2,{data:concatenateTypedArrays(a.sent()),specs:t}]}})})}function decodeWeights(e,t){for(var n={},r=0,o=function(t){var o=t.name,a=t.dtype,i=t.shape,s=sizeFromShape(i),u=void 0;if("quantization"in t){var l=t.quantization;if("uint8"!==l.dtype&&"uint16"!==l.dtype)throw new Error("Weight "+t.name+" has unknown quantization dtype "+l.dtype+". Supported quantization dtypes are: 'uint8' and 'uint16'.");var c=DTYPE_VALUE_SIZE_MAP[l.dtype],p=e.slice(r,r+s*c),d="uint8"===l.dtype?new Uint8Array(p):new Uint16Array(p);if("float32"===a)u=Float32Array.from(d,function(e){return e*l.scale+l.min});else{if("int32"!==a)throw new Error("Unsupported dtype in weight '"+o+"': "+a);u=Int32Array.from(d,function(e){return Math.round(e*l.scale+l.min)});}r+=s*c;}else{var h=DTYPE_VALUE_SIZE_MAP[a];p=e.slice(r,r+s*h);if("float32"===a)u=new Float32Array(p);else if("int32"===a)u=new Int32Array(p);else{if("bool"!==a)throw new Error("Unsupported dtype in weight '"+o+"': "+a);u=new Uint8Array(p);}r+=s*h;}var f=void 0;if("float32"===a)f=tensor(u,i,"float32");else if("int32"===a)f=tensor(u,i,"int32");else{if("bool"!==a)throw new Error("Unsupported dtype in weight '"+o+"': "+a);f=tensor(u,i,"bool");}n[o]=f;},a=0,i=t;a<i.length;a++){o(i[a]);}return n}function concatenateTypedArrays(e){if(null===e)throw new Error("Invalid input value: "+JSON.stringify(e));var t=0,n=[];e.forEach(function(e){if(t+=e.byteLength,n.push(e.byteLength===e.buffer.byteLength?e:new e.constructor(e)),!(e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array))throw new Error("Unsupported TypedArray subtype: "+e.constructor.name)});var r=new Uint8Array(t),o=0;return n.forEach(function(e){r.set(new Uint8Array(e.buffer),o),o+=e.byteLength;}),r.buffer}var useNodeBuffer="undefined"!=typeof Buffer&&("undefined"==typeof Blob||"undefined"==typeof atob||"undefined"==typeof btoa);function stringByteLength(e){return useNodeBuffer?Buffer.byteLength(e):new Blob([e]).size}function arrayBufferToBase64String(e){return useNodeBuffer?Buffer.from(e).toString("base64"):btoa(String.fromCharCode.apply(null,new Uint8Array(e)))}function base64StringToArrayBuffer(e){if(useNodeBuffer){var t=Buffer.from(e,"base64");return t.buffer.slice(t.byteOffset,t.byteOffset+t.byteLength)}for(var n=atob(e),r=new Uint8Array(n.length),o=0;o<n.length;++o)r.set([n.charCodeAt(o)],o);return r.buffer}function concatenateArrayBuffers(e){var t=0;e.forEach(function(e){t+=e.byteLength;});var n=new Uint8Array(t),r=0;return e.forEach(function(e){n.set(new Uint8Array(e),r),r+=e.byteLength;}),n.buffer}function basename(e){for(e=e.trim();e.endsWith("/");)e=e.slice(0,e.length-1);var t=e.split("/");return t[t.length-1]}function getModelArtifactsInfoForJSON(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error("Expected JSON model topology, received ArrayBuffer.");return {dateSaved:new Date,modelTopologyType:"JSON",modelTopologyBytes:null==e.modelTopology?0:stringByteLength(JSON.stringify(e.modelTopology)),weightSpecsBytes:null==e.weightSpecs?0:stringByteLength(JSON.stringify(e.weightSpecs)),weightDataBytes:null==e.weightData?0:e.weightData.byteLength}}var IORouterRegistry=function(){function e(){this.saveRouters=[],this.loadRouters=[];}return e.getInstance=function(){return null==e.instance&&(e.instance=new e),e.instance},e.registerSaveRouter=function(t){e.getInstance().saveRouters.push(t);},e.registerLoadRouter=function(t){e.getInstance().loadRouters.push(t);},e.getSaveHandlers=function(t){return e.getHandlers(t,"save")},e.getLoadHandlers=function(t){return e.getHandlers(t,"load")},e.getHandlers=function(e,t){var n=[];return ("load"===t?this.getInstance().loadRouters:this.getInstance().saveRouters).forEach(function(t){var r=t(e);null!==r&&n.push(r);}),n},e}(),URL_SCHEME_SUFFIX="://",ModelStoreManagerRegistry=function(){function e(){this.managers={};}return e.getInstance=function(){return null==e.instance&&(e.instance=new e),e.instance},e.registerManager=function(t,n){assert(null!=t,"scheme must not be undefined or null."),t.endsWith(URL_SCHEME_SUFFIX)&&(t=t.slice(0,t.indexOf(URL_SCHEME_SUFFIX))),assert(t.length>0,"scheme must not be an empty string.");var r=e.getInstance();assert(null==r.managers[t],"A model store manager is already registered for scheme '"+t+"'."),r.managers[t]=n;},e.getManager=function(e){var t=this.getInstance().managers[e];if(null==t)throw new Error("Cannot find model manager for scheme '"+e+"'");return t},e.getSchemes=function(){return Object.keys(this.getInstance().managers)},e}();function parseURL(e){if(-1===e.indexOf(URL_SCHEME_SUFFIX))throw new Error("The url string provided does not contain a scheme. Supported schemes are: "+ModelStoreManagerRegistry.getSchemes().join(","));return {scheme:e.split(URL_SCHEME_SUFFIX)[0],path:e.split(URL_SCHEME_SUFFIX)[1]}}function cloneModelInternal(e,t,n){return void 0===n&&(n=!1),__awaiter(this,void 0,void 0,function(){var r,o,a,i,s,u,l,c,p;return __generator(this,function(d){switch(d.label){case 0:return assert(e!==t,"Old path and new path are the same: '"+e+"'"),assert((r=IORouterRegistry.getLoadHandlers(e)).length>0,"Copying failed because no load handler is found for source URL "+e+"."),assert(r.length<2,"Copying failed because more than one ("+r.length+") load handlers for source URL "+e+"."),o=r[0],assert((a=IORouterRegistry.getSaveHandlers(t)).length>0,"Copying failed because no save handler is found for destination URL "+t+"."),assert(a.length<2,"Copying failed because more than one ("+r.length+") save handlers for destination URL "+t+"."),i=a[0],s=parseURL(e).scheme,u=parseURL(e).path,l=s===parseURL(e).scheme,[4,o.load()];case 1:return c=d.sent(),n&&l?[4,ModelStoreManagerRegistry.getManager(s).removeModel(u)]:[3,3];case 2:d.sent(),d.label=3;case 3:return [4,i.save(c)];case 4:return p=d.sent(),!n||l?[3,6]:[4,ModelStoreManagerRegistry.getManager(s).removeModel(u)];case 5:d.sent(),d.label=6;case 6:return [2,p.modelArtifactsInfo]}})})}function listModels(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o,a,i;return __generator(this,function(s){switch(s.label){case 0:e=ModelStoreManagerRegistry.getSchemes(),t={},n=0,r=e,s.label=1;case 1:return n<r.length?(o=r[n],[4,ModelStoreManagerRegistry.getManager(o).listModels()]):[3,4];case 2:for(i in a=s.sent())t[o+URL_SCHEME_SUFFIX+i]=a[i];s.label=3;case 3:return n++,[3,1];case 4:return [2,t]}})})}function removeModel(e){return __awaiter(this,void 0,void 0,function(){var t;return __generator(this,function(n){switch(n.label){case 0:return t=parseURL(e),[4,ModelStoreManagerRegistry.getManager(t.scheme).removeModel(t.path)];case 1:return [2,n.sent()]}})})}function copyModel(e,t){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(n){switch(n.label){case 0:return [4,cloneModelInternal(e,t,!1)];case 1:return [2,n.sent()]}})})}function moveModel(e,t){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(n){switch(n.label){case 0:return [4,cloneModelInternal(e,t,!0)];case 1:return [2,n.sent()]}})})}var DATABASE_NAME="tensorflowjs",DATABASE_VERSION=1,MODEL_STORE_NAME="models_store",INFO_STORE_NAME="model_info_store";function getIndexedDBFactory(){if(!ENV.get("IS_BROWSER"))throw new Error("Failed to obtain IndexedDB factory because the current environmentis not a web browser.");var e=window,t=e.indexedDB||e.mozIndexedDB||e.webkitIndexedDB||e.msIndexedDB||e.shimIndexedDB;if(null==t)throw new Error("The current browser does not appear to support IndexedDB.");return t}function setUpDatabase(e){var t=e.result;t.createObjectStore(MODEL_STORE_NAME,{keyPath:"modelPath"}),t.createObjectStore(INFO_STORE_NAME,{keyPath:"modelPath"});}var BrowserIndexedDB=function(){function e(e){if(this.indexedDB=getIndexedDBFactory(),null==e||!e)throw new Error("For IndexedDB, modelPath must not be null, undefined or empty.");this.modelPath=e;}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(t){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");return [2,this.databaseAction(this.modelPath,e)]})})},e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(e){return [2,this.databaseAction(this.modelPath)]})})},e.prototype.databaseAction=function(e,t){var n=this;return new Promise(function(e,r){var o=n.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);o.onupgradeneeded=function(){return setUpDatabase(o)},o.onsuccess=function(){var a=o.result;if(null==t){var i=a.transaction(MODEL_STORE_NAME,"readonly"),s=i.objectStore(MODEL_STORE_NAME).get(n.modelPath);s.onsuccess=function(){if(null==s.result)return a.close(),r(new Error("Cannot find model with path '"+n.modelPath+"' in IndexedDB."));e(s.result.modelArtifacts);},s.onerror=function(e){return a.close(),r(s.error)},i.oncomplete=function(){return a.close()};}else{var u,l=getModelArtifactsInfoForJSON(t),c=a.transaction(INFO_STORE_NAME,"readwrite"),p=c.objectStore(INFO_STORE_NAME),d=p.put({modelPath:n.modelPath,modelArtifactsInfo:l});d.onsuccess=function(){var o=(u=a.transaction(MODEL_STORE_NAME,"readwrite")).objectStore(MODEL_STORE_NAME).put({modelPath:n.modelPath,modelArtifacts:t,modelArtifactsInfo:l});o.onsuccess=function(){return e({modelArtifactsInfo:l})},o.onerror=function(e){var t=(p=c.objectStore(INFO_STORE_NAME)).delete(n.modelPath);t.onsuccess=function(){return a.close(),r(o.error)},t.onerror=function(e){return a.close(),r(o.error)};};},d.onerror=function(e){return a.close(),r(d.error)},c.oncomplete=function(){null==u?a.close():u.oncomplete=function(){return a.close()};};}},o.onerror=function(e){return r(o.error)};})},e.URL_SCHEME="indexeddb://",e}(),indexedDBRouter=function(e){return ENV.get("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(BrowserIndexedDB.URL_SCHEME)?browserIndexedDB(e.slice(BrowserIndexedDB.URL_SCHEME.length)):null};function browserIndexedDB(e){return new BrowserIndexedDB(e)}function maybeStripScheme(e){return e.startsWith(BrowserIndexedDB.URL_SCHEME)?e.slice(BrowserIndexedDB.URL_SCHEME.length):e}IORouterRegistry.registerSaveRouter(indexedDBRouter),IORouterRegistry.registerLoadRouter(indexedDBRouter);var BrowserIndexedDBManager=function(){function e(){this.indexedDB=getIndexedDBFactory();}return e.prototype.listModels=function(){return __awaiter(this,void 0,void 0,function(){var e=this;return __generator(this,function(t){return [2,new Promise(function(t,n){var r=e.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);r.onupgradeneeded=function(){return setUpDatabase(r)},r.onsuccess=function(){var e=r.result,o=e.transaction(INFO_STORE_NAME,"readonly"),a=o.objectStore(INFO_STORE_NAME).getAll();a.onsuccess=function(){for(var e={},n=0,r=a.result;n<r.length;n++){var o=r[n];e[o.modelPath]=o.modelArtifactsInfo;}t(e);},a.onerror=function(t){return e.close(),n(a.error)},o.oncomplete=function(){return e.close()};},r.onerror=function(e){return n(r.error)};})]})})},e.prototype.removeModel=function(e){return __awaiter(this,void 0,void 0,function(){var t=this;return __generator(this,function(n){return e=maybeStripScheme(e),[2,new Promise(function(n,r){var o=t.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);o.onupgradeneeded=function(){return setUpDatabase(o)},o.onsuccess=function(){var t,a=o.result,i=a.transaction(INFO_STORE_NAME,"readwrite"),s=i.objectStore(INFO_STORE_NAME),u=s.get(e);u.onsuccess=function(){if(null==u.result)return a.close(),r(new Error("Cannot find model with path '"+e+"' in IndexedDB."));var o=s.delete(e),i=function(){var o=(t=a.transaction(MODEL_STORE_NAME,"readwrite")).objectStore(MODEL_STORE_NAME).delete(e);o.onsuccess=function(){return n(u.result.modelArtifactsInfo)},o.onerror=function(e){return r(u.error)};};o.onsuccess=i,o.onerror=function(e){return i(),a.close(),r(u.error)};},u.onerror=function(e){return a.close(),r(u.error)},i.oncomplete=function(){null==t?a.close():t.oncomplete=function(){return a.close()};};},o.onerror=function(e){return r(o.error)};})]})})},e}();if(ENV.get("IS_BROWSER"))try{ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME,new BrowserIndexedDBManager);}catch(e){}var PATH_SEPARATOR="/",PATH_PREFIX="tensorflowjs_models",INFO_SUFFIX="info",MODEL_TOPOLOGY_SUFFIX="model_topology",WEIGHT_SPECS_SUFFIX="weight_specs",WEIGHT_DATA_SUFFIX="weight_data";function getModelKeys(e){return {info:[PATH_PREFIX,e,INFO_SUFFIX].join(PATH_SEPARATOR),topology:[PATH_PREFIX,e,MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),weightSpecs:[PATH_PREFIX,e,WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),weightData:[PATH_PREFIX,e,WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR)}}function getModelPathFromKey(e){var t=e.split(PATH_SEPARATOR);if(t.length<3)throw new Error("Invalid key format: "+e);return t.slice(1,t.length-1).join(PATH_SEPARATOR)}function maybeStripScheme$1(e){return e.startsWith(BrowserLocalStorage.URL_SCHEME)?e.slice(BrowserLocalStorage.URL_SCHEME.length):e}var BrowserLocalStorage=function(){function e(e){if(!ENV.get("IS_BROWSER")||void 0===window.localStorage)throw new Error("The current environment does not support local storage.");if(this.LS=window.localStorage,null==e||!e)throw new Error("For local storage, modelPath must not be null, undefined or empty.");this.modelPath=e,this.keys=getModelKeys(this.modelPath);}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o;return __generator(this,function(a){if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserLocalStorage.save() does not support saving model topology in binary formats yet.");t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),r=getModelArtifactsInfoForJSON(e);try{return this.LS.setItem(this.keys.info,JSON.stringify(r)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,arrayBufferToBase64String(e.weightData)),[2,{modelArtifactsInfo:r}]}catch(e){for(o in this.keys)this.LS.removeItem(this.keys[o]);throw new Error("Failed to save model '"+this.modelPath+"' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes="+r.modelTopologyBytes+", weightSpecsBytes="+r.weightSpecsBytes+", weightDataBytes="+r.weightDataBytes+".")}return [2]})})},e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o;return __generator(this,function(a){if(null==(e=JSON.parse(this.LS.getItem(this.keys.info))))throw new Error("In local storage, there is no model with name '"+this.modelPath+"'");if("JSON"!==e.modelTopologyType)throw new Error("BrowserLocalStorage does not support loading non-JSON model topology yet.");if(t={},null==(n=JSON.parse(this.LS.getItem(this.keys.topology))))throw new Error("In local storage, the topology of model '"+this.modelPath+"' is missing.");if(t.modelTopology=n,null==(r=JSON.parse(this.LS.getItem(this.keys.weightSpecs))))throw new Error("In local storage, the weight specs of model '"+this.modelPath+"' are missing.");if(t.weightSpecs=r,null==(o=this.LS.getItem(this.keys.weightData)))throw new Error("In local storage, the binary weight values of model '"+this.modelPath+"' are missing.");return t.weightData=base64StringToArrayBuffer(o),[2,t]})})},e.URL_SCHEME="localstorage://",e}(),localStorageRouter=function(e){return ENV.get("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(BrowserLocalStorage.URL_SCHEME)?browserLocalStorage(e.slice(BrowserLocalStorage.URL_SCHEME.length)):null};function browserLocalStorage(e){return new BrowserLocalStorage(e)}IORouterRegistry.registerSaveRouter(localStorageRouter),IORouterRegistry.registerLoadRouter(localStorageRouter);var BrowserLocalStorageManager=function(){function e(){assert(ENV.get("IS_BROWSER"),"Current environment is not a web browser"),assert(void 0!==window.localStorage,"Current browser does not appear to support localStorage"),this.LS=window.localStorage;}return e.prototype.listModels=function(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o,a;return __generator(this,function(i){for(e={},t=PATH_PREFIX+PATH_SEPARATOR,n=PATH_SEPARATOR+INFO_SUFFIX,r=0;r<this.LS.length;++r)(o=this.LS.key(r)).startsWith(t)&&o.endsWith(n)&&(a=getModelPathFromKey(o),e[a]=JSON.parse(this.LS.getItem(o)));return [2,e]})})},e.prototype.removeModel=function(e){return __awaiter(this,void 0,void 0,function(){var t,n;return __generator(this,function(r){if(e=maybeStripScheme$1(e),t=getModelKeys(e),null==this.LS.getItem(t.info))throw new Error("Cannot find model at path '"+e+"'");return n=JSON.parse(this.LS.getItem(t.info)),this.LS.removeItem(t.info),this.LS.removeItem(t.topology),this.LS.removeItem(t.weightSpecs),this.LS.removeItem(t.weightData),[2,n]})})},e}();if(ENV.get("IS_BROWSER"))try{ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME,new BrowserLocalStorageManager);}catch(e){}var DEFAULT_FILE_NAME_PREFIX="model",DEFAULT_JSON_EXTENSION_NAME=".json",DEFAULT_WEIGHT_DATA_EXTENSION_NAME=".weights.bin",BrowserDownloads=function(){function e(t){if(!ENV.get("IS_BROWSER"))throw new Error("browserDownloads() cannot proceed because the current environment is not a browser.");t.startsWith(e.URL_SCHEME)&&(t=t.slice(e.URL_SCHEME.length)),null!=t&&0!==t.length||(t=DEFAULT_FILE_NAME_PREFIX),this.modelTopologyFileName=t+DEFAULT_JSON_EXTENSION_NAME,this.weightDataFileName=t+DEFAULT_WEIGHT_DATA_EXTENSION_NAME;}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o,a,i;return __generator(this,function(s){if(t=window.URL.createObjectURL(new Blob([e.weightData],{type:"application/octet-stream"})),e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserDownloads.save() does not support saving model topology in binary formats yet.");return n=[{paths:["./"+this.weightDataFileName],weights:e.weightSpecs}],r={modelTopology:e.modelTopology,weightsManifest:n},o=window.URL.createObjectURL(new Blob([JSON.stringify(r)],{type:"application/json"})),(a=null==this.jsonAnchor?document.createElement("a"):this.jsonAnchor).download=this.modelTopologyFileName,a.href=o,a.click(),null!=e.weightData&&((i=null==this.weightDataAnchor?document.createElement("a"):this.weightDataAnchor).download=this.weightDataFileName,i.href=t,i.click()),[2,{modelArtifactsInfo:getModelArtifactsInfoForJSON(e)}]})})},e.URL_SCHEME="downloads://",e}(),BrowserFiles=function(){function e(e){if(null==e||e.length<1)throw new Error("When calling browserFiles, at least 1 file is required, but received "+e);this.files=e;}return e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){var e,t,n=this;return __generator(this,function(r){return e=this.files[0],t=this.files.slice(1),[2,new Promise(function(r,o){var a=new FileReader;a.onload=function(a){var i=JSON.parse(a.target.result),s=i.modelTopology;if(null!=s){0===t.length&&r({modelTopology:s});var u=i.weightsManifest;if(null!=u){var l;try{l=n.checkManifestAndWeightFiles(u,t);}catch(e){return void o(e)}var c=[],p=[],d=[];u.forEach(function(e){e.paths.forEach(function(e){p.push(e),d.push(null);}),c.push.apply(c,e.weights);}),u.forEach(function(e){e.paths.forEach(function(e){var t=new FileReader;t.onload=function(t){var n=t.target.result,o=p.indexOf(e);d[o]=n,-1===d.indexOf(null)&&r({modelTopology:s,weightSpecs:c,weightData:concatenateArrayBuffers(d)});},t.onerror=function(t){return o("Failed to weights data from file of path '"+e+"'.")},t.readAsArrayBuffer(l[e]);});});}else o(new Error("weightManifest field is missing from file "+e.name));}else o(new Error("modelTopology field is missing from file "+e.name));},a.onerror=function(t){return o("Failed to read model topology and weights manifest JSON from file '"+e.name+"'. BrowserFiles supports loading Keras-style tf.Model artifacts only.")},a.readAsText(e);})]})})},e.prototype.checkManifestAndWeightFiles=function(e,t){for(var n=[],r=t.map(function(e){return basename(e.name)}),o={},a=0,i=e;a<i.length;a++){i[a].paths.forEach(function(e){var a=basename(e);if(-1!==n.indexOf(a))throw new Error("Duplicate file basename found in weights manifest: '"+a+"'");if(n.push(a),-1===r.indexOf(a))throw new Error("Weight file with basename '"+a+"' is not provided.");o[e]=t[r.indexOf(a)];});}if(n.length!==t.length)throw new Error("Mismatch in the number of files in weights manifest ("+n.length+") and the number of weight files provided ("+t.length+").");return o},e}(),browserDownloadsRouter=function(e){return ENV.get("IS_BROWSER")&&!Array.isArray(e)&&e.startsWith(BrowserDownloads.URL_SCHEME)?browserDownloads(e.slice(BrowserDownloads.URL_SCHEME.length)):null};function browserDownloads(e){return void 0===e&&(e="model"),new BrowserDownloads(e)}function browserFiles(e){return new BrowserFiles(e)}function loadWeightsAsArrayBuffer(e,t,n){return __awaiter(this,void 0,void 0,function(){var r,o;return __generator(this,function(a){switch(a.label){case 0:return null==n&&(n=fetch),r=e.map(function(e){return n(e,t)}),[4,Promise.all(r)];case 1:return o=a.sent(),[4,Promise.all(o.map(function(e){return e.arrayBuffer()}))];case 2:return [2,a.sent()]}})})}function loadWeights(e,t,n,r){return void 0===t&&(t=""),__awaiter(this,void 0,void 0,function(){return __generator(this,function(o){return [2,weightsLoaderFactory(function(e){return loadWeightsAsArrayBuffer(e,r)})(e,t,n)]})})}function weightsLoaderFactory(e){var t=this;return function(n,r,o){return void 0===r&&(r=""),__awaiter(t,void 0,void 0,function(){var t,a,i,s,u,l,c,p,d,h;return __generator(this,function(f){switch(f.label){case 0:if(t=n.map(function(){return !1}),a={},i=null!=o?o.map(function(){return !1}):[],s=[],n.forEach(function(e,n){var r=0;e.weights.forEach(function(e){var u="quantization"in e?e.quantization.dtype:e.dtype,l=DTYPE_VALUE_SIZE_MAP[u]*sizeFromShape(e.shape),c=function(){t[n]=!0,null==a[n]&&(a[n]=[]),a[n].push({manifestEntry:e,groupOffset:r,sizeBytes:l});};null!=o?o.forEach(function(t,n){t===e.name&&(c(),i[n]=!0);}):c(),s.push(e.name),r+=l;});}),!i.every(function(e){return e}))throw u=o.filter(function(e,t){return !i[t]}),new Error("Could not find weights in manifest with names: "+u.join(", ")+". \nManifest JSON has weights with names: "+s.join(", ")+".");return l=t.reduce(function(e,t,n){return t&&e.push(n),e},[]),c=[],l.forEach(function(e){n[e].paths.forEach(function(e){var t=r+(r.endsWith("/")?"":"/")+e;c.push(t);});}),[4,e(c)];case 1:return p=f.sent(),d={},h=0,l.forEach(function(e){for(var t=n[e].paths.length,r=0,o=0;o<t;o++)r+=p[h+o].byteLength;for(var i=new ArrayBuffer(r),s=new Uint8Array(i),u=0,l=0;l<t;l++){var c=new Uint8Array(p[h+l]);s.set(c,u),u+=c.byteLength;}a[e].forEach(function(e){var t=decodeWeights(i.slice(e.groupOffset,e.groupOffset+e.sizeBytes),[e.manifestEntry]);for(var n in t)d[n]=t[n];}),h+=t;}),[2,d]}})})}}IORouterRegistry.registerSaveRouter(browserDownloadsRouter);var BrowserHTTPRequest=function(){function e(e,t,n,r){if(this.weightPathPrefix=n,this.DEFAULT_METHOD="POST",null==r){if("undefined"==typeof fetch)throw new Error("browserHTTPRequest is not supported outside the web browser without a fetch polyfill.");this.fetchFunc=fetch.bind("undefined"==typeof window?null:window);}else assert("function"==typeof r,"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)"),this.fetchFunc=r;if(assert(null!=e&&e.length>0,"URL path for browserHTTPRequest must not be null, undefined or empty."),Array.isArray(e)&&assert(2===e.length,"URL paths for browserHTTPRequest must have a length of 2, (actual length is "+e.length+")."),this.path=e,null!=t&&null!=t.body)throw new Error("requestInit is expected to have no pre-existing body, but has one.");this.requestInit=t||{};}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o;return __generator(this,function(a){switch(a.label){case 0:if(e.modelTopology instanceof ArrayBuffer)throw new Error("BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.");return (t=Object.assign({method:this.DEFAULT_METHOD},this.requestInit)).body=new FormData,n=[{paths:["./model.weights.bin"],weights:e.weightSpecs}],r={modelTopology:e.modelTopology,weightsManifest:n},t.body.append("model.json",new Blob([JSON.stringify(r)],{type:"application/json"}),"model.json"),null!=e.weightData&&t.body.append("model.weights.bin",new Blob([e.weightData],{type:"application/octet-stream"}),"model.weights.bin"),[4,this.getFetchFunc()(this.path,t)];case 1:if((o=a.sent()).ok)return [2,{modelArtifactsInfo:getModelArtifactsInfoForJSON(e),responses:[o]}];throw new Error("BrowserHTTPRequest.save() failed due to HTTP response status "+o.status+".")}})})},e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(e){return [2,Array.isArray(this.path)?this.loadBinaryModel():this.loadJSONModel()]})})},e.prototype.loadBinaryTopology=function(){return __awaiter(this,void 0,void 0,function(){var e,t;return __generator(this,function(n){switch(n.label){case 0:return n.trys.push([0,3,,4]),[4,this.getFetchFunc()(this.path[0],this.requestInit)];case 1:if(!(e=n.sent()).ok)throw new Error("BrowserHTTPRequest.load() failed due to HTTP response: "+e.statusText);return [4,e.arrayBuffer()];case 2:return [2,n.sent()];case 3:throw t=n.sent(),new Error(this.path[0]+" not found. "+t);case 4:return [2]}})})},e.prototype.loadBinaryModel=function(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o,a,i,s;return __generator(this,function(u){switch(u.label){case 0:return e=this.loadBinaryTopology(),[4,this.getFetchFunc()(this.path[1],this.requestInit)];case 1:if(!(t=u.sent()).ok)throw new Error("BrowserHTTPRequest.load() failed due to HTTP response: "+t.statusText);return [4,Promise.all([e,t])];case 2:return n=u.sent(),r=n[0],[4,n[1].json()];case 3:return null==(o=u.sent())?[3,5]:[4,this.loadWeights(o)];case 4:s=u.sent(),a=s[0],i=s[1],u.label=5;case 5:return [2,{modelTopology:r,weightSpecs:a,weightData:i}]}})})},e.prototype.loadJSONModel=function(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o,a,i,s;return __generator(this,function(u){switch(u.label){case 0:return [4,this.getFetchFunc()(this.path,this.requestInit)];case 1:if(!(e=u.sent()).ok)throw new Error("BrowserHTTPRequest.load() failed due to HTTP response: "+e.statusText);return [4,e.json()];case 2:if(t=u.sent(),n=t.modelTopology,r=t.weightsManifest,null==n&&null==r)throw new Error("The JSON from HTTP path "+this.path+" contains neither model topology or manifest for weights.");return null==r?[3,4]:(i=t.weightsManifest,[4,this.loadWeights(i)]);case 3:s=u.sent(),o=s[0],a=s[1],u.label=4;case 4:return [2,{modelTopology:n,weightSpecs:o,weightData:a}]}})})},e.prototype.loadWeights=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o,a,i,s,u,l,c,p,d;return __generator(this,function(h){switch(h.label){case 0:for(t=Array.isArray(this.path)?this.path[1]:this.path,n=parseUrl(t),r=n[0],o=n[1],a=this.weightPathPrefix||r,i=[],s=0,u=e;s<u.length;s++)l=u[s],i.push.apply(i,l.weights);return c=[],e.forEach(function(e){e.paths.forEach(function(e){c.push(a+e+o);});}),p=[i],d=concatenateArrayBuffers,[4,loadWeightsAsArrayBuffer(c,this.requestInit,this.getFetchFunc())];case 1:return [2,p.concat([d.apply(void 0,[h.sent()])])]}})})},e.prototype.getFetchFunc=function(){return this.fetchFunc},e.URL_SCHEME_REGEX=/^https?:\/\//,e}();function parseUrl(e){var t=e.lastIndexOf("/"),n=e.lastIndexOf("?");return [e.substring(0,t)+"/",n>t?e.substring(n):""]}function isHTTPScheme(e){return null!=e.match(BrowserHTTPRequest.URL_SCHEME_REGEX)}var httpRequestRouter=function(e){if("undefined"==typeof fetch)return null;return (Array.isArray(e)?e.every(function(e){return isHTTPScheme(e)}):isHTTPScheme(e))?browserHTTPRequest(e):null};function browserHTTPRequest(e,t,n,r){return new BrowserHTTPRequest(e,t,n,r)}IORouterRegistry.registerSaveRouter(httpRequestRouter),IORouterRegistry.registerLoadRouter(httpRequestRouter);var PassthroughLoader=function(){function e(e,t,n){this.modelTopology=e,this.weightSpecs=t,this.weightData=n;}return e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){var e;return __generator(this,function(t){return e={},null!=this.modelTopology&&(e=__assign({modelTopology:this.modelTopology},e)),null!=this.weightSpecs&&this.weightSpecs.length>0&&(e=__assign({weightSpecs:this.weightSpecs},e)),null!=this.weightData&&this.weightData.byteLength>0&&(e=__assign({weightData:this.weightData},e)),[2,e]})})},e}(),PassthroughSaver=function(){function e(e){this.saveHandler=e;}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(t){return [2,this.saveHandler(e)]})})},e}();function fromMemory(e,t,n){return new PassthroughLoader(e,t,n)}function withSaveHandler(e){return new PassthroughSaver(e)}var registerSaveRouter=IORouterRegistry.registerSaveRouter,registerLoadRouter=IORouterRegistry.registerLoadRouter,getSaveHandlers=IORouterRegistry.getSaveHandlers,getLoadHandlers=IORouterRegistry.getLoadHandlers,io=Object.freeze({browserFiles:browserFiles,browserHTTPRequest:browserHTTPRequest,concatenateArrayBuffers:concatenateArrayBuffers,decodeWeights:decodeWeights,encodeWeights:encodeWeights,fromMemory:fromMemory,getLoadHandlers:getLoadHandlers,getModelArtifactsInfoForJSON:getModelArtifactsInfoForJSON,getSaveHandlers:getSaveHandlers,isHTTPScheme:isHTTPScheme,loadWeights:loadWeights,registerLoadRouter:registerLoadRouter,registerSaveRouter:registerSaveRouter,weightsLoaderFactory:weightsLoaderFactory,withSaveHandler:withSaveHandler,copyModel:copyModel,listModels:listModels,moveModel:moveModel,removeModel:removeModel});function confusionMatrix_(e,t,n){var r=convertToTensor(e,"labels","confusionMatrix"),o=convertToTensor(t,"predictions","confusionMatrix");assert(null==n||n>0&&Number.isInteger(n),"If provided, numClasses must be a positive integer, but got "+n),assert(1===r.rank,"Expected the rank of labels to be 1, but got "+r.rank),assert(1===o.rank,"Expected the rank of predictions to be 1, but got "+o.rank),assert(r.shape[0]===o.shape[0],"Mismatch in the number of examples: "+r.shape[0]+" vs. "+o.shape[0]+". Labels and predictions should have the same number of elements."),assert(n>0&&Number.isInteger(n),"numClasses is required to be a positive integer, but got "+n);var a=oneHot(r.asType("int32"),n),i=oneHot(o.asType("int32"),n);return a.transpose().matMul(i).asType("int32")}var confusionMatrix=op({confusionMatrix_:confusionMatrix_}),math=Object.freeze({confusionMatrix:confusionMatrix}),Serializable=function(){function e(){}return e.prototype.getClassName=function(){return this.constructor.className},e.fromConfig=function(e,t){return new e(t)},e}(),SerializationMap=function(){function e(){this.classNameMap={};}return e.getMap=function(){return null==e.instance&&(e.instance=new e),e.instance},e.register=function(t){e.getMap().classNameMap[t.className]=[t,t.fromConfig];},e}();function registerClass(e){assert(null!=e.className,"Class being registered does not have the static className property defined."),assert("string"==typeof e.className,"className is required to be a string, but got type "+typeof e.className),assert(e.className.length>0,"Class being registered has an empty-string as its className, which is disallowed."),SerializationMap.register(e);}var serialization=Object.freeze({Serializable:Serializable,SerializationMap:SerializationMap,registerClass:registerClass}),WEBGL_ENVS={HAS_WEBGL:!0},NODE_ENVS={IS_NODE:!0},CHROME_ENVS={IS_CHROME:!0},BROWSER_ENVS={IS_BROWSER:!0},CPU_ENVS={HAS_WEBGL:!1},ALL_ENVS={};function expectArraysClose(e,t,n){return null==n&&(n=ENV.get("TEST_EPSILON")),expectArraysPredicate(e,t,function(e,t){return areClose(e,Number(t),n)})}function expectArraysPredicate(e,t,n){if(e instanceof Tensor||t instanceof Tensor){if(e instanceof Tensor&&t instanceof Tensor){if(e.dtype!==t.dtype)throw new Error("Arrays are of different type actual: "+e.dtype+" vs expected: "+t.dtype+".");if(!arraysEqual(e.shape,t.shape))throw new Error("Arrays are of different shape actual: "+e.shape+" vs expected: "+t.shape+".")}}else{var r=e.constructor.name,o=t.constructor.name;if(r!==o)throw new Error("Arrays are of different type actual: "+r+" vs expected: "+o)}var a,i;if(a=e instanceof Tensor?e.dataSync():e,i=t instanceof Tensor?t.dataSync():t,a.length!==i.length)throw new Error("Arrays have different lengths actual: "+a.length+" vs expected: "+i.length+".\nActual:   "+a+".\nExpected: "+i+".");for(var s=0;s<i.length;++s){var u=a[s],l=i[s];if(!n(u,l))throw new Error("Arrays differ: actual["+s+"] = "+u+", expected["+s+"] = "+l+".\nActual:   "+a+".\nExpected: "+i+".")}}function expectPromiseToFail(e,t){e().then(function(){return t.fail()},function(){return t()});}function expectArraysEqual(e,t){return e instanceof Tensor&&"string"===e.dtype||t instanceof Tensor&&"string"===t.dtype||e instanceof Array&&isString(e[0])||t instanceof Array&&isString(t[0])?expectArraysPredicate(e,t,function(e,t){return e==t}):expectArraysClose(e,t,0)}function expectNumbersClose(e,t,n){if(null==n&&(n=ENV.get("TEST_EPSILON")),!areClose(e,t,n))throw new Error("Numbers differ: actual === "+e+", expected === "+t)}function areClose(e,t,n){return !(!isNaN(e)||!isNaN(t))||!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function expectValuesInRange(e,t,n){var r;r=e instanceof Tensor?e.dataSync():e;for(var o=0;o<r.length;o++)if(r[o]<t||r[o]>n)throw new Error("Value out of range:"+r[o]+" low: "+t+", high: "+n)}function expectArrayBuffersEqual(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t));}var test_util=Object.freeze({WEBGL_ENVS:WEBGL_ENVS,NODE_ENVS:NODE_ENVS,CHROME_ENVS:CHROME_ENVS,BROWSER_ENVS:BROWSER_ENVS,CPU_ENVS:CPU_ENVS,ALL_ENVS:ALL_ENVS,expectArraysClose:expectArraysClose,expectPromiseToFail:expectPromiseToFail,expectArraysEqual:expectArraysEqual,expectNumbersClose:expectNumbersClose,expectValuesInRange:expectValuesInRange,expectArrayBuffersEqual:expectArrayBuffersEqual}),version="0.14.2",webgl=Object.freeze({gpgpu_util:gpgpu_util,webgl_util:webgl_util,MathBackendWebGL:MathBackendWebGL,GPGPUContext:GPGPUContext}),Optimizer=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends(t,e),t.prototype.minimize=function(e,t,n){void 0===t&&(t=!1);var r=this.computeGradients(e,n),o=r.value,a=r.grads;return this.applyGradients(a),Object.keys(a).forEach(function(e){return a[e].dispose()}),t?o:(o.dispose(),null)},t.prototype.computeGradients=function(e,t){return variableGrads(e,t)},t}(Serializable),AdadeltaOptimizer=function(e){function t(t,n,r){void 0===r&&(r=null);var o=e.call(this)||this;return o.learningRate=t,o.rho=n,o.epsilon=r,o.accumulatedGrads={},o.accumulatedUpdates={},o.c=keep(scalar(-t)),o.rhoScalar=keep(scalar(n)),o.oneMinusRho=keep(scalar(1-n)),null===r&&(r=ENV.get("EPSILON")),o.epsilonScalar=keep(scalar(r)),o}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulatedGrads[n]){tidy(function(){t.accumulatedGrads[n]=zerosLike(o).variable(!1);});}if(null==r.accumulatedUpdates[n]){tidy(function(){t.accumulatedUpdates[n]=zerosLike(o).variable(!1);});}var a=e[n],i=r.accumulatedGrads[n],s=r.accumulatedUpdates[n];tidy(function(){var e=t.rhoScalar.mul(i).add(t.oneMinusRho.mul(a.square())),r=s.add(t.epsilonScalar).sqrt().div(i.add(t.epsilonScalar).sqrt()).mul(a),u=t.rhoScalar.mul(s).add(t.oneMinusRho.mul(r.square()));t.accumulatedGrads[n].assign(e),t.accumulatedUpdates[n].assign(u);var l=t.c.mul(r).add(o);o.assign(l);});},r=this;for(var o in e)n(o);},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsilonScalar.dispose(),this.rhoScalar.dispose(),this.oneMinusRho.dispose(),null!=this.accumulatedUpdates&&(Object.keys(this.accumulatedUpdates).forEach(function(t){return e.accumulatedUpdates[t].dispose()}),Object.keys(this.accumulatedGrads).forEach(function(t){return e.accumulatedGrads[t].dispose()}));},t.prototype.getConfig=function(){return {learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}},t.fromConfig=function(e,t){return new e(t.learningRate,t.rho,t.epsilon)},t.className="AdadeltaOptimizer",t}(Optimizer);registerClass(AdadeltaOptimizer);var AdagradOptimizer=function(e){function t(t,n){void 0===n&&(n=.1);var r=e.call(this)||this;return r.learningRate=t,r.initialAccumulatorValue=n,r.accumulatedGrads={},r.c=keep(scalar(-t)),r.epsilon=keep(scalar(ENV.get("EPSILON"))),r}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulatedGrads[n]){tidy(function(){t.accumulatedGrads[n]=fill(o.shape,t.initialAccumulatorValue).variable(!1);});}var a=e[n],i=r.accumulatedGrads[n];tidy(function(){var e=i.add(a.square());t.accumulatedGrads[n].assign(e);var r=t.c.mul(a.div(e.add(t.epsilon).sqrt())).add(o);o.assign(r);});},r=this;for(var o in e)n(o);},t.prototype.dispose=function(){var e=this;this.epsilon.dispose(),this.c.dispose(),null!=this.accumulatedGrads&&Object.keys(this.accumulatedGrads).forEach(function(t){return e.accumulatedGrads[t].dispose()});},t.prototype.getConfig=function(){return {learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}},t.fromConfig=function(e,t){return new e(t.learningRate,t.initialAccumulatorValue)},t.className="AdagradOptimizer",t}(Optimizer);registerClass(AdagradOptimizer);var AdamOptimizer=function(e){function t(t,n,r,o){void 0===o&&(o=null);var a=e.call(this)||this;return a.learningRate=t,a.beta1=n,a.beta2=r,a.epsilon=o,a.accumulatedFirstMoment={},a.accumulatedSecondMoment={},a.c=keep(scalar(-t)),a.beta1Scalar=keep(scalar(n)),a.beta2Scalar=keep(scalar(r)),tidy(function(){a.accBeta1=scalar(n).variable(),a.accBeta2=scalar(r).variable();}),a.oneMinusBeta1=keep(scalar(1-n)),a.oneMinusBeta2=keep(scalar(1-r)),a.one=keep(scalar(1)),null===o&&(o=ENV.get("EPSILON")),a.epsScalar=keep(scalar(o)),a}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this;tidy(function(){var n=t.one.sub(t.accBeta1),r=t.one.sub(t.accBeta2);for(var o in e){var a=ENV.engine.registeredVariables[o];if(null==t.accumulatedFirstMoment[o]){var i=!1;t.accumulatedFirstMoment[o]=zerosLike(a).variable(i);}if(null==t.accumulatedSecondMoment[o]){i=!1;t.accumulatedSecondMoment[o]=zerosLike(a).variable(i);}var s=e[o],u=t.accumulatedFirstMoment[o],l=t.accumulatedSecondMoment[o],c=t.beta1Scalar.mul(u).add(t.oneMinusBeta1.mul(s)),p=t.beta2Scalar.mul(l).add(t.oneMinusBeta2.mul(s.square())),d=c.div(n),h=p.div(r);t.accumulatedFirstMoment[o].assign(c),t.accumulatedSecondMoment[o].assign(p);var f=t.c.mul(d.div(t.epsScalar.add(h.sqrt()))).add(a);a.assign(f);}t.accBeta1.assign(t.accBeta1.mul(t.beta1Scalar)),t.accBeta2.assign(t.accBeta2.mul(t.beta2Scalar));});},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsScalar.dispose(),this.beta1Scalar.dispose(),this.beta2Scalar.dispose(),this.accBeta1.dispose(),this.accBeta2.dispose(),this.oneMinusBeta1.dispose(),this.oneMinusBeta2.dispose(),this.one.dispose(),null!=this.accumulatedFirstMoment&&Object.keys(this.accumulatedFirstMoment).forEach(function(t){return e.accumulatedFirstMoment[t].dispose()}),null!=this.accumulatedSecondMoment&&Object.keys(this.accumulatedSecondMoment).forEach(function(t){return e.accumulatedSecondMoment[t].dispose()});},t.prototype.getConfig=function(){return {learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}},t.fromConfig=function(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)},t.className="AdamOptimizer",t}(Optimizer);registerClass(AdamOptimizer);var AdamaxOptimizer=function(e){function t(t,n,r,o,a){void 0===o&&(o=null),void 0===a&&(a=0);var i=e.call(this)||this;return i.learningRate=t,i.beta1=n,i.beta2=r,i.epsilon=o,i.decay=a,i.accumulatedFirstMoment={},i.accumulatedWeightedInfNorm={},i.c=keep(scalar(-t)),i.beta1Scalar=keep(scalar(n)),i.beta2Scalar=keep(scalar(r)),i.decayScalar=keep(scalar(a)),tidy(function(){i.iteration=scalar(0).variable(),i.accBeta1=scalar(n).variable();}),i.oneMinusBeta1=keep(scalar(1-n)),i.one=keep(scalar(1)),null===o&&(o=ENV.get("EPSILON")),i.epsScalar=keep(scalar(o)),i}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this;tidy(function(){var n=t.one.sub(t.accBeta1),r=t.c.div(t.one.add(t.decayScalar.mul(t.iteration)));for(var o in e){var a=ENV.engine.registeredVariables[o];if(null==t.accumulatedFirstMoment[o]){var i=!1;t.accumulatedFirstMoment[o]=zerosLike(a).variable(i);}if(null==t.accumulatedWeightedInfNorm[o]){i=!1;t.accumulatedWeightedInfNorm[o]=zerosLike(a).variable(i);}var s=e[o],u=t.accumulatedFirstMoment[o],l=t.accumulatedWeightedInfNorm[o],c=t.beta1Scalar.mul(u).add(t.oneMinusBeta1.mul(s)),p=t.beta2Scalar.mul(l),d=s.abs(),h=p.maximum(d);t.accumulatedFirstMoment[o].assign(c),t.accumulatedWeightedInfNorm[o].assign(h);var f=r.div(n).mul(c.div(t.epsScalar.add(h))).add(a);a.assign(f);}t.iteration.assign(t.iteration.add(t.one)),t.accBeta1.assign(t.accBeta1.mul(t.beta1Scalar));});},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsScalar.dispose(),this.accBeta1.dispose(),this.beta1Scalar.dispose(),this.beta2Scalar.dispose(),this.oneMinusBeta1.dispose(),this.decayScalar.dispose(),this.iteration.dispose(),this.one.dispose(),null!=this.accumulatedFirstMoment&&Object.keys(this.accumulatedFirstMoment).forEach(function(t){return e.accumulatedFirstMoment[t].dispose()}),null!=this.accumulatedWeightedInfNorm&&Object.keys(this.accumulatedWeightedInfNorm).forEach(function(t){return e.accumulatedWeightedInfNorm[t].dispose()});},t.prototype.getConfig=function(){return {learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}},t.fromConfig=function(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)},t.className="AdamaxOptimizer",t}(Optimizer);registerClass(AdamaxOptimizer);var SGDOptimizer=function(e){function t(t){var n=e.call(this)||this;return n.learningRate=t,n.setLearningRate(t),n}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this;Object.keys(e).forEach(function(n){var r=e[n],o=ENV.engine.registeredVariables[n];tidy(function(){var e=t.c.mul(r).add(o);o.assign(e);});});},t.prototype.setLearningRate=function(e){this.learningRate=e,null!=this.c&&this.c.dispose(),this.c=keep(scalar(-e));},t.prototype.dispose=function(){this.c.dispose();},t.prototype.getConfig=function(){return {learningRate:this.learningRate}},t.fromConfig=function(e,t){return new e(t.learningRate)},t.className="SGDOptimizer",t}(Optimizer);registerClass(SGDOptimizer);var MomentumOptimizer=function(e){function t(t,n,r){void 0===r&&(r=!1);var o=e.call(this,t)||this;return o.learningRate=t,o.momentum=n,o.useNesterov=r,o.m=scalar(o.momentum),o.accumulations={},o}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulations[n]){tidy(function(){t.accumulations[n]=zerosLike(o).variable(!1);});}var a=r.accumulations[n],i=e[n];tidy(function(){var e,r=t.m.mul(a).add(i);e=t.useNesterov?t.c.mul(i.add(r.mul(t.m))).add(o):t.c.mul(r).add(o),t.accumulations[n].assign(r),o.assign(e);});},r=this;for(var o in e)n(o);},t.prototype.dispose=function(){if(e.prototype.dispose.call(this),this.m.dispose(),null!=this.accumulations)for(var t in this.accumulations)this.accumulations[t].dispose();},t.prototype.setMomentum=function(e){this.momentum=e;},t.prototype.getConfig=function(){return {learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}},t.fromConfig=function(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)},t.className="MomentumOptimizer",t}(SGDOptimizer);registerClass(MomentumOptimizer);var RMSPropOptimizer=function(e){function t(t,n,r,o,a){void 0===n&&(n=.9),void 0===r&&(r=0),void 0===o&&(o=null),void 0===a&&(a=!1);var i=e.call(this)||this;return i.learningRate=t,i.decay=n,i.momentum=r,i.epsilon=o,i.accumulatedMeanSquares={},i.accumulatedMeanGrads={},i.accumulatedMoments={},i.c=keep(scalar(t)),i.decayScalar=keep(scalar(n)),i.momentumScalar=keep(scalar(r)),i.oneMinusDecay=keep(scalar(1-n)),i.centered=a,null===o&&(o=ENV.get("EPSILON")),i.epsilonScalar=keep(scalar(o)),i}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulatedMeanSquares[n]){tidy(function(){t.accumulatedMeanSquares[n]=zerosLike(o).variable(!1);});}if(null==r.accumulatedMeanGrads[n]&&r.centered){tidy(function(){t.accumulatedMeanGrads[n]=zerosLike(o).variable(!1);});}if(null==r.accumulatedMoments[n]){tidy(function(){t.accumulatedMoments[n]=zerosLike(o).variable(!1);});}var a=r.accumulatedMeanSquares[n],i=r.accumulatedMeanGrads[n],s=r.accumulatedMoments[n],u=e[n];tidy(function(){var e=t.decayScalar.mul(a).add(t.oneMinusDecay.mul(u.square()));if(t.centered){var r=t.decayScalar.mul(i).add(t.oneMinusDecay.mul(u)),l=t.momentumScalar.mul(s).add(t.c.mul(u).div(e.sub(r.square().add(t.epsilonScalar)).sqrt()));t.accumulatedMeanSquares[n].assign(e),t.accumulatedMeanGrads[n].assign(r),t.accumulatedMoments[n].assign(l);var c=o.sub(l);o.assign(c);}else{var p=t.decayScalar.mul(a).add(t.oneMinusDecay.mul(u.square()));l=t.momentumScalar.mul(s).add(t.c.mul(u).div(p.add(t.epsilonScalar).sqrt()));t.accumulatedMeanSquares[n].assign(p),t.accumulatedMoments[n].assign(l);c=o.sub(l);o.assign(c);}});},r=this;for(var o in e)n(o);},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsilonScalar.dispose(),this.decayScalar.dispose(),this.momentumScalar.dispose(),this.oneMinusDecay.dispose(),null!=this.accumulatedMeanSquares&&Object.keys(this.accumulatedMeanSquares).forEach(function(t){return e.accumulatedMeanSquares[t].dispose()}),null!=this.accumulatedMeanGrads&&this.centered&&Object.keys(this.accumulatedMeanGrads).forEach(function(t){return e.accumulatedMeanGrads[t].dispose()}),null!=this.accumulatedMoments&&Object.keys(this.accumulatedMoments).forEach(function(t){return e.accumulatedMoments[t].dispose()});},t.prototype.getConfig=function(){return {learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}},t.fromConfig=function(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)},t.className="RMSPropOptimizer",t}(Optimizer);registerClass(RMSPropOptimizer);var OptimizerConstructors=function(){function e(){}return e.sgd=function(e){return new SGDOptimizer(e)},e.momentum=function(e,t,n){return void 0===n&&(n=!1),new MomentumOptimizer(e,t,n)},e.rmsprop=function(e,t,n,r,o){return void 0===t&&(t=.9),void 0===n&&(n=0),void 0===r&&(r=null),void 0===o&&(o=!1),new RMSPropOptimizer(e,t,n,r,o)},e.adam=function(e,t,n,r){return void 0===e&&(e=.001),void 0===t&&(t=.9),void 0===n&&(n=.999),void 0===r&&(r=null),new AdamOptimizer(e,t,n,r)},e.adadelta=function(e,t,n){return void 0===e&&(e=.001),void 0===t&&(t=.95),void 0===n&&(n=null),new AdadeltaOptimizer(e,t,n)},e.adamax=function(e,t,n,r,o){return void 0===e&&(e=.002),void 0===t&&(t=.9),void 0===n&&(n=.999),void 0===r&&(r=null),void 0===o&&(o=0),new AdamaxOptimizer(e,t,n,r,o)},e.adagrad=function(e,t){return void 0===t&&(t=.1),new AdagradOptimizer(e,t)},e}(),train={sgd:OptimizerConstructors.sgd,momentum:OptimizerConstructors.momentum,adadelta:OptimizerConstructors.adadelta,adagrad:OptimizerConstructors.adagrad,rmsprop:OptimizerConstructors.rmsprop,adamax:OptimizerConstructors.adamax,adam:OptimizerConstructors.adam},setBackend=Environment.setBackend,getBackend=Environment.getBackend,disposeVariables=Environment.disposeVariables,memory=Environment.memory;setOpHandler(ops);
+
+    var tfCore_esm = /*#__PURE__*/Object.freeze({
+        setBackend: setBackend,
+        getBackend: getBackend,
+        disposeVariables: disposeVariables,
+        memory: memory,
+        version_core: version,
+        nextFrame: nextFrame,
+        environment: environment,
+        io: io,
+        math: math,
+        serialization: serialization,
+        test_util: test_util,
+        util: util,
+        webgl: webgl,
+        AdadeltaOptimizer: AdadeltaOptimizer,
+        AdagradOptimizer: AdagradOptimizer,
+        AdamOptimizer: AdamOptimizer,
+        AdamaxOptimizer: AdamaxOptimizer,
+        MomentumOptimizer: MomentumOptimizer,
+        Optimizer: Optimizer,
+        RMSPropOptimizer: RMSPropOptimizer,
+        SGDOptimizer: SGDOptimizer,
+        Tensor: Tensor,
+        TensorBuffer: TensorBuffer,
+        variable: variable,
+        Variable: Variable,
+        get Rank () { return Rank; },
+        get Reduction () { return Reduction; },
+        ENV: ENV,
+        Environment: Environment,
+        KernelBackend: KernelBackend,
+        DataStorage: DataStorage,
+        image: image_ops,
+        linalg: linalg_ops,
+        losses: loss_ops,
+        spectral: spectral_ops,
+        op: op,
+        batchNormalization2d: batchNormalization2d,
+        batchNormalization3d: batchNormalization3d,
+        batchNormalization4d: batchNormalization4d,
+        batchNormalization: batchNormalization,
+        complex: complex,
+        real: real,
+        imag: imag,
+        concat: concat,
+        concat1d: concat1d,
+        concat2d: concat2d,
+        concat3d: concat3d,
+        concat4d: concat4d,
+        split: split$1,
+        conv1d: conv1d,
+        conv2d: conv2d,
+        conv3d: conv3d,
+        conv2dDerFilter: conv2dDerFilter,
+        depthwiseConv2d: depthwiseConv2d,
+        separableConv2d: separableConv2d,
+        conv2dTranspose: conv2dTranspose,
+        matMul: matMul,
+        dot: dot,
+        outerProduct: outerProduct,
+        reverse: reverse,
+        reverse1d: reverse1d,
+        reverse2d: reverse2d,
+        reverse3d: reverse3d,
+        reverse4d: reverse4d,
+        maxPool: maxPool,
+        avgPool: avgPool,
+        pool: pool,
+        slice: slice,
+        slice1d: slice1d,
+        slice2d: slice2d,
+        slice3d: slice3d,
+        slice4d: slice4d,
+        abs: abs,
+        acos: acos,
+        acosh: acosh,
+        asin: asin,
+        asinh: asinh,
+        atan: atan,
+        atanh: atanh,
+        ceil: ceil,
+        clipByValue: clipByValue,
+        cos: cos,
+        cosh: cosh,
+        erf: erf,
+        exp: exp,
+        expm1: expm1,
+        floor: floor,
+        log: log$1,
+        log1p: log1p,
+        logSigmoid: logSigmoid,
+        neg: neg,
+        reciprocal: reciprocal,
+        round: round,
+        rsqrt: rsqrt,
+        sigmoid: sigmoid,
+        sign: sign,
+        sin: sin,
+        sinh: sinh,
+        softplus: softplus,
+        sqrt: sqrt,
+        square: square,
+        step: step,
+        tan: tan,
+        tanh: tanh$1,
+        all: all,
+        any: any,
+        argMax: argMax,
+        argMin: argMin,
+        logSumExp: logSumExp,
+        max: max,
+        mean: mean,
+        min: min,
+        moments: moments,
+        sum: sum$1,
+        prod: prod,
+        equal: equal,
+        equalStrict: equalStrict,
+        greater: greater,
+        greaterEqual: greaterEqual,
+        greaterEqualStrict: greaterEqualStrict,
+        greaterStrict: greaterStrict,
+        less: less,
+        lessEqual: lessEqual,
+        lessEqualStrict: lessEqualStrict,
+        lessStrict: lessStrict,
+        notEqual: notEqual,
+        notEqualStrict: notEqualStrict,
+        add: add,
+        addN: addN,
+        addStrict: addStrict,
+        atan2: atan2,
+        div: div,
+        divStrict: divStrict,
+        floorDiv: floorDiv,
+        maximum: maximum,
+        maximumStrict: maximumStrict,
+        minimum: minimum,
+        minimumStrict: minimumStrict,
+        mod: mod,
+        modStrict: modStrict,
+        mul: mul,
+        mulStrict: mulStrict,
+        pow: pow,
+        powStrict: powStrict,
+        squaredDifference: squaredDifference,
+        squaredDifferenceStrict: squaredDifferenceStrict,
+        sub: sub,
+        subStrict: subStrict,
+        elu: elu,
+        leakyRelu: leakyRelu,
+        prelu: prelu,
+        relu: relu,
+        selu: selu,
+        logicalAnd: logicalAnd,
+        logicalNot: logicalNot,
+        logicalOr: logicalOr,
+        logicalXor: logicalXor,
+        where: where,
+        whereAsync: whereAsync,
+        buffer: buffer,
+        toPixels: toPixels,
+        print: print,
+        batchToSpaceND: batchToSpaceND,
+        cast: cast,
+        clone: clone,
+        cumsum: cumsum,
+        depthToSpace: depthToSpace,
+        expandDims: expandDims,
+        eye: eye,
+        fromPixels: fromPixels,
+        multinomial: multinomial,
+        oneHot: oneHot,
+        pad: pad,
+        pad1d: pad1d,
+        pad2d: pad2d,
+        pad3d: pad3d,
+        pad4d: pad4d,
+        rand: rand,
+        randomNormal: randomNormal,
+        randomUniform: randomUniform,
+        reshape: reshape,
+        spaceToBatchND: spaceToBatchND,
+        squeeze: squeeze,
+        stack: stack,
+        tile: tile,
+        truncatedNormal: truncatedNormal,
+        unstack: unstack,
+        setdiff1dAsync: setdiff1dAsync,
+        fill: fill,
+        linspace: linspace,
+        ones: ones$1,
+        range: range,
+        scalar: scalar,
+        tensor: tensor,
+        tensor1d: tensor1d,
+        tensor2d: tensor2d,
+        tensor3d: tensor3d,
+        tensor4d: tensor4d,
+        tensor5d: tensor5d,
+        tensor6d: tensor6d,
+        zeros: zeros,
+        onesLike: onesLike,
+        zerosLike: zerosLike,
+        transpose: transpose,
+        softmax: softmax,
+        logSoftmax: logSoftmax,
+        localResponseNormalization: localResponseNormalization,
+        norm: norm,
+        gather: gather,
+        unsortedSegmentSum: unsortedSegmentSum,
+        basicLSTMCell: basicLSTMCell,
+        multiRNNCell: multiRNNCell,
+        movingAverage: movingAverage,
+        stridedSlice: stridedSlice,
+        topk: topk,
+        scatterND: scatterND,
+        fft: fft,
+        ifft: ifft,
+        rfft: rfft,
+        irfft: irfft,
+        sparseToDense: sparseToDense,
+        gatherND: gatherND,
+        train: train,
+        tidy: tidy,
+        keep: keep,
+        dispose: dispose,
+        time: time,
+        profile: profile,
+        customGrad: customGrad,
+        grad: grad,
+        grads: grads,
+        valueAndGrad: valueAndGrad,
+        valueAndGrads: valueAndGrads,
+        variableGrads: variableGrads
+    });
+
+    /*! *****************************************************************************
+    Copyright (c) Microsoft Corporation. All rights reserved.
+    Licensed under the Apache License, Version 2.0 (the "License"); you may not use
+    this file except in compliance with the License. You may obtain a copy of the
+    License at http://www.apache.org/licenses/LICENSE-2.0
+
+    THIS CODE IS PROVIDED ON AN *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+    KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
+    WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
+    MERCHANTABLITY OR NON-INFRINGEMENT.
+
+    See the Apache Version 2.0 License for specific language governing permissions
+    and limitations under the License.
+    ***************************************************************************** */
+    /* global Reflect, Promise */
+
+    var extendStatics$1 = function(d, b) {
+        extendStatics$1 = Object.setPrototypeOf ||
+            ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
+            function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
+        return extendStatics$1(d, b);
+    };
+
+    function __extends$1(d, b) {
+        extendStatics$1(d, b);
+        function __() { this.constructor = d; }
+        d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
+    }
+
+    var __assign$1 = function() {
+        __assign$1 = Object.assign || function __assign(t) {
+            for (var s, i = 1, n = arguments.length; i < n; i++) {
+                s = arguments[i];
+                for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];
+            }
+            return t;
+        };
+        return __assign$1.apply(this, arguments);
+    };
+
+    function __awaiter$1(thisArg, _arguments, P, generator) {
+        return new (P || (P = Promise))(function (resolve, reject) {
+            function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+            function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+            function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+            step((generator = generator.apply(thisArg, _arguments || [])).next());
+        });
+    }
+
+    function __generator$1(thisArg, body) {
+        var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+        return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+        function verb(n) { return function (v) { return step([n, v]); }; }
+        function step(op) {
+            if (f) throw new TypeError("Generator is already executing.");
+            while (_) try {
+                if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+                if (y = 0, t) op = [op[0] & 2, t.value];
+                switch (op[0]) {
+                    case 0: case 1: t = op; break;
+                    case 4: _.label++; return { value: op[1], done: false };
+                    case 5: _.label++; y = op[1]; op = [0]; continue;
+                    case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                    default:
+                        if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                        if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                        if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                        if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                        if (t[2]) _.ops.pop();
+                        _.trys.pop(); continue;
+                }
+                op = body.call(thisArg, _);
+            } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+            if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+        }
+    }
+
+    var Dimensions = /** @class */ (function () {
+        function Dimensions(width, height) {
+            if (!isValidNumber(width) || !isValidNumber(height)) {
+                throw new Error("Dimensions.constructor - expected width and height to be valid numbers, instead have " + JSON.stringify({ width: width, height: height }));
+            }
+            this._width = width;
+            this._height = height;
+        }
+        Object.defineProperty(Dimensions.prototype, "width", {
+            get: function () { return this._width; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(Dimensions.prototype, "height", {
+            get: function () { return this._height; },
+            enumerable: true,
+            configurable: true
+        });
+        Dimensions.prototype.reverse = function () {
+            return new Dimensions(1 / this.width, 1 / this.height);
+        };
+        return Dimensions;
+    }());
+
+    var Point = /** @class */ (function () {
+        function Point(x, y) {
+            this._x = x;
+            this._y = y;
+        }
+        Object.defineProperty(Point.prototype, "x", {
+            get: function () { return this._x; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(Point.prototype, "y", {
+            get: function () { return this._y; },
+            enumerable: true,
+            configurable: true
+        });
+        Point.prototype.add = function (pt) {
+            return new Point(this.x + pt.x, this.y + pt.y);
+        };
+        Point.prototype.sub = function (pt) {
+            return new Point(this.x - pt.x, this.y - pt.y);
+        };
+        Point.prototype.mul = function (pt) {
+            return new Point(this.x * pt.x, this.y * pt.y);
+        };
+        Point.prototype.div = function (pt) {
+            return new Point(this.x / pt.x, this.y / pt.y);
+        };
+        Point.prototype.abs = function () {
+            return new Point(Math.abs(this.x), Math.abs(this.y));
+        };
+        Point.prototype.magnitude = function () {
+            return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2));
+        };
+        Point.prototype.floor = function () {
+            return new Point(Math.floor(this.x), Math.floor(this.y));
+        };
+        return Point;
+    }());
+
+    function isTensor(tensor$$1, dim) {
+        return tensor$$1 instanceof Tensor && tensor$$1.shape.length === dim;
+    }
+    function isTensor1D(tensor$$1) {
+        return isTensor(tensor$$1, 1);
+    }
+    function isTensor2D(tensor$$1) {
+        return isTensor(tensor$$1, 2);
+    }
+    function isTensor3D(tensor$$1) {
+        return isTensor(tensor$$1, 3);
+    }
+    function isTensor4D(tensor$$1) {
+        return isTensor(tensor$$1, 4);
+    }
+    function isFloat(num) {
+        return num % 1 !== 0;
+    }
+    function isEven$1(num) {
+        return num % 2 === 0;
+    }
+    function round$1(num, prec) {
+        if (prec === void 0) { prec = 2; }
+        var f = Math.pow(10, prec);
+        return Math.floor(num * f) / f;
+    }
+    function isDimensions(obj) {
+        return obj && obj.width && obj.height;
+    }
+    function computeReshapedDimensions(_a, inputSize) {
+        var width = _a.width, height = _a.height;
+        var scale = inputSize / Math.max(height, width);
+        return new Dimensions(Math.round(width * scale), Math.round(height * scale));
+    }
+    function getCenterPoint(pts) {
+        return pts.reduce(function (sum, pt) { return sum.add(pt); }, new Point(0, 0))
+            .div(new Point(pts.length, pts.length));
+    }
+    function range$1(num, start, step$$1) {
+        return Array(num).fill(0).map(function (_, i) { return start + (i * step$$1); });
+    }
+    function isValidNumber(num) {
+        return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0;
+    }
+    function isValidProbablitiy(num) {
+        return isValidNumber(num) && 0 <= num && num <= 1.0;
+    }
+
+    var Box = /** @class */ (function () {
+        // TODO: MTCNN boxes sometimes have negative widths or heights, figure out why and remove
+        // allowNegativeDimensions flag again
+        function Box(_box, allowNegativeDimensions) {
+            if (allowNegativeDimensions === void 0) { allowNegativeDimensions = false; }
+            var box = (_box || {});
+            var isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);
+            var isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);
+            if (!isRect && !isBbox) {
+                throw new Error("Box.constructor - expected box to be IBoundingBox | IRect, instead have " + JSON.stringify(box));
+            }
+            var _a = isRect
+                ? [box.x, box.y, box.width, box.height]
+                : [box.left, box.top, box.right - box.left, box.bottom - box.top], x = _a[0], y = _a[1], width = _a[2], height = _a[3];
+            Box.assertIsValidBox({ x: x, y: y, width: width, height: height }, 'Box.constructor', allowNegativeDimensions);
+            this._x = x;
+            this._y = y;
+            this._width = width;
+            this._height = height;
+        }
+        Box.isRect = function (rect) {
+            return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);
+        };
+        Box.assertIsValidBox = function (box, callee, allowNegativeDimensions) {
+            if (allowNegativeDimensions === void 0) { allowNegativeDimensions = false; }
+            if (!Box.isRect(box)) {
+                throw new Error(callee + " - invalid box: " + JSON.stringify(box) + ", expected object with properties x, y, width, height");
+            }
+            if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {
+                throw new Error(callee + " - width (" + box.width + ") and height (" + box.height + ") must be positive numbers");
+            }
+        };
+        Object.defineProperty(Box.prototype, "x", {
+            get: function () { return this._x; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(Box.prototype, "y", {
+            get: function () { return this._y; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(Box.prototype, "width", {
+            get: function () { return this._width; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(Box.prototype, "height", {
+            get: function () { return this._height; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(Box.prototype, "left", {
+            get: function () { return this.x; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(Box.prototype, "top", {
+            get: function () { return this.y; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(Box.prototype, "right", {
+            get: function () { return this.x + this.width; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(Box.prototype, "bottom", {
+            get: function () { return this.y + this.height; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(Box.prototype, "area", {
+            get: function () { return this.width * this.height; },
+            enumerable: true,
+            configurable: true
+        });
+        Box.prototype.round = function () {
+            var _a = [this.x, this.y, this.width, this.height]
+                .map(function (val) { return Math.round(val); }), x = _a[0], y = _a[1], width = _a[2], height = _a[3];
+            return new Box({ x: x, y: y, width: width, height: height });
+        };
+        Box.prototype.floor = function () {
+            var _a = [this.x, this.y, this.width, this.height]
+                .map(function (val) { return Math.floor(val); }), x = _a[0], y = _a[1], width = _a[2], height = _a[3];
+            return new Box({ x: x, y: y, width: width, height: height });
+        };
+        Box.prototype.toSquare = function () {
+            var _a = this, x = _a.x, y = _a.y, width = _a.width, height = _a.height;
+            var diff = Math.abs(width - height);
+            if (width < height) {
+                x -= (diff / 2);
+                width += diff;
+            }
+            if (height < width) {
+                y -= (diff / 2);
+                height += diff;
+            }
+            return new Box({ x: x, y: y, width: width, height: height });
+        };
+        Box.prototype.rescale = function (s) {
+            var scaleX = isDimensions(s) ? s.width : s;
+            var scaleY = isDimensions(s) ? s.height : s;
+            return new Box({
+                x: this.x * scaleX,
+                y: this.y * scaleY,
+                width: this.width * scaleX,
+                height: this.height * scaleY
+            });
+        };
+        Box.prototype.pad = function (padX, padY) {
+            var _a = [
+                this.x - (padX / 2),
+                this.y - (padY / 2),
+                this.width + padX,
+                this.height + padY
+            ], x = _a[0], y = _a[1], width = _a[2], height = _a[3];
+            return new Box({ x: x, y: y, width: width, height: height });
+        };
+        Box.prototype.clipAtImageBorders = function (imgWidth, imgHeight) {
+            var _a = this, x = _a.x, y = _a.y, right = _a.right, bottom = _a.bottom;
+            var clippedX = Math.max(x, 0);
+            var clippedY = Math.max(y, 0);
+            var newWidth = right - clippedX;
+            var newHeight = bottom - clippedY;
+            var clippedWidth = Math.min(newWidth, imgWidth - clippedX);
+            var clippedHeight = Math.min(newHeight, imgHeight - clippedY);
+            return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight })).floor();
+        };
+        Box.prototype.shift = function (sx, sy) {
+            var _a = this, width = _a.width, height = _a.height;
+            var x = this.x + sx;
+            var y = this.y + sy;
+            return new Box({ x: x, y: y, width: width, height: height });
+        };
+        Box.prototype.padAtBorders = function (imageHeight, imageWidth) {
+            var w = this.width + 1;
+            var h = this.height + 1;
+            var dx = 1;
+            var dy = 1;
+            var edx = w;
+            var edy = h;
+            var x = this.left;
+            var y = this.top;
+            var ex = this.right;
+            var ey = this.bottom;
+            if (ex > imageWidth) {
+                edx = -ex + imageWidth + w;
+                ex = imageWidth;
+            }
+            if (ey > imageHeight) {
+                edy = -ey + imageHeight + h;
+                ey = imageHeight;
+            }
+            if (x < 1) {
+                edy = 2 - x;
+                x = 1;
+            }
+            if (y < 1) {
+                edy = 2 - y;
+                y = 1;
+            }
+            return { dy: dy, edy: edy, dx: dx, edx: edx, y: y, ey: ey, x: x, ex: ex, w: w, h: h };
+        };
+        Box.prototype.calibrate = function (region) {
+            return new Box({
+                left: this.left + (region.left * this.width),
+                top: this.top + (region.top * this.height),
+                right: this.right + (region.right * this.width),
+                bottom: this.bottom + (region.bottom * this.height)
+            }).toSquare().round();
+        };
+        return Box;
+    }());
+
+    var BoundingBox = /** @class */ (function (_super) {
+        __extends$1(BoundingBox, _super);
+        function BoundingBox(left, top, right, bottom) {
+            return _super.call(this, { left: left, top: top, right: right, bottom: bottom }) || this;
+        }
+        return BoundingBox;
+    }(Box));
+
+    var BoxWithText = /** @class */ (function (_super) {
+        __extends$1(BoxWithText, _super);
+        function BoxWithText(box, text) {
+            var _this = _super.call(this, box) || this;
+            _this._text = text;
+            return _this;
+        }
+        Object.defineProperty(BoxWithText.prototype, "text", {
+            get: function () { return this._text; },
+            enumerable: true,
+            configurable: true
+        });
+        return BoxWithText;
+    }(Box));
+
+    var LabeledBox = /** @class */ (function (_super) {
+        __extends$1(LabeledBox, _super);
+        function LabeledBox(box, label) {
+            var _this = _super.call(this, box) || this;
+            _this._label = label;
+            return _this;
+        }
+        LabeledBox.assertIsValidLabeledBox = function (box, callee) {
+            Box.assertIsValidBox(box, callee);
+            if (!isValidNumber(box.label)) {
+                throw new Error(callee + " - expected property label (" + box.label + ") to be a number");
+            }
+        };
+        Object.defineProperty(LabeledBox.prototype, "label", {
+            get: function () { return this._label; },
+            enumerable: true,
+            configurable: true
+        });
+        return LabeledBox;
+    }(Box));
+
+    var ObjectDetection = /** @class */ (function () {
+        function ObjectDetection(score, classScore, className, relativeBox, imageDims) {
+            this._imageDims = new Dimensions(imageDims.width, imageDims.height);
+            this._score = score;
+            this._classScore = classScore;
+            this._className = className;
+            this._box = new Box(relativeBox).rescale(this._imageDims);
+        }
+        Object.defineProperty(ObjectDetection.prototype, "score", {
+            get: function () { return this._score; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(ObjectDetection.prototype, "classScore", {
+            get: function () { return this._classScore; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(ObjectDetection.prototype, "className", {
+            get: function () { return this._className; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(ObjectDetection.prototype, "box", {
+            get: function () { return this._box; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(ObjectDetection.prototype, "imageDims", {
+            get: function () { return this._imageDims; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(ObjectDetection.prototype, "imageWidth", {
+            get: function () { return this.imageDims.width; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(ObjectDetection.prototype, "imageHeight", {
+            get: function () { return this.imageDims.height; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(ObjectDetection.prototype, "relativeBox", {
+            get: function () { return new Box(this._box).rescale(this.imageDims.reverse()); },
+            enumerable: true,
+            configurable: true
+        });
+        ObjectDetection.prototype.forSize = function (width, height) {
+            return new ObjectDetection(this.score, this.classScore, this.className, this.relativeBox, { width: width, height: height });
+        };
+        return ObjectDetection;
+    }());
+
+    var PredictedBox = /** @class */ (function (_super) {
+        __extends$1(PredictedBox, _super);
+        function PredictedBox(box, label, score, classScore) {
+            var _this = _super.call(this, box, label) || this;
+            _this._score = score;
+            _this._classScore = classScore;
+            return _this;
+        }
+        PredictedBox.assertIsValidPredictedBox = function (box, callee) {
+            LabeledBox.assertIsValidLabeledBox(box, callee);
+            if (!isValidProbablitiy(box.score)
+                || !isValidProbablitiy(box.classScore)) {
+                throw new Error(callee + " - expected properties score (" + box.score + ") and (" + box.classScore + ") to be a number between [0, 1]");
+            }
+        };
+        Object.defineProperty(PredictedBox.prototype, "score", {
+            get: function () { return this._score; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(PredictedBox.prototype, "classScore", {
+            get: function () { return this._classScore; },
+            enumerable: true,
+            configurable: true
+        });
+        return PredictedBox;
+    }(LabeledBox));
+
+    var Rect = /** @class */ (function (_super) {
+        __extends$1(Rect, _super);
+        function Rect(x, y, width, height) {
+            return _super.call(this, { x: x, y: y, width: width, height: height }) || this;
+        }
+        return Rect;
+    }(Box));
+
+    function disposeUnusedWeightTensors(weightMap, paramMappings) {
+        Object.keys(weightMap).forEach(function (path) {
+            if (!paramMappings.some(function (pm) { return pm.originalPath === path; })) {
+                weightMap[path].dispose();
+            }
+        });
+    }
+
+    function extractWeightEntryFactory(weightMap, paramMappings) {
+        return function (originalPath, paramRank, mappedPath) {
+            var tensor = weightMap[originalPath];
+            if (!isTensor(tensor, paramRank)) {
+                throw new Error("expected weightMap[" + originalPath + "] to be a Tensor" + paramRank + "D, instead have " + tensor);
+            }
+            paramMappings.push({ originalPath: originalPath, paramPath: mappedPath || originalPath });
+            return tensor;
+        };
+    }
+
+    function extractWeightsFactory(weights) {
+        var remainingWeights = weights;
+        function extractWeights(numWeights) {
+            var ret = remainingWeights.slice(0, numWeights);
+            remainingWeights = remainingWeights.slice(numWeights);
+            return ret;
+        }
+        function getRemainingWeights() {
+            return remainingWeights;
+        }
+        return {
+            extractWeights: extractWeights,
+            getRemainingWeights: getRemainingWeights
+        };
+    }
+
+    function getModelUris(uri, defaultModelName) {
+        var defaultManifestFilename = defaultModelName + "-weights_manifest.json";
+        if (!uri) {
+            return {
+                modelBaseUri: '',
+                manifestUri: defaultManifestFilename
+            };
+        }
+        if (uri === '/') {
+            return {
+                modelBaseUri: '/',
+                manifestUri: "/" + defaultManifestFilename
+            };
+        }
+        var protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';
+        uri = uri.replace(protocol, '');
+        var parts = uri.split('/').filter(function (s) { return s; });
+        var manifestFile = uri.endsWith('.json')
+            ? parts[parts.length - 1]
+            : defaultManifestFilename;
+        var modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');
+        modelBaseUri = uri.startsWith('/') ? "/" + modelBaseUri : modelBaseUri;
+        return {
+            modelBaseUri: modelBaseUri,
+            manifestUri: modelBaseUri === '/' ? "/" + manifestFile : modelBaseUri + "/" + manifestFile
+        };
+    }
+
+    function isNodejs() {
+        return typeof global === 'object'
+            && typeof require === 'function'
+            && typeof module !== 'undefined'
+            // issues with gatsby.js: module.exports is undefined
+            // && !!module.exports
+            && typeof process !== 'undefined' && !!process.version;
+    }
+
+    function isBrowser() {
+        return typeof window === 'object'
+            && typeof document !== 'undefined'
+            && typeof HTMLImageElement !== 'undefined'
+            && typeof HTMLCanvasElement !== 'undefined'
+            && typeof HTMLVideoElement !== 'undefined'
+            && typeof ImageData !== 'undefined';
+    }
+
+    function initializeEnvironment() {
+        // check for isBrowser() first to prevent electron renderer process
+        // to be initialized with wrong environment due to isNodejs() returning true
+        if (isBrowser()) {
+            return initializeBrowserEnv();
+        }
+        if (isNodejs()) {
+            return initializeNodejsEnv();
+        }
+        return null;
+    }
+    function initializeNodejsEnv() {
+        var Canvas = global['Canvas'] || global['HTMLCanvasElement'];
+        var Image = global['Image'] || global['HTMLImageElement'];
+        var createCanvasElement = function () {
+            if (Canvas) {
+                return new Canvas();
+            }
+            throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');
+        };
+        var createImageElement = function () {
+            if (Image) {
+                return new Image();
+            }
+            throw new Error('createImageElement - missing Image implementation for nodejs environment');
+        };
+        var fetch = global['fetch'] || function () {
+            throw new Error('fetch - missing fetch implementation for nodejs environment');
+        };
+        var fs = null, requireFsError = '';
+        try {
+            fs = require('fs');
+        }
+        catch (err) {
+            requireFsError = err.toString();
+        }
+        var readFile = fs
+            ? function (filePath) {
+                return new Promise(function (res, rej) {
+                    fs.readFile(filePath, function (err, buffer) {
+                        return err ? rej(err) : res(buffer);
+                    });
+                });
+            }
+            : function () {
+                throw new Error("readFile - failed to require fs in nodejs environment with error: " + requireFsError);
+            };
+        return {
+            Canvas: Canvas || /** @class */ (function () {
+                function class_1() {
+                }
+                return class_1;
+            }()),
+            Image: Image || /** @class */ (function () {
+                function class_2() {
+                }
+                return class_2;
+            }()),
+            ImageData: global['ImageData'] || /** @class */ (function () {
+                function class_3() {
+                }
+                return class_3;
+            }()),
+            Video: global['HTMLVideoElement'] || /** @class */ (function () {
+                function class_4() {
+                }
+                return class_4;
+            }()),
+            createCanvasElement: createCanvasElement,
+            createImageElement: createImageElement,
+            fetch: fetch,
+            readFile: readFile
+        };
+    }
+    function initializeBrowserEnv() {
+        var fetch = window['fetch'] || function () {
+            throw new Error('fetch - missing fetch implementation for browser environment');
+        };
+        var readFile = function () {
+            throw new Error('readFile - filesystem not available for browser environment');
+        };
+        return {
+            Canvas: HTMLCanvasElement,
+            Image: HTMLImageElement,
+            ImageData: ImageData,
+            Video: HTMLVideoElement,
+            createCanvasElement: function () { return document.createElement('canvas'); },
+            createImageElement: function () { return document.createElement('img'); },
+            fetch: fetch,
+            readFile: readFile
+        };
+    }
+
+    var environment$1;
+    function initialize() {
+        environment$1 = initializeEnvironment();
+    }
+    function getEnv() {
+        if (!environment$1) {
+            throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');
+        }
+        return environment$1;
+    }
+    function monkeyPatch(env) {
+        environment$1 = environment$1 || initializeEnvironment();
+        if (!environment$1) {
+            throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');
+        }
+        var _a = env.Canvas, Canvas = _a === void 0 ? environment$1.Canvas : _a, _b = env.Image, Image = _b === void 0 ? environment$1.Image : _b;
+        environment$1.Canvas = Canvas;
+        environment$1.Image = Image;
+        environment$1.createCanvasElement = env.createCanvasElement || (function () { return new Canvas(); });
+        environment$1.createImageElement = env.createImageElement || (function () { return new Image(); });
+        environment$1.ImageData = env.ImageData || environment$1.ImageData;
+        environment$1.Video = env.Video || environment$1.Video;
+        environment$1.fetch = env.fetch || environment$1.fetch;
+        environment$1.readFile = env.readFile || environment$1.readFile;
+    }
+    var env = {
+        getEnv: getEnv,
+        initialize: initialize,
+        initializeBrowserEnv: initializeBrowserEnv,
+        initializeNodejsEnv: initializeNodejsEnv,
+        monkeyPatch: monkeyPatch,
+        isBrowser: isBrowser,
+        isNodejs: isNodejs
+    };
+    initialize();
+
+    function isMediaLoaded(media) {
+        var _a = env.getEnv(), Image = _a.Image, Video = _a.Video;
+        return (media instanceof Image && media.complete)
+            || (media instanceof Video && media.readyState >= 3);
+    }
+
+    function awaitMediaLoaded(media) {
+        return new Promise(function (resolve, reject) {
+            if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {
+                return resolve();
+            }
+            function onLoad(e) {
+                if (!e.currentTarget)
+                    return;
+                e.currentTarget.removeEventListener('load', onLoad);
+                e.currentTarget.removeEventListener('error', onError);
+                resolve(e);
+            }
+            function onError(e) {
+                if (!e.currentTarget)
+                    return;
+                e.currentTarget.removeEventListener('load', onLoad);
+                e.currentTarget.removeEventListener('error', onError);
+                reject(e);
+            }
+            media.addEventListener('load', onLoad);
+            media.addEventListener('error', onError);
+        });
+    }
+
+    function bufferToImage(buf) {
+        return new Promise(function (resolve, reject) {
+            if (!(buf instanceof Blob)) {
+                return reject('bufferToImage - expected buf to be of type: Blob');
+            }
+            var reader = new FileReader();
+            reader.onload = function () {
+                if (typeof reader.result !== 'string') {
+                    return reject('bufferToImage - expected reader.result to be a string, in onload');
+                }
+                var img = env.getEnv().createImageElement();
+                img.onload = function () { return resolve(img); };
+                img.onerror = reject;
+                img.src = reader.result;
+            };
+            reader.onerror = reject;
+            reader.readAsDataURL(buf);
+        });
+    }
+
+    function getContext2dOrThrow(canvas) {
+        var ctx = canvas.getContext('2d');
+        if (!ctx) {
+            throw new Error('canvas 2d context is null');
+        }
+        return ctx;
+    }
+
+    function getMediaDimensions(input) {
+        var _a = env.getEnv(), Image = _a.Image, Video = _a.Video;
+        if (input instanceof Image) {
+            return new Dimensions(input.naturalWidth, input.naturalHeight);
+        }
+        if (input instanceof Video) {
+            return new Dimensions(input.videoWidth, input.videoHeight);
+        }
+        return new Dimensions(input.width, input.height);
+    }
+
+    function createCanvas(_a) {
+        var width = _a.width, height = _a.height;
+        var createCanvasElement = env.getEnv().createCanvasElement;
+        var canvas = createCanvasElement();
+        canvas.width = width;
+        canvas.height = height;
+        return canvas;
+    }
+    function createCanvasFromMedia(media, dims) {
+        var ImageData = env.getEnv().ImageData;
+        if (!(media instanceof ImageData) && !isMediaLoaded(media)) {
+            throw new Error('createCanvasFromMedia - media has not finished loading yet');
+        }
+        var _a = dims || getMediaDimensions(media), width = _a.width, height = _a.height;
+        var canvas = createCanvas({ width: width, height: height });
+        if (media instanceof ImageData) {
+            getContext2dOrThrow(canvas).putImageData(media, 0, 0);
+        }
+        else {
+            getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);
+        }
+        return canvas;
+    }
+
+    function getDefaultDrawOptions(options) {
+        if (options === void 0) { options = {}; }
+        return Object.assign({}, {
+            boxColor: 'blue',
+            textColor: 'red',
+            lineWidth: 2,
+            fontSize: 20,
+            fontStyle: 'Georgia',
+            withScore: true,
+            withClassName: true
+        }, options);
+    }
+
+    function drawBox(ctx, x, y, w, h, options) {
+        var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));
+        ctx.strokeStyle = drawOptions.boxColor;
+        ctx.lineWidth = drawOptions.lineWidth;
+        ctx.strokeRect(x, y, w, h);
+    }
+
+    function drawText(ctx, x, y, text, options) {
+        if (options === void 0) { options = {}; }
+        var drawOptions = Object.assign(getDefaultDrawOptions(), options);
+        var padText = 2 + drawOptions.lineWidth;
+        ctx.fillStyle = drawOptions.textColor;
+        ctx.font = drawOptions.fontSize + "px " + drawOptions.fontStyle;
+        ctx.fillText(text, x + padText, y + padText + (drawOptions.fontSize * 0.6));
+    }
+
+    function resolveInput(arg) {
+        if (!env.isNodejs() && typeof arg === 'string') {
+            return document.getElementById(arg);
+        }
+        return arg;
+    }
+
+    function drawDetection(canvasArg, detection, options) {
+        var Canvas = env.getEnv().Canvas;
+        var canvas = resolveInput(canvasArg);
+        if (!(canvas instanceof Canvas)) {
+            throw new Error('drawDetection - expected canvas to be of type: HTMLCanvasElement');
+        }
+        var detectionArray = Array.isArray(detection)
+            ? detection
+            : [detection];
+        detectionArray.forEach(function (det) {
+            var _a = det instanceof ObjectDetection ? det.box : det, x = _a.x, y = _a.y, width = _a.width, height = _a.height;
+            var drawOptions = getDefaultDrawOptions(options);
+            var ctx = getContext2dOrThrow(canvas);
+            drawBox(ctx, x, y, width, height, drawOptions);
+            var withScore = drawOptions.withScore;
+            var text = det instanceof BoxWithText
+                ? det.text
+                : ((withScore && det instanceof PredictedBox)
+                    ? "" + round$1(det.score)
+                    : (det instanceof ObjectDetection
+                        ? "" + det.className + (withScore ? " (" + round$1(det.score) + ")" : '')
+                        : ''));
+            if (text) {
+                drawText(ctx, x, y + height, text, drawOptions);
+            }
+        });
+    }
+
+    function fetchOrThrow(url, init) {
+        return __awaiter$1(this, void 0, void 0, function () {
+            var fetch, res;
+            return __generator$1(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        fetch = env.getEnv().fetch;
+                        return [4 /*yield*/, fetch(url, init)];
+                    case 1:
+                        res = _a.sent();
+                        if (!(res.status < 400)) {
+                            throw new Error("failed to fetch: (" + res.status + ") " + res.statusText + ", from url: " + res.url);
+                        }
+                        return [2 /*return*/, res];
+                }
+            });
+        });
+    }
+
+    function fetchImage(uri) {
+        return __awaiter$1(this, void 0, void 0, function () {
+            var res, blob;
+            return __generator$1(this, function (_a) {
+                switch (_a.label) {
+                    case 0: return [4 /*yield*/, fetchOrThrow(uri)];
+                    case 1:
+                        res = _a.sent();
+                        return [4 /*yield*/, (res).blob()];
+                    case 2:
+                        blob = _a.sent();
+                        if (!blob.type.startsWith('image/')) {
+                            throw new Error("fetchImage - expected blob type to be of type image/*, instead have: " + blob.type + ", for url: " + res.url);
+                        }
+                        return [2 /*return*/, bufferToImage(blob)];
+                }
+            });
+        });
+    }
+
+    function fetchJson(uri) {
+        return __awaiter$1(this, void 0, void 0, function () {
+            return __generator$1(this, function (_a) {
+                switch (_a.label) {
+                    case 0: return [4 /*yield*/, fetchOrThrow(uri)];
+                    case 1: return [2 /*return*/, (_a.sent()).json()];
+                }
+            });
+        });
+    }
+
+    function fetchNetWeights(uri) {
+        return __awaiter$1(this, void 0, void 0, function () {
+            var _a;
+            return __generator$1(this, function (_b) {
+                switch (_b.label) {
+                    case 0:
+                        _a = Float32Array.bind;
+                        return [4 /*yield*/, fetchOrThrow(uri)];
+                    case 1: return [4 /*yield*/, (_b.sent()).arrayBuffer()];
+                    case 2: return [2 /*return*/, new (_a.apply(Float32Array, [void 0, _b.sent()]))()];
+                }
+            });
+        });
+    }
+
+    function imageTensorToCanvas(imgTensor, canvas) {
+        return __awaiter$1(this, void 0, void 0, function () {
+            var targetCanvas, _a, height, width, numChannels, imgTensor3D;
+            return __generator$1(this, function (_b) {
+                switch (_b.label) {
+                    case 0:
+                        targetCanvas = canvas || env.getEnv().createCanvasElement();
+                        _a = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0), height = _a[0], width = _a[1], numChannels = _a[2];
+                        imgTensor3D = tidy(function () { return imgTensor.as3D(height, width, numChannels).toInt(); });
+                        return [4 /*yield*/, toPixels(imgTensor3D, targetCanvas)];
+                    case 1:
+                        _b.sent();
+                        imgTensor3D.dispose();
+                        return [2 /*return*/, targetCanvas];
+                }
+            });
+        });
+    }
+
+    function imageToSquare(input, inputSize, centerImage) {
+        if (centerImage === void 0) { centerImage = false; }
+        var _a = env.getEnv(), Image = _a.Image, Canvas = _a.Canvas;
+        if (!(input instanceof Image || input instanceof Canvas)) {
+            throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');
+        }
+        var dims = getMediaDimensions(input);
+        var scale = inputSize / Math.max(dims.height, dims.width);
+        var width = scale * dims.width;
+        var height = scale * dims.height;
+        var targetCanvas = createCanvas({ width: inputSize, height: inputSize });
+        var inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);
+        var offset = Math.abs(width - height) / 2;
+        var dx = centerImage && width < height ? offset : 0;
+        var dy = centerImage && height < width ? offset : 0;
+        getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);
+        return targetCanvas;
+    }
+
+    function isMediaElement(input) {
+        var _a = env.getEnv(), Image = _a.Image, Canvas = _a.Canvas, Video = _a.Video;
+        return input instanceof Image
+            || input instanceof Canvas
+            || input instanceof Video;
+    }
+
+    function loadWeightMap(uri, defaultModelName) {
+        return __awaiter$1(this, void 0, void 0, function () {
+            var _a, manifestUri, modelBaseUri, manifest;
+            return __generator$1(this, function (_b) {
+                switch (_b.label) {
+                    case 0:
+                        _a = getModelUris(uri, defaultModelName), manifestUri = _a.manifestUri, modelBaseUri = _a.modelBaseUri;
+                        return [4 /*yield*/, fetchJson(manifestUri)];
+                    case 1:
+                        manifest = _b.sent();
+                        return [2 /*return*/, io.loadWeights(manifest, modelBaseUri)];
+                }
+            });
+        });
+    }
+
+    /**
+     * Pads the smaller dimension of an image tensor with zeros, such that width === height.
+     *
+     * @param imgTensor The image tensor.
+     * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on
+     * both sides of the minor dimension oof the image.
+     * @returns The padded tensor with width === height.
+     */
+    function padToSquare(imgTensor, isCenterImage) {
+        if (isCenterImage === void 0) { isCenterImage = false; }
+        return tidy(function () {
+            var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];
+            if (height === width) {
+                return imgTensor;
+            }
+            var dimDiff = Math.abs(height - width);
+            var paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));
+            var paddingAxis = height > width ? 2 : 1;
+            var createPaddingTensor = function (paddingAmount) {
+                var paddingTensorShape = imgTensor.shape.slice();
+                paddingTensorShape[paddingAxis] = paddingAmount;
+                return fill(paddingTensorShape, 0);
+            };
+            var paddingTensorAppend = createPaddingTensor(paddingAmount);
+            var remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];
+            var paddingTensorPrepend = isCenterImage && remainingPaddingAmount
+                ? createPaddingTensor(remainingPaddingAmount)
+                : null;
+            var tensorsToStack = [
+                paddingTensorPrepend,
+                imgTensor,
+                paddingTensorAppend
+            ]
+                .filter(function (t) { return !!t; })
+                .map(function (t) { return t.toFloat(); });
+            return concat(tensorsToStack, paddingAxis);
+        });
+    }
+
+    var NetInput = /** @class */ (function () {
+        function NetInput(inputs, treatAsBatchInput) {
+            if (treatAsBatchInput === void 0) { treatAsBatchInput = false; }
+            var _this = this;
+            this._imageTensors = [];
+            this._canvases = [];
+            this._treatAsBatchInput = false;
+            this._inputDimensions = [];
+            if (!Array.isArray(inputs)) {
+                throw new Error("NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have " + inputs);
+            }
+            this._treatAsBatchInput = treatAsBatchInput;
+            this._batchSize = inputs.length;
+            inputs.forEach(function (input, idx) {
+                if (isTensor3D(input)) {
+                    _this._imageTensors[idx] = input;
+                    _this._inputDimensions[idx] = input.shape;
+                    return;
+                }
+                if (isTensor4D(input)) {
+                    var batchSize = input.shape[0];
+                    if (batchSize !== 1) {
+                        throw new Error("NetInput - tf.Tensor4D with batchSize " + batchSize + " passed, but not supported in input array");
+                    }
+                    _this._imageTensors[idx] = input;
+                    _this._inputDimensions[idx] = input.shape.slice(1);
+                    return;
+                }
+                var canvas = input instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);
+                _this._canvases[idx] = canvas;
+                _this._inputDimensions[idx] = [canvas.height, canvas.width, 3];
+            });
+        }
+        Object.defineProperty(NetInput.prototype, "imageTensors", {
+            get: function () {
+                return this._imageTensors;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(NetInput.prototype, "canvases", {
+            get: function () {
+                return this._canvases;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(NetInput.prototype, "isBatchInput", {
+            get: function () {
+                return this.batchSize > 1 || this._treatAsBatchInput;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(NetInput.prototype, "batchSize", {
+            get: function () {
+                return this._batchSize;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(NetInput.prototype, "inputDimensions", {
+            get: function () {
+                return this._inputDimensions;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(NetInput.prototype, "inputSize", {
+            get: function () {
+                return this._inputSize;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(NetInput.prototype, "reshapedInputDimensions", {
+            get: function () {
+                var _this = this;
+                return range$1(this.batchSize, 0, 1).map(function (_, batchIdx) { return _this.getReshapedInputDimensions(batchIdx); });
+            },
+            enumerable: true,
+            configurable: true
+        });
+        NetInput.prototype.getInput = function (batchIdx) {
+            return this.canvases[batchIdx] || this.imageTensors[batchIdx];
+        };
+        NetInput.prototype.getInputDimensions = function (batchIdx) {
+            return this._inputDimensions[batchIdx];
+        };
+        NetInput.prototype.getInputHeight = function (batchIdx) {
+            return this._inputDimensions[batchIdx][0];
+        };
+        NetInput.prototype.getInputWidth = function (batchIdx) {
+            return this._inputDimensions[batchIdx][1];
+        };
+        NetInput.prototype.getReshapedInputDimensions = function (batchIdx) {
+            if (typeof this.inputSize !== 'number') {
+                throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');
+            }
+            var width = this.getInputWidth(batchIdx);
+            var height = this.getInputHeight(batchIdx);
+            return computeReshapedDimensions({ width: width, height: height }, this.inputSize);
+        };
+        /**
+         * Create a batch tensor from all input canvases and tensors
+         * with size [batchSize, inputSize, inputSize, 3].
+         *
+         * @param inputSize Height and width of the tensor.
+         * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on
+         * both sides of the minor dimension oof the image.
+         * @returns The batch tensor.
+         */
+        NetInput.prototype.toBatchTensor = function (inputSize, isCenterInputs) {
+            var _this = this;
+            if (isCenterInputs === void 0) { isCenterInputs = true; }
+            this._inputSize = inputSize;
+            return tidy(function () {
+                var inputTensors = range$1(_this.batchSize, 0, 1).map(function (batchIdx) {
+                    var input = _this.getInput(batchIdx);
+                    if (input instanceof Tensor) {
+                        var imgTensor = isTensor4D(input) ? input : input.expandDims();
+                        imgTensor = padToSquare(imgTensor, isCenterInputs);
+                        if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {
+                            imgTensor = image_ops.resizeBilinear(imgTensor, [inputSize, inputSize]);
+                        }
+                        return imgTensor.as3D(inputSize, inputSize, 3);
+                    }
+                    if (input instanceof env.getEnv().Canvas) {
+                        return fromPixels(imageToSquare(input, inputSize, isCenterInputs));
+                    }
+                    throw new Error("toBatchTensor - at batchIdx " + batchIdx + ", expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have " + input);
+                });
+                var batchTensor = stack(inputTensors.map(function (t) { return t.toFloat(); })).as4D(_this.batchSize, inputSize, inputSize, 3);
+                return batchTensor;
+            });
+        };
+        return NetInput;
+    }());
+
+    /**
+     * Validates the input to make sure, they are valid net inputs and awaits all media elements
+     * to be finished loading.
+     *
+     * @param input The input, which can be a media element or an array of different media elements.
+     * @returns A NetInput instance, which can be passed into one of the neural networks.
+     */
+    function toNetInput(inputs) {
+        return __awaiter$1(this, void 0, void 0, function () {
+            var inputArgArray, getIdxHint, inputArray;
+            return __generator$1(this, function (_a) {
+                switch (_a.label) {
+                    case 0:
+                        if (inputs instanceof NetInput) {
+                            return [2 /*return*/, inputs];
+                        }
+                        inputArgArray = Array.isArray(inputs)
+                            ? inputs
+                            : [inputs];
+                        if (!inputArgArray.length) {
+                            throw new Error('toNetInput - empty array passed as input');
+                        }
+                        getIdxHint = function (idx) { return Array.isArray(inputs) ? " at input index " + idx + ":" : ''; };
+                        inputArray = inputArgArray.map(resolveInput);
+                        inputArray.forEach(function (input, i) {
+                            if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {
+                                if (typeof inputArgArray[i] === 'string') {
+                                    throw new Error("toNetInput -" + getIdxHint(i) + " string passed, but could not resolve HTMLElement for element id " + inputArgArray[i]);
+                                }
+                                throw new Error("toNetInput -" + getIdxHint(i) + " expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id");
+                            }
+                            if (isTensor4D(input)) {
+                                // if tf.Tensor4D is passed in the input array, the batch size has to be 1
+                                var batchSize = input.shape[0];
+                                if (batchSize !== 1) {
+                                    throw new Error("toNetInput -" + getIdxHint(i) + " tf.Tensor4D with batchSize " + batchSize + " passed, but not supported in input array");
+                                }
+                            }
+                        });
+                        // wait for all media elements being loaded
+                        return [4 /*yield*/, Promise.all(inputArray.map(function (input) { return isMediaElement(input) && awaitMediaLoaded(input); }))];
+                    case 1:
+                        // wait for all media elements being loaded
+                        _a.sent();
+                        return [2 /*return*/, new NetInput(inputArray, Array.isArray(inputs))];
+                }
+            });
+        });
+    }
+
+    function iou(box1, box2, isIOU) {
+        if (isIOU === void 0) { isIOU = true; }
+        var width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));
+        var height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));
+        var interSection = width * height;
+        return isIOU
+            ? interSection / (box1.area + box2.area - interSection)
+            : interSection / Math.min(box1.area, box2.area);
+    }
+
+    function nonMaxSuppression$1(boxes, scores, iouThreshold, isIOU) {
+        if (isIOU === void 0) { isIOU = true; }
+        var indicesSortedByScore = scores
+            .map(function (score, boxIndex) { return ({ score: score, boxIndex: boxIndex }); })
+            .sort(function (c1, c2) { return c1.score - c2.score; })
+            .map(function (c) { return c.boxIndex; });
+        var pick = [];
+        var _loop_1 = function () {
+            var curr = indicesSortedByScore.pop();
+            pick.push(curr);
+            var indices = indicesSortedByScore;
+            var outputs = [];
+            for (var i = 0; i < indices.length; i++) {
+                var idx = indices[i];
+                var currBox = boxes[curr];
+                var idxBox = boxes[idx];
+                outputs.push(iou(currBox, idxBox, isIOU));
+            }
+            indicesSortedByScore = indicesSortedByScore.filter(function (_, j) { return outputs[j] <= iouThreshold; });
+        };
+        while (indicesSortedByScore.length > 0) {
+            _loop_1();
+        }
+        return pick;
+    }
+
+    function normalize(x, meanRgb) {
+        return tidy(function () {
+            var r = meanRgb[0], g = meanRgb[1], b = meanRgb[2];
+            var avg_r = fill(x.shape.slice(0, 3).concat([1]), r);
+            var avg_g = fill(x.shape.slice(0, 3).concat([1]), g);
+            var avg_b = fill(x.shape.slice(0, 3).concat([1]), b);
+            var avg_rgb = concat([avg_r, avg_g, avg_b], 3);
+            return sub(x, avg_rgb);
+        });
+    }
+
+    function shuffleArray(inputArray) {
+        var array = inputArray.slice();
+        for (var i = array.length - 1; i > 0; i--) {
+            var j = Math.floor(Math.random() * (i + 1));
+            var x = array[i];
+            array[i] = array[j];
+            array[j] = x;
+        }
+        return array;
+    }
+
+    function sigmoid$1(x) {
+        return 1 / (1 + Math.exp(-x));
+    }
+    function inverseSigmoid(x) {
+        return Math.log(x / (1 - x));
+    }
+
+    var NeuralNetwork = /** @class */ (function () {
+        function NeuralNetwork(_name) {
+            this._name = _name;
+            this._params = undefined;
+            this._paramMappings = [];
+        }
+        Object.defineProperty(NeuralNetwork.prototype, "params", {
+            get: function () { return this._params; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(NeuralNetwork.prototype, "paramMappings", {
+            get: function () { return this._paramMappings; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(NeuralNetwork.prototype, "isLoaded", {
+            get: function () { return !!this.params; },
+            enumerable: true,
+            configurable: true
+        });
+        NeuralNetwork.prototype.getParamFromPath = function (paramPath) {
+            var _a = this.traversePropertyPath(paramPath), obj = _a.obj, objProp = _a.objProp;
+            return obj[objProp];
+        };
+        NeuralNetwork.prototype.reassignParamFromPath = function (paramPath, tensor$$1) {
+            var _a = this.traversePropertyPath(paramPath), obj = _a.obj, objProp = _a.objProp;
+            obj[objProp].dispose();
+            obj[objProp] = tensor$$1;
+        };
+        NeuralNetwork.prototype.getParamList = function () {
+            var _this = this;
+            return this._paramMappings.map(function (_a) {
+                var paramPath = _a.paramPath;
+                return ({
+                    path: paramPath,
+                    tensor: _this.getParamFromPath(paramPath)
+                });
+            });
+        };
+        NeuralNetwork.prototype.getTrainableParams = function () {
+            return this.getParamList().filter(function (param) { return param.tensor instanceof Variable; });
+        };
+        NeuralNetwork.prototype.getFrozenParams = function () {
+            return this.getParamList().filter(function (param) { return !(param.tensor instanceof Variable); });
+        };
+        NeuralNetwork.prototype.variable = function () {
+            var _this = this;
+            this.getFrozenParams().forEach(function (_a) {
+                var path = _a.path, tensor$$1 = _a.tensor;
+                _this.reassignParamFromPath(path, tensor$$1.variable());
+            });
+        };
+        NeuralNetwork.prototype.freeze = function () {
+            var _this = this;
+            this.getTrainableParams().forEach(function (_a) {
+                var path = _a.path, variable$$1 = _a.tensor;
+                var tensor$$1 = tensor(variable$$1.dataSync());
+                variable$$1.dispose();
+                _this.reassignParamFromPath(path, tensor$$1);
+            });
+        };
+        NeuralNetwork.prototype.dispose = function (throwOnRedispose) {
+            if (throwOnRedispose === void 0) { throwOnRedispose = true; }
+            this.getParamList().forEach(function (param) {
+                if (throwOnRedispose && param.tensor.isDisposed) {
+                    throw new Error("param tensor has already been disposed for path " + param.path);
+                }
+                param.tensor.dispose();
+            });
+            this._params = undefined;
+        };
+        NeuralNetwork.prototype.serializeParams = function () {
+            return new Float32Array(this.getParamList()
+                .map(function (_a) {
+                var tensor$$1 = _a.tensor;
+                return Array.from(tensor$$1.dataSync());
+            })
+                .reduce(function (flat, arr) { return flat.concat(arr); }));
+        };
+        NeuralNetwork.prototype.load = function (weightsOrUrl) {
+            return __awaiter$1(this, void 0, void 0, function () {
+                return __generator$1(this, function (_a) {
+                    switch (_a.label) {
+                        case 0:
+                            if (weightsOrUrl instanceof Float32Array) {
+                                this.extractWeights(weightsOrUrl);
+                                return [2 /*return*/];
+                            }
+                            return [4 /*yield*/, this.loadFromUri(weightsOrUrl)];
+                        case 1:
+                            _a.sent();
+                            return [2 /*return*/];
+                    }
+                });
+            });
+        };
+        NeuralNetwork.prototype.loadFromUri = function (uri) {
+            return __awaiter$1(this, void 0, void 0, function () {
+                var weightMap;
+                return __generator$1(this, function (_a) {
+                    switch (_a.label) {
+                        case 0:
+                            if (uri && typeof uri !== 'string') {
+                                throw new Error(this._name + ".loadFromUri - expected model uri");
+                            }
+                            return [4 /*yield*/, loadWeightMap(uri, this.getDefaultModelName())];
+                        case 1:
+                            weightMap = _a.sent();
+                            this.loadFromWeightMap(weightMap);
+                            return [2 /*return*/];
+                    }
+                });
+            });
+        };
+        NeuralNetwork.prototype.loadFromDisk = function (filePath) {
+            return __awaiter$1(this, void 0, void 0, function () {
+                var readFile, _a, manifestUri, modelBaseUri, fetchWeightsFromDisk, loadWeights, manifest, _b, _c, weightMap;
+                return __generator$1(this, function (_d) {
+                    switch (_d.label) {
+                        case 0:
+                            if (filePath && typeof filePath !== 'string') {
+                                throw new Error(this._name + ".loadFromDisk - expected model file path");
+                            }
+                            readFile = env.getEnv().readFile;
+                            _a = getModelUris(filePath, this.getDefaultModelName()), manifestUri = _a.manifestUri, modelBaseUri = _a.modelBaseUri;
+                            fetchWeightsFromDisk = function (filePaths) { return Promise.all(filePaths.map(function (filePath) { return readFile(filePath).then(function (buf) { return buf.buffer; }); })); };
+                            loadWeights = io.weightsLoaderFactory(fetchWeightsFromDisk);
+                            _c = (_b = JSON).parse;
+                            return [4 /*yield*/, readFile(manifestUri)];
+                        case 1:
+                            manifest = _c.apply(_b, [(_d.sent()).toString()]);
+                            return [4 /*yield*/, loadWeights(manifest, modelBaseUri)];
+                        case 2:
+                            weightMap = _d.sent();
+                            this.loadFromWeightMap(weightMap);
+                            return [2 /*return*/];
+                    }
+                });
+            });
+        };
+        NeuralNetwork.prototype.loadFromWeightMap = function (weightMap) {
+            var _a = this.extractParamsFromWeigthMap(weightMap), paramMappings = _a.paramMappings, params = _a.params;
+            this._paramMappings = paramMappings;
+            this._params = params;
+        };
+        NeuralNetwork.prototype.extractWeights = function (weights) {
+            var _a = this.extractParams(weights), paramMappings = _a.paramMappings, params = _a.params;
+            this._paramMappings = paramMappings;
+            this._params = params;
+        };
+        NeuralNetwork.prototype.traversePropertyPath = function (paramPath) {
+            if (!this.params) {
+                throw new Error("traversePropertyPath - model has no loaded params");
+            }
+            var result = paramPath.split('/').reduce(function (res, objProp) {
+                if (!res.nextObj.hasOwnProperty(objProp)) {
+                    throw new Error("traversePropertyPath - object does not have property " + objProp + ", for path " + paramPath);
+                }
+                return { obj: res.nextObj, objProp: objProp, nextObj: res.nextObj[objProp] };
+            }, { nextObj: this.params });
+            var obj = result.obj, objProp = result.objProp;
+            if (!obj || !objProp || !(obj[objProp] instanceof Tensor)) {
+                throw new Error("traversePropertyPath - parameter is not a tensor, for path " + paramPath);
+            }
+            return { obj: obj, objProp: objProp };
+        };
+        return NeuralNetwork;
+    }());
+
+    function convLayer(x, params, padding, withRelu) {
+        if (padding === void 0) { padding = 'same'; }
+        if (withRelu === void 0) { withRelu = false; }
+        return tidy(function () {
+            var out = add(conv2d(x, params.filters, [1, 1], padding), params.bias);
+            return withRelu ? relu(out) : out;
+        });
+    }
+
+    function extractConvParamsFactory(extractWeights, paramMappings) {
+        return function (channelsIn, channelsOut, filterSize, mappedPrefix) {
+            var filters = tensor4d(extractWeights(channelsIn * channelsOut * filterSize * filterSize), [filterSize, filterSize, channelsIn, channelsOut]);
+            var bias = tensor1d(extractWeights(channelsOut));
+            paramMappings.push({ paramPath: mappedPrefix + "/filters" }, { paramPath: mappedPrefix + "/bias" });
+            return { filters: filters, bias: bias };
+        };
+    }
+
+    function extractFCParamsFactory(extractWeights, paramMappings) {
+        return function (channelsIn, channelsOut, mappedPrefix) {
+            var fc_weights = tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut]);
+            var fc_bias = tensor1d(extractWeights(channelsOut));
+            paramMappings.push({ paramPath: mappedPrefix + "/weights" }, { paramPath: mappedPrefix + "/bias" });
+            return {
+                weights: fc_weights,
+                bias: fc_bias
+            };
+        };
+    }
+
+    var SeparableConvParams = /** @class */ (function () {
+        function SeparableConvParams(depthwise_filter, pointwise_filter, bias) {
+            this.depthwise_filter = depthwise_filter;
+            this.pointwise_filter = pointwise_filter;
+            this.bias = bias;
+        }
+        return SeparableConvParams;
+    }());
+
+    function extractSeparableConvParamsFactory(extractWeights, paramMappings) {
+        return function (channelsIn, channelsOut, mappedPrefix) {
+            var depthwise_filter = tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1]);
+            var pointwise_filter = tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut]);
+            var bias = tensor1d(extractWeights(channelsOut));
+            paramMappings.push({ paramPath: mappedPrefix + "/depthwise_filter" }, { paramPath: mappedPrefix + "/pointwise_filter" }, { paramPath: mappedPrefix + "/bias" });
+            return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
+        };
+    }
+    function loadSeparableConvParamsFactory(extractWeightEntry) {
+        return function (prefix) {
+            var depthwise_filter = extractWeightEntry(prefix + "/depthwise_filter", 4);
+            var pointwise_filter = extractWeightEntry(prefix + "/pointwise_filter", 4);
+            var bias = extractWeightEntry(prefix + "/bias", 1);
+            return new SeparableConvParams(depthwise_filter, pointwise_filter, bias);
+        };
+    }
+
+    var isNumber$1 = function (arg) { return typeof arg === 'number'; };
+    function validateConfig(config) {
+        if (!config) {
+            throw new Error("invalid config: " + config);
+        }
+        if (typeof config.withSeparableConvs !== 'boolean') {
+            throw new Error("config.withSeparableConvs has to be a boolean, have: " + config.withSeparableConvs);
+        }
+        if (!isNumber$1(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {
+            throw new Error("config.iouThreshold has to be a number between [0, 1], have: " + config.iouThreshold);
+        }
+        if (!Array.isArray(config.classes)
+            || !config.classes.length
+            || !config.classes.every(function (c) { return typeof c === 'string'; })) {
+            throw new Error("config.classes has to be an array class names: string[], have: " + JSON.stringify(config.classes));
+        }
+        if (!Array.isArray(config.anchors)
+            || !config.anchors.length
+            || !config.anchors.map(function (a) { return a || {}; }).every(function (a) { return isNumber$1(a.x) && isNumber$1(a.y); })) {
+            throw new Error("config.anchors has to be an array of { x: number, y: number }, have: " + JSON.stringify(config.anchors));
+        }
+        if (config.meanRgb && (!Array.isArray(config.meanRgb)
+            || config.meanRgb.length !== 3
+            || !config.meanRgb.every(isNumber$1))) {
+            throw new Error("config.meanRgb has to be an array of shape [number, number, number], have: " + JSON.stringify(config.meanRgb));
+        }
+    }
+    function validateTrainConfig(config) {
+        if (![config.noObjectScale, config.objectScale, config.coordScale, config.classScale].every(isNumber$1)) {
+            throw new Error("for training you have to specify noObjectScale, objectScale, coordScale, classScale parameters in your config.json file");
+        }
+        return config;
+    }
+
+    var CELL_SIZE = 32;
+    var DEFAULT_FILTER_SIZES = [
+        3, 16, 32, 64, 128, 256, 512, 1024, 1024
+    ];
+
+    function leaky(x) {
+        return tidy(function () {
+            var min$$1 = mul(x, scalar(0.10000000149011612));
+            return add(relu(sub(x, min$$1)), min$$1);
+            //return tf.maximum(x, min)
+        });
+    }
+
+    function convWithBatchNorm(x, params) {
+        return tidy(function () {
+            var out = pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
+            out = conv2d(out, params.conv.filters, [1, 1], 'valid');
+            out = sub(out, params.bn.sub);
+            out = mul(out, params.bn.truediv);
+            out = add(out, params.conv.bias);
+            return leaky(out);
+        });
+    }
+
+    function depthwiseSeparableConv(x, params) {
+        return tidy(function () {
+            var out = pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]);
+            out = separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid');
+            out = add(out, params.bias);
+            return leaky(out);
+        });
+    }
+
+    function extractorsFactory(extractWeights, paramMappings) {
+        var extractConvParams = extractConvParamsFactory(extractWeights, paramMappings);
+        function extractBatchNormParams(size, mappedPrefix) {
+            var sub$$1 = tensor1d(extractWeights(size));
+            var truediv = tensor1d(extractWeights(size));
+            paramMappings.push({ paramPath: mappedPrefix + "/sub" }, { paramPath: mappedPrefix + "/truediv" });
+            return { sub: sub$$1, truediv: truediv };
+        }
+        function extractConvWithBatchNormParams(channelsIn, channelsOut, mappedPrefix) {
+            var conv = extractConvParams(channelsIn, channelsOut, 3, mappedPrefix + "/conv");
+            var bn = extractBatchNormParams(channelsOut, mappedPrefix + "/bn");
+            return { conv: conv, bn: bn };
+        }
+        var extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings);
+        return {
+            extractConvParams: extractConvParams,
+            extractConvWithBatchNormParams: extractConvWithBatchNormParams,
+            extractSeparableConvParams: extractSeparableConvParams
+        };
+    }
+    function extractParams(weights, config, boxEncodingSize, filterSizes) {
+        var _a = extractWeightsFactory(weights), extractWeights = _a.extractWeights, getRemainingWeights = _a.getRemainingWeights;
+        var paramMappings = [];
+        var _b = extractorsFactory(extractWeights, paramMappings), extractConvParams = _b.extractConvParams, extractConvWithBatchNormParams = _b.extractConvWithBatchNormParams, extractSeparableConvParams = _b.extractSeparableConvParams;
+        var params;
+        if (config.withSeparableConvs) {
+            var s0 = filterSizes[0], s1 = filterSizes[1], s2 = filterSizes[2], s3 = filterSizes[3], s4 = filterSizes[4], s5 = filterSizes[5], s6 = filterSizes[6], s7 = filterSizes[7], s8 = filterSizes[8];
+            var conv0 = config.isFirstLayerConv2d
+                ? extractConvParams(s0, s1, 3, 'conv0')
+                : extractSeparableConvParams(s0, s1, 'conv0');
+            var conv1 = extractSeparableConvParams(s1, s2, 'conv1');
+            var conv2 = extractSeparableConvParams(s2, s3, 'conv2');
+            var conv3 = extractSeparableConvParams(s3, s4, 'conv3');
+            var conv4 = extractSeparableConvParams(s4, s5, 'conv4');
+            var conv5 = extractSeparableConvParams(s5, s6, 'conv5');
+            var conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined;
+            var conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined;
+            var conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8');
+            params = { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3, conv4: conv4, conv5: conv5, conv6: conv6, conv7: conv7, conv8: conv8 };
+        }
+        else {
+            var s0 = filterSizes[0], s1 = filterSizes[1], s2 = filterSizes[2], s3 = filterSizes[3], s4 = filterSizes[4], s5 = filterSizes[5], s6 = filterSizes[6], s7 = filterSizes[7], s8 = filterSizes[8];
+            var conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0');
+            var conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1');
+            var conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2');
+            var conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3');
+            var conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4');
+            var conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5');
+            var conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6');
+            var conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7');
+            var conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8');
+            params = { conv0: conv0, conv1: conv1, conv2: conv2, conv3: conv3, conv4: conv4, conv5: conv5, conv6: conv6, conv7: conv7, conv8: conv8 };
+        }
+        if (getRemainingWeights().length !== 0) {
+            throw new Error("weights remaing after extract: " + getRemainingWeights().length);
+        }
+        return { params: params, paramMappings: paramMappings };
+    }
+
+    function extractorsFactory$1(weightMap, paramMappings) {
+        var extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings);
+        function extractBatchNormParams(prefix) {
+            var sub = extractWeightEntry(prefix + "/sub", 1);
+            var truediv = extractWeightEntry(prefix + "/truediv", 1);
+            return { sub: sub, truediv: truediv };
+        }
+        function extractConvParams(prefix) {
+            var filters = extractWeightEntry(prefix + "/filters", 4);
+            var bias = extractWeightEntry(prefix + "/bias", 1);
+            return { filters: filters, bias: bias };
+        }
+        function extractConvWithBatchNormParams(prefix) {
+            var conv = extractConvParams(prefix + "/conv");
+            var bn = extractBatchNormParams(prefix + "/bn");
+            return { conv: conv, bn: bn };
+        }
+        var extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry);
+        return {
+            extractConvParams: extractConvParams,
+            extractConvWithBatchNormParams: extractConvWithBatchNormParams,
+            extractSeparableConvParams: extractSeparableConvParams
+        };
+    }
+    function extractParamsFromWeigthMap(weightMap, config) {
+        var paramMappings = [];
+        var _a = extractorsFactory$1(weightMap, paramMappings), extractConvParams = _a.extractConvParams, extractConvWithBatchNormParams = _a.extractConvWithBatchNormParams, extractSeparableConvParams = _a.extractSeparableConvParams;
+        var params;
+        if (config.withSeparableConvs) {
+            var numFilters = (config.filterSizes && config.filterSizes.length || 9);
+            params = {
+                conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),
+                conv1: extractSeparableConvParams('conv1'),
+                conv2: extractSeparableConvParams('conv2'),
+                conv3: extractSeparableConvParams('conv3'),
+                conv4: extractSeparableConvParams('conv4'),
+                conv5: extractSeparableConvParams('conv5'),
+                conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,
+                conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,
+                conv8: extractConvParams('conv8')
+            };
+        }
+        else {
+            params = {
+                conv0: extractConvWithBatchNormParams('conv0'),
+                conv1: extractConvWithBatchNormParams('conv1'),
+                conv2: extractConvWithBatchNormParams('conv2'),
+                conv3: extractConvWithBatchNormParams('conv3'),
+                conv4: extractConvWithBatchNormParams('conv4'),
+                conv5: extractConvWithBatchNormParams('conv5'),
+                conv6: extractConvWithBatchNormParams('conv6'),
+                conv7: extractConvWithBatchNormParams('conv7'),
+                conv8: extractConvParams('conv8')
+            };
+        }
+        disposeUnusedWeightTensors(weightMap, paramMappings);
+        return { params: params, paramMappings: paramMappings };
+    }
+
+    (function (TinyYolov2SizeType) {
+        TinyYolov2SizeType[TinyYolov2SizeType["XS"] = 224] = "XS";
+        TinyYolov2SizeType[TinyYolov2SizeType["SM"] = 320] = "SM";
+        TinyYolov2SizeType[TinyYolov2SizeType["MD"] = 416] = "MD";
+        TinyYolov2SizeType[TinyYolov2SizeType["LG"] = 608] = "LG";
+    })(exports.TinyYolov2SizeType || (exports.TinyYolov2SizeType = {}));
+    var TinyYolov2Options = /** @class */ (function () {
+        function TinyYolov2Options(_a) {
+            var _b = _a === void 0 ? {} : _a, inputSize = _b.inputSize, scoreThreshold = _b.scoreThreshold;
+            this._name = 'TinyYolov2Options';
+            this._inputSize = inputSize || 416;
+            this._scoreThreshold = scoreThreshold || 0.5;
+            if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {
+                throw new Error(this._name + " - expected inputSize to be a number divisible by 32");
+            }
+            if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {
+                throw new Error(this._name + " - expected scoreThreshold to be a number between 0 and 1");
+            }
+        }
+        Object.defineProperty(TinyYolov2Options.prototype, "inputSize", {
+            get: function () { return this._inputSize; },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2Options.prototype, "scoreThreshold", {
+            get: function () { return this._scoreThreshold; },
+            enumerable: true,
+            configurable: true
+        });
+        return TinyYolov2Options;
+    }());
+
+    var TinyYolov2 = /** @class */ (function (_super) {
+        __extends$1(TinyYolov2, _super);
+        function TinyYolov2(config) {
+            var _this = _super.call(this, 'TinyYolov2') || this;
+            validateConfig(config);
+            _this._config = config;
+            return _this;
+        }
+        Object.defineProperty(TinyYolov2.prototype, "config", {
+            get: function () {
+                return this._config;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2.prototype, "withClassScores", {
+            get: function () {
+                return this.config.withClassScores || this.config.classes.length > 1;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2.prototype, "boxEncodingSize", {
+            get: function () {
+                return 5 + (this.withClassScores ? this.config.classes.length : 0);
+            },
+            enumerable: true,
+            configurable: true
+        });
+        TinyYolov2.prototype.runTinyYolov2 = function (x, params) {
+            var out = convWithBatchNorm(x, params.conv0);
+            out = maxPool(out, [2, 2], [2, 2], 'same');
+            out = convWithBatchNorm(out, params.conv1);
+            out = maxPool(out, [2, 2], [2, 2], 'same');
+            out = convWithBatchNorm(out, params.conv2);
+            out = maxPool(out, [2, 2], [2, 2], 'same');
+            out = convWithBatchNorm(out, params.conv3);
+            out = maxPool(out, [2, 2], [2, 2], 'same');
+            out = convWithBatchNorm(out, params.conv4);
+            out = maxPool(out, [2, 2], [2, 2], 'same');
+            out = convWithBatchNorm(out, params.conv5);
+            out = maxPool(out, [2, 2], [1, 1], 'same');
+            out = convWithBatchNorm(out, params.conv6);
+            out = convWithBatchNorm(out, params.conv7);
+            return convLayer(out, params.conv8, 'valid', false);
+        };
+        TinyYolov2.prototype.runMobilenet = function (x, params) {
+            var out = this.config.isFirstLayerConv2d
+                ? leaky(convLayer(x, params.conv0, 'valid', false))
+                : depthwiseSeparableConv(x, params.conv0);
+            out = maxPool(out, [2, 2], [2, 2], 'same');
+            out = depthwiseSeparableConv(out, params.conv1);
+            out = maxPool(out, [2, 2], [2, 2], 'same');
+            out = depthwiseSeparableConv(out, params.conv2);
+            out = maxPool(out, [2, 2], [2, 2], 'same');
+            out = depthwiseSeparableConv(out, params.conv3);
+            out = maxPool(out, [2, 2], [2, 2], 'same');
+            out = depthwiseSeparableConv(out, params.conv4);
+            out = maxPool(out, [2, 2], [2, 2], 'same');
+            out = depthwiseSeparableConv(out, params.conv5);
+            out = maxPool(out, [2, 2], [1, 1], 'same');
+            out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out;
+            out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out;
+            return convLayer(out, params.conv8, 'valid', false);
+        };
+        TinyYolov2.prototype.forwardInput = function (input, inputSize) {
+            var _this = this;
+            var params = this.params;
+            if (!params) {
+                throw new Error('TinyYolov2 - load model before inference');
+            }
+            return tidy(function () {
+                var batchTensor = input.toBatchTensor(inputSize, false).toFloat();
+                batchTensor = _this.config.meanRgb
+                    ? normalize(batchTensor, _this.config.meanRgb)
+                    : batchTensor;
+                batchTensor = batchTensor.div(scalar(256));
+                return _this.config.withSeparableConvs
+                    ? _this.runMobilenet(batchTensor, params)
+                    : _this.runTinyYolov2(batchTensor, params);
+            });
+        };
+        TinyYolov2.prototype.forward = function (input, inputSize) {
+            return __awaiter$1(this, void 0, void 0, function () {
+                var _a;
+                return __generator$1(this, function (_b) {
+                    switch (_b.label) {
+                        case 0:
+                            _a = this.forwardInput;
+                            return [4 /*yield*/, toNetInput(input)];
+                        case 1: return [4 /*yield*/, _a.apply(this, [_b.sent(), inputSize])];
+                        case 2: return [2 /*return*/, _b.sent()];
+                    }
+                });
+            });
+        };
+        TinyYolov2.prototype.detect = function (input, forwardParams) {
+            if (forwardParams === void 0) { forwardParams = {}; }
+            return __awaiter$1(this, void 0, void 0, function () {
+                var _this = this;
+                var _a, inputSize, scoreThreshold, netInput, out, out0, inputDimensions, results, boxes, scores, classScores, classNames, indices, detections;
+                return __generator$1(this, function (_b) {
+                    switch (_b.label) {
+                        case 0:
+                            _a = new TinyYolov2Options(forwardParams), inputSize = _a.inputSize, scoreThreshold = _a.scoreThreshold;
+                            return [4 /*yield*/, toNetInput(input)];
+                        case 1:
+                            netInput = _b.sent();
+                            return [4 /*yield*/, this.forwardInput(netInput, inputSize)];
+                        case 2:
+                            out = _b.sent();
+                            out0 = tidy(function () { return unstack(out)[0].expandDims(); });
+                            inputDimensions = {
+                                width: netInput.getInputWidth(0),
+                                height: netInput.getInputHeight(0)
+                            };
+                            results = this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold);
+                            out.dispose();
+                            out0.dispose();
+                            boxes = results.map(function (res) { return res.box; });
+                            scores = results.map(function (res) { return res.score; });
+                            classScores = results.map(function (res) { return res.classScore; });
+                            classNames = results.map(function (res) { return _this.config.classes[res.label]; });
+                            indices = nonMaxSuppression$1(boxes.map(function (box) { return box.rescale(inputSize); }), scores, this.config.iouThreshold, true);
+                            detections = indices.map(function (idx) {
+                                return new ObjectDetection(scores[idx], classScores[idx], classNames[idx], boxes[idx], inputDimensions);
+                            });
+                            return [2 /*return*/, detections];
+                    }
+                });
+            });
+        };
+        TinyYolov2.prototype.getDefaultModelName = function () {
+            return '';
+        };
+        TinyYolov2.prototype.extractParamsFromWeigthMap = function (weightMap) {
+            return extractParamsFromWeigthMap(weightMap, this.config);
+        };
+        TinyYolov2.prototype.extractParams = function (weights) {
+            var filterSizes = this.config.filterSizes || DEFAULT_FILTER_SIZES;
+            var numFilters = filterSizes ? filterSizes.length : undefined;
+            if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {
+                throw new Error("TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found " + numFilters + " filterSizes in config");
+            }
+            return extractParams(weights, this.config, this.boxEncodingSize, filterSizes);
+        };
+        TinyYolov2.prototype.extractBoxes = function (outputTensor, inputBlobDimensions, scoreThreshold) {
+            var _this = this;
+            var width = inputBlobDimensions.width, height = inputBlobDimensions.height;
+            var inputSize = Math.max(width, height);
+            var correctionFactorX = inputSize / width;
+            var correctionFactorY = inputSize / height;
+            var numCells = outputTensor.shape[1];
+            var numBoxes = this.config.anchors.length;
+            var _a = tidy(function () {
+                var reshaped = outputTensor.reshape([numCells, numCells, numBoxes, _this.boxEncodingSize]);
+                var boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4]);
+                var scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1]);
+                var classScores = _this.withClassScores
+                    ? softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, _this.config.classes.length]), 3)
+                    : scalar(0);
+                return [boxes, scores, classScores];
+            }), boxesTensor = _a[0], scoresTensor = _a[1], classScoresTensor = _a[2];
+            var results = [];
+            for (var row = 0; row < numCells; row++) {
+                for (var col = 0; col < numCells; col++) {
+                    for (var anchor = 0; anchor < numBoxes; anchor++) {
+                        var score = sigmoid$1(scoresTensor.get(row, col, anchor, 0));
+                        if (!scoreThreshold || score > scoreThreshold) {
+                            var ctX = ((col + sigmoid$1(boxesTensor.get(row, col, anchor, 0))) / numCells) * correctionFactorX;
+                            var ctY = ((row + sigmoid$1(boxesTensor.get(row, col, anchor, 1))) / numCells) * correctionFactorY;
+                            var width_1 = ((Math.exp(boxesTensor.get(row, col, anchor, 2)) * this.config.anchors[anchor].x) / numCells) * correctionFactorX;
+                            var height_1 = ((Math.exp(boxesTensor.get(row, col, anchor, 3)) * this.config.anchors[anchor].y) / numCells) * correctionFactorY;
+                            var x = (ctX - (width_1 / 2));
+                            var y = (ctY - (height_1 / 2));
+                            var pos = { row: row, col: col, anchor: anchor };
+                            var _b = this.withClassScores
+                                ? this.extractPredictedClass(classScoresTensor, pos)
+                                : { classScore: 1, label: 0 }, classScore = _b.classScore, label = _b.label;
+                            results.push(__assign$1({ box: new BoundingBox(x, y, x + width_1, y + height_1), score: score, classScore: score * classScore, label: label }, pos));
+                        }
+                    }
+                }
+            }
+            boxesTensor.dispose();
+            scoresTensor.dispose();
+            classScoresTensor.dispose();
+            return results;
+        };
+        TinyYolov2.prototype.extractPredictedClass = function (classesTensor, pos) {
+            var row = pos.row, col = pos.col, anchor = pos.anchor;
+            return Array(this.config.classes.length).fill(0)
+                .map(function (_, i) { return classesTensor.get(row, col, anchor, i); })
+                .map(function (classScore, label) { return ({
+                classScore: classScore,
+                label: label
+            }); })
+                .reduce(function (max$$1, curr) { return max$$1.classScore > curr.classScore ? max$$1 : curr; });
+        };
+        return TinyYolov2;
+    }(NeuralNetwork));
+
+    var TinyYolov2LossFunction = /** @class */ (function () {
+        function TinyYolov2LossFunction(outputTensor, groundTruth, predictedBoxes, reshapedImgDims, config) {
+            this._config = config;
+            this._reshapedImgDims = new Dimensions(reshapedImgDims.width, reshapedImgDims.height);
+            this._outputTensor = outputTensor;
+            this._predictedBoxes = predictedBoxes;
+            this.validateGroundTruthBoxes(groundTruth);
+            this._groundTruth = this.assignGroundTruthToAnchors(groundTruth);
+            var groundTruthMask = this.createGroundTruthMask();
+            var _a = this.createCoordAndScoreMasks(), coordBoxOffsetMask = _a.coordBoxOffsetMask, coordBoxSizeMask = _a.coordBoxSizeMask, scoreMask = _a.scoreMask;
+            this.noObjectLossMask = tidy(function () { return mul(scoreMask, sub(scalar(1), groundTruthMask)); });
+            this.objectLossMask = tidy(function () { return mul(scoreMask, groundTruthMask); });
+            this.coordBoxOffsetMask = tidy(function () { return mul(coordBoxOffsetMask, groundTruthMask); });
+            this.coordBoxSizeMask = tidy(function () { return mul(coordBoxSizeMask, groundTruthMask); });
+            var classScoresMask = tidy(function () { return sub(scalar(1), coordBoxOffsetMask.add(coordBoxSizeMask).add(scoreMask)); });
+            this.groundTruthClassScoresMask = tidy(function () { return mul(classScoresMask, groundTruthMask); });
+        }
+        Object.defineProperty(TinyYolov2LossFunction.prototype, "config", {
+            get: function () {
+                return this._config;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2LossFunction.prototype, "reshapedImgDims", {
+            get: function () {
+                return this._reshapedImgDims;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2LossFunction.prototype, "outputTensor", {
+            get: function () {
+                return this._outputTensor;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2LossFunction.prototype, "groundTruth", {
+            get: function () {
+                return this._groundTruth;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2LossFunction.prototype, "predictedBoxes", {
+            get: function () {
+                return this._predictedBoxes;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2LossFunction.prototype, "inputSize", {
+            get: function () {
+                return Math.max(this.reshapedImgDims.width, this.reshapedImgDims.height);
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2LossFunction.prototype, "withClassScores", {
+            get: function () {
+                return this._config.withClassScores || this._config.classes.length > 1;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2LossFunction.prototype, "boxEncodingSize", {
+            get: function () {
+                return 5 + (this.withClassScores ? this._config.classes.length : 0);
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2LossFunction.prototype, "anchors", {
+            get: function () {
+                return this._config.anchors;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2LossFunction.prototype, "numBoxes", {
+            get: function () {
+                return this.anchors.length;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2LossFunction.prototype, "numCells", {
+            get: function () {
+                return this.inputSize / CELL_SIZE;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2LossFunction.prototype, "gridCellEncodingSize", {
+            get: function () {
+                return this.boxEncodingSize * this.numBoxes;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        TinyYolov2LossFunction.prototype.toOutputTensorShape = function (tensor$$1) {
+            var _this = this;
+            return tidy(function () { return tensor$$1.reshape([1, _this.numCells, _this.numCells, _this.gridCellEncodingSize]); });
+        };
+        TinyYolov2LossFunction.prototype.computeLoss = function () {
+            var _this = this;
+            return tidy(function () {
+                var noObjectLoss = _this.computeNoObjectLoss();
+                var objectLoss = _this.computeObjectLoss();
+                var coordLoss = _this.computeCoordLoss();
+                var classLoss = _this.withClassScores
+                    ? _this.computeClassLoss()
+                    : scalar(0);
+                var totalLoss = tidy(function () { return noObjectLoss.add(objectLoss).add(coordLoss).add(classLoss); });
+                return {
+                    noObjectLoss: noObjectLoss,
+                    objectLoss: objectLoss,
+                    coordLoss: coordLoss,
+                    classLoss: classLoss,
+                    totalLoss: totalLoss
+                };
+            });
+        };
+        TinyYolov2LossFunction.prototype.computeNoObjectLoss = function () {
+            var _this = this;
+            return tidy(function () {
+                return _this.computeLossTerm(_this.config.noObjectScale, _this.toOutputTensorShape(_this.noObjectLossMask), sigmoid(_this.outputTensor));
+            });
+        };
+        TinyYolov2LossFunction.prototype.computeObjectLoss = function () {
+            var _this = this;
+            return tidy(function () {
+                return _this.computeLossTerm(_this.config.objectScale, _this.toOutputTensorShape(_this.objectLossMask), sub(_this.toOutputTensorShape(_this.computeIous()), sigmoid(_this.outputTensor)));
+            });
+        };
+        TinyYolov2LossFunction.prototype.computeClassLoss = function () {
+            var _this = this;
+            return tidy(function () {
+                var classLossTensor = tidy(function () {
+                    var predClassScores = mul(softmax(_this.outputTensor.reshape([_this.numCells, _this.numCells, _this.numBoxes, _this.boxEncodingSize]), 3), _this.groundTruthClassScoresMask);
+                    var gtClassScores = _this.createOneHotClassScoreMask();
+                    return sub(gtClassScores, predClassScores);
+                });
+                return _this.computeLossTerm(_this.config.classScale, scalar(1), classLossTensor);
+            });
+        };
+        TinyYolov2LossFunction.prototype.computeCoordLoss = function () {
+            var _this = this;
+            return tidy(function () {
+                return _this.computeLossTerm(_this.config.coordScale, scalar(1), add(_this.computeCoordBoxOffsetError(), _this.computeCoordBoxSizeError()));
+            });
+        };
+        TinyYolov2LossFunction.prototype.computeCoordBoxOffsetError = function () {
+            var _this = this;
+            return tidy(function () {
+                var mask = _this.toOutputTensorShape(_this.coordBoxOffsetMask);
+                var gtBoxOffsets = mul(mask, _this.toOutputTensorShape(_this.computeCoordBoxOffsets()));
+                var predBoxOffsets = mul(mask, sigmoid(_this.outputTensor));
+                return sub(gtBoxOffsets, predBoxOffsets);
+            });
+        };
+        TinyYolov2LossFunction.prototype.computeCoordBoxSizeError = function () {
+            var _this = this;
+            return tidy(function () {
+                var mask = _this.toOutputTensorShape(_this.coordBoxSizeMask);
+                var gtBoxSizes = mul(mask, _this.toOutputTensorShape(_this.computeCoordBoxSizes()));
+                var predBoxSizes = mul(mask, _this.outputTensor);
+                return sub(gtBoxSizes, predBoxSizes);
+            });
+        };
+        TinyYolov2LossFunction.prototype.computeLossTerm = function (scale, mask, lossTensor) {
+            var _this = this;
+            return tidy(function () { return mul(scalar(scale), _this.squaredSumOverMask(mask, lossTensor)); });
+        };
+        TinyYolov2LossFunction.prototype.squaredSumOverMask = function (mask, lossTensor) {
+            return tidy(function () { return sum$1(square(mul(mask, lossTensor))); });
+        };
+        TinyYolov2LossFunction.prototype.validateGroundTruthBoxes = function (groundTruth) {
+            var _this = this;
+            groundTruth.forEach(function (_a) {
+                var x = _a.x, y = _a.y, width = _a.width, height = _a.height, label = _a.label;
+                if (typeof label !== 'number' || label < 0 || label > (_this.config.classes.length - 1)) {
+                    throw new Error("invalid ground truth data, expected label to be a number in [0, " + (_this.config.classes.length - 1) + "]");
+                }
+                if (x < 0 || x > 1 || y < 0 || y > 1 || width < 0 || (x + width) > 1 || height < 0 || (y + height) > 1) {
+                    throw new Error("invalid ground truth data, box is out of image boundaries " + JSON.stringify({ x: x, y: y, width: width, height: height }));
+                }
+            });
+        };
+        TinyYolov2LossFunction.prototype.assignGroundTruthToAnchors = function (groundTruth) {
+            var _this = this;
+            var groundTruthBoxes = groundTruth
+                .map(function (_a) {
+                var x = _a.x, y = _a.y, width = _a.width, height = _a.height, label = _a.label;
+                return ({
+                    box: new Rect(x, y, width, height),
+                    label: label
+                });
+            });
+            return groundTruthBoxes.map(function (_a) {
+                var box = _a.box, label = _a.label;
+                var _b = box.rescale(_this.reshapedImgDims), left = _b.left, top = _b.top, bottom = _b.bottom, right = _b.right, x = _b.x, y = _b.y, width = _b.width, height = _b.height;
+                var ctX = left + (width / 2);
+                var ctY = top + (height / 2);
+                var col = Math.floor((ctX / _this.inputSize) * _this.numCells);
+                var row = Math.floor((ctY / _this.inputSize) * _this.numCells);
+                var anchorsByIou = _this.anchors.map(function (anchor, idx) { return ({
+                    idx: idx,
+                    iou: iou(new BoundingBox(0, 0, anchor.x * CELL_SIZE, anchor.y * CELL_SIZE), new BoundingBox(0, 0, width, height))
+                }); }).sort(function (a1, a2) { return a2.iou - a1.iou; });
+                var anchor = anchorsByIou[0].idx;
+                return { row: row, col: col, anchor: anchor, box: box, label: label };
+            });
+        };
+        TinyYolov2LossFunction.prototype.createGroundTruthMask = function () {
+            var _this = this;
+            var mask = zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize]);
+            var buf = mask.buffer();
+            this.groundTruth.forEach(function (_a) {
+                var row = _a.row, col = _a.col, anchor = _a.anchor;
+                for (var i = 0; i < _this.boxEncodingSize; i++) {
+                    buf.set(1, row, col, anchor, i);
+                }
+            });
+            return mask;
+        };
+        TinyYolov2LossFunction.prototype.createCoordAndScoreMasks = function () {
+            var _this = this;
+            return tidy(function () {
+                var coordBoxOffsetMask = zeros([_this.numCells, _this.numCells, _this.numBoxes, _this.boxEncodingSize]);
+                var coordBoxSizeMask = zeros([_this.numCells, _this.numCells, _this.numBoxes, _this.boxEncodingSize]);
+                var scoreMask = zeros([_this.numCells, _this.numCells, _this.numBoxes, _this.boxEncodingSize]);
+                var coordBoxOffsetBuf = coordBoxOffsetMask.buffer();
+                var coordBoxSizeBuf = coordBoxSizeMask.buffer();
+                var scoreBuf = scoreMask.buffer();
+                for (var row = 0; row < _this.numCells; row++) {
+                    for (var col = 0; col < _this.numCells; col++) {
+                        for (var anchor = 0; anchor < _this.numBoxes; anchor++) {
+                            coordBoxOffsetBuf.set(1, row, col, anchor, 0);
+                            coordBoxOffsetBuf.set(1, row, col, anchor, 1);
+                            coordBoxSizeBuf.set(1, row, col, anchor, 2);
+                            coordBoxSizeBuf.set(1, row, col, anchor, 3);
+                            scoreBuf.set(1, row, col, anchor, 4);
+                        }
+                    }
+                }
+                return { coordBoxOffsetMask: coordBoxOffsetMask, coordBoxSizeMask: coordBoxSizeMask, scoreMask: scoreMask };
+            });
+        };
+        TinyYolov2LossFunction.prototype.createOneHotClassScoreMask = function () {
+            var mask = zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize]);
+            var buf = mask.buffer();
+            var classValuesOffset = 5;
+            this.groundTruth.forEach(function (_a) {
+                var row = _a.row, col = _a.col, anchor = _a.anchor, label = _a.label;
+                buf.set(1, row, col, anchor, classValuesOffset + label);
+            });
+            return mask;
+        };
+        TinyYolov2LossFunction.prototype.computeIous = function () {
+            var _this = this;
+            var isSameAnchor = function (p1) { return function (p2) {
+                return p1.row === p2.row
+                    && p1.col === p2.col
+                    && p1.anchor === p2.anchor;
+            }; };
+            var ious = zeros([this.numCells, this.numCells, this.gridCellEncodingSize]);
+            var buf = ious.buffer();
+            this.groundTruth.forEach(function (_a) {
+                var row = _a.row, col = _a.col, anchor = _a.anchor, box = _a.box;
+                var predBox = _this.predictedBoxes.find(isSameAnchor({ row: row, col: col, anchor: anchor }));
+                if (!predBox) {
+                    throw new Error("no output box found for: row " + row + ", col " + col + ", anchor " + anchor);
+                }
+                var boxIou = iou(box.rescale(_this.reshapedImgDims), predBox.box.rescale(_this.reshapedImgDims));
+                var anchorOffset = _this.boxEncodingSize * anchor;
+                var scoreValueOffset = 4;
+                buf.set(boxIou, row, col, anchorOffset + scoreValueOffset);
+            });
+            return ious;
+        };
+        TinyYolov2LossFunction.prototype.computeCoordBoxOffsets = function () {
+            var _this = this;
+            var offsets = zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize]);
+            var buf = offsets.buffer();
+            this.groundTruth.forEach(function (_a) {
+                var row = _a.row, col = _a.col, anchor = _a.anchor, box = _a.box;
+                var _b = box.rescale(_this.reshapedImgDims), left = _b.left, top = _b.top, right = _b.right, bottom = _b.bottom;
+                var centerX = (left + right) / 2;
+                var centerY = (top + bottom) / 2;
+                var dCenterX = centerX - (col * CELL_SIZE);
+                var dCenterY = centerY - (row * CELL_SIZE);
+                // inverseSigmoid(0) === -Infinity, inverseSigmoid(1) === Infinity
+                //const dx = inverseSigmoid(Math.min(0.999, Math.max(0.001, dCenterX / CELL_SIZE)))
+                //const dy = inverseSigmoid(Math.min(0.999, Math.max(0.001, dCenterY / CELL_SIZE)))
+                var dx = dCenterX / CELL_SIZE;
+                var dy = dCenterY / CELL_SIZE;
+                buf.set(dx, row, col, anchor, 0);
+                buf.set(dy, row, col, anchor, 1);
+            });
+            return offsets;
+        };
+        TinyYolov2LossFunction.prototype.computeCoordBoxSizes = function () {
+            var _this = this;
+            var sizes = zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize]);
+            var buf = sizes.buffer();
+            this.groundTruth.forEach(function (_a) {
+                var row = _a.row, col = _a.col, anchor = _a.anchor, box = _a.box;
+                var _b = box.rescale(_this.reshapedImgDims), width = _b.width, height = _b.height;
+                var dw = Math.log(width / (_this.anchors[anchor].x * CELL_SIZE));
+                var dh = Math.log(height / (_this.anchors[anchor].y * CELL_SIZE));
+                buf.set(dw, row, col, anchor, 2);
+                buf.set(dh, row, col, anchor, 3);
+            });
+            return sizes;
+        };
+        return TinyYolov2LossFunction;
+    }());
+
+    function getDefaultBackwardOptions(options) {
+        return Object.assign({}, {
+            minBoxSize: CELL_SIZE
+        }, options);
+    }
+
+    var TinyYolov2Trainable = /** @class */ (function (_super) {
+        __extends$1(TinyYolov2Trainable, _super);
+        function TinyYolov2Trainable(trainableConfig, optimizer) {
+            var _this = _super.call(this, trainableConfig) || this;
+            _this._trainableConfig = validateTrainConfig(trainableConfig);
+            _this._optimizer = optimizer;
+            return _this;
+        }
+        Object.defineProperty(TinyYolov2Trainable.prototype, "trainableConfig", {
+            get: function () {
+                return this._trainableConfig;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        Object.defineProperty(TinyYolov2Trainable.prototype, "optimizer", {
+            get: function () {
+                return this._optimizer;
+            },
+            enumerable: true,
+            configurable: true
+        });
+        TinyYolov2Trainable.prototype.backward = function (img, groundTruth, inputSize, options) {
+            if (options === void 0) { options = {}; }
+            return __awaiter$1(this, void 0, void 0, function () {
+                var _this = this;
+                var _a, minBoxSize, reportLosses, reshapedImgDims, filteredGroundTruthBoxes, netInput, loss;
+                return __generator$1(this, function (_b) {
+                    switch (_b.label) {
+                        case 0:
+                            _a = getDefaultBackwardOptions(options), minBoxSize = _a.minBoxSize, reportLosses = _a.reportLosses;
+                            reshapedImgDims = computeReshapedDimensions(getMediaDimensions(img), inputSize);
+                            filteredGroundTruthBoxes = this.filterGroundTruthBoxes(groundTruth, reshapedImgDims, minBoxSize);
+                            if (!filteredGroundTruthBoxes.length) {
+                                return [2 /*return*/, null];
+                            }
+                            return [4 /*yield*/, toNetInput(imageToSquare(img, inputSize))];
+                        case 1:
+                            netInput = _b.sent();
+                            loss = this.optimizer.minimize(function () {
+                                var _a = _this.computeLoss(_this.forwardInput(netInput, inputSize), filteredGroundTruthBoxes, reshapedImgDims), noObjectLoss = _a.noObjectLoss, objectLoss = _a.objectLoss, coordLoss = _a.coordLoss, classLoss = _a.classLoss, totalLoss = _a.totalLoss;
+                                if (reportLosses) {
+                                    var losses = {
+                                        totalLoss: totalLoss.dataSync()[0],
+                                        noObjectLoss: noObjectLoss.dataSync()[0],
+                                        objectLoss: objectLoss.dataSync()[0],
+                                        coordLoss: coordLoss.dataSync()[0],
+                                        classLoss: classLoss.dataSync()[0]
+                                    };
+                                    var report = {
+                                        losses: losses,
+                                        numBoxes: filteredGroundTruthBoxes.length,
+                                        inputSize: inputSize
+                                    };
+                                    reportLosses(report);
+                                }
+                                return totalLoss;
+                            }, true);
+                            return [2 /*return*/, loss];
+                    }
+                });
+            });
+        };
+        TinyYolov2Trainable.prototype.computeLoss = function (outputTensor, groundTruth, reshapedImgDims) {
+            var config = validateTrainConfig(this.config);
+            var inputSize = Math.max(reshapedImgDims.width, reshapedImgDims.height);
+            if (!inputSize) {
+                throw new Error("computeLoss - invalid inputSize: " + inputSize);
+            }
+            var predictedBoxes = this.extractBoxes(outputTensor, reshapedImgDims);
+            return tidy(function () {
+                var lossFunction = new TinyYolov2LossFunction(outputTensor, groundTruth, predictedBoxes, reshapedImgDims, config);
+                return lossFunction.computeLoss();
+            });
+        };
+        TinyYolov2Trainable.prototype.filterGroundTruthBoxes = function (groundTruth, imgDims, minBoxSize) {
+            var imgHeight = imgDims.height, imgWidth = imgDims.width;
+            return groundTruth.filter(function (_a) {
+                var x = _a.x, y = _a.y, width = _a.width, height = _a.height;
+                var box = (new Rect(x, y, width, height))
+                    .rescale({ height: imgHeight, width: imgWidth });
+                var isTooTiny = box.width < minBoxSize || box.height < minBoxSize;
+                return !isTooTiny;
+            });
+        };
+        TinyYolov2Trainable.prototype.load = function (weightsOrUrl) {
+            return __awaiter$1(this, void 0, void 0, function () {
+                return __generator$1(this, function (_a) {
+                    switch (_a.label) {
+                        case 0: return [4 /*yield*/, _super.prototype.load.call(this, weightsOrUrl)];
+                        case 1:
+                            _a.sent();
+                            this.variable();
+                            return [2 /*return*/];
+                    }
+                });
+            });
+        };
+        return TinyYolov2Trainable;
+    }(TinyYolov2));
+
+    exports.tf = tfCore_esm;
+    exports.BoundingBox = BoundingBox;
+    exports.Box = Box;
+    exports.BoxWithText = BoxWithText;
+    exports.Dimensions = Dimensions;
+    exports.LabeledBox = LabeledBox;
+    exports.ObjectDetection = ObjectDetection;
+    exports.Point = Point;
+    exports.PredictedBox = PredictedBox;
+    exports.Rect = Rect;
+    exports.disposeUnusedWeightTensors = disposeUnusedWeightTensors;
+    exports.extractWeightEntryFactory = extractWeightEntryFactory;
+    exports.extractWeightsFactory = extractWeightsFactory;
+    exports.getModelUris = getModelUris;
+    exports.awaitMediaLoaded = awaitMediaLoaded;
+    exports.bufferToImage = bufferToImage;
+    exports.createCanvas = createCanvas;
+    exports.createCanvasFromMedia = createCanvasFromMedia;
+    exports.drawBox = drawBox;
+    exports.drawDetection = drawDetection;
+    exports.drawText = drawText;
+    exports.fetchImage = fetchImage;
+    exports.fetchJson = fetchJson;
+    exports.fetchNetWeights = fetchNetWeights;
+    exports.fetchOrThrow = fetchOrThrow;
+    exports.getContext2dOrThrow = getContext2dOrThrow;
+    exports.getDefaultDrawOptions = getDefaultDrawOptions;
+    exports.getMediaDimensions = getMediaDimensions;
+    exports.imageTensorToCanvas = imageTensorToCanvas;
+    exports.imageToSquare = imageToSquare;
+    exports.isMediaElement = isMediaElement;
+    exports.isMediaLoaded = isMediaLoaded;
+    exports.loadWeightMap = loadWeightMap;
+    exports.NetInput = NetInput;
+    exports.resolveInput = resolveInput;
+    exports.toNetInput = toNetInput;
+    exports.env = env;
+    exports.sigmoid = sigmoid$1;
+    exports.inverseSigmoid = inverseSigmoid;
+    exports.iou = iou;
+    exports.nonMaxSuppression = nonMaxSuppression$1;
+    exports.normalize = normalize;
+    exports.padToSquare = padToSquare;
+    exports.shuffleArray = shuffleArray;
+    exports.isTensor = isTensor;
+    exports.isTensor1D = isTensor1D;
+    exports.isTensor2D = isTensor2D;
+    exports.isTensor3D = isTensor3D;
+    exports.isTensor4D = isTensor4D;
+    exports.isFloat = isFloat;
+    exports.isEven = isEven$1;
+    exports.round = round$1;
+    exports.isDimensions = isDimensions;
+    exports.computeReshapedDimensions = computeReshapedDimensions;
+    exports.getCenterPoint = getCenterPoint;
+    exports.range = range$1;
+    exports.isValidNumber = isValidNumber;
+    exports.isValidProbablitiy = isValidProbablitiy;
+    exports.NeuralNetwork = NeuralNetwork;
+    exports.convLayer = convLayer;
+    exports.extractConvParamsFactory = extractConvParamsFactory;
+    exports.extractFCParamsFactory = extractFCParamsFactory;
+    exports.extractSeparableConvParamsFactory = extractSeparableConvParamsFactory;
+    exports.loadSeparableConvParamsFactory = loadSeparableConvParamsFactory;
+    exports.SeparableConvParams = SeparableConvParams;
+    exports.TinyYolov2 = TinyYolov2;
+    exports.TinyYolov2LossFunction = TinyYolov2LossFunction;
+    exports.TinyYolov2Trainable = TinyYolov2Trainable;
+    exports.TinyYolov2Options = TinyYolov2Options;
+
+    Object.defineProperty(exports, '__esModule', { value: true });
+
+})));
+//# sourceMappingURL=tiny-yolov2.js.map
diff --git a/node_modules/tfjs-tiny-yolov2/dist/tiny-yolov2.js.map b/node_modules/tfjs-tiny-yolov2/dist/tiny-yolov2.js.map
new file mode 100644
index 0000000..9fec250
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/dist/tiny-yolov2.js.map
@@ -0,0 +1 @@
+{"version":3,"file":"tiny-yolov2.js","sources":["../node_modules/@tensorflow/tfjs-core/dist/tf-core.esm.js","../node_modules/tfjs-image-recognition-base/build/es6/classes/Dimensions.js","../node_modules/tfjs-image-recognition-base/build/es6/classes/Point.js","../node_modules/tfjs-image-recognition-base/build/es6/utils/index.js","../node_modules/tfjs-image-recognition-base/build/es6/classes/Box.js","../node_modules/tfjs-image-recognition-base/build/es6/classes/BoundingBox.js","../node_modules/tfjs-image-recognition-base/build/es6/classes/BoxWithText.js","../node_modules/tfjs-image-recognition-base/build/es6/classes/LabeledBox.js","../node_modules/tfjs-image-recognition-base/build/es6/classes/ObjectDetection.js","../node_modules/tfjs-image-recognition-base/build/es6/classes/PredictedBox.js","../node_modules/tfjs-image-recognition-base/build/es6/classes/Rect.js","../node_modules/tfjs-image-recognition-base/build/es6/common/disposeUnusedWeightTensors.js","../node_modules/tfjs-image-recognition-base/build/es6/common/extractWeightEntryFactory.js","../node_modules/tfjs-image-recognition-base/build/es6/common/extractWeightsFactory.js","../node_modules/tfjs-image-recognition-base/build/es6/common/getModelUris.js","../node_modules/tfjs-image-recognition-base/build/es6/env/isNodejs.js","../node_modules/tfjs-image-recognition-base/build/es6/env/isBrowser.js","../node_modules/tfjs-image-recognition-base/build/es6/env/initialize.js","../node_modules/tfjs-image-recognition-base/build/es6/env/index.js","../node_modules/tfjs-image-recognition-base/build/es6/dom/isMediaLoaded.js","../node_modules/tfjs-image-recognition-base/build/es6/dom/awaitMediaLoaded.js","../node_modules/tfjs-image-recognition-base/build/es6/dom/bufferToImage.js","../node_modules/tfjs-image-recognition-base/build/es6/dom/getContext2dOrThrow.js","../node_modules/tfjs-image-recognition-base/build/es6/dom/getMediaDimensions.js","../node_modules/tfjs-image-recognition-base/build/es6/dom/createCanvas.js","../node_modules/tfjs-image-recognition-base/build/es6/dom/getDefaultDrawOptions.js","../node_modules/tfjs-image-recognition-base/build/es6/dom/drawBox.js","../node_modules/tfjs-image-recognition-base/build/es6/dom/drawText.js","../node_modules/tfjs-image-recognition-base/build/es6/dom/resolveInput.js","../node_modules/tfjs-image-recognition-base/build/es6/dom/drawDetection.js","../node_modules/tfjs-image-recognition-base/build/es6/dom/fetchOrThrow.js","../node_modules/tfjs-image-recognition-base/build/es6/dom/fetchImage.js","../node_modules/tfjs-image-recognition-base/build/es6/dom/fetchJson.js","../node_modules/tfjs-image-recognition-base/build/es6/dom/fetchNetWeights.js","../node_modules/tfjs-image-recognition-base/build/es6/dom/imageTensorToCanvas.js","../node_modules/tfjs-image-recognition-base/build/es6/dom/imageToSquare.js","../node_modules/tfjs-image-recognition-base/build/es6/dom/isMediaElement.js","../node_modules/tfjs-image-recognition-base/build/es6/dom/loadWeightMap.js","../node_modules/tfjs-image-recognition-base/build/es6/ops/padToSquare.js","../node_modules/tfjs-image-recognition-base/build/es6/dom/NetInput.js","../node_modules/tfjs-image-recognition-base/build/es6/dom/toNetInput.js","../node_modules/tfjs-image-recognition-base/build/es6/ops/iou.js","../node_modules/tfjs-image-recognition-base/build/es6/ops/nonMaxSuppression.js","../node_modules/tfjs-image-recognition-base/build/es6/ops/normalize.js","../node_modules/tfjs-image-recognition-base/build/es6/ops/shuffleArray.js","../node_modules/tfjs-image-recognition-base/build/es6/ops/index.js","../node_modules/tfjs-image-recognition-base/build/es6/NeuralNetwork.js","../src/common/convLayer.ts","../src/common/extractConvParamsFactory.ts","../src/common/extractFCParamsFactory.ts","../src/common/types.ts","../src/common/extractSeparableConvParamsFactory.ts","../src/tinyYolov2/config.ts","../src/tinyYolov2/const.ts","../src/tinyYolov2/leaky.ts","../src/tinyYolov2/convWithBatchNorm.ts","../src/tinyYolov2/depthwiseSeparableConv.ts","../src/tinyYolov2/extractParams.ts","../src/tinyYolov2/extractParamsFromWeigthMap.ts","../src/tinyYolov2/TinyYolov2Options.ts","../src/tinyYolov2/TinyYolov2.ts","../src/tinyYolov2/TinyYolov2LossFunction.ts","../src/tinyYolov2/getDefaultBackwardOptions.ts","../src/tinyYolov2/TinyYolov2Trainable.ts"],"sourcesContent":["/**\n * @license\n * Copyright 2018 Google LLC. All Rights Reserved.\n * Licensed under the Apache License, Version 2.0 (the \"License\");\n * you may not use this file except in compliance with the License.\n * You may obtain a copy of the License at\n *\n * http://www.apache.org/licenses/LICENSE-2.0\n *\n * Unless required by applicable law or agreed to in writing, software\n * distributed under the License is distributed on an \"AS IS\" BASIS,\n * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n * See the License for the specific language governing permissions and\n * limitations under the License.\n * =============================================================================\n */\nvar extendStatics=Object.setPrototypeOf||{__proto__:[]}instanceof Array&&function(e,t){e.__proto__=t}||function(e,t){for(var n in t)t.hasOwnProperty(n)&&(e[n]=t[n])};function __extends(e,t){function n(){this.constructor=e}extendStatics(e,t),e.prototype=null===t?Object.create(t):(n.prototype=t.prototype,new n)}var 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n=0;n<e.length;n++)if(e[n]!==t[n])return!1;return!0}function isInt(e){return e%1==0}function tanh(e){if(null!=Math.tanh)return Math.tanh(e);if(e===1/0)return 1;if(e===-1/0)return-1;var t=Math.exp(2*e);return(t-1)/(t+1)}function sizeToSquarishShape(e){for(var t=Math.floor(Math.sqrt(e));t>1;--t)if(e%t==0)return[t,e/t];return[1,e]}function createShuffledIndices(e){for(var t=new Uint32Array(e),n=0;n<e;++n)t[n]=n;return shuffle(t),t}function rightPad(e,t){return t<=e.length?e:e+\" \".repeat(t-e.length)}function repeatedTry(e,t,n){return void 0===t&&(t=function(e){return 0}),new Promise(function(r,o){var a=0,i=function(){if(e())r();else{var s=t(++a);null!=n&&a>=n?o():setTimeout(i,s)}};i()})}function inferFromImplicitShape(e,t){for(var n=1,r=-1,o=0;o<e.length;++o)if(e[o]>=0)n*=e[o];else if(-1===e[o]){if(-1!==r)throw Error(\"Shapes can only have 1 implicit size. Found -1 at dim \"+r+\" and dim \"+o);r=o}else if(e[o]<0)throw Error(\"Shapes can not be < 0. Found \"+e[o]+\" at dim \"+o);if(-1===r){if(t>0&&t!==n)throw Error(\"Size(\"+t+\") must match the product of shape \"+e);return e}if(0===n)throw Error(\"Cannot infer the missing size in [\"+e+\"] when there are 0 elements\");if(t%n!=0)throw Error(\"The implicit shape can't be a fractional number. Got \"+t+\" / \"+n);var a=e.slice();return a[r]=t/n,a}function squeezeShape(e,t){for(var n=[],r=[],o=0,a=0;a<e.length;++a){if(null!=t){if(t[o]===a&&1!==e[a])throw new Error(\"Can't squeeze axis \"+a+\" since its dim '\"+e[a]+\"' is not 1\");(null==t[o]||t[o]>a)&&1===e[a]&&(n.push(e[a]),r.push(a)),t[o]<=a&&o++}1!==e[a]&&(n.push(e[a]),r.push(a))}return{newShape:n,keptDims:r}}function getTypedArrayFromDType(e,t){var n=null;if(null==e||\"float32\"===e)n=new Float32Array(t);else if(\"int32\"===e)n=new Int32Array(t);else{if(\"bool\"!==e)throw new Error(\"Unknown data type \"+e);n=new Uint8Array(t)}return n}function getArrayFromDType(e,t){var n=null;if(null==e||\"float32\"===e)n=new Float32Array(t);else if(\"int32\"===e)n=new Int32Array(t);else if(\"bool\"===e)n=new Uint8Array(t);else{if(\"string\"!==e)throw new Error(\"Unknown data type \"+e);n=new Array(t)}return n}function checkComputationForNaN(e,t,n){if(\"float32\"===t)for(var r=0;r<e.length;r++)if(isNaN(e[r]))throw Error(\"The result of the '\"+n+\"' has NaNs.\")}function checkConversionForNaN(e,t){if(\"float32\"!==t)for(var n=0;n<e.length;n++)if(isNaN(e[n]))throw Error(\"NaN is not a valid value for dtype: '\"+t+\"'.\")}function hasEncodingLoss(e,t){return\"complex64\"!==t&&((\"float32\"!==t||\"complex64\"===e)&&((\"int32\"!==t||\"float32\"===e||\"complex64\"===e)&&(\"bool\"!==t||\"bool\"!==e)))}function isTypedArray(e){return e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array}function bytesPerElement(e){if(\"float32\"===e||\"int32\"===e)return 4;if(\"complex64\"===e)return 8;if(\"bool\"===e)return 1;throw new Error(\"Unknown dtype \"+e)}function bytesFromStringArray(e){if(null==e)return 0;var t=0;return e.forEach(function(e){return t+=2*e.length}),t}function isString(e){return\"string\"==typeof e||e instanceof String}function isBoolean(e){return\"boolean\"==typeof e}function isNumber(e){return\"number\"==typeof e}function inferDtype(e){return e instanceof Array?inferDtype(e[0]):e instanceof Float32Array?\"float32\":e instanceof Int32Array||e instanceof Uint8Array?\"int32\":isNumber(e)?\"float32\":isString(e)?\"string\":isBoolean(e)?\"bool\":\"float32\"}function isFunction(e){return!!(e&&e.constructor&&e.call&&e.apply)}function nearestDivisor(e,t){for(var n=t;n<e;++n)if(e%n==0)return n;return e}function computeStrides(e){var t=e.length;if(t<2)return[];var n=new Array(t-1);n[t-2]=e[t-1];for(var r=t-3;r>=0;--r)n[r]=n[r+1]*e[r+1];return n}function toTypedArray(e,t,n){if(\"string\"===t)throw new Error(\"Cannot convert a string[] to a TypedArray\");if(noConversionNeeded(e,t))return e;if(Array.isArray(e)&&(e=flatten(e)),null==t||\"float32\"===t||\"complex64\"===t)return new Float32Array(e);if(\"int32\"===t)return n&&checkConversionForNaN(e,t),new Int32Array(e);if(\"bool\"===t){for(var r=new Uint8Array(e.length),o=0;o<r.length;++o)0!==Math.round(e[o])&&(r[o]=1);return r}throw new Error(\"Unknown data type \"+t)}function noConversionNeeded(e,t){return e instanceof Float32Array&&\"float32\"===t||e instanceof Int32Array&&\"int32\"===t||e instanceof Uint8Array&&\"bool\"===t}function makeOnesTypedArray(e,t){for(var n=makeZerosTypedArray(e,t),r=0;r<n.length;r++)n[r]=1;return n}function makeZerosTypedArray(e,t){if(null==t||\"float32\"===t||\"complex64\"===t)return new Float32Array(e);if(\"int32\"===t)return new Int32Array(e);if(\"bool\"===t)return new Uint8Array(e);throw new Error(\"Unknown data type \"+t)}function now(){if(\"undefined\"!=typeof performance)return performance.now();if(\"undefined\"!=typeof process){var e=process.hrtime();return 1e3*e[0]+e[1]/1e6}throw new Error(\"Cannot measure time in this environment. You should run tf.js in the browser or in Node.js\")}var util=Object.freeze({shuffle:shuffle,clamp:clamp,nearestLargerEven:nearestLargerEven,sum:sum,randUniform:randUniform,distSquared:distSquared,assert:assert,assertShapesMatch:assertShapesMatch,assertNonNull:assertNonNull,flatten:flatten,sizeFromShape:sizeFromShape,isScalarShape:isScalarShape,arraysEqual:arraysEqual,isInt:isInt,tanh:tanh,sizeToSquarishShape:sizeToSquarishShape,createShuffledIndices:createShuffledIndices,rightPad:rightPad,repeatedTry:repeatedTry,inferFromImplicitShape:inferFromImplicitShape,squeezeShape:squeezeShape,getTypedArrayFromDType:getTypedArrayFromDType,getArrayFromDType:getArrayFromDType,checkComputationForNaN:checkComputationForNaN,checkConversionForNaN:checkConversionForNaN,hasEncodingLoss:hasEncodingLoss,isTypedArray:isTypedArray,bytesPerElement:bytesPerElement,bytesFromStringArray:bytesFromStringArray,isString:isString,isBoolean:isBoolean,isNumber:isNumber,inferDtype:inferDtype,isFunction:isFunction,nearestDivisor:nearestDivisor,computeStrides:computeStrides,toTypedArray:toTypedArray,makeOnesTypedArray:makeOnesTypedArray,makeZerosTypedArray:makeZerosTypedArray,now:now}),Profiler=function(){function e(e,t){this.backendTimer=e,this.logger=t,null==t&&(this.logger=new Logger)}return e.prototype.profileKernel=function(e,t){var n,r=this,o=this.backendTimer.time(function(){n=t()});return(Array.isArray(n)?n:[n]).forEach(function(t){var n=t.dataSync();checkComputationForNaN(n,t.dtype,e),o.then(function(o){var a=\"\";null!=o.getExtraProfileInfo&&(a=o.getExtraProfileInfo()),r.logger.logKernelProfile(e,t,n,o.kernelMs,a)})}),n},e}(),Logger=function(){function e(){}return e.prototype.logKernelProfile=function(e,t,n,r,o){var a=rightPad(r+\"ms\",9),i=rightPad(e,25),s=t.rank,u=t.size,l=rightPad(t.shape.toString(),14);console.log(\"%c\"+i+\"\\t%c\"+a+\"\\t%c\"+s+\"D \"+l+\"\\t%c\"+u+\"\\t%c\"+o,\"font-weight:bold\",\"color:red\",\"color:blue\",\"color: orange\",\"color: green\")},e}(),FORMAT_LIMIT_NUM_VALS=20,FORMAT_NUM_FIRST_LAST_VALS=3,FORMAT_NUM_SIG_DIGITS=7;function tensorToString(e,t,n,r){var o=computeStrides(t),a=computeMaxSizePerColumn(e,t,n,o),i=t.length,s=subTensorToString(e,t,n,o,a),u=[\"Tensor\"];return r&&(u.push(\"  dtype: \"+n),u.push(\"  rank: \"+i),u.push(\"  shape: [\"+t+\"]\"),u.push(\"  values:\")),u.push(s.map(function(e){return\"    \"+e}).join(\"\\n\")),u.join(\"\\n\")}function computeMaxSizePerColumn(e,t,n,r){var o=sizeFromShape(t),a=r[r.length-1],i=new Array(a).fill(0),s=t.length,u=\"complex64\"===n?createComplexTuples(e):e;if(s>1)for(var l=0;l<o/a;l++)for(var c=l*a,p=0;p<a;p++)i[p]=Math.max(i[p],valToString(u[c+p],0).length);return i}function valToString(e,t){return rightPad(Array.isArray(e)?parseFloat(e[0].toFixed(FORMAT_NUM_SIG_DIGITS))+\" + \"+parseFloat(e[1].toFixed(FORMAT_NUM_SIG_DIGITS))+\"j\":isString(e)?\"'\"+e+\"'\":parseFloat(e.toFixed(FORMAT_NUM_SIG_DIGITS)).toString(),t)}function subTensorToString(e,t,n,r,o,a){void 0===a&&(a=!0);var i=\"complex64\"===n?2:1,s=t[0],u=t.length;if(0===u)return\"complex64\"===n?[valToString(createComplexTuples(e)[0],0)]:[e[0].toString()];if(1===u){if(s>FORMAT_LIMIT_NUM_VALS){var l=FORMAT_NUM_FIRST_LAST_VALS*i,c=Array.from(e.slice(0,l)),p=Array.from(e.slice(s-FORMAT_NUM_FIRST_LAST_VALS*i,s));return\"complex64\"===n&&(c=createComplexTuples(c),p=createComplexTuples(p)),[\"[\"+c.map(function(e,t){return valToString(e,o[t])}).join(\", \")+\", ..., \"+p.map(function(e,t){return valToString(e,o[s-FORMAT_NUM_FIRST_LAST_VALS+t])}).join(\", \")+\"]\"]}return[\"[\"+(\"complex64\"===n?createComplexTuples(e):Array.from(e)).map(function(e,t){return valToString(e,o[t])}).join(\", \")+\"]\"]}var d=t.slice(1),h=r.slice(1),f=r[0]*i,m=[];if(s>FORMAT_LIMIT_NUM_VALS){for(var g=0;g<FORMAT_NUM_FIRST_LAST_VALS;g++){var v=(y=g*f)+f;m.push.apply(m,subTensorToString(e.slice(y,v),d,n,h,o,!1))}m.push(\"...\");for(g=s-FORMAT_NUM_FIRST_LAST_VALS;g<s;g++){v=(y=g*f)+f;m.push.apply(m,subTensorToString(e.slice(y,v),d,n,h,o,g===s-1))}}else for(g=0;g<s;g++){var y;v=(y=g*f)+f;m.push.apply(m,subTensorToString(e.slice(y,v),d,n,h,o,g===s-1))}var x=2===u?\",\":\"\";m[0]=\"[\"+m[0]+x;for(g=1;g<m.length-1;g++)m[g]=\" \"+m[g]+x;var T=\",\\n\";for(g=2;g<u;g++)T+=\"\\n\";return m[m.length-1]=\" \"+m[m.length-1]+\"]\"+(a?\"\":T),m}function createComplexTuples(e){for(var t=[],n=0;n<e.length;n+=2)t.push([e[n],e[n+1]]);return t}var TensorBuffer=function(){function e(e,t,n){if(this.dtype=t,this.shape=e.slice(),this.size=sizeFromShape(e),null!=n){var r=n.length;assert(r===this.size,\"Length of values '\"+r+\"' does not match the size inferred by the shape '\"+this.size+\"'.\")}if(\"complex64\"===t)throw new Error(\"complex64 dtype TensorBuffers are not supported. Please create a TensorBuffer for the real and imaginary parts separately and call tf.complex(real, imag).\");this.values=n||getArrayFromDType(t,sizeFromShape(this.shape)),this.strides=computeStrides(e)}return e.prototype.set=function(e){for(var t=[],n=1;n<arguments.length;n++)t[n-1]=arguments[n];0===t.length&&(t=[0]),assert(t.length===this.rank,\"The number of provided coordinates (\"+t.length+\") must match the rank (\"+this.rank+\")\");var r=this.locToIndex(t);this.values[r]=e},e.prototype.get=function(){for(var e=[],t=0;t<arguments.length;t++)e[t]=arguments[t];0===e.length&&(e=[0]);for(var n=e[e.length-1],r=0;r<e.length-1;++r)n+=this.strides[r]*e[r];return this.values[n]},e.prototype.locToIndex=function(e){if(0===this.rank)return 0;if(1===this.rank)return e[0];for(var t=e[e.length-1],n=0;n<e.length-1;++n)t+=this.strides[n]*e[n];return t},e.prototype.indexToLoc=function(e){if(0===this.rank)return[];if(1===this.rank)return[e];for(var t=new Array(this.shape.length),n=0;n<t.length-1;++n)t[n]=Math.floor(e/this.strides[n]),e-=t[n]*this.strides[n];return t[t.length-1]=e,t},Object.defineProperty(e.prototype,\"rank\",{get:function(){return this.shape.length},enumerable:!0,configurable:!0}),e.prototype.toTensor=function(){return Tensor.make(this.shape,{values:this.values},this.dtype)},e}(),trackerFn=null,opHandler=null;function setTensorTracker(e){trackerFn=e}function setOpHandler(e){opHandler=e}var Tensor=function(){function e(e,t,n,r){this.isDisposedInternal=!1,this.shape=e.slice(),this.dtype=t||\"float32\",this.size=sizeFromShape(e),this.strides=computeStrides(e),this.dataId=null!=r?r:{},this.id=trackerFn().nextTensorId(),this.rankType=this.rank<5?this.rank.toString():\"higher\",trackerFn().registerTensor(this),null!=n&&trackerFn().write(this.dataId,n)}return e.make=function(t,n,r){return new e(t,r,n.values,n.dataId)},e.prototype.flatten=function(){return this.throwIfDisposed(),this.as1D()},e.prototype.asScalar=function(){return this.throwIfDisposed(),assert(1===this.size,\"The array must have only 1 element.\"),this.reshape([])},e.prototype.as1D=function(){return this.throwIfDisposed(),this.reshape([this.size])},e.prototype.as2D=function(e,t){return this.throwIfDisposed(),this.reshape([e,t])},e.prototype.as3D=function(e,t,n){return this.throwIfDisposed(),this.reshape([e,t,n])},e.prototype.as4D=function(e,t,n,r){return this.throwIfDisposed(),this.reshape([e,t,n,r])},e.prototype.as5D=function(e,t,n,r,o){return this.throwIfDisposed(),this.reshape([e,t,n,r,o])},e.prototype.asType=function(e){return this.throwIfDisposed(),opHandler.cast(this,e)},Object.defineProperty(e.prototype,\"rank\",{get:function(){return this.shape.length},enumerable:!0,configurable:!0}),e.prototype.get=function(){for(var e=[],t=0;t<arguments.length;t++)e[t]=arguments[t];assert(e.length===this.rank,\"Number of coordinates in get() must match the rank of the tensor\"),assert(\"complex64\"!==this.dtype,\"Tensor.get() is not supported for complex64 tensors yet.\"),this.throwIfDisposed(),0===e.length&&(e=[0]);for(var n=e[e.length-1],r=0;r<e.length-1;++r)n+=this.strides[r]*e[r];return this.dataSync()[n]},e.prototype.buffer=function(){return opHandler.buffer(this.shape,this.dtype,this.dataSync())},e.prototype.data=function(){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(e){return this.throwIfDisposed(),[2,trackerFn().read(this.dataId)]})})},e.prototype.dataSync=function(){return this.throwIfDisposed(),trackerFn().readSync(this.dataId)},e.prototype.dispose=function(){this.isDisposed||(trackerFn().disposeTensor(this),this.isDisposedInternal=!0)},Object.defineProperty(e.prototype,\"isDisposed\",{get:function(){return this.isDisposedInternal},enumerable:!0,configurable:!0}),e.prototype.throwIfDisposed=function(){if(this.isDisposed)throw new Error(\"Tensor is disposed.\")},e.prototype.toFloat=function(){return this.asType(\"float32\")},e.prototype.toInt=function(){return this.asType(\"int32\")},e.prototype.toBool=function(){return this.asType(\"bool\")},e.prototype.print=function(e){return void 0===e&&(e=!1),opHandler.print(this,e)},e.prototype.reshape=function(e){return this.throwIfDisposed(),opHandler.reshape(this,e)},e.prototype.reshapeAs=function(e){return this.throwIfDisposed(),this.reshape(e.shape)},e.prototype.expandDims=function(e){return void 0===e&&(e=0),opHandler.expandDims(this,e)},e.prototype.cumsum=function(e,t,n){return void 0===e&&(e=0),void 0===t&&(t=!1),void 0===n&&(n=!1),opHandler.cumsum(this,e,t,n)},e.prototype.squeeze=function(e){return this.throwIfDisposed(),opHandler.squeeze(this,e)},e.prototype.clone=function(){return this.throwIfDisposed(),opHandler.clone(this)},e.prototype.toString=function(e){return void 0===e&&(e=!1),tensorToString(this.dataSync(),this.shape,this.dtype,e)},e.prototype.tile=function(e){return this.throwIfDisposed(),opHandler.tile(this,e)},e.prototype.gather=function(e,t){return void 0===t&&(t=0),this.throwIfDisposed(),opHandler.gather(this,e,t)},e.prototype.matMul=function(e,t,n){return void 0===t&&(t=!1),void 0===n&&(n=!1),this.throwIfDisposed(),opHandler.matMul(this,e,t,n)},e.prototype.dot=function(e){return this.throwIfDisposed(),opHandler.dot(this,e)},e.prototype.norm=function(e,t,n){return void 0===e&&(e=\"euclidean\"),void 0===t&&(t=null),void 0===n&&(n=!1),this.throwIfDisposed(),opHandler.norm(this,e,t,n)},e.prototype.slice=function(e,t){return this.throwIfDisposed(),opHandler.slice(this,e,t)},e.prototype.reverse=function(e){return this.throwIfDisposed(),opHandler.reverse(this,e)},e.prototype.concat=function(t,n){return void 0===n&&(n=0),this.throwIfDisposed(),t instanceof e&&(t=[t]),opHandler.concat([this].concat(t),n)},e.prototype.split=function(e,t){return void 0===t&&(t=0),this.throwIfDisposed(),opHandler.split(this,e,t)},e.prototype.stack=function(e,t){return void 0===t&&(t=0),opHandler.stack([this,e],t)},e.prototype.unstack=function(e,t){return void 0===t&&(t=0),opHandler.unstack(this,t)},e.prototype.pad=function(e,t){return void 0===t&&(t=0),opHandler.pad(this,e,t)},e.prototype.batchNormalization=function(e,t,n,r,o){return void 0===n&&(n=.001),this.throwIfDisposed(),opHandler.batchNormalization(this,e,t,n,r,o)},e.prototype.all=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.all(this,e,t)},e.prototype.any=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.any(this,e,t)},e.prototype.logSumExp=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.logSumExp(this,e,t)},e.prototype.sum=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.sum(this,e,t)},e.prototype.prod=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.prod(this,e,t)},e.prototype.mean=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.mean(this,e,t)},e.prototype.min=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.min(this,e,t)},e.prototype.max=function(e,t){return void 0===e&&(e=null),void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.max(this,e,t)},e.prototype.argMin=function(e){return void 0===e&&(e=null),this.throwIfDisposed(),opHandler.argMin(this,e)},e.prototype.argMax=function(e){return void 0===e&&(e=null),this.throwIfDisposed(),opHandler.argMax(this,e)},e.prototype.cast=function(e){return this.throwIfDisposed(),opHandler.cast(this,e)},e.prototype.add=function(e){return this.throwIfDisposed(),opHandler.add(this,e)},e.prototype.addStrict=function(e){return this.throwIfDisposed(),opHandler.addStrict(this,e)},e.prototype.atan2=function(e){return this.throwIfDisposed(),opHandler.atan2(this,e)},e.prototype.sub=function(e){return this.throwIfDisposed(),opHandler.sub(this,e)},e.prototype.subStrict=function(e){return this.throwIfDisposed(),opHandler.subStrict(this,e)},e.prototype.pow=function(e){return this.throwIfDisposed(),opHandler.pow(this,e)},e.prototype.powStrict=function(e){return this.throwIfDisposed(),opHandler.powStrict(this,e)},e.prototype.mul=function(e){return this.throwIfDisposed(),opHandler.mul(this,e)},e.prototype.mulStrict=function(e){return this.throwIfDisposed(),opHandler.mulStrict(this,e)},e.prototype.div=function(e){return this.throwIfDisposed(),opHandler.div(this,e)},e.prototype.floorDiv=function(e){return this.throwIfDisposed(),opHandler.floorDiv(this,e)},e.prototype.divStrict=function(e){return this.throwIfDisposed(),opHandler.divStrict(this,e)},e.prototype.minimum=function(e){return this.throwIfDisposed(),opHandler.minimum(this,e)},e.prototype.minimumStrict=function(e){return this.throwIfDisposed(),opHandler.minimumStrict(this,e)},e.prototype.maximum=function(e){return this.throwIfDisposed(),opHandler.maximum(this,e)},e.prototype.maximumStrict=function(e){return this.throwIfDisposed(),opHandler.maximumStrict(this,e)},e.prototype.mod=function(e){return this.throwIfDisposed(),opHandler.mod(this,e)},e.prototype.modStrict=function(e){return this.throwIfDisposed(),opHandler.modStrict(this,e)},e.prototype.squaredDifference=function(e){return this.throwIfDisposed(),opHandler.squaredDifference(this,e)},e.prototype.squaredDifferenceStrict=function(e){return this.throwIfDisposed(),opHandler.squaredDifferenceStrict(this,e)},e.prototype.transpose=function(e){return this.throwIfDisposed(),opHandler.transpose(this,e)},e.prototype.notEqual=function(e){return this.throwIfDisposed(),opHandler.notEqual(this,e)},e.prototype.notEqualStrict=function(e){return this.throwIfDisposed(),opHandler.notEqualStrict(this,e)},e.prototype.less=function(e){return this.throwIfDisposed(),opHandler.less(this,e)},e.prototype.lessStrict=function(e){return this.throwIfDisposed(),opHandler.lessStrict(this,e)},e.prototype.equal=function(e){return this.throwIfDisposed(),opHandler.equal(this,e)},e.prototype.equalStrict=function(e){return this.throwIfDisposed(),opHandler.equalStrict(this,e)},e.prototype.lessEqual=function(e){return this.throwIfDisposed(),opHandler.lessEqual(this,e)},e.prototype.lessEqualStrict=function(e){return this.throwIfDisposed(),opHandler.lessEqualStrict(this,e)},e.prototype.greater=function(e){return this.throwIfDisposed(),opHandler.greater(this,e)},e.prototype.greaterStrict=function(e){return this.throwIfDisposed(),opHandler.greaterStrict(this,e)},e.prototype.greaterEqual=function(e){return this.throwIfDisposed(),opHandler.greaterEqual(this,e)},e.prototype.greaterEqualStrict=function(e){return this.throwIfDisposed(),opHandler.greaterEqualStrict(this,e)},e.prototype.logicalAnd=function(e){return this.throwIfDisposed(),opHandler.logicalAnd(this,e)},e.prototype.logicalOr=function(e){return this.throwIfDisposed(),opHandler.logicalOr(this,e)},e.prototype.logicalNot=function(){return this.throwIfDisposed(),opHandler.logicalNot(this)},e.prototype.logicalXor=function(e){return this.throwIfDisposed(),opHandler.logicalXor(this,e)},e.prototype.where=function(e,t){return this.throwIfDisposed(),opHandler.where(e,this,t)},e.prototype.neg=function(){return this.throwIfDisposed(),opHandler.neg(this)},e.prototype.ceil=function(){return this.throwIfDisposed(),opHandler.ceil(this)},e.prototype.floor=function(){return this.throwIfDisposed(),opHandler.floor(this)},e.prototype.sign=function(){return this.throwIfDisposed(),opHandler.sign(this)},e.prototype.exp=function(){return this.throwIfDisposed(),opHandler.exp(this)},e.prototype.expm1=function(){return this.throwIfDisposed(),opHandler.expm1(this)},e.prototype.log=function(){return this.throwIfDisposed(),opHandler.log(this)},e.prototype.log1p=function(){return this.throwIfDisposed(),opHandler.log1p(this)},e.prototype.sqrt=function(){return this.throwIfDisposed(),opHandler.sqrt(this)},e.prototype.rsqrt=function(){return this.throwIfDisposed(),opHandler.rsqrt(this)},e.prototype.square=function(){return this.throwIfDisposed(),opHandler.square(this)},e.prototype.reciprocal=function(){return this.throwIfDisposed(),opHandler.reciprocal(this)},e.prototype.abs=function(){return this.throwIfDisposed(),opHandler.abs(this)},e.prototype.clipByValue=function(e,t){return this.throwIfDisposed(),opHandler.clipByValue(this,e,t)},e.prototype.relu=function(){return this.throwIfDisposed(),opHandler.relu(this)},e.prototype.elu=function(){return this.throwIfDisposed(),opHandler.elu(this)},e.prototype.selu=function(){return this.throwIfDisposed(),opHandler.selu(this)},e.prototype.leakyRelu=function(e){return void 0===e&&(e=.2),this.throwIfDisposed(),opHandler.leakyRelu(this,e)},e.prototype.prelu=function(e){return this.throwIfDisposed(),opHandler.prelu(this,e)},e.prototype.sigmoid=function(){return this.throwIfDisposed(),opHandler.sigmoid(this)},e.prototype.logSigmoid=function(){return this.throwIfDisposed(),opHandler.logSigmoid(this)},e.prototype.softplus=function(){return this.throwIfDisposed(),opHandler.softplus(this)},e.prototype.zerosLike=function(){return this.throwIfDisposed(),opHandler.zerosLike(this)},e.prototype.onesLike=function(){return this.throwIfDisposed(),opHandler.onesLike(this)},e.prototype.sin=function(){return this.throwIfDisposed(),opHandler.sin(this)},e.prototype.cos=function(){return this.throwIfDisposed(),opHandler.cos(this)},e.prototype.tan=function(){return this.throwIfDisposed(),opHandler.tan(this)},e.prototype.asin=function(){return this.throwIfDisposed(),opHandler.asin(this)},e.prototype.acos=function(){return this.throwIfDisposed(),opHandler.acos(this)},e.prototype.atan=function(){return this.throwIfDisposed(),opHandler.atan(this)},e.prototype.sinh=function(){return this.throwIfDisposed(),opHandler.sinh(this)},e.prototype.cosh=function(){return this.throwIfDisposed(),opHandler.cosh(this)},e.prototype.tanh=function(){return this.throwIfDisposed(),opHandler.tanh(this)},e.prototype.asinh=function(){return this.throwIfDisposed(),opHandler.asinh(this)},e.prototype.acosh=function(){return this.throwIfDisposed(),opHandler.acosh(this)},e.prototype.atanh=function(){return this.throwIfDisposed(),opHandler.atanh(this)},e.prototype.erf=function(){return this.throwIfDisposed(),opHandler.erf(this)},e.prototype.round=function(){return this.throwIfDisposed(),opHandler.round(this)},e.prototype.step=function(e){return void 0===e&&(e=0),this.throwIfDisposed(),opHandler.step(this,e)},e.prototype.softmax=function(e){return void 0===e&&(e=-1),this.throwIfDisposed(),opHandler.softmax(this,e)},e.prototype.logSoftmax=function(e){return void 0===e&&(e=-1),this.throwIfDisposed(),opHandler.logSoftmax(this,e)},e.prototype.resizeBilinear=function(e,t){return void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.image.resizeBilinear(this,e,t)},e.prototype.resizeNearestNeighbor=function(e,t){return void 0===t&&(t=!1),this.throwIfDisposed(),opHandler.image.resizeNearestNeighbor(this,e,t)},e.prototype.conv1d=function(e,t,n,r,o,a){return void 0===r&&(r=\"NWC\"),void 0===o&&(o=1),this.throwIfDisposed(),opHandler.conv1d(this,e,t,n,r,o,a)},e.prototype.conv2d=function(e,t,n,r,o,a){return void 0===r&&(r=\"NHWC\"),void 0===o&&(o=[1,1]),this.throwIfDisposed(),opHandler.conv2d(this,e,t,n,r,o,a)},e.prototype.conv2dTranspose=function(e,t,n,r,o){return this.throwIfDisposed(),opHandler.conv2dTranspose(this,e,t,n,r,o)},e.prototype.depthwiseConv2D=function(e,t,n,r,o,a){return void 0===r&&(r=\"NHWC\"),void 0===o&&(o=[1,1]),this.throwIfDisposed(),opHandler.depthwiseConv2d(this,e,t,n,r,o,a)},e.prototype.separableConv2d=function(e,t,n,r,o,a){return void 0===o&&(o=[1,1]),void 0===a&&(a=\"NHWC\"),this.throwIfDisposed(),opHandler.separableConv2d(this,e,t,n,r,o,a)},e.prototype.avgPool=function(e,t,n,r){return this.throwIfDisposed(),opHandler.avgPool(this,e,t,n,r)},e.prototype.maxPool=function(e,t,n,r){return this.throwIfDisposed(),opHandler.maxPool(this,e,t,n,r)},e.prototype.localResponseNormalization=function(e,t,n,r){return void 0===e&&(e=5),void 0===t&&(t=1),void 0===n&&(n=1),void 0===r&&(r=.5),opHandler.localResponseNormalization(this,e,t,n,r)},e.prototype.pool=function(e,t,n,r,o){return this.throwIfDisposed(),opHandler.pool(this,e,t,n,r,o)},e.prototype.variable=function(e,t,n){return void 0===e&&(e=!0),this.throwIfDisposed(),Variable.variable(this,e,t,n)},e.prototype.unsortedSegmentSum=function(e,t){return this.throwIfDisposed(),opHandler.unsortedSegmentSum(this,e,t)},e.prototype.batchToSpaceND=function(e,t){return this.throwIfDisposed(),opHandler.batchToSpaceND(this,e,t)},e.prototype.spaceToBatchND=function(e,t){return this.throwIfDisposed(),opHandler.spaceToBatchND(this,e,t)},e.prototype.topk=function(e,t){return void 0===e&&(e=1),void 0===t&&(t=!0),this.throwIfDisposed(),opHandler.topk(this,e,t)},e.prototype.stridedSlice=function(e,t,n,r,o){return void 0===r&&(r=0),void 0===o&&(o=0),this.throwIfDisposed(),opHandler.stridedSlice(this,e,t,n,r,o)},e.prototype.depthToSpace=function(e,t){return this.throwIfDisposed(),opHandler.depthToSpace(this,e,t)},e.prototype.fft=function(){return this.throwIfDisposed(),opHandler.spectral.fft(this)},e.prototype.ifft=function(){return this.throwIfDisposed(),opHandler.spectral.ifft(this)},e}();Object.defineProperty(Tensor,Symbol.hasInstance,{value:function(e){return!!e&&null!=e.shape&&null!=e.dtype}});var Variable=function(e){function t(t,n,r){void 0===n&&(n=!0);var o=e.call(this,t.shape,t.dtype,null,t.dataId)||this;o.trainable=n,o.name=r,null==o.name&&(o.name=trackerFn().nextVariableId().toString());try{trackerFn().registerVariable(o)}catch(e){throw trackerFn().disposeTensor(o),e}return o}return __extends(t,e),t.variable=function(e,n,r,o){return void 0===n&&(n=!0),null!=o&&o!==e.dtype&&(e=e.asType(o)),new t(e,n,r)},t.prototype.assign=function(e){if(e.dtype!==this.dtype)throw new Error(\"dtype of the new value (\"+e.dtype+\") and previous value (\"+this.dtype+\") must match\");if(!arraysEqual(e.shape,this.shape))throw new Error(\"shape of the new value (\"+e.shape+\") and previous value (\"+this.shape+\") must match\");trackerFn().disposeTensor(this),this.dataId=e.dataId,trackerFn().registerTensor(this)},t}(Tensor);Object.defineProperty(Variable,Symbol.hasInstance,{value:function(e){return e instanceof Tensor&&null!=e.assign&&e.assign instanceof Function}});var Rank,UpcastInt32AndMap,UpcastBoolAndMap,UpcastFloat32AndMap,UpcastComplex64AndMap,variable=Variable.variable;function getFilteredNodesXToY(e,t,n){for(var r={},o={},a=0;a<t.length;a++)r[t[a].id]=!0;for(a=0;a<e.length;a++){var i=(f=e[a]).inputs;for(var s in i){for(var u=i[s],l=!1,c=0;c<t.length;c++)if(r[u.id]){f.outputs.forEach(function(e){return r[e.id]=!0}),l=!0,o[f.id]=!0;break}if(l)break}}var p={};p[n.id]=!0;var d={};for(a=e.length-1;a>=0;a--)for(i=(f=e[a]).inputs,c=0;c<f.outputs.length;c++)if(p[f.outputs[c].id]){for(var s in i)p[i[s].id]=!0,d[f.id]=!0;break}var h=[];for(a=0;a<e.length;a++){var f;if(o[(f=e[a]).id]&&d[f.id]){var m={};for(var s in f.inputs){var g=f.inputs[s];r[g.id]&&(m[s]=g)}var v=Object.assign({},f);v.inputs=m,v.outputs=f.outputs,h.push(v)}}return h}function backpropagateGradients(e,t){for(var n=function(n){var r=t[n],o=[];if(r.outputs.forEach(function(t){var n=e[t.id];if(null!=n)o.push(n);else{var r=Tensor.make(t.shape,{values:makeZerosTypedArray(t.size,t.dtype)},t.dtype);o.push(r)}}),null==r.gradient)throw new Error(\"Cannot compute gradient: gradient function not found for \"+r.name+\".\");var a=r.gradient(1===r.outputs.length?o[0]:o);for(var i in r.inputs){if(!(i in a))throw new Error(\"Cannot backprop through input \"+i+\". Available gradients found: \"+Object.keys(a)+\".\");var s=a[i]();if(\"float32\"!==s.dtype)throw new Error(\"Error in gradient for op \"+r.name+\". The gradient of input \"+i+\" must have 'float32' dtype, but has '\"+s.dtype+\"'\");var u=r.inputs[i];if(!arraysEqual(s.shape,u.shape))throw new Error(\"Error in gradient for op \"+r.name+\". The gradient of input '\"+i+\"' has shape '\"+s.shape+\"', which does not match the shape of the input '\"+u.shape+\"'\");if(null==e[u.id])e[u.id]=s;else{var l=e[u.id];e[u.id]=l.add(s),l.dispose()}}},r=t.length-1;r>=0;r--)n(r)}!function(e){e.R0=\"R0\",e.R1=\"R1\",e.R2=\"R2\",e.R3=\"R3\",e.R4=\"R4\",e.R5=\"R5\",e.R6=\"R6\"}(Rank||(Rank={})),function(e){e.float32=\"float32\",e.int32=\"int32\",e.bool=\"int32\",e.complex64=\"complex64\"}(UpcastInt32AndMap||(UpcastInt32AndMap={})),function(e){e.float32=\"float32\",e.int32=\"int32\",e.bool=\"bool\",e.complex64=\"complex64\"}(UpcastBoolAndMap||(UpcastBoolAndMap={})),function(e){e.float32=\"float32\",e.int32=\"float32\",e.bool=\"float32\",e.complex64=\"complex64\"}(UpcastFloat32AndMap||(UpcastFloat32AndMap={})),function(e){e.float32=\"complex64\",e.int32=\"complex64\",e.bool=\"complex64\",e.complex64=\"complex64\"}(UpcastComplex64AndMap||(UpcastComplex64AndMap={}));var upcastTypeMap={float32:UpcastFloat32AndMap,int32:UpcastInt32AndMap,bool:UpcastBoolAndMap,complex64:UpcastComplex64AndMap};function upcastType(e,t){if(\"string\"===e||\"string\"===t){if(\"string\"===e&&\"string\"===t)return\"string\";throw new Error(\"Can not upcast \"+e+\" with \"+t)}return upcastTypeMap[e][t]}function sumOutType(e){return upcastType(e,\"int32\")}function makeTypesMatch(e,t){if(e.dtype===t.dtype)return[e,t];var n=upcastType(e.dtype,t.dtype);return[e.cast(n),t.cast(n)]}function assertTypesMatch(e,t){assert(e.dtype===t.dtype,\"The dtypes of the first(\"+e.dtype+\") and second(\"+t.dtype+\") input must match\")}function isTensorInList(e,t){for(var n=0;n<t.length;n++)if(t[n].id===e.id)return!0;return!1}function getTensorsInContainer(e){var t=[];return walkTensorContainer(e,t,new Set),t}function walkTensorContainer(e,t,n){if(null!=e)if(e instanceof Tensor)t.push(e);else if(isIterable(e)){var r=e;for(var o in r){var a=r[o];n.has(a)||(n.add(a),walkTensorContainer(a,t,n))}}}function isIterable(e){return Array.isArray(e)||\"object\"==typeof e}var Type,Engine=function(){function e(e,t,n){this.backend=e,this.safeMode=t,this.debugMode=n,this.registeredVariables={},this.nextTapeNodeId=0,this.numBytes=0,this.numTensors=0,this.numStringTensors=0,this.numDataBuffers=0,this.profiling=!1,this.gradientScopeCount=0,this.customGradientDepth=0,this.scopeStack=[],this.keepTensors=new Set,this.tensorInfo=new WeakMap,this.profiler=new Profiler(e),this.activeProfile={newBytes:0,newTensors:0,peakBytes:0,kernels:[],result:null}}return e.prototype.moveData=function(e){this.write(e,this.readSync(e))},e.prototype.tidy=function(e,t,n){var r=this;void 0===n&&(n=!1);var o,a=null;if(null==t){if(\"function\"!=typeof e)throw new Error(\"Please provide a function to tidy()\");t=e}else{if(\"string\"!=typeof e&&!(e instanceof String))throw new Error(\"When calling with two arguments, the first argument to tidy() must be a string\");if(\"function\"!=typeof t)throw new Error(\"When calling with two arguments, the 2nd argument to tidy() must be a function\");a=e}return this.scopedRun(function(){return r.startScope(a,n)},function(){return r.endScope(o,n)},function(){return(o=t())instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),o})},e.prototype.scopedRun=function(e,t,n){e();try{var r=n();return t(),r}catch(e){throw t(),e}},e.prototype.nextTensorId=function(){return e.nextTensorId++},e.prototype.nextVariableId=function(){return e.nextVariableId++},e.prototype.runKernel=function(e,t,n){var r,o=this,a=[],i=function(e){return a.push(e),e},s=this.activeScope.name,u=this.numBytes,l=this.numTensors;if(this.scopedRun(function(){return o.customGradientDepth++},function(){return o.customGradientDepth--},function(){r=o.debugMode()?o.profiler.profileKernel(s,function(){return e(o.backend,i)}):e(o.backend,i)}),this.shouldRecord()){var c={id:this.nextTapeNodeId++,name:s,inputs:t,outputs:Array.isArray(r)?r:[r]};null!=n&&(c.gradient=function(e){return n(e,a)}),this.activeTape.push(c)}return this.profiling&&this.activeProfile.kernels.push({name:s,bytesAdded:this.numBytes-u,totalBytesSnapshot:this.numBytes,tensorsAdded:this.numTensors-l,totalTensorsSnapshot:this.numTensors,inputShapes:Object.keys(t).map(function(e){return t[e].shape}),outputShape:Array.isArray(r)?r.map(function(e){return e.shape}):r.shape}),r},e.prototype.registerTensor=function(e){var t=this.tensorInfo.has(e.dataId)?this.tensorInfo.get(e.dataId).refCount:0;if(this.numTensors++,\"string\"===e.dtype&&this.numStringTensors++,0===t){this.numDataBuffers++;var n=0;\"complex64\"!==e.dtype&&\"string\"!==e.dtype&&(n=sizeFromShape(e.shape)*bytesPerElement(e.dtype)),this.tensorInfo.set(e.dataId,{backend:this.backend,dtype:e.dtype,shape:e.shape,bytes:n,refCount:0}),this.numBytes+=n,this.backend.register(e.dataId,e.shape,e.dtype)}this.tensorInfo.get(e.dataId).refCount++,e instanceof Variable||this.track(e)},e.prototype.registerVariable=function(e){if(null!=this.registeredVariables[e.name])throw new Error(\"Variable with name \"+e.name+\" was already registered\");this.registeredVariables[e.name]=e},e.prototype.disposeTensor=function(e){if(this.tensorInfo.has(e.dataId)){this.keepTensors.has(e.id)&&this.keepTensors.delete(e.id),this.numTensors--,\"string\"===e.dtype&&this.numStringTensors--;var t=this.tensorInfo.get(e.dataId);t.refCount<=1?(\"complex64\"!==e.dtype&&(this.numBytes-=t.bytes),this.numDataBuffers--,t.backend.disposeData(e.dataId),this.tensorInfo.delete(e.dataId)):this.tensorInfo.get(e.dataId).refCount--}},e.prototype.disposeVariables=function(){for(var e in this.registeredVariables){var t=this.registeredVariables[e];this.disposeTensor(t),delete this.registeredVariables[e]}},e.prototype.memory=function(){var e=this.backend.memory();return e.numTensors=this.numTensors,e.numDataBuffers=this.numDataBuffers,e.numBytes=this.numBytes,this.numStringTensors>0&&(e.unreliable=!0,null==e.reasons&&(e.reasons=[]),e.reasons.push(\"Memory usage by string tensors is approximate (2 bytes per character)\")),e},e.prototype.profile=function(e){return __awaiter(this,void 0,void 0,function(){var t,n;return __generator(this,function(r){return this.profiling=!0,t=this.numBytes,n=this.numTensors,this.activeProfile.kernels=[],this.activeProfile.result=e(),this.profiling=!1,this.activeProfile.peakBytes=Math.max.apply(Math,this.activeProfile.kernels.map(function(e){return e.totalBytesSnapshot})),this.activeProfile.newBytes=this.numBytes-t,this.activeProfile.newTensors=this.numTensors-n,[2,this.activeProfile]})})},e.prototype.shouldRecord=function(){return null!=this.activeTape&&0===this.customGradientDepth},e.prototype.addTapeNode=function(e,t,n){var r={};e.forEach(function(e,t){r[t]=e});var o={id:this.nextTapeNodeId++,name:this.activeScope.name,inputs:r,outputs:[t],gradient:function(e){var t={};return n(e).forEach(function(e,n){t[n]=function(){return e}}),t}};this.activeTape.push(o)},e.prototype.keep=function(e){if(1===this.scopeStack.length&&this.safeMode)throw new Error(\"Safe mode is ON. Enclose all tensor operations inside tf.tidy(): tf.tidy(() => {...}) to avoid memory leaks.\");return this.keepTensors.add(e.id),e},e.prototype.startScope=function(e,t){void 0===t&&(t=!1),t&&0===this.gradientScopeCount&&(this.activeTape=[]),t&&this.gradientScopeCount++;var n={track:[],name:\"unnamed scope\"};e&&(n.name=e),this.scopeStack.push(n),this.activeScope=n},e.prototype.endScope=function(e,t){var n=this;void 0===t&&(t=!1),t&&(this.gradientScopeCount--,0===this.gradientScopeCount&&(this.activeTape=null));var r=new Set(this.keepTensors),o=getTensorsInContainer(e);o.forEach(function(e){return r.add(e.id)});for(var a=0;a<this.activeScope.track.length;a++){var i=this.activeScope.track[a];r.has(i.id)||(null!=this.activeTape?o.push(i):i.dispose())}var s=this.scopeStack.pop();this.activeScope=0===this.scopeStack.length?null:this.scopeStack[this.scopeStack.length-1],o.forEach(function(e){!n.keepTensors.has(e.id)&&isTensorInList(e,s.track)&&n.track(e)})},e.prototype.gradients=function(e,t,n,r){var o=this;if(void 0===r&&(r=!1),assert(t.length>0,\"gradients() received an empty list of xs.\"),null!=n&&\"float32\"!==n.dtype)throw new Error(\"dy must have 'float32' dtype, but has '\"+n.dtype+\"'\");return this.tidy(\"gradients\",function(){var a=e();assert(a instanceof Tensor,\"The result y returned by f() must be a tensor.\");var i=getFilteredNodesXToY(o.activeTape,t,a);if(!r&&0===i.length&&t.length>0)throw new Error(\"Cannot compute gradient of y=f(x) with respect to x. Make sure that the f you passed encloses all operations that lead from x to y.\");var s={};return s[a.id]=null==n?ones(a.shape):n,backpropagateGradients(s,i),{value:a,grads:t.map(function(e){return s[e.id]})}},!0)},e.prototype.customGrad=function(e){var t=this;return assert(isFunction(e),\"The f passed in customGrad(f) must be a function.\"),function(){for(var n,r,o=[],a=0;a<arguments.length;a++)o[a]=arguments[a];if(assert(o.every(function(e){return e instanceof Tensor}),\"The args passed in customGrad(f)(x1, x2,...) must all be tensors\"),t.scopedRun(function(){return t.customGradientDepth++},function(){return t.customGradientDepth--},function(){r=t.tidy(e.name,function(){var t=e.apply(void 0,o),r=t.value,a=t.gradFunc;return assert(r instanceof Tensor,\"The function f passed in customGrad(f) must return an object where `obj.value` is a tensor\"),assert(isFunction(a),\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function.\"),n=a,r},!0)}),t.shouldRecord()){t.addTapeNode(o,r,function(e){var t=n(e),r=Array.isArray(t)?t:[t];return assert(r.length===o.length,\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns the same number of tensors as inputs passed to f(...).\"),assert(r.every(function(e){return e instanceof Tensor}),\"The function f passed in customGrad(f) must return an object where `obj.gradFunc` is a function that returns a list of only tensors.\"),r})}return r}},e.prototype.write=function(e,t){var n=this.tensorInfo.get(e);if(\"string\"===n.dtype){var r=bytesFromStringArray(t);this.numBytes+=r-n.bytes,n.bytes=r}this.backend!==n.backend&&(n.backend.disposeData(e),n.backend=this.backend,this.backend.register(e,n.shape,n.dtype)),this.backend.write(e,t)},e.prototype.readSync=function(e){return this.tensorInfo.get(e).backend.readSync(e)},e.prototype.read=function(e){return this.tensorInfo.get(e).backend.read(e)},e.prototype.fromPixels=function(e,t){return this.backend.fromPixels(e,t)},e.prototype.time=function(e){return __awaiter(this,void 0,void 0,function(){var t,n;return __generator(this,function(r){switch(r.label){case 0:return t=now(),[4,this.backend.time(e)];case 1:return(n=r.sent()).wallMs=now()-t,[2,n]}})})},e.prototype.track=function(e){if(1===this.scopeStack.length&&this.safeMode)throw new Error(\"Safe mode is ON. Enclose all tensor operations inside tf.tidy(): tf.tidy(() => {op();...}); to avoid memory leaks.\");return null!=this.activeScope&&this.activeScope.track.push(e),e},e.nextTensorId=0,e.nextVariableId=0,e}();function ones(e){var t=makeOnesTypedArray(sizeFromShape(e),\"float32\");return Tensor.make(e,{values:t})}!function(e){e[e.NUMBER=0]=\"NUMBER\",e[e.BOOLEAN=1]=\"BOOLEAN\",e[e.STRING=2]=\"STRING\"}(Type||(Type={}));var MAX_TEXTURE_SIZE,URL_PROPERTIES=[{name:\"DEBUG\",type:Type.BOOLEAN},{name:\"IS_BROWSER\",type:Type.BOOLEAN},{name:\"WEBGL_LAZILY_UNPACK\",type:Type.BOOLEAN},{name:\"WEBGL_CPU_FORWARD\",type:Type.BOOLEAN},{name:\"WEBGL_PACK\",type:Type.BOOLEAN},{name:\"WEBGL_PACK_BATCHNORMALIZATION\",type:Type.BOOLEAN},{name:\"WEBGL_PACK_CLIP\",type:Type.BOOLEAN},{name:\"WEBGL_PACK_DEPTHWISECONV\",type:Type.BOOLEAN},{name:\"WEBGL_CONV_IM2COL\",type:Type.BOOLEAN},{name:\"WEBGL_MAX_TEXTURE_SIZE\",type:Type.NUMBER},{name:\"WEBGL_PAGING_ENABLED\",type:Type.BOOLEAN},{name:\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\",type:Type.NUMBER},{name:\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\",type:Type.BOOLEAN},{name:\"WEBGL_VERSION\",type:Type.NUMBER},{name:\"WEBGL_RENDER_FLOAT32_ENABLED\",type:Type.BOOLEAN},{name:\"WEBGL_DOWNLOAD_FLOAT_ENABLED\",type:Type.BOOLEAN},{name:\"WEBGL_FENCE_API_ENABLED\",type:Type.BOOLEAN},{name:\"WEBGL_SIZE_UPLOAD_UNIFORM\",type:Type.NUMBER},{name:\"BACKEND\",type:Type.STRING},{name:\"EPSILON\",type:Type.NUMBER},{name:\"PROD\",type:Type.BOOLEAN},{name:\"TENSORLIKE_CHECK_SHAPE_CONSISTENCY\",type:Type.BOOLEAN}];function isWebGLVersionEnabled(e){try{if(null!=getWebGLContext(e))return!0}catch(e){return!1}return!1}function getWebGLMaxTextureSize(e){if(null==MAX_TEXTURE_SIZE){var t=getWebGLContext(e);MAX_TEXTURE_SIZE=t.getParameter(t.MAX_TEXTURE_SIZE)}return MAX_TEXTURE_SIZE}function getWebGLDisjointQueryTimerVersion(e){if(0===e)return 0;var t=getWebGLContext(e);return hasExtension(t,\"EXT_disjoint_timer_query_webgl2\")&&2===e?2:hasExtension(t,\"EXT_disjoint_timer_query\")?1:0}function isRenderToFloatTextureEnabled(e){if(0===e)return!1;var t=getWebGLContext(e);if(1===e){if(!hasExtension(t,\"OES_texture_float\"))return!1}else if(!hasExtension(t,\"EXT_color_buffer_float\"))return!1;return createFloatTextureAndBindToFramebuffer(t,e)}function isDownloadFloatTextureEnabled(e){if(0===e)return!1;var t=getWebGLContext(e);if(1===e){if(!hasExtension(t,\"OES_texture_float\"))return!1;if(!hasExtension(t,\"WEBGL_color_buffer_float\"))return!1}else if(!hasExtension(t,\"EXT_color_buffer_float\"))return!1;return createFloatTextureAndBindToFramebuffer(t,e)}function isWebGLFenceEnabled(e){return 2===e&&null!=getWebGLContext(e).fenceSync}function isChrome(){return\"undefined\"!=typeof navigator&&null!=navigator&&null!=navigator.userAgent&&/Chrome/.test(navigator.userAgent)&&/Google Inc/.test(navigator.vendor)}var TENSORFLOWJS_FLAGS_PREFIX=\"tfjsflags\";function getFeaturesFromURL(){var e={};if(\"undefined\"==typeof window||void 0===window.location||void 0===window.location.search)return e;var t=getQueryParams(window.location.search);if(TENSORFLOWJS_FLAGS_PREFIX in t){var n={};t[TENSORFLOWJS_FLAGS_PREFIX].split(\",\").forEach(function(e){var t=e.split(\":\"),r=t[0],o=t[1];n[r]=o}),URL_PROPERTIES.forEach(function(t){t.name in n&&(console.log(\"Setting feature override from URL \"+t.name+\": \"+n[t.name]),t.type===Type.NUMBER?e[t.name]=+n[t.name]:t.type===Type.BOOLEAN?e[t.name]=\"true\"===n[t.name]:t.type===Type.STRING?e[t.name]=n[t.name]:console.warn(\"Unknown URL param: \"+t.name+\".\"))})}return e}function hasExtension(e,t){return null!=e.getExtension(t)}function createFloatTextureAndBindToFramebuffer(e,t){var n=e.createFramebuffer(),r=e.createTexture();e.bindTexture(e.TEXTURE_2D,r);var o=2===t?e.RGBA32F:e.RGBA;e.texImage2D(e.TEXTURE_2D,0,o,1,1,0,e.RGBA,e.FLOAT,null),e.bindFramebuffer(e.FRAMEBUFFER,n),e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,r,0);var a=e.checkFramebufferStatus(e.FRAMEBUFFER)===e.FRAMEBUFFER_COMPLETE;return e.bindTexture(e.TEXTURE_2D,null),e.bindFramebuffer(e.FRAMEBUFFER,null),e.deleteTexture(r),e.deleteFramebuffer(n),a}function getQueryParams(e){var t={};return e.replace(/[?&]([^=?&]+)(?:=([^&]*))?/g,function(e){for(var n=[],r=1;r<arguments.length;r++)n[r-1]=arguments[r];return decodeParam(t,n[0],n[1]),n.join(\"=\")}),t}function decodeParam(e,t,n){e[decodeURIComponent(t)]=decodeURIComponent(n||\"\")}var EPSILON_FLOAT16=1e-4,TEST_EPSILON_FLOAT16=.1,EPSILON_FLOAT32=1e-7,TEST_EPSILON_FLOAT32=.001,Environment=function(){function e(e){this.features={},this.registry={},null!=e&&(this.features=e),this.get(\"DEBUG\")&&console.warn(\"Debugging mode is ON. The output of every math call will be downloaded to CPU and checked for NaNs. This significantly impacts performance.\")}return e.setBackend=function(e,t){if(void 0===t&&(t=!1),!(e in ENV.registry))throw new Error(\"Backend name '\"+e+\"' not found in registry\");ENV.engine.backend=ENV.findBackend(e),ENV.backendName=e},e.getBackend=function(){return ENV.initEngine(),ENV.backendName},e.disposeVariables=function(){ENV.engine.disposeVariables()},e.memory=function(){return ENV.engine.memory()},e.profile=function(e){return ENV.engine.profile(e)},e.tidy=function(e,t){return ENV.engine.tidy(e,t)},e.dispose=function(e){getTensorsInContainer(e).forEach(function(e){return e.dispose()})},e.keep=function(e){return ENV.engine.keep(e)},e.time=function(e){return ENV.engine.time(e)},e.prototype.get=function(e){return e in this.features?this.features[e]:(this.features[e]=this.evaluateFeature(e),this.features[e])},e.prototype.getFeatures=function(){return this.features},e.prototype.set=function(e,t){this.features[e]=t},e.prototype.getBestBackendName=function(){var e=this;if(0===Object.keys(this.registry).length)throw new Error(\"No backend found in registry.\");return Object.keys(this.registry).map(function(t){return{name:t,entry:e.registry[t]}}).sort(function(e,t){return t.entry.priority-e.entry.priority})[0].name},e.prototype.evaluateFeature=function(e){if(\"DEBUG\"===e)return!1;if(\"IS_BROWSER\"===e)return\"undefined\"!=typeof window;if(\"IS_NODE\"===e)return\"undefined\"!=typeof process&&void 0!==process.versions&&void 0!==process.versions.node;if(\"IS_CHROME\"===e)return isChrome();if(\"WEBGL_CPU_FORWARD\"===e)return!0;if(\"WEBGL_PACK\"===e)return!1;if(\"WEBGL_PACK_BATCHNORMALIZATION\"===e)return this.get(\"WEBGL_PACK\");if(\"WEBGL_PACK_CLIP\"===e)return this.get(\"WEBGL_PACK\");if(\"WEBGL_PACK_DEPTHWISECONV\"===e)return this.get(\"WEBGL_PACK\");if(\"WEBGL_LAZILY_UNPACK\"===e)return this.get(\"WEBGL_PACK\");if(\"WEBGL_CONV_IM2COL\"===e)return this.get(\"WEBGL_PACK\");if(\"WEBGL_PAGING_ENABLED\"===e)return this.get(\"IS_BROWSER\")&&!this.get(\"PROD\");if(\"WEBGL_MAX_TEXTURE_SIZE\"===e)return getWebGLMaxTextureSize(this.get(\"WEBGL_VERSION\"));if(\"IS_TEST\"===e)return!1;if(\"BACKEND\"===e)return this.getBestBackendName();if(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"===e){var t=this.get(\"WEBGL_VERSION\");return 0===t?0:getWebGLDisjointQueryTimerVersion(t)}if(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_RELIABLE\"===e)return this.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0&&!isMobile();if(\"HAS_WEBGL\"===e)return this.get(\"WEBGL_VERSION\")>0;if(\"WEBGL_VERSION\"===e)return isWebGLVersionEnabled(2)?2:isWebGLVersionEnabled(1)?1:0;if(\"WEBGL_RENDER_FLOAT32_ENABLED\"===e)return isRenderToFloatTextureEnabled(this.get(\"WEBGL_VERSION\"));if(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\"===e)return isDownloadFloatTextureEnabled(this.get(\"WEBGL_VERSION\"));if(\"WEBGL_FENCE_API_ENABLED\"===e)return isWebGLFenceEnabled(this.get(\"WEBGL_VERSION\"));if(\"WEBGL_SIZE_UPLOAD_UNIFORM\"===e)return this.get(\"WEBGL_RENDER_FLOAT32_ENABLED\")?4:0;if(\"TEST_EPSILON\"===e)return 32===this.backend.floatPrecision()?TEST_EPSILON_FLOAT32:TEST_EPSILON_FLOAT16;if(\"EPSILON\"===e)return 32===this.backend.floatPrecision()?EPSILON_FLOAT32:EPSILON_FLOAT16;if(\"PROD\"===e)return!1;if(\"TENSORLIKE_CHECK_SHAPE_CONSISTENCY\"===e)return!this.get(\"PROD\");throw new Error(\"Unknown feature \"+e+\".\")},e.prototype.setFeatures=function(e){this.features=Object.assign({},e)},e.prototype.reset=function(){this.features=getFeaturesFromURL(),null!=this.globalEngine&&(this.globalEngine=null)},Object.defineProperty(e.prototype,\"backend\",{get:function(){return this.engine.backend},enumerable:!0,configurable:!0}),e.prototype.findBackend=function(e){return e in this.registry?this.registry[e].backend:null},e.prototype.registerBackend=function(e,t,n,r){var o=this;if(void 0===n&&(n=1),e in this.registry)return console.warn(e+\" backend was already registered. Reusing existing backend\"),null!=r&&r(function(){return o.engine}),!1;try{var a=t();return a.setDataMover({moveData:function(e){return o.engine.moveData(e)}}),this.registry[e]={backend:a,priority:n},!0}catch(t){return console.warn(\"Registration of backend \"+e+\" failed\"),console.warn(t.stack||t.message),!1}},e.prototype.removeBackend=function(e){if(!(e in this.registry))throw new Error(e+\" backend not found in registry\");this.registry[e].backend.dispose(),delete this.registry[e]},Object.defineProperty(e.prototype,\"engine\",{get:function(){return this.initEngine(),this.globalEngine},enumerable:!0,configurable:!0}),e.prototype.initEngine=function(){var e=this;if(null==this.globalEngine){this.backendName=this.get(\"BACKEND\");var t=this.findBackend(this.backendName);this.globalEngine=new Engine(t,!1,function(){return e.get(\"DEBUG\")})}},e}();function getGlobalNamespace(){var e;if(\"undefined\"!=typeof window)e=window;else{if(\"undefined\"==typeof process)throw new Error(\"Could not find a global object\");e=process}return e}function getOrMakeEnvironment(){var e=getGlobalNamespace();return null==e.ENV&&(e.ENV=new Environment(getFeaturesFromURL()),setTensorTracker(function(){return e.ENV.engine})),e.ENV}var ENV=getOrMakeEnvironment(),environment=Object.freeze({EPSILON_FLOAT16:EPSILON_FLOAT16,EPSILON_FLOAT32:EPSILON_FLOAT32,Environment:Environment,ENV:ENV});function grad(e){return assert(isFunction(e),\"The f passed in grad(f) must be a function\"),function(t,n){return assert(t instanceof Tensor,\"The x passed in grad(f)(x) must be a tensor\"),assert(null==n||n instanceof Tensor,\"The dy passed in grad(f)(x, dy) must be a tensor\"),ENV.engine.tidy(function(){var r=ENV.engine.gradients(function(){return e(t)},[t],n),o=r.value,a=r.grads;return null!=n&&assertShapesMatch(o.shape,n.shape,\"The shape of dy passed in grad(f)(x, dy) must match the shape returned by f(x)\"),checkGrads(a),a[0]})}}function grads(e){return assert(isFunction(e),\"The f passed in grads(f) must be a function\"),function(t,n){return assert(Array.isArray(t)&&t.every(function(e){return e instanceof Tensor}),\"The args passed in grads(f)(args) must be an array of tensors\"),assert(null==n||n instanceof Tensor,\"The dy passed in grads(f)(args, dy) must be a tensor\"),ENV.engine.tidy(function(){var r=ENV.engine.gradients(function(){return e.apply(void 0,t)},t,n),o=r.value,a=r.grads;return null!=n&&assertShapesMatch(o.shape,n.shape,\"The shape of dy passed in grads(f)([x1,...], dy) must match the shape returned by f([x1,...])\"),checkGrads(a),a})}}function valueAndGrad(e){return assert(isFunction(e),\"The f passed in valueAndGrad(f) must be a function\"),function(t,n){assert(t instanceof Tensor,\"The x passed in valueAndGrad(f)(x) must be a tensor\"),assert(null==n||n instanceof Tensor,\"The dy passed in valueAndGrad(f)(x, dy) must be a tensor\");var r=ENV.engine.gradients(function(){return e(t)},[t],n),o=r.grads,a=r.value;return checkGrads(o),{grad:o[0],value:a}}}function valueAndGrads(e){return assert(isFunction(e),\"The f passed in valueAndGrads(f) must be a function\"),function(t,n){assert(Array.isArray(t)&&t.every(function(e){return e instanceof Tensor}),\"The args passed in valueAndGrads(f)(args) must be array of tensors\"),assert(null==n||n instanceof Tensor,\"The dy passed in valueAndGrads(f)(args, dy) must be a tensor\");var r=ENV.engine.gradients(function(){return e.apply(void 0,t)},t,n);return null!=n&&assertShapesMatch(r.value.shape,n.shape,\"The shape of dy passed in valueAndGrads(f)([x1,...], dy) must match the shape returned by f([x1,...])\"),checkGrads(r.grads),r}}function variableGrads(e,t){if(assert(isFunction(e),\"The f passed in variableGrads(f) must be a function\"),assert(null==t||Array.isArray(t)&&t.every(function(e){return e instanceof Variable}),\"The varList passed in variableGrads(f, varList) must be an array of variables\"),null==t)for(var n in t=[],ENV.engine.registeredVariables)t.push(ENV.engine.registeredVariables[n]);var r=t.length;assert((t=t.filter(function(e){return e.trainable})).length>0,\"variableGrads() expects at least one of the input variables to be trainable, but none of the \"+r+\" variables is trainable.\");var o=ENV.engine.gradients(e,t,null,!0),a=o.value,i=o.grads;assert(i.some(function(e){return null!=e}),\"Cannot find a connection between any variable and the result of the loss function y=f(x). Please make sure the operations that use variables are inside the function f passed to minimize().\"),assert(0===a.rank,\"The f passed in variableGrads(f) must return a scalar, but it returned a rank-\"+a.rank+\" tensor\");var s={};return t.forEach(function(e,t){null!=i[t]&&(s[e.name]=i[t])}),{value:a,grads:s}}function customGrad(e){return ENV.engine.customGrad(e)}function checkGrads(e){if(e.filter(function(e){return null==e}).length>0)throw new Error(\"Cannot compute gradient of y=f(x) with respect to x. Make sure that\\n    the f you passed encloses all operations that lead from x to y.\")}var tidy=Environment.tidy,keep=Environment.keep,dispose=Environment.dispose,time=Environment.time,profile=Environment.profile;function warn(){for(var e=[],t=0;t<arguments.length;t++)e[t]=arguments[t];ENV.get(\"IS_TEST\")||console.warn.apply(console,e)}function getReshaped(e,t,n,r){void 0===r&&(r=!0);var o=[];if(r)(o=o.concat(t.slice(0))).push(e[0]/n),o=o.concat(e.slice(1));else{o=o.concat(e[0]);for(var a=t.length,i=0;i<a;++i)o=o.concat([e[i+1]/t[i],t[i]]);o=o.concat(e.slice(a+1))}return o}function getPermuted(e,t,n){void 0===n&&(n=!0);var r=[];if(n){r.push(t);for(var o=t+1;o<e;++o)o<=2*t?(r.push(o),r.push(o-(t+1))):r.push(o)}else{var a=[],i=[];for(o=1;o<e;++o)o>=2*t+1||o%2==1?i.push(o):a.push(o);r.push.apply(r,a),r.push(0),r.push.apply(r,i)}return r}function getReshapedPermuted(e,t,n,r){void 0===r&&(r=!0);var o=[];r?o.push(e[0]/n):o.push(e[0]*n);for(var a=1;a<e.length;++a)a<=t.length?r?o.push(t[a-1]*e[a]):o.push(e[a]/t[a-1]):o.push(e[a]);return o}function getSliceBeginCoords(e,t){for(var n=[0],r=0;r<t;++r)n.push(e[r][0]);return n}function getSliceSize(e,t,n){for(var r=e.slice(0,1),o=0;o<n;++o)r.push(e[o+1]-t[o][0]-t[o][1]);return r}function axesAreInnerMostDims(e,t){for(var n=0;n<e.length;++n)if(e[e.length-n-1]!==t-1-n)return!1;return!0}function combineLocations(e,t,n){for(var r=e.length+t.length,o=[],a=0,i=0,s=0;s<r;s++)-1===n.indexOf(s)?o.push(e[a++]):o.push(t[i++]);return o}function computeOutAndReduceShapes(e,t){for(var n=[],r=e.length,o=0;o<r;o++)-1===t.indexOf(o)&&n.push(e[o]);return[n,t.map(function(t){return e[t]})]}function expandShapeToKeepDim(e,t){return combineLocations(e,t.map(function(e){return 1}),t)}function parseAxisParam(e,t){var n=t.length;return assert((e=null==e?t.map(function(e,t){return t}):[].concat(e)).every(function(e){return e>=-n&&e<n}),\"All values in axis param must be in range [-\"+n+\", \"+n+\") but got axis \"+e),assert(e.every(function(e){return isInt(e)}),\"All values in axis param must be integers but got axis \"+e),e.map(function(e){return e<0?n+e:e})}function assertAxesAreInnerMostDims(e,t,n){assert(axesAreInnerMostDims(t,n),e+\" supports only inner-most axes for now. Got axes \"+t+\" and rank-\"+n+\" input.\")}function getAxesPermutation(e,t){if(axesAreInnerMostDims(e,t))return null;for(var n=[],r=0;r<t;++r)-1===e.indexOf(r)&&n.push(r);return e.forEach(function(e){return n.push(e)}),n}function getUndoAxesPermutation(e){return e.map(function(e,t){return[t,e]}).sort(function(e,t){return e[1]-t[1]}).map(function(e){return e[0]})}function getInnerMostAxes(e,t){for(var n=[],r=t-e;r<t;++r)n.push(r);return n}function assertParamsConsistent(e,t){var n=e[0].length;e.forEach(function(e,t){assert(e.length===n,\"Error in concat\"+n+\"D: rank of tensors[\"+t+\"] must be the same as the rank of the rest (\"+n+\")\")}),assert(t>=0&&t<n,\"Error in concat\"+n+\"D: axis must be between 0 and \"+(n-1)+\".\");var r=e[0];e.forEach(function(e,o){for(var a=0;a<n;a++)assert(a===t||e[a]===r[a],\"Error in concat\"+n+\"D: Shape of tensors[\"+o+\"] (\"+e+\") does not match the shape of the rest (\"+r+\") along the non-concatenated axis \"+o+\".\")})}function computeOutShape(e,t){for(var n=e[0].slice(),r=1;r<e.length;r++)n[t]+=e[r][t];return n}function prepareAndValidate(e,t){if(e.rank<1)throw new Error(\"tf.gatherND() expects the input to be rank 1 or higher, but the rank was \"+e.rank+\".\");if(t.rank<1)throw new Error(\"tf.gatherND() expects the indices to be rank 1 or higher, but the rank was \"+t.rank+\".\");if(\"int32\"!==t.dtype)throw new Error(\"tf.gatherND() expects the indices to be int32 type, but the dtype was \"+t.dtype+\".\");if(t.shape[t.rank-1]>e.rank)throw new Error(\"index innermost dimension length must be <= tensor rank; saw: \"+t.shape[t.rank-1]+\" vs. \"+e.rank);if(0===e.size)throw new Error(\"Requested more than 0 entries, but input is empty. Input shape: \"+e.shape+\".\");for(var n=t.shape,r=n[n.length-1],o=1,a=0;a<n.length-1;++a)o*=n[a];var i=e.shape,s=n.slice();s.pop();var u=1;for(a=r;a<e.rank;++a)u*=i[a],s.push(i[a]);var l=computeStrides(e.shape).map(function(e){return e/u}).concat([1]).slice(0,r);return[s,o,u,l]}var PARALLELIZE_THRESHOLD=30;function computeOptimalWindowSize(e){return e<=PARALLELIZE_THRESHOLD?e:nearestDivisor(e,Math.floor(Math.sqrt(e)))}function validateUpdateShape(e,t,n){var r=t.rank>1?t.shape[t.rank-1]:1,o=t.rank>1?t.rank-1:1,a=\"Must have updates.shape = indices.shape[:batchDim] + shape[sliceDim:], got updates.shape: \"+n.shape+\", indices.shape: \"+t.shape+\", shape: \"+e+\", sliceDim: \"+r+\", and batchDim: \"+o+\".\";if(n.rank<o)throw new Error(a+\" update.rank < \"+o+\". \");if(e.length<r+(n.rank-o))throw new Error(a+\" Output shape length < \"+(r+(n.rank-o)));if(n.rank!==o+e.length-r)throw new Error(a+\" update.rank != \"+(o+e.length-r));for(var i=0;i<o;++i)if(n.shape[i]!==t.shape[i])throw new Error(a+\" updates.shape[\"+i+\"] (\"+n.shape[i]+\") != indices.shape[\"+i+\"] (\"+t.shape[i]+\").\");for(i=0;i<n.rank-o;++i)if(n.shape[i+o]!==e[i+r])throw new Error(a+\" updates.shape[\"+(i+o)+\"] (\"+n.shape[i+o]+\") != shape[\"+(i+o)+\"] (\"+e[i+o]+\")\")}function validateInput(e,t,n){if(t.rank<1)throw new Error(\"tf.scatterND() expects the indices to be rank 1 or higher, but the rank was \"+t.rank+\".\");if(e.rank<1)throw new Error(\"tf.scatterND() expects the updates to be rank 1 or higher, but the rank was \"+e.rank+\".\");if(\"int32\"!==t.dtype)throw new Error(\"The dtype of 'indices' should be int32, but got dtype: \"+t.dtype);if(n.length<1)throw new Error(\"Output rank must be greater or equal to 1, but got shape: \"+n);if(0===n.length){if(0===t.size)throw new Error(\"Indices specified for empty output. indices shape: \"+t.shape);if(0===e.size)throw new Error(\"Updates specified for empty output. updates shape: \"+e.shape)}validateUpdateShape(n,t,e)}function calculateShapes(e,t,n){for(var r=t.rank>1?t.shape[t.rank-1]:1,o=n.length,a=1,i=r;i<o;++i)a*=n[i];var s=r<1?1:r,u=t.size/s,l=computeStrides(n).concat([1]);return{sliceRank:r,numUpdates:u,sliceSize:a,strides:l.slice(l.length-r,l.length),outputSize:sizeFromShape(n)}}function segOpComputeOptimalWindowSize(e,t){var n,r=!1;for(e<=PARALLELIZE_THRESHOLD?(n=e,r=!0):n=nearestDivisor(e,Math.floor(Math.sqrt(e)));!r;){if(n>t||n===e){r=!0;break}n=nearestDivisor(e,n+1)}return n}function computeOutShape$1(e,t,n){for(var r=[],o=e.length,a=0;a<o;a++)a!==t?r.push(e[a]):r.push(n);return r}function assertParamsValid(e,t,n){assert(e.rank===t.length,\"Error in slice\"+e.rank+\"D: Length of begin \"+t+\" must match the rank of the array (\"+e.rank+\").\"),assert(e.rank===n.length,\"Error in slice\"+e.rank+\"D: Length of size \"+n+\" must match the rank of the array (\"+e.rank+\").\");for(var r=0;r<e.rank;++r)assert(t[r]+n[r]<=e.shape[r],\"Error in slice\"+e.rank+\"D: begin[\"+r+\"] + size[\"+r+\"] (\"+(t[r]+n[r])+\") would overflow input.shape[\"+r+\"] (\"+e.shape[r]+\")\")}function getStridedSlicedInfo(e,t,n,r,o,a,i,s,u){if(void 0===o&&(o=0),void 0===a&&(a=0),void 0===i&&(i=0),void 0===s&&(s=0),void 0===u&&(u=0),0!==i)throw new Error(\"ellipsis mask is not yet supported\");if(0!==s)throw new Error(\"new axis mask is not yet supported\");for(var l=[],c=[],p=[],d=0;d<e.length;d++)l[d]=startForAxis(o,t,r,e,d),c[d]=stopForAxis(a,n,r,e,d),u&1<<d&&(c[d]=l[d]+1,p.push(d));var h=new Array(e.length).fill(0);return h=h.map(function(e,t){for(var n=0,o=l[t];!(r[t]>0?o>=c[t]:o<=c[t]);o+=r[t])n+=1;return n}),[l,h,p]}function startForAxis(e,t,n,r,o){var a=t[o];e&1<<o&&(a=n[o]>0?Number.MIN_SAFE_INTEGER:Number.MAX_SAFE_INTEGER);var i=r[o];return a<0&&(a+=i),a=clamp(0,a,i-1)}function stopForAxis(e,t,n,r,o){var a=t[o];e&1<<o&&(a=n[o]>0?Number.MAX_SAFE_INTEGER:Number.MIN_SAFE_INTEGER);var i=r[o];return a<0&&(a+=i),a=n[o]>0?clamp(0,a,i):clamp(-1,a,i-1)}function inferShape(e){var t=e;if(isTypedArray(e))return[e.length];if(!Array.isArray(e))return[];for(var n=[];t instanceof Array||isTypedArray(t);)n.push(t.length),t=t[0];return e instanceof Array&&ENV.get(\"TENSORLIKE_CHECK_SHAPE_CONSISTENCY\")&&deepAssertShapeConsistency(e,n,[]),n}function deepAssertShapeConsistency(e,t,n){if(n=n||[],e instanceof Array||isTypedArray(e)){assert(t.length>0,function(){return\"Element arr[\"+n.join(\"][\")+\"] should be a primitive, but is an array of \"+e.length+\" elements\"}),assert(e.length===t[0],function(){return\"Element arr[\"+n.join(\"][\")+\"] should have \"+t[0]+\" elements, but has \"+e.length+\" elements\"});for(var r=t.slice(1),o=0;o<e.length;++o)deepAssertShapeConsistency(e[o],r,n.concat(o))}else assert(0===t.length,function(){return\"Element arr[\"+n.join(\"][\")+\"] is a primitive, but should be an array/TypedArray of \"+t[0]+\" elements\"})}function assertDtype(e,t,n,r){if(null!=e&&(\"numeric\"!==e&&e!==t||\"numeric\"===e&&\"string\"===t))throw new Error(\"Argument '\"+n+\"' passed to '\"+r+\"' must be \"+e+\" tensor, but got \"+t+\" tensor\")}function convertToTensor(e,t,n,r){if(void 0===r&&(r=\"numeric\"),e instanceof Tensor)return assertDtype(r,e.dtype,t,n),e;var o=inferDtype(e);if(\"string\"!==o&&[\"bool\",\"int32\",\"float32\"].indexOf(r)>=0&&(o=r),assertDtype(r,o,t,n),!isTypedArray(e)&&!Array.isArray(e)&&\"number\"!=typeof e&&\"boolean\"!=typeof e&&\"string\"!=typeof e)throw new Error(\"Argument '\"+t+\"' passed to '\"+n+\"' must be a Tensor or TensorLike, but got '\"+e.constructor.name+\"'\");var a=inferShape(e);isTypedArray(e)||Array.isArray(e)||(e=[e]);var i=\"string\"!==o?toTypedArray(e,o,ENV.get(\"DEBUG\")):flatten(e);return Tensor.make(a,{values:i},o)}function convertToTensorArray(e,t,n){if(!Array.isArray(e))throw new Error(\"Argument \"+t+\" passed to \"+n+\" must be a `Tensor[]` or `TensorLike[]`\");return e.map(function(e,r){return convertToTensor(e,t+\"[\"+r+\"]\",n)})}function op(e){var t=Object.keys(e);if(1!==t.length)throw new Error(\"Please provide an object with a single key (operation name) mapping to a function. Got an object with \"+t.length+\" keys.\");var n=t[0],r=e[n];n.endsWith(\"_\")&&(n=n.substring(0,n.length-1));var o=function(){for(var e=[],t=0;t<arguments.length;t++)e[t]=arguments[t];ENV.engine.startScope(n);try{var o=r.apply(void 0,e);return o instanceof Promise&&console.error(\"Cannot return a Promise inside of tidy.\"),ENV.engine.endScope(o),o}catch(e){throw ENV.engine.endScope(null),e}};return Object.defineProperty(o,\"name\",{value:n,configurable:!0}),o}function softmax_(e,t){void 0===t&&(t=-1);var n=convertToTensor(e,\"logits\",\"softmax\");if(-1===t&&(t=n.rank-1),t!==n.rank-1)throw Error(\"Softmax along a non-last dimension is not yet supported. Logits was rank \"+n.rank+\" and dim was \"+t);return customGrad(function(e){var n=e.logSumExp([t],!0),r=e.toFloat().sub(n).exp();return{value:r,gradFunc:function(e){var n=e.mul(r);return n.sub(n.sum([t],!0).mul(r))}}})(n)}function logSoftmax_(e,t){void 0===t&&(t=-1);var n=convertToTensor(e,\"logits\",\"logSoftmax\");if(-1===t&&(t=n.rank-1),t!==n.rank-1)throw Error(\"Log Softmax along a non-last dimension is not yet supported. Logits was rank \"+n.rank+\" and axis was \"+t);return customGrad(function(e){var n=e.max(t,!0),r=e.sub(n),o=r.toFloat().sub(r.exp().sum(t,!0).log());return{value:o,gradFunc:function(e){var n=o.exp();return e.sub(e.sum(t,!0).mul(n))}}})(n)}var softmax=op({softmax_:softmax_}),logSoftmax=op({logSoftmax_:logSoftmax_});function complex_(e,t){var n=convertToTensor(e,\"real\",\"complex\"),r=convertToTensor(t,\"imag\",\"complex\");return assertShapesMatch(n.shape,r.shape,\"real and imag shapes, \"+n.shape+\" and \"+r.shape+\", must match in call to tf.complex().\"),ENV.engine.runKernel(function(e){return e.complex(n,r)},{$real:n,$imag:r})}function real_(e){var t=convertToTensor(e,\"input\",\"real\");return ENV.engine.runKernel(function(e){return e.real(t)},{$input:t})}function imag_(e){var t=convertToTensor(e,\"input\",\"imag\");return ENV.engine.runKernel(function(e){return e.imag(t)},{$input:t})}var complex=op({complex_:complex_}),real=op({real_:real_}),imag=op({imag_:imag_});function tensor(e,t,n){if(null==n&&(n=inferDtype(e)),\"complex64\"===n)throw new Error(\"Cannot construct a complex64 tensor directly. Please use tf.complex(real, imag).\");if(!isTypedArray(e)&&!Array.isArray(e)&&\"number\"!=typeof e&&\"boolean\"!=typeof e&&\"string\"!=typeof e)throw new Error(\"values passed to tensor(values) must be a number/boolean/string or an array of numbers/booleans/strings, or a TypedArray\");var r=inferShape(e);if(null!=t){var o=sizeFromShape(t),a=sizeFromShape(r);assert(o===a,function(){return\"Based on the provided shape, [\"+t+\"], the tensor should have \"+o+\" values but has \"+a});for(var i=0;i<r.length;++i){var s=r[i],u=i!==r.length-1||s!==sizeFromShape(t.slice(i));assert(r[i]===t[i]||!u,function(){return\"Error creating a new Tensor. Inferred shape (\"+r+\") does not match the provided shape (\"+t+\"). \"})}}return isTypedArray(e)||Array.isArray(e)||(e=[e]),t=t||r,e=\"string\"!==n?toTypedArray(e,n,ENV.get(\"DEBUG\")):flatten(e),Tensor.make(t,{values:e},n)}function scalar(e,t){if((isTypedArray(e)||Array.isArray(e))&&\"complex64\"!==t)throw new Error(\"Error creating a new Scalar: value must be a primitive (number|boolean|string)\");return tensor(e,[],t)}function tensor1d(e,t){assertNonNull(e);var n=inferShape(e);if(1!==n.length)throw new Error(\"tensor1d() requires values to be a flat/TypedArray\");return tensor(e,n,t)}function tensor2d(e,t,n){if(assertNonNull(e),null!=t&&2!==t.length)throw new Error(\"tensor2d() requires shape to have two numbers\");var r=inferShape(e);if(2!==r.length&&1!==r.length)throw new Error(\"tensor2d() requires values to be number[][] or flat/TypedArray\");if(1===r.length&&null==t)throw new Error(\"tensor2d() requires shape to be provided when `values` are a flat/TypedArray\");return tensor(e,t=t||r,n)}function tensor3d(e,t,n){if(assertNonNull(e),null!=t&&3!==t.length)throw new Error(\"tensor3d() requires shape to have three numbers\");var r=inferShape(e);if(3!==r.length&&1!==r.length)throw new Error(\"tensor3d() requires values to be number[][][] or flat/TypedArray\");if(1===r.length&&null==t)throw new Error(\"tensor3d() requires shape to be provided when `values` are a flat array\");return tensor(e,t=t||r,n)}function tensor4d(e,t,n){if(assertNonNull(e),null!=t&&4!==t.length)throw new Error(\"tensor4d() requires shape to have four numbers\");var r=inferShape(e);if(4!==r.length&&1!==r.length)throw new Error(\"tensor4d() requires values to be number[][][][] or flat/TypedArray\");if(1===r.length&&null==t)throw new Error(\"tensor4d() requires shape to be provided when `values` are a flat array\");return tensor(e,t=t||r,n)}function tensor5d(e,t,n){if(assertNonNull(e),null!=t&&5!==t.length)throw new Error(\"tensor5d() requires shape to have five numbers\");var r=inferShape(e);if(5!==r.length&&1!==r.length)throw new Error(\"tensor5d() requires values to be number[][][][][] or flat/TypedArray\");if(1===r.length&&null==t)throw new Error(\"tensor5d() requires shape to be provided when `values` are a flat array\");return tensor(e,t=t||r,n)}function tensor6d(e,t,n){if(assertNonNull(e),null!=t&&6!==t.length)throw new Error(\"tensor6d() requires shape to have six numbers\");var r=inferShape(e);if(6!==r.length&&1!==r.length)throw new Error(\"tensor6d() requires values to be number[][][][] or flat/TypedArray\");if(1===r.length&&null==t)throw new Error(\"tensor6d() requires shape to be provided when `values` are a flat array\");return tensor(e,t=t||r,n)}function ones$1(e,t){if(void 0===t&&(t=\"float32\"),\"complex64\"===t){var n=ones$1(e,\"float32\"),r=ones$1(e,\"float32\");return complex(n,r)}var o=makeOnesTypedArray(sizeFromShape(e),t);return Tensor.make(e,{values:o},t)}function zeros(e,t){if(void 0===t&&(t=\"float32\"),\"complex64\"===t){var n=zeros(e,\"float32\"),r=zeros(e,\"float32\");return complex(n,r)}var o=makeZerosTypedArray(sizeFromShape(e),t);return Tensor.make(e,{values:o},t)}function fill(e,t,n){var r=getArrayFromDType(n=n||inferDtype(t),sizeFromShape(e));return r.fill(t),Tensor.make(e,{values:r},n)}function onesLike_(e){var t=convertToTensor(e,\"x\",\"onesLike\");return ones$1(t.shape,t.dtype)}function zerosLike_(e){var t=convertToTensor(e,\"x\",\"zerosLike\");return zeros(t.shape,t.dtype)}function linspace(e,t,n){if(0===n)throw new Error(\"Cannot request zero samples\");var r=(t-e)/(n-1),o=makeZerosTypedArray(n,\"float32\");o[0]=e;for(var a=1;a<o.length;a++)o[a]=o[a-1]+r;return tensor1d(o,\"float32\")}function range(e,t,n,r){if(void 0===n&&(n=1),void 0===r&&(r=\"float32\"),0===n)throw new Error(\"Cannot have a step of zero\");if(e===t||e<t&&n<0||t<e&&n>1)return zeros([0],r);var o=makeZerosTypedArray(Math.abs(Math.ceil((t-e)/n)),r);t<e&&1===n&&(n=-1),o[0]=e;for(var a=1;a<o.length;a++)o[a]=o[a-1]+n;return tensor1d(o,r)}var onesLike=op({onesLike_:onesLike_}),zerosLike=op({zerosLike_:zerosLike_}),DataStorage=function(){function e(e){this.dataMover=e,this.data=new WeakMap}return e.prototype.get=function(e){return this.data.has(e)||this.dataMover.moveData(e),this.data.get(e)},e.prototype.set=function(e,t){this.data.set(e,t)},e.prototype.has=function(e){return this.data.has(e)},e.prototype.delete=function(e){return this.data.delete(e)},e}(),KernelBackend=function(){function e(){}return e.prototype.time=function(e){throw new Error(\"Not yet implemented.\")},e.prototype.read=function(e){throw new Error(\"Not yet implemented.\")},e.prototype.readSync=function(e){throw new Error(\"Not yet implemented.\")},e.prototype.disposeData=function(e){throw new Error(\"Not yet implemented.\")},e.prototype.write=function(e,t){throw new Error(\"Not yet implemented.\")},e.prototype.fromPixels=function(e,t){throw new Error(\"Not yet implemented.\")},e.prototype.register=function(e,t,n){throw new Error(\"Not yet implemented.\")},e.prototype.memory=function(){throw new Error(\"Not yet implemented.\")},e.prototype.floatPrecision=function(){throw new Error(\"Not yet implemented\")},e.prototype.batchMatMul=function(e,t,n,r){throw new Error(\"Not yet implemented\")},e.prototype.slice=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.stridedSlice=function(e,t,n,r,o,a,i,s,u){throw new Error(\"Not yet implemented\")},e.prototype.reverse=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.concat=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.neg=function(e){throw new Error(\"Not yet implemented\")},e.prototype.add=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.addN=function(e){throw new Error(\"Not yet implemented\")},e.prototype.subtract=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.multiply=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.realDivide=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.floorDiv=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.sum=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.prod=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.unsortedSegmentSum=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.argMin=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.argMax=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.equal=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.notEqual=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.less=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.lessEqual=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.greater=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.greaterEqual=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.logicalNot=function(e){throw new Error(\"Not yet implemented\")},e.prototype.logicalAnd=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.logicalOr=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.where=function(e){throw new Error(\"Not yet implemented\")},e.prototype.select=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.topk=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.min=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.minimum=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.mod=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.max=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.maximum=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.all=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.any=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.squaredDifference=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.ceil=function(e){throw new Error(\"Not yet implemented\")},e.prototype.floor=function(e){throw new Error(\"Not yet implemented\")},e.prototype.round=function(e){throw new Error(\"Not yet implemented\")},e.prototype.sign=function(e){throw new Error(\"Not yet implemented\")},e.prototype.pow=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.exp=function(e){throw new Error(\"Not yet implemented\")},e.prototype.expm1=function(e){throw new Error(\"Not yet implemented\")},e.prototype.log=function(e){throw new Error(\"Not yet implemented\")},e.prototype.log1p=function(e){throw new Error(\"Not yet implemented\")},e.prototype.sqrt=function(e){throw new Error(\"Not yet implemented\")},e.prototype.rsqrt=function(e){throw new Error(\"Not yet implemented\")},e.prototype.square=function(e){throw new Error(\"Not yet implemented\")},e.prototype.reciprocal=function(e){throw new Error(\"Not yet implemented\")},e.prototype.relu=function(e){throw new Error(\"Not yet implemented\")},e.prototype.elu=function(e){throw new Error(\"Not yet implemented\")},e.prototype.eluDer=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.selu=function(e){throw new Error(\"Not yet implemented\")},e.prototype.int=function(e){throw new Error(\"Not yet implemented\")},e.prototype.clip=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.abs=function(e){throw new Error(\"Not yet implemented\")},e.prototype.complexAbs=function(e){throw new Error(\"Not yet implemented\")},e.prototype.sigmoid=function(e){throw new Error(\"Not yet implemented\")},e.prototype.softplus=function(e){throw new Error(\"Not yet implemented\")},e.prototype.sin=function(e){throw new Error(\"Not yet implemented\")},e.prototype.cos=function(e){throw new Error(\"Not yet implemented\")},e.prototype.tan=function(e){throw new Error(\"Not yet implemented\")},e.prototype.asin=function(e){throw new Error(\"Not yet implemented\")},e.prototype.acos=function(e){throw new Error(\"Not yet implemented\")},e.prototype.atan=function(e){throw new Error(\"Not yet implemented\")},e.prototype.atan2=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.sinh=function(e){throw new Error(\"Not yet implemented\")},e.prototype.cosh=function(e){throw new Error(\"Not yet implemented\")},e.prototype.tanh=function(e){throw new Error(\"Not yet implemented\")},e.prototype.asinh=function(e){throw new Error(\"Not yet implemented\")},e.prototype.acosh=function(e){throw new Error(\"Not yet implemented\")},e.prototype.atanh=function(e){throw new Error(\"Not yet implemented\")},e.prototype.erf=function(e){throw new Error(\"Not yet implemented\")},e.prototype.step=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.conv2d=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.conv2dDerInput=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.conv2dDerFilter=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.depthwiseConv2D=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.depthwiseConv2DDerInput=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.depthwiseConv2DDerFilter=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.conv3d=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.conv3dDerInput=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.conv3dDerFilter=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.maxPool=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.maxPoolBackprop=function(e,t,n,r){throw new Error(\"Not yet implemented\")},e.prototype.avgPool=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.avgPoolBackprop=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.reshape=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.cast=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.tile=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.pad=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.transpose=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.gather=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.gatherND=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.scatterND=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.batchToSpaceND=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.spaceToBatchND=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.resizeBilinear=function(e,t,n,r){throw new Error(\"Not yet implemented\")},e.prototype.resizeBilinearBackprop=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.resizeNearestNeighbor=function(e,t,n,r){throw new Error(\"Not yet implemented\")},e.prototype.resizeNearestNeighborBackprop=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.batchNormalization=function(e,t,n,r,o,a){throw new Error(\"Not yet implemented\")},e.prototype.localResponseNormalization4D=function(e,t,n,r,o){throw new Error(\"Not yet implemented\")},e.prototype.LRNGrad=function(e,t,n,r,o,a,i){throw new Error(\"Not yet implemented\")},e.prototype.multinomial=function(e,t,n,r){throw new Error(\"Not yet implemented\")},e.prototype.oneHot=function(e,t,n,r){throw new Error(\"Not yet implemented\")},e.prototype.cumsum=function(e,t,n,r){throw new Error(\"Not yet implemented\")},e.prototype.nonMaxSuppression=function(e,t,n,r,o){throw new Error(\"Not yet implemented\")},e.prototype.fft=function(e){throw new Error(\"Not yet implemented\")},e.prototype.ifft=function(e){throw new Error(\"Not yet implemented\")},e.prototype.complex=function(e,t){throw new Error(\"Not yet implemented\")},e.prototype.real=function(e){throw new Error(\"Not yet implemented\")},e.prototype.imag=function(e){throw new Error(\"Not yet implemented\")},e.prototype.cropAndResize=function(e,t,n,r,o,a){throw new Error(\"Not yet implemented\")},e.prototype.depthToSpace=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.split=function(e,t,n){throw new Error(\"Not yet implemented\")},e.prototype.sparseToDense=function(e,t,n,r){throw new Error(\"Not yet implemented\")},e.prototype.setDataMover=function(e){throw new Error(\"Not yet implemented\")},e.prototype.dispose=function(){throw new Error(\"Not yet implemented\")},e}();function castTensor(e,t,n){if(\"complex64\"===t){if(\"complex64\"===e.dtype)return e.clone();var r=zeros(e.shape),o=e.toFloat(),a=n.complex(o,r);return r.dispose(),o.dispose(),a}if(!hasEncodingLoss(e.dtype,t))return Tensor.make(e.shape,{dataId:e.dataId},t);if(\"complex64\"===e.dtype){var i=n.real(e);a=i.cast(t);return i.dispose(),a}if(\"int32\"===t)return n.int(e);if(\"bool\"===t){var s=scalar(0,e.dtype);a=n.notEqual(e,s);return s.dispose(),a}throw new Error(\"Error in Cast: unknown dtype argument (\"+t+\")\")}function reshapeTensor(e,t){return Tensor.make(t,{dataId:e.dataId},e.dtype)}function mergeRealAndImagArrays(e,t){if(e.length!==t.length)throw new Error(\"Cannot merge real and imag arrays of different lengths. real:\"+e.length+\", imag: \"+t.length+\".\");for(var n=new Float32Array(2*e.length),r=0;r<n.length;r+=2)n[r]=e[r/2],n[r+1]=t[r/2];return n}function splitRealAndImagArrays(e){for(var t=new Float32Array(e.length/2),n=new Float32Array(e.length/2),r=0;r<e.length;r+=2)t[r/2]=e[r],n[r/2]=e[r+1];return{real:t,imag:n}}function complexWithEvenIndex(e){for(var t=Math.ceil(e.length/4),n=new Float32Array(t),r=new Float32Array(t),o=0;o<e.length;o+=4)n[Math.floor(o/4)]=e[o],r[Math.floor(o/4)]=e[o+1];return{real:n,imag:r}}function complexWithOddIndex(e){for(var t=Math.floor(e.length/4),n=new Float32Array(t),r=new Float32Array(t),o=2;o<e.length;o+=4)n[Math.floor(o/4)]=e[o],r[Math.floor(o/4)]=e[o+1];return{real:n,imag:r}}function getComplexWithIndex(e,t){return{real:e[2*t],imag:e[2*t+1]}}function assignToTypedArray(e,t,n,r){e[2*r]=t,e[2*r+1]=n}function exponents(e,t){for(var n=new Float32Array(e/2),r=new Float32Array(e/2),o=0;o<Math.ceil(e/2);o++){var a=(t?2:-2)*Math.PI*(o/e);n[o]=Math.cos(a),r[o]=Math.sin(a)}return{real:n,imag:r}}function exponent(e,t,n){var r=(n?2:-2)*Math.PI*(e/t);return{real:Math.cos(r),imag:Math.sin(r)}}function nonMaxSuppressionImpl(e,t,n,r,o){for(var a=Array.from(t).map(function(e,t){return{score:e,boxIndex:t}}).filter(function(e){return e.score>o}).sort(function(e,t){return t.score-e.score}),i=[],s=0;s<a.length;s++){var u=a[s],l=u.score,c=u.boxIndex;if(l<o)break;for(var p=!1,d=i.length-1;d>=0;--d){if(intersectionOverUnion(e,c,i[d])>=r){p=!0;break}}if(!p&&(i.push(c),i.length>=n))break}return tensor1d(i,\"int32\")}function intersectionOverUnion(e,t,n){var r=e.subarray(4*t,4*t+4),o=e.subarray(4*n,4*n+4),a=Math.min(r[0],r[2]),i=Math.min(r[1],r[3]),s=Math.max(r[0],r[2]),u=Math.max(r[1],r[3]),l=Math.min(o[0],o[2]),c=Math.min(o[1],o[3]),p=Math.max(o[0],o[2]),d=Math.max(o[1],o[3]),h=(s-a)*(u-i),f=(p-l)*(d-c);if(h<=0||f<=0)return 0;var m=Math.max(a,l),g=Math.max(i,c),v=Math.min(s,p),y=Math.min(u,d),x=Math.max(v-m,0)*Math.max(y-g,0);return x/(h+f-x)}function split(e,t,n){var r=Array(e.rank).fill(0),o=e.shape.slice();return t.map(function(t){o[n]=t;var a=e.slice(r,o);return r[n]+=t,a})}function topkImpl(e,t,n,r,o){for(var a=t[t.length-1],i=[e.length/a,a],s=i[0],u=i[1],l=getTypedArrayFromDType(n,s*r),c=getTypedArrayFromDType(\"int32\",s*r),p=0;p<s;p++){for(var d=p*u,h=e.subarray(d,d+u),f=[],m=0;m<h.length;m++)f.push({value:h[m],index:m});f.sort(function(e,t){return t.value-e.value});var g=p*r,v=l.subarray(g,g+r),y=c.subarray(g,g+r);for(m=0;m<r;m++)v[m]=f[m].value,y[m]=f[m].index}var x=t.slice();return x[x.length-1]=r,[tensor(l,x,n),tensor(c,x,\"int32\")]}var ArgMinMaxProgram=function(){return function(e,t,n){this.variableNames=[\"A\"];var r=e.windowSize,o=e.batchSize,a=e.inSize,i=Math.ceil(a/r);n||this.variableNames.push(\"bestIndicesA\"),this.outputShape=[o,i];var s=\"max\"===t?\">\":\"<\",u=n?\"inOffset + i;\":\"round(getBestIndicesA(batch, inOffset + i));\";this.userCode=\"\\n      void main() {\\n        ivec2 coords = getOutputCoords();\\n        int batch = coords[0];\\n        int outIdx = coords[1];\\n        int inOffset = outIdx * \"+r+\";\\n\\n        int bestIndex = inOffset;\\n        float bestValue = getA(batch, bestIndex);\\n\\n        for (int i = 0; i < \"+r+\"; i++) {\\n          int inIdx = \"+u+\";\\n          float candidate = getA(batch, inIdx);\\n          if (candidate \"+s+\" bestValue) {\\n            bestValue = candidate;\\n            bestIndex = inIdx;\\n          }\\n        }\\n        setOutput(float(bestIndex));\\n      }\\n    \"}}(),AvgPool2DBackpropProgram=function(){return function(e){this.variableNames=[\"dy\"],this.outputShape=e.inShape;var t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,o=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,s=e.effectiveFilterHeight,u=e.effectiveFilterWidth,l=s-1-e.padInfo.top,c=u-1-e.padInfo.left,p=1/(t*n);this.userCode=\"\\n      const ivec2 pads = ivec2(\"+l+\", \"+c+\");\\n      const float avgMultiplier = float(\"+p+\");\\n\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int b = coords[0];\\n        int d = coords[3];\\n\\n        ivec2 dyRCCorner = coords.yz - pads;\\n        int dyRCorner = dyRCCorner.x;\\n        int dyCCorner = dyRCCorner.y;\\n\\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\\n        // ? = to be determined. : = across all values in that axis.\\n        float dotProd = 0.0;\\n        for (int wR = 0; wR < \"+s+\";\\n            wR += \"+a+\") {\\n          float dyR = float(dyRCorner + wR) / \"+r+\".0;\\n\\n          if (dyR < 0.0 || dyR >= \"+e.outHeight+\".0 || fract(dyR) > 0.0) {\\n            continue;\\n          }\\n          int idyR = int(dyR);\\n\\n          for (int wC = 0; wC < \"+u+\";\\n            wC+= \"+i+\") {\\n            float dyC = float(dyCCorner + wC) / \"+o+\".0;\\n\\n            if (dyC < 0.0 || dyC >= \"+e.outWidth+\".0 ||\\n                fract(dyC) > 0.0) {\\n              continue;\\n            }\\n            int idyC = int(dyC);\\n\\n            float dyValue = getDy(b, idyR, idyC, d);\\n\\n            dotProd += dyValue * avgMultiplier;\\n          }\\n        }\\n        setOutput(dotProd);\\n      }\\n    \"}}();function getBroadcastDims(e,t){for(var n=e.length,r=[],o=0;o<n;o++){var a=n-1-o,i=e[a]||1;(t[t.length-1-o]||1)>1&&1===i&&r.unshift(a)}return r}function getReductionAxes(e,t){for(var n=[],r=0;r<t.length;r++){var o=e[e.length-r-1],a=t.length-r-1,i=t[a];(null==o||1===o&&i>1)&&n.unshift(a)}return n}function broadcastDimsAreOuter(e){for(var t=0;t<e.length;t++)if(e[t]!==t)return!1;return!0}function assertAndGetBroadcastShape(e,t){for(var n=[],r=Math.max(e.length,t.length),o=0;o<r;o++){var a=e[e.length-o-1];null==a&&(a=1);var i=t[t.length-o-1];if(null==i&&(i=1),1===a)n.unshift(i);else if(1===i)n.unshift(a);else{if(a!==i)throw Error(\"Operands could not be broadcast together with shapes \"+e+\" and \"+t+\".\");n.unshift(a)}}return n}var BatchNormProgram=function(){return function(e,t,n,r,o,a){this.outputShape=[],this.supportsBroadcasting=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],assertAndGetBroadcastShape(e,t),assertAndGetBroadcastShape(e,n);var i=\"0.0\";null!=r&&(assertAndGetBroadcastShape(e,r),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");var s=\"1.0\";null!=o&&(assertAndGetBroadcastShape(e,o),this.variableNames.push(\"scale\"),s=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=\"\\n      void main() {\\n        float x = getXAtOutCoords();\\n        float mean = getMeanAtOutCoords();\\n        float variance = getVarianceAtOutCoords();\\n        float offset = \"+i+\";\\n        float scale = \"+s+\";\\n        float inv = scale * inversesqrt(variance + float(\"+a+\"));\\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\\n      }\\n    \"}}(),BatchNormPackedProgram=function(){return function(e,t,n,r,o,a){this.supportsBroadcasting=!0,this.usesPackedTextures=!0,this.variableNames=[\"x\",\"mean\",\"variance\"],assertAndGetBroadcastShape(e,t),assertAndGetBroadcastShape(e,n);var i=\"vec4(0.0)\";null!=r&&(assertAndGetBroadcastShape(e,r),this.variableNames.push(\"offset\"),i=\"getOffsetAtOutCoords()\");var s=\"vec4(1.0)\";null!=o&&(assertAndGetBroadcastShape(e,o),this.variableNames.push(\"scale\"),s=\"getScaleAtOutCoords()\"),this.outputShape=e,this.userCode=\"\\n      void main() {\\n        vec4 offset = \"+i+\";\\n        vec4 scale = \"+s+\";\\n\\n        vec4 x = getXAtOutCoords();\\n        vec4 mean = getMeanAtOutCoords();\\n        vec4 variance = getVarianceAtOutCoords();\\n\\n        vec4 inv = scale * inversesqrt(variance + vec4(\"+a+\"));\\n\\n        setOutput((x - mean) * inv + offset);\\n      }\\n    \"}}(),COMPLEX_MULTIPLY={REAL:\"return areal * breal - aimag * bimag;\",IMAG:\"return areal * bimag + aimag * breal;\"},BinaryOpComplexProgram=function(){return function(e,t,n){this.variableNames=[\"AReal\",\"AImag\",\"BReal\",\"BImag\"],this.supportsBroadcasting=!0,this.outputShape=assertAndGetBroadcastShape(t,n),this.userCode=\"\\n      float binaryOpComplex(\\n          float areal, float aimag, float breal, float bimag) {\\n        \"+e+\"\\n      }\\n\\n      void main() {\\n        float areal = getARealAtOutCoords();\\n        float aimag = getAImagAtOutCoords();\\n        float breal = getBRealAtOutCoords();\\n        float bimag = getBImagAtOutCoords();\\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\\n      }\\n    \"}}(),CHECK_NAN_SNIPPET=\"\\n  if (isNaN(a)) return a;\\n  if (isNaN(b)) return b;\\n\",ADD=\"return a + b;\",SUB=\"return a - b;\",MUL=\"return a * b;\",DIV=\"if (a == b) return 1.0;\\n  return a / b;\",INT_DIV=\"\\n  float resultSign = sign(a) * sign(b);\\n  int ia = round(a);\\n  int ib = round(b);\\n  int result = ia / ib;\\n  int amodb = ia - ib * result;\\n\\n  if (resultSign < 0.0 && amodb != 0) {\\n    result -= 1;\\n  }\\n  return float(result);\\n\",POW=\"\\nif(a < 0.0 && floor(b) < b){\\n  return NAN;\\n}\\nreturn (round(mod(b, 2.0)) == 0 || round(mod(b, 2.0)) == 2) ?\\n    pow(abs(a), b) : sign(a) * pow(abs(a), b);\\n\",SQUARED_DIFFERENCE=\"return (a - b) * (a - b);\",EQUAL=\"return float(a == b);\",NOT_EQUAL=\"return float(a != b);\",LESS=\"return float(a < b);\",LESS_EQUAL=\"return float(a <= b);\",GREATER=\"return float(a > b);\",GREATER_EQUAL=\"return float(a >= b);\",LOGICAL_AND=\"return float(a >= 1.0 && b >= 1.0);\",LOGICAL_OR=\"return float(a >= 1.0 || b >= 1.0);\",MAX=CHECK_NAN_SNIPPET+\"\\n  return max(a, b);\\n\",MIN=CHECK_NAN_SNIPPET+\"\\n  return min(a, b);\\n\",MOD=\"if (b == 0.0) return NAN;\\n  return mod(a, b);\",ATAN2=CHECK_NAN_SNIPPET+\"\\n  return atan(a, b);\\n\",ELU_DER=\"return (b >= 1.0) ? a : a * (b + 1.0);\",BinaryOpProgram=function(){function e(e,t,n){this.variableNames=[\"A\",\"B\"],this.supportsBroadcasting=!0,this.outputShape=assertAndGetBroadcastShape(t,n),this.userCode=\"\\n      uniform float NAN;\\n      float binaryOperation(float a, float b) {\\n        \"+e+\"\\n      }\\n\\n      void main() {\\n        float a = getAAtOutCoords();\\n        float b = getBAtOutCoords();\\n        setOutput(binaryOperation(a, b));\\n      }\\n    \"}return e.prototype.getCustomSetupFunc=function(){var e=this;return function(t,n){null==e.startLoc&&(e.startLoc=t.getUniformLocationNoThrow(n,\"NAN\"),null==e.startLoc)||t.gl.uniform1f(e.startLoc,NaN)}},e}(),ClipProgram=function(){return function(e,t,n){this.variableNames=[\"A\"],this.outputShape=e,this.userCode=\"\\n      void main() {\\n        float value = getAAtOutCoords();\\n        if (isNaN(value)) {\\n          setOutput(value);\\n          return;\\n        }\\n\\n        setOutput(clamp(value, float(\"+t+\"), float(\"+n+\")));\\n      }\\n    \"}}(),ClipPackedProgram=function(){return function(e,t,n){this.variableNames=[\"A\"],this.usesPackedTextures=!0,this.outputShape=e,this.userCode=\"\\n      void main() {\\n        vec4 value = getAAtOutCoords();\\n\\n        if (hasNaN(value)) {\\n          setOutput(value);\\n          return;\\n        }\\n\\n        setOutput(clamp(value, vec4(\"+t+\"), vec4(\"+n+\")));\\n      }\\n    \"}}(),ComplexAbsProgram=function(){return function(e){this.variableNames=[\"real\",\"imag\"],this.outputShape=e,this.userCode=\"\\n      void main() {\\n        float re = abs(getRealAtOutCoords());\\n        float im = abs(getImagAtOutCoords());\\n        float mx = max(re, im);\\n\\n        // sadly the length function in glsl is not underflow-safe\\n        // (at least not on Intel GPUs). So the safe solution is\\n        // to ensure underflow-safety in all cases.\\n        setOutput(\\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\\n        );\\n      }\\n    \"}}(),ConcatProgram=function(){return function(e,t){this.variableNames=[\"A\",\"B\"],this.outputShape=[],this.outputShape=computeOutShape([e,t],1),this.userCode=\"\\n      void main() {\\n        ivec2 coords = getOutputCoords();\\n        int yR = coords.x;\\n        int yC = coords.y;\\n\\n        float value = 0.0;\\n        if (yC < \"+e[1]+\") {\\n          value = getA(yR, yC);\\n        } else {\\n          yC -= \"+e[1]+\";\\n          value = getB(yR, yC);\\n        }\\n\\n        setOutput(value);\\n      }\\n    \"}}(),Conv2DDerFilterProgram=function(){return function(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;var t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,o=e.padInfo.left;this.userCode=\"\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int wR = coords.x;\\n        int wC = coords.y;\\n        int d1 = coords.z;\\n        int d2 = coords.w;\\n\\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\\n        // ? = to be determined. : = across all values in that axis.\\n        float dotProd = 0.0;\\n\\n        for (int b = 0; b < \"+e.batchSize+\"; b++) {\\n          for (int yR = 0; yR < \"+e.outHeight+\"; yR++) {\\n            int xR = wR + yR * \"+t+\" - \"+r+\";\\n\\n            if (xR < 0 || xR >= \"+e.inHeight+\") {\\n              continue;\\n            }\\n\\n            for (int yC = 0; yC < \"+e.outWidth+\"; yC++) {\\n              int xC = wC + yC * \"+n+\" - \"+o+\";\\n\\n              if (xC < 0 || xC >= \"+e.inWidth+\") {\\n                continue;\\n              }\\n\\n              float dyValue = getDy(b, yR, yC, d2);\\n              float xValue = getX(b, xR, xC, d1);\\n              dotProd += (xValue * dyValue);\\n            }\\n          }\\n        }\\n        setOutput(dotProd);\\n      }\\n    \"}}(),Conv2DDerInputProgram=function(){return function(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;var t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,o=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left;this.userCode=\"\\n      const ivec2 pads = ivec2(\"+a+\", \"+i+\");\\n\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int batch = coords[0];\\n        int d1 = coords[3];\\n\\n        ivec2 dyCorner = coords.yz - pads;\\n        int dyRCorner = dyCorner.x;\\n        int dyCCorner = dyCorner.y;\\n\\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\\n        // ? = to be determined. : = across all values in that axis.\\n        float dotProd = 0.0;\\n        for (int wR = 0; wR < \"+t+\"; wR++) {\\n          float dyR = float(dyRCorner + wR) / \"+r+\".0;\\n\\n          if (dyR < 0.0 || dyR >= \"+e.outHeight+\".0 || fract(dyR) > 0.0) {\\n            continue;\\n          }\\n          int idyR = int(dyR);\\n\\n          int wRPerm = \"+t+\" - 1 - wR;\\n\\n          for (int wC = 0; wC < \"+n+\"; wC++) {\\n            float dyC = float(dyCCorner + wC) / \"+o+\".0;\\n\\n            if (dyC < 0.0 || dyC >= \"+e.outWidth+\".0 ||\\n                fract(dyC) > 0.0) {\\n              continue;\\n            }\\n            int idyC = int(dyC);\\n\\n            int wCPerm = \"+n+\" - 1 - wC;\\n\\n            for (int d2 = 0; d2 < \"+e.outChannels+\"; d2++) {\\n              float xValue = getDy(batch, idyR, idyC, d2);\\n              float wValue = getW(wRPerm, wCPerm, d1, d2);\\n              dotProd += xValue * wValue;\\n            }\\n          }\\n        }\\n        setOutput(dotProd);\\n      }\\n    \"}}(),Conv3DDerFilterProgram=function(){return function(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;var t=e.strideDepth,n=e.strideHeight,r=e.strideWidth,o=e.padInfo.front,a=e.padInfo.top,i=e.padInfo.left;this.userCode=\"\\n      void main() {\\n        ivec5 coords = getOutputCoords();\\n        int wF = coords.x;\\n        int wR = coords.y;\\n        int wC = coords.z;\\n        int d1 = coords.w;\\n        int d2 = coords.u;\\n\\n        float dotProd = 0.0;\\n\\n        for (int b = 0; b < \"+e.batchSize+\"; b++) {\\n          for (int yF = 0; yF < \"+e.outDepth+\"; yF++) {\\n            int xF = wF + yF * \"+t+\" - \"+o+\";\\n\\n            if (xF < 0 || xF >= \"+e.inDepth+\") {\\n              continue;\\n            }\\n\\n            for (int yR = 0; yR < \"+e.outHeight+\"; yR++) {\\n              int xR = wR + yR * \"+n+\" - \"+a+\";\\n\\n              if (xR < 0 || xR >= \"+e.inHeight+\") {\\n                continue;\\n              }\\n\\n              for (int yC = 0; yC < \"+e.outWidth+\"; yC++) {\\n                int xC = wC + yC * \"+r+\" - \"+i+\";\\n\\n                if (xC < 0 || xC >= \"+e.inWidth+\") {\\n                  continue;\\n                }\\n\\n                float dyValue = getDy(b, yF, yR, yC, d2);\\n                float xValue = getX(b, xF, xR, xC, d1);\\n                dotProd += (xValue * dyValue);\\n              }\\n            }\\n          }\\n        }\\n        setOutput(dotProd);\\n      }\\n    \"}}(),Conv3DDerInputProgram=function(){return function(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;var t=e.filterDepth,n=e.filterHeight,r=e.filterWidth,o=e.strideDepth,a=e.strideHeight,i=e.strideWidth,s=t-1-e.padInfo.front,u=n-1-e.padInfo.top,l=r-1-e.padInfo.left;this.userCode=\"\\n      const ivec3 pads = ivec3(\"+s+\", \"+u+\", \"+l+\");\\n\\n      void main() {\\n        ivec5 coords = getOutputCoords();\\n        int batch = coords.x;\\n        int d1 = coords.u;\\n\\n\\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\\n        int dyFCorner = dyCorner.x;\\n        int dyRCorner = dyCorner.y;\\n        int dyCCorner = dyCorner.z;\\n\\n        float dotProd = 0.0;\\n        for (int wF = 0; wF < \"+t+\"; wF++) {\\n          float dyF = float(dyFCorner + wF) / \"+o+\".0;\\n\\n          if (dyF < 0.0 || dyF >= \"+e.outDepth+\".0 || fract(dyF) > 0.0) {\\n            continue;\\n          }\\n          int idyF = int(dyF);\\n\\n          int wFPerm = \"+t+\" - 1 - wF;\\n\\n          for (int wR = 0; wR < \"+n+\"; wR++) {\\n            float dyR = float(dyRCorner + wR) / \"+a+\".0;\\n\\n            if (dyR < 0.0 || dyR >= \"+e.outHeight+\".0 ||\\n              fract(dyR) > 0.0) {\\n              continue;\\n            }\\n            int idyR = int(dyR);\\n\\n            int wRPerm = \"+n+\" - 1 - wR;\\n\\n            for (int wC = 0; wC < \"+r+\"; wC++) {\\n              float dyC = float(dyCCorner + wC) / \"+i+\".0;\\n\\n              if (dyC < 0.0 || dyC >= \"+e.outWidth+\".0 ||\\n                  fract(dyC) > 0.0) {\\n                continue;\\n              }\\n              int idyC = int(dyC);\\n\\n              int wCPerm = \"+r+\" - 1 - wC;\\n\\n              for (int d2 = 0; d2 < \"+e.outChannels+\"; d2++) {\\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\\n                dotProd += xValue * wValue;\\n              }\\n            }\\n          }\\n        }\\n        setOutput(dotProd);\\n      }\\n    \"}}(),DepthwiseConv2DDerFilterProgram=function(){return function(e){this.variableNames=[\"x\",\"dy\"],this.outputShape=e.filterShape;var t=e.strideHeight,n=e.strideWidth,r=e.padInfo.top,o=e.padInfo.left,a=e.outChannels/e.inChannels;this.userCode=\"\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int wR = coords.x;\\n        int wC = coords.y;\\n        int d1 = coords.z;\\n        int dm = coords.w;\\n        int d2 = d1 * \"+a+\" + dm;\\n\\n        float dotProd = 0.0;\\n\\n        // TODO: Vec4 over the batch size\\n        for (int b = 0; b < \"+e.batchSize+\"; b++) {\\n          for (int yR = 0; yR < \"+e.outHeight+\"; yR++) {\\n            int xR = wR + yR * \"+t+\" - \"+r+\";\\n\\n            if (xR < 0 || xR >= \"+e.inHeight+\") {\\n              continue;\\n            }\\n\\n            for (int yC = 0; yC < \"+e.outWidth+\"; yC++) {\\n              int xC = wC + yC * \"+n+\" - \"+o+\";\\n\\n              if (xC < 0 || xC >= \"+e.inWidth+\") {\\n                continue;\\n              }\\n\\n              float dyValue = getDy(b, yR, yC, d2);\\n              float xValue = getX(b, xR, xC, d1);\\n              dotProd += (xValue * dyValue);\\n            }\\n          }\\n        }\\n        setOutput(dotProd);\\n      }\\n    \"}}(),DepthwiseConv2DDerInputProgram=function(){return function(e){this.variableNames=[\"dy\",\"W\"],this.outputShape=e.inShape;var t=e.filterHeight,n=e.filterWidth,r=e.strideHeight,o=e.strideWidth,a=t-1-e.padInfo.top,i=n-1-e.padInfo.left,s=e.outChannels/e.inChannels;this.userCode=\"\\n      const ivec2 pads = ivec2(\"+a+\", \"+i+\");\\n\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int batch = coords[0];\\n        int d1 = coords[3];\\n        ivec2 dyCorner = coords.yz - pads;\\n        int dyRCorner = dyCorner.x;\\n        int dyCCorner = dyCorner.y;\\n\\n        float dotProd = 0.0;\\n\\n        for (int wR = 0; wR < \"+t+\"; wR++) {\\n          float dyR = float(dyRCorner + wR) / \"+r+\".0;\\n\\n          if (dyR < 0.0 || dyR >= \"+e.outHeight+\".0 || fract(dyR) > 0.0) {\\n            continue;\\n          }\\n          int idyR = int(dyR);\\n\\n          int wRPerm = \"+t+\" - 1 - wR;\\n\\n          for (int wC = 0; wC < \"+n+\"; wC++) {\\n            float dyC = float(dyCCorner + wC) / \"+o+\".0;\\n\\n            if (dyC < 0.0 || dyC >= \"+e.outWidth+\".0 ||\\n                fract(dyC) > 0.0) {\\n              continue;\\n            }\\n            int idyC = int(dyC);\\n\\n            int wCPerm = \"+n+\" - 1 - wC;\\n\\n            // TODO: Vec4 over the channelMul\\n            for (int dm = 0; dm < \"+s+\"; dm++) {\\n              int d2 = d1 * \"+s+\" + dm;\\n              float xValue = getDy(batch, idyR, idyC, d2);\\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\\n              dotProd += xValue * wValue;\\n            }\\n          }\\n        }\\n        setOutput(dotProd);\\n      }\\n    \"}}(),Conv2DProgram=function(){return function(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;var t=e.padInfo.top,n=e.padInfo.left,r=e.strideHeight,o=e.strideWidth,a=e.dilationHeight,i=e.dilationWidth,s=e.filterHeight,u=e.filterWidth,l=4*Math.floor(e.inChannels/4),c=e.inChannels%4;this.userCode=\"\\n      const ivec2 strides = ivec2(\"+r+\", \"+o+\");\\n      const ivec2 pads = ivec2(\"+t+\", \"+n+\");\\n\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int batch = coords[0];\\n        int d2 = coords[3];\\n\\n        ivec2 xRCCorner = coords.yz * strides - pads;\\n        int xRCorner = xRCCorner.x;\\n        int xCCorner = xRCCorner.y;\\n\\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\\n        // ? = to be determined. : = across all values in that axis.\\n        float dotProd = 0.0;\\n        for (int wR = 0; wR < \"+s+\"; wR++) {\\n          int xR = xRCorner + wR * \"+a+\";\\n\\n          if (xR < 0 || xR >= \"+e.inHeight+\") {\\n            continue;\\n          }\\n\\n          for (int wC = 0; wC < \"+u+\"; wC++) {\\n            int xC = xCCorner + wC * \"+i+\";\\n\\n            if (xC < 0 || xC >= \"+e.inWidth+\") {\\n              continue;\\n            }\\n\\n            for (int d1 = 0; d1 < \"+l+\"; d1 += 4) {\\n              vec4 xValues = vec4(\\n                getX(batch, xR, xC, d1),\\n                getX(batch, xR, xC, d1 + 1),\\n                getX(batch, xR, xC, d1 + 2),\\n                getX(batch, xR, xC, d1 + 3)\\n              );\\n              vec4 wValues = vec4(\\n                getW(wR, wC, d1, d2),\\n                getW(wR, wC, d1 + 1, d2),\\n                getW(wR, wC, d1 + 2, d2),\\n                getW(wR, wC, d1 + 3, d2)\\n              );\\n\\n              dotProd += dot(xValues, wValues);\\n            }\\n\\n            if (\"+(1===c)+\") {\\n              dotProd +=\\n                getX(batch, xR, xC, \"+l+\") *\\n                getW(wR, wC, \"+l+\", d2);\\n            } else if (\"+(2===c)+\") {\\n              vec2 xValues = vec2(\\n                getX(batch, xR, xC, \"+l+\"),\\n                getX(batch, xR, xC, \"+l+\" + 1)\\n              );\\n              vec2 wValues = vec2(\\n                getW(wR, wC, \"+l+\", d2),\\n                getW(wR, wC, \"+l+\" + 1, d2)\\n              );\\n              dotProd += dot(xValues, wValues);\\n            } else if (\"+(3===c)+\") {\\n              vec3 xValues = vec3(\\n                getX(batch, xR, xC, \"+l+\"),\\n                getX(batch, xR, xC, \"+l+\" + 1),\\n                getX(batch, xR, xC, \"+l+\" + 2)\\n              );\\n              vec3 wValues = vec3(\\n                getW(wR, wC, \"+l+\", d2),\\n                getW(wR, wC, \"+l+\" + 1, d2),\\n                getW(wR, wC, \"+l+\" + 2, d2)\\n              );\\n              dotProd += dot(xValues, wValues);\\n            }\\n          }\\n        }\\n        setOutput(dotProd);\\n      }\\n    \"}}(),Conv3DProgram=function(){return function(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;var t=e.padInfo.front,n=e.padInfo.top,r=e.padInfo.left,o=e.strideDepth,a=e.strideHeight,i=e.strideWidth,s=e.dilationDepth,u=e.dilationHeight,l=e.dilationWidth,c=e.filterDepth,p=e.filterHeight,d=e.filterWidth,h=4*Math.floor(e.inChannels/4),f=e.inChannels%4;this.userCode=\"\\n      const ivec3 strides = ivec3(\"+o+\", \"+a+\", \"+i+\");\\n      const ivec3 pads = ivec3(\"+t+\", \"+n+\", \"+r+\");\\n\\n      void main() {\\n        ivec5 coords = getOutputCoords();\\n        int batch = coords.x;\\n        int d2 = coords.u;\\n\\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\\n        int xFCorner = xFRCCorner.x;\\n        int xRCorner = xFRCCorner.y;\\n        int xCCorner = xFRCCorner.z;\\n\\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\\n        // values in that axis.\\n        float dotProd = 0.0;\\n        for (int wF = 0; wF < \"+c+\"; wF++) {\\n          int xF = xFCorner + wF * \"+s+\";\\n\\n          if (xF < 0 || xF >= \"+e.inDepth+\") {\\n            continue;\\n          }\\n\\n          for (int wR = 0; wR < \"+p+\"; wR++) {\\n            int xR = xRCorner + wR * \"+u+\";\\n\\n            if (xR < 0 || xR >= \"+e.inHeight+\") {\\n              continue;\\n            }\\n\\n            for (int wC = 0; wC < \"+d+\"; wC++) {\\n              int xC = xCCorner + wC * \"+l+\";\\n\\n              if (xC < 0 || xC >= \"+e.inWidth+\") {\\n                continue;\\n              }\\n\\n              for (int d1 = 0; d1 < \"+h+\"; d1 += 4) {\\n                vec4 xValues = vec4(\\n                  getX(batch, xF, xR, xC, d1),\\n                  getX(batch, xF, xR, xC, d1 + 1),\\n                  getX(batch, xF, xR, xC, d1 + 2),\\n                  getX(batch, xF, xR, xC, d1 + 3)\\n                );\\n                vec4 wValues = vec4(\\n                  getW(wF, wR, wC, d1, d2),\\n                  getW(wF, wR, wC, d1 + 1, d2),\\n                  getW(wF, wR, wC, d1 + 2, d2),\\n                  getW(wF, wR, wC, d1 + 3, d2)\\n                );\\n\\n                dotProd += dot(xValues, wValues);\\n              }\\n\\n              if (\"+(1===f)+\") {\\n                dotProd +=\\n                  getX(batch, xF, xR, xC, \"+h+\") *\\n                  getW(wF, wR, wC, \"+h+\", d2);\\n              } else if (\"+(2===f)+\") {\\n                vec2 xValues = vec2(\\n                  getX(batch, xF, xR, xC, \"+h+\"),\\n                  getX(batch, xF, xR, xC, \"+h+\" + 1)\\n                );\\n                vec2 wValues = vec2(\\n                  getW(wF, wR, wC, \"+h+\", d2),\\n                  getW(wF, wR, wC, \"+h+\" + 1, d2)\\n                );\\n                dotProd += dot(xValues, wValues);\\n              } else if (\"+(3===f)+\") {\\n                vec3 xValues = vec3(\\n                  getX(batch, xF, xR, xC, \"+h+\"),\\n                  getX(batch, xF, xR, xC, \"+h+\" + 1),\\n                  getX(batch, xF, xR, xC, \"+h+\" + 2)\\n                );\\n                vec3 wValues = vec3(\\n                  getW(wF, wR, wC, \"+h+\", d2),\\n                  getW(wF, wR, wC, \"+h+\" + 1, d2),\\n                  getW(wF, wR, wC, \"+h+\" + 2, d2)\\n                );\\n                dotProd += dot(xValues, wValues);\\n              }\\n            }\\n          }\\n        }\\n        setOutput(dotProd);\\n      }\\n    \"}}(),DepthwiseConv2DProgram=function(){return function(e){this.variableNames=[\"x\",\"W\"],this.outputShape=e.outShape;var t=e.inHeight,n=e.inWidth,r=e.padInfo.top,o=e.padInfo.left,a=e.strideHeight,i=e.strideWidth,s=e.dilationHeight,u=e.dilationWidth,l=e.filterHeight,c=e.filterWidth,p=e.outChannels/e.inChannels;this.userCode=\"\\n      const ivec2 strides = ivec2(\"+a+\", \"+i+\");\\n      const ivec2 pads = ivec2(\"+r+\", \"+o+\");\\n\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int batch = coords.x;\\n        ivec2 xRCCorner = coords.yz * strides - pads;\\n        int d2 = coords.w;\\n        int d1 = d2 / \"+p+\";\\n        int q = d2 - d1 * \"+p+\";\\n\\n        int xRCorner = xRCCorner.x;\\n        int xCCorner = xRCCorner.y;\\n\\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\\n        // ? = to be determined. : = across all values in that axis.\\n        float dotProd = 0.0;\\n        // TODO(dsmilkov): Flatten the two for loops and vec4 the operations.\\n        for (int wR = 0; wR < \"+l+\"; wR++) {\\n          int xR = xRCorner + wR * \"+s+\";\\n\\n          if (xR < 0 || xR >= \"+t+\") {\\n            continue;\\n          }\\n\\n          for (int wC = 0; wC < \"+c+\"; wC++) {\\n            int xC = xCCorner + wC * \"+u+\";\\n\\n            if (xC < 0 || xC >= \"+n+\") {\\n              continue;\\n            }\\n\\n            float xVal = getX(batch, xR, xC, d1);\\n            float wVal = getW(wR, wC, d1, q);\\n            dotProd += xVal * wVal;\\n          }\\n        }\\n        setOutput(dotProd);\\n      }\\n    \"}}(),DepthwiseConvPacked2DProgram=function(){return function(e){this.variableNames=[\"x\",\"W\"],this.usesPackedTextures=!0,this.outputShape=e.outShape;for(var t=e.inHeight,n=e.inWidth,r=e.padInfo.top,o=e.padInfo.left,a=e.strideHeight,i=e.strideWidth,s=e.filterHeight,u=e.filterWidth,l=Math.ceil((u+1)/2),c=\"int xR; int xC;\",p=0;p<s;p++){for(var d=-o;d<2*l;d++)c+=\"vec4 \"+xTexelName(p,d)+\" = vec4(0.);\";for(d=0;d<u;d++)c+=\"\\n          vec4 wR\"+p+\"C\"+d+\" = vec4(0.);\\n          vec4 xR\"+p+\"C\"+d+\" = vec4(0.);\"}for(p=0;p<s;p++)for(d=0;d<l;d++){var h=2*d,f=2*d+o;c+=\"\\n          xR = xRCorner + \"+p+\";\\n          xC = xCCorner + \"+f+\";\\n\\n          if(xR >= 0 && xR < \"+t+\" && xC >= 0 && xC < \"+n+\") {\\n            \"+xTexelName(p,f)+\" = getX(batch, xR, xC, d1);\\n          }\",0===o?h<u&&d===l-1&&(i>1&&(c+=\"\\n                vec4 \"+xTexelName(p,f+2)+\" = vec4(0.);\\n\\n                if(xR >= 0 && xR < \"+t+\" && xC + 2 < \"+n+\") {\\n                  \"+xTexelName(p,f+2)+\" = getX(batch, xR, xC + 2, d1);\\n                }\"),c+=\"\\n              xR\"+p+\"C\"+f+\" = \"+constructTexel(p,f,i,o)+\";\\n            \"):0===d&&(c+=\"\\n            if(xR >= 0 && xR < \"+t+\" && xC - 2 >= 0) {\\n              \"+xTexelName(p,f-2)+\" = getX(batch, xR, xC - 2, d1);\\n            }\"),h>0&&(c+=\"xR\"+p+\"C\"+(f-2)+\" =\\n            \"+constructTexel(p,f-2,i,o)+\";\"),f-1>=0&&f-1<u&&(c+=\"xR\"+p+\"C\"+(f-1)+\" =\\n              \"+constructTexel(p,f-1,i,o)+\";\"),h<u&&(c+=\"\\n            vec4 wTexel\"+p+\"C\"+h+\" = getW(\"+p+\", \"+h+\", d1, q);\\n            wR\"+p+\"C\"+h+\" = vec4(wTexel\"+p+\"C\"+h+\".xz, wTexel\"+p+\"C\"+h+\".xz);\\n          \",h+1<u&&(c+=\"\\n              vec4 wTexelR\"+p+\"C\"+(h+1)+\" = getW(\"+p+\", \"+(h+1)+\", d1, q);\\n              wR\"+p+\"C\"+(h+1)+\" =\\n                vec4(wTexelR\"+p+\"C\"+(h+1)+\".xz, wTexelR\"+p+\"C\"+(h+1)+\".xz);\"))}for(p=0;p<s;p++)for(d=0;d<u;d++)c+=\"result += xR\"+p+\"C\"+d+\" * wR\"+p+\"C\"+d+\";\";this.userCode=\"\\n      const ivec2 strides = ivec2(\"+a+\", \"+i+\");\\n      const ivec2 pads = ivec2(\"+r+\", \"+o+\");\\n\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int batch = coords.x;\\n        ivec2 xRCCorner = coords.yz * strides - pads;\\n        int d2 = coords.w;\\n        int d1 = d2;\\n        int q = 0;\\n        int xRCorner = xRCCorner.x;\\n        int xCCorner = xRCCorner.y;\\n\\n        vec4 result = vec4(0.);\\n\\n        \"+c+\"\\n\\n        setOutput(result);\\n      }\\n    \"}}();function xTexelName(e,t){return\"xTexelR\"+e+\"C\"+(t<0?\"minus\"+Math.abs(t).toString():t)}function constructTexel(e,t,n,r){return 1===n?r%2==t%2?xTexelName(e,t):\"vec4(\"+xTexelName(e,t-1)+\".zw, \"+xTexelName(e,t+1)+\".xy)\":r%2==t%2?\"vec4(\"+xTexelName(e,t)+\".xy, \"+xTexelName(e,t+2)+\".xy)\":\"vec4(\"+xTexelName(e,t-1)+\".zw, \"+xTexelName(e,t+1)+\".zw)\"}var CropAndResizeProgram=function(){return function(e,t,n,r,o){this.variableNames=[\"Image\",\"Boxes\",\"BoxInd\"],this.outputShape=[];var a=e[0],i=e[1],s=e[2],u=e[3],l=t[0],c=n[0],p=n[1];this.outputShape=[l,c,p,u];var d=\"bilinear\"===r?1:0,h=[i-1+\".0\",s-1+\".0\"],f=h[0],m=h[1],g=c>1?[\"\"+(i-1)/(c-1),\"(y2-y1) * height_ratio\",\"y1*\"+f+\" + float(y)*(height_scale)\"]:[\"0.0\",\"0.0\",\"0.5 * (y1+y2) * \"+f],v=g[0],y=g[1],x=g[2],T=p>1?[\"\"+(s-1)/(p-1),\"(x2-x1) * width_ratio\",\"x1*\"+m+\" + float(x)*(width_scale)\"]:[\"0.0\",\"0.0\",\"0.5 * (x1+x2) * \"+m],w=T[0],E=T[1],S=T[2];this.userCode=\"\\n      const float height_ratio = float(\"+v+\");\\n      const float width_ratio = float(\"+w+\");\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int b = coords[0];\\n        int y = coords[1];\\n        int x = coords[2];\\n        int d = coords[3];\\n\\n        // get box vals\\n        float y1 = getBoxes(b,0);\\n        float x1 = getBoxes(b,1);\\n        float y2 = getBoxes(b,2);\\n        float x2 = getBoxes(b,3);\\n\\n        // get image in batch index\\n        int bInd = round(getBoxInd(b));\\n        if(bInd < 0 || bInd >= \"+a+\") {\\n          return;\\n        }\\n\\n        float height_scale = \"+y+\";\\n        float width_scale = \"+E+\";\\n\\n        float in_y = \"+x+\";\\n        if( in_y < 0.0 || in_y > \"+f+\" ) {\\n          setOutput(float(\"+o+\"));\\n          return;\\n        }\\n        float in_x = \"+S+\";\\n        if( in_x < 0.0 || in_x > \"+m+\" ) {\\n          setOutput(float(\"+o+\"));\\n          return;\\n        }\\n\\n        vec2 sourceFracIndexRC = vec2(in_y,in_x);\\n        if(\"+d+\" == 1) {\\n          // Compute the four integer indices.\\n          ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\\n          ivec2 sourceCeilRC = ivec2(ceil(sourceFracIndexRC));\\n\\n          float topLeft = getImage(b, sourceFloorRC.x, sourceFloorRC.y, d);\\n          float bottomLeft = getImage(b, sourceCeilRC.x, sourceFloorRC.y, d);\\n          float topRight = getImage(b, sourceFloorRC.x, sourceCeilRC.y, d);\\n          float bottomRight = getImage(b, sourceCeilRC.x, sourceCeilRC.y, d);\\n\\n          vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\\n\\n          float top = topLeft + (topRight - topLeft) * fracRC.y;\\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\\n          float newValue = top + (bottom - top) * fracRC.x;\\n          setOutput(newValue);\\n        } else {\\n          // Compute the coordinators of nearest neighbor point.\\n          ivec2 sourceNearestRC = ivec2(floor(\\n            sourceFracIndexRC + vec2(0.5,0.5)));\\n          float newValue = getImage(b, sourceNearestRC.x, sourceNearestRC.y, d);\\n          setOutput(newValue);\\n        }\\n      }\\n    \"}}();function getLogicalCoordinatesFromFlatIndex(e,t,n){void 0===n&&(n=\"index\");var r=computeStrides(t);return r.map(function(t,o){return\"int \"+e[o]+\" = \"+n+\" / \"+t+\"; \"+(o===r.length-1?\"int \"+e[o+1]+\" = \"+n+\" - \"+e[o]+\" * \"+t:\"index -= \"+e[o]+\" * \"+t)+\";\"}).join(\"\")}function buildVec(e){return 1===e.length?\"\"+e[0]:\"vec\"+e.length+\"(\"+e.join(\",\")+\")\"}function dotify(e,t){if(e.length!==t.length)throw new Error(\"Vectors to be dotted must be of the same length -got \"+e.length+\" and \"+t.length);for(var n=[],r=Math.floor(e.length/4),o=e.length%4,a=0;a<r;a++){var i=e.slice(4*a,4*a+4),s=t.slice(4*a,4*a+4);n.push(buildVec(i)+\", \"+buildVec(s))}if(0!==o){i=e.slice(4*r),s=t.slice(4*r);1===i.length&&(i=i.map(function(e){return\"float(\"+e+\")\"}),s=s.map(function(e){return\"float(\"+e+\")\"})),n.push(buildVec(i)+\", \"+buildVec(s))}return n.map(function(e,t){return\"dot(\"+e+\")\"}).join(\"+\")}function makeShader(e,t,n,r,o){var a=e.map(function(e){var t=sizeFromShape(e.shapeInfo.logicalShape);return e.shapeInfo.isUniform?\"uniform float \"+e.name+(t>1?\"[\"+t+\"]\":\"\")+\";\":\"uniform sampler2D \"+e.name+\";\"});a=a.join(\"\\n\");var i,s,u=e.map(function(e){return getInputSamplingSnippet(e,t,r,o)}).join(\"\\n\"),l=t.texShape,c=SHADER_PREFIX;return t.isPacked?(i=getPackedOutputSamplingSnippet(t.logicalShape,l),s=FLOAT_TEXTURE_SET_RGBA_SNIPPET):(i=getOutputSamplingSnippet(t.logicalShape,l),s=FLOAT_TEXTURE_SET_R_SNIPPET),o&&(c+=SHADER_PACKED_PREFIX),[c,FLOAT_TEXTURE_SAMPLE_SNIPPET,s,a,i,u,n].join(\"\\n\")}function getSamplerFromInInfo(e){var t=e.shapeInfo.logicalShape;switch(t.length){case 0:return getSamplerScalar(e);case 1:return getSampler1D(e);case 2:return getSampler2D(e);case 3:return getSampler3D(e);case 4:return getSampler4D(e);case 5:return getSampler5D(e);case 6:return getSampler6D(e);default:throw new Error(t.length+\"-D input sampling is not yet supported\")}}function getPackedSamplerFromInInfo(e){var t=e.shapeInfo.logicalShape;switch(t.length){case 0:return getPackedSamplerScalar(e);case 1:return getPackedSampler1D(e);case 2:return getPackedSampler2D(e);case 3:return getPackedSampler3D(e);case 4:return getPackedSampler4D(e);default:throw new Error(\"Packed \"+t.length+\"-D input sampling is not yet supported\")}}function getInputSamplingSnippet(e,t,n,r){void 0===r&&(r=!1);var o=getSamplerFlat(e);return o+=r?getPackedSamplerFromInInfo(e):getSamplerFromInInfo(e),(n||arraysEqual(e.shapeInfo.logicalShape,t.logicalShape))&&(o+=r?getPackedSamplerAtOutputCoords(e,t,n):getSamplerAtOutputCoords(e,t,n)),o}function getPackedOutputSamplingSnippet(e,t){switch(e.length){case 0:return getOutputScalarCoords();case 1:return getOutputPacked1DCoords(e,t);case 2:return getOutputPacked2DCoords(e,t);case 3:return getOutputPacked3DCoords(e,t);case 4:return getOutputPacked4DCoords(e,t);default:throw new Error(e.length+\"-D packed output coordinate fetching is not yet supported\")}}function getOutputSamplingSnippet(e,t){switch(e.length){case 0:return getOutputScalarCoords();case 1:return getOutput1DCoords(e,t);case 2:return getOutput2DCoords(e,t);case 3:return getOutput3DCoords(e,t);case 4:return getOutput4DCoords(e,t);case 5:return getOutput5DCoords(e,t);case 6:return getOutput6DCoords(e,t);default:throw new Error(e.length+\"-D output sampling is not yet supported\")}}var SAMPLE_1D_SNIPPET=\"\\nvec2 UVfrom1D(int texNumR, int texNumC, int index) {\\n  int texR = index / texNumC;\\n  int texC = index - texR * texNumC;\\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\\n  int texelIndex = index / 2;\\n  int texR = texelIndex / texNumC;\\n  int texC = texelIndex - texR * texNumC;\\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\n\",SAMPLE_2D_SNIPPET=\"\\nvec2 UVfrom2D(int texNumR, int texNumC, int numC, int row, int col) {\\n  int index = row * numC + col;\\n  int texR = index / texNumC;\\n  int texC = index - texR * texNumC;\\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\\n  int texNumC, int row, int col) {\\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\\n  int texR = texelIndex / texNumC;\\n  int texC = texelIndex - texR * texNumC;\\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\n\",SAMPLE_3D_SNIPPET=\"\\nvec2 UVfrom3D(int texNumR, int texNumC, int stride0,\\n    int stride1, int row, int col, int depth) {\\n  // Explicitly use integer operations as dot() only works on floats.\\n  int index = row * stride0 + col * stride1 + depth;\\n  int texR = index / texNumC;\\n  int texC = index - texR * texNumC;\\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\nvec2 packedUVfrom3D(int texNumR, int texNumC,\\n    int texelsInBatch, int texelsInLogicalRow, int b,\\n    int row, int col) {\\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\\n  int texR = index / texNumC;\\n  int texC = index - texR * texNumC;\\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\n\",SAMPLE_4D_SNIPPET=\"\\nvec2 UVfrom4D(int texNumR, int texNumC, int stride0,\\n    int stride1, int stride2, int row, int col, int depth,\\n    int depth2) {\\n  // Explicitly use integer operations as dot() only works on floats.\\n  int index = row * stride0 + col * stride1 + depth * stride2 + depth2;\\n  int texR = index / texNumC;\\n  int texC = index - texR * texNumC;\\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\nvec2 packedUVfrom4D(int texNumR, int texNumC, int texelsInBatch2,\\n    int texelsInBatch, int texelsInLogicalRow, int b2, int b,\\n    int row, int col) {\\n  int index = b2 * texelsInBatch2 + b * texelsInBatch +\\n    (row / 2) * texelsInLogicalRow + (col / 2);\\n  int texR = index / texNumC;\\n  int texC = index - texR * texNumC;\\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\n\",SAMPLE_5D_SNIPPET=\"\\nvec2 UVfrom5D(int texNumR, int texNumC, int stride0,\\n    int stride1, int stride2, int stride3, int row, int col, int depth,\\n    int depth2, int depth3) {\\n  // Explicitly use integer operations as dot() only works on floats.\\n  int index = row * stride0 + col * stride1 +\\n              depth * stride2 + depth2 * stride3 + depth3;\\n  int texR = index / texNumC;\\n  int texC = index - texR * texNumC;\\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\n\",SAMPLE_6D_SNIPPET=\"\\nvec2 UVfrom6D(int texNumR, int texNumC, int stride0,\\n    int stride1, int stride2, int stride3, int stride4,\\n    int row, int col, int depth, int depth2, int depth3, int depth4) {\\n  // Explicitly use integer operations as dot() only works on floats.\\n  int index = row * stride0 + col * stride1 + depth * stride2 + depth2 *\\n    stride3 + depth3 * stride4 + depth4;\\n  int texR = index / texNumC;\\n  int texC = index - texR * texNumC;\\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\\n}\\n\",FLOAT_TEXTURE_SAMPLE_SNIPPET=\"\\n  float sampleTexture(sampler2D textureSampler, vec2 uv) {\\n    return texture2D(textureSampler, uv).r;\\n  }\\n\",FLOAT_TEXTURE_SET_R_SNIPPET=\"\\n  void setOutput(float val) {\\n    gl_FragColor = vec4(val, 0, 0, 0);\\n  }\\n\",FLOAT_TEXTURE_SET_RGBA_SNIPPET=\"\\n  void setOutput(vec4 val) {\\n    gl_FragColor = val;\\n  }\\n\",NAN_CHECKS=\"\",SHADER_PREFIX=\"\\n  precision highp float;\\n  precision highp int;\\n  varying vec2 resultUV;\\n  const vec2 halfCR = vec2(0.5, 0.5);\\n\\n  struct ivec5\\n  {\\n    int x;\\n    int y;\\n    int z;\\n    int w;\\n    int u;\\n  };\\n\\n  struct ivec6\\n  {\\n    int x;\\n    int y;\\n    int z;\\n    int w;\\n    int u;\\n    int v;\\n  };\\n\\n  \"+(NAN_CHECKS=ENV.get(\"PROD\")?\"\\n    bool isNaN(float val) {\\n      return false;\\n    }\\n\\n    bool hasNaN(vec4 values) {\\n      return false;\\n    }\\n  \":\"\\n    bool isNaN(float val) {\\n      return (val < 1.0 || 0.0 < val || val == 0.0) ? false : true;\\n    }\\n\\n    bool hasNaN(vec4 values) {\\n      return any(bvec4(\\n        isNaN(values.x),\\n        isNaN(values.y),\\n        isNaN(values.z),\\n        isNaN(values.w)\\n      ));\\n    }\\n  \")+\"\\n\\n  float getNaN(vec4 values) {\\n    return dot(vec4(1), values);\\n  }\\n\\n  int round(float value) {\\n    return int(floor(value + 0.5));\\n  }\\n\\n  int imod(int x, int y) {\\n    return x - y * (x / y);\\n  }\\n\\n  //Based on the work of Dave Hoskins\\n  //https://www.shadertoy.com/view/4djSRW\\n  #define HASHSCALE1 443.8975\\n  float random(float seed){\\n    vec2 p = resultUV * seed;\\n    vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\\n    p3 += dot(p3, p3.yzx + 19.19);\\n    return fract((p3.x + p3.y) * p3.z);\\n  }\\n\\n  \"+SAMPLE_1D_SNIPPET+\"\\n  \"+SAMPLE_2D_SNIPPET+\"\\n  \"+SAMPLE_3D_SNIPPET+\"\\n  \"+SAMPLE_4D_SNIPPET+\"\\n  \"+SAMPLE_5D_SNIPPET+\"\\n  \"+SAMPLE_6D_SNIPPET+\"\\n\",SHADER_PACKED_PREFIX=\"\\n  float getChannel(vec4 frag, vec2 innerDims) {\\n    vec2 modCoord = mod(innerDims, 2.);\\n    return modCoord.x == 0. ?\\n      (modCoord.y == 0. ? frag.r : frag.g) :\\n      (modCoord.y == 0. ? frag.b : frag.a);\\n  }\\n  float getChannel(vec4 frag, int dim) {\\n    float modCoord = mod(float(dim), 2.);\\n    return modCoord == 0. ? frag.r : frag.g;\\n  }\\n\";function getOutputScalarCoords(){return\"\\n    int getOutputCoords() {\\n      return 0;\\n    }\\n  \"}function getOutputPacked1DCoords(e,t){var n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];return 1===t[0]?\"\\n      int getOutputCoords() {\\n        return 2 * int(resultUV.x * \"+n[1]+\".0);\\n      }\\n    \":1===t[1]?\"\\n      int getOutputCoords() {\\n        return 2 * int(resultUV.y * \"+n[0]+\".0);\\n      }\\n    \":\"\\n    int getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n                             vec2(\"+n[0]+\", \"+n[1]+\"));\\n      return resTexRC.x * \"+n[1]+\" + resTexRC.y;\\n    }\\n  \"}function getOutput1DCoords(e,t){return 1===t[0]?\"\\n      int getOutputCoords() {\\n        return int(resultUV.x * \"+t[1]+\".0);\\n      }\\n    \":1===t[1]?\"\\n      int getOutputCoords() {\\n        return int(resultUV.y * \"+t[0]+\".0);\\n      }\\n    \":\"\\n    int getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n                             vec2(\"+t[0]+\", \"+t[1]+\"));\\n      return resTexRC.x * \"+t[1]+\" + resTexRC.y;\\n    }\\n  \"}function getOutputPacked3DCoords(e,t){var n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[2]/2),o=r*Math.ceil(e[1]/2);return\"\\n    ivec3 getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n                             vec2(\"+n[0]+\", \"+n[1]+\"));\\n      int index = resTexRC.x * \"+n[1]+\" + resTexRC.y;\\n\\n      int b = index / \"+o+\";\\n      index -= b * \"+o+\";\\n\\n      int r = 2 * (index / \"+r+\");\\n      int c = imod(index, \"+r+\") * 2;\\n\\n      return ivec3(b, r, c);\\n    }\\n  \"}function getOutput3DCoords(e,t){var n=getLogicalCoordinatesFromFlatIndex([\"r\",\"c\",\"d\"],e);return\"\\n    ivec3 getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n                             vec2(\"+t[0]+\", \"+t[1]+\"));\\n      int index = resTexRC.x * \"+t[1]+\" + resTexRC.y;\\n      \"+n+\"\\n      return ivec3(r, c, d);\\n    }\\n  \"}function getOutputPacked4DCoords(e,t){var n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)],r=Math.ceil(e[3]/2),o=r*Math.ceil(e[2]/2),a=o*e[1];return\"\\n    ivec4 getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n                             vec2(\"+n[0]+\", \"+n[1]+\"));\\n      int index = resTexRC.x * \"+n[1]+\" + resTexRC.y;\\n\\n      int b2 = index / \"+a+\";\\n      index -= b2 * \"+a+\";\\n\\n      int b = index / \"+o+\";\\n      index -= b * \"+o+\";\\n\\n      int r = 2 * (index / \"+r+\");\\n      int c = imod(index, \"+r+\") * 2;\\n\\n      return ivec4(b2, b, r, c);\\n    }\\n  \"}function getOutput4DCoords(e,t){var n=getLogicalCoordinatesFromFlatIndex([\"r\",\"c\",\"d\",\"d2\"],e);return\"\\n    ivec4 getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n        vec2(\"+t[0]+\", \"+t[1]+\"));\\n      int index = resTexRC.x * \"+t[1]+\" + resTexRC.y;\\n      \"+n+\"\\n      return ivec4(r, c, d, d2);\\n    }\\n  \"}function getOutput5DCoords(e,t){var n=getLogicalCoordinatesFromFlatIndex([\"r\",\"c\",\"d\",\"d2\",\"d3\"],e);return\"\\n    ivec5 getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2(\"+t[0]+\",\\n                             \"+t[1]+\"));\\n\\n      int index = resTexRC.x * \"+t[1]+\" + resTexRC.y;\\n\\n      \"+n+\"\\n\\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\\n      return outShape;\\n    }\\n  \"}function getOutput6DCoords(e,t){var n=getLogicalCoordinatesFromFlatIndex([\"r\",\"c\",\"d\",\"d2\",\"d3\",\"d4\"],e);return\"\\n    ivec6 getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n        vec2(\"+t[0]+\", \"+t[1]+\"));\\n      int index = resTexRC.x * \"+t[1]+\" + resTexRC.y;\\n\\n      \"+n+\"\\n\\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\\n      return result;\\n    }\\n  \"}function getOutputPacked2DCoords(e,t){var n=[Math.ceil(t[0]/2),Math.ceil(t[1]/2)];if(arraysEqual(e,t))return\"\\n      ivec2 getOutputCoords() {\\n        return 2 * ivec2(resultUV.yx * vec2(\"+n[0]+\", \"+n[1]+\"));\\n      }\\n    \";var r=Math.ceil(e[1]/2);return\"\\n    ivec2 getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n                             vec2(\"+n[0]+\", \"+n[1]+\"));\\n\\n      int index = resTexRC.x * \"+n[1]+\" + resTexRC.y;\\n      int r = 2 * (index / \"+r+\");\\n      int c = imod(index, \"+r+\") * 2;\\n\\n      return ivec2(r, c);\\n    }\\n  \"}function getOutput2DCoords(e,t){return arraysEqual(e,t)?\"\\n      ivec2 getOutputCoords() {\\n        return ivec2(resultUV.yx * vec2(\"+t[0]+\", \"+t[1]+\"));\\n      }\\n    \":1===e[1]?\"\\n      ivec2 getOutputCoords() {\\n        ivec2 resTexRC = ivec2(resultUV.yx *\\n                               vec2(\"+t[0]+\", \"+t[1]+\"));\\n        int index = resTexRC.x * \"+t[1]+\" + resTexRC.y;\\n        return ivec2(index, 0);\\n      }\\n    \":1===e[0]?\"\\n      ivec2 getOutputCoords() {\\n        ivec2 resTexRC = ivec2(resultUV.yx *\\n                               vec2(\"+t[0]+\", \"+t[1]+\"));\\n        int index = resTexRC.x * \"+t[1]+\" + resTexRC.y;\\n        return ivec2(0, index);\\n      }\\n    \":\"\\n    ivec2 getOutputCoords() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n                             vec2(\"+t[0]+\", \"+t[1]+\"));\\n      int index = resTexRC.x * \"+t[1]+\" + resTexRC.y;\\n      int r = index / \"+e[1]+\";\\n      int c = index - r * \"+e[1]+\";\\n      return ivec2(r, c);\\n    }\\n  \"}function getPackedSamplerScalar(e){var t=e.name;return\"\\n    vec4 \"+(\"get\"+t.charAt(0).toUpperCase()+t.slice(1))+\"() {\\n      return texture2D(\"+t+\", halfCR);\\n    }\\n  \"}function getSamplerScalar(e){var t=e.name,n=\"get\"+t.charAt(0).toUpperCase()+t.slice(1);return e.shapeInfo.isUniform?\"float \"+n+\"() {return \"+t+\";}\":\"\\n    float \"+n+\"() {\\n      return sampleTexture(\"+t+\", halfCR);\\n    }\\n  \"}function getPackedSampler1D(e){var t=e.name,n=\"get\"+t.charAt(0).toUpperCase()+t.slice(1),r=e.shapeInfo.texShape,o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)];return\"\\n    vec4 \"+n+\"(int index) {\\n      vec2 uv = packedUVfrom1D(\\n        \"+o[0]+\", \"+o[1]+\", index);\\n      return texture2D(\"+t+\", uv);\\n    }\\n  \"}function getSampler1D(e){var t=e.name,n=\"get\"+t.charAt(0).toUpperCase()+t.slice(1);return\"\\n    float \"+n+\"(int index) {\\n      return \"+n+\"Flat(index);\\n    }\\n  \"}function getPackedSampler2D(e){var t=e.shapeInfo.logicalShape,n=e.name,r=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),o=e.shapeInfo.texShape,a=o[0],i=o[1];if(null!=o&&arraysEqual(t,o))return\"\\n      vec4 \"+r+\"(int row, int col) {\\n        vec2 uv = (vec2(col, row) + halfCR) / vec2(\"+i+\".0, \"+a+\".0);\\n\\n        return texture2D(\"+n+\", uv);\\n      }\\n    \";var s=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];return\"\\n    vec4 \"+r+\"(int row, int col) {\\n      vec2 uv = packedUVfrom2D(\"+Math.ceil(t[1]/2)+\", \"+s[0]+\", \"+s[1]+\", row, col);\\n      return texture2D(\"+n+\", uv);\\n    }\\n  \"}function getSampler2D(e){var t=e.shapeInfo.logicalShape,n=e.name,r=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),o=e.shapeInfo.texShape;if(null!=o&&arraysEqual(t,o)){var a=o[0];return\"\\n    float \"+r+\"(int row, int col) {\\n      vec2 uv = (vec2(col, row) + halfCR) / vec2(\"+o[1]+\".0, \"+a+\".0);\\n      return sampleTexture(\"+n+\", uv);\\n    }\\n  \"}var i=squeezeShape(t),s=i.newShape,u=i.keptDims,l=s;if(l.length<t.length){return\"\\n      \"+getSamplerFromInInfo(squeezeInputInfo(e,l))+\"\\n      float \"+r+\"(int row, int col) {\\n        return \"+r+\"(\"+getSqueezedParams([\"row\",\"col\"],u)+\");\\n      }\\n    \"}if(e.shapeInfo.isUniform)return\"\\n      float \"+r+\"(int row, int col) {\\n        float index = dot(vec2(row, col), vec2(\"+t[1]+\", 1));\\n        return \"+r+\"Flat(round(index));\\n      }\\n    \";var c=o[0],p=o[1];return 1===p?\"\\n    float \"+r+\"(int row, int col) {\\n      float index = dot(vec2(row, col), vec2(\"+t[1]+\", 1));\\n      vec2 uv = vec2(0.5, (index + 0.5) / \"+c+\".0);\\n      return sampleTexture(\"+n+\", uv);\\n    }\\n  \":1===c?\"\\n    float \"+r+\"(int row, int col) {\\n      float index = dot(vec2(row, col), vec2(\"+t[1]+\", 1));\\n      vec2 uv = vec2((index + 0.5) / \"+p+\".0, 0.5);\\n      return sampleTexture(\"+n+\", uv);\\n    }\\n  \":\"\\n  float \"+r+\"(int row, int col) {\\n    vec2 uv = UVfrom2D(\"+c+\", \"+p+\", \"+t[1]+\", row, col);\\n    return sampleTexture(\"+n+\", uv);\\n  }\\n\"}function getPackedSampler3D(e){var t=e.shapeInfo.logicalShape,n=e.name,r=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),o=e.shapeInfo.texShape,a=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];if(1===t[0]){return\"\\n        \"+getPackedSamplerFromInInfo(squeezeInputInfo(e,t.slice(1)))+\"\\n        vec4 \"+r+\"(int b, int row, int col) {\\n          return \"+r+\"(\"+getSqueezedParams([\"b\",\"row\",\"col\"],[1,2])+\");\\n        }\\n      \"}var i=a[0],s=a[1],u=Math.ceil(t[2]/2);return\"\\n    vec4 \"+r+\"(int b, int row, int col) {\\n      vec2 uv = packedUVfrom3D(\\n        \"+i+\", \"+s+\", \"+u*Math.ceil(t[1]/2)+\", \"+u+\", b, row, col);\\n      return texture2D(\"+n+\", uv);\\n    }\\n  \"}function getSampler3D(e){var t=e.shapeInfo.logicalShape,n=e.name,r=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),o=t[1]*t[2],a=t[2],i=squeezeShape(t),s=i.newShape,u=i.keptDims,l=s;if(l.length<t.length){return\"\\n        \"+getSamplerFromInInfo(squeezeInputInfo(e,l))+\"\\n        float \"+r+\"(int row, int col, int depth) {\\n          return \"+r+\"(\"+getSqueezedParams([\"row\",\"col\",\"depth\"],u)+\");\\n        }\\n      \"}if(e.shapeInfo.isUniform)return\"\\n      float \"+r+\"(int row, int col, int depth) {\\n        float index = dot(vec3(row, col, depth),\\n                          vec3(\"+o+\", \"+a+\", 1));\\n        return \"+r+\"Flat(round(index));\\n      }\\n    \";var c=e.shapeInfo.texShape,p=c[0],d=c[1];return d===o?\"\\n        float \"+r+\"(int row, int col, int depth) {\\n          float texR = float(row);\\n          float texC = dot(vec2(col, depth), vec2(\"+a+\", 1));\\n          vec2 uv = (vec2(texC, texR) + halfCR) /\\n                     vec2(\"+d+\".0, \"+p+\".0);\\n          return sampleTexture(\"+n+\", uv);\\n        }\\n      \":d===a?\"\\n    float \"+r+\"(int row, int col, int depth) {\\n      float texR = dot(vec2(row, col), vec2(\"+t[1]+\", 1));\\n      float texC = float(depth);\\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(\"+d+\".0, \"+p+\".0);\\n      return sampleTexture(\"+n+\", uv);\\n    }\\n  \":\"\\n      float \"+r+\"(int row, int col, int depth) {\\n        vec2 uv = UVfrom3D(\\n            \"+p+\", \"+d+\", \"+o+\", \"+a+\", row, col, depth);\\n        return sampleTexture(\"+n+\", uv);\\n      }\\n  \"}function getPackedSampler4D(e){var t=e.shapeInfo.logicalShape,n=e.name,r=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),o=e.shapeInfo.texShape,a=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)],i=a[0],s=a[1],u=Math.ceil(t[3]/2),l=u*Math.ceil(t[2]/2);return\"\\n    vec4 \"+r+\"(int b2, int b, int row, int col) {\\n      vec2 uv = packedUVfrom4D(\\n        \"+i+\", \"+s+\", \"+l*t[1]+\",\\n        \"+l+\", \"+u+\", b2, b, row, col);\\n      return texture2D(\"+n+\", uv);\\n    }\\n  \"}function getSampler4D(e){var t=e.shapeInfo.logicalShape,n=e.name,r=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),o=t[3],a=t[2]*o,i=t[1]*a,s=squeezeShape(t),u=s.newShape,l=s.keptDims;if(u.length<t.length){return\"\\n      \"+getSamplerFromInInfo(squeezeInputInfo(e,u))+\"\\n      float \"+r+\"(int row, int col, int depth, int depth2) {\\n        return \"+r+\"(\"+getSqueezedParams([\"row\",\"col\",\"depth\",\"depth2\"],l)+\");\\n      }\\n    \"}if(e.shapeInfo.isUniform)return\"\\n      float \"+r+\"(int row, int col, int depth, int depth2) {\\n        float index = dot(vec4(row, col, depth, depth2),\\n                          vec4(\"+i+\", \"+a+\", \"+o+\", 1));\\n        return \"+r+\"Flat(round(index));\\n      }\\n    \";var c=e.shapeInfo.texShape,p=c[0],d=c[1];return d===i?\"\\n      float \"+r+\"(int row, int col, int depth, int depth2) {\\n        float texR = float(row);\\n        float texC =\\n            dot(vec3(col, depth, depth2), vec3(\"+a+\", \"+o+\", 1));\\n        vec2 uv = (vec2(texC, texR) + halfCR) /\\n                   vec2(\"+d+\".0, \"+p+\".0);\\n        return sampleTexture(\"+n+\", uv);\\n      }\\n    \":d===o?\"\\n      float \"+r+\"(int row, int col, int depth, int depth2) {\\n        float texR = dot(vec3(row, col, depth),\\n                         vec3(\"+t[1]*t[2]+\", \"+t[2]+\", 1));\\n        float texC = float(depth2);\\n        vec2 uv = (vec2(texC, texR) + halfCR) /\\n                  vec2(\"+d+\".0, \"+p+\".0);\\n        return sampleTexture(\"+n+\", uv);\\n      }\\n    \":\"\\n    float \"+r+\"(int row, int col, int depth, int depth2) {\\n      vec2 uv = UVfrom4D(\"+p+\", \"+d+\", \"+i+\", \"+a+\",\\n          \"+o+\", row, col, depth, depth2);\\n      return sampleTexture(\"+n+\", uv);\\n    }\\n  \"}function getSampler5D(e){var t=e.shapeInfo.logicalShape,n=e.name,r=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),o=t[4],a=t[3]*o,i=t[2]*a,s=t[1]*i,u=squeezeShape(t),l=u.newShape,c=u.keptDims;if(l.length<t.length){return\"\\n      \"+getSamplerFromInInfo(squeezeInputInfo(e,l))+\"\\n      float \"+r+\"(int row, int col, int depth, int depth2, int depth3) {\\n        return \"+r+\"(\"+getSqueezedParams([\"row\",\"col\",\"depth\",\"depth2\",\"depth3\"],c)+\");\\n      }\\n    \"}if(e.shapeInfo.isUniform)return\"\\n      float \"+r+\"(int row, int col, int depth, int depth2, int depth3) {\\n        float index = dot(\\n          vec4(row, col, depth, depth2),\\n          vec4(\"+s+\", \"+i+\", \"+a+\", \"+o+\")) +\\n          depth3;\\n        return \"+r+\"Flat(index);\\n      }\\n    \";var p=e.shapeInfo.texShape,d=p[0],h=p[1];return h===s?\"\\n      float \"+r+\"(int row, int col, int depth, int depth2, int depth3) {\\n        int texR = row;\\n        float texC = dot(\\n          vec4(col, depth, depth2, depth3),\\n          vec4(\"+i+\", \"+a+\", \"+o+\", 1));\\n        vec2 uv = (vec2(texC, texR) + halfCR) /\\n                   vec2(\"+h+\".0, \"+d+\".0);\\n        return sampleTexture(\"+n+\", uv);\\n      }\\n    \":h===o?\"\\n      float \"+r+\"(int row, int col, int depth, int depth2, int depth3) {\\n        float texR = dot(\\n          vec4(row, col, depth, depth2),\\n          vec4(\"+t[1]*t[2]*t[3]+\", \"+t[2]*t[3]+\",\\n            \"+t[3]+\", 1));\\n        int texC = depth3;\\n        vec2 uv = (vec2(texC, texR) + halfCR) /\\n                  vec2(\"+h+\".0, \"+d+\".0);\\n        return sampleTexture(\"+n+\", uv);\\n      }\\n    \":\"\\n    float \"+r+\"(int row, int col, int depth, int depth2, int depth3) {\\n      vec2 uv = UVfrom5D(\"+d+\", \"+h+\", \"+s+\", \"+i+\",\\n          \"+a+\", \"+o+\", row, col, depth, depth2, depth3);\\n      return sampleTexture(\"+n+\", uv);\\n    }\\n  \"}function getSampler6D(e){var t=e.shapeInfo.logicalShape,n=e.name,r=\"get\"+n.charAt(0).toUpperCase()+n.slice(1),o=t[5],a=t[4]*o,i=t[3]*a,s=t[2]*i,u=t[1]*s,l=squeezeShape(t),c=l.newShape,p=l.keptDims;if(c.length<t.length){return\"\\n      \"+getSamplerFromInInfo(squeezeInputInfo(e,c))+\"\\n      float \"+r+\"(int row, int col, int depth,\\n                    int depth2, int depth3, int depth4) {\\n        return \"+r+\"(\"+getSqueezedParams([\"row\",\"col\",\"depth\",\"depth2\",\"depth3\",\"depth4\"],p)+\");\\n      }\\n    \"}if(e.shapeInfo.isUniform)return\"\\n      float \"+r+\"(int row, int col, int depth,\\n                  int depth2, int depth3, int depth4) {\\n        float index = dot(\\n          vec4(row, col, depth, depth2),\\n          vec4(\"+u+\", \"+s+\", \"+i+\", \"+a+\")) +\\n          dot(\\n            vec2(depth3, depth4),\\n            vec2(\"+o+\", 1));\\n        return \"+r+\"Flat(index);\\n      }\\n    \";var d=e.shapeInfo.texShape,h=d[0],f=d[1];return f===u?\"\\n      float \"+r+\"(int row, int col, int depth,\\n                    int depth2, int depth3, int depth4) {\\n        int texR = row;\\n        float texC = dot(\\n          vec4(col, depth, depth2, depth3),\\n          vec4(\"+s+\", \"+i+\", \"+a+\", \"+o+\")) + depth4;\\n        vec2 uv = (vec2(texC, texR) + halfCR) /\\n                   vec2(\"+f+\".0, \"+h+\".0);\\n        return sampleTexture(\"+n+\", uv);\\n      }\\n    \":f===o?\"\\n      float \"+r+\"(int row, int col, int depth,\\n                    int depth2, int depth3, int depth4) {\\n        float texR = dot(\\n          vec4(row, col, depth, depth2),\\n          vec4(\"+t[1]*t[2]*t[3]*t[4]+\",\\n               \"+t[2]*t[3]*t[4]+\",\\n               \"+t[3]*t[4]+\",\\n               \"+t[4]+\")) + depth3;\\n        int texC = depth4;\\n        vec2 uv = (vec2(texC, texR) + halfCR) /\\n                  vec2(\"+f+\".0, \"+h+\".0);\\n        return sampleTexture(\"+n+\", uv);\\n      }\\n    \":\"\\n    float \"+r+\"(int row, int col, int depth,\\n                  int depth2, int depth3, int depth4) {\\n      vec2 uv = UVfrom6D(\"+h+\", \"+f+\", \"+u+\", \"+s+\",\\n          \"+i+\", \"+a+\", \"+o+\"\\n          ,row, col, depth, depth2, depth3, depth4);\\n      return sampleTexture(\"+n+\", uv);\\n    }\\n  \"}function getSamplerFlat(e){var t=e.name,n=\"get\"+t.charAt(0).toUpperCase()+t.slice(1)+\"Flat\",r=sizeFromShape(e.shapeInfo.logicalShape);if(e.shapeInfo.isUniform)return 1===r?\"float \"+n+\"(int index) {return \"+t+\";}\":\"\\n      float \"+n+\"(int index) {\\n        for (int i = 0; i < \"+r+\"; i++) {\\n          if (i == index) {\\n            return \"+t+\"[i];\\n          }\\n        }\\n      }\\n    \";var o=e.shapeInfo.texShape,a=o[0],i=o[1];return 1===i&&1===a?\"\\n      float \"+n+\"(int index) {\\n        return sampleTexture(\"+t+\", halfCR);\\n      }\\n    \":1===i?\"\\n      float \"+n+\"(int index) {\\n        vec2 uv = vec2(0.5, (float(index) + 0.5) / \"+a+\".0);\\n        return sampleTexture(\"+t+\", uv);\\n      }\\n    \":1===a?\"\\n      float \"+n+\"(int index) {\\n        vec2 uv = vec2((float(index) + 0.5) / \"+i+\".0, 0.5);\\n        return sampleTexture(\"+t+\", uv);\\n      }\\n    \":\"\\n    float \"+n+\"(int index) {\\n      vec2 uv = UVfrom1D(\"+a+\", \"+i+\", index);\\n      return sampleTexture(\"+t+\", uv);\\n    }\\n  \"}function getBroadcastOutputCoordsSampler(e,t,n,r){var o=e.shapeInfo.logicalShape.length,a=t.logicalShape.length,i=\"int\";2===a?i=\"ivec2\":3===a?i=\"ivec3\":4===a&&(i=\"ivec4\");var s=getBroadcastDims(e.shapeInfo.logicalShape,t.logicalShape),u=a-o;return\"\\n    float \"+r+\"() {\\n      \"+i+\" coords = getOutputCoords();\\n      \"+(0===o?\"\":a<2&&s.length>=1?\"coords = 0;\":s.map(function(e){return\"coords[\"+(e+u)+\"] = 0;\"}).join(\"\\n\"))+\"\\n      return get\"+n+\"(\"+(a<2&&o>0?\"coords\":e.shapeInfo.logicalShape.map(function(e,t){return\"coords[\"+(t+u)+\"]\"}).join(\", \"))+\");\\n    }\\n  \"}function getPackedSamplerAtOutputCoords(e,t,n){var r=e.name,o=r.charAt(0).toUpperCase()+r.slice(1),a=e.shapeInfo.texShape,i=\"get\"+o+\"AtOutCoords\",s=t.texShape,u=[Math.ceil(a[0]/2),Math.ceil(a[1]/2)],l=u[0],c=u[1],p=getBroadcastDims(e.shapeInfo.logicalShape,t.logicalShape),d=e.shapeInfo.logicalShape.length,h=t.logicalShape.length;if(p.length)throw Error(\"Packed broadcast sampling is not implemented yet.\");if(arraysEqual(e.shapeInfo.texShape,s))return\"\\n      vec4 \"+i+\"() {\\n        return texture2D(\"+r+\", resultUV);\\n      }\\n    \";var f=\"return texture2D(\"+r+\", uv)\";return 1===d&&h>1?f=\"\\n      vec4 sample = texture2D(\"+r+\", uv);\\n      return vec4(sample.xy, sample.xy);\\n    \":0===d&&h>0&&(f=1===h?\"\\n        vec4 sample = texture2D(\"+r+\", uv);\\n        return vec4(sample.x, sample.x, 0., 0.);\\n      \":\"\\n        vec4 sample = texture2D(\"+r+\", uv);\\n        return vec4(sample.x);\\n      \"),\"\\n    vec4 \"+i+\"() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n                             vec2(\"+u[0]+\", \"+u[1]+\"));\\n      int index = resTexRC.x * \"+u[1]+\" + resTexRC.y;\\n\\n      int texR = index / \"+c+\";\\n      int texC = index - texR * \"+c+\";\\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2(\"+c+\", \"+l+\");\\n\\n      \"+f+\";\\n    }\\n  \"}function getSamplerAtOutputCoords(e,t,n){var r=e.name,o=r.charAt(0).toUpperCase()+r.slice(1),a=\"get\"+o+\"AtOutCoords\",i=getBroadcastDims(e.shapeInfo.logicalShape,t.logicalShape),s=e.shapeInfo.logicalShape.length,u=t.logicalShape.length,l=n&&(u>s||i.length>0),c=broadcastDimsAreOuter(i),p=e.shapeInfo.isUniform;if(l&&!c)return getBroadcastOutputCoordsSampler(e,t,o,a);var d=sizeFromShape(e.shapeInfo.logicalShape),h=\"\";l&&c&&(h=\"\\n        int mainPart = index / \"+d+\";\\n        index -= mainPart * \"+d+\";\\n      \");var f=t.texShape;if(p)return 1===d?\"float \"+a+\"() {return \"+r+\";}\":\"\\n      float \"+a+\"() {\\n        ivec2 resTexRC = ivec2(resultUV.yx *\\n                              vec2(\"+f[0]+\", \"+f[1]+\"));\\n        int index = resTexRC.x * \"+f[1]+\" + resTexRC.y;\\n        \"+h+\"\\n        return get\"+o+\"Flat(index);\\n      }\\n    \";var m=e.shapeInfo.texShape;return arraysEqual(m,f)?\"\\n      float \"+a+\"() {\\n        return sampleTexture(\"+r+\", resultUV);\\n      }\\n    \":\"\\n    float \"+a+\"() {\\n      ivec2 resTexRC = ivec2(resultUV.yx *\\n                             vec2(\"+f[0]+\", \"+f[1]+\"));\\n      int index = resTexRC.x * \"+f[1]+\" + resTexRC.y;\\n      \"+h+\"\\n      int texR = index / \"+m[1]+\";\\n      int texC = index - texR * \"+m[1]+\";\\n      vec2 uv = (vec2(texC, texR) + halfCR) /\\n                 vec2(\"+m[1]+\".0, \"+m[0]+\".0);\\n\\n      return sampleTexture(\"+r+\", uv);\\n    }\\n  \"}function getCoordsDataType(e){if(e<=1)return\"int\";if(2===e)return\"ivec2\";if(3===e)return\"ivec3\";if(4===e)return\"ivec4\";if(5===e)return\"ivec5\";if(6===e)return\"ivec6\";throw Error(\"GPU for rank \"+e+\" is not yet supported\")}function squeezeInputInfo(e,t){var n=JSON.parse(JSON.stringify(e));return n.shapeInfo.logicalShape=t,n}function getSqueezedParams(e,t){return t.map(function(t){return e[t]}).join(\", \")}var CumSumProgram=function(){return function(e,t,n){this.variableNames=[\"x\"],this.outputShape=e;var r=e.length,o=e[e.length-1],a=n?\"<\":\">\";this.userCode=\"\\n      int getIndex(int i) {\\n        \"+(n?\"return \"+o+\" -i - 1;\":\"return i;\")+\"\\n      }\\n\\n      void main() {\\n        \"+getCoordsDataType(r)+\" coords = getOutputCoords();\\n        int end = \"+getFinalCoord(r,\"coords\")+\";\\n        float val = 0.0;\\n        for (int i = \"+o+\" - 1; i >= 0; i -= 1) {\\n          int idx = getIndex(i);\\n          if (idx \"+a+\" end) {\\n            continue;\\n          }\\n          if (idx == end && \"+t+\") {\\n            continue;\\n          }\\n          \"+getFinalCoord(r,\"coords\")+\" = idx;\\n          val += getX(\"+getCoords(r,\"coords\")+\");\\n        }\\n        setOutput(val);\\n      }\\n    \"}}();function getCoords(e,t){if(1===e)return\"\"+t;if(2===e)return t+\".x, \"+t+\".y\";if(3===e)return t+\".x, \"+t+\".y, \"+t+\".z\";if(4===e)return t+\".x, \"+t+\".y, \"+t+\".z, \"+t+\".w\";throw Error(\"Cumulative sum for rank \"+e+\" is not yet supported\")}function getFinalCoord(e,t){if(1===e)return\"\"+t;if(2===e)return t+\".y\";if(3===e)return t+\".z\";if(4===e)return t+\".w\";throw Error(\"Cumulative sum for rank \"+e+\" is not yet supported\")}var DepthToSpaceProgram=function(){function e(e,t,n){this.variableNames=[\"x\"],this.outputShape=[],this.outputShape=e,this.blockSize=t,this.dataFormat=n,this.userCode=\"\\n    void main() {\\n      ivec4 coords = getOutputCoords();\\n      int b = coords[0];\\n      int h = \"+this.getHeightCoordString()+\";\\n      int w = \"+this.getWidthCoordString()+\";\\n      int d = \"+this.getDepthCoordString()+\";\\n\\n      int in_h = h / \"+t+\";\\n      int offset_h = imod(h, \"+t+\");\\n      int in_w = w / \"+t+\";\\n      int offset_w = imod(w, \"+t+\");\\n      int offset_d = (offset_h * \"+t+\" + offset_w) *\\n        \"+this.getOutputDepthSize()+\";\\n      int in_d = d + offset_d;\\n\\n      float result = \"+this.getInputSamplingString()+\";\\n      setOutput(result);\\n    }\\n  \"}return e.prototype.getHeightCoordString=function(){return\"NHWC\"===this.dataFormat?\"coords[1]\":\"coords[2]\"},e.prototype.getWidthCoordString=function(){return\"NHWC\"===this.dataFormat?\"coords[2]\":\"coords[3]\"},e.prototype.getDepthCoordString=function(){return\"NHWC\"===this.dataFormat?\"coords[3]\":\"coords[1]\"},e.prototype.getOutputDepthSize=function(){return\"NHWC\"===this.dataFormat?this.outputShape[3]:this.outputShape[1]},e.prototype.getInputSamplingString=function(){return\"NHWC\"===this.dataFormat?\"getX(b, in_h, in_w, in_d)\":\"getX(b, in_d, in_h, in_w)\"},e}(),EncodeFloatProgram=function(){return function(e){this.variableNames=[\"A\"],this.outputShape=e,this.userCode=\"\\n      const float FLOAT_MAX = 1.70141184e38;\\n      const float FLOAT_MIN = 1.17549435e-38;\\n\\n      lowp vec4 encode_float(highp float v) {\\n        if (isNaN(v)) {\\n          return vec4(255, 255, 255, 255);\\n        }\\n\\n        highp float av = abs(v);\\n\\n        if(av < FLOAT_MIN) {\\n          return vec4(0.0, 0.0, 0.0, 0.0);\\n        } else if(v > FLOAT_MAX) {\\n          return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\\n        } else if(v < -FLOAT_MAX) {\\n          return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\\n        }\\n\\n        highp vec4 c = vec4(0,0,0,0);\\n\\n        highp float e = floor(log2(av));\\n        highp float m = exp2(fract(log2(av))) - 1.0;\\n\\n        c[2] = floor(128.0 * m);\\n        m -= c[2] / 128.0;\\n        c[1] = floor(32768.0 * m);\\n        m -= c[1] / 32768.0;\\n        c[0] = floor(8388608.0 * m);\\n\\n        highp float ebias = e + 127.0;\\n        c[3] = floor(ebias / 2.0);\\n        ebias -= c[3] * 2.0;\\n        c[2] += floor(ebias) * 128.0;\\n\\n        c[3] += 128.0 * step(0.0, -v);\\n\\n        return c / 255.0;\\n      }\\n\\n      void main() {\\n        float x = getAAtOutCoords();\\n        gl_FragColor = encode_float(x);\\n      }\\n    \"}}(),COMPLEX_FFT={REAL:\"return real * expR - imag * expI;\",IMAG:\"return real * expI + imag * expR;\"},FFTProgram=function(){return function(e,t,n){this.variableNames=[\"real\",\"imag\"];var r=t[1];this.outputShape=t;var o=n?\"2.0 * \"+Math.PI:\"-2.0 * \"+Math.PI,a=n?r+\".0\":\"1.0\";this.userCode=\"\\n      const float exponentMultiplier = \"+o+\";\\n\\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\\n        \"+e+\"\\n      }\\n\\n      float mulMatDFT(int batch, int index) {\\n        float indexRatio = float(index) / float(\"+r+\");\\n        float exponentMultiplierTimesIndexRatio =\\n            exponentMultiplier * indexRatio;\\n\\n        float result = 0.0;\\n\\n        for (int i = 0; i < \"+r+\"; i++) {\\n          // x = (-2|2 * PI / N) * index * i;\\n          float x = exponentMultiplierTimesIndexRatio * float(i);\\n          float expR = cos(x);\\n          float expI = sin(x);\\n          float real = getReal(batch, i);\\n          float imag = getImag(batch, i);\\n\\n          result +=\\n              unaryOpComplex(real, expR, imag, expI) / \"+a+\";\\n        }\\n\\n        return result;\\n      }\\n\\n      void main() {\\n        ivec2 coords = getOutputCoords();\\n        setOutput(mulMatDFT(coords[0], coords[1]));\\n      }\\n    \"}}(),FromPixelsProgram=function(){return function(e){this.variableNames=[\"A\"];var t=e[0],n=e[1];this.outputShape=e,this.userCode=\"\\n      void main() {\\n        ivec3 coords = getOutputCoords();\\n        int texR = coords[0];\\n        int texC = coords[1];\\n        int depth = coords[2];\\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2(\"+n+\".0, \"+t+\".0);\\n\\n        vec4 values = texture2D(A, uv);\\n        float value;\\n        if (depth == 0) {\\n          value = values.r;\\n        } else if (depth == 1) {\\n          value = values.g;\\n        } else if (depth == 2) {\\n          value = values.b;\\n        } else if (depth == 3) {\\n          value = values.a;\\n        }\\n\\n        setOutput(floor(value * 255.0 + 0.5));\\n      }\\n    \"}}(),GatherProgram=function(){return function(e,t,n){this.variableNames=[\"A\",\"indices\"];var r=e.slice();r[n]=t,this.outputShape=r,this.rank=r.length;var o=getCoordsDataType(this.rank),a=getSourceCoords(e,n);this.userCode=\"\\n      void main() {\\n        \"+o+\" resRC = getOutputCoords();\\n        setOutput(getA(\"+a+\"));\\n      }\\n    \"}}();function getSourceCoords(e,t){var n=e.length;if(n>4)throw Error(\"Gather for rank \"+n+\" is not yet supported\");if(1===n)return\"int(getIndices(resRC))\";for(var r=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],o=[],a=0;a<e.length;a++)a===t?o.push(\"int(getIndices(\"+r[a]+\"))\"):o.push(\"\"+r[a]);return o.join()}var TextureUsage,PhysicalTextureType,GatherNDProgram=function(){return function(e,t,n){this.sliceDim=e,this.strides=t,this.variableNames=[\"x\",\"indices\"],this.outputShape=n;var r=getCoordsDataType(t.length),o=getCoordsDataType(n.length),a=this.sliceDim>1?\"strides[j]\":\"strides\";this.userCode=\"\\n        \"+r+\" strides = \"+r+\"(\"+this.strides+\");\\n         void main() {\\n          \"+o+\" coords = getOutputCoords();\\n          int flattenIndex = 0;\\n          for (int j = 0; j < \"+this.sliceDim+\"; j++) {\\n            int index = round(getIndices(coords[0], j));\\n            flattenIndex += index * \"+a+\";\\n          }\\n          setOutput(getX(flattenIndex, coords[1]));\\n        }\\n      \"}}();function getUnpackedMatrixTextureShapeWidthHeight(e,t){return[t,e]}function getUnpackedArraySizeFromMatrixSize(e,t){return e*t}function getMatrixSizeFromUnpackedArraySize(e,t){if(e%t!=0)throw new Error(\"unpackedSize (\"+e+\") must be a multiple of \"+t);return e/t}function encodeMatrixToUnpackedArray(e,t,n){var r=getUnpackedArraySizeFromMatrixSize(e.length,n);if(t.length<r)throw new Error(\"unpackedArray length (\"+t.length+\") must be >= \"+r);for(var o=0,a=0;a<e.length;++a)t[o]=e[a],o+=n}function decodeMatrixFromUnpackedArray(e,t,n){var r=getMatrixSizeFromUnpackedArraySize(e.length,n);if(t.length<r)throw new Error(\"matrix length (\"+t.length+\") must be >= \"+r);for(var o=0,a=0;a<e.length;a+=n)t[o++]=e[a]}function getPackedMatrixTextureShapeWidthHeight(e,t){return[Math.max(1,Math.ceil(t/2)),Math.max(1,Math.ceil(e/2))]}function getPackedRGBAArraySizeFromMatrixShape(e,t){var n=getPackedMatrixTextureShapeWidthHeight(e,t);return n[0]*n[1]*4}function encodeMatrixToPackedRGBA(e,t,n,r,o){var a=getPackedRGBAArraySizeFromMatrixShape(n,r);if(o.length<a)throw new Error(\"packedRGBA length (\"+o.length+\") must be >=\\n        \"+a);for(var i=r%2==1,s=n%2==1,u=Math.floor(r/2),l=Math.floor(n/2),c=Math.ceil(r/2),p=c*Math.ceil(n/2),d=nearestLargerEven(n)*nearestLargerEven(r),h=0;h<t;h++){for(var f=h*n*r,m=h*d,g=i?4:0,v=r,y=m,x=0;x<l;++x){for(var T=2*x*r,w=0;w<u;++w){var E=f+T+2*w;o[y]=e[E],o[y+1]=e[E+1],o[y+2]=e[E+v],o[y+3]=e[E+v+1],y+=4}y+=g}if(i){E=f+r-1,y=m+4*(c-1);var S=2*r;for(g=4*c,x=0;x<l;++x)o[y]=e[E],o[y+2]=e[E+r],E+=S,y+=g}if(s){for(E=f+(n-1)*r,y=m+4*(p-c),w=0;w<u;++w)o[y++]=e[E++],o[y++]=e[E++],y+=2;i&&s&&(o[m+d-4]=e[E])}}return o}function decodeMatrixFromPackedRGBA(e,t,n,r,o){var a=n*r;if(o.length<a)throw new Error(\"matrix length (\"+o.length+\") must be >= \"+a);for(var i=r%2==1,s=n%2==1,u=Math.floor(r/2),l=Math.floor(n/2),c=Math.ceil(r/2),p=c*Math.ceil(n/2),d=nearestLargerEven(n)*nearestLargerEven(r),h=0;h<t;h++){for(var f=h*n*r,m=h*d,g=i?4:0,v=r+(i?1:0),y=m,x=f,T=f+r,w=0;w<l;++w){for(var E=0;E<u;++E)o[x++]=e[y++],o[x++]=e[y++],o[T++]=e[y++],o[T++]=e[y++];y+=g,x+=v,T+=v}if(i){y=m+4*(c-1);var S=f+r-1;for(g=4*c,v=2*r,w=0;w<l;++w)o[S]=e[y],o[S+r]=e[y+2],y+=g,S+=v}if(s){for(y=m+4*(p-c),S=f+(n-1)*r,E=0;E<u;++E)o[S++]=e[y++],o[S++]=e[y++],y+=2;i&&(o[f+n*r-1]=e[y])}}return o}function callAndCheck(e,t){var n=t();return checkWebGLError(e),n}!function(e){e[e.RENDER=0]=\"RENDER\",e[e.UPLOAD=1]=\"UPLOAD\",e[e.PIXELS=2]=\"PIXELS\",e[e.DOWNLOAD=3]=\"DOWNLOAD\"}(TextureUsage||(TextureUsage={})),function(e){e[e.UNPACKED_FLOAT16=0]=\"UNPACKED_FLOAT16\",e[e.UNPACKED_FLOAT32=1]=\"UNPACKED_FLOAT32\",e[e.PACKED_4X1_UNSIGNED_BYTE=2]=\"PACKED_4X1_UNSIGNED_BYTE\",e[e.PACKED_2X2_FLOAT32=3]=\"PACKED_2X2_FLOAT32\",e[e.PACKED_2X2_FLOAT16=4]=\"PACKED_2X2_FLOAT16\"}(PhysicalTextureType||(PhysicalTextureType={}));var webGLDebugErrorCheckingEnabled=!1;function enableDebugWebGLErrorChecking(e){webGLDebugErrorCheckingEnabled=e}function checkWebGLError(e){if(webGLDebugErrorCheckingEnabled){var t=e.getError();if(t!==e.NO_ERROR)throw new Error(\"WebGL Error: \"+getWebGLErrorMessage(e,t))}}function getWebGLErrorMessage(e,t){switch(t){case e.NO_ERROR:return\"NO_ERROR\";case e.INVALID_ENUM:return\"INVALID_ENUM\";case e.INVALID_VALUE:return\"INVALID_VALUE\";case e.INVALID_OPERATION:return\"INVALID_OPERATION\";case e.INVALID_FRAMEBUFFER_OPERATION:return\"INVALID_FRAMEBUFFER_OPERATION\";case e.OUT_OF_MEMORY:return\"OUT_OF_MEMORY\";case e.CONTEXT_LOST_WEBGL:return\"CONTEXT_LOST_WEBGL\";default:return\"Unknown error code \"+t}}function getExtensionOrThrow(e,t){return throwIfNull(e,function(){return e.getExtension(t)},'Extension \"'+t+'\" not supported on this browser.')}function createVertexShader(e,t){var n=throwIfNull(e,function(){return e.createShader(e.VERTEX_SHADER)},\"Unable to create vertex WebGLShader.\");if(callAndCheck(e,function(){return e.shaderSource(n,t)}),callAndCheck(e,function(){return e.compileShader(n)}),!1===e.getShaderParameter(n,e.COMPILE_STATUS))throw console.log(e.getShaderInfoLog(n)),new Error(\"Failed to compile vertex shader.\");return n}function createFragmentShader(e,t){var n=throwIfNull(e,function(){return e.createShader(e.FRAGMENT_SHADER)},\"Unable to create fragment WebGLShader.\");if(callAndCheck(e,function(){return e.shaderSource(n,t)}),callAndCheck(e,function(){return e.compileShader(n)}),!1===e.getShaderParameter(n,e.COMPILE_STATUS))throw logShaderSourceAndInfoLog(t,e.getShaderInfoLog(n)),new Error(\"Failed to compile fragment shader.\");return n}var lineNumberRegex=/ERROR: [0-9]+:([0-9]+):/g;function logShaderSourceAndInfoLog(e,t){var n=lineNumberRegex.exec(t);if(null==n)return console.log(\"Couldn't parse line number in error: \"+t),void console.log(e);for(var r=+n[1],o=e.split(\"\\n\"),a=o.length.toString().length+2,i=o.map(function(e,t){return rightPad((t+1).toString(),a)+e}),s=0,u=0;u<i.length;u++)s=Math.max(i[u].length,s);var l=i.slice(0,r-1),c=i.slice(r-1,r),p=i.slice(r);console.log(l.join(\"\\n\")),console.log(t.split(\"\\n\")[0]),console.log(\"%c \"+rightPad(c[0],s),\"border:1px solid red; background-color:#e3d2d2; color:#a61717\"),console.log(p.join(\"\\n\"))}function createProgram(e){return throwIfNull(e,function(){return e.createProgram()},\"Unable to create WebGLProgram.\")}function linkProgram(e,t){if(callAndCheck(e,function(){return e.linkProgram(t)}),!1===e.getProgramParameter(t,e.LINK_STATUS))throw console.log(e.getProgramInfoLog(t)),new Error(\"Failed to link vertex and fragment shaders.\")}function validateProgram(e,t){if(callAndCheck(e,function(){return e.validateProgram(t)}),!1===e.getProgramParameter(t,e.VALIDATE_STATUS))throw console.log(e.getProgramInfoLog(t)),new Error(\"Shader program validation failed.\")}function createStaticVertexBuffer(e,t){var n=throwIfNull(e,function(){return e.createBuffer()},\"Unable to create WebGLBuffer\");return callAndCheck(e,function(){return e.bindBuffer(e.ARRAY_BUFFER,n)}),callAndCheck(e,function(){return e.bufferData(e.ARRAY_BUFFER,t,e.STATIC_DRAW)}),n}function createStaticIndexBuffer(e,t){var n=throwIfNull(e,function(){return e.createBuffer()},\"Unable to create WebGLBuffer\");return callAndCheck(e,function(){return e.bindBuffer(e.ELEMENT_ARRAY_BUFFER,n)}),callAndCheck(e,function(){return e.bufferData(e.ELEMENT_ARRAY_BUFFER,t,e.STATIC_DRAW)}),n}function getNumChannels(){return 2===ENV.get(\"WEBGL_VERSION\")?1:4}function createTexture(e){return throwIfNull(e,function(){return e.createTexture()},\"Unable to create WebGLTexture.\")}function validateTextureSize(e,t){var n=ENV.get(\"WEBGL_MAX_TEXTURE_SIZE\");if(e<=0||t<=0){var r=\"[\"+e+\"x\"+t+\"]\";throw new Error(\"Requested texture size \"+r+\" is invalid.\")}if(e>n||t>n){r=\"[\"+e+\"x\"+t+\"]\";throw new Error(\"Requested texture size \"+r+\" greater than WebGL maximum on this browser / GPU \"+(\"[\"+n+\"x\"+n+\"]\")+\".\")}}function createFramebuffer(e){return throwIfNull(e,function(){return e.createFramebuffer()},\"Unable to create WebGLFramebuffer.\")}function bindVertexBufferToProgramAttribute(e,t,n,r,o,a,i){var s=e.getAttribLocation(t,n);return-1!==s&&(callAndCheck(e,function(){return e.bindBuffer(e.ARRAY_BUFFER,r)}),callAndCheck(e,function(){return e.vertexAttribPointer(s,o,e.FLOAT,!1,a,i)}),callAndCheck(e,function(){return e.enableVertexAttribArray(s)}),!0)}function bindTextureUnit(e,t,n){validateTextureUnit(e,n),callAndCheck(e,function(){return e.activeTexture(e.TEXTURE0+n)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,t)})}function unbindTextureUnit(e,t){validateTextureUnit(e,t),callAndCheck(e,function(){return e.activeTexture(e.TEXTURE0+t)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)})}function getProgramUniformLocationOrThrow(e,t,n){return throwIfNull(e,function(){return e.getUniformLocation(t,n)},'uniform \"'+n+'\" not present in program.')}function getProgramUniformLocation(e,t,n){return e.getUniformLocation(t,n)}function bindTextureToProgramUniformSampler(e,t,n,r,o){callAndCheck(e,function(){return bindTextureUnit(e,n,o)}),callAndCheck(e,function(){return e.uniform1i(r,o)})}function bindCanvasToFramebuffer(e){callAndCheck(e,function(){return e.bindFramebuffer(e.FRAMEBUFFER,null)}),callAndCheck(e,function(){return e.viewport(0,0,e.canvas.width,e.canvas.height)}),callAndCheck(e,function(){return e.scissor(0,0,e.canvas.width,e.canvas.height)})}function bindColorTextureToFramebuffer(e,t,n){callAndCheck(e,function(){return e.bindFramebuffer(e.FRAMEBUFFER,n)}),callAndCheck(e,function(){return e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,t,0)})}function unbindColorTextureFromFramebuffer(e,t){callAndCheck(e,function(){return e.bindFramebuffer(e.FRAMEBUFFER,t)}),callAndCheck(e,function(){return e.framebufferTexture2D(e.FRAMEBUFFER,e.COLOR_ATTACHMENT0,e.TEXTURE_2D,null,0)})}function validateFramebuffer(e){var t=e.checkFramebufferStatus(e.FRAMEBUFFER);if(t!==e.FRAMEBUFFER_COMPLETE)throw new Error(\"Error binding framebuffer: \"+getFramebufferErrorMessage(e,t))}function getFramebufferErrorMessage(e,t){switch(t){case e.FRAMEBUFFER_INCOMPLETE_ATTACHMENT:return\"FRAMEBUFFER_INCOMPLETE_ATTACHMENT\";case e.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT:return\"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT\";case e.FRAMEBUFFER_INCOMPLETE_DIMENSIONS:return\"FRAMEBUFFER_INCOMPLETE_DIMENSIONS\";case e.FRAMEBUFFER_UNSUPPORTED:return\"FRAMEBUFFER_UNSUPPORTED\";default:return\"unknown error \"+t}}function throwIfNull(e,t,n){var r=callAndCheck(e,function(){return t()});if(null==r)throw new Error(n);return r}function validateTextureUnit(e,t){var n=e.MAX_COMBINED_TEXTURE_IMAGE_UNITS-1,r=t+e.TEXTURE0;if(r<e.TEXTURE0||r>n)throw new Error(\"textureUnit must be in \"+(\"[gl.TEXTURE0, gl.TEXTURE\"+n+\"]\")+\".\")}function getTextureShapeFromLogicalShape(e,t){void 0===t&&(t=!1);var n=ENV.get(\"WEBGL_MAX_TEXTURE_SIZE\");if(t&&(n*=2,e=e.map(function(t,n){return n>=e.length-2?nearestLargerEven(e[n]):e[n]})),2!==e.length){var r=squeezeShape(e);e=r.newShape}var o=sizeFromShape(e);return e.length<=1&&o<=n?[1,o]:2===e.length&&e[0]<=n&&e[1]<=n?e:3===e.length&&e[0]*e[1]<=n&&e[2]<=n?[e[0]*e[1],e[2]]:3===e.length&&e[0]<=n&&e[1]*e[2]<=n?[e[0],e[1]*e[2]]:4===e.length&&e[0]*e[1]*e[2]<=n&&e[3]<=n?[e[0]*e[1]*e[2],e[3]]:4===e.length&&e[0]<=n&&e[1]*e[2]*e[3]<=n?[e[0],e[1]*e[2]*e[3]]:sizeToSquarishShape(o)}function isEven(e){return e%2==0}function isReshapeFree(e,t){if(arraysEqual(e=e.slice(-2),t=t.slice(-2)))return!0;if(!e.length||!t.length)return!0;if(0===e[0]||0===e[1]||0===t[0]||0===t[1])return!0;if(e.length!==t.length){var n=e.slice(-1)[0],r=t.slice(-1)[0];if(n===r)return!0;if(isEven(n)&&isEven(r)&&(1===e[0]||1===t[0]))return!0}else if(isEven(e[0])&&isEven(t[0])){if(isEven(e[1])&&isEven(t[1]))return!0;if(e[1]===t[1])return!0}return!1}var webgl_util=Object.freeze({callAndCheck:callAndCheck,enableDebugWebGLErrorChecking:enableDebugWebGLErrorChecking,checkWebGLError:checkWebGLError,getWebGLErrorMessage:getWebGLErrorMessage,getExtensionOrThrow:getExtensionOrThrow,createVertexShader:createVertexShader,createFragmentShader:createFragmentShader,createProgram:createProgram,linkProgram:linkProgram,validateProgram:validateProgram,createStaticVertexBuffer:createStaticVertexBuffer,createStaticIndexBuffer:createStaticIndexBuffer,getNumChannels:getNumChannels,createTexture:createTexture,validateTextureSize:validateTextureSize,createFramebuffer:createFramebuffer,bindVertexBufferToProgramAttribute:bindVertexBufferToProgramAttribute,bindTextureUnit:bindTextureUnit,unbindTextureUnit:unbindTextureUnit,getProgramUniformLocationOrThrow:getProgramUniformLocationOrThrow,getProgramUniformLocation:getProgramUniformLocation,bindTextureToProgramUniformSampler:bindTextureToProgramUniformSampler,bindCanvasToFramebuffer:bindCanvasToFramebuffer,bindColorTextureToFramebuffer:bindColorTextureToFramebuffer,unbindColorTextureFromFramebuffer:unbindColorTextureFromFramebuffer,validateFramebuffer:validateFramebuffer,getFramebufferErrorMessage:getFramebufferErrorMessage,getTextureShapeFromLogicalShape:getTextureShapeFromLogicalShape,isReshapeFree:isReshapeFree});function createVertexShader$1(e){return createVertexShader(e,\"\\n    precision highp float;\\n    attribute vec3 clipSpacePos;\\n    attribute vec2 uv;\\n    varying vec2 resultUV;\\n\\n    void main() {\\n      gl_Position = vec4(clipSpacePos, 1);\\n      resultUV = uv;\\n    }\")}function createVertexBuffer(e){return createStaticVertexBuffer(e,new Float32Array([-1,1,0,0,1,-1,-1,0,0,0,1,1,0,1,1,1,-1,0,1,0]))}function createIndexBuffer(e){return createStaticIndexBuffer(e,new Uint16Array([0,1,2,2,1,3]))}function getTextureConfig(e,t){var n,r,o,a,i,s,u,l=e;return 2===ENV.get(\"WEBGL_VERSION\")?(n=l.R32F,r=l.R16F,o=l.RGBA32F,a=l.RED,i=4,s=1,u=l.HALF_FLOAT):(n=e.RGBA,r=e.RGBA,o=l.RGBA,a=e.RGBA,i=4,s=4,u=null!=t?t.HALF_FLOAT_OES:null),{internalFormatFloat:n,internalFormatHalfFloat:r,internalFormatPackedFloat:o,textureFormatFloat:a,downloadTextureFormat:e.RGBA,downloadUnpackNumChannels:i,defaultNumChannels:s,textureTypeHalfFloat:u}}function createAndConfigureTexture(e,t,n,r,o,a){validateTextureSize(t,n);var i=createTexture(e),s=e.TEXTURE_2D;return callAndCheck(e,function(){return e.bindTexture(s,i)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_WRAP_S,e.CLAMP_TO_EDGE)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_WRAP_T,e.CLAMP_TO_EDGE)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_MIN_FILTER,e.NEAREST)}),callAndCheck(e,function(){return e.texParameteri(s,e.TEXTURE_MAG_FILTER,e.NEAREST)}),callAndCheck(e,function(){return e.texImage2D(s,0,r,t,n,0,o,a,null)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)}),i}function createFloat32MatrixTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],r.internalFormatFloat,r.textureFormatFloat,e.FLOAT)}function createFloat16MatrixTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],r.internalFormatFloat,r.textureFormatFloat,r.textureTypeHalfFloat)}function createUnsignedBytesMatrixTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],e.RGBA,e.RGBA,e.UNSIGNED_BYTE)}function createPackedMatrixTexture(e,t,n,r){var o=getPackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],r.internalFormatPackedFloat,e.RGBA,e.FLOAT)}function createFloat16PackedMatrixTexture(e,t,n,r){var o=getPackedMatrixTextureShapeWidthHeight(t,n);return createAndConfigureTexture(e,o[0],o[1],r.internalFormatHalfFloat,e.RGBA,r.textureTypeHalfFloat)}function bindVertexProgramAttributeStreams(e,t,n){return callAndCheck(e,function(){return e.bindBuffer(e.ARRAY_BUFFER,n)}),bindVertexBufferToProgramAttribute(e,t,\"clipSpacePos\",n,3,20,0)&&bindVertexBufferToProgramAttribute(e,t,\"uv\",n,2,20,12)}function uploadPixelDataToTexture(e,t,n){callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,t)}),callAndCheck(e,function(){return e.texImage2D(e.TEXTURE_2D,0,e.RGBA,e.RGBA,e.UNSIGNED_BYTE,n)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)})}function uploadDataToTexture(e,t,n,r,o,a){validateTextureSize(n,r),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,t)}),callAndCheck(e,function(){return e.texSubImage2D(e.TEXTURE_2D,0,0,0,n,r,a,e.FLOAT,o)}),callAndCheck(e,function(){return e.bindTexture(e.TEXTURE_2D,null)})}function uploadMatrixToTexture(e,t,n,r,o,a,i){var s,u=getUnpackedMatrixTextureShapeWidthHeight(n,r),l=u[0],c=u[1];1===i.defaultNumChannels?s=o:encodeMatrixToUnpackedArray(o,s=new Float32Array(getUnpackedArraySizeFromMatrixSize(o.length,a)),a),uploadDataToTexture(e,t,l,c,s,i.textureFormatFloat)}function uploadMatrixToPackedTexture(e,t,n,r,o,a,i,s,u){var l=getPackedMatrixTextureShapeWidthHeight(a,i),c=l[0],p=l[1],d=new Float32Array(n*getPackedRGBAArraySizeFromMatrixShape(r,o));encodeMatrixToPackedRGBA(s,n,r,o,d),uploadDataToTexture(e,t,c,p,d,e.RGBA)}function maybeCreateBufferFromOutputTexture(e,t,n,r,o){var a=t;if(2===ENV.get(\"WEBGL_VERSION\")){var i=e,s=i.createBuffer();callAndCheck(e,function(){return e.bindBuffer(i.PIXEL_PACK_BUFFER,s)});var u=4*getUnpackedArraySizeFromMatrixSize(n*r,o.downloadUnpackNumChannels);callAndCheck(e,function(){return e.bufferData(i.PIXEL_PACK_BUFFER,u,e.STATIC_DRAW)}),callAndCheck(e,function(){return i.readPixels(0,0,r,n,e.RGBA,e.FLOAT,0)}),callAndCheck(e,function(){return e.bindBuffer(i.PIXEL_PACK_BUFFER,null)}),a=s}return a}function downloadFloat32MatrixFromBuffer(e,t,n,r,o){var a=e,i=new Float32Array(getUnpackedArraySizeFromMatrixSize(n*r,o.downloadUnpackNumChannels));a.bindBuffer(e.ARRAY_BUFFER,t),a.getBufferSubData(e.ARRAY_BUFFER,0,i),a.bindBuffer(e.ARRAY_BUFFER,null);var s=new Float32Array(n*r);return decodeMatrixFromUnpackedArray(i,s,o.downloadUnpackNumChannels),s}function downloadFloat32MatrixFromOutputTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n),a=o[0],i=o[1],s=new Float32Array(getUnpackedArraySizeFromMatrixSize(t*n,r.downloadUnpackNumChannels));callAndCheck(e,function(){return e.readPixels(0,0,a,i,r.downloadTextureFormat,e.FLOAT,s)});var u=new Float32Array(t*n);return decodeMatrixFromUnpackedArray(s,u,r.downloadUnpackNumChannels),u}function downloadByteEncodedFloatMatrixFromOutputTexture(e,t,n,r){var o=getUnpackedMatrixTextureShapeWidthHeight(t,n),a=o[0],i=o[1],s=new Uint8Array(getUnpackedArraySizeFromMatrixSize(t*n,4));return callAndCheck(e,function(){return e.readPixels(0,0,a,i,r.downloadTextureFormat,e.UNSIGNED_BYTE,s)}),new Float32Array(s.buffer)}function downloadPackedMatrixFromBuffer(e,t,n,r,o,a,i,s){var u=e,l=new Float32Array(getPackedRGBAArraySizeFromMatrixShape(a,i));u.bindBuffer(e.ARRAY_BUFFER,t),u.getBufferSubData(e.ARRAY_BUFFER,0,l),u.bindBuffer(e.ARRAY_BUFFER,null);var c=new Float32Array(sizeFromShape([n,r,o]));return decodeMatrixFromPackedRGBA(l,n,r,o,c),c}function downloadMatrixFromPackedOutputTexture(e,t,n,r,o,a,i){var s=getPackedMatrixTextureShapeWidthHeight(o,a),u=s[0],l=s[1],c=new Float32Array(getPackedRGBAArraySizeFromMatrixShape(o,a));callAndCheck(e,function(){return e.readPixels(0,0,u,l,e.RGBA,e.FLOAT,c)});var p=new Float32Array(sizeFromShape([t,n,r]));return decodeMatrixFromPackedRGBA(c,t,n,r,p)}var gpgpu_util=Object.freeze({createVertexShader:createVertexShader$1,createVertexBuffer:createVertexBuffer,createIndexBuffer:createIndexBuffer,getTextureConfig:getTextureConfig,createFloat32MatrixTexture:createFloat32MatrixTexture,createFloat16MatrixTexture:createFloat16MatrixTexture,createUnsignedBytesMatrixTexture:createUnsignedBytesMatrixTexture,createPackedMatrixTexture:createPackedMatrixTexture,createFloat16PackedMatrixTexture:createFloat16PackedMatrixTexture,bindVertexProgramAttributeStreams:bindVertexProgramAttributeStreams,uploadPixelDataToTexture:uploadPixelDataToTexture,uploadMatrixToTexture:uploadMatrixToTexture,uploadMatrixToPackedTexture:uploadMatrixToPackedTexture,maybeCreateBufferFromOutputTexture:maybeCreateBufferFromOutputTexture,downloadFloat32MatrixFromBuffer:downloadFloat32MatrixFromBuffer,downloadFloat32MatrixFromOutputTexture:downloadFloat32MatrixFromOutputTexture,downloadByteEncodedFloatMatrixFromOutputTexture:downloadByteEncodedFloatMatrixFromOutputTexture,downloadPackedMatrixFromBuffer:downloadPackedMatrixFromBuffer,downloadMatrixFromPackedOutputTexture:downloadMatrixFromPackedOutputTexture}),GPGPUContext=function(){function e(e){this.outputTexture=null,this.program=null,this.disposed=!1,this.autoDebugValidate=!1,this.vertexAttrsAreBound=!1,this.itemsToPoll=[],this.gl=null!=e?e:getWebGLContext(ENV.get(\"WEBGL_VERSION\")),1===ENV.get(\"WEBGL_VERSION\")?(this.textureFloatExtension=getExtensionOrThrow(this.gl,\"OES_texture_float\"),this.colorBufferFloatExtension=this.gl.getExtension(\"WEBGL_color_buffer_float\"),ENV.get(\"WEBGL_RENDER_FLOAT32_ENABLED\")||(this.textureHalfFloatExtension=getExtensionOrThrow(this.gl,\"OES_texture_half_float\"),this.colorBufferHalfFloatExtension=this.gl.getExtension(\"EXT_color_buffer_half_float\"))):this.colorBufferFloatExtension=getExtensionOrThrow(this.gl,\"EXT_color_buffer_float\"),this.vertexBuffer=createVertexBuffer(this.gl),this.indexBuffer=createIndexBuffer(this.gl),this.framebuffer=createFramebuffer(this.gl),this.textureConfig=getTextureConfig(this.gl,this.textureHalfFloatExtension)}return e.prototype.dispose=function(){var e=this;if(!this.disposed){null!=this.program&&console.warn(\"Disposing a GPGPUContext that still has a bound WebGLProgram. This is probably a resource leak, delete the program with GPGPUContext.deleteProgram before disposing.\"),null!=this.outputTexture&&console.warn(\"Disposing a GPGPUContext that still has a bound output matrix texture.  This is probably a resource leak, delete the output matrix texture with GPGPUContext.deleteMatrixTexture before disposing.\");var t=this.gl;callAndCheck(t,function(){return t.finish()}),callAndCheck(t,function(){return t.bindFramebuffer(t.FRAMEBUFFER,null)}),callAndCheck(t,function(){return t.deleteFramebuffer(e.framebuffer)}),callAndCheck(t,function(){return t.bindBuffer(t.ARRAY_BUFFER,null)}),callAndCheck(t,function(){return t.bindBuffer(t.ELEMENT_ARRAY_BUFFER,null)}),callAndCheck(t,function(){return t.deleteBuffer(e.indexBuffer)}),this.disposed=!0}},e.prototype.enableAutomaticDebugValidation=function(e){this.autoDebugValidate=e,enableDebugWebGLErrorChecking(e)},e.prototype.createFloat32MatrixTexture=function(e,t){return this.throwIfDisposed(),createFloat32MatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.createFloat16MatrixTexture=function(e,t){return this.throwIfDisposed(),createFloat16MatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.createUnsignedBytesMatrixTexture=function(e,t){return this.throwIfDisposed(),createUnsignedBytesMatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.uploadPixelDataToTexture=function(e,t){this.throwIfDisposed(),uploadPixelDataToTexture(this.gl,e,t)},e.prototype.createFloat16PackedMatrixTexture=function(e,t){return this.throwIfDisposed(),createFloat16PackedMatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.createPackedMatrixTexture=function(e,t){return this.throwIfDisposed(),createPackedMatrixTexture(this.gl,e,t,this.textureConfig)},e.prototype.deleteMatrixTexture=function(e){var t=this;this.throwIfDisposed(),this.outputTexture===e&&(unbindColorTextureFromFramebuffer(this.gl,this.framebuffer),this.outputTexture=null),callAndCheck(this.gl,function(){return t.gl.deleteTexture(e)})},e.prototype.uploadMatrixToTexture=function(e,t,n,r){this.throwIfDisposed();var o=getNumChannels();return uploadMatrixToTexture(this.gl,e,t,n,r,o,this.textureConfig)},e.prototype.uploadMatrixToPackedTexture=function(e,t,n,r,o,a,i){return this.throwIfDisposed(),uploadMatrixToPackedTexture(this.gl,e,t,n,r,o,a,i,this.textureConfig)},e.prototype.downloadFloat32MatrixFromOutputTexture=function(e,t,n){var r=this;return this.downloadMatrixDriver(e,function(){return downloadFloat32MatrixFromOutputTexture(r.gl,t,n,r.textureConfig)})},e.prototype.downloadByteEncodedFloatMatrixFromOutputTexture=function(e,t,n){var r=this;return this.downloadMatrixDriver(e,function(){return downloadByteEncodedFloatMatrixFromOutputTexture(r.gl,t,n,r.textureConfig)})},e.prototype.downloadPackedMatrixFromBuffer=function(e,t,n,r,o,a){return downloadPackedMatrixFromBuffer(this.gl,e,t,n,r,o,a,this.textureConfig)},e.prototype.downloadFloat32MatrixFromBuffer=function(e,t,n){return downloadFloat32MatrixFromBuffer(this.gl,e,t,n,this.textureConfig)},e.prototype.maybeCreateBufferFromTexture=function(e,t,n){this.bindTextureToFrameBuffer(e);var r=maybeCreateBufferFromOutputTexture(this.gl,e,t,n,this.textureConfig);return this.unbindTextureToFrameBuffer(),r},e.prototype.createAndWaitForFence=function(){var e=this.createFence(this.gl);return this.pollFence(e)},e.prototype.createFence=function(e){var t,n,r=this;if(ENV.get(\"WEBGL_FENCE_API_ENABLED\")){var o=e,a=o.fenceSync(o.SYNC_GPU_COMMANDS_COMPLETE,0);e.flush(),n=function(){var e=o.clientWaitSync(a,0,0);return e===o.ALREADY_SIGNALED||e===o.CONDITION_SATISFIED},t=a}else ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?(t=this.beginQuery(),this.endQuery(),n=function(){return r.isQueryAvailable(t,ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))}):n=function(){return!0};return{query:t,isFencePassed:n}},e.prototype.downloadMatrixFromPackedTexture=function(e,t,n,r,o,a){var i=this;return this.downloadMatrixDriver(e,function(){return downloadMatrixFromPackedOutputTexture(i.gl,t,n,r,o,a,i.textureConfig)})},e.prototype.createProgram=function(e){this.throwIfDisposed();var t=this.gl,n=createFragmentShader(t,e),r=createVertexShader$1(t),o=createProgram(t);return callAndCheck(t,function(){return t.attachShader(o,r)}),callAndCheck(t,function(){return t.attachShader(o,n)}),linkProgram(t,o),this.autoDebugValidate&&validateProgram(t,o),this.vertexAttrsAreBound||(this.setProgram(o),this.vertexAttrsAreBound=bindVertexProgramAttributeStreams(t,this.program,this.vertexBuffer)),o},e.prototype.deleteProgram=function(e){var t=this;this.throwIfDisposed(),e===this.program&&(this.program=null),null!=e&&callAndCheck(this.gl,function(){return t.gl.deleteProgram(e)})},e.prototype.setProgram=function(e){var t=this;this.throwIfDisposed(),this.program=e,null!=this.program&&this.autoDebugValidate&&validateProgram(this.gl,this.program),callAndCheck(this.gl,function(){return t.gl.useProgram(e)})},e.prototype.getUniformLocation=function(e,t,n){return void 0===n&&(n=!0),this.throwIfDisposed(),n?getProgramUniformLocationOrThrow(this.gl,e,t):getProgramUniformLocation(this.gl,e,t)},e.prototype.getAttributeLocation=function(e,t){var n=this;return this.throwIfDisposed(),callAndCheck(this.gl,function(){return n.gl.getAttribLocation(e,t)})},e.prototype.getUniformLocationNoThrow=function(e,t){return this.throwIfDisposed(),this.gl.getUniformLocation(e,t)},e.prototype.setInputMatrixTexture=function(e,t,n){this.throwIfDisposed(),this.throwIfNoProgram(),bindTextureToProgramUniformSampler(this.gl,this.program,e,t,n)},e.prototype.setOutputMatrixTexture=function(e,t,n){this.setOutputMatrixTextureDriver(e,n,t)},e.prototype.setOutputPackedMatrixTexture=function(e,t,n){this.throwIfDisposed();var r=getPackedMatrixTextureShapeWidthHeight(t,n),o=r[0],a=r[1];this.setOutputMatrixTextureDriver(e,o,a)},e.prototype.setOutputMatrixWriteRegion=function(e,t,n,r){this.setOutputMatrixWriteRegionDriver(n,e,r,t)},e.prototype.setOutputPackedMatrixWriteRegion=function(e,t,n,r){throw new Error(\"setOutputPackedMatrixWriteRegion not implemented.\")},e.prototype.debugValidate=function(){null!=this.program&&validateProgram(this.gl,this.program),validateFramebuffer(this.gl)},e.prototype.executeProgram=function(){this.throwIfDisposed(),this.throwIfNoProgram();var e=this.gl;this.autoDebugValidate&&this.debugValidate(),callAndCheck(e,function(){return e.drawElements(e.TRIANGLES,6,e.UNSIGNED_SHORT,0)})},e.prototype.blockUntilAllProgramsCompleted=function(){var e=this;this.throwIfDisposed(),callAndCheck(this.gl,function(){return e.gl.finish()})},e.prototype.getQueryTimerExtension=function(){return null==this.disjointQueryTimerExtension&&(this.disjointQueryTimerExtension=getExtensionOrThrow(this.gl,2===ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")?\"EXT_disjoint_timer_query_webgl2\":\"EXT_disjoint_timer_query\")),this.disjointQueryTimerExtension},e.prototype.getQueryTimerExtensionWebGL2=function(){return this.getQueryTimerExtension()},e.prototype.getQueryTimerExtensionWebGL1=function(){return this.getQueryTimerExtension()},e.prototype.beginQuery=function(){if(2===ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")){var e=this.gl,t=this.getQueryTimerExtensionWebGL2(),n=e.createQuery();return e.beginQuery(t.TIME_ELAPSED_EXT,n),n}var r=this.getQueryTimerExtensionWebGL1(),o=r.createQueryEXT();return r.beginQueryEXT(r.TIME_ELAPSED_EXT,o),o},e.prototype.endQuery=function(){if(2!==ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")){var e=this.getQueryTimerExtensionWebGL1();e.endQueryEXT(e.TIME_ELAPSED_EXT)}else{var t=this.gl,n=this.getQueryTimerExtensionWebGL2();t.endQuery(n.TIME_ELAPSED_EXT)}},e.prototype.waitForQueryAndGetTime=function(e){return __awaiter(this,void 0,void 0,function(){var t=this;return __generator(this,function(n){switch(n.label){case 0:return[4,repeatedTry(function(){return t.disposed||t.isQueryAvailable(e,ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))})];case 1:return n.sent(),[2,this.getQueryTime(e,ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\"))]}})})},e.prototype.getQueryTime=function(e,t){if(0===t)return null;if(2===t){var n=this.gl;return n.getQueryParameter(e,n.QUERY_RESULT)/1e6}var r=this.getQueryTimerExtensionWebGL1();return r.getQueryObjectEXT(e,r.QUERY_RESULT_EXT)/1e6},e.prototype.isQueryAvailable=function(e,t){if(0===t)return!0;if(2===t){var n=this.gl,r=this.getQueryTimerExtensionWebGL2(),o=n.getQueryParameter(e,n.QUERY_RESULT_AVAILABLE);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint}o=(r=this.getQueryTimerExtensionWebGL1()).getQueryObjectEXT(e,r.QUERY_RESULT_AVAILABLE_EXT);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint},e.prototype.pollFence=function(e){var t=this;return new Promise(function(n){t.addItemToPoll(function(){return e.isFencePassed()},function(){return n()})})},e.prototype.pollItems=function(){for(var e=binSearchLastTrue(this.itemsToPoll.map(function(e){return e.isDoneFn})),t=0;t<=e;++t){(0,this.itemsToPoll[t].resolveFn)()}this.itemsToPoll=this.itemsToPoll.slice(e+1)},e.prototype.addItemToPoll=function(e,t){var n=this;this.itemsToPoll.push({isDoneFn:e,resolveFn:t}),this.itemsToPoll.length>1||repeatedTry(function(){return n.pollItems(),0===n.itemsToPoll.length})},e.prototype.bindTextureToFrameBuffer=function(e){this.throwIfDisposed(),bindColorTextureToFramebuffer(this.gl,e,this.framebuffer),this.autoDebugValidate&&validateFramebuffer(this.gl)},e.prototype.unbindTextureToFrameBuffer=function(){null!=this.outputTexture?(bindColorTextureToFramebuffer(this.gl,this.outputTexture,this.framebuffer),this.autoDebugValidate&&validateFramebuffer(this.gl)):unbindColorTextureFromFramebuffer(this.gl,this.framebuffer)},e.prototype.downloadMatrixDriver=function(e,t){this.bindTextureToFrameBuffer(e);var n=t();return this.unbindTextureToFrameBuffer(),n},e.prototype.setOutputMatrixTextureDriver=function(e,t,n){this.throwIfDisposed();var r=this.gl;bindColorTextureToFramebuffer(r,e,this.framebuffer),this.autoDebugValidate&&validateFramebuffer(r),this.outputTexture=e,callAndCheck(r,function(){return r.viewport(0,0,t,n)}),callAndCheck(r,function(){return r.scissor(0,0,t,n)})},e.prototype.setOutputMatrixWriteRegionDriver=function(e,t,n,r){var o=this;this.throwIfDisposed(),callAndCheck(this.gl,function(){return o.gl.scissor(e,t,n,r)})},e.prototype.throwIfDisposed=function(){if(this.disposed)throw new Error(\"Attempted to use disposed GPGPUContext.\")},e.prototype.throwIfNoProgram=function(){if(null==this.program)throw new Error(\"No GPU program is currently set.\")},e}();function binSearchLastTrue(e){for(var t=0,n=e.length-1,r=-1;t<=n;){var o=t+n>>1;e[o]()?(r=o,t=o+1):n=o-1}return r}function compileProgram(e,t,n,r){for(var o=t.userCode,a=n.map(function(e,n){var r={logicalShape:e.shape,texShape:e.isUniform?null:e.texData.texShape,isUniform:e.isUniform,isPacked:!e.isUniform&&e.texData.isPacked};return{name:t.variableNames[n],shapeInfo:r}}),i=a.map(function(e){return e.shapeInfo}),s={logicalShape:r.shape,texShape:r.texData.texShape,isUniform:!1,isPacked:r.texData.isPacked},u=makeShader(a,s,o,!0===t.supportsBroadcasting,t.usesPackedTextures),l=e.createProgram(u),c={},p=0;p<t.variableNames.length;p++){var d=t.variableNames[p];c[d]=e.getUniformLocation(l,d,!1)}return{program:t,source:u,webGLProgram:l,uniformLocations:c,gpgpu:e,inShapeInfos:i,outShapeInfo:s}}function validateBinaryAndProgram(e,t){if(e.length!==t.length)throw Error(\"Binary was compiled with \"+e.length+\" inputs, but was executed with \"+t.length+\" inputs\");e.forEach(function(e,n){var r=e.logicalShape,o=t[n],a=o.shape;if(!arraysEqual(r,a))throw Error(\"Binary was compiled with different shapes than the current args. Shapes \"+r+\" and \"+a+\" must match\");if(!e.isUniform||!o.isUniform){var i=e.texShape,s=o.isUniform?null:o.texData.texShape;if(!arraysEqual(i,s))throw Error(\"Binary was compiled with different texture shapes than the current args. Shape \"+i+\" and \"+s+\" must match\")}})}function runProgram(e,t,n,r){validateBinaryAndProgram(e.inShapeInfos,t),validateBinaryAndProgram([e.outShapeInfo],[n]);var o=n.texData.texture,a=n.texData.texShape,i=e.gpgpu;n.texData.isPacked?i.setOutputPackedMatrixTexture(o,a[0],a[1]):i.setOutputMatrixTexture(o,a[0],a[1]),i.setProgram(e.webGLProgram),t.forEach(function(t,n){var r=e.program.variableNames[n],o=e.uniformLocations[r];if(null!=o){if(t.isUniform){if(1===sizeFromShape(t.shape))i.gl.uniform1f(o,t.uniformValues[0]);else{var a=t.uniformValues;a instanceof Float32Array||(a=new Float32Array(a)),i.gl.uniform1fv(o,a)}return}var s=t.texData.texture;i.setInputMatrixTexture(s,o,n)}}),null!=r&&r(i,e.webGLProgram),i.executeProgram()}function makeShaderKey(e,t,n){var r=\"\";t.concat(n).forEach(function(e){r+=e.shape+\"_\"+(e.isUniform?\"uniform\":e.texData.texShape)});var o=e.userCode,a=(!0===e.supportsBroadcasting).toString(),i=e.constructor.name;return i+=\"_\"+a+\"_\"+r+\"_\"+o}var Im2ColProgram=function(){return function(e,t,n){this.variableNames=[\"A\"],this.outputShape=e;var r=n.filterWidth,o=n.inChannels,a=n.strideWidth,i=n.strideHeight,s=n.padInfo,u=n.outWidth,l=n.dilationWidth,c=n.dilationHeight,p=s.left,d=s.top,h=o*r;this.userCode=\"\\n      void main() {\\n        ivec2 rc = getOutputCoords();\\n\\n        vec4 result = vec4(0);\\n\\n        for(int row=0; row<=1; row++) {\\n          for(int col=0; col<=1; col++) {\\n            int blockIndex = rc.y + col;\\n            int pos = rc.x + row;\\n\\n            if(blockIndex >= \"+e[1]+\" || pos >= \"+e[0]+\") continue;\\n\\n            int offsetY = int(blockIndex / (\"+u+\")) * \"+i+\" - \"+d+\";\\n            int d0 = offsetY + \"+c+\" * (pos / \"+h+\");\\n\\n            if(d0 >= \"+t[0]+\" || d0 < 0) continue;\\n\\n            int offsetX = int(mod(float(blockIndex), \"+u+\".) * \"+a+\". - \"+p+\".);\\n            int d1 = offsetX + \"+l+\" * (int(mod(float(pos), \"+h+\".) / \"+o+\".));\\n\\n            if(d1 >= \"+t[1]+\" || d1 < 0) continue;\\n\\n            result[row * 2 + col] = getA(d0, d1, int(mod(float(pos), \"+o+\".)));\\n          }\\n        }\\n\\n        gl_FragColor = result;\\n      }\\n    \"}}(),LRNProgram=function(){return function(e,t,n,r,o){this.variableNames=[\"x\"],this.outputShape=[];var a,i=t,s=e[3]-1;this.outputShape=e;var u=\"float(\"+n+\") + float(\"+r+\") * sum\";a=.5===o?\"inversesqrt(\"+u+\")\":1===o?\"1.0/(\"+u+\")\":\"exp(log(\"+u+\") * float(-\"+o+\"));\",this.userCode=\"\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int b = coords[0];\\n        int r = coords[1];\\n        int c = coords[2];\\n        int d = coords[3];\\n        float x = getX(b, r, c, d);\\n        float sum = 0.0;\\n        for (int j = -\"+i+\"; j <= \"+i+\"; j++) {\\n          int idx = d + j;\\n          if (idx >= 0 && idx <=  \"+s+\") {\\n            float z = getX(b, r, c, idx);\\n            sum += z * z;\\n          }\\n        }\\n        float val = x * \"+a+\";\\n        setOutput(val);\\n      }\\n    \"}}(),LRNGradProgram=function(){return function(e,t,n,r,o){this.variableNames=[\"inputImage\",\"outputImage\",\"dy\"],this.outputShape=[],this.outputShape=e,this.depth=e[3],this.depthRadius=t,this.bias=n,this.alpha=r,this.beta=o,this.userCode=\"\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int b = coords[0];\\n        int r = coords[1];\\n        int c = coords[2];\\n\\n        float result = 0.0;\\n        for (int d = 0; d < \"+this.depth+\"; ++d) {\\n          int depthBegin = int(max(0.0, float(d - \"+t+\")));\\n          int depthEnd = int(min(float(\"+this.depth+\"),\\n              float(d + \"+t+\" + 1)));\\n\\n          const int MIN_DEPTH_BEGIN = 0;\\n          const int MAX_DEPTH_END = \"+this.depth+\";\\n\\n          float norm = 0.0;\\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\\n            if (k < depthBegin){\\n              continue;\\n            }\\n            else if (k >= depthBegin && k < depthEnd) {\\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\\n            }\\n            else {\\n              break;\\n            }\\n          }\\n\\n          norm = float(\"+r+\") * norm + float(\"+n+\");\\n\\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\\n            if (k < depthBegin){\\n              continue;\\n            }\\n            else if (k >= depthBegin && k < depthEnd){\\n              float dyi = -2.0 * float(\"+r+\")\\n                * float(\"+o+\")\\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\\n                / norm;\\n              if (k == d) {\\n                dyi += pow(norm, -1.0 * \"+o+\");\\n              }\\n              if (k == coords[3]) {\\n                dyi *= getDy(b, r, c, d);\\n                result += dyi;\\n              }\\n            }\\n            else {\\n              break;\\n            }\\n          }\\n      }\\n      setOutput(result);\\n      }\\n    \"}}(),MaxPool2DBackpropProgram=function(){return function(e){this.variableNames=[\"dy\",\"maxPos\"],this.outputShape=e.inShape;var t=e.strideHeight,n=e.strideWidth,r=e.dilationHeight,o=e.effectiveFilterHeight,a=e.effectiveFilterWidth,i=o-1-e.padInfo.top,s=a-1-e.padInfo.left,u=o*a-1;this.userCode=\"\\n      const ivec2 pads = ivec2(\"+i+\", \"+s+\");\\n\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int b = coords[0];\\n        int d = coords[3];\\n\\n        ivec2 dyRCCorner = coords.yz - pads;\\n        int dyRCorner = dyRCCorner.x;\\n        int dyCCorner = dyRCCorner.y;\\n\\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\\n        // ? = to be determined. : = across all values in that axis.\\n        float dotProd = 0.0;\\n        for (int wR = 0; wR < \"+o+\";\\n          wR += \"+r+\") {\\n          float dyR = float(dyRCorner + wR) / \"+t+\".0;\\n\\n          if (dyR < 0.0 || dyR >= \"+e.outHeight+\".0 || fract(dyR) > 0.0) {\\n            continue;\\n          }\\n          int idyR = int(dyR);\\n\\n          for (int wC = 0; wC < \"+a+\"; wC++) {\\n            float dyC = float(dyCCorner + wC) / \"+n+\".0;\\n\\n            if (dyC < 0.0 || dyC >= \"+e.outWidth+\".0 ||\\n                fract(dyC) > 0.0) {\\n              continue;\\n            }\\n            int idyC = int(dyC);\\n\\n            float dyValue = getDy(b, idyR, idyC, d);\\n            int maxPosValue = \"+u+\" - int(getMaxPos(b, idyR, idyC, d));\\n\\n            // Get the current value, check it against the value from the\\n            // position matrix.\\n            int curPosValue = wR * \"+a+\" + wC;\\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\\n\\n            dotProd += dyValue * mask;\\n          }\\n        }\\n        setOutput(dotProd);\\n      }\\n    \"}}(),MatMulProgram=function(){return function(e,t,n,r){void 0===n&&(n=!1),void 0===r&&(r=!1),this.variableNames=[\"matrixA\",\"matrixB\"];var o=e[0],a=n?e[2]:e[1],i=r?t[1]:t[2],s=n?e[1]:e[2];this.outputShape=[o,a,i];var u=function(e,t){return n?\"batch, \"+t+\" + \"+e+\", aRow\":\"batch, aRow, \"+t+\" + \"+e},l=function(e,t){return r?\"batch, bCol, \"+t+\" + \"+e:\"batch, \"+t+\" + \"+e+\", bCol\"},c=4*Math.floor(s/4),p=s%4;this.userCode=\" float dotARowBCol(int batch, int aRow, int bCol) {\\n      float result = 0.0;\\n      for (int i = 0; i < \"+c+\"; i += 4) {\\n        vec4 a = vec4(\\n          getMatrixA(\"+u(0,\"i\")+\"),\\n          getMatrixA(\"+u(1,\"i\")+\"),\\n          getMatrixA(\"+u(2,\"i\")+\"),\\n          getMatrixA(\"+u(3,\"i\")+\")\\n        );\\n        vec4 b = vec4(\\n          getMatrixB(\"+l(0,\"i\")+\"),\\n          getMatrixB(\"+l(1,\"i\")+\"),\\n          getMatrixB(\"+l(2,\"i\")+\"),\\n          getMatrixB(\"+l(3,\"i\")+\")\\n        );\\n\\n        result += dot(a, b);\\n      }\\n\\n      if (\"+(1===p)+\") {\\n        result += getMatrixA(\"+u(0,c)+\") *\\n          getMatrixB(\"+l(0,c)+\");\\n      } else if (\"+(2===p)+\") {\\n        vec2 a = vec2(\\n          getMatrixA(\"+u(0,c)+\"),\\n          getMatrixA(\"+u(1,c)+\")\\n        );\\n        vec2 b = vec2(\\n          getMatrixB(\"+l(0,c)+\"),\\n          getMatrixB(\"+l(1,c)+\")\\n        );\\n        result += dot(a, b);\\n      } else if (\"+(3===p)+\") {\\n        vec3 a = vec3(\\n          getMatrixA(\"+u(0,c)+\"),\\n          getMatrixA(\"+u(1,c)+\"),\\n          getMatrixA(\"+u(2,c)+\")\\n        );\\n        vec3 b = vec3(\\n          getMatrixB(\"+l(0,c)+\"),\\n          getMatrixB(\"+l(1,c)+\"),\\n          getMatrixB(\"+l(2,c)+\")\\n        );\\n        result += dot(a, b);\\n      }\\n\\n      return result;\\n    }\\n\\n    void main() {\\n      ivec3 resBRC = getOutputCoords();\\n      setOutput(dotARowBCol(resBRC.x, resBRC.y, resBRC.z));\\n    }\\n    \"}}(),MatMulPackedProgram=function(){return function(e,t,n,r,o){void 0===r&&(r=!1),void 0===o&&(o=!1),this.variableNames=[\"matrixA\",\"matrixB\"],this.usesPackedTextures=!0,this.outputShape=n;var a=r?e[0]:e[1],i=Math.ceil(a/2),s=r?\"i * 2, rc.x\":\"rc.x, i * 2\",u=o?\"rc.y, i * 2\":\"i * 2, rc.y\",l=r?[\"a.xxyy\",\"a.zzww\"]:[\"a.xxzz\",\"a.yyww\"],c=o?[\"b.xzxz\",\"b.ywyw\"]:[\"b.xyxy\",\"b.zwzw\"];this.userCode=\"\\n      const float sharedDimension = \"+i+\".0;\\n\\n      vec4 dot2x2ARowBCol(ivec2 rc) {\\n        vec4 result = vec4(0);\\n        for (int i = 0; i < \"+i+\"; i++) {\\n          vec4 a = getMatrixA(\"+s+\");\\n          vec4 b = getMatrixB(\"+u+\");\\n\\n          result += (\"+l[0]+\" * \"+c[0]+\") + (\"+l[1]+\" * \"+c[1]+\");\\n        }\\n        return result;\\n      }\\n\\n      void main() {\\n        ivec2 rc = getOutputCoords();\\n        setOutput(dot2x2ARowBCol(rc));\\n      }\\n    \"}}(),MultinomialProgram=function(){function e(e,t,n){this.variableNames=[\"probs\"],this.outputShape=[e,n],this.userCode=\"\\n      uniform float seed;\\n\\n      void main() {\\n        ivec2 coords = getOutputCoords();\\n        int batch = coords[0];\\n\\n        float r = random(seed);\\n        float cdf = 0.0;\\n\\n        for (int i = 0; i < \"+(t-1)+\"; i++) {\\n          cdf += getProbs(batch, i);\\n\\n          if (r < cdf) {\\n            setOutput(float(i));\\n            return;\\n          }\\n        }\\n\\n        // If no other event happened, last event happened.\\n        setOutput(float(\"+(t-1)+\"));\\n      }\\n    \"}return e.prototype.getCustomSetupFunc=function(e){var t=this;return function(n,r){null==t.seedLoc&&(t.seedLoc=n.getUniformLocation(r,\"seed\")),n.gl.uniform1f(t.seedLoc,e)}},e}(),OneHotProgram=function(){return function(e,t,n,r){this.variableNames=[\"indices\"],this.outputShape=[e,t],this.userCode=\"\\n      void main() {\\n        ivec2 coords = getOutputCoords();\\n        int index = round(getIndices(coords.x));\\n        setOutput(mix(float(\"+r+\"), float(\"+n+\"),\\n                      float(index == coords.y)));\\n      }\\n    \"}}();function getVecChannels(e,t){return[\"x\",\"y\",\"z\",\"w\",\"u\",\"v\"].slice(0,t).map(function(t){return e+\".\"+t})}function getChannels(e,t){return 1===t?[e]:getVecChannels(e,t)}function getSourceCoords$1(e,t){if(1===e)return\"rc\";for(var n=\"\",r=0;r<e;r++)n+=t[r],r<e-1&&(n+=\",\");return n}var PackProgram=function(){return function(e){this.variableNames=[\"A\"],this.isPackShader=!0,this.outputShape=e;var t=e.length;if(0===t)this.userCode=\"\\n        void main() {\\n          setOutput(vec4(getA(), 0., 0., 0.));\\n        }\\n      \";else{var n=getChannels(\"rc\",t),r=getCoordsDataType(t),o=getOutOfBoundsCondition(t,e,n),a=getSetup(t,e[e.length-1],e[e.length-2],n),i=getOutput(e,n);this.userCode=\"\\n        void main() {\\n          \"+r+\" rc = getOutputCoords();\\n\\n          if(\"+o+\") {\\n            setOutput(vec4(0));\\n          } else {\\n            \"+a+\"\\n\\n            setOutput(vec4(\"+i+\"));\\n          }\\n        }\\n      \"}}}();function getSourceCoordsArr(e,t){for(var n=[],r=0;r<=1;r++)for(var o=0;o<=1;o++){for(var a=(0===r?\"r\":\"rp1\")+\", \"+(0===o?\"c\":\"cp1\"),i=2;i<e;i++)a=t[t.length-1-i]+\",\"+a;n.push(a)}return n}function getOutOfBoundsCondition(e,t,n){if(1===e)return\"rc > \"+t[0];for(var r=\"\",o=e-2;o<e;o++)r+=n[o]+\" >= \"+t[o],o<e-1&&(r+=\"||\");return r}function getSetup(e,t,n,r){if(1===e)return\"\";var o=r.slice(-2);return\"\\n    int r = \"+o[0]+\";\\n    int c = \"+o[1]+\";\\n    int rp1 = r + 1;\\n    int cp1 = c + 1;\\n\\n    bool cEdge = cp1 >= \"+t+\";\\n    bool rEdge = rp1 >= \"+n+\";\\n  \"}function getOutput(e,t){var n=e.length,r=getSourceCoordsArr(n,t);return 1===n?\"getA(rc),\\n            rc + 1 >= \"+e[0]+\" ? 0. : getA(rc + 1),\\n            0, 0\":\"getA(\"+r[0]+\"),\\n          cEdge ? 0. : getA(\"+r[1]+\"),\\n          rEdge ? 0. : getA(\"+r[2]+\"),\\n          rEdge || cEdge ? 0. : getA(\"+r[3]+\")\"}var PadProgram=function(){return function(e,t,n){this.variableNames=[\"x\"],this.outputShape=t.map(function(t,n){return t[0]+e[n]+t[1]});var r=e.length,o=getCoordsDataType(r),a=t.map(function(e){return e[0]}).join(\",\"),i=t.map(function(t,n){return t[0]+e[n]}).join(\",\"),s=[\"coords[0]\",\"coords[1]\",\"coords[2]\",\"coords[3]\"].slice(0,r);this.userCode=1!==r?\"\\n      \"+o+\" start = \"+o+\"(\"+a+\");\\n      \"+o+\" end = \"+o+\"(\"+i+\");\\n\\n      void main() {\\n        \"+o+\" outC = getOutputCoords();\\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\\n          setOutput(float(\"+n+\"));\\n        } else {\\n          \"+o+\" coords = outC - start;\\n          setOutput(getX(\"+s+\"));\\n        }\\n      }\\n    \":\"\\n        int start = \"+a+\";\\n        int end = \"+i+\";\\n\\n        void main() {\\n          int outC = getOutputCoords();\\n          if (outC < start || outC >= end) {\\n            setOutput(float(\"+n+\"));\\n          } else {\\n            setOutput(getX(outC - start));\\n          }\\n        }\\n      \"}}(),Pool2DProgram=function(){return function(e,t,n){if(this.variableNames=[\"x\"],\"avg\"===t&&n)throw new Error(\"Cannot compute positions for average pool.\");var r=e.filterWidth,o=e.strideHeight,a=e.strideWidth,i=e.dilationHeight,s=e.dilationWidth,u=e.effectiveFilterHeight,l=e.effectiveFilterWidth,c=e.padInfo.top,p=e.padInfo.left;this.outputShape=e.outShape;var d=\"avg\"===t,h=\"0.0\";if(d||(h=\"-1.0 / 0.0\"),n)this.userCode=\"\\n        const ivec2 strides = ivec2(\"+o+\", \"+a+\");\\n        const ivec2 pads = ivec2(\"+c+\", \"+p+\");\\n\\n        void main() {\\n          ivec4 coords = getOutputCoords();\\n          int batch = coords[0];\\n          int d = coords[3];\\n\\n          ivec2 xRCCorner = coords.yz * strides - pads;\\n          int xRCorner = xRCCorner.x;\\n          int xCCorner = xRCCorner.y;\\n\\n          // max/min x(?, ?, d) to get y(yR, yC, d).\\n          // ? = to be determined\\n          float minMaxValue = 0.0;\\n          float minMaxValueFound = 0.0;\\n          int minMaxPosition = 0;\\n          float avgValue = 0.0;\\n\\n          for (int wR = 0; wR < \"+u+\";\\n              wR += \"+i+\") {\\n            int xR = xRCorner + wR;\\n\\n            if (xR < 0 || xR >= \"+e.inHeight+\") {\\n              continue;\\n            }\\n\\n            for (int wC = 0; wC < \"+l+\";\\n                wC += \"+s+\") {\\n              int xC = xCCorner + wC;\\n\\n              if (xC < 0 || xC >= \"+e.inWidth+\") {\\n                continue;\\n              }\\n\\n              float value = getX(batch, xR, xC, d);\\n\\n              // If a min / max value has already been found, use it. If not,\\n              // use the current value.\\n              float currMinMaxValue = mix(\\n                  value, minMaxValue, minMaxValueFound);\\n              if (value >= currMinMaxValue) {\\n                minMaxValue = value;\\n                minMaxValueFound = 1.0;\\n                minMaxPosition = wR * \"+l+\" + wC;\\n              }\\n            }\\n          }\\n          setOutput(float(minMaxPosition));\\n        }\\n      \";else{var f=t+\"(\"+t+\"(\"+t+\"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])\";\"avg\"===t&&(f=\"avgValue / count\");var m=4*Math.floor(r/4),g=r%4,v=\"\\n      if (\"+d+\") {\\n        avgValue += dot(values, ones);\\n      } else {\\n        minMaxValue = max(values, minMaxValue);\\n      }\\n    \";this.userCode=\"\\n      const ivec2 strides = ivec2(\"+o+\", \"+a+\");\\n      const ivec2 pads = ivec2(\"+c+\", \"+p+\");\\n      const float initializationValue = \"+h+\";\\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\\n\\n      float count = 0.0;\\n\\n      float getValue(int batch, int xR, int xC, int d) {\\n        if (xC < 0 || xC >= \"+e.inWidth+\") {\\n          return initializationValue;\\n        }\\n        count += 1.0;\\n        return getX(batch, xR, xC, d);\\n      }\\n\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int batch = coords[0];\\n        int d = coords[3];\\n\\n        ivec2 xRCCorner = coords.yz * strides - pads;\\n        int xRCorner = xRCCorner.x;\\n        int xCCorner = xRCCorner.y;\\n\\n        // max/min x(?, ?, d) to get y(yR, yC, d).\\n        // ? = to be determined\\n        vec4 minMaxValue = vec4(\"+h+\");\\n        float avgValue = 0.0;\\n        count = 0.0;\\n\\n        for (int wR = 0; wR < \"+u+\";\\n            wR += \"+i+\") {\\n          int xR = xRCorner + wR;\\n\\n          if (xR < 0 || xR >= \"+e.inHeight+\") {\\n            continue;\\n          }\\n\\n          for (int wC = 0; wC < \"+m+\"; wC += 4) {\\n            int xC = xCCorner + wC * \"+s+\";\\n\\n            vec4 values = vec4(\\n              getValue(batch, xR, xC, d),\\n              getValue(batch, xR, xC + \"+s+\", d),\\n              getValue(batch, xR, xC + 2 * \"+s+\", d),\\n              getValue(batch, xR, xC + 3 * \"+s+\", d)\\n            );\\n\\n            \"+v+\"\\n          }\\n\\n          int xC = xCCorner + \"+m+\";\\n          if (\"+(1===g)+\") {\\n            vec4 values = vec4(\\n              getValue(batch, xR, xC, d),\\n              initializationValue,\\n              initializationValue,\\n              initializationValue\\n            );\\n\\n            \"+v+\"\\n          } else if (\"+(2===g)+\") {\\n            vec4 values = vec4(\\n              getValue(batch, xR, xC, d),\\n              getValue(batch, xR, xC + \"+s+\", d),\\n              initializationValue,\\n              initializationValue\\n            );\\n\\n            \"+v+\"\\n          } else if (\"+(3===g)+\") {\\n            vec4 values = vec4(\\n              getValue(batch, xR, xC, d),\\n              getValue(batch, xR, xC + \"+s+\", d),\\n              getValue(batch, xR, xC + 2 * \"+s+\", d),\\n              initializationValue\\n            );\\n\\n            \"+v+\"\\n          }\\n        }\\n        setOutput(\"+f+\");\\n      }\\n    \"}}}(),ReduceProgram=function(){return function(e,t){this.variableNames=[\"x\"];var n=e.windowSize,r=e.batchSize,o=e.inSize,a=Math.ceil(o/n);this.outputShape=[r,a];var i=\"0.0\",s=\"\";\"prod\"===t?i=\"1.0\":\"min\"===t?(i=\"1.0 / 0.0\",s=\"min\"):\"max\"===t&&(i=\"-1.0 / 0.0\",s=\"max\");var u=t+\"(\"+t+\"(\"+t+\"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])\";\"sum\"===t?u=\"sumValue\":\"prod\"===t?u=\"prodValue\":\"all\"===t?u=\"allValue\":\"any\"===t&&(u=\"anyValue\");var l=4*Math.floor(n/4),c=n%4,p=\"\\n      if (\"+(\"sum\"===t)+\") {\\n        sumValue += dot(values, ones);\\n      } else if (\"+(\"prod\"===t)+\") {\\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\\n        prodValue *= tmp[0] * tmp[1];\\n      } else {\\n        minMaxValue = \"+s+\"(values, minMaxValue);\\n      }\\n    \",d=\"vec4\";\"all\"===t?(i=\"1.0\",p=\"\\n        bool reducedAllValue = all(values);\\n        float floatedReducedAllValue = float(reducedAllValue);\\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\\n      \",d=\"bvec4\"):\"any\"===t&&(i=\"0.0\",p=\"\\n        bool reducedAnyValue = any(values);\\n        float floatedReducedAnyValue = float(reducedAnyValue);\\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\\n      \",d=\"bvec4\");var h=\"\";o%n>0&&(h=\"\\n        if (inIdx < 0 || inIdx >= \"+o+\") {\\n          return initializationValue;\\n        }\\n      \"),this.userCode=\"\\n      const float initializationValue = \"+i+\";\\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\\n\\n      float getValue(int batch, int inIdx) {\\n        \"+h+\"\\n        return getX(batch, inIdx);\\n      }\\n\\n      void main() {\\n        ivec2 coords = getOutputCoords();\\n        int batch = coords[0];\\n        int outIdx = coords[1];\\n        int inOffset = outIdx * \"+n+\";\\n\\n        vec4 minMaxValue = vec4(\"+i+\");\\n        float prodValue = 1.0;\\n        float sumValue = 0.0;\\n        float allValue = 1.0;\\n        float anyValue = 0.0;\\n\\n        for (int i = 0; i < \"+l+\"; i += 4) {\\n          int inIdx = inOffset + i;\\n          \"+d+\" values = \"+d+\"(\\n            getValue(batch, inIdx),\\n            getValue(batch, inIdx + 1),\\n            getValue(batch, inIdx + 2),\\n            getValue(batch, inIdx + 3)\\n          );\\n\\n          \"+p+\"\\n        }\\n\\n        int inIdx = inOffset + \"+l+\";\\n        if (\"+(1===c)+\") {\\n          \"+d+\" values = \"+d+\"(\\n            getValue(batch, inIdx),\\n            initializationValue,\\n            initializationValue,\\n            initializationValue\\n          );\\n\\n          \"+p+\"\\n        } else if (\"+(2===c)+\") {\\n          \"+d+\" values = \"+d+\"(\\n            getValue(batch, inIdx),\\n            getValue(batch, inIdx + 1),\\n            initializationValue,\\n            initializationValue\\n          );\\n\\n          \"+p+\"\\n        } else if (\"+(3===c)+\") {\\n          \"+d+\" values = \"+d+\"(\\n            getValue(batch, inIdx),\\n            getValue(batch, inIdx + 1),\\n            getValue(batch, inIdx + 2),\\n            initializationValue\\n          );\\n\\n          \"+p+\"\\n        }\\n        setOutput(\"+u+\");\\n      }\\n    \"}}(),ReshapePackedProgram=function(){return function(e,t){this.variableNames=[\"A\"],this.usesPackedTextures=!0,this.outputShape=e;for(var n=\"\",r=0;r<4;r++){var o=\"thisRC = rc;\";r%2==1&&(o+=\"thisRC.z += 1;\"),r>1&&(o+=\"thisRC.y += 1;\"),n+=\"\\n        \"+o+\"\\n        \"+(r>0?\"if(thisRC.y < rows && thisRC.z < cols){\":\"\")+\"\\n          int flatIndex = getFlatIndex(thisRC);\\n\\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\\n\\n          result[\"+r+\"] =\\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\\n        \"+(r>0?\"}\":\"\")+\"\\n      \"}this.userCode=\"\\n      \"+getReshapedInputCoords(t)+\"\\n      \"+getFlatIndex(e)+\"\\n\\n      void main() {\\n        ivec3 rc = getOutputCoords();\\n\\n        vec4 result = vec4(0.);\\n\\n        ivec3 thisRC;\\n        int rows = \"+e[1]+\";\\n        int cols = \"+e[2]+\";\\n\\n        \"+n+\"\\n\\n        setOutput(result);\\n      }\\n    \"}}();function getFlatIndex(e){return\"\\n    int getFlatIndex(ivec3 coords) {\\n      return round(\"+dotify([\"coords.x\",\"coords.y\",\"coords.z\"],computeStrides(e).map(function(e){return e.toString()}).concat([\"1.\"]))+\");\\n    }\\n  \"}function getReshapedInputCoords(e){return\"\\n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\\n      \"+getLogicalCoordinatesFromFlatIndex([\"r\",\"c\",\"d\"],e)+\"\\n      return ivec3(r, c, d);\\n    }\\n  \"}var ResizeBilinearBackpropProgram=function(){return function(e,t,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t.shape;var r=t.shape,o=r[1],a=r[2],i=e.shape,s=i[1],u=i[2],l=[n&&s>1?o-1:o,n&&u>1?a-1:a],c=[n&&s>1?s-1:s,n&&u>1?u-1:u],p=l[0]/c[0],d=l[1]/c[1],h=1/p,f=1/d,m=2*Math.ceil(h)+2,g=2*Math.ceil(f)+2;this.userCode=\"\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int b = coords[0];\\n        int d = coords[3];\\n        int r = coords[1];\\n        int c = coords[2];\\n\\n        float accumulator = 0.0;\\n\\n        const float heightScale = float(\"+p+\");\\n        const float widthScale = float(\"+d+\");\\n\\n        const float invHeightScale = float(\"+h+\");\\n        const float invWidthScale = float(\"+f+\");\\n\\n        const int winHeight = int(\"+m+\");\\n        const int winWidth = int(\"+g+\");\\n\\n        // Compute bounds for where in dy we will look\\n        float startRLerp = floor(float(r) * invHeightScale);\\n        int startDyR = int(startRLerp - float(winHeight / 2));\\n\\n        float startCLerp = floor(float(c) * invWidthScale);\\n        int startDyC = int(startCLerp - float(winWidth / 2));\\n\\n        // Loop over dy\\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\\n          int dyR = dyROffset + startDyR;\\n\\n          // Guard against the window exceeding the bounds of dy\\n          if (dyR < 0 || dyR >= \"+s+\") {\\n            continue;\\n          }\\n\\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\\n            int dyC = dyCOffset + startDyC;\\n\\n            // Guard against the window exceeding the bounds of dy\\n            if (dyC < 0 || dyC >= \"+u+\") {\\n              continue;\\n            }\\n\\n            float dxR = float(dyR) * heightScale;\\n            int topDxRIndex = int(floor(dxR));\\n            int bottomDxRIndex = int(min(ceil(dxR), \"+(o-1)+\".0));\\n            float dxRLerp = dxR - float(topDxRIndex);\\n            float inverseDxRLerp = 1.0 - dxRLerp;\\n\\n            float dxC = float(dyC) * widthScale;\\n            int leftDxCIndex = int(floor(dxC));\\n            int rightDxCIndex = int(min(ceil(dxC), \"+(a-1)+\".0));\\n            float dxCLerp = dxC - float(leftDxCIndex);\\n            float inverseDxCLerp = 1.0 - dxCLerp;\\n\\n            if (r == topDxRIndex && c == leftDxCIndex) {\\n              // topLeft\\n              accumulator +=\\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\\n            }\\n\\n            if (r == topDxRIndex && c == rightDxCIndex) {\\n              // topRight\\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\\n            }\\n\\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\\n              // bottomLeft\\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\\n            }\\n\\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\\n              // bottomRight\\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\\n            }\\n          }\\n        }\\n        // End loop over dy\\n\\n        setOutput(accumulator);\\n      }\\n    \"}}(),ResizeBilinearProgram=function(){return function(e,t,n,r){this.variableNames=[\"A\"],this.outputShape=[];var o=e[0],a=e[1],i=e[2],s=e[3];this.outputShape=[o,t,n,s];var u=[r&&t>1?a-1:a,r&&n>1?i-1:i],l=[r&&t>1?t-1:t,r&&n>1?n-1:n];this.userCode=\"\\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\\n          \"+u[0]/l[0]+\",\\n          \"+u[1]/l[1]+\");\\n      const vec2 inputShapeRC = vec2(\"+a+\".0, \"+i+\".0);\\n\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int b = coords[0];\\n        int d = coords[3];\\n        ivec2 yRC = coords.yz;\\n\\n        // Fractional source index.\\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\\n\\n        // Compute the four integer indices.\\n        ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\\n        ivec2 sourceCeilRC = ivec2(\\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\\n\\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\\n\\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\\n\\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\\n        float newValue = top + (bottom - top) * fracRC.x;\\n\\n        setOutput(newValue);\\n      }\\n    \"}}(),ResizeNearestNeigborBackpropProgram=function(){return function(e,t,n){this.variableNames=[\"dy\"],this.outputShape=[],this.outputShape=t.shape;var r=t.shape,o=r[1],a=r[2],i=e.shape,s=i[1],u=i[2],l=[n&&s>1?o-1:o,n&&u>1?a-1:a],c=[n&&s>1?s-1:s,n&&u>1?u-1:u],p=l[0]/c[0],d=l[1]/c[1],h=1/p,f=1/d,m=2*Math.ceil(h)+2,g=2*Math.ceil(f)+2;this.userCode=\"\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int b = coords[0];\\n        int d = coords[3];\\n        int r = coords[1];\\n        int c = coords[2];\\n\\n        float accumulator = 0.0;\\n\\n        const float heightScale = float(\"+p+\");\\n        const float widthScale = float(\"+d+\");\\n\\n        const float invHeightScale = float(\"+h+\");\\n        const float invWidthScale = float(\"+f+\");\\n\\n        const int winHeight = int(\"+m+\");\\n        const int winWidth = int(\"+g+\");\\n\\n        // Compute bounds for where in dy we will look\\n        float startRLerp = floor(float(r) * invHeightScale);\\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\\n\\n        float startCLerp = floor(float(c) * invWidthScale);\\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\\n\\n        // Loop over dy\\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\\n          int dyR = dyROffset + startDyR;\\n\\n          // Guard against the window exceeding the bounds of dy\\n          if (dyR < 0 || dyR >= \"+s+\") {\\n            continue;\\n          }\\n\\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\\n            int dyC = dyCOffset + startDyC;\\n\\n            // Guard against the window exceeding the bounds of dy\\n            if (dyC < 0 || dyC >= \"+u+\") {\\n              continue;\\n            }\\n\\n            float sourceFracRow =\\n              float(\"+l[0]+\") *\\n                (float(dyR) / float(\"+c[0]+\"));\\n\\n            float sourceFracCol =\\n                float(\"+l[1]+\") *\\n                  (float(dyC) / float(\"+c[1]+\"));\\n\\n            int sourceNearestRow = int(min(\\n                float(int(\"+o+\") - 1),\\n                \"+n+\" ? float(round(sourceFracRow)) :\\n                                  float(floor(sourceFracRow))));\\n\\n            int sourceNearestCol = int(min(\\n                float(int(\"+a+\") - 1),\\n                \"+n+\" ? float(round(sourceFracCol)) :\\n                                  float(floor(sourceFracCol))));\\n\\n            if (r == sourceNearestRow && c == sourceNearestCol) {\\n              accumulator += getDy(b, dyR, dyC, d);\\n            }\\n          }\\n        }\\n        // End loop over dy\\n\\n        setOutput(accumulator);\\n      }\\n    \"}}(),ResizeNearestNeighborProgram=function(){return function(e,t,n,r){this.variableNames=[\"A\"],this.outputShape=[];var o=e[0],a=e[1],i=e[2],s=e[3];this.outputShape=[o,t,n,s];var u=[r&&t>1?a-1:a,r&&n>1?i-1:i],l=[r&&t>1?t-1:t,r&&n>1?n-1:n],c=r?\"0.5\":\"0.0\";this.userCode=\"\\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\\n          \"+u[0]/l[0]+\",\\n          \"+u[1]/l[1]+\");\\n      const vec2 inputShapeRC = vec2(\"+a+\".0, \"+i+\".0);\\n\\n      void main() {\\n        ivec4 coords = getOutputCoords();\\n        int b = coords[0];\\n        int d = coords[3];\\n        ivec2 yRC = coords.yz;\\n\\n        // Fractional source index.\\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\\n\\n        // Compute the coordinators of nearest neighbor point.\\n        ivec2 sourceNearestRC = ivec2(\\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + \"+c+\")));\\n\\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\\n\\n        setOutput(newValue);\\n      }\\n    \"}}(),ReverseProgram=function(){return function(e,t){this.variableNames=[\"x\"];var n=e.length;if(n>4)throw new Error(\"WebGL backend: Reverse of rank-\"+n+\" tensor is not yet supported\");if(this.outputShape=e,1!==n){var r=e.map(function(n,r){return function(n){return-1!==t.indexOf(n)&&1!==e[n]?e[n]+\" - coords[\"+n+\"] - 1\":\"coords[\"+n+\"]\"}(r)}).join(\",\"),o=getCoordsDataType(n);this.userCode=\"\\n      void main() {\\n        \"+o+\" coords = getOutputCoords();\\n        setOutput(getX(\"+r+\"));\\n      }\\n    \"}else this.userCode=\"\\n        void main() {\\n          int coord = getOutputCoords();\\n          setOutput(getX(\"+e[0]+\" - coord - 1));\\n        }\\n      \"}}(),ScatterProgram=function(){return function(e,t,n,r,o,a,i){void 0===i&&(i=!0),this.variableNames=[\"updates\",\"indices\",\"defaultValue\"],this.outputShape=a;var s=getCoordsDataType(o.length),u=getCoordsDataType(a.length),l=\"\";1===n?l=\"i\":2===n&&(l=\"i, j\");var c=\"getIndices(\"+l+\")\",p=\"\";1===r?p=\"i\":2===r&&(p=\"i, coords[1]\");var d=\"getUpdates(\"+p+\")\",h=t>1?\"strides[j]\":\"strides\";this.userCode=\"\\n        \"+s+\" strides = \"+s+\"(\"+o+\");\\n\\n        void main() {\\n          \"+u+\" coords = getOutputCoords();\\n          float sum = 0.0;\\n          bool found = false;\\n          for (int i = 0; i < \"+e+\"; i++) {\\n            int flattenedIndex = 0;\\n            for (int j = 0; j < \"+t+\"; j++) {\\n              int index = round(\"+c+\");\\n              flattenedIndex += index * \"+h+\";\\n            }\\n            if (flattenedIndex == coords[0]) {\\n              sum += \"+d+\";\\n              found = true;\\n            }\\n          }\\n          setOutput(mix(getDefaultValue(), sum, float(found)));\\n        }\\n      \"}}(),SegmentOpProgram=function(){return function(e,t){this.variableNames=[\"x\",\"segmentIds\"];var n=e.windowSize,r=e.batchSize,o=e.inSize,a=e.numSegments,i=a*Math.ceil(o/n);this.outputShape=[r,i];var s=4*Math.floor(n/4),u=n%4,l=\"\\n        sumValue += dot(values, filter);\\n    \",c=\"\";o%n>0&&(c=\"\\n        if (inIdx < 0 || inIdx >= \"+o+\") {\\n          return initializationValue;\\n        }\\n      \");var p=\"\";o%n>0&&(p=\"\\n        if (inIdx < 0 || inIdx >= \"+o+\") {\\n          return -1.0;\\n        }\\n      \"),this.userCode=\"\\n      const float initializationValue = 0.0;\\n\\n      float getValue(int batch, int inIdx) {\\n        \"+c+\"\\n        return getX(batch, inIdx);\\n      }\\n\\n      float getSegmentIdAtIndex(int inIdx) {\\n        \"+p+\"\\n        return getSegmentIds(inIdx);\\n      }\\n\\n      void main() {\\n        ivec2 coords = getOutputCoords();\\n        int batch = coords[0];\\n        int outIdx = coords[1];\\n        int inOffset = int(floor(float(outIdx) / float(\\n          \"+a+\")) * float(\"+n+\"));\\n        int currentSeg = int(mod(float(outIdx), float(\"+a+\")));\\n\\n        float sumValue = 0.0;\\n\\n        for (int i = 0; i < \"+s+\"; i += 4) {\\n          int inIdx = inOffset + i;\\n          vec4 values = vec4(\\n            getValue(batch, inIdx),\\n            getValue(batch, inIdx + 1),\\n            getValue(batch, inIdx + 2),\\n            getValue(batch, inIdx + 3)\\n          );\\n\\n          vec4 filter = vec4(\\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\\n          );\\n\\n          \"+l+\"\\n        }\\n\\n        int inIdx = inOffset + \"+s+\";\\n        if (\"+(1===u)+\") {\\n          vec4 values = vec4(\\n            getValue(batch, inIdx),\\n            initializationValue,\\n            initializationValue,\\n            initializationValue\\n          );\\n\\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\\n\\n          vec4 filter = vec4(\\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\\n            0,\\n            0,\\n            0\\n          );\\n\\n          \"+l+\"\\n        } else if (\"+(2===u)+\") {\\n          vec4 values = vec4(\\n            getValue(batch, inIdx),\\n            getValue(batch, inIdx + 1),\\n            initializationValue,\\n            initializationValue\\n          );\\n\\n          vec4 filter = vec4(\\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\\n              0,\\n              0\\n          );\\n\\n          \"+l+\"\\n        } else if (\"+(3===u)+\") {\\n          vec4 values = vec4(\\n            getValue(batch, inIdx),\\n            getValue(batch, inIdx + 1),\\n            getValue(batch, inIdx + 2),\\n            initializationValue\\n          );\\n\\n          vec4 filter = vec4(\\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\\n            0\\n          );\\n\\n          \"+l+\"\\n        }\\n        setOutput(sumValue);\\n      }\\n    \"}}(),SelectProgram=function(){return function(e,t,n){var r,o;if(this.variableNames=[\"c\",\"a\",\"b\"],this.outputShape=t,n>4)throw Error(\"Where for rank \"+n+\" is not yet supported\");if(1===n)o=\"resRC\",r=\"resRC\";else{for(var a=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\"],i=[],s=[],u=0;u<t.length;u++)s.push(\"\"+a[u]),u<e&&i.push(\"\"+a[u]);r=i.join(),o=s.join()}var l=getCoordsDataType(n);this.userCode=\"\\n      void main() {\\n        \"+l+\" resRC = getOutputCoords();\\n        float cVal = getC(\"+r+\");\\n        if (cVal >= 1.0) {\\n          setOutput(getA(\"+o+\"));\\n        } else {\\n          setOutput(getB(\"+o+\"));\\n        }\\n      }\\n    \"}}(),SliceProgram=function(){function e(e){this.variableNames=[\"source\"],this.outputShape=e,this.rank=e.length;var t=getCoordsDataType(this.rank),n=getCoords$1(this.rank);this.userCode=\"\\n      uniform \"+t+\" start;\\n\\n      void main() {\\n        \"+t+\" sourceLoc = start + getOutputCoords();\\n        setOutput(getSource(\"+n+\"));\\n      }\\n    \"}return e.prototype.getCustomSetupFunc=function(e){var t=this;if(e.length!==this.rank)throw Error(\"The rank (\"+this.rank+\") of the program must match the length of start (\"+e.length+\")\");return function(n,r){if(null!=t.startLoc||(t.startLoc=n.getUniformLocationNoThrow(r,\"start\"),null!=t.startLoc))if(1===t.rank)n.gl.uniform1i(t.startLoc,e[0]);else if(2===t.rank)n.gl.uniform2i(t.startLoc,e[0],e[1]);else if(3===t.rank)n.gl.uniform3i(t.startLoc,e[0],e[1],e[2]);else{if(4!==t.rank)throw Error(\"Slicing for rank \"+t.rank+\" is not yet supported\");n.gl.uniform4i(t.startLoc,e[0],e[1],e[2],e[3])}}},e}();function getCoords$1(e){if(1===e)return\"sourceLoc\";if(2===e)return\"sourceLoc.x, sourceLoc.y\";if(3===e)return\"sourceLoc.x, sourceLoc.y, sourceLoc.z\";if(4===e)return\"sourceLoc.x, sourceLoc.y, sourceLoc.z, sourceLoc.w\";throw Error(\"Slicing for rank \"+e+\" is not yet supported\")}var StridedSliceProgram=function(){return function(e,t,n,r){this.variableNames=[\"x\"];var o=n.filter(function(e,t){return-1===r.indexOf(t)});this.outputShape=o;var a=n.length,i=getCoordsDataType(n.length),s=getCoordsDataType(o.length),u=\"\";if(1===a)u=\"coords * strides + begin\";else{var l=0;u=n.map(function(e,t){return-1===r.indexOf(t)?(l++,1===o.length?\"coords * strides[\"+t+\"] + begin[\"+t+\"]\":\"coords[\"+(l-1)+\"] * strides[\"+t+\"] + begin[\"+t+\"]\"):\"begin[\"+t+\"]\"}).join(\",\")}this.userCode=\"\\n      \"+i+\" begin = \"+i+\"(\"+e+\");\\n      \"+i+\" strides = \"+i+\"(\"+t+\");\\n\\n      void main() {\\n        \"+s+\" coords = getOutputCoords();\\n        setOutput(getX(\"+u+\"));\\n      }\\n    \"}}(),TextureManager=function(){function e(e){this.gpgpu=e,this.numUsedTextures=0,this.numFreeTextures=0,this.freeTextures={},this.logEnabled=!1,this.usedTextures={}}return e.prototype.acquireTexture=function(e,t,n){var r,o=getPhysicalFromLogicalTextureType(t,n),a=getKeyFromTextureShape(e,o,n);if(a in this.freeTextures||(this.freeTextures[a]=[]),a in this.usedTextures||(this.usedTextures[a]=[]),this.freeTextures[a].length>0){this.numFreeTextures--,this.numUsedTextures++,this.log();var i=this.freeTextures[a].shift();return this.usedTextures[a].push(i),i}return this.numUsedTextures++,this.log(),o===PhysicalTextureType.PACKED_2X2_FLOAT32?r=this.gpgpu.createPackedMatrixTexture(e[0],e[1]):o===PhysicalTextureType.PACKED_2X2_FLOAT16?r=this.gpgpu.createFloat16PackedMatrixTexture(e[0],e[1]):o===PhysicalTextureType.UNPACKED_FLOAT32?r=this.gpgpu.createFloat32MatrixTexture(e[0],e[1]):o===PhysicalTextureType.UNPACKED_FLOAT16?r=this.gpgpu.createFloat16MatrixTexture(e[0],e[1]):o===PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE&&(r=this.gpgpu.createUnsignedBytesMatrixTexture(e[0],e[1])),this.usedTextures[a].push(r),r},e.prototype.releaseTexture=function(e,t,n,r){if(null!=this.freeTextures){var o=getKeyFromTextureShape(t,getPhysicalFromLogicalTextureType(n,r),r);o in this.freeTextures||(this.freeTextures[o]=[]),this.freeTextures[o].push(e),this.numFreeTextures++,this.numUsedTextures--;var a=this.usedTextures[o],i=a.indexOf(e);if(i<0)throw new Error(\"Cannot release a texture that was never provided by this texture manager\");a.splice(i,1),this.log()}},e.prototype.log=function(){if(this.logEnabled){var e=this.numFreeTextures+this.numUsedTextures;console.log(\"Free/Used\",this.numFreeTextures+\" / \"+this.numUsedTextures,\"(\"+e+\")\")}},e.prototype.getNumUsedTextures=function(){return this.numUsedTextures},e.prototype.getNumFreeTextures=function(){return this.numFreeTextures},e.prototype.dispose=function(){var e=this;if(null!=this.freeTextures){for(var t in this.freeTextures)this.freeTextures[t].forEach(function(t){e.gpgpu.deleteMatrixTexture(t)});for(var t in this.usedTextures)this.usedTextures[t].forEach(function(t){e.gpgpu.deleteMatrixTexture(t)});this.freeTextures=null,this.usedTextures=null,this.numUsedTextures=0,this.numFreeTextures=0}},e}();function getPhysicalFromLogicalTextureType(e,t){if(e===TextureUsage.UPLOAD)return t?PhysicalTextureType.PACKED_2X2_FLOAT32:PhysicalTextureType.UNPACKED_FLOAT32;if(e===TextureUsage.RENDER||null==e)return t?ENV.get(\"WEBGL_RENDER_FLOAT32_ENABLED\")?PhysicalTextureType.PACKED_2X2_FLOAT32:PhysicalTextureType.PACKED_2X2_FLOAT16:ENV.get(\"WEBGL_RENDER_FLOAT32_ENABLED\")?PhysicalTextureType.UNPACKED_FLOAT32:PhysicalTextureType.UNPACKED_FLOAT16;if(e===TextureUsage.DOWNLOAD||e===TextureUsage.PIXELS)return PhysicalTextureType.PACKED_4X1_UNSIGNED_BYTE;throw new Error(\"Unknown logical texture type \"+e)}function getKeyFromTextureShape(e,t,n){return e[0]+\"_\"+e[1]+\"_\"+t+\"_\"+n}var TileProgram=function(){return function(e,t){this.variableNames=[\"A\"];for(var n=new Array(e.length),r=0;r<n.length;r++)n[r]=e[r]*t[r];this.outputShape=n,this.rank=n.length;var o=getCoordsDataType(this.rank),a=getSourceCoords$2(e);this.userCode=\"\\n      void main() {\\n        \"+o+\" resRC = getOutputCoords();\\n        setOutput(getA(\"+a+\"));\\n      }\\n    \"}}();function getSourceCoords$2(e){var t=e.length;if(t>5)throw Error(\"Tile for rank \"+t+\" is not yet supported\");if(1===t)return\"imod(resRC, \"+e[0]+\")\";for(var n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\"],r=[],o=0;o<e.length;o++)r.push(\"imod(\"+n[o]+\", \"+e[o]+\")\");return r.join()}var TransposeProgram=function(){return function(e,t){this.variableNames=[\"A\"];for(var n=new Array(e.length),r=0;r<n.length;r++)n[r]=e[t[r]];this.outputShape=n,this.rank=n.length;var o=getCoordsDataType(this.rank),a=getSwitchedCoords(t);this.userCode=\"\\n    void main() {\\n      \"+o+\" resRC = getOutputCoords();\\n      setOutput(getA(\"+a+\"));\\n    }\\n    \"}}();function getSwitchedCoords(e){var t=e.length;if(t>6)throw Error(\"Transpose for rank \"+t+\" is not yet supported\");for(var n=[\"resRC.x\",\"resRC.y\",\"resRC.z\",\"resRC.w\",\"resRC.u\",\"resRC.v\"],r=new Array(t),o=0;o<e.length;o++)r[e[o]]=n[o];return r.join()}var ERF_P=.3275911,ERF_A1=.254829592,ERF_A2=-.284496736,ERF_A3=1.421413741,ERF_A4=-1.453152027,ERF_A5=1.061405429,SELU_SCALEALPHA=1.7580993408473768,SELU_SCALE=1.0507009873554805,UnaryOpProgram=function(){function e(e,t){this.variableNames=[\"A\"],this.outputShape=e,this.userCode=\"\\n      uniform float NAN;\\n      float unaryOperation(float x) {\\n        \"+t+\"\\n      }\\n\\n      void main() {\\n        float x = getAAtOutCoords();\\n        float y = unaryOperation(x);\\n\\n        setOutput(y);\\n      }\\n    \"}return e.prototype.getCustomSetupFunc=function(){var e=this;return function(t,n){null==e.startLoc&&(e.startLoc=t.getUniformLocationNoThrow(n,\"NAN\"),null==e.startLoc)||t.gl.uniform1f(e.startLoc,NaN)}},e}(),CHECK_NAN_SNIPPET$1=\"if (isNaN(x)) return x;\",ABS=\"return abs(x);\",RELU=CHECK_NAN_SNIPPET$1+\"\\n  return (x < 0.0) ? 0.0 : x;\\n\",ELU=\"return (x >= 0.0) ? x : (exp(x) - 1.0);\",SELU=\"\\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\\n  // see: https://arxiv.org/abs/1706.02515\\n  float scaleAlpha = \"+SELU_SCALEALPHA+\";\\n  float scale = \"+SELU_SCALE+\";\\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\\n\";function STEP(e){return void 0===e&&(e=0),CHECK_NAN_SNIPPET$1+\"\\n    return x > 0.0 ? 1.0 : float(\"+e+\");\\n  \"}var NEG=\"return -x;\",CEIL=\"return ceil(x);\",FLOOR=\"return floor(x);\",SIGN=\"\\n  if (isNaN(x)) { return 0.0; }\\n  return sign(x);\\n\",ROUND=\"\\n  // OpenGL ES does not support round function.\\n  // The algorithm is based on banker's rounding.\\n  float base = floor(x);\\n  if ((x - base) < 0.5) {\\n    return floor(x);\\n  } else if ((x - base) > 0.5) {\\n    return ceil(x);\\n  } else {\\n    if (mod(base, 2.0) == 0.0) {\\n      return base;\\n    } else {\\n      return base + 1.0;\\n    }\\n  }\\n\",EXP=\"return exp(x);\",EXPM1=\"return exp(x) - 1.0;\",LOG=\"if (x < 0.0) return NAN;\\n  return log(x);\",LOG1P=\"return log(1.0 + x);\",SQRT=\"return sqrt(x);\",RSQRT=\"return inversesqrt(x);\",SIGMOID=\"return 1.0 / (1.0 + exp(-1.0 * x));\",SOFTPLUS=\"\\n  float epsilon = 1.1920928955078125e-7;\\n  float threshold = log(epsilon) + 2.0;\\n\\n  bool too_large = x > -threshold;\\n  bool too_small = x < threshold;\\n\\n  float result;\\n  float exp_x = exp(x);\\n\\n  if (too_large){\\n    result = x;\\n  }\\n  else if (too_small){\\n    result = exp_x;\\n  }\\n  else{\\n    result = log(exp_x + 1.0);\\n  }\\n  return result;\\n\",SIN=CHECK_NAN_SNIPPET$1+\"\\n  return sin(x);\\n\",COS=CHECK_NAN_SNIPPET$1+\"\\n  return cos(x);\\n\",TAN=\"return tan(x);\",ASIN=\"return asin(x);\",ACOS=\"return acos(x);\",ATAN=CHECK_NAN_SNIPPET$1+\"\\n  return atan(x);\\n\",SINH=\"\\n  float e2x = exp(x);\\n  return (e2x - 1.0 / e2x) / 2.0;\\n\",COSH=\"\\n  float e2x = exp(-x);\\n  return (e2x + 1.0 / e2x) / 2.0;\\n\",TANH=\"\\n  float e2x = exp(-2.0 * abs(x));\\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\\n\",ASINH=\"return log(x + sqrt(x * x + 1.0));\",ACOSH=CHECK_NAN_SNIPPET$1+\"\\n  if (x < 1.0) return NAN;\\n  return log(x + sqrt(x * x - 1.0));\",ATANH=CHECK_NAN_SNIPPET$1+\"\\n  if ((x < -1.0) || (x > 1.0)) return NAN;\\n  return (log(1.0 + x) - log(1.0 - x)) / 2.0;\",ERF='\\n  // Error function is calculated approximately with elementary function.\\n  // See \"Handbook of Mathematical Functions with Formulas,\\n  // Graphs, and Mathematical Tables\", Abramowitz and Stegun.\\n  float p = '+ERF_P+\";\\n  float a1 = \"+ERF_A1+\";\\n  float a2 = \"+ERF_A2+\";\\n  float a3 = \"+ERF_A3+\";\\n  float a4 = \"+ERF_A4+\";\\n  float a5 = \"+ERF_A5+\";\\n\\n  float t = 1.0 / (1.0 + p * x);\\n  return 1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x);\\n\",SQUARE=\"return x * x;\",RECIPROCAL=\"return 1.0 / x;\",LOGICAL_NOT=\"return float(!(x >= 1.0));\",TO_INT=\"return float(int(x));\",UnpackProgram=function(){return function(e){this.variableNames=[\"A\"],this.usesPackedTextures=!0,this.outputShape=e;var t=e.length,n=getChannels(\"rc\",t),r=getCoordsDataType(t),o=getSourceCoords$1(t,n),a=n.slice(-2),i=t<=1?\"rc\":\"vec2(\"+a.join(\",\")+\")\";this.userCode=\"\\n      void main() {\\n        \"+r+\" rc = getOutputCoords();\\n        vec4 packedInput = getA(\"+o+\");\\n\\n        setOutput(getChannel(packedInput, \"+i+\"));\\n      }\\n    \"}}();function concat1d_(e){return concat(e,0)}function concat2d_(e,t){return concat(e,t)}function concat3d_(e,t){return concat(e,t)}function concat4d_(e,t){return concat(e,t)}function concat_(e,t){void 0===t&&(t=0),assert(e.length>=1,\"Pass at least one tensor to concat\");var n=convertToTensorArray(e,\"tensors\",\"concat\");t=parseAxisParam(t,n[0].shape)[0];var r=computeOutShape(n.map(function(e){return e.shape}),t);if(0===sizeFromShape(r))return tensor([],r);if(1===(n=n.filter(function(e){return e.size>0})).length)return n[0];var o=n.map(function(e){return e.shape});assertParamsConsistent(o,t);var a=n;return ENV.engine.runKernel(function(e){return e.concat(n,t)},a,function(e){var n=o.map(function(e){return e[t]});return split$1(e,n,t).map(function(e){return function(){return e}})})}function split_(e,t,n){void 0===n&&(n=0);var r,o=convertToTensor(e,\"x\",\"split\");n=parseAxisParam(n,o.shape)[0],\"number\"==typeof t?(assert(o.shape[n]%t==0,\"Number of splits must evenly divide the axis.\"),r=Array(t).fill(o.shape[n]/t)):(assert(o.shape[n]===t.reduce(function(e,t){return e+t}),\"The sum of sizes must match the size of the axis dimension.\"),r=t);return ENV.engine.runKernel(function(e){return e.split(o,r,n)},{$x:o},function(e){return{$x:function(){return concat(e,n)}}})}var concat=op({concat_:concat_}),concat1d=op({concat1d_:concat1d_}),concat2d=op({concat2d_:concat2d_}),concat3d=op({concat3d_:concat3d_}),concat4d=op({concat4d_:concat4d_}),split$1=op({split_:split_}),commonjsGlobal=\"undefined\"!=typeof window?window:\"undefined\"!=typeof global?global:\"undefined\"!=typeof self?self:{};function createCommonjsModule(e,t){return e(t={exports:{}},t.exports),t.exports}var alea=createCommonjsModule(function(e){!function(e,t,n){function r(e,t){return t.c=e.c,t.s0=e.s0,t.s1=e.s1,t.s2=e.s2,t}function o(e,t){var n=new function(e){var t,n=this,r=(t=4022871197,function(e){e=e.toString();for(var n=0;n<e.length;n++){var r=.02519603282416938*(t+=e.charCodeAt(n));r-=t=r>>>0,t=(r*=t)>>>0,t+=4294967296*(r-=t)}return 2.3283064365386963e-10*(t>>>0)});n.next=function(){var e=2091639*n.s0+2.3283064365386963e-10*n.c;return n.s0=n.s1,n.s1=n.s2,n.s2=e-(n.c=0|e)},n.c=1,n.s0=r(\" \"),n.s1=r(\" \"),n.s2=r(\" \"),n.s0-=r(e),n.s0<0&&(n.s0+=1),n.s1-=r(e),n.s1<0&&(n.s1+=1),n.s2-=r(e),n.s2<0&&(n.s2+=1),r=null}(e),o=t&&t.state,a=n.next;return a.int32=function(){return 4294967296*n.next()|0},a.double=function(){return a()+1.1102230246251565e-16*(2097152*a()|0)},a.quick=a,o&&(\"object\"==typeof o&&r(o,n),a.state=function(){return r(n,{})}),a}t&&t.exports?t.exports=o:n&&n.amd?n(function(){return 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Pass non-scalar to pad\");var o=t.map(function(e){return e[0]});return ENV.engine.runKernel(function(e){return e.pad(r,t,n)},{$x:r},function(e){return{$x:function(){return e.slice(o,r.shape)}}})}function stack_(e,t){void 0===t&&(t=0);var n=convertToTensorArray(e,\"tensors\",\"stack\");if(assert(n.length>=1,\"Pass at least one tensor to tf.stack\"),1===n.length)return n[0].expandDims(t);var r=n[0].rank,o=n[0].shape,a=n[0].dtype;assert(t<=r,\"Axis must be <= rank of the tensor\"),n.forEach(function(e){assertShapesMatch(o,e.shape,\"All tensors passed to stack must have matching shapes\")}),n.forEach(function(e){assert(a===e.dtype,\"All tensors passed to stack must have matching dtypes\")});var i=n.map(function(e){return e.expandDims(t)});return concat(i,t)}function batchToSpaceND_(e,t,n){var r=convertToTensor(e,\"x\",\"batchToSpaceND\"),o=t.reduce(function(e,t){return e*t});assert(r.rank>=1+t.length,\"input rank is \"+r.rank+\" but should be > than blockShape.length \"+t.length),assert(n.length===t.length,\"crops.length is \"+n.length+\" but should be equal to blockShape.length  \"+t.length),assert(r.shape[0]%o==0,\"input tensor batch is \"+r.shape[0]+\" but is not divisible by the product of the elements of blockShape \"+t.join(\" * \")+\" === \"+o);return ENV.engine.runKernel(function(e){return e.batchToSpaceND(r,t,n)},{$x:r},function(e){return{$x:function(){return e.spaceToBatchND(t,n)}}})}function spaceToBatchND_(e,t,n){var r=convertToTensor(e,\"x\",\"spaceToBatchND\");assert(r.rank>=1+t.length,\"input rank \"+r.rank+\" should be > than [blockShape] \"+t.length),assert(n.length===t.length,\"paddings.shape[0] \"+n.length+\" must be equal to [blockShape] \"+t.length),assert(r.shape.reduce(function(e,r,o){return o>0&&o<=t.length?e&&(r+n[o-1][0]+n[o-1][1])%t[o-1]==0:e},!0),\"input spatial dimensions \"+r.shape.slice(1)+\" with paddings \"+n.toString()+\" must be divisible by blockShapes \"+t.toString());return ENV.engine.runKernel(function(e){return e.spaceToBatchND(r,t,n)},{$x:r},function(e){return{$x:function(){return e.batchToSpaceND(t,n)}}})}function unstack_(e,t){void 0===t&&(t=0);for(var n,r=convertToTensor(e,\"x\",\"unstack\"),o=r.shape[t],a=Array(r.rank-1).fill(0),i=0,s=0;s<r.rank;s++)s!==t&&(a[i]=r.shape[s],i++);n=Array(o).fill(1);var u=Array(r.rank).fill(0),l=r.shape.slice();return n.map(function(e){l[t]=e;var n=r.slice(u,l);return u[t]+=e,n.reshape(a)})}function cumsum_(e,t,n,r){void 0===t&&(t=0),void 0===n&&(n=!1),void 0===r&&(r=!1);var o=convertToTensor(e,\"x\",\"cumsum\"),a=getAxesPermutation([t|=0],o.rank),i=o;null!=a&&(i=o.transpose(a));var s=getInnerMostAxes(1,o.rank)[0],u=ENV.engine.runKernel(function(e){return e.cumsum(i,s,n,r)},{permutedX:i},function(e){return{permutedX:function(){return e.cumsum(t,n,!r)}}});return null!=a&&(u=u.transpose(a)),u}function expandDims_(e,t){void 0===t&&(t=0);var n=convertToTensor(e,\"x\",\"expandDims\");assert(t<=n.rank,\"Axis must be <= rank of the tensor\");var r=n.shape.slice();return t<0&&(assert(-(n.rank+1)<=t,\"Axis must be in the interval [\"+-(n.rank+1)+\", \"+n.rank+\"]\"),t=n.rank+t+1),r.splice(t,0,1),reshape(n,r)}function depthToSpace_(e,t,n){void 0===n&&(n=\"NHWC\");var r=convertToTensor(e,\"x\",\"depthToSpace\"),o=\"NHWC\"===n?r.shape[1]:r.shape[2],a=\"NHWC\"===n?r.shape[2]:r.shape[3],i=\"NHWC\"===n?r.shape[3]:r.shape[1];return assert(o*t>=0,\"Negative dimension size caused by overflow when multiplying\\n      \"+o+\" and \"+t+\"  for depthToSpace with input shape\\n      \"+r.shape),assert(a*t>=0,\"Negative dimension size caused by overflow when multiplying\\n      \"+a+\" and \"+t+\" for depthToSpace with input shape\\n          \"+r.shape),assert(i%(t*t)==0,\"Dimension size must be evenly divisible by \"+t*t+\" but is \"+i+\" for depthToSpace with input shape \"+r.shape),ENV.engine.runKernel(function(e){return e.depthToSpace(r,t,n)},{$x:r})}function setdiff1dAsync_(e,t){return __awaiter(this,void 0,void 0,function(){var n,r,o,a,i,s,u,l,c,p;return __generator(this,function(d){switch(d.label){case 0:return n=convertToTensor(e,\"x\",\"setdiff1d\"),r=convertToTensor(t,\"y\",\"setdiff1d\"),assert(n.dtype===r.dtype,\"x and y should have the same dtype, but got x (\"+n.dtype+\") and y (\"+r.dtype+\").\"),assert(1===n.rank,\"x should be 1D tensor, but got x (\"+n.shape+\").\"),assert(1===r.rank,\"y should be 1D tensor, but got y (\"+r.shape+\").\"),[4,n.data()];case 1:return o=d.sent(),[4,r.data()];case 2:for(a=d.sent(),i=new Set(a),s=0,c=0;c<o.length;c++)i.has(o[c])||s++;for(u=new TensorBuffer([s],n.dtype),l=new TensorBuffer([s],\"int32\"),c=0,p=0;c<o.length;c++)i.has(o[c])||(u.values[p]=o[c],l.values[p]=c,p++);return[2,[u.toTensor(),l.toTensor()]]}})})}function buffer(e,t,n){return void 0===t&&(t=\"float32\"),new TensorBuffer(e,t=t||\"float32\",n)}function print(e,t){void 0===t&&(t=!1),console.log(e.toString(t))}var batchToSpaceND=op({batchToSpaceND_:batchToSpaceND_}),cast=op({cast_:cast_}),clone=op({clone_:clone_}),cumsum=op({cumsum_:cumsum_}),depthToSpace=op({depthToSpace_:depthToSpace_}),expandDims=op({expandDims_:expandDims_}),eye=op({eye_:eye_}),fromPixels=op({fromPixels_:fromPixels_}),multinomial=op({multinomial_:multinomial_}),oneHot=op({oneHot_:oneHot_}),pad=op({pad_:pad_}),pad1d=op({pad1d_:pad1d_}),pad2d=op({pad2d_:pad2d_}),pad3d=op({pad3d_:pad3d_}),pad4d=op({pad4d_:pad4d_}),rand=op({rand_:rand_}),randomNormal=op({randomNormal_:randomNormal_}),randomUniform=op({randomUniform_:randomUniform_}),reshape=op({reshape_:reshape_}),spaceToBatchND=op({spaceToBatchND_:spaceToBatchND_}),squeeze=op({squeeze_:squeeze_}),stack=op({stack_:stack_}),tile=op({tile_:tile_}),truncatedNormal=op({truncatedNormal_:truncatedNormal_}),unstack=op({unstack_:unstack_}),setdiff1dAsync=setdiff1dAsync_;function whereImpl(e,t){for(var n=[],r=0;r<t.length;r++)t[r]&&n.push(r);var o=buffer(e,\"int32\"),a=buffer([n.length,e.length],\"int32\");for(r=0;r<n.length;r++){var i=o.indexToLoc(n[r]),s=r*e.length;a.values.set(i,s)}return a.toTensor()}var CPU_HANDOFF_SIZE_THRESHOLD=10,BEFORE_PAGING_CONSTANT=300,MATMUL_SHARED_DIM_THRESHOLD=1e3,MathBackendWebGL=function(){function e(e,t){if(void 0===t&&(t=!0),this.gpgpu=e,this.delayedStorage=t,this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.lruDataGPU=[],this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.binaryCache={},this.disposed=!1,ENV.get(\"WEBGL_VERSION\")<1)throw new Error(\"WebGL is not supported on this device\");if(null==e){var n=getWebGLContext(ENV.get(\"WEBGL_VERSION\"));this.gpgpu=new GPGPUContext(n),this.canvas=n.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpuCreatedLocally=!1,this.canvas=e.gl.canvas;ENV.get(\"WEBGL_PAGING_ENABLED\")&&(this.NUM_BYTES_BEFORE_PAGING=window.screen.height*window.screen.width*window.devicePixelRatio*BEFORE_PAGING_CONSTANT),this.textureManager=new TextureManager(this.gpgpu)}return e.prototype.register=function(e,t,n){if(this.texData.has(e))throw new Error(\"Data buffer is already registered\");this.texData.set(e,{shape:t,dtype:n})},e.prototype.setDataMover=function(e){this.texData=new DataStorage(e)},e.prototype.fromPixels=function(e,t){if(null==e)throw new Error(\"pixels passed to tf.fromPixels() can not be null\");var n=[e.height,e.width],r=[e.height,e.width,t];if(!(e instanceof HTMLVideoElement||e instanceof HTMLImageElement||e instanceof HTMLCanvasElement||e instanceof ImageData))throw new Error(\"pixels passed to tf.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ImageData, but was \"+e.constructor.name);if(e instanceof HTMLVideoElement){if(null==this.fromPixels2DContext){if(!ENV.get(\"IS_BROWSER\"))throw new Error(\"Can't read pixels from HTMLImageElement outside the browser.\");if(\"complete\"!==document.readyState)throw new Error(\"The DOM is not ready yet. Please call tf.fromPixels() once the DOM is ready. One way to do that is to add an event listener for `DOMContentLoaded` on the document object\");this.fromPixels2DContext=document.createElement(\"canvas\").getContext(\"2d\")}this.fromPixels2DContext.canvas.width=e.width,this.fromPixels2DContext.canvas.height=e.height,this.fromPixels2DContext.drawImage(e,0,0,e.width,e.height),e=this.fromPixels2DContext.canvas}var o=this.makeTensorHandle(n,\"int32\");this.texData.get(o.dataId).usage=TextureUsage.PIXELS,this.gpgpu.uploadPixelDataToTexture(this.getTexture(o.dataId),e);var a=new FromPixelsProgram(r),i=this.compileAndRun(a,[o]);return this.disposeData(o.dataId),i},e.prototype.makeTensorHandle=function(e,t){var n={};return this.register(n,e,t),{dataId:n,shape:e,dtype:t}},e.prototype.write=function(e,t){if(null==t)throw new Error(\"MathBackendWebGL.write(): values can not be null\");var n=this.texData.get(e),r=n.texture,o=n.texShape,a=n.usage,i=n.dtype,s=n.isPacked;if(\"complex64\"===i)throw new Error(\"Cannot write to a complex64 dtype. Please use tf.complex(real, imag).\");null!=r&&(this.releaseTexture(e,r,o,a,s),n.texture=null,n.texShape=null),n.usage=TextureUsage.UPLOAD,n.values=t,this.delayedStorage||this.uploadToGPU(e)},e.prototype.readSync=function(e){var t=this.texData.get(e),n=t.values,r=t.dtype,o=t.complexTensors;if(null!=n)return this.convertAndCacheOnCPU(e);if(\"string\"===r)return n;var a,i,s=null!=this.activeTimers;(s&&(a=performance.now()),\"complex64\"===r)?i=mergeRealAndImagArrays(o.real.dataSync(),o.imag.dataSync()):i=this.getValuesFromTexture(e);return s&&(this.downloadWaitMs+=performance.now()-a),this.convertAndCacheOnCPU(e,i)},e.prototype.read=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o,a,i,s,u,l,c,p,d,h,f,m,g,v,y;return __generator(this,function(x){switch(x.label){case 0:if(this.pendingRead.has(e))return r=this.pendingRead.get(e),[2,new Promise(function(e){return r.push(e)})];if(o=this.texData.get(e),a=o.texture,i=o.values,s=o.texShape,u=o.isPacked,l=o.shape,null!=i)return[2,this.convertAndCacheOnCPU(e)];if(this.pendingRead.set(e,[]),!ENV.get(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")&&2===ENV.get(\"WEBGL_VERSION\"))throw new Error(\"tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.\");return c=s[1],p=s[0],u&&(t=getPackedMatrixTextureShapeWidthHeight(s[0],s[1]),c=t[0],p=t[1]),d=this.gpgpu.maybeCreateBufferFromTexture(a,p,c),[4,this.gpgpu.createAndWaitForFence()];case 1:return x.sent(),d instanceof WebGLTexture?h=this.getValuesFromTexture(e):u?(f=this.getBatchDim(l),m=1,g=1,l.length&&(n=this.getRowsCols(l),m=n[0],g=n[1]),h=this.gpgpu.downloadPackedMatrixFromBuffer(d,f,m,g,s[0],s[1])):h=this.gpgpu.downloadFloat32MatrixFromBuffer(d,s[0],s[1]),v=this.convertAndCacheOnCPU(e,h),y=this.pendingRead.get(e),this.pendingRead.delete(e),y.forEach(function(e){return e(v)}),this.pendingDisposal.has(e)&&(this.pendingDisposal.delete(e),this.disposeData(e)),[2,v]}})})},e.prototype.getValuesFromTexture=function(e){var t,n=this.texData.get(e),r=n.shape,o=n.dtype,a=n.texture,i=n.texShape;if(ENV.get(\"WEBGL_DOWNLOAD_FLOAT_ENABLED\")){if(this.texData.get(e).isPacked){var s=this.getBatchDim(r),u=1,l=1;return r.length&&(u=(t=this.getRowsCols(r))[0],l=t[1]),this.gpgpu.downloadMatrixFromPackedTexture(a,s,u,l,i[0],i[1])}return this.gpgpu.downloadFloat32MatrixFromOutputTexture(a,i[0],i[1])}var c=this.makeTensorHandle(r,\"float32\");c.size=sizeFromShape(r),this.texData.get(c.dataId).usage=TextureUsage.DOWNLOAD;var p=new EncodeFloatProgram(r);this.compileAndRun(p,[{shape:r,dtype:o,dataId:e}],c,null,!1);var d=this.texData.get(c.dataId),h=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(d.texture,d.texShape[0],d.texShape[1]);return this.disposeData(c.dataId),h},e.prototype.time=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o,a,i,s;return __generator(this,function(u){switch(u.label){case 0:return t=this.activeTimers,n=[],r=!1,null==this.programTimersStack?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,e(),o=flatten(this.activeTimers.map(function(e){return e.query})).filter(function(e){return null!=e}),a=flatten(this.activeTimers.map(function(e){return e.name})).filter(function(e){return null!=e}),this.activeTimers=t,r&&(this.programTimersStack=null),[4,Promise.all(o)];case 1:return i=u.sent(),s={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:sum(i),getExtraProfileInfo:function(){return i.map(function(e,t){return{name:a[t],ms:e}}).map(function(e){return e.name+\": \"+e.ms}).join(\", \")},wallMs:null},this.uploadWaitMs=0,this.downloadWaitMs=0,[2,s]}})})},e.prototype.memory=function(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU}},e.prototype.startTimer=function(){return ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?this.gpgpu.beginQuery():{startMs:performance.now(),endMs:null}},e.prototype.endTimer=function(e){return ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?(this.gpgpu.endQuery(),e):(e.endMs=performance.now(),e)},e.prototype.getQueryTime=function(e){return __awaiter(this,void 0,void 0,function(){var t;return __generator(this,function(n){return ENV.get(\"WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION\")>0?[2,this.gpgpu.waitForQueryAndGetTime(e)]:[2,(t=e).endMs-t.startMs]})})},e.prototype.disposeData=function(e){if(!this.pendingDisposal.has(e))if(this.pendingRead.has(e))this.pendingDisposal.add(e);else if(this.texData.has(e)){var t=this.texData.get(e),n=t.texture,r=t.texShape,o=t.usage,a=t.complexTensors,i=t.isPacked;null!=n&&this.releaseTexture(e,n,r,o,i),null!=a&&(a.real.dispose(),a.imag.dispose()),this.texData.delete(e)}},e.prototype.getTexture=function(e){return this.uploadToGPU(e),this.texData.get(e).texture},e.prototype.getCPUBackend=function(){return ENV.get(\"WEBGL_CPU_FORWARD\")?(null==this.cpuBackend&&(this.cpuBackend=ENV.findBackend(\"cpu\")),this.cpuBackend):null},e.prototype.shouldExecuteOnCPU=function(e,t){var n=this;return void 0===t&&(t=CPU_HANDOFF_SIZE_THRESHOLD),null!=this.getCPUBackend()&&e.every(function(e){return null==n.texData.get(e.dataId).texture&&e.size<t})},e.prototype.getGPGPUContext=function(){return this.gpgpu},e.prototype.getCanvas=function(){return this.canvas},e.prototype.complex=function(e,t){var n=this.makeOutputArray(e.shape,\"complex64\");return this.texData.get(n.dataId).complexTensors={real:ENV.engine.keep(e.clone()),imag:ENV.engine.keep(t.clone())},n},e.prototype.real=function(e){return this.texData.get(e.dataId).complexTensors.real.clone()},e.prototype.imag=function(e){return this.texData.get(e.dataId).complexTensors.imag.clone()},e.prototype.slice=function(e,t,n){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.slice(e,t,n);var r=new SliceProgram(n),o=r.getCustomSetupFunc(t);return this.compileAndRun(r,[e],null,o)},e.prototype.stridedSlice=function(e,t,n,r,o,a,i,s,u){if(this.shouldExecuteOnCPU([e]))return this.cpuBackend.stridedSlice(e,t,n,r,o,a,i,s,u);var l=getStridedSlicedInfo(e.shape,t,n,r,o,a,i,s,u),c=l[0],p=l[1],d=l[2],h=p.filter(function(e,t){return-1===d.indexOf(t)});if(h.some(function(e){return 0===e}))return tensor([],h);var f=new StridedSliceProgram(c,r,p,d);return this.compileAndRun(f,[e])},e.prototype.reverse=function(e,t){var n=new ReverseProgram(e.shape,t);return this.compileAndRun(n,[e])},e.prototype.concat2Tensors=function(e,t,n){var r=computeOutShape([e.shape,t.shape],n),o=e.as2D(-1,sizeFromShape(e.shape.slice(n))),a=t.as2D(-1,sizeFromShape(t.shape.slice(n))),i=new ConcatProgram(o.shape,a.shape);return this.compileAndRun(i,[o,a]).reshape(r)},e.prototype.concat=function(e,t){if(this.shouldExecuteOnCPU(e))return this.cpuBackend.concat(e,t);if(1===e.length)return e[0];for(var n=e[0],r=1;r<e.length;++r)n=this.concat2Tensors(n,e[r],t);return n},e.prototype.neg=function(e){var t=new UnaryOpProgram(e.shape,NEG);return this.compileAndRun(t,[e])},e.prototype.batchMatMul=function(e,t,n,r){var o=n?e.shape[2]:e.shape[1],a=r?t.shape[1]:t.shape[2],i=n?e.shape[1]:e.shape[2],s=e.shape[0];if((1===o||1===a)&&i>MATMUL_SHARED_DIM_THRESHOLD){n&&(e=e.transpose([0,2,1])),r&&(t=t.transpose([0,2,1]));var u=1===a?e:e.as3D(s,i,1),l=1===a?2:1,c=1===a?t.as3D(s,1,i):t;return this.multiply(u,c).sum(l,!0)}var p=upcastType(e.dtype,t.dtype);if(1===s){var d=e.as2D(e.shape[1],e.shape[2]),h=t.as2D(t.shape[1],t.shape[2]),f=new MatMulPackedProgram(d.shape,h.shape,[o,a],n,r),m=this.makePackedTensor(f.outputShape,p),g=this.compileAndRun(f,[d,h],m);return g.reshape([1,g.shape[0],g.shape[1]])}f=new MatMulProgram(e.shape,t.shape,n,r),m=this.makeOutputArray(f.outputShape,p);return this.compileAndRun(f,[e,t],m)},e.prototype.multiply=function(e,t){if(\"complex64\"===e.dtype){var n=this.texData.get(e.dataId),r=this.texData.get(t.dataId),o=new BinaryOpComplexProgram(COMPLEX_MULTIPLY.REAL,e.shape,t.shape),a=new BinaryOpComplexProgram(COMPLEX_MULTIPLY.IMAG,e.shape,t.shape),i=[this.makeComplexComponentTensorHandle(e,n.complexTensors.real),this.makeComplexComponentTensorHandle(e,n.complexTensors.imag),this.makeComplexComponentTensorHandle(t,r.complexTensors.real),this.makeComplexComponentTensorHandle(t,r.complexTensors.imag)],s=this.compileAndRun(o,i),u=this.compileAndRun(a,i),l=this.complex(s,u);return s.dispose(),u.dispose(),l}if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.multiply(e,t);var c=new BinaryOpProgram(MUL,e.shape,t.shape),p=this.makeOutputArray(c.outputShape,e.dtype);return this.compileAndRun(c,[e,t],p)},e.prototype.batchNormalization=function(e,t,n,r,o,a){var i=[e,t,n],s=null;null!=a&&(s=a.shape,i.push(a));var u=null;if(null!=o&&(u=o.shape,i.push(o)),ENV.get(\"WEBGL_PACK_BATCHNORMALIZATION\")){var l=new BatchNormPackedProgram(e.shape,t.shape,n.shape,s,u,r);return this.compileAndRun(l,i)}var c=new BatchNormProgram(e.shape,t.shape,n.shape,s,u,r);return this.compileAndRun(c,i)},e.prototype.localResponseNormalization4D=function(e,t,n,r,o){var a=new LRNProgram(e.shape,t,n,r,o);return this.compileAndRun(a,[e])},e.prototype.LRNGrad=function(e,t,n,r,o,a,i){var s=new LRNGradProgram(t.shape,r,o,a,i);return this.compileAndRun(s,[t,n,e])},e.prototype.tile=function(e,t){var n=new TileProgram(e.shape,t);return this.compileAndRun(n,[e])},e.prototype.pad=function(e,t,n){var r=new PadProgram(e.shape,t,n);return this.compileAndRun(r,[e])},e.prototype.transpose=function(e,t){var n=new TransposeProgram(e.shape,t);return this.compileAndRun(n,[e])},e.prototype.gather=function(e,t,n){var r=new GatherProgram(e.shape,t.size,n);return this.compileAndRun(r,[e,t])},e.prototype.batchToSpaceND=function(e,t,n){assert(e.rank<=4,\"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet\");var r=t.reduce(function(e,t){return e*t}),o=getReshaped(e.shape,t,r),a=getPermuted(o.length,t.length),i=getReshapedPermuted(e.shape,t,r),s=getSliceBeginCoords(n,t.length),u=getSliceSize(i,n,t.length);return e.reshape(o).transpose(a).reshape(i).slice(s,u)},e.prototype.spaceToBatchND=function(e,t,n){assert(e.rank<=4,\"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet\");var r=t.reduce(function(e,t){return e*t}),o=[[0,0]];o.push.apply(o,n);for(var a=1+t.length;a<e.shape.length;++a)o.push([0,0]);var i=e.pad(o),s=getReshaped(i.shape,t,r,!1),u=getPermuted(s.length,t.length,!1),l=getReshapedPermuted(i.shape,t,r,!1);return i.reshape(s).transpose(u).reshape(l)},e.prototype.reduce=function(e,t,n){var r=e.shape[0],o=e.shape[1],a=computeOptimalWindowSize(o),i=new ReduceProgram({windowSize:a,inSize:o,batchSize:r},t),s=i.outputShape,u=s[0],l=s[1],c=this.makeOutputArray([u,l],n);return this.compileAndRun(i,[e],c),1===c.shape[1]?c:this.reduce(c,t,n)},e.prototype.argReduce=function(e,t,n){void 0===n&&(n=null);var r=e.shape[0],o=e.shape[1];null!=n&&(r=n.shape[0],o=n.shape[1]);var a=computeOptimalWindowSize(o),i=new ArgMinMaxProgram({windowSize:a,inSize:o,batchSize:r},t,null==n),s=i.outputShape,u=s[0],l=s[1],c=this.makeOutputArray([u,l],\"int32\"),p=[e];return null!=n&&p.push(n),this.compileAndRun(i,p,c),1===c.shape[1]?c:this.argReduce(e,t,c)},e.prototype.sum=function(e,t){assertAxesAreInnerMostDims(\"sum\",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o),i=sumOutType(e.dtype);return this.reduce(a,\"sum\",i).reshape(r)},e.prototype.prod=function(e,t){var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o),i=sumOutType(e.dtype);return this.reduce(a,\"prod\",i).reshape(r)},e.prototype.unsortedSegmentSum=function(e,t,n){var r=0,o=getAxesPermutation([r],e.rank),a=e;null!=o&&(a=e.transpose(o),r=getInnerMostAxes(1,e.rank)[0]);var i=computeOutShape$1(a.shape,r,n),s=sizeFromShape([a.shape[r]]),u=a.as2D(-1,s),l=sumOutType(e.dtype),c=this.segOpCompute(u,\"unsortedSegmentSum\",t,l,n).reshape(i);return null!=o&&(c=c.transpose(getUndoAxesPermutation(o))),c},e.prototype.segOpCompute=function(e,t,n,r,o){var a=e.shape[0],i=e.shape[1],s=segOpComputeOptimalWindowSize(i,o),u=new SegmentOpProgram({windowSize:s,inSize:i,batchSize:a,numSegments:o},t),l=u.outputShape,c=l[0],p=l[1],d=this.makeOutputArray([c,p],r);return this.compileAndRun(u,[e,n],d),d.shape[1]===o?d:(n=range(0,o).tile([i/s]),this.segOpCompute(d,t,n,r,o))},e.prototype.argMin=function(e,t){var n=[t];assertAxesAreInnerMostDims(\"argMin\",n,e.rank);var r=computeOutAndReduceShapes(e.shape,n),o=r[0],a=sizeFromShape(r[1]),i=e.as2D(-1,a);return this.argReduce(i,\"min\").reshape(o)},e.prototype.argMax=function(e,t){var n=[t];assertAxesAreInnerMostDims(\"argMax\",n,e.rank);var r=computeOutAndReduceShapes(e.shape,n),o=r[0],a=sizeFromShape(r[1]),i=e.as2D(-1,a);return this.argReduce(i,\"max\").reshape(o)},e.prototype.cumsum=function(e,t,n,r){if(t!==e.rank-1)throw new Error(\"WebGL cumsum shader expects an inner-most axis=\"+(e.rank-1)+\" but got axis=\"+t);var o=new CumSumProgram(e.shape,n,r);return this.compileAndRun(o,[e])},e.prototype.equal=function(e,t){var n=new BinaryOpProgram(EQUAL,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,\"bool\");return this.compileAndRun(n,[e,t],r)},e.prototype.notEqual=function(e,t){var n=new BinaryOpProgram(NOT_EQUAL,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,\"bool\");return this.compileAndRun(n,[e,t],r)},e.prototype.less=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.less(e,t);var n=new BinaryOpProgram(LESS,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,\"bool\");return this.compileAndRun(n,[e,t],r)},e.prototype.lessEqual=function(e,t){var n=new BinaryOpProgram(LESS_EQUAL,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,\"bool\");return this.compileAndRun(n,[e,t],r)},e.prototype.greater=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.greater(e,t);var n=new BinaryOpProgram(GREATER,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,\"bool\");return this.compileAndRun(n,[e,t],r)},e.prototype.greaterEqual=function(e,t){var n=new BinaryOpProgram(GREATER_EQUAL,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,\"bool\");return this.compileAndRun(n,[e,t],r)},e.prototype.logicalNot=function(e){var t=new UnaryOpProgram(e.shape,LOGICAL_NOT);return this.compileAndRun(t,[e])},e.prototype.logicalAnd=function(e,t){var n=new BinaryOpProgram(LOGICAL_AND,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,\"bool\");return this.compileAndRun(n,[e,t],r)},e.prototype.logicalOr=function(e,t){var n=new BinaryOpProgram(LOGICAL_OR,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,\"bool\");return this.compileAndRun(n,[e,t],r)},e.prototype.select=function(e,t,n){var r=new SelectProgram(e.rank,t.shape,t.rank),o=this.makeOutputArray(r.outputShape,upcastType(t.dtype,n.dtype));return this.compileAndRun(r,[e,t,n],o)},e.prototype.where=function(e){warn(\"tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead\");var t=e.dataSync();return whereImpl(e.shape,t)},e.prototype.topk=function(e,t,n){return topkImpl(e.dataSync(),e.shape,e.dtype,t,n)},e.prototype.min=function(e,t){assertAxesAreInnerMostDims(\"min\",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o);return this.reduce(a,\"min\",a.dtype).reshape(r)},e.prototype.minimum=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.minimum(e,t);var n=new BinaryOpProgram(MIN,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.mod=function(e,t){var n=new BinaryOpProgram(MOD,e.shape,t.shape),r=n.getCustomSetupFunc();return this.compileAndRun(n,[e,t],null,r)},e.prototype.max=function(e,t){assertAxesAreInnerMostDims(\"max\",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o);return this.reduce(a,\"max\",a.dtype).reshape(r)},e.prototype.maximum=function(e,t){if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.maximum(e,t);var n=new BinaryOpProgram(MAX,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.all=function(e,t){assertAxesAreInnerMostDims(\"all\",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o);return this.reduce(a,\"all\",a.dtype).reshape(r)},e.prototype.any=function(e,t){assertAxesAreInnerMostDims(\"any\",t,e.rank);var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=sizeFromShape(n[1]),a=e.as2D(-1,o);return this.reduce(a,\"any\",a.dtype).reshape(r)},e.prototype.squaredDifference=function(e,t){var n=new BinaryOpProgram(SQUARED_DIFFERENCE,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.realDivide=function(e,t){var n=new BinaryOpProgram(DIV,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,\"float32\");return this.compileAndRun(n,[e,t],r)},e.prototype.floorDiv=function(e,t){var n=new BinaryOpProgram(INT_DIV,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,\"int32\");return this.compileAndRun(n,[e,t],r)},e.prototype.add=function(e,t){if(\"complex64\"===e.dtype&&\"complex64\"===t.dtype)return this.complexSeparableBinaryOp(e,t,ADD);var n=new BinaryOpProgram(ADD,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,upcastType(e.dtype,t.dtype));return this.compileAndRun(n,[e,t],r)},e.prototype.complexSeparableBinaryOp=function(e,t,n){var r=this,o=this.texData.get(e.dataId),a=this.texData.get(t.dataId),i=[[o.complexTensors.real,a.complexTensors.real],[o.complexTensors.imag,a.complexTensors.imag]].map(function(o){var a=o[0],i=o[1],s=new BinaryOpProgram(n,e.shape,t.shape),u=r.makeOutputArray(s.outputShape,upcastType(a.dtype,i.dtype)),l=r.makeComplexComponentTensorHandle(e,a),c=r.makeComplexComponentTensorHandle(t,i);return r.compileAndRun(s,[l,c],u)}),s=i[0],u=i[1],l=this.complex(s,u);return s.dispose(),u.dispose(),l},e.prototype.makeComplexComponentTensorHandle=function(e,t){return{dataId:t.dataId,dtype:t.dtype,shape:e.shape}},e.prototype.addN=function(e){for(var t=e[0],n=1;n<e.length;n++)t=this.add(t,e[n]);return t},e.prototype.subtract=function(e,t){if(\"complex64\"===e.dtype&&\"complex64\"===t.dtype)return this.complexSeparableBinaryOp(e,t,SUB);if(this.shouldExecuteOnCPU([e,t]))return this.cpuBackend.subtract(e,t);var n=new BinaryOpProgram(SUB,e.shape,t.shape),r=this.makeOutputArray(n.outputShape,upcastType(e.dtype,t.dtype));return this.compileAndRun(n,[e,t],r)},e.prototype.pow=function(e,t){var n=new BinaryOpProgram(POW,e.shape,t.shape),r=n.getCustomSetupFunc(),o=this.makeOutputArray(n.outputShape,upcastType(e.dtype,t.dtype));return this.compileAndRun(n,[e,t],o,r)},e.prototype.ceil=function(e){var t=new UnaryOpProgram(e.shape,CEIL);return this.compileAndRun(t,[e])},e.prototype.floor=function(e){var t=new UnaryOpProgram(e.shape,FLOOR);return this.compileAndRun(t,[e])},e.prototype.sign=function(e){var t=new UnaryOpProgram(e.shape,SIGN);return this.compileAndRun(t,[e])},e.prototype.round=function(e){var t=new UnaryOpProgram(e.shape,ROUND);return this.compileAndRun(t,[e])},e.prototype.exp=function(e){var t=new UnaryOpProgram(e.shape,EXP);return this.compileAndRun(t,[e])},e.prototype.expm1=function(e){var t=new UnaryOpProgram(e.shape,EXPM1);return this.compileAndRun(t,[e])},e.prototype.log=function(e){var t=new UnaryOpProgram(e.shape,LOG),n=t.getCustomSetupFunc();return this.compileAndRun(t,[e],null,n)},e.prototype.log1p=function(e){var t=new UnaryOpProgram(e.shape,LOG1P);return this.compileAndRun(t,[e])},e.prototype.sqrt=function(e){var t=new UnaryOpProgram(e.shape,SQRT);return this.compileAndRun(t,[e])},e.prototype.rsqrt=function(e){var t=new UnaryOpProgram(e.shape,RSQRT);return this.compileAndRun(t,[e])},e.prototype.square=function(e){var t=new UnaryOpProgram(e.shape,SQUARE);return this.compileAndRun(t,[e])},e.prototype.reciprocal=function(e){var t=new UnaryOpProgram(e.shape,RECIPROCAL);return this.compileAndRun(t,[e])},e.prototype.relu=function(e){var t=new UnaryOpProgram(e.shape,RELU);return this.compileAndRun(t,[e])},e.prototype.elu=function(e){var t=new UnaryOpProgram(e.shape,ELU);return this.compileAndRun(t,[e])},e.prototype.eluDer=function(e,t){var n=new BinaryOpProgram(ELU_DER,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.selu=function(e){var t=new UnaryOpProgram(e.shape,SELU);return this.compileAndRun(t,[e])},e.prototype.int=function(e){var t=new UnaryOpProgram(e.shape,TO_INT),n=this.makeOutputArray(t.outputShape,\"int32\");return this.compileAndRun(t,[e],n)},e.prototype.clip=function(e,t,n){var r;return r=ENV.get(\"WEBGL_PACK_CLIP\")?new ClipPackedProgram(e.shape,t,n):new ClipProgram(e.shape,t,n),this.compileAndRun(r,[e])},e.prototype.abs=function(e){var t=new UnaryOpProgram(e.shape,ABS);return this.compileAndRun(t,[e])},e.prototype.complexAbs=function(e){var t=this.texData.get(e.dataId),n=new ComplexAbsProgram(e.shape),r=[this.makeComplexComponentTensorHandle(e,t.complexTensors.real),this.makeComplexComponentTensorHandle(e,t.complexTensors.imag)];return this.compileAndRun(n,r)},e.prototype.sigmoid=function(e){var t=new UnaryOpProgram(e.shape,SIGMOID);return this.compileAndRun(t,[e])},e.prototype.softplus=function(e){var t=new UnaryOpProgram(e.shape,SOFTPLUS);return this.compileAndRun(t,[e])},e.prototype.sin=function(e){var t=new UnaryOpProgram(e.shape,SIN);return this.compileAndRun(t,[e])},e.prototype.cos=function(e){var t=new UnaryOpProgram(e.shape,COS);return this.compileAndRun(t,[e])},e.prototype.tan=function(e){var t=new UnaryOpProgram(e.shape,TAN);return this.compileAndRun(t,[e])},e.prototype.asin=function(e){var t=new UnaryOpProgram(e.shape,ASIN);return this.compileAndRun(t,[e])},e.prototype.acos=function(e){var t=new UnaryOpProgram(e.shape,ACOS);return this.compileAndRun(t,[e])},e.prototype.atan=function(e){var t=new UnaryOpProgram(e.shape,ATAN);return this.compileAndRun(t,[e])},e.prototype.atan2=function(e,t){var n=new BinaryOpProgram(ATAN2,e.shape,t.shape);return this.compileAndRun(n,[e,t])},e.prototype.sinh=function(e){var t=new UnaryOpProgram(e.shape,SINH);return this.compileAndRun(t,[e])},e.prototype.cosh=function(e){var t=new UnaryOpProgram(e.shape,COSH);return this.compileAndRun(t,[e])},e.prototype.tanh=function(e){var t=new UnaryOpProgram(e.shape,TANH);return this.compileAndRun(t,[e])},e.prototype.asinh=function(e){var t=new UnaryOpProgram(e.shape,ASINH);return this.compileAndRun(t,[e])},e.prototype.acosh=function(e){var t=new UnaryOpProgram(e.shape,ACOSH),n=t.getCustomSetupFunc();return this.compileAndRun(t,[e],null,n)},e.prototype.atanh=function(e){var t=new UnaryOpProgram(e.shape,ATANH),n=t.getCustomSetupFunc();return this.compileAndRun(t,[e],null,n)},e.prototype.erf=function(e){var t=new UnaryOpProgram(e.shape,ERF);return this.compileAndRun(t,[e])},e.prototype.step=function(e,t){var n=new UnaryOpProgram(e.shape,STEP(t));return this.compileAndRun(n,[e])},e.prototype.conv2dWithIm2Row=function(e,t,n){var r=n.filterWidth,o=n.filterHeight,a=n.inChannels,i=n.outWidth,s=n.outHeight,u=r*o*a,l=s*i,c=[u,l],p=e.squeeze([0]),d=t.reshape([u,-1]),h=new Im2ColProgram(c,p.shape,n),f=this.compileAndRun(h,[p]),m=new MatMulPackedProgram(f.shape,d.shape,[l,n.outChannels],!0,!1);return this.compileAndRun(m,[f,d]).reshape([1,s,i,n.outChannels])},e.prototype.conv2d=function(e,t,n){if(ENV.get(\"WEBGL_CONV_IM2COL\")&&1===e.shape[0])return this.conv2dWithIm2Row(e,t,n);var r=new Conv2DProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv2dDerInput=function(e,t,n){var r=new Conv2DDerInputProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv2dDerFilter=function(e,t,n){var r=new Conv2DDerFilterProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.depthwiseConv2D=function(e,t,n){var r;return ENV.get(\"WEBGL_PACK_DEPTHWISECONV\")&&1===n.dilationWidth&&1===n.dilationHeight&&n.padInfo.left<=1&&n.strideWidth<=2&&n.outChannels/n.inChannels==1?(r=new DepthwiseConvPacked2DProgram(n),this.compileAndRun(r,[e,t],this.makePackedTensor(n.outShape,e.dtype))):(r=new DepthwiseConv2DProgram(n),this.compileAndRun(r,[e,t]))},e.prototype.depthwiseConv2DDerInput=function(e,t,n){var r=new DepthwiseConv2DDerInputProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.depthwiseConv2DDerFilter=function(e,t,n){var r=new DepthwiseConv2DDerFilterProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv3d=function(e,t,n){var r=new Conv3DProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv3dDerInput=function(e,t,n){var r=new Conv3DDerInputProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.conv3dDerFilter=function(e,t,n){var r=new Conv3DDerFilterProgram(n);return this.compileAndRun(r,[e,t])},e.prototype.maxPool=function(e,t){var n=new Pool2DProgram(t,\"max\",!1),r=this.makeOutputArray(n.outputShape,e.dtype);return this.compileAndRun(n,[e],r)},e.prototype.avgPool=function(e,t){var n=new Pool2DProgram(t,\"avg\",!1),r=this.makeOutputArray(n.outputShape,\"float32\");return this.compileAndRun(n,[e],r)},e.prototype.maxPoolBackprop=function(e,t,n,r){var o=new Pool2DProgram(r,\"max\",!0),a=this.compileAndRun(o,[t]),i=new MaxPool2DBackpropProgram(r),s=this.makeOutputArray(i.outputShape,t.dtype),u=this.compileAndRun(i,[e,a],s);return a.dispose(),u},e.prototype.avgPoolBackprop=function(e,t,n){var r=new AvgPool2DBackpropProgram(n),o=this.makeOutputArray(r.outputShape,t.dtype);return this.compileAndRun(r,[e],o)},e.prototype.cast=function(e,t){return castTensor(e,t,this)},e.prototype.reshape=function(e,t){return this.texData.get(e.dataId).isPacked&&!isReshapeFree(e.shape,t)?this.packedReshape(e,t):reshapeTensor(e,t)},e.prototype.resizeBilinear=function(e,t,n,r){var o=new ResizeBilinearProgram(e.shape,t,n,r);return this.compileAndRun(o,[e])},e.prototype.resizeBilinearBackprop=function(e,t,n){var r=new ResizeBilinearBackpropProgram(e,t,n);return this.compileAndRun(r,[e])},e.prototype.resizeNearestNeighbor=function(e,t,n,r){var o=new ResizeNearestNeighborProgram(e.shape,t,n,r);return this.compileAndRun(o,[e])},e.prototype.resizeNearestNeighborBackprop=function(e,t,n){var r=new ResizeNearestNeigborBackpropProgram(e,t,n);return this.compileAndRun(r,[e])},e.prototype.multinomial=function(e,t,n,r){var o=t?e:softmax(e),a=o.shape[0],i=o.shape[1],s=new MultinomialProgram(a,i,n),u=this.makeOutputArray(s.outputShape,\"int32\"),l=s.getCustomSetupFunc(r);return this.compileAndRun(s,[o],u,l)},e.prototype.oneHot=function(e,t,n,r){var o=new OneHotProgram(e.size,t,n,r);return this.compileAndRun(o,[e])},e.prototype.nonMaxSuppression=function(e,t,n,r,o){return warn(\"tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead\"),nonMaxSuppressionImpl(e.dataSync(),t.dataSync(),n,r,o)},e.prototype.cropAndResize=function(e,t,n,r,o,a){var i=new CropAndResizeProgram(e.shape,t.shape,r,o,a);return this.compileAndRun(i,[e,t,n])},e.prototype.depthToSpace=function(e,t,n){assert(t>1,\"blockSize should be > 1 for depthToSpace, but was: \"+t);var r=e.shape[0],o=(\"NHWC\"===n?e.shape[1]:e.shape[2])*t,a=(\"NHWC\"===n?e.shape[2]:e.shape[3])*t,i=(\"NHWC\"===n?e.shape[3]:e.shape[1])/(t*t),s=new DepthToSpaceProgram(\"NHWC\"===n?[r,o,a,i]:[r,i,o,a],t,n);return this.compileAndRun(s,[e])},e.prototype.split=function(e,t,n){return split(e,t,n)},e.prototype.scatterND=function(e,t,n){var r=calculateShapes(t,e,n),o=r.sliceRank,a=r.numUpdates,i=r.sliceSize,s=r.strides,u=r.outputSize,l=[u/i,i],c=e.reshape([a,o]),p=t.reshape([a,i]);if(0===u)return reshapeTensor(tensor([]),n);var d=scalar(0),h=new ScatterProgram(a,o,c.rank,p.rank,s,l);return this.compileAndRun(h,[p,c,d]).reshape(n)},e.prototype.sparseToDense=function(e,t,n,r){var o=calculateShapes(t,e,n),a=o.sliceRank,i=o.numUpdates,s=o.strides,u=o.outputSize,l=new ScatterProgram(i,a,e.rank,t.rank,s,[u,1],!1);return this.compileAndRun(l,[t,e,r]).reshape(n)},e.prototype.fft=function(e){return this.fftImpl(e,!1)},e.prototype.ifft=function(e){return this.fftImpl(e,!0)},e.prototype.fftImpl=function(e,t){var n=this.texData.get(e.dataId),r=new FFTProgram(COMPLEX_FFT.REAL,e.shape,t),o=new FFTProgram(COMPLEX_FFT.IMAG,e.shape,t),a=[this.makeComplexComponentTensorHandle(e,n.complexTensors.real),this.makeComplexComponentTensorHandle(e,n.complexTensors.imag)],i=this.compileAndRun(r,a),s=this.compileAndRun(o,a),u=this.complex(i,s).as2D(e.shape[0],e.shape[1]);return i.dispose(),s.dispose(),u},e.prototype.gatherND=function(e,t){var n=t.shape,r=n[n.length-1],o=prepareAndValidate(e,t),a=o[0],i=o[1],s=o[2],u=o[3],l=t.reshape([i,r]),c=e.reshape([e.size/s,s]),p=new GatherNDProgram(r,u,[i,s]);return this.compileAndRun(p,[c,l]).reshape(a)},e.prototype.makeOutputArray=function(e,t){return Tensor.make(e,{},t)},e.prototype.makePackedTensor=function(e,t){var n=Tensor.make(e,{},t);return this.texData.get(n.dataId).isPacked=!0,n},e.prototype.unpackTensor=function(e){var t=new UnpackProgram(e.shape);return this.compileAndRun(t,[e],Tensor.make(t.outputShape,{},e.dtype))},e.prototype.getBatchDim=function(e,t){return void 0===t&&(t=2),sizeFromShape(e.slice(0,e.length-t))},e.prototype.getRowsCols=function(e){if(0===e.length)throw Error(\"Cannot get rows and columns of an empty shape array.\");return[e.length>1?e[e.length-2]:1,e[e.length-1]]},e.prototype.packedReshape=function(e,t){var n=e.reshape([this.getBatchDim(e.shape)].concat(this.getRowsCols(e.shape))),r=[this.getBatchDim(t)].concat(this.getRowsCols(t)),o=new ReshapePackedProgram(r,n.shape);return this.compileAndRun(o,[n]).reshape(t)},e.prototype.compileAndRun=function(e,t,n,r,o){var a=this;if(void 0===o&&(o=!0),null==n&&(n=e.usesPackedTextures?this.makePackedTensor(e.outputShape,t[0].dtype):this.makeOutputArray(e.outputShape,t[0].dtype)),0===n.size)return this.texData.get(n.dataId).values=getTypedArrayFromDType(n.dtype,0),n;var i=t.map(function(t){if(\"complex64\"===t.dtype)throw new Error(\"GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.\");var n=a.texData.get(t.dataId);if(null==n.texture){if(!e.usesPackedTextures&&sizeFromShape(t.shape)<=ENV.get(\"WEBGL_SIZE_UPLOAD_UNIFORM\"))return{shape:t.shape,texData:null,isUniform:!0,uniformValues:a.readSync(t.dataId)};e.usesPackedTextures&&(n.isPacked=!0,n.shape=t.shape)}else if(!!n.isPacked!=!!e.usesPackedTextures){var r=void 0,o=void 0;n.isPacked?(r=new UnpackProgram(t.shape),o=a.compileAndRun(r,[t],Tensor.make(r.outputShape,{},t.dtype))):(r=new PackProgram(t.shape),o=a.compileAndRun(r,[t],a.makePackedTensor(t.shape,t.dtype))),n=a.texData.get(o.dataId),t=o}else if(n.isPacked&&!isReshapeFree(n.shape,t.shape)){a.delayedStorage=!1;var i=t.dataSync();a.delayedStorage=!0,t=Tensor.make(t.shape,{values:i},t.dtype),(n=a.texData.get(t.dataId)).isPacked=!0}return a.uploadToGPU(t.dataId),{shape:t.shape,texData:n,isUniform:!1}});this.uploadToGPU(n.dataId);var s,u={shape:n.shape,texData:this.texData.get(n.dataId),isUniform:!1},l=makeShaderKey(e,i,u),c=this.getAndSaveBinary(l,function(){return compileProgram(a.gpgpu,e,i,u)}),p=null!=this.activeTimers;if(p&&(s=this.startTimer()),runProgram(c,i,u,r),ENV.get(\"WEBGL_PAGING_ENABLED\")&&o&&this.numBytesInGPU>this.NUM_BYTES_BEFORE_PAGING)for(var d=this.numBytesInGPU-this.NUM_BYTES_BEFORE_PAGING;d>0&&this.lruDataGPU.length>0;){var h=this.lruDataGPU.shift(),f=this.texData.get(h),m=f.shape,g=f.dtype;d-=this.computeBytes(m,g),this.read(h)}return p&&(s=this.endTimer(s),this.activeTimers.push({name:e.constructor.name,query:this.getQueryTime(s)})),ENV.get(\"WEBGL_LAZILY_UNPACK\")||!this.texData.get(n.dataId).isPacked||e.isPackShader?n:this.unpackTensor(n)},e.prototype.getAndSaveBinary=function(e,t){return e in this.binaryCache||(this.binaryCache[e]=t()),this.binaryCache[e]},e.prototype.getTextureManager=function(){return this.textureManager},e.prototype.dispose=function(){if(!this.disposed){for(var e in this.binaryCache)this.gpgpu.deleteProgram(this.binaryCache[e].webGLProgram);this.textureManager.dispose(),this.canvas.remove(),null!=this.fromPixels2DContext&&this.fromPixels2DContext.canvas.remove(),this.gpgpuCreatedLocally&&this.gpgpu.dispose(),this.disposed=!0}},e.prototype.floatPrecision=function(){var e=this;return tidy(function(){return e.abs(scalar(1e-8)).get()>0?32:16})},e.prototype.uploadToGPU=function(e){var t,n=this.texData.get(e),r=n.shape,o=n.values,a=n.texture,i=n.usage,s=n.isPacked;if(null==a){var u,l=null!=this.activeTimers;l&&(u=performance.now());var c=getTextureShapeFromLogicalShape(r,s);n.texShape=c;var p=this.acquireTexture(e,c,i,s);if(n.texture=p,null!=o){if(s){var d=this.getBatchDim(r),h=1,f=1;r.length&&(h=(t=this.getRowsCols(r))[0],f=t[1]),this.gpgpu.uploadMatrixToPackedTexture(p,d,h,f,c[0],c[1],typedArrayToFloat32(o))}else this.gpgpu.uploadMatrixToTexture(p,c[0],c[1],typedArrayToFloat32(o));n.values=null,l&&(this.uploadWaitMs+=performance.now()-u)}}else{ENV.get(\"WEBGL_PAGING_ENABLED\")&&this.lruDataGPU.indexOf(e)>=0&&(this.lruDataGPU.splice(this.lruDataGPU.indexOf(e),1),this.lruDataGPU.push(e))}},e.prototype.convertAndCacheOnCPU=function(e,t){var n=this.delayedStorage,r=this.texData.get(e),o=r.texture,a=r.texShape,i=r.dtype,s=r.usage,u=r.isPacked;return n&&null!=o&&(this.releaseTexture(e,o,a,s,u),r.texture=null,r.texShape=null),r.usage=TextureUsage.UPLOAD,null!=t&&(r.values=float32ToTypedArray(t,i)),r.values},e.prototype.releaseTexture=function(e,t,n,r,o){var a=this.texData.get(e),i=a.shape,s=a.dtype;if(ENV.get(\"WEBGL_PAGING_ENABLED\")){var u=this.lruDataGPU.indexOf(e);u>=0&&this.lruDataGPU.splice(u,1)}this.numBytesInGPU-=this.computeBytes(i,s),this.textureManager.releaseTexture(t,n,r,o)},e.prototype.acquireTexture=function(e,t,n,r){var o=this.texData.get(e),a=o.shape,i=o.dtype;return ENV.get(\"WEBGL_PAGING_ENABLED\")&&this.lruDataGPU.push(e),this.numBytesInGPU+=this.computeBytes(a,i),this.textureManager.acquireTexture(t,n,r)},e.prototype.computeBytes=function(e,t){return sizeFromShape(e)*bytesPerElement(t)},e}();function float32ToTypedArray(e,t){if(\"float32\"===t||\"complex64\"===t)return e;if(\"int32\"===t||\"bool\"===t){for(var n=\"int32\"===t?new Int32Array(e.length):new Uint8Array(e.length),r=0;r<n.length;++r)n[r]=Math.round(e[r]);return n}throw new Error(\"Unknown dtype \"+t)}function typedArrayToFloat32(e){return e instanceof Float32Array?e:new Float32Array(e)}function neg_(e){var t=convertToTensor(e,\"x\",\"neg\");return ENV.engine.runKernel(function(e){return e.neg(t)},{$x:t},function(e){return{$x:function(){return e.neg()}}})}function ceil_(e){var t=convertToTensor(e,\"x\",\"ceil\");return ENV.engine.runKernel(function(e){return e.ceil(t)},{$x:t},function(e){return{$x:function(){return zerosLike(e)}}})}function floor_(e){var t=convertToTensor(e,\"x\",\"floor\");return ENV.engine.runKernel(function(e){return e.floor(t)},{$x:t},function(e){return{$x:function(){return zerosLike(e)}}})}function sign_(e){var t=convertToTensor(e,\"x\",\"sign\");return ENV.engine.runKernel(function(e){return e.sign(t)},{$x:t},function(e){return{$x:function(){return zerosLike(e)}}})}function round_(e){var t=convertToTensor(e,\"x\",\"round\");return ENV.engine.runKernel(function(e){return e.round(t)},{$x:t},function(e){return{$x:function(){return zerosLike(e)}}})}function exp_(e){var t=convertToTensor(e,\"x\",\"exp\");return ENV.engine.runKernel(function(e,n){return n(e.exp(t))},{$x:t},function(e,t){var n=t[0];return{$x:function(){return e.mulStrict(n)}}})}function expm1_(e){var t=convertToTensor(e,\"x\",\"expm1\");return ENV.engine.runKernel(function(e){return e.expm1(t)},{$x:t},function(e){return{$x:function(){return e.mulStrict(t.exp())}}})}function log_(e){var t=convertToTensor(e,\"x\",\"log\");return ENV.engine.runKernel(function(e){return e.log(t)},{$x:t},function(e){return{$x:function(){return e.divStrict(t.toFloat())}}})}function log1p_(e){var t=convertToTensor(e,\"x\",\"log1p\");return ENV.engine.runKernel(function(e){return e.log1p(t)},{$x:t},function(e){return{$x:function(){return e.div(t.add(1))}}})}function sqrt_(e){var t=convertToTensor(e,\"x\",\"sqrt\");return ENV.engine.runKernel(function(e){return e.sqrt(t)},{$x:t},function(e){return{$x:function(){return e.div(t.toFloat().sqrt().mul(2))}}})}function rsqrt_(e){var t=convertToTensor(e,\"x\",\"rsqrt\");return ENV.engine.runKernel(function(e){return e.rsqrt(t)},{$x:t},function(e){return{$x:function(){return e.div(t.pow(1.5).mul(2)).neg()}}})}function square_(e){var t=convertToTensor(e,\"x\",\"square\");return ENV.engine.runKernel(function(e){return e.square(t)},{$x:t},function(e){return{$x:function(){return e.mul(t.toFloat().mul(2))}}})}function reciprocal_(e){var t=convertToTensor(e,\"x\",\"reciprocal\");return ENV.engine.runKernel(function(e){return e.reciprocal(t)},{$x:t},function(e){return{$x:function(){return e.divStrict(t.square().neg())}}})}function abs_(e){var t=convertToTensor(e,\"x\",\"abs\");if(\"complex64\"===t.dtype)return ENV.engine.runKernel(function(e){return e.complexAbs(t)},{$x:t});return ENV.engine.runKernel(function(e){return e.abs(t)},{$x:t},function(e){return{$x:function(){return e.mulStrict(t.toFloat().step(-1))}}})}function clipByValue_(e,t,n){var r=convertToTensor(e,\"x\",\"clipByValue\");assert(t<=n,\"Error in clip: min (\"+t+\") must be less than or equal to max (\"+n+\").\");return ENV.engine.runKernel(function(e){return e.clip(r,t,n)},{$x:r},function(e){return{$x:function(){return e.where(r.greaterEqual(t).logicalAnd(r.lessEqual(n)),zerosLike(e))}}})}function sigmoid_(e){var t=convertToTensor(e,\"x\",\"sigmoid\");return ENV.engine.runKernel(function(e,n){return n(e.sigmoid(t))},{$x:t},function(e,t){var n=t[0];return{$x:function(){return e.mul(n.mul(scalar(1).sub(n)))}}})}function logSigmoid_(e){var t=convertToTensor(e,\"x\",\"logSigmoid\");return ENV.engine.runKernel(function(e){return e.softplus(t.neg()).neg()},{$x:t},function(e){return{$x:function(){return e.mulStrict(t.neg().sigmoid())}}})}function softplus_(e){var t=convertToTensor(e,\"x\",\"softplus\");return ENV.engine.runKernel(function(e){return e.softplus(t)},{$x:t},function(e){return{$x:function(){return e.mulStrict(t.sigmoid())}}})}function sin_(e){var t=convertToTensor(e,\"x\",\"sin\");return ENV.engine.runKernel(function(e){return e.sin(t)},{$x:t},function(e){return{$x:function(){return t.toFloat().cos().mulStrict(e)}}})}function cos_(e){var t=convertToTensor(e,\"x\",\"cos\");return ENV.engine.runKernel(function(e){return e.cos(t)},{$x:t},function(e){return{$x:function(){return t.toFloat().sin().neg().mulStrict(e)}}})}function tan_(e){var t=convertToTensor(e,\"x\",\"tan\");return ENV.engine.runKernel(function(e){return e.tan(t)},{$x:t},function(e){return{$x:function(){return e.divStrict(t.cos().square())}}})}function asin_(e){var t=convertToTensor(e,\"x\",\"asin\");return ENV.engine.runKernel(function(e){return e.asin(t)},{$x:t},function(e){return{$x:function(){return e.divStrict(scalar(1).sub(t.toFloat().square()).sqrt())}}})}function acos_(e){var t=convertToTensor(e,\"x\",\"acos\");return ENV.engine.runKernel(function(e){return e.acos(t)},{$x:t},function(e){return{$x:function(){return e.divStrict(scalar(1).sub(t.toFloat().square()).sqrt()).neg()}}})}function atan_(e){var t=convertToTensor(e,\"x\",\"atan\");return ENV.engine.runKernel(function(e){return e.atan(t)},{$x:t},function(e){return{$x:function(){return e.div(t.toFloat().square().add(1))}}})}function sinh_(e){var t=convertToTensor(e,\"x\",\"sinh\");return ENV.engine.runKernel(function(e){return e.sinh(t)},{$x:t},function(e){return{$x:function(){return t.toFloat().cosh().mulStrict(e)}}})}function cosh_(e){var t=convertToTensor(e,\"x\",\"cosh\");return ENV.engine.runKernel(function(e){return e.cosh(t)},{$x:t},function(e){return{$x:function(){return t.toFloat().sinh().mulStrict(e)}}})}function tanh_(e){var t=convertToTensor(e,\"x\",\"tanh\");return ENV.engine.runKernel(function(e,n){return n(e.tanh(t))},{$x:t},function(e,t){var n=t[0];return{$x:function(){return scalar(1).sub(n.square()).mulStrict(e)}}})}function asinh_(e){var t=convertToTensor(e,\"x\",\"asinh\");return ENV.engine.runKernel(function(e){return e.asinh(t)},{$x:t},function(e){return{$x:function(){return e.divStrict(scalar(1).add(t.toFloat().square()).sqrt())}}})}function acosh_(e){var t=convertToTensor(e,\"x\",\"acosh\");return ENV.engine.runKernel(function(e){return e.acosh(t)},{$x:t},function(e){return{$x:function(){return e.divStrict(t.toFloat().square().sub(1).sqrt())}}})}function atanh_(e){var t=convertToTensor(e,\"x\",\"atanh\");return ENV.engine.runKernel(function(e){return e.atanh(t)},{$x:t},function(e){return{$x:function(){return e.div(scalar(1).sub(t.toFloat().square()))}}})}function erf_(e){var t=convertToTensor(e,\"x\",\"erf\");assert(\"int32\"===t.dtype||\"float32\"===t.dtype,\"Input dtype must be `int32` or `float32`.\"),\"int32\"===t.dtype&&(t=t.toFloat());return ENV.engine.runKernel(function(e){return e.erf(t)},{$x:t},function(e){return{$x:function(){return e.mul(t.square().neg().exp().mul(2/Math.sqrt(Math.PI)))}}})}function step_(e,t){void 0===t&&(t=0);var n=convertToTensor(e,\"x\",\"step\");return ENV.engine.runKernel(function(e){return e.step(n,t)},{$x:n},function(e){return{$x:function(){return zerosLike(e)}}})}ENV.get(\"IS_BROWSER\")&&ENV.registerBackend(\"webgl\",function(){return new MathBackendWebGL},2,setTensorTracker);var abs=op({abs_:abs_}),acos=op({acos_:acos_}),acosh=op({acosh_:acosh_}),asin=op({asin_:asin_}),asinh=op({asinh_:asinh_}),atan=op({atan_:atan_}),atanh=op({atanh_:atanh_}),ceil=op({ceil_:ceil_}),clipByValue=op({clipByValue_:clipByValue_}),cos=op({cos_:cos_}),cosh=op({cosh_:cosh_}),erf=op({erf_:erf_}),exp=op({exp_:exp_}),expm1=op({expm1_:expm1_}),floor=op({floor_:floor_}),log$1=op({log_:log_}),log1p=op({log1p_:log1p_}),logSigmoid=op({logSigmoid_:logSigmoid_}),neg=op({neg_:neg_}),reciprocal=op({reciprocal_:reciprocal_}),round=op({round_:round_}),rsqrt=op({rsqrt_:rsqrt_}),sigmoid=op({sigmoid_:sigmoid_}),sign=op({sign_:sign_}),sin=op({sin_:sin_}),sinh=op({sinh_:sinh_}),softplus=op({softplus_:softplus_}),sqrt=op({sqrt_:sqrt_}),square=op({square_:square_}),step=op({step_:step_}),tan=op({tan_:tan_}),tanh$1=op({tanh_:tanh_});function batchNormalization2d_(e,t,n,r,o,a){void 0===r&&(r=.001);var i,s,u=convertToTensor(e,\"x\",\"batchNormalization\"),l=convertToTensor(t,\"mean\",\"batchNormalization\"),c=convertToTensor(n,\"variance\",\"batchNormalization\");return null!=o&&(i=convertToTensor(o,\"scale\",\"batchNormalization\")),null!=a&&(s=convertToTensor(a,\"offset\",\"batchNormalization\")),assert(2===u.rank,\"Error in batchNormalization3D: x must be rank 3 but got rank \"+u.rank+\".\"),assert(2===l.rank||1===l.rank,\"Error in batchNormalization2D: mean must be rank 2 or rank 1 but got rank \"+l.rank+\".\"),assert(2===c.rank||1===c.rank,\"Error in batchNormalization2D: variance must be rank 2 or rank 1 but got rank \"+c.rank+\".\"),null!=i&&assert(2===i.rank||1===i.rank,\"Error in batchNormalization2D: scale must be rank 2 or rank 1 but got rank \"+i.rank+\".\"),null!=s&&assert(2===s.rank||1===s.rank,\"Error in batchNormalization2D: offset must be rank 2 or rank 1 but got rank \"+s.rank+\".\"),batchNormalization(u,l,c,r,i,s)}function batchNormalization3d_(e,t,n,r,o,a){void 0===r&&(r=.001);var i,s,u=convertToTensor(e,\"x\",\"batchNormalization\"),l=convertToTensor(t,\"mean\",\"batchNormalization\"),c=convertToTensor(n,\"variance\",\"batchNormalization\");return null!=o&&(i=convertToTensor(o,\"scale\",\"batchNormalization\")),null!=a&&(s=convertToTensor(a,\"offset\",\"batchNormalization\")),assert(3===u.rank,\"Error in batchNormalization3D: x must be rank 3 but got rank \"+u.rank+\".\"),assert(3===l.rank||1===l.rank,\"Error in batchNormalization3D: mean must be rank 3 or rank 1 but got rank \"+l.rank+\".\"),assert(3===c.rank||1===c.rank,\"Error in batchNormalization3D: variance must be rank 3 or rank 1 but got rank \"+c.rank+\".\"),null!=i&&assert(3===i.rank||1===i.rank,\"Error in batchNormalization3D: scale must be rank 3 or rank 1 but got rank \"+i.rank+\".\"),null!=s&&assert(3===s.rank||1===s.rank,\"Error in batchNormalization3D: offset must be rank 3 or rank 1 but got rank \"+s.rank+\".\"),batchNormalization(u,l,c,r,i,s)}function batchNormalization4d_(e,t,n,r,o,a){void 0===r&&(r=.001);var i,s,u=convertToTensor(e,\"x\",\"batchNormalization\"),l=convertToTensor(t,\"mean\",\"batchNormalization\"),c=convertToTensor(n,\"variance\",\"batchNormalization\");return null!=o&&(i=convertToTensor(o,\"scale\",\"batchNormalization\")),null!=a&&(s=convertToTensor(a,\"offset\",\"batchNormalization\")),assert(4===u.rank,\"Error in batchNormalization4D: x must be rank 4 but got rank \"+u.rank+\".\"),assert(4===l.rank||1===l.rank,\"Error in batchNormalization4D: mean must be rank 4 or rank 1 but got rank \"+l.rank+\".\"),assert(4===c.rank||1===c.rank,\"Error in batchNormalization4D: variance must be rank 4 or rank 1 but got rank \"+c.rank+\".\"),null!=i&&assert(4===i.rank||1===i.rank,\"Error in batchNormalization4D: scale must be rank 4 or rank 1 but got rank \"+i.rank+\".\"),null!=s&&assert(4===s.rank||1===s.rank,\"Error in batchNormalization4D: offset must be rank 4 or rank 1 but got rank \"+s.rank+\".\"),batchNormalization(u,l,c,r,i,s)}function batchNormalization_(e,t,n,r,o,a){void 0===r&&(r=.001);var i,s,u,l=convertToTensor(e,\"x\",\"batchNormalization\"),c=convertToTensor(t,\"mean\",\"batchNormalization\"),p=convertToTensor(n,\"variance\",\"batchNormalization\");null!=o&&(i=convertToTensor(o,\"scale\",\"batchNormalization\")),null!=a&&(s=convertToTensor(a,\"offset\",\"batchNormalization\")),assert(c.rank===p.rank,\"Batch normalization gradient requires mean and variance to have equal ranks.\"),assert(null==s||c.rank===s.rank,\"Batch normalization gradient requires mean and offset to have equal ranks.\"),assert(null==i||c.rank===i.rank,\"Batch normalization gradient requires mean and scale to have equal ranks.\"),u=0===l.rank||1===l.rank?l.as4D(1,1,1,l.size):2===l.rank?l.as4D(1,1,l.shape[0],l.shape[1]):3===l.rank?l.as4D(1,l.shape[0],l.shape[1],l.shape[2]):l;return ENV.engine.runKernel(function(e){return e.batchNormalization(u,batchnormReshape4D(c),batchnormReshape4D(p),r,batchnormReshape4D(i),batchnormReshape4D(s))},{$x:l,$mean:c,$variance:p,$scale:i,$offset:s},function(e){var t=null==i?scalar(1):i,n=getReductionAxes(c.shape,u.shape),o=[];if(1===c.rank){for(var a=0;a<u.shape.length-1;++a)o.push(u.shape[a]);o.push(1)}var s=l.sub(c),d=e.mul(t),h=rsqrt(p.add(scalar(r))),f=h.mul(h).mul(h).mul(scalar(-.5));return{$x:function(){return 1===c.rank?e.mul(tile(h.as4D(1,1,1,c.shape[0]),o)).mul(t).reshape(l.shape):e.mul(h).mul(t).reshape(l.shape)},$mean:function(){var e=h.mul(scalar(-1)).mul(d);return 1===c.rank&&(e=e.sum(n)),e.reshape(c.shape)},$variance:function(){var e=f.mul(s).mul(d);return 1===c.rank&&(e=e.sum(n)),e.reshape(c.shape)},$scale:function(){var t=s.mul(h),r=e.mul(t);return 1===c.rank&&(r=r.sum(n)),r.reshape(c.shape)},$offset:function(){var t=e;return 1===c.rank&&(t=t.sum(n)),t.reshape(c.shape)}}}).reshape(l.shape)}function batchnormReshape4D(e){return null==e?null:0===e.rank?e.as1D():1===e.rank?e:2===e.rank?e.as4D(1,1,e.shape[0],e.shape[1]):3===e.rank?e.as4D(1,e.shape[0],e.shape[1],e.shape[2]):e}var batchNormalization2d=op({batchNormalization2d_:batchNormalization2d_}),batchNormalization3d=op({batchNormalization3d_:batchNormalization3d_}),batchNormalization4d=op({batchNormalization4d_:batchNormalization4d_}),batchNormalization=op({batchNormalization_:batchNormalization_});function computePool2DInfo(e,t,n,r,o,a,i){void 0===i&&(i=\"channelsLast\");var s,u=parseTupleParam(t),l=u[0],c=u[1];if(\"channelsLast\"===i)s=[l,c,e[3],e[3]];else{if(\"channelsFirst\"!==i)throw new Error(\"Unknown dataFormat \"+i);s=[l,c,e[1],e[1]]}return computeConv2DInfo(e,s,n,r,o,a,!1,i)}function computeConv2DInfo(e,t,n,r,o,a,i,s){void 0===i&&(i=!1),void 0===s&&(s=\"channelsLast\");var u=[-1,-1,-1,-1],l=u[0],c=u[1],p=u[2],d=u[3];if(\"channelsLast\"===s)l=e[0],c=e[1],p=e[2],d=e[3];else{if(\"channelsFirst\"!==s)throw new Error(\"Unknown dataFormat \"+s);l=e[0],d=e[1],c=e[2],p=e[3]}var h,f=t[0],m=t[1],g=t[3],v=parseTupleParam(n),y=v[0],x=v[1],T=parseTupleParam(r),w=T[0],E=T[1],S=getEffectiveFilterSize(f,w),b=getEffectiveFilterSize(m,E),C=getPadAndOutInfo(o,c,p,y,x,S,b,a),A=C.padInfo,_=C.outHeight,N=C.outWidth,R=i?g*d:g;return\"channelsFirst\"===s?h=[l,R,_,N]:\"channelsLast\"===s&&(h=[l,_,N,R]),{batchSize:l,dataFormat:s,inHeight:c,inWidth:p,inChannels:d,outHeight:_,outWidth:N,outChannels:R,padInfo:A,strideHeight:y,strideWidth:x,filterHeight:f,filterWidth:m,effectiveFilterHeight:S,effectiveFilterWidth:b,dilationHeight:w,dilationWidth:E,inShape:e,outShape:h,filterShape:t}}function computeConv3DInfo(e,t,n,r,o,a,i){void 0===a&&(a=!1),void 0===i&&(i=\"channelsLast\");var s=[-1,-1,-1,-1,-1],u=s[0],l=s[1],c=s[2],p=s[3],d=s[4];if(\"channelsLast\"===i)u=e[0],l=e[1],c=e[2],p=e[3],d=e[4];else{if(\"channelsFirst\"!==i)throw new Error(\"Unknown dataFormat \"+i);u=e[0],d=e[1],l=e[2],c=e[3],p=e[4]}var h,f=t[0],m=t[1],g=t[2],v=t[4],y=parse3TupleParam(n),x=y[0],T=y[1],w=y[2],E=parse3TupleParam(r),S=E[0],b=E[1],C=E[2],A=get3DPadAndOutInfo(o,l,c,p,x,T,w,getEffectiveFilterSize(f,S),getEffectiveFilterSize(m,b),getEffectiveFilterSize(g,C)),_=A.padInfo,N=A.outDepth,R=A.outHeight,k=A.outWidth,I=a?v*d:v;return\"channelsFirst\"===i?h=[u,I,N,R,k]:\"channelsLast\"===i&&(h=[u,N,R,k,I]),{batchSize:u,dataFormat:i,inDepth:l,inHeight:c,inWidth:p,inChannels:d,outDepth:N,outHeight:R,outWidth:k,outChannels:I,padInfo:_,strideDepth:x,strideHeight:T,strideWidth:w,filterDepth:f,filterHeight:m,filterWidth:g,dilationDepth:S,dilationHeight:b,dilationWidth:C,inShape:e,outShape:h,filterShape:t}}function computeOutputShape3D(e,t,n,r,o,a){null==o&&(o=computeDefaultPad(e,t,r));var i=e[0],s=e[1],u=conditionalRound((i-t+2*o)/r+1,a);assert(isInt(u),\"The output # of rows (\"+u+\") must be an integer. Change the stride and/or zero pad parameters\");var l=conditionalRound((s-t+2*o)/r+1,a);return assert(isInt(l),\"The output # of columns (\"+l+\") must be an integer. Change the stride and/or zero pad parameters\"),[u,l,n]}function computeDefaultPad(e,t,n,r){void 0===r&&(r=1);var o=getEffectiveFilterSize(t,r);return Math.floor((e[0]*(n-1)-n+o)/2)}function parseTupleParam(e){return\"number\"==typeof e?[e,e]:e}function parse3TupleParam(e){return\"number\"==typeof e?[e,e,e]:e}function getEffectiveFilterSize(e,t){return t<=1?e:e+(e-1)*(t-1)}function getPadAndOutInfo(e,t,n,r,o,a,i,s){var u,l,c;if(\"number\"==typeof e){u={top:e,bottom:e,left:e,right:e,type:0===e?\"VALID\":\"NUMBER\"};var p=computeOutputShape3D([t,n,1],a,1,r,e,s);l=p[0],c=p[1]}else if(\"same\"===e){var d=((l=Math.ceil(t/r))-1)*r+a-t,h=((c=Math.ceil(n/o))-1)*o+i-n,f=Math.floor(d/2),m=d-f,g=Math.floor(h/2);u={top:f,bottom:m,left:g,right:h-g,type:\"SAME\"}}else{if(\"valid\"!==e)throw Error(\"Unknown padding parameter: \"+e);u={top:0,bottom:0,left:0,right:0,type:\"VALID\"},l=Math.ceil((t-a+1)/r),c=Math.ceil((n-i+1)/o)}return{padInfo:u,outHeight:l,outWidth:c}}function get3DPadAndOutInfo(e,t,n,r,o,a,i,s,u,l){var c,p,d,h;if(\"same\"===e){var f=((p=Math.ceil(t/o))-1)*o+s-t,m=((d=Math.ceil(n/a))-1)*a+u-n,g=((h=Math.ceil(r/i))-1)*i+l-r,v=Math.floor(f/2),y=f-v,x=Math.floor(m/2),T=m-x,w=Math.floor(g/2);c={top:x,bottom:T,left:w,right:g-w,front:v,back:y,type:\"SAME\"}}else{if(\"valid\"!==e)throw Error(\"Unknown padding parameter: \"+e);c={top:0,bottom:0,left:0,right:0,front:0,back:0,type:\"VALID\"},p=Math.ceil((t-s+1)/o),d=Math.ceil((n-u+1)/a),h=Math.ceil((r-l+1)/i)}return{padInfo:c,outDepth:p,outHeight:d,outWidth:h}}function conditionalRound(e,t){if(!t)return e;switch(t){case\"round\":return Math.round(e);case\"ceil\":return Math.ceil(e);case\"floor\":return Math.floor(e);default:throw new Error(\"Unknown roundingMode \"+t)}}function tupleValuesAreOne(e){var t=parseTupleParam(e),n=t[0],r=t[1];return 1===n&&1===r}function eitherStridesOrDilationsAreOne(e,t){return tupleValuesAreOne(e)||tupleValuesAreOne(t)}function matMul_(e,t,n,r){var o;void 0===n&&(n=!1),void 0===r&&(r=!1);var a=convertToTensor(e,\"a\",\"matMul\"),i=convertToTensor(t,\"b\",\"matMul\");a=(o=makeTypesMatch(a,i))[0],i=o[1];var s=n?a.shape[a.rank-2]:a.shape[a.rank-1],u=r?i.shape[i.rank-1]:i.shape[i.rank-2],l=n?a.shape[a.rank-1]:a.shape[a.rank-2],c=r?i.shape[i.rank-2]:i.shape[i.rank-1],p=a.shape.slice(0,-2),d=i.shape.slice(0,-2),h=sizeFromShape(p),f=sizeFromShape(d);assert(a.rank>=2&&i.rank>=2&&a.rank===i.rank,\"Error in matMul: inputs must have the same rank of at least 2, got ranks \"+a.rank+\" and \"+i.rank+\".\"),assert(arraysEqual(p,d),\"Error in matMul: outer dimensions (\"+p+\") and (\"+d+\") of Tensors with shapes \"+a.shape+\" and \"+i.shape+\" must match.\"),assert(s===u,\"Error in matMul: inner shapes (\"+s+\") and (\"+u+\") of Tensors with shapes \"+a.shape+\" and \"+i.shape+\" and transposeA=\"+n+\" and transposeB=\"+r+\" must match.\");var m=a.shape.slice(0,-2).concat([l,c]),g=n?a.as3D(h,s,l):a.as3D(h,l,s),v=r?i.as3D(f,c,u):i.as3D(f,u,c);return ENV.engine.runKernel(function(e){return e.batchMatMul(g,v,n,r)},{$a:g,$b:v},function(e){return n||r?!n&&r?{$a:function(){return e.matMul(v,!1,!1)},$b:function(){return e.matMul(g,!0,!1)}}:n&&!r?{$a:function(){return v.matMul(e,!1,!0)},$b:function(){return g.matMul(e,!1,!1)}}:{$a:function(){return v.matMul(e,!0,!0)},$b:function(){return e.matMul(g,!0,!0)}}:{$a:function(){return e.matMul(v,!1,!0)},$b:function(){return g.matMul(e,!0,!1)}}}).reshape(m)}function outerProduct_(e,t){var n=convertToTensor(e,\"v1\",\"outerProduct\"),r=convertToTensor(t,\"v2\",\"outerProduct\");return assert(1===n.rank&&1===r.rank,\"Error in outerProduct: inputs must be rank 1, but got ranks \"+n.rank+\" and \"+r.rank+\".\"),n.as2D(-1,1).matMul(r.as2D(1,-1))}function dot_(e,t){var n=convertToTensor(e,\"t1\",\"dot\"),r=convertToTensor(t,\"t2\",\"dot\");assert(!(1!==n.rank&&2!==n.rank||1!==r.rank&&2!==r.rank),\"Error in dot: inputs must all be rank 1 or 2, but got ranks \"+n.rank+\" and \"+r.rank+\".\");var o=1===n.rank?n.size:n.shape[1],a=1===r.rank?r.size:r.shape[0];return assert(o===a,\"Error in dot: inner dimensions of inputs must match, but got \"+o+\" and \"+a+\".\"),1===n.rank&&1===r.rank?n.as2D(1,-1).matMul(r.as2D(-1,1)).asScalar():1===n.rank&&2===r.rank?n.as2D(1,-1).matMul(r.as2D(r.shape[0],r.shape[1])).as1D():2===n.rank&&1===r.rank?n.matMul(r.as2D(-1,1)).as1D():n.matMul(r.as2D(r.shape[0],r.shape[1]))}var matMul=op({matMul_:matMul_}),dot=op({dot_:dot_}),outerProduct=op({outerProduct_:outerProduct_});function conv1d_(e,t,n,r,o,a,i){void 0===o&&(o=\"NWC\"),void 0===a&&(a=1);var s=convertToTensor(e,\"x\",\"conv1d\"),u=convertToTensor(t,\"filter\",\"conv1d\"),l=s,c=!1;2===s.rank&&(c=!0,l=s.as3D(1,s.shape[0],s.shape[1])),assert(3===l.rank,\"Error in conv1d: input must be rank 3, but got rank \"+l.rank+\".\"),assert(3===u.rank,\"Error in conv1d: filter must be rank 3, but got rank \"+u.rank+\".\"),null!=i&&assert(isInt(r),\"Error in conv1d: pad must be an integer when using, dimRoundingMode \"+i+\" but got pad \"+r+\".\"),assert(l.shape[2]===u.shape[1],\"Error in conv1d: depth of input (\"+l.shape[2]+\") must match input depth for filter \"+u.shape[1]+\".\"),assert(eitherStridesOrDilationsAreOne(n,a),\"Error in conv1D: Either stride or dilation must be 1. Got stride \"+n+\" and dilation '\"+a+\"'\"),assert(\"NWC\"===o,\"Error in conv1d: got dataFormat of \"+o+\" but only NWC is currently supported.\");var p=u.as4D(1,u.shape[0],u.shape[1],u.shape[2]),d=l.as4D(l.shape[0],1,l.shape[1],l.shape[2]),h=conv2d(d,p,[1,n],r,\"NHWC\",[1,a],i);return c?h.as2D(h.shape[2],h.shape[3]):h.as3D(h.shape[0],h.shape[2],h.shape[3])}function conv2d_(e,t,n,r,o,a,i){void 0===o&&(o=\"NHWC\"),void 0===a&&(a=[1,1]);var s=convertToTensor(e,\"x\",\"conv2d\"),u=convertToTensor(t,\"filter\",\"conv2d\"),l=s,c=!1;3===s.rank&&(c=!0,l=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),assert(4===l.rank,\"Error in conv2d: input must be rank 4, but got rank \"+l.rank+\".\"),assert(4===u.rank,\"Error in conv2d: filter must be rank 4, but got rank \"+u.rank+\".\"),null!=i&&assert(isInt(r),\"Error in conv2d: pad must be an integer when using, dimRoundingMode \"+i+\" but got pad \"+r+\".\"),assert(l.shape[3]===u.shape[2],\"Error in conv2d: depth of input (\"+l.shape[3]+\") must match input depth for filter \"+u.shape[2]+\".\"),assert(eitherStridesOrDilationsAreOne(n,a),\"Error in conv2D: Either strides or dilations must be 1. Got strides \"+n+\" and dilations '\"+a+\"'\"),assert(\"NHWC\"===o,\"Error in conv2d: got dataFormat of \"+o+\" but only NHWC is currently supported.\");var p,d=computeConv2DInfo(l.shape,u.shape,n,a,r,i);if(1!==d.filterHeight||1!==d.filterWidth||1!==d.dilationHeight||1!==d.dilationWidth||1!==d.strideHeight||1!==d.strideWidth||\"SAME\"!==d.padInfo.type&&\"VALID\"!==d.padInfo.type){p=ENV.engine.runKernel(function(e){return e.conv2d(l,u,d)},{x:l,$filter:u},function(e){return assert(tupleValuesAreOne(a),\"Error in gradient of conv2D: dilation rates greater than 1 are notyet supported in gradients. Got dilations '\"+a+\"'\"),{x:function(){return conv2dDerInput_(l.shape,e,u,n,r)},$filter:function(){return conv2dDerFilter_(l,e,u.shape,n,r)}}})}else{var h=l.reshape([-1,d.inChannels]),f=u.reshape([d.inChannels,d.outChannels]);p=matMul(h,f).reshape(d.outShape)}return c?p.as3D(p.shape[1],p.shape[2],p.shape[3]):p}function conv2dDerInput_(e,t,n,r,o,a){assert(e.length===t.rank,\"Length of inShape (\"+e.length+\") and rank of dy (\"+t.rank+\") must match\");var i=e,s=t,u=!1;3===t.rank&&(u=!0,s=t.as4D(1,t.shape[0],t.shape[1],t.shape[2]),i=[1,e[0],e[1],e[2]]);var l=i[3],c=s.shape[3];assert(4===i.length,\"Error in conv2dDerInput: inShape must be length 4, but got length \"+i.length+\".\"),assert(4===s.rank,\"Error in conv2dDerInput: dy must be rank 4, but got rank \"+s.rank),assert(4===n.rank,\"Error in conv2dDerInput: filter must be rank 4, but got rank \"+n.rank),assert(l===n.shape[2],\"Error in conv2dDerInput: depth of input (\"+l+\") must match input depth for filter \"+n.shape[2]+\".\"),assert(c===n.shape[3],\"Error in conv2dDerInput: depth of output (\"+c+\") must match output depth for filter \"+n.shape[3]+\".\"),null!=a&&assert(isInt(o),\"Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode \"+a+\" but got pad \"+o+\".\");var p=computeConv2DInfo(i,n.shape,r,1,o,a),d=ENV.engine.runKernel(function(e){return e.conv2dDerInput(s,n,p)},{dy4D:s,filter:n},function(e){return{dy4D:function(){return conv2d(e,n,r,o,\"NHWC\",1,a)},filter:function(){return conv2dDerFilter(e,s,n.shape,r,o,a)}}});return u?d.as3D(d.shape[1],d.shape[2],d.shape[3]):d}function conv2dDerFilter_(e,t,n,r,o,a){var i=e;3===e.rank&&(i=e.as4D(1,e.shape[0],e.shape[1],e.shape[2]));var s=t;3===s.rank&&(s=t.as4D(1,t.shape[0],t.shape[1],t.shape[2])),assert(4===i.rank,\"Error in conv2dDerFilter: input must be rank 4, but got shape \"+i.shape+\".\"),assert(4===s.rank,\"Error in conv2dDerFilter: dy must be rank 4, but got shape \"+s.shape+\".\"),assert(4===n.length,\"Error in conv2dDerFilter: filterShape must be length 4, but got \"+n+\".\"),assert(i.shape[3]===n[2],\"Error in conv2dDerFilter: depth of input \"+i.shape[3]+\") must match input depth in filter (\"+n[2]+\".\"),assert(s.shape[3]===n[3],\"Error in conv2dDerFilter: depth of dy (\"+s.shape[3]+\") must match output depth for filter (\"+n[3]+\").\"),null!=a&&assert(isInt(o),\"Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode \"+a+\" but got pad \"+o+\".\");var u=computeConv2DInfo(i.shape,n,r,1,o,a);return ENV.engine.runKernel(function(e){return e.conv2dDerFilter(i,s,u)},{x4D:i,dy4D:s})}function conv2dTranspose_(e,t,n,r,o,a){return conv2dDerInput_(n,convertToTensor(e,\"x\",\"conv2dTranspose\"),convertToTensor(t,\"filter\",\"conv2dTranspose\"),r,o,a)}function depthwiseConv2d_(e,t,n,r,o,a,i){void 0===o&&(o=\"NHWC\"),void 0===a&&(a=[1,1]);var s=convertToTensor(e,\"x\",\"depthwiseConv2d\"),u=convertToTensor(t,\"filter\",\"depthwiseConv2d\"),l=s,c=!1;3===s.rank&&(c=!0,l=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),assert(4===l.rank,\"Error in depthwiseConv2d: input must be rank 4, but got rank \"+l.rank+\".\"),assert(4===u.rank,\"Error in depthwiseConv2d: filter must be rank 4, but got rank \"+u.rank+\".\"),assert(l.shape[3]===u.shape[2],\"Error in depthwiseConv2d: number of input channels (\"+l.shape[3]+\") must match the inChannels dimension in filter \"+u.shape[2]+\".\"),null==a&&(a=[1,1]),assert(eitherStridesOrDilationsAreOne(n,a),\"Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides \"+n+\" and dilations '\"+a+\"'\"),null!=i&&assert(isInt(r),\"Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode \"+i+\" but got pad \"+r+\".\");var p=computeConv2DInfo(l.shape,u.shape,n,a,r,i,!0),d=ENV.engine.runKernel(function(e){return e.depthwiseConv2D(l,u,p)},{x:l,$filter:u},function(e){return assert(tupleValuesAreOne(a),\"Error in gradient of depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '\"+a+\"'\"),{x:function(){return depthwiseConv2dDerInput(l.shape,e,u,p)},$filter:function(){return depthwiseConv2dDerFilter(l,e,u.shape,p)}}});return c?d.as3D(d.shape[1],d.shape[2],d.shape[3]):d}function separableConv2d_(e,t,n,r,o,a,i){void 0===a&&(a=[1,1]),void 0===i&&(i=\"NHWC\");var s=convertToTensor(e,\"x\",\"separableConv2d\"),u=convertToTensor(t,\"depthwiseFilter\",\"separableConv2d\"),l=convertToTensor(n,\"pointwiseFilter\",\"separableConv2d\"),c=s,p=!1;if(3===s.rank&&(p=!0,c=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),\"NCHW\"===i)throw new Error(\"separableConv2d currently does not support dataFormat NCHW; only NHWC is supported\");assert(4===c.rank,\"Error in separableConv2d: input must be rank 4, but got rank \"+c.rank+\".\"),assert(4===u.rank,\"Error in separableConv2d: depthwise filter must be rank 4, but got rank \"+u.rank+\".\"),assert(4===l.rank,\"Error in separableConv2d: pointwise filter must be rank 4, but got rank \"+u.rank+\".\"),assert(1===l.shape[0],\"Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got \"+l.shape[0]+\".\"),assert(1===l.shape[1],\"Error in separableConv2d: the second dimension of pointwise filter  must be 1, but got \"+l.shape[1]+\".\");var d=u.shape[2],h=u.shape[3];assert(l.shape[2]===d*h,\"Error in separableConv2d: the third dimension of pointwise filter must be \"+d*h+\", but got \"+l.shape[2]+\".\");var f=depthwiseConv2d(c,u,r,o,i,a),m=conv2d(f,l,1,\"valid\",i);return p?m.as3D(m.shape[1],m.shape[2],m.shape[3]):m}function parseTupleParam$1(e){return\"number\"==typeof e?[e,e,e]:2===e.length?[e[0],e[1],1]:e}function tupleValuesAreOne$1(e){var t=parseTupleParam$1(e),n=t[0],r=t[1],o=t[2];return 1===n&&1===r&&1===o}function eitherStridesOrDilationsAreOne$1(e,t){return tupleValuesAreOne$1(e)||tupleValuesAreOne$1(t)}function depthwiseConv2dDerInput(e,t,n,r){var o=t,a=!1;3===t.rank&&(a=!0,o=t.as4D(1,t.shape[0],t.shape[1],t.shape[2]));var i=ENV.engine.runKernel(function(e){return e.depthwiseConv2DDerInput(o,n,r)},{dy4D:o});return a?i.as3D(i.shape[1],i.shape[2],i.shape[3]):i}function depthwiseConv2dDerFilter(e,t,n,r){var o=e;3===e.rank&&(o=e.as4D(1,e.shape[0],e.shape[1],e.shape[2]));var a=t;return 3===a.rank&&(a=t.as4D(1,t.shape[0],t.shape[1],t.shape[2])),ENV.engine.runKernel(function(e){return e.depthwiseConv2DDerFilter(o,a,r)},{x4D:o,dy4D:a})}function conv3d_(e,t,n,r,o,a){void 0===o&&(o=\"NHWC\"),void 0===a&&(a=[1,1,1]);var i=convertToTensor(e,\"x\",\"conv3d\"),s=convertToTensor(t,\"filter\",\"conv3d\"),u=i,l=!1;4===i.rank&&(l=!0,u=i.as5D(1,i.shape[0],i.shape[1],i.shape[2],i.shape[3])),assert(5===u.rank,\"Error in conv3d: input must be rank 5, but got rank \"+u.rank+\".\"),assert(5===s.rank,\"Error in conv3d: filter must be rank 5, but got rank \"+s.rank+\".\"),assert(u.shape[4]===s.shape[3],\"Error in conv3d: depth of input (\"+u.shape[4]+\") must match input depth for filter \"+s.shape[3]+\".\"),assert(eitherStridesOrDilationsAreOne$1(n,a),\"Error in conv3D: Either strides or dilations must be 1. Got strides \"+n+\" and dilations '\"+a+\"'\"),assert(\"NHWC\"===o,\"Error in conv3d: got dataFormat of \"+o+\" but only NHWC is currently supported.\");var c=computeConv3DInfo(u.shape,s.shape,n,a,r),p=ENV.engine.runKernel(function(e){return e.conv3d(u,s,c)},{x:u,$filter:s},function(e){return assert(tupleValuesAreOne$1(a),\"Error in gradient of conv3D: dilation rates greater than 1 are notyet supported in gradients. Got dilations '\"+a+\"'\"),{x:function(){return conv3dDerInput_(u.shape,e,s,n,r)},$filter:function(){return conv3dDerFilter_(u,e,s.shape,n,r)}}});return l?p.as4D(p.shape[1],p.shape[2],p.shape[3],p.shape[4]):p}function conv3dDerInput_(e,t,n,r,o){assert(e.length===t.rank,\"Length of inShape (\"+e.length+\") and rank of dy (\"+t.rank+\") must match\");var a=e,i=t,s=!1;4===t.rank&&(s=!0,i=t.as5D(1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]),a=[1,e[0],e[1],e[2],e[3]]);var u=a[4],l=i.shape[4];assert(5===a.length,\"Error in conv3dDerInput: inShape must be length 5, but got length \"+a.length+\".\"),assert(5===i.rank,\"Error in conv3dDerInput: dy must be rank 5, but got rank \"+i.rank),assert(5===n.rank,\"Error in conv3dDerInput: filter must be rank 5, but got rank \"+n.rank),assert(u===n.shape[3],\"Error in conv3dDerInput: depth of input (\"+u+\") must match input depth for filter \"+n.shape[3]+\".\"),assert(l===n.shape[4],\"Error in conv3dDerInput: depth of output (\"+l+\") must match output depth for filter \"+n.shape[4]+\".\");var c=computeConv3DInfo(a,n.shape,r,1,o),p=ENV.engine.runKernel(function(e){return e.conv3dDerInput(i,n,c)},{dy5D:i});return s?p.as4D(p.shape[1],p.shape[2],p.shape[3],p.shape[4]):p}function conv3dDerFilter_(e,t,n,r,o){var a=e;4===e.rank&&(a=e.as5D(1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]));var i=t;4===i.rank&&(i=t.as5D(1,t.shape[0],t.shape[1],t.shape[2],t.shape[3])),assert(5===a.rank,\"Error in conv3dDerFilter: input must be rank 5, but got shape \"+a.shape+\".\"),assert(5===i.rank,\"Error in conv3dDerFilter: dy must be rank 5, but got shape \"+i.shape+\".\"),assert(5===n.length,\"Error in conv3dDerFilter: filterShape must be length 5, but got \"+n+\".\"),assert(a.shape[4]===n[3],\"Error in conv3dDerFilter: depth of input \"+a.shape[4]+\") must match input depth in filter (\"+n[3]+\".\"),assert(i.shape[4]===n[4],\"Error in conv3dDerFilter: depth of dy (\"+i.shape[4]+\") must match output depth for filter (\"+n[4]+\").\");var s=computeConv3DInfo(a.shape,n,r,1,o);return ENV.engine.runKernel(function(e){return e.conv3dDerFilter(a,i,s)},{x5D:a,dy5D:i})}var conv1d=op({conv1d_:conv1d_}),conv2d=op({conv2d_:conv2d_}),conv3d=op({conv3d_:conv3d_}),conv2dDerFilter=op({conv2dDerFilter_:conv2dDerFilter_}),depthwiseConv2d=op({depthwiseConv2d_:depthwiseConv2d_}),separableConv2d=op({separableConv2d_:separableConv2d_}),conv2dTranspose=op({conv2dTranspose_:conv2dTranspose_});function reverse1d_(e){var t=convertToTensor(e,\"x\",\"reverse\");return assert(1===t.rank,\"Error in reverse1D: x must be rank 1 but got\\n             rank \"+t.rank+\".\"),reverse(t,0)}function reverse2d_(e,t){var n=convertToTensor(e,\"x\",\"reverse\");return assert(2===n.rank,\"Error in reverse2D: x must be rank 2 but got\\n             rank \"+n.rank+\".\"),reverse(n,t)}function reverse3d_(e,t){var n=convertToTensor(e,\"x\",\"reverse\");return assert(3===n.rank,\"Error in reverse3D: x must be rank 3 but got\\n             rank \"+n.rank+\".\"),reverse(n,t)}function reverse4d_(e,t){var n=convertToTensor(e,\"x\",\"reverse\");return assert(4===n.rank,\"Error in reverse4D: x must be rank 4 but got\\n             rank \"+n.rank+\".\"),reverse(n,t)}function reverse_(e,t){var n=convertToTensor(e,\"x\",\"reverse\");if(0===n.rank)return n.clone();var r=parseAxisParam(t,n.shape);return ENV.engine.runKernel(function(e){return e.reverse(n,r)},{$x:n},function(e){return{$x:function(){return e.reverse(r)}}}).reshapeAs(n)}var reverse=op({reverse_:reverse_}),reverse1d=op({reverse1d_:reverse1d_}),reverse2d=op({reverse2d_:reverse2d_}),reverse3d=op({reverse3d_:reverse3d_}),reverse4d=op({reverse4d_:reverse4d_});function maxPoolImpl_(e,t,n,r,o,a){var i=convertToTensor(e,\"x\",\"maxPool\"),s=i,u=!1;3===i.rank&&(u=!0,s=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),null==r&&(r=[1,1]),assert(4===s.rank,\"Error in maxPool: input must be rank 4 but got rank \"+s.rank+\".\"),assert(eitherStridesOrDilationsAreOne(n,r),\"Error in maxPool: Either strides or dilations must be 1. Got strides \"+n+\" and dilations '\"+r+\"'\"),null!=a&&assert(isInt(o),\"Error in maxPool: pad must be an integer when using, dimRoundingMode \"+a+\" but got pad \"+o+\".\");var l=computePool2DInfo(s.shape,t,n,r,o,a),c=ENV.engine.runKernel(function(e,t){return t(e.maxPool(s,l))},{x:s},function(e,a){var i=a[0];return{x:function(){return maxPoolBackprop(e,s,i,t,n,r,o)}}});return u?c.as3D(c.shape[1],c.shape[2],c.shape[3]):c}function maxPool_(e,t,n,r,o){return maxPoolImpl_(e,t,n,1,r,o)}function avgPoolImpl_(e,t,n,r,o,a){var i=convertToTensor(e,\"x\",\"avgPool\",\"float32\");null==r&&(r=[1,1]),assert(eitherStridesOrDilationsAreOne(n,r),\"Error in avgPool: Either strides or dilations must be 1. Got strides \"+n+\" and dilations '\"+r+\"'\");var s=i,u=!1;3===i.rank&&(u=!0,s=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),assert(4===s.rank,\"Error in avgPool: x must be rank 4 but got rank \"+s.rank+\".\"),null!=a&&assert(isInt(o),\"Error in avgPool: pad must be an integer when using, dimRoundingMode \"+a+\" but got pad \"+o+\".\");var l=computePool2DInfo(s.shape,t,n,r,o),c=ENV.engine.runKernel(function(e){return e.avgPool(s,l)},{x:s},function(e){return{x:function(){return avgPoolBackprop(e,s,t,n,r,o)}}});return c=c.cast(i.dtype),u?c.as3D(c.shape[1],c.shape[2],c.shape[3]):c}function avgPool_(e,t,n,r,o){return avgPoolImpl_(e,t,n,1,r,o)}function pool_(e,t,n,r,o,a){null==o&&(o=[1,1]),null==a&&(a=1),0===r&&(r=\"valid\");var i=convertToTensor(e,\"x\",\"maxPool\"),s=i,u=!1;3===i.rank&&(u=!0,s=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),assert(eitherStridesOrDilationsAreOne(a,o),\"Error in pool: Either strides or dilations must be 1. Got strides \"+a+\" and dilations '\"+o+\"'\");var l,c=computePool2DInfo(s.shape,t,a,o,r),p=[c.dilationHeight,c.dilationWidth];l=\"same\"===r?withSpaceToBatchBasePaddings([c.filterHeight,c.filterWidth],p):[[0,0],[0,0]];var d=1===p[0]&&1===p[1],h=requiredSpaceToBatchPaddings([c.inHeight,c.inWidth],p,l),f=h[0],m=h[1],g=d?r:\"valid\",v=d?s:spaceToBatchND(s,p,f),y=(\"avg\"===n?function(){return avgPoolImpl_(v,t,a,1,g)}:function(){return maxPoolImpl_(v,t,a,1,g)})(),x=d?y:batchToSpaceND(y,p,m);return u?x.as3D(x.shape[1],x.shape[2],x.shape[3]):x}function maxPoolBackprop(e,t,n,r,o,a,i,s){var u=convertToTensor(e,\"dy\",\"maxPoolBackprop\"),l=convertToTensor(t,\"input\",\"maxPoolBackprop\"),c=convertToTensor(n,\"output\",\"maxPoolBackprop\");assert(l.rank===u.rank,\"Rank of input (\"+l.rank+\") does not match rank of dy (\"+u.rank+\")\"),null==a&&(a=[1,1]),assert(eitherStridesOrDilationsAreOne(o,a),\"Error in maxPoolBackProp: Either strides or dilations must be 1. Got strides \"+o+\" and dilations '\"+a+\"'\"),assert(4===u.rank,\"Error in maxPoolBackprop: dy must be rank 4 but got rank \"+u.rank+\".\"),assert(4===l.rank,\"Error in maxPoolBackprop: input must be rank 4 but got rank \"+l.rank+\".\"),null!=s&&assert(isInt(i),\"Error in maxPoolBackprop: pad must be an integer when using, dimRoundingMode \"+s+\" but got pad \"+i+\".\");var p=computePool2DInfo(l.shape,r,o,a,i,s);return ENV.engine.runKernel(function(e){return e.maxPoolBackprop(u,l,c,p)},{$dy:u,$input:l})}function avgPoolBackprop(e,t,n,r,o,a){var i=convertToTensor(e,\"dy\",\"avgPoolBackprop\"),s=convertToTensor(t,\"input\",\"avgPoolBackprop\");assert(s.rank===i.rank,\"Rank of input (\"+s.rank+\") does not match rank of dy (\"+i.rank+\")\"),null==o&&(o=[1,1]),assert(eitherStridesOrDilationsAreOne(r,o),\"Error in avgPoolBackprop: Either strides or dilations must be 1. Got strides \"+r+\" and dilations '\"+o+\"'\");var u=s,l=i,c=!1;3===s.rank&&(c=!0,u=s.as4D(1,s.shape[0],s.shape[1],s.shape[2]),l=i.as4D(1,i.shape[0],i.shape[1],i.shape[2])),assert(4===l.rank,\"Error in avgPoolBackprop: dy must be rank 4 but got rank \"+l.rank+\".\"),assert(4===u.rank,\"Error in avgPoolBackprop: input must be rank 4 but got rank \"+u.rank+\".\");var p=computePool2DInfo(u.shape,n,r,o,a),d=ENV.engine.runKernel(function(e){return e.avgPoolBackprop(l,u,p)},{dy4D:l,input4D:u});return c?d.as3D(d.shape[1],d.shape[2],d.shape[3]):d}function requiredSpaceToBatchPaddings(e,t,n){var r=n.map(function(e){return e[0]}),o=n.map(function(e){return e[1]}),a=e.concat(r,o),i=t.map(function(e,t){return(e-a[t]%e)%e}),s=o.map(function(e,t){return e+i[t]});return[t.map(function(e,t){return[r[t],s[t]]}),t.map(function(e,t){return[0,i[t]]})]}function withSpaceToBatchBasePaddings(e,t){var n=e.map(function(e,n){return e+(e-1)*(t[n]-1)}).map(function(e){return e-1}),r=n.map(function(e){return Math.floor(e/2)}),o=n.map(function(e,t){return e-r[t]});return n.map(function(e,t){return[r[t],o[t]]})}var maxPool=op({maxPool_:maxPool_}),avgPool=op({avgPool_:avgPool_}),pool=op({pool_:pool_});function slice1d_(e,t,n){var r=convertToTensor(e,\"x\",\"slice1d\");return assert(1===r.rank,\"slice1d expects a rank-1 tensor, but got a rank-\"+r.rank+\" tensor\"),slice(r,[t],[n])}function slice2d_(e,t,n){var r=convertToTensor(e,\"x\",\"slice2d\");return assert(2===r.rank,\"slice2d expects a rank-2 tensor, but got a rank-\"+r.rank+\" tensor\"),slice(r,t,n)}function slice3d_(e,t,n){var r=convertToTensor(e,\"x\",\"slice3d\");return assert(3===r.rank,\"slice3d expects a rank-3 tensor, but got a rank-\"+r.rank+\" tensor\"),slice(r,t,n)}function slice4d_(e,t,n){var r=convertToTensor(e,\"x\",\"slice4d\");return assert(4===r.rank,\"slice4d expects a rank-4 tensor, but got a rank-\"+r.rank+\" tensor\"),slice(r,t,n)}function slice_(e,t,n){var r,o,a=convertToTensor(e,\"x\",\"slice\");if(0===a.rank)throw new Error(\"Slicing scalar is not possible\");r=\"number\"==typeof t?[t].concat(new Array(a.rank-1).fill(0)):t.length<a.rank?t.concat(new Array(a.rank-t.length).fill(0)):t.slice(),o=(o=null==n?new Array(a.rank).fill(-1):\"number\"==typeof n?[n].concat(new Array(a.rank-1).fill(-1)):n.length<a.rank?n.concat(new Array(a.rank-n.length).fill(-1)):n).map(function(e,t){return e>=0?e:(assert(-1===e,\"Bad value in size\"),a.shape[t]-r[t])}),assertParamsValid(a,r,o);var i=a.shape;return ENV.engine.runKernel(function(e){return e.slice(a,r,o)},{$x:a},function(e){for(var t=[],n=0;n<e.rank;n++)t.push([r[n],i[n]-r[n]-o[n]]);return{$x:function(){return e.pad(t)}}})}var slice=op({slice_:slice_}),slice1d=op({slice1d_:slice1d_}),slice2d=op({slice2d_:slice2d_}),slice3d=op({slice3d_:slice3d_}),slice4d=op({slice4d_:slice4d_});function logSumExp_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,\"x\",\"logSumExp\"),o=parseAxisParam(t,r.shape),a=r.max(o,!0),i=r.sub(a).exp().sum(o).log(),s=a.reshape(i.shape).add(i);if(n){var u=expandShapeToKeepDim(s.shape,o);return s.reshape(u)}return s}function sum_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,\"x\",\"sum\");\"bool\"===r.dtype&&(r=r.toInt());var o=parseAxisParam(t,r.shape);return customGrad(function(e){var t=getAxesPermutation(o,e.rank),r=o,a=e;null!=t&&(a=e.transpose(t),r=getInnerMostAxes(r.length,e.rank));var i=ENV.engine.runKernel(function(e){return e.sum(a,r)},{permutedX:a});if(n){var s=expandShapeToKeepDim(i.shape,o);i=i.reshape(s)}return{value:i,gradFunc:function(t){var n=e.shape.slice();return o.forEach(function(e){n[e]=1}),t.reshape(n).mul(ones$1(e.shape,\"float32\"))}}})(r)}function prod_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,\"x\",\"prod\");\"bool\"===r.dtype&&(r=r.toInt());var o=parseAxisParam(t,r.shape),a=getAxesPermutation(o,r.rank),i=o,s=r;null!=a&&(s=r.transpose(a),i=getInnerMostAxes(i.length,r.rank));var u=ENV.engine.runKernel(function(e){return e.prod(s,i)},{permutedX:s});if(n){var l=expandShapeToKeepDim(u.shape,o);u=u.reshape(l)}return u}function mean_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,\"x\",\"mean\"),o=parseAxisParam(t,r.shape),a=sizeFromShape(computeOutAndReduceShapes(r.shape,o)[1]);return customGrad(function(e){var r=scalar(a);return{value:(r.dtype===e.dtype?e:e.cast(r.dtype)).div(r).sum(t,n),gradFunc:function(t){var n=e.shape.slice();return o.forEach(function(e){n[e]=1}),t.reshape(n).mul(ones$1(e.shape,\"float32\")).div(r)}}})(r)}function gradForMinAndMax(e,t,n,r,o){var a=t[0];return a.rank<n.rank&&(a=a.reshape(expandShapeToKeepDim(a.shape,r))),e.rank<n.rank&&(e=e.reshape(expandShapeToKeepDim(e.shape,r))),{$x:function(){var t=e.mul(n.equal(a).cast(e.dtype));return null==o?t:t.transpose(o)}}}function min_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,\"x\",\"min\"),o=r,a=parseAxisParam(t,r.shape),i=a,s=getAxesPermutation(i,r.rank);null!=s&&(r=r.transpose(s),i=getInnerMostAxes(i.length,r.rank));var u=ENV.engine.runKernel(function(e,t){return t(e.min(r,i))},{$x:r},function(e,t){return gradForMinAndMax(e,t,o,a,s)});if(n){var l=expandShapeToKeepDim(u.shape,a);u=u.reshape(l)}return u}function max_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,\"x\",\"max\"),o=r,a=parseAxisParam(t,r.shape),i=a,s=getAxesPermutation(i,r.rank);null!=s&&(r=r.transpose(s),i=getInnerMostAxes(i.length,r.rank));var u=ENV.engine.runKernel(function(e,t){return t(e.max(r,i))},{$x:r},function(e,t){return gradForMinAndMax(e,t,o,a,s)});if(n){var l=expandShapeToKeepDim(u.shape,a);u=u.reshape(l)}return u}function argMin_(e,t){void 0===t&&(t=0);var n=convertToTensor(e,\"x\",\"argMin\");null==t&&(t=0);var r=parseAxisParam(t,n.shape),o=getAxesPermutation(r,n.rank);null!=o&&(n=n.transpose(o),r=getInnerMostAxes(r.length,n.rank));return ENV.engine.runKernel(function(e){return e.argMin(n,r[0])},{$x:n},function(e){return{$x:function(){return zerosLike(n)}}})}function argMax_(e,t){void 0===t&&(t=0);var n=convertToTensor(e,\"x\",\"argMax\");null==t&&(t=0);var r=parseAxisParam(t,n.shape),o=getAxesPermutation(r,n.rank);null!=o&&(n=n.transpose(o),r=getInnerMostAxes(r.length,n.rank));return ENV.engine.runKernel(function(e){return e.argMax(n,r[0])},{$x:n},function(e){return{$x:function(){return zerosLike(n)}}})}function all_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,\"x\",\"all\",\"bool\"),o=parseAxisParam(t,r.shape),a=o,i=getAxesPermutation(a,r.rank);null!=i&&(r=r.transpose(i),a=getInnerMostAxes(a.length,r.rank));var s=ENV.engine.runKernel(function(e){return e.all(r,a)},{$x:r});if(n){var u=expandShapeToKeepDim(s.shape,o);return s.reshape(u)}return s}function any_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=convertToTensor(e,\"x\",\"any\",\"bool\"),o=parseAxisParam(t,r.shape),a=o,i=getAxesPermutation(a,r.rank);null!=i&&(r=r.transpose(i),a=getInnerMostAxes(a.length,r.rank));var s=ENV.engine.runKernel(function(e){return e.any(r,a)},{$x:r});if(n){var u=expandShapeToKeepDim(s.shape,o);return s.reshape(u)}return s}function moments_(e,t,n){void 0===t&&(t=null),void 0===n&&(n=!1);var r=parseAxisParam(t,(e=convertToTensor(e,\"x\",\"moments\")).shape),o=e.mean(r,n),a=o.shape;return n||(a=expandShapeToKeepDim(o.shape,r)),{mean:o,variance:e.toFloat().sub(o.reshape(a)).square().mean(r,n)}}var all=op({all_:all_}),any=op({any_:any_}),argMax=op({argMax_:argMax_}),argMin=op({argMin_:argMin_}),logSumExp=op({logSumExp_:logSumExp_}),max=op({max_:max_}),mean=op({mean_:mean_}),min=op({min_:min_}),moments=op({moments_:moments_}),sum$1=op({sum_:sum_}),prod=op({prod_:prod_});function notEqual_(e,t){var n,r=convertToTensor(e,\"a\",\"notEqual\"),o=convertToTensor(t,\"b\",\"notEqual\");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.notEqual(r,o)},{$a:r,$b:o})}function notEqualStrict_(e,t){var n=convertToTensor(e,\"a\",\"notEqualStrict\"),r=convertToTensor(t,\"b\",\"notEqualStrict\");return assertShapesMatch(n.shape,r.shape,\"Error in notEqualStrict: \"),n.notEqual(r)}function less_(e,t){var n,r=convertToTensor(e,\"a\",\"less\"),o=convertToTensor(t,\"b\",\"less\");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.less(r,o)},{$a:r,$b:o})}function lessStrict_(e,t){var n=convertToTensor(e,\"a\",\"lessStrict\"),r=convertToTensor(t,\"b\",\"lessStrict\");return assertShapesMatch(n.shape,r.shape,\"Error in lessStrict: \"),n.less(r)}function equal_(e,t){var n,r=convertToTensor(e,\"a\",\"equal\"),o=convertToTensor(t,\"b\",\"equal\");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.equal(r,o)},{$a:r,$b:o})}function equalStrict_(e,t){var n=convertToTensor(e,\"a\",\"equalStrict\"),r=convertToTensor(t,\"b\",\"equalStrict\");return assertShapesMatch(n.shape,r.shape,\"Error in equalStrict: \"),n.equal(r)}function lessEqual_(e,t){var n,r=convertToTensor(e,\"a\",\"lessEqual\"),o=convertToTensor(t,\"b\",\"lessEqual\");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.lessEqual(r,o)},{$a:r,$b:o})}function lessEqualStrict_(e,t){var n=convertToTensor(e,\"a\",\"lessEqualStrict\"),r=convertToTensor(t,\"b\",\"lessEqualStrict\");return assertShapesMatch(n.shape,r.shape,\"Error in lessEqualStrict: \"),n.lessEqual(r)}function greater_(e,t){var n,r=convertToTensor(e,\"a\",\"greater\"),o=convertToTensor(t,\"b\",\"greater\");return n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape),ENV.engine.runKernel(function(e){return e.greater(r,o)},{$a:r,$b:o})}function greaterStrict_(e,t){var n=convertToTensor(e,\"a\",\"greaterStrict\"),r=convertToTensor(t,\"b\",\"greaterStrict\");return assertShapesMatch(n.shape,r.shape,\"Error in greaterStrict: \"),n.greater(r)}function greaterEqual_(e,t){var n,r=convertToTensor(e,\"a\",\"greaterEqual\"),o=convertToTensor(t,\"b\",\"greaterEqual\");n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.greaterEqual(r,o)},{$a:r,$b:o},function(e){return{$a:function(){return zerosLike(r)},$b:function(){return zerosLike(o)}}})}function greaterEqualStrict_(e,t){var n=convertToTensor(e,\"a\",\"greaterEqualStrict\"),r=convertToTensor(t,\"b\",\"greaterEqualStrict\");return assertShapesMatch(n.shape,r.shape,\"Error in greaterEqualStrict: \"),n.greaterEqual(r)}var equal=op({equal_:equal_}),equalStrict=op({equalStrict_:equalStrict_}),greater=op({greater_:greater_}),greaterEqual=op({greaterEqual_:greaterEqual_}),greaterEqualStrict=op({greaterEqualStrict_:greaterEqualStrict_}),greaterStrict=op({greaterStrict_:greaterStrict_}),less=op({less_:less_}),lessEqual=op({lessEqual_:lessEqual_}),lessEqualStrict=op({lessEqualStrict_:lessEqualStrict_}),lessStrict=op({lessStrict_:lessStrict_}),notEqual=op({notEqual_:notEqual_}),notEqualStrict=op({notEqualStrict_:notEqualStrict_});function add_(e,t){var n,r=convertToTensor(e,\"a\",\"add\"),o=convertToTensor(t,\"b\",\"add\");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.add(r,o)},{$a:r,$b:o},function(e){return{$a:function(){var t=e,n=getReductionAxes(r.shape,a);return n.length>0&&(t=t.sum(n)),t.reshape(r.shape)},$b:function(){var t=e,n=getReductionAxes(o.shape,a);return n.length>0&&(t=t.sum(n)),t.reshape(o.shape)}}})}function addN_(e){assert(Array.isArray(e),function(){return\"The argument passed to tf.addN() must be a list of tensors\"}),assert(e.length>=1,function(){return\"Must pass at least one tensor to tf.addN(), but got \"+e.length});var t=e.map(function(e,t){return convertToTensor(e,\"tensors\"+t,\"addN\")}),n=t[0];t.forEach(function(e){if(e.dtype!==n.dtype)throw new Error(\"All tensors passed to tf.addN() must have the same dtype\")}),t.forEach(function(e){if(!arraysEqual(e.shape,n.shape))throw new Error(\"All tensors passed to tf.addN() must have the same shape\")});var r=t;return ENV.engine.runKernel(function(e){return e.addN(t)},r,function(e){var n={};return t.forEach(function(t,r){n[r]=function(){return e.clone()}}),n})}function addStrict_(e,t){var n=convertToTensor(e,\"a\",\"addStrict\"),r=convertToTensor(t,\"b\",\"addStrict\");return assertShapesMatch(n.shape,r.shape,\"Error in addStrict: \"),n.add(r)}function sub_(e,t){var n,r=convertToTensor(e,\"a\",\"sub\"),o=convertToTensor(t,\"b\",\"sub\");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.subtract(r,o)},{$a:r,$b:o},function(e){return{$a:function(){var t=e,n=getReductionAxes(r.shape,a);return n.length>0&&(t=t.sum(n)),t.reshape(r.shape)},$b:function(){var t=e,n=getReductionAxes(o.shape,a);return n.length>0&&(t=t.sum(n)),t.neg().reshape(o.shape)}}})}function subStrict_(e,t){var n=convertToTensor(e,\"a\",\"subStrict\"),r=convertToTensor(t,\"b\",\"subStrict\");return assertShapesMatch(n.shape,r.shape,\"Error in subStrict: \"),n.sub(r)}function pow_(e,t){var n=convertToTensor(e,\"base\",\"pow\"),r=convertToTensor(t,\"exp\",\"pow\"),o=assertAndGetBroadcastShape(n.shape,r.shape);e=n.cast(upcastType(n.dtype,r.dtype)),t=r.cast(upcastType(n.dtype,r.dtype));return ENV.engine.runKernel(function(e,t){return t(e.pow(n,r))},{$base:n,$exp:r},function(e,t){var a=t[0];return{$base:function(){var t=r.toFloat(),a=e.mul(t.mul(n.pow(t.sub(scalar(1))))),i=getReductionAxes(n.shape,o);return i.length>0&&(a=a.sum(i)),a.reshape(n.shape)},$exp:function(){var t=e.mul(a.mul(n.log()).toFloat()),i=getReductionAxes(r.shape,o);return i.length>0&&(t=t.sum(i)),t.reshape(r.shape)}}})}function powStrict_(e,t){return assertShapesMatch(e.shape,t.shape,\"Error in powStrict: \"),e.pow(t)}function mul_(e,t){var n,r=convertToTensor(e,\"a\",\"mul\"),o=convertToTensor(t,\"b\",\"mul\");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.multiply(r,o)},{$a:r,$b:o},function(e){return{$a:function(){var t=e.mul(o.toFloat()),n=getReductionAxes(r.shape,a);return n.length>0?t.sum(n).reshape(r.shape):t},$b:function(){var t=e.mul(r.toFloat()),n=getReductionAxes(o.shape,a);return n.length>0?t.sum(n).reshape(o.shape):t}}})}function mulStrict_(e,t){var n=convertToTensor(e,\"a\",\"mul\"),r=convertToTensor(t,\"b\",\"mul\");return assertShapesMatch(n.shape,r.shape,\"Error in multiplyStrict: \"),n.mul(r)}function div_(e,t){var n,r,o=convertToTensor(e,\"a\",\"div\"),a=convertToTensor(t,\"b\",\"div\");if(n=makeTypesMatch(o,a),o=n[0],a=n[1],\"int32\"===o.dtype&&\"int32\"===a.dtype)return floorDiv(o,a);r=function(e){return e.realDivide(o,a)};var i=assertAndGetBroadcastShape(o.shape,a.shape);return ENV.engine.runKernel(r,{$a:o,$b:a},function(e){return{$a:function(){var t=e.div(a.toFloat()),n=getReductionAxes(o.shape,i);return n.length>0?t.sum(n).reshape(o.shape):t},$b:function(){var t=e.mul(o.toFloat()),n=getReductionAxes(a.shape,i);n.length>0&&(t=t.sum(n).reshape(a.shape));var r=a.square();return t.div(r.toFloat()).neg()}}})}function floorDiv_(e,t){var n,r=convertToTensor(e,\"a\",\"floorDiv\"),o=convertToTensor(t,\"b\",\"floorDiv\");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.floorDiv(r,o)},{$a:r,$b:o},function(e){return{$a:function(){var t=e.div(o.toFloat()),n=getReductionAxes(r.shape,a);return n.length>0?t.sum(n).reshape(r.shape):t},$b:function(){var t=e.mul(r.toFloat()),n=getReductionAxes(o.shape,a);n.length>0&&(t=t.sum(n).reshape(o.shape));var i=o.square();return t.div(i.toFloat()).neg()}}})}function divStrict_(e,t){var n=convertToTensor(e,\"a\",\"div\"),r=convertToTensor(t,\"b\",\"div\");return assertShapesMatch(n.shape,r.shape,\"Error in divideStrict: \"),n.div(r)}function mod_(e,t){var n,r=convertToTensor(e,\"a\",\"mod\"),o=convertToTensor(t,\"b\",\"mod\");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.mod(r,o)},{$a:r,$b:o},function(e){return{$a:function(){var t=getReductionAxes(r.shape,a);return t.length>0?e.sum(t).reshape(r.shape):e},$b:function(){var t=e.mul(r.div(o).floor().neg()),n=getReductionAxes(o.shape,a);return n.length>0?t.sum(n).reshape(o.shape):t}}})}function modStrict_(e,t){var n=convertToTensor(e,\"a\",\"modStrict\"),r=convertToTensor(t,\"b\",\"modStrict\");return assertShapesMatch(n.shape,r.shape,\"Error in modStrict: \"),n.mod(r)}function minimum_(e,t){var n,r=convertToTensor(e,\"a\",\"minimum\"),o=convertToTensor(t,\"b\",\"minimum\");n=makeTypesMatch(r,o),r=n[0],o=n[1],\"bool\"===r.dtype&&(r=r.toInt(),o=o.toInt()),assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.minimum(r,o)},{$a:r,$b:o},function(e){return{$a:function(){return e.mul(r.lessEqual(o).toFloat())},$b:function(){return e.mul(r.greater(o).toFloat())}}})}function minimumStrict_(e,t){var n=convertToTensor(e,\"a\",\"minimumStrict\"),r=convertToTensor(t,\"b\",\"minimumStrict\");return assertShapesMatch(n.shape,r.shape,\"Error in minimumStrict: \"),n.minimum(r)}function maximum_(e,t){var n,r=convertToTensor(e,\"a\",\"maximum\"),o=convertToTensor(t,\"b\",\"maximum\");n=makeTypesMatch(r,o),r=n[0],o=n[1],\"bool\"===r.dtype&&(r=r.toInt(),o=o.toInt()),assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.maximum(r,o)},{$a:r,$b:o},function(e){return{$a:function(){return e.mul(r.greaterEqual(o).toFloat())},$b:function(){return e.mul(r.less(o).toFloat())}}})}function maximumStrict_(e,t){var n=convertToTensor(e,\"a\",\"maximumStrict\"),r=convertToTensor(t,\"b\",\"maximumStrict\");return assertShapesMatch(n.shape,r.shape,\"Error in maximumStrict: \"),n.maximum(r)}function squaredDifference_(e,t){var n,r=convertToTensor(e,\"a\",\"squaredDifference\"),o=convertToTensor(t,\"b\",\"squaredDifference\");n=makeTypesMatch(r,o),r=n[0],o=n[1],assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.squaredDifference(r,o)},{$a:r,$b:o},function(e){var t=scalar(2);return{$a:function(){return e.mul(r.sub(o).mul(t))},$b:function(){return e.mul(o.sub(r).mul(t))}}})}function squaredDifferenceStrict_(e,t){var n=convertToTensor(e,\"a\",\"squaredDifferenceStrict\"),r=convertToTensor(t,\"b\",\"squaredDifferenceStrict\");return assertShapesMatch(n.shape,r.shape,\"Error in squaredDifferenceStrict: \"),n.squaredDifference(r)}function atan2_(e,t){var n,r=convertToTensor(e,\"a\",\"atan2\"),o=convertToTensor(t,\"b\",\"atan2\");n=makeTypesMatch(r,o),r=n[0],o=n[1];var a=assertAndGetBroadcastShape(r.shape,o.shape);return ENV.engine.runKernel(function(e){return e.atan2(r,o)},{$a:r,$b:o},function(e){return{$a:function(){var t=add(r.square(),o.square()),n=e.mul(o.div(t)),i=getReductionAxes(r.shape,a);return i.length>0&&(n=n.sum(i)),n.reshape(r.shape)},$b:function(){var t=add(r.square(),o.square()),n=neg(e.mul(r.div(t))),i=getReductionAxes(o.shape,a);return i.length>0&&(n=n.sum(i)),n.reshape(o.shape)}}})}var add=op({add_:add_}),addN=op({addN_:addN_}),addStrict=op({addStrict_:addStrict_}),atan2=op({atan2_:atan2_}),div=op({div_:div_}),divStrict=op({divStrict_:divStrict_}),floorDiv=op({floorDiv_:floorDiv_}),maximum=op({maximum_:maximum_}),maximumStrict=op({maximumStrict_:maximumStrict_}),minimum=op({minimum_:minimum_}),minimumStrict=op({minimumStrict_:minimumStrict_}),mod=op({mod_:mod_}),modStrict=op({modStrict_:modStrict_}),mul=op({mul_:mul_}),mulStrict=op({mulStrict_:mulStrict_}),pow=op({pow_:pow_}),powStrict=op({powStrict_:powStrict_}),squaredDifference=op({squaredDifference_:squaredDifference_}),squaredDifferenceStrict=op({squaredDifferenceStrict_:squaredDifferenceStrict_}),sub=op({sub_:sub_}),subStrict=op({subStrict_:subStrict_});function logicalNot_(e){var t=convertToTensor(e,\"x\",\"logicalNot\",\"bool\");return ENV.engine.runKernel(function(e){return e.logicalNot(t)},{$x:t})}function logicalAnd_(e,t){var n=convertToTensor(e,\"a\",\"logicalAnd\",\"bool\"),r=convertToTensor(t,\"b\",\"logicalAnd\",\"bool\");return assertAndGetBroadcastShape(n.shape,r.shape),ENV.engine.runKernel(function(e){return e.logicalAnd(n,r)},{$a:n,$b:r})}function logicalOr_(e,t){var n=convertToTensor(e,\"a\",\"logicalOr\",\"bool\"),r=convertToTensor(t,\"b\",\"logicalOr\",\"bool\");return assertAndGetBroadcastShape(n.shape,r.shape),ENV.engine.runKernel(function(e){return e.logicalOr(n,r)},{$a:n,$b:r})}function logicalXor_(e,t){var n=convertToTensor(e,\"a\",\"logicalXor\",\"bool\"),r=convertToTensor(t,\"b\",\"logicalXor\",\"bool\");return assertAndGetBroadcastShape(n.shape,r.shape),logicalOr(e,t).logicalAnd(logicalAnd(e,t).logicalNot())}function where_(e,t,n){var r=convertToTensor(t,\"a\",\"where\"),o=convertToTensor(n,\"b\",\"where\"),a=convertToTensor(e,\"condition\",\"where\",\"bool\");assertShapesMatch(r.shape,o.shape,\"Error in where: \"),1===a.rank?assert(a.shape[0]===r.shape[0],\"The first dimension of `a` must match the size of `condition`.\"):assertShapesMatch(a.shape,o.shape,\"Error in where: \");return ENV.engine.runKernel(function(e){return e.select(a,r,o)},{$condition:a,$a:r,$b:o},function(e){return{$condition:function(){return zerosLike(a).toFloat()},$a:function(){return e.mul(a.cast(e.dtype))},$b:function(){return e.mul(a.logicalNot().cast(e.dtype))}}})}function whereAsync_(e){return __awaiter(this,void 0,void 0,function(){var t,n,r;return __generator(this,function(o){switch(o.label){case 0:return[4,(t=convertToTensor(e,\"condition\",\"whereAsync\",\"bool\")).data()];case 1:return n=o.sent(),r=whereImpl(t.shape,n),e!==t&&t.dispose(),[2,r]}})})}var logicalAnd=op({logicalAnd_:logicalAnd_}),logicalNot=op({logicalNot_:logicalNot_}),logicalOr=op({logicalOr_:logicalOr_}),logicalXor=op({logicalXor_:logicalXor_}),where=op({where_:where_}),whereAsync=whereAsync_;function relu_(e){var t=convertToTensor(e,\"x\",\"relu\");if(\"bool\"===t.dtype)return t.toInt();return ENV.engine.runKernel(function(e){return e.relu(t)},{$x:t},function(e){var n=t.step();return{$x:function(){return e.mulStrict(n.toFloat())}}})}function elu_(e){var t=convertToTensor(e,\"x\",\"elu\");return ENV.engine.runKernel(function(e,n){return n(e.elu(t))},{$x:t},function(e,t){var n=t[0];return{$x:function(){return ENV.engine.runKernel(function(t){return t.eluDer(e,n)},{dy:e,y:n})}}})}function selu_(e){var t=convertToTensor(e,\"x\",\"selu\");return ENV.engine.runKernel(function(e){return e.selu(t)},{$x:t},function(e){return{$x:function(){var n=t.greater(scalar(0)),r=scalar(SELU_SCALEALPHA),o=scalar(SELU_SCALE),a=e.mul(o),i=e.mul(r).mul(t.toFloat().exp());return where(n,a,i)}}})}function leakyRelu_(e,t){void 0===t&&(t=.2);var n=convertToTensor(e,\"x\",\"leakyRelu\");return maximum(scalar(t).mul(n),n)}function prelu_(e,t){var n=convertToTensor(e,\"x\",\"prelu\"),r=convertToTensor(t,\"alpha\",\"prelu\"),o=scalar(0);return maximum(o,n).add(r.mul(minimum(o,n)))}var elu=op({elu_:elu_}),leakyRelu=op({leakyRelu_:leakyRelu_}),prelu=op({prelu_:prelu_}),relu=op({relu_:relu_}),selu=op({selu_:selu_});function transpose_(e,t){var n=convertToTensor(e,\"x\",\"transpose\");if(null==t&&(t=n.shape.map(function(e,t){return t}).reverse()),assert(n.rank===t.length,\"Error in transpose: rank of input \"+n.rank+\" must match length of perm \"+t+\".\"),t.forEach(function(e){assert(e>=0&&e<n.rank,\"All entries in 'perm' must be between 0 and \"+(n.rank-1)+\" but got \"+t)}),n.rank<=1)return n.clone();return ENV.engine.runKernel(function(e){return e.transpose(n,t)},{$x:n},function(e){var n=getUndoAxesPermutation(t);return{$x:function(){return e.transpose(n)}}})}var transpose=op({transpose_:transpose_});function localResponseNormalization_(e,t,n,r,o){void 0===t&&(t=5),void 0===n&&(n=1),void 0===r&&(r=1),void 0===o&&(o=.5);var a=convertToTensor(e,\"x\",\"localResponseNormalization\");assert(4===a.rank||3===a.rank,\"Error in localResponseNormalization: x must be rank 3 or 4 but got\\n               rank \"+a.rank+\".\"),assert(isInt(t),\"Error in localResponseNormalization: depthRadius must be an integer\\n                     but got depthRadius \"+t+\".\");var i=a,s=!1;3===a.rank&&(s=!0,i=a.as4D(1,a.shape[0],a.shape[1],a.shape[2]));var u=ENV.engine.runKernel(function(e,a){return a(e.localResponseNormalization4D(i,t,n,r,o))},{x4D:i},function(e,a){var s=a[0];return{x4D:function(){return ENV.engine.runKernel(function(a){return a.LRNGrad(e,i,s,t,n,r,o)},{})}}});return s?u.as3D(u.shape[1],u.shape[2],u.shape[3]):u}var localResponseNormalization=op({localResponseNormalization_:localResponseNormalization_});function norm_(e,t,n,r){void 0===t&&(t=\"euclidean\"),void 0===n&&(n=null),void 0===r&&(r=!1);var o=normImpl(e=convertToTensor(e,\"x\",\"norm\"),t,n),a=o.shape;if(r){var i=parseAxisParam(n,e.shape);a=expandShapeToKeepDim(o.shape,i)}return o.reshape(a)}function normImpl(e,t,n){if(void 0===n&&(n=null),0===e.rank)return e.abs();if(1!==e.rank&&null===n)return normImpl(e.reshape([-1]),t,n);if(1===e.rank||\"number\"==typeof n||n instanceof Array&&1===n.length){if(1===t)return e.abs().sum(n);if(t===1/0)return e.abs().max(n);if(t===-1/0)return e.abs().min(n);if(\"euclidean\"===t||2===t)return e.abs().pow(scalar(2,\"int32\")).sum(n).sqrt();throw new Error(\"Error in norm: invalid ord value: \"+t)}if(n instanceof Array&&2===n.length){if(1===t)return e.abs().sum(n[0]).max(n[1]-1);if(t===1/0)return e.abs().sum(n[1]).max(n[0]);if(t===-1/0)return e.abs().sum(n[1]).min(n[0]);if(\"fro\"===t||\"euclidean\"===t)return e.square().sum(n).sqrt();throw new Error(\"Error in norm: invalid ord value: \"+t)}throw new Error(\"Error in norm: invalid axis: \"+n)}var norm=op({norm_:norm_});function unsortedSegmentSum_(e,t,n){var r=convertToTensor(e,\"x\",\"unsortedSegmentSum\"),o=convertToTensor(t,\"segmentIds\",\"unsortedSegmentSum\",\"int32\");assert(isInt(n),\"numSegments must be of dtype int\");return ENV.engine.runKernel(function(e){return e.unsortedSegmentSum(r,o,n)},{$x:r},function(e){return{$x:function(){return gatherDropNegatives(e,o)}}})}function gather_(e,t,n){void 0===n&&(n=0);var r=convertToTensor(e,\"x\",\"gather\"),o=convertToTensor(t,\"indices\",\"gather\",\"int32\");n=parseAxisParam(n,r.shape)[0];return ENV.engine.runKernel(function(e){return e.gather(r,o,n)},{$x:r},function(e){return{$x:function(){if(0===n)return unsortedSegmentSum(e,o,r.shape[n]);var t=r.shape,a=o.size,i=t.slice(0,n),s=i.length,u=t.slice(n,t.length).slice(1),l=u.length,c=arrayRange(0,s),p=arrayRange(s+1,s+1+l),d=arrayConcat([i,[a],u]),h=e.reshape(d),f=o.reshape([a]),m=arrayConcat([[s],c,p]),g=h.transpose(m),v=unsortedSegmentSum(g,f,r.shape[n]),y=getUndoAxesPermutation(m);return v=v.transpose(y)}}})}function arrayRange(e,t){for(var n=[],r=e;r<t;++r)n.push(r);return n}function arrayConcat(e){for(var t=[],n=0;n<e.length;++n)for(var r=0;r<e[n].length;++r)t.push(e[n][r]);return t}function gatherDropNegatives(e,t){for(var n=maximum(t,zerosLike(t)),r=gather(e,n),o=greaterEqual(t,scalar(0,\"int32\")),a=r.rank-o.rank,i=0;i<a;++i)o=expandDims(o,i+1);o=logicalAnd(o,ones$1(r.shape,\"bool\"));var s=zerosLike(r);return where(o,r,s)}var gather=op({gather_:gather_}),unsortedSegmentSum=op({unsortedSegmentSum_:unsortedSegmentSum_});function multiRNNCell_(e,t,n,r){for(var o=convertToTensor(t,\"data\",\"multiRNNCell\"),a=convertToTensorArray(n,\"c\",\"multiRNNCell\"),i=convertToTensorArray(r,\"h\",\"multiRNNCell\"),s=o,u=[],l=0;l<e.length;l++){var c=e[l](s,a[l],i[l]);u.push(c[0]),u.push(c[1]),s=c[1]}var p=[],d=[];for(l=0;l<u.length;l+=2)p.push(u[l]),d.push(u[l+1]);return[p,d]}function basicLSTMCell_(e,t,n,r,o,a){var i=convertToTensor(e,\"forgetBias\",\"basicLSTMCell\"),s=convertToTensor(t,\"lstmKernel\",\"basicLSTMCell\"),u=convertToTensor(n,\"lstmBias\",\"basicLSTMCell\"),l=convertToTensor(r,\"data\",\"basicLSTMCell\"),c=convertToTensor(o,\"c\",\"basicLSTMCell\"),p=convertToTensor(a,\"h\",\"basicLSTMCell\"),d=l.concat(p,1).matMul(s).add(u),h=d.shape[0],f=d.shape[1]/4,m=[h,f],g=d.slice([0,0],m),v=d.slice([0,f],m),y=d.slice([0,2*f],m),x=d.slice([0,3*f],m),T=g.sigmoid().mulStrict(v.tanh()).addStrict(c.mulStrict(i.add(y).sigmoid()));return[T,T.tanh().mulStrict(x.sigmoid())]}var basicLSTMCell=op({basicLSTMCell_:basicLSTMCell_}),multiRNNCell=op({multiRNNCell_:multiRNNCell_});function movingAverage_(e,t,n,r,o){void 0===o&&(o=!0);var a=convertToTensor(e,\"v\",\"movingAverage\"),i=convertToTensor(t,\"x\",\"movingAverage\"),s=convertToTensor(n,\"decay\",\"movingAverage\");assertTypesMatch(a,i),assert(arraysEqual(a.shape,i.shape),\"Shape mismatch in v and x\");var u=scalar(1),l=u.sub(s),c=i.sub(a).mul(l);if(o){assert(null!=r,\"When using zeroDebias: true, step is required.\");var p=convertToTensor(r,\"step\",\"movingAverage\");c=c.div(u.sub(pow(s,p)))}return a.add(c)}var movingAverage=op({movingAverage_:movingAverage_});function stridedSlice_(e,t,n,r,o,a,i,s,u){if(void 0===o&&(o=0),void 0===a&&(a=0),void 0===i&&(i=0),void 0===s&&(s=0),void 0===u&&(u=0),0!==i)throw new Error(\"ellipsis mask is not yet supported\");if(0!==s)throw new Error(\"new axis mask is not yet supported\");var l=convertToTensor(e,\"x\",\"stridedSlice\");return ENV.engine.runKernel(function(e){return e.stridedSlice(l,t,n,r,o,a,i,s,u)},{$x:l})}var stridedSlice=op({stridedSlice_:stridedSlice_});function topk_(e,t,n){void 0===t&&(t=1),void 0===n&&(n=!0);var r=convertToTensor(e,\"x\",\"topk\");if(0===r.rank)throw new Error(\"topk() expects the input to be of rank 1 or higher\");var o=r.shape[r.shape.length-1];if(t>o)throw new Error(\"'k' passed to topk() must be <= the last dimension (\"+o+\") but got \"+t);var a=ENV.engine.runKernel(function(e){return e.topk(r,t,n)},{$x:r});return{values:a[0],indices:a[1]}}var topk=op({topk_:topk_});function scatterND_(e,t,n){var r=convertToTensor(e,\"indices\",\"scatterND\",\"int32\"),o=convertToTensor(t,\"updates\",\"scatterND\");return validateInput(o,r,n),ENV.engine.runKernel(function(e){return e.scatterND(r,o,n)},{$indices:r,$updates:o})}var scatterND=op({scatterND_:scatterND_});function fft_(e){assert(\"complex64\"===e.dtype,\"The dtype for tf.spectral.fft() must be complex64 but got \"+e.dtype+\".\");var t=e.shape[e.shape.length-1],n=e.size/t,r=e.as2D(n,t);return ENV.engine.runKernel(function(e){return e.fft(r)},{input:e}).reshape(e.shape)}function ifft_(e){assert(\"complex64\"===e.dtype,\"The dtype for tf.spectral.ifft() must be complex64 but got \"+e.dtype+\".\");var t=e.shape[e.shape.length-1],n=e.size/t,r=e.as2D(n,t);return ENV.engine.runKernel(function(e){return e.ifft(r)},{input:e}).reshape(e.shape)}function rfft_(e){assert(\"float32\"===e.dtype,\"The dtype for rfft() must be real value but\\n    got \"+e.dtype);var t=e.shape[e.shape.length-1],n=e.size/t,r=e.zerosLike(),o=complex(e,r).as2D(n,t),a=fft(o),i=Math.floor(t/2)+1,s=real(a),u=imag(a),l=s.split([i,t-i],s.shape.length-1),c=u.split([i,t-i],u.shape.length-1),p=e.shape.slice();return p[e.shape.length-1]=i,complex(l[0],c[0]).reshape(p)}function irfft_(e){var t=e.shape[e.shape.length-1],n=e.size/t;if(t<=2){var r=e.as2D(n,t),o=ifft(r);return real(o)}var a=[n,2*(t-1)],i=real(e).as2D(n,t),s=imag(e).as2D(n,t),u=i.slice([0,1],[n,t-2]).reverse(1),l=s.slice([0,1],[n,t-2]).reverse(1).mul(scalar(-1)),c=i.concat(u,1),p=s.concat(l,1);r=complex(c,p).as2D(a[0],a[1]),o=ifft(r);return real(o)}var fft=op({fft_:fft_}),ifft=op({ifft_:ifft_}),rfft=op({rfft_:rfft_}),irfft=op({irfft_:irfft_}),spectral_ops=Object.freeze({fft:fft,ifft:ifft,rfft:rfft,irfft:irfft});function validateInput$1(e,t,n,r){if(\"int32\"!==e.dtype)throw new Error(\"tf.sparseToDense() expects the indices to be int32 type, but the dtype was \"+e.dtype+\".\");if(e.rank>2)throw new Error(\"sparseIndices should be a scalar, vector, or matrix, but got shape \"+e.shape+\".\");var o=e.rank>0?e.shape[0]:1,a=e.rank>1?e.shape[1]:1;if(n.length!==a)throw new Error(\"outputShape has incorrect number of elements:, \"+n.length+\", should be: \"+a+\".\");var i=t.size;if(0!==t.rank&&(1!==t.rank||i!==o))throw new Error(\"sparseValues has incorrect shape \"+t.shape+\", should be [] or [\"+o+\"]\");if(t.dtype!==r.dtype)throw new Error(\"sparseValues.dtype must match defaultValues.dtype\")}function sparseToDense_(e,t,n,r){var o=convertToTensor(e,\"sparseIndices\",\"sparseToDense\",\"int32\"),a=convertToTensor(t,\"sparseValues\",\"sparseToDense\"),i=convertToTensor(r,\"defaultValue\",\"sparseToDense\",a.dtype);return validateInput$1(o,a,n,i),ENV.engine.runKernel(function(e){return e.sparseToDense(o,a,n,i)},{$sparseIndices:o,$sparseValues:a,$defaultValue:i})}var sparseToDense=op({sparseToDense_:sparseToDense_});function gatherND_(e,t){var n=convertToTensor(t,\"indices\",\"gatherND\",\"int32\"),r=convertToTensor(e,\"x\",\"gatherND\");return ENV.engine.runKernel(function(e){return e.gatherND(r,n)},{$x:r,$indices:n})}var Reduction,gatherND=op({gatherND_:gatherND_});function computeWeightedLoss_(e,t,n){void 0===n&&(n=Reduction.SUM_BY_NONZERO_WEIGHTS);var r=convertToTensor(e,\"losses\",\"computeWeightedLoss\"),o=null;null!=t&&(o=convertToTensor(t,\"weights\",\"computeWeightedLoss\"));var a=null==o?r:r.mul(o);if(n===Reduction.NONE)return a;if(n===Reduction.SUM)return a.sum();if(n===Reduction.MEAN){if(null==o)return a.mean();var i=sizeFromShape(r.shape)/sizeFromShape(o.shape),s=a.sum().div(o.sum());return i>1?s.div(scalar(i)):s}if(n===Reduction.SUM_BY_NONZERO_WEIGHTS){if(null==o)return a.sum().div(scalar(r.size));var u=o.mul(ones$1(r.shape)).notEqual(scalar(0)).sum().toFloat();return a.sum().div(u)}throw Error(\"Unknown reduction: \"+n)}function absoluteDifference_(e,t,n,r){void 0===r&&(r=Reduction.SUM_BY_NONZERO_WEIGHTS);var o=convertToTensor(e,\"labels\",\"absoluteDifference\"),a=convertToTensor(t,\"predictions\",\"absoluteDifference\"),i=null;null!=n&&(i=convertToTensor(n,\"weights\",\"absoluteDifference\")),assertShapesMatch(o.shape,a.shape,\"Error in absoluteDifference: \");var s=o.sub(a).abs();return computeWeightedLoss(s,i,r)}function meanSquaredError_(e,t,n,r){void 0===r&&(r=Reduction.SUM_BY_NONZERO_WEIGHTS);var o=convertToTensor(e,\"labels\",\"meanSquaredError\"),a=convertToTensor(t,\"predictions\",\"meanSquaredError\"),i=null;null!=n&&(i=convertToTensor(n,\"weights\",\"meanSquaredError\")),assertShapesMatch(o.shape,a.shape,\"Error in meanSquaredError: \");var s=o.squaredDifference(a);return computeWeightedLoss(s,i,r)}function cosineDistance_(e,t,n,r,o){void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,\"labels\",\"cosineDistance\"),i=convertToTensor(t,\"predictions\",\"cosineDistance\"),s=null;null!=r&&(s=convertToTensor(r,\"weights\",\"cosineDistance\")),assertShapesMatch(a.shape,i.shape,\"Error in cosineDistance: \");var u=scalar(1).sub(a.mul(i).sum(n,!0));return computeWeightedLoss(u,s,o)}function hingeLoss_(e,t,n,r){void 0===r&&(r=Reduction.SUM_BY_NONZERO_WEIGHTS);var o=convertToTensor(e,\"labels\",\"hingeLoss\"),a=convertToTensor(t,\"predictions\",\"hingeLoss\"),i=null;null!=n&&(i=convertToTensor(n,\"weights\",\"hingeLoss\")),assertShapesMatch(o.shape,a.shape,\"Error in hingeLoss: \");var s=scalar(1);o=scalar(2).mul(o).sub(s);var u=s.sub(o.mul(a)).relu();return computeWeightedLoss(u,i,r)}function logLoss_(e,t,n,r,o){void 0===r&&(r=1e-7),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,\"labels\",\"logLoss\"),i=convertToTensor(t,\"predictions\",\"logLoss\"),s=null;null!=n&&(s=convertToTensor(n,\"weights\",\"logLoss\")),assertShapesMatch(a.shape,i.shape,\"Error in logLoss: \");var u=scalar(1),l=scalar(r),c=a.mul(i.add(l).log()).neg().sub(u.sub(a).mul(u.sub(i).add(l).log()));return computeWeightedLoss(c,s,o)}function sigmoidCrossEntropyWithLogits_(e,t){var n=convertToTensor(e,\"labels\",\"sigmoidCrossEntropyWithLogits\"),r=convertToTensor(t,\"logits\",\"sigmoidCrossEntropyWithLogits\");assertShapesMatch(n.shape,r.shape,\"Error in sigmoidCrossEntropyWithLogits: \");var o=r.relu(),a=r.mul(n),i=r.abs().neg().exp().log1p();return o.sub(a).add(i)}function sigmoidCrossEntropy_(e,t,n,r,o){void 0===r&&(r=0),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,\"multiClassLabels\",\"sigmoidCrossEntropy\"),i=convertToTensor(t,\"logits\",\"sigmoidCrossEntropy\"),s=null;if(null!=n&&(s=convertToTensor(n,\"weights\",\"sigmoidCrossEntropy\")),assertShapesMatch(a.shape,i.shape,\"Error in sigmoidCrossEntropy: \"),r>0){var u=scalar(r),l=scalar(1),c=scalar(.5);a=a.mul(l.sub(u)).add(c.mul(u))}var p=sigmoidCrossEntropyWithLogits_(a,i);return computeWeightedLoss(p,s,o)}function huberLoss_(e,t,n,r,o){void 0===r&&(r=1),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,\"labels\",\"huberLoss\"),i=convertToTensor(t,\"predictions\",\"huberLoss\"),s=null;null!=n&&(s=convertToTensor(n,\"weights\",\"huberLoss\")),assertShapesMatch(a.shape,i.shape,\"Error in huberLoss: \");var u=scalar(r),l=i.sub(a).abs(),c=minimum(l,u),p=l.sub(c),d=scalar(.5).mul(c.square()).add(u.mul(p));return computeWeightedLoss(d,s,o)}function softmaxCrossEntropyWithLogits_(e,t,n){if(void 0===n&&(n=-1),-1===n&&(n=t.rank-1),n!==t.rank-1)throw Error(\"Softmax cross entropy along a non-last dimension is not yet supported. Labels / logits was rank \"+t.rank+\" and dim was \"+n);return customGrad(function(e,t){var r=t.logSumExp([n],!0),o=t.toFloat().sub(r);return{value:o.mul(e).neg().sum([n]),gradFunc:function(t){var r=expandShapeToKeepDim(t.shape,[n]);return[t.reshape(r).mul(e.toFloat().sub(o.exp())),t.reshape(r).mul(o.exp().sub(e.toFloat()))]}}})(e,t)}function softmaxCrossEntropy_(e,t,n,r,o){void 0===r&&(r=0),void 0===o&&(o=Reduction.SUM_BY_NONZERO_WEIGHTS);var a=convertToTensor(e,\"onehotLabels\",\"softmaxCrossEntropy\"),i=convertToTensor(t,\"logits\",\"softmaxCrossEntropy\"),s=null;if(null!=n&&(s=convertToTensor(n,\"weights\",\"softmaxCrossEntropy\")),assertShapesMatch(a.shape,i.shape,\"Error in softmaxCrossEntropy: \"),r>0){var u=scalar(r),l=scalar(1),c=scalar(a.shape[1]);a=a.mul(l.sub(u)).add(u.div(c))}var p=softmaxCrossEntropyWithLogits_(a,i);return computeWeightedLoss(p,s,o)}!function(e){e[e.NONE=0]=\"NONE\",e[e.MEAN=1]=\"MEAN\",e[e.SUM=2]=\"SUM\",e[e.SUM_BY_NONZERO_WEIGHTS=3]=\"SUM_BY_NONZERO_WEIGHTS\"}(Reduction||(Reduction={}));var absoluteDifference=op({absoluteDifference_:absoluteDifference_}),computeWeightedLoss=op({computeWeightedLoss_:computeWeightedLoss_}),cosineDistance=op({cosineDistance_:cosineDistance_}),hingeLoss=op({hingeLoss_:hingeLoss_}),huberLoss=op({huberLoss_:huberLoss_}),logLoss=op({logLoss_:logLoss_}),meanSquaredError=op({meanSquaredError_:meanSquaredError_}),sigmoidCrossEntropy=op({sigmoidCrossEntropy_:sigmoidCrossEntropy_}),softmaxCrossEntropy=op({softmaxCrossEntropy_:softmaxCrossEntropy_}),loss_ops=Object.freeze({get Reduction(){return Reduction},absoluteDifference:absoluteDifference,computeWeightedLoss:computeWeightedLoss,cosineDistance:cosineDistance,hingeLoss:hingeLoss,huberLoss:huberLoss,logLoss:logLoss,meanSquaredError:meanSquaredError,sigmoidCrossEntropy:sigmoidCrossEntropy,softmaxCrossEntropy:softmaxCrossEntropy});function gramSchmidt_(e){var t;if(Array.isArray(e)){t=!1,assert(null!=e&&e.length>0,\"Gram-Schmidt process: input must not be null, undefined, or empty\");for(var n=e[0].shape[0],r=1;r<e.length;++r)assert(e[r].shape[0]===n,\"Gram-Schmidt: Non-unique lengths found in the input vectors: (\"+e[r].shape[0]+\" vs. \"+n+\")\")}else t=!0,e=split$1(e,e.shape[0],0).map(function(e){return squeeze(e,[0])});assert(e.length<=e[0].shape[0],\"Gram-Schmidt: Number of vectors (\"+e.length+\") exceeds number of dimensions (\"+e[0].shape[0]+\").\");var o=[],a=e,i=function(e){o.push(ENV.engine.tidy(function(){var t=a[e];if(e>0)for(var n=0;n<e;++n){var r=sum$1(o[n].mulStrict(t)).mul(o[n]);t=t.sub(r)}return t.div(norm(t,\"euclidean\"))}))};for(r=0;r<e.length;++r)i(r);return t?stack(o,0):o}function qr_(e,t){if(void 0===t&&(t=!1),e.rank<2)throw new Error(\"qr() requires input tensor to have a rank >= 2, but got rank \"+e.rank);if(2===e.rank)return qr2d(e,t);var n=e.shape.slice(0,e.shape.length-2).reduce(function(e,t){return e*t}),r=[],o=[];return unstack(e.reshape([n,e.shape[e.shape.length-2],e.shape[e.shape.length-1]]),0).forEach(function(e){var n=qr2d(e,t),a=n[0],i=n[1];r.push(a),o.push(i)}),[stack(r,0).reshape(e.shape),stack(o,0).reshape(e.shape)]}function qr2d(e,t){return void 0===t&&(t=!1),ENV.engine.tidy(function(){if(2!==e.shape.length)throw new Error(\"qr2d() requires a 2D Tensor, but got a \"+e.shape.length+\"D Tensor.\");for(var n=e.shape[0],r=e.shape[1],o=eye(n),a=e.clone(),i=tensor2d([[1]],[1,1]),s=i.clone(),u=n>=r?r:n,l=function(e){var t,u=a,l=s,c=o;t=ENV.engine.tidy(function(){var t=a.slice([e,e],[n-e,1]),u=t.norm(),l=a.slice([e,e],[1,1]),c=l.sign().neg(),p=l.sub(c.mul(u)),d=t.div(p);s=1===d.shape[0]?i.clone():i.concat(d.slice([1,0],[d.shape[0]-1,d.shape[1]]),0);var h=c.matMul(p).div(u).neg(),f=a.slice([e,0],[n-e,r]),m=h.mul(s);a=0===e?f.sub(m.matMul(s.transpose().matMul(f))):a.slice([0,0],[e,r]).concat(f.sub(m.matMul(s.transpose().matMul(f))),0);var g=o.slice([0,e],[n,o.shape[1]-e]);return o=0===e?g.sub(g.matMul(s).matMul(m.transpose())):o.slice([0,0],[n,e]).concat(g.sub(g.matMul(s).matMul(m.transpose())),1),[s,a,o]}),s=t[0],a=t[1],o=t[2],dispose([u,l,c])},c=0;c<u;++c)l(c);return!t&&n>r&&(o=o.slice([0,0],[n,r]),a=a.slice([0,0],[r,r])),[o,a]})}var gramSchmidt=op({gramSchmidt_:gramSchmidt_}),qr=op({qr_:qr_}),linalg_ops=Object.freeze({gramSchmidt:gramSchmidt,qr:qr});function resizeBilinear_(e,t,n){void 0===n&&(n=!1);var r=convertToTensor(e,\"images\",\"resizeBilinear\");assert(3===r.rank||4===r.rank,\"Error in resizeBilinear: x must be rank 3 or 4, but got rank \"+r.rank+\".\"),assert(2===t.length,\"Error in resizeBilinear: new shape must 2D, but got shape \"+t+\".\");var o=r,a=!1;3===r.rank&&(a=!0,o=r.as4D(1,r.shape[0],r.shape[1],r.shape[2]));var i=t[0],s=t[1],u=ENV.engine.runKernel(function(e,t){return e.resizeBilinear(o,i,s,n)},{batchImages:o},function(e,t){return{batchImages:function(){return ENV.engine.runKernel(function(t){return t.resizeBilinearBackprop(e,o,n)},{})}}});return a?u.as3D(u.shape[1],u.shape[2],u.shape[3]):u}function resizeNearestNeighbor_(e,t,n){void 0===n&&(n=!1);var r=convertToTensor(e,\"images\",\"resizeNearestNeighbor\");assert(3===r.rank||4===r.rank,\"Error in resizeNearestNeighbor: x must be rank 3 or 4, but got rank \"+r.rank+\".\"),assert(2===t.length,\"Error in resizeNearestNeighbor: new shape must 2D, but got shape \"+t+\".\"),assert(\"float32\"===r.dtype||\"int32\"===r.dtype,\"`images` must have `int32` or `float32` as dtype\");var o=r,a=!1;3===r.rank&&(a=!0,o=r.as4D(1,r.shape[0],r.shape[1],r.shape[2]));var i=t[0],s=t[1],u=ENV.engine.runKernel(function(e,t){return e.resizeNearestNeighbor(o,i,s,n)},{batchImages:o},function(e,t){return{batchImages:function(){return ENV.engine.runKernel(function(t){return t.resizeNearestNeighborBackprop(e,o,n)},{})}}});return a?u.as3D(u.shape[1],u.shape[2],u.shape[3]):u}function nonMaxSuppression_(e,t,n,r,o){void 0===r&&(r=.5),void 0===o&&(o=Number.NEGATIVE_INFINITY);var a=convertToTensor(e,\"boxes\",\"nonMaxSuppression\"),i=convertToTensor(t,\"scores\",\"nonMaxSuppression\"),s=nonMaxSuppSanityCheck(a,i,n,r,o);return n=s.maxOutputSize,r=s.iouThreshold,o=s.scoreThreshold,ENV.engine.runKernel(function(e){return e.nonMaxSuppression(a,i,n,r,o)},{$boxes:a})}function nonMaxSuppressionAsync_(e,t,n,r,o){return void 0===r&&(r=.5),void 0===o&&(o=Number.NEGATIVE_INFINITY),__awaiter(this,void 0,void 0,function(){var a,i,s,u,l,c;return __generator(this,function(p){switch(p.label){case 0:return a=convertToTensor(e,\"boxes\",\"nonMaxSuppressionAsync\"),i=convertToTensor(t,\"scores\",\"nonMaxSuppressionAsync\"),s=nonMaxSuppSanityCheck(a,i,n,r,o),n=s.maxOutputSize,r=s.iouThreshold,o=s.scoreThreshold,[4,a.data()];case 1:return u=p.sent(),[4,i.data()];case 2:return l=p.sent(),c=nonMaxSuppressionImpl(u,l,n,r,o),a!==e&&a.dispose(),i!==t&&i.dispose(),[2,c]}})})}function nonMaxSuppSanityCheck(e,t,n,r,o){null==r&&(r=.5),null==o&&(o=Number.NEGATIVE_INFINITY);var a=e.shape[0];return n=Math.min(n,a),assert(0<=r&&r<=1,\"iouThreshold must be in [0, 1], but was '\"+r+\"'\"),assert(2===e.rank,\"boxes must be a 2D tensor, but was of rank '\"+e.rank+\"'\"),assert(4===e.shape[1],\"boxes must have 4 columns, but 2nd dimension was \"+e.shape[1]),assert(1===t.rank,\"scores must be a 1D tensor\"),assert(t.shape[0]===a,\"scores has incompatible shape with boxes. Expected \"+a+\", but was \"+t.shape[0]),{maxOutputSize:n,iouThreshold:r,scoreThreshold:o}}function cropAndResize_(e,t,n,r,o,a){var i=convertToTensor(e,\"image\",\"cropAndResize\",\"float32\"),s=convertToTensor(t,\"boxes\",\"cropAndResize\",\"float32\"),u=convertToTensor(n,\"boxInd\",\"cropAndResize\",\"int32\");o=o||\"bilinear\",a=a||0;var l=s.shape[0];assert(4===i.rank,\"Error in cropAndResize: image must be rank 4,but got rank \"+i.rank+\".\"),assert(2===s.rank&&4===s.shape[1],\"Error in cropAndResize: boxes must be have size [\"+l+\",4] but had shape \"+s.shape+\".\"),assert(1===u.rank&&u.shape[0]===l,\"Error in cropAndResize: boxInd must be have size [\"+l+\"] but had shape \"+s.shape+\".\"),assert(2===r.length,\"Error in cropAndResize: cropSize must be of length 2, but got length \"+r.length+\".\"),assert(r[0]>=1&&r[1]>=1,\"cropSize must be atleast [1,1], but was \"+r),assert(\"bilinear\"===o||\"nearest\"===o,\"method must be bilinear or nearest, but was \"+o);return ENV.engine.runKernel(function(e,t){return e.cropAndResize(i,s,u,r,o,a)},{$image:i,$boxes:s})}var resizeBilinear=op({resizeBilinear_:resizeBilinear_}),resizeNearestNeighbor=op({resizeNearestNeighbor_:resizeNearestNeighbor_}),nonMaxSuppression=op({nonMaxSuppression_:nonMaxSuppression_}),nonMaxSuppressionAsync=nonMaxSuppressionAsync_,cropAndResize=cropAndResize_,image_ops=Object.freeze({resizeBilinear:resizeBilinear,resizeNearestNeighbor:resizeNearestNeighbor,nonMaxSuppression:nonMaxSuppression,nonMaxSuppressionAsync:nonMaxSuppressionAsync,cropAndResize:cropAndResize}),ops=Object.freeze({image:image_ops,linalg:linalg_ops,losses:loss_ops,spectral:spectral_ops,op:op,batchNormalization2d:batchNormalization2d,batchNormalization3d:batchNormalization3d,batchNormalization4d:batchNormalization4d,batchNormalization:batchNormalization,complex:complex,real:real,imag:imag,concat:concat,concat1d:concat1d,concat2d:concat2d,concat3d:concat3d,concat4d:concat4d,split:split$1,conv1d:conv1d,conv2d:conv2d,conv3d:conv3d,conv2dDerFilter:conv2dDerFilter,depthwiseConv2d:depthwiseConv2d,separableConv2d:separableConv2d,conv2dTranspose:conv2dTranspose,matMul:matMul,dot:dot,outerProduct:outerProduct,reverse:reverse,reverse1d:reverse1d,reverse2d:reverse2d,reverse3d:reverse3d,reverse4d:reverse4d,maxPool:maxPool,avgPool:avgPool,pool:pool,slice:slice,slice1d:slice1d,slice2d:slice2d,slice3d:slice3d,slice4d:slice4d,abs:abs,acos:acos,acosh:acosh,asin:asin,asinh:asinh,atan:atan,atanh:atanh,ceil:ceil,clipByValue:clipByValue,cos:cos,cosh:cosh,erf:erf,exp:exp,expm1:expm1,floor:floor,log:log$1,log1p:log1p,logSigmoid:logSigmoid,neg:neg,reciprocal:reciprocal,round:round,rsqrt:rsqrt,sigmoid:sigmoid,sign:sign,sin:sin,sinh:sinh,softplus:softplus,sqrt:sqrt,square:square,step:step,tan:tan,tanh:tanh$1,all:all,any:any,argMax:argMax,argMin:argMin,logSumExp:logSumExp,max:max,mean:mean,min:min,moments:moments,sum:sum$1,prod:prod,equal:equal,equalStrict:equalStrict,greater:greater,greaterEqual:greaterEqual,greaterEqualStrict:greaterEqualStrict,greaterStrict:greaterStrict,less:less,lessEqual:lessEqual,lessEqualStrict:lessEqualStrict,lessStrict:lessStrict,notEqual:notEqual,notEqualStrict:notEqualStrict,add:add,addN:addN,addStrict:addStrict,atan2:atan2,div:div,divStrict:divStrict,floorDiv:floorDiv,maximum:maximum,maximumStrict:maximumStrict,minimum:minimum,minimumStrict:minimumStrict,mod:mod,modStrict:modStrict,mul:mul,mulStrict:mulStrict,pow:pow,powStrict:powStrict,squaredDifference:squaredDifference,squaredDifferenceStrict:squaredDifferenceStrict,sub:sub,subStrict:subStrict,elu:elu,leakyRelu:leakyRelu,prelu:prelu,relu:relu,selu:selu,logicalAnd:logicalAnd,logicalNot:logicalNot,logicalOr:logicalOr,logicalXor:logicalXor,where:where,whereAsync:whereAsync,buffer:buffer,toPixels:toPixels,print:print,batchToSpaceND:batchToSpaceND,cast:cast,clone:clone,cumsum:cumsum,depthToSpace:depthToSpace,expandDims:expandDims,eye:eye,fromPixels:fromPixels,multinomial:multinomial,oneHot:oneHot,pad:pad,pad1d:pad1d,pad2d:pad2d,pad3d:pad3d,pad4d:pad4d,rand:rand,randomNormal:randomNormal,randomUniform:randomUniform,reshape:reshape,spaceToBatchND:spaceToBatchND,squeeze:squeeze,stack:stack,tile:tile,truncatedNormal:truncatedNormal,unstack:unstack,setdiff1dAsync:setdiff1dAsync,fill:fill,linspace:linspace,ones:ones$1,range:range,scalar:scalar,tensor:tensor,tensor1d:tensor1d,tensor2d:tensor2d,tensor3d:tensor3d,tensor4d:tensor4d,tensor5d:tensor5d,tensor6d:tensor6d,zeros:zeros,onesLike:onesLike,zerosLike:zerosLike,transpose:transpose,softmax:softmax,logSoftmax:logSoftmax,localResponseNormalization:localResponseNormalization,norm:norm,gather:gather,unsortedSegmentSum:unsortedSegmentSum,basicLSTMCell:basicLSTMCell,multiRNNCell:multiRNNCell,movingAverage:movingAverage,stridedSlice:stridedSlice,topk:topk,scatterND:scatterND,fft:fft,ifft:ifft,rfft:rfft,irfft:irfft,sparseToDense:sparseToDense,gatherND:gatherND}),MathBackendCPU=function(){function e(){this.blockSize=48,this.firstUse=!0,ENV.get(\"IS_BROWSER\")&&(this.fromPixels2DContext=document.createElement(\"canvas\").getContext(\"2d\"))}return e.prototype.setDataMover=function(e){this.data=new DataStorage(e)},e.prototype.register=function(e,t,n){if(this.firstUse&&(this.firstUse=!1,ENV.get(\"IS_NODE\")&&warn(\"\\n============================\\nHi there 👋. Looks like you are running TensorFlow.js in Node.js. To speed things up dramatically, install our node backend, which binds to TensorFlow C++, by running npm i @tensorflow/tfjs-node, or npm i @tensorflow/tfjs-node-gpu if you have CUDA. Then call require('@tensorflow/tfjs-node'); (-gpu suffix for CUDA) at the start of your program. Visit https://github.com/tensorflow/tfjs-node for more details.\\n============================\\n\")),this.data.has(e))throw new Error(\"Data buffer is already registered\");this.data.set(e,{dtype:n})},e.prototype.write=function(e,t){if(null==t)throw new Error(\"MathBackendCPU.write(): values can not be null\");this.data.get(e).values=t},e.prototype.fromPixels=function(e,t){if(null==e)throw new Error(\"pixels passed to tf.fromPixels() can not be null\");var n,r;if(ENV.get(\"IS_NODE\")&&null==e.getContext)throw new Error(\"When running in node, pixels must be an HTMLCanvasElement like the one returned by the `canvas` npm package\");if(null!=e.getContext)n=e.getContext(\"2d\").getImageData(0,0,e.width,e.height).data;else if(e instanceof ImageData)n=e.data;else{if(!(e instanceof HTMLImageElement||e instanceof HTMLVideoElement))throw new Error(\"pixels passed to tf.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ImageData, but was \"+e.constructor.name);if(null==this.fromPixels2DContext)throw new Error(\"Can't read pixels from HTMLImageElement outside the browser.\");this.fromPixels2DContext.canvas.width=e.width,this.fromPixels2DContext.canvas.height=e.height,this.fromPixels2DContext.drawImage(e,0,0,e.width,e.height),n=this.fromPixels2DContext.getImageData(0,0,e.width,e.height).data}if(4===t)r=new Int32Array(n);else{var o=e.width*e.height;r=new Int32Array(o*t);for(var a=0;a<o;a++)for(var i=0;i<t;++i)r[a*t+i]=n[4*a+i]}return tensor3d(r,[e.height,e.width,t],\"int32\")},e.prototype.read=function(e){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(t){return[2,this.readSync(e)]})})},e.prototype.readSync=function(e){var t=this.data.get(e),n=t.dtype,r=t.complexTensors;return\"complex64\"===n?mergeRealAndImagArrays(r.real.dataSync(),r.imag.dataSync()):this.data.get(e).values},e.prototype.disposeData=function(e){if(this.data.has(e)){var t=this.data.get(e).complexTensors;null!=t&&(t.real.dispose(),t.imag.dispose()),this.data.delete(e)}},e.prototype.time=function(e){return __awaiter(this,void 0,void 0,function(){var t;return __generator(this,function(n){return t=now(),e(),[2,{kernelMs:now()-t}]})})},e.prototype.memory=function(){return{unreliable:!0,reasons:[\"The reported memory is an upper bound. Due to automatic garbage collection, the true allocated memory may be less.\"]}},e.prototype.complex=function(e,t){var n=Tensor.make(e.shape,{},\"complex64\");return this.data.get(n.dataId).complexTensors={real:ENV.engine.keep(e.clone()),imag:ENV.engine.keep(t.clone())},n},e.prototype.real=function(e){return this.data.get(e.dataId).complexTensors.real.clone()},e.prototype.imag=function(e){return this.data.get(e.dataId).complexTensors.imag.clone()},e.prototype.assertNotComplex=function(e,t){Array.isArray(e)||(e=[e]),e.forEach(function(e){null!=e&&assert(\"complex64\"!==e.dtype,t+\" does not support complex64 tensors.\")})},e.prototype.slice=function(e,t,n){this.assertNotComplex(e,\"slice\");for(var r=buffer(n,e.dtype),o=0;o<r.size;++o){var a=r.indexToLoc(o),i=a.map(function(e,n){return e+t[n]});r.set.apply(r,[e.get.apply(e,i)].concat(a))}return r.toTensor()},e.prototype.stridedSlice=function(e,t,n,r,o,a,i,s,u){this.assertNotComplex(e,\"stridedSlice\");var l=getStridedSlicedInfo(e.shape,t,n,r,o,a,i,s,u),c=l[0],p=l[1],d=l[2],h=p.filter(function(e,t){return-1===d.indexOf(t)});if(h.some(function(e){return 0===e}))return tensor([],h);for(var f=buffer(p,e.dtype),m=0;m<f.size;m++){for(var g=f.indexToLoc(m),v=new Array(g.length),y=0;y<v.length;y++)v[y]=g[y]*r[y]+c[y];f.set.apply(f,[e.get.apply(e,v)].concat(g))}return f.toTensor().reshape(h)},e.prototype.reverse=function(e,t){this.assertNotComplex(e,\"reverse\");for(var n=buffer(e.shape,e.dtype),r=e.buffer(),o=function(o){var a=n.indexToLoc(o),i=a.slice();t.forEach(function(t){return i[t]=e.shape[t]-1-i[t]}),n.set.apply(n,[r.get.apply(r,i)].concat(a))},a=0;a<n.size;a++)o(a);return n.toTensor()},e.prototype.concat=function(e,t){this.assertNotComplex(e,\"concat\");var n=e.map(function(e){var n=sizeFromShape(e.shape.slice(t));return e.as2D(-1,n)}),r=computeOutShape(n.map(function(e){return e.shape}),1),o=buffer(r,e[0].dtype).values;if(1===n[0].shape[0]){var a=0;n.forEach(function(e){o.set(e.dataSync(),a),a+=e.size})}else{var i=0;n.forEach(function(e){for(var t=e.dataSync(),n=0,a=0;a<e.shape[0];++a)for(var s=a*r[1]+i,u=0;u<e.shape[1];++u)o[s+u]=t[n++];i+=e.shape[1]})}var s=computeOutShape(e.map(function(e){return e.shape}),t);return tensor(o,s,e[0].dtype)},e.prototype.neg=function(e){return this.assertNotComplex(e,\"neg\"),this.multiply(scalar(-1),e)},e.prototype.add=function(e,t){return\"complex64\"===e.dtype||\"complex64\"===t.dtype?this.broadcastedBinaryComplexOp(e.cast(\"complex64\"),t.cast(\"complex64\"),function(e,t,n,r){return{real:e+n,imag:t+r}}):this.broadcastedBinaryOp(e,t,upcastType(e.dtype,t.dtype),function(e,t){return e+t})},e.prototype.addN=function(e){this.assertNotComplex(e,\"addN\");for(var t=e.map(function(e){return e.dataSync()}),n=buffer(e[0].shape,e[0].dtype),r=n.values,o=0;o<e.length;o++)for(var a=t[o],i=0;i<r.length;i++)r[i]+=a[i];return n.toTensor()},e.prototype.subtract=function(e,t){return\"complex64\"===e.dtype||\"complex64\"===t.dtype?this.broadcastedBinaryComplexOp(e.cast(\"complex64\"),t.cast(\"complex64\"),function(e,t,n,r){return{real:e-n,imag:t-r}}):this.broadcastedBinaryOp(e,t,upcastType(e.dtype,t.dtype),function(e,t){return e-t})},e.prototype.pow=function(e,t){return this.assertNotComplex([e,t],\"pow\"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){return Math.pow(e,t)})},e.prototype.batchMatMul=function(e,t,n,r){this.assertNotComplex([e,t],\"matMul\");for(var o=n?e.shape[1]:e.shape[2],a=n?e.shape[2]:e.shape[1],i=r?t.shape[1]:t.shape[2],s=e.shape[0],u=e.dataSync(),l=t.dataSync(),c=n?[e.strides[0],1,e.strides[1]]:[e.strides[0],e.strides[1],1],p=c[0],d=c[1],h=c[2],f=r?[1,t.strides[1],t.strides[0]]:[t.strides[1],1,t.strides[0]],m=f[0],g=f[1],v=f[2],y=a*i,x=buffer([s,a,i],e.dtype),T=x.values,w=this.blockSize,E=0;E<s;E++)for(var S=0;S<a;S+=w)for(var b=0;b<i;b+=w)for(var C=0;C<o;C+=w)for(var 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n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,upcastType(e.dtype,\"int32\")),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),l=0;l<s.length;++l){for(var c=l*i,p=0,d=0;d<i;++d)p+=u[c+d];s[l]=p}return a},e.prototype.prod=function(e,t){this.assertNotComplex(e,\"sum\");for(var n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,upcastType(e.dtype,\"int32\")),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),l=0;l<s.length;++l){for(var c=l*i,p=1,d=0;d<i;++d)p*=u[c+d];s[l]=p}return a},e.prototype.unsortedSegmentSum=function(e,t,n){this.assertNotComplex(e,\"unsortedSegmentSum\");for(var r=[],o=e.rank-t.rank,a=0;a<o;++a)t=t.expandDims(a+1);for(a=0;a<n;++a){var i=scalar(a,\"int32\"),s=equal(i,t).asType(\"float32\").mul(e).sum(0);r.push(s)}return stack(r)},e.prototype.argMin=function(e,t){this.assertNotComplex(e,\"argMin\");var n=[t];assertAxesAreInnerMostDims(\"argMin\",n,e.rank);for(var r=computeOutAndReduceShapes(e.shape,n),o=r[0],a=r[1],i=zeros(o,\"int32\"),s=sizeFromShape(a),u=i.dataSync(),l=e.dataSync(),c=0;c<u.length;++c){for(var p=c*s,d=l[p],h=0,f=0;f<s;++f){var m=l[p+f];m<d&&(d=m,h=f)}u[c]=h}return i},e.prototype.argMax=function(e,t){this.assertNotComplex(e,\"argMax\");var n=[t];assertAxesAreInnerMostDims(\"argMax\",n,e.rank);for(var r=computeOutAndReduceShapes(e.shape,n),o=r[0],a=r[1],i=zeros(o,\"int32\"),s=sizeFromShape(a),u=i.dataSync(),l=e.dataSync(),c=0;c<u.length;++c){for(var p=c*s,d=l[p],h=0,f=0;f<s;++f){var m=l[p+f];m>d&&(d=m,h=f)}u[c]=h}return i},e.prototype.cumsum=function(e,t,n,r){if(this.assertNotComplex(e,\"cumsum\"),t!==e.rank-1)throw new Error(\"backend.cumsum in CPU expects an inner-most axis=\"+(e.rank-1)+\" but got axis=\"+t);for(var o=upcastType(e.dtype,\"int32\"),a=zeros(e.shape,o),i=a.dataSync(),s=e.dataSync(),u=e.shape[e.rank-1],l=r?function(e,t){return e+u-t-1}:function(e,t){return e+t},c=0;c<s.length;c+=u)for(var p=0;p<u;p++){var 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n=computeOutAndReduceShapes(e.shape,t),r=n[0],o=n[1],a=zeros(r,e.dtype),i=sizeFromShape(o),s=a.dataSync(),u=e.dataSync(),l=0;l<s.length;++l){for(var c=l*i,p=u[c],d=0;d<i;++d){var h=u[c+d];p=p||h}s[l]=p}return a},e.prototype.squaredDifference=function(e,t){return this.assertNotComplex([e,t],\"squaredDifference\"),this.broadcastedBinaryOp(e,t,e.dtype,function(e,t){var n=e-t;return n*n})},e.prototype.ceil=function(e){this.assertNotComplex(e,\"ceil\");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)n[r]=Math.ceil(t[r]);return Tensor.make(e.shape,{values:n})},e.prototype.floor=function(e){this.assertNotComplex(e,\"floor\");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)n[r]=Math.floor(t[r]);return Tensor.make(e.shape,{values:n})},e.prototype.sign=function(e){this.assertNotComplex(e,\"x\");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)t[r]<0?n[r]=-1:t[r]>0?n[r]=1:n[r]=0;return 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Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=Math.log1p(o)}return Tensor.make(e.shape,{values:n})},e.prototype.sqrt=function(e){this.assertNotComplex(e,\"sqrt\");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=Math.sqrt(o)}return Tensor.make(e.shape,{values:n})},e.prototype.rsqrt=function(e){this.assertNotComplex(e,\"rsqrt\");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=1/Math.sqrt(o)}return Tensor.make(e.shape,{values:n})},e.prototype.square=function(e){this.assertNotComplex(e,\"square\");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r){var o=t[r];n[r]=o*o}return Tensor.make(e.shape,{values:n})},e.prototype.reciprocal=function(e){this.assertNotComplex(e,\"reciprocal\");for(var t=e.dataSync(),n=new Float32Array(t.length),r=0;r<t.length;++r)n[r]=1/t[r];return Tensor.make(e.shape,{values:n})},e.prototype.relu=function(e){this.assertNotComplex(e,\"relu\");for(var t=zeros(e.shape,e.dtype),n=t.dataSync(),r=e.dataSync(),o=0;o<r.length;++o)n[o]=Math.max(0,r[o]);return t},e.prototype.elu=function(e){this.assertNotComplex(e,\"elu\");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r){var o=n[r];t[r]=o>=0?o:Math.exp(o)-1}return Tensor.make(e.shape,{values:t})},e.prototype.eluDer=function(e,t){this.assertNotComplex([e,t],\"eluDer\");for(var n=new Float32Array(t.size),r=t.dataSync(),o=e.dataSync(),a=0;a<r.length;++a){var i=r[a];n[a]=i>=1?o[a]:o[a]*(i+1)}return Tensor.make(t.shape,{values:n})},e.prototype.selu=function(e){this.assertNotComplex(e,\"selu\");for(var t=SELU_SCALEALPHA,n=SELU_SCALE,r=new Float32Array(e.size),o=e.dataSync(),a=0;a<o.length;++a){var i=o[a];r[a]=i>=0?n*i:t*(Math.exp(i)-1)}return Tensor.make(e.shape,{values:r})},e.prototype.clip=function(e,t,n){this.assertNotComplex(e,\"clip\");for(var r=new Float32Array(e.size),o=e.dataSync(),a=0;a<o.length;++a){var i=o[a];r[a]=i>n?n:i<t?t:i}return 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Tensor.make(e.shape,{values:t})},e.prototype.asinh=function(e){this.assertNotComplex(e,\"asinh\");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.asinh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.acosh=function(e){this.assertNotComplex(e,\"acosh\");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.acosh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.atanh=function(e){this.assertNotComplex(e,\"atanh\");for(var t=new Float32Array(e.size),n=e.dataSync(),r=0;r<n.length;++r)t[r]=Math.atanh(n[r]);return Tensor.make(e.shape,{values:t})},e.prototype.erf=function(e){this.assertNotComplex(e,\"erf\");for(var t=new Float32Array(e.size),n=e.dataSync(),r=ERF_P,o=ERF_A1,a=ERF_A2,i=ERF_A3,s=ERF_A4,u=ERF_A5,l=0;l<n.length;++l){var c=n[l],p=1/(1+r*c);t[l]=1-((((u*p+s)*p+i)*p+a)*p+o)*p*Math.exp(-c*c)}return Tensor.make(e.shape,{values:t})},e.prototype.step=function(e,t){void 0===t&&(t=0),this.assertNotComplex(e,\"step\");for(var n=new Float32Array(e.size),r=e.dataSync(),o=0;o<r.length;++o){var a=r[o];isNaN(a)?n[o]=NaN:n[o]=a>0?1:t}return Tensor.make(e.shape,{values:n})},e.prototype.conv2d=function(e,t,n){this.assertNotComplex([e,t],\"conv2d\");for(var r=n.filterHeight,o=n.filterWidth,a=n.dilationHeight,i=n.dilationWidth,s=n.padInfo.left,u=n.padInfo.top,l=buffer(n.outShape,e.dtype),c=e.dataSync(),p=t.dataSync(),d=l.values,h=0;h<n.batchSize;++h)for(var f=h*e.strides[0],m=h*l.strides[0],g=0;g<n.outHeight;++g)for(var v=m+g*l.strides[1],y=g*n.strideHeight-s,x=0;x<r;x++){var T=y+x*a;if(!(T<0||T>=n.inHeight))for(var w=x*t.strides[0],E=f+T*e.strides[1],S=0;S<n.outWidth;++S)for(var b=v+S*n.outChannels,C=S*n.strideWidth-u,A=0;A<o;A++){var _=C+A*i;if(!(_<0||_>=n.inWidth))for(var N=w+A*t.strides[1],R=E+_*n.inChannels,k=N,I=0;I<n.inChannels;++I){for(var D=c[R+I],M=0;M<n.outChannels;++M)d[b+M]+=D*p[k+M];k+=n.outChannels}}}return l.toTensor()},e.prototype.conv3d=function(e,t,n){for(var r=n.filterDepth,o=n.filterHeight,a=n.filterWidth,i=n.dilationDepth,s=n.dilationHeight,u=n.dilationWidth,l=n.padInfo.front,c=n.padInfo.left,p=n.padInfo.top,d=buffer(n.outShape,e.dtype),h=e.dataSync(),f=t.dataSync(),m=d.values,g=0;g<n.batchSize;++g)for(var v=g*e.strides[0],y=g*d.strides[0],x=0;x<n.outDepth;++x)for(var T=y+x*d.strides[1],w=x*n.strideDepth-l,E=0;E<r;E++){var S=w+E*i;if(!(S<0||S>=n.inDepth))for(var b=E*t.strides[0],C=v+S*e.strides[1],A=0;A<n.outHeight;++A)for(var _=T+A*d.strides[2],N=A*n.strideHeight-p,R=0;R<o;R++){var k=N+R*s;if(!(k<0||k>=n.inHeight))for(var I=b+R*t.strides[1],D=C+k*e.strides[2],M=0;M<n.outWidth;++M)for(var P=_+M*n.outChannels,O=M*n.strideWidth-c,F=0;F<a;F++){var L=O+F*u;if(!(L<0||L>=n.inWidth))for(var B=I+F*t.strides[2],U=D+L*n.inChannels,V=B,z=0;z<n.inChannels;++z){for(var H=h[U+z],W=0;W<n.outChannels;++W)m[P+W]+=H*f[V+W];V+=n.outChannels}}}}return d.toTensor()},e.prototype.conv2dDerInput=function(e,t,n){this.assertNotComplex([e,t],\"conv2dDerInput\");for(var r=buffer(n.inShape,\"float32\"),o=r.values,a=r.strides,i=a[0],s=a[1],u=a[2],l=e.dataSync(),c=e.strides,p=c[0],d=c[1],h=c[2],f=t.dataSync(),m=t.strides,g=m[0],v=m[1],y=m[2],x=n.batchSize,T=n.filterHeight,w=n.filterWidth,E=n.inChannels,S=n.inHeight,b=n.inWidth,C=n.outChannels,A=n.outHeight,_=n.outWidth,N=n.strideHeight,R=n.strideWidth,k=T-1-n.padInfo.top,I=w-1-n.padInfo.left,D=0;D<x;++D)for(var M=0;M<E;++M)for(var P=0;P<S;++P)for(var O=P-k,F=Math.max(0,Math.ceil(O/N)),L=Math.min(A,(T+O)/N),B=0;B<b;++B){for(var U=B-I,V=Math.max(0,Math.ceil(U/R)),z=Math.min(_,(w+U)/R),H=0,W=F;W<L;++W)for(var G=W*N-O,q=V;q<z;++q)for(var $=p*D+d*W+h*q,X=g*(T-1-G)+v*(w-1-(q*R-U))+y*M,K=0;K<C;++K){H+=l[$+K]*f[X+K]}o[i*D+s*P+u*B+M]=H}return r.toTensor()},e.prototype.conv3dDerInput=function(e,t,n){for(var r=buffer(n.inShape,\"float32\"),o=r.values,a=r.strides,i=a[0],s=a[1],u=a[2],l=a[3],c=e.dataSync(),p=e.strides,d=p[0],h=p[1],f=p[2],m=p[3],g=t.dataSync(),v=t.strides,y=v[0],x=v[1],T=v[2],w=v[3],E=n.batchSize,S=n.filterDepth,b=n.filterHeight,C=n.filterWidth,A=n.inChannels,_=n.inDepth,N=n.inHeight,R=n.inWidth,k=n.outChannels,I=n.outDepth,D=n.outHeight,M=n.outWidth,P=n.strideDepth,O=n.strideHeight,F=n.strideWidth,L=S-1-n.padInfo.front,B=b-1-n.padInfo.top,U=C-1-n.padInfo.left,V=0;V<E;++V)for(var z=0;z<A;++z)for(var H=0;H<_;++H)for(var W=H-L,G=Math.max(0,Math.ceil(W/P)),q=Math.min(I,(S+W)/P),$=0;$<N;++$)for(var X=$-B,K=Math.max(0,Math.ceil(X/O)),j=Math.min(D,(b+X)/O),Y=0;Y<R;++Y){for(var Q=Y-U,J=Math.max(0,Math.ceil(Q/F)),Z=Math.min(M,(C+Q)/F),ee=0,te=G;te<q;++te)for(var ne=te*P-W,re=K;re<j;++re)for(var oe=re*O-X,ae=J;ae<Z;++ae)for(var ie=d*V+h*te+f*re+m*ae,se=y*(S-1-ne)+x*(b-1-oe)+T*(C-1-(ae*F-Q))+w*z,ue=0;ue<k;++ue){ee+=c[ie+ue]*g[se+ue]}o[i*V+s*H+u*$+l*Y+z]=ee}return r.toTensor()},e.prototype.conv2dDerFilter=function(e,t,n){this.assertNotComplex([e,t],\"conv2dDerFilter\");for(var r=n.strideHeight,o=n.strideWidth,a=n.filterHeight,i=n.filterWidth,s=buffer(n.filterShape,\"float32\"),u=n.padInfo.left,l=n.padInfo.top,c=0;c<a;++c)for(var p=Math.max(0,Math.ceil((l-c)/r)),d=Math.min(n.outHeight,(n.inHeight+l-c)/r),h=0;h<i;++h)for(var f=Math.max(0,Math.ceil((u-h)/o)),m=Math.min(n.outWidth,(n.inWidth+u-h)/o),g=0;g<n.inChannels;++g)for(var v=0;v<n.outChannels;++v){for(var y=0,x=0;x<n.batchSize;++x)for(var T=p;T<d;++T)for(var w=c+T*r-l,E=f;E<m;++E){var S=h+E*o-u;y+=e.get(x,w,S,g)*t.get(x,T,E,v)}s.set(y,c,h,g,v)}return s.toTensor()},e.prototype.conv3dDerFilter=function(e,t,n){for(var r=n.strideDepth,o=n.strideHeight,a=n.strideWidth,i=n.filterDepth,s=n.filterHeight,u=n.filterWidth,l=buffer(n.filterShape,\"float32\"),c=l.values,p=l.strides,d=p[0],h=p[1],f=p[2],m=p[3],g=t.dataSync(),v=t.strides,y=v[0],x=v[1],T=v[2],w=v[3],E=e.dataSync(),S=e.strides,b=S[0],C=S[1],A=S[2],_=S[3],N=n.padInfo.front,R=n.padInfo.left,k=n.padInfo.top,I=0;I<i;++I)for(var D=Math.max(0,Math.ceil((N-I)/r)),M=Math.min(n.outDepth,(n.inDepth+N-I)/r),P=I*d,O=0;O<s;++O)for(var F=Math.max(0,Math.ceil((k-O)/o)),L=Math.min(n.outHeight,(n.inHeight+k-O)/o),B=O*h+P,U=0;U<u;++U)for(var V=Math.max(0,Math.ceil((R-U)/a)),z=Math.min(n.outWidth,(n.inWidth+R-U)/a),H=U*f+B,W=0;W<n.inChannels;++W)for(var G=W*m+H,q=0;q<n.outChannels;++q){for(var $=0,X=0;X<n.batchSize;++X)for(var K=X*b,j=X*y,Y=D;Y<M;++Y)for(var Q=(I+Y*r-N)*C+K,J=Y*x+j,Z=F;Z<L;++Z)for(var ee=(O+Z*o-k)*A+Q,te=Z*T+J,ne=V;ne<z;++ne){var re=ne*w+te;$+=E[(U+ne*a-R)*_+ee+W]*g[re+q]}c[G+q]=$}return l.toTensor()},e.prototype.depthwiseConv2D=function(e,t,n){this.assertNotComplex([e,t],\"depthwiseConv2D\");for(var r=n.filterHeight,o=n.filterWidth,a=n.dilationHeight,i=n.dilationWidth,s=n.padInfo.left,u=n.padInfo.top,l=n.outChannels/n.inChannels,c=buffer(n.outShape,e.dtype),p=e.dataSync(),d=t.dataSync(),h=c.values,f=0;f<n.batchSize;++f)for(var m=f*e.strides[0],g=f*c.strides[0],v=0;v<n.outHeight;++v)for(var y=g+v*c.strides[1],x=v*n.strideHeight-s,T=0;T<r;++T){var w=x+T*a;if(!(w<0||w>=n.inHeight))for(var E=T*t.strides[0],S=m+w*e.strides[1],b=0;b<n.outWidth;++b)for(var C=y+b*c.strides[2],A=b*n.strideWidth-u,_=0;_<o;++_){var N=A+_*i;if(!(N<0||N>=n.inWidth))for(var R=E+_*t.strides[1],k=S+N*n.inChannels,I=C,D=R,M=0;M<n.inChannels;++M){for(var P=p[k+M],O=0;O<l;++O)h[I+O]+=P*d[D+O];I+=l,D+=l}}}return c.toTensor()},e.prototype.depthwiseConv2DDerInput=function(e,t,n){this.assertNotComplex([e,t],\"depthwiseConv2DDerInput\");for(var r=buffer(n.inShape,\"float32\"),o=r.values,a=r.strides,i=a[0],s=a[1],u=a[2],l=e.dataSync(),c=e.strides,p=c[0],d=c[1],h=c[2],f=t.dataSync(),m=t.strides,g=m[0],v=m[1],y=m[2],x=n.batchSize,T=n.filterHeight,w=n.filterWidth,E=n.inChannels,S=n.inHeight,b=n.inWidth,C=n.outChannels,A=n.outHeight,_=n.outWidth,N=n.strideHeight,R=n.strideWidth,k=T-1-n.padInfo.top,I=w-1-n.padInfo.left,D=C/E,M=0;M<x;++M)for(var P=0;P<E;++P)for(var O=0;O<S;++O)for(var F=O-k,L=Math.max(0,Math.ceil(F/N)),B=Math.min(A,(T+F)/N),U=0;U<b;++U){for(var V=U-I,z=Math.max(0,Math.ceil(V/R)),H=Math.min(_,(w+V)/R),W=0,G=L;G<B;++G)for(var q=G*N-F,$=z;$<H;++$)for(var X=p*M+d*G+h*$,K=g*(T-1-q)+v*(w-1-($*R-V))+y*P,j=0;j<D;++j){W+=l[X+(P*D+j)]*f[K+j]}o[i*M+s*O+u*U+P]=W}return r.toTensor()},e.prototype.depthwiseConv2DDerFilter=function(e,t,n){this.assertNotComplex([e,t],\"depthwiseConv2DDerFilter\");for(var r=n.strideHeight,o=n.strideWidth,a=n.filterHeight,i=n.filterWidth,s=buffer(n.filterShape,\"float32\"),u=n.padInfo.left,l=n.padInfo.top,c=n.outChannels/n.inChannels,p=0;p<a;++p)for(var d=Math.max(0,Math.ceil((l-p)/r)),h=Math.min(n.outHeight,(n.inHeight+l-p)/r),f=0;f<i;++f)for(var m=Math.max(0,Math.ceil((u-f)/o)),g=Math.min(n.outWidth,(n.inWidth+u-f)/o),v=0;v<n.outChannels;++v){for(var y=Math.trunc(v/c),x=v%c,T=0,w=0;w<n.batchSize;++w)for(var E=d;E<h;++E)for(var S=p+E*r-l,b=m;b<g;++b){var C=f+b*o-u;T+=e.get(w,S,C,y)*t.get(w,E,b,v)}s.set(T,p,f,y,x)}return s.toTensor()},e.prototype.tile=function(e,t){this.assertNotComplex(e,\"tile\");for(var n=new Array(e.rank),r=0;r<n.length;r++)n[r]=e.shape[r]*t[r];var o=buffer(n,e.dtype),a=e.buffer();for(r=0;r<o.values.length;++r){for(var i=o.indexToLoc(r),s=new Array(e.rank),u=0;u<s.length;u++)s[u]=i[u]%e.shape[u];var l=a.locToIndex(s);o.values[r]=a.values[l]}return o.toTensor()},e.prototype.pad=function(e,t,n){this.assertNotComplex(e,\"pad\");var r=t.map(function(t,n){return t[0]+e.shape[n]+t[1]}),o=t.map(function(e){return e[0]}),a=e.buffer(),i=buffer(r,e.dtype);0!==n&&i.values.fill(n);for(var s=0;s<e.size;s++){var u=a.indexToLoc(s),l=u.map(function(e,t){return e+o[t]});i.set.apply(i,[e.get.apply(e,u)].concat(l))}return i.toTensor()},e.prototype.transpose=function(e,t){this.assertNotComplex(e,\"transpose\");for(var n=new Array(e.rank),r=0;r<n.length;r++)n[r]=e.shape[t[r]];var o=e.dataSync(),a=buffer(n,e.dtype),i=e.buffer();for(r=0;r<e.size;++r){for(var s=i.indexToLoc(r),u=new Array(s.length),l=0;l<u.length;l++)u[l]=s[t[l]];var c=a.locToIndex(u);a.values[c]=o[r]}return a.toTensor()},e.prototype.gather=function(e,t,n){this.assertNotComplex([e,t],\"gather\");var r=e.shape.slice(),o=t.dataSync();r[n]=o.length;for(var a=buffer(r,e.dtype),i=e.buffer(),s=0;s<a.size;++s){var u=a.indexToLoc(s),l=u.slice();l[n]=o[u[n]];var c=i.locToIndex(l);a.values[s]=i.values[c]}return a.toTensor()},e.prototype.batchToSpaceND=function(e,t,n){this.assertNotComplex([e],\"batchToSpaceND\");var r=t.reduce(function(e,t){return e*t}),o=getReshaped(e.shape,t,r),a=getPermuted(o.length,t.length),i=getReshapedPermuted(e.shape,t,r),s=getSliceBeginCoords(n,t.length),u=getSliceSize(i,n,t.length);return e.reshape(o).transpose(a).reshape(i).slice(s,u)},e.prototype.spaceToBatchND=function(e,t,n){this.assertNotComplex([e],\"spaceToBatchND\");var r=t.reduce(function(e,t){return e*t}),o=[[0,0]];o.push.apply(o,n);for(var a=1+t.length;a<e.shape.length;++a)o.push([0,0]);var i=e.pad(o),s=getReshaped(i.shape,t,r,!1),u=getPermuted(s.length,t.length,!1),l=getReshapedPermuted(i.shape,t,r,!1);return i.reshape(s).transpose(u).reshape(l)},e.prototype.pool=function(e,t,n){this.assertNotComplex(e,\"pool\");for(var r=t.strideHeight,o=t.strideWidth,a=t.dilationHeight,i=t.dilationWidth,s=t.effectiveFilterHeight,u=t.effectiveFilterWidth,l=t.padInfo.top,c=t.padInfo.left,p=\"max\"===n?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,d=e.dataSync(),h=buffer(t.outShape,e.dtype),f=h.values,m=t.outShape[1]*t.outShape[2]*t.outShape[3],g=t.outShape[2]*t.outShape[3],v=t.outShape[3],y=0;y<t.batchSize;++y)for(var x=y*m,T=y*e.strides[0],w=0;w<t.inChannels;++w)for(var E=0;E<t.outHeight;++E)for(var S=E*r-l,b=Math.max(0,S),C=Math.min(t.inHeight,s+S),A=x+E*g,_=0;_<t.outWidth;++_){for(var N=_*o-c,R=Math.max(0,N),k=Math.min(t.inWidth,u+N),I=p,D=0,M=0,P=b;P<C;P+=a){for(var O=T+P*e.strides[1],F=R;F<k;F+=i){var L=d[O+F*e.strides[2]+w];\"max\"===n&&L>I?I=L:\"avg\"===n&&(D+=L,M++)}if(isNaN(I))break}f[A+_*v+w]=\"avg\"===n?D/M:I}return h.toTensor()},e.prototype.maxPool=function(e,t){return this.pool(e,t,\"max\")},e.prototype.maxPoolPositions=function(e,t){for(var n=buffer(t.outShape,\"int32\"),r=t.strideHeight,o=t.strideWidth,a=t.dilationHeight,i=t.dilationWidth,s=t.effectiveFilterHeight,u=t.effectiveFilterWidth,l=t.padInfo.top,c=t.padInfo.left,p=0;p<t.batchSize;++p)for(var d=0;d<t.inChannels;++d)for(var h=0;h<t.outHeight;++h){for(var f=h*r-l,m=f;m<0;)m+=a;for(var g=Math.min(t.inHeight,s+f),v=0;v<t.outWidth;++v){for(var y=v*o-c,x=y;x<0;)x+=i;for(var T=Math.min(t.inWidth,u+y),w=Number.NEGATIVE_INFINITY,E=-1,S=m;S<g;S+=a)for(var b=S-f,C=x;C<T;C+=i){var A=C-y,_=e.get(p,S,C,d);_>w&&(w=_,E=b*u+A)}n.set(E,p,h,v,d)}}return n.toTensor()},e.prototype.maxPoolBackprop=function(e,t,n,r){this.assertNotComplex([t,n],\"maxPoolBackprop\");for(var o=this.maxPoolPositions(t,r),a=r.strideHeight,i=r.strideWidth,s=r.dilationHeight,u=r.dilationWidth,l=r.effectiveFilterHeight,c=r.effectiveFilterWidth,p=c-1-r.padInfo.left,d=l-1-r.padInfo.top,h=buffer(t.shape,\"float32\"),f=0;f<r.batchSize;++f)for(var m=0;m<r.inChannels;++m)for(var g=0;g<r.inHeight;++g)for(var v=0;v<r.inWidth;++v){for(var y=g-d,x=v-p,T=0,w=0;w<l;w+=s){var E=(y+w)/a;if(!(E<0||E>=r.outHeight||Math.floor(E)!==E))for(var S=0;S<c;S+=u){var b=(x+S)/i;if(!(b<0||b>=r.outWidth||Math.floor(b)!==b)){var C=l*c-1-o.get(f,E,b,m)===w*c+S?1:0;if(0!==C)T+=e.get(f,E,b,m)*C}}}h.set(T,f,g,v,m)}return h.toTensor()},e.prototype.avgPoolBackprop=function(e,t,n){this.assertNotComplex([e,t],\"avgPoolBackprop\");for(var r=n.strideHeight,o=n.strideWidth,a=n.filterHeight,i=n.filterWidth,s=n.dilationHeight,u=n.dilationWidth,l=n.effectiveFilterHeight,c=n.effectiveFilterWidth,p=c-1-n.padInfo.left,d=l-1-n.padInfo.top,h=buffer(t.shape,\"float32\"),f=1/(a*i),m=0;m<n.batchSize;++m)for(var g=0;g<n.inChannels;++g)for(var v=0;v<n.inHeight;++v)for(var y=0;y<n.inWidth;++y){for(var x=v-d,T=y-p,w=0,E=0;E<l;E+=s){var S=(x+E)/r;if(!(S<0||S>=n.outHeight||Math.floor(S)!==S))for(var b=0;b<c;b+=u){var C=(T+b)/o;if(!(C<0||C>=n.outWidth||Math.floor(C)!==C))w+=e.get(m,S,C,g)}}h.set(w*f,m,v,y,g)}return h.toTensor()},e.prototype.cast=function(e,t){return castTensor(e,t,this)},e.prototype.reshape=function(e,t){return reshapeTensor(e,t)},e.prototype.avgPool=function(e,t){return this.assertNotComplex(e,\"avgPool\"),this.pool(e,t,\"avg\").toFloat()},e.prototype.resizeBilinear=function(e,t,n,r){this.assertNotComplex(e,\"resizeBilinear\");for(var o=e.shape,a=o[0],i=o[1],s=o[2],u=o[3],l=e.dataSync(),c=new Float32Array(sizeFromShape([a,t,n,u])),p=[r&&t>1?i-1:i,r&&n>1?s-1:s],d=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=0,f=p[0]/d[0],m=p[1]/d[1],g=0;g<a;g++)for(var v=0;v<t;v++)for(var y=f*v,x=Math.floor(y),T=y-x,w=Math.min(i-1,Math.ceil(y)),E=g*e.strides[0]+x*e.strides[1],S=g*e.strides[0]+w*e.strides[1],b=0;b<n;b++)for(var C=m*b,A=Math.floor(C),_=C-A,N=Math.min(s-1,Math.ceil(C)),R=E+A*e.strides[2],k=S+A*e.strides[2],I=E+ +N*e.strides[2],D=S+N*e.strides[2],M=0;M<u;M++){var P=l[R+M],O=l[k+M],F=P+(l[I+M]-P)*_,L=F+(O+(l[D+M]-O)*_-F)*T;c[h++]=L}return tensor(c,[a,t,n,u])},e.prototype.resizeBilinearBackprop=function(e,t,n){this.assertNotComplex([e,t],\"resizeBilinearBackprop\");for(var r=t.shape,o=r[0],a=r[1],i=r[2],s=r[3],u=e.shape,l=u[1],c=u[2],p=new Float32Array(o*a*i*s),d=[n&&l>1?a-1:a,n&&c>1?i-1:i],h=[n&&l>1?l-1:l,n&&c>1?c-1:c],f=d[0]/h[0],m=d[1]/h[1],g=e.dataSync(),v=0,y=0;y<o;y++)for(var x=y*t.strides[0],T=0;T<l;T++)for(var w=T*f,E=Math.floor(w),S=Math.min(Math.ceil(w),a-1),b=x+E*t.strides[1],C=x+S*t.strides[1],A=w-E,_=1-A,N=0;N<c;N++)for(var R=N*m,k=Math.floor(R),I=Math.min(Math.ceil(R),i-1),D=R-k,M=1-D,P=b+k*t.strides[2],O=b+I*t.strides[2],F=C+k*t.strides[2],L=C+I*t.strides[2],B=_*M,U=_*D,V=A*M,z=A*D,H=0;H<s;H++){var W=g[v++];p[P+H]+=W*B,p[O+H]+=W*U,p[F+H]+=W*V,p[L+H]+=W*z}return tensor4d(p,[o,i,a,s],t.dtype)},e.prototype.resizeNearestNeighbor=function(e,t,n,r){this.assertNotComplex(e,\"resizeNearestNeighbor\");for(var o=e.shape,a=o[0],i=o[1],s=o[2],u=o[3],l=e.dataSync(),c=new Float32Array(a*t*n*u),p=[r&&t>1?i-1:i,r&&n>1?s-1:s],d=[r&&t>1?t-1:t,r&&n>1?n-1:n],h=p[0]/d[0],f=p[1]/d[1],m=0,g=0;g<a;g++)for(var v=g*e.strides[0],y=0;y<t;y++)for(var x=h*y,T=v+Math.min(i-1,r?Math.round(x):Math.floor(x))*e.strides[1],w=0;w<n;w++)for(var E=f*w,S=T+Math.min(s-1,r?Math.round(E):Math.floor(E))*e.strides[2],b=0;b<u;b++){var C=l[S+b];c[m++]=C}return tensor(c,[a,t,n,u],e.dtype)},e.prototype.resizeNearestNeighborBackprop=function(e,t,n){this.assertNotComplex([e,t],\"resizeNearestNeighborBackprop\");for(var r=t.shape,o=r[0],a=r[1],i=r[2],s=r[3],u=e.shape,l=u[1],c=u[2],p=new Float32Array(o*a*i*s),d=e.dataSync(),h=[n&&l>1?a-1:a,n&&c>1?i-1:i],f=[n&&l>1?l-1:l,n&&c>1?c-1:c],m=h[0]/f[0],g=h[1]/f[1],v=1/m,y=1/g,x=2*Math.ceil(v)+2,T=2*Math.ceil(y)+2,w=0;w<o;w++)for(var E=w*t.strides[0],S=0;S<a;S++)for(var b=E+S*t.strides[1],C=Math.floor(S*v),A=Math.floor(C-x/2),_=0;_<i;_++)for(var N=b+_*t.strides[2],R=Math.floor(_*y),k=Math.floor(R-T/2),I=0;I<s;I++){for(var D=0,M=0;M<x;M++){var P=M+A;if(!(P<0||P>=l)){var O=E+P*e.strides[1],F=P*m;if(S===Math.min(a-1,n?Math.round(F):Math.floor(F)))for(var L=0;L<T;L++){var B=L+k;if(!(B<0||B>=c)){var U=O+B*e.strides[2],V=B*g;_===Math.min(i-1,n?Math.round(V):Math.floor(V))&&(D+=d[U+I])}}}}p[N+I]=D}return tensor4d(p,t.shape,t.dtype)},e.prototype.batchNormalization=function(e,t,n,r,o,a){this.assertNotComplex([e,t,n,o,a],\"batchNormalization\");for(var i=e.dataSync(),s=t.dataSync(),u=n.dataSync(),l=o?o.dataSync():new Float32Array([1]),c=a?a.dataSync():new Float32Array([0]),p=new Float32Array(i.length),d=c.length,h=l.length,f=u.length,m=s.length,g=0,v=0,y=0,x=0,T=0;T<i.length;++T)p[T]=c[g++]+(i[T]-s[v++])*l[y++]/Math.sqrt(u[x++]+r),g>=d&&(g=0),v>=m&&(v=0),y>=h&&(y=0),x>=f&&(x=0);return tensor4d(p,e.shape)},e.prototype.localResponseNormalization4D=function(e,t,n,r,o){this.assertNotComplex(e,\"localResponseNormalization4D\");var a=e.shape[3],i=a-1,s=e.dataSync(),u=sizeFromShape(e.shape),l=new Float32Array(u);function c(e){for(var n=e%a,r=e-n+Math.max(0,n-t),o=e-n+Math.min(n+t,i),u=0;r<=o;r++){var l=s[r];u+=l*l}return u}for(var p=0;p<u;p++){var d=c(p),h=s[p]*Math.pow(n+r*d,-o);l[p]=h}return tensor4d(l,e.shape)},e.prototype.LRNGrad=function(e,t,n,r,o,a,i){this.assertNotComplex(e,\"LRNGrad\");for(var s=e.shape[3],u=e.dataSync(),l=t.dataSync(),c=n.dataSync(),p=new Float32Array(sizeFromShape(e.shape)),d=sizeFromShape(e.shape),h=0;h<d;h++){for(var f=h%s,m=h-f+Math.max(0,f-r),g=h-f+Math.min(s,f+r+1),v=0,y=m;y<g;y++)v+=Math.pow(l[y],2);v=a*v+o;for(y=m;y<g;y++){var x=-2*a*i*l[y]*c[h]/v;h===y&&(x+=Math.pow(v,-i)),x*=u[h],p[y]+=x}}return tensor4d(p,e.shape)},e.prototype.multinomial=function(e,t,n,r){this.assertNotComplex(e,\"multinomial\");for(var o=t?e:softmax(e),a=o.shape[0],i=o.shape[1],s=zeros([a,n],\"int32\"),u=s.dataSync(),l=o.dataSync(),c=0;c<a;++c){var p=c*i,d=new Float32Array(i-1);d[0]=l[p];for(var h=1;h<d.length;++h)d[h]=d[h-1]+l[p+h];for(var f=seedrandom_1(r.toString()),m=c*n,g=0;g<n;++g){var v=f();u[m+g]=d.length;for(var y=0;y<d.length;y++)if(v<d[y]){u[m+g]=y;break}}}return s},e.prototype.oneHot=function(e,t,n,r){this.assertNotComplex(e,\"oneHot\");var o=new Float32Array(e.size*t);o.fill(r);for(var a=0;a<e.size;++a)e.get(a)>=0&&e.get(a)<t&&(o[a*t+e.get(a)]=n);return tensor2d(o,[e.size,t],\"int32\")},e.prototype.nonMaxSuppression=function(e,t,n,r,o){return this.assertNotComplex(e,\"nonMaxSuppression\"),nonMaxSuppressionImpl(e.dataSync(),t.dataSync(),n,r,o)},e.prototype.fft=function(e){return this.fftBatch(e,!1)},e.prototype.ifft=function(e){return this.fftBatch(e,!0)},e.prototype.fftBatch=function(e,t){for(var n=e.shape[0],r=e.shape[1],o=buffer(e.shape,\"float32\"),a=buffer(e.shape,\"float32\"),i=real(e).as2D(n,r),s=imag(e).as2D(n,r),u=0;u<n;u++)for(var l=i.slice([u,0],[1,r]),c=s.slice([u,0],[1,r]),p=complex(l,c),d=this.fftImpl(p,t).dataSync(),h=0;h<r;h++){var f=getComplexWithIndex(d,h);o.values[u*r+h]=f.real,a.values[u*r+h]=f.imag}return complex(o.toTensor(),a.toTensor()).as2D(n,r)},e.prototype.fftImpl=function(e,t){var n=e.as1D(),r=n.size;if(this.isExponentOf2(r)){var o=this.fftRadix2(n,r,t).as2D(e.shape[0],e.shape[1]);return t&&(o=complex(real(o).div(scalar(r)),imag(o).div(scalar(r)))),o}var a=e.dataSync(),i=splitRealAndImagArrays(this.fourierTransformByMatmul(a,r,t));return complex(i.real,i.imag).as2D(e.shape[0],e.shape[1])},e.prototype.isExponentOf2=function(e){return 0==(e&e-1)},e.prototype.fftRadix2=function(e,t,n){if(1===t)return e;var r=e.dataSync(),o=t/2,a=complexWithEvenIndex(r),i=complex(a.real,a.imag).as1D(),s=complexWithOddIndex(r),u=complex(s.real,s.imag).as1D();i=this.fftRadix2(i,o,n),u=this.fftRadix2(u,o,n);var l=exponents(t,n),c=complex(l.real,l.imag).mul(u),p=i.add(c),d=i.sub(c),h=real(p).concat(real(d)),f=imag(p).concat(imag(d));return complex(h,f).as1D()},e.prototype.fourierTransformByMatmul=function(e,t,n){for(var r=new Float32Array(2*t),o=0;o<t;o++){for(var a=0,i=0,s=0;s<t;s++){var u=exponent(o*s,t,n),l=getComplexWithIndex(e,s);a+=l.real*u.real-l.imag*u.imag,i+=l.real*u.imag+l.imag*u.real}n&&(a/=t,i/=t),assignToTypedArray(r,a,i,o)}return r},e.prototype.depthToSpace=function(e,t,n){assert(\"NHWC\"===n,\"Only NHWC dataFormat supported on CPU for depthToSpace. Got \"+n),assert(t>1,\"blockSize should be > 1 for depthToSpace, but was: \"+t);for(var r=e.shape[0],o=e.shape[1],a=e.shape[2],i=e.shape[3],s=o*t,u=a*t,l=i/(t*t),c=e.dataSync(),p=new Float32Array(r*s*u*l),d=0,h=0;h<r;++h)for(var f=0;f<s;++f)for(var m=Math.floor(f/t),g=f%t,v=0;v<u;++v)for(var y=Math.floor(v/t),x=(g*t+v%t)*l,T=0;T<l;++T){var w=T+x+i*(y+a*(m+o*h));p[d++]=c[w]}return tensor4d(p,[r,s,u,l])},e.prototype.broadcastedBinaryOp=function(e,t,n,r){var o=assertAndGetBroadcastShape(e.shape,t.shape),a=buffer(o,n),i=e.dataSync(),s=t.dataSync(),u=getBroadcastDims(e.shape,o),l=getBroadcastDims(t.shape,o),c=a.values;if(u.length+l.length===0)for(var p=0;p<c.length;++p)c[p]=r(i[p%i.length],s[p%s.length]);else{var d=e.buffer(),h=t.buffer(),f=function(n){var o=a.indexToLoc(n),p=o.slice(-e.rank);u.forEach(function(e){return p[e]=0});var f=d.locToIndex(p),m=o.slice(-t.rank);l.forEach(function(e){return m[e]=0});var g=h.locToIndex(m);c[n]=r(i[f],s[g])};for(p=0;p<c.length;++p)f(p)}return a.toTensor()},e.prototype.broadcastedBinaryComplexOp=function(e,t,n){var r=assertAndGetBroadcastShape(e.shape,t.shape),o=buffer(r,\"float32\"),a=buffer(r,\"float32\"),i=e.dataSync(),s=t.dataSync(),u=getBroadcastDims(e.shape,r),l=getBroadcastDims(t.shape,r),c=o.values,p=a.values;if(u.length+l.length===0)for(var d=0;d<c.length;d++){var h=d%i.length,f=d%s.length,m=n(i[2*h],i[2*h+1],s[2*f],s[2*f+1]);c[d]=m.real,p[d]=m.imag}else{var g=this.data.get(e.dataId).complexTensors.real.buffer(),v=this.data.get(t.dataId).complexTensors.real.buffer(),y=function(r){var a=o.indexToLoc(r),d=a.slice(-e.rank);u.forEach(function(e){return d[e]=0});var h=g.locToIndex(d),f=a.slice(-t.rank);l.forEach(function(e){return f[e]=0});var m=v.locToIndex(f),y=n(i[2*h],i[2*h+1],s[2*m],s[2*m+1]);c[r]=y.real,p[r]=y.imag};for(d=0;d<c.length;d++)y(d)}return this.complex(o.toTensor(),a.toTensor())},e.prototype.split=function(e,t,n){return split(e,t,n)},e.prototype.dispose=function(){},e.prototype.floatPrecision=function(){return 32},e.prototype.cropAndResize=function(e,t,n,r,o,a){for(var i=e.shape,s=i[0],u=i[1],l=i[2],c=i[3],p=t.shape[0],d=r[0],h=r[1],f=buffer([p,d,h,c],e.dtype),m=t.dataSync(),g=n.dataSync(),v=e.dataSync(),y=e.strides,x=f.strides,T=0;T<p;T++){var w=4*T,E=m[w],S=m[w+1],b=m[w+2],C=m[w+3],A=g[T];if(!(A>=s))for(var _=d>1?(b-E)*(u-1)/(d-1):0,N=h>1?(C-S)*(l-1)/(h-1):0,R=0;R<d;R++){var k=d>1?E*(u-1)+R*_:.5*(E+b)*(u-1);if(k<0||k>u-1)for(var I=0;I<h;I++)for(var D=0;D<c;D++){var M=D+I*x[2]+R*x[1]+T*x[0];f.values[M]=a}else if(\"bilinear\"===o){var P=Math.floor(k),O=Math.ceil(k),F=k-P;for(I=0;I<h;I++){if((q=h>1?S*(l-1)+I*N:.5*(S+C)*(l-1))<0||q>l-1)for(D=0;D<c;D++){M=D+I*x[2]+R*x[1]+T*x[0];f.values[M]=a}else{var L=Math.floor(q),B=Math.ceil(q),U=q-L;for(D=0;D<c;D++){var V=v[M=D+L*y[2]+P*y[1]+A*y[0]],z=v[M=D+B*y[2]+P*y[1]+A*y[0]],H=v[M=D+L*y[2]+O*y[1]+A*y[0]],W=V+(z-V)*U,G=H+(v[M=D+B*y[2]+O*y[1]+A*y[0]]-H)*U;M=D+I*x[2]+R*x[1]+T*x[0],f.values[M]=W+(G-W)*F}}}}else for(I=0;I<h;++I){var q;if((q=h>1?S*(l-1)+I*N:.5*(S+C)*(l-1))<0||q>l-1)for(D=0;D<c;D++){M=D+I*x[2]+R*x[1]+T*x[0];f.values[M]=a}else{var $=Math.round(q),X=Math.round(k);for(D=0;D<c;D++){var K=D+$*y[2]+X*y[1]+A*y[0],j=D+I*x[2]+R*x[1]+T*x[0];f.values[j]=v[K]}}}}}return f.toTensor()},e.prototype.sparseToDense=function(e,t,n,r){var o=calculateShapes(t,e,n),a=o.sliceRank,i=o.numUpdates,s=o.sliceSize,u=o.strides,l=o.outputSize;return this.scatter(e,t,n,l,s,i,a,u,r,!1)},e.prototype.gatherND=function(e,t){var n=t.shape,r=n[n.length-1],o=prepareAndValidate(e,t),a=o[0],i=o[1],s=o[2],u=o[3];if(0===i)return tensor([],a,e.dtype);for(var l=new TensorBuffer([i,s],e.dtype),c=t.dataSync(),p=e.dataSync(),d=0;d<i;d++){for(var h=[],f=0,m=0;m<r;m++){var g=c[d*r+m];f+=g*u[m],h.push(g)}if(f<0||f>=e.size/s)throw new Error(\"Invalid indices: \"+h+\" does not index into \"+e.shape);for(var v=0;v<s;v++)l.values[d*s+v]=p[f*s+v]}return l.toTensor().reshape(a)},e.prototype.scatterND=function(e,t,n){var r=calculateShapes(t,e,n),o=r.sliceRank,a=r.numUpdates,i=r.sliceSize,s=r.strides,u=r.outputSize,l=scalar(0);return this.scatter(e,t,n,u,i,a,o,s,l,!0)},e.prototype.scatter=function(e,t,n,r,o,a,i,s,u,l){var c=[r/o,o],p=e.dataSync(),d=t.dataSync();if(0===r)return tensor([],n,t.dtype);var h=new TensorBuffer(c,t.dtype);h.values.fill(u.dataSync()[0]);for(var f=0;f<a;f++){for(var m=[],g=0,v=0;v<i;v++){var y=p[f*i+v];m.push(y),g+=y*s[v]}if(g<0||g>=r/o)throw new Error(\"Invalid indices: \"+m+\" does not index into \"+n);for(var x=0;x<o;x++)l?h.values[g*o+x]+=d[f*o+x]:h.values[g*o+x]=0===t.rank?d[0]:d[f*o+x]}return h.toTensor().reshape(n)},e}();ENV.registerBackend(\"cpu\",function(){return new MathBackendCPU},1,setTensorTracker);var delayCallback=\"undefined\"!=typeof requestAnimationFrame?requestAnimationFrame:setImmediate;function nextFrame(){return new Promise(function(e){return delayCallback(function(){return e()})})}var DTYPE_VALUE_SIZE_MAP={float32:4,int32:4,uint16:2,uint8:1,bool:1};function encodeWeights(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o;return __generator(this,function(a){switch(a.label){case 0:for(r in t=[],n=[],e){if(\"float32\"!==(o=e[r]).dtype&&\"int32\"!==o.dtype&&\"bool\"!==o.dtype)throw new Error(\"Unsupported dtype in weight '\"+r+\"': \"+o.dtype);t.push({name:r,shape:o.shape,dtype:o.dtype}),n.push(o.data())}return[4,Promise.all(n)];case 1:return[2,{data:concatenateTypedArrays(a.sent()),specs:t}]}})})}function decodeWeights(e,t){for(var n={},r=0,o=function(t){var o=t.name,a=t.dtype,i=t.shape,s=sizeFromShape(i),u=void 0;if(\"quantization\"in t){var l=t.quantization;if(\"uint8\"!==l.dtype&&\"uint16\"!==l.dtype)throw new Error(\"Weight \"+t.name+\" has unknown quantization dtype \"+l.dtype+\". Supported quantization dtypes are: 'uint8' and 'uint16'.\");var c=DTYPE_VALUE_SIZE_MAP[l.dtype],p=e.slice(r,r+s*c),d=\"uint8\"===l.dtype?new Uint8Array(p):new Uint16Array(p);if(\"float32\"===a)u=Float32Array.from(d,function(e){return e*l.scale+l.min});else{if(\"int32\"!==a)throw new Error(\"Unsupported dtype in weight '\"+o+\"': \"+a);u=Int32Array.from(d,function(e){return Math.round(e*l.scale+l.min)})}r+=s*c}else{var h=DTYPE_VALUE_SIZE_MAP[a];p=e.slice(r,r+s*h);if(\"float32\"===a)u=new Float32Array(p);else if(\"int32\"===a)u=new Int32Array(p);else{if(\"bool\"!==a)throw new Error(\"Unsupported dtype in weight '\"+o+\"': \"+a);u=new Uint8Array(p)}r+=s*h}var f=void 0;if(\"float32\"===a)f=tensor(u,i,\"float32\");else if(\"int32\"===a)f=tensor(u,i,\"int32\");else{if(\"bool\"!==a)throw new Error(\"Unsupported dtype in weight '\"+o+\"': \"+a);f=tensor(u,i,\"bool\")}n[o]=f},a=0,i=t;a<i.length;a++){o(i[a])}return n}function concatenateTypedArrays(e){if(null===e)throw new Error(\"Invalid input value: \"+JSON.stringify(e));var t=0,n=[];e.forEach(function(e){if(t+=e.byteLength,n.push(e.byteLength===e.buffer.byteLength?e:new e.constructor(e)),!(e instanceof Float32Array||e instanceof Int32Array||e instanceof Uint8Array))throw new Error(\"Unsupported TypedArray subtype: \"+e.constructor.name)});var r=new Uint8Array(t),o=0;return n.forEach(function(e){r.set(new Uint8Array(e.buffer),o),o+=e.byteLength}),r.buffer}var useNodeBuffer=\"undefined\"!=typeof Buffer&&(\"undefined\"==typeof Blob||\"undefined\"==typeof atob||\"undefined\"==typeof btoa);function stringByteLength(e){return useNodeBuffer?Buffer.byteLength(e):new Blob([e]).size}function arrayBufferToBase64String(e){return useNodeBuffer?Buffer.from(e).toString(\"base64\"):btoa(String.fromCharCode.apply(null,new Uint8Array(e)))}function base64StringToArrayBuffer(e){if(useNodeBuffer){var t=Buffer.from(e,\"base64\");return t.buffer.slice(t.byteOffset,t.byteOffset+t.byteLength)}for(var n=atob(e),r=new Uint8Array(n.length),o=0;o<n.length;++o)r.set([n.charCodeAt(o)],o);return r.buffer}function concatenateArrayBuffers(e){var t=0;e.forEach(function(e){t+=e.byteLength});var n=new Uint8Array(t),r=0;return e.forEach(function(e){n.set(new Uint8Array(e),r),r+=e.byteLength}),n.buffer}function basename(e){for(e=e.trim();e.endsWith(\"/\");)e=e.slice(0,e.length-1);var t=e.split(\"/\");return t[t.length-1]}function getModelArtifactsInfoForJSON(e){if(e.modelTopology instanceof ArrayBuffer)throw new Error(\"Expected JSON model topology, received ArrayBuffer.\");return{dateSaved:new Date,modelTopologyType:\"JSON\",modelTopologyBytes:null==e.modelTopology?0:stringByteLength(JSON.stringify(e.modelTopology)),weightSpecsBytes:null==e.weightSpecs?0:stringByteLength(JSON.stringify(e.weightSpecs)),weightDataBytes:null==e.weightData?0:e.weightData.byteLength}}var IORouterRegistry=function(){function e(){this.saveRouters=[],this.loadRouters=[]}return e.getInstance=function(){return null==e.instance&&(e.instance=new e),e.instance},e.registerSaveRouter=function(t){e.getInstance().saveRouters.push(t)},e.registerLoadRouter=function(t){e.getInstance().loadRouters.push(t)},e.getSaveHandlers=function(t){return e.getHandlers(t,\"save\")},e.getLoadHandlers=function(t){return e.getHandlers(t,\"load\")},e.getHandlers=function(e,t){var n=[];return(\"load\"===t?this.getInstance().loadRouters:this.getInstance().saveRouters).forEach(function(t){var r=t(e);null!==r&&n.push(r)}),n},e}(),URL_SCHEME_SUFFIX=\"://\",ModelStoreManagerRegistry=function(){function e(){this.managers={}}return e.getInstance=function(){return null==e.instance&&(e.instance=new e),e.instance},e.registerManager=function(t,n){assert(null!=t,\"scheme must not be undefined or null.\"),t.endsWith(URL_SCHEME_SUFFIX)&&(t=t.slice(0,t.indexOf(URL_SCHEME_SUFFIX))),assert(t.length>0,\"scheme must not be an empty string.\");var r=e.getInstance();assert(null==r.managers[t],\"A model store manager is already registered for scheme '\"+t+\"'.\"),r.managers[t]=n},e.getManager=function(e){var t=this.getInstance().managers[e];if(null==t)throw new Error(\"Cannot find model manager for scheme '\"+e+\"'\");return t},e.getSchemes=function(){return Object.keys(this.getInstance().managers)},e}();function parseURL(e){if(-1===e.indexOf(URL_SCHEME_SUFFIX))throw new Error(\"The url string provided does not contain a scheme. Supported schemes are: \"+ModelStoreManagerRegistry.getSchemes().join(\",\"));return{scheme:e.split(URL_SCHEME_SUFFIX)[0],path:e.split(URL_SCHEME_SUFFIX)[1]}}function cloneModelInternal(e,t,n){return void 0===n&&(n=!1),__awaiter(this,void 0,void 0,function(){var r,o,a,i,s,u,l,c,p;return __generator(this,function(d){switch(d.label){case 0:return assert(e!==t,\"Old path and new path are the same: '\"+e+\"'\"),assert((r=IORouterRegistry.getLoadHandlers(e)).length>0,\"Copying failed because no load handler is found for source URL \"+e+\".\"),assert(r.length<2,\"Copying failed because more than one (\"+r.length+\") load handlers for source URL \"+e+\".\"),o=r[0],assert((a=IORouterRegistry.getSaveHandlers(t)).length>0,\"Copying failed because no save handler is found for destination URL \"+t+\".\"),assert(a.length<2,\"Copying failed because more than one (\"+r.length+\") save handlers for destination URL \"+t+\".\"),i=a[0],s=parseURL(e).scheme,u=parseURL(e).path,l=s===parseURL(e).scheme,[4,o.load()];case 1:return c=d.sent(),n&&l?[4,ModelStoreManagerRegistry.getManager(s).removeModel(u)]:[3,3];case 2:d.sent(),d.label=3;case 3:return[4,i.save(c)];case 4:return p=d.sent(),!n||l?[3,6]:[4,ModelStoreManagerRegistry.getManager(s).removeModel(u)];case 5:d.sent(),d.label=6;case 6:return[2,p.modelArtifactsInfo]}})})}function listModels(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o,a,i;return __generator(this,function(s){switch(s.label){case 0:e=ModelStoreManagerRegistry.getSchemes(),t={},n=0,r=e,s.label=1;case 1:return n<r.length?(o=r[n],[4,ModelStoreManagerRegistry.getManager(o).listModels()]):[3,4];case 2:for(i in a=s.sent())t[o+URL_SCHEME_SUFFIX+i]=a[i];s.label=3;case 3:return n++,[3,1];case 4:return[2,t]}})})}function removeModel(e){return __awaiter(this,void 0,void 0,function(){var t;return __generator(this,function(n){switch(n.label){case 0:return t=parseURL(e),[4,ModelStoreManagerRegistry.getManager(t.scheme).removeModel(t.path)];case 1:return[2,n.sent()]}})})}function copyModel(e,t){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(n){switch(n.label){case 0:return[4,cloneModelInternal(e,t,!1)];case 1:return[2,n.sent()]}})})}function moveModel(e,t){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(n){switch(n.label){case 0:return[4,cloneModelInternal(e,t,!0)];case 1:return[2,n.sent()]}})})}var DATABASE_NAME=\"tensorflowjs\",DATABASE_VERSION=1,MODEL_STORE_NAME=\"models_store\",INFO_STORE_NAME=\"model_info_store\";function getIndexedDBFactory(){if(!ENV.get(\"IS_BROWSER\"))throw new Error(\"Failed to obtain IndexedDB factory because the current environmentis not a web browser.\");var e=window,t=e.indexedDB||e.mozIndexedDB||e.webkitIndexedDB||e.msIndexedDB||e.shimIndexedDB;if(null==t)throw new Error(\"The current browser does not appear to support IndexedDB.\");return t}function setUpDatabase(e){var t=e.result;t.createObjectStore(MODEL_STORE_NAME,{keyPath:\"modelPath\"}),t.createObjectStore(INFO_STORE_NAME,{keyPath:\"modelPath\"})}var BrowserIndexedDB=function(){function e(e){if(this.indexedDB=getIndexedDBFactory(),null==e||!e)throw new Error(\"For IndexedDB, modelPath must not be null, undefined or empty.\");this.modelPath=e}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(t){if(e.modelTopology instanceof ArrayBuffer)throw new Error(\"BrowserLocalStorage.save() does not support saving model topology in binary formats yet.\");return[2,this.databaseAction(this.modelPath,e)]})})},e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(e){return[2,this.databaseAction(this.modelPath)]})})},e.prototype.databaseAction=function(e,t){var n=this;return new Promise(function(e,r){var o=n.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);o.onupgradeneeded=function(){return setUpDatabase(o)},o.onsuccess=function(){var a=o.result;if(null==t){var i=a.transaction(MODEL_STORE_NAME,\"readonly\"),s=i.objectStore(MODEL_STORE_NAME).get(n.modelPath);s.onsuccess=function(){if(null==s.result)return a.close(),r(new Error(\"Cannot find model with path '\"+n.modelPath+\"' in IndexedDB.\"));e(s.result.modelArtifacts)},s.onerror=function(e){return a.close(),r(s.error)},i.oncomplete=function(){return a.close()}}else{var u,l=getModelArtifactsInfoForJSON(t),c=a.transaction(INFO_STORE_NAME,\"readwrite\"),p=c.objectStore(INFO_STORE_NAME),d=p.put({modelPath:n.modelPath,modelArtifactsInfo:l});d.onsuccess=function(){var o=(u=a.transaction(MODEL_STORE_NAME,\"readwrite\")).objectStore(MODEL_STORE_NAME).put({modelPath:n.modelPath,modelArtifacts:t,modelArtifactsInfo:l});o.onsuccess=function(){return e({modelArtifactsInfo:l})},o.onerror=function(e){var t=(p=c.objectStore(INFO_STORE_NAME)).delete(n.modelPath);t.onsuccess=function(){return a.close(),r(o.error)},t.onerror=function(e){return a.close(),r(o.error)}}},d.onerror=function(e){return a.close(),r(d.error)},c.oncomplete=function(){null==u?a.close():u.oncomplete=function(){return a.close()}}}},o.onerror=function(e){return r(o.error)}})},e.URL_SCHEME=\"indexeddb://\",e}(),indexedDBRouter=function(e){return ENV.get(\"IS_BROWSER\")&&!Array.isArray(e)&&e.startsWith(BrowserIndexedDB.URL_SCHEME)?browserIndexedDB(e.slice(BrowserIndexedDB.URL_SCHEME.length)):null};function browserIndexedDB(e){return new BrowserIndexedDB(e)}function maybeStripScheme(e){return e.startsWith(BrowserIndexedDB.URL_SCHEME)?e.slice(BrowserIndexedDB.URL_SCHEME.length):e}IORouterRegistry.registerSaveRouter(indexedDBRouter),IORouterRegistry.registerLoadRouter(indexedDBRouter);var BrowserIndexedDBManager=function(){function e(){this.indexedDB=getIndexedDBFactory()}return e.prototype.listModels=function(){return __awaiter(this,void 0,void 0,function(){var e=this;return __generator(this,function(t){return[2,new Promise(function(t,n){var r=e.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);r.onupgradeneeded=function(){return setUpDatabase(r)},r.onsuccess=function(){var e=r.result,o=e.transaction(INFO_STORE_NAME,\"readonly\"),a=o.objectStore(INFO_STORE_NAME).getAll();a.onsuccess=function(){for(var e={},n=0,r=a.result;n<r.length;n++){var o=r[n];e[o.modelPath]=o.modelArtifactsInfo}t(e)},a.onerror=function(t){return e.close(),n(a.error)},o.oncomplete=function(){return e.close()}},r.onerror=function(e){return n(r.error)}})]})})},e.prototype.removeModel=function(e){return __awaiter(this,void 0,void 0,function(){var t=this;return __generator(this,function(n){return e=maybeStripScheme(e),[2,new Promise(function(n,r){var o=t.indexedDB.open(DATABASE_NAME,DATABASE_VERSION);o.onupgradeneeded=function(){return setUpDatabase(o)},o.onsuccess=function(){var t,a=o.result,i=a.transaction(INFO_STORE_NAME,\"readwrite\"),s=i.objectStore(INFO_STORE_NAME),u=s.get(e);u.onsuccess=function(){if(null==u.result)return a.close(),r(new Error(\"Cannot find model with path '\"+e+\"' in IndexedDB.\"));var o=s.delete(e),i=function(){var o=(t=a.transaction(MODEL_STORE_NAME,\"readwrite\")).objectStore(MODEL_STORE_NAME).delete(e);o.onsuccess=function(){return n(u.result.modelArtifactsInfo)},o.onerror=function(e){return r(u.error)}};o.onsuccess=i,o.onerror=function(e){return i(),a.close(),r(u.error)}},u.onerror=function(e){return a.close(),r(u.error)},i.oncomplete=function(){null==t?a.close():t.oncomplete=function(){return a.close()}}},o.onerror=function(e){return r(o.error)}})]})})},e}();if(ENV.get(\"IS_BROWSER\"))try{ModelStoreManagerRegistry.registerManager(BrowserIndexedDB.URL_SCHEME,new BrowserIndexedDBManager)}catch(e){}var PATH_SEPARATOR=\"/\",PATH_PREFIX=\"tensorflowjs_models\",INFO_SUFFIX=\"info\",MODEL_TOPOLOGY_SUFFIX=\"model_topology\",WEIGHT_SPECS_SUFFIX=\"weight_specs\",WEIGHT_DATA_SUFFIX=\"weight_data\";function getModelKeys(e){return{info:[PATH_PREFIX,e,INFO_SUFFIX].join(PATH_SEPARATOR),topology:[PATH_PREFIX,e,MODEL_TOPOLOGY_SUFFIX].join(PATH_SEPARATOR),weightSpecs:[PATH_PREFIX,e,WEIGHT_SPECS_SUFFIX].join(PATH_SEPARATOR),weightData:[PATH_PREFIX,e,WEIGHT_DATA_SUFFIX].join(PATH_SEPARATOR)}}function getModelPathFromKey(e){var t=e.split(PATH_SEPARATOR);if(t.length<3)throw new Error(\"Invalid key format: \"+e);return t.slice(1,t.length-1).join(PATH_SEPARATOR)}function maybeStripScheme$1(e){return e.startsWith(BrowserLocalStorage.URL_SCHEME)?e.slice(BrowserLocalStorage.URL_SCHEME.length):e}var BrowserLocalStorage=function(){function e(e){if(!ENV.get(\"IS_BROWSER\")||void 0===window.localStorage)throw new Error(\"The current environment does not support local storage.\");if(this.LS=window.localStorage,null==e||!e)throw new Error(\"For local storage, modelPath must not be null, undefined or empty.\");this.modelPath=e,this.keys=getModelKeys(this.modelPath)}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o;return __generator(this,function(a){if(e.modelTopology instanceof ArrayBuffer)throw new Error(\"BrowserLocalStorage.save() does not support saving model topology in binary formats yet.\");t=JSON.stringify(e.modelTopology),n=JSON.stringify(e.weightSpecs),r=getModelArtifactsInfoForJSON(e);try{return this.LS.setItem(this.keys.info,JSON.stringify(r)),this.LS.setItem(this.keys.topology,t),this.LS.setItem(this.keys.weightSpecs,n),this.LS.setItem(this.keys.weightData,arrayBufferToBase64String(e.weightData)),[2,{modelArtifactsInfo:r}]}catch(e){for(o in this.keys)this.LS.removeItem(this.keys[o]);throw new Error(\"Failed to save model '\"+this.modelPath+\"' to local storage: size quota being exceeded is a possible cause of this failure: modelTopologyBytes=\"+r.modelTopologyBytes+\", weightSpecsBytes=\"+r.weightSpecsBytes+\", weightDataBytes=\"+r.weightDataBytes+\".\")}return[2]})})},e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o;return __generator(this,function(a){if(null==(e=JSON.parse(this.LS.getItem(this.keys.info))))throw new Error(\"In local storage, there is no model with name '\"+this.modelPath+\"'\");if(\"JSON\"!==e.modelTopologyType)throw new Error(\"BrowserLocalStorage does not support loading non-JSON model topology yet.\");if(t={},null==(n=JSON.parse(this.LS.getItem(this.keys.topology))))throw new Error(\"In local storage, the topology of model '\"+this.modelPath+\"' is missing.\");if(t.modelTopology=n,null==(r=JSON.parse(this.LS.getItem(this.keys.weightSpecs))))throw new Error(\"In local storage, the weight specs of model '\"+this.modelPath+\"' are missing.\");if(t.weightSpecs=r,null==(o=this.LS.getItem(this.keys.weightData)))throw new Error(\"In local storage, the binary weight values of model '\"+this.modelPath+\"' are missing.\");return t.weightData=base64StringToArrayBuffer(o),[2,t]})})},e.URL_SCHEME=\"localstorage://\",e}(),localStorageRouter=function(e){return ENV.get(\"IS_BROWSER\")&&!Array.isArray(e)&&e.startsWith(BrowserLocalStorage.URL_SCHEME)?browserLocalStorage(e.slice(BrowserLocalStorage.URL_SCHEME.length)):null};function browserLocalStorage(e){return new BrowserLocalStorage(e)}IORouterRegistry.registerSaveRouter(localStorageRouter),IORouterRegistry.registerLoadRouter(localStorageRouter);var BrowserLocalStorageManager=function(){function e(){assert(ENV.get(\"IS_BROWSER\"),\"Current environment is not a web browser\"),assert(void 0!==window.localStorage,\"Current browser does not appear to support localStorage\"),this.LS=window.localStorage}return e.prototype.listModels=function(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o,a;return __generator(this,function(i){for(e={},t=PATH_PREFIX+PATH_SEPARATOR,n=PATH_SEPARATOR+INFO_SUFFIX,r=0;r<this.LS.length;++r)(o=this.LS.key(r)).startsWith(t)&&o.endsWith(n)&&(a=getModelPathFromKey(o),e[a]=JSON.parse(this.LS.getItem(o)));return[2,e]})})},e.prototype.removeModel=function(e){return __awaiter(this,void 0,void 0,function(){var t,n;return __generator(this,function(r){if(e=maybeStripScheme$1(e),t=getModelKeys(e),null==this.LS.getItem(t.info))throw new Error(\"Cannot find model at path '\"+e+\"'\");return n=JSON.parse(this.LS.getItem(t.info)),this.LS.removeItem(t.info),this.LS.removeItem(t.topology),this.LS.removeItem(t.weightSpecs),this.LS.removeItem(t.weightData),[2,n]})})},e}();if(ENV.get(\"IS_BROWSER\"))try{ModelStoreManagerRegistry.registerManager(BrowserLocalStorage.URL_SCHEME,new BrowserLocalStorageManager)}catch(e){}var DEFAULT_FILE_NAME_PREFIX=\"model\",DEFAULT_JSON_EXTENSION_NAME=\".json\",DEFAULT_WEIGHT_DATA_EXTENSION_NAME=\".weights.bin\",BrowserDownloads=function(){function e(t){if(!ENV.get(\"IS_BROWSER\"))throw new Error(\"browserDownloads() cannot proceed because the current environment is not a browser.\");t.startsWith(e.URL_SCHEME)&&(t=t.slice(e.URL_SCHEME.length)),null!=t&&0!==t.length||(t=DEFAULT_FILE_NAME_PREFIX),this.modelTopologyFileName=t+DEFAULT_JSON_EXTENSION_NAME,this.weightDataFileName=t+DEFAULT_WEIGHT_DATA_EXTENSION_NAME}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o,a,i;return __generator(this,function(s){if(t=window.URL.createObjectURL(new Blob([e.weightData],{type:\"application/octet-stream\"})),e.modelTopology instanceof ArrayBuffer)throw new Error(\"BrowserDownloads.save() does not support saving model topology in binary formats yet.\");return n=[{paths:[\"./\"+this.weightDataFileName],weights:e.weightSpecs}],r={modelTopology:e.modelTopology,weightsManifest:n},o=window.URL.createObjectURL(new Blob([JSON.stringify(r)],{type:\"application/json\"})),(a=null==this.jsonAnchor?document.createElement(\"a\"):this.jsonAnchor).download=this.modelTopologyFileName,a.href=o,a.click(),null!=e.weightData&&((i=null==this.weightDataAnchor?document.createElement(\"a\"):this.weightDataAnchor).download=this.weightDataFileName,i.href=t,i.click()),[2,{modelArtifactsInfo:getModelArtifactsInfoForJSON(e)}]})})},e.URL_SCHEME=\"downloads://\",e}(),BrowserFiles=function(){function e(e){if(null==e||e.length<1)throw new Error(\"When calling browserFiles, at least 1 file is required, but received \"+e);this.files=e}return e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){var e,t,n=this;return __generator(this,function(r){return e=this.files[0],t=this.files.slice(1),[2,new Promise(function(r,o){var a=new FileReader;a.onload=function(a){var i=JSON.parse(a.target.result),s=i.modelTopology;if(null!=s){0===t.length&&r({modelTopology:s});var u=i.weightsManifest;if(null!=u){var l;try{l=n.checkManifestAndWeightFiles(u,t)}catch(e){return void o(e)}var c=[],p=[],d=[];u.forEach(function(e){e.paths.forEach(function(e){p.push(e),d.push(null)}),c.push.apply(c,e.weights)}),u.forEach(function(e){e.paths.forEach(function(e){var t=new FileReader;t.onload=function(t){var n=t.target.result,o=p.indexOf(e);d[o]=n,-1===d.indexOf(null)&&r({modelTopology:s,weightSpecs:c,weightData:concatenateArrayBuffers(d)})},t.onerror=function(t){return o(\"Failed to weights data from file of path '\"+e+\"'.\")},t.readAsArrayBuffer(l[e])})})}else o(new Error(\"weightManifest field is missing from file \"+e.name))}else o(new Error(\"modelTopology field is missing from file \"+e.name))},a.onerror=function(t){return o(\"Failed to read model topology and weights manifest JSON from file '\"+e.name+\"'. BrowserFiles supports loading Keras-style tf.Model artifacts only.\")},a.readAsText(e)})]})})},e.prototype.checkManifestAndWeightFiles=function(e,t){for(var n=[],r=t.map(function(e){return basename(e.name)}),o={},a=0,i=e;a<i.length;a++){i[a].paths.forEach(function(e){var a=basename(e);if(-1!==n.indexOf(a))throw new Error(\"Duplicate file basename found in weights manifest: '\"+a+\"'\");if(n.push(a),-1===r.indexOf(a))throw new Error(\"Weight file with basename '\"+a+\"' is not provided.\");o[e]=t[r.indexOf(a)]})}if(n.length!==t.length)throw new Error(\"Mismatch in the number of files in weights manifest (\"+n.length+\") and the number of weight files provided (\"+t.length+\").\");return o},e}(),browserDownloadsRouter=function(e){return ENV.get(\"IS_BROWSER\")&&!Array.isArray(e)&&e.startsWith(BrowserDownloads.URL_SCHEME)?browserDownloads(e.slice(BrowserDownloads.URL_SCHEME.length)):null};function browserDownloads(e){return void 0===e&&(e=\"model\"),new BrowserDownloads(e)}function browserFiles(e){return new BrowserFiles(e)}function loadWeightsAsArrayBuffer(e,t,n){return __awaiter(this,void 0,void 0,function(){var r,o;return __generator(this,function(a){switch(a.label){case 0:return null==n&&(n=fetch),r=e.map(function(e){return n(e,t)}),[4,Promise.all(r)];case 1:return o=a.sent(),[4,Promise.all(o.map(function(e){return e.arrayBuffer()}))];case 2:return[2,a.sent()]}})})}function loadWeights(e,t,n,r){return void 0===t&&(t=\"\"),__awaiter(this,void 0,void 0,function(){return __generator(this,function(o){return[2,weightsLoaderFactory(function(e){return loadWeightsAsArrayBuffer(e,r)})(e,t,n)]})})}function weightsLoaderFactory(e){var t=this;return function(n,r,o){return void 0===r&&(r=\"\"),__awaiter(t,void 0,void 0,function(){var t,a,i,s,u,l,c,p,d,h;return __generator(this,function(f){switch(f.label){case 0:if(t=n.map(function(){return!1}),a={},i=null!=o?o.map(function(){return!1}):[],s=[],n.forEach(function(e,n){var r=0;e.weights.forEach(function(e){var u=\"quantization\"in e?e.quantization.dtype:e.dtype,l=DTYPE_VALUE_SIZE_MAP[u]*sizeFromShape(e.shape),c=function(){t[n]=!0,null==a[n]&&(a[n]=[]),a[n].push({manifestEntry:e,groupOffset:r,sizeBytes:l})};null!=o?o.forEach(function(t,n){t===e.name&&(c(),i[n]=!0)}):c(),s.push(e.name),r+=l})}),!i.every(function(e){return e}))throw u=o.filter(function(e,t){return!i[t]}),new Error(\"Could not find weights in manifest with names: \"+u.join(\", \")+\". \\nManifest JSON has weights with names: \"+s.join(\", \")+\".\");return l=t.reduce(function(e,t,n){return t&&e.push(n),e},[]),c=[],l.forEach(function(e){n[e].paths.forEach(function(e){var t=r+(r.endsWith(\"/\")?\"\":\"/\")+e;c.push(t)})}),[4,e(c)];case 1:return p=f.sent(),d={},h=0,l.forEach(function(e){for(var t=n[e].paths.length,r=0,o=0;o<t;o++)r+=p[h+o].byteLength;for(var i=new ArrayBuffer(r),s=new Uint8Array(i),u=0,l=0;l<t;l++){var c=new Uint8Array(p[h+l]);s.set(c,u),u+=c.byteLength}a[e].forEach(function(e){var t=decodeWeights(i.slice(e.groupOffset,e.groupOffset+e.sizeBytes),[e.manifestEntry]);for(var n in t)d[n]=t[n]}),h+=t}),[2,d]}})})}}IORouterRegistry.registerSaveRouter(browserDownloadsRouter);var BrowserHTTPRequest=function(){function e(e,t,n,r){if(this.weightPathPrefix=n,this.DEFAULT_METHOD=\"POST\",null==r){if(\"undefined\"==typeof fetch)throw new Error(\"browserHTTPRequest is not supported outside the web browser without a fetch polyfill.\");this.fetchFunc=fetch.bind(\"undefined\"==typeof window?null:window)}else assert(\"function\"==typeof r,\"Must pass a function that matches the signature of `fetch` (see https://developer.mozilla.org/en-US/docs/Web/API/Fetch_API)\"),this.fetchFunc=r;if(assert(null!=e&&e.length>0,\"URL path for browserHTTPRequest must not be null, undefined or empty.\"),Array.isArray(e)&&assert(2===e.length,\"URL paths for browserHTTPRequest must have a length of 2, (actual length is \"+e.length+\").\"),this.path=e,null!=t&&null!=t.body)throw new Error(\"requestInit is expected to have no pre-existing body, but has one.\");this.requestInit=t||{}}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o;return __generator(this,function(a){switch(a.label){case 0:if(e.modelTopology instanceof ArrayBuffer)throw new Error(\"BrowserHTTPRequest.save() does not support saving model topology in binary formats yet.\");return(t=Object.assign({method:this.DEFAULT_METHOD},this.requestInit)).body=new FormData,n=[{paths:[\"./model.weights.bin\"],weights:e.weightSpecs}],r={modelTopology:e.modelTopology,weightsManifest:n},t.body.append(\"model.json\",new Blob([JSON.stringify(r)],{type:\"application/json\"}),\"model.json\"),null!=e.weightData&&t.body.append(\"model.weights.bin\",new Blob([e.weightData],{type:\"application/octet-stream\"}),\"model.weights.bin\"),[4,this.getFetchFunc()(this.path,t)];case 1:if((o=a.sent()).ok)return[2,{modelArtifactsInfo:getModelArtifactsInfoForJSON(e),responses:[o]}];throw new Error(\"BrowserHTTPRequest.save() failed due to HTTP response status \"+o.status+\".\")}})})},e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(e){return[2,Array.isArray(this.path)?this.loadBinaryModel():this.loadJSONModel()]})})},e.prototype.loadBinaryTopology=function(){return __awaiter(this,void 0,void 0,function(){var e,t;return __generator(this,function(n){switch(n.label){case 0:return n.trys.push([0,3,,4]),[4,this.getFetchFunc()(this.path[0],this.requestInit)];case 1:if(!(e=n.sent()).ok)throw new Error(\"BrowserHTTPRequest.load() failed due to HTTP response: \"+e.statusText);return[4,e.arrayBuffer()];case 2:return[2,n.sent()];case 3:throw t=n.sent(),new Error(this.path[0]+\" not found. \"+t);case 4:return[2]}})})},e.prototype.loadBinaryModel=function(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o,a,i,s;return __generator(this,function(u){switch(u.label){case 0:return e=this.loadBinaryTopology(),[4,this.getFetchFunc()(this.path[1],this.requestInit)];case 1:if(!(t=u.sent()).ok)throw new Error(\"BrowserHTTPRequest.load() failed due to HTTP response: \"+t.statusText);return[4,Promise.all([e,t])];case 2:return n=u.sent(),r=n[0],[4,n[1].json()];case 3:return null==(o=u.sent())?[3,5]:[4,this.loadWeights(o)];case 4:s=u.sent(),a=s[0],i=s[1],u.label=5;case 5:return[2,{modelTopology:r,weightSpecs:a,weightData:i}]}})})},e.prototype.loadJSONModel=function(){return __awaiter(this,void 0,void 0,function(){var e,t,n,r,o,a,i,s;return __generator(this,function(u){switch(u.label){case 0:return[4,this.getFetchFunc()(this.path,this.requestInit)];case 1:if(!(e=u.sent()).ok)throw new Error(\"BrowserHTTPRequest.load() failed due to HTTP response: \"+e.statusText);return[4,e.json()];case 2:if(t=u.sent(),n=t.modelTopology,r=t.weightsManifest,null==n&&null==r)throw new Error(\"The JSON from HTTP path \"+this.path+\" contains neither model topology or manifest for weights.\");return null==r?[3,4]:(i=t.weightsManifest,[4,this.loadWeights(i)]);case 3:s=u.sent(),o=s[0],a=s[1],u.label=4;case 4:return[2,{modelTopology:n,weightSpecs:o,weightData:a}]}})})},e.prototype.loadWeights=function(e){return __awaiter(this,void 0,void 0,function(){var t,n,r,o,a,i,s,u,l,c,p,d;return __generator(this,function(h){switch(h.label){case 0:for(t=Array.isArray(this.path)?this.path[1]:this.path,n=parseUrl(t),r=n[0],o=n[1],a=this.weightPathPrefix||r,i=[],s=0,u=e;s<u.length;s++)l=u[s],i.push.apply(i,l.weights);return c=[],e.forEach(function(e){e.paths.forEach(function(e){c.push(a+e+o)})}),p=[i],d=concatenateArrayBuffers,[4,loadWeightsAsArrayBuffer(c,this.requestInit,this.getFetchFunc())];case 1:return[2,p.concat([d.apply(void 0,[h.sent()])])]}})})},e.prototype.getFetchFunc=function(){return this.fetchFunc},e.URL_SCHEME_REGEX=/^https?:\\/\\//,e}();function parseUrl(e){var t=e.lastIndexOf(\"/\"),n=e.lastIndexOf(\"?\");return[e.substring(0,t)+\"/\",n>t?e.substring(n):\"\"]}function isHTTPScheme(e){return null!=e.match(BrowserHTTPRequest.URL_SCHEME_REGEX)}var httpRequestRouter=function(e){if(\"undefined\"==typeof fetch)return null;return(Array.isArray(e)?e.every(function(e){return isHTTPScheme(e)}):isHTTPScheme(e))?browserHTTPRequest(e):null};function browserHTTPRequest(e,t,n,r){return new BrowserHTTPRequest(e,t,n,r)}IORouterRegistry.registerSaveRouter(httpRequestRouter),IORouterRegistry.registerLoadRouter(httpRequestRouter);var PassthroughLoader=function(){function e(e,t,n){this.modelTopology=e,this.weightSpecs=t,this.weightData=n}return e.prototype.load=function(){return __awaiter(this,void 0,void 0,function(){var e;return __generator(this,function(t){return e={},null!=this.modelTopology&&(e=__assign({modelTopology:this.modelTopology},e)),null!=this.weightSpecs&&this.weightSpecs.length>0&&(e=__assign({weightSpecs:this.weightSpecs},e)),null!=this.weightData&&this.weightData.byteLength>0&&(e=__assign({weightData:this.weightData},e)),[2,e]})})},e}(),PassthroughSaver=function(){function e(e){this.saveHandler=e}return e.prototype.save=function(e){return __awaiter(this,void 0,void 0,function(){return __generator(this,function(t){return[2,this.saveHandler(e)]})})},e}();function fromMemory(e,t,n){return new PassthroughLoader(e,t,n)}function withSaveHandler(e){return new PassthroughSaver(e)}var registerSaveRouter=IORouterRegistry.registerSaveRouter,registerLoadRouter=IORouterRegistry.registerLoadRouter,getSaveHandlers=IORouterRegistry.getSaveHandlers,getLoadHandlers=IORouterRegistry.getLoadHandlers,io=Object.freeze({browserFiles:browserFiles,browserHTTPRequest:browserHTTPRequest,concatenateArrayBuffers:concatenateArrayBuffers,decodeWeights:decodeWeights,encodeWeights:encodeWeights,fromMemory:fromMemory,getLoadHandlers:getLoadHandlers,getModelArtifactsInfoForJSON:getModelArtifactsInfoForJSON,getSaveHandlers:getSaveHandlers,isHTTPScheme:isHTTPScheme,loadWeights:loadWeights,registerLoadRouter:registerLoadRouter,registerSaveRouter:registerSaveRouter,weightsLoaderFactory:weightsLoaderFactory,withSaveHandler:withSaveHandler,copyModel:copyModel,listModels:listModels,moveModel:moveModel,removeModel:removeModel});function confusionMatrix_(e,t,n){var r=convertToTensor(e,\"labels\",\"confusionMatrix\"),o=convertToTensor(t,\"predictions\",\"confusionMatrix\");assert(null==n||n>0&&Number.isInteger(n),\"If provided, numClasses must be a positive integer, but got \"+n),assert(1===r.rank,\"Expected the rank of labels to be 1, but got \"+r.rank),assert(1===o.rank,\"Expected the rank of predictions to be 1, but got \"+o.rank),assert(r.shape[0]===o.shape[0],\"Mismatch in the number of examples: \"+r.shape[0]+\" vs. \"+o.shape[0]+\". Labels and predictions should have the same number of elements.\"),assert(n>0&&Number.isInteger(n),\"numClasses is required to be a positive integer, but got \"+n);var a=oneHot(r.asType(\"int32\"),n),i=oneHot(o.asType(\"int32\"),n);return a.transpose().matMul(i).asType(\"int32\")}var confusionMatrix=op({confusionMatrix_:confusionMatrix_}),math=Object.freeze({confusionMatrix:confusionMatrix}),Serializable=function(){function e(){}return e.prototype.getClassName=function(){return this.constructor.className},e.fromConfig=function(e,t){return new e(t)},e}(),SerializationMap=function(){function e(){this.classNameMap={}}return e.getMap=function(){return null==e.instance&&(e.instance=new e),e.instance},e.register=function(t){e.getMap().classNameMap[t.className]=[t,t.fromConfig]},e}();function registerClass(e){assert(null!=e.className,\"Class being registered does not have the static className property defined.\"),assert(\"string\"==typeof e.className,\"className is required to be a string, but got type \"+typeof e.className),assert(e.className.length>0,\"Class being registered has an empty-string as its className, which is disallowed.\"),SerializationMap.register(e)}var serialization=Object.freeze({Serializable:Serializable,SerializationMap:SerializationMap,registerClass:registerClass}),WEBGL_ENVS={HAS_WEBGL:!0},NODE_ENVS={IS_NODE:!0},CHROME_ENVS={IS_CHROME:!0},BROWSER_ENVS={IS_BROWSER:!0},CPU_ENVS={HAS_WEBGL:!1},ALL_ENVS={};function expectArraysClose(e,t,n){return null==n&&(n=ENV.get(\"TEST_EPSILON\")),expectArraysPredicate(e,t,function(e,t){return areClose(e,Number(t),n)})}function expectArraysPredicate(e,t,n){if(e instanceof Tensor||t instanceof Tensor){if(e instanceof Tensor&&t instanceof Tensor){if(e.dtype!==t.dtype)throw new Error(\"Arrays are of different type actual: \"+e.dtype+\" vs expected: \"+t.dtype+\".\");if(!arraysEqual(e.shape,t.shape))throw new Error(\"Arrays are of different shape actual: \"+e.shape+\" vs expected: \"+t.shape+\".\")}}else{var r=e.constructor.name,o=t.constructor.name;if(r!==o)throw new Error(\"Arrays are of different type actual: \"+r+\" vs expected: \"+o)}var a,i;if(a=e instanceof Tensor?e.dataSync():e,i=t instanceof Tensor?t.dataSync():t,a.length!==i.length)throw new Error(\"Arrays have different lengths actual: \"+a.length+\" vs expected: \"+i.length+\".\\nActual:   \"+a+\".\\nExpected: \"+i+\".\");for(var s=0;s<i.length;++s){var u=a[s],l=i[s];if(!n(u,l))throw new Error(\"Arrays differ: actual[\"+s+\"] = \"+u+\", expected[\"+s+\"] = \"+l+\".\\nActual:   \"+a+\".\\nExpected: \"+i+\".\")}}function expectPromiseToFail(e,t){e().then(function(){return t.fail()},function(){return t()})}function expectArraysEqual(e,t){return e instanceof Tensor&&\"string\"===e.dtype||t instanceof Tensor&&\"string\"===t.dtype||e instanceof Array&&isString(e[0])||t instanceof Array&&isString(t[0])?expectArraysPredicate(e,t,function(e,t){return e==t}):expectArraysClose(e,t,0)}function expectNumbersClose(e,t,n){if(null==n&&(n=ENV.get(\"TEST_EPSILON\")),!areClose(e,t,n))throw new Error(\"Numbers differ: actual === \"+e+\", expected === \"+t)}function areClose(e,t,n){return!(!isNaN(e)||!isNaN(t))||!(isNaN(e)||isNaN(t)||Math.abs(e-t)>n)}function expectValuesInRange(e,t,n){var r;r=e instanceof Tensor?e.dataSync():e;for(var o=0;o<r.length;o++)if(r[o]<t||r[o]>n)throw new Error(\"Value out of range:\"+r[o]+\" low: \"+t+\", high: \"+n)}function expectArrayBuffersEqual(e,t){expect(new Float32Array(e)).toEqual(new Float32Array(t))}var test_util=Object.freeze({WEBGL_ENVS:WEBGL_ENVS,NODE_ENVS:NODE_ENVS,CHROME_ENVS:CHROME_ENVS,BROWSER_ENVS:BROWSER_ENVS,CPU_ENVS:CPU_ENVS,ALL_ENVS:ALL_ENVS,expectArraysClose:expectArraysClose,expectPromiseToFail:expectPromiseToFail,expectArraysEqual:expectArraysEqual,expectNumbersClose:expectNumbersClose,expectValuesInRange:expectValuesInRange,expectArrayBuffersEqual:expectArrayBuffersEqual}),version=\"0.14.2\",webgl=Object.freeze({gpgpu_util:gpgpu_util,webgl_util:webgl_util,MathBackendWebGL:MathBackendWebGL,GPGPUContext:GPGPUContext}),Optimizer=function(e){function t(){return null!==e&&e.apply(this,arguments)||this}return __extends(t,e),t.prototype.minimize=function(e,t,n){void 0===t&&(t=!1);var r=this.computeGradients(e,n),o=r.value,a=r.grads;return this.applyGradients(a),Object.keys(a).forEach(function(e){return a[e].dispose()}),t?o:(o.dispose(),null)},t.prototype.computeGradients=function(e,t){return variableGrads(e,t)},t}(Serializable),AdadeltaOptimizer=function(e){function t(t,n,r){void 0===r&&(r=null);var o=e.call(this)||this;return o.learningRate=t,o.rho=n,o.epsilon=r,o.accumulatedGrads={},o.accumulatedUpdates={},o.c=keep(scalar(-t)),o.rhoScalar=keep(scalar(n)),o.oneMinusRho=keep(scalar(1-n)),null===r&&(r=ENV.get(\"EPSILON\")),o.epsilonScalar=keep(scalar(r)),o}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulatedGrads[n]){tidy(function(){t.accumulatedGrads[n]=zerosLike(o).variable(!1)})}if(null==r.accumulatedUpdates[n]){tidy(function(){t.accumulatedUpdates[n]=zerosLike(o).variable(!1)})}var a=e[n],i=r.accumulatedGrads[n],s=r.accumulatedUpdates[n];tidy(function(){var e=t.rhoScalar.mul(i).add(t.oneMinusRho.mul(a.square())),r=s.add(t.epsilonScalar).sqrt().div(i.add(t.epsilonScalar).sqrt()).mul(a),u=t.rhoScalar.mul(s).add(t.oneMinusRho.mul(r.square()));t.accumulatedGrads[n].assign(e),t.accumulatedUpdates[n].assign(u);var l=t.c.mul(r).add(o);o.assign(l)})},r=this;for(var o in e)n(o)},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsilonScalar.dispose(),this.rhoScalar.dispose(),this.oneMinusRho.dispose(),null!=this.accumulatedUpdates&&(Object.keys(this.accumulatedUpdates).forEach(function(t){return e.accumulatedUpdates[t].dispose()}),Object.keys(this.accumulatedGrads).forEach(function(t){return e.accumulatedGrads[t].dispose()}))},t.prototype.getConfig=function(){return{learningRate:this.learningRate,rho:this.rho,epsilon:this.epsilon}},t.fromConfig=function(e,t){return new e(t.learningRate,t.rho,t.epsilon)},t.className=\"AdadeltaOptimizer\",t}(Optimizer);registerClass(AdadeltaOptimizer);var AdagradOptimizer=function(e){function t(t,n){void 0===n&&(n=.1);var r=e.call(this)||this;return r.learningRate=t,r.initialAccumulatorValue=n,r.accumulatedGrads={},r.c=keep(scalar(-t)),r.epsilon=keep(scalar(ENV.get(\"EPSILON\"))),r}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulatedGrads[n]){tidy(function(){t.accumulatedGrads[n]=fill(o.shape,t.initialAccumulatorValue).variable(!1)})}var a=e[n],i=r.accumulatedGrads[n];tidy(function(){var e=i.add(a.square());t.accumulatedGrads[n].assign(e);var r=t.c.mul(a.div(e.add(t.epsilon).sqrt())).add(o);o.assign(r)})},r=this;for(var o in e)n(o)},t.prototype.dispose=function(){var e=this;this.epsilon.dispose(),this.c.dispose(),null!=this.accumulatedGrads&&Object.keys(this.accumulatedGrads).forEach(function(t){return e.accumulatedGrads[t].dispose()})},t.prototype.getConfig=function(){return{learningRate:this.learningRate,initialAccumulatorValue:this.initialAccumulatorValue}},t.fromConfig=function(e,t){return new e(t.learningRate,t.initialAccumulatorValue)},t.className=\"AdagradOptimizer\",t}(Optimizer);registerClass(AdagradOptimizer);var AdamOptimizer=function(e){function t(t,n,r,o){void 0===o&&(o=null);var a=e.call(this)||this;return a.learningRate=t,a.beta1=n,a.beta2=r,a.epsilon=o,a.accumulatedFirstMoment={},a.accumulatedSecondMoment={},a.c=keep(scalar(-t)),a.beta1Scalar=keep(scalar(n)),a.beta2Scalar=keep(scalar(r)),tidy(function(){a.accBeta1=scalar(n).variable(),a.accBeta2=scalar(r).variable()}),a.oneMinusBeta1=keep(scalar(1-n)),a.oneMinusBeta2=keep(scalar(1-r)),a.one=keep(scalar(1)),null===o&&(o=ENV.get(\"EPSILON\")),a.epsScalar=keep(scalar(o)),a}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this;tidy(function(){var n=t.one.sub(t.accBeta1),r=t.one.sub(t.accBeta2);for(var o in e){var a=ENV.engine.registeredVariables[o];if(null==t.accumulatedFirstMoment[o]){var i=!1;t.accumulatedFirstMoment[o]=zerosLike(a).variable(i)}if(null==t.accumulatedSecondMoment[o]){i=!1;t.accumulatedSecondMoment[o]=zerosLike(a).variable(i)}var s=e[o],u=t.accumulatedFirstMoment[o],l=t.accumulatedSecondMoment[o],c=t.beta1Scalar.mul(u).add(t.oneMinusBeta1.mul(s)),p=t.beta2Scalar.mul(l).add(t.oneMinusBeta2.mul(s.square())),d=c.div(n),h=p.div(r);t.accumulatedFirstMoment[o].assign(c),t.accumulatedSecondMoment[o].assign(p);var f=t.c.mul(d.div(t.epsScalar.add(h.sqrt()))).add(a);a.assign(f)}t.accBeta1.assign(t.accBeta1.mul(t.beta1Scalar)),t.accBeta2.assign(t.accBeta2.mul(t.beta2Scalar))})},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsScalar.dispose(),this.beta1Scalar.dispose(),this.beta2Scalar.dispose(),this.accBeta1.dispose(),this.accBeta2.dispose(),this.oneMinusBeta1.dispose(),this.oneMinusBeta2.dispose(),this.one.dispose(),null!=this.accumulatedFirstMoment&&Object.keys(this.accumulatedFirstMoment).forEach(function(t){return e.accumulatedFirstMoment[t].dispose()}),null!=this.accumulatedSecondMoment&&Object.keys(this.accumulatedSecondMoment).forEach(function(t){return e.accumulatedSecondMoment[t].dispose()})},t.prototype.getConfig=function(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon}},t.fromConfig=function(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon)},t.className=\"AdamOptimizer\",t}(Optimizer);registerClass(AdamOptimizer);var AdamaxOptimizer=function(e){function t(t,n,r,o,a){void 0===o&&(o=null),void 0===a&&(a=0);var i=e.call(this)||this;return i.learningRate=t,i.beta1=n,i.beta2=r,i.epsilon=o,i.decay=a,i.accumulatedFirstMoment={},i.accumulatedWeightedInfNorm={},i.c=keep(scalar(-t)),i.beta1Scalar=keep(scalar(n)),i.beta2Scalar=keep(scalar(r)),i.decayScalar=keep(scalar(a)),tidy(function(){i.iteration=scalar(0).variable(),i.accBeta1=scalar(n).variable()}),i.oneMinusBeta1=keep(scalar(1-n)),i.one=keep(scalar(1)),null===o&&(o=ENV.get(\"EPSILON\")),i.epsScalar=keep(scalar(o)),i}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this;tidy(function(){var n=t.one.sub(t.accBeta1),r=t.c.div(t.one.add(t.decayScalar.mul(t.iteration)));for(var o in e){var a=ENV.engine.registeredVariables[o];if(null==t.accumulatedFirstMoment[o]){var i=!1;t.accumulatedFirstMoment[o]=zerosLike(a).variable(i)}if(null==t.accumulatedWeightedInfNorm[o]){i=!1;t.accumulatedWeightedInfNorm[o]=zerosLike(a).variable(i)}var s=e[o],u=t.accumulatedFirstMoment[o],l=t.accumulatedWeightedInfNorm[o],c=t.beta1Scalar.mul(u).add(t.oneMinusBeta1.mul(s)),p=t.beta2Scalar.mul(l),d=s.abs(),h=p.maximum(d);t.accumulatedFirstMoment[o].assign(c),t.accumulatedWeightedInfNorm[o].assign(h);var f=r.div(n).mul(c.div(t.epsScalar.add(h))).add(a);a.assign(f)}t.iteration.assign(t.iteration.add(t.one)),t.accBeta1.assign(t.accBeta1.mul(t.beta1Scalar))})},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsScalar.dispose(),this.accBeta1.dispose(),this.beta1Scalar.dispose(),this.beta2Scalar.dispose(),this.oneMinusBeta1.dispose(),this.decayScalar.dispose(),this.iteration.dispose(),this.one.dispose(),null!=this.accumulatedFirstMoment&&Object.keys(this.accumulatedFirstMoment).forEach(function(t){return e.accumulatedFirstMoment[t].dispose()}),null!=this.accumulatedWeightedInfNorm&&Object.keys(this.accumulatedWeightedInfNorm).forEach(function(t){return e.accumulatedWeightedInfNorm[t].dispose()})},t.prototype.getConfig=function(){return{learningRate:this.learningRate,beta1:this.beta1,beta2:this.beta2,epsilon:this.epsilon,decay:this.decay}},t.fromConfig=function(e,t){return new e(t.learningRate,t.beta1,t.beta2,t.epsilon,t.decay)},t.className=\"AdamaxOptimizer\",t}(Optimizer);registerClass(AdamaxOptimizer);var SGDOptimizer=function(e){function t(t){var n=e.call(this)||this;return n.learningRate=t,n.setLearningRate(t),n}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this;Object.keys(e).forEach(function(n){var r=e[n],o=ENV.engine.registeredVariables[n];tidy(function(){var e=t.c.mul(r).add(o);o.assign(e)})})},t.prototype.setLearningRate=function(e){this.learningRate=e,null!=this.c&&this.c.dispose(),this.c=keep(scalar(-e))},t.prototype.dispose=function(){this.c.dispose()},t.prototype.getConfig=function(){return{learningRate:this.learningRate}},t.fromConfig=function(e,t){return new e(t.learningRate)},t.className=\"SGDOptimizer\",t}(Optimizer);registerClass(SGDOptimizer);var MomentumOptimizer=function(e){function t(t,n,r){void 0===r&&(r=!1);var o=e.call(this,t)||this;return o.learningRate=t,o.momentum=n,o.useNesterov=r,o.m=scalar(o.momentum),o.accumulations={},o}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulations[n]){tidy(function(){t.accumulations[n]=zerosLike(o).variable(!1)})}var a=r.accumulations[n],i=e[n];tidy(function(){var e,r=t.m.mul(a).add(i);e=t.useNesterov?t.c.mul(i.add(r.mul(t.m))).add(o):t.c.mul(r).add(o),t.accumulations[n].assign(r),o.assign(e)})},r=this;for(var o in e)n(o)},t.prototype.dispose=function(){if(e.prototype.dispose.call(this),this.m.dispose(),null!=this.accumulations)for(var t in this.accumulations)this.accumulations[t].dispose()},t.prototype.setMomentum=function(e){this.momentum=e},t.prototype.getConfig=function(){return{learningRate:this.learningRate,momentum:this.momentum,useNesterov:this.useNesterov}},t.fromConfig=function(e,t){return new e(t.learningRate,t.momentum,t.useNesterov)},t.className=\"MomentumOptimizer\",t}(SGDOptimizer);registerClass(MomentumOptimizer);var RMSPropOptimizer=function(e){function t(t,n,r,o,a){void 0===n&&(n=.9),void 0===r&&(r=0),void 0===o&&(o=null),void 0===a&&(a=!1);var i=e.call(this)||this;return i.learningRate=t,i.decay=n,i.momentum=r,i.epsilon=o,i.accumulatedMeanSquares={},i.accumulatedMeanGrads={},i.accumulatedMoments={},i.c=keep(scalar(t)),i.decayScalar=keep(scalar(n)),i.momentumScalar=keep(scalar(r)),i.oneMinusDecay=keep(scalar(1-n)),i.centered=a,null===o&&(o=ENV.get(\"EPSILON\")),i.epsilonScalar=keep(scalar(o)),i}return __extends(t,e),t.prototype.applyGradients=function(e){var t=this,n=function(n){var o=ENV.engine.registeredVariables[n];if(null==r.accumulatedMeanSquares[n]){tidy(function(){t.accumulatedMeanSquares[n]=zerosLike(o).variable(!1)})}if(null==r.accumulatedMeanGrads[n]&&r.centered){tidy(function(){t.accumulatedMeanGrads[n]=zerosLike(o).variable(!1)})}if(null==r.accumulatedMoments[n]){tidy(function(){t.accumulatedMoments[n]=zerosLike(o).variable(!1)})}var a=r.accumulatedMeanSquares[n],i=r.accumulatedMeanGrads[n],s=r.accumulatedMoments[n],u=e[n];tidy(function(){var e=t.decayScalar.mul(a).add(t.oneMinusDecay.mul(u.square()));if(t.centered){var r=t.decayScalar.mul(i).add(t.oneMinusDecay.mul(u)),l=t.momentumScalar.mul(s).add(t.c.mul(u).div(e.sub(r.square().add(t.epsilonScalar)).sqrt()));t.accumulatedMeanSquares[n].assign(e),t.accumulatedMeanGrads[n].assign(r),t.accumulatedMoments[n].assign(l);var c=o.sub(l);o.assign(c)}else{var p=t.decayScalar.mul(a).add(t.oneMinusDecay.mul(u.square()));l=t.momentumScalar.mul(s).add(t.c.mul(u).div(p.add(t.epsilonScalar).sqrt()));t.accumulatedMeanSquares[n].assign(p),t.accumulatedMoments[n].assign(l);c=o.sub(l);o.assign(c)}})},r=this;for(var o in e)n(o)},t.prototype.dispose=function(){var e=this;this.c.dispose(),this.epsilonScalar.dispose(),this.decayScalar.dispose(),this.momentumScalar.dispose(),this.oneMinusDecay.dispose(),null!=this.accumulatedMeanSquares&&Object.keys(this.accumulatedMeanSquares).forEach(function(t){return e.accumulatedMeanSquares[t].dispose()}),null!=this.accumulatedMeanGrads&&this.centered&&Object.keys(this.accumulatedMeanGrads).forEach(function(t){return e.accumulatedMeanGrads[t].dispose()}),null!=this.accumulatedMoments&&Object.keys(this.accumulatedMoments).forEach(function(t){return e.accumulatedMoments[t].dispose()})},t.prototype.getConfig=function(){return{learningRate:this.learningRate,decay:this.decay,momentum:this.momentum,epsilon:this.epsilon,centered:this.centered}},t.fromConfig=function(e,t){return new e(t.learningRate,t.decay,t.momentum,t.epsilon,t.centered)},t.className=\"RMSPropOptimizer\",t}(Optimizer);registerClass(RMSPropOptimizer);var OptimizerConstructors=function(){function e(){}return e.sgd=function(e){return new SGDOptimizer(e)},e.momentum=function(e,t,n){return void 0===n&&(n=!1),new MomentumOptimizer(e,t,n)},e.rmsprop=function(e,t,n,r,o){return void 0===t&&(t=.9),void 0===n&&(n=0),void 0===r&&(r=null),void 0===o&&(o=!1),new RMSPropOptimizer(e,t,n,r,o)},e.adam=function(e,t,n,r){return void 0===e&&(e=.001),void 0===t&&(t=.9),void 0===n&&(n=.999),void 0===r&&(r=null),new AdamOptimizer(e,t,n,r)},e.adadelta=function(e,t,n){return void 0===e&&(e=.001),void 0===t&&(t=.95),void 0===n&&(n=null),new AdadeltaOptimizer(e,t,n)},e.adamax=function(e,t,n,r,o){return void 0===e&&(e=.002),void 0===t&&(t=.9),void 0===n&&(n=.999),void 0===r&&(r=null),void 0===o&&(o=0),new AdamaxOptimizer(e,t,n,r,o)},e.adagrad=function(e,t){return void 0===t&&(t=.1),new AdagradOptimizer(e,t)},e}(),train={sgd:OptimizerConstructors.sgd,momentum:OptimizerConstructors.momentum,adadelta:OptimizerConstructors.adadelta,adagrad:OptimizerConstructors.adagrad,rmsprop:OptimizerConstructors.rmsprop,adamax:OptimizerConstructors.adamax,adam:OptimizerConstructors.adam},setBackend=Environment.setBackend,getBackend=Environment.getBackend,disposeVariables=Environment.disposeVariables,memory=Environment.memory;setOpHandler(ops);export{setBackend,getBackend,disposeVariables,memory,version as version_core,nextFrame,environment,io,math,serialization,test_util,util,webgl,AdadeltaOptimizer,AdagradOptimizer,AdamOptimizer,AdamaxOptimizer,MomentumOptimizer,Optimizer,RMSPropOptimizer,SGDOptimizer,Tensor,TensorBuffer,variable,Variable,Rank,Reduction,ENV,Environment,KernelBackend,DataStorage,image_ops as image,linalg_ops as linalg,loss_ops as losses,spectral_ops as spectral,op,batchNormalization2d,batchNormalization3d,batchNormalization4d,batchNormalization,complex,real,imag,concat,concat1d,concat2d,concat3d,concat4d,split$1 as split,conv1d,conv2d,conv3d,conv2dDerFilter,depthwiseConv2d,separableConv2d,conv2dTranspose,matMul,dot,outerProduct,reverse,reverse1d,reverse2d,reverse3d,reverse4d,maxPool,avgPool,pool,slice,slice1d,slice2d,slice3d,slice4d,abs,acos,acosh,asin,asinh,atan,atanh,ceil,clipByValue,cos,cosh,erf,exp,expm1,floor,log$1 as log,log1p,logSigmoid,neg,reciprocal,round,rsqrt,sigmoid,sign,sin,sinh,softplus,sqrt,square,step,tan,tanh$1 as tanh,all,any,argMax,argMin,logSumExp,max,mean,min,moments,sum$1 as sum,prod,equal,equalStrict,greater,greaterEqual,greaterEqualStrict,greaterStrict,less,lessEqual,lessEqualStrict,lessStrict,notEqual,notEqualStrict,add,addN,addStrict,atan2,div,divStrict,floorDiv,maximum,maximumStrict,minimum,minimumStrict,mod,modStrict,mul,mulStrict,pow,powStrict,squaredDifference,squaredDifferenceStrict,sub,subStrict,elu,leakyRelu,prelu,relu,selu,logicalAnd,logicalNot,logicalOr,logicalXor,where,whereAsync,buffer,toPixels,print,batchToSpaceND,cast,clone,cumsum,depthToSpace,expandDims,eye,fromPixels,multinomial,oneHot,pad,pad1d,pad2d,pad3d,pad4d,rand,randomNormal,randomUniform,reshape,spaceToBatchND,squeeze,stack,tile,truncatedNormal,unstack,setdiff1dAsync,fill,linspace,ones$1 as ones,range,scalar,tensor,tensor1d,tensor2d,tensor3d,tensor4d,tensor5d,tensor6d,zeros,onesLike,zerosLike,transpose,softmax,logSoftmax,localResponseNormalization,norm,gather,unsortedSegmentSum,basicLSTMCell,multiRNNCell,movingAverage,stridedSlice,topk,scatterND,fft,ifft,rfft,irfft,sparseToDense,gatherND,train,tidy,keep,dispose,time,profile,customGrad,grad,grads,valueAndGrad,valueAndGrads,variableGrads};\n//# sourceMappingURL=tf-core.esm.js.map\n","import { isValidNumber } from '../utils';\r\nvar Dimensions = /** @class */ (function () {\r\n    function Dimensions(width, height) {\r\n        if (!isValidNumber(width) || !isValidNumber(height)) {\r\n            throw new Error(\"Dimensions.constructor - expected width and height to be valid numbers, instead have \" + JSON.stringify({ width: width, height: height }));\r\n        }\r\n        this._width = width;\r\n        this._height = height;\r\n    }\r\n    Object.defineProperty(Dimensions.prototype, \"width\", {\r\n        get: function () { return this._width; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Dimensions.prototype, \"height\", {\r\n        get: function () { return this._height; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Dimensions.prototype.reverse = function () {\r\n        return new Dimensions(1 / this.width, 1 / this.height);\r\n    };\r\n    return Dimensions;\r\n}());\r\nexport { Dimensions };\r\n//# sourceMappingURL=Dimensions.js.map","var Point = /** @class */ (function () {\r\n    function Point(x, y) {\r\n        this._x = x;\r\n        this._y = y;\r\n    }\r\n    Object.defineProperty(Point.prototype, \"x\", {\r\n        get: function () { return this._x; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Point.prototype, \"y\", {\r\n        get: function () { return this._y; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Point.prototype.add = function (pt) {\r\n        return new Point(this.x + pt.x, this.y + pt.y);\r\n    };\r\n    Point.prototype.sub = function (pt) {\r\n        return new Point(this.x - pt.x, this.y - pt.y);\r\n    };\r\n    Point.prototype.mul = function (pt) {\r\n        return new Point(this.x * pt.x, this.y * pt.y);\r\n    };\r\n    Point.prototype.div = function (pt) {\r\n        return new Point(this.x / pt.x, this.y / pt.y);\r\n    };\r\n    Point.prototype.abs = function () {\r\n        return new Point(Math.abs(this.x), Math.abs(this.y));\r\n    };\r\n    Point.prototype.magnitude = function () {\r\n        return Math.sqrt(Math.pow(this.x, 2) + Math.pow(this.y, 2));\r\n    };\r\n    Point.prototype.floor = function () {\r\n        return new Point(Math.floor(this.x), Math.floor(this.y));\r\n    };\r\n    return Point;\r\n}());\r\nexport { Point };\r\n//# sourceMappingURL=Point.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { Dimensions } from '../classes/Dimensions';\r\nimport { Point } from '../classes/Point';\r\nexport function isTensor(tensor, dim) {\r\n    return tensor instanceof tf.Tensor && tensor.shape.length === dim;\r\n}\r\nexport function isTensor1D(tensor) {\r\n    return isTensor(tensor, 1);\r\n}\r\nexport function isTensor2D(tensor) {\r\n    return isTensor(tensor, 2);\r\n}\r\nexport function isTensor3D(tensor) {\r\n    return isTensor(tensor, 3);\r\n}\r\nexport function isTensor4D(tensor) {\r\n    return isTensor(tensor, 4);\r\n}\r\nexport function isFloat(num) {\r\n    return num % 1 !== 0;\r\n}\r\nexport function isEven(num) {\r\n    return num % 2 === 0;\r\n}\r\nexport function round(num, prec) {\r\n    if (prec === void 0) { prec = 2; }\r\n    var f = Math.pow(10, prec);\r\n    return Math.floor(num * f) / f;\r\n}\r\nexport function isDimensions(obj) {\r\n    return obj && obj.width && obj.height;\r\n}\r\nexport function computeReshapedDimensions(_a, inputSize) {\r\n    var width = _a.width, height = _a.height;\r\n    var scale = inputSize / Math.max(height, width);\r\n    return new Dimensions(Math.round(width * scale), Math.round(height * scale));\r\n}\r\nexport function getCenterPoint(pts) {\r\n    return pts.reduce(function (sum, pt) { return sum.add(pt); }, new Point(0, 0))\r\n        .div(new Point(pts.length, pts.length));\r\n}\r\nexport function range(num, start, step) {\r\n    return Array(num).fill(0).map(function (_, i) { return start + (i * step); });\r\n}\r\nexport function isValidNumber(num) {\r\n    return !!num && num !== Infinity && num !== -Infinity && !isNaN(num) || num === 0;\r\n}\r\nexport function isValidProbablitiy(num) {\r\n    return isValidNumber(num) && 0 <= num && num <= 1.0;\r\n}\r\n//# sourceMappingURL=index.js.map","import { isDimensions, isValidNumber } from '../utils';\r\nvar Box = /** @class */ (function () {\r\n    // TODO: MTCNN boxes sometimes have negative widths or heights, figure out why and remove\r\n    // allowNegativeDimensions flag again\r\n    function Box(_box, allowNegativeDimensions) {\r\n        if (allowNegativeDimensions === void 0) { allowNegativeDimensions = false; }\r\n        var box = (_box || {});\r\n        var isBbox = [box.left, box.top, box.right, box.bottom].every(isValidNumber);\r\n        var isRect = [box.x, box.y, box.width, box.height].every(isValidNumber);\r\n        if (!isRect && !isBbox) {\r\n            throw new Error(\"Box.constructor - expected box to be IBoundingBox | IRect, instead have \" + JSON.stringify(box));\r\n        }\r\n        var _a = isRect\r\n            ? [box.x, box.y, box.width, box.height]\r\n            : [box.left, box.top, box.right - box.left, box.bottom - box.top], x = _a[0], y = _a[1], width = _a[2], height = _a[3];\r\n        Box.assertIsValidBox({ x: x, y: y, width: width, height: height }, 'Box.constructor', allowNegativeDimensions);\r\n        this._x = x;\r\n        this._y = y;\r\n        this._width = width;\r\n        this._height = height;\r\n    }\r\n    Box.isRect = function (rect) {\r\n        return !!rect && [rect.x, rect.y, rect.width, rect.height].every(isValidNumber);\r\n    };\r\n    Box.assertIsValidBox = function (box, callee, allowNegativeDimensions) {\r\n        if (allowNegativeDimensions === void 0) { allowNegativeDimensions = false; }\r\n        if (!Box.isRect(box)) {\r\n            throw new Error(callee + \" - invalid box: \" + JSON.stringify(box) + \", expected object with properties x, y, width, height\");\r\n        }\r\n        if (!allowNegativeDimensions && (box.width < 0 || box.height < 0)) {\r\n            throw new Error(callee + \" - width (\" + box.width + \") and height (\" + box.height + \") must be positive numbers\");\r\n        }\r\n    };\r\n    Object.defineProperty(Box.prototype, \"x\", {\r\n        get: function () { return this._x; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"y\", {\r\n        get: function () { return this._y; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"width\", {\r\n        get: function () { return this._width; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"height\", {\r\n        get: function () { return this._height; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"left\", {\r\n        get: function () { return this.x; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"top\", {\r\n        get: function () { return this.y; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"right\", {\r\n        get: function () { return this.x + this.width; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"bottom\", {\r\n        get: function () { return this.y + this.height; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(Box.prototype, \"area\", {\r\n        get: function () { return this.width * this.height; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Box.prototype.round = function () {\r\n        var _a = [this.x, this.y, this.width, this.height]\r\n            .map(function (val) { return Math.round(val); }), x = _a[0], y = _a[1], width = _a[2], height = _a[3];\r\n        return new Box({ x: x, y: y, width: width, height: height });\r\n    };\r\n    Box.prototype.floor = function () {\r\n        var _a = [this.x, this.y, this.width, this.height]\r\n            .map(function (val) { return Math.floor(val); }), x = _a[0], y = _a[1], width = _a[2], height = _a[3];\r\n        return new Box({ x: x, y: y, width: width, height: height });\r\n    };\r\n    Box.prototype.toSquare = function () {\r\n        var _a = this, x = _a.x, y = _a.y, width = _a.width, height = _a.height;\r\n        var diff = Math.abs(width - height);\r\n        if (width < height) {\r\n            x -= (diff / 2);\r\n            width += diff;\r\n        }\r\n        if (height < width) {\r\n            y -= (diff / 2);\r\n            height += diff;\r\n        }\r\n        return new Box({ x: x, y: y, width: width, height: height });\r\n    };\r\n    Box.prototype.rescale = function (s) {\r\n        var scaleX = isDimensions(s) ? s.width : s;\r\n        var scaleY = isDimensions(s) ? s.height : s;\r\n        return new Box({\r\n            x: this.x * scaleX,\r\n            y: this.y * scaleY,\r\n            width: this.width * scaleX,\r\n            height: this.height * scaleY\r\n        });\r\n    };\r\n    Box.prototype.pad = function (padX, padY) {\r\n        var _a = [\r\n            this.x - (padX / 2),\r\n            this.y - (padY / 2),\r\n            this.width + padX,\r\n            this.height + padY\r\n        ], x = _a[0], y = _a[1], width = _a[2], height = _a[3];\r\n        return new Box({ x: x, y: y, width: width, height: height });\r\n    };\r\n    Box.prototype.clipAtImageBorders = function (imgWidth, imgHeight) {\r\n        var _a = this, x = _a.x, y = _a.y, right = _a.right, bottom = _a.bottom;\r\n        var clippedX = Math.max(x, 0);\r\n        var clippedY = Math.max(y, 0);\r\n        var newWidth = right - clippedX;\r\n        var newHeight = bottom - clippedY;\r\n        var clippedWidth = Math.min(newWidth, imgWidth - clippedX);\r\n        var clippedHeight = Math.min(newHeight, imgHeight - clippedY);\r\n        return (new Box({ x: clippedX, y: clippedY, width: clippedWidth, height: clippedHeight })).floor();\r\n    };\r\n    Box.prototype.shift = function (sx, sy) {\r\n        var _a = this, width = _a.width, height = _a.height;\r\n        var x = this.x + sx;\r\n        var y = this.y + sy;\r\n        return new Box({ x: x, y: y, width: width, height: height });\r\n    };\r\n    Box.prototype.padAtBorders = function (imageHeight, imageWidth) {\r\n        var w = this.width + 1;\r\n        var h = this.height + 1;\r\n        var dx = 1;\r\n        var dy = 1;\r\n        var edx = w;\r\n        var edy = h;\r\n        var x = this.left;\r\n        var y = this.top;\r\n        var ex = this.right;\r\n        var ey = this.bottom;\r\n        if (ex > imageWidth) {\r\n            edx = -ex + imageWidth + w;\r\n            ex = imageWidth;\r\n        }\r\n        if (ey > imageHeight) {\r\n            edy = -ey + imageHeight + h;\r\n            ey = imageHeight;\r\n        }\r\n        if (x < 1) {\r\n            edy = 2 - x;\r\n            x = 1;\r\n        }\r\n        if (y < 1) {\r\n            edy = 2 - y;\r\n            y = 1;\r\n        }\r\n        return { dy: dy, edy: edy, dx: dx, edx: edx, y: y, ey: ey, x: x, ex: ex, w: w, h: h };\r\n    };\r\n    Box.prototype.calibrate = function (region) {\r\n        return new Box({\r\n            left: this.left + (region.left * this.width),\r\n            top: this.top + (region.top * this.height),\r\n            right: this.right + (region.right * this.width),\r\n            bottom: this.bottom + (region.bottom * this.height)\r\n        }).toSquare().round();\r\n    };\r\n    return Box;\r\n}());\r\nexport { Box };\r\n//# sourceMappingURL=Box.js.map","import * as tslib_1 from \"tslib\";\r\nimport { Box } from './Box';\r\nvar BoundingBox = /** @class */ (function (_super) {\r\n    tslib_1.__extends(BoundingBox, _super);\r\n    function BoundingBox(left, top, right, bottom) {\r\n        return _super.call(this, { left: left, top: top, right: right, bottom: bottom }) || this;\r\n    }\r\n    return BoundingBox;\r\n}(Box));\r\nexport { BoundingBox };\r\n//# sourceMappingURL=BoundingBox.js.map","import * as tslib_1 from \"tslib\";\r\nimport { Box } from './Box';\r\nvar BoxWithText = /** @class */ (function (_super) {\r\n    tslib_1.__extends(BoxWithText, _super);\r\n    function BoxWithText(box, text) {\r\n        var _this = _super.call(this, box) || this;\r\n        _this._text = text;\r\n        return _this;\r\n    }\r\n    Object.defineProperty(BoxWithText.prototype, \"text\", {\r\n        get: function () { return this._text; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    return BoxWithText;\r\n}(Box));\r\nexport { BoxWithText };\r\n//# sourceMappingURL=BoxWithText.js.map","import * as tslib_1 from \"tslib\";\r\nimport { Box } from './Box';\r\nimport { isValidNumber } from '../utils/index';\r\nvar LabeledBox = /** @class */ (function (_super) {\r\n    tslib_1.__extends(LabeledBox, _super);\r\n    function LabeledBox(box, label) {\r\n        var _this = _super.call(this, box) || this;\r\n        _this._label = label;\r\n        return _this;\r\n    }\r\n    LabeledBox.assertIsValidLabeledBox = function (box, callee) {\r\n        Box.assertIsValidBox(box, callee);\r\n        if (!isValidNumber(box.label)) {\r\n            throw new Error(callee + \" - expected property label (\" + box.label + \") to be a number\");\r\n        }\r\n    };\r\n    Object.defineProperty(LabeledBox.prototype, \"label\", {\r\n        get: function () { return this._label; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    return LabeledBox;\r\n}(Box));\r\nexport { LabeledBox };\r\n//# sourceMappingURL=LabeledBox.js.map","import { Box } from './Box';\r\nimport { Dimensions } from './Dimensions';\r\nvar ObjectDetection = /** @class */ (function () {\r\n    function ObjectDetection(score, classScore, className, relativeBox, imageDims) {\r\n        this._imageDims = new Dimensions(imageDims.width, imageDims.height);\r\n        this._score = score;\r\n        this._classScore = classScore;\r\n        this._className = className;\r\n        this._box = new Box(relativeBox).rescale(this._imageDims);\r\n    }\r\n    Object.defineProperty(ObjectDetection.prototype, \"score\", {\r\n        get: function () { return this._score; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(ObjectDetection.prototype, \"classScore\", {\r\n        get: function () { return this._classScore; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(ObjectDetection.prototype, \"className\", {\r\n        get: function () { return this._className; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(ObjectDetection.prototype, \"box\", {\r\n        get: function () { return this._box; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(ObjectDetection.prototype, \"imageDims\", {\r\n        get: function () { return this._imageDims; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(ObjectDetection.prototype, \"imageWidth\", {\r\n        get: function () { return this.imageDims.width; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(ObjectDetection.prototype, \"imageHeight\", {\r\n        get: function () { return this.imageDims.height; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(ObjectDetection.prototype, \"relativeBox\", {\r\n        get: function () { return new Box(this._box).rescale(this.imageDims.reverse()); },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    ObjectDetection.prototype.forSize = function (width, height) {\r\n        return new ObjectDetection(this.score, this.classScore, this.className, this.relativeBox, { width: width, height: height });\r\n    };\r\n    return ObjectDetection;\r\n}());\r\nexport { ObjectDetection };\r\n//# sourceMappingURL=ObjectDetection.js.map","import * as tslib_1 from \"tslib\";\r\nimport { isValidProbablitiy } from '../utils';\r\nimport { LabeledBox } from './LabeledBox';\r\nvar PredictedBox = /** @class */ (function (_super) {\r\n    tslib_1.__extends(PredictedBox, _super);\r\n    function PredictedBox(box, label, score, classScore) {\r\n        var _this = _super.call(this, box, label) || this;\r\n        _this._score = score;\r\n        _this._classScore = classScore;\r\n        return _this;\r\n    }\r\n    PredictedBox.assertIsValidPredictedBox = function (box, callee) {\r\n        LabeledBox.assertIsValidLabeledBox(box, callee);\r\n        if (!isValidProbablitiy(box.score)\r\n            || !isValidProbablitiy(box.classScore)) {\r\n            throw new Error(callee + \" - expected properties score (\" + box.score + \") and (\" + box.classScore + \") to be a number between [0, 1]\");\r\n        }\r\n    };\r\n    Object.defineProperty(PredictedBox.prototype, \"score\", {\r\n        get: function () { return this._score; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(PredictedBox.prototype, \"classScore\", {\r\n        get: function () { return this._classScore; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    return PredictedBox;\r\n}(LabeledBox));\r\nexport { PredictedBox };\r\n//# sourceMappingURL=PredictedBox.js.map","import * as tslib_1 from \"tslib\";\r\nimport { Box } from './Box';\r\nvar Rect = /** @class */ (function (_super) {\r\n    tslib_1.__extends(Rect, _super);\r\n    function Rect(x, y, width, height) {\r\n        return _super.call(this, { x: x, y: y, width: width, height: height }) || this;\r\n    }\r\n    return Rect;\r\n}(Box));\r\nexport { Rect };\r\n//# sourceMappingURL=Rect.js.map","export function disposeUnusedWeightTensors(weightMap, paramMappings) {\r\n    Object.keys(weightMap).forEach(function (path) {\r\n        if (!paramMappings.some(function (pm) { return pm.originalPath === path; })) {\r\n            weightMap[path].dispose();\r\n        }\r\n    });\r\n}\r\n//# sourceMappingURL=disposeUnusedWeightTensors.js.map","import { isTensor } from '../utils';\r\nexport function extractWeightEntryFactory(weightMap, paramMappings) {\r\n    return function (originalPath, paramRank, mappedPath) {\r\n        var tensor = weightMap[originalPath];\r\n        if (!isTensor(tensor, paramRank)) {\r\n            throw new Error(\"expected weightMap[\" + originalPath + \"] to be a Tensor\" + paramRank + \"D, instead have \" + tensor);\r\n        }\r\n        paramMappings.push({ originalPath: originalPath, paramPath: mappedPath || originalPath });\r\n        return tensor;\r\n    };\r\n}\r\n//# sourceMappingURL=extractWeightEntryFactory.js.map","export function extractWeightsFactory(weights) {\r\n    var remainingWeights = weights;\r\n    function extractWeights(numWeights) {\r\n        var ret = remainingWeights.slice(0, numWeights);\r\n        remainingWeights = remainingWeights.slice(numWeights);\r\n        return ret;\r\n    }\r\n    function getRemainingWeights() {\r\n        return remainingWeights;\r\n    }\r\n    return {\r\n        extractWeights: extractWeights,\r\n        getRemainingWeights: getRemainingWeights\r\n    };\r\n}\r\n//# sourceMappingURL=extractWeightsFactory.js.map","export function getModelUris(uri, defaultModelName) {\r\n    var defaultManifestFilename = defaultModelName + \"-weights_manifest.json\";\r\n    if (!uri) {\r\n        return {\r\n            modelBaseUri: '',\r\n            manifestUri: defaultManifestFilename\r\n        };\r\n    }\r\n    if (uri === '/') {\r\n        return {\r\n            modelBaseUri: '/',\r\n            manifestUri: \"/\" + defaultManifestFilename\r\n        };\r\n    }\r\n    var protocol = uri.startsWith('http://') ? 'http://' : uri.startsWith('https://') ? 'https://' : '';\r\n    uri = uri.replace(protocol, '');\r\n    var parts = uri.split('/').filter(function (s) { return s; });\r\n    var manifestFile = uri.endsWith('.json')\r\n        ? parts[parts.length - 1]\r\n        : defaultManifestFilename;\r\n    var modelBaseUri = protocol + (uri.endsWith('.json') ? parts.slice(0, parts.length - 1) : parts).join('/');\r\n    modelBaseUri = uri.startsWith('/') ? \"/\" + modelBaseUri : modelBaseUri;\r\n    return {\r\n        modelBaseUri: modelBaseUri,\r\n        manifestUri: modelBaseUri === '/' ? \"/\" + manifestFile : modelBaseUri + \"/\" + manifestFile\r\n    };\r\n}\r\n//# sourceMappingURL=getModelUris.js.map","export function isNodejs() {\r\n    return typeof global === 'object'\r\n        && typeof require === 'function'\r\n        && typeof module !== 'undefined'\r\n        // issues with gatsby.js: module.exports is undefined\r\n        // && !!module.exports\r\n        && typeof process !== 'undefined' && !!process.version;\r\n}\r\n//# sourceMappingURL=isNodejs.js.map","export function isBrowser() {\r\n    return typeof window === 'object'\r\n        && typeof document !== 'undefined'\r\n        && typeof HTMLImageElement !== 'undefined'\r\n        && typeof HTMLCanvasElement !== 'undefined'\r\n        && typeof HTMLVideoElement !== 'undefined'\r\n        && typeof ImageData !== 'undefined';\r\n}\r\n//# sourceMappingURL=isBrowser.js.map","import { isNodejs } from './isNodejs';\r\nimport { isBrowser } from './isBrowser';\r\nexport function initializeEnvironment() {\r\n    // check for isBrowser() first to prevent electron renderer process\r\n    // to be initialized with wrong environment due to isNodejs() returning true\r\n    if (isBrowser()) {\r\n        return initializeBrowserEnv();\r\n    }\r\n    if (isNodejs()) {\r\n        return initializeNodejsEnv();\r\n    }\r\n    return null;\r\n}\r\nexport function initializeNodejsEnv() {\r\n    var Canvas = global['Canvas'] || global['HTMLCanvasElement'];\r\n    var Image = global['Image'] || global['HTMLImageElement'];\r\n    var createCanvasElement = function () {\r\n        if (Canvas) {\r\n            return new Canvas();\r\n        }\r\n        throw new Error('createCanvasElement - missing Canvas implementation for nodejs environment');\r\n    };\r\n    var createImageElement = function () {\r\n        if (Image) {\r\n            return new Image();\r\n        }\r\n        throw new Error('createImageElement - missing Image implementation for nodejs environment');\r\n    };\r\n    var fetch = global['fetch'] || function () {\r\n        throw new Error('fetch - missing fetch implementation for nodejs environment');\r\n    };\r\n    var fs = null, requireFsError = '';\r\n    try {\r\n        fs = require('fs');\r\n    }\r\n    catch (err) {\r\n        requireFsError = err.toString();\r\n    }\r\n    var readFile = fs\r\n        ? function (filePath) {\r\n            return new Promise(function (res, rej) {\r\n                fs.readFile(filePath, function (err, buffer) {\r\n                    return err ? rej(err) : res(buffer);\r\n                });\r\n            });\r\n        }\r\n        : function () {\r\n            throw new Error(\"readFile - failed to require fs in nodejs environment with error: \" + requireFsError);\r\n        };\r\n    return {\r\n        Canvas: Canvas || /** @class */ (function () {\r\n            function class_1() {\r\n            }\r\n            return class_1;\r\n        }()),\r\n        Image: Image || /** @class */ (function () {\r\n            function class_2() {\r\n            }\r\n            return class_2;\r\n        }()),\r\n        ImageData: global['ImageData'] || /** @class */ (function () {\r\n            function class_3() {\r\n            }\r\n            return class_3;\r\n        }()),\r\n        Video: global['HTMLVideoElement'] || /** @class */ (function () {\r\n            function class_4() {\r\n            }\r\n            return class_4;\r\n        }()),\r\n        createCanvasElement: createCanvasElement,\r\n        createImageElement: createImageElement,\r\n        fetch: fetch,\r\n        readFile: readFile\r\n    };\r\n}\r\nexport function initializeBrowserEnv() {\r\n    var fetch = window['fetch'] || function () {\r\n        throw new Error('fetch - missing fetch implementation for browser environment');\r\n    };\r\n    var readFile = function () {\r\n        throw new Error('readFile - filesystem not available for browser environment');\r\n    };\r\n    return {\r\n        Canvas: HTMLCanvasElement,\r\n        Image: HTMLImageElement,\r\n        ImageData: ImageData,\r\n        Video: HTMLVideoElement,\r\n        createCanvasElement: function () { return document.createElement('canvas'); },\r\n        createImageElement: function () { return document.createElement('img'); },\r\n        fetch: fetch,\r\n        readFile: readFile\r\n    };\r\n}\r\n//# sourceMappingURL=initialize.js.map","import { initializeBrowserEnv, initializeEnvironment, initializeNodejsEnv } from './initialize';\r\nimport { isBrowser } from './isBrowser';\r\nimport { isNodejs } from './isNodejs';\r\nvar environment;\r\nfunction initialize() {\r\n    environment = initializeEnvironment();\r\n}\r\nfunction getEnv() {\r\n    if (!environment) {\r\n        throw new Error('getEnv - environment is not defined, check isNodejs() and isBrowser()');\r\n    }\r\n    return environment;\r\n}\r\nfunction monkeyPatch(env) {\r\n    environment = environment || initializeEnvironment();\r\n    if (!environment) {\r\n        throw new Error('monkeyPatch - environment is not defined, check isNodejs() and isBrowser()');\r\n    }\r\n    var _a = env.Canvas, Canvas = _a === void 0 ? environment.Canvas : _a, _b = env.Image, Image = _b === void 0 ? environment.Image : _b;\r\n    environment.Canvas = Canvas;\r\n    environment.Image = Image;\r\n    environment.createCanvasElement = env.createCanvasElement || (function () { return new Canvas(); });\r\n    environment.createImageElement = env.createImageElement || (function () { return new Image(); });\r\n    environment.ImageData = env.ImageData || environment.ImageData;\r\n    environment.Video = env.Video || environment.Video;\r\n    environment.fetch = env.fetch || environment.fetch;\r\n    environment.readFile = env.readFile || environment.readFile;\r\n}\r\nexport var env = {\r\n    getEnv: getEnv,\r\n    initialize: initialize,\r\n    initializeBrowserEnv: initializeBrowserEnv,\r\n    initializeNodejsEnv: initializeNodejsEnv,\r\n    monkeyPatch: monkeyPatch,\r\n    isBrowser: isBrowser,\r\n    isNodejs: isNodejs\r\n};\r\ninitialize();\r\n//# sourceMappingURL=index.js.map","import { env } from '../env';\r\nexport function isMediaLoaded(media) {\r\n    var _a = env.getEnv(), Image = _a.Image, Video = _a.Video;\r\n    return (media instanceof Image && media.complete)\r\n        || (media instanceof Video && media.readyState >= 3);\r\n}\r\n//# sourceMappingURL=isMediaLoaded.js.map","import { env } from '../env';\r\nimport { isMediaLoaded } from './isMediaLoaded';\r\nexport function awaitMediaLoaded(media) {\r\n    return new Promise(function (resolve, reject) {\r\n        if (media instanceof env.getEnv().Canvas || isMediaLoaded(media)) {\r\n            return resolve();\r\n        }\r\n        function onLoad(e) {\r\n            if (!e.currentTarget)\r\n                return;\r\n            e.currentTarget.removeEventListener('load', onLoad);\r\n            e.currentTarget.removeEventListener('error', onError);\r\n            resolve(e);\r\n        }\r\n        function onError(e) {\r\n            if (!e.currentTarget)\r\n                return;\r\n            e.currentTarget.removeEventListener('load', onLoad);\r\n            e.currentTarget.removeEventListener('error', onError);\r\n            reject(e);\r\n        }\r\n        media.addEventListener('load', onLoad);\r\n        media.addEventListener('error', onError);\r\n    });\r\n}\r\n//# sourceMappingURL=awaitMediaLoaded.js.map","import { env } from '../env';\r\nexport function bufferToImage(buf) {\r\n    return new Promise(function (resolve, reject) {\r\n        if (!(buf instanceof Blob)) {\r\n            return reject('bufferToImage - expected buf to be of type: Blob');\r\n        }\r\n        var reader = new FileReader();\r\n        reader.onload = function () {\r\n            if (typeof reader.result !== 'string') {\r\n                return reject('bufferToImage - expected reader.result to be a string, in onload');\r\n            }\r\n            var img = env.getEnv().createImageElement();\r\n            img.onload = function () { return resolve(img); };\r\n            img.onerror = reject;\r\n            img.src = reader.result;\r\n        };\r\n        reader.onerror = reject;\r\n        reader.readAsDataURL(buf);\r\n    });\r\n}\r\n//# sourceMappingURL=bufferToImage.js.map","export function getContext2dOrThrow(canvas) {\r\n    var ctx = canvas.getContext('2d');\r\n    if (!ctx) {\r\n        throw new Error('canvas 2d context is null');\r\n    }\r\n    return ctx;\r\n}\r\n//# sourceMappingURL=getContext2dOrThrow.js.map","import { Dimensions } from '../classes/Dimensions';\r\nimport { env } from '../env';\r\nexport function getMediaDimensions(input) {\r\n    var _a = env.getEnv(), Image = _a.Image, Video = _a.Video;\r\n    if (input instanceof Image) {\r\n        return new Dimensions(input.naturalWidth, input.naturalHeight);\r\n    }\r\n    if (input instanceof Video) {\r\n        return new Dimensions(input.videoWidth, input.videoHeight);\r\n    }\r\n    return new Dimensions(input.width, input.height);\r\n}\r\n//# sourceMappingURL=getMediaDimensions.js.map","import { env } from '../env';\r\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\r\nimport { getMediaDimensions } from './getMediaDimensions';\r\nimport { isMediaLoaded } from './isMediaLoaded';\r\nexport function createCanvas(_a) {\r\n    var width = _a.width, height = _a.height;\r\n    var createCanvasElement = env.getEnv().createCanvasElement;\r\n    var canvas = createCanvasElement();\r\n    canvas.width = width;\r\n    canvas.height = height;\r\n    return canvas;\r\n}\r\nexport function createCanvasFromMedia(media, dims) {\r\n    var ImageData = env.getEnv().ImageData;\r\n    if (!(media instanceof ImageData) && !isMediaLoaded(media)) {\r\n        throw new Error('createCanvasFromMedia - media has not finished loading yet');\r\n    }\r\n    var _a = dims || getMediaDimensions(media), width = _a.width, height = _a.height;\r\n    var canvas = createCanvas({ width: width, height: height });\r\n    if (media instanceof ImageData) {\r\n        getContext2dOrThrow(canvas).putImageData(media, 0, 0);\r\n    }\r\n    else {\r\n        getContext2dOrThrow(canvas).drawImage(media, 0, 0, width, height);\r\n    }\r\n    return canvas;\r\n}\r\n//# sourceMappingURL=createCanvas.js.map","export function getDefaultDrawOptions(options) {\r\n    if (options === void 0) { options = {}; }\r\n    return Object.assign({}, {\r\n        boxColor: 'blue',\r\n        textColor: 'red',\r\n        lineWidth: 2,\r\n        fontSize: 20,\r\n        fontStyle: 'Georgia',\r\n        withScore: true,\r\n        withClassName: true\r\n    }, options);\r\n}\r\n//# sourceMappingURL=getDefaultDrawOptions.js.map","import { getDefaultDrawOptions } from './getDefaultDrawOptions';\r\nexport function drawBox(ctx, x, y, w, h, options) {\r\n    var drawOptions = Object.assign(getDefaultDrawOptions(), (options || {}));\r\n    ctx.strokeStyle = drawOptions.boxColor;\r\n    ctx.lineWidth = drawOptions.lineWidth;\r\n    ctx.strokeRect(x, y, w, h);\r\n}\r\n//# sourceMappingURL=drawBox.js.map","import { getDefaultDrawOptions } from './getDefaultDrawOptions';\r\nexport function drawText(ctx, x, y, text, options) {\r\n    if (options === void 0) { options = {}; }\r\n    var drawOptions = Object.assign(getDefaultDrawOptions(), options);\r\n    var padText = 2 + drawOptions.lineWidth;\r\n    ctx.fillStyle = drawOptions.textColor;\r\n    ctx.font = drawOptions.fontSize + \"px \" + drawOptions.fontStyle;\r\n    ctx.fillText(text, x + padText, y + padText + (drawOptions.fontSize * 0.6));\r\n}\r\n//# sourceMappingURL=drawText.js.map","import { env } from '../env';\r\nexport function resolveInput(arg) {\r\n    if (!env.isNodejs() && typeof arg === 'string') {\r\n        return document.getElementById(arg);\r\n    }\r\n    return arg;\r\n}\r\n//# sourceMappingURL=resolveInput.js.map","import { BoxWithText } from '../classes/BoxWithText';\r\nimport { ObjectDetection } from '../classes/ObjectDetection';\r\nimport { PredictedBox } from '../classes/PredictedBox';\r\nimport { env } from '../env';\r\nimport { round } from '../utils';\r\nimport { drawBox } from './drawBox';\r\nimport { drawText } from './drawText';\r\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\r\nimport { getDefaultDrawOptions } from './getDefaultDrawOptions';\r\nimport { resolveInput } from './resolveInput';\r\nexport function drawDetection(canvasArg, detection, options) {\r\n    var Canvas = env.getEnv().Canvas;\r\n    var canvas = resolveInput(canvasArg);\r\n    if (!(canvas instanceof Canvas)) {\r\n        throw new Error('drawDetection - expected canvas to be of type: HTMLCanvasElement');\r\n    }\r\n    var detectionArray = Array.isArray(detection)\r\n        ? detection\r\n        : [detection];\r\n    detectionArray.forEach(function (det) {\r\n        var _a = det instanceof ObjectDetection ? det.box : det, x = _a.x, y = _a.y, width = _a.width, height = _a.height;\r\n        var drawOptions = getDefaultDrawOptions(options);\r\n        var ctx = getContext2dOrThrow(canvas);\r\n        drawBox(ctx, x, y, width, height, drawOptions);\r\n        var withScore = drawOptions.withScore;\r\n        var text = det instanceof BoxWithText\r\n            ? det.text\r\n            : ((withScore && det instanceof PredictedBox)\r\n                ? \"\" + round(det.score)\r\n                : (det instanceof ObjectDetection\r\n                    ? \"\" + det.className + (withScore ? \" (\" + round(det.score) + \")\" : '')\r\n                    : ''));\r\n        if (text) {\r\n            drawText(ctx, x, y + height, text, drawOptions);\r\n        }\r\n    });\r\n}\r\n//# sourceMappingURL=drawDetection.js.map","import * as tslib_1 from \"tslib\";\r\nimport { env } from '../env';\r\nexport function fetchOrThrow(url, init) {\r\n    return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n        var fetch, res;\r\n        return tslib_1.__generator(this, function (_a) {\r\n            switch (_a.label) {\r\n                case 0:\r\n                    fetch = env.getEnv().fetch;\r\n                    return [4 /*yield*/, fetch(url, init)];\r\n                case 1:\r\n                    res = _a.sent();\r\n                    if (!(res.status < 400)) {\r\n                        throw new Error(\"failed to fetch: (\" + res.status + \") \" + res.statusText + \", from url: \" + res.url);\r\n                    }\r\n                    return [2 /*return*/, res];\r\n            }\r\n        });\r\n    });\r\n}\r\n//# sourceMappingURL=fetchOrThrow.js.map","import * as tslib_1 from \"tslib\";\r\nimport { bufferToImage } from './bufferToImage';\r\nimport { fetchOrThrow } from './fetchOrThrow';\r\nexport function fetchImage(uri) {\r\n    return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n        var res, blob;\r\n        return tslib_1.__generator(this, function (_a) {\r\n            switch (_a.label) {\r\n                case 0: return [4 /*yield*/, fetchOrThrow(uri)];\r\n                case 1:\r\n                    res = _a.sent();\r\n                    return [4 /*yield*/, (res).blob()];\r\n                case 2:\r\n                    blob = _a.sent();\r\n                    if (!blob.type.startsWith('image/')) {\r\n                        throw new Error(\"fetchImage - expected blob type to be of type image/*, instead have: \" + blob.type + \", for url: \" + res.url);\r\n                    }\r\n                    return [2 /*return*/, bufferToImage(blob)];\r\n            }\r\n        });\r\n    });\r\n}\r\n//# sourceMappingURL=fetchImage.js.map","import * as tslib_1 from \"tslib\";\r\nimport { fetchOrThrow } from './fetchOrThrow';\r\nexport function fetchJson(uri) {\r\n    return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n        return tslib_1.__generator(this, function (_a) {\r\n            switch (_a.label) {\r\n                case 0: return [4 /*yield*/, fetchOrThrow(uri)];\r\n                case 1: return [2 /*return*/, (_a.sent()).json()];\r\n            }\r\n        });\r\n    });\r\n}\r\n//# sourceMappingURL=fetchJson.js.map","import * as tslib_1 from \"tslib\";\r\nimport { fetchOrThrow } from './fetchOrThrow';\r\nexport function fetchNetWeights(uri) {\r\n    return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n        var _a;\r\n        return tslib_1.__generator(this, function (_b) {\r\n            switch (_b.label) {\r\n                case 0:\r\n                    _a = Float32Array.bind;\r\n                    return [4 /*yield*/, fetchOrThrow(uri)];\r\n                case 1: return [4 /*yield*/, (_b.sent()).arrayBuffer()];\r\n                case 2: return [2 /*return*/, new (_a.apply(Float32Array, [void 0, _b.sent()]))()];\r\n            }\r\n        });\r\n    });\r\n}\r\n//# sourceMappingURL=fetchNetWeights.js.map","import * as tslib_1 from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { env } from '../env';\r\nimport { isTensor4D } from '../utils';\r\nexport function imageTensorToCanvas(imgTensor, canvas) {\r\n    return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n        var targetCanvas, _a, height, width, numChannels, imgTensor3D;\r\n        return tslib_1.__generator(this, function (_b) {\r\n            switch (_b.label) {\r\n                case 0:\r\n                    targetCanvas = canvas || env.getEnv().createCanvasElement();\r\n                    _a = imgTensor.shape.slice(isTensor4D(imgTensor) ? 1 : 0), height = _a[0], width = _a[1], numChannels = _a[2];\r\n                    imgTensor3D = tf.tidy(function () { return imgTensor.as3D(height, width, numChannels).toInt(); });\r\n                    return [4 /*yield*/, tf.toPixels(imgTensor3D, targetCanvas)];\r\n                case 1:\r\n                    _b.sent();\r\n                    imgTensor3D.dispose();\r\n                    return [2 /*return*/, targetCanvas];\r\n            }\r\n        });\r\n    });\r\n}\r\n//# sourceMappingURL=imageTensorToCanvas.js.map","import { env } from '../env';\r\nimport { createCanvas, createCanvasFromMedia } from './createCanvas';\r\nimport { getContext2dOrThrow } from './getContext2dOrThrow';\r\nimport { getMediaDimensions } from './getMediaDimensions';\r\nexport function imageToSquare(input, inputSize, centerImage) {\r\n    if (centerImage === void 0) { centerImage = false; }\r\n    var _a = env.getEnv(), Image = _a.Image, Canvas = _a.Canvas;\r\n    if (!(input instanceof Image || input instanceof Canvas)) {\r\n        throw new Error('imageToSquare - expected arg0 to be HTMLImageElement | HTMLCanvasElement');\r\n    }\r\n    var dims = getMediaDimensions(input);\r\n    var scale = inputSize / Math.max(dims.height, dims.width);\r\n    var width = scale * dims.width;\r\n    var height = scale * dims.height;\r\n    var targetCanvas = createCanvas({ width: inputSize, height: inputSize });\r\n    var inputCanvas = input instanceof Canvas ? input : createCanvasFromMedia(input);\r\n    var offset = Math.abs(width - height) / 2;\r\n    var dx = centerImage && width < height ? offset : 0;\r\n    var dy = centerImage && height < width ? offset : 0;\r\n    getContext2dOrThrow(targetCanvas).drawImage(inputCanvas, dx, dy, width, height);\r\n    return targetCanvas;\r\n}\r\n//# sourceMappingURL=imageToSquare.js.map","import { env } from '../env';\r\nexport function isMediaElement(input) {\r\n    var _a = env.getEnv(), Image = _a.Image, Canvas = _a.Canvas, Video = _a.Video;\r\n    return input instanceof Image\r\n        || input instanceof Canvas\r\n        || input instanceof Video;\r\n}\r\n//# sourceMappingURL=isMediaElement.js.map","import * as tslib_1 from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { getModelUris } from '../common/getModelUris';\r\nimport { fetchJson } from './fetchJson';\r\nexport function loadWeightMap(uri, defaultModelName) {\r\n    return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n        var _a, manifestUri, modelBaseUri, manifest;\r\n        return tslib_1.__generator(this, function (_b) {\r\n            switch (_b.label) {\r\n                case 0:\r\n                    _a = getModelUris(uri, defaultModelName), manifestUri = _a.manifestUri, modelBaseUri = _a.modelBaseUri;\r\n                    return [4 /*yield*/, fetchJson(manifestUri)];\r\n                case 1:\r\n                    manifest = _b.sent();\r\n                    return [2 /*return*/, tf.io.loadWeights(manifest, modelBaseUri)];\r\n            }\r\n        });\r\n    });\r\n}\r\n//# sourceMappingURL=loadWeightMap.js.map","import * as tf from '@tensorflow/tfjs-core';\r\n/**\r\n * Pads the smaller dimension of an image tensor with zeros, such that width === height.\r\n *\r\n * @param imgTensor The image tensor.\r\n * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\r\n * both sides of the minor dimension oof the image.\r\n * @returns The padded tensor with width === height.\r\n */\r\nexport function padToSquare(imgTensor, isCenterImage) {\r\n    if (isCenterImage === void 0) { isCenterImage = false; }\r\n    return tf.tidy(function () {\r\n        var _a = imgTensor.shape.slice(1), height = _a[0], width = _a[1];\r\n        if (height === width) {\r\n            return imgTensor;\r\n        }\r\n        var dimDiff = Math.abs(height - width);\r\n        var paddingAmount = Math.round(dimDiff * (isCenterImage ? 0.5 : 1));\r\n        var paddingAxis = height > width ? 2 : 1;\r\n        var createPaddingTensor = function (paddingAmount) {\r\n            var paddingTensorShape = imgTensor.shape.slice();\r\n            paddingTensorShape[paddingAxis] = paddingAmount;\r\n            return tf.fill(paddingTensorShape, 0);\r\n        };\r\n        var paddingTensorAppend = createPaddingTensor(paddingAmount);\r\n        var remainingPaddingAmount = dimDiff - paddingTensorAppend.shape[paddingAxis];\r\n        var paddingTensorPrepend = isCenterImage && remainingPaddingAmount\r\n            ? createPaddingTensor(remainingPaddingAmount)\r\n            : null;\r\n        var tensorsToStack = [\r\n            paddingTensorPrepend,\r\n            imgTensor,\r\n            paddingTensorAppend\r\n        ]\r\n            .filter(function (t) { return !!t; })\r\n            .map(function (t) { return t.toFloat(); });\r\n        return tf.concat(tensorsToStack, paddingAxis);\r\n    });\r\n}\r\n//# sourceMappingURL=padToSquare.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nimport { env } from '../env';\r\nimport { padToSquare } from '../ops/padToSquare';\r\nimport { computeReshapedDimensions, isTensor3D, isTensor4D, range } from '../utils';\r\nimport { createCanvasFromMedia } from './createCanvas';\r\nimport { imageToSquare } from './imageToSquare';\r\nvar NetInput = /** @class */ (function () {\r\n    function NetInput(inputs, treatAsBatchInput) {\r\n        if (treatAsBatchInput === void 0) { treatAsBatchInput = false; }\r\n        var _this = this;\r\n        this._imageTensors = [];\r\n        this._canvases = [];\r\n        this._treatAsBatchInput = false;\r\n        this._inputDimensions = [];\r\n        if (!Array.isArray(inputs)) {\r\n            throw new Error(\"NetInput.constructor - expected inputs to be an Array of TResolvedNetInput or to be instanceof tf.Tensor4D, instead have \" + inputs);\r\n        }\r\n        this._treatAsBatchInput = treatAsBatchInput;\r\n        this._batchSize = inputs.length;\r\n        inputs.forEach(function (input, idx) {\r\n            if (isTensor3D(input)) {\r\n                _this._imageTensors[idx] = input;\r\n                _this._inputDimensions[idx] = input.shape;\r\n                return;\r\n            }\r\n            if (isTensor4D(input)) {\r\n                var batchSize = input.shape[0];\r\n                if (batchSize !== 1) {\r\n                    throw new Error(\"NetInput - tf.Tensor4D with batchSize \" + batchSize + \" passed, but not supported in input array\");\r\n                }\r\n                _this._imageTensors[idx] = input;\r\n                _this._inputDimensions[idx] = input.shape.slice(1);\r\n                return;\r\n            }\r\n            var canvas = input instanceof env.getEnv().Canvas ? input : createCanvasFromMedia(input);\r\n            _this._canvases[idx] = canvas;\r\n            _this._inputDimensions[idx] = [canvas.height, canvas.width, 3];\r\n        });\r\n    }\r\n    Object.defineProperty(NetInput.prototype, \"imageTensors\", {\r\n        get: function () {\r\n            return this._imageTensors;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(NetInput.prototype, \"canvases\", {\r\n        get: function () {\r\n            return this._canvases;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(NetInput.prototype, \"isBatchInput\", {\r\n        get: function () {\r\n            return this.batchSize > 1 || this._treatAsBatchInput;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(NetInput.prototype, \"batchSize\", {\r\n        get: function () {\r\n            return this._batchSize;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(NetInput.prototype, \"inputDimensions\", {\r\n        get: function () {\r\n            return this._inputDimensions;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(NetInput.prototype, \"inputSize\", {\r\n        get: function () {\r\n            return this._inputSize;\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(NetInput.prototype, \"reshapedInputDimensions\", {\r\n        get: function () {\r\n            var _this = this;\r\n            return range(this.batchSize, 0, 1).map(function (_, batchIdx) { return _this.getReshapedInputDimensions(batchIdx); });\r\n        },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    NetInput.prototype.getInput = function (batchIdx) {\r\n        return this.canvases[batchIdx] || this.imageTensors[batchIdx];\r\n    };\r\n    NetInput.prototype.getInputDimensions = function (batchIdx) {\r\n        return this._inputDimensions[batchIdx];\r\n    };\r\n    NetInput.prototype.getInputHeight = function (batchIdx) {\r\n        return this._inputDimensions[batchIdx][0];\r\n    };\r\n    NetInput.prototype.getInputWidth = function (batchIdx) {\r\n        return this._inputDimensions[batchIdx][1];\r\n    };\r\n    NetInput.prototype.getReshapedInputDimensions = function (batchIdx) {\r\n        if (typeof this.inputSize !== 'number') {\r\n            throw new Error('getReshapedInputDimensions - inputSize not set, toBatchTensor has not been called yet');\r\n        }\r\n        var width = this.getInputWidth(batchIdx);\r\n        var height = this.getInputHeight(batchIdx);\r\n        return computeReshapedDimensions({ width: width, height: height }, this.inputSize);\r\n    };\r\n    /**\r\n     * Create a batch tensor from all input canvases and tensors\r\n     * with size [batchSize, inputSize, inputSize, 3].\r\n     *\r\n     * @param inputSize Height and width of the tensor.\r\n     * @param isCenterImage (optional, default: false) If true, add an equal amount of padding on\r\n     * both sides of the minor dimension oof the image.\r\n     * @returns The batch tensor.\r\n     */\r\n    NetInput.prototype.toBatchTensor = function (inputSize, isCenterInputs) {\r\n        var _this = this;\r\n        if (isCenterInputs === void 0) { isCenterInputs = true; }\r\n        this._inputSize = inputSize;\r\n        return tf.tidy(function () {\r\n            var inputTensors = range(_this.batchSize, 0, 1).map(function (batchIdx) {\r\n                var input = _this.getInput(batchIdx);\r\n                if (input instanceof tf.Tensor) {\r\n                    var imgTensor = isTensor4D(input) ? input : input.expandDims();\r\n                    imgTensor = padToSquare(imgTensor, isCenterInputs);\r\n                    if (imgTensor.shape[1] !== inputSize || imgTensor.shape[2] !== inputSize) {\r\n                        imgTensor = tf.image.resizeBilinear(imgTensor, [inputSize, inputSize]);\r\n                    }\r\n                    return imgTensor.as3D(inputSize, inputSize, 3);\r\n                }\r\n                if (input instanceof env.getEnv().Canvas) {\r\n                    return tf.fromPixels(imageToSquare(input, inputSize, isCenterInputs));\r\n                }\r\n                throw new Error(\"toBatchTensor - at batchIdx \" + batchIdx + \", expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have \" + input);\r\n            });\r\n            var batchTensor = tf.stack(inputTensors.map(function (t) { return t.toFloat(); })).as4D(_this.batchSize, inputSize, inputSize, 3);\r\n            return batchTensor;\r\n        });\r\n    };\r\n    return NetInput;\r\n}());\r\nexport { NetInput };\r\n//# sourceMappingURL=NetInput.js.map","import * as tslib_1 from \"tslib\";\r\nimport { isTensor3D, isTensor4D } from '../utils';\r\nimport { awaitMediaLoaded } from './awaitMediaLoaded';\r\nimport { isMediaElement } from './isMediaElement';\r\nimport { NetInput } from './NetInput';\r\nimport { resolveInput } from './resolveInput';\r\n/**\r\n * Validates the input to make sure, they are valid net inputs and awaits all media elements\r\n * to be finished loading.\r\n *\r\n * @param input The input, which can be a media element or an array of different media elements.\r\n * @returns A NetInput instance, which can be passed into one of the neural networks.\r\n */\r\nexport function toNetInput(inputs) {\r\n    return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n        var inputArgArray, getIdxHint, inputArray;\r\n        return tslib_1.__generator(this, function (_a) {\r\n            switch (_a.label) {\r\n                case 0:\r\n                    if (inputs instanceof NetInput) {\r\n                        return [2 /*return*/, inputs];\r\n                    }\r\n                    inputArgArray = Array.isArray(inputs)\r\n                        ? inputs\r\n                        : [inputs];\r\n                    if (!inputArgArray.length) {\r\n                        throw new Error('toNetInput - empty array passed as input');\r\n                    }\r\n                    getIdxHint = function (idx) { return Array.isArray(inputs) ? \" at input index \" + idx + \":\" : ''; };\r\n                    inputArray = inputArgArray.map(resolveInput);\r\n                    inputArray.forEach(function (input, i) {\r\n                        if (!isMediaElement(input) && !isTensor3D(input) && !isTensor4D(input)) {\r\n                            if (typeof inputArgArray[i] === 'string') {\r\n                                throw new Error(\"toNetInput -\" + getIdxHint(i) + \" string passed, but could not resolve HTMLElement for element id \" + inputArgArray[i]);\r\n                            }\r\n                            throw new Error(\"toNetInput -\" + getIdxHint(i) + \" expected media to be of type HTMLImageElement | HTMLVideoElement | HTMLCanvasElement | tf.Tensor3D, or to be an element id\");\r\n                        }\r\n                        if (isTensor4D(input)) {\r\n                            // if tf.Tensor4D is passed in the input array, the batch size has to be 1\r\n                            var batchSize = input.shape[0];\r\n                            if (batchSize !== 1) {\r\n                                throw new Error(\"toNetInput -\" + getIdxHint(i) + \" tf.Tensor4D with batchSize \" + batchSize + \" passed, but not supported in input array\");\r\n                            }\r\n                        }\r\n                    });\r\n                    // wait for all media elements being loaded\r\n                    return [4 /*yield*/, Promise.all(inputArray.map(function (input) { return isMediaElement(input) && awaitMediaLoaded(input); }))];\r\n                case 1:\r\n                    // wait for all media elements being loaded\r\n                    _a.sent();\r\n                    return [2 /*return*/, new NetInput(inputArray, Array.isArray(inputs))];\r\n            }\r\n        });\r\n    });\r\n}\r\n//# sourceMappingURL=toNetInput.js.map","export function iou(box1, box2, isIOU) {\r\n    if (isIOU === void 0) { isIOU = true; }\r\n    var width = Math.max(0.0, Math.min(box1.right, box2.right) - Math.max(box1.left, box2.left));\r\n    var height = Math.max(0.0, Math.min(box1.bottom, box2.bottom) - Math.max(box1.top, box2.top));\r\n    var interSection = width * height;\r\n    return isIOU\r\n        ? interSection / (box1.area + box2.area - interSection)\r\n        : interSection / Math.min(box1.area, box2.area);\r\n}\r\n//# sourceMappingURL=iou.js.map","import { iou } from './iou';\r\nexport function nonMaxSuppression(boxes, scores, iouThreshold, isIOU) {\r\n    if (isIOU === void 0) { isIOU = true; }\r\n    var indicesSortedByScore = scores\r\n        .map(function (score, boxIndex) { return ({ score: score, boxIndex: boxIndex }); })\r\n        .sort(function (c1, c2) { return c1.score - c2.score; })\r\n        .map(function (c) { return c.boxIndex; });\r\n    var pick = [];\r\n    var _loop_1 = function () {\r\n        var curr = indicesSortedByScore.pop();\r\n        pick.push(curr);\r\n        var indices = indicesSortedByScore;\r\n        var outputs = [];\r\n        for (var i = 0; i < indices.length; i++) {\r\n            var idx = indices[i];\r\n            var currBox = boxes[curr];\r\n            var idxBox = boxes[idx];\r\n            outputs.push(iou(currBox, idxBox, isIOU));\r\n        }\r\n        indicesSortedByScore = indicesSortedByScore.filter(function (_, j) { return outputs[j] <= iouThreshold; });\r\n    };\r\n    while (indicesSortedByScore.length > 0) {\r\n        _loop_1();\r\n    }\r\n    return pick;\r\n}\r\n//# sourceMappingURL=nonMaxSuppression.js.map","import * as tf from '@tensorflow/tfjs-core';\r\nexport function normalize(x, meanRgb) {\r\n    return tf.tidy(function () {\r\n        var r = meanRgb[0], g = meanRgb[1], b = meanRgb[2];\r\n        var avg_r = tf.fill(x.shape.slice(0, 3).concat([1]), r);\r\n        var avg_g = tf.fill(x.shape.slice(0, 3).concat([1]), g);\r\n        var avg_b = tf.fill(x.shape.slice(0, 3).concat([1]), b);\r\n        var avg_rgb = tf.concat([avg_r, avg_g, avg_b], 3);\r\n        return tf.sub(x, avg_rgb);\r\n    });\r\n}\r\n//# sourceMappingURL=normalize.js.map","export function shuffleArray(inputArray) {\r\n    var array = inputArray.slice();\r\n    for (var i = array.length - 1; i > 0; i--) {\r\n        var j = Math.floor(Math.random() * (i + 1));\r\n        var x = array[i];\r\n        array[i] = array[j];\r\n        array[j] = x;\r\n    }\r\n    return array;\r\n}\r\n//# sourceMappingURL=shuffleArray.js.map","export * from './iou';\r\nexport * from './nonMaxSuppression';\r\nexport * from './normalize';\r\nexport * from './padToSquare';\r\nexport * from './shuffleArray';\r\nexport function sigmoid(x) {\r\n    return 1 / (1 + Math.exp(-x));\r\n}\r\nexport function inverseSigmoid(x) {\r\n    return Math.log(x / (1 - x));\r\n}\r\n//# sourceMappingURL=index.js.map","import * as tslib_1 from \"tslib\";\r\nimport * as tf from '@tensorflow/tfjs-core';\r\nimport { getModelUris } from './common/getModelUris';\r\nimport { loadWeightMap } from './dom';\r\nimport { env } from './env';\r\nvar NeuralNetwork = /** @class */ (function () {\r\n    function NeuralNetwork(_name) {\r\n        this._name = _name;\r\n        this._params = undefined;\r\n        this._paramMappings = [];\r\n    }\r\n    Object.defineProperty(NeuralNetwork.prototype, \"params\", {\r\n        get: function () { return this._params; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(NeuralNetwork.prototype, \"paramMappings\", {\r\n        get: function () { return this._paramMappings; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    Object.defineProperty(NeuralNetwork.prototype, \"isLoaded\", {\r\n        get: function () { return !!this.params; },\r\n        enumerable: true,\r\n        configurable: true\r\n    });\r\n    NeuralNetwork.prototype.getParamFromPath = function (paramPath) {\r\n        var _a = this.traversePropertyPath(paramPath), obj = _a.obj, objProp = _a.objProp;\r\n        return obj[objProp];\r\n    };\r\n    NeuralNetwork.prototype.reassignParamFromPath = function (paramPath, tensor) {\r\n        var _a = this.traversePropertyPath(paramPath), obj = _a.obj, objProp = _a.objProp;\r\n        obj[objProp].dispose();\r\n        obj[objProp] = tensor;\r\n    };\r\n    NeuralNetwork.prototype.getParamList = function () {\r\n        var _this = this;\r\n        return this._paramMappings.map(function (_a) {\r\n            var paramPath = _a.paramPath;\r\n            return ({\r\n                path: paramPath,\r\n                tensor: _this.getParamFromPath(paramPath)\r\n            });\r\n        });\r\n    };\r\n    NeuralNetwork.prototype.getTrainableParams = function () {\r\n        return this.getParamList().filter(function (param) { return param.tensor instanceof tf.Variable; });\r\n    };\r\n    NeuralNetwork.prototype.getFrozenParams = function () {\r\n        return this.getParamList().filter(function (param) { return !(param.tensor instanceof tf.Variable); });\r\n    };\r\n    NeuralNetwork.prototype.variable = function () {\r\n        var _this = this;\r\n        this.getFrozenParams().forEach(function (_a) {\r\n            var path = _a.path, tensor = _a.tensor;\r\n            _this.reassignParamFromPath(path, tensor.variable());\r\n        });\r\n    };\r\n    NeuralNetwork.prototype.freeze = function () {\r\n        var _this = this;\r\n        this.getTrainableParams().forEach(function (_a) {\r\n            var path = _a.path, variable = _a.tensor;\r\n            var tensor = tf.tensor(variable.dataSync());\r\n            variable.dispose();\r\n            _this.reassignParamFromPath(path, tensor);\r\n        });\r\n    };\r\n    NeuralNetwork.prototype.dispose = function (throwOnRedispose) {\r\n        if (throwOnRedispose === void 0) { throwOnRedispose = true; }\r\n        this.getParamList().forEach(function (param) {\r\n            if (throwOnRedispose && param.tensor.isDisposed) {\r\n                throw new Error(\"param tensor has already been disposed for path \" + param.path);\r\n            }\r\n            param.tensor.dispose();\r\n        });\r\n        this._params = undefined;\r\n    };\r\n    NeuralNetwork.prototype.serializeParams = function () {\r\n        return new Float32Array(this.getParamList()\r\n            .map(function (_a) {\r\n            var tensor = _a.tensor;\r\n            return Array.from(tensor.dataSync());\r\n        })\r\n            .reduce(function (flat, arr) { return flat.concat(arr); }));\r\n    };\r\n    NeuralNetwork.prototype.load = function (weightsOrUrl) {\r\n        return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n            return tslib_1.__generator(this, function (_a) {\r\n                switch (_a.label) {\r\n                    case 0:\r\n                        if (weightsOrUrl instanceof Float32Array) {\r\n                            this.extractWeights(weightsOrUrl);\r\n                            return [2 /*return*/];\r\n                        }\r\n                        return [4 /*yield*/, this.loadFromUri(weightsOrUrl)];\r\n                    case 1:\r\n                        _a.sent();\r\n                        return [2 /*return*/];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    NeuralNetwork.prototype.loadFromUri = function (uri) {\r\n        return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n            var weightMap;\r\n            return tslib_1.__generator(this, function (_a) {\r\n                switch (_a.label) {\r\n                    case 0:\r\n                        if (uri && typeof uri !== 'string') {\r\n                            throw new Error(this._name + \".loadFromUri - expected model uri\");\r\n                        }\r\n                        return [4 /*yield*/, loadWeightMap(uri, this.getDefaultModelName())];\r\n                    case 1:\r\n                        weightMap = _a.sent();\r\n                        this.loadFromWeightMap(weightMap);\r\n                        return [2 /*return*/];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    NeuralNetwork.prototype.loadFromDisk = function (filePath) {\r\n        return tslib_1.__awaiter(this, void 0, void 0, function () {\r\n            var readFile, _a, manifestUri, modelBaseUri, fetchWeightsFromDisk, loadWeights, manifest, _b, _c, weightMap;\r\n            return tslib_1.__generator(this, function (_d) {\r\n                switch (_d.label) {\r\n                    case 0:\r\n                        if (filePath && typeof filePath !== 'string') {\r\n                            throw new Error(this._name + \".loadFromDisk - expected model file path\");\r\n                        }\r\n                        readFile = env.getEnv().readFile;\r\n                        _a = getModelUris(filePath, this.getDefaultModelName()), manifestUri = _a.manifestUri, modelBaseUri = _a.modelBaseUri;\r\n                        fetchWeightsFromDisk = function (filePaths) { return Promise.all(filePaths.map(function (filePath) { return readFile(filePath).then(function (buf) { return buf.buffer; }); })); };\r\n                        loadWeights = tf.io.weightsLoaderFactory(fetchWeightsFromDisk);\r\n                        _c = (_b = JSON).parse;\r\n                        return [4 /*yield*/, readFile(manifestUri)];\r\n                    case 1:\r\n                        manifest = _c.apply(_b, [(_d.sent()).toString()]);\r\n                        return [4 /*yield*/, loadWeights(manifest, modelBaseUri)];\r\n                    case 2:\r\n                        weightMap = _d.sent();\r\n                        this.loadFromWeightMap(weightMap);\r\n                        return [2 /*return*/];\r\n                }\r\n            });\r\n        });\r\n    };\r\n    NeuralNetwork.prototype.loadFromWeightMap = function (weightMap) {\r\n        var _a = this.extractParamsFromWeigthMap(weightMap), paramMappings = _a.paramMappings, params = _a.params;\r\n        this._paramMappings = paramMappings;\r\n        this._params = params;\r\n    };\r\n    NeuralNetwork.prototype.extractWeights = function (weights) {\r\n        var _a = this.extractParams(weights), paramMappings = _a.paramMappings, params = _a.params;\r\n        this._paramMappings = paramMappings;\r\n        this._params = params;\r\n    };\r\n    NeuralNetwork.prototype.traversePropertyPath = function (paramPath) {\r\n        if (!this.params) {\r\n            throw new Error(\"traversePropertyPath - model has no loaded params\");\r\n        }\r\n        var result = paramPath.split('/').reduce(function (res, objProp) {\r\n            if (!res.nextObj.hasOwnProperty(objProp)) {\r\n                throw new Error(\"traversePropertyPath - object does not have property \" + objProp + \", for path \" + paramPath);\r\n            }\r\n            return { obj: res.nextObj, objProp: objProp, nextObj: res.nextObj[objProp] };\r\n        }, { nextObj: this.params });\r\n        var obj = result.obj, objProp = result.objProp;\r\n        if (!obj || !objProp || !(obj[objProp] instanceof tf.Tensor)) {\r\n            throw new Error(\"traversePropertyPath - parameter is not a tensor, for path \" + paramPath);\r\n        }\r\n        return { obj: obj, objProp: objProp };\r\n    };\r\n    return NeuralNetwork;\r\n}());\r\nexport { NeuralNetwork };\r\n//# sourceMappingURL=NeuralNetwork.js.map","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { ConvParams } from './types';\r\n\r\nexport function convLayer(\r\n  x: tf.Tensor4D,\r\n  params: ConvParams,\r\n  padding: 'valid' | 'same' = 'same',\r\n  withRelu: boolean = false\r\n): tf.Tensor4D {\r\n  return tf.tidy(() => {\r\n    const out = tf.add(\r\n      tf.conv2d(x, params.filters, [1, 1], padding),\r\n      params.bias\r\n    ) as tf.Tensor4D\r\n\r\n    return withRelu ? tf.relu(out) : out\r\n  })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { ExtractWeightsFunction, ParamMapping } from 'tfjs-image-recognition-base';\r\n\r\nimport { ConvParams } from './types';\r\n\r\nexport function extractConvParamsFactory(\r\n  extractWeights: ExtractWeightsFunction,\r\n  paramMappings: ParamMapping[]\r\n) {\r\n\r\n  return function(\r\n    channelsIn: number,\r\n    channelsOut: number,\r\n    filterSize: number,\r\n    mappedPrefix: string\r\n  ): ConvParams {\r\n\r\n    const filters = tf.tensor4d(\r\n      extractWeights(channelsIn * channelsOut * filterSize * filterSize),\r\n      [filterSize, filterSize, channelsIn, channelsOut]\r\n    )\r\n    const bias = tf.tensor1d(extractWeights(channelsOut))\r\n\r\n    paramMappings.push(\r\n      { paramPath: `${mappedPrefix}/filters` },\r\n      { paramPath: `${mappedPrefix}/bias` }\r\n    )\r\n\r\n    return { filters, bias }\r\n  }\r\n\r\n}\r\n","import * as tf from '@tensorflow/tfjs-core';\r\nimport { ExtractWeightsFunction, ParamMapping } from 'tfjs-image-recognition-base';\r\n\r\nimport { FCParams } from './types';\r\n\r\n\r\nexport function extractFCParamsFactory(\r\n  extractWeights: ExtractWeightsFunction,\r\n  paramMappings: ParamMapping[]\r\n) {\r\n\r\n  return function(\r\n    channelsIn: number,\r\n    channelsOut: number,\r\n    mappedPrefix: string\r\n  ): FCParams {\r\n\r\n    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut])\r\n    const fc_bias = tf.tensor1d(extractWeights(channelsOut))\r\n\r\n    paramMappings.push(\r\n      { paramPath: `${mappedPrefix}/weights` },\r\n      { paramPath: `${mappedPrefix}/bias` }\r\n    )\r\n\r\n    return {\r\n      weights: fc_weights,\r\n      bias: fc_bias\r\n    }\r\n  }\r\n\r\n}\r\n","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nexport type ConvParams = {\r\n  filters: tf.Tensor4D\r\n  bias: tf.Tensor1D\r\n}\r\n\r\nexport type FCParams = {\r\n  weights: tf.Tensor2D\r\n  bias: tf.Tensor1D\r\n}\r\n\r\nexport class SeparableConvParams {\r\n  constructor(\r\n    public depthwise_filter: tf.Tensor4D,\r\n    public pointwise_filter: tf.Tensor4D,\r\n    public bias: tf.Tensor1D\r\n  ) {}\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { ExtractWeightsFunction, ParamMapping } from 'tfjs-image-recognition-base';\r\n\r\nimport { SeparableConvParams } from './types';\r\n\r\nexport function extractSeparableConvParamsFactory(\r\n  extractWeights: ExtractWeightsFunction,\r\n  paramMappings: ParamMapping[]\r\n) {\r\n\r\n  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {\r\n    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])\r\n    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])\r\n    const bias = tf.tensor1d(extractWeights(channelsOut))\r\n\r\n    paramMappings.push(\r\n      { paramPath: `${mappedPrefix}/depthwise_filter` },\r\n      { paramPath: `${mappedPrefix}/pointwise_filter` },\r\n      { paramPath: `${mappedPrefix}/bias` }\r\n    )\r\n\r\n    return new SeparableConvParams(\r\n      depthwise_filter,\r\n      pointwise_filter,\r\n      bias\r\n    )\r\n  }\r\n\r\n}\r\n\r\nexport function loadSeparableConvParamsFactory(\r\n  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T\r\n) {\r\n\r\n  return function (prefix: string): SeparableConvParams {\r\n    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)\r\n    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)\r\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\r\n\r\n    return new SeparableConvParams(\r\n      depthwise_filter,\r\n      pointwise_filter,\r\n      bias\r\n    )\r\n  }\r\n\r\n}\r\n","import { Point } from 'tfjs-image-recognition-base';\r\n\r\nexport type TinyYolov2Config = {\r\n  withSeparableConvs: boolean\r\n  iouThreshold: number\r\n  anchors: Point[]\r\n  classes: string[]\r\n  meanRgb?: [number, number, number]\r\n  withClassScores?: boolean,\r\n  filterSizes?: number[]\r\n  isFirstLayerConv2d?: boolean\r\n}\r\n\r\nexport type TinyYolov2TrainableConfig = TinyYolov2Config & {\r\n  // hyper params for training\r\n  noObjectScale: number\r\n  objectScale: number\r\n  coordScale: number\r\n  classScale: number\r\n}\r\n\r\nconst isNumber = (arg: any) => typeof arg === 'number'\r\n\r\nexport function validateConfig(config: any) {\r\n  if (!config) {\r\n    throw new Error(`invalid config: ${config}`)\r\n  }\r\n\r\n  if (typeof config.withSeparableConvs !== 'boolean') {\r\n    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`)\r\n  }\r\n\r\n  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {\r\n    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`)\r\n  }\r\n\r\n  if (\r\n    !Array.isArray(config.classes)\r\n    || !config.classes.length\r\n    || !config.classes.every((c: any) => typeof c === 'string')\r\n  ) {\r\n\r\n    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`)\r\n  }\r\n\r\n  if (\r\n    !Array.isArray(config.anchors)\r\n    || !config.anchors.length\r\n    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))\r\n  ) {\r\n\r\n    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`)\r\n  }\r\n\r\n  if (config.meanRgb && (\r\n    !Array.isArray(config.meanRgb)\r\n    || config.meanRgb.length !== 3\r\n    || !config.meanRgb.every(isNumber)\r\n  )) {\r\n\r\n    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)\r\n  }\r\n}\r\n\r\nexport function validateTrainConfig(config: any): TinyYolov2TrainableConfig {\r\n  if (![config.noObjectScale, config.objectScale, config.coordScale, config.classScale].every(isNumber)) {\r\n    throw new Error(`for training you have to specify noObjectScale, objectScale, coordScale, classScale parameters in your config.json file`)\r\n  }\r\n\r\n  return config\r\n}","export const CELL_SIZE = 32\r\n\r\nexport const DEFAULT_FILTER_SIZES = [\r\n  3, 16, 32, 64, 128, 256, 512, 1024, 1024\r\n]","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nexport function leaky(x: tf.Tensor4D): tf.Tensor4D {\r\n  return tf.tidy(() => {\r\n    const min = tf.mul(x, tf.scalar(0.10000000149011612))\r\n    return tf.add(tf.relu(tf.sub(x, min)), min)\r\n    //return tf.maximum(x, min)\r\n  })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { leaky } from './leaky';\r\nimport { ConvWithBatchNorm } from './types';\r\n\r\nexport function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {\r\n  return tf.tidy(() => {\r\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\r\n\r\n    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')\r\n    out = tf.sub(out, params.bn.sub)\r\n    out = tf.mul(out, params.bn.truediv)\r\n    out = tf.add(out, params.conv.bias)\r\n\r\n    return leaky(out)\r\n  })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\n\r\nimport { SeparableConvParams } from '../common/types';\r\nimport { leaky } from './leaky';\r\n\r\nexport function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {\r\n  return tf.tidy(() => {\r\n    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D\r\n\r\n    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')\r\n    out = tf.add(out, params.bias)\r\n\r\n    return leaky(out)\r\n  })\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from 'tfjs-image-recognition-base';\r\n\r\nimport { extractConvParamsFactory } from '../common';\r\nimport { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\r\nimport { TinyYolov2Config } from './config';\r\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\r\n\r\nfunction extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {\r\n\r\n  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)\r\n\r\n  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {\r\n\r\n    const sub = tf.tensor1d(extractWeights(size))\r\n    const truediv = tf.tensor1d(extractWeights(size))\r\n\r\n    paramMappings.push(\r\n      { paramPath: `${mappedPrefix}/sub` },\r\n      { paramPath: `${mappedPrefix}/truediv` }\r\n    )\r\n\r\n    return { sub, truediv }\r\n  }\r\n\r\n  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {\r\n\r\n    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)\r\n    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)\r\n\r\n    return { conv, bn }\r\n  }\r\n  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)\r\n\r\n  return {\r\n    extractConvParams,\r\n    extractConvWithBatchNormParams,\r\n    extractSeparableConvParams\r\n  }\r\n\r\n}\r\n\r\nexport function extractParams(\r\n  weights: Float32Array,\r\n  config: TinyYolov2Config,\r\n  boxEncodingSize: number,\r\n  filterSizes: number[]\r\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\r\n\r\n  const {\r\n    extractWeights,\r\n    getRemainingWeights\r\n  } = extractWeightsFactory(weights)\r\n\r\n  const paramMappings: ParamMapping[] = []\r\n\r\n  const {\r\n    extractConvParams,\r\n    extractConvWithBatchNormParams,\r\n    extractSeparableConvParams\r\n  } = extractorsFactory(extractWeights, paramMappings)\r\n\r\n  let params: TinyYolov2NetParams\r\n\r\n  if (config.withSeparableConvs) {\r\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\r\n\r\n    const conv0 = config.isFirstLayerConv2d\r\n      ? extractConvParams(s0, s1, 3, 'conv0')\r\n      : extractSeparableConvParams(s0, s1, 'conv0')\r\n    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')\r\n    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')\r\n    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')\r\n    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')\r\n    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')\r\n    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined\r\n    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined\r\n    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')\r\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\r\n  } else {\r\n    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes\r\n    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)\r\n    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')\r\n    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')\r\n    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')\r\n    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')\r\n    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')\r\n    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')\r\n    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')\r\n    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')\r\n    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }\r\n  }\r\n\r\n  if (getRemainingWeights().length !== 0) {\r\n    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)\r\n  }\r\n\r\n\r\n  return { params, paramMappings }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from 'tfjs-image-recognition-base';\r\n\r\nimport { ConvParams } from '../common';\r\nimport { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';\r\nimport { TinyYolov2Config } from './config';\r\nimport { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';\r\n\r\nfunction extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {\r\n\r\n  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)\r\n\r\n  function extractBatchNormParams(prefix: string): BatchNorm {\r\n    const sub = extractWeightEntry<tf.Tensor1D>(`${prefix}/sub`, 1)\r\n    const truediv = extractWeightEntry<tf.Tensor1D>(`${prefix}/truediv`, 1)\r\n    return { sub, truediv }\r\n  }\r\n\r\n  function extractConvParams(prefix: string): ConvParams {\r\n    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)\r\n    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)\r\n    return { filters, bias }\r\n  }\r\n\r\n  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {\r\n    const conv = extractConvParams(`${prefix}/conv`)\r\n    const bn = extractBatchNormParams(`${prefix}/bn`)\r\n    return { conv, bn }\r\n  }\r\n\r\n  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)\r\n\r\n  return {\r\n    extractConvParams,\r\n    extractConvWithBatchNormParams,\r\n    extractSeparableConvParams\r\n  }\r\n\r\n}\r\n\r\nexport function extractParamsFromWeigthMap(\r\n  weightMap: tf.NamedTensorMap,\r\n  config: TinyYolov2Config\r\n): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {\r\n\r\n  const paramMappings: ParamMapping[] = []\r\n\r\n  const {\r\n    extractConvParams,\r\n    extractConvWithBatchNormParams,\r\n    extractSeparableConvParams\r\n  } = extractorsFactory(weightMap, paramMappings)\r\n\r\n  let params: TinyYolov2NetParams\r\n\r\n  if (config.withSeparableConvs) {\r\n    const numFilters = (config.filterSizes && config.filterSizes.length || 9)\r\n    params = {\r\n      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),\r\n      conv1: extractSeparableConvParams('conv1'),\r\n      conv2: extractSeparableConvParams('conv2'),\r\n      conv3: extractSeparableConvParams('conv3'),\r\n      conv4: extractSeparableConvParams('conv4'),\r\n      conv5: extractSeparableConvParams('conv5'),\r\n      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,\r\n      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,\r\n      conv8: extractConvParams('conv8')\r\n    }\r\n  } else {\r\n    params = {\r\n      conv0: extractConvWithBatchNormParams('conv0'),\r\n      conv1: extractConvWithBatchNormParams('conv1'),\r\n      conv2: extractConvWithBatchNormParams('conv2'),\r\n      conv3: extractConvWithBatchNormParams('conv3'),\r\n      conv4: extractConvWithBatchNormParams('conv4'),\r\n      conv5: extractConvWithBatchNormParams('conv5'),\r\n      conv6: extractConvWithBatchNormParams('conv6'),\r\n      conv7: extractConvWithBatchNormParams('conv7'),\r\n      conv8: extractConvParams('conv8')\r\n    }\r\n  }\r\n\r\n  disposeUnusedWeightTensors(weightMap, paramMappings)\r\n\r\n  return { params, paramMappings }\r\n}","export enum TinyYolov2SizeType {\r\n  XS = 224,\r\n  SM = 320,\r\n  MD = 416,\r\n  LG = 608\r\n}\r\n\r\nexport interface ITinyYolov2Options {\r\n  inputSize?: number\r\n  scoreThreshold?: number\r\n}\r\n\r\nexport class TinyYolov2Options {\r\n  protected _name: string = 'TinyYolov2Options'\r\n\r\n  private _inputSize: number\r\n  private _scoreThreshold: number\r\n\r\n  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {\r\n    this._inputSize = inputSize || 416\r\n    this._scoreThreshold = scoreThreshold || 0.5\r\n\r\n    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {\r\n      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`)\r\n    }\r\n\r\n    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {\r\n      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)\r\n    }\r\n  }\r\n\r\n  get inputSize(): number { return this._inputSize }\r\n  get scoreThreshold(): number { return this._scoreThreshold }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport {\r\n  BoundingBox,\r\n  Dimensions,\r\n  NetInput,\r\n  NeuralNetwork,\r\n  nonMaxSuppression,\r\n  normalize,\r\n  ObjectDetection,\r\n  sigmoid,\r\n  TNetInput,\r\n  toNetInput,\r\n} from 'tfjs-image-recognition-base';\r\n\r\nimport { convLayer } from '../common';\r\nimport { ConvParams, SeparableConvParams } from '../common/types';\r\nimport { TinyYolov2Config, validateConfig } from './config';\r\nimport { DEFAULT_FILTER_SIZES } from './const';\r\nimport { convWithBatchNorm } from './convWithBatchNorm';\r\nimport { depthwiseSeparableConv } from './depthwiseSeparableConv';\r\nimport { extractParams } from './extractParams';\r\nimport { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';\r\nimport { leaky } from './leaky';\r\nimport { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';\r\nimport { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';\r\n\r\nexport class TinyYolov2 extends NeuralNetwork<TinyYolov2NetParams> {\r\n\r\n  private _config: TinyYolov2Config\r\n\r\n  constructor(config: TinyYolov2Config) {\r\n    super('TinyYolov2')\r\n    validateConfig(config)\r\n    this._config = config\r\n  }\r\n\r\n  public get config(): TinyYolov2Config {\r\n    return this._config\r\n  }\r\n\r\n  public get withClassScores(): boolean {\r\n    return this.config.withClassScores || this.config.classes.length > 1\r\n  }\r\n\r\n  public get boxEncodingSize(): number {\r\n    return 5 + (this.withClassScores ? this.config.classes.length : 0)\r\n  }\r\n\r\n  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {\r\n\r\n    let out = convWithBatchNorm(x, params.conv0)\r\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\r\n    out = convWithBatchNorm(out, params.conv1)\r\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\r\n    out = convWithBatchNorm(out, params.conv2)\r\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\r\n    out = convWithBatchNorm(out, params.conv3)\r\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\r\n    out = convWithBatchNorm(out, params.conv4)\r\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\r\n    out = convWithBatchNorm(out, params.conv5)\r\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\r\n    out = convWithBatchNorm(out, params.conv6)\r\n    out = convWithBatchNorm(out, params.conv7)\r\n\r\n    return convLayer(out, params.conv8, 'valid', false)\r\n  }\r\n\r\n  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {\r\n\r\n    let out = this.config.isFirstLayerConv2d\r\n      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))\r\n      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)\r\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\r\n    out = depthwiseSeparableConv(out, params.conv1)\r\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\r\n    out = depthwiseSeparableConv(out, params.conv2)\r\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\r\n    out = depthwiseSeparableConv(out, params.conv3)\r\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\r\n    out = depthwiseSeparableConv(out, params.conv4)\r\n    out = tf.maxPool(out, [2, 2], [2, 2], 'same')\r\n    out = depthwiseSeparableConv(out, params.conv5)\r\n    out = tf.maxPool(out, [2, 2], [1, 1], 'same')\r\n    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out\r\n    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out\r\n\r\n    return convLayer(out, params.conv8, 'valid', false)\r\n  }\r\n\r\n  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {\r\n\r\n    const { params } = this\r\n\r\n    if (!params) {\r\n      throw new Error('TinyYolov2 - load model before inference')\r\n    }\r\n\r\n    return tf.tidy(() => {\r\n\r\n      let batchTensor = input.toBatchTensor(inputSize, false).toFloat()\r\n      batchTensor = this.config.meanRgb\r\n        ? normalize(batchTensor, this.config.meanRgb)\r\n        : batchTensor\r\n      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D\r\n\r\n      return this.config.withSeparableConvs\r\n        ? this.runMobilenet(batchTensor, params as MobilenetParams)\r\n        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)\r\n    })\r\n  }\r\n\r\n  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {\r\n    return await this.forwardInput(await toNetInput(input), inputSize)\r\n  }\r\n\r\n  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {\r\n\r\n    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)\r\n\r\n    const netInput = await toNetInput(input)\r\n    const out = await this.forwardInput(netInput, inputSize)\r\n    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D\r\n\r\n    const inputDimensions = {\r\n      width: netInput.getInputWidth(0),\r\n      height: netInput.getInputHeight(0)\r\n    }\r\n\r\n    const results = this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)\r\n    out.dispose()\r\n    out0.dispose()\r\n\r\n    const boxes = results.map(res => res.box)\r\n    const scores = results.map(res => res.score)\r\n    const classScores = results.map(res => res.classScore)\r\n    const classNames = results.map(res => this.config.classes[res.label])\r\n\r\n    const indices = nonMaxSuppression(\r\n      boxes.map(box => box.rescale(inputSize)),\r\n      scores,\r\n      this.config.iouThreshold,\r\n      true\r\n    )\r\n\r\n    const detections = indices.map(idx =>\r\n      new ObjectDetection(\r\n        scores[idx],\r\n        classScores[idx],\r\n        classNames[idx],\r\n        boxes[idx],\r\n        inputDimensions\r\n      )\r\n    )\r\n\r\n    return detections\r\n  }\r\n\r\n  protected getDefaultModelName(): string {\r\n    return ''\r\n  }\r\n\r\n  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {\r\n    return extractParamsFromWeigthMap(weightMap, this.config)\r\n  }\r\n\r\n  protected extractParams(weights: Float32Array) {\r\n    const filterSizes = this.config.filterSizes || DEFAULT_FILTER_SIZES\r\n\r\n    const numFilters = filterSizes ? filterSizes.length : undefined\r\n    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {\r\n      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)\r\n    }\r\n    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)\r\n  }\r\n\r\n  protected extractBoxes(\r\n    outputTensor: tf.Tensor4D,\r\n    inputBlobDimensions: Dimensions,\r\n    scoreThreshold?: number\r\n  ) {\r\n\r\n    const { width, height } = inputBlobDimensions\r\n    const inputSize = Math.max(width, height)\r\n    const correctionFactorX = inputSize / width\r\n    const correctionFactorY = inputSize / height\r\n\r\n    const numCells = outputTensor.shape[1]\r\n    const numBoxes = this.config.anchors.length\r\n\r\n    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {\r\n      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])\r\n\r\n      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])\r\n      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])\r\n      const classScores = this.withClassScores\r\n        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)\r\n        : tf.scalar(0)\r\n      return [boxes, scores, classScores]\r\n    })\r\n\r\n    const results = []\r\n\r\n    for (let row = 0; row < numCells; row ++) {\r\n      for (let col = 0; col < numCells; col ++) {\r\n        for (let anchor = 0; anchor < numBoxes; anchor ++) {\r\n          const score = sigmoid(scoresTensor.get(row, col, anchor, 0))\r\n          if (!scoreThreshold || score > scoreThreshold) {\r\n            const ctX = ((col + sigmoid(boxesTensor.get(row, col, anchor, 0))) / numCells) * correctionFactorX\r\n            const ctY = ((row + sigmoid(boxesTensor.get(row, col, anchor, 1))) / numCells) * correctionFactorY\r\n            const width = ((Math.exp(boxesTensor.get(row, col, anchor, 2)) * this.config.anchors[anchor].x) / numCells) * correctionFactorX\r\n            const height = ((Math.exp(boxesTensor.get(row, col, anchor, 3)) * this.config.anchors[anchor].y) / numCells) * correctionFactorY\r\n\r\n            const x = (ctX - (width / 2))\r\n            const y = (ctY - (height / 2))\r\n\r\n            const pos = { row, col, anchor }\r\n            const { classScore, label } = this.withClassScores\r\n              ? this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)\r\n              : { classScore: 1, label: 0 }\r\n\r\n            results.push({\r\n              box: new BoundingBox(x, y, x + width, y + height),\r\n              score: score,\r\n              classScore: score * classScore,\r\n              label,\r\n              ...pos\r\n            })\r\n          }\r\n        }\r\n      }\r\n    }\r\n\r\n    boxesTensor.dispose()\r\n    scoresTensor.dispose()\r\n    classScoresTensor.dispose()\r\n\r\n    return results\r\n  }\r\n\r\n  private extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number }) {\r\n    const { row, col, anchor } = pos\r\n    return Array(this.config.classes.length).fill(0)\r\n      .map((_, i) => classesTensor.get(row, col, anchor, i))\r\n      .map((classScore, label) => ({\r\n        classScore,\r\n        label\r\n      }))\r\n      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)\r\n  }\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport { BoundingBox, Dimensions, IDimensions, iou, Point, Rect } from 'tfjs-image-recognition-base';\r\n\r\nimport { TinyYolov2TrainableConfig } from './config';\r\nimport { CELL_SIZE } from './const';\r\nimport { GridPosition, GroundTruth, GroundTruthWithGridPosition } from './types';\r\n\r\nexport class TinyYolov2LossFunction {\r\n\r\n  private _config: TinyYolov2TrainableConfig\r\n  private _reshapedImgDims: Dimensions\r\n  private _outputTensor: tf.Tensor4D\r\n  private _groundTruth: GroundTruthWithGridPosition[]\r\n  private _predictedBoxes: GroundTruthWithGridPosition[]\r\n\r\n  public noObjectLossMask: tf.Tensor4D\r\n  public objectLossMask: tf.Tensor4D\r\n  public coordBoxOffsetMask: tf.Tensor4D\r\n  public coordBoxSizeMask: tf.Tensor4D\r\n  public groundTruthClassScoresMask: tf.Tensor4D\r\n\r\n  constructor(\r\n    outputTensor: tf.Tensor4D,\r\n    groundTruth: GroundTruth[],\r\n    predictedBoxes: GroundTruthWithGridPosition[],\r\n    reshapedImgDims: IDimensions,\r\n    config: TinyYolov2TrainableConfig\r\n  ) {\r\n    this._config = config\r\n    this._reshapedImgDims = new Dimensions(reshapedImgDims.width, reshapedImgDims.height)\r\n    this._outputTensor = outputTensor\r\n    this._predictedBoxes = predictedBoxes\r\n\r\n    this.validateGroundTruthBoxes(groundTruth)\r\n    this._groundTruth = this.assignGroundTruthToAnchors(groundTruth)\r\n\r\n    const groundTruthMask = this.createGroundTruthMask()\r\n    const { coordBoxOffsetMask, coordBoxSizeMask, scoreMask } = this.createCoordAndScoreMasks()\r\n\r\n    this.noObjectLossMask = tf.tidy(() => tf.mul(scoreMask, tf.sub(tf.scalar(1), groundTruthMask))) as tf.Tensor4D\r\n    this.objectLossMask = tf.tidy(() => tf.mul(scoreMask, groundTruthMask)) as tf.Tensor4D\r\n    this.coordBoxOffsetMask = tf.tidy(() => tf.mul(coordBoxOffsetMask, groundTruthMask)) as tf.Tensor4D\r\n    this.coordBoxSizeMask = tf.tidy(() => tf.mul(coordBoxSizeMask, groundTruthMask)) as tf.Tensor4D\r\n\r\n    const classScoresMask = tf.tidy(() => tf.sub(tf.scalar(1), coordBoxOffsetMask.add(coordBoxSizeMask).add(scoreMask)))\r\n    this.groundTruthClassScoresMask = tf.tidy(() => tf.mul(classScoresMask, groundTruthMask)) as tf.Tensor4D\r\n  }\r\n\r\n  public get config(): TinyYolov2TrainableConfig {\r\n    return this._config\r\n  }\r\n\r\n  public get reshapedImgDims(): Dimensions {\r\n    return this._reshapedImgDims\r\n  }\r\n\r\n  public get outputTensor(): tf.Tensor4D {\r\n    return this._outputTensor\r\n  }\r\n\r\n  public get groundTruth(): GroundTruthWithGridPosition[] {\r\n    return this._groundTruth\r\n  }\r\n\r\n  public get predictedBoxes(): GroundTruthWithGridPosition[] {\r\n    return this._predictedBoxes\r\n  }\r\n\r\n  public get inputSize(): number {\r\n    return Math.max(this.reshapedImgDims.width, this.reshapedImgDims.height)\r\n  }\r\n\r\n  public get withClassScores(): boolean {\r\n    return this._config.withClassScores || this._config.classes.length > 1\r\n  }\r\n\r\n  public get boxEncodingSize(): number {\r\n    return 5 + (this.withClassScores ? this._config.classes.length : 0)\r\n  }\r\n\r\n  public get anchors(): Point[] {\r\n    return this._config.anchors\r\n  }\r\n\r\n  public get numBoxes(): number {\r\n    return this.anchors.length\r\n  }\r\n\r\n  public get numCells(): number {\r\n    return this.inputSize / CELL_SIZE\r\n  }\r\n\r\n  public get gridCellEncodingSize(): number {\r\n    return this.boxEncodingSize * this.numBoxes\r\n  }\r\n\r\n  public toOutputTensorShape(tensor: tf.Tensor) {\r\n    return tf.tidy(() => tensor.reshape([1, this.numCells, this.numCells, this.gridCellEncodingSize]))\r\n  }\r\n\r\n  public computeLoss() {\r\n    return tf.tidy(() => {\r\n\r\n      const noObjectLoss = this.computeNoObjectLoss()\r\n      const objectLoss = this.computeObjectLoss()\r\n      const coordLoss = this.computeCoordLoss()\r\n      const classLoss = this.withClassScores\r\n        ? this.computeClassLoss()\r\n        : tf.scalar(0)\r\n\r\n      const totalLoss: tf.Tensor<tf.Rank.R0> = tf.tidy(() => noObjectLoss.add(objectLoss).add(coordLoss).add(classLoss))\r\n\r\n      return {\r\n        noObjectLoss,\r\n        objectLoss,\r\n        coordLoss,\r\n        classLoss,\r\n        totalLoss\r\n      }\r\n    })\r\n  }\r\n\r\n  public computeNoObjectLoss(): tf.Tensor<tf.Rank.R0> {\r\n    return tf.tidy(() =>\r\n      this.computeLossTerm(\r\n        this.config.noObjectScale,\r\n        this.toOutputTensorShape(this.noObjectLossMask),\r\n        tf.sigmoid(this.outputTensor)\r\n      )\r\n    )\r\n  }\r\n\r\n  public computeObjectLoss(): tf.Tensor<tf.Rank.R0> {\r\n    return tf.tidy(() =>\r\n      this.computeLossTerm(\r\n        this.config.objectScale,\r\n        this.toOutputTensorShape(this.objectLossMask),\r\n        tf.sub(this.toOutputTensorShape(this.computeIous()), tf.sigmoid(this.outputTensor))\r\n      )\r\n    )\r\n  }\r\n\r\n  public computeClassLoss(): tf.Tensor<tf.Rank.R0> {\r\n    return tf.tidy(() => {\r\n\r\n      const classLossTensor = tf.tidy(() => {\r\n\r\n        const predClassScores = tf.mul(\r\n          tf.softmax(this.outputTensor.reshape([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize]), 3),\r\n          this.groundTruthClassScoresMask\r\n        )\r\n\r\n        const gtClassScores = this.createOneHotClassScoreMask()\r\n\r\n        return tf.sub(gtClassScores, predClassScores)\r\n      })\r\n\r\n      return this.computeLossTerm(\r\n        this.config.classScale,\r\n        tf.scalar(1),\r\n        classLossTensor as tf.Tensor4D\r\n      )\r\n    })\r\n  }\r\n\r\n  public computeCoordLoss(): tf.Tensor<tf.Rank.R0> {\r\n    return tf.tidy(() =>\r\n      this.computeLossTerm(\r\n        this.config.coordScale,\r\n        tf.scalar(1),\r\n        tf.add(this.computeCoordBoxOffsetError(), this.computeCoordBoxSizeError())\r\n      )\r\n    )\r\n  }\r\n\r\n  public computeCoordBoxOffsetError(): tf.Tensor4D {\r\n    return tf.tidy(() => {\r\n\r\n      const mask = this.toOutputTensorShape(this.coordBoxOffsetMask)\r\n      const gtBoxOffsets = tf.mul(mask, this.toOutputTensorShape(this.computeCoordBoxOffsets()))\r\n      const predBoxOffsets = tf.mul(mask, tf.sigmoid(this.outputTensor))\r\n\r\n      return tf.sub(gtBoxOffsets, predBoxOffsets)\r\n\r\n    })\r\n  }\r\n\r\n  public computeCoordBoxSizeError(): tf.Tensor4D {\r\n    return tf.tidy(() => {\r\n\r\n      const mask = this.toOutputTensorShape(this.coordBoxSizeMask)\r\n      const gtBoxSizes = tf.mul(mask, this.toOutputTensorShape(this.computeCoordBoxSizes()))\r\n      const predBoxSizes = tf.mul(mask, this.outputTensor)\r\n\r\n      return tf.sub(gtBoxSizes, predBoxSizes)\r\n\r\n    })\r\n  }\r\n\r\n  private computeLossTerm(scale: number, mask: tf.Tensor<tf.Rank>, lossTensor: tf.Tensor4D): tf.Tensor<tf.Rank.R0> {\r\n    return tf.tidy(() => tf.mul(tf.scalar(scale), this.squaredSumOverMask(mask, lossTensor)))\r\n  }\r\n\r\n  private squaredSumOverMask(mask: tf.Tensor<tf.Rank>, lossTensor: tf.Tensor4D): tf.Tensor4D {\r\n    return tf.tidy(() => tf.sum(tf.square(tf.mul(mask, lossTensor))))\r\n  }\r\n\r\n  private validateGroundTruthBoxes(groundTruth: GroundTruth[]) {\r\n    groundTruth.forEach(({ x, y, width, height, label }) => {\r\n      if (typeof label !== 'number' || label < 0 || label > (this.config.classes.length - 1)) {\r\n        throw new Error(`invalid ground truth data, expected label to be a number in [0, ${this.config.classes.length - 1}]`)\r\n      }\r\n\r\n      if (x < 0 || x > 1 || y < 0 || y > 1 || width < 0 || (x + width) > 1 || height < 0 || (y + height) > 1) {\r\n        throw new Error(`invalid ground truth data, box is out of image boundaries ${JSON.stringify({ x, y, width, height})}`)\r\n      }\r\n    })\r\n  }\r\n\r\n  private assignGroundTruthToAnchors(groundTruth: GroundTruth[]) {\r\n    const groundTruthBoxes = groundTruth\r\n      .map(({ x, y, width, height, label }) => ({\r\n        box: new Rect(x, y, width, height),\r\n        label\r\n      }))\r\n\r\n    return groundTruthBoxes.map(({ box, label }) => {\r\n      const { left, top, bottom, right, x, y, width, height } = box.rescale(this.reshapedImgDims)\r\n\r\n      const ctX = left + (width / 2)\r\n      const ctY = top + (height / 2)\r\n\r\n      const col = Math.floor((ctX / this.inputSize) * this.numCells)\r\n      const row = Math.floor((ctY / this.inputSize) * this.numCells)\r\n\r\n      const anchorsByIou = this.anchors.map((anchor, idx) => ({\r\n        idx,\r\n        iou: iou(\r\n          new BoundingBox(0, 0, anchor.x * CELL_SIZE, anchor.y * CELL_SIZE),\r\n          new BoundingBox(0, 0, width, height)\r\n        )\r\n      })).sort((a1, a2) => a2.iou - a1.iou)\r\n\r\n      const anchor = anchorsByIou[0].idx\r\n\r\n      return { row, col, anchor, box, label }\r\n    })\r\n  }\r\n\r\n  private createGroundTruthMask() {\r\n\r\n    const mask = tf.zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize])\r\n    const buf = mask.buffer()\r\n\r\n    this.groundTruth.forEach(({ row, col, anchor }) => {\r\n      for (let i = 0; i < this.boxEncodingSize; i++) {\r\n        buf.set(1, row, col, anchor, i)\r\n      }\r\n    })\r\n\r\n    return mask\r\n  }\r\n\r\n  private createCoordAndScoreMasks() {\r\n    return tf.tidy(() => {\r\n\r\n      const coordBoxOffsetMask = tf.zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize])\r\n      const coordBoxSizeMask = tf.zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize])\r\n      const scoreMask = tf.zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize])\r\n\r\n      const coordBoxOffsetBuf = coordBoxOffsetMask.buffer()\r\n      const coordBoxSizeBuf = coordBoxSizeMask.buffer()\r\n      const scoreBuf = scoreMask.buffer()\r\n\r\n      for (let row = 0; row < this.numCells; row++) {\r\n        for (let col = 0; col < this.numCells; col++) {\r\n          for (let anchor = 0; anchor < this.numBoxes; anchor++) {\r\n            coordBoxOffsetBuf.set(1, row, col, anchor, 0)\r\n            coordBoxOffsetBuf.set(1, row, col, anchor, 1)\r\n            coordBoxSizeBuf.set(1, row, col, anchor, 2)\r\n            coordBoxSizeBuf.set(1, row, col, anchor, 3)\r\n            scoreBuf.set(1, row, col, anchor, 4)\r\n          }\r\n        }\r\n      }\r\n\r\n      return { coordBoxOffsetMask, coordBoxSizeMask, scoreMask }\r\n    })\r\n\r\n  }\r\n\r\n  private createOneHotClassScoreMask() {\r\n    const mask = tf.zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize])\r\n    const buf = mask.buffer()\r\n\r\n    const classValuesOffset = 5\r\n    this.groundTruth.forEach(({ row, col, anchor, label }) => {\r\n      buf.set(1, row, col, anchor, classValuesOffset + label)\r\n    })\r\n\r\n    return mask\r\n  }\r\n\r\n  private computeIous() {\r\n\r\n    const isSameAnchor = (p1: GridPosition) => (p2: GridPosition) =>\r\n      p1.row === p2.row\r\n        && p1.col === p2.col\r\n        && p1.anchor === p2.anchor\r\n\r\n    const ious = tf.zeros([this.numCells, this.numCells, this.gridCellEncodingSize])\r\n    const buf = ious.buffer()\r\n\r\n    this.groundTruth.forEach(({ row, col, anchor, box }) => {\r\n      const predBox = this.predictedBoxes.find(isSameAnchor({ row, col, anchor }))\r\n\r\n      if (!predBox) {\r\n        throw new Error(`no output box found for: row ${row}, col ${col}, anchor ${anchor}`)\r\n      }\r\n\r\n      const boxIou = iou(\r\n        box.rescale(this.reshapedImgDims),\r\n        predBox.box.rescale(this.reshapedImgDims)\r\n      )\r\n\r\n      const anchorOffset = this.boxEncodingSize * anchor\r\n      const scoreValueOffset = 4\r\n      buf.set(boxIou, row, col, anchorOffset + scoreValueOffset)\r\n    })\r\n\r\n    return ious\r\n  }\r\n\r\n  public computeCoordBoxOffsets() {\r\n\r\n    const offsets = tf.zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize])\r\n    const buf = offsets.buffer()\r\n\r\n    this.groundTruth.forEach(({ row, col, anchor, box }) => {\r\n      const { left, top, right, bottom } = box.rescale(this.reshapedImgDims)\r\n\r\n      const centerX = (left + right) / 2\r\n      const centerY = (top + bottom) / 2\r\n\r\n      const dCenterX = centerX - (col * CELL_SIZE)\r\n      const dCenterY = centerY - (row * CELL_SIZE)\r\n\r\n      // inverseSigmoid(0) === -Infinity, inverseSigmoid(1) === Infinity\r\n      //const dx = inverseSigmoid(Math.min(0.999, Math.max(0.001, dCenterX / CELL_SIZE)))\r\n      //const dy = inverseSigmoid(Math.min(0.999, Math.max(0.001, dCenterY / CELL_SIZE)))\r\n      const dx = dCenterX / CELL_SIZE\r\n      const dy = dCenterY / CELL_SIZE\r\n\r\n      buf.set(dx, row, col, anchor, 0)\r\n      buf.set(dy, row, col, anchor, 1)\r\n    })\r\n\r\n    return offsets\r\n  }\r\n\r\n  public computeCoordBoxSizes() {\r\n\r\n    const sizes = tf.zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize])\r\n    const buf = sizes.buffer()\r\n\r\n    this.groundTruth.forEach(({ row, col, anchor, box }) => {\r\n      const { width, height } = box.rescale(this.reshapedImgDims)\r\n      const dw = Math.log(width / (this.anchors[anchor].x * CELL_SIZE))\r\n      const dh = Math.log(height / (this.anchors[anchor].y * CELL_SIZE))\r\n\r\n      buf.set(dw, row, col, anchor, 2)\r\n      buf.set(dh, row, col, anchor, 3)\r\n    })\r\n\r\n    return sizes\r\n  }\r\n\r\n}\r\n","import { CELL_SIZE } from './const';\r\nimport { TinyYolov2BackwardOptions } from './types';\r\n\r\nexport function getDefaultBackwardOptions(options: TinyYolov2BackwardOptions) {\r\n  return Object.assign(\r\n    {},\r\n    {\r\n      minBoxSize: CELL_SIZE\r\n    },\r\n    options\r\n  )\r\n}","import * as tf from '@tensorflow/tfjs-core';\r\nimport {\r\n  computeReshapedDimensions,\r\n  Dimensions,\r\n  getMediaDimensions,\r\n  imageToSquare,\r\n  Rect,\r\n  toNetInput,\r\n} from 'tfjs-image-recognition-base';\r\n\r\nimport { TinyYolov2TrainableConfig, validateTrainConfig } from './config';\r\nimport { getDefaultBackwardOptions } from './getDefaultBackwardOptions';\r\nimport { TinyYolov2 } from './TinyYolov2';\r\nimport { TinyYolov2LossFunction } from './TinyYolov2LossFunction';\r\nimport { GroundTruth, TinyYolov2BackwardOptions } from './types';\r\n\r\nexport class TinyYolov2Trainable extends TinyYolov2 {\r\n\r\n  private _trainableConfig: TinyYolov2TrainableConfig\r\n  private _optimizer: tf.Optimizer\r\n\r\n  constructor(trainableConfig: TinyYolov2TrainableConfig, optimizer: tf.Optimizer) {\r\n    super(trainableConfig)\r\n    this._trainableConfig = validateTrainConfig(trainableConfig)\r\n    this._optimizer = optimizer\r\n  }\r\n\r\n  public get trainableConfig(): TinyYolov2TrainableConfig {\r\n    return this._trainableConfig\r\n  }\r\n\r\n  public get optimizer(): tf.Optimizer {\r\n    return this._optimizer\r\n  }\r\n\r\n  public async backward(\r\n    img: HTMLImageElement | HTMLCanvasElement,\r\n    groundTruth: GroundTruth[],\r\n    inputSize: number,\r\n    options: TinyYolov2BackwardOptions = {}\r\n  ): Promise<tf.Tensor<tf.Rank.R0> | null> {\r\n\r\n    const { minBoxSize, reportLosses } = getDefaultBackwardOptions(options)\r\n    const reshapedImgDims = computeReshapedDimensions(getMediaDimensions(img), inputSize)\r\n    const filteredGroundTruthBoxes = this.filterGroundTruthBoxes(groundTruth, reshapedImgDims, minBoxSize)\r\n\r\n    if (!filteredGroundTruthBoxes.length) {\r\n      return null\r\n    }\r\n\r\n    // square input images before creating tensor to prevent gpu memory overflow bug\r\n    const netInput = await toNetInput(imageToSquare(img, inputSize))\r\n\r\n    const loss = this.optimizer.minimize(() => {\r\n\r\n      const {\r\n        noObjectLoss,\r\n        objectLoss,\r\n        coordLoss,\r\n        classLoss,\r\n        totalLoss\r\n      } = this.computeLoss(\r\n        this.forwardInput(netInput, inputSize),\r\n        filteredGroundTruthBoxes,\r\n        reshapedImgDims\r\n      )\r\n\r\n      if (reportLosses) {\r\n        const losses = {\r\n          totalLoss: totalLoss.dataSync()[0],\r\n          noObjectLoss: noObjectLoss.dataSync()[0],\r\n          objectLoss: objectLoss.dataSync()[0],\r\n          coordLoss: coordLoss.dataSync()[0],\r\n          classLoss: classLoss.dataSync()[0]\r\n        }\r\n\r\n        const report = {\r\n          losses,\r\n          numBoxes: filteredGroundTruthBoxes.length,\r\n          inputSize\r\n        }\r\n\r\n        reportLosses(report)\r\n      }\r\n\r\n      return totalLoss\r\n    }, true)\r\n\r\n    return loss\r\n  }\r\n\r\n  public computeLoss(outputTensor: tf.Tensor4D, groundTruth: GroundTruth[], reshapedImgDims: Dimensions) {\r\n\r\n    const config = validateTrainConfig(this.config)\r\n\r\n    const inputSize = Math.max(reshapedImgDims.width, reshapedImgDims.height)\r\n\r\n    if (!inputSize) {\r\n      throw new Error(`computeLoss - invalid inputSize: ${inputSize}`)\r\n    }\r\n\r\n    const predictedBoxes = this.extractBoxes(outputTensor, reshapedImgDims)\r\n\r\n    return tf.tidy(() => {\r\n      const lossFunction = new TinyYolov2LossFunction(outputTensor, groundTruth, predictedBoxes, reshapedImgDims, config)\r\n      return lossFunction.computeLoss()\r\n    })\r\n  }\r\n\r\n  public filterGroundTruthBoxes(groundTruth: GroundTruth[], imgDims: Dimensions, minBoxSize: number) {\r\n\r\n    const { height: imgHeight, width: imgWidth } = imgDims\r\n\r\n    return groundTruth.filter(({ x, y, width, height }) => {\r\n      const box = (new Rect(x, y, width, height))\r\n        .rescale({ height: imgHeight, width: imgWidth })\r\n\r\n      const isTooTiny = box.width < minBoxSize || box.height < minBoxSize\r\n      return !isTooTiny\r\n    })\r\n  }\r\n\r\n  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {\r\n    await super.load(weightsOrUrl)\r\n    this.variable()\r\n  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\ No newline at end of file
diff --git a/node_modules/tfjs-tiny-yolov2/dist/tiny-yolov2.min.js b/node_modules/tfjs-tiny-yolov2/dist/tiny-yolov2.min.js
new file mode 100644
index 0000000..9d27d95
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/dist/tiny-yolov2.min.js
@@ -0,0 +1 @@
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this.throwIfDisposed(),rt.greater(this,t)},r.prototype.greaterStrict=function(t){return this.throwIfDisposed(),rt.greaterStrict(this,t)},r.prototype.greaterEqual=function(t){return this.throwIfDisposed(),rt.greaterEqual(this,t)},r.prototype.greaterEqualStrict=function(t){return this.throwIfDisposed(),rt.greaterEqualStrict(this,t)},r.prototype.logicalAnd=function(t){return this.throwIfDisposed(),rt.logicalAnd(this,t)},r.prototype.logicalOr=function(t){return this.throwIfDisposed(),rt.logicalOr(this,t)},r.prototype.logicalNot=function(){return this.throwIfDisposed(),rt.logicalNot(this)},r.prototype.logicalXor=function(t){return this.throwIfDisposed(),rt.logicalXor(this,t)},r.prototype.where=function(t,e){return this.throwIfDisposed(),rt.where(t,this,e)},r.prototype.neg=function(){return this.throwIfDisposed(),rt.neg(this)},r.prototype.ceil=function(){return this.throwIfDisposed(),rt.ceil(this)},r.prototype.floor=function(){return 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this.throwIfDisposed(),rt.elu(this)},r.prototype.selu=function(){return this.throwIfDisposed(),rt.selu(this)},r.prototype.leakyRelu=function(t){return void 0===t&&(t=.2),this.throwIfDisposed(),rt.leakyRelu(this,t)},r.prototype.prelu=function(t){return this.throwIfDisposed(),rt.prelu(this,t)},r.prototype.sigmoid=function(){return this.throwIfDisposed(),rt.sigmoid(this)},r.prototype.logSigmoid=function(){return this.throwIfDisposed(),rt.logSigmoid(this)},r.prototype.softplus=function(){return this.throwIfDisposed(),rt.softplus(this)},r.prototype.zerosLike=function(){return this.throwIfDisposed(),rt.zerosLike(this)},r.prototype.onesLike=function(){return this.throwIfDisposed(),rt.onesLike(this)},r.prototype.sin=function(){return this.throwIfDisposed(),rt.sin(this)},r.prototype.cos=function(){return this.throwIfDisposed(),rt.cos(this)},r.prototype.tan=function(){return this.throwIfDisposed(),rt.tan(this)},r.prototype.asin=function(){return this.throwIfDisposed(),rt.asin(this)},r.prototype.acos=function(){return this.throwIfDisposed(),rt.acos(this)},r.prototype.atan=function(){return this.throwIfDisposed(),rt.atan(this)},r.prototype.sinh=function(){return this.throwIfDisposed(),rt.sinh(this)},r.prototype.cosh=function(){return this.throwIfDisposed(),rt.cosh(this)},r.prototype.tanh=function(){return this.throwIfDisposed(),rt.tanh(this)},r.prototype.asinh=function(){return this.throwIfDisposed(),rt.asinh(this)},r.prototype.acosh=function(){return this.throwIfDisposed(),rt.acosh(this)},r.prototype.atanh=function(){return this.throwIfDisposed(),rt.atanh(this)},r.prototype.erf=function(){return this.throwIfDisposed(),rt.erf(this)},r.prototype.round=function(){return this.throwIfDisposed(),rt.round(this)},r.prototype.step=function(t){return void 0===t&&(t=0),this.throwIfDisposed(),rt.step(this,t)},r.prototype.softmax=function(t){return void 0===t&&(t=-1),this.throwIfDisposed(),rt.softmax(this,t)},r.prototype.logSoftmax=function(t){return void 0===t&&(t=-1),this.throwIfDisposed(),rt.logSoftmax(this,t)},r.prototype.resizeBilinear=function(t,e){return void 0===e&&(e=!1),this.throwIfDisposed(),rt.image.resizeBilinear(this,t,e)},r.prototype.resizeNearestNeighbor=function(t,e){return void 0===e&&(e=!1),this.throwIfDisposed(),rt.image.resizeNearestNeighbor(this,t,e)},r.prototype.conv1d=function(t,e,n,r,o,i){return void 0===r&&(r="NWC"),void 0===o&&(o=1),this.throwIfDisposed(),rt.conv1d(this,t,e,n,r,o,i)},r.prototype.conv2d=function(t,e,n,r,o,i){return void 0===r&&(r="NHWC"),void 0===o&&(o=[1,1]),this.throwIfDisposed(),rt.conv2d(this,t,e,n,r,o,i)},r.prototype.conv2dTranspose=function(t,e,n,r,o){return this.throwIfDisposed(),rt.conv2dTranspose(this,t,e,n,r,o)},r.prototype.depthwiseConv2D=function(t,e,n,r,o,i){return void 0===r&&(r="NHWC"),void 0===o&&(o=[1,1]),this.throwIfDisposed(),rt.depthwiseConv2d(this,t,e,n,r,o,i)},r.prototype.separableConv2d=function(t,e,n,r,o,i){return void 0===o&&(o=[1,1]),void 0===i&&(i="NHWC"),this.throwIfDisposed(),rt.separableConv2d(this,t,e,n,r,o,i)},r.prototype.avgPool=function(t,e,n,r){return this.throwIfDisposed(),rt.avgPool(this,t,e,n,r)},r.prototype.maxPool=function(t,e,n,r){return this.throwIfDisposed(),rt.maxPool(this,t,e,n,r)},r.prototype.localResponseNormalization=function(t,e,n,r){return void 0===t&&(t=5),void 0===e&&(e=1),void 0===n&&(n=1),void 0===r&&(r=.5),rt.localResponseNormalization(this,t,e,n,r)},r.prototype.pool=function(t,e,n,r,o){return this.throwIfDisposed(),rt.pool(this,t,e,n,r,o)},r.prototype.variable=function(t,e,n){return void 0===t&&(t=!0),this.throwIfDisposed(),at.variable(this,t,e,n)},r.prototype.unsortedSegmentSum=function(t,e){return this.throwIfDisposed(),rt.unsortedSegmentSum(this,t,e)},r.prototype.batchToSpaceND=function(t,e){return this.throwIfDisposed(),rt.batchToSpaceND(this,t,e)},r.prototype.spaceToBatchND=function(t,e){return this.throwIfDisposed(),rt.spaceToBatchND(this,t,e)},r.prototype.topk=function(t,e){return void 0===t&&(t=1),void 0===e&&(e=!0),this.throwIfDisposed(),rt.topk(this,t,e)},r.prototype.stridedSlice=function(t,e,n,r,o){return void 0===r&&(r=0),void 0===o&&(o=0),this.throwIfDisposed(),rt.stridedSlice(this,t,e,n,r,o)},r.prototype.depthToSpace=function(t,e){return this.throwIfDisposed(),rt.depthToSpace(this,t,e)},r.prototype.fft=function(){return this.throwIfDisposed(),rt.spectral.fft(this)},r.prototype.ifft=function(){return this.throwIfDisposed(),rt.spectral.ifft(this)},r}();Object.defineProperty(it,Symbol.hasInstance,{value:function(t){return!!t&&null!=t.shape&&null!=t.dtype}});var at=function(o){function i(t,e,n){void 0===e&&(e=!0);var r=o.call(this,t.shape,t.dtype,null,t.dataId)||this;r.trainable=e,r.name=n,null==r.name&&(r.name=nt().nextVariableId().toString());try{nt().registerVariable(r)}catch(t){throw nt().disposeTensor(r),t}return r}return s(i,o),i.variable=function(t,e,n,r){return void 0===e&&(e=!0),null!=r&&r!==t.dtype&&(t=t.asType(r)),new i(t,e,n)},i.prototype.assign=function(t){if(t.dtype!==this.dtype)throw new Error("dtype of the new value ("+t.dtype+") and previous value ("+this.dtype+") must match");if(!y(t.shape,this.shape))throw new Error("shape of the new value ("+t.shape+") and previous value ("+this.shape+") must match");nt().disposeTensor(this),this.dataId=t.dataId,nt().registerTensor(this)},i}(it);Object.defineProperty(at,Symbol.hasInstance,{value:function(t){return t instanceof it&&null!=t.assign&&t.assign instanceof Function}});var st,ut,ct,lt,ht,pt,ft,dt,mt,vt,gt=at.variable;(vt=st||(st={})).R0="R0",vt.R1="R1",vt.R2="R2",vt.R3="R3",vt.R4="R4",vt.R5="R5",vt.R6="R6",(mt=ut||(ut={})).float32="float32",mt.int32="int32",mt.bool="int32",mt.complex64="complex64",(dt=ct||(ct={})).float32="float32",dt.int32="int32",dt.bool="bool",dt.complex64="complex64",(ft=lt||(lt={})).float32="float32",ft.int32="float32",ft.bool="float32",ft.complex64="complex64",(pt=ht||(ht={})).float32="complex64",pt.int32="complex64",pt.bool="complex64",pt.complex64="complex64";var yt={float32:lt,int32:ut,bool:ct,complex64:ht};function xt(t,e){if("string"!==t&&"string"!==e)return yt[t][e];if("string"===t&&"string"===e)return"string";throw new Error("Can not upcast "+t+" with "+e)}function wt(t){return xt(t,"int32")}function bt(t,e){if(t.dtype===e.dtype)return[t,e];var n=xt(t.dtype,e.dtype);return[t.cast(n),e.cast(n)]}function Et(t){var e=[];return function t(e,n,r){if(null!=e)if(e instanceof it)n.push(e);else 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l={id:this.nextTapeNodeId++,name:s,inputs:e,outputs:Array.isArray(r)?r:[r]};null!=n&&(l.gradient=function(t){return n(t,i)}),this.activeTape.push(l)}return this.profiling&&this.activeProfile.kernels.push({name:s,bytesAdded:this.numBytes-u,totalBytesSnapshot:this.numBytes,tensorsAdded:this.numTensors-c,totalTensorsSnapshot:this.numTensors,inputShapes:Object.keys(e).map(function(t){return e[t].shape}),outputShape:Array.isArray(r)?r.map(function(t){return t.shape}):r.shape}),r},t.prototype.registerTensor=function(t){var e=this.tensorInfo.has(t.dataId)?this.tensorInfo.get(t.dataId).refCount:0;if(this.numTensors++,"string"===t.dtype&&this.numStringTensors++,0===e){this.numDataBuffers++;var n=0;"complex64"!==t.dtype&&"string"!==t.dtype&&(n=L(t.shape)*_(t.dtype)),this.tensorInfo.set(t.dataId,{backend:this.backend,dtype:t.dtype,shape:t.shape,bytes:n,refCount:0}),this.numBytes+=n,this.backend.register(t.dataId,t.shape,t.dtype)}this.tensorInfo.get(t.dataId).refCount++,t instanceof at||this.track(t)},t.prototype.registerVariable=function(t){if(null!=this.registeredVariables[t.name])throw new Error("Variable with name "+t.name+" was already registered");this.registeredVariables[t.name]=t},t.prototype.disposeTensor=function(t){if(this.tensorInfo.has(t.dataId)){this.keepTensors.has(t.id)&&this.keepTensors.delete(t.id),this.numTensors--,"string"===t.dtype&&this.numStringTensors--;var e=this.tensorInfo.get(t.dataId);e.refCount<=1?("complex64"!==t.dtype&&(this.numBytes-=e.bytes),this.numDataBuffers--,e.backend.disposeData(t.dataId),this.tensorInfo.delete(t.dataId)):this.tensorInfo.get(t.dataId).refCount--}},t.prototype.disposeVariables=function(){for(var t in this.registeredVariables){var e=this.registeredVariables[t];this.disposeTensor(e),delete this.registeredVariables[t]}},t.prototype.memory=function(){var t=this.backend.memory();return t.numTensors=this.numTensors,t.numDataBuffers=this.numDataBuffers,t.numBytes=this.numBytes,0<this.numStringTensors&&(t.unreliable=!0,null==t.reasons&&(t.reasons=[]),t.reasons.push("Memory usage by string tensors is approximate (2 bytes per character)")),t},t.prototype.profile=function(r){return m(this,void 0,void 0,function(){var e,n;return C(this,function(t){return this.profiling=!0,e=this.numBytes,n=this.numTensors,this.activeProfile.kernels=[],this.activeProfile.result=r(),this.profiling=!1,this.activeProfile.peakBytes=Math.max.apply(Math,this.activeProfile.kernels.map(function(t){return t.totalBytesSnapshot})),this.activeProfile.newBytes=this.numBytes-e,this.activeProfile.newTensors=this.numTensors-n,[2,this.activeProfile]})})},t.prototype.shouldRecord=function(){return null!=this.activeTape&&0===this.customGradientDepth},t.prototype.addTapeNode=function(t,e,r){var n={};t.forEach(function(t,e){n[e]=t});var o={id:this.nextTapeNodeId++,name:this.activeScope.name,inputs:n,outputs:[e],gradient:function(t){var n={};return r(t).forEach(function(t,e){n[e]=function(){return t}}),n}};this.activeTape.push(o)},t.prototype.keep=function(t){if(1===this.scopeStack.length&&this.safeMode)throw new Error("Safe mode is ON. 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implemented")},t.prototype.rsqrt=function(t){throw new Error("Not yet implemented")},t.prototype.square=function(t){throw new Error("Not yet implemented")},t.prototype.reciprocal=function(t){throw new Error("Not yet implemented")},t.prototype.relu=function(t){throw new Error("Not yet implemented")},t.prototype.elu=function(t){throw new Error("Not yet implemented")},t.prototype.eluDer=function(t,e){throw new Error("Not yet implemented")},t.prototype.selu=function(t){throw new Error("Not yet implemented")},t.prototype.int=function(t){throw new Error("Not yet implemented")},t.prototype.clip=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.abs=function(t){throw new Error("Not yet implemented")},t.prototype.complexAbs=function(t){throw new Error("Not yet implemented")},t.prototype.sigmoid=function(t){throw new Error("Not yet implemented")},t.prototype.softplus=function(t){throw new Error("Not yet implemented")},t.prototype.sin=function(t){throw new Error("Not yet implemented")},t.prototype.cos=function(t){throw new Error("Not yet implemented")},t.prototype.tan=function(t){throw new Error("Not yet implemented")},t.prototype.asin=function(t){throw new Error("Not yet implemented")},t.prototype.acos=function(t){throw new Error("Not yet implemented")},t.prototype.atan=function(t){throw new Error("Not yet implemented")},t.prototype.atan2=function(t,e){throw new Error("Not yet implemented")},t.prototype.sinh=function(t){throw new Error("Not yet implemented")},t.prototype.cosh=function(t){throw new Error("Not yet implemented")},t.prototype.tanh=function(t){throw new Error("Not yet implemented")},t.prototype.asinh=function(t){throw new Error("Not yet implemented")},t.prototype.acosh=function(t){throw new Error("Not yet implemented")},t.prototype.atanh=function(t){throw new Error("Not yet implemented")},t.prototype.erf=function(t){throw new Error("Not yet implemented")},t.prototype.step=function(t,e){throw new Error("Not yet implemented")},t.prototype.conv2d=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.conv2dDerInput=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.conv2dDerFilter=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.depthwiseConv2D=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.depthwiseConv2DDerInput=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.depthwiseConv2DDerFilter=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.conv3d=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.conv3dDerInput=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.conv3dDerFilter=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.maxPool=function(t,e){throw new Error("Not yet implemented")},t.prototype.maxPoolBackprop=function(t,e,n,r){throw new Error("Not yet implemented")},t.prototype.avgPool=function(t,e){throw new Error("Not yet implemented")},t.prototype.avgPoolBackprop=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.reshape=function(t,e){throw new Error("Not yet implemented")},t.prototype.cast=function(t,e){throw new Error("Not yet implemented")},t.prototype.tile=function(t,e){throw new Error("Not yet implemented")},t.prototype.pad=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.transpose=function(t,e){throw new Error("Not yet implemented")},t.prototype.gather=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.gatherND=function(t,e){throw new Error("Not yet implemented")},t.prototype.scatterND=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.batchToSpaceND=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.spaceToBatchND=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.resizeBilinear=function(t,e,n,r){throw new Error("Not yet implemented")},t.prototype.resizeBilinearBackprop=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.resizeNearestNeighbor=function(t,e,n,r){throw new Error("Not yet implemented")},t.prototype.resizeNearestNeighborBackprop=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.batchNormalization=function(t,e,n,r,o,i){throw new Error("Not yet implemented")},t.prototype.localResponseNormalization4D=function(t,e,n,r,o){throw new Error("Not yet implemented")},t.prototype.LRNGrad=function(t,e,n,r,o,i,a){throw new Error("Not yet implemented")},t.prototype.multinomial=function(t,e,n,r){throw new Error("Not yet implemented")},t.prototype.oneHot=function(t,e,n,r){throw new Error("Not yet implemented")},t.prototype.cumsum=function(t,e,n,r){throw new Error("Not yet implemented")},t.prototype.nonMaxSuppression=function(t,e,n,r,o){throw new Error("Not yet implemented")},t.prototype.fft=function(t){throw new Error("Not yet implemented")},t.prototype.ifft=function(t){throw new Error("Not yet implemented")},t.prototype.complex=function(t,e){throw new Error("Not yet implemented")},t.prototype.real=function(t){throw new Error("Not yet implemented")},t.prototype.imag=function(t){throw new Error("Not yet implemented")},t.prototype.cropAndResize=function(t,e,n,r,o,i){throw new Error("Not yet implemented")},t.prototype.depthToSpace=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.split=function(t,e,n){throw new Error("Not yet implemented")},t.prototype.sparseToDense=function(t,e,n,r){throw new Error("Not yet implemented")},t.prototype.setDataMover=function(t){throw new Error("Not yet implemented")},t.prototype.dispose=function(){throw new Error("Not yet implemented")},t}();function We(t,e,n){if("complex64"===e){if("complex64"===t.dtype)return t.clone();var r=Me(t.shape),o=t.toFloat(),i=n.complex(o,r);return r.dispose(),o.dispose(),i}if(!I(t.dtype,e))return it.make(t.shape,{dataId:t.dataId},e);if("complex64"===t.dtype){var a=n.real(t);return i=a.cast(e),a.dispose(),i}if("int32"===e)return n.int(t);if("bool"!==e)throw new Error("Error in Cast: unknown dtype argument ("+e+")");var s=Se(0,t.dtype);return i=n.notEqual(t,s),s.dispose(),i}function Ue(t,e){return it.make(e,{dataId:t.dataId},t.dtype)}function Ve(t,e){if(t.length!==e.length)throw new Error("Cannot merge real and imag arrays of different lengths. real:"+t.length+", imag: "+e.length+".");for(var n=new Float32Array(2*t.length),r=0;r<n.length;r+=2)n[r]=t[r/2],n[r+1]=e[r/2];return n}function Ge(t,e){return{real:t[2*e],imag:t[2*e+1]}}function He(t,e,n,r,o){for(var i=Array.from(e).map(function(t,e){return{score:t,boxIndex:e}}).filter(function(t){return t.score>o}).sort(function(t,e){return e.score-t.score}),a=[],s=0;s<i.length;s++){var u=i[s],c=u.score,l=u.boxIndex;if(c<o)break;for(var h=!1,p=a.length-1;0<=p;--p)if(qe(t,l,a[p])>=r){h=!0;break}if(!h&&(a.push(l),a.length>=n))break}return Re(a,"int32")}function qe(t,e,n){var r=t.subarray(4*e,4*e+4),o=t.subarray(4*n,4*n+4),i=Math.min(r[0],r[2]),a=Math.min(r[1],r[3]),s=Math.max(r[0],r[2]),u=Math.max(r[1],r[3]),c=Math.min(o[0],o[2]),l=Math.min(o[1],o[3]),h=Math.max(o[0],o[2]),p=Math.max(o[1],o[3]),f=(s-i)*(u-a),d=(h-c)*(p-l);if(f<=0||d<=0)return 0;var m=Math.max(i,c),v=Math.max(a,l),g=Math.min(s,h),y=Math.min(u,p),x=Math.max(g-m,0)*Math.max(y-v,0);return x/(f+d-x)}function je(n,t,r){var o=Array(n.rank).fill(0),i=n.shape.slice();return t.map(function(t){i[r]=t;var e=n.slice(o,i);return o[r]+=t,e})}function $e(t,e,n,r,o){for(var i=e[e.length-1],a=[t.length/i,i],s=a[0],u=a[1],c=R(n,s*r),l=R("int32",s*r),h=0;h<s;h++){for(var p=h*u,f=t.subarray(p,p+u),d=[],m=0;m<f.length;m++)d.push({value:f[m],index:m});d.sort(function(t,e){return e.value-t.value});var v=h*r,g=c.subarray(v,v+r),y=l.subarray(v,v+r);for(m=0;m<r;m++)g[m]=d[m].value,y[m]=d[m].index}var x=e.slice();return x[x.length-1]=r,[Ce(c,x,n),Ce(l,x,"int32")]}var Ke=function(t,e,n){this.variableNames=["A"];var r=t.windowSize,o=t.batchSize,i=t.inSize,a=Math.ceil(i/r);n||this.variableNames.push("bestIndicesA"),this.outputShape=[o,a];var s="max"===e?">":"<",u=n?"inOffset + i;":"round(getBestIndicesA(batch, inOffset + i));";this.userCode="\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * "+r+";\n\n        int bestIndex = inOffset;\n        float bestValue = getA(batch, bestIndex);\n\n        for (int i = 0; i < "+r+"; i++) {\n          int inIdx = "+u+";\n          float candidate = getA(batch, inIdx);\n          if (candidate "+s+" bestValue) {\n            bestValue = candidate;\n            bestIndex = inIdx;\n          }\n        }\n        setOutput(float(bestIndex));\n      }\n    "},Xe=function(t){this.variableNames=["dy"],this.outputShape=t.inShape;var e=t.filterHeight,n=t.filterWidth,r=t.strideHeight,o=t.strideWidth,i=t.dilationHeight,a=t.dilationWidth,s=t.effectiveFilterHeight,u=t.effectiveFilterWidth,c=s-1-t.padInfo.top,l=u-1-t.padInfo.left,h=1/(e*n);this.userCode="\n      const ivec2 pads = ivec2("+c+", "+l+");\n      const float avgMultiplier = float("+h+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+s+";\n            wR += "+i+") {\n          float dyR = float(dyRCorner + wR) / "+r+".0;\n\n          if (dyR < 0.0 || dyR >= "+t.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < "+u+";\n            wC+= "+a+") {\n            float dyC = float(dyCCorner + wC) / "+o+".0;\n\n            if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n\n            dotProd += dyValue * avgMultiplier;\n          }\n        }\n        setOutput(dotProd);\n      }\n    "};function Ye(t,e){for(var n=t.length,r=[],o=0;o<n;o++){var i=n-1-o,a=t[i]||1;1<(e[e.length-1-o]||1)&&1===a&&r.unshift(i)}return r}function Qe(t,e){for(var n=[],r=0;r<e.length;r++){var o=t[t.length-r-1],i=e.length-r-1,a=e[i];(null==o||1===o&&1<a)&&n.unshift(i)}return n}function Je(t,e){for(var n=[],r=Math.max(t.length,e.length),o=0;o<r;o++){var i=t[t.length-o-1];null==i&&(i=1);var a=e[e.length-o-1];if(null==a&&(a=1),1===i)n.unshift(a);else if(1===a)n.unshift(i);else{if(i!==a)throw Error("Operands could not be broadcast together with shapes "+t+" and "+e+".");n.unshift(i)}}return n}var Ze=function(t,e,n,r,o,i){this.outputShape=[],this.supportsBroadcasting=!0,this.variableNames=["x","mean","variance"],Je(t,e),Je(t,n);var a="0.0";null!=r&&(Je(t,r),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");var s="1.0";null!=o&&(Je(t,o),this.variableNames.push("scale"),s="getScaleAtOutCoords()"),this.outputShape=t,this.userCode="\n      void main() {\n        float x = getXAtOutCoords();\n        float mean = getMeanAtOutCoords();\n        float variance = getVarianceAtOutCoords();\n        float offset = "+a+";\n        float scale = "+s+";\n        float inv = scale * inversesqrt(variance + float("+i+"));\n        setOutput(dot(vec3(x, -mean, offset), vec3(inv, inv, 1)));\n      }\n    "},tn=function(t,e,n,r,o,i){this.supportsBroadcasting=!0,this.usesPackedTextures=!0,this.variableNames=["x","mean","variance"],Je(t,e),Je(t,n);var a="vec4(0.0)";null!=r&&(Je(t,r),this.variableNames.push("offset"),a="getOffsetAtOutCoords()");var s="vec4(1.0)";null!=o&&(Je(t,o),this.variableNames.push("scale"),s="getScaleAtOutCoords()"),this.outputShape=t,this.userCode="\n      void main() {\n        vec4 offset = "+a+";\n        vec4 scale = "+s+";\n\n        vec4 x = getXAtOutCoords();\n        vec4 mean = getMeanAtOutCoords();\n        vec4 variance = getVarianceAtOutCoords();\n\n        vec4 inv = scale * inversesqrt(variance + vec4("+i+"));\n\n        setOutput((x - mean) * inv + offset);\n      }\n    "},en="return areal * breal - aimag * bimag;",nn="return areal * bimag + aimag * breal;",rn=function(t,e,n){this.variableNames=["AReal","AImag","BReal","BImag"],this.supportsBroadcasting=!0,this.outputShape=Je(e,n),this.userCode="\n      float binaryOpComplex(\n          float areal, float aimag, float breal, float bimag) {\n        "+t+"\n      }\n\n      void main() {\n        float areal = getARealAtOutCoords();\n        float aimag = getAImagAtOutCoords();\n        float breal = getBRealAtOutCoords();\n        float bimag = getBImagAtOutCoords();\n        setOutput(binaryOpComplex(areal, aimag, breal, bimag));\n      }\n    "},on="return a + b;",an="return a - b;",sn=function(){function t(t,e,n){this.variableNames=["A","B"],this.supportsBroadcasting=!0,this.outputShape=Je(e,n),this.userCode="\n      uniform float NAN;\n      float binaryOperation(float a, float b) {\n        "+t+"\n      }\n\n      void main() {\n        float a = getAAtOutCoords();\n        float b = getBAtOutCoords();\n        setOutput(binaryOperation(a, b));\n      }\n    "}return t.prototype.getCustomSetupFunc=function(){var n=this;return function(t,e){null==n.startLoc&&(n.startLoc=t.getUniformLocationNoThrow(e,"NAN"),null==n.startLoc)||t.gl.uniform1f(n.startLoc,NaN)}},t}(),un=function(t,e,n){this.variableNames=["A"],this.outputShape=t,this.userCode="\n      void main() {\n        float value = getAAtOutCoords();\n        if (isNaN(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, float("+e+"), float("+n+")));\n      }\n    "},cn=function(t,e,n){this.variableNames=["A"],this.usesPackedTextures=!0,this.outputShape=t,this.userCode="\n      void main() {\n        vec4 value = getAAtOutCoords();\n\n        if (hasNaN(value)) {\n          setOutput(value);\n          return;\n        }\n\n        setOutput(clamp(value, vec4("+e+"), vec4("+n+")));\n      }\n    "},ln=function(t){this.variableNames=["real","imag"],this.outputShape=t,this.userCode="\n      void main() {\n        float re = abs(getRealAtOutCoords());\n        float im = abs(getImagAtOutCoords());\n        float mx = max(re, im);\n\n        // sadly the length function in glsl is not underflow-safe\n        // (at least not on Intel GPUs). So the safe solution is\n        // to ensure underflow-safety in all cases.\n        setOutput(\n          mx == 0.0 ? 0.0 : mx * length(vec2(1, min(re, im)/mx))\n        );\n      }\n    "},hn=function(t,e){this.variableNames=["A","B"],this.outputShape=[],this.outputShape=ie([t,e],1),this.userCode="\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int yR = coords.x;\n        int yC = coords.y;\n\n        float value = 0.0;\n        if (yC < "+t[1]+") {\n          value = getA(yR, yC);\n        } else {\n          yC -= "+t[1]+";\n          value = getB(yR, yC);\n        }\n\n        setOutput(value);\n      }\n    "},pn=function(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;var e=t.strideHeight,n=t.strideWidth,r=t.padInfo.top,o=t.padInfo.left;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int d2 = coords.w;\n\n        // Convolve x(?, ?, d1) with dy(:, :, d2) to get dw(wR, wC, d1, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n\n        for (int b = 0; b < "+t.batchSize+"; b++) {\n          for (int yR = 0; yR < "+t.outHeight+"; yR++) {\n            int xR = wR + yR * "+e+" - "+r+";\n\n            if (xR < 0 || xR >= "+t.inHeight+") {\n              continue;\n            }\n\n            for (int yC = 0; yC < "+t.outWidth+"; yC++) {\n              int xC = wC + yC * "+n+" - "+o+";\n\n              if (xC < 0 || xC >= "+t.inWidth+") {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},fn=function(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;var e=t.filterHeight,n=t.filterWidth,r=t.strideHeight,o=t.strideWidth,i=e-1-t.padInfo.top,a=n-1-t.padInfo.left;this.userCode="\n      const ivec2 pads = ivec2("+i+", "+a+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        // Convolve dy(?, ?, d2) with w(:, :, d1, d2) to compute dx(xR, xC, d1).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+e+"; wR++) {\n          float dyR = float(dyRCorner + wR) / "+r+".0;\n\n          if (dyR < 0.0 || dyR >= "+t.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = "+e+" - 1 - wR;\n\n          for (int wC = 0; wC < "+n+"; wC++) {\n            float dyC = float(dyCCorner + wC) / "+o+".0;\n\n            if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = "+n+" - 1 - wC;\n\n            for (int d2 = 0; d2 < "+t.outChannels+"; d2++) {\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, d2);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},dn=function(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;var e=t.strideDepth,n=t.strideHeight,r=t.strideWidth,o=t.padInfo.front,i=t.padInfo.top,a=t.padInfo.left;this.userCode="\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int wF = coords.x;\n        int wR = coords.y;\n        int wC = coords.z;\n        int d1 = coords.w;\n        int d2 = coords.u;\n\n        float dotProd = 0.0;\n\n        for (int b = 0; b < "+t.batchSize+"; b++) {\n          for (int yF = 0; yF < "+t.outDepth+"; yF++) {\n            int xF = wF + yF * "+e+" - "+o+";\n\n            if (xF < 0 || xF >= "+t.inDepth+") {\n              continue;\n            }\n\n            for (int yR = 0; yR < "+t.outHeight+"; yR++) {\n              int xR = wR + yR * "+n+" - "+i+";\n\n              if (xR < 0 || xR >= "+t.inHeight+") {\n                continue;\n              }\n\n              for (int yC = 0; yC < "+t.outWidth+"; yC++) {\n                int xC = wC + yC * "+r+" - "+a+";\n\n                if (xC < 0 || xC >= "+t.inWidth+") {\n                  continue;\n                }\n\n                float dyValue = getDy(b, yF, yR, yC, d2);\n                float xValue = getX(b, xF, xR, xC, d1);\n                dotProd += (xValue * dyValue);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},mn=function(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;var e=t.filterDepth,n=t.filterHeight,r=t.filterWidth,o=t.strideDepth,i=t.strideHeight,a=t.strideWidth,s=e-1-t.padInfo.front,u=n-1-t.padInfo.top,c=r-1-t.padInfo.left;this.userCode="\n      const ivec3 pads = ivec3("+s+", "+u+", "+c+");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d1 = coords.u;\n\n\n        ivec3 dyCorner = ivec3(coords.y, coords.z, coords.w) - pads;\n        int dyFCorner = dyCorner.x;\n        int dyRCorner = dyCorner.y;\n        int dyCCorner = dyCorner.z;\n\n        float dotProd = 0.0;\n        for (int wF = 0; wF < "+e+"; wF++) {\n          float dyF = float(dyFCorner + wF) / "+o+".0;\n\n          if (dyF < 0.0 || dyF >= "+t.outDepth+".0 || fract(dyF) > 0.0) {\n            continue;\n          }\n          int idyF = int(dyF);\n\n          int wFPerm = "+e+" - 1 - wF;\n\n          for (int wR = 0; wR < "+n+"; wR++) {\n            float dyR = float(dyRCorner + wR) / "+i+".0;\n\n            if (dyR < 0.0 || dyR >= "+t.outHeight+".0 ||\n              fract(dyR) > 0.0) {\n              continue;\n            }\n            int idyR = int(dyR);\n\n            int wRPerm = "+n+" - 1 - wR;\n\n            for (int wC = 0; wC < "+r+"; wC++) {\n              float dyC = float(dyCCorner + wC) / "+a+".0;\n\n              if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n                  fract(dyC) > 0.0) {\n                continue;\n              }\n              int idyC = int(dyC);\n\n              int wCPerm = "+r+" - 1 - wC;\n\n              for (int d2 = 0; d2 < "+t.outChannels+"; d2++) {\n                float xValue = getDy(batch, idyF, idyR, idyC, d2);\n                float wValue = getW(wFPerm, wRPerm, wCPerm, d1, d2);\n                dotProd += xValue * wValue;\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},vn=function(t){this.variableNames=["x","dy"],this.outputShape=t.filterShape;var e=t.strideHeight,n=t.strideWidth,r=t.padInfo.top,o=t.padInfo.left,i=t.outChannels/t.inChannels;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int wR = coords.x;\n        int wC = coords.y;\n        int d1 = coords.z;\n        int dm = coords.w;\n        int d2 = d1 * "+i+" + dm;\n\n        float dotProd = 0.0;\n\n        // TODO: Vec4 over the batch size\n        for (int b = 0; b < "+t.batchSize+"; b++) {\n          for (int yR = 0; yR < "+t.outHeight+"; yR++) {\n            int xR = wR + yR * "+e+" - "+r+";\n\n            if (xR < 0 || xR >= "+t.inHeight+") {\n              continue;\n            }\n\n            for (int yC = 0; yC < "+t.outWidth+"; yC++) {\n              int xC = wC + yC * "+n+" - "+o+";\n\n              if (xC < 0 || xC >= "+t.inWidth+") {\n                continue;\n              }\n\n              float dyValue = getDy(b, yR, yC, d2);\n              float xValue = getX(b, xR, xC, d1);\n              dotProd += (xValue * dyValue);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},gn=function(t){this.variableNames=["dy","W"],this.outputShape=t.inShape;var e=t.filterHeight,n=t.filterWidth,r=t.strideHeight,o=t.strideWidth,i=e-1-t.padInfo.top,a=n-1-t.padInfo.left,s=t.outChannels/t.inChannels;this.userCode="\n      const ivec2 pads = ivec2("+i+", "+a+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d1 = coords[3];\n        ivec2 dyCorner = coords.yz - pads;\n        int dyRCorner = dyCorner.x;\n        int dyCCorner = dyCorner.y;\n\n        float dotProd = 0.0;\n\n        for (int wR = 0; wR < "+e+"; wR++) {\n          float dyR = float(dyRCorner + wR) / "+r+".0;\n\n          if (dyR < 0.0 || dyR >= "+t.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          int wRPerm = "+e+" - 1 - wR;\n\n          for (int wC = 0; wC < "+n+"; wC++) {\n            float dyC = float(dyCCorner + wC) / "+o+".0;\n\n            if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            int wCPerm = "+n+" - 1 - wC;\n\n            // TODO: Vec4 over the channelMul\n            for (int dm = 0; dm < "+s+"; dm++) {\n              int d2 = d1 * "+s+" + dm;\n              float xValue = getDy(batch, idyR, idyC, d2);\n              float wValue = getW(wRPerm, wCPerm, d1, dm);\n              dotProd += xValue * wValue;\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},yn=function(t){this.variableNames=["x","W"],this.outputShape=t.outShape;var e=t.padInfo.top,n=t.padInfo.left,r=t.strideHeight,o=t.strideWidth,i=t.dilationHeight,a=t.dilationWidth,s=t.filterHeight,u=t.filterWidth,c=4*Math.floor(t.inChannels/4),l=t.inChannels%4;this.userCode="\n      const ivec2 strides = ivec2("+r+", "+o+");\n      const ivec2 pads = ivec2("+e+", "+n+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d2 = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, d2) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+s+"; wR++) {\n          int xR = xRCorner + wR * "+i+";\n\n          if (xR < 0 || xR >= "+t.inHeight+") {\n            continue;\n          }\n\n          for (int wC = 0; wC < "+u+"; wC++) {\n            int xC = xCCorner + wC * "+a+";\n\n            if (xC < 0 || xC >= "+t.inWidth+") {\n              continue;\n            }\n\n            for (int d1 = 0; d1 < "+c+"; d1 += 4) {\n              vec4 xValues = vec4(\n                getX(batch, xR, xC, d1),\n                getX(batch, xR, xC, d1 + 1),\n                getX(batch, xR, xC, d1 + 2),\n                getX(batch, xR, xC, d1 + 3)\n              );\n              vec4 wValues = vec4(\n                getW(wR, wC, d1, d2),\n                getW(wR, wC, d1 + 1, d2),\n                getW(wR, wC, d1 + 2, d2),\n                getW(wR, wC, d1 + 3, d2)\n              );\n\n              dotProd += dot(xValues, wValues);\n            }\n\n            if ("+(1===l)+") {\n              dotProd +=\n                getX(batch, xR, xC, "+c+") *\n                getW(wR, wC, "+c+", d2);\n            } else if ("+(2===l)+") {\n              vec2 xValues = vec2(\n                getX(batch, xR, xC, "+c+"),\n                getX(batch, xR, xC, "+c+" + 1)\n              );\n              vec2 wValues = vec2(\n                getW(wR, wC, "+c+", d2),\n                getW(wR, wC, "+c+" + 1, d2)\n              );\n              dotProd += dot(xValues, wValues);\n            } else if ("+(3===l)+") {\n              vec3 xValues = vec3(\n                getX(batch, xR, xC, "+c+"),\n                getX(batch, xR, xC, "+c+" + 1),\n                getX(batch, xR, xC, "+c+" + 2)\n              );\n              vec3 wValues = vec3(\n                getW(wR, wC, "+c+", d2),\n                getW(wR, wC, "+c+" + 1, d2),\n                getW(wR, wC, "+c+" + 2, d2)\n              );\n              dotProd += dot(xValues, wValues);\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},xn=function(t){this.variableNames=["x","W"],this.outputShape=t.outShape;var e=t.padInfo.front,n=t.padInfo.top,r=t.padInfo.left,o=t.strideDepth,i=t.strideHeight,a=t.strideWidth,s=t.dilationDepth,u=t.dilationHeight,c=t.dilationWidth,l=t.filterDepth,h=t.filterHeight,p=t.filterWidth,f=4*Math.floor(t.inChannels/4),d=t.inChannels%4;this.userCode="\n      const ivec3 strides = ivec3("+o+", "+i+", "+a+");\n      const ivec3 pads = ivec3("+e+", "+n+", "+r+");\n\n      void main() {\n        ivec5 coords = getOutputCoords();\n        int batch = coords.x;\n        int d2 = coords.u;\n\n        ivec3 xFRCCorner = ivec3(coords.y, coords.z, coords.w) * strides - pads;\n        int xFCorner = xFRCCorner.x;\n        int xRCorner = xFRCCorner.y;\n        int xCCorner = xFRCCorner.z;\n\n        // Convolve x(?, ?, ?, d1) with w(:, :, :, d1, d2) to get\n        // y(yF, yR, yC, d2). ? = to be determined. : = across all\n        // values in that axis.\n        float dotProd = 0.0;\n        for (int wF = 0; wF < "+l+"; wF++) {\n          int xF = xFCorner + wF * "+s+";\n\n          if (xF < 0 || xF >= "+t.inDepth+") {\n            continue;\n          }\n\n          for (int wR = 0; wR < "+h+"; wR++) {\n            int xR = xRCorner + wR * "+u+";\n\n            if (xR < 0 || xR >= "+t.inHeight+") {\n              continue;\n            }\n\n            for (int wC = 0; wC < "+p+"; wC++) {\n              int xC = xCCorner + wC * "+c+";\n\n              if (xC < 0 || xC >= "+t.inWidth+") {\n                continue;\n              }\n\n              for (int d1 = 0; d1 < "+f+"; d1 += 4) {\n                vec4 xValues = vec4(\n                  getX(batch, xF, xR, xC, d1),\n                  getX(batch, xF, xR, xC, d1 + 1),\n                  getX(batch, xF, xR, xC, d1 + 2),\n                  getX(batch, xF, xR, xC, d1 + 3)\n                );\n                vec4 wValues = vec4(\n                  getW(wF, wR, wC, d1, d2),\n                  getW(wF, wR, wC, d1 + 1, d2),\n                  getW(wF, wR, wC, d1 + 2, d2),\n                  getW(wF, wR, wC, d1 + 3, d2)\n                );\n\n                dotProd += dot(xValues, wValues);\n              }\n\n              if ("+(1===d)+") {\n                dotProd +=\n                  getX(batch, xF, xR, xC, "+f+") *\n                  getW(wF, wR, wC, "+f+", d2);\n              } else if ("+(2===d)+") {\n                vec2 xValues = vec2(\n                  getX(batch, xF, xR, xC, "+f+"),\n                  getX(batch, xF, xR, xC, "+f+" + 1)\n                );\n                vec2 wValues = vec2(\n                  getW(wF, wR, wC, "+f+", d2),\n                  getW(wF, wR, wC, "+f+" + 1, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              } else if ("+(3===d)+") {\n                vec3 xValues = vec3(\n                  getX(batch, xF, xR, xC, "+f+"),\n                  getX(batch, xF, xR, xC, "+f+" + 1),\n                  getX(batch, xF, xR, xC, "+f+" + 2)\n                );\n                vec3 wValues = vec3(\n                  getW(wF, wR, wC, "+f+", d2),\n                  getW(wF, wR, wC, "+f+" + 1, d2),\n                  getW(wF, wR, wC, "+f+" + 2, d2)\n                );\n                dotProd += dot(xValues, wValues);\n              }\n            }\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},wn=function(t){this.variableNames=["x","W"],this.outputShape=t.outShape;var e=t.inHeight,n=t.inWidth,r=t.padInfo.top,o=t.padInfo.left,i=t.strideHeight,a=t.strideWidth,s=t.dilationHeight,u=t.dilationWidth,c=t.filterHeight,l=t.filterWidth,h=t.outChannels/t.inChannels;this.userCode="\n      const ivec2 strides = ivec2("+i+", "+a+");\n      const ivec2 pads = ivec2("+r+", "+o+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2 / "+h+";\n        int q = d2 - d1 * "+h+";\n\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // Convolve x(?, ?, d1) with w(:, :, d1, q) to get y(yR, yC, d2).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        // TODO(dsmilkov): Flatten the two for loops and vec4 the operations.\n        for (int wR = 0; wR < "+c+"; wR++) {\n          int xR = xRCorner + wR * "+s+";\n\n          if (xR < 0 || xR >= "+e+") {\n            continue;\n          }\n\n          for (int wC = 0; wC < "+l+"; wC++) {\n            int xC = xCCorner + wC * "+u+";\n\n            if (xC < 0 || xC >= "+n+") {\n              continue;\n            }\n\n            float xVal = getX(batch, xR, xC, d1);\n            float wVal = getW(wR, wC, d1, q);\n            dotProd += xVal * wVal;\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},bn=function(t){this.variableNames=["x","W"],this.usesPackedTextures=!0,this.outputShape=t.outShape;for(var e=t.inHeight,n=t.inWidth,r=t.padInfo.top,o=t.padInfo.left,i=t.strideHeight,a=t.strideWidth,s=t.filterHeight,u=t.filterWidth,c=Math.ceil((u+1)/2),l="int xR; int xC;",h=0;h<s;h++){for(var p=-o;p<2*c;p++)l+="vec4 "+En(h,p)+" = vec4(0.);";for(p=0;p<u;p++)l+="\n          vec4 wR"+h+"C"+p+" = vec4(0.);\n          vec4 xR"+h+"C"+p+" = vec4(0.);"}for(h=0;h<s;h++)for(p=0;p<c;p++){var f=2*p,d=2*p+o;l+="\n          xR = xRCorner + "+h+";\n          xC = xCCorner + "+d+";\n\n          if(xR >= 0 && xR < "+e+" && xC >= 0 && xC < "+n+") {\n            "+En(h,d)+" = getX(batch, xR, xC, d1);\n          }",0===o?f<u&&p===c-1&&(1<a&&(l+="\n                vec4 "+En(h,d+2)+" = vec4(0.);\n\n                if(xR >= 0 && xR < "+e+" && xC + 2 < "+n+") {\n                  "+En(h,d+2)+" = getX(batch, xR, xC + 2, d1);\n                }"),l+="\n              xR"+h+"C"+d+" = "+Cn(h,d,a,o)+";\n            "):0===p&&(l+="\n            if(xR >= 0 && xR < "+e+" && xC - 2 >= 0) {\n              "+En(h,d-2)+" = getX(batch, xR, xC - 2, d1);\n            }"),0<f&&(l+="xR"+h+"C"+(d-2)+" =\n            "+Cn(h,d-2,a,o)+";"),0<=d-1&&d-1<u&&(l+="xR"+h+"C"+(d-1)+" =\n              "+Cn(h,d-1,a,o)+";"),f<u&&(l+="\n            vec4 wTexel"+h+"C"+f+" = getW("+h+", "+f+", d1, q);\n            wR"+h+"C"+f+" = vec4(wTexel"+h+"C"+f+".xz, wTexel"+h+"C"+f+".xz);\n          ",f+1<u&&(l+="\n              vec4 wTexelR"+h+"C"+(f+1)+" = getW("+h+", "+(f+1)+", d1, q);\n              wR"+h+"C"+(f+1)+" =\n                vec4(wTexelR"+h+"C"+(f+1)+".xz, wTexelR"+h+"C"+(f+1)+".xz);"))}for(h=0;h<s;h++)for(p=0;p<u;p++)l+="result += xR"+h+"C"+p+" * wR"+h+"C"+p+";";this.userCode="\n      const ivec2 strides = ivec2("+i+", "+a+");\n      const ivec2 pads = ivec2("+r+", "+o+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords.x;\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int d2 = coords.w;\n        int d1 = d2;\n        int q = 0;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        vec4 result = vec4(0.);\n\n        "+l+"\n\n        setOutput(result);\n      }\n    "};function En(t,e){return"xTexelR"+t+"C"+(e<0?"minus"+Math.abs(e).toString():e)}function Cn(t,e,n,r){return 1===n?r%2==e%2?En(t,e):"vec4("+En(t,e-1)+".zw, "+En(t,e+1)+".xy)":r%2==e%2?"vec4("+En(t,e)+".xy, "+En(t,e+2)+".xy)":"vec4("+En(t,e-1)+".zw, "+En(t,e+1)+".zw)"}var Sn=function(t,e,n,r,o){this.variableNames=["Image","Boxes","BoxInd"],this.outputShape=[];var i=t[0],a=t[1],s=t[2],u=t[3],c=e[0],l=n[0],h=n[1];this.outputShape=[c,l,h,u];var p="bilinear"===r?1:0,f=[a-1+".0",s-1+".0"],d=f[0],m=f[1],v=1<l?[""+(a-1)/(l-1),"(y2-y1) * height_ratio","y1*"+d+" + float(y)*(height_scale)"]:["0.0","0.0","0.5 * (y1+y2) * "+d],g=v[0],y=v[1],x=v[2],w=1<h?[""+(s-1)/(h-1),"(x2-x1) * width_ratio","x1*"+m+" + float(x)*(width_scale)"]:["0.0","0.0","0.5 * (x1+x2) * "+m],b=w[0],E=w[1],C=w[2];this.userCode="\n      const float height_ratio = float("+g+");\n      const float width_ratio = float("+b+");\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int y = coords[1];\n        int x = coords[2];\n        int d = coords[3];\n\n        // get box vals\n        float y1 = getBoxes(b,0);\n        float x1 = getBoxes(b,1);\n        float y2 = getBoxes(b,2);\n        float x2 = getBoxes(b,3);\n\n        // get image in batch index\n        int bInd = round(getBoxInd(b));\n        if(bInd < 0 || bInd >= "+i+") {\n          return;\n        }\n\n        float height_scale = "+y+";\n        float width_scale = "+E+";\n\n        float in_y = "+x+";\n        if( in_y < 0.0 || in_y > "+d+" ) {\n          setOutput(float("+o+"));\n          return;\n        }\n        float in_x = "+C+";\n        if( in_x < 0.0 || in_x > "+m+" ) {\n          setOutput(float("+o+"));\n          return;\n        }\n\n        vec2 sourceFracIndexRC = vec2(in_y,in_x);\n        if("+p+" == 1) {\n          // Compute the four integer indices.\n          ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n          ivec2 sourceCeilRC = ivec2(ceil(sourceFracIndexRC));\n\n          float topLeft = getImage(b, sourceFloorRC.x, sourceFloorRC.y, d);\n          float bottomLeft = getImage(b, sourceCeilRC.x, sourceFloorRC.y, d);\n          float topRight = getImage(b, sourceFloorRC.x, sourceCeilRC.y, d);\n          float bottomRight = getImage(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n          vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n          float top = topLeft + (topRight - topLeft) * fracRC.y;\n          float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n          float newValue = top + (bottom - top) * fracRC.x;\n          setOutput(newValue);\n        } else {\n          // Compute the coordinators of nearest neighbor point.\n          ivec2 sourceNearestRC = ivec2(floor(\n            sourceFracIndexRC + vec2(0.5,0.5)));\n          float newValue = getImage(b, sourceNearestRC.x, sourceNearestRC.y, d);\n          setOutput(newValue);\n        }\n      }\n    "};function Rn(n,t,r){void 0===r&&(r="index");var o=U(t);return o.map(function(t,e){return"int "+n[e]+" = "+r+" / "+t+"; "+(e===o.length-1?"int "+n[e+1]+" = "+r+" - "+n[e]+" * "+t:"index -= "+n[e]+" * "+t)+";"}).join("")}function Nn(t){return 1===t.length?""+t[0]:"vec"+t.length+"("+t.join(",")+")"}function Tn(t,e,n,r,o){var i=t.map(function(t){var e=L(t.shapeInfo.logicalShape);return t.shapeInfo.isUniform?"uniform float "+t.name+(1<e?"["+e+"]":"")+";":"uniform sampler2D "+t.name+";"});i=i.join("\n");var a,s,u=t.map(function(t){return function(t,e,n,r){void 0===r&&(r=!1);var o=function(t){var e=t.name,n="get"+e.charAt(0).toUpperCase()+e.slice(1)+"Flat",r=L(t.shapeInfo.logicalShape);if(t.shapeInfo.isUniform)return 1===r?"float "+n+"(int index) {return "+e+";}":"\n      float "+n+"(int index) {\n        for (int i = 0; i < "+r+"; i++) {\n          if (i == index) {\n            return "+e+"[i];\n          }\n        }\n      }\n    ";var o=t.shapeInfo.texShape,i=o[0],a=o[1];return 1===a&&1===i?"\n      float "+n+"(int index) {\n        return sampleTexture("+e+", halfCR);\n      }\n    ":1===a?"\n      float "+n+"(int index) {\n        vec2 uv = vec2(0.5, (float(index) + 0.5) / "+i+".0);\n        return sampleTexture("+e+", uv);\n      }\n    ":1===i?"\n      float "+n+"(int index) {\n        vec2 uv = vec2((float(index) + 0.5) / "+a+".0, 0.5);\n        return sampleTexture("+e+", uv);\n      }\n    ":"\n    float "+n+"(int index) {\n      vec2 uv = UVfrom1D("+i+", "+a+", index);\n      return sampleTexture("+e+", uv);\n    }\n  "}(t);return o+=r?In(t):kn(t),(n||y(t.shapeInfo.logicalShape,e.logicalShape))&&(o+=r?function(t,e,n){var r=t.name,o=r.charAt(0).toUpperCase()+r.slice(1),i=t.shapeInfo.texShape,a="get"+o+"AtOutCoords",s=e.texShape,u=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],c=u[0],l=u[1],h=Ye(t.shapeInfo.logicalShape,e.logicalShape),p=t.shapeInfo.logicalShape.length,f=e.logicalShape.length;if(h.length)throw Error("Packed broadcast sampling is not implemented yet.");if(y(t.shapeInfo.texShape,s))return"\n      vec4 "+a+"() {\n        return texture2D("+r+", resultUV);\n      }\n    ";var d="return texture2D("+r+", uv)";return 1===p&&1<f?d="\n      vec4 sample = texture2D("+r+", uv);\n      return vec4(sample.xy, sample.xy);\n    ":0===p&&0<f&&(d=1===f?"\n        vec4 sample = texture2D("+r+", uv);\n        return vec4(sample.x, sample.x, 0., 0.);\n      ":"\n        vec4 sample = texture2D("+r+", uv);\n        return vec4(sample.x);\n      "),"\n    vec4 "+a+"() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+u[0]+", "+u[1]+"));\n      int index = resTexRC.x * "+u[1]+" + resTexRC.y;\n\n      int texR = index / "+l+";\n      int texC = index - texR * "+l+";\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2("+l+", "+c+");\n\n      "+d+";\n    }\n  "}(t,e):function(t,e,n){var r=t.name,o=r.charAt(0).toUpperCase()+r.slice(1),i="get"+o+"AtOutCoords",a=Ye(t.shapeInfo.logicalShape,e.logicalShape),s=t.shapeInfo.logicalShape.length,u=e.logicalShape.length,c=n&&(s<u||0<a.length),l=function(t){for(var e=0;e<t.length;e++)if(t[e]!==e)return!1;return!0}(a),h=t.shapeInfo.isUniform;if(c&&!l)return function(t,e,n,r){var o=t.shapeInfo.logicalShape.length,i=e.logicalShape.length,a="int";2===i?a="ivec2":3===i?a="ivec3":4===i&&(a="ivec4");var s=Ye(t.shapeInfo.logicalShape,e.logicalShape),u=i-o;return"\n    float "+r+"() {\n      "+a+" coords = getOutputCoords();\n      "+(0===o?"":i<2&&1<=s.length?"coords = 0;":s.map(function(t){return"coords["+(t+u)+"] = 0;"}).join("\n"))+"\n      return get"+n+"("+(i<2&&0<o?"coords":t.shapeInfo.logicalShape.map(function(t,e){return"coords["+(e+u)+"]"}).join(", "))+");\n    }\n  "}(t,e,o,i);var p=L(t.shapeInfo.logicalShape),f="";c&&l&&(f="\n        int mainPart = index / "+p+";\n        index -= mainPart * "+p+";\n      ");var d=e.texShape;if(h)return 1===p?"float "+i+"() {return "+r+";}":"\n      float "+i+"() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                              vec2("+d[0]+", "+d[1]+"));\n        int index = resTexRC.x * "+d[1]+" + resTexRC.y;\n        "+f+"\n        return get"+o+"Flat(index);\n      }\n    ";var m=t.shapeInfo.texShape;return y(m,d)?"\n      float "+i+"() {\n        return sampleTexture("+r+", resultUV);\n      }\n    ":"\n    float "+i+"() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+d[0]+", "+d[1]+"));\n      int index = resTexRC.x * "+d[1]+" + resTexRC.y;\n      "+f+"\n      int texR = index / "+m[1]+";\n      int texC = index - texR * "+m[1]+";\n      vec2 uv = (vec2(texC, texR) + halfCR) /\n                 vec2("+m[1]+".0, "+m[0]+".0);\n\n      return sampleTexture("+r+", uv);\n    }\n  "}(t,e,n)),o}(t,e,r,o)}).join("\n"),c=e.texShape,l=_n;return s=e.isPacked?(a=function(t,e){switch(t.length){case 0:return"\n    int getOutputCoords() {\n      return 0;\n    }\n  ";case 1:return f=e,d=[Math.ceil(f[0]/2),Math.ceil(f[1]/2)],1===f[0]?"\n      int getOutputCoords() {\n        return 2 * int(resultUV.x * "+d[1]+".0);\n      }\n    ":1===f[1]?"\n      int getOutputCoords() {\n        return 2 * int(resultUV.y * "+d[0]+".0);\n      }\n    ":"\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+d[0]+", "+d[1]+"));\n      return resTexRC.x * "+d[1]+" + resTexRC.y;\n    }\n  ";case 2:return function(t,e){var n=[Math.ceil(e[0]/2),Math.ceil(e[1]/2)];if(y(t,e))return"\n      ivec2 getOutputCoords() {\n        return 2 * ivec2(resultUV.yx * vec2("+n[0]+", "+n[1]+"));\n      }\n    ";var r=Math.ceil(t[1]/2);return"\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+n[0]+", "+n[1]+"));\n\n      int index = resTexRC.x * "+n[1]+" + resTexRC.y;\n      int r = 2 * (index / "+r+");\n      int c = imod(index, "+r+") * 2;\n\n      return ivec2(r, c);\n    }\n  "}(t,e);case 3:return u=t,c=e,l=[Math.ceil(c[0]/2),Math.ceil(c[1]/2)],h=Math.ceil(u[2]/2),p=h*Math.ceil(u[1]/2),"\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+l[0]+", "+l[1]+"));\n      int index = resTexRC.x * "+l[1]+" + resTexRC.y;\n\n      int b = index / "+p+";\n      index -= b * "+p+";\n\n      int r = 2 * (index / "+h+");\n      int c = imod(index, "+h+") * 2;\n\n      return ivec3(b, r, c);\n    }\n  ";case 4:return n=t,r=e,o=[Math.ceil(r[0]/2),Math.ceil(r[1]/2)],i=Math.ceil(n[3]/2),a=i*Math.ceil(n[2]/2),s=a*n[1],"\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+o[0]+", "+o[1]+"));\n      int index = resTexRC.x * "+o[1]+" + resTexRC.y;\n\n      int b2 = index / "+s+";\n      index -= b2 * "+s+";\n\n      int b = index / "+a+";\n      index -= b * "+a+";\n\n      int r = 2 * (index / "+i+");\n      int c = imod(index, "+i+") * 2;\n\n      return ivec4(b2, b, r, c);\n    }\n  ";default:throw new Error(t.length+"-D packed output coordinate fetching is not yet supported")}var n,r,o,i,a,s;var u,c,l,h,p;var f,d}(e.logicalShape,c),Mn):(a=function(t,e){switch(t.length){case 0:return"\n    int getOutputCoords() {\n      return 0;\n    }\n  ";case 1:return 1===(p=e)[0]?"\n      int getOutputCoords() {\n        return int(resultUV.x * "+p[1]+".0);\n      }\n    ":1===p[1]?"\n      int getOutputCoords() {\n        return int(resultUV.y * "+p[0]+".0);\n      }\n    ":"\n    int getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+p[0]+", "+p[1]+"));\n      return resTexRC.x * "+p[1]+" + resTexRC.y;\n    }\n  ";case 2:return y(l=t,h=e)?"\n      ivec2 getOutputCoords() {\n        return ivec2(resultUV.yx * vec2("+h[0]+", "+h[1]+"));\n      }\n    ":1===l[1]?"\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2("+h[0]+", "+h[1]+"));\n        int index = resTexRC.x * "+h[1]+" + resTexRC.y;\n        return ivec2(index, 0);\n      }\n    ":1===l[0]?"\n      ivec2 getOutputCoords() {\n        ivec2 resTexRC = ivec2(resultUV.yx *\n                               vec2("+h[0]+", "+h[1]+"));\n        int index = resTexRC.x * "+h[1]+" + resTexRC.y;\n        return ivec2(0, index);\n      }\n    ":"\n    ivec2 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+h[0]+", "+h[1]+"));\n      int index = resTexRC.x * "+h[1]+" + resTexRC.y;\n      int r = index / "+l[1]+";\n      int c = index - r * "+l[1]+";\n      return ivec2(r, c);\n    }\n  ";case 3:return u=e,c=Rn(["r","c","d"],t),"\n    ivec3 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n                             vec2("+u[0]+", "+u[1]+"));\n      int index = resTexRC.x * "+u[1]+" + resTexRC.y;\n      "+c+"\n      return ivec3(r, c, d);\n    }\n  ";case 4:return a=e,s=Rn(["r","c","d","d2"],t),"\n    ivec4 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2("+a[0]+", "+a[1]+"));\n      int index = resTexRC.x * "+a[1]+" + resTexRC.y;\n      "+s+"\n      return ivec4(r, c, d, d2);\n    }\n  ";case 5:return o=e,i=Rn(["r","c","d","d2","d3"],t),"\n    ivec5 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx * vec2("+o[0]+",\n                             "+o[1]+"));\n\n      int index = resTexRC.x * "+o[1]+" + resTexRC.y;\n\n      "+i+"\n\n      ivec5 outShape = ivec5(r, c, d, d2, d3);\n      return outShape;\n    }\n  ";case 6:return n=e,r=Rn(["r","c","d","d2","d3","d4"],t),"\n    ivec6 getOutputCoords() {\n      ivec2 resTexRC = ivec2(resultUV.yx *\n        vec2("+n[0]+", "+n[1]+"));\n      int index = resTexRC.x * "+n[1]+" + resTexRC.y;\n\n      "+r+"\n\n      ivec6 result = ivec6(r, c, d, d2, d3, d4);\n      return result;\n    }\n  ";default:throw new Error(t.length+"-D output sampling is not yet supported")}var n,r;var o,i;var a,s;var u,c;var l,h;var p}(e.logicalShape,c),An),o&&(l+=On),[l,Dn,s,i,a,u,n].join("\n")}function kn(t){var e,n,r,o,i,a=t.shapeInfo.logicalShape;switch(a.length){case 0:return o=(r=t).name,i="get"+o.charAt(0).toUpperCase()+o.slice(1),r.shapeInfo.isUniform?"float "+i+"() {return "+o+";}":"\n    float "+i+"() {\n      return sampleTexture("+o+", halfCR);\n    }\n  ";case 1:return e=t.name,"\n    float "+(n="get"+e.charAt(0).toUpperCase()+e.slice(1))+"(int index) {\n      return "+n+"Flat(index);\n    }\n  ";case 2:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=t.shapeInfo.texShape;if(null!=o&&y(e,o)){var i=o[0];return"\n    float "+r+"(int row, int col) {\n      vec2 uv = (vec2(col, row) + halfCR) / vec2("+o[1]+".0, "+i+".0);\n      return sampleTexture("+n+", uv);\n    }\n  "}var a=E(e),s=a.newShape,u=a.keptDims,c=s;if(c.length<e.length)return"\n      "+kn(Pn(t,c))+"\n      float "+r+"(int row, int col) {\n        return "+r+"("+Bn(["row","col"],u)+");\n      }\n    ";if(t.shapeInfo.isUniform)return"\n      float "+r+"(int row, int col) {\n        float index = dot(vec2(row, col), vec2("+e[1]+", 1));\n        return "+r+"Flat(round(index));\n      }\n    ";var l=o[0],h=o[1];return 1===h?"\n    float "+r+"(int row, int col) {\n      float index = dot(vec2(row, col), vec2("+e[1]+", 1));\n      vec2 uv = vec2(0.5, (index + 0.5) / "+l+".0);\n      return sampleTexture("+n+", uv);\n    }\n  ":1===l?"\n    float "+r+"(int row, int col) {\n      float index = dot(vec2(row, col), vec2("+e[1]+", 1));\n      vec2 uv = vec2((index + 0.5) / "+h+".0, 0.5);\n      return sampleTexture("+n+", uv);\n    }\n  ":"\n  float "+r+"(int row, int col) {\n    vec2 uv = UVfrom2D("+l+", "+h+", "+e[1]+", row, col);\n    return sampleTexture("+n+", uv);\n  }\n"}(t);case 3:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e[1]*e[2],i=e[2],a=E(e),s=a.newShape,u=a.keptDims,c=s;if(c.length<e.length)return"\n        "+kn(Pn(t,c))+"\n        float "+r+"(int row, int col, int depth) {\n          return "+r+"("+Bn(["row","col","depth"],u)+");\n        }\n      ";if(t.shapeInfo.isUniform)return"\n      float "+r+"(int row, int col, int depth) {\n        float index = dot(vec3(row, col, depth),\n                          vec3("+o+", "+i+", 1));\n        return "+r+"Flat(round(index));\n      }\n    ";var l=t.shapeInfo.texShape,h=l[0],p=l[1];return p===o?"\n        float "+r+"(int row, int col, int depth) {\n          float texR = float(row);\n          float texC = dot(vec2(col, depth), vec2("+i+", 1));\n          vec2 uv = (vec2(texC, texR) + halfCR) /\n                     vec2("+p+".0, "+h+".0);\n          return sampleTexture("+n+", uv);\n        }\n      ":p===i?"\n    float "+r+"(int row, int col, int depth) {\n      float texR = dot(vec2(row, col), vec2("+e[1]+", 1));\n      float texC = float(depth);\n      vec2 uv = (vec2(texC, texR) + halfCR) / vec2("+p+".0, "+h+".0);\n      return sampleTexture("+n+", uv);\n    }\n  ":"\n      float "+r+"(int row, int col, int depth) {\n        vec2 uv = UVfrom3D(\n            "+h+", "+p+", "+o+", "+i+", row, col, depth);\n        return sampleTexture("+n+", uv);\n      }\n  "}(t);case 4:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e[3],i=e[2]*o,a=e[1]*i,s=E(e),u=s.newShape,c=s.keptDims;if(u.length<e.length)return"\n      "+kn(Pn(t,u))+"\n      float "+r+"(int row, int col, int depth, int depth2) {\n        return "+r+"("+Bn(["row","col","depth","depth2"],c)+");\n      }\n    ";if(t.shapeInfo.isUniform)return"\n      float "+r+"(int row, int col, int depth, int depth2) {\n        float index = dot(vec4(row, col, depth, depth2),\n                          vec4("+a+", "+i+", "+o+", 1));\n        return "+r+"Flat(round(index));\n      }\n    ";var l=t.shapeInfo.texShape,h=l[0],p=l[1];return p===a?"\n      float "+r+"(int row, int col, int depth, int depth2) {\n        float texR = float(row);\n        float texC =\n            dot(vec3(col, depth, depth2), vec3("+i+", "+o+", 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2("+p+".0, "+h+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":p===o?"\n      float "+r+"(int row, int col, int depth, int depth2) {\n        float texR = dot(vec3(row, col, depth),\n                         vec3("+e[1]*e[2]+", "+e[2]+", 1));\n        float texC = float(depth2);\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2("+p+".0, "+h+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":"\n    float "+r+"(int row, int col, int depth, int depth2) {\n      vec2 uv = UVfrom4D("+h+", "+p+", "+a+", "+i+",\n          "+o+", row, col, depth, depth2);\n      return sampleTexture("+n+", uv);\n    }\n  "}(t);case 5:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e[4],i=e[3]*o,a=e[2]*i,s=e[1]*a,u=E(e),c=u.newShape,l=u.keptDims;if(c.length<e.length)return"\n      "+kn(Pn(t,c))+"\n      float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n        return "+r+"("+Bn(["row","col","depth","depth2","depth3"],l)+");\n      }\n    ";if(t.shapeInfo.isUniform)return"\n      float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4("+s+", "+a+", "+i+", "+o+")) +\n          depth3;\n        return "+r+"Flat(index);\n      }\n    ";var h=t.shapeInfo.texShape,p=h[0],f=h[1];return f===s?"\n      float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n        int texR = row;\n        float texC = dot(\n          vec4(col, depth, depth2, depth3),\n          vec4("+a+", "+i+", "+o+", 1));\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2("+f+".0, "+p+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":f===o?"\n      float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4("+e[1]*e[2]*e[3]+", "+e[2]*e[3]+",\n            "+e[3]+", 1));\n        int texC = depth3;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2("+f+".0, "+p+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":"\n    float "+r+"(int row, int col, int depth, int depth2, int depth3) {\n      vec2 uv = UVfrom5D("+p+", "+f+", "+s+", "+a+",\n          "+i+", "+o+", row, col, depth, depth2, depth3);\n      return sampleTexture("+n+", uv);\n    }\n  "}(t);case 6:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=e[5],i=e[4]*o,a=e[3]*i,s=e[2]*a,u=e[1]*s,c=E(e),l=c.newShape,h=c.keptDims;if(l.length<e.length)return"\n      "+kn(Pn(t,l))+"\n      float "+r+"(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        return "+r+"("+Bn(["row","col","depth","depth2","depth3","depth4"],h)+");\n      }\n    ";if(t.shapeInfo.isUniform)return"\n      float "+r+"(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n        float index = dot(\n          vec4(row, col, depth, depth2),\n          vec4("+u+", "+s+", "+a+", "+i+")) +\n          dot(\n            vec2(depth3, depth4),\n            vec2("+o+", 1));\n        return "+r+"Flat(index);\n      }\n    ";var p=t.shapeInfo.texShape,f=p[0],d=p[1];return d===u?"\n      float "+r+"(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        int texR = row;\n        float texC = dot(\n          vec4(col, depth, depth2, depth3),\n          vec4("+s+", "+a+", "+i+", "+o+")) + depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                   vec2("+d+".0, "+f+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":d===o?"\n      float "+r+"(int row, int col, int depth,\n                    int depth2, int depth3, int depth4) {\n        float texR = dot(\n          vec4(row, col, depth, depth2),\n          vec4("+e[1]*e[2]*e[3]*e[4]+",\n               "+e[2]*e[3]*e[4]+",\n               "+e[3]*e[4]+",\n               "+e[4]+")) + depth3;\n        int texC = depth4;\n        vec2 uv = (vec2(texC, texR) + halfCR) /\n                  vec2("+d+".0, "+f+".0);\n        return sampleTexture("+n+", uv);\n      }\n    ":"\n    float "+r+"(int row, int col, int depth,\n                  int depth2, int depth3, int depth4) {\n      vec2 uv = UVfrom6D("+f+", "+d+", "+u+", "+s+",\n          "+a+", "+i+", "+o+"\n          ,row, col, depth, depth2, depth3, depth4);\n      return sampleTexture("+n+", uv);\n    }\n  "}(t);default:throw new Error(a.length+"-D input sampling is not yet supported")}}function In(t){var e,n,r,o,i,a,s,u,c,l,h,p,f,d,m,v,g=t.shapeInfo.logicalShape;switch(g.length){case 0:return"\n    vec4 get"+(v=t.name).charAt(0).toUpperCase()+v.slice(1)+"() {\n      return texture2D("+v+", halfCR);\n    }\n  ";case 1:return p=(h=t).name,f="get"+p.charAt(0).toUpperCase()+p.slice(1),d=h.shapeInfo.texShape,m=[Math.ceil(d[0]/2),Math.ceil(d[1]/2)],"\n    vec4 "+f+"(int index) {\n      vec2 uv = packedUVfrom1D(\n        "+m[0]+", "+m[1]+", index);\n      return texture2D("+p+", uv);\n    }\n  ";case 2:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=t.shapeInfo.texShape,i=o[0],a=o[1];if(null!=o&&y(e,o))return"\n      vec4 "+r+"(int row, int col) {\n        vec2 uv = (vec2(col, row) + halfCR) / vec2("+a+".0, "+i+".0);\n\n        return texture2D("+n+", uv);\n      }\n    ";var s=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];return"\n    vec4 "+r+"(int row, int col) {\n      vec2 uv = packedUVfrom2D("+Math.ceil(e[1]/2)+", "+s[0]+", "+s[1]+", row, col);\n      return texture2D("+n+", uv);\n    }\n  "}(t);case 3:return function(t){var e=t.shapeInfo.logicalShape,n=t.name,r="get"+n.charAt(0).toUpperCase()+n.slice(1),o=t.shapeInfo.texShape,i=[Math.ceil(o[0]/2),Math.ceil(o[1]/2)];if(1===e[0])return"\n        "+In(Pn(t,e.slice(1)))+"\n        vec4 "+r+"(int b, int row, int col) {\n          return "+r+"("+Bn(["b","row","col"],[1,2])+");\n        }\n      ";var a=i[0],s=i[1],u=Math.ceil(e[2]/2);return"\n    vec4 "+r+"(int b, int row, int col) {\n      vec2 uv = packedUVfrom3D(\n        "+a+", "+s+", "+u*Math.ceil(e[1]/2)+", "+u+", b, row, col);\n      return texture2D("+n+", uv);\n    }\n  "}(t);case 4:return n=(e=t).shapeInfo.logicalShape,r=e.name,o="get"+r.charAt(0).toUpperCase()+r.slice(1),i=e.shapeInfo.texShape,a=[Math.ceil(i[0]/2),Math.ceil(i[1]/2)],s=a[0],u=a[1],c=Math.ceil(n[3]/2),l=c*Math.ceil(n[2]/2),"\n    vec4 "+o+"(int b2, int b, int row, int col) {\n      vec2 uv = packedUVfrom4D(\n        "+s+", "+u+", "+l*n[1]+",\n        "+l+", "+c+", b2, b, row, col);\n      return texture2D("+r+", uv);\n    }\n  ";default:throw new Error("Packed "+g.length+"-D input sampling is not yet supported")}}var Dn="\n  float sampleTexture(sampler2D textureSampler, vec2 uv) {\n    return texture2D(textureSampler, uv).r;\n  }\n",An="\n  void setOutput(float val) {\n    gl_FragColor = vec4(val, 0, 0, 0);\n  }\n",Mn="\n  void setOutput(vec4 val) {\n    gl_FragColor = val;\n  }\n",_n="\n  precision highp float;\n  precision highp int;\n  varying vec2 resultUV;\n  const vec2 halfCR = vec2(0.5, 0.5);\n\n  struct ivec5\n  {\n    int x;\n    int y;\n    int z;\n    int w;\n    int u;\n  };\n\n  struct ivec6\n  {\n    int x;\n    int y;\n    int z;\n    int w;\n    int u;\n    int v;\n  };\n\n  "+(Ft.get("PROD")?"\n    bool isNaN(float val) {\n      return false;\n    }\n\n    bool hasNaN(vec4 values) {\n      return false;\n    }\n  ":"\n    bool isNaN(float val) {\n      return (val < 1.0 || 0.0 < val || val == 0.0) ? false : true;\n    }\n\n    bool hasNaN(vec4 values) {\n      return any(bvec4(\n        isNaN(values.x),\n        isNaN(values.y),\n        isNaN(values.z),\n        isNaN(values.w)\n      ));\n    }\n  ")+"\n\n  float getNaN(vec4 values) {\n    return dot(vec4(1), values);\n  }\n\n  int round(float value) {\n    return int(floor(value + 0.5));\n  }\n\n  int imod(int x, int y) {\n    return x - y * (x / y);\n  }\n\n  //Based on the work of Dave Hoskins\n  //https://www.shadertoy.com/view/4djSRW\n  #define HASHSCALE1 443.8975\n  float random(float seed){\n    vec2 p = resultUV * seed;\n    vec3 p3  = fract(vec3(p.xyx) * HASHSCALE1);\n    p3 += dot(p3, p3.yzx + 19.19);\n    return fract((p3.x + p3.y) * p3.z);\n  }\n\n  \nvec2 UVfrom1D(int texNumR, int texNumC, int index) {\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom1D(int texNumR, int texNumC, int index) {\n  int texelIndex = index / 2;\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n\n  \nvec2 UVfrom2D(int texNumR, int texNumC, int numC, int row, int col) {\n  int index = row * numC + col;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom2D(int texelsInLogicalRow, int texNumR,\n  int texNumC, int row, int col) {\n  int texelIndex = (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = texelIndex / texNumC;\n  int texC = texelIndex - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n\n  \nvec2 UVfrom3D(int texNumR, int texNumC, int stride0,\n    int stride1, int row, int col, int depth) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom3D(int texNumR, int texNumC,\n    int texelsInBatch, int texelsInLogicalRow, int b,\n    int row, int col) {\n  int index = b * texelsInBatch + (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n\n  \nvec2 UVfrom4D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int row, int col, int depth,\n    int depth2) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth * stride2 + depth2;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\nvec2 packedUVfrom4D(int texNumR, int texNumC, int texelsInBatch2,\n    int texelsInBatch, int texelsInLogicalRow, int b2, int b,\n    int row, int col) {\n  int index = b2 * texelsInBatch2 + b * texelsInBatch +\n    (row / 2) * texelsInLogicalRow + (col / 2);\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n\n  \nvec2 UVfrom5D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int stride3, int row, int col, int depth,\n    int depth2, int depth3) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 +\n              depth * stride2 + depth2 * stride3 + depth3;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n\n  \nvec2 UVfrom6D(int texNumR, int texNumC, int stride0,\n    int stride1, int stride2, int stride3, int stride4,\n    int row, int col, int depth, int depth2, int depth3, int depth4) {\n  // Explicitly use integer operations as dot() only works on floats.\n  int index = row * stride0 + col * stride1 + depth * stride2 + depth2 *\n    stride3 + depth3 * stride4 + depth4;\n  int texR = index / texNumC;\n  int texC = index - texR * texNumC;\n  return (vec2(texC, texR) + halfCR) / vec2(texNumC, texNumR);\n}\n\n",On="\n  float getChannel(vec4 frag, vec2 innerDims) {\n    vec2 modCoord = mod(innerDims, 2.);\n    return modCoord.x == 0. ?\n      (modCoord.y == 0. ? frag.r : frag.g) :\n      (modCoord.y == 0. ? frag.b : frag.a);\n  }\n  float getChannel(vec4 frag, int dim) {\n    float modCoord = mod(float(dim), 2.);\n    return modCoord == 0. ? frag.r : frag.g;\n  }\n";function Fn(t){if(t<=1)return"int";if(2===t)return"ivec2";if(3===t)return"ivec3";if(4===t)return"ivec4";if(5===t)return"ivec5";if(6===t)return"ivec6";throw Error("GPU for rank "+t+" is not yet supported")}function Pn(t,e){var n=JSON.parse(JSON.stringify(t));return n.shapeInfo.logicalShape=e,n}function Bn(e,t){return t.map(function(t){return e[t]}).join(", ")}var Ln=function(t,e,n){this.variableNames=["x"];var r=(this.outputShape=t).length,o=t[t.length-1],i=n?"<":">";this.userCode="\n      int getIndex(int i) {\n        "+(n?"return "+o+" -i - 1;":"return i;")+"\n      }\n\n      void main() {\n        "+Fn(r)+" coords = getOutputCoords();\n        int end = "+zn(r,"coords")+";\n        float val = 0.0;\n        for (int i = "+o+" - 1; i >= 0; i -= 1) {\n          int idx = getIndex(i);\n          if (idx "+i+" end) {\n            continue;\n          }\n          if (idx == end && "+e+") {\n            continue;\n          }\n          "+zn(r,"coords")+" = idx;\n          val += getX("+function(t,e){if(1===t)return""+e;if(2===t)return e+".x, "+e+".y";if(3===t)return e+".x, "+e+".y, "+e+".z";if(4===t)return e+".x, "+e+".y, "+e+".z, "+e+".w";throw Error("Cumulative sum for rank "+t+" is not yet supported")}(r,"coords")+");\n        }\n        setOutput(val);\n      }\n    "};function zn(t,e){if(1===t)return""+e;if(2===t)return e+".y";if(3===t)return e+".z";if(4===t)return e+".w";throw Error("Cumulative sum for rank "+t+" is not yet supported")}var Wn=function(){function t(t,e,n){this.variableNames=["x"],this.outputShape=[],this.outputShape=t,this.blockSize=e,this.dataFormat=n,this.userCode="\n    void main() {\n      ivec4 coords = getOutputCoords();\n      int b = coords[0];\n      int h = "+this.getHeightCoordString()+";\n      int w = "+this.getWidthCoordString()+";\n      int d = "+this.getDepthCoordString()+";\n\n      int in_h = h / "+e+";\n      int offset_h = imod(h, "+e+");\n      int in_w = w / "+e+";\n      int offset_w = imod(w, "+e+");\n      int offset_d = (offset_h * "+e+" + offset_w) *\n        "+this.getOutputDepthSize()+";\n      int in_d = d + offset_d;\n\n      float result = "+this.getInputSamplingString()+";\n      setOutput(result);\n    }\n  "}return t.prototype.getHeightCoordString=function(){return"NHWC"===this.dataFormat?"coords[1]":"coords[2]"},t.prototype.getWidthCoordString=function(){return"NHWC"===this.dataFormat?"coords[2]":"coords[3]"},t.prototype.getDepthCoordString=function(){return"NHWC"===this.dataFormat?"coords[3]":"coords[1]"},t.prototype.getOutputDepthSize=function(){return"NHWC"===this.dataFormat?this.outputShape[3]:this.outputShape[1]},t.prototype.getInputSamplingString=function(){return"NHWC"===this.dataFormat?"getX(b, in_h, in_w, in_d)":"getX(b, in_d, in_h, in_w)"},t}(),Un=function(t){this.variableNames=["A"],this.outputShape=t,this.userCode="\n      const float FLOAT_MAX = 1.70141184e38;\n      const float FLOAT_MIN = 1.17549435e-38;\n\n      lowp vec4 encode_float(highp float v) {\n        if (isNaN(v)) {\n          return vec4(255, 255, 255, 255);\n        }\n\n        highp float av = abs(v);\n\n        if(av < FLOAT_MIN) {\n          return vec4(0.0, 0.0, 0.0, 0.0);\n        } else if(v > FLOAT_MAX) {\n          return vec4(0.0, 0.0, 128.0, 127.0) / 255.0;\n        } else if(v < -FLOAT_MAX) {\n          return vec4(0.0, 0.0,  128.0, 255.0) / 255.0;\n        }\n\n        highp vec4 c = vec4(0,0,0,0);\n\n        highp float e = floor(log2(av));\n        highp float m = exp2(fract(log2(av))) - 1.0;\n\n        c[2] = floor(128.0 * m);\n        m -= c[2] / 128.0;\n        c[1] = floor(32768.0 * m);\n        m -= c[1] / 32768.0;\n        c[0] = floor(8388608.0 * m);\n\n        highp float ebias = e + 127.0;\n        c[3] = floor(ebias / 2.0);\n        ebias -= c[3] * 2.0;\n        c[2] += floor(ebias) * 128.0;\n\n        c[3] += 128.0 * step(0.0, -v);\n\n        return c / 255.0;\n      }\n\n      void main() {\n        float x = getAAtOutCoords();\n        gl_FragColor = encode_float(x);\n      }\n    "},Vn="return real * expR - imag * expI;",Gn="return real * expI + imag * expR;",Hn=function(t,e,n){this.variableNames=["real","imag"];var r=e[1];this.outputShape=e;var o=n?"2.0 * "+Math.PI:"-2.0 * "+Math.PI,i=n?r+".0":"1.0";this.userCode="\n      const float exponentMultiplier = "+o+";\n\n      float unaryOpComplex(float real, float expR, float imag, float expI) {\n        "+t+"\n      }\n\n      float mulMatDFT(int batch, int index) {\n        float indexRatio = float(index) / float("+r+");\n        float exponentMultiplierTimesIndexRatio =\n            exponentMultiplier * indexRatio;\n\n        float result = 0.0;\n\n        for (int i = 0; i < "+r+"; i++) {\n          // x = (-2|2 * PI / N) * index * i;\n          float x = exponentMultiplierTimesIndexRatio * float(i);\n          float expR = cos(x);\n          float expI = sin(x);\n          float real = getReal(batch, i);\n          float imag = getImag(batch, i);\n\n          result +=\n              unaryOpComplex(real, expR, imag, expI) / "+i+";\n        }\n\n        return result;\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        setOutput(mulMatDFT(coords[0], coords[1]));\n      }\n    "},qn=function(t){this.variableNames=["A"];var e=t[0],n=t[1];this.outputShape=t,this.userCode="\n      void main() {\n        ivec3 coords = getOutputCoords();\n        int texR = coords[0];\n        int texC = coords[1];\n        int depth = coords[2];\n        vec2 uv = (vec2(texC, texR) + halfCR) / vec2("+n+".0, "+e+".0);\n\n        vec4 values = texture2D(A, uv);\n        float value;\n        if (depth == 0) {\n          value = values.r;\n        } else if (depth == 1) {\n          value = values.g;\n        } else if (depth == 2) {\n          value = values.b;\n        } else if (depth == 3) {\n          value = values.a;\n        }\n\n        setOutput(floor(value * 255.0 + 0.5));\n      }\n    "},jn=function(t,e,n){this.variableNames=["A","indices"];var r=t.slice();r[n]=e,this.outputShape=r,this.rank=r.length;var o=Fn(this.rank),i=function(t,e){var n=t.length;if(4<n)throw Error("Gather for rank "+n+" is not yet supported");if(1===n)return"int(getIndices(resRC))";for(var r=["resRC.x","resRC.y","resRC.z","resRC.w"],o=[],i=0;i<t.length;i++)i===e?o.push("int(getIndices("+r[i]+"))"):o.push(""+r[i]);return o.join()}(t,n);this.userCode="\n      void main() {\n        "+o+" resRC = getOutputCoords();\n        setOutput(getA("+i+"));\n      }\n    "};var $n,Kn,Xn,Yn,Qn=function(t,e,n){this.sliceDim=t,this.strides=e,this.variableNames=["x","indices"],this.outputShape=n;var r=Fn(e.length),o=Fn(n.length),i=1<this.sliceDim?"strides[j]":"strides";this.userCode="\n        "+r+" strides = "+r+"("+this.strides+");\n         void main() {\n          "+o+" coords = getOutputCoords();\n          int flattenIndex = 0;\n          for (int j = 0; j < "+this.sliceDim+"; j++) {\n            int index = round(getIndices(coords[0], j));\n            flattenIndex += index * "+i+";\n          }\n          setOutput(getX(flattenIndex, coords[1]));\n        }\n      "};function Jn(t,e){return[e,t]}function Zn(t,e){return t*e}function tr(t,e,n){var r=function(t,e){if(t%e!=0)throw new Error("unpackedSize ("+t+") must be a multiple of "+e);return t/e}(t.length,n);if(e.length<r)throw new Error("matrix length ("+e.length+") must be >= "+r);for(var o=0,i=0;i<t.length;i+=n)e[o++]=t[i]}function er(t,e){return[Math.max(1,Math.ceil(e/2)),Math.max(1,Math.ceil(t/2))]}function nr(t,e){var n=er(t,e);return n[0]*n[1]*4}function rr(t,e,n,r,o){var i=n*r;if(o.length<i)throw new Error("matrix length ("+o.length+") must be >= "+i);for(var 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e.gl.deleteProgram(t)})},t.prototype.setProgram=function(t){var e=this;this.throwIfDisposed(),this.program=t,null!=this.program&&this.autoDebugValidate&&mr(this.gl,this.program),or(this.gl,function(){return e.gl.useProgram(t)})},t.prototype.getUniformLocation=function(t,e,n){return void 0===n&&(n=!0),this.throwIfDisposed(),n?Sr(this.gl,t,e):Rr(this.gl,t,e)},t.prototype.getAttributeLocation=function(t,e){var n=this;return this.throwIfDisposed(),or(this.gl,function(){return n.gl.getAttribLocation(t,e)})},t.prototype.getUniformLocationNoThrow=function(t,e){return this.throwIfDisposed(),this.gl.getUniformLocation(t,e)},t.prototype.setInputMatrixTexture=function(t,e,n){this.throwIfDisposed(),this.throwIfNoProgram(),Nr(this.gl,this.program,t,e,n)},t.prototype.setOutputMatrixTexture=function(t,e,n){this.setOutputMatrixTextureDriver(t,n,e)},t.prototype.setOutputPackedMatrixTexture=function(t,e,n){this.throwIfDisposed();var r=er(e,n),o=r[0],i=r[1];this.setOutputMatrixTextureDriver(t,o,i)},t.prototype.setOutputMatrixWriteRegion=function(t,e,n,r){this.setOutputMatrixWriteRegionDriver(n,t,r,e)},t.prototype.setOutputPackedMatrixWriteRegion=function(t,e,n,r){throw new Error("setOutputPackedMatrixWriteRegion not implemented.")},t.prototype.debugValidate=function(){null!=this.program&&mr(this.gl,this.program),Ir(this.gl)},t.prototype.executeProgram=function(){this.throwIfDisposed(),this.throwIfNoProgram();var t=this.gl;this.autoDebugValidate&&this.debugValidate(),or(t,function(){return t.drawElements(t.TRIANGLES,6,t.UNSIGNED_SHORT,0)})},t.prototype.blockUntilAllProgramsCompleted=function(){var t=this;this.throwIfDisposed(),or(this.gl,function(){return t.gl.finish()})},t.prototype.getQueryTimerExtension=function(){return null==this.disjointQueryTimerExtension&&(this.disjointQueryTimerExtension=cr(this.gl,2===Ft.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")?"EXT_disjoint_timer_query_webgl2":"EXT_disjoint_timer_query")),this.disjointQueryTimerExtension},t.prototype.getQueryTimerExtensionWebGL2=function(){return this.getQueryTimerExtension()},t.prototype.getQueryTimerExtensionWebGL1=function(){return this.getQueryTimerExtension()},t.prototype.beginQuery=function(){if(2===Ft.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){var t=this.gl,e=this.getQueryTimerExtensionWebGL2(),n=t.createQuery();return t.beginQuery(e.TIME_ELAPSED_EXT,n),n}var r=this.getQueryTimerExtensionWebGL1(),o=r.createQueryEXT();return r.beginQueryEXT(r.TIME_ELAPSED_EXT,o),o},t.prototype.endQuery=function(){if(2!==Ft.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")){var t=this.getQueryTimerExtensionWebGL1();t.endQueryEXT(t.TIME_ELAPSED_EXT)}else{var e=this.gl,n=this.getQueryTimerExtensionWebGL2();e.endQuery(n.TIME_ELAPSED_EXT)}},t.prototype.waitForQueryAndGetTime=function(n){return m(this,void 0,void 0,function(){var e=this;return C(this,function(t){switch(t.label){case 0:return[4,w(function(){return e.disposed||e.isQueryAvailable(n,Ft.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))})];case 1:return t.sent(),[2,this.getQueryTime(n,Ft.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION"))]}})})},t.prototype.getQueryTime=function(t,e){if(0===e)return null;if(2===e){var n=this.gl;return n.getQueryParameter(t,n.QUERY_RESULT)/1e6}var r=this.getQueryTimerExtensionWebGL1();return r.getQueryObjectEXT(t,r.QUERY_RESULT_EXT)/1e6},t.prototype.isQueryAvailable=function(t,e){if(0===e)return!0;if(2!==e)return o=(r=this.getQueryTimerExtensionWebGL1()).getQueryObjectEXT(t,r.QUERY_RESULT_AVAILABLE_EXT),null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint;var n=this.gl,r=this.getQueryTimerExtensionWebGL2(),o=n.getQueryParameter(t,n.QUERY_RESULT_AVAILABLE);return null==this.disjoint&&(this.disjoint=this.gl.getParameter(r.GPU_DISJOINT_EXT)),o&&!this.disjoint},t.prototype.pollFence=function(e){var n=this;return new Promise(function(t){n.addItemToPoll(function(){return e.isFencePassed()},function(){return t()})})},t.prototype.pollItems=function(){for(var t=function(t){for(var e=0,n=t.length-1,r=-1;e<=n;){var o=e+n>>1;t[o]()?e=(r=o)+1:n=o-1}return r}(this.itemsToPoll.map(function(t){return t.isDoneFn})),e=0;e<=t;++e)(0,this.itemsToPoll[e].resolveFn)();this.itemsToPoll=this.itemsToPoll.slice(t+1)},t.prototype.addItemToPoll=function(t,e){var n=this;this.itemsToPoll.push({isDoneFn:t,resolveFn:e}),1<this.itemsToPoll.length||w(function(){return n.pollItems(),0===n.itemsToPoll.length})},t.prototype.bindTextureToFrameBuffer=function(t){this.throwIfDisposed(),Tr(this.gl,t,this.framebuffer),this.autoDebugValidate&&Ir(this.gl)},t.prototype.unbindTextureToFrameBuffer=function(){null!=this.outputTexture?(Tr(this.gl,this.outputTexture,this.framebuffer),this.autoDebugValidate&&Ir(this.gl)):kr(this.gl,this.framebuffer)},t.prototype.downloadMatrixDriver=function(t,e){this.bindTextureToFrameBuffer(t);var n=e();return this.unbindTextureToFrameBuffer(),n},t.prototype.setOutputMatrixTextureDriver=function(t,e,n){this.throwIfDisposed();var r=this.gl;Tr(r,t,this.framebuffer),this.autoDebugValidate&&Ir(r),this.outputTexture=t,or(r,function(){return r.viewport(0,0,e,n)}),or(r,function(){return r.scissor(0,0,e,n)})},t.prototype.setOutputMatrixWriteRegionDriver=function(t,e,n,r){var o=this;this.throwIfDisposed(),or(this.gl,function(){return o.gl.scissor(t,e,n,r)})},t.prototype.throwIfDisposed=function(){if(this.disposed)throw new Error("Attempted to use disposed GPGPUContext.")},t.prototype.throwIfNoProgram=function(){if(null==this.program)throw new Error("No GPU program is currently set.")},t}();function ao(t,s){if(t.length!==s.length)throw Error("Binary was compiled with "+t.length+" inputs, but was executed with "+s.length+" inputs");t.forEach(function(t,e){var n=t.logicalShape,r=s[e],o=r.shape;if(!y(n,o))throw Error("Binary was compiled with different shapes than the current args. Shapes "+n+" and "+o+" must match");if(!t.isUniform||!r.isUniform){var i=t.texShape,a=r.isUniform?null:r.texData.texShape;if(!y(i,a))throw Error("Binary was compiled with different texture shapes than the current args. Shape "+i+" and "+a+" must match")}})}var so=function(t,e,n){this.variableNames=["A"],this.outputShape=t;var r=n.filterWidth,o=n.inChannels,i=n.strideWidth,a=n.strideHeight,s=n.padInfo,u=n.outWidth,c=n.dilationWidth,l=n.dilationHeight,h=s.left,p=s.top,f=o*r;this.userCode="\n      void main() {\n        ivec2 rc = getOutputCoords();\n\n        vec4 result = vec4(0);\n\n        for(int row=0; row<=1; row++) {\n          for(int col=0; col<=1; col++) {\n            int blockIndex = rc.y + col;\n            int pos = rc.x + row;\n\n            if(blockIndex >= "+t[1]+" || pos >= "+t[0]+") continue;\n\n            int offsetY = int(blockIndex / ("+u+")) * "+a+" - "+p+";\n            int d0 = offsetY + "+l+" * (pos / "+f+");\n\n            if(d0 >= "+e[0]+" || d0 < 0) continue;\n\n            int offsetX = int(mod(float(blockIndex), "+u+".) * "+i+". - "+h+".);\n            int d1 = offsetX + "+c+" * (int(mod(float(pos), "+f+".) / "+o+".));\n\n            if(d1 >= "+e[1]+" || d1 < 0) continue;\n\n            result[row * 2 + col] = getA(d0, d1, int(mod(float(pos), "+o+".)));\n          }\n        }\n\n        gl_FragColor = result;\n      }\n    "},uo=function(t,e,n,r,o){this.variableNames=["x"],this.outputShape=[];var i,a=e,s=t[3]-1;this.outputShape=t;var u="float("+n+") + float("+r+") * sum";i=.5===o?"inversesqrt("+u+")":1===o?"1.0/("+u+")":"exp(log("+u+") * float(-"+o+"));",this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n        int d = coords[3];\n        float x = getX(b, r, c, d);\n        float sum = 0.0;\n        for (int j = -"+a+"; j <= "+a+"; j++) {\n          int idx = d + j;\n          if (idx >= 0 && idx <=  "+s+") {\n            float z = getX(b, r, c, idx);\n            sum += z * z;\n          }\n        }\n        float val = x * "+i+";\n        setOutput(val);\n      }\n    "},co=function(t,e,n,r,o){this.variableNames=["inputImage","outputImage","dy"],this.outputShape=[],this.outputShape=t,this.depth=t[3],this.depthRadius=e,this.bias=n,this.alpha=r,this.beta=o,this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int r = coords[1];\n        int c = coords[2];\n\n        float result = 0.0;\n        for (int d = 0; d < "+this.depth+"; ++d) {\n          int depthBegin = int(max(0.0, float(d - "+e+")));\n          int depthEnd = int(min(float("+this.depth+"),\n              float(d + "+e+" + 1)));\n\n          const int MIN_DEPTH_BEGIN = 0;\n          const int MAX_DEPTH_END = "+this.depth+";\n\n          float norm = 0.0;\n          for (int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k) {\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd) {\n              norm += getInputImage(b, r, c, k) * getInputImage(b, r, c, k);\n            }\n            else {\n              break;\n            }\n          }\n\n          norm = float("+r+") * norm + float("+n+");\n\n          for(int k = MIN_DEPTH_BEGIN; k < MAX_DEPTH_END; ++k){\n            if (k < depthBegin){\n              continue;\n            }\n            else if (k >= depthBegin && k < depthEnd){\n              float dyi = -2.0 * float("+r+")\n                * float("+o+")\n                * getInputImage(b ,r ,c, k) * getOutputImage(b, r, c, d)\n                / norm;\n              if (k == d) {\n                dyi += pow(norm, -1.0 * "+o+");\n              }\n              if (k == coords[3]) {\n                dyi *= getDy(b, r, c, d);\n                result += dyi;\n              }\n            }\n            else {\n              break;\n            }\n          }\n      }\n      setOutput(result);\n      }\n    "},lo=function(t){this.variableNames=["dy","maxPos"],this.outputShape=t.inShape;var e=t.strideHeight,n=t.strideWidth,r=t.dilationHeight,o=t.effectiveFilterHeight,i=t.effectiveFilterWidth,a=o-1-t.padInfo.top,s=i-1-t.padInfo.left,u=o*i-1;this.userCode="\n      const ivec2 pads = ivec2("+a+", "+s+");\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n\n        ivec2 dyRCCorner = coords.yz - pads;\n        int dyRCorner = dyRCCorner.x;\n        int dyCCorner = dyRCCorner.y;\n\n        // Convolve dy(?, ?, d) with pos mask(:, :, d) to get dx(xR, xC, d).\n        // ? = to be determined. : = across all values in that axis.\n        float dotProd = 0.0;\n        for (int wR = 0; wR < "+o+";\n          wR += "+r+") {\n          float dyR = float(dyRCorner + wR) / "+e+".0;\n\n          if (dyR < 0.0 || dyR >= "+t.outHeight+".0 || fract(dyR) > 0.0) {\n            continue;\n          }\n          int idyR = int(dyR);\n\n          for (int wC = 0; wC < "+i+"; wC++) {\n            float dyC = float(dyCCorner + wC) / "+n+".0;\n\n            if (dyC < 0.0 || dyC >= "+t.outWidth+".0 ||\n                fract(dyC) > 0.0) {\n              continue;\n            }\n            int idyC = int(dyC);\n\n            float dyValue = getDy(b, idyR, idyC, d);\n            int maxPosValue = "+u+" - int(getMaxPos(b, idyR, idyC, d));\n\n            // Get the current value, check it against the value from the\n            // position matrix.\n            int curPosValue = wR * "+i+" + wC;\n            float mask = float(maxPosValue == curPosValue ? 1.0 : 0.0);\n\n            dotProd += dyValue * mask;\n          }\n        }\n        setOutput(dotProd);\n      }\n    "},ho=function(t,e,n,r){void 0===n&&(n=!1),void 0===r&&(r=!1),this.variableNames=["matrixA","matrixB"];var o=t[0],i=n?t[2]:t[1],a=r?e[1]:e[2],s=n?t[1]:t[2];this.outputShape=[o,i,a];var u=function(t,e){return n?"batch, "+e+" + "+t+", aRow":"batch, aRow, "+e+" + "+t},c=function(t,e){return r?"batch, bCol, "+e+" + "+t:"batch, "+e+" + "+t+", bCol"},l=4*Math.floor(s/4),h=s%4;this.userCode=" float dotARowBCol(int batch, int aRow, int bCol) {\n      float result = 0.0;\n      for (int i = 0; i < "+l+"; i += 4) {\n        vec4 a = vec4(\n          getMatrixA("+u(0,"i")+"),\n          getMatrixA("+u(1,"i")+"),\n          getMatrixA("+u(2,"i")+"),\n          getMatrixA("+u(3,"i")+")\n        );\n        vec4 b = vec4(\n          getMatrixB("+c(0,"i")+"),\n          getMatrixB("+c(1,"i")+"),\n          getMatrixB("+c(2,"i")+"),\n          getMatrixB("+c(3,"i")+")\n        );\n\n        result += dot(a, b);\n      }\n\n      if ("+(1===h)+") {\n        result += getMatrixA("+u(0,l)+") *\n          getMatrixB("+c(0,l)+");\n      } else if ("+(2===h)+") {\n        vec2 a = vec2(\n          getMatrixA("+u(0,l)+"),\n          getMatrixA("+u(1,l)+")\n        );\n        vec2 b = vec2(\n          getMatrixB("+c(0,l)+"),\n          getMatrixB("+c(1,l)+")\n        );\n        result += dot(a, b);\n      } else if ("+(3===h)+") {\n        vec3 a = vec3(\n          getMatrixA("+u(0,l)+"),\n          getMatrixA("+u(1,l)+"),\n          getMatrixA("+u(2,l)+")\n        );\n        vec3 b = vec3(\n          getMatrixB("+c(0,l)+"),\n          getMatrixB("+c(1,l)+"),\n          getMatrixB("+c(2,l)+")\n        );\n        result += dot(a, b);\n      }\n\n      return result;\n    }\n\n    void main() {\n      ivec3 resBRC = getOutputCoords();\n      setOutput(dotARowBCol(resBRC.x, resBRC.y, resBRC.z));\n    }\n    "},po=function(t,e,n,r,o){void 0===r&&(r=!1),void 0===o&&(o=!1),this.variableNames=["matrixA","matrixB"],this.usesPackedTextures=!0,this.outputShape=n;var i=r?t[0]:t[1],a=Math.ceil(i/2),s=r?"i * 2, rc.x":"rc.x, i * 2",u=o?"rc.y, i * 2":"i * 2, rc.y",c=r?["a.xxyy","a.zzww"]:["a.xxzz","a.yyww"],l=o?["b.xzxz","b.ywyw"]:["b.xyxy","b.zwzw"];this.userCode="\n      const float sharedDimension = "+a+".0;\n\n      vec4 dot2x2ARowBCol(ivec2 rc) {\n        vec4 result = vec4(0);\n        for (int i = 0; i < "+a+"; i++) {\n          vec4 a = getMatrixA("+s+");\n          vec4 b = getMatrixB("+u+");\n\n          result += ("+c[0]+" * "+l[0]+") + ("+c[1]+" * "+l[1]+");\n        }\n        return result;\n      }\n\n      void main() {\n        ivec2 rc = getOutputCoords();\n        setOutput(dot2x2ARowBCol(rc));\n      }\n    "},fo=function(){function t(t,e,n){this.variableNames=["probs"],this.outputShape=[t,n],this.userCode="\n      uniform float seed;\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n\n        float r = random(seed);\n        float cdf = 0.0;\n\n        for (int i = 0; i < "+(e-1)+"; i++) {\n          cdf += getProbs(batch, i);\n\n          if (r < cdf) {\n            setOutput(float(i));\n            return;\n          }\n        }\n\n        // If no other event happened, last event happened.\n        setOutput(float("+(e-1)+"));\n      }\n    "}return t.prototype.getCustomSetupFunc=function(n){var r=this;return function(t,e){null==r.seedLoc&&(r.seedLoc=t.getUniformLocation(e,"seed")),t.gl.uniform1f(r.seedLoc,n)}},t}(),mo=function(t,e,n,r){this.variableNames=["indices"],this.outputShape=[t,e],this.userCode="\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int index = round(getIndices(coords.x));\n        setOutput(mix(float("+r+"), float("+n+"),\n                      float(index == coords.y)));\n      }\n    "};function vo(t,e){return 1===e?[t]:(n=t,["x","y","z","w","u","v"].slice(0,e).map(function(t){return n+"."+t}));var n}var go=function(t){this.variableNames=["A"],this.isPackShader=!0;var e,n,r,o,i=(this.outputShape=t).length;if(0===i)this.userCode="\n        void main() {\n          setOutput(vec4(getA(), 0., 0., 0.));\n        }\n      ";else{var a=vo("rc",i),s=Fn(i),u=function(t,e,n){if(1===t)return"rc > "+e[0];for(var r="",o=t-2;o<t;o++)r+=n[o]+" >= "+e[o],o<t-1&&(r+="||");return r}(i,t,a),c=function(t,e,n,r){if(1===t)return"";var o=r.slice(-2);return"\n    int r = "+o[0]+";\n    int c = "+o[1]+";\n    int rp1 = r + 1;\n    int cp1 = c + 1;\n\n    bool cEdge = cp1 >= "+e+";\n    bool rEdge = rp1 >= "+n+";\n  "}(i,t[t.length-1],t[t.length-2],a),l=(n=a,r=(e=t).length,o=function(t,e){for(var n=[],r=0;r<=1;r++)for(var o=0;o<=1;o++){for(var i=(0===r?"r":"rp1")+", "+(0===o?"c":"cp1"),a=2;a<t;a++)i=e[e.length-1-a]+","+i;n.push(i)}return n}(r,n),1===r?"getA(rc),\n            rc + 1 >= "+e[0]+" ? 0. : getA(rc + 1),\n            0, 0":"getA("+o[0]+"),\n          cEdge ? 0. : getA("+o[1]+"),\n          rEdge ? 0. : getA("+o[2]+"),\n          rEdge || cEdge ? 0. : getA("+o[3]+")");this.userCode="\n        void main() {\n          "+s+" rc = getOutputCoords();\n\n          if("+u+") {\n            setOutput(vec4(0));\n          } else {\n            "+c+"\n\n            setOutput(vec4("+l+"));\n          }\n        }\n      "}};var yo=function(n,t,e){this.variableNames=["x"],this.outputShape=t.map(function(t,e){return t[0]+n[e]+t[1]});var r=n.length,o=Fn(r),i=t.map(function(t){return t[0]}).join(","),a=t.map(function(t,e){return t[0]+n[e]}).join(","),s=["coords[0]","coords[1]","coords[2]","coords[3]"].slice(0,r);this.userCode=1!==r?"\n      "+o+" start = "+o+"("+i+");\n      "+o+" end = "+o+"("+a+");\n\n      void main() {\n        "+o+" outC = getOutputCoords();\n        if (any(lessThan(outC, start)) || any(greaterThanEqual(outC, end))) {\n          setOutput(float("+e+"));\n        } else {\n          "+o+" coords = outC - start;\n          setOutput(getX("+s+"));\n        }\n      }\n    ":"\n        int start = "+i+";\n        int end = "+a+";\n\n        void main() {\n          int outC = getOutputCoords();\n          if (outC < start || outC >= end) {\n            setOutput(float("+e+"));\n          } else {\n            setOutput(getX(outC - start));\n          }\n        }\n      "},xo=function(t,e,n){if(this.variableNames=["x"],"avg"===e&&n)throw new Error("Cannot compute positions for average pool.");var r=t.filterWidth,o=t.strideHeight,i=t.strideWidth,a=t.dilationHeight,s=t.dilationWidth,u=t.effectiveFilterHeight,c=t.effectiveFilterWidth,l=t.padInfo.top,h=t.padInfo.left;this.outputShape=t.outShape;var p="avg"===e,f="0.0";if(p||(f="-1.0 / 0.0"),n)this.userCode="\n        const ivec2 strides = ivec2("+o+", "+i+");\n        const ivec2 pads = ivec2("+l+", "+h+");\n\n        void main() {\n          ivec4 coords = getOutputCoords();\n          int batch = coords[0];\n          int d = coords[3];\n\n          ivec2 xRCCorner = coords.yz * strides - pads;\n          int xRCorner = xRCCorner.x;\n          int xCCorner = xRCCorner.y;\n\n          // max/min x(?, ?, d) to get y(yR, yC, d).\n          // ? = to be determined\n          float minMaxValue = 0.0;\n          float minMaxValueFound = 0.0;\n          int minMaxPosition = 0;\n          float avgValue = 0.0;\n\n          for (int wR = 0; wR < "+u+";\n              wR += "+a+") {\n            int xR = xRCorner + wR;\n\n            if (xR < 0 || xR >= "+t.inHeight+") {\n              continue;\n            }\n\n            for (int wC = 0; wC < "+c+";\n                wC += "+s+") {\n              int xC = xCCorner + wC;\n\n              if (xC < 0 || xC >= "+t.inWidth+") {\n                continue;\n              }\n\n              float value = getX(batch, xR, xC, d);\n\n              // If a min / max value has already been found, use it. If not,\n              // use the current value.\n              float currMinMaxValue = mix(\n                  value, minMaxValue, minMaxValueFound);\n              if (value >= currMinMaxValue) {\n                minMaxValue = value;\n                minMaxValueFound = 1.0;\n                minMaxPosition = wR * "+c+" + wC;\n              }\n            }\n          }\n          setOutput(float(minMaxPosition));\n        }\n      ";else{var d=e+"("+e+"("+e+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"avg"===e&&(d="avgValue / count");var m=4*Math.floor(r/4),v=r%4,g="\n      if ("+p+") {\n        avgValue += dot(values, ones);\n      } else {\n        minMaxValue = max(values, minMaxValue);\n      }\n    ";this.userCode="\n      const ivec2 strides = ivec2("+o+", "+i+");\n      const ivec2 pads = ivec2("+l+", "+h+");\n      const float initializationValue = "+f+";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float count = 0.0;\n\n      float getValue(int batch, int xR, int xC, int d) {\n        if (xC < 0 || xC >= "+t.inWidth+") {\n          return initializationValue;\n        }\n        count += 1.0;\n        return getX(batch, xR, xC, d);\n      }\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int batch = coords[0];\n        int d = coords[3];\n\n        ivec2 xRCCorner = coords.yz * strides - pads;\n        int xRCorner = xRCCorner.x;\n        int xCCorner = xRCCorner.y;\n\n        // max/min x(?, ?, d) to get y(yR, yC, d).\n        // ? = to be determined\n        vec4 minMaxValue = vec4("+f+");\n        float avgValue = 0.0;\n        count = 0.0;\n\n        for (int wR = 0; wR < "+u+";\n            wR += "+a+") {\n          int xR = xRCorner + wR;\n\n          if (xR < 0 || xR >= "+t.inHeight+") {\n            continue;\n          }\n\n          for (int wC = 0; wC < "+m+"; wC += 4) {\n            int xC = xCCorner + wC * "+s+";\n\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + "+s+", d),\n              getValue(batch, xR, xC + 2 * "+s+", d),\n              getValue(batch, xR, xC + 3 * "+s+", d)\n            );\n\n            "+g+"\n          }\n\n          int xC = xCCorner + "+m+";\n          if ("+(1===v)+") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              initializationValue,\n              initializationValue,\n              initializationValue\n            );\n\n            "+g+"\n          } else if ("+(2===v)+") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + "+s+", d),\n              initializationValue,\n              initializationValue\n            );\n\n            "+g+"\n          } else if ("+(3===v)+") {\n            vec4 values = vec4(\n              getValue(batch, xR, xC, d),\n              getValue(batch, xR, xC + "+s+", d),\n              getValue(batch, xR, xC + 2 * "+s+", d),\n              initializationValue\n            );\n\n            "+g+"\n          }\n        }\n        setOutput("+d+");\n      }\n    "}},wo=function(t,e){this.variableNames=["x"];var n=t.windowSize,r=t.batchSize,o=t.inSize,i=Math.ceil(o/n);this.outputShape=[r,i];var a="0.0",s="";"prod"===e?a="1.0":"min"===e?(a="1.0 / 0.0",s="min"):"max"===e&&(a="-1.0 / 0.0",s="max");var u=e+"("+e+"("+e+"(minMaxValue[0], minMaxValue[1]), minMaxValue[2]), minMaxValue[3])";"sum"===e?u="sumValue":"prod"===e?u="prodValue":"all"===e?u="allValue":"any"===e&&(u="anyValue");var c=4*Math.floor(n/4),l=n%4,h="\n      if ("+("sum"===e)+") {\n        sumValue += dot(values, ones);\n      } else if ("+("prod"===e)+") {\n        vec2 tmp = vec2(values[0], values[1]) * vec2(values[2], values[3]);\n        prodValue *= tmp[0] * tmp[1];\n      } else {\n        minMaxValue = "+s+"(values, minMaxValue);\n      }\n    ",p="vec4";"all"===e?(a="1.0",h="\n        bool reducedAllValue = all(values);\n        float floatedReducedAllValue = float(reducedAllValue);\n        allValue = float(allValue >= 1.0 && floatedReducedAllValue >= 1.0);\n      ",p="bvec4"):"any"===e&&(a="0.0",h="\n        bool reducedAnyValue = any(values);\n        float floatedReducedAnyValue = float(reducedAnyValue);\n        anyValue = float(anyValue >= 1.0 || floatedReducedAnyValue >= 1.0);\n      ",p="bvec4");var f="";0<o%n&&(f="\n        if (inIdx < 0 || inIdx >= "+o+") {\n          return initializationValue;\n        }\n      "),this.userCode="\n      const float initializationValue = "+a+";\n      const vec4 ones = vec4(1.0, 1.0, 1.0, 1.0);\n\n      float getValue(int batch, int inIdx) {\n        "+f+"\n        return getX(batch, inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = outIdx * "+n+";\n\n        vec4 minMaxValue = vec4("+a+");\n        float prodValue = 1.0;\n        float sumValue = 0.0;\n        float allValue = 1.0;\n        float anyValue = 0.0;\n\n        for (int i = 0; i < "+c+"; i += 4) {\n          int inIdx = inOffset + i;\n          "+p+" values = "+p+"(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          "+h+"\n        }\n\n        int inIdx = inOffset + "+c+";\n        if ("+(1===l)+") {\n          "+p+" values = "+p+"(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          "+h+"\n        } else if ("+(2===l)+") {\n          "+p+" values = "+p+"(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          "+h+"\n        } else if ("+(3===l)+") {\n          "+p+" values = "+p+"(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          "+h+"\n        }\n        setOutput("+u+");\n      }\n    "},bo=function(t,e){this.variableNames=["A"],this.usesPackedTextures=!0,this.outputShape=t;for(var n="",r=0;r<4;r++){var o="thisRC = rc;";r%2==1&&(o+="thisRC.z += 1;"),1<r&&(o+="thisRC.y += 1;"),n+="\n        "+o+"\n        "+(0<r?"if(thisRC.y < rows && thisRC.z < cols){":"")+"\n          int flatIndex = getFlatIndex(thisRC);\n\n          ivec3 inputRC = inputCoordsFromReshapedOutCoords(flatIndex);\n          vec2 inputRCInnerDims = vec2(float(inputRC.y),float(inputRC.z));\n\n          result["+r+"] =\n            getChannel(getA(inputRC.x, inputRC.y, inputRC.z), inputRCInnerDims);\n        "+(0<r?"}":"")+"\n      "}this.userCode="\n      \n    ivec3 inputCoordsFromReshapedOutCoords(int index) {\n      "+Rn(["r","c","d"],e)+"\n      return ivec3(r, c, d);\n    }\n  \n      \n    int getFlatIndex(ivec3 coords) {\n      return round("+function(t,e){if(t.length!==e.length)throw new Error("Vectors to be dotted must be of the same length -got "+t.length+" and "+e.length);for(var n=[],r=Math.floor(t.length/4),o=t.length%4,i=0;i<r;i++){var a=t.slice(4*i,4*i+4),s=e.slice(4*i,4*i+4);n.push(Nn(a)+", "+Nn(s))}return 0!==o&&(a=t.slice(4*r),s=e.slice(4*r),1===a.length&&(a=a.map(function(t){return"float("+t+")"}),s=s.map(function(t){return"float("+t+")"})),n.push(Nn(a)+", "+Nn(s))),n.map(function(t,e){return"dot("+t+")"}).join("+")}(["coords.x","coords.y","coords.z"],U(t).map(function(t){return t.toString()}).concat(["1."]))+");\n    }\n  \n\n      void main() {\n        ivec3 rc = getOutputCoords();\n\n        vec4 result = vec4(0.);\n\n        ivec3 thisRC;\n        int rows = "+t[1]+";\n        int cols = "+t[2]+";\n\n        "+n+"\n\n        setOutput(result);\n      }\n    "};var Eo=function(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e.shape;var r=e.shape,o=r[1],i=r[2],a=t.shape,s=a[1],u=a[2],c=[n&&1<s?o-1:o,n&&1<u?i-1:i],l=[n&&1<s?s-1:s,n&&1<u?u-1:u],h=c[0]/l[0],p=c[1]/l[1],f=1/h,d=1/p,m=2*Math.ceil(f)+2,v=2*Math.ceil(d)+2;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float("+h+");\n        const float widthScale = float("+p+");\n\n        const float invHeightScale = float("+f+");\n        const float invWidthScale = float("+d+");\n\n        const int winHeight = int("+m+");\n        const int winWidth = int("+v+");\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(startRLerp - float(winHeight / 2));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(startCLerp - float(winWidth / 2));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= "+s+") {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= "+u+") {\n              continue;\n            }\n\n            float dxR = float(dyR) * heightScale;\n            int topDxRIndex = int(floor(dxR));\n            int bottomDxRIndex = int(min(ceil(dxR), "+(o-1)+".0));\n            float dxRLerp = dxR - float(topDxRIndex);\n            float inverseDxRLerp = 1.0 - dxRLerp;\n\n            float dxC = float(dyC) * widthScale;\n            int leftDxCIndex = int(floor(dxC));\n            int rightDxCIndex = int(min(ceil(dxC), "+(i-1)+".0));\n            float dxCLerp = dxC - float(leftDxCIndex);\n            float inverseDxCLerp = 1.0 - dxCLerp;\n\n            if (r == topDxRIndex && c == leftDxCIndex) {\n              // topLeft\n              accumulator +=\n                getDy(b, dyR, dyC, d) * inverseDxRLerp * inverseDxCLerp;\n            }\n\n            if (r == topDxRIndex && c == rightDxCIndex) {\n              // topRight\n              accumulator += getDy(b, dyR, dyC, d) * inverseDxRLerp * dxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == leftDxCIndex) {\n              // bottomLeft\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * inverseDxCLerp;\n            }\n\n            if (r == bottomDxRIndex && c == rightDxCIndex) {\n              // bottomRight\n              accumulator += getDy(b, dyR, dyC, d) * dxRLerp * dxCLerp;\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    "},Co=function(t,e,n,r){this.variableNames=["A"],this.outputShape=[];var o=t[0],i=t[1],a=t[2],s=t[3];this.outputShape=[o,e,n,s];var u=[r&&1<e?i-1:i,r&&1<n?a-1:a],c=[r&&1<e?e-1:e,r&&1<n?n-1:n];this.userCode="\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          "+u[0]/c[0]+",\n          "+u[1]/c[1]+");\n      const vec2 inputShapeRC = vec2("+i+".0, "+a+".0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the four integer indices.\n        ivec2 sourceFloorRC = ivec2(sourceFracIndexRC);\n        ivec2 sourceCeilRC = ivec2(\n          min(inputShapeRC - 1.0, ceil(sourceFracIndexRC)));\n\n        float topLeft = getA(b, sourceFloorRC.x, sourceFloorRC.y, d);\n        float bottomLeft = getA(b, sourceCeilRC.x, sourceFloorRC.y, d);\n        float topRight = getA(b, sourceFloorRC.x, sourceCeilRC.y, d);\n        float bottomRight = getA(b, sourceCeilRC.x, sourceCeilRC.y, d);\n\n        vec2 fracRC = sourceFracIndexRC - vec2(sourceFloorRC);\n\n        float top = topLeft + (topRight - topLeft) * fracRC.y;\n        float bottom = bottomLeft + (bottomRight - bottomLeft) * fracRC.y;\n        float newValue = top + (bottom - top) * fracRC.x;\n\n        setOutput(newValue);\n      }\n    "},So=function(t,e,n){this.variableNames=["dy"],this.outputShape=[],this.outputShape=e.shape;var r=e.shape,o=r[1],i=r[2],a=t.shape,s=a[1],u=a[2],c=[n&&1<s?o-1:o,n&&1<u?i-1:i],l=[n&&1<s?s-1:s,n&&1<u?u-1:u],h=c[0]/l[0],p=c[1]/l[1],f=1/h,d=1/p,m=2*Math.ceil(f)+2,v=2*Math.ceil(d)+2;this.userCode="\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        int r = coords[1];\n        int c = coords[2];\n\n        float accumulator = 0.0;\n\n        const float heightScale = float("+h+");\n        const float widthScale = float("+p+");\n\n        const float invHeightScale = float("+f+");\n        const float invWidthScale = float("+d+");\n\n        const int winHeight = int("+m+");\n        const int winWidth = int("+v+");\n\n        // Compute bounds for where in dy we will look\n        float startRLerp = floor(float(r) * invHeightScale);\n        int startDyR = int(floor(startRLerp - float(winHeight / 2)));\n\n        float startCLerp = floor(float(c) * invWidthScale);\n        int startDyC = int(floor(startCLerp - float(winWidth / 2)));\n\n        // Loop over dy\n        for (int dyROffset = 0; dyROffset < winHeight; dyROffset++) {\n          int dyR = dyROffset + startDyR;\n\n          // Guard against the window exceeding the bounds of dy\n          if (dyR < 0 || dyR >= "+s+") {\n            continue;\n          }\n\n          for (int dyCOffset = 0; dyCOffset < winWidth; dyCOffset++) {\n            int dyC = dyCOffset + startDyC;\n\n            // Guard against the window exceeding the bounds of dy\n            if (dyC < 0 || dyC >= "+u+") {\n              continue;\n            }\n\n            float sourceFracRow =\n              float("+c[0]+") *\n                (float(dyR) / float("+l[0]+"));\n\n            float sourceFracCol =\n                float("+c[1]+") *\n                  (float(dyC) / float("+l[1]+"));\n\n            int sourceNearestRow = int(min(\n                float(int("+o+") - 1),\n                "+n+" ? float(round(sourceFracRow)) :\n                                  float(floor(sourceFracRow))));\n\n            int sourceNearestCol = int(min(\n                float(int("+i+") - 1),\n                "+n+" ? float(round(sourceFracCol)) :\n                                  float(floor(sourceFracCol))));\n\n            if (r == sourceNearestRow && c == sourceNearestCol) {\n              accumulator += getDy(b, dyR, dyC, d);\n            }\n          }\n        }\n        // End loop over dy\n\n        setOutput(accumulator);\n      }\n    "},Ro=function(t,e,n,r){this.variableNames=["A"],this.outputShape=[];var o=t[0],i=t[1],a=t[2],s=t[3];this.outputShape=[o,e,n,s];var u=[r&&1<e?i-1:i,r&&1<n?a-1:a],c=[r&&1<e?e-1:e,r&&1<n?n-1:n],l=r?"0.5":"0.0";this.userCode="\n      const vec2 effectiveInputOverOutputRatioRC = vec2(\n          "+u[0]/c[0]+",\n          "+u[1]/c[1]+");\n      const vec2 inputShapeRC = vec2("+i+".0, "+a+".0);\n\n      void main() {\n        ivec4 coords = getOutputCoords();\n        int b = coords[0];\n        int d = coords[3];\n        ivec2 yRC = coords.yz;\n\n        // Fractional source index.\n        vec2 sourceFracIndexRC = vec2(yRC) * effectiveInputOverOutputRatioRC;\n\n        // Compute the coordinators of nearest neighbor point.\n        ivec2 sourceNearestRC = ivec2(\n          min(inputShapeRC - 1.0, floor(sourceFracIndexRC + "+l+")));\n\n        float newValue = getA(b, sourceNearestRC.x, sourceNearestRC.y, d);\n\n        setOutput(newValue);\n      }\n    "},No=function(r,o){this.variableNames=["x"];var t=r.length;if(4<t)throw new Error("WebGL backend: Reverse of rank-"+t+" tensor is not yet supported");if(this.outputShape=r,1!==t){var e=r.map(function(t,e){return n=e,-1!==o.indexOf(n)&&1!==r[n]?r[n]+" - coords["+n+"] - 1":"coords["+n+"]";var n}).join(","),n=Fn(t);this.userCode="\n      void main() {\n        "+n+" coords = getOutputCoords();\n        setOutput(getX("+e+"));\n      }\n    "}else this.userCode="\n        void main() {\n          int coord = getOutputCoords();\n          setOutput(getX("+r[0]+" - coord - 1));\n        }\n      "},To=function(t,e,n,r,o,i,a){void 0===a&&(a=!0),this.variableNames=["updates","indices","defaultValue"],this.outputShape=i;var s=Fn(o.length),u=Fn(i.length),c="";1===n?c="i":2===n&&(c="i, j");var l="getIndices("+c+")",h="";1===r?h="i":2===r&&(h="i, coords[1]");var p="getUpdates("+h+")",f=1<e?"strides[j]":"strides";this.userCode="\n        "+s+" strides = "+s+"("+o+");\n\n        void main() {\n          "+u+" coords = getOutputCoords();\n          float sum = 0.0;\n          bool found = false;\n          for (int i = 0; i < "+t+"; i++) {\n            int flattenedIndex = 0;\n            for (int j = 0; j < "+e+"; j++) {\n              int index = round("+l+");\n              flattenedIndex += index * "+f+";\n            }\n            if (flattenedIndex == coords[0]) {\n              sum += "+p+";\n              found = true;\n            }\n          }\n          setOutput(mix(getDefaultValue(), sum, float(found)));\n        }\n      "},ko=function(t,e){this.variableNames=["x","segmentIds"];var n=t.windowSize,r=t.batchSize,o=t.inSize,i=t.numSegments,a=i*Math.ceil(o/n);this.outputShape=[r,a];var s=4*Math.floor(n/4),u=n%4,c="\n        sumValue += dot(values, filter);\n    ",l="";0<o%n&&(l="\n        if (inIdx < 0 || inIdx >= "+o+") {\n          return initializationValue;\n        }\n      ");var h="";0<o%n&&(h="\n        if (inIdx < 0 || inIdx >= "+o+") {\n          return -1.0;\n        }\n      "),this.userCode="\n      const float initializationValue = 0.0;\n\n      float getValue(int batch, int inIdx) {\n        "+l+"\n        return getX(batch, inIdx);\n      }\n\n      float getSegmentIdAtIndex(int inIdx) {\n        "+h+"\n        return getSegmentIds(inIdx);\n      }\n\n      void main() {\n        ivec2 coords = getOutputCoords();\n        int batch = coords[0];\n        int outIdx = coords[1];\n        int inOffset = int(floor(float(outIdx) / float(\n          "+i+")) * float("+n+"));\n        int currentSeg = int(mod(float(outIdx), float("+i+")));\n\n        float sumValue = 0.0;\n\n        for (int i = 0; i < "+s+"; i += 4) {\n          int inIdx = inOffset + i;\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            getValue(batch, inIdx + 3)\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 3)) == currentSeg ? 1 : 0\n          );\n\n          "+c+"\n        }\n\n        int inIdx = inOffset + "+s+";\n        if ("+(1===u)+") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            initializationValue,\n            initializationValue,\n            initializationValue\n          );\n\n          int inIdxSeg = int(getSegmentIdAtIndex(inIdx));\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            0,\n            0,\n            0\n          );\n\n          "+c+"\n        } else if ("+(2===u)+") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            initializationValue,\n            initializationValue\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n              0,\n              0\n          );\n\n          "+c+"\n        } else if ("+(3===u)+") {\n          vec4 values = vec4(\n            getValue(batch, inIdx),\n            getValue(batch, inIdx + 1),\n            getValue(batch, inIdx + 2),\n            initializationValue\n          );\n\n          vec4 filter = vec4(\n            int(getSegmentIdAtIndex(inIdx)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 1)) == currentSeg ? 1 : 0,\n            int(getSegmentIdAtIndex(inIdx + 2)) == currentSeg ? 1 : 0,\n            0\n          );\n\n          "+c+"\n        }\n        setOutput(sumValue);\n      }\n    "},Io=function(t,e,n){var r,o;if(this.variableNames=["c","a","b"],this.outputShape=e,4<n)throw Error("Where for rank "+n+" is not yet supported");if(1===n)r=o="resRC";else{for(var i=["resRC.x","resRC.y","resRC.z","resRC.w"],a=[],s=[],u=0;u<e.length;u++)s.push(""+i[u]),u<t&&a.push(""+i[u]);r=a.join(),o=s.join()}var c=Fn(n);this.userCode="\n      void main() {\n        "+c+" resRC = getOutputCoords();\n        float cVal = getC("+r+");\n        if (cVal >= 1.0) {\n          setOutput(getA("+o+"));\n        } else {\n          setOutput(getB("+o+"));\n        }\n      }\n    "},Do=function(){function t(t){this.variableNames=["source"],this.outputShape=t,this.rank=t.length;var e=Fn(this.rank),n=function(t){if(1===t)return"sourceLoc";if(2===t)return"sourceLoc.x, sourceLoc.y";if(3===t)return"sourceLoc.x, sourceLoc.y, sourceLoc.z";if(4===t)return"sourceLoc.x, sourceLoc.y, sourceLoc.z, sourceLoc.w";throw Error("Slicing for rank "+t+" is not yet supported")}(this.rank);this.userCode="\n      uniform "+e+" start;\n\n     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this.truncated&&!this.isValidTruncated(n)||(this.nextVal=this.convertValue(n)),this.convertValue(e)},t.prototype.convertValue=function(t){return null==this.dtype||"float32"===this.dtype?t:Math.round(t)},t.prototype.isValidTruncated=function(t){return t<=this.upper&&t>=this.lower},t}();function oi(b,E){return m(this,void 0,void 0,function(){var e,n,r,o,i,a,s,u,c,l,h,p,f,d,m,v,g,y,x,w;return C(this,function(t){switch(t.label){case 0:if(e=me(b,"img","toPixels"),b instanceof it||(e=e.toInt()),2!==e.rank&&3!==e.rank)throw new Error("toPixels only supports rank 2 or 3 tensors, got rank "+e.rank+".");if(n=e.shape.slice(0,2),r=n[0],o=n[1],4<(i=2===e.rank?1:e.shape[2])||2===i)throw new Error("toPixels only supports depth of size 1, 3 or 4 but got "+i);return a=e.min(),s=e.max(),[4,a.data()];case 1:return u=t.sent()[0],[4,s.data()];case 2:if(c=t.sent()[0],a.dispose(),s.dispose(),"float32"===e.dtype){if(u<0||1<c)throw new Error("Tensor values for a float32 Tensor must be in the range [0 - 1] but got range ["+u+" - "+c+"].")}else{if("int32"!==e.dtype)throw new Error("Unsupported type for toPixels: "+e.dtype+". Please use float32 or int32 tensors.");if(u<0||255<c)throw new Error("Tensor values for a int32 Tensor must be in the range [0 - 255] but got range ["+u+" - "+c+"].")}return[4,e.data()];case 3:for(l=t.sent(),h="float32"===e.dtype?255:1,p=new Uint8ClampedArray(o*r*4),f=0;f<r*o;++f)g=v=m=d=void 0,1===i?(d=l[f]*h,m=l[f]*h,v=l[f]*h,g=255):3===i?(d=l[3*f]*h,m=l[3*f+1]*h,v=l[3*f+2]*h,g=255):4===i&&(d=l[4*f]*h,m=l[4*f+1]*h,v=l[4*f+2]*h,g=l[4*f+3]*h),p[0+(y=4*f)]=Math.round(d),p[y+1]=Math.round(m),p[y+2]=Math.round(v),p[y+3]=Math.round(g);return null!=E&&(E.width=o,E.height=r,x=E.getContext("2d"),w=new ImageData(p,o,r),x.putImageData(w,0,0)),e!==b&&e.dispose(),[2,p]}})})}function ii(t,e,n){return void 0===e&&(e="float32"),new et(t,e=e||"float32",n)}function ai(t,e){void 0===e&&(e=!1),console.log(t.toString(e))}var si=ge({batchToSpaceND_:function(t,e,n){var r=me(t,"x","batchToSpaceND"),o=e.reduce(function(t,e){return t*e});return M(r.rank>=1+e.length,"input rank is "+r.rank+" but should be > than blockShape.length "+e.length),M(n.length===e.length,"crops.length is "+n.length+" but should be equal to blockShape.length  "+e.length),M(r.shape[0]%o==0,"input tensor batch is "+r.shape[0]+" but is not divisible by the product of the elements of blockShape "+e.join(" * ")+" === "+o),Ft.engine.runKernel(function(t){return t.batchToSpaceND(r,e,n)},{$x:r},function(t){return{$x:function(){return t.spaceToBatchND(e,n)}}})}}),ui=ge({cast_:function(t,e){var n=me(t,"x","cast");return Ft.engine.runKernel(function(t){return t.cast(n,e)},{$x:n},function(t){return{$x:function(){return t.clone()}}})}}),ci=ge({clone_:function(t){var e=me(t,"x","clone",null);return Ft.engine.runKernel(function(t){return it.make(e.shape,{dataId:e.dataId},e.dtype)},{$x:e},function(t){return{$x:function(){return t.toFloat()}}})}}),li=ge({cumsum_:function(t,e,n,r){void 0===e&&(e=0),void 0===n&&(n=!1),void 0===r&&(r=!1);var o=me(t,"x","cumsum"),i=ne([e|=0],o.rank),a=o;null!=i&&(a=o.transpose(i));var s=oe(1,o.rank)[0],u=Ft.engine.runKernel(function(t){return t.cumsum(a,s,n,r)},{permutedX:a},function(t){return{permutedX:function(){return t.cumsum(e,n,!r)}}});return null!=i&&(u=u.transpose(i)),u}}),hi=ge({depthToSpace_:function(t,e,n){void 0===n&&(n="NHWC");var r=me(t,"x","depthToSpace"),o="NHWC"===n?r.shape[1]:r.shape[2],i="NHWC"===n?r.shape[2]:r.shape[3],a="NHWC"===n?r.shape[3]:r.shape[1];return M(0<=o*e,"Negative dimension size caused by overflow when multiplying\n      "+o+" and "+e+"  for depthToSpace with input shape\n      "+r.shape),M(0<=i*e,"Negative dimension size caused by overflow when multiplying\n      "+i+" and "+e+" for depthToSpace with input shape\n          "+r.shape),M(a%(e*e)==0,"Dimension size must be evenly divisible by "+e*e+" but is "+a+" for depthToSpace with input shape "+r.shape),Ft.engine.runKernel(function(t){return t.depthToSpace(r,e,n)},{$x:r})}}),pi=ge({expandDims_:function(t,e){void 0===e&&(e=0);var n=me(t,"x","expandDims");M(e<=n.rank,"Axis must be <= rank of the tensor");var r=n.shape.slice();return e<0&&(M(-(n.rank+1)<=e,"Axis must be in the interval ["+-(n.rank+1)+", "+n.rank+"]"),e=n.rank+e+1),r.splice(e,0,1),Ri(n,r)}}),fi=ge({eye_:function(t,e,n,r){void 0===r&&(r="float32"),null==e&&(e=t);for(var o=ii([t,e],r),i=t<=e?t:e,a=0;a<i;++a)o.set(1,a,a);var s=o.toTensor().as2D(t,e);if(null==n)return s;if(1===n.length)return Ii(pi(s,0),[n[0],1,1]);if(2===n.length)return Ii(pi(pi(s,0),0),[n[0],n[1],1,1]);if(3===n.length)return Ii(pi(pi(pi(s,0),0),0),[n[0],n[1],n[2],1,1]);throw new Error("eye() currently supports only 1D and 2D batchShapes, but received "+n.length+"D.")}}),di=ge({fromPixels_:function(t,e){if(void 0===e&&(e=3),4<e)throw new Error("Cannot construct Tensor with more than 4 channels from pixels.");return Ft.engine.fromPixels(t,e)}}),mi=ge({multinomial_:function(t,e,n,r){void 0===r&&(r=!1);var o=me(t,"logits","multinomial"),i=o.size,a=o.rank;if(i<2)throw new Error("Error in multinomial: you need at least 2 outcomes, but got "+i+".");if(2<a)throw new Error("Rank of probabilities must be 1 or 2, but is "+a);n=n||Math.random();var s=1===a?o.as2D(1,-1):o,u=Ft.engine.runKernel(function(t){return t.multinomial(s,r,e,n)},{logits2D:s});return 1===a?u.as1D():u}}),vi=ge({oneHot_:function(t,e,n,r){void 0===n&&(n=1),void 0===r&&(r=0);var o=me(t,"indices","oneHot","int32");if(e<2)throw new Error("Error in oneHot: depth must be >=2, but it is "+e);return Ft.engine.runKernel(function(t){return t.oneHot(o,e,n,r)},{$indices:o},function(t){return{$indices:function(){return Me(o.shape,"float32")}}})}}),gi=ge({pad_:function(t,e,n){void 0===n&&(n=0);var r=me(t,"x","pad");if(0===r.rank)throw new Error("pad(scalar) is not defined. Pass non-scalar to pad");var o=e.map(function(t){return t[0]});return Ft.engine.runKernel(function(t){return t.pad(r,e,n)},{$x:r},function(t){return{$x:function(){return t.slice(o,r.shape)}}})}}),yi=ge({pad1d_:function(t,e,n){return void 0===n&&(n=0),M(2===e.length,"Invalid number of paddings. Must be length of 2."),gi(t,[e],n)}}),xi=ge({pad2d_:function(t,e,n){return void 0===n&&(n=0),M(2===e.length&&2===e[0].length&&2===e[1].length,"Invalid number of paddings. Must be length of 2 each."),gi(t,e,n)}}),wi=ge({pad3d_:function(t,e,n){return void 0===n&&(n=0),M(3===e.length&&2===e[0].length&&2===e[1].length&&2===e[2].length,"Invalid number of paddings. Must be length of 2 each."),gi(t,e,n)}}),bi=ge({pad4d_:function(t,e,n){return void 0===n&&(n=0),M(4===e.length&&2===e[0].length&&2===e[1].length&&2===e[2].length&&2===e[3].length,"Invalid number of paddings. Must be length of 2 each."),gi(t,e,n)}}),Ei=ge({rand_:function(t,e,n){var r=L(t),o=null;if(null==n||"float32"===n)o=new Float32Array(r);else if("int32"===n)o=new Int32Array(r);else{if("bool"!==n)throw new Error("Unknown data type "+n);o=new Uint8Array(r)}for(var i=0;i<r;i++)o[i]=e();return it.make(t,{values:o},n)}}),Ci=ge({randomNormal_:function(t,e,n,r,o){if(void 0===e&&(e=0),void 0===n&&(n=1),null!=r&&"bool"===r)throw new Error("Unsupported data type "+r);for(var i=new ri(e,n,r,!1,o),a=ii(t,r),s=0;s<a.values.length;s++)a.values[s]=i.nextValue();return a.toTensor()}}),Si=ge({randomUniform_:function(t,e,n,r){void 0===e&&(e=0),void 0===n&&(n=1),void 0===r&&(r="float32");for(var o=ii(t,r),i=0;i<o.values.length;i++)o.values[i]=h(e,n);return o.toTensor()}}),Ri=ge({reshape_:function(t,e){var n=me(t,"x","reshape",null);return e=b(e,n.size),M(n.size===L(e),"new shape and old shape must have the same number of elements."),Ft.engine.runKernel(function(t){return t.reshape(n,e)},{$x:n},function(t){return{$x:function(){return t.reshape(n.shape)}}})}}),Ni=ge({spaceToBatchND_:function(t,r,o){var e=me(t,"x","spaceToBatchND");return M(e.rank>=1+r.length,"input rank "+e.rank+" should be > than [blockShape] "+r.length),M(o.length===r.length,"paddings.shape[0] "+o.length+" must be equal to [blockShape] "+r.length),M(e.shape.reduce(function(t,e,n){return 0<n&&n<=r.length?t&&(e+o[n-1][0]+o[n-1][1])%r[n-1]==0:t},!0),"input spatial dimensions "+e.shape.slice(1)+" with paddings "+o.toString()+" must be divisible by blockShapes "+r.toString()),Ft.engine.runKernel(function(t){return t.spaceToBatchND(e,r,o)},{$x:e},function(t){return{$x:function(){return t.batchToSpaceND(r,o)}}})}}),Ti=ge({squeeze_:function(t,e){var n=me(t,"x","squeeze");return Ri(n,E(n.shape,e).newShape)}}),ki=ge({stack_:function(t,e){void 0===e&&(e=0);var n=ve(t,"tensors","stack");if(M(1<=n.length,"Pass at least one tensor to tf.stack"),1===n.length)return n[0].expandDims(e);var r=n[0].rank,o=n[0].shape,i=n[0].dtype;M(e<=r,"Axis must be <= rank of the tensor"),n.forEach(function(t){f(o,t.shape,"All tensors passed to stack must have matching shapes")}),n.forEach(function(t){M(i===t.dtype,"All tensors passed to stack must have matching dtypes")});var a=n.map(function(t){return t.expandDims(e)});return Vo(a,e)}}),Ii=ge({tile_:function(t,a){var s=me(t,"x","tile");return M(s.rank===a.length,"Error in transpose: rank of input "+s.rank+" must match length of reps "+a+"."),Ft.engine.runKernel(function(t){return t.tile(s,a)},{$x:s},function(i){return{$x:function(){var t=Be(s);if(1===s.rank)for(var e=0;e<a[0];++e)t=t.add(i.slice([e*s.shape[0]],[s.shape[0]]));else if(2===s.rank)for(e=0;e<a[0];++e)for(var n=0;n<a[1];++n)t=t.add(i.slice([e*s.shape[0],n*s.shape[1]],[s.shape[0],s.shape[1]]));else if(3===s.rank)for(e=0;e<a[0];++e)for(n=0;n<a[1];++n)for(var r=0;r<a[2];++r)t=t.add(i.slice([e*s.shape[0],n*s.shape[1],r*s.shape[2]],[s.shape[0],s.shape[1],s.shape[2]]));else{if(4!==s.rank)throw new Error("Gradient for tile operation is not implemented for rank-"+s.rank+" tensors yet.");for(e=0;e<a[0];++e)for(n=0;n<a[1];++n)for(r=0;r<a[2];++r)for(var o=0;o<a[3];++o)t=t.add(i.slice([e*s.shape[0],n*s.shape[1],r*s.shape[2],o*s.shape[3]],[s.shape[0],s.shape[1],s.shape[2],s.shape[3]]))}return t}}})}}),Di=ge({truncatedNormal_:function(t,e,n,r,o){if(void 0===e&&(e=0),void 0===n&&(n=1),null!=r&&"bool"===r)throw new Error("Unsupported data type "+r);for(var i=new ri(e,n,r,!0,o),a=ii(t,r),s=0;s<a.values.length;s++)a.values[s]=i.nextValue();return a.toTensor()}}),Ai=ge({unstack_:function(t,n){void 0===n&&(n=0);for(var e,r=me(t,"x","unstack"),o=r.shape[n],i=Array(r.rank-1).fill(0),a=0,s=0;s<r.rank;s++)s!==n&&(i[a]=r.shape[s],a++);e=Array(o).fill(1);var u=Array(r.rank).fill(0),c=r.shape.slice();return e.map(function(t){c[n]=t;var e=r.slice(u,c);return u[n]+=t,e.reshape(i)})}}),Mi=function(h,p){return m(this,void 0,void 0,function(){var e,n,r,o,i,a,s,u,c,l;return C(this,function(t){switch(t.label){case 0:return e=me(h,"x","setdiff1d"),n=me(p,"y","setdiff1d"),M(e.dtype===n.dtype,"x and y should have the same dtype, but got x ("+e.dtype+") and y ("+n.dtype+")."),M(1===e.rank,"x should be 1D tensor, but got x ("+e.shape+")."),M(1===n.rank,"y should be 1D tensor, but got y ("+n.shape+")."),[4,e.data()];case 1:return r=t.sent(),[4,n.data()];case 2:for(o=t.sent(),i=new Set(o),c=a=0;c<r.length;c++)i.has(r[c])||a++;for(s=new et([a],e.dtype),u=new et([a],"int32"),l=c=0;c<r.length;c++)i.has(r[c])||(s.values[l]=r[c],u.values[l]=c,l++);return[2,[s.toTensor(),u.toTensor()]]}})})};function _i(t,e){for(var n=[],r=0;r<e.length;r++)e[r]&&n.push(r);var o=ii(t,"int32"),i=ii([n.length,t.length],"int32");for(r=0;r<n.length;r++){var a=o.indexToLoc(n[r]),s=r*t.length;i.values.set(a,s)}return i.toTensor()}var Oi=function(){function t(t,e){if(void 0===e&&(e=!0),this.gpgpu=t,this.delayedStorage=e,this.pendingRead=new WeakMap,this.pendingDisposal=new WeakSet,this.lruDataGPU=[],this.numBytesInGPU=0,this.uploadWaitMs=0,this.downloadWaitMs=0,this.binaryCache={},this.disposed=!1,Ft.get("WEBGL_VERSION")<1)throw new Error("WebGL is not supported on this device");if(null==t){var n=a(Ft.get("WEBGL_VERSION"));this.gpgpu=new io(n),this.canvas=n.canvas,this.gpgpuCreatedLocally=!0}else this.gpgpuCreatedLocally=!1,this.canvas=t.gl.canvas;Ft.get("WEBGL_PAGING_ENABLED")&&(this.NUM_BYTES_BEFORE_PAGING=window.screen.height*window.screen.width*window.devicePixelRatio*300),this.textureManager=new Mo(this.gpgpu)}return t.prototype.register=function(t,e,n){if(this.texData.has(t))throw new Error("Data buffer is already registered");this.texData.set(t,{shape:e,dtype:n})},t.prototype.setDataMover=function(t){this.texData=new Le(t)},t.prototype.fromPixels=function(t,e){if(null==t)throw new Error("pixels passed to tf.fromPixels() can not be null");var n=[t.height,t.width],r=[t.height,t.width,e];if(!(t instanceof HTMLVideoElement||t instanceof HTMLImageElement||t instanceof HTMLCanvasElement||t instanceof ImageData))throw new Error("pixels passed to tf.fromPixels() must be either an HTMLVideoElement, HTMLImageElement, HTMLCanvasElement or ImageData, but was "+t.constructor.name);if(t instanceof HTMLVideoElement){if(null==this.fromPixels2DContext){if(!Ft.get("IS_BROWSER"))throw new Error("Can't read pixels from HTMLImageElement outside the browser.");if("complete"!==document.readyState)throw new Error("The DOM is not ready yet. Please call tf.fromPixels() once the DOM is ready. One way to do that is to add an event listener for `DOMContentLoaded` on the document object");this.fromPixels2DContext=document.createElement("canvas").getContext("2d")}this.fromPixels2DContext.canvas.width=t.width,this.fromPixels2DContext.canvas.height=t.height,this.fromPixels2DContext.drawImage(t,0,0,t.width,t.height),t=this.fromPixels2DContext.canvas}var o=this.makeTensorHandle(n,"int32");this.texData.get(o.dataId).usage=$n.PIXELS,this.gpgpu.uploadPixelDataToTexture(this.getTexture(o.dataId),t);var i=new qn(r),a=this.compileAndRun(i,[o]);return this.disposeData(o.dataId),a},t.prototype.makeTensorHandle=function(t,e){var n={};return this.register(n,t,e),{dataId:n,shape:t,dtype:e}},t.prototype.write=function(t,e){if(null==e)throw new Error("MathBackendWebGL.write(): values can not be null");var n=this.texData.get(t),r=n.texture,o=n.texShape,i=n.usage,a=n.dtype,s=n.isPacked;if("complex64"===a)throw new Error("Cannot write to a complex64 dtype. Please use tf.complex(real, imag).");null!=r&&(this.releaseTexture(t,r,o,i,s),n.texture=null,n.texShape=null),n.usage=$n.UPLOAD,n.values=e,this.delayedStorage||this.uploadToGPU(t)},t.prototype.readSync=function(t){var e=this.texData.get(t),n=e.values,r=e.dtype,o=e.complexTensors;if(null!=n)return this.convertAndCacheOnCPU(t);if("string"===r)return n;var i,a,s=null!=this.activeTimers;return s&&(i=performance.now()),a="complex64"===r?Ve(o.real.dataSync(),o.imag.dataSync()):this.getValuesFromTexture(t),s&&(this.downloadWaitMs+=performance.now()-i),this.convertAndCacheOnCPU(t,a)},t.prototype.read=function(x){return m(this,void 0,void 0,function(){var e,n,r,o,i,a,s,u,c,l,h,p,f,d,m,v,g,y;return C(this,function(t){switch(t.label){case 0:if(this.pendingRead.has(x))return r=this.pendingRead.get(x),[2,new Promise(function(t){return r.push(t)})];if(o=this.texData.get(x),i=o.texture,a=o.values,s=o.texShape,u=o.isPacked,c=o.shape,null!=a)return[2,this.convertAndCacheOnCPU(x)];if(this.pendingRead.set(x,[]),!Ft.get("WEBGL_DOWNLOAD_FLOAT_ENABLED")&&2===Ft.get("WEBGL_VERSION"))throw new Error("tensor.data() with WEBGL_DOWNLOAD_FLOAT_ENABLED=false and WEBGL_VERSION=2 not yet supported.");return l=s[1],h=s[0],u&&(e=er(s[0],s[1]),l=e[0],h=e[1]),p=this.gpgpu.maybeCreateBufferFromTexture(i,h,l),[4,this.gpgpu.createAndWaitForFence()];case 1:return t.sent(),f=p instanceof WebGLTexture?this.getValuesFromTexture(x):u?(d=this.getBatchDim(c),v=m=1,c.length&&(n=this.getRowsCols(c),m=n[0],v=n[1]),this.gpgpu.downloadPackedMatrixFromBuffer(p,d,m,v,s[0],s[1])):this.gpgpu.downloadFloat32MatrixFromBuffer(p,s[0],s[1]),g=this.convertAndCacheOnCPU(x,f),y=this.pendingRead.get(x),this.pendingRead.delete(x),y.forEach(function(t){return t(g)}),this.pendingDisposal.has(x)&&(this.pendingDisposal.delete(x),this.disposeData(x)),[2,g]}})})},t.prototype.getValuesFromTexture=function(t){var e,n=this.texData.get(t),r=n.shape,o=n.dtype,i=n.texture,a=n.texShape;if(Ft.get("WEBGL_DOWNLOAD_FLOAT_ENABLED")){if(this.texData.get(t).isPacked){var s=this.getBatchDim(r),u=1,c=1;return r.length&&(u=(e=this.getRowsCols(r))[0],c=e[1]),this.gpgpu.downloadMatrixFromPackedTexture(i,s,u,c,a[0],a[1])}return this.gpgpu.downloadFloat32MatrixFromOutputTexture(i,a[0],a[1])}var l=this.makeTensorHandle(r,"float32");l.size=L(r),this.texData.get(l.dataId).usage=$n.DOWNLOAD;var h=new Un(r);this.compileAndRun(h,[{shape:r,dtype:o,dataId:t}],l,null,!1);var p=this.texData.get(l.dataId),f=this.gpgpu.downloadByteEncodedFloatMatrixFromOutputTexture(p.texture,p.texShape[0],p.texShape[1]);return this.disposeData(l.dataId),f},t.prototype.time=function(u){return m(this,void 0,void 0,function(){var e,n,r,o,i,a,s;return C(this,function(t){switch(t.label){case 0:return e=this.activeTimers,r=!(n=[]),null==this.programTimersStack?(this.programTimersStack=n,r=!0):this.activeTimers.push(n),this.activeTimers=n,u(),o=d(this.activeTimers.map(function(t){return t.query})).filter(function(t){return null!=t}),i=d(this.activeTimers.map(function(t){return t.name})).filter(function(t){return null!=t}),this.activeTimers=e,r&&(this.programTimersStack=null),[4,Promise.all(o)];case 1:return a=t.sent(),s={uploadWaitMs:this.uploadWaitMs,downloadWaitMs:this.downloadWaitMs,kernelMs:l(a),getExtraProfileInfo:function(){return a.map(function(t,e){return{name:i[e],ms:t}}).map(function(t){return t.name+": "+t.ms}).join(", ")},wallMs:null},this.uploadWaitMs=0,this.downloadWaitMs=0,[2,s]}})})},t.prototype.memory=function(){return{unreliable:!1,numBytesInGPU:this.numBytesInGPU}},t.prototype.startTimer=function(){return 0<Ft.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")?this.gpgpu.beginQuery():{startMs:performance.now(),endMs:null}},t.prototype.endTimer=function(t){return 0<Ft.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")?this.gpgpu.endQuery():t.endMs=performance.now(),t},t.prototype.getQueryTime=function(n){return m(this,void 0,void 0,function(){var e;return C(this,function(t){return 0<Ft.get("WEBGL_DISJOINT_QUERY_TIMER_EXTENSION_VERSION")?[2,this.gpgpu.waitForQueryAndGetTime(n)]:[2,(e=n).endMs-e.startMs]})})},t.prototype.disposeData=function(t){if(!this.pendingDisposal.has(t))if(this.pendingRead.has(t))this.pendingDisposal.add(t);else if(this.texData.has(t)){var e=this.texData.get(t),n=e.texture,r=e.texShape,o=e.usage,i=e.complexTensors,a=e.isPacked;null!=n&&this.releaseTexture(t,n,r,o,a),null!=i&&(i.real.dispose(),i.imag.dispose()),this.texData.delete(t)}},t.prototype.getTexture=function(t){return this.uploadToGPU(t),this.texData.get(t).texture},t.prototype.getCPUBackend=function(){return Ft.get("WEBGL_CPU_FORWARD")?(null==this.cpuBackend&&(this.cpuBackend=Ft.findBackend("cpu")),this.cpuBackend):null},t.prototype.shouldExecuteOnCPU=function(t,e){var n=this;return void 0===e&&(e=10),null!=this.getCPUBackend()&&t.every(function(t){return null==n.texData.get(t.dataId).texture&&t.size<e})},t.prototype.getGPGPUContext=function(){return this.gpgpu},t.prototype.getCanvas=function(){return this.canvas},t.prototype.complex=function(t,e){var n=this.makeOutputArray(t.shape,"complex64");return this.texData.get(n.dataId).complexTensors={real:Ft.engine.keep(t.clone()),imag:Ft.engine.keep(e.clone())},n},t.prototype.real=function(t){return this.texData.get(t.dataId).complexTensors.real.clone()},t.prototype.imag=function(t){return this.texData.get(t.dataId).complexTensors.imag.clone()},t.prototype.slice=function(t,e,n){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.slice(t,e,n);var r=new Do(n),o=r.getCustomSetupFunc(e);return this.compileAndRun(r,[t],null,o)},t.prototype.stridedSlice=function(t,e,n,r,o,i,a,s,u){if(this.shouldExecuteOnCPU([t]))return this.cpuBackend.stridedSlice(t,e,n,r,o,i,a,s,u);var c=le(t.shape,e,n,r,o,i,a,s,u),l=c[0],h=c[1],p=c[2],f=h.filter(function(t,e){return-1===p.indexOf(e)});if(f.some(function(t){return 0===t}))return Ce([],f);var d=new Ao(l,r,h,p);return this.compileAndRun(d,[t])},t.prototype.reverse=function(t,e){var n=new No(t.shape,e);return this.compileAndRun(n,[t])},t.prototype.concat2Tensors=function(t,e,n){var r=ie([t.shape,e.shape],n),o=t.as2D(-1,L(t.shape.slice(n))),i=e.as2D(-1,L(e.shape.slice(n))),a=new hn(o.shape,i.shape);return this.compileAndRun(a,[o,i]).reshape(r)},t.prototype.concat=function(t,e){if(this.shouldExecuteOnCPU(t))return this.cpuBackend.concat(t,e);if(1===t.length)return t[0];for(var n=t[0],r=1;r<t.length;++r)n=this.concat2Tensors(n,t[r],e);return n},t.prototype.neg=function(t){var e=new zo(t.shape,"return -x;");return this.compileAndRun(e,[t])},t.prototype.batchMatMul=function(t,e,n,r){var o=n?t.shape[2]:t.shape[1],i=r?e.shape[1]:e.shape[2],a=n?t.shape[1]:t.shape[2],s=t.shape[0];if((1===o||1===i)&&1e3<a){n&&(t=t.transpose([0,2,1])),r&&(e=e.transpose([0,2,1]));var u=1===i?t:t.as3D(s,a,1),c=1===i?2:1,l=1===i?e.as3D(s,1,a):e;return this.multiply(u,l).sum(c,!0)}var h=xt(t.dtype,e.dtype);if(1!==s)return d=new ho(t.shape,e.shape,n,r),m=this.makeOutputArray(d.outputShape,h),this.compileAndRun(d,[t,e],m);var p=t.as2D(t.shape[1],t.shape[2]),f=e.as2D(e.shape[1],e.shape[2]),d=new po(p.shape,f.shape,[o,i],n,r),m=this.makePackedTensor(d.outputShape,h),v=this.compileAndRun(d,[p,f],m);return v.reshape([1,v.shape[0],v.shape[1]])},t.prototype.multiply=function(t,e){if("complex64"===t.dtype){var n=this.texData.get(t.dataId),r=this.texData.get(e.dataId),o=new rn(en,t.shape,e.shape),i=new rn(nn,t.shape,e.shape),a=[this.makeComplexComponentTensorHandle(t,n.complexTensors.real),this.makeComplexComponentTensorHandle(t,n.complexTensors.imag),this.makeComplexComponentTensorHandle(e,r.complexTensors.real),this.makeComplexComponentTensorHandle(e,r.complexTensors.imag)],s=this.compileAndRun(o,a),u=this.compileAndRun(i,a),c=this.complex(s,u);return s.dispose(),u.dispose(),c}if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.multiply(t,e);var l=new sn("return a * b;",t.shape,e.shape),h=this.makeOutputArray(l.outputShape,t.dtype);return this.compileAndRun(l,[t,e],h)},t.prototype.batchNormalization=function(t,e,n,r,o,i){var a=[t,e,n],s=null;null!=i&&(s=i.shape,a.push(i));var u=null;if(null!=o&&(u=o.shape,a.push(o)),Ft.get("WEBGL_PACK_BATCHNORMALIZATION")){var c=new tn(t.shape,e.shape,n.shape,s,u,r);return this.compileAndRun(c,a)}var l=new Ze(t.shape,e.shape,n.shape,s,u,r);return this.compileAndRun(l,a)},t.prototype.localResponseNormalization4D=function(t,e,n,r,o){var i=new uo(t.shape,e,n,r,o);return this.compileAndRun(i,[t])},t.prototype.LRNGrad=function(t,e,n,r,o,i,a){var s=new co(e.shape,r,o,i,a);return this.compileAndRun(s,[e,n,t])},t.prototype.tile=function(t,e){var n=new Fo(t.shape,e);return this.compileAndRun(n,[t])},t.prototype.pad=function(t,e,n){var r=new yo(t.shape,e,n);return this.compileAndRun(r,[t])},t.prototype.transpose=function(t,e){var n=new Po(t.shape,e);return this.compileAndRun(n,[t])},t.prototype.gather=function(t,e,n){var r=new jn(t.shape,e.size,n);return this.compileAndRun(r,[t,e])},t.prototype.batchToSpaceND=function(t,e,n){M(t.rank<=4,"batchToSpaceND for rank > 4 with a WebGL backend not implemented yet");var r=e.reduce(function(t,e){return t*e}),o=jt(t.shape,e,r),i=$t(o.length,e.length),a=Kt(t.shape,e,r),s=Xt(n,e.length),u=Yt(a,n,e.length);return t.reshape(o).transpose(i).reshape(a).slice(s,u)},t.prototype.spaceToBatchND=function(t,e,n){M(t.rank<=4,"spaceToBatchND for rank > 4 with a WebGL backend not implemented yet");var r=e.reduce(function(t,e){return t*e}),o=[[0,0]];o.push.apply(o,n);for(var i=1+e.length;i<t.shape.length;++i)o.push([0,0]);var a=t.pad(o),s=jt(a.shape,e,r,!1),u=$t(s.length,e.length,!1),c=Kt(a.shape,e,r,!1);return a.reshape(s).transpose(u).reshape(c)},t.prototype.reduce=function(t,e,n){var r=t.shape[0],o=t.shape[1],i=se(o),a=new wo({windowSize:i,inSize:o,batchSize:r},e),s=a.outputShape,u=s[0],c=s[1],l=this.makeOutputArray([u,c],n);return this.compileAndRun(a,[t],l),1===l.shape[1]?l:this.reduce(l,e,n)},t.prototype.argReduce=function(t,e,n){void 0===n&&(n=null);var r=t.shape[0],o=t.shape[1];null!=n&&(r=n.shape[0],o=n.shape[1]);var i=se(o),a=new Ke({windowSize:i,inSize:o,batchSize:r},e,null==n),s=a.outputShape,u=s[0],c=s[1],l=this.makeOutputArray([u,c],"int32"),h=[t];return null!=n&&h.push(n),this.compileAndRun(a,h,l),1===l.shape[1]?l:this.argReduce(t,e,l)},t.prototype.sum=function(t,e){ee("sum",e,t.rank);var n=Jt(t.shape,e),r=n[0],o=L(n[1]),i=t.as2D(-1,o),a=wt(t.dtype);return this.reduce(i,"sum",a).reshape(r)},t.prototype.prod=function(t,e){var n=Jt(t.shape,e),r=n[0],o=L(n[1]),i=t.as2D(-1,o),a=wt(t.dtype);return this.reduce(i,"prod",a).reshape(r)},t.prototype.unsortedSegmentSum=function(t,e,n){var r=0,o=ne([r],t.rank),i=t;null!=o&&(i=t.transpose(o),r=oe(1,t.rank)[0]);var a=function(t,e,n){for(var r=[],o=t.length,i=0;i<o;i++)i!==e?r.push(t[i]):r.push(n);return r}(i.shape,r,n),s=L([i.shape[r]]),u=i.as2D(-1,s),c=wt(t.dtype),l=this.segOpCompute(u,"unsortedSegmentSum",e,c,n).reshape(a);return null!=o&&(l=l.transpose(re(o))),l},t.prototype.segOpCompute=function(t,e,n,r,o){var i=t.shape[0],a=t.shape[1],s=function(t,e){var n,r=!1;for(t<=30?(n=t,r=!0):n=W(t,Math.floor(Math.sqrt(t)));!r;){if(e<n||n===t){r=!0;break}n=W(t,n+1)}return n}(a,o),u=new ko({windowSize:s,inSize:a,batchSize:i,numSegments:o},e),c=u.outputShape,l=c[0],h=c[1],p=this.makeOutputArray([l,h],r);return this.compileAndRun(u,[t,n],p),p.shape[1]===o?p:(n=Fe(0,o).tile([a/s]),this.segOpCompute(p,e,n,r,o))},t.prototype.argMin=function(t,e){var n=[e];ee("argMin",n,t.rank);var r=Jt(t.shape,n),o=r[0],i=L(r[1]),a=t.as2D(-1,i);return this.argReduce(a,"min").reshape(o)},t.prototype.argMax=function(t,e){var n=[e];ee("argMax",n,t.rank);var r=Jt(t.shape,n),o=r[0],i=L(r[1]),a=t.as2D(-1,i);return this.argReduce(a,"max").reshape(o)},t.prototype.cumsum=function(t,e,n,r){if(e!==t.rank-1)throw new Error("WebGL cumsum shader expects an inner-most axis="+(t.rank-1)+" but got axis="+e);var o=new Ln(t.shape,n,r);return this.compileAndRun(o,[t])},t.prototype.equal=function(t,e){var n=new sn("return float(a == b);",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[t,e],r)},t.prototype.notEqual=function(t,e){var n=new sn("return float(a != b);",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[t,e],r)},t.prototype.less=function(t,e){if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.less(t,e);var n=new sn("return float(a < b);",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[t,e],r)},t.prototype.lessEqual=function(t,e){var n=new sn("return float(a <= b);",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[t,e],r)},t.prototype.greater=function(t,e){if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.greater(t,e);var n=new sn("return float(a > b);",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[t,e],r)},t.prototype.greaterEqual=function(t,e){var n=new sn("return float(a >= b);",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[t,e],r)},t.prototype.logicalNot=function(t){var e=new zo(t.shape,"return float(!(x >= 1.0));");return this.compileAndRun(e,[t])},t.prototype.logicalAnd=function(t,e){var n=new sn("return float(a >= 1.0 && b >= 1.0);",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[t,e],r)},t.prototype.logicalOr=function(t,e){var n=new sn("return float(a >= 1.0 || b >= 1.0);",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"bool");return this.compileAndRun(n,[t,e],r)},t.prototype.select=function(t,e,n){var r=new Io(t.rank,e.shape,e.rank),o=this.makeOutputArray(r.outputShape,xt(e.dtype,n.dtype));return this.compileAndRun(r,[t,e,n],o)},t.prototype.where=function(t){qt("tf.where() in webgl locks the UI thread. Call tf.whereAsync() instead");var e=t.dataSync();return _i(t.shape,e)},t.prototype.topk=function(t,e,n){return $e(t.dataSync(),t.shape,t.dtype,e)},t.prototype.min=function(t,e){ee("min",e,t.rank);var n=Jt(t.shape,e),r=n[0],o=L(n[1]),i=t.as2D(-1,o);return this.reduce(i,"min",i.dtype).reshape(r)},t.prototype.minimum=function(t,e){if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.minimum(t,e);var n=new sn("\n  if (isNaN(a)) return a;\n  if (isNaN(b)) return b;\n\n  return min(a, b);\n",t.shape,e.shape);return this.compileAndRun(n,[t,e])},t.prototype.mod=function(t,e){var n=new sn("if (b == 0.0) return NAN;\n  return mod(a, b);",t.shape,e.shape),r=n.getCustomSetupFunc();return this.compileAndRun(n,[t,e],null,r)},t.prototype.max=function(t,e){ee("max",e,t.rank);var n=Jt(t.shape,e),r=n[0],o=L(n[1]),i=t.as2D(-1,o);return this.reduce(i,"max",i.dtype).reshape(r)},t.prototype.maximum=function(t,e){if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.maximum(t,e);var n=new sn("\n  if (isNaN(a)) return a;\n  if (isNaN(b)) return b;\n\n  return max(a, b);\n",t.shape,e.shape);return this.compileAndRun(n,[t,e])},t.prototype.all=function(t,e){ee("all",e,t.rank);var n=Jt(t.shape,e),r=n[0],o=L(n[1]),i=t.as2D(-1,o);return this.reduce(i,"all",i.dtype).reshape(r)},t.prototype.any=function(t,e){ee("any",e,t.rank);var n=Jt(t.shape,e),r=n[0],o=L(n[1]),i=t.as2D(-1,o);return this.reduce(i,"any",i.dtype).reshape(r)},t.prototype.squaredDifference=function(t,e){var n=new sn("return (a - b) * (a - b);",t.shape,e.shape);return this.compileAndRun(n,[t,e])},t.prototype.realDivide=function(t,e){var n=new sn("if (a == b) return 1.0;\n  return a / b;",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"float32");return this.compileAndRun(n,[t,e],r)},t.prototype.floorDiv=function(t,e){var n=new sn("\n  float resultSign = sign(a) * sign(b);\n  int ia = round(a);\n  int ib = round(b);\n  int result = ia / ib;\n  int amodb = ia - ib * result;\n\n  if (resultSign < 0.0 && amodb != 0) {\n    result -= 1;\n  }\n  return float(result);\n",t.shape,e.shape),r=this.makeOutputArray(n.outputShape,"int32");return this.compileAndRun(n,[t,e],r)},t.prototype.add=function(t,e){if("complex64"===t.dtype&&"complex64"===e.dtype)return this.complexSeparableBinaryOp(t,e,on);var n=new sn(on,t.shape,e.shape),r=this.makeOutputArray(n.outputShape,xt(t.dtype,e.dtype));return this.compileAndRun(n,[t,e],r)},t.prototype.complexSeparableBinaryOp=function(s,u,c){var l=this,t=this.texData.get(s.dataId),e=this.texData.get(u.dataId),n=[[t.complexTensors.real,e.complexTensors.real],[t.complexTensors.imag,e.complexTensors.imag]].map(function(t){var e=t[0],n=t[1],r=new sn(c,s.shape,u.shape),o=l.makeOutputArray(r.outputShape,xt(e.dtype,n.dtype)),i=l.makeComplexComponentTensorHandle(s,e),a=l.makeComplexComponentTensorHandle(u,n);return l.compileAndRun(r,[i,a],o)}),r=n[0],o=n[1],i=this.complex(r,o);return r.dispose(),o.dispose(),i},t.prototype.makeComplexComponentTensorHandle=function(t,e){return{dataId:e.dataId,dtype:e.dtype,shape:t.shape}},t.prototype.addN=function(t){for(var e=t[0],n=1;n<t.length;n++)e=this.add(e,t[n]);return e},t.prototype.subtract=function(t,e){if("complex64"===t.dtype&&"complex64"===e.dtype)return this.complexSeparableBinaryOp(t,e,an);if(this.shouldExecuteOnCPU([t,e]))return this.cpuBackend.subtract(t,e);var n=new sn(an,t.shape,e.shape),r=this.makeOutputArray(n.outputShape,xt(t.dtype,e.dtype));return this.compileAndRun(n,[t,e],r)},t.prototype.pow=function(t,e){var n=new sn("\nif(a < 0.0 && floor(b) < b){\n  return NAN;\n}\nreturn (round(mod(b, 2.0)) == 0 || round(mod(b, 2.0)) == 2) ?\n    pow(abs(a), b) : sign(a) * pow(abs(a), b);\n",t.shape,e.shape),r=n.getCustomSetupFunc(),o=this.makeOutputArray(n.outputShape,xt(t.dtype,e.dtype));return this.compileAndRun(n,[t,e],o,r)},t.prototype.ceil=function(t){var e=new zo(t.shape,"return ceil(x);");return this.compileAndRun(e,[t])},t.prototype.floor=function(t){var e=new zo(t.shape,"return floor(x);");return this.compileAndRun(e,[t])},t.prototype.sign=function(t){var e=new zo(t.shape,"\n  if (isNaN(x)) { return 0.0; }\n  return sign(x);\n");return this.compileAndRun(e,[t])},t.prototype.round=function(t){var e=new zo(t.shape,"\n  // OpenGL ES does not support round function.\n  // The algorithm is based on banker's rounding.\n  float base = floor(x);\n  if ((x - base) < 0.5) {\n    return floor(x);\n  } else if ((x - base) > 0.5) {\n    return ceil(x);\n  } else {\n    if (mod(base, 2.0) == 0.0) {\n      return base;\n    } else {\n      return base + 1.0;\n    }\n  }\n");return this.compileAndRun(e,[t])},t.prototype.exp=function(t){var e=new zo(t.shape,"return exp(x);");return this.compileAndRun(e,[t])},t.prototype.expm1=function(t){var e=new zo(t.shape,"return exp(x) - 1.0;");return this.compileAndRun(e,[t])},t.prototype.log=function(t){var e=new zo(t.shape,"if (x < 0.0) return NAN;\n  return log(x);"),n=e.getCustomSetupFunc();return this.compileAndRun(e,[t],null,n)},t.prototype.log1p=function(t){var e=new zo(t.shape,"return log(1.0 + x);");return this.compileAndRun(e,[t])},t.prototype.sqrt=function(t){var e=new zo(t.shape,"return sqrt(x);");return this.compileAndRun(e,[t])},t.prototype.rsqrt=function(t){var e=new zo(t.shape,"return inversesqrt(x);");return this.compileAndRun(e,[t])},t.prototype.square=function(t){var e=new zo(t.shape,"return x * x;");return this.compileAndRun(e,[t])},t.prototype.reciprocal=function(t){var e=new zo(t.shape,"return 1.0 / x;");return this.compileAndRun(e,[t])},t.prototype.relu=function(t){var e=new zo(t.shape,"if (isNaN(x)) return x;\n  return (x < 0.0) ? 0.0 : x;\n");return this.compileAndRun(e,[t])},t.prototype.elu=function(t){var e=new zo(t.shape,"return (x >= 0.0) ? x : (exp(x) - 1.0);");return this.compileAndRun(e,[t])},t.prototype.eluDer=function(t,e){var n=new sn("return (b >= 1.0) ? a : a * (b + 1.0);",t.shape,e.shape);return this.compileAndRun(n,[t,e])},t.prototype.selu=function(t){var e=new zo(t.shape,"\n  // Stable and Attracting Fixed Point (0, 1) for Normalized Weights.\n  // see: https://arxiv.org/abs/1706.02515\n  float scaleAlpha = 1.7580993408473768;\n  float scale = 1.0507009873554805;\n  return (x >= 0.0) ? scale * x : scaleAlpha * (exp(x) - 1.0);\n");return this.compileAndRun(e,[t])},t.prototype.int=function(t){var e=new zo(t.shape,"return float(int(x));"),n=this.makeOutputArray(e.outputShape,"int32");return this.compileAndRun(e,[t],n)},t.prototype.clip=function(t,e,n){var r;return r=Ft.get("WEBGL_PACK_CLIP")?new cn(t.shape,e,n):new un(t.shape,e,n),this.compileAndRun(r,[t])},t.prototype.abs=function(t){var e=new zo(t.shape,"return abs(x);");return this.compileAndRun(e,[t])},t.prototype.complexAbs=function(t){var e=this.texData.get(t.dataId),n=new ln(t.shape),r=[this.makeComplexComponentTensorHandle(t,e.complexTensors.real),this.makeComplexComponentTensorHandle(t,e.complexTensors.imag)];return this.compileAndRun(n,r)},t.prototype.sigmoid=function(t){var e=new zo(t.shape,"return 1.0 / (1.0 + exp(-1.0 * x));");return this.compileAndRun(e,[t])},t.prototype.softplus=function(t){var e=new zo(t.shape,"\n  float epsilon = 1.1920928955078125e-7;\n  float threshold = log(epsilon) + 2.0;\n\n  bool too_large = x > -threshold;\n  bool too_small = x < threshold;\n\n  float result;\n  float exp_x = exp(x);\n\n  if (too_large){\n    result = x;\n  }\n  else if (too_small){\n    result = exp_x;\n  }\n  else{\n    result = log(exp_x + 1.0);\n  }\n  return result;\n");return this.compileAndRun(e,[t])},t.prototype.sin=function(t){var e=new zo(t.shape,"if (isNaN(x)) return x;\n  return sin(x);\n");return this.compileAndRun(e,[t])},t.prototype.cos=function(t){var e=new zo(t.shape,"if (isNaN(x)) return x;\n  return cos(x);\n");return this.compileAndRun(e,[t])},t.prototype.tan=function(t){var e=new zo(t.shape,"return tan(x);");return this.compileAndRun(e,[t])},t.prototype.asin=function(t){var e=new zo(t.shape,"return asin(x);");return this.compileAndRun(e,[t])},t.prototype.acos=function(t){var e=new zo(t.shape,"return acos(x);");return this.compileAndRun(e,[t])},t.prototype.atan=function(t){var e=new zo(t.shape,"if (isNaN(x)) return x;\n  return atan(x);\n");return this.compileAndRun(e,[t])},t.prototype.atan2=function(t,e){var n=new sn("\n  if (isNaN(a)) return a;\n  if (isNaN(b)) return b;\n\n  return atan(a, b);\n",t.shape,e.shape);return this.compileAndRun(n,[t,e])},t.prototype.sinh=function(t){var e=new zo(t.shape,"\n  float e2x = exp(x);\n  return (e2x - 1.0 / e2x) / 2.0;\n");return this.compileAndRun(e,[t])},t.prototype.cosh=function(t){var e=new zo(t.shape,"\n  float e2x = exp(-x);\n  return (e2x + 1.0 / e2x) / 2.0;\n");return this.compileAndRun(e,[t])},t.prototype.tanh=function(t){var e=new zo(t.shape,"\n  float e2x = exp(-2.0 * abs(x));\n  return sign(x) * (1.0 - e2x) / (1.0 + e2x);\n");return this.compileAndRun(e,[t])},t.prototype.asinh=function(t){var e=new zo(t.shape,"return log(x + sqrt(x * x + 1.0));");return this.compileAndRun(e,[t])},t.prototype.acosh=function(t){var e=new zo(t.shape,"if (isNaN(x)) return x;\n  if (x < 1.0) return NAN;\n  return log(x + sqrt(x * x - 1.0));"),n=e.getCustomSetupFunc();return this.compileAndRun(e,[t],null,n)},t.prototype.atanh=function(t){var e=new zo(t.shape,"if (isNaN(x)) return x;\n  if ((x < -1.0) || (x > 1.0)) return NAN;\n  return (log(1.0 + x) - log(1.0 - x)) / 2.0;"),n=e.getCustomSetupFunc();return this.compileAndRun(e,[t],null,n)},t.prototype.erf=function(t){var e=new zo(t.shape,'\n  // Error function is calculated approximately with elementary function.\n  // See "Handbook of Mathematical Functions with Formulas,\n  // Graphs, and Mathematical Tables", Abramowitz and Stegun.\n  float p = 0.3275911;\n  float a1 = 0.254829592;\n  float a2 = -0.284496736;\n  float a3 = 1.421413741;\n  float a4 = -1.453152027;\n  float a5 = 1.061405429;\n\n  float t = 1.0 / (1.0 + p * x);\n  return 1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*exp(-x*x);\n');return this.compileAndRun(e,[t])},t.prototype.step=function(t,e){var n,r=new zo(t.shape,(void 0===(n=e)&&(n=0),Wo+"\n    return x > 0.0 ? 1.0 : float("+n+");\n  "));return this.compileAndRun(r,[t])},t.prototype.conv2dWithIm2Row=function(t,e,n){var r=n.filterWidth,o=n.filterHeight,i=n.inChannels,a=n.outWidth,s=n.outHeight,u=r*o*i,c=s*a,l=[u,c],h=t.squeeze([0]),p=e.reshape([u,-1]),f=new so(l,h.shape,n),d=this.compileAndRun(f,[h]),m=new po(d.shape,p.shape,[c,n.outChannels],!0,!1);return this.compileAndRun(m,[d,p]).reshape([1,s,a,n.outChannels])},t.prototype.conv2d=function(t,e,n){if(Ft.get("WEBGL_CONV_IM2COL")&&1===t.shape[0])return this.conv2dWithIm2Row(t,e,n);var r=new yn(n);return this.compileAndRun(r,[t,e])},t.prototype.conv2dDerInput=function(t,e,n){var r=new fn(n);return this.compileAndRun(r,[t,e])},t.prototype.conv2dDerFilter=function(t,e,n){var r=new pn(n);return this.compileAndRun(r,[t,e])},t.prototype.depthwiseConv2D=function(t,e,n){var r;return Ft.get("WEBGL_PACK_DEPTHWISECONV")&&1===n.dilationWidth&&1===n.dilationHeight&&n.padInfo.left<=1&&n.strideWidth<=2&&n.outChannels/n.inChannels==1?(r=new bn(n),this.compileAndRun(r,[t,e],this.makePackedTensor(n.outShape,t.dtype))):(r=new wn(n),this.compileAndRun(r,[t,e]))},t.prototype.depthwiseConv2DDerInput=function(t,e,n){var r=new gn(n);return this.compileAndRun(r,[t,e])},t.prototype.depthwiseConv2DDerFilter=function(t,e,n){var r=new vn(n);return this.compileAndRun(r,[t,e])},t.prototype.conv3d=function(t,e,n){var r=new xn(n);return this.compileAndRun(r,[t,e])},t.prototype.conv3dDerInput=function(t,e,n){var r=new mn(n);return this.compileAndRun(r,[t,e])},t.prototype.conv3dDerFilter=function(t,e,n){var r=new dn(n);return this.compileAndRun(r,[t,e])},t.prototype.maxPool=function(t,e){var n=new xo(e,"max",!1),r=this.makeOutputArray(n.outputShape,t.dtype);return this.compileAndRun(n,[t],r)},t.prototype.avgPool=function(t,e){var n=new xo(e,"avg",!1),r=this.makeOutputArray(n.outputShape,"float32");return this.compileAndRun(n,[t],r)},t.prototype.maxPoolBackprop=function(t,e,n,r){var o=new xo(r,"max",!0),i=this.compileAndRun(o,[e]),a=new lo(r),s=this.makeOutputArray(a.outputShape,e.dtype),u=this.compileAndRun(a,[t,i],s);return i.dispose(),u},t.prototype.avgPoolBackprop=function(t,e,n){var r=new Xe(n),o=this.makeOutputArray(r.outputShape,e.dtype);return this.compileAndRun(r,[t],o)},t.prototype.cast=function(t,e){return We(t,e,this)},t.prototype.reshape=function(t,e){return this.texData.get(t.dataId).isPacked&&!Fr(t.shape,e)?this.packedReshape(t,e):Ue(t,e)},t.prototype.resizeBilinear=function(t,e,n,r){var o=new Co(t.shape,e,n,r);return this.compileAndRun(o,[t])},t.prototype.resizeBilinearBackprop=function(t,e,n){var r=new Eo(t,e,n);return this.compileAndRun(r,[t])},t.prototype.resizeNearestNeighbor=function(t,e,n,r){var o=new Ro(t.shape,e,n,r);return this.compileAndRun(o,[t])},t.prototype.resizeNearestNeighborBackprop=function(t,e,n){var r=new So(t,e,n);return this.compileAndRun(r,[t])},t.prototype.multinomial=function(t,e,n,r){var o=e?t:ye(t),i=o.shape[0],a=o.shape[1],s=new fo(i,a,n),u=this.makeOutputArray(s.outputShape,"int32"),c=s.getCustomSetupFunc(r);return this.compileAndRun(s,[o],u,c)},t.prototype.oneHot=function(t,e,n,r){var o=new mo(t.size,e,n,r);return this.compileAndRun(o,[t])},t.prototype.nonMaxSuppression=function(t,e,n,r,o){return qt("tf.nonMaxSuppression() in webgl locks the UI thread. Call tf.nonMaxSuppressionAsync() instead"),He(t.dataSync(),e.dataSync(),n,r,o)},t.prototype.cropAndResize=function(t,e,n,r,o,i){var a=new Sn(t.shape,e.shape,r,o,i);return this.compileAndRun(a,[t,e,n])},t.prototype.depthToSpace=function(t,e,n){M(1<e,"blockSize should be > 1 for depthToSpace, but was: "+e);var r=t.shape[0],o=("NHWC"===n?t.shape[1]:t.shape[2])*e,i=("NHWC"===n?t.shape[2]:t.shape[3])*e,a=("NHWC"===n?t.shape[3]:t.shape[1])/(e*e),s=new Wn("NHWC"===n?[r,o,i,a]:[r,a,o,i],e,n);return this.compileAndRun(s,[t])},t.prototype.split=function(t,e,n){return je(t,e,n)},t.prototype.scatterND=function(t,e,n){var r=ce(0,t,n),o=r.sliceRank,i=r.numUpdates,a=r.sliceSize,s=r.strides,u=r.outputSize,c=[u/a,a],l=t.reshape([i,o]),h=e.reshape([i,a]);if(0===u)return Ue(Ce([]),n);var p=Se(0),f=new To(i,o,l.rank,h.rank,s,c);return this.compileAndRun(f,[h,l,p]).reshape(n)},t.prototype.sparseToDense=function(t,e,n,r){var o=ce(0,t,n),i=o.sliceRank,a=o.numUpdates,s=o.strides,u=o.outputSize,c=new To(a,i,t.rank,e.rank,s,[u,1],!1);return this.compileAndRun(c,[e,t,r]).reshape(n)},t.prototype.fft=function(t){return this.fftImpl(t,!1)},t.prototype.ifft=function(t){return this.fftImpl(t,!0)},t.prototype.fftImpl=function(t,e){var n=this.texData.get(t.dataId),r=new Hn(Vn,t.shape,e),o=new Hn(Gn,t.shape,e),i=[this.makeComplexComponentTensorHandle(t,n.complexTensors.real),this.makeComplexComponentTensorHandle(t,n.complexTensors.imag)],a=this.compileAndRun(r,i),s=this.compileAndRun(o,i),u=this.complex(a,s).as2D(t.shape[0],t.shape[1]);return a.dispose(),s.dispose(),u},t.prototype.gatherND=function(t,e){var n=e.shape,r=n[n.length-1],o=ae(t,e),i=o[0],a=o[1],s=o[2],u=o[3],c=e.reshape([a,r]),l=t.reshape([t.size/s,s]),h=new Qn(r,u,[a,s]);return this.compileAndRun(h,[l,c]).reshape(i)},t.prototype.makeOutputArray=function(t,e){return it.make(t,{},e)},t.prototype.makePackedTensor=function(t,e){var n=it.make(t,{},e);return this.texData.get(n.dataId).isPacked=!0,n},t.prototype.unpackTensor=function(t){var e=new Uo(t.shape);return this.compileAndRun(e,[t],it.make(e.outputShape,{},t.dtype))},t.prototype.getBatchDim=function(t,e){return void 0===e&&(e=2),L(t.slice(0,t.length-e))},t.prototype.getRowsCols=function(t){if(0===t.length)throw Error("Cannot get rows and columns of an empty shape array.");return[1<t.length?t[t.length-2]:1,t[t.length-1]]},t.prototype.packedReshape=function(t,e){var n=t.reshape([this.getBatchDim(t.shape)].concat(this.getRowsCols(t.shape))),r=[this.getBatchDim(e)].concat(this.getRowsCols(e)),o=new bo(r,n.shape);return this.compileAndRun(o,[n]).reshape(e)},t.prototype.compileAndRun=function(i,t,e,n,r){var a=this;if(void 0===r&&(r=!0),null==e&&(e=i.usesPackedTextures?this.makePackedTensor(i.outputShape,t[0].dtype):this.makeOutputArray(i.outputShape,t[0].dtype)),0===e.size)return this.texData.get(e.dataId).values=R(e.dtype,0),e;var o=t.map(function(t){if("complex64"===t.dtype)throw new Error("GPGPUProgram does not support complex64 input. For complex64 dtypes, please separate the program into real and imaginary parts.");var e=a.texData.get(t.dataId);if(null==e.texture){if(!i.usesPackedTextures&&L(t.shape)<=Ft.get("WEBGL_SIZE_UPLOAD_UNIFORM"))return{shape:t.shape,texData:null,isUniform:!0,uniformValues:a.readSync(t.dataId)};i.usesPackedTextures&&(e.isPacked=!0,e.shape=t.shape)}else if(!!e.isPacked!=!!i.usesPackedTextures){var n=void 0,r=void 0;r=e.isPacked?(n=new Uo(t.shape),a.compileAndRun(n,[t],it.make(n.outputShape,{},t.dtype))):(n=new go(t.shape),a.compileAndRun(n,[t],a.makePackedTensor(t.shape,t.dtype))),e=a.texData.get(r.dataId),t=r}else if(e.isPacked&&!Fr(e.shape,t.shape)){a.delayedStorage=!1;var o=t.dataSync();a.delayedStorage=!0,t=it.make(t.shape,{values:o},t.dtype),(e=a.texData.get(t.dataId)).isPacked=!0}return a.uploadToGPU(t.dataId),{shape:t.shape,texData:e,isUniform:!1}});this.uploadToGPU(e.dataId);var s,u={shape:e.shape,texData:this.texData.get(e.dataId),isUniform:!1},c=function(t,e,n){var r="";e.concat(n).forEach(function(t){r+=t.shape+"_"+(t.isUniform?"uniform":t.texData.texShape)});var o=t.userCode,i=(!0===t.supportsBroadcasting).toString();return t.constructor.name+"_"+i+"_"+r+"_"+o}(i,o,u),l=this.getAndSaveBinary(c,function(){return function(t,r,e,n){for(var o=r.userCode,i=e.map(function(t,e){var n={logicalShape:t.shape,texShape:t.isUniform?null:t.texData.texShape,isUniform:t.isUniform,isPacked:!t.isUniform&&t.texData.isPacked};return{name:r.variableNames[e],shapeInfo:n}}),a=i.map(function(t){return t.shapeInfo}),s={logicalShape:n.shape,texShape:n.texData.texShape,isUniform:!1,isPacked:n.texData.isPacked},u=Tn(i,s,o,!0===r.supportsBroadcasting,r.usesPackedTextures),c=t.createProgram(u),l={},h=0;h<r.variableNames.length;h++){var p=r.variableNames[h];l[p]=t.getUniformLocation(c,p,!1)}return{program:r,source:u,webGLProgram:c,uniformLocations:l,gpgpu:t,inShapeInfos:a,outShapeInfo:s}}(a.gpgpu,i,o,u)}),h=null!=this.activeTimers;if(h&&(s=this.startTimer()),function(a,t,e,n){ao(a.inShapeInfos,t),ao([a.outShapeInfo],[e]);var r=e.texData.texture,o=e.texData.texShape,s=a.gpgpu;e.texData.isPacked?s.setOutputPackedMatrixTexture(r,o[0],o[1]):s.setOutputMatrixTexture(r,o[0],o[1]),s.setProgram(a.webGLProgram),t.forEach(function(t,e){var n=a.program.variableNames[e],r=a.uniformLocations[n];if(null!=r){if(t.isUniform){if(1===L(t.shape))s.gl.uniform1f(r,t.uniformValues[0]);else{var o=t.uniformValues;o instanceof Float32Array||(o=new Float32Array(o)),s.gl.uniform1fv(r,o)}return}var i=t.texData.texture;s.setInputMatrixTexture(i,r,e)}}),null!=n&&n(s,a.webGLProgram),s.executeProgram()}(l,o,u,n),Ft.get("WEBGL_PAGING_ENABLED")&&r&&this.numBytesInGPU>this.NUM_BYTES_BEFORE_PAGING)for(var p=this.numBytesInGPU-this.NUM_BYTES_BEFORE_PAGING;0<p&&0<this.lruDataGPU.length;){var f=this.lruDataGPU.shift(),d=this.texData.get(f),m=d.shape,v=d.dtype;p-=this.computeBytes(m,v),this.read(f)}return h&&(s=this.endTimer(s),this.activeTimers.push({name:i.constructor.name,query:this.getQueryTime(s)})),Ft.get("WEBGL_LAZILY_UNPACK")||!this.texData.get(e.dataId).isPacked||i.isPackShader?e:this.unpackTensor(e)},t.prototype.getAndSaveBinary=function(t,e){return t in this.binaryCache||(this.binaryCache[t]=e()),this.binaryCache[t]},t.prototype.getTextureManager=function(){return this.textureManager},t.prototype.dispose=function(){if(!this.disposed){for(var t in this.binaryCache)this.gpgpu.deleteProgram(this.binaryCache[t].webGLProgram);this.textureManager.dispose(),this.canvas.remove(),null!=this.fromPixels2DContext&&this.fromPixels2DContext.canvas.remove(),this.gpgpuCreatedLocally&&this.gpgpu.dispose(),this.disposed=!0}},t.prototype.floatPrecision=function(){var t=this;return Wt(function(){return 0<t.abs(Se(1e-8)).get()?32:16})},t.prototype.uploadToGPU=function(t){var e,n=this.texData.get(t),r=n.shape,o=n.values,i=n.texture,a=n.usage,s=n.isPacked;if(null==i){var u,c=null!=this.activeTimers;c&&(u=performance.now());var l=_r(r,s);n.texShape=l;var h=this.acquireTexture(t,l,a,s);if(n.texture=h,null!=o){if(s){var p=this.getBatchDim(r),f=1,d=1;r.length&&(f=(e=this.getRowsCols(r))[0],d=e[1]),this.gpgpu.uploadMatrixToPackedTexture(h,p,f,d,l[0],l[1],Fi(o))}else this.gpgpu.uploadMatrixToTexture(h,l[0],l[1],Fi(o));n.values=null,c&&(this.uploadWaitMs+=performance.now()-u)}}else Ft.get("WEBGL_PAGING_ENABLED")&&0<=this.lruDataGPU.indexOf(t)&&(this.lruDataGPU.splice(this.lruDataGPU.indexOf(t),1),this.lruDataGPU.push(t))},t.prototype.convertAndCacheOnCPU=function(t,e){var n=this.delayedStorage,r=this.texData.get(t),o=r.texture,i=r.texShape,a=r.dtype,s=r.usage,u=r.isPacked;return n&&null!=o&&(this.releaseTexture(t,o,i,s,u),r.texture=null,r.texShape=null),r.usage=$n.UPLOAD,null!=e&&(r.values=function(t,e){if("float32"===e||"complex64"===e)return t;if("int32"!==e&&"bool"!==e)throw new Error("Unknown dtype "+e);for(var n="int32"===e?new Int32Array(t.length):new Uint8Array(t.length),r=0;r<n.length;++r)n[r]=Math.round(t[r]);return n}(e,a)),r.values},t.prototype.releaseTexture=function(t,e,n,r,o){var i=this.texData.get(t),a=i.shape,s=i.dtype;if(Ft.get("WEBGL_PAGING_ENABLED")){var u=this.lruDataGPU.indexOf(t);0<=u&&this.lruDataGPU.splice(u,1)}this.numBytesInGPU-=this.computeBytes(a,s),this.textureManager.releaseTexture(e,n,r,o)},t.prototype.acquireTexture=function(t,e,n,r){var o=this.texData.get(t),i=o.shape,a=o.dtype;return Ft.get("WEBGL_PAGING_ENABLED")&&this.lruDataGPU.push(t),this.numBytesInGPU+=this.computeBytes(i,a),this.textureManager.acquireTexture(e,n,r)},t.prototype.computeBytes=function(t,e){return L(t)*_(e)},t}();function Fi(t){return t instanceof Float32Array?t:new Float32Array(t)}Ft.get("IS_BROWSER")&&Ft.registerBackend("webgl",function(){return new Oi},2,ot);var Pi=ge({abs_:function(t){var e=me(t,"x","abs");return"complex64"===e.dtype?Ft.engine.runKernel(function(t){return t.complexAbs(e)},{$x:e}):Ft.engine.runKernel(function(t){return t.abs(e)},{$x:e},function(t){return{$x:function(){return t.mulStrict(e.toFloat().step(-1))}}})}}),Bi=ge({acos_:function(t){var e=me(t,"x","acos");return Ft.engine.runKernel(function(t){return t.acos(e)},{$x:e},function(t){return{$x:function(){return t.divStrict(Se(1).sub(e.toFloat().square()).sqrt()).neg()}}})}}),Li=ge({acosh_:function(t){var e=me(t,"x","acosh");return Ft.engine.runKernel(function(t){return t.acosh(e)},{$x:e},function(t){return{$x:function(){return t.divStrict(e.toFloat().square().sub(1).sqrt())}}})}}),zi=ge({asin_:function(t){var e=me(t,"x","asin");return Ft.engine.runKernel(function(t){return t.asin(e)},{$x:e},function(t){return{$x:function(){return 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("+n+")."),Ft.engine.runKernel(function(t){return t.clip(r,e,n)},{$x:r},function(t){return{$x:function(){return t.where(r.greaterEqual(e).logicalAnd(r.lessEqual(n)),Be(t))}}})}}),qi=ge({cos_:function(t){var e=me(t,"x","cos");return Ft.engine.runKernel(function(t){return t.cos(e)},{$x:e},function(t){return{$x:function(){return e.toFloat().sin().neg().mulStrict(t)}}})}}),ji=ge({cosh_:function(t){var e=me(t,"x","cosh");return Ft.engine.runKernel(function(t){return t.cosh(e)},{$x:e},function(t){return{$x:function(){return e.toFloat().sinh().mulStrict(t)}}})}}),$i=ge({erf_:function(t){var e=me(t,"x","erf");return M("int32"===e.dtype||"float32"===e.dtype,"Input dtype must be `int32` or `float32`."),"int32"===e.dtype&&(e=e.toFloat()),Ft.engine.runKernel(function(t){return t.erf(e)},{$x:e},function(t){return{$x:function(){return t.mul(e.square().neg().exp().mul(2/Math.sqrt(Math.PI)))}}})}}),Ki=ge({exp_:function(t){var n=me(t,"x","exp");return Ft.engine.runKernel(function(t,e){return e(t.exp(n))},{$x:n},function(t,e){var n=e[0];return{$x:function(){return t.mulStrict(n)}}})}}),Xi=ge({expm1_:function(t){var e=me(t,"x","expm1");return Ft.engine.runKernel(function(t){return t.expm1(e)},{$x:e},function(t){return{$x:function(){return t.mulStrict(e.exp())}}})}}),Yi=ge({floor_:function(t){var e=me(t,"x","floor");return Ft.engine.runKernel(function(t){return t.floor(e)},{$x:e},function(t){return{$x:function(){return Be(t)}}})}}),Qi=ge({log_:function(t){var e=me(t,"x","log");return Ft.engine.runKernel(function(t){return t.log(e)},{$x:e},function(t){return{$x:function(){return t.divStrict(e.toFloat())}}})}}),Ji=ge({log1p_:function(t){var e=me(t,"x","log1p");return Ft.engine.runKernel(function(t){return t.log1p(e)},{$x:e},function(t){return{$x:function(){return t.div(e.add(1))}}})}}),Zi=ge({logSigmoid_:function(t){var e=me(t,"x","logSigmoid");return Ft.engine.runKernel(function(t){return t.softplus(e.neg()).neg()},{$x:e},function(t){return{$x:function(){return t.mulStrict(e.neg().sigmoid())}}})}}),ta=ge({neg_:function(t){var e=me(t,"x","neg");return Ft.engine.runKernel(function(t){return t.neg(e)},{$x:e},function(t){return{$x:function(){return t.neg()}}})}}),ea=ge({reciprocal_:function(t){var e=me(t,"x","reciprocal");return Ft.engine.runKernel(function(t){return t.reciprocal(e)},{$x:e},function(t){return{$x:function(){return t.divStrict(e.square().neg())}}})}}),na=ge({round_:function(t){var e=me(t,"x","round");return Ft.engine.runKernel(function(t){return t.round(e)},{$x:e},function(t){return{$x:function(){return Be(t)}}})}}),ra=ge({rsqrt_:function(t){var e=me(t,"x","rsqrt");return Ft.engine.runKernel(function(t){return t.rsqrt(e)},{$x:e},function(t){return{$x:function(){return t.div(e.pow(1.5).mul(2)).neg()}}})}}),oa=ge({sigmoid_:function(t){var n=me(t,"x","sigmoid");return Ft.engine.runKernel(function(t,e){return e(t.sigmoid(n))},{$x:n},function(t,e){var n=e[0];return{$x:function(){return t.mul(n.mul(Se(1).sub(n)))}}})}}),ia=ge({sign_:function(t){var e=me(t,"x","sign");return Ft.engine.runKernel(function(t){return t.sign(e)},{$x:e},function(t){return{$x:function(){return Be(t)}}})}}),aa=ge({sin_:function(t){var e=me(t,"x","sin");return Ft.engine.runKernel(function(t){return t.sin(e)},{$x:e},function(t){return{$x:function(){return e.toFloat().cos().mulStrict(t)}}})}}),sa=ge({sinh_:function(t){var e=me(t,"x","sinh");return Ft.engine.runKernel(function(t){return t.sinh(e)},{$x:e},function(t){return{$x:function(){return e.toFloat().cosh().mulStrict(t)}}})}}),ua=ge({softplus_:function(t){var e=me(t,"x","softplus");return Ft.engine.runKernel(function(t){return t.softplus(e)},{$x:e},function(t){return{$x:function(){return t.mulStrict(e.sigmoid())}}})}}),ca=ge({sqrt_:function(t){var e=me(t,"x","sqrt");return Ft.engine.runKernel(function(t){return t.sqrt(e)},{$x:e},function(t){return{$x:function(){return t.div(e.toFloat().sqrt().mul(2))}}})}}),la=ge({square_:function(t){var e=me(t,"x","square");return Ft.engine.runKernel(function(t){return t.square(e)},{$x:e},function(t){return{$x:function(){return t.mul(e.toFloat().mul(2))}}})}}),ha=ge({step_:function(t,e){void 0===e&&(e=0);var n=me(t,"x","step");return Ft.engine.runKernel(function(t){return t.step(n,e)},{$x:n},function(t){return{$x:function(){return Be(t)}}})}}),pa=ge({tan_:function(t){var e=me(t,"x","tan");return Ft.engine.runKernel(function(t){return t.tan(e)},{$x:e},function(t){return{$x:function(){return t.divStrict(e.cos().square())}}})}}),fa=ge({tanh_:function(t){var n=me(t,"x","tanh");return Ft.engine.runKernel(function(t,e){return e(t.tanh(n))},{$x:n},function(t,e){var n=e[0];return{$x:function(){return Se(1).sub(n.square()).mulStrict(t)}}})}});function da(t){return null==t?null:0===t.rank?t.as1D():1===t.rank?t:2===t.rank?t.as4D(1,1,t.shape[0],t.shape[1]):3===t.rank?t.as4D(1,t.shape[0],t.shape[1],t.shape[2]):t}var ma=ge({batchNormalization2d_:function(t,e,n,r,o,i){void 0===r&&(r=.001);var a,s,u=me(t,"x","batchNormalization"),c=me(e,"mean","batchNormalization"),l=me(n,"variance","batchNormalization");return null!=o&&(a=me(o,"scale","batchNormalization")),null!=i&&(s=me(i,"offset","batchNormalization")),M(2===u.rank,"Error in batchNormalization3D: x must be rank 3 but got rank "+u.rank+"."),M(2===c.rank||1===c.rank,"Error in batchNormalization2D: mean must be rank 2 or rank 1 but got rank "+c.rank+"."),M(2===l.rank||1===l.rank,"Error in batchNormalization2D: variance must be rank 2 or rank 1 but got rank "+l.rank+"."),null!=a&&M(2===a.rank||1===a.rank,"Error in batchNormalization2D: scale must be rank 2 or rank 1 but got rank "+a.rank+"."),null!=s&&M(2===s.rank||1===s.rank,"Error in batchNormalization2D: offset must be rank 2 or rank 1 but got rank "+s.rank+"."),ya(u,c,l,r,a,s)}}),va=ge({batchNormalization3d_:function(t,e,n,r,o,i){void 0===r&&(r=.001);var a,s,u=me(t,"x","batchNormalization"),c=me(e,"mean","batchNormalization"),l=me(n,"variance","batchNormalization");return null!=o&&(a=me(o,"scale","batchNormalization")),null!=i&&(s=me(i,"offset","batchNormalization")),M(3===u.rank,"Error in batchNormalization3D: x must be rank 3 but got rank "+u.rank+"."),M(3===c.rank||1===c.rank,"Error in batchNormalization3D: mean must be rank 3 or rank 1 but got rank "+c.rank+"."),M(3===l.rank||1===l.rank,"Error in batchNormalization3D: variance must be rank 3 or rank 1 but got rank "+l.rank+"."),null!=a&&M(3===a.rank||1===a.rank,"Error in batchNormalization3D: scale must be rank 3 or rank 1 but got rank "+a.rank+"."),null!=s&&M(3===s.rank||1===s.rank,"Error in batchNormalization3D: offset must be rank 3 or rank 1 but got rank "+s.rank+"."),ya(u,c,l,r,a,s)}}),ga=ge({batchNormalization4d_:function(t,e,n,r,o,i){void 0===r&&(r=.001);var a,s,u=me(t,"x","batchNormalization"),c=me(e,"mean","batchNormalization"),l=me(n,"variance","batchNormalization");return null!=o&&(a=me(o,"scale","batchNormalization")),null!=i&&(s=me(i,"offset","batchNormalization")),M(4===u.rank,"Error in batchNormalization4D: x must be rank 4 but got rank "+u.rank+"."),M(4===c.rank||1===c.rank,"Error in batchNormalization4D: mean must be rank 4 or rank 1 but got rank "+c.rank+"."),M(4===l.rank||1===l.rank,"Error in batchNormalization4D: variance must be rank 4 or rank 1 but got rank "+l.rank+"."),null!=a&&M(4===a.rank||1===a.rank,"Error in batchNormalization4D: scale must be rank 4 or rank 1 but got rank "+a.rank+"."),null!=s&&M(4===s.rank||1===s.rank,"Error in batchNormalization4D: offset must be rank 4 or rank 1 but got rank "+s.rank+"."),ya(u,c,l,r,a,s)}}),ya=ge({batchNormalization_:function(t,e,n,c,r,o){void 0===c&&(c=.001);var l,i,h,p=me(t,"x","batchNormalization"),f=me(e,"mean","batchNormalization"),d=me(n,"variance","batchNormalization");return null!=r&&(l=me(r,"scale","batchNormalization")),null!=o&&(i=me(o,"offset","batchNormalization")),M(f.rank===d.rank,"Batch normalization gradient requires mean and variance to have equal ranks."),M(null==i||f.rank===i.rank,"Batch normalization gradient requires mean and offset to have equal ranks."),M(null==l||f.rank===l.rank,"Batch normalization gradient requires mean and scale to have equal ranks."),h=0===p.rank||1===p.rank?p.as4D(1,1,1,p.size):2===p.rank?p.as4D(1,1,p.shape[0],p.shape[1]):3===p.rank?p.as4D(1,p.shape[0],p.shape[1],p.shape[2]):p,Ft.engine.runKernel(function(t){return t.batchNormalization(h,da(f),da(d),c,da(l),da(i))},{$x:p,$mean:f,$variance:d,$scale:l,$offset:i},function(n){var t=null==l?Se(1):l,r=Qe(f.shape,h.shape),e=[];if(1===f.rank){for(var o=0;o<h.shape.length-1;++o)e.push(h.shape[o]);e.push(1)}var i=p.sub(f),a=n.mul(t),s=ra(d.add(Se(c))),u=s.mul(s).mul(s).mul(Se(-.5));return{$x:function(){return 1===f.rank?n.mul(Ii(s.as4D(1,1,1,f.shape[0]),e)).mul(t).reshape(p.shape):n.mul(s).mul(t).reshape(p.shape)},$mean:function(){var t=s.mul(Se(-1)).mul(a);return 1===f.rank&&(t=t.sum(r)),t.reshape(f.shape)},$variance:function(){var t=u.mul(i).mul(a);return 1===f.rank&&(t=t.sum(r)),t.reshape(f.shape)},$scale:function(){var t=i.mul(s),e=n.mul(t);return 1===f.rank&&(e=e.sum(r)),e.reshape(f.shape)},$offset:function(){var t=n;return 1===f.rank&&(t=t.sum(r)),t.reshape(f.shape)}}}).reshape(p.shape)}});function xa(t,e,n,r,o,i,a){void 0===a&&(a="channelsLast");var s,u=Ea(e),c=u[0],l=u[1];if("channelsLast"===a)s=[c,l,t[3],t[3]];else{if("channelsFirst"!==a)throw new Error("Unknown dataFormat "+a);s=[c,l,t[1],t[1]]}return wa(t,s,n,r,o,i,!1,a)}function wa(t,e,n,r,o,i,a,s){void 0===a&&(a=!1),void 0===s&&(s="channelsLast");var u=[-1,-1,-1,-1],c=u[0],l=u[1],h=u[2],p=u[3];if("channelsLast"===s)c=t[0],l=t[1],h=t[2],p=t[3];else{if("channelsFirst"!==s)throw new Error("Unknown dataFormat "+s);c=t[0],p=t[1],l=t[2],h=t[3]}var f,d=e[0],m=e[1],v=e[3],g=Ea(n),y=g[0],x=g[1],w=Ea(r),b=w[0],E=w[1],C=Sa(d,b),S=Sa(m,E),R=function(t,e,n,r,o,i,a,s){var u,c,l;if("number"==typeof t){u={top:t,bottom:t,left:t,right:t,type:0===t?"VALID":"NUMBER"};var h=function(t,e,n,r,o,i){null==o&&(o=function(t,e,n,r){void 0===r&&(r=1);var o=Sa(e,r);return Math.floor((t[0]*(n-1)-n+o)/2)}(t,e,r));var a=t[0],s=t[1],u=Ra((a-e+2*o)/r+1,i);M(D(u),"The output # of rows ("+u+") must be an integer. Change the stride and/or zero pad parameters");var c=Ra((s-e+2*o)/r+1,i);return M(D(c),"The output # of columns ("+c+") must be an integer. Change the stride and/or zero pad parameters"),[u,c,n]}([e,n,1],i,1,r,t,s);c=h[0],l=h[1]}else if("same"===t){var p=((c=Math.ceil(e/r))-1)*r+i-e,f=((l=Math.ceil(n/o))-1)*o+a-n,d=Math.floor(p/2),m=p-d,v=Math.floor(f/2);u={top:d,bottom:m,left:v,right:f-v,type:"SAME"}}else{if("valid"!==t)throw Error("Unknown padding parameter: "+t);u={top:0,bottom:0,left:0,right:0,type:"VALID"},c=Math.ceil((e-i+1)/r),l=Math.ceil((n-a+1)/o)}return{padInfo:u,outHeight:c,outWidth:l}}(o,l,h,y,x,C,S,i),N=R.padInfo,T=R.outHeight,k=R.outWidth,I=a?v*p:v;return"channelsFirst"===s?f=[c,I,T,k]:"channelsLast"===s&&(f=[c,T,k,I]),{batchSize:c,dataFormat:s,inHeight:l,inWidth:h,inChannels:p,outHeight:T,outWidth:k,outChannels:I,padInfo:N,strideHeight:y,strideWidth:x,filterHeight:d,filterWidth:m,effectiveFilterHeight:C,effectiveFilterWidth:S,dilationHeight:b,dilationWidth:E,inShape:t,outShape:f,filterShape:e}}function ba(t,e,n,r,o,i,a){void 0===i&&(i=!1),void 0===a&&(a="channelsLast");var s=[-1,-1,-1,-1,-1],u=s[0],c=s[1],l=s[2],h=s[3],p=s[4];if("channelsLast"===a)u=t[0],c=t[1],l=t[2],h=t[3],p=t[4];else{if("channelsFirst"!==a)throw new Error("Unknown dataFormat "+a);u=t[0],p=t[1],c=t[2],l=t[3],h=t[4]}var f,d=e[0],m=e[1],v=e[2],g=e[4],y=Ca(n),x=y[0],w=y[1],b=y[2],E=Ca(r),C=E[0],S=E[1],R=E[2],N=function(t,e,n,r,o,i,a,s,u,c){var l,h,p,f;if("same"===t){var d=((h=Math.ceil(e/o))-1)*o+s-e,m=((p=Math.ceil(n/i))-1)*i+u-n,v=((f=Math.ceil(r/a))-1)*a+c-r,g=Math.floor(d/2),y=d-g,x=Math.floor(m/2),w=m-x,b=Math.floor(v/2);l={top:x,bottom:w,left:b,right:v-b,front:g,back:y,type:"SAME"}}else{if("valid"!==t)throw Error("Unknown padding parameter: "+t);l={top:0,bottom:0,left:0,right:0,front:0,back:0,type:"VALID"},h=Math.ceil((e-s+1)/o),p=Math.ceil((n-u+1)/i),f=Math.ceil((r-c+1)/a)}return{padInfo:l,outDepth:h,outHeight:p,outWidth:f}}(o,c,l,h,x,w,b,Sa(d,C),Sa(m,S),Sa(v,R)),T=N.padInfo,k=N.outDepth,I=N.outHeight,D=N.outWidth,A=i?g*p:g;return"channelsFirst"===a?f=[u,A,k,I,D]:"channelsLast"===a&&(f=[u,k,I,D,A]),{batchSize:u,dataFormat:a,inDepth:c,inHeight:l,inWidth:h,inChannels:p,outDepth:k,outHeight:I,outWidth:D,outChannels:A,padInfo:T,strideDepth:x,strideHeight:w,strideWidth:b,filterDepth:d,filterHeight:m,filterWidth:v,dilationDepth:C,dilationHeight:S,dilationWidth:R,inShape:t,outShape:f,filterShape:e}}function Ea(t){return"number"==typeof t?[t,t]:t}function Ca(t){return"number"==typeof t?[t,t,t]:t}function Sa(t,e){return e<=1?t:t+(t-1)*(e-1)}function Ra(t,e){if(!e)return t;switch(e){case"round":return Math.round(t);case"ceil":return Math.ceil(t);case"floor":return Math.floor(t);default:throw new Error("Unknown roundingMode "+e)}}function Na(t){var e=Ea(t),n=e[0],r=e[1];return 1===n&&1===r}function Ta(t,e){return Na(t)||Na(e)}var ka=ge({matMul_:function(t,e,n,r){var o;void 0===n&&(n=!1),void 0===r&&(r=!1);var i=me(t,"a","matMul"),a=me(e,"b","matMul");i=(o=bt(i,a))[0],a=o[1];var s=n?i.shape[i.rank-2]:i.shape[i.rank-1],u=r?a.shape[a.rank-1]:a.shape[a.rank-2],c=n?i.shape[i.rank-1]:i.shape[i.rank-2],l=r?a.shape[a.rank-2]:a.shape[a.rank-1],h=i.shape.slice(0,-2),p=a.shape.slice(0,-2),f=L(h),d=L(p);M(2<=i.rank&&2<=a.rank&&i.rank===a.rank,"Error in matMul: inputs must have the same rank of at least 2, got ranks "+i.rank+" and "+a.rank+"."),M(y(h,p),"Error in matMul: outer dimensions ("+h+") and ("+p+") of Tensors with shapes "+i.shape+" and "+a.shape+" must match."),M(s===u,"Error in matMul: inner shapes ("+s+") and ("+u+") of Tensors with shapes "+i.shape+" and "+a.shape+" and transposeA="+n+" and transposeB="+r+" must match.");var m=i.shape.slice(0,-2).concat([c,l]),v=n?i.as3D(f,s,c):i.as3D(f,c,s),g=r?a.as3D(d,l,u):a.as3D(d,u,l);return Ft.engine.runKernel(function(t){return t.batchMatMul(v,g,n,r)},{$a:v,$b:g},function(t){return n||r?!n&&r?{$a:function(){return t.matMul(g,!1,!1)},$b:function(){return t.matMul(v,!0,!1)}}:n&&!r?{$a:function(){return g.matMul(t,!1,!0)},$b:function(){return v.matMul(t,!1,!1)}}:{$a:function(){return g.matMul(t,!0,!0)},$b:function(){return t.matMul(v,!0,!0)}}:{$a:function(){return t.matMul(g,!1,!0)},$b:function(){return v.matMul(t,!0,!1)}}}).reshape(m)}}),Ia=ge({dot_:function(t,e){var n=me(t,"t1","dot"),r=me(e,"t2","dot");M(!(1!==n.rank&&2!==n.rank||1!==r.rank&&2!==r.rank),"Error in dot: inputs must all be rank 1 or 2, but got ranks "+n.rank+" and "+r.rank+".");var o=1===n.rank?n.size:n.shape[1],i=1===r.rank?r.size:r.shape[0];return M(o===i,"Error in dot: inner dimensions of inputs must match, but got "+o+" and "+i+"."),1===n.rank&&1===r.rank?n.as2D(1,-1).matMul(r.as2D(-1,1)).asScalar():1===n.rank&&2===r.rank?n.as2D(1,-1).matMul(r.as2D(r.shape[0],r.shape[1])).as1D():2===n.rank&&1===r.rank?n.matMul(r.as2D(-1,1)).as1D():n.matMul(r.as2D(r.shape[0],r.shape[1]))}}),Da=ge({outerProduct_:function(t,e){var n=me(t,"v1","outerProduct"),r=me(e,"v2","outerProduct");return M(1===n.rank&&1===r.rank,"Error in outerProduct: inputs must be rank 1, but got ranks "+n.rank+" and "+r.rank+"."),n.as2D(-1,1).matMul(r.as2D(1,-1))}});function Aa(t,e,n,r,o,i){M(t.length===e.rank,"Length of inShape ("+t.length+") and rank of dy ("+e.rank+") must match");var a=t,s=e,u=!1;3===e.rank&&(u=!0,s=e.as4D(1,e.shape[0],e.shape[1],e.shape[2]),a=[1,t[0],t[1],t[2]]);var c=a[3],l=s.shape[3];M(4===a.length,"Error in conv2dDerInput: inShape must be length 4, but got length "+a.length+"."),M(4===s.rank,"Error in conv2dDerInput: dy must be rank 4, but got rank "+s.rank),M(4===n.rank,"Error in conv2dDerInput: filter must be rank 4, but got rank "+n.rank),M(c===n.shape[2],"Error in conv2dDerInput: depth of input ("+c+") must match input depth for filter "+n.shape[2]+"."),M(l===n.shape[3],"Error in conv2dDerInput: depth of output ("+l+") must match output depth for filter "+n.shape[3]+"."),null!=i&&M(D(o),"Error in conv2dDerInput: pad must be an integer when using, dimRoundingMode "+i+" but got pad "+o+".");var h=wa(a,n.shape,r,1,o,i),p=Ft.engine.runKernel(function(t){return t.conv2dDerInput(s,n,h)},{dy4D:s,filter:n},function(t){return{dy4D:function(){return Fa(t,n,r,o,"NHWC",1,i)},filter:function(){return Ba(t,s,n.shape,r,o,i)}}});return u?p.as3D(p.shape[1],p.shape[2],p.shape[3]):p}function Ma(t,e,n,r,o,i){var a=t;3===t.rank&&(a=t.as4D(1,t.shape[0],t.shape[1],t.shape[2]));var s=e;3===s.rank&&(s=e.as4D(1,e.shape[0],e.shape[1],e.shape[2])),M(4===a.rank,"Error in conv2dDerFilter: input must be rank 4, but got shape "+a.shape+"."),M(4===s.rank,"Error in conv2dDerFilter: dy must be rank 4, but got shape "+s.shape+"."),M(4===n.length,"Error in conv2dDerFilter: filterShape must be length 4, but got "+n+"."),M(a.shape[3]===n[2],"Error in conv2dDerFilter: depth of input "+a.shape[3]+") must match input depth in filter ("+n[2]+"."),M(s.shape[3]===n[3],"Error in conv2dDerFilter: depth of dy ("+s.shape[3]+") must match output depth for filter ("+n[3]+")."),null!=i&&M(D(o),"Error in conv2dDerFilter: pad must be an integer when using, dimRoundingMode "+i+" but got pad "+o+".");var u=wa(a.shape,n,r,1,o,i);return Ft.engine.runKernel(function(t){return t.conv2dDerFilter(a,s,u)},{x4D:a,dy4D:s})}function _a(t){var e,n="number"==typeof(e=t)?[e,e,e]:2===e.length?[e[0],e[1],1]:e,r=n[0],o=n[1],i=n[2];return 1===r&&1===o&&1===i}var Oa=ge({conv1d_:function(t,e,n,r,o,i,a){void 0===o&&(o="NWC"),void 0===i&&(i=1);var s=me(t,"x","conv1d"),u=me(e,"filter","conv1d"),c=s,l=!1;2===s.rank&&(l=!0,c=s.as3D(1,s.shape[0],s.shape[1])),M(3===c.rank,"Error in conv1d: input must be rank 3, but got rank 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Got stride "+n+" and dilation '"+i+"'"),M("NWC"===o,"Error in conv1d: got dataFormat of "+o+" but only NWC is currently supported.");var h=u.as4D(1,u.shape[0],u.shape[1],u.shape[2]),p=c.as4D(c.shape[0],1,c.shape[1],c.shape[2]),f=Fa(p,h,[1,n],r,"NHWC",[1,i],a);return l?f.as2D(f.shape[2],f.shape[3]):f.as3D(f.shape[0],f.shape[2],f.shape[3])}}),Fa=ge({conv2d_:function(t,e,n,r,o,i,a){void 0===o&&(o="NHWC"),void 0===i&&(i=[1,1]);var s=me(t,"x","conv2d"),u=me(e,"filter","conv2d"),c=s,l=!1;3===s.rank&&(l=!0,c=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),M(4===c.rank,"Error in conv2d: input must be rank 4, but got rank "+c.rank+"."),M(4===u.rank,"Error in conv2d: filter must be rank 4, but got rank "+u.rank+"."),null!=a&&M(D(r),"Error in conv2d: pad must be an integer when using, dimRoundingMode "+a+" but got pad "+r+"."),M(c.shape[3]===u.shape[2],"Error in conv2d: depth of input ("+c.shape[3]+") must match input depth for filter "+u.shape[2]+"."),M(Ta(n,i),"Error in conv2D: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+i+"'"),M("NHWC"===o,"Error in conv2d: got dataFormat of "+o+" but only NHWC is currently supported.");var h,p=wa(c.shape,u.shape,n,i,r,a);if(1!==p.filterHeight||1!==p.filterWidth||1!==p.dilationHeight||1!==p.dilationWidth||1!==p.strideHeight||1!==p.strideWidth||"SAME"!==p.padInfo.type&&"VALID"!==p.padInfo.type)h=Ft.engine.runKernel(function(t){return t.conv2d(c,u,p)},{x:c,$filter:u},function(t){return M(Na(i),"Error in gradient of conv2D: dilation rates greater than 1 are notyet supported in gradients. Got dilations '"+i+"'"),{x:function(){return Aa(c.shape,t,u,n,r)},$filter:function(){return Ma(c,t,u.shape,n,r)}}});else{var f=c.reshape([-1,p.inChannels]),d=u.reshape([p.inChannels,p.outChannels]);h=ka(f,d).reshape(p.outShape)}return l?h.as3D(h.shape[1],h.shape[2],h.shape[3]):h}}),Pa=ge({conv3d_:function(t,e,n,r,o,i){void 0===o&&(o="NHWC"),void 0===i&&(i=[1,1,1]);var a,s=me(t,"x","conv3d"),u=me(e,"filter","conv3d"),c=s,l=!1;4===s.rank&&(l=!0,c=s.as5D(1,s.shape[0],s.shape[1],s.shape[2],s.shape[3])),M(5===c.rank,"Error in conv3d: input must be rank 5, but got rank "+c.rank+"."),M(5===u.rank,"Error in conv3d: filter must be rank 5, but got rank "+u.rank+"."),M(c.shape[4]===u.shape[3],"Error in conv3d: depth of input ("+c.shape[4]+") must match input depth for filter "+u.shape[3]+"."),M((a=i,_a(n)||_a(a)),"Error in conv3D: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+i+"'"),M("NHWC"===o,"Error in conv3d: got dataFormat of "+o+" but only NHWC is currently supported.");var h=ba(c.shape,u.shape,n,i,r),p=Ft.engine.runKernel(function(t){return t.conv3d(c,u,h)},{x:c,$filter:u},function(t){return M(_a(i),"Error in gradient of conv3D: dilation rates greater than 1 are notyet supported in gradients. Got dilations '"+i+"'"),{x:function(){return function(t,e,n,r,o){M(t.length===e.rank,"Length of inShape ("+t.length+") and rank of dy ("+e.rank+") must match");var i=t,a=e,s=!1;4===e.rank&&(s=!0,a=e.as5D(1,e.shape[0],e.shape[1],e.shape[2],e.shape[3]),i=[1,t[0],t[1],t[2],t[3]]);var u=i[4],c=a.shape[4];M(5===i.length,"Error in conv3dDerInput: inShape must be length 5, but got length "+i.length+"."),M(5===a.rank,"Error in conv3dDerInput: dy must be rank 5, but got rank "+a.rank),M(5===n.rank,"Error in conv3dDerInput: filter must be rank 5, but got rank "+n.rank),M(u===n.shape[3],"Error in conv3dDerInput: depth of input ("+u+") must match input depth for filter "+n.shape[3]+"."),M(c===n.shape[4],"Error in conv3dDerInput: depth of output ("+c+") must match output depth for filter "+n.shape[4]+".");var l=ba(i,n.shape,r,1,o),h=Ft.engine.runKernel(function(t){return t.conv3dDerInput(a,n,l)},{dy5D:a});return s?h.as4D(h.shape[1],h.shape[2],h.shape[3],h.shape[4]):h}(c.shape,t,u,n,r)},$filter:function(){return function(t,e,n,r,o){var i=t;4===t.rank&&(i=t.as5D(1,t.shape[0],t.shape[1],t.shape[2],t.shape[3]));var a=e;4===a.rank&&(a=e.as5D(1,e.shape[0],e.shape[1],e.shape[2],e.shape[3])),M(5===i.rank,"Error in conv3dDerFilter: input must be rank 5, but got shape "+i.shape+"."),M(5===a.rank,"Error in conv3dDerFilter: dy must be rank 5, but got shape "+a.shape+"."),M(5===n.length,"Error in conv3dDerFilter: filterShape must be length 5, but got "+n+"."),M(i.shape[4]===n[3],"Error in conv3dDerFilter: depth of input "+i.shape[4]+") must match input depth in filter ("+n[3]+"."),M(a.shape[4]===n[4],"Error in conv3dDerFilter: depth of dy ("+a.shape[4]+") must match output depth for filter ("+n[4]+").");var s=ba(i.shape,n,r,1,o);return Ft.engine.runKernel(function(t){return t.conv3dDerFilter(i,a,s)},{x5D:i,dy5D:a})}(c,t,u.shape,n,r)}}});return l?p.as4D(p.shape[1],p.shape[2],p.shape[3],p.shape[4]):p}}),Ba=ge({conv2dDerFilter_:Ma}),La=ge({depthwiseConv2d_:function(t,e,n,r,o,i,a){void 0===o&&(o="NHWC"),void 0===i&&(i=[1,1]);var s=me(t,"x","depthwiseConv2d"),u=me(e,"filter","depthwiseConv2d"),c=s,l=!1;3===s.rank&&(l=!0,c=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),M(4===c.rank,"Error in depthwiseConv2d: input must be rank 4, but got rank "+c.rank+"."),M(4===u.rank,"Error in depthwiseConv2d: filter must be rank 4, but got rank "+u.rank+"."),M(c.shape[3]===u.shape[2],"Error in depthwiseConv2d: number of input channels ("+c.shape[3]+") must match the inChannels dimension in filter "+u.shape[2]+"."),null==i&&(i=[1,1]),M(Ta(n,i),"Error in depthwiseConv2d: Either strides or dilations must be 1. Got strides "+n+" and dilations '"+i+"'"),null!=a&&M(D(r),"Error in depthwiseConv2d: pad must be an integer when using, dimRoundingMode "+a+" but got pad "+r+".");var h=wa(c.shape,u.shape,n,i,r,a,!0),p=Ft.engine.runKernel(function(t){return t.depthwiseConv2D(c,u,h)},{x:c,$filter:u},function(t){return M(Na(i),"Error in gradient of depthwiseConv2d: dilation rates greater than 1 are not yet supported. Got dilations '"+i+"'"),{x:function(){return function(t,e,n,r){var o=e,i=!1;3===e.rank&&(i=!0,o=e.as4D(1,e.shape[0],e.shape[1],e.shape[2]));var a=Ft.engine.runKernel(function(t){return t.depthwiseConv2DDerInput(o,n,r)},{dy4D:o});return i?a.as3D(a.shape[1],a.shape[2],a.shape[3]):a}(c.shape,t,u,h)},$filter:function(){return function(t,e,n,r){var o=t;3===t.rank&&(o=t.as4D(1,t.shape[0],t.shape[1],t.shape[2]));var i=e;return 3===i.rank&&(i=e.as4D(1,e.shape[0],e.shape[1],e.shape[2])),Ft.engine.runKernel(function(t){return t.depthwiseConv2DDerFilter(o,i,r)},{x4D:o,dy4D:i})}(c,t,u.shape,h)}}});return l?p.as3D(p.shape[1],p.shape[2],p.shape[3]):p}}),za=ge({separableConv2d_:function(t,e,n,r,o,i,a){void 0===i&&(i=[1,1]),void 0===a&&(a="NHWC");var s=me(t,"x","separableConv2d"),u=me(e,"depthwiseFilter","separableConv2d"),c=me(n,"pointwiseFilter","separableConv2d"),l=s,h=!1;if(3===s.rank&&(h=!0,l=s.as4D(1,s.shape[0],s.shape[1],s.shape[2])),"NCHW"===a)throw new Error("separableConv2d currently does not support dataFormat NCHW; only NHWC is supported");M(4===l.rank,"Error in separableConv2d: input must be rank 4, but got rank "+l.rank+"."),M(4===u.rank,"Error in separableConv2d: depthwise filter must be rank 4, but got rank "+u.rank+"."),M(4===c.rank,"Error in separableConv2d: pointwise filter must be rank 4, but got rank "+u.rank+"."),M(1===c.shape[0],"Error in separableConv2d: the first dimension of pointwise filter  must be 1, but got "+c.shape[0]+"."),M(1===c.shape[1],"Error in separableConv2d: the second dimension of pointwise filter  must be 1, but got "+c.shape[1]+".");var p=u.shape[2],f=u.shape[3];M(c.shape[2]===p*f,"Error in separableConv2d: the third dimension of pointwise filter must be "+p*f+", but got "+c.shape[2]+".");var d=La(l,u,r,o,a,i),m=Fa(d,c,1,"valid",a);return h?m.as3D(m.shape[1],m.shape[2],m.shape[3]):m}}),Wa=ge({conv2dTranspose_:function(t,e,n,r,o,i){return Aa(n,me(t,"x","conv2dTranspose"),me(e,"filter","conv2dTranspose"),r,o,i)}});var Ua=ge({reverse_:function(t,e){var n=me(t,"x","reverse");if(0===n.rank)return n.clone();var r=te(e,n.shape);return Ft.engine.runKernel(function(t){return t.reverse(n,r)},{$x:n},function(t){return{$x:function(){return t.reverse(r)}}}).reshapeAs(n)}}),Va=ge({reverse1d_:function(t){var e=me(t,"x","reverse");return M(1===e.rank,"Error in reverse1D: x must be rank 1 but got\n             rank "+e.rank+"."),Ua(e,0)}}),Ga=ge({reverse2d_:function(t,e){var n=me(t,"x","reverse");return M(2===n.rank,"Error in reverse2D: x must be rank 2 but got\n             rank "+n.rank+"."),Ua(n,e)}}),Ha=ge({reverse3d_:function(t,e){var n=me(t,"x","reverse");return M(3===n.rank,"Error in reverse3D: x must be rank 3 but got\n             rank "+n.rank+"."),Ua(n,e)}}),qa=ge({reverse4d_:function(t,e){var n=me(t,"x","reverse");return M(4===n.rank,"Error in reverse4D: x must be rank 4 but got\n             rank "+n.rank+"."),Ua(n,e)}});function ja(t,r,o,i,a,e){var n=me(t,"x","maxPool"),s=n,u=!1;3===n.rank&&(u=!0,s=n.as4D(1,n.shape[0],n.shape[1],n.shape[2])),null==i&&(i=[1,1]),M(4===s.rank,"Error in maxPool: input must be rank 4 but got rank "+s.rank+"."),M(Ta(o,i),"Error in maxPool: Either strides or dilations must be 1. Got strides "+o+" and dilations '"+i+"'"),null!=e&&M(D(a),"Error in maxPool: pad must be an integer when using, dimRoundingMode "+e+" but got pad "+a+".");var c=xa(s.shape,r,o,i,a,e),l=Ft.engine.runKernel(function(t,e){return e(t.maxPool(s,c))},{x:s},function(t,e){var n=e[0];return{x:function(){return function(t,e,n,r,o,i,a,s){var u=me(t,"dy","maxPoolBackprop"),c=me(e,"input","maxPoolBackprop"),l=me(n,"output","maxPoolBackprop");M(c.rank===u.rank,"Rank of input ("+c.rank+") does not match rank of dy ("+u.rank+")"),null==i&&(i=[1,1]),M(Ta(o,i),"Error in maxPoolBackProp: Either strides or dilations must be 1. Got strides "+o+" and dilations '"+i+"'"),M(4===u.rank,"Error in maxPoolBackprop: dy must be rank 4 but got rank "+u.rank+"."),M(4===c.rank,"Error in maxPoolBackprop: input must be rank 4 but got rank "+c.rank+"."),null!=s&&M(D(a),"Error in maxPoolBackprop: pad must be an integer when using, dimRoundingMode "+s+" but got pad "+a+".");var h=xa(c.shape,r,o,i,a,s);return Ft.engine.runKernel(function(t){return t.maxPoolBackprop(u,c,l,h)},{$dy:u,$input:c})}(t,s,n,r,o,i,a)}}});return u?l.as3D(l.shape[1],l.shape[2],l.shape[3]):l}function $a(t,e,n,r,o,i){var a=me(t,"x","avgPool","float32");null==r&&(r=[1,1]),M(Ta(n,r),"Error in avgPool: Either strides or dilations must be 1. 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hu=ge({basicLSTMCell_:function(t,e,n,r,o,i){var a=me(t,"forgetBias","basicLSTMCell"),s=me(e,"lstmKernel","basicLSTMCell"),u=me(n,"lstmBias","basicLSTMCell"),c=me(r,"data","basicLSTMCell"),l=me(o,"c","basicLSTMCell"),h=me(i,"h","basicLSTMCell"),p=c.concat(h,1).matMul(s).add(u),f=p.shape[0],d=p.shape[1]/4,m=[f,d],v=p.slice([0,0],m),g=p.slice([0,d],m),y=p.slice([0,2*d],m),x=p.slice([0,3*d],m),w=v.sigmoid().mulStrict(g.tanh()).addStrict(l.mulStrict(a.add(y).sigmoid()));return[w,w.tanh().mulStrict(x.sigmoid())]}}),pu=ge({multiRNNCell_:function(t,e,n,r){for(var o=me(e,"data","multiRNNCell"),i=ve(n,"c","multiRNNCell"),a=ve(r,"h","multiRNNCell"),s=o,u=[],c=0;c<t.length;c++){var l=t[c](s,i[c],a[c]);u.push(l[0]),u.push(l[1]),s=l[1]}var h=[],p=[];for(c=0;c<u.length;c+=2)h.push(u[c]),p.push(u[c+1]);return[h,p]}});var fu=ge({movingAverage_:function(t,e,n,r,o){void 0===o&&(o=!0);var 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Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=Math.acos(n[r]);return it.make(t.shape,{values:e})},t.prototype.atan=function(t){this.assertNotComplex(t,"atan");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=Math.atan(n[r]);return it.make(t.shape,{values:e})},t.prototype.atan2=function(t,e){return this.assertNotComplex([t,e],"atan2"),this.broadcastedBinaryOp(t,e,t.dtype,function(t,e){return Math.atan2(t,e)})},t.prototype.sinh=function(t){this.assertNotComplex(t,"sinh");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=Math.sinh(n[r]);return it.make(t.shape,{values:e})},t.prototype.cosh=function(t){this.assertNotComplex(t,"cosh");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=Math.cosh(n[r]);return it.make(t.shape,{values:e})},t.prototype.tanh=function(t){this.assertNotComplex(t,"tanh");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=v(n[r]);return it.make(t.shape,{values:e})},t.prototype.asinh=function(t){this.assertNotComplex(t,"asinh");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=Math.asinh(n[r]);return it.make(t.shape,{values:e})},t.prototype.acosh=function(t){this.assertNotComplex(t,"acosh");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=Math.acosh(n[r]);return it.make(t.shape,{values:e})},t.prototype.atanh=function(t){this.assertNotComplex(t,"atanh");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r)e[r]=Math.atanh(n[r]);return it.make(t.shape,{values:e})},t.prototype.erf=function(t){this.assertNotComplex(t,"erf");for(var e=new Float32Array(t.size),n=t.dataSync(),r=0;r<n.length;++r){var o=n[r],i=1/(1+.3275911*o);e[r]=1-((((1.061405429*i-1.453152027)*i+1.421413741)*i-.284496736)*i+.254829592)*i*Math.exp(-o*o)}return it.make(t.shape,{values:e})},t.prototype.step=function(t,e){void 0===e&&(e=0),this.assertNotComplex(t,"step");for(var n=new Float32Array(t.size),r=t.dataSync(),o=0;o<r.length;++o){var i=r[o];isNaN(i)?n[o]=NaN:n[o]=0<i?1:e}return it.make(t.shape,{values:n})},t.prototype.conv2d=function(t,e,n){this.assertNotComplex([t,e],"conv2d");for(var r=n.filterHeight,o=n.filterWidth,i=n.dilationHeight,a=n.dilationWidth,s=n.padInfo.left,u=n.padInfo.top,c=ii(n.outShape,t.dtype),l=t.dataSync(),h=e.dataSync(),p=c.values,f=0;f<n.batchSize;++f)for(var d=f*t.strides[0],m=f*c.strides[0],v=0;v<n.outHeight;++v)for(var g=m+v*c.strides[1],y=v*n.strideHeight-s,x=0;x<r;x++){var w=y+x*i;if(!(w<0||w>=n.inHeight))for(var b=x*e.strides[0],E=d+w*t.strides[1],C=0;C<n.outWidth;++C)for(var S=g+C*n.outChannels,R=C*n.strideWidth-u,N=0;N<o;N++){var T=R+N*a;if(!(T<0||T>=n.inWidth))for(var k=b+N*e.strides[1],I=E+T*n.inChannels,D=k,A=0;A<n.inChannels;++A){for(var M=l[I+A],_=0;_<n.outChannels;++_)p[S+_]+=M*h[D+_];D+=n.outChannels}}}return c.toTensor()},t.prototype.conv3d=function(t,e,n){for(var r=n.filterDepth,o=n.filterHeight,i=n.filterWidth,a=n.dilationDepth,s=n.dilationHeight,u=n.dilationWidth,c=n.padInfo.front,l=n.padInfo.left,h=n.padInfo.top,p=ii(n.outShape,t.dtype),f=t.dataSync(),d=e.dataSync(),m=p.values,v=0;v<n.batchSize;++v)for(var g=v*t.strides[0],y=v*p.strides[0],x=0;x<n.outDepth;++x)for(var w=y+x*p.strides[1],b=x*n.strideDepth-c,E=0;E<r;E++){var C=b+E*a;if(!(C<0||C>=n.inDepth))for(var S=E*e.strides[0],R=g+C*t.strides[1],N=0;N<n.outHeight;++N)for(var T=w+N*p.strides[2],k=N*n.strideHeight-h,I=0;I<o;I++){var D=k+I*s;if(!(D<0||D>=n.inHeight))for(var A=S+I*e.strides[1],M=R+D*t.strides[2],_=0;_<n.outWidth;++_)for(var O=T+_*n.outChannels,F=_*n.strideWidth-l,P=0;P<i;P++){var B=F+P*u;if(!(B<0||B>=n.inWidth))for(var L=A+P*e.strides[2],z=M+B*n.inChannels,W=L,U=0;U<n.inChannels;++U){for(var V=f[z+U],G=0;G<n.outChannels;++G)m[O+G]+=V*d[W+G];W+=n.outChannels}}}}return p.toTensor()},t.prototype.conv2dDerInput=function(t,e,n){this.assertNotComplex([t,e],"conv2dDerInput");for(var r=ii(n.inShape,"float32"),o=r.values,i=r.strides,a=i[0],s=i[1],u=i[2],c=t.dataSync(),l=t.strides,h=l[0],p=l[1],f=l[2],d=e.dataSync(),m=e.strides,v=m[0],g=m[1],y=m[2],x=n.batchSize,w=n.filterHeight,b=n.filterWidth,E=n.inChannels,C=n.inHeight,S=n.inWidth,R=n.outChannels,N=n.outHeight,T=n.outWidth,k=n.strideHeight,I=n.strideWidth,D=w-1-n.padInfo.top,A=b-1-n.padInfo.left,M=0;M<x;++M)for(var _=0;_<E;++_)for(var O=0;O<C;++O)for(var F=O-D,P=Math.max(0,Math.ceil(F/k)),B=Math.min(N,(w+F)/k),L=0;L<S;++L){for(var z=L-A,W=Math.max(0,Math.ceil(z/I)),U=Math.min(T,(b+z)/I),V=0,G=P;G<B;++G)for(var H=G*k-F,q=W;q<U;++q)for(var j=h*M+p*G+f*q,$=v*(w-1-H)+g*(b-1-(q*I-z))+y*_,K=0;K<R;++K)V+=c[j+K]*d[$+K];o[a*M+s*O+u*L+_]=V}return r.toTensor()},t.prototype.conv3dDerInput=function(t,e,n){for(var r=ii(n.inShape,"float32"),o=r.values,i=r.strides,a=i[0],s=i[1],u=i[2],c=i[3],l=t.dataSync(),h=t.strides,p=h[0],f=h[1],d=h[2],m=h[3],v=e.dataSync(),g=e.strides,y=g[0],x=g[1],w=g[2],b=g[3],E=n.batchSize,C=n.filterDepth,S=n.filterHeight,R=n.filterWidth,N=n.inChannels,T=n.inDepth,k=n.inHeight,I=n.inWidth,D=n.outChannels,A=n.outDepth,M=n.outHeight,_=n.outWidth,O=n.strideDepth,F=n.strideHeight,P=n.strideWidth,B=C-1-n.padInfo.front,L=S-1-n.padInfo.top,z=R-1-n.padInfo.left,W=0;W<E;++W)for(var U=0;U<N;++U)for(var V=0;V<T;++V)for(var G=V-B,H=Math.max(0,Math.ceil(G/O)),q=Math.min(A,(C+G)/O),j=0;j<k;++j)for(var $=j-L,K=Math.max(0,Math.ceil($/F)),X=Math.min(M,(S+$)/F),Y=0;Y<I;++Y){for(var Q=Y-z,J=Math.max(0,Math.ceil(Q/P)),Z=Math.min(_,(R+Q)/P),tt=0,et=H;et<q;++et)for(var nt=et*O-G,rt=K;rt<X;++rt)for(var ot=rt*F-$,it=J;it<Z;++it)for(var at=p*W+f*et+d*rt+m*it,st=y*(C-1-nt)+x*(S-1-ot)+w*(R-1-(it*P-Q))+b*U,ut=0;ut<D;++ut)tt+=l[at+ut]*v[st+ut];o[a*W+s*V+u*j+c*Y+U]=tt}return r.toTensor()},t.prototype.conv2dDerFilter=function(t,e,n){this.assertNotComplex([t,e],"conv2dDerFilter");for(var r=n.strideHeight,o=n.strideWidth,i=n.filterHeight,a=n.filterWidth,s=ii(n.filterShape,"float32"),u=n.padInfo.left,c=n.padInfo.top,l=0;l<i;++l)for(var h=Math.max(0,Math.ceil((c-l)/r)),p=Math.min(n.outHeight,(n.inHeight+c-l)/r),f=0;f<a;++f)for(var d=Math.max(0,Math.ceil((u-f)/o)),m=Math.min(n.outWidth,(n.inWidth+u-f)/o),v=0;v<n.inChannels;++v)for(var g=0;g<n.outChannels;++g){for(var y=0,x=0;x<n.batchSize;++x)for(var w=h;w<p;++w)for(var b=l+w*r-c,E=d;E<m;++E){var C=f+E*o-u;y+=t.get(x,b,C,v)*e.get(x,w,E,g)}s.set(y,l,f,v,g)}return s.toTensor()},t.prototype.conv3dDerFilter=function(t,e,n){for(var r=n.strideDepth,o=n.strideHeight,i=n.strideWidth,a=n.filterDepth,s=n.filterHeight,u=n.filterWidth,c=ii(n.filterShape,"float32"),l=c.values,h=c.strides,p=h[0],f=h[1],d=h[2],m=h[3],v=e.dataSync(),g=e.strides,y=g[0],x=g[1],w=g[2],b=g[3],E=t.dataSync(),C=t.strides,S=C[0],R=C[1],N=C[2],T=C[3],k=n.padInfo.front,I=n.padInfo.left,D=n.padInfo.top,A=0;A<a;++A)for(var M=Math.max(0,Math.ceil((k-A)/r)),_=Math.min(n.outDepth,(n.inDepth+k-A)/r),O=A*p,F=0;F<s;++F)for(var P=Math.max(0,Math.ceil((D-F)/o)),B=Math.min(n.outHeight,(n.inHeight+D-F)/o),L=F*f+O,z=0;z<u;++z)for(var W=Math.max(0,Math.ceil((I-z)/i)),U=Math.min(n.outWidth,(n.inWidth+I-z)/i),V=z*d+L,G=0;G<n.inChannels;++G)for(var H=G*m+V,q=0;q<n.outChannels;++q){for(var j=0,$=0;$<n.batchSize;++$)for(var K=$*S,X=$*y,Y=M;Y<_;++Y)for(var Q=(A+Y*r-k)*R+K,J=Y*x+X,Z=P;Z<B;++Z)for(var tt=(F+Z*o-D)*N+Q,et=Z*w+J,nt=W;nt<U;++nt){var rt=nt*b+et;j+=E[(z+nt*i-I)*T+tt+G]*v[rt+q]}l[H+q]=j}return c.toTensor()},t.prototype.depthwiseConv2D=function(t,e,n){this.assertNotComplex([t,e],"depthwiseConv2D");for(var r=n.filterHeight,o=n.filterWidth,i=n.dilationHeight,a=n.dilationWidth,s=n.padInfo.left,u=n.padInfo.top,c=n.outChannels/n.inChannels,l=ii(n.outShape,t.dtype),h=t.dataSync(),p=e.dataSync(),f=l.values,d=0;d<n.batchSize;++d)for(var m=d*t.strides[0],v=d*l.strides[0],g=0;g<n.outHeight;++g)for(var y=v+g*l.strides[1],x=g*n.strideHeight-s,w=0;w<r;++w){var b=x+w*i;if(!(b<0||b>=n.inHeight))for(var E=w*e.strides[0],C=m+b*t.strides[1],S=0;S<n.outWidth;++S)for(var R=y+S*l.strides[2],N=S*n.strideWidth-u,T=0;T<o;++T){var k=N+T*a;if(!(k<0||k>=n.inWidth))for(var I=E+T*e.strides[1],D=C+k*n.inChannels,A=R,M=I,_=0;_<n.inChannels;++_){for(var O=h[D+_],F=0;F<c;++F)f[A+F]+=O*p[M+F];A+=c,M+=c}}}return l.toTensor()},t.prototype.depthwiseConv2DDerInput=function(t,e,n){this.assertNotComplex([t,e],"depthwiseConv2DDerInput");for(var r=ii(n.inShape,"float32"),o=r.values,i=r.strides,a=i[0],s=i[1],u=i[2],c=t.dataSync(),l=t.strides,h=l[0],p=l[1],f=l[2],d=e.dataSync(),m=e.strides,v=m[0],g=m[1],y=m[2],x=n.batchSize,w=n.filterHeight,b=n.filterWidth,E=n.inChannels,C=n.inHeight,S=n.inWidth,R=n.outChannels,N=n.outHeight,T=n.outWidth,k=n.strideHeight,I=n.strideWidth,D=w-1-n.padInfo.top,A=b-1-n.padInfo.left,M=R/E,_=0;_<x;++_)for(var O=0;O<E;++O)for(var F=0;F<C;++F)for(var P=F-D,B=Math.max(0,Math.ceil(P/k)),L=Math.min(N,(w+P)/k),z=0;z<S;++z){for(var W=z-A,U=Math.max(0,Math.ceil(W/I)),V=Math.min(T,(b+W)/I),G=0,H=B;H<L;++H)for(var q=H*k-P,j=U;j<V;++j)for(var $=h*_+p*H+f*j,K=v*(w-1-q)+g*(b-1-(j*I-W))+y*O,X=0;X<M;++X)G+=c[$+(O*M+X)]*d[K+X];o[a*_+s*F+u*z+O]=G}return r.toTensor()},t.prototype.depthwiseConv2DDerFilter=function(t,e,n){this.assertNotComplex([t,e],"depthwiseConv2DDerFilter");for(var r=n.strideHeight,o=n.strideWidth,i=n.filterHeight,a=n.filterWidth,s=ii(n.filterShape,"float32"),u=n.padInfo.left,c=n.padInfo.top,l=n.outChannels/n.inChannels,h=0;h<i;++h)for(var p=Math.max(0,Math.ceil((c-h)/r)),f=Math.min(n.outHeight,(n.inHeight+c-h)/r),d=0;d<a;++d)for(var m=Math.max(0,Math.ceil((u-d)/o)),v=Math.min(n.outWidth,(n.inWidth+u-d)/o),g=0;g<n.outChannels;++g){for(var y=Math.trunc(g/l),x=g%l,w=0,b=0;b<n.batchSize;++b)for(var E=p;E<f;++E)for(var C=h+E*r-c,S=m;S<v;++S){var R=d+S*o-u;w+=t.get(b,C,R,y)*e.get(b,E,S,g)}s.set(w,h,d,y,x)}return s.toTensor()},t.prototype.tile=function(t,e){this.assertNotComplex(t,"tile");for(var n=new Array(t.rank),r=0;r<n.length;r++)n[r]=t.shape[r]*e[r];var o=ii(n,t.dtype),i=t.buffer();for(r=0;r<o.values.length;++r){for(var a=o.indexToLoc(r),s=new Array(t.rank),u=0;u<s.length;u++)s[u]=a[u]%t.shape[u];var c=i.locToIndex(s);o.values[r]=i.values[c]}return o.toTensor()},t.prototype.pad=function(n,t,e){this.assertNotComplex(n,"pad");var r=t.map(function(t,e){return t[0]+n.shape[e]+t[1]}),o=t.map(function(t){return t[0]}),i=n.buffer(),a=ii(r,n.dtype);0!==e&&a.values.fill(e);for(var s=0;s<n.size;s++){var u=i.indexToLoc(s),c=u.map(function(t,e){return t+o[e]});a.set.apply(a,[n.get.apply(n,u)].concat(c))}return a.toTensor()},t.prototype.transpose=function(t,e){this.assertNotComplex(t,"transpose");for(var n=new Array(t.rank),r=0;r<n.length;r++)n[r]=t.shape[e[r]];var o=t.dataSync(),i=ii(n,t.dtype),a=t.buffer();for(r=0;r<t.size;++r){for(var s=a.indexToLoc(r),u=new Array(s.length),c=0;c<u.length;c++)u[c]=s[e[c]];var l=i.locToIndex(u);i.values[l]=o[r]}return i.toTensor()},t.prototype.gather=function(t,e,n){this.assertNotComplex([t,e],"gather");var r=t.shape.slice(),o=e.dataSync();r[n]=o.length;for(var i=ii(r,t.dtype),a=t.buffer(),s=0;s<i.size;++s){var u=i.indexToLoc(s),c=u.slice();c[n]=o[u[n]];var l=a.locToIndex(c);i.values[s]=a.values[l]}return i.toTensor()},t.prototype.batchToSpaceND=function(t,e,n){this.assertNotComplex([t],"batchToSpaceND");var r=e.reduce(function(t,e){return t*e}),o=jt(t.shape,e,r),i=$t(o.length,e.length),a=Kt(t.shape,e,r),s=Xt(n,e.length),u=Yt(a,n,e.length);return t.reshape(o).transpose(i).reshape(a).slice(s,u)},t.prototype.spaceToBatchND=function(t,e,n){this.assertNotComplex([t],"spaceToBatchND");var r=e.reduce(function(t,e){return t*e}),o=[[0,0]];o.push.apply(o,n);for(var i=1+e.length;i<t.shape.length;++i)o.push([0,0]);var a=t.pad(o),s=jt(a.shape,e,r,!1),u=$t(s.length,e.length,!1),c=Kt(a.shape,e,r,!1);return a.reshape(s).transpose(u).reshape(c)},t.prototype.pool=function(t,e,n){this.assertNotComplex(t,"pool");for(var r=e.strideHeight,o=e.strideWidth,i=e.dilationHeight,a=e.dilationWidth,s=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=e.padInfo.top,l=e.padInfo.left,h="max"===n?Number.NEGATIVE_INFINITY:Number.POSITIVE_INFINITY,p=t.dataSync(),f=ii(e.outShape,t.dtype),d=f.values,m=e.outShape[1]*e.outShape[2]*e.outShape[3],v=e.outShape[2]*e.outShape[3],g=e.outShape[3],y=0;y<e.batchSize;++y)for(var x=y*m,w=y*t.strides[0],b=0;b<e.inChannels;++b)for(var E=0;E<e.outHeight;++E)for(var C=E*r-c,S=Math.max(0,C),R=Math.min(e.inHeight,s+C),N=x+E*v,T=0;T<e.outWidth;++T){for(var k=T*o-l,I=Math.max(0,k),D=Math.min(e.inWidth,u+k),A=h,M=0,_=0,O=S;O<R;O+=i){for(var F=w+O*t.strides[1],P=I;P<D;P+=a){var B=p[F+P*t.strides[2]+b];"max"===n&&A<B?A=B:"avg"===n&&(M+=B,_++)}if(isNaN(A))break}d[N+T*g+b]="avg"===n?M/_:A}return f.toTensor()},t.prototype.maxPool=function(t,e){return this.pool(t,e,"max")},t.prototype.maxPoolPositions=function(t,e){for(var n=ii(e.outShape,"int32"),r=e.strideHeight,o=e.strideWidth,i=e.dilationHeight,a=e.dilationWidth,s=e.effectiveFilterHeight,u=e.effectiveFilterWidth,c=e.padInfo.top,l=e.padInfo.left,h=0;h<e.batchSize;++h)for(var p=0;p<e.inChannels;++p)for(var f=0;f<e.outHeight;++f){for(var d=f*r-c,m=d;m<0;)m+=i;for(var v=Math.min(e.inHeight,s+d),g=0;g<e.outWidth;++g){for(var y=g*o-l,x=y;x<0;)x+=a;for(var w=Math.min(e.inWidth,u+y),b=Number.NEGATIVE_INFINITY,E=-1,C=m;C<v;C+=i)for(var S=C-d,R=x;R<w;R+=a){var N=R-y,T=t.get(h,C,R,p);b<T&&(b=T,E=S*u+N)}n.set(E,h,f,g,p)}}return n.toTensor()},t.prototype.maxPoolBackprop=function(t,e,n,r){this.assertNotComplex([e,n],"maxPoolBackprop");for(var o=this.maxPoolPositions(e,r),i=r.strideHeight,a=r.strideWidth,s=r.dilationHeight,u=r.dilationWidth,c=r.effectiveFilterHeight,l=r.effectiveFilterWidth,h=l-1-r.padInfo.left,p=c-1-r.padInfo.top,f=ii(e.shape,"float32"),d=0;d<r.batchSize;++d)for(var m=0;m<r.inChannels;++m)for(var v=0;v<r.inHeight;++v)for(var g=0;g<r.inWidth;++g){for(var y=v-p,x=g-h,w=0,b=0;b<c;b+=s){var E=(y+b)/i;if(!(E<0||E>=r.outHeight||Math.floor(E)!==E))for(var C=0;C<l;C+=u){var S=(x+C)/a;if(!(S<0||S>=r.outWidth||Math.floor(S)!==S)){var R=c*l-1-o.get(d,E,S,m)===b*l+C?1:0;0!==R&&(w+=t.get(d,E,S,m)*R)}}}f.set(w,d,v,g,m)}return f.toTensor()},t.prototype.avgPoolBackprop=function(t,e,n){this.assertNotComplex([t,e],"avgPoolBackprop");for(var r=n.strideHeight,o=n.strideWidth,i=n.filterHeight,a=n.filterWidth,s=n.dilationHeight,u=n.dilationWidth,c=n.effectiveFilterHeight,l=n.effectiveFilterWidth,h=l-1-n.padInfo.left,p=c-1-n.padInfo.top,f=ii(e.shape,"float32"),d=1/(i*a),m=0;m<n.batchSize;++m)for(var v=0;v<n.inChannels;++v)for(var g=0;g<n.inHeight;++g)for(var y=0;y<n.inWidth;++y){for(var x=g-p,w=y-h,b=0,E=0;E<c;E+=s){var C=(x+E)/r;if(!(C<0||C>=n.outHeight||Math.floor(C)!==C))for(var S=0;S<l;S+=u){var R=(w+S)/o;R<0||R>=n.outWidth||Math.floor(R)!==R||(b+=t.get(m,C,R,v))}}f.set(b*d,m,g,y,v)}return f.toTensor()},t.prototype.cast=function(t,e){return We(t,e,this)},t.prototype.reshape=function(t,e){return Ue(t,e)},t.prototype.avgPool=function(t,e){return this.assertNotComplex(t,"avgPool"),this.pool(t,e,"avg").toFloat()},t.prototype.resizeBilinear=function(t,e,n,r){this.assertNotComplex(t,"resizeBilinear");for(var o=t.shape,i=o[0],a=o[1],s=o[2],u=o[3],c=t.dataSync(),l=new Float32Array(L([i,e,n,u])),h=[r&&1<e?a-1:a,r&&1<n?s-1:s],p=[r&&1<e?e-1:e,r&&1<n?n-1:n],f=0,d=h[0]/p[0],m=h[1]/p[1],v=0;v<i;v++)for(var g=0;g<e;g++)for(var y=d*g,x=Math.floor(y),w=y-x,b=Math.min(a-1,Math.ceil(y)),E=v*t.strides[0]+x*t.strides[1],C=v*t.strides[0]+b*t.strides[1],S=0;S<n;S++)for(var R=m*S,N=Math.floor(R),T=R-N,k=Math.min(s-1,Math.ceil(R)),I=E+N*t.strides[2],D=C+N*t.strides[2],A=E+ +k*t.strides[2],M=C+k*t.strides[2],_=0;_<u;_++){var O=c[I+_],F=c[D+_],P=O+(c[A+_]-O)*T,B=P+(F+(c[M+_]-F)*T-P)*w;l[f++]=B}return Ce(l,[i,e,n,u])},t.prototype.resizeBilinearBackprop=function(t,e,n){this.assertNotComplex([t,e],"resizeBilinearBackprop");for(var r=e.shape,o=r[0],i=r[1],a=r[2],s=r[3],u=t.shape,c=u[1],l=u[2],h=new Float32Array(o*i*a*s),p=[n&&1<c?i-1:i,n&&1<l?a-1:a],f=[n&&1<c?c-1:c,n&&1<l?l-1:l],d=p[0]/f[0],m=p[1]/f[1],v=t.dataSync(),g=0,y=0;y<o;y++)for(var x=y*e.strides[0],w=0;w<c;w++)for(var b=w*d,E=Math.floor(b),C=Math.min(Math.ceil(b),i-1),S=x+E*e.strides[1],R=x+C*e.strides[1],N=b-E,T=1-N,k=0;k<l;k++)for(var I=k*m,D=Math.floor(I),A=Math.min(Math.ceil(I),a-1),M=I-D,_=1-M,O=S+D*e.strides[2],F=S+A*e.strides[2],P=R+D*e.strides[2],B=R+A*e.strides[2],L=T*_,z=T*M,W=N*_,U=N*M,V=0;V<s;V++){var G=v[g++];h[O+V]+=G*L,h[F+V]+=G*z,h[P+V]+=G*W,h[B+V]+=G*U}return ke(h,[o,a,i,s],e.dtype)},t.prototype.resizeNearestNeighbor=function(t,e,n,r){this.assertNotComplex(t,"resizeNearestNeighbor");for(var o=t.shape,i=o[0],a=o[1],s=o[2],u=o[3],c=t.dataSync(),l=new Float32Array(i*e*n*u),h=[r&&1<e?a-1:a,r&&1<n?s-1:s],p=[r&&1<e?e-1:e,r&&1<n?n-1:n],f=h[0]/p[0],d=h[1]/p[1],m=0,v=0;v<i;v++)for(var g=v*t.strides[0],y=0;y<e;y++)for(var x=f*y,w=g+Math.min(a-1,r?Math.round(x):Math.floor(x))*t.strides[1],b=0;b<n;b++)for(var E=d*b,C=w+Math.min(s-1,r?Math.round(E):Math.floor(E))*t.strides[2],S=0;S<u;S++){var R=c[C+S];l[m++]=R}return Ce(l,[i,e,n,u],t.dtype)},t.prototype.resizeNearestNeighborBackprop=function(t,e,n){this.assertNotComplex([t,e],"resizeNearestNeighborBackprop");for(var r=e.shape,o=r[0],i=r[1],a=r[2],s=r[3],u=t.shape,c=u[1],l=u[2],h=new Float32Array(o*i*a*s),p=t.dataSync(),f=[n&&1<c?i-1:i,n&&1<l?a-1:a],d=[n&&1<c?c-1:c,n&&1<l?l-1:l],m=f[0]/d[0],v=f[1]/d[1],g=1/m,y=1/v,x=2*Math.ceil(g)+2,w=2*Math.ceil(y)+2,b=0;b<o;b++)for(var E=b*e.strides[0],C=0;C<i;C++)for(var S=E+C*e.strides[1],R=Math.floor(C*g),N=Math.floor(R-x/2),T=0;T<a;T++)for(var k=S+T*e.strides[2],I=Math.floor(T*y),D=Math.floor(I-w/2),A=0;A<s;A++){for(var M=0,_=0;_<x;_++){var O=_+N;if(!(O<0||c<=O)){var F=E+O*t.strides[1],P=O*m;if(C===Math.min(i-1,n?Math.round(P):Math.floor(P)))for(var B=0;B<w;B++){var L=B+D;if(!(L<0||l<=L)){var z=F+L*t.strides[2],W=L*v;T===Math.min(a-1,n?Math.round(W):Math.floor(W))&&(M+=p[z+A])}}}}h[k+A]=M}return ke(h,e.shape,e.dtype)},t.prototype.batchNormalization=function(t,e,n,r,o,i){this.assertNotComplex([t,e,n,o,i],"batchNormalization");for(var a=t.dataSync(),s=e.dataSync(),u=n.dataSync(),c=o?o.dataSync():new Float32Array([1]),l=i?i.dataSync():new Float32Array([0]),h=new Float32Array(a.length),p=l.length,f=c.length,d=u.length,m=s.length,v=0,g=0,y=0,x=0,w=0;w<a.length;++w)h[w]=l[v++]+(a[w]-s[g++])*c[y++]/Math.sqrt(u[x++]+r),p<=v&&(v=0),m<=g&&(g=0),f<=y&&(y=0),d<=x&&(x=0);return ke(h,t.shape)},t.prototype.localResponseNormalization4D=function(t,a,e,n,r){this.assertNotComplex(t,"localResponseNormalization4D");var s=t.shape[3],u=s-1,c=t.dataSync(),o=L(t.shape),i=new Float32Array(o);function l(t){for(var e=t%s,n=t-e+Math.max(0,e-a),r=t-e+Math.min(e+a,u),o=0;n<=r;n++){var i=c[n];o+=i*i}return o}for(var h=0;h<o;h++){var p=l(h),f=c[h]*Math.pow(e+n*p,-r);i[h]=f}return ke(i,t.shape)},t.prototype.LRNGrad=function(t,e,n,r,o,i,a){this.assertNotComplex(t,"LRNGrad");for(var s=t.shape[3],u=t.dataSync(),c=e.dataSync(),l=n.dataSync(),h=new Float32Array(L(t.shape)),p=L(t.shape),f=0;f<p;f++){for(var d=f%s,m=f-d+Math.max(0,d-r),v=f-d+Math.min(s,d+r+1),g=0,y=m;y<v;y++)g+=Math.pow(c[y],2);for(g=i*g+o,y=m;y<v;y++){var x=-2*i*a*c[y]*l[f]/g;f===y&&(x+=Math.pow(g,-a)),x*=u[f],h[y]+=x}}return ke(h,t.shape)},t.prototype.multinomial=function(t,e,n,r){this.assertNotComplex(t,"multinomial");for(var o=e?t:ye(t),i=o.shape[0],a=o.shape[1],s=Me([i,n],"int32"),u=s.dataSync(),c=o.dataSync(),l=0;l<i;++l){var h=l*a,p=new Float32Array(a-1);p[0]=c[h];for(var f=1;f<p.length;++f)p[f]=p[f-1]+c[h+f];for(var d=ni(r.toString()),m=l*n,v=0;v<n;++v){var g=d();u[m+v]=p.length;for(var y=0;y<p.length;y++)if(g<p[y]){u[m+v]=y;break}}}return s},t.prototype.oneHot=function(t,e,n,r){this.assertNotComplex(t,"oneHot");var o=new Float32Array(t.size*e);o.fill(r);for(var i=0;i<t.size;++i)0<=t.get(i)&&t.get(i)<e&&(o[i*e+t.get(i)]=n);return Ne(o,[t.size,e],"int32")},t.prototype.nonMaxSuppression=function(t,e,n,r,o){return this.assertNotComplex(t,"nonMaxSuppression"),He(t.dataSync(),e.dataSync(),n,r,o)},t.prototype.fft=function(t){return this.fftBatch(t,!1)},t.prototype.ifft=function(t){return this.fftBatch(t,!0)},t.prototype.fftBatch=function(t,e){for(var n=t.shape[0],r=t.shape[1],o=ii(t.shape,"float32"),i=ii(t.shape,"float32"),a=be(t).as2D(n,r),s=Ee(t).as2D(n,r),u=0;u<n;u++)for(var c=a.slice([u,0],[1,r]),l=s.slice([u,0],[1,r]),h=we(c,l),p=this.fftImpl(h,e).dataSync(),f=0;f<r;f++){var d=Ge(p,f);o.values[u*r+f]=d.real,i.values[u*r+f]=d.imag}return we(o.toTensor(),i.toTensor()).as2D(n,r)},t.prototype.fftImpl=function(t,e){var n=t.as1D(),r=n.size;if(this.isExponentOf2(r)){var o=this.fftRadix2(n,r,e).as2D(t.shape[0],t.shape[1]);return e&&(o=we(be(o).div(Se(r)),Ee(o).div(Se(r)))),o}var i=t.dataSync(),a=function(t){for(var e=new Float32Array(t.length/2),n=new Float32Array(t.length/2),r=0;r<t.length;r+=2)e[r/2]=t[r],n[r/2]=t[r+1];return{real:e,imag:n}}(this.fourierTransformByMatmul(i,r,e));return we(a.real,a.imag).as2D(t.shape[0],t.shape[1])},t.prototype.isExponentOf2=function(t){return 0==(t&t-1)},t.prototype.fftRadix2=function(t,e,n){if(1===e)return t;var r=t.dataSync(),o=e/2,i=function(t){for(var e=Math.ceil(t.length/4),n=new Float32Array(e),r=new Float32Array(e),o=0;o<t.length;o+=4)n[Math.floor(o/4)]=t[o],r[Math.floor(o/4)]=t[o+1];return{real:n,imag:r}}(r),a=we(i.real,i.imag).as1D(),s=function(t){for(var e=Math.floor(t.length/4),n=new Float32Array(e),r=new Float32Array(e),o=2;o<t.length;o+=4)n[Math.floor(o/4)]=t[o],r[Math.floor(o/4)]=t[o+1];return{real:n,imag:r}}(r),u=we(s.real,s.imag).as1D();a=this.fftRadix2(a,o,n),u=this.fftRadix2(u,o,n);var c=function(t,e){for(var n=new Float32Array(t/2),r=new Float32Array(t/2),o=0;o<Math.ceil(t/2);o++){var i=(e?2:-2)*Math.PI*(o/t);n[o]=Math.cos(i),r[o]=Math.sin(i)}return{real:n,imag:r}}(e,n),l=we(c.real,c.imag).mul(u),h=a.add(l),p=a.sub(l),f=be(h).concat(be(p)),d=Ee(h).concat(Ee(p));return we(f,d).as1D()},t.prototype.fourierTransformByMatmul=function(t,e,n){for(var r=new Float32Array(2*e),o=0;o<e;o++){for(var i=0,a=0,s=0;s<e;s++){var u=(d=o*s,m=e,void 0,v=(n?2:-2)*Math.PI*(d/m),{real:Math.cos(v),imag:Math.sin(v)}),c=Ge(t,s);i+=c.real*u.real-c.imag*u.imag,a+=c.real*u.imag+c.imag*u.real}n&&(i/=e,a/=e),h=i,p=a,(l=r)[2*(f=o)]=h,l[2*f+1]=p}var l,h,p,f,d,m,v;return r},t.prototype.depthToSpace=function(t,e,n){M("NHWC"===n,"Only NHWC dataFormat supported on CPU for depthToSpace. Got "+n),M(1<e,"blockSize should be > 1 for depthToSpace, but was: "+e);for(var r=t.shape[0],o=t.shape[1],i=t.shape[2],a=t.shape[3],s=o*e,u=i*e,c=a/(e*e),l=t.dataSync(),h=new Float32Array(r*s*u*c),p=0,f=0;f<r;++f)for(var d=0;d<s;++d)for(var m=Math.floor(d/e),v=d%e,g=0;g<u;++g)for(var y=Math.floor(g/e),x=(v*e+g%e)*c,w=0;w<c;++w){var b=w+x+a*(y+i*(m+o*f));h[p++]=l[b]}return ke(h,[r,s,u,c])},t.prototype.broadcastedBinaryOp=function(a,s,t,u){var e=Je(a.shape,s.shape),c=ii(e,t),l=a.dataSync(),h=s.dataSync(),p=Ye(a.shape,e),f=Ye(s.shape,e),d=c.values;if(p.length+f.length===0)for(var n=0;n<d.length;++n)d[n]=u(l[n%l.length],h[n%h.length]);else{var m=a.buffer(),v=s.buffer(),r=function(t){var e=c.indexToLoc(t),n=e.slice(-a.rank);p.forEach(function(t){return n[t]=0});var r=m.locToIndex(n),o=e.slice(-s.rank);f.forEach(function(t){return o[t]=0});var i=v.locToIndex(o);d[t]=u(l[r],h[i])};for(n=0;n<d.length;++n)r(n)}return c.toTensor()},t.prototype.broadcastedBinaryComplexOp=function(s,u,c){var t=Je(s.shape,u.shape),l=ii(t,"float32"),e=ii(t,"float32"),h=s.dataSync(),p=u.dataSync(),f=Ye(s.shape,t),d=Ye(u.shape,t),m=l.values,v=e.values;if(f.length+d.length===0)for(var n=0;n<m.length;n++){var r=n%h.length,o=n%p.length,i=c(h[2*r],h[2*r+1],p[2*o],p[2*o+1]);m[n]=i.real,v[n]=i.imag}else{var g=this.data.get(s.dataId).complexTensors.real.buffer(),y=this.data.get(u.dataId).complexTensors.real.buffer(),a=function(t){var e=l.indexToLoc(t),n=e.slice(-s.rank);f.forEach(function(t){return n[t]=0});var r=g.locToIndex(n),o=e.slice(-u.rank);d.forEach(function(t){return o[t]=0});var i=y.locToIndex(o),a=c(h[2*r],h[2*r+1],p[2*i],p[2*i+1]);m[t]=a.real,v[t]=a.imag};for(n=0;n<m.length;n++)a(n)}return this.complex(l.toTensor(),e.toTensor())},t.prototype.split=function(t,e,n){return je(t,e,n)},t.prototype.dispose=function(){},t.prototype.floatPrecision=function(){return 32},t.prototype.cropAndResize=function(t,e,n,r,o,i){for(var a=t.shape,s=a[0],u=a[1],c=a[2],l=a[3],h=e.shape[0],p=r[0],f=r[1],d=ii([h,p,f,l],t.dtype),m=e.dataSync(),v=n.dataSync(),g=t.dataSync(),y=t.strides,x=d.strides,w=0;w<h;w++){var b=4*w,E=m[b],C=m[b+1],S=m[b+2],R=m[b+3],N=v[w];if(!(s<=N))for(var T=1<p?(S-E)*(u-1)/(p-1):0,k=1<f?(R-C)*(c-1)/(f-1):0,I=0;I<p;I++){var D=1<p?E*(u-1)+I*T:.5*(E+S)*(u-1);if(D<0||u-1<D)for(var A=0;A<f;A++)for(var M=0;M<l;M++){var _=M+A*x[2]+I*x[1]+w*x[0];d.values[_]=i}else if("bilinear"===o){var O=Math.floor(D),F=Math.ceil(D),P=D-O;for(A=0;A<f;A++)if((q=1<f?C*(c-1)+A*k:.5*(C+R)*(c-1))<0||c-1<q)for(M=0;M<l;M++)_=M+A*x[2]+I*x[1]+w*x[0],d.values[_]=i;else{var B=Math.floor(q),L=Math.ceil(q),z=q-B;for(M=0;M<l;M++){var W=g[_=M+B*y[2]+O*y[1]+N*y[0]],U=g[_=M+L*y[2]+O*y[1]+N*y[0]],V=g[_=M+B*y[2]+F*y[1]+N*y[0]],G=W+(U-W)*z,H=V+(g[_=M+L*y[2]+F*y[1]+N*y[0]]-V)*z;_=M+A*x[2]+I*x[1]+w*x[0],d.values[_]=G+(H-G)*P}}}else for(A=0;A<f;++A){var q;if((q=1<f?C*(c-1)+A*k:.5*(C+R)*(c-1))<0||c-1<q)for(M=0;M<l;M++)_=M+A*x[2]+I*x[1]+w*x[0],d.values[_]=i;else{var j=Math.round(q),$=Math.round(D);for(M=0;M<l;M++){var K=M+j*y[2]+$*y[1]+N*y[0],X=M+A*x[2]+I*x[1]+w*x[0];d.values[X]=g[K]}}}}}return d.toTensor()},t.prototype.sparseToDense=function(t,e,n,r){var o=ce(0,t,n),i=o.sliceRank,a=o.numUpdates,s=o.sliceSize,u=o.strides,c=o.outputSize;return this.scatter(t,e,n,c,s,a,i,u,r,!1)},t.prototype.gatherND=function(t,e){var n=e.shape,r=n[n.length-1],o=ae(t,e),i=o[0],a=o[1],s=o[2],u=o[3];if(0===a)return Ce([],i,t.dtype);for(var c=new et([a,s],t.dtype),l=e.dataSync(),h=t.dataSync(),p=0;p<a;p++){for(var f=[],d=0,m=0;m<r;m++){var v=l[p*r+m];d+=v*u[m],f.push(v)}if(d<0||d>=t.size/s)throw new Error("Invalid indices: "+f+" does not index into "+t.shape);for(var g=0;g<s;g++)c.values[p*s+g]=h[d*s+g]}return c.toTensor().reshape(i)},t.prototype.scatterND=function(t,e,n){var r=ce(0,t,n),o=r.sliceRank,i=r.numUpdates,a=r.sliceSize,s=r.strides,u=r.outputSize,c=Se(0);return this.scatter(t,e,n,u,a,i,o,s,c,!0)},t.prototype.scatter=function(t,e,n,r,o,i,a,s,u,c){var l=[r/o,o],h=t.dataSync(),p=e.dataSync();if(0===r)return Ce([],n,e.dtype);var f=new et(l,e.dtype);f.values.fill(u.dataSync()[0]);for(var d=0;d<i;d++){for(var m=[],v=0,g=0;g<a;g++){var y=h[d*a+g];m.push(y),v+=y*s[g]}if(v<0||r/o<=v)throw new Error("Invalid indices: "+m+" does not index into "+n);for(var x=0;x<o;x++)c?f.values[v*o+x]+=p[d*o+x]:f.values[v*o+x]=0===e.rank?p[0]:p[d*o+x]}return f.toTensor().reshape(n)},t}();Ft.registerBackend("cpu",function(){return new Ku},1,ot);var Xu="undefined"!=typeof requestAnimationFrame?requestAnimationFrame:setImmediate;var Yu={float32:4,int32:4,uint16:2,uint8:1,bool:1};function Qu(p,t){for(var f={},d=0,e=function(t){var e=t.name,n=t.dtype,r=t.shape,o=L(r),i=void 0;if("quantization"in t){var a=t.quantization;if("uint8"!==a.dtype&&"uint16"!==a.dtype)throw new Error("Weight "+t.name+" has unknown quantization dtype "+a.dtype+". 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Nl({width:this.getInputWidth(t),height:this.getInputHeight(t)},this.inputSize)},t.prototype.toBatchTensor=function(r,o){var i=this;return void 0===o&&(o=!0),this._inputSize=r,Wt(function(){var t=Tl(i.batchSize,0,1).map(function(t){var e=i.getInput(t);if(e instanceof it){var n=Cl(e)?e:e.expandDims();return(n=hh(n,o)).shape[1]===r&&n.shape[2]===r||(n=ju.resizeBilinear(n,[r,r])),n.as3D(r,r,3)}if(e instanceof Kl.getEnv().Canvas)return di(uh(e,r,o));throw new Error("toBatchTensor - at batchIdx "+t+", expected input to be instanceof tf.Tensor or instanceof HTMLCanvasElement, instead have "+e)});return ki(t.map(function(t){return t.toFloat()})).as4D(i.batchSize,r,r,3)})},t}();function fh(n){return gl(this,void 0,void 0,function(){var r,o,e;return yl(this,function(t){switch(t.label){case 0:if(n instanceof ph)return[2,n];if(!(r=Array.isArray(n)?n:[n]).length)throw new Error("toNetInput - empty array passed as input");return o=function(t){return Array.isArray(n)?" at input index 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t.score-e.score}).map(function(t){return t.boxIndex}),h=[],e=function(){var t=l.pop();h.push(t);for(var e=l,n=[],r=0;r<e.length;r++){var o=e[r],i=s[t],a=s[o];n.push(dh(i,a,c))}l=l.filter(function(t,e){return n[e]<=u})};0<l.length;)e();return h}function vh(s,u){return Wt(function(){var t=u[0],e=u[1],n=u[2],r=_e(s.shape.slice(0,3).concat([1]),t),o=_e(s.shape.slice(0,3).concat([1]),e),i=_e(s.shape.slice(0,3).concat([1]),n),a=Vo([r,o,i],3);return qs(s,a)})}function gh(t){return 1/(1+Math.exp(-t))}var yh=function(){function t(t){this._name=t,this._params=void 0,this._paramMappings=[]}return Object.defineProperty(t.prototype,"params",{get:function(){return this._params},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"paramMappings",{get:function(){return this._paramMappings},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"isLoaded",{get:function(){return!!this.params},enumerable:!0,configurable:!0}),t.prototype.getParamFromPath=function(t){var e=this.traversePropertyPath(t);return e.obj[e.objProp]},t.prototype.reassignParamFromPath=function(t,e){var n=this.traversePropertyPath(t),r=n.obj,o=n.objProp;r[o].dispose(),r[o]=e},t.prototype.getParamList=function(){var n=this;return this._paramMappings.map(function(t){var e=t.paramPath;return{path:e,tensor:n.getParamFromPath(e)}})},t.prototype.getTrainableParams=function(){return this.getParamList().filter(function(t){return t.tensor instanceof at})},t.prototype.getFrozenParams=function(){return this.getParamList().filter(function(t){return!(t.tensor instanceof at)})},t.prototype.variable=function(){var r=this;this.getFrozenParams().forEach(function(t){var e=t.path,n=t.tensor;r.reassignParamFromPath(e,n.variable())})},t.prototype.freeze=function(){var o=this;this.getTrainableParams().forEach(function(t){var e=t.path,n=t.tensor,r=Ce(n.dataSync());n.dispose(),o.reassignParamFromPath(e,r)})},t.prototype.dispose=function(e){void 0===e&&(e=!0),this.getParamList().forEach(function(t){if(e&&t.tensor.isDisposed)throw new Error("param tensor has already been disposed for path "+t.path);t.tensor.dispose()}),this._params=void 0},t.prototype.serializeParams=function(){return new Float32Array(this.getParamList().map(function(t){var e=t.tensor;return Array.from(e.dataSync())}).reduce(function(t,e){return t.concat(e)}))},t.prototype.load=function(e){return gl(this,void 0,void 0,function(){return yl(this,function(t){switch(t.label){case 0:return e instanceof Float32Array?(this.extractWeights(e),[2]):[4,this.loadFromUri(e)];case 1:return t.sent(),[2]}})})},t.prototype.loadFromUri=function(n){return gl(this,void 0,void 0,function(){var e;return yl(this,function(t){switch(t.label){case 0:if(n&&"string"!=typeof n)throw new Error(this._name+".loadFromUri - expected model uri");return[4,lh(n,this.getDefaultModelName())];case 1:return e=t.sent(),this.loadFromWeightMap(e),[2]}})})},t.prototype.loadFromDisk=function(h){return gl(this,void 0,void 0,function(){var e,n,r,o,i,a,s,u,c,l;return yl(this,function(t){switch(t.label){case 0:if(h&&"string"!=typeof h)throw new Error(this._name+".loadFromDisk - expected model file path");return e=Kl.getEnv().readFile,n=Ul(h,this.getDefaultModelName()),r=n.manifestUri,o=n.modelBaseUri,i=function(t){return Promise.all(t.map(function(t){return e(t).then(function(t){return t.buffer})}))},a=Uc.weightsLoaderFactory(i),c=(u=JSON).parse,[4,e(r)];case 1:return s=c.apply(u,[t.sent().toString()]),[4,a(s,o)];case 2:return l=t.sent(),this.loadFromWeightMap(l),[2]}})})},t.prototype.loadFromWeightMap=function(t){var e=this.extractParamsFromWeigthMap(t),n=e.paramMappings,r=e.params;this._paramMappings=n,this._params=r},t.prototype.extractWeights=function(t){var e=this.extractParams(t),n=e.paramMappings,r=e.params;this._paramMappings=n,this._params=r},t.prototype.traversePropertyPath=function(n){if(!this.params)throw new Error("traversePropertyPath - model has no loaded params");var t=n.split("/").reduce(function(t,e){if(!t.nextObj.hasOwnProperty(e))throw new Error("traversePropertyPath - object does not have property "+e+", for path "+n);return{obj:t.nextObj,objProp:e,nextObj:t.nextObj[e]}},{nextObj:this.params}),e=t.obj,r=t.objProp;if(!(e&&r&&e[r]instanceof it))throw new Error("traversePropertyPath - parameter is not a tensor, for path "+n);return{obj:e,objProp:r}},t}();function xh(e,n,r,o){return void 0===r&&(r="same"),void 0===o&&(o=!1),Wt(function(){var t=Ns(Fa(e,n.filters,[1,1],r),n.bias);return o?nu(t):t})}function wh(a,s){return function(t,e,n,r){var o=ke(a(t*e*n*n),[n,n,t,e]),i=Re(a(e));return s.push({paramPath:r+"/filters"},{paramPath:r+"/bias"}),{filters:o,bias:i}}}var bh=function(t,e,n){this.depthwise_filter=t,this.pointwise_filter=e,this.bias=n};function Eh(a,s){return function(t,e,n){var r=ke(a(9*t),[3,3,t,1]),o=ke(a(t*e),[1,1,t,e]),i=Re(a(e));return s.push({paramPath:n+"/depthwise_filter"},{paramPath:n+"/pointwise_filter"},{paramPath:n+"/bias"}),new bh(r,o,i)}}function Ch(o){return function(t){var e=o(t+"/depthwise_filter",4),n=o(t+"/pointwise_filter",4),r=o(t+"/bias",1);return new bh(e,n,r)}}var Sh=function(t){return"number"==typeof t};function Rh(t){if(![t.noObjectScale,t.objectScale,t.coordScale,t.classScale].every(Sh))throw new Error("for training you have to specify noObjectScale, objectScale, coordScale, classScale parameters in your config.json file");return t}var Nh,Th=[3,16,32,64,128,256,512,1024,1024];function kh(e){return Wt(function(){var t=zs(e,Se(.10000000149011612));return Ns(nu(qs(e,t)),t)})}function Ih(e,n){return Wt(function(){var t=gi(e,[[0,0],[1,1],[1,1],[0,0]]);return t=Fa(t,n.conv.filters,[1,1],"valid"),t=qs(t,n.bn.sub),t=zs(t,n.bn.truediv),kh(t=Ns(t,n.conv.bias))})}function Dh(e,n){return Wt(function(){var t=gi(e,[[0,0],[1,1],[1,1],[0,0]]);return t=za(t,n.depthwise_filter,n.pointwise_filter,[1,1],"valid"),kh(t=Ns(t,n.bias))})}function Ah(s,u){var c=wh(s,u);var t=Eh(s,u);return{extractConvParams:c,extractConvWithBatchNormParams:function(t,e,n){var r,o,i,a;return{conv:c(t,e,3,n+"/conv"),bn:(o=n+"/bn",i=Re(s(r=e)),a=Re(s(r)),u.push({paramPath:o+"/sub"},{paramPath:o+"/truediv"}),{sub:i,truediv:a})}},extractSeparableConvParams:t}}function Mh(t,e){var n=zl(t,e);function r(t){return{filters:n(t+"/filters",4),bias:n(t+"/bias",1)}}return{extractConvParams:r,extractConvWithBatchNormParams:function(t){var e;return{conv:r(t+"/conv"),bn:{sub:n((e=t+"/bn")+"/sub",1),truediv:n(e+"/truediv",1)}}},extractSeparableConvParams:Ch(n)}}(Nh=t.TinyYolov2SizeType||(t.TinyYolov2SizeType={}))[Nh.XS=224]="XS",Nh[Nh.SM=320]="SM",Nh[Nh.MD=416]="MD",Nh[Nh.LG=608]="LG";var _h=function(){function t(t){var e=void 0===t?{}:t,n=e.inputSize,r=e.scoreThreshold;if(this._name="TinyYolov2Options",this._inputSize=n||416,this._scoreThreshold=r||.5,"number"!=typeof this._inputSize||this._inputSize%32!=0)throw new Error(this._name+" - expected inputSize to be a number divisible by 32");if("number"!=typeof this._scoreThreshold||this._scoreThreshold<=0||1<=this._scoreThreshold)throw new Error(this._name+" - expected scoreThreshold to be a number between 0 and 1")}return Object.defineProperty(t.prototype,"inputSize",{get:function(){return this._inputSize},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"scoreThreshold",{get:function(){return this._scoreThreshold},enumerable:!0,configurable:!0}),t}(),Oh=function(n){function t(t){var e=n.call(this,"TinyYolov2")||this;return function(t){if(!t)throw new Error("invalid config: "+t);if("boolean"!=typeof t.withSeparableConvs)throw new Error("config.withSeparableConvs has to be a boolean, have: "+t.withSeparableConvs);if(!Sh(t.iouThreshold)||t.iouThreshold<0||1<t.iouThreshold)throw new Error("config.iouThreshold has to be a number between [0, 1], have: "+t.iouThreshold);if(!Array.isArray(t.classes)||!t.classes.length||!t.classes.every(function(t){return"string"==typeof t}))throw new Error("config.classes has to be an array class names: string[], have: "+JSON.stringify(t.classes));if(!Array.isArray(t.anchors)||!t.anchors.length||!t.anchors.map(function(t){return t||{}}).every(function(t){return Sh(t.x)&&Sh(t.y)}))throw new Error("config.anchors has to be an array of { x: number, y: number }, have: "+JSON.stringify(t.anchors));if(t.meanRgb&&(!Array.isArray(t.meanRgb)||3!==t.meanRgb.length||!t.meanRgb.every(Sh)))throw new Error("config.meanRgb has to be an array of shape [number, number, number], have: "+JSON.stringify(t.meanRgb))}(t),e._config=t,e}return ml(t,n),Object.defineProperty(t.prototype,"config",{get:function(){return this._config},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"withClassScores",{get:function(){return this.config.withClassScores||1<this.config.classes.length},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"boxEncodingSize",{get:function(){return 5+(this.withClassScores?this.config.classes.length:0)},enumerable:!0,configurable:!0}),t.prototype.runTinyYolov2=function(t,e){var n=Ih(t,e.conv0);return n=Ih(n=Ka(n,[2,2],[2,2],"same"),e.conv1),n=Ih(n=Ka(n,[2,2],[2,2],"same"),e.conv2),n=Ih(n=Ka(n,[2,2],[2,2],"same"),e.conv3),n=Ih(n=Ka(n,[2,2],[2,2],"same"),e.conv4),n=Ih(n=Ka(n,[2,2],[2,2],"same"),e.conv5),xh(n=Ih(n=Ih(n=Ka(n,[2,2],[1,1],"same"),e.conv6),e.conv7),e.conv8,"valid",!1)},t.prototype.runMobilenet=function(t,e){var n=this.config.isFirstLayerConv2d?kh(xh(t,e.conv0,"valid",!1)):Dh(t,e.conv0);return n=Dh(n=Ka(n,[2,2],[2,2],"same"),e.conv1),n=Dh(n=Ka(n,[2,2],[2,2],"same"),e.conv2),n=Dh(n=Ka(n,[2,2],[2,2],"same"),e.conv3),n=Dh(n=Ka(n,[2,2],[2,2],"same"),e.conv4),n=Dh(n=Ka(n,[2,2],[2,2],"same"),e.conv5),n=Ka(n,[2,2],[1,1],"same"),n=e.conv6?Dh(n,e.conv6):n,xh(n=e.conv7?Dh(n,e.conv7):n,e.conv8,"valid",!1)},t.prototype.forwardInput=function(e,n){var r=this,o=this.params;if(!o)throw new Error("TinyYolov2 - load model before inference");return Wt(function(){var t=e.toBatchTensor(n,!1).toFloat();return t=(t=r.config.meanRgb?vh(t,r.config.meanRgb):t).div(Se(256)),r.config.withSeparableConvs?r.runMobilenet(t,o):r.runTinyYolov2(t,o)})},t.prototype.forward=function(n,r){return gl(this,void 0,void 0,function(){var e;return yl(this,function(t){switch(t.label){case 0:return e=this.forwardInput,[4,fh(n)];case 1:return[4,e.apply(this,[t.sent(),r])];case 2:return[2,t.sent()]}})})},t.prototype.detect=function(m,v){return void 0===v&&(v={}),gl(this,void 0,void 0,function(){var e,n,r,o,i,a,s,u,c,l,h,p,f,d=this;return yl(this,function(t){switch(t.label){case 0:return e=new _h(v),n=e.inputSize,r=e.scoreThreshold,[4,fh(m)];case 1:return o=t.sent(),[4,this.forwardInput(o,n)];case 2:return i=t.sent(),a=Wt(function(){return Ai(i)[0].expandDims()}),s={width:o.getInputWidth(0),height:o.getInputHeight(0)},u=this.extractBoxes(a,o.getReshapedInputDimensions(0),r),i.dispose(),a.dispose(),c=u.map(function(t){return t.box}),l=u.map(function(t){return t.score}),h=u.map(function(t){return t.classScore}),p=u.map(function(t){return d.config.classes[t.label]}),f=mh(c.map(function(t){return t.rescale(n)}),l,this.config.iouThreshold,!0),[2,f.map(function(t){return new Fl(l[t],h[t],p[t],c[t],s)})]}})})},t.prototype.getDefaultModelName=function(){return""},t.prototype.extractParamsFromWeigthMap=function(t){return function(t,e){var n,r=[],o=Mh(t,r),i=o.extractConvParams,a=o.extractConvWithBatchNormParams,s=o.extractSeparableConvParams;if(e.withSeparableConvs){var u=e.filterSizes&&e.filterSizes.length||9;n={conv0:e.isFirstLayerConv2d?i("conv0"):s("conv0"),conv1:s("conv1"),conv2:s("conv2"),conv3:s("conv3"),conv4:s("conv4"),conv5:s("conv5"),conv6:7<u?s("conv6"):void 0,conv7:8<u?s("conv7"):void 0,conv8:i("conv8")}}else n={conv0:a("conv0"),conv1:a("conv1"),conv2:a("conv2"),conv3:a("conv3"),conv4:a("conv4"),conv5:a("conv5"),conv6:a("conv6"),conv7:a("conv7"),conv8:i("conv8")};return Ll(t,r),{params:n,paramMappings:r}}(t,this.config)},t.prototype.extractParams=function(t){var e=this.config.filterSizes||Th,n=e?e.length:void 0;if(7!==n&&8!==n&&9!==n)throw new Error("TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found "+n+" filterSizes in config");return function(t,e,n,r){var o,i=Wl(t),a=i.extractWeights,s=i.getRemainingWeights,u=[],c=Ah(a,u),l=c.extractConvParams,h=c.extractConvWithBatchNormParams,p=c.extractSeparableConvParams;if(e.withSeparableConvs){var f=r[0],d=r[1],m=r[2],v=r[3],g=r[4],y=r[5],x=r[6],w=r[7],b=r[8];o={conv0:e.isFirstLayerConv2d?l(f,d,3,"conv0"):p(f,d,"conv0"),conv1:p(d,m,"conv1"),conv2:p(m,v,"conv2"),conv3:p(v,g,"conv3"),conv4:p(g,y,"conv4"),conv5:p(y,x,"conv5"),conv6:w?p(x,w,"conv6"):void 0,conv7:b?p(w,b,"conv7"):void 0,conv8:l(b||w||x,5*n,1,"conv8")}}else f=r[0],d=r[1],m=r[2],v=r[3],g=r[4],y=r[5],x=r[6],w=r[7],b=r[8],o={conv0:h(f,d,"conv0"),conv1:h(d,m,"conv1"),conv2:h(m,v,"conv2"),conv3:h(v,g,"conv3"),conv4:h(g,y,"conv4"),conv5:h(y,x,"conv5"),conv6:h(x,w,"conv6"),conv7:h(w,b,"conv7"),conv8:l(b,5*n,1,"conv8")};if(0!==s().length)throw new Error("weights remaing after extract: "+s().length);return{params:o,paramMappings:u}}(t,this.config,this.boxEncodingSize,e)},t.prototype.extractBoxes=function(e,t,n){for(var r=this,o=t.width,i=t.height,a=Math.max(o,i),s=a/o,u=a/i,c=e.shape[1],l=this.config.anchors.length,h=Wt(function(){var t=e.reshape([c,c,l,r.boxEncodingSize]);return[t.slice([0,0,0,0],[c,c,l,4]),t.slice([0,0,0,4],[c,c,l,1]),r.withClassScores?ye(t.slice([0,0,0,5],[c,c,l,r.config.classes.length]),3):Se(0)]}),p=h[0],f=h[1],d=h[2],m=[],v=0;v<c;v++)for(var g=0;g<c;g++)for(var y=0;y<l;y++){var x=gh(f.get(v,g,y,0));if(!n||n<x){var w=(g+gh(p.get(v,g,y,0)))/c*s,b=(v+gh(p.get(v,g,y,1)))/c*u,E=Math.exp(p.get(v,g,y,2))*this.config.anchors[y].x/c*s,C=Math.exp(p.get(v,g,y,3))*this.config.anchors[y].y/c*u,S=w-E/2,R=b-C/2,N={row:v,col:g,anchor:y},T=this.withClassScores?this.extractPredictedClass(d,N):{classScore:1,label:0},k=T.classScore,I=T.label;m.push(vl({box:new Ml(S,R,S+E,R+C),score:x,classScore:x*k,label:I},N))}}return p.dispose(),f.dispose(),d.dispose(),m},t.prototype.extractPredictedClass=function(n,t){var r=t.row,o=t.col,i=t.anchor;return Array(this.config.classes.length).fill(0).map(function(t,e){return n.get(r,o,i,e)}).map(function(t,e){return{classScore:t,label:e}}).reduce(function(t,e){return t.classScore>e.classScore?t:e})},t}(yh),Fh=function(){function t(t,e,n,r,o){this._config=o,this._reshapedImgDims=new xl(r.width,r.height),this._outputTensor=t,this._predictedBoxes=n,this.validateGroundTruthBoxes(e),this._groundTruth=this.assignGroundTruthToAnchors(e);var i=this.createGroundTruthMask(),a=this.createCoordAndScoreMasks(),s=a.coordBoxOffsetMask,u=a.coordBoxSizeMask,c=a.scoreMask;this.noObjectLossMask=Wt(function(){return zs(c,qs(Se(1),i))}),this.objectLossMask=Wt(function(){return zs(c,i)}),this.coordBoxOffsetMask=Wt(function(){return zs(s,i)}),this.coordBoxSizeMask=Wt(function(){return zs(u,i)});var l=Wt(function(){return qs(Se(1),s.add(u).add(c))});this.groundTruthClassScoresMask=Wt(function(){return zs(l,i)})}return Object.defineProperty(t.prototype,"config",{get:function(){return this._config},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"reshapedImgDims",{get:function(){return this._reshapedImgDims},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"outputTensor",{get:function(){return this._outputTensor},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"groundTruth",{get:function(){return this._groundTruth},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"predictedBoxes",{get:function(){return this._predictedBoxes},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"inputSize",{get:function(){return Math.max(this.reshapedImgDims.width,this.reshapedImgDims.height)},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"withClassScores",{get:function(){return this._config.withClassScores||1<this._config.classes.length},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"boxEncodingSize",{get:function(){return 5+(this.withClassScores?this._config.classes.length:0)},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"anchors",{get:function(){return this._config.anchors},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"numBoxes",{get:function(){return this.anchors.length},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"numCells",{get:function(){return this.inputSize/32},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"gridCellEncodingSize",{get:function(){return this.boxEncodingSize*this.numBoxes},enumerable:!0,configurable:!0}),t.prototype.toOutputTensorShape=function(t){var e=this;return Wt(function(){return t.reshape([1,e.numCells,e.numCells,e.gridCellEncodingSize])})},t.prototype.computeLoss=function(){var i=this;return Wt(function(){var t=i.computeNoObjectLoss(),e=i.computeObjectLoss(),n=i.computeCoordLoss(),r=i.withClassScores?i.computeClassLoss():Se(0),o=Wt(function(){return t.add(e).add(n).add(r)});return{noObjectLoss:t,objectLoss:e,coordLoss:n,classLoss:r,totalLoss:o}})},t.prototype.computeNoObjectLoss=function(){var t=this;return Wt(function(){return t.computeLossTerm(t.config.noObjectScale,t.toOutputTensorShape(t.noObjectLossMask),oa(t.outputTensor))})},t.prototype.computeObjectLoss=function(){var t=this;return Wt(function(){return t.computeLossTerm(t.config.objectScale,t.toOutputTensorShape(t.objectLossMask),qs(t.toOutputTensorShape(t.computeIous()),oa(t.outputTensor)))})},t.prototype.computeClassLoss=function(){var n=this;return Wt(function(){var t=Wt(function(){var t=zs(ye(n.outputTensor.reshape([n.numCells,n.numCells,n.numBoxes,n.boxEncodingSize]),3),n.groundTruthClassScoresMask),e=n.createOneHotClassScoreMask();return qs(e,t)});return n.computeLossTerm(n.config.classScale,Se(1),t)})},t.prototype.computeCoordLoss=function(){var t=this;return Wt(function(){return t.computeLossTerm(t.config.coordScale,Se(1),Ns(t.computeCoordBoxOffsetError(),t.computeCoordBoxSizeError()))})},t.prototype.computeCoordBoxOffsetError=function(){var r=this;return Wt(function(){var t=r.toOutputTensorShape(r.coordBoxOffsetMask),e=zs(t,r.toOutputTensorShape(r.computeCoordBoxOffsets())),n=zs(t,oa(r.outputTensor));return qs(e,n)})},t.prototype.computeCoordBoxSizeError=function(){var r=this;return Wt(function(){var t=r.toOutputTensorShape(r.coordBoxSizeMask),e=zs(t,r.toOutputTensorShape(r.computeCoordBoxSizes())),n=zs(t,r.outputTensor);return qs(e,n)})},t.prototype.computeLossTerm=function(t,e,n){var r=this;return Wt(function(){return zs(Se(t),r.squaredSumOverMask(e,n))})},t.prototype.squaredSumOverMask=function(t,e){return Wt(function(){return ps(la(zs(t,e)))})},t.prototype.validateGroundTruthBoxes=function(t){var a=this;t.forEach(function(t){var e=t.x,n=t.y,r=t.width,o=t.height,i=t.label;if("number"!=typeof i||i<0||i>a.config.classes.length-1)throw new Error("invalid ground truth data, expected label to be a number in [0, "+(a.config.classes.length-1)+"]");if(e<0||1<e||n<0||1<n||r<0||1<e+r||o<0||1<n+o)throw new Error("invalid ground truth data, box is out of image boundaries "+JSON.stringify({x:e,y:n,width:r,height:o}))})},t.prototype.assignGroundTruthToAnchors=function(t){var h=this;return t.map(function(t){var e=t.x,n=t.y,r=t.width,o=t.height,i=t.label;return{box:new Bl(e,n,r,o),label:i}}).map(function(t){var e=t.box,n=t.label,r=e.rescale(h.reshapedImgDims),o=r.left,i=r.top,a=(r.bottom,r.right,r.x,r.y,r.width),s=r.height,u=o+a/2,c=i+s/2,l=Math.floor(u/h.inputSize*h.numCells);return{row:Math.floor(c/h.inputSize*h.numCells),col:l,anchor:h.anchors.map(function(t,e){return{idx:e,iou:dh(new Ml(0,0,32*t.x,32*t.y),new Ml(0,0,a,s))}}).sort(function(t,e){return e.iou-t.iou})[0].idx,box:e,label:n}})},t.prototype.createGroundTruthMask=function(){var i=this,t=Me([this.numCells,this.numCells,this.numBoxes,this.boxEncodingSize]),a=t.buffer();return this.groundTruth.forEach(function(t){for(var e=t.row,n=t.col,r=t.anchor,o=0;o<i.boxEncodingSize;o++)a.set(1,e,n,r,o)}),t},t.prototype.createCoordAndScoreMasks=function(){var c=this;return Wt(function(){for(var t=Me([c.numCells,c.numCells,c.numBoxes,c.boxEncodingSize]),e=Me([c.numCells,c.numCells,c.numBoxes,c.boxEncodingSize]),n=Me([c.numCells,c.numCells,c.numBoxes,c.boxEncodingSize]),r=t.buffer(),o=e.buffer(),i=n.buffer(),a=0;a<c.numCells;a++)for(var s=0;s<c.numCells;s++)for(var u=0;u<c.numBoxes;u++)r.set(1,a,s,u,0),r.set(1,a,s,u,1),o.set(1,a,s,u,2),o.set(1,a,s,u,3),i.set(1,a,s,u,4);return{coordBoxOffsetMask:t,coordBoxSizeMask:e,scoreMask:n}})},t.prototype.createOneHotClassScoreMask=function(){var t=Me([this.numCells,this.numCells,this.numBoxes,this.boxEncodingSize]),i=t.buffer();return this.groundTruth.forEach(function(t){var e=t.row,n=t.col,r=t.anchor,o=t.label;i.set(1,e,n,r,5+o)}),t},t.prototype.computeIous=function(){var c=this,t=Me([this.numCells,this.numCells,this.gridCellEncodingSize]),l=t.buffer();return this.groundTruth.forEach(function(t){var e,n=t.row,r=t.col,o=t.anchor,i=t.box,a=c.predictedBoxes.find((e={row:n,col:r,anchor:o},function(t){return e.row===t.row&&e.col===t.col&&e.anchor===t.anchor}));if(!a)throw new Error("no output box found for: row "+n+", col "+r+", anchor "+o);var s=dh(i.rescale(c.reshapedImgDims),a.box.rescale(c.reshapedImgDims)),u=c.boxEncodingSize*o;l.set(s,n,r,u+4)}),t},t.prototype.computeCoordBoxOffsets=function(){var c=this,t=Me([this.numCells,this.numCells,this.numBoxes,this.boxEncodingSize]),l=t.buffer();return this.groundTruth.forEach(function(t){var e=t.row,n=t.col,r=t.anchor,o=t.box.rescale(c.reshapedImgDims),i=o.left,a=o.top,s=((i+o.right)/2-32*n)/32,u=((a+o.bottom)/2-32*e)/32;l.set(s,e,n,r,0),l.set(u,e,n,r,1)}),t},t.prototype.computeCoordBoxSizes=function(){var c=this,t=Me([this.numCells,this.numCells,this.numBoxes,this.boxEncodingSize]),l=t.buffer();return this.groundTruth.forEach(function(t){var e=t.row,n=t.col,r=t.anchor,o=t.box.rescale(c.reshapedImgDims),i=o.width,a=o.height,s=Math.log(i/(32*c.anchors[r].x)),u=Math.log(a/(32*c.anchors[r].y));l.set(s,e,n,r,2),l.set(u,e,n,r,3)}),t},t}();var Ph=function(r){function t(t,e){var n=r.call(this,t)||this;return n._trainableConfig=Rh(t),n._optimizer=e,n}return ml(t,r),Object.defineProperty(t.prototype,"trainableConfig",{get:function(){return this._trainableConfig},enumerable:!0,configurable:!0}),Object.defineProperty(t.prototype,"optimizer",{get:function(){return this._optimizer},enumerable:!0,configurable:!0}),t.prototype.backward=function(o,i,p,a){return void 0===a&&(a={}),gl(this,void 0,void 0,function(){var n,r,s,u,c,l,h=this;return yl(this,function(t){switch(t.label){case 0:return e=a,n=Object.assign({},{minBoxSize:32},e),r=n.minBoxSize,s=n.reportLosses,u=Nl(Zl(o),p),(c=this.filterGroundTruthBoxes(i,u,r)).length?[4,fh(uh(o,p))]:[2,null];case 1:return l=t.sent(),[2,this.optimizer.minimize(function(){var t=h.computeLoss(h.forwardInput(l,p),c,u),e=t.noObjectLoss,n=t.objectLoss,r=t.coordLoss,o=t.classLoss,i=t.totalLoss;if(s){var a={losses:{totalLoss:i.dataSync()[0],noObjectLoss:e.dataSync()[0],objectLoss:n.dataSync()[0],coordLoss:r.dataSync()[0],classLoss:o.dataSync()[0]},numBoxes:c.length,inputSize:p};s(a)}return i},!0)]}var e})})},t.prototype.computeLoss=function(t,e,n){var r=Rh(this.config),o=Math.max(n.width,n.height);if(!o)throw new Error("computeLoss - invalid inputSize: "+o);var i=this.extractBoxes(t,n);return Wt(function(){return new Fh(t,e,i,n,r).computeLoss()})},t.prototype.filterGroundTruthBoxes=function(t,e,a){var s=e.height,u=e.width;return t.filter(function(t){var e=t.x,n=t.y,r=t.width,o=t.height,i=new Bl(e,n,r,o).rescale({height:s,width:u});return!(i.width<a||i.height<a)})},t.prototype.load=function(e){return gl(this,void 0,void 0,function(){return yl(this,function(t){switch(t.label){case 0:return[4,r.prototype.load.call(this,e)];case 1:return t.sent(),this.variable(),[2]}})})},t}(Oh);t.tf=fl,t.BoundingBox=Ml,t.Box=Al,t.BoxWithText=_l,t.Dimensions=xl,t.LabeledBox=Ol,t.ObjectDetection=Fl,t.Point=wl,t.PredictedBox=Pl,t.Rect=Bl,t.disposeUnusedWeightTensors=Ll,t.extractWeightEntryFactory=zl,t.extractWeightsFactory=Wl,t.getModelUris=Ul,t.awaitMediaLoaded=Yl,t.bufferToImage=Ql,t.createCanvas=th,t.createCanvasFromMedia=eh,t.drawBox=rh,t.drawDetection=function(t,e,l){var n=Kl.getEnv().Canvas,h=ih(t);if(!(h instanceof n))throw new Error("drawDetection - expected canvas to be of type: HTMLCanvasElement");(Array.isArray(e)?e:[e]).forEach(function(t){var e=t instanceof Fl?t.box:t,n=e.x,r=e.y,o=e.width,i=e.height,a=nh(l),s=Jl(h);rh(s,n,r,o,i,a);var u=a.withScore,c=t instanceof _l?t.text:u&&t instanceof Pl?""+Sl(t.score):t instanceof Fl?t.className+(u?" ("+Sl(t.score)+")":""):"";c&&oh(s,n,r+i,c,a)})},t.drawText=oh,t.fetchImage=function(r){return gl(this,void 0,void 0,function(){var e,n;return yl(this,function(t){switch(t.label){case 0:return[4,ah(r)];case 1:return[4,(e=t.sent()).blob()];case 2:if(!(n=t.sent()).type.startsWith("image/"))throw new Error("fetchImage - expected blob type to be of type image/*, instead have: "+n.type+", for url: "+e.url);return[2,Ql(n)]}})})},t.fetchJson=sh,t.fetchNetWeights=function(n){return gl(this,void 0,void 0,function(){var e;return yl(this,function(t){switch(t.label){case 0:return e=Float32Array.bind,[4,ah(n)];case 1:return[4,t.sent().arrayBuffer()];case 2:return[2,new(e.apply(Float32Array,[void 0,t.sent()]))]}})})},t.fetchOrThrow=ah,t.getContext2dOrThrow=Jl,t.getDefaultDrawOptions=nh,t.getMediaDimensions=Zl,t.imageTensorToCanvas=function(s,u){return gl(this,void 0,void 0,function(){var e,n,r,o,i,a;return yl(this,function(t){switch(t.label){case 0:return e=u||Kl.getEnv().createCanvasElement(),n=s.shape.slice(Cl(s)?1:0),r=n[0],o=n[1],i=n[2],[4,oi(a=Wt(function(){return s.as3D(r,o,i).toInt()}),e)];case 1:return t.sent(),a.dispose(),[2,e]}})})},t.imageToSquare=uh,t.isMediaElement=ch,t.isMediaLoaded=Xl,t.loadWeightMap=lh,t.NetInput=ph,t.resolveInput=ih,t.toNetInput=fh,t.env=Kl,t.sigmoid=gh,t.inverseSigmoid=function(t){return Math.log(t/(1-t))},t.iou=dh,t.nonMaxSuppression=mh,t.normalize=vh,t.padToSquare=hh,t.shuffleArray=function(t){for(var e=t.slice(),n=e.length-1;0<n;n--){var r=Math.floor(Math.random()*(n+1)),o=e[n];e[n]=e[r],e[r]=o}return e},t.isTensor=bl,t.isTensor1D=function(t){return bl(t,1)},t.isTensor2D=function(t){return bl(t,2)},t.isTensor3D=El,t.isTensor4D=Cl,t.isFloat=function(t){return t%1!=0},t.isEven=function(t){return t%2==0},t.round=Sl,t.isDimensions=Rl,t.computeReshapedDimensions=Nl,t.getCenterPoint=function(t){return t.reduce(function(t,e){return t.add(e)},new wl(0,0)).div(new wl(t.length,t.length))},t.range=Tl,t.isValidNumber=kl,t.isValidProbablitiy=Il,t.NeuralNetwork=yh,t.convLayer=xh,t.extractConvParamsFactory=wh,t.extractFCParamsFactory=function(i,a){return function(t,e,n){var r=Ne(i(t*e),[t,e]),o=Re(i(e));return a.push({paramPath:n+"/weights"},{paramPath:n+"/bias"}),{weights:r,bias:o}}},t.extractSeparableConvParamsFactory=Eh,t.loadSeparableConvParamsFactory=Ch,t.SeparableConvParams=bh,t.TinyYolov2=Oh,t.TinyYolov2LossFunction=Fh,t.TinyYolov2Trainable=Ph,t.TinyYolov2Options=_h,Object.defineProperty(t,"__esModule",{value:!0})});
diff --git a/node_modules/tfjs-tiny-yolov2/karma.conf.js b/node_modules/tfjs-tiny-yolov2/karma.conf.js
new file mode 100644
index 0000000..12f8980
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/karma.conf.js
@@ -0,0 +1,40 @@
+const dataFiles = [
+].map(pattern => ({
+  pattern,
+  watched: false,
+  included: false,
+  served: true,
+  nocache: false
+}))
+
+module.exports = function(config) {
+  config.set({
+    frameworks: ['jasmine', 'karma-typescript'],
+    files: [
+      'src/**/*.ts',
+      'test/**/*.ts'
+    ].concat(dataFiles),
+    preprocessors: {
+      '**/*.ts': ['karma-typescript']
+    },
+    karmaTypescriptConfig: {
+      tsconfig: 'tsconfig.test.json'
+    },
+    browsers: process.env.KARMA_BROWSERS
+      ? process.env.KARMA_BROWSERS.split(',')
+      : ['Chrome'],
+    browserNoActivityTimeout: 120000,
+    captureTimeout: 60000,
+    client: {
+      jasmine: {
+        timeoutInterval: 60000
+      }
+    },
+    customLaunchers: {
+      ChromeNoSandbox: {
+        base: 'Chrome',
+        flags: ['--no-sandbox']
+      }
+    }
+  })
+}
diff --git a/node_modules/tfjs-tiny-yolov2/package.json b/node_modules/tfjs-tiny-yolov2/package.json
new file mode 100644
index 0000000..e7adb1f
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/package.json
@@ -0,0 +1,75 @@
+{
+  "_args": [
+    [
+      "tfjs-tiny-yolov2@0.4.0",
+      "/Users/zamua/Dropbox/workspace/madify"
+    ]
+  ],
+  "_from": "tfjs-tiny-yolov2@0.4.0",
+  "_id": "tfjs-tiny-yolov2@0.4.0",
+  "_inBundle": false,
+  "_integrity": "sha512-jCsAB9cn3TCikiYr1i4rqBsS5onPJ4E6VnxfRiqHGZRC4crWZ3blmyPPOiVUQx+UXMJQ77rEWfiVQE0/84VmtA==",
+  "_location": "/tfjs-tiny-yolov2",
+  "_phantomChildren": {},
+  "_requested": {
+    "type": "version",
+    "registry": true,
+    "raw": "tfjs-tiny-yolov2@0.4.0",
+    "name": "tfjs-tiny-yolov2",
+    "escapedName": "tfjs-tiny-yolov2",
+    "rawSpec": "0.4.0",
+    "saveSpec": null,
+    "fetchSpec": "0.4.0"
+  },
+  "_requiredBy": [
+    "/face-api.js"
+  ],
+  "_resolved": "https://registry.npmjs.org/tfjs-tiny-yolov2/-/tfjs-tiny-yolov2-0.4.0.tgz",
+  "_spec": "0.4.0",
+  "_where": "/Users/zamua/Dropbox/workspace/madify",
+  "author": {
+    "name": "justadudewhohacks"
+  },
+  "dependencies": {
+    "@tensorflow/tfjs-core": "0.14.2",
+    "tfjs-image-recognition-base": "^0.3.0",
+    "tslib": "^1.9.3"
+  },
+  "description": "Tiny YOLO v2 object detection with tensorflow.js.",
+  "devDependencies": {
+    "@types/jasmine": "^2.8.8",
+    "@types/node": "^10.12.12",
+    "jasmine-core": "^3.2.1",
+    "karma": "^3.0.0",
+    "karma-chrome-launcher": "^2.2.0",
+    "karma-jasmine": "^1.1.2",
+    "karma-typescript": "^3.0.12",
+    "rollup": "^0.65.0",
+    "rollup-plugin-commonjs": "^9.1.6",
+    "rollup-plugin-node-resolve": "^3.3.0",
+    "rollup-plugin-typescript2": "^0.16.1",
+    "rollup-plugin-uglify": "^4.0.0",
+    "typescript": "2.8.4"
+  },
+  "keywords": [
+    "tiny",
+    "yolo",
+    "tensorflow",
+    "tf"
+  ],
+  "license": "MIT",
+  "main": "./build/commonjs/index.js",
+  "module": "./build/es6/index.js",
+  "name": "tfjs-tiny-yolov2",
+  "scripts": {
+    "build": "rm -rf ./build && rm -rf ./dist && npm run rollup && npm run rollup-min && npm run tsc && npm run tsc-es6",
+    "rollup": "rollup -c rollup.config.js",
+    "rollup-min": "rollup -c rollup.config.js --environment minify:true",
+    "test": "karma start",
+    "test-travis": "set KARMA_BROWSERS=ChromeHeadlessNoSandbox&& karma start --single-run",
+    "tsc": "tsc",
+    "tsc-es6": "tsc --p tsconfig.es6.json"
+  },
+  "typings": "./build/commonjs/index.d.ts",
+  "version": "0.4.0"
+}
diff --git a/node_modules/tfjs-tiny-yolov2/rollup.config.js b/node_modules/tfjs-tiny-yolov2/rollup.config.js
new file mode 100644
index 0000000..5a163ab
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/rollup.config.js
@@ -0,0 +1,46 @@
+import commonjs from 'rollup-plugin-commonjs';
+import node from 'rollup-plugin-node-resolve';
+import typescript from 'rollup-plugin-typescript2';
+import { uglify } from 'rollup-plugin-uglify';
+import path from 'path';
+
+const { minify } = process.env
+
+export default {
+  input: 'src/index.ts',
+  plugins: [
+    typescript({
+      tsconfigOverride: {
+        compilerOptions: {
+          module: 'ES2015',
+          declaration: false
+        }
+      }
+    }),
+    node(),
+    commonjs({
+      include: 'node_modules/**'
+    })
+  ].concat(minify ? uglify() : []),
+  output: {
+    extend: true,
+    file: `dist/tiny-yolov2${minify ? '.min' : ''}.js`,
+    format: 'umd',
+    name: 'yolo',
+    globals: {
+      'crypto': 'crypto'
+    },
+    sourcemap: minify ? false : true
+  },
+  external: ['crypto'],
+  onwarn: (warning) => {
+    const ignoreWarnings = ['CIRCULAR_DEPENDENCY', 'CIRCULAR', 'THIS_IS_UNDEFINED']
+    if (ignoreWarnings.some(w => w === warning.code))
+      return
+
+    if (warning.missing === 'alea')
+      return
+
+    console.warn(warning.message)
+  }
+}
diff --git a/node_modules/tfjs-tiny-yolov2/src/common/convLayer.ts b/node_modules/tfjs-tiny-yolov2/src/common/convLayer.ts
new file mode 100644
index 0000000..60e00da
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/src/common/convLayer.ts
@@ -0,0 +1,19 @@
+import * as tf from '@tensorflow/tfjs-core';
+
+import { ConvParams } from './types';
+
+export function convLayer(
+  x: tf.Tensor4D,
+  params: ConvParams,
+  padding: 'valid' | 'same' = 'same',
+  withRelu: boolean = false
+): tf.Tensor4D {
+  return tf.tidy(() => {
+    const out = tf.add(
+      tf.conv2d(x, params.filters, [1, 1], padding),
+      params.bias
+    ) as tf.Tensor4D
+
+    return withRelu ? tf.relu(out) : out
+  })
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-tiny-yolov2/src/common/extractConvParamsFactory.ts b/node_modules/tfjs-tiny-yolov2/src/common/extractConvParamsFactory.ts
new file mode 100644
index 0000000..a6a9817
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/src/common/extractConvParamsFactory.ts
@@ -0,0 +1,32 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { ExtractWeightsFunction, ParamMapping } from 'tfjs-image-recognition-base';
+
+import { ConvParams } from './types';
+
+export function extractConvParamsFactory(
+  extractWeights: ExtractWeightsFunction,
+  paramMappings: ParamMapping[]
+) {
+
+  return function(
+    channelsIn: number,
+    channelsOut: number,
+    filterSize: number,
+    mappedPrefix: string
+  ): ConvParams {
+
+    const filters = tf.tensor4d(
+      extractWeights(channelsIn * channelsOut * filterSize * filterSize),
+      [filterSize, filterSize, channelsIn, channelsOut]
+    )
+    const bias = tf.tensor1d(extractWeights(channelsOut))
+
+    paramMappings.push(
+      { paramPath: `${mappedPrefix}/filters` },
+      { paramPath: `${mappedPrefix}/bias` }
+    )
+
+    return { filters, bias }
+  }
+
+}
diff --git a/node_modules/tfjs-tiny-yolov2/src/common/extractFCParamsFactory.ts b/node_modules/tfjs-tiny-yolov2/src/common/extractFCParamsFactory.ts
new file mode 100644
index 0000000..1630f7d
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/src/common/extractFCParamsFactory.ts
@@ -0,0 +1,32 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { ExtractWeightsFunction, ParamMapping } from 'tfjs-image-recognition-base';
+
+import { FCParams } from './types';
+
+
+export function extractFCParamsFactory(
+  extractWeights: ExtractWeightsFunction,
+  paramMappings: ParamMapping[]
+) {
+
+  return function(
+    channelsIn: number,
+    channelsOut: number,
+    mappedPrefix: string
+  ): FCParams {
+
+    const fc_weights = tf.tensor2d(extractWeights(channelsIn * channelsOut), [channelsIn, channelsOut])
+    const fc_bias = tf.tensor1d(extractWeights(channelsOut))
+
+    paramMappings.push(
+      { paramPath: `${mappedPrefix}/weights` },
+      { paramPath: `${mappedPrefix}/bias` }
+    )
+
+    return {
+      weights: fc_weights,
+      bias: fc_bias
+    }
+  }
+
+}
diff --git a/node_modules/tfjs-tiny-yolov2/src/common/extractSeparableConvParamsFactory.ts b/node_modules/tfjs-tiny-yolov2/src/common/extractSeparableConvParamsFactory.ts
new file mode 100644
index 0000000..1b2e405
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/src/common/extractSeparableConvParamsFactory.ts
@@ -0,0 +1,47 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { ExtractWeightsFunction, ParamMapping } from 'tfjs-image-recognition-base';
+
+import { SeparableConvParams } from './types';
+
+export function extractSeparableConvParamsFactory(
+  extractWeights: ExtractWeightsFunction,
+  paramMappings: ParamMapping[]
+) {
+
+  return function(channelsIn: number, channelsOut: number, mappedPrefix: string): SeparableConvParams {
+    const depthwise_filter = tf.tensor4d(extractWeights(3 * 3 * channelsIn), [3, 3, channelsIn, 1])
+    const pointwise_filter = tf.tensor4d(extractWeights(channelsIn * channelsOut), [1, 1, channelsIn, channelsOut])
+    const bias = tf.tensor1d(extractWeights(channelsOut))
+
+    paramMappings.push(
+      { paramPath: `${mappedPrefix}/depthwise_filter` },
+      { paramPath: `${mappedPrefix}/pointwise_filter` },
+      { paramPath: `${mappedPrefix}/bias` }
+    )
+
+    return new SeparableConvParams(
+      depthwise_filter,
+      pointwise_filter,
+      bias
+    )
+  }
+
+}
+
+export function loadSeparableConvParamsFactory(
+  extractWeightEntry: <T>(originalPath: string, paramRank: number) => T
+) {
+
+  return function (prefix: string): SeparableConvParams {
+    const depthwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/depthwise_filter`, 4)
+    const pointwise_filter = extractWeightEntry<tf.Tensor4D>(`${prefix}/pointwise_filter`, 4)
+    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)
+
+    return new SeparableConvParams(
+      depthwise_filter,
+      pointwise_filter,
+      bias
+    )
+  }
+
+}
diff --git a/node_modules/tfjs-tiny-yolov2/src/common/index.ts b/node_modules/tfjs-tiny-yolov2/src/common/index.ts
new file mode 100644
index 0000000..23748ef
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/src/common/index.ts
@@ -0,0 +1,5 @@
+export * from './convLayer';
+export * from './extractConvParamsFactory';
+export * from './extractFCParamsFactory';
+export * from './extractSeparableConvParamsFactory';
+export * from './types';
diff --git a/node_modules/tfjs-tiny-yolov2/src/common/types.ts b/node_modules/tfjs-tiny-yolov2/src/common/types.ts
new file mode 100644
index 0000000..37d340c
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/src/common/types.ts
@@ -0,0 +1,19 @@
+import * as tf from '@tensorflow/tfjs-core';
+
+export type ConvParams = {
+  filters: tf.Tensor4D
+  bias: tf.Tensor1D
+}
+
+export type FCParams = {
+  weights: tf.Tensor2D
+  bias: tf.Tensor1D
+}
+
+export class SeparableConvParams {
+  constructor(
+    public depthwise_filter: tf.Tensor4D,
+    public pointwise_filter: tf.Tensor4D,
+    public bias: tf.Tensor1D
+  ) {}
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-tiny-yolov2/src/index.ts b/node_modules/tfjs-tiny-yolov2/src/index.ts
new file mode 100644
index 0000000..ba6eb88
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/src/index.ts
@@ -0,0 +1,7 @@
+import * as tf from '@tensorflow/tfjs-core';
+
+export * from 'tfjs-image-recognition-base';
+export * from './common';
+export * from './tinyYolov2';
+
+export { tf }
\ No newline at end of file
diff --git a/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/TinyYolov2.ts b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/TinyYolov2.ts
new file mode 100644
index 0000000..e50329a
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/TinyYolov2.ts
@@ -0,0 +1,251 @@
+import * as tf from '@tensorflow/tfjs-core';
+import {
+  BoundingBox,
+  Dimensions,
+  NetInput,
+  NeuralNetwork,
+  nonMaxSuppression,
+  normalize,
+  ObjectDetection,
+  sigmoid,
+  TNetInput,
+  toNetInput,
+} from 'tfjs-image-recognition-base';
+
+import { convLayer } from '../common';
+import { ConvParams, SeparableConvParams } from '../common/types';
+import { TinyYolov2Config, validateConfig } from './config';
+import { DEFAULT_FILTER_SIZES } from './const';
+import { convWithBatchNorm } from './convWithBatchNorm';
+import { depthwiseSeparableConv } from './depthwiseSeparableConv';
+import { extractParams } from './extractParams';
+import { extractParamsFromWeigthMap } from './extractParamsFromWeigthMap';
+import { leaky } from './leaky';
+import { ITinyYolov2Options, TinyYolov2Options } from './TinyYolov2Options';
+import { DefaultTinyYolov2NetParams, MobilenetParams, TinyYolov2NetParams } from './types';
+
+export class TinyYolov2 extends NeuralNetwork<TinyYolov2NetParams> {
+
+  private _config: TinyYolov2Config
+
+  constructor(config: TinyYolov2Config) {
+    super('TinyYolov2')
+    validateConfig(config)
+    this._config = config
+  }
+
+  public get config(): TinyYolov2Config {
+    return this._config
+  }
+
+  public get withClassScores(): boolean {
+    return this.config.withClassScores || this.config.classes.length > 1
+  }
+
+  public get boxEncodingSize(): number {
+    return 5 + (this.withClassScores ? this.config.classes.length : 0)
+  }
+
+  public runTinyYolov2(x: tf.Tensor4D, params: DefaultTinyYolov2NetParams): tf.Tensor4D {
+
+    let out = convWithBatchNorm(x, params.conv0)
+    out = tf.maxPool(out, [2, 2], [2, 2], 'same')
+    out = convWithBatchNorm(out, params.conv1)
+    out = tf.maxPool(out, [2, 2], [2, 2], 'same')
+    out = convWithBatchNorm(out, params.conv2)
+    out = tf.maxPool(out, [2, 2], [2, 2], 'same')
+    out = convWithBatchNorm(out, params.conv3)
+    out = tf.maxPool(out, [2, 2], [2, 2], 'same')
+    out = convWithBatchNorm(out, params.conv4)
+    out = tf.maxPool(out, [2, 2], [2, 2], 'same')
+    out = convWithBatchNorm(out, params.conv5)
+    out = tf.maxPool(out, [2, 2], [1, 1], 'same')
+    out = convWithBatchNorm(out, params.conv6)
+    out = convWithBatchNorm(out, params.conv7)
+
+    return convLayer(out, params.conv8, 'valid', false)
+  }
+
+  public runMobilenet(x: tf.Tensor4D, params: MobilenetParams): tf.Tensor4D {
+
+    let out = this.config.isFirstLayerConv2d
+      ? leaky(convLayer(x, params.conv0 as ConvParams, 'valid', false))
+      : depthwiseSeparableConv(x, params.conv0 as SeparableConvParams)
+    out = tf.maxPool(out, [2, 2], [2, 2], 'same')
+    out = depthwiseSeparableConv(out, params.conv1)
+    out = tf.maxPool(out, [2, 2], [2, 2], 'same')
+    out = depthwiseSeparableConv(out, params.conv2)
+    out = tf.maxPool(out, [2, 2], [2, 2], 'same')
+    out = depthwiseSeparableConv(out, params.conv3)
+    out = tf.maxPool(out, [2, 2], [2, 2], 'same')
+    out = depthwiseSeparableConv(out, params.conv4)
+    out = tf.maxPool(out, [2, 2], [2, 2], 'same')
+    out = depthwiseSeparableConv(out, params.conv5)
+    out = tf.maxPool(out, [2, 2], [1, 1], 'same')
+    out = params.conv6 ? depthwiseSeparableConv(out, params.conv6) : out
+    out = params.conv7 ? depthwiseSeparableConv(out, params.conv7) : out
+
+    return convLayer(out, params.conv8, 'valid', false)
+  }
+
+  public forwardInput(input: NetInput, inputSize: number): tf.Tensor4D {
+
+    const { params } = this
+
+    if (!params) {
+      throw new Error('TinyYolov2 - load model before inference')
+    }
+
+    return tf.tidy(() => {
+
+      let batchTensor = input.toBatchTensor(inputSize, false).toFloat()
+      batchTensor = this.config.meanRgb
+        ? normalize(batchTensor, this.config.meanRgb)
+        : batchTensor
+      batchTensor = batchTensor.div(tf.scalar(256)) as tf.Tensor4D
+
+      return this.config.withSeparableConvs
+        ? this.runMobilenet(batchTensor, params as MobilenetParams)
+        : this.runTinyYolov2(batchTensor, params as DefaultTinyYolov2NetParams)
+    })
+  }
+
+  public async forward(input: TNetInput, inputSize: number): Promise<tf.Tensor4D> {
+    return await this.forwardInput(await toNetInput(input), inputSize)
+  }
+
+  public async detect(input: TNetInput, forwardParams: ITinyYolov2Options = {}): Promise<ObjectDetection[]> {
+
+    const { inputSize, scoreThreshold } = new TinyYolov2Options(forwardParams)
+
+    const netInput = await toNetInput(input)
+    const out = await this.forwardInput(netInput, inputSize)
+    const out0 = tf.tidy(() => tf.unstack(out)[0].expandDims()) as tf.Tensor4D
+
+    const inputDimensions = {
+      width: netInput.getInputWidth(0),
+      height: netInput.getInputHeight(0)
+    }
+
+    const results = this.extractBoxes(out0, netInput.getReshapedInputDimensions(0), scoreThreshold)
+    out.dispose()
+    out0.dispose()
+
+    const boxes = results.map(res => res.box)
+    const scores = results.map(res => res.score)
+    const classScores = results.map(res => res.classScore)
+    const classNames = results.map(res => this.config.classes[res.label])
+
+    const indices = nonMaxSuppression(
+      boxes.map(box => box.rescale(inputSize)),
+      scores,
+      this.config.iouThreshold,
+      true
+    )
+
+    const detections = indices.map(idx =>
+      new ObjectDetection(
+        scores[idx],
+        classScores[idx],
+        classNames[idx],
+        boxes[idx],
+        inputDimensions
+      )
+    )
+
+    return detections
+  }
+
+  protected getDefaultModelName(): string {
+    return ''
+  }
+
+  protected extractParamsFromWeigthMap(weightMap: tf.NamedTensorMap) {
+    return extractParamsFromWeigthMap(weightMap, this.config)
+  }
+
+  protected extractParams(weights: Float32Array) {
+    const filterSizes = this.config.filterSizes || DEFAULT_FILTER_SIZES
+
+    const numFilters = filterSizes ? filterSizes.length : undefined
+    if (numFilters !== 7 && numFilters !== 8 && numFilters !== 9) {
+      throw new Error(`TinyYolov2 - expected 7 | 8 | 9 convolutional filters, but found ${numFilters} filterSizes in config`)
+    }
+    return extractParams(weights, this.config, this.boxEncodingSize, filterSizes)
+  }
+
+  protected extractBoxes(
+    outputTensor: tf.Tensor4D,
+    inputBlobDimensions: Dimensions,
+    scoreThreshold?: number
+  ) {
+
+    const { width, height } = inputBlobDimensions
+    const inputSize = Math.max(width, height)
+    const correctionFactorX = inputSize / width
+    const correctionFactorY = inputSize / height
+
+    const numCells = outputTensor.shape[1]
+    const numBoxes = this.config.anchors.length
+
+    const [boxesTensor, scoresTensor, classScoresTensor] = tf.tidy(() => {
+      const reshaped = outputTensor.reshape([numCells, numCells, numBoxes, this.boxEncodingSize])
+
+      const boxes = reshaped.slice([0, 0, 0, 0], [numCells, numCells, numBoxes, 4])
+      const scores = reshaped.slice([0, 0, 0, 4], [numCells, numCells, numBoxes, 1])
+      const classScores = this.withClassScores
+        ? tf.softmax(reshaped.slice([0, 0, 0, 5], [numCells, numCells, numBoxes, this.config.classes.length]), 3)
+        : tf.scalar(0)
+      return [boxes, scores, classScores]
+    })
+
+    const results = []
+
+    for (let row = 0; row < numCells; row ++) {
+      for (let col = 0; col < numCells; col ++) {
+        for (let anchor = 0; anchor < numBoxes; anchor ++) {
+          const score = sigmoid(scoresTensor.get(row, col, anchor, 0))
+          if (!scoreThreshold || score > scoreThreshold) {
+            const ctX = ((col + sigmoid(boxesTensor.get(row, col, anchor, 0))) / numCells) * correctionFactorX
+            const ctY = ((row + sigmoid(boxesTensor.get(row, col, anchor, 1))) / numCells) * correctionFactorY
+            const width = ((Math.exp(boxesTensor.get(row, col, anchor, 2)) * this.config.anchors[anchor].x) / numCells) * correctionFactorX
+            const height = ((Math.exp(boxesTensor.get(row, col, anchor, 3)) * this.config.anchors[anchor].y) / numCells) * correctionFactorY
+
+            const x = (ctX - (width / 2))
+            const y = (ctY - (height / 2))
+
+            const pos = { row, col, anchor }
+            const { classScore, label } = this.withClassScores
+              ? this.extractPredictedClass(classScoresTensor as tf.Tensor4D, pos)
+              : { classScore: 1, label: 0 }
+
+            results.push({
+              box: new BoundingBox(x, y, x + width, y + height),
+              score: score,
+              classScore: score * classScore,
+              label,
+              ...pos
+            })
+          }
+        }
+      }
+    }
+
+    boxesTensor.dispose()
+    scoresTensor.dispose()
+    classScoresTensor.dispose()
+
+    return results
+  }
+
+  private extractPredictedClass(classesTensor: tf.Tensor4D, pos: { row: number, col: number, anchor: number }) {
+    const { row, col, anchor } = pos
+    return Array(this.config.classes.length).fill(0)
+      .map((_, i) => classesTensor.get(row, col, anchor, i))
+      .map((classScore, label) => ({
+        classScore,
+        label
+      }))
+      .reduce((max, curr) => max.classScore > curr.classScore ? max : curr)
+  }
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/TinyYolov2LossFunction.ts b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/TinyYolov2LossFunction.ts
new file mode 100644
index 0000000..b82d622
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/TinyYolov2LossFunction.ts
@@ -0,0 +1,378 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { BoundingBox, Dimensions, IDimensions, iou, Point, Rect } from 'tfjs-image-recognition-base';
+
+import { TinyYolov2TrainableConfig } from './config';
+import { CELL_SIZE } from './const';
+import { GridPosition, GroundTruth, GroundTruthWithGridPosition } from './types';
+
+export class TinyYolov2LossFunction {
+
+  private _config: TinyYolov2TrainableConfig
+  private _reshapedImgDims: Dimensions
+  private _outputTensor: tf.Tensor4D
+  private _groundTruth: GroundTruthWithGridPosition[]
+  private _predictedBoxes: GroundTruthWithGridPosition[]
+
+  public noObjectLossMask: tf.Tensor4D
+  public objectLossMask: tf.Tensor4D
+  public coordBoxOffsetMask: tf.Tensor4D
+  public coordBoxSizeMask: tf.Tensor4D
+  public groundTruthClassScoresMask: tf.Tensor4D
+
+  constructor(
+    outputTensor: tf.Tensor4D,
+    groundTruth: GroundTruth[],
+    predictedBoxes: GroundTruthWithGridPosition[],
+    reshapedImgDims: IDimensions,
+    config: TinyYolov2TrainableConfig
+  ) {
+    this._config = config
+    this._reshapedImgDims = new Dimensions(reshapedImgDims.width, reshapedImgDims.height)
+    this._outputTensor = outputTensor
+    this._predictedBoxes = predictedBoxes
+
+    this.validateGroundTruthBoxes(groundTruth)
+    this._groundTruth = this.assignGroundTruthToAnchors(groundTruth)
+
+    const groundTruthMask = this.createGroundTruthMask()
+    const { coordBoxOffsetMask, coordBoxSizeMask, scoreMask } = this.createCoordAndScoreMasks()
+
+    this.noObjectLossMask = tf.tidy(() => tf.mul(scoreMask, tf.sub(tf.scalar(1), groundTruthMask))) as tf.Tensor4D
+    this.objectLossMask = tf.tidy(() => tf.mul(scoreMask, groundTruthMask)) as tf.Tensor4D
+    this.coordBoxOffsetMask = tf.tidy(() => tf.mul(coordBoxOffsetMask, groundTruthMask)) as tf.Tensor4D
+    this.coordBoxSizeMask = tf.tidy(() => tf.mul(coordBoxSizeMask, groundTruthMask)) as tf.Tensor4D
+
+    const classScoresMask = tf.tidy(() => tf.sub(tf.scalar(1), coordBoxOffsetMask.add(coordBoxSizeMask).add(scoreMask)))
+    this.groundTruthClassScoresMask = tf.tidy(() => tf.mul(classScoresMask, groundTruthMask)) as tf.Tensor4D
+  }
+
+  public get config(): TinyYolov2TrainableConfig {
+    return this._config
+  }
+
+  public get reshapedImgDims(): Dimensions {
+    return this._reshapedImgDims
+  }
+
+  public get outputTensor(): tf.Tensor4D {
+    return this._outputTensor
+  }
+
+  public get groundTruth(): GroundTruthWithGridPosition[] {
+    return this._groundTruth
+  }
+
+  public get predictedBoxes(): GroundTruthWithGridPosition[] {
+    return this._predictedBoxes
+  }
+
+  public get inputSize(): number {
+    return Math.max(this.reshapedImgDims.width, this.reshapedImgDims.height)
+  }
+
+  public get withClassScores(): boolean {
+    return this._config.withClassScores || this._config.classes.length > 1
+  }
+
+  public get boxEncodingSize(): number {
+    return 5 + (this.withClassScores ? this._config.classes.length : 0)
+  }
+
+  public get anchors(): Point[] {
+    return this._config.anchors
+  }
+
+  public get numBoxes(): number {
+    return this.anchors.length
+  }
+
+  public get numCells(): number {
+    return this.inputSize / CELL_SIZE
+  }
+
+  public get gridCellEncodingSize(): number {
+    return this.boxEncodingSize * this.numBoxes
+  }
+
+  public toOutputTensorShape(tensor: tf.Tensor) {
+    return tf.tidy(() => tensor.reshape([1, this.numCells, this.numCells, this.gridCellEncodingSize]))
+  }
+
+  public computeLoss() {
+    return tf.tidy(() => {
+
+      const noObjectLoss = this.computeNoObjectLoss()
+      const objectLoss = this.computeObjectLoss()
+      const coordLoss = this.computeCoordLoss()
+      const classLoss = this.withClassScores
+        ? this.computeClassLoss()
+        : tf.scalar(0)
+
+      const totalLoss: tf.Tensor<tf.Rank.R0> = tf.tidy(() => noObjectLoss.add(objectLoss).add(coordLoss).add(classLoss))
+
+      return {
+        noObjectLoss,
+        objectLoss,
+        coordLoss,
+        classLoss,
+        totalLoss
+      }
+    })
+  }
+
+  public computeNoObjectLoss(): tf.Tensor<tf.Rank.R0> {
+    return tf.tidy(() =>
+      this.computeLossTerm(
+        this.config.noObjectScale,
+        this.toOutputTensorShape(this.noObjectLossMask),
+        tf.sigmoid(this.outputTensor)
+      )
+    )
+  }
+
+  public computeObjectLoss(): tf.Tensor<tf.Rank.R0> {
+    return tf.tidy(() =>
+      this.computeLossTerm(
+        this.config.objectScale,
+        this.toOutputTensorShape(this.objectLossMask),
+        tf.sub(this.toOutputTensorShape(this.computeIous()), tf.sigmoid(this.outputTensor))
+      )
+    )
+  }
+
+  public computeClassLoss(): tf.Tensor<tf.Rank.R0> {
+    return tf.tidy(() => {
+
+      const classLossTensor = tf.tidy(() => {
+
+        const predClassScores = tf.mul(
+          tf.softmax(this.outputTensor.reshape([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize]), 3),
+          this.groundTruthClassScoresMask
+        )
+
+        const gtClassScores = this.createOneHotClassScoreMask()
+
+        return tf.sub(gtClassScores, predClassScores)
+      })
+
+      return this.computeLossTerm(
+        this.config.classScale,
+        tf.scalar(1),
+        classLossTensor as tf.Tensor4D
+      )
+    })
+  }
+
+  public computeCoordLoss(): tf.Tensor<tf.Rank.R0> {
+    return tf.tidy(() =>
+      this.computeLossTerm(
+        this.config.coordScale,
+        tf.scalar(1),
+        tf.add(this.computeCoordBoxOffsetError(), this.computeCoordBoxSizeError())
+      )
+    )
+  }
+
+  public computeCoordBoxOffsetError(): tf.Tensor4D {
+    return tf.tidy(() => {
+
+      const mask = this.toOutputTensorShape(this.coordBoxOffsetMask)
+      const gtBoxOffsets = tf.mul(mask, this.toOutputTensorShape(this.computeCoordBoxOffsets()))
+      const predBoxOffsets = tf.mul(mask, tf.sigmoid(this.outputTensor))
+
+      return tf.sub(gtBoxOffsets, predBoxOffsets)
+
+    })
+  }
+
+  public computeCoordBoxSizeError(): tf.Tensor4D {
+    return tf.tidy(() => {
+
+      const mask = this.toOutputTensorShape(this.coordBoxSizeMask)
+      const gtBoxSizes = tf.mul(mask, this.toOutputTensorShape(this.computeCoordBoxSizes()))
+      const predBoxSizes = tf.mul(mask, this.outputTensor)
+
+      return tf.sub(gtBoxSizes, predBoxSizes)
+
+    })
+  }
+
+  private computeLossTerm(scale: number, mask: tf.Tensor<tf.Rank>, lossTensor: tf.Tensor4D): tf.Tensor<tf.Rank.R0> {
+    return tf.tidy(() => tf.mul(tf.scalar(scale), this.squaredSumOverMask(mask, lossTensor)))
+  }
+
+  private squaredSumOverMask(mask: tf.Tensor<tf.Rank>, lossTensor: tf.Tensor4D): tf.Tensor4D {
+    return tf.tidy(() => tf.sum(tf.square(tf.mul(mask, lossTensor))))
+  }
+
+  private validateGroundTruthBoxes(groundTruth: GroundTruth[]) {
+    groundTruth.forEach(({ x, y, width, height, label }) => {
+      if (typeof label !== 'number' || label < 0 || label > (this.config.classes.length - 1)) {
+        throw new Error(`invalid ground truth data, expected label to be a number in [0, ${this.config.classes.length - 1}]`)
+      }
+
+      if (x < 0 || x > 1 || y < 0 || y > 1 || width < 0 || (x + width) > 1 || height < 0 || (y + height) > 1) {
+        throw new Error(`invalid ground truth data, box is out of image boundaries ${JSON.stringify({ x, y, width, height})}`)
+      }
+    })
+  }
+
+  private assignGroundTruthToAnchors(groundTruth: GroundTruth[]) {
+    const groundTruthBoxes = groundTruth
+      .map(({ x, y, width, height, label }) => ({
+        box: new Rect(x, y, width, height),
+        label
+      }))
+
+    return groundTruthBoxes.map(({ box, label }) => {
+      const { left, top, bottom, right, x, y, width, height } = box.rescale(this.reshapedImgDims)
+
+      const ctX = left + (width / 2)
+      const ctY = top + (height / 2)
+
+      const col = Math.floor((ctX / this.inputSize) * this.numCells)
+      const row = Math.floor((ctY / this.inputSize) * this.numCells)
+
+      const anchorsByIou = this.anchors.map((anchor, idx) => ({
+        idx,
+        iou: iou(
+          new BoundingBox(0, 0, anchor.x * CELL_SIZE, anchor.y * CELL_SIZE),
+          new BoundingBox(0, 0, width, height)
+        )
+      })).sort((a1, a2) => a2.iou - a1.iou)
+
+      const anchor = anchorsByIou[0].idx
+
+      return { row, col, anchor, box, label }
+    })
+  }
+
+  private createGroundTruthMask() {
+
+    const mask = tf.zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize])
+    const buf = mask.buffer()
+
+    this.groundTruth.forEach(({ row, col, anchor }) => {
+      for (let i = 0; i < this.boxEncodingSize; i++) {
+        buf.set(1, row, col, anchor, i)
+      }
+    })
+
+    return mask
+  }
+
+  private createCoordAndScoreMasks() {
+    return tf.tidy(() => {
+
+      const coordBoxOffsetMask = tf.zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize])
+      const coordBoxSizeMask = tf.zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize])
+      const scoreMask = tf.zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize])
+
+      const coordBoxOffsetBuf = coordBoxOffsetMask.buffer()
+      const coordBoxSizeBuf = coordBoxSizeMask.buffer()
+      const scoreBuf = scoreMask.buffer()
+
+      for (let row = 0; row < this.numCells; row++) {
+        for (let col = 0; col < this.numCells; col++) {
+          for (let anchor = 0; anchor < this.numBoxes; anchor++) {
+            coordBoxOffsetBuf.set(1, row, col, anchor, 0)
+            coordBoxOffsetBuf.set(1, row, col, anchor, 1)
+            coordBoxSizeBuf.set(1, row, col, anchor, 2)
+            coordBoxSizeBuf.set(1, row, col, anchor, 3)
+            scoreBuf.set(1, row, col, anchor, 4)
+          }
+        }
+      }
+
+      return { coordBoxOffsetMask, coordBoxSizeMask, scoreMask }
+    })
+
+  }
+
+  private createOneHotClassScoreMask() {
+    const mask = tf.zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize])
+    const buf = mask.buffer()
+
+    const classValuesOffset = 5
+    this.groundTruth.forEach(({ row, col, anchor, label }) => {
+      buf.set(1, row, col, anchor, classValuesOffset + label)
+    })
+
+    return mask
+  }
+
+  private computeIous() {
+
+    const isSameAnchor = (p1: GridPosition) => (p2: GridPosition) =>
+      p1.row === p2.row
+        && p1.col === p2.col
+        && p1.anchor === p2.anchor
+
+    const ious = tf.zeros([this.numCells, this.numCells, this.gridCellEncodingSize])
+    const buf = ious.buffer()
+
+    this.groundTruth.forEach(({ row, col, anchor, box }) => {
+      const predBox = this.predictedBoxes.find(isSameAnchor({ row, col, anchor }))
+
+      if (!predBox) {
+        throw new Error(`no output box found for: row ${row}, col ${col}, anchor ${anchor}`)
+      }
+
+      const boxIou = iou(
+        box.rescale(this.reshapedImgDims),
+        predBox.box.rescale(this.reshapedImgDims)
+      )
+
+      const anchorOffset = this.boxEncodingSize * anchor
+      const scoreValueOffset = 4
+      buf.set(boxIou, row, col, anchorOffset + scoreValueOffset)
+    })
+
+    return ious
+  }
+
+  public computeCoordBoxOffsets() {
+
+    const offsets = tf.zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize])
+    const buf = offsets.buffer()
+
+    this.groundTruth.forEach(({ row, col, anchor, box }) => {
+      const { left, top, right, bottom } = box.rescale(this.reshapedImgDims)
+
+      const centerX = (left + right) / 2
+      const centerY = (top + bottom) / 2
+
+      const dCenterX = centerX - (col * CELL_SIZE)
+      const dCenterY = centerY - (row * CELL_SIZE)
+
+      // inverseSigmoid(0) === -Infinity, inverseSigmoid(1) === Infinity
+      //const dx = inverseSigmoid(Math.min(0.999, Math.max(0.001, dCenterX / CELL_SIZE)))
+      //const dy = inverseSigmoid(Math.min(0.999, Math.max(0.001, dCenterY / CELL_SIZE)))
+      const dx = dCenterX / CELL_SIZE
+      const dy = dCenterY / CELL_SIZE
+
+      buf.set(dx, row, col, anchor, 0)
+      buf.set(dy, row, col, anchor, 1)
+    })
+
+    return offsets
+  }
+
+  public computeCoordBoxSizes() {
+
+    const sizes = tf.zeros([this.numCells, this.numCells, this.numBoxes, this.boxEncodingSize])
+    const buf = sizes.buffer()
+
+    this.groundTruth.forEach(({ row, col, anchor, box }) => {
+      const { width, height } = box.rescale(this.reshapedImgDims)
+      const dw = Math.log(width / (this.anchors[anchor].x * CELL_SIZE))
+      const dh = Math.log(height / (this.anchors[anchor].y * CELL_SIZE))
+
+      buf.set(dw, row, col, anchor, 2)
+      buf.set(dh, row, col, anchor, 3)
+    })
+
+    return sizes
+  }
+
+}
diff --git a/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/TinyYolov2Options.ts b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/TinyYolov2Options.ts
new file mode 100644
index 0000000..d84a4a2
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/TinyYolov2Options.ts
@@ -0,0 +1,34 @@
+export enum TinyYolov2SizeType {
+  XS = 224,
+  SM = 320,
+  MD = 416,
+  LG = 608
+}
+
+export interface ITinyYolov2Options {
+  inputSize?: number
+  scoreThreshold?: number
+}
+
+export class TinyYolov2Options {
+  protected _name: string = 'TinyYolov2Options'
+
+  private _inputSize: number
+  private _scoreThreshold: number
+
+  constructor({ inputSize, scoreThreshold }: ITinyYolov2Options = {}) {
+    this._inputSize = inputSize || 416
+    this._scoreThreshold = scoreThreshold || 0.5
+
+    if (typeof this._inputSize !== 'number' || this._inputSize % 32 !== 0) {
+      throw new Error(`${this._name} - expected inputSize to be a number divisible by 32`)
+    }
+
+    if (typeof this._scoreThreshold !== 'number' || this._scoreThreshold <= 0 || this._scoreThreshold >= 1) {
+      throw new Error(`${this._name} - expected scoreThreshold to be a number between 0 and 1`)
+    }
+  }
+
+  get inputSize(): number { return this._inputSize }
+  get scoreThreshold(): number { return this._scoreThreshold }
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/TinyYolov2Trainable.ts b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/TinyYolov2Trainable.ts
new file mode 100644
index 0000000..c51264b
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/TinyYolov2Trainable.ts
@@ -0,0 +1,127 @@
+import * as tf from '@tensorflow/tfjs-core';
+import {
+  computeReshapedDimensions,
+  Dimensions,
+  getMediaDimensions,
+  imageToSquare,
+  Rect,
+  toNetInput,
+} from 'tfjs-image-recognition-base';
+
+import { TinyYolov2TrainableConfig, validateTrainConfig } from './config';
+import { getDefaultBackwardOptions } from './getDefaultBackwardOptions';
+import { TinyYolov2 } from './TinyYolov2';
+import { TinyYolov2LossFunction } from './TinyYolov2LossFunction';
+import { GroundTruth, TinyYolov2BackwardOptions } from './types';
+
+export class TinyYolov2Trainable extends TinyYolov2 {
+
+  private _trainableConfig: TinyYolov2TrainableConfig
+  private _optimizer: tf.Optimizer
+
+  constructor(trainableConfig: TinyYolov2TrainableConfig, optimizer: tf.Optimizer) {
+    super(trainableConfig)
+    this._trainableConfig = validateTrainConfig(trainableConfig)
+    this._optimizer = optimizer
+  }
+
+  public get trainableConfig(): TinyYolov2TrainableConfig {
+    return this._trainableConfig
+  }
+
+  public get optimizer(): tf.Optimizer {
+    return this._optimizer
+  }
+
+  public async backward(
+    img: HTMLImageElement | HTMLCanvasElement,
+    groundTruth: GroundTruth[],
+    inputSize: number,
+    options: TinyYolov2BackwardOptions = {}
+  ): Promise<tf.Tensor<tf.Rank.R0> | null> {
+
+    const { minBoxSize, reportLosses } = getDefaultBackwardOptions(options)
+    const reshapedImgDims = computeReshapedDimensions(getMediaDimensions(img), inputSize)
+    const filteredGroundTruthBoxes = this.filterGroundTruthBoxes(groundTruth, reshapedImgDims, minBoxSize)
+
+    if (!filteredGroundTruthBoxes.length) {
+      return null
+    }
+
+    // square input images before creating tensor to prevent gpu memory overflow bug
+    const netInput = await toNetInput(imageToSquare(img, inputSize))
+
+    const loss = this.optimizer.minimize(() => {
+
+      const {
+        noObjectLoss,
+        objectLoss,
+        coordLoss,
+        classLoss,
+        totalLoss
+      } = this.computeLoss(
+        this.forwardInput(netInput, inputSize),
+        filteredGroundTruthBoxes,
+        reshapedImgDims
+      )
+
+      if (reportLosses) {
+        const losses = {
+          totalLoss: totalLoss.dataSync()[0],
+          noObjectLoss: noObjectLoss.dataSync()[0],
+          objectLoss: objectLoss.dataSync()[0],
+          coordLoss: coordLoss.dataSync()[0],
+          classLoss: classLoss.dataSync()[0]
+        }
+
+        const report = {
+          losses,
+          numBoxes: filteredGroundTruthBoxes.length,
+          inputSize
+        }
+
+        reportLosses(report)
+      }
+
+      return totalLoss
+    }, true)
+
+    return loss
+  }
+
+  public computeLoss(outputTensor: tf.Tensor4D, groundTruth: GroundTruth[], reshapedImgDims: Dimensions) {
+
+    const config = validateTrainConfig(this.config)
+
+    const inputSize = Math.max(reshapedImgDims.width, reshapedImgDims.height)
+
+    if (!inputSize) {
+      throw new Error(`computeLoss - invalid inputSize: ${inputSize}`)
+    }
+
+    const predictedBoxes = this.extractBoxes(outputTensor, reshapedImgDims)
+
+    return tf.tidy(() => {
+      const lossFunction = new TinyYolov2LossFunction(outputTensor, groundTruth, predictedBoxes, reshapedImgDims, config)
+      return lossFunction.computeLoss()
+    })
+  }
+
+  public filterGroundTruthBoxes(groundTruth: GroundTruth[], imgDims: Dimensions, minBoxSize: number) {
+
+    const { height: imgHeight, width: imgWidth } = imgDims
+
+    return groundTruth.filter(({ x, y, width, height }) => {
+      const box = (new Rect(x, y, width, height))
+        .rescale({ height: imgHeight, width: imgWidth })
+
+      const isTooTiny = box.width < minBoxSize || box.height < minBoxSize
+      return !isTooTiny
+    })
+  }
+
+  public async load(weightsOrUrl: Float32Array | string | undefined): Promise<void> {
+    await super.load(weightsOrUrl)
+    this.variable()
+  }
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/config.ts b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/config.ts
new file mode 100644
index 0000000..268a285
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/config.ts
@@ -0,0 +1,71 @@
+import { Point } from 'tfjs-image-recognition-base';
+
+export type TinyYolov2Config = {
+  withSeparableConvs: boolean
+  iouThreshold: number
+  anchors: Point[]
+  classes: string[]
+  meanRgb?: [number, number, number]
+  withClassScores?: boolean,
+  filterSizes?: number[]
+  isFirstLayerConv2d?: boolean
+}
+
+export type TinyYolov2TrainableConfig = TinyYolov2Config & {
+  // hyper params for training
+  noObjectScale: number
+  objectScale: number
+  coordScale: number
+  classScale: number
+}
+
+const isNumber = (arg: any) => typeof arg === 'number'
+
+export function validateConfig(config: any) {
+  if (!config) {
+    throw new Error(`invalid config: ${config}`)
+  }
+
+  if (typeof config.withSeparableConvs !== 'boolean') {
+    throw new Error(`config.withSeparableConvs has to be a boolean, have: ${config.withSeparableConvs}`)
+  }
+
+  if (!isNumber(config.iouThreshold) || config.iouThreshold < 0 || config.iouThreshold > 1.0) {
+    throw new Error(`config.iouThreshold has to be a number between [0, 1], have: ${config.iouThreshold}`)
+  }
+
+  if (
+    !Array.isArray(config.classes)
+    || !config.classes.length
+    || !config.classes.every((c: any) => typeof c === 'string')
+  ) {
+
+    throw new Error(`config.classes has to be an array class names: string[], have: ${JSON.stringify(config.classes)}`)
+  }
+
+  if (
+    !Array.isArray(config.anchors)
+    || !config.anchors.length
+    || !config.anchors.map((a: any) => a || {}).every((a: any) => isNumber(a.x) && isNumber(a.y))
+  ) {
+
+    throw new Error(`config.anchors has to be an array of { x: number, y: number }, have: ${JSON.stringify(config.anchors)}`)
+  }
+
+  if (config.meanRgb && (
+    !Array.isArray(config.meanRgb)
+    || config.meanRgb.length !== 3
+    || !config.meanRgb.every(isNumber)
+  )) {
+
+    throw new Error(`config.meanRgb has to be an array of shape [number, number, number], have: ${JSON.stringify(config.meanRgb)}`)
+  }
+}
+
+export function validateTrainConfig(config: any): TinyYolov2TrainableConfig {
+  if (![config.noObjectScale, config.objectScale, config.coordScale, config.classScale].every(isNumber)) {
+    throw new Error(`for training you have to specify noObjectScale, objectScale, coordScale, classScale parameters in your config.json file`)
+  }
+
+  return config
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/const.ts b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/const.ts
new file mode 100644
index 0000000..60643bc
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/const.ts
@@ -0,0 +1,5 @@
+export const CELL_SIZE = 32
+
+export const DEFAULT_FILTER_SIZES = [
+  3, 16, 32, 64, 128, 256, 512, 1024, 1024
+]
\ No newline at end of file
diff --git a/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/convWithBatchNorm.ts b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/convWithBatchNorm.ts
new file mode 100644
index 0000000..8351174
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/convWithBatchNorm.ts
@@ -0,0 +1,17 @@
+import * as tf from '@tensorflow/tfjs-core';
+
+import { leaky } from './leaky';
+import { ConvWithBatchNorm } from './types';
+
+export function convWithBatchNorm(x: tf.Tensor4D, params: ConvWithBatchNorm): tf.Tensor4D  {
+  return tf.tidy(() => {
+    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D
+
+    out = tf.conv2d(out, params.conv.filters, [1, 1], 'valid')
+    out = tf.sub(out, params.bn.sub)
+    out = tf.mul(out, params.bn.truediv)
+    out = tf.add(out, params.conv.bias)
+
+    return leaky(out)
+  })
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/depthwiseSeparableConv.ts b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/depthwiseSeparableConv.ts
new file mode 100644
index 0000000..25c5547
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/depthwiseSeparableConv.ts
@@ -0,0 +1,15 @@
+import * as tf from '@tensorflow/tfjs-core';
+
+import { SeparableConvParams } from '../common/types';
+import { leaky } from './leaky';
+
+export function depthwiseSeparableConv(x: tf.Tensor4D, params: SeparableConvParams): tf.Tensor4D  {
+  return tf.tidy(() => {
+    let out = tf.pad(x, [[0, 0], [1, 1], [1, 1], [0, 0]]) as tf.Tensor4D
+
+    out = tf.separableConv2d(out, params.depthwise_filter, params.pointwise_filter, [1, 1], 'valid')
+    out = tf.add(out, params.bias)
+
+    return leaky(out)
+  })
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/extractParams.ts b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/extractParams.ts
new file mode 100644
index 0000000..0ee4f3a
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/extractParams.ts
@@ -0,0 +1,100 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { extractWeightsFactory, ExtractWeightsFunction, ParamMapping } from 'tfjs-image-recognition-base';
+
+import { extractConvParamsFactory } from '../common';
+import { extractSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';
+import { TinyYolov2Config } from './config';
+import { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';
+
+function extractorsFactory(extractWeights: ExtractWeightsFunction, paramMappings: ParamMapping[]) {
+
+  const extractConvParams = extractConvParamsFactory(extractWeights, paramMappings)
+
+  function extractBatchNormParams(size: number, mappedPrefix: string): BatchNorm {
+
+    const sub = tf.tensor1d(extractWeights(size))
+    const truediv = tf.tensor1d(extractWeights(size))
+
+    paramMappings.push(
+      { paramPath: `${mappedPrefix}/sub` },
+      { paramPath: `${mappedPrefix}/truediv` }
+    )
+
+    return { sub, truediv }
+  }
+
+  function extractConvWithBatchNormParams(channelsIn: number, channelsOut: number, mappedPrefix: string): ConvWithBatchNorm {
+
+    const conv = extractConvParams(channelsIn, channelsOut, 3, `${mappedPrefix}/conv`)
+    const bn = extractBatchNormParams(channelsOut, `${mappedPrefix}/bn`)
+
+    return { conv, bn }
+  }
+  const extractSeparableConvParams = extractSeparableConvParamsFactory(extractWeights, paramMappings)
+
+  return {
+    extractConvParams,
+    extractConvWithBatchNormParams,
+    extractSeparableConvParams
+  }
+
+}
+
+export function extractParams(
+  weights: Float32Array,
+  config: TinyYolov2Config,
+  boxEncodingSize: number,
+  filterSizes: number[]
+): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {
+
+  const {
+    extractWeights,
+    getRemainingWeights
+  } = extractWeightsFactory(weights)
+
+  const paramMappings: ParamMapping[] = []
+
+  const {
+    extractConvParams,
+    extractConvWithBatchNormParams,
+    extractSeparableConvParams
+  } = extractorsFactory(extractWeights, paramMappings)
+
+  let params: TinyYolov2NetParams
+
+  if (config.withSeparableConvs) {
+    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes
+
+    const conv0 = config.isFirstLayerConv2d
+      ? extractConvParams(s0, s1, 3, 'conv0')
+      : extractSeparableConvParams(s0, s1, 'conv0')
+    const conv1 = extractSeparableConvParams(s1, s2, 'conv1')
+    const conv2 = extractSeparableConvParams(s2, s3, 'conv2')
+    const conv3 = extractSeparableConvParams(s3, s4, 'conv3')
+    const conv4 = extractSeparableConvParams(s4, s5, 'conv4')
+    const conv5 = extractSeparableConvParams(s5, s6, 'conv5')
+    const conv6 = s7 ? extractSeparableConvParams(s6, s7, 'conv6') : undefined
+    const conv7 = s8 ? extractSeparableConvParams(s7, s8, 'conv7') : undefined
+    const conv8 = extractConvParams(s8 || s7 || s6, 5 * boxEncodingSize, 1, 'conv8')
+    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }
+  } else {
+    const [s0, s1, s2, s3, s4, s5, s6, s7, s8] = filterSizes
+    const conv0 = extractConvWithBatchNormParams(s0, s1, 'conv0',)
+    const conv1 = extractConvWithBatchNormParams(s1, s2, 'conv1')
+    const conv2 = extractConvWithBatchNormParams(s2, s3, 'conv2')
+    const conv3 = extractConvWithBatchNormParams(s3, s4, 'conv3')
+    const conv4 = extractConvWithBatchNormParams(s4, s5, 'conv4')
+    const conv5 = extractConvWithBatchNormParams(s5, s6, 'conv5')
+    const conv6 = extractConvWithBatchNormParams(s6, s7, 'conv6')
+    const conv7 = extractConvWithBatchNormParams(s7, s8, 'conv7')
+    const conv8 = extractConvParams(s8, 5 * boxEncodingSize, 1, 'conv8')
+    params = { conv0, conv1, conv2, conv3, conv4, conv5, conv6, conv7, conv8 }
+  }
+
+  if (getRemainingWeights().length !== 0) {
+    throw new Error(`weights remaing after extract: ${getRemainingWeights().length}`)
+  }
+
+
+  return { params, paramMappings }
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/extractParamsFromWeigthMap.ts b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/extractParamsFromWeigthMap.ts
new file mode 100644
index 0000000..d74bd59
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/extractParamsFromWeigthMap.ts
@@ -0,0 +1,86 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { disposeUnusedWeightTensors, extractWeightEntryFactory, ParamMapping } from 'tfjs-image-recognition-base';
+
+import { ConvParams } from '../common';
+import { loadSeparableConvParamsFactory } from '../common/extractSeparableConvParamsFactory';
+import { TinyYolov2Config } from './config';
+import { BatchNorm, ConvWithBatchNorm, TinyYolov2NetParams } from './types';
+
+function extractorsFactory(weightMap: any, paramMappings: ParamMapping[]) {
+
+  const extractWeightEntry = extractWeightEntryFactory(weightMap, paramMappings)
+
+  function extractBatchNormParams(prefix: string): BatchNorm {
+    const sub = extractWeightEntry<tf.Tensor1D>(`${prefix}/sub`, 1)
+    const truediv = extractWeightEntry<tf.Tensor1D>(`${prefix}/truediv`, 1)
+    return { sub, truediv }
+  }
+
+  function extractConvParams(prefix: string): ConvParams {
+    const filters = extractWeightEntry<tf.Tensor4D>(`${prefix}/filters`, 4)
+    const bias = extractWeightEntry<tf.Tensor1D>(`${prefix}/bias`, 1)
+    return { filters, bias }
+  }
+
+  function extractConvWithBatchNormParams(prefix: string): ConvWithBatchNorm {
+    const conv = extractConvParams(`${prefix}/conv`)
+    const bn = extractBatchNormParams(`${prefix}/bn`)
+    return { conv, bn }
+  }
+
+  const extractSeparableConvParams = loadSeparableConvParamsFactory(extractWeightEntry)
+
+  return {
+    extractConvParams,
+    extractConvWithBatchNormParams,
+    extractSeparableConvParams
+  }
+
+}
+
+export function extractParamsFromWeigthMap(
+  weightMap: tf.NamedTensorMap,
+  config: TinyYolov2Config
+): { params: TinyYolov2NetParams, paramMappings: ParamMapping[] } {
+
+  const paramMappings: ParamMapping[] = []
+
+  const {
+    extractConvParams,
+    extractConvWithBatchNormParams,
+    extractSeparableConvParams
+  } = extractorsFactory(weightMap, paramMappings)
+
+  let params: TinyYolov2NetParams
+
+  if (config.withSeparableConvs) {
+    const numFilters = (config.filterSizes && config.filterSizes.length || 9)
+    params = {
+      conv0: config.isFirstLayerConv2d ? extractConvParams('conv0') : extractSeparableConvParams('conv0'),
+      conv1: extractSeparableConvParams('conv1'),
+      conv2: extractSeparableConvParams('conv2'),
+      conv3: extractSeparableConvParams('conv3'),
+      conv4: extractSeparableConvParams('conv4'),
+      conv5: extractSeparableConvParams('conv5'),
+      conv6: numFilters > 7 ? extractSeparableConvParams('conv6') : undefined,
+      conv7: numFilters > 8 ? extractSeparableConvParams('conv7') : undefined,
+      conv8: extractConvParams('conv8')
+    }
+  } else {
+    params = {
+      conv0: extractConvWithBatchNormParams('conv0'),
+      conv1: extractConvWithBatchNormParams('conv1'),
+      conv2: extractConvWithBatchNormParams('conv2'),
+      conv3: extractConvWithBatchNormParams('conv3'),
+      conv4: extractConvWithBatchNormParams('conv4'),
+      conv5: extractConvWithBatchNormParams('conv5'),
+      conv6: extractConvWithBatchNormParams('conv6'),
+      conv7: extractConvWithBatchNormParams('conv7'),
+      conv8: extractConvParams('conv8')
+    }
+  }
+
+  disposeUnusedWeightTensors(weightMap, paramMappings)
+
+  return { params, paramMappings }
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/getDefaultBackwardOptions.ts b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/getDefaultBackwardOptions.ts
new file mode 100644
index 0000000..dd071f1
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/getDefaultBackwardOptions.ts
@@ -0,0 +1,12 @@
+import { CELL_SIZE } from './const';
+import { TinyYolov2BackwardOptions } from './types';
+
+export function getDefaultBackwardOptions(options: TinyYolov2BackwardOptions) {
+  return Object.assign(
+    {},
+    {
+      minBoxSize: CELL_SIZE
+    },
+    options
+  )
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/index.ts b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/index.ts
new file mode 100644
index 0000000..cb42342
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/index.ts
@@ -0,0 +1,6 @@
+export * from './TinyYolov2';
+export * from './TinyYolov2LossFunction';
+export * from './TinyYolov2Trainable';
+export * from './TinyYolov2Options';
+
+export { TinyYolov2NetParams } from './types'
\ No newline at end of file
diff --git a/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/leaky.ts b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/leaky.ts
new file mode 100644
index 0000000..20176cb
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/leaky.ts
@@ -0,0 +1,9 @@
+import * as tf from '@tensorflow/tfjs-core';
+
+export function leaky(x: tf.Tensor4D): tf.Tensor4D {
+  return tf.tidy(() => {
+    const min = tf.mul(x, tf.scalar(0.10000000149011612))
+    return tf.add(tf.relu(tf.sub(x, min)), min)
+    //return tf.maximum(x, min)
+  })
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/types.ts b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/types.ts
new file mode 100644
index 0000000..4a31288
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/src/tinyYolov2/types.ts
@@ -0,0 +1,75 @@
+import * as tf from '@tensorflow/tfjs-core';
+import { Box, IRect } from 'tfjs-image-recognition-base';
+
+import { ConvParams } from '../common';
+import { SeparableConvParams } from '../common/types';
+
+export type BatchNorm = {
+  sub: tf.Tensor1D
+  truediv: tf.Tensor1D
+}
+
+export type ConvWithBatchNorm = {
+  conv: ConvParams
+  bn: BatchNorm
+}
+
+export type MobilenetParams = {
+  conv0: SeparableConvParams | ConvParams
+  conv1: SeparableConvParams
+  conv2: SeparableConvParams
+  conv3: SeparableConvParams
+  conv4: SeparableConvParams
+  conv5: SeparableConvParams
+  conv6?: SeparableConvParams
+  conv7?: SeparableConvParams
+  conv8: ConvParams
+}
+
+export type DefaultTinyYolov2NetParams = {
+  conv0: ConvWithBatchNorm
+  conv1: ConvWithBatchNorm
+  conv2: ConvWithBatchNorm
+  conv3: ConvWithBatchNorm
+  conv4: ConvWithBatchNorm
+  conv5: ConvWithBatchNorm
+  conv6: ConvWithBatchNorm
+  conv7: ConvWithBatchNorm
+  conv8: ConvParams
+}
+
+export type TinyYolov2NetParams = DefaultTinyYolov2NetParams | MobilenetParams
+
+export type GridPosition = {
+  row: number
+  col: number
+  anchor: number
+}
+
+export type GroundTruthWithGridPosition = GridPosition & {
+  box: Box
+  label: number
+}
+
+export type GroundTruth = IRect & {
+  label: number
+}
+
+export type YoloLoss = {
+  totalLoss: number
+  noObjectLoss: number
+  objectLoss: number
+  coordLoss: number
+  classLoss: number
+}
+
+export type LossReport = {
+  losses: YoloLoss
+  numBoxes: number
+  inputSize: number
+}
+
+export type TinyYolov2BackwardOptions = {
+  minBoxSize?: number
+  reportLosses?: (report: LossReport) => void
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-tiny-yolov2/tsconfig.es6.json b/node_modules/tfjs-tiny-yolov2/tsconfig.es6.json
new file mode 100644
index 0000000..621477b
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/tsconfig.es6.json
@@ -0,0 +1,11 @@
+{
+  "extends": "./tsconfig.json",
+  "compilerOptions": {
+    "outDir": "build/es6",
+    "module": "es6",
+    "target": "es5"
+  },
+  "include": [
+    "src"
+  ]
+}
diff --git a/node_modules/tfjs-tiny-yolov2/tsconfig.json b/node_modules/tfjs-tiny-yolov2/tsconfig.json
new file mode 100644
index 0000000..dfbd7ca
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/tsconfig.json
@@ -0,0 +1,35 @@
+{
+  "compilerOptions": {
+
+    "removeComments": false,
+    "preserveConstEnums": true,
+    "emitDecoratorMetadata": true,
+    "experimentalDecorators": true,
+    "sourceMap": true,
+    "declaration": true,
+    "noImplicitAny": true,
+    "noImplicitReturns": true,
+    "noImplicitThis": true,
+    "noFallthroughCasesInSwitch": true,
+    "suppressImplicitAnyIndexErrors": true,
+    "strictNullChecks": true,
+    "importHelpers": true,
+    "outDir": "build/commonjs",
+    "module": "commonjs",
+    "target": "es5",
+    "moduleResolution": "node",
+    "lib": ["es2015", "dom"],
+    "typeRoots": [
+      "typings",
+      "node_modules/@types"
+    ],
+    "baseUrl": "."
+  },
+  "formatCodeOptions": {
+    "indentSize": 2,
+    "tabSize": 2
+  },
+  "include": [
+    "src"
+  ]
+}
\ No newline at end of file
diff --git a/node_modules/tfjs-tiny-yolov2/tsconfig.test.json b/node_modules/tfjs-tiny-yolov2/tsconfig.test.json
new file mode 100644
index 0000000..f885fbc
--- /dev/null
+++ b/node_modules/tfjs-tiny-yolov2/tsconfig.test.json
@@ -0,0 +1,7 @@
+{
+  "extends": "./tsconfig.json",
+  "include": [
+    "src",
+    "test"
+  ]
+}
diff --git a/node_modules/tslib/.gitattributes b/node_modules/tslib/.gitattributes
new file mode 100644
index 0000000..74f5f4a
--- /dev/null
+++ b/node_modules/tslib/.gitattributes
@@ -0,0 +1 @@
+*.js linguist-language=TypeScript
\ No newline at end of file
diff --git a/node_modules/tslib/CopyrightNotice.txt b/node_modules/tslib/CopyrightNotice.txt
new file mode 100644
index 0000000..0f6db1f
--- /dev/null
+++ b/node_modules/tslib/CopyrightNotice.txt
@@ -0,0 +1,15 @@
+/*! *****************************************************************************
+Copyright (c) Microsoft Corporation. All rights reserved. 
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use
+this file except in compliance with the License. You may obtain a copy of the
+License at http://www.apache.org/licenses/LICENSE-2.0  
+ 
+THIS CODE IS PROVIDED ON AN *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
+WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE, 
+MERCHANTABLITY OR NON-INFRINGEMENT. 
+ 
+See the Apache Version 2.0 License for specific language governing permissions
+and limitations under the License.
+***************************************************************************** */
+
diff --git a/node_modules/tslib/LICENSE.txt b/node_modules/tslib/LICENSE.txt
new file mode 100644
index 0000000..8746124
--- /dev/null
+++ b/node_modules/tslib/LICENSE.txt
@@ -0,0 +1,55 @@
+Apache License
+
+Version 2.0, January 2004
+
+http://www.apache.org/licenses/ 
+
+TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+1. Definitions.
+
+"License" shall mean the terms and conditions for use, reproduction, and distribution as defined by Sections 1 through 9 of this document.
+
+"Licensor" shall mean the copyright owner or entity authorized by the copyright owner that is granting the License.
+
+"Legal Entity" shall mean the union of the acting entity and all other entities that control, are controlled by, or are under common control with that entity. For the purposes of this definition, "control" means (i) the power, direct or indirect, to cause the direction or management of such entity, whether by contract or otherwise, or (ii) ownership of fifty percent (50%) or more of the outstanding shares, or (iii) beneficial ownership of such entity.
+
+"You" (or "Your") shall mean an individual or Legal Entity exercising permissions granted by this License.
+
+"Source" form shall mean the preferred form for making modifications, including but not limited to software source code, documentation source, and configuration files.
+
+"Object" form shall mean any form resulting from mechanical transformation or translation of a Source form, including but not limited to compiled object code, generated documentation, and conversions to other media types.
+
+"Work" shall mean the work of authorship, whether in Source or Object form, made available under the License, as indicated by a copyright notice that is included in or attached to the work (an example is provided in the Appendix below).
+
+"Derivative Works" shall mean any work, whether in Source or Object form, that is based on (or derived from) the Work and for which the editorial revisions, annotations, elaborations, or other modifications represent, as a whole, an original work of authorship. For the purposes of this License, Derivative Works shall not include works that remain separable from, or merely link (or bind by name) to the interfaces of, the Work and Derivative Works thereof.
+
+"Contribution" shall mean any work of authorship, including the original version of the Work and any modifications or additions to that Work or Derivative Works thereof, that is intentionally submitted to Licensor for inclusion in the Work by the copyright owner or by an individual or Legal Entity authorized to submit on behalf of the copyright owner. For the purposes of this definition, "submitted" means any form of electronic, verbal, or written communication sent to the Licensor or its representatives, including but not limited to communication on electronic mailing lists, source code control systems, and issue tracking systems that are managed by, or on behalf of, the Licensor for the purpose of discussing and improving the Work, but excluding communication that is conspicuously marked or otherwise designated in writing by the copyright owner as "Not a Contribution."
+
+"Contributor" shall mean Licensor and any individual or Legal Entity on behalf of whom a Contribution has been received by Licensor and subsequently incorporated within the Work.
+
+2. Grant of Copyright License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable copyright license to reproduce, prepare Derivative Works of, publicly display, publicly perform, sublicense, and distribute the Work and such Derivative Works in Source or Object form.
+
+3. Grant of Patent License. Subject to the terms and conditions of this License, each Contributor hereby grants to You a perpetual, worldwide, non-exclusive, no-charge, royalty-free, irrevocable (except as stated in this section) patent license to make, have made, use, offer to sell, sell, import, and otherwise transfer the Work, where such license applies only to those patent claims licensable by such Contributor that are necessarily infringed by their Contribution(s) alone or by combination of their Contribution(s) with the Work to which such Contribution(s) was submitted. If You institute patent litigation against any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Work or a Contribution incorporated within the Work constitutes direct or contributory patent infringement, then any patent licenses granted to You under this License for that Work shall terminate as of the date such litigation is filed.
+
+4. Redistribution. You may reproduce and distribute copies of the Work or Derivative Works thereof in any medium, with or without modifications, and in Source or Object form, provided that You meet the following conditions:
+
+You must give any other recipients of the Work or Derivative Works a copy of this License; and
+
+You must cause any modified files to carry prominent notices stating that You changed the files; and
+
+You must retain, in the Source form of any Derivative Works that You distribute, all copyright, patent, trademark, and attribution notices from the Source form of the Work, excluding those notices that do not pertain to any part of the Derivative Works; and
+
+If the Work includes a "NOTICE" text file as part of its distribution, then any Derivative Works that You distribute must include a readable copy of the attribution notices contained within such NOTICE file, excluding those notices that do not pertain to any part of the Derivative Works, in at least one of the following places: within a NOTICE text file distributed as part of the Derivative Works; within the Source form or documentation, if provided along with the Derivative Works; or, within a display generated by the Derivative Works, if and wherever such third-party notices normally appear. The contents of the NOTICE file are for informational purposes only and do not modify the License. You may add Your own attribution notices within Derivative Works that You distribute, alongside or as an addendum to the NOTICE text from the Work, provided that such additional attribution notices cannot be construed as modifying the License. You may add Your own copyright statement to Your modifications and may provide additional or different license terms and conditions for use, reproduction, or distribution of Your modifications, or for any such Derivative Works as a whole, provided Your use, reproduction, and distribution of the Work otherwise complies with the conditions stated in this License.
+
+5. Submission of Contributions. Unless You explicitly state otherwise, any Contribution intentionally submitted for inclusion in the Work by You to the Licensor shall be under the terms and conditions of this License, without any additional terms or conditions. Notwithstanding the above, nothing herein shall supersede or modify the terms of any separate license agreement you may have executed with Licensor regarding such Contributions.
+
+6. Trademarks. This License does not grant permission to use the trade names, trademarks, service marks, or product names of the Licensor, except as required for reasonable and customary use in describing the origin of the Work and reproducing the content of the NOTICE file.
+
+7. Disclaimer of Warranty. Unless required by applicable law or agreed to in writing, Licensor provides the Work (and each Contributor provides its Contributions) on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied, including, without limitation, any warranties or conditions of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A PARTICULAR PURPOSE. You are solely responsible for determining the appropriateness of using or redistributing the Work and assume any risks associated with Your exercise of permissions under this License.
+
+8. Limitation of Liability. In no event and under no legal theory, whether in tort (including negligence), contract, or otherwise, unless required by applicable law (such as deliberate and grossly negligent acts) or agreed to in writing, shall any Contributor be liable to You for damages, including any direct, indirect, special, incidental, or consequential damages of any character arising as a result of this License or out of the use or inability to use the Work (including but not limited to damages for loss of goodwill, work stoppage, computer failure or malfunction, or any and all other commercial damages or losses), even if such Contributor has been advised of the possibility of such damages.
+
+9. Accepting Warranty or Additional Liability. While redistributing the Work or Derivative Works thereof, You may choose to offer, and charge a fee for, acceptance of support, warranty, indemnity, or other liability obligations and/or rights consistent with this License. However, in accepting such obligations, You may act only on Your own behalf and on Your sole responsibility, not on behalf of any other Contributor, and only if You agree to indemnify, defend, and hold each Contributor harmless for any liability incurred by, or claims asserted against, such Contributor by reason of your accepting any such warranty or additional liability.
+
+END OF TERMS AND CONDITIONS
diff --git a/node_modules/tslib/README.md b/node_modules/tslib/README.md
new file mode 100644
index 0000000..ae595fe
--- /dev/null
+++ b/node_modules/tslib/README.md
@@ -0,0 +1,134 @@
+# tslib
+
+This is a runtime library for [TypeScript](http://www.typescriptlang.org/) that contains all of the TypeScript helper functions.
+
+This library is primarily used by the `--importHelpers` flag in TypeScript.
+When using `--importHelpers`, a module that uses helper functions like `__extends` and `__assign` in the following emitted file:
+
+```ts
+var __assign = (this && this.__assign) || Object.assign || function(t) {
+    for (var s, i = 1, n = arguments.length; i < n; i++) {
+        s = arguments[i];
+        for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p))
+            t[p] = s[p];
+    }
+    return t;
+};
+exports.x = {};
+exports.y = __assign({}, exports.x);
+
+```
+
+will instead be emitted as something like the following:
+
+```ts
+var tslib_1 = require("tslib");
+exports.x = {};
+exports.y = tslib_1.__assign({}, exports.x);
+```
+
+Because this can avoid duplicate declarations of things like `__extends`, `__assign`, etc., this means delivering users smaller files on average, as well as less runtime overhead.
+For optimized bundles with TypeScript, you should absolutely consider using `tslib` and `--importHelpers`.
+
+# Installing
+
+For the latest stable version, run:
+
+## npm
+
+```sh
+# TypeScript 2.3.3 or later
+npm install --save tslib
+
+# TypeScript 2.3.2 or earlier
+npm install --save tslib@1.6.1
+```
+
+## bower
+
+```sh
+# TypeScript 2.3.3 or later
+bower install tslib
+
+# TypeScript 2.3.2 or earlier
+bower install tslib@1.6.1
+```
+
+## JSPM
+
+```sh
+# TypeScript 2.3.3 or later
+jspm install tslib
+
+# TypeScript 2.3.2 or earlier
+jspm install tslib@1.6.1
+```
+
+# Usage
+
+Set the `importHelpers` compiler option on the command line:
+
+```
+tsc --importHelpers file.ts
+```
+
+or in your tsconfig.json:
+
+```json
+{
+    "compilerOptions": {
+        "importHelpers": true
+    }
+}
+```
+
+#### For bower and JSPM users
+
+You will need to add a `paths` mapping for `tslib`, e.g. For Bower users:
+
+```json
+{
+    "compilerOptions": {
+        "module": "amd",
+        "importHelpers": true,
+        "baseUrl": "./",
+        "paths": {
+            "tslib" : ["bower_components/tslib/tslib.d.ts"]
+        }
+    }
+}
+```
+
+For JSPM users:
+
+```json
+{
+    "compilerOptions": {
+        "module": "system",
+        "importHelpers": true,
+        "baseUrl": "./",
+        "paths": {
+            "tslib" : ["jspm_packages/npm/tslib@1.9.3/tslib.d.ts"]
+        }
+    }
+}
+```
+
+
+# Contribute
+
+There are many ways to [contribute](https://github.com/Microsoft/TypeScript/blob/master/CONTRIBUTING.md) to TypeScript.
+
+* [Submit bugs](https://github.com/Microsoft/TypeScript/issues) and help us verify fixes as they are checked in.
+* Review the [source code changes](https://github.com/Microsoft/TypeScript/pulls).
+* Engage with other TypeScript users and developers on [StackOverflow](http://stackoverflow.com/questions/tagged/typescript).
+* Join the [#typescript](http://twitter.com/#!/search/realtime/%23typescript) discussion on Twitter.
+* [Contribute bug fixes](https://github.com/Microsoft/TypeScript/blob/master/CONTRIBUTING.md).
+* Read the language specification ([docx](http://go.microsoft.com/fwlink/?LinkId=267121), [pdf](http://go.microsoft.com/fwlink/?LinkId=267238)).
+
+# Documentation
+
+* [Quick tutorial](http://www.typescriptlang.org/Tutorial)
+* [Programming handbook](http://www.typescriptlang.org/Handbook)
+* [Language specification](https://github.com/Microsoft/TypeScript/blob/master/doc/spec.md)
+* [Homepage](http://www.typescriptlang.org/)
diff --git a/node_modules/tslib/bower.json b/node_modules/tslib/bower.json
new file mode 100644
index 0000000..d3d8ec1
--- /dev/null
+++ b/node_modules/tslib/bower.json
@@ -0,0 +1,34 @@
+{
+  "name": "tslib",
+  "authors": [
+    "Microsoft Corp."
+  ],
+  "homepage": "http://typescriptlang.org/",
+  "version": "1.9.3",
+  "license": "Apache-2.0",
+  "description": "Runtime library for TypeScript helper functions",
+  "keywords": [
+    "TypeScript",
+    "Microsoft",
+    "compiler",
+    "language",
+    "javascript",
+    "tslib",
+    "runtime"
+  ],
+  "repository": {
+    "type": "git",
+    "url": "https://github.com/Microsoft/tslib.git"
+  },
+  "main": "tslib.js",
+  "ignore": [
+    "**/.*",
+    "node_modules",
+    "bower_components",
+    "docs",
+    "package.json",
+    ".npmignore",
+    ".gitignore",
+    ".gitattributes"
+  ]
+}
diff --git a/node_modules/tslib/docs/generator.md b/node_modules/tslib/docs/generator.md
new file mode 100644
index 0000000..88ffb08
--- /dev/null
+++ b/node_modules/tslib/docs/generator.md
@@ -0,0 +1,486 @@
+# The `__generator` helper
+
+The `__generator` helper is a function designed to support TypeScript's down-level emit for
+async functions when targeting ES5 and earlier. But how, exactly, does it work?
+
+Here's the body of the `__generator` helper:
+
+```js
+__generator = function (thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t;
+    return { next: verb(0), "throw": verb(1), "return": verb(2) };
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = y[op[0] & 2 ? "return" : op[0] ? "throw" : "next"]) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [0, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+};
+```
+
+And here's an example of it in use:
+
+```ts
+// source
+async function func(x) {
+    try {
+        await x;
+    }
+    catch (e) {
+        console.error(e);
+    }
+    finally {
+        console.log("finally");
+    }
+}
+
+// generated
+function func(x) {
+    return __awaiter(this, void 0, void 0, function () {
+        var e_1;
+        return __generator(this, function (_a) {
+            switch (_a.label) {
+                case 0:
+                    _a.trys.push([0, 1, 3, 4]);
+                    return [4 /*yield*/, x];
+                case 1:
+                    _a.sent();
+                    return [3 /*break*/, 4];
+                case 2:
+                    e_1 = _a.sent();
+                    console.error(e_1);
+                    return [3 /*break*/, 4];
+                case 3:
+                    console.log("finally");
+                    return [7 /*endfinally*/];
+                case 4: return [2 /*return*/];
+            }
+        });
+    });
+}
+```
+
+There is a lot going on in this function, so the following will break down what each part of the
+`__generator` helper does and how it works.
+
+# Opcodes
+
+The `__generator` helper uses opcodes which represent various operations that are interpreted by
+the helper to affect its internal state. The following table lists the various opcodes, their
+arguments, and their purpose:
+
+| Opcode         | Arguments | Purpose                                                                                                                        |
+|----------------|-----------|--------------------------------------------------------------------------------------------------------------------------------|
+| 0 (next)       | *value*   | Starts the generator, or resumes the generator with *value* as the result of the `AwaitExpression` where execution was paused. |
+| 1 (throw)      | *value*   | Resumes the generator, throwing *value* at `AwaitExpression` where execution was paused.                                       |
+| 2 (return)     | *value*   | Exits the generator, executing any `finally` blocks starting at the `AwaitExpression` where execution was paused.              |
+| 3 (break)      | *label*   | Performs an unconditional jump to the specified label, executing any `finally` between the current instruction and the label.  |
+| 4 (yield)      | *value*   | Suspends the generator, setting the resume point at the next label and yielding the value.                                     |
+| 5 (yieldstar)  | *value*   | Suspends the generator, setting the resume point at the next label and delegating operations to the supplied value.            |
+| 6 (catch)      | *error*   | An internal instruction used to indicate an exception that was thrown from the body of the generator.                          |
+| 7 (endfinally) |           | Exits a finally block, resuming any previous operation (such as a break, return, throw, etc.)                                  |
+
+# State
+The `_`, `f`, `y`, and `t` variables make up the persistent state of the `__generator` function. Each variable
+has a specific purpose, as described in the following sections:
+
+## The `_` variable
+The `__generator` helper must share state between its internal `step` orchestration function and
+the `body` function passed to the helper.
+
+```ts
+var _ = {
+    label: 0,
+    sent: function() {
+        if (t[0] & 1) // NOTE: true for `throw`, but not `next` or `catch`
+            throw t[1];
+        return sent[1];
+    },
+    trys: [],
+    ops: []
+};
+```
+
+The following table describes the members of the `_` state object and their purpose:
+
+| Name    | Description                                                                                                               |
+|---------|---------------------------------------------------------------------------------------------------------------------------|
+| `label` | Specifies the next switch case to execute in the `body` function.                                                         |
+| `sent`  | Handles the completion result passed to the generator.                                                                    |
+| `trys`  | A stack of **Protected Regions**, which are 4-tuples that describe the labels that make up a `try..catch..finally` block. |
+| `ops`   | A stack of pending operations used for `try..finally` blocks.                                                             |
+
+The `__generator` helper passes this state object to the `body` function for use with switching
+between switch cases in the body, handling completions from `AwaitExpression`, etc.
+
+## The `f` variable
+The `f` variable indicates whether the generator is currently executing, to prevent re-entry of
+the same generator during its execution.
+
+## The `y` variable
+The `y` variable stores the iterator passed to a `yieldstar` instruction to which operations should be delegated.
+
+## The `t` variable
+The `t` variable is a temporary variable that stores one of the following values:
+
+- The completion value when resuming from a `yield` or `yield*`.
+- The error value for a catch block.
+- The current **Protected Region**.
+- The verb (`next`, `throw`, or `return` method) to delegate to the expression of a `yield*`.
+- The result of evaluating the verb delegated to the expression of a `yield*`.
+
+> NOTE: None of the above cases overlap.
+
+# Protected Regions
+A **Protected Region** is a region within the `body` function that indicates a
+`try..catch..finally` statement. It consists of a 4-tuple that contains 4 labels:
+
+| Offset | Description                                                                             |
+|--------|-----------------------------------------------------------------------------------------|
+| 0      | *Required* The label that indicates the beginning of a `try..catch..finally` statement. |
+| 1      | *Optional* The label that indicates the beginning of a `catch` clause.                  |
+| 2      | *Optional* The label that indicates the beginning of a `finally` clause.                |
+| 3      | *Required* The label that indicates the end of the `try..catch..finally` statement.     |
+
+# The generator object
+The final step of the `__generator` helper is the allocation of an object that implements the
+`Generator` protocol, to be used by the `__awaiter` helper:
+
+```ts
+return { next: verb(0), "throw": verb(1), "return": verb(2) };
+function verb(n) { return function (v) { return step([n, v]); }; }
+```
+
+This object translates calls to `next`, `throw`, and `return` to the appropriate Opcodes and
+invokes the `step` orchestration function to continue execution. The `throw` and `return` method
+names are quoted to better support ES3.
+
+# Orchestration
+The `step` function is the main orechestration mechanism for the `__generator` helper. It
+interprets opcodes, handles **protected regions**, and communicates results back to the caller.
+
+Here's a closer look at the `step` function:
+
+```ts
+function step(op) {
+    if (f) throw new TypeError("Generator is already executing.");
+    while (_) try {
+        if (f = 1, y && (t = y[op[0] & 2 ? "return" : op[0] ? "throw" : "next"]) && !(t = t.call(y, op[1])).done) return t;
+        if (y = 0, t) op = [0, t.value];
+        switch (op[0]) {
+            case 0: case 1: t = op; break;
+            case 4: _.label++; return { value: op[1], done: false };
+            case 5: _.label++; y = op[1]; op = [0]; continue;
+            case 7: op = _.ops.pop(); _.trys.pop(); continue;
+            default:
+                if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                if (t[2]) _.ops.pop();
+                _.trys.pop(); continue;
+        }
+        op = body.call(thisArg, _);
+    } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+    if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+}
+```
+
+The main body of `step` exists in a `while` loop. This allows us to continually interpret
+operations until we have reached some completion value, be it a `return`, `await`, or `throw`.
+
+## Preventing re-entry
+The first part of the `step` function is used as a check to prevent re-entry into a currently
+executing generator:
+
+```ts
+if (f) throw new TypeError("Generator is already executing.");
+```
+
+## Running the generator
+The main body of the `step` function consists of a `while` loop which continues to evaluate
+instructions until the generator exits or is suspended:
+
+```ts
+while (_) try ...
+```
+
+When the generator has run to completion, the `_` state variable will be cleared, forcing the loop
+to exit.
+
+## Evaluating the generator body.
+```ts
+try {
+    ...
+    op = body.call(thisArg, _);
+}
+catch (e) {
+    op = [6, e];
+    y = 0;
+}
+finally {
+    f = t = 0;
+}
+```
+
+Depending on the current operation, we re-enter the generator body to start or continue execution.
+Here we invoke `body` with `thisArg` as the `this` binding and the `_` state object as the only
+argument. The result is a tuple that contains the next Opcode and argument.
+
+If evaluation of the body resulted in an exception, we convert this into an Opcode 6 ("catch")
+operation to be handled in the next spin of the `while` loop. We also clear the `y` variable in
+case it is set to ensure we are no longer delegating operations as the exception occurred in
+user code *outside* of, or at the function boundary of, the delegated iterator (otherwise the
+iterator would have handled the exception itself).
+
+After executing user code, we clear the `f` flag that indicates we are executing the generator,
+as well as the `t` temporary value so that we don't hold onto values sent to the generator for
+longer than necessary.
+
+Inside of the `try..finally` statement are a series of statements that are used to evaluate the
+operations of the transformed generator body.
+
+The first thing we do is mark the generator as executing:
+
+```ts
+if (f = 1, ...)
+```
+
+Despite the fact this expression is part of the head of an `if` statement, the comma operator
+causes it to be evaluated and the result thrown out. This is a minification added purely to
+reduce the overall footprint of the helper.
+
+## Delegating `yield*`
+
+The first two statements of the `try..finally` statement handle delegation for `yield*`:
+
+```ts
+if (f = 1, y && (t = y[op[0] & 2 ? "return" : op[0] ? "throw" : "next"]) && !(t = t.call(y, op[1])).done) return t;
+if (y = 0, t) op = [0, t.value];
+```
+
+If the `y` variable is set, and `y` has a `next`, `throw`, or `return` method (depending on the
+current operation), we invoke this method and store the return value (an IteratorResult) in `t`.
+
+If `t` indicates it is a yielded value (e.g. `t.done === false`), we return `t` to the caller.
+If `t` indicates it is a returned value (e.g. `t.done === true`), we mark the operation with the
+`next` Opcode, and the returned value.
+If `y` did not have the appropriate method, or `t` was a returned value, we reset `y` to a falsey
+value and continue processing the operation.
+
+## Handling operations
+
+The various Opcodes are handled in the following switch statement:
+
+```ts
+switch (op[0]) {
+    case 0: case 1: t = op; break;
+    case 4: _.label++; return { value: op[1], done: false };
+    case 5: _.label++; y = op[1]; op = [0]; continue;
+    case 7: op = _.ops.pop(); _.trys.pop(); continue;
+    default:
+        if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+        if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+        if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+        if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+        if (t[2]) _.ops.pop();
+        _.trys.pop(); continue;
+}
+```
+
+The following sections describe the various Opcodes:
+
+### Opcode 0 ("next") and Opcode 1 ("throw")
+```ts
+case 0: // next
+case 1: // throw
+    t = op;
+    break;
+```
+
+Both Opcode 0 ("next") and Opcode 1 ("throw") have the same behavior. The current operation is
+stored in the `t` variable and the `body` function is invoked. The `body` function should call
+`_.sent()` which will evaluate the appropriate completion result.
+
+### Opcode 4 ("yield")
+```ts
+case 4: // yield
+    _.label++;
+    return { value: op[1], done: false };
+```
+
+When we encounter Opcode 4 ("yield"), we increment the label by one to indicate the point at which
+the generator will resume execution. We then return an `IteratorResult` whose `value` is the
+yielded value, and `done` is `false`.
+
+### Opcode 5 ("yieldstar")
+```ts
+case 5: // yieldstar
+    _.label++;
+    y = op[1];
+    op = [0];
+    continue;
+```
+
+When we receive Opcode 5 ("yieldstar"), we increment the label by one to indicate the point at which
+the generator will resume execution. We then store the iterator in `op[1]` in the `y` variable, and
+set the operation to delegate to Opcode 0 ("next") with no value. Finally, we continue execution at
+the top of the loop to start delegation.
+
+### Opcode 7 ("endfinally")
+```ts
+case 7:
+    op = _.ops.pop();
+    _.trys.pop();
+    continue;
+```
+
+Opcode 7 ("endfinally") indicates that we have hit the end of a `finally` clause, and that the last
+operation recorded before entering the `finally` block should be evaluated.
+
+### Opcode 2 ("return"), Opcode 3 ("break"), and Opcode 6 ("catch")
+```ts
+default:
+    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) {
+        _ = 0;
+        continue;
+    }
+    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) {
+        _.label = op[1];
+        break;
+    }
+    if (op[0] === 6 && _.label < t[1]) {
+        _.label = t[1];
+        t = op;
+        break;
+    }
+    if (t && _.label < t[2]) {
+        _.label = t[2];
+        _.ops.push(op);
+        break;
+    }
+    if (t[2])
+        _.ops.pop();
+    _.trys.pop();
+    continue;
+}
+```
+
+The handling for Opcode 2 ("return"), Opcode 3 ("break") and Opcode 6 ("catch") is more
+complicated, as we must obey the specified runtime semantics of generators. The first line in this
+clause gets the current **Protected Region** if found and stores it in the `t` temp variable:
+
+```ts
+if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && ...) ...
+```
+
+The remainder of this statement, as well as the following by several `if` statements test for more
+complex conditions. The first of these is the following:
+
+```ts
+if (!(t = ...) && (op[0] === 6 || op[0] === 2)) {
+    _ = 0;
+    continue;
+}
+```
+
+If we encounter an Opcode 6 ("catch") or Opcode 2 ("return"), and we are not in a protected region,
+then this operation completes the generator by setting the `_` variable to a falsey value. The
+`continue` statement resumes execution at the top of the `while` statement, which will exit the loop
+so that we continue execution at the statement following the loop.
+
+```ts
+if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) {
+    _.label = op[1];
+    break;
+}
+```
+
+The `if` statement above handles Opcode 3 ("break") when we are either not in a **protected region**, or
+are performing an unconditional jump to a label inside of the current **protected region**. In this case
+we can unconditionally jump to the specified label.
+
+```ts
+if (op[0] === 6 && _.label < t[1]) {
+    _.label = t[1];
+    t = op;
+    break;
+}
+```
+
+The `if` statement above handles Opcode 6 ("catch") when inside the `try` block of a **protected
+region**. In this case we jump to the `catch` block, if present. We replace the value of `t` with
+the operation so that the exception can be read as the first statement of the transformed `catch`
+clause of the transformed generator body.
+
+```ts
+if (t && _.label < t[2]) {
+    _.label = t[2];
+    _.ops.push(op);
+    break;
+}
+```
+
+This `if` statement handles all Opcodes when in a **protected region** with a `finally` clause.
+As long as we are not already inside the `finally` clause, we jump to the `finally` clause and
+push the pending operation onto the `_.ops` stack. This allows us to resume execution of the
+pending operation once we have completed execution of the `finally` clause, as long as it does not
+supersede this operation with its own completion value.
+
+```ts
+if (t[2])
+    _.ops.pop();
+```
+
+Any other completion value inside of a `finally` clause will supersede the pending completion value
+from the `try` or `catch` clauses. The above `if` statement pops the pending completion from the
+stack.
+
+```ts
+_.trys.pop();
+continue;
+```
+
+The remaining statements handle the point at which we exit a **protected region**. Here we pop the
+current **protected region** from the stack and spin the `while` statement to evaluate the current
+operation again in the next **protected region** or at the function boundary.
+
+## Handling a completed generator
+Once the generator has completed, the `_` state variable will be falsey. As a result, the `while`
+loop will terminate and hand control off to the final statement of the orchestration function,
+which deals with how a completed generator is evaluated:
+
+```ts
+if (op[0] & 5)
+    throw op[1];
+return { value: op[0] ? op[1] : void 0, done: true };
+```
+
+If the caller calls `throw` on the generator it will send Opcode 1 ("throw"). If an exception
+is uncaught within the body of the generator, it will send Opcode 6 ("catch"). As the generator has
+completed, it throws the exception. Both of these cases are caught by the bitmask `5`, which does
+not collide with the only two other valid completion Opcodes.
+
+If the caller calls `next` on the generator, it will send Opcode 0 ("next"). As the generator has
+completed, it returns an `IteratorResult` where `value` is `undefined` and `done` is true.
+
+If the caller calls `return` on the generator, it will send Opcode 2 ("return"). As the generator
+has completed, it returns an `IteratorResult` where `value` is the value provided to `return`, and
+`done` is true.
\ No newline at end of file
diff --git a/node_modules/tslib/package.json b/node_modules/tslib/package.json
new file mode 100644
index 0000000..7de7e6f
--- /dev/null
+++ b/node_modules/tslib/package.json
@@ -0,0 +1,60 @@
+{
+  "_args": [
+    [
+      "tslib@1.9.3",
+      "/Users/zamua/Dropbox/workspace/madify"
+    ]
+  ],
+  "_from": "tslib@1.9.3",
+  "_id": "tslib@1.9.3",
+  "_inBundle": false,
+  "_integrity": "sha512-4krF8scpejhaOgqzBEcGM7yDIEfi0/8+8zDRZhNZZ2kjmHJ4hv3zCbQWxoJGz1iw5U0Jl0nma13xzHXcncMavQ==",
+  "_location": "/tslib",
+  "_phantomChildren": {},
+  "_requested": {
+    "type": "version",
+    "registry": true,
+    "raw": "tslib@1.9.3",
+    "name": "tslib",
+    "escapedName": "tslib",
+    "rawSpec": "1.9.3",
+    "saveSpec": null,
+    "fetchSpec": "1.9.3"
+  },
+  "_requiredBy": [
+    "/face-api.js",
+    "/tfjs-image-recognition-base",
+    "/tfjs-tiny-yolov2"
+  ],
+  "_resolved": "https://registry.npmjs.org/tslib/-/tslib-1.9.3.tgz",
+  "_spec": "1.9.3",
+  "_where": "/Users/zamua/Dropbox/workspace/madify",
+  "author": {
+    "name": "Microsoft Corp."
+  },
+  "bugs": {
+    "url": "https://github.com/Microsoft/TypeScript/issues"
+  },
+  "description": "Runtime library for TypeScript helper functions",
+  "homepage": "http://typescriptlang.org/",
+  "jsnext:main": "tslib.es6.js",
+  "keywords": [
+    "TypeScript",
+    "Microsoft",
+    "compiler",
+    "language",
+    "javascript",
+    "tslib",
+    "runtime"
+  ],
+  "license": "Apache-2.0",
+  "main": "tslib.js",
+  "module": "tslib.es6.js",
+  "name": "tslib",
+  "repository": {
+    "type": "git",
+    "url": "git+https://github.com/Microsoft/tslib.git"
+  },
+  "typings": "tslib.d.ts",
+  "version": "1.9.3"
+}
diff --git a/node_modules/tslib/tslib.d.ts b/node_modules/tslib/tslib.d.ts
new file mode 100644
index 0000000..c4a0e11
--- /dev/null
+++ b/node_modules/tslib/tslib.d.ts
@@ -0,0 +1,33 @@
+/*! *****************************************************************************
+Copyright (c) Microsoft Corporation. All rights reserved.
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use
+this file except in compliance with the License. You may obtain a copy of the
+License at http://www.apache.org/licenses/LICENSE-2.0
+
+THIS CODE IS PROVIDED ON AN *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
+WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
+MERCHANTABLITY OR NON-INFRINGEMENT.
+
+See the Apache Version 2.0 License for specific language governing permissions
+and limitations under the License.
+***************************************************************************** */
+export declare function __extends(d: Function, b: Function): void;
+export declare function __assign(t: any, ...sources: any[]): any;
+export declare function __rest(t: any, propertyNames: (string | symbol)[]): any;
+export declare function __decorate(decorators: Function[], target: any, key?: string | symbol, desc?: any): any;
+export declare function __param(paramIndex: number, decorator: Function): Function;
+export declare function __metadata(metadataKey: any, metadataValue: any): Function;
+export declare function __awaiter(thisArg: any, _arguments: any, P: Function, generator: Function): any;
+export declare function __generator(thisArg: any, body: Function): any;
+export declare function __exportStar(m: any, exports: any): void;
+export declare function __values(o: any): any;
+export declare function __read(o: any, n?: number): any[];
+export declare function __spread(...args: any[]): any[];
+export declare function __await(v: any): any;
+export declare function __asyncGenerator(thisArg: any, _arguments: any, generator: Function): any;
+export declare function __asyncDelegator(o: any): any;
+export declare function __asyncValues(o: any): any;
+export declare function __makeTemplateObject(cooked: string[], raw: string[]): TemplateStringsArray;
+export declare function __importStar<T>(mod: T): T;
+export declare function __importDefault<T>(mod: T): T | { default: T };
diff --git a/node_modules/tslib/tslib.es6.html b/node_modules/tslib/tslib.es6.html
new file mode 100644
index 0000000..b122e41
--- /dev/null
+++ b/node_modules/tslib/tslib.es6.html
@@ -0,0 +1 @@
+<script src="tslib.es6.js"></script>
\ No newline at end of file
diff --git a/node_modules/tslib/tslib.es6.js b/node_modules/tslib/tslib.es6.js
new file mode 100644
index 0000000..6da273e
--- /dev/null
+++ b/node_modules/tslib/tslib.es6.js
@@ -0,0 +1,186 @@
+/*! *****************************************************************************
+Copyright (c) Microsoft Corporation. All rights reserved.
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use
+this file except in compliance with the License. You may obtain a copy of the
+License at http://www.apache.org/licenses/LICENSE-2.0
+
+THIS CODE IS PROVIDED ON AN *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
+WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
+MERCHANTABLITY OR NON-INFRINGEMENT.
+
+See the Apache Version 2.0 License for specific language governing permissions
+and limitations under the License.
+***************************************************************************** */
+/* global Reflect, Promise */
+
+var extendStatics = function(d, b) {
+    extendStatics = Object.setPrototypeOf ||
+        ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
+        function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
+    return extendStatics(d, b);
+};
+
+export function __extends(d, b) {
+    extendStatics(d, b);
+    function __() { this.constructor = d; }
+    d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
+}
+
+export var __assign = function() {
+    __assign = Object.assign || function __assign(t) {
+        for (var s, i = 1, n = arguments.length; i < n; i++) {
+            s = arguments[i];
+            for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];
+        }
+        return t;
+    }
+    return __assign.apply(this, arguments);
+}
+
+export function __rest(s, e) {
+    var t = {};
+    for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0)
+        t[p] = s[p];
+    if (s != null && typeof Object.getOwnPropertySymbols === "function")
+        for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) if (e.indexOf(p[i]) < 0)
+            t[p[i]] = s[p[i]];
+    return t;
+}
+
+export function __decorate(decorators, target, key, desc) {
+    var c = arguments.length, r = c < 3 ? target : desc === null ? desc = Object.getOwnPropertyDescriptor(target, key) : desc, d;
+    if (typeof Reflect === "object" && typeof Reflect.decorate === "function") r = Reflect.decorate(decorators, target, key, desc);
+    else for (var i = decorators.length - 1; i >= 0; i--) if (d = decorators[i]) r = (c < 3 ? d(r) : c > 3 ? d(target, key, r) : d(target, key)) || r;
+    return c > 3 && r && Object.defineProperty(target, key, r), r;
+}
+
+export function __param(paramIndex, decorator) {
+    return function (target, key) { decorator(target, key, paramIndex); }
+}
+
+export function __metadata(metadataKey, metadataValue) {
+    if (typeof Reflect === "object" && typeof Reflect.metadata === "function") return Reflect.metadata(metadataKey, metadataValue);
+}
+
+export function __awaiter(thisArg, _arguments, P, generator) {
+    return new (P || (P = Promise))(function (resolve, reject) {
+        function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+        function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+        function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+        step((generator = generator.apply(thisArg, _arguments || [])).next());
+    });
+}
+
+export function __generator(thisArg, body) {
+    var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+    return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+    function verb(n) { return function (v) { return step([n, v]); }; }
+    function step(op) {
+        if (f) throw new TypeError("Generator is already executing.");
+        while (_) try {
+            if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+            if (y = 0, t) op = [op[0] & 2, t.value];
+            switch (op[0]) {
+                case 0: case 1: t = op; break;
+                case 4: _.label++; return { value: op[1], done: false };
+                case 5: _.label++; y = op[1]; op = [0]; continue;
+                case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                default:
+                    if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                    if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                    if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                    if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                    if (t[2]) _.ops.pop();
+                    _.trys.pop(); continue;
+            }
+            op = body.call(thisArg, _);
+        } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+        if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+    }
+}
+
+export function __exportStar(m, exports) {
+    for (var p in m) if (!exports.hasOwnProperty(p)) exports[p] = m[p];
+}
+
+export function __values(o) {
+    var m = typeof Symbol === "function" && o[Symbol.iterator], i = 0;
+    if (m) return m.call(o);
+    return {
+        next: function () {
+            if (o && i >= o.length) o = void 0;
+            return { value: o && o[i++], done: !o };
+        }
+    };
+}
+
+export function __read(o, n) {
+    var m = typeof Symbol === "function" && o[Symbol.iterator];
+    if (!m) return o;
+    var i = m.call(o), r, ar = [], e;
+    try {
+        while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value);
+    }
+    catch (error) { e = { error: error }; }
+    finally {
+        try {
+            if (r && !r.done && (m = i["return"])) m.call(i);
+        }
+        finally { if (e) throw e.error; }
+    }
+    return ar;
+}
+
+export function __spread() {
+    for (var ar = [], i = 0; i < arguments.length; i++)
+        ar = ar.concat(__read(arguments[i]));
+    return ar;
+}
+
+export function __await(v) {
+    return this instanceof __await ? (this.v = v, this) : new __await(v);
+}
+
+export function __asyncGenerator(thisArg, _arguments, generator) {
+    if (!Symbol.asyncIterator) throw new TypeError("Symbol.asyncIterator is not defined.");
+    var g = generator.apply(thisArg, _arguments || []), i, q = [];
+    return i = {}, verb("next"), verb("throw"), verb("return"), i[Symbol.asyncIterator] = function () { return this; }, i;
+    function verb(n) { if (g[n]) i[n] = function (v) { return new Promise(function (a, b) { q.push([n, v, a, b]) > 1 || resume(n, v); }); }; }
+    function resume(n, v) { try { step(g[n](v)); } catch (e) { settle(q[0][3], e); } }
+    function step(r) { r.value instanceof __await ? Promise.resolve(r.value.v).then(fulfill, reject) : settle(q[0][2], r); }
+    function fulfill(value) { resume("next", value); }
+    function reject(value) { resume("throw", value); }
+    function settle(f, v) { if (f(v), q.shift(), q.length) resume(q[0][0], q[0][1]); }
+}
+
+export function __asyncDelegator(o) {
+    var i, p;
+    return i = {}, verb("next"), verb("throw", function (e) { throw e; }), verb("return"), i[Symbol.iterator] = function () { return this; }, i;
+    function verb(n, f) { i[n] = o[n] ? function (v) { return (p = !p) ? { value: __await(o[n](v)), done: n === "return" } : f ? f(v) : v; } : f; }
+}
+
+export function __asyncValues(o) {
+    if (!Symbol.asyncIterator) throw new TypeError("Symbol.asyncIterator is not defined.");
+    var m = o[Symbol.asyncIterator], i;
+    return m ? m.call(o) : (o = typeof __values === "function" ? __values(o) : o[Symbol.iterator](), i = {}, verb("next"), verb("throw"), verb("return"), i[Symbol.asyncIterator] = function () { return this; }, i);
+    function verb(n) { i[n] = o[n] && function (v) { return new Promise(function (resolve, reject) { v = o[n](v), settle(resolve, reject, v.done, v.value); }); }; }
+    function settle(resolve, reject, d, v) { Promise.resolve(v).then(function(v) { resolve({ value: v, done: d }); }, reject); }
+}
+
+export function __makeTemplateObject(cooked, raw) {
+    if (Object.defineProperty) { Object.defineProperty(cooked, "raw", { value: raw }); } else { cooked.raw = raw; }
+    return cooked;
+};
+
+export function __importStar(mod) {
+    if (mod && mod.__esModule) return mod;
+    var result = {};
+    if (mod != null) for (var k in mod) if (Object.hasOwnProperty.call(mod, k)) result[k] = mod[k];
+    result.default = mod;
+    return result;
+}
+
+export function __importDefault(mod) {
+    return (mod && mod.__esModule) ? mod : { default: mod };
+}
diff --git a/node_modules/tslib/tslib.html b/node_modules/tslib/tslib.html
new file mode 100644
index 0000000..44c9ba5
--- /dev/null
+++ b/node_modules/tslib/tslib.html
@@ -0,0 +1 @@
+<script src="tslib.js"></script>
\ No newline at end of file
diff --git a/node_modules/tslib/tslib.js b/node_modules/tslib/tslib.js
new file mode 100644
index 0000000..b0b1ff3
--- /dev/null
+++ b/node_modules/tslib/tslib.js
@@ -0,0 +1,243 @@
+/*! *****************************************************************************
+Copyright (c) Microsoft Corporation. All rights reserved.
+Licensed under the Apache License, Version 2.0 (the "License"); you may not use
+this file except in compliance with the License. You may obtain a copy of the
+License at http://www.apache.org/licenses/LICENSE-2.0
+
+THIS CODE IS PROVIDED ON AN *AS IS* BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+KIND, EITHER EXPRESS OR IMPLIED, INCLUDING WITHOUT LIMITATION ANY IMPLIED
+WARRANTIES OR CONDITIONS OF TITLE, FITNESS FOR A PARTICULAR PURPOSE,
+MERCHANTABLITY OR NON-INFRINGEMENT.
+
+See the Apache Version 2.0 License for specific language governing permissions
+and limitations under the License.
+***************************************************************************** */
+/* global global, define, System, Reflect, Promise */
+var __extends;
+var __assign;
+var __rest;
+var __decorate;
+var __param;
+var __metadata;
+var __awaiter;
+var __generator;
+var __exportStar;
+var __values;
+var __read;
+var __spread;
+var __await;
+var __asyncGenerator;
+var __asyncDelegator;
+var __asyncValues;
+var __makeTemplateObject;
+var __importStar;
+var __importDefault;
+(function (factory) {
+    var root = typeof global === "object" ? global : typeof self === "object" ? self : typeof this === "object" ? this : {};
+    if (typeof define === "function" && define.amd) {
+        define("tslib", ["exports"], function (exports) { factory(createExporter(root, createExporter(exports))); });
+    }
+    else if (typeof module === "object" && typeof module.exports === "object") {
+        factory(createExporter(root, createExporter(module.exports)));
+    }
+    else {
+        factory(createExporter(root));
+    }
+    function createExporter(exports, previous) {
+        if (exports !== root) {
+            if (typeof Object.create === "function") {
+                Object.defineProperty(exports, "__esModule", { value: true });
+            }
+            else {
+                exports.__esModule = true;
+            }
+        }
+        return function (id, v) { return exports[id] = previous ? previous(id, v) : v; };
+    }
+})
+(function (exporter) {
+    var extendStatics = Object.setPrototypeOf ||
+        ({ __proto__: [] } instanceof Array && function (d, b) { d.__proto__ = b; }) ||
+        function (d, b) { for (var p in b) if (b.hasOwnProperty(p)) d[p] = b[p]; };
+
+    __extends = function (d, b) {
+        extendStatics(d, b);
+        function __() { this.constructor = d; }
+        d.prototype = b === null ? Object.create(b) : (__.prototype = b.prototype, new __());
+    };
+
+    __assign = Object.assign || function (t) {
+        for (var s, i = 1, n = arguments.length; i < n; i++) {
+            s = arguments[i];
+            for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p)) t[p] = s[p];
+        }
+        return t;
+    };
+
+    __rest = function (s, e) {
+        var t = {};
+        for (var p in s) if (Object.prototype.hasOwnProperty.call(s, p) && e.indexOf(p) < 0)
+            t[p] = s[p];
+        if (s != null && typeof Object.getOwnPropertySymbols === "function")
+            for (var i = 0, p = Object.getOwnPropertySymbols(s); i < p.length; i++) if (e.indexOf(p[i]) < 0)
+                t[p[i]] = s[p[i]];
+        return t;
+    };
+
+    __decorate = function (decorators, target, key, desc) {
+        var c = arguments.length, r = c < 3 ? target : desc === null ? desc = Object.getOwnPropertyDescriptor(target, key) : desc, d;
+        if (typeof Reflect === "object" && typeof Reflect.decorate === "function") r = Reflect.decorate(decorators, target, key, desc);
+        else for (var i = decorators.length - 1; i >= 0; i--) if (d = decorators[i]) r = (c < 3 ? d(r) : c > 3 ? d(target, key, r) : d(target, key)) || r;
+        return c > 3 && r && Object.defineProperty(target, key, r), r;
+    };
+
+    __param = function (paramIndex, decorator) {
+        return function (target, key) { decorator(target, key, paramIndex); }
+    };
+
+    __metadata = function (metadataKey, metadataValue) {
+        if (typeof Reflect === "object" && typeof Reflect.metadata === "function") return Reflect.metadata(metadataKey, metadataValue);
+    };
+
+    __awaiter = function (thisArg, _arguments, P, generator) {
+        return new (P || (P = Promise))(function (resolve, reject) {
+            function fulfilled(value) { try { step(generator.next(value)); } catch (e) { reject(e); } }
+            function rejected(value) { try { step(generator["throw"](value)); } catch (e) { reject(e); } }
+            function step(result) { result.done ? resolve(result.value) : new P(function (resolve) { resolve(result.value); }).then(fulfilled, rejected); }
+            step((generator = generator.apply(thisArg, _arguments || [])).next());
+        });
+    };
+
+    __generator = function (thisArg, body) {
+        var _ = { label: 0, sent: function() { if (t[0] & 1) throw t[1]; return t[1]; }, trys: [], ops: [] }, f, y, t, g;
+        return g = { next: verb(0), "throw": verb(1), "return": verb(2) }, typeof Symbol === "function" && (g[Symbol.iterator] = function() { return this; }), g;
+        function verb(n) { return function (v) { return step([n, v]); }; }
+        function step(op) {
+            if (f) throw new TypeError("Generator is already executing.");
+            while (_) try {
+                if (f = 1, y && (t = op[0] & 2 ? y["return"] : op[0] ? y["throw"] || ((t = y["return"]) && t.call(y), 0) : y.next) && !(t = t.call(y, op[1])).done) return t;
+                if (y = 0, t) op = [op[0] & 2, t.value];
+                switch (op[0]) {
+                    case 0: case 1: t = op; break;
+                    case 4: _.label++; return { value: op[1], done: false };
+                    case 5: _.label++; y = op[1]; op = [0]; continue;
+                    case 7: op = _.ops.pop(); _.trys.pop(); continue;
+                    default:
+                        if (!(t = _.trys, t = t.length > 0 && t[t.length - 1]) && (op[0] === 6 || op[0] === 2)) { _ = 0; continue; }
+                        if (op[0] === 3 && (!t || (op[1] > t[0] && op[1] < t[3]))) { _.label = op[1]; break; }
+                        if (op[0] === 6 && _.label < t[1]) { _.label = t[1]; t = op; break; }
+                        if (t && _.label < t[2]) { _.label = t[2]; _.ops.push(op); break; }
+                        if (t[2]) _.ops.pop();
+                        _.trys.pop(); continue;
+                }
+                op = body.call(thisArg, _);
+            } catch (e) { op = [6, e]; y = 0; } finally { f = t = 0; }
+            if (op[0] & 5) throw op[1]; return { value: op[0] ? op[1] : void 0, done: true };
+        }
+    };
+
+    __exportStar = function (m, exports) {
+        for (var p in m) if (!exports.hasOwnProperty(p)) exports[p] = m[p];
+    };
+
+    __values = function (o) {
+        var m = typeof Symbol === "function" && o[Symbol.iterator], i = 0;
+        if (m) return m.call(o);
+        return {
+            next: function () {
+                if (o && i >= o.length) o = void 0;
+                return { value: o && o[i++], done: !o };
+            }
+        };
+    };
+
+    __read = function (o, n) {
+        var m = typeof Symbol === "function" && o[Symbol.iterator];
+        if (!m) return o;
+        var i = m.call(o), r, ar = [], e;
+        try {
+            while ((n === void 0 || n-- > 0) && !(r = i.next()).done) ar.push(r.value);
+        }
+        catch (error) { e = { error: error }; }
+        finally {
+            try {
+                if (r && !r.done && (m = i["return"])) m.call(i);
+            }
+            finally { if (e) throw e.error; }
+        }
+        return ar;
+    };
+
+    __spread = function () {
+        for (var ar = [], i = 0; i < arguments.length; i++)
+            ar = ar.concat(__read(arguments[i]));
+        return ar;
+    };
+
+    __await = function (v) {
+        return this instanceof __await ? (this.v = v, this) : new __await(v);
+    };
+
+    __asyncGenerator = function (thisArg, _arguments, generator) {
+        if (!Symbol.asyncIterator) throw new TypeError("Symbol.asyncIterator is not defined.");
+        var g = generator.apply(thisArg, _arguments || []), i, q = [];
+        return i = {}, verb("next"), verb("throw"), verb("return"), i[Symbol.asyncIterator] = function () { return this; }, i;
+        function verb(n) { if (g[n]) i[n] = function (v) { return new Promise(function (a, b) { q.push([n, v, a, b]) > 1 || resume(n, v); }); }; }
+        function resume(n, v) { try { step(g[n](v)); } catch (e) { settle(q[0][3], e); } }
+        function step(r) { r.value instanceof __await ? Promise.resolve(r.value.v).then(fulfill, reject) : settle(q[0][2], r);  }
+        function fulfill(value) { resume("next", value); }
+        function reject(value) { resume("throw", value); }
+        function settle(f, v) { if (f(v), q.shift(), q.length) resume(q[0][0], q[0][1]); }
+    };
+
+    __asyncDelegator = function (o) {
+        var i, p;
+        return i = {}, verb("next"), verb("throw", function (e) { throw e; }), verb("return"), i[Symbol.iterator] = function () { return this; }, i;
+        function verb(n, f) { i[n] = o[n] ? function (v) { return (p = !p) ? { value: __await(o[n](v)), done: n === "return" } : f ? f(v) : v; } : f; }
+    };
+
+    __asyncValues = function (o) {
+        if (!Symbol.asyncIterator) throw new TypeError("Symbol.asyncIterator is not defined.");
+        var m = o[Symbol.asyncIterator], i;
+        return m ? m.call(o) : (o = typeof __values === "function" ? __values(o) : o[Symbol.iterator](), i = {}, verb("next"), verb("throw"), verb("return"), i[Symbol.asyncIterator] = function () { return this; }, i);
+        function verb(n) { i[n] = o[n] && function (v) { return new Promise(function (resolve, reject) { v = o[n](v), settle(resolve, reject, v.done, v.value); }); }; }
+        function settle(resolve, reject, d, v) { Promise.resolve(v).then(function(v) { resolve({ value: v, done: d }); }, reject); }
+    };
+
+    __makeTemplateObject = function (cooked, raw) {
+        if (Object.defineProperty) { Object.defineProperty(cooked, "raw", { value: raw }); } else { cooked.raw = raw; }
+        return cooked;
+    };
+
+    __importStar = function (mod) {
+        if (mod && mod.__esModule) return mod;
+        var result = {};
+        if (mod != null) for (var k in mod) if (Object.hasOwnProperty.call(mod, k)) result[k] = mod[k];
+        result["default"] = mod;
+        return result;
+    };
+
+    __importDefault = function (mod) {
+        return (mod && mod.__esModule) ? mod : { "default": mod };
+    };
+
+    exporter("__extends", __extends);
+    exporter("__assign", __assign);
+    exporter("__rest", __rest);
+    exporter("__decorate", __decorate);
+    exporter("__param", __param);
+    exporter("__metadata", __metadata);
+    exporter("__awaiter", __awaiter);
+    exporter("__generator", __generator);
+    exporter("__exportStar", __exportStar);
+    exporter("__values", __values);
+    exporter("__read", __read);
+    exporter("__spread", __spread);
+    exporter("__await", __await);
+    exporter("__asyncGenerator", __asyncGenerator);
+    exporter("__asyncDelegator", __asyncDelegator);
+    exporter("__asyncValues", __asyncValues);
+    exporter("__makeTemplateObject", __makeTemplateObject);
+    exporter("__importStar", __importStar);
+    exporter("__importDefault", __importDefault);
+});
diff --git a/package.json b/package.json
index 82a270c..261240a 100644
--- a/package.json
+++ b/package.json
@@ -17,6 +17,7 @@
     "bootstrap": "^4.1.3",
     "caman": "^4.1.2",
     "dropzone": "^5.5.1",
+    "face-api.js": "^0.17.1",
     "jquery": "^3.3.1"
   }
 }
diff --git a/weights/face_expression_model-shard1 b/weights/face_expression_model-shard1
new file mode 100644
index 0000000..619cdf6
Binary files /dev/null and b/weights/face_expression_model-shard1 differ
diff --git a/weights/face_expression_model-weights_manifest.json b/weights/face_expression_model-weights_manifest.json
new file mode 100644
index 0000000..7b74b5a
--- /dev/null
+++ b/weights/face_expression_model-weights_manifest.json
@@ -0,0 +1 @@
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+++ b/weights/face_recognition_model-shard2
@@ -0,0 +1,6 @@
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diff --git a/weights/face_recognition_model-weights_manifest.json b/weights/face_recognition_model-weights_manifest.json
new file mode 100644
index 0000000..554d3e4
--- /dev/null
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diff --git a/weights/ssd_mobilenetv1_model-shard1 b/weights/ssd_mobilenetv1_model-shard1
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diff --git a/weights/ssd_mobilenetv1_model-shard2 b/weights/ssd_mobilenetv1_model-shard2
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index 0000000..75123b5
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